diff --git a/FasterTransformer/fastertransformer/cuda/CMakeLists.txt b/FasterTransformer/fastertransformer/cuda/CMakeLists.txt index 3245a710..dccf44ad 100644 --- a/FasterTransformer/fastertransformer/cuda/CMakeLists.txt +++ b/FasterTransformer/fastertransformer/cuda/CMakeLists.txt @@ -19,5 +19,6 @@ set(cuda_kernel_files ) add_library(fastertransformer STATIC ${cuda_kernel_files}) +set_target_properties(fastertransformer PROPERTIES CUDA_RESOLVE_DEVICE_SYMBOLS ON) target_link_libraries(fastertransformer PUBLIC -lcublas -lcudart ${CMAKE_THREAD_LIBS_INIT}) diff --git a/FasterTransformer/fastertransformer/cuda/cuda_kernels.cu b/FasterTransformer/fastertransformer/cuda/cuda_kernels.cu index 684dc791..d20b699c 100644 --- a/FasterTransformer/fastertransformer/cuda/cuda_kernels.cu +++ b/FasterTransformer/fastertransformer/cuda/cuda_kernels.cu @@ -197,11 +197,9 @@ void add_bias_input_layernorm(__half* out, const __half* input, const __half* bi template void add_bias_act_kernelLauncher(T* out, const T* bias, int m, int n, cudaStream_t stream) { -// dim3 grid(m / 64); dim3 grid(m / 4); dim3 block(n / 4); - assert(block.x > 1024); -// dim3 block(n); + assert(block.x <= 1024); add_bias_act<<>>(out, bias, m, n); } @@ -209,9 +207,9 @@ template void add_bias_input_layernorm_kernelLauncher(T* out, const T* input, const T* bias, const T* gamma, const T* beta, int m, int n, cudaStream_t stream) { - assert(n > 1024); dim3 grid(m); dim3 block(n); + assert(block.x <= 1024); add_bias_input_layernorm<<>>(out, input, bias, gamma, beta, m, n); } @@ -220,9 +218,9 @@ template <> void add_bias_input_layernorm_kernelLauncher(__half* out, const __half* input, const __half* bias, const __half* gamma, const __half* beta, int m, int n, cudaStream_t stream) { - assert(n / 2 > 1024); dim3 grid(m); dim3 block(n / 2); + assert(block.x <= 1024); add_bias_input_layernorm<__half><<>>(out, input, bias, gamma, beta, m, n); } diff --git a/FasterTransformer/fastertransformer/cuda/open_attention.cu b/FasterTransformer/fastertransformer/cuda/open_attention.cu index dce4b228..bd565c20 100644 --- a/FasterTransformer/fastertransformer/cuda/open_attention.cu +++ b/FasterTransformer/fastertransformer/cuda/open_attention.cu @@ -88,7 +88,7 @@ T blockReduceMax(T val) __syncthreads(); - val = (threadIdx.x < (blockDim.x >> 5 )) ? shared[lane] : 0; + val = (threadIdx.x < (blockDim.x >> 5 )) ? shared[lane] : -1e20f; val = warpReduceMax(val); return val; @@ -204,7 +204,7 @@ void softmax_kernel(T* qk_buf_, const T* attr_mask, const int batch_size, const mask_val = (1.0f - mask_val) * -10000.0f; - float tmp = threadIdx.x < seq_len ? (float)(qk * (float)scaler + mask_val): -1e-20f; + float tmp = threadIdx.x < seq_len ? (float)(qk * (float)scaler + mask_val): -1e20f; float max_val = blockReduceMax(tmp); @@ -248,7 +248,7 @@ void softmax_kernel_v2(T* qk_buf_, const T* attr_mask, const int batch_size, con mask_val = (1.0f - mask_val) * -10000.0f; - float tmp = threadIdx.x < seq_len ? (float)(qk * (float)scaler + mask_val) : -1e-20f; + float tmp = threadIdx.x < seq_len ? (float)(qk * (float)scaler + mask_val) : -1e20f; float max_val = blockReduceMax(tmp); if(threadIdx.x == 0) s_max = max_val; @@ -324,10 +324,9 @@ void OpenMultiHeadAttention::multiHeadAttr_nofuse_kernelLauncher( if(OpType_ == OperationType::FP32) { -// const int word_per_block = 32; const int word_per_block = 1; - assert(k > 1024); - assert(m / word_per_block * 3 > 65536); + assert(k <= 1024); + assert(m / word_per_block * 3 <= 65536); dim3 grid(m / word_per_block * 3); dim3 block(k); @@ -340,8 +339,6 @@ void OpenMultiHeadAttention::multiHeadAttr_nofuse_kernelLauncher( grid.x = batch_size * seq_len / word_per_block; block.x = head_num * size_per_head * word_per_block / 2; - assert(block.x); - add_QKV_bias<<>>(Q, bias_Q, K, bias_K, V, bias_V, q_buf_, k_buf_, v_buf_, batch_size, seq_len, head_num, size_per_head / 2, word_per_block); } @@ -400,11 +397,10 @@ void OpenMultiHeadAttention::multiHeadAttr_nofuse_kernelLauncher( if(OpType_ == OperationType::HALF) { const int seq_per_block = 4; - // const int seq_per_block = 1; grid.x = batch_size * head_num * seq_len / seq_per_block; block.x = seq_per_block * size_per_head / 2; - assert(grid.x * seq_per_block != batch_size * head_num * seq_len); + assert(grid.x * seq_per_block == batch_size * head_num * seq_len); transpose<<>>(transpose_dst_, dst, batch_size, seq_len, head_num, size_per_head / 2); diff --git a/FasterTransformer/fastertransformer/tf_op/CMakeLists.txt b/FasterTransformer/fastertransformer/tf_op/CMakeLists.txt index 908b5d9c..85c1b943 100644 --- a/FasterTransformer/fastertransformer/tf_op/CMakeLists.txt +++ b/FasterTransformer/fastertransformer/tf_op/CMakeLists.txt @@ -25,4 +25,5 @@ add_definitions(-DGOOGLE_CUDA=1) add_definitions(-DNDEBUG) add_library(tf_fastertransformer SHARED ${tf_bert_transformer_files}) +set_target_properties(tf_fastertransformer PROPERTIES CUDA_RESOLVE_DEVICE_SYMBOLS ON) target_link_libraries(tf_fastertransformer PRIVATE -lcublas -lcudart -ltensorflow_framework ${CMAKE_THREAD_LIBS_INIT}) diff --git a/FasterTransformer/sample/tensorflow/transformer_fp16.py b/FasterTransformer/sample/tensorflow/transformer_fp16.py index 6d3dbaf4..fd561d5c 100644 --- a/FasterTransformer/sample/tensorflow/transformer_fp16.py +++ b/FasterTransformer/sample/tensorflow/transformer_fp16.py @@ -363,7 +363,7 @@ with tf.Session(config=config) as sess: print("#################################") np_val1 = sess.run(output) np_val2 = sess.run(output_own) - print("cross_check " + str(np.allclose(np_val1, np_val2, atol = 1e-5))) + print("cross_check " + str(np.allclose(np_val1, np_val2, atol = 1e-1))) print("max diff " + str(np.fabs(np_val1 - np_val2).max())) print("min diff " + str(np.fabs(np_val1 - np_val2).min())) print np_val1 diff --git a/FasterTransformer/sample/tensorflow/transformer_fp32.py b/FasterTransformer/sample/tensorflow/transformer_fp32.py index 1d01567d..1dd10d69 100644 --- a/FasterTransformer/sample/tensorflow/transformer_fp32.py +++ b/FasterTransformer/sample/tensorflow/transformer_fp32.py @@ -361,7 +361,7 @@ with tf.Session(config=config) as sess: print("#################################") np_val1 = sess.run(output) np_val2 = sess.run(output_own) - print("cross_check " + str(np.allclose(np_val1, np_val2, atol = 1e-5))) + print("cross_check " + str(np.allclose(np_val1, np_val2, atol = 1e-4))) print("max diff " + str(np.fabs(np_val1 - np_val2).max())) print("min diff " + str(np.fabs(np_val1 - np_val2).min())) diff --git a/FasterTransformer/tools/gemm_test/CMakeLists.txt b/FasterTransformer/tools/gemm_test/CMakeLists.txt index 67e109f3..9d947886 100644 --- a/FasterTransformer/tools/gemm_test/CMakeLists.txt +++ b/FasterTransformer/tools/gemm_test/CMakeLists.txt @@ -22,7 +22,9 @@ set(gemm_fp32_files ) add_executable(gemm_fp32 ${gemm_fp32_files}) +set_target_properties(gemm_fp32 PROPERTIES CUDA_RESOLVE_DEVICE_SYMBOLS ON) target_link_libraries(gemm_fp32 PUBLIC -lcublas -lcudart ${CMAKE_THREAD_LIBS_INIT}) add_executable(gemm_fp16 ${gemm_fp16_files}) +set_target_properties(gemm_fp16 PROPERTIES CUDA_RESOLVE_DEVICE_SYMBOLS ON) target_link_libraries(gemm_fp16 PUBLIC -lcublas -lcudart ${CMAKE_THREAD_LIBS_INIT}) diff --git a/MxNet/Classification/RN50v1.5/Dockerfile b/MxNet/Classification/RN50v1.5/Dockerfile new file mode 100644 index 00000000..8d8f0226 --- /dev/null +++ b/MxNet/Classification/RN50v1.5/Dockerfile @@ -0,0 +1,3 @@ +FROM nvcr.io/nvidia/mxnet:19.07-py3 +COPY . /workspace/rn50 +WORKDIR /workspace/rn50 diff --git a/MxNet/Classification/RN50v1.5/LICENSE b/MxNet/Classification/RN50v1.5/LICENSE index 261eeb9e..d6456956 100644 --- a/MxNet/Classification/RN50v1.5/LICENSE +++ b/MxNet/Classification/RN50v1.5/LICENSE @@ -1,3 +1,4 @@ + Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ diff --git a/MxNet/Classification/RN50v1.5/README.md b/MxNet/Classification/RN50v1.5/README.md index 9198a0ff..ff131bfc 100644 --- a/MxNet/Classification/RN50v1.5/README.md +++ b/MxNet/Classification/RN50v1.5/README.md @@ -1,6 +1,46 @@ -# ResNet50 v1.5 For MXNet +# ResNet50 v1.5 for MXNet -## The model +This repository provides a script and recipe to train the ResNet50 v1.5 model to achieve state of the art accuracy, and is tested and maintained by NVIDIA. + +## Table Of Contents +- [Model overview](#model-overview) + * [Default configuration](#default-configuration) + * [Feature support matrix](#feature-support-matrix) + * [Features](#features) + * [Mixed precision training](#mixed-precision-training) + * [Enabling mixed precision](#enabling-mixed-precision) +- [Setup](#setup) + * [Requirements](#requirements) +- [Quick Start Guide](#quick-start-guide) +- [Advanced](#advanced) + * [Scripts and sample code](#scripts-and-sample-code) + * [Parameters](#parameters) + * [Command-line options](#command-line-options) + * [Getting the data](#getting-the-data) + * [Dataset guidelines](#dataset-guidelines) + * [Multi-dataset](#multi-dataset) + * [Training process](#training-process) + * [Inference process](#inference-process) +- [Performance](#performance) + * [Benchmarking](#benchmarking) + * [Training performance benchmark](#training-performance-benchmark) + * [Inference performance benchmark](#inference-performance-benchmark) + * [Results](#results) + * [Training accuracy results](#training-accuracy-results) + * [Training accuracy: NVIDIA DGX-1 (8x V100 16G)](#training-accuracy-nvidia-dgx-1-(8x-v100-16G)) + * [Training stability test](#training-stability-test) + * [Training performance results](#training-performance-results) + * [Training performance: NVIDIA DGX-1 (8x V100 16G)](#training-performance-nvidia-dgx-1-(8x-v100-16G)) + * [Training performance: NVIDIA DGX-2 (16x V100 32G)](#training-performance-nvidia-dgx-2-(16x-v100-32G)) + * [Inference performance results](#inference-performance-results) + * [Inference performance: NVIDIA DGX-1 (8x V100 16G)](#inference-performance-nvidia-dgx-1-(8x-v100-16G)) + * [Inference performance: NVIDIA T4](#inference-performance-nvidia-t4) +- [Release notes](#release-notes) + * [Changelog](#changelog) + * [Known issues](#known-issues) + + +## Model overview The ResNet50 v1.5 model is a modified version of the [original ResNet50 v1 model](https://arxiv.org/abs/1512.03385). The difference between v1 and v1.5 is in the bottleneck blocks which require @@ -9,96 +49,448 @@ v1.5 has stride = 2 in the 3x3 convolution This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec). -## Training procedure +This model is trained with mixed precision using Tensor Cores on NVIDIA Volta and Turing GPUs. Therefore, researchers can get results 3.5x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. -### Optimizer +### Default configuration -This model trains for 90 epochs, with the standard ResNet v1.5 setup: +**Optimizer:** -* SGD with momentum (0.9) +* SGD with momentum (0.875) +* Learning rate = 0.256 for 256 batch size, for other batch sizes we lineary scale the learning rate. +* Learning rate schedule -- we use cosine LR schedule +* Linear warmup of the learning rate during first 5 epochs according to [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677). +* Weight decay: 3.0517578125e-05 (1/32768). +* We do not apply WD on Batch Norm trainable parameters (gamma/bias) +* Label Smoothing: 0.1 +* We train for: + * 50 Epochs -> configuration that reaches 75.9% top1 accuracy + * 90 Epochs -> 90 epochs is a standard for ResNet50 + * 250 Epochs -> best possible accuracy. For 250 epoch training we also use [MixUp regularization](https://arxiv.org/pdf/1710.09412.pdf). -* Learning rate = 0.1 for 256 batch size, for other batch sizes we linearly -scale the learning rate. +**Data augmentation:** -* Learning rate decay - multiply by 0.1 after 30, 60, and 80 epochs +This model uses the following data augmentation: -* Linear warmup of the learning rate during first 5 epochs -according to [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677). - -* Weight decay: 1e-4 - -### Data Augmentation - -During training, we perform the following augmentation techniques: +For training: * Normalization * Random resized crop to 224x224 -* Scale from 5% to 100% +* Scale from 8% to 100% * Aspect ratio from 3/4 to 4/3 * Random horizontal flip -During inference, we perform the following augmentation techniques: +For inference: * Normalization * Scale to 256x256 * Center crop to 224x224 -See `data.py` for more info. +### Feature support matrix -# Setup - -## Requirements - -Ensure your environment meets the following requirements: - -* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) -* [MXNet 18.12-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia%2Fmxnet) or newer -* [NVIDIA-DALI 0.5.0](https://github.com/NVIDIA/DALI) -- included in the MXNet container -* [Python 3.5](https://www.python.org) -- included in the MXNet container -* [CUDA 10](https://developer.nvidia.com/cuda-toolkit) -- included in the MXNet container -* [cuDNN 7.4.1](https://developer.nvidia.com/cudnn) -- included in the the MXNet container -* (optional) NVIDIA Volta or Turing GPU (see section below) -- for best training performance using FP16 - -For more information about how to get started with NGC containers, see the -following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation: -* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html) -* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry) -* [Running MXNet](https://docs.nvidia.com/deeplearning/dgx/mxnet-release-notes/running.html#running) - -## Training using mixed precision with Tensor Cores - -### Hardware requirements -Training with mixed precision on NVIDIA Tensor Cores, requires an NVIDIA Volta-based or Turing-based GPU. +| **Feature** | **ResNet50 MXNet** | +|:---:|:--------:| +|[DALI](https://docs.nvidia.com/deeplearning/sdk/dali-release-notes/index.html)|yes| +|Horovod Multi-GPU|yes| -### Software changes +#### Features +The following features are supported by this model. -For information about how to train using mixed precision, see the -[Mixed Precision Training paper](https://arxiv.org/abs/1710.03740) -and -[Training With Mixed Precision documentation](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). +NVIDIA DALI - NVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks, and an execution engine, to accelerate the pre-processing of the input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be easily integrated into different deep learning training and inference applications. + +Horovod Multi-GPU - Horovod is a distributed training framework for TensorFlow, Keras, PyTorch and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the [Horovod: Official repository](https://github.com/horovod/horovod). -# Quick start guide -## Docker +### Mixed precision training -To run docker MXNet container, run: +Mixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architecture, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps: +1. Porting the model to use the FP16 data type where appropriate. +2. Adding loss scaling to preserve small gradient values. -`nvidia-docker run --rm -it --ipc=host -v :/workspace/resnet50 -v :/data/imagenet/train-val-recordio-passthrough nvcr.io/nvidia/mxnet:18.12-py3` +The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in [CUDA 8](https://devblogs.nvidia.com/parallelforall/tag/fp16/) in the NVIDIA Deep Learning SDK. -It will also automatically start downloading the MXNet container if you haven't downloaded it yet. You can also download it manually by running: +For information about: +- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation. +- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog. -`nvidia-docker pull nvcr.io/nvidia/mxnet:18.12-py3` -If you haven't prepared dataset yet (see section below), download raw ImageNet dataset (see section below), and run: -`nvidia-docker run --rm -it --ipc=host -v :/workspace/resnet50 -v :/data/imagenet/train-val-recordio-passthrough -v :/data/imagenet/raw nvcr.io/nvidia/mxnet:18.12-py3` +#### Enabling mixed precision +Using the Gluon API, ensure you perform the following steps to convert a model that supports computation with float16. -and follow step from Prepare Dataset section. +1. Cast Gluon Block‘s parameters and expected input type to float16 by calling the cast method of the Block representing the network. + ```python + net = net.cast('float16') + ``` -## Prepare Dataset +2. Ensure the data input to the network is of float16 type. If your DataLoader or Iterator produces output in another datatype, then you have to cast your data. There are different ways you can do this. The easiest way is to use the `astype` method of NDArrays. + ```python + data = data.astype('float16', copy=False) + ``` + +3. If you are using images and DataLoader, you can also use a Cast transform. It is preferable to use multi_precision mode of optimizer when training in float16. This mode of optimizer maintains a master copy of the weights in float32 even when the training (forward and backward pass) is in float16. This helps increase precision of the weight updates and can lead to faster convergence in some scenarios. + ```python + optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01) + ``` + +## Setup +The following section lists the requirements in order to start training the ResNet50 v1.5 model. + +### Requirements + +This repository contains Dockerfile which extends the MXNet NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components: +- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) +- [MXNet 19.07-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia%2Fmxnet) +- [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) or [Turing](https://www.nvidia.com/en-us/geforce/turing/) based GPU + +For more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation: +- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html) +- [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#accessing_registry) +- [Running MXNet](https://docs.nvidia.com/deeplearning/dgx/mxnet-release-notes/running.html#running) + +For those unable to use the MXNet NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html). + + +## Quick Start Guide + +**1. Clone the repository.** +```bash +git clone https://github.com/NVIDIA/DeepLearningExamples +cd DeepLearningExamples/MxNet/Classification/RN50v1.5 +``` + +**2. Build the ResNet50 MXNet NGC container.** +After Docker is setup, you can build the ResNet50 image with: +```bash +docker build . -t nvidia_rn50_mx +``` + +**3. Start an interactive session in the NGC container to run preprocessing/training/inference.** +```bash +nvidia-docker run --rm -it --ipc=host :/data/imagenet/train-val-recordio-passthrough nvidia_rn50_mx +``` + +**4. Download and preprocess the data.** +* Download the images from http://image-net.org/download-images. +* Extract the training and validation data: + ```bash + mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train + tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar + find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done + cd .. + ``` + +**5. Extract the validation data and move the images to subfolders.** +```bash +mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar +wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash +``` + +**6. Preprocess the dataset.** +```bash +./scripts/prepare_imagenet.sh +``` + +**7. Start training.** +```bash +./runner -n -b +``` + +**8. Start validation/evaluation.** +```bash +./runner -n -b --load --mode val +``` + +**9. Start inference/predictions.** +```bash +./runner --load --mode pred --data-pred +``` + + +## Advanced + +The following sections provide greater details of the dataset, running training and inference, and the training results. + +### Scripts and sample code + +In the root directory, the most important files are: +* `runner`: A wrapper on the `train.py` script which is the main executable script for training/validation/predicting +* `benchmark.py`: A script for benchmarking +* `Dockerfile`: Container to build the container +* `fit.py`: A file containing most of the training and validation logic +* `data.py`: Data loading and preprocessing code +* `dali.py`: Data loading and preprocessing code using DALI +* `models.py`: The model architecture +* `report.py`: A file containing JSON report structure and description of fields + +In the `scripts` directory, the most important files are: +* `prepare_imagenet.sh`: A script that converts raw dataset format to RecordIO format + + + + +### Parameters + +The complete list of available parameters contains: +``` +Model: + --arch {resnetv1,resnetv15,resnextv1,resnextv15,xception} + model architecture (default: resnetv15) + --num-layers NUM_LAYERS + number of layers in the neural network, required by + some networks such as resnet (default: 50) + --num-groups NUM_GROUPS + number of groups for grouped convolutions, required by + some networks such as resnext (default: 32) + --num-classes NUM_CLASSES + the number of classes (default: 1000) + --batchnorm-eps BATCHNORM_EPS + the amount added to the batchnorm variance to prevent + output explosion. (default: 1e-05) + --batchnorm-mom BATCHNORM_MOM + the leaky-integrator factor controling the batchnorm + mean and variance. (default: 0.9) + --fuse-bn-relu FUSE_BN_RELU + have batchnorm kernel perform activation relu + (default: 0) + --fuse-bn-add-relu FUSE_BN_ADD_RELU + have batchnorm kernel perform add followed by + activation relu (default: 0) + +Training: + --mode {train_val,train,val,pred} + mode (default: train_val) + --seed SEED random seed (default: None) + -n NGPUS, --ngpus NGPUS + number of GPUs to use (default: 1) + --kv-store {device,horovod} + key-value store type (default: horovod) + --dtype {float32,float16} + Precision (default: float16) + --amp If enabled, turn on AMP (Automatic Mixed Precision) + (default: False) + -b BATCH_SIZE, --batch-size BATCH_SIZE + batch size per GPU (default: 192) + -e NUM_EPOCHS, --num-epochs NUM_EPOCHS + number of epochs (default: 90) + -l LR, --lr LR learning rate; IMPORTANT: true learning rate will be + calculated as `lr * batch_size / 256` (default: 0.256) + --lr-schedule {multistep,cosine} + learning rate schedule (default: cosine) + --lr-factor LR_FACTOR + the ratio to reduce lr on each step (default: 0.256) + --lr-steps LR_STEPS the epochs to reduce the lr, e.g. 30,60 (default: []) + --warmup-epochs WARMUP_EPOCHS + the epochs to ramp-up lr to scaled large-batch value + (default: 5) + --optimizer OPTIMIZER + the optimizer type (default: sgd) + --mom MOM momentum for sgd (default: 0.875) + --wd WD weight decay for sgd (default: 3.0517578125e-05) + --label-smoothing LABEL_SMOOTHING + label smoothing factor (default: 0.1) + --mixup MIXUP alpha parameter for mixup (if 0 then mixup is not + applied) (default: 0) + --disp-batches DISP_BATCHES + show progress for every n batches (default: 20) + --model-prefix MODEL_PREFIX + model checkpoint prefix (default: model) + --save-frequency SAVE_FREQUENCY + frequency of saving model in epochs (--model-prefix + must be specified). If -1 then save only best model. + If 0 then do not save anything. (default: -1) + --begin-epoch BEGIN_EPOCH + start the model from an epoch (default: 0) + --load LOAD checkpoint to load (default: None) + --test-io test reading speed without training (default: False) + --test-io-mode {train,val} + data to test (default: train) + --log LOG file where to save the log from the experiment + (default: log.log) + --report REPORT file where to save report (default: report.json) + --no-metrics do not calculate evaluation metrics (for benchmarking) + (default: False) + --benchmark-iters BENCHMARK_ITERS + run only benchmark-iters iterations from each epoch + (default: None) + +Data: + --data-root DATA_ROOT + Directory with RecordIO data files (default: + /data/imagenet/train-val-recordio-passthrough) + --data-backend {dali,mxnet,synthetic} + data backend (default: dali) + --image-shape IMAGE_SHAPE + the image shape feed into the network (default: [3, + 224, 224]) + --rgb-mean RGB_MEAN a tuple of size 3 for the mean rgb (default: [123.68, + 116.779, 103.939]) + --rgb-std RGB_STD a tuple of size 3 for the std rgb (default: [58.393, + 57.12, 57.375]) + --input-layout {NCHW,NHWC} + the layout of the input data (default: NCHW) + --conv-layout {NCHW,NHWC} + the layout of the data assumed by the conv operation + (default: NCHW) + --batchnorm-layout {NCHW,NHWC} + the layout of the data assumed by the batchnorm + operation (default: NCHW) + --pooling-layout {NCHW,NHWC} + the layout of the data assumed by the pooling + operation (default: NCHW) + --num-examples NUM_EXAMPLES + the number of training examples (doesn't work with + mxnet data backend) (default: 1281167) + --data-val-resize DATA_VAL_RESIZE + base length of shorter edge for validation dataset + (default: 256) + +DALI data backend: + entire group applies only to dali data backend + + --dali-separ-val each process will perform independent validation on + whole val-set (default: False) + --dali-threads DALI_THREADS + number of threadsper GPU for DALI (default: 3) + --dali-validation-threads DALI_VALIDATION_THREADS + number of threadsper GPU for DALI for validation + (default: 10) + --dali-prefetch-queue DALI_PREFETCH_QUEUE + DALI prefetch queue depth (default: 2) + --dali-nvjpeg-memory-padding DALI_NVJPEG_MEMORY_PADDING + Memory padding value for nvJPEG (in MB) (default: 64) + +MXNet data backend: + entire group applies only to mxnet data backend + + --data-mxnet-threads DATA_MXNET_THREADS + number of threads for data decoding for mxnet data + backend (default: 40) + --random-crop RANDOM_CROP + if or not randomly crop the image (default: 0) + --random-mirror RANDOM_MIRROR + if or not randomly flip horizontally (default: 1) + --max-random-h MAX_RANDOM_H + max change of hue, whose range is [0, 180] (default: + 0) + --max-random-s MAX_RANDOM_S + max change of saturation, whose range is [0, 255] + (default: 0) + --max-random-l MAX_RANDOM_L + max change of intensity, whose range is [0, 255] + (default: 0) + --min-random-aspect-ratio MIN_RANDOM_ASPECT_RATIO + min value of aspect ratio, whose value is either None + or a positive value. (default: 0.75) + --max-random-aspect-ratio MAX_RANDOM_ASPECT_RATIO + max value of aspect ratio. If min_random_aspect_ratio + is None, the aspect ratio range is + [1-max_random_aspect_ratio, + 1+max_random_aspect_ratio], otherwise it is + [min_random_aspect_ratio, max_random_aspect_ratio]. + (default: 1.33) + --max-random-rotate-angle MAX_RANDOM_ROTATE_ANGLE + max angle to rotate, whose range is [0, 360] (default: + 0) + --max-random-shear-ratio MAX_RANDOM_SHEAR_RATIO + max ratio to shear, whose range is [0, 1] (default: 0) + --max-random-scale MAX_RANDOM_SCALE + max ratio to scale (default: 1) + --min-random-scale MIN_RANDOM_SCALE + min ratio to scale, should >= img_size/input_shape. + otherwise use --pad-size (default: 1) + --max-random-area MAX_RANDOM_AREA + max area to crop in random resized crop, whose range + is [0, 1] (default: 1) + --min-random-area MIN_RANDOM_AREA + min area to crop in random resized crop, whose range + is [0, 1] (default: 0.05) + --min-crop-size MIN_CROP_SIZE + Crop both width and height into a random size in + [min_crop_size, max_crop_size] (default: -1) + --max-crop-size MAX_CROP_SIZE + Crop both width and height into a random size in + [min_crop_size, max_crop_size] (default: -1) + --brightness BRIGHTNESS + brightness jittering, whose range is [0, 1] (default: + 0) + --contrast CONTRAST contrast jittering, whose range is [0, 1] (default: 0) + --saturation SATURATION + saturation jittering, whose range is [0, 1] (default: + 0) + --pca-noise PCA_NOISE + pca noise, whose range is [0, 1] (default: 0) + --random-resized-crop RANDOM_RESIZED_CROP + whether to use random resized crop (default: 1) +``` + +### Command-line options + +To see the full list of available options and their descriptions, use the `-h` or `--help` command line option: `./runner --help` and `python train.py --help`. `./runner` acts as a wrapper on `train.py` and all additional flags will be passed to `train.py`. + +`./runner` command-line options: +``` +usage: runner [-h] [-n NGPUS] [-b BATCH_SIZE] [-e NUM_EPOCHS] [-l LR] + [--data-root DATA_ROOT] [--dtype {float32,float16}] + [--kv-store {device,horovod}] + [--data-backend {dali,mxnet,synthetic}] +``` + +`train.py` command-line options: +``` +usage: train.py [-h] + [--arch {resnetv1,resnetv15,resnextv1,resnextv15,xception}] + [--num-layers NUM_LAYERS] [--num-groups NUM_GROUPS] + [--num-classes NUM_CLASSES] [--batchnorm-eps BATCHNORM_EPS] + [--batchnorm-mom BATCHNORM_MOM] [--fuse-bn-relu FUSE_BN_RELU] + [--fuse-bn-add-relu FUSE_BN_ADD_RELU] + [--mode {train_val,train,val,pred}] [--seed SEED] + [--gpus GPUS] [--kv-store {device,horovod}] + [--dtype {float32,float16}] [--amp] [--batch-size BATCH_SIZE] + [--num-epochs NUM_EPOCHS] [--lr LR] + [--lr-schedule {multistep,cosine}] [--lr-factor LR_FACTOR] + [--lr-steps LR_STEPS] [--warmup-epochs WARMUP_EPOCHS] + [--optimizer OPTIMIZER] [--mom MOM] [--wd WD] + [--label-smoothing LABEL_SMOOTHING] [--mixup MIXUP] + [--disp-batches DISP_BATCHES] [--model-prefix MODEL_PREFIX] + [--save-frequency SAVE_FREQUENCY] [--begin-epoch BEGIN_EPOCH] + [--load LOAD] [--test-io] [--test-io-mode {train,val}] + [--log LOG] [--report REPORT] [--no-metrics] + [--benchmark-iters BENCHMARK_ITERS] [--data-train DATA_TRAIN] + [--data-train-idx DATA_TRAIN_IDX] [--data-val DATA_VAL] + [--data-val-idx DATA_VAL_IDX] [--data-pred DATA_PRED] + [--data-backend {dali,mxnet,synthetic}] + [--image-shape IMAGE_SHAPE] [--rgb-mean RGB_MEAN] + [--rgb-std RGB_STD] [--input-layout {NCHW,NHWC}] + [--conv-layout {NCHW,NHWC}] [--batchnorm-layout {NCHW,NHWC}] + [--pooling-layout {NCHW,NHWC}] [--num-examples NUM_EXAMPLES] + [--data-val-resize DATA_VAL_RESIZE] [--dali-separ-val] + [--dali-threads DALI_THREADS] + [--dali-validation-threads DALI_VALIDATION_THREADS] + [--dali-prefetch-queue DALI_PREFETCH_QUEUE] + [--dali-nvjpeg-memory-padding DALI_NVJPEG_MEMORY_PADDING] + [--data-mxnet-threads DATA_MXNET_THREADS] + [--random-crop RANDOM_CROP] [--random-mirror RANDOM_MIRROR] + [--max-random-h MAX_RANDOM_H] [--max-random-s MAX_RANDOM_S] + [--max-random-l MAX_RANDOM_L] + [--min-random-aspect-ratio MIN_RANDOM_ASPECT_RATIO] + [--max-random-aspect-ratio MAX_RANDOM_ASPECT_RATIO] + [--max-random-rotate-angle MAX_RANDOM_ROTATE_ANGLE] + [--max-random-shear-ratio MAX_RANDOM_SHEAR_RATIO] + [--max-random-scale MAX_RANDOM_SCALE] + [--min-random-scale MIN_RANDOM_SCALE] + [--max-random-area MAX_RANDOM_AREA] + [--min-random-area MIN_RANDOM_AREA] + [--min-crop-size MIN_CROP_SIZE] + [--max-crop-size MAX_CROP_SIZE] [--brightness BRIGHTNESS] + [--contrast CONTRAST] [--saturation SATURATION] + [--pca-noise PCA_NOISE] + [--random-resized-crop RANDOM_RESIZED_CROP] +``` + +### Getting the data The MXNet ResNet50 v1.5 script operates on ImageNet 1k, a widely popular image classification dataset from ILSVRC challenge. -You can download the images from http://image-net.org/download-images +You can download the images from http://image-net.org/download-images. The recommended data format is [RecordIO](http://mxnet.io/architecture/note_data_loading.html), which @@ -106,7 +498,7 @@ concatenates multiple examples into seekable binary files for better read efficiency. MXNet provides a tool called `im2rec.py` located in the `/opt/mxnet/tools/` directory. The tool converts individual images into `.rec` files. -To prepare RecordIO file containing ImageNet data, we first need to create .lst files +To prepare a RecordIO file containing ImageNet data, we first need to create `.lst` files which consist of the labels and image paths. We assume that the original images were downloaded to `/data/imagenet/raw/train-jpeg` and `/data/imagenet/raw/val-jpeg`. @@ -115,121 +507,216 @@ python /opt/mxnet/tools/im2rec.py --list --recursive train /data/imagenet/raw/tr python /opt/mxnet/tools/im2rec.py --list --recursive val /data/imagenet/raw/val-jpeg ``` -Then we generate the `.rec` (RecordIO files with data) and `.idx` (indexes required by DALI +Next, we generate the `.rec` (RecordIO files with data) and `.idx` (indexes required by DALI to speed up data loading) files. To obtain the best training accuracy -we do not preprocess the images when creating RecordIO file. +we do not preprocess the images when creating the RecordIO file. ```bash python /opt/mxnet/tools/im2rec.py --pass-through --num-thread 40 train /data/imagenet/raw/train-jpeg python /opt/mxnet/tools/im2rec.py --pass-through --num-thread 40 val /data/imagenet/raw/val-jpeg ``` -## Running training +#### Dataset guidelines +The process of loading, normalizing and augmenting the data contained in the dataset can be found in the `data.py` and `dali.py` files. -To run training for a standard configuration (1/4/8 GPUs, FP16/FP32), -run one of the scripts in the `./examples` directory -called `./examples/RN50_{FP16, FP32}_{1, 4, 8}GPU.sh`. -By default the training scripts run the validation and save checkpoint after each epoch. -Checkpoints will be stored in `model-symbol.json` and `model-.params` files. +The data is read from RecordIO format, which concatenates multiple examples into seekable binary files for better read efficiency. -If imagenet is mounted in the `/data/imagenet/train-val-recordio-passthrough` directory, you don't have to specify `--data-root` flag. +Data augmentation techniques are described in the [Default configuration](#default-configuration) section. -To run a non standard configuration use: +#### Multi-dataset -`./runner -n -b --data-root --dtype --model-prefix ` +In most cases, to train a model on a different dataset, no changes in the code are required, but the dataset has to be converted into RecordIO format. -Checkpoints will be stored in `-symbol.json` and `-.params` files. -To generate JSON report with performance and accuracy stats, use `--report ` flag (see `report.py` for info about JSON report file structure). -Use `./runner -h` and `python ./train.py -h` to obtain the list of available options. - -## Running inference - -To run inference on a checkpointed model run: -* For FP16 - `./examples/SCORE_FP16.sh ` -* For FP32 - `./examples/SCORE_FP32.sh ` +To convert a custom dataset, follow the steps from [Getting the data](#getting-the-data) section, and refer to the `scripts/prepare_dataset.py` script. -## Benchmark scripts +### Training process + +To start training, run: +`./runner -n -b --data-root --dtype ` + +By default the training script runs the validation after each epoch: +* the best checkpoint will be stored in the `model_best.params` file in the working directory +* the log from training will be saved in the `log.log` file in the working directory +* the JSON report with statistics will be saved in the `report.json` file in the working directory + +If ImageNet is mounted in the `/data/imagenet/train-val-recordio-passthrough` directory, you don't have to specify the `--data-root` flag. + +### Inference process + +To start validation, run: +`./runner -n -b --data-root --dtype --mode val` + +By default: +* the log from validation will be saved in the `log.log` file in the working directory +* the JSON report with statistics will be saved in the `report.json` file in the working directory + +## Performance + +### Benchmarking To benchmark training and inference, run: +`python benchmark.py -n -b --data-root --dtype -o ` -`python benchmark.py -n -b --data-root --dtype -o ` - -To control benchmark length per epoch, use `-i` flag (defaults to 100 iterations). -To control number of epochs, use `-e` flag. -To control number of warmup epochs (epochs which are not taken into account), use `-w` flag. -To limit length of dataset, use `--num-examples` flag. -To benchmark only inference, use `--only-inference` flag. +To control the benchmark length per epoch, use the `-i` flag (defaults to 100 iterations). +To control the number of epochs, use the `-e` flag. +To control the number of warmup epochs (epochs which are not taken into account), use the `-w` flag. +To limit the length of the dataset, use the `--num-examples` flag. By default, the same parameters as in `./runner` will be used. Additional flags will be passed to `./runner`. +#### Training performance benchmark +To benchmark only training, use the `--mode train` flag. -## Training accuracy results - -The following results were obtained by running the `./examples/RN50_{FP16, FP32}_{1, 4, 8}GPU.sh` scripts in the -mxnet-18.12-py3 Docker container on NVIDIA DGX-1 with 8 V100 16G GPUs. - -| **number of GPUs** | **FP16 top1** | **FP16 training time** | **FP32 top1** | **FP32 training time** | -|:------------------:|:-------------:|:----------------------:|:-------------:|:----------------------:| -| 1 | 76.424 | 22.9h | 76.462 | 82.0h | -| 4 | 76.328 | 6.2h | 76.448 | 21.1h | -| 8 | 76.490 | 3.3h | 76.668 | 11.1h | - -Here are example graphs of FP32 and FP16 training on 8 GPU configuration: - -![TrainingLoss](./img/training_loss.png) - -![TrainingAccuracy](./img/training_accuracy.png) - -![ValidationAccuracy](./img/validation_accuracy.png) +#### Inference performance benchmark +To benchmark only inference, use the `--mode val` flag. -## Training performance results +### Results + +The following sections provide details on how we achieved our performance and accuracy in training and inference. + +#### Training accuracy results + +##### Training accuracy: NVIDIA DGX-1 (8x V100 16G) + +90 epochs configuration +Our results were obtained by running the `./runner -n -b 96 --dtype float32` script for FP32 and the `./runner -n -b 192` script for mixed precision in the in the mxnet-19.07-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. + on NVIDIA DGX-1 with (8x V100 16G) GPUs. + +| **GPUs** | **Accuracy - mixed precision** | **Accuracy - FP32** | **Time to train - mixed precision** | **Time to train - FP32** | **Time to train - speedup** | +|:---:|:---:|:---:|:---:|:---:|:---:| +|1|77.208|77.160|24.2|84.5|3.49| +|4|77.296|77.280|6.0|21.4|3.59| +|8|77.308|77.292|3.0|10.7|3.54| + +##### Training stability test + +Our results were obtained by running the following commands 8 times with different seeds. + +* For 50 epochs + * `./runner -n 8 -b 96 --dtype float32 --num-epochs 50` for FP32 + * `./runner -n 8 -b 192 --num-epochs 50` for mixed precision +* For 90 epochs + * `./runner -n 8 -b 96 --dtype float32` for FP32 + * `./runner -n 8 -b 192` for mixed precision +* For 250 epochs + * `./runner -n 8 -b 96 --dtype float32 --num-epochs 250 --mixup 0.2` for FP32 + * `./runner -n 8 -b 192 --num-epochs 250 --mixup 0.2` for mixed precision + +| **# of epochs** | **mixed precision avg top1** | **FP32 avg top1** | **mixed precision standard deviation** | **FP32 standard deviation** | **mixed precision minimum top1** | **FP32 minimum top1** | **mixed precision maximum top1** | **FP32 maximum top1** | +|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +|50|76.156|76.185|0.118|0.082|76.010|76.062|76.370|76.304| +|90|77.105|77.224|0.097|0.060|76.982|77.134|77.308|77.292| +|250|78.317|78.400|0.073|0.102|78.202|78.316|78.432|78.570| + + +Plots for 250 epoch configuration +Here are example graphs of FP32 and mixed precision training on 8 GPU 250 epochs configuration: + +![TrainingLoss](./img/dgx1-16g_250e_training_loss.png) + +![TrainingAccuracy](./img/dgx1-16g_250e_validation_top1.png) + +![ValidationAccuracy](./img/dgx1-16g_250e_validation_top5.png) + + +#### Training performance results + +##### Training performance: NVIDIA DGX-1 (8x V100 16G) + +The following results were obtained by running the +`python benchmark.py -n 1,2,4,8 -b 192 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for mixed precision and the +`python benchmark.py -n 1,2,4,8 -b 96 --dtype float32 -o benchmark_report_fp32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for FP32 in the mxnet-19.07-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. -The following results were obtained by running -`python benchmark.py -n 1,4,8 -b 208 --dtype float16 -o benchmark_report_fp16.json --data-root -i 100 -e 12 -w 4 --num-examples 25600` for FP16, and -`python benchmark.py -n 1,4,8 -b 96 --dtype float32 -o benchmark_report_fp32.json --data-root -i 100 -e 12 -w 4 --num-examples 12800` for FP32 -in the mxnet-18.12-py3 Docker container on NVIDIA DGX-1 with V100 16G GPUs. Training performance reported as Total IPS (data + compute time taken into account). Weak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU. -| **number of GPUs** | **FP16 img/s** | **FP32 img/s** | **FP16 speedup** | **FP16 weak scaling** | **FP32 weak scaling** | -|:------------------:|:--------------:|:--------------:|:----------------:|:---------------------:|:---------------------:| -| 1 | 1442.6 | 400.2 | 3.60 | 1.00 | 1.00 | -| 4 | 5391.8 | 1558.6 | 3.46 | 3.74 | 3.89 | -| 8 | 10263.2 | 2957.4 | 3.47 | 7.11 | 7.39 | +| **GPUs** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** | +|:---:|:---:|:---:|:---:|:---:|:---:| +|1|1427|385|3.71|1.00|1.00| +|2|2820|768|3.67|1.98|2.00| +|4|5560|1513|3.68|3.90|3.93| +|8|10931|3023|3.62|7.66|7.86| +##### Training performance: NVIDIA DGX-2 (16x V100 32G) -## Inference performance results +The following results were obtained by running the +`python benchmark.py -n 1,4,8,16 -b 256 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for mixed precision and the +`python benchmark.py -n 1,4,8,16 -b 128 --dtype float32 -o benchmark_report_fp32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for FP32 in the mxnet-19.07-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. + +Training performance reported as Total IPS (data + compute time taken into account). +Weak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU. + +| **GPUs** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** | +|:---:|:---:|:---:|:---:|:---:|:---:| +|1|1438|409|3.52|1.00|1.00| +|2|2868|817|3.51|1.99|2.00| +|4|5624|1617|3.48|3.91|3.96| +|8|11174|3214|3.48|7.77|7.86| +|16|20530|6356|3.23|14.28|15.54| + +#### Inference performance results + +##### Inference performance: NVIDIA DGX-1 (8x V100 16G) + +The following results were obtained by running the +`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val` script for mixed precision and the +`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_fp32.json -i 500 -e 3 -w 1 --mode val` script for FP32 in the mxnet-19.07-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. -The following results were obtained by running -`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,96,128,192,208 --dtype float16 -o inferbenchmark_report_fp16.json --data-root -i 200 -e 12 -w 4 --only-inference` for FP16, and -`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,96 --dtype float32 -o inferbenchmark_report_fp32.json --data-root -i 200 -e 12 -w 4 --only-inference` for FP32 -in the mxnet-18.12-py3 Docker container on NVIDIA DGX-1 using one V100 16G GPU. Inference performance reported as Total IPS (data + compute time taken into account). -| **batch size** | **FP16 img/s** | **FP32 img/s** | -|:--------------:|:--------------:|:--------------:| -| 1 | 314 | 252 | -| 2 | 555 | 393 | -| 4 | 1024 | 601 | -| 8 | 1642 | 824 | -| 16 | 2144 | 1028 | -| 32 | 2954 | 1138 | -| 64 | 3428 | 1236 | -| 96 | 3546 | 1282 | -| 128 | 3690 | | -| 192 | 3828 | | -| 208 | 3832 | | +Reported mixed precision speedups are relative to FP32 numbers for corresponding configuration. +| **Batch size** | **Throughput (img/sec) - mixed precision** | **Throughput - speedup** | **Avg latency (ms) - mixed precision** | **Avg latency - speedup** | **50% latency (ms) - mixed precision** | **50% latency - speedup** | **90% latency (ms) - mixed precision** | **90% latency - speedup** | **95% latency (ms) - mixed precision** | **95% latency - speedup** | **99% latency (ms) - mixed precision** | **99% latency - speedup** | **100% latency (ms) - mixed precision** | **100% latency - speedup** | +|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +| 1 | 397 | 1.65 | 2.5 | 1.65 | 2.5 | 1.67 | 2.7 | 1.59 | 2.8 | 1.56 | 3.2 | 1.51 | 15.8 | 0.84 | +| 2 | 732 | 1.81 | 2.7 | 1.81 | 2.6 | 1.88 | 3.0 | 1.67 | 3.3 | 1.52 | 4.9 | 1.10 | 18.8 | 0.83 | +| 4 | 1269 | 2.08 | 3.2 | 2.08 | 3.0 | 2.21 | 3.5 | 1.92 | 4.0 | 1.72 | 7.5 | 0.97 | 14.5 | 0.54 | +| 8 | 2012 | 2.53 | 4.0 | 2.53 | 3.9 | 2.59 | 4.2 | 2.45 | 4.4 | 2.37 | 8.3 | 1.29 | 15.3 | 0.72 | +| 16 | 2667 | 2.64 | 6.0 | 2.64 | 5.9 | 2.66 | 6.3 | 2.54 | 6.4 | 2.52 | 8.3 | 2.02 | 16.9 | 1.05 | +| 32 | 3240 | 2.86 | 9.9 | 2.86 | 9.8 | 2.87 | 10.3 | 2.79 | 10.4 | 2.76 | 11.5 | 2.53 | 28.4 | 1.12 | +| 64 | 3776 | 3.10 | 17.0 | 3.10 | 17.0 | 3.09 | 17.5 | 3.03 | 17.7 | 3.01 | 18.1 | 3.01 | 18.7 | 2.99 | +| 128 | 3734 | 3.02 | 34.3 | 3.02 | 33.8 | 3.05 | 35.5 | 2.93 | 36.3 | 2.88 | 42.4 | 2.79 | 51.7 | 2.38 | +| 192 | 3641 | 2.90 | 52.7 | 2.90 | 52.4 | 2.90 | 55.2 | 2.77 | 56.2 | 2.74 | 65.4 | 2.76 | 77.1 | 2.41 | +| 256 | 3463 | 2.73 | 73.9 | 2.73 | 72.8 | 2.75 | 77.3 | 2.61 | 79.9 | 2.54 | 100.8 | 2.39 | 104.1 | 2.35 | -# Changelog +##### Inference performance: NVIDIA T4 -1. Dec 19, 2018 +The following results were obtained by running the +`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val` script for mixed precision and the +`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_fp32.json -i 500 -e 3 -w 1 --mode val` script for FP32 in the mxnet-19.07-py3 NGC container on an NVIDIA T4 GPU. + +Inference performance reported as Total IPS (data + compute time taken into account). + +Reported mixed precision speedups are relative to FP32 numbers for corresponding configuration. + +| **Batch size** | **Throughput (img/sec) - mixed precision** | **Throughput - speedup** | **Avg latency (ms) - mixed precision** | **Avg latency - speedup** | **50% latency (ms) - mixed precision** | **50% latency - speedup** | **90% latency (ms) - mixed precision** | **90% latency - speedup** | **95% latency (ms) - mixed precision** | **95% latency - speedup** | **99% latency (ms) - mixed precision** | **99% latency - speedup** | **100% latency (ms) - mixed precision** | **100% latency - speedup** | +|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +| 1 | 348 | 1.88 | 2.9 | 1.88 | 2.8 | 1.91 | 2.9 | 1.88 | 3.0 | 1.90 | 3.9 | 1.82 | 17.6 | 0.74 | +| 2 | 594 | 2.30 | 3.4 | 2.30 | 3.3 | 2.35 | 3.4 | 2.34 | 3.5 | 2.38 | 5.7 | 1.55 | 20.2 | 0.74 | +| 4 | 858 | 2.93 | 4.7 | 2.93 | 4.6 | 2.97 | 4.9 | 2.86 | 5.0 | 2.81 | 6.0 | 2.46 | 13.7 | 1.12 | +| 8 | 1047 | 3.17 | 7.6 | 3.17 | 7.6 | 3.19 | 7.9 | 3.10 | 8.2 | 3.02 | 9.1 | 2.77 | 15.0 | 1.72 | +| 16 | 1163 | 3.16 | 13.8 | 3.16 | 13.7 | 3.17 | 14.1 | 3.13 | 14.4 | 3.07 | 15.4 | 2.90 | 17.5 | 2.62 | +| 32 | 1225 | 3.22 | 26.1 | 3.22 | 26.1 | 3.22 | 27.0 | 3.15 | 27.3 | 3.12 | 28.3 | 3.05 | 30.5 | 2.89 | +| 64 | 1230 | 3.15 | 52.0 | 3.15 | 51.8 | 3.16 | 52.9 | 3.12 | 53.3 | 3.10 | 54.4 | 3.08 | 58.8 | 2.90 | +| 128 | 1260 | 3.21 | 101.6 | 3.21 | 101.3 | 3.22 | 102.7 | 3.21 | 103.2 | 3.20 | 115.0 | 2.89 | 121.8 | 2.86 | +| 192 | 1252 | 3.20 | 153.3 | 3.20 | 153.1 | 3.20 | 154.7 | 3.19 | 155.5 | 3.21 | 156.9 | 3.20 | 182.3 | 2.81 | +| 256 | 1251 | 3.22 | 204.6 | 3.22 | 204.3 | 3.23 | 206.4 | 3.21 | 207.1 | 3.21 | 209.3 | 3.18 | 241.9 | 2.76 | + +## Release notes + +### Changelog + +1. Dec, 2018 * Initial release (based on https://github.com/apache/incubator-mxnet/tree/master/example/image-classification) +2. June, 2019 + * Code refactor + * Label smoothing + * Cosine LR schedule + * MixUp regularization + * Better configurations -# Known Issues +### Known Issues There are no known issues with this model. diff --git a/MxNet/Classification/RN50v1.5/__init__.py b/MxNet/Classification/RN50v1.5/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/MxNet/Classification/RN50v1.5/benchmark.py b/MxNet/Classification/RN50v1.5/benchmark.py old mode 100644 new mode 100755 index 68471333..1bcc9b1e --- a/MxNet/Classification/RN50v1.5/benchmark.py +++ b/MxNet/Classification/RN50v1.5/benchmark.py @@ -1,3 +1,5 @@ +#!/usr/bin/env python3 + # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -18,14 +20,21 @@ import sys import tempfile import json import os +import traceback +import numpy as np from collections import OrderedDict from subprocess import Popen -parser = argparse.ArgumentParser(description='Benchmark') +def int_list(x): + return list(map(int, x.split(','))) + +parser = argparse.ArgumentParser(description='Benchmark', + formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--executable', default='./runner', help='path to runner') -parser.add_argument('-n', '--ngpus', metavar='N1,[N2,...]', +parser.add_argument('-o', '--output', metavar='OUT', required=True, help="path to benchmark report") +parser.add_argument('-n', '--ngpus', metavar='N1,[N2,...]', type=int_list, required=True, help='numbers of gpus separated by comma') -parser.add_argument('-b', '--batch-sizes', metavar='B1,[B2,...]', +parser.add_argument('-b', '--batch-sizes', metavar='B1,[B2,...]', type=int_list, required=True, help='batch sizes separated by comma') parser.add_argument('-i', '--benchmark-iters', metavar='I', type=int, default=100, help='iterations') @@ -33,57 +42,83 @@ parser.add_argument('-e', '--epochs', metavar='E', type=int, default=1, help='number of epochs') parser.add_argument('-w', '--warmup', metavar='N', type=int, default=0, help='warmup epochs') -parser.add_argument('-o', '--output', metavar='OUT', required=True, help="path to benchmark report") -parser.add_argument('--only-inference', action='store_true', help="benchmark inference only") +parser.add_argument('--timeout', metavar='T', + type=str, default='inf', help='timeout for each run') +parser.add_argument('--mode', metavar='MODE', choices=('train_val', 'train', 'val'), default='train_val', + help="benchmark mode") args, other_args = parser.parse_known_args() -ngpus = list(map(int, args.ngpus.split(','))) -batch_sizes = list(map(int, args.batch_sizes.split(','))) - +latency_percentiles = ['avg', 50, 90, 95, 99, 100] +harmonic_mean_metrics = ['train.total_ips', 'val.total_ips'] res = OrderedDict() res['model'] = '' -res['ngpus'] = ngpus -res['bs'] = batch_sizes -if args.only_inference: - res['metric_keys'] = ['val.total_ips'] -else: - res['metric_keys'] = ['train.total_ips', 'val.total_ips'] +res['ngpus'] = args.ngpus +res['bs'] = args.batch_sizes +res['metric_keys'] = [] +if args.mode == 'train' or args.mode == 'train_val': + res['metric_keys'].append('train.total_ips') + for percentile in latency_percentiles: + res['metric_keys'].append('train.latency_{}'.format(percentile)) +if args.mode == 'val' or args.mode == 'train_val': + res['metric_keys'].append('val.total_ips') + for percentile in latency_percentiles: + res['metric_keys'].append('val.latency_{}'.format(percentile)) + res['metrics'] = OrderedDict() -for n in ngpus: +for n in args.ngpus: res['metrics'][str(n)] = OrderedDict() - for bs in batch_sizes: + for bs in args.batch_sizes: res['metrics'][str(n)][str(bs)] = OrderedDict() report_file = args.output + '-{},{}'.format(n, bs) - Popen([args.executable, '-n', str(n), '-b', str(bs), + Popen(['timeout', args.timeout, args.executable, '-n', str(n), '-b', str(bs), '--benchmark-iters', str(args.benchmark_iters), '-e', str(args.epochs), '--report', report_file, - *([] if not args.only_inference else ['--only-inference']), - '--no-metrics'] + other_args, stdout=sys.stderr).wait() + '--mode', args.mode, '--no-metrics'] + other_args, + stdout=sys.stderr).wait() - with open(report_file, 'r') as f: - report = json.load(f) + try: + for suffix in ['', *['-{}'.format(i) for i in range(1, n)]]: + try: + with open(report_file + suffix, 'r') as f: + report = json.load(f) + break + except FileNotFoundError: + pass + else: + with open(report_file, 'r') as f: + report = json.load(f) - for metric in res['metric_keys']: - data = report['metrics'][metric][args.warmup:] - avg = len(data) / sum(map(lambda x: 1 / x, data)) - res['metrics'][str(n)][str(bs)][metric] = avg + for metric in res['metric_keys']: + if len(report['metrics'][metric]) != args.epochs: + raise ValueError('Wrong number epochs in report') + data = report['metrics'][metric][args.warmup:] + if metric in harmonic_mean_metrics: + avg = len(data) / sum(map(lambda x: 1 / x, data)) + else: + avg = np.mean(data) + res['metrics'][str(n)][str(bs)][metric] = avg + except Exception as e: + traceback.print_exc() + + for metric in res['metric_keys']: + res['metrics'][str(n)][str(bs)][metric] = float('nan') -column_len = 7 +column_len = 11 for m in res['metric_keys']: print(m, file=sys.stderr) print(' ' * column_len, end='|', file=sys.stderr) - for bs in batch_sizes: + for bs in args.batch_sizes: print(str(bs).center(column_len), end='|', file=sys.stderr) print(file=sys.stderr) - print('-' * (len(batch_sizes) + 1) * (column_len + 1), file=sys.stderr) - for n in ngpus: + print('-' * (len(args.batch_sizes) + 1) * (column_len + 1), file=sys.stderr) + for n in args.ngpus: print(str(n).center(column_len), end='|', file=sys.stderr) - for bs in batch_sizes: - print(str(round(res['metrics'][str(n)][str(bs)][m])).center(column_len), end='|', file=sys.stderr) + for bs in args.batch_sizes: + print('{:.5g}'.format(res['metrics'][str(n)][str(bs)][m]).center(column_len), end='|', file=sys.stderr) print(file=sys.stderr) print(file=sys.stderr) diff --git a/MxNet/Classification/RN50v1.5/benchmarking.py b/MxNet/Classification/RN50v1.5/benchmarking.py index 2d40b36a..94ccc3e9 100644 --- a/MxNet/Classification/RN50v1.5/benchmarking.py +++ b/MxNet/Classification/RN50v1.5/benchmarking.py @@ -52,11 +52,14 @@ class BenchmarkingDataIter: def __getattr__(self, attr): return getattr(self.data_iter, attr) - def get_avg_time_and_clear(self): + def get_avg_time(self): if self.num <= 1: avg = float('nan') else: avg = self.overall_time / (self.num - 1) + return avg + + def reset(self): self.overall_time = 0 self.num = 0 - return avg + self.data_iter.reset() diff --git a/MxNet/Classification/RN50v1.5/dali.py b/MxNet/Classification/RN50v1.5/dali.py index f600491a..fbd047b9 100644 --- a/MxNet/Classification/RN50v1.5/dali.py +++ b/MxNet/Classification/RN50v1.5/dali.py @@ -18,146 +18,166 @@ from nvidia.dali.pipeline import Pipeline import nvidia.dali.ops as ops import nvidia.dali.types as types from nvidia.dali.plugin.mxnet import DALIClassificationIterator +import horovod.mxnet as hvd def add_dali_args(parser): - group = parser.add_argument_group('DALI', 'pipeline and augumentation') - group.add_argument('--use-dali', action='store_true', - help='use dalli pipeline and augunetation') - group.add_argument('--separ-val', action='store_true', + group = parser.add_argument_group('DALI data backend', 'entire group applies only to dali data backend') + group.add_argument('--dali-separ-val', action='store_true', help='each process will perform independent validation on whole val-set') group.add_argument('--dali-threads', type=int, default=3, help="number of threads" +\ "per GPU for DALI") - group.add_argument('--validation-dali-threads', type=int, default=10, help="number of threads" +\ + group.add_argument('--dali-validation-threads', type=int, default=10, help="number of threads" +\ "per GPU for DALI for validation") - group.add_argument('--dali-prefetch-queue', type=int, default=3, help="DALI prefetch queue depth") - group.add_argument('--dali-nvjpeg-memory-padding', type=int, default=16, help="Memory padding value for nvJPEG (in MB)") + group.add_argument('--dali-prefetch-queue', type=int, default=2, help="DALI prefetch queue depth") + group.add_argument('--dali-nvjpeg-memory-padding', type=int, default=64, help="Memory padding value for nvJPEG (in MB)") + group.add_argument('--dali-fuse-decoder', type=int, default=1, help="0 or 1 whether to fuse decoder or not") return parser -_mean_pixel = [255 * x for x in (0.485, 0.456, 0.406)] -_std_pixel = [255 * x for x in (0.229, 0.224, 0.225)] - class HybridTrainPipe(Pipeline): - def __init__(self, batch_size, num_threads, device_id, rec_path, idx_path, - shard_id, num_shards, crop_shape, - nvjpeg_padding, prefetch_queue=3, - output_layout=types.NCHW, pad_output=True, dtype='float16'): - super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed = 12 + device_id, prefetch_queue_depth = prefetch_queue) - self.input = ops.MXNetReader(path = [rec_path], index_path=[idx_path], + def __init__(self, args, batch_size, num_threads, device_id, rec_path, idx_path, + shard_id, num_shards, crop_shape, nvjpeg_padding, prefetch_queue=3, + output_layout=types.NCHW, pad_output=True, dtype='float16', dali_cpu=False): + super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id, prefetch_queue_depth = prefetch_queue) + self.input = ops.MXNetReader(path=[rec_path], index_path=[idx_path], random_shuffle=True, shard_id=shard_id, num_shards=num_shards) - self.decode = ops.nvJPEGDecoder(device = "mixed", output_type = types.RGB, - device_memory_padding = nvjpeg_padding, - host_memory_padding = nvjpeg_padding) - self.rrc = ops.RandomResizedCrop(device = "gpu", size = crop_shape) - self.cmnp = ops.CropMirrorNormalize(device = "gpu", - output_dtype = types.FLOAT16 if dtype == 'float16' else types.FLOAT, - output_layout = output_layout, - crop = crop_shape, - pad_output = pad_output, - image_type = types.RGB, - mean = _mean_pixel, - std = _std_pixel) - self.coin = ops.CoinFlip(probability = 0.5) + if dali_cpu: + dali_device = "cpu" + if args.dali_fuse_decoder: + self.decode = ops.HostDecoderRandomCrop(device=dali_device, output_type=types.RGB) + else: + self.decode = ops.HostDecoder(device=dali_device, output_type=types.RGB) + else: + dali_device = "gpu" + if args.dali_fuse_decoder: + self.decode = ops.nvJPEGDecoderRandomCrop(device="mixed", output_type=types.RGB, + device_memory_padding=nvjpeg_padding, host_memory_padding=nvjpeg_padding) + else: + self.decode = ops.nvJPEGDecoder(device="mixed", output_type=types.RGB, + device_memory_padding=nvjpeg_padding, host_memory_padding=nvjpeg_padding) + + if args.dali_fuse_decoder: + self.resize = ops.Resize(device=dali_device, resize_x=crop_shape[1], resize_y=crop_shape[0]) + else: + self.resize = ops.RandomResizedCrop(device=dali_device, size=crop_shape) + + self.cmnp = ops.CropMirrorNormalize(device="gpu", + output_dtype=types.FLOAT16 if dtype == 'float16' else types.FLOAT, + output_layout=output_layout, crop=crop_shape, pad_output=pad_output, + image_type=types.RGB, mean=args.rgb_mean, std=args.rgb_std) + self.coin = ops.CoinFlip(probability=0.5) def define_graph(self): rng = self.coin() - self.jpegs, self.labels = self.input(name = "Reader") + self.jpegs, self.labels = self.input(name="Reader") images = self.decode(self.jpegs) - images = self.rrc(images) - output = self.cmnp(images, mirror = rng) + images = self.resize(images) + output = self.cmnp(images.gpu(), mirror=rng) return [output, self.labels] class HybridValPipe(Pipeline): - def __init__(self, batch_size, num_threads, device_id, rec_path, idx_path, - shard_id, num_shards, crop_shape, - nvjpeg_padding, prefetch_queue=3, - resize_shp=None, - output_layout=types.NCHW, pad_output=True, dtype='float16'): - super(HybridValPipe, self).__init__(batch_size, num_threads, device_id, seed = 12 + device_id, prefetch_queue_depth = prefetch_queue) - self.input = ops.MXNetReader(path = [rec_path], index_path=[idx_path], + def __init__(self, args, batch_size, num_threads, device_id, rec_path, idx_path, + shard_id, num_shards, crop_shape, nvjpeg_padding, prefetch_queue=3, resize_shp=None, + output_layout=types.NCHW, pad_output=True, dtype='float16', dali_cpu=False): + super(HybridValPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id, prefetch_queue_depth=prefetch_queue) + self.input = ops.MXNetReader(path=[rec_path], index_path=[idx_path], random_shuffle=False, shard_id=shard_id, num_shards=num_shards) - self.decode = ops.nvJPEGDecoder(device = "mixed", output_type = types.RGB, - device_memory_padding = nvjpeg_padding, - host_memory_padding = nvjpeg_padding) - self.resize = ops.Resize(device = "gpu", resize_shorter=resize_shp) if resize_shp else None - self.cmnp = ops.CropMirrorNormalize(device = "gpu", - output_dtype = types.FLOAT16 if dtype == 'float16' else types.FLOAT, - output_layout = output_layout, - crop = crop_shape, - pad_output = pad_output, - image_type = types.RGB, - mean = _mean_pixel, - std = _std_pixel) + + if dali_cpu: + dali_device = "cpu" + self.decode = ops.HostDecoder(device=dali_device, output_type=types.RGB) + else: + dali_device = "gpu" + self.decode = ops.nvJPEGDecoder(device="mixed", output_type=types.RGB, + device_memory_padding=nvjpeg_padding, + host_memory_padding=nvjpeg_padding) + self.resize = ops.Resize(device=dali_device, resize_shorter=resize_shp) if resize_shp else None + self.cmnp = ops.CropMirrorNormalize(device="gpu", + output_dtype=types.FLOAT16 if dtype == 'float16' else types.FLOAT, + output_layout=output_layout, crop=crop_shape, pad_output=pad_output, + image_type=types.RGB, mean=args.rgb_mean, std=args.rgb_std) def define_graph(self): - self.jpegs, self.labels = self.input(name = "Reader") + self.jpegs, self.labels = self.input(name="Reader") images = self.decode(self.jpegs) if self.resize: images = self.resize(images) - output = self.cmnp(images) + output = self.cmnp(images.gpu()) return [output, self.labels] -def get_rec_iter(args, kv=None): - # resize is default base length of shorter edge for dataset; - # all images will be reshaped to this size - resize = int(args.resize) - # target shape is final shape of images pipelined to network; - # all images will be cropped to this size - target_shape = tuple([int(l) for l in args.image_shape.split(',')]) - pad_output = target_shape[0] == 4 - gpus = list(map(int, filter(None, args.gpus.split(',')))) # filter to not encount eventually empty strings - batch_size = args.batch_size//len(gpus) +def get_rec_iter(args, kv=None, dali_cpu=False): + gpus = args.gpus num_threads = args.dali_threads - num_validation_threads = args.validation_dali_threads - #db_folder = "/data/imagenet/train-480-val-256-recordio/" + num_validation_threads = args.dali_validation_threads + pad_output = (args.image_shape[0] == 4) # the input_layout w.r.t. the model is the output_layout of the image pipeline output_layout = types.NHWC if args.input_layout == 'NHWC' else types.NCHW - rank = kv.rank if kv else 0 - nWrk = kv.num_workers if kv else 1 + if 'horovod' in args.kv_store: + rank = hvd.rank() + nWrk = hvd.size() + else: + rank = kv.rank if kv else 0 + nWrk = kv.num_workers if kv else 1 - trainpipes = [HybridTrainPipe(batch_size = batch_size, + batch_size = args.batch_size // nWrk // len(gpus) + + trainpipes = [HybridTrainPipe(args = args, + batch_size = batch_size, num_threads = num_threads, device_id = gpu_id, rec_path = args.data_train, idx_path = args.data_train_idx, shard_id = gpus.index(gpu_id) + len(gpus)*rank, num_shards = len(gpus)*nWrk, - crop_shape = target_shape[1:], + crop_shape = args.image_shape[1:], output_layout = output_layout, - pad_output = pad_output, dtype = args.dtype, + pad_output = pad_output, + dali_cpu = dali_cpu, nvjpeg_padding = args.dali_nvjpeg_memory_padding * 1024 * 1024, prefetch_queue = args.dali_prefetch_queue) for gpu_id in gpus] - valpipes = [HybridValPipe(batch_size = batch_size, - num_threads = num_validation_threads, - device_id = gpu_id, - rec_path = args.data_val, - idx_path = args.data_val_idx, - shard_id = 0 if args.separ_val - else gpus.index(gpu_id) + len(gpus)*rank, - num_shards = 1 if args.separ_val else len(gpus)*nWrk, - crop_shape = target_shape[1:], - resize_shp = resize, - output_layout = output_layout, - pad_output = pad_output, - dtype = args.dtype, - nvjpeg_padding = args.dali_nvjpeg_memory_padding * 1024 * 1024, - prefetch_queue = args.dali_prefetch_queue) for gpu_id in gpus] if args.data_val else None + if args.data_val: + valpipes = [HybridValPipe(args = args, + batch_size = batch_size, + num_threads = num_validation_threads, + device_id = gpu_id, + rec_path = args.data_val, + idx_path = args.data_val_idx, + shard_id = 0 if args.dali_separ_val + else gpus.index(gpu_id) + len(gpus)*rank, + num_shards = 1 if args.dali_separ_val else len(gpus)*nWrk, + crop_shape = args.image_shape[1:], + resize_shp = args.data_val_resize, + output_layout = output_layout, + dtype = args.dtype, + pad_output = pad_output, + dali_cpu = dali_cpu, + nvjpeg_padding = args.dali_nvjpeg_memory_padding * 1024 * 1024, + prefetch_queue = args.dali_prefetch_queue) for gpu_id in gpus] if args.data_val else None trainpipes[0].build() if args.data_val: valpipes[0].build() + worker_val_examples = valpipes[0].epoch_size("Reader") + if not args.dali_separ_val: + worker_val_examples = worker_val_examples // nWrk + if rank < valpipes[0].epoch_size("Reader") % nWrk: + worker_val_examples += 1 if args.num_examples < trainpipes[0].epoch_size("Reader"): warnings.warn("{} training examples will be used, although full training set contains {} examples".format(args.num_examples, trainpipes[0].epoch_size("Reader"))) dali_train_iter = DALIClassificationIterator(trainpipes, args.num_examples // nWrk) - dali_val_iter = DALIClassificationIterator(valpipes, valpipes[0].epoch_size("Reader") // (1 if args.separ_val else nWrk), fill_last_batch = False) if args.data_val else None - return dali_train_iter, dali_val_iter + if args.data_val: + dali_val_iter = DALIClassificationIterator(valpipes, worker_val_examples, fill_last_batch = False) if args.data_val else None + else: + dali_val_iter = None + + return dali_train_iter, dali_val_iter diff --git a/MxNet/Classification/RN50v1.5/data.py b/MxNet/Classification/RN50v1.5/data.py index f3fd7812..36d3d3c0 100644 --- a/MxNet/Classification/RN50v1.5/data.py +++ b/MxNet/Classification/RN50v1.5/data.py @@ -1,7 +1,5 @@ -# ----------------------------------------------------------------------- # Copyright 2017-2018 The Apache Software Foundation # -# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information @@ -36,128 +34,61 @@ # limitations under the License. import mxnet as mx +import mxnet.ndarray as nd import random import argparse from mxnet.io import DataBatch, DataIter import numpy as np +import horovod.mxnet as hvd + +import dali def add_data_args(parser): - data = parser.add_argument_group('Data', 'the input images') + def float_list(x): + return list(map(float, x.split(','))) + def int_list(x): + return list(map(int, x.split(','))) + + data = parser.add_argument_group('Data') data.add_argument('--data-train', type=str, help='the training data') data.add_argument('--data-train-idx', type=str, default='', help='the index of training data') data.add_argument('--data-val', type=str, help='the validation data') data.add_argument('--data-val-idx', type=str, default='', help='the index of validation data') - data.add_argument('--rgb-mean', type=str, default='123.68,116.779,103.939', + data.add_argument('--data-pred', type=str, help='the image on which run inference (only for pred mode)') + + data.add_argument('--data-backend', choices=('dali-gpu', 'dali-cpu', 'mxnet', 'synthetic'), default='dali-gpu', + help='set data loading & augmentation backend') + data.add_argument('--image-shape', type=int_list, default=[3, 224, 224], + help='the image shape feed into the network') + data.add_argument('--rgb-mean', type=float_list, default=[123.68, 116.779, 103.939], help='a tuple of size 3 for the mean rgb') - data.add_argument('--rgb-std', type=str, default='1,1,1', + data.add_argument('--rgb-std', type=float_list, default=[58.393, 57.12, 57.375], help='a tuple of size 3 for the std rgb') - data.add_argument('--pad-size', type=int, default=0, - help='padding the input image') - data.add_argument('--fill-value', type=int, default=127, - help='Set the padding pixels value to fill_value') - data.add_argument('--image-shape', type=str, - help='the image shape feed into the network, e.g. (3,224,224)') - data.add_argument('--num-classes', type=int, help='the number of classes') - data.add_argument('--num-examples', type=int, help='the number of training examples') - data.add_argument('--data-nthreads', type=int, default=4, - help='number of threads for data decoding') - data.add_argument('--benchmark-iters', type=int, default=None, - help='run only benchmark-iters iterations from each epoch') - data.add_argument('--input-layout', type=str, default='NCHW', - help='the layout of the input data (e.g. NCHW)') - data.add_argument('--conv-layout', type=str, default='NCHW', - help='the layout of the data assumed by the conv operation (e.g. NCHW)') - data.add_argument('--conv-algo', type=int, default=-1, - help='set the convolution algos (fwd, dgrad, wgrad)') - data.add_argument('--batchnorm-layout', type=str, default='NCHW', - help='the layout of the data assumed by the batchnorm operation (e.g. NCHW)') - data.add_argument('--batchnorm-eps', type=float, default=2e-5, - help='the amount added to the batchnorm variance to prevent output explosion.') - data.add_argument('--batchnorm-mom', type=float, default=0.9, - help='the leaky-integrator factor controling the batchnorm mean and variance.') - data.add_argument('--pooling-layout', type=str, default='NCHW', - help='the layout of the data assumed by the pooling operation (e.g. NCHW)') - data.add_argument('--verbose', type=int, default=0, - help='turn on reporting of chosen algos for convolution, etc.') - data.add_argument('--seed', type=int, default=None, - help='set the seed for python, nd and mxnet rngs') - data.add_argument('--custom-bn-off', type=int, default=0, - help='disable use of custom batchnorm kernel') - data.add_argument('--fuse-bn-relu', type=int, default=0, - help='have batchnorm kernel perform activation relu') - data.add_argument('--fuse-bn-add-relu', type=int, default=0, - help='have batchnorm kernel perform add followed by activation relu') - data.add_argument('--force-tensor-core', type=int, default=0, - help='require conv algos to be tensor core') + + data.add_argument('--input-layout', type=str, default='NCHW', choices=('NCHW', 'NHWC'), + help='the layout of the input data') + data.add_argument('--conv-layout', type=str, default='NCHW', choices=('NCHW', 'NHWC'), + help='the layout of the data assumed by the conv operation') + data.add_argument('--batchnorm-layout', type=str, default='NCHW', choices=('NCHW', 'NHWC'), + help='the layout of the data assumed by the batchnorm operation') + data.add_argument('--pooling-layout', type=str, default='NCHW', choices=('NCHW', 'NHWC'), + help='the layout of the data assumed by the pooling operation') + + data.add_argument('--num-examples', type=int, default=1281167, + help="the number of training examples (doesn't work with mxnet data backend)") + data.add_argument('--data-val-resize', type=int, default=256, + help='base length of shorter edge for validation dataset') + return data -# Action to translate --set-resnet-aug flag to its component settings. -class SetResnetAugAction(argparse.Action): - def __init__(self, nargs=0, **kwargs): - if nargs != 0: - raise ValueError('nargs for SetResnetAug must be 0.') - super(SetResnetAugAction, self).__init__(nargs=nargs, **kwargs) - def __call__(self, parser, namespace, values, option_string=None): - # standard data augmentation setting for resnet training - setattr(namespace, 'random_crop', 1) - setattr(namespace, 'random_resized_crop', 1) - setattr(namespace, 'random_mirror', 1) - setattr(namespace, 'min_random_area', 0.08) - setattr(namespace, 'max_random_aspect_ratio', 4./3.) - setattr(namespace, 'min_random_aspect_ratio', 3./4.) - setattr(namespace, 'brightness', 0.4) - setattr(namespace, 'contrast', 0.4) - setattr(namespace, 'saturation', 0.4) - setattr(namespace, 'pca_noise', 0.1) - # record that this --set-resnet-aug 'macro arg' has been invoked - setattr(namespace, self.dest, 1) - -# Similar to the above, but suitable for calling within a training script to set the defaults. -def set_resnet_aug(aug): - # standard data augmentation setting for resnet training - aug.set_defaults(random_crop=0, random_resized_crop=1) - aug.set_defaults(random_mirror=1) - aug.set_defaults(min_random_area=0.08) - aug.set_defaults(max_random_aspect_ratio=4./3., min_random_aspect_ratio=3./4.) - aug.set_defaults(brightness=0.4, contrast=0.4, saturation=0.4, pca_noise=0.1) - -# Action to translate --set-data-aug-level arg to its component settings. -class SetDataAugLevelAction(argparse.Action): - def __init__(self, option_strings, dest, nargs=None, **kwargs): - if nargs is not None: - raise ValueError("nargs not allowed") - super(SetDataAugLevelAction, self).__init__(option_strings, dest, **kwargs) - def __call__(self, parser, namespace, values, option_string=None): - level = values - # record that this --set-data-aug-level 'macro arg' has been invoked - setattr(namespace, self.dest, level) - if level >= 1: - setattr(namespace, 'random_crop', 1) - setattr(namespace, 'random_mirror', 1) - if level >= 2: - setattr(namespace, 'max_random_h', 36) - setattr(namespace, 'max_random_s', 50) - setattr(namespace, 'max_random_l', 50) - if level >= 3: - setattr(namespace, 'max_random_rotate_angle', 10) - setattr(namespace, 'max_random_shear_ratio', 0.1) - setattr(namespace, 'max_random_aspect_ratio', 0.25) - -# Similar to the above, but suitable for calling within a training script to set the defaults. -def set_data_aug_level(aug, level): - if level >= 1: - aug.set_defaults(random_crop=1, random_mirror=1) - if level >= 2: - aug.set_defaults(max_random_h=36, max_random_s=50, max_random_l=50) - if level >= 3: - aug.set_defaults(max_random_rotate_angle=10, max_random_shear_ratio=0.1, max_random_aspect_ratio=0.25) - def add_data_aug_args(parser): aug = parser.add_argument_group( - 'Image augmentations', 'implemented in src/io/image_aug_default.cc') + 'MXNet data backend', 'entire group applies only to mxnet data backend') + aug.add_argument('--data-mxnet-threads', type=int, default=40, + help='number of threads for data decoding for mxnet data backend') aug.add_argument('--random-crop', type=int, default=0, help='if or not randomly crop the image') - aug.add_argument('--random-mirror', type=int, default=0, + aug.add_argument('--random-mirror', type=int, default=1, help='if or not randomly flip horizontally') aug.add_argument('--max-random-h', type=int, default=0, help='max change of hue, whose range is [0, 180]') @@ -165,9 +96,9 @@ def add_data_aug_args(parser): help='max change of saturation, whose range is [0, 255]') aug.add_argument('--max-random-l', type=int, default=0, help='max change of intensity, whose range is [0, 255]') - aug.add_argument('--min-random-aspect-ratio', type=float, default=None, + aug.add_argument('--min-random-aspect-ratio', type=float, default=0.75, help='min value of aspect ratio, whose value is either None or a positive value.') - aug.add_argument('--max-random-aspect-ratio', type=float, default=0, + aug.add_argument('--max-random-aspect-ratio', type=float, default=1.33, help='max value of aspect ratio. If min_random_aspect_ratio is None, ' 'the aspect ratio range is [1-max_random_aspect_ratio, ' '1+max_random_aspect_ratio], otherwise it is ' @@ -183,7 +114,7 @@ def add_data_aug_args(parser): 'otherwise use --pad-size') aug.add_argument('--max-random-area', type=float, default=1, help='max area to crop in random resized crop, whose range is [0, 1]') - aug.add_argument('--min-random-area', type=float, default=1, + aug.add_argument('--min-random-area', type=float, default=0.05, help='min area to crop in random resized crop, whose range is [0, 1]') aug.add_argument('--min-crop-size', type=int, default=-1, help='Crop both width and height into a random size in ' @@ -199,87 +130,200 @@ def add_data_aug_args(parser): help='saturation jittering, whose range is [0, 1]') aug.add_argument('--pca-noise', type=float, default=0, help='pca noise, whose range is [0, 1]') - aug.add_argument('--random-resized-crop', type=int, default=0, + aug.add_argument('--random-resized-crop', type=int, default=1, help='whether to use random resized crop') - aug.add_argument('--set-resnet-aug', action=SetResnetAugAction, - help='whether to employ standard resnet augmentations (see data.py)') - aug.add_argument('--set-data-aug-level', type=int, default=None, action=SetDataAugLevelAction, - help='set multiple data augmentations based on a `level` (see data.py)') return aug +def get_data_loader(args): + if args.data_backend == 'dali-gpu': + return (lambda *args, **kwargs: dali.get_rec_iter(*args, **kwargs, dali_cpu=False)) + if args.data_backend == 'dali-cpu': + return (lambda *args, **kwargs: dali.get_rec_iter(*args, **kwargs, dali_cpu=True)) + if args.data_backend == 'synthetic': + return get_synthetic_rec_iter + if args.data_backend == 'mxnet': + return get_rec_iter + raise ValueError('Wrong data backend') + +class DataGPUSplit: + def __init__(self, dataloader, ctx, dtype): + self.dataloader = dataloader + self.ctx = ctx + self.dtype = dtype + self.batch_size = dataloader.batch_size // len(ctx) + self._num_gpus = len(ctx) + + def __iter__(self): + return DataGPUSplit(iter(self.dataloader), self.ctx, self.dtype) + + def __next__(self): + data = next(self.dataloader) + ret = [] + for i in range(len(self.ctx)): + start = i * len(data.data[0]) // len(self.ctx) + end = (i + 1) * len(data.data[0]) // len(self.ctx) + pad = max(0, min(data.pad - (len(self.ctx) - i - 1) * self.batch_size, self.batch_size)) + ret.append(mx.io.DataBatch( + [data.data[0][start:end].as_in_context(self.ctx[i]).astype(self.dtype)], + [data.label[0][start:end].as_in_context(self.ctx[i])], + pad=pad)) + return ret + + def next(self): + return next(self) + + def reset(self): + self.dataloader.reset() + def get_rec_iter(args, kv=None): - image_shape = tuple([int(l) for l in args.image_shape.split(',')]) - if args.input_layout == 'NHWC': - image_shape = image_shape[1:] + (image_shape[0],) - if kv: - (rank, nworker) = (kv.rank, kv.num_workers) + gpus = args.gpus + if 'horovod' in args.kv_store: + rank = hvd.rank() + nworker = hvd.size() + gpus = [gpus[0]] + batch_size = args.batch_size // hvd.size() else: - (rank, nworker) = (0, 1) - rgb_mean = [float(i) for i in args.rgb_mean.split(',')] - rgb_std = [float(i) for i in args.rgb_std.split(',')] + rank = kv.rank if kv else 0 + nworker = kv.num_workers if kv else 1 + batch_size = args.batch_size + if args.input_layout == 'NHWC': raise ValueError('ImageRecordIter cannot handle layout {}'.format(args.input_layout)) - train = mx.io.ImageRecordIter( - path_imgrec = args.data_train, - path_imgidx = args.data_train_idx, - label_width = 1, - mean_r = rgb_mean[0], - mean_g = rgb_mean[1], - mean_b = rgb_mean[2], - std_r = rgb_std[0], - std_g = rgb_std[1], - std_b = rgb_std[2], - data_name = 'data', - label_name = 'softmax_label', - data_shape = image_shape, - batch_size = args.batch_size, - rand_crop = args.random_crop, - max_random_scale = args.max_random_scale, - pad = args.pad_size, - fill_value = args.fill_value, - random_resized_crop = args.random_resized_crop, - min_random_scale = args.min_random_scale, - max_aspect_ratio = args.max_random_aspect_ratio, - min_aspect_ratio = args.min_random_aspect_ratio, - max_random_area = args.max_random_area, - min_random_area = args.min_random_area, - min_crop_size = args.min_crop_size, - max_crop_size = args.max_crop_size, - brightness = args.brightness, - contrast = args.contrast, - saturation = args.saturation, - pca_noise = args.pca_noise, - random_h = args.max_random_h, - random_s = args.max_random_s, - random_l = args.max_random_l, - max_rotate_angle = args.max_random_rotate_angle, - max_shear_ratio = args.max_random_shear_ratio, - rand_mirror = args.random_mirror, - preprocess_threads = args.data_nthreads, - shuffle = True, - num_parts = nworker, - part_index = rank) + + train = DataGPUSplit(mx.io.ImageRecordIter( + path_imgrec = args.data_train, + path_imgidx = args.data_train_idx, + label_width = 1, + mean_r = args.rgb_mean[0], + mean_g = args.rgb_mean[1], + mean_b = args.rgb_mean[2], + std_r = args.rgb_std[0], + std_g = args.rgb_std[1], + std_b = args.rgb_std[2], + data_name = 'data', + label_name = 'softmax_label', + data_shape = args.image_shape, + batch_size = batch_size, + rand_crop = args.random_crop, + max_random_scale = args.max_random_scale, + random_resized_crop = args.random_resized_crop, + min_random_scale = args.min_random_scale, + max_aspect_ratio = args.max_random_aspect_ratio, + min_aspect_ratio = args.min_random_aspect_ratio, + max_random_area = args.max_random_area, + min_random_area = args.min_random_area, + min_crop_size = args.min_crop_size, + max_crop_size = args.max_crop_size, + brightness = args.brightness, + contrast = args.contrast, + saturation = args.saturation, + pca_noise = args.pca_noise, + random_h = args.max_random_h, + random_s = args.max_random_s, + random_l = args.max_random_l, + max_rotate_angle = args.max_random_rotate_angle, + max_shear_ratio = args.max_random_shear_ratio, + rand_mirror = args.random_mirror, + preprocess_threads = args.data_mxnet_threads, + shuffle = True, + num_parts = nworker, + part_index = rank, + seed = args.seed or '0', + ), [mx.gpu(gpu) for gpu in gpus], args.dtype) if args.data_val is None: return (train, None) - val = mx.io.ImageRecordIter( - path_imgrec = args.data_val, - path_imgidx = args.data_val_idx, - label_width = 1, - mean_r = rgb_mean[0], - mean_g = rgb_mean[1], - mean_b = rgb_mean[2], - std_r = rgb_std[0], - std_g = rgb_std[1], - std_b = rgb_std[2], - data_name = 'data', - label_name = 'softmax_label', - batch_size = args.batch_size, - round_batch = False, - data_shape = image_shape, - preprocess_threads = args.data_nthreads, - rand_crop = False, - rand_mirror = False, - num_parts = nworker, - part_index = rank) + val = DataGPUSplit(mx.io.ImageRecordIter( + path_imgrec = args.data_val, + path_imgidx = args.data_val_idx, + label_width = 1, + mean_r = args.rgb_mean[0], + mean_g = args.rgb_mean[1], + mean_b = args.rgb_mean[2], + std_r = args.rgb_std[0], + std_g = args.rgb_std[1], + std_b = args.rgb_std[2], + data_name = 'data', + label_name = 'softmax_label', + batch_size = batch_size, + round_batch = False, + data_shape = args.image_shape, + preprocess_threads = args.data_mxnet_threads, + rand_crop = False, + rand_mirror = False, + num_parts = nworker, + part_index = rank, + resize = args.data_val_resize, + ), [mx.gpu(gpu) for gpu in gpus], args.dtype) return (train, val) + + +class SyntheticDataIter(DataIter): + def __init__(self, num_classes, data_shape, max_iter, ctx, dtype): + self.batch_size = data_shape[0] + self.cur_iter = 0 + self.max_iter = max_iter + self.dtype = dtype + label = np.random.randint(0, num_classes, [self.batch_size,]) + data = np.random.uniform(-1, 1, data_shape) + self.data = [] + self.label = [] + self._num_gpus = len(ctx) + for dev in ctx: + self.data.append(mx.nd.array(data, dtype=self.dtype, ctx=dev)) + self.label.append(mx.nd.array(label, dtype=self.dtype, ctx=dev)) + + def __iter__(self): + return self + + def next(self): + self.cur_iter += 1 + if self.cur_iter <= self.max_iter: + return [DataBatch(data=(data,), label=(label,), pad=0) for data, label in zip(self.data, self.label)] + else: + raise StopIteration + + def __next__(self): + return self.next() + + def reset(self): + self.cur_iter = 0 + +def get_synthetic_rec_iter(args, kv=None): + gpus = args.gpus + if 'horovod' in args.kv_store: + gpus = [gpus[0]] + batch_size = args.batch_size // hvd.size() + else: + batch_size = args.batch_size + + if args.input_layout == 'NCHW': + data_shape = (batch_size, *args.image_shape) + elif args.input_layout == 'NHWC': + data_shape = (batch_size, *args.image_shape[1:], args.image_shape[0]) + else: + raise ValueError('Wrong input layout') + + train = SyntheticDataIter(args.num_classes, data_shape, + args.num_examples // args.batch_size, + [mx.gpu(gpu) for gpu in gpus], args.dtype) + if args.data_val is None: + return (train, None) + + val = SyntheticDataIter(args.num_classes, data_shape, + args.num_examples // args.batch_size, + [mx.gpu(gpu) for gpu in gpus], args.dtype) + return (train, val) + +def load_image(args, path, ctx=mx.cpu()): + image = mx.image.imread(path).astype('float32') + image = mx.image.imresize(image, *args.image_shape[1:]) + image = (image - nd.array(args.rgb_mean)) / nd.array(args.rgb_std) + image = image.as_in_context(ctx) + if args.input_layout == 'NCHW': + image = image.transpose((2, 0, 1)) + image = image.astype(args.dtype) + if args.image_shape[0] == 4: + dim = 0 if args.input_layout == 'NCHW' else 2 + image = nd.concat(image, nd.zeros((1, *image.shape[1:]), dtype=image.dtype, ctx=image.context), dim=dim) + return image diff --git a/MxNet/Classification/RN50v1.5/examples/INFER_BENCHMARK_FP16.sh b/MxNet/Classification/RN50v1.5/examples/INFER_BENCHMARK_FP16.sh deleted file mode 100644 index e395640b..00000000 --- a/MxNet/Classification/RN50v1.5/examples/INFER_BENCHMARK_FP16.sh +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# This script launches ResNet50 inference benchmark in FP16 on 1 GPU with 1,2,4,64,128,192,208 batch size -# Usage ./INFER_BENCHMARK_FP16.sh - -python benchmark.py -n 1 -b 1,2,4,64,128,192,208 --only-inference -e 3 -w 1 -i 100 -o report.json $@ diff --git a/MxNet/Classification/RN50v1.5/examples/RN50_FP16_1GPU.sh b/MxNet/Classification/RN50v1.5/examples/RN50_FP16_1GPU.sh deleted file mode 100644 index d69f486e..00000000 --- a/MxNet/Classification/RN50v1.5/examples/RN50_FP16_1GPU.sh +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# This script launches ResNet50 training in FP16 on 1 GPUs using 208 batch size (208 per GPU) -# Usage ./RN50_FP16_1GPU.sh - -"$1/runner" -n 1 -b 208 --model-prefix model ${@:2} diff --git a/MxNet/Classification/RN50v1.5/examples/RN50_FP16_8GPU.sh b/MxNet/Classification/RN50v1.5/examples/RN50_FP16_8GPU.sh deleted file mode 100644 index 4a53c347..00000000 --- a/MxNet/Classification/RN50v1.5/examples/RN50_FP16_8GPU.sh +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# This script launches ResNet50 training in FP16 on 8 GPUs using 1664 batch size (208 per GPU) -# Usage ./RN50_FP16_8GPU.sh - -"$1/runner" -n 8 -b 208 --model-prefix model ${@:2} diff --git a/MxNet/Classification/RN50v1.5/examples/RN50_FP32_1GPU.sh b/MxNet/Classification/RN50v1.5/examples/RN50_FP32_1GPU.sh deleted file mode 100644 index da266b44..00000000 --- a/MxNet/Classification/RN50v1.5/examples/RN50_FP32_1GPU.sh +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# This script launches ResNet50 training in FP32 on 1 GPUs using 96 batch size (96 per GPU) -# Usage ./RN50_FP32_1GPU.sh - -"$1/runner" -n 1 -b 96 --dtype float32 --model-prefix model ${@:2} diff --git a/MxNet/Classification/RN50v1.5/examples/RN50_FP32_4GPU.sh b/MxNet/Classification/RN50v1.5/examples/RN50_FP32_4GPU.sh deleted file mode 100644 index d9eae80b..00000000 --- a/MxNet/Classification/RN50v1.5/examples/RN50_FP32_4GPU.sh +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# This script launches ResNet50 training in FP32 on 4 GPUs using 384 batch size (96 per GPU) -# Usage ./RN50_FP32_4GPU.sh - -"$1/runner" -n 4 -b 96 --dtype float32 --model-prefix model ${@:2} diff --git a/MxNet/Classification/RN50v1.5/examples/RN50_FP32_8GPU.sh b/MxNet/Classification/RN50v1.5/examples/RN50_FP32_8GPU.sh deleted file mode 100644 index 47ce7bf6..00000000 --- a/MxNet/Classification/RN50v1.5/examples/RN50_FP32_8GPU.sh +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# This script launches ResNet50 training in FP32 on 8 GPUs using 768 batch size (96 per GPU) -# Usage ./RN50_FP32_8GPU.sh - -"$1/runner" -n 8 -b 96 --dtype float32 --model-prefix model ${@:2} diff --git a/MxNet/Classification/RN50v1.5/examples/SCORE_FP16.sh b/MxNet/Classification/RN50v1.5/examples/SCORE_FP16.sh deleted file mode 100644 index bd3740b5..00000000 --- a/MxNet/Classification/RN50v1.5/examples/SCORE_FP16.sh +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# This script score ResNet50 checkpoint in FP16 on 1 GPUs using 128 batch size -# Usage ./SCORE_FP16.sh - -./runner -n 1 -b 128 --only-inference --model-prefix $1 --load-epoch $2 -e 1 ${@:3} diff --git a/MxNet/Classification/RN50v1.5/examples/SCORE_FP32.sh b/MxNet/Classification/RN50v1.5/examples/SCORE_FP32.sh deleted file mode 100644 index 8b7dd3d4..00000000 --- a/MxNet/Classification/RN50v1.5/examples/SCORE_FP32.sh +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# This script score ResNet50 checkpoint in FP32 on 1 GPUs using 64 batch size -# Usage ./SCORE_FP32.sh - -./runner -n 1 -b 64 --dtype float32 --only-inference --model-prefix $1 --load-epoch $2 -e 1 ${@:3} diff --git a/MxNet/Classification/RN50v1.5/fit.py b/MxNet/Classification/RN50v1.5/fit.py index 17267ab9..69404784 100644 --- a/MxNet/Classification/RN50v1.5/fit.py +++ b/MxNet/Classification/RN50v1.5/fit.py @@ -33,197 +33,408 @@ # See the License for the specific language governing permissions and # limitations under the License. -""" example train fit utility """ +""" train fit utility """ import logging import os import time import re import math import sys +import random +from itertools import starmap +import numpy as np import mxnet as mx +import mxnet.ndarray as nd +import horovod.mxnet as hvd +import mxnet.contrib.amp as amp +from mxnet import autograd as ag +from mxnet import gluon from report import Report from benchmarking import BenchmarkingDataIter - -def get_epoch_size(args, kv): - return math.ceil(int(args.num_examples / kv.num_workers) / args.batch_size) - -def _get_lr_scheduler(args, kv): - if 'lr_factor' not in args or args.lr_factor >= 1: - return (args.lr, None) - epoch_size = get_epoch_size(args, kv) - begin_epoch = args.load_epoch if args.load_epoch else 0 - if 'pow' in args.lr_step_epochs: - lr = args.lr - max_up = args.num_epochs * epoch_size - pwr = float(re.sub('pow[- ]*', '', args.lr_step_epochs)) - poly_sched = mx.lr_scheduler.PolyScheduler(max_up, lr, pwr) - return (lr, poly_sched) - step_epochs = [int(l) for l in args.lr_step_epochs.split(',')] - lr = args.lr - for s in step_epochs: - if begin_epoch >= s: - lr *= args.lr_factor - if lr != args.lr: - logging.info('Adjust learning rate to %e for epoch %d', - lr, begin_epoch) - - steps = [epoch_size * (x - begin_epoch) - for x in step_epochs if x - begin_epoch > 0] - if steps: - if kv: - num_workers = kv.num_workers - else: - num_workers = 1 - epoch_size = math.ceil(int(args.num_examples/num_workers)/args.batch_size) - return (lr, mx.lr_scheduler.MultiFactorScheduler(step=steps, factor=args.lr_factor, - base_lr=args.lr, warmup_steps=epoch_size * args.warmup_epochs, - warmup_mode=args.warmup_strategy)) - else: - return (lr, None) - -def _load_model(args, rank=0): - if 'load_epoch' not in args or args.load_epoch is None: - return (None, None, None) - assert args.model_prefix is not None - model_prefix = args.model_prefix - if rank > 0 and os.path.exists("%s-%d-symbol.json" % (model_prefix, rank)): - model_prefix += "-%d" % (rank) - sym, arg_params, aux_params = mx.model.load_checkpoint( - model_prefix, args.load_epoch) - logging.info('Loaded model %s_%04d.params', model_prefix, args.load_epoch) - return (sym, arg_params, aux_params) - - -def _save_model(args, rank=0): - if args.model_prefix is None: - return None - return mx.callback.do_checkpoint(args.model_prefix if rank == 0 else "%s-%d" % ( - args.model_prefix, rank), period=args.save_period) - +import data def add_fit_args(parser): - """ - parser : argparse.ArgumentParser - return a parser added with args required by fit - """ - train = parser.add_argument_group('Training', 'model training') - train.add_argument('--num-layers', type=int, - help='number of layers in the neural network, \ - required by some networks such as resnet') - train.add_argument('--gpus', type=str, - help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu') - train.add_argument('--kv-store', type=str, default='device', + def int_list(x): + return list(map(int, x.split(','))) + + def float_list(x): + return list(map(float, x.split(','))) + + train = parser.add_argument_group('Training') + train.add_argument('--mode', default='train_val', choices=('train_val', 'train', 'val', 'pred'), + help='mode') + train.add_argument('--seed', type=int, default=None, + help='random seed') + + train.add_argument('--gpus', type=int_list, default=[0], + help='list of gpus to run, e.g. 0 or 0,2,5') + train.add_argument('--kv-store', type=str, default='device', choices=('device', 'horovod'), help='key-value store type') - train.add_argument('--num-epochs', type=int, default=100, - help='max num of epochs') + + train.add_argument('--dtype', type=str, default='float16', choices=('float32', 'float16'), + help='precision') + train.add_argument('--amp', action='store_true', + help='If enabled, turn on AMP (Automatic Mixed Precision)') + train.add_argument('--batch-size', type=int, default=192, + help='the batch size') + train.add_argument('--num-epochs', type=int, default=90, + help='number of epochs') train.add_argument('--lr', type=float, default=0.1, help='initial learning rate') - train.add_argument('--lr-factor', type=float, default=0.1, + train.add_argument('--lr-schedule', choices=('multistep', 'cosine'), default='cosine', + help='learning rate schedule') + train.add_argument('--lr-factor', type=float, default=0.256, help='the ratio to reduce lr on each step') - train.add_argument('--lr-step-epochs', type=str, + train.add_argument('--lr-steps', type=float_list, default=[], help='the epochs to reduce the lr, e.g. 30,60') - train.add_argument('--initializer', type=str, default='default', - help='the initializer type') - train.add_argument('--optimizer', type=str, default='sgd', - help='the optimizer type') - train.add_argument('--mom', type=float, default=0.9, - help='momentum for sgd') - train.add_argument('--wd', type=float, default=0.0001, - help='weight decay for sgd') - train.add_argument('--batch-size', type=int, default=208, - help='the batch size') - train.add_argument('--disp-batches', type=int, default=20, - help='show progress for every n batches') - train.add_argument('--model-prefix', type=str, - help='model prefix') - train.add_argument('--save-period', type=int, default=1, help='params saving period') - parser.add_argument('--monitor', dest='monitor', type=int, default=0, - help='log network parameters every N iters if larger than 0') - train.add_argument('--load-epoch', type=int, - help='load the model on an epoch using the model-load-prefix') - train.add_argument('--loss', type=str, default='', - help='show the cross-entropy or nll loss. ce strands for cross-entropy, nll-loss stands for likelihood loss') - train.add_argument('--test-io', type=int, default=0, - help='1 means test reading speed without training') - train.add_argument('--dtype', type=str, default='float16', - help='precision: float32 or float16') - train.add_argument('--gc-type', type=str, default='none', - help='type of gradient compression to use, \ - takes `2bit` or `none` for now') - train.add_argument('--gc-threshold', type=float, default=0.5, - help='threshold for 2bit gradient compression') - # additional parameters for large batch sgd - train.add_argument('--macrobatch-size', type=int, default=0, - help='distributed effective batch size') train.add_argument('--warmup-epochs', type=int, default=5, help='the epochs to ramp-up lr to scaled large-batch value') - train.add_argument('--warmup-strategy', type=str, default='linear', - help='the ramping-up strategy for large batch sgd') - train.add_argument('--logging-dir', type=str, default='logs') - train.add_argument('--log', type=str, default='') - train.add_argument('--bn-gamma-init0', action='store_true') - train.add_argument('--epoch-size',type=int, default=0, - help='set number of batches in an epoch. useful for debugging') - #train.add_argument('--tensorboard', type=str, default='', - # help='log parameters to visualize in tensorboard every epoch. takes name to specify as tensorboard run. Empty means tensorboard logging is disabled') - train.add_argument('--profile-worker-suffix', type=str, default='', - help='profile workers actions into this file. During distributed training\ - filename saved will be rank1_ followed by this suffix') - train.add_argument('--profile-server-suffix', type=str, default='', - help='profile server actions into a file with name like rank1_ followed by this suffix \ - during distributed training') - train.add_argument('--report', type=str, help='file where to save report') - train.add_argument('--only-inference', action='store_true', help='do not train, only inference (for benchmarking)') + train.add_argument('--optimizer', type=str, default='sgd', + help='the optimizer type') + train.add_argument('--mom', type=float, default=0.875, + help='momentum for sgd') + train.add_argument('--wd', type=float, default=1 / 32768, + help='weight decay for sgd') + train.add_argument('--label-smoothing', type=float, default=0.1, + help='label smoothing factor') + train.add_argument('--mixup', type=float, default=0, + help='alpha parameter for mixup (if 0 then mixup is not applied)') + + train.add_argument('--disp-batches', type=int, default=20, + help='show progress for every n batches') + train.add_argument('--model-prefix', type=str, default='model', + help='model checkpoint prefix') + train.add_argument('--save-frequency', type=int, default=-1, + help='frequency of saving model in epochs (--model-prefix must be specified). ' + 'If -1 then save only best model. If 0 then do not save anything.') + train.add_argument('--begin-epoch', type=int, default=0, + help='start the model from an epoch') + train.add_argument('--load', help='checkpoint to load') + + train.add_argument('--test-io', action='store_true', + help='test reading speed without training') + train.add_argument('--test-io-mode', default='train', choices=('train', 'val'), + help='data to test') + + train.add_argument('--log', type=str, default='log.log', + help='file where to save the log from the experiment') + train.add_argument('--report', default='report.json', help='file where to save report') + train.add_argument('--no-metrics', action='store_true', help='do not calculate evaluation metrics (for benchmarking)') + train.add_argument('--benchmark-iters', type=int, default=None, + help='run only benchmark-iters iterations from each epoch') return train +def get_epoch_size(args, kv): + return math.ceil(args.num_examples / args.batch_size) -def fit(args, network, data_loader, **kwargs): +def get_lr_scheduler(args): + def multistep_schedule(x): + lr = args.lr * (args.lr_factor ** (len(list(filter(lambda step: step <= x, args.lr_steps))))) + warmup_coeff = min(1, x / args.warmup_epochs) + return warmup_coeff * lr + + def cosine_schedule(x): + steps = args.lr_steps + if not steps or steps[0] > args.warmup_epochs: + steps = [args.warmup_epochs] + steps + elif not steps or steps[0] != 0: + steps = [0] + steps + + if steps[-1] != args.num_epochs: + steps.append(args.num_epochs) + + if x < args.warmup_epochs: + return args.lr * x / args.warmup_epochs + + for i, (step, next_step) in enumerate(zip(steps, steps[1:])): + if next_step > x: + return args.lr * 0.5 * (1 + math.cos(math.pi * (x - step) / (next_step - step))) * (args.lr_factor ** i) + return 0 + + schedules = { + 'multistep': multistep_schedule, + 'cosine': cosine_schedule, + } + return schedules[args.lr_schedule] + +def load_model(args, model): + if args.load is None: + return False + model.load_parameters(args.load) + logging.info('Loaded model {}'.format(args.load)) + return True + +def save_checkpoint(net, epoch, top1, best_acc, model_prefix, save_frequency, kvstore): + if model_prefix is None or save_frequency == 0 or ('horovod' in kvstore and hvd.rank() != 0): + return + if save_frequency > 0 and (epoch + 1) % save_frequency == 0: + fname = '{}_{:04}.params'.format(model_prefix, epoch) + net.save_parameters(fname) + logging.info('[Epoch {}] Saving checkpoint to {} with Accuracy: {:.4f}'.format(epoch, fname, top1)) + if top1 > best_acc: + fname = '{}_best.params'.format(model_prefix) + net.save_parameters(fname) + logging.info('[Epoch {}] Saving checkpoint to {} with Accuracy: {:.4f}'.format(epoch, fname, top1)) + +def add_metrics_to_report(report, mode, metric, durations, total_batch_size, loss=None, warmup=20): + if report is None: + return + + top1 = metric.get('accuracy', None) + if top1 is not None: + report.add_value('{}.top1'.format(mode), top1) + + top5 = metric.get('top_k_accuracy_5', None) + if top5 is not None: + report.add_value('{}.top5'.format(mode), top5) + + if loss is not None: + report.add_value('{}.loss'.format(mode), loss.get_global()[1]) + + if len(durations) > warmup: + durations = durations[warmup:] + duration = np.mean(durations) + total_ips = total_batch_size / duration + report.add_value('{}.latency_avg'.format(mode), duration) + for percentile in [50, 90, 95, 99, 100]: + report.add_value('{}.latency_{}'.format(mode, percentile), np.percentile(durations, percentile)) + report.add_value('{}.total_ips'.format(mode), total_ips) + +def model_pred(args, model, image): + from imagenet_classes import classes + output = model(image.reshape(-1, *image.shape))[0].softmax().as_in_context(mx.cpu()) + top = output.argsort(is_ascend=False)[:10] + for i, ind in enumerate(top): + ind = int(ind.asscalar()) + logging.info('{:2d}. {:5.2f}% -> {}'.format(i + 1, output[ind].asscalar() * 100, classes[ind])) + +def reduce_metrics(args, metrics, kvstore): + if 'horovod' not in kvstore or not metrics[0] or hvd.size() == 1: + return metrics + + m = mx.ndarray.array(metrics[1], ctx=mx.gpu(args.gpus[0])) + reduced = hvd.allreduce(m) + values = reduced.as_in_context(mx.cpu()).asnumpy().tolist() + return (metrics[0], values) + +def model_score(args, net, val_data, metric, kvstore, report=None): + if val_data is None: + logging.info('Omitting validation: no data') + return [], [] + + if not isinstance(metric, mx.metric.EvalMetric): + metric = mx.metric.create(metric) + metric.reset() + + val_data.reset() + + total_batch_size = val_data.batch_size * val_data._num_gpus * (hvd.size() if 'horovod' in kvstore else 1) + + durations = [] + tic = time.time() + outputs = [] + for batches in val_data: + # synchronize to previous iteration + for o in outputs: + o.wait_to_read() + + data = [b.data[0] for b in batches] + label = [b.label[0][:len(b.data[0]) - b.pad] for b in batches if len(b.data[0]) != b.pad] + outputs = [net(X) for X, b in zip(data, batches)] + outputs = [o[:len(b.data[0]) - b.pad] for o, b in zip(outputs, batches) if len(b.data[0]) != b.pad] + metric.update(label, outputs) + + durations.append(time.time() - tic) + tic = time.time() + + metric = reduce_metrics(args, metric.get_global(), kvstore) + add_metrics_to_report(report, 'val', dict(zip(*metric)), durations, total_batch_size) + return metric + +class ScalarMetric(mx.metric.Loss): + def update(self, _, scalar): + self.sum_metric += scalar + self.global_sum_metric += scalar + self.num_inst += 1 + self.global_num_inst += 1 + +def label_smoothing(labels, classes, eta): + return labels.one_hot(classes, on_value=1 - eta + eta / classes, off_value=eta / classes) + +def model_fit(args, net, train_data, eval_metric, optimizer, + optimizer_params, lr_scheduler, eval_data, kvstore, kv, + begin_epoch, num_epoch, model_prefix, report, print_loss): + + if not isinstance(eval_metric, mx.metric.EvalMetric): + eval_metric = mx.metric.create(eval_metric) + loss_metric = ScalarMetric() + + if 'horovod' in kvstore: + trainer = hvd.DistributedTrainer(net.collect_params(), optimizer, optimizer_params) + else: + trainer = gluon.Trainer(net.collect_params(), optimizer, optimizer_params, + kvstore=kv, update_on_kvstore=False) + + if args.amp: + amp.init_trainer(trainer) + + sparse_label_loss = (args.label_smoothing == 0 and args.mixup == 0) + loss = gluon.loss.SoftmaxCrossEntropyLoss(sparse_label=sparse_label_loss) + loss.hybridize(static_shape=True, static_alloc=True) + + local_batch_size = train_data.batch_size + total_batch_size = local_batch_size * train_data._num_gpus * (hvd.size() if 'horovod' in kvstore else 1) + durations = [] + + epoch_size = get_epoch_size(args, kv) + + def transform_data(images, labels): + if args.mixup != 0: + coeffs = mx.nd.array(np.random.beta(args.mixup, args.mixup, size=images.shape[0])).as_in_context(images.context) + image_coeffs = coeffs.astype(images.dtype, copy=False).reshape(*coeffs.shape, 1, 1, 1) + ret_images = image_coeffs * images + (1 - image_coeffs) * images[::-1] + + ret_labels = label_smoothing(labels, args.num_classes, args.label_smoothing) + label_coeffs = coeffs.reshape(*coeffs.shape, 1) + ret_labels = label_coeffs * ret_labels + (1 - label_coeffs) * ret_labels[::-1] + else: + ret_images = images + if not sparse_label_loss: + ret_labels = label_smoothing(labels, args.num_classes, args.label_smoothing) + else: + ret_labels = labels + + return ret_images, ret_labels + + + best_accuracy = -1 + for epoch in range(begin_epoch, num_epoch): + tic = time.time() + train_data.reset() + eval_metric.reset() + loss_metric.reset() + btic = time.time() + + logging.info('Starting epoch {}'.format(epoch)) + outputs = [] + for i, batches in enumerate(train_data): + # synchronize to previous iteration + for o in outputs: + o.wait_to_read() + + trainer.set_learning_rate(lr_scheduler(epoch + i / epoch_size)) + + data = [b.data[0] for b in batches] + label = [b.label[0].as_in_context(b.data[0].context) for b in batches] + orig_label = label + + data, label = zip(*starmap(transform_data, zip(data, label))) + + outputs = [] + Ls = [] + with ag.record(): + for x, y in zip(data, label): + z = net(x) + L = loss(z, y) + # store the loss and do backward after we have done forward + # on all GPUs for better speed on multiple GPUs. + Ls.append(L) + outputs.append(z) + + if args.amp: + with amp.scale_loss(Ls, trainer) as scaled_loss: + ag.backward(scaled_loss) + else: + ag.backward(Ls) + + if 'horovod' in kvstore: + trainer.step(local_batch_size) + else: + trainer.step(total_batch_size) + + if print_loss: + loss_metric.update(..., np.mean([l.asnumpy() for l in Ls]).item()) + eval_metric.update(orig_label, outputs) + + if args.disp_batches and not (i + 1) % args.disp_batches: + name, acc = eval_metric.get() + if print_loss: + name = [loss_metric.get()[0]] + name + acc = [loss_metric.get()[1]] + acc + + logging.info('Epoch[{}] Batch [{}-{}]\tSpeed: {} samples/sec\tLR: {}\t{}'.format( + epoch, (i // args.disp_batches) * args.disp_batches, i, + args.disp_batches * total_batch_size / (time.time() - btic), trainer.learning_rate, + '\t'.join(list(map(lambda x: '{}: {:.6f}'.format(*x), zip(name, acc)))))) + eval_metric.reset_local() + loss_metric.reset_local() + btic = time.time() + + durations.append(time.time() - tic) + tic = time.time() + + + add_metrics_to_report(report, 'train', dict(eval_metric.get_global_name_value()), durations, total_batch_size, loss_metric if print_loss else None) + + if args.mode == 'train_val': + logging.info('Validating epoch {}'.format(epoch)) + score = model_score(args, net, eval_data, eval_metric, kvstore, report) + for name, value in zip(*score): + logging.info('Epoch[{}] Validation {:20}: {}'.format(epoch, name, value)) + + score = dict(zip(*score)) + accuracy = score.get('accuracy', -1) + save_checkpoint(net, epoch, accuracy, best_accuracy, model_prefix, args.save_frequency, kvstore) + best_accuracy = max(best_accuracy, accuracy) + + +def fit(args, model, data_loader): """ train a model args : argparse returns - network : the symbol definition of the nerual network + model : the the neural network model data_loader : function that returns the train and val data iterators """ start_time = time.time() + report = Report(args.arch, len(args.gpus), sys.argv) + + # select gpu for horovod process + if 'horovod' in args.kv_store: + hvd.init() + args.gpus = [args.gpus[hvd.local_rank()]] + + if args.amp: + amp.init() + + if args.seed is not None: + logging.info('Setting seeds to {}'.format(args.seed)) + random.seed(args.seed) + np.random.seed(args.seed) + mx.random.seed(args.seed) + # kvstore - kv = mx.kvstore.create(args.kv_store) - if args.gc_type != 'none': - kv.set_gradient_compression({'type': args.gc_type, - 'threshold': args.gc_threshold}) - if args.profile_server_suffix: - mx.profiler.set_config(filename=args.profile_server_suffix, profile_all=True, profile_process='server') - mx.profiler.set_state(state='run', profile_process='server') - - if args.profile_worker_suffix: - if kv.num_workers > 1: - filename = 'rank' + str(kv.rank) + '_' + args.profile_worker_suffix - else: - filename = args.profile_worker_suffix - mx.profiler.set_config(filename=filename, profile_all=True, profile_process='worker') - mx.profiler.set_state(state='run', profile_process='worker') - - # logging - head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s' - logging.basicConfig(level=logging.DEBUG, format=head) - logging.info('start with arguments %s', args) - - epoch_size = get_epoch_size(args, kv) - - # data iterators - (train, val) = data_loader(args, kv) - if 'dist' in args.kv_store and not 'async' in args.kv_store: - logging.info('Resizing training data to %d batches per machine', epoch_size) - # resize train iter to ensure each machine has same number of batches per epoch - # if not, dist_sync can hang at the end with one machine waiting for other machines - if not args.use_dali: - train = mx.io.ResizeIter(train, epoch_size) + if 'horovod' in args.kv_store: + kv = None + rank = hvd.rank() + num_workers = hvd.size() + else: + kv = mx.kvstore.create(args.kv_store) + rank = kv.rank + num_workers = kv.num_workers if args.test_io: + train, val = data_loader(args, kv) + + if args.test_io_mode == 'train': + data_iter = train + else: + data_iter = val + tic = time.time() - for i, batch in enumerate(train): + for i, batch in enumerate(data_iter): if isinstance(batch, list): for b in batch: for j in b.data: @@ -232,232 +443,90 @@ def fit(args, network, data_loader, **kwargs): for j in batch.data: j.wait_to_read() if (i + 1) % args.disp_batches == 0: - logging.info('Batch [%d]\tSpeed: %.2f samples/sec', i, - args.disp_batches * args.batch_size / (time.time() - tic)) + logging.info('Batch [{}]\tSpeed: {:.2f} samples/sec'.format( + i, args.disp_batches * args.batch_size / (time.time() - tic))) tic = time.time() return - # load model - if 'arg_params' in kwargs and 'aux_params' in kwargs: - arg_params = kwargs['arg_params'] - aux_params = kwargs['aux_params'] - else: - sym, arg_params, aux_params = _load_model(args, kv.rank) - - # save model - checkpoint = _save_model(args, kv.rank) - epoch_end_callbacks = [] - if checkpoint: - epoch_end_callbacks.append(checkpoint) + if not load_model(args, model): + # all initializers should be specified in the model definition. + # if not, this will raise an error + model.initialize(mx.init.Initializer()) # devices for training - devs = mx.cpu() if args.gpus is None or args.gpus == "" else [ - mx.gpu(int(i)) for i in args.gpus.split(',')] + devs = list(map(mx.gpu, args.gpus)) + model.collect_params().reset_ctx(devs) + + if args.mode == 'pred': + logging.info('Infering image {}'.format(args.data_pred)) + model_pred(args, model, data.load_image(args, args.data_pred, devs[0])) + return # learning rate - lr, lr_scheduler = _get_lr_scheduler(args, kv) - - # create model - model = mx.mod.Module( - context=devs, - symbol=network - ) + lr_scheduler = get_lr_scheduler(args) optimizer_params = { - 'learning_rate': lr, + 'learning_rate': 0, 'wd': args.wd, - 'lr_scheduler': lr_scheduler, - 'multi_precision': True} + 'multi_precision': True, + } # Only a limited number of optimizers have 'momentum' property has_momentum = {'sgd', 'dcasgd', 'nag', 'signum', 'lbsgd'} if args.optimizer in has_momentum: optimizer_params['momentum'] = args.mom - monitor = mx.mon.Monitor( - args.monitor, pattern=".*") if args.monitor > 0 else None - - # A limited number of optimizers have a warmup period - has_warmup = {'lbsgd', 'lbnag'} - if args.optimizer in has_warmup: - if 'dist' in args.kv_store: - nworkers = kv.num_workers - else: - nworkers = 1 - epoch_size = args.num_examples / args.batch_size / nworkers - - if epoch_size < 1: - epoch_size = 1 - macrobatch_size = args.macrobatch_size - if macrobatch_size < args.batch_size * nworkers: - macrobatch_size = args.batch_size * nworkers - #batch_scale = round(float(macrobatch_size) / args.batch_size / nworkers +0.4999) - batch_scale = math.ceil( - float(macrobatch_size) / args.batch_size / nworkers) - optimizer_params['updates_per_epoch'] = epoch_size - optimizer_params['begin_epoch'] = args.load_epoch if args.load_epoch else 0 - optimizer_params['batch_scale'] = batch_scale - optimizer_params['warmup_strategy'] = args.warmup_strategy - optimizer_params['warmup_epochs'] = args.warmup_epochs - optimizer_params['num_epochs'] = args.num_epochs - - if args.initializer == 'default': - initializer = mx.init.Xavier( - rnd_type='gaussian', factor_type="in", magnitude=2) - # initializer = mx.init.Xavier(factor_type="in", magnitude=2.34), - elif args.initializer == 'xavier': - initializer = mx.init.Xavier() - elif args.initializer == 'msra': - initializer = mx.init.MSRAPrelu() - elif args.initializer == 'orthogonal': - initializer = mx.init.Orthogonal() - elif args.initializer == 'normal': - initializer = mx.init.Normal() - elif args.initializer == 'uniform': - initializer = mx.init.Uniform() - elif args.initializer == 'one': - initializer = mx.init.One() - elif args.initializer == 'zero': - initializer = mx.init.Zero() - # evaluation metrices if not args.no_metrics: - eval_metrics = ['crossentropy', 'accuracy'] + eval_metrics = ['accuracy'] eval_metrics.append(mx.metric.create( 'top_k_accuracy', top_k=5)) else: eval_metrics = [] - supported_loss = ['ce', 'nll_loss'] - if len(args.loss) > 0: - # ce or nll loss is only applicable to softmax output - loss_type_list = args.loss.split(',') - if 'softmax_output' in network.list_outputs(): - for loss_type in loss_type_list: - loss_type = loss_type.strip() - if loss_type == 'nll': - loss_type = 'nll_loss' - if loss_type not in supported_loss: - logging.warning(loss_type + ' is not an valid loss type, only cross-entropy or ' \ - 'negative likelihood loss is supported!') - else: - eval_metrics.append(mx.metric.create(loss_type)) - else: - logging.warning("The output is not softmax_output, loss argument will be skipped!") - - # callbacks that run after each batch - batch_end_callbacks = [] - batch_end_callbacks.append(mx.callback.Speedometer( - args.batch_size, args.disp_batches)) - - if 'batch_end_callback' in kwargs: - cbs = kwargs['batch_end_callback'] - batch_end_callbacks += cbs if isinstance(cbs, list) else [cbs] - - - report = Report('resnet{}'.format(args.num_layers), len(args.gpus.split(',')), sys.argv) - + train, val = data_loader(args, kv) train = BenchmarkingDataIter(train, args.benchmark_iters) - val = BenchmarkingDataIter(val, args.benchmark_iters) + if val is not None: + val = BenchmarkingDataIter(val, args.benchmark_iters) - class Gatherer: - def __init__(self, report, mode, data_iter, total_bs=None): - self.report = report - self.mode = mode - self.total_bs = total_bs - self.data_iter = data_iter - self.clear() - - def clear(self): - self.num = 0 - self.top1 = 0 - self.top5 = 0 - self.loss = 0 - self.time = 0 - self.tic = 0 - - def gather_metrics(self, data): - params = dict(data.eval_metric.get_global_name_value()) - - if self.num != 0: - self.time += time.time() - self.tic - self.num += 1 - if not args.no_metrics: - self.top1 = params['accuracy'] - self.top5 = params['top_k_accuracy_5'] - self.loss = params['cross-entropy'] - - self.tic = time.time() - - def add_metrics(self, *a, **k): - top1 = self.top1 * 100 - top5 = self.top5 * 100 - loss = self.loss - if self.num <= 1: - time = float('nan') - else: - time = self.time / (self.num - 1) - data = self.data_iter.get_avg_time_and_clear() - if self.total_bs is not None: - compute_ips = self.total_bs / (time - data) - total_ips = self.total_bs / time - - if not args.no_metrics: - self.report.add_value('{}.top1'.format(self.mode), top1) - self.report.add_value('{}.top5'.format(self.mode), top5) - self.report.add_value('{}.loss'.format(self.mode), loss) - self.report.add_value('{}.time'.format(self.mode), time) - # self.report.add_value('{}.data'.format(self.mode), data) - if self.total_bs is not None: - # self.report.add_value('{}.compute_ips'.format(self.mode), compute_ips) - self.report.add_value('{}.total_ips'.format(self.mode), total_ips) - self.clear() - - def save_report(*a, **k): - report.set_total_duration(time.time() - start_time) - if args.report: - report.save(args.report) - - train_gatherer = Gatherer(report, 'train', train, args.batch_size) - eval_gatherer = Gatherer(report, 'val', val, args.batch_size) - - batch_end_callbacks = [train_gatherer.gather_metrics] + batch_end_callbacks - epoch_end_callbacks = [train_gatherer.add_metrics, save_report] + epoch_end_callbacks - - eval_batch_end_callbacks = [eval_gatherer.gather_metrics] - eval_end_callbacks = [eval_gatherer.add_metrics, save_report] + if 'horovod' in args.kv_store: + # Fetch and broadcast parameters + params = model.collect_params() + if params is not None: + hvd.broadcast_parameters(params, root_rank=0) # run - model.fit(train, - begin_epoch=args.load_epoch if args.load_epoch else 0, - num_epoch=args.num_epochs if not args.only_inference else 0, - eval_data=val, - eval_metric=eval_metrics, - kvstore=kv, - optimizer=args.optimizer, - optimizer_params=optimizer_params, - initializer=initializer, - arg_params=arg_params, - aux_params=aux_params, - batch_end_callback=batch_end_callbacks, - epoch_end_callback=epoch_end_callbacks, #checkpoint if args.use_dali else ,, - eval_batch_end_callback=eval_batch_end_callbacks, - eval_end_callback=eval_end_callbacks, - allow_missing=True, - monitor=monitor) + if args.mode in ['train_val', 'train']: + model_fit( + args, + model, + train, + begin_epoch=args.begin_epoch, + num_epoch=args.num_epochs, + eval_data=val, + eval_metric=eval_metrics, + kvstore=args.kv_store, + kv=kv, + optimizer=args.optimizer, + optimizer_params=optimizer_params, + lr_scheduler=lr_scheduler, + report=report, + model_prefix=args.model_prefix, + print_loss=not args.no_metrics, + ) + elif args.mode == 'val': + for epoch in range(args.num_epochs): # loop for benchmarking + score = model_score(args, model, val, eval_metrics, args.kv_store, report=report) + for name, value in zip(*score): + logging.info('Validation {:20}: {}'.format(name, value)) + else: + raise ValueError('Wrong mode') - if args.only_inference: - for epoch in range(args.num_epochs): - score = model.score(val, eval_metrics, batch_end_callback=eval_batch_end_callbacks, score_end_callback=eval_end_callbacks, epoch=epoch) - print('-------------') - for name, value in score: - print('{}: {}'.format(name, value)) + mx.nd.waitall() - if args.profile_server_suffix: - mx.profiler.set_state(state='run', profile_process='server') - if args.profile_worker_suffix: - mx.profiler.set_state(state='run', profile_process='worker') + report.set_total_duration(time.time() - start_time) + if args.report: + suffix = '-{}'.format(hvd.rank()) if 'horovod' in args.kv_store and hvd.rank() != 0 else '' + report.save(args.report + suffix) - save_report() - - print('Experiment took: {} sec'.format(report.total_duration)) + logging.info('Experiment took: {} sec'.format(report.total_duration)) diff --git a/MxNet/Classification/RN50v1.5/imagenet_classes.py b/MxNet/Classification/RN50v1.5/imagenet_classes.py new file mode 100644 index 00000000..82e6888b --- /dev/null +++ b/MxNet/Classification/RN50v1.5/imagenet_classes.py @@ -0,0 +1,1002 @@ +classes = { + 0: 'tench, Tinca tinca', + 1: 'goldfish, Carassius auratus', + 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', + 3: 'tiger shark, Galeocerdo cuvieri', + 4: 'hammerhead, hammerhead shark', + 5: 'electric ray, crampfish, numbfish, torpedo', + 6: 'stingray', + 7: 'cock', + 8: 'hen', + 9: 'ostrich, Struthio camelus', + 10: 'brambling, Fringilla montifringilla', + 11: 'goldfinch, Carduelis carduelis', + 12: 'house finch, linnet, Carpodacus mexicanus', + 13: 'junco, snowbird', + 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', + 15: 'robin, American robin, Turdus migratorius', + 16: 'bulbul', + 17: 'jay', + 18: 'magpie', + 19: 'chickadee', + 20: 'water ouzel, dipper', + 21: 'kite', + 22: 'bald eagle, American eagle, Haliaeetus leucocephalus', + 23: 'vulture', + 24: 'great grey owl, great gray owl, Strix nebulosa', + 25: 'European fire salamander, Salamandra salamandra', + 26: 'common newt, Triturus vulgaris', + 27: 'eft', + 28: 'spotted salamander, Ambystoma maculatum', + 29: 'axolotl, mud puppy, Ambystoma mexicanum', + 30: 'bullfrog, Rana catesbeiana', + 31: 'tree frog, tree-frog', + 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', + 33: 'loggerhead, loggerhead turtle, Caretta caretta', + 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', + 35: 'mud turtle', + 36: 'terrapin', + 37: 'box turtle, box tortoise', + 38: 'banded gecko', + 39: 'common iguana, iguana, Iguana iguana', + 40: 'American chameleon, anole, Anolis carolinensis', + 41: 'whiptail, whiptail lizard', + 42: 'agama', + 43: 'frilled lizard, Chlamydosaurus kingi', + 44: 'alligator lizard', + 45: 'Gila monster, Heloderma suspectum', + 46: 'green lizard, Lacerta viridis', + 47: 'African chameleon, Chamaeleo chamaeleon', + 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', + 49: 'African crocodile, Nile crocodile, Crocodylus niloticus', + 50: 'American alligator, Alligator mississipiensis', + 51: 'triceratops', + 52: 'thunder snake, worm snake, Carphophis amoenus', + 53: 'ringneck snake, ring-necked snake, ring snake', + 54: 'hognose snake, puff adder, sand viper', + 55: 'green snake, grass snake', + 56: 'king snake, kingsnake', + 57: 'garter snake, grass snake', + 58: 'water snake', + 59: 'vine snake', + 60: 'night snake, Hypsiglena torquata', + 61: 'boa constrictor, Constrictor constrictor', + 62: 'rock python, rock snake, Python sebae', + 63: 'Indian cobra, Naja naja', + 64: 'green mamba', + 65: 'sea snake', + 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', + 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus', + 68: 'sidewinder, horned rattlesnake, Crotalus cerastes', + 69: 'trilobite', + 70: 'harvestman, daddy longlegs, Phalangium opilio', + 71: 'scorpion', + 72: 'black and gold garden spider, Argiope aurantia', + 73: 'barn spider, Araneus cavaticus', + 74: 'garden spider, Aranea diademata', + 75: 'black widow, Latrodectus mactans', + 76: 'tarantula', + 77: 'wolf spider, hunting spider', + 78: 'tick', + 79: 'centipede', + 80: 'black grouse', + 81: 'ptarmigan', + 82: 'ruffed grouse, partridge, Bonasa umbellus', + 83: 'prairie chicken, prairie grouse, prairie fowl', + 84: 'peacock', + 85: 'quail', + 86: 'partridge', + 87: 'African grey, African gray, Psittacus erithacus', + 88: 'macaw', + 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', + 90: 'lorikeet', + 91: 'coucal', + 92: 'bee eater', + 93: 'hornbill', + 94: 'hummingbird', + 95: 'jacamar', + 96: 'toucan', + 97: 'drake', + 98: 'red-breasted merganser, Mergus serrator', + 99: 'goose', + 100: 'black swan, Cygnus atratus', + 101: 'tusker', + 102: 'echidna, spiny anteater, anteater', + 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', + 104: 'wallaby, brush kangaroo', + 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', + 106: 'wombat', + 107: 'jellyfish', + 108: 'sea anemone, anemone', + 109: 'brain coral', + 110: 'flatworm, platyhelminth', + 111: 'nematode, nematode worm, roundworm', + 112: 'conch', + 113: 'snail', + 114: 'slug', + 115: 'sea slug, nudibranch', + 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore', + 117: 'chambered nautilus, pearly nautilus, nautilus', + 118: 'Dungeness crab, Cancer magister', + 119: 'rock crab, Cancer irroratus', + 120: 'fiddler crab', + 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', + 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus', + 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', + 124: 'crayfish, crawfish, crawdad, crawdaddy', + 125: 'hermit crab', + 126: 'isopod', + 127: 'white stork, Ciconia ciconia', + 128: 'black stork, Ciconia nigra', + 129: 'spoonbill', + 130: 'flamingo', + 131: 'little blue heron, Egretta caerulea', + 132: 'American egret, great white heron, Egretta albus', + 133: 'bittern', + 134: 'crane', + 135: 'limpkin, Aramus pictus', + 136: 'European gallinule, Porphyrio porphyrio', + 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana', + 138: 'bustard', + 139: 'ruddy turnstone, Arenaria interpres', + 140: 'red-backed sandpiper, dunlin, Erolia alpina', + 141: 'redshank, Tringa totanus', + 142: 'dowitcher', + 143: 'oystercatcher, oyster catcher', + 144: 'pelican', + 145: 'king penguin, Aptenodytes patagonica', + 146: 'albatross, mollymawk', + 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', + 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', + 149: 'dugong, Dugong dugon', + 150: 'sea lion', + 151: 'Chihuahua', + 152: 'Japanese spaniel', + 153: 'Maltese dog, Maltese terrier, Maltese', + 154: 'Pekinese, Pekingese, Peke', + 155: 'Shih-Tzu', + 156: 'Blenheim spaniel', + 157: 'papillon', + 158: 'toy terrier', + 159: 'Rhodesian ridgeback', + 160: 'Afghan hound, Afghan', + 161: 'basset, basset hound', + 162: 'beagle', + 163: 'bloodhound, sleuthhound', + 164: 'bluetick', + 165: 'black-and-tan coonhound', + 166: 'Walker hound, Walker foxhound', + 167: 'English foxhound', + 168: 'redbone', + 169: 'borzoi, Russian wolfhound', + 170: 'Irish wolfhound', + 171: 'Italian greyhound', + 172: 'whippet', + 173: 'Ibizan hound, Ibizan Podenco', + 174: 'Norwegian elkhound, elkhound', + 175: 'otterhound, otter hound', + 176: 'Saluki, gazelle hound', + 177: 'Scottish deerhound, deerhound', + 178: 'Weimaraner', + 179: 'Staffordshire bullterrier, Staffordshire bull terrier', + 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', + 181: 'Bedlington terrier', + 182: 'Border terrier', + 183: 'Kerry blue terrier', + 184: 'Irish terrier', + 185: 'Norfolk terrier', + 186: 'Norwich terrier', + 187: 'Yorkshire terrier', + 188: 'wire-haired fox terrier', + 189: 'Lakeland terrier', + 190: 'Sealyham terrier, Sealyham', + 191: 'Airedale, Airedale terrier', + 192: 'cairn, cairn terrier', + 193: 'Australian terrier', + 194: 'Dandie Dinmont, Dandie Dinmont terrier', + 195: 'Boston bull, Boston terrier', + 196: 'miniature schnauzer', + 197: 'giant schnauzer', + 198: 'standard schnauzer', + 199: 'Scotch terrier, Scottish terrier, Scottie', + 200: 'Tibetan terrier, chrysanthemum dog', + 201: 'silky terrier, Sydney silky', + 202: 'soft-coated wheaten terrier', + 203: 'West Highland white terrier', + 204: 'Lhasa, Lhasa apso', + 205: 'flat-coated retriever', + 206: 'curly-coated retriever', + 207: 'golden retriever', + 208: 'Labrador retriever', + 209: 'Chesapeake Bay retriever', + 210: 'German short-haired pointer', + 211: 'vizsla, Hungarian pointer', + 212: 'English setter', + 213: 'Irish setter, red setter', + 214: 'Gordon setter', + 215: 'Brittany spaniel', + 216: 'clumber, clumber spaniel', + 217: 'English springer, English springer spaniel', + 218: 'Welsh springer spaniel', + 219: 'cocker spaniel, English cocker spaniel, cocker', + 220: 'Sussex spaniel', + 221: 'Irish water spaniel', + 222: 'kuvasz', + 223: 'schipperke', + 224: 'groenendael', + 225: 'malinois', + 226: 'briard', + 227: 'kelpie', + 228: 'komondor', + 229: 'Old English sheepdog, bobtail', + 230: 'Shetland sheepdog, Shetland sheep dog, Shetland', + 231: 'collie', + 232: 'Border collie', + 233: 'Bouvier des Flandres, Bouviers des Flandres', + 234: 'Rottweiler', + 235: 'German shepherd, German shepherd dog, German police dog, alsatian', + 236: 'Doberman, Doberman pinscher', + 237: 'miniature pinscher', + 238: 'Greater Swiss Mountain dog', + 239: 'Bernese mountain dog', + 240: 'Appenzeller', + 241: 'EntleBucher', + 242: 'boxer', + 243: 'bull mastiff', + 244: 'Tibetan mastiff', + 245: 'French bulldog', + 246: 'Great Dane', + 247: 'Saint Bernard, St Bernard', + 248: 'Eskimo dog, husky', + 249: 'malamute, malemute, Alaskan malamute', + 250: 'Siberian husky', + 251: 'dalmatian, coach dog, carriage dog', + 252: 'affenpinscher, monkey pinscher, monkey dog', + 253: 'basenji', + 254: 'pug, pug-dog', + 255: 'Leonberg', + 256: 'Newfoundland, Newfoundland dog', + 257: 'Great Pyrenees', + 258: 'Samoyed, Samoyede', + 259: 'Pomeranian', + 260: 'chow, chow chow', + 261: 'keeshond', + 262: 'Brabancon griffon', + 263: 'Pembroke, Pembroke Welsh corgi', + 264: 'Cardigan, Cardigan Welsh corgi', + 265: 'toy poodle', + 266: 'miniature poodle', + 267: 'standard poodle', + 268: 'Mexican hairless', + 269: 'timber wolf, grey wolf, gray wolf, Canis lupus', + 270: 'white wolf, Arctic wolf, Canis lupus tundrarum', + 271: 'red wolf, maned wolf, Canis rufus, Canis niger', + 272: 'coyote, prairie wolf, brush wolf, Canis latrans', + 273: 'dingo, warrigal, warragal, Canis dingo', + 274: 'dhole, Cuon alpinus', + 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', + 276: 'hyena, hyaena', + 277: 'red fox, Vulpes vulpes', + 278: 'kit fox, Vulpes macrotis', + 279: 'Arctic fox, white fox, Alopex lagopus', + 280: 'grey fox, gray fox, Urocyon cinereoargenteus', + 281: 'tabby, tabby cat', + 282: 'tiger cat', + 283: 'Persian cat', + 284: 'Siamese cat, Siamese', + 285: 'Egyptian cat', + 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', + 287: 'lynx, catamount', + 288: 'leopard, Panthera pardus', + 289: 'snow leopard, ounce, Panthera uncia', + 290: 'jaguar, panther, Panthera onca, Felis onca', + 291: 'lion, king of beasts, Panthera leo', + 292: 'tiger, Panthera tigris', + 293: 'cheetah, chetah, Acinonyx jubatus', + 294: 'brown bear, bruin, Ursus arctos', + 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus', + 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', + 297: 'sloth bear, Melursus ursinus, Ursus ursinus', + 298: 'mongoose', + 299: 'meerkat, mierkat', + 300: 'tiger beetle', + 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', + 302: 'ground beetle, carabid beetle', + 303: 'long-horned beetle, longicorn, longicorn beetle', + 304: 'leaf beetle, chrysomelid', + 305: 'dung beetle', + 306: 'rhinoceros beetle', + 307: 'weevil', + 308: 'fly', + 309: 'bee', + 310: 'ant, emmet, pismire', + 311: 'grasshopper, hopper', + 312: 'cricket', + 313: 'walking stick, walkingstick, stick insect', + 314: 'cockroach, roach', + 315: 'mantis, mantid', + 316: 'cicada, cicala', + 317: 'leafhopper', + 318: 'lacewing, lacewing fly', + 319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", + 320: 'damselfly', + 321: 'admiral', + 322: 'ringlet, ringlet butterfly', + 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', + 324: 'cabbage butterfly', + 325: 'sulphur butterfly, sulfur butterfly', + 326: 'lycaenid, lycaenid butterfly', + 327: 'starfish, sea star', + 328: 'sea urchin', + 329: 'sea cucumber, holothurian', + 330: 'wood rabbit, cottontail, cottontail rabbit', + 331: 'hare', + 332: 'Angora, Angora rabbit', + 333: 'hamster', + 334: 'porcupine, hedgehog', + 335: 'fox squirrel, eastern fox squirrel, Sciurus niger', + 336: 'marmot', + 337: 'beaver', + 338: 'guinea pig, Cavia cobaya', + 339: 'sorrel', + 340: 'zebra', + 341: 'hog, pig, grunter, squealer, Sus scrofa', + 342: 'wild boar, boar, Sus scrofa', + 343: 'warthog', + 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius', + 345: 'ox', + 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', + 347: 'bison', + 348: 'ram, tup', + 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', + 350: 'ibex, Capra ibex', + 351: 'hartebeest', + 352: 'impala, Aepyceros melampus', + 353: 'gazelle', + 354: 'Arabian camel, dromedary, Camelus dromedarius', + 355: 'llama', + 356: 'weasel', + 357: 'mink', + 358: 'polecat, fitch, foulmart, foumart, Mustela putorius', + 359: 'black-footed ferret, ferret, Mustela nigripes', + 360: 'otter', + 361: 'skunk, polecat, wood pussy', + 362: 'badger', + 363: 'armadillo', + 364: 'three-toed sloth, ai, Bradypus tridactylus', + 365: 'orangutan, orang, orangutang, Pongo pygmaeus', + 366: 'gorilla, Gorilla gorilla', + 367: 'chimpanzee, chimp, Pan troglodytes', + 368: 'gibbon, Hylobates lar', + 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus', + 370: 'guenon, guenon monkey', + 371: 'patas, hussar monkey, Erythrocebus patas', + 372: 'baboon', + 373: 'macaque', + 374: 'langur', + 375: 'colobus, colobus monkey', + 376: 'proboscis monkey, Nasalis larvatus', + 377: 'marmoset', + 378: 'capuchin, ringtail, Cebus capucinus', + 379: 'howler monkey, howler', + 380: 'titi, titi monkey', + 381: 'spider monkey, Ateles geoffroyi', + 382: 'squirrel monkey, Saimiri sciureus', + 383: 'Madagascar cat, ring-tailed lemur, Lemur catta', + 384: 'indri, indris, Indri indri, Indri brevicaudatus', + 385: 'Indian elephant, Elephas maximus', + 386: 'African elephant, Loxodonta africana', + 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', + 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', + 389: 'barracouta, snoek', + 390: 'eel', + 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', + 392: 'rock beauty, Holocanthus tricolor', + 393: 'anemone fish', + 394: 'sturgeon', + 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus', + 396: 'lionfish', + 397: 'puffer, pufferfish, blowfish, globefish', + 398: 'abacus', + 399: 'abaya', + 400: "academic gown, academic robe, judge's robe", + 401: 'accordion, piano accordion, squeeze box', + 402: 'acoustic guitar', + 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier', + 404: 'airliner', + 405: 'airship, dirigible', + 406: 'altar', + 407: 'ambulance', + 408: 'amphibian, amphibious vehicle', + 409: 'analog clock', + 410: 'apiary, bee house', + 411: 'apron', + 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', + 413: 'assault rifle, assault gun', + 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack', + 415: 'bakery, bakeshop, bakehouse', + 416: 'balance beam, beam', + 417: 'balloon', + 418: 'ballpoint, ballpoint pen, ballpen, Biro', + 419: 'Band Aid', + 420: 'banjo', + 421: 'bannister, banister, balustrade, balusters, handrail', + 422: 'barbell', + 423: 'barber chair', + 424: 'barbershop', + 425: 'barn', + 426: 'barometer', + 427: 'barrel, cask', + 428: 'barrow, garden cart, lawn cart, wheelbarrow', + 429: 'baseball', + 430: 'basketball', + 431: 'bassinet', + 432: 'bassoon', + 433: 'bathing cap, swimming cap', + 434: 'bath towel', + 435: 'bathtub, bathing tub, bath, tub', + 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', + 437: 'beacon, lighthouse, beacon light, pharos', + 438: 'beaker', + 439: 'bearskin, busby, shako', + 440: 'beer bottle', + 441: 'beer glass', + 442: 'bell cote, bell cot', + 443: 'bib', + 444: 'bicycle-built-for-two, tandem bicycle, tandem', + 445: 'bikini, two-piece', + 446: 'binder, ring-binder', + 447: 'binoculars, field glasses, opera glasses', + 448: 'birdhouse', + 449: 'boathouse', + 450: 'bobsled, bobsleigh, bob', + 451: 'bolo tie, bolo, bola tie, bola', + 452: 'bonnet, poke bonnet', + 453: 'bookcase', + 454: 'bookshop, bookstore, bookstall', + 455: 'bottlecap', + 456: 'bow', + 457: 'bow tie, bow-tie, bowtie', + 458: 'brass, memorial tablet, plaque', + 459: 'brassiere, bra, bandeau', + 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', + 461: 'breastplate, aegis, egis', + 462: 'broom', + 463: 'bucket, pail', + 464: 'buckle', + 465: 'bulletproof vest', + 466: 'bullet train, bullet', + 467: 'butcher shop, meat market', + 468: 'cab, hack, taxi, taxicab', + 469: 'caldron, cauldron', + 470: 'candle, taper, wax light', + 471: 'cannon', + 472: 'canoe', + 473: 'can opener, tin opener', + 474: 'cardigan', + 475: 'car mirror', + 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig', + 477: "carpenter's kit, tool kit", + 478: 'carton', + 479: 'car wheel', + 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', + 481: 'cassette', + 482: 'cassette player', + 483: 'castle', + 484: 'catamaran', + 485: 'CD player', + 486: 'cello, violoncello', + 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone', + 488: 'chain', + 489: 'chainlink fence', + 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', + 491: 'chain saw, chainsaw', + 492: 'chest', + 493: 'chiffonier, commode', + 494: 'chime, bell, gong', + 495: 'china cabinet, china closet', + 496: 'Christmas stocking', + 497: 'church, church building', + 498: 'cinema, movie theater, movie theatre, movie house, picture palace', + 499: 'cleaver, meat cleaver, chopper', + 500: 'cliff dwelling', + 501: 'cloak', + 502: 'clog, geta, patten, sabot', + 503: 'cocktail shaker', + 504: 'coffee mug', + 505: 'coffeepot', + 506: 'coil, spiral, volute, whorl, helix', + 507: 'combination lock', + 508: 'computer keyboard, keypad', + 509: 'confectionery, confectionary, candy store', + 510: 'container ship, containership, container vessel', + 511: 'convertible', + 512: 'corkscrew, bottle screw', + 513: 'cornet, horn, trumpet, trump', + 514: 'cowboy boot', + 515: 'cowboy hat, ten-gallon hat', + 516: 'cradle', + 517: 'crane', + 518: 'crash helmet', + 519: 'crate', + 520: 'crib, cot', + 521: 'Crock Pot', + 522: 'croquet ball', + 523: 'crutch', + 524: 'cuirass', + 525: 'dam, dike, dyke', + 526: 'desk', + 527: 'desktop computer', + 528: 'dial telephone, dial phone', + 529: 'diaper, nappy, napkin', + 530: 'digital clock', + 531: 'digital watch', + 532: 'dining table, board', + 533: 'dishrag, dishcloth', + 534: 'dishwasher, dish washer, dishwashing machine', + 535: 'disk brake, disc brake', + 536: 'dock, dockage, docking facility', + 537: 'dogsled, dog sled, dog sleigh', + 538: 'dome', + 539: 'doormat, welcome mat', + 540: 'drilling platform, offshore rig', + 541: 'drum, membranophone, tympan', + 542: 'drumstick', + 543: 'dumbbell', + 544: 'Dutch oven', + 545: 'electric fan, blower', + 546: 'electric guitar', + 547: 'electric locomotive', + 548: 'entertainment center', + 549: 'envelope', + 550: 'espresso maker', + 551: 'face powder', + 552: 'feather boa, boa', + 553: 'file, file cabinet, filing cabinet', + 554: 'fireboat', + 555: 'fire engine, fire truck', + 556: 'fire screen, fireguard', + 557: 'flagpole, flagstaff', + 558: 'flute, transverse flute', + 559: 'folding chair', + 560: 'football helmet', + 561: 'forklift', + 562: 'fountain', + 563: 'fountain pen', + 564: 'four-poster', + 565: 'freight car', + 566: 'French horn, horn', + 567: 'frying pan, frypan, skillet', + 568: 'fur coat', + 569: 'garbage truck, dustcart', + 570: 'gasmask, respirator, gas helmet', + 571: 'gas pump, gasoline pump, petrol pump, island dispenser', + 572: 'goblet', + 573: 'go-kart', + 574: 'golf ball', + 575: 'golfcart, golf cart', + 576: 'gondola', + 577: 'gong, tam-tam', + 578: 'gown', + 579: 'grand piano, grand', + 580: 'greenhouse, nursery, glasshouse', + 581: 'grille, radiator grille', + 582: 'grocery store, grocery, food market, market', + 583: 'guillotine', + 584: 'hair slide', + 585: 'hair spray', + 586: 'half track', + 587: 'hammer', + 588: 'hamper', + 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier', + 590: 'hand-held computer, hand-held microcomputer', + 591: 'handkerchief, hankie, hanky, hankey', + 592: 'hard disc, hard disk, fixed disk', + 593: 'harmonica, mouth organ, harp, mouth harp', + 594: 'harp', + 595: 'harvester, reaper', + 596: 'hatchet', + 597: 'holster', + 598: 'home theater, home theatre', + 599: 'honeycomb', + 600: 'hook, claw', + 601: 'hoopskirt, crinoline', + 602: 'horizontal bar, high bar', + 603: 'horse cart, horse-cart', + 604: 'hourglass', + 605: 'iPod', + 606: 'iron, smoothing iron', + 607: "jack-o'-lantern", + 608: 'jean, blue jean, denim', + 609: 'jeep, landrover', + 610: 'jersey, T-shirt, tee shirt', + 611: 'jigsaw puzzle', + 612: 'jinrikisha, ricksha, rickshaw', + 613: 'joystick', + 614: 'kimono', + 615: 'knee pad', + 616: 'knot', + 617: 'lab coat, laboratory coat', + 618: 'ladle', + 619: 'lampshade, lamp shade', + 620: 'laptop, laptop computer', + 621: 'lawn mower, mower', + 622: 'lens cap, lens cover', + 623: 'letter opener, paper knife, paperknife', + 624: 'library', + 625: 'lifeboat', + 626: 'lighter, light, igniter, ignitor', + 627: 'limousine, limo', + 628: 'liner, ocean liner', + 629: 'lipstick, lip rouge', + 630: 'Loafer', + 631: 'lotion', + 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', + 633: "loupe, jeweler's loupe", + 634: 'lumbermill, sawmill', + 635: 'magnetic compass', + 636: 'mailbag, postbag', + 637: 'mailbox, letter box', + 638: 'maillot', + 639: 'maillot, tank suit', + 640: 'manhole cover', + 641: 'maraca', + 642: 'marimba, xylophone', + 643: 'mask', + 644: 'matchstick', + 645: 'maypole', + 646: 'maze, labyrinth', + 647: 'measuring cup', + 648: 'medicine chest, medicine cabinet', + 649: 'megalith, megalithic structure', + 650: 'microphone, mike', + 651: 'microwave, microwave oven', + 652: 'military uniform', + 653: 'milk can', + 654: 'minibus', + 655: 'miniskirt, mini', + 656: 'minivan', + 657: 'missile', + 658: 'mitten', + 659: 'mixing bowl', + 660: 'mobile home, manufactured home', + 661: 'Model T', + 662: 'modem', + 663: 'monastery', + 664: 'monitor', + 665: 'moped', + 666: 'mortar', + 667: 'mortarboard', + 668: 'mosque', + 669: 'mosquito net', + 670: 'motor scooter, scooter', + 671: 'mountain bike, all-terrain bike, off-roader', + 672: 'mountain tent', + 673: 'mouse, computer mouse', + 674: 'mousetrap', + 675: 'moving van', + 676: 'muzzle', + 677: 'nail', + 678: 'neck brace', + 679: 'necklace', + 680: 'nipple', + 681: 'notebook, notebook computer', + 682: 'obelisk', + 683: 'oboe, hautboy, hautbois', + 684: 'ocarina, sweet potato', + 685: 'odometer, hodometer, mileometer, milometer', + 686: 'oil filter', + 687: 'organ, pipe organ', + 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO', + 689: 'overskirt', + 690: 'oxcart', + 691: 'oxygen mask', + 692: 'packet', + 693: 'paddle, boat paddle', + 694: 'paddlewheel, paddle wheel', + 695: 'padlock', + 696: 'paintbrush', + 697: "pajama, pyjama, pj's, jammies", + 698: 'palace', + 699: 'panpipe, pandean pipe, syrinx', + 700: 'paper towel', + 701: 'parachute, chute', + 702: 'parallel bars, bars', + 703: 'park bench', + 704: 'parking meter', + 705: 'passenger car, coach, carriage', + 706: 'patio, terrace', + 707: 'pay-phone, pay-station', + 708: 'pedestal, plinth, footstall', + 709: 'pencil box, pencil case', + 710: 'pencil sharpener', + 711: 'perfume, essence', + 712: 'Petri dish', + 713: 'photocopier', + 714: 'pick, plectrum, plectron', + 715: 'pickelhaube', + 716: 'picket fence, paling', + 717: 'pickup, pickup truck', + 718: 'pier', + 719: 'piggy bank, penny bank', + 720: 'pill bottle', + 721: 'pillow', + 722: 'ping-pong ball', + 723: 'pinwheel', + 724: 'pirate, pirate ship', + 725: 'pitcher, ewer', + 726: "plane, carpenter's plane, woodworking plane", + 727: 'planetarium', + 728: 'plastic bag', + 729: 'plate rack', + 730: 'plow, plough', + 731: "plunger, plumber's helper", + 732: 'Polaroid camera, Polaroid Land camera', + 733: 'pole', + 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', + 735: 'poncho', + 736: 'pool table, billiard table, snooker table', + 737: 'pop bottle, soda bottle', + 738: 'pot, flowerpot', + 739: "potter's wheel", + 740: 'power drill', + 741: 'prayer rug, prayer mat', + 742: 'printer', + 743: 'prison, prison house', + 744: 'projectile, missile', + 745: 'projector', + 746: 'puck, hockey puck', + 747: 'punching bag, punch bag, punching ball, punchball', + 748: 'purse', + 749: 'quill, quill pen', + 750: 'quilt, comforter, comfort, puff', + 751: 'racer, race car, racing car', + 752: 'racket, racquet', + 753: 'radiator', + 754: 'radio, wireless', + 755: 'radio telescope, radio reflector', + 756: 'rain barrel', + 757: 'recreational vehicle, RV, R.V.', + 758: 'reel', + 759: 'reflex camera', + 760: 'refrigerator, icebox', + 761: 'remote control, remote', + 762: 'restaurant, eating house, eating place, eatery', + 763: 'revolver, six-gun, six-shooter', + 764: 'rifle', + 765: 'rocking chair, rocker', + 766: 'rotisserie', + 767: 'rubber eraser, rubber, pencil eraser', + 768: 'rugby ball', + 769: 'rule, ruler', + 770: 'running shoe', + 771: 'safe', + 772: 'safety pin', + 773: 'saltshaker, salt shaker', + 774: 'sandal', + 775: 'sarong', + 776: 'sax, saxophone', + 777: 'scabbard', + 778: 'scale, weighing machine', + 779: 'school bus', + 780: 'schooner', + 781: 'scoreboard', + 782: 'screen, CRT screen', + 783: 'screw', + 784: 'screwdriver', + 785: 'seat belt, seatbelt', + 786: 'sewing machine', + 787: 'shield, buckler', + 788: 'shoe shop, shoe-shop, shoe store', + 789: 'shoji', + 790: 'shopping basket', + 791: 'shopping cart', + 792: 'shovel', + 793: 'shower cap', + 794: 'shower curtain', + 795: 'ski', + 796: 'ski mask', + 797: 'sleeping bag', + 798: 'slide rule, slipstick', + 799: 'sliding door', + 800: 'slot, one-armed bandit', + 801: 'snorkel', + 802: 'snowmobile', + 803: 'snowplow, snowplough', + 804: 'soap dispenser', + 805: 'soccer ball', + 806: 'sock', + 807: 'solar dish, solar collector, solar furnace', + 808: 'sombrero', + 809: 'soup bowl', + 810: 'space bar', + 811: 'space heater', + 812: 'space shuttle', + 813: 'spatula', + 814: 'speedboat', + 815: "spider web, spider's web", + 816: 'spindle', + 817: 'sports car, sport car', + 818: 'spotlight, spot', + 819: 'stage', + 820: 'steam locomotive', + 821: 'steel arch bridge', + 822: 'steel drum', + 823: 'stethoscope', + 824: 'stole', + 825: 'stone wall', + 826: 'stopwatch, stop watch', + 827: 'stove', + 828: 'strainer', + 829: 'streetcar, tram, tramcar, trolley, trolley car', + 830: 'stretcher', + 831: 'studio couch, day bed', + 832: 'stupa, tope', + 833: 'submarine, pigboat, sub, U-boat', + 834: 'suit, suit of clothes', + 835: 'sundial', + 836: 'sunglass', + 837: 'sunglasses, dark glasses, shades', + 838: 'sunscreen, sunblock, sun blocker', + 839: 'suspension bridge', + 840: 'swab, swob, mop', + 841: 'sweatshirt', + 842: 'swimming trunks, bathing trunks', + 843: 'swing', + 844: 'switch, electric switch, electrical switch', + 845: 'syringe', + 846: 'table lamp', + 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle', + 848: 'tape player', + 849: 'teapot', + 850: 'teddy, teddy bear', + 851: 'television, television system', + 852: 'tennis ball', + 853: 'thatch, thatched roof', + 854: 'theater curtain, theatre curtain', + 855: 'thimble', + 856: 'thresher, thrasher, threshing machine', + 857: 'throne', + 858: 'tile roof', + 859: 'toaster', + 860: 'tobacco shop, tobacconist shop, tobacconist', + 861: 'toilet seat', + 862: 'torch', + 863: 'totem pole', + 864: 'tow truck, tow car, wrecker', + 865: 'toyshop', + 866: 'tractor', + 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', + 868: 'tray', + 869: 'trench coat', + 870: 'tricycle, trike, velocipede', + 871: 'trimaran', + 872: 'tripod', + 873: 'triumphal arch', + 874: 'trolleybus, trolley coach, trackless trolley', + 875: 'trombone', + 876: 'tub, vat', + 877: 'turnstile', + 878: 'typewriter keyboard', + 879: 'umbrella', + 880: 'unicycle, monocycle', + 881: 'upright, upright piano', + 882: 'vacuum, vacuum cleaner', + 883: 'vase', + 884: 'vault', + 885: 'velvet', + 886: 'vending machine', + 887: 'vestment', + 888: 'viaduct', + 889: 'violin, fiddle', + 890: 'volleyball', + 891: 'waffle iron', + 892: 'wall clock', + 893: 'wallet, billfold, notecase, pocketbook', + 894: 'wardrobe, closet, press', + 895: 'warplane, military plane', + 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', + 897: 'washer, automatic washer, washing machine', + 898: 'water bottle', + 899: 'water jug', + 900: 'water tower', + 901: 'whiskey jug', + 902: 'whistle', + 903: 'wig', + 904: 'window screen', + 905: 'window shade', + 906: 'Windsor tie', + 907: 'wine bottle', + 908: 'wing', + 909: 'wok', + 910: 'wooden spoon', + 911: 'wool, woolen, woollen', + 912: 'worm fence, snake fence, snake-rail fence, Virginia fence', + 913: 'wreck', + 914: 'yawl', + 915: 'yurt', + 916: 'web site, website, internet site, site', + 917: 'comic book', + 918: 'crossword puzzle, crossword', + 919: 'street sign', + 920: 'traffic light, traffic signal, stoplight', + 921: 'book jacket, dust cover, dust jacket, dust wrapper', + 922: 'menu', + 923: 'plate', + 924: 'guacamole', + 925: 'consomme', + 926: 'hot pot, hotpot', + 927: 'trifle', + 928: 'ice cream, icecream', + 929: 'ice lolly, lolly, lollipop, popsicle', + 930: 'French loaf', + 931: 'bagel, beigel', + 932: 'pretzel', + 933: 'cheeseburger', + 934: 'hotdog, hot dog, red hot', + 935: 'mashed potato', + 936: 'head cabbage', + 937: 'broccoli', + 938: 'cauliflower', + 939: 'zucchini, courgette', + 940: 'spaghetti squash', + 941: 'acorn squash', + 942: 'butternut squash', + 943: 'cucumber, cuke', + 944: 'artichoke, globe artichoke', + 945: 'bell pepper', + 946: 'cardoon', + 947: 'mushroom', + 948: 'Granny Smith', + 949: 'strawberry', + 950: 'orange', + 951: 'lemon', + 952: 'fig', + 953: 'pineapple, ananas', + 954: 'banana', + 955: 'jackfruit, jak, jack', + 956: 'custard apple', + 957: 'pomegranate', + 958: 'hay', + 959: 'carbonara', + 960: 'chocolate sauce, chocolate syrup', + 961: 'dough', + 962: 'meat loaf, meatloaf', + 963: 'pizza, pizza pie', + 964: 'potpie', + 965: 'burrito', + 966: 'red wine', + 967: 'espresso', + 968: 'cup', + 969: 'eggnog', + 970: 'alp', + 971: 'bubble', + 972: 'cliff, drop, drop-off', + 973: 'coral reef', + 974: 'geyser', + 975: 'lakeside, lakeshore', + 976: 'promontory, headland, head, foreland', + 977: 'sandbar, sand bar', + 978: 'seashore, coast, seacoast, sea-coast', + 979: 'valley, vale', + 980: 'volcano', + 981: 'ballplayer, baseball player', + 982: 'groom, bridegroom', + 983: 'scuba diver', + 984: 'rapeseed', + 985: 'daisy', + 986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", + 987: 'corn', + 988: 'acorn', + 989: 'hip, rose hip, rosehip', + 990: 'buckeye, horse chestnut, conker', + 991: 'coral fungus', + 992: 'agaric', + 993: 'gyromitra', + 994: 'stinkhorn, carrion fungus', + 995: 'earthstar', + 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', + 997: 'bolete', + 998: 'ear, spike, capitulum', + 999: 'toilet tissue, toilet paper, bathroom tissue' +} diff --git a/MxNet/Classification/RN50v1.5/img/training_loss.png b/MxNet/Classification/RN50v1.5/img/dgx1-16g_250e_training_loss.png similarity index 100% rename from MxNet/Classification/RN50v1.5/img/training_loss.png rename to MxNet/Classification/RN50v1.5/img/dgx1-16g_250e_training_loss.png diff --git a/MxNet/Classification/RN50v1.5/img/dgx1-16g_250e_validation_top1.png b/MxNet/Classification/RN50v1.5/img/dgx1-16g_250e_validation_top1.png new file mode 100644 index 00000000..5ac6716d Binary files /dev/null and b/MxNet/Classification/RN50v1.5/img/dgx1-16g_250e_validation_top1.png differ diff --git a/MxNet/Classification/RN50v1.5/img/dgx1-16g_250e_validation_top5.png b/MxNet/Classification/RN50v1.5/img/dgx1-16g_250e_validation_top5.png new file mode 100644 index 00000000..fee347a6 Binary files /dev/null and b/MxNet/Classification/RN50v1.5/img/dgx1-16g_250e_validation_top5.png differ diff --git a/MxNet/Classification/RN50v1.5/img/training_accuracy.png b/MxNet/Classification/RN50v1.5/img/training_accuracy.png deleted file mode 100644 index 9673d11a..00000000 Binary files a/MxNet/Classification/RN50v1.5/img/training_accuracy.png and /dev/null differ diff --git a/MxNet/Classification/RN50v1.5/img/validation_accuracy.png b/MxNet/Classification/RN50v1.5/img/validation_accuracy.png deleted file mode 100644 index 02685ea3..00000000 Binary files a/MxNet/Classification/RN50v1.5/img/validation_accuracy.png and /dev/null differ diff --git a/MxNet/Classification/RN50v1.5/models.py b/MxNet/Classification/RN50v1.5/models.py new file mode 100644 index 00000000..43b5ae09 --- /dev/null +++ b/MxNet/Classification/RN50v1.5/models.py @@ -0,0 +1,522 @@ +# Copyright 2017-2018 The Apache Software Foundation +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +# ----------------------------------------------------------------------- +# +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import copy + +import mxnet as mx +from mxnet.gluon.block import HybridBlock +from mxnet.gluon import nn + +def add_model_args(parser): + model = parser.add_argument_group('Model') + model.add_argument('--arch', default='resnetv15', + choices=['resnetv1', 'resnetv15', + 'resnextv1', 'resnextv15', + 'xception'], + help='model architecture') + model.add_argument('--num-layers', type=int, default=50, + help='number of layers in the neural network, \ + required by some networks such as resnet') + model.add_argument('--num-groups', type=int, default=32, + help='number of groups for grouped convolutions, \ + required by some networks such as resnext') + model.add_argument('--num-classes', type=int, default=1000, + help='the number of classes') + model.add_argument('--batchnorm-eps', type=float, default=1e-5, + help='the amount added to the batchnorm variance to prevent output explosion.') + model.add_argument('--batchnorm-mom', type=float, default=0.9, + help='the leaky-integrator factor controling the batchnorm mean and variance.') + model.add_argument('--fuse-bn-relu', type=int, default=0, + help='have batchnorm kernel perform activation relu') + model.add_argument('--fuse-bn-add-relu', type=int, default=0, + help='have batchnorm kernel perform add followed by activation relu') + return model + +class Builder: + def __init__(self, dtype, input_layout, conv_layout, bn_layout, + pooling_layout, bn_eps, bn_mom, fuse_bn_relu, fuse_bn_add_relu): + self.dtype = dtype + self.input_layout = input_layout + self.conv_layout = conv_layout + self.bn_layout = bn_layout + self.pooling_layout = pooling_layout + self.bn_eps = bn_eps + self.bn_mom = bn_mom + self.fuse_bn_relu = fuse_bn_relu + self.fuse_bn_add_relu = fuse_bn_add_relu + + self.act_type = 'relu' + self.bn_gamma_initializer = lambda last: 'zeros' if last else 'ones' + self.linear_initializer = lambda groups=1: mx.init.Xavier(rnd_type='gaussian', factor_type="in", + magnitude=2 * (groups ** 0.5)) + + self.last_layout = self.input_layout + + def copy(self): + return copy.copy(self) + + def batchnorm(self, last=False): + gamma_initializer = self.bn_gamma_initializer(last) + bn_axis = 3 if self.bn_layout == 'NHWC' else 1 + return self.sequence( + self.transpose(self.bn_layout), + nn.BatchNorm(axis=bn_axis, momentum=self.bn_mom, epsilon=self.bn_eps, + gamma_initializer=gamma_initializer, + running_variance_initializer=gamma_initializer) + ) + + def batchnorm_add_relu(self, last=False): + gamma_initializer = self.bn_gamma_initializer(last) + if self.fuse_bn_add_relu: + bn_axis = 3 if self.bn_layout == 'NHWC' else 1 + return self.sequence( + self.transpose(self.bn_layout), + BatchNormAddRelu(axis=bn_axis, momentum=self.bn_mom, + epsilon=self.bn_eps, act_type=self.act_type, + gamma_initializer=gamma_initializer, + running_variance_initializer=gamma_initializer) + ) + return NonFusedBatchNormAddRelu(self, last=last) + + def batchnorm_relu(self, last=False): + gamma_initializer = self.bn_gamma_initializer(last) + if self.fuse_bn_relu: + bn_axis = 3 if self.bn_layout == 'NHWC' else 1 + return self.sequence( + self.transpose(self.bn_layout), + nn.BatchNorm(axis=bn_axis, momentum=self.bn_mom, + epsilon=self.bn_eps, act_type=self.act_type, + gamma_initializer=gamma_initializer, + running_variance_initializer=gamma_initializer) + ) + + return self.sequence(self.batchnorm(last=last), self.activation()) + + def activation(self): + return nn.Activation(self.act_type) + + def global_avg_pool(self): + return self.sequence( + self.transpose(self.pooling_layout), + nn.GlobalAvgPool2D(layout=self.pooling_layout) + ) + + def max_pool(self, pool_size, strides=1, padding=True): + padding = pool_size // 2 if padding is True else int(padding) + return self.sequence( + self.transpose(self.pooling_layout), + nn.MaxPool2D(pool_size, strides=strides, padding=padding, + layout=self.pooling_layout) + ) + + def conv(self, channels, kernel_size, padding=True, strides=1, groups=1, in_channels=0): + padding = kernel_size // 2 if padding is True else int(padding) + initializer = self.linear_initializer(groups=groups) + return self.sequence( + self.transpose(self.conv_layout), + nn.Conv2D(channels, kernel_size=kernel_size, strides=strides, + padding=padding, use_bias=False, groups=groups, + in_channels=in_channels, layout=self.conv_layout, + weight_initializer=initializer) + ) + + def separable_conv(self, channels, kernel_size, in_channels, padding=True, strides=1): + return self.sequence( + self.conv(in_channels, kernel_size, padding=padding, + strides=strides, groups=in_channels, in_channels=in_channels), + self.conv(channels, 1, in_channels=in_channels) + ) + + def dense(self, units, in_units=0): + return nn.Dense(units, in_units=in_units, + weight_initializer=self.linear_initializer()) + + def transpose(self, to_layout): + if self.last_layout == to_layout: + return None + ret = Transpose(self.last_layout, to_layout) + self.last_layout = to_layout + return ret + + def sequence(self, *seq): + seq = list(filter(lambda x: x is not None, seq)) + if len(seq) == 1: + return seq[0] + ret = nn.HybridSequential() + ret.add(*seq) + return ret + + +class Transpose(HybridBlock): + def __init__(self, from_layout, to_layout): + super().__init__() + supported_layouts = ['NCHW', 'NHWC'] + if from_layout not in supported_layouts: + raise ValueError('Not prepared to handle layout: {}'.format(from_layout)) + if to_layout not in supported_layouts: + raise ValueError('Not prepared to handle layout: {}'.format(to_layout)) + self.from_layout = from_layout + self.to_layout = to_layout + + def hybrid_forward(self, F, x): + # Insert transpose if from_layout and to_layout don't match + if self.from_layout == 'NCHW' and self.to_layout == 'NHWC': + return F.transpose(x, axes=(0, 2, 3, 1)) + elif self.from_layout == 'NHWC' and self.to_layout == 'NCHW': + return F.transpose(x, axes=(0, 3, 1, 2)) + else: + return x + + def __repr__(self): + s = '{name}({content})' + if self.from_layout == self.to_layout: + content = 'passthrough ' + self.from_layout + else: + content = self.from_layout + ' -> ' + self.to_layout + return s.format(name=self.__class__.__name__, + content=content) + +class LayoutWrapper(HybridBlock): + def __init__(self, op, io_layout, op_layout, **kwargs): + super(LayoutWrapper, self).__init__(**kwargs) + with self.name_scope(): + self.layout1 = Transpose(io_layout, op_layout) + self.op = op + self.layout2 = Transpose(op_layout, io_layout) + + def hybrid_forward(self, F, *x): + return self.layout2(self.op(*(self.layout1(y) for y in x))) + +class BatchNormAddRelu(nn.BatchNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if self._kwargs.pop('act_type') != 'relu': + raise ValueError('BatchNormAddRelu can be used only with ReLU as activation') + + def hybrid_forward(self, F, x, y, gamma, beta, running_mean, running_var): + return F.BatchNormAddRelu(data=x, addend=y, gamma=gamma, beta=beta, + moving_mean=running_mean, moving_var=running_var, name='fwd', **self._kwargs) + +class NonFusedBatchNormAddRelu(HybridBlock): + def __init__(self, builder, **kwargs): + super().__init__() + self.bn = builder.batchnorm(**kwargs) + self.act = builder.activation() + + def hybrid_forward(self, F, x, y): + return self.act(self.bn(x) + y) + + +# Blocks +class ResNetBasicBlock(HybridBlock): + def __init__(self, builder, channels, stride, downsample=False, in_channels=0, + version='1', resnext_groups=None, **kwargs): + super().__init__() + assert not resnext_groups + + self.transpose = builder.transpose(builder.conv_layout) + builder_copy = builder.copy() + + body = [ + builder.conv(channels, 3, strides=stride, in_channels=in_channels), + builder.batchnorm_relu(), + builder.conv(channels, 3), + ] + + self.body = builder.sequence(*body) + self.bn_add_relu = builder.batchnorm_add_relu(last=True) + + builder = builder_copy + if downsample: + self.downsample = builder.sequence( + builder.conv(channels, 1, strides=stride, in_channels=in_channels), + builder.batchnorm() + ) + else: + self.downsample = None + + def hybrid_forward(self, F, x): + if self.transpose is not None: + x = self.transpose(x) + residual = x + + x = self.body(x) + + if self.downsample: + residual = self.downsample(residual) + + x = self.bn_add_relu(x, residual) + return x + + +class ResNetBottleNeck(HybridBlock): + def __init__(self, builder, channels, stride, downsample=False, in_channels=0, + version='1', resnext_groups=None): + super().__init__() + stride1 = stride if version == '1' else 1 + stride2 = 1 if version == '1' else stride + + mult = 2 if resnext_groups else 1 + groups = resnext_groups or 1 + + self.transpose = builder.transpose(builder.conv_layout) + builder_copy = builder.copy() + + body = [ + builder.conv(channels * mult // 4, 1, strides=stride1, in_channels=in_channels), + builder.batchnorm_relu(), + builder.conv(channels * mult // 4, 3, strides=stride2), + builder.batchnorm_relu(), + builder.conv(channels, 1) + ] + + self.body = builder.sequence(*body) + self.bn_add_relu = builder.batchnorm_add_relu(last=True) + + builder = builder_copy + if downsample: + self.downsample = builder.sequence( + builder.conv(channels, 1, strides=stride, in_channels=in_channels), + builder.batchnorm() + ) + else: + self.downsample = None + + def hybrid_forward(self, F, x): + if self.transpose is not None: + x = self.transpose(x) + residual = x + + x = self.body(x) + + if self.downsample: + residual = self.downsample(residual) + + x = self.bn_add_relu(x, residual) + return x + + +class XceptionBlock(HybridBlock): + def __init__(self, builder, definition, in_channels, relu_at_beginning=True): + super().__init__() + + self.transpose = builder.transpose(builder.conv_layout) + builder_copy = builder.copy() + + body = [] + if relu_at_beginning: + body.append(builder.activation()) + + last_channels = in_channels + for channels1, channels2 in zip(definition, definition[1:] + [0]): + if channels1 > 0: + body.append(builder.separable_conv(channels1, 3, in_channels=last_channels)) + if channels2 > 0: + body.append(builder.batchnorm_relu()) + else: + body.append(builder.batchnorm(last=True)) + + last_channels = channels1 + else: + body.append(builder.max_pool(3, 2)) + + self.body = builder.sequence(*body) + + builder = builder_copy + if any(map(lambda x: x <= 0, definition)): + self.shortcut = builder.sequence( + builder.conv(last_channels, 1, strides=2, in_channels=in_channels), + builder.batchnorm(), + ) + else: + self.shortcut = builder.sequence() + + def hybrid_forward(self, F, x): + return self.shortcut(x) + self.body(x) + +# Nets +class ResNet(HybridBlock): + def __init__(self, builder, block, layers, channels, classes=1000, + version='1', resnext_groups=None): + super().__init__() + assert len(layers) == len(channels) - 1 + + self.version = version + with self.name_scope(): + features = [ + builder.conv(channels[0], 7, strides=2), + builder.batchnorm_relu(), + builder.max_pool(3, 2), + ] + + for i, num_layer in enumerate(layers): + stride = 1 if i == 0 else 2 + features.append(self.make_layer(builder, block, num_layer, channels[i+1], + stride, in_channels=channels[i], + resnext_groups=resnext_groups)) + features.append(builder.global_avg_pool()) + + self.features = builder.sequence(*features) + self.output = builder.dense(classes, in_units=channels[-1]) + + def make_layer(self, builder, block, layers, channels, stride, + in_channels=0, resnext_groups=None): + layer = [] + layer.append(block(builder, channels, stride, channels != in_channels, + in_channels=in_channels, version=self.version, + resnext_groups=resnext_groups)) + for _ in range(layers-1): + layer.append(block(builder, channels, 1, False, in_channels=channels, + version=self.version, resnext_groups=resnext_groups)) + return builder.sequence(*layer) + + def hybrid_forward(self, F, x): + x = self.features(x) + x = self.output(x) + return x + + +class Xception(HybridBlock): + def __init__(self, builder, + definition=([32, 64], + [[128, 128, 0], [256, 256, 0], [728, 728, 0], + *([[728, 728, 728]] * 8), [728, 1024, 0]], + [1536, 2048]), + classes=1000): + super().__init__() + + definition1, definition2, definition3 = definition + + with self.name_scope(): + features = [] + last_channels = 0 + for i, channels in enumerate(definition1): + features += [ + builder.conv(channels, 3, strides=(2 if i == 0 else 1), in_channels=last_channels), + builder.batchnorm_relu(), + ] + last_channels = channels + + for i, block_definition in enumerate(definition2): + features.append(XceptionBlock(builder, block_definition, in_channels=last_channels, + relu_at_beginning=False if i == 0 else True)) + last_channels = list(filter(lambda x: x > 0, block_definition))[-1] + + for i, channels in enumerate(definition3): + features += [ + builder.separable_conv(channels, 3, in_channels=last_channels), + builder.batchnorm_relu(), + ] + last_channels = channels + + features.append(builder.global_avg_pool()) + + self.features = builder.sequence(*features) + self.output = builder.dense(classes, in_units=last_channels) + + def hybrid_forward(self, F, x): + x = self.features(x) + x = self.output(x) + + return x + + +resnet_spec = {18: (ResNetBasicBlock, [2, 2, 2, 2], [64, 64, 128, 256, 512]), + 34: (ResNetBasicBlock, [3, 4, 6, 3], [64, 64, 128, 256, 512]), + 50: (ResNetBottleNeck, [3, 4, 6, 3], [64, 256, 512, 1024, 2048]), + 101: (ResNetBottleNeck, [3, 4, 23, 3], [64, 256, 512, 1024, 2048]), + 152: (ResNetBottleNeck, [3, 8, 36, 3], [64, 256, 512, 1024, 2048])} + +def create_resnet(builder, version, num_layers=50, resnext=False, classes=1000): + assert num_layers in resnet_spec, \ + "Invalid number of layers: {}. Options are {}".format( + num_layers, str(resnet_spec.keys())) + block_class, layers, channels = resnet_spec[num_layers] + assert not resnext or num_layers >= 50, \ + "Cannot create resnext with less then 50 layers" + net = ResNet(builder, block_class, layers, channels, version=version, + resnext_groups=args.num_groups if resnext else None) + return net + +class fp16_model(mx.gluon.block.HybridBlock): + def __init__(self, net, **kwargs): + super(fp16_model, self).__init__(**kwargs) + with self.name_scope(): + self._net = net + + def hybrid_forward(self, F, x): + y = self._net(x) + y = F.cast(y, dtype='float32') + return y + +def get_model(arch, num_classes, num_layers, image_shape, dtype, amp, + input_layout, conv_layout, batchnorm_layout, pooling_layout, + batchnorm_eps, batchnorm_mom, fuse_bn_relu, fuse_bn_add_relu, **kwargs): + + builder = Builder( + dtype = dtype, + input_layout = input_layout, + conv_layout = conv_layout, + bn_layout = batchnorm_layout, + pooling_layout = pooling_layout, + bn_eps = batchnorm_eps, + bn_mom = batchnorm_mom, + fuse_bn_relu = fuse_bn_relu, + fuse_bn_add_relu = fuse_bn_add_relu, + ) + + if arch.startswith('resnet') or arch.startswith('resnext'): + version = '1' if arch in {'resnetv1', 'resnextv1'} else '1.5' + net = create_resnet( + builder = builder, + version = version, + resnext = arch.startswith('resnext'), + num_layers = num_layers, + classes = num_classes, + ) + elif arch == 'xception': + net = Xception(builder, classes=num_classes) + else: + raise ValueError('Wrong model architecture') + + net.hybridize(static_shape=True, static_alloc=True) + + if not amp: + net.cast(dtype) + if dtype == 'float16': + net = fp16_model(net) + + return net diff --git a/MxNet/Classification/RN50v1.5/report.py b/MxNet/Classification/RN50v1.5/report.py index d2a29441..9abbeaaa 100644 --- a/MxNet/Classification/RN50v1.5/report.py +++ b/MxNet/Classification/RN50v1.5/report.py @@ -21,15 +21,21 @@ # - "metrics" : per epoch metrics for train and validation # (some of below metrics may not exist in the report, # depending on application arguments) -# - "train.top1" : training top1 accuracy in epoch. -# - "train.top5" : training top5 accuracy in epoch. -# - "train.loss" : training loss in epoch. -# - "train.time" : average training time of iteration in seconds. -# - "train.total_ips" : training speed (data and compute time taken into account) for epoch in images/sec. -# - "val.top1", "val.top5", "val.loss", "val.time", "val.total_ips" : the same but for validation. +# - "train.top1" : training top1 accuracy in epoch. +# - "train.top5" : training top5 accuracy in epoch. +# - "train.loss" : training loss in epoch. +# - "train.total_ips" : training speed (data and compute time taken into account) for epoch in images/sec. +# - "train.latency_avg" : average latency of one iteration in seconds. +# - "train.latency_50" : median latency of one iteration in seconds. +# - "train.latency_90" : 90th percentile latency of one iteration in seconds. +# - "train.latency_95" : 95th percentile latency of one iteration in seconds. +# - "train.latency_99" : 99th percentile latency of one iteration in seconds. +# - "train.latency_100" : highest observed latency of one iteration in seconds. +# - "val.top1", "val.top5", "val.time", "val.total_ips", "val.latency_avg", "val.latency_50", +# "val.latency_90", "val.latency_95", "val.latency_99", "val.latency_100" : the same but for validation. import json -from collections import defaultdict, OrderedDict +from collections import OrderedDict class Report: def __init__(self, model_name, ngpus, cmd): @@ -37,15 +43,21 @@ class Report: self.ngpus = ngpus self.cmd = cmd self.total_duration = 0 - self.metrics = defaultdict(lambda: []) + self.metrics = OrderedDict() def add_value(self, metric, value): + if metric not in self.metrics: + self.metrics[metric] = [] self.metrics[metric].append(value) def set_total_duration(self, duration): self.total_duration = duration def save(self, filename): + with open(filename, 'w') as f: + f.write(self.get_report()) + + def get_report(self): report = OrderedDict([ ('model', self.model_name), ('ngpus', self.ngpus), @@ -53,5 +65,4 @@ class Report: ('cmd', self.cmd), ('metrics', self.metrics), ]) - with open(filename, 'w') as f: - json.dump(report, f, indent=4) + return json.dumps(report, indent=4) diff --git a/MxNet/Classification/RN50v1.5/resnet.py b/MxNet/Classification/RN50v1.5/resnet.py deleted file mode 100644 index 94920142..00000000 --- a/MxNet/Classification/RN50v1.5/resnet.py +++ /dev/null @@ -1,376 +0,0 @@ -# Copyright 2017-2018 The Apache Software Foundation -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# -# ----------------------------------------------------------------------- -# -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -''' -Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py -(Original author Wei Wu) by Antti-Pekka Hynninen - -"Flexible Layout" (fl) version created by Dick Carter. - -Implementing the original resnet ILSVRC 2015 winning network from: - -Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition" -''' -import mxnet as mx -import numpy as np -import random - -# Transform a symbol from one layout to another, or do nothing if they have the same layout -def transform_layout(data, from_layout, to_layout): - supported_layouts = ['NCHW', 'NHWC'] - if from_layout not in supported_layouts: - raise ValueError('Not prepared to handle layout: {}'.format(from_layout)) - if to_layout not in supported_layouts: - raise ValueError('Not prepared to handle layout: {}'.format(to_layout)) - - # Insert transpose if from_layout and to_layout don't match - if from_layout == 'NCHW' and to_layout == 'NHWC': - return mx.sym.transpose(data, axes=(0, 2, 3, 1)) - elif from_layout == 'NHWC' and to_layout == 'NCHW': - return mx.sym.transpose(data, axes=(0, 3, 1, 2)) - else: - return data - -# A BatchNorm wrapper that responds to the input layout -def batchnorm(data, io_layout, batchnorm_layout, **kwargs): - # Transpose as needed to batchnorm_layout - transposed_as_needed = transform_layout(data, io_layout, batchnorm_layout) - bn_axis = 3 if batchnorm_layout == 'NHWC' else 1 - batchnormed = mx.sym.BatchNorm(data=transposed_as_needed, axis=bn_axis, **kwargs) - # Transpose back to i/o layout as needed - return transform_layout(batchnormed, batchnorm_layout, io_layout) - -# A BatchNormAddRelu wrapper that responds to the input layout -def batchnorm_add_relu(data, addend, io_layout, batchnorm_layout, **kwargs): - # Transpose as needed to batchnorm_layout - transposed_data_as_needed = transform_layout(data, io_layout, batchnorm_layout) - transposed_addend_as_needed = transform_layout(addend, io_layout, batchnorm_layout) - bn_axis = 3 if batchnorm_layout == 'NHWC' else 1 - batchnormed = mx.sym.BatchNormAddRelu(data=transposed_data_as_needed, - addend=transposed_addend_as_needed, - axis=bn_axis, **kwargs) - # Transpose back to i/o layout as needed - return transform_layout(batchnormed, batchnorm_layout, io_layout) - -# A Pooling wrapper that responds to the input layout -def pooling(data, io_layout, pooling_layout, **kwargs): - # Pooling kernel, as specified by pooling_layout, may be in conflict with i/o layout. - transposed_as_needed = transform_layout(data, io_layout, pooling_layout) - pooled = mx.sym.Pooling(data=transposed_as_needed, layout=pooling_layout, **kwargs) - # Transpose back to i/o layout as needed - return transform_layout(pooled, pooling_layout, io_layout) - -# Assumption is that data comes in and out in the 'conv_layout' format. -# If this format is different from the 'batchnorm_layout' format, then the batchnorm() routine -# will introduce transposes on both sides of the mx.sym.BatchNorm symbol -def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, - workspace=256, memonger=False, conv_layout='NCHW', batchnorm_layout='NCHW', - verbose=False, cudnn_bn_off=False, bn_eps=2e-5, bn_mom=0.9, conv_algo=-1, - fuse_bn_relu=False, fuse_bn_add_relu=False, cudnn_tensor_core_only=False): - """Return ResNet Unit symbol for building ResNet - Parameters - ---------- - data : str - Input data - num_filter : int - Number of output channels - bnf : int - Bottle neck channels factor with regard to num_filter - stride : tuple - Stride used in convolution - dim_match : Boolean - True means channel number between input and output is the same, otherwise means differ - name : str - Base name of the operators - workspace : int - Workspace used in convolution operator - """ - - act = 'relu' if fuse_bn_relu else None - if bottle_neck: - conv1 = mx.sym.Convolution(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0), - no_bias=True, workspace=workspace, name=name + '_conv1', layout=conv_layout, - cudnn_algo_verbose=verbose, - cudnn_algo_fwd=conv_algo, cudnn_algo_bwd_data=conv_algo, cudnn_algo_bwd_filter=conv_algo, - cudnn_tensor_core_only=cudnn_tensor_core_only) - bn1 = batchnorm(data=conv1, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, eps=bn_eps, momentum=bn_mom, name=name + '_bn1', cudnn_off=cudnn_bn_off, act_type=act) - act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1') if not fuse_bn_relu else bn1 - conv2 = mx.sym.Convolution(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=stride, pad=(1,1), - no_bias=True, workspace=workspace, name=name + '_conv2', layout=conv_layout, - cudnn_algo_verbose=verbose, - cudnn_algo_fwd=conv_algo, cudnn_algo_bwd_data=conv_algo, cudnn_algo_bwd_filter=conv_algo, - cudnn_tensor_core_only=cudnn_tensor_core_only) - bn2 = batchnorm(data=conv2, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, eps=bn_eps, momentum=bn_mom, name=name + '_bn2', cudnn_off=cudnn_bn_off, act_type=act) - act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2') if not fuse_bn_relu else bn2 - conv3 = mx.sym.Convolution(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, - workspace=workspace, name=name + '_conv3', layout=conv_layout, - cudnn_algo_verbose=verbose, - cudnn_algo_fwd=conv_algo, cudnn_algo_bwd_data=conv_algo, cudnn_algo_bwd_filter=conv_algo, - cudnn_tensor_core_only=cudnn_tensor_core_only) - - if dim_match: - shortcut = data - else: - conv1sc = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, - workspace=workspace, name=name+'_conv1sc', layout=conv_layout, - cudnn_algo_verbose=verbose, - cudnn_algo_fwd=conv_algo, cudnn_algo_bwd_data=conv_algo, cudnn_algo_bwd_filter=conv_algo, - cudnn_tensor_core_only=cudnn_tensor_core_only) - shortcut = batchnorm(data=conv1sc, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, eps=bn_eps, momentum=bn_mom, name=name + '_sc', cudnn_off=cudnn_bn_off) - if memonger: - shortcut._set_attr(mirror_stage='True') - - if fuse_bn_add_relu: - return batchnorm_add_relu(data=conv3, addend=shortcut, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, eps=bn_eps, momentum=bn_mom, name=name + '_bn3', cudnn_off=cudnn_bn_off) - else: - bn3 = batchnorm(data=conv3, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, eps=bn_eps, momentum=bn_mom, name=name + '_bn3', cudnn_off=cudnn_bn_off) - return mx.sym.Activation(data=bn3 + shortcut, act_type='relu', name=name + '_relu3') - - else: - conv1 = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1), - no_bias=True, workspace=workspace, name=name + '_conv1', layout=conv_layout, - cudnn_algo_verbose=verbose, - cudnn_algo_fwd=conv_algo, cudnn_algo_bwd_data=conv_algo, cudnn_algo_bwd_filter=conv_algo, - cudnn_tensor_core_only=cudnn_tensor_core_only) - bn1 = batchnorm(data=conv1, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, momentum=bn_mom, eps=bn_eps, name=name + '_bn1', cudnn_off=cudnn_bn_off, act_type=act) - act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1') if not fuse_bn_relu else bn1 - conv2 = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), - no_bias=True, workspace=workspace, name=name + '_conv2', layout=conv_layout, - cudnn_algo_verbose=verbose, - cudnn_algo_fwd=conv_algo, cudnn_algo_bwd_data=conv_algo, cudnn_algo_bwd_filter=conv_algo, - cudnn_tensor_core_only=cudnn_tensor_core_only) - - if dim_match: - shortcut = data - else: - conv1sc = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, - workspace=workspace, name=name+'_conv1sc', layout=conv_layout, - cudnn_algo_verbose=verbose, - cudnn_algo_fwd=conv_algo, cudnn_algo_bwd_data=conv_algo, cudnn_algo_bwd_filter=conv_algo, - cudnn_tensor_core_only=cudnn_tensor_core_only) - shortcut = batchnorm(data=conv1sc, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, momentum=bn_mom, eps=bn_eps, name=name + '_sc', cudnn_off=cudnn_bn_off) - if memonger: - shortcut._set_attr(mirror_stage='True') - - if fuse_bn_add_relu: - return batchnorm_add_relu(data=conv2, addend=shortcut, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, momentum=bn_mom, eps=bn_eps, name=name + '_bn2', cudnn_off=cudnn_bn_off) - else: - bn2 = batchnorm(data=conv2, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, momentum=bn_mom, eps=bn_eps, name=name + '_bn2', cudnn_off=cudnn_bn_off) - return mx.sym.Activation(data=bn2 + shortcut, act_type='relu', name=name + '_relu2') - -def resnet(units, num_stages, filter_list, num_classes, image_shape, bottle_neck=True, workspace=256, dtype='float32', memonger=False, - input_layout='NCHW', conv_layout='NCHW', batchnorm_layout='NCHW', pooling_layout='NCHW', verbose=False, - cudnn_bn_off=False, bn_eps=2e-5, bn_mom=0.9, conv_algo=-1, - fuse_bn_relu=False, fuse_bn_add_relu=False, force_tensor_core=False, use_dali=True): - """Return ResNet symbol of - Parameters - ---------- - units : list - Number of units in each stage - num_stages : int - Number of stage - filter_list : list - Channel size of each stage - num_classes : int - Ouput size of symbol - dataset : str - Dataset type, only cifar10 and imagenet supports - workspace : int - Workspace used in convolution operator - dtype : str - Precision (float32 or float16) - memonger : boolean - Activates "memory monger" to reduce the model's memory footprint - input_layout : str - interpretation (e.g. NCHW vs NHWC) of data provided by the i/o pipeline (may introduce transposes - if in conflict with 'layout' above) - conv_layout : str - interpretation (e.g. NCHW vs NHWC) of data for convolution operation. - batchnorm_layout : str - directs which kernel performs the batchnorm (may introduce transposes if in conflict with 'conv_layout' above) - pooling_layout : str - directs which kernel performs the pooling (may introduce transposes if in conflict with 'conv_layout' above) - """ - - act = 'relu' if fuse_bn_relu else None - num_unit = len(units) - assert(num_unit == num_stages) - data = mx.sym.Variable(name='data') - if not use_dali: - # double buffering of data - if dtype == 'float32': - data = mx.sym.identity(data=data, name='id') - else: - if dtype == 'float16': - data = mx.sym.Cast(data=data, dtype=np.float16) - (nchannel, height, width) = image_shape - - # Insert transpose as needed to get the input layout to match the desired processing layout - data = transform_layout(data, input_layout, conv_layout) - - if height <= 32: # such as cifar10 - body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1,1), pad=(1, 1), - no_bias=True, name="conv0", workspace=workspace, layout=conv_layout, - cudnn_algo_verbose=verbose, - cudnn_algo_fwd=conv_algo, cudnn_algo_bwd_data=conv_algo, cudnn_algo_bwd_filter=conv_algo, - cudnn_tensor_core_only=force_tensor_core) - # Is this BatchNorm supposed to be here? - body = batchnorm(data=body, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, eps=bn_eps, momentum=bn_mom, name='bn0', cudnn_off=cudnn_bn_off) - else: # often expected to be 224 such as imagenet - body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3), - no_bias=True, name="conv0", workspace=workspace, layout=conv_layout, - cudnn_algo_verbose=verbose, - cudnn_algo_fwd=conv_algo, cudnn_algo_bwd_data=conv_algo, cudnn_algo_bwd_filter=conv_algo, - cudnn_tensor_core_only=force_tensor_core) - body = batchnorm(data=body, io_layout=conv_layout, batchnorm_layout=batchnorm_layout, - fix_gamma=False, eps=bn_eps, momentum=bn_mom, name='bn0', cudnn_off=cudnn_bn_off, act_type=act) - if not fuse_bn_relu: - body = mx.sym.Activation(data=body, act_type='relu', name='relu0') - body = pooling(data=body, io_layout=conv_layout, pooling_layout=pooling_layout, - kernel=(3, 3), stride=(2, 2), pad=(1, 1), pool_type='max') - - for i in range(num_stages): - body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False, - name='stage%d_unit%d' % (i + 1, 1), - bottle_neck=bottle_neck, workspace=workspace, - memonger=memonger, conv_layout=conv_layout, batchnorm_layout=batchnorm_layout, - verbose=verbose, cudnn_bn_off=cudnn_bn_off, bn_eps=bn_eps, bn_mom=bn_mom, - conv_algo=conv_algo, fuse_bn_relu=fuse_bn_relu, fuse_bn_add_relu=fuse_bn_add_relu, - cudnn_tensor_core_only=force_tensor_core) - for j in range(units[i]-1): - body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i + 1, j + 2), - bottle_neck=bottle_neck, workspace=workspace, - memonger=memonger, conv_layout=conv_layout, batchnorm_layout=batchnorm_layout, - verbose=verbose, cudnn_bn_off=cudnn_bn_off, bn_eps = bn_eps, bn_mom=bn_mom, - conv_algo=conv_algo, fuse_bn_relu=fuse_bn_relu, fuse_bn_add_relu=fuse_bn_add_relu, - cudnn_tensor_core_only=force_tensor_core) - # bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1') - # relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1') - # Although kernel is not used here when global_pool=True, we should put one - pool1 = pooling(data=body, io_layout=conv_layout, pooling_layout=pooling_layout, - global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1') - flat = mx.sym.Flatten(data=pool1) - fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='fc1', cublas_algo_verbose=verbose) - if dtype == 'float16': - fc1 = mx.sym.Cast(data=fc1, dtype=np.float32) - return mx.sym.SoftmaxOutput(data=fc1, name='softmax') - -def get_symbol(num_classes, num_layers, image_shape, conv_workspace=256, dtype='float32', - input_layout='NCHW', conv_layout='NCHW', batchnorm_layout='NCHW', pooling_layout='NCHW', - verbose=False, seed=None, cudnn_bn_off=False, batchnorm_eps=2e-5, batchnorm_mom=0.9, - conv_algo=-1, fuse_bn_relu=False, fuse_bn_add_relu=False, force_tensor_core=False, use_dali=True, **kwargs): - """ - Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py - (Original author Wei Wu) by Antti-Pekka Hynninen - Implementing the original resnet ILSVRC 2015 winning network from: - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition" - """ - if seed is not None: - print('Setting seeds to %s' % (seed,)) - random.seed(seed) - np.random.seed(seed) - mx.random.seed(seed) - - image_shape = [int(l) for l in image_shape.split(',')] - (nchannel, height, width) = image_shape - if height <= 28: - num_stages = 3 - if (num_layers-2) % 9 == 0 and num_layers >= 164: - per_unit = [(num_layers-2)//9] - filter_list = [16, 64, 128, 256] - bottle_neck = True - elif (num_layers-2) % 6 == 0 and num_layers < 164: - per_unit = [(num_layers-2)//6] - filter_list = [16, 16, 32, 64] - bottle_neck = False - else: - raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers)) - units = per_unit * num_stages - else: - if num_layers >= 50: - filter_list = [64, 256, 512, 1024, 2048] - bottle_neck = True - else: - filter_list = [64, 64, 128, 256, 512] - bottle_neck = False - num_stages = 4 - if num_layers == 18: - units = [2, 2, 2, 2] - elif num_layers == 34: - units = [3, 4, 6, 3] - elif num_layers == 50: - units = [3, 4, 6, 3] - elif num_layers == 101: - units = [3, 4, 23, 3] - elif num_layers == 152: - units = [3, 8, 36, 3] - elif num_layers == 200: - units = [3, 24, 36, 3] - elif num_layers == 269: - units = [3, 30, 48, 8] - else: - raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers)) - - return resnet(units = units, - num_stages = num_stages, - filter_list = filter_list, - num_classes = num_classes, - image_shape = image_shape, - bottle_neck = bottle_neck, - workspace = conv_workspace, - dtype = dtype, - input_layout = input_layout, - conv_layout = conv_layout, - batchnorm_layout = batchnorm_layout, - pooling_layout = pooling_layout, - verbose = verbose, - cudnn_bn_off = cudnn_bn_off, - bn_eps = batchnorm_eps, - bn_mom = batchnorm_mom, - conv_algo = conv_algo, - fuse_bn_relu = fuse_bn_relu, - fuse_bn_add_relu = fuse_bn_add_relu, - force_tensor_core = force_tensor_core, - use_dali = use_dali) diff --git a/MxNet/Classification/RN50v1.5/runner b/MxNet/Classification/RN50v1.5/runner index 40be0e7b..c70a5309 100755 --- a/MxNet/Classification/RN50v1.5/runner +++ b/MxNet/Classification/RN50v1.5/runner @@ -14,77 +14,56 @@ # See the License for the specific language governing permissions and # limitations under the License. -import os, socket -from argparse import ArgumentParser -import warnings +import os +import argparse +from pathlib import Path - -optparser = ArgumentParser(description="train resnet50 with MXNet") -optparser.add_argument("-n", "--n-GPUs", type=int, default=8, help="number of GPUs to use; " +\ - "default = 8") -optparser.add_argument("-b", "--batch-size", type=int, default=208, help="batch size per GPU; " +\ - "default = 208") -optparser.add_argument("-e", "--num-epochs", type=int, default=90, help="number of epochs; " +\ - "default = 90") -optparser.add_argument("-l", "--lr", type=float, default=0.1, help="learning rate; default = 0.1; " +\ - "IMPORTANT: true learning rate will be calculated as `lr * batch_size/256`") -optparser.add_argument("--no-val", action="store_true", - help="if set no validation will be performed") -optparser.add_argument("--no-dali", action="store_true", default=False, - help="use default MXNet pipeline instead of DALI") -optparser.add_argument("--data-root", type=str, help="Directory with RecordIO data files", default="/data/imagenet/train-val-recordio-passthrough") -optparser.add_argument("--data-nthreads", type=int, help="number of threads for data loading; default = 40", default=40) -optparser.add_argument("--dtype", type=str, help="Precision, float16 or float32", default="float16") +optparser = argparse.ArgumentParser(description='Train classification models on ImageNet', + formatter_class=argparse.ArgumentDefaultsHelpFormatter) +optparser.add_argument('-n', '--ngpus', type=int, default=1, help='number of GPUs to use') +optparser.add_argument('-b', '--batch-size', type=int, default=192, help='batch size per GPU') +optparser.add_argument('-e', '--num-epochs', type=int, default=90, help='number of epochs') +optparser.add_argument('-l', '--lr', type=float, default=0.256, help='learning rate; ' + 'IMPORTANT: true learning rate will be calculated as `lr * batch_size / 256`') +optparser.add_argument('--data-root', type=Path, help='Directory with RecordIO data files', default=Path('/data/imagenet/train-val-recordio-passthrough')) +optparser.add_argument('--dtype', help='Precision', default='float16', choices=('float32', 'float16')) +optparser.add_argument('--kv-store', default='horovod', choices=('device', 'horovod'), help='key-value store type') +optparser.add_argument('--data-backend', default='dali-gpu', choices=('dali-gpu', 'dali-cpu', 'mxnet', 'synthetic'), help='data backend') opts, args = optparser.parse_known_args() -if opts.dtype == "float16": - n_ch = str(4 - int(opts.no_dali)) +if opts.dtype == 'float16': + n_ch = str(4 - int(opts.data_backend == 'mxnet')) else: n_ch = str(3) -opts.batch_size *= opts.n_GPUs +opts.batch_size *= opts.ngpus +opts.lr *= opts.batch_size / 256 -opts.lr *= opts.batch_size/256 - -command = "" -command += "python "+os.path.dirname(__file__)+"/train.py" -command += " --num-layers 50" -command += " --data-train " + opts.data_root + "/train.rec" -command += " --data-train-idx " + opts.data_root + "/train.idx" -if not opts.no_val: - command += " --data-val " + opts.data_root + "/val.rec" - command += " --data-val-idx " + opts.data_root + "/val.idx" -command += " --data-nthreads " + str(opts.data_nthreads) -command += " --optimizer sgd --dtype " + opts.dtype -command += " --lr-step-epochs 30,60,80 --max-random-area 1" -command += " --min-random-area 0.05 --max-random-scale 1" -command += " --min-random-scale 1 --min-random-aspect-ratio 0.75" -command += " --max-random-aspect-ratio 1.33 --max-random-shear-ratio 0" -command += " --max-random-rotate-angle 0 --random-resized-crop 1" -command += " --random-crop 0 --random-mirror 1" -command += " --image-shape "+n_ch+",224,224 --warmup-epochs 5" -command += " --disp-batches 20" -command += " --batchnorm-mom 0.9 --batchnorm-eps 1e-5" +command = [] +if 'horovod' in opts.kv_store: + command += ['horovodrun', '-np', str(opts.ngpus)] +command += ['python', str(Path(__file__).parent / "train.py")] +command += ['--data-train', str(opts.data_root / "train.rec")] +command += ['--data-train-idx', str(opts.data_root / "train.idx")] +command += ['--data-val', str(opts.data_root / "val.rec")] +command += ['--data-val-idx', str(opts.data_root / "val.idx")] +command += ['--dtype', opts.dtype] +command += ['--image-shape', n_ch + ',224,224'] if opts.dtype == 'float16': - command += " --fuse-bn-relu 1" - command += " --input-layout NHWC --conv-layout NHWC" - command += " --batchnorm-layout NHWC --pooling-layout NHWC" - command += " --conv-algo 1 --force-tensor-core 1" - command += " --fuse-bn-add-relu 1" + command += '--fuse-bn-relu 1 --fuse-bn-add-relu 1'.split() + command += '--input-layout NCHW --conv-layout NHWC ' \ + '--batchnorm-layout NHWC --pooling-layout NHWC'.split() -command += " --kv-store device" -if not opts.no_dali: - command += " --use-dali" - command += " --dali-prefetch-queue 2 --dali-nvjpeg-memory-padding 64" -command += " --lr "+str(opts.lr) -command += " --gpus " + str(list(range(opts.n_GPUs))).replace(' ', '').replace('[', '').replace(']', '') -command += " --batch-size " + str(opts.batch_size) -command += " --num-epochs " + str(opts.num_epochs) +command += ['--kv-store', opts.kv_store] +command += ['--data-backend', opts.data_backend] +command += ['--lr', str(opts.lr)] +command += ['--gpus', ','.join(list(map(str, range(opts.ngpus))))] +command += ['--batch-size', str(opts.batch_size)] +command += ['--num-epochs', str(opts.num_epochs)] +command += args -for arg in args: - command += " " + arg os.environ['MXNET_UPDATE_ON_KVSTORE'] = "0" os.environ['MXNET_EXEC_ENABLE_ADDTO'] = "1" @@ -92,5 +71,11 @@ os.environ['MXNET_USE_TENSORRT'] = "0" os.environ['MXNET_GPU_WORKER_NTHREADS'] = "2" os.environ['MXNET_GPU_COPY_NTHREADS'] = "1" os.environ['MXNET_OPTIMIZER_AGGREGATION_SIZE'] = "54" +os.environ['HOROVOD_CYCLE_TIME'] = "0.1" +os.environ['HOROVOD_FUSION_THRESHOLD'] = "67108864" +os.environ['HOROVOD_NUM_NCCL_STREAMS'] = "2" +os.environ['MXNET_HOROVOD_NUM_GROUPS'] = "16" +os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD'] = "999" +os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD'] = "25" -exit(os.system('/bin/bash -c "'+command+'"')) +os.execvp(command[0], command) diff --git a/MxNet/Classification/RN50v1.5/examples/BENCHMARK_FP16.sh b/MxNet/Classification/RN50v1.5/scripts/prepare_imagenet.sh old mode 100644 new mode 100755 similarity index 57% rename from MxNet/Classification/RN50v1.5/examples/BENCHMARK_FP16.sh rename to MxNet/Classification/RN50v1.5/scripts/prepare_imagenet.sh index 797b7e36..4eb4700e --- a/MxNet/Classification/RN50v1.5/examples/BENCHMARK_FP16.sh +++ b/MxNet/Classification/RN50v1.5/scripts/prepare_imagenet.sh @@ -1,3 +1,4 @@ +#!/bin/bash # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,8 +13,14 @@ # See the License for the specific language governing permissions and # limitations under the License. +if [ $# -lt 2 ] ; then + echo "usage: $0 raw_dataset prepared_dataset" + exit 1 +fi -# This script launches ResNet50 benchmark in FP16 on 1,4,8 GPUs with 64,128,192,208 batch size -# Usage ./BENCHMARK_FP16.sh - -python benchmark.py -n 1,4,8 -b 64,128,192,208 -e 2 -w 1 -i 100 -o report.json $@ +cd "$2" && +python /opt/mxnet/tools/im2rec.py --list --recursive train "$1/train" && +python /opt/mxnet/tools/im2rec.py --list --recursive val "$1/val" && +python /opt/mxnet/tools/im2rec.py --pass-through --num-thread 40 train "$1/train" && +python /opt/mxnet/tools/im2rec.py --pass-through --num-thread 40 val "$1/val" && +echo "Dataset was prepared succesfully!" diff --git a/MxNet/Classification/RN50v1.5/train.py b/MxNet/Classification/RN50v1.5/train.py index b7f5e555..33fff8fd 100644 --- a/MxNet/Classification/RN50v1.5/train.py +++ b/MxNet/Classification/RN50v1.5/train.py @@ -34,58 +34,37 @@ # limitations under the License. import os +import sys import argparse import logging -logging.basicConfig(level=logging.DEBUG) -import data, dali, fit import mxnet as mx import numpy as np -def set_imagenet_aug(aug): - # standard data augmentation setting for imagenet training - aug.set_defaults(rgb_mean='123.68,116.779,103.939', rgb_std='58.393,57.12,57.375') - aug.set_defaults(random_crop=0, random_resized_crop=1, random_mirror=1) - aug.set_defaults(min_random_area=0.08) - aug.set_defaults(max_random_aspect_ratio=4./3., min_random_aspect_ratio=3./4.) - aug.set_defaults(brightness=0.4, contrast=0.4, saturation=0.4, pca_noise=0.1) +import data, dali +import fit +import models -if __name__ == '__main__': - # parse args - parser = argparse.ArgumentParser(description="train resnet on imagenet", +def parse_args(): + parser = argparse.ArgumentParser(description="Train classification models on ImageNet", formatter_class=argparse.ArgumentDefaultsHelpFormatter) + models.add_model_args(parser) fit.add_fit_args(parser) data.add_data_args(parser) dali.add_dali_args(parser) data.add_data_aug_args(parser) - - # Instead, to get standard resnet augmentation on a per-use basis, invoke as in: - # train_imagenet.py --set-resnet-aug ... - # Finally, to get the legacy MXNet v1.2 training settings on a per-use basis, invoke as in: - # train_imagenet.py --set-data-aug-level 3 - parser.set_defaults( - # network - num_layers = 50, + return parser.parse_args() - # data - resize = 256, - num_classes = 1000, - num_examples = 1281167, - image_shape = '3,224,224', - min_random_scale = 1, # if input image has min size k, suggest to use - # 256.0/x, e.g. 0.533 for 480 - # train - num_epochs = 90, - lr_step_epochs = '30,60,80', - dtype = 'float32' - ) - args = parser.parse_args() +def setup_logging(args): + head = '{asctime}:{levelname}: {message}' + logging.basicConfig(level=logging.DEBUG, format=head, style='{', + handlers=[logging.StreamHandler(sys.stderr), logging.FileHandler(args.log)]) + logging.info('Start with arguments {}'.format(args)) - if not args.use_dali: - data.set_data_aug_level(parser, 0) +if __name__ == '__main__': + args = parse_args() + setup_logging(args) - # load network - import resnet as net - sym = net.get_symbol(**vars(args)) + model = models.get_model(**vars(args)) + data_loader = data.get_data_loader(args) - # train - fit.fit(args, sym, dali.get_rec_iter) + fit.fit(args, model, data_loader) diff --git a/PyTorch/Detection/SSD/.gitignore b/PyTorch/Detection/SSD/.gitignore new file mode 100644 index 00000000..eeb8a6ec --- /dev/null +++ b/PyTorch/Detection/SSD/.gitignore @@ -0,0 +1 @@ +**/__pycache__ diff --git a/PyTorch/Detection/SSD/Dockerfile b/PyTorch/Detection/SSD/Dockerfile index 32328c62..9e795b60 100755 --- a/PyTorch/Detection/SSD/Dockerfile +++ b/PyTorch/Detection/SSD/Dockerfile @@ -1,11 +1,11 @@ -FROM nvcr.io/nvidia/pytorch:19.05-py3 +FROM nvcr.io/nvidia/pytorch:19.08-py3 # Set working directory WORKDIR /workspace ENV PYTHONPATH "${PYTHONPATH}:/workspace" -RUN apt-get update && apt-get install -y python3-tk python-pip git tmux htop tree +RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y python3-tk python-pip git tmux htop tree # Necessary pip packages RUN pip install --upgrade pip diff --git a/PyTorch/Detection/SSD/README.md b/PyTorch/Detection/SSD/README.md index d7457809..1ce01bdc 100644 --- a/PyTorch/Detection/SSD/README.md +++ b/PyTorch/Detection/SSD/README.md @@ -242,11 +242,11 @@ The following section lists the requirements in order to start training the SSD3 ### Requirements -This repository contains `Dockerfile` which extends the PyTorch 19.06 NGC container +This repository contains `Dockerfile` which extends the PyTorch 19.08 NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following software: * [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) -* [PyTorch 19.06-py3+ NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch) +* [PyTorch 19.08-py3+ NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch) * [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) or [Turing](https://www.nvidia.com/en-us/geforce/turing/) based GPU For more information about how to get started with NGC containers, see the @@ -256,7 +256,7 @@ Documentation: * [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry) * [Running PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running) -For those unable to use the [PyTorch 19.06-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch), +For those unable to use the [PyTorch 19.08-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch), to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html). @@ -537,9 +537,9 @@ The flag `--save` flag enables storing checkpoints after each epoch under `./mod Our scripts for SSD300 v1.1 presents two ways to run inference. To get meaningful results, you need a pre-trained model checkpoint. -One way is to run an interactive session on Jupyter notebook, as described in a [Quick Start Guide](#8-start-inferencepredictions). +One way is to run an interactive session on Jupyter notebook, as described in a 8th step of the [Quick Start Guide](#quick-start-guide). -Another way is to run a script `src/SSD300_inference.py`. It contains the logic from the notebook, wrapped into a Python script. The script contains sample usage. +Another way is to run a script `examples/SSD300_inference.py`. It contains the logic from the notebook, wrapped into a Python script. The script contains sample usage. To use the inference example script in your own code, you can call the `main` function, providing input image URIs as an argument. The result will be a list of detections for each input image. @@ -597,16 +597,18 @@ The following sections provide details on how we achieved our performance and ac ##### NVIDIA DGX-1 (8x V100 16G) Our results were obtained by running the `./examples/SSD300_FP{16,32}_{1,4,8}GPU.sh` -script in the `pytorch-19.06-py3` NGC container on NVIDIA DGX-1 with 8x +script in the `pytorch-19.08-py3` NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch. -| **Number of GPUs** | **Mixed precision mAP** | **Training time with mixed precision** | **FP32 mAP** | **Training time with FP32** | -|:------------------:|:------------------------:|:-------------------------------------:|:------------:|:---------------------------:| -| 1 | 0.2494 | 10h 39min | 0.2483 | 21h 40min | -| 4 | 0.2495 | 2h 53min | 0.2478 | 5h 52min | -| 8 | 0.2489 | 1h 31min | 0.2475 | 2h 54min | - +|GPUs |Batch size / GPU|Accuracy - FP32|Accuracy - mixed precision|Time to train - FP32|Time to train - mixed precision|Time to train speedup (FP32 to mixed precision)| +|-----------|----------------|---------------|---------------------------|--------------------|--------------------------------|------------------------------------------------| +|1 |32 |0.250 |0.250 |20:20:13 |10:23:46 |195.62% | +|4 |32 |0.249 |0.250 |5:11:17 |2:39:28 |195.20% | +|8 |32 |0.250 |0.250 |2:37:35 |1:25:38 |184.01% | +|1 |64 | |0.252 | |9:27:33 |215.00% | +|4 |64 | |0.251 | |2:24:43 |215.10% | +|8 |64 | |0.252 | |1:13:01 |215.85% | Here are example graphs of FP32 and FP16 training on 8 GPU configuration: @@ -620,15 +622,18 @@ Here are example graphs of FP32 and FP16 training on 8 GPU configuration: ##### NVIDIA DGX-1 (8x V100 16G) Our results were obtained by running the `main.py` script with the `--mode -benchmark-training` flag in the `pytorch-19.06-py3` NGC container on NVIDIA +benchmark-training` flag in the `pytorch-19.08-py3` NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch. -| **Number of GPUs** | **Batch size per GPU** | **Mixed precision img/s (median)** | **FP32 img/s (median)** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with mixed precision** | **Multi-gpu weak scaling with FP32** | -|:------------------:|:----------------------:|:----------------------------------:|:-----------------------:|:---------------------------------:|:-----------------------------------------------:|:------------------------------------:| -| 1 | 32 | 217.052 | 102.495 | 2.12 | 1.00 | 1.00 | -| 4 | 32 | 838.457 | 397.797 | 2.11 | 3.86 | 3.88 | -| 8 | 32 | 1639.843 | 789.695 | 2.08 | 7.56 | 7.70 | +|GPUs |Batch size / GPU|Throughput - FP32|Throughput - mixed precision|Throughput speedup (FP32 - mixed precision)|Weak scaling - FP32 |Weak scaling - mixed precision | +|-----------|----------------|-----------------|-----------------------------|-------------------------------------------|--------------------------------|------------------------------------------------| +|1 |32 |133.67 |215.30 |161.07% |100.00% |100.00% | +|4 |32 |532.05 |828.63 |155.74% |398.04% |384.88% | +|8 |32 |1,060.33 |1,647.74 |155.40% |793.27% |765.33% | +|1 |64 | |232.22 |173.73% | |100.00% | +|4 |64 | |910.77 |171.18% | |392.20% | +|8 |64 | |1,769.48 |166.88% | |761.99% | To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. @@ -638,16 +643,16 @@ To achieve these same results, follow the [Quick Start Guide](#quick-start-guide ##### NVIDIA DGX-1 (1x V100 16G) Our results were obtained by running the `main.py` script with `--mode -benchmark-inference` flag in the pytorch-19.06-py3 NGC container on NVIDIA +benchmark-inference` flag in the pytorch-19.08-py3 NGC container on NVIDIA DGX-1 with (1x V100 16G) GPUs. -| **Batch size** | **Mixed precision img/s (median)** | **FP32 img/s (median)** | -|:--------------:|:----------------------------------:|:-----------------------:| -| 2 | 163.12 | 147.91 | -| 4 | 296.60 | 201.62 | -| 8 | 412.52 | 228.16 | -| 16 | 470.10 | 280.57 | -| 32 | 520.54 | 302.43 | +|Batch size |Throughput - FP32|Throughput - mixed precision|Throughput speedup (FP32 - mixed precision)|Weak scaling - FP32 |Weak scaling - mixed precision | +|-----------|-----------------|-----------------------------|-------------------------------------------|--------------------|--------------------------------| +|2 |148.99 |186.60 |125.24% |100.00% |100.00% | +|4 |203.35 |326.69 |160.66% |136.48% |175.08% | +|8 |227.32 |433.45 |190.68% |152.57% |232.29% | +|16 |278.02 |493.19 |177.39% |186.60% |264.31% | +|32 |299.81 |545.84 |182.06% |201.23% |292.53% | To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. @@ -655,6 +660,13 @@ To achieve these same results, follow the [Quick Start Guide](#quick-start-guide ### Changelog +August 2019 + * upgrade the PyTorch container to 19.08 + * update Results section in the README + * code updated to use DALI 0.12.0 + * checkpoint loading fix + * fixed links in the README + July 2019 * script and notebook for inference * use AMP instead of hand-crafted FP16 support @@ -666,7 +678,7 @@ July 2019 March 2019 * Initial release -### Known issues +## Known issues There are no known issues with this model. diff --git a/PyTorch/Detection/SSD/csrc/box_encoder_cuda.cu b/PyTorch/Detection/SSD/csrc/box_encoder_cuda.cu index b5a2f03f..b740a18d 100644 --- a/PyTorch/Detection/SSD/csrc/box_encoder_cuda.cu +++ b/PyTorch/Detection/SSD/csrc/box_encoder_cuda.cu @@ -1,6 +1,6 @@ /****************************************************************************** * -* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +* Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/PyTorch/Detection/SSD/csrc/interface.cpp b/PyTorch/Detection/SSD/csrc/interface.cpp index 0ca51e3f..a8dea4e4 100644 --- a/PyTorch/Detection/SSD/csrc/interface.cpp +++ b/PyTorch/Detection/SSD/csrc/interface.cpp @@ -1,6 +1,6 @@ /****************************************************************************** * -* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +* Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/PyTorch/Detection/SSD/csrc/random_horiz_flip.cu b/PyTorch/Detection/SSD/csrc/random_horiz_flip.cu index 6964bfee..de8a681e 100644 --- a/PyTorch/Detection/SSD/csrc/random_horiz_flip.cu +++ b/PyTorch/Detection/SSD/csrc/random_horiz_flip.cu @@ -1,6 +1,6 @@ /****************************************************************************** * -* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +* Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/PyTorch/Detection/SSD/dle/inference.py b/PyTorch/Detection/SSD/dle/inference.py index ad54c2c1..bb009331 100644 --- a/PyTorch/Detection/SSD/dle/inference.py +++ b/PyTorch/Detection/SSD/dle/inference.py @@ -1,3 +1,17 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import numpy as np import skimage diff --git a/PyTorch/Detection/SSD/examples/SSD300_inference.py b/PyTorch/Detection/SSD/examples/SSD300_inference.py index b6f7a536..148454be 100644 --- a/PyTorch/Detection/SSD/examples/SSD300_inference.py +++ b/PyTorch/Detection/SSD/examples/SSD300_inference.py @@ -1,3 +1,17 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import torch import numpy as np @@ -10,7 +24,6 @@ from src.utils import dboxes300_coco, Encoder def load_checkpoint(model, model_file): cp = torch.load(model_file)['model'] - cp = { k.replace('module.1.', ''): cp[k] for k in cp } model.load_state_dict(cp) diff --git a/PyTorch/Detection/SSD/examples/inference.ipynb b/PyTorch/Detection/SSD/examples/inference.ipynb index d0278a16..efdb5da8 100644 --- a/PyTorch/Detection/SSD/examples/inference.ipynb +++ b/PyTorch/Detection/SSD/examples/inference.ipynb @@ -74,7 +74,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -83,7 +83,7 @@ }, { "data": { - "image/png": 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5I8HPLACDAKrQt7F3RXDu7+/zmTeu8BM/+R/zyJPPEmMmhGqUfjHTVVSyXJkX+Q4c3SKuB24dRjYxMjShaeY5URlSKjjmgKoSk22+Q45QNvNYTPVUMM6cM3/se7/vy8w8VyXGhCp03cJwCDCtMmUGTY3boiTiEFl1++SiIUrOhlMJDH3P/mrFiy88S07KYrXPrTu3ufLGGzxy4REunD1g+fSTvPDs0zjpGIbEa699mo9/6lMMwzCbgDGDF8VjGqFzGQREFc0mNB2GkSHKZhjwgFs61inz4Y+9yqVLr5BdxGUhZ8W7wBAzVE91gmHIOA/BB6O3dGKmvnccHR7SD6ZROREeeeQR074BTtn0pprgvT2CbU7eyfMKhV/kR4G4y+wGm+5Ts3y0ZCc/2Op7O5dMrj6x2RsgIAYm51SFp7T+1g0iZ505du6nbXNQdxxh56/44Hb/tppONvRpK1MGUcg6CrBtzmbFOHEjDC2FIQK0DbxqmTnnhoUChBCImtuYCqapJldgBsoeqg5xC9IQefLRA/6nn/vLXLl5zJ/4kR/nxRdfbNS+L3prc9ODc/iyMQZxJOcMgy0c7kzClzEIriPmRGibui80xWJJiUOcoqKkbXz/Lu3BEJpqHEap6LxKo9GQC62460qEQ/2RIxVzVTGzxXlPTongPBpjQUIcKR5zZhV48bmnCF1AxJFSz5U3r3FwcMBiscc7nn+c59/2FM4JSZVXX32VT3/mdY4OlS6YNjAQjeJTzR0t3k98iTpSI8Rn836rOPosBBfJcYDBow6SZlQMs5IcKcwkBIgaySkRi1YqzrEs9CYR8GHBzevXiSiqmdVixcHBAd6vSJuIX3aIt7HJIoRm+4+L34k3wS/FKG8Y59S5MhcWWTOEMnmr9ueo+50J0TI5AwY/qErxWFP6LxNNWYiUIAZs4eepFiqTqKICTG5jvVX7zaoTWW/HBO9af3SCf1g/d5nk2/jlXJtsvFWpmvVJ8Sgw14yrdl3D3cqcqSZ2VvBODG/0owCuGrVZCq7N60pVctmUCbIiSYkBwDBXG7OxfxVOYlvAqxq+3/puz8aLkLOQCbDpubjn+eVf/Mt85vIN/vP/6r8niUeBiLAM3WSTmAc/fP6aQ5pAy2Rv8yV4ISp0WYhB0CgoQvQBzRHx5R5xaPFhiDoTeOrJZIuMK2N0P+3BEJoIZqZ6yDW8yb5xzry55qxJpkFVbWwyb33oSDEWnM/UcVXMw113elV8MpV/4T1PP36REBYADMPA1cufwofAwflzvP35p3jbMxcJywPu3D7mzavXuHrlJm/euI5IYrEI5GwOJs2AeqramUlt0aNi60FNM8lSJrKMQkqreaoZSbngrCDOGykmRuu/c+S8wTlhSJHVamX9vnbNFhWe849cYO/MCrzDZZ142SfTuwrJoqGp6syshG3feWlbWpfbqVdV7ewk1/EkjUlMq9RTrgeNFH638MFp8xUDnFnEcyrRHFucC8td/TytnRzZsSlQMBugkpTK8VlnB2oG8eNZXIUnqsme0kRTN0ZJSgmHoG6CBU960oIPygZvXv/JVniKleBm42YRcUTPpXNn+Ct/6c/x5vUj/uyf+Q848/Sz7T4FTBu8t0f0WTcVyhoSgrM9PFaYzAmigncW9+WKv0JwINpw4jr3Oz9u9jijy91reyCEpnkGl2UClXAxN+4CzgXDMsWwP3OCTDh2CjnFdr7ReaAgyfAtVwB9MaEcHDjnCyHdBNszzzxpZyw7/yJ4bt2+yY2rV3j68ad56YUXuBOP0JRZrlbcvH6bD3/kw1y7sSZ05hzoYzSepnNkzSiCD0tS0mYyCSZf605o5mRCMe+oIxdwC8gO70JbcEmTQQbO0/dD8SDauCBw48Y1bt6m7LYecsfZgwPOnzuPkklpEh2lOuKIlEXdtGdT612jGMsJwTal/NTnoMVSaOZyfZ4zR5HOhAJOmpDb1vK88yOmNQkTNMeLYb52ii2McQtnnV57PKaa9tP34+t2qGUVrFNK2mnMBXHOFICc8UUlb46zUGhtpSvZ0aT8tjY+Ox8m1HKJrqoQCDCLKtKsTfrpOLAzPV1hJw90Oj4tZNllfOiQQbl0dsFf//n/jhs3b/PKN/1+vvW7/ii+20fENxfiF6JpWRn4DpXeMH6Gxg2u/5zTiuFADTFGSCkRQiiaN3Wyg1SK4b17zx8IoWmKiZkkrnBDVPtmVqWUmsaS6yJxpp1qGQFbIBPTS2xK5RxxLjQzrWJRNaJHXBGoGD9F1EI1RRyhEy5eOMfB3ooQloQlvPnaZYs8WO1xbu+Ab/i6ryUhLJZLPv7xV7l85Sq3Dg/RlOmcIxbQ3QUgVpOpdBGKbDFTw0nBuHxxDKiiE6xFxTpcoqBba2Pi631jHmAdQITbhze4dfuamfqLBefOPspi0TUBWr2I9rtRsguVh3lSYH627TSHRaXV7PIwjzc6TwKS8lQAlvPoKDB3CYLPZ3NT8FWMm9lITLkCN3OSO0BQdwIyHp0zowCox4iM52NLI1JoAlez4XOgM5igdM+OkcnvdrUTmwqQE4inl4LD5sTBvuOTH/kgP/vR3+X3f+Db+Nqvf4U7Pewt9/H+FL7t59q0OMWcQyThxCMy16bVa/NzgqCTsFPNlYdtI1BZJQL4XXzdU9oDITTt5pjttlJ2TucDOUUjIReNrP4d49oEYHlfPWO12QJ0aLaBSskWnQtmWlduphAQZ+Z7TJkuBPOkOwNQFiGQicQh8eyTFxn6TPALfAhcu3qDT33qk1y8dIlnn3iURx65YDHoCa5cvcKn33gTzZEcI54lAGEiCGPR4YL31JgblSl4e9KT62A263cJGYcJQiXaWKqZeEdHd9hsMk5gs+k5OHeWvb09lqvVqU9Hcx6xxc+x7Ypfr6MROOmVnv0WnTtTJpSc8aOR2fCFaq7MTyabjiBmYk8gZFfiy1N5WL5s1C7TaGYVVpxqS1XT3BX1U9vU0SMpo5Uwr3NM8343jGk/6m+9OrJkVCy40SPFYTTQOcev/eP/k3/0y3+Hd7z7/Xznv/qH2v1/ocx1cYJ3jiAwbPE0RbQJ0jyxWsxZJCN+WWhejm1myT1c/8GgHJ3V737/u2daRqV4VM6cquInhF1LQDHNVlNe86hmO1e1VF92ojzhzW2bc66o7nHE7LMzw9SZFqF59EBWT53Z+p71+ojVYoXzgWGIXH7jMgcHB+yfWYFEE/ZZStIOw1qUTFZ2aAULEOMkWvifUa1qFqU5VljHbA7Ej9+f9HhWDLVqHan6IdSTc+LcufOcO3cO733JBeDw3rcJN00MsQtE3+kk2YpMGqNsRjPasjWNC9bJ6CEGGkywq2VGITMzbSUX5cx4uTtx1bv0+27fnWR8jvilICQ/x2rvFiffyOvVJD6lb7t6KM41OlKDfpojcfc9TaOSlC06k0jz6pcPZr8pHTLLrGCF9fsosI7wQz/yY5x79DlCcAwp4r2UZ3c/5vvoADI34AYhktd34HBD6gf6IXIUM5toSkLaRFKxnFQgpkjVvHM2uC8lLVmQjLFCoRF+4we+48uJciQnJo3gEadlBykcskJsRwyj8F1oJjclXrp53cnFVDMifJGCLepoau577xuEWE1g1eK9LdinxtSiYEQcrnE/LPFH9BEYTCuWzKVLF8r9WPSKF0fWofAwbTIL5hU9sYiEdn4hGC7mqwf9dBNWZY5TzdkGk5YNAqB48V35zHkQCdy5c4dbt262wy9evMRqSxPdtbBb93cIh23ies7aOLWTs867aQO14wbur5n1ISccWae1t9JSRxtgYo5PWsWEK3owFeannb9qStuY4nafJmjAeFzO5hCqAuktsBTNFYKSNu+nE0W3L1DXynwLgAx+4sAKwSEaEZf523/jfyQtzvCjf+pPQ1LzWXyeNE/nHDhn2rXUsSlrwhc4V7BN0nlS4V83Uz6rdT9Lo43dj/L4gAhNa7OwrzaBRrh7ik16H2Y4nFGALNSqZTwqg5q3EkjY4AVUwfsJ4F0SOdQ0YGbGW1y68x5NieoHVUyD0ezo+0jwS6A6CjKLEEi5OKeyaVOuODxCCMVpohZvPktdp005lGyOIXEyEzpTwrPBalocXUUbLlq5B6PxbGtXTqHE21tMttFeckqFuO6aRi4iXL9+HVWl7zecO3eeixcvAha5tVgsyu6dmgZaBcCuNv9cZ8c2jHDqYKl9P9XhYotBtjTqmdVSMCxO2Wy2zdFdwmpXy8VhN40yEidl4z2dJ7trge7aDHc5hNo5mOCUmLmvbFssbqfGeZr2uX3f1RKZHT7V/p0YLl40zj4OCB7vApp69M4tfuYv/qe47gz/3o/9WVjt4Zxrjtr75X1KU4wEJmY3JELwDEPEhWKqq5hFtwX7GVPEiP1OoDJy7qc9MEJze7EZ5lhMTimvLpMm2XJGC7XwJHHkAgI7t6Dt+WWQlcnOUv7ZJjUB9AE0mJaQJxOzYkV+9NLnZKZukAUQ7SGJt+vlXAJuzJunuS9ZZ6pmbDu+JcS1SKjqBMsScYTqL2x9no1XhRkkUb3WWnZSC9qR4mk9uUA9QioaZlaFVD25CU2eTGwOCbt3+91isWS9XvOxj/0ei8WyhWmeO3eORx99lFQiMU55wluvo6AbnTufL3fTvbepoNwpnHaZylU7PuVea/jm6Mi9v0W5awy3Be2UZaCqZQrXjaZ2dHKePBfsp7UmpCcm99RPUIEgX+aqUVArVJNQMVhF1CGsWHroZOCv/7X/mk9fvswP/smf4Llnn8c0g8+d22kbddm8nIOSqEadoMnw7VzxzRmEoxMI7v4gygdDaBYMYpbT0BnGpapIGrVDU5zMIUTdqcQ0Od/AX18mU7UrTKC2RL5ac3DGyXAJNWpANTfWgqt4qFACE8Y+2q6mhrNkwyedmlDNToo72+7PhKuFfoaiwWYqrlg4nGbxEAiG54VKM9mKX3YOlcFw0uJBNNEzxhzP1JASNOCct/HRjCuLMJfPk1QRXY28UWCOUIbdW9ctEBFC6Lhz+zYpRm7evMm1mzd48skneeqpp1BVhvWGvdWKaYxvTY0Xuo5YBUE2+MWL0Zu2Md7akZNx47OvZ9qaeaJLmZP2zIx3cD+axdShYucsGmAdoa1TJSnYZbm16t2+m+Y9zsfd2e5P03xn+Kdi1C1sTvl2TP3dyWvnEjwgBcN0xYJpmLIz3K9ad7Y+Cu2v3HoVsPY+WAamEp0rztD7SIY88NTj5/jVv/+LXLt5gx/7Mz8J3VlcWLX+DzHRdcIYUDumIxRNZKlKSQDXWzpChK74B5K0RYq4jFOjpHkCokXhUnOSKhNoolJF7rE9GELTiXm2yluLkLGUbio1fnqkxVS1Pqk2Mm7TY+rOO5to7sSkrZ71XW166DbONG25JAAQb8lpnSS8WDqrqdaUS7SG3atrQhgno8BzgBTDR7RM/irYJv1xrq1WkYpzlvvY1c3iUKLmfizXbmOtu382bW3hTRbg0Z07OBdYLpb0w0Barwmh48aNG1y+/AY5K3uLJc899xznzp1DVEk50y0WtkmmZM9YqyZdOKPbAuaeBZxuvd5bu1cBWgUmnFxf+XNTlk5td8PZ7qYVAy266m63N9UqT7tWFZQirq27qaDf1nirkiol0UdVcqwfnthvWAXP3/i5v8Zn3rzFD/27P8qlp58r+RgqVclhqeGgxZ9PwPpqopuWOc4XJ4JO4IyKY9Y+uiJkZ0PyWTy7B0No6lwh0EyjueQUcV3HMERCOUBL9mXv/NR+BmiCYZZhZgcuFHX0RtdWIzVmwtQZpqqqOwByy2xDzqgTJFd6icwEHVhJigzFE55HYVZwAJGawQeyKBQeGmyzagp+mcOISdZ7kvkcyDLSeFIqqfdStrAzVdusxFKoyV1Wfh2jO7fvsFx2rFZncN4zbAZSEMtd6hw5R0vHJ44QHInM7736cXyJAY8xsre3z0svvWRpvjQ2bb5qmFPTsIzcqf2atmlqZdMC72017HJW7T7/xCzH5uA2Z/Lz3e7Wn6kJv8vBtK2rh+k+VL4ccmwhni25dVGh61wXwHnD4lMNeBB7LlOhWc34KRbe8o1O6l85CezteYTI809c4Ff+7i9w/bjnbS+8ne/+nu8FumLrhPJbu5sTI1Gu6VwyB6bEiC0ZAAAgAElEQVTWfxOeK1WA17+r6+5e48t2tweDcvToOf3Df/AbySm1wYlkQrk170qChElXbdEXPKLw5ap5Xp0x8ySyND6X6lhXaDsN3Ihx5bZbwt3jU81DXs6zg1IEtLyOVdMDmrdbi7Mpi53FuYKHiTOqk052b+vpnIozGRcLDDIHUtZsHsQ84RNOAP6WORxL4lvv284ZC4xgDiAnnsWyY71eFwxZ21hOE+W2sSz9q7LYlxUYB9MgYowEB+986WUuXLhATIlhvWF/f5/1eo2IWJq+SnM6ZZpXR1Ado5Njtf2DcVFPX2eHbH3WhPE2sXzieW0LNZ9cT9Os8ncl738em7oKUZQ11DLD2/eCZRGrm1SlDlXzHGjOxWqST9eRmwgle3Ul3nsUnG5S+NC0TrEkN65CHR7njfLWLVbcurPmPe//Vr7m699NSlWzHR2vyho0IamHoyMkZfoh0veWlX1Qs/6qYzIPI82oWp/2d549pro+vvY97//yoRxVT3ILPSufZwdBTGtzbh6VojUMCpDOkhckzOljsmk0yf0ueVcE7UwQ75jL0/Rlp3H8EoISLPMRzjIhlRuoJSgEIUhEkVYQroBCE3JujbgoE04xZ4mkNnl37b2u0D5covBKrZCXcUlz8YgXvqeO4XsFISjPoDIRRuB/s+nx3rFYLLlzeMjRZk3XdYCSSlKSqe9BMFxWSnKN3J4TI0Bfvuu6QE7KRz72cWLfs9n0PPPE4zz//PN0oSOjTWDeS9PCjqwllktv7vn3n02rvMYxlp8mRXY54D4XgXk3iteuJnk0laEkh2HczIRc/KsTgU7R1pogNH1dpMzJOs8mz72Wt7bNOLdIu/rdeGaPiLFQXMlqmGKxvtKA08jBXuBD/+8/5df+73/I+779u3j5q762YZQUZaAK39nNlWsJuSlNUDZUnY+Z/W0aZ233E3cOD4jQbBZ2iysGr2buqisLzQMqjUJRH6B5wBTnxVLXYxELKjZJZrG6E4pSG7JTAfoKQBcQHDGp6kZNthnDCcvpKBm8hW5J9gipkLqjmZ7Bk2MyzdR7C9lULf2zDSM4R3QFC0omCJq25gSS4NUcDoiFjeVkC9iJaegpKi3EfDIfpp5ib4x9hhQRX5I8O8dms8E5z2KxBI2sj9fAAIUpMAwWKZUKjD5macTg9RYiKogmvHhqfiJVynOCIUaCD6ZVYomZr9y4wZUbNwDTRF9++Z08/thFRCq7YNwIq4UwauLz55iFVm5jqnVaNJU0CTGNGrnf1oTljkU3j8mf43GnXauG9bkJRljbZ9O/nTzPqVAJARGZOI20wBpmyXh1eFxxFkqxfqSkBKzm+PhMmgOu9nVy/XGMau5To+wJgi6FhIU7pzhw/swBv/1rv8o//vt/l+/54z/AE297kZgzKUVWnVhQiRdcqkKcprkGDxnzomdnMsX5WtK39i3N6oa5L0vv+Y7WcJId2oI4E5D1byl8MeccbuvwLMWMmkwagFyEbp7o6WGHU6QWorfdjZZOasb7rLSLid/ZaB22M2pJYJGyTUqRUOg+4843tpIzXevCG6NMXDl51RAzVTA4Urb4Gt3q32nTQct5xAc2mzWqjuVygVt0xGFgfdiX35dQVD9WvcmauBvpvBKI28aDlOOtJktS82rXjPRtHCfTMbjARz78UT6UP8RytWKzXrNYLnnxxRd56vHHSxar8ixznnH+2ma5674LNc3G/XOnvNxP2yX4ptq0EwsP3AWZ3U3TvJfvdp0zxqGY5Gaa+xDIZJxaJqFanjk68A7wVSiW+3F+ZDfJaMLXkFsvc/Fix4ylg6uZHhszoZDlRFDWXHr0DP/wl/9Xlnv7bIaBP/FDP4LGYTT/GPFUKYwGzUy0TaNEkWWimGb7/cTE3MnWuEt7YIUmUHbb079X86EYXWWSvX3HiUaS+A7MqZ1PdghNX+qIFEzIl7yfuzSBhn0BY/GWQglymRStDIRr1k1CNLWLjqFrbkZe3qYAZzEB44jkbDu+5qmgdO0co1N5EpNvYoUYk+W07JaIE47Wa+O2BY/vKm5ZOpIqPqYNMwVaATcbg9GUUrRBDlO80XmB5EtNpdFcB1Ad2rlCMETbuVCyMUFMiY9+7GP8zu/8FqrKpUuXeOWVV9j0m3EMTz7WL3Lb1YO54JoKt228M+V815pP99vu6rNIlnMVEQtJJhJCZ3QchSQF3qnPuygOCk0b9t437dKwbbFoM0AngqmZ8DYCVIDJFIDQlIe65mPucM5xbr8j9j37eP7Wz/8cOXi+9/u/Dy0eK+cKNptrzHnNfuZLqsgC2LgKSwlTmAtojsp7bW8pNEXkZ4HvBi6r6rvKZ48C/zPwAvAq8MdV9brYnf+3wHcBR8APq+o/f8teSNmFwljOIRdNwrkaqmZ/p0lsuUz+ac6FZTh+ZxELaSzVULLkVG9gzsZXbAJwYmak4p3fnujTmi8tflfr79uYbQlUC4UkALUERKZFNlQHUfVAStWExGZo01+zhZcq2cxqsmmapbyuncbM5m0N3TkrVRxCoO83pDTyRdMwP9a8+5MPtGjO2c7vfHGoTTzW02aTtYarSXsFyGmSTFjG89r78dkOzUHnSEnpFsEEbE5Woyc4bty4xa/+6j/h9u3bnLtwjq944UUeu/Co1b+JkbAMpFQ5tOad//Rrn2Z93LNarXj0sUc4d/78W1J76uI+kYG9arqT42tU4eycWzjnjOFwyrV34efbVLq3+k393fS7WgoaQEloMhw+O4ckj4uezhUNLVTubC7aaHECVY+796M14R2dG5WJqgFO+2F9qWtsdzRQ02TLufqhxztbG4EAKvztX/glHr94kfd+4P34vqc/PjKFKFWHD6AWAt3ImEXbRPJOtPt+HOL3omn+HPDTwM9PPvsp4FdU9S+IyE+V9z8J/CHgK8u/bwR+przetVVPaUypOBpAa4IIZyZoc+qEQj73gqTS/UwzjZ1Raal2bJaT5JOKg9ZrV3NSmdcjb98XXGe73XUCy/Q4LP5dekvVWVRAyUqqYN+k1XxHSQGp+RjthIPmsnjNUVTNUIVGjlYcFNNKcvX+q4WZOQ8hMByvWW8GXGelEApV0jL13MP8ESemIaTx4Bjj5Ijd+Qmbtl9+lvNIANHJk2oUmGRPI8cp79WDmjkZM5w7/yhJIh/9xMf59Gdep1/3iMKzzz7FY489Ztct1zh3cIFrV19jGDJ7e2c4d/7u91kF1Qy33BKeOmFgpHrfM57YPFXaW2XV2RnFtY3ZTo+52zyszIl2qAm0lBKClbuNCVzXIWQWK4+6DukK9OUtwbcvSVvqeplimS74OslBrNjfNuw0cjmrZlHHdL7x1tu0nLKChI4691UznQouZw5vXeWX/97/wvU37/AH3vt+Hn3iCY76o+aIymSklO4WV4lGFbOd43BV0bjXdk+UIxF5Afh7E03zw8AHVPV1EXkK+L9U9WUR+Svl77+5fdzdzn/psQv6r3/HN+NDIMWI856YBgQh5jTLuMzWDl6LIlXzb1onqx4qSVvo2DQWt/5dyypMaz3n5kQwrbNqq1NP/JTWVBOHjITaYs1MvNFSo4VSLuGTJtlizqC5/WaadaMWXIsxzpwImkvJWsZF4WSsRlivOZQonnrGmCPppPyfzxkpwjg3HWv8YmtybTPophEkuYz7dJyhYJDOM8ShjXv9bbtvqUXm6nknmcxRQgiTYl/MMp9vtxQjZw4OeNtzz7HfrTg+PkbE03UB7zN7qxWb9UDoLIu/r8B4cxZUALguMCNcO1lOWAeequELhlX6klhiHKy8870TaRU+Z1aKlkAJydRNqHp9ZxZQsjGcruUktEqdpm0lpiVDck74XDZ37VgsIbmOV77h93P58mWW3qNJiZueLMZpnpriwZmvwDnXEq8IOmbiKgl3xnuax9FPNdKT3m0mG+lJ+TQNKxaxqqAxJV588UVe+IqXOTw6ZLHaY71em5iMmZTFkoKXsVTmyZ5VlZe+9j1fUMrRExNB+BngifL3M8AnJ8e9Vj67q9BUtfC6lGMJHdSWZXnhjZoSgrRFCCb8IhlxHqeuCC8174qWMEsKjhZK6d0Znlm4ZMWbK04sBlvqonYMMRp5/ZQmzYNofLJKNZomRJ7ism0X9W4UbOIJQFYZOaOTSWS8s9jI+nc3z6xqYL/p2c6+sxkilgJyHMO7AYC5KJy1bBNbf+8cj+bwmY4Ro9aVLfM8MtdK9ZSTTpOueLHEZz74+y6Y5n3g+PCYj3/842zuHLPoOi5eusQTTzxJt+joY0SCkDEhI01YTqEgE/7jxmXHN+FFagLVvi+lRtIcw5Q8CgNVRbIUxoefbU3z6+f2LzNurmZpj3zZVEq5VLnechyoMgzHTUtMxaJTPBKE4D0+JPrNhtXeAW97/oAQAteuXOX27dssSkIXK2Oc2z07NU1WnBQHUn2WAqVCwt3aCW87o7Dc9b5B5rMqABkXjGX85ptv8vFX/wXv+tpXWCws1LdCYM47qyZbqIpjUby3Xgvb7XN2BKmqyrRQ9D02EflR4EcB9veW3L59x8ydgtE5B13XIc7RBctuJFuu8c7iEoxUm4vG6IrG6DvbWVRJWryrLRS8TCYxbch5I/EOlCqGwTFYbWBUdYZtTs20VEps1Ik4JdNPMZsmXCWM5SbUNRveEtqOQt7SdG47D3wDvyv/0tZuvbYY8RwI3cLA9BiJ2HzIRasNPpAKlrUzccPko23tbbrptMgmbOJlHQMTRoeUtg1p2wHngx/LNQM4oQuBoR/MERU8XejmeTh3OEjG4mr55Hu1ZxOTJSDZbHpc1xGB115/nddef50Qlhyvj1kExzPPPsNzTz9jVVFzwndqjoRSwiNnCH7RhJGU+uvbDstGgZoYRy2wYKv/YyWCBdOgDNVc/pkEtA11fGZ930+ei9W4qpmmgEa3CiHgvePc/v7sug2WkozIgBNltdoDZ7kCBhUOLj7JweNPsRSPpoE3Pv1J8wGQGdLGKG4xsnAg6oo17pnR8cZezrTME7j/ltRy3hVLsqyr6bOf/M57cxT6EOiHgdWy46Mf/iCI5+joiPe+7wNEEVIc8L4r9cNy82+cdv27tc9WaL4hIk9NzPPL5fNPAc9Njnu2fHaiqepfBf4qwGMXzqptzmUXJRNjbppX730bZO99y6m5ciOmiY7xqGMpU0AgbCW8ULQVpFKnDRwPYjHwNqBl8ut4bphHBlWn0LazaDfHrkzQSZXEer+oPbQUbaGkbMl2fTDtY9eWlFNuOTE3xQzr9lYMfc+tw1ssFpYIIWGaQOVT1nDKUzPdVKxxOkd3KHbGf3NMbX2zCIIJUJ2YWBOB6cSRYmLQHifeNH2Frpjbi8Vi1CjFEXM8ce3ZOGxpqVVYatYm2E0Q2WJOW9jEOiYkLEga+dSn3+BjH/0Em/Waxx9/jJdffom9/SXOw2Z9h9VqzzL5VNS85QYYzyltnpyELaYhhu3zZi73DT8dI+PKd9hnOScrvVscHjOnTk54cXTBG2whynKxINcgDqbXpGXDUrGNAa1FyEyFrWwQVdtwU4bHHn+Wcwf73LhxjWvXrpBSxIXOzFxnCbaNjuZxYjSlcW1MNo57gARzyp9VKtWaiEYls1ot+PV/9k9JQ+bd3/hNxCGSsxb/yfAWZzq9fbZC838Hfgj4C+X170w+/9Mi8guYA+jmW+GZrRUSe6U0THejacbwlJJhNQprIzriBILvWCwXLIoApXjTrWzGZOFJweFKGduUkpVHcFIysyvUHJvZ6kfPyMmzDW/+8GsS5dPJy7RyrO0Qm7k4F9rDCEUDSzGR0ZFUXM+DhZ7lPlrBqEVHP0SGfk1OGb9a0qcx4YEUW7s6tah4zsz0mQu4XYJy2uoi2T6sshuq0KoOtilWLF7wxZG16lZmRSDt+NAFUk5GaJ9eoWr505Rn2zbt5JFUgalqwkHLYwdqgBKJNagp+sMQOXfuPId95Dc//HH+nw9+pCXBfs+738nXvOtr6PvDZtptC8bpOG4rRmbaj2PT+jihI83/WYZxGlZZioNByw9rVliH95kQljSOJEBNcF3GddofASydmjeLqM4L3xXlQHCtFIyQRQlFOB4PCbfY5+nn3o4jc/mN1+n7Nd47YlqXVAqF/rNjGXyuhP0Tg3tKc6q4EOj7HiXzO7/zm6CBl7/qncQ4fPbXhrd2BInI3wQ+AFwE3gD+HPC/Ab8IvA34BEY5ulYoRz8NfCdGOfp3VPXX36oTT1y8oN/7ne/lqN+QNZGT0g+9aTI64Xiptl1dsxrOVUyerFpiuTO+YImuaDDN21cWna/hmlWr0oqDFoDflcztanHVU/zNO4vlNmjEJnSqWGU1xwoGNBlD+3xrwlQguhaOizGXcNFUO0blO+ZkGkZKiaFPeG9a3WazIQE5AHH+LAVGqk91EE0xTWchl857w6YKHcghpJmskmZiTptmnQnN6pCrpUMa7jZxBNmzLBtIpa+0zk5PvuOzXa12Kc/+LPdRQkmTYalBgjkZYzTuZ0qE1VkOD+9w5eo1jo+OOHPmDCmaxzUUiyanhPOG+YUQ2F/u8Z5v+iYuPrIkxoG+H1gsAjDg1QTPkKI5MJ0CVk4ZrNb7CAyn4jxU49vmyfxWndQgKlqqCEsf8J0zTLJuUIzz0yxZB9q1Z3diyAomKnTgLKoqdEKfAt/w/j8G6k1ZaNUDyrkngmaapq9bdGw2G66/WQRoCAzDhs4lm1feLJ3OCxlvjiNvkUVelEx1JG3hmVMM8y5Cbsp13W4mH6SMhUW87e/v886vfheb9UC3XNFvBsQH3vHV774nR9ADkbDjiYuP6A/+kW9p2o1m2ylCCMQJnWO9XrNer1uy2zipH0RxDkgVCs41LcNC5yaCQ5WleJxzrPb2AJpWCiVKo+74k35WjXfKl2taMDa5alTOzATbFjY65k+099ZPi2qaC83qqBp/C5vNQKjY1mYAL/QpFgxzWmPGNfdC1fZSMZ/AQhnxhaBcahBNmQamLYwldFt8fA2NnHj862dT+KKOd+OfVo3SS8NUK5xyP0JzamBX8zMUpoWY78VwruJ9Nye11T/yIfD665e5fv0mXVjQdZ3Ns8JhjS07P6Ppq4rvgjlQQmfHeo/LVgLl2tUrvPTyC/y+d38dC4mlJjkWPabJNDpX+MfqMK5tEejl4aaYkAmNJ4TAcjlu+NUycBU+cnNI6LS51qhbbu7QFBGCdI1i3nnQsM+73vMdTeAGN38Iuxw2xmQoTI5i+uYcuXr1Goc3r1iVglr9QIqqX3igDsGL0LUKqqNjyAI4yrXchLe81SwRjp4qOKvQnJ67Hwa6sODw8JAXvuIdPPn0MxwfHfOOr/nGLx+h+eSlR/QH/vC3zD7zTYCOZl7NVm4eO0dMZsL1fc9mGMw0LIsoScGaCoZoXug01tqZmo/FHFqUsK+UlOWyI3QC6psnv06aGsI31YAFWi3zjCUfqYl3YS5Ip06eamo1M1IVxLTtMTWSbxpDSskcUDIm3lBVqw6cM6lcM6aqTY0mM1IwRcM/6IcNn7l8mXPnz3Ow2qNbdIV9kPFb5OPqLEsxNa6sXZsTJriihas3mt27BKAmnfNCJ8c4KXXjJ5/FGPE+FDpUyVZTcNQYNzgf2MRIFxakwkxwIsSo/N7Hfg/vvfGA1aMqiAsEObkJquqoaTLf5JxzzfJQahnoZHOFSL85Ym+14J1v/0qee+Yp0hDxmkm5JwSB7JuGiNMCRzhWqxUiY8VKaydZBeL8TABMnT9w0qqpG2CqfZ16q9VyNDgcuMzB2Yu84+v/AIuwTz/Yxlyf4bwPE4VgK8WWiNWjB/DiuH3nDjeuXjNIJSvknpgSrhRac2R8t2jOw3ovQYadkVFSKU7tu92bxNavZuPTqE54VEwJecdLL/HV3/AtXz5Zjna1RHG+eCmZyvNohnhQMkGE0AW6znPOnSlxvI6cEpu+J6VE3/emgWSQZAFh3nvU2yKcAs5RM2SH+AWbOLAeIiRKJUYlBIcLnVGGfOHgiVgJ1eJgct5M/ypQZ9noVZune7tVgZmzWsJ3V5KmOptchp8lE5YuN6El5b8glfhvRN4swehFYlhUP/TkZK+m1Vps+NkzBxzsnwG0RE+VaTgPWykkfPPgViHsZIQhaqSVfR5mnvcqALeb1IvtwiOLvT1dEjVMwfKtVitXSDGhPnA0DCwXeyRVLl++zvXbNwl+YVrb3kHRqLXdXCpe5+lzMifKuDnXZ5MmEI1zzhx6reOBOPTglCwrNoPjgx/5BL/x//02sR947MJ53vXV72TVCXth4GBvhQ9C1wWjkU0rqPpRgOtUOFXlplJ6KIlh7mK2Tr+z1Hxj5UgxkhSCEJzR9fZXe9D3pORZho40CXWcPrOZxjl5zmUvbnNznWC5f8BTewd4L1y98ibHt2/gvStZrEyrj70l73DONfyeQguacXFz0XBzCVoRIRMbj/t+wk/tOgazrVaBT7/26j3/9oEQmnVh1HUqFDqPuDG9UyOmjruFTqKyq0/TeSu5u+f3ysl1VvSrahEpJdbrdaNqDEMkagK1hBklsRnB1xrp5tGPw0DoOnJZROIcw9ATfGDZLcy7v+hmO7rImKXHKA/VNMuEkq269s05m6iu8DxdA+vMM1lTceVkpT9amQKheHYtjjgJlBsxGGK5AoEDOWAYekQcm9jzyIVzHN45Au8YNj2U3KUzLhzZuNEp44Mj9Rmc4J2wCotRm8SulTXPqD87ze/yoHUaVjn7yqhj/RSbAMgJLVpfJrNcLLl+4wZXr11j6Hv29s8zJCu8t7/YL6UnDOLIzXOejJtIQrqxvLOIENNg2aQmzYSk3SfBE3Mip8RCIjFa3H8I5tjyznDnqJnV3lnYg9sp8au/8VtcPLvi29/7CqvOakVJCcMy87NWYqWNvU68cW1fkdh2ypqopAY6TI8Use9aQpDCT8xCY5msVgvEG43KtOQN61s3efTRfVI2XGtKHqoCc+ZUms6TbI6oFmFecNhc5vq5849y/vwFVqsVN27d4sa1q6R+wEtPTpEUjTrkxOaw5Hm8UCcJqddwVQ/X5l+4FwfRdnNqAR+a7/23D4TQnKv/toRqAlPNMBTSscM3fCcrbXJ7JjzAdDIe2tcYQVXLeRkT6jYsVspi5THpsmiTL8ZMjJCiEtMaspI14QhEwSgaBUDXnAmLBaqZTY6QI3l9jLjR1Fr6QBeC7fQTjMooVUJNsZVJhVU+RhmplrBIwDeeZ8ZJrINXLGpFZcyQ7XNJoLClKthCK55rvyA74ez5cya8VvtlCDP90XFzrqVoMIdikRXOuxJNE3A7zk+FRsYHPH28459FU6TCEo1eZJqIacQGB7gwhtXGPnP58hXu3LmD9x2Lbknw+yz3D1CUzokBmeonZXQnm5iriZktrVzKFj5rr75ZHg4p96HFKQe6GXBQtLNACOW4Mr+MIWHXiZtIZXc4t+LMcsXSWxo0YcSbBVey9dchqlrlfB5Xk7I25zCP/BThEGfrQccMSs45FqsV3llggG1+RumyxBsO1QWZzMHBGQgdbiFI9LgMGsZigNtt6vyzCKBRw/VV63R2l+odWYV1TJzZP+Dc2fPEfuDqtcscHt7CeUtVqDmV0GErelG1UFVfIqKELnuUwUoIq53fPPdTfPSkqd5oTzJyZl0tBXOP7YEQmjDBIpo2CSlSXNIWYbPddmYsqqbN5H1N5wY0bDIQZgu7xp9673GaWXWr8qBWpGT4ZT8MRDzHdw6JySasyaV6nqolgmZHKrhbHCwEMkglvWvTOr0Po2lStMM8bMrZDNvNZf82zKvwT/2i8OKqlzoXzUQqi8nizv2iOLZyc0c6SnKTqpE666sv8f4igt/bG7VFB8PQG6HaSSscllPE4U9gXDsjjipTIU/HfDR/40xC1GcoZPV4Oq5fu8mNWzcZhoH9PRPu+/v7DbfMBR5p4bYiOJnWsaFg28xixQGCN20zWD1nq18ExC2TrzIwgvc29qWfFT82rN0WeS6OTMNOAQKrvc4cUnXWCJMa3bsW7facf4uF7b0dIxbl42q6POcIzjc831eTd8bwACSzt7+PXyzo2SDe4yW3yqyEmuhmjKiZ9W6Ctdb3203Lv6FYbji48NiTXHrqGY6Pj3njjdfRnBpvlX4A58iSUG/jK6oQinWWxmJ0UjcvANEWcnqvpU/utT0YQrNM9EQhIdcbd8Yx9MGjsWgIibJL2/GjNkDb1WvSYW1kgwkh3ReeW2LGATReZgKX8KKgQ+PyeRyimVXoyDj2L4xZHlJxVsQY6YeBoe+JMSJqEEMW89yKUBJmjDQjiydft91ZiTixgDrna4IEe1/TVyW11GGaa7ameWJecdoYB0YVKnrqLPRsXH6uzmKT9MUBJ+jEEeRF6PyK/VJZcnSMpAJdGFWmmr8tUXT1jJcmTBgD2fKL5gJDDCmy6SNnz57l8PCYK1euGE+xZGPCCULALwyrLb3HYAv7exvXkuZNrIvVnC7kOmdcox8VaWDCxrl2diduokWaYOwnQrfRpnLGlWQzOUeC88SkOIE0ZFQSYXkWajy9c5aEmlpqZHQOjicfHVDmec9mbXmHd77BIuIW5gNwrgUxeFecJqFaHmVDlZla2sztmrt2sb9AVKjuaxVFcsAHDLrZcpw1I7kFEJyM/Kl/T6uRNgAULG/XEFmGjrc98yxgNbxu37zB0a1bWEeUvlDtyBZ+6pw5QJ1j1KLV1oA9N7MSWvE10y7K5B/nyv1yNh8MoSkYJaNMiJhr4gN7kJbJZ0Qw6z36wvkjuYmH3QbV1lOe/2Dye8MAJx5tAO+LxjJqtlJI8NVEdprHmjhFg0GVzjs6vyQtbOF4DPsCOFofM8RIVl8Ejf1+WubC+pCLde0Y+ohIRBU6P8ky7YyjVwdDREyjLZoizsZJJZFVCJiAnWrVWf1k8pi2Oh02oFUzHAeHMimzLSoBUUeoRdoAOmZ4bUqxJYs2LXAMbRQEJx3DYFg4u1UAACAASURBVJrEjRuHXL9xHeeuELoOh6cLzqR6iezJO7SbFqml9hzy7Ltink7yB6Q4oCUkstFVRU6ltcQSJDATBM7yt6acm2k+wkaCL/QmEwweLyALx2K5KhQjKxmiYqZ90pEKVzV1w8EhBI93nYUUy5gMxvpCwbg9lcspTnAZQhgxxTpvToYvCuMtZ0JnMIjLgvOBOGRyqQYLZnEgzAQjUCiAibtqmDpupNWqazjtJOqr/tbJkrPnLvHE489w/fp1rl59s0Qel5DhOJgwdM5wZKe4bJsJajQjg0pGi+b+Ec/d7cEQmtjDz4OF3yVNLSlD6Do6qcW7RqwPRpNeXMUEa50dKciHLe5t5TwXsqzolpOBeo3xc3vARTjrHCZQVfOiVsGQa6Uao1LUrH17y47VqkNYtN/2fc8wDGw2/czjCDRhWxMfD5PQPwdsjo5xJZ1brffeLRYkV7JqOgHEhHAqlt/0VkMose6l+BtWtkLy6JWfzjAv5tUcaoji1qKpCzHG2FLree/NiVbOF2NsFKxN0cZf+8Rl1AndagkCq8WemZTZxmexWJHFMkw5P3q4Xa1Ih2l1FpCwMAty4m2tZrZOxq8V6yqJMqoJ27J3b1Hwgg+TDXkUOnWjimqQQNVQrfyGLWzvHd5ZMuWYezRHqtE4dd1oFmI2R2HowiRUuCPnREymxXnvraTKXGpiNpOUzUasBk8ebMNsgsy1n0wfbhW8kAnBGbl/48lZEEk2H0otH8s4OB+faWjziXwJW95/02YbBDk+p+lxVFPbzrdJ0O3t8+wLbyfFY65ee9MyL+VsmGY0fGPUfrUUKyx0NqZa8f1rlbvagyM0y66At4wvqEAw7WRTPagxGges7LaupeUyioplXhfwBiNnV4jDubcdmKJ8VmKwr6ZSKLWAlCRmAknFK6eOC0rShKyo8wVAhySu5ipo3Elz2NSqfDV2rzxEepadsuwCZ8+UXItSTNZipg5DJubUtETDx8zUExGSeFuwKeO9cHy8GbE7YOEd4KyQlQiho9GaVJXsDN4IwCLbwrNaRG6820IWt2xMsMx2C1ETOVtSFVSK0jqWN6hrwEplFM2hUHRCCHzmzVvcub2BvT1b2KnkeHTGT3XOEZaBXChmDbwvdK08MTGdsygfjYkgjjxsaS1SPPtNblbAJhXeZYkHdyPsEMJq9uBV1ZgVaTyvOIcveK4lmtECT9jz2SRQHUB7xC8gDZzff5TDdaZbFDM7eELocCvX0gXOOJca8a46Mi3DkNOtYxCDMstGbRCVkMXPKEuWdUgZ0xcW6EY8pmUGnCxxEshe0ezxvqSc8xlRRXI2K6ZtmiVyLhess6Y7LI7MPk4sOakhntXnPc41CV1xBGob90zCBSHGfswnEQKPX3qKII4bN25w/do1CJAYiBrNQTfYpqPO3OuaPOJDuWdt+RocZQMSUzS+LB1BOMFpCZdSG+S6G1XsMQegZhwS41WenriNBnQH35HKhMGZOZWzTQTnA0ViGYYqjGY929aaOSaUjGhJvOFk5miqWo3FebOVUPakZmval5HuLX0dnFmt0L0xF4b4gMNoS0aRGojRolGERCqCwlUzXhybGAGFvh81crHvTJ4KoXh5a5RazuP9ZjwTHKK8GHUruNBkUE21BeC74vEcqiVQInImWnRKkZQsOEFyQoKn8m+ndX7G8R8fgPd+i2lhGC8pNczRbUW+1JwG9Vd5grfav5IHswgCy7zUl59KE4qLblwqvlKCogUyaLZorGmJ35wjzz/3DE8+csBzLzzL2YOOBd6e1fSeQnFChbbTTO55yg0dPbyuUvGKuS5mgzYTvNz2zBTfFgpOXEnDCIuFUaDCYsFisSAOG7pFh2pqm2zTIksgQwVCNGuL5DMMfRScvqulSirurSWZixomXzfpibBsEUw6wgoNR08Gn/XA+UcusX9wgeXK8+abb3J8fMQQN2iKNifKPVdKVhBHCJbz05xxkVGMz8OB36o9GEKzKAJ5S7VTtaqJdaF48aScCCXRqRGDtWFy1WvcWk1CkYrWU/1p4gp/ubLKCq2k4ZiWE0YYH17tJ1LC4IoJVox3mxSMESziyiOZhGSKTMzfekoRFoulLToxpxdajSBp42BAP3SdZ7X05BIbr1mJKZZIoVSEEv8/de8dbMlxnXn+0lTduvbd5/u1N+hGE00HghQAmhENRIokSEojiZTh0Eghand2djQRilkpZhWSIiY2RjFSSKFV7GwsJ3ZDox1pZZcjihoZDjUUvQVAEiTYsO3wul8/b66rqszcPzKrbt3XrwFoQn9gkwF29zV1y2SePOc73/lOmbktrIUJi1pKAlE9VPfIAt8cIqUee4uVBUhInhEoVBKJFuE4jCuqwHvYRUlbiW8aGwoSxvxPn/EGa7IQIvuEzv4qHJ/oC4mDohbfFYpWyuutVsR9q/fXn39QTNpnbAsjkLsxUd/heYICX7xQeJTO2tAMLiQsnfFRQe4jACFE2XGAMAcTlfCau19KIwIhc1ye+rSl0BOG3eWu9MLHLxZzqCovGIwkHrdXykdcstCRFeBVlwojSTlHq9dbHqdkbGgEiiiOkToOWpsa52SJE08YzeCRE3ilKHwXWFt42bLUKpABRZHOh+AKgbV+7frjeLpdUVFXPc9JCCGcc9hQLI7M5CAFaeaY6s4wPTNHmo5Yv3mt5ETnNvN2Jc+xSpOmPgJTSJQOLYVl4HxOog7POV4cRpMyci1HwbOyxqJESJoEE+XLFD2ALirfE2Kye+W4HtVn21zwrEw4jpSBo1f+pmMcQtxm75kgfcN+WkhhZ6qCBmUVkCuyeYVJ9uMWeTMRGqsJ6RNWeJK7EyErGqADAkKklSVSCqUjrHXk1sMU1lpsan2tdByD83w7h0E4b/i93fPH8V5v4VUOKZR0VCBf630YlcKiVUg04Q2cETlZ+Tz856NYlMpUrjx+8AwdIFzgMk56iYV3UoScY/6fKIsErJ1szVzFNH1iLBjdCcWg8ShEObRSpRdVPN1CWX7/ENIbYh20GavN5YogRWlFLRLURO7nKxpK0109BzsxFyZ+h0rJJAShC1sxlpSFBJMJnoOxO1/5VHhwPvyWEpTyGWalvYatKaKOoK3oI+exN1jFLj2u78oowbmg0+kCrh48TRWutHg6zgUeqZXkwfs044V84HUIoICzC+fEBSEUkMRxncNHz5DnOcNhn52dbUZp359H5jtApMZ6GMcGz1yaUpH+hY4XhdG01jHM8nDyhVEJ2XMpMEJ4EYbiBgWMUIfJklN0dAyU4X0LI1eZx1SkxBnjdTNNMX2DDxrJYEzCjlxkuPdVCjjGdB7wk6TA7mSlgsNn54tqmODFHFRO6KJg+0y5QIrwwgsXq1JQ1jlwQgeilS//E0EtxlnP05RSoAlZfSWxUgPxpDgGhtzkeDuW+13bxSilA46piYT3CGxufX1ykVAICVmtauS5wcgcUWE2aHRYIMELcQZjPGaWo8ilIc9H1JI6WTpu4VtWDbmxav8YuxPjDKgQJfVFaIUSnn5WJB2qAi/G5GgUuTFejFcojPO0orJ+3LtTZIU+aRhSCCId4UtjA38zWERrw0YUQvsqtcYn4jyrII4UyhqEDFVfB2zEBWeyavTKEtyCIUGAEYQAYfZtIArIvDGRPgVa1W22yIDbWaQujKSHObSIENoSRynOCerxTPBCXdiYkjCHc99grdINQQjG1K/SE5XltQA4bKgSIvRJBx1oSc4GcW8ctTymYDml1gTII8WV0ZSY2DwKMQ8nCA23ClqYBCcRMqbe0NQbvnTWZJa19Zuk6YAsH3lEwFgwltSk1Gq1CSfn+caLwmgKMW7aVIhY5Ln3yorsXNErB3yo6DtTenUTLb12oBUB4xKU3gn4bKuVEiUFJvNxfElDDF5WSYwuvI0i0bBPuMJ7gGEXLfySfR4yEEoe8cbChNp3fBXGQa2GqztdtcslUGYmi3/7aFWXKY3C2fVrLlS7FLUC+xvB40sxa3HsRWqdp4sQqo2c9YvD5BmIcUMvHRckfIWOCljE+Z8XBYFcYosmSs5zZjGVCptIIY0gqUUMRmlx4Z4sHriIrmKcwBtBXelPU3ynMJJV3ieMw/vqvayFxWmch0ryPC8/X83wlvNFepm2EhsztvwuIbmktUY4SgPvz8WHe8ZZ8twRxxEivVXstvqsi/ldfe4FBQpRVNsU5weIsCHiKPsGORWmgCqTMIYhjggpfVsLrUMraTKcM0SxAEZkRlCrTxHJiGGeUavVGfSHJTzhNT0L/HeMLzrniCPpRa1dUfsd1me5wflIxW/6XhVKCO3XjSwwUovQ4yo+KZ0XEhYyKFQ5Xy1VgGjFM6DwOAsu7n6jJ8Nc8utofuEwYNne3mRnexth8wCvKfLcev7nCxwvCqOJGAO+PoTQCDFeRIWXYSt1yB63EBgny4yJsD5ZJKU3snlRRmYBJzCOcGxxSx27DUC6V30Jp+Vgv2NYVNeOFYgoPZGqh6sodlTry9oIU9xRYoPAGMOVY4NQBcWdc2U/9vLogUxd4LROgTAFM+BguMCfmymPYq31+J21QW2dsmIEFJGuB1qNKdXOi+6BzpmA3WdINAjf68+HwiXT03M0rUMjMRgE1nsxOLTy1UgqXGOpnl8aPU/ZqZadQmAlKFFGAONKslvDK2csZeO5qpEUopQzq45CiKSIDqQIxHIlPDlcELKv4UnYKqRR+XsQ/a3VEoaDXVCTy2w/Xcv/JeCBhcShsD6kl/65+dO24BJvYEQlrLe6PAYiBwcxTZw0wAgVOayIUdpvPnHcQKuErd4uTz5xjc9+5s+5vnydzFl0/Td58MEHefDBd/DKu19Br9dDaRnoZH4eO0dZq100UpOVTb1QyLIhySWtBw2llFBqs4Zn5EKNufBckxoC4xzSgFNeZ3SsVC/KXrMl8hkcBf+iHK9BYSk6U/pkniJzjsbUPM3OIrkZsLm2ikmHZHlKlh/cPfWg8eIwmgdk+IoFfHC/EV8xNg5/IZIKJx3SWYR15Gla7nj7W6JqqXBqn3GRAoPPbvg2Dm4iq3YLtmU9H0xQrUiqHI9CgMNjb4UUVr7/4RS7JmOMaHxbQuJhXwWKN6S+VwtC+J1cgwwNwarOsRCT5GwZOJy+L7ktNyrvvATPRoKzColEao2zo3COFbqXkAhhCPlIBDYYPi8C7RNm1gsyBy/EmwFJZnw1UDXpV5QyFur8PsoYU2OK89dajUvl8J6oFKJ8PuMqIE8vKTPdcrxZOOPJ0NX7X4hYFM9OKBlk5cZ90/0PVtx6S4iARImRFRlE5wxZloXKlHDfK9dTJZtLIcrzrm4Cfh8VgC4zzUJm5ZyI4zgkMQxpahDEtOsz4BT9rMfVa+s89NWHuHzpGhubPYSE6Zkmp06d4IG3fB9Lhxa5776TvPrVd5PnKRvbA9781n/M3OwMzVaT3/v9/8hv//Zvc3N1me994xv5lV/+ZbrdLv1+H2e881H0eYqiaGKe+rLhEDZLA1KgnCgTb/4zRXsbFYxu0Ax14AJLRiJRIZlorSPCRyW5G7f8KJNJwh+zCpf4cN1DDON8hEWKiNn5w0gMuIztrQ1e6HhxGM19CwPGXuVBu3JhTJTSoZKIEB7IkHH0E7m4RZLQyiLoLzocWchWFNiW93D9MVXI+oqQ9asqjE+G1mHB3Rpt+xCkWEDlQmIsFLp/VByH/T2BitYD/nPeRLmyP5IpjdbB4H+x+dwaflhrvchxRahinH12qCCSUc61yiFcqGUvOJMCiXSe1l92oyg8YiE8gTkkqHwtqwNsmdApzrHgER6sixiuqbI7ySJKuY0OgRddHnvvCK/3WEIglVr50pt1dmwcw1AVzKtsDVJotwZRbPDOnne2vWHMQnmh70w6aSjLFIZSCFmQQINXL4oOq4CTZWWViCVax2gR4Zxgr7fHxcef4juPXeTixcv0h9BJBEdPH+f+e1/FD/3ID9Bs1TCjoa/akQZZZMDZJB31iZQAkTMzXafdabCzt02apbz2/tfxfQ98P0pDu93m137t3/LHf/yn1Gox/9O//Hne+MY3YowhSZJyM68KfReFDj716HyPrKpykwvYgxhHarKQWQxaCp7EHjZE6eEwJRXO+s1MiaKL6zgK8ve4AIQdPhTzxq6o93Pl5gTWCqa6c7edb/vHi8Jo+tzXpOdRUBoKLcsC34R9+F9l54bCODiqu7kNNXPG4KVXECTBeDkdBdxIkjvfeMsrfwe6TGbCTuqpIn5H9dUTBe5aXfhFKKm0xBiQJghqFK0oPIFufP5hcdrK9UdCkwlP4bDSosWYzmKdx9dKwWMbvBOhQjKpCr0F6CL8vg9TvPCsD399ykZJwDlvnGUh6uBL81xFJk4yZpo6gSdCW7xsH/64DlCh1NQhsconrjVgpA21K3ngoXg+o/dyqxzLkBALxP1yShy0KRQsgWKxVvrihC+VmOn+2vQszbxIdbgoHeacDqIq1tryeRfeJDhcES7iQg29P4AKFDiEQisQROikhTUjijYf4FABIywelhSOzGTEsReHcc76BE6UENVirIUbK6t889FvcfXSCleuPItSsLg4y/mXnOWVF87xmnvuQVjJaLiHdTlR7PcsrYaY4QChI+JIlvCUUirwUEGQENcsWTpEAvWa7+feajc9FGMFm5ub/PiP/zgf+MAHme5Oc/3mDX79N3+dj/3Jx/iB97yHD/6TD3Pk8CL90ZDU5FghMKMhUS3GCelxcofvDVVAHxPrvZizISMfvIyCtuaUZ5JgrW+1jcJIv0nnylduWCdQYghOkIXCEMrCADyh3QYYQQIViE7KF24KXxRG00/F/RhTUc87aTBvN6q0k/IIFWpE1Xvxme7xZ1zQfBQSotDWQEqvoxnHYdcsvRH/9zy3oY684Eea8j0ZDHRxKgoQ2nebZF8O1RZtgkWABJwlVd7jUkiklRPhaJWaJaVEaR0atYXdOJTElceX/jVZCxgrARsrxxgb9NMr1FOHmnilFTYYfFGe/bi/kheCDZ5u8PCFC76gMCEn53B5cLeRmFwghSbHkhvKRVS9L95gPrfIbjGklCXVqFiI1e6h1Yx68b4NGXVUhcFZ0pLGcynP8/JcJrPb/j74Tp8Kk5vQKlpgXMYoTXFkOGfRqha+G55R7OX+hPQFBFInXH1mmcuXnuRrD32VvV3D3FyDl144z9lzd7K4uMjxpeMcXTyKy3dDMsRHTlEU42ROOtzygtw1v9lqnfiQXhTiHSpUHIlx4tV57VkpJM7maJWUGKVzDl2Bb6WUtNodAHr9HvPz8/yLf/6zfOSnPkI6GnHk8DFG6YB//rM/y8WLF3nPD/4AP/NTP0Oz1aI36KPweGQpJO1cCeNXR4HdWzcEKVGi0tHA+VbeToaKnmAzIucfnXPgcl+qLI31xjqUEZdecIUHPC5mncgYPO94URjN5x7jbN3ft250bDSLEKmgTcgKfaIwqCL0JRsblLKhvAj6hGHxeONrgoBwNPEe+L8XBtXmjmGWgfDUKC0UtTJEt16n0hh2ez1iqb0IhBbESQyB21fw16oJqiIEMoHI6zOBvnWrX59hYeFCgiBMxiAyUb0+fyahbXDwiKpMhTHmXGB+cpwhE8J7CU56PJnQqhiCgQblBC7OwUkvoiFyRKSI0jwI5eYh5B7X5ivlEwA6Hk/Rg0LwAnv01VSuTPQUxlFJTzNSoYqmvF7rDV6W5xNGU8pKjXvYtJViIuKBoPDEWGDD4YL6VBH1RN4bMganFTqKybKM5eVlHn74O1y5dhVroNFMuP/e+zl54jhve9v38v1v/0eMRoPQiTMjjiPSdI9Bb9eLdhh/vgWbwKR90LVQDSbRKi5Ft5UqoABAEeCn4trwCRpMKAGWyLyaHR9v1cU60kqGJJAlHY6QStFsNmm12/QGfXCGX/3Vf0O326XZbvOXf/6X/Lt/979hgfe+972890ffR683oKAclc91kn7i5zUhYetk5RykV963AhMiEGe94IxyElMwQWUwmgYyESqRrECIMafWY8wyzDU1EeQ+33hx9AhamHU/9sMP+ORJCIu8hzLGMKv1ssDE34vPUPlO9bXi+9XPS2MmtR+BXEyqyPjjeWOxv5LndqMq1lqEmFhfJTMwOdpa3CglHQ3IjWHQ32Nt5SbOWrbWN2jUGyzMzXD42HG2Bj26s3PjpliFIMiElzyGJipngY85fciyf6+pgAOUm4QXSKok8f3mkOf5WMzWOo9NOheqebwsSZE08yuqIBVYnMuDAbdk1nuDJrV849vfYW84otFogU8hhdDaBi+o0gq5Qi+qdrWcCMGLq5GTlTX7scoiPC87UWpdsIFLVkQhVlwwBookg99ky5wtBTpWGOlyGH9fkyRiJhGsXL+EcXVmZmY4f/48x44dY2am5dXLt9a8spC0KFtkescOQileUc1ZCTNOesmCaSIo8GGCsIcMGL0M4sAelxeei2tyfDVVjFMWLWuAZWAkr3vTu8u5XlJ8GM+38k9baFd6d9QiKDoASinJsrG0YqPVRErJH/3xH/Fbv/W/cvLECX7xF3+RCxcukGUZe3t75XGdc9RqNYYmm+hyACAKPN+5ktdZFKJYwj4eGA3OjculPVvMkYbQ3DiLCRq3zokgeC544Pu+9/9fPYIEno4gbCG3NsapXtD3/55eKMISGoVUjqFuCW9v/Z0Kgf0AQ1qS3m1IjvgXvfyXhWefucLujZu02g2yPGV7u0dRybK4cIQsy0gaM6yub/PU5UvMzm+wcOK49zKsIxIieJqqhBzGNAsFYvKcirB5clSgkNJ9laU1dRW5unChQODfuWID8YswN+PQRiJCBC7LXdw6z+Oba7U8kbheY2mmzfXVPtJ5YvEo9ZlNp2OMsaGBWgijGHslxXUUIiT++JObvqnQ0gocWVXoRV6SDuIkmUjeCOkFbY1hIhwvSeQlFaiAGfx15sZM7LLeQ7Nsbe3wkf/xI2B8B1Xw/Yi0Foz6m9g8Jom8p6eECPQgygSGV3MPXmDw3P3UDCIhIXKSQqNl4CYrH1VIFZgPojCsgjiE5Z64iC/wwCCl13FVWqFswcUsxDzGGe5bhhAeAwZMyHgjVKkWpbWm1m6QpiMvXg287a1v453veCdaa5qNJr/267/Gn/zpn/KuBx/kJ3/yJzl+/Dg7Ozuko7TkJpccZeuCSnsw3CJ0c5ES8DJ9SIlykAeM28MlCpH7fkjSGgwSYwVW41u4WN8vq0i0vZDxQvqeHwN+F1jEr46POud+SwgxA/whcBK4hO99vin8rPot4B343ucfcs499Fy/MTvTdfd/zyu4fn2Z1dU1bt7cQmuo1yNmZmY4ceIUi4uLzHbrRFE0NqQuJcu8MEWRiImiiDRNy8meZTkq8juj1trX+gpNyUp3ptyVUwsEN1+oYBwsZdYz3LFbjeU+Yq1zFiO9TY4jRU3XeOqJp7j40DeRCGKtaTQaDEcjchy5sCx0ugwHGSqps723QV3HzM61yYSlFif0+3ssHD2MStpYk6OlRklJZk2gxUyqaRfhW41xt8syoRPKC02eetwUEJHwLmIQQZFSeTUj40jzcWdAUWBxOHCCPM9wNoRQeMPq4YocpGI0GFETcOLIDMP+DkpKcifpD3PMIKVvUoSBWGm2hxmPXr4CtRbkEi2NN8BU4ZkKqBvOCEDqA0SIK5tFWbxQGVWqUnUc9FrhDVfpRy4fMzwKL8lYh5AxsXT89AfehXaTZZ7VP/0/PAZZ/vM5Nn8Pw0x2oyw8SKFcSHL6Z1GTCmMEcazRErRJybIBo/42vd09ZqcOsTfq0ZmZhpoG3aBZX+DV9z1QJk+L6rccg0T5fEpgWFgcuXAYDNJqhFNM2J3goe8f1deKNZtlhunpaQB+7ud+ji9+8Yu85S1v4cMf/jBnzpxhOBoyHAyRtai8l8Wz0Dk+2VgJ8gsj6wKGjvWQlTXFJu7pS0XvoswYDJZ/9IY3/4N5mjnwc865h4QQbeDrQohPAh8CPuWc+1UhxC8AvwD8PPB24Gz4717gfw9/3v4ktODUiRnOnJotqwp8G90GSa3BcGAwxrHX32ZjY4UbN1ZYX1vn6SsrFEnIubkO586d4/SJw0xNTdFutXx7ijxnNBqgaxqT5ziVktsUIWIfNusInOd9idCPRxEy4EphMWXW0z8QWxrRkmwvzL4rciQiIktTbJ7zt5/9NBJBq9MizYb0Bn36uxmNehOU5sb169xY2UZGgs7sFAvTi9y8fo3cpbSSiFoDZGb42me/xANvf5A9n7rGSEGso1CV4iYy+nme4XdgyPNROHc/oXStTqQl1qogO+bDUS1DqGJzLwRtvR0VQf6t8EJMZgGDlBGIyEdoQThFqYgst9QUrK3eQFhHp9vmqccfB+3xR4VCaU3U6pBu7DHqDT1rQcErX3KW7zx+mRTlqz0mNqgSKS1fKVoFe0xx0vMs9hAbEm1SMG4BccBwlZB8/5jgEBfYbjTGAMFDMwJHXtw/50qRif3H2H/sgwzMfuNdJHHGnNtxYqekKgVhDBlwEmVyNpevMNOokY0GPPLww2xt7XDi6AmUiXhWOe6+/zU4bZGxZRR4vUnNixBjvHwgwpJLi5Aa62QQ4nAhOygQTqHkeB0YO3n2E6F9cR/Ds4jiGsPhECEE//pf/2viOGZqaoonn3yS9773R1hdXeP9738/P/H+n0CqqKR4JVGN1Hk9WiWDVmdRNScd5WZktZ+f0uFc5I2mXwyeiWIOrtK73Xheo+mcuw5cD3/fFUI8BhwB3gO8MXzsPwCfxhvN9wC/6/zd+ZIQoiuEWArHuc2PhKZhudfQAxA4RsM9JI40TbE5tJqKWq3LkSOzWGuIROQVtI0vuk+ShN7I0dsb8N3HnmBtbY1Lly6ztZ3hHDSbglOnTnD02FHmD80xNzdHJBWj0Yh8OMS6UMMbdlopdDlJSzBcao/hGFNGtrcI1DnLpWcukQ36XLt6GZU6mu0ug8EAmHn1xwAAIABJREFULRzNeoKwgqmkCYkkH3ZJRyP2egMW6jE7q8vk2ZBjx85gBn0OHbuTr3ztqyS1GR766tc5c9d5knaDPMvJIfSnFiFp4c9Ka0WeG5SK0VHi0SdZlMYZMptjjcGkRWWRp1Fp6UVBtY4wLicbpUiCALHIyykjCX3nTQjfXB76j1v6O3tkvT3qSUKW5zz06GM0Gm0GwwEzMzM04gibDxB7fchAGYlQEYN8l249JnYZqXI4agjGzAcbBCSqHrU3WqZUsTuIT6nDd511E4LE+73P0hhWjlH+lhQBeRgvMJsbij7pBcfU80AibD5CWAMTCagCjB4buUIkppoMqWKW1SEDB7nqaRbYs1ctCkbVgZIR6eZVNrdWwWV869IuWtVI84g7zr2CL3z5S8zPTHPhrgs89dhFer0+SXeG48fvQuuYVGtUlKDjyGOqwmGFQAsvWF1A9xLhNzcZ1kgh6SYE+7c7mJT/K9kIwrM2nPMqXoN+j+Gwz8xMl49+9P8giiKmpqb49x/9KL/3H3+PmdkZfumXfolz585Rq2nSPPfRISACPjvGxC3SCKxwWOPxZ4fHYK31+qRKirEI9QsYf69EkBDiJPAZ4KXAFedcN7wugE3nXFcI8QngV51znwvvfQr4eefc1/Yd6yPARwDarcY9H/6JdxSvhx06sPqFo9AVFEWrUjGeyGX/lOLPCr3IOV9nrCOJRJM7i9Y18iwj15pW0kQIQRIlbGxucOXqNZ5+6jLXl5fZ2xmiFLQamgsXLrC4uMj8/DytqQQlJb1+H6xlmKZoaQM9qQYBfP76332ONB8irAsaiopB6iX6p6bauOGQGEer1SJSGqVjssygraA52+TqyhZaR6zv9NjY2MJIzez0FCeWuuzs7ZB0pzl+7DhCy7I/NQGi3Z/I2j+k0J6r6bxSkWTcdldIixaKTEpslqGMxZiUItLB+rSJJxErTB5EFPIMLWFl5TrZXoZUcOXGGlZo0iyjVqsRCcDlJHGNvUGfI0dmMUhc7ileMYJWKyFOEh7+7uPYuH1LMUGRqKkmB8Olhz/HF194mBPzTo69NP++L9vD+ZsnhSj1SYGSoymkLI1sUepZ9KPP87zcpHChyMLl/Hcfeg/KCIws7nsBY4w9soOggOp5ipCcLDBsIVRpHIuiCyEESI9Pklu0VDz7zGV6V5+mFktSC4sLS4DjP//1X/CSC3fRarZBaWa702xurFNvJqhanaluA4tkbnGJ1swi9UaLuaUTGKnpzi2Rp7kXZJHSJ4NcjpXKMzcKKpq7NXHq3P7CkPGY6CNVSf4CEyXKWqkycXT48GHW1tb42Mf/jN//vd/n3vvu5ad/+qeZXVikVquRpmmoGnQhTyKRMgqbnG+xLArLb33F0ZmX3fMPmwgSQrSAPwX+hXNuZ1+W2gmxv47luYdz7qPARwEWF2ZctcmRA6wIZU/O0zcAJuRboNwdJjAef2zvRRVcPCNCDbIlz3u+b3naZy/tg4W+1GRZxuHDXY4cnUdaR56NiOMYrRK01mit2dnZ4YnHb7K2usraxgbXl5fp9XKaCcwvLHDq5EkOHz5MkiRkvT5aCSxFAzVDPhwiVMzas6scn+9wcmmec0sztBotnt1aY303I+tnRGbAPSfmyWzON7ef5W1vfz1//qlP06y1ODzfIiLnmWvLHFk8hJZxWZteXX63ZMyrxqNoFYHxYibkICKEEmjtBWnREi0h7/e9lxGy6yjrZeWsXyBKxgjny9S2NzfIBn3yLKa/t8f65g5TnTaJy7lzrsmhToskgmHuWN+x7KUZOo5IbU42GtJudunv7dGoa975+tfy1199mOFEy9qxwbQhMVCGfQXkXLkLZX35AbtIobmYBVzNe3uepF/1hgocVemqB+u97KDfg5TCZ4udD4ktFjMalAyQghcr5HiRhiuqHHPygRXGsoh0qmWXpXdZ4poCpEQrr4a0fnOdm5eeZHn5JlEU0axHbG4MWV2/zoMPvoPpmSmuXV1G64SLFy+S5zlnTp1iY3mNVjyPBFae/C4sbmIaDQbrz+CSGWL9OqJ607dLwYLwgjm+jNh677lwdoIRHQ978A5+IPujYjwrBjXNMqSUdKam2Ov1aDSb/MiPvo/3ve99tNttrl69yj/7px9hfWOTD33gQ7ztbW+n0WhgspGfAtar3wvn5eFcaIwlhEDcegq3HS/IaAohIrzB/D3n3P8bXl4pwm4hxBJwM7z+LHCs8vWj4bXnHB6PcJUJrorNnxJP3PedoqFUoVe5n3YURV5GX1gHWniRCAuZMWOB1CDAUItij006g8MSR77CJs97WKsYDj22ubQww9LCjD+fQlknlgipQUeQ56xevUI2ynF1371yOLLoJKY7M82hRsSdi10OL02ztNilm7TZ3d3l+o09FpotdiTMNGocWppjaBztpAZZn0PTU0T1Ot965Jvce+/9PPHkdZ567CIv/Z57yE2OLkqiJ8Q1AFFU54T3BegAkisRUex1Eh/mmtxBBDL3UndSKFAFDzEL610iiTEuB2EZ7u2S9zM2tlI2t3aZ7bS5ub3LfLfL8RbceWSWEzMJc3MzNNuaer2Gy+Hi1RU+++hV+kOoNZps9XeY6kwhDNTUiAdedSef+OzXSFoLWJWRjwCpsCIPquPjRXhQS1nMOEwrmRgBfxX4UK2szMErOlWmm3+22vMzjU0pjJzNfda1oLQQQkOFw+qQWsu9Yrh1Xm2oODtpS2ZkmNOFhmuVxVFwgcelsePijEkP02OYkgwYGsONpy/z8Fe+ztbKOrNzi+xu76KFZmpKcufpk6yvrHBzZYUshZwd5mZmiJMGl67doN/vsXhohuFwyOHDczzx5EXOnz+LvbGFbO3w2ENDLnzPaxG1I17oWlZq8kP9qJSEe+7AVUNxQVUwJrx46zMb3wSosBeg0nqYkMQTEIdmjMN+n/nZWX7jN36TRrNJp93mkYce5ld++Vd47evu44Mf/DBKKSId+3xF4ktTlfItUsTtoe5bxvMazRB6/5/AY86536i89XHgg8Cvhj//rPL6PxNC/AE+AbT9nHgmQCBgVzOfBaB7m3MKn7m1CuggsN0KEM7hchMmtwpqPPt2wjAmlLVdkbktFKnT8VcKEQinsHmGSQckUvLkdx9j4dAs/f4eVsLeXsZMvc6dRxu87PQxXnH6FEkt8nW/9YhWc44nnniKmzdv8LJXvoIjh2bpTE2xsbFFt55w6dnrvOLUAtvDnOujFrN1yV2HBPPHOihGXmkIhxS2FKYoCh6t9E1dReGVhzheKV023Sp0QIulKwqP0jqkCqIogW6E9c3DfBgas7F6neFuj9X1XYajnGarwcZOnzOzTV6+0OTOI00OzSQ0O21krGh0W9S7bYSKOHR0hvOnFrm+MeDjn38M02oiRc7uzg6rasCrX/UKap3X8Zef+ipWd3zJuslClngyFD+IJeY9vaAxqgtyOhO6mcaOEwZjSn7lERebjx1/R0svGiKlA3z3VOMgdQJtc6w15DbDaYmXRPPJmeIcJ7zKQHeaxFELo1iE5FVDGt4XBY6pEErR1LD87DKf/ORn2dwZAoonHnuabrPJZn9EL/MaqnvpCGuMb0edC2yWsrA4T9JJ2O4N+MKXv0USKy5fucrpU0t846FvouOEmhI0atdZu3KZl73hrRw6ecGLsBRkTCcQBFFo4QWzBeNE2XhTO2CNV4LU0gjbMVxSPs8q6aBggsjCs/X/bk9NIfGN+S7cdYE/+MM/YJSlJLWEKIr52Mc+xp9//BO8/o3fy4c+9CGydMTIps+ZINw/Xoin+TrgnwDfEkI8El77V3hj+UdCiJ8CLgPvDe/9Zzzd6Ek85ejDz/8TwmOOlXapKmBI+zFXWRHylWIy61jFuGCytFKI0IAxd9Rqml46JK5F2ED0LVuu+iNPHBPGxhNubYUqXQ5qzIMb9HdpJ74Xj4o1UkCzBqcOz3HXnSdpNb3AQa3exlp4duUG586e4tTplOOnlogbLQDanTpCOFR0BJOPuH5jndEwpaHhx3/onXzq81/mi5/9HJluc8e583SnOyHUEJiQpPAepO9J3kjq2CwnMwalFanJibUvARRSE4sE63KU02QmJVIaa/AlpVhw/vlYl9Oo1+nv7rGxepNWq8Pa1hatVgfynENTNU7XNXcf6TA3E9NqJIEfGaFVjNZN6p0ug3jItI2ZmclpxHX+8LOPYIHVrT2cixn1h0zFjlNHuzx6dVjWystKS47wkCbmiDM2NNLzHmaem8rcuNUwFgu5qAY6yAHyc8QneozJPPk9jhnu7bE4Pc3e3g7teo3BXkYtqZEPM9J0SBIHFSQqeH1BUbKulIKrlvhWvcsSvpeVMB1Rck+FEOhIUo8j/uYvPsn1lU1yp8ldTtLuELcaKCm4tr6FjiMfaeWOmbk5MIZOq0WaZvSHjt09i+pKermjt9Unf2KFpBaDGLC1uU5S26Rzs83Qxrzr6HmcDtEgIJ3FhPYqDlnCRcX1+JzDAUUYQLXbghQFRHf7eHm/wn9VVlIEJosIFV+xVES1VqlP8O53v5sHH3w3SZJgjOGTf/NX/OEf/j/82Pt/7La/t3+8kOz555iEy6rjLQd83gH/wws+g3KIMdhdZhMnjWDZureaIRXl7972yC5Uqggh2RnuQRT7vjFCYVxeepLPeXb7jOf+ss5is8yGI1pxnf5wSDupkSGII5iKNYeabRpCoPFJFKFiUF7wIc0GHL/jJO3pLrreJh+lKJ0wSg3HZuboba5CZtgb5mxubvPw7g5x1OIH3nEfV7eG1FsdUpOxurrK1tYWjz/+OGtrOwjnhR1OnTrFdGeKRqNBvZ1gkNjcYVE4FCiH1YY8zXEmB+2JxUJ61fVslPrMpDVkmSVTOZubm+QWtntDkmYLnCHCsZQITk23qNegVk8QKkJKi1QObEo66HlKaOswd95/D9ce/RpL/R5ves0F/urvvorJI7Z6OU9deZaXnD/Om19/P0/8wae9vuftxGKrBHNcKBQJ2fNKWDc2SLd8DaCkbD3XXPIYN+TCcWimy3SiuevEnUhl2dzaI0kShDxEIgTkXns0iqKSmeEKGlJABqrqRxPheIXEX76PC/ibCd/zeOwXPvdVVta20HEdjGOq1SLPLTvbW9hYk9TqIBQCg0Ry8alLTLWaXFu+QRLXvIyejunvZTSbdWKt6ecpsiZJB5b24jwbWzvofMD2xgp5nnkmiVChgMJ7hk74AMxVFwVQFl3cIhYM3GIgbx9l3u6ZlEcK91bg+7976TzvPBQJNGdh1O8R1SLe865384Y3vI7ZuRngZ1/Q771IKoIKDyDs8sJ7A/szi/szpAUWJMJkLDLo+4d0vlLk1OkzXL12nU9+6r9y151nWVycr+CgFvZTh6pnWISycpKbNz5/Px55+GFGwyHEjplGi62dLVrNhIXZaTrtNsbmpHihVoVAasvp88eIdB1dn8KiQQqiWox0KSdPnmGnv8vepsRmI+44cZjHrtzkkUce4+zZszz5xOO0l46xN9jEOUenk9DtHubo0Xm/OE2ofsHRaNYZZRnrN7e5+uyzrK2tsbG2SjZyLCzNcvjQEkePLlGv11E4UqswWYzJJCurfZavP87u9h4ba9u0GjFnTp+kmbT45neepDszTaI13YamW5e0GpK4rsmwRDic9NQnLWLIwQ0Ns0e6qFoL4oTFU6d4TXuTzc0d/vahZ1AqBhFxY3mVl997H1ONBn0zZLCvcsOXwlJawTKcE6KcH4VCkbWufMQuL5Iz++aLEpNSfMUTtqLEveJIE9c0ygw4MjdNIjQ3l68GLYIcJ3Nm5+b44uc+B8bR7XY5dcdpGo0mWkelZvDYwx3LwJXzTClkUCwvsEsBlF0vhJ+zQkh62zt84hN/hY5bpKOe96LSEbs7PWIpaTRqLCaaVj0iFpBieXYjY5ArtExI8xSbZkhheOv995BmhqcvXyEdOSSxL57YHbK5ukWzETNMc4+TCw99FfuYkBZdNvGRZC/U9h10v2+zqd36uUlZSVUYzHBvlQ3tcAAtfCUbElRNk9Ri+sMhU+0u6SC/3U/cMl4kRhOvlDNh8BRO5mUIY41FFG0RKrXIfu8l9EeZHKV3KCGOaly5contfs6l5RUe++4yv/Avf4a1m1cQIg679u1FQcYE5nFpZ2k4jUNEilatwdbKOlmWEZmYy+kmNnccnm0wHQvIBmRDaNS7vupGAdbLf+m4ho5qGCdQgIo0NJtEUcTw+i4r165y4vBRHn38cV52+ji4iEefeZKdvuXlc4dxenze1lq/OK3HOI3z0v79Xg9rLVNTMd3uaeAOCGJtKEWiFOlohI40tVbC1uYmtSQmz1IW5jocXXqVr3oxI5YvX6XfG3J5fQOVJMRacLIrWWxpFhNHFDkazWlfQhpLdLOBjGooHTPKBI16g521a6ysXCJyloVjdzDIHud777mTyysbfPv6NgLJdmZZu7FCqx6xu5UhhZ0Iz2z4vyLEs3K82jw3VZRYphdp8G/LEFruX6+yUhqZh3Bc4jmZOo9pxY7XvvQErZrmO09dZmX5WbY2Nrnrrpcw1enw0Ne+QaPVZ293GyUjarUavd6Ab3zlYRbmOxw+cYrGbLeECoSQXtl/H3ZZ9TqlsAjlu5lKlYS57euwa5Hmf/5X/wvEbeTIkCQt+sMBcZwQRzEvO7HAa84ucWhxns5Ug2anjhSCUap5/JlLZKnl9LHjIHJOnTyOqivWnr2Oy1MeuXidft73/F0DjaTFpUvXaU51aSY1kBF5qMl3gbmCoFR1L3B158ZO54SrUREFfq5R3JdiVOlmZTgfMnjC2fLeFvNBCl9U4c/B/5aSgjQdooO37w5KJN5mvGiM5n7agQtqJlIH5RgVAeOyOgiephShEu3WLakoH3SiKLMUuFFGJ4px8YDLl5+h3YzZr6V3O8O5//0SmI8E/VHK1vZN8maCzhL6W1tkSYuGEJyab/CSk4eJXU4+TMFKdgZDTG6ZXVhASR0WcUastY8ppEIrjXWKpN3grle9EkXE2+88z+bmOts9wyMXh2z3UnQUkbnJftrVc93Pa/QfKyaWJ2MpAVmeobUEkTPq7VBXAjMaEAuJjiTIjNwYoghu3HiWTqfLyFimOi0Oz7ZYTIYcaUK7pqnXNKN0RK3ZJkoaQUdRYVDIJAYdYUxOSyT0B30uP3uTmSMnyUYD7jy+wJbVrKxvcOToIbIcbm5tkRqFrCR0/DMIi+NAOsutL5XdNgvJweq9CliwkrJa2IgAYl0jdoZX3XmahWadwV6PfJgxN93lxOEFmq0Gy9ee4d57X8m1a5fp9frkLkdLydzh4zz6rUc4e/oOnrj4FHe+8jydzhQ46T02pUoSP8KWz8hXx1nfr0dO4p0yYLa7u3s0OrOsbQ/I0xHdVos4aZOnAxr1BlMJdBLH0uEuSb1GHMc4JJ2pmPnp89QaTaJmCx1HDAY9hrll6fhR7lzd4SuPXmaYacBQr2taccTs9Dw7231gn/Bz4WFWhhR4zia27OVzkOf4fHoOL2QcRHi8RUyFiiPFJI789+Grv0iMpm8DWw2VhBrvVoX6OE76MDrQEZwMKkUFERhgXzsJEWgkQghcbujU6/zgux9E1WqMhjvEapxYUkqXN9AGpffiteqoloQ550BqEiFwSrK+tkM7amB0TJaPOHu4y1tefZa6gMxIrixv8tmvPE5mI5548gp5ZFlYOMTr7r+XN772fnCw29uh3lTkEpCGJOmyeLyLjpugBHsi48TSHIszXVaHW6hYwWhMiSnOrQolVLGywmCqShbZV2XJUkXJyAglLVZSbjhFnT44ojgmdwKF48TxI0zZIVM1sHkKuokVGhVFOJOTDvrUWlOYPEfVYkyaIxKLlZqBybDKYUd7bA9iFpaWmOk8ylwjZqdvaTda3Lx5k82dLRqdhfJkqxVC5SUYy6T8W+XiKnOt+tpEeB6uraCwaek37HpSo6XhFWfvoK0Mjz32XYappaYTjiwtsL52nVZjmiSWdKcaCI5w8eJFlpYOAfClL36O++6/h43NNb776Dc4fWyekRDIZgOhamincMo7BKqS1HDSltinK+ayGFOolNIIBOkwRyPQQXfB4itrdKSYabfYvLlO7e7ICypbb3CdsNSaLVQ9Jm43sKmh3Zqm7gwYw/mX3MHi57/B5dU9bm5ssRTV2MmHvOKOC9SbsT9HHVowW1GS2/2dDbmHwrMTvgDDR5PVdXQrzrk/tzCx9AIcUXQNQOA9dECJyXle4NoFdFOUTUba64VOFD24Ymt8YeNFYTSFCGK3t1h7OVbOQXoytigaedlJylephFO5Gb4YGSGCRJkKXqrLUUhqSiBV5EN8MakMX9RZV1398EuV8/avp9ZglaUVaZrOkokBqY1Y6ipec8c8NZHRT2F7O2Np6Q5m5o5jMTzw5jexMdjj6Wcu89DXvs7q5We469xZjp04w6Wrl1g6fpTBaMBTV5fZ3dpBSVg8eog7zx0nPpFy9tRxLq0N6Q32fPOuA3fx4twnZh9jx75gGHh6jAhJKim9BIKQnhAMHss34Vns9vqY3SH1ep3+zjaHF1pM1SNa0hLHfoEO84xWvUm73SauJaT4nuiRjkkHGe2laTpz0/TTlOFej+Fgm7zVYXZ2mjPbkCaHiOqave0e58+c5MqNLU+hEqI0bFUBB/ZFK+OpMAldTIyJCMU3wpNFItKCs5Y8yzk822F7fZljd96JERHfffpxEun5rVoL8nTEzPQ8W+sbzM3NcfbUaVqdhOnpKV750vP8zd/8F+675+W87YF7SaTj0a9+kXte9waiWoIhBBfCK1IJAUJ6ta8xpaZIGPlnGsUxWmiM6ZEaS5Ik4Az9wQhrHKN+yvmu5q5Tixyen4LM4shRkUY4h3aC3GaIkWPj8jOkw4wsM0x1u9RbHZr1Bh9433v4xX/zW1grUTWNMDHLq+v8+9/8XfpDfCXVuLdJOZeK15TzsJsTlgO7FD5Hwu3gUdDDxjSxMYne4Rt932rS1AtUSnuh40VhNH0hxcE3sMrBkqGjtm+FC6ZajnYAJiGFxzQEApv7PjhOWaSK/E7jJi9fBqnrQpRDKXHrIps8O4SAxGUooWg4w0//0A/xsb/9a27sGVw6oDPdxRAhpWHpUEI6WEXKiJvXV3j2qas4Jxn1h5w9foZup0Ge5vR21mg0aqxcv84XvvRlvvLEMuQ5naRGt13nS7MN3v/e76fdaZJnGWY0RLU745Ya+87xlvuCYJ/z7K/XmbBNWZz1i8tWJ6gYlsdsdzrs7vZAanZ3d8lbmtyNiJs1GkoinCKq1dG1Bk4niCgmVglCR5jMoaRm2O8zuJ6SyZjp6WmifI/13R063RkWZuGbl1aJOgnz3Q4uu+zbaCDwtRZVZX9f4bM/Ihh7Lbd6mlWB6WKUFLIA+yAFEkU2zNja2uPQfIuvP/QwN27c5PzLznFjeYeNrU3SUQpCY21ObgyrN9e8AIVKaTQirl66zOFDR9hcv8m5s6eoT03xhkP309vrI3UN00qQziCFKilIk9dR/KkoDITJc6SSXnZNKHIHeRqUpXJDbHd40/fcTbdZI45quCwnzTIvTKwjsr4CI7ixvs53vv0NlI44fPQE8WnIc99GJRKCd7/tAf7Dn32awTBFmBHbvSE72wN0re69YRxjM7Kf++xdHevkAe/8w40iOhIFz2nfMJU1/EKlJp9rvCiMpsdvJheyn7/7FZW9AS3e02LsbZZeonShd7cLaT3f01vGYPOgLWm9wbWMs5KCcfmcc+PkgU8yVWAD1KR3IiUY5XGi7UtceeoKs5Fiw2W0Gw1q2lKjBtoRa81mbwsrY55aMXz+ke+wdO4sF5bmydMBw17KkSNnmZpuMxhZdlav02p2eN3LW3RnF6lHkCQJzXaH3/mdP2Hp1MuptadJqOP2CSUL4ZMeeQn27IMY8BtVLLyOpDA5LtLe6xEKEfBLq4wPixxY5w2pcNCuN8kz2N3boduIWZhWtJHkcY2dkaYeRYjdDGMH7PYz5ufnMSZFRzAYDLHZALOiQGua3QZutM3hI8fZvnyJudk5hkM4kiZsjrw3K2oRmfZcz/BUSm/TP3cvjOG5qWPcu7j2/RVD5fNVQBHWh6jCSq/JKG1GLGF+KsEqw9XldVyacuj4cQYjxTPXVtnc6dNq1eiPbmBSS6zBktKeamJ2HGtbO1y44xxra2tcfOIZZmYXGVy9yuL8PDOzC1xfeYaF+ktA6X315gInlG9VERIe0llcSILFkcJZSI1lOOih43pQ8jFExvJP//H3MRtlrN5YZXtzl0ZdE9caWAStZotmu8Pm1jrDbEh7apaF+XniesLGVp96Dq3pDpFUvPbVL+d3/uTjtGpHOHlsjqPHz9FotUlHI3x9kvCJRmV8/QOunGme7u6wzmALndqDKEfihZlTF4y0ADS+p5WzWXh2EicUTNT1Bz7vvo2xyrQR0t3KeHqe8aIwmn747nPlv0oFlNthDfZAL8FY3z9EKTkWRijwOkWpyD0cDn1IUxlVia+i3YKQQQWlugaVmFAB1/UEMcgx17e4a36GLO4hdUS3FZNIhdSGerOJVLB0ZB5HxJFjS7zy7gvsjRxHFmZ59ulv8+pXnWdrawunZunMTPPod58EXePzX7/I8vo3eMWZY9wxP8PZC6d4w5vfxue+8m22hwOc1r6G/Dl2UTlh+P3/jT1OEcoSi8/KfSZHlHzAom7m0KEFdvYu00oUZxe71HPHHgnsKE7ccQ5jtpFNTc8IHBGXvvgI890ZpuemMc5y88ZNGvUmi0fPsreRejUdcRqX1HjsiW9wduksl258naZKWF1ZYXd7j0jp4DXcOifG5YiTrxbXU21VMXE/ROiUKcZ9o6wxJJEkJmK602Z7a5OBtSgU6TDHrO8xGm2StDoMnSPfG1JvQTYYEmmFEpZ6W6CtYNjLuHT5OuvbeyDqfPmbT9CdbnN9c5kzxy3tTotdsBUPAAAgAElEQVR6EjOyk4kebyjHbVp8wFWI2BQqXBEnTpwAkaFUi9wJZiPD9732VYidTS7v5NTihHxrj62tTZKaZn66y+lzJzh05jzJ2iZf+a9fYHkl5//6Tx8nlzV+9J1vIZIZd5w7wrEzp5hamOX+V7+E7b0Rd8dt3vPj72Q4GpWiJb5M2YOHbp8vaT1E7F2dgrt5AGPhhY4Dkz1KTEJyB4wJucD/tp+eGP+wwf5/8yhCwGplgHhOg1nIP1UTHaLyHee8Ik2BZxhrSwdRIOjt9by6deV/WEFZqO1AK10hF1d/ObwgJVIpRqnvvnf4+Cwnj3c5f2yJk8e62DylFkdI6eHUuNZkdn6amW6ddjRirj5ksZYy2rjG4myH3d4uSaNOrRYzM79Au9shiiVHlo6z2x9x6iUv4z/9xWd4+rGncDUVhHEdMpZYKZFKlB0ni/+UkmWDsvF/cuKapApleWLsbZfyZGLsaXulb/9+u90CLFPtLq36LI8/s4FsHyZqznLt0jK9nuRbT67z+KU1+ut9onqTv/vs59nc2WR1Y516e4q9/oiHnrzCjT24sTzi0W9fwck2973+rfzfv/8xnFH00yFJq8lgkIIBVbbWrVTWuEA9sy6Uw3njWG29XO39o9Q4rBd46TgtRGil5ClfbpihEazdXPUYYWq4sbbB2s4umZWkmWU47JONhkRaUJOQRI5DMzNMJQmmP2S6NcXC9Cwrq5tEUUIsFVnm2FjrsbU74lvffZr+YMTuxo6nzuD1OJX0nqWSQQMUERIfnh8jhUAFbNcayw/+wIMYLGnmmJueYbS1SqTg2GyLqD7Fk8sb3NzssbHVY31jnd5ej+m5GfpunaNnppmZ0Xzw/e/iwbfex9KRDq+85wLT01PEdS9W8/2vfwMLnRrHO9M89nefQcZxKYpTCGqUJc1UlKeq1XrOd/ss/g7eeBZUoVv/O3jZl56iCLX/ouB/jKEWwb4wvOKAjZO8bvI7z8OYqY4XkadZGbfBJkS4wT57Zit8sIMv2t8kM9GX3IZjW2tu+bwX0fUdIH1Hh3HP77F4yK0jkoJBDvVjR2nqPv0nvsniVJe1vR7KCYzJAKgJ5VWBGhIRO+Y681grEbkP6/rDTZJ6jDNQrzc4evQoAklbK77nwntYnJ/jFb/437O6doNGU9FtRXQiRxILdnJDpELb3cq5lqFSldZhx68JUamiKCedQCgRmlXh1WEQoRLLIaSkVovo9Xp0koTl61c4f+Y4Lsvo50OevPIs9dU6u0LiXE5v2OfYTIvjJ46zuDjL8uoKTz79JDvDIc+uj6i3mtx/7918+r/8Je96z7uIWOL9H3gvX/7CQ6TbuywcWqTdaLAxyDFqsgihfO5VfmYZgh28+CZa8lY+owujKjRJvYlJUx+/C+2l73RMvdlhmI5IkoS9nV3qcR2dQwtB1GhwrNthNesjI41KRww3tziyOMPe1h5nTizhgGs3txgay8raTXp7huWvfo373vaWiespEj5FdYt/wRPefVgJBMbJO97xAJ/5zBfYGmXU44hEKfoG/uKL32RXTLO8ss7P/vADfOtbj3iRmywnFobZzgzM55xJLcNhyrkLp2gf7pLETeJGnaReRyrBm970WjYvX+R7Hngzf/yJP+Pu0QCjov23dfxMyuTteBRcTSUKpwNwPrGoDqQLPr8RK5Z/iA+9hkIV4isz6AdEJqXDtZ949vzjxWM0xW3wjspwhX9ffOV5bqwQwlMsDjpWoCNUE3pSSIx1QZS08hkH1lifGDigsF9LkEmDq9t7HD3R5fChOQ6dvYtvfeXbuJElqxlyB8JIIhUhI6jFwiunazAjFQSYNSMcLetpL9PTHebnZ3nm6ae5cmmFzWvXaM93ePM738DW5ipmsMvL7jgDeeYpW9p3JDxIfKS6SUipA5naK3xPlOohSgEJEURUiqMJJcB4SbZsmGFdjpaC08cXaMoei90pVno5d7/kBAmQzMwj4xquP8C6EfWFOYxJmZnp8sXPP8Rr7n89LzWKVqeBizIefN8PMzPVYGd3lampKe556bn/j7r3DrIsu+s8P8dc82y+tJXlu6uqu7rVXi2pjbyXBhm0AgSLD5hh2NkNdmcHYmeJwOwMBJqYQbODxISEhEAIEAjBSsAAI6bVUgt1q9WtUnvvymZmpX32unPO/nHuM5lZ1QaYiJ5T8SJf3nez3jXn/s7v9/19f98fvW/3uOmV1/P5/3YvRkhyaQi2RYHjMHt83+WFVY/K6zH0irQeG1ZhfedKQUl8R5CkKZEO6Q0GJP0+YVyh1+/TrMZUqhGilrN/cZ7u+SUaoabVimhGIWkcElZjrrn8Stq9PnefOMHcdJNDC00WFvew+vX7Wdvaojm/yLcffIzFg/Pj6y8mSO44Ss6Mn7M7zkWU4bpyGT/1oz/If/hPn0IjiJWkv7lEpVLn1ddchu3tZXV1ibnpFoGy5HmKyFPqQmMbLYJLNFkywCloNqpUG7NMzc6NWh9nzjBtDU+snOG93/3dhFoyeB47MzToiLFn6CZmpmQSeXxpXt4/9nDOja7jix0vE6Mp8D2NRelFAshdmS5rh6VOEi9DtbukUaBGz5EpHEqFOIqRC48Yl1hlWU6gxyumUKCNd/tRouTH+dVrfCyTFAvASbpSUk8z1oIK3VTROrCPbvsM73jTtRQuRbgKLhNQFxRpQagaqDgCZ9ACVJyzsryEEhCpCKEFWVGgagHWOa669kZufE2IDjRKa7Z66zSiJusrq7zh7R/gqyefpiEj8qHMm9TbhCHAc1BhOImH9fs+Q+zK/VRZPyyEw8oApEWWeFUhS2Vs5UV7Y2u5qtXk8GyV+VgjspSN1bMcP3SAQguKrCDSCbbooyqKtbUuc80a8w3N8be9n0ZzhkCDTVKslBQyJg46tDfXqDWa2EGHSkvx2uuv4G8++XESk4NURDbGyol7OcHBA8ayb9vmjSs9y3GzPomHYYZN1bwGQI6UYIsUqUM2NteoVepkWUYc1MlMwaCXsNBo0e90kL0NDu5r0Tq2l3OrSxyYmmHpzLPccOUVbLQ3WNxT4/j0IscvmQetCCpVpmfnOHq2zem77yWXjo12n2tbM6RpSqVS8X2shnzD4X0pM/kj/FlKn/gSAAaTOa66/irmpyrMTldZmJ8G0+TIYU2t6qjP7qHf69PuQGdjk1h7vmYnM6A0rUaTolpBKUWlNsP09BxJJ0NXAozSCKV49fvez1N33c7C3CxohciHxrysmJqsyho+ohMlr0KWaqXOYYQENNIaLi7Du1ush9L7ds6VLAr/ZapkfDCMRGGstTs0cc5hkV4gS5jy8XUofO7CvgS7/TIxmhcfL6V3x84xxPJAleICtlRSktQb9YvSD/yExYPbbmIbw2dzbDABtNFYabFWsZblzDRiDhw+Rveb91GkCWtra1TiGr1On1pdkyWpF6zAstndJM8SAqFQTtDf6NOYVrg8RxrBVnuTymydQVKgcoUpLCiDSzNWV9e48+GHEa2pkqAvxopNbD9uUYbWO4VG/DmVId/EuQuh/IQTpvR8oFBQyaCvoF4kHK1J1GALUZkijmNfGmoNjbAJkcepk36fIhnQjDVaSXInWVtZ4+pXvZonHvw2YZmMqy/MMb1vnnNnlwi0X0AHvT6212V6Zobk9Blk4JWQR62XXfmYTuARSsvtLW8ZetsWa30WXQqBEqpcm8d9sIeLtkF5geLMYCLre02lfVwYoiKYrVriIieYXkAqyxVH9vKq645z5vRpKocWmWpWeP2bb6VbpFSnmjTq0xD4vkhCK665+hJuv/NuttZTqtOzWCnJk5RGtbarA+sINhHjpWBnwsgpR2ESrj9+KWqwiZKOWqyp1iKajQphpMkzCAOwIqNar7F2fp2gEpP1ewy22jhbILQiDqcYdPqIICQ3DqS/nqk0VKZbZAQgI5ydkEgsx/PRiiY5teNtkp2Z852GcqeK2UsZzjnfoWC4xkofjg8ZMwrh29aUnvCLHS8To1mqjzj/HvyE31lSNU5glOWRE2Du5PtthOdyWGuRyodgWmuq1FBSYMpeykPIT8jSc0GA88kjKeTE/z9UfCgrEpwgLKDQFkvMc51VDrdiVs89R6OmSNCEuklhDcYUDPp9wkiwuemluqy1NOotHvrOd/jOt04w05rl+p6jPrWKxTG3OMPm+jr1erVMDGjywYA8SfiBH/ghvnxqydeDl0CwcxcWLdluMMciFpNDbmMJDJvHjb3/UvkLKTVKSaab3uCVnWOZatYJgoDZmWmUkgwGfbSz5A4ykVKrRYRBzPrZ02wFFRYOXMrDd33T8zBTw9ml8+xZWKDdbvskg7OsLJ/j6te8huKBv0KpACMkerJH0HCyDI9aqVID0J/D5HkqNWFQd1ydcXWRv5Zebd8hhKKfZtQbFXAhM3FMPUk5MteguafBob172Lc4Q1iJaTQvQzfqTDemCSpVZoMpOr0BT5xeIxm0mWs1uOaqK5huBVx6YIHvPLlBu5dx6twy8VSN6emWn2+lMPKwJceLGUWRcWh+mpWnlnG2QMmQahwTBwGB0oQ4hCmQBrY2Njl36gxOK+pRxMqzp6nWY6J6jY5cJ81BRBE6jpGRRddrnFvd4PpbXs+5tsEOm5eV9B87jA6tZ+DZHUjbboGbcqEu309O1+HctfbiOhDjUuDdY9IOjOloYwjHOjsqFZMTiakL4Z4XGy8To3nxsfPiDAUNrIVRN/qJMSIoT/YDl3YE+Dvr6dphqeo+EhWwjFRsJj3NXVUM20q+hplahZEJQSHZ1HUePT/gwEzMysYyzWqFIBJI4cjypDzGkl9aWOKpBgSSK2+4luPXXU9nq8O5s+cphObAocMorXns0YfZt3cvcVjBGIcShqVeh33HjjI4fZrIaDLNdiWuXbNqMrwTo5B1cgLaMvushC27Mo0XKYElMJAr0EZialUCsUCyuu69X2dQ0htIW2T0k5zN9S2WT56iIkPqsy3WNrqkRUBlNcWFFbCO+ZkFHrvvBHs2N5k6epg161sRnDp9kkMHD1CYPjOX7KdwgtBZCpyvRNkxQYa4bJHnYIctFAyj7oTDhwmBHhVLTIRvgKMAFGHZrM8K6TVHowgpQ6rk3HL5ArccmyFWFTr9nJPLW6z2Brzi+KVUwxCFYzAY8MCDj3LigUdYb7fJCnjD627mifMrPPzg/bzqNVfyA9/7bm7/V79G6+Ax1lY7hM88y/XXXjeifrlSXERJiRKeKSEuormJ0mitkEmP6UCRdPpUwyFrQJXea0A1niFNOnzzWw/RSaEWNDhz7iRXXXUZg7Rgq7PC1HROxYKuNyjSAZX6FItzU0RK8uTyBte88e0Yl3PRcZEk7vONiyXsLvoVbvwlz+d8+v18ttxj9W4M3Ul//6WjhA1e/Pe/bIymENude1lObCcmtlqLELtD9p0Uh6Hwgfe6vDoMlElgwBTG8zWFGNWiIm3JIZNjqpP1Ld18F75S9MJuW0I9pqYGaCsxyof+jyQVnjoDD913lve/6lLQChlqRNkeVQQK5zIIJFpYQhdiCkEQxTT2TtOYn2fv/AFkHLK6vMx8pUJvfZNwVpGnKVuDnPkDe7n38ccJ0L4Gn6EBv9hwEz/LMPUCfWmctRjh0EpSlF6PNQKJpFAQp5JumFMksDXIqIcFxkYU0tB3fcKiQqefYApDnvaYm2+xfr7N3V++lyDQHD58CVGzwdr6JoPOFu9431s5fPlxlk4/R7VaI52Zol8RtGZb5AJuetc72drqEAcR1vbRhGU57EVOr0zNWuvvqSrVw2WZgS4wIEA559dRORYzmewyCQYnLNW45qt7jKNvHYdnK2AUv/WX3yQO60SBYGom5Nv3P8prr72W+cUW83sWmW41ufrqa3j2zBpJmvPw4ye58tB+jl95gHB2lrAjueU1V/ON+56kdeAAxhUUEnRhsQGAZoiLGOxY+ajMQg8FPoQArWNfV64ympFms8iYyhQuN5AW5Dis6JPnhReHIeTs2dO85W1v4+jVx1hc3Me5c6dwNqe/tY7sbiLDgCyMMXmfio6QjSlstYazooxq/AJkrBlFdHaEK1KWNbsLeJkvzqJeTEBjJ1a/HX4qj2oSavKV+IxcYeHvu3MOKxyFNQQvpUEQLyOjCZMnzrbnf2hOlfJ43YUEgycN5zgD6cMtnxxyCOWNgDFlu1ljdmAmO/qZ43vETHptFwwZyqwrwo44YlZKjl51JQV97NBjCz2mpaoB2cDQ3ujylT+9HS0DkAoda1CC619xIyefXmbv4YMc3ruX86dOcXblPP3cEgSKta0e+669jgf+61dharY0g0N1gote3V1bLuCwlUmi3cYU59BeQQRlFTaWqKJO0O2jrCCUIXJQkImcZNADFFLVCFTITKvFda9rYY2h3eng9IBj1x+lIoDYe95WQWINrt/HJo5Ko0JaJJhBwFavTzfZoh4HCCZaLFzgfkwuBJZJepGd+PzCod9wmypV+IMgoN1po7Wmmxkun6kQSc0d9z7KQrPCq268nNbsfp589jl0kZD0E0IdMje7wJmVJR687wSbnYRvPXqSo5fsZ+Pcs7Q7R7h58VZmWzPc9MqrOfHIM9i8j+0UxGVSMrN5GeiI0Z0b04/K9x5UQghIXY5JLYv7L+XsqW8RTDVJXEpqQrqJr9yxeYiSmrnZaRrNCnsumSM3A5r1JltJl6DZoNPdpDY9RWYFSZaiw5jAadppwg23vo3capzSvuUImmFnhYvlHS5UfDI5rLU4cTE46aWN0THsBLQZzvzdRRFD6MO659OJ3z1eFkZTUK6ekxfPuZHalBPecDrne9sML9CosqBMVOAE0g3r08UYXymTq846CpNz6uRJjhw5QhSG5Fk+6rUC/hi29wiCwhboMvt8YbiwrI4QwkuUOUPP5lQaNazIsc73TJFSYsjQlQZBEfDsyWW+9egZ3v/BD3DLW95A0Ihp9wcYmyOeXeErX/grEpFz0xtu4cClR8nSAefXz5NHdf7LnScIZxYZCDAmZ/x4jQ5q4uqyy6vceS7DEJySarR9v5IrWBQUMiRyYKQiFYpQSNJQkpEh4ip9UxDlvgunJSOxAzKbUW/WiOOIQ8f24VRAEMYsLi6QJAX9bo/Wnr10uwOiapVACbJBnzhUNGqCy296E/XqFzyndce5DJ/FYaXXpHKTEn6xNMYSaj1uBzvCtLYnLiYX3jzPmJ6eZv38JghHHEYsNqvIwjG7Zz+v3DfLbV9/gEee+DrHX7EXknXe/KqrqIZV5hcXIVS8951v5tmnn+V9b7mZVKTUmk0uu+pKwjgiMwNuvf4K7vjGd5hpxLzuuuvIkgE2itBCjRI/O0PyYZ90/14hEARCYKXmNe94J7c/9wwSS5YWpGnu93ES0yvIs5xKJJmZnWV2apozp88Rh3WczD0cYXLObp0niKscWtxPJjW5NWwZRSxiDAGRFGAKRKkANRS3gQtEgBN8zQsZTSmlJ7i/SNz2+cbwGMwF+Nd+Pg+jT7wDVW6yQxLU/4jh+S49TdxQOmKMN0yEUgAjyTh2hOaUNajOS21R1sRaWaAQzM7OAv4m60Bv+/tdbj8QSB8qCSG2wajj7KzGCXwNLmDLrnf0c98qohiUob2X/yqyjGww4MjRw/zMjx/j7rvv53PfeYSnz50HHZCYlGeXNpiqSj7wnn9CYTKCsEGeQqXZ4Klz5+mIOlYqCmc8f3IEGwzxnh2rqnUXCMflrmzt9s9Fmezy74tQoq1BOt/h0+mQRqPFYHMDXdEEYYhIcnQIWeHIMlhb6aCoIFZWqNcr9LSmOTNHTsJDy5soFdNs1OgPEpq1ClILihBsLrCDgjPJCr/+iV8lkiE9A+gUOQGRDO/T8NyMGXuU1pkyOVHWGu+4Mj6E383RE0IQBKFvaVEUVKox2iZU6wfQWI7vm+Y7D97Pa669kve+Y4686ON6PaKswOYD4jDEGkdUDbjy6mNYnRNXWuzbfwgdVOgPutgcanGFpoZ3v/4WdBTSEc43/JNMsMC3G/OxkLJkiMoGhSQLLOe6myRxSB1LvpWRdnOkCpDC0Uk2sAVUoiq9do89CwdoBo61M5tstbv00i5LZ5/l6huvotFsetpdoCgQ4AKsLRDCkKcgCUY2Rg5b+E4YnSFZfJI0PlnvPbnN2AuXQz/fmKwAdCULZeQiXACqs6UIt3RypI7mdxvDCi+Fo/OyMZq+DG586FpITGk4xx7l9sk9eYHV0Du0lC66BoqS1lCGZM6htKTVam278Bc8nqHRdH4+uNLjvdDuPokksGUySTqBthJpLKHUYD1tRUSCwAYEukLc8pVA7Y1lbrn2UrTRtLOc+tQMRZZSC2LyWJJPBahahawo6OcpVkd0VYyQCmcccqjYLsWIL7ctRH2RILs/3xcIUoREO0mhDBpFN9I8M1BQQG1QYKYllUqFJO9x+PBRnn76HPc/8DDSVrnpDbdghMNIQZuYwjpqpsLayhp/8aX/wne9761UpmsenzW+dLA7SPn1L/wNK5tVemkHqWsj8v0FDs5fhiELYkgrKn+XCIRSSDNe9eyO/2fkrZghzUoQRhHGGJwt2D/fpFXLqTci3nHrjWR5hivWmVuYJ9CzPPHo40jpcMZSiWKi6iyFScBKWo0FcAFFkmMKR2BDBtJAssVgfYnjb3wDT51fxyJ8lcwF78/w52REJnEKIuvoO8nbvv/HyQc9Tnz9q7j1FfI8Jww1fefobG1h+5usr6xx4p4HuPLIUZqzM1x1/Q2kwvLmt76Js5tnyK2lnyYEMkYoR2drncUDhzC2jKj+u2oWvfC4EA1puOlC1T1DDNSYoVC3xTd/K3Zxel/MeFkYzZFNVHJUKeBGHlP5+wSBeXShhm08cTjMLmmoEWdRBhibo7QkDD0mVuS7xYon3zvhCfSupBsNUSSzA4MZUpmEs8SlaIGwDukKwqCGinrIRJMkGVpFhHWByXMq1QppnhIu1ii6jo2VLVqVWUKpES3Fmklp7p0hiAVYR3etS24jTnUGSAKykpQuR0RofzxKbfe4/HWY6JHDkL/qscRtrRXKczVCEyApnCsJyw6cJHCWQtnywc6RNqDfmIXlNdKtLs25aWo1Se5y+tkWU7OS7/metzDY6NHfXGK6OUet1gAhmJmZJd67yOllydU3fggRa/LBJhkBQRhQKwxLqeKJMwPiusapAEuBNJbc5hMLnpjALHc+AGOIQpdYjdtmfLYPY3yCsCAE1YUi4oYjezn15LN069O0Io3TOVIkNOtNYumJ3lGoWFlZwQlI0x79tE1Ui9lY2WDj/ApTlZjMeQUpY3yr5cw48jThDa+/mWtfeytzB4/x3MoJMiSBkwhbbHv8x5U1CodBDTE6IbBCYoUgFhGdIkeEETe+7hbu+NxnPR85Uiwe3svUdAPXzVncN8v8wgJxI6YaTZGHAlkN2aRPpd5A5zkmHZAbqM0skLY3MCZDqgqFNUQ6JEuHVsqXNBtlGTY/KlNAODNOzjhKRSpXcjOFwbmdLW7GBu75xmRk5EV5xmIh2xJFzhcBOEepTypKr3RMzStMMXoGXux4MX3PY+BrQFTu/yfOuV8UQlwKfA6YBe4Fftg5lwkhIuAzwI3AGvAh59yzL/Q9vlzvwmuYQCCVHJGah4LDatseYvczQ5lcKjFRIaHdbvtugRe5SFLI8rJSrkq73f2ht+OsG3u4w7psSiBWWCwJQghqU022Om3avS4EEtVrI3SNOA6J5xfJG4ZoZpaVlTaBTqjEAfW5eVQUEQhYWl6jWzhWBwPOD7oYHTFmG1yYs7ZN1UgItJ5cgC52F4aYrnhBR2KYbOvl4MKQpgtZOb9CJV4kRDHo9tFxhFTC13LXKrjCUQS+KddW0UMWA6b3zSMlSC3Jm1Xaa5skg4SNrS5ff2qLuFalKAxBEPkkoPWY86Q4r8e61a4HbltYW6rSXwh/m3xolHLEhaIQAVI56gz4p+95LR//izt5bm2T+ahOgUDJGnquhdKSvN/jmVOnmJmdp9GYo7M1QMY1Fg8eZXZhPw/dezdPPfAMl77iCuL5adJSwTwbJFx+2XHm5hZYXdvweV6ly3nkw/Th4rC9xe8wCXTxSKmImlgl0Tah6IIsQBgJWqOEIi1yGISkyRaiVkfpKsIKVKBRkabf74GwNJXCFpZf/YWfZ6MX8P/+p9+kP9hESTWuIR+JxE0MhxcffjGT7h9xKKXI8xylxrKSkw6XFxvfDse9VEz1xSSNUuAtzrnrgOuBdwkhbgY+DHzEOXcM2AB+otz/J4CNcvtHyv1eYIiRmzxEJIdjWBsqh33QS8MppGCoCiMmcDzJsOuyp40oAQLrVxoYldKNv1qMXsMkghehlaP/czhplVK73HmhfD8ZKUuGt/RUFytihDUIJ1ChoFJVWCfotPt0u336Wz2yXoIw0KhUMUnOK648xpGjB1k8uI/pRpVKGJBllm5nQLcoOJ9mmLiOGWFdQyVveYHX9odqlAx7wflx8RV3CJPI8o4hBTKsMHvJ5WRBzOZmmzNLKww6ffrtLpFUKAE6FLigIG5FiIokqAVEUwEZGUZaROgneG/gsDm0N7qc28r44lfu8ZlN6VuVYN2u8GvoURtjKEoq2fBVFAVFUfgyufK+e2/SjH53zpFlOUmSkOc5RVGQFIaskPSs4OTSOlUp+eDN1/Loo4+RZlDkKWRAkuAGKSfuuoepapNYx7TPtzn31Ckeuvd+PvvpP2B5eY25A5dSaU3z0EMP0O+0SdMMlxXYNCeMI1a3NnnkiScxznnHbfgs7LiHO6Oh5/OOciWZv/w6NlPLYJCS9HKcExTSYZUlzRPWNjc4u3SOftLBihylQ4SOSQpHmkO9WQdTIK3h+97/Tv78r/+cW299LfWFhTFdeYLq5WT5Kp/K3fPqpYfCL3UYY0qDuZtMP/kabvv7jBc0ms6PbvlrUL4c8BbgT8rtvwt8d/n+/eXvlJ+/VbyA7+vKf6VfDxAgnN4AACAASURBVI6R8QtUKVxgTfmw+vdYi5IOifWvkkoCjMDeIdfTK5J7L7bT7SL1iMXuZbiG3mRpjHxP9KHnIUu5tHLiKonwyrAI5TmdKigNSEmK9n8HSuaQG8JQU63EBFFEnhd0Nvusr26xtuS9qjQr2Le4D5taAqepyJAwsdBL2NzYojtIaM7OI+IqVghUFEx4IJOZfjt6TawFCMGEpzK+7ruN6/aHU5Sq92O6i0Y6/xIEGDS2cPQKTTR/CIIaa+c7bHT6rK13WTq7RJZmdPs9qvU6URRSq9cJaxFWSlSoiOMYLRRKaHqdAevnOyyvJdz75Dlm9uwb1QRrKdDaN6DTWo28w2GprO9Frra9htJw/nc9WviUGn+mlN83iqLxtqoh1r6fjNERYa3BoZamKg1OKUxW0O1vkQ0Siizn0CWXIMOIh598gm9++9ucuP8+sAXXv/IGgkqVXlIwe/QSpvctsHR2mVgLTJayvLLCE2fO8vhzp5Fh4FuviFIdUu02mENPemeyw69fOzpYGsdVt74ZM72X892U7iClcH4CCOmYX5zn+CuO8ppbb2DfoX1U6zVU5BMrvf4AJxR5miNtQRAo5qcint14gFMrJ3ndTa/mwUefIIwr4LyhcjjyPB0tRj4y8O+V9NY0z7ZDYhczWi8UKr9QaeXOSsHJ/ceaududp5diQF8Upil86cu9+J6vHwOeAjadc0MFjdPA/vL9fuBUeSCFEGILH8Kv7vg//xnwzwCmyjpwYcd8qSEccqFExkgX0SvqYaUqqf0CqdyoHYkohR2k9VkcCUw1mz7cLy/2sIxSTYSzpjCloO94m1IlSXoXBgPWhqUiDVQrFZyx9LKnmaluEVgQVKmFdcT0gLY09Lp98iylvbbJytIqUVij2ZgliGqEcY1uvwfasNneopdkHDpyOQ+cPEPuIq/SI3MuzFV9/r7tF3rwxpNOjEJeWdYbSyF9ssT6cBAXeiNcLkhOAsKggikefOJpDsUNVCflsXPr1Csh4ZJlcd8eZlvTTLVChA6JanVEFIOQ5NZQdBPSbsLZpSVOLp2nv5WwVGgeXuthVO7lz4aIsoPMmF3QA+xmP0zOHSEceZYTaDVa1Dy/0KE12/QVnXP0c0u9cKAzqNW47ZH7eOfRA7zrltfw+OlTvOrYATayNoFp8NRDj7F/bg+VKObowSNsbfWZm5mjUig651ZJNnqce+45Hn3iMd7+9tdSnZ8nS3KkDEgrVdTMNLlseGUtZImcW7YvZpNh+phSN05y7h5BEFHkfW5693uYi0M++5GPEA86LE5XiZSm3e0wKBKwitZehax45kKvN8BYR1ytEIeKPOvTqtbJ22ucO30Hp059lRtf+b387M/9PP1um/nZBvv3L1BgaVZCms0W061ZXvnKV2FkweHDh1lc3Ectjuh2d9er/0PHUN7tggnaCxhCv/927GlnVv+Fxosyms45A1wvhGgBfwZc8aK/4eL/5yeATwDsX1xwCnByrP1s7UQ2dFjRM1pZh4kgL1smhMNZ41WzkT48kBOrCwalRQkIe/7muO7Uejm44SQEpFZlN6KSniB9WCslFM4RRxFJ4vtBF8YQBlXCqKBV3cvP/tz/xfy04tc++pO0n+7gNn2YjiqIwhrVKtgc8qwgDzSDbp/VzYRnzqyhwtiL1eoAFyp0qJieajE9U2f9oQFyuuaPGQ0lF8hx4Ru+czV+/tV599+HUvpqKCSSUo1GZH5P53vZFMYSBDV63YQv/909vP2GV3BEG7TUbCQWgWHruRUW1nsoE1Cvh2ysbFBrTCNkCFFICjz29NP0Njc9lFHRVPQC8txpikxSb7XIkpwsL5DCa14O25T487TeAy6rrczoXCSh9gugcD56QEqvMg4EQUBR+CKHyevlnKMuLU4ZQgJsVvDQsxt89803M7MgWHnwcThsMdKBFszO7uH0yga3/e1d7D+8wKV7D7DcXmXTpVxy7DJaM7Mk6RY//MYfJCkMMggpbI88L3yJp47LJKb2sJQo52iZ5BnyMf1c9bxTkAiticKQdruNM9BoNEZEc6UU6aDD7N6D3Hf/w/yHz3ySo62QopvxuM040GwRkxFmEdYZNvMzhJUauhKgI02jWiMKBK4AgoCN7hoVGWLX1mhUezjRxhbTxLUmBsnyuS2mWlVEaEm3NlkZdPjCFx6j1+0z1aj7hGMY86sf/nXa/YTSv8D63tuAxNnhYj5c+C8cQu9cJI3wQMAILZgwiEPi/DCRq6TCFWLXAvtSDCa8xOy5c25TCPEV4BagJYTQpbd5ADhT7nYGOAicFp5IOYVPCD3vEKNEih+Rv6yl0mh5oXb+kfTSTlLIMgVzYZxn8u+SZEC9XsUNm6oJH0pDCVyPJOckQnpSuqPM8yAInCz9AUWROhqNFpXqNP/qX/88zUaVZhX+93/6QQ4eabGWNgn6bSAGvJdYq9UwucGYBIocZEgUa5wsUFojtcBqSRBXaDQaLO7fxyDXBI1pUhzaGQrhq6e9ZOGFb/jk5NqJhU3stctr8+G8KLlsZQLClmLMQwVnocidpNqc4aO/8XGEVQwSy9fuepDrP/Rmtk49ibQpVgUMsoKz57fY2HiAyy87QiUK2ErAOEUmUp4+swQWWvUW1Cz7LjnKZz72p3zwB97LPScepZsMSPIe9XiKJElBBoDBWoMOZIlp2tFDM9arMQjUiIcpYCTUMLwuSilsQSmKPoR3LLrUdChUjowVlhr/7o9vQ8Uhq+favPk6ST0UbK5tsLh/H3N7ZlAyJO12OXbsAHN79xNUa/SygoU9M2S9S7BhAMrrtYYy5Hw6oLlnD73Si/bzb2hMhjN+e7TjhB2V80oHST9Fy4BKI2YwGNCoT5XFHI57TtzHJ372l0AKknabvTdcQSuM6Q+6nE7aaD0gjHpEUYSOE6p5TlNO0Ww2iKK4xL59QlAIBSqjP0g5u/wYjXqF7qCLUgGD1NKqtchW20RBkwA87FKtsto+TyhLKCSH79xzD5dfed34XK2XZHMC7xi58pnHd4IdPaDbZ/b2X60BMa742mZkd9gCM0E3+/skgIbjxWTP54G8NJgV4O345M5XgO/BZ9B/FPhi+SdfKn+/s/z8Nvcijk6WmJpgmPzxD6gQY/BflBSFMZYzDmOYNBLsyJxKn2gQQDJIUTLAlkbTlvXsUiqMy7znJgN0oMnSnEDLMkHgb069McXS0hJ/+oX/j81Nn4k3xtBsaILAkGVwen2dV7SPsPfAcdbP3Oc11RRgLVqHtFozBEEfugmZzUlzQ1DxGWGEpNqImJlpsWfPIkI4uonlE5/4DJdecy3veuvrSQebSBWWKk1jys1OVSd//yaxzAvXmw/n1iTGqbRAFB7W0FqR5d6TT1IDSvLpT3+OIKySIamEASEpRlf5zS9+jZ95/2uRgxXObLTJ0oLcSlLg7keeJFCSSqWK1CFKQRBoqtWYqAJzizNcdvUrObn2eX7v83/Nd73+emqtQ/T6CWfOrrPVE/T6PSxQFL43qUQh0V4x1VqiwIfgQ2MvpRwZJVHqUg7nBTDR/dHiXAElwdzjuZa8yNAyoletYIocPTPLWj8hDkJsb8Dq+jKVesDefTMk7ZjOaodk6xmCSoNwaobz+jxRs0YmLTqOGXQGmLTw9fe5RQTRaO4KHE55ao5UEc4W4Moa6aKg0aojdR2c5I477uQTH/8kBw8dpp8MSPoZUVyh101YXNzDVnedzW5BN+lRi+tcdfMNnLn3Ieabi/TWN8icI08Nqc2YqVZRgSYIlGcmDKUUhw6L0Dhyqk1DIh/jyFWKu/+uS1xrUTjDqeVlqnFEqGG6USVp95Gyx2Yn8ZGKlERhxIc//O/5w8//MVudtveGbY5WCucEzgmkEmVeYUwf29bfR4zbVSBE2RIHX2hxkZKei2XJ/yHJoBfjae4FfrfENSXwx865vxBCPAx8Tgjxb4ETwKfK/T8F/J4Q4klgHfj+F/oCgfAdt4VEOutLEYd0BeGXoqEhHSWNoOxgudsI7BoTMvhxpYoxbiwFV2afh3ilDgKEUEhXNmPTIa6wVOt1/uiP/oizyxtoren1eug4IneWshU7whWoqModDzzITe+J6CYFhVuloefQIhhxBKXWVKoNUqHJRIFKbSl8AGEYU2/WWJxdQAsPPcwthPz1F/8z7/ngT/NHy0t83w99AGMsWZahA1lW+1wopzc2kkPS++7rtf3ajXFPg3MGnCPPc6yVRNVZvnnP3/Ho088RV2tYIxDOkBt/7FY5EiR//tW7+eAtR1hoNrGVgHbfsNEbIBAY48ic51tWgxqtVpPFxRkqVUklCJFZl0BApqrsWTxIf7BBd3OdK44dJi0y2u0t1ts98sxnxrvdHlJo1trrVOIqWZ5j8dKBmiEvtVxEhtHgxBgJ8btSh8CBlQ7lJLpcaKVUvoGeDn01jKow6BlqLehttgl0w4fZoUWEmjAM0LEibEm6skMtaHjjYD0n8OzmeY4cewX9zoDMf8EIZhLCQ1RKKJyQVOt10rzgWyfu5vc/9znm919Cr9Oj2x3QWjzA8noPVI5xzuuy1iKWN9cY9DOMNdTrU2yurWGqAzZ751BilmolYGAyn6wMIFCSQCi0DEY1Ms4ZrPOGXBagqyEDvURmzvOWd13H12+/C5Vm6FhTn2pw9vRJDh9YYKvX95CakaTW0kkL4lCzub5Go9nip37qpzi3vMQXv/hFmmHA+vp6eR+EFxRRyj+voxs1gVGP3o7ZMs/XuveFxt/X23xBo+mcux+44QLbnwZec4HtCfC9L+UghoX//jqUQPjQPS9rbX3ib3uYLsYu0uj/mkzUjABeMYp3qFVr3jC4cf3xUJtTCE0c1Ljttts4v7LK5tYmP/0z/xsf/ejHGAxSWlMzaKHIBilREJS17c4bXaGxI2p+Gx1vENWmaF11EB4Hr5cUjEDrMAypCkFiHFJ4yq2SklqtQaNVR+DD0MIV5MEqerDKbV/4Db7rQ/+S3/2DP+Oqyw9x8803kxcZWZZdEKecNJJehGLsefp7ZXcllKTwXpa0fhFxElABQkl+5d99gj0LUyR5QRRWyAc9X7FlBTqOsblBacVzPUM/tVSDEMiR0xXmp2fIsoLcWmrTdYLYe1JHjxyiWg1xZJ7Sla7RIOHc8hKf+cLf8qMffDvLS49w2bErcd2MmUbMe9/3Tm677XZ6vR69bkCgaxyN97Kx0ebkmWXQGuPG0EVJz8c4N6KzDa9LUSSjcx+HhF5Q2joH2mJdhhzWNDtDZSok21xHDwJqtYj2xha1hqdTVYIaU1NTRJUK1fk5VCX2ZYYGet2E9fUtGguzZHnCXL3J0iApDWbp8UuIw5izp5f59Y/9Fu1ej6npObq9lCCeZ2V5y5+XkGxstgHKOvOUmZlZ1tttoigiy31v9CKX5Dk88PT97D04jRyAKryhjJBI6aU31DAF5QyFLcBZAq3KTP6AQaowrgdC87o33IrkHtIsJ3c5adJh/769bGxsMl2vjp49qTV5lpURliZNMzqd89jc8iM/+KN02l2+9OdfYnPQLrHY4fNsYaLMZeIGDaf20NsZldSWeeAXPYYGc6j6f/EmjruH+Mcolv+Hjv1797if/pEPsU3FeThJ5eRF89JrPgkit3822mfoQlpw446VUoWYAorCMNVs0k16FEXBnj17EUJw//3388277uGaa66mKAxSKRr1Or/3+T9BhgGxDrZBA9uytC5Ayh7CSRABIu7xW5+92feNiecpvlEnaw/QUQUhK2jt4YJ+Lukk+QhrCYKAOI4J45hYeSZBLlOSxibzx2YJlmuc39T8i1/4JIWyFCan1WoA8KM/9kOcPX3K95i2Dh0EWAvW5ERRWLa+9UPtuGxCCKwwCKER+K6WlUCQphkCxZe/fBvPPHOubKZlyzJDhTWQZRmFs+hQUeQpgVTECG44PMMHbtiPUBopYKufUIsraOn9v0q9TrVeRylJVLE0mhERijwOOLsp+OGf+ySHD+8ntAm33nozt33lr/jg//R+hIVnTj7Ddddfy9LSKV73utfzldv+Dmt61JvTLOw7yG//zh8yM3uYftLxyaUAUuOXLYnnzOmhWLEucGikK5sAu5xpM+Atb3g9i1MNHJK1rT5fvvMrSB0RhJLXHt5HUxtMZhBYolgTVTT1qRr1So0oqrCw/1IyFSHiEKVha7PH+toWzknmDuwjkoqk6DLbqmGpc//jZ/jt3/8cT55uE1R887ZCGTBeEzQru3AaV5QhrQNjy7kjCUOJ0hJjHEWeYwqNEGBcl0EPfuL/vJJaR1NdjqirnLzQCGnQSlNpNKg1p6hUq1TikGq16udhGJaLi6HfXaH51oKsktHUV/Bdb/wIiIAwiMiSlGpco9/b4tL9e9HaC2D0k4w8z1BhVLbskARRlfW1DZRWBDpAKkGtEvKp3/kUqclQSiNRo9Y2FxNhls6/JqE4/3N3wnOyZXBRlPKMbneIvv+KW+51zr3qgl84MV42ZZRSlApB5ZBSA6VKtBiCvMKrvozC7Rcy+P4iGmOQCur1Br1eD4AzJ89y2223cfjwYZqNFvVGnde++sbyexxBEJCmCUXhUMqADna58mPvrhgxe7WO6HR7CF3HiIKe7XF6vc2xYBEntI8HwypSCCraYZWn8XgJM0kQhkhKgnZZHHZq9Qyz11fI1zUH5ubpbJ6mNncAiWLQLwjCiF/6hQ/z+tffTBRHHL/8UoJA0+sNCAJNUZRhz6gP/O5QXqG9Py98GNtLBTjNxz76W1RrTaxUpTfuWQRKSYJqDB3IBn2MkTTqUwx6PVIJT5xb4eTBOnvnW1QjyezMDEjpj0lp0jSlGlcojKFSiygKi9MB2iXUnaESwtLaeXqdjFdc2+OyY/t45fXX8ou/9O+Zn5nln7zjEh66/z7uO/Etjl9+CJyiP+jS3lzmX/zzn+Azn/lTVKjHXF2ZgxFI5eloxuS4wkKee5EqW2CSHj/zfe9gWmVUqyFFuuUJ4Xadf/697+DgJfup1pv87R9/iSCOCELLoNslyyyDLCXJJWomIity3PkNZFgDJXBC0esnZLllbn4eTYYUIe2VZfKTCfsOXcb73nYjn/r4f6QS13HCkeQJ1oArDEpoTG6QgUYIjy/7zo6CorC+1bQMvXaDszg37pUe6IBUGrJ8wPz0UU49dJYr5zWVSJJbzyqwJscVOUpCFCgEFpOnOCXKJEvG6vp5WnLGQ1eqXIBLT00phbGGXr/vF+fcAoogqrLZGVCR4JTPkAtlSbOMiIgs6zHVbHF+dZ0f+ZEf44//8HP00+QCxPgXNzzeaUYc3pFRnNjHC5iPnam/z3gpMnL/XcfO8NIzMOW4/lGIchWaOORRCYJk1LNcDA2qLHnyYkQveeyxxwiDkLPnzpFnGUePHgV8MqDT6eCE4NEnnmR5ZZW777mH3iBhZsb3v/EJpd3qOsOyQyn16IZUooh67MsDhVYwX9CnT6AlFAkWQyEkRkC1ElGvVanXKtSqMVEgCcKA3BoyaxFa8dSTT0CYQK1Auk32TjXIihSLIzeGfj9hfmEP37zrPp579iyPP/oMf/mXf8XHf/OTfPVrd1Cr1gHQIsBkBUpIj9eWIZkXtZU+I62U9wSDKp/+7d+n2ZjFFGPai3OOeq2OMTmDfpdarUK9GmOLHKxl0M1Ic0eSA85RpBl5ZsH6Kpy5vfMs7ttLpRIzSBOarQY60gSRL7kMDETOUKtImlNTBGHE2maXD3zg+/mTL3yOn/k/fpLXvOZaPvvZP0DIFg8+8hxRrcFX77iTuFrn+uuuIY4Elx3Zhy1y8jz3i6BUREIihUJL0FoTxJp6vUFQbdKKNf/rB9/IXGCJlQZjkUHIIM2ohDF52mdtdZl00EapnKy7Shgqms0maZKTZob19S6rG13a3QHLqxs8d+osTz/1LGdPnqHT6dKYmiKoBgQzLWqNKnf8t7v5pV/9LI/c/xBZO+ejH/41Pv+xX+Tdrz5CPesiMgMyopNJjBDYIiE3zgvDlJ6+Qvumea5UPRIKJQO01iPjYIwhDDJSnfJnX3+QLVenGoZIJTh37gxhqKlVQkIFAkOgQCuwJqPIE2xSUBQZQvYQeYYzA5SUI7qWEMJ7cFKx1e6TFRZjvLFKkoTuoE9mCtIiJ01Tr2OLQwchW502IozptAe8+53vxVnBIE2wE6Hz6DV67v1nzrlSociC9BTBSUL7ZNXXsBrMWouZfG+K3VWCLzBeNkYTKLNhQ+K5KrHKEQq13c/eOSYy6OPTKvGO0pjFccwzzz7D0tISl152jCuvvoqNzQ4PPPQQz506w10n7me90+Xu++7n2bPLPPLkM2USxE58zXaSuN9oJ77TIoRCmwPEcUhgW1z/pjdy+0MJWzamNuVl6WSgCOMQrQVCeDxHDrnK1qCDgLhWJYqrFMkAjaSxqMlkhz1ze0mytGymZr12pTPUGnW+8537OXrZZRw5dITjx49RiZt858SDRDLGFtZnRS9wLgBSaQZZzp133ctvfvxTgCDPU6Qn0aKDgDAM0WFAvdHwtfORotGoUW9UqVarLO6dIw41OqqTuoB2N6UwCikskfahMaKg1oyJmxGdpE2WJRQmpaYVW/2CR545T6M1x1S1Qm4ybv/6PfzH3/gUhy85zrNPP8K+gy3e9q43Ua/XuffEw3zms3/Cwr4D7D9wKY8/fpL11TVe99qb0NpDHknqcd+iyCjShCzLSJIBWZLSGSSk3S3e+uormbY9Qi0JRBn6FZZKECIwnH1miVMnE776zecwosL83CFCDWEkabTqhIFGAOfXNnnu1DnOnFli5fwa3SShn+fM7FkgqsXoKMR1DYNByk/+8v9Ne+8cf/bnX+WeO77MwYVpFkPNj7/njZz460+xX2ZE3U3CwlfzZDIoZ9gwsVVyFKX09wgvHiuVXxTCMBwZB+UUGV3SCnziS19ngKRWrXLs2DHCQGNt4dkD1uBMgTM5zhSEWtKoR+iwRp7HFEkVHcQjg1kUPrnV6/fZv/8gSofkxpIUKUmSUKlUUKUEYVFk9AcDWtPTWOdKEXBLUVis1Oi4yi//m3+LyYuRodxW+jixDbc7G36xEsmdn423W9+qe2dLmxcYLyujKSb+SeG5kN6IypKMrthemS5HLykUUuz8zP/0mU9Ne6vtvUtn+crtd/Bf//Z28gKiqIEpNTff+e53M7+4h7e/8x30kj5hGFKpVBj2HJo0NNu8Y6dAWMLI92T5hZ//JF2zBrLDoHGO3/iLv+LH/vXH+NqJ5wiDGqGKiVSA0ooojogrEWEUEISaKIqYmZ9lbn6e1vQ09XodJWp03RZiXvHEs6fBhUgRYo1ES+09KC04cuww9957LzfddAvXXHkdWsVccslRvnHnXayeX8NOtncZJckESOgnAz79O7/LU0+f8n14tMAEBYXIMNphjSHPc9rtTarVmFqtRpIlzMxOcfWVxwmjgJnZFml7wPl+wW//zaNkuoE1BqUhVECeIpXFaQgjQaNaoaoErj9gc2OVzUzye3/5NQ4cvZzl5SWsgUq1wtT8Pq6+5lUoanQ2Okg7YH5e8fa33Mzllx7n2OVX8IlP/T6b7ZQ777qHxx9/mLlmgzzJUDpAhpogDonDgDiKieOIMI6oNOtcc7DO8aZDhVWky4lrMdWovD9IokrMN06e5ec++nnOqCpZ1KWqN2lN1WlN1WjVImZadWZadeI4RlqLSAqqUcjCwh4uvfxKwkqVMI6RSiN0yEaRkeg1br/zt3mgk3LPF+/lW39zF3/z5b9F5oZTJx7nl7/7Jj7y/bfy17/6v1DtbWILQ24yCpN5eAHDMM01THo6Z0biJcOfQgimpjULc12mpiCvNPl/Pn07aZoSRSH1Wp04DsugzhtdqSCMtJ+TQZU/+KOvc3j/99GqvYVIX4nAM03qtZqnKQlBkuWsbq6TFY4kKei0O2Rpyq/8m39D0h9gC0ueG5JBSr+XkCU5oEjSjEFm6CN48KGHOXv6zDZDdyHPcZjEGb7fXullL/z3zmGs9X3ChgYT95IN58vGaHoCshy9Rl4nXnkaK0aUDCVl2Zlx6JkOs21mXIteEpmkkEjjf1ZjzVSzyQOPPUmrNc3inj0IDdffcA2taV9euXxuif/5Q9/PJYcO8653vJOrL7sCjdhlLIXwYb8QAk2AEA6pIq9fqEJOPLhBrC8ntSk10+QD3/sqmIr5z1+6jR/+l79CHtUwaorSHcLJgLjWpNacpTE7SxjVaTSmkUHEpXvmGRQJ9YolWJiir6bAaorck7GLIiUvMpwTmBTu+OpdfOOue/nqnV9n//w8g06HN7z2TaSppdFqYHI8VmVywkARaMWXv/wVfv/3/hRFnayfIRzY3CKMRKExRYbQDqX+f+reO0zS7Crz/F3zuTDpy2e57q72Vt2SkBnkJeRXAlZiMULDADvzYJZFYvHCDDAsM4+GZxYnEMiglYQkWg41oh0SLdOq9qaqy3X5qqz0JiI+d83+cSMis7pL0OzuH+L2E11ZWVGRFfF999xzzvue940wleLCuTkmxkdIlWTu/AUiqVmaX+DUyTOMTY6gbI/2tkk+fveDZBO7KbsVaWRpphBLyGSFJBqW7Z1OD1El/NgvfZwHzzi+8rX76RlPFXnyXs6eyy/ngx/+a7JMUhQl1hgmRyeYmtpEXixx/1fu5iUvuYnltVU2bd3N2NgUP/D930fd529WvR55lZObkqLsURQVlbf4+bO8/ZUvoqEjRpOYrJGg0widCXTqSTJNljX4n1/2arQWPO/W7dCQiHgM6QWRlLTaKZFWNNOUiVbK5k1TTG1qMzU1wdTEZqQwKCVCW8QDVclIrFlZPYZIlxnfrfmrxw/wF5+9kz//0Gd55P5HyJKEL33tKY6eXuab99zPcgWidCQ2RtqQKJjahV60CKPDtl+2W++xFhwl+Ihms8m+vTuYGh/nlht34KkwLcXvf+YbfP6BY5SmQLuKVITXUN7STiQR7P0R0wAAIABJREFUsHXLND/6K7/HN04vsmLO4apNdJa7wWuJAR9YkDaaVL0eyysdKmNxPlRA7bExfvUX38svv/dnSFONdTXG1szOLiJVjDEWZSW+stRFQV0Y3vO/v5cYj65DjxbvkNaHzNoThFt8YJxsHIG1LuAAFo/xG8py43DWgXV4Y8CXWFWCLhGyRMoC4YpLRKVLr++YoLlOoruYTLduIHWppq1lnZpwqT8Lp0plC6T3oDS/+bv/BU3M0uIK09t38eY3vZVrr72eSMcoNI8/9iSf/9wX+fSnb+f40ycCTfTbNIw3nmgXS3cJtI9oyKtopdcw1ryGH//J78NLRyFiOs2t/MBP/w7v/KlfYmbeMtLeRas5SSwjZF2TSkEzlpiiy1izjUq34OtJludHsaJB4TvBXKxvfmZ8OC48ltLWOO+56Zabed1rX0+UpCyvrHLn3XfhgUcfexwRBdixMhZPxBe++A+cOXeeNGuBDKLPWZYRJxk+aKOQRClYSLOMLMtoNts8+eRB1ro5QiqKsqDXK7DWMr55M3kBeV2z4jR/8LHP48b2UNDES4X1CqHbNKVA+BLnSyLveezRo2zbMkmcpZTekldh9lwDvdVV9j9wmJ2795KkGlMVrCzN00w0u3ZNU1vN8SOnwDq0jPncl/6Re776T1x3+Q7asSTL2mRxQpYmpGlC0moQKcU7X34bunOBkZEUoSCKG0gZAJQkTWm2GzSbDepild1bPfufvgeZQK+qkHGEihOSRpN2u00UxyitUUpgbEWsHHXZQ9QOvWEsEh0Fryht6ZoZRjcL0st281BlWJjYwxe/fpC77nuIO07P86XzS5xujDM23qLMu5RFFy0kmNC6cn3/92H5SgCIpPRDLmscx+TLlqn2Fl74XdejIwuRJG+O8JWnTvNfPnIncmIvIkohL+gtrSF8g4/d9QhXv/FnOKdHWHI1F848xUjb0atqnAlldVUZfH+PGmuoTChlvBDEOqWb58zNLYHQnDs/B0BVlHgseS9HCM1AYKbMiyBnlyT81Uc/RqfI1zNE4fo4R7DBGT76f36pnuRGSlH4jAbleN95wAfVtIEJ3HNd3xHo+cY1lMXqh/MgleWHfELn15u96xSljf3OPkosHFIIrINWu413io998nbSVps0Tih6BQtz85jasDA/z803PY+i7lEVJQCX272Y2qB0NAyOlwKCNk6cDL7vnKOVjPChD36eH3n3bbg6YnLSUvkKVRWAw8QJZdzgJ/7bH6M8JEpRd9dw1uG8QyqHTjKkkrQnVvnyv/8P6KrBfGeGkTSlEtWwHSGEx7oKiwQpSFoNhNRMTG7i6FOHWFxc4pWveC0XZs9z7vwZpFA0shYf++inMTZ8Ts4JShPeu1IKa4LK9Vh7lMrW9IqSVqPJ2vIKWbNBXpZs2bKDo8dOM7094+y5C0R9W+SnDhxl02TGbbfeyNfvf4hee5T3/l9/w9teehXf+8pbEMUa7XgE2WpR9jSmLjl+6hToBiKCRkvSWwGNxFvHK199K2OpYvu2Ub750JNsGYkpK4fwkEYpkYfxiTHqqmDz5Bj/eN9+JjftYGG1x03XXsWFCwusrXaJM03Vq/E4VKyJegX7to8RRYbCVEFn1RZEyQRxkhDZcDDGiWdaaP7TD7yCQ/osu27eB4/1MMIRJY3gBpkJZNKh0+1S1464PzCR9NstSikSpXHeU4uayEjKbo+OOccP/eRb+I1f+TLNuEHRK3lSxDx630NsvfIGnjh5lEPn7+TlL7mNrCE4N7/CFZdfywc+8Ne0xxrktot0QdjZWkekNN4EfVLnII41ZZVz3dXPBwy3PV8go6+GplZu8Srm6cLwA7/1QaStQSi0kCg8IvKkU6OQQzODXdMv4uSxkyytdXBe4UqD0grf34+VdUxNbWal02Gs1aIyBrSmPTHBr/3GfybNWkNpx+ldOzhz5hyjTJKpOJD6rWd+fonp6W186tOf58ff/W56vS5OBgV+ZaE/37shYIRD46LvPSuoiIuBJMBbt3HX/nMh6VnrOyTTHGhiPjvTHH4t3fBEe9afMRgLGEi+DV4rfFCd5R4f+KsPo7Ik3BCxZM/e3TTbTcq6ZPv0NrrdNVaX12g2WxRFxcryGuNjk2zZvHkoJ/bteiUbVxRFQSrLRHzx9ofodaDoSGzRYmwS6sr1aS+OWETETiAd2MriSInTUXSrTdoaR8oIFBQOhCoZHRkNPTFx8efkvUBLBc73UUzBl+64g0NPPUW3KOnmFU8+dYgHHn6Ux544QGUdf/JnH6SobBBc1opGlpKlMVmWIKUnjaIwPmlyGs2MRhqxurpMe6TF9I5tZFnC8soyV151OQ6B6Y9rSClptjSlcfzTP30tmIUJ6EYpn33wFD/6e5/g5/747/gfX3qceTtKq9kmMl2m2hmnFzsYqamqipFGEykll+3ZzPETR9i2pcn0rnG+8c0HaY2No+MEoSMWl1dptkdoNRPqyvD08ZN0u10uzMzypS/fz2e+dC/XXHc9zYZGRopGo0GWJkhXMxYLYhX0U2MZEQmFUAlRJKmqEhTIWBBFnkhLxpMWrVxQZxU+roPmgQCvNFYJRCMhbY2Rttpk7TZpo4nSGiIVPKQY9NYKhIGTh09Tm4qrrt2Nd4KygkgHtwHdSPAy56rLp4kbCd968DF6uaSZxZx8+iBvev13s3N6G0JqjLFUVT3UEx1MiA1EMNI0Zq27yukzM1S2i6tNUIyKJZVwRAo8BqclXnpqBUWkqVSKkyk1Pa67eTsPPnQH7dEZLpw7jqktxnms8cGwDkjimEazFfQ7qxKd6ECa04rSGTxBkNr2tQwqY6iNxWtBYSqMNYCgKg2WiF9736/3d1U/YZIiPMQ6Web/7drIVfZsdBn6l9d3SNAU38b3ZT1gbnjmpZ/zrJdcDypTE5tY6fWQcYIzloWlecpejkLQzhpIwhzzwsIyJ06cYeb8HCdOnuHsuRmcs0MASA880vn2JXtZln174C5KxYyNT7Jr9zRaa3703a9HZopKeqJ+z8gqRS0EhRKYRkyRKKyMcTIFlSGkpt1I6S0v0ltbwzjJcqeDcZbKeirLehdXBqqWUIo3vP71TE5OsWnTFp544gDnz5+n1QgI9733fIXhbK9zlFVJlRekcYLysHlynKouwBnarSYKaDQbtFsNVleWOHTkIBD6R3Pzc/SKgjiKhohqUTlWuxXOh8NmdKTJRGIpjOV8CbN6knsPXOCt7/ljXvQzf8pvfvpbdBs7OTGzymKn7INaGoult1oS6zbzMwtcec01rK0GFSqLwViH0oq6LolkAAp7eY3SEUVecuVVl3H01HlWeyVTU6PkRU5ZlkBNmkhe9uLbSDC0YoVGEYmEKGsjhcZ7yBojCDRra13SNGW8PcGIb7JSrrGwMh8oVdbiEcgkZmxqgubYGGm7TZI2+uRwhdASncTrN4kPLIbDB4+gtGbz5ikmtMBIS2FAlhppI7qVosgdrdFRFjurnDh3Bik0WguWV+ZptVq0xyYwfl2I2Xs/PDyD9J1GCEGjEXPt9dfQaIbrpKXAaxfAS6uJfQROUsoMKwSSmhjXN9Fr88Jbn8eVV06xUp7mvgceRkdR0Mmkj6ALiXXh2pRlSRRF1GXdN6MQJH2vpaqqAEldVTQaDcqypNenIlnjsVVNVRR4qfnWQw/2BXMkTrCOePh1cvv/lzUYwfzXcja/Q4Jm6JuJvgSDGArrhq8FUX+sULJRCEoIxUVSUqIGafqUDA1WoJD84Z//GVqlJCJstvNzi6AEp8+d4cBTT9Fqt9m+cyebN0+wc+d29uzdyc03Xc+hQwd48uBBVlZW+j9PXCRgOwikQsLAMjOQ8oNVb1lonnhkjqJcZmrLNG97+xuI44JUJSipMSoi1pIk1qSRpqElmRIkOhCTnXQIpSGJabd2cOTkUWyxQNIco7aGsirpdrusrK2ysLzMwnKXqrRIB3MX5vja17/B3XfdxeTkJqZ37CQvCpIk5dTpGbq9HmkjYbTRZLI9SprFrK0tMT29HS1jLr9sJxLDrh3bUDikKdm5bYoXP/9Wdm/dwbVXXEkiNRJHLD2xDhtTKEh0RKqDQ6dQmrqoWS4EUdZkcqSFrXsslx1klKNaCX9/ZImf/KMv8PDMGo1mRGNkPPAoo5TSGYTSTGzazM6RKcpiDlM4prfsIUsylJYUZQ4a4jT4xwulSZoZeb7Gnh2TLM5f4Bv3P8G/e9GLsUowO9Pl2JHzvOiabSwtddCNUUpTY4UkTWKKvBsmTXREY2SMv/zDj6KjiGxkDE2Ejyoue/n1rPoudc/grAEdIaMWrfExxjdtYmzbFtKxNrIhkNqDFDgFXlhGkzZ4w/lzJS7fwdpKSrS5JpaaOI0wiadXdzFFj6KsMIVh8+atHD5ygvm5Jara08xGWDo3w9te8d3EMtz/Fk9ta6xwVFVJHEfkeR4I6Sm0mhndtWUsOc5HJE6hhaYUDqM8UkPkemAqqspg8BgMMkl42WuvoluUbJq4goe/+QASjdZ9Qr1T4BVJHJN3OoyPjbO0tIZOM2QfsNFRSq9b4mWEwWE8bN26naLMcXXw73LCYKVnudsJAU02GR0dI1Yu0Jb6GeHGTNMKgUOscznd+sMN/bvXK1JHFfqYw76oH3Jwnuv6Dgmag3IaAkt1oJT+jIcfBEi3IZO8+CEw4U15jfUCJzxVWdLr9SjraiiaURQV11x7HShBWZd84hOf5PDRY/ztZ2/nxKmTFFXJjulpsixldHT0kuj5Rq6m7JN9B+2DLFVoHfGxj36W2uTMzMxT1T3ysgdEgVYl/UVjYgNKxOB76+6cjtPnH0VEM6ioIooFzSyQ4dutJu1mg3azQRzrMDtsLWkjpT3SZGLTBK2RNqudNSye2bkFaluhtabIexRVaMpvmZoiUpKjRw6TxZqi0+W6K6/ClgU7t21my9QkvjT4sqQZRZw4cRytJc4ZpJasra6gVESSZGgdIYQkjlKiKOLMyZPrWUkdsqEkS2g0RmhoyUQjI8oaLOW9/tRLH0yLNcZULCwu8vTJ4yjl2bljC2fOn+fBBx8gz0O1IKzv210YTG2CGrzWrCwvM9HKeO0rXsYrX/ZSPvmp2yk6FZ2iJKprRhsJnV6N0BmtsUnyPKcxMsL5s6dR1uAFqKjNUqUwdQ2VIZYpa8s9ZDvjkTM5NLehqYm8IPIJSkOSKCIFaQTNSKC9IkGRomglLXzV5bFDJ/jiP56mkV3N1Mjz2LJlG9YYirygKiymshjjMQ5wgroOo6tzy6uUdQgIWkK7odm7c0cAUZzFCI+VYLF4GTK0vCzBJczOzqNIkArqimHbadB6gqCJMKAqlWU5tAfZvWc7eEldO+ZnDZVxdLsFFoGpLFUZACCBYGxinNXVVcqiDMyXvhYVzmFrgzIeWxuKorjoZw9oQ3UdRLarsuZPP/BBemWBs98O8H1mKLkEGf4ZMeOZikjPog/+C+s7I2iKPqAixQb1oQ3CEm7DYXHRChdEiOC/AxLr7XrQ6Y94lVVF2siGN4PUiompydAz27uXkydOcePNN2FMxc6dO2k0Mq666ipe/ZpXI5BUVTXkgj1z4mBjuT646EIIvAmTNocPzXNh4Rwzs2dZWVll+44MrXS/P7suFLBRMGCg3jTIaquyYmwyRcY9smYISEJalAKpAmiktB/+HYRnbmmRsi7p5Tmrayt864H9HDlyDNPvdzVbDZqNJkoLunmPszNnuPnG62gmmkamuWzPXsqiCLPIacTq8hJxorkwN8OVV+8Db0OG1X8Pg4xbqeD8KISgk/fIkpiDjz/MRCtDOIvWfWBEwsTEFKNTWxhtjzLVaLDcKZEyQnkoyxznHOPjo/z4j/0kk5NbePLAE4yNNlnrdpBRFMQg5MVmWtbZwPvr5QgUvdzw+c/fwQMPPUmzOUFRGoyWjKcROhKBIpY0ydIRTGVxQrK4sIItAhNAi5SVXoWoDNSCJx45zo37XkBpPHc//ji/8P4PI1p7UFELEZfo9iQyG0WmLVTWxqoEGTlEImhOjpEL+PiXv85v/9GHkaOj1CJH2oydO3dgfQBDgoOqwrgaZ2uMq5FK0B5psbzapawMq2tdhNLkRYX30GxlWBsUp4w31LbCOkeUxCAF7ZFNrK6u0mw22LQZVGSoqmpo+QHr5X0cxyRJMrzXe70OSjtWVlcwtacqodPpUNYVRZ5TVCXWO/JOQV1bep2cNGtiaouQQVpQ9m1jTB2cRGU/KDebzeE8+EbA1dShxP+bT36admtkuAef+diYvFySvN4fABk+n8Bl3cgJH8rNPcf1nRE0/XrJDR5kX5aMARWgn0mKwWkTRic3HiqB/C6QVgd7C2oQDhXF6wFNhhRfKcWRo0eZnZ9jctMmRifGGBkdodfpMTE2jqktE2PjLMzNccW+ff0+TFiDk3fjBdt4Yg+zUDRCOmKV0VmtmZzczOT4ND//np8Olhx9HqnzgXc6uJGGZH4pkUqFh07QMsHZ8NxYJ0Fb1Aukl0gv+2IhQdLLe8/ffOoTPPbkE5w8c5JNW7cED/lIo+OY0lQsLi+xvBYcEJvNJjfecD2nTpxk+9bNpJHigQce7IMWNZUpuPnmG6md4drrruGppw7gTMXmqQnwAqlUOAAkpGmM0gJU6GNlacQvvvdn+d63vA5bdcE7TFVT9kpWV2cpigKNpjQGNOzds4dYaLSUNLOUOEr5kw/8JQ8++gRXXXMlW6Y2IXwQk17rdelUBU5LjKmDrqi1RFFEVVfU1tApa46du0BuHGVtMFYgopgrd+4kEo6JsRGyLAuZnHHUJVx/0/ODbz0eV5WkzYjmSAPnE44dX2N5DrZsu5y3/8h3s4jkHT/7e3z8Hx5nbM/VNJIktFqUQtWGFIEymqZq0VuxvO833s8n7nwMl6W0WqN4m1OaJXbu3IG3FUPFAWfZOLxhjENJjTUuAChekI6O8/Tps8SNhK3bt5IkCc5UxEqDD9lqmqR461heGOPaq17JWOsKbrzuRoyJ+1M6hoHWgtZBpyDPA9Wn0Wigtaaqc85deDpoDmhBXYHUgQZovKE2JWUZjOnqsqIoCiYnJ6hNmEFHBsAszsJ4cIHFiXDYJUlySYC16PbI0iZZs0Vd1zTT7NKh41mTQes+WYNka51aNBgGYCgxOVBY+9f0NL8zKEcCoiTG1B4pNc5CUa4hdNQ3dhiQelwfExc8ExLy3uE8DBTZlfJUdc3n/u5LOJkEsnZVh1fyngvnZ/iRH/phnn76aWZmLrCwsMDzbrkJKSWTkxPcc/edrCwvs9bJSeNgurXxBhsETeg3k4eqYn2ZrciCTZCyZvfu20hIWekt89Y37+P3fv0TVEicsei+yZvH9xXow3/OFWEswyqUahNRkaqMolhG+BwtUkBQUfdnfx2CmEiBSiK2bdvGI48+wZV7trKyuIjuz5p7DzoO/SgtFd54VpY73PeNh3jTa1/F4QMHUDLibe/4fu679yuMjLbRLc2hI4dIoyaHDx1jx85ppNLs2rWDk+dmSLMULVVffk4ghSGfL1heLnnrG1/L0rmTNGPJ3q1jnF8yNMZiYtnAeUdVFBhToKUki1MWZhdCLyzv0Gq2kHhG2k1e9fo3cMedf8dt1+yjNQYLy+CkYnF+kcaYRIqIOAZrPA5NpyqocSytrQWNVO/QQuF8hSgNV1+3j2Kugyam7OWY2uMMKAGbN29j4XhOZ3UB0dQsLVXkawWyMcV9R1b4qU1N/EqT1/27F/PHjTsoGOWv7/waH73zm4h6De89k+PjTIyPU5c151cXGVRESinSqIFKATRjIzuIVcnYRLiJYhVRmQqIkbZGKoUzBmkFOCiqirIypDLC4Tl5dhapBTIS6FhR+5jKGpyP0BEUvVDW/93t/8gPvesG2tl2nnfLDm6//THiJGRwWdpASklZWbQO5Xmv16OuLa1WC5HUpImi6NTUboVN27bjKjDOYo1F9Q0FhQNjHLUpqSrN8uIykYpI4hjrDWmq6VU1OIV3AiscjUaLTqdzUZbpvcdLQV5YalXwx3/+p/ynd/+vCLUBgB1sOOeHGhNwsVCa6Es34kRgB2ADRuI2BsmLRcufy/qOyDQXFhb5nd/9r/z+H7yf9/3m7/Gff/f3WesELxGDx3of6A1DAeJByr3+cM4Pe4CuL5nVarV46uChi4Cb4UlWlfzlhz7Ehbk59uzazfT2aawXnD07wz33fJUzZ84zuWkLTx9/mjiOL7qoz+prDmQAPRvKHR3QdtPk9OmTCJWzbfsmevlan4cZSuuNosrfbiltcMLhhETgA5+yz00LKHNY3nvKMoBDi4uLZGlEt+hx8swZemVBN++GiSqh8bXD1xWTE6Ncue8Krrv6Su665x50HJFXFd5YnjxwlLgxwuOPPkFeGC7fdyVp1uDkmXPEWTj5pQBTmdB8t55YKkYaTRIdkTUimlmDLVu2UBYFL37BdzHWbqGdxFYVpqqQSpGl2fBzO3HiBFVVIpUi1ppGo0WcZHzms1/gp372Pazmnq3b9nDLLTfRGEkRsabOuwgl0UrTqwpqU/fRY7eOKIcPKGwgEfGx2+9gtTT0TE3hDCKLmZ1fpCx6IOHBhx+H0mOtp3Bw9lSPRE6SasXk5DY2796GxzA60Z/zTkBFDpuk+CRlxViOnZvhzPJyONDEBnI7AmvDIWx9j9n5Q2zeOoXQjqIscTKwIRCiT7wm8DC971tGO7wWCC0xYtDakcO+epg3N1hbY22N1pIDRx5krTjJzMKjXHP9Lt74ptcwNjaCUoIo1iA8jUaKUgLnwgST1hJjKsoCiqoLwmJry9yFeVZWVjHGIpQiimJarTZxlpI0MoRSVM6SZVlILrRAao0gVGIDERUlJWVVDq/PQGTEOUdlakxdENsGH/vQZxgZHwjGXLqMvmR5fomnenepMv7fIHoeFJsTBJo4ilAq4oN/9RE6RYEjgDleeAyDgt3hhdsIlIVS0vbTfAJ/bHZ+kTjNLkr7BwG0MDUnTp9EKcHhI0eY3r6DRtZASMHLX/EKfuRd76LZDLPEwTt7vV+5MQhvzDalJ2SZUqJkjFYSW2d86MN/zvzyk6z2nmZ+6TDGFyAkUiuElEHubsMSUm6wYYCiXsF6B0KRV12SLA4bCk9lg19OoHOUOGcRAqqqRCnJytoatTXUtqZ2QTQ2iRJazRZJktBZXeLIoSc5ffo4I6MtJrdspXSOmbNnufWFL+T+Bx/leTfexr4rr+PC/DIiSjDW02y1yfMc7QWtOCHVKojaOkcSpURKI/FEceBdKgcjrRau7gXdSuGJ42AO5lUQqZWRplvkoa3SF6EwpsJUJa3WGL/6vt/lbe/4YYwaYfPkBPt27UQIRXdllU7eCcyI2QvY/rSKdwq8xhjwToVfvcCjWLCaI/NdTj7xJKtnz7FpfJzDBw9DXmA9PHnwKMVShyJf43/7hZ/mlz76t/yH3/qv1B5mzq7RaMZs2byL73njC6lLAWiUUsQ6IdIJdWkQKLwNLA/v1x1Qa1vjrMd6KMsOqFWSFjhhkXHUp9konJbUOIyECtdHnV3wTfchQ3ImtJ2870sL9hW9RJ+r7LzBecPJU+cQSQeZzoMqefLAYUbaKddcvY9GkpD3VtmyeYIf+sF3EGlIE0XeW+UlL34B3kNRd7H0cD4iilJMDWurOavLXeZml5ibXaLbK0Ao4ixFxxFT27fQ6XX7lCSF1jFxf1gEJQNtbgNesNHDBx9RGoPTJR/88Ec4f2Glv4cHJfgz+5vuWQF1KG7+jO/BxSIe/zaBIKAyJaUp8NJifZDvf/9//xO+/o37AY0TGu9FX1hDsHGqcpBhejzWBlUiD3zqM38bEPRLSD8lSYLSmr/59O1UdYWQkmYj4/LL9rI4P8+37v8mf/Ppz1wE+AyAmUtZfkop+6Ko6xmoo0ZpwVe/cj8157iw8E1U4zTzq/Ncec1VyCgL5Xw/aEoVMoU4CtmCkpI4iWk0RkjTmCT16Mhhq/DmTb8nFSwePEoJ4lgTx5q6LsOkkIcoSVBRRJxokiSUf3Pzs3S7a2zfOc1rvud7uO7qqxkdHeXkqVPMXJhn/0OPsGPXTs7PzPLAIwd5/MmDJGnK8VMnmdq0iaosEELSTGKaacJIo0mkBYnSaCGJIxW6C9ZgTE3UaLK6tsyVV+wCJHEaEUe672cukTqUl+DQiSJLE7AW4ypQniLvkbUa/OL7fp3FnuBTn7yDYweeDiUrjrPn55lfXkOphG5e0CvKYeZS98GHcK9IlPTYJOK9/+N2pvZcwbFDh1ldXOKFL3sZjz9yiAvn5njPb/4GcwvLzM6c5sYrNnF+YY2jC3NEWcZ//PE/oCh7aNXkB3/47USJxbugei5xSOFJ0ggVh0GKOErRKh4q+zhZ46QnjmJMXeKNIs4sSI91QWjY+j7pWob7YjjeqmNsbYiEAg9Vng+BlEEQGPQJB98zxnDoSIEVUPgFkqZgdm4mbH5raDQSpibG6XbWuPeuO3nRC17ATTdcz0tf/F2cPX2K8QmwvqL2FYuLNWPtCVqtEZrNNkmS0Wi0EEKR5yVLSyv0ujlxFLGwsEjSbIDWKBUhUYy126GXOjjoi4JGo3FRuwsg8hYyePE7d/D7f/YeXvuKt9DtdsMM+SVW8LfaAB5vCJiun0hdmgvOv2FpOC/RKupnigEsiBTc95WvUxYGVznw/Q6nAemCqRL9DNNZN8y4jLVY58nLKiiHD4CWjcZjNpi9ttoNGiMt7n94P2cvnGX/w/s5eOQgjx98jKlNo+Gf1g+Yz6QYDTPOvpK2lgo9+Blego9AlAg3wdjYWAikPueyy9uMT43xhje/mU63g/eeG264AWeDn3VV1kxPT1MUBb1ujyq3eJdTlHNY2wMchn5ze0Npv9FMbvB1pAKoEimBUgLrQm9p1/QOLtuzl+NHj/O5T3+OI0eOsnXTVnZu3870ls284nVvYHVlgeffdgvzy72Qcfe1B5X0VFWITQVEAAAgAElEQVROHEkmJiexziElJHESzLkQNBoZWRyR5z3m5+fxtUGUlisv30umM/AOZ8L1ct4jlSBNUrJmg6qq6V9camPwlsBr7Svqf/zjf4ua3MlTM0ssr62CjKmMYGmpQKAw1tKPI0Ao+wblv8MjTE67HaHG2vzR330Tozbz1METbN6+k4e++QCLc7NcWJ7l8NMn6a056rkFPvJ//gojcY1WKcaN0stzYt2mu2bQcY5UQWkquF0Fn5vB0IGU4d6JddIXmwmmZdYYlPbgFWOjI+H6e0J/WFxMsRlkqTqOqExNWQfFfPrgYZgEcsP36xwoFRFHKdZ4hILaZgiZkFcd4kQyPz8PQFWWTE1N4aylrGsWFhdI04QjR45SVxWjYw1WV1coakddaebm5rHOkSQJjUawt0iShCwLVjJFUbCwsEB7dAQnYWlpOQQ1JJKQGBjnhuOXsB64Bo+8zFlb7ZJF4xw/dgGZ9K+j+udD1kZw55nruUz0PZf1HRM0g/NeQMwnJ0eY3rGVKFZkjZj55XkK1adRIKi9x3mBqzzeBJuCQLMIWWamFU+fPEkkgz2D6J/UA3Js+D0YV2Gc4djRYxw6+CR33nsPp86c5dyFWc4vLNNzHh8rjLPDoDugZ1yU3vu+L7gm2AALh5IgbBIGO12Lbp1QulWUavPv3/19nD1xgqefOsD3vuP70GlEc3SE7/p3L6Jnuuim5rqbb+RNb3szuhExMTaCRSEoKPOCAkdVGywDN8511D7wFR2Li2uUpRmWP84F/qJSERhPZ6XL+XPz7Lvmal771teyZfs2vnrf19n/0CMcPXmGxx7az8y5Oa697nrm5peYmbtAUVcIIXFWUHZLqrLisulteFPjcMSZptmOkKlnciyAQ8dPnmSl2yVKI2pnaCYZuA7OBuBDCYkWoAWkWtBKInorK7QaGVEc0UwaRHEMOGKlkEiM6/H393wFPb6dtD1OtdZDyRaFh9zVGAwIsC7cG94FgQ2jJDLRiLRFaUFGkl5jkt+5/Su878NfYP+RGV7wguez//5HOXv8DG94y+s5+PADHD78FK1ikZddtS/M+1tHc/wG4pHN3HzDW9BxAZFCSjskTQshiJUOSPZwfDL4UCmd45wAlWB9jWGNOB5DRx4VKTwxXlVDicSgRxBeQ+swd18LRWXBSIlDI4n6KkoCqgD6marGOIuOI6TLaMUjQIxH0kpSHIrVlYqtW7chnWSkOUHdtZRrnqXZLru2T7O2vMa2rW3SNKWsO8wvzpG1Y/Jej7WVNerKECcpUgWKUpoGyUAhBEuLCzSSBCGhxiJSjdWCKIlRQhBt2EsbqzkAJWNkZfnEf/sqGVtoTe1GoMEalPOofjvsIrR8Q3m+MWt13uOsxNkYfIzsOxcMyv1nDm7/S+s7Aj0fzTKev32SrNmiWxomp7ZwdG6O0xdmcToM1+s6UJHqymC9oRL9MURnMTYQa521eNtltZDcc89X8EL3TzN3UYY44FuG8bIMZx2NkTaveeWr+ydd+BD//s5/wApHHMdD9HyQtQxe75m+JINlsSgRXGmMgS987m5e/Zor6HW6vP2dL+Qv/vRuZs8vU5icPXt2c+rUcRCeV7/6VeR5wdPHnqbZavLSl76E+aUjGCPw0hPFIqDNanQIdgzWxX0a+tJ1DqWCuEmZ90giTXNkE9M7pkgjwcGnjnLi+DFMDSMjY1jr2XfF1XS6i+RVwYEnD/C613w33/jG1zh//nx43wpKU9Pp9ti9czfi9CkajTC7Do6JMY2Om5w5O8fMuRlwFUXtkbbAljlVXiCbI8Q6cAgRoYR2JoB3pq7pdYItiUQhlQgz9y4ohGsZYeoVvvT3/8DvvO+X+ae7vkzRW6GoimGvTw6mtESoXAKH06DlOthgrKeKNc1t21hYXOC3P/E57Pwq73jTq/mPv/1XvPSF13Pr9bcyFzf52P/9OR47cQ43to3RtMUv//yv8uu/+fO0YsF1N0zz6MMW4+SwmrmYBnNx6VnVjjgKAsFxojFV4JgiBdZY6AMmzocKKjArBI5A/K6qCmv6BmgulPxa0R9fTIcMj8HPttaSNsfR8SYoTgMFY5MpKIcxBcvLS0QqwntH2tJcWDyHkKF1EqUZV+zbwsrqCt7BQ986Ak6SZUkAycoustYoqfseRRbV3yN5nnPu3Dm2btvK/Pw8U1NTlEW5XvH16Xp1XQ+/N7BftsKivCSJNFfv2cdrX/s63vKWt/GpT36UkWYz0LLCDXLRurTaUfjfIAMV3hHcR/+ZoPTPrOccNEWYV3wAOOu9f5MQYi/B83wSeBD4Ye99JYRIgI8AtwILwDu89yf+uddWSnD1FdNU1oBqcurcHNPbtvPE0cMBGa96mEKjYsm52Qs475jetoPS1oHWKUUQIbAWLwzWaXTaxBiP7dumwnp/Z73UDqVE3i3Ys2sH9/7jvUgEreYIhTEsLa8xMtpAKTW88a21F82ghwB60efUV1UxIAK5tyhrvvmNg7zyNVeAVCyvnSBOBe1Gg9pUxDoDIWk2UpaWlqiqmi1bt1AUBefOnccKWF7tIpXE9os+3xdTlQNFIi7eqOPjo6yurqG0Q6JpJCmpVmgtuTA/w9zsaS7btYvn33IzFkdZOJZW1oiiBGcrDhw4RJRlrHW6JFmDJMno5UW/PAoZe1Wb4FueRrSSGK2CaIO3FbbXAVMzs7TK9PQO1vIuU1nM3Ow5bnneTTx57CQApqrx0g3Br6IoSNMUh0ciULEY9qpqZyiqiiRKGB1rEpWe/+OXf5u3vvU1VBcWKHtF8B8yhuEkbn8lSRKAqw0IrTOWWBpkHDG+eZyiLomzmC8dOMqMjvjCY0e598hpjPU0mpp4dBIjHEVV8A93foVf/a13YoAfevfbePThj2MqQRyvb/7B8t4/A+yTWOcRXuF9TS9fRCebQm9eEijJ/SwTFdggQgjiKA4Ec2MuCqiqDwQN7mtrzbN+bm26LCxVFEUXIRR11WV6+jJmzuZUpkCKoIYkEbSbI/S6HVQksabkhpv2MTY2g/eCQ4efGPbbkySmtiFbU1r2HSUD91jIYFeyvLTEyvLKEDXXWuO8o9vtDseQy7IcttAG+9M53597F7zv136dn/vZn0OpGKmyMDYZjkLEM3LEizLMYV/3EjpGThL8lL5dVPr2619Tnv8scHDD738feL/3/gpgCfix/vd/DFjqf//9/ef9s0sIwXhrlNhJIiEYabd44sCjaAm2NHQ6C5w4cZCZMyeYPXeCxQszFPkati4wZRdT9TC2oKoKOqsFq8urGFf3g5e4CLgZADmDpZSi2WxQFAVSKzpFj2Mnj3P0xEnGx0cu6rkMAufGLPOZTeRhticivAgNb4D995/A+uC/s5qvcfzsSWpqvIckTRhpj7Jt647gWaMj6rrG2jBqVteGNMsojAMpqfo6g8Fk7mLJuqqqqOuabrc7bCfUtSHWmjRNacQJu7ZvY3r7XpYXetx771fZv38/p06dYHF+lpWVRQ4dPULWGmV0dJT5xQXAsrDUAaCZNcjLLt57Ot2C8+fPs2fXLhIJrTSjkTbJsiZpnHDtvquRHq677npWOnkAM5zjir278XWgBmmtUf0IN8jknQmfmVSh/+e9oygKut0c6xzG1GDB1jXtsVGESjF1TRRHRDpCEayHw4CEI4411tZ9b+16OJDgfdh4xvgAqNQ1MtLEoqYdw0SrSaQ8jUTgfIS3oKXAiuAdFre6zK8d4Mabd4Ou0Gka7pX+fTIE6ZzD1PVFY7FCBNqZdQYd2fX7pk8xuoiG1gcwjLMkScLoaKgyBp9XXhYYZ4iSADYJta6LMLjnoce5M2tkjTYYxcnzq4xtGqFTFqhEMLM4S2HWWF5bCaO+yuOFIW3G3HbrDTTTSWztmbswi5LgnBkGONkPlIN90oe4cdbS6gM/g7FMHWviJB4eXoN9JKUcTigFnmYYeEzijJ/5mZ9mbHyUojC88Y1vQ0hB5QxS6mdNC14KFX8maDtA28PPXn881/WcgqYQYhp4I/AX/d8L4JXAp/tP+TDwP/W/fmv/9/T//FXiX8Dzy8pw5PgFHCPkBUxMTrK0tAI1bN80SXd1lTRNsEqweesOpJL0Ojl1XmBqR94tePrYKR586BH273+M+7/1EF56nFynCTnnngUIDdDwJElI05SpTZvYvXs31113Pbfd9jzyPB/+GzdejI0mTX5D0AoAVH+O1gmcD9msjhTOQ5RMYISlk/cQEURJm16notsrmZ9f5Onjx5FCU9VhqmJ+boFms8lqZwHjLQ5Bp1dTm0sfjxsb6VVlqKpAlYp1HHqbxtHIWkyMj7J751Ze9OLn8+KXvIy9l13L5k1buerKq7nxxlsYGxtHq4i1tS5Zo8H80tJg3xLFEWmWgpJ4KZlf6VF7TdocQ+oU4zTGa6yTTExOMNJqc9WV16CwoBOitEGsQdgcIWWwHKYvaScESgZjuSQOEyqVq+iWOXlZgBRoHYfetJVoqbBll7u+fBfXXn8dRVkGa16CO+/gM4FwmCilhpXCYEBB9vukri9erHXKalGASnEypkBjZIKTHgiiz4iY9shmzp0KfMOV3gkcq2i9fl1MfzJJ9w8rpQK9TArB0tICxgbrZu/C9NvgXrMmEMbtM8pM54Pu6eB+q/plelEU4UDp5Rc9fzDTPbjnIyE48Ohp2q0pdu/ey60v2cXIJkvpFvjRn3gnt77weUzt3MoLv/tWzs+fpTHWJm6OcPT4adbWurTTa8j0FczP2L69b9rfC4MN8uzAVFUV3rngE6RUmO3PGnjnh3tugPYPrs/gmql+yMirkgsL8ywsLTE6NsnU1FZq64giFbCJwc/7tqPWF6/hHma9lznoQz/X9Vwzzf8O/ALr/dJJYNl7P8AozwA7+l/vAE73/4EGWOk//6IlhPgJIcQDQogHCuOYvOxq5hw8fPoMH/n8HVTeMT7SJIk1IopBpXTWCpbml5kYm8KUNSa3LC+ucPDAIY4ePsHiSkFRe5bzHFlblAlI70ZO5cWomaQsSypTcezkKc6cPMXMudP08iX27N1FNtIalvSDvzM44S9SPLIi6GG6Pg/QCaQSOCGpnAo8NTNKYTJ6dQcZaX7wXa8iQrN3317GJkbJmk3SrM2ho0epa0FtBXOLi6TtEcYnd6AjBzYGafv0CtH3OllvoocAIXEu9DS1ljSSFIXH1hULiwtoHTM7t8Q39j/C7V+8g3u+eh/f2v8QDz9xkINHjnH69GlmZi+wY+cWGo2Uy3ftweUGpTR1XTM2NkZ3rcIbT6+XU1Q1p8/Pcnapw2ynYqXyVFbjZEqWJEzv2MSpM0fIolGWVjqgJTNzs9x44y3UVUW3DPPL3oesM4pjojjGCyjKkt6qoeg4tEhQ6KBeY8NBGEURDsFa3uVzX7wXrKe2BpU0iPobRMpwjQfBUykRMvWiQlhHLEKPqshzTFWBlBSdCq0CTzSSCmctGoVRgXepfIIoEn7pF/+IKNVY73nNm6/nB/6Xt+Kw5Pkq73rXDxJFCXjPtu3bedkrXo4xhkazwVvf/v00WhF1aenmixhbU7kaYwVKhikySbwh23QIEUZatQ6816r/nkT/3l5cWAr3Hw6pgoqPcXbDvZ7xxS/cw0OPPsV9X3+c5kjJE4dO87q3vZw77/sChbPMzM2wVsyw87JdxE3B2FSL9liDyfHtdJcUOza/gE4Hut0OHvoUsX4YEX6Ysm1UABsEz1A5WU6fPo2Q4dpJIXn5y18eqjwpqet1AyvrPd5brIBOWTK3OEvP1fSE461vfidYA3Ux1IgLvpT2ov09fKjBvzK8pvcX925C1vn/I7ldCPEmYNZ7/+BzftXnsLz3H/De3+a9v81aw9/fexd33HsXJ86dASXYMjXBy17yXRS9NYQBUzi8lGzduhVlPa6oyFdz8uWC86dnsaWlzkuKosR5iZRxIBd/myNkkJENLlan0+G6Gy7jxS+5jWuvuZL52RnKvkf6IGgO1iBL6Xa7lGVJUVdBEFVJ/EAkta9WJIXAOIsUmlPH5hlpbqHdGOctb349cRo8Y0xesml8gmOHDzPWHKHdSCnyNdrNFk8fOUyz3aK2BudiqlrhhBtmLv1rNHxPJkSUMAgQhfcfScWNV13L9LbteCzOQqvZDEIZE5N0c0+v22V+eZm5pSWuuvpanJNs2rSZbq/DSF/lKZD8Qy/QuX5fyVjq2lDXBms9dW3oVRW92uBVxJYd03zz/v08eeQpojgh1ilVr2Drls108w6RDrQYQYzSEcaFXmlZGnp5Eewa+l5M3vvhphwcWANgrq5LXv/6tyBERJykONYrjEEvbaDzOJi5jpKYTl5SmADYpVGKdoEgPqxG+joAl9qMD+8/w8pSiRQ13/e9b+Tuu+/mla96FVdcdSV333svV1x1Bfuu3ofTjm89tJ+bbr2J8ckxjh05zsT4FAJLWZbUxlIUBmNC68E5Hybc+og77uINPWBECCGoq4perzcUv+7vLWC9FeV9mKY7eGSJkdYmpiZH2Ll9BwcfPcUj+5/g3OlZms2IolphcakiGxcUbpHZpQWuvXWayqzQzBrYWiGJsNZRVnkIcsIH1ou3Q+2EjcpdA1AqSVPiOA6An3VcffXVNFtNHnn4EeI4HrpaDpZ0FinWDeNwkhRLQ0GWBHO6oh/8vPO4SxDeh0ZsJnBPhPfB7+uZJPh/JfXouQBBLwHeIoR4A5ACI8AfAmNCCN3PJqeBs/3nnwV2AmeEEBoYJQBC33Y5H1R5DKBcaPNORZAvnuXWa/dSdXsgNKvn5zm3chRT11RFEC/VUURVW7xziFaTOgoe5NYMEMtnk9AHv5ZliRBBWCJJEo4dO89xeQHnBKudDub/oe49gyxL0/rO32uOuS7zZmZllvddXdXV3k73WGbQMBgBgw0ZFtAihUACKQJpdyFAERvsarVE7GrFbgQb2gBhAidAywwMzAAD43pMd8+0ra52ZbtcZqW7mdcd85r98J5z81ZNo2GEPvSeiYzKup2Tde8x7/s8/+dvSo/SdrKwCiEmF74oChqN4CyDEBNKUyC1+2Br7B3Wh6rUGsHH/+hz/KN/+gRaRTSasJ3dJBkmrK5uUZQF87vmMM5y6c0rLCx0GQwy9u7dg/FjhkPPrl138ua117G+WsirG6pWU0xuEimx1qOVxFfUDoljrt1ibEp27+qGk+ElKzdv8v53Pcz2cEhv0Ofls6/R7c5y9NgdCBTtTpfu3BLnLl+nLMNDqXWEVoos80gdo3VMlhVEUUyRG6IIjPLY4ZBG2qTd7OCanrnFRQbrKzSSNAzc2mFYpyuXcRHFSCyj4Qhjw0Yj5Q4mHdzI3QTLrQ9RPahffOYreBM8U+vrXrd/9fcQWrE8L5idnWW1t0UjSTHOB6MKC0IpqrYh/Ls+iCm8q+4pFyYL890FDh64n5u9Zzh15z6SVHLx0nnm5ucpS09eFiztWWRzc4PNDYeQQaq4ub0JeLa2bwYmiHWMRlnlHwCh6hFvuWCKmm/qHEkcPlc94JpgwlM4e/2ZrQiskO7sfnx5kTuPHeL0kW0O7j/OmbPP8/L6S6xcsTzywJ1cufYKi0v72NwoWe9tcvnyczx4/2O8ceEsKnFIL3E2OIopF/iTASOmuj803u0Mpowxkw3ee8/6+jqXL10mTVPOnj3L7qXdjEajyeJZ/5y1DpPnNFuzFM4i0fQ31/n0p/+S/tY6utnAl+XOVHxq3lDfFwDOlkgd4V2Ah2sroOnj61k0v2al6b3/ae/9Ae/9EeDvAH/pvf/7wKeA761+7IeAj1bf/2H1d6r//pf+a7wjIQS2ktxJIZhvdWAoePX5izz/zOucefY1nvn8c1w+f5Wb69tsDDIGxmOsIM9tcCMVCqcEuXUhlQ8QeFQFik9PwOtdqJad1dnmK5tbbGwN6G1v0+wklL6cVDVptVOORiOMMZUzTnmLpHL6AtSgf3jYNd5L/vCPvsD22KJ1G69GLB6f59zKK7x69QwHTi3ypZef55lXnuKNG2d477c9xErvIo+8625eeulLdOLTqPIIdx5+H51WO/zuqQ0g4DR+QhgWhM4ljuMQdiVhfr7LfKfJyvIV5rpNhCg5sH8RbzOywQYP3HOa+0+fYu++vbz44stcuHiZ0TBjnGcoFTa30TBDaYVSGjyURUZZZEgJeZ5hbMFwNGY4GjEcjRiNxuxd2s1P/st/wYULb7K0Zx9RI6XX36Y7O0v4NUEFNhgM6PW2qocvELbrW0erHcYCEyhiB5fOjeGp559jnA9oxRrjdh6g2muzxtUajQZxmjAYDSkNOKFRSUrhPNvDAc47rHcTd6tp+77ajUoA1qX8X7/4UbxPuXbtdXK3zOr6MkJKVpZXWFxcYLO3xkynTaORVhDJGqPhFtevX6U738Q7R5rMc/XydaSKK3XYlF2g3Fn8YUf9liQJpSnZ2tqaLKL1vV1vMDuTaIeSjnajTbu5C+ccx+9cIk4M16+c4wPvfB8//Pe+j3/1kz/KUifi3qP3kbqUPfNt3nz9BkrE9Ht9mk2NKRVl2UCgkSLCWUFRGMbjPAy+isouLsvY2tpic3OT0WjEeDSiLIpgX1eW3Fi+wfLyMnNzc9z/wP0Mh6PJxg9ghULIoIwrswE2G1FsLvPclz7L1nCLWHjI+xMIYyJnfovhT00zhHoz+eo2/us5/ibk9v8B+EkhxDkCZvnL1eu/DCxUr/8k8FNf6xd5ghuz1GqyS64NhmznltxrTKIppaHUAiN3biDng5mH847CGsrKN8+Jiqdnaw3uznGLXrzaBaH6na5EAJGIiGUjBHtVuFg9kVZKEUURWZbt/P+mP4uvK9z61LpqmqoQMkboBjdXtvBS8J8+8gW+/x98GNEpuf9dd9HZA3c8cBCRwtr2MosHl9gYrDI7E9FQB+iterTvMuoPv/ocVi3YhEPqAh6Hc0RpQmFLBqMBcRTT6cxy4XzY6be2+pSmZP/+fVy5dJFut42W8OCDDyCE5I1zb7CyvEJpqTwzJc4wNZ0NN3loKw3GFJRYMluQWcPYFMhI85nPfIbzFy6wtt7DOEtuDZ1Om+GwH/TlJsPYQGGy1uBdoGvV59/YHYlPDZfUbXeoJoMe//4H7qYsMvxUgEw9/CnLcnLttNYY7yiMpbQO6wlGGVH4fRMRQz3pqFveqYfROMuv/8bHKUpF3BA8+I47gwGyDP6RV66+SafTYXn9Jq1um2sr1ylcSac1S6xjsiyj3y+C+GGU4a0Ludz+rQ13fd1JVJvG9vb2pNOoH/7plrz+M4oihDUUWc5wK6PVaqNiy9bYkZuUZ5+7wNrqBsP+mI5OSSz0bvS4fn4NM4LFpRms9Uhi4jQBmVcdlkfIepxiJkYc9fuoO7gQ+7Fjo2iMCX6cec4HP/hBrl2/hvfcYsEonAotuVBoY1Flzr/72X/G+OYlpAh0tLdOqH3rwxEWZOvLr4vI/lbH10Vu995/Gvh09f0F4LG3+JkM+L6v6/cC3vrgluMdadLAeI+MJEIBpQehA8lZiMqUIkxahQ0VllEB0JWVysQ6i4girPcgqwtW3WD1jjw9TS2KIlSseHSqKMqcONIBHzThhjDG0mgkjMfDCkuraS3yluo1EHQ9qsJUBR7rBJGJiUSTXr5C3Ic9s7CydpVv/tvvZG3rBsKAMIZus0V/NGT3kVmWtzaQyS7aszEz7Tu53jtDd2ZvJRsN1ZhS1WbgQkvjvEIIi5QCfIGSEVeuXOHek6co8xHNNGU8GnPujfPcd//DvH7+HFv9bU7fcx8319YBjfBw6MABbq6GjJywoTu8KbBNXU2GBdZ58iIjIcXIwKksrav8TXMgKEU+9clP8c/++U/w1OefJHYKijHNtLLcc+H6Kk9F7gacr4YxEuerFn5qGEfVQVhXkaGdB2E5df+9vPLKq6RReG9ah2ud5zmNRuMW491YBU20liI49lDxK6MYa2yYbbjQrVgCnurYAXxSrVjZgM5sC2Hn+PD37eLzn/x90iTlziOnUQ3Ln/zpx3jkoYfo9zMW5vfQHwzZ7F0iTXex1V+mndxDI1qgt3YxbBJyiPBtPMWkM5dK4sxOu+2cQ8YRMo3JyxJdVb5Rld5Z/1yNcwZv1iZCjRlkhiiKGPRHnHnuTeb3znDz6jYvnX2Vxx87xZU3rtE3q3jV4fChJVQMIopotFJWVq+SbSmipMB5iSsDjp4kCVonUBl2A5PObhpfnUh7K1MR7z2ff/LzlKZkZqZNNgrSXCcKklgxHPR48ORx/uF3fzv72gnWDrh65RKNw3eTa4lwFllFV9Sbmp3a1GpMO6rXGEy4dr5iGn+dFWZ9vC0UQfgg1ZbOI53H1NO3qkWDnfJb1XhW9XcfbAZJmg2cDhEXOI8WGutdhUW5W4Ym9cJWL5wTLAjL9nbgI9YX3Bo7wT3j2E9A97oS+aqP4neqvWmlgxAhviFNZmjPGIgcJ+/bz/ZGzh9/9DP845/4EY4cPcAdd5xiMCh5+tmXeeTxexCyYGQ0X3rxzziweIKN7EYIY8syvK+UI67GM8P0XnjQkUIJj5Mah2JmpoPzjkhqCmuYnZkDOeKpL3+FuYVdnLjzBG9eu45xkDSbnDt/gTiKmJ+fI8tLPOy4bgNCSZIoJjdhmm+MCcmLOCwmxAmj8T7He0u70+bmyiqf+dwX+PC3/W2kLFFRUcU5hN1fOBHUXzrY6tXXuMbI6klsbdBcTslbIx02pt/9nf+IsFSDnh2/gBqLbjabkxYxjuOKmlXc0s5a73AikMZLUSlJBAjvJwtmgGA8C7OHcXaOzd467dYsuxZnefPqRZqNJa68eonHH3832+s5rUaHjfVt0kZEd+4IpYe93SV2zd/NaNtz9co6Za5JtWKqqAbnsVWVJKTEm7AxSxH8WKPq/QoERuws6NNtaiCjRzSbKb/6K3/Mv/m5f4joneMDHz7AaNymv7ZNp5Fw7txlBuWA1rxH6gITZew/2kSJeS6cW+Vqb8ARzlkAACAASURBVBOtwRmDkMnk99fUp2De0Qx0qSkIZfr91JBYURTEcSDrR6mi0Yg4vnsXp3cvsWd+lt3dNs00Yu/SArHK8XaMimLWV5Y5fOgUeIUPMo/goVktgGraUNOHaxYqboez1bDJiwkOutMZ/vWPt4/2HLjzwD6+8/3fyFJt5+YcwdbIVyYMhrwoglOMDV5+KtL4ygGmbrWFVpPFSwiBMGHxjJSecCyn/TWnp89aB/lXnY9SX+iyLCftXb2D7fwbgcrgXIADnDOTvxdFFqgTtqTMBZubW8x2Y8Z5n4fecRd2lFIOEz7y25/gnrsfZf/eO3jHI48xWi/5g9/8GM984RkeOP0gp47dA06QRjHKxcRRSDes8Trv/SR/x3tPJFXYhY3FVFWyc45Gs4kSCuscC/PzdGZmWF6+wZmXzhInKXfddZprV6+QtmLmFme5cvUi46wPMGm/dBQI50KGdEAIPELnavO+cC1KYyrNNMRJzK/82q/zEz/xk1y5dgPjdRgmWIv14ToY7yBSk86gboVD5+AopjDkmj0wgQlciURw48YqKEWr3Z4shDUME9IPS6TUBIHbjtCh/mzWWkyNDwpC3o4SFWwud7xThUA4QzNu4FwLpCcre1i1SWMuZnV7jYPHlhibFa6tn2Nz+CbrW6vMzTfZc3SW9eEKJ+4+xJnXPk9/uMLFC1eIombF9pgqEKSYcDadtROPy42NVfJshC1CVr0DyikVUj3onCht8oLheIuvPHcGLxVnXnqVpX0zJGkTLSArxqStJviIK+ctP/4jP8XLX3mTBx94gO5CyjueeJi1m9soKVAq3sFYvZ9wUZ1z9Ho9xqNReA9K0Wq2EFISVVLkepha48tRFEEp8BZu3FgmkY7E5diyQGGRLvBd0THCGpQ3xEqCs7fAEMEeyqGFRWGQvkQLixbBF0KIKcjF2yrlu2rzq+//usfbYtFMkojdnZTTJ44z3NrEDMeTE6xqOVlV4dRfglBBFsagIh0iRavEOmNM5dZNME3QEaI6KUHju9Pq3e6LWb9eluYWbidwywN7e6WplCKO41temybSh+oo4uzLb7C5MaIcCB5/5HHiPOIffPgHeedd70IPYy6fuYzMm3zg0W/iB779xzi66yHuvOMYexb2cvXSRTAlaawxzt4SShUeekLJDmgVdLvtVkyzEZPlI9bW1zE45rptOp2ULNtm3+557jhygMFwyI0b13n+hWc5cvQQWmvWe+vsP3QQJxxxHD5PiMCtHLGhSuIUVYRrqMxDdKysdvc6GkSyvt6jPdthVGTIVJMVJXlZcWCrRUlHEahwHSE8eNY7yoooXT90IURth9fnfdjgms0mp++6m1E+nlyDNE0RQjAcDicmvTX2lyQJURRNNkMhxKQ6mVRKFfn59pxt6yyjfp9f//X/l8IJtLSUcouNbJmzl14gXYBedp1zN69z1xOHOXf1Ck+ffZJTD+1j79FZukspIh4ws+CDqW/FZfS24vtO4ajTwgXvPTPtDs0kDXaE1QJPjQEjblnUAEwZFq5773mYcxcuM989wPHDx3nys8+yuG8Xmc1RkWDQ2+bQ7nn+5CN/yhMPv5O7Th+gP8xYXV3n7JnLIExYnG5RG+0snp2ZGeI4ZjgcMhoO6W31sBXToz6fss6+KorwNQ40wdwLyirDSlEiXY4iR7kCbU2oK8uMyBtwDiluhQDE1PNbwzjGlFNClLfmY369w6C3xaJpSsPj9z7A8y+8wIWVaxQVYCwqo63pqmP6GBcZ1juyssDiJzuYlBId6QmuApDGCcJ9tfP6tF3UdJqklDsL3jQG6m9/oAiEaXATp+yiyDCmmFScdX5ObjY5e+Yi5dY8WW+Wpt5Fb2sbh+HYidMs7D4IsaQYGXbv6uCMYbA15s5Th9kabPLwQ4+yvLxOrzeACqerq6PBoKSs8lCUCNzMSGmaiaTbSWk2UyyeN6/dQIqSpfkZ7jpxjCOH96OV5fjJ42xsb6CbCQ7P8uoGrUaXrd6QPPNYs/MAFmWg9ITFJq5eK3DGTsj/WZYhxE7FqVREnCb8/P/287z08ktkeZhiJ0kyWSB1HGMrnmlouaPQZVTsBlG1dnV1nSTJzqADj1RQFIajx48Rx/HkmoUY27DYFnkZDHFVhLPhwQrRDuWEunWLV6ufUpAIsHWlCaRxB/D86Z9/nm53PzOtWW72NvjAtz7B+77lQTLbp7PQRQt488JrfNOHHuG973mQ3//IR5FJxt6Du4jkLvr9Aaury5QmcB/t1KBz+v4L2HrO7GwHJeHkyRM8+shD7N69RGkNQslJlTythNJak7ZKwNJsLtJsNtm7dy+nDh/j2z70Ptb7a8g4YWV1DWdhvbfN57/0JG9cu8jioQWuLD/Di2c/y2uvvYI3FuHTSZs9zUZxLmxuAK12m0ajQdpoTBZI2OkGvfd0u93KTKSBiiPSZoPRcMhou4ftF2A8pXMV0d2jpCDRmt7G+lcNwPCBnuWduO1LTjx2J4fwAWpxU39+HZXm2wLTlAjWNgYYEVGMc6BEiBDilBcliYyC5lgEx+vChZt3oRtz5NhR5toNcDDTaXNj5TIvvnoF5hcYCwMiOMqMS4N20BASAzjlMLZASIEVATcqM4v1buL4ooTHCldN/cC5gpBrzsScoF4k6io0vB4cZnaq1zBNlzTpzLdZ2LPEaDyE1ph0yfNbv/97zKctTj54gtdeeYPLV1c4sPcQF6+8yQ/9k7+N84LLF/osLMKNK2s0m4r1bYNGUFqL9aAimI1SjCnJShviuYTDCAVC0UyDAsPh2dwu2J102Nrqc/7SWe66+x6iSKG94czLr/GOx99LI4nYXF+jPxwEjpwAKzRGQOw9BUFRFUDjekhSVUbWh4WnCE7eXscUxpFGKWvLm7zrXe/ClYokTQP+LENLrl2QVMbKo7WqfFUD5qi0pllFbIzGY3AeYT266kiHQiOcA1lRkYSn9DVLwtNoNBgMRlXMcYl3AqVhPC5JkuQWgw0XbK4BJhCN1jro/JUk98FZviwMQhiuXsoYFyN6gy3e+d6TRI02X3jyy3zXd38I05ek6TkOHr4fX6YYbtBu7eXQkaPce9fDDLa38fEAa2aqoaICCiglNdfbVIICWzpKqTDbI7TyDAdbKGNpaMl8s8U4DxHVVjCZVE+I/MQkUvKpP/k03/HhE3j1Bs3ZBme+8gUef/BxXnn1da5f3WB2tkWvN+TRx+5mZWVMp92kPxzim2tsbTiSdoItJKM8r4ahelLhTcsgZV1VCoETIuDdFRRWMxKiKGJ1dZVOp4G2Bqc8V/pbLHY0ha+0+sUYF3mUSBFEaFewde083fYC0g4JQ1gQMsAt1u10HzXAG1UVuLe1Pr56H1JMOib/FhXoX3W8LRZN7+H66jJpGjM7u8DFN14HG1qn7lyL/fv2kKYplgIlNaPC8fqFS+ze00XbHFlYcq+Q44LvvHcv3/7gUb587jpfOnuJ9VwQN0PsrXEORGjN8tLgkKgoCgKs0gEBiwm7J0itcW4neTK81yl9cYUlTrfg03zQ2w8dG7YHAw7tO8auxhKv3zjL2uAa3/LdH+Dpv3wB3YG9R/awtrmBnrW88/QdlGpAPhywd0+HM2c/x4svvk5WhCGGzQuoxADCh+lgpEFEEqUtcZwQyZC109CaRjMEaG31tplptVFxgktjvvjC88xozeNPvIuZ7gIXLlykmTbY3t5GCY21OYpqslxVPF4KhAPvbHDbqaoMISWKwLsVNiwyxlq0jiZ812/4hvfx5Gc/x2yaokXI5pYiTNEjVQ05rAuZ7GWoJOIoClN0pcBXeuWyvIU0XcMh586dI89z0rRJWeakacJwOCRJUm6n88RxsGirN0BgwoKY8F8rHPzWe9aDVCHMziXMNBdY2+7xgW96Dz/3s7+KGklOH32IqxevcfquUzRVl9/6rY/yxDuPcfehk2SbBc04YteuffSG1ygLi3BBAht07hZdb9A+5J6XpaU3HvENjz3MnoUuM/NzzMx02b24h1/+D7/K0IwRKiwcwhriSFGWDmdLnNaUdox1Hf7kY5/lQ9+lePP8iIfvf5hO8wDNWPJ3v/ckzz37ElcvX+fmtWuIWJDEPZLmDNsDx+aGZe98ip3SatfnXUxYIzvPwMTIo2K6TJ/LiW+mUkgVoXyoEtd6A/q75pjNhvTHgm4uSbRERhprJZG3yDLDm1HlwhRa7nrxmxaRT/Oy/0s5mW91vC3acyE9qIIo8YyyTQ4cXuKu++7g2MmDHDy8hIwMWbFNng0psjHSFRw7dICV3ha5cfRHJb1xTrcRMyM1ixK+9dQBfu7vfJB/9R3v5Hhq2SUymtqgkphR5a+oJbgiI7IFkRlXgHBFGq+GS/WDOD39g50o31r1UB/TE7npSTqAs4pXzl7l5bMvcP7CKzg34CvPneHQsb3k1tDrj5lfPIwA3vmexzlx6i6e/uKLFOWQZ5/9PCIuOHPmAkqoSQvmnEMCSgRA21obTFYtYfJaPew6TVFxRO4NQ5ezvLZGmqa00yaNNGF1a8jH/uwvOPvqBeYXl4ikoFkZTSilUFH4N4u8CFpuPIUxWMDiKSvuZWFKrHdIRKAMyWCIGymNEhKhFVlZ8Ozzz01aTllRewB0nCJ1jHGechzaqigK7kVSCEaj0QSXnDApps6ztZYsG0+qiTAhL75q6Dd9nWrq2bSMr37Ap4eEE7ines340HLOtPdgxh1mmnewOLuXo3tO8N0f+ntcObuMKuAd9zwBmeRbv/FDRGWX/saQl5+9iJCeRtJCCRiNckBTlJaiKAN1LGS7gAdjLNY69uzdRawVsZQ044Q0brC9NeQ7P/w9JHETGcdIFREnraBl12mgoFlJI4pJNLzx8kXSyLJ7scn5186xtdnj2MGj9DeHnDi2n0cevpfjR46gyEHkGDsmSVLKHG6u3mQ8Dgmturqm3obrHVfKKwi4pbNBWlnjn7WRsjGGdrs94csmcfDi1IlGpTH9vGBQGrK8oMhyrC3wNkR8SBQRhlQKpJ+2fwvGJ966yRfOh+/fYqGs2Q9h/RG3Zdv+54+3RaUppWB+vkOn3UBHimw4YJwFhUGaNqsdWKC0DBZbIiglEqHojweIZoInYk8npp0K4liEKV8Kc7Ntfu6O95P5CBvP8uWzN/j4p77M+UEP1Wgw8ILCg/FgzAgpA1ZS2+rXrfZEqVDRFmqT12ncdPqoH8jph80XjgtvXGVuzxIzaoEr65c4dnwfv/bLv4M0iiuXrlMuWlrdNsvXVjh4+DDkDRYWFtm/a47LN57l+TPnaJsu6HCDBumvI1YhE1sgQIUKSqqIWAm0UmxvbrJuLahwE69tbZO9+hp7Dx5gXIw4dOwo166uoKMG6xsbZMUYUWXLl6VFJtWNj2eUZUjjKE0JppqiK4XNC5ySVZBWFQ7nDBJDsxWTJAn9bMwv/MIv8NjDD9Pv92k0m2TDsBA2Z9qMxiNwdqK+qQdsSiuyrNKi3zZwC/eQnLAb1tbWKMv6mrlKSx6iUurrO91KTmOA0wtnuN471dPk/1dhnForpLWYAv7gD/6U7/++v48Y3KS7EKI+vNNg4cDeA/zuf/pdrl69itSKH/6n38PTrz3HcNTjyvAiumMCfco7rACDwZucyMeTxXtYBjFFubaBP3qYUgSxRRy1mV1o4/OMI0cP8cprL6OUIPcq4LAuRGIYN0SYBkI4zr4wQPsGVo4xsuRP/uyTLM7O8yM/+oN84uOf4M47TrC0dy+X1l5is3cToUdIldDtdnFFjikFvXKLOI6JkwQdRxVn1k28AJz3xEkyGW7VfOjaOLuu5KMo4tq1G+xZ6JLbEisk13t9WhrSWDLf18RxRJI2iF1QuUXOkG2toloz+AkLMzwM3v0VwoBqkaxzgqajYrzz//9bNAVUuKBiMBigvUDEvmoJCyQK58C5wN+zPiTwqbhJKQw2TXCZJkoaYbGUMSqCSDq0AlRMR0k8W3zDvQs8durbiaNZNvKcL716njcuX2OrN+KV9XVW1zfDQqd1aBunwHjglgd2erp+O+drWp1Rv9ZpLbHNJc69+STD3ga7j7TYt2cPF798icRFDHpj2kdSChxl6Rj2BxiTszU6y7bp0pjNQYDWTQxTGJxzaKkobDlp1ZVSzLQaxBJGW33iJGKh2yVqpGT9EXE7QeiY2e4CV1ZuEEWadqPF1tYmB/bvZXmcIUQUcsR9wJcng6CiCF65PpTmktBSR2lCWRbgwYkwwEFqtKq4ttX5GOdj/u4P/AC/8ku/hKyqwWbaYH1rO1QcUuIrSzupJUoqirJkNBrRarVuaZXrB7SGVLTWtNtt0rQmU9fmt4I4DvEndXcQOgcx+T3TUEt9TLd00xugAIRVgMW6jOXVmyTNBlIXdPfGtKMmv/trf8h7n3ic3/u//y1KtejnY97zxBP86ef+kJJNSjPk0NGHef3qi1jnsJVTj/VhMq91sJiDEKKnlCSWChkJnAyDqcAYscSR4oPvfx8XXj9DHGlknFIUBYPBgDgOJHhBK2waDpyXFIXh7BtbvP99j/DZT3yZ5bUrvPjS6zz7/Ks88sg9PPHuB/Eqp9NuI2S8c//LlNx5xnnOOM9J0zAYalWYs9aaoizJswxbWeRN079qCXJ93o0J95BWMXjB9qggN4bBMGPclBRFIwy4fDDxKIsBo+0Nuq0mkBAWTQf+q804hBCTwdot15Vbh236a2QPTR9vi/bcI5Ay4B+RCEMBjETqOEwsFZCAkkExpJ1AORcwzijCZYA2zMaSSICiJBYiZASJOHQ4XiB9gnSClIKyWKZttnj8yC6+69E7+Y53n0LmWYjvdhZhC0I66q3RFs4K8Io6lnVarlb/CWG45a1DeInAIaXj6B3zfOAb3gPNAY3dMBwPSFVMqxMhopz77jtJbg2+MHz+818gbrTJii3yYpOhW2GQZVCIibOME6E9Vw5sFcolZMAOF7pNsmxEv7+NUJIoSoh0TEMnpM2UtBFwvueffZ77776fNy9dY35XlziRLK/coDAlTgpKDEoKtNA0UlllMTnycUYxHFMURVWlBU6oEjJsaFqjdRIiLXA4L2h1GiRpkNe9+uqrXLn6JnEsGRcjbqyuIKsquLQukN4l6CiaUI4mmd74iSZbSgnO4VBIqcBbFmbneM/xQ/zgE/fy/Y/czanZLirPaGhX4ZYSoWLQUdBPC4fAgBcUU9PdW9tzAziMKQgYjkd4g1GeInN87tOfwbtt9u19gDhp8eyLT9JuO9649ArN+VlG2Rbf+z3fysxCzOtfXmfY8+Ss8fKVP+TypfOQRQjv0DpGedBeY0uLEgpTGCRVR9GISeIWM+kssdZkboiUJVp5OrHj3//Mj/FTf+db+c77jnOkCTaH3Fuka+HNEImk0Qx+JHPzBzh6WPDHn/gyM3v28Myrn+LwXYeQkSJttJnbpYi0RMqchYU5Oo2IWDWQUfDIrHPNayepjV6PjY0N1tbWyIZhPlA7gtWMB1lxNuNp3qaO2MrK4BdQOEofkRtJ6QrWxo7R2JBnRWi3TYkTMXK4ThNwwoFx4CzW29t8MsUt3NXpKhN2Fsxwff/64sq3RaXpPGRFyYzJ8FiQKVrmGFdVMdIDAWP0wQUWpAzJgyaoT5pJQqII9TuiwiUljtD+i6CQrBIf5SSUiWxEYixm2Ge9NyZpxTtSO7FTfUwAb3Hr4EEIbuFs/lWtujWWeFeT1e0trIuwZgAq5cbyFYbbfRIluXDhAnfffx9FPubuB+/gytp5Tt67i0azySjTaHYTi1msMUi9YxRiramA8JrB6pjvdrAuR5sQDKe0xOHY7G8xGI0oCo9WEUVe8NQXn+HRBx/lwuXzxFFMRnDcycsBRWmrKa5DqsqFRtZ4VcAr6+gITTjf+NAGSakmmTdZPqRV24MZxy/90v/Dw48+wurqCmz3SOPWJJJCKRUusVITjmJRFDvGwRWWaSo8F4ISpMQhhGRutk1vFebigoVuxHtOvoPuoUMs7t1Hb5DxWx/9OBcvX+PmRsmGsfiKqqQE8HVUHM4FBVSqW+RjyaXrL1CaFe6/bx9vnlumP+ijSDh+4hgvbva5cvUGeeXn2J1T6CjHscEb5zeQUgdnKl1DPoFjGAZVZnLfNXTKxto6B+fnAuwRS8rc4ISkqwV+fY187Rr7GnByscPZ61uUeFQSFmGR5+zdu5fzNzbQfoXH3nUfmXmTxV0L7Ns3y66727z2yiU2+2PuvvdRRvnrrG+t0NuYwZmEYjwOC0wUDJXbzebEBEVHUbg3p4ZzNfsAoNlsVoXHTmRMFEWMx2OG/UFIbJVByLA+HjOjNb3BkI1EksSCTjtFpTNoBNI4Vi6do3X4LjwyLKhVVMjkuSOo8pIprPW/xvG2qDQBChumsMHC3sNEDlUZXgACGUDb6l1LIYLJB0GznagqhEsrRDUjqClCt2ciOy8oCRk7DkEuJJYQZxBaPYFkp92+3Tnl9mO6Gq3/Pn3zKK2Zneuyvj6g215EKgvCcfr0KbSOKXNDf2sD4QRznQW++UPfwp7FXTz6jpOM8wByL18d4Z1EVI4/t1e50+/RGosrSqQMElBjDOPxmI3eJtZaut0OC3MzHDqwn3ba5MWXvkKzkWCMwxU17hfdMiCpc4nc1IMQ64g4ipFK7cgfqf1K7aQNikRCUsVhCC/JhjnvfOIdKBmUL5FWk0qk/hxxFOGAwpQIJZG66kKmW+Zw0qGaig9HJXGiEa7EFwWNSJGIkqawjDdu0vUj/tF3fxM//y/+W37+J36YRDqEs+g4RFUo8dXSuulrOuHxCgEqqKCkcLQbXWScUbDM4WOz3PfQXYwzgymg252lLEtmO116GwNMYXnwwaMYN8D7Bq+8di5cM7vD1AjX1+6o3IQIrv5FgbdukqW0sbmJchIlNE99+ctBwJokOOGJI0WqFcICQiEIm05ZCrqdJZrdWR56/CSz6RyDjS0++QdfYHNtg7vv3sel86/zwAPHWdzVoTs7y/K1AVpDZyZGKhuynaqBSyNJsaUJG2kFwUx7ezq3I41VSiErE21Zmd/U/GpdxZ5IpdjOCwrjKYxlkI/RUlQzv3BubDmmv7mOdCEJFQTiNvWBkCFXyTt/yxdUVaavVIfef12V5tti0fSAFBFlGT6UpUQITekM1odFLkxpXch+qdq/WAdD2UAidGAN1gciuQ8S07ALEapK56ZMWl2IRBgNM3IjyEWK08GoQymBFypwAW+bmk8T49/KnHb6v00vAM5avuWDH0QWoKwkjRzOjHn9tdd4zxPvpZG22L/vIHlhedcT7+Nzf/405159k9Onj1Iaj5Cep7/wHEqGBX0aCrj1vYSp4XiQIUnoDbZAeKJY4WzJrtkOS3NzzLWbdGdbLC7Os7h7F41WgzRNSZOELMuY7c5XC3FdRbsKlgh2YNYKmmmbOI7RWtGMEqRW4WwLQVFLN72nNCWukq61m63JQnzzxjLSgcKHFnsKM5yuDsqyRE5RuRzB0UlphVcCIypSsw+45dZmD+EsTSQ+L6oW3hKpUI1bPK4coooBu2abeGfIsnzy79UP/u345uR+9cEc2GLRMkFSEMctytLi0RiT8+Vnv8juQwsYO2Y4GBBFEQcOHODchXPMNGe5774HAA3CsbraQ4kQuyE8wcVH+OoeqhZtH+6lJInwhHjiVnMWa0BHPgzPEs3Gdg9pS7RXKARtFSPqabJUGGEpjGHP/AKdJGX/3ohilHNk8RCnDp2CYZuHTj/IYw89iBJj5mcX2b/nOMU4ZXuwiVOGzlyXpJGCFORlMSHWF1k+gWvs1IbeaDZJ03SCaUolJ8YsUgTan1IqpHLKQEEbW8iMp3QVB9h5XFFii5qH6cKmEGuErnWCf9X6svO//xrH26I9xzuyYsw4aRBpSYIjFwqkrkwgoOZeOARSOpwv8MrjrKMjU5p6Gy2D4YeUOpCqhUI7DUKHhdcFTz2bSZwrEUSUFAxHhnNXx+jKjUjrKtNHx6GIqW6A0IbfjoPIqRakroAcWImXQfNqnSROEn7qx3+aY0dPcvXSCo2l4DSytLREixkee+I9CDNiz5GjJLJJb2OLK6trzM93idILCDHP0198HSu28G4WX9rKmb4il0+lEYpIsro1YM51OHb4EOdefwOpIprNFqKZoHVKnhn6/YxGoyRupBw4uB8zHHLi2EHWeqskaZut62skSQ1RxJRlHuJzjcMryVpvgyhKsGWQw504fgxnLKNRn5mZOZI4IrdBz48yZHnIXk8UZNbxm7/zm3SarYrv6lCNJt5XZHfrcFLQ2w5TWipDZZxHesLgJFI4U0U7EHi4rbjB2dfPM5NvU+zfhZVQShBlibahcm0awXBcsN1bI9JtyqKP9SXWCzQJ3oWuJmwatdy2qoJqAYOUmFLhyYMPQDHkjz/2Kd79/v1YtcK1jYu89/Hv4Fd/+Q+wwoAv+b3f+W3e946HSffMcPKBWSK1TVYO2FoXCG+QhM1ca40TVVpiaYK+X4Xz4QtBFEs++eTnaT/7ImlD8cKrr5GiuXb9Go/MPorLczCWNJIstRXX+gmxMwgJkU4pswHt5n6yso8ZK06c3su4J2i1Ul578RILi22c0Xzf+3+RU/fNI1LNO9/5rQwHn8AUBqH6LM4fYDAYEDVD9ZqmKaNsNNn0aq2/1jrQ1ExBGichh12qwEWtHNeTJGFrMER6aDYa2KIgl4rVTLJnViCkwdkMS4ktR/hcIBspMRm9GxfRiyewMhh31LglVJvNW7Ts4ZtbO8Ov53hbVJpJEhPXssfqM5RlSTSVWAf1zhtwRCEk3ltKU4CCdrNRGWZMt9NVxYmfVJy5KbE4yup7LRVbgwFfeemFr2rDgtGG/Zontl6sbnF1qabNUiuECIYjjjb9sWVlZQMsKCRHDu9msDVkadce5nYtMhw4iqxg//79HD16iLm5bmXuWrKx0UMJHSyxpqqg2jF+enBonWUwGPDm9RucPH0PBw4e7LsH5gAAIABJREFUYDgasL3Vo9/bQGDQCpzJ0TjK3ib7l/aAgdnGLKPRaKL0qNtFiwgOQHisd1g81uaULqfRTri2cpXM5SFnZzzGOk8Sxcjq3CsdE2lN2mqH2InScfLkyapa1WE6wQ7UUZblJG5CTlX4kzZ56sav28GyLFm5uYYxLgzH2HH+sba2fwv8z/E4pyhysqycdAU1l/D2o+bE1pSZsizxaoQXlkHh2OgN+MSfPYk1EpMb7j/9IB/5/U8grOTG9TUWF3Zz3/33MBoOuXj+DRppqBh1FGEyP2ldJ/fPbdxfYyyRVCSNmC89fabS6ENpLKbIwj2uJVvFiEJYBJJGEjPb7oThnGsQKtuSPHP8m5/5LD/8XZ/kx//Rb7Dn4F6urp3n/OUreF3RqVLDf/+z/5JIH0Kxm7NnztNuzYGPMaVn+eYypS1JGymNZgMktNoBl26kDTrtDlJIirwAGcjtrlJSGWeJowil1aSDkFKSpukkygPnGORjrAVfDTp3WAx20jXmo+HXpBl91bX8GxLc3xaLJni63XZlRmvwSlYlee3OHd5muHFrcb5AaEmiJMZnzLcbKFW1xwi8kIS0jbCoTFp8B6UtMNaHYK+tDN3qkLlygrt4bxFymjj7nz/J0yTpyWuVaqKwBovH+JzdJ2bYdTRldeNmmGYnTU6cPMaV6xd5+cwFVjbXubZ8mZX1NQw5he1TlkOGWU5eGLa3RtXU3k9cnerwLKZggZ334RgMx7xx4Ty9Xo/TJ+/k4L69KC1Z39xks7dBVubM75rnrtN3c2n5Bi+9cY7eKK8A9BoPrhZNV021fQDeEx2xtLSXOGqhSHBGs70dptxZnoMPiyvVgq61xhMimr0TRFHCE0+8C2uDmXRNksbu6KZrLwEIBPhJvO8UNCJEhT9XZOfxeEyrM4uQQZQonEUgKGvFT2mwZaDjrG9soBTBOJi/Wj0yjU/XeJwQEY64YggI9iwuoWJPpC13HjlMMZa894l3849/5J9w/z2PcfTgCQ7sO8q4t42SOVlpkDoK+e6TFM3bcc3Ktaoaiq1vb5I2GjhvgIKytKGD0oJMuOB4jkYpSSQUc51OOGciwCvhrsgozDatWXjv+5/gzSs32LN3P4XP6A97XFle48bGgI//2Uf4wAcfonTr3PPAHoTuEyeSKJaBC5qPubF8jZury/T7W4yGQcNfmnIiL/aEijKquJxChk6uxmrrcyuECBtItfkpKclKw6gwlBbywuFq8w0T8soFMB5sV3j2V2OS01zM2x7Y/+zz/LWOt0V77p3DmYIojkEqhqOSpJlOuJvWVhPz+uc9lLZERZpyZDEuZy6ZByUQSlQGtRHWgZYBzzROUFooraf0BmN1ZTfmuD7cYt+hAyyfeR3diCaEdsQU8D/Rld/mrPIWVWh4LeCBEoVUjtm5Gb7x297L7Hybp576GI99wx6kHxG1UxYOKp771Av84Hd+B7sWGvz8//jb7Nk7i5HrbA8fmPzO3jYstgVeuIDhVdCBlwL8zrSyoWO0FqSNiKB7V3jrWV3dQGnJ4oEjHJvpcP3GTV555RW+8tJrzHSXcNJzc3UNJTXjcYFSINXOcEkJDdU03DuDjBWD9S2WZuZZ31hH6giFxLgSV00uI4KtWpwkqDihHIcY4Wa6jXOOP/yjP0JGmvFgTKxifGUEMr04SsQEFXHOVeYt4pZrs4M5Bwhl9969SGFQKlgC4i060pi8QFowRc6gP2SchyrTWwtK3yKfrCGX+t+drnKdtWgZI5RnptPgxuqIM19cxZY5UhlOHDnEt3/n99ONUmKpaKRtdKRoJ5bFuXmEy2lFLYwzYHa8Cm5XJwXFV6CvmaIkiTSlK/nABz7IXLfF8o0NTt51kmazw8/+T/+adtrB9EuSSBApQaQ1wlu8GOBJwSd4m+OKFkvdA9hxymuvv8qJ4yeII8V3fe83McoNLzx3nl3dO/nf/9df4q4H7+Df/dt/TyQ6SNUICqMqrrjWt+d5TlkG8r/WwXVMa42WCiEFSiosgXER4ojDn7Vqra7i6whm7xxOKzIrKEpPVpRBg1+WmChCGYfxjlhHIWG0ZlK42we+nqmO/W9cZcLbpNIUCLRQDAYj+sOMKGl9VTrd9BGqTYEj7FpSa+biBkpGKCUrzmeVzVP1Z8ZYSuPJbUlmDCYrKZ1lWFg+8/RXWL52vVqcQ1RGFKlbbPqnlSPTOmcp5S2cs8kD7D24yrDXW0aDHr/3ax/l2afO8PRnLyLzJtYUaO04esdRxvkaNt7i6Wefxds+Dz90ipkZwcJMm32zCyw2u8zEICy3VJo14O6YjjsIumPwWAG97T5LBw/R2bWXp196jb/49Bf50794kjNnz+N0kwPHToIv2dOd486jd5A0W1T0x1sWCuvB2Mq6Swm6nQ4/8g9/iMGoR7vbQSuPUKGyr6fo3gcHmdEwYzQag1I02m3StIEzjvW1DX7sR38MFQVJnhShis3zPDyAwZYeCG5Y4frsuIBPWnMXABhnAxl/z579SAVppGkm0cTBSgqPNSVFluGlQifBOMT7gJXebv9WH2+tWw4Le3/YZ2HXHDPtFHxKJJvs3Rfz8uUv8tSZv+AzT32GmQOWP/rL38dqw5eeukSzETPbmOPkoZOUJn7Lf2v6SwkxkZo++ug9rK1dZ9wf0ko8tijJhzmd9gyffekMxczs5BrsmusitMLVC7MsaHdSpLa8eekq+/cucuTwIusbVzBmwG/+yu/xkd/9OI8+dDen7pzl0FKbrd6QVnM3adpAaYOOHKlKaCctWnGTmUaHbmuWZrNJkiSTTcVVVaOzDmtsGPJaO0mPnP689TNTd01KSNAp2+OCrPCUBvLROKSeGlsBbgLtDM6WYdD7Vwx6XDW4+6+xYMLbZdEUwQWo3W6TJBFKg9YxO7zD+isMg7RWKCVIvcQREZuYpKWIpEHGgbSsVVy12gbhHUo7rDPYQkChKYCyzOn7iBN338vS4cPEsSaJUpKkhdYxQilqTWttNPxWFKSdqXkVbyEiPDrQZCQIIlypObjnIHm/ZLThSNsRrWaXA/v28Yv/7vd54N0P8OSff4Ujh49AktHvjzl+cJ6ZRoPZ9mF27zqEKCuSfhSMhIUJC4UBnBAQWRAm0He8ZVwUrNzc5MFHn+BLz7zAF5/5Cq12h90H9jM/t4jNDYy2eP+jd/G+x57gxvIKb1w4z2ZvG1zgqQovgjuME5R5hlGakfd4Lzh14k5ef/U1tI7Z3t4EUZJq0KUmRuGlpBSO0kGZlZTDEUpA2myh4winSoSHP//jT5L1x6HCEmKSxSQrrq3WOrTuYkfHPH3vOOcqpkQg/kdasNCdAW8QOgbVCC5WZdDlZ+M+vWFGd36WWCiUKZFeY0VQlEyT2utNo8at6yFHjXFaPMPccu3mG/zIj38v892DzMYNEqW4+4Hd3PvBu7iyfJmFQym9vMeZ1y7xzd9+imfPbDPMYbNnGBfDWyrcCb5aKX6EB1MxOawMJjGRiPFlGaSrRclgmGGLkk2r+OTnnmZx9z4sniQqSHBhmOIczipKI7l5Y53TR45hB+tcemGFY/vu5O//N9/Ch777Ed79t07y1NN/ybNf+AIffPdDPPfMy6wvr+CMp9Pu4m1M7gy2NCFiBU8h/IR7aW2JMQV5PmY0GlCUOUVZmXGbHGtynC1wtsBTTja+yUzAewoc0pf0xp5+5sjLIa5w2NJXvuR+Yr4jzADBlCP7LZPyyolLhH6+duG3HvzkdRt403/N423RngNEcTQ5abVGdfoIFdSOjC+EfIWqJtKCdjMJLYPUCKERtbO30jgHxpUURUmeF+A1TnicBesda5sbXLi8HPLKq5FRvQ/u3AhvDTbDrVXIdGUWXttp8We6kkbL0JnRLO06zPbmFjevhcjbb/zGJzjz8ivsnj/CD/7QD7B6Zch73/cP+J9/5j8iGiPuPX0vrc4ipnAIG7JqwtR5UoiFOYoWAQ+unNrvue8ePvvZTzPb6dJspighWL52GWsdDz/8GHv37ec//ObvMTc7Q2e2gxMSLzQ3b96sOHFMWsbwWW0Y1mM5fscRfvO3fhekJo5jnCtDuyYJTu7eV3mpISysKB0iy0iTBvPzC/QHG0ipuHz1EsePH2djfYPRaERaVX71eQt563Zyfqc3q+lrUP9srCNarTZWa4TYsSgTcscqbTQc0mx2QASaUmbLSVjYW0nqpq//Ds6qAEeaxGg1z//xv/wGH/tbP832QHJjdQ2QnDpykl/f/ihPP/Mi9z9wmmwL3vfub+Jf/8z/yaHFA8hGRiudwxc7dLWarREqaEKkcOVnum9pCS0CLjsajGg0EmIlsXmJLcb0t4ccWtrDS+cvcHCuw2Cjh88Nos4WFwodeYzJeOPaKt/8LXfy3/3zH+WPPvEX3Exj8kELUxguvnyTJ77nIba311ia0+zZcwghIpZXbjI/t0iv1wszCO/xkQqUo2pAVjeI9edwdUSJuJUaFNp0FZgs1WdPk5TheISMJcZ7hibH/H/MvXewned93/l5yltOvedWABcdIEgQFEVQpChTsiWKki3JKo7jXjZex4lnx/FkvJG9sZ0db5LdnU029qztxPHGJXa8rrIl2ZYlq5qiKkWKIkSKYgGIXi9uPfUtT9k/nvecewBSETM7O8Nn5s69eAHc097n9/zKt1Any0rKMnh1xcYyGgxptMNhOOz3iWaCLfV0Z1NW+F05bu2EYzVUny7MLWxRIoWn/hJ6Bt9ovSIyTUTo3dhqo495qeO1XfqGryjSiIoxUuBRFNR0hVdUNWSUIKIYrzRCxZTe44VCxrLCcVYMISW4srWFSGusbw7xYmzZ4ELjXLiJIIaqysaXWuPmPWwHzenBwXhtXNHU/V52NG/lh97+b/nVf/FFPvPBK7zzjW9h9dwW109lnHtqk6tn+pSjksF1zRtf8w8YXT2C6x2h3x+CiPDYib+1UlN40KofVOYlo2FOp9XmwtkzHL3tVoaDHnuWl7HecMdtt/A93/0eHn748/T7Xe697x6ajZg77zyK0oZmXaGjoDeJrOA9k81s8bZgYXGWtNGAKCgS9YYj0noDEBRjg+nKX8cYg7El3hvKLKcsSlqz7dDaiBTZaMAP/8j3M6q45YjA6BLV1Hyc2Y2vy6khyWS5sVCtxliHUDFCyMCmkduiG2FHO7SshKZF4EJ/M4TENC52MiCsfAQSqWnGLdpqln/3S4/xA+/+Az72kVU2ejm/87u/w1vfcR+7d+7m8MGDCL3JL/7Mr7OjvkQx6HHk4BL9je4ku522YUmjuEItWNJahJKeXUsL4AwaSZaFPmJrZo7Ll8/x5je8gUHhuD6yPHrqPGVhkOWIhgdrPXGU4Lzl0OF9fNub7uPxL32GO259NatnT/Ghj/wlv/RzP8+//Of/jH/98z/Lwx//a1772tcwyuGHv/Pd/P5//A1++zd+jdsO7mc06DHTbtNst9BxjCIc0kpr0lptMlAdIwLGX8bZ6jB1k/LcOHuD/UwURzhrQ6vMe3KpuLQ1YGgEhQFbFPisQBgXGE5CkG1tgTdT8LvwFQr2YEA4QUWIkMgoYUi05/rl83zgD/+Yzz/0d980TE3i0cv5R0KIs0KIp4QQJ4QQX66uzQkhPiGEOFl9n62uCyHErwshTgkhnhRCvOabPkB17yuttgOkCE3s6RtWSYWS4ymjZ5CNyDJDK42JcdRrLZSsPHy8CQ1AAB1RVDatIUMNDpHGlMSNJr0sJ0lC8Bkr2WgviAjiEZFSREqhpgYPN2eX03228XMeN/bHf3/xzEUeefhRFtstfuA9/x0PvuEtnHvmHDNxg83LQ+645VZkWTKjZ2npJu//o/fxib/9KE9+4fP88W/9EbEK5bdj2wxs/PjhuYRNbIxhcXGRdrsDSIQIr/urTzxBf6vPVj/j/R/4G+46doz1axd44Zkv89pX34EsM+qRJBtucWDvEocO7iaOBEp6vDWhNeAFWipedewY585dZFhkGDwy0jgXPhOkx1XtDOsMOlII6Yi0RlYDIu+h1ZxBVuX3Bz/4wW0VnLHs2/SNqlTQZKwC1vZrvvE+Cr9bsNnt4b0jjjVgUVqElstk2GIoKkuM8e8bT7BvnlyPr91MaggN5jCozLI++BFZb5V/9N0/yGLR4i/+7y+wMz3Et732Af76Tz5GW3c4tOcg73z7cZY7cxzZcx+mt4tEbvfOpx8zqBaEbC3LCnYuLaCwRBJymyEiTeEsvcEQJxU7d+6kzHKyvES3O0RCoyXMxCEjdt5O2gsf/vCnKPwad937KtoLio0rX6PRzLFik9IO8dJw/O67ee/P/yI//VP/BJxDS8/P/tx7MUWJKUq0VhNldi3kRLV9zBAbs8Rk1ZcOlFhwPny31ldwou2hX5ZljEajqiUDyIiR82RG0c9KShuqHQBvXFBRykfBCmP6fqiM8awtA557rGTjLLVY8ujnPsef/8kfc/rUCxw4eAAd/f+Tab7Ze3/ce39v9eefBz7lvT8CfIptf/N3AEeqr58EfvO/4TEmq5zYJGzDeKyz4XoZDLy0ilnd7BMpGZwrpyZnwvsJowehcJXCc0WxCKeQkJw6d56TZ8+SJnUELggUOIvGI6y5oUy82RfopaAhL7WiKEJHEV44EBEOzd994ks8/NBD7Nq9kyiOac/MoZSizB1aSQQxSzuWuHjpDAVdtoYDVBRYMlLYF21mgCiJK7sDhRYabxwzzRmeePxrzM8t4pznyC1HMMazZ3kXg6zL7Ow8r77zHpwzzHdavOn138Ib7rmbW4/spxh1mZ9psTg/U/X6fKVIA8vLe3jmuWfD52FNML4rDVonoeyqzO3C9ZLSGYwNlhhj6E+z2SKOUpSKuHT58qTCmH5NthoojAc501jKGyBB1Y9ChBK8yEJgHpuxQTWJ1opGK1gxdDrtgAWt2g83B65JeTnV43ypz9hUIiKliyjKlK889TTHXn03//gffj9pVOfxh5/iR979I3R0k7mow5H9d3Fo1xLd3hk+99lPIfyNXbLtx6+cU12waZYi+MuLSKAizSgrKT00kpgdO5YYbG7ywLe8FootvNSUViO0Zsf87A2//9y5CxQ5zC4tcOric8wd7vPwid/ks4/9Ry5sfZR0pofWmka9AyIiMzbAUJTkvvvuwxpPUYZ2l4o1jUaDer0e0C+EvqGWatuixge64lgizlXfp1/rOLkY+z8hBcqBljHDrKSXFRgPWVkESxAX1KCcc/giiKhIPGOapfeeosiopDuQwhE5w6Of+TTv+/3fw+YFi505ZmZmUHHE9fWNl9y7L7X+v/Q0vwt4oPr5vxD80P95df0PfLi7HhFCdIQQu7z3V77RL3K+ssqVYgJGHYe/ceoebhxN6Tw6ajAYDLm2soLPIpbas0Q2AlEGvQXnESqwepy3KASx1lgLRhfB3yWKWc9g4CV5IYlF2Jh6LNKhgpujxTCmEorK2sFPNqjAOYNS41J5SluzkqlyRtA1Pe5583H2zi5z8vnnabVTdh9YZm+nQ1SXrG/GfOivPo6MhnzPD/4Qjz76GQ4eOMjS3gaDYY0f+4l/wS/8wv/O7MIuinKEtwKsreBAgQ2ElHTaHVavXUPFkv5oxJlzF0mbKXv3LTPqb7E4O0Ovu0E7jdm7dzenzrzAC6dPsX/fPtJ6nc3eAO8dc+02J596isXFRa6vrNFqz9Dt9mk3W1xdXSdKWuS54ckTz1KfmQ0WAy5okhajPlJXDojG4a2l8AZpY5wuiROJNgVloYnjBFf3FM4w2LjOLbce4NzZiyRpE1SQljNF1YqYIhkIQj9s3NYBApzFB7C3UpIkBZONcLmm7PawnRZpBPgCkSgO7dvN+qalnTYwOiaRYJQL/GXhpzLLccDdDuZuMojIUCLBWEcazxLXDZfOXuJVR/fy8Q/+CWl7juWlvWANw35GFMPqtR7veftR8u4MzZNz1Bd3c+KLjwDbDo9jGI8j3PvBR0ySZX2EaeFLSSEN9VoC1tEf9ogENJOYg50WT8uYvJRk2hHZGjsWGui1QTC5Q1Dmkte94TgvPH+Cj//Np/iffu5/HEM4EVaCS3EuxnpNWebEkaahAq15Y30DhKM/HACQVtqnAFqpquwWwSWBMEWXSiEmg5nwZ1sdnGVZIl3l0S5gWObM7VisKr5gICi9ZKtU9AcZnXZCYQ2RKYmIUDK0VpySKKvwkSbPcyIlsZml1ajzxYcf4vz5syx0OsS1lJ07d7Kyvk6e51xb36AsitAWepnr5WaaHvi4EOJxIcRPVtd2TAXCq8CO6ufdwIWp/3uxuvYNl/OwnhnOXlvnanfISi+jX8BWCb0Czl5b5+SFq3ztzFWeOX2Bp0+e5sK1da73hkDBjrkagjAUGftje+8nfOfSBPc6peSEnWIddEcZq1s9vJL4WCGdRjiNdwqswnuFlNP6mTdmGdNwpGkJufB3EUIENlBacyztSnnm6pPcft9RnIaPffIjHD60l3e+7UE+8IE/pVdssHRoiSdOfpkz1y7xkU9+nGO37OHw7h28731/wUyngTUFURQj5bY4xjQsS2kNSpLUgpBDqzPDbGcWvGf//v3s2ruP1bU19uzfx6c+/RAmKzm4Z28QMM5KIqmopTVWNzdYXNzBtcvXePCBB7B5wXyzwdJch6OH93P8VXdw9dplokhhyxzwRNGYZqhDZlkYBDoYWxmJKS1lYYKPvfO4wqBkgqOkLIKX/P3330+apts33RT/e5xdvlTvcdJrDDMwojTBohhlhqIwuGoQJRHhUJQC60rOnz/Fvr07GeYjsrIMPdObEsnpknz6sYUQeKfCvRKeBdevX+Ht3/k2njrxNA/c+xruO3aIA3sXGWRd/vgv/oyvPPkUu5bneO7Jr3D90gXe+bbv4Dd+9deoN2du6IuPs95JmS4F1joWFhZCIuBASR16dQ5yU+Bt8HHylHiTgwji01KDxldKYQ6pBB7DmdOn+U//4S947z/9XxFmDmFmoZwD2wYfrHytLYmTUNrHcYTSAijZuTSPUBKhJFmeMcozsiJnOBpOhKLHkKNxgJwmIoxdRW/WRR1PNaM4pjcYTEp6KwRrgyG59eR5EVTtTTAK8z6U6N4YjDOYbEAkQTrH17/2VT7yNx8izzMWFxYpnWNlZYWNjU1MUaCEwBYltiwx+Y1zlP/aermZ5rd67y8JIZaATwghnp3+S++9FzdHlG+yquA7DsC8cPF69cYOA+6xOpmyLEcoSVm6qq8F2JJWU0OUUiszdnVSpMsqr5pxQ5gJxEAT1Nl9pTLuLNjCMSqh2wu6fw5QY2iTqxg9DnAS5+22cMLU2p7obtMot6Eq2+VzfzikXq/x5U88y8kvvUA59DQ7KevDyzz+hcvcd9+dnLx0lde/6R5+9//5A/wG7Ows8PwXH+HVh27jAx/5PFJoSpMjRPDV8WyXj+O12esCMBgNaTabSK3JsiGmKIni/QwGfTqdOT7y8Y9zeP9e7rj9GJfOvMDyrt0IoYhizdnz55nptLl47hLf973fx+c//RlmmzXedP+9PHvyebyKef7kM0Q6RiUROFuNIzX4sVJ9yNaCw6MHH3Q4jQNdBoWeRr2BUgmOlNEwIs9zDh06ENwJaw2MzfGuKtOFqBr744moDwiKWrLtSLiNfyfLMppzC1w1UDiPtxblxqqBQS086US0mwlH9i3w+NlzqCSuptUCyXYbZlrRfXrjCxE4ut4H9kqzWeMdf+9HObtykmixyUCGDKozt5Nf/+XfRKSKNI4x2Rb/9l/9IpuDguOveR0irbE4v8TFK5cnfdUxRnm6BWOt56677uLkM88ihAhDHmHQSUpWlsw1WqxcvRLEM3TI4NJIkzvPTLOOMZ4oSoJilJD0ukM++9mH0dH/gpKGrMgDwE/VGXOZvfdsbfU5e/Y0w/6Ajc113vau9/DTP/0/8LP/87+qpPqYuudfbP3iqhnB5ACogujNAVNWbTNf8fqttaA8RV6SxBErgwE95yhKD1XmTSmhtGil8GWJUIqttVUe/sxnaNZqCCFpNZsMRyP6mxvgPVme08+GKKWJooCMabXaE7fMl7NeVtD03l+qvq8IIT4I3AdcG5fdQohdwEr1zy8Be6f++57q2s2/87eA3wKQQnhvtnsb3nhyH/qZMg6gXAlEUgaAr5aUtsAZweGlJj7vYSJN5CvguRQBg0XobXofaHLl1DWH5Np6j25vBIlGRdFEGckR7EKVUlg8UugJr11MlYTfaOIabgBb7fWgSO2s4J3f9y20mzv4xF9+mltvPciJx55gebbNyedOstYfsXnlOj/5gz/Ga47dz2ijS371DFcuX+XcCxcQWjK3uERdJRMQcBDlHcNTPGtrG2igmTZCdoSg2+9z+MAhnnvuOUZFTl6U7FzeyfzCPC+ceQFRGnrDATENLpx8ngOH9nHq7Bke+LY3cOLLjzHbaRFHmuFgwMED+3nk8Sdw3tJs1Wh1Wgy7A66trKDSyou8MKS1oHKkRKWSJHzlBGgYjPp4W9CLBO2ZhaAj6cMB1Ov1CAOLckKnE1WQHGcdVAdFFAWH0u2Dwwf1Kzy58ays9xjknjFyzdkSb0pEJBEibNzdyzspG/A7H/oCPtJEcYT07obgOG2UN53pBmZSCK5aa7pbXU585Qn+6sN/xtaGYbB+md/+D7/O8cVbcfRwGYw2I4Qp+Ge/+H+gSkmOxTvHbYf3c/7SxanMUt4QnIUTJImiXq+jpKTIc6I4piwNaQob62vM7W6RtppsjAYUDlIlMaUhijSRzbHGIypco7MeoSKkCtz3fFggRBjCenJAIqRGS00S19m/fz8Lc/MoJciM4a1vfpAk+Tc3sKRu6BtPDShvJqjkWXbDPpEyQIsC2iJAAHNT0pmZwbmgZGSdx6uY9RwWBiN2zLUDdMyWKBRJpLl89jSPPf5V2p0OnVZz8lgrKyEpKp2ZqE1GUUxcVWlKhNqz3Wy+5F5+qfVNg6YQogFI731NPel/AAAgAElEQVSv+vk7gH8N/DXwY8C/qb7/VfVf/hr4aSHEnwKvA7b+a/3M8BghM1OqgogIhcRWnsQOlAwb0BLc9jxYL6hbwevvuJXUWywJEoOvgltIKiTeBTUgCMOzMQZzs8j5+pkL6HoN7wLI2uuQ33oJThFKei2qkjIA7b3fHlZsS7+piaz/NDTFV8On5d07mV9s85E//QRzMwtcOHWWrOjz57//n3j08S/z+FdPcvXyFsr30T5iWI742w9+gLd+5/fTnFmg3xuR1BXRWPig2O53leU2QynViuN33snTT54gihT5qGB5eSfXrl9lOBzhEbRm2njvOXn6NPU0ZdfSEhevXSVK6iRRzNWLV7j3ruNsXL/K0myHpF6jNIZra6vsml/k9sNHOHn2DK0qyJihYM/yMlvDEdkobE7vwsDKykqpx5eUOLAhC80oiUeSWrMRWFwioVZr8bWnn2Q0GmGD/h9SbAcPORWsnB+LHG8D0LVWZCVIESbmf/hnf86Du1rk1mJLC8YGVo0IAw1vHO3mDOsnHuP2vR2u9DWbxRZ6crfcuG4OaN77gBJwFucViY45d/4Cly9e5cD8Epd7Z/mWdy1Tj87xlTO/xZ0H/h6aPYgoRmQ2YI6FB3L+wX//Y3z0oc9MfMSFEGRVcJm0fiob4rwYkqR1nDOVT5VjMOxRGoMRmg9+/JN09hwIIiQCwKK9Q8gAtXOE2UFRFMjYsra5SS0KOrTOC/DR+BXjnEUpQbs1i6k8oXzAoKGVIK8y4vAeCESFAhjvgbFH/fRQb3wITQ/YxkO+6UoN5xn0u8y1ZjFCYmLF0+evcsudh8nLgpYCqcI+9aXh1Kmvs2tugZXNDTY3N6k1GzTqLfLCEMcaIRWmyIOcYPUYYYBVw5hvjMF+qfVyepo7gM8JIb4KPAp82Hv/UUKw/HYhxEngrdWfAT4CnAZOAb8N/NQ3fQQhSFVCJqYFQWU1CRNgQXqJnso6NQIRw77ZGKFjFMMqZc+3yxobhBqUc0gPygecn3HgipzVqrQzCIywoVkkqya2CxAnb0B6tY3XVMGka5v9o140BAofvEH6QH1zkeXslUsIL6irGdauddla6/Ke7/sh/v3v/T5ZdpWt4aN0+32chrTW4F0//CPUdiwjkiT0aaIIlaRY5wJ8wgmsCQIaQii8DaZcsckYlZAZgfWGtbUNsqzEE2ihWkl6/QGIAFe5uHKF2kydNHLsPngYoWucO3MVlTaI6y26vQFra2vYwvDY408x6vV5z3e+DaSl3aox00hoJnB4eQlbFmS2pD/shc/KS7wTOCsRVf+tLC1ZbugOBmz0thDBnwTnLZsbPfK8vEGGz8pJtQjhZYf7w3kow+dnK7hVUL0SNGuwudFjw5QUpQkHnivJzZCqZxM2riwZjUa86/57qbdCvzsaa1oGyesXwY5umJ57EUSLrcKQk0aeZ557mi8+8WnqM4bZRpNUL7CjcSt1tYREICqxEy/CtFerlFcdPx68foydOIoCQVfWK7SweJdw6foVkliCDQBaZ3N8JWeXlQXdtVUKlVJkA4abq1hTIKI6IpLYoqzuy+2D3suUTz702RAc8QjlqlcdvsYHlvMW611odAlFlnWJpSSttZAyJdK1iYfQuF0yFk4ZExOm+fTjQDmxL6kwuJOs1HkSoYh0DSsVloCXzpyja3LyssT4AD1SLkIIT10rVvt9jPU0mi1wgtFwgLMlpvAoIpJ6gzhNaTaa1Nst4jRFVO4DsXipo/Kl1zfNNL33p4G7XuL6GvCWl7jugX/ysp8BBKVpKVDGY5UgcUyc9LZPLoByUgp477F5QT3ReF/iKxA2agxjIExclcYz7kuBrxRYMuPZ3NwMAxvGSuHxRCjVOReC3tQJOF2ylWW5fSq+xBJe45XA43jT/W9mvbsCpMzMzJE0R/zlX/17duxUzHTqRIlBi4RXH76PYV9S5hlaiKBwDZNpfnBCDGVMmKqOOeFBgq4oLM88fZK3Hz/KQ8+epVZPGBZZeM4yDLYGvTDtxwm8FczOzDHfWWRrc5VHv/w483Nz1NKEjX6XrY1NWq0Goyzj9ttvY8/uvZx67lnOvHCaZr0VdJ+tZzAYcuvRO+hnhkFh0DIILGRZoKYF4YQw4bbWBq1M4XCbW8x2OtRqNfpbXfKsoFZLJsB9WZXb8GKhBYfHeId329xlJUFIzeLiDgb9i2R5iSnBekNpSuKswEYlJFFV1SgOH9nDalmjU0u5bFbxIrQLxvjRl2rUTyBBOLwfH5aCwji+8ujjxD7hZ37mn7Kn8wCYhCzvYvIEKm8hJ0XFTQk92tlmO5if5fmNPdNxTuMlcax5+ulnWGzWcEYS6fB6x9J5W1sb3H7sGF94/HGk1BTDAcpbRlmG1qCTKPRK9ZgOGshjf/6+9/Fdb38wDMHENs8ftplPzm/jKMefy+bGBunMfNgbbGeOsN22UlE02U9QmcNNtTzGr3Xs7Dr+t8ZZQBCnSejdV+0YjAqUXGvATn/2osL4jplHmtIUKB2RagVeMcoG1OvpxOQtTdOgfFWGKtP+N4xkXhE0Sicj9MFdNM6eR3lNLkp09abe3BQf0xqdc3RS0N4g/LaclKeaoAs3CTjOBVWgoOsaaHTdUUlhwXmDEA5vJF6NT9aqvJ7KMsY3kK1KwnEpbq0lSZKJYvX4+UkiHA6nPE899izGGHYt7mPf3iW+8viXeM2xt9CqzzKzkFBLmxQjTW/D4BghSUKGLCTWhQnkGDgcWg/bTpe26sFpqWjWUob9EfceXOJzp04xHBrQmkg5nB8S6SCmq6MYHQXL27MXznLu4jkajRY7d89Rq9VY29gkzgQ7du7g6qVL3Hb0CN3uBkvLe7lF3oYzhka9yWAwYmOrz+xMm+eeeYbBYMTc7AJoTRODkjAalfSHGciqfPM23KhGIkeGtasbwfta1+n1u8zPzzPMSgpjKuZW1dMkHAy5KRGV8pIU1dAEwAqasWaQOb7jbd/Of/7N36NfWoaFIy8zbNYAkePKAqlAKU2SCA7sW+ah932C3mqXepRQZAaZjCFT00Fmuopgcn8465FJEDCJdIuHPvEl/vwP34cZhsxXuowkjvDOVmWhqmKhwBlDFMUMu72AHX5Rr3wb7mR9zrPPnOO13/tuzp+7UE29VOBYe0M9SSnLkuEwI66lLLZaNFLNcBhQFULJKguUSO+DeZvWnL90kbTZIh+OkD5CqRvv++k+7riP7r1nbnaWAoX1BkRwqZwuu8d7YQyfmgimcCM6YIJKqOYZY2fV8OoF2WgUDhEEWmrWBhllRwXee9WHtdYx6PaQMiXPxu6Y0GzUKMuCWj1FKovWEe12i9FoSGkMshItbtbrL6m7+Y3WK4JGKZDEnSVKYylNhnHbfZCoksK/Wc/QOUc9EtR0aOR6F/Qeb17GmJBtVCWCJ0jnD3MblN21nsiQwYuHO0KIiVDqzWtMDxtz4WuVhSlsl5TSSza7PdY3umidkaZNZpr72b3zKIsLywgzTzHSeKOC4qerBccTD2VeTDj4eZ5PGEtSyolYqxACZGCOZFlGCcTtefbunGX3jjlMliGFIE3r1RNyeGlZ31xjNApCEe1WmyROiZOIjc11bGlYmO1w5eJFDh48TJ4ZjFV84bEnOH3+Eh5FlKS05+bodrvcf//9DHpdvu31ryWNPZEviCiZqWs6rRpLczMUwwEaj/aCVEX4IlAqi6JgOBgyGgV2ztzs3I3+MtZWQZOJmhMyiCGP35sxRbKZpmH6qhOSWGIsDIUky0aYUVEp5BShBWQdCEmcptTSlEacsnv37jD5H3/uU/1Lf9NBun1N4p0JyA2rGfaLCn4VhlOuYg2FXmG4h8s8x+Op1+sBCWANnU7nRRPz4BQG+MporYS8CCaEWZ7hTVA4qsVpCLres2ffPrz3HNy3n2G/j1CKkaWiMd4YlIfDgjiqkRcl4ib42jdbr737NfQ3tyaZp6xor0prkiShUW8gCYlMGseBTVclQZGOXtzDHO8prSlNiVSSwhlyE8gHSkoMgu7QkI0B8gZEpeTfarfp9/uTKnCsX1CrNxgNB6RpSqNRJ89ypFAkOoggx3HMVr/PqMhf+oW+xHpFBE28x2WKqN2irEHi/SQ4TG+M8ek0ebOtmzTtpRIVZOjGNS5ztpkCocfWy7LAUfaBYveNJuKT6zfh9Laf+vZkdZwVSymDtWi10nqNzsIs7dldLCwtsWvPjkpwI4wdhG8hCFCiSMaTYDw7O0u72kxZlt2gtAPcMNl1eErj0anm+ZUBs+2UmSji9tt24/IcX0bYMqbXLeht9WnV2izMLbE0v4NEpySxZOXqFlnfUaunFKOSAwcOcebcRU6evsKzZ67hZYpOm2x2uyA1w2zE7j17ArOmGNHbXGN+tskD33oPCzMpi/M1WnWBtDmvu+c4o34PScHe5R0c2LuHHbvmSFOJcyOczxiNMuYX5idDkHHQHA8Wxv2v0tobAoCUkiSK2bW4EDKSOKJWq6F0zOYgI8sH2LxkZApsmeNcGJJopZCR5NixowyGWxgyUGbSh5sulb9R8LzxZhC0Ox1mF2ZCsAx3ZgV1KhlLySVxzPr1VU6cOMGHP/xhSmN4w+tfH2w9mGYdTZXKWlCrpTz62OOh+iDQFmWFOQ1WAJIf//EfZ6vb5x1vfwetVgfjLKPSVL7papvjjkARYQwkjSY+4OtetHe+UQvqJ/7hTwStzErurSiKGyiuSgejNF1pNkgEiY6IkwSkQCs9GQzd8BaOh0NRjJbqhoESUtPP8sp/qsQaN4kTo8GAWpqGzLKWUqulFS4b2jNNREUlBtCVdkUUhWqwXq+jopdfdL8igqazOb0LZ2kcOkZr91Fs1Kqyy9CHlNajLWipiYUirvBgDQMlBVpLIi8DmJoA8IXAlRZeoJQM2VEe6JSFLzlzYQUrPdJJtItQRiAKS+QEsQVKOykzvHOT6a0yHlk6lAlZ03QwHy8hBNL5MOF0isO3HeDypYs88cUvsX/3MhpFjYTYQewE0hQIXwaPd5kjhOfy6gof+tjH+Ppzp7DecfnSleo5yDAYq8oopQI0JBURkQxaop987BE67R0gBbEQ3HJwH93eFr1hFy8gzwXGepw3eJuTZV26W10Srdm9PM+OhVleuLzGZx85wdWVDUbDETO1hFYkSfB02h0uXrrAkydOcP+rbyfb2iRK2sw323TSGs99/Xm+/S2v50BnmQfuvZs4TViab3Pb/mWKoeHQ4b1EIsePhjSbKYeXEhY6Hcxmn06ziZIxpRcI6cmVwKhqKCE9TjjSWJNEYQrqEBTGksQxed5nfraJK13IxgRcWt1iMIrZyHr4zJNlA4qixAgwXqC94OC+XTRqMbY0WBMyYeVerNw/DTcKPwR6nhmVSKeIPBw8up/NYR/Kqi/rFF76ifNiYUc470lrKTt37uDNDz6IdI4f+J7vCYcEFipShvCuUkf0YMG6gsvXLofHUhEyVmTeYNEg63Qas3zu0w8xU0s4s7ZGmRUoL+llHi2rimw8/FQBLTIqC2ppkOobugyRSryWSF2ptThBpDRKgHeSPDPkmWF+aYDSHis9Q9PFlCFwmrIkz3Pyogiol0gTxXEISjJgbrWQUBhSoW8o56cPJOeC6nuz3WLsyeSFYKMsGFSSAdJ7ZJUq6ShiZrbFwYMHA21ZR4HzDsRRitahYo3iiGaziaMauBmDMCWzU1XiN1uviJ4m1iBcRi41enE39VqH1edOUMMzNprzIoCFvPcIJYmkGIMqkV4GKIRwOBfU0q3bxmha60IQEx7pAhToyup6kM2fegemM0XgRRnOONMbL+fcpIIar0nwFAJFgo1y1ovLZMMuSTrD8p69jJH3Y9du6x1CBSERKQTWFCwuLjIzM0scpTg74ty5c1UGawgVeXBVHE8dRVUmmdKx1euztr6K0oJGWmOr3+We47dgnOTZZ09SAt3+gK1enz27dzC0lrmkQV7kXLy4ymAwAmfZvy9kkRKHEI60FpMPC048dQJTZPzo9343o/46sXK0ajGp1qxf72K95InHn6CRtNBKkeqYcy+c4p577mZz6/PM1CJ27Zjn+ZNbmGzAoWMHOP/Ik/zkt9/Ja+6/lU62yl987EvQWUAUI+J6RH+0idaS3bt2cHD/PsrScPnKVV44c5F2u8PuXbOUxYA56uSDUKZF9TbDcoOt3LFYQL8Y0i6D+6Q3JTKJcElEpxOBLZBje1c8UgW4WlmWk37deE2XsdZaksoV1ThDqxXxhS9+nm+981vx0oZANb4lgFoUU5YltSShUauFHp5SHH/1q4kjRZmZoGE6pS8wgV1JyWjkyIzFYWi2W3z58SdRSrC8tAOkpNZOWd69g2FeYAnq6KWxTDDQVaUmpcR6G7aQ9Vy7co3LV64wyvrc+5rXonUU9hOGsurPOtkjGznqM9d57vxH0WlJXkgiX8f7Au9CvzNJkkDZlJLcGGw14CmKAqEU9ThBR7rCQ29LQY73WZwk1bVgwjbs9mjW6lXLK6KXWbDgCHJ+wstqGBRRFAVxFBxShRCkaUqWZSRJMvFnd5VFTJALDEnPaAo/+s3WKyJoCg9JFOMQZMaTtBrM33acYuUCdv0aMriQUSLxPjSfY6HJtSeOU+I4ClhLIcBJrPHIKFDiwn0XWCEeiSkMHsVqP0PJBJQPXr5iG/4wzh7jJMEaM5mUj8sbWake5WVxw1R9OuO0wiCcpLAl7cUG9WYLVwahZW8svuKr4yVOyAnwVgCy8jaqp7VgASAVmxsbUyWWoSy29Ua9rQDFtkTXU7CQ1Oo0lCAbDjh8cD+9UY/NzQF333k7z549x+ZGEPq9trKK8yUL8ztZuXqBhc4ie3ftYH4+pdVqk0Qp1lhWr1/n5PnTbHWHHLnlKLcduYX+5gZRrFlcWKDTqgeOcqNFXlq8dew5tINrK+tEKubI7bfzsU9/Bq1ivva1r7F75zL1WoqSgiNHDvPw57/Mwd2LxMMr/OiDB/mRB2+F2jLPnb3M+z78If7gsc8SNWbI+wVCVOLLxgS4Sb3Or/5fv8oHP/hB9izt5tqFcxNdzc3uiI0iKPWPsh61eoQZ5US6htMl6BitHDP1GFdlJlkZetRREk+GfePB2+Q995W+AWEaHq6Bp+DvHvokb3jVGypRZR+wodbhXVBgV3E6Ea0QPmgqpLGupsQJ+SibDPvG99P4vkzilGvXV6nVFE99/XnipE6tkTAqMqTWqEIS64jPff4LHLxrF07GdPuDbTRCFTi11uQmRwvN+9//fv7+u9/K3uXdlLZgMBzhykDOCPKIJTpWmDKls3iFLz7xx+TJFUZmSJw2cFmCKe1EOT3P8xAks/yGfmek9MSUz3tf2dIwISlMD53KogzlvlQMh0MiqVBJcJjtFg5nQs40PsBqtTq90pHW0mp+4SjLgiROaDSbDAeDsD+dp7AlrVaLbDiilqQUWTbhwr+c9YoImgCjrKDuLFF1Aom0hohrFCIi1ileSOZvuZ1hFsRDa7Gi3T+DNx5TFkSRwJYCHSkg4ALxvoIlQCQkA0okgmFRMjSgI4XBBB8huY3Lm0ymjZkEwzHgOK3SeFNtpNLeaMsxyUicwWkdpt5lyVBuMTfT4eChZYSvcJ7SgPFVcK9gTYDTASsoXBChcB5Onz4NIsjbFa4MAVvJIKFmHcKNMwmB8Z7uVhfvM2ppk82NHoNhHy3BmCEHdy3CrkWGpeGFFy7Sbs9w5vxFbFmQRD2aacyTT53DOcfu5b006w127dpNr4R6vaDfHfHoY08Qp5pIRVy5tsGh227n9IVz1GdmMcMBZphy6ep1+qOcjY1VzlxISFuznDt1nre88R189cQzKOHYvWMenKKRpOxYToiEIypjiiLHuTPcc/ssb7z7H7HyqQ/QLUHMdDh6x3FqrRmGNmdgLSvDdX7g+9/Fu979AP/bL/2fNBqKSCdsbq6ze2mJC1sjjrRTdOkZ9gekaR2fFnitKVRgmr35/vv4yIkzIBxprUZRFOR5HrKmlxhYwDae0lmL0cHMrMg8Tz79GAiDQCJFhPEGhCeSCoknL4obYEXee4aDLrYsMEFfK3izTzGeJoMT7dka9lnrFiAkxluElGAcEkc9rnP7rbdz9z33kn/1oxg/Ii/Niw73sizx0lEYy6/8yr/j1v0zRDrFWMGRW48yGg6pPAFwUuFswXr+COcu/hW6k1FPS97w+n08+qUecRqR1ts46ycDvXFgBm5IKJxzDAaDSfYupURXMKDxezrujQZYV7AH7nQ6rG2tIaOErrGY0mLtdmtMa40mwPQa7QbdbpcoirHOUgyKCsZmJ/TjQKIRlEWJc34iQ/ly1isiaApASFdNgQVZmRFtDthcvc58c4Z6o4NXmnyYESU1vIC1teu41ctYt48oSfC+BC8QFbLP+xBtPCB88Iap/sQoK7BAFPRyQqlS/R8zFSin9RUnajpVCa+UwgmBEjdO1rehUTEGTyQFWhnuvv8oR5ZupdNpM9owgXciLEJWLoreTfCidup3+Ypqd+3ateratq2wEEH0YDwAszawXqSUtDptir5lkI0QShPFdfKsjylG7F3ezfXVVeZadZa/9fV85fGvIqwhiRtsdXsUuWU09NTqNXpDw/Onn6F15hzohEhINJa03aTbG9HUUGSryKTB9Y1Nbl3ejTSGlY0BhakxN7sQbGe/8FUOHTtEGsNw0KPVTJmfX6LWqnPp8hXmOvOMihajcotmNEJrSz1uUPYsG70NCmNp1lvUB47zj36CAkUR1zn2+jeR90dk+RCHYWamRq1VI46TyqgrJmnW2Rga4oakkVqcsdi8RMYlQiXESnH8jmN87MRZlIzIy5IojoOQQ/V53ww1gjA20UJUJa4lLwo+9/lHAkpASwpTienqcIDnZYEWgJLbAiSykrorCxYWF7lyfQuwk4Dyomm9cjgh8CKgLQ4dOsDxO24njTTWGhYXdhPJhIsXL7JDB/PAG7DHY4yztURJhBeKaytdAqG7BJHx9ae/yPLyLtJaHPaH6SKU5OLFP0LFA8SwScE1vvd7/jGf/fT70GmGEJ5YpbRaLYqyHKfdk6Wqx5U+IFbKKumQUk5EdmB74DseAFvnSNOUtfV1dBQTxzGFybDWVV+WOI0nOGshBRsbG9TrdTY3N8nznFqtNiU7GPqnw+EIWxgiLZhptRkNBi87Xr0igiaEwJEIgfWWWr9gbeUiM1HKzI49bI6GkGf43KF6a0jlmDOWmVotyJHlljgGpyxWum0BWymQDvAl1mkiL5DlgGv9DCFBqhJhFChF4J+EfmlhSuKqT4UMkJ6gAm7J8zz0W5wlShMofSCZ+O3MYNw/UkIikiYXv5axa9deLr+wgaKGYIvAflcEhXgQFWXQ41Eu9G8FBifBeMfVlRXK3CC8RkqFl8HRT4iQFRsFIpZIX+CUZLTZo9OZw/W7xFqS5yNqaYrzCd3uEK1ThBfY4Savv/d2ikLxxRNfR8gaubV47XDScf7aFaRSuCRFohjkI2ZadXJXopUis0Gh6NKlcxy69Q7Wrq0x02phGNAdlWx1L7N//z6ImuTFCGOgnxXsObAHV2RsXt+kmeyiPlOnziDIynmPSmoYJHm3RzEskE6xmWSMZhroOGbU7zO/tMQLjzxMbX6RqJZy26vu49rqBmxmGJ+RDUtktMTmqMtJVzA7N082yOk3huhUkxIjM0tfjdi3ewmyTVqdGa6vrWJhwliBSsKsuq/G31UViJwLeNxIKxbai0RRTNSqc+XsOZJanU5cJzTmbeiriwTDAGsiCj/E+pxLK1/kyKsSzn10iFQp0huM22bQQEX2KCVWWExheeubvgUNDDZWaCzuJopbrK+vceDAAZ4/dZbZxTmw17je36b+jrNWIQTKepwK/cG7bnN85tO/gi9nyUc9tk6n6KjO9fUVupvr7Nm7wCizrF4Pwh6L++q8/R23gB1iirETgiSJE2KpK2WpsdqYD71D7wO21gWVdznuc1YH/fT+iXREURZIB04I+sMBO+cWcV7Syx0jM2LkHA4HJdQbKVc2tmjWZgKlNsuI45gkSRgMh0EAW8pJAM1HIySQpnWMczQqu4yXs14RQVNIhc0zRpcu0s8ylLNEZY7QnvVLp5BJRJwmJF6GJywkxhc0my3wEmt9VQq9NBVKCoGp7BCK0nDm0qXJjTONM0OKMPGLg2WCKYsAlK1aBs6AsA5TKUB7UyLktoiEtXbKwVKipCZN6pR9UDbh5OlTRGkNKzfQyDDAEtsn7KQcEyYQf3yAq2gFg94QKWMKY0JfTKpqwBV6t1JQZSBhc3XmZ9na2qBer1GOMZ1SEmsdpuzj5rspaDTqWDdglPWCt451CNQkQ9Fasr52DSk1zXqLWjrD5WuXkMKyf+eOMCxJUta73cCqciVKSbq9EVtbW3z91HluO3oURo5aO+HyyhrDYUa9npJZx/Wt6+xa3o2OAzVU+CA43ep08PmAVq3DE4+eYNeuvdhsiNB9hJIM1ntEDYf2ElGrc+38abpbXa5evcBoVFBLNDLSJGmdbORZKS1NLTCDjCKtYfICIaFZrzHMuvzL9/4U3//ef3tDWRlF0USoeBp6NL2MMRMBkY2NLvMLc0RRAk7Q2+rz/Nef5a47j5HGKaUpGORbSOlwXlGWgtXB59j0n+d1bzzEJz98Fu81vjrGb16hrIQD+5awNkge+qhOZ36W0TDcr6vXVzl29Dby9XNkBJUtK/WLWgyjLEMnKWna4AN/+TjvePt301mss379Kp2ZGdZWM6K0yfr1a2TZkKIsOKYTytKysZbzpje/l7ysoVwNomCWBqEXL50kEpK8LIijCFO5RYbYGqCE5VQAn86ClVJB/8F74lrCKBuhk5jCGbQPNGdjPEk1/BFKEktJs95AK42zDhGF3zkajpifmyPPchrNBpubm0gZlK5ELCjLgixzlbD5y1uviKDpnaWhHXbtIk0tgjeYVOgIYiXxbohwJcoFZehOBzsAACAASURBVOfMlCjpKlc6hyCuRAN8UGznRrylwIN3mMLTLzznVjZuwKxBRccSEjzhTXehvJATIVWJEoJSAFVfpKzYCmOhhWAuNmY/eIwpiNt1ZOxAGvYd3EdpHCiNt2EoYKf2xYR+hsC6EDyklOi6Yr3fBa9QPvRghFLhexXws3yIkyK8/ooCqLWiNIZmJY8Fob2AlGSjPnGkaTUaZPmQIs+RUmFKh5CSmU6LXm+LmZk2WRa8e0LZVHDp8iWEDOIqW0XJsLvBktLsWmqQakFpB8y2mlxd2+TshRUO7T/AKOuidRo8YbzAICoDM8f5s2fpNlcpjh0PIH4g0jGz7R3U59uYwnL7va+ivznk7Avn0Y0m3X6ferPB8q4dNNf7NDsLnD71OWY7M3zne/4+m5eu82u/+z5a9SZFf4SutTi92qezUKeuc+IoZxiPaNZLXFYSo0nzderC0UVVYiglURQRVXTAcYtmHECnh4beB7pfVg658/jr+NuP/iUPvvHbybOMpR1tsqzE5B68wCZDnG/jxQaro0e4Vv4Nhcg5fv9xkmZKMSrASrzfLqvHS0WaWiRp1VOUMOgkoXCGCxfOs7i4RLvdYH19gyIb4ZIWg6RFd+Tw9e3Defw9crPgRsi65Rd++f38wi9D5A1YAc6jk4DWsJWKjUBTr9fRURBH8elOonQVMTAIH4ZieZ5TZFUvGLk9QKtmBrDtCzSNSNmmS4fM1NswUzDOVGZ9ArSgMDmyXmXuzuCtw6ngDa8RDIeD7daM1sRJxNr6Okkc0+v1JtJ7KopRWjHsZuhI02y3X3a8ekUETS0lnWYN402YLEtBLU6qyZpFJ5XyihSkUuFEQaxASUGWj5CtOKi4iLHIKThMKGMJfRHnKg1KGXF1bYhIA5DYeY93IUOUSlW2qaHX6XDkzkyoY9OsCgkIrcEFbcdpONL4Jo/jiNZsg+/6vrfzqU8/RC2u4yhxBaH8x7yoH+q9p6xuAOssfTPi3NZZLveuYr1CUPV+q8zRWo8Q2znJeHsNswyhNWkcs9UNxl0LCwvhxpGSudkOW5ubZPkAREqSNkJv15VEStGaqdHtrXH33XfxyU/+HVpr6vWUZrMahBmHKWPW1zYoRwMW5hZ5/uTz3PXqO0FaVldWuL6+wdziIkmkMYUhNwWxjuh2N0ljyabNkUrTSNu00w5lJqCUlKbAKTh/+iyJiEgbKWm7gUjaHNvxanqbI9TKKmdOnSUbdYnjhHhmnSJKsV7xe//lD3nntz1Is6lQKkI5Qb3WgjhmddBnMbHkmaHIcsqiDz6ohs+3aszUBH2zDTsbQ45u7i9Ol5TbAiPgnOHqtQv859/7bRbbiyAMBw8fBVcgpQ2UQ5FSZAWF/DrD/iNQbCC7DqHOYoqy8ldyEwYYTA1THNTrdRq1GnGUBtSHMWxubdButzBFRqIjJI7MOZLFfZTcOIwZ36c2GUDpiVRMooIKvNHBghqgFDnIFO0SvLBYWzIYDGg067SSmNxKYAbVkmxlW8SiViECCGpVSuMrZEGtVptMzW/GvU5/D/oKngiJUeG+cd6RxjFpoilKT+HBKAkygOaFUkRSU5Z9hAiMICXHlZJkfm4usOWm9CJ6/R5Hjx4NFOiypN9/+Ra+rwhwu/AEA3jv0UmK0jGmslQ1zoFQ+NJjhCAz2f9L3ZsHW5re9X2fZ3m3s9196W26Z3p69tEIrWgYscksASGEwRS2CYtjKCd2XMZJGZJUkcR2vIa4wHEoUxAXxhSYkgwIggUSElpGCxpGg1ojjWamp3u6+3bfvn3Xs73bs+SP533PPT1aGKpIlfJW3ekz9567nPM+z+/5Ld8lAIAlaC2p65JjrUuBsY2RvFT4piSflc8OSlOjs1CGOwFCB83M2lksjZitkjgVfKcLU2MbSOhss9AswhCR75j8zT4MyCjh3vvv4QtXniPqSS5dfZ6ympLqmKquoMlSZkK6zUdR1uRFRV7lkGr2zRE7k0NEpBBaUXsXTt4onMBWgH3FnWwnlL1uh7IsAREWcrMJp3lBlMRN3y5ko2HCaMjzguHRHoNBjw99+I943eteR5EbiqJif3+ffDqirkukEMRSsdDtcePmTfaGQz797EX2xmNKV6GzjP6gj1RQTStGR0OUUhTllO3tbSbTwF2/sb3LaFgwHk6D2LSIKcvA5beJZuwNO8NDclNRuxodW06sL/NN3/AWFgc9zp7bZG0lwycVLp/w+gsP8kv/4TcZLC+xc3uXu+8+R5SkSJ2yO5piLJRVAGGbKg8ZY6SwtuKtT7x5lj22A795Ezs4HgTNT6NbWqvxIUA8+9nPNboEGdvXr3K0v4XwRwi/T9/eILPPshh/kgfO7nJ+uWLZQs8eEhEmyULdKRLTfkgZQOexjlBSIUWgLva6Pa5ee5k0TRkMgvxfmqbc+/Bj+LnSfP6Qtk7hbIJQEU5ZRCZRMiGKIuJI4J3GVj1KZXGJxESSHMH+xDA2BT62CBnjXUqvc1eARCVJAy7XoY0lA0OrMjVVUd7BtGqFWWa9T+dmbDCsxxlLWRRIIZFKMR1PWGxeW1lXBOfYpo0iVUi4msNGNLoFy8vLWOfodrt478mylBMnTtAb9Ll0+SUqa/C4L0nB/nLXV0Wm6YQnSSNEw/s1pgIvWRh0ODwaESuJiMGqIAUVC4mWisJMOVIJytrm5lsiKZA+eJ0HX2XwXqOEIxKeq0djahchItko3VhUFKaM0jtkI2ggpAx9kbpuQPKNIsscQBgBItbEzZCIBvYjCDJok4MxN2/fwG2VPPP0c3zXX34z1kzZPdgj0imrvRVGlcV6iyumSBmGQ8pniGTEnzz/PqITfZAbYCVCh8AuZZvVuIA6EA1MyodOmARKLxg4x3g4ZnNtjao2TMZjOlmHRGtKUxDrhGKaMxj0GB0dYqsguba+uszhwSFl6dAatq5fY2Vzkdc+8ig3b97k+eefD46dskRFOsCjhAgix7v77BwcoFWgMu7u7dHppOhYc3bzLvb395EOojhmWhYcHu2RJh2kPQJ/inxaEaWaJOsTdzJ6WcrwsOCzz3yWCxcuIKViYiqyNEUtpNx94X4cjspWnO72qTSMiyH0E7Z3hmg8ozMLFONJ2MjpgK2iYsPXxPES2XJJojRO9QDLD3zb1/PuDzwDiUB7HSQDm4yvNf2at5ie94SP45iOCMpSuRoyOvjfkbXg5S98BiFrbi6s0xn0ufeBx+j2BvSXzzLc3+DyZ5/mk09u8Qd/9Alqs4xVHi89rm4PZIcQIIRFRTFCC2SiyMsJsU6pa8PUGHwUcfPmNusnNgjkCc3O9i0WFwccluPZQdBe0jnQjsr4RoBG4HUww8M1nH6RB8V9oVGRCoNG5xiPHWIyIssy4kTgmRBnEUVRBEsN75Feo6SaaTvMZ7ttZZYkyR0q7m1WH8S/g4OoJuiyVqXD5AZh8kDRpIH8OYfTIYkaTws2NjaZjMM0/PDwkG63y3g8xprgO3T9+nV6vR6FdeTN8/48vPuviqCplULpwFF1Lvg94wRlXpBEMdJBJ0mZFFN8pDClC9TJJGM6LbErKfFXeNFO+OAD5B3X9w8RWiFsmzXKpr8okbYRPXbMbqyXxzJW86IhcCxd57mTCw6gKIk6EYU74sb1LZIEpuMJVSHoD1ZwxnNwawRRGDJIEeGcxOHQWc1TL3+QcmGKyk7gpzVCNXJwVY5AYb1sekQCZ4MyjDGNB4x3TCY5p1cGFNMpab9HVY/ZWN9AR5rx8JBOnLC+cYLx6JCDg32yXhDKiOKEra19HnrgHvI8x+E52N+jMjUf/qMPoZTi8ccf5+jwiIuffRaXB4GKVlhF2KCF6nAsdfpsbp7k1q2bDDoZxXTM6OAwDEmmJc7W9HrLOONZ7HfpdhOs10H0Wbqg7hNLVs6s8Q0b34LAUhU1n3r/+zh37jz9pXUmkxqtNTt7+9x938Os9hL2hyXf8pe+mfd+8ElWlteCOqTSjCdTTmye5NLlzxEPBiQ6pzfJqGRMlhmEgpQChcHTQdFIEgqFwc3aMG2G1K6Fth9nTFBIOjoc8cRb38xrH/lG1lcltdrkN/7j+3nm4ssM92GafxZnwNSg4y5SdRnlB6SdZYZ2n1SlKKebIWEwKAuXRMuAYNJCgIXhZIjCUcsEnSYUeU5eTEmVhGjAiy98nvWTaxxeHn9RWdw+rut65jelhMIKG/ZjA+nx3lGWNZ1OByFC5t3KsLVydr1uD1BY56iaz1lTzzLKWYbbOCK4pgfcKooJKdEN1Mh5jxEO2fDBvQStIuIo4o2ve4wXP/0kSilK09AvG9jdYLDI1BxRVhVVXTVCH4qqJadoNZvWW+Podns4GwDxo8Yq5lXFq1f9zP8Pr7AAgypMrBVSapwxIGmGEzXTyqB0K9UPYHFWM8xzajsgtQ4vPTpSs/6eaFTFlPRUzULZ2h9j8cSNeHDUbIC2/xFK+UZlSQTOrncNrW6ul3XH397qW873J2VCaSrOnT/Pzs1DSnObvCp4+aVL5EdHnL77NEnaResBxpUU0xLvDEmqub77J0zqz4PYpD4IToPSOaqqQFqLFHGQvQOQirysgkamD3JyTgYxh0jFHBYHVLVBCcl4OsFbh1BBhHk4GjEtCnQSMzwahTKzNKytLfKFy5dRwnPh3nt54N7zfP7iRaxQ7B0c8PGPfBwV6ZBZdTrkeU5elbOhWKwykJrJdEoxHrO00KOfBErf+gP3cuXyDYqyDO2PaY6OY/Cwde0Gp06dAuFQKOrCMj0cclDts7pxGqkcVlu+6W3fzGg04gMfeD9xkrK2ug6p55NPX6Se1sSxJnIjtKtYXhxwsHfA+toGO/t75HXJEYq90hAdHbGZL+A6HudKvA9iGm968BxPX9rB2hpB0zv7Uv1Ff0xJdC4E1SqTvPj5q7z4efjmD/4ccTdif6/Ge4VzgeqL75GkCTI21ARfrCgODJ0kBu8UzkuEqGgDZivM7aqaXmeVqqhQKsIhGE8Lbu/dDurrVQWdjKuXLjGaTFlcXGHt5CYvXLnyRftuvk/b4lGdDbCgcFaEgZioa6QIX1NSk8SBVtmu97Isg+pYt08cd5rX6pDCzgYyx4Oz0FIIoPnjJMQ2kK7Akgu9SieCgIdtuOdCay6cP8PhRY8Wbfsi4LC9tUxHIWucToKqUZ7nyFgyGg5J05SyLBk0Ax/vjmFkh4eHdyiU/VnXV0XQFFKgVUjFnQkCo9CkzDLId0lb4bwg1RHOGXSqSOIFJsUeTgT4jlR3ZpuCdpFaoNGP3N7Dzz23hYu0WeIr+5PSByiTsw6hvvQw4Eu+pligvKeaTHj96x7kNT/yPfzh7/w+StYsL/Y42j2gLC7z0COrFOUOSVqRqIL9vaucX98hrU5y9fqE/aMv8N3v+F70EGwvDCiUl1Q+WNtaZ1CRxjuI0ARMf9AavH17hyxNmE5GxHEExmCdQWpNmqVMyrx5/YokFTPVGVM7sijmnnN3Y+uCj3zoSZJIcO8953nr41/Lhz78EdY3N3ACnvvCJZRSdDvdmQhCWZZMxxN6OqLbzVhbWiRJBC+++DzjaYGxGmOCpFfak2QLCWtrK3Q6Xfb3C1SsGCzGHI2H/OmHXkCgePzrBlg3xZgK3ctYWzvBN37T2/DesbNzm4s3rtA/ewq0ZjEesNLpc//6CvfedZLRSHN5a4vTd59lMtwn7Z3ixnifhS5sX72B0ALV0Yh0QKI1/8vf+at864/+I0Q/ucMHaB7nOA8Rm5eyKwtPr5dRmhInBxw6ixVLaFUjZcJ0OkRFMYfTEqUFdV2itUS7Dl5METYNNh/SNMyYtq8ZIEhJnFLXFqliDodjrl7ZIso6CFfxxCMXeOsDF/joJz/F7avX+Oa3vIH3/+nzPHPtJt0sOV6br8g02xZDuxfaxNY2A65UhxLaOoNrRGq6vV6wWG6cJq217O/t0e/3yToZzjpsbcI0vcnQAUxdU7nyizLQdv+1gsS20ZmQWjX6uOBtycWnPsq9q33WFrqolnrswxbPi5IoTedoz5LxeDzzZS+KgrxBvBhjZu4AaZLMWEiv5vqqCJo4h7QGLWXYTABK4JEkMkzCC1PQ6/Yxpg5K4JVgUh0w6SRYW1CImNg6bNQAgpv/WueJfISpCjwlVW2Io2CiJKVCKT07cUNm6tBRI4ZBAz/yoRFNM70UBHvUuq4D8UEdi3sopUCAq2qUjvjDd3+MJNJ88g+eYTQ8YHz7fTz08BI6qbj+uc9y8ckp3k/Y2X2J9c0VlpbXMHKDR+57Iw8/0qNyHb7nnT9Br5vipMCJDOOqAFuShCxTB4kuLz0Ijzee0WhC7+w6uArQKKkprUFKzUJ3gHUlg0GHg4OKYjoh6a2Qxh0sJUU9YX1lg6vXX8ZjeNMTb+DSs89zY2+X59/7EoNel5WlBaI05vrlayyvrnD7YJ9pFSA6yimSKOPl61vcfe40F198ATAkSUZuCsp8hFYxWnnKSYmuHfXaMnlfUZsxEoeMBJtn7uHR174GqTN+9mf+LWudLvc9egHtI/btTlB/VzEXLlzgKD7B1XrKwqkzlDsHvHjpJU6e3ODm9mVMbdhYX6GcHlJWjls3DxmOdzkXJTA4zXQ0IU4T1u5aJpYSbw55y2tW+fClIVIHG0sdyp5glTvHEJr3ugk6jpoymBWhYo0uPMTTGeul210IK7PJUrO4Gw6r2AIJCNNkcSBEMltXbcAu6oqrV7Z44doNVroxtU5ZW054cGmNRwcxS4z5gW95M57HOTw84Lc+epFE3Tm0mj/wZwflnByeUookSWaZtAiKOTjfevwEGmPbk2yHYHEcM51Og6NokqLjCNX4kLcHi5YK62kCVjPcUvJOqqoQWGPRcYRp/j4pJFVuSEaHrJ1cRkcdRNJDIHEeoiTFTse4Rk2prGukihAyBPbhcMj6+jqj4Yja1ESNnmZry/FKibqvdH11BE3RKAzpoPSDCCKxQgdXQx0pkjRgr+KGTyqEwNWGo6MRUXQyaBY2l5QivJkz8GyYgmIUVeXxskY2E8WWkjibmCoZBEHieNb/cM6BDZmrjo7lrKQKgsbzi/GYDhZ+drfbpZlvEXuYTN7Ne37jNo+96RF6Hc0bHnqAtH+C4d7X8d73f45fe9dTfPDDH2U0eTdl0POg1+3jCIuyBeFqrWe9qFlJI8WstZBlCc5ZbA1pqvAYzp7bZDwZU5eGsppwY2tIp9shihSZVuTFGBlpsjSa+Zrfc/5ubt64wWg65nWvfx2LC4v80R9+gGcvXsTiWVpY4MI95+ntdPnCpcsoIdCxpjaG0sDy6irPfeELTCY5UTQkiTSNUy4VstEC8Pw/Tz8LX/d6ejLm9EKKOZrQ8S9xZbrMyTMn+dH/+vsYH+Y8+9kXufa5y6ysLtDpRywvrXFxkvP8omVz+S5eXN5kMDUcDcdcv3GVs3dvUtQlolCYuiJOY4gMl7fHlPeskJc1SSNSXZQlLk5xDn7y7/4Yn/z7/woZRZg6D9WJlMhXmIXN4wvbKXqkE7qdDpPxBE+jXNU8tw1MLaY3rJXW7g+Chw9AI/TBndJ0TkBv0EVbT10XlNZy5fkj3vzWDosDTafTQ3iJERYhNePp5I4A2WZ3Mwpuk2W2mV77uCzL4+zQGJwPDgXQ+mGF113XljhOZ+V9izYwxpCmKTqK6Ha7OO8wxuKNvbMMbwaYMGeXrBRahbacVBpBwHamiebM5jrrg4RuJ0HHoQcrZUhgkjSlVhHOOzppRr/fI8+7jMcjijxAjjwhOSrKgoV0gTzP6fX7jEejVx2uvjqCJgKldDhdtAYPcRJT1AWJbIWIHVpJqrpCR1HQuLQOrxSm8kgVQOzehUgjdJDIl0KCdIgmU7QetFY0GPAgVCpnHcIgV6UUlWm+h0BjlI1LYQtzkJI7AuUXDYe8D8pNLgDrlVI4k/Ct3/Y/cLi3y6+/6738+i//Mbl7Cil73N6fsLh+mu3dgrroISOHjyxSaoZFQdbtMcmnxDpC+OP+Ks3kelZSYZEyIi+DdWmapoCjrCq2t2+ytLxIVYbpb6/fRSCpTUmdD3E+6BLWxpOmMb1ej5s3b2LriieeeJwvfOHzXDw85NTJE9x3/31sbd/ihc89x6efforewgL9bofVtTWGxYTbt2+zvNLnuRcuMZ4WxFFEt9NHSMtP/+RP8c//+b+kLCeMi5I46zOqHM8c1Fz8+Cf5D7/wr9lcX+O3/+//k/sOLWokWFhZQBDx2GOP4WK4snOFb3/TW1jspuSby/z+Bz/C24s1PrbeYbsaUYiKex+4h8PDPbrdHsZYjJNoDPfcd55PPX+VA5+FQOjB1obpdIJIErLeCnE1RdQFxAmdKKIwIqhJaXXH/Z4Hn4fHALLJNlWwBRLHz5vHdrbr5EvuiLZfJ+60yG3tN+p8wjv/8jtwVc7Wxc9zz8qAtcU+XjjwFVpojHNMcwuJmgXMeaD8vOfP/LBzPphCaJ8pFGWZz8gbWseznxMqrmOrizbYlmXJdBrWoVKKLMsQMCvB2yt4Ah1n7VJKvGyQCd4hnEBLxehwnzMnXs+KDgIbVs3rb1o6nQ79E5tcfvllRJZxdHTI7u4eZ84ER3FjgqaAmZpZhgxQ5FN6ve5XiE93Xl8VQVMQJujeB8Wifq9PXdtgYdFws5VW1FUArUZZCLAq8hgpcCJC0dj2emYYLVTIOIOTnsQg8QJqLLFXMzA3QiBnrocOS8hOHQI06AYwG7LRuYBFgEhYe6d6e7sAO1nMdDKik6ZMpyVx3/DoG/8JhS+DVD+CysdBK8F3mO7s4qXAx5LK6KDH6WqESCgLGzyDrA3QrIa22aq2BDg+hOlZY/5mamItsAa8E9SlYjS0VKVBKo3WQWOw21lASs13fefX87v/+cN0+4H1MZ5OyPOSfrdDXlRM8zGdLOH1b3gNf/j+DxJnKb2FHoPFBU6dOcvFzz/LlWuXkZEiy2LAoHXM4uIiGhEGBrbkxNoKi72M7/2xH+b/+rmfJ4kilNf0+z30oMtf+3s/CVGMwJJWhrOLKd/3+HlsPkSrjKGZgKgRieNPnvkkwwfu57oa8In8NkVyktH4kMVBD3DEUUKaJYxHE9K0yyDWvHxji7r2fPz5K7zh8ftC1lTWkNdEiyEzjJOUf/+v/ld+8Cf+MYWOSJJ4Niz0c/1N7/0dJWpVGSaTKf1BJ2SYHqwzszUz38NrA2d43GIxQ+CadyxoqyAhBLWxiCSjrg2/+e7f4ke/8wnuffAkrzl/miSSyDhCSEtRWKZVhRUEBaz4lW6p3DHcaj8/D+qfz6aFaE0Fw2NT53dk3BBGEUEVqlX+Osa7ChFwwq2MoffHfkOyKZNbWBdNFowI90LYwPRbWFphcXmDqDgCGSbu1tsmw6+pqoob17foZx3KPMd6T9btkOdTJpMJ0J0Npqw1rK2fZG93Fykkpn71Nr6vKmgKIRaBXwQeIaRkfwP4AvAfgXPAFeD7vfcHIryLPwt8BzAFfsR7//RX/PkIbGXpdXvUZY4pC5ROg4eHVrPJYr/fwTT0K+cdiRJUHoLUq6TGEDUBZJYByiCGIVQQ7hUKjK3xjYsfIvRoZqmaasheUiAa9e4W3BxOtGDKFS4/O7lb3Nn8gh+PRnS7XWxtkEJjzQlyP8U5j/UC65bChjW+8aZx1JUjSgUhMbaBESW7OG+at95hCarVcOfCF0LifAP0lR6LYHFxha3rW2TdhDRNcY01Qhxrqqqg3+8QRTF17XDFhO982+vYOZzy+Rdepq4tp06dwVnHpz71FKtLPd75znfwu+/5HfqDDidOniSNOjz16WfY2dmBKGZ9bRWtO1R1xWg0wpTNoEAGoWgdRTz4yMPc2r3Nr/7Kr3HqrnMc7O/hbUG/t8y4qul2u0wPhoyN5eTaCjtO8IFnr/MPfvyvUua73P3wJpPDMZ2VlHNnXsOb/u7/xF/6W3+TLcYsVwWFqJFAMZmysrjMcHLAow8/wMXPPAdpSpQpMim4fLsgx7GSplgcdZGDqfH5kCJZ5HwfElOzm9esRTFpkpI3FtEOZmr+bUBs73tVVVibBRiNVvgWGjN3z+aDaJvhtVlcu7Hbn9d+D0BkakCSZDGRNXTrgkcvnCHtRGgV6MfOa/BTirpiYXmJg+HR7GffmRXfKTAz/1rmhbhb2JMx1SzYxzpBCn2HV7sXrsGwuuZ7j4VsZq9jLuO21gaKavPvjJLaDttEI9foLFIq0iiakU+UBIVEikAZToRCjAWdNKbT7XJze5ul9VW2bt7AW8dgMJjdo1B9wu2dnZBc2IpO5y8+0/xZ4L3e++8TQsRAB/gfgT/03v8zIcRPAT8F/CTwXwAXmo83Az/f/PtlLyEEaRwwZjqKcNailSFt8H5lWWKsxYjg3+KaNyo3jkzXjKqKpVTTpRPKJwXIcCOsJ2SrqsIXilQLKq8x3lCZmk43DYvcQ2hNt72UVsLKzN7sli/bOlB664KSkQwfeB9A3akkn05AHuPCpFYUbhR6ox6cCSWyEmrmUSQQpJHEKRkGOz4wNJwwodeLQMuIuqyCugvHU0ctFWVdIFRQpleixHvLcDRibWOFyeSI0XifhYU+y50FptMJSiriOGE0HIHWDVbO89qHH+Tpz75EJ+kwHo6o65rTd51jPNrjXe/6T2yurvO1b3kTT378SbZvPM9Cv0s26LO0ts7BwZAXX3yR2sLK8tps02nlMdaTRj02N06RZRlH0zE3trfp9brESUzcjWfZR7rQZ7x7yNr6EkVheO7Q8N/8zG+gxIRHXncXpxZXuH3zJh9/9jL4LieSJV78zEW4VbK2eAJRllQWDEF0+vJLL9NNIpxOOXsqpnJB2fzQas5HYGJFaWucz1A6uBnGieBDv/F/8Pj3/n0Oh0O8CoyXQb/HeDrCN5lU21tuByrG1kynUzqdDoYvHTDn2znt97Vf+1Liw7OSOcpQZU7cXeBMVHDv8iKD3iLdXoeqLpFYbF1TlxXDYsKbX3s/7/nATI5vqAAAIABJREFUJ0kTcccB71xYQaplpXk/E3tRQqLbIC5V4I1LiRDHrYm6br2bwsERRRHGOYTQCCGD3USTQKimNysJTDyPxziHlkE3No3TmQeWb4gls+QFgU4C4L0qDEknAbOPcAoZpUg0VmhirUiUYlLXVMMhQgjGR0cs9xcoyzAMMsaQdTsIAflk2ijMC+I4ZTT6C6RRCiEWgK8HfgnAe1957w+B7wZ+uXnaLwPvbB5/N/Dvfbg+ASwKIU58pd/hvUPJoKajlSDSzbSsCSxSSNIkwTR9GSFDEKtNFXpVxiAVYQpnQQiFVlEwLWtEXqUI5vTzorJtOR3gDw0EqSnDW7Mo50I/sxMlpCqaWfdOp9O5UzTw1713OG8xtkZFQT6u9UYxzuJMAM4bY1FSE+mYKNJEkSZJYtI0IYo02gU/6xbmEvxiQLrQe9NzJdt8eaVlFLi4OB59+CGW+j2got/vopQmjhK8k+TTkizt0e31mU4Lur0+aZrS6/TIJxOuXHmZKFL8tR/4Kzz8yEMMhxOORoccHB1hpeK+Bx/k43/8x1y/uUOv32N5ZYXT6+uMbu+ye32LWDlObCwRRZbNjQXuOrnC6tIqsYrZ2dnl3b/5WxS1oa4si4NFOmkXVztWlpaCCpNSxEojgeFhQZHnxKllmB9R0uHJj23zn973HL//6W18Z5NuvMzoyc8Rv7iH3x1Rjsbsjw6IIs30cEgqZWCGydBn6yjFo/edIY1Tbk3gcGyxBWBgMhmRj6b4Rn1e25Lf/eWfYaWXEfmgdXBweAuNoNfvz+5RG+zaezKdTsOBibizvH6FRue8rsErKbV30icbiJOpmRrDYhTxV972DSwvZ2gdqpI01QT2kEfqMFV+/etfy2ChN/O1ir1E1g5tIRWKyAm0hciBNA5vjzPR9vfOZ4vH5bYnioKLgFIBOjVbh02W7ExgyUkP0gchmuDa6ZFe4Gwgs9RFQRbHMwUu5T3aE0RDkCgHpg54lsm4oCo9dSj5Zr9TKUmWpNQmOHuGuBJ6+hcuXMB7N4MdeU/Tl9VUdUVZFk3v/9Vdr4Y7dDdwG/h3QohPCyF+UQjRBTa89zeb52wDG83jU8C1ue+/3nzuy19C4HEsLS/icehYBQokgm6ng5CCfr+PM7YBxoa+ZZqmwSog7YYSHIVWMUKopiEvECKINkA4RcuyCJRNEVz8wknODLelmyZ1OwlUImR97SJoezHdbnfmGjnfq/Lezwyd2oU+GwJg0dKTxoo4UaiG+qW1bHpZIIRHuSAaYuumF9YETQjiJq39Qvua2isSclbSRIA1FXedPc1odNR4QYfJdVEWTKZjqio4+02nE8bjMVJAN+tw34V7iZTnvb//e+STIWfu2iBJEqSOmBQlL1y9xgsvb7F5+iwPPvwoSHjqT55mPBwivePMyXPcd88F3vyGt5DqlMkw58qlK5jS0Ov3+cf/5H9jeW2Dk5sbvPWJtwbxWCHYvnETbx2RCmVfuziTJIFI4aMEr2OkdI0akiITgoyS0WRKZSGNI/q9lJOry5y56wSdVNNNNc4UpN0gLHy0v8vbv+2bEK7mtz/4DEdjj6s00gexkPZe1lVNJB1racm//Yd/D10FVoxMUySe4dFRqJKaDdf2Adv1UNc1pvGtp1kD7aHdjMjvqF7my+SwLcQdH9570kST4xht3eBkpohij44gbIuw1qJYURrP0vICxeSQKs9nQbpdj1rrcMAKESxWEMRKE+twWLU45TugQBwHTanAY0nSiNqUeI4P77a94I0lalpZs2zTH2ND27UaRzGmNlR5EcgXBMO7SChoWENtK2R4VLB3e0xZ2plqkveBctrr9vBecHvnNpUxOAdZb8DTTz89Uz7q9Xqze5PnOf1+H6W+WDbvK12vJmhq4HXAz3vvvwaYEErx2eXDKvvSY8AvcwkhflwI8ZQQ4iljLc4bRuMhxlSUZR7sJLRqQLWByN/thUZu0JK0pGlMXVpM1S4wFaboonWg9LMMEiStf7r3FoRr3ryyaeKHjHVear/tKUVR4FFn3c6sMT8PaA64uqACU1UFVXm8SOez2U6sSCNJkijSRJHEEtEcw0qLsPCEQzSN8eZ9Qqgw3LHWUs/BQ9rA3v49s8a8daSdmCiSXLr0YgiIDc3SeVhb38RayPOKM6fvQkpNXVf00i6dJGVra4ssCV4r169vYaxjNJqQZh3W1je4vr3NcFQznZRcvrbFC5eusrp5gte89mt4/RvexHhyyKef+RM+9vGPMjw6ZDQesrq2xumzZ/jOt7+dx17zGGdObNLJUj78oQ9iTYUzNU8/82myboeyDpAVHUlKM0FFmukEBv0VamuYeEftPcrHjEYlTna46/Qmi4NFrDF0OxllDVevXsXhWF1bo6hrep0uK0tLrK6u8fKLn6GXeg6BHM1oNMFZh6saUQlT45vWEPWEB9cl7/uVf4E/GuOdwrgwADo6OsJ7z8Li4ow33R6WRVEEg7E5KE3bx2v7dm0Amx/OzPcU26tdi9574iThoXtPIsojev2F5kAMB2j42YIoThHC88CFcxSlaTwI/Wwt+VaERgDtGlNB00E0WZ4SEuPsjG8/L0iDlygZ4SxEOkGrY/thIcLQr3Y2gNmrKny/c+goVBJ4hzE1pq4oyynO1WHvSt8QUoKvVxAlCe+RTmOeu/Qyz166xnA0Cfu7oVoKH7LSOEuJ0pTBYIGllRX2dncZDAYYY5hMp2itOTo6Cra9KvgPxUnyF849vw5c995/svn/dxGC5i0hxAnv/c2m/N5pvr4FnJn7/tPN5+64vPe/APwCQC+NvPeCoqiCF7WQwaSpCThJ4ycSsrjw4nQUURYlQqqgKamaEkd6EI0dBAohQ7kupWuazeH3twZlYbEGipr3HttknGHhCmxRUYpgBuVkUPWB5sRtuy7iWLVdqMDHtfVxH6tlHElvSHTcSGSF81014gZubiJfC7CeY3hL02vyjVSen5/gyyaLFWImaOxEaNA6YGGwQr/f5/r163Q6nZkNQL8fxIZvbN1AKsniwiJFHoymlM4C9q6q0TpGJQkQoUTN3t4BDsnd95zFGs/BUU53YYWrN29xMJ5y5sxZ1jdOU5qbTCcFC4MVHn70PCvry2xt3eQjH/4jbu9so6MgwLy5vkEcx4zzIds7O7NyVcdBfaqsoawdaZIwmYzwzpGpDJRFRZKyGJMPx7x45RIby30SrRkPx8QkrJ89zdWr19ja36fT6TEZTsj6HV6+vsXa2hJv/7av41d/5xMknT7ejCltSWYc9bQClSOVwvigKiSzmFXGPPkr/4zv+Nv/mFHDwe73+4xGI4qioNPpzADebd/w4OCAxcVFkjTFNNCceZiRVArXHIQB91jf0dd8JZbY+lDu3rU54MTGEkm6RBQFkz8IvUTnoJtIIqG4/Nxn0RxXKkF4KMCnVKTx1jXYRRGCpwuCMHXj2Ni6NbasnhYWZPhiuFSb5LRX6HOGEj13JZ0kBeqwt5VE6TDZj9QxW6kdVunGhK2FDjolINZ8/uZNzi/3ef76DUQs2dzcpJ+k4W+ylsODQyIVkXQybty4CULwyIP3c/3GDSaTCcPhkI2NDcbjo9k+bmmXr/b6M8Or934buCaEuL/51NuAzwHvAX64+dwPA7/dPH4P8EMiXF8LHM2V8V/yEkKQxAlpkoTgoiTdTmfGBmjfzDiOwwS4Cai5swgCHi1CYUUwiwpeQ82L8wIhQaOJVaBVBo6DJ9YRWkYoEYNTCBMYC845lIdYhPI7kiqUjEJia0NdVlRFGU7TuqasTaMML5pgKUK25IJDn3EW4yyV90zqmtI6jPUggsCBcD64p0EIzEKRKD37nBKhYT7LlAXH2UILI1ESiW/sjBWxVWiZUNc1BwcH9HodHn74QYwtiAVgPVmcoXCcObVBXeVUpuRgPGRpsUNtHaW1GKCsKuJYoaMuUdRheWEZYzxFXVFXFWVpiLIB48rx+Rde4tnngmtlUdYURU1eWi5dusa1K1vs3r6NjhMWltc4c/oMZVVTlFXgPMegIkmSpQgl6fYGnLv7LEJCbgqmZUFtDDUFDoepSipi7HjCQuLwRY4G4jijEhUvPPscrq5Z6vRCdi4E+dGIfrfH9tbtoGEgSjwFKlM43yjfU1ILA1iknSJciSpqKilIXcE/+MFvC3RBnZAIGwRsVcroaIRSgsFgQBwls6pmMjnCO9NA244HQc65WRY6D5BvM1UlFHgRqJyNXbXWkijSPHJuHStTlIDSSerJBMYjKlMGZperWFxcRWp48PzS7EC+Y5rtjn+fty6UxI1AjVAhcWlL+lbNflbmN1mr1AFLiQxsuXY/t8lAK6QtdRisejRShtaAbkknjWNC64suIx3wqAQcskKBsQhXMdERB1mXpy/dIp868nwK1lDmNdvTKcurq8hYoeOINE1Jk4SD0YjaBgM6KWTQSpgULPQW6ff7JEnMn6dQfrXT8/8W+NVmcv4S8KNNTPoNIcR/BbwMfH/z3N8jwI1eJECOfvTP+uEeHzw90mR28hVFwcb6+mwKOZ1OZ70frYOkfa/bpWsIyj6NQZpoXlJ4gxrhgVYcQIeg3KoYtaVSG4zyyTQoLYUEDgj9NOsDfU015fA8Pi00+4OCUltyeQispub/Wwyfb8qMeX/1ONHESjcK1E3zHRc80GUoj1oQ7pd9/9pJa9O2cM6RFwWS0CA3Jkx3r1y9Qj4t6KYJUglW1wbsypKrVy8jEBR+wurSIrd3bgfesdZBJgyoyhKt4pCRFjlJktDtdoiEIi8KclNRmgpbG1Qn+LD4quL67jZbe7eazS9wdcCbuuEEJxRx1md3f59aRMQ6+F1LFaGlZn29w+7t26hIMxwOwXmSrIMjvEacpZP1OXfyAgvdPtU0Z3VlhSvXrtHtd1g9vcjS0hIvXHqBLOtQVAWLC4vgBRvrMTaKSRFE1mJdBTpk/lorbIOWaP2mKpsjSoGSCd/xjW/kcy9c4/c++QK1UDhfEiuByxJ2d/dYXFwKGeh4iBC+sZOtGyuQYzhcm7m1a3E+YEKQwRZSUFtL3JTw03pMV/S4Z20DZceUro8dlnziIx/jDY89Spwm1LIizTJuvXiFKpI88fjXcfld77tjwDS/buB4oi5FcCAwTWraWn3MkzjaaX+eF2RZekdlFqlohpgQzWEPTX/f2NAG8J6wY4+tL2ZBWQWYFhaECsaFURJBCbFOORgOec1DD2CWdxkbByVUk4oqjrEyDE9XVoLYNoQAfnR0FDQ+I03WyZoYEuG9ZzKZIBBMpn/Bxmre+2eAN3yJL73tSzzXA3/7Vf8FBJymkGKmuye1Qkk5I/uXRZjMRXHE0tISB4eH4D11UTKpapJ+htQW3wCEQ5NdYo1FxgHDKYUn8TW9LGE0AeeqQEu0psluYrr9oNgzLQuMpZkMhhsRq9DAN6aixa0Fjm6Es2LWe4WmDynbwZALtE4R/g5n7Wz6rZTCOqi8a/Q4G9l/HLW1M6bE/JR19p7NNa5nLBMfRF+lgE6WMJ3UVFXJ4uIiWSfj2rVrQZWoyOn1FIeHezhrZiXRZDxGAWl3gGoWYFlVdBpLW+8Et2/vzMDcURTRTdNw7wg9PhEn1M4RJQmZD/YHOoooJzmmrkkaFITxjr3Dg1kFMUjSwNwiiEcnnYQszbDWMJ4EWTPVMlhEOBSFUkzzMYPOIvu391gcDNje3uah++/npSuXiTLN7e1bZGmHTppRTKdEccTNrW1Wl1fCoACPcjXWFVRV6DFXVYFoMq4A9JUoX+Nrh1GKWI74ib/+XWTZB3j3R56mmy0wmRyAd3Q6GcPhkDgKftt4y3g85ujoiMFggNJ65js0H8C+HPfZN1mdcS54Q+kOnaM9lrqaKOpgS8s//Ke/xPd/39sZjQyrA4FMIFYampbV4qB/x9qZn/S3VxsInWsUpur6jiHmvMXHPCa1HaCG1xKon1GkEEJTNYQMlDyGM6kWTnVsmytlyEqtc2jrsEKgmiI4iiNMbahNzTQXpFHG7sGQ/sZJ9suKJEk5nIw4sglTBX4cXkO328U3lZoxhqqqSJJkFky73S47O7dQSpOlGb1+71XHq68K5XZP6H9IrQJla27QEscxnW4nBA3n2d/fD6d/0+tJ0xiHReos6DRyzESIogghNUpFWFujJCx0e8g4QqsMQYRSEUKowDDSkl63w2K3H05OpWZlSouXyzodsiybLTIhRMONj+l2O6RNFjc/EW0XaKQhjsK/UlicLcGF8h0ZoEWmqpmURWhBcCzKOo/1m1/s81N7T1PluAotKiIVBj+1Cc14qcB7SxxnaB0TdBIN3kt0nBInHSpjGDcK26Hh7++Y7Pb7DTyp1yOKNMPReGYVIFXAq8ZKEQtJFsUs9wf00ozlpQErywskkaSTxXTSiE4cPiIBGk8sokYhNByIWze2GI+GTCZh0g++EY2IgkSgEGAqzp87QRLHgefc73N7d5fFxSWWFhfpD/o4GwLXE1//VkbTgm4n0EOF8/RjTSwFUSTRiaaY5lhr8NhZoMALYpliipr9W3sc7R+BqPmhd7yJf/rf/ZeQD+mmXfqdIL7R6/W4desW49GI4XBIkmTB2raqwlBDHesezKMf7tgTzYDRmYpIBz8ccGT7I/7l3/l+ep2YJFvk00/9KXqwwb97939me+82dmoRXtLp9IijDmVhEK74ImvqV07E2/s7jwmN43j23Hmccjtw0lpR12a2V40Jw6+qMrNAq7UmkgGF0iq6ywbXPE+bFFISN18PPf7gttry2KWSMyWjj3zsU3z60mWevrKFSRPEygJ1J4Km/E6SoBtRViVSSTZPnCBNUybjScBqZil7B/sgQjBPOim9fv9Vx6uviqDZ3r5X3tiqDFlmVYby1DZePhB0D6MoCeBF6RH6WNLtGPYTPJWVN1QqYb+O8DIhVr7BcGrwEuvAOE8xnTAdjzB1HQRYjcFx3G9yzmGqAiUhiTVpEiGFp65zjCmwrkQqR5Io+v0uvV6HhYU+WZaQZQkq7iCjDNTxY9FM3bWWeGzjma1ni3a+LGoDMIRN1eJI20sricRwcmOV4dEBWZrOWBzdbkYSJ5RVifOessgZjsJAyHtPbT1R0qE2HiF1I87QMI7melrt+xB+d7BUSLN01jIxdR0GVniMD0rzppGvQwg63R5RGtokUZyGoVajAYoluGuiuLVzCyFC+6N9P9oN7Ruh46DjaKirEBQODw9YWFpk/+CAw6MjBv0+xnsWBgOkVnz8Yx8PQhAuQE/yvGBaGvIy+CnNenJzaIRw70Nf11BTjkaMd0cMhxNSnfHaM6v84Lc+TorH2pBt13VNr9eb/b37+/sURdVMlXNqU89ezysZOu2kOay18K+rw77w1vFj3/kEvsqZjCrKgyGXr28zPbrFqdOrSFXgHBS1w1qPc5LTp8/hfHFHz7QNVq/83W0Am0eIzFc5r4REhfUhjr9HqIaTLqkq0xA05jjtTSbbVg3ze11LFQZvjUEiPjgvVI3fDz707cvaUlYGJ2NuD0fUKmJoLFVlSNEUZUFV1RRFwF6OR2OuX7tGmqacPXcXy8vL5HmBVpokjRkMBvR6XaL41TPKvyq454gmA9IxthHEaHuXGt2A1oNLovdBISbPc7yOkJHG0GVSRqhMYaUP7B4J02pKnA4Yi5hf/b33kXdWuf/Rx/iTd/8uC/0UJVNcHSxxJ2WFSWNofr6OXOC82jCVDBO9GJzG1g4VR6FHFylkYympms0NbcM8lPEzGIkzqFhRlQ7vbMBiCkmUJqFnqhVWBJZQG5yOwcTzYrTM8KLt4/AgQZoxF04NUFqR12MWen0O9/cpJlPuPnc3W9evYwGHJs0W2T885PTps2xf38aTc8/dd2HjHvazV2ZujO39kFLiCaDh8MdIkDo07I1FWIci6DBqqWbVgRSSqjYgFE5CrBKMCJkTPogxOO+olCMSko7ULPYGjJpgJoBEaZQOvd/SGmKpsFHCvesLHB3copv0qGuDqXNW1xZYW11jUlR4B9euXWNhaUCSaHxZo4Sm008ofEAq1E7jMAg3QEUa6YMbYl0p0Am6ZfX7lKIe89JLB5welaxtLLKwMuBH3vE2Fvs9fu2Tz3J16xZJops+dvALyvMhZV1T2kavoBnURkoHh9OyQlhJgcV4QyP0hZcSIk3Ha0ov6E/3uGslhbzEJT12Dg84fWIV5UHoml62gtaK8UHOLUZsF1PinV2Uj3jLGx/hE089Ry2mZKoLwgAxSEktg3qN8wbVaBmEvn/Yg8ZXyAiwYM2xvcqXYi5537KewkGoG8C6rQ0q1aimFPc03HoR4U2IAVgfsnAVBkDOOnSzn5RUICyyQbKcOrHGwU3D9rQmjUqWVzYoJ1M6nYw4jhkOhyglWFldZjweU+YFo6NhMzMIZGQda4yruXnr5v8PpeEIp7trAsG8fJWQgjiKKH1FJEMZnZswiIjjmGvbN/g37/kD7r3rLg4Pdrn77nu4cvkq586e49Lll1haXGffwP/8L34WnSb8+N/4myyuLlAOJzhf0etldFLFxuI6B4dHx1g7anwrMtGUGkU5DbxXpajqYyxmHMczqbYZJKM5VY9VYcLEMDCONN4HOwcpI8rCYJ1vID4aM6eKHd6H40b9PHukvdrnSmF54mvfSKZqNjbXefbiZxj0Q8mSZinP/OmfIoWg0++Br1la7lMbze7uLaS2LAz63NrZ4tZRGFy1PaAgLNuhLMqZQEiWZURxDC4MwZIkYToJWMcKFzKG8jgLbm0MtAzZhBRB+CHSEUhBLGPKosDYmlExxDPHsml+hhKSKEuCpqp1aAXf8S1v4+aVi5zcOMnoaMx0UnDq9CYvvXQZJTO2trY4f+89OFdzdHTEow+dZ2V9hd9/7++ysHIaFyt82qOXGIQFFSVUxpFpgZIeKRzeW/LpmN3xIn/rZ36dUX9AVkz58e94gjdfOMF95yVveuhefvPJ58l6i4zHY7zQVLVBGkOURMRxzKjpzeZ5gVINIkIIYh2Rpllgv4i4eY8aYoMV1NqQRTHvePRNXDh7jt0bu/zeRz/N297yNbzmVEJPwgPnz5PnY8bllO3xlD0h6C0vk8YZtTX80F//fp56+h8hY03rG+2cQ3iBlCr0I/2dAO82A/XOooXCiqC89ErA+/w1U2Nq9oYxNdZ5klg35bfEGBEon0LgvANh0VJRV2HCraxEKDGTZ2z3U5xGCCl4+JFHWFxc5M2vfYyckrqqqadHnDpxks88e5Fut8vJkyfYvb1HmkryPLCzFhYWiOOI4XCEVKENuLy8HHQT/hzXV0XQVErRSTNAUZsqqCu3zBchqY2h0+kwGo3CtJtwKuTFPj/90z+JqRxv//bv5jVveTNbxQu87w8+yDQveef3vJMr12/xiz//b9jd3eHEqZMURUWaZPioQkQJZVnjncERqJfGWnxVEwQKgrCFEAH+kKYpw3KCsSZI1Ak9K7HiOCZJU2zTYpgPbsYYptMpkdIEHcLgDjmjc8LMt8g7E2T+5/uXAqq6DBROG/qUUoeealBZCllgosIQaHh4gKlrNtY3mUxGKCk42N9lc3Md7yy7u3skSUYxzlHCMzw65NSpkxzs77K0NGDxxFkubz2NbPpQeZ7PPKStDYF7d3eXbrcLXpKmKdYaRsNRyJDjAEhGBCk/OB4+5C0XX4aBgccHYRThyJI0GK9JSVlWRGkSQM5NsJ31iQk0VGdqbl5/CVMX7NzaptsdsLa2SVlWLAyWuH59myRJ2L29w/0P3Mfh4QE7t26wvrHEmdObZIMNjPccOrhHxgwn+9hqARnFjXdUiTMxUkLlKv77n/vXpHefQwnDIFrml97/KcbDh+hhOXISaycsLq+xvHqC4fiQajrBGct4ckjVDEw6nQ5lEXCctqpDZqSDEEXU9vKFRQiPkoLKCKQTnD15nidfus43fvu3sPjYA1z9zPN85LmLfM+b3shKuUcWS9L+Bhdvj1i/9wGuPP85otpwNPl/qXvzUMmy/M7vc5a7xB7x1tzXqspaumvr6mlJ3S2pWwIjxpLMYOwxM8bggQHbMB77L2GwB2MYkNE/Iy9jDAMe2TDMCFsMRpJH063ulnpavdRelbVkVWXlnm9/sd/tLP7j3IgXWSpJJZChfJIg33sZLzLi3nt+97d8lyFf+vKLvPvRTbytMEbinEEjlggRV3uLK0mAXX2iDaS9wHgHtR+8XRESWV2L6mgZMG1FEsWoWsJtPp8udRvSRnMJqZOIIDSi68yyVmqsyiqI+dSsKlNapITxaEQ2aDOZjhAtzVqnSaPd4tW3X+WLTz/LvXv32Nvbp9PpcHR0jBSh1VUUBbPZbLlvptPp0uPor5pG+f/5ctZS1Txza2oYUD1dFguZs6qi0WgsT2hVVkRaYvI5//0//O+4f+dDNtYGtFJBrBwP793if/pHv0GawrUnHuM7/+oPONUZUB0dcO30FmWZ4XyFSmOE0jgjcDaImkZxRBTHJEkU9CuDNEOgGSYRg06bZpIGQRDrlzCkqoYWaa2DS1+dtQohaDQa9cQ8BAytFXEc0YgT4igK/R/nENYFK1JjA37TOoT3wYvdOZIoCkIHQtSqRW7Jh4+UX/ZbqYHSOgrq2XGUYKoKISSdTgdrLcPRkMPDIRcvXiLL5mxvbdHvr/PBjZt475lMJ/T7PZrNJsfHx8xm85pIoJb83vl8ztHREcYYer3w3DiKloInaZrSbIThWSNtBA+hOK7FpEMG4fGUVcXt27cDva3I0XFoC3hfH3sp0TWlNar97JV0TI8P2djcxFhHr9fjzTffZjKZcXAwpN/vo5Si3ely69Ytms0W0/mMl199gwsXHidpNOn1Brz5wW2KsmRWZUFxSgah4aODA7wLaAkpB8jOgKgZWFOlVjRPbfE7P3yFmwdzNja2+KVf+HkinYCEdrdPv79OvzugP9hE6ZizZ87RbreDArmOaDabyxaL0OqElusEQmk21rd54vFGgjXdAAAgAElEQVQv8OyzX0aKNun5J/j13/5X/IP/7V9w+aUXaJ87zQMJmdAcl4YHk4wz157k5v09mo023hqmsyGv/OhPsMUMrcDW0mzL/F24ALOzVRAIdqGvLp1FeYeq6cxayCUVEnkiLLIqMrI4X4sqMUiwVfXPwzwiSRIQgjwzlJUJkDpT1FbciyrNYqwNWpqLnqkQ6ChUbMaWdVJVMp7lOJ0wnky4fOUx7t69w/raOmVZcnh4GKjWWi05/1EtJJLN5jx+9TGuXr5S7+3/n7lRBiaOxZowNXYLGIPzCBl0+OI4xhob4C3OkaYJYipIoy7/7t/6D/CdiOlsQr+racTw/ttv8u//zX+P//Tv/Ed845tfpxU10UcH/KO//x8i3Yxfd2M+vLtDVs5QxlFQInSwD06SaFkORzqIfiAMWkWkQjPLMrrNBoWx6DgFFyANQRLMLkv2RqOxhJhYa3H1eYkAZxfUOLWcKnoT+LRSn1AwF8dnVTBWSkmlxdLvpKoqNjY3WU81WoSJe14GYYS4KcjmOVJJjo4P6Pa6VIXj6uWr3L5zEyUV/W6fhw/voaTio5u32D/MSZKAtRyPJzSbTdbW1iiKgvFo9EgwbDZaGOOI44h8ngU2lK5bFrEOAiXOIF2Q40obwQMnVBCivskoDnYOaLfbgb+tZRBnEbW3fN2eWGgAeDxSRBTzjERLqrKg1Qr0zzzPuXTxCgf7r5LnOZ1Oh+lkzJWrl/n444/pdltkueXNN99HtXqkUcK7N+8yf3KdpJ0iTI0jd5bh0TFrm5sorZn1uug0pjJzBJqmaDCXGebCNf7h7/2Qq//mLXZllwIJWoberZQYD1JrOp0OKMlmf5PIRxzs74cbqa+Fdm0NlzOKSMckaYQVTfKDnNl0jkMSeyitR6opnSjipS+8xKvvfEgmUx5M5jQ7Tfbee5OtrbOYccE0n9Lf6FFO57TbMRfOnuHW4SHUkodCgfcKLxwaFZAL7qQsDxKLoOrkwDmBW9E9WLaF6u8X1+ziOlUqiDJ770FQT+UFoEK1JCPwVfiZ9yF5qNNMKQITyLuFXbYIgjf44L5ZlpjKolTKaJLhskPW1gYAHB0fATCdTlhbW6MsC5QK84Ysy0jihEYz4cGDBxhjGAwGSwTIZ1mfi6AppCRtBUweTtJttsmrUAI7QNTl76zKWBt0KQ6HRL02Vy5t8Mff/w7/9+/9Ib/3u98FI4lsm+eefpHHLl7kn/3j/4ULp87y3/ztv835U1vY6THGZJQ25u/83M/yX/2T/4N22kGnAoFGa4lzyTLwGU+YqIqg0WlsBVqjVYQ1Hi0jKC1OBGm7qsY8Gh+yvNlshlKBmRBHCUo6hNR4C15bdASm8ggRfK19VF+syi9B5UsIiFCoOFif5rZAmMAYitKEZqNNLCTtRhKm+FnO2voprM8xzqO1YzabcenSJawtGbk5lcnZXNtgOp1w8+MP6DSa5LMpg26PGx/v42JBXHPgFxi3ZrNBq9ViNpsxm80odEGkNHEUI+v2RaRjjKx7mdbXMn8KtKgtD8xS1q604eIf7+4He4SmqsHPQV1fIZbgcm8sUkuiOGQvXgo219bwAmaT4KB55857PP/88xwe7SOk5cLZC1TWsrtruP3RbRQKW4JQho31bWamoPQZ4zkc5xkX2n1KX9GMBYoWSXTMbDInMYqd4T3a/Q1aVMStHqUpaVVNcmdon3qScW4Z9HvIceif4TymKDFCkE2mvPTic7z3/rs8fPAAhWZtY31pZlZbP4JQyFghVYxC4wpDLh2pTElq6T7wmEoQmxnv35iwOWjzMM+IWy32Do/o9Xu4fEKSCuQwDDPb3S5VbvmVX/5F/sd/8i8wzoWdLxbybytBUNSW1LWQt3cuaBYgcC5AfyprHylRF8FyVQ/BOYewEC+GlaIWBLegtUBKDwQBcFvfOI21SKGRSgVVKuuXqvGibj05Y9l5uMOFs6eY5CXN3gC855nnXmTn3q0lB15KyebmFnv7uyHZSVPKsmJtbQ2tNXkZGH393hqj4ehPqcn/eetzETSdc8xnc5CCRpws4QJJqpnMpkRaMhoPacQJs9EIqQXlbMozV9bI8wl/71d+nkanhdYRG2uhROy2EkyeI02FIMGZISUGgcZrRYcRT50/zQc7kxDssPgkHI5FuRuvYNaW0+uFUIdzWOuCiPEytZcYF+AeqsaJggwuePM8DBakrLGhgeOr9AL8vrhLgxfhLr+kkEZpLYXnalvTYOzW6Q2wiBpqJNja2qCaHtLp9JhlGQKLNVMunL3InTv3ONoNboGddpPJcIitSpqNJuPpmKjRIs+DG6ExBqEXXjAnXOlQboUyuKpKiiJI83lfYI2vL2xFJELgMwTucl4US6B+WZtsKaWo6gGSFxAlMaIWZwjlo0J6lkyvRY+4LEuUjKisJ04a9PtdhseH3H8wYfv0Bh/cvFHrB0im0zFpu8VgrUc2DfziIrfkZcG9e/d5/KnHwQVBW60btNtdysog4gQ3r0BGVBhazRY//vbLjOcFcQTKWLTQNAZN+lHE0eER8yrn6N6cLAvKUe1GEwjVRJI0+Ojjm+zs7AR1rCSlsh5jHFGU0Gm1SJJW0DUlWL2YGu6WxvGS/+2MCU4CCtqdNqe2+rz66qus9Te4cOESadqk0UgZjycMR0O++c1f5Ec/+hFSKq499SSvvXOL6Tyn2Wnggy/Bp+7F1bWcjtclt10MgVbA8ou16FUvUBerwPglkN9ZhA+CHPhaY1OK2jpYnoDfhQIV+OTLKb2o5R+dQUmJVIJ8PqYVt7n58W021zrYLEAUm40G1hiaSYJQEbNZYBYeHR0RRRHNdgtnHcPhcDlo+qzrcxE0BcHQbNGgFdZhsjki0qRSoZsJm4M+RVGwsbHB4eEx+eSQq6f6dLs9BAorDE4IIl8gjcHPYiIZKHmlL4JTowr4SwkMmpqfefpx3nv4Y4SK8N6c4ESlXA4gFrjPBabM2IAxs85hrENHgjLPA4tJqdrgXkM9MMnzssYwepwt8T4oYEeRpigypA4iIgExAKBqvKDEVIspZ15P3D3CS9I4QhKEYqV3KOGJhcflIxSG4eGEn/nqz/DO9Xd45gtf5OObd8ALnnv2Oa5ff4uiyjlz6jSTyYRZFpTkkyShalq2NrZpt++Q+xW6aH1M5rOMJE0o8gKp6sxSBYUka8FUBq0DLEXp8PPCmkeslfv9fqDMRjHG+4CRDNJUYXM4B06gZC3BI/1y0y3l+2pLj06nzWw+ZmtjnXs7OzR8g/W1NfI8ZzQac/fhDvM8Y3t7i+3NbYSQ7O7foTfocDQfcv36u0wmE7yFSMuaACHAJ0RJxLjMSCtDFLUwBoo8VDxVUVIZw6wsSOIA27FCU5gMgabVSEGrgJBA88wXn+M73/59Gs2UJEnJFygJ53GVwUympIWj0WwE6TJsrUwVo7Ukrod+UiucdLQTyXh4QFVOcTjm2ZjXXnuZdrvNzk5AdTQabd5++x1arQ5PP/00R+MD1notmhqKIgvQKqjZO3UraIWIsQDfA/XXJzhPQUgaVuFvqyIjksDuE0m0fO1FCb9ExiwuCeFxPmTQAEq4upyue7y6HgTVA1VvPN1ej9l8ilYqOBBYg5kbpLB00yZCK9rtLpPpGKk1o9EQKRTdbpdmsxmm5UIwzzOuXrqMtYb9/YPPHK8+F4MgUW+AZqN5wrJJG1jnmM5zrPVMJnN8aXmwu4OwlmevXqTTlETkJCIjISf2BhMXuNjgpMU7g7AV2oPyggiJdhZVzrCm5MUL55HVHCWCh8oCaLuAC30amFfrGKUikjhdAu0bSUKiAqTIFRW+DCyJhaL3osfZqUVr0zRd3pUXFK/5fL58ZEVFUVkqG0DnC4GDJErpNlokMiKJYmxRBnFX75Decu3yOb7w5FM8/dRTfHjjffCWV159g6woWN9Y4/q7byC04/z5y4xGM5548hlmucHLiOmkoNVoh6ljtxXYUHWmoJWmKkt0HAVqqgqbyNYNe+8CtlRHwSrYKkHpbbjppSlxEtNqNknjBGcdcRQHqIl1lHkRsgYp8SZgZhWBkrcImIvhkaif5yQo6bl6dpMkStg/nKGIqXLPZJix1t3E5I40bfDiiy9x6vQ57ty+x91bd2k2GrQ6DZ5+6hpbGwOUCjctpSLm8xyAyGkqEfHbf/ATXFZQZFOeunqVVEqU8VQlSHRQIC9LysKQ5xVN3ThBTNTHpb/e543X30DImDhqkuUBSrS4NrQOItSlycnyoGta5IZOu0+71aTdatLptNnc2uDU9hZbgwFXTm/iypIir9ha32Q8mrC5ucnu3i4bG+tsrG/wcO8B3X6bS1cu8MOf/Am379ykGTv+h9/4r2k3U5Q4GeJ8kh0Ej/LSV3++uhYA/cUDCMLBQhJ9IrQskA+VdSAVnoCucAiU1EFAW6laPi4KYHfEUnx7VVGsXbfydncfMptMyUtPFLcxVuK849KlqxSVYZZl6DhmY2MTqSSHh4ccHx/RSBs474jqAD4cDpdkjs+yPhdBE2A6nRElcdAgdJa8yCmsAS0prMGrcKFKFRRPnr56uR6eENzqvEQR0a6apKaJshGICKs0ojZNM1JSCUFBKINToKElkff4yi7vbs208YjG4WLCCUFWa5GBBXFUj/Qq4BWNJ5IRWsf13b5Rl7QnmoQBp1YRktk6OBCEkBcPIf1SWzBONO1miyRJSKI4iLR68MajpUYKhbCBVvpwb4cHO3vcvHWLuNXAGU+/v4ZzcHBwwMbGBuB5sLuLjmJefeU1tk+dotFu1/x5yfb2Fo20EbxZ6kwbGTJN7xxKK4yxoWEvg38LBHEFIMjeidrBc8HblzpI2tXZxOJvJSW2rILTpwxhKJYKHS1MwB61isAHCT0rYH3Q5fhol/lkSoWg3+lSFgVnTp1if2+PbqdNouDmB+9x//ZNymwCvmSSTbl79z7vvX+d3b2HzGcl3kmqKojWKhVji4JZmfPB/T2qPGc83Gej3yHSkiLPA7wraWBLE7C1kQ7QoTQljiTeVeAq4jgoxncH/VB1lEFLIYlCNmrKAhnCBxCGg1VpKHJDUbCc+GZZxmg84ujwiNHwmLVel+16QjyfTuj2++R5wdpgndu371JWFRdqB8Y33ngdWaM3bn70IaPhEaaslkH7EdUjPj1ormaKq2tRhS2es/jdhbXE6lqyjEzg83tfKxotLDRYKH0FsY88n+OsCUB6BQi/vLkmtf0FwGyWIYTCo5Ai5rkXXuCV119ld38P4yzHoyGTySSIgABnzpzF2PDZT58+XfdfP/3z/Vnr8xE0vaChm+TTPExVrQuue/UmwoVBTdxJaTS7dHTF5VYKSFACI8JG8tJTAl5YhKhCxuJBmHq650H5UAIopVCR56vPPouREuOqOkh4SlMrVstA3XKCEw66CIGhsqGv4rzA+Iok0TTiKNh1RIo0bRJFSRC1aAWaltQRpuaaF5XB+kUfiHrDRkRphFYxymsSH6ErR1GZgO9c0NrqYGSweBWgHS8+c5k0SphMjoljzdH+IdN8xmDQI0016+tr7O0dkKYNqnkW5ObwVLMpkQFnc/J8zubGGq1YImONUEFlSWu9nGZa8wkHQkBoibcn4gi2VsvJ8xznF2OfsIw7ETaJ4uhRPKpUgYJpbdDAER6Po6yKYIbng94ppmI8OmJ4fEyz16HMx6huk2lVcW/nIbN5Qbe3RdLoYazjzNmLpN0+l564ilSKdtrAFZ5Yd4m8J049ptFBSgJPRSryecZ4aqkcuOmE0egYtESnjTAcs0G8VugI6xyRpO43KjrNDqe3T2Oc48HOPW7eeIc4CuK+URQxL3O8gK1T2wHrai1pEtfCKUGd39sMISxeQOlzTBn42LPZMU9caCGbKXlZ4KwmzzJmsxlbW1s899xzKBX66Hfv3uHatSdpt9vk04o4brO12eLX/ov/hFbapqqFcBZW1UEsJjxq/5jw8BpXebBhELPqNeRtsMjQ8qQi0zVixNV8ee/tMjnQkcT6kEAEuwuWPcWqNo3zDpK4gZMy7BdrUTX5wxiDkIqsnDMvpszLgtlkjDMVWVHxxtvX6ffXOHv6DK2kzZlTp2i0mnT7fYyzDMcjoiRZQgODQ6rD/AVKYqvrcxE0F/2q8I2gMo7NrQ0uXbpEv9sm1gKM4Xgypjg44ld+7nkiN/tTr/FnsRTQgfGAsBRVQVFUOCtoRnB/9x7OGbxun2Q1PkAljKklumzQypRSh2nwSuYZQN/1SYgi4iQJ+ptIIhSRCF/HIpRt8/l86T+0WLKmm2kZhiix1sSRJo4j4jQh1tGyRKVuxkdJ7SHkHJWZcvXyBebzGd475rOMjY0NlNLs7e1gTDD6iuOYqiyRwpJnUyIFX3rxeQ72d+h120gc77z9NnGimc9my2w5q72rpQrZZZBvq7PDxWZRodwikksKZbPRPFHEcSFIpmlCGidIJTk+HuLrIdgCpvLJHvKqR82SiSI1vVbocx8fDum0OoyHE65euYLUgv5Gi7SlUEpy+fIVxqMxRTHn3XffxVSW6bwibrZI4phYOZJGAyET4qiBLYIzKrqNa8aYqMF0OgvKV96HwKgDlMrZkE1LX9v0GsNgbQ2nDO9+8DbGVMzG2bLlAzXd0BiK2ZzhwSFVUdBMAzRNR5JmM0GnGh1HKKGxpgJv0XGEt4ZvvniNG9df5/7dW+jI46XB+Yoolqyt99CRoCizJY73zu27wRensiRJyp2bt5Cz+6SxeYT/vrp/Vsv21cn4QgMAeEQicemR7jxiRaNzEVxXnQ5kPS9YDIqoX38xS3DW1i5XfomcwAcFJCED2WJ3ZydoRlRg8gqTF5RFgdIRR8djZvOSrCgxrmR3dzf05OvQMB6POXPmDHmec/v2bXZ2doJ2Qnzi4/4Xrc9F0BQCrLdEdb+v32szHA7Zvf8Q8Gytb+Bt6DM2JXSlJ46D9JuzJzaknywVfK3A4F2J9gZhDaPZjNE0Y1bMuftwj5sPRvVGrSiNXWaCISMKG9h5j62HM4uS5pEpovcBjF0HFSVlAAwbE2BBSodAIWXQKlzR01wGBwSJjkijmFhJlPB4avECZ/C2oqxyEOFubdyJL/Zav8Prr71CUYSh0/rGOsfHQ1qtNtYF4oD3juFoGASeWyk6kpw7f4Zbt27yxS8+w/HRBOck589fJI2by2npQt0m0CBr98IlRKUWfKCWvlMi4OF0EJJeDIBULUALNYdYBsOr4WRcZzf+EYGIVbzfqljKohdorSVRglObA6TQ5NmMYjbl9gcfsNbvMhmP2N/b5eBgn7zIKMqMc+fOU5Q5SZKgdLgB5VVJGcy6+TevvEZmyuBFpAV3d4+gEZNZSZK2cFKidGAwOWfQMsIZE7IUY0jjlG63w8OdXYaTCa12h35/wGwyWZ7nRY88UhpRg8atsTUuM/R+S1eCDIInFomSGqmDZmU3laTKcWrzNDhJu9VCaEWj0WBtbcDrr7/OjRs38N6zPhjwhae/WF+Lkkazi5QR2bSglST82n/595bva/UYL9YqFnPx3o058W9fZf5AjfusBToW2Wgcp7h63wQny9DSWkQw7/1SgzaU6gFkvmjfWBeA9wsKsqqD+XyeM53OMcbiPLWyUkFeVqSNPiqKGQ6HCAGVCd5D0/E0eHxZx4N794l1xKDXp9lqMcvmHI+GnzlefS6CppQSJYK5XLMVkRcZvs5M1gcDjg73wFliCc1GjEZS2TBAWp3MBhZCtBxiaBXVvNrAsjBO8MaN24wKzXCSkxkYm/rO6U8uAmdBqogoSk7ukELg3Qn1Ma59msN7l5RVFcycrMVWdVbhPUkUskPnXKCCGbP0eF69eysVyjdvHVqA1pAkEWncOBH8kCEQOTymKhAiwC/Ond0mjWM6nS7eUwcfyPOM7a3TAVNZGFrNNlub26TNNqPxlPFkxu3b99g/PCSSEVcuXQY87XaTdru9bMovAlpZFKFfVVYLBhy+vsFUtTK5F/V5EXJ5QxMIjDPL73Gwu7OL1AqpP10oYbEZVzNOCBlO2kj40gvP8Owz1xgOj3juC8+wtTng577xdfb39thY2+DCuYtICWdOn+Hg8JD5fEIcxwzW+lhhORoOWVvv8fjlS0gZMZqVGOuJ05RGr837H92iuz5gPJsjk6B7KrSkWcsCCilp1srgg16fNE05PjrGeUdRBpsOU1bY6kQmbYnLFAt/oACtmc5zSmvQSUK72yZOI5TWuFqw3TrwvuArX7pCpxvhlCdJU3YfPEQKQZGXPHywx5NPPkO/t8YzT3+RXq9f23CUXL50lbTRoNkIE+TSCo4Odsjz/BGTPuCRBGRx7D9trT5nEUBxJ0rtC9LFIuB+2mstkSp1wFz+vH4/ru6nB1vkEwm67a1tJuMpVeUYZhmuFi2fzzMm85KygrTVpNfr8uSTj5OmAbLX7wdR6qIo8DY4hpoyGAsm8WcfBH0uIEfOO3SiSRtNZvN53dgtKYzk3t4BXsc01vuYoiDpJhzujEjais2tFl5rdBTjvQpeI7YCq/BaYWVN4bKeWTbh4KDig2mDud/n6qk1hoVEJA5hE0rhSRZBLY5rQQm51P9z9V0Rr2ql6ZMSRimFq6o6u1I44ZBaoJ3EqzAlRoV+qYriQI0UQcrGoVBeoCIFSlKZYLWhZVQDfSFW8RIjJ0WwQZUiwZDT1CnaFeBKDo/nXL18mTs3b9NsNpCxZjyc0W0NcAacURw8nJDlc1549kXefOt1HnvsMs477oyH3L5zg3JmWNteR8sAjYqjBOMNWsWUVYXznmYjRdR+7wbwbpHl13atYiVAADKWxCrGFYY40rx24x06/Q5S6Hoj+LqX9yiCYSGOLJDYsoapSKjykuHBMd+7d5fBoMONjz5kOJwwmUzROmF4NGRzfZM4Trjx3nWuXLrAdDKnmluGw0PSRky316Z0hq2u5iBvo/xD0hxGoymDQclPrr+P6m6xN844m8QMpyXt1oBW6jFK48swqGw2GhwPx8xnFonGeUcjjUlaTRINSpR4NLbO5ow1mKRJmtYVB8HD3kxLFI71jU3wEhGDFeCqEi1jtrqShh2jpSRN23zhC1/kBz/4Ae1mG5OVSKl5cGeHwaDD++++y3g+Y3g8oduPubdzk7VOj9t3H/LMM88wmU25tHaKJAahIipbEKmYyoehKJwA1hcrlNt2WcGt4ohtHTSdFKg6WfXeI30Y1EoV7IR1FOxgopqkIQM1CSV9PbcAhEbZMPxdmKZJHeGAbDbDCUe31SDLMh7sHDLotZDO0mp3aUodmtIqWGSMjocMj4/QcYSOY7wQSOUpreHK1Us8fPgw9P031sjrFtRnWZ+LTFMgEF4xHo7BSqrckKYtqjIwBMrSMBmNMUVBVRpy49jZ2UOjSVQDYxZafxKtgttd4CmDcJKidBg0D4czHhzsY+OUuRMczws8wcflkfK+LgsXG7+sqpq3HXCDOjqxMF08QmsvuGoKGZgwnjB9dj6oqishg4rnit+K9ISBh3U4G/i5i15OZaolJMnUpb6zDmMXk3gFOCKlmIymDAZ97t66s3TgTJIEKyBpBEqjjjyVmbM2WGNvf49Gs8nh4SH7e3s0Gk06vTbNZoP1jc265Az2E4v3o+p+lKkV9p0NzqBixUp12euqP2eko/AcIdBpEEkQ9bBHyEe1QlfXArqzKrXnXWCVCBl6iHEzBSKKQtDp9zk8GjKazBisbXI8nrG+ucXVx69xNBzjEMRpg/FsQqKC+Rpo0t4aEotNOzS2zyGdIS89eeVppA329vYoZgUPD49YWOSWeYGoxasn0zlp2kAIH9AOUUScxCgl2d3fJU71I22YNE1pNZpLwd3FZ2u322itGY9HzPMZVVWBs0gZ4UzFtYtnmM/mTGcjrl27xh//8R8RRRFFzdO/9NhlvHQ02i3ysuQrX/kKp05vce7cRUxl2Nvbo9lskKQpeZ7zwQc30Eri63O5EJz+s9Ynz88CWbGQcFzte662VuBEFWlVJ2GRjCzO8yOZqHAg6gFRve/Migum9Y7t7W2arRZFUTAZT9nb2w1K7VKg4ojpPKMz6C99xRZkhzzPSZOE+/fvE0UR62trFHlOp9v9zPHqcxE0PZ4krlW7FXR6bZwpSJKEIs9qkGuwHRhPpjw8njIt4fhgxvg4w+YOicJ6E6TxKxvYMzUN0hee4azg//zW99idDHntg4/ZyRx/8Cc/Cic/1MXAJ/Bon9i0q5Jvi7JmkRUtvl4l/i/0PxcXj1YyKNdUBToKqkpSLXpCJsCZapwqIgQd50+m0lkeBGyllERJcEmMlOf+vTsh2zUVG5sDLl++RJbP2N7eZFbmDCczjkdHtJsNuu2Ujc01ZrNxMImrKjqdDqdPbTMcDrHegD5x/Vy6C65gVo0xFHkRGFF1zymNYhpJSmFKhAxUPCFPbhBCBkpe2mqianm9R66BTwmaAIIT7/jFMc6yMa1Ok95gjbjZYuP0afprG5y7eJlef0BmKsaTKXt7e7zyyitBjHY2JcumXLp8kfOnzzJo9fAGjJJQ5rx1+5D//f/5PspYfNpDtlrkpWFUSTauPMP9oxGdbhNf93WzLKvPq2A+y+ubAHTXBkRRxPhoyOHhUdjEC2ZXnTlPxmPKPAyAFqIuSkcYEwgTAKUp8VWJQ9BOoKVyvvnNb6B1xB9+51tYY7l8+RIvvvgizluuv/0WV69e4sb775Okmtdee41GI+XocEhRhuvz6OiYWx/fpCxLZrM526dO1cB1D8hAY/60/fkpwXS1T7vai161tl585tXBUlBxCkiQZSAUcnnNQ0Bk6JrWuBDBVoQgOp1OyWbFks0zGAyweO7ef8Cde/eZzWaUZYW1kqPhDGsFcaRptlLWN9bw3nL1sUuBHlpVzKZTIq15+ODBp372T1ufi6CppKDVkvR6MWv9FGemgAGfIyjodVO0tFSmpIo0r+3s8ebOPod7Uz547xb3bu+QTedUsyIAw0uLtfYk7PsAACAASURBVB5bQlkYiumE4WzOQ+Pp91MyY/jeq2/y1V/8t4hqho5iBX9Zl1LeudCMrodBWgdBDImvG/keZ02ghTmLguA6uJCyMiaIUywyNQw4E/CWizuzVMRaESmFs1XwAHeupmmGC3BhG5AkyRI/KhOF9JIzmz22Buvs7O6z93CX+WzM7t49huNDbt+9w2w6DyIFSYPJeEblPPcf3CbPM3r9ALbf29tjd28/AL11wr17D3B1KbmaBS6a+FKFgYg1FjMvMHnOfDonzwNkDBdU9suiZDqfkWUZ0+kMFceUNjBbyqKgqkqkFMRxQhwnyPoPDkxpMDVJQAiFc2CtR+uYXtohz+eMsxFG5Ny5f4PDoyOEUmxtbTGZTNk+tc1kfMhLX3qefD7izKlNlPR00gYf3ngX4Rz3795mc3sdjCXp9XnnKOPil7/BH13/kMHlKzT769wn5dd/97uowQbz6Zh5ltf99xpf6ATWQpoGxaLh8TFVWZDqmNFo9KcGLdZaFIIyyzHWLm2Vy7wKg9C6faOFoCoDy2VzLcEXI777ve/SSFukSRMdRXz4wUf86Ic/Jk5TOr0ub7z9Or21PsPRjHNnzpJnM/J5ThpFTMZjtra2aLXa9Pt9er0eW+sDyjyU5tLXvkIra3neV4Y+q8PWVSGZT+I6F+uTli8hsQgDIbV8TXFC6pQe5+u2Tl2eYx2KhfWFD+gXBP1+l7feuk6apFgPD/d3g12Jg9ILslJgbTBN29nd4eKFixhjePhwh63NU3gnqEqLNZ6nnnzmM8erz0XQBJhnoacwqz2IZf3W4jgm0hFbp7ZJtCYrSqYO8ihBaEmj1eDo6Ijd+/scPdzncPeQ8dGQfDgjG46ZH47Iy4L7u/vkIqaqClCKuJGyc7DLN37mq8EdscYffhLysrpCE18sH66enC8ECow9oWH6OvC52jTNO0eiNThb49YEtp7Ehosy9PSSKD4B1tcN8uWf+rUFgsIURDqhlWiqMmf71BbNdotOp4PzhvMXLlBYw1o7ZWujy9bGOmkrxVSGLJsRJwGp0Ol0WBusoWSCdZ5sXuDRS8risskPoaSUJ5CTkEV6nHE4ZynKMgTK2ZQ8L5bCFUDwb9GKRru1LM9WJ+WrU9hVlklg7Jx4PpVlyRNXLmHKPIimqAat5oC0EeOqnKOjA4o8486tj9na2KKRJDz95FMc7u7z/BefZ6M3oLSG2/c/RisoyxG5sRRmhmk3+Y//29/gB9c/Ymqho2N0olG9BlZHy7bHyXRYg3C0Wz0inXJ0cMzm+gY2r8A5pGNZhaxCdwSQJkmAytRYyW63i5S6PqZBzYc4YTo65OqFc+RFRa+zRlUaKmN48cWXSNMmAo2oz5dWCVpHNNIW+3v7XLt2DSEEX/nKT6OjYEpYVRXD4ZDZbMbVq5eXCAnhRSiLV9YnA+OnrcVUfXWYtAqQXx0WrcLGFgGTeh+EiypoKEglMEuwOxhr62FP0L7M5kVQOTKGixcvEtdZ6f7+ER/eucl3f/ADfv873+X3v/Vt7u3sApJm0uZPfvAjOp1Auz4+PEIh2d7cWlpyf9b1uQia3ntMUWGKEi2DN5BIJEprkijm+PCAsiggDt7lvY0gAVUZh7OGaVExnBt2ZzNm5Zj9wyFZYagoKaRDqISslIynGdMMhHDkxnPr9l1UGvHz3/gamSkxzlHaMmgA1mDchSvjYpmqIFSuDlc/r7QmiGwoT62PHC6WeoC07NnETUq3AA8HcQLhgk4mIvg9IxVlVS3xbM4HjOgChhFG+56GShDeIITjwtlLNNI2aZJwcDykv3Ga7e2z9JtNTm+dYTqcUpU5RZZhDAwGG3S7ffb3D9nbOaLIHSoRtNM2rY7GyQRjqGEqoQ3gnMOZColHC8DWNFUfetJRGrLgJE0CDU5r0kZKq90KzCilyOYzbt28SRxHVFW5clzDECn4y+m6d6lQMkJUFm/BFDmlE+T5nC+/8ARnLl7BOsfx0ZjZNOf5p57i3JkzjIdHbKwPuHjpIvujgu98/yf8+JXX2D8e8857H3H9ow+xwiFjePGlZ3BHU44nx1QuYlrM0BtnsY0OsdNMzRxnXWA5EVEaD1rjhEQISVEY4qiBjiSj4YjLl68G+xSpgxQatpZHO1E6D95PQZl9OfBSmtJkCAlSObQW4B2lKVhPoO0zorjFvJzhPEQ64b333iPPc/YP9zg+OuDC2fOsDXpB0xXDhcuXuPH+h3SaLd55822+8tWvs7N7wDwryLKc9fUBp9Y7pJEgFRGREIHw4B59YGuuuQ/d+9KFAOZ9ECe2tiIvPdZIvK9Agat7+ShH5QqEDuy2gGipE4AacoWzaF8LIjuPdTbgo11Q/7fWBj1MUxFHCVUVTO+qqiLPCg4nE16//h6dQZ+HR/u8+c677B/ukxdzhvmM7/34NeZlQV7OAcFwOOfw6Ji03cLUxIvt06cZDv8KIUdCiGtCiNdXHmMhxN8XQqwJIf61EOKD+u9B/XwhhPhNIcSHQog3hRAv/kX/h/ee3FYU3mKEw9eBp6ozl0ajyf7+fpgaW8d0NCNt9dmbDBnlQQ1aO08rCmZpWWn5/p/8BCljkiQNAU1KKhtMtKazOd6WIAxIg5COr/7Ui6Gf5yWV9UHIQgQqW1VVUAc/HUUUtavgIrAtsItiBR4R4psNsIn6ucU8x1UmUCFrcWFYwcQte59qqQQkVzKUxdJSBTkvW6GAaTYlSWKuXLlKnufs7e8zGk2YTqcMj49qncqCXr9FsxVgWAhBkedsn9oOr6ljRuMRw+GQ4XC49KN2daYRAlyYglaVxREgWAuoiHcukAiUIq5FpKkzLWvCMWg0mhwdTR7JYlftQYT0INxyuIZwFFWOUNBoxiSJQirLyz/8PrduvM9sdEwrjWmlmldff4W7926TpDHGlty+9zGmnNFuN5hmOXEaMZ2NKfI5s8mUa49f49bNO/z1f/uXgsiIteR5zu7BPh/fvUVlDYVxoDTW6eAc6g24CudKhDe1lXGQGOsNeigtKcoCIUWtAcqyLF/wzJUKqlgCSeWCWKhUGkTtfGpduEEKyeZgjZ//2k8zGu5T5hm2tPR6baSELJvS7XcYDLp0Om3G0yGdToez586itebDDz+gMBUWy5NfeIq3336Lq49dZjg65pkvPMNkOqLRCNnzLAtq5qY6OR+rfcp6Xy/7ysuqQECj0aLfaWLKYrmXhVgIENtllurciccXK1jN8DthCOXq3irIGrMZrv2iNtfLsgIhoNPpLPukVZGzttbllVdexRtBWZusOe+Jo4S8rEK1ohStdpPt7W163S7FdM758+fY29vFGkPU+Cvknnvv3/feP++9fx74EjAHfgf4NeDb3vvHgW/X3wP8EvB4/fi7wD/+C9+FEAESgCDLS6azjPksI4pj2p023jta7TYRgk6nixCa7/74bf75aze5Po25l6VksoEXEOuEWVHy8b0HxCoOdzHlEdIxzz3NZhDhtT7wwN966w3W+j3Onz3NC08/Bc6hdYzgRO15MfwBKIsKrWNsLaSBUKGR7gWmCl4niwlyOHG1vp+QlFkW4EeVRRiHMv6RgLnwQymKRy/AxVq4QvpaqHm93+DMqW3OnNmmyGe8/+77PP/sC0gPB3v7aKmD3JsObpFnz57h7t2gjp4mCY89/jhFXqAizWwyR6GC7JspqKoqsILqNsSijFu8p2BRIHBS4EN8rDdeuZya6/hR5lRVlbRa8SPWsJ+4DADPwivemIq4kVD6igqHsTmxcvTbMWdObRALQStNsFXOqbMXGU4zdNTEeYkg4pmnLtNot+j0+mxs9Lhy6RSnt7dpN5sc7Q+5fP4y3//B92i2U9JmG5WkJK0mXjjuPXjAwcEhd+/ucvfeAR/fPuThzpSPP97n4cMJd+8N2Xm4y3B4zHg8ZvfhDg8e3A/HyAXZs0UPGsLNdzEQKq0Mhm1JSpy2mJdV4GNbQ5zERLoRrJizMeODu9gio5FGKBHx8OFDirII03Z1Ikw9PB6z83CPd955l8loznPPPUekNcZY3nrrTTY31/nwxg20Urx7/S0GvR7nz2ySzXPSZqPuKT4q3LHUTFi5/iULKnL9nNLzP//mbxI5E37bhQxyiSuWK8ru3tfGia626a6W/1cgkQjwGnwELqglRSoohuE8UaSWw7SqqkiShMtXLvIL3/gpTm+ewpSWvCgZDsc0kw5PPP4UX/ny1/jjH/wInaQ4LM5XQfsBx53bHzNY6xGlmvFs9BeGqcX6y5bnvwB85L2/Dfwq8E/rn/9T4N+pv/5V4Ld8WD8E+kKI03/ei8ZRhM1Lus0mwjoiAohbScl8Pse5gPpvDQboTpu9gyFZbjh79WmOMsvEeeZljiknmHxOUZY4paiMQVhLNsuRQpPqMKg5tbWBKTxV5SkKx/7OIbGIeezcac5trYMrkYCowuFZUL5EPQRZBA9X93NCjzNYBlfGUlULjng9ea/vvKU1S/vaRZ9nlYq2WKreaKv/vjQlMyaINqcxly5sMxuNuHn7I7rdNlEkufHBe5iyJIkDxtM5R6PRIssKrl9/h4sXrpAkMbPZjN2dXcqqREnJeHTA2bOnccBsFqTbFv/vgsYWcJWhQ+D8ItMM79k7R6qCaVee50vqpHUWJJR5QVLbMC6FQOq1CldZhacshwwubOBYKy6dOcXp06eYTzO0TjkeT6ms56Nb7/Pkk1eZzY/ZeXiXL7/0LK++9jqnt7axzjEaT5kXJdfffoe//su/TJqm7B8dsvPgAXGkcMaEm6UQNNIG/V6fdjsYcWktiRuK3MyRMThpkDG1ZW+w8tjcXq9VnsJ5bLVaQAiWCybYAqLjvSBNWzgX2i5SgpKB6z+ejMnzAiEEP/uV58FNSeMGX/v6TyGk5+7du5w/d46NjQ0ODvaRUjKbzmm32jjr0Sql2ery/vs3iGQECLJpxt7uDlEUs9ZfYzabMTwecuvDj0LSJ33AitYVwOL6XCVVCCECBhcRQOxCLTPo2x++y3/+n/2tkAx4HlEIW2Tay8l5vWfm86CUHrDttXycqHuoIgwdF9lpgL0FoZhVJEIcReAcO/fu8PWf/jJFYbl44RzddpPLF85zenObfq/HE9eeRcoULxW7u3tEUYyOIrwTjIaT2qf9swfBv2zQ/JvAP6u/3vbeP6y/3gG266/PAndXfude/bNHlhDi7wohXhZCvJzlJa12k6LI2dzcoNVuLjM0aw1aK/b39/ndP/gh3/r+yzw8HHJ2ezsYNilPJeEoN1inmWaGSmlEo8ksrwJf2ECqE1qNhEYUsb29sXwfcRSxv79PLDVaSV74a18gajiMc3jUktmwWFpF4QIXQfcyjpKlWjT1oEbWF55WdVboPZWpKL3FSvBa4lTNnqmD8UK4dfF/rUIwvD/h4S5uJuPJCGErjvaP+Bt/41epTIE3hn63zdb2RrijW8Ng0GUyPWYw6LG+vonzlm63R57nNFvN2v8744UXnmNnZwfvPTs7B0uOuXdBGk4tPtdKeQbU4rGBAmqKkkToJYXSE5SYVM1Fd/jlQGcBtVl8ViGC82cURY+0IrwtEFgknjgSPHH1HKODA0xV4I1DOGg2GzSSlDdff4Onrj3Fz371Z/n273+LJ65d48c/+iGpjhgM1iBKSJsd/vW3/pAXvvQiO3sP2Vrrs97tIjzY0uKsZzqb1z3XAIeLY8X6epuNQZON9TZbmx3WBy1arXYdEMKparebAZMqHx1qLUrVxXFLopg8n4EzNJoxWgZolRSeONJLuigu46f/2ktEUZM//O638MLx+OOPc3h0xN07d6mqwC7b2Opx+uwmDoNSnqrKaKcp8/mUc6dP89yzX0SKiO3t00xnc9bXNllbW0ci2FhrkhUhSAt9QlcF+CQszFob9Gg9SwnH+XxOIxWc3moss+rVAdAq1Cicb4e1VT3UpO7Z+6DWrgSOCkeF9NRqXpZYRzVe8wTC5JyrER4OYTVJ4vnyS09w9swGa4MOBzv3sdmMSHmiqM08t0gRhLurwjCczhisr4fXMo61Tv9PBbs/a33moCmEiIFfAX77k//mF3SQv8Ty3v+v3vuXvPcvRToYpanaBreqqgAByisKZ3jj/Qd8fL+g2W4DksHWGleuXcLLWu5Neu4Op1w/Ery8m/HW3SPuVhU3bj8gPxqTO80r73xENitodbo04pQ4DeWBwzOdThnPJ/T6fbqtFl/50ktgKkyRL7M950ywu1BBANevKB95b+ppOYH3VmdmC0UfJSVRnWkopYJSNR6nBEaEprZE1BRLdUIpq8uapQmWkkiRECUp5zcGwVmzn/J//ct/SWEquhtdUJIL5y+RZyVxI2Ww0WEyG3Hx0inGk0M2NzfZP9xlc2sdFcE0G/Jz3/waH334EZ1OcJc0JlAmF8Znzgfolal530KIJRDa1Re+Lc3yM+RlRbFyo6mqCun9YkZGaSqsf1TEFkBFOii818fX4XFERAJ01GR8/ICo1kGcZxnr22tsbA4o5znD4ZBIN3jz1Xf53h//EV/+2gsMj8ecPXcerQXzvGJ9bZskSpmPC37y8qtYV2KB0xu9MNQwDq0s7SRFWMd8Gix3m80GzTRl0Omx3h/QaDSJ04ROt01v0EZoUEpjjcdRIiQUZYWSCVJGj7RZoigKBIkaQhbEpIMLK8ZSFqEf3m9A7EZcf+d9JtmIbmuTKi/Ji4K1/oC01UBqRV4W3Lm/w717D2gmCcZW9Nc67B/t452j2+ny45+8jNaKnZ17WFtRFCWj8RiL4Wd++meIVc2WkZJSOAoXDM9OfIJqzGWtcYmrmV82XMOPnd7AYFEqovIOLTVGeKrcIJxE+oXQsQVOEBH1SQYn8MbjrQ14aR/8zWtoNkWRBb6/Dp701vjw2U2OFo5JWXHr7gHj4Zx33/yA577wHEcHx9y/twfOYE3BfD6nsg4tPUkzmBrOxhPy6Yzh0RHTyWf3CPrLZJq/BLzqvd+tv99dlN313wvz4PvA+ZXfO1f/7M9dgkAPdDbY9kqlKE1FpGN29o6xtsL4Ah3BpYtnsWVBLAjCpSrmSCjenubc9jmHWnL3uOC3vvMy//zd+/yD3/odXr67h2pFTKcjSpvT7fUCOBsYjUbBfxuIhWazv8bXv/o1hDoB6xrjcEYsy+M/ay37N4SLzC/u3O6kQf7I517CblaVYcJDhvi8nMRDuNOaIqPfD3Jzs+kMJYJavHeSB3d3eOftd0jTJt3ugFu3HtDrDnj1lTdopCn37t3HGkKmUpYcHRwxGo44ODxge2v7kdZDkecnpXSdESyZHjUUCkBKgdAKqTVRGtFvdxh0e0uxkMUxHI1H4XddYECp2uFwsRE/eVwWxAOvg0bllXNn8WXFpStX+dmvfx1nHTdvfkySpPQG/297ZxZb13Wd4W+d+Q6kLkkp1mBZ1mDFlSPJMjzISFAUjY0GQZOXpkCEAM1D3hqgaVGgqNGnvrVA0TgPRdAJfSiK1khaNIFbNEDcJK2DRK2dpLYsWfJAWxIHkRSnO517ptWHvS95ZSuw6Mi6JH0+4AD3DCT35j53n7PWXutfDfbds497Du7nN37z87TbxsRvtlfxoggkp7W6TJanVGohpx48ge9XGG2MMb7rLlzfJ4qqZKmYh3PgUK3XCSoV/CikKAo6SWz1Xo26keuZNtZrNXzfXU9i8Dyaqyuo5vaBa/5nYRjat66UajWiXq+Q5wme51CJAgpcRneM4kmPR04cpdvt4vs+4+MTNJtNDh85TOD7NFtN4+srlHazxY56jYnGDqCg3Vyh3Vyl1+7gug6vvPIye3bvptVqs7rSpNNpU6vVcBxoNHZw/0fvQ5wcJUPEqq7379l3LEAOvkH2CX2HS69doBL6tFe7hK5nXB0qN5jlgzGfOrAQpKrrmXX2bxYD91tmH75Jtu4TXisyKA6uF9KOU869+ioFLotLq1ybv87Bg4eZm7tGmhaIeGS5ic5oNdu0Wx2iSoVOp4NjrZ6o8sGU8D3DumkO8G3gi/bzF4FvDRz/LbuKfhpYGTDjb4oqiOfh+AHdNCGz/pUoivCtmY6jRFHA8eMfAxsEnWEmGykgcHw0FXqpcOHS22QacLWT8m8/OU9RmyATFzfP6DVXCZyAsfExiiLHcTzCoEKna7I6AtejUR1levoqhdiYTVzrzzOTetzt3uCTG0T6pkOek+WmZGjf1TA4ofbJc/Pk7N9IgzepiFgBYIXclO9VMqoeOKQ4mrF37924bggqOE7A2Pg4cZyxf99+enHK8lKTpaUWR4/+EgcOHGRiYoyJsZ1EUZUwqDA+vpO333qLh06dYn7BPPdyFM91qdXrN8h6ua5rzSjX3NQ2lq7fZC8KcGzaZavVwhETURCGIWEYMj01jea5iUV13DU/pj+QUtinb9qqmLGXPOHJX36cSlRhanaey5cv43jC7t27cAIPzTyuTc/Sbi3x/e9/l7nZGdIkYWp6liiK2LtnP1NXpmk0GqRZj4sXX8NzTHWApeUlU9jLKjrheYRRRFSvUavX6MRdVlabdNs9stz486KouqYq79qsH3EKXNfErs7Nz9rce13zE2ZpQa06QhjWKAoX4w4PcZyAIksIw4ikl7B/1zhJc5mTJ05x5fIU1xeuc/jQId588w2CKCTPcibfmuQLZ84w0RhD04Q8S/A9hwN37+PBB47bRUOXSqVKmiY0RmqcOH6MerXK9YV54k6HkZFRnn766wM+dkxarxW6fvf3dED71Hwijnt4XsHM9BSjdZOVh+NQiEkJ7t/zg6vqvu+bCAsrfHKj+ruL5wZGHAeHLFdwXLKsWEvR7IvexEmPdivmzdcn6XZSlpZXKPC4vrhCTkG93mBlpUkvSRC7YJspRtW90yasRriOs7bweqvc0qQpIjXgSeBfBg7/CfCkiLwGPGH3Af4deBN4Hfhr4Lff6/crUBSO8VsgZFmO4/sUTkGSguv5VEciAhwkM287ru9D7uAU9gYOPHK/4NLrV+i5LitxE2fEozYastxdAqegMlIlc12ml1eJwhqFfbuL45jFxSXSNOUHz/+IH549Sy/tMVqv4DliMnVsqNBg1lBfE7Nf+GowqBfMEzHLM1zPJe7FawPe9+cZcV/reMcsJPXDIzzXM/nbjksQBUZN3nEQCg7su4vRapUoiFhptjh9+jQ7bRlSEePzmZu/Rp6njI5GRFHET3/2MufPX+DylSuEQUA1iOh121RCq9kIjI7usPGVFRCh1WyuuSf6b58AWZreENQs4pBhUtyWm6vEq21c++UzdaoLG9KTmC9QnqN5bsKmisKuuK6n5sF6ga/A1rCpBQ5Vt2B2dpbZuQUm357ksUcfAcdBXA/JgDwnT7vs3jWC5yWMjjXYu2snRWIqg47uHGdxZRmVgNmFBfBcpq9dJ4iq5OQ4rlJoztzCPGlizOQ0TejFPQqFpMjINCfJEuIkwfVMca9u3KEoMiqVKlmqNg61Sxh561ECVq18acmEXNXr9bUXgjiOydMYcmXEU3bvcJjYUefFF3/Co48+xomTx8EpUIxSVBzHuMA3nnnGrtbDzOw1eknG6kqLcy/9H3ft2s3C3Dy9NGZh8RqFKjMzMzQadR579CGOHj3EzNQ8RYENqXMockHzgsD1bjDL+9bGoE6A0Q0Q8gxO3v8AR+45wmOPnCTJM3JRCll3v/Qfuv0HYZIk9KxiltqkkCwz95QpXOiQWdnB9QqmfX0J830xkSk+jYkJcoUsV5aWVwnCiKnZaTpxB1Px0iFJTOxnmhekqVH0qDV2mBRX31tLmrhVbmnSVNW2qk6o6srAseuq+klVvU9Vn1DVRXtcVfXLqnpYVY+r6gvv9fuFfpaNqR0SRiG+FXqdnZ0zMY69mGNHD+O7JgYu1Rz1HXIH0iSH3GFmeoFW3KOXJowENTwvJCnA8X2SPAfxKBDOX7xo3pSKgm4nRsTUBn/uuedYbjVJMBqQjkClEqJqRTkceVfbswFTQu3v7C8G9ReEsiwjjuO1JyW8U/psPX7NpJeZmMh+LZXcZhq54lKt1bh3/x4CL6DT7dLptHn++edpNVeoVquoZraaYUIctynULGiM1OscOXwf9x44yNUrbwMFjgs7d44zO3OVqctXmLp6FWBNqT2KbjRZtCjW/K35miN+QM3IdShETPplbuPyCpPHXq3XiALfBFArxrlvJ93BrJP+5rquCZK31x+6Zz/t5ipplvKZz3yWkXqDcy+9jOsKju/iR+btsNVOgIjF5SarnRVGR2q8cek87e4yiXaIRkMKr6CTxpx4+BhZ6vFf//1jWu2YIIqY2DlBr9Mxb2NxTOiH1Oo1ikIJg5AwiHDwCILQZmw5jIyO2HYb6yjPU8Jo3Zc56NYwvkyHbneFokjwPKMN64cFrptx8oHDjI16tOMWfhAwOTnJ/PwCCwsLOI5LkibU6jU+8pG7SJIUX4RqdQR1PBzfJ04SgqjG0sJ19u7dy/h4g8bYGHGna5X3M378ox8yPzeLOMqevWMEoYuSArruTmL9wTW4Er6eJbR+X7x87gIL86t84vTjJnG80DXzHLjBukqSZO2FQZy+NquRIDSK/s7aKn7aT+O1Ai99074/CVcqFS6+eYkMpZvFBLUKaW78sVmeADmLi4t04x7iesYqdT1wPRYWr5Pk5u9EUXSDhfee89VGLv6gEJEmcHHY7bjD7ARuvQTe1qfs7/Znq/f5gKrueq+LNoWeJnBRVR8ediPuJCLywoepz2V/tz8flj5vitzzkpKSkq1COWmWlJSUbIDNMmn+1bAbMAQ+bH0u+7v9+VD0eVMsBJWUlJRsFTbLm2ZJSUnJlmDok6aIfEpELlr9zT9875/Y/IjIfhH5noicF5FXROQr9vht0yDdjIiIKyI/FZFn7f5BETlr+/WM1S9AREK7/7o9f+8w2/1+EZGGiHxTRF4VkQsi8vh2HmMR+T17P58TkX8UkWi7j/HNGOqkKSIu8BeYvPZjwBkROTbMNt0mcb+9lAAAAr1JREFUMuD3VfUYcBr4su3X7dMg3Zx8BbgwsP+nwFdV9QiwBHzJHv8SsGSPf9VetxX5GvAfqno/cBLT9205xiKyD/gd4GFV/RimRsHn2f5j/G4Go/zv9AY8DnxnYP8p4KlhtukD6ue3MGmoF4E99tgeTHwqwF8CZwauX7tuq2wYYZbngF8FnsUo2i4A3jvHGvgO8Lj97NnrZNh92GB/dwCT72z3dh1j1iUfx+2YPQv82nYe45+3Dds8vyXtza2MNUtOAWf5BTVINzlPA3+AkWgCmACWVbVfF3awT2v9tedX7PVbiYPAPPB31iXxN1ajYVuOsapOAX8GXAZmMGP2Itt7jG/KsCfNbY2I1IF/Bn5XVVcHz6l5BG+L0AUR+XVgTlVfHHZb7iAe8BDwdVU9BbRZN8WBbTfGY5iqDAeBvUAN+NRQGzUkhj1pvi/tza2AiPiYCfMfVLWvDnVbNUg3ER8HPisibwH/hDHRv4YpddJP1R3s01p/7fkdwPU72eDbwFXgqqqetfvfxEyi23WMnwAmVXVeVVOM4tnH2d5jfFOGPWn+L3CfXYELMI7lbw+5Tb8wYiSM/ha4oKp/PnDqtmmQbiZU9SlVvVtV78WM4X+q6heA7wGfs5e9s7/9/8Pn7PVb6o1MVWeBKyLyUXvok8B5tukYY8zy0yJStfd3v7/bdox/LsN2qgKfBi4BbwB/NOz23KY+fQJjlr0E/Mxun8b4dJ4DXgO+C4zb6wUTRfAG8DJmhXLo/Xifff8V4Fn7+RDwPxht1W8AoT0e2f3X7flDw273++zrg8ALdpz/FRjbzmMM/DHwKnAO+Hsg3O5jfLOtzAgqKSkp2QDDNs9LSkpKthTlpFlSUlKyAcpJs6SkpGQDlJNmSUlJyQYoJ82SkpKSDVBOmiUlJSUboJw0S0pKSjZAOWmWlJSUbID/ByPT/Iv89YCuAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -229,7 +229,7 @@ "output_type": "stream", "text": [ "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.torch/models/resnet50-19c8e357.pth\n", - "102502400it [00:09, 10819016.16it/s]\n" + "102502400it [00:09, 10362562.63it/s]\n" ] } ], @@ -446,7 +446,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/PyTorch/Detection/SSD/main.py b/PyTorch/Detection/SSD/main.py index 0e4733d0..81b2009a 100644 --- a/PyTorch/Detection/SSD/main.py +++ b/PyTorch/Detection/SSD/main.py @@ -1,3 +1,17 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import os import time from argparse import ArgumentParser @@ -157,13 +171,12 @@ def train(train_loop_func, logger, args): if args.checkpoint is not None: if os.path.isfile(args.checkpoint): - load_checkpoint(ssd300, args.checkpoint) + load_checkpoint(ssd300.module if args.distributed else ssd300, args.checkpoint) checkpoint = torch.load(args.checkpoint, map_location=lambda storage, loc: storage.cuda(torch.cuda.current_device())) start_epoch = checkpoint['epoch'] iteration = checkpoint['iteration'] scheduler.load_state_dict(checkpoint['scheduler']) - ssd300.load_state_dict(checkpoint['model']) optimizer.load_state_dict(checkpoint['optimizer']) else: print('Provided checkpoint is not path to a file') diff --git a/PyTorch/Detection/SSD/setup.py b/PyTorch/Detection/SSD/setup.py index 7bc21aa1..c96bde14 100644 --- a/PyTorch/Detection/SSD/setup.py +++ b/PyTorch/Detection/SSD/setup.py @@ -1,6 +1,6 @@ #!/usr/bin/env python -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/PyTorch/Detection/SSD/src/coco.py b/PyTorch/Detection/SSD/src/coco.py old mode 100755 new mode 100644 index dd0e880b..60d7eede --- a/PyTorch/Detection/SSD/src/coco.py +++ b/PyTorch/Detection/SSD/src/coco.py @@ -1,3 +1,17 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + __author__ = 'tylin' __version__ = '2.0' # Interface for accessing the Microsoft COCO dataset. diff --git a/PyTorch/Detection/SSD/src/coco_pipeline.py b/PyTorch/Detection/SSD/src/coco_pipeline.py index 82acae90..3c5a6b4c 100644 --- a/PyTorch/Detection/SSD/src/coco_pipeline.py +++ b/PyTorch/Detection/SSD/src/coco_pipeline.py @@ -1,4 +1,4 @@ -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -43,7 +43,7 @@ class COCOPipeline(Pipeline): self.input = ops.COCOReader(file_root = file_root, annotations_file = annotations_file, shard_id = shard_id, num_shards = num_gpus, ratio=True, ltrb=True, random_shuffle=True, skip_empty=True) - self.decode = ops.HostDecoder(device = "cpu", output_type = types.RGB) + self.decode = ops.ImageDecoder(device = "cpu", output_type = types.RGB) # Augumentation techniques self.crop = ops.SSDRandomCrop(device="cpu", num_attempts=1) @@ -163,7 +163,7 @@ class DALICOCOIterator(object): for p in self._pipes: p._prefetch() for p in self._pipes: - outputs.append(p._share_outputs()) + outputs.append(p.share_outputs()) for i in range(self._num_gpus): dev_id = self._pipes[i].device_id out_images = [] @@ -237,8 +237,8 @@ class DALICOCOIterator(object): pyt_offsets[j] = torch.IntTensor(bbox_offsets[j]) for p in self._pipes: - p._release_outputs() - p._run() + p.release_outputs() + p.schedule_run() copy_db_index = self._current_data_batch # Change index for double buffering diff --git a/PyTorch/Detection/SSD/src/data.py b/PyTorch/Detection/SSD/src/data.py index 622af5d9..10b87dd1 100644 --- a/PyTorch/Detection/SSD/src/data.py +++ b/PyTorch/Detection/SSD/src/data.py @@ -1,3 +1,17 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import os import torch @@ -18,7 +32,7 @@ def get_train_loader(args, local_seed): output_fp16=args.amp, output_nhwc=False, pad_output=False, seed=local_seed) train_pipe.build() - test_run = train_pipe.run() + test_run = train_pipe.schedule_run(), train_pipe.share_outputs(), train_pipe.release_outputs() train_loader = DALICOCOIterator(train_pipe, 118287 / args.N_gpu) return train_loader diff --git a/PyTorch/Detection/SSD/src/evaluate.py b/PyTorch/Detection/SSD/src/evaluate.py index f9d6560a..923faa43 100644 --- a/PyTorch/Detection/SSD/src/evaluate.py +++ b/PyTorch/Detection/SSD/src/evaluate.py @@ -1,3 +1,17 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import torch import time import numpy as np diff --git a/PyTorch/Detection/SSD/src/logger.py b/PyTorch/Detection/SSD/src/logger.py index b9c19de2..f4c7dd87 100644 --- a/PyTorch/Detection/SSD/src/logger.py +++ b/PyTorch/Detection/SSD/src/logger.py @@ -1,3 +1,17 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import math import numpy as np diff --git a/PyTorch/Detection/SSD/src/model.py b/PyTorch/Detection/SSD/src/model.py index f5738eb8..91fdb9d4 100644 --- a/PyTorch/Detection/SSD/src/model.py +++ b/PyTorch/Detection/SSD/src/model.py @@ -1,3 +1,17 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import torch import torch.nn as nn from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet101, resnet152 diff --git a/PyTorch/Detection/SSD/src/train.py b/PyTorch/Detection/SSD/src/train.py index 014404c5..85c92193 100644 --- a/PyTorch/Detection/SSD/src/train.py +++ b/PyTorch/Detection/SSD/src/train.py @@ -1,3 +1,17 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from torch.autograd import Variable import torch import time diff --git a/PyTorch/Detection/SSD/src/utils.py b/PyTorch/Detection/SSD/src/utils.py index b36a1c96..6d7efbff 100644 --- a/PyTorch/Detection/SSD/src/utils.py +++ b/PyTorch/Detection/SSD/src/utils.py @@ -1,3 +1,17 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import torch import torchvision.transforms as transforms import torch.utils.data as data diff --git a/PyTorch/Detection/SSD/ssd/__pycache__/argparse.cpython-36.pyc b/PyTorch/Detection/SSD/ssd/__pycache__/argparse.cpython-36.pyc deleted file mode 100644 index e55b995c..00000000 Binary files a/PyTorch/Detection/SSD/ssd/__pycache__/argparse.cpython-36.pyc and /dev/null differ diff --git a/PyTorch/LanguageModeling/BERT/.dockerignore b/PyTorch/LanguageModeling/BERT/.dockerignore index 0da97b53..594940d2 100644 --- a/PyTorch/LanguageModeling/BERT/.dockerignore +++ b/PyTorch/LanguageModeling/BERT/.dockerignore @@ -1,8 +1,20 @@ -data/download/ -data/extracted/ -data/formatted_one_article_per_line/ -data/sharded/ -data/hdf5/ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +data/download +data/extracted +data/formatted_one_article_per_line +data/sharded +data/hdf5 vocab/ -results/ -checkpoints/* +results/ \ No newline at end of file diff --git a/PyTorch/LanguageModeling/BERT/.gitignore b/PyTorch/LanguageModeling/BERT/.gitignore index 5269e69c..52579bcf 100644 --- a/PyTorch/LanguageModeling/BERT/.gitignore +++ b/PyTorch/LanguageModeling/BERT/.gitignore @@ -8,14 +8,11 @@ __pycache__/ # C extensions *.so -#Data +#Data checkpoints and results data/*/*/ data/*/*.zip -data/* - -#checkpoints and results -checkpoints/* -results/* +checkpoints/ +results/ # Distribution / packaging .Python diff --git a/PyTorch/LanguageModeling/BERT/Dockerfile b/PyTorch/LanguageModeling/BERT/Dockerfile index 0130c6b4..0dabe400 100755 --- a/PyTorch/LanguageModeling/BERT/Dockerfile +++ b/PyTorch/LanguageModeling/BERT/Dockerfile @@ -1,24 +1,22 @@ -ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:19.07-py3 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:19.08-py3 FROM ${FROM_IMAGE_NAME} RUN apt-get update && apt-get install -y pbzip2 pv bzip2 cabextract ENV BERT_PREP_WORKING_DIR /workspace/bert/data -WORKDIR /opt -RUN rm -rf /opt/pytorch/apex ; \ - git clone https://github.com/NVIDIA/apex.git pytorch/apex ; \ - cd pytorch/apex ; \ - pip uninstall --yes apex; \ - git checkout 880ab925bce9f817a93988b021e12db5f67f7787; \ - git pull; \ - pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . - -#WORKDIR /opt -#RUN cd pytorch/apex \ -# && git fetch origin pull/334/head:multi_tensor_lamb_optimizer \ -# && git checkout multi_tensor_lamb_optimizer \ -# && python setup.py develop --cuda_ext --cpp_ext - WORKDIR /workspace RUN git clone https://github.com/attardi/wikiextractor.git RUN git clone https://github.com/soskek/bookcorpus.git diff --git a/PyTorch/LanguageModeling/BERT/LICENSE b/PyTorch/LanguageModeling/BERT/LICENSE index d6456956..de609f66 100755 --- a/PyTorch/LanguageModeling/BERT/LICENSE +++ b/PyTorch/LanguageModeling/BERT/LICENSE @@ -1,4 +1,3 @@ - Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ @@ -176,6 +175,8 @@ END OF TERMS AND CONDITIONS + Copyright 2019 NVIDIA CORPORATION. All rights reserved. + APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following diff --git a/PyTorch/LanguageModeling/BERT/README.md b/PyTorch/LanguageModeling/BERT/README.md index be8ac3c8..318f2de2 100755 --- a/PyTorch/LanguageModeling/BERT/README.md +++ b/PyTorch/LanguageModeling/BERT/README.md @@ -1,8 +1,8 @@ # BERT For PyTorch -This repository provides a script and recipe to train the BERT model to achieve state of the art accuracy, and is tested and maintained by NVIDIA. +This repository provides a script and recipe to train the BERT model for PyTorch to achieve state-of-the-art accuracy, and is tested and maintained by NVIDIA. -**Table Of Contents** +## Table Of Contents - [Model overview](#model-overview) * [Model architecture](#model-architecture) @@ -11,6 +11,7 @@ This repository provides a script and recipe to train the BERT model to achieve * [Features](#features) * [Mixed precision training](#mixed-precision-training) * [Enabling mixed precision](#enabling-mixed-precision) + * [Glossary](#glossary) - [Setup](#setup) * [Requirements](#requirements) - [Quick Start Guide](#quick-start-guide) @@ -18,14 +19,12 @@ This repository provides a script and recipe to train the BERT model to achieve * [Scripts and sample code](#scripts-and-sample-code) * [Parameters](#parameters) * [Pre-training parameters](#pre-training-parameters) + * [Multi-node](#multi-node) * [Fine-tuning parameters](#fine-tuning-parameters) * [Command-line options](#command-line-options) * [Getting the data](#getting-the-data) * [Dataset guidelines](#dataset-guidelines) * [Multi-dataset](#multi-dataset) - * [Relocating hdf5 files](#relocating-hdf5-files) - * [Inter sequence-pair mixing](#inter-sequence-pair-mixing) - * [Retaining document-level granularity](#retaining-document-level-granularity) * [Training process](#training-process) * [Pre-training](#pre-training) * [Fine-tuning](#fine-tuning) @@ -43,31 +42,34 @@ This repository provides a script and recipe to train the BERT model to achieve * [Training stability test](#training-stability-test) * [Pre-training stability test](#pre-training-stability-test) * [Fine-tuning stability test](#fine-tuning-stability-test) - * [Training performance results](#training-performance-results) - * [Training performance: NVIDIA DGX-1 (8x V100 16G)](#training-performance-nvidia-dgx-1-8x-v100-16g) - * [Pre-training NVIDIA DGX-1 With 16G](#pre-training-nvidia-dgx-1-with-16g) - * [Fine-tuning NVIDIA DGX-1 With 16G](#fine-tuning-nvidia-dgx-1-with-16g) - * [Training performance: NVIDIA DGX-1 (8x V100 32G)](#training-performance-nvidia-dgx-1-8x-v100-32g) - * [Pre-training NVIDIA DGX-1 With 32G](#pre-training-nvidia-dgx-1-with-32g) - * [Fine-tuning NVIDIA DGX-1 With 32G](#fine-tuning-nvidia-dgx-1-with-32g) - * [Training performance: NVIDIA DGX-2 (16x V100 32G)](#training-performance-nvidia-dgx-2-16x-v100-32g) - * [Pre-training NVIDIA DGX-2 With 32G](#pre-training-nvidia-dgx-2-with-32g) - * [Fine-tuning NVIDIA DGX-2 With 32G](#fine-tuning-nvidia-dgx-2-with-32g) - * [Inference performance results](#inference-performance-results) - * [Inference performance: NVIDIA DGX-1 (1x V100 16G)](#inference-performance-nvidia-dgx-1-1x-v100-16g) - * [Pre-training inference on NVIDIA DGX-1 with 16G](#pre-training-inference-on-nvidia-dgx-1-with-16g) - * [Fine-tuning inference on NVIDIA DGX-1 with 16G](#fine-tuning-inference-on-nvidia-dgx-1-with-16g) - * [Inference performance: NVIDIA DGX-1 (1x V100 32G)](#inference-performance-nvidia-dgx-1-1x-v100-32g) - * [Pre-training inference on NVIDIA DGX-1 with 32G](#pre-training-inference-on-nvidia-dgx-1-with-32g) - * [Fine-tuning inference on NVIDIA DGX-1 with 32G](#fine-tuning-inference-on-nvidia-dgx-1-with-32g) - * [Inference performance: NVIDIA DGX-2 (1x V100 32G)](#inference-performance-nvidia-dgx-2-1x-v100-32g) - * [Pre-training inference on NVIDIA DGX-2 with 32G](#pre-training-inference-on-nvidia-dgx-2-with-32g) - * [Fine-tuning inference on NVIDIA DGX-2 with 32G](#fine-tuning-inference-on-nvidia-dgx-2-with-32g) + * [Training performance results](#training-performance-results) + * [Training performance: NVIDIA DGX-1 (8x V100 16G)](#training-performance-nvidia-dgx-1-8x-v100-16g) + * [Pre-training NVIDIA DGX-1 With 16G](#pre-training-nvidia-dgx-1-with-16g) + * [Pre-training on multiple NVIDIA DGX-1 With 16G](#pre-training-on-multiple-nvidia-dgx-1-with-16g) + * [Fine-tuning NVIDIA DGX-1 With 16G](#fine-tuning-nvidia-dgx-1-with-16g) + * [Training performance: NVIDIA DGX-1 (8x V100 32G)](#training-performance-nvidia-dgx-1-8x-v100-32g) + * [Pre-training NVIDIA DGX-1 With 32G](#pre-training-nvidia-dgx-1-with-32g) + * [Fine-tuning NVIDIA DGX-1 With 32G](#fine-tuning-nvidia-dgx-1-with-32g) + * [Training performance: NVIDIA DGX-2 (16x V100 32G)](#training-performance-nvidia-dgx-2-16x-v100-32g) + * [Pre-training NVIDIA DGX-2 With 32G](#pre-training-nvidia-dgx-2-with-32g) + * [Pre-training on multiple NVIDIA DGX-2H With 32G](#pre-training-on-multiple-nvidia-dgx-2h-with-32g) + * [Fine-tuning NVIDIA DGX-2 With 32G](#fine-tuning-nvidia-dgx-2-with-32g) + * [Inference performance results](#inference-performance-results) + * [Inference performance: NVIDIA DGX-1 (1x V100 16G)](#inference-performance-nvidia-dgx-1-1x-v100-16g) + * [Pre-training inference on NVIDIA DGX-1 with 16G](#pre-training-inference-on-nvidia-dgx-1-with-16g) + * [Fine-tuning inference on NVIDIA DGX-1 with 16G](#fine-tuning-inference-on-nvidia-dgx-1-with-16g) + * [Inference performance: NVIDIA DGX-1 (1x V100 32G)](#inference-performance-nvidia-dgx-1-1x-v100-32g) + * [Pre-training inference on NVIDIA DGX-1 with 32G](#pre-training-inference-on-nvidia-dgx-1-with-32g) + * [Fine-tuning inference on NVIDIA DGX-1 with 32G](#fine-tuning-inference-on-nvidia-dgx-1-with-32g) + * [Inference performance: NVIDIA DGX-2 (1x V100 32G)](#inference-performance-nvidia-dgx-2-1x-v100-32g) + * [Pre-training inference on NVIDIA DGX-2 with 32G](#pre-training-inference-on-nvidia-dgx-2-with-32g) + * [Fine-tuning inference on NVIDIA DGX-2 with 32G](#fine-tuning-inference-on-nvidia-dgx-2-with-32g) - [Release notes](#release-notes) * [Changelog](#changelog) * [Known issues](#known-issues) + ## Model overview BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) paper. NVIDIA's implementation of BERT is an optimized version of the [Hugging Face implementation](https://github.com/huggingface/pytorch-pretrained-BERT), leveraging mixed precision arithmetic and Tensor Cores on V100 GPUs for faster training times while maintaining target accuracy. @@ -75,22 +77,25 @@ BERT, or Bidirectional Encoder Representations from Transformers, is a new metho The repository also contains scripts to interactively launch data download, training, benchmarking and inference routines in a Docker container for both pre-training and fine-tuning for tasks such as question answering. The major differences between the original implementation of the paper and this version of BERT are as follows: - Scripts to download Wikipedia and BookCorpus datasets -- Scripts to preprocess downloaded data or a custom corpus into inputs and targets for pre-training in a modular fashion. +- Scripts to preprocess downloaded data or a custom corpus into inputs and targets for pre-training in a modular fashion - Fused [LAMB](https://arxiv.org/pdf/1904.00962.pdf) optimizer to support training with larger batches - Fused Adam optimizer for fine tuning tasks - Fused CUDA kernels for better performance LayerNorm -- Automatic Mixed precision training support +- Automatic mixed precision (AMP) training support +- Scripts to launch on multiple number of nodes Other publicly available implementations of BERT include: - -1. [Google's official implementation](https://github.com/google-research/bert) -2. [codertimo](https://github.com/codertimo/BERT-pytorch) +1. [NVIDIA Tensorflow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT) +2. [Hugging Face](https://github.com/huggingface/pytorch-pretrained-BERT) +3. [codertimo](https://github.com/codertimo/BERT-pytorch) +4. [gluon-nlp](https://github.com/dmlc/gluon-nlp/tree/master/scripts/bert) +5. [Google's implementation](https://github.com/google-research/bert) This model trains with mixed precision Tensor Cores on Volta and provides a push-button solution to pretraining on a corpus of choice. As a result, researchers can get results 4x faster than training without Tensor Cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. ### Model architecture -The BERT architecture uses the same architecture as the encoder half of the Transformer. Input sequences are projected into an embedding space before being fed into the encoder structure. Additionally, a positional and segment encodings are added to the embeddings to preserve positional information. The encoder structure is simply a stack of Transformer blocks, which consist of a multi-head attention layer followed by successive stages of feed-forward networks and layer normalization. The multi-head attention layer accomplishes self-attention on multiple input representations. +The BERT architecture uses the same architecture as the encoder half of the Transformer. Input sequences are projected into an embedding space before being fed into the encoder structure. Additionally, positional and segment encodings are added to the embeddings to preserve positional information. The encoder structure is simply a stack of Transformer blocks, which consist of a multi-head attention layer followed by successive stages of feed-forward networks and layer normalization. The multi-head attention layer accomplishes self-attention on multiple input representations. An illustration of the architecture taken from the [Transformer paper](https://arxiv.org/pdf/1706.03762.pdf) is shown below. @@ -100,14 +105,14 @@ An illustration of the architecture taken from the [Transformer paper](https://a The architecture of the BERT model is almost identical to the Transformer model that was first introduced in the [Attention Is All You Need paper](https://arxiv.org/pdf/1706.03762.pdf). The main innovation of BERT lies in the pre-training step, where the model is trained on two unsupervised prediction tasks using a large text corpus. Training on these unsupervised tasks produces a generic language model, which can then be quickly fine-tuned to achieve state-of-the-art performance on language processing tasks such as question answering. -The BERT paper reports results two configurations of BERT, each corresponding to a unique model size. This implementation provides the same configurations by default, which are described in the table below. +The BERT paper reports the results for two configurations of BERT, each corresponding to a unique model size. This implementation provides the same configurations by default, which are described in the table below. | **Model** | **Hidden layers** | **Hidden unit size** | **Attention heads** | **Feedforward filter size** | **Max sequence length** | **Parameters** | |:---------:|:----------:|:----:|:---:|:--------:|:---:|:----:| |BERTBASE |12 encoder| 768| 12|4 x 768|512|110M| |BERTLARGE|24 encoder|1024| 16|4 x 1024|512|330M| -Additionally, this implementation supports training on multiple GPUs. Mixed precision training and inference with dynamic loss scaling is also supported. + ### Feature support matrix @@ -118,12 +123,13 @@ The following features are supported by this model. |APEX AMP|Yes| |APEX DDP|Yes| |LAMB|Yes| +|Multi-node|Yes| #### Features -[APEX](https://github.com/NVIDIA/apex) is a Pytorch extension with NVIDIA-maintained utilities to streamline mixed precision and distributed training. +[APEX](https://github.com/NVIDIA/apex) is a Pytorch extension with NVIDIA-maintained utilities to streamline mixed precision and distributed training, whereas [AMP](https://nvidia.github.io/apex/amp.html) is an abbreviation used for automatic mixed precision training. -[DDP](https://nvidia.github.io/apex/parallel.html) stands for DistributedDataParallel and is used for multi-GPU training, where as [AMP](https://nvidia.github.io/apex/amp.html) is an abbreviation used for automatic mixed precision training. +[DDP](https://nvidia.github.io/apex/parallel.html) stands for DistributedDataParallel and is used for multi-GPU training. [LAMB](https://arxiv.org/pdf/1904.00962.pdf) stands for Layerwise Adaptive Moments based optimizer, is a large batch optimization technique that helps accelerate training of deep neural networks using large minibatches. It allows using a global batch size of 65536 and 32768 on sequence lengths 128 and 512 respectively, compared to a batch size of 256 for Adam. The optimized implementation accumulates 1024 gradients batches in phase 1 and 4096 steps in phase 2 before updating weights once. This results in 15% training speedup. On multi-node systems, LAMB allows scaling up to 1024 GPUs resulting in training speedups of up to 72x in comparison to [Adam](https://arxiv.org/pdf/1412.6980.pdf). Adam has limitations on the learning rate that can be used since it is applied globally on all parameters whereas LAMB follows a layerwise learning rate strategy. @@ -135,10 +141,9 @@ Mixed precision is the combined use of different numerical precisions in a compu 2. Adding loss scaling to preserve small gradient values. For information about: - - How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation. - Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog. -- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/). +- APEX tools for mixed precision training, see the [NVIDIA APEX: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/). #### Enabling mixed precision @@ -149,15 +154,35 @@ Automatic mixed precision can be enabled with the following code changes: ``` from apex import amp if fp16: - # Wrap optimizer and model - model, optimizer = amp.initialize(model, optimizer, opt_level=, loss_scale=”dynamic”) + # Wrap optimizer and model + model, optimizer = amp.initialize(model, optimizer, opt_level=, loss_scale=”dynamic”) if fp16: - with amp.scale_loss(loss, optimizer) as scaled_loss: - scaled_loss.backward() + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() ``` -Where `` is the optimization level. In the pretraining, “O2” is set as the optimization level. Mixed precision training can be turned on by passing the `fp16` argument to the pre-training and fine-tuning Python scripts. Shell scripts all have a positional argument available to enable mixed precision training. +Where `` is the optimization level. In the pretraining, `O2` is set as the optimization level. Mixed precision training can be turned on by passing the `fp16` argument to the `run_pretraining.py` and `run_squad.py`. All shell scripts have a positional argument available to enable mixed precision training. + +### Glossary + +**Fine-tuning** +Training an already pretrained model further using a task specific dataset for subject-specific refinements, by adding task-specific layers on top if required. + +**Language Model** +Assigns a probability distribution over a sequence of words. Given a sequence of words, it assigns a probability to the whole sequence. + +**Pre-training** +Training a model on vast amounts of data on the same (or different) task to build general understandings. + +**Transformer** +The paper [Attention Is All You Need](https://arxiv.org/abs/1706.03762) introduces a novel architecture called Transformer that uses an attention mechanism and transforms one sequence into another. + +**Phase1** +Pretraining on samples of sequence length 128 and 20 masked predictions per sequence. + +**Phase2** +Pretraining on samples of sequence length 512 and 80 masked predictions per sequence. ## Setup @@ -178,9 +203,14 @@ For more information about how to get started with NGC containers, see the follo For those unable to use the PyTorch NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/dgx/support-matrix/index.html). +For multi-node, the sample provided in this repository requires [Enroot](https://github.com/NVIDIA/enroot) and [Pyxis](https://github.com/NVIDIA/pyxis) set up on a [SLURM](https://slurm.schedmd.com) cluster. + +More information on how to set up and launch can be found in the [Multi-node Documentation](https://docs.nvidia.com/ngc/multi-node-bert-user-guide). + + ## Quick Start Guide -To train your model using mixed precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the BERT model. The default parameters for pretraining have been set to run on 8 x V100 32G cards. For the specifics concerning training and inference, see [Advanced](#advanced). +To train your model using mixed precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the BERT model. The default parameters for pretraining have been set to run on 8 x V100 32G cards. For the specifics concerning training and inference, see the [Advanced](#advanced) section. 1. Clone the repository. @@ -190,11 +220,11 @@ To train your model using mixed precision with Tensor Cores or using FP32, perfo `cd DeepLearningExamples/PyTorch/LanguageModeling/BERT` -2. Download NVIDIA pretrained checkpoint. +2. Download the NVIDIA pretrained checkpoint. -If you want to use a pretrained checkpoint, visit [NGC](https://ngc.nvidia.com/catalog/models) and browse the available models. This downloaded checkpoint is used to fine-tune on SQuAD. Make sure to place the downloaded checkpoint in `checkpoints/` folder. +If you want to use a pretrained checkpoint, visit [NGC](https://ngc.nvidia.com/catalog/models) and browse the available models. This downloaded checkpoint is used to fine-tune on SQuAD. Ensure you place the downloaded checkpoint in the `checkpoints/` folder. -3. Build the BERT 19.07 NGC container. +3. Build the BERT 19.08 NGC container. `bash scripts/docker/build.sh` @@ -202,7 +232,7 @@ If you want to use a pretrained checkpoint, visit [NGC](https://ngc.nvidia.com/c `bash scripts/docker/launch.sh` -Resultant logs and checkpoints of pretraining and finetuning routines get stored in the `results/` folder. +Resultant logs and checkpoints of pretraining and fine-tuning routines get stored in the `results/` folder. `data` and `vocab.txt` are downloaded in `data/` directory by default. Refer to the [Getting the data](#getting-the-data) section for more details on how to process a custom corpus as required for BERT pretraining. @@ -214,25 +244,29 @@ This repository provides scripts to download, verify and extract the following d - Wikipedia (pre-training) - BookCorpus (pre-training) -To download, verify, extract the datasets, and create the shards in hdf5 format, run: +To download, verify, extract the datasets, and create the shards in hdf5 format, run: `/workspace/bert/data/create_datasets_from_start.sh` -6. Start pre-training. +Depending on the speed of your internet connection, this process takes about a day to complete. -BERT is designed to pre-train deep bidirectional representations for language representations. The following scripts are to replicate pretraining on Wikipedia+Book Corpus from this [paper](https://arxiv.org/pdf/1810.04805.pdf). These scripts are general and can be used for pre-training language representations on any corpus of choice. +6. Start pretraining. -From within the container, you can use the following script to run pre-training. +BERT is designed to pre-train deep bidirectional networks for language representations. The following scripts replicate pretraining on Wikipedia + BookCorpus from this [paper](https://arxiv.org/pdf/1810.04805.pdf). These scripts are general and can be used for pre-training language representations on any corpus of choice. + +To run on a single node, from within the container, you can use the following script to run pre-training. `bash scripts/run_pretraining.sh` -More details can be found in Details/Training Process - -7. Start fine-tuning with the SQUAD dataset. +The default hyperparameters are set to run on 8 x V100 32G cards. -The above pretrained BERT representations can be fine tuned with just one additional output layer for a state-of-the-art question answering system. Running the following script launches fine-tuning for question answering with the SQuaD dataset. +To run on multiple nodes, see the [Multi-node](#multi-node) section. + +7. Start fine-tuning with the SQuAD dataset. + +The above pretrained BERT representations can be fine tuned with just one additional output layer for a state-of-the-art question answering system. Running the following script launches fine-tuning for question answering with the SQuAD dataset. `bash scripts/run_squad.sh /workspace/checkpoints/` -Default arguments are listed below in order, +Default arguments are listed below in the order the scripts expects: - Initial checkpoint - The default is `/workspace/checkpoints/bert_uncased.pt`. - Number of training Epochs - The default is `2`. @@ -244,18 +278,18 @@ Default arguments are listed below in order, - SQuAD directory - The default is `/workspace/bert/data/v1.1`. - Vocabulary file (token to ID mapping) - The default is `/workspace/bert/vocab/vocab`. - Output directory for result - The default is `/results/SQuAD`. -- Mode (“train”, “eval”, “train eval”, "predict") - The default is `train`. -- Config file for the bert model (It should be the same as the pretrained model) - The default is `/workspace/bert/bert_config.json`. +- Mode (`train`, `eval`, `train eval`, `predict`) - The default is `train`. +- Config file for the BERT model (It should be the same as the pretrained model) - The default is `/workspace/bert/bert_config.json`. -The script will save the final checkpoint to the `/results/SQuAD/pytorch_model.bin` file. +The script saves the final checkpoint to the `/results/SQuAD/pytorch_model.bin` file. 9. Start validation/evaluation. -Validation can be performed with the same script as above, setting `Mode` to "prediction". +Validation can be performed with the same script as above, setting `Mode` to `prediction`. 10. Start inference/predictions. -Inference can be performed with the same script as above, setting `Mode` to `eval`. Inference predictions get saved to `/predictions.json`. +Inference can be performed with the same script as above, setting `Mode` to `eval`. Inference predictions are saved to `/predictions.json`. ## Advanced @@ -273,7 +307,7 @@ Descriptions of the key scripts and folders are provided below. - `create_pretraining_data.py` - Creates `.hdf5` files from shared text files in the final step of dataset creation. - `model.py` - Implements the BERT pre-training and fine-tuning model architectures with PyTorch. - `optimization.py` - Implements the LAMB optimizer with PyTorch. -- `run_squad.py` - Implements fine tuning training and evaluation for question answering on the [SQuaD](https://rajpurkar.github.io/SQuAD-explorer/) dataset. +- `run_squad.py` - Implements fine tuning training and evaluation for question answering on the [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) dataset. - `run_pretraining.py` - Implements BERT pre-training. - `run_pretraining_inference.py` - Implements evaluation of a BERT pre-trained model. @@ -284,145 +318,169 @@ Descriptions of the key scripts and folders are provided below. The complete list of the available parameters for the `run_pretraining.py` script are: ``` - --input_dir INPUT_DIR - The input data directory. - Should contain .hdf5 files for the task. + --input_dir INPUT_DIR - The input data directory. + Should contain .hdf5 files for the task. - --config_file CONFIG_FILE - Path to a json file describing the BERT model - configuration. This file configures the model - architecture, such as the number of transformer - blocks, number of attention heads, etc. + --config_file CONFIG_FILE - Path to a json file describing the BERT model + configuration. This file configures the model + architecture, such as the number of transformer + blocks, number of attention heads, etc. - --bert_model BERT_MODEL - Specifies the type of BERT model to use; - should be one of the following: - bert-base-uncased - bert-large-uncased - bert-base-cased - bert-base-multilingual - bert-base-chinese + --bert_model BERT_MODEL - Specifies the type of BERT model to use; + should be one of the following: + bert-base-uncased + bert-large-uncased + bert-base-cased + bert-base-multilingual + bert-base-chinese - --output_dir OUTPUT_DIR - Path to the output directory where the model - checkpoints will be written. + --output_dir OUTPUT_DIR - Path to the output directory where the model + checkpoints will be written. --max_seq_length MAX_SEQ_LENGTH - - The maximum total input sequence length after - WordPiece tokenization. Sequences longer than - this will be truncated, and sequences shorter - than this will be padded. + - The maximum total input sequence length after + WordPiece tokenization. Sequences longer than + this will be truncated, and sequences shorter + than this will be padded. --max_predictions_per_seq MAX_PREDICTIONS_PER_SEQ - - The maximum total of masked tokens per input - sequence for Masked LM. + - The maximum total of masked tokens per input + sequence for Masked LM. --train_batch_size TRAIN_BATCH_SIZE - - Batch size per GPU for training. + - Batch size per GPU for training. --learning_rate LEARNING_RATE - - The initial learning rate for LAMB optimizer. + - The initial learning rate for LAMB optimizer. - --max_steps MAX_STEPS - Total number of training steps to perform. + --max_steps MAX_STEPS - Total number of training steps to perform. --warmup_proportion WARMUP_PROPORTION - - Proportion of training to perform linear learning - rate warmup for. For example, 0.1 = 10% of training. + - Proportion of training to perform linear learning + rate warmup for. For example, 0.1 = 10% of training. - --seed SEED - Sets the seed to use for random number generation. + --seed SEED - Sets the seed to use for random number generation. --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS - - Number of update steps to accumulate before - performing a backward/update pass. + - Number of update steps to accumulate before + performing a backward/update pass. - --fp16 - If set, will perform computations using - automatic mixed precision. + --fp16 - If set, will perform computations using + automatic mixed precision. - --loss_scale LOSS_SCALE - Sets the loss scaling value to use when - mixed precision is used. The default value (0) - tells the script to use dynamic loss scaling - instead of fixed loss scaling. + --loss_scale LOSS_SCALE - Sets the loss scaling value to use when + mixed precision is used. The default value (0) + tells the script to use dynamic loss scaling + instead of fixed loss scaling. - --log_freq LOG_FREQ - If set, the script will output the training - loss every LOG_FREQ steps. + --log_freq LOG_FREQ - If set, the script will output the training + loss every LOG_FREQ steps. - --resume_from_checkpoint - If set, training will resume from a checkpoint - that currently exists in OUTPUT_DIR. + --resume_from_checkpoint - If set, training will resume from a checkpoint + that currently exists in OUTPUT_DIR. --num_steps_per_checkpoint NUM_STEPS_PER_CHECKPOINT - - Number of update steps until a model checkpoint - is saved to disk.` + - Number of update steps until a model checkpoint + is saved to disk. + --phase2 - Specified if training on phase 2 only. If not specified, default pretraining is on phase 1. + --phase1_end_step - The number of steps phase 1 was trained for. In order to + resume phase 2 the correct way, phase1_end_step should correspond to the --max_steps phase 1 was trained for. ``` + +#### Multi-node + +Multi-node runs can be launched on a pyxis/enroot Slurm cluster (see [Requirements](#requirements)) with the `run.sub` script with the following command for a 4-node DGX1 example for both phase 1 and phase 2: +``` +BATCHSIZE=2048 LR=6e-3 GRADIENT_STEPS=128 PHASE=1 sbatch -N4 --ntasks-per-node=8 run.sub +BATCHSIZE=1024 LR=4e-3 GRADIENT_STEPS=256 PHASE=2 sbatch -N4 --ntasks-per-node=8 run.sub +``` + + +Checkpoint after phase 1 will be saved in `checkpointdir` specified in `run.sub`. The checkpoint will be automatically picked up to resume training on phase 2. Note that phase 2 should be run after phase 1. + +Variables to re-run the [Training performance results](#training-performance-results) are available in the `configurations.yml` file. + +The batch variables `BATCHSIZE`, `LR`, `GRADIENT_STEPS`,`PHASE` refer to the Python arguments `train_batch_size`, `learning_rate`, `gradient_accumulation_steps`, `phase2` respectively. + +Note that the `run.sub` script is a starting point that has to be adapted depending on the environment. In particular, variables such as `datadir` handle the location of the files for each phase. + +Refer to the files contents to see the full list of variables to adjust for your system. + + #### Fine-tuning parameters -The run_squad.py script contains many of the same arguments as `run_pretraining.py`. +The `run_squad.py` script contains many of the same arguments as `run_pretraining.py`. The main script specific parameters are: ``` - --bert_model BERT_MODEL - Specifies the type of BERT model to use; - should be one of the following: - bert-base-uncased - bert-large-uncased - bert-base-cased - bert-base-multilingual - bert-base-chinese + --bert_model BERT_MODEL - Specifies the type of BERT model to use; + should be one of the following: + bert-base-uncased + bert-large-uncased + bert-base-cased + bert-base-multilingual + bert-base-chinese - --train_file TRAIN_FILE - Path to the SQuAD json for training. - For example, train-v1.1.json. + --train_file TRAIN_FILE - Path to the SQuAD json for training. + For example, train-v1.1.json. - --predict_file PREDICT_FILE - Path to the SQuAD json for predictions. - For example, dev-v1.1.json or test-v1.1.json. + --predict_file PREDICT_FILE - Path to the SQuAD json for predictions. + For example, dev-v1.1.json or test-v1.1.json. --max_seq_length MAX_SEQ_LENGTH - - The maximum total input sequence length - after WordPiece tokenization. - Sequences longer than this will be truncated, - and sequences shorter than this will be padded. + - The maximum total input sequence length + after WordPiece tokenization. + Sequences longer than this will be truncated, + and sequences shorter than this will be padded. - --doc_stride DOC_STRIDE - When splitting up a long document into chunks - this parameters sets how much stride to take - between chunks of tokens. + --doc_stride DOC_STRIDE - When splitting up a long document into chunks + this parameters sets how much stride to take + between chunks of tokens. --max_query_length MAX_QUERY_LENGTH - - The maximum number of tokens for the question. - Questions longer than - will be truncated to the value specified. + - The maximum number of tokens for the question. + Questions longer than + will be truncated to the value specified. - --n_best_size N_BEST_SIZE - The total number of n-best predictions to - generate in the nbest_predictions.json - output file. + --n_best_size N_BEST_SIZE - The total number of n-best predictions to + generate in the nbest_predictions.json + output file. --max_answer_length MAX_ANSWER_LENGTH - - The maximum length of an answer that can be - generated. This is needed because the start and - end predictions are not conditioned on one another. + - The maximum length of an answer that can be + generated. This is needed because the start and + end predictions are not conditioned on one another. - --verbose_logging - If true, all the warnings related to data - processing will be printed. A number of warnings - are expected for a normal SQuAD evaluation. + --verbose_logging - If true, all the warnings related to data + processing will be printed. A number of warnings + are expected for a normal SQuAD evaluation. - --do_lower_case - Whether to lower case the input text. Set to - true for uncased models and false for cased models. + --do_lower_case - Whether to lower case the input text. Set to + true for uncased models and false for cased models. - --version_2_with_negative - If true, the SQuAD examples contain questions - that do not have an answer. + --version_2_with_negative - If true, the SQuAD examples contain questions + that do not have an answer. --null_score_diff_threshold NULL_SCORE_DIFF_THRES HOLD - - A null answer will be predicted if null_score if - best_non_null is greater than NULL_SCORE_DIFF_THRESHOLD. + - A null answer will be predicted if null_score if + best_non_null is greater than NULL_SCORE_DIFF_THRESHOLD. ``` ### Command-line options -To see the full list of available options and their descriptions, use the -h or --help command line option, for example: +To see the full list of available options and their descriptions, use the `-h` or `--help` command line option, for example: `python run_pretraining.py --help` `python run_squad.py --help` -Detailed descriptions of command line options can be found in the Parameters section above. +Detailed descriptions of command-line options can be found in the [Parameters](#parameters) section. ### Getting the data -For pre-training BERT, we use the concatenation of Wikipedia (2500M words) as well as Book Corpus (800M words). For Wikipedia, we extract only the text passages and ignore headers, lists, and tables. BERT requires that datasets are structured as a document level corpus rather than a shuffled sentence level corpus because it is critical to extract long contiguous sentences. +For pre-training BERT, we use the concatenation of Wikipedia (2500M words) as well as BookCorpus (800M words). For Wikipedia, we extract only the text passages and ignore headers, lists, and tables. BERT requires that datasets are structured as a document level corpus rather than a shuffled sentence level corpus because it is critical to extract long contiguous sentences. The preparation of pre-training dataset is described in the `bertPrep.py` script found in the `data/` folder. The component steps in the automated scripts to prepare the datasets are as follows: @@ -436,12 +494,11 @@ The preparation of pre-training dataset is described in the `bertPrep.py` script 5. hdf5 file creation - each text file shard is processed by the `create_pretraining_data.py` script to produce a corresponding hdf5 file. The script generates input data and labels for masked language modeling and sentence prediction tasks for the input text shard. -The tools used for preparing the Bookcorpus and Wikipedia datasets can be applied to prepare an arbitrary corpus. The `create_datasets_from_start.sh` script in the `data/` directory applies sentence segmentation, sharding, and hdf5 file creation given an arbitrary text file containing a document-separated text corpus. - +The tools used for preparing the BookCorpus and Wikipedia datasets can be applied to prepare an arbitrary corpus. The `create_datasets_from_start.sh` script in the `data/` directory applies sentence segmentation, sharding, and hdf5 file creation given an arbitrary text file containing a document-separated text corpus. For fine-tuning a pre-trained BERT model for specific tasks, by default this repository prepares the following dataset: -- [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/): for question answering +- [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/): for question answering #### Dataset guidelines @@ -469,7 +526,7 @@ The training process consists of two steps: pre-training and fine-tuning. Pre-training is performed using the `run_pretraining.py` script along with parameters defined in the `scripts/run_pretraining.sh`. -The `run_pretraining.sh` script runs a job on a single node that trains the BERT-large model from scratch using the Wikipedia and BookCorpus datasets as training data using LAMB optimizer. By default, the training script runs two phases of training with a hyperparameter recipe specific to 8 x V100 32G cards: +The `run_pretraining.sh` script runs a job on a single node that trains the BERT-large model from scratch using Wikipedia and BookCorpus datasets as training data using the LAMB optimizer. By default, the training script runs two phases of training with a hyperparameter recipe specific to 8 x V100 32G cards: Phase 1: (Maximum sequence length of 128) - Runs on 8 GPUs with training batch size of 64 per GPU @@ -487,7 +544,7 @@ Phase 2: (Maximum sequence length of 512) - Saves a checkpoint every 200 iterations (keeps only the latest 3 checkpoints) and at the end of training. All checkpoints, and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory). - Creates a log file containing all the output -These parameters will train on Wikipedia and BooksCorpus to SoTA accuracy on a DGX-1 with 32GB V100 cards. +These parameters will train on Wikipedia and BookCorpus to SoTA accuracy on a DGX-1 with 32GB V100 cards. `bash run_pretraining.sh ` @@ -496,20 +553,23 @@ Where: - `` is per-GPU batch size used for training. Larger batch sizes run more efficiently, but require more memory. - `` is the base learning rate for training - `` is the type of math in your model, can be either `fp32` or `fp16`. The options mean: - - FP32: 32-bit IEEE single precision floats. - - FP16: Mixed precision 16 and 32 bit floats. + - FP32: 32-bit IEEE single precision floats. + - FP16: Mixed precision 16 and 32 bit floats. - `` is the number of GPUs to use for training. Must be equal to or smaller than the number of GPUs attached to your node. - `` is the percentage of training steps used for warm-up at the start of training. - `` is the total number of training steps. - `` controls how often checkpoints are saved. -- `` if set to true, training should resume from latest model in /results/checkpoints. Default is false. -- `` a flag indicating if output should be written to a log file or not (acceptable values are true or false. true indicates output should be saved to a log file.) +- `` if set to `true`, training should resume from latest model in `/results/checkpoints`. Default is `false`. +- `` a flag indicating if output should be written to a log file or not (acceptable values are `true` or 'false`. `true` indicates output should be saved to a log file.) - `` a flag indicating whether a larger batch should be simulated with gradient accumulation. - `` an integer indicating the number of steps to accumulate gradients over. Effective batch size = `training_batch_size` / `gradient_accumulation_steps`. - `` random seed for the run. - `` - If set to `true`, performs allreduce only after the defined number of gradient accumulation steps. - `` - If set to `true`, performs allreduce after gradient accumulation steps in FP16. - `` - If set to `true`, accumulates/sums the gradients in FP16. + + Note: The above three options need to be set to false when running on fp32. + - `` is per-GPU batch size used for training in phase 2. Larger batch sizes run more efficiently, but require more memory. - `` is the base learning rate for training phase 2. - `` is the percentage of training steps used for warm-up at the start of training. @@ -522,7 +582,7 @@ For example: Trains BERT-large from scratch on a DGX-1 32G using FP16 arithmetic. 90% of the training steps are done with sequence length 128 (phase1 of training) and 10% of the training steps are done with sequence length 512 (phase2 of training). -In order to train on a DGX-1 16G, set `gradient_accumulation_steps` to `512` and `gradient_accumulation_steps_phase2` to `1024` in `scripts/run_pretraining.sh` +In order to train on a DGX-1 16G, set `gradient_accumulation_steps` to `512` and `gradient_accumulation_steps_phase2` to `1024` in `scripts/run_pretraining.sh`. In order to train on a DGX-2 32G, set `train_batch_size` to `4096`, `train_batch_size_phase2` to `2048`, `num_gpus` to `16`, `gradient_accumulation_steps` to `64` and `gradient_accumulation_steps_phase2` to `256` in `scripts/run_pretraining.sh` @@ -538,17 +598,17 @@ By default, each Python script implements fine-tuning a pre-trained BERT model f - Has FP16 precision enabled - Saves a checkpoint at the end of training to the `/results/` folder -Fine-tuning Python scripts implement support for mixed precision and multi-GPU training through NVIDIA’s [Apex](https://github.com/NVIDIA/apex) library. For a full list of parameters and associated explanations, consult the [Parameters](#parameters) section. +Fine-tuning Python scripts implement support for mixed precision and multi-GPU training through NVIDIA’s [APEX](https://github.com/NVIDIA/apex) library. For a full list of parameters and associated explanations, see the [Parameters](#parameters) section. All fine-tuning shell scripts have the same positional arguments, outlined below: -`bash scripts/run_squad.sh ` +```bash scripts/run_squad.sh ``` By default, the mode positional argument is set to train eval. See the [Quick Start Guide](#quick-start-guide) for explanations of each positional argument. Note: The first positional argument (the path to the checkpoint to load) is required. -Each fine-tuning script assumes that the corresponding dataset files exist in the `data/` directory or separate path can be a command line input to `run_squad.sh`. +Each fine-tuning script assumes that the corresponding dataset files exist in the `data/` directory or separate path can be a command-line input to `run_squad.sh`. ### Inference process @@ -578,13 +638,13 @@ Where: - `` is per-GPU batch size used for inference. Larger batch sizes run more efficiently, but require more memory. - `` is the type of math in your model, can be either `fp32` or `fp16`. The options mean: - - `fp32`: 32-bit IEEE single precision floats - - `fp16`: 16-bit floats for 3.2x faster inference + - `fp32`: 32-bit IEEE single precision floats + - `fp16`: 16-bit floats for 3.2x faster inference - `` is the number of GPUs to use for inference. Must be equal to or smaller than the number of GPUs attached to your node. - `` is either `--eval` for evaluation or `--prediction` for inference -- `` is the model checkpoint to run inference on. Default is `-1`, which takes the most recent model checkpoint from the checkpoints folder. +- `` is the model checkpoint to run inference on. Default is `-1`, which takes the most recent model checkpoint from the `checkpoints` folder. - `` is the total number of inference steps per process. Default is `-1`, which iterates over the entire dataset. -- `` a flag indicating if output should be written to a logfile or not (acceptable values are true or false. true indicates output should be saved to a logfile.) +- `` a flag indicating if output should be written to a logfile or not (acceptable values are `true` or `false`. `true` indicates output should be saved to a logfile.) For example: @@ -598,11 +658,10 @@ Evaluation fine-tuning is enabled by the same scripts as training: The mode positional argument of the shell script is used to run in evaluation mode. The fine-tuned BERT model will be run on the evaluation dataset, and the evaluation loss and accuracy will be displayed. -Each inference shell script expects dataset files to exist in the same locations as the corresponding training scripts. The inference scripts can be run with default settings. By setting `mode` variable in the script to either `eval` or `prediction` flag, you can choose between running evaluation on a given dataset or doing prediction. +Each inference shell script expects dataset files to exist in the same locations as the corresponding training scripts. The inference scripts can be run with default settings. By setting `mode` variable in the script to either `eval` or `prediction` flag, you can choose between running prediction and evaluating them on a given dataset or just the former. `bash scripts/run_squad.sh ` -Note: Fine-tuning evaluation is only supported on single GPU. ## Performance @@ -612,11 +671,11 @@ The following section shows how to run benchmarks measuring the model performanc #### Training performance benchmark -Training performance benchmarks for both pretraining and fine-tuning can be obtained by running `scripts/run_pretraining.sh` and `scripts/run_squad.sh` respectively. The required parameters can be passed through the command line as described in [Training process](#training-process). +Training performance benchmarks for both pretraining and fine-tuning can be obtained by running `scripts/run_pretraining.sh` and `scripts/run_squad.sh` respectively. The required parameters can be passed through the command-line as described in [Training process](#training-process). To benchmark the training performance on a specific batch size, run: -`bash scripts/run_squad.sh train ` +`bash scripts/run_squad.sh train ` An example call used to generate throughput numbers: @@ -626,11 +685,11 @@ An example call used to generate throughput numbers: #### Inference performance benchmark -Inference performance benchmarks for both pretraining and fine-tuning can be obtained by running `scripts/run_pretraining_inference.sh` and `scripts/run_squad.sh` respectively. The required parameters can be passed through the command line as described in [Inference process](#inference-process). +Inference performance benchmarks for both pretraining and fine-tuning can be obtained by running `scripts/run_pretraining_inference.sh` and `scripts/run_squad.sh` respectively. The required parameters can be passed through the command-line as described in [Inference process](#inference-process). To benchmark the inference performance on a specific batch size, run: -`bash scripts/run_squad.sh eval ` +`bash scripts/run_squad.sh eval ` An example call used to generate throughput numbers: @@ -644,18 +703,20 @@ An example call used to generate throughput numbers: #### Training accuracy results - -Our results were obtained by running `scripts/run_squad.sh` and `scripts/run_pretraining.sh` training scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-2 with (16x V100 32G) GPUs for pretraining and NVIDIA DGX-1 with (8x V100 16G) GPUs for fine-tuning. - -Note: Pretraining results were obtained with a dataset that was created using an earlier version of the data preprocessing scripts than are currently in this repository, and with an an earlier snapshot of wikidumps. The results in the table will be updated soon with results using the latest data prep scripts. Early data show the results are quite similar. +Our results were obtained by running the `scripts/run_squad.sh` and `scripts/run_pretraining.sh` training scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-2 with (16x V100 32G) GPUs for pretraining and NVIDIA DGX-1 with (8x V100 16G) GPUs for fine-tuning. ##### Pre-training loss results -| DGX System | GPUs | Accumulated Batch size / GPU (Phase 1 and Phase 2) | Accumulation steps (Phase 1 and Phase 2) | Final Loss - FP32 | Final Loss - mixed precision | Time to train(days) - FP32 | Time to train(days) - mixed precision | Time to train speedup (FP32 to mixed precision) +| DGX System | GPUs | Accumulated Batch size / GPU (Phase 1 and Phase 2) | Accumulation steps (Phase 1 and Phase 2) | Final Loss - FP32 | Final Loss - mixed precision | Time to train(hours) - FP32 | Time to train(hours) - mixed precision | Time to train speedup (FP32 to mixed precision) |---|---|---|---|---|---|---|---|--- -| NVIDIA DGX-1 With 16G|8|8192 and 4196 |512 and 1024|-|1.53|-|6.84|- -| NVIDIA DGX-2 With 32G|16|4096 and 2048 |64 and 256|-|1.52|-|2.71|- +| 1 x NVIDIA DGX-1 With 16G|8|8192 and 4196 |512 and 1024|-|1.36|-|153.16|- +| 1 x NVIDIA DGX-2H With 32G|16|4096 and 2048 |64 and 256|-|1.35|-|58.4|- +| 4 x NVIDIA DGX-1 With 16G|8|2048 and 1024 |128 and 256|-|1.34|-|39.27|- +| 4 x NVIDIA DGX-2H With 32G|16|1024 and 512 |16 and 64|-|1.33|-|15.35|- +| 16 x NVIDIA DGX-1 With 16G|8|512 and 256 |32 and 64|-|1.329|-|10.36|- +| 16 x NVIDIA DGX-2H With 32G|16|256 and 128 |4 and 16|-|1.33|-|3.94|- +| 64 x NVIDIA DGX-2H With 32G|16|64 and 32 |(1 and 4)FP16 and (2 and 8)FP32|1.33|1.331|4.338|1.124|3.85 ##### Fine-tuning accuracy results @@ -667,9 +728,9 @@ Note: Pretraining results were obtained with a dataset that was created using an ###### Pre-training stability test -| Accuracy Metric | Seed 1 -|---|--- -| Final Loss | 1.52 +| Accuracy Metric | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Mean | Standard Deviation +|---|---|---|---|---|---|---|--- +|Final Loss| 1.344 | 1.328 | 1.324 | 1.326 | 1.333 | 1.331 | 0.009 ###### Fine-tuning stability test @@ -680,11 +741,12 @@ Training stability with 8 GPUs, FP16 computations, batch size of 4: |Exact Match %| 84.50 | 84.07 | 84.52 | 84.23 | 84.17 | 84.30 | .200 | f1 % | 91.29 | 91.01 | 91.14 | 91.10 | 90.85 | 91.08 | 0.162 + #### Training performance results ##### Training performance: NVIDIA DGX-1 (8x V100 16G) -Our results were obtained by running `scripts/run_pretraining.sh` and `scripts/run_squad.shtraining scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. Performance numbers (in sequences per second) were averaged over a predefined number of training iterations. +Our results were obtained by running the `scripts/run_pretraining.sh` and `scripts/run_squad.sh` training scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. Performance numbers (in sequences per second) were averaged over a predefined number of training iterations. ###### Pre-training NVIDIA DGX-1 With 16G @@ -698,6 +760,18 @@ Our results were obtained by running `scripts/run_pretraining.sh` and `scripts/r | 8| 2| 4| 512| 56.16 |194.56 | 3.46| 7.43| 7.30 +###### Pre-training on multiple NVIDIA DGX-1 With 16G + +| Nodes | GPUs | Batch size / GPU (FP32) | Batch size / GPU (FP16) | Sequence length | Throughput - FP32(sequences/sec) | Throughput - mixed precision(sequences/sec) | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision +|------------------|----------------------|----------------------|-------------------|-----------------------------------------------|------------------------------------|---------------------------------|----------------------|----------------------------------------------|-------------- +|1 |8 | N/A | 16| 128| N/A |874.24 |N/A |N/A | 1.00 +|4 |8 | N/A | 16| 128| N/A |3089.76 | N/A| N/A| 3.53 +|16 |8 | N/A | 16| 128| N/A |12144.64 | N/A| N/A| 13.89 +|1 |8 | N/A | 4| 512| N/A |195.93 |N/A |N/A | 1.00 +|4 |8 | N/A | 4| 512| N/A |700.16 | N/A| N/A| 3.57 +|16| 8| N/A | 4| 512| N/A |2746.368 | N/A| N/A| 14.02 + + ###### Fine-tuning NVIDIA DGX-1 With 16G @@ -713,7 +787,7 @@ Our results were obtained by running `scripts/run_pretraining.sh` and `scripts/r ##### Training performance: NVIDIA DGX-1 (8x V100 32G) -Our results were obtained by running `scripts/run_pretraining.sh` and `scripts/run_squad.sh` training scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-1 with (8x V100 32G) GPUs. Performance numbers (in sequences per second) were averaged over an entire training epoch. +Our results were obtained by running the `scripts/run_pretraining.sh` and `scripts/run_squad.sh` training scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-1 with (8x V100 32G) GPUs. Performance numbers (in sequences per second) were averaged over an entire training epoch. ###### Pre-training NVIDIA DGX-1 With 32G @@ -729,6 +803,7 @@ Our results were obtained by running `scripts/run_pretraining.sh` and `scripts/r |4 |N/A | 10| 512|N/A |164.00 | N/A| N/A| 3.57 | 8|N/A | 10| 512|N/A |325.60| N/A| N/A| 7.08 + ###### Fine-tuning NVIDIA DGX-1 With 32G | GPUs | Batch size / GPU | Throughput - FP32(sequences/sec) | Throughput - mixed precision(sequences/sec) | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision @@ -743,7 +818,7 @@ Our results were obtained by running `scripts/run_pretraining.sh` and `scripts/r ##### Training performance: NVIDIA DGX-2 (16x V100 32G) -Our results were obtained by running `scripts/run_pretraining.sh` and `scripts/run_squad.sh` training scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-2 with (16x V100 32G) GPUs. Performance numbers (in sequences per second) were averaged over an entire training epoch. +Our results were obtained by running the `scripts/run_pretraining.sh` and `scripts/run_squad.sh` training scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-2 with (16x V100 32G) GPUs. Performance numbers (in sequences per second) were averaged over an entire training epoch. ###### Pre-training NVIDIA DGX-2 With 32G @@ -762,6 +837,22 @@ Our results were obtained by running `scripts/run_pretraining.sh` and `scripts/r |8 | N/A | 10| 512| N/A| 325.60| N/A| N/A| 6.87 |16 | N/A | 10| 512| N/A| 648.00| N/A| N/A| 13.67 +###### Pre-training on multiple NVIDIA DGX-2H With 32G + +Note: Multi-node performance numbers below are on DGX-2H whereas the single node performance numbers above are on DGX-2. + + +| Nodes | GPUs | Batch size / GPU (FP32) | Batch size / GPU (FP16) | Sequence length | Throughput - FP32(sequences/sec) | Throughput - mixed precision(sequences/sec) | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision +|------------------|----------------------|----------------------|-------------------|-----------------------------------------------|------------------------------------|---------------------------------|----------------------|----------------------------------------------|--------------------- +|1 |16 | N/A | 64| 128| N/A |3379.2 |N/A |N/A | 1.00 +|4 |16 | N/A | 64| 128| N/A |12709.88 | N/A| N/A| 3.76 +|16 |16 | N/A | 64| 128| N/A |51937.28 | N/A| N/A| 15.37 +|64 |16 | 32 | 64| 128| 46628.86 |188088.32 | 4.03 | N/A| 55.66 +|1 |16 | N/A | 8| 512| N/A |625.66 |N/A |N/A | 1.00 +|4 |16 | N/A | 8| 512| N/A |2386.38 | N/A| N/A| 3.81 +|16| 16| N/A | 8| 512| N/A |9932.8 | N/A| N/A| 15.87 +|64| 16| 4 | 8| 512| 9543.68 |37478.4 | 3.92| N/A| 59.9 + ###### Fine-tuning NVIDIA DGX-2 With 32G | GPUs | Batch size / GPU | Throughput - FP32(sequences/sec) | Throughput - mixed precision(sequences/sec) | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision @@ -781,7 +872,7 @@ To achieve these same results, follow the steps in the [Quick Start Guide](#quic ##### Inference performance: NVIDIA DGX-1 (1x V100 16G) -Our results were obtained by running `scripts/run_pretraining_inference.sh` on data of sequence length 512 and `scripts/run_squad.sh` scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-1 with (1x V100 16G) GPUs. +Our results were obtained by running the `scripts/run_pretraining_inference.sh` script on data of sequence length 512 and the `scripts/run_squad.sh` script in the pytorch:19.07-py3 NGC container on NVIDIA DGX-1 with (1x V100 16G) GPUs. ###### Pre-training inference on NVIDIA DGX-1 with 16G @@ -797,7 +888,7 @@ Our results were obtained by running `scripts/run_pretraining_inference.sh` on d ##### Inference performance: NVIDIA DGX-1 (1x V100 32G) -Our results were obtained by running `scripts/run_pretraining_inference.sh` and `scripts/run_squad.sh` scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-1 with (1x V100 32G) GPUs. +Our results were obtained by running the `scripts/run_pretraining_inference.sh` and `scripts/run_squad.sh` scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-1 with (1x V100 32G) GPUs. ###### Pre-training inference on NVIDIA DGX-1 with 32G @@ -813,13 +904,13 @@ Our results were obtained by running `scripts/run_pretraining_inference.sh` and ##### Inference performance: NVIDIA DGX-2 (1x V100 32G) -Our results were obtained by running `scripts/run_pretraining_inference.sh` and `scripts/run_squad.sh` scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-2 with (1x V100 32G) GPUs. +Our results were obtained by running the `scripts/run_pretraining_inference.sh` and `scripts/run_squad.sh` scripts in the pytorch:19.07-py3 NGC container on NVIDIA DGX-2 with (1x V100 32G) GPUs. ###### Pre-training inference on NVIDIA DGX-2 with 32G |GPUs | Throughput - FP32(sequences/sec)|Throughput - Mixed Precision(sequences/sec) |---------- |---------|--------------- -| 1| 30.24 97.72 +| 1| 30.24| 97.72 ###### Fine-tuning inference on NVIDIA DGX-2 with 32G @@ -835,16 +926,20 @@ The inference performance metrics used were items/second. ### Changelog +September 2019 +- Scripts to support multi-node launch +- Update pretraining loss results based on the latest data preparation scripts + August 2019 - -- Pretraining support with LAMB optimizer - +- Pre-training support with LAMB optimizer - Updated Data download and Preprocessing July 2019 - - Initial release ### Known issues There are no known issues with this model. + + + diff --git a/PyTorch/LanguageModeling/BERT/bind_pyt.py b/PyTorch/LanguageModeling/BERT/bind_pyt.py index 9e907f47..211467aa 100755 --- a/PyTorch/LanguageModeling/BERT/bind_pyt.py +++ b/PyTorch/LanguageModeling/BERT/bind_pyt.py @@ -1,3 +1,16 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import sys import subprocess import os diff --git a/PyTorch/LanguageModeling/BERT/configurations.yml b/PyTorch/LanguageModeling/BERT/configurations.yml new file mode 100644 index 00000000..5ae89482 --- /dev/null +++ b/PyTorch/LanguageModeling/BERT/configurations.yml @@ -0,0 +1,182 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +#1 DGX1 phase1 +bert--DGX1: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "1" + BATCHSIZE: "8192" + LR: "6e-3" + GRADIENT_STEPS: "512" + PHASE: "1" + +#4 DGX1 phase1 +bert--DGX1_4x8x16x128: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "4" + BATCHSIZE: "2048" + LR: "6e-3" + GRADIENT_STEPS: "128" + PHASE: "1" + +#16 DGX1 phase1 +bert--DGX1_16x8x16x32: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "16" + BATCHSIZE: "512" + LR: "6e-3" + GRADIENT_STEPS: "32" + PHASE: "1" + +#1 DGX2 phase1 +bert--DGX2: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "1" + BATCHSIZE: "4096" + LR: "6e-3" + GRADIENT_STEPS: "64" + PHASE: "1" + +#4 DGX2 phase1 +bert--DGX2_4x16x64x16: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "4" + BATCHSIZE: "1024" + LR: "6e-3" + GRADIENT_STEPS: "16" + PHASE: "1" + +#16 DGX2 phase1 +bert--DGX2_16x16x64x4: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "16" + BATCHSIZE: "256" + LR: "6e-3" + GRADIENT_STEPS: "4" + PHASE: "1" + +#64 DGX2 phase1 +bert--DGX2_64x16x64: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "64" + BATCHSIZE: "64" + LR: "6e-3" + GRADIENT_STEPS: "1" + PHASE: "1" + +#1 DGX1 phase2 +bert--DGX1_1x8x4x1024: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "1" + BATCHSIZE: "4096" + LR: "4e-3" + GRADIENT_STEPS: "1024" + PHASE: "2" + +#4 DGX1 phase2 +bert--DGX1_4x8x4x256: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "4" + BATCHSIZE: "1024" + LR: "4e-3" + GRADIENT_STEPS: "256" + PHASE: "2" + +#16 DGX1 phase2 +bert--DGX1_16x8x4x64: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "16" + BATCHSIZE: "256" + LR: "4e-3" + GRADIENT_STEPS: "64" + PHASE: "2" + +#1 DGX2 phase2 +bert--DGX2_1x16x8x256: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "1" + BATCHSIZE: "2048" + LR: "4e-3" + GRADIENT_STEPS: "256" + PHASE: "2" + +#4 DGX2 phase2 +bert--DGX2_4x16x8x64: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "4" + BATCHSIZE: "512" + LR: "4e-3" + GRADIENT_STEPS: "64" + PHASE: "2" + +#16 DGX2 phase2 +bert--DGX2_16x16x8x16: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "16" + BATCHSIZE: "128" + LR: "4e-3" + GRADIENT_STEPS: "16" + PHASE: "2" + +#64 DGX2 phase2 +bert--DGX2_64x16x8x4: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "64" + BATCHSIZE: "32" + LR: "4e-3" + GRADIENT_STEPS: "4" + PHASE: "2" + diff --git a/PyTorch/LanguageModeling/BERT/create_pretraining_data.py b/PyTorch/LanguageModeling/BERT/create_pretraining_data.py index de2bc5b5..7370d790 100755 --- a/PyTorch/LanguageModeling/BERT/create_pretraining_data.py +++ b/PyTorch/LanguageModeling/BERT/create_pretraining_data.py @@ -1,6 +1,6 @@ # coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors. -# +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """Create masked LM/next sentence masked_lm TF examples for BERT.""" from __future__ import absolute_import, division, print_function, unicode_literals diff --git a/PyTorch/LanguageModeling/BERT/data/BooksDownloader.py b/PyTorch/LanguageModeling/BERT/data/BooksDownloader.py index c79bfa1a..a10ebde0 100644 --- a/PyTorch/LanguageModeling/BERT/data/BooksDownloader.py +++ b/PyTorch/LanguageModeling/BERT/data/BooksDownloader.py @@ -1,4 +1,16 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import subprocess class BooksDownloader: diff --git a/PyTorch/LanguageModeling/BERT/data/BookscorpusTextFormatting.py b/PyTorch/LanguageModeling/BERT/data/BookscorpusTextFormatting.py index 71c67a9b..22e48d4b 100644 --- a/PyTorch/LanguageModeling/BERT/data/BookscorpusTextFormatting.py +++ b/PyTorch/LanguageModeling/BERT/data/BookscorpusTextFormatting.py @@ -1,4 +1,16 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import glob import os diff --git a/PyTorch/LanguageModeling/BERT/data/Downloader.py b/PyTorch/LanguageModeling/BERT/data/Downloader.py index d6b25f0e..ebbd43d6 100644 --- a/PyTorch/LanguageModeling/BERT/data/Downloader.py +++ b/PyTorch/LanguageModeling/BERT/data/Downloader.py @@ -1,4 +1,16 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from GooglePretrainedWeightDownloader import GooglePretrainedWeightDownloader from NVIDIAPretrainedWeightDownloader import NVIDIAPretrainedWeightDownloader from WikiDownloader import WikiDownloader diff --git a/PyTorch/LanguageModeling/BERT/data/GooglePretrainedWeightDownloader.py b/PyTorch/LanguageModeling/BERT/data/GooglePretrainedWeightDownloader.py index f833759a..bb0684d3 100644 --- a/PyTorch/LanguageModeling/BERT/data/GooglePretrainedWeightDownloader.py +++ b/PyTorch/LanguageModeling/BERT/data/GooglePretrainedWeightDownloader.py @@ -1,4 +1,15 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import hashlib import os diff --git a/PyTorch/LanguageModeling/BERT/data/MRPCDownloader.py b/PyTorch/LanguageModeling/BERT/data/MRPCDownloader.py index f20ffe2e..42dd4227 100644 --- a/PyTorch/LanguageModeling/BERT/data/MRPCDownloader.py +++ b/PyTorch/LanguageModeling/BERT/data/MRPCDownloader.py @@ -1,4 +1,15 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import bz2 import os diff --git a/PyTorch/LanguageModeling/BERT/data/NVIDIAPretrainedWeightDownloader.py b/PyTorch/LanguageModeling/BERT/data/NVIDIAPretrainedWeightDownloader.py index 0d4fc020..13c9a320 100644 --- a/PyTorch/LanguageModeling/BERT/data/NVIDIAPretrainedWeightDownloader.py +++ b/PyTorch/LanguageModeling/BERT/data/NVIDIAPretrainedWeightDownloader.py @@ -1,4 +1,15 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import os diff --git a/PyTorch/LanguageModeling/BERT/data/SquadDownloader.py b/PyTorch/LanguageModeling/BERT/data/SquadDownloader.py index 2d97fc41..6d64ffc6 100644 --- a/PyTorch/LanguageModeling/BERT/data/SquadDownloader.py +++ b/PyTorch/LanguageModeling/BERT/data/SquadDownloader.py @@ -1,4 +1,15 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import bz2 import os diff --git a/PyTorch/LanguageModeling/BERT/data/TextSharding.py b/PyTorch/LanguageModeling/BERT/data/TextSharding.py index e690aa3b..0753e742 100644 --- a/PyTorch/LanguageModeling/BERT/data/TextSharding.py +++ b/PyTorch/LanguageModeling/BERT/data/TextSharding.py @@ -1,4 +1,15 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. from collections import defaultdict from itertools import islice diff --git a/PyTorch/LanguageModeling/BERT/data/WikiDownloader.py b/PyTorch/LanguageModeling/BERT/data/WikiDownloader.py index be85ac8f..505ec76c 100644 --- a/PyTorch/LanguageModeling/BERT/data/WikiDownloader.py +++ b/PyTorch/LanguageModeling/BERT/data/WikiDownloader.py @@ -1,4 +1,15 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import bz2 import os @@ -43,6 +54,4 @@ class WikiDownloader: subprocess.run('bzip2 -dk ' + self.save_path + '/' + filename, shell=True, check=True) else: - assert False, 'WikiDownloader not implemented for this language yet.' - - + assert False, 'WikiDownloader not implemented for this language yet.' \ No newline at end of file diff --git a/PyTorch/LanguageModeling/BERT/data/WikicorpusTextFormatting.py b/PyTorch/LanguageModeling/BERT/data/WikicorpusTextFormatting.py index 9e0c7222..9d356b13 100644 --- a/PyTorch/LanguageModeling/BERT/data/WikicorpusTextFormatting.py +++ b/PyTorch/LanguageModeling/BERT/data/WikicorpusTextFormatting.py @@ -1,4 +1,15 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import glob import os diff --git a/PyTorch/LanguageModeling/BERT/data/__init__.py b/PyTorch/LanguageModeling/BERT/data/__init__.py index e69de29b..98386fd4 100644 --- a/PyTorch/LanguageModeling/BERT/data/__init__.py +++ b/PyTorch/LanguageModeling/BERT/data/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/PyTorch/LanguageModeling/BERT/data/bertPrep.py b/PyTorch/LanguageModeling/BERT/data/bertPrep.py index 7960111c..bd7496da 100644 --- a/PyTorch/LanguageModeling/BERT/data/bertPrep.py +++ b/PyTorch/LanguageModeling/BERT/data/bertPrep.py @@ -1,4 +1,15 @@ # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import BookscorpusTextFormatting import Downloader @@ -70,14 +81,13 @@ def main(args): wikiextractor_command = path_to_wikiextractor_in_container + ' ' + directory_structure['download'] + '/' + args.dataset + '/wikicorpus_en.xml ' + '-b 100M --processes ' + str(args.n_processes) + ' -o ' + directory_structure['extracted'] + '/' + args.dataset print('WikiExtractor Command:', wikiextractor_command) wikiextractor_process = subprocess.run(wikiextractor_command, shell=True, check=True) + #wikiextractor_process.communicate() wiki_path = directory_structure['extracted'] + '/wikicorpus_en' output_filename = directory_structure['formatted'] + '/wikicorpus_en_one_article_per_line.txt' wiki_formatter = WikicorpusTextFormatting.WikicorpusTextFormatting(wiki_path, output_filename, recursive=True) wiki_formatter.merge() - assert os.stat(output_filename).st_size > 0, 'File glob did not pick up extracted wiki files from WikiExtractor.' - elif args.dataset == 'wikicorpus_zh': assert False, 'wikicorpus_zh not fully supported at this time. The simplified/tradition Chinese data needs to be translated and properly segmented still, and should work once this step is added.' if args.skip_wikiextractor == 0: @@ -85,6 +95,7 @@ def main(args): wikiextractor_command = path_to_wikiextractor_in_container + ' ' + directory_structure['download'] + '/' + args.dataset + '/wikicorpus_zh.xml ' + '-b 100M --processes ' + str(args.n_processes) + ' -o ' + directory_structure['extracted'] + '/' + args.dataset print('WikiExtractor Command:', wikiextractor_command) wikiextractor_process = subprocess.run(wikiextractor_command, shell=True, check=True) + #wikiextractor_process.communicate() wiki_path = directory_structure['extracted'] + '/wikicorpus_zh' output_filename = directory_structure['formatted'] + '/wikicorpus_zh_one_article_per_line.txt' diff --git a/PyTorch/LanguageModeling/BERT/data/create_datasets_from_start.sh b/PyTorch/LanguageModeling/BERT/data/create_datasets_from_start.sh index 414d8d03..756cec20 100755 --- a/PyTorch/LanguageModeling/BERT/data/create_datasets_from_start.sh +++ b/PyTorch/LanguageModeling/BERT/data/create_datasets_from_start.sh @@ -1,5 +1,18 @@ #!/bin/bash + # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # Download python3 /workspace/bert/data/bertPrep.py --action download --dataset bookscorpus python3 /workspace/bert/data/bertPrep.py --action download --dataset wikicorpus_en @@ -26,4 +39,4 @@ python3 /workspace/bert/data/bertPrep.py --action create_hdf5_files --dataset bo # Create HDF5 files Phase 2 python3 /workspace/bert/data/bertPrep.py --action create_hdf5_files --dataset books_wiki_en_corpus --max_seq_length 512 \ - --max_predictions_per_seq 80 --vocab_file $BERT_PREP_WORKING_DIR/download/google_pretrained_weights/uncased_L-24_H-1024_A-16/vocab.txt --do_lower_case 1 + --max_predictions_per_seq 80 --vocab_file $BERT_PREP_WORKING_DIR/download/google_pretrained_weights/uncased_L-24_H-1024_A-16/vocab.txt --do_lower_case 1 \ No newline at end of file diff --git a/PyTorch/LanguageModeling/BERT/data/glue/download_mrpc.sh b/PyTorch/LanguageModeling/BERT/data/glue/download_mrpc.sh index d6faedb4..65f3446b 100755 --- a/PyTorch/LanguageModeling/BERT/data/glue/download_mrpc.sh +++ b/PyTorch/LanguageModeling/BERT/data/glue/download_mrpc.sh @@ -1,5 +1,18 @@ #!/usr/bin/env bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + echo "Downloading MRPC data" wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py diff --git a/PyTorch/LanguageModeling/BERT/data/squad/squad_download.sh b/PyTorch/LanguageModeling/BERT/data/squad/squad_download.sh index 249778f5..7aa6f268 100755 --- a/PyTorch/LanguageModeling/BERT/data/squad/squad_download.sh +++ b/PyTorch/LanguageModeling/BERT/data/squad/squad_download.sh @@ -1,5 +1,18 @@ #!/usr/bin/env bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + echo "Downloading dataset for squad..." # Download SQuAD diff --git a/PyTorch/LanguageModeling/BERT/extract_features.py b/PyTorch/LanguageModeling/BERT/extract_features.py index c41d4517..e26cfe94 100755 --- a/PyTorch/LanguageModeling/BERT/extract_features.py +++ b/PyTorch/LanguageModeling/BERT/extract_features.py @@ -12,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """Extract pre-computed feature vectors from a PyTorch BERT model.""" from __future__ import absolute_import diff --git a/PyTorch/LanguageModeling/BERT/file_utils.py b/PyTorch/LanguageModeling/BERT/file_utils.py index b475d450..cdefb125 100755 --- a/PyTorch/LanguageModeling/BERT/file_utils.py +++ b/PyTorch/LanguageModeling/BERT/file_utils.py @@ -1,8 +1,22 @@ +# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """ Utilities for working with the local dataset cache. This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp Copyright by the AllenNLP authors. """ + from __future__ import (absolute_import, division, print_function, unicode_literals) import json diff --git a/PyTorch/LanguageModeling/BERT/modeling.py b/PyTorch/LanguageModeling/BERT/modeling.py index 8d644821..fa19fbdc 100755 --- a/PyTorch/LanguageModeling/BERT/modeling.py +++ b/PyTorch/LanguageModeling/BERT/modeling.py @@ -1,7 +1,6 @@ # coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """PyTorch BERT model.""" from __future__ import absolute_import, division, print_function, unicode_literals diff --git a/PyTorch/LanguageModeling/BERT/optimization.py b/PyTorch/LanguageModeling/BERT/optimization.py index cdbbba84..ac5b64f9 100755 --- a/PyTorch/LanguageModeling/BERT/optimization.py +++ b/PyTorch/LanguageModeling/BERT/optimization.py @@ -13,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """PyTorch optimization for BERT model.""" import math @@ -24,6 +25,7 @@ from torch.nn.utils import clip_grad_norm_ from apex.optimizers import FusedAdam from apex.multi_tensor_apply import multi_tensor_applier import amp_C + multi_tensor_l2norm = amp_C.multi_tensor_l2norm lamb_compute_update = amp_C.multi_tensor_lamb_stage1_cuda lamb_apply_update = amp_C.multi_tensor_lamb_stage2_cuda diff --git a/PyTorch/LanguageModeling/BERT/run.sub b/PyTorch/LanguageModeling/BERT/run.sub new file mode 100644 index 00000000..dd5ad17a --- /dev/null +++ b/PyTorch/LanguageModeling/BERT/run.sub @@ -0,0 +1,74 @@ +#!/bin/bash +#SBATCH --exclusive +#SBATCH --mem=0 +#SBATCH --overcommit + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -eux + +# The following variables variables need to be set +# Base container to be used +readonly docker_image="nvcr.io/nvidia/pytorch:19.08-py3" +# Location of dataset for phase 1 +readonly datadir="/raid/datasets/bert/hdf5/shard_1472_test_split_10/seq_128_pred_20_dupe_5/training" +# Location of dataset for phase 2 +readonly datadir_phase2="/raid/datasets/bert/hdf5/shard_1472_test_split_10/seq_512_pred_80_dupe_5/training" +# Path to where trained checkpoints will be saved on the system +readonly checkpointdir="$PWD/checkpoints" + +readonly mounts=".:/workspace/bert,${datadir}:/workspace/data,${datadir_phase2}:/workspace/data_phase2,${checkpointdir}:/results" + +srun --ntasks="${SLURM_JOB_NUM_NODES}" --ntasks-per-node=1 mkdir -p "${checkpointdir}" + +PHASE1="\ + --train_batch_size=${BATCHSIZE:-16} \ + --learning_rate=${LR:-6e-3} \ + --warmup_proportion=${WARMUP_UPDATES:-0.2843} \ + --input_dir=/workspace/data \ + --max_seq_length=128 \ + --max_predictions_per_seq=20 \ + --max_steps=7038 \ + --num_steps_per_checkpoint=2500 \ + " +PHASE2="\ + --train_batch_size=${BATCHSIZE:-4096} \ + --learning_rate=${LR:-4e-3} \ + --warmup_proportion=${WARMUP_UPDATES:-0.128} \ + --input_dir=/workspace/data_phase2 \ + --phase2 \ + --max_seq_length=512 \ + --max_predictions_per_seq=80 \ + --max_steps=1563 \ + --num_steps_per_checkpoint=1000 \ + --resume_from_checkpoint --phase1_end_step=7038 \ + " +PHASES=( "$PHASE1" "$PHASE2" ) + +PHASE=${PHASE:-1} + +BERT_CMD="\ + python -u /workspace/bert/run_pretraining.py \ + --seed=42 \ + ${PHASES[$((PHASE-1))]} \ + --do_train \ + --config_file=/workspace/bert/bert_config.json \ + --output_dir=/results \ + --fp16 \ + --allreduce_post_accumulation --allreduce_post_accumulation_fp16 \ + --gradient_accumulation_steps=${GRADIENT_STEPS:-2} \ + --log_freq=1 \ + --local_rank=\${SLURM_LOCALID}" + +srun -l --container-image="${docker_image}" --container-mounts="${mounts}" sh -c "${BERT_CMD}" diff --git a/PyTorch/LanguageModeling/BERT/run_glue.py b/PyTorch/LanguageModeling/BERT/run_glue.py index 7c33a4a3..b00ab587 100755 --- a/PyTorch/LanguageModeling/BERT/run_glue.py +++ b/PyTorch/LanguageModeling/BERT/run_glue.py @@ -1,7 +1,6 @@ # coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """BERT finetuning runner.""" from __future__ import absolute_import, division, print_function diff --git a/PyTorch/LanguageModeling/BERT/run_pretraining.py b/PyTorch/LanguageModeling/BERT/run_pretraining.py index 6a2c6806..fe926f3f 100755 --- a/PyTorch/LanguageModeling/BERT/run_pretraining.py +++ b/PyTorch/LanguageModeling/BERT/run_pretraining.py @@ -13,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """BERT finetuning runner.""" from __future__ import absolute_import @@ -65,7 +66,6 @@ def create_pretraining_dataset(input_file, max_pred_length, shared_list, args): train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size * args.n_gpu, num_workers=4, pin_memory=True) - # shared_list["0"] = (train_dataloader, input_file) return train_dataloader, input_file class pretraining_dataset(Dataset): @@ -179,7 +179,7 @@ def parse_arguments(): type=float, default=0.0, help='Loss scaling, positive power of 2 values can improve fp16 convergence.') parser.add_argument('--log_freq', - type=float, default=50.0, + type=float, default=1.0, help='frequency of logging loss.') parser.add_argument('--checkpoint_activations', default=False, @@ -253,7 +253,7 @@ def setup_training(args): raise ValueError(" `do_train` must be True.") if not args.resume_from_checkpoint and os.path.exists(args.output_dir) and ( - os.listdir(args.output_dir) and os.listdir(args.output_dir) != ['logfile.txt']): + os.listdir(args.output_dir) and any([i.startswith('ckpt') for i in os.listdir(args.output_dir)])): raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) if not args.resume_from_checkpoint: @@ -478,8 +478,7 @@ def main(): for f_id in range(f_start_id + 1 , len(files)): - # torch.cuda.synchronize() - # f_start = time.time() + if torch.distributed.get_world_size() > num_files: data_file = files[(f_id*torch.distributed.get_world_size()+torch.distributed.get_rank() + remainder*f_id)%num_files] else: @@ -489,23 +488,10 @@ def main(): previous_file = data_file - # train_dataloader = shared_file_list["0"][0] - - # thread = multiprocessing.Process( - # name="LOAD DATA:" + str(f_id) + ":" + str(data_file), - # target=create_pretraining_dataset, - # args=(data_file, args.max_predictions_per_seq, shared_file_list, args, n_gpu) - # ) - # thread.start() dataset_future = pool.submit(create_pretraining_dataset, data_file, args.max_predictions_per_seq, shared_file_list, args) - # torch.cuda.synchronize() - # f_end = time.time() - # print('[{}] : shard overhead {}'.format(torch.distributed.get_rank(), f_end - f_start)) train_iter = tqdm(train_dataloader, desc="Iteration") if is_main_process() else train_dataloader for step, batch in enumerate(train_iter): - # torch.cuda.synchronize() - # iter_start = time.time() training_steps += 1 batch = [t.to(device) for t in batch] @@ -533,7 +519,7 @@ def main(): global_step = take_optimizer_step(args, optimizer, model, overflow_buf, global_step) if global_step >= args.max_steps: - last_num_steps = global_step % args.log_freq + last_num_steps = int(training_steps / args.gradient_accumulation_steps) % args.log_freq last_num_steps = args.log_freq if last_num_steps == 0 else last_num_steps average_loss = torch.tensor(average_loss, dtype=torch.float32).cuda() average_loss = average_loss / (last_num_steps * divisor) @@ -541,7 +527,7 @@ def main(): average_loss /= torch.distributed.get_world_size() torch.distributed.all_reduce(average_loss) if is_main_process(): - logger.info("Total Steps:{} Final Loss = {}".format(training_steps, average_loss.item())) + logger.info("Total Steps:{} Final Loss = {}".format(training_steps / args.gradient_accumulation_steps, average_loss.item())) elif training_steps % (args.log_freq * args.gradient_accumulation_steps) == 0: if is_main_process(): print("Step:{} Average Loss = {} Step Loss = {} LR {}".format(global_step, average_loss / ( @@ -578,13 +564,6 @@ def main(): # thread.join() return args - - # torch.cuda.synchronize() - # iter_end = time.time() - - # if torch.distributed.get_rank() == 0: - # print('step {} : {}'.format(global_step, iter_end - iter_start)) - del train_dataloader # thread.join() # Make sure pool has finished and switch train_dataloader diff --git a/PyTorch/LanguageModeling/BERT/run_pretraining_inference.py b/PyTorch/LanguageModeling/BERT/run_pretraining_inference.py index 678e7f66..b776ce34 100755 --- a/PyTorch/LanguageModeling/BERT/run_pretraining_inference.py +++ b/PyTorch/LanguageModeling/BERT/run_pretraining_inference.py @@ -12,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """BERT finetuning runner.""" from __future__ import absolute_import diff --git a/PyTorch/LanguageModeling/BERT/run_squad.py b/PyTorch/LanguageModeling/BERT/run_squad.py index 8f3ba8b4..f78fbd6c 100755 --- a/PyTorch/LanguageModeling/BERT/run_squad.py +++ b/PyTorch/LanguageModeling/BERT/run_squad.py @@ -1,7 +1,6 @@ # coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """Run BERT on SQuAD.""" from __future__ import absolute_import, division, print_function @@ -40,6 +40,7 @@ from file_utils import PYTORCH_PRETRAINED_BERT_CACHE from modeling import BertForQuestionAnswering, BertConfig, WEIGHTS_NAME, CONFIG_NAME from optimization import BertAdam, warmup_linear from tokenization import (BasicTokenizer, BertTokenizer, whitespace_tokenize) +from utils import is_main_process if sys.version_info[0] == 2: import cPickle as pickle @@ -923,9 +924,11 @@ def main(): model = BertForQuestionAnswering(config) # model = BertForQuestionAnswering.from_pretrained(args.bert_model, # cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank))) - print("USING CHECKOINT") + if is_main_process(): + print("LOADING CHECKOINT") model.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu')["model"], strict=False) - print("USED CHECKPOINT \n\n") + if is_main_process(): + print("LOADED CHECKPOINT") model.to(device) if args.fp16 and args.old: model.half() diff --git a/PyTorch/LanguageModeling/BERT/run_swag.py b/PyTorch/LanguageModeling/BERT/run_swag.py index cb8ea149..ebc608f6 100755 --- a/PyTorch/LanguageModeling/BERT/run_swag.py +++ b/PyTorch/LanguageModeling/BERT/run_swag.py @@ -1,7 +1,6 @@ # coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -13,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """BERT finetuning runner.""" import argparse diff --git a/PyTorch/LanguageModeling/BERT/schedulers.py b/PyTorch/LanguageModeling/BERT/schedulers.py index 0333bbd1..2cf38841 100755 --- a/PyTorch/LanguageModeling/BERT/schedulers.py +++ b/PyTorch/LanguageModeling/BERT/schedulers.py @@ -1,3 +1,17 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import math import torch from torch.optim.optimizer import Optimizer diff --git a/PyTorch/LanguageModeling/BERT/scripts/data_download.sh b/PyTorch/LanguageModeling/BERT/scripts/data_download.sh index 36ad14e4..a66727e5 100755 --- a/PyTorch/LanguageModeling/BERT/scripts/data_download.sh +++ b/PyTorch/LanguageModeling/BERT/scripts/data_download.sh @@ -1,4 +1,18 @@ #!/usr/bin/env bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + DATA_DIR=${1:-/workspace/bert/data} # Download vocab files from pretrained model diff --git a/PyTorch/LanguageModeling/BERT/scripts/run_glue.sh b/PyTorch/LanguageModeling/BERT/scripts/run_glue.sh index 5fe89e05..8a9a11c8 100755 --- a/PyTorch/LanguageModeling/BERT/scripts/run_glue.sh +++ b/PyTorch/LanguageModeling/BERT/scripts/run_glue.sh @@ -1,5 +1,18 @@ #!/bin/bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + MRPC_DIR=/workspace/bert/data/glue/MRPC OUT_DIR=/results/MRPC @@ -55,7 +68,8 @@ CMD+="$use_fp16" LOGFILE=$OUT_DIR/logfile $CMD |& tee $LOGFILE -sed -r 's/ |(\[A)/\n/g' $LOGFILE > $LOGFILE.edit +sed -r 's/ +|(\[A)/\n/g' $LOGFILE > $LOGFILE.edit throughput=`cat $LOGFILE.edit | grep -E 'Iteration.*[0-9.]+(s/it|it/s)' | tail -1 | egrep -o '[0-9.]+(s/it|it/s)'` diff --git a/PyTorch/LanguageModeling/BERT/scripts/run_pretraining.sh b/PyTorch/LanguageModeling/BERT/scripts/run_pretraining.sh index 4d15ded6..4c160a17 100644 --- a/PyTorch/LanguageModeling/BERT/scripts/run_pretraining.sh +++ b/PyTorch/LanguageModeling/BERT/scripts/run_pretraining.sh @@ -1,5 +1,18 @@ #!/bin/bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + echo "Container nvidia build = " $NVIDIA_BUILD_ID train_batch_size=${1:-8192} learning_rate=${2:-"6e-3"} @@ -18,11 +31,11 @@ allreduce_post_accumulation=${14:-"true"} allreduce_post_accumulation_fp16=${15:-"true"} accumulate_into_fp16=${16:-"false"} -train_batch_size_phase2=${1:-4096} -learning_rate_phase2=${2:-"4e-3"} -warmup_proportion_phase2=${5:-"0.128"} -train_steps_phase2=${6:-1563} -gradient_accumulation_steps_phase2=${11:-512} +train_batch_size_phase2=${17:-4096} +learning_rate_phase2=${18:-"4e-3"} +warmup_proportion_phase2=${19:-"0.128"} +train_steps_phase2=${20:-1563} +gradient_accumulation_steps_phase2=${21:-512} DATASET=hdf5_lower_case_1_seq_len_128_max_pred_20_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5/books_wiki_en_corpus # change this for other datasets DATA_DIR=$BERT_PREP_WORKING_DIR/${DATASET}/ @@ -108,13 +121,7 @@ CMD+=" $ALL_REDUCE_POST_ACCUMULATION" CMD+=" $ALL_REDUCE_POST_ACCUMULATION_FP16" CMD+=" $ACCUMULATE_INTO_FP16" CMD+=" --do_train" - -if [ "$num_gpus" -gt 1 ] ; then - CMD="python3 -m torch.distributed.launch --nproc_per_node=$num_gpus $CMD" -else - CMD="python3 $CMD" -fi - +CMD="python3 -m torch.distributed.launch --nproc_per_node=$num_gpus $CMD" if [ "$create_logfile" = "true" ] ; then export GBS=$(expr $train_batch_size \* $num_gpus) @@ -145,7 +152,7 @@ throughput=`cat $LOGFILE | grep Iteration | tail -1 | awk -F'it/s' '{print $1}' loss=`cat $LOGFILE | grep 'Average Loss' | tail -1 | awk -F'Average Loss =' '{print $2}' | awk -F' ' '{print $1}' | egrep -o [0-9.]+` final_loss=`cat $LOGFILE | grep 'Total Steps' | tail -1 | awk -F'Final Loss =' '{print $2}' | awk -F' ' '{print $1}' | egrep -o [0-9.]+` -train_perf=$(awk 'BEGIN {print ('$throughput' * '$num_gpus' * '$train_batch_size')}') +train_perf=$(awk 'BEGIN {print ('$throughput' * '$num_gpus' * '$train_batch_size' / '$gradient_accumulation_steps' )}') echo " training throughput phase1: $train_perf sequences/second" echo "average loss: $loss" echo "final loss: $final_loss" @@ -207,13 +214,7 @@ CMD+=" $ALL_REDUCE_POST_ACCUMULATION" CMD+=" $ALL_REDUCE_POST_ACCUMULATION_FP16" CMD+=" $ACCUMULATE_INTO_FP16" CMD+=" --do_train --phase2 --resume_from_checkpoint --phase1_end_step=$train_steps" - -if [ "$num_gpus" -gt 1 ] ; then - CMD="python3 -m torch.distributed.launch --nproc_per_node=$num_gpus $CMD" -else - CMD="python3 $CMD" -fi - +CMD="python3 -m torch.distributed.launch --nproc_per_node=$num_gpus $CMD" if [ "$create_logfile" = "true" ] ; then export GBS=$(expr $train_batch_size_phase2 \* $num_gpus) @@ -239,7 +240,8 @@ throughput=`cat $LOGFILE | grep Iteration | tail -1 | awk -F'it/s' '{print $1}' loss=`cat $LOGFILE | grep 'Average Loss' | tail -1 | awk -F'Average Loss =' '{print $2}' | awk -F' ' '{print $1}' | egrep -o [0-9.]+` final_loss=`cat $LOGFILE | grep 'Total Steps' | tail -1 | awk -F'Final Loss =' '{print $2}' | awk -F' ' '{print $1}' | egrep -o [0-9.]+` -train_perf=$(awk 'BEGIN {print ('$throughput' * '$num_gpus' * '$train_batch_size_phase2')}') +train_perf=$(awk 'BEGIN {print ('$throughput' * '$num_gpus' * '$train_batch_size_phase2' / '$gradient_accumulation_steps_phase2')}') + echo " training throughput phase2: $train_perf sequences/second" echo "average loss: $loss" echo "final loss: $final_loss" diff --git a/PyTorch/LanguageModeling/BERT/scripts/run_pretraining_inference.sh b/PyTorch/LanguageModeling/BERT/scripts/run_pretraining_inference.sh index 7e98c756..6eee728e 100644 --- a/PyTorch/LanguageModeling/BERT/scripts/run_pretraining_inference.sh +++ b/PyTorch/LanguageModeling/BERT/scripts/run_pretraining_inference.sh @@ -1,5 +1,18 @@ #!/bin/bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + echo "Container nvidia build = " $NVIDIA_BUILD_ID DATASET=wikipedia_corpus # change this for other datasets diff --git a/PyTorch/LanguageModeling/BERT/scripts/run_squad.sh b/PyTorch/LanguageModeling/BERT/scripts/run_squad.sh index 8a99d7d7..3e71d553 100755 --- a/PyTorch/LanguageModeling/BERT/scripts/run_squad.sh +++ b/PyTorch/LanguageModeling/BERT/scripts/run_squad.sh @@ -1,7 +1,19 @@ #!/usr/bin/env bash -#OUT_DIR=/results/SQuAD +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +#OUT_DIR=/results/SQuAD echo "Container nvidia build = " $NVIDIA_BUILD_ID diff --git a/PyTorch/LanguageModeling/BERT/scripts/run_swag.sh b/PyTorch/LanguageModeling/BERT/scripts/run_swag.sh index 8a854bb1..377834ee 100755 --- a/PyTorch/LanguageModeling/BERT/scripts/run_swag.sh +++ b/PyTorch/LanguageModeling/BERT/scripts/run_swag.sh @@ -1,5 +1,18 @@ #!/bin/bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + SWAG_DIR=/workspace/bert/data/swag OUT_DIR=/results/SWAG @@ -54,7 +67,8 @@ CMD+="$use_fp16" LOGFILE=$OUT_DIR/logfile $CMD |& tee $LOGFILE -sed -r 's/ |(\[A)/\n/g' $LOGFILE > $LOGFILE.edit +sed -r 's/ +|(\[A)/\n/g' $LOGFILE > $LOGFILE.edit throughput=`cat $LOGFILE.edit | grep -E 'Iteration.*[0-9.]+(s/it|it/s)' | tail -1 | egrep -o '[0-9.]+(s/it|it/s)'` diff --git a/PyTorch/LanguageModeling/BERT/scripts/start_pretraining.sh b/PyTorch/LanguageModeling/BERT/scripts/start_pretraining.sh index a3155bfe..6ddc2985 100644 --- a/PyTorch/LanguageModeling/BERT/scripts/start_pretraining.sh +++ b/PyTorch/LanguageModeling/BERT/scripts/start_pretraining.sh @@ -1,4 +1,18 @@ #!/bin/bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # purpose: for multinode training on slurm clusters node_type=${1:-"dgx1"} num_nodes=${2:-1} diff --git a/PyTorch/LanguageModeling/BERT/tokenization.py b/PyTorch/LanguageModeling/BERT/tokenization.py index 5f364385..c25c323e 100755 --- a/PyTorch/LanguageModeling/BERT/tokenization.py +++ b/PyTorch/LanguageModeling/BERT/tokenization.py @@ -1,6 +1,6 @@ # coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. -# # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """Tokenization classes.""" from __future__ import absolute_import, division, print_function, unicode_literals diff --git a/PyTorch/LanguageModeling/BERT/utils.py b/PyTorch/LanguageModeling/BERT/utils.py index 4fae2889..4f8e0d86 100755 --- a/PyTorch/LanguageModeling/BERT/utils.py +++ b/PyTorch/LanguageModeling/BERT/utils.py @@ -1,3 +1,16 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import torch import torch.distributed as dist diff --git a/PyTorch/Recommendation/NCF/Dockerfile b/PyTorch/Recommendation/NCF/Dockerfile index 68740232..9ea311ab 100644 --- a/PyTorch/Recommendation/NCF/Dockerfile +++ b/PyTorch/Recommendation/NCF/Dockerfile @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:19.06-py3 +ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:19.09-py3 FROM ${FROM_IMAGE_NAME} RUN apt-get update && \ diff --git a/PyTorch/Recommendation/NCF/README.md b/PyTorch/Recommendation/NCF/README.md index bb8fb33a..73baeb04 100644 --- a/PyTorch/Recommendation/NCF/README.md +++ b/PyTorch/Recommendation/NCF/README.md @@ -225,7 +225,7 @@ This will result in a checkpoint file being written to `/data/checkpoints/model. The trained model can be evaluated by passing the `--mode` test flag to the `run.sh` script: ```bash -python -m torch.distributed.launch --nproc_per_node=8 ncf.py --data /data/cache/ml-20m --mode test --checkpoint-path /data/checkpoints/model.pth +python -m torch.distributed.launch --nproc_per_node=1 ncf.py --data /data/cache/ml-20m --mode test --load_checkpoint_path /data/checkpoints/model.pth ``` @@ -552,6 +552,9 @@ The following table shows the best inference throughput: * Updated performance tables. * Default container changed to PyTorch 19.06-py3. * Caching validation negatives between runs +4. September, 2019 + * Adjusting for API changes in PyTorch and APEX + * Checkpoints loading fix ### Known issues diff --git a/PyTorch/Recommendation/NCF/dataloading.py b/PyTorch/Recommendation/NCF/dataloading.py index 51d1f1b7..85905a01 100644 --- a/PyTorch/Recommendation/NCF/dataloading.py +++ b/PyTorch/Recommendation/NCF/dataloading.py @@ -50,10 +50,11 @@ def create_test_data(test_ratings, test_negs, args): stable_indices = torch.gather(indices, 1, indices_order) #[0,1,3,2] # produce -1 mask dup_mask = (sorted_items[:,0:-1] == sorted_items[:,1:]) - dup_mask = torch.cat((torch.zeros_like(test_pos, dtype=torch.uint8), dup_mask),dim=1) - dup_mask = torch.gather(dup_mask,1,stable_indices.sort()[1]) + dup_mask = dup_mask.type(torch.uint8) + dup_mask = torch.cat((torch.zeros_like(test_pos, dtype=torch.uint8), dup_mask), dim=1) + dup_mask = torch.gather(dup_mask, 1, stable_indices.sort()[1]) # produce real sample indices to later check in topk - sorted_items, indices = (test_items != test_pos).sort() + sorted_items, indices = (test_items != test_pos).type(torch.uint8).sort() sum_item_indices = sorted_items.float()+indices.float()/len(indices[0]) indices_order = torch.sort(sum_item_indices)[1] stable_indices = torch.gather(indices, 1, indices_order) diff --git a/PyTorch/Recommendation/NCF/inference.py b/PyTorch/Recommendation/NCF/inference.py index a963d3c9..b51b8e90 100644 --- a/PyTorch/Recommendation/NCF/inference.py +++ b/PyTorch/Recommendation/NCF/inference.py @@ -72,7 +72,8 @@ def main(): if args.opt_level == "O2": model = amp.initialize(model, opt_level=args.opt_level, keep_batchnorm_fp32=False, loss_scale='dynamic') - + model.eval() + users = torch.cuda.LongTensor(args.batch_size).random_(0, args.n_users) items = torch.cuda.LongTensor(args.batch_size).random_(0, args.n_items) diff --git a/PyTorch/Recommendation/NCF/ncf.py b/PyTorch/Recommendation/NCF/ncf.py index e2355f52..4fbb9ffb 100644 --- a/PyTorch/Recommendation/NCF/ncf.py +++ b/PyTorch/Recommendation/NCF/ncf.py @@ -223,7 +223,7 @@ def main(): dropout=args.dropout) optimizer = FusedAdam(model.parameters(), lr=args.learning_rate, - betas=(args.beta1, args.beta2), eps=args.eps, eps_inside_sqrt=False) + betas=(args.beta1, args.beta2), eps=args.eps) criterion = nn.BCEWithLogitsLoss(reduction='none') # use torch.mean() with dim later to avoid copy to host # Move model and loss to GPU @@ -252,6 +252,7 @@ def main(): if args.load_checkpoint_path: state_dict = torch.load(args.load_checkpoint_path) + state_dict = {k.replace('module.', '') : v for k,v in state_dict.items()} model.load_state_dict(state_dict) if args.mode == 'test': diff --git a/PyTorch/Segmentation/MaskRCNN/README.md b/PyTorch/Segmentation/MaskRCNN/README.md index 992b2c09..33ba1ad7 100755 --- a/PyTorch/Segmentation/MaskRCNN/README.md +++ b/PyTorch/Segmentation/MaskRCNN/README.md @@ -2,30 +2,36 @@ This repository provides a script and recipe to train and infer on MaskRCNN to achieve state of the art accuracy, and is tested and maintained by NVIDIA. ## Table Of Contents -* [The model](#the-model) +* [Model overview](#model-overview) + * [Model Architecture](#model-architecture) * [Default configuration](#default-configuration) + * [Mixed precision training](#mixed-precision-training) + * [Enabling mixed precision](#enabling-mixed-precision) * [Setup](#setup) * [Requirements](#requirements) * [Quick start guide](#quick-start-guide) -* [Details](#details) +* [Advanced](#advanced) * [Command line arguments](#command-line-arguments) * [Getting the data](#getting-the-data) * [Training process](#training-process) - * [Enabling mixed precision](#enabling-mixed-precision) -* [Benchmarking](#benchmarking) -* [Results](#results) - * [Training accuracy results](#training-accuracy-results) - * [Training stability test](#training-stability-test) - * [Training performance results](#training-performance-results) - * [NVIDIA DGX-1 (8x V100 16G)](#nvidia-dgx-1-8x-v100-16g) - * [NVIDIA DGX-1 (8x V100 32G)](#nvidia-dgx-1-8x-v100-32g) - * [Inference performance results](#inference-performance-results) - * [NVIDIA DGX-1 16G (1x V100 16G)](#nvidia-dgx-1-16g-1x-v100-16g) - * [NVIDIA DGX-1 32G (1x V100 32G)](#nvidia-dgx-1-32g-1x-v100-32g) -* [Changelog](#changelog) -* [Known issues](#known-issues) +* [Performance](#performance) + * [Benchmarking](#benchmarking) + * [Training performance benchmark](#training-performance-benchmark) + * [Inference performance benchmark](#inference-performance-benchmark) + * [Results](#results) + * [Training accuracy results](#training-accuracy-results) + * [Training stability test](#training-stability-test) + * [Training performance results](#training-performance-results) + * [NVIDIA DGX-1 (8x V100 16G)](#nvidia-dgx-1-8x-v100-16g) + * [NVIDIA DGX-1 (8x V100 32G)](#nvidia-dgx-1-8x-v100-32g) + * [Inference performance results](#inference-performance-results) + * [NVIDIA DGX-1 16G (1x V100 16G)](#nvidia-dgx-1-16g-1x-v100-16g) + * [NVIDIA DGX-1 32G (1x V100 32G)](#nvidia-dgx-1-32g-1x-v100-32g) +* [Release notes](#release-notes) + * [Changelog](#changelog) + * [Known issues](#known-issues) -## The model +## Model overview Mask R-CNN is a convolution based neural network for the task of object instance segmentation. The paper describing the model can be found [here](https://arxiv.org/abs/1703.06870). NVIDIA’s Mask R-CNN 19.2 is an optimized version of [Facebook’s implementation](https://github.com/facebookresearch/maskrcnn-benchmark), leveraging mixed precision arithmetic and tensor cores on V100 GPUs for 1.3x faster training times while maintaining target accuracy. Because this model trains with mixed precision tensor cores on Volta, researchers can get results much faster than training without tensor cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. @@ -43,6 +49,17 @@ Other publicly available implementations of Mask R-CNN include: - [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) - [Google’s tensorflow model](https://github.com/tensorflow/models/tree/master/research/object_detection) +### Model architecture + +MaskRCNN builds on top of FasterRCNN adding an additional mask head for the task of image segmentation. + +The architecture consists of following: +- R-50 backbone with FPN +- RPN head +- RoI ALign +- Bounding and classification box head +- Mask head + ### Default Configuration The default configuration of this model can be found at `pytorch/maskrcnn_benchmark/config/defaults.py`. The default hyper-parameters are as follows: - General: @@ -82,6 +99,68 @@ This repository implements multi-gpu and gradient accumulation to support larger - Pre NMS box selection - Selection of RoIs based on objectness score before NMS is applied. The source files can be found under `maskrcnn_benchmark/csrc/cuda`. + +### Feature support matrix + +The following features are supported by this model. + +| **Feature** | **MaskRCNN** | +|:---------:|:----------:| +|APEX AMP|Yes| +|APEX DDP|Yes| + +#### Features +APEX is a Pytorch extension with NVIDIA-maintained utilities to streamline mixed precision and distributed training. + +### Mixed precision training + + + + +Mixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architecture, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps: + +1. Porting the model to use the FP16 data type where appropriate. + +2. Adding loss scaling to preserve small gradient values. + + + + +For information about: + +- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation. + +- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog. + + +APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/). + + +#### Enabling mixed precision + + + + +In this repository, mixed precision training is enabled by NVIDIA’s [APEX](https://github.com/NVIDIA/apex) library. The APEX library has an automatic mixed precision module that allows mixed precision to be enabled with minimal code changes. + + + +Automatic mixed precision can be enabled with the following code changes: + +``` +from apex import amp +if fp16: + # Wrap optimizer and model + model, optimizer = amp.initialize(model, optimizer, opt_level=, loss_scale=”dynamic”) + +if fp16: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + ``` + +Where is the optimization level. In the MaskRCNN, “O1” is set as the optimization level. Mixed precision training can be turned on by passing in the argument fp16 to the pre-training and fine-tuning Python scripts. Shell scripts all have a positional argument available to enable mixed precision training. + + ## Setup The following sections list the requirements in order to start training the Mask R-CNN model. ### Requirements @@ -184,10 +263,40 @@ Model predictions get saved in the `/inference` directory. To perform inference and skip computation of mAP scores, issue the `--skip-eval` flag. Performance is reported in seconds per iteration per GPU. The benchmarking scripts can be used to extract frames per second on training and inference. -## Details +## Advanced The following sections provide greater details of the dataset, running training and inference, and the training results. -### Command line arguments +### Scripts and sample code + + +Descriptions of the key scripts and folders are provided below. + + + +- maskrcnn_benchmark - Contains scripts for to build individual components of the model such as backbone, FPN, RPN, mask and bbox heads etc., +- download_dataset.sh - Launches download and processing of required datasets. + +- scripts/ - Contains shell scripts to launch data download, train the model and perform inferences. + + + - train.sh - Launches model training + + - eval.sh - Performs inference and compute mAP of predictions. + + - inference.sh - Performs inference on given data. + + - train_benchmark.sh - To benchmark training performance. + + - inference_benchmark.sh - To benchmark inference performance. + - docker/ - Scripts to build the docker image and to start an interactive session. + +- tools/ + - train_net.py - End to end to script to load data, build and train the model. + - test.net.py - End to end script to load data, checkpoint and perform inference and compute mAP score. + + +### Parameters +#### train_net.py script parameters You can modify the training behaviour through the various flags in both the `train_net.py` script and through overriding specific parameters in the YAML config files. Flags in the `train_net.py` script are as follows: `--config_file` - path to config file containing model params @@ -208,12 +317,23 @@ python -m torch.distributed.launch --nproc_per_node=2 tools/train_net.py \ SOLVER.BASE_LR 0.002 \ SOLVER.STEPS “(360000, 480000)” ``` + +### Command-line options + +To see the full list of available options and their descriptions, use the -h or --help command line option, for example: + + + +`python tools/train_net.py --help` + ### Getting the data The Mask R-CNN model was trained on the [COCO 2014](http://cocodataset.org/#download) dataset. This dataset comes with a training and validation set. This repository contains the `./download_dataset.sh`,`./verify_dataset.sh`, and `./extract_dataset.sh` scripts which automatically download and preprocess the training and validation sets. +#### Dataset guidelines + In order to run on your own dataset, ensure your dataset is present/mounted to the Docker container with the following hierarchy: ``` my_dataset/ @@ -327,60 +447,28 @@ __Note__: The score is always the Average Precision(AP) at - Area = all - include small, medium and large - maxDets = 100 -## Enabling mixed precision -[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) previously required two steps: -1. Porting the model to use the FP16 data type where appropriate. -2. Manually adding loss scaling to preserve small gradient values. +## Performance -Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP), library from [APEX](https://github.com/NVIDIA/apex) that casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients. In PyTorch, loss scaling can be easily applied by using scale_loss() method provided by amp. The scaling value to be used can be [dynamic](https://nvidia.github.io/apex/fp16_utils.html#apex.fp16_utils.DynamicLossScaler) or fixed. - -For an in-depth walk through on AMP, check out sample usage [here](https://github.com/NVIDIA/apex/tree/master/apex/amp#usage-and-getting-started). [APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage tensor cores performance. - -To enable mixed precision, you can: - - Import AMP from APEX, for example: - ``` - from apex import amp - ``` - - Initialize an AMP handle, for example: - ``` - amp_handle = amp.init(enabled=True, verbose=True) - ``` - - Wrap your optimizer with the AMP handle, for example: - ``` - optimizer = amp_handle.wrap_optimizer(optimizer) - ``` - - Scale loss before backpropagation (assuming loss is stored in a variable called losses) - - Default backpropagate for FP32: - ``` - losses.backward() - ``` - - Scale loss and backpropagate with AMP: - ``` - with optimizer.scale_loss(losses) as scaled_losses: - scaled_losses.backward() - ``` - -For information about: -- how to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation. -- Techniques used for [mixed precision training, see the Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog. -- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training). - -## Benchmarking +### Benchmarking Benchmarking can be performed for both training and inference. Both scripts run the Mask R-CNN model using the parameters defined in `configs/e2e_mask_rcnn_R_50_FPN_1x.yaml`. You can specify whether benchmarking is performed in FP16 or FP32 by specifying it as an argument to the benchmarking scripts. +#### Training performance benchmark Training benchmarking can performed by running the script: ``` scripts/train_benchmark.sh ``` +#### Inference performance benchmark Inference benchmarking can be performed by running the script: ``` scripts/inference_benchmark.sh ``` -## Results +### Results The following sections provide details on how we achieved our performance and accuracy in training and inference. -### Training Accuracy Results +#### Training Accuracy Results + +##### NVIDIA DGX-1 (8x V100 16G) Our results were obtained by running the `tools/train_net.py` training script in the PyTorch 19.02-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. | **number of GPUs** | **batch size/GPU** | **Training time with FP16(hours)** | **Training time with FP32(hours)** | @@ -397,7 +485,7 @@ ACCURACY CURVE: ![Accuracy Curve](./img/accuracy_curve.png) -#### Training Stability Test +##### Training Stability Test The following tables compare mAP scores across 5 different training runs with different seeds, for both FP16 and FP32 respectively. The runs showcase consistent convergence on all 5 seeds with very little deviation. | **Config** | **Seed #1** | **Seed #2** | **Seed #3** | **Seed #4** | **Seed #5** | **mean** | **std** | @@ -410,8 +498,8 @@ The following tables compare mAP scores across 5 different training runs with di | 8 GPUs, fp32, final AP BBox | 0.377 | 0.377 | 0.376 | 0.378 | 0.378 | 0.377 | 0.001 | | 8 GPUs, fp32, final AP Segm | 0.344 | 0.342 | 0.343 | 0.343 | 0.343 | 0.342 | 0.001 | -### Training Performance Results -#### NVIDIA DGX-1 (8x V100 16G) +#### Training Performance Results +##### NVIDIA DGX-1 (8x V100 16G) Our results were obtained by running the `scripts/train.sh` training script in the PyTorch 19.02-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance numbers (in tokens per second) were averaged over an entire training epoch. | **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** | @@ -428,7 +516,7 @@ Our results were obtained by running the `scripts/train.sh` training script in t To achieve these same results, follow the [Quick start guide](#quick-start-guide) outlined above. -#### NVIDIA DGX-1 (8x V100 32G) +##### NVIDIA DGX-1 (8x V100 32G) Our results were obtained by running the `scripts/train.sh` training script in the PyTorch 19.02-py3 NGC container on NVIDIA DGX-1 with 8x V100 32G GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch. | **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with FP32** | **Multi-gpu weak scaling with FP16** | @@ -453,8 +541,8 @@ It should be noted that respective values for FP32 runs using a batch size of 16 To achieve these same results, follow the [Quick start guide](#quick-start-guide) outlined above. -### Inference performance results -#### NVIDIA DGX-1 16G (1x V100 16G) +#### Inference performance results +##### NVIDIA DGX-1 16G (1x V100 16G) Our results were obtained by running the `scripts/inference.sh` training script in the PyTorch 19.02-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch. | **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speedup** | @@ -463,7 +551,7 @@ Our results were obtained by running the `scripts/inference.sh` training script To achieve these same results, follow the [Quick start guide](#quick-start-guide) outlined above. -#### NVIDIA DGX-1 32G (1x V100 32G) +##### NVIDIA DGX-1 32G (1x V100 32G) Our results were obtained by running the `scripts/inference.sh ` training script in the PyTorch 19.02-py3 NGC container on NVIDIA DGX-1 with 1x V100 32G GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch. | **number of GPUs** | **batch size/GPU** | **FP 32 items/sec** | **FP16 items/sec** | **Speedup** | @@ -472,9 +560,24 @@ Our results were obtained by running the `scripts/inference.sh 0 else item item = item.tolist() diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/notebooks/README.md b/PyTorch/Segmentation/MaskRCNN/pytorch/notebooks/README.md new file mode 100644 index 00000000..161b7f1b --- /dev/null +++ b/PyTorch/Segmentation/MaskRCNN/pytorch/notebooks/README.md @@ -0,0 +1,37 @@ +## Jupyter demo notebooks +This folder contains demo notebooks for the MaskRCNN model. + +1 - pytorch_MaskRCNN_pyt_train_and_inference.ipynb: end to end training and inference demo. + +The most convenient way to make use of this notebook is via a docker container, which provides a self-contained, isolated and re-producible environment for all experiments. The steps to follow are: + +First, clone the repository: + +``` +git clone https://github.com/NVIDIA/DeepLearningExamples.git +cd DeepLearningExamples/PyTorch/Segmentation/MaskRCNN +``` + +Next, build the NVIDIA Mask R-CNN container: + +``` +cd pytorch +docker build --rm -t nvidia_joc_maskrcnn_pt . +``` + +Then launch the container with: + +``` +PATH_TO_COCO='/path/to/coco-2014' +MOUNT_LOCATION='/datasets/data' +NAME='nvidia_maskrcnn' + +docker run --it --runtime=nvidia -p 8888:8888 -v $PATH_TO_COCO:/$MOUNT_LOCATION --rm --name=$NAME --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --ipc=host nvidia_joc_maskrcnn_pt +``` +where `/path/to/coco-2014` is the path on the host machine where the data was/is to be downloaded. + +Within the docker interactive bash session, start Jupyter with + +`jupyter notebook --ip 0.0.0.0 --port 8888` + +Then open the Jupyter GUI interface on your host machine at http://localhost:8888. Within the container, this notebook itself is located at /workspace/object_detection/notebooks. \ No newline at end of file diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/notebooks/pytorch_MaskRCNN_pyt_train_and_inference.ipynb b/PyTorch/Segmentation/MaskRCNN/pytorch/notebooks/pytorch_MaskRCNN_pyt_train_and_inference.ipynb new file mode 100644 index 00000000..f188e584 --- /dev/null +++ b/PyTorch/Segmentation/MaskRCNN/pytorch/notebooks/pytorch_MaskRCNN_pyt_train_and_inference.ipynb @@ -0,0 +1,677 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Mask R-CNN Training and Inference Demo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "Mask R-CNN is a convolution-based neural network architecture for the task of object instance segmentation. The original paper describing the model can be found at [https://arxiv.org/abs/1703.06870](https://arxiv.org/abs/1703.06870). NVIDIA’s Mask R-CNN is an optimized version of [Facebook’s implementation](https://github.com/facebookresearch/maskrcnn-benchmark), leveraging mixed precision arithmetic and tensor cores for faster training while maintaining comparable accuracy with single precision (FP32) training.\n", + "\n", + "The major differences between the official implementation of the paper and our version of Mask R-CNN are as follows:\n", + "\n", + "- Mixed precision support with [PyTorch automatic mixed precision (AMP)](https://github.com/NVIDIA/apex).\n", + "- Gradient accumulation to simulate larger batches.\n", + "- Custom fused CUDA kernels for faster computations.\n", + "\n", + "\n", + "### Learning objectives\n", + "\n", + "This notebook demonstrates the steps for training a Mask R-CNN model using 1 or multiple GPUs. We then employ the trained model to make inference on new images.\n", + "\n", + "## Content\n", + "1. [Requirements](#1)\n", + "1. [Data download and preprocessing](#2)\n", + "1. [Training](#3)\n", + "1. [Testing trained model](#4)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 1. Requirements\n", + "\n", + "\n", + "### 1.1 Docker container\n", + "The most convenient way to make use of the NVIDIA Mask R-CNN model is via a docker container, which provides a self-contained, isolated and re-producible environment for all experiments. Refer to the [Quick Start Guide section](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/MaskRCNN#requirements) of the Readme documentation for a comprehensive guide. We briefly summarize the steps here.\n", + "\n", + "First, clone the repository:\n", + "\n", + "```\n", + "git clone https://github.com/NVIDIA/DeepLearningExamples.git\n", + "cd DeepLearningExamples/PyTorch/Segmentation/MaskRCNN\n", + "```\n", + "\n", + "Next, build the NVIDIA Mask R-CNN container:\n", + "\n", + "```\n", + "cd pytorch\n", + "docker build --rm -t nvidia_joc_maskrcnn_pt .\n", + "```\n", + "\n", + "Then launch the container with:\n", + "\n", + "```\n", + "PATH_TO_COCO='/path/to/coco-2014'\n", + "MOUNT_LOCATION='/datasets/data'\n", + "NAME='nvidia_maskrcnn'\n", + "\n", + "docker run --it --runtime=nvidia -p 8888:8888 -v $PATH_TO_COCO:/$MOUNT_LOCATION --rm --name=$NAME --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --ipc=host nvidia_joc_maskrcnn_pt\n", + "```\n", + "where `/path/to/coco-2014` is the path on the host machine where the data was/is to be downloaded. More on data set preparation in the next section.\n", + "\n", + "Within the docker interactive bash session, start Jupyter with\n", + "\n", + "```\n", + "jupyter notebook --ip 0.0.0.0 --port 8888\n", + "```\n", + "\n", + "Then open the Jupyter GUI interface on your host machine at http://localhost:8888. Within the container, this notebook itself is located at `/workspace/object_detection/demo`.\n", + "\n", + "### 1.2 Hardware\n", + "This notebook can be executed on any CUDA-enabled NVIDIA GPU, although for efficient mixed precision training, a [Tensor Core NVIDIA GPU](https://www.nvidia.com/en-us/data-center/tensorcore/) is desired (Volta, Turing or newer architectures). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 2. Data download and preprocessing\n", + "\n", + "This notebook demonstrates training and validation of the Mask R-CNN model on the [COCO 2014 dataset](http://cocodataset.org/#download). If not already available locally, the following [script](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Segmentation/MaskRCNN/download_dataset.sh) in the repository provides a convenient way to download and extract all the necessary data in one go. Be mindful of the size of the raw data (~20GB). The script makes use of `wget` and will automatically resume if disrupted. Once downloaded, the script invokes `dtrx` to extract the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! wget https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Segmentation/MaskRCNN/download_dataset.sh -P /workspace/object_detection/\n", + "! bash /workspace/object_detection/download_dataset.sh /datasets/data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within the docker container, the final data directory should look like:\n", + "\n", + "```\n", + "/datasets/data\n", + " annotations/\n", + " instances_train2014.json\n", + " instances_val2014.json\n", + " train2014/\n", + " COCO_train2014_*.jpg\n", + " val2014/\n", + " COCO_val2014_*.jpg\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 3. Training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The shell script [train.sh](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/train.sh) provides a convenient interface to launch training tasks. \n", + "By default, invoking [train.sh](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/train.sh) will make use of 8 GPUs, saves checkpoints every 2500 iterations and uses mixed precision training.\n", + "\n", + "```\n", + "cd /workspace/object_detection/\n", + "bash scripts/train.sh\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that, within [train.sh](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/train.sh), it invokes the following Python command:\n", + "\n", + "```python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file \"configs/e2e_mask_rcnn_R_50_FPN_1x.yaml\" DTYPE \"float16\"```\n", + "\n", + "which launches pytorch distributed training with 8 GPUs, using the train script in [tools/train_net.py](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Segmentation/MaskRCNN/pytorch/tools/train_net.py). Various sample training configurations are available within the [configs](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/MaskRCNN/pytorch/configs) directory, for example, a [configuration file](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Segmentation/MaskRCNN/pytorch/configs/e2e_mask_rcnn_R_50_FPN_1x_1GPU.yaml) for training using 1 GPU. \n", + "\n", + "### 3.1 Training with 1 GPU\n", + "We will now take a closer look at training a Mask-RCNN model using 1 GPU, using the below custom config script." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "echo 'MODEL:\n", + " META_ARCHITECTURE: \"GeneralizedRCNN\"\n", + " WEIGHT: \"catalog://ImageNetPretrained/MSRA/R-50\"\n", + " BACKBONE:\n", + " CONV_BODY: \"R-50-FPN\"\n", + " OUT_CHANNELS: 256\n", + " RPN:\n", + " USE_FPN: True\n", + " ANCHOR_STRIDE: (4, 8, 16, 32, 64)\n", + " PRE_NMS_TOP_N_TRAIN: 2000\n", + " PRE_NMS_TOP_N_TEST: 1000\n", + " POST_NMS_TOP_N_TEST: 1000\n", + " FPN_POST_NMS_TOP_N_TEST: 1000\n", + " ROI_HEADS:\n", + " USE_FPN: True\n", + " ROI_BOX_HEAD:\n", + " POOLER_RESOLUTION: 7\n", + " POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)\n", + " POOLER_SAMPLING_RATIO: 2\n", + " FEATURE_EXTRACTOR: \"FPN2MLPFeatureExtractor\"\n", + " PREDICTOR: \"FPNPredictor\"\n", + " ROI_MASK_HEAD:\n", + " POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)\n", + " FEATURE_EXTRACTOR: \"MaskRCNNFPNFeatureExtractor\"\n", + " PREDICTOR: \"MaskRCNNC4Predictor\"\n", + " POOLER_RESOLUTION: 14\n", + " POOLER_SAMPLING_RATIO: 2\n", + " RESOLUTION: 28\n", + " SHARE_BOX_FEATURE_EXTRACTOR: False\n", + " MASK_ON: True\n", + "DATASETS:\n", + " TRAIN: (\"coco_2014_train\", \"coco_2014_valminusminival\")\n", + " TEST: (\"coco_2014_minival\",)\n", + "DATALOADER:\n", + " SIZE_DIVISIBILITY: 32\n", + "SOLVER:\n", + " BASE_LR: 0.005\n", + " WEIGHT_DECAY: 0.0001\n", + " STEPS: (240000, 320000)\n", + " MAX_ITER: 360000\n", + " IMS_PER_BATCH: 4\n", + "TEST:\n", + " IMS_PER_BATCH: 16\n", + "' > /workspace/object_detection/configs/custom_config.yml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Various configurable options within the above configuration file can be modified, for example, the learning rate `BASE_LR`, training batch size `IMS_PER_BATCH` or number of train iterations `MAX_ITER`. The training process will start from a pre-trained Resnet-50 backbone model downloaded from `https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl`.\n", + "\n", + "#### Training with full precision (FP32)\n", + "Next, we launch the training script using the [custom configuration script](../configs/custom_config.yml) just created above. A full training cycle with 360000 iterations on the COCO-2014 data on a single GPU might take as much as 1.5 days on an NVIDIA V100 GPU. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%run ../tools/train_net.py --config-file \"/workspace/object_detection/configs/custom_config.yml\" DTYPE \"float32\" OUTPUT_DIR ./results/1GPU-FP32/ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upon completion, the final model is saved to `./results/1GPU-FP32/model_final.pth`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training with mixed-precision\n", + "Next, we launch the training script using the same [custom configuration script](../configs/custom_config.yml) created above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%run ../tools/train_net.py --config-file \"/workspace/object_detection/configs/custom_config.yml\" DTYPE \"float16\" OUTPUT_DIR ./results/1GPU-FP16/ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On compatible NVIDIA GPUs, the training script makes use of the Tensor cores for higher FP16 arithmetic throughput. On a V100 GPU, this shortens the training time by about 30%." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Training with 8 GPUs\n", + "We will now configure a training script to train a Mask-RCNN model using 8 GPUs. Thanks to having 8 GPUs, we can reduce the number of training iterations by a factor of 8, while the learning rate is also increased by 8 times to account for the larger global batch size." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "echo 'MODEL:\n", + " META_ARCHITECTURE: \"GeneralizedRCNN\"\n", + " WEIGHT: \"catalog://ImageNetPretrained/MSRA/R-50\"\n", + " BACKBONE:\n", + " CONV_BODY: \"R-50-FPN\"\n", + " OUT_CHANNELS: 256\n", + " RPN:\n", + " USE_FPN: True\n", + " ANCHOR_STRIDE: (4, 8, 16, 32, 64)\n", + " PRE_NMS_TOP_N_TRAIN: 2000\n", + " PRE_NMS_TOP_N_TEST: 1000\n", + " POST_NMS_TOP_N_TEST: 1000\n", + " FPN_POST_NMS_TOP_N_TEST: 1000\n", + " FPN_POST_NMS_TOP_N_TRAIN: 4000\n", + " ROI_HEADS:\n", + " USE_FPN: True\n", + " ROI_BOX_HEAD:\n", + " POOLER_RESOLUTION: 7\n", + " POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)\n", + " POOLER_SAMPLING_RATIO: 2\n", + " FEATURE_EXTRACTOR: \"FPN2MLPFeatureExtractor\"\n", + " PREDICTOR: \"FPNPredictor\"\n", + " ROI_MASK_HEAD:\n", + " POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)\n", + " FEATURE_EXTRACTOR: \"MaskRCNNFPNFeatureExtractor\"\n", + " PREDICTOR: \"MaskRCNNC4Predictor\"\n", + " POOLER_RESOLUTION: 14\n", + " POOLER_SAMPLING_RATIO: 2\n", + " RESOLUTION: 28\n", + " SHARE_BOX_FEATURE_EXTRACTOR: False\n", + " MASK_ON: True\n", + "DATASETS:\n", + " TRAIN: (\"coco_2014_train\", \"coco_2014_valminusminival\")\n", + " TEST: (\"coco_2014_minival\",)\n", + "DATALOADER:\n", + " SIZE_DIVISIBILITY: 32\n", + "SOLVER:\n", + " BASE_LR: 0.04\n", + " WEIGHT_DECAY: 0.0001\n", + " STEPS: (36000, 48000)\n", + " MAX_ITER: 50000\n", + " IMS_PER_BATCH: 32\n", + "TEST:\n", + " IMS_PER_BATCH: 8\n", + "' > /workspace/object_detection/configs/custom_config_8_GPUs.yml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training with full precision" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run -m torch.distributed.launch -- --nproc_per_node=8 ../tools/train_net.py --config-file \"/workspace/object_detection/configs/custom_config_8_GPUs.yml\" DTYPE \"float32\" OUTPUT_DIR ./results/8GPU-FP32/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the Jupyter graphical interface does not update the training progress on the fly, you can observe the information being printed in the shell window from where you launched Jupyter. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training with mixed-precision\n", + "We now launch the training process using mixed precision. Observe the information being printed in the shell window from where you launched Jupyter. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run -m torch.distributed.launch -- --nproc_per_node=8 ../tools/train_net.py --config-file \"/workspace/object_detection/configs/custom_config_8_GPUs.yml\" DTYPE \"float16\" OUTPUT_DIR ./results/8GPU-FP16/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 4. Testing trained model\n", + "\n", + "After model training has completed, we can test the trained model against the COCO-2014 validation set. First, we create a new configuration file for the test. Note: you must point the model `WEIGHT` parameter to a final model checkpoint, e.g. `./results/8GPU-FP32/model_final.pth`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "echo 'MODEL:\n", + " META_ARCHITECTURE: \"GeneralizedRCNN\"\n", + " WEIGHT: \"./results/8GPU-FP16/model_final.pth\"\n", + " BACKBONE:\n", + " CONV_BODY: \"R-50-FPN\"\n", + " OUT_CHANNELS: 256\n", + " RPN:\n", + " USE_FPN: True\n", + " ANCHOR_STRIDE: (4, 8, 16, 32, 64)\n", + " PRE_NMS_TOP_N_TRAIN: 2000\n", + " PRE_NMS_TOP_N_TEST: 1000\n", + " POST_NMS_TOP_N_TEST: 1000\n", + " FPN_POST_NMS_TOP_N_TEST: 1000\n", + " ROI_HEADS:\n", + " USE_FPN: True\n", + " ROI_BOX_HEAD:\n", + " POOLER_RESOLUTION: 7\n", + " POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)\n", + " POOLER_SAMPLING_RATIO: 2\n", + " FEATURE_EXTRACTOR: \"FPN2MLPFeatureExtractor\"\n", + " PREDICTOR: \"FPNPredictor\"\n", + " ROI_MASK_HEAD:\n", + " POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)\n", + " FEATURE_EXTRACTOR: \"MaskRCNNFPNFeatureExtractor\"\n", + " PREDICTOR: \"MaskRCNNC4Predictor\"\n", + " POOLER_RESOLUTION: 14\n", + " POOLER_SAMPLING_RATIO: 2\n", + " RESOLUTION: 28\n", + " SHARE_BOX_FEATURE_EXTRACTOR: False\n", + " MASK_ON: True\n", + "DATASETS:\n", + " TRAIN: (\"coco_2014_train\", \"coco_2014_valminusminival\")\n", + " TEST: (\"coco_2014_minival\",)\n", + "DATALOADER:\n", + " SIZE_DIVISIBILITY: 32\n", + "SOLVER:\n", + " BASE_LR: 0.005\n", + " WEIGHT_DECAY: 0.0001\n", + " STEPS: (240000, 360000)\n", + " MAX_ITER: 360000\n", + " IMS_PER_BATCH: 4\n", + "TEST:\n", + " IMS_PER_BATCH: 16\n", + "' > /workspace/object_detection/configs/test_custom_config.yml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validating on the COCO-2014 mini evaluation data set\n", + "Next, we launch the evaluation script, which will read the COCO-2014 mini evaluation dataset of 5000 images and evaluate various quality metrics, such as recall, precision and IoU at various thresholds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "%run ../tools/test_net.py \\\n", + " --config-file /workspace/object_detection/configs/test_custom_config.yml \\\n", + " DTYPE \"float16\" \\\n", + " DATASETS.TEST \"(\\\"coco_2014_minival\\\",)\" \\\n", + " OUTPUT_DIR ./results/8GPU-FP16/evaluation \\\n", + " TEST.IMS_PER_BATCH 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing on new images\n", + "\n", + "We will now launch an interactive testing, where you can load new test images. First, we load some required libraries and define some helper functions to load images." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "attempted relative import beyond top-level package", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmaskrcnn_benchmark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdemo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredictor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCOCODemo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: attempted relative import beyond top-level package" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.pylab as pylab\n", + "\n", + "import requests\n", + "from io import BytesIO\n", + "from PIL import Image\n", + "import numpy as np\n", + "\n", + "# this makes our figures bigger\n", + "pylab.rcParams['figure.figsize'] = 20, 20\n", + "\n", + "from maskrcnn_benchmark.config import cfg\n", + "from ..demo.predictor import COCODemo\n", + "\n", + "def load(url):\n", + " \"\"\"\n", + " Given an url of an image, downloads the image and\n", + " returns a PIL image\n", + " \"\"\"\n", + " response = requests.get(url)\n", + " pil_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n", + " # convert to BGR format\n", + " image = np.array(pil_image)[:, :, [2, 1, 0]]\n", + " return image\n", + "\n", + "def imshow(img):\n", + " plt.imshow(img[:, :, [2, 1, 0]])\n", + " plt.axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we load the trained model specified in the test configuration file, e.g. from `./results/8GPU-FP32/model_final.pth`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "config_file = \"/workspace/object_detection/configs/test_custom_config.yml\"\n", + "\n", + "# update the config options with the config file\n", + "cfg.merge_from_file(config_file)\n", + "# manual override some options\n", + "cfg.merge_from_list([\"MODEL.DEVICE\", \"cuda\"])\n", + "\n", + "coco_demo = COCODemo(\n", + " cfg,\n", + " min_image_size=800,\n", + " confidence_threshold=0.7,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "User now can load a test image from any public URL." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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JuvibNuXhxU2omnAcmWjUtZqml0w4yg9RgknS+D7bYt527GLfuN6CJhtbr72uh5Ypoo3mJxM9S5N+m+bf0y3G3jUTFDmxfHeOQ23K+J1gPJt+3GquA3eyIVSMJGXRCly64VIAwEArDconDvBQtXnjG7FIDkFf/jYRlJvXrceK1fwuJ4bhlgt40H3w7keweTOROhsu5iFx5RJ6yZO5FPY+x7l2xSre7wbZN32D/YhGxWg8KofZMr77Y/k0EGHPJhzOGxFBhGiIIlbD68JhHhb7EmyDayxFTSUPr7k0nQWWPG+0pw+33fo5AEB1OedFTQ5OiwML8fabeVDtGOahOdHI3+46eR/WbWoBAIyHOI9/8/vfQY3LeVCXsdrbxXUilzEw2Md/uw7nrsXNnGs3bl6JEZlP7S6Wlemi0WgkH8BIhsq1wyxzTBA1mWwEXR1EiRoG2zwgSAkgg0iY7UknxYtksG5WwEAhT/1NHh/5fBbpNPWuy+HQEoSn67qwLIXwyXrfsb1FdIEr76yLgpTjwHEmohZN0/RQfabyEqo5Ip9BeTnXh4IphqRyHnjLIxYeO0y0bPXandTjZh7Adz91HNkUjSNtJ7h2bLqIa8nCxYuRTAzLdVyva6qps5bF1UgM0Wg5Vsn7rTG2Mzk+gvqVRHktXMXxtWkF19bNW5tx/5M0Hn7xc18DACxp4jj78de/iJWruNa/+Ra+U3H+iebaMhx/nG0oyIuzoL4ZvV1EqAzRHoqGxhoAQNTUUbWQ9VlQS0NNaz+NTMuaK1DTwHF07DiNgwNdvL+pbgOy4HeJDD9XldOQdLr3acRcVmjJIo7xhEMdD+aOIRSiHgZfoBHza23so4vf9ptYK3uZtTvfzLLE6HbbJz6BNRezT9aG2K6r19Go89ShB9Fzkm0+UM93OFlLZEzrYAceG5dDwDjHwJ5HfoWaIBWhyfv11G4afDes3Yq6ahqAFlZxTtl+OZHUD+z5Pi669F0sS2sHAFgmjWl7nvsmchmiwN00++nEMRoTy0eHEcmxf6tk/igPsy63fe2b2Nq8hHVJ0Ug0KuOl4fKLsC1LY9mJ49RV51mOszXN9Uh200DYtIhj5vAuGoYqLzIRbKBOD/6aRrNdu77Ntlx3A8zoOurfpkFusb4GBbHMHurjXq28jmWubFyD/AjLymXYb5GgGIFCLvJJtaGX9UEWR8PQPDSgI++sMhrlso7nHT9Xme9e6cUOZ+eLzN/hdv60Ya5yLnV+tZv3Soyz80nOHZFEfTjTeXK9MqeiS4CiIVip1CiBrivnrhcB4BlzZu4D1506HnRdGSZ0uM5ERH+p8UftY2c6i0333bnOVbOhbSbW+dzODJPr/HLKizmBp36vPPyTr50Z7KBpxTYrjdrnOA59DiFffPHFF1988cUXX3zxxRdffPHFl9eZnBcIoaxwzJimhmCYKJl0llZIW/GLuCbGRukZDYYE8eOm5ZosKmppJUuNEXbugl7KULwf+TRROGaA3tL+XoZtVZSFsOECempOHKO3VlnYQvEQIKAcXSDoo2nCzw2tgNomfjcm+F5XEw9uUENnJz163Z18jpVhnQp2BtlxfldfvRQAsHYzPZABYxxGht67zSuvAwC84bIrkNPoYcsJx1EgR/0Eyg2cFU8XxugVs3sI0e9PjmN8mM8JC7zapupQXlWOTJ51LggiQ1meo9GAF46hQg/GE/w7FAaC4v0PFlR4HXX3mb/8DFoILsBp4RpwdHq5YuV1GB6WED/hhwpJPxRcFR5YlBe1zs7y81ToY0kYziyW0tmeORPvzFzl1fAs8RnipS153OQmezZmTYOmrOxTbMIuNBXf7SoiHYF4ajpcTSEIJsJFNWgwlHegGKnsleN5Kgz1Pk8DnVTfeY/VoClE0jz7z/vFLbZjij9Cmwb1o031PmiYeM10qKPSmszkzSKcdcaaTr2+hPdgindFlNXT2YvFDZxTIlnOj9f+9hUAAD1i4vmOYwCA2np6y5uMWjx74CgAYPPV9NTXV9HzfoUbwbEDDL1Yt+JCAIDVxFCfYHoMVTIGhsboTQ8EuYzEYhEvlKisjIiFXE5QPfEIUuMS1iv3W+L91nIWrttJfo/uMc6dHX3kSut64RlU1HEuKYtICFKcZSOfxJsuZnjc/scYLrfzUoYGHXvhNJpbVgIAHjv0DAAg8SBDRoYSA7j1c2x7hSBRdy5fjrTDeWlgkPNVXxfn19PtXYjLPFrI04sfj3EUPXvgIPoEVRCLcK5ctoRohZGeMRhDXEQuWU1UxJGjZwEAdqIcZwtcl6IRzqf5tIJQB1CQ9UTFhzuaitd3vXGlPGtqri4UClDOupCskWoSGB4agxXkOpEXZEsoxDYZuolclmUofiYVlhYOh5GVd2FsjGsKoMNU72iA/wionYRb8LyEMUEdhWTxSSfHEJTx+sRDREosXkiE0IXr1mL300TULlxMJE0uy/X9yccfx8ggx1HzYiJ+LruM6LRwqBz3/OwJAMCOlRy/e/qJkuod7cFl6xkW1n2K0JuP/eQXAICbP9CHyhCf/b9v+SgAYOeV5HT69k+/iDMFjr9n+4jyuPughDJVNiOoE6kTjhF5ls+Pe6RjhsMx2nWaYyidGUFFhfRlnuN/9YYGuS+PVFLxK0lYe4LrfSjajapK6r1vgH0x0sX9R3PdMnT1sl7aGOu5bC0RO3ueyuDqzeTfMRrYXzsPsh96fv5rZKu4b7nt+38FAPjZzxha/7Frfw81YxwX69aQe+zACMfqfbfdhhWVRCSND7LNux8iB9GS0Cqc/Ob9AIBd7UT1/e7VN2LFhm0AgP1n7mKZy1h2MpfB2p3vBQAEw2zzg92/AgA89PQzePt1RHhffS0RTPu7GIrXcXwXRtuIDmvQOSd9/Nr/wbo8/Ws0byUnz4lWPuexX5AHKVJfBUfex/d/6C8BAIPdfF/v/MqPsGP7FgDAySPU40WXvQUAsKJlOe5rJ3Jso8w3R/aTX+hQNIhlF9zCdq2jrp45Qt4qO7UUsYLwSx76RwDA0Mhv4c0byYXUeh/D8SKVDHlc0fhmJME+j4VlnZV3KTkORAQNacj7r97vfCHroUV14aBQiKFC4dzcwhPWsXkCMGbGRGNatPM5lf0ybqH+KyJ9zkVeLS6ml3r/fDl9ABThLlPqcm7hOOejuK47bViY938PmV8UY9J+tIj614CJcQHQvX236+2tvac4U/+tVCpBPNB0DZOpgCbSHUyPMZkNITSdlPbfTGFXL3d/v1Zzw+S6T6erCRyj6jdMPItpmlYSXcFPw5jK8zSb+AghX3zxxRdffPHFF1988cUXX3zxxZfXmZwXCKGAkE8ahoF8VryfOr1qmUzGuy4WoUdT0XuE6+j5Kos0oK+DnrZsnkgalSEmn65ENErCv6BkXkgPCOdFXx/WrqHn+4QQMCYz9OzpAR2OBOCFJCuFEaZnULfzGBqkd7eqkt6nVJqeKVfLIzFGD1FTDT1gl64jT0A0oiMeY7tywk8UiEjceFzHjkuYiePk8yRUbevtxpLV9MzlRmj7C8bZZY8e+xl27ToFAFhrkeQ1eJbtzNaE0HmanvbaRnqKTZN1GBvIQKgyIAlGkBKPcW7QRk09vWG9/fQuZnIqztOFJaSWmST7ZPO6KwEA1YvTONVNb6IlGcRMQQgNjwwhLPoLCilpNiPZY8y5Db/pLLezW1XnVOycypq3J0NTVv1zjUs9dys1CbX5b1VfR5u5pAkZCpQ70lFoMadYZ1WjaThzvNq6kunEMaAJMsjwHlBEN7gCXSp6GoRLyNVQmALDKokRdhTZ3gyNKbneq1Opricgps5hzMyB+FIrIedWdvsJBNznQEg5vWd2alaGyeNIT/OdXLl4GdxGIoRqr6J3PZniexYvj6Ja0DmXbeFcu6B6KUaeI68HBqW/xUO9YMVyZIR7bFiQifEg5760DYSFcD4msBBFQFwo5Dw+HFM4M8aF9yeeBCxBvXgtkAxclhmBHSBKZtlSQg3veYjkqx1DHTjdSW/60sWcT1esIEebG3XwQic9+8OCFr3oTeQ8Cm3ow+e/+h8AgEXVbPOlS4giOHi4B6MF1ssVxY/oKejiFnMkM5fSdUW8AlUVbP+Q8BEp7pxCzsLiheQM6TrDOTAzTv4YzcjisjeSYPaaa4go+NKXyTnyzN6DHlJyXNaLYJDrk65ZHomjoSsiZ6mv60KXLJYe+bvU0wyYHsI1ImiqVErQOZmkh8DL5Vh2UK1rhoasIIRygswNuIrDT0dFBfsmk5YMBdARFO6qoKDDqqu43pqmhYz0ecCUTGmyjr77vTfirp/dK+VKVrge6nzRgjg2bG4BAAz3US/dvUScVMWrYKe4LltB1uVsL9EyS5rr0CuZNH/+DJEfdhfvX7qoAQelTy7dRtTMP172uwCAo20HULee/XRSMs1Fg7IWb9+G1p8ye1+ujzq76WLe/+hjD6O6mmTIS8ukvxDDMckcFghwj2Lq/M3VgKYmfrd6LRG17d1ct1et24gDe7hO9/SQg+iC9dyPuGYX6qq5ZldXUC/tQ0SXrF9SA7eOY2bUpl5GeskXFs/q2HQhEX/Dkmhq+8dJYDx6rA0PfYuIG/MU++TW6/8CALDt6ndiVFLc3PkkETsrY3xftt58E779jdsBAFeEmgAAz58iYuqpujD+/tPkz3nLSXL69CCJhguJ1Kt02L6D+/cAABobM9jz5OcBAA1RvqvaEDciO7dehyt3kNPnjh8QnfPUk9RV2FiEG97y29TRCt53+eW8dvUbN+Kz/+ePqX+L42PHReQiMrR21MteMF5DxGCNIPlWL70CP3yUnGqdLvW3JE80YVlCw0f+9N8BFJFxkTryKDnlART6yJGUrOdY+60bibg6fXI/lq/fCgCwouRgGjMqcbyb2WIXLOT+6P67v8JrnGV4w0U3AwBG03yvVSKQiGUjlRHknszHHom7aXrcHYo7SM2rpqnBnYU8YspeZMKl88v+NBeZyz5uputexlqc29WKymO+yKlXGXQwXT3nVId5Q8Pm2cCXEcDz3wn1NfG8oTj+VPuKzKCaXrrPlPd/LvtM7zzieqj7yZEVLEb2547apxfPMV4Ws0lIHV2fHSk4E1fQK4XuefXnlpcmc+Ew00rPdZOap7uYcljzzoVzbLaPEPLFF1988cUXX3zxxRdffPHFF198eZ3JeYEQamik97VQMNHdR96GeJl4OsvpeauuNtFQR09S6wl6/SIVRM/0ncnBlaZU1xCZkhTeoEi4HNkcPS8jksK3QmLCR0eTuFji5VtaJD3qAJ8XDMaRlUwrTp4uGycl0BrbRrlF/gFNEDTVlfQCjo+PA+IJj5XRIvrum5nqubX1JDRdvOJRSRUvmVhOtrXjC1/5ZwBANCRplu3V+PPL/hAAEBBYT16jfu554C6EXT4z7i4EAPzdJ/8OAND8lnrceRe9dvfcx1j/M2eZucUJFhATj3laUidXVFEfwz2jCIXpkbIkA1ghJx5qU0MmJygSyRCVEE6nGjMAzaXXFOIVTtv04AatGELiTU6M0wsaDteLGpXHeXqZbzrQl5oqdEK85gwm01cqRnlyuXNlvtcxsc06pvIfqfSUpc4glU2raFd2YMizAobKGCBjwbHhSqpblXVAeQQMFwiIspQXU+nOcRy44DhKC4+GggppWgBmkTRIvtNV4z1zuFe/c42RVzfqRd/FTNxKE66f8OX0z5yrU20udZ6A2tJm8dKqNJLqWsncV1leAUMQJ4k030tHUognbQcrVhJ5c2gfPdW2FcXbrmcq+rzMA2aYfTLupLFoRYs8TtA/2TFpi4VcntcrfjfFOxMIBJFJ8Z1Oy7sdj8lclkujIHwzBUmvnMoJP1puHGM5zherquhV33IxUQ2Nzz+K+++7BwCw95dE55RXE9kRjFWgq4+oEEs4fn79DLmE2gbOYniUZcYCrOevH3+YetEyMONEDfT18/6g6yKUyUkb2V8KQRMKhtDfR/RlJiWol3GZ92tCGBIUxJhEcMfywoOXi6L7EOfdo4PkrjnQzmudGh1xmedTUqZ6bj6fRzYrKBtBYZmsCj10gupzpG+cEoIthRpQmcRsQX0EgyGY8l7Z8r4VskX0QSAgqCMZqraksk4ls179FFdRMBhGXrhhPw0eAAAgAElEQVSi8uP8LV7GtVk3DCTTRGu5MdazYRHn+31HD+C0IEnDFr+rrOS68eDje1BWKetLgmXlMvw7ncoWM5dJvQ49R/TQvj3tSKRYr4ZRrv1GJdtiW3l85H1/AADYc4D9ULWe/ETRSBcOH9sFAOhTnENHySsV0BO47mJy+z36GJFCT0s/Gg2VOJUgH5fbxvG1csVqGKZkzHPYUQHha8rlkhgb5vvYdopl9Ih+KipzaBTd9PeTe280SRTQtm0Xw7R43/E2olacIY7ZntYkVq1/OwAg1kw04OoV5BB6+Ce34oFHvgwAeD7FMXv87eRIWpithan4pmzuGVZc/1vU1QIde+5lVq33vO2dAICUxXF1+9f/HG4/x9PeqPTfSnKRZTsG8Zmv/hMAYPtOzjF3feEbWLbgswCAxgWCujO4Vzt80kY2Qc6nnmqWtXLZe9gWswKHjz8JAAia7JNl1S0AgERfFutXkzto1ZZNAIAO2Y/s73sOMYNI7Z07mAYeMY7xS9bUYniMaJ5shnPY6s1EFn3nm9/AL+5i5rB1q9nfGQ4BLF23CZEqIqQGU0QpRaPcJ+1pfQZZ4QA6fPjHAIDNl5DLLLywBvueI8J741oikspyGfz6iS8CAKorOUeETPbDvfd9HuMOx9GWLW8DAFSEicLS8nFYQXmfZY6283xBQ1YYQYtlZYUzyC5IukjTmOKnfy0yrb6c171c952rvFZUNPP21M8T6ePOU5/z5wJ6ZRX7Us8Ak8splVeSn2gC6tyDdSiOGN3LIDbn7lI8oCXIIPXpFCTrs+wnStE9qo2KE9L1sofZCFtqrzARhTKXtk1bxVn4gl5umS0a5NWQ+aKWSrOMeYgsr0w11h1vvvL65hzrp50P5FvrL4q6AJDLxjA4yE2QcBiibiH/bqg3EDYltXCOsOCf3sMNViFrICTke1GTG4OcHDA0Mw2nwE2XJaTIeYGfr1uzEDsuJ9y/s49lPfwYiS3hVsNOUzeFNK+vraURKGRlEHC5OevqIlnlb7yPKVuPt55GSg4LPb0kZr10IzdP2y7cjHvvFch8mJveijpCiKNVVchn2YZHHuIG8JIl78EXPk8ixMACvoTtNsu8+V1vw5m9PDRsqubh6b67mUYXtQCk7q4cIH/ys+8CAG790j/jTA8PJfFaMTKBh/RwsAqD42zPynXU48AAybajwQAqLXZK3GFK6lve9GkAwIIdzcg7PNzkFOOYbE4CGhAwuRErSEiRA27KdZcQ/ZlkrqRjk6939YmLgeu6mLpFmttzXqpB6NxJ/krCpeZYluu63mGv9K7JKdC1ksnCW2qUwUUMPZZmIyIbzaAcRpUKcgXHWyBSEpqi5SXsxNFhiXEzbLEsw5T+dgPIF3hdQQxCjiZ/Q0fOUUYpRaorldIcL2RsLuncp03tKG0uvX8uBiF1/WxlTielzzmXvjemIY6eYhYqMRR5di4xQrj5AiJB9T5LKIGhQoRyMBUZaYIHrVA0Dl1Cv2xpUDrJuTZmWZ5RL6fJwV/nZxAhOJI6XYUnqbBe07QQiXBey2b5nUpPHowGcWaABh1dCJkf2kXi16HBM6gSEuruDh5ia+Qw9o6Na/Hpv/gTAEBzM+ffhZIG+nTXAJ45wPl6dHRUPmngWbdoIc4Kqb+yphhRti/nZmCIMb0uznkuPTKAguhPhU+pV9A0LKQlXEqFdannaZqGSFgdsqkzy+LaoCOMcTmEug7nOlsOb0EzDjM4MUWr6w0eHdn8RB0HrCKBtGkE5Tcx1NpFQ5YyBAUkPFdtCE0zAFuMTHkJiVMjLBaLwRKjvaurEE3de15CEiGEpO2WFfHuzeao0/Jyrsm2C4yOsK0ZCVNsaqQxpyJuoa5GDCB9vGZUwuUGhvthSYhTLM61JJ2Use3oSKbpCKmoYtvranloLuQNdJ3luGpYxHUlHFRrfx5rL5S04qe45vW10lG0Y8cVONJKA0h1LcfA1pVMa27a4zjayv3AoxLSlRfnUKU1jnLR+2BG5lzHQH0913G1/KmwWd3JwjRlPpT3Nx2ScZgdR2M9G11fT/2ZBj/Xr9uBw0dodOgbofFr4xL26eljA4jVkAy5VQywDU0t1I8bQDzMdy9WLQ60o9TdXQ8dwcrNbOsWl/UdMKj/U7EUGkBd9bkMEz3z3CEAQBNsJHPsr8Yox9AFqxje/vzpvUie5vhoHeNeobypBtddTILupia+C8+1MZzsRHsrmps41uIx6uV0F+uwZPUt2HnR1QCAb956OwBg7VI67BoWLsBF26/gs7eQsFr46vHwrh+hIcBw2eXL1gAAxuyk6LUOIwPsQy0sYaw23/0n734ay5fQuKwX2ObVq2nUcoNVyJu87vBBGpmzCRrrjp44gHSIfTk+ylCzRBnngy0tlwDtnIOSNSwzVaiBMSJ9UsnrXzjKcN3yyjhqWzjvBIItAIAVCz4EAGhZuBNN9dzzFlQIh7RZK5gwnEkHMhWRobsvIRvG/ELGzleD02st8yZdnvcT57cvne9wOR8MQucLGfAEmSWt+2wy4RZvDyrh45pbNOgUd/BQuA6P6kHt892SAjV1dTFlfDavjEPGhPY4tus5lpQhSO0HdB0Iyn6qmLxnSpVfUZku7bz3/BfR+VwInF8NeXEj2MSkPUpsB14Perl/PNCA61Fy6F7I30SnPgCYpjljQ/2QMV988cUXX3zxxRdffPHFF1988cWX15mcFyFjwRjdHqlMEgFJhVtw6FEJSdiDY2dgixn7Ex/+FABg6WqiZf79S19CUKcnCjkJeTIVrKoOSUlvPjJCqHY8zDLTWRu9vfSebdxMz9D+gyQp7e3KIyzEkHX19HBu3UovlO20YtsFJG784r8zFakpEOCKShcd7SSovkrSOX/tiz8DACxZtQGXv4mw4MOHCRFvFdLLnuf2eF5PRxNofq4WAZPtcnV+d+cDfN7RQ93Y1ESkzniOhI0/+zEJS6//2DsAIerWBOVx8zsZHnLze34Tf/6p/0W9ffVWAMDCJfQkZhwNEYses75uejErqum5zyTSaOugF2yNkGRGJSTACkaQEi+y49KjFTAUNDoN5FlmJi/DLSYhEjn3VbPKvlzPeanIoHNFPc31WZMhgi6msdaXOAs82KGkhtbEA27pQCTA32IhgZKKyyGfB/JCSJtTUFJBJhnQYEk4ZNDip0QwwbFdzzseLWN4aFZIL5M5F66E6ijou2fx1t1ZPQ7n0qeaOzvKaCrqR1nWS6Gxk3Q8nYeiFLI5S7jfXKCrxmSkkKZNuS9vCarKcZGXECyvLPEOGIbhIT9iVdS/FsghUyCCwxYC4VBECOh1C8mEpFeXcNJsmh73nJ32Uo4rf0JE5uxs3vEIhQ25aHCY4RrOcB5rly0HAAz0En2RO8L5G6MnMSz1aztOlMHpINFAh599Bhddzznz+b0MNakRJSczaVREiahY2UzC6TMdnFcXb1mFzjGG+xg2B6IiWHYNHZrJOa+rXwjubSAgqk1JCFJNDed9u1BAUEKAFOKp6DHToWW5TpSVRURHfJfyyECTdUF1YnmU+h8f0ZAX1FAsxjkzkUgoxUKT+VN5eBQJc8EGbIEJRGNlcrWkk89nIa+lh/hRIVaZTBqWIFsseb+zOQUHz3spaAuO+o5/5/JZxOPUsXqBUqmUty4HgmotVWt4FpmsJFiIMhysN8EyB8YG0LKKY6BG0FFlNtueOuF4CQw0mXciAeo/M55FUHSlkGt5SeLQfaYH4RDbf/Ykw76Xb2KZeqQMd931AAAgJCFEMZl/1m7diaPHuZ499exDAIBVGxiS9MzufTh5lGFhtoSJxyV8EHoIvSlBbQUFtVVIok9QKCpkPVYuaOaChvXrWO7jjzIcSm274nEdBUHjAERFDQ5zDNz/2K+QSLKtR47xmkiM/bD2wmWIO0T6DO9ljNPBXQyrXH/JRhw7SuTTpsVELR85TITLB674IDYsJgrIWcs1+KffI3nz2Pda0VnJ5xTyfAfry9l/TSu24rndDK1a/Kalosdl0pLDsBawb65ewbCrYGAR9u1mmFXFdUT4jEiopDYcwBEJM2xZTv1tXs2yQmED6Odvf/Q+7lFa1jA8zKmIYSTL97e7m3uNBunTt657CwZCEsJZK8jrFOekXieF8haiDdtPEJ0zPEoU4k1v34p/u/0bAID3fJJ7ynSE/R3M2FjgcL8Y3UBi+GdPtwAAtje9CeNniXg6FmEI7gsd1PnxVAgf//BfAwDuu5tE08Pps6gzWK+OzmdZZrxRdLwe2QGSVQ+mSNR9/AXOYaHGMC5e+bcAgJVLqMfFDXx/NBfIiYc/KO+zQjPnnRwMcC4/V5mvh3g+uytN02Cf457nvESDzCLnI5LpfKzTfOR8b8dc6R6myGRkv0LJ6y48ZJU29dLJhM6uNhWhVnqNSkmuUK0K/V/I217dFZm9pRJQ6POfI84lXKv02smIoLkgg0rPRLO9u9M9R59rjvaXIPMPlUVxCHhFlJ76KMWEA3bJp0KLz2z28RFCvvjiiy+++OKLL7744osvvvjiiy+vMzkvEELJAXo/M4MGXIeeaOVZVSloCwXAMsgN8NDj9FYtWEQP1oqlZRgYoHc8I3H9hk6PTD6bRVhQMkiL+zRDS9qhtjM4O0xvkTPIOvzG9ncDAP79rh+hq6cdAPCbb3wvAKB2OT3ATz99AlaIfDpbd7AOp04ybrx+oQETbENNjJa7us3kNrj1S9/CX/4+48Mby+jtS+QFnWM1ofssvZ5aloSP66sXATEhXgWRTPt/Qs6MlfGlqK5huwZOs14PP0IP0/UfeS8KoKfNgaAGhCTatA38wz/+CwBg9Qp6C7/93e8BANp7TiAjZLAVddS7IaiB3vYx1IRZr4hwZGST4k12AggaioSM97sSzJh2LBgGv7MMqZOQQU5L0KOVWtWnWnb1YmDt1HsnMxB7llPDQ7mcq3jp0b1YzKlcLx7ni3q863ppokOCnlDpuO2c7ZGzemRuHvJE9yo/FwNyqZXZFoSP4pTRXB0GFHeFfAqCR4fmxQ2PS9p4CGKnkM8jJ4QztiB9LEktresuNI3tiiSlPdLvjpZFXuzLqbygwwR5EjBchAKCHgqovhfeFdhwhOdEWegLko7cAGCK99MQ5J9pUZ95x0HGViSagt4QFJLjODCEP0cXVJrr2kXSai/wvcjhotBDxfSMAe835ULRpuRuLI7fItHbNBwMyrMxS7xyEQU0C4dDadyxqmVedGAYKEzJ6ikoFrd4R04hXLI6ArqMTQUoEm9CupCCKXwb6Sy95AGPaNxExlTV4XcR8VRHYSAr5WsOK1MV5FzRmu3G1378XflNCJpDnDPHhlI4dog8MIqcN17G304fOY3+VqIgyuNVUr+YtCUO1yTK48BBokMW1PF5nc+fhZsScmOT80/YkrZrmsdl1dJCZNHw8FkIHzAq68lzMy6k+ZlcDpYgnsZVCuow65lOZpC3ODbLQvwuOSJrGAyY8n4ZZlx0JnwAkTScvPD0iN5NS9CwjoOQoFjVuMzLfBIMmTDl/YyE5X0RckjLCCAe5nqkOIgKMkQDZhAFIaQNBgU9YBDZEQhogPRbXgjJg4KgspwIwvLOlpUTXZNIJJGR9zGbFV6cceo6k8mhpop1yKRkzEg9A5EQHn78MQDAsmWLpV6SXCGXQnaMZSYcPkc3iFQJBy2MJfickM3f1ByT1LKIVXA8NDQuAQB0nCYCLR6LwEzK+y8vR38f16A7vvkDLFnOdTmfJIKkdS+RGokzZ2BmqY9FS8hXlRimroaGB6HL3JUbI/K1YBkYz3NMR3MbAAA9PRyPZn4U/WUctxdvIdJk8QYiYvbueRLZDFE5Y8P8TKeoj8pYI6qqBEkXZ59kNfIGnexqxxVbeN2GC6jH8gq+B7FMFG+/nOnSt1/KujSEiS5JhRaiu4zjqPshouf+4B1Eszy/7DDuufdOAEB9Fev3Qhef+8kP/BGM95N8+it3/BsAAM2y31kWxVgP36XqMPX4O5/6M9zy/t8AAHzhh7cDAK69/EoAwLYtb8Cx4z8HAARt1mtUCOt7BvrxtrddQb0vbgEACNANowmgd5Qoo8VriFJKgjqIhIGAJP7oHmS90mmOl/bTfTBluO/eR/7Gy64gb+QglqNiMcfM8cNEK1ZLso8161bgTJbjKkowFlrvJ0djfz6DSy65DgCwopeF3/cTosCjKzP47B1EmYd6+LmwLIpEg6DCHPZXblQI1ysW4Ugr5wtD3vG1FZzDMgMn8HAfkUv3PcoX+Q8+9HUAwJLaS5ESoumIcMHlMwotGgSM+XEBzecuTdPmxwjzEvhLX3VP9qwbspm19mphWIpoiJl/m/jdxL/nzwU0P5k3QuJlrseLyWzoklnR64brIW9U0geFyCn9TpfNl8rIrsGB7ah1nQUoNL7uFlO3e/ti3ZyKRJ+tYiUw+YDa92kKSc6/g4bhlaWeN1t3zYaE8niJHGfKd0qYyl6b9j7XdT3U8mwyBcWvad5ZqHSf75WvOVOuP18QZ67rFhPrTBINLnTRh+nI4ij7VrugF6d9Oeslx4iKHUk4WLBw5Ys+20cI+eKLL7744osvvvjiiy+++OKLL768zuS8QAjpwo/QsMhAMEBPal8fvVOpMdqs6psswKEn6W/+kh6m5tWS5SYNBAKMwQ+H6OHJ5uh1iZdbsMWbqRn0pqf6JNNOpBrpAapg7y5m1/rge/4GAHDtu/4QX/0q08CfOU5Pz6K1KwAAy5c1Ipni9ctW0n20dy89fIFQLYaGWKah05O1eRE5fp7rq8Q/3E4Pzwc/Sg9Tz3F61wa7CghIOuasRW+fGQsB4s3VQB1tXLMVADB2ZhSOoKlCcd534Iikcz3agYY19EQVbJXhS3hgCnlEwtTVe3/7FgDAe95Hb97RY124444fAgC+8wN+nj1Oz2co3AChbcDYGL35VpRtdhwHrjPRS1Iav1mMzZxoxZ3W7PxKGGk1Z4rnZK7xqyqVaylniLrGmdRmZU03DNNj8x8ZoMeyIOVEI3GEBV3gSGNVnKemaZgCQpm9YVPq7AiCKaABAY8zRFIhK4NyiQdAy6q0OBIrrGkIitdCpRy3BdHl2DZcW3k7FAeRPF/TPXSLJl4IR7i74OrehQodpZBWuutCl/tMqHHiSr1NBEOSylv4qhQaKJ23vUx6edGt46XYNDy4li4m84Ljwp2SoUGf8n9D1d1L7+56/CqTxwzb8F/fpq4y8OkqLblW5PZyVfy6SmceMBAt8LeCQokIl0pOcxEUfTjSz4gKB8uZLJZEyJHWO8xxdXwP+YU2LNmAykX08DcuJLrkJ7+8nXWLVjK1AoDus/R2VFYQmXHRRRfi/vuInFSol/XricLYf/gwlq/ifH1c5u94jPNe0DSQlZTmvX1EoSxeUofOs5x3ewY5v2UzHFehUMRD/5kSS28JmscKRDCeJHJVZfgqeHHbBeQkFZLKfKj4gkzT9BCS2dzEdPeBQMCbL7xYfnmuaZpwJctbTrh61DsFR4OmssIVJmaX4N/sp5FR1jcqWTfTqSziMeqvsYGomYE+ydSlGZ6ncmiI871mGl79VDy6al9dXQ0sU9WVdU8J/1TMDGPdBUSlKj6nQ89JqnejDIagqVJJrllVwl/lIodyQQHZhbzUhfdHgmUYH6X+VNbt5AivTQ6mERfenaVLiHoZywzIte0YGRbEqqCJ+4eIcorF6lBWRj3EI5Lufkw4DTUbhRyv2345ETidXUDvEHlfRgaI4K2PcR9St7AW/SGO0f6gjIskUU6RshaM9zCDWMFimzuHyT/YYSVRWcX+MWqIRi5PsU/KqiP40SPfFn0zW1igTibicsBZz/fsgTT1t+09XOe//C+fwRO7mLnsT/6KqeKH69mWziMvIBtmHQ72ktfwhg/9bwBAX5mJ6gbqr11n3+a6uK7pegB9GY77rhG+Z/f+wRXo6aW+cily5Ty3m32UWXMMJzpY1y1rWeaaBZLpLxLCwqUtrFeC1wcsfj70yC9x9AQRXBd0M5NYIkX9XHf5DSgvYx8iy7KPn+K4sgIWlrcQBbS4ifucEyfYR4dOfhfPHiD3z7Ll1NXTx4jKalxeiXie/bTvSWYEcwQBePUll+E73/4qy2zke2O1sI92Lrochx4ib1VbJTPajbqALnPLyf1Ejm3bSm6ks9V7YUSo91WLuCdcvph8lnc/+P8QznD8tiziXvKO/7gNAPCpTyxHfQWfOZ6UdVa281ZIh6sSCb6Ccr541F/PMt8+KKanfhkr44snpUeDyZw0pWeRyRlGbccp4X9RiHdeq6HIIalAM5rmwJgpFfI0Mt140afZ305G3Ewn03H6TN0jF6/xUthPd9+k89mEayadG6er53RILsctTFsmgGkhcR5625gCtX/VZTa+JNVfBZd7IFOiPWBqGBsnkrlfopt6+toAANsu2Q7oWSlhZn6588Ig9MJhBQ0PoUZIjBUpaSbBRpw4ksCGddz8fOB3uIE42MrNlJ230NbKDUo2zU8VTRKyLLgOywoHuFEMRRgqMZ7NISQjcfU6bqJOtNF4846d78Vtt/4HAODpux8BABw7RQLBS7auRk42yRXCRPorgRM3ZoPI5AVePkJjSqyWoVb1TXnkbC7iX/36fbx+IeHLRthEcIxQ4VUG4dXX3nSNp6NRCV/4n59kGvqdW6/Av37xM/xRoMa9Q9yAPPXAo7hx5fv4m4RzGGEJGTMNZNPcgCSTEg4iBoPlyyvxp5/6YwDAlZdzE9nRwQ1S59lDeOjXbGPnaRqxUknq0bbz3gFfQd00dypMT4n3Ek/4cjbCsZLZdS75x6eVieVPB3Ocrq623Gc79oTrA7rh6c2RkA11+EvlksUwxbzAPRVsVC+CI9V96u+gZSFvq83duUkx7ExNmi5MMdoE5WCmwvp03YEmxpNoQOCHUgnLML0QIiWOLEyatBsAAmKoKU1X6hHuqlSVqm66A82b4JQepc2GXtycyLvkyAIQDumISuhSOCQHZCnTsgy48vCUMlypcDnNQF707pjyvJI1szhG5UvHgVaSyB0oGpJcuCWhXlMXCi8NqHqAW/rbpMVxyt3nJi/nBlwDvJ2JM2nx0bWiyUz1jYdE1XVoYiC0RFdq2c2ZDlKSuj4e5oE4FOUcsWDNUizSeYAOx1n66R4efp/a8wQuXMffmtfwsJe+iwaX8eQAyuNcE0aEcPfoER6qCgUHWQmbUItkezvnq2DEQHcXjfZhCQPZtJFpxdtPt2E0wblZ0zk+BkeGMdCvwsEkhbohxg5HQy7HVo6Ps31il8CyZSsw3sY1JyWp6dU7YVgBRENcfNXcoGRsbAzREJ/jiCXDkvDSQCCATHbiiU5Nj5lMxgtfy4pBKCeGudJQUNuz86rQRweGvAvxOA/PtpBKBwJBaBrbqoitFcG4U3CRSGZEj7yvKl4OQ09OaFdlZbn3dy4loSxCOF0uBMuZdBoD3TwYZyTUMZOQcLKY4b0g5TEhqk6OS+NzaGjg+jw6Qv33DbGcSDiKYJAH9fERGl5MjetopEzHoBj3tjcy3CownJTnazj0DEOQ4lySUSf6X7QojoYG1vmZXQxxW7+Oqc5PHckhGlzF5/WTeHrHpuXIjEkqdL0FADCcpfFMj7ZhTIjLy1I0MNx/708BAAvrWrB8MR03S5fT0dMywtCl+x9+ClaIBMLdPTRi6RvY70GrGadp08BAnOPYFoj41ot78A9//VbW9XnqePFGvlN14TDKhVT++//5ZQBAyqF+zFQeazYyhOvgCyS/fuRBhoc989QCHDlJY08uzeeVbWR4WPPCCDplsCUT7BvjVB2WVXNMNm+hAXB0mHUZam3DJ28hUXKfpJvf9+Aj1Gc0j0BGDHB5jqvyCMdlbnwceg/H9unH2M/ZHMfcj8781Hv3xiXkcdlKGoNbVjbj6BANXJ29nG/2PEuj2PDpg2gWo8r3v05Dy8Gz3FOe7OnG0mYabX71AB2Qm7fTKHP25DC2VHM8Pr6bhN2nWznXrHr/pYDkN2n9HgmkxxBCTN61FQtYr46TfI5WVoVAueyHTI6Zjn6G8wWryhAd5tz3xvVvAQAcDnODf+cPb8P73vVnvM/m+LfEoZgvZGGqOIJXQHxD0IvLq6GjF3vGXOrwEqL2XpLMl6T7v4q4rlt0IntUEsXfJotyuti2PSXUTLFUTOxOFUZlQDOmhj+di8zX+DO5HQx1mkSF4BRpHGYzCM0WMqark4TCEZQaiCY7+osFvMh+Wx0Izr9xOFs/lP6m6+r8Lft4uCiLqyRQXNeGha7l1MlOlNdxH9FUecGM5f/Xd2/74osvvvjiiy+++OKLL7744osvvvhyTqKdD5basqpqFwCyScCxiVqxIuIhFae8YQJV5bQ2Xr2T6db/80EidlatrISpS6hZD72845LqtpDRoWu0pCkvaEUVvVypnIGEhI+98yrCdePlLGfxBWtww/W/CQCot4hIGh2lRypgjSEgaXNPdT0KAPid3/1DAEBdo4Xhbv62bBEJKeu209t6z3eOoaWGcOD+BD2BqTzbW6VvxZog07b+7Ufp+Vl0UxkyNj1eTpAetogr0GjTwJlOtv/Wf/8cAGDX/fRm7lh8A/7pG1/gdZWC5NBVOIPreZtzGXpyFfw/kR5APEY0U1rStjouPU0VZTqOH+1mm1+gx21ZMy2N2fq0VwbEQ60JkkRZhVnlieTBswktodOR9b26NkzNUqkZJ6bx0xzXQ/ioUJFx8a6n02k0NxDNZkioh0ITabpZLMvLH6hCzQw4zsRQkWnrNI0F2VHoH/EqWACiUveYSgM/wZMgfaHCtVR4WUlKQtWnebsE1qrQNV7UX5F8zoOeChxTl/a5sL3UmZqh0jcX21ewWUZOpcGWIRO0DIQkBbWpqbAw/ma7BpIZzhHJtArLCUqbdCSF5DanFz2lU4jnlBfCLnikyQp+a5uK2K/goS1c1b4SpJCHgpqDV2YuhJ3arOyO01sFpI4AACAASURBVKHt5vc+GCWhnFPGmqZNCyNW16rxXl1Ob0ReUo4PaxmggvNTmaAtvvP5LwIAEvY4LruWyIWGJZxjOk7tBQD8xcc+gIi0Y+e1NwEAsgbLvvNf/wELFjB8R/Xp4hYiJ8ZSGdx0840AgF/fSy9+RJgSm5YsxI9/9EsAwCoJHVOoj0gkhId+/QwAoF4IpMcS/SgIAbljsy6jgniIRCLIZjlXGgKli4RV6FcAVlCQahIGlRa0Qj5ne6TyZkAQeYKyGRkZgp2TcDB551QYmm3byAlSUH2n+iOTyXhlKBis6g+zBOqsxqwirs8VbGg6y1Rk1gGzBOEpOC9NPm0pu6KiEslswbsOAMrLy6FGs4eSlDmtp6fHC4kNC5JJuUitYNjzeqYEdaSg8iEr6KH6kpKWPMCmo6wy5Ok9lZSQU2EAT49nEQgImglc+9Xza+vCMAwiiTZvJfrluYPHeW2hFokR6lEPjclz+IyGBgtZQfgkRrk/iBqcz//ww5/GxVuYzv3w00Ry/Mdd/xeX7PhtAEChQFTTjVddDADo7XgEP77rDtERywjabNih0X1oeQOJHiMBjmljnOnaz7buQmcH9VfRxPE/3MXxXxddgWYh0B7LES1TtZjImqd3P4Www/JbquU9sbluHx9wUN1Mz+Fg5yEAQFM59zZmIIyeYa7r6wWtFxDSyvqyKPrPEpkyLsidTgmvs3ULLzxLPSwoI2rm7e+/CmuWU+9nBcr0xDPsr7qN6/AbNzOl/NoaooyOPvIwAOBYfw7r11Nvy5ZSL6Yg+MxCFolhtrWukWFohbwMEAfo6+fzRhPUv3C4o6CN4XP/+lkAwP6DROzU1rGem9ZsRnqE7RhsY/t2bCeSbEiz8f0HHgIAWAIHjEvIXqYygJuv/jAA4MgzDGN7pOMpAMC7fu+jWGWzL56570cAgFQkjViQ9SurJXzICnH+6O3thmNxvEcbqKPFS9knqUQfrtrwewCAtnaOzfpm6qxlRTPqK9hPas0WHn/kChnorujmZZRXAvVyLqvX+YtMmojQeKVlLkiO81dXlHnSkL/6Ms+IBMe1YegTEeWl2yzVPWo9zBdkbXYcDxE0eV9mlOzLFP2GrjMVPP+tT/s5XVkvNj4m7w2LSXVK6ATmcI4rvc8LbZ8kpaTS06GVJu+xZ0M0lX43eb894fopYWilYW+vfcjYnEQamBPaj0BQhy32kzy3LRju53qYKnShabnsXQMbZux8HyHkiy+++OKLL7744osvvvjiiy+++PI6k/MDIVRd7gJAPjfmWb1cl95LxdUTLwsjJ5w3pnjo9fKsV0ZcSByjMd4XidBDEgg6cDQhXxKOkmHhoojFK70UbkGHZZVX0WvY2T+OtUuvAgD8zz/4vwCAJkmV2993FlXlJDY1Ld53988fBADsfWYXjh2Q+HBBPMSW0Dv0wgvjHr9BTDyrkTS9Qcvqt+Of/oX8QJWbxaqaTcOO0piXFi9wxGH9cpkswnXC+5AkD8CffPTjAIBwdyP+5p8/AwCoWk4PeMEUQu102iMZzuWUl1YFqZZhZJReOOWhVumFTSOMqnIhsBTUxdAAkVYjro6cPREhhGkQQkXyM4+RBDPL9HiKV9t6m1ep21W6R62IGLLzyqtOUd5yy7KQF3IvU3g0FKqkNIWiLjxDtljRc7mcV4aS2TiYSmUyQigMF2UhQSXwlUDQLHqyPK4RfRJpNjTPql9Qqeila23Hy04P3S1411M/mlf3YEB0ZZpe2QoNpQtKT5Fua7rrcQE5Qlasnm/qgAdYEr4fRZrtOA4ywuuSE6SWIRfb0DAmaK3xfIXSkKcrXVNcR3Kf7iASZPkhQXQlpS7ZbB5pldrdVWPbLOrRI9lRipnZ81I6omf00MyWdn5ahND8PFgmpiKEvLL0mVNwuq6LrLzrau4MhzjGj5xpxc8fux8A0FhFJMLZg0QPRMwQsuXCcRHiff1HSPw6fvgYFtWSTPq0zMMdgpSKtHUjEiHiQ80lC1vI5ZEpuFi0iOiJoPTpk4/Tq58vAIU8ddm0kLwpmqSob2pqwqEDREO0t5K/rr6+FnqQ/VpbQwTB8WOnvOeGhAsok02JImS8hMJwofiBJnrmctlCkWheEC4K8TM8PIiAINoUIbaHyMvnYUpdJiNwXNf13iE1e6p53NSLZPHZ/MQ62S4QkDXACnCtLCh+OcOALvNHQRCrqr2WFcRIQqFLcyXPU6noCcWoqiIqImiFMDjIdUHNEWruMy3L4xwqCOoiLYTTARPYtJGIh9bj/K68kvUcSw6gr79byud3FUIs3tPV7c1Bqt8yNvutLBaDZrCsG9+1EQBw333kCMyM1nnE8c0LOFYV2i6fczEmaLSaGj7HkXfviit2Yu06EpcnK4kArtcX4NRecsmMJzmuIEku1mzahGf2MTlFY4z3/fbbyaFz8+9dhOhyjodt62/g5zKO5/t/+iUM9Qn/1io+Jymwr2Md/bh0x+UAgCNHiAr+xQ/uBgBcsPgKVFUQEfedH38FAPD5T38IAPCfTz6EWCP7q0zjO+UKh1Pdoihaz7ZTDwXqsbWfHEvXv3ktljZx3/HoY3xnjxynzp890Ik3voH1u2oHdaW5wzglBPDheur/2AtE9ehWBitW8boNq8gv5ArSxzQX4503E2llGrXSF2oNCyA1zvc3JJxgCt2KvA3LEM4yWS+ygkC7/Wu34rn9RO+sXk4S6+pK7rn2tZ/E4ADL/zsh2bZlL/X1O/4NtnBTXbjhEgBAjaDQv/bLr+B/fOQfAACxFPdLf/aP1DEWrsPnP03C72/8/V8DAOqjIxgNEZF1z17q772/RUR5oXsIZdVs/65T3ENGKjnHNJeFcfGlv8+6ryRavVvIupcuW4jxEbY/KmtX3qauNd0E9JefQ8hHCE0v2mRG3Ff8eeenHs5F/rsjhKbj03EVt2YJj6hCxWYyE7kQWcbE+42SZB/FBD0udG0iGXLpvru0PtPVcaa6l+43Jl87E9/PdN+VIowmp4+fmCJetWviHkrTNBhzTAA0uUx3Mtre1YvoJm3q9a+VTKtjffqZsVAoePvEkHBCjo/wrNne2Yp164iE1lyuXZDoqKefuBPPn7oHAPChD33TRwj54osvvvjiiy+++OKLL7744osvvvhCOS+yjCmLlwHTywBWWUNvTF6j16OzYxRlYXqZchl+56bpFQoEdBTSkho3yd/6HDJqr1hXDivM70ZStKhFJa3I4HA/qmvEkyKcRdkwvaF1TeWIBOhZXlhL72Jfkl6eaHmkmMY9TUvc+9/7QX6+64PoaafXaLSX3qZf/Ireu+rsIJoaWOf6GiJ3Ltp0MwBg21V1AB1XSGZo8QtoGWgar/e4WgT5FDZ1JMYF6RQjcuczf00vVyhRgaoV/A6TGOgDhgVd0CGuBJ1rYlm2cyaiUXJ3hMTrbaQkc9Y4MCTIqmBIrMcSF6/nq2A4iulcQbxmtrwWEQlTfsLcmFZmktnund72+WJZxkwvI5U8QWVPs50p13vImkIBukUPu630Iqmi3RL0hUpp7z3LNKdBiJTGtmpTvvNap/St0rnrGoqAD3V9MROc8iYUSxLkDdxifK1C/6gsCY7uIWL0wkRrvw4HcBTXiHynrPymAVOTbEeqTl5qMRu6oCdMQd4oPepwAEF+QLiHNC8FQxaGtMs07IntdICQoAFzAmmybcfjZ1IZGiSJFMIWEJEMLZGQ9E1KvSM28sJxpHhZFP+R7mqe16eoyZkRbC/VC/FyejFs2y56R4yJfEHTpgiV3/Kmg8oQ56TxLOfVQpyKbOtsxb3f+i4AYM0qcoBcsI2Zegb6Mhg9Qd6008eY5WdJHTnI6hrfgD/8X58EALz7g2/i9aPM2tNc1exx5KiQ/M6zRC5ogQBa21hmbpyZvt5yzZUAgEOHTyIl6bC3XcT5+4VjzJ7U1n4CjvCsXHAB63Dk6HFU13Pu65W028kUuWU0FBE0Cs1jBSRT5XgSQYElKF0pZFIul/N0qjiE8hkiTwrZnJdKXrVP6dyyLESEe0ghajLCYRQKhZBKCYpH7lcZSnRTmxFhaFmWl3Wwro4LTXc316dgyPSy6tmC1mtaSJ6nVDKDzm4ifhrruW4ODeVRK8iZs2epqwrJbhEtD6Nb6twgzxkY4Frs5HIoryJiLzHKMisFSbakZSEiYeo2Xsb3dOuFHDtHjh+ELe+sev3HxsjxA60AS/Rg6xwzW7axbmfa0xgd4m979pA7SAP3FT29o2hczMLKK4VbsJLonM6OQWzfQi6Zxx57GgCwaAlRN4F4JY61M5snjjHL2JGUCzdEXd50I/cBh/cRpRQPB5E3JNNcBZ99WOb4y276Y9z+VaKPWxqojx90tAMArrj+zzA0xD4fGCKq5OSeXQCA3fuO4NBhInBaT/K35S1EH731po/gmqvJhziS5fr8s31EyBhOCpEk392q5eybsxbruWDlOsBk3zy9n9xe1Qv4juw72YanDnH8HthFPUYLbNMtV16Kq66lV3I0/wQArn91Bt//sXZmJzOGOMbTkQIMQfM0V/NdXVHD+59uuxNtrUQ8LV9BdHYwwHpmcw6yeb4TelYQzsrjHi5gNMe9SXaE62xvJ5F/Ky+6FjUtRJ6tF+/p7meojxY7giuv4Xd6M8f9w09wbnrjzTdj9BTHbdcwMxfuBuedy991BTTZp+7q5XN3XEV+tM5jbcjGqff3fvaPAAB7HroPnY8zw1k01gIAOCj9d+HCCzHcRfTb2qUccz3D1Nkl69+NJ/cTYRmr4Jhbvozz1eg4EJU1KyVZ/crKBD2etV/SLmo6+e+ASvmvLn4fvASZd3bi+Ymua8WsYtO8jSqz62yZt4Dp6uxO+HRdZsKdq5SiwdU+e0rdHGdKfUrvm5XlcgbuIWD69Pbq75k4hHRdn3JQnJDpbHIFiuSm3q/e8zR3xnNpaZmv9nt2Ls9zHMezl3Sd4d6rppZ7qNraWqSFf9GVPVi0nONrdKQHA32DL1r+eWEQCprcXJhaJRwh1yqrYsMal3HxN8NRjPfJATAsBJ82f8vnMyhkpKwgF0X1/g8NFFDVxMU7FOIBJmTSYFPpFGHzEUk/mkjyt4itYXyAC3V6lJu9phUtAICzHWcRjQpsSw4I7Wc7vLY0LObGqmkpN6ZrdjJNLXoCkP0oEJKUujo3R6lcCm6BGwlo3IhZoTDGhDA3aLFdjhjDNAMIxfhdNsO6NC1aLvebgOgxJZB8TRF8OhryeQUHlAOgVKmQ7/VCFbJpbqwCEp5QXVXuHXRU2EQgwDABveS1LL5Y6rNoANFKJrGpMnFymvCSlBpJ5o00nan80olAvimdHNSkPmnyNAzDC/9QZXlk0SXXqsm69NDtXSfGG2WcCQQCsAsT01PPZbLQNM0zUmglE55K/+54adnlb0332qPCJFTYlluia1WWMgjpuuaFTblmEf7Ka6aOATVxuZoB01L18irND/mPhalrhAjddTGZxFrZWzRX89JRhuRwn1eQVNdBOKjCCsQwlCsgn1fhf7zPsji2QwFAzvvIq/TgyliquV64j1uQsLX/z957B8h1VnfDv3vv9Jmd7buSVr33YlsucpHcbWxsAm50CJ1ASCAkvOEl+b4UCCTh40sIb4BQTK+2MbaxjWy5SG6yJKt3aaVV216mz23vH7/z3Ck7u5LlghLf88/sztzy9Oc85/zO75TVU1VbbWjlZrJXK+38eIbAsxVXQ0nfqJqQtut6Cotn0BAYciCqYSDLQ1Be4xrxjKSN3vrMeqxavAgAsPrKKwAA9677HQBAr29Fg9gqbr2KKZRjjQzhaF2yBH/17X8AAKSKXBeXtjE8tzudx+zZMwAA9XKg3vAsCaGzZsFL697ayhAd2+FGUHDSMMQ4sv8wD19FW/aBWBQNjTTIi50Fll6EFmadZ06dDgCYNJkGgn37DnghgYpYeURCUhLxOuSKXA9VKJdqszpF/gx4xsi8rMehYNCbO5YKPfXGZWjUHFKk7JZleb8p449HdK9pnuFJK1b2XzwRhSXk393iqFB1SaWzCAQqIdtdR6lsRCIJz5Su3tMxaQIaG+ql/Vj3jJD59vT0Ih6rq7i+sYX7RG9vL4aHabgzAiqkmLNp//7DGBjgATwg4S4dUzmG+lL1GM7yvpFhMahJOFpjYz1cMbYLPzjCUXlvcwDdcmDv72MZMsMcL5ruoGDS4PfSNhqXwuGc165dp9YCAM47n+ngA2He77phrLqEBpfGAPf3of5OdPfw4N7TyXo1TaUxq36ag+hTDIe097Isvzj0RQDA5rXPQT/Oej3yo/8PAFA3hYa4devWobuPRoQp7XSENeX4vpsWXANYLPPQXpI2t4j/59sP/Tmi80iQfOpp1ufCS0iCveWpZzFjBst1MsdrNr1Eg+qcyctw/CTHZqMQH58Sw9e+E2kUMzT4/dGNNHjlhmlIuuzCy7F0DtvoUUkHb9v9MOs4P7JBFiwhhObz2hNYNochaRdesRIA4ASpe11W92b0jPAZ2UHpZ5OGncbGKNobWIZ0hmMmGhHi9eEYdNmkigbHxeylTXItMJSi/mWCpNCRBs6NVcuuw7EuGl/+5Z++wLaew3DUeEMCt9/xVgDA0V0MM/zif/wIAPDdF+/F1M9xbDZrnAeZMMmeV908HX/y2WsBAPkW9vufnP8hIMK+Wz6D7fGu9zJkLJ+tw8zJbI9vfvejAICGKMfA5t025kxnopGnnuQ6OmES10Jdi3s6bjzB/S+XU84T/VULyXmtD0dnQlB7rhpCXq9inav192V88YwoVQTGQGlfdzw9XHTeirCqSme+ixqh/JoDpcjVMsZUh34p0XV9TAJyTdPGJaEej06gWsrnt2EYNX8DMMogVB46Vm3L09WZxXVrmsy859SqnyjsY9/5h5MzWQt1Xfd08UlTuRdkctQF2js64EiItZ7k5/PrfwsAGEz1YPHCC05bBj9kzBdffPHFF1988cUXX3zxxRdffPHlDSbnBEIon6PnrK4ug1hM0nQL+iJn0asTb9TQ0yUhXwF6lODwPkPTPEiQmadFNBShN2lkaBgTJCWr4yhCUFrgGpMhKFrqwRQ9UwJWQixgoSipTE+Ih3laGz11DYk4NJ1eyWyeMKxgXKXyDaEndVLKSQ+xPSJlag7BFMunk6G1NCFe9ljIQCYlpGAJeliLhSwMgyFphYIid5awJFjISdrtmCGeaEEKFUMmTFdSEUfF6uilSzc8K6lCSGQK9MbVRcMoFnhdJCDufDHqDg11IhpLSv35OTxEL6DybLNpK8PBNM31UCglcIhYgcuttON5Qdyaf565aE5FqvDTXl5eFreSsM3DP5WlUay2tOu6Dttrksp66a7rtVfQ8wqwTwuFAoLjkBKPWV6UIKilu51RKckFBISApkGX/tUUX7LkerfLCOWUt8Krn1N6elGuDwdVyuzSUlLySmilz6qO00qjYVR9FD8ztBIJnKlCGFUYW8DwkD4emW9BIYRsLxW1EeQYzQUAy1Ske4LAUym94cAyeV0+y3lTVKTbNqC7Ci1UZcF3Ha8eykOtSLfPda9eIFDWX+qzzMOkPDRBIUVXoVIbnnwc+3NEciTaGVK7/jGSodbnLQ/NN5zh2rl3CxEM582fgxOCLNq8k175qbOJQjn0269h4CQRB1MS9KQPDwv60Unj2DGuv339LEtCQiOiiCEgyLiiybV9/wEiHhra4hgY5HjYsfsQAKCujmtpvmhCc4k88MITwy5ysg9lC0S71Atq1HVtD1HX1CTlG2D9CoUCQmFJLS9tptYFx3ZRFAhSQBCaDQ1EX0ybNg1HjzDESaWr90JJbdsLC1P9oFKp23ARF5b4uro6uQbyPrOiX1XZ+f4AgrLd9w1wzxoZYR06JreisYnPGuintyknYSjplOn1fTxK1N3K81fg0YeJWJg9e6aUi2U/cLATySTr2N1HhIlaO9PZFPQA945ASPY1QZ3aRRsdE4lwnTlHwqQD7IftO19AVxfL1SDJHFSZgkEDlrR3fzcbIhBRoWAJxCR8Z0AQJxo4dhqaQ7Adyc3qSj8jIs/U0dPPcXSiT0LNAlwXDp54DLs7f852n3gpAOCjd/wTNHDsHD7GELNkcBUA4Om9G+C6RIXMX0nkyEcWLAAAfCfXgMRNJAvuzm0GANz/2O/ZVr39SIa5Rid0hl/devvbAQCrr7wWX/8mU7fPXkZEntXH9fGSlUn88D8/x/vifwQA+PSbPgEAWDBtNbR6jrXffp6ew5su4DVd+45gyw6WoTm5EAAw2MXN4cL5szChgTpUUCcS+qp3EeV37Y0fx/HD1NGMOoZiLW1vxe9f+C4AwB5iSNb1lzA0/vjxTcAwy9B9gAioRCP7dEvXZrhB9lO2l+O2vp6E1Zs37sXSBZcBANqbiSzMy2Jr6g4MIUx3IPQAOUGWh0LoINAPW3YyZNR0iDB8fuNmLJnLENXJQT4zmSeq51e//y0aF/O7VSuIZPrse/8PAOB39/01nnv4btb1itsBAO9c+V62T+o4nnmYdW+q53o1deUEJOr5rHabBNVL5jE87O77f40VFzMccfmsNQCAgkHUWH8igqsvZrvdt/Y/AQDbtnKtveyiW7wwUkvmtS0IxUAQgPXKEELn+v71hxYfHeTLeFKZYl6dC/i/bdseetbTPyQevjw1+2g0EOCFinlJWuCdfZVeWp7IpxohVK7D1kIGKVF6R62wsjMZk7UQRmMhkmqVr+JdGsb+bTzRxsa7aOcQQmis8LxyKQ+lU8jw7m7qJvWt1IV279+FWJT719GDu+V62iJmzW/AocNdpy2LjxDyxRdffPHFF1988cUXX3zxxRdffHmDyTmBELrxndcAAPZvfxZtDbRR9Q3Rs9cQFsupbiEaoRdTK9JbqOnC+4N+LxV9XBeeG0nj1zqrAaZBT6xkiIctRM15F9Di9FYJPx8EUIMT/UEsbyVXwPniuTwmaCXHcTy0RMigF8iwBUHiOggFxJsrz9QVX0LWQVCQBJoQ4driUc9aLnQhEnUUIZIGGK4gI+RZlrJsOgHxZwKOTa9aQa4x3CDCktLYMRXJs1iPNddLAe6INTogdsGMZXmp+lQoq11U6ZWbvPTjpqQmDoSlLqaDoKFiPuW+cn4ilSpcWUCFacU9Y+9HGQKpjB8MKLOcuoCCM1XHnLqaAccdI+WhW8PyXPZ3NXqi5jNqEJQZTu26MbbVuxAAylIuGhUcPmO+rxYKxVVxteJ5gAuhx0JYOKkMXb3HhQZ2piUPsxXPUJl3QSF8HEWqbDvw+kKrRAHZrubxCGleGkxeGjBsaB4UqdrKX47ckphpvWQpV80e9OpcVmXVjioNutwXDWvQBHkHmQeGFoBtcC4pPhe1+GlGALYthNbiZbUc8bo6TsnZKnX2Ypg1DbaK11Y0SOPQeZ6R9b2WY3c8foXq1JpnLLqH/lPoF1vIiUNaCJp43zXheEmNcO07cvgY9ALX0//85j0AgFWTVwMA/vydf4pvPvxtAMDBrk4AwIRpXKP3dx1HWzuRAPmeAwCAPafIY5KIxjAhQh4NU1BcwxbRJQ2ROtTFydORbEgAAAo9RAwZjoVIRPGosZ937iY/zpSZdd5alBomGkjXuH+MjIwgIOk4TVk0G8ITkRvhdYf3kCy4aBLd1NrchoEBolVaW+mBUWT9qdQwtALXe8VhZQrhuqbbMIRwWhFUx5OCXAg7GO4lUicS4/2GhzCyEZR+DUUERajoQZwgYLMvknKfrZACugFTuINiUeFUcrPSPn1oncjx77ok2wlAyoIM2pt4/dQO/rZnP71J/f0BJBKsa7vwnxxNDUJvEgSuoGxMIca++pbrsXkX2685wH12qEdIppOTMXsu4Rq7d9G7pdKihhNhJIUHJzmB3z34e6ZJPXpkCCFNuH9MljlvC39MNAII+ipT4DX7D1F3WDBvJi4+jyievbuYECIjaeFnTJ0CyyTyZo+Q+hYyHFczZy6GbhLFc2wHx/rb30eUyEOPPQprmHW2R+4FAPyvT69F+2QiPmZPI0fPdbeRtyZqLMKpHr6ztZ16RJ7NgsW33ArLFWTibnbwnm1El8yfthC26DK5YXr7XthHBM7DG3+M9etI/Pytr/+U5XrkGwCAeP0h5DdznrzjznewPleSQ2jzb/4VT/2YCS6Ql3U/QF1q/4GjGOzj/LCGOFbffjN5ckIT6zAoHE7FNNGB0NkGXX1d2HGC83jm+UTznNq3H8kOlqG7yPYLN3F8zIheiuNWJwBgOMIOnyZ8VDfOuRXrdhC5tHkz5/FnPkUEzvZt38aBU5KCPsk5WBSOzPb2AHKCELItQTIFBDXWO4KRPiIEH/nVvwAAmiYLp5XdgJPCMXX8BJE3V15MNNXwsTb8+p+ICDLfxTKsvJzonk+c/yjWPU0kV9ch1iWxfAXL/cRerLrwVgBA5wDfO9y3Bem0kOxfRMRURvbfm665EcIzj6vvZOp6hQ4smgVE6jkXliy4mc2uPPdFIB6KyPXC96jLumOdvff7D4VIOReQMOOC1N3qff3MynsmvCDj3fdyRTtLdWB8zuVx9I+ze93rLrWaczzS5xIRseYdAkp9qHk/VccdlC6xoQnqR4H+S5SNupcExmtZ0dsNzSnjbaxMI++6bomXUyVu0QLQJBmLJbq7Stii6e4oHh2lb1uWDVUxheAtH6vV41WdC2zb9jj+qpFPNUUrIZR0D7HjVnxomga96vzoPVNzvDKPh5ypJe44g/q1XG5UW+m6UcZLq34THtOADtX+tupviUSADe+wkBFe4UhEznCugUwvddH0KUEvp9kfO55cj2Od1PcaGql3d6eIxrciQ+iXvfvOO8Yuu48Q8sUXX3zxxRdffPHFF1988cUXX3x5g8k5gRD62MffDwB43x0PI6LR+1PI0yKm+AHa28M4sYNepiiE6MehpUx3MwiKJ9Vw6LmMxnnthGkhDIsXOJEUpMogvYvpsIU6lQ5bxW7q4rWyfvir2gAAIABJREFUV+Hd19wFAMh1iEvPHTir+pXiRPWKv4GqFHqj4ijdMWMrx5NyC++ozxoZwcbzXpRf41nUlbVTZVjSDS/TmOMoLht1X1mWMc9cWmUdf1lSac13y6zMr6ecCx6tclFWdM1DGJXGQCmOeLT9t8QXVLLkl8aDiiMuHzMqo1H1+0tp7j3OqFoIqqqx5lb/jjIvV41sBzXnS1WdWBbxXngDBB5kSSXc8xBqjg2tyltypnl7X60MYGc/ns7O86hpGkzLrHiC4mUJ62GP1waCdsmKG/tE3ynYYXrVrWP0OFz9wTUAgI6rpmHZEfKPnDjJrEtb4lw7+/ceQ16eGZW09VFJ3RzQgN4BIhAcWVRiMV5j2xbmLyTaYtIUcrg9+BARHelMBjnxjhuG8lqxc+fOWYBTkj4+HCJaoameKaw1J4h8mnuAyqpYLOSQjE+W59IrU58kR9LgYMbLRtbTzd8CkvXR0Gykc8JzJ1nFNIGzZfNFxMSLP3EikTHHjxN503W8F1HFl5RnO7a2CYdNIImBAdYjKfebFvc6y05hahPRVKmM1EH4eLIjaRSFC0uN42ic/R2rC6J5IhEc/SeJ2nAKihdpNpZcRPTDrt1Ee1hWQd6fhiXoq5de2AEAGMoOYu5ctmVAp8crFOX7dr70Aro6WZ/JbYoPin07Y9kUJBt43fyFzCJ3uJO/hcNRbNpEJMxAH/shNSy8MKaBZBORS4pjQZyvyGXTgJRBd4R/ULj7Xtq6E9d97i0AgIuuvBAA8LvHieoZHO7HrgM7AQAT4kSMTJzE8XXo4HHcfO2H5DqOtRdefBwAMKm9A1M7lgEAjhx+CQAwrTGAPds2sf12dQIA7HZBKsPA7A6ijdKdTwAA3nHVB/meY/sQDbJ/fvS9HwIArr6C6db37zuM932Y133vFz8AAERN4TfrzeOiZcwY8swGIlWSwndlhHVceJX0s8Hy3buO3EDdXafQ2sx2PzCBnIkPrd8CAHC7i/jkXe9jHWdw3G/eR86d7JHdiIYl+2ucSJcly98MADjcsws7d5BPyurlOFk0bwVyoT0AgPp6wni27CDaa8WaO6EfZH+1S+r6rOwzjR3TcV0j67xiHueU0BuiPboSwV626cSl5Ki658DXAQCtAzNx8jjRU8MBzpely68HAGx6+Hu47WY+c8GVRO7Ei1xb1v70u2g9n+iptonso6Zp1D9npWbhTe+4QH4jsksSmAHFIlat5n1PbXkBANB1gujC1gkzYOa4zk1bxGdu2LIBl12yWtqDbWuIVz8QcEZ5kdXcjehRSGJATJrEOa/4I1zXRSZjVnwXlLGUyRS873x5deXl7tMvFxnky2sj43WDBn1saFVNPVCdbUo6ZEW2c1APqf5Oge9N04QlupfSrb1svYbhIYKUPqb4Bx2ntP+VZylT2UKVqPUjEDA8Qs5SynfD+7/6vnJeo1qcQ0q8zKej9PvxkFZnHmFRLuQ6qry+1hlg1PtO89zXQ1x39Lgoob1sFCWjtC4oaYU6L+RO4PnNzHKqxbjnxxPU8QJmBBYBvLj7od8AAJYs4/70xx/8EPQR4RPuJoL33t8yM+Z963+DJVdfetoynxMGoTo5NASDIRzpFGh9nYR1ZdlIRauIcJjGHleIo4NBRTKd9Ehds30crBPniALTPghLGsm1eZCJNAj5sg0YkqYXAjdP76cyeselH8aay5cAAPYHhTi6LCP4eNAqR6UaL2EE+X0Ngi4l5YO3NBnHMxaNPeDL31PLIFQy7FQZhDSUQo+8R7tenZRBwXKqftPgTfiS0Wd0GbUqg4SLyjCuM5GSEeAsIbXjhV155Rp9/dm842zkrCHGyjin2thx4UhH2QLHtBWkXNOhEqTbduXYrAwZk98ELu06JZLnarI613U92Gf1Qu+6rheWVF1PDaX2HtVuTik5ZHm5yt8LwEvJXfHeGuk5Da/TlTGWZTLtEpG2rtJSqrKXF3BUOWsYbM+g384F46VVNBGOKOJ4CVUSkvqca3sDKixrc2+Gh/xZ5y9EyOEaPbic6abTIS6Mn/js53DedB44i80MP9ktRoQYdFgWlZGkhIj09DAsDI6NsBAWq3DWgT4aCubPn42uEwzleUEOX42NPFRpegD79zL8rEEOWsk6HhafeeY5L736zOnTAAD5NMfQYG8KkRDXexXKNWHCBOQKbI9582iA6h+g4SSVGkJIQpdvuI7EwPc/8CAAhnfoEv5bNLkJqzERCpSUucFBGpLe+lbidXv6erF3O+G8Ha1CklvgNb3HexAJsR65XioEygAye84kDPZJyC3UPijjGTpicdYhGuWnAwknzqcQSwpp88zpAICDWRJ5T5rSgqEsn3HJlQxNObSH4TLNsWbsPsC+10IcR1MnNCEiJMuT2mhM6TpC41suG0FDiO+eOpXhMZo4cA4f3YtB2rVwySqGI+05yPsygyZWLON3thg+OvfTYJOItaKQF3JsIbHPSYhaIZXzSMZtaf9JE6dKm0Xw819QIXI13l8wOVYLVs6D8rc185kF0TV6ezN44BGGQ9745otYvyHqDqFYAEMjhGrvOsrxO7tlCj78ZoaXP3jfIwCA3/0N08hHJzRjU53uvRMALl5Ewukjp7bigkU0OrzpBpIHB2TN3rz139E7Qr0jXs+x8OAzNP689da7sHULDTnPbGSo03lLaKQyzBa89ALDriJXsN96t5EI+qIbPoWnv0fD1r4NnDefvP29AIA116/G7l4acVLiXNsxzDabGjIRl7IHhbj/v77xdalfAUc7OY4Xz6TB5Z13fQJ/9Wf3AQBOnmQdpl3McTI0fAq3voXGtv48DbVpMXYOWTnE1FoekaQFcbbHjEmX4uBuGgzVVpARPXB4078i69BgkhY97u6nmNZ98aJr8euHOFfTWRqph3Ms0+K58zFnDudEywQSVpsxMXwP92DNVCEwl8CQzDDXilxdHbJFrpXz5yxmG3fTILRswWLMmkej+LEjDJ1s2n0C02eQODoc5jpnqXrqAS+JgqMMTjLZDb10+IpESoTnAA+HSkpOHd4XCoV8Q8SrLK/lnv2HPri+EaT8cF77gto/lpMvVxs3NMctc5CO1n2r3+fROZQZXFRiFGXADYUC3nejna4lg0J5AgrLqq1b63q5UarScOW6uneeq3YKW5blla9kSCoVxvFCvirPYuXnVXUfvLIEoY0VW6Zppd+U3l2Vhr5W/SrOAOfQHPKcymWi1utSNTWEgwS9eGfuHHWa9U9tQGcX9+f2aZK4Ze0GAMCNa27CI/fQARPRqcsone9nv/8Z3nYNk1QompCMhHq/7aa3YcriZactux8y5osvvvjiiy+++OKLL7744osvvvjyBpNzAiE0bRK9NAsXLsSWZ+m1tE1auE51CUQ/GFagBgQjtAxawpqbLYSgW6xKQpzec2YSTTSU6kJYPJso8kczTu9OMhWBsokN9dOqd37kSgDAXbddj0wjPWsQy52hlci0xiMUrg7VUaLrWomoV34qWTtrWTnPMG6l+q5xEEJE+oyNQildX/W/o435mwOrjMBLLN/Kgu2WiIhHve8VGHXPJI1iuXgp7l8Hx5nruq+A7Pf0MMoxXlp2d6kcQBkhnApT1CzYbqWnUSGFKuGYCgXkfeO9YXQ42vjWes/joMZOmSlaq7qmPP259509Gk3meVlqtZmHepN2sV0PUVG6TrlPbM+7UR3WWC6aIi0v+86oGlevFF32asiZvNcwjJJHSAh6XXFVB42I56mxXXqoDx09AgBYMHc6fvRf3wQA7DrK9fHUDxnSsmDBQhwYJprnR7/5GQBgdlKI/82c9x7lQVFp04eGhjwodCxG1EA0StTHW++8DVu3ktx5734iGGbNIqLGNG0EJFQsEFAEqwyZcgM6pk5m6EzPSXrvbYHoRiIhWIJCqa9X9w0gnJCU8IIamGoTbeC60/HkEwyfWfs4YboqrEyDDtfzq/AzLGFoplVAPCpIpAwRJvfd/2sAwNy5c9HRwfCUjvZlcjfve/rpezGSomdn8kSiDa68kqFEebMHxdnsmwceuB8AMEHIuh3HRbHA/rrsMoaADQ0TDdHZdRC6KaEsU0h8PG8+6zmQP4SRYYYQrXuUbdzaKvNtOAnLZd+oUJhFkxchWc+xcvjQzoq6G9AQC3Hvbp/Avj96lM9sak5iyjSGmnUdJfqqq4shWZFIHcIhoic2bmZa8EiUz8mlhzFJwt0GBkiC3CTp5+fMuxwvvkiC5XCSfdrbzzrX19VhUMLjmuupD7QF2ebpjIHmgOzFJp+payxvMpkEonzG1n1E5Vx+OeHWG554CsUsnzkBrMvAELBfGI4vfx9RMn8+n+nMf/id7yHncvxFkizD97/3z2zrxx/Dm75HNM+S84lEOryXIXumATz0O46VqCJVlzZOnejD+rXrWIZJfGbHBIa7nepOYbjAcTRYlLCCBoYu/fY/foODDzN87LMf/EsAwJe+8XcAgH/7wX+gc4T1uvbCPwYAXGJxDD754+dw9R1E3NxyB1Pa/+oRjr2hgb2YN4HvmT2faK/0SAZvu+4zAIDHt3K+PHuAiKGO1la8tJv9+sxuopxCCSKt6sM34BJp5+kNDMna2ynjy0hgykqibDaeeBgAcN75DF976YkcDnYR/WOkieruCPK3W656N/oOc+1KdbO/HttKAu7Dvcdx4YWcx4uWUO/bvOMJAEBzQwd0SSwgoDLUC+qxbyQD3WFfTJLvwjrrNFIs6T7hBPvkzts/hGKE60BR1jnXow4IeAkJSjqNJCFxHQ8toJBCai8PBoMIh4VWQcJ6i9LfoVAA1itMO/9Gktdymx6P8uFcQjX8T5daTT0e0XTp/9K5wpDTskcIrZWj2xVSSEUg6GVRDIKSkd+CAR0BSUbkfcpeVCtTulcmt5QYRinQjm1XoIWA0lphBDQY2miEjxKl+1efAfi/0vMr68BQuEo9uhIVVTneyxFGY6WNH+88zTL8913LSmTS6vyi2gWwFUG4IO4DEUkSktMwsYnI08IQr7nuMiYcSOoJvO+OTwEA7rnnVwCArY9wHz2SOYAXnqBeMLeDOnKyjTrtwvMug4P205bXRwj54osvvvjiiy+++OKLL7744osvvrzB5JxACBkOvcGtbfUec9/UqdMBAMd6JMVaNujFWBdtenUKFu/LmxnoOXpgOlqFAHqYMevF4V7kg/QaJ0Bvzsk0CQsjhQCcHE148aGlAIC3f5LesfhS4MAIvabJAD10Vg1S3mpxXXcUaqKcQHosUula3EDlhMwlKecaGrMUY5PxamNbXB27FB+qyM68tNoOvPTbyqJcAmFYnvVb99KSSxpBaB7iQ6U4d5VL7BU4SMayNo99Q+042/EgQ+NZpk/v3Tnbyr0cVFjpHYa0u0pXHdA16Hpl3LDqBzijkT0lVFktwrYK3FFFCWoRk4/rDVPj3eP9KT1T8ce4NRBCmlPjmVIux8vrqbhUNO83hXJyNIzitVNzMKAF4Lhj8VmNPec1TSvVx4M+lb/klYyfM5Wze45hGMgXc1IW4QsK00MBR0c2x9+SDUQgxITU7vOf/DiSjfQ0zJpF5MGhTfRQ3PXlL+Dvv07ulOARQeVM4LXnn7cce/bQi3/8OJEEUUHPAIDmqnEr/8t8/cY3voFMhtww9YIoOnqUKKT0cBrxOL9THpi8eMk1y8Zwnut+fZKoo6B42wcGe9E6kQiTJcuIKOjsPAQ3yHt37iLixLaIiKlLJD2+pZGREXkfy2sWLWigl8/J8X16knvQ8GAKHR1MUtDQxL3q5CnuKbaWx+U3EU3yzNNExOQyvD/c2oCgQSRBMcy6O3Vsj3iwA08/RLRFXqALxyRteiQURFMz+6u3h3tkSxtRCt0v7kQC7JP5NxAZdLCXqdiPnDyJhhDbryXE9twgSJW33/FXiHWsBwCsffhJts/2A7jsCqZhv/Y6pjZ3wfLu2LoX/X3cs2dMJ5H2Aw+yvxYumoNe4UQ6dJDosnCIKkhDYxxPrye/zewZ5PHr6WF5gyEgVyCKp30i65eIs0+vWLMSe/YSQVbXyH44fkRSpAc1TJrI8ddzgnxSiy4gcmrFshW4//6fAwBMSU4xfQa9aUa0DwPDbL/UMPvt9w9vZN03DeGOtzBO/+LrmGr8XX/6UbSuJJIrI/H8d3/pfwMAbr/x7QhrnDuPPfkLAEBfD9sjpgN/9mny6TgukUsT29lmE2c0Y89mElXPnEROq0tWEj2zceMLWLqQcy9XZLt0HibnQCzegOEBjqNtLxCVdvII05/Pa1mKzTvI99M4g5w79z3Lsf6L3/0cdpacQ4e3ErETCPD/hbMvR+sc8js9e5ik6N3Cx9UWmoOckMUfF86nvc37MdjBcTcxR+RTeBPH+GB3Dv09JPZuCbOfGurZxkeOrcWD95Ir4f1vZ78d2svyxoKzcXSYZf3tg/8JAJjeyndcMLcdNy8nT9gDTxLJ1zL1/QCAyS0z0NREdNkTGzmHPvwB8jx96zd34lA3eckuLZB8fFLTTD67aSnCLufCiGyN+QLbMx6LQysKf1E/18lYjH2ccRxvEYskOZ5sALbwfDgqXbRKSa1r3pZRvV/btuYt7+Ewx5XiCzFNs4xM2qj4rVAwPa6Rc13ONk36WctZI7dfnlRzVdZSBWrt3C+DitCXlyFlqponpYQ0lddV/KaX+PsCVcByQPO4bvRqPbEM6lM6B/L/UCg0imxYiW3bozhoal2r+IgsqwjL1uRvRTStEE0a9ECJhJ6/8f5isYhisSjfVXIIAaNT0Vei75WS5ta4pvJcVcExOgaHUE2epqrfy6XWWXmsz9dVvCiBsnOTFEOt0eonM5fFwAD31/vv/xYA4LrVRBXHYibiddQHpkzn3t/cRHSrkyrg/p/9EgCgt/Fhd614NwBgcus83H8f98+kTv020kR9qyc7gpb2yGmr4COEfPHFF1988cUXX3zxxRdffPHFF1/eYHJOuBE0h57i6TOmIFugF1JxSqBIr5+djpQ8yuL9COj0yhjBYdRFadtaOptx5m6OFrKJydl4bi9j8U8I4zZC5AroNtOYlJ0BAHjb6s8CAM6/nplRThb3I2zTuwuHVjbNOL310XVdD1WjrguWZWZSVlJljS3nwKm2oGplaJ5qZvXTleFM4i6rn2m7TpklWVl2S9dUf6fsySG9ZL1WXi6Vkt5yS1nJbLFcu7qKqT1tEUfXDaORLFKbmn+ejZQ7dcZ61Ok4i15LqfXeQIDtEpBGDetAUKG0PNSFQmiVkGCu4g4q49fRqtx23lgqq3M1wq08c0I1D5KmaXBVIQTJoUKTK8Zq1aftljwWmlPpcahoB6lndSYyALAVV1c58sqbQypbU4kHxpJUnE4Z71Ulh1KJj8p13TLk2dhj4VziClBlsWwXumRsMqrWNcs2vXopT7PyZgyl+9FqETF5+R8x9bQmPENf/ecvYdcmetxnNNI77kTZxs89t97LBKkbbMdkPT3wwZCOkATohyRD1fAQkQ/9g4PkdAFgF/msvjTLYhUsaOIBy+WIpEkkeG0uMwxN3pPPEQESbWS5l69YjISgjfrkPUPpLBKC7Jk5g+iLTRuf996b4mUwdO5B9YJMGrJHMHU695DZs4hSeFjSgU6ZMhEjknK9UFAZsOj5iUcjuOfn5GGxNWatCksay9QgENSIgAlFeN/jT/yY7dHnoqmez1BpaVsaiYa1zDxTzQKISna4NVeRe8hGGJ2b+R5HULjLzmPGtCNdXRjqI8IkEid66Mt/T/TM1EVz4DxEdM1zz7I9AnoU27d1AgDe8V4iXLbvIHrjyMknERS1Yud2InfmzGT7bNu8C41NRMBkUuybufO4X8fiYQwLKqe3m0iMpnqiRPL5LMwi2yGfY0ec6iHq5ft3H0JdE3WECNj34QDHyZx5k7FjD8ugMnX1uEQHNy7owC0T3wMA2PwEeXwyBXIdRWPDSAonoWuyrQJhfra2xbFiBZE6oWb21yVLZmHHfU8BANaniHKaOIPtaOdNbNxM3p4mg16+G9/KTG5f/fcv4uQJorQKCmG1m3Nk0ax5SCY4XsOgZ68ny3HsRg20tbNt9uwkmqetmfXrO3EYEQJ20LeLbdbawPa54l234cUCuY5676WeNXLoCABgccMU5Js5nvYfYgr3kEsU0Tvf+UF0TOd4OnGKmbPmNgnCbs5CbNzOftveyXlZfHodGqazrksWk8tnynv+FQCwbfej2NHNFPRhSfGObmYrXDolhBNSnh98jV7PRSuvBAAc7tsJZ5j1+ccPkQNo9xFmMLziwvegrYnZWDL4KQDg0En283fu+QmcBBGJz2xi3+yZw4xijm0jne8EAJgukX/trcxQp+UAXVRQmWbIyRrlFi04sm8q5KSr+EWKjpcJTGXGyxaKiArfj9o31XbouhiFGi9tF46HRDTk3eoa27bL9rtKb/5Ynvj/WXJ6JPXpMsi+2nKm+/z4uvurVRpfqqUazV7SH2v1ieh6DqApNL1WPT8BHQrNUz0ey3RXbfS5bnTZ5HlnOHfVdaFQCHbeqvitQu+WB6vztGlKts1CAY4o4WodKY9o0TyuInlWmU7v8QQpPIlS5iuyYisdWZXJHaWfn2mK+LHOsuXcQ7WyoY1GWr3Oevg4r9N1HRHRXedNI/q28wDR44cPHcDUWdQtWmzZI4PUBQpOFrPmUZ9as+ouAEBjHRG2Rzdtx3s+cDsvb+V3IynufYVUDt/52t0AgM/928oxy3VOGIRU2t5ILIygIdD6k1RCDTkUttc3Y3iEm3xBDiC6SbhuOBJAY4QKeqxIuPn1N7Bhrn3HtfjVOirFf/s3fw0ACAhhdX2xDauXMQ3wez9G+L7RIml6zTzqw+yMnEmNQHMlxMItmwNVg6zWNlVt/Cn/u/yz+rpaa8PZpiUf7xmVC0hpkeR3sojqgO4K2aHAblWYR8RwS4ucgjur4jmOt/CUZsgo7OVZ18ETt3KBH3V9VWryWq1X67uzXUTGuu+1IkjTIaEyZZV3qmL7vH+1sjFZHUZluBhTbdIcb3yUoLVivLFt2Ha1AWq0glodOua6rgexrA5j09zyDWZ0mar7stw4pZ5VEMLNUCBYRr5nqtryOYbuKd5FZUGSpdGBDXfsYVUqi2cLO3eMP0pqEgpq8FJ92raQ26n5HAjDEGPRkBg01q5dCwBYNGsGgj1co7fvpBFhzVtuAQB87W8/A3uIa7Qtho/8MNfxWDThPd9yFaSZ/zc0JmEVhAxaEVwLkXFDXQOSdTzoD0oq+rCQFodiESQSNJz0D/E30ylKnYNe+vKLVzFspb+fZTlx4gTqGniAPN7NQ+zy8y9EPsND4ZEj3JjrknQI9HcPwbG5ecejQvxn89nFwjB6BhmyldrGEKcZMxmiEo+FkUnTgJHJKKs4635g3yFAkiLMXcA6LFjAEJiTE0bQfTIrbcOD/vxFJILevfsIrrjyJgDAz3/KEKQ9OxmKd9FFy9AiisAzL/DAr8e4Zs+duxTLFrMdvvx1hjMtW34NAOBzn/kHfOUfadi59EqGzkyfy3To61/4JR64j2lOmxvoSClmXPT3sp9/8QspwwEaXhzHQTDMd3Yfp6Fg3z6Ok5UrV2D/XrZVfYL7dXMz+2HX7p0IaDR8KOLoQpb7bTQWQTRCg4TrKuJcjoHLL18FLch2fPBn4kyy2XbRqIvZM3nAP9TVyfYPsNwPPfpDZCVc/MCml1iWCWLguWQ+enp43b69vM/WWd7GpiAyFo04kZMcoytnLkTfIMdR3WQaatY+yz655/c/RVsL7w0UOI6zohgP9eY9ovqOdvZbVnSaVP+wBzM/corP6t9CY6tuhHEqyuQbEzo4PoaGlW5kY1BCHRubOAebVzAc/rFf/wQzYzQexmJsa30ajT63r1iGtY/RmLLHZL81zuRY37b/MdhFklwGTNblrbd+km3QrKGz+DQAoCHF+TOpI4oL5kwHAHznp/8HAPDmmz4NAFgRvAjpPQwX3NtHA00hRiNhuOFWrLmIIYh2gEa0xzcSHh+LB9AUZ7hfm4SAPbrhUQDA3eZPEWmgjtaznQar1RfTsXcqN4RjO5iu/tpG6njf/db/AgA4hobr3kZFu2iQUDugsY9cPYeoy/oXsjRQhuuEjgAuAhGuj+k814EgJHzLdWGnuJYl42zjghYeRR+ghKEflQfV8nOTWrsLBbPi/mAwWFpPJVTEc0AGDW8/e73krA9b/8MNIOeSM+iNLI7rwFH0AdXnLOijdFZ4Oqnt6c0yzUr3aVqZIaLaUe+UzXX1PnmyW2tcjHb0VyclKhdl7DAMA46rwk8lsYM43gzDKIUxVenPhmHA0CoNJmodKac2UVKhy1cd37wQqRrl9c6W0EYb4tyya0aF85U5fscw/pYbhGqtr2diXHst5metM6YpqAhDJV0KBFHfwL37igsZKjY4QP10YlsHUib30oOH+V1zCx1MgVgM7dO5/zU2SgiYQ0dJbEY9zAjfs38/988W2RcbovX43Kf/4rRlfyO4EnzxxRdffPHFF1988cUXX3zxxRdffCmTcwIhZIpHKxwOomjSRtWUoDcmnxUSTj2HN99Iz+hQip7YPiEuHRjIY8XcVQCAVbPfBAC4Zo3AumODeO97PgYAiIrH5mMfI3H0svAluOEaXt92IcsynKFFLqnFMWSTQDFqEPbsOrmzqp87DtJhLM8RRRt1XcWvr9C6OZ511a36S3ddwK32bvG3kG7DrSqLIvPVXLcsLbO6ptIyfTZlPtPvz/T3V6scJRnLun86l9jZldNxJNRJoWAcwBUGS63cEo9KhFCgCmpJ74WU1EPxlJWuGhmk3qG5sFRIkHR3BTJIocpqFn5sSGj137VSVaoQxGqEEQDYwoTuBnQvZadlqetZdh06HI+Qurruo0PG/rvIuDBcrdQXXj+JZ7pYLHoE8LEmrsONjURybLr3Rdzwyf8HADC9iaktl8wkymDi1A50ZSUteAu9H1aH+2qWAAAgAElEQVSEqIuhlI6ooGtUqFqJ1NBCMS9oT0F+JCTkqVAMolsIgZukDPkc7wsEgKwQ2irvVkjSO0ejMQwPMTxm27ZtAICIePVN2wIy9OyHY0QI9A9kkB7qlrryuyZBEXUf70WdIJH0AOdZOsdnt0+Koi9NdFIiwTCaFRcQzbPhyccRkzonhWC2p5fXzp+/ABmd+8uBLtY9K+nPJ0y2EWknymDr7sMAgNU3vZ91DyYxNEAERyzMNmpvZXjOqVPHceQ4nx8IcS7s3iPol/ZmZBroSYrU8ze7SLTOP33xC/ij24kQ6u2WdTvO9nH1ITQ3EMHROInoknkz5+Ib3/46AGDjiyScXrZyEQDgmXXrsepa7tOf/JN/BAB8+V+Y4rxv4DiaW9i2XUf57osvZLjQsaNHcayPSIxQVBDAQs5bzLtobmQZVGhgjpdi08a9WLCYaJCshOg0Crm05uTQIW2DAsOt8ieJrLn6vPl4aRPDzkydbdZ7ivcd3DOAOXMFedNNlFhrI/WL62+6GYeOMzzsl+tJTrxs/nJc3LwGAHD/d4iYunE622rpBUvwn78kiXJnWiDhPSRKbmmbhIUTSWJ8+CDHqCEoqVSfhQlTOP7MiewLM9gi7b8ElhCKDwx2AgAyg2yQwd40LruMykxM+nBehBDxxe+fiAEJGbv0PMLNf//U79lWrS34wEf+inX9EdshZRORtGv7BqxZxfDCA0Jg/p1n2f8tE+ZCz3DcTswSyfTLdS8h1sBnXSMhi5Ygn2KT5yL+EufHNVM5Fp7ay9CvSfOG0LqCyDR3mDrXkm5B9fU9hqUrifDplhD+RAPbI2oUgcMcM+kCEULPb2Do2bz5s9DdzTJPWclyfuELJL7f8/wOPL/9AQDA4xsY5nnjGo4lO5hBoMC5W5/gmlLIy3jUXC/BhiUhmopwNqwZCEooQHqEdTai4XKaUdZP6Vya64VnlPackoffSwjhjO27rSaAtSznDY1Meb3r/mq+70woIXx5+WLb9qgEKiVUj4HqM4n6zbSsGveVEEJq7lWHLGma7YXbV9Mr1EII1UIG1SJIri6LruteGUrflT2z6mynaFc0DSiaqv6V5zPSP6iyuN71UoIa54Py8606D1RGB7yaYaxnEl5WHi5Xfs59PeaV1y7QyqhUZHzIudi1TNgF7uGDA9QLspLs6tixY0hJqPpNt3GfNrilYKjnBBJN/Kcgu0pPP/f0Ka1TcGQn0bctOvWPtnbq5ul0Ebk4dZr6ccruI4R88cUXX3zxxRdffPHFF1988cUXX95gck4ghPI5emJaZ81FJEZPV0C+0/SgfIYA8ifiU3eRs2LxPHqot57cjZFTTAG7YA3TCGfr6RXqOXUCSPGZt7/5rQAA+xir/eTaPbjidqZ1O9TzHACgvp0eu2wqgoBFa2osRAucFaEltpB3oOuq6cRrVKRHNxIJIZMd8f4GABvkBwhqGjSLVkFDEBPBoFzjOIChiAfFw2k4KJqSilg8UY7wfei6jnCQbZSVOHZImQKGCVvIw8KhuNwnseea7hEgZgv0riuOlKARQ6HIsgdDwrOisf0zfTkEdHrJVeypMvqmkEejcBK4EuteUGmgHR1WUP1G62hQFytpWSrxUVxMYxhyx7LvOhjb+lvr21rpTkdfp5GBuUb5Tsf+p41R0rG+L4kieFPXK++fUbI8OxwDhrI+GxqC6gZxXdquDlvu9ez/UmlDdyqJkVFmwYbuIWdKhHBl3gu3KNereSkeC7goyth0FBlnoOS51BVvj9znCFePC9djF1d9Um7Z9zwNgZAUoDT+lWPDkbFmqFS+WgCacCpFo3yfaZooWMrjIml6bXW/5bWSStlpKo+spsPQq9Fe7qi/3TNAD70m8com6xIKhZAV4l09LJ5i5a0xAEMayxJyQQP1sKSjwtKHAek/CzogSBuHyw8+fst7AQA/MHtx36+JDpgxm970acv+DADQdaoPRZnrjoxDTSeKJZGMeYggU9aIgPKkp9OIhLm25IU4OpdTYymHWIy/mVJXTZIJZHMF6LpaU/isvKz1SEYQqmcdMibb42Q/kSATJrZAEw+MIWtm5/7dHtqo6+gpeSbHXDgYR0HS2btC4BiV9OxBhNFex+cPnuQ+8fQT5FRJZ0YwYw4RJoUC1+he4THq6elBQXhpcinen5G9oM90MZxhWVuaec1X/+0rAICFSy5HfRPRDyeOEGnS1kzemky+D+Ei26i9gzHneZfv3f/c0zCSfP7SqdzjZs8mJ1DniUMYGCRaZup08gwlBMW1bPpVOL6cZT6+jyib2254H+6/l4TYp06Se+XS84jemBq/EPf/jFw0TY3f5/OPCGF1VEOTxM3vEK6XJ55ZBwAYyA4hLyy+CYM8Lqk0+2NCSwJOkWiLRiHxjQXIDXTBwivR2sK/19xINNCm5+4DAEyb0YrhftbDMjguL1pNPp2nNryIVStJaH0yxT1yeIhzpLN7BK0z2W/J2WyzupZOAEDXyHr8+l6SLl84n6iZNXOuwaGj5KlZfgPLMn8+x2xdIIwrzieJ8ZyjbKtNB3htLhdGyxUklByJs79TIyzD6hXnYdO2HwAA5s4gZ8DyBSzvnkPHkM5yrpvDbLMLp5Pj52S4G6suuxEA8NHP/w0AYMsWosQ2bVyL/gEhjN5OdNp1V38QAPDFL38Fq2/ke/omEnm9WOPYaZ6go7ebKJ5nn6YHMgv2af/Jbtx2A3kYG4SseUF6B374ky8BAL7wFZKhD6U5F1/a9hyWXk9U2JLpnwIAXJvj+P/Sf30QBftzAICJ9SSsv/66jwIAuk8sQsQlIihgsb8uX3I9AOBA5w7ct+mrAIB4lGPg4pXkx+o6+hImt8wAAGx+gp7Yu776GbbxBXE0/5Ltd/e3vgwAaEqwP65YcQnSGhFFlqAWo2mOpePGECbJ3G2SNaxnQPa3BhuOjDXb5qdhuAhkOS+DEa4ftsX+MwJJOLLfZR3qXggIi7UWg+5UEr6W9utACUHgwVr5EdJ0mKgkcD1T0c8SofxasCGPv1+eJbfjWRZz3LK8ilUv8WCewUPdV0+fGP9145SpRhlqIUeq0S4lnXQ0lt5Dp9RoWEPTPNSKSlKhxHGcUVw+isiYvFrqb85ZXanMdtEb9wEhb7eKLJVZhl7XdKV/COLHcVE01TyrQsZoLkyzUrcuJ5QfpXdrJXTOeKnTa/0WDKrv5GDnKuS76zW0JfpOoai4yAIo2pVoIyXlZVAop3KCZs2ubGMvzU9ZNEip68siDzy9vrIuGKVfV5QGmhaq/KriXFKd1KX0OR7S6jVBCql6KQLusp8MD3oj7aOHYMi+0prkXgKLusPURcsxNEQ91sxzT8ikhCeoPg7b4vWFPkkyMSyI74TjobhVZETBZv9F66MwBVUMVLVnmfgIIV988cUXX3zxxRdffPHFF1988cWXN5icEwghJ0crWMj2MrwjWMdINy89tR5EwBD+hh56SGOXkrcgkk+ipY2xcnmdzxo5QatZY2wSBtP0Lh44cAwA8KY7yBu05OLVMCO0pDUmmOHlwD7G8k9snYq4xFvmxTtpOvSoGoEQChITH0/Q2myJxTFj5WBIRpSCWChDklLWcQ3YFi2tEeF/GMlJ5jLNgRakR1QX1nDNDCESqoxDVdkA8/m8ZwGNyPvSObkfAQRDEe86oJSVxbIdD9kTVUgCsSaadh6hcEDuU54syTwUTHpoLUcyAOXy5LKIxOsxMsD3eKnOAyyTbVqwHWHEl5TNZrHEAfJqyR8i5vrVyPg2+pnKRutWfACOhwjy0ChelUfHGLvlaeA9r0mJx6dkpT87m3ApTrZU92oOnwoeHi9FpfJ4Vno+K69XzymLv1YeT+8aG5qr5oT0A0ocRrogfAy5z9Jd6MojUpZSvnRf5W+eaON7L85EXsuxaYWkDnYeiTDREznJ2BVPcg3N5LIwxUNUJ8iW7uwgIpINyhL3hRPlhNSKgJHlHM0brOOU5fSyz9s9E9t2M8vSru1bAADvfTszAxWyOdTXsQzplGTfUalaQw5SaVkvIlyb6iT1ezqd9hCWij8jleK18UjcQxZpGvsyIAHVhuF63EGKhyoUkrVwOIuAh7hkveJRIgvq6xpgSgYytT46lo2GZGV0tTeerRL/QIkzQHF1OVDJOmLCUaeLJ9F1Asim1X2CeJUU4v19KTRMIBJm0iR+FgQVFI43ImIyQ1SxwDLXhVn3BXMmYeM6Zmeq0/jbu9/OFN2dfXvwy1/+EABw0VVEazzy1JMAgCO9fbjz2nd7dQWAw4fYj+lsCnlp/5YJRBs9/PgzAICPfOCd6DpBNE93F9El5126Al/4278DALzvw0zd/tD95Mm5+7s/xRWrybczcSL31GyWGbgsO4NQgG28ZTNRMvt3kiMpmysgHpL2FxRtRBBTxbyFgng/1X4YlM+//+I/4lvf+j4AYNNG8sFEA+Te2bf7KNrbiIZyC/ScLVxAhNCxzl14aj2RXIvmkq8mFGCbP//8bhzY3Mn2XsY9Py6etj1b12LpMu7PzR1ETj218R4MjhBVNpIlR5GtE2E0a0YWc85nPRYuY5as0Eaiaw4fBNavJwpo0kQinDsmcGzvP/gjHD1ApM7QcY6ny5exPac1tOJXTxDRvHAWuZVc0WOuvPUK2HHOnb/7V6Jt7n+OfTOjbgKWTGFZ1q37FQDggbWCjG6agice/TkAoCnM/touWdeK2QSeeYC8O7A5v5Ytvpxly2cQk8xlq9dwHZjVuBGPPs8x+v2vE6XU3k40YXN4Dp7acg8AoP+iF/kMmd83X3k9Du1itrreoZ8AADbtoK42bdp8BF22ezJJVLfWyrK8uPcnaJ5GxNPSeUQGvfdOZkF74qmncKSL4/wD71/N658j79WkKctx27uZBS1tEW3et5ftYc+Zi0hMuJSEFy3cRB2z68F78fUdHGt3fVCyp7WwPdxUEFmpT7SB9ztmBtl6zlVLdCcjwDmvmxqKGa4l8SjRc1Hx+I8MZKDHOB48T72HTgBsp3KfVR74ouMgEPB9vb68yuJq40KJtCp9tDbChZ8KIa5pmsc/WtJJ1VPGChOo5Las9Z7y7LcA9UiFDPJ+E7SMY8M7CavrTYV2dyzv+YbohgpZxOJVZcZVPDxubT24vNzlZR8PFVSrXpWi0E0KbVTKblbi+oTUS9rdslCwVB0r9dhAoIQ+rNbpdV2Hhsq6emd0uKOQgkqX59Va1W9eSMB4NR6FvldRCW5ZGUrtV0Ihvdy2/UOJIzB83eB+pulAsom6sQvqqWrfCBhRDEkW1iHJ4Dt7tqDB81lI0BAMabJwVM5ZsBE0RFceByF0ThiEBjZTMWicnICTZKP05+RAIRC+lmgQ0YAMzhg79qXDJHfsyo1g4XQeWEZcOVgEqCjZbhCRqBgkimyIE0NUmPQ2E/uGCKd+6p67AQCP/Joki3/+x9/Cm28hoVNXZhMAIBYiHLmYz8EVI08hqwwnQmoaiMA2FUmrGHOC/K1omTDCLIMZZP0iMRpJwmEDjoRzFMQAFQmE4UiITFaMWoGgLteHvUWrUFRpdvnsbM7xCDkDIYEqh9hmxVzRi8ey7MqFwLJNhAWurLtq8RRFJBDyYOohMSTVCXw/n88jEafCo8LAMmJQCodCXoiZLe8LSSiRO+5heywZI4TrNSL8PRMS6+prXg0DUQlaW7bAqfVTkTd7xN1O2WajVmfXM6JUp4p/VUQZdCqMUfxUm4il0mGWQVCrN8daqTVrfle1eQVdrWQIUpuxbBQGdNje3xS9bGPRnMrNzoHmhVuW8KhnMzZffwnIyp8p5OEqhUDWTHOIa0YIGowQQ1hSErpU1xbGQ/cytGbuggUAgPlLSPbqjFgw5LqghF09vJMEs70Dpzyr/XA/QzA0h2tFWA8iPSDQ1iDvC0ZpALGsIpJJHoLUmqYIgoPBoGd4VsafqISgaq7mrV3qPk3qHDAMWKLAWYogUba0WLQOrhii1fgPiwEsGo16xqV83vGeHTIqod1qfBTd0vg1FHusMswXc55RKpGgQSMY4LoYjTjo7yvVkWUOy2fACwepk/0pn+H639OdQUMDD9lDp44DANon0sjRe/wlvCQhWDObuOc5kn60a38vJk+ZBgDYsoF7lpHiO5qdOB5/kgadWJBjYdE8GkISyWZs3bIPADBpMg00dpjP3HVsJyJhvvtf/o1hOU4YuFKSMVx7JcN21j1NguWvfO2L+OK/MKTw5D46YP73XzN86NDhPWhtoVHjwQfWAgCGhWhZswy4CoouRshohErR0GAa3/rG91jHLoYsrRWjWCQawwUXkjT5909x/7twEfdtBI6gqYVl376Vjp4vfZl1OG9lDBevIeHx8y/QQJCI9UkbJJEaYbkmtdEg9NCDHP8TJ8yBHRQnTpRtBs1EX4bw7UiQ13f30zGVaDBh59gHw/3st5ZmDp45s6fj4D5ZwwzOqYLO0KwpU2xMbKWit3sLdZqdx1imaGwyIvU0HixZKQTSJvvr2aeeQ8ccluFYN8t5mYQUxoypCFtsoxViSHr+Seo/DWjApHl0sG09yL4c7pzM+0KtmDeb+3s8xjl8zRXvBAB86ANLMCKOoRN91I/al34MV4W4pvQe/h0AYGSYbfvOt78dew8xdG7rVupa1hGOueL8EFZczHF14CSNzaEs22fg5EasH+CYmTuTYW6Nw2yfWH0WLY1ibNMuAQA8tZkGm+7cS3jr20hwHQ1zDm4c7gQA2PZuzJjKtrnzrk8AAP71a38KANh3Yi+WLKBh082x7rmskHTPX4K53Wyje378RQBA+pYvAABWTV+Fhow4CTPUy/JxF4ZA/nVR8F1DQq+1KJJiODIgxmkJQ4nHNOShqALAMlslBV8Zp4NBpU9BLtKA1zntvC9vEFGDrMowVK5bjseu4NkAylJza1W/lUfNjYpIK/nuyp6pvnC8p6m9Wxl4XFfz9n/LUg5tIX3H6PTlllAcaE6JRFkpk6VU6qV6OFV6bfnf1bquURbqNp6xohbBeC0ntC3nwFJYl7pGQ1GMXiq0zbKUvmOj1JCiT4nRWYEHgFKoeyn8Soepzh2lvPOejGeGrj4XnYlDnRnpq8JfKygllBFMOd7K27OyNK95yNhZincO1oSWAIZXdKUvxj1HgIGmejqumlu43+Zy3Dcs2/Acowr0cVxoDHQdSEhilLr42GXx3Qi++OKLL7744osvvvjiiy+++OKLL28wOScQQvt2EtJrmmHoAuGf3EQz1sgALVwhy0QmTa9bfRO9W4kmWtb+7pOfw6XnbwYArLiakPBElN4tLWUhIpa0xqCkXZN0yeHWbfjKF/8CAPD0bwk5vmoJPW6pod042U3PVahuIQAgnybxZl1dHSxLEDiKyEtIm8286XnRiuI5NoU0K9kSgx6lZyiVo8ds2y5C5wdPpnHNZUzXGzIVWXQG4TAthCptsZeq2bYRlrAwZcU15TMcjnnfKa+Y+h9uyZLsiOdShZwhFPLIx1S9HFesz8EAQoJOcpVXXgy30bCOrBBpuzKkdEEpBHQblklPmTKxK2CSLtbPlyN6Dav0aypVSJGSZfk00NmzJPwby2quayTUA+BFKrllbJJu1fs03YWmIK5QJHzqWfoZoYXOlNROldcW5I1e5Z0xNQ2OpiC85qj7xhPHS9OoiMgFJaKXEU6XeWzkrlHPLy+trYig5Uvb0UoRemc0rk6PHnrdPBCSgr2hoQEDEjKalPkckrnrmkW4EnKUmEjUxbMvPIwn1tPj/uxzGwAAq68i2uOO225HV5qw1GcefwwAsG47URSDW5+Dq7rQZDu0NnJtGhlKobGez1frlPJ+mGYKra2t8reERIwIKZ7reuTpKpRQeamKWbOEFtKU907I8VwXIUdIIBW6Ugirw0bQ86orxI8p13R3n4Sms22CIa6PdclIyftejV7THA8ZFAhUIslsBFHMcs8aGhyRurPtAkYQOYV0DaoQM0kqYJgws6zXiM52CAWICipkgZEA4cALV6g6MEmCmcvgwgVsx95OhhR997vfBgDMWDIL85YxbXenpHVfcD5Dpjq3bUFvHxE7i2aTgHjPLqJDbG0IDW3cI1MpvvfCVQy9uf/Be3Hb5XcCABKNgtgpZDBhCr1Uf/mZvwYA7NpFBM3v1j6IW7Z9AABw9XIW/mgXy2laIcyZy3Cfiy4houO3v2U4UygS9dBdhsZ+yqQ4nmdOnYM33UDkyDe++V0AwNtuY5misSiGJRRx2TLu/Z/7y78FAPz///HXuP8hhie97S4SGP/w10SeTJ09G+kCx0UmIugeSS/eFpuD+iaip9auYwjR4DDRX/H6CWiXOfTc00QoT5wBBCQc+tBBIrpSIyx70FkI2yY6JJtj+8+dSmRXzjqMtskMQwoJurenR8IpQzNg1DEN7eo3U1c4sp1jdueO7XjfO0lofbiTiKmOWfz/go4Z2PAsQ+E++5f/DgAYCrNM5lAA+X4J24yyTHd9hKGF2Wwfug5tBADYfRzbFyxnP2Ttw3CkjfQ4x/GLh0kqfmCgE7bLsre3cuzcs/k7yHTzWTdeSJRNLEtC6E/d/RdYdS1Jr5OrGMJ13ls+DgB45MHfYNFk9mHrVI6vx5/4LQBgzczVmDBAlNfezf8AAFgwnyGQkXAKk6dwDZo/m8TYx/uIQsrmG9A9zHnZ1sZnXnH9zWyXE/0YcYalvTkW3nUXCfLt+nqIKgp7RBCG7Zy7jW3zcN5S1kvfzpDMf/8cyzLw0b/EJWveAgCIO5xT9XkNAriD5dBLq4nqUwRQLLK980IIHxBIf1gzIOpfCVnhIR50uOINVoiiUiIOF9pZ6B/nitf8XCmHL+NIDdT5eLrTKFL0QBn5sMeOUEUQrI1+qA6UhSxViuOWdEFPPZfDRr6Y996t9ICiJIoIBHRA6StVqBwVGg6M1jNd1y0L16yqb0W7VP5o2yXE8ahU9rrhFb7UZiXCapUQpXyOBKq4N5TencsXPT3MFsSgVRZm6iWlqZEafiz6B8uySuvMqDOK5mF5jKqkNfwHY4g7qt0r14BqfbsUHqaKrJBB5eTXo85E5+i6omvcHIqif+plCYRUO+pCk2BaDoKS3CovyaQSCbm/oKEgxNF1CeooSi+Jqg3odGV5ZVXxxRdffPHFF1988cUXX3zxxRdffPHlv5ucEwihr/z8+wCAiy5dik/fzNTwfZ1EBu2I0VJ2omcYqycT9ZPpomVs8nlECs2Y0Yyf/uBhAMCvHubnqsuYonXxjCmwI0TshISUMBYhr0Ah0otD2+jJawnRa1oXbZH3bcaJPnJqTG6m9y0snDnZogmVjn1gkJ6zqHgXG9risCR9aMSgxa8vw/+3bd2N5158Qv4mb9JAN4lE424CfYfp1frwe+gxC2txmOL9yWSED0TSOluOBlv4L0JCUJ3PC6l00PGQLcoarpA1RiCCnKCFLDH1FrN8NqB7CI641Me0FLLIRDhCz5Vt8TdLPP1mMYt4QlAC8qxsju0SjAShKWuzmI+TSXq401lJEV0m5cRrZ2vRHS8e9/WSs7ZGi1Xb8LiSxFLsOKXUhbqKA1ZWf9vj2PG4TvRSHKn6rEQ3nZ0HcTwiPyUKAabZpdhfL6a7Bj9Qrfd4n4qXyCPLLutHgYAEVTpQhezQ9JI3SNrFsl04Hr+SeGcUubSml/EfjY7bLnuhKuE4v72+NnZbEQjmimgQRrmhYa6d9YLoMJ0iUia/OySEtg8/8iDe98ckBP7l95km/IVHHwUAvPO227GpaycA4Cf3k9w1fYIoAzeTQl7SgSdiRE2MSIrMYiGHllautTmL60AqQ7RMNBxBJsP1VyESFQoom8l7CBqVYj6dFvQQ7DL+Hsh3yvtU4o3SjUrPUjgSQDqVk+vVNieINctGcwvLnkzyfUNDQxjq5fUKFVnOZaW8Z+UklQCg6Toa6lulPHx3OqXqGUFByJqFHxlN9XxfJKojMyTvsTl+U2leG63TUVcvcf1BlqG5kW2WTQ+iaQJ5gmZN5v63o5NtNZLKwsjxWZ0HuZf09BNd0hC04BS5lp88wTHQ1sJn1zXmEZbMp9EQ+/Lzf/F5AMDffP6rWHoe99J9R4mijSfr0NPHZ119A9N0X3cDUR/f+9VP8M9f+ScAwOU/I0lxUGLXp89dCAF0IdnMF+aE3DvR0OihWMPi8VQomzfdfAt0ab/rb+R7EsI1mMrmsXjpEgDA+me4d+/YtwsAsPLSK/H48w8CALbvIcLN0DlHNjx9AFaB+/JQlvvZSJjt/+hLD6O1keN4ymx63K667lIAQD7fhIPH9gMAli4kd9HJU0dRAOfXnJnUUcwM0SjHDvdh2XL2Uz5HBNjRE5xbjmMhECUv0NQ6opanzGK9BgeP48BujqNo/ZC0H/thwuQ25IvsC0fnb4f7iZC+4cY3wwrzWf/+jT8HALzQybl759veis98gojo5zYQFfjAYyS1ntiRRSjE+k+fRwTT9KlESQ2lUjjYTXROfzfL29tL8ux4ogDLYDlTU8npc03rSjyRIbLnwBEirGZOYhr5P1p6MzY99BsAQMtU6jJPdBIlndXm4aYg+YF27SI6WwuRp6kheguuXvL/AgAefYH60fp13+FvdSEkWtmXWw4RxR0OXAkAGBiKI2WT8LtZSDsV11rjjGZYIELIEJ2mKcDUv1kXEHA1kpM5r3f2sywP//oJ9B4iMmjxZI7jmy5iWz2/8dvY1U/E+5++jUTcKRt49CDXWMdhnQNB6qA6oli2gOOoPsQyZEe4LkQjQE68wGq9CQsXmaHB0w2LouOpjT4YDglHyPhyLnjMz4Uy+PLKxXXH5ojhx3jIcsVJU43O0RGoGh6GoZfpoKV3q2eV31v+aZs28gWJrrAq54amGV56ec1LTiFRFGU6QC3UjPd7ld5Y3hLVqP9y1NFo4uOSDlpqz/KnVZ9jtFFof8UXVCxaKCjUs4oqUDoyXLjCPVTiDBLOxGLe03Oq32vbDhCoJOcuvwm5EoAAACAASURBVKK6f1/pGU7XtQrUT3k5dV0v6WHee8rPF6PRVOeiaMLT61qi50Y0qPmi+jIgbRAI6ChIJEBqkHpI5yCRxJOnTUVA+mbTxucBAOevpN6SHuz1gl3iza1jlsVHCPniiy+++OKLL7744osvvvjiiy++vMHknEAIdR6n16nznrW4UlKoXbGE/APZOG1WuVwXTnUf4Q3L6RFsm8DfVq5cgHqbsfhOHT0wjz7+YwBA5kQBkVamgA1q9OI1JOkl3L17GAFtOgAg2UZrZ6pIb1LXqRg2byUvQk7SwU/pIO9BoiHhmaeTjcKVE6a1dVvnc3hhM71HBw8zY8jOTfRY9vUNoD5Jz2E8RI6k7KBkrlg4D0eP0rP0b9/43wCAW2/7CKZMJQfEqR6W3ctoU1ePkRF6xRS/RUg4hXK5NGIxPt/Mi4VRsv44sGFCUlA3yzU2PYNa0fDi2dV7FADB0W1YprCZm7RCBgP0/OaLAaSEK0NAQ2iaQA+Y4ZqwxfLZP0BP1mAvyx1K6OMid2qx7I8lp7vm7K3EY5dhXJb8s85SVWkNVykuNTgeh5B6b9FzjTiwVPypruKcyzMnqPJ6hXvFbTteZrVSpgb+77iOh3QaP1YYFXUob9ZiFULI1QDNrvSgKJ4iaA4s+a0gZTDtspwKKn7bSxFalrHBy6KhvnnlaefPRM52fFqSOSNUdOGMKK4Mzr1jJj0Hx4eO4jc/vhsAYPaQz+W8VVdjwzpZp3YRsVAYJkJm596t6BL+tN5TRCI0pjj300UNQZ2edj1Ej5viG2trb0L/INdyW+N6UF8v6ZV1w/Nom0UiClQMv2EEvd9UrLuKi9f0gLcWKT4pQzz8luWgKKgXhc5R3i6rkIct8dQKUafJZzFnoZjjeihZ4THQOwDHCkpZxdMu2XvKPVTKc6Yc8JrjoiCetniC7RKVdNWOY6FQEGSnxHCHBXYQCjvos4gO1d1muY/vravPIij12LaRCJCZs4nA7JgRRX+eXC27JGV7J0ERiJ/qQTzLsna0k0tF7XXDp/bj4x9hiuz7fklenUSUvxnaKbS3El1z/2++BwCYPpmI2YGeDegfPh8A0NrCa/K5IhzJiFG02Y6f/gumF398w/NYL0izn91PhNCdt5Dv53Bn1psxc+YRxRKJl3jwdMkuFgyzXEWL6I3l562ADA80t5OXxZE9LFPIYc7c6QCAN930XgBAKs/xtXHLDmRybP+DBznGr1p1KwBg6551SAsSQ0sR5aEl+JJPfuh9SEv2s8EUkbzZFFFVxzpdFP8ve+8dINdZnY0/997pZdts79Kqd0sucrfcCzbFQAihmC+BJPz4QgKEwEcIIQQIkOQDEvIRQuiYZoJNcS+yJTfZklWtLm1fbS8zO/WW3x/Pee+d2SJsY8Akc/6ZnZ1b3v6e95znPEf2upPj1EfMfAGhMPuw5yTnnKHzc2lnE3p7+KyTx4nUWbmGKKKOpVUYnyaSa2CQ+7vmZ3/HqwJYtZlIsKF+jpkDZ+gRbK6tRSRAnQYy99JZzv3mpg6017Ntn3/8bwEAr1m7GQDQEMzgZ/cQ8efk+MxNq4iIGRx8ED5ZN0xBo+USTBXv90WR72UdWgS5hjGOx+7jTyHYyN+G/UTQzCQqEI3xubsPMLNdfVsnAOCKrX+N89eSK3HHDqZ870sxI15dezfe97e875pLbwMArKkjd9RPvvev8NVyPnetpG54Yxuzyd378D8hLdleszqRYIl6tpUWbMU3vk2+o/XnEKWUzBIF57MHsbSFfEJtNcywVp/gGK+oqUXW5PsOH9oHAPijd/4JAODyG6/Bbbcyrf2xvRwfJ8e5luYKA4j3ss4PPUSuo+OjU9jzEBFCLW3Ub6sSRIv1DQ2i90Ki3m66luioygr2e7JQQMRFZct8k/XO0HQ3e2tQwQ+LkAdn25le6R7zsvwWxE1h++LGRjE6x33UImnki6/PCqrNtm0XlaP0PoWGMOB4SB/1DA3zEDHFZVgcpaR5eoTKVOtCjjU3U5cSlwPH9upmzUXn2PPf56J/NHvePFN6RDHqxfufh83w0sarMhXNa+HQtCyPg8gW5HPeVFEhokuZtosMmtseBjSEQtx71acqi2maRRnRSlE2lmUhK1na5va949jQnbmdXtwGpWieYjTV3P+p9jAMzc3QOl/mc4UWA5v03xG4S26W+0wwqpBaJkwV/eBXJHL80DS4GXL7TncDAA4dIcI2l8vDkOvXrdsIABgZoL4f0MNIiQ4fTSxeFu03HUqzkGzpWuYAQK85AzNFJble0kAvX0aDyNZzz0VCDB66HBaWb+Vg/dpPv4ltm6jsJrp4/Vvf8r/4nFg1xuVgUEX7BWybCk91dRDV1QLFEsWqqYmKdEWFjoY6Kj3T45yEup+Hm2uuuwrJjCjzQbbf4RM0+nztW9+ALmnVZyTt6NpOKmiBQAg5SS3c1UHFICWEhwP9PWhrYQrUNWtJvBkPtuBVN5OgsLmZ9UqLgUfTA/MgjJqa/bqDQECUuqykT5Qy5cwUIlWcYEMC+x6bIgFpY6gFLU0s1/iEkKzZnFXhSMA93CjyKzjyWywAWw5PpsH3HTlGJe++X9yBi85lWMElooynZjlop4UQt7gOHqTRk5KFVVss7fyvR15I2vm5UgxdfdHvE4OQLi1gSJydHxZ8bpiKMrhI6uxCHmau1CBk6IAh6bn9bjptvkPXNLfvvIW3aGPS5oyrBQje9Bew2i5k9FGkeMX3n81IpH7LqM3HVBBKHUEZ08ogWnxwV6GSs3n+Zpqmy6rtbm4ujNYLH3MhwPoL6b+F1O4Xtwv9quPYDSl0dNeIkpKDTLSJC95HPvo+7N3BNN2rhMS2Y+l5+PpX/x0A8KEPcO3sH+aB9ckD+1DdyF0jOUwD+UwvCXFtf9Ttk7SQ/kajKi1zCrkcD+MNdTzwJIXwF4bu9k9O1jAVWhWLxVzyQ9UeyriiOyj6TcazKAimabthRmobM3SPRD8UYv3nku8nU1MwfLxBKUNwdJjyjJAcwpRxSXMsV5lUYa+O7SlkBbkxEOT/sjm2i1mw4TisswpNC4T4gHRmEsEo95OBXoZ1ReW9lTU+N5lAazP3jm4JU6pMWKiLcNyNDUqIms59amw6hWBWjMWS5GDtRTTmnDqxF/WVPOxGfTSqbLv8SgDAY0/8BFuvYJrw/fsZehQRsu2lHc249uZPAgA2rmEIdSFbgGVL6l4h8W5v4Xj51Mc/g8/8PR0a572Oz//x95iiu5AGokJuvmsXycpvvo57QjAQha7zWX43lJBtfudP78EFW+mMmZrifhmtkKQTMzMIhGhACvs5/k/1chwfO3EYn/mHPwUA6LO8pjXB0J6jPQ/i8qtInP30Hs4N1TdbNl2Jd7+T4UnfuZ2E3bpBo8eBg7shZ3Kc7KNhobVdx8ykrClZ9nPGopXulldfCNtkWcdGOC4mpli+eOWkWw+/kBpPTagQ11l0rVPGRM6FEwdYh5gvj846hhntOUDD056eIwCAdRdchdve/C4AwM5HaJDbc4zGrJnp04gY7KeKGHUNI8yy6BjGVRcwzKr/OImq+02GIuYno6ivZttUCqHlzAiNR82tMYymOWaqq2jMydojqNVIuvzcsyS4XnsVr9/dN4mVq24FAFywmkaYB//r39g+s91Y3sz5uHYpjTeOwzC5g0e+jfVLqJv1nOQ4Fs5mrFvSgJPHjgIAGupYv4EzDFUbmRrE6o0cO8cH2A7TQgJfGcqgo+G1bAcxCB3qowH8Vde+Cf1HaKwLpjm/z2vn+O+bOoXn+mkkuuXVpDjY//A9AIAdD/0LJhzqU/VtdFxWVbRiS+2lLNcIn9/Yxfl1ZOBJjIghf+my6wEAW85l+7S2rERQDoWG7NeOJP2wCjZ8spf6hHDUlEUwbxYQ8M0Ns/DklWQQ+k2X5ZVU97PKXJbiheQlJi75pe97Cc89m0HIcTxHivqcVVQUtqcrB0Q/CAa4jgd9fpcmYaFQLCV5Od/lc+a8ECK156fztruHe0TEhlsGN5zLWFwHdZ2zRY7gubqroYw5RQahucYfn8/T1VzHVzEZttturteJxbRtFPIqHEyMP3kLWUclGlL3Sf00X1FZ1bkR7nur45GFy4DFdH/+Py3rp9KV1TmkOLxuQUfvnBAu95oi57nqP6999Hmk2cVl8c7A88vgGlPOUqbflhTPl4khOvbgkz22tgLpDHWhUIh6dEDCnQHb6+gsx/PRw7Q9mLAQFL25okpS09dwny9kTeRzKrlVaNGG+B2xoZWlLGUpS1nKUpaylKUsZSlLWcpSlrKU5eWSVwRCqLOW8VoTAR/iEVr1QgL1Hp4kPH7L+g5cvqoTANDVRC/33n7CrHvSR3Gml16WTVfT03PvTyUMYjILu5LPdDK0qKV1eupCVgWsLL1gCnLsC9I7uXpVPWqE0PqqC+it2riGMO3Dxw7gWDetcukCvdxr1jPd7/CYhaeepLcqk5E0un4+O1EdRyhIG9zQID2I522lB3dqZhp79zPEbMt55/N9S1ciK1a9W255IwCgrZOe3JnpWYTDEvIlxJw+Madr8CGdFpRAjF5oR8zbyewYwtUsz0f/nilWt+98AABw/fmvwoc/+Bm2jY/euKTA6m1YMHy0WtbU0gqpiWV3OnsGT+1lmtm7H/k5AGBiguijE88/h+oQrZUXbqU37e3vILHlrJBsAi8cIaTQMS9WXm7r8G8KIeQTa7BPs6Ccfm4abUm/nc/nUcgp9JAgi4oQQj5dwTAhv+m/VoTQPK9Ckd35bCkuF0IIqd9mVfiZIIT8BuCX1KWKVLoYIZQTWGtOpSU3TWBOyk5F3mdDnxf+d3Yn2dkA+S/Mxv5yjceIkLjnq2M4Y0nYphCRDh8iauDxu+9Cop5hMXuPMe22lYzh1dcRIdEzzLWoN00UxIMP3o8ag556v7RRXuNYm5pKuwgdj7DQ6zefi3Zm+9clOPdHpoahCwozGmVZZoWcOpvNu+1ROTdtvVUMX+b9auzYtu2WRf0vmxViaMtw05irtVB5CAMBHYavlCTaZwSRkfTgYQmlUojLbDrlER4Lqb8i99dhwJTxEAqryisvnoV8VvWz8hzKuMynYBuVUmdBlygYnBlFzmRfNC3lmG1q7gQA7HisF+dt5bp/+fkMNbnzfoarpDPTaDbYfn4/UUPHhogAqamIAHEJK6plONjVV3Bfm82NIZkhtHjlUu4vyTGiXyqCQay/kiE+y5vXSPkcWAbbuSD1qojwfaPdQ/iDVxNpsj/Dd3/5Cz8CALz2puthCsp0VMbc9dcwZCczmwdkjOWlPaurSH74xJO7EBAi8ryELedlfIVDXuKFqKBUf3wnEUmhqA+f/RTDfP7js0xvb06xvAe7f4jJJNvj5zsYGp7Mcc+qrK7Bv32RiI/pSZblVM92AMBdP/06Th1jW7U0sR2fP/4IDF0QZBnqCNNTHNtveOs6zGaJGBkfZH/PphlmZGarseFc7s/w8xpFqp7P5zEu6d+7ljBE6lSGekVHtQNtnGicg5KK/tF9RORGqpdgZTN1ERSIKjNHGcrlSxjom2S7GeC8WbucYyke8mMmLXOwiWVKD1E3itdUwZCEGsk058GMEH5vXLsKI31E3mi2JLWojaJV4NhBm/c9tZv6WH8yi/5BluHqCy/nM8dZdgfL8JGP/xUA4GFJDqLLGlEd1XDkSDcAYGiYZWjv5PgI1zTgwnOuAABcft6bAQB7d38DAPDQw3cjXsO2OjpBGoGO1Zwjp4Z9yAsSOmUwjK+uk203fnIIr7mZIYhXXXQt3yOQ+3ufegCnh7mOdnQxHM9vU5dJDezFk/dyvJ+7kfOmY+UVWH8+9byKKrZxMse63P5fn0BBl+Qafo4dAcGhsbURa5cSQdfSwN9q41xPHcuBKeg8QxBCuk+FdQD6IqTSryQvOVBGCC0qv02E0Mv8fNM0XcS2+sxK4gbN8ULAg0KIG1Jh1YbPS6SiiuRY0CV6ROlq2WxWPvPzEELu+yzN3es9XZfPtArmPBSPm4xH1xdFsOuYryO774c1T7cuRggtGjJWhBDy9EyVkMVCVqIqlE6TyxZgBwVBY6myFD3TVqFUslaI3hMO+FElupaShXT6eVEomgYzX4r2UuFNlmWV0C/MFYUQmkcEfRaEkN9vLHrWKEZoqXIWI4YUhUqxvFLmf7HdZbiH+sD2nQ8CAF79+tfCkXTxQ7IHtzYycsdvBJCRkPiZftpGagXNf/T0cTS0EjZbLUjZjOzXs8ks8nlBc7ctLSOEylKWspSlLGUpS1nKUpaylKUsZSlLWcpCeUWQSqeEgFRLz8IRPoR4PREqKaGZGepNor+CnpSwRsvYyuWMJV8TPQ8/GWOs+uRRGr+uvITeu3vu2wuMEVHUWkkvUEzIQ5yADqOOHqzqMK2Jy+toYXvk4OPwr2NZ7nzwTgBAYzu9T1NIYTBDboxAgNc/+ji9cJdetAXbtpH3ISXerdFhckTMzs66SI5kihbyQ4cZU37u+Vuw/zA9lI89To9lXaAJS5Yx9v4nP2GK1dfcTH6A1o51ODNJD2y4gqimnPBnGDUBxDTWKyhW40lJwZxoq8XnvvohAMDXv0Nk0MYu1mHPzvvwQ+Eqescff4xt1UArshE10D9OD+X+448BAE508/uDT/wYpw6y7BGLXtCgpFeNOglA57sfE69dPkIyzre/9t/h2LRk5gulaCrTnEVQyEVNIb12kIFu06OpZCGLr73Ab5pLRiz/U8TFGuYF3ypCOV3zaN3O9r65ommaG+usK89BieFckSDDK4PcpysvhkLzaAoaY7lkc7p4FRyb/V2wLdgSR2zYLlEQNPUChYzxWJWLvB1zbMKaDdWCXlU9z4MmKA/dkJbRWAYNBky7lJvHKoF5CZGfkOEpuJOjOyhI+zuOQh8pJI8B21LlE8I1eW/ecVzuFpdI21Jx5hZsVRaXUbuIU8G1zs/nMXph8sLRUQvJQuSHAVvaI+jDTIHrhl/qFZNY6EI6C1Px6Mh6Vajj+nD66Gnse5qe9q2bLgAAVAT425+++//ggZ8xDfRrzicCxFffjGveeAUA4JrreP30aa5FNZE6dFZxrXTAsjx5hjwhQSsGW9YZR3irHJkloXAYZk5567imj4EkufD54UjZVSp6RYgbioRdL7ep0rcqb7fjQzgadtuNzxZye8vGrHAWKa+Y4t6BCWQELWSapd4xTdddckY1dnTDQkg4aGZnuc9YBUH+BCPQxEsVCsfkmUKoHQC0gnAqCTmg4iCKxcIoBPnuUUkKoOoZDFTAFqSq7ghSqI6f+XwaNYLwGT/Dvc4RIunWWj9OHWD5GuP0KMVsQgp6B2egL20CALz+DW8AAER3EbEZnK1EfR3b6BfPcf1+XdNbAQCdznL88xeYKl6/mO/dtGkDAGDl1g50NpJ3x7TZ1o6dcKeA3+DelppiO3atasKFN10HADjwpX8BAHzzq18EANx0y/Vw/OzLqhqOr0SCY7Rn9jScgrSxeDxVHw1PjqO5kQihoCHrf55jIJfJQxdUcSHJfnjdHxD59O9f/hwqqjoBACcDbOsdg3sBADUVTTBlnQ/Nsl7rNpPz6KF9j+MXe0i8bcr6c/t/khS458BxXLyJKJJ0huM4NxuBLutbOMS+iCeoHxzcfwhVVUTcTE9LqvYouWjODA7g2T1EBi1dxjoXROGpTgSxtIvjaPcepoifHOc1rdfWwagkp1eskuOxM0QUzPT4BM7YjwAAzrv4CpZpmSD68gX4DHIORULkFti0ljw3MM4gnyMv4uQI63Kim2UZfHovVm9qAQDoYY6hUz3kGQoYY6gMyJ6fJ2Jnsn8cYydZ9pVr+duqdbz/8Hf3YHklUdzP7yIi2vRzPG85tw2HjnE8bd72DgBA91GisaYHj6FnlNfbghLbvJnj997d9+PyFup4+wV9teIa8vDUrrwZjcJ/9E//j+1/7CARTfbUFIaEiNVJE+EcPcP3HToVxNvfQlSOIWvYnuMki37yyc/AFvS47md/r2y/iu0RiqFzIxFdvjbqNvGuTqRmhDQ8xPu+/IO/AwAcPP09rG3nfJkYYP30LMf47HgB9+/9Dut6PlHjF28l+k4rBBAVXgm/zMWpNMe4FgE00Q3mksLatr0oIlfTtBe5D3rykj3vv6EAhd8aMuCFIH1eAc/WFInyi3yk0mttzdMXldqnUL7pbN5FcbpIE4UqNjxtSvOJ/qdQ6JrlobgVB5FTcMe04jdVaNGCY8MqqEgDhZaT74YfluKOdBOwKNSuz32+q3+4erGOkpTr/AsAYGhaSQp5ALBFl1UJEvgeXqMQ/obu6biqDF6zO0Vf1LyUs5HuQ0D4lRSpNBwdSkVWt7loGdNDCbqIJHmvBQ0FS+lAUgbLK4XLNyplt6XuZsF028iyS3l7dN2AMwepo8Tn87n/s5Seryk0lcaohaL3qlFUKNjzUFTFCKO5a5g7Nhzfb3zOuzxNjlH0P35aWumY0+EdLmureebODHJvfuqeu3HONvLJdXQwsYMuiZx0Exg+xevGZogir+ng+46fOIxjp57gfZ3U2TobubfEQhqcwOQvrUMZIVSWspSlLGUpS1nKUpaylKUsZSlLWcryP0xeEQghx6SHLxKMIjvLmLlRjegXv3ikxqan8NRh8g7s2kfP74hDL3Q05kNMGNN7R8mbUTtBZEsw78OrLiV3zRf/4RMAAItOSkzPAhV0qGKoh546K0mkj/6TYbRsYNzelz5DD92pXnrQ4zW16BP+ouEhem5nxmgBfP7AMXS007O0fiM9gedvEQ+raSMvlvKaBGPqZ2bpYTp04Gl0tBAV1S1lefLZ7ahvp/deC9H7863v09v6R+/4OyxZShTU+BQtf8oq/vDd38f9P2Wq1U1raGGMVNDCGG+swYhYGLdtoSfLSUtGJiuF+3cydr9xOT16lfVEYT346H3Y8QRRWGOj9H5mhZMp6oSh++hxnBSLaLpAT19lhQ81UT4jFqQn9vgBIpNut96BV9/8l3IdY/EnZ9in/qAPmRzHgl/4TAJGhes19mTx7AYlv6EUIeR6Qoo8Iu59Ki7VKXnYryQeR02R50B5V7xCFsUnz/GooMjy78ZjC7+TZS6S0euFlF01wPz75n8aRbHSCmEl3gUb0FweKJR8lmQvEBu0mybSsd3sKG42B1vd54ZAu2mmi7mmvLZUHgPv/XbpT694UbHXZiGHiCCCbIn7zgifWsDngyNeHYWq2r2bqKB7fvpzxCRm+rVvZuacU49z3XLSNprOJWpx3QquB+0tjfjLT3M9rAoRwdC0kutJ1gBaO5cAAE4eZDadwjTLEgxabp8oPi/Vu5aVgym8Vo6MX8vm90KGKUQBwC8ZrPy+kNzv98a2o1LDqtSpJnIuQk3NDclK4vO7f6sU9rPJlLzPyzjiIoOKvOTemJR2N02ks4LOqKZnf2qK69zsbBpVcbZRVlKOV0j8fS6Xcb2QKiuZGnupZNpFObbI2j44yKxtkXAUKckSodBNKcnapukWcsJt17mU62qiTtpKy+LQcY4HBIkk+Pz//b8AgA/81d+gZ5j70d2CKIXO9y1tXgfUsu/fso1eo/2PEmHUPZbHH7yOqeGvvvAKXn8OvVa33/VFnE7QE3XNNqb5zjoANMk0YhFRpMlEK+TzeO+7PwgA+PnPiKzdv5t8K4/c9yCuv5G8VTmbbRWUNnPyNoxwUOrIBuzt53577PhRrOjieOzvZv2a69kuMzMzMCQ2Xjk4q+TZh3tO4fgY9YgnvnUH63qKyBZ9QzuW1hBdc82byM93z0O/AACkJpP4+ueZEn34DPfKzDT7vSpehWf2cM4pXob6hjqoVdwnPHttLZxv/X2nXM6LnGSGyZgHWE8jgqCfe+PFF7DdH7yH/DOp9CRWbiDqpbaB9/ef4JjrO2Fj+Wpy1kzPMkPJsnXUMUaHAzB9wu9ksv0Cko7ryJHnUF/bAQC47FJ6IH9xL3l1QpEkOju459fUsW3Xn8fxFTmsoSIkCM8A69cY5TU1sSbMZMin2LmO7Wn3JLDnGPWo+hbO4yVLOOcvvXwZjh0k4iYmYK94LZ81mzyJb/7HF9h+S1mfVJLtP957Cmd6ugEA52wkumzXvi8BAKqDfvz1B8j385Y/pj4RkTp86RffhV9QUSpz4XlNHIPJqREEjvOZiS4qgqE4C7X56nYMj7Cf7uhnfwct6jSj2QEMDvI+BIiKSlQyM+yR3odhOWz/nsPUEYPNDhJrbgQAnDzB/opnqfd1+VuRHSba69ariNI7bzPLxwIJh96UzPk81wHbb8AUPTYlXEKVogNbsznk9FJujWIejrkcHsU8HK8Ujo2XU/471unllpdKJesmZTXgKQIKCSKoG8cqeLqdrrhhlE7pwJiDsilODz8X7WI5tosmUfpfNi97uK25fIaWi+qR98CCoyJD5uZiB9x06Y5WOl8czM8kpipqY7566c0tZwHuoKLvc5H56sm/ZKyq5yvuRE3TkJb9T7X/Qhl8lZ5TPNeTyWTJs4L+gPt9bjnskj5djH/UB0uuU+iw4vvOnplY9U3pmqTr+nzOoRcgv405byv9UsaZ39DdPnHPnw77anIqiWrJBOazqVt0rSb687LrrsbIAPeekVki8yubqI9F/RVYuoI6V+AMdcSgQ930xstfjR1PMxIgKPaC6gbqsLqWQHr8lx+GXhEGoVBQlOvMDAzZyAp5boCawPi1aBSjBUWSS8XDF2yRJwTgj1BpHRrk5+QRdkpLdRTd3VRMv/kDKpOJ1VSMly5vhx6gcoFqKg0Dkt7XiDr48Z0/5rMm2GEjo1QIaltXIxgk1N0vadaXdFCxS02P4PhRKqG5HHfs7iOHAQCbN29C3ma9LthK2HRvPxXU2bSJoQFJP9pGpc0wDBw9QQhzYzOh16PTfPa3fvBprF1PWH8iQeVn6VLWKzeVhl9CUHZs5wBZvoblG38ug0iQg2vLKhqsWuuEZHFs3njsiwAAIABJREFUPfbspfL+8U9SOUnnWKaZpIXaBJWlsMYBGQtIul87iFmTB4+YpLSvEkU3l09hYpp1DAd5X26avz1/6BGkJV3vrbd+mnVpYJrZZCYFQ0jmwnJAzmcLbijJ2WTBxUCFFc2xVjhFRhgXJupe4sE3z7bALPTbYlcvBMtWIWq2YxfxyTkL3CeboxgF7IJnJNHnlKGkTNp868hCBqTFfvOgmkbR4qzgorxHBQ+xPAJJVzDkor/dMDl44WVe+nfZhBWSFx7xs6WVbmgk7laKgftQVQP3+dpvCpP+ImShNjck7bTteP2qus2nUqmbedS1cO48tIPQ0B/d8W0AwOnjx9AlaZk/+7mP8n99vfJCHadPdgMAVnTxoLVx5Qb8+Du3AwB+71UMsRkbZ5rlU+O9WLKJB/D77r4LABAF1wjbLLhGKQ+m6xlaIH8HNH/Jb7oRcY09loRpWBJ2ZZl5Vwl0SdENNfEs5CV0SykuRhGppCIfVIqHIoMMBAKuzuUaHy0PQq3uU9BtwEZVnGv6zCQVJVOMTPW19Zia4iFWEZmPjXCficejLsx5YoJ7SE0ND+szMykEAnl5D99XVcU1MJvNImdyL9i0Ym1J2Y8dOwGfrH0T41x/q2tZh9nsCOoq+PyjPeyvHz5AQ/2a1Rdj386vsn5S3nwF6zBVGEClEAq3NVDxGz1DR8KNW2/Fn/3F+wAAw6LIHU3xkHry+BGsTNCgEBKOxpn0JILSDgYknE8M9ZPjeXSIYeHq62l0+Ob/+yYA4Cff+z4uv4GH3XCE97W3MNRp/+7nEKri/qKbao6zv759+7fdZxkRtst4ivULBHTXMGM3co/86dPbAQCHdu/F5g6O93/+188DAPbsYcjYP37t89iX5eE8YbE9jkrIZFCPoEqFPFfSuTOUYX+nJqYRr6ECZjjclwKGD9ksFbDuAeoYFZUSsh2KIpNRYQR8pkwfBMM2bLAPVBjkpvVX8BqMo39gPwBg6UrOeV+OY2dwEHhykOO2S/bw6UmOhWrHh9k02/HYIeoy61dyvITjNs67kCFiXSvZR8Z2KpXJ7AzGJqigZnPse93H+sXiNirEiBvwsa1a1m0EADz29H6E61i+Kalne2c9An7qaMkU23hyguvAxk2rMTZCA0u8jgaMaDXr8sS9z2LpMirFq1rZb0/sEv1l6QrcdutfAADuvOvr0kYcq8eeOYNzLyPB+nCS7f+pD/0xy54HBiXBxYoNXNNyQsz8xOF70Bbhu7dcwJAvK0Kj+JM77kbPM3SOjefpyKoQHaK9og3+GOfz8JGdAIB/uZPrZDjagS6Zz20trN/2H2/Hik0s34rVdPAd2EkdrKlyEoVKPt/fwrbNSobhyYk0/LYyost4jLG8aZi4/zANuock7fD5y7cCAC5ffxH0XOkaXbxvLnbAsm37lxzafrekbAj69YuX5MXz3i1EtOyXq5SGG1TnOr3YkSdnP2W4Mc0ig4JymDnQhcDfpSZQ4WTQvFT38psphiSjKEW5EjetO+Y7Lx1VF91LEKN5ZBRubebeV/y5GKk0w88XG5sLJSzxnKjKIa7aTNeL3mmous8vn2eZUAYhByoHi2q/AmQftW34/aVlLg49zeVL15bietrzjGfzZaGzx2LE3cVhYQvJ3LPUQs9c7PeXWxw5a+hi+LNswBB91pLETwGJ762ujuHUca7bM72kXmldwTmxe+/3cHQf9+BUTtq4gmHg1RVxXCJ7yHhKKA1MGn2qIwbOdHOvm5GhHRfnZkf1Jhx//mEAwMb61y9ah/8+q39ZylKWspSlLGUpS1nKUpaylKUsZSlLWV6QvCIQQnpYrLEFE3nxlPkF9u0IY1Y4XuF6ApXn1zBpRauLdmHTWnpcnjUkjegoPT1/9t6/QEsVodcPPEw49tGnCA3P5IeRtOhpHOmld/fKc+mh+uLtX8Unv/ApAMDoEXrfXNh//xCWtPB9nQ1CCBqhdzlRE0ROSGEVIaVt07voj/hRKWng7/w5yT5b2li2jvYujE/QU6RYhifT/ThznFbAXbsUPJ3evtnUAfT004MYDkiq2wm+b836y/Dm3/9fAICHHyaCoCBlaG/pRO9pIp2SOaKNbIuewLZla9A3RO9i3yC913U1LF9jrYGcEHk6Nj9rq+m1Ghg9g4oqli9eQY92Ji1orJoEbIvtlhW0UV0r6xSdXYHWxEW8roIQe80R95hTgE88lCrNng82XuqQVYRwhosckdfYjodIUWFQRcgWlx5sAaCJa22e611wHBcJU0Qv5v7loXnskt8MTYNPuTbk01AoDF1zkTC2XSi536cVI3/mh4B5UvSbgsa6qdjFQu9obhiOIn3zPotRP6VhWtoCoXsuGTV091eX+E5Beh3AdhQRnIJmudAOD+lzFiST9917jvfT3BDDX4/8qp6HrCnE+oBLuBgSSIaC3+oBP6ZmOS8feuwhAEC7rDtJI4iefQzvGj3NsNljJ0meunTZCphjDF94VlAQp/fswdolXMNuvoUhZj8QtNGRx/bhn48RsupI2vRYSBCQ+TGYZiliR9dVqJTpkQPKNFX97fcFUFDIIAUldxRM2INThyN8VjTGus9MTrnjwlDjyfEIDlUq+5DAnQ14hOQKceOmsC+CIc9NV6ppGrJptm2tEB2r9PN9fX2ud7Cykut3MiWp5QtZVNXy+rggXM4Mcr+oq69122g2JWE8BX76AwaqK7jWTQsaZ9kyIhgOHZ6BZvM9/jjX9tOnuGavWN2FcSG9burkNd/9LsOI//AN78efvefPAQB3/JghTz7pm+HTBWTDRGuMnOb+NNbH9qmuPoJfCES5LsT9pdFiuS/ZdgN+cOf3AQAbVkg6c81AYx3RqJqfe4EiivRpFTAlMcBr30hP1H0/IMrj2Z07cOQEx+b65by/rZVoWM12UJBn+KXfqoWM+dEd2zE0NSJtSrSSWjl9RX8LnTieeoyk2c889hSufRcTQXzgZ19hu3yRoXT9T+5CpJ7tn54VJFi1pEoP+DHcz/eZeeXhY3/XJhLI2ypkl3M2k/EhneYe1dQo88Ttbwe1CbZpVp413s/3VSWAkITE73qGKK+6at6v64AOIn/vv7cbALCxi+WLxHX09hHZsynKUGufw/tq4hbGhrnXq5CAmQxD0OsaDegBlmv7znsAALEqvn9N60XIzvD5h44w1LQj0QkACAcnkMtxniUk5KyunWNoQ2EJ9h/g+w7vYZt1dMzAbxD5Zec4jgZ6WJZEWMeqNXzGwDjHXFM7Udrbrl0CQ/S+/h7qNicO85pNr9uA/iHOgcZGInAUQXbbhlZYg6QT2LeXiJ2chH5tufxGXOu/HADwiKAdTwySvPmCTRcinmW5nryXHti6LlmHCn60Choq1c/w/ngTx168sg4RQXRecfWfsLzsBjx55GHkNZbr/f/7W2zPx4fwna8QtVlnUBc6p5N65tHeE8g6XJvve4ahn8EmoqSraxpQBY4xS0b5jufZN5/7948jp/O+C8+9GADQ2ckU9TY8j76SYoTQ3DCyuYSsv1F5qcTIZ0mJfta9+NdJ8vy7LC+gXRZCXKj93rIsb4wp3dcNlfK5Id3qDKfQPTp0V4dUe4iL+HG0IlJpXm/aHhJpbqpxTdOgSTYWW6VqF+y6vsB48cKTrCLsj1byqZse0bGLiHERTTZsN3yq9NkLUTe8EB3Rdkxo8NA48l+pp+WlejdVe1rumcatlwIDFSW3cRE4RWcVwygtdDGCam6djRK0k1lyfTGKy1R9V4TG5vPOEp2g2fPOVx76CEWoqDmRHMACjB7FD/rNIgQdS4XuydlN97u0D7NTXKuPdnOfgq7BF6DGckZoUk5sp25++Ng+fODv/xoAEArTvnDwae6Hp08dwHCGyN+NWxj6rwbvw9//KqKiP586zWt8EaJxC80JPLGTyao2XlFGCJWlLGUpS1nKUpaylKUsZSlLWcpSlrKUReQVgRDyVUvKVsMHY1oQIpKG3JZUtKnxGUTiEj+dJqon5NCDazuNeNU2kgqemnw/AODMOL3ln/j8W/Cet30GAPCpL3wWADAsXD3JmSk8e4BxfI/cvx0AcGAP47JvuvlGxBvoMWtM0KszO8X7ck4GXZ30ahkOuTXCEhMbjphIF2iZjEqsemWC8fr79h/BPY88JtfRa3i6l88cnTyM+loSFDbU0RPVZtRhz1PkAbGFcHN4iJbGeDyOGiGDbW6gp/+ma+kpalt+AeLV9Hw99yV6ZJ99lp/brrgIbR18fixGa2pG2vOZpx7FyuVMS93XTfRQbz+9i20dna6FNmPROzkt6V8DiSwsaQfbYR0q45I625hGMCKEr/wX9AK9d10tt+HKi/8AAJDPqRSS5FTQ/T6YOSFrlbb16UkUrFIv1kJW9/kkcFAkN276aNcCDtuNF1YGZWVjNovi7Ys9bHNlHiJGK0YGLVBOp9R67lr4PQoWD620gGdv4bjZualkHSxmIWfc9yK/Fd3nkgaXtHFpGYrbWpfr/WLBN4tiyhXprDLp224ddO+ZKsWl8rrwvwvUdW6Zz+IJcM5i89bsxX9a/K5F3vureR59ErNtmzb8hqSgFqTEjKAPahqq8NQepj5WZKuZUc7zS867AN+/ndwaAZmL9RGuX5hOY3Uj16t0mHN2anoYuiBnugeJUDk1QA9Fwu9HSDh8nApJs14Q4kAtgIIlXms1bMUTaBfxXilkV0GQT4ZRgOpnvXQowDE8Xi3dUJ+8NhAIuN4wL5Usv5umCaECgt8Il7TnzEzavU/NYZWSXtM0FzWkrjEMw213S4ixx8eFdDHgoYw2byFvm0I03X333bCFKLClheu8ItLOpTMwVf2F+yweD8mzx6GBe93xY8I5l+G6unL5EgwKAmTDmvMAAPc+SFRrenYSSeEqOldIeW+4ktwymfxJXHDlTQCAPYMkyU2e4f7im9ZQ3cr/FQRltmklvw88dgy/dxFRFN/9LkmG29ZsAQBU16/A+Ru4v+x/ZjsAYGh0Am/6/fewbaUd1Loc9AFjwrdz0YWXAABWLCf6c8fTT+OB++4GAKwRhFCigQgoGD6X362Q5hiNVNM7NpZJ4023cZ/4wCf+BgDw/Gl62h5/8gmcOEEk3NAAeeyCh3sAAOs6l+DgHfcDAA5/k4iYhOyt/tZ2ZAps44R41fKzfO/k1CSCYfZTczORLqdPE10VNwzYwm9YWcnyzcykEBPkr+JP6utn+aamkshnuYfmhQcjJGTqE2M56Fpc6iwk4u3cG3p6TyJXIOpn7dKb5Fn0EiKQxcVXsH+OHePcDQd4ny/Yj4ZWItbGJyTRwzjrZQRzOHq4m2URboKJca4tTn4SqQleV1XJcZEZZT0vvX4tRiaIzpnOsm9PHKG3saW5A0u72H6njxMVHF+3GqdPss7tgqpev5a6yZ5du2H4+dxGQVf3nGBbJSor0NnCthkdYfkuvZwcSQhO4YHt5KJ68+/dJm3Ma8YO9+HP3v9OAMBdPyMv2hM7ifi5s68bN7zxbQCArbfxvlXLOPaGBiewagnb8bFHyBf5o3uJrKtINCCe4wRLhEX/06ib9CT3oTrOdeO5bnJSpaaFBFsLI5Rgm/7oUSL3nOkQ1lYtkfqQr62tletIYNcIdD+fse8w1/ZffIvE2BP5HDrX8brhE3x33x6O7RtqV8EvxNTTz3E9eDpCdNTEZVVYGxWeqzlp54s5DBfSI15q2vmy/PeTFzIWFHrD1jR3Q1e3KX2gYDkozEGOZCQhjW54vJSm4shUCCBHw1ySYQfeGLUVml70OE330Ebq09CKUS8L6/JOCXpIK/m04LhoeEO910XFLy6GYcybe8XiIZSFi9MlLrXcCIK5/F+W5SGESs8Cxbr0wqTZLhinCHmj9EvVLj5BWQcCAfh8pXpzMSJJ9U8x+Tef7XEILURm79ZdDmMK6aNpmltnl3PSTXuve/ySiyS7WUx+0xxifl3xWbINUjNTsDOCxh7gPv3Ew7QvdK1cgeZ2ImVXrGbyqqXbXsMHaSE4Otvovp//FwCgUp69btWFCAoP5eAQ99jnn+TZPh7tQscy6jeJAnXdnlGiW08NVGHp6iW/tA5lhFBZylKWspSlLGUpS1nKUpaylKUsZSnL/zB5RSCELryC2RG+9x8/QE1YPBtuimEWsbayAvWN9Ng4Di1kxwcZsz5rz6CvX7xg4jXNFIh6qQv58dlPvxcAsKSLnqErr6HXyR/O4ZZl5wMA3vQGZjFxxBP8lZ/9Ez7yif8DAFhRQS/mwQNkA29b0gjNpkfPp/H6mGTqspFBRjw3MxmW6fHnugEABw6chGXTK1wb5KfiDRoZS2JwgOifcJAesw3rV+GSS1i+gqJCkZSmg0MDOHWSHv2ClPn4CcaXR+s2o7OL7Tg4LmmOY/TGPfn0UfT20yOdydKzdM4mspY3N6xCwMf7YnFaGCMRWnqTqRFAYk4rKol8Gh6jhTLsiyMqGcimZtgudQkiC6LRANIpetqrNLb71qX05l118xsQklSpEynJUhYTpJBtwdZosVbW/mwhD0MvRQK8UFFZhYLKGq4rD4IDXfHhSP0Ul4VpazAdxQO1kNV9YQt0SdavudcUxcuqBA2uT0LT4FMpOOU3xX1kmtY8hJCLS3IsF/lUjFYqyQCxSNk9j8r8+1wLvnqOoxW9W+F3FNrJSweqvATud8vxPD3KY1Pk+fGyOMy3T2vzEEXzxfNkWYte83LKr8PzoPhIAlrAfb5K4dm6hDxeI1PDGBmhp6Fastx0rSKvy/jwIEZm6EU2DbZDKMT5M52cQSgQcf8GAKtgwzHpIVIZzpJJrplR+BCSMTMlHsB0nvNacxw3v7fK/Ga5WaF0d6zMjf238rmisaI8fOr+onTzss5ZwsNm6BE3+9xc71hxBgprjgfSsgqup1J56hSfj2marndMjR2f4Uc6w7YpSPavpUs7AQAnTp1ApaBVDj5P/pH2diJHlnQtxeQMszwUhGdGZRRbs2YNjh5jhsm1q5mWemKSCAvdsDAyzDVWl0wop47yOfFYCh0dvP6ZZ5lNLhrh/tLa0oZsjmivoQGWpW0DvULf/OGdOHKUz//oh4iUveNHRBaZm+ux/bEHAAAzgnz6+Be/DAA4b8P5eN1rLwMAPLKT3FTVy8ljcnTfMex8jOiaC89nanoDefzn1z8HAHj/+/4RAJDNsw6zmVHMTnMc1TYSaXLDq4hwefTpnfivH/4QAPC+9zCr2bpzmK0qnc+hwUdkS064fCZlrFaFI+h/7BkAwB9tIvdKUDCYddEKNPo4toeT3Nfe9Hss59U3XIP3/TkzU739LW8FANx6M3lWPvOv/4z7ntgOAIhInH9DC9sxbccwmeK7J4SvqSBr2fjUNJYvaZc6c3/PZsdhCLrs6BFm+TAtjt9opAp5aRtHMn8GwhyHydQkrAL30kKW++fzB8g/1daxHI5wCHUtJ2fOzp1sg3whBsvk3NYDTO+et4ngKWRyCAtSrbaJ9ZoYkqxyIzmMDhON0t7ONaWqUri6ps5gZIy6yDrJZDghXERPPjGCq15FhM/RXtYv6usEwP2psortcOXFzNR1fOgILtx6LQDg8SceBQBMT3LdqopEkBR0oyVrTEWYCLSh8dMwdOo+jYklUi+WfWx6Hy6/kejj/SfJBVkV5fh6+x/eihPj1AWzGuv12Q/+BwBgamgPvn4Pr7/XT+9s6wrOLT0NrN7E9k6Osl6JauqWR44PYttm1jk9zfL6K9lvmYkMKmo5RrsHn2XZe4k0P/D8KLZewbnz1FPkEIJZwJ/fRqRaRTsR2Lv7uS5Ut2/Gnu1sI32yRt73NNugowYnHuDzJ0yOzTe++28BAJesuwERyUQ/ckr4k47RGzy08wks33YlyyxrntJ/zoZWKN6LX/GyAOfNXKR2WV64vFRkmErvrumG2/6m6AM52WOz+YKrx6qsnnlB+eqOA90o3dctV9XWPbSRWzy7iGtSIU08xIjiF1SslZpC6di2yzfoRgAsNE5cvVQVwdN5LYViUcAkTYezyHzRdX1xJIvjuNnP3Oxpoiix/KW8l+roURwlUPr73Oxpaj7PL5dXJszjPnWRQj6fi9Rxn1Wkc7moppJU9ICueyixuXV2nMXPI7quu+ceL6OxdxaYizYqHqtzUVSldV08AuBs411xkb5YcTKCuBLuyXi8CoeOc39eu4wooCCYbXJ0ehKr1nBf3/Uo94aswz181eot0Avch1Y1dgIA6jr5PVrTCExzz35iH8/7l19FHl5/LI7MKM8AX/1H6mdV9YLgrj6JHcKteN2tf7poHbRXAkR098AhBwDe+dbbcOIgN1ZNwq40h5O4pqYGmkwUTRS4ghCqZk8n8Q+f5Ob7bPLfAAD/9nk28nktSzE7RkWxYQU75cv/wnSi0egIZoVUWrOomNl5UQ7PieGDf0PCwGfv2s4yxKloJeqjaGmhMtLYQKVN17g7O0YaAyN85lO7CWVXk8kwosjk1cGYHaXSzeZm0y7xWS5DA09NpY1aSflbmeDhYfUGwperKuswOsBD5NQEDxLJNA00FbUJZHP8rallidSP7Xj3z+/DQJ8onQJZ61xCuHO8ykJdAw8eQ8Ms+4QoQ6lkFlPTPBT6Q4q+UxaEiQ5Ao+IcCnFQB4Jsx0R0NTavIdz/0ot5MFi7kf0waE7DsX3SNqx7Oiv3BwIIh6mYzkqIQzCqw8qdbSKL8WCBNdov/wuK0UeFxAQNIBQUQlq/MkaJASqXw2SmNK118eI7d8EvSe069zf3JtvVZVyDkPquOQj6SuGsxcS4LrmuUUrCbFmeQUgvMvQYbmpK2cgUObRRtPjrpbBWXffCW+YpkY5HxltM4AwAdpExxnY3VVEQbMfd1NR1lgr/cfQi4uj5RJgLhv95b5pz/fy+secF7xXJWTaMs8mvqjQvdL8iCg/5Q7BlAM9KWvaZAg+nD26/B08+ykP9gWefAgBsu/pGPtMycXDvcwAAXeo1MsJD0tq1a3GmjwZXJ8f3mJoPzdU8YB0eZBjC6DRDPlbUVKM6QQPI0UGuKdV+fk9lsi5BpCIkN/xFRlp5t6PJIVj6284VilLQy7WO2uA9pcvwqZAxIU/MG/PGe/E80Od0r2rbQt5x5w5QqmRYluWGgCkjZCAQgE8WCZVCfOUqHuwGBwcwOMy2jEWjJc8EgOtuYB/097Idd+8mkV9LUwOiUTlwT49Le7Dt2ttbceR5rnVhIQ9fsXyp3FeHPfvYl2odz8hYGJ+YxAfe/jH+rbG/fvAACZPNZAUqNK7Nf/e33LsukBCVf/3GN/DAtxkOViXr3awkDPjTT38aNWGW6x/eRzLD66/mfR/64Hvxwb+8BgBw7mYqM6tWb8HhE9ynN29i3RvruKb/5GdfcY1g733XhwAAPodlv/b6a3FCCLfv3c7wljVLufesaG1FbQ0P2VkVhiB7sU/3IyfKvyEOhIu30TB09ZVX4cBeEg/33sED/x/9I+vwyc//I06d4EH/3R/+AAAgJGNu98M70L6aoWw/vZ1GKsWp62g2psQYFQyxTIlqHtaHzgygqkpCMUVisQqMjbEvFbFkICCKWD6Lmmru4ePj1AvCEupdUZt3x1pnG/f1M0Mkg1zSuQLRKkkWkWWbTY7z2opYE6ZmeF3nUpkTWfb7xGAVpsXR09DFPdme4DxvaWnCVJLlzJgq9JzjOeCrcedHWzvHY2qSukAmVUBTs0DQB6gXzCT5vaIyCJ9B/WO0X0LwOmOorabRZVBCzlMTXH98poXly1megBi1R1kkRJuArDiUDGnHFWvYLiMzAxiTOaQ7HL/1VdTB0pECsgd5/a3Xkx7A30lD+Y5nfoarl1MHeugnd7Ktqji+jh67H7PicKyMUReq0mjsy81aCFcqigAJ+ZODUDTSgXidrNcR9lEsyrZ6+JGvIzVtSjvSAZZoyyHefiHfbXLO3Xsvy3LO8lUwJziP62Is54aLqFMOTR3FUC8NeNkA28EO0YD6jrd+ArUGxxFXZkBskpicHofm5zxRe/hCoRsLHtp+wwahl/q+s6WbLsuLl5d6DlT6nGkDtsSQ58R7nRNdIyd7LVBEZC7fNc0bc/N1bB22pRxM6l/2fD1A6b7wHESuD9Otl+06apQUbM+JpLlJUtT5DCXfWWZxHsm/DN07t/iNUioKf8ALQQ8EhPKiyMiSF+eRciIVxECmOfP7ovR76TzWNM0lcvbK6l0zP5279yyl56vf1FkgGAy6aec93d+1HiEtId050U2UnqVpBvRFiOk1TXPf576/iHTca7/5RNdzQ/+LyfDPbthZfE04+28vkVxfDVJluAIwO8nNLSb1O/Acjf3BykpE62hzMFKip1bISq7lkBtniPrpw5KSvovGIyOio0/2hDC4J+gx2jdOd0+gws/969AeSWk/zT4anDiDNedx73jv+z6yaOXLIWNlKUtZylKWspSlLGUpS1nKUpaylKUs/8PkFREyFgnRg/a2t78LHxOouyakSgrxMDYzAZ9D+GtIyEHjGq1f0LIY6aE357o3Xw0A+No3mdZ9KjsMvzj0nu8hbPmTnya59Bc//0FkkgqpwGfXtdOz8v2ffhV3/oJQ+TqB6I9m6QkLx4NISlrZcFhSokuq3ROnj+LZA/RK2ga9R3VxWv4KORM93fQih8VzrIyK2dk8wgavCwfYHmuab0K8gl6gjefTs7T36B0AgP7+/aiO0ZO0buMaAIChMyTuwL5dCOvV8mDWq2ATSvbW378RDwkMubeH/9vxKL21eSOAgJBThiK0vNbWVEn5/ACI2DHygrIJc/hMa0kE8vz7svW3AQC66mnR3HrhxdhwKYkRRy2+bygl6e6DARTS9PaFQQ9bNCwWd5+JjJCsGn62VSaTR/AFpEZdEBZtqfTI8l3+7/PpiATFkh9iW+Vca72NqewvJzBb0KK/UKjYYuVVZLuOBk1BR12COI+kbS60swSZNC+V/QvzmC2EwJmLfCqBatoLt4dWRPbnQkP4QywtAAAgAElEQVRdaK3mwjcdl2ha3WljPgV3sQhUFQF5WHE7qvqXEmrzGtXRL18q3V+nF1JB+3O5HBxp77yQG//0bob9TE4PYnqKHodIkNfseOA+AEB9TTVC4uFRHqNENdeAoYEBDAip3ZYN5wAAgk1tOPkUkSwRQcY0SLr1gm0jo7xGQrgb0VU4pVFEOCjbhyIDt20vpbxLHOgRNyoPnd/npYYHGDKmvEDZTEbqwE/bCSImqBCftJGjSNk1zU0vq8ZoSOC6Ggqup9ELD/NCx/ySUro4jatCqvn93F/6erlO+YIB1Ndxrc1kuOdMC5LB5/PhoQe3AyDKCABsIViemU67z5+eJlrjvPOZn1pzbNxyKxFI9/6c9+95Zg8AYN1b3oY3vPaNAIDv/eg7rIPabyIhfG0fw2FaTaIaQtxSYPtMjMVZ17d/6hMAgIt+xj7e/+gDeJOEbvWYRGs89X2Gqnzk+lcjFGWdP/rXfwcAWLqKyJ3v/uLbOOc8hp/sPcTU6De9+jZ09xExNT7Ol6/tWg8AeO2Nr8fTCRJHP/U4w93e/BbWZfmaFTjRw+u/8/VvAgD+7UtfAgDUNNQhL8TFTgVRK5agonx2ASG11qXY/s89zD1rZaIVDX5e33seET+ty4lW6uvpdxFBly5n+Q6NMMz6F889gT/rlLAkSf3uFPi+qkgEBUHXtTfQ4xYIcA/y+/3Iq1AFWcuSsyl3H6+q4BwKBAUBnJlFXpBO0TAVETVGgTwMg2O6rYPvWb2G6BnHCuHAISJHzkyzXfJZPnPozHGE/HzhyQMcc2aS331+G2kpSzxKhIqZJsKoMlaNdEZQQ2B/nz4mIYyOhWCIbTUxwnqNprnfI5tCPsf9ubmZSCmJ6kO2oKOintfF6ohg2ru/GzMzfEZlmHpLxyoiZJJjo/AbHMvZtJAiC7F1uPJCWEmW5/JL6c2czXMOBuFDRz3rU8iyPRtr2O+PPfgE/J1s9ydyQrr/CMfHyOBJaH4SR5+Msq0aRafyJSqQHCVqK6dJmvqLSIS+99E7cGSEXtpgE+vXFud9k5MHceoI2yNUxTHT3MJGr2tqhiYRH2nROczZEQwfJCKoNUxk26sk1FfTj+DUNJ9xcpwhX4cHuA5kjQF0NLPPU6MsS2Uly6lPHke8mfWSpROzQooej1fCUuExc0heF9IjfpeljAz67YgaVxIdhmw2h7TsjWo8Oq5+5oWWqTD1gKx7GnRXGXRDo9xP20OWy5av+4tQ6krNtDwqBTWiLZWkQunBul2Cjimug2MXPWyBZC4eTYLSlSHfPUSSikpw78tbgEI7uwlsPD16bvr4grSLjoX08Pnk1G64lunA8RRtqUJRWva5mXKKwuTcyDsX5e/pcfYcPd9FdMFDHSqUtddW9ryQsZJwvkXmqqY5C59p5HPuOrVQ33jP0uZdt9Bv88vwMqwj8ohsXuwSPj9igizOj3MvVyN0xZo1yIoeHchTv9UFUTZ5Zhrjgr66aNsauZ8626m+UVxyKSlw7vsOEd/PClVM3m7E628kmjuyns+KiV6369iTsEKloYgLye/+jlCWspSlLGUpS1nKUpaylKUsZSlLWcpSlhclrwiE0PgZWq5Wn7sOgQaxMvfTshbw0XuUszT43LhoepjSSVpO+wMOZidpcT2/4ToAQEMHvSXJYzrsHJE6fvHsnX6EXrJje0fQ2EWzXiYn78nxvZtWXYpZKVfUT+ttMEAET8bSYIUkNi9JNNCxI4wzHx/JwdDp5UuJx3NMCFlTMz4EQ/TwFDIRqT29aolKP5IDfE/zUr7n3e+8GHoD4+SHdcYS/kD4KSaOpRAoMCX97FY+f/MF9Kol6pph2PRmmUKuLTQVSGV0XHEdvWAjI7z/4XvIR9Ld5yAkCJxARjyIB9hW9a2VSOeJMjAD9HC21tJDdU67Hy0Jpkf+m4//PV8kVBup/DT6JujdcmwWIi6k4JmcjYBPpa0kekh5EgomXBJrHbREBwxAVwgTRb5cZA23XFQIPxQ2RNMd+MXKLvy5UCAHn8/vPUMChxXJtN8IuH2v4qILit1O97mEck6Rh0KVU1MohjllMXy661m2CiqdoliKDb9r7Xfjo8XsHPR5SBdDL0UbaT7AMZUl3uNk0uaghjzPhu56K1SqeGWENwzD5YZRUmxpV+nAPYeDh/hRXgWXSNjxLlF8QgVB+jiYb8nX3Peo8jpumRXpsFZ0n+onlwhQOUPsIg+JlpX6+aC5y51wyTgeN40iu3akPQJFzgWPxPCXx9kvRHKnUI5u7SzL47lS4zgn6BLNRKSGcy+d5bgP1nCtCOWjSJ7hfF6b6OTtbVxrnt65C9Uheq1TQupbVc1J6AtqiFVzzo7n+dvwziE4wvsVDLHDIoLAsQtBjAzzt7o4Pe9TSfJ3mDAU9Q806RPVqj6fDlP6JyOoCDcWPaCjIPw5IeEgU96w1OwEIrIuKk4xCPl+wB+AIf05M0lulFilwBMMzfMSisdsepLriB4Iw5QfQxFen5yakfeHYEt/m8Jvo0U0RGXRsiVVPExBGNlZWGpdjPFZWzcSAXnwwF7YI3zGZIjohupWrr1BPYZAjs+oEhSiZnCt1qtj6BO0ixVgO7R3sM6J6hguvexN/E3n/+64gwTQvmwFjj5Djp5xcP2O1XMM5Ow8AjJmqiSd+eEd2wEAf/W/P4QzIOrloc+RM2d5E5GvF286D8s2El0aa2Pf/OfP/5XvMyzcfCURnmNJeqK+/p33IJskamLVinewjdR4D8Rw5c1/xPIMc08tyAi5+TVvwPa7SVq9/ec/Yfu9nwSH/qYKDE8QyVInqFZTeKiyAQPpWY6njrp2aQ+mY91/5FncJaTBF15E9NWu54hMgt/GqMTwP7SPe1yHcPXETBtjMxzTuUnOCV9ASINDU6hsZHw/QsKzdZKojZa2MNJJSZMsC5yh2YiFWMcZ8QSqlPTJyVFcfd0VAID+gW4AQEUd6/Xcs6NoFvRJQkiKY2Gim/Yd3ouZPNsjl+H7woJcM6084hWcz7qg0YyIkLXmZ9HcwP118gzrPj7NNTBvHEJCUDITQ9JfGeo7OT2CpPBZDEwKW7FN3aYisBw9Axyjo0l6Kpe2EV1VV12JgW7yGTU3EoW1ZoWF6R7qNZEQU+JWy5zNWXXYcYj9c9WFrwcANGWp2/QfGEa8hWvEkEl9qndc0t3PzKKqguO3rpJrUns70TZLNmawdy/LOt5PJE42w7m8cvV67HiAY6W5lhxd04Is7x3tg1AkYvx5kn+u7iB3V0XnOvgOsN3HDrA96jazTml/NdIO15LkMN8bC3AutTavgW2S5Hmgj/rS0ooV8EVYr6k85264lXXoPj6C5RuoRw328vrJCb7vynP/GLuPfB0A0NZGpNXKdUwAct/On6Nt2T4AwMa1JPOuixMxmEqnERX0tjUnz0Kxt92awz2i6/o8r/oLlZd630v1zJeRQQtLsf6hu8jdUs6d4n72uBYlIgOai8B1k5iIfprNW8gIh6ciWM5LIgZAdzkBjbn8ko4Dv5TFZyheSo8nyE0WoRKouINWg610PFFJdXs+9t1NG685sN1xoZA7mlsG0zbn3Ocp545KdDHnGjiaq6doeumzLdsB3PuEJ1Z+CwQNNxGQJfyQiu/QcRw3EYflciSJfmzbRVw5al6qwljz55kGaAoFr3QhR/XbQkh+SfSiAZrljYdiKRQK7hgIiE7oHpEcAI5w+wknkiWIIcs+CypHs+FYpchExRsU8Bsu+qpQUKnsvfvnRgAU10mfW7+itWzu+sbye2Ny7m+/8poijRT0C++sU4ApY1oTfWDNJZfzN82HoEJ+SfCDKSixeEMEa1upX472SeIoiaBJF0ZxfDfRr7l8NwBg3YZL+bl+CzqqqK9sP0U7wW7hc3QCNrYsW/VLq1BGCJWlLGUpS1nKUpaylKUsZSlLWcpSlrL8D5NXBEJIsZvHo5Woq6OFa3hQWS+Fod3ncZRk0vTwVFbQAlih53D4+f0AAKPidQCA87Yy9exTzw/hXW9l6tk16+np7KijdyZSl0ZOLNX+KC1440nyBDlBG2/4g1sAAHd/hxnLjCg9ic+fzKNniNeHgrTcjY7QG6rBj3glvbqmIFuSk8KmHjQxOSWesyC9eJqYQtNjBupqWS472A0A+ORnP4qxGK2WB/uYYrhWMmFV2bUYT7P+Dz16QNqMZdh87ko01ausKPT0j4zTFRaP++EP8N2NtfQ6ve1tbwEAHDh9ANsfpBc4N8t2r22gBzObzcIfiEnZw9L+RPys3BhCX892AMDffJp8Ee/8kw+zfeIN0DRlZRb7oxtTq7mW+bPJC7XcepkFlHlZIU901xrtF76UoDD/+/2am6XAdUYoNFHQQcJgX6bT4h2U1IIF24FjK74U8RT7FdLHh6wgJJRlPWB4KR1VusyC8qSYympd8Lh23IxZLywTls8/17ZrQ3ky9LkpHQ2WuuR/RZ9ePO98K/pi3ESM5+XfprLCK+8HNHiwkuLyAYCXnlONBK3Ey6PitxdIt7lIkYqL66jMEo5eNNZUfLnKnGDDVnVdoH5nS205Vxa6xv2fSltqGB7yScoQFE6QTCyGY0P0wt95O3lWnnmQnCyjY0NYv4kcQFsuYKrJr3yBSI6AEYLmI4LAkjpHY1yjaipiyIvXv9DH9S0YCkP3cf4aPq5TVlEaUYVamxKunJBwqOiahmlB2oSDnBsq20R1dbWbNVGWPi+TRMCHXEbSdKdZFjXOwuEwXI+etEdIII2apiOV4hpWk+BaNDpBJI7Pp6NC+EBM4f2KCYKn4AD5POuTy3NddAQtli9oLq+QPyQcLIUCxg1e11RPdMik8MjkUjkYBts24OPc7TtIVEQoZSLeTC6gqhDbqn+sGwCwoqsdkUrxmgpn0WSKCKaR/pPYvJqp3psk09NsivvLkdGTePOFRDNc2UYUxfHj9PQU+mdw3TlMh/39ndyXksPSH5qFUC3bw5E2Dgg65GcP343WWtbhslWMS9+1l5wlszdsQ2QlkTNTA0RkNCdZ3iVL29EYITdPcxW9Vt39R1DfyPKd6CUCZMUSeqkChQL8k7JGKn4EQZ5cf+MN+OIK3nf8JFEU2+8mB9a6hnaMPck9PNfMceuT9TUCg65hAG/7k9vYRkO8/6lnn8GpA0SYXHQZvW8f++uPASASr66e6OA+ed8zO5h6VYeDwf5utlWeY65GUo4nasPo7iFCZWSQHrrqOPe8JW3NGOrnOBkWNFooXIvMjHBfObxu8AzH6C2vuRqXb9sKAPjhD4noMgucE7X1cdQ2cg5lC5zz4TjrPDpxHLq4DuMx9qktHB3RSDXGR/l8hZ6LCnrZMAJIiX6Unmb5KoUHMB6rgV+gsdEYPwdHOcZ1v4FKec9slu2RiAiXUyEL3RY+J0GgBoKiC8RacLKP6CktzLWlq24DciH2Se0SooYsg0jn1EwvJgdZru5uXt+4lOXTp08gn2X7fe+r5ICsqeaY3bT5XDy/jyiebBfbP2/9FAAAx0I8zLIeeJ6cQ5bwr9UnxmAWZC74qKvlU3xHNplAJCxrXy3b7L77+V5rthENLbJ3aNSdeo6yzWp9FViWIAfQ/uPM+NI9yfFlZ9JudtTaRvbz8MxJrG+nd3bXLo7x0SnWZXlXK46fos5lzrIOA8NsHyOoY9tV1GczOc7xuCDRjnTvwvBxlrV7N7Prbb2AaLtlKy51Mzwp8VJKe3uPh07w1v2XyitURuy88mQxdaWYe8XTV1RmJNsdF0oH0ER3s4uyc+YUUZCLQLPdceW46vT8MaHAPD7N2+9d3VN0XkO9zwEMlJbTcRYan0pnPouOpp1dd3sxup2npy5QEvlfoWC6vy80pTxOL/Xskl/PUrb5evDcdlZtvNDRyq2Drrmp7hdi2VSoK4WkUe/g/fMznfFzAUSSQuygNKsYPwUhU3QO9BA+3plFIdwWQghh3jimFHOlLVSeheTlWsM83iUNqq3O+mg5uKp+s03LbauI8NZFEgn3coVAapkmp151O3W3Zas34Pnt5Hl8/mAPAGA8y33t0mu3Ye9zR+XvxYvyijAIWaKchwJhNDRSgRvYywqpBOd+wwdL4HymKJj5NDf4Gn8UmQwP7JLxHW96M9PuHr3ro7jp1STIbF7GtPGzKSq9OV8OKVGonKyktovzjWNT03h2Lzdvf5jvmZ4Q4tJQDLMznJizOhVuX1BB8XJICfl0QWZ7VKOylrdOwJDwgICEF9gZDoabr/owPvxXNKI8+vS3+d5oFFY76/rO/+8P+SxbCKpTafgrhDBa2uj4UXZ4Oj+BbVdyQNU1kAw1VkGl63T3MVRIHXVJWxqr4LVbLm3Dozs5gEJOTNpaDpJmDp0dhCT75bTX0kLFajI9imXn8JmPbr8LAPCXH+Vh4w/f/nFcfDHTFQ+dkdTL7gFZsHIl/8OLEgU1tIs2OV1TUH7vuybhKj6dbWYo+0RR6kJH+ssLGfPBVmNTpcwOuoFo89JlKtF1DT7X6qVIb/nV7/fIbVVYWV4R8TqOS7hrlywqpURx81IzwiP69UgkvY1WpXuca+hhe5QahAxddw1kC5HweQR0qt3VKqZ50GQ3xbxASh0P9jl/j7LnpXm0F9i89QXKMndrNBbYMCxZ4mw4rsFQpWV3ocAaXHJir1Qvjox6rtGoZFzN2azY1qXK+GyA4+yZR3dhaIhhJ7WVnLN1jVx/rNwZVIY5z370Ix6G3v+adwEAVm3eiAee4mE33kbDcrCaRpVvfOcbSMuBNiRht+b0NPxGaTzBzBQNL2F/hTtGc2LgiUg4Wiadco0wblSkyT+SySRSKR5cdL/a9NUct10YsjIQK7Esy4X+qqGdnOVzYpEKV4FQRpxIJOLep8iaK6KSsjwjRIc+H8KSXl2mLuKS5rq5sQl9AsVtaKRRvLKyGkePMKV2QOZjgxyknSodoxN8z+gED2vhAPvBZ/hhSxheUPYCXULNBnsG3PkfiohxI8p2rQo3onuS66E/wbJXSRjQnQ/fhW+tYHtfceWbAQDnVzGN92c+/G5gGQ+cf/I4D5Uf+PD7AAAPPfsIYgLpH7a5bvsb2FYnn3wGxjkM6/3AR/4BAPDMDioPlYaBnd9l2FWzEDPf282xdHn9Fqyb4H0r6kgMvPv4ndAsGjd0nUawnJwMrJgf0xK+Gmhl+yVlv/jaj2+Hr5r1CkoI0Q9+zCQJzZUJ1zhniYGmIIdazcm7nfjlr9AA+sa3vhUAcNfP7kRXE3WG/n4aA8w81/qKUBi2KEQ1EqZ4dJRz68rLLsb+PQwPsqXNAuKYakjUofckn9XaTEUsk+Mzn3jsIFYtp1ErlZbQzKoq5KXPfeJouORyGoFCsTQOHWE7T6YYEnTOklcBAKLRWhw6RGh3jRC6z2Y5vqoqAxjqpyFC09h+WdFtMG2iuo4OHxWXm5rhNeFwGJpA800JvxyXOifilTh4nIbMG2/inhyvox5y5Ei3ayBvqGCdK/1suzOpHhgBMQ5luaYEw9RDHnjkWTS1MlQ9EGMb7O8bQG0LldSjA0flmWy/FSs2ofc4594tlzG8f+cuEqcv6axAcpJjoCZKA5KW57hKT+roaqNBMpVlHZJJ6oihfCNmkzTg1VTx/lyOa8ypk4OoFKX6se0c05GYkL4nM2hsZrtvvYCheidOcA042DuCcER0BYPt4NdYJsSqcMub3gMAuCHH8f8f3/4AAKC/dxRNNdQzcxkxMHdUwbapcwZ9TBgSAstUyJroWM55Pyvk0pYYrk73n8KaGElCsxaNRk/svhcA0Fy3FTddzvD8ikq29ZHe02yf/ADqI1zX5ie8mE8m+3KEjJXllSdKt5h7kC7uY0/vVjqf4dEIiPGlYKnkDJZLkK/CZT1fl+bus+6z3e+OG0o1zzmmae7ebysd13VEOl7YmuiWxbrhC0n0ovROo0h/PtsYnxfyBM2lcVC6uDL6aI4D21G0FnMMV5oG13jjRsAVPdspLbsywBia51j1DCGLl1fTNPfM5FImLNS/6rsKe7MdF4wwN129ptmwLDkPCEWEuVAwkau3e3rkvCQ1RaGBnlFa0VooI6TlhnepMiiDCMmoS3Xx4nG8YEIfqJDJhQ1XC8nLYwyacw7UfHC0OTG7RQZNL+GKcs7z/0Yw4Dpn/WFxjAa5FzW2t7vXJZqX/P/sfWeAHFeV7ldVnae7J0dpNKMcLEuWJVvOGScM2MYGkxYwGBOM10SzLCywyz5232PXsPBgwYCJBmySbYyNI46SJVuSrZxHYUaTY+fuqno/vnOrqntassxjwbvU+THd01V1695bN5w65zvf4RexKYz2DqOhnvvL+2/8EABg016+hw9MTeLyy9/8si3wQ8Z88cUXX3zxxRdffPHFF1988cUXX/7K5FWCEBJiT4TQ3EELV8mmFwiSDt7QNFiKTFdXxGT0+iWTYWRsSc06TM/oym566D70oU/g7vseBgAsWU4XW/d8XhesrUMsJl5gscbqQXp5JyZHsPWlHgDAnLpuAEAsKWigQN4JjVBktAr6MJnKwxJvYUSIUqHTUxQKNCBk0LsbMiTlqoRkDPSNoVayvF5wGaHvoRktuOETrwMAFKckXWxMPJbFPGJCvFrMiIfJ5P/7ewaReYBQ/NUrSSC9YvkK6ccUpiZZli5hGcl6Xvf8viGMjtKimRAPfzDMhgWiOobF0whBDW02WE4gEcVJIYb6tcyiZ+rAHnqr/v5zb8Xff4Jpks88640AgCND9L5qYfFyHqdomuZauCtE11xLvkIGBT2og4DlmN1ZlkOc5xIv2g6iSEK5dA2mtFUZ6aMhF3qtLNDKuu2Q41klBMW7qNJKOigi23RTOMrYMQIK8qpBN8TC7phqvZDD6p4ewwAMFRkl9TRgeDx/8JThQoHLDqrU8pobnOVNk6nKrvReKGSNabl+Agc9pLxCmsczYZdb0TUYUJZ1a9qj9XpUKlJPuo4RR1TJhsf7osirNdgOf3NIpVQ3XG+GKdeoZ1g4ToTQUSGyHm+rVcnsCXc8qPHR18+QkYhp48KziMT4/QaGBD3x0joAwCkzuvHSs88BAC5+I639j0zwuk17SzjrDRcAAHZvI/Lhru/fAQAY2ncAtTrXotMl7fmzU2sQkkGjEGEqnXMkYDvoqXCYa+VkimtYc0sLlixhyNGLGxkuYYrXMJ2eQl0d19PaBnrehyS0JaADEZWWXSHxpK9KADQFm5WHFI1zTSvk8k4az0iMdQlLaFw6n0ZdQlAosj6q1O8BLYh0ToWKsMyubnruTbPoPOcd24hgqKurR02M9yxm2FY1omLJeswQMuL2mURJbtsmZLlNjSjaRITW1/L6+Z3sn00b9wJB/qZJyvHUIJ/30OgkpiyibOpaFQ6W9zuxox1z53AzGN1EYuH33v41AMDmwDiCkyxz1jlEvt71BENHLjj7DDzXw2cSrOPzDgtKxG6pwwuDHCvfvutnAIBrr7kKANDR2YwZ5xN9sXUDCQsbDfZjz57dSL2ZbZ/ZTfJaq1nDc0/fyfukiVx488UfAwAc6M9hZHJc+o17wZf/5fMAgO9+6V9x7mncjwak/IN7etgfiUFYgpRKFLh22kIePJmdgCHk5Ef2cl8ZO8TrMukpfP1HRE189bav8DfZ32o0DTUSYvPgfUSuxiWl/ZKF8/CHRxhqk0gQkTFnLsfH4iULMTrK/XnTRqKE33QdUT2ve30jfnn3AwCABXMlvP3IAMwix1i+SKTy6WdfBAA41LcZvYcZlnXqyqUAgHXrOJ+bWmoQjxJ1EtZJlr1pPed8bV0LinmVnl7I2CWZQzSRcLysSQlzCwckcUV6EnEJ5UzW8FhdnGPp0L4hjI6yrO272I9agHPXsosoClp5cFjCBhcTXXzpZWdj7Tp6GrNptvPgfiJpRsd7MT7FvlqeZBu2HtiFzDrOr6Z6zqVsh4RTL+7Au28hEtoSovB3XU1v5trtP8OWSc6Jxg7WOZ2XMNhIO4ZHibDqkJDCsf0cZ83NOtraqRcFo5zz0Sif5ehYCX191DciSfZZc0uN9OoYgpIApPeAoIAEaTt/YRyGEHkW8jKeouw7s6GEJ7Y8Jr+xfm3ziDBqTLZgyzoi/y67mMTwO/ffg4xCMNWyXvv3su5WMYTZczneo0nWoamD99l7YCOWrzwPAJAZor65cx/n8FWfugZG62IAwOFxjq8lKzi+NGhqyExDMZumWRUZpP73EUL/vaUaOqQSIVQtnMbZk3XDoelQyF+lo5TyBcZxAQgK0r7k0edsp0ylL7qkvpYHucFPL2Knsu4OxNzRu12V1UXeuE3w6sOqjOrIkZf7rVI0TO9PJ0QNmod4W/pY9BiVOKayNOfbURAplucsD8DKU0TlM/XUscotlU7t1N1TL12xXFfgQixbg2mr9UIh2V2ybV10Y2dNUY/LEynhrC2qngHtqKnlLdvyRHeUr0nVkD5e0fTKd5TpqPxqxNHTkEz/RaJVvkdUuZ1pq8XaG60h73bBQNkxLRyGGn95SYQV0PgeX9ccg56QZ5OiTto/Qv2xqbMTs7pnvmx9fYSQL7744osvvvjiiy+++OKLL7744stfmbwqEEKRAK3Nuh5C+0x6UMQADV1ZoosmbHF7GHK+GaQ3LmukoDXQEvbMPd8DAJx9LklXr7jqdciL13mUThok4rxuslBEPiuplvMqdl+IMLMZtDXSwxYs0gI3cliRoZqICXGgbdPblMvRMtfZ1ozaZvGi9ZIIOl8U73WgHpoi1goKIXaM3tcVJ65CSs7Likf70Y334/EH6MXsStZLWfSshuqbkJmgJ6omJsTPkj46HGzEvh560wp5chSkhFz0gvMWQ7fYV1Hh1NAjRGjd95tnEAqwzSr9HyR+3tAs5ItCqpigpTE9wXMOHNiDtEmLZKKOZbV18/K582J4YQM9qsuXkaS7JklkkHBfAvBYblHdsuuK8g4YZefYtumkkhfgBwJirQ/ouuP5Myos5rZtO/GatrGukWMAACAASURBVMOT51q6DUlFDA/PD+tZgq1St6usj4oIDYZjFxZuXcfSbmseZI8yxxremFgXucQGKsSQ7XLzVHAWAZrjZXHRQMe2fFclaJM6VMaee/tKffemlOen5sZ5w0UbqWPuY52OllEHnXBj5WHSPd4gu9zSrgPT0GKKI6g85NolzgtJERGZX4qvyQZQMMtj1at5W46HXLqah8LxdnjOqTyvTZf53ZjDxj6iEtauIefF688k10bqYD9GAlwjnlrL9N1amB7ujlgMt/+vfwUAbH7xebZPOIJiiThM4VBJLqYXP7f5MQSETygvCJyokMVrNlBfTy98UFJd9/bSy55KpbBbeEj6+riOtLW0OG0dFnRBJi98OuJtNII6ArJuj4xw3QoISkQPBpzzdJ2TsKmJa1o+k3fiybNZrr8ZQSvZJhzCy5ikllfjP58rIFmTlOt4fl8vPek1NTXo6yWqYf78bgDArJldOCgptRWiYkiQBUcGBzB7FomjVywnn87eDURMzK5rw4JVJHnetZXIrIYaPsuZzR04IATh3YuJIGiqYV995AN/h0338xnecf93AQCHZQ2djIQx2sb2XHvjLQAAbQ7XzKGRg/jt98m70ySIllUrmCzhypv/BlP/QeTBxBG21ZS+Gw4CEGLlHuGGuXU9UaQzl5+A9k6u6Qe3EjE1eJDPzyxO4dOfIaJj6UKiWbqaOvC2t/0zAOCpp7g/Deb43Os7WvHkLiJyv/mZDwIAtjzDNOMLmjtwwkzGvffvIP/LwKjw5NgaCpN8Thnlncx7kIOy/tY1CQeWIHFi0RA+fBPvs2Mzn0m9pHjVtRJiUZ7fc1g2f53lPP30004Ci/PPJ7Ju00tERyV7I7hK0FMFi/cZm6J+cPWb34YLLyY675c//yEA4O67foPaWqJJWts45l5Yz7KiMROpSUEWl1hWOs+9ud5uQ1LqevAAETsuM78GrcC5ZxiSlCHA/hkcGsXcBRyPJSFPrqvneAkETDQLv1BBeKTCsm7Nnd0N3eoBAESCwtXTxrGemiqilOXca2thWUZcdI2aBGzhvOns5PqxYjXHXt/QfiQUmi9FlNJF80/ES6McRzObiNzpnEcOrMefX4P5q4igS4u+Y+dZdiCewNZdHKMlmcids3nuvp690ISP4fRTmXq9fz/n28OPrMO8BURA59Kce9u2E2kUiJoYnyJK8bRFpwIAxkf4HJJ1OiYnuRb17CFa6aKLWU6fvRmjA0IUXmBdeg5xfJ0777UYOcQ19okdQp7dJkiv4CxcfOUbAAC1Tey/lsCJmDrIZxiPyzNJin5WbMJgH+vcOpPPu76Fa+HgSAZ7D60HACRirMPcVo7ZX931RRzOCOm9zn58zeq3AADedMFnkBN0pFo7yzk5pu/53s/jFZ9I+i8vR3tm1VLLK7Esy0EYOuUotIdtOcqnq56Woza8ZeqaKsfyoO4ruW9sh39H8Q6alnvM0Smt8nT3XryCU5RuoxwR5O2D6X3xShKCHK849fWg1NU7h2qfZZfcfrfLkTH8rr6U6/IBzfAQTKuTPOfY5Yc0fXpinmpp1qv977ZDvdMoMm8Lmsn1oiT7pRexA0t4pEou6hDgsFH3luXG5Q3SDQe97Y4jN7rG5TAt1/PLUV/T5ZWsQcdKMf9ft5ZVS/ZDcd8Dpa/lHcSLmDIExa96oGSZKJW454dEp0/luO/G9SAgqev3HaI+0TqL+9Lw+KijY8xduuK4a+uLL7744osvvvjiiy+++OKLL7744sv/cHlVIIQUX0WpaKO5lR7UgLjzjZJ48c0iIB5vXbiDTBC5k82mkdHoBdq5iV7Pk7okM8+MIAYk00hXE9E4qQnFoROHWVK8QGJ1k1TMVroBpQl6weZ2kffg6nOZJri1vRaNLfRqGcIREQnRyzN3/hwkm2jp23+I6Jx//pcvsOxoHfYcegIAkJuiZ6q1nl6rSy57A3JiPcwYRB395sGfA3RmIRxmvXLCo5TKZ9EYU151eqHNANuSyTSjrp6WwYERejbXPc+61MUsnLCUmWRsyXjzzAv0dvUdGkV9DT16tvS1yl5VNC0Us7z3jIVMJfvj75BH4vQr27B9F/mFTljOfp+UtMcNM7Joq+Xzykv2mLwXgVJhmHVT9lUX15LrppoExHEg5nnFiRIQu2oAZlmWAQAoKS+EqcNU9ldloVWIIdtGSDhU1OleUMq09OyCC7Jt2/WuOKkX5JhmHDXe1YscUZkeXIO+4eSHrEQI0eunvDLTvRGV9yk7ppwYThyy7THIV7MXlyO5nKhqzVOWVX6Mt9W8l0+PrYU3XttSLfakrdQ8R1h2pdPHiTe33Zqr6wO6hYgggiJBGR/yaVpA0VLtqh7nXHaf4/AmeOP09SpIK3UsGBSEYpLjbGRiH3a8RGThS78hT8W73vJ2AMCDh45gRKcHoGOCyIqcoF92jIxheD+z99zytusBAAf7egAAv3v8IdTN5rzetZfcN6lUyhlH2TQXmYYGIgvS6TSam8q3BsUHVSyaGB8nCqVVMhiqTGTQLIdzSGXNcPpPN5CTlLWKA06xaWUmUg6PS0TWueEBer8nUhNobWqVenJNUee2trbjyBEiP0LybFVaXMssYGqK3zuF/6dvgOvkactXYd5ccua8+OKLAID+oWEMDPG41sR+iAgyI2/kkS6wjV/6N6KwhgXl9NTjf8Cb38EMkIf3sc433fIJAMCPfvhTLCtxr8kob36ez23OiTOx/OLPAgCu/hwzYr7mXCIfhjYdwMHnicJ6+J7NAIA3/+2HAQDX3HgNxtNca5/6HpGXn93Jc05dugTzmogg2DPEZ7RvkqijqBZDzWyiUQbAtjQK39PUCzuw79fcl2LNPKdOuGmWLliAeJj9bYxxnNz+w5/igtcwE2Y6TpTGmz/6bgDARZdeiak0PVZr7v0dy1hIdNQpq1fh/DeREy8sXHO3/RP7U9NsxAUxlpHMJhHxUmqpAmoauCeOmER2be7hOC6W0tj6zBq2Q9AyXZKptLkxCcvmb++/kvXTBfH2/PpNaJHzLr+SyNV9fRwL8foEApKh7rRzmF5802ZyM+0/dBjBIOtw7y/YZ8VMEK1ziLD66Mc4FvbtXwsA2Lt/G+Z2EXHT3kR01IWvJcLoW//5DSTjnM+2znExew4RTeGQiU7J1PXII9yfa4Psg5OWL0EkLs9S5smwzEmzlEcywzZ2drFOdUJOONZfQH8v+6q9k2O7rkD9ZWI8hWUnkoMmnshJHViXibSOvn4+09POIG9NTYzzOh63MH8h50trM7OvxacaUTyZ7VqwjGVu38W1qS0O/Pg28mFZAa5J3YtYz9lzJxFvJi/W1Aj1ucxoD/8fyCAQ4vkPPkj+opWricy75OIbcPt3iQw/coTzbUIywCVqa9DeRHTSUD/nwtgQ29LWWQsryme5eKmgnse4/re3zcLYEFFGK1axDZs2c86HkMRoinWwi4LI62Vf1Xc3o0HWos3PECl3+PBL6OrmeNeMYbkf9cXDByZQVy8orTTbXCc8kYvnnYZ8VriUMqxnto/PprfvaRgJ3nP2yZIhbSvH7+Oh+3HuKUSVOim95dOyrKp6wCsRHxn06pBj8eJoWvl3ADBNl7dQIYRcRIfihTGhmZJxWTKEqj09UrSRk6x/JcUpY3u5cxSau/y+3gy5donHCro7Bt3MuKoNLhfRtAxpXm6eSs6hKuJGHvxxohD7Vcv09H+pEqXj5bBxypre5sqaefXG6rrn0c93fiu/oqwsB9GvefR09b5jeNcFiYiQjJWGh0PILMo4MsujGbx8P3rFuDIM46hcQLquOwgyJeXvRNWfrxft+P+7Jnl18j+dVGuvi45yx6b0lZonNjNce0WhsHRdd3RkVVY8Ql3NsIBclvvDVJZlzlnM1PSnNjXh4JY9L1vjV4VByCEvKwTQ0MSNUqVnN2wX6hqUl2uV6q8k8Kg6o95JCzueY1m9h/jysCIwE3otr5sQpSkR43UZaxKaKKHKiDBFnQGL5p2Fz32aJJVXn0OCyEA9rwsELWQLPFF3CK5FsZqcRFYe8JxFfBjfu/0uAMCOPUfwyFM/BwA8/RAVs9QBKovRujyCdSx/+wGGEtzzi7txQgcNO1lJ2d7QxnamikBmQlLTmUKSbVNx1wOAaUpYncylosD8nn5yA+JxntcoYQgbNxNKFjWiUBxWhig1mRwnZigSh1HDwtY8z1CWb33nDgDAd26/D+9+P0k3+wdYzyRvAZTcwayMSwVJzWsXSl5mOOnHKgYh7+KgzqtYKy3bhG5ZznfAY5DQdCdQyVmUVcpDzXII4NQLstfgUlILolQoJG3QQwYCAvVTxGaWLFyWaaOk4LMOalS1S/NseCplpRhHNAua7hqV2B9VjDhV0pgrErhjkap5SSSdxagihM6rMFZCXb0Lesm5n5qTtscIoG4s13uqa8pvXnOQ0zJbGQp5QVDTXIOapsLR+FmyLAfq6pQj/wYM3TEgqTDCgG4gKgagUFApMfw0LRuW1L1qes1XIFVTYlaBbOsyv4Iy4YY0GnYe/80DWCXhFV/8/JcBAOMBvojPr8lg6HcM06oVIrlQlGW/5bpr8W9f/g8AQFrW02vf9k4AwP2PP4ZShhuFJeFW8ZoE8pKiHbLGBjWuhZqVdkK/VMrZvBBOL1u2FD09PdIQ3iefF0LWYBDRKNdB0fschYBQV0W2H5P7SrhXOOaQ9Eciar6xX2bP6YYl4auKnHtE6tbU2IjaJNfPXRLGpoxawbCFguwJ/UN8aZ6cYj+euvoMnH322ey3t5D4dc78OWiI08jf18v10BIFqTaZxJyFNKJ/4wffAQCccTnDjF7ctx3xWjox5i9bCQA4+ULuF7979hmcsowva1/72v8CACwWcv9SbQAHdnODDrewz8Yj7Md3vO8GvPN8Gg0+IYagu/7hH/n5ja+gVsKMTjmdBqQlAf7f9/QLyC6mEeHy698EAHjpURoTnlyzHgmDe2tikv0yIcaEmUtmo2M2rysO04GgR9n/6w9uQm2QBrlP/8PNAIDelIZ3v+e9rPMEjVPFDMfH8/f9FnaeY/Oqt18DAEiL8WzfwCF891fc//b3MBwPMicLlg1dVyT7YqgVxTMcroHwmiIoLymjg7xv2NDQ1E4j2I6DPXK9rCNBG0Ny73fcSIPQ6CiNAV/9+rfwjuv+BgBQE2O/JIXE+umn1mL9czTKnnASX+QnU+yrF7dswJo1NIKNSsi2BRvN7TQu1SRYl+FR9l8g2I7XXHge+72GBp2n1qyVutfj8AGGzp1/AVO3HzxEw1P3iW3YtWM7ACAWYXvq6nn9a6+4DONTHCvPPcuyVl90MQDg3l/9AsNHaFhIC7n2jR/6JABgvDWDwV7er/8QjYidM1jvzo45GFNGZpvtWi0h3i+s34ITT6QTqEY29j9ICnc9GMPQGMs8fJjz8vzzbsJZyzlfdmxmG4KyrzUGDVx2BY2Ca55lHxf7ed/HDmyEmZL6NLPfu2axH09c0Ir77qXO1NfL+6gUu8WBKPp7ewAA2TzHTmsz+6pYtGAKR3wwKnqIzb4bOAwsWc71YnSc/bF+A0PW6uvrsXAxjcbZEudEWzv/33vkCJYuZ8jeUIrHbrr+7wEA5535Zjz/NA35mWYamdprgKY5dC4eHuAaM5ri+J/RUkBMHHPyro1sirrr8kVL0NfHZ1if5Lrx2tfR2J8dGsb6x/gMcmn2x65hIRC9uNNZd/UqSTiq6Q9HO+YV3xD06pDjCVn3Pio3EYhKH190wncqdUNNt51080oPcxKIWCUncYujXXudcIZWdp1btg5LkRMrHVk5WHUNrpOx3HijaZrHqWg793G+e847eoe4BNB/KjkuMmrD4xRTv3mMU26dlX6rKAvc/qh0fvPe5dfZtmtYcFgmqhhJHP3e89wcx7d6fzGVAxMO6bdtS3iYR6/VzHIy6WMZmG11ruamlp/2bqPbjoPZLKlzvGVNd4ADEhZZ8Zu3vf9d1ivLUuFy8oNrW3W5OaCAAh7HuZqnckYpB0D0o6wo4PXNjc51TRJWfizxQ8Z88cUXX3zxxRdffPHFF1988cUXX/7K5FWBEFLoHEsPICkw9rBAtgMlfgaNPEwJlyiKKyUYFcLjjI4a8e7u6N8LACj8nnD6k99yHYKWeMJj/CxkhYA0HHeslQUhZlJEy6FwCZe/gemfc5K6bUxS2oeDSeSLEvagwp8MetX0YAmaEEZPpenWrLfpNezsno2Pr6Yn6Uby/2HvS4S+a+FR9A+zXt1zzgcAXHHWdXjoxwyBW7WESKFoEz2VowUgE5PwthTrEikImWRwFBPiZQrXED1ULLJ9g5M6nn+B5IgnRAnP3iCpjZvCM2Dms9Iu6eMAPYK5QhrBMPu7VtK2fvHfPg4AeOCS3fjiF/4TAPCeG69m2XNZ33wqh1gbPa9agH1dFLLLAIJHRVEcrzjWZst2EAuOV8BjYS8qS6tejvghkkPOqoCsaprtyYUuyBt1XcmErRBCCuDjoJtcD4KbetwtR92nMvRLkYd6RVnRlcePJbjpPAFBCKkIOLHCM71suafA9Rq6duCCpdIO285nZb0U5NeG7SDilPfedpBCLpl0ZcCf934uYbf3nHIPg0MGbugI6uVIppIiwoMFrQIVpTlwVSAgdVap1YOG7aDfAmq9cVBStoMgsyrq/kpT8TqImJILsXXJBT0hY3JMhYw9eu9vAABNZggfei/RDF//PdNpf/NHJB2Ojo1gYZIhBONTRNQUDa5pz27bgL/94ucAAL+6934AQGYtyXxb583F0CGGP3QV2dKamgQmxhhuVS+E9Yqovb6+HuMClVRoTIVK2bx5s0PuHA0rSDk/TdNySPDU880Kc3ykJohoDde3KSEPVmnoV560AuueI9KhJGu7QvBs2LIOMzuE8LiHbYhEeF1TU5MTtqbGbybLda6zswWHDhNFooBQiqj6m9+6Hd/4Fter0848DQCwY9sWhGrYxlIN19WkrK+FQsFBsT73h6fYOhkSizu7nXTW172PoX2fvY2Ey3sO70c2R/RDfSvXQ4UienbHbvzyGYZUvWE1Q5JntXL93j8xhODF/G3uLUTZbP4m296NWcjLmv7wD37Jds3g/pI2gIGXSHLb38/wt5QgZmNt9QgKQeRQjv0fjkbk3F5EBCU2OcVjmjybXKGEpmXslw9/lKFwVj6HuhhRXjMM2Zda+Lwns/vQMoPoicMjHF9ZeQ6FVAa7nmCaeolaQ22IYyJk68hL0ohAjg9Ml2MlLQxL0MBaRpI+DBLtG0mEMSAIiWSc+5IaA6neMeTFw3njh5navLVlptTFxjnnEuW1VaDUVoltqK9vQk2MbZ6cZNktrdRL1qx9Elu28lnMaKfOMWt2N04/l8/1gYeZNv5IP9t8yikL8fvHfgsAGBtn2NRUioiausZu1MYZZjU+xDZ3d/C5awUgpEnWhTzrdepqhq8dOHAAaUHutrURSZMV5N8nP/ZJ3Hbbv7HfJES7VOT1r33t6xASJGLRJNpl/QYS2M+bezJ2HSCarK2L47A2wrmlWbsxkSLqaNMmoqP6j3ANqGlOIGtKCL6EFg7lSrjiVI7bQ8+zb0sa2xdtmoHnJPyuroVr364N7OP+sXEUheA+m+Gz1KLdAIBzVi/GNW/iWEtw+COscXw8sf7XOFnCpnSNdR6b6gEAzJ0/E4d7WP78eSosjOPzmQ3b0dPDZ3LJ60mW3d7AMrdunMLGF4g61KNE87Q2EaXTVmtg93bqUDMlCcruw3ymyW27sexsjqvELI6PdevvQ3Mdid8zKa61Y1QXYSCFsX6Gg83ooj5W1Dm2t2z/HSZHqO8daGKSlNws9tkFp5yJ9wiZfb6HOu/T27neb9+2CbNb2G8q3EftM7quTwvd8KKD/9xhZH6a+z+fOGnCTdOTZl4oIWSdDGi6kyTFFN0wJxtoOp1CKS+6WUgmoUraoeswFApeKw9H0ywNJSi9VOoScJEu+jREzPQwLRe17jmvgiah7LwKSon/KrFQfgOlQ/GIeh9Rep+XlNtFxwBwSber3kVlivHo5Zp7zJL3HvsYc1DVs3y+VU/ZbsGGURFi5qCQLMtJmVONusIhqpYXkqJHR6/sK4X+DwRdAvSS817hfWepjhACXJ1fybEI8l9ujaqGrvtzSBkyCEymYQSd7EA8x9Griw6KSrMl8Qe4tgdDYehRfj/jfO4XYwUiWJHPYOuL3B/O6pp91Lr4CCFffPHFF1988cUXX3zxxRdffPHFl78yeVUghDICjIilS2gK0tMTaaRVcHKYnimjlEQBtFjHJBV6cZJepNFYDhEh92uaSY/bJvHg/N0nP4xPf/YbAIBgs1gtxatumGkEhVytKLwiykMdNgIYHWWZelCIsYS3o4A8tFBlmjxphB2CVVTxruK1ljS/xWIvMn28T1hSPM9czTj4UslCjVgDQyla329+y8exYiZ5Ig4N0zN9cOTXAIBo3ka8lt5SO6dS8dI73JJsQki4fAwwhlATXoV0bR4DRXr3DjzI1Lid9TzHMgZRKgi/R1GRw5J3I2SWEISko7XZ1mQ9y9z5hw246ham6X3dNeSuWPsoeZMSy4BDEcblT2b4LEMxeiBzhjmd30Z6s8xS6THZKqJoy0HCcEyYlul4CiyxGgtlCUqWQ2fjeiPks+AhS1PWeh2uB8LSFBGuQp+IVd0IwFZWWxU77dTdhlUs5wlSSBrTLFSxYgtnhmk6udNdtJFCl1geEutA2fUl03YQC077dMPDiSR95XhNXC9hQBG6e3iUVAx5JaLI0hhHDgAWZHw45NKm64Wo8EbYumutDxyFII7tcpFBABDSbQjVDoqyVAXkoeqa5vD+KC+BsrQzLl2epVGStujQhZvEdriDpD/MEgJCpBhUxOISi2trQFo8ZLEY1xuX7ybikBgrJFKxxLluh3QUVJmKS0nKDgZDyAqPxdAYvdfxNPvzjJveits3EDnyv7/2aQDA+BZ6gK9YfQ7mzesGAHzrx+TvCoi3ZrB3ErBYv2SEv02M0bM9NTmAWIJr1+g4vdGlbB5tLZyHCuGTTtPTb2lAqahI7GQMyDgJBqKoa+d1U1M8vyAei1Ao7IkTZ9tDQbY5GitBqMOQkuGQkbV2y+69KErMs5Vi3569lOvelr2H0dpGPpISgR8YAe/7ox/ei2Qt23XDjTcAAJ56gqiqPYNp1LdwbW0Iy9oiY3xiNIXly4i2qBGCvqGhETSNyL3P4b1fPEKEwPkdq7HpKfKdfPUHPwAA3P+TnwAAXtqzAZecQ0RnspZr5y8O89k8cd+PELqCZTV0koPlOz9iyvjtO26FIe1PX0L+kv5hjolSYT8uXUTkQUC4yJqS7PO+qX7oee4dyWYiMmxBwDaG6rFoybls/w4iM7SJfun/HDIypgM5xeEm3jwDGC0QOVJTw7IVWXkyEkHPSySrjQTZV7atIzPJdTee4B5ZkDXQiMYxZxl5YF54juiTvl7Zi+waNMd5zBYy5aIkdciXSg6XlfIP6+IR1e0CgiHuORlBDGfyMu9iQWji3a6Jcw4pVFpXVzfGJzi/dm/axr6eybGTaI7iG9/5FgBgbifbFTXY9njIxNw5RIzkbe6NL24kqmVgzzDagkRkmTnW78iBA+jv5/jIaeSfijUQCbL5xRxOX0WC30M95Jbps7mPzj55BYJJzpOf3kmE4NndwsGlzQFqWVezlWVeeynRxfO7wvj854UXKMM6JGqIfnvjW96Kh57i+vGitPnZtdyLh4YOwVJ7VVzqJ0irfeObsWpFFzs+x+v+z/fJl/X686/Edad8EACw5jl6GXfuu4/9X6qDOczx1BBnP/Yc+QO+81PqDavOvAwA8JtffxUAMDqwHeefcy0A4JLXXAkAePQJlrXrtnEsXU6ET05I3A/tJdLqzkPfwsFD/P7PX/zfAIBskei7WfNm4jXnkJdo937yL/7s50Q7He6twZwTiYZKmdQJp8JEPyYaDuHaay5lf5eIxAlHOO5XrFiJvKDr9u/lWAsLP5FRCCMaIPpndifHycxu9uezL/0eC07getXUQERTyKrBE/t+DADYupHPsknQ2fFgBJrOuRqJEoW2Zyv1pLZZcZy0lAjGXT28bvvvubYcfu5ZvP1tROy1ziPp9eoZSwAA84b6Hb5GFylRzt9R+R3gvllJ4FruVT8Wd81RD+Gv2e/8x6Y9/2ORU44ekivCCgknm+hQRoH/b9u7ByeuJqIxqfj5LEF+mxYygtLICVIzn+f6WjAN2Ea5vmhrilxah65xjVYRHxBdsWSWnAESDpejyC2r4KC/tQrd17Y1B3WuiSKo296+0SrOd9H+Co1jORxC+jRkkRLbNj1cmuXoJB3VUPvHASGxAy6IR9222nVOfdW5lvO+UzkCKomXHZG+cWIQlN5dFqlQwVnkrYI3IkLOUZxPtoqQ8KCTDL2c69M7VpW+qHR41WbTtlx4mIj6V+Ua8Z6v3jlM0/QQ46PsmO7pz2nPTYdn2VFj1XJ4d6rL/+869Uqud8+1TOormk79RQvrKEmCjIBERZVEH7NMDaEa7hP5AvfbgCCrM4Ux6LKvD/dQ5xrdwX1j877HsG4/9Y+zXv/2P0kLfPHFF1988cUXX3zxxRdffPHFF198+R8grwqEkBeBEInQ6qVin7PKw4+AG2MnFs24ZAtDIIzxAUE81Ak/TYSer9GR3bj7J3cDAD78d+Tm6M0eknIsJ1ZS1cGJo7RtD1qj/FO3NQf1oFXzqGjlVnDbIafRnd+8XC/eT9aB3+saW/C+m24BAKzbyCwwn/rHXwEAurqjmByUDC1Z9lE0TGt2ujCMCB1YiAjyKVZDz+rUVBHhAF3tWlqlrua5ybpaOIgW8dxqumsFLuV5XX0dvby7dh4GAGzd/iQuGmNmnZtv+DoAIDBET+dU7z60LXktACCf4W/RJJ9xsZRx+w3TRXOs515LdzlvDKzpnhQnjtUT42pUSf1d5UJ+xqfc4AAAIABJREFUuD9UqdX0+6gsDC5fUGnab3aVuGglrmVed5BPjq3eyd1p08J9lHpUjt+yLFcV9bWr8ASV8QxVZBfzXu+UIZwe7vXWdI+IQnNZlvOjyuxVXjfxZDmeFPXp2qt19dw9McMBJ6tFuVfBW2+9SpY2l6uI5wcDAZjBcrSWykKXzReQkLWoKK6MsEIV5nIIqbTxgsKKWYJgyBRQVyNoO/Gy5ItFaYuJeCwo5fM+s68gX9m/fP3f8eAvmYlp2RyiHRtmkvtizbaN+MMecng0nbyIZR+kJ2ByqoC7f/kzAMDHbiV6YGiE83N8bAKNdUQ69PWT1yUSMJwxahjCAyCuxHw+56y/igpJpZrXAoz5BtzUmJamvDkWCgVBRUm/hAXZkcsVYEi2zNoE14HhMa4/PXt7sXgevduTg1yM5s1lhsa3vq8e376N2dZOWsDsa/sOsg2b9+6CKXW+/8k/AADe+653AgD+8JM70TGLiIeHH6VnpEnnojiRyiN8IdewdVvYn+2tLYjVso/CEdYvKBl+mk5ux7s+wfXNln65/lMfAwB85Pp3INfENo4I79L1H/sAAGDT/rXoP8gyHr6HyCJbeGqWdM2CJamkzVF+Xvs6ohxu/MAH8P73MYvXxk1EOkxJZrDxqSkkZNxGhUvIycoX0XHrZ28FADRI+vnLLnsNAKB/5AjiwrNXI5ngFLqtZBYdZJAaA9Eoy7Ys0+FesgRRlMnk0NHO/UjTJZPbANFoiboGvPg8kVWarCOhAPtHg+6MHU15LC3eL2cVHIRQOFSulnCtUOg6QesGWN9IJAJb1BjF26X4zQaODKKunm2eIXw1/b2sZ1DTMDVJ9NTQKAdmXYLPf2JqEFu38rmdeBKzf61fQ09bIhbAsoVMvb58Fs9HvY29kjVNoWAz40T1hbANff2cX4lm9uOBXRzjS1bMxCkLOe9rLiWvTSm/CwDw1NNrMesEzvEl3ZwLOZ2exOTCEzFn1XkAgFPDkiFNeP2+cNs/YTjN5/rJD/4DAOAnd/0fAEC2ZwPOPYXjITdGtA0GOK9HAhlMzmNZO3aTD+eUhcx0d8N7P4h9+9merMkxnpEMa1pRQ1MHPZZjabarqwgMHaKOtXZcxphwLn74llsxNS7PYpjz/4RlJwMAmprvweAQr2uW7CiKJ6E+GUaxhbxOv7j7p2xzPctZteREPLeeHGQHe4m2W3oi+7Pn0DA2rFWcRZxnVpFr9KpTViCXIcfC8AT7vb2zm23oOBH5NJFcb7r2jQCA3VvZL9GWPLZv7QEAHNghHnGpZ+/oKG7+FDMDds3mGtPaVo+WIZZ7wOT9VJrq2rZuDPdSrxrey35c1sGMcYViEQf3Ea118UXMhvijn98jdT8ZedH7inHJxhfh3GpqrIdtKK96+SfwMjqQL39S+XP3sdrTA4EAioJCNtXzFoRyb28vTsIpPCb6ZkEQwaVSyUUEOfuDvPdouvNqo9AqSqs1NPddyEmfLXuJZlvO+5LSuWxN8dC4OqWjwzpoHZeTRvPw1hwtfTmvKR/bzrm295jTW+qQ866hleW/9ermZXeRY0dHCun29ONHRfiU3a/ab9N117LaHEfWuenvMtP5jLz3cPBEzmlHr/vx1EnTNOed43gQVl6Oq8p3FadM3eUeqpaNeVqUBX88Zj3/EqLLWp2RF/FIOIqAoLnHhrhHbtvOfe3Mc85HXvEsSeSRXuB8DSeCDufhzNnUNWbWcR/ctON+pKY8UKyjyKvCIORyiVmIJqjYRGsIhx/XhLAwqMMSQjMV+pJWOeKNGFasotKkUoFPiXISDBex8bn1AIBs/1tZVo16ocvDkpc7pQgX5aXNG6KjQkwsd6XyzK/KF33N/c0uf7ksIwauMmGUOBMgEMTAMNs4ew5DHJobqYzu37MLEXnBUbFRtkUDS23CQEHCESZSVNwCUZlgYUCXNNZxFaonxaSmJpCskxdcRTKn0nYbOiYnZUJLCMaiZQxjCEX6kZ6kIlwXlxeRixnC0WDEcfLpVKpHS2zLyBTh2aFYwkVvVpmXKh28FwpZkjAcZ3G3XGOC7Rgg1HXu5qCI0LwEigDHngM5dX5zWb6mLepVFrPKdPWWh9D5aEaZ6mVZxzTiVEsnqY4dK/Wjm+5yurGossyApsOuAA5aLgu2u5tWXK9rcEgFlSgeb822XUOes+S4EHbDMeiIUcppmAlbxrZZAQnVNM15Ti6EVNVpetstTZ9GpKiOBXUNZkC9qCqDI8dZvC6O4XGO21iIL45pCVeJxiKYnORLhjKg2Bo/zVIOqQxhnupFtbWZLwj7D+zDnBkMK9i6kamDn9tAw0TfSy+hQ0Jzsv2cU8kkJ2h9U6NDYJ6WUI+REZ5zzgUXYelSvlitXcf0zEeOHJZe0BGLqnBPbiL5dMYJ/1NRr5EI6z6VSSPgEJyz3y3pD6too6Q2oLAySLAfM+mcQwpoyBptqvU1EENG0r7nMuzPC84l8d3atWudZ3fSCq5z/cM89x3vfCu+/q//AgA4/zV8UZo9my/KyUgcRySl/BlnkoQaQiy8ZfQQxnslNKeb4RkLAiTgzWdMDG/nC3xaQmtTE6NobOLz+cWvGb5z+kkkkb39lz/H1/7xPwAAKxczpPmgzjFw8iXn4yf3MHzMlFDcZx9/AgCwc1cf+vb1AAAWzaZRb143DSmDRwYxYwHLmn8CQ1rOu+AcAMBrX3c5+gdI9B8X8mY11utrG7BYNnulzCuCzz17e3HV20jqDxVWKv0abapDxJKwqwz7Vq0BxWLRSfne1Nwg54hhTzcQCElYrsxPPV/E4cMMA66tZR9bErqbmhhHLCZk0BKOEI+yDZYFFMWpoMZe0KDiEwqEYJnla4OhB6UNmvNyks1KCHOYgzYYTKK+obGsP/oO0dAzPjqJorzcNDZxDjUkhSx6agqjMj9qaxmmpFIxpzMjaGrmPqte/BWRuWbDMVqML2NI4nDfOsQMtrG9VghV22mwam6NITdIJe3QC7yuvWE2AGD3pu04++S3AQDe8FaO+/Z2zs8dN70eC+Z0AwCmxmhEWDvAFO6Nu1tgFjnOl11KA8/ACMPFR5/uw63vZyhRUedefM3VfwMAqG8+GZdeyhCp9U/QSLp1E8P6Tl91OkoJjodD+5jo4sPv+qTUqRP/9OXPAwASsvevXsa5MXTksLO3RurFmVew8J7r3wMAmDuXoZJPPkui+2hNDP1HRuQY59v11zPMvK+/Fx/4EMPJdm9nCF5qjONkxQnLMTjEtTYQoeFqzwGGgO2K7Eed6DDRGI+tf54Goo7Oedi3h9c1t3Ce5LJ8Nna+HhvWsa15i+NyxSqe89y6dThymG1997tIkJ22JNwrvhRnreTcPXvl5QCAF9ZRYU8U96I1xOuee5ph/rk5XXjzNeyPF7avAwD0730cAHD+2TfhvEtoQEpPsO7nrWCY2O71T+DBZ2hoamlgP553JsdXe9NcdLVT2VchkhDjezBowNIrX2y9YTXVdc9XmkDBl7+8TNcX+WkYmqOAKXVW6SH79x9wNDxFq2DKWl0qFmEWudYqXSgie7hmVDMquuPMCempTBISKB+LPFdAAJZ7zNXV+L9l2rAqk7ro5jF18qMZOzXL67BV5x7LMCE6ZRWKAzdJSPU68P9jHZte3+OVanq+246j0zFMl6MTyGu2d02oWouXrV91f/vLG9IqnbvHJLr3OMIr60Sj1vEY4Kq/p/05JSe6k24o4EjGsVAeGaAjpqWDOk7BLiHnZNihnmOOcd/Nx5vRkKDu9eiDPwIAbNpCWphEPoZrrr7lZevih4z54osvvvjiiy+++OKLL7744osvvvyVyasCIaSLZbNUshEWL3w8TktXUazaoYgFU6yAQbH8hcK0moViYRweoEcpFCSyKJ2lp2fzlu1oqRES1AEhg10iFrV83rUMSviJQ3CmWW54iiLkg7KUazAqraTqHEvzpPgTb7mtyHm93V2R2ttjRFd1ChoBpMWrXtdE79HHbmb4xK23vh8FU5Hi0ruVjBNynM2mYdu8VyGrwkLoPSoW00ilFCku75dI0PtZyOsOYa6TVVKF1YRr0NTKskaG6XFLJtivzW2t0CWN5FiKXtDFK+iNT9gtGBD0UMYgqiEmxFfeMLmqlkmHF9GFESnYq/Iwu9BQN32lw8Nsu1buY1mnK9MNev+vRJNUt+4fPwRb022HqK3a+UbgaMds5z6VZntNgztwnd+0adZv/RjW8HLLvJBym+ZRz1fIMQXVNAzDkz9R2iIeKlNzUVsllItmA4ZeXj9NoUtM05O+UpEcu6ggBxEkZbngJdsNMVNhTZblIJZULRwEIGw4WVClCUHJUT+ZGkdQzssL+ZshYZjpXBqxZI2cL2gGCWVJhKIwBY2j1pZv/fAOAMCCRQtx8CWGbPyf/7wNANAwwjpduOpk/GovQ24SjUQzKPLmpvomDA3SK5BPc12ICYJk947t2LGdYQ99fTynpoZ162zphOJ+LGSy0mc6MhnJ/e2E47ANkUjEIRVUqCqFBsrn8w7CRKEpdUGjFPN5F9El4Y3qWZa0PBISilVKc7HbsYmIh7AWw/tvJGntC+uJdCgIYqU9EsXHbvoIAOB7d7D/zlxJZBEmUjj3JELfDx/p4f0kjKQ1Fce7zyRp7R2/IanugncRRfSRj34cH7yBIVknxYnUOrS7iPOXk2gzW8fnVRplO7XBcdz5G4bjrRZUUzrLZ7JvchCPf+mbrGsr17yePfTqFNMpzJ1DYtn6Rq55U3mOiZxWQlM3kSnLziCy6yvf/hoA4EDfQSQk6YCWE3SOIF3qkvU4+wKmtf71rxg+Uixy/W9oakamj+Mql+dzTia5j9bEE6gTFMvBg0ybrjy+Mzs7UCpJeICsqwUh9W1qbkARRKgkarm/1MRiCBqEZJgS9lsr6JD6pjps3UL0UFjIlyNR7i+WZUKTxBD5fHnor2VZCIgnWoXX5qWvbFtzxqZCyyk+/0wmg3yedZ2/kKiNSYVEy5vQdd5b7Vm1DdQPVq5YgrPO5nj46reZpr25gUTehh7G8+uJnAkICrc+SVRWqTCG1acT0bVoAdFe2yNbYckePyxzb+UchjeGtQzScY6n+VeSYPyiC98FAPjhL36F+lms+2NbSeA8r0T0W2hOEP1Fot9OWMlU9F2ziKjZvH8ngvVsx8QAn/dlF5C4emTgCJ7eRF3o9JXnAwBmzuV9W+Yuxie/SkLmj36IiJUrM+8HACzvOgG9/Vx3tu8nWnH5KURJrVm3Br37+EwvOv88AMCOfqIf5y5Yjvo2rg07DzM0q6tzFh56mIigd87oBgA0trJvhwb6ERDyzKKgvYR3Hhdc/BpMTHEs79nL8KxYiPOg79AATElkMJHiOePj7Luh5CDqmrhWZsZkn5AwxbraKK67hqGYY5NETu6REM2evaNYuIDILImoxcgIy16y6GxcdjGPWWCI6oNP3AkAOK3uVlx4OteK7ZNEDc1cQQRgcU8We58iWmjFIvbfc2sfw/8VtFte4/hvbuWz3PD8WhyZ4jifMYfPac0eItpXnDYbq0tEgPX1sr/ndDF9/eH+YUc9ioRUeKdCirvIwEodoBpC+b8nQuiVoCG88uf2gb98Pf+UhNOWJ7RKnWeoBCpZzpfhwSFkFAJapYoXfSmgazAkyUS1UHyls5cc5IxLVaBVIPMdlapKl08PSXJ/1Mr0atEhnegMD/2Aoy96ZTpKnHXQHB1UN9SeIwct77ivqGw1moaqIVyV74PHOFZW42MdkzOOe3xMjzh4JVL2/uQhmAbcMcT6VKebeKXIp+Mi54Z37ar43/PdpZmoHkXxapYARNezufcc2vMi9uyh/pE3qcd1zuVeYmgFB82XGud1s5LUswIJDbs3cu9++jHuIe8VaoP8roP48k+pY1x22RuPWhcfIeSLL7744osvvvjiiy+++OKLL7748lcmrwqEkEolnjdNJ3VhnfAClMSSrWm2g6LRJUViUayWqbEp1EXp4ZzKikfVpjczmBhD/yC9W9tfpKfn1JO7eF0qNc1z4k196NipFcJCkaRpmsPH4iARFPOJbsMh+qgklYYnTraCYKvMZqzSiefSqE3Ss5ybonV/5VLyTHz8pn/CRz5C8tJaOgsREVN8TVsNlBFXpbSzTCHdNgxYglRShKD5Ao8lkhGMjvE+CSF+zadYs4l0Ds2t9AI31tPTPDXOshcvfh2g8XnZmvCWhFhmKpuB8OzC1olYKBbiUpeJMmtvWX9ommOktjxW9EoCtGoWzcq0lDyrusXay83j3mO6Zb/S02ZZluMtqUy/qGkaFJ2Oi1LyfKo07FWQQtXIoSulOsppetumcQ0dI/a3qnW/og7evlJ8P96UkFpF+eoczbQcEk2FGFHxvV5gUaCiWZYJmArWo09/RsfjBfDWvaSyoQowTXm0dNvDk6QQLQqMFdAQjXC85wR5kC1w3McTtU461B07iWzZO8A1JhzRUTJ5/qOPPgwAmJiiZ3o41Y/1a8lxkU8Lr5Z4u9dv2Yi8zJ3DggaqC3OCHzxwAEWFIBNUX0AIZAuZLHJZfm+p5ZqRFQTQnM452KdQRzWce6lcznHdFaVDUhnFQRZHRpBECoWoFpRoNOpwESiEkOKPsUolGLK2qHGhUB/RWgtBWYNOXUki3e5OIjru/d0DqK0jWmbBEt5n9Vn0hE9awNnnnAcA+M5//l8ALuFsx8JujJpsY0yQI3v38jl8+zu/xuqLTuWx2UTp/OS3JKNtnBVH5zwiPtY+zJTqLe0tSC7gedF19NTkBZX2hksuwe8eJ+fKdx8m4fdD9zJVdnhwAotmsO69B4Wkf5SImrPOXeL01cK5ZwIAJsZlDBWH8fxGEkYHE0Q37N692+k7lT7Ylr6NCFJi/77D2Lqd6ImzzyN/yXe//V3WOzTlIAxrJcFASNao4vgU9k8STaLmTULizYeGhlCyeJ+ooHla2+h10oM6AhrXbYes37RgCal8IMDyF59AAvS6xjgCIT6Tnt0yVj18YdEI75lR41Z4AdPpHHQ9J2UK75QHDeiSnwpqSFIom5bhoKAmhKRbjetsqYCojHfFczMyQqQF0IfJFPm7UnLZ00/Su1afSKIk4L68lZW+Yv+3dTQ7nHvtCaLEXuidgg2mH08GySv00maiYcemetE6m20ci/G5aft/CABYfu4CPPvs9wAA9z/AcXXKSe/mfWoWomMWn8HWLUSc7f8tkV1NLV1oa5R05zM5jmGyr7e8tA+nn0cU25Ex6j2PPEoegXdclUQXuId/6VOfBQDEO4lUiS/oxvq9JDC+5sp3su2yvIZDAaxcRETM619Djp8rLmN/bN/1Eh5/gnXvqKPO1djeiUiYdV/zAlE558kc3rVjNxbOIQqqPiYIxlbOu+ve+g7ccjPvrfjaUOJY6OhYgueeJ/Jmyw5yBzW1cV3sPZJCvkBOsM4WoraWLiPyb97Cdjz62z/wvMMvsGyd3Dt17cCR/h4AwOAwx1PXXB6b1d2GsTE+w/patrW+jiigUtbClAyaO+7i3HvTG9kvP/zlHVg2j+iw004k8s8cNfGbe4lSXHw6uS4vfhNRj1EjjGCenEr2GD+PjHNu7R88jDecRD4hI8g6ZGSNb22d7azRUVkbCiXFyxV09OZqUo2D8C8nfyzS57/L/abLf2kKek2t0aajGyv9amqCYzafz2NomLxmEUk0oNZXXdcdrlOXn8VFcypdV7042m5IBSytXIdylXqvli76n9JzLc9viufTKduT/ETpytp0nfxY4uircPVRs4Jzh4TH8l6m+IXklGoE1sfDgaOVrCpYHxEvSq/ikFUlSU7Ve3vKODoVklaVMNr5fjRwkmY7/6hXFdtBXr2y/j8eORZpdvmx6ddVch1537Osakimo0SK8P+/DKooIHUZ7KP+uObR57BXePzOvJAoZsMkCvTJxx5Cayv1zYWLeWzHs9zXgnMbYAla/JJLiCrO2tzDdh/Ygs7O1peti48Q8sUXX3zxxRdffPHFF1988cUXX3z5K5NXBUJIxa8GEXBQAm0t9BqpVNSGobloBCiEED0k8WQdIJaxsE6vUcqit1HXgWiUXDf9/co7SAl4LNe2yrjjpMfWXZ4aJ3xVrOiwHaSD+k3Zg20NsCEZehxrpTfOsbqF0rY8/0t2MkPXoJkq2xeP9x6mZf+Ky98MS2AMX/n3fwQAjI/zWLhWg2XTk6R4Vkoltj0QshCOsP/CUd4nl6U7NBjOQcAQ0CTLmMoCUBPXMJWiRzQu/BbJMDkzioWIG7MusfK5PPsubASgSTpUXaz9pRyflW643oCqSJ8Kq69t207aShUA7CC1vDGu0pcl8VgEPKW7iKTqscZeqYaacTOKuedNdyLYHsSTXfHpsXgbqmx1me5k2LIrPiuv9f5v2zbMY8TxVra5rAz1k3gmLM2Gm1RMeXrcurvll3tZvClClXjLUfcxHK+M4g1yLeQOLktlaNN0d1xVqbsCr1gOR5JbT1NKCygPh645/Vwyy3vXtu1pPETKyRWMRDEinjVDuBoSgtgZGBzEo48+CgDYuoN8OG2zOSeSsSB+cRezT6nxUZQKH9q6E5bMuYisH9leegcOTY4h2UpP+/CwcG9JVi8rbyIREnSdIHGGs1wDQ4ESSoLYaW2k16+tmR7toBHBm65l2uJvfpsom3A0hjrx6EeiLCuXI3qlVDJRLChUhxdlR54hB8Ehvaa4Xopwx1pWyjIFSdI1pwHIc3FJT3FtmDObKKC3/81sbBP+o98/8iAAIJVln7/l1lsxdwm99suWMI66BEmf2ZhE7xj7aDhFpNX+TUQPzLh8OdprxMtVx/VqYYxInOfv/DU6Tmaa+1HJDDa3azbu++3vAAC1wlVy0bvYZ3tf3Oqgk277HVFGe7cxxvsdF16BO+//MQAgFiTaIpFkv/b19TgotGXz2GcffPf7AAAPP3Y3UqNEkw3sI/Kjo55oj4kjo2isV9m+2I+ZHPs40FiDjRvIERMJEjmSm2AfJ1rC0AQRqjiHikWFsNWc7C3z5xOZtWQJ++D7378T9Q2ypgvSalzQbA0NdZgQXpZinp8hI4LREY7XSJTtS9bRE/Xilu2IyP5iSLY8QzbxQsF09gWVOjWZIJoNAPIF7lHKW1sUNJBhGE6WvIZG9q0p2SYnJsYwPiaZy4KsS2oq61x35EiftEvQqTJHeo+U0HOAiKmEpKiKyfweHRzFxATropLorDyNqJt4vIQtO4gqOzJORFddzVJ84n3MQheuYx/d9Bly9Gzdm8aqM8gDs28bx3gqR7SZmXoJmtSnuYlryhkriCo5fGAmdj9PZE+7ELb16uQUCqdq8O733cw2xzmm8zIXLzrtQnz96+Qle3IXEVBvfOPbAQBLly/DGSeTc2tC+uULX/l7AMCGJ+7FtVcyC2tbHRHUWYP9P2/+fFz/N+QaqqsnAureR7juvbR7A6JRPsOFc4nKaZ+xAHPncWw9+BDPGx3neHnpxa04dSnr0DGT69P7bmDZ/37bf6ClgeXPmc25PruTGQLbZjbipCLbumgRn8Vvf0/UTTzR5vCsjYS4HrS0EOl2qH87ooKyPvX0iwAAgwRq4Qv//HF857tfAgBk8uzbXJE61BNrfoBZneQQKpq8b1MjkYmztBwOPEfk0ylziQYyZDzOmNmGlauJ6lnQxevntJ2F5igH0lM7ngEAbHuB2caWLzsLi4Q7qC3J/ti1h+vBWOEwtu0h4nHVCqIqbU24uuojyAh3VkFQkoonMhjSYJaO39tdDQHsy59G/lj01R+N2lIRCHbJ1ffkQ82RUqmEwUGO91ZZV03hkDMMA7a8fyiKT29G30qOFlVNzXZ1SQeZ4dFPXaTNdB3WLq9mGcLf5dt0+W2OhyfmWCg4w1FpVCYrQJN3Bd1Q/cfzDeOVzgfpK72axurW7WjP9+V4eKr/pgg9jyete9kbBf/qlcc0DzJIRUGUP+9qdS5/P7CnHat2fuV1xz4mer4HOXysbGiVvLHlen719/C/hJREV2uZwX33unffgsfvewAAMLdrKc8R9HQYU5jVwP3M0okiqm3nXpfJGtizg7/94oGvsPBZ1MXMwxrmzj37ZevyqjAIKUMBtKCzqLQ1UzFQLx/BgO4YcDQ1ASSGTNdKiIFKXS7LjbkuwXMmRmph5AgjnruQHVkQ2K03LEm9vRrOwmXDlEGjUtkHrILzf9BQA8olcQOAgqWhKMaUkmiThoL/HyPleLWwoUg4jlFJeZ2s4wuCUaLSOjSaxmVXEKa8cDGVr5s+dCMAoJgaREEUzViCinBNRO4XyiA1IS8Xqi7yJRzWEQwJia/0Y30DX+I6O6OYkJCD3r1UuNtrqQx1zGxEJMbNBhLaFtT4PAJBDeks2xAK8UaxGNuat1xLilqAVa94E1WqkDELNjRlCHJi4uS6gGswdMgVnYVy+kJa1TiiPj2LzNEWqrKypqVxnW5Aco9NX/y8ol6yjzdUzJEKJHS19LLVyrGt6WFh1lHqQHumGvfl6R69JIZu2+H5X23oriEIIJxZbfrlieUBy9Zgq9TTEtKirIS2aUOv2AAtTzhbJdwZHhp4ZQ9SMGmvMVZ9K0rqVdvWEJDU2PkcfwuK1fqF59ahReblWz/zOQBAJsL7PfnwA9BlDo5LKEC8RkIq4k0oCrFyWO699PXn8v9kEOu3EwI6McyX7Lxah8JhJ+wq389jtQ0MzRgbH0ZjPctfJC/6+/YxjCIYjaGxnQb2CTHGdjU0OCG4al4qA3g6nUZJ5aMVQ5Amn8ViEUG5UOloaoyHQgEHbp4XAkvhdsXUaAZZCbm78YZ3AQBefxXJ7aK1CdQ28UXnYB9DTteu5QvTB4wABnv523/ecTsA4O3veTcA4Nu3fwO/vZ8GpJuvv4F1kRTMpQM9GNhJA8sPf0DD3NsuZ4hVb/8R6GI0iyb5/LZs3YkP3sgwjv8Q8u9fvJ0vqt/93rew9DJeO/nv/wIAuPVSGovu2rWHx+OLAAAgAElEQVQepqxFuaIKuWPZ2XQKhsyTHTtpPNixl4bDJctPRLSZyvjPfs409wPyTJOxGmSFXLcgxraBAY6hlpntCMlM2Sthig3yolosFh3CzHxBDIUSehMMhBFNci3ftpOGiZWn8kV06fJF6JN+V1NBkThPTEzAlnVak0/T1p1U7ekMX/R79vNFvL65zgmptA15yRBCclgWslP8bXxMnDkyJ+K1EYTEeJWdkvlsKcVPQ1FiuDKZ8nXVtnUn9M2W8LOxQe43sWgAySTbMbOTe7+aw6l0EUaQ/RGRl/Ws7IswbXSLYdeISLr7Avc+O6vDkg3zhXVs+1VvuhRWh4T09TIMcJkQhutDM7G4meF0M0+fDQB44lkaFRM1OpafwmMHB4XQefhuAMA1V/0bTuu9EABQSO0BAHzjOzQmPnRoNy7s4Xg/dTHvk0/TgHfyuasw9W3OvYWNbENB+nqskEJWVsHWLh77h1s+CgB4/A8P4dAehphZczmeYrV8Dol4LWJN7O90kWPvsstpXEk+a2DXboZydXQvBwDM6ToZTbIuzmqjkpuTEMGP3PwxxIRc3pJYltMuYFmNd34bjXGuUxedfT4A4NnnaPxsaGrC7p37AQC1cRrPOhrE8Nrfh7Y20QXBY7rGuZ/NHMbIBOdOMMRjCVlrfnDHrzA5IST7YkzZvZPGmLw2jKyEci2ZT2PM7C4abHqmnoUtcfBdnVxrn1nXw762gdo26psTMhdnn9SM9y74VwDAgke+DwB4/kWSfi7unoEtPZx7ydVMLb/sJPZdKRXHwYIY2IvKAcZ6Do9NoqGe60xJ9NKSPJt0zkYgeHQ9wpf/OVI9+Yh6tzG9Pzl7SalUwoQ4d9U7jqsC2a6z1a5ca20n8YdKJOE6gnXnPrapDEpczw23Cm4Il9IdbBuOO9jRH0UftC1Ympv4Q9XhaGO62jGvMcDSKs9326wSLLjuL6lnlfeE45pTL2NoOGrKd4+x6PhJl9UX9b5zrHcHr3O3el28BNLHTsJz9Hejas7rY113LIOQS2p+dINQtVDQqg50u/qxv6QEIqzU1BATMkUMDedffR4AqFxVGB7hXrS0ZhkG9lIf+N7XSRL9mosuBQB0d8/Bw4/9CgBglejEGKffB2Ymi+D+bS9bFz9kzBdffPHFF1988cUXX3zxxRdffPHlr0xeFQghhfIwNM1JOaig6woh5LUwOp8lepqgpxAEvUVF8ZKExTvTllyJq6/8AgDgtDPoFevLTzr3dUl84dxH/aAMrYqMLCHIgEgogIiQdiqPuErRnSoUkRFofa6kvDoSVuYhuVIWdiW2hxlMFw98Kp1HRNJK5wWxEBIixlw+j8FRepQ7ZjKk4lu3MzXyt2/7Ap7fQLj4YB8ti5EEvYbtnWHYNRKSFmS9VBq7kml4kBsSciPVHBwcQHsH/xk7SE/bkjn07M3pmoHd/bQ+BqR+kaAgtnKjMIL0ECt0ky3ea0Q1eHKBl4kF11rpfe62ECdaZjmU8VhWZlvTYAtqS4mN47PCuyTKetmnFz00PQzQLvOqeD8BG7oeKDvfWweFgqvm6ThWXa1iOb6mjJBPRNemW/CVF8d7XeUTqXZf1/Oi6lvyhNO5YWRyNlz3kRzTlPfJJb6zHW+QfGqaG2+lVwbTEUHE0vmp0L3eviobO3JdSdYbFTqmUtsDbv+r+Zkt5KHJvSOGpPeVLrvgzPMQT3B+NjVwvN/wz58BAEwNH4FZ0Z5LLibK5MF7HsKcJnqbb76eXv/5b7kCAPC5T92MmZKiuUHQE49JeEKwqQEHheh08TKi8/Qi1yEtoCFZR6REQpBCi5fS07xy9Wr85re/BAC0zOQaWF9Xi6EhojrSaYXgo9e7WCwiYNCLbklIW6HARtdEYk54rQq/UcTJtbUJNDc1yXX8LRoV5MTOXVh2wgoAQH8/ybLDcd4vZ2YRF7LbBYsZKtLWwno2TwKBGvZtShPC+1q2c5YRw48/TcROeyf77HVnk3R/Mq7hBz8g0keXMTqe5+dFF1+DIz09AIDfHeaaed65ZyK0nGS3syx6V974zr8FAGxKj+Hr77wGAHDVeQz/OfViIhh6f5bByC6GqWUzRPVo4bi0vRMDEvbXfQKf10iJhNOPPb4R+SDX9FEJ8xqa4rNtiTWgNsL+qGtiGNr8mUSXjKbTGJ8k8iugSTiY7SK0wjW8Ll8UdKqQ0ba3d2LDxucAAIsWsZ0bX2Sa8IGhQQcRlE4zVKpkCroHNvSQgpYrstEARkbYjnic9zPBuhwZGESijvtDUp5voaBCv3TU1rJvJ8Z4/tgEPdU1MaBO0Kh2SfZLCfs2TQu2kOQqfcCUcOlsJu94RlXyhpkSbl4y0+iaTaRxU7OkaRfPeCZTQl8v90YzokK8+VHfkMT8uSRt7h0lmkoLsA0TUzmHEFsRod9793cxLt63mgQLUWTsic5m9E4QFVbMUe9YsIzXBYshRAvsj9MXXgkAWL+N+3bn7J2wZAz88tl/AgC0d7Ds61a+AXUxtmsoJ6TjAsIqjIwiJmiSkrBfnzaXIUzNoShKgtra2UdvZLKRa0R80RJs6+O8POEsrh9aioi38VQGsRael89yjDfV8Ibp8WEMDbJd7d2c38jHENY57lokxGximOd0tHSjKGjPtITNJuOcL5eeewZsg89ZhTOOT/A5n3P26Xh+DdFXt9z0QQDARz/+TunrQYQDnP9nn3kZ++UIr3vq+d3YtYdtbRXC6WgD69LWHsNkhnXJCJtAdop1WXnWidjwIon/2+p4cP9OzuWxmnU4cxXbum8HPbJRi6FtF5zwFtRm2TcdzRz/er4fdoTzeOYJgvoKUmfr7joVyRYihIIRjs3xEuug181GmyE0BYJIUpyzyUQSeQl3DQRUmAv7LhSOO1QGSo6FTn81ecuPX/7npJ33yitBdFU7V4XbBnUdmoLE2JXnWA5629FnPQlzHKoLOd8wDed+05PwuCnpp9dPIT2nJ9XxkjVXtsN0fve883lIpY+WcOWY6CEN069zqAlc9EmlTm6Vpj+/Yz0jLwrmlSQ9ccU45nV/POJv+vvOUSMVbLc/KkXXNAdVVk2O/k509HXneNef44nWUGLbNrTKRDRwx/arSXIl6jm9w9Q1tm99An0S13zVddcDADrauR8iG8VDP/8tAMCSff6OO5mkYmT/IM5ZxUiDzCEimgPybn/tO67GwgafVNoXX3zxxRdffPHFF1988cUXX3zxxZcKeVUghIoaPSJ5YwLNGXrywk2SxjwiHmojimiOHr1SXJA6OXpucloMKZ3nRcTbp+cYu371quvwts/Q69w7Rk94SaXDNgwYtkIU8beQeGQMDTCkLGUFD4gVPRzSnfN1QwixJVV0MBSCrgmhalpidm16+nRddyzdFeANWJrlopWkLeGAAdM5URAxUF4hl2OhmKKXqzFB7/w/fukObH6RcYY7d5H8dNfB+/l56C7E61mvUEBxQ7DPiwULpTA9iO2tPGdqkF6rYk0MhQy9VB06rZVvOY8e9F57FJEoPYGWeEYLGj3Ieth2UF8WWKYWZb+axaLT5qDhZQ2iV0GhrqySm7ZYie0GwDrnK1I1N97YjaFWvFPOZdLHmq55UqgrL4nLk6PisHUHheJFCJXz6ShPSNEsQnPQK0o838Sjr6uy1O+ai5ZxUrZXsYZPQ8rZNiKh8jqYpgUL5SglE94yhH/HUPHe01Fs0wmk3bKKpXIEjma7HiGHu6mKWLryykh9NR3T7dKqDjYg3mRD6mWqfvFcoxBCcPiQLOgyX0zFpWCWoJw9ako5pNSevlUpSW0ZCyUj5KAADU3x43BOhGIhZHXW79EXib44tJPcEKk9wyj2cr2J1HDe3P0LolEmxoexfCURI3step3fdQpRG/l0Fu0NwuER5PLcIuTPU6NTqBfWd4Wgicc4d3fs2YITTiIHxcEjRJCMjfL+CBVRKvL7nFn0EtQlkjhwkGtEMCBoIFPI+ksBBzWoxkVLC+f3VH4KQen7QoqejajO65vDHUiGiSQ4cdUJAIDeERInP/LUGtzwQXov/vbGTwAAhgboqY8kapAe4pp5ppCndsygR70fKQeJVB9jP5w0n6S0ph5B5yquRb3jXG9s4YP5wpc/j3t+SK6WK087DwBwz6/vBAAM61NoFkTSGy8kMuu0C87Hr39CUunl5zJF/Hs+8SEAwGf+4e9w4AEiN77yyFMAgPmriRA4+czTcN/v7wEAxEJEZLS3s49zhXFY0u9Nwo3SliCS4cn+e7BB1uiuerZ1IifrWx0QaeJzXjyPHDP79/4/9r47QK+yyvt373379JlMpmQmnRSSACEhARKB0KSLiICuWBY/y1rW7uru6rrrisq69nXtBRUQUYo0KaGEXgIkJIT0yWQyvc/b773fH7/zPPe+ZSYTROX7vOePvJn3veXpz3nO+Z3fIX9K38FOxENCgpxgHVzhuBrJHMSihcJTVc89tWc/+yg5MIpElM/sPkgkyHAf20zcpwCAlPRptSDfAAcpnYuX4yM7biMs7ecKGtYSsignbyAzyOsm5L6wcFRZlqGRCw31LPP4GPfwVDKKdFI4J0wiLPI5tfZFNJIxI4gYQ1gvo9EoIhGWdSwpJOx54fwza2GHWZ+0IcTnsjZl7HFUSCr5nlGOw9ED/G3J3DrEItwH08JJNTSokkg4GDzI66obed/C2QvR1813v+mN7wcAbJG0sb+7/TrUt5FseaifJOA17WyP6lALcmwGtLUTxTOz7SIAwI9//PcI2ayX4/JZM1YSVbju1MVYuIDzf3CI7R5P8EGD7gC2ddDTeJVwbS04TYgpYwmEpCvnz+AY6tgnxJRmDts6WAdbCKDDcV4Tyztwx4U/UZAqrsP3HrXkWOzeS29mwuW6GK2qwUSG1ze2sMzpNPshlU0hKpxShnDDT0xw7p975VU4sJscPpseICpy1ToSSG/c/Cxis7i2pEWP+MBV5D/64f9+Ew1hrk+PbyQR99A469Le2IJ5c9jPkQqO93nzSK75oXd9Ep/5Evtk4x1837vedTkAoLmiAnNnEFH0zDNso7TJ+To3sgK797Dvl8x9AwDgpOXkc8zEkpi7mHMvnOPanAyHEcqysq1tcwEAs9qJ+LPhoibONcFVaGmlx9iuh8CFIofnJY4LGMInpEC+IeGqdIpQv7xvep5xs8j5/+r40ydHyRTzukxXzCl0jClLcgTvezX4l8qVc6rnKl2mIMfMpNeXVkYhKCPJFBzZCxUhZ3Rc9P50ViMuoxJ5kBXdKZfLaqSwQvcqBHAqldL6pUbMu4pDdfLyOo7hS6de+Om6XpCA1stE57BdR6eiV5SVpuHxxhSj8MuJRiS5HhLc1Do1dP1iUteszFN/JECZh5bUtThxi2mUG59lUDNFl9nI+34vjBZwXdcD2vvqHNJtVIpyMope4K+PRuYXpWc3TVOXq4QXFEAkXGg28J7p+niJSpFjxQTdHgrJ9ZUZJZ8h6/BnITUX/O91neLFzJhyQdPnxTIyvfWztM9tGdzFdU+lMojHuQ9u/OUNAICOYUHOZ3NAjNygO54nr1/NeuqPh558BHfcQz123htOAwCsiXOfyswAqoX7cG07992c8BO95ZJzkXqx8xXUIJBAAgkkkEACCSSQQAIJJJBAAgkkkP+v5TWBEFKWuZBlaetwXR2zgkTEm5TPZmGGxGsvFuuozttt6tTHdRI3r0ycjU2N2uNiOyqLiFgTHVtnKlLpzFV697BlwggpzgTeFtbXWJrSRBkOLfWfkIGYxP4rjpNkXngPbBu2eEh1AjzF1WMY0NGzymBruJrl3iljGdbWUNUMUpdkuh9Ll9FqePzxRCLY7usBAHfccyxu+O3XAACjIOJKAAmIWzYyghDKCc8NQnxmTaIG6W5aH88/858AANE5khLZcUqs9VPy3fi8DMU8QR7CxdbIII1ecRyEfTwx/Jz0NdMSxh1P/Xv5zzLWc9UGzuTlOlxscUmM9jQQQocTxzP3l9ynLNgeuscf81vIr+VvK0Olj5dr83D0H8UsROXrO52O87JalHpGXB0Drtyaao4YpqtRP4bOdOavo55g3jO1J0/xChGJYJqWx40kz9QebtNgmjR4nrllK5hpp8/dh/2ZfQCA1RuIOLn38XsBAIsWz8eL28kr0t1Pr/r5Z3J+3nvPPRgeoie7P000oMrKE6uIIitpu595mlm4miV72AlrT8CEpGqfP5+okvEk/77rj3egfbZ4GA7RS+C2tsMyVbp5eu8rElGpXwQ5ycZkCYqwfTY9Dvu7OjDYz3WjsYmIxLoaLiDv+8A/YNmxRAZt3LQRAHDPo8wCdvzy+fj2d5kK89LLmQZ7xjyuUX1DgwjLIrZ8FVPRO/L+VD6PcFTSakqa5bdeyfuH+0fQNyAZpWrIXXbXQ3cDALY+/iQWzqTnffMmttVZks1o4qmd2LidWZ1OPINogVxbJda+jqifD7yXvE7HnMG/Tz1zA8Y7iLoaHmabzp7FNk7WTqBakAtDh5iyukoQZZF8ArMS9PR07SPyYeFyIgMOjaYxfyafMX8R1+gnNpOL6Li2BagRPig3xnoZklkpYsVx+VuvBAB8+Wqu422zyXfT0rYQEyMsZ3MD+zuX5RrtZCdQVSFIXNkrx8fJB5OIRRAWTrylR7Esg4NEWIyNj6BCPMapDOfBwe5uNDWw7xU/kA3Ol2g8iqgga/M5ldWGv4XDFhJqf87zmqzw+eVztl7nI7LvqjmVyzma40hxU7l6vwGSwt2kkG5Dwq2Xz42isY3vmzufYyEl2bgqK2NIpwSdJPwxiUrOs5w9jgMHiJKpjrP9w4K+aGpqxOc/RA6b73/zcwCAuroZ6BrnXP3f35FTZs1xRPzFYWBc0G/z5qwCAOzdTa9fpLUfezIsa1fyewAA0+L4aGyagSqH7ZBwmIo+EyPy5/cP/hT9OaLKliwkwi1nso82Pn0XnATb44W95Nw59RSibHbuuQ0p4Z0RwA/GR7imnXnKOYjWEsmUEa4/0+G14UhUZ2ZNVHgchgBwzDHHYN48orH37We96meGvUypogv5uUPUelq8t+XyBmYJgubssznee0c4v3e+tB3v/vurAAADBzjGZ81meTeceS4S1eRiWr2eKMmf/ZwZCefOa0Uqz7k3OMZU23t2Em33/N4tmD2XZa8ME0n9ix//EgCw9oTj8Y53sm1vvYPIwaVHE3131IJz0DfG69vaud4vW0X04z13/xZPDJFPav06jrmQMwZASJ7+H5H/N3mFXh35c2ZmO9JnH+n1GVlro5aJUVkXYzWcg3lRcTKurX+rEPSx00+dIx6P6/mZSqUKnh2Px/UarXQGj0PI0fpV+axThWgXhfh0XdeHECrioITrUxOVHljKEzRVRl71mwVDI72L32MYhq6XEvWbZVlTZjUrfl/5vwtRLPIGVZ3C+8zSawpQPgoF5EOelSa49+v5xX2h/p7euCruS9M0S97noXkM37nHLfjNX4+pM4lNNYYm/246Z6KCtPOTXlUqR7oWKpRZJBLRyCClcymdRqGDAODcC4nkRZJngUeffAC7skwPNjNC/fGRTcw8/OSDm5BM81mPbPwZAODYE3i+eN+l/4hbf0B+ocXLiAp+ad/zAIBrf/Q99Iii8+llx01a9teEQUgNzogVQk5gfTPr2BBWTFI+j+cQjkgKagX9M0s7Sk16Uz6rqyu1wcQRg4slg8Z0HY+zVsoQMj14uyZYtlS4kL+8MjiLFjPDNBGWQ1Rcyu4mVSiRgwxU+JM8U+B+pmHo9OpqcQqFTThQqX6VUctbXFyz/MJoOC4yGQmhyE/I1dwUzj/tg1gukOkHHmE65tvv+jEAoLYuhXg1D3eRmGwmomgNd7bhgnUfBgCcczEVpZeSVLSiThwlUpQSvFw5Ld9vxUYVx3EKDEH6miMwpnjGDc9YUXzt4QwT5cpFMbUhsxyEdSoCtalI0aaz2ZX7Ti2ofgK/YkOQXxxN+FcKk5yWwWqKJVUv0t4X+jdFSKc2Fcs1vQ1FGWr0ZuVA9Y8Kd9OQatf3BmX00aFjHvTR8ZVTGYKcArJreIYleGNGwe6j8TBsRdIqhuiwLAS5XA6JCh5cVGr6vm4aS/buP4gL3vhGAEAqRCUtUsF5Mjg0pOHR23YwHKFjH8MRcpmMVpbqhaB5sJ8HmaamJkxMUEnr7eV3VVUkMx0bG8KBTh7IRsdYBkMWPivkYteuXXI9jQId+w8gk2G5ampofFeGLtu2kZVGDcvhfO9ehlZk8jkdqpQ3eH/K4YF3zB7BroM8tO4+QIPXoqVzAQDDew+iWwwmn/nCpwAA13z3u2zrShvVCZZhpJ8H5JjL9gyZlt5gFZl1bUWNtHkUlsCXlfF+50tMxe5mMmhL8Lr5J/OQeNXb3w0AWLLqBBw6wPrc/vwjAIB/u+aLWCiW8eoZ7KeHH70fAPC7G27Cv1z1IbZVHfvkK1+hMWb9ulNgidG8+xAhv1deyDCSi06/DL+8jinll6yU0LaDPMwuXbwa+Q4edlccvwYAsHr3fj5nXxdmHMsN/ZSzSZL7m98yHXnzzFbEKmiAamplaFp7G8NlWmc14Kknach58VmOBdNgX41P9CMs5P41VUKwLIftXC6nDS1797MMKgS6YWYjHDHsZMdpTEnEQ8gLEaJyjKg+MsIm0mNCUh4tTBebTzvIp6n0ZCQ8ToVQJxIJ/ayxYY4ny1TE5g5MMyxllRBeSRphmAZyMo7dHL9rbJgpzw5jeIghiwcO8D193Sx306wGjCcltDvD/q6tEANMDRCNsewzGmkI7e1j3ybCCSQnuDcm0yzT2MG9OEpCPh95ZDPbcds+AMA7Lnsvdu5+FACw7SXO9bpZNNZ1HTBw8gk0OmbwoLQ/r62rOgEtLRwDO/fSIHHl4m8BADqGHsfWzd8HAMxpZnt852oqgrfdfBeWtNGoV53leLYs9scdd/0UkPC9UJyfzfUM0bz/zidw0bmnAwCyKban7bJ9MqmsNsilVUIOWWGj0SjSEjZYJSHrli/8W4WfaL3MZxAqTtSAUAUisv62NLPv47JeDfb2wBRHXlv7XABAUw3nwdJlR2PbDo73xYtp4Fm6lIpwW/NM7O/i2JzZxPlyy60ki370mefQ2sR+s9L3AQAMCXV7aUcPbr+b4aGz53CNTQ/TmL676wnUJdhG3YM0DKXBNj5xzQZs3U69KCVbTzhtwA2V31Nfa2nh/5YNQa+m+B2Hf8r9Rypa94qEUC3Gd6VHV9dzj0XIwoA4nYbSnOMpWbNramr0nDWMcMGzbdvW+0RUHAh5269zq/8rndKvVxWGJRX8rfRhfRbzn62U49EsuNZ7Z6lRoJy4ro2QhDV7fSNggJCly6zOeuoa0zSnNDZMlT7e91fRr57xrFzymOL/e/U0y5bBSxNfamjXhhxZkgsML5q+RJ1b1Hmm1HijlmiriNpjsjKXO/8UG338f09lCHKdwrHjl+mcl8r+PqUBb/LvphYvuQdAp7JqL+87BXJwdFnyMzguMyles/rEtUjs2AIAmBmlHWSXUL+MvJTGsmPpPH5p720AgK0v0bl53X03401X8Mzx0vN0LlYL9YDphlFnthy2BkHIWCCBBBJIIIEEEkgggQQSSCCBBBLI35i8JhBCKsU5HBdZsZzGKyQFrYRk5LMZRBRSxykk43UcRxOg2YqENqQemYVpKcSNIJEUIsECQqZYiS0ho1aGaDieXVe9L6wIp/1hP4VExo7jwhTLX1hQMpGQsnj7qHDFUGmFlbXU0CTRtkL+hMIavJAVokxFyunAgFkU5qMsvVFUwbAm5Dt6MyvjQkCdAhbNJQR97Sp6ptceswEA8LPrP4ODHfRuNYpHtipHQu4zz/4Mzj2PabP399BbmhePaqyAJG2qUKdCcmjbnjzsysnbZa3nxZ4GNQocx9Gok1IrsVHilfQb7Utgh77n2D7iM37Ic1wPrYKierk60Xh5yOWk7z0MGki3kUbP+iphT5bm3nefHi9+WG7peyYTFx5EuKQbiq4DfMgpwzcO7OL3OJ4XTf+kEHwuDFN5PWRCqzobQLE9WxfJ9aDGyrngejcBh0GFAT5PiFMm1aoixLaBvIQ1dnUS+VEhYSvpiTSaWxhi+egWEk43NNBjv3vri4AQ5oYllf1gF9NEVtVWIx7n2pcVWLZa27q7u7Fg3lwAXvrn554iIiEaN1HTwPtmtTKcZPcuoj0mxsY1uWVFhCEfQxMjiEb53EpJ+6xSjpsWEBKUY20tvYwDA0Qd1dXWIiahZaksy6dI8H99w7VoaKTX3paU4U8/Q2/8gobZeOtbSLx638NE5Vz5Zqbanr90Mc67gKia8069EAAwNkjPZTqVRExyasvyiDEJdYqG4xiV/zdVE53Q201ESHJ0DNuEiPniz74JANAsqJuDE0MYrOczOycYGhTqH8UTu4humiOp3g9sIcrp/DPPxsOPEbnxng+TSH9uB8O9nn/mSYQkbGp+O9txXMK0Vqw/FV8+g6iLb36P6I7rriUq081a+OxnPwsAWLaSYXbf//7/AAD6B/rQdS8RC8eeRLLhWY307tSGYjDC9OCee/45bAdhCr77zjsRsViGtnlENezrYBtEY7bue4WoUYg8F47eexS0OZWW8K1YFI6EIppCoB6y8miZRW/zqKBlFgpqo2+gF2khKjWEpDwcVmTlKaQElZMT1F0kHJNrwshKOFMszvrlsgq276Uf1okXhBTVskyEwhKeFJFxIuGehhFGxOKcyGe5n/Uc4jjO5g2MjrCcVUJIHAoTvTSztQ4HO4kGuVBIjc0Qy/TMM89gTFC3F3/ggwCAh++7DhOCzlskKLNHnyTku2vAwZuvJNH0tb/8TwDAQJJrxchoFAN9/P+Fr2c4VLKbIWe2tRmbHuNePJKmN//Wu7mOrD2tFeE02/0HP/wJAOBBmVOzF1WgtoZt9Za3/x0AYMteIt22b9+LOpm7c1rZfwNdHON2rBnm6bb6ngwAACAASURBVOKxzHN/N6VdDMtGvigJgV7bAcREV5sQKDsATQSv+ktB5f2poYs9xnk3jIzcVylo8BkScolsHnEJP2ucwblQleBYd/ITqJHvOjo5nxcuXCnPMXCol0hBpQPNbOC6bI7FcPdGzmsjK4ikEOdIxKnEy1vYhy2CEjNFB6tw40iPsR339rFP7mkj2vG04z6ENSfz3Umb91fG6pEu5Xn+s0oxOfS05S8MEHrF5fwzyHTQ2H+elOCl979ST31YzhrJVApJQXEmhKT/6WcZdhIKhdAvCOPeAaJ2G2VuRSKREn1HoQNN09QIBy/tvKe86dTwRQgctllhyJhleX+bWp8t/8lnyB8FgPaivpgCqeKUUViVjhcOhzXCVX3np6QoLZcXDjV1qFh5cd1S1JFeE1EmgsDw31v6HksPFg+FqT4V4qkYjcmzVGlkgyelRNMA22eqOk8V/TBZWJj//+UQP8XPms48LXdfOXl1kEEUpUOpI57j2FDHiOLzn+saeg6MdVJPD7VS19432okVZxC5e1CoBs6/lLry6+Yeg59cS1LpdJZUAafMJvq8vnEhOkaomwxL2vlf3M9ENu/51CdxnrHssHUIEEKBBBJIIIEEEkgggQQSSCCBBBJIIH9j8ppACLlCSGnkbThhIZQUREBYeDcMd0ynhFbcGP7YQs/qqCyt4pJx85o4WlnPo8rabHippFWIt7K28jnFVmWPeNZ7n3AO+Wrj3cdvJcs6olYYTlGTa8SI68XW5yWVYzpnwxaruyLHVWRiJm3Jhc9ScZ+ug5CyfivEgngZo+GERtL0dLHdzz7rEgDAsuOOwef/nWlX+yTV6hVv/wQAYNW556FzlF7TiiohosOgvNnxGefLWVeL0p+LlyGXs0st5XYxV4+vfjDgTMJN5Liuj4y79N7idKOWObkl219Ozc2jHm2UjjnPY+Cho6ZDjlZWisnefF4JW/9R6G0BPH4bJY5bSl6tOXQcx4cWKu+pKFf2qerg/0vFgKsnWUX1KKiXwX4tfHcpukm9wO818cBGak7IODNM/ZuL0v5S4uhHGSV9qBAy2VxO/+ahCPmb37O0by+RGM1V5KlYt3Y9WlvpWV5s05u/+SV66DITSYQlbXNK+q2ikl72XCajn98wg158xYM0NDCIri56nRXfT1WU60k0ZuGc00iafP2NTGPpSCPEIxXICdlkdoxeQ8uyUF0j81gTufOaWCyCvBA4K2JJRQ4bhomIrC15mQsT4+T9GRsbgS0oj8aZRApVCrpnRnMLDGnwefVso60vMs75rBPX4aafXAsAuPO3JKH+0n9eAwBoqpiBQUmDHa2IFrzXdvKaz0mtzGkpb0tFPVZeQLK93/yBsdanveOtAIBEXTUGOtiOX/vi1QCA1TWNaG4kCezKlnkAgAMHDgAA1p59PL79CEl/b7ibnEArV5GYb9Gp6/FvXyRKwBXOBdNiW/VPjCMeJTqhpontMSbIju9+5fuYtZKkuF/8zKd5vyBPPvYvH0VvF7mAbv71dSz76acCAPaP9GPwmacAANu3czwdf6x4iGY0YLCPz6+vJ8opFGVZnnjyIdQKCXhfP71HaUnhvuLYpRgcJnKmf5Bt3dRMdMjw8CjWriQnTasgz5555hmN+Gg06dUyBJJbmUkA46p/hHxdrTu2CbUnhkP8TaHhotEIHFdQJTL38jlvrKr1Tb037+OXU98p7q1R4cMIh6tR30TER9TiXDKEKHngUBJ2nm0TlZT0ra1EAc1sq8Dlb+O+19/P33513W/Y1mvWwq7gfc8+TVROXcNMzJtB75tlsj77Om8HAGx86BZU1RMVFZP0sNueJiqnuaUJXSP01D/3Mufq+rPeBwC48ZarEDP4rIow2//RJ0h4nHKOQlv7QgBAZojjvzpCPenohTPQ20+Ez69++WUAQEMLx+Wi9iWIR9gXg938XL+O4+q8iy5DTkis7RA5piaG2B/V1ZV6v8wKKlChJHO5HGLCldje3qJ/UwghJcrzXm5/8ZC1BkxTcYIJqizDOXHpGy/B7Ba2ny06VzbPOmQzWfQJ0iGd4fhva5vDOowM4ZgVJPOeyPKaN1xI3qBEug4HY1wD159Gj+wLW+8BAFRGLNQI51ZFgoiisy8k0tCdOIi86KntI+QZeuhOjo+aipNx4orTWOck78+GYzDMVwYR+ksgaP5/5w0qGHNF7UmkRCF+2H9fScsU8db4n28UXFaeq2Sycv2ponXWFNerqqoKbH+S++ut190EABjq5PhHRRRbn34OAHCzrMeLBRV7wgknYM4czh2l2yjUqJ942UM6ePq4I7qWFwVRiLbh9fzU+rhhwHTU/FeHHLm2gKdS6jcF8sP/van1e/nbMvW5QPHpaJQSbC9JkKn0P2+9mgy5dDg9fzJxHW/MFaNzLNOc4lkOStPIGwXnDtbLQwppxJNZOm7heqhN9fziazRyycdBZBrlzQbTPeNMD0XlOwNM9dsUc6gY7fKnlulwopFuMk3CobAufFIIoxXXaCQaRVL4QNNdPJvve4JJIB549kHEF1FfXCP72FNb7wQArFi0FDNX86H/Zyk5MaOD3CPvfv5FLD+Ve93aJqJUd67YBwBoOWoebv420ekfPPvESevwmjAIKYUx5BrISYPFRWGcOYuHh7HtI3A17EqRn3KhMi1TKyqxGlFQFdwxBIQEZxiBgrwrcjFDG4DUgFc7sGmYJTA7RcCbNw0dulWOZEuJOhib4VJCtGJxXAOOKGSWrJapbE4bSJQRIGT6M595MEM+XhYEc0IbzWDzs76OC35yYgRmiAOxShS57i4aeppmz8O3vkRyyk9fwUw2q5ZxgNU0At3qUJnkAA4LqWxeYP9AuQnqIzn2ZRDTf6vNoNwC6RQuYq7hQT/9oVcA4Dree0shl6UGIr8BRG9S2gg1xeKgUjWEnBKF1oNElid/K353ue+L1nsvFNEwdB1VG/jrkMtPQbxWtDQyZEz1QXEZCu8ECjdXLwys8EbL9S+qU9Rdb17qG0Nf7pFRy+YPQ5MJGtrY44V9ee0vB07vifoA4y+l907XdyUAwzNo6jZV+rvjIiQhL8p4nFUhNyEDMQnfmdnIderhR7ion/m6MzBDvjv3OB7ebrmDilnYCqFOCI/nNHBe7ulkuFIkGkVSwqBU51xxxRUAgN3zF+Ce+/4IgIc0wDMQR0IR9PbwMN/c1C63S+1tBzlZH7NZPjvrmr5wDn7W13MTSqeTOsujUgrVAW94bBQDI1QsZ89ulev57MGhMbzUT1LnnVE2aP0MHrgQisIW0t/Fc3kgs8fYyFseex7rzqIxa9cBGmo+9tGPAAC+/eWveZnsxCjgSLgRHCAh4So5h+Vsa2abz7PX4h3vYbawT/3HfwAA3GHWfWZNLX54I9e5BY2sw5Ily7B5K8n5OsYZxvPBT34MAJBJmFi1lpvow488BABoFAPP4I7dqGmi4W/jH7cCAN7+Jj6zPp5ASso1PM41c1iyu/zh3tsweB9DsdauJel1cwPH1623/xZrVq0HALzlchro7/gjFYKTLzgdTz9CQ1BeQtNMCa9Zc/LJ6O2hca6xgaE2+zeRPDscjmC3hNAtX8H2bxajTzqb0hnjRp5kRrZUkn0aCoXwwgt0DqhQwbXrjsXdd9NwV1vD92SFELGvbwDJCTHkgMaYeILlq0xUIiShV+NimBwZoaI0OmrDcbmf18jYVqFgY6kUDPAZllVIdOpX5FTSiEohIs5lTfT1Dkp91NhRhKk2ohIKkQhzPzx5PTPcdfTuxwMPbgIA7N3DsTAmoYXVdXVw5L7YGN/XMLsRjStoROzt4nWJOu6JF559ATo7Seg+NE4j39KjTgAA7NnfiX/9zBcAADfe9L8AgG3Psx3Hulbigx/4F9ZDjFg//ykNeZkBG0vW0JjYHhNDmRCEr1q4HIvOY+arHbs41sYGuSZNOEn0pVi+k85huNtx688GAHz1ui/hdeto8Fg4m8pkwwy27dDIGCwUhn7pTK2mhagYRyYm2JfReFVJeMBUGXnUb1ErhLwYBceSVHIt2RAvvPhSDA1zDlVVc20aS0lIbSyGE9ayTYeHuDbFZU4M9saw/WXOgQM9NHq+851UpDP7epBx+Z6lxzHE8mfXsq/yI2Gccz7XpE2baRhOplnul57fhg1vYLj9iBjN5sr8ufWu72BOy1EAgDlxGpgdC1NtiZPKn9tQ86o+v4yO9deWYmcSvyzWhQ4ffjLls1/F345Uyj0rLhk509kMZjRwnrzt0ssAAL8Rw9ChfBrN4uA5cwPHeHsVz0319fV6ry8Oo4pEIiX6gJdoByWkzX49XKkiSifJ+XRYraYXK78o1HEB6qJTEReXIywGgHDIC3dT5zH1zImJCVRI9kTt/DN9GcmKdMPid5Z7n0esDRSHy8HyGZd8IUT6an1cLDY2eWfNAufzJGUxTdMLtVMf/sQ3RUmZCs67RmEWNL+xafJlw/WBFMqN88MHJJXr08mMZ1PtKZM90zsTTt+AdzjR4121lQ4Zy2FkhHtWRPajiGQMhmvDtFifluO437Ys5+dJ512KZx5+AADwq298BwAQnUdd7e7btmJYzt1fOZlz/ZvX0ZnauGIF1p7Cs0afyb24cjv1499/+3q0iYNpKglCxgIJJJBAAgkkkEACCSSQQAIJJJBA/sbktYEQUintXFOnna8R1EnrbHpft6dfQKhaUqiJ8VX7+X1WRAWxVXDAXCqFsOAELJ3CsBRyptJhqxR+NNgqe5kghAQ2kMvbOv27aZdHrPA9qnye96SEFLmYUQ2e1TIaATLiDTekzqZY4S0YmkBboaFCYq4PRU3PwyukmukJWvYdOwRXrJXqoRXVtDwfOrgb7U30lH3q818CADz14gMAgDPXvA2VMYHmK/iERQLNUfEa+MVLYVhKoqyrDkNzBJdLIVkOjqmQWa5rlFw/2XvgehZvr3ye1bnU4+D3bJQjXAMM2/I6pUjKpaqcLqTUj3Tyi+Mj43ScQou54wt5KoRaesnX/b8x/EzazVb9VFquyYje+GPptbqORfcXwLT1QNaF1NZ6HQKmQuIMV6eS14ThUKTqJtQK4BbDaOEhg9T1XCOK4baYtH6qja2QB+FVoVsKqWKZlg5xWLqYYR3ZE+iBv/iSN+PQuKAFmhVKRoh7c1nUt9Gj/K3v/QAAcN6l9NQlkxmExSNXFSfSYfNTzwIATl5/siZ1vetehjY0CNJoIjWBA91E17zvg0yRfs01DLsa6DuEeIRlnz+P6+neAyPo7WVoiUIbKcnn84goaGuE67BKUW+HgQZBhSgvYXUFvYuxcAVcCR3qGSIaIi+ox7HRJJaduRoAcPzRJLWPR7k21bfMwJsuvxQA8K9f+HcAwKJFDIPav3cfliwnsf3wBFEQsRjvs3M2hgbp9W+vYr1m1nFNSnbn0CMExscsI5n0PEFs7d2zB1X1LHNCiLU3nH8e9u8kQe9n/oNlmHU8y/CpL/wzGmJso0UtJJMe7SO6556ND+CElazPKasZoja7WRBQRkYj1QaGGBrUs5999PPvfQtHr2PYWfdL7N9cmvUb6hvBHbcwzO3qr34TAHCshJp19fej6wAROyrESiEmjj5uJZ7/Fb1FQzL2Fi3let7T04PXnUi05/gEy97QyDH4xz8S7QN4KDHlFR4YGEBM2u2d734vAOCW264FJMFCopLtt/u5fQCAkeEcaqrZBzGFrJN9KZfLIZvl3FHjSYUitrU1Y0Yjx/KuXdtYvnqWb6B/J2ISGqnmnvJuhkJhZCT0rbmVIUsjo4NS9mHEVOIDeU8uwzaOxh3MqOM4qquTuV7B8RKKm3j0MaJqqiSkbUY17//Db3+HSIIosY+9h+FdW/c+hY98lITRixewDq2NRPA89viDyBvsu5M3EIH03LMM5YhXhfCVa/4VAPDevyc6Z/uTRCR17etEpaRVf2wr0UozFxOF9OxTe/ChxW9hfdJEuPRPsA129e3BiMmynrSeKWhTu9kGv7vrt0hZ1AtefvlBAMCmJxja9uzWJ2AKinDnDBLBV8fY/mduOBuWoLLTE+x3RXQ/NjKuEUJRQUs6jlOCnC7eB/3f6b/zGa3LuKLnKFbOVDqDykrO2VSK/aTmVj4HrReolNkKJRY2wzjYw3q0y1wHZL1qNxDfzrUsEq6SMkg4rBvBWCYl7+FusvdFjstL3/Z+PPHwLQCAQx1s/zXrieir7OxFVzeJ/ucsp1c3mxxEONSAyeRIiItfDXm1n+9HebwW5NVE4fyp7/1Ll0Wtj+NuBk1NXLcrmrkWrd5Ngtrr77kLlbWcE7UNXGOqZX1k2Lgkp5HU8gpZk8mkdH1UGm2tC1lWCUrGj4xR13lhZ2X0TRSFN7keWlx9qnDiwvd4f0+G2AmFvNAqhXJU4fCjo8NoEDSV3l80kMRHBjKJTu//f/EZQu4seCYweQSB/9xTSsJc/kxU3JKF1xSW2f9pFNdDoZpc1ztcq2sUasl1p4x0Uf1UWqojk1dK9lwYNlguvE5FIUz/mVOJvx9ToqMpfSeTTSKZIkKosmqmXEUU9ODQEOrruff07SEFQCe4r3Vu3ordHUxSYc7hsy685DQAQM/Wvcg18Tyx6zkiX5uaePZYPPso/P4PPwMAxIUq4MoPUEepDzfj0L6XD1ufACEUSCCBBBJIIIEEEkgggQQSSCCBBPI3Jq8JhJDjKMKOMMbFq9so3vzKanpzkuOjmlRUpxBX5NKuqz1DuRwtcFGxJI8NDWrCaMnwTgJiKBSL+r94mxSfkQMYlrJUF3LEuK6rLd0KaRHykdJ6qc0LERaO40ulrriKhNjLMA2YYp9TZYjFw3ANRQTM2xQKyACQywj6QXMjCcdJykJFQlLBSly/4l6orKzFqPA2VFbzu2RG2rymBXsO0vs8/wR6udsXkY9gdKQHtXG+eyJLb9egzfsAs4yltdRSq/pNXeo4TonlWvOeOG75sPsiArSpZCprsR9tU2yR91vAHR18qz4N/WkUldAfPTuVB2DqQisPSlF5YcB2C8eCJk4uk1KTpNHlvQM2DO+eovKYZrm+LC1/WS+J+lTtKd9Yfq+w4lBXngTT1ATXuv2UdwaGJh90hYtFo54MX1c4pWVxC/OTiuelGCU2uT1cxZDbhkeqqPhPFNoAlpeqXa0/GzacAQCYyKQRr6TXeftOWuZTKh13yNJlv3/jRgBeTL0DF7WCDDCkXiuWEOGyatVqrH4d02Dfu4moAcfi2jmRHEdHF7kufnbtL/ibvGNGSyMiltRBwoiXLFmEwX6iaxIJ1ieV5nxe2LYQe3bSQzE+rrwewg9g5JEVT1LEZJ3PP5+p4u+79wE0tc8BAJzYQu/HH+4iV0/XwV787FoSJC+/hnw8CeEpqmlpxs9/cyMA4J8//ikAwHuuIs/Haaecit4BSQ0vRI+ZLD17phtCpSA4NOBMkAK5cAj1ghjZu2ePtK2gGxa0Ir+L7x6RvWf7tpfxn5//HNvmJCJ+PvcL8rps2r4FZ8whabA5yrV223NEeew71IPffp2p6Jv/mUgmCOCqO9+DfX30yg6M0lO0bhXjxC9atRr3bqNnqGfkEAAgn+VYqgtXYyzHGv3g1yQCrBUuvbtvvwWtEZZBEWjfeTd5pa6/5WaMjfE9MeVdlNTtp607A5e/mVwSL764BQDwX1/7GgBg4cI5mDuX/dbZSYRKdy/5aqKROGRLxm233a1/U8SjBjgGqquJjIlH4kiOs3wZR/EQsSy2m4criLF8XhGQcoz39vaiu4fjt7WV47+9bS4AYPu2PZpEXfFVKRRdyIpgIs92GB5NSjvKHh4KoaWFnrn2WUzRenAfUR6VFUBdA8f98mPZth1d/A1WveapiiZkn85z7jZXN8MMs64P7SOSZuLQAI5p4lhb1kYUT7ha9v4ocOAQEVnPPMu9NTJMnqfeAzkcfSGRJcN5lm/2Co6hcy45D+EMOWievZf9NWsB0Wnnn34Cbv4NSYyXLSUy7tD+/QCAAwd34IBwFt3/CNPTjh5ke55+yjpUxNhuB/dwzITynBPH181DnyCRdqXJNbJ1PxE1vX0H8I4r6GlUyKxUStBpFRWYGGP9KkQ/S2bzOlW1Rlfqvd/P/Va4bltwNcecQgblZZMLG5ZGJLoyZysqONHsrIG8cBolRO9JJ7neZVJZNLVwrNbKupUUQHMuEsfM2eTvqksQMbFoPtfXZce+Dn1jXLeXL+UYWtBMhOOEM4YlsziP33zh6QCA624l10O6N4rEUvZbRnivYmiCYxSm61YyFarkT/FeT3XnVL8dCZ7l1UC/FJM8T/vdfyFE0qvRF69EpuyjSUiwAcCV80E0GsfwIa7hCeHMyQrpfjqTQY2l9lJOhpTi8YpGNbpP8QWpT8MwtI6sOYTKEBh7uracZ/L5Ev7QvE/3MpxClLrperyRpRxCvvtKG6b8/wHYtlOwBgGeXjc6OqrLXsBv6quf/5Heo/1RCYXfGaYLtziTjU8mjRjwn0eMIoSQWQ4h5BagmEpF/VYuEkD9v5A71fVFI3hnCx8v0RRvU1Lu9DatMT0FMmg6vxUgp8ogyDR30CTvn+w9k5XXL7ajuDQVKigOAc3i4CHqoAP9/G10dBzLlnHveeR+ckJufISo3T37dyMxm3v2O8+4AADw4CPcm8dSaRxVS0RQrornixPX8JrqbBh/3ESdsvZo6gXHHkfdZs++Dtxx908BAJ983eSk0gFCKJBAAgkkkEACCSSQQAIJJJBAAgnkb0xeEwghQ+LFk7aNlqhYJtP04IYjtHAmY0CLeCOTYvTMmrTI1VotyA3TO+i00hKaTjPbxKHREJxheo2GJKNElSs8CYaJkKms0pI1TAyiVsiEspzmVUYxH9u74sZxbMWaz2tCpgVtsJXYc+VJt2HCNRQHiogGxDja4ByS9+TyQES6KKv4i9Ip3W6KO8UVT1QuKw9wbCQnFF8SvXbKWp1OjiIeUxnI+BG3aGnMZDJoqJT0yGO0nlthial1LEhSD9iWtKMYUkfhpfz1ImjF2uy4UBVTGUNsxcmU97iHNOeQ+tufG0ub5gFLpTxUAJeiVJXqXqAQ2aWM9YpjSmWrsh1onhrLlwUAkCxxRS6swpT0crlCjolXIZ/L6THgzzym7veeUc49Vmjtt7Ul2vX4mLSHyGuXbJH3wnV9dZVPQ9xqIdPV7ZVVHhA1dGCUxP9aZSz0VpEV3fTFN6vMEyo7Wcj08RHlI/o9QCFKbKoEBY7MIb9oVn/DayP1rx5/Ki7d9V5g6vtkDPg5xaQMKpGV4RiIhJS3G3K9eHvzrn7WjAquV1YNL8qGcghLqmFTUDYL5zADQL5jEKlecpm0t9P7PK+BqLuXD+xDNsy51yyZARcspYf7he7dqJpJi3+VIDIywhtUURlD2uV8Ghjgd/Oa6Knu2NOBvKTK7hlhxebMiiLuMLPG/v2MRTZVCtrFi3HBhecCALZuIR/GRJIogJ6DLvKSsal7jGvRzffdCwDoOtSFbfv3AgCWzmemnUvOIHooXFmD7duZbvu+R+5j2cVD99GPfhStzazr5/7j8wCAf/nsPwMA5h+1CDX1bFvlzYyojshmvUwhgno878yL2HY19RgJs65Jycrwx230xETnNuKX3/sRAMDoJProph/9Am3r1gIAdj1Pjph8P9E9b1+zFj/52o8BAMevpnflgrezXrtH9qMjwfa47wUiKzbufgwA0JKwcGgLkURdvSyfW8u96B1f+joeuOIsljVGVElVHWPD9x7Yg/a5HA9PPMRnLmhne9Zls6iRsTIuKcvdIT7bmBhHWw3X8llziFgZFb6gHfs242vfYBa17//v/7D5cqz7ow8/jKveQQ6nb3+HnEVJ4ZxraqqFI3vAffeQNyVeYWsUj5Nn+zfU8rOq0kB6jGNtKMk5mxK0xsREFlU1nEtVwgeYlcxzw4N5jI9ybY9GOa7iUXrTohETYYH55mVxkS0ZqXwWkQTHcdiiDpB2+f68OY6OAXrJTYPfVcdY7lx2CDPncD2tamY75PvZZj2HcpjVxvmVSLAOHXvZxoloBnOb+VtohDxZg929mL2IyJ4LLmN2zl/94lqpcw8WLeT8HZB08HOOJ5fQoL0Nbk7KnCHC5/6H6CX8zU1JfO/bRNR97moiuQ4+xQxw0bo69Amab/Va8lAdu5Lj8tnH7kNyjLpPXx/f92KeSJeVK9bBMNj+PYOcp/Wz2AZ/d+mXkR3nHDzUT06rjkP7AAB79nYhP84+rIhwjCZzoseEDZ3lLp1V6Oy4XsPVnqi4GQU+C6DUS8uskrJe20pn8LzYhuylEeECymVFLwsZcCTjbDYrCImY6D2WgRqLWRfVeh8RHgdzPIflS+hJ7enhOHn9Re8CADQ1NeHAAaK2hgXRtexYenQHhwfgLuK6bQhnWtzkHJ6xoALHzJvLEgtPVhpphF3FX1Qoh/dGF/tsp0BG+7kqp3rkNDLyTAf94/GsGPCjC45EjhTp46FjXhlix4/yV59TldnLqlr4vQVDZxHWCBpBtiQSUQwPc73IKq61qEKyGjBtNRdUlIA807KQyXNsOopb1PKygSq0uiPoSsNxte6pdWvRj42JNKIy14fSLEtO9KRwyERO9PWknEeyck4wso7OymwLn05ezjiGZcKRTMMR4U5UEQuu68KxC1Fw6kyQyWSQlfs8Tk7v6Kk0fYXu9bILl+IVHCfvQxIpZKEvEqCYt1L+Tro5RAQ5aiQ4F+1ezusXezpwXIL8f6bw0cFU5fM92ywc44VImqKCuqXcQ74fS37zkC06JsGHslFnqVJeIcDUETDlRCPsi9DwDjwOIfX88uiawvc5jgvTLKzPlBm+CuZpEUJfbxJT4VK8c7g/0xng56Mq817DgGFO37wx1TLsurYXlVTS/g5yMrYrI9TLuvYz0+4N2x/GRIaH5vPaiToP13D/7LcsjEf4jIsv/TsAQG2OOsoziw7gY//4UQBAVsajdS31wN2VQ3jmq/vWwgAAIABJREFUSWazfcu73wEAOGkxUa2mAbQexTK0zOSz//gsEfq5fA3WtV122HZ4TRiENDzPNOEIFFF1dtMMhh5EKyIe5E86WqXdTbqjqBaosCthXgqKPjrR74M8ygA0/TC4wsFWTsqlSXUctcAVXss1qnyzMp2frnVB3R3HMwipQWfbtgdh1HxfvsVEHUzdIgh28Tt9nyGz9DtDvy+vy4UyC09xCzk+I45Z3H5qIbJLw8Fs38bh+gwefplsidDhRJoITX3hkUNbeoFz9acKx1N979XLLYEte4u8DcMpXCy1sckwtAJQTvJqU3PV+/ybQOGzCuCKUyhWTtH1hRuTVM/1KzxFhhxDGUw8gr1IqHSslrzbKS1nsTpvGKY2kCmoqxVSBiIv/aWhlaHCmvH5njmxuCxHAtmeLiFd2bS0Za6f7N1l3yNtlc/ZMGS8n7iaKZFf3MmNYtvGJ+Bk+NtsCWWpmctDy6zBMVRWMXxh9z6G0Nz+q98DAOacuhrJHoYXYZBKXljSpUbCIcRlrI0O02C7K8n749UJGMLEnxbjwY7B7UjLoVKNi6ZWHgg3b96sCSmTEoKrgnqtEBAWwulZYnAKC8HsOWedjU0PPQIAeOIJQlcP7mMI0u/+cAeuu+EGAMCPf/BDAMBb3kJi3G99/Rv4yEeYZn5WC1O2f/rTn9ZFU+2swoXythgrGmdidJQGD0cOQzNns0zdB/YDNdwT+uUg/tYLzgcAfOo/PofTj2HIx+OHaHS49J1XoutFGkz+/fs0ikREWX7g9vvw9jddBQC45uv/DQC4+SZu0L179+OqD7Ie+TDbcXmMffLcWCcOTPBw3jgiabCH2JIf//j/wUu7aIhbvYbj4/3v/wAA4H/+51u4S9K6n7iGYSrj4zSSjKfSqK6l8eHUDW8FANxyE8OHRsZ3IF7B54cjNHLMaeB7K6pqsJGoY3zlWzRWnHk6leD7N23Cd37wLQBAWysNStVRGj3mzm7Aky+x/RrrGaITr85r8s2hHglzq+XYqW8AGo+mAS/tcB149mm2q2lUws6xPKlxScMaVetdN8Jx7ql9vbxvdIjj03ZMZLMSFi1hmyEJScrZeSiNOCxW+GySBtjqqhrkxtm/A2A/1NZzXNtuAuMjHE8WeLjv7aGRZObMhchleFhISruHRXmrq89hw1ls/1/cwNBMw56J007jd1+8+pMAgPVraFBdvfosbHzgZgBAQz0JvhurqPitXLgAE4fYtjsfJIn1msUM1X5x30v44U0ch8tPYFjS7BNoUJ5V3YgVYiB/ccfzAAAk2D6d+RGksjQ27ztIg2ZdnGvMY4/djUSc/Xv+WRxrm597DgDw8s69WLuWUPTWeYSgrwqz7Z59dgs2beK8Pu001stQB8msA0tCOcOWcpK9OuFEk0nxnpDP50sUdf9hQYWvFV+TSCSQlHjIBjGyqmuTySRmzBAy6vb2gmfG4xVwJKTTkHDI449n6F80GkU4xjbOZUPyTPfIYrH+ivJK+u0vRZx8uPdMpxxOscI+TVGH0OQY15aammqMyf8VPYN6fyqTgSlrUrUYKGNxjoVkNgtbjIiGjLWMkJeHDBdRMUBnM7LjOi7yjiI6LjwQGyFD6xuKBkMZbODakOmIzgOdAIBtW+mcqKqohJ3jM7skRLixmfumGYrAVgl5igw8pi9kTKlqtk4nb8PT2gsd6bm8AztfaDhRoaGFfVbqfC3RsXw+WqU/K5XScFzt6HWNwjXCsUxdnqwY1Lp7uSd09/QhJYY4U86fENJsw5ncSDqVMbFckqGpwpK8Ne3I9M1y5Sr4bbJrpiCHnposu3T9Lff+kvPSn0gyPd1ylhpsjlzK1ac4SUJeG0uzek0JZ2VeSoKHVOcArrjyCgBAvJNrxTd+QBqCU99+MZyD3PsHK6lX5RtoLPrg31+KFza/wPfW8rc1Z3J/ufHqz+CctzFZxLHHUB9wQR3MdaJoWUqnFnJ0VqlELG+64EIMPtd/2LoHIWOBBBJIIIEEEkgggQQSSCCBBBJIIH9j8tpCCIVC2uSs4IfNEkqQSCTgJBV6hR+1dfRipvqzyDgKMklrfU5gwQOjB3WK28p6em6ctIJ/lVo9y8IFypRXWQgdDajR8UMl12uLqunCNRWKpTQtq6NhHp6XS4UVFSCJVEk1SkbFTymrrPebytKoiD396SE9dJQqgwHlWGP6ScDUacktTfBoqz5SRXFcH8xTWei9uilkiyJFVp8qTMzfRr7KodhjYBiGDjXSrSdtZcCf9tz1/8RwJrOwf/3UdMW/aVSPa0LnW/QFw/E37ysVjmeY3rWuRoAVIqD8qSB1enX1p4/4WKN/DBT87RevHV2YEjZYbhz7RoyUEzpMrpxF2IPkyhdm6ZxwBTPix43p+6RzFJTVND34r2oi1y1GBRU+nb9hOtOx5P1lv3N99SkS0/WgtX4Cv+LylH9n4d8RIV9Oj48iJEhGO822OvZohoqcseFMrF9B5Meq45kK3I3SM7hixUpUx0ikXxcm0uLc19Mrv+oNG3DVR0juuuEkokqe2kf0RefLO9EsabqrJYRmQhqvZ3gIdZVcF/MTXBdj0UqEbJavuZWoiVnN9C5sGdyKBx9+AACQkjAQ5cWsjUQxb/4cllmQQjt3EVlRX1WHxfOJgnjX5W8HAPzb574AAHjrZVfga4KuSQgp9+mnE/lw9Ve+gg9/8EMAgJ/8kKFcC+bNY4PaliburhRPSk68eQNDg6iolnTRkvr6S1//KsttpLFqOdu7XdKrpxcz/e6+g3sRljCXrfuFOHbrC3jnFf8HAPA7CWucI2ijG391Iz78SSKWlh/PEJ3wTo7fA3tfgDtMcu7xXiIq+k8gEX8om8CqCbZtTjzEPcP0yN55x22wJC37C1sZSvffXyMipPtQJwSYhbx4VitUyu1cDs/vYOjQ9t0M54uGeE3b3AbYNj1CjiBxFs1lmIvrujhjLcfAlueJ9qiMEyEzf/F8jA4z/OwpQUlBUFgDE0AswvsG++lhaq2sRDgsCR5std6z/XsH96N3kOTJ7bPZ3uEwvWN520GnpOmOhmWMJvl3dXUlYjH2r2UJ8ahsD6YRRiTMMqj5OTzKetbUVGF0jGiexYvFY2YLcWMyhZAQQLsSnlxRw0/bBQ7sF/RWs6C3pB9jsWq88JyM6ToilGsbWLZEFXD/gwyds9Mh+S2Hp59i+vaxUT5zx04ib2LRMC44j3OhtoZlmRhk+YbGH8OSlQwFTE2w/eYuIXH02jNeh+clzXmVhNXlB4iG29bbi3gd289uYHsc3Ee0WcfeHejYsUfKx3Un1CBjKNeCpgUc089vISLpnVcRmj6ecpCWfd21BRUl68PyFavR3EfEmSsJLBQ62bJCGo2dyym9yj2idVuLn2j2CIg9HddLc18OIaQI/4sJrtN2xtMfNCpC0mO7DmKSulft60kZq6FITEMmsxne39K6WL8jk5YyKER72PwTPK/TQLQY07jGJ38touRXKlMiII40RO0Vopkc2Qcrq7gOpdJJJCQMe0wQiYr4PhwLY2CE82U8xzW2vZK/wXZgyuIelvmmUqk7EQM941zLZoS53rg+RIyHEFfIHV8yBfktJOM4Go0hJzr8wQ4ihTs79gEAmubNw34p36YHGB696m0MWwlFLFhC0xGRcZ+XMWiFwx4Ng0zwtJytXNcrlwrBt33nBa0Gq4WhAKk1VRikW/JnMWFxAWJegb6LkC1O3oDlqL2K9Roa4h4yODyCnApRVUWR851llK5J0wkxfMVoNt+UnIoo2f9ZnFTGCxPz2mM6ZS73XTmEkFP0vqnQ936k0CtFCU2WyMafAGcq4ukjldI+NAE9phUlDee1IlcHgEQNdTQzznVhzaoTEJa9p23l0QCAc99yMQAg3lCHWkGS7++hrnDiG3kWePy5u/Ff//5FAMBlH2ByFXMmUao9fduws4NhzrmbGOa86FjqpHNmtuPemxki9qNf/QQAMO8o6mB1sSpseYz64sfWfmvSugcIoUACCSSQQAIJJJBAAgkkkEACCSSQvzF5TSCETG3FtTWJoLLOVQhHhv87ZaXLpgSlYEY0YscShFAoTC9Xzh5GapxeQUd4/RQCxDHgkW1NwSNSGjvqSx+PQqslU9sp9AOvyArZKxwPlVQS62gaOo+4IzHDtu2WIIOmSlOqyhIyfXwuRXxBlmX4UA2F9sBQKKTfozzTql1dw9VpzlXcsOLJge1qwmLFKOzF+TraGTBVXbxU5epvQCNafERxyu+nrOCW737Ll/KR3/ms+2URSJC+Uu8uRAHxu0LSZU3kbJglnDeqnobhaM+jW2Khd319X/BouNBAKxR1qUb0+J/lcUf5gEwFyKCi1PXak+ihlNRY03cbhoekKbK2F1jfdTOotnY1kEjzBSnvq+k9SnlyXT0mPJLAYiCOP556MnRPQbnKXFPO91TOQ1r8jHJzf8r79Pv4xkRVJSIyWPvFu97YSLTIhRe9EScLQqi7n17/884gCqh3dxfaHaJe/u4CctOccOnZAIC7nnoA7ZWMM04LWeXYEO9vbZsFW7x1tuZk4/JuOi7snPJW03vhON54bWwkMkjF+RuOqz1jqVF6P2fPngsAqKsMYaF4HbZtJ7rmc0IA/c1vfBejwm10wVnk6/nR94n4+cjHP4F//IcPAwDuf/ABAEBUPClXf/FqfP7znwcAfPWr/wUA+NjHPsb2GBjU6eINQYHGBWFk5/KaZD8S4rpfV0UvzcYf/RYVb5S+E+6bfUNEwez5+XXYsJZEfG86k+3+/e/9FFlJWvC+TxE10SzIokc2P4cdo/TG2DO5iTz6W3Ik1dXXYt1KPuuFreSIeu4htstbN1yAj37nU/xtkF7ae67/HQDgljvuQE6SIvR1M957x/NEdpxz9ga88XwiRe68m8iTnCwMDTMaYVn0bCaF5b9KymnCxoI5RCrsepmIkSeFv+ezn/o8jllGz9Lnv/ifAAA3xL7d17kHV76DHEnf/jrTZ1cIymwsZaNSJms4TELmaKQGOUn13tBEFFs2R/TEeCqlU3+PC7nxrDY+a/HSGejtJWfL8CD7dKCX93fuH8VwH7+raBDyYEXm7joadqi8jIoDI2RZiEpuV02f4Sh4VQ6Gxf5qnMX3tM4lSmfHjp2YNYvonM7OfXwvpx3S+S60zOI4gk3PfnMTeYbGxw8iOcYxXhkn70w+34FMmkiiuW1E4FTVEQWUd4aweyfb5oMffhMA4Oovs/0ffOYpfOGSywEAg/2s349+/XMAwCWXrERNLQsUlom6ehHRXo/v2AxbuPAeffxhAMDuF0gE3WhFMSPKNaKjm3xjR2/gfa1Na4AQy2LHOdb29+wHAMyfvwoT0qZZh+tVboJtF4qYaGnl2BkSFFVC86Y4yAnBdEzmpdIZXom8Eg+v31M8nWeqdTzvOLrMimdS8wRVVOj/D49xjVUIEAAwhVMqk+F90UiFvl/vxYLcsywLbt5DLL1qcoTIoGKZDlLoSJEF03nfdJ/xaiKD/lSxRK/NSX+blom0LDhVsuckBQ0UMoDHn3kCAJCRJAJN7US8RqOAo58ha5qgDIxMDrURjjFbxotpmjBV1EE5/VmeERIuSJV6w85lNB/W4kVETs5pJ5dY38goXFHij5q/QMolpNIwPP0SHhIJALL5HPJC5B6SQZ7W49ooc05SCD74FVT58fBj139LMZE0UEr0XYgdKtS/DcfViCWV5GBomPN6eHRc8wspHVaj+3w66HTSn+uSvOLx6Z1HfE/Xn6XnCUMn6dHf6WvKRJNMQw5X9iNB40yZTl7V67DPMcteNhXf0qsv6vzMvxI+3jBVhoFh6ohzFlF3G9o+hnvu3ggAOPs0ouGtSuofu7btg1nFvfgbN3wFALC7izri0UtXYP5RywEA2X7O4d8++DgA4G1v/hds3b+L1+8gufSDD1EH2PzYE4hIpMj685n0ZFYj9ZLhvgOItx1+zgUIoUACCSSQQAIJJJBAAgkkkEACCSSQvzF5TSCElDc6m8sijPKW1lwmC9el9Uvz94gFdGZ1HUzJeiGOS8SqLLlvAPk0n5EI0+OWStPD6hZwoyiLsLDLm65GOpiKFAVe6jnlMVCiLPSWZen7lCVfpUd1YWsrp858EfK4hJRHSvMGwUM4KOSUUw6lUMRoHw1HPCuuPF6Vt9CbVsRXU4bPRcejwoXjFmYf8LKmG/pZCslhl0kr7ogV3s+DVMzQ48E8DI3Y8SzJntegNJOYofl2ylDelKQ39WzuXn+pG1WWLKK9vChtfinWeJ+13igqC6E+8t9ixI+/hrpMnjdjqiwYrgfBKXm2jpVW1/qRLWXaQ/VruMhzY/pitEs8Pr6yqeRk/nhenWbe336qhiXoH8/LoMFauvR+j4c3Hl6JlEP6aCSYar9y46UMMqj4b8ZvF87HceHaqa6twYSgd6KS5jQU55yfd9RCjGfpTRwWT51KMb+lazMueP3rAAAnnUOvwnM9TCN/8x1/wPyF9Oj9/mZmlppVTXTPmvVrcMsdtwGA5qaJ5Fmmlqo6DImXu76FqIaxoWGd2vbAQT7ftVmmuXPacewKeigOSJaw3bv2AWDWsMsuY+aEWJTr6egIkSbveff7cPcdTEG/Zi0zWC07mrHT+zsPorOT2U7uvPNOAMA55xGds3jxUfinf/onXrefiIW8eE0dy9V8NWrdz8jCUxWNIpVie+clG9SH3/N+1rm6Hnu2MFPDj/+VvEJnP8ZY7f0TnZjVNhcAUGGyb576/d343p3XAwC+fg2v/4d/+AeWIZ3GZz9BxNJnPvlxAMD/dNIb1NrchMVzWMeVx7DfZvz6VgDAp773bTz4ApFEOx6mx7hR+JfGczlUR9lP9TOI6KiJ0Xt04fkXoa+f6I4HNt7P99QTnTM2MYFEhGPFMulhGu2nxzOSyKOrm/1VUS8onQz3uh9f/31s2EDU1oPPkpvmE58hL9JzO/bhpt/cyDLIvjSvVRAxg8PYdZDIKtuWjGXVQN6klzuV2sd3S7aweGIGxsa46eTH2d/RCtZzIj2oY9r37SEqamYz39PeVouXX6KHrKefCJy0rCM11Q0awaEQhtXV9MqPjg6jslJi8QURU1nBayoQwfAIEUkDwxxPy+uJ4Fl/xin44x1E1Qz2sY3WnU4UUdvsOmQmOKZPP/U8AEDHAXrj9j76IpYtPkb6ggio+hmzkRwnCi0T4hhdvpzz7JT169FSz3F+qJNjtPsQEWTrVi3BvTcy49vC2USZJSRN7a9++VNUz+YzGts5FxeuPRUA0Nndg999lRwBQ0Ps75zF+5Zf+iasfd1ZAIBtT0tWoVre3zJ/DlI227i5igjF/nHW/fFbfoS5C4ggWLWciEQFTMjlchgWb7qXtlvt5Z7uM1Ua4OkI0aZH7lk3TbNk3yzk2Ci/n4XDYT2ulCi+If+1Cj3hZX/NeTx5YVV2toFru4hGBV2ZTcmzIq8czTNZJtM/ER1U8Iopsh+9ms/+S0vZdxcrItMUlY3OEjRiIprQGslz28gBd0j26b1796Kvt1vexwGyczN51aL11Vi8YhkAYNF8ooZmVQhnICLID5JDKCWcgo7jwiwe2/JpWpZeD4uzIFmuiZTM7XlHESHUPou6wr6nN6OikXvNGslwGU8IMimd984h8lqFTs1msshIGuyQvC/n+PW4IuSIobJCl6LbrSnGWTlkkBITXlp2VWPvHGeU6HLqfSErglyW+0rfQck8Ocy2Hhuf0PWKealy5X1GSTRHuYxbr3SclyDMXcM777hF5x+fnq/AP4bhrcX6mb7/GcUcTIfhIyp3XbGUy6CmZDK+H9d1S1T46XIK/bW4zgp4k4rSzntcqK7WS7PCa2hGqAs0JxowV/Tzjpeo104kuSccP/c4PHM70UNDhxg5sOFY6vstc5rw1rdTx77lp9QPXi9ngaOXL8DlFfz/9V8nmvgR0VtXLV6F152ygdetoT4xUzjP/nj7z7Gzb+th6/yaMAjpwee4fsweP+xSI4JOPy+LUjadQSIskC6H37W08bDSt/Mgdr9M2PxxR3Eh9qB0lrdoFS1igJ+wSp/85RfTN9E8smaAyHZvbSg0DDmOR25sWvKpwn4cV4dweIqVL123+sY3GUvSx8u14bBVMqF1KnDTf+BHgeQcWxPCeeTXHCIODOTVb/IsWzaKcBmFTJsmHNcjIdMWDP4dKlhY1AJiqpqXbiyGoTcSr37w/V1oMNGGGtPVepVnSCqzMOr7Tf0cb1EoWrh9z9BAVV8TOEULsTYeGYavPVAqxdBa/0/KcOR9oZ4OL5u7flEJ2ZyvNPp/VnE54ZZCM8vYZAyxNHpc6p5BqGSjcb17i2HI3CjKt4ffWFQyWH3Pn+oQoVOm+i8pgZ4aOqRQGcr0vHS9YL2Sjcn3TPVLSMiKx8fHYRZtjoqALhqPIWwxVCEe553DQzyArj92LY5eRZK47hQVmFiMytq5Z52NH333GwCAQ4d4oL787UxBOWFnMTjOQ1uLhKZVSnhYeiwNV8JBUikaDyoqopjRwMN4IsZNIznGUBE7l8XYEP+/9CiSRDtpdnRdKobuQwwbOef1hKX+1zUkQ37fez+oQ3s2v7AFALDiOB6e//EjH4ElbdM3wIO0gswfOtSDRYv4nvnzCbfNZIT8OhbT7VYRo/FGHdZzeVMTVI+mqbCHTb5jd283UpLCtyvMuh9zKlN3Njy1FTMraIS58N3vAgBYVdX4+ut5SL7tNhrWfvaznwEALr7kDbj+N9x8u99EUsA3nHcRAGBx21xcfjnDfqoaaah5toP7zdU/uQaDe/YCAM4+/TQAwH9+6xrWITeEqngr26OXBpdVq7mJZ5081p9C49JDmx4AAPR084BRU1mJtiYalfYdYNiPK+1YUxVFzuaYscW4V1nJfXBkbBAH9/I981p5QPjmV0kuWFNXjW4xClbIQWQkSWV5YKQbrsM9oKaa46W/px/RKio20YSk1o5yXo+PJBFyJYUx5rJ8Qrw7ODyiiU2rqtn+EQlhqq+twTnnc9z/7kYaz3JZzptMehyhsBhVlfFBFqV82vWMRWLMCosuMGf2LDz+OI2AiQRDnnbtppEqn7NRV0cD0NL5fO/oEMdshzuh5+ymR+8BADg5tsfs9gYMixFmxTEnAgBS6TEk4lQGUymWZVYDD1qbnziEujM4V1tbGbr1ifd9AgCwe/8WzG5nXxw1h9dXmJzr+7qa8fBTDwAAep97DABw5RNUEi+9+A14w5WXsW2SPNw8/Qyv2XugE/MXsz4rzzwNANBSy/749W++jSw49zacTqPRE0/fDDbaCLa8xHV4eIB13bD2UrZ5JKz1FUunm5ewF9PU7Z1O8zvVR38pcV23RP/wk0wXG6pMT5v3hajLCq5D5vPeYVuuV+uQZVmw85xfpuyD/oOQbXPtsnUI0avYHlMagvy/vTLw/5EcbP9cxp5X+tzp3FdsD5rKGObXDWtruV6pEbOzoxM33nQTAKCrl/NbOSdqKiv0GIvm1Zzi3pzt7sOe3TwcbhQjzEmrjwcAnHbiSaiR8GZL1j7XtnX4knJkeePSgis6eF4IpA0hqDZDIWQlpC2uiHDVOhkJwRAH8USS+4VpMLTENR3PKa4oOoRWIGcDOX0w5lqbzXvrgFXkCPSSjJSmjy97yC9KFV/OQOHX47zzgXe9o6exOLtFSYtaJtJZtkdHR0fB/eFwWOulau1S9AphK1JSHv9cn4rKQ0nxb39KqGX5EKwiB6z/3FqUzMVfl2Ij4istn3cu+fMZlv/a4oEaCu0FuVxWE0yHKsRxUEPnXVtNPWxJ5jJzFnXyeELi0jNAzJFEQAupc73lzdzTEQohJPHrb72AYeZbDlHn2LWzD7lO6nGPPP4MAODoBdQr3vX+q7D8eOqwB/bwenuIe3lnZyfaFrUetp5ByFgggQQSSCCBBBJIIIEEEkgggQQSyN+YvDYQQo4KzfIhTeRTWWzD4bBmFFbWuZCEWxmujXohn16xcCUA4MwN9GbubhvCQB+h5K67WB7tkbMpUmmVHtwQE7Nt216wirKq6uihUuunR3jt+P7vkUPzGu9ZRdWE4wB5TdbsvVdZ6yFW+gILrxRIpZRXXomQD2XiKtLfotTqAOC4hegrP4m1TomuU8a7cFRKemWxFkSIY3iIIvUW18sz6YV+SbnClnggysB3PXJpfzp4zwKuPJTKAO0qb8aUcEcThsoTW2RFB5nJCq/X/W16pONF4sDRqBXtOfAPC6fI26Hb3S0TmleEUisjU/m+HAOA4aHKAEHBFXlV/I9XyKApSSOneKm2lKv5YzgwTRVHpi5SZXdLCOFt7Vnxl6EQEeY6BkpHrarLkXsgJkX6oFz7qv7zv3Oqp/N6hbaZmBhHTYLevmROwpqk7mYOMEL0JgwIWmb5EoYdtVbVQ7JZY9gWAlghML3119dj97MMAzn1RKJd0uK5f+yxx1ElSKJcive54oHMwkZCwtbiQsxcV5fAgITKHHc0Q0qSE0RMjIz14qR1JwEAaqrp2fjoxz8HALj+Nz/FnXfcDQCY3bYEAHDxG+jZOOfc8/D8C4TPD47QMzE0PCZ1zwISJhcSJIdCDBmOjVFB/URDhWHBuZyj1zU/KoH/sZBU66KkAH65m4icH9z0S1x1Eb0rn/wkERk7d7Bs77r4MjTOIcrm13cyhXhuJIOf3fBdAMC73/sePrOFe8rPb78BEyH24b1PMk3vjIXzAAALTjsLO4clBXqI/VRdw/vqsjlsFm7ir99Ocu0G2QtGbBt5QRm0ttFzs+UlQnrThoMbbmUI11A/Q9OS40SCuLk8WtsEAVavQlP4jrHkBGY00pOtUrH3d9EzHQvb6Kkh0fRbLyG66b+/SbRZxAbCLsdKIsExEK1iuNL+rXuwUMK6KhLcb21UwA0p9I54pmXfrKuIo76a3q22ZiJVnnqWKc7XrDkXmzYxhO7gfparpkbCuyIWRkY5bk9acywA4GUVpniitHoXAAAgAElEQVSwTyPv1F6n9sjq6mqOLQBjQvY8s57e7vmzW3FgH73xigy25yDbvLaqEtWVnBM19Szv85vYxpU1QFWVkATHWL+BHv62bt1qjI+w7C9tY/jVaRtOwWiKhOKOzfId3Mv7/vGj/4SOLoaLdBwgGeTKFWcAAJrnrgAkBGVIEl9sOIeIv6hzDkLj7BOVStqaw74ZPNSJTiHOnhgdkHoR4XXM0mV48Xmioo5fT5TZL24gEqyuPoENp/09AOCxRx4EAGQmiJiKRG0gyfc5SQn1i/G9I+NjiIselhfkdizCtsvnsz40X2Fo53RlMhTudMVP7Dmd3/zI1Ygi8dXh+vyMhMLeniXoSqW3WGYEhoRMKr1Rq0SmBcdl30civD5kRJB3C0PTpi3TCg179cLH/tryl0ojPx2ybIbBy94jaJs77mJI9F33b4Qtx6cKIYpVlBapjI2orFMZeVYiwWsi2SxCgrZNCIL38Y2PAgB27NqJiy4lAnVxnHPdsICwij7QFApSB39CEKlPVgjeLdNFTPb8ri6uDUOSat6BDUfWzL0d3C8XNnDNjJhhWGGPwgMA0mnO76zjwnElVMxW+pFC4nn6r1sUXuO6ng6qfnML4sJUFEThPOXcLdQA/UEFalr7kekaySG/qVNcJByGLevSgf1ECFWKrlFZkdDPDAlyKu+U6uTFaCA/HcZktALlxL8mFV9nmta01kPHd77SLSSJBiyo86AX7qYoQ8pFRiiZrk7tFNdN9furSED/1xRbj23VVqUhmUoikRgiEUlMAOoMeQlmzOSzqG2h7pQ1OfYOdVIHjYSimHk8Q7TX1W8AACQlaiCSc5EbZxkq584FAKxews9wBngp9SQA4OIPEDG8RlBB/ekU8lGO5fZmIqIfuJXI4dnz56Mr23nYugcIoUACCSSQQAIJJJBAAgkkkEACCSSQvzF5TSCEHB8ayJE4XFc8NYrQLxaLAXTuaWtdRkj7jHAIdo4cEs319GyuWEIuiuVzqvHwZlq405mxgvc5Th62LRZuzV3kK1cRAsT0W1ndQguvJp0yXM3dorxN2ayynDowXcW9UhjX7jh+viRIe/hIaxWFmp9PxyqMKVZoKieX91AWReV0XVenxnZ88fJ8R0R7GjS6SdXF+b/svXecHeV1Pv7MzJ3b9m4varvqvYEEElWAMTYYg20wNrjF2PnZsePYTvKNW/xNnGo7zjdOdRKXuOEaN4xNMZgiQEICIRDqEup1V9t3b78z8/vjOe87c8uuVjJgGebw+XC1U955eznnOc8xURTroKvyoi0APgKkWhPsalJujVSxFE9Rtc+ppmsygggh/5rusBVEb2Uh1g2r6j1UcD5p3qUAasvn5lFpGmV+seXlM/0w7hVFdg1UcROUEdFVkHn7VhZDh5evrW33EVnBZzwv6L8rCCqYwRKVvW+YnrY2KZRdsH/of9ewiCjR9ad8kx2fj8ut6KtBCaLz+FvNaRXoFVXood/E0jCRd6vQSqjdhmO9lxPSuEQkitwoEQ9x8S32xMJsew4KQlLZ2kJLYEsD+Wf6kEVOIB9TWjmXve+vSWR8eOt2fPlTnwUA3PE4UTq7jhD1ceLYEXS20cqnyN7rmghPycOEJ6FWb7ia6IRndzyJESnOJatWAwB27yGHSt2Ihf4RIiJ6B1ie1zfSwrni/FVorKfV47JLrwQAJOt4b++eQ3jtddcBAPbtY77yYsU0LdPv72I5z0jIeNu2dRhctwIZapRKsCNEUSiS1jpBgw6NDMMVS2xTPcv++a/9OwDg+ulLcNuaawAA//a/JOBduUo4ar7zXXzz6yTra5Rw9X2He7BqMlE/W+59GAAQi3AsHd/0DKYJIiI1wHab1sV22/bUVsSmEOGTf5ok1jdcxTp4YtcTeOpf/4fPp/j8vgGiiaZ2zcb02UT67DlApMnxXvqGH39iABBEY3uTkBUX1PrWhu4RPjcqfU1ZtGbP7MIxCaFuurRoN0r4+J6T3Tg6yvXve8KRlCmyrgsnetDeRL/3t7z+jQCAr/zXfwMALl1yPvoHyFU0dRKRNNFkDAcPEYEUixKRZBrCT2EMwQQ5NY52E73SPo352/j0XegVkueovBeJCJpo+myUiszfqy8jN893v0eUVCZbwNAo+69hZuQ99ol8PqvXQcXzMSKIrVIuDRN8z46wHs6/kGkfO3oQI33kZZo1l8SP06d3AQCy2RwcqZtEA+ulrZljeMvmQ5jRxbGazrOcQ4MZJGL0428VgvB2IZWub8wg1itoowLvDSr+mXg9bBmD8Ub247jMx46XROtK8mr98K7vMq0hjrPISB5XLiXvyJS2mcxDPfvXxp3PoVXWumeeYqjanhPsJ60NU7B1I9vt/e/4J9bxD/+K5eswEAPLZWTZJiM5QZfFbRSzQroqpMtqrxKJRDVXjuLYmegc/UJajSuJX2tZ7ivvmYbpkwXLHioaKJ/aF1VyNebzDqJx2WvJOPOC/CeKW1H6aDHv4YWkEfptyItBPD0eUuJM8nM279V6v4r7JoAQuvPnDBRw/wNcG+oaWuEIYXQ6zfFlWfzbKbnIy/65IKhRxa/VmKhDUZA36UGiYhvqhBuvewj3ff9nAICpt5GXri6eQEK483TAFuHCcRxXr5OmIChdyXt2dAgJOTs9tn4dAODwMSIEko0NGBE0ycNrGbTg8oVEZdqpqM+j43HuLJUUZ2vED6ajEXE+GkhtsRXts+LlMT2rah9dXv+127IWebNhliM3+G3ov3WyFXvYaMRCSfiOuk8QMRWrbwPAsa54YJBU5zPFEVOs6jO19sNni7apft5EAMtedi+4Nw/uT3XdKG8G7ehgBdBTknpgnqzkwxkvRHxQxntvomlMVE43zl8M5JHq48GgPZV9VK1/QS6mqPKqUZ47rom6eq75h/dtAQDs2cFAFh2TJiErJOdTBUW0fYBoveRwDqkU9+6pGdRjjMg+psUroadIjsqeKOePp3YTgT13xSqMytny2QfJfdhznFxCT+57Gt0O+z0+OHbZQ4RQKKGEEkoooYQSSiihhBJKKKGEEsorTM4Ju0WkyGyMNqQR9WhNM4ap6ZraTl+4xs4EhoapUauL0GpaEj6MvGshLVFVRrvJwzAySP+8rmXAlJN8L1OgxcYSa6bjAnlxwy0pP33ROFoeAv6rYlHSwBhzTM1kUPOq/DyLkkzEMDU3j4qgUhB/c3iOz42kfk1aAQBoVE8wJLpCAmjrukKHOB4q0SQ6WpnlI5NKSqOvNJulouZ2UffUr+O52kfYUvwqUOHnLf09P5oWK8u2q30vo7ayrgU07IqgSCGgPMBEpbbZ1RFvVD3oaGiGH3KyEpfiwYMJH4UD+P7Y1McbZWlpX2gARpX1wit7ppaYHrSqVaGBzMALujSSF0ff8vxQml45Osc0NEZMh4o3AvksuOW63XEtYJ6pX1ZdXHE+uY7jW0IqXJnNiN+WtljHvAAPVVEVRCHAAn7iqq3zajCUwaqkDf1q0HWgrrnjhLocTyxtLhmfh6nq+RoyEaukrawmrgskON8UpG4txRdmWjAkulgsx/obdommsMw6ZISvIAn+rkgQ/fL62z6AE9NovT98QKIn2US4rFy8BPuOEGlS30irRHMjrQxutoRsmmmdv4zIgohpY9qNMwAACyU0/P98maiQt7/5rVjSSXTCll0MUT7US+ti0UyhYNLX2UpwrrbiLGcJRUwSvpmT3eRtU5FXUom4X7NicYwpzqnAfKVEzw2Goa13iQQtnupvy7IQkbkkkyEqZP9zzO97Xv0mHOhjne7a+DgA4APvJ9Jq0aIlOBFhGWYsJg/S89lhzF62iOnKp1PCiTIl1YAh4UTKii/4T+78IQDgutffjCkjrNuW2azHe9bSmrzv8GNYfj7RJwf2MC/TOrkuHe4+BJxk/WXS7ANT22ixLBbziMhi4wpSpT5BpIplpzB88hAAIJ9j27dNIrLlZE8fPFnHYkmmNTzCta5t8gx07yeHzeAQLUsdU/neiZOnUBriGvmNH7Bc888jr5RTLOD59WLJmsZ2njl1PvbupjWrpUmianWwz/X1Z2HmiYxrn0FE1tx57HMPPLgRdRZRJ0ODRNfk0/x7w4bt+NvPkdMoM0hL1oy5RN1s2XlYW519VKyaJEqIiu+90GLAsdluj27citUXMURrWu51zWBUr1Wrl+OrX/tnAMDDj9wPAGhuZDQuuHVoaJD5qkCOr8PCCdQ1eSn6pV81NbKvT5kyCRdfRjTajuc5TpavIRLPSEzD9JlEkDWmhH9jmGkVPRMFWUNLgiQwpT/nRou44VXkerJGmJf77/82AODCq65GwwyOz1fdcDPLLAiq4W98GQf3k6dgpkS9W7iQfRzFQzjZS+Tej37Fci1efhEA4OTzO9HUxf4wbdEV/K7sUbY++yxaBa04axZRdEVBxpSKBY2M8PkR3XEtty+GVXcs5EeQB68Ku+y55KYMSJD/qJI3QlmFTRsoOQohG5O7RT8vYJpFlZRVqPH1F1LObm2sxRE41r7mxWizIKLgjN8d57Xx0lRhxUuKtzQaw8go58M64aHzBIlqWsDGhzg3PPwQuc8SSc7DpZILV/FCSZ8pFQWNaNl6PxWVSEIKcZ8rZDUnmCcoUMvhmI8ZEWCY175zPxHAH3zzbRjNEsVWL+ufj9LxUBJ+QWQ455ZUV3AduDnmYc70OQCA+3OP8VbCgywrmL+EkZdtQSE5jod0JldWj6ZAkwquvy5pBLza7wfGvGFV9EcvgCKRS2awAVUalWcc+HvzoLdAZdjzWsh1FcFU7XOT0RiGBrjWpIdYvuM5cirVJWw4bTxTjhaE32lQzlS2f16q4vvBOKHba6Djy1F2KHvepw0KRvmtQM6PgdJTh3jl5KKrx3PgybnAqZgfWU+150zPq54HanG0VhfQDGCbgoin4N9nJr9N6qFyStnyjNSMpFlxpoomIrrxps87T365rypkB/HkU+T3mbeM+/uRrVy3dw/3Y/WVlwEAMuDc1CJryv989csYOMG1e09+DwDg4gvI95kdGcSsTo71vM05I99OVPG82FW4qK31tGU+JxRC6qDpuT4JVkSw1CkJ0zZ37nxs3sNDkHgJoOD6Llmx9pRKDQAwPMzBv2dXP9LDnOg7KpQrQXJfvaFQygEjGFqeYjgKSjo2iWEwLX+iUoM+sMHQrl+6FgLky/wtlUpjhjUMToqVIVdN19HKDTUQNRF0CShVhCP38xt0Mauuo6pNjVce7r5WPscTlkHyYiqCcOhfX3kT+GSF8qZsItYKK0qwlGO11lnD289wolLkdhb80JgT+Z6uA8PQE77Oy1nCrINtpEhCtRIscE/3R6sirGXg077K1NPKM0cpPjwVYt4nK59I2bX8BhvGF0peajI824qgvZ0LxJFTVPDs3MuJ/zXvvQ77xE1FKXFf90aGfv/2d7/jH2BkM3po30EAwJL5SzFgcRG5/BKSUS9ffZFWHhw7wUPs/KU8wF934016w9YrZLz9w1xgpk+fjj45EKtFUbmKTJ06FUePMi1VBhXauyjuYcCZ1Wk0EUchyw2cOqwF57u4hJ0/dIRlbZJwnSuuXYPHn2P48sZOHtwf28QFePuJ3fjaV74PAPjoH74HALBhy7OYJGSaHeJ695Mf/gAA8IV/+Cy2biXk939/zFDDF4m7XN9gNy64+AIAwMJlDFrQY7Ie7/z1NzGtlYqZmbOobNqw/hkAgF0XhSsHj8F+tunll/Mg/vTTT8OzFDmoEEfLoaCttQlxObi0tFLZNHkqFRl7D2zRddMnCqySuPqkEpOQyUr9idtydpRpLpo7H9u20l1wupS9s4Vpz5jehani1rh9JzcsLU0J3PqW2wEAvSeonHp+D0OgTp3ehb0HaJTplEPGJZexruYuuAzRGPvMX/7FnzAvGebpVa++ChddROXE3s10dXrrG0mw/PzOvdi6jd+24uyXo6JIqq+vR07IymNxtn3JYbl6+3px/BRJuRcsZrCJDRvZJ97xezeha/pMAMDwCBVQRSF7PXJsP4aGmc9kXPYVEdajmejBZVdwDA2WWMfrd/wKp0ocEzPnkODxn75CEvbfe9cforODCpmhQbr6NdWLa1o6j7jN9kpGeKCDkMIePz4Cz+Q3L38z62/lRVTGbHxuBzY/y/ZatpwucP0nmfbUrtnomkEj2v5DhJbH29l+nbE1uGApN6Q/vY99e98+KvZuufk96BT3s+Z6HjzzcpiYOXOmHmdqrKt5ORKJjBmSN5RQfhsynhvaSNY3JgCAWyoiJS5buZxSDFFRnx4axJ0SQMG2OQ94eo8OeHL+UCoDRTHhFPJ6f+665WuWUTRQyDuSZrm7fsn2CYUH9hwEAKzdsgmvWUmX7twojR6OKIRihoWIKIILEkDCLIkrZ8xAdsCfIwFfmXtiZBCOw/cmT56sKo15MaBdq5SrmC3rfMQyfXJosUbbNfaGleO/3NWJEtQZVboZ+b/Bd8RIHjHHbF/DMHxX84o8uHCQHWH7ukKC7wrpdq6Qw89/RPfkD7/p7XzBVtrc2kqYymuVSipvjOfHes+nJYlMaP4se6YigI1p+c9U1mXwe+NtrccDPFSZVr3x+sArZy1wSuVBTwwrAicvhhMhZn/yKbpvHjtyECMjHM9DMhZXX0Ql0KSDR/Ho9+4FAMTFcDyY4z7m0acfxOKV3GfefP4NAICoGGj37TqAQ3u4H1u1kkqizU9z37pi9YWYv3DBacsQuoyFEkoooYQSSiihhBJKKKGEEkooobzC5JxACLkC/3ZLrnZBUYiFpJB+rli+As/eL9ZVgShbYkGzIzGMCuxTuRP099N6d3D3dnS10KKXF/K3SFK09TC0et9HT4hm33IDiJ1y7SqvjW2FGMtiVvI8RCR9syKcuWF4Gutnauh18H61prXy2xpNBJ80WEHt1b2iG3AJqkjbKQGeCluukDgqvKHhQwx956VqORProGd4Vf5dXgBCqYjodBYMowoZFIDNVOmiVS5rQZM1FDVIzjaB8oznKqbyBgCGW1v97sDzXdTkmrK6uEYNJFiQ4LASoFWDCLrW/VrP+/kpbwDDMDRir5JMM1gXJafcrczzPEChygQe7Y8Rx++bxth9p6YE/eJeQnmprdwxIS3uHx5Ee5KIlnWP0tWpb5AEzyd6jyPWTGvA/oNE4uw7ThTR5M5p8FwiHXpPEHFy7dXXAgDcPDBlEd1WGpqIWNm5cxsWL6dLzpyFhI3PW8C/p7R1aAueISF1i9JPGhoa8MY30jJx8iS/44c2tbQ1sru7W1/j+2dXpyqUOOCHcVZuHoVSEXUS8vPEEZLnTe0gUfPktlZkTgmBcyNRL7s2knxvxSWLMRTjxL//aboCvOHyNdizj0isr3/zWwCARYtoUfnyt76ON99CsuVZC2llPXyMRMtPb7oTQybbp/XZRwAA27dvBwDs29eD4W5af+oFwpsZ4W/Ss1BwuGYloqyjnVtJSt2QrAtYEMV6LEEThof7UddEa3CDEE7v3898pzNpNKR4rX+YeVowj+iU0ZF+DA1wbbzo4lUsw1Fak7ZtfVbXW1JIdbc+S8vSvK7ZWLaM7kknjjNc7769O5AQmO5kcRGsb2H/GhgdQsdkwpZ37qKL2oYNRPauueJN2LOXLn1NAl8ePc51eueendj9PBEt+w+wbrdsZh7+7KP/B9/97vcAAPf/mmSJ9c10UfM8D7aEDs/I2i9eV2ht68CTTxG5dMFFRF/dex9dQP71S5/D8ZNEzRXSJJeO27SWz5reDpgSDr6P/SRZxzkt0ZxHLMV7dpZ9r6nFQS7LcfiDO7hHef4Y837y5F6YLtFW8+bQRfMTf/IZAEAaQ0gmWA8GOM7ufeBuAAzrfETCRXfNJOJn3iQ+s33n82iaxPq+UwjCJ7dwXLc3NeNVV70WALDwJOeIR+5jnVkLOtC6mG3/vhnzAAD9xw7ymUcfx5SbiDRLC4zTEBcp5S4G+PsrTWZrmho1VGklDyWU08lZI4BroEMqpda1mHgcqK7quQUUhbC/TpBCSu74/o/RPypE6VGOBeUCbUYsHYpeob/1Pr/oakRdQQjX9XzuAem0zPvyzGCRc6Bt20jJ/O0KauDOu34BU5BIr7qQCMq8IIVSsTiKeaZfKBCd4ArhdMKy9Nppy5weFRfo0ROjQJJrqCJRL4m/bSKe1CgBn5BZ3CotE54gkNSmNRKpHvPVCKFqMLtpKuLqYGj58vf5W02d4LtlGVXvWTX2qgBdcRNqry/tVjJYlqH0MGYJN0ZU4O1FszbypzLtyr2x3vO61WHda6VR9f44dCRj0kCYgX/X+JW7VWmM6+UyzjNmJUN+zWReeWuBpT2dxN08l4EtcK1f/II0ArkMUXuJuI0r13BPkm/hmM/mBbE9ewY6U9y7/93ffxoAsO8U91JGm4VilK2z7pFHAQCzxQ1+RtcsRCzOYQcPcz/y+hvpdl7f1IjOzq7TluGV12qhhBJKKKGEEkoooYQSSiihhBJKKK9wOScQQlob6UCH8lboELNIDduiBUtgxlQ4N9GACrjBM1xkxXKVFZ89Q55xS47WcCclNKOydsMDXD9WIsr/YcIzA0giAEYA+TOeH2ulBDkvikrTLdAkRWJrmn5YZtdQIe2squ94NfKgRN2zLMMP/ShIFcUb5Do1SJelDhzH18grfuAgMXA1MkihnaoJ1CaCBijB8xE+FYgaF2RVkg8wL0xYfWDM7wRD1+sXK60JCNyrzHPg0cpwmbVE8+IEte/6/WoZyyoW5E2quud6uq9WItA8161u1Ir8jHk94G8MsB9aitSvAiEU/LYDp+zvWvkKXlcE62cKEFJytnidl/q92g1xer17NkuLYGOqXodCftvNbwMAXHoeSXm/+t9fRl09EZPv/r33AgCGskSC7N62C21N5ArxhM100QKSHN9445vwq189AgDIC2JnzoIFuPvue/jvOUR0XLyaFsjevj7NoxBP0aLanOLfxWwGPT0KJcByKStjf3+vfu/SS8ltkk6TG+VsEVfxZMJ/1y3vc06xpJGQimeoWcJ15qMR7NpLq8qm58hp8+UvMQT8v/z73+BEt1hq2liGXz1yP/70A+S16T9Fi+2vhFD05re8AZsEMTNNiHe/+AUScN92yxvwxBMM3bt3N60yboF5SkRSyA6xP2TyRKGkxDI7d84MFIT75vhxoqn6+2j59VxT8+mk6mnxuede+pTf/u63I+sx73d866cAgM5pRC25SGJmF1EoCZPkp26ReWltbQc8WogP7iexMAwVOtxB/zA5aF5/Ay1TU9uJatm2dSdG+tjHBgVxNXfWFBSyJOS8/1dEWF12JUmV29ra8NWv/hcA4B3vfRcAoKeX77VOmobcTqKnBoZYBoVG7O0/hVPC9zNzOXlujvaTl2fqvFn41Gf+AgCwZYcEjRBEMCK25u5QUi88UiPDA2iUsK/33MO+PnPuTP79i3tw8ABRZW+8lpa6I4eIGIKZx9z5DPf67Ja9kk+muW//EdQ3EfE0cJx1NmnKCHpybF97lEidN1zyOgDAfWt/jqSENz64g225fxuRYKk2B7PmLwcAXPfaP+C1DraljV585xv/BgD4y4/9PwCAUDKho2MychK6+vhhlqFrEtFNQ0ND6O1l/x3qJ5rh+pvJM1ayUrAK3PJZJVolp88igmzWyAhO9LOdz2skImkox3azDN9qbVvyviI9Lzk6qIUl95wa+wEl41rNzxYs8jtAU/G7UraXOp9n/b3KvyeIfFDMMJlBjpHG+hhyQsgcTxCx861v3QEAuPPue9E1i+jIkvDOjKpn43E/qEsA3QEAhUJOI+hcWYt9ElpPc+GVBIGXkbXfLrmIRLiWZrOc36xsBl8VdGSHcIMtETL7Qr6EYdmHRQuq7DIuXVej/GMSfj4vZYBpIJnkPuLgPqIxF04l4jDREUVS8uB7OKh9p6PX4KiUp+T5pMtj8cYw9HnlecnnUB2LO8gwfA5TTVhf8nQo9UoEOwC4jtqDlu8V8rEc6mISfEDqvyjzVDQaxdWXXAoASMkkO2hwD2b4HPM1yjk2Kseo4U1S6+9KhJBnuFVnFd+jo5YXAPxw81X3xkAUgZ8wTa/mvZp5nvCO+JWLMSlKv7JF32BZwNpH1gIALpH+NUkCh0TiETzzJLkSjwqPX3sL987ff+hudHbyObOB4/nNV70TALBt1wF0mkQEWVPYR1999XUAgN5TI3Blj7fqotcAAKLS52N1Sc3pOp68clsvlFBCCSWUUEIJJZRQQgkllFBCCeUVKucEQkihDeJGFLYKJ6vCYEtwmikdnbBTEsJxiJo4xaaeGc1gWgv96/OCEMoV+MyMGbPQc5QhkDsyonVv8EONegpJpIlc1B3XR6iItrSowo6O4+dpmkYAzaO/whSDqB5NgSOcPTWQFk4wBHjFr2EYNTXkALmKVBR3Hd3JZ6zRYJxaHDM+J4ziEjo9b5DKz5mKGdQ6m37+qtI+45THljLUUMW12lansU1YXiWpzwsgikOrQnkvI6S8r+hfx6uKEja+dcD3b67sO4Zh+qgwSVQBfsbiKvLzXrsNeVml+VLroMe2Vo8vZ9a24/d/H3U4lijOAcd19afXP0wOoYY2WvMmd7Zh0yPrAQB/8P4/AgD8/v9Hy8H1170OjlgAB44R6XD//US4fORP/wyT99ASeLiPKIy5s2cgEec8OKmFKIqCWCWjUUs3k+YzE24C27a1pVFx+ShehcbGRs3lpvqV4heJ1uAamIgUi377OQ7TUqFvY7GYtlwp619RkEJJI4JPffgTAAA13c9fQOSJbdkaFaW4dg4dPI5f/PiXAIC3vOlWAMCll1wJALj3wbthmDTBPrWBoXuvvZpRHLx8Aaf2EhmUKioOLeFn8GLoEkRRTy/5esQIjbaWFNatY6SopkZag1avZoSqU6f6cOKEoFcuIbrj1w8TIbRrz27kDdVOtOQODRBJM2VqGw7u5w83TDIAACAASURBVHc6Wog0GRoiyuPYiaOICW/D5Cn8XrSO/ap9cj32H6SV6uAJ/t5065sBANe88XX46dfvBADMnk4L9TNPPYKLLmL5r72WdbVsJRFhyUQKG9cTBbR0KaOvXXc9kW5FL4GYfHPGbPIzHT1KBM70GbPxzLNPAgCufjPLnImxbfuLo4h6zPvSCxnBbf3jHAe2bfvrINgvh3qJXorFYojEWEezupj3ZkG81cdTmDeTPDoXrboeAFAq0if/gV/fjeEM9wwXXMRwsevWsl7f9taPYyCzGwCwYgHRNQM9h9EhfE53/5JWv0vPfz3L7A5i9wFGt1t5PqOIzO2i9W79tq/g4Ue+yDz3s73fcfNfAwB6D6fx0fex/7o5ohKmTGJ+m5Ip7NxNLqbzFjB63ROPEaX2yY99Uu9bnljH7+479ggA4A03fwKd7ewXDoiQkIBHOG/5VSikJWpSlugt1b+Cc75GPqh9hetWXXsFBZaZsIyJCn6ZRGQ76/DxL8J3xnvGkT284rorFtNoEo6se+7i/H/nz8nL1dIxDf1DtMJL0GN9vrAsW6PbdUQxiTKUzaXhqR2bRnGrqJEGisL3ExEkQUl4eSwr4nN3yt5reHAEJdDq3z/Eed9oJwq0v/8kEo1EFRQkYqQre7ei5yIr/EKeoERzsm6bRkSvy7tkHlmzklEYc7kcYonyaKVQYe5dP7qyQpEX3RoQmglIJYocCKJlqs9C/gWj5rvq2eC8FHzf8ICslD8qiCkVPTdmxRCTuU6VLyN7moQV0+nX8goZm+/HGYPLp1yqoglb1feC5at1z6vgqp3IdwH4qP1xEEy1kUEhjqSWRCxu7rKyB4VrYM3lr+I92Wv3neS+IlVKYuEi7mWWyl7owFHuvc5feSlO9pI/8NobuHfKpdnG77jxtUjYnLtOuQcBAHUN5H/snLkCpkQcc91yL49cIa/Plgm/S1eX4cyL/cJLSTb6MTMCU4E6pc+VsqyIRDyFuiZOfqMnSZZmCPw+FksEBivfU64K07saMHqMFZFWZGyptsDXZTCpcOumiplt6gqsGpiB/yspVyqU50Wdr4OpKEWUo0mRAVMPeHUQr3YLCw72WmS/AMNFKjirGvSuViQBrq4r/zsA1zc11SoFg4Zqwq0iwtZPG26V2mQiG52zmVaqIMZB9y5d4dWuX+OHUSx/fyJbG9fAaYgNDf+5MUSH8AwsWoqYWbdDmaLQrbrm36z1fPmEH1QCVZFyq3LBg6E39uULYFAhZEQmvlC8XDa9lVK7XOO5jI2tGNJKN7eER+Ww25LgxD84yHlr3cZHsWzqTADAFRdzofmrP/87AEB6dATHjvPQesnla/j8eh5O/+cbX8eFEm7+0BEuNOctWYC5M7mxVK60EbXBjdva7UOT/Mu0XDR8omilCNIEmoWCH7JX5l/1t9osT1S0i4pp6QOn45QrpR3HQXqUa0FdgoqGBjnwRwsuUuJu/PEP/xkAYHCI+e072o01r6Ky4pEnHgQA3HbdzRhNcyH/2U+oAPnon9KF7KGHHsTmZ0hOnLAJKW9upBKtvaUdq5dRkTMyQEVcUx1JrPfv78HIEBVOcdlkR+Ms17p1T+Bd73wPAGCfKGO2baMiZVrndCxcSGXDBz/0AQDArn0Mu7527VoI/zi6uqYDAM5bTEXXrp3bML2TbfqaV5ME25WDyY9/+k1IBFOMZtk20YQp9Whg7lzCkLMF1tEHP/JBAMDcuXPxrf/6XwDA/Xf+GADw7W98HV/5Cv/d1kmFREZcI1yk8Y//8nkAwHd+8H0AwJObqeT4v5/5LHbspLtULMp2mjKJCorGxmY8t43uZ49t2wzAdwNsbW7BXT/6Cd8Tl4jl59PV6skNT6KpQVwl5XCi+mcsGsWQuKad6qEi9HkhDo9Go/jQB6lUXXMlIdf7Dh9k2f/oQ7jn/m8AAHJ5jr0jh+kSFjFbkIjTLXHdMyQpz/QXkRnluGpupetW9zDr8ca3vBXd36a74UCam8E++V153hWY2cl6OH6Yypuf3UESyfe88/OYPpMKyZOnqPBTQTSKuQKuueIqAMCOXVQq3ng9lUzdPSewchnDVN/+jncDAO5/gH3gvnu+CyPGNK5eRQi7I6S5da6LhgaOs+Ec+0XC9A0Qle7Ayu3FsiztkqIJd8cjYj1LF6GXq5BUd5wD48vc1Ww8OVsl03jvRYRnQillYtEYvKIEGBD3y1iCyt3RkTTsBMd6yRUiYleRS6d1uykDhVYwmBGtfHfVwqmoGwpFOBLOPS/zmyNzZ8myURBDdiEtRpemJrTI+rrxHip9626k8mLetE5kTnF98SSEvTrAx+04Ipa4nykieFGO27aNguR9hqwhTY0tci+gALH8YBH675LaG6oDRpBOQL+JoNRqDtcL7C2lTRynXLHBdizf89pWFK5X8bxbYw9fsffNDaaxfc8u1k0j27ckbZIeHkZe6j0tSjRXSKY9s3o/rUtpVAer0eVzvUp2i5pS+YwbULqZFXtzI+C6q2lMDAOeUapIq/a5MHiNv+p+jV9vApk/IyEJyMtZVN0mEtyguW51iVs7psjDwM5tVMZO6+Besr6Z966YuQyJBPfNkEAqxVEhnk9GcfQQqRCSWeoxCiWOa8eLQjzJYdv8cknmtnjURjF3+j34y7uFQgkllFBCCSWUUEIJJZRQQgkllFBCqZJzAiGkrNC2F0W+ILBG0bp7OSExTNpoEah7/w5qy2ICzY/ZUR3KMdaSkPf5XmNjI5qW0mpXSkqIxQDpoaW1okrjLcgOy4OptdjVmuuxSKVrEU4bondzAZ1mpanMdYCSctMIhLEc27XHqLqm4LCO5yOQvAqdX9k9ZbQzVLj7QH6Um1tA2++KJn8iWsRKDf14z5zJcxFVl7WqsSINV18emxKtrP0moAx3teK8+uEyq9wYRirPgLYWKYovK/CsNxYcFr6l5XTuYErGCokZRAhVEtV5nudXgyY5Vx/xi1XtQFhNPO3nxUQAwlSVz4nI2aOMXmKTtGa6PzNdu9LkJ5I2LrmE7jiT40Q+HM4xfHT7pFZ07yYKyCixPj7woY8CADY88QR+8UuSDL/j3bcDAE6cIunt5k2bsHwZ4anIEVqeK+TR3k4kSzFPy4Fqt9FMHlEJwatC1aKo0JLVlhCFFghCthVqSEHTrQk2X2U7u8WSRq4XnfLw1mq+A4DFi0mgPWOGwOndYXiC83cEldOb4fsncyNo7CIxp7WdyKI58+Zj/ZMbAQDTxL1o3WN02fvc3/4Nbn3bmwAAz+9j6PV5Qk5c15REfZbt5HpE3rQ0M829+/NoaKXldcXFJIm++66fAwA6Js3CiW4iihTSZ/tOWoy2bH0GJ/uISCl9nvV54Sq+v3XnHljiGpUe5vdGxJUgMzqKwV5emzeP7kUDI3SfmrNwDhbNYt387GfMQ5sQHJ7q6cOllxIxcu/9v2IdTKO1avDkKdxxJ5+f3sxyzlw4Dw+sfRgA8Lb3EZ2UHxYrpWWhQwhKX3cdQ55+9p8+CwD4yV13oL6JfaaQZ5vMnX2elHk9skXm9YmHiMb61Kf/nPk7dBB7dxE91dpE946oIe6Ora0oiXXXSrLPDQ8SFVRyHCRSbItjPRxD23YQUbPmqjVYdj7dJFqmMk1HJuJsaRi3v/f3AQD//E//CQBYJfX/1a98EV1zWA+zFxGq3dIcw7atRP0kWjhentm1DgDw7MEB9PeLK5uEjh0a4XduuvFtOH8JCeNPniBZ9oYniJIqmD3wIkRt1UuZ02minG596ztw4Hkixg4naUnsHySKqKl1MobFbbO9mfueW971cQBAzxc/hs33ErX12lVXAQAGpR6jdh4xsf5HpG1KUZ8QV43xKhLUwJgP5XdfzjoM/G9ZauW71jXLlfDq4rt7ovsk/v6zRDR6chxqE0L9k72D/l5NCPijYrE3YehgNfGoIpHlrshMpXxUqyBjBczCZ2SjqNdNWac8x0UuK2uxzfdTsTgi4iXx9ONcn/Z0cy77i099ApOaSZUxNECkkAraUTAAR/Y+ysVbrcW5XA6O7NfUeqlc6FiG8jGuzgJEjgvSSfYDJbdWfxkPOS9PKAYBeKjmoFfv+zd88H81jUYZqXSl+5TsZo8fPYG9h7l2ey1EfdniRlFnJ5CVvUVe0qyr2NvUKseZ7khPj9hRqZZ7cNS6F0QBmVZlfaPsmfI8W/o95dZY09VsTFc4b6xboVSI4xRRkv6k3K/zJQl6ZRhYsJR7VphshwZpG6cAfwioM7rJOWY4PYh4I/vmjA7uPxoE3ee5FiJRK/iahvbnBgbQe4p7ys7554+Z5xAhFEoooYQSSiihhBJKKKGEEkoooYTyCpNzAiGkNOsRRJAuKasU75me8FtEomjuaJF/S7hj0dBnh/KoE82YSktZj+vq6uAWqZ3rzdPH3ZYQyqdDpWgl3ThcKpVWiFohug1DCmO4WmtvKaSLq97zdJ69QFq1LHJKKgnUtMXBjPqEdxVExK4LTVynPqSIYz3DqfImrcmJ8gIRA1vBkPbjtUXQ4qB47io4esrqRaV5lvkKplXtx3v691mWCuSNUf3vWn73VRYObenwrR21eIkMbUHx3xszJKb8pxNGRf1VoKDMQKF9gFslaq46T14NNJx2yT5DpNC5FFp4Qnk23MA4OT2ptB1T4WbTsKO0XubFWtjYRCvgnr278P6Libqoi3EOO3SYqIELVl+CzjlEmmzdQm6Tm958MwDgJz/4Mdrr6Te/aAbDaQ/ns2hukRCYijZNfhPJFLLijKyI323daR2fl0xbPaVfuh5KJUWYqZaWiY3Gseo0Ylm6/1mKs0HIrA3D0NZPVywvytKZGR1GEiyzLfxCP1nH0OPZwUFMa6Df9uw2WoMvmbMU2w4QdRGtY1uc6CGnRKo+iU984mMAgI99kois5w+TDLloFrFzH8O450eIyOoWRMe0ObOREaLpjqn8Tq7Av3NDw/jlURJFv/3ttwEAbnwD2/bOX96JnHD53P/rX0uZaRVyHCAp5RkcJhKms5OIpmVL5+OpjeToUQihO77/TQDAQ488iP4+8v1EBPW18kKiXrZseg49QkT+sT/6UwDA1i0Mrf74urU4dJKososuJJpl+rwujLpEpBQNIpKK2kppIS9cT9O7ZgEAbriBIdj3H9yJzZtZx80J9tVf3EXi80Qqj9EcLbjvuYGE1osms1x/94+fxxVCYn333XcDAD79qf8LAHjikSewQciTjcaYpCVcg5kMkoIQUkSPLe3cQxztPoEv/gvDunfOJ6LpX//1v/m9f3g/Nmwgj1HMYh4GxAJf8k6hLsFyHRO0WOe0aWgTS31vL68Nj3Jcjo6OoEMCXhQy5CM6PEAui3vv+TlmzlsCAJgilnqzmdxR3ZkhNA4cBADUxWjRrqtnv4o1NiE7QpT04aPso6vnME9LV5yHpjjRbyMSdj4uXFpvu+V2PLT+PgDA1kPPAADmdpJLyxwGHAm1bDVzfLls2jKuDL1Xk/GdzWaRE46SWtxBE5Gx0NahvDJlIoErJoIor4mkdhTig/Pwz376c/Se4lia0sUxODgqhMKxOPoHyFFXNLnOKPLhqG2jOcVxGbPF88Dh2ue6LopFeV7OKEVBGEWsCFIS8n00I0TQskTm8kU4JRmDrZzvG5wIHI9r27RZHOMZOcc8t2MnVizinD6rmWjfQTlD5EaHfAJoQRvZdoBDSPKqzgw54Rdx845GJ2kkriJoNj29BnsCeSq41ch0JcEQ55X3fe+L4N6xcu/r49ANzTPkjNsHKsmktRQ91DWxvfJ13Bek5Pjb2NaEbbsYKGB2C1EXUSHiNqwxEDQVZawquzfOvRrvj5d2LZ7T4DkhYo2VRo3vB6qllteJ/KP6tXBuPq0oTyc1pmzb1tWdL3KNjMo+HzBRcnxkPQA4nuy5oxHkFPeYjLO8w/cTdiNsSD92uEAXcpxr4rEUinlB8spYsiSgyskjB3D3z+lB8KG/CBFCoYQSSiihhBJKKKGEEkoooYQSSiihiJwTCKGohF0cMdMwPFr3SpYwYot2vNWaiuYWWr48W7TtpoQKjI/CAd87VqAv/xU2LW+ZY3k0TKdGPZIWpnpBfXiub8VXli+lVXVdDyWz3F9T+R/ncjmf9V6s1erXgK/xs1Q4ygD3hWbs1/AQ0WjD89FAOoyuCa9Ca+sJt5Lj+Yiiko6QJnxLThFRiTBgiPZYM/J7qIp65oimMmL5nEHK51lFX3PhWwmNCtSQFdQoe+WIpKBK2jDLNfuW6QYie1W+F/yKr7d0KrloAlpwVaXlLclnx9LWK16koAS1pJXWS4VwodUU5fcC0QEUL5OK2OU6yl/X0/1Cl0lFFnNdXf8KmWF5gb8lr9oYb/oWgUquqCBCKHCR+YWPutJoMd3P/Me98qqumVYt8b9b/aJbpYKemOVhvCgm41oLa6Cxqq1V1fecGvmaEDLI86fUsZ/3UFnuqIzrTAmIRDge8yk+kxQr44oZq9CnwIYxog0EOILj3UfRLBwvs2YSZXDhBQxzPWnSLAxIuNwOQU+YuQwcKbij5gPVtx0XMX/w8HltrDP0/FTZQUwErE7S79XccFqWkTEsUJbh6vHhh/dVVhAAaq6MEh1SkLXEthrgSiEyg7S63nAJ68P4w0+j5EhEJUFRZFIels9h+M977mfksT/4wIcAAGlE0DSd1uPZTUS2LF5Oy+ypjjhG714LAJgyl+grN8/8njywC3UttEbeex8jl3XNZRj6+R2z8Oh6ojRc6TMLJFpYy7p1GOglAkfB9fJpsRwbFoYzLGNXJzli5iwg+uixxx9BxzSiXYrCBzO9cyYARudyBpnWV/7lqwCAp57m9+fMnodkHa1Ot9z2HgBAJH4XAGDqzKXomErE2n//y/8DACxfdQXefjujn6moFnE1btwiikW2ZVrycOllrwUAnJdNo//kNwAAzz1HLp/m9piUZTpG01zfX/+RPwQA/PUfE620fP4i9B0jEsaS8K1P7eT7cxcvwmO/Yrj41laWISPW7ogVRVEQWV6JHbj3xCkAwIkjhxCNse/8+j6ilj7zV/+H9w6fxL13MdJfMk7E2aQOonOuuvotuOuXPwQApGTAxKIl9GfI27NgNvvFoZ3kArpw+VRkChIdrJ6orXkNbKNcsQeP/YqcTZdeQw6nXc+zXLNmLcHcWYxeVyo1SBkEiWNb8AQh+J73sq76+8m/1HfkFJrnEPnnCjcBxOLf1rUcr72W/VdFBPNUKOqUjWJekATym4/wfddz/GgnMhaL0vCeFeCjU+ufdxqURg3EdfD6mcrZRrgKIp8mkk99/ayxx7XlbKzvp6urFyPq13ii5+bKcN+BfFain03TPGP+Kd0mrlX2t1PMa067kfSwZIp5SKQSfv7kyLNbIjv2DPVi2kyiACWAGBqTHPNDwxlEBd1vS302SdSxVCoKx+HYyeY5N0ctPhuNRGDL2aQoaFrl1QAjDwOcDxrqhWdI8jQ06GBQ+M+aHc65VlMchRE+H5dVtEkQqOt+cT82PkSusiXLlwEALl5JXpL6hnoMDOSlrgSJIKiliOOgXhCy2QLrcUTmzIRlIxbjt3Myxo2ILP6eAVleEZE8W1IxTrGk61jtpxxZpw07otfprHDExiVKrWVZQKkcfaj2HE7J1ecqR/h+MgZQJwhqlMr5CUtuEXmZIye3c23cuoWI2XxfEXHhSzt2khxMI0n2lxP93WjLcf685ZKrAQBuROZ2w9DcS6p8tqDEisUiTMmzymdBotdZETNwkJCxIGMi4vp7c7UfVmBy1wzsRyvOJiYMXVZd16apN7mKd1dxPpmmP7+VHK/sGcd1EQkgsPi98fb0Y96aoLz8sScK0WbbAWSb/MbseNXztswXanxGDUEPeYApCLVCgQNOIRMt20I0JUg/h/23DI0vZwdH5gpHEEOT2mdi1tQlpy3DOaEQCkLdtJpEHSg832VKEZWqTq4Gn+u6KMhEUCiwsnp6CIFffcFiZCSUoFZcSEPAc/TBWIdll+978LRrhOrKwYWsktCszLUn8Fzle+q8VEn4ReJj5b/jH0qNikVUHXAd1/VdxnSIaN6M25Z244A+n0n+TFcPbrOCGtgw3DHP50bwDFsxOZRtaCo2N+MScDv+JrIycYajrKQuNjV0dDx5McKc1yYtrH2PRJvl0M6ggqzW82N9o2ZeqnU31YR3NRRCNcn9JvTFF05eyI3qi9rOL3zS40pBNkq2bcMQRV9mlJDQjBD2zpkzD7tlg9PfRxcWS0JgR6NRjIwQ3q4UQ6p+rr76an0AHBY3I3XAA1DlbvhiyEu9HbAsC6OjLKtyI5s0iXDw6667Dhs2bAAALF9O+Gw8nsJl118LAFi7nQS/9/36AQDA7H17cGiEhgZL3Ivy4lb2tX/8d3zsA38AAGiK0BVgzxa+v3nns6hroovSkmUMk37fz6gASKMVGXEPUATSd/3iFwCA0Uway1YyX09uoGKhqYkb+HQ6jcYGroPtbTzIHDvIdKa2L9LlUkrFG15/DQDgO9/tREEUNNNmzAQADIxI+PlYEps20UWqW0KcHxEFzFve8lY88yxdEFNCYLxnzx7cdRcVRm98wy0AAEdOCpZtaXeJppS472XYrx5f+yhyGa7FUydRMdYiMP69e3YCEj73zq9/BwAwdyYVbJddtgqPr6PSrfUIFWw7NpF42lzkwhSiar3Jljk3Eonq0Ml1Eeb9uec4fl53/TXY9hzd4o4NMyz8zoNU+m16bBcuv5R9wfDo+jVnFl0yTu7fgz/+vT8DAIwICfYdP/wZ2qRvLV3MTddwH90Ii14SJ46zLmfMZN+JTmJbFnpG0F7Ptj+5nXmpj7KfHT1+LxLJ9wEAYoYQURaF6LSYRWsrD4XdJzmuZ86cCQCYPKVL74vUIUW5VXqep/dQiuRSH6gdR/9bQd4TmsC02lXBdx2z9bWiKL68SOW67UtNQ8U5LqFL228mv+l+p5YoA4UrWpx4PK7dOuslrLga+6WSiyOiBPjlPVT2b99OkvqIYSIpLlwZCbgwOiIKnngdMgUaXmbKXORBjDXFHBwx9FoqPHtUEbTHdFCbWFH5VfOnUMjBdMuNn6bD/tVYl0JEjANpUTAfPZxDYx3zl5e9b0y+A8fBYC8V3I+vfQQA8MzTnP+vu+46TJZ9gCKHz4o7d7aUh2UkpSLFUCn1akctTQdg6jONUFK4gKlIpJVioahCnhuIaKJjlJUPjqsvKqWZeNDBKXn6QGtU7BJs26gis096NiBBBNQOJivGkub2Nnge85WWNpwyiWukkypi0wamofpJS8R3f73i8st5rZFz86C45LqOWxU8Qxnik8mknluVpFLseznpSyp9wFcAmK7nk1UrN3hxJTICJN2mUX7GNAObUj2GXE/bIEuu6o8KNOBo413ELFe2mZbx0m/+Q6kpRg1Xa0v6TKzinmmafp9Rt8S97NixI7Ak3Lwj1wqj4mrmRrBHqA2uHycvL3+1XSihhBJKKKGEEkoooYQSSiihhBJKKGVyTiCElJimD7NzlVuRaDuD1i1FYtjWRoubm3EREReppFglu3tovRsZyWp19GiWWveWCGHqLhxtYfBRSgJfRBAmJygP9YxlaVLjShQQENTsQsoQCBE4JvFimaOSlLmaQE3BAYPE1r7Li7ixWb5bh/+8o58xKsLMq7I4ZfWgHg+6ttWGCJWFj69xzS9D+d+ugRpswXIPZuDb5ZaHWnLmVsdxkEZGMO8VuSsjFi+/Vg6PVr8KIaTq36iCSas+6LruhMoxXpjI8Qjvaqd9ZmaC8dzBQjl7UeEobdtCRFnRYrRMJcV6dNutb8MPC4rMsdxtq1gs6rnSFutCRkgrDcPQVipFBulgYmit35bF4DdFEThuUSODlDVuVBAMrW0dWL36YgBA9PIrAADNre1wovxmq7jjbPoFrcmrP/wBNIs7184dRJM8+SBD0i+sn4TePloTFy2eAwDobSE6dd7SFbDibJPNDzwJAPjgu4gmijQ0ofAYLb2eWGnXrn0MADB56hQ8LUiwaTPo4lMqcM3LO1lk8/ze6Aiv9Z6i5fdTn/o4+vpIfn3nz+nWdOXlawAAq1auRt8grZkNQia+8kKpg9gOlKQ/tbfTbevCC1fxG6MZrLn8SgDA7t10rUok6rT1OCooFFfed5wi4uK+p/pXqo71ueWZ5zDQMyjXaIk1PT4LJ6LRuku6ZgIAks187+ipI7j0Crr7bd5MtJItc+a73/t2bD+4h/WwjyiAphaiZzLZvIb7K7LzeJRpNqYaEYvx269dwrQ3PUiC6zWrLsdH/pjk4f/2n/8FAJg2my4ZG7Z/H6eeIjnjhz9wOwDg2qGrsOM59ouNG0hcvuB8oo327DoCO05LfUcb67Y4xL3J5mcOY9UaEnV3JJm/+S3sj9nRNP7+b24FAFz/7r8GAKyaTJfGklPQe6DeXqY1cwbrEzCQFot5XYrXFHy8VCppZJCaD4IWblu3JZ9XFu1g6GX1nnoml8vpeUfvz2QuA15c1OFvKsE91ETkXC7Lb1vGJPMdR34zxJVysxdSZKeIViGMHx1l/0/Vc6xvWL8RP/gh58NciX00k+b8Mam9GTlB57a2E+WXKxDRNzjSj6kz6I5bdFT4eIVY8RBNMi0136m9fdEBXEHOWDIvRiw1P3pQQWY8FW9d1uRENAEzKagVQcdHIzYaBCE0Oixu4k2cTzLZLJws866I9B1xpfvl3fdhchtRjRefzzmmTt7Ld3fDlKAFpkIUKIRyIgFXXMUqqQ0ipn92UOUxXZ+AupJKImb5c4Ur92xB5SjvBNd1NRpC7VfUfFJfX6/nOdW/kpEIqT4AjURVyN+RbEafAw3lESFTVywW02moulJzoZf1MEsIuxVBs/Imicaj+rmCYjHRocQjyAvBtzo3qv6YSNTpg1lJ2rkkyCLbNJBIJcruKfRWnR0LnDHKvwczUvO8qcLHq/Dzys/Gg6f/7coz5yR+gAAAIABJREFUGkVkRmCdW8f/V7CUjzPP8zTCsJa6QJ83FZpMkIPrH3sY+w4TBbRkKferF6xg4JDJk6bjne9+7xnmJJRQQgkllFBCCSWUUEIJJZRQQgkllJe9nBMqQoVigWUGUDXlYeRLpRLi4uubFU6g4QFaGzNOFnGrXu5R09p9ipazE8dP4YKF1JaNHi5n8DIsrxp1EiA+trTGWwjsFKeQ4RMpVoYlh+FqtZ7yYTZMScczalhFtCOg/neQq9VHBiHwnLypUCE1tMauVx7STiFUPM/z8T2iPtd0Ri5gqHDinvJbpSh8y1g5r5SJ+N27ZT6x1QgXHxk0HtnZRO9NgLywBmRiLH/3WoSUmtPJdX1eFp2Q/7df/+W8VQYTCz5eBosaM0SlYcBTROnB/FW8V7MlJmCkOxcsoy9EHs6Fcowl0QCnj0I1KAuYcBSjoaEFi5eSW2bHToY9v/gqIgr6+voClixBQ4jFfnh4WKNlNLnmOETrL4aVYKLcUTpPag78DZpM+fqr+lDhgYtFB1OnkcNGhdvNZoo40E2EyfIZ8wEA7/zL1wAAui5fiQ0HyFkzsPMQAOCmy3hv0CvAVcijaeQriA1y7TEP5DG3jeSWa+YRcXPrO28HAPTGTDzyNPmEtjxHzqH5QkrdPziKjIQ+ztaRF8oTy56Hgg6h7pmcv6+4ejUA4Omtj2PFheSw6eslGnbX8/sBAFe/5vXYvY95z4tFulHISefMm4dZs+YCAHbsYEj0lSuYZjqdRkrCK09qoyW2VCphzux5+j4AGCC6pFQswhIrbS6rLO+03C9cvAQlR60LstbJ2rpq9aVobiL/wkf+mETJT28mf9LWO7Zix04ipubOJwqrro79+fvf/w6WLGZ7PbCTZVVi2zYyOdZjTFB2qv9/6Uv/iZUryOtU30Ui6GWrWJ/P7X0YX/4fjsdVy68DALzvdubp2IF92Ln/XgDAz+77LgCgyepALMr6zo3Sir/paXJ72NEOrFxMfoq3v/n3Wa6t5EPq6c3i6Fb2uQWvoSVvwYUkNZ05fTEe30BOqW2//CUAYN4ttPh3tLejJCiczmlEkClkl+caiMe5T1JjSSF/isViFSdHLcSyem9UUGme58HQ6EOFPBa+oYKLguwtCmLljpyGgK1y3azkY3yppDaK+dxdI4IyHgn2WPdfTJkIOXQtxPLZ5tPRgVrUXsjUaBKV4gMPcZz9+Cd3IiEoxZynQqnzmWy+oIOqZLp7mJYih44lEFEExo6yylejsoX3VXMDWYalUS+KSyYm3y3B8BHACrknJNOGBzjKO0COZkXHxdAI5xZ1bjHFw6FUGkE2w3ddTzhU0xyD0biN505yLh8+xXNSbpC/yWQSabXHEG5BO6rKHIEl66bybDCFANo1AEvma5+Txhc1t6i1Xu1HXNdFsSSE1upMJLuMTDEPTwIHJYWXTwX0GM4MwnB9vh4AMCwDsQjbpFEI/48dPw4AiNelEBVkRUQiZSQa+Mz69Rtw6NBBlrUlqfMFkMtU7bl03gXJZMC/p8SWtcQzoPmFFNq0qGBEpSIseU4jL6XPGiVXn7NsxfETZzpeyfW5ZCfIAarrveJMahj+2cKS+rYC7RZi/M8NCXr9KDHHWEM9eJozOCLE048+/GsAwLNPb8bNt97M52S9NgWNiIILR/Zo40mIEAollFBCCSWUUEIJJZRQQgkllFBCeYXJOYEQqqWprOTocRwHLS20NFqicVX36uMpmB6vDQ1Tm96aoBY5m83BEo2piryiwrwBQfREBQIkmD8dgUwhVlz9ba8C5aHjBwbeUyH/gjgbFaJOR83yTFSiWAwzkK+KSjIC6CE/GoCKGFLSlgy3QtEYtMKZFWmawRzqfKFKAiUMlMeovFR+PXDTr3Mz8Fz1dwwNK/CtMtrv2n+q+sUJ3Qs+No6uvCL6XBU6B9ARxZRvMwxDh6OstDwahqERWTqgVTDKXo3n+X3Djw7nh6rTz9RCcPlFGM8WMLZl7+XEF3Smlt/KsQFMrDdVjreJShDBp0aYJdp9VyKVFD0HK4XbZVCsfQqhEY/Hfet/Rd9paGiost4p9OK5Ii+0Zd4NRGFUVjzN8ZAvaZ6J4Hh7VDh8ls9i6N6pK4gsHSymMZIRS6oM3nf9PtEe20d68NDXvgcAmCURpo7L3HngwBGsXnUJAKBrNpEc9iQiOf7jC3+HA7uJ8rIFBhUT62Z2cBDNgugakvDz8ZjilDBgCmKnZ+AwAOBb3/9nAED3qQP45Cf+guUy+f5Nt7wLAPDgg/frSDsf/uhHAAB//rFPMd/T58CRPuZJB1Z8DvFkAgVB+rz6aoaP3/Lcs2gSPgrLVFYn1ksqlUKuyD6ZUBFXBM1SKJUwIuimWIxW2kiE7w+OjmqejmNplvnH95Crp64ugdUXs99v37UDANDSRh6kvTv2oLmeeYkKIk61czozpFFyCm1XkvDz7a0d6OtltJnHjpM76H3vp4/9RRdcjhuufwvLECOKautORu/ZuXMnzr+QqLyM9IULlixF7xGiwpYsJ3LsnrW02o3mS7j+OqbVNZmoqy99698BAI11HehokTDJHayPZ3Yy+tFzz+/H7bcSlfTMWiIdRke5t2lrbYUlKINJk8ijaErI2lwhr/cWCjEhUedhWVbVWuCHdXa0JVyhshMSftozgKKgBhTnkKrjeDyuV/GioLMr91JAOWJ4Ytx2vx35XUMKBeVs0TZlHJAvULknks5vFGVM7b8DHHr19Y0AgP/9NqMUPryWPG+Tp3TBFWRLusjIk9kckZeREU9zDQ0PcN6ybaI9LDOOkWGOobpETL6nIpc5yOXK+UcjEf4dsaJ671qSnZkjodWLJQd5icjoKgRrXtCfXl5HA3XEqyBqRTTvTptwox3v4fw4ODCs0SqDQ5yvbVnX82lDI3wU+t7TmxMD9YKYqpfIkfE6jvVYLIa42l8q3ssAf6luV7d8j2Gapt5baMSfgFEM04Ap6AQVkjsn83F7WyOK8p1YMqbzBwAlt4SYyfLlBWF0fHAAucGMlJlz7uLFi3X5nJyKyCiofVWfxQIKMncVc7KWCoIpGvd5eyJROZdJkDDTZORMwEcUqeopFvOar1EhoBQ6J2bbyAsiyBF0r+bWswAIqrgknhxQiCTXn39RI1qjK3XlqDOH58EWRG6gAcrqEQAc2f9ZERXa3MTLYV//chAD5WPpdKLmvuEBopGHR9j3/uQjH0PbNKKIXVPpBoSnMm/gwKEjAIBJCzrHTPucUAjp0KfwdPh3s4p418WkKYTfK8WQmZb3XBdFgSsbCU5w7bLxrk814uQJQuWTCRKHldTAjljwzPLBo3iaPMOtCsdsaIWGWXVY1oTTnqcnXk/DP2uVWU0uAacsr3yRsyrd0QIZVFRhUjkq0UBe1AYMkpbvfjW28sYbc44onz6q3adq/btKKu/VKF9t3YzqCzUgk+Ns4Ca00TmNL0ul61uZYsdA1bXK96reD5zDK/Nkmqbf9kohGpgsfChoOaTUMAzfJe0c38iaE3HdqyHeRJV7FeL39XHauVbS3tkqTILlm3ieg66xlvRJu2IjUnIKWgE9aTLnQhUetaGhQafhyuKvlECe52n3qbqEhLB1fKX4REqq9xpnq/A63XsvcLd1XVfDzNUhVvWFQqmAqM0NcJ1yJcjlcOsaugfZUyXEuCiBOuqSKB7nJnz4FJUIhhwa5s6Yh596XHuUcm55J92vvr5lFw6sJKHnPTs2AgCuzjCdvn374fKsgYYEFTwN4upTF49pd2VFKF4U8sqlC5chJy8eO0qo/IUXSKjz0TZ8+45vAgCyw1TGDPSznRcumgMPzOeUKSzff/zHv/H3X/8TA6M8KE2aRBJVRbQ6ODyEpLhNdEydxjLnsppcWElBlD4uHN3XMnkeujraqbzpG+iHYcu8ZisCVsXUWUIsSSXW5774DwCA3VsYiv3jH/kojh89CADYd5i/Tz1Fd7KFs+bj2AFudJRiXilCVAhgAHBkf6AUX6bn4rXXvJH1N3cGACCWFKLraBLHjvKwcdevvg4AmD6Xipf2eV1Ii3u6M8w62vrcHlx06eVSVj634DCVfdNmt+LHd5KYuu3d3JNccDHdPr/5lR/ii18kCfjeHSTGfucNVNbFGhqRydHtbPkV7Jejo4NSVybsCJWc2vUiwzIHDzcJcQMpSN+JRCK+kapU7lIedPlR80gxzfdc+C5jMdXuUtdOOqsXtbicAHOGn1YtV2sllS5iL7Wb05m6LI337Lm+7gZlvHKcbRtUkt2WB9+ovUerpRycqKjuqlwnGxoa9Jzw618/BABoFpLo/qFhFGTcl8S1Kh4TYme3iMwo56nmJiqUjhzmvJrO5DRBvSM0FUoMw9DjRCkWlORyOb3mxOoUebsiQi7665GUQc2lpVIJJXGRysrYa5o6GSVRHmREmd7d3Q0AKBRKaGlj/gxL3EKzXIMaWpoxIu5qJ47TFa5R1n7PsDE0SOWy/k3z13KBrnY5VBbLzyhGoG+oubYk+4hI1IYpCgzlzqfmGAueVk5FpM5aJfhPyfCw/wDPZ6MZ5j0nYeUHBwf1mrp3924AwLotz+r8FBzW46rVVLT/wfvfj5gYGFxRDIneHAvnzkPXVK5te0f7y8sVaEu911JKnEhM178KDR900XIUwXWOeWlsbNT1YopySZ03s0WZo2HqNo8oo5+0u2eZ2qio3OV0ncOD2l/6hN+GdkVTRmjlAsn5jU8ppeDZ72tDebFkvBmw0q06OF8mGzmGbnjzTfKwo6kFlBuaI+6YhUwOy86bf9q8hL0jlFBCCSWUUEIJJZRQQgkllFBCCeUVJucEQsirhYSpkFKphKYmWvBSQhRmgxrbkcKgDitr23zGFnheLp/BwCAtACWL1xqaSd5pWlFYAtMvVRE7GVWuF9qtIxDOr0pMU6M1lBbdrEHApiClNd2F5BnXM6pCOWpckufB9GEh/FF/woQn7+nSeL5GWd9TzwdAUmMjKmoQcAfyMhYMfHyrWnXYV6XIrgXcGc94dcaw53GRQcE6r235CkqldawMhVXje9WoI/ULmJ5V9oxZBics7wvaumsQtVZVxAm0QS35XbJ2vlxEoSqcYhGGWI8saVMV/tmImhgWt5GYIBsbGhr0+8q6peDBikg6m61GdABnhgway7X2xZKz7btKTNPU9aGsjApZGnQ9HRoe0O80CFlnVlwBzJKQ95U8rFhIFM7bbnorAKBeQnp395zA/m4iVHoO8ndqK1Eir7vmtViwgKHCn72bxNFf+8I/AQBWzF2IjouvBABcJO5Q2Twtuv/4xS/AtHyLMgD84YfpPpRL5/DE+jsBAB/7oz8BABw6TAvwk5ufRlbCCZ84QSv3RptW4d+//VY8+TTdmJ56ah0A4F23vpv1MzoCQ9a2TIb9qyhWxubmZpRy/PewrKNTO6chryywQtTtFljHnmEiKagrpMWFUeq3pbkRu3YwdH28Wa3PYn01PXhi6XzgRz8BAFzzWrpfffs738LRI7QiX3kJQ8SfqlOh3CdhaJj1ZpoMuarcoEbSo9p6r+qxTkI4u04RT20gauua668HAFx2GdPevn0rli5eyOebmdbnP/c3Uv+9aJpH97+DB58HAFxy6eW4/qY3AAAeeuBuAEAsImTbORsrV7LvHOnfzGtx5mnughhWLHknAGDFQtbSgMeQ19/7yf1oiPLbN7+OrnoRgfvncgXt1uIIO64lCAHDMFAQMmhl5VZuDWqOAfxxpQjXI5GIrjdlAY81cW7x7dK+qDYtlYpwxILuCFrDikXHJI7+XZLfxTyfTk6HDnqh1/4Xwx0tKGocDAwM4b777gMQQGlIP3ZcA/UpXhseVuTBQjeRrNPPJWS8xaPiAmYn0CBEx4pkWKF7Ck5Rky1HtIcP/47aJpJJIvhccRUryFiM2FEUBHlqimtng6BlguNzdISIzdb6BoyM0DUkKum3NMua7zHADQDYcsZoqCdiaCSf14iRgozensEB/R03xrIe6+4FAOw7dID1mKzHpGbOrZ7MmWrv4MF3YzfE1UkjERxHu1Lpfam4y1kRW4dxzwnJ//Y9nDuf2fIsHn6Mrn0Daa4vhSLfGxkZ1YhmFYY7Go0jJ2tcQuan9RuIDMvDwO/d+jYAQJPF90qC7Iqafh0p4udCwUcyKdcttWeIqEZ1HezcSTflOQsYSEG5JhumqcsVr0/p5wGgN92HmKw1iSjvKdQ/PA9FKaMie1Z7PniezoPao3jKm8QKBgUp6LpWJOhZh/USl3XChQtDXCWVy16hyLWSbo3V59JQzjUxyn6D07eVFASa9CUTLkzZ70Q0iYgEiUq4OHmK+9O6Sa1jfi1ECIUSSiihhBJKKKGEEkoooYQSSiihvMLknEAI1eJl0agLcagslUqIiN+vJgctFv2/hYStINcUOZtt21i2jNa+bc9TQ14SQjPTEoIvBOhtlHbWMDVyyTTKtXR8YAxLSw1SaR8hVG0t8fmBjQpiWSKMFHeKWYFoKaurCgJuz/MJ5LTraDC/imRO2f00QqgWB06N918gcQ0/71VIIc8bAyZUG4lU9e4Y985UJoZ0Kq9/0zTLrD2V6VTnL8CrYJSjjcrCDypyv4r3Pc+DUQNl51W8p68HrXZjlCWUl1aUD7th2zo8urJc5oUDJJaIIyHEi5kcEQgR0yfYV/3PEiuBsmZalqWtTgoRgNOQSlcig35TGY85yjCMwBRUzrt2tl/3ApY2ZTFWpLyxmB3g4+KXotEoskla/vIZCZErXx+OA4cKfPeyVxHVA7Emp2ZNxZo1awAADWI5XDiXlsT5n/w4ckXhp5Aa+Mp/fgkAcMub34h8kW05mqUFuE4sfIuWLMTz+xgyuK2D1pwnNpLUeOX5F+D8JbMAAC11RDzd9H6ih44834fJM2lt/uGPfgAAWLaEaNj9+3fh4CFaZXNiiZ0qnAp1qSSynli+FSpKypfOZhEzlUWUdZVJ+3waVoTXRqRuGyP1yEj9RS2+lxF+optueAOeEmtuVPpfUnh+XCePOTOJvLnuQiJ1Fgm66p61D2DGDJZ5RhvRV6dSRNIsXrwUO46TLFv1bTW/JZNJn09HiMVHRlj2ttZm5AWdFCuwzP1HyM2x6rzVuPOXPwIAHDvFUPbzz58JAHjssUOYu5IcQPEY6yGfc7FrDwm+U4LKSwoy68D2XlywkuVIC9Lq1tXLAABHtu/B2v0Psw0ayLP0+c98AADwmktX4TXX/DkAYCgj6GfhsLAsH03sSMCKeJx9KV/M+RZ9o3zU5XI5HR5ZIadUncViMT32Bga4T/r5/XfxG/BwqpdIAjWGFswnH8HVa67E5HrmPZ1l3bZEIn5I54pf4NxC3tTitRlLJsJX+EKvn78pUlLJi8EbFBSfcLecBzMolfukILfdmYsiNWd/3rRpE/bv51hNNXHOjAiqxI4nMDzC9bKYVbw2fuAGxXVWLPD5hnrOFblCHnVJsywtzxWuUtPQRP8RS/F15CRtCzEZq47qFw7Lbpk20l75uUKRtxeLRY3S87LC7dPXg6iceyw544wO9kkVWEg1EKka0ShAOf/kMxjV5NV8PGr6nEdZQcccPMq5s7OTY7hYl4WjkCkyZE2NWPFgKISKXAuiCvV7iqBZEE22HYEn7azQup/+NIMf1KVScNTeR46jppSlubUOppyncoKqimQMNArX29AA68hL8P1165/EsUMnAAB/9+dMPy68ULmeo/q8qHmGVBni/tynQTOyP6hLNeDAAaKnZszhGtQoa0kul9N78Yis/UcOHgQA/O1/fQGjwgPVOYPr2vXXEom6evkFiEoZc7JHiwtfpOO6GoVtWuUIcWJ+2H+zwreUzabx0MbHdH4AYN487j8S0YTmBOxo5q8iurbs6OnoU0N5qWQC9KZly4o87xhqryZ9p+gntHMPUW29PdwnTWtvwo9+wOAnn/jMf4z5vRAhFEoooYQSSiihhBJKKKGEEkoooYTyCpNzAiGkop84RQBRQceIT2cR9Hl0HAudceH+mUbt++hxRt3wbAuwJSyq+LHbSf7d3XMY3UcZ3aMhxbQPbKf1tWvlcjgS/tbRVk0JK2m62qod0SHclXXBqkJyBEujIgGp2NWO64en1MgnzRLv28J9DiD5ntRL8Jr6sWD5/DI67Dm/Uwyq+VQEN/UZz8+zY5anaTiejpbm8xkFeW5qW3/MMnSKcKFU5Bcg6qpMSiUNkVLWEm1JNMaytk3cAleGhKnwbx6fCiXA6+SWX/PT9v9dM1rKGOr3mvxC8H15PeW6LB8O2s8qEUXBdNR4qX62Rh4C7WWcZdgo1eeC3xsrel35g2f1ubMWKwiMG8sSWquuAtb1yoiCL4oELKyKv0VbisSipfzOASAmoUYz0u5Rx0NUxqeyuiqUR6FU1OG3FWrINk0/TH2FVdc0Td1ORcUnIsgC7ywtuuNaHoLcFS8QeiBiWvCEX0VZ8aJSj57j163iRHCKLoy8IEtMxccia9FAFgs6ZpRdGxS+n1jOw6sufh0AYPbcOQCAklit+wb6MU1QOE/tIdri3R/+JABg7vyl2L5zCwBgtI/5nNpBC+R7b3sP7vj5FwEARYNWxlM9RK/cdsuH8MDdPwQAfPPHDMvePIcWwc996bP43F+R6+bKS14FAMiIpbl3pBfzu4hMObmfUVY2PkEOnWtf80ZkVMQbiSjmSvnqbBMlU5CrsoiYsGAJBwUkatiARMBqbKpHVBBFBUE8mTK/tbS1omMyET7Fgoqcx2SSdSnNzXDbez4EADhyhFauXY89iXe/kyioi1Zfw/qQ6Dg93adw45W0vG5+6FGmKZZ604iiJJFyVFQ5FYEln89i2OS/tx6TiGCrVwAAjsND3XSGZv3mFz7F54fIydTc3Iz1ax9hHV90NfMet3BkP3mc9h+RaG0zGfnm9devwoqVzPOmfURHHcww+tGCxavwb3/B9K+4nM9fvIhR6aKRLhwbZJrTOonGMfLCO2FHdFS3SFz4I4q0GMfsuA7/HhFztyX9PxWN63UwLdGI6iRSyfpnNmHXQSIs1j1BNNqzT28CAEyePFkjq9R88OhDDwIA9u3bq8fEwcPkeXrVa67BO/5/9t483q7zqhJcZ7zzfbP0JNmWPI+x4ziTM5GJBgJJIKEpmqGLqQpCQ9G/qm66q6G7fhCqCrqaLigSQieETJBAksoESWzieIhjO5ZjOx5kW7ZlzdKT3njne8/Yf+y1v3PvfU9Pz4qdUlXO/udK7557zne++dt77bV+8McBAMVYNZrlmhAJUs4GisBziQLoRAODONA5aVjRxyDD2YbDykAbKXYCMhca5c4xJaHna9+NlLoTcE6mfHmge7U4MoiIAt89HijPTYoY2fsDgF8QdMJgMMAglHYulomyG0JyJESDOKw/l7+Pgj5sj+pzyhHD+c53fDRBFAXbpKTqVUEfNteTXshxrYhUGyiQz7MZy9jzGLV2BqEp35fuFA6zI6uCcPn5d/0sCm0i+ckVE6UJIvZfn6gJjYC3Bx2AynkOUf5LlPt+8pmn4QZSnh7rOuTiXwlCFFWu28321ABgOxGq1fJI/bdVkbNQNBNUn5xuOt+laWq2bQn3CoGW2/fRoyJYh++XKZKlqE7I8xSd12xJfbiui1Zbfqc9dPvMrBknyic6ST6k/mAAS1UaiewME2kHL3FR4pawO1AFZvKOhUCB615wmkhjro1X3XAdrAmZK0sWpejZP7qNhiZiwOJGteOyv3gOSrb0zV6LayP5nXrNHmrst/ffLmiW1JV7e6UaAiokhjqnKVdat2+QjDPk/xtEIZbXZK2pVuSeLlXJtk1NYuecXPfMCZnLLr1S5k53+7RBSil3UIVV5zsuDq4KAvJ662q5XrfTjQCnnpR73fYqUQ/9uYqgqbxyGXrZA98RfrhPfvTjAICTR5cwTYTsU4uiIHnPM7LO/Nv3/gFunr4QAFBOpR0aDVk36pU6AqJ/3KoibAWxecfdd+HRJx8DAINa6jSaWFmTF5nbJv3i1ltvBQDU5iYwPS9rzY+8VTjubr5a1hknctF15He6R9Y+AcCo3RnOKN0QRglIR2TmZm0vIEMp6X7RMuc6wNZDqDN2lrKAiOPMtZVDlV8mMHtRDmFY+vwE4LACiwJOGQiCFL5nDtQ026jiOeMqqYOBKbMebWyzJU1NgULWi/JRIRgqq96S/48TmI1vh4p/Ze5x5MXkQVFM7jKzSU4QB3K9w+sD3jwcBKhYihSWHzS60ncmkgiHbZlLCnzPr332qwCA1ZWncbL1GO9/ZoTQeeEQGj0ujB4dzKKTZAfOMqH9KySSLpRqJvVKD0MKe4Z1IVTHr14bJW/r9vuwfRl8Ng8PvpNtSPSwrGdDc5DekPA4+6MeNgyh4gbX6eKdpQatP4BuJmNuj9TT6HdWeoZUMVrMHj+GKJe7pKN/HHmvFxjqrYvksI07V8bte4423wquciN/wiaej803luvTwc7+m/XF3OzqF9In819Litn5lKawkW21fOME5j1uOB9/Yj8uvlCgyUrEWNL5yraNbPRGJK8b9bWMXFFlUc/tEPVfys5Wnxv223VpvEN1vS61RB1jKS6+WBxBmmI8nMq8siqb1507xMGw51IJapTLZVxDouorr5KNiEMhhCNHLHihbsplPapNCIGmlUZ4/RvEEbGwLPf++j9+DQBw2y1fxTve8WMAgCcf3wcA+MtPfAwA8PTBI+i3Za17/eslJUsJMf/ywx/CL/7yewAAi0uybk5wsx31OxJwGbLhlNgCnV+LiyKRPj87h2JVyupZ3GAhS4U2wYhYiRDBOgOqFfndtTdcBwC4Z69sbKe2bcev/5bIsZcrsob/yiXiaPujP/5DnGpIPfg8gKgTyPazfqyOUB0/g8EAF+3eBQD45lf+nuWVsq31+vjyl4XY2klkYzXDNJRafRq7dkgb+nSehW4X02WSxxbk+rnt4vj6uztuwXGO0alQDh1fvVUccW9JsWQwAAAgAElEQVR+02vxqitkg37tzmsAAG9917sBAE89vB/RomwK67t5MLDl/0EQmHo06aU85IRxgNiSuvVLTB2lJHISR0hJclulI+jYoqRYvP/978fTh8ShMzUjh6qdc1LHDhxUC0IUXpmVw732/73fvNf091275Ppbb7kFzz4lh5+3/7A46669VA5mTgyAju02D4AFEvCW/ZK5l8qJGydQFMHhCVw37koLUCoU0elJ3YxLgANZWouacZhvQLS/0fw4Ppecy5pn8zDe037oZuk7+s6NLg8NZZm/rdQykax4jCC/UChgoiDjpdmQlFPjTLMs45AJmCKijii3UEWf49FiypPNcXpyeQmT03KoLHDeafOg6lgWfFsPIiQz1YCCZWPAlJ5ilamIPBhW/BLuuEXG8V/+9UcAADO79wAAfuztP4GdE9KvunRuJbYNp8hABg81iNRx6KDkSV/5d3/whwCAg5QQt/o2ikX5LmD5+nSWxJaLiP+2eIjSYKrrutn6N9auSZKYd4yi0XkrCiPEDuvR1XMCrwkygYcK04uMU9K2TR/QzWzEe3qOA49lX21Lm/b6ZVQ4z2jpdEz4vm8oJTR1NKUUu2VJarS8P9d1W88cqekrLp33993zEOvahsYW2w2Z0xvL8vnfveWtqNK5p/VX5QH5sX374HEcb9shc98q+8RkrYguHfh3fEWIv9U5s7rWNO3senog15Qpx9Tb8rIcdFPbMilUmrpcYH8JBwM8/qise/1A6uEtPySE/PHpDjoMsBenaryXlKHX7WDfI48CAN71CkkJV2EOVDw89Yw4dC46SNnuuRsAAP/fBz+MZ0jGfeg5+WyvSbuV57ZhQHEKBQZUSR4ehgOkdMr2mfpv16QdmssLmJidAwB87lOfAgB85etSZ4utFvqRrqVS/3PTs5gUDnAj0GDx3qdPNuAWZDwr+bo64wdhH0WOZwU3qPMzjFO42nfkGwQ9qc9ysWDGhJYhSbLAYrGoc6q6yjQoF8HmWhXzb3qGTQDQ52hk0h0GM6IkNIENy2fgkndOrQh9OkKLrlIp9FjOEiLeP+IcWHQ8uAYZwOfx936hYErsWirOkjmPdb5ObQU8aLqWBUTqeBpdZ5CmZpzUipzToc+NtFvA9TqsMQZNEht+UddZucZnOduN48A26Ud+Ks+boIP483/2ScTzTGPvyFp+5KAQtt/0A69HdXkCZ7M8ZSy33HLLLbfccsstt9xyyy233HLL7fvMzguEkK0ka6k9lKo0Jr9tZ5gYhQ8uuhLdspIYnaZAmou+eKktW0nW0kwqckU+d10k0bjy1CQCRpSsgfgHPcLZgjRBQs91HPHJGxCxbhhRMggheu+zL2DTNb5O0h6W8V4qeac19D2GZOPlnlm0NTGQUv1/YjygG5dPbDzqb28Q+cp+dWZZ82FLNsmuscbQR7btDHmbR2HdwyiFkeedMxPa9xbJsmnK1ibfjct6n68InPFybfpOI2iUF61I35em0cJDRw6jSNj3/DaBCWtksDYxgS6jz8WCylVnqV8btZ1BgPB6nUM3i6qf6V5bsTMhel6s/r/h84isSDUipWgsAOPpsmmq8q+JkajtE5qv6VQTE1PoEyp8xVVCLFwnCWir3TVRrWdPyTq242JBeH3pH76KS7YLSmaRcuv1GWnThZNH8dKbXg0A+MVfkHXwI3/zYQDAvd+8G2+idPrxBUkx+59/81/J75aW8b73i+R9n9Lyv/EeScO6/c770O20WQmjcveuXzARXJ16HcfJ4P2so8OMlO65YBdmp+Qdgx6j6ZRctqws4mvkzo1ARN9ErWOGqDuM9l3/spswxT7d7kg/PLIgKVydoI3duy5n2SlNzAhpFKVGjj1itM9hdDkYBAiZanDj5YLw6pwSAsav3XEbfuNXfwEA8NEPCQn49jlBCJ04uYrLLpW2OfDc4wCAQa8Fx5ao7s//j/8MAPD0gbvkOacfwbdul6jxL/6SpIf90DslwtxuNPCbV0kan6LSjjwraWJX3XgzIu5JVpgi4TNCWioV0eN47rEtC36GqijXBHURMHJrEbVRRBkRo+RNQtJ/53f/LwASbb9gXhBTyyuCunCLvE88QLFIJFJP0zqkLAXYBjXQXZU9mA0L9x2U9L0GEXL/9nf/jfwuiFAj4XZC5EMrZHpNHGSIRE2XJerO8TKiakU/a9Q2DEMUia7T+VDRQ3oNkPU57WdbRT2e6xw0/LtWL0P2AIDnZmS8jpUhPgDA47u0O21THw7TMmw7S3nUCHatohLnGTVBryPIngpJ258+ICiHh558HK99syAMGx3pAy0SLn/xs5/Dy24SwvRrLhM0xI4Z6ff1ShUtpp96LKfKaidxjNIUUftMb6kQOdhba+Ev/vwDAIAu97UrzxwAABw9cgzxrKCcXKZAT1QnYXGuVXL6hNHxquMBHBOzVZljHjkiZO575nfDJmH0afangUqOu57Z/g3vLwHpV+N/UzQtLMsMTEXiaZ/xfd+kYevvh+9j9rNWJjQCSN8LiNjT9tY0G8eyDa2Cikc4jmP6gPZfTduM0sSkpmlPLmmqDpws5wWKxrSya5U9mR+2JX3oi1+4BXffJWM3pdBAREGFSr2GKSILFbEz68n/3/fBv8BF18ga59SlX7zjHZKm5BW2IeY8NVGX9i5wTeiHATzOA34xS4eUd/eMGEDCvtrpdTA1JUgHrUc9G/V7AUpEKe3ftx8AcOjAYX7XwQzf0ZqUMnelCHBdF5N1+duphsy/u5nidmjf4zh4SPrY1Cnp22sr8vl3n/4sJrZJ+pgi+SZm5P8nO01sLxLJwfF8alHq7Nt79+I1TAP2SWB+uifz5EXbptFckrX7zm/cAQBYOC3zcWlyCm5Z+wzXyJ6DhOi3xKRwMSW3UsapE1LWBx8SBNT1l10vdVuyYIUZPYd8SmfwvNTMv/plgQjDTq9rEOja6/pML3ec7NlhJP2y2ZL3mpmawv2PPAgAuOoqEXlaa8kcdfz4cdx4g6RrKzKoF0p/SZIEK6elTXQPq583Xv8yWNyzPfCIpDf7XN/L5TLueFje+Z+++2flnlEfJaYq4gypY/KddIyT3HudPHUCT1OQw+I6+O53/iQA4JkjB3DJhUIn4EPaW+k+bNvJiNn13qxzx/Fx4qCkIv7jnZL6v+AsAAB+4I0/iOsv/SEAQMRU64mO9IkHvv4PuPF/+EUAQNGSub25IHX82tdei8//40cBAEfWRGL+ja9+IwDg2GCAuR3z6991zHKEUG655ZZbbrnllltuueWWW2655Zbb95mdFwih0WitYf8FMOS8hJDsAcAcvbD76KGsFAoo05NcL4v3uEJPdKPVQchIpUV0Tmm7eAlt2zIyjU6q8qiM/FpxFkVwMlTOUKnH38K8S4bA0dzd7J003dDmd3ZGUGTQNYqFcmwHY9y9Q1Eye4hQZ4zoNc38fFnVap70MB/R2L3TsftAorrr/zaWa71RAG0LkvFJkhgkwgihLUajLKPfnSuXyffa97lxOaXuzoxyej4AqJHafB5RzM0kYbf87O+CYPNc7FxbbytlGOY2+W5tPbPX1mwzKeNhbp/xv22fF6//ddddh5Q55HPbhURfZWDjNBtnkXKXbfDM4XuPc5yZ/29UnUN/e6HxPOd6v+F3G+8DZ+IHsZgfrhwGVjqWE44M9ZmaucxGSP4Gh1HkkHwYlmUZhMrcnCBcNLqbWkCbKJxLLrsYAHDXvUK4+dAjj+LTH/ooAODh+4Vv5sJLBD1kl130mkQG1IQ84Nd+WaTKTx0/hvvu3wsA+NV/LpxA37pfuCHWlg/j1/75rwIAHiUB5iqRID/xjrebuX9yUiLvp04KWmZ+bhZgBDIdihoqb4DOc0eOHgIANJvXwfOEYwcC0jARbt93UVIuFUqpFxklb7cbBrmh6A6tO8dzDfdBhWiIHTukPienauh25D2WT0sEdscu4Wvq9bsoMndfSZhtbW/LwenTwulz1zeljttEFsxdNI+UjAI//DaJ1H3rXrnm4ksuw/x2QdLc/PKbAQBXXn4ZHt4n3BUf/ozwpLzsWkEt/ad/90kcYQTv7iflmpfc8HIAwMBxUCmRV4L9YmanRP0GUYwuCXN9koum7Ff9ft/Uo5I9K6qqWK1gYUWiicdPS8TxkX2CZLryssuxY4fw/Hzogx8EkBFBe24hk7Ancq1N5EgcxxnJc6AcJdwbuRZqpYopFyD8DVWShz/5kBCnf/nv/wEA8DPv/El0KF0fMsJfJT9WPx7AN6gfzl1hRtQbcTPUYWS5wL6Tpta6fYQhwbewjg9tMyn3rSB7t7quDV9XIOJDJa+7LakDB5ZBiijhacCIu+c7Gc9jMorc8wu+GSehES3RPbMFW5liuX17jiS0f/Knf4q//7qQO59akvFic887VaubSPhvvEfmlMvIj9aLB3Aq0g8XlmTcfOMOQZLUyhW84hWvAABMbp9hgVnXXh+zs7IeNRuCVoi4p3zff/wzdHoydl/5SuHSWlxexbXXCrfaz7zzv5d35R3XVpYwVRdUZEI0kE/eDst1sEium5jlVHSJb9lm7x4H2k+yarXtTPIbyOYd13UzRKw9xtNpWev49YaRQvpdrynt7GpfjWKDYtC+YFFIJ4oiI9ah6KMkiRASTaPCIVoG13FhRfLMcokkzaz3QRQafpQoVITm0N5aybI5vtaWZB4p+C4SHlImVdKe0u2f+uRn0Wyujb4rEVuTs3MYEM1w/Gnpa1/4vIz5f/1b/xJ7DwqCY3UgfZaaAojiTJ5eswUMB5/lwCE3WswyFUol5c82xOluQd653W4bMRu9VxKS4LroYK0j75g25J5lCgqlvQj33SNr77t+TDj4vLLMaU8+8ywijqs215dvkmw/sR1EcZbBAgAN8hqVq1Wc4vVTM1KPMgMAX/3sFxAdlzFUJR/udw4Ioul9v/v7+MRnPgsAOHxyifeSdb7VjQyPbZpwnkRihCPqPPOqmIaHEpZPy5j40he+KPeqSF299YfejF2OoFoz4RVy2MQROh0u3syKqbE+KiUHh48I2nD3RYKMKZKLbGnltKmHL31Jnrf3AREo2LlzJ+79ttTbxATP6OT0XV5dwUUXyf7mh35QOJ/miEz8/Oc/j2cPypykbarzw5PPPo3Dz8n6dT+FEF76UkE4Tk1N4VuPCYlyqyHt8LpXvxYvueY6vhaRe0RZerANuf7ee+4FAHzko4K8jpBihaIZ1e3SFqtEYF573dVwidwNevJ55cWy9vejniGf1jk2I/e18f4/+U8AgIcPyX7Mu0Dab+/9D+LGV30ZAPBLvywo7uqq9JO7v/Iw3B94EwDgNVOylj/x7e8AABYOH8D0pNzjEAfYNs7HB48tord4dndPjhDKLbfccsstt9xyyy233HLLLbfccvs+s/MCIaQ2ouZiYDYa9UiN7O12srD3yNAO10ZIz39AvcAm5RujWRvLyxKFqE8wz5we/SQMDdu7mkZKkzQxXnBF7MQboD62Fi1SnqDsvdJUJUL5nW0ZxQCjRLYBcsqUYAiJlI4hb+wNECh6yUbKZYrwsUagPptxBo1H0baIBBmDFyRJOiKVutHnf2v2QqBRXkheFaNuN4zy2Mr9t1SG9UgMK/dBb2hbVZMbty5VZyqVCrqhRC1qFUEZdBiNC+MYNUZjBowqW7a9acR7XKL5bNxB3+17nMleyHngbIqJ65CPGpGFM4QMGuc1c9bxU8Sp8uKEBvWi34XktSiVSgi4tkVU8JgrS3TnX7znXwBlaa8rXiUolL33i1rE7t07gJpEyQssU4Wo2H/2K+8x3A6TjMpPT8la+c63/Tg+/nfCh/O1r4lk+E03SlT+8iuuRj+Ud1VVobk5+V2704bjjfInbSTzrSpjjUbDIGK1zlxGxG3bNhFEm1HhTGbcVRAEdhKBc+P1wrUTdAcmyv3cIYk+33W3RNAefehBPPboEyNlMfwxVoYcUf4NVWBJU2DAKNrd33kKAPBr/5Pw//zAW16Dvd8WDqByUdptuSU8QK963RuxsHgMAFCqye8/+X9/Cf/0Z34KAPDWmyU6+PHPfgEA8MCBRfzEj7wLAPCWl0n08oFv3QcAuOmmm0DgEsrk69HoeLfZxdSM9AeVWW8zAu+6juFfUBREkbwOASL8L//6t6XMq7Lv6TOyOjc3Z/gvikSzzW0XpFW31TUSw42GRNJV07dQ8NFhnSpPRY08RUhjrFBZR1ENh5pLKA/k30XyzPztF0S1rZEM8OPveCcAYKYq0ctB0De/H1eDS3WvFseG/0zR1ao2lHiOaXOVL6qSO6fX65k+oOpdzWZz5P/A5vPMRkjNzWyje0VEb7rKl8n3K/oFc722ic4wwSCER26dPuvIp6KS7Xko8/qjpwXNt2ObjJsBAnjkllGU6JNPCwKhWq2jsSTR7mnOMT6PAd21Fqx59jnW49988dMAgGOHj6BLdNhTTwlCYJrKe2mQ4HNfE6XDy64THpnf/BUZS2XXxwmiFCK+s0tUz/GDRxGmguC47ZbbpexBYJBgiujYTRTsu9/8w+Y84BKZoWibKE4M/46udcqh56WpSNYjUxIz80IUGWUvgypR/irXMRxU2l7DvFN6D4PYcbN5Tv9dILJC5/9Op4PUVgU48hFx7KaDAXxeN0ik/ZCuVyQ2cvdpDFUKL5I7xyGqNR0MzFjSzb9uv1NYiKmapvPPZEUVviKjNqXpDCFVAVeXm5idmRt5H39eyr62sorOQUEm1pSP5znhxvs//4/fQ5vzTofrTI8EPlESGXSk7SmHDft4aqHZEPSLmRdKjpkPddzo/2E5aLWVz0bebxu53xrLCxlHKt+nG8u9bTioso6WqO6277iso3sf/g5SlufAo7JO/PX9MpYqtSmDpCkUyCdFdGUcpqjXJvlvab8yeW1d38G379s7UnaL3ED/++/9IZ55WsaXRUn6oMv3tS1UOceqqiSiAB4RlmX2tTYRmGvLq6jzO4rz4dsPPgAAeOvb3ozY0v6uiCLp453uKhaJAtzBsefwu2efeArfelRQn3feLfX4trcJqur33/tenFqQsa5opZUVmWtOnFjBJPctXbZRMOC8XCzjyAHhafrQgb8EAEzWZU8zGAyQUgVxinuZY0fkGQ8/+IRJjimX5d6XXy6cfI888gg8zmEPPyTv3B50cOU1wh+sfL0+9zZWaqF7ShC1z+w/wL9Jm1xwwTwKbSlPUpB+8vQzwv/T7w/wLe7pfus9/xIAEIIIN3donTAUwFLX99x+Gw4dkb3M1IXyXqtNab+SY2Pvtz4jZSZP8q/cKFx/b37LT+OCaaKIl2Ude/UPvg4AcOgR4L1/9jH5blbK+2wifeHiC6/Byy9VrsUz23niEBqiLtYNt8mVyjaXCR1Cs9OyiSkSGgpkzhuVG1whyXR1YhIBySNnJgRK3uUzLAfwOAn5nEBsJVuNI0McqoSUqWv0iM/yPmNOLf1rmv3JGpO/S5GlpCk8flg+fv3NnCydbqw4o78bKRGA4RSvdOQD9vr0MDXhA/zuDnn22GEqsbIDwlYOaJIyc36kfo3amdN9NksFekEdO+d6bt6gCC9G+c5XcuzzxYYPHZsRdg9D1oHsoLvvsccNEejJk7KI6Fy4fddO9EnEt5kNP0c3ZboRMykY8X8d8vMbEcZuVI/D/88ye3VeVJnTFEMz98jvbHvocMFNmtaVlIObrliJBnVNiUyAoc60oRZl5PtRiIg7ndvuuRMAcOqYbJiuf8m1cEnC6XEz027KJnbnjt3YeaGka62Q4PeKq2UDNDkxg5+2fg4AEBPm/5m//TsAwNt/7F3YeZGkhiyyDOqYKBQKiDQlRUlJE8B19aAq/a/VlAPX6moDJnhhnP0MqMQxYqY/GHlxN5Os1b6MWK6/cP5CAMCRnUfgefLsL35RHAura1IfayuruOLSy+Qee2RD9dhjkprlF0sY0CmqMukuRSdayy3Tx6s1SUF/6EEhoXzo4ftx9TVSj/uXhLx6O6WUv/3wXtz40psAAHfdK9efWDqGex6Won/zHoGuFygF+8z+O/HpnhyUfu7dsmHcdcUeAMD9Tz6K668Tp9djT0mZ1Vnx2le/Bp+/TZxe6vR502uFDLgbdFGblk1kzM3n3Q+Ik+nrd3zdpGIpoa0SaS4cPYl6XeVsmQbBw4Nru4h4WJueloOM7qHSNDFCF15x1MFZKJRNW2rKmGXZ8JnS4BMq32c63j13fgOPPCwQ920VqaPf/IVfASCpBD1urvVQr23UbrczonMetru8NrRS1GtyAInZzk0e7ACRpZcyW7y39BNzYMbG88GZ7cxr+plMSa8NYS4dkwES9NVJz2dqKpxj20aEpEqSZofvstRcw99+TlJLjp6UgwyHDX74bT+C171E+tXe70jHfIBpBb7lISYx+GpLDr3bJ+QwVbRdgA6oD7z/LwAAa0uZo7fAQMPMnDibS5RGXuusocjvbrtN0sjanH9+9FVvQIPk0AHnKyeVvlEvVrHclDYM2JZewcfT++Tg/aWvSP+/9hohof2RN78FDveoFRIQa8pkWizCovNGRVYiHvwrpRKKnL8DEiUPByLVSRSOOXj0ewBwKNsdso/LOi3XeGxLJduO4xiJ0j6MpykCcL2MUHz4GieOsyB0SOfBkJBNzLJoH2oPehgwnVedX5MVmctSK+tjeg8T7I4igNnQWi4l/62WMiepOjLUYTZVm0G/yTRPOlw7moKXOLAGTFFbYx1zDVtpD7C8Kg4GDVgUfRmDMpaTkfooeFq41NS/Ehn3o75ZUx0S6Xcpb++6Lkokg9YD/8JRGRvbJ6po0akKOiK8mtzTjhJ0lmS++MP/8P8AAGZaTFltdlEnRUmrQWcTvSurzSZqFSVFl3Gpa1jBcZCynGWS7ut63261YTPNbZZOjm7Ic2u7jwsvlLU4DTVFlul8pRLAe7qcBxDFcClZr1uzrtJuuI5xfpX5rkvHZTx/5pOfRZHexN1Mse6ukeT5yGFMTUlq1CRJxBeOidP52HOHcGCRjr+azN/feVTWrkNHThgxqAZT4mw+w/NKaK/Rac/x2euS/L2fmH2Rrln9NqlcHM+k4h98TtbiqQk62iIXJc6rAYWf/vbTkqpWqVQwz3VslcCRbdvm8R8/9D4AwATnqze9UpwpV8xfiC/9/ZcAAP94uwTfZpi2Zts+uh0pz+JJ6b/1GXn3qBvgFTdJCvid98jc97nPCUn09de9xARnn33yGSkX++dTjz2J02sSsAlsaRvyi6PkOlhblj63Z+drAAAXXy+0AmFnFSnHECDz8N67JDXzA+/7PcTbZVz9+z/6cwDAhdYeef4T+9A9+TjOZnm4Prfccsstt9xyyy233HLLLbfccsvt+8zOE4RQMvQ5FoFVDsjUMilN04xoxPRAO7ZrorIhoYjLjJo++sSTqBevBgBMEsa2qhGZXt/AohXmnyhCyLYM5MvakDWZ5bLHUpxSK4vim9fLUq0cKxn7fYYmyrLklER5BDs1+rtN/gc7+8vzSblI0nRd7GtY2v5M3sPhJ6yvqWRT9MowifTIPTeQsk7TDaBP56mdq+z8OT/re5xiN96vNnqn50vCmZvY85FgP35U0lcee+QRFDlCVxlBqJG874KLLjRpIxOMpGsk/kzP12c1GjKPzs5KxMLapHwbvcNW+/qLKTu/FcQVkCEzU4PU1HnYHkrj1SiyrBuplaWuxJYSYFIGNklMJNpEwBSJaqUAZeqTklwzMSvI12t2bsPDe++R67sStfvRHxRyYwtF9CjpSj5QFBxKDVspOkSHTM7IGrlKydqg2zVRLk0j+/G3SyrT3PSMST00BLfsL7GVmne1Gb1L4wQ+EQurTDHrMe1tba0J1SYw6WFDn3p/lacdlnEeT33eQ0Lbl7zsenz8bz8OALj0yj0AgLvuFhTBzTffjJ96988AAD71cUE8PfSQkFcWK0Wk3COMS42nlm3SJWpEHxXYbi+98To0+5LioGkTe/bIcx9//FFctFtg9P5RShw/fjvue0QILFfBaPJhidrOF+Zx+rBENn/99/+VvMMFktpz+tQifvu3Jb3rgx8UqLymMR1eW8QHPyAoDY3WbmdKkON7uP8hSTlQkuhPfepvAAAH9j+NEuu4zCiy68nnIO0YdJgifVQy2HV8eCTVDJXMmrvDXq+DGhEB5RIl5ok4XG13hhBx0qblKIHPvVPQl73WBKWeX3/dy3CKqJMd7O9+iYS7RQ+1okRlA5ahQPREZXICfd7LYd9LQsqTd1pYOy11PD0pdeXymjiKDKG4zmWaerah5PAW7Gzow42+U5RdzPHvFOSdG7026pynNUW1RzLvol9Awgi/po6dOCHv+Qd/+EdYJsFvhekVfSIU//rDn8AnbCE3P/qspDZMEY01aAfwmV5UqUg0udWXtiyXi1g4KMi7EhGAUyVZL4q2b1Jcg5bMP0efk7WnUiijvUZC96rMLY9+W6LR++5+ELGmabHaYqIo+s0ubLaTp+kVYYoekQQXbBdUnqafNLttVJkeq2cGRdVbngt4o+mhER/YSXroG6QmiYv56SUxCrbOnyyDojiHRE9izn2DQCXRHbMmaNvqZxiGBumj86i5No7gEs0TcAKPCZyIosj0rZBrQ7VaRsTfKsF6l+3V6/eQkhFYiYUtIvJsKzXzLiymyxKZBQtI2dd0fqyQrLuxtmYoJ4pEYelzpyfrBuWlAgxxT8pb8nxUCkp0L+Ms5dnGgYse54sa57cwytYS1a2xUm0/pke6BRQ4CVWYUjvr1NDmvSx+VyQ5dxAEsDhelHTf5XzVH6RIOQ9WiDhpE6FSSoGY+6Ed1wnqIlgQ2e7FRsOQSjucH/dQMKD7yD6D/jH7AdZ/wbYQsXwqOqHo6kqxakjG2y3pT4ogjLp9dBK2V6zpslI/i4vL6LPtJ4n0LDgunDWmnXFOqTFNbKJWR3tZ1rGwLfd02dfv+NI3zPlW0XYFprRFUWjQ5UrL4vP9Jjg1HqsAACAASURBVGtV9Hryu8FA+tzDR2Ut3jY3b4QJPJ/trEjgODXE4LoWu0SwRUlq5Ob73EekRU0ftBFEMkB0bg/7JNZ3fJNmPuB6oWfnKAWafY6lQNrk1s//I1abUh+KMr3vVkEDXXrJJQZZCyINq3xeu9vHGtFTHvtOhe83PzOP2758KwDg6AlZ1yZJ7Pz3+7+MSpXE9kwlHBAtacUWZnYKkXYnlrKUXfl9s9OGGwrq+RufexIA0Dr4ewCAm67Yg/QJKddf3P7vpSxECv3MO38LtzwnhNj/+a8/BAD4kdcKSffK6iou2yMoo80sRwjllltuueWWW2655ZZbbrnllltuuX2f2XmBEDLRYTvZgJ+HHmk4xoNfopRsjVGnsJ+i36HnnrcqM/rxxP6n8Orrheiu36OEaZF5vY5riO5S1YPHkIQnc29TS+XYzx6ttixrHcWyQTlZFgwjnDVGFGfBvLshi0aKM/nspM4MDmjs2zOTXydWxgWx/hrLlGEdwijFBm2jZm1QhrNbmg5FMcbLOURcOow0OVPZzz87N9n588lH+0JxCQ3LpZ/zPc7x51sFlL1g7zpMzv28fnjmqzcjQ76Y0p9veMMbsHpKIgy7yHcyQ/n5KAhRZhSo1yGXkJv1s/F7xkNcBsrRoFKhvrteiv18tPF5Zdw24/Ya7wsb138mcz1gZF4j7kpwayUpKopU4JhXnhXXdVHiWFdyUY85+YkFXLtb+HPmGWW0SV5Zm92NBhFCHpGnVqyR6ox0ttGUdlZEThgOcPPNki/vM3Jb4zpqWRkpr8dIrG/4MEIkKuygayWyCFuHyCKNBHY6HRNVjyLl6iPng2OtQ4RqfXiea+5BOgV4zLdfWl1EfVLKdZAy6c22ROyue+Ur8PG/FnRMQMnfHqOoNUzAJ8+EtlGL6ItqtWpQF5VUIp3XXijj5rnHvgOPJKsNIrSuuEz2EAsLx/C+9/8pAOBl10ukuFq7DNGacCzccKFE2h9ZkTp4bq2AlBzNeyYkQt08JvwKBcvGX/zxn43UX0oxjM9/7JO4dP4i1pHsW/7fPxWZ2iAJsW+/RA4VJT1Rlb1QoViEw0mvo1FNlwi0ch1LlOCtWnK9jvPBYIABpOJL5FkqMoJbLkyYNm2Q92BiQqKUpWLRoCACoo8GYYAu90xWgXwpidz7c1/8At7ylrdIfb9SSLa/fUJIPLetTBjehocfFCneKc47N9xwAwKiPNKYXEIs35dvvQWf+6zw6fzUT4pU+c/9k5/lOxTMiqpz4PDcMD7m9T2H0X3jwhfAmdcJy7I2/G7vQw8CAK64VtDqTxyWd/7wJz6Gt/+okLK+8TUyPstE/LhIYavmOsfsp8n71Vpdw7YpqSuX6IKab5l3uHef8FtNEsEwQYLbPbv24NAxQT8sUwZ+/kJBma2uraBqk4+EpNSKmHdToKvowzlp+7Ivfci1HHiJtEWT/ES1Ajli7ALWOPY6RB0op43rugiJvIm5Ny85TrYDItrimaeExPehB76NnT9AHi2iRErkES2Uilg4LmPQZjsr2q7V7YOAFESUIdc+67qu4ega7wtRFJn+YCeja6Tv++Yeyumjn1EUGdScQYbpfWzbIG5CIteU+NtxHMRj13teAXGY3RfIEFBBNIBLTqUBObo0m8GxhlCRKc895J6zUgspuccSlqHTlvvUJucQBj2WXd61WlLkzxoCknPXKlJnNY8cOraDXlu+iwz6iEirIMQM+3SLRPcgWqfbbcIlOkTXFOX8KpfLCPpEqpLo2o5T7CCxdU/rheWsba8jYB/tELlar0lf7acpOlyHlENIeVzLbgHKnPQwyZ4v5/zolYvw2F898qIdJCp7amYOJb7HHDndlL/KSUOA/LSnTgnnzoljwu04OTmJOaLfFhaE42iqLshSx+mjuSpl13m/Q1RJo93Bth2CTq2w/sNBADdRRDP5tchrFAaxkVIP+9r/5JoL5y4xKKB6VcaqckWtNFbR7gnKy1IOK7ZNs58AFjnYOK4nJmX+6Id6TgYi9nEdS/1Bx0iwa9/OUHOxQcHqO/d5TRgnSCwl1Zbf616lXp80+4cGM4KmiIwOwx5OrXE/wesnixXsKKkgFfmLenLvpw8fxalVWRsvr8u+eXH5NO+VvZfZoxBd9vS+JxFyb7Z9kgTcbPeKXzXzgI7F0Fcer8TsP8C51k6l3fz6BGpEIvU7Ml7u/LogkL/x9VvQWZD+dNWb3ggA+J3/VVDGj3zyNrz+Na8FAMzPyrzf6kq93Hf/bfj4U8Ij94GPfBNnsvPn9JlbbrnllltuueWWW2655ZZbbrnlltv3xM4PhJDFHPI0NRF236MXkt4zeKFRGauVxcuHElU0ejFsevlXIBGALj3mL7/2GsxfIB5Mh1wPvUQ8vLPORVhjDmzsMTrmiWc4HQSw6OmGQ2WNodxx/ZeFLC/XfCoHkP7JkOicOcK0ccT6zGiSzZAm8RA2YQSBhM1RC5nw/UZfpibKvb6gG/7T3NXgq8be0UkxpCY3Xj5rCCqVDn03Lvt8Zu6aETNKOVu4Vi1NcSb6qK2oop3pdy80v5Dc8+zqTxuhbLYCvNkIPfN8ED/DUVOLUYhUI2awTLkyfg/+0LaM8oe/iQLepvW50TtvwH9k7pFRgZ2TbaWONlQN2+Q3yt0CAKmJBsnnBPPFr776aixQfWEH0SW23tW2MgUn/imJQxQZfg4dRcJwhAUBIuY+H6bK0nXbZA7tef7zlmEGgDTOIoCq+KQWxtFQ1H5UccRJM+SNRmdUcntiYmJEzhfIFL426i3KaRAEgVF6GW5nVUE07xVn76fP8VhOnQuTOELCiFCb3AcF5vIHvb5S0BiUjUe1Gsu20ffkHTUKNDeboXP6iUQeJ68SnhVFqy531lBVxRvmzdu+PN+KYjM9Kq9CP2AUzgJa5FDZfd1LzXMAYLHVgF9kmWMiA4Yi1fWCRO86lIGu1Stgl8HCEeEcAZU8jx05jkZLEDflirR3n5HzxPLgkJukx6hbkZHOVreDBsk0koI8x+O6++mPfQ4XMjI6S76kH3/D2wAAX/j8f8bllwjX0E/96m8AAO68SyJgzWbXqIqphK9FnifbSxE7fNeilH1ySuLEnnc13vNvROb1fR//D1K+jnCxbJ/w4V7xCgBAuy0onSiooF6TyFxzTaKLzVWJ6E34bRRdKnsNqGrmy/4lGgSo2pSlppytRkoTS7h7AMBjW7bIGZMCmKOEr+4/NOra6XSN8kqvT/6NgnSKXrNl2rzHqHM/UHSCa8Znu0tEAamBqn4ROya552KkXiXmzbwCoFCXe6/2O7A5XhbWBLXYVQnx3bvxxH6RVX7mgNSpKrEEdoyVhrzjgUMHAQDTcxI5v+yyy4wCUkR+lUnyobl9FzN16R+PPSZqLh+ByKU7ZQ/wZAyUqLRTJf/G7voOgw5RdbcqpZunpqZQJ/9ICqJJuF320xC2Ep8QxRKwz/vVKmLl3yCSr1qr4pn98q5/+RHhwoo41zx18AAeflj6yl/tkXn7p3/2p6XOem2w6bBnl6DFvv20KPq4lQoGijqkkl6f80Gn08JcWdaCAetqqSH1v23XhThNCXBdG1cXZP5xHBu9UPrcgHNttaAcKS4cRcoT0VgoKoI7NRLWWl5F3C8PWkhL0n8L3E87nKRKZRedgTwn4L0DC4ZzMyLnWcrrH97/FF5DhNCzvrxXT9GZnT7ckkqny7O1L9XrdYMossjPEhvenxALp06M1IdBD1kZd9yAEm5domds14Gna4ciIc0ex8XpFUEbuNo/OE4cxzFoL6PwRUW8arUsmQkAQp3IC0BC9T9FXQRGMdEz66YiJXTcifIZSdJ4zx45XwDbPNuz9PoO3yXAzhnpOwH7Vci6i+PQIC77nO99qkSGYQxSocCCqps55j2VP8YicqxGla3J6naDLNQ13C8QuRo5CMiNpOPUKpWwFqhSHPg7KWc3iE39OUTWLhHp4qVFVMlV5vFeLrlwvKKqVQOXp6pAyHUiiVFheXR/FK9KXXeSHuLKgPUn301MSd01G6tmfgJ5eOpE8E0WbHTIeeaTt661IughJ04NKs9n287U5fmTlQJS9t+InEBJHKNHpInF/tEnUkvOBRkPotSZ/H9h4QR6HI9hImvOFZeJsuazB5406tvaB9rk47FdB2msfIhyjUNeqcEgNGNHOZUUhZUmLiLdXLMMNrl6PN836EOP/cIye8QEkXKXEc1mKVqpq6plwNS0rLuuozxIEepcN5OSItBsUx/KG6a8eU7koVCUtSbgfJ8GVJWDg6qvPgqqmnWkfP0Q6Aa8v6M+BOlf1WIRvu4z+T6K1rWLDmyuSy4RTJUq+71jZQqYVJpL24o4TDB/wUvkuiXpt//bbwuH4pHjj+Of/MSvAwBufIn87a/+6qMAgP1PP4VuN8LZ7LxwCGW2Aam0dmTEZnIuKLETF/jmySVYrPgiN5ExF8JSwcNJQkmvvWQPAIHyA0CaxKgTat1R5q4NznCGGHTLaVFnvi47PG3lXt/79KgXQ3L8xbj3+SBjfj6U4b+UPZ93H3FyjMFFsYFzxFaYqj3kqEm2OvZeeDuXfruZw2zYKbjRdZulLI1fo5uoKIrMv03ag2bius66FKpCwYeXqjNFDgsKP69VKugwLePYMXGet3eL9K/DA/xGtllqYGoBYRyMXm9ScjOJWj1oNld5GIaNpDea/jRPIt1er4c231nTA3Su7gVBJuGrhM6cT23bRjpEZgxI/UWUSNX+l/mjUwRjaSO60bFsB7bK4KrjTg9MhcLoQQDZhr3f78PmJn6WG0eVpu+ttM1mWqH9ahO+j/Zg1Pll8UDS73TNod4t0/nA9IlSuSwOo6F30NSFankKtkr98pokziDfTb57mWSJnUEXqcKi+bfqlKyj9ekJ1LgpTnh4cgnVT4IQAyXMpYiDzw2PlTooULZ4wHpYW5RD1aVXXmWIGl/5OpFhrVSkvU91WkZG/Jmjkk42MS9OtOXmQZT4Xq2WHHrNwSKMUaJjrUgHwdfvvhsA8PM/90u44647AQB33HU/AOD6a+SeySDGs8+Jk+OGq2Uj7VWmcOyIHD5jbrJLrqTzeJYLK+TGGUq+LG1aLLkoFrMNLAC0+4Tq27Yhj22z/8/OSR0EUYjW2ig5poafbCR63kSJ6YMe+0kcxAh4AHFYLzpuLMs2qe2uksFyw1ooFIzUsx6a1Tnr+z4STefgOJusVhCxz8zVpS+stVusAwdzc+Jcaq2R5NnTMb+I4wfEwagpcE4kZTn41EHMTokT7KKdQq596ohA59PER42kuCsr0g733SPtVq6WcOioOJdOnJR7X3yxpNlG01OYnxdHks6dOmnWKiVMTkrZdY+4bZscOman69g2K+8wMyWHiMUlSS+490v34plnn5V78ZB47Uuuw757xUnZWJJ+aMaNV0KhJv3v2HNSvj/5oz+WOltbw1VXSFpipyEH44ov187NVDFRl/73xH5xEq0yBaw+VTehvYDtdPykHEArlRouuUQISxcpHz1NB8DCiZOwOR9qACbkvrjf75g5LGFH0VTXYrFopLFbHSlnsyv16XiukUtPyOdQMk6FwDjPda0Lggghx0KJQYmUZbjnrm/i4HNSt6f3y1hXB3u32zX3UKfeDNeShYUFk/K81sgOy/K8wBAeK+mvmu+4WcpLqAdd+c6xLTNfh5oKp+uHm2QBLx5Qdd4ZNl3/ZtivHcdCi84hfe7a8goG/d7I73SNK5fLSLhKuVwDBiTcjeOYTiHDkZ0910qhq1tWD1wPy45ZE0sMEhS03ZNoKLWSKWd0tPeCnlnrzDUjVA9iKuyjB3PPK6BEx6Ku/SZdOQlNmptZ3wexcVCpgIKRW+92zT5iclrau0hnc9yNTb/Sdb1IcnUV2gCydCTt25OV6lAATMo5PS3t5RcLCLhOqkN5mYIeYdBHo0QHPu/l8/063Z4ZS/o3fa4L25Dda3/W1MI0ldRIYDTVN7Wckes1NctxHIzHUdMhGo4e1yE9T2v7lSoV0w+1ziJtyyBCwjnZMangfJdCFlTQPahZlzwHDudWnVMifa8whKVk+2Z8aYkTRLH2I97RACwS0za2HfFzvYBFVpYE2vpaV0oYniLNyKTpmLE1rBjFGu/KSOLjLHCp9ebVZZ3QMR/BMqIK2r6a2gnHNk43JfPucK7uNbN9rbZfkWl9E/UJzG2X/rpwVObCnRdKH99z4Rvxxc99AQDw2b+5g0WnAyvsmz3UZpanjOWWW2655ZZbbrnllltuueWWW265fZ/ZeYUQcix7iMhv1PuYppGR31MJQocQ0TAOYBGe21cYGeG+Va9o4FcBPfmxRSKq1WUTreiFJAIlzMxzXdiE9AeK+98iSGEzwsGtXns2O1vq0fB15/KcrciKn6tlHvfnf99zfecXGvn0YqGDzpl8+XsAVtpqn9vMxmW4gYxU0Xj7FXaeDv1tkxd8vm27GaHwd/t+59p+W0EKbXZ/27bXkaAqYmUjmeRGu4WKS/QISRy1juN+AKvCCDHlWDsqH/0861g/Xd9GHG98DWzboIfKjGqePCkQ6tmpacyRHHttTSJu/TVGZF03Iz0kqqHPqGG5XB4hiJX3g/n/EaY6XUopTtd1DaphXb0nFqxUU/vWf2pKj6ImtLbTNDWpaV3WX5HIlur0pJHUVkv4y3JcgecURr4LGZG1LAt1WyL6EZTMV+5dqEyZiLkiAp45cJBlsZAwtaxWk0iWRrZ6vY6JpnmMbqnUcxiG2EkURHdZIrOLS6fwmpffDAAoTTDFmu0QpDH+/EMfAAC88tWvAgDcdMONrCuYKFxB0zuibD7QKPoS2/ljn5D0mn7Qxw//qKSIffVuQVqcWBDU76VXXI4HHxTC3iWiQrpK9hqFhghbUcGOErjGFiqO9PsG4d8NyiUfXz6JO275ipSLdKOrDamfatHH3E7KuPelnFOTE6hWFVlVYx1T5nuQEb9qm1olactutwubqAntC40W+3jYR5cpiGoMQmN+5w5MT8n9e71MBhsAOu2eib6XyowOchyU6mWkIMk2kQTaZ60kW0J0nKjksBPEKE/I+yu57nCE2iLay3aIQItimEgs0y6LjkLte+gWKFm/LO+a6P4qBJyIdRUR3UDkvOPY8GKmZi4LGqWzKmiURpgYNI/F90uJPKv5NVx1wRVyTwIUe6eYhhIUcfhpIdrUVL0dRJctHT2GdiQolAqj8vvalPYuuygyFVGj6yqFfOzIcUwRcaPIruYzJzFDAvdt04IcSznOwl4A32N6lsfUFNbxJbv24PTCMuuUbToj/WvQD3FgWdLQuh0lZpd3X1leg2drigNRM1UZz6eXFjHLMlQqcq/Tp4TkPIVtpLg1el+pSruVa0UjKd0lsjGBtEMYWya1XpFgmt7hpEmW/sv5tEsy4EG/j6I32p9c30GUkDScY7fIvXzY7OMJytkrVbhPqfk4kr4v7yz33L5dkKRLp04gYWS+MxhF0aZD5OEKQdDyxkFo1oAi5ytdZ7xS0ZSvrYiYQFFEliEw3z4na5eS5Q4GAzP/ahpJxHHe7baNvLdT0PTq1MzJZn2JdJ1PEbPsJSJpitUhgmzSCGQk/5H5Tv8WEiXquko4HRu0XJpkqXP8AyIiDPX3rlPi+/XN/bUf6ppiW7bZ7xkESZqlrziUPy9ao+gXRMkQKa98VsoFhEwx67UFbWdxTelHIWLOfdVE5se5aRnXds02Z0qtR9XVaHc76/ZIE5xPet320H5A0+s0XTxEl6mirY5MzpqCPl2fyNIUOb3p+tbv9xH0VPKeCC/OI1EYoD/QNnFH6jNN06wPsLyu66IbjKKIY03tDLPrFNGVmi2XjV6iUvSkCohlXPrFAtI2K4d9T+ekOI6Rsi2jWPeZ8p1v2bB4L11DNIXL932DPlbUp6YdhkkE0x1UuMJscy1E0eie1+gcpbFJbddxOowKV1SOjhFNsZcL9TmaqZAh47w+96xct604MfOZqX9WcZIkcLX/0b+ge700TpByv6FIPj3rRGlk2qbOtcELVAAkNKn+PlMfNS01bjXQHsiacIEA1XDysCA9G80APilv6qTVWVyU8k5NbkOvrymjZ7YcIZRbbrnllltuueWWW2655ZZbbrnl9n1m5wVCyDYxVdu4U9Vz5xgvnYNUo8gkGa1R/vJQ8BQKJKaeZS75PGUOl5eWUJvga/KjwNzAsN+DT++en2S5h4B4RNULrpFtexNi283se801Y6fr/7ZRCcbRBpsR6W4uuXp2QuMz33f0/2evq623wYvJh/Tfio2TLp+VLHsLdbopfw5GET9pmq67fjhaY3hmxsnHXySUjymLoTh6fu9+rrYZX9BmNhw90oiSIj8qJGFNkGaoIUZQKtUqbBL7dhlJUd6IsuMrl50hq6xVlGskIyocYtlhGbJyZRRRKj0brytDqkEoKzWcFwWibU6dEIRQ1BvgEkXxjM2/lXIFbUbtVW5WI7PBoGfqJmJkUGWr/aKPBnPkSyRsXFlZQYVRSOWGUHJS27Jg26N5/co1kiQJOoxYagRQo0lesYAO61b5dzRCdNvdd6I1JiesPAmlYoZqVdPvXNc1RIMlRq01stVut/Hkk0J0fO+93wIAHD4sKIdSuWqQGGZtZTuUy2V4RA89/bQQ/mpk1raz6K5Gq1718lfg0EFBWB0nx9Ts7DZT1nu+IVw8txDN87KbrgcAvP61r8MaI8xHl0/x2RW+SxF3PvIAAOAzt38VAPDoo98x9XjXt+5n2aXPzu+U6P99DzyMI5Sin/KJHGYk1/eLpr0U8dTrSn8pFovoEv221pT6rxWIshl0UCa3SRLL5+lTjBBO+qgyn78ySc6naIBKXYWLGeXmIhz3ukgJn1heEdSW8vdUKmUE3FusUvL21Ipw0UxM1TFgPzq1KH978+veKL93i+hS3ltRHsr15ftFBJHyvkhdr7aJZJqqoVhSHXMxjQgO+qGJ4Cq3QZV91nc8Q+yu48xyFI2ybLi5SoxyW66FGsnup6uC7pgi74blelhV7iDOT3GiaDsbs9Mk9uS7T9QFzTI5OYk65ZSPsE+7JFMtFhx0miQA1Qgxy3T61DJmiBLYvUtIUw8fEdScM7AwWZR94iRFTGq2jGGnMoMu0S4VkoTu2CVl61mJkdh2SLBar5A3ayZCle9cSKSOpmdmsLomKBwlVXf5WSnX0CWyxfFG+TeWO8sokd9ECaOXTwtPUMttG8RknRxYOs8Vfd+g2mcp0b20JM/vdQcoEWHvOorilDprtdbgK+eYP0qijyQxHJoqg77alvHTcjqmX/X4LloHrU4n4wbU9Ylzm2N7CIkGUsJ/a0hkotchd9yA6CHPRtlWAuDRddL3fQwoDKMcKiHRQJVSAWFP5eZHZd2tioMJoqd0fdI1pd/vG8nxFvmZFOHoDrrm2V32vQpRY75vGQTpiQVZxww3TT/Erl3CgeXoXsioaqQolzKCY7UaxwmsUUGDicnpjDeJ/CWKjIvjjGBZ33WYz07HSUCUhqcIqCQ2/CjavwZEoEZBaN4jSZUjKVvDDMcr1zjluymXi6ZudV1TVE8Yx0jCUYSLkrdbqb2ONLvb7qFPtEuJ8/Ak0X2e5xlUXoefA+2jrQ527ZI1QxFaXa4F3XbbrK8qYqKI6FKhsI5/MeV63Wg3DWeWtkOFnEVhGK5DZimJsl8sICDEpMf6qCnfjWsjMgcx7R/yvzhJDF+bHtw924ZLdFi2N4my5/LHPuewxMq4byL2BwXnaF91LBut7ijazuHcnlouUu2v9uieNUxig7pS0n3dsll2qt3XrC865u3ARhhrKYhSHzpSpsmoGImOLZkoiG5KRsVILMsy53ZFEQ3igZk1dG+o+zHANjzEing1xNi2jTQloTvrLCRiB45tiKN7XMxD7nGiKDLnF4Oa4/7WTj1T1v5gNBMpjmMkidatovw5hgcJQnIztotEKLak7zrYbuaupVD2V1HIfaeToD+GJNvIcoRQbrnllltuueWWW2655ZZbbrnlltv3mZ0XCCF1OFpIDQu6ok5Ust2KASvRKLJ4RKeIEEqt1Hhx1ROnihDXXLkbtRnxfqvyAVNVEQc9ePSUF+ih1WToJE6RkrHeYSRqyyJjL5idDXmzMVJhI66XrQAPNkIWbfyADUryAqEmzo7yeP5opFHkwuj9z8aLs1mdrPvd8LWbVMfmNXWuaKtz56J6oWxL91KFPztDKwznNQ9fA2QcDeOIic3abXM02/OzjcdS1ofOlWvrTL8bvudmnGN6jUZG+v2++ZvmrqvaijV0vYm4BQEsRgfL5B1wqDoW9nrwihJFU0WNiNEF17I2RTOdSSHNhYXYUiUkKjyEWcTSgI7Y9mtETExVakj4bEUwaZSm027DG+PhCTUKmtrI1O3JX0DUR3cwwCNPPQUAuP4VIiFenp6Gyyi8wT1lFEIGzmTpO7Ocbmqj5kqEpscccuXT6UchfKqyOQVZl/7gve8FADz66KOYopytKpQYrg1Y2b9V2lXVybo9+Fy2tXyK3LFt20QlS7z35ZMSFU2SxEhd6xqp9Tjp1wyC5qLrpb0VIRQEIfY+86g8uyWRqQf3PoiHHngIAFBjH9tOefDOWtNwUimK5Y6vfB0A8J379qJLjhJFmEzNSD0eO3UCJ08dG6mHnfNS9n6/b1Aoqqy0dkL4IzzXxZUXiBJTm5w0scNIWBhjwMiyS2nckHXguzYCVeihOtAqpaLXVhqYJ4fEg48RjVKXsiwOuqhTnjaxpCzNboga0SsWZeQTKqNUMIFikTwsU1U+T9pGkR0AMD0lSJgaP6v1KtqMUluW1FW7x+hnv42pSdnfvPnNIsN96623yu+qVbS4z1lcFDW0yZq8C+LY8M343NNoxNLybTgFoqkYefer5FSAbWSYe0QIpeTM8Utlw1VWZLvP7dxmuJtijfgquiGOMSAysUxkhva1UydPIlG0UUWQO+Wy1JFIS0s79VkWx+Fa6bqILYTENgAAIABJREFUlVeFEXRFzTXbHSyclmipqi6V2LZeoWiQT0oKcWhpQZ7XbhoenUmS2hWpyhOEieFZ0XmrSNnuMAnRa8l47rAd3H4FHlEeXXKHtJsSyY1TINRwONfEAlXX2oMIjSW5l1FDYwTY8wq47DJRCzty5BAAGCWtVrsByx5FG6myWxQm2MZ/q7qkzi29XgdxqNww5DQZKPdNz6wFM9uEZwkpJZsdDw6ft31GvisQDXHy5Ekz3yjihDRR6PcGSJUuxsx9TqYEqGBdIkIG7b6BDrjkNqoRQeX7PupEZ+jv11aoxGc7GZed3NL0e29ISczIzbvZfGq4WlS5SRX70mQdn0uZ46ZQKKFLdJ72bS1Tr9M1ql8D8n9VuUYUCgUkQ4pSWqaE3E0puWFC/r/TahsUYJP9yTZoXcuMgXF0jud55jtdAyIioPTvQNZ3VOmrVKoYZJvec5nqcnEcZTwuLIOiYKwoMNcHbMsCuWUsN4UdjfK5mHawXFhQbjSqLlXKKE1Jm9dsmX/LRPaWikX4LWnX1UVBxIU1KmnWaub9BxyDZaqoeRMTsDgGLLZpqcJ6iEIzPsw5lfNkpV7DvCPrgnl39nXXcg0Karz+HccximpqhusnjTLuJXYURWWGYYgyf6djajAYIIhG13W1IOib+xrVOo7BdmvZqJfpWOgPKdQZtDnn08jsyW3zO+XOMZxPSMymRLmEDFdVEBtE4rASmFwcGlSaOf2kBmoEBfGkYyx3jmON7Jvledn7ZlkIrNskU7dTZJDWHZDA5n600+VeT79JkgwZFCoPF+dJ2832iUZtkH3B9cxYHXSDkedalmXe37KJVmK/T5DC4/xrp5ohJc9w4MEh19zikvx+cVGeNzUxi5jzcLGuHESyJjQaTVils5/Pzg+H0PDhXA+KmjaSDB0w6Kyxmd5lyNlsCwk74DHC3g6zk//YW1+DxJbJ8uiJowCAK66UFIQostDjwj6w2Jh2tlFS4r9EgVRxRoQ2VPqtvOEZ33lzL9Pm9z6btPXoc85u381B+sUkoX5BzBpztAyXbxPHwvh7jP7/e+4hPKNtyRlzjsV9PuTGZ7Px+kw2KFSPqRxpnKyD656tXOuvic96zdksS386u5P1TNee3bJ0qM363Lpxpg5zK0VoUpDInqp9Pk2hS63Wh+faSEnSqofkPn/nuRaCnsyZKsnbWJWNRGHX/BAp/NbnnwQYkkAdIxG3bbh8D71GIdxREA4dDEZTzizXMb1HNx7O0GZeF2qXi6RNp0y/20bEzWqTaSHlYgmHluUwaHuaYsz5P0kMoaqBxfOeBbcAN5E1RNejZymN3Ol1sbIqG+0vfEHkQA8elHSVyclJJB2p70t37gaQETp3Oi1Dgjze/33PA4ZSxKR82UFGofUOHS4Fn8GQlSb8ierIO0RcT3desic7+HDjo07F+tw0LmheAADmAHrsyFGTgqKOJ88m6WexguVEHBFYknrZXZcDqBXHKLExXaZX9Fek/vtrq9hRE2eIVRg9QNYrVXMwbZ4SR5BuYqt+GYskVVTncZEBI8dyoX1NHRoKcw+jCJUJEhEzrSAkrH7h+BJuuOpKqePdPNyUZeN/7MRxrCxIuRpNwr8tH7ZuUuk8sLmh6wUOeoGShEodqfRss9WBxfKpY6JaJJl3P4ZHmfqds1L/p09Knc/MzKDMtJ8jh2RPoyTHNhyTWuVCytBYkfYoFFxYLjfe3GD6LNNkpZQdhDHqbHZsFwsnJW1Nx43ut6674Xp43NDWSI4ZWRF63FxPUVK70ZA0Mct2MT0r7WzSRwjN3za/0xBzar9XZ2mj0TBknU6RDlHOB0GnY65X/lFHZYwdC5Eh7+VLD+Q7z3LRCZi+xsOhOsWTMDVEnik/VxdlLBfsEkp8Zz28tjsNPs8xTgCVs/Zdz8yxic30hZDpUIUSJrRf6PvRQbltdg6rq3LfafbV666+BgBw6tQpzM1JPSpJdLstdbVj+zz2H3hG/taU38/P75Tnu4mRm1cnv6YEzW/bjqUlGV8rdKbMz4vjcXpqDisr8p0NeY6SNsdxjJBtOU2n1KAnY39ucsI4m11oWo3MkwunF7HYXubf6IT0feNQ1r2/UkqkcYIS57OYuSnVWtm0gz5nbVnKqXuLen0yk5Rn/xo+dOs8qv1RCcpd180kpS2mA3NNdVwb23eIQyLm3FLxWZYwMWk82p9NSksSm3ua5wba7oBH536/zXHgucbhquteHKsYQA9RlB0wgSxNDgBijkcd6+rEtG3bOMg1BUzvrY4rAAhM+o2mDzpY43pmBBuYWhingM9UIJVzV+Lo5ZVWRjjta9qg/L9QzJxTmuqn36VpFugP6ahcXjqNGY6FqRqdxRw3g8EALc4X7Y78TdcQNyga8QVdOy7YKU7WSqls5pY++0CFa0mcpkiUOiQcbUvf9029dYdIw6XOLNPOmj6rv1tdXTVrfRboIQF9v2/6v7aFmduCcJ1wSBAEqBQ1HQwjZegN+iZYVK8qkTnXs37f9E0U1wc8iwz0JKxHTV9Oktgcl+yxdK00ipHolyYdFbxmmAibf2T/kBQp3RPKV0YMIrVN6n5qnJ26p3dgaz7dWFmSGMa7oXtEy7JMupv2Rx0/aQqkTFG1XKZwsf2SMDLPUaebjp/ISpGybkqc9zMXUwr1lelYcgwdhp2BDbTh1Onj2mb9ggn4SlnSJDb7+tSRcTAxK2vs6lrbBB4dJgK2uzK/1ut1rDWyueFMlqeM5ZZbbrnllltuueWWW2655ZZbbrl9n9n5gRCipyxOI9jOmBSvkQy2YDPkqxKcO3YIPDUMQxTK9BDSY/3ccSG9/Npdd2DnBRK1uGAHCQv74gksFB0jmafeafVGOq5jyLY04q6w9c2i/+f63Ytl56Ps/Faf/7165guZNqX3O+8QUi+AvRDvpdGu4XtqBETHuqITgv4gk+yslHEm2xpSKCv7mVLAXkjbDF22FWTN8/2+UqmYCOAw0bR+ZtFFRlfS1Fy3THlp12e67cx23PvoXgDA7bdLus8liUSh3nTtlWdECA2/73hdD5LASLcbyDCROKnnIGS0WpEFdRLBVrwKPKZ6La5J5LdA2HQSR+gQTaZy7ikjx91BgNPLgqjYv38/ABjC5ebqmpGL/fjKR+W7ffsw8EalhRUZGqeJgTA7LHuBUX3PcVGCRNqUCFcj1YmVRetOHhaZ9KsvFgnsXruj/MMYWErKyHSBgm/IRbX+2oTmdwfdLOrkq5QvI4qTE7C6A/OOANBl23bSAJWm3EMjjwnhz/ufeAJ9pi8Y8mC+S2N1zaSwfOehRwAArVbLRGxVDtjdJp+dTg91TVGaZH2mRK6FMcDob0SEhK6pOyam0WOkXIlRd5Cot1wuY7nHSNeUoI00qnz69GlMTkpfCYkAmZiS383MzaJL5IZ2zTIj/d1+z6z5KVMArK78//Dhk3jtDS8BAFRL7E/8fOmNN8Luyz1XG0y5rBRQZVS3wqh1bYJoJ7gGVaPR+FKZEXHHRZ9RSUXiKQE1kI0TjXKfYB9qJpaJWj+3T1IfZ2dlb9MIAxNdNWgxlYW3MySRq5FEIpk8t4CIyAMlSp1gO6ZJakjVVVjD1ZQHx0fCOV2TevuDEG2meHSIvjp1SlAp7XYbFfY/g+rTtP1SxaBrNIKrxK/hoA+7Ks/U8aXXJmFi7tVYo+w50QCAjSlG0PU5zRYRXoPQjAWVZdbPYrmEtTV5ow5lwSc5JyH2DAl4g/dSUuU0SdDsEIXCMdxY7SNsq1wxEYb8rlT0NZiOtKqIIqnrWr2CHbOCYtA+HXG+m56oZ5LXnBsyAlgHF++5aOSdT58UlFipVEG/p4SxUmfbGGGem5sx5K6KmJjbLsii2Zk59PtCOK+0DjovO0mE2CGqwyaihhk3vltEmkh91GtMdymQiHuihMiZZrmydjDrA48CVqoEtSlqk7KXX1iS99EoPqwEUyQXVsSIV2DqmeMZ1HGBa56mGm+0nrpDachaf5yaEQdEETguakxf1ZRmRZfBjlBh3+yFMg6UODwMQ/hMIXV6TNVThJBjw+O4tIhiKZbKBkCQiSRkaPeiSRfmoxU9l1goFDSFiClZyNIpFTnicr3U9OpysWTmK4wJKfiOC1fne0fqrRXI+yVJArDMisYKOYY77bZJv9Qxm3K2cPs2qioSRIStIiCS2DLopIB17NkOQqJFW4mggTRNFMiI2a97xcsAABUiY3zbRkHRVy0p88JJmU/XsJqhcfj7VSKhXNfNpL/ZDpp6bYUhgljTfPT5Sj0SZ/L27E+aclmtVtFqME2c6WA1pjtOzW0zc58Ss2sqaBiGaJKQX/fKhULBoGe1fxiEsz2UlsQyDzj2u6USJkKSa3NsDPgZJJGZUyKu85nIk2toVoywhqbrJikMHifRuZ19CA6ClPsb1plBfqcpXEvTp3gWgD10T5O8xc+MaNkg1RItH8eP5ZhF3yDRXQeKxNffeYpkQpYeqsT/tqJhkWbM3kTBRSxLHEZIUkXSyfwWaZpckmR0GNYoIgwxEPP+ro6bVJ8XItDUtGSU4NrzqqYt2wHHRiLoXcdP4LqKmGQdhTxTtdooexM4m+UIodxyyy233HLLLbfccsstt9xyyy237zM7LxBCasNR9SxfMONSsMbyBJV00vUzb2CZ8os1Xnty8TQuuIRyofTE3/tNkcW95CXTuGinRFJ85pB3upR3SwNYJC3ciOx23F4MZNBGpLVb+t0W/6b2XSMkxvl5MJq3ea628T22Ljuf24tnZ5WnP9vvhqIDqRnzYsMyqRoteSEk5Z+PvZDPe171ssGzNyNv1iiU7/uGb8Md4r4BpF7HiYSTMAIY5VNOj/0HhPvm9tu+jlUiK3bsIGmiybVO1yGENiO/1s/a1OQ6gkNt537Ux+llifjajLwrGiiwuiYiV2UEUYmau8EAO2cFMXJoQbhUfud3fgcAcGKtYepDEQnbeO2OmTlcc6VwcUSM3Na8MtxIIjw9JdAlMiBOsrx321GuEuWE6CMuEWHFem83KUntOCa//6brbgQAnCZSYtfcDsS+3HON76rRo4labYgcVD41etrv9w0/QoWoKM1PX1k4bUiDNRJYK5EcvFo3kU7Nm68wAtnr9QwCb0CERMQoXtBqoh8MRwCF6FfJPpVw1BDjIjEorZ6vufGKWAlgsx6V92SNEU/btk1duY7KYEuUMkx7mJ2SNVw5oraRZDe1bMMVEnO9dhmBtxwbK2sS6d2xXdDEir4I+yFsQoDrUyW+O6PenZ5B18Dk4ktf6kUW6owOXnPF1VLHdR+2q3LsRHR1BTnieBXM7xSUhfJVKeGvY9koMYpfZ/tqHQT90PSnbayr+W0SYR70+6b+qmUSeHdVbrln5I1NvfTlPvv3P4HlJgmWGQUdJhvVSKfKAndbRGoGAQpFlbaV66uMdp9eWTVcEj6jwXEUmOhvi/ONkqEG3Z65fnpG2rRMdJVbrKBE+WsTAS9K/Tz++KOIiLZrkMvGIxo8iBLMTJIMvUiiU3JBlvyC6U/KzXERuXe6/cDw4ihBtdJbJGGEmZKMj0pJEAwnDws/JVIPJdb7FMmvU0vq7tjJ46b+64l8t7y8jMmScPHofD1F8nBviPC4XqMIgM6L/a5wRgA4fUqebSLbnmf6qNaVohxWl9fglJQHKuXzpkxZZvhvXVv1971eF7VpQSJNcq5MyDN34PAxxIxyu6x3Rel4voUS15cTpwWVuW1C+nMcBVglp88E26jFsVStl+GXBBXVJsH18loT1brUwxS5V7ScS0unsdpYY32TJJpjpNFcNW1ZZrupiEC73TWk1wPKxWvd2bZtUJ8qGGDmnTBDKznk41KOqvZqA432PgBCpA/AcNtYqZ1xtUSKxrLMu+hY1cwDv+Cy3CWD7pguS105joNmR+6vEtYloklspEjZH3pEGnrkgwniCCHRtv8/e2/WM0t2XYmtmDMjp2++97tjDbcGkRRLFEWp1S3bDXVDkt0G3JLh9oMHGH7wL/FTPxgN2IAbguAG7J7UUmtsSU0NpERREiVSYrHIKtZcdedvHnKKOcIPe+0dmd8dWKTUQgHM81BZ98vIiBPn7DPtvfZaun3Ic+WXc0zWuuI8HzqhtaeihbxmeV133ZaAWzleXc6ZTgOURJlOSpW35jV1g1BlyHl9SQKVJEmsgkq0rE8tmxoOr9f+ajwXmeqJs62Ub7BqalScfzevyvowpXx67PiP2LsiRD3Pa9Eh9fKZr0Z7JlXCaOWTqet6YU+j9kg+td7Q0EqKDFWUZDfqGFKtIRJkW3nAisLOmYqEUv61PM9t/lCkVdiJ0PGIqiSMTZFZVVO3+2f2s8d36UYd2xNq/ULyc/lhYH9TzicldnYcz9A/aju4wK8DACVRW5WJYzgoHM254TyCdv+o+ypFoOoepagqOJo1pPaoCLIaxpupoLlmgTsuzxU91PJ+aV8+ItrhulZXfXf9HVzHMpdq49ejDS5kOFSsl9Y3cFpEaGkoqpaXT1HmMxWLUP5BtxXaqZTzk31cwUVmPGhEvJV7fD8HRc7npXIDRcUhd1HX39nds0IIrcqqrMqqrMqqrMqqrMqqrMqqrMqqrMqqfJ+VjwVCqKBHtNsdmge0obctg3iGu34Ev6YE9blcf2kk6B5nO0a4J95Ul97zDycPAADbQYDDDxhR/ZRELbYj8R4nswetUsVcc7rleWWRwoPcy/GYN7zgMf9ueEgat732qTLmT/7q8fd90jOdRRRN/eRrFdmjjtfmKf7Bp7zfYuTg0VI/8TvncVV6HM/LgnJTjWXeAZPD9n37jaIg9BrXdVFd7K8nvs3ys93vASEi9Xx8cfEosuJx7XCxNN+hGhfv8Thlqu8V1bNUD95DlRo0N9/zvKWoymKp69qiwjmjBIFKXc4zdJn3njAC3KEMeq8TAQmjaORGyYbkboGLkE56ptKaZ3+9N8B5KfUKWmkr4weLwmUpe89p5bq1v3OdkwIPriodlhoRYd5+GOGM0b6IEf6AUbnpfIbRxmipXgo09BzHohE+/9gwWpCmqSneaNjD8qTLwjheNMJR1dKOs6RASHTBLJE6ra8p70QJT6NwFeVK8xy9qxKh/5NvfxMA8M//5b+S56QFfmBb5shPbwhC6NOfeFbea3wfI0azAvKRKOKhhodD8vZopHPC6On0sMYb35LnfPPrrwIALu3I84/2D5BR8rRL6d4z/i6bpvird0Qxx2dU/ubzz8m/w8DWi02qGZWU+/7BzWdRbxBhxfmwpAJLczjFB5M3AACn5OGYZgUGvE45IXa35N3heDinLG9F204VoQUfA0Z4NXJbEXXkljXG5FKJqYYDX+zjYL6PYSGR+s2R1H06leh313NsvtUhO6NdRYMunFqjXPJdxahQv78NQJ6XZxJVjPrk6AgjnDAKH5FzSNVt1rZ2TIJa54OYSjFJkiCOiCBIVTVlA5WpORF1VKi0a4kR0QGeI22rUsNJlmJIvp89Rm675Ee49dyzOHggsvM9RsdPEnKWhAG6VEiLaHMlJa+v797CPtW35rms+S7RLDeuvIi9u4JiSMnr4g/IAdXxcZ+oshcjQW9c3RGbf/+993CHnCtXrokN3Nx6Xp5R+OhyDXr9vvD34IGHLGd0cdYiDwBgbdjHg9/9ffktVYVeflbutbGxhueef0bagciWtBI7m6QT7O8dso2JxiJqIxr08dpteXaTK3+B9FuSJFhX5A0RYFfJQ9M/6mOayrjqsh1NTrec42QsCAufkd4h7TLsBcatExE95Jbk9klrzBkp1jZoXOcR6eSS3FG51yCibTXk0dkjIuPovfeNT0f5IXMqsjm+i0uUO9f5UHmKwgY4eCAImnPeS3labt26ZYp4J2NpW0XPzbIU61QCUznzwjhB2shvlrEviaKbezXCrth9SNTRjPxcu5e2URFG0iF6LtoM8OBIeB6U6ykeyO/n8zm6nO/H0wPWRepwdnoM12llwAGYdHM2bZV5lI+o4dwXBrVFpjOiCeeFzAf9OEbB9UE55xRhOB6PkXLjomgI3Xc0RW6IkXJSLrXVYG2EjV2xzWNyKn1wLm1+7dpVOJwf71Hlt9+Xdelo/wAe1Yx0X9Hrhvac6SklzYl86PdbHsGKFQ24bjrxJiIipBQ9NOH7FUWG4ZAKixfUnQKv3TcadQ6Uw8i1vbtJzPOdg6YBCISJI1UXU2RH1qIGKuV5I29St4uKSJ0++WYaIjADz4HPPXhSybtPp1N0uebrWUXRMn4Y2D6ATEoo58qFVRuaz+U9HfKKVEWNlLxuY/KFdTtsnyw19MmQiJ1mATlVcV+lKKfKa1E2EXm0PMLsdH9VAXDJ7ZKxHb2QdfMchHyejhuX6NPI901dydSqnPYfLveQilBxagcTIk7/4kuSBdLhWnBlZ/MRjkpVQ5tMplgnukvRhAXniMOjE+Pw2dyUfY/aRDqbGr+b7vGUJ2g0GrSqfLQF/X1el5hQhW/K9p/Txt3aNUTQ+qbUKaykXdM0RaEKc5kqugUGa1QUz94DQYycTyfWlxucy5VvyXVdeFScrHgoVd6wqmlQNVofuadvaCoHda3qmuTg4/64zBq05035nSJpqqKEz3mu4jpRc0wEoYeCfVMkRD3SFjwnNBVVO79w3+N5HoJQ549lrqMahXEbteO7MRXblmeTCpR1bpxltaejiRt2x0HlqG0q55AipyqwqZBSQdJzte6uKYg5nJsrVXhtKqRsXD+46EtwoRsst+aelKi7pqng8zklSfsCh3aZOXbGARGvWaVtUMFxJ/hO5WPhECLCFpPxmS1uaoC+y81rlqLLRVgn5c2+bC7X19dx/K5sBvuhbEYVfuj7fksSR8P96f/qZwAAb+49wORcUhTiHcJ1zSEhUs6AGDMA+M6Tm+txB+u/birWd/r9RyGp/dsoH7Wej/nhE695HClv0zR2YG+q5cG+mMqikG0lKGvQwoE/Sv3+UyQlPc7VZo6hv4EnXrSFx5Eo68L0UdN+Hlf0ek1/UAcFsOBgWXDEAeKsCkhY7HKiV6nWQdRFwUXq4UPZ1L//7ntyzTzFj33mswAAn2lCk7rdmKW8h6fSs1x88rJoXVILaZ76hjM6AbSejefYAqGHjQ1uVJM8wbjQDRjnJJ92VmWI+Y4+yVNr8uj2uh0jjNXnZtzcjAZDcwzrhs5p5Pdx6NnkrxuYHtMo8rzBjNLCRhIdc6Hu+Fhbo7wvH6iH/G6/h9OKsGAeasL+JRzzgPrL//IXAQDBoVwz7I5w9005nJ/3ZM77w/ffl7rMD/GVr30dAFBw4/7Dn/txqUMQ4qtf/SoASXeQl2CaETzkXOwVk75PZ1+e5wb7HnFj6o/kvd7Yf8/6+TU6kr74h1+ytjtiisIlHpZvXL8OADg+2rMDrUrCOlw3JvO5ze1BV57bjWK8cEukxpUwt+YmNk1T3HDk/iolfc6+ff3Nd8zZ0JB8ucN3QVki1fQPc1zLNa7no3Lkd/f25SDY7cl379y7DXBMXL92EwBwxPQdN6sMZq7yvAr1nkxaed/1daayVITxT+cIeQDpxnL9gKkZSZJY/XRje/OmPHdj57KRcnc7srbmeQnUuivjAYREsb1eD2enshG+c/dDAMDnPisEn+nOGmrajKZnaVrf/Yf3MGU6yIxt+6M/8kNS9/kUB0cCt99hmniWyHOPTvexxVSqz/0Xcr06H+7euYO/fFXI0XPOGynHVhh3kbKtNB3vzdsiROGhwZ17Yv8DkvnuXpO2i/oj1CQ6Tg+ZvlI7iEhgO4yV8JiHlabCp2+KA0jHxPZVeYcgCIwgtszVUS590+8NceTJ/c8oHX76QBxEs8kUJaHyHfapHj5qB7ieMmC2K3V480CcR1VRYnt7254NtPMPvLYd8prOR0rbe45jpJ+hHr7UyVqkZv9KRJ4kmR1E9PC6SCB9si97Lt2j6XNHg75dr4c2nfuiXs/+dnwsv7f0CS+AowfTwF/6/Z07d1pybUcPxPxdp2uOzMGazDu7TKc6OjnGBlPL7n1wGwBwmXNM7DZ2aNO0Bw1MdbwAjq8pj5QVTws7KM7nUpcPeM9et4OOLzamwYuUaU2+67UJ8ka0y4NTphTegEtHSI9z2WgwtECNpqK0Mte+kOSjpWj98EOx+/F0grUNcVgpsbiuqU7VoKN7c85zLnMui7TAdEqSeD34a3rf+bh1aHZ1nZbnNq5n1ykhvOu6RmCe0GmsDqHBYGByzFHcktgDQNSJLZXn6EQcfyHH4Mbauu1bOl35W71AzJzxOUZSvkDYq/2sqTaNnii9xpziJe2pUgLeqrb5QNM6Eo79buSb3WsPhNwb1WhMBGBatmujOudszWf/JZOJ7bGMkJmn0zxJF1J4mPbG9c91XURMp1sfkjie49lxnDZl/2Lg0nHatC7+bc7OLMsSSbKcyqJCDE3ToGJjeZ7OO3JtEAR2PrN0Hq5vdVHKWoOFgKfntkTF6gTnepFlmbWNih3oPqsoWsl2kxxXMvCFlLHiwjVra2vmONWi7VrXtY1rC9Cx7R7ef2B11rGnBNKu6yJkn/RYT02Nc33HAoI6X9kcjZYMvVxw4lZsd4/3irgmXNraNsd8rzewNgLEMdTU6vhYdig5aKAzj+Pq2cGxK81JrJ9KLl239dTyuAD/xbPG4lhUG9dAqef4rZPeHEOLv9N7PWqrRvWy8NyLxxzHwBPVwjm2tVt5kGOE7DWfp3bl1I7R22jF2vdpbAzggiCV67TO5sBbvgZOZe/YKDm03bJGY06pZbBHXTcLZz3eyhAozVI7P6msUsZWZVVWZVVWZVVWZVVWZVVWZVVWZVVWZVW+z8rHAiFUEMbcibqWzpFcQBsArkXYasKXlQzu8pVd3E0EVRAwCh30xVN2Phlj71AisJu74kG9c0ciIv31GA6hiwqvnBnBVse8bI2GAtSB9xRv52L5blKCHneXj4r8eRRJUz3hSv2Bhmi+cx0+SvleU5G+0zWPQ6/yBumjAAAgAElEQVRoW5b1cupY0zQWHViUDdXvntoHF5+z8O/mqcllTy4fJdXMZBcXnvHXISBfLIsooMW/6Wd9gTzvo0q3a1FExyJBsKaRXZQ9z4rcIraRpySNCuOsUNHTrTKbKt18mqSYM1JWEPaSEy3S6cQIWYe5I7bwNkmRf/C5F+ENKDWs0uELUZYef+fSdtI0bUn0VIqdsqPdXgcxUVAVn5Mz0uM3DvqEi9dEoVSNQsR9OIw+dy5Ar5MkschEU2kkRFPWXJSc3zSyrSkFeZkjYsRcI1HzRp77e1/8PK6QtFOx7xp5RMfHWiyR7y9+408BAK9+/XV89Ut/Iu9MArqhI++ZTieYUbZ5krJtf+d3AQAvXdlBSNJOnaM//+ufBwDMshxjRlQvMdLeRqg9OA3J9jQqRrSYX7sIHKnr3r09vru8181nnzEi0ZhR1z6Jko+Pj7HOlKiOI/0wOSShc+DhmGleSi747M1npN6Tc5wSUbE5FCRNXjs4ncv1XiX3UoThIO5iciaohNdf+4q8K6OG0zTHxkDeVSVhldw1CAJ0BsspESoVfXS0j0kpz1NE42VfyIcrt8GYEffkriBVEkYzx/M5hkwFnFG6tkvUUb/TxSaJWC9dkjQoTfU5PDlGGEq/aYrIKdtnlqQteSoRWgU0Sl+iDuV53TXK1J4dY8B39DjnuiQk3lgfGDJNUzfmXLfPz07RjeX+8UDaXVMDz87OLK3xlKjdr31V2hqoDdF1diBr+eGR9F9vsIbJFvcPWyQX5bjrDvpYJ0w/ycSGlKQxDELrX021HHL+2Z88xJiogbff/0Da9soLAIAHhw/RZQT2R35Y0Ith2LE1RxE+k6nYwvPP3URA8vY5UZVl2BK863yrkVtF2YzHU+wT/XZMUt4ia+eTnW1BGSlKJiMx5XCwZgS4+/cEVbWxKf32wksvWlRc5xYlG/7gg/dwRGL3PtPJEpW+LSsETAdRyLwuBVXVIGWanMpj+42HdKJkupo+1UbV+4xWX79xdakOyTyziGaWqUx3i5TQObzL+XSHaKeqrG190fQRjdwHQdDK2rPf9LvpLMMp94bHx6dL9R2uj2zevnpdxmWXSLxOWaOYa2oDyUmtPUqUrLMiA8J+gH2Oe107pkqMOxoYcjFwNU1AvovDACXTiyI+WyXYI0+QaQDg8PpkQvJhP7D0HUWUJky5zvPcUGlbTJFUsuJLu1ctZWPAVJseRVqyeWKptFOmx2lw3QsD3LlDRB3nO0VenZ5MDEWccS3JM6YbFhWiPtGYdYuKdV0lJZaia7LjOJgS1aqpQEqkuz3sWxQ9VIQ4kQuzWWLru6Zu6XjN5gmuXhU71HtrGmVZFOhoOlOuJLlEkGQlPNq0kpUvIhIUuad1HzINfnNrHYoMUrJt3V+4rmt2vM51Ke72cc5+VdvW1LnxuLb7q0w6mJLvwoFL4niV1jbUgOcuCXcAsGuX6BL4uUjEq/NTRhvXlDjZTyoxMO1e0VFZvkTiDQA0Y6FXuIA4aVN8KpuvllLjKiXmles8k+QOMCQBvNqc3ivwHTszBBcQV2EY2hioq2UUheu6di9F22jbVVVl99fvFGUWeA6aSsaO7hUU+R2GIQIiGTtsD+uH0IfnqLgCU9NypRfw4HN9SUhWXiSFURoo8lFR2a7ntGmh+TKCqWkaW2+VvLrINT3Pac+uRnei6fcLUvbaT4tZGxeEl2xf4TSY6zvquVrPclVtIhW6X9en1mgMFdjKzS8SNCvy5lGE0OOyJvRo7FyAwjSNY/VxzSBZv7pF7Ni5RdMwF89YdnxUxI6DFofpLl0DtPNTRfSRyzQvZ/E9dC6zVDjfUiZ5ZDACb5k7l+c3I0BvShs3TysrhNCqrMqqrMqqrMqqrMqqrMqqrMqqrMqqrMr3WflYIIQy5sg33Xwhd1C8uCqjFgQdNFAZRaKAVPq2E5snbUh0QeVO7XchycPUI3fGyOr1K0Mcz8RL34kloqqEh7VTY5rIPYYkOtVo3Hdbnob0Wc6AvPC7x13/1+UqeoxE/FMv/yiS2Y/7myFOvqvHPf7+C++npFnGIbQgsX2xHSrz1NYILrqEF8ojHEKPQSR913V+ArLIcZyn3vNJffmdqlHVy6iw72QnF738H6UsRY+McJN50XDgBvI3jXLpNd1e3EbdGErVyFvTNBgxAjMmye2DPeESaoqqJVAkMmabkbZqmhghYkwOkbt3hbTy+a1dbG4JqiEd85o4tnsZ2SGjJXGv174bPer+iJHOqgTmywTV5HRD1O9anf0BOTMYGc+nE0NRaQS402e0NUkRM0qu0aopCV3nWY4OeX52SEB6TDRLAcfkvXskMH54W5CRr772LRxscQ4jV8Mzn3gZAPAHf/SH+PaHct2X/vTL8g5paFHgwpf6zdkejt9gROLPqpD2W1NeqFmBPJf+nZwzUsznXd64jLBgNDKRew2cFi3mehJFU3JdbZ8ir4woeTzWeVt+d3j/IeaJRhcpGa4ko50hnJKoUa4FR2fy+7pJAPIVxESl7J+Q4Pr0CBFt9TKj/3cfPgR5/1CSM6pFX5yjnssaFThix2t9ufeVy+vIEqK9+HsNaNVNhcmUXC+09y7b6tL2JbgTRvaJxPiQUXbP8yyHv+IKETNSH6HGKUlhNbIc8D1HgxFmHBNvvym8P9tXxIb80MNDkut2WIeafbO+NUKhETBGtsf8Q5qmGF4S8YaAMvJBlhrqpKEtzJWnxfNM7nlIfoo7d2RczqYTQ/XBETSKIoS6UYh1ciitEenWJ28Y6qaVZx8KAkrr2xutGRdQRjuZTCQq2o97GK5R+pscg0qk2Qk72L3U9j0AXCE/0dH+fcwUsXMobXZ0KMi1h/eOcfPGLfl/jtm6abk1Tk8F3dQwMvjsp57HnESsDuXt//7nfgIA8OUvfxlvvyuE6SadTDTywwf7OD6VthmMBEFzbVtIlZMkQePq+iq2vbYh15wcjw3poPc8JsJu7fzMkBJdIhvVrjpxF+UBEba6RWBEO89zQ25XNHKtZ1FUFn3W6HhRFUao3EbXKUKQzkxaXhE0ipQQ/h95uL6D8kWGoW/P1AjxObli5nn+CD9ItsCxo3yUE+77lGR6ONwwuWhF0uk6NZ/PTdhE+ZoUmV7mlaE2z8bLyNKyapCSvP4mEYlwXRx/WwjSQ6LQlLemqWqL/mp7KDdEXeYY6hgPWlSZtkvNLY0ikuaJcpT4ODoip49ychABEkY+HKJclL8rJZcb3BS+8oEQ+aftmCQJhuTVm5EsfqryzPPEyIYHfRmfMbm0qrpGzsGapIr2UIRHYETOiqosy9rWYrUdtSs4tdmv2r8SpzeojLNJ0RqGKklS487a3pT5QNf+w8NDfPZHPiPvRTSF8gadHp+0+xsii5Q7Ma1qhMrLRPRWixCqrd2VD0rf4ezspOX54Jll+5LUra5rnI1lrD57+YbV88Pbwt/3/K0XAbTkxEdH3zDEk2YFROGjaCVF8irBbyfomj0V5D1KFsbLxT2o1reoqke4ciqV0fYcuIpw85Qsl2e5soFrktr8W9DyJmkbK0drwH2C77jIuN42fF6n37f/Vy4VbcemaWzNnnKsJ1wTti9vPPJ++g6+77diRkYur7Litdlmi7aROm1tbLb9zPVP14GN7S2URN4omkrRM2EYPiJ8o7+rPMfQHSZRTihI4PstLxSr5/s+PFfJxonwUeLivDDEth/q/oPI3iBAzjlW+bsI8iXtDc9ZylNDc3IBFHaGXD5LNlVt7aecO2o7Hhy4JgnPeY73rJpWDt53lR+KELLaeVSEZ/HMYv+vaCAiYmqYPS71t/LMuu7SJxp75faMqc9tWn6l2upCe4YD4+hSoummnUO1Ps0jWTm18SkqB5MiPYW/uCW0Xiy105jPIFzIjtHiOhezY/R/3AW+pCeXFUJoVVZlVVZlVVZlVVZlVVZlVVZlVVZlVVbl+6x8LBBCAzquTo4ewid8IhowP5yR0rp24TDSUzE0ohz6WZFjxtxs9TYfjyXqt33pBlKyk9+/L3+bUvWjLBIUVOQIyU0SKRrIzQwe0tTLbN7fK8/LYvmb4Ov5rurxkZBBj16zlCL5xOc92a/4dHTU996OmvO72Ceuc+Fv/PQW8kn/tsoTUUDNk1mJ/jp1NNn0xyB/LvaB67Ye+Y/CPP+4kuZUiWA0o6lqu5dFONByCM0oaelsSGT6/v59AMC7b76FOx/eBgCLlt+l2s9a3Mc3viKqVdduiHrULqWH/7v/+r8xNQblCHtwW5AI5UufRlNqsjA5OvL8kahAq2pRtAp1qkRDlRYPjkXHNSJYOeSSGPTxF18V+fKcuft//gVB4Kyvr+PKlWvyruSBOT2VaE0cx7hONawrVwSlcIOqTq7r4rf+w28DAH74h0Wd6dtvC9rj9OzMeJaOz8j/ckRFFa+DV98QNaHX3xE00K/+tvD+3H7/A8SMLF3bFhSR0+2hoPTslGpX4ZCKjkmKbiztcHVD6jc+lrnT7cQ4JDIo8xjRYDRofnqIlIipIREBc6qJ+K5v9jGmKoincsvJDIenEhnVyMj1G9IeB3v72OhJVHfKqLrKW1eOh719ifaTxgG7N6TNfS/CCWWLS4Z3Tsby3K2tHfhEmx6fiu28/8FbSOZEOhA5pVFdr86x1pW1p8uop0aqs0mJsqLcsVTBIuK9Qd9UX4Y9QXAMehI573Zj3MykbU/I8/HeXRkHB8dH8Mm/oJH30yPyELgN/D4lublmKTfB7tomrl4Ru6r53A5RN+eTM7imgiZ10Mhq1O0hI1cD/GUVnjAewKO0q0fJ2+5gaCg0VaLxGBlMihoOOaJUdabiXNHt9IzrZs6/hQHlz5MUD2lXn/6McHpcuSKInfOTUziMgA9Hsi9QFaTjk3McUnUqYMTsjMiaSdzHGrk4VG5XERcoKoD2e3AitrdNufZP/cDL+OYbrwMAnn3+OanDsdjJRn/Q8kHNW+nma9ekL+dEk+xekj568723MFdlQNr261+XOWOR9874xeaqfOPbWNcoY5LKOzeeg5JR45RcO4Yw8nLE5E8zSjxGIN96520ktBmVPz84I8opzxBSRUrX5T77OHV8BL7yZ2j0k6iWsGMKR/o5TzOTas7IgaMR9DDuwifaSxXf/FB5IxxkjKprJFxV3jzPs3s43G8YmjxJLdKuHD2LHHeqkKbID430b66t4/5U1ppbz8mecJ9IsO3tbcyIEG/5GDQS3O53XKLzCkZ3G7c22dykaNE1/XgZMZXFiogpEVnUf3kNR1lZOxeKdlH586aBz7FqUfyiRTkplUcUqSoRUUTjFA6j8CVkPhytbbHuHvJ8xrrL9WfkA8uyDGvkLotHyiWmqlUOrqyJveu+zDhR3AABUYd9ri+F1bM0hIvaepYVhkLRdtC5tigzGyeFoozk1pjP56j4jn2ilEq2Z1FEePFFQdco2kLlwvOyxIM96XNFH+rn7qXLphRaEN10/4EgDafzmSkDzolSNRR0mdt+OzQUkKy7s9ncUBrann0ih/0owJToz4GiZ4vSePiUf1GRe02RI51yPLJNE87bVVXZfHNRbakej5f4DHlR+/0Ffkn9LIrC+Odc5enMles1hOMqqoyoXeWFQoOAYzwIqE7IbWdeFpjxnXVu6ZOjMPICe7YiaNKqQujr34g01PETVYipjKh7tcmEiK40tzlCt7z6vKpqVdDcx/AgKTfmTLmLHEU5+e0+mqu/Src/2Hto84buP3zyBhVNjZS2YtxzpvpYPKK6tsh5pP22yH9UV616GQBErgyKxoGhknReNMROVdl8ocW4V13HxuDF80HTNK0E+wX7cD3PuKEu0tg2C89ur1/kbF1G/xiqpyyemMHhuq6hspd5hRZuK1fy307LB8wPXeucxmnP+c7F3zvG02RnXb2N49g/TBWuWTx71YuXXziLcd1T5UJFJqE2NJ89VhX73AYOUxR85SGt9ROonWWeJX2y44iy7XcqHwuH0OxAFuUodJHPhAjx6EQg5WuXZKHuRhs24SpxI89p+PQPvYKv/dqfyb24gFV6GEWNfUKmRyQjfe8DOaB1t2r0t2XDF7JTFD5X+qVJM+pzFXG1aNwXD/eO4zxyAHda8dCnukAuOhHqhbt/z6liTyCQ1ics3W+xnk953t+6c2XRucEX8WncllLk+49Ahd2nOEWedP+/sXKBXA3ud35GjcZIO7/b8jRH0MXPx8k8frdFSdyUOHA2nSLiuLT0CR6EnKrCcFs2P//05/9vAMCf/dEfAwCKWYK1/nL6lMvcnfHpGJknC6xu4L41FQfRT3/272Lz5WcBAPOJHAh1nD68dx+967JB9TiGZ2mKPkmJtR10AUySBMEF24kVguoH8JiG+s49made+9Y3pe5FgTe+If9/85ocxH0ehouiwjtvi2Pmww8+WHruZz/7WZNOfp9y7vkX/0jaynPt4PKlL//pUntevnIFb/KeWrI9uU9/cx0x5zedqN7/UBxko7CP65tykJ7T8YJBx4hA19bl/e7cEYfEoNeHM5e+m4/p3OgR2pw6iGgzA/a3Ohqa2sHYZVoe4cgzbf+swOXLQmK6tinwbUXFbm1vW6qYEkQ+ZBqPD6cl1ONhdkjp99PJ1CQ7U6Z5HVPy3KsizOnkUHlPj7uU9dEaDvekT+7cZvpIv4OeqxtYbuDo6CrLDCWJt0suvmAqS68/QETC7vOxOOeUBNTvBwg6JEsk9P3o6L611ZROvbUdOZBpCkyRZuhy81hZykxodZvSPnohU/V25LDhOr4FRpSIMWGanON5uLq7xXdoDw0AMD47QshUg2Sm17cHhJKpUS7oMEODkzNNo8mX6pcXpc1Faxw3Dng4rwojPy251sQkdG5cFzFTUlTWukuy7qvXnzXC53s6Bt/4S7kmjm3eCPm7T35SnIk3b95EzvSdux+KszmhXZd1gyjWDTAJzXkw/Im/++P4q9fFIZRp2iDnso2NPj54W8jrEx58PN+BS+eX2nTiyzi79/Y7yEmqbZt5myNaR7QS23q+zkNtGqUefDRV0nU99IfSVh3d53BsVE2FPp0O6gjJKF07TzJzmmk/aPpUFHgIXU3ZYJoiD0J5WWJK51dpG/d23VDHqR7q4bl2OIzpCBnRiQC0gYMg1ANa6xTT1CY9OOoymiWppSPYmsX6drtdWzdVgtoCAk2DhHNDSDuJSGKdJjOTjb/9gcyrow1Zp6bTMWINDjI4EKtcuOubU2l9S9N3pB2LvETI9Ayz9bKEzzkoCjSAwnQjp5UKnvM5Pc6Z3U5sogrNhTErY4ypPGxZ/S4rK1uXdanXNo7jDno6f56pmAudWmWNWaEpcEzbYR95ngPXmp1EurTLqBej4YElT1sbBYC8rHB8esgakqSfxPeduIu6VKeZfM7nczuMO3yOtnVZlmYzuYo4kLw6CjyM6MgxEmqmwNROm7KhDhB1bMB17D1U3lpJdsuyNJvbpF2ow3A+n5tTvMM5TMdBU5cmka3k6GqfoR+YjeuY1VT3wWBgpLWvfv3rcr3Xpgm9/7akHWq6eC/qWF1zBsKbSuqQpSmCjvxOHU+635nP58jmmjbPgzdU0rsxO9Ki9llV1YKse7B0TYPKDtdqaznHHRaIiE06vGqJiAs6uJSIXMnS66gDj+tQj87mUT9Gjyn1mpo8n8veocwbhJ1lom/lB06mE3Ni1eWyQ6MoCptTHHPG8qMs7X20f7U9zs7OFsbVssjKeDKxfZsevpSQu4QDR8eno8TRBDSUBQJtY44hZ+GQf9FBUxZtuuBFh4uQKDMlkONF7d/1PfjO8pyia74Lv70H9/me2873vgpJqIPc2q6B4yynTS2eu3K+o+ZfmSNj4b+FepKUMLkqEWhapDo33AXnEfv3sQTSjTqs2nap6wv10gVmYT2zOXbhLOYuOHmA1q6kjfi7Ur1E+kZNe4a3r9Tx2BJOazu0EZyW4Noxku2FIISNRz3PaUW8R51g0DXTX+rDJ5VVytiqrMqqrMqqrMqqrMqqrMqqrMqqrMqqrMr3WflYIIRuXZWI8cZzu0hJ3PfmHcrLHkgk99kX1pCp9CM9jDUJXF946SUE9PJnE0LR+/T6e8CU0F3HF0/5kOlo0+kUfl+8y11Gk7tbEj06Ssbo0GPaJaw9q1rY3ZPQMo9Foiz87YnpQs1j0EYLqJLvhjhapVufXL5zmtCTiOUeW5fvAGp5EgKnWUDRPO6ax/2t1mg/P5XkL45jg+IqUkTJJJumMQLGx9bvKf/+XkmlH6m5eqadpyOSvldSae+CfPxiJOERyPACAbde91FRUk3rugbQImrgOAZv13QOhbnH60Nk9Pzfe1dQKBtdieZduX4LFUl1T0ie7HiKSumafGuP47mzZlhjkxE9mkg0/pDpDw8O9vH3mDqkEF4/Ck12MWA0YEJSzU6nY975lNe7axIdrh3g+Fzu+yu/8ZsAgK985S8AAC89dwtDphdR/R33PhRIue+4yIgMmFGmV6NqB3fuWzRYkUKKgNrc3LQ0sg/eklSxiDLQ+3fvP9Jvl9clQl1nBRqmmxSV3PvapiByzk9PUDPKsvuskFW+v3eA9S1KrpMEfItkvk3ZWPrUyRnH14BR6+IUV64+AwAYRArLZpu5IYYB5d8Vis5I/a1btzAmykJh3BrF7na7ONgTZGjMMbzJujw8O8IW3zFnGlSlKJaqRp9ROH1eyLltOPKxBvnukKS1k4l8nh8E8JWwkZGoum5AXnA0hPmXmp4RNOiGmjLGOYW2fXh4iPVLilaUSHZTEDKfRQhqkumes284D/SGI7x4U9IYfCJpvvn2t6QCTtkSoCeaBsFoZlVinYi6zXVpozVKuJdlbel0bbSQaK5ujMmMxNi0Q227AD4cpg+m82O+i7Sn53lotJEUBeC4TLhrofkOUQfJfGby4CVTKUZEWniuh9oRlNJ0Jnc4n0idimQOMOJ7xBQu+BI5//o3X2sjluzfnGN/d20NBdOSrlyXMa+pma+/+W34SrhZ6thQxFUH51w7XI6vQ5LZH+ztY5uS3Ep6jW15RpPOMSEKbW1zjW3sYkLknUa0T/YPrf005dRXAlJG56Mowow28+BBizyQawMj3w2JbNzYEDsZDIc457j8gIgpRZf1un3MfXn/SKWkGUnMqxoxU+g0tU3FFuq8xiyTd43Yp8FQnlfXQE4kWGlyxK6904ARe0UDXd7dMsLii6nMp6enRuCsaWWlEiUvbEeV1LViPbvdniH97jH13wiyO6EhrGYkk92/SyRe0xgyQlOBtI3jTohedxk1qmlycdTBOft0nYii+VR+l+aJETFrepKmkUVBYONLJendjmuR4g5tfEx0oO8HJNOGCQ3o/qx0HOQN00y499TQtFsDCe1eUwpdIpM6QbMk6Qy07T8YDNBhP2kfnZMIPS9rxEz1zRtN26ItdCKUeSsgAcBQT1WRIYylL3UNV+oGxw9sLoq64dJ3J6en6ARtOod8NuhSOlxtp7dArK2ok6pWhJW8Z5bO4RKgliRMr2WbVw1Q0H59RQITxZlkqaE1dD8aMXWyzgtDheRM3dX7pEUKXEiNV9lvP3Dh8W8DjiFtl7oGas4p1rZcU1y33buuU1p+Np6Y/Sl6qMkUNdbY+HWJiGlcpn1WdYsY05S7BbJitdGIc0uSL6cULf6/IiDyPIdXLyOEAkM7ZoZi83xF/mnaS2MohoJ7PUtnQ1sWkYL6XBULilj3rCyMcL6cyL00TR1Ng5J22+VYcLhmLaKeFE2l4y5NU0t3tTRF7s/KPGsRNGwPI0Cvq0eQ+Vr3YTxsz4gcJyZmgsbQQ/rOWd2usYo0qVVXXAmam8aQ+YrKSbL5Qh0uSJTXdTtWdZ7nHT3HRaFjyVArigZEi7xxLyCn6gZNqciiCwgjt7F9dCtJzweiQcS9vGVLGHHyo4gibbMgCNDU5dJ37R0ffV677/ENObV0/lHSfG1j/tltqoWUMc3+4b0edzbXP3gt/UZTNku/l7pdsGlDDy20Ua3NoY3lwFLoan0v1+qiST+1pZvbDeA67ZlXPmH//ig+hBVCaFVWZVVWZVVWZVVWZVVWZVVWZVVWZVVW5fusfCwQQmUq0ZLpQQ5nKJ7dS1viIf/tP/g9AMD6zjX0RhIRzcgLoOiBMAyN96FD7/6kkJzuvCngMUf6w7sSTXth8wcAAM+8NMKI0dbzE4mqdXYFPdTpdODQ49eiIOSjaZpHPML/qXl1Lj7vccXq8j0+42mE1Yvv/LTfPen3j/tukV75qdKCC79Tb6h6difML/c8r72vSh/Sq+o0DWo8uf2ehhDy/xrE148rggT7m7eZ+gLHANDmjGuUS/+9mCduMq7fBQINaCVylSAyDEN7TkLeg0uXBA3w8PgQv/Av/h8AwHRfxnpMMs4mKdHhvWIi8ZTAcX190yIcBeXPg5RRr3mCEaWW3/g9QdK8/b7wQGwFMf7pP/s/rF4AcLx/gB0iTf63/+V/BdBy83iehynt6MZNQdD8yy8LsfOv/NtfgpeLHW335fc/9MxLAIC6rFGSByBhO0xI8lwUuUVEOsqxQ5TC17/2lxiuybyj0abr23Lv8/NzvPUNQRfscG7S3Pc8L7FDvhjtv2MiLIb9nkXHz/cFXTm8Is8vyhx75FmqYvKDhD7uHkmkXVFUo6HMr5uXdjElqmlnl2S5ROXEVzyMlahQc7MZ6a+rBt0+I+4jEscy8vXqq6/ipZcEEbNBPoZ794TDaO/hQ0MGKc9TQk6hwfYGTogo6jK6rqijwHcx4Hy/xXsG/H2/54Bdg7qgFGyfxJ6TM1TkHRgxguv7PjbXpM6KsjllRK8bBvAYeVSuo4AR2Tju4IjE1LUSJTNV22tKBBzjx0SvJTkRK0WK+am8f0U7KUGukzBQiiL4HBNn54JO2NnZwYvXn+F3Mt72KI3uBCH6RBkouqRLlMh0koBq34aacQux+Y1eZLwN/VDGFNWP0emE0MBeThRtkjaoNHLFsav8QqS9yDsAACAASURBVHWeWIRy55L0iRK0n56eAOQYiDoypjY8if7HO2tIiNjRsaF8EKNgwzi+Mtrq5qbU8+VbzxuP1xkRaN/6tpA2Z1mGLcpMN0TIKVlut9u1uWuwscP6yXh49+13sLEjdn/7Q+ELyonk8VFjfSTvVXAVOjw8NLSLSnFrCX3XyI91zVKkiuu6trcYKC8I0RCdqGu8Khr5ddnmx4enuH9AclutFyO5fuPg7FBs7Ud/+LPyzrTZt9573xABLvsoIkphc2sDO5xPA8YJFX0Ep8EsU44LRv0Xo/kc42xinJ0e2lqjSDdt67quW1Jh47siYjMrbL5XwQCdv4dRhPlExoCuWWtEEVZlil5nmah3m98FQWDXax+lJgNdoOMtk6grWnU+TQwFsXdf2vrwQMZ5liWG7ohJgOsb2iNAxj7R5T1JZuhynulvSb3GY6lvhcYQkxbdZvjZ6XSxT3SjttEauZj6cW8BiccIeq08Zy062DdkXbveKwrFkA7cMoSBg4BWqmtwQB4fz/OQUNZd22VIYmz4gaE29J6KaimbFsWt/e7RNuI4hsd6dYi07fUG1heTM+7JSRQ8HPaxf8C2Je9XzDrMZxN7R5NU1xFXFvA4KTec22OS7V+9umuIIt0D5ZQq73W6hmbbI0p4pqi0ODYuH21PRUxsrq9ji/08z+Xd80zW9M3NDUMwVpxPFZUWhiFqrjM522x9OEDMtcbhnG793dS4vivIOEUgnRJ5GXe6qPReev2CzLq+68z2zy2hsO3BL+x9PdeHUst1Od7imCIXx8fIciJjlQaGz6vxKEp9kVLTiI5pF8rp5IehISR0e5onOSZTab+EyKCYe45+v49MUS98trZtUbRk5TonRQ5FNIocowt8OFrCMLQ5SBE+RnQdBnYvRR2q/TtVjapeRrhpC6RpamgyLR0+Iy2rdi4q2zlT2qBBN2oJpq1O7JRgUUIdy5kA2oAqrgC07azjUjulcdsMGLdRHkWi4KrKEKQtFyG5Gmu0k56nqCU+3gEatkBt0vT8PZoFRAttAcs8T/r+i6XBo9AdnZdlbb3ArQQHlRFMl/Y+8u+WX8h4o+28/5hMkQUDbs/mitRpz7AOltFyiyTTmsjj+BdwOY7TZpQ0ihQC71PCXtpbRgg1TW3ZFYpuUs6uum7v9bSyQgityqqsyqqsyqqsyqqsyqqsyqqsyqqsyqp8n5WPBULoD18TpZ0rl9aMS6MKJRLi0TX3f/7z/x39nkTve554xf/Jz/23AIDrnRG2b5Gf4w2JVKzXIlmbnCU4KiTyl7mCWLifSdRv7f2biGOJBO4VEmXspxLZcOvQlFpyTzzDnUY8qjUW5WKXoxJ5nhsrunoVQ+Zln53PEZM7pWIecKvwkKDi8ypPczkDi9aVc8pgq3Si55hHXT28yj7euKV5wzXyotGIPM+trhpV1KhEXTrG66HvZzmQDoyJXeugimxh5OKMHAYahSvoNS6aGjFD0xUjnl1GieH5xgWhBO2hqnDkJQJqimresR+FcOYSOfEuS2Ti9pFwLzy4cxc3N0RZKmXDp420Wb/fM2+s5pz76q0uS5MPVW+u5yqyomX175AnQblf8qpEl+9Vs23psIVbV6g0d1f7iLnnQScwNIqqVEzZt51OjLpZjqg69BSLGo8qebQcFPJODqa+Rq4oLR2GSGdy3bfeEdt+4w2JnK9vb+Pms88AAK5dEkScKkC98a3XLeqp0uhnY4nIeGFoDvkJo0HhSN7hZDLBGZVWjo4kWvXHf/wn8i5ZiYcPhSNmjVwj6qsez2bY3JY6IGdeOiNih/uHpv43XJf54NSX9nk9O8X5a68CAHY8icq8woj4vHHxlc9/EUArC3x2tI8donC21qUO/+jn/jEA4BvvvoVvvifoopMvyXu9/QVRMQoy16TCj+7zHV54FgDw8OE++gOJSL/z538FACgTQeKsDUdwqQo0Z/ROkQXP7+xY1HozkvbTaFcvCOByTOj8o0ovQTfAxi5RkrTZWUmZ4PQUXkRei02JSM1U9tWLUKeM+n9TONmKUJSWAKA3IkcB54UHDz+0qNiMQiGn5Jt4/+0D3Lol7//yJz8BANjnnP3Nd97HuitjUNvsnHwroevhkEgWRXMq2sAJQnufPJF5RBFo2Z0J0rm84+CyzN+KJKngwCNXgPLLqbLK3sM78Cgzs71BXqNTykgHPjIii+48kLVh9+oOippcRQVVwlT/opyDoigIOL7SjNG/UR/dfJPvIX+bHEs0eBb4xu3QX5f2dBMirSIgLMnBNJPfdT3KOc+SVhqX88CoL7bz7I0XkXAQVnPyYtCG6rpGwrlFlVgKcl+4LlBTFS5TdAjnplmWY0i03NaazN9dRpOO9g9Q9IjmS6XdB6GDnFuHvBJbc4hEGq71kYyln0+PZbwoR8bOVscifork6sWUFXcji0JOKlWRIzJs2MUZ7cL0bBVpFPnY2xcEx+lt4Y3RqOT62tDW4t0tmd8KIt/KJEM3UJSi9Jc/lHFw52SCZ7rk0+E6Nua8v9Z1Mbgm7XhyKJ30yZc/CXANXRvJXFY6VODzIzSV9gFV2jhnlnmFtURVmcixcyLzR9ANACKzbJ2hTURlF+/elvnKI8dMLyLCqNvDiCixMTnMEnJvlQGQUdVpvqDqBACXLm+jIGdFxnlDFafWN7dxZfsFAECu3D7sK98F5uwnHZeypyECbEPGrKIxj46OFhAOy1HgqqmsPhrIjtiOjuejS8TkCy8Lwls5ksrxGY4fkrOJqGBFpXR7MeKB2Og7H8o+88bNmwCAoq5xTFRZTMTJlCjLIAhwyL4YXRYE2Quf+DQA4MEH7xti7fiUKm0qI48Sk0TWy+0tefe6LDGhhD04JkqSziXnE1yi3elap9xWXTh4rjtaaiMXUs+OHyHleK6U3oP7pI4fmWpRQFRlyvk1yVLjZ4p076mIiTjEbEL0PRXjfI7d8WRifeMaN2Or3gPeX9fbhP2XlLnVfZ2cZxr9L4sMtS92ofv+sBPZvlRVxhxfrp9nc+MAUvtShamo08GMe2pdO0zivKgxPZB+Gp9Plp6XZClOyfelqCb9/fH0gUXqpzMZC4oG+PQPvGz/P6HNKLKo0+kgJ2IqIpJgnbxIg06MmSvX1zE5n2jX02RufRN4nKsd3+aWcED1RVX57MTIqKo3ocKlw71rWdXI+LuC6M2KY2MQDZGSm8tle1Zck+NuhJSLfq52QY61Bp6hiXfIIxj7cu/jkwxlTa4i7l/sLFE1Sj9nJTRUi2d75Iyw2ElGdT43QqAbRdpXVQZYJ9+PL12JmnyHZTXDuY5nIsCee+UVAMBR4drevUM1yx7V7sK4h0xRK4QVL/JYKSeM6yqyn/ZfAR77ST+1/ZOisD18n3sh5TMqstL4+FQN26etz5PMzirK5+XRjoumRkibURv108K41RRM43GzEgZdm+eVf1FRt54HEKwMn/OIITad2hBFFcd4EJH/Lk8B7nMMJaOfTdNCuZS7TBXI4KDUM3PVIp4WP6X9eAbmv6uqMtvpkYtwRpsPnNrQaLDzHM/VnY6dH/VwOZlN7TylKF3lk+rGXfsuorqm1ioMO7Zm6bnY5TW+7xsqrFGlPqLa5pOZ8bTlF9BHtVOjpny8k8tc0fIkurbWu/yBt5DtonNSSdiQDi0HjWUhWPYNr6nr2vZXTysfC4fQT/3UTwEAqvzc/nbnQP7/nC3z3u0PcUbJR5dQzQf3ZAN4/Ud3MSS5391MJvrtNSWDm+GIk7/HiURJUH/x134d/xMPdJ3LskmIKNHqRBEmlLDvrckGTgk+fd9vCegiMdJSSRq9EAUdBLpgpCTzHK1v2cLSI2mcpr81TQnH4WTOw03jlPB54KkUDssFvq4rzOaaSkFyUIURFhVCl+8/W4Zld8KuLbiLku0AEHZCg0UWRuYoxu45rn1Xe8HS7+qqrfOcG+7dXXHIHZ+fIWV7r5HQUlNbKq+Gy0mP+1rkhLhFXdccVQ49LbNijI7KoHKCun1f0i7e+LOv4eq2HEY/82PiGDgnFPjw8BAp7UglvYfcJE7OzrG5KQe6QV/qZ2RfvgNfOYyZMqMQVKfMUPDwZdBLpmIUqDHivVQWVB2BJydH6A/kHroB2eaQnk6nKLgxfZsSowMuXrvXr+GYDpdrzwh5qhISjycTbNDJccIJ6N994dfxxT8RaXd1PsyP5bu1oItrJE3d/YQc7p9/7jkAwO/+x89bn//jn/05AC2s0gt8fPnPRAp977YcwpSkdTpLkNDu19ekPXWePzw8xIDEtwXh1Rltb2O0ZgvnmGTUM3UAdGLMuIi7MzGQDjfe/+pf/Ru8w3SOl39ADggeD4llXuEHXv4kAMChjR7sjVDxmb/9W78DAPhDSr2fTWco2OdHhKkPPemjjbVNzOgQW6ez84ww/t1L2zg8lu821+U7r9s6TXXTM+LmQsdgEIZwuBmf5ITAQyU4XWQFxz+dPaANXbtxHffuSrsb+azKXE9naAplSGa+ERdev3FQcLPqcAWNMgd9phw1TMOrSVIah5HZ5o1nnpF357/3HjzAm98W8uPf/X1J553RodkEXWQc60dckFQ+fT4b44hpUzpehutMvSkKnBNGH2iaI6G/tR8Y/FjJZHd5QLt6/ZoREKvtldwQDJs2nSDgjuKEtvfg4BTXb4rNfOYl+TyZnOE2ia05taDI1Rkboaad6zynm+t6XqEKuUDz/gElwWdFivGR9GE/lnl7Ux3maYLtHXEe7JCQ9YM/EbJySc/lhplOi+eflwO57/smd66k157Ox55nm8IpN8Yhib9Dz0cyWd4UFlz+O93ASN/HnOM36AzeG5+Z7fSUkLUq4DOPLNfTqOvbZzyUfh3luoHWg7/TShLrAdVIlGuT605K6dM519/57Axxj8/jQTfmIWp/f9+InEvOxzq/oqgw5hrpc53pcN4X5wUdcaWM/cs7sn7cTu9aOioqqd94SsfreIaTGQliXZnn3ChARlnfkxlz7ejIqBqgqZlKyINtNZXUozQpcHggc3iXhy6DyI8G8HkaOpvKAUk3lXEcW4qUBnw0PbWqKkt70pRM11J0+kgztRnP7gUADx/s402mYnRI/qvS7ZNZgoesp8okaz96cOBy0xlzXDuobW2qLjgfyrI0m1ans/7bhY+Q7aCpInqfoigQ6sGRUt52gAlCczhNmeu4fyhjuTPp4EZHbHlwWRwSORf1IGvM0auBuU4o7R9HHfihfLnJ+cblfuT5Z57Fybk8RyXDldS6dmqMGPTQFKaqEyPk3lPfWee0XreLiPuxKdcel0TLJ7MzDLm/jGM6DehoODs7Q8ZDfMg9ifbp0cmREeaq8ELKuamsG6uDpvvYAa1qLDCk91oUMdCUiotjOM/K9uDB8emrnaBBxr9VRqTNuc1zkdH50KVTWwjXVSpZ6mDS6FXZOg853y/uYU32vWoPQfK7yvqpzepoA3Z6T71Xw7Hkuq6lY/icRzUF72x8aumNZr88O5RNCZ/9m+g9+bwkmeHKFRLW0znVnh0cm8tD1qF22qBkQ8dARFt3owBOzvWS54mK+4iyrtBR6XWuxXP2w3w8QYcOZN2Xzensd5oGUYcONU9+r6nCYTey4IOm8O8fHlubK2n+I3QCvo+K6W4atC5rJdJ2rb9KzsMFHTzVpEas+wFH5ysfOfeEnspv83OWZIhCqV+HAYcH9+XM6Dc+ulzYw0DGlM9+Q1mgYB3Asacq5KHn22E8MUGClnrB54U+542Szv+syW1vrHu8kv9OqgxZtpwOpuvTbDpFh0TaQYdrkHIvl5UFRGrdhxSZOXaCUM9qbfqqkmXP6dxvnDZlSfPSi2bZedA0zhL1BABUVWvrtX6nP7MUqUVpdHfpb67rGkCiYuBBSferqk1tczhuXF/z7l2UDGKkqZ5NuYYF3gLtxnI6VJZllh6nzpxhr2/pezsMQhs4wmnTt7tMV9Y9dlEUyJUwnkEInWvqurY6sEswm0pbZ3m6IO7zqDNGgyoh37VRo4P5C80hZzZQLzjQuEYaqTfaILIRjavT3vPbNn1KWaWMrcqqrMqqrMqqrMqqrMqqrMqqrMqqrMqqfJ+VjwVCKGZEyu8Pzav3yXWB237ylU8BAP7ej38WP//zvwgA2GPkq8g0Sj5EvyfRn6IQKLUSqWVVbRFphaCmqXjtrr/0Mt65I8SJn4jFY/jnv/cFAMA//Cf/PQp6Ywsig5SYcp6lCBjVUbRAnxDboq4QdeT/jTByKJ7byTxFTdKnKWFioWZPuT66/B2m8jzHLVHSO28eZPWOe555O0tGJ3OFDIc9e3ZIr2O4kF6m0SL1jhqSKcssYthlepxB5arKIksaxTCSNd/BnBFpJ2dKRZ/Ih7KN/kwI9fMY/Wg8ANEydLKoSdDXOJgT7RIz4ttEtaGoHEbAdhht+QZq/Ltf+VUAwL/+tX8PADignGq/EyEm4uOIKJt1Ijrm07FFxRQZc4lEfT/7sz+LiuSnU6KNIuI5o8BHTs91w4ij25V2OT+b4L1v38Zi2XsgNvt3f+zH8MY7bwIA3n1TyJC3N5kKM5vjR3/yH8jz5uJlfu1bku51f+8h7tz9EABw46oghBTZdHR0hJd/6IcBAP/XL/wLAMBXv/Lnlp41YjR5RKTb2mCIc0Ya9/6joGT+8Pw35N0vXcKU9vELb/4zaWuVje20Us0gzHTIFJNBGMNzxDaTkzHrJ2Mq2rxs5IUayR3GCv+cIWekwuX48omCOzw7wWXeo6bLfO9A4N2D0RBXLknkdzYhsoXR8jzJ8Na3pG17itxxgYjtMGYqQEkEg1s7WGN0ZWtT2kyZgaOoYxGXmHDdMeXLD/cPcJ9y0RsbUs9LRKUURWHRgW5PIaVSpnkKl/ZrkO2MUVDXRX+NhJyM7qpdTidzHBwzHedUoq23booteHAQazSIstZjouFGww2Dax0SWj5AAL9mCqKmbfJzXpeYkUBUo6ZDkrX21oYYbojdWVoqx/fNm89hzohvyrH7yideludd2bT5YkiUzDEjxnt7B0i2xT5Oj5gyQ8RJE0XIOPYczk0Fo7avvvaatY1CeFW6uUGJlMinvGEUjxFPBwliztcb6xL9d7wIsymj1Yy2hkQidHwHE75PSkl5JXsu6wBTElkmRKMoMsx1G0MNKSpHM5682rfU0cBV1Ke8l+M46IRS1x5tWtEaBwcHKBtKwhNNaND0uJ33M6YghgUjzp5vqbE6x2ck103yFCOitWZE5eicmDg+Ot5yZBpNCTdgRP9UnpMypWs8PYdPROc008iXRoBz5EShtWlDGtGeGCmrS4SbXnvj+lVMjnl/2v3upyV9591330U8lD7fZrQ87rdpmBnH+KWd3aV7vjc5Q1HI83pMOdU5yUGDU6KKd4ikvHfnQ2mzeYJ770ra5aUXZJw+PDxAwPlijamIpycyTx2fHGIwlL9FRJxoakncHYCXY9BjipkSSDdARJRvQhTx+eTM6qBo255KlVeK2sgR9+S7DhFX33zrLT6vbwS4OqdrKvRkMrHop4bHAyIF5kmGgtHS5iL5quMgYkqbrkcdP7DUcSUcVeLkqi5REMWn6Uia7unWgUU9PV9TpOS95tMxzrj3UVi87l+24w1D0vYJNb6xrijVGg7rqmNQo6f1IMRgnehNy35orB1HTKV1iQC8d09Szja2riFnesD1W4IsbxRBEriYsa1GFEFx4wgu1xCP+zhNvo6jEAkFTe4dC3Ksx/3OeDJBsy6/Gw22+Dum96c5ZmzTOduzN5A5LSlT+GhlugEgzbTf26i6Iqw0RaipS8zHYmtzEmObvHgU2f5PkUEOG204iA25VDAVThEynuOg4FifE6nsE4UxGAzg2NzQphHqM3Xt0e+iKMI50eWa1qHF9Vp7b9E/RGH1eoaSNkJgzmUnZ6dtOh4/bZ8bRUi5D9PUr0VkQP1IGolnz1N0wZBiB4aw3duztKsbXLsf7guabRj3F4itpR1rpzbyXV0/Fwmhdc0IuMfQTcZGf4C+zoN8d0Xb7U+O0elJXeeafm2ojdxQ7YoWqA3hGaDgmnF2LvNb39UUxgCOnpc4xyi6vi5yBOwvXddTUg7EcR8xszu0/RQ54ToNOry+wz7dm5/BUdUGOzupkETc9qWiVxR1WsxRcD2HorInfE4QIuBE3BsuixAEVWsX2ZhISKYbVU1piBYd1zr2t92BpW93NXWO7dkEATwoAkY+FBHm7niG8Cw5HSdM/c2rEoHf1btI2waukTybbXI+TbLU9gaWQso5dzgc2p6mRXLxPOguVIylWCDBVrJxI1peEFvSoqgVFS9ovAWEzAU0kIdFAudlFGLd1GYzmtLlL6COdD1z0SLO9PdKxu525W+XL182aXkdE0Wqwg2OtZ+us0bF4rpwdbljmlej59SyJXmudC+Tt2f00ZrYSsKxm3JfVtWNIeMKbRrL/WrbXlPouCzC8V00RBp7pRJkE9lVNy2Jt6LuFM3labLx08sKIbQqq7Iqq7Iqq7Iqq7Iqq7Iqq7Iqq7Iqq/J9Vj4WCCGHcrtABo8eOM3vnE/FE/38S1fxD35ceEH+6A+Imrj3IQBg57iHK7s3eY+vyp1KeuJQoyKq5IzRz8MT+fcrP/ppQ6b89q/9EgBgtCWcKn/vv/wZlPSaK7loQVLItY0BPCIW+tsSuVEugCgMMaUUdFcjNiT2apymlQik13LvriBJHn74EJ/99OcAACGYR99U1jKKiNEIVlM35knWXFrHbRFMGgFRLqBTcvv0B7HxjqgbXaVyvSDAnKSn6qGN6N123DZio9FBlRzujQY4J8dOyXtpnrTnN3ACth/rGxHJ0Il8TBj9/70v/AEA4I//6EtynzSzKOhwRM6ohw8R0VX6d376HwJoeRU6QYg335ZIqPISDfi7Yp4jYK70zjpRK/SGb24NMWQUPpmShPOBRGyeGW3g7VPhbBkQ+aFe7fPZBFuXhXPi9XfkuQX75ne/+Pv48uflPW7cECJ05S761MufwltvCT/Qr/3qrwEA1vmeZ6en+M0vfBnAgnw83+/38i8gZaRgkwgSjYC5rovk10UmvSBH1fPXnkdGueH5wTKPRh50LYc5pjzvteduAQDSeWJ5x1rnCbl9tnZ34RzL2Bl1eM2EBLxehSHl1WeV1OGMnCzdbhc+EQ4VCUs3iDJxisK4ngISATqM1kzPx0bup3VRdFpZ1vBJ5FeRO6cmMiHwQvTWGflmm9WuC7eR31ac9lz+Hk2DuaKMQnXXyzXT8dRyyO+QwHJ7W9p/OOrjBrlW9vdIqM3o/4svvmgSqCoJq0EXP/DhaF7/TKOmSkraWOTWuA3YV/P5HFtb5LNQzhZGE8qmgsPrFIFTufL8WZqgZFSy01Ei8sDyojUyNyAvS1SWxuU155g/JwfDw68f4HmSsnpGgi9V6XYjm3f2OB94jAYfHu7jB18RVMeX//QrAIAHD2RsjUbrOKKk85B1uEqU3rdvf2gcCCpFHZNc8OH+Hq5elyirRXNYJ2e0hjn7nkuKIYW6UYz0TOr3+gNZL4bDNYCR4TmRIxptjfwu1knUvXNZ7Y+oqqJG4FDGmWvWwYGgxhq3MoncRudaciVt9oa4dE3WjtxRNBpRmahbvinec8r2nE5SVFyH1tdJyEpC/163b+NEEasaQcurFH1yhkzJ0bXNPPpmfI4ex9kREVpvvy1I21u3bhki45Sy391+FwUJrR8SsTcjGquocrgaDaylfibtito4jnQetYhg1co2D0cyFpSjrt/toyT5chSoVC7JTM8nxvFyaZfoPg2+zj2TXta2NfSAGxhZs3LaKRrgyuVd3CeisxcLt9rpuYz9PM2wyfnx5EA4rQbra2bnx0TwKf9R6LU8cEok7HpEQDUzuKGiV6RefUbLPQSIibxTdMnO1ct8xjE8hmWn42VCykG/Z6g5RUZ7bLNZmlg7ZEQR1kR1Nk2DLXJaLfL2AMJBooSqSlbZyue6j8gBHx8fGxLLIrZED/T7fZPC1b7Q53Q7HTjGn6g2RPtoSkPJKVmo2vNRMkHBaPosI6+ip7ZXoMPoe5/zou5tBkGEkmIPSg69syPrEpwK20R3aAR+GMre8OvfetPea2dX+kRJ3N0owCWiUlWMoZzPUZI8OVKOL6IV6rrGEZHMug6iw/1VN8QJkRTlnvBlKp/rYDBAp46X7nVMXiPP8xDaOCOyBYru9m0MqQ1p1By1Y9LkGpLW9/QcB1OuIbqf7ZMr7Oazz+C1V78hz1MJ8YVIfcs5wr5kP+ZphvW+rKVzzklhFLWiJcUyz0qapi2fIfcyykW4vrZj+5bDw3bfAQifpa41SvKq8+oiIumiNHpd14bKmTKyv4iG02ffvy99o3NtEAU4OJL1TLmlFjmdTpSsnKTGp5wzQv/SAgpL7hV1AriBiqtw7BEFMMtajhJFi+p6E0UBglQRGBeIfqsGTqPIOxkvKcfkoNeilBQN2COitEZ75hhxfek5JBGfzuCqRDz3Y8rTMplMsMX9nrZ13Sh6eR0DItl1L5PVbVaCS+4hR5Eg0QbOiMLRLJAOkVCj9U2k5DPMuJdX9EqapsZtNFAp+lQRXi0iK+zLuwfcZ54fn5hdKKK3y3nH8Vxbx/S8Gmg2ShgaGfiM9p4yoyXLc0O02HrETp3Ox4a86RDtWLJuRVWiipVcm/vpaY65S0EZIjyVI7Yqa0OWqD3pvtZzXXTYHkqGvMgbRnNC5HeWft80zSOiACqJ7jiO7dUM4VMr6tdFS4ezzPcj8uxStL66zrhNy9tYcw3yWG+naRHXyvGlKNUgCNDjPKf7CNd1LVMh5D10vBVFYe9Y1IqII/Kq220RZ1BOINa3Ko1kXzm36li5pkK7v54B0uO5tZ0hchfagQ9u729rqwpGNUZqrj4IbY/A85beFWj3Wb7v2pz5tLJCCK3KqqzKqqzKqqzKqqzKqqzKqqzKqqzKqnyflY8FQihLJcoV9xwk/P/0mPny9NBX6SFuXJZIcADPnwAAIABJREFU20/+/c8AAN4TJzz6cYxXPil/+83ubwFo0RNV7VmEXZXfNIc3qQoc70nk+94dUSz6Gd7nW699FZ/8O4LYcemJU8nWr/3lX2DvLhXOrghC4LmbzwAAhp0hGnKioNTn0rPZCdEwOqiqB1/8/OelTofn+PqXRer6J39CVNc+9blPISPCR1VOlP/BrWvzHGtUXmX9vK6LWSGe/5hR9euXhUPh5OQEabksv7i1y6hY3Zhii0pOlnmrUqZRMJUZ1DrNqwQJEVkeERbbVyU6cPv+fZxPpS8H5Kl4cCBt9+pffBVT8gK8+ppIohcpOXq8PhzyX5SJeEQ3166inEg05etf/RoA4JgR95HTwQvPC8pFc+o1MhgUJaaeSmFShpx2FRUOsnPppzwR7+05eSr+/S/9Mv7zfyRIpIRRiw8ZOb787A28+q5wAf3iL/5bea/b0qeXdnaw25f3r86lLmuhREF+/Zf/A84ZpbpyVSKOqkqSFQ5qQsJUqWvQbzkeNKKdF8q6r4gtB85MoyUScZslc4yVW4ARS5XunO2foEvvt78uUbT9PYn0R1GEvJD+Um+6evnv3r5nHu/TufSD2t76cIBTRkTX+uTjoezudDo3z33roRc7e+WTn8AZI6Nfo4z8KTkVemEH5+To6hAVol78sqjhM3KuXDn9jlzz3DM3zCO/Ru6byXyGIFrmVVAp6fX+GuIBkRjkUCjJz9Dr9U3BR5UBb916BgDwg5/+JA4OxVb+v//3X8vvyVM2PN9qObpCVShh1LWhfCRgiDcFpXhegJyKEBrhPDyUvtnY2LIIv6GHGCna2drAKSPNriohkGOs8Qv0qXylKlfj6czyqLVNPfIQrEXREp8N0EZrR+truMS+PD3hBMy4wvH5mak/ubSBKaNP77z/AY6JxHj99delPXzffq92of2lkY0oihCy7idsB1WtGo7WjZukVZtQnoUKVbUcbVXoSDwcodvnd7SFTtdHlSrPhFynioJlWRk6T/Ps9bm9boyEfBtTjbaSK632gYIRzim54Gbke2u8ELvk7nA7qt6l4TEXDX+nXE7DISXiZzNDwFR9jcIZ5BMNlVpqGlRZtNE8l/Z/+FDmX41QN3mJ4z0qXxEJoxwHfbeDg0S+e0CUwuVrVzEhwkSl4QNyHgVRaPxY/VpVgojsCD1E5Aczrgzlm/AD48SLGFlW5M7RyRliqi1t71wBANzfJwrL800Z8CbRczr2Dw8PW6WQTKK6Xq3Sy12URPPV7G/t0/XNNey5imyTMdXri12enZ3hUy8LL9YX/4JrVp4g4fjY2pKx8ROfo9Ll2SH29sl5SBYJj21QZqlxXpVEfSUlUW1NZNwkqowW98T2+t3YIvX3S6ITFvhFFEFn3IBqe5MxNkbyHoruqwtFIkStamizrCTmuC3vjGeRW+V+KVC4ywihzfURptNlDgrdV3S7XRvrqialXA9rowFmicpoyzUpkSNR1MFoQGQsw9em0OO71s8q0Xz1+hX7TpUHfVNZ4b7ifIrdK3LdMCY6lXNLdzDAdXL1lURs7qWCPPnPPvsZPHwo6LBbzwuC7O4DosX8Hk7IjZadyjrqZhnCdbE/RQYoCrRMMpQc6wNyk0yJcvCiAKUvNpNxbumoYuL52JBBiohOK5XM7qLiPKUqdCrPXJaVtV/E/Z9y76Rpauu78n6pWlBd14jJwRdRwemM6LS/+tpfGr+HqkT6dct743H8d8PliHiVZDivzuz+gNjsmOuSzXP8/Ww2wzNUvby0uWHvCkg0XvlRgkD6VNesqqoMPaTISZWdjvu9RxBJelDoRhEGVDQuDZ3Qqoh2uRe/du2a1R2QfY/OgboHUp7Ebr+Hly/JPKUoBX0nqSN5RRbm7ZKcPgnHgvJ+OY4Dn2g53fsPyOnoeZ6NsxERLe2aUKAqFHHJ/Ry54LoR4PrL0uYJ0Znj6XmL5iOitI7aca4rsHIuGtyjbqx/lWuq61FtFlNktHdVAi45N3XirqmNKiJxpz/C0UPZBzREm4dSFeSnE0PXxLTxjOuZ4wGVngPZxqo+Grh+O0DmVCvlP4dR3CKmyDWnyB00QE5kbEVZd71nNc+N46Xm/NvoNqRwrA76UTZEPU3nlr3ghZzfuKZXeQOHCmwB5+iqcDDnHKH11HWjqCvbg+q+URW04k4XnnK3KS6E42eWzFFSuswhV47toeoafri8hqvdu65ryPdWeYzrhucZekjHl/LcVHWLurmIjPY8z+ZKQxtB1ycsqHhJ0fkgCALktKuNDZkrxuOx7XmzCwppQLsfNU4fRadWLjwalqKvlAetqV00bL+CaNNF7iGdk7StdN8eB4GhyczYtDSu8XnqvKH/Rt3AZRupkrfO2S4cU2FVFJeWGo3tBZ9WPhYOoQ/uygbubPoAl3ak815k+lS+Lxse1yuwscmNG0kWNzlQ3nrr2wad3iAprJer0QRmiCE3AmeURj7cP8DxoWx2N66S2LeWe//5n3wJ79wXwun3738IAGi4Qp0cHZss6pczSfH5n/+H/1HqfesFbCtBJ9O03D5TabI5/s0v/zupw6lMauscqNujTcxP5B3GJ3LY9jwHFQ/xw6FM6irrnpxPcJWEl2MeqJtIScE8DDakHX7nN34TAIxQ7ZVXXrEF/Zf+/a8AAHavyILme44RXnZprJryFAQBTglJDriBW+eivNn//9l7sx7Lsuw87DvzOXe+ETfGnCuzRnZ3NWkOItmmTUmGZUCCYBC0HwxDD/5RBgQYfrFlw4YEkiJESpANDk2RzR6q2ayqrKqch8gY7407n/kcP+xvrRORTfarC+i7XyIz4t4z7GHttdf61vftY0y519csxXD5Xp8/+hKfsURKFrk4/ucvXmIwImmwnIhz06/rZQx413/X6nfR7RmnsM1SPUfKQCq3cYAlOML+uLV/iEta4xfPDCHoJQOOoeNhSKdcIIY+4aJ//Af/Dg9fPDfPQOLzlMSg9vdC/J//lyE5f/82Azue2YxXRzN0KDvsE/Z/eNsEDj/77BHOCBnu8qCLWghxe3D5bxnnNy9NkGk47KvTJYu9w34syxI2CY9nlDJFWWmwoeZGKJKpCH2saWS9ac1nMX1gWRbOuSbU4Ig0o+vqQWS2Mk6vRSf7+auXSmp5/NrAs4WcrR21cHhonKA4JTkgS4Ra3Ra+8a1vAgD+4nt/CQBYzGVsQvRJ+imPrgeZqkSbZXxpYn7K4aaqC5XbloNIt9tWGVAxygJhncWrK2WQ5nd7PFzmeYGK/RjQyXt1ZA54k+mloLB1E253zBz44tFj9Pssf2Jpocr1xmuVaBYi20s6vWla4CZh5mL4mw23IZOWjS3hXGh3M1hSTsfPv3ht5k6vN0DAsXn8zBCjpo6lJRjipLlTIaD3lGyvfGsTKYoCz3j93R1z+P2t3/6H5tovXyPle5Tsq6fHZi6cn4/x2WcPATSExUI8a1kWRnSSpeRJEgFBp42KzyCO9zHh/1ZdY74wtk83c5aq3N25hRVLgJYki+4MzXgskgUenbzkvU2fHb1qZITlgOrVxo6vlyskPHDKoSjnxnt4sA+bh9+Xj02ZlQTMrdBRQv3litLj/NvFbIHTc9rTlpkXwwFLdi7nStApDnsT1KoASr2vWcIlBwuUtQasJLApeG7fd3FK8lJpE5YG2XUDkXchAUDzvMcnR+gL3J+Brmi4A4SUeyYZe5cHLdtxUIrkLO33VaeoIYE0bSjOjG03h4ZpI60NAOPzib7XLa7ZYwaEgihSAtwDBr6FLHsVr1XmWA6V/a4Z0yJN8PLZIwBNwFyJdBcWbh2aNfjqyBz073IfPD85Uln7j+6Z+Xg0mSHmepGx+M6v/wYAYDTo4Lvf+wsAwPc+MQmfC9r/VRKjk4iIgnmv2jLz37N9dCKz1904MH0rpVyj0Uj7490HJiAxmZi+evL0KULaxZAHwX2Wgq3aLQ1WSim42JEirhAfJ9f6T+d6nqOiQyoOsdjVusrg0U4NJKAxvdQEgNguJZUuK7R5yBY7LPtZXhb6b3WceegO/BYskdSmryCl8lEFJd7d2zW29jd+yQTkRrvbeu9U90GWCHUDzGbGnnZY3isnyn63hzUTXg59mXs3TKCh1Q6xTXEKeb8P7hibhtrGBwy0/ul3jW94OZ2CsRrU9Oc8ut6u62CLSUUp4frsK5NoitcZPAqSrCXw7crBLkSXPonP55PEgVVUiFh+LWS1MZOZeZbB6Zj7DEl4KsG92Wym5aTNwUxIgNOG+oDP2WaCqj3awnomdpiBfK7r1AHAQ2ibiS9Zi+tVogmNkgm3KAqUiFwOQ7Ln93od3H3HlCt33zroJkmChHNZZdlln69rDQRJk7+5rquHNTlMykE1DAItb5OSKmlx3ATPdjjnJAATJylGFMPIuJakP7e2tvT9fUmmOUKI21xfhEbSNNG9UPY2qcgt8hIWbauU7UipVOT6gOzFtMcSSLoZ3ML2DsUUxD4ymYmq1r1UDtKyfgI/1HeWROBqTuGWTkft94BBKknAHRweYpulmCuW3ce08ZblINZkCctyGXzLHAe+kNczaJo7GexD81wdISWmnYNtw5UABN/ZLSik0osQLyUQzcQQEz6dfg+OEBDTn00596y8bOgzNHnBa/seKiaRtJxJAqJZiaArCUDz+b4EQqrqSoD2evlVbVVawin2wGbSys1zOPQnxI6skrWufwkapHFT2inXV7vNfiniVP8tpckO12kYta+QSLNsUGgZylKpSSQIKc2yLO0rDUjYVwJCEvSVsn66LVfzCUoK7rn6PQkwvk2qXmZ5Q8zMoGmTbAD6DFrK/H369Knud0LyrsJMRYEl6S9akZlPsg48z1NxAyVrzoXIuVnbGc/Y4jvZVg2f69Pnfiv3q+pa/XX7SjJY+jGj3ZH7VWUzTyx+QcShrgo8yLiJmIkYlRrlFR/y72+bkrFN27RN27RN27RN27RN27RN27RN27RN27Sfs/a1QAj9xz//FABQuTFu3ZDIoiFbdgnFe+fBAU6OTFlXTKTPs6eGXPrLyTGO5wZl1GUUPCmZwaxtJYkqGGl1SDCMHLAYJaVCLp4RFl8VAQ7umuzbObPW1kpgcI5KmYrE7R/84R+beyS/jzv7Jrv4L4gaWhAOnpRNxHY9NdHE0ZaJTEeeC8lB7I9M9LJ2CtiekMYxQ0ei6zJewiOJrk3SZrdvIrzf/fO/xK2bJpt1RiTGJ9//awDAw799iA8++ND87q9Nic6fzE0mK4oiRdVIdlAInYuyxCXLFwrBZRJOsVrP0PKECNv86Y/+g5Ez96MQbRJlzscms18ymjvodrBYmAzskuVPAnHe2RoqPN3xmNHqh5iP+QxEwggZXF0WGmqW5xP4+TpYCl8sbu2azMsHt0ymqapsvHhm0AKnZybrt01YvRt18PlzMw/fkKgwY6TY8Vx8dMeUDqwvTWQ5S82zHe7saWatYDT79al5z6jTxg2SJou8ukTvLQ/wXSEnNZmXARFvrXZLI+xCUPuEaCfP85DwexGjzHvdHkZEn0ike06CytwG2ltmDSQr039CDt3tdnF4aLI5ghx5RdLDu7fvICXWskjMfW6yVHLx5CsEzPzWJLmtOT/a3R5+8qkpE7r7ninrswg1fn70Aru3DRLJUal3Zv2WiWYMFkSnyf/7/QG2iVzos0xJSPssC+gQnVMQVhw5vqJPdpgdcFyTJbu4uNAysh0S1Nq1SERnihiRUow5pdtfvHoIh/N+/6bJ8uZCDOr7ik44IfGuEDmOBv2GnJzZi5r3SNYrJJwXgmyU/ri8vFREVpdlJDnLcqbjuZa07VBy+ckzg2Qajy9xwZKgi1OzBv1WS7MpQnor2YvT6blmDgVqv2Dfdft9lMw2S39KWVMWZ1gzC5GwpOjizKybwPPw/gdGFEAyNguiOofDoZZ8rJihTphZ/Ue/+it49MggOYYsJ/vioUEa5XmOLWbjG4is6dcbt/ZxzHI+hGZeDkgGHiwulETWYca9XuaInOvQ3dmlyI+WaLMcUTLvgi6DY2sprKIi+KfpYo6QWeeI5Iw99qfvuMgIy15x3kYsiaknC0WgirSxkOwO+n0l125IULn2Qw9Obd61LEm4zvkfBAF2iKqUJmUvdV0j4nwKiVpccDy8Xhs2jecWEaml7cNtEXnH+RhwPqd5gZgZsvHU2DBBhpZlqRk6yfpJVr6qKqzWZl6MXGObcinpHHb1eyuKHmwPuvxeCd82z1cSpdOnD5D2+jqnNTtGos/OoIeQiIWFlE8QlVUXpZILH78x62ZJtFla5Di+MOikDsl/49UM7faA9zHX+sH3fwQA2Bv10R+Yfv+d3/kfAADnb4yP8cVXX+AropRS7lU1/RH4FWKihS7GzNj3tpp3ob0S4YVd7mtHJ8f46rHxkxyiQyQTGbgeMs4jgcAPaSfn6xgR7bdDJEJMNJFVX81gC7kr4e5VjYLvLJ8fjy/U11qz3wTtFYVtLaNZkSj9jPtup9NSOywIBE+y17ajYh2C2uhybKc5tCT2izdmT/z8fzdovVWygqwPIfFcs1TTC1wt9+x1GrQtAIS+r/ShObPjQgxcI4Mr2V3ayZpS2e12W5FZgqiuLQftRGSUhbCXZLKWi4jS8qnIuUuJSF6jz7KT+++8b67PfWKdxNgiQvPDjz4CALx8at79kx/8EAHtWiUS3rQVg0FXy6VKznch3y/KFMKkLyixmv5mEHiK9EulxIG+nms76NOfqFieJ+VGge9oBjuibydI3TAMMSESuljK+izh0Q/IuYeIrx21AoXCyJz76iuDPpeMPwCMXxifrcf94vDwEBdEdAm5tErMXymV3N3d5SNcQUzx+U7pAwm6dTgcqj92fmpsxAveN89zneOCBrx/z5wlWq0WHhNJ2qHNldJ/13W1ZLrN/aIsCrTpD9gk8Za9eZwWKNkPWXrdvtVFjZKwwzH36VykwNGUWEo5Tnm1NCgh8o5llDJ+rX5fkVaKWrZYKuW4uk7E3sueHPU66JKgPRN5+6BBbLlSbkgUVl5LqSrgC+kyd9W5neDGvRt8ZvP95AoKS0UUaMNCVkMsZwt4LSKXiM4WG7Yo1/Bodywp2+bFLdfRMlRBxIlf3I4igEIqMqfl2pXbIF4rQZxdQa7FQsXBPhAfL0gLRSqLcIUQVmdZhjxlH1GMJLuskdH/0hIsloJlaaHjJGWhoZRHpilcIo1XRLakcyLleh0VJICWgppnR1GikNJnQaBdKRNTAYm3yskc19Xnk/mkpXFXoHFvCxTUuELyrH+ifQR+ikRZCattS9dVQ9AeKnpQ9NmlbzvdbkPHwDmq8z5oKfJG/JCSc7soKq0qEPvYok9jWTXquimXB4CgIAl7kinyXfZ8WwigHRcefyclYHKdqgIc7mcOz3xS9VZVlfqLKcdUWom6ESr6GW2DENq0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0Tfs5a9bbEbn/P9q/+pf/Ww0A59PniCITQfv0Rwa9EhJd81/+w1/G33xh5L0/e2Iy7t+8Z7hbFqGHL8iN8fgzQ/Q4PzHZ4Rwu1pTI7QpfDzNhERJ8/Mvvme8dmxra2ZmJrH3jnY+xYBp4NTDf27VMhs6PQq2fFLSGEIfFswVG5KT5n/7Hf2E+c99kRi4nc/zL//l/BQDc2zMIHp/Ry7Zl4/FD8373+F7f+d1/rCTG/+mvjFTzf/4rvwYA6DmNPKdHvqBXM5PF+KM/+BOsifh4cNcgYVJybZwcH6PXNe9REyBWssY17ITKGZKlgtghmsXzEDP7M2fdrJAjb3W6mil7yVpkqcUdbI20hlb4UixmEiwfiioRIkuRzdwfbUNU8rKcmUqnhl8RxUCiavDabS/AbG2epyLPT8T+uTfcReqR44EZg4yZs6K08NXj5wCAhNkS4U8Z9Ppazyx1vSWlKltegIhR94JR+4jZuyiKME/MWEj0t2JGvIAFicNKhFjkWD3bwjozGakOOX1kjH3f14yLZPalptbzPKTrtyTEB33N/L1d11umjXxrXV5HG6RF2vDpWIJSEBRWpbwNImX8KyRPvbi40Ch6wui0IHd2RluaoXhDUnDws55rw2MGTAjCJxMzjh23hYxr1yJxnmT8e92BovtcTpScmaISZfOunEM2HI26H+4YRJJkMS8mY80yiTVM5PlsT9EuPrV817EZo62tLbw5NtlBixL2k5cmS9judbTefsFM+IBjevNwHxV5EoTHZ501WYnXzGIKf4b0T1HWytPYIGLMnOt2Wlhz7ZSCTLogP0anhzWzhMOeueZosKXIG+EJ2yIh7qeffoo+US8fkED3yy+NbSpQ4/4dg4ZaMzN1SA6y8/EcXNo6FheUYI+rCp4Q8jGzURBJkuepvqugnKQFgafZTOlPIfr1PE+zRjL3Dkmev8ICkwvzPdcz1xQkXo0cViHr2Xzfrn04kXmeLSLPSoUB2crnpBlI2qS7d+8iZ+3+DfJOSdbvhz/8ITJBn/C95PtWVcJmVtGNzLzwQmPHLddByHV8fmLW+q1bt/iZCCXvLZkyQVyFYahZsR2ileRvjmvh1g0zzmI/XlMu2Q8DFJz5XaJTT6cTvmeOakFuLs7DyXIJJrzgCs8H8a1xkiGmvdkluqOxU0HD5cFs4ZwZeMPzQURjIEgMZsnK4qcy0wltvWM33GHv3jMItEuSTH/+8EtF7nkhDQGzf+cXx/jiC+MrBCRTd2i3tsIAB3tmTk/IN7hKBNmUwSVK5pc++hgA8IOHT5C6JHDnfPyv/4vfAmBQRBfcJ8MB+RF3zRx9+PgLfP8Tg9xt9cnXUwtRso9Oy8yHkNnZg32zl1sA+oJooR0QhMRiucZf/cAILghCU7hfBr2Oog2EP6JHEtonz55hl7xJn9PPEpRwu91WPgvhMZFsqnWFMFZQEWm2VuSX7KnSAj9ClwhtQRRosxoEiAgoVESItyNPs59Vaa5JM4J7tz7E4S3Tp2/Oje08mxgf8eXrV1iSF3FrSIQckRNtNFlqQRoK4uJsfIHDG7d4LWNHFyuKn3RDfeTLc7OfCYIw8CNMaGvFt+70ukiJApR9Rvbp6WSGgMilKZHGFTk24HrY6TTcfkCzloq60My+zyx5i3tsP2o3cvXssz5Rz+88eNBwOJKH66svDeKyLiv4kRAW0yfxZG/NG1JXEv3KtS3LVmJrQdR4Igrgu42su/hAVwikY+6tEIRhmiKm79kiQkX6LM9z3H/XIIz3yQUmMvLhFcSrolfYL8EVKXtBqilvULv53g7J7AVl4FgWUs4dIZpV1ECrpfuScmCJz+B5uj4EUSB8eUmy1jFUnizan+VyqXvcinv3mzdvUBM5I/0vtnAdp4pIyehLepxXRVkqWle2MeEOXc4XCDnvxd+fVuY52+2u7hmec50EvC4blJe8z4B2K01T5fjy/es+ZdRrKcn70YlZnydEeip5GhqkiYj+rBYLeEJIrpLogXJFKc+N8KxUBSBrx2pIzQFgu9tXNInsycL7mCQJBkRKtujXKteOVTfEzOyzVxQU2t7eVkSjoIaEQ6jb7TacUnxeOUvBtjCnLRGESpeIVxQlenImEtJ8niuWq5VWV2wRkXp5eo6ibZ5Z1n+VkksoyVVqXDhoD4gSjhwPIf2ix88NsvDpa1MpEXRaiISIXBCTRESWaYa8vj4fpVmu0yBJ3yJRdn1PkZ3iBwoyyK5/Ghmk6GfHRl5cv598NkkS9b3kb3t7hs/L932dFzI2vu+rbZDKGRVSqCpds90WkZBpQ2YtaDtFN3GelGWp18y4n9XaL5XOHTmjiE9Zw4bHa4gYgPBFhl6gc3tOoZ4ZffsKpV5rNLhOTm/VQKk+NRFvgoRyXVgUvPm9f/2vGibtt9rXomQszo3z6YchXELyT7mIapaH/S//+kcIHFHrMc7Pw6/MwaB0c7To8FkS5OAErjNLSQRXAqXrGSP/X7ltDDzjOL88N9DTCy6K33j/N5HuGgfJJzyvbDOIAMADy30YwKo7LE3pe4g4Hx49NQ7nP/72PwEAjLMEc5Lxtgtz7ZzqLMfLS/QORvy3cTK+/JvvorTMwvzb75vne/6l+Vu/10JJSaoOyQGPCV1tt26gUxkDc3xkPr9cmT6+1e1D0N4pDxYRIcrWeoyKB7MVPf4GHj/Aioz90yXJt3jArdMKHh1A2EJMSyPlBLi4MM81YZnGwS3jbEeWhYrlJ7IZtFscq/VUkKvw6Pkl0zWqUqBwskOTTNarFBIecLxFUaayQsxj8+w5S/UkiNMJO9incR3HLHUiUWpkRVgzyCaG36IztIpzjEZUd+Mm1Oqa75WuC1jmmgE3bIEs+76r/WHTmAVc4HVRoiDsMGG5i68kno4ak8mFED+yJCAIlHxP4KnZKkeZXg8ESflPu7urm1S7cx1a3oo6SiItQSMxyI5noyCT/stzMy8ePTHlCbPzMbZZ0jDaNutTSM9qABckin7z2ARMxKj7vt+ofGQaTTB9jgVcbsJyGMoY5EqWCy0R03IQjo3j+ujSYbF5zYODHRy9Mc/8H/70T0y/yfyyHbTpfO6R5PyCil2X4wlcEpe36VDJZjU5udCDph+ZZ777wbcBAOt4hidPzcFK+rrVN7am8ro4vWBpKkt6Wtxw4qLCqLPNf5NMsCJ8PElgyebIQ14WmD6Yz8ZoMxAd04kVkvSD7T4WAYlA6TzsbI+wSozj57L0pd1j4PtgCy0ecCqOxe7AjGmcZ4bwHUBGu5HT5mZloUSSsp4rlmFFrq9j7nBjSlIpxfAVci2OrWzwcZI25SO0lTdvG9u5Wq1049sdGkfg9NiM8Wh3B/NL43wWhRlLcYj7vaEqidUsya3sDI4rTieDFVS4GwyHqOSQQUbPvhzWAx8OP//VC+NYjXjwrL0IU5JnRhxfgRcDQEl4+ogOXw3uIb6nXvySB2o5lG5tWVjT8Vgx6P+GSYOIxPIANMAjc2KdZerwiZOytbejnxdnSJynLcc804vXp+qv//yNAAAgAElEQVRUDFmqtr+7o3NaA8l0nlpO3hB807HfuWkCUWEYokWiR7FFXz78nP1aqdpi7l1RVANQphXSzFyrS9L20r1yELHMv1eEVfsMvvW2uuizTMB1rpdAdlDiZWkOwrJfl9yzLuMcHSkB4H40o7jFew8e4DWJ/l0mHr55/x0cn/CgyXl19x4Dk9kSNcvZ37BUJOKadzwbt+4Yu/aSym/i7Fm2i5MLE7i7f/8+AGDQZYnDKtV9zyVZZUq7PNxq41vvm0Pz69dmLdi2KEfZqBgInTFgIGIJSVHiEYO+EozsUXmx1+/Dkr2G3x+yX3uhjZBrXtTdzvIaIp8jyqTi79eWhYUEK/iZCkIunZtTJwA3N3NbREZWVYaCB+OYh6Ja1sjxY3z3q0/M84lzLntep4+9kbEXErCSues7GbZINL81NHNPTv7BeU9J7N8NTH8+/MJQFDy4cV8DvE+ePgcA/PZv/zYAc2A6ZXnu/8t95uad29jeN2tNyIK3+8aePv3yCf7Nv/k9jgHnO+2qXXpYkxZASk07EYMIURd1zXI67p8L2n0rrJHOTR+LCMeSa3FyOUYnIsEp56+UxmZ5BcYsdbxW8UT7dchSZBFZaDGQnaYpHPqiUnoEllK3Wj2sSSQ8pViKxYNP1O4B3LtfUqhhdLADWxR86MsEtZC7+mi55p4uk4t7DOQBNVo89Ip9E1sGWOiQtkCCkRIkycoMo4O965+nXfbcEEMGTGMSuUqga7maotUn4TnX0JqqRnGS4oz+Q4/lymdMjORpcoV427zXupayEAstqnOuL0xAs9eKUJB4PhC/jKXGo7CDhHa3Td835Tk6sF10uWYd+lBJJuy1IbY57+eXpEKI+UwFUNH2+/x+xLKouq7ghrJWTV8tZrx/FEFEQ+uKCQA+03I6wVP6bbIB9mLzLOPx+FrgFAB2WuZn98a+lixJIK/IEyxJDj+lT7/mvK/rWoOVYgeCypUOwZolnA/uGnvgciyfPH2Bnm3m05LlpBHnEuJLtAOzKERYYpulfgfbQw1qP+b+nLNsNoz6WC5MP3TYD3t9YwM8z8MXp8bW5jlVRENzD7tlYX1p7IcqrpKKIl0vtWT09I3Zy7OiMqVrANZMXshnPN9BzGCqKE2+pppwmWYajJK9u82zn+/6sOh3SAK4zUDgKsvUH1vTDst6g+UgYqJCSiXlDND2A0TRdXWyFc/V3W5Xg3zyLKp2mNeIF9fpIkQ9ut8KVFxB6BgqUg4UywVWnO8ZBQNK30fFcreIPoaUo8WrBBmT/cs5/TLuF35oqdKfo+tL3qUh/7Y8rlN59qLW5GzK/ShgoqPIUwSyV5XiB4vyalPuJf2RMnDe7XZR0G+2JSBKY5ZXFdb8nKjnOrQxoWurANPPapuSsU3btE3btE3btE3btE3btE3btE3btE3btJ+z9rVACEnWqsgzVEsT/RJiptWKGfQoUAhYSciwxzINN7CR1iYKKJllgc6jtOEzsyxRs45F0lbfVXjqBwcmi3nCrMmrszeIK0b6hNhpbj5r1w6sSsqtWJri8DnDAGCU7vFnpowt9k3k79H6Eo5tnvnZK5MRsZjVDVsBWoR9l7mJEP7wb5/jhCTK2/smW3U5M5mzcrmGx2z160fPAQAR4cXx5QR9Ri0DIqUErpeuY81QSPTYqgXS6yNZm89NcyFnM9eJqwwZs2fdjnlOm2iWyPKUUFJILgXWGi9XCh8fMlsoMOtoNNLvCQRVECie4ylppGTCt/sDhfC6hLOHfOeT8wuNKscxMwhTkj2XQEJkkdxPZPmeLV/iv/vd3wUAbBFWLdkI23NxdGIyO6eUdJ1SXjXLMhREM6S85it+z3EcJUgebLGviAaaT1cKxRdiT5G3teoakIi31cCqAQMBlBIAkQNtUx6x1+thn6SiAve1PRdlJWUZph9ckScsqyb74DJCLpKyVaXExaWWtHAOoUbOteqT7DIU+OLN+1qS9d//junPEeWOK8/CmBmzFUsOJJs8nc11rU6ZZTl+bTLjs+kUUoCzWpFEnJDIw71dbG2bzPlKsgscj/lihQtey+H3x94cC45dh/22SzSQ79pajiDR/S1mEr3KUZLg2yQiv33TZJg++cknWBItZwnDYWGes9920WO/gc+85Du/LixcMgPYEsQOSUPhOVrWuKZtmhI2Ol7M0CGSqT00a8MWJGWcKkKuYKZZUB+rVXxFgpeQ2fNzuAKP5hiKxHYQtWEx3SeZWymHTPNE7aj09y7fL4h8nWsF7aJcu0hKWKzvms2aEifAZJgqyf4LeTDRIbdu3NRyGI+E97I+Dw8PsVxcL4uUDOTp+VjvLe9iX4HAS5ZK0CyLxUJh2wtmCTvMrmd5hS5LFBK++ylt2OtXp3CIvpI5fYOiAufn5w2xZotlaMzqOK6NmETCyyXn3FZDGizPNSRZq0qtlqWWNAg5bK9nyvraUYDLy0t9H6DZBx3HwyXLaOQ+QqJaFIV+XspJBNE4Go00AygZM9u2FZUnKEKx977v6zOU3DdFth5oiMRzlqFKKZxl1VoaUZN0X+ZCVdRKwq7lGbSntufCEZSolKzKXjQYNGWX3PMuSdYfhj4+/tiUfH3yk0+uvbsbufoOgugIuNctZlNsEZH7+pVBCn38C78MSerV/NyPf2Ak5qNuC10iKd799rsAgOV6pn0lYyilc2InHctWZPOCyNyzgChTr6XjuiDRshBaDnodlR+WdXP5xqAALieXiGnTV7Qt5yTLTbJCs4OCOHn1nKgn5xQW+1hKDnYHd819fUfHRjL8X70+0ZILldKl/XE8T/cxsSNivz2/jQnf3yeK8MbOr2pfvTo3mfM5+2PAsshivURBOyzPPuBeHjg2QCTphCgsaXkAZJS6DgWVkomIh40x10BABJRA+udZjHrGkl8ipV6c0Xa6HtYsc7v5viES7vZ7as+kdLpLpJzt2fjGNwwp9PsfmjUeEv1iOw5WRFMJmkSy40We4jnFMBKOaUSSaS8M0M/MtfLyeplFURRKyCzZZ+mz7d4QNt+1IqLRrxs/xPVFHt18xr8ijjGb81q+eXYRW7CCFtpEXAf0Gx3usXllo0c0idARFGWhJfsV5xyT+mh1e/oeYu8//9wgLbrdzk/JKstnDw4O8YwCHDlRPFLXaweeio/cuWXOABlRxWVS4Nmx8eFXfM4bNwzyL8sTTKfGRpyemvW1XIs9XyraWdbBAUtZDvZ3UFHIQ3w7KT1rtVoYj801W7RXnWEXvkWEDwnalajXcRBSRED291xK/SygTAWJyFLfVwYxmxYFFlz/S/pzEdGZtuMhot/gCt0Byc6LKoNV8axAhEXIsSyyDGlcXnsW6f/Feo2aqGKxTTdYntfpdPTzYj/E12i12/CuUCYAQJLE6HRZNsX1L2g223XgCpJRyN7p79iejSIza9An/YDFLz548AA+UUkJ5cRt+hrZaqDrg2B6LY0Loi68wHz+1p3g2js7boktRwRAzLUbhF2Fw7vXJdTlM2lWYUC0s201axYAev0ddDlOnr/gezoobJbUE51a0a6enZ3BJlL4jAhXj1Z+2O1hLSIz3Del9LTTavw48UPWaVN6Lmuv22JFBG1FWuRIWQonKH9XSseKQknKZV8/Pza+ZTu4C5eLfEo0UJ/k7ZZloRWaPafZUzgnHBcWrldBCI2EA8AXVB/v22qFf0dpmnm/JM4agSkieENFt+ZwaBfv0vff4vkJlaX2NGR/5ELwDlv9UfEbJV7g2xaeETF8xqoVJSGvqp9aQ1p2a9sa/xDaAiGcNh0g/74+x+vagS3llD+jbRBCm7Zpm7Zpm7Zpm7Zpm7Zpm7Zpm7Zpm7ZpP2fta4EQktrbJEuQL64ThS2ZubRaTU2xLwR0zE66vguPUW1BPswm5nvxMtXsc0nypja5Zdr723j6ynCavH/TZHP+yT8zteD/7vf+DGfMjn/8zW8CAMKWud+zZ69wzgyxkrxaJHm1KvRFUpE1ib/3B0aSfooSfaITJFtd1yLlWePV2ERH/+bLTwEAHacFi/W7fkQiLQYAk8UaOfl3Om0TNV6uyFOBFD1PovzutZ91ZcMPTBZnLRJ67BdUkRKCrqSO3RUSZqBgvWXIay8ZDe4HDv7bf/7PzeeYeTkmYeFPfvw3SsYmUc+bB4e8jqvR6TXlunvMnPX7fVBtHkNG7x0bqIfXZdL75O25nLmIWc8rhI0+M22l48GXrBbladfMKJ4en2hdacKIa1KYv3mOj9vkK7lxwzxzxT4YX1ziRyQ+F64XqTPtb29jyBptybS3WVvbjkKtndVIO9EoWVYAgs4h6kLoyTzPU6SVZJWXsm7SNU4pay/cLa7rKmmeyDn3iKbKy1xlOWup+2YmJQpCJR+r8+skfxWAilmL3p6J2g+Iohh0e0q+PLxHzhBmfM7nYyTkLXjxwmSpzom4Or+YNESN5K3pU9467PQ1K1sXHBtm82zPVhSWS+boY8q7O36A3/jOb5p3XzbIkzDq6b8BYEpOqzwp0ee8kOi+S+6yGwc3VD54wozginM1iiJYjM6LbPRyasbBsoA+U0p72waRsZgSrVFkOCTawuLcC0jmOZnPMCYKUFAQS147qwosSCQaF8x0yhjFKYpSJD5Nn/mCEIrX8DimIlm+zjKVFk2YSUyYpW0HbUUP3X5HuHnINVAWuHvXoBVXnH9zEt4VRYVaaveZIT5PDGrm9uENRZM8f/4cAK5le98mDJSMyLMnTxAfmrUnhNPKLVMUSDORnBUyR2P/L2YX6Ii8ciZZbpKlu57uL5J5dBxHpcUPSA49JMrg4nKCnH3r++Sn6ZvxuywmcF1zrQf3jUCB1N3v7u4iIXeCzPf+QBAhU3SYgZIM0yU5i4qiuJIRNX2ccowW8ym6/evzWOy36zrax5nyAghyp1YEjKBmJKNu27ZmApUviOiSqqr0d9KKomgEDd7i64iiSD8vyFwl/0QjlSrjLciWqqoaEmlmadO4QSE1SEmiiJjoq1BjQQSd7P2CjCvTVO2vZC5brOGPV0ssuM4EAyj7kwVLyXFv7Jm5cJvk1Gcnb3DjwNin5Wys75cThZLH5lrTubEV8Gz0uOc/Lwza4KOPDfn1zt6OctMJklHIKh3YEJXYmmgDzSInpaLkZA50uQ/mea7Iu0Py1mxx3L94/AQTSt4L4W7DBeWqJHpARIfbJqFwXakP5ZLnoN0hn0mRIRMkAnkVdrZHSs4sOUeRWUdt6f7sMWMp89+uS9zeF+lvZlmJVPb8CO/dvWs+X14nLM2yBQbknpRMcU3HLFlOdd4X5MmT+ZHGBRYkJy9WIt1sbhsFkc5tmf50YzC5fIGUc1qQBJdnFJEoK9jctWvO9dPzE3iVeeejFwbV85S+Sbxaw6VdEj6/mr5kkq6UI6ooBElDW50kui+LGENWNWTKAbPdCfezXc7ZVquFillqsQO7RMEtk0z7tKqvS0SXqFUqO4vN/QRFYNUN2svi3OlvmXFcJekV20C+Quk7P8Au911BGE6nE7UXggiohH8KjT2cUyJbSbaLQhGXci3hicvzvBlzjnNEHzjNU0VtFryWrEGrAiJyiHlE603I/3j79m38FYVexDZtb5n1Fi8T7BDhIOijHm3TYLilgi2CZBRuu+FwW58TRJlNJ5eISficiXgJ13eS5eoTCjH+amLswtZoG2sQ4cf5aItDXduYsx/7O2ac0jXR5PEKIasWxA8U9LRrWYrYkbWR0X6IPwk0qHgh6e6VJWwS4wt3U49+SKfTAewG8QQ0e4NBw5om83KwNbxCZiwS4w0a0bavYxzEr83zGBF5esZnxgfNOd6D7S1F/gvqWex5vz1S5K/YFpGDPzo+vib6AjS2BZYNh0inFdfg6WMj4uG6vlZEyOfFPwBacMVW0v8TvyWtCtQgIpyoMTh2U0FxYM4qHc6P5XQKPxQC4p50iHm/eI3dXYNYEoJl4bPstH0E5E2S55S1niQZIvJiyT5zRHGKJEn0dx1WQUizbRsF54iI1ByQw9CqSxUIuEUEnhJqu45yjilZvIyxZYHgSFTKCSlS7A68QOaF+MWuog/FTxJWHcux1acQHj85F2Zlrn7c6ZTiOORQdS0bHe4v06fGLgp6qCpqPWO3eRYSJPbOcKB8qIWgqZyGL1YqpOTasp85jtPYXdoFOUNYjoMoEOGWpt8BwPcdhDI3f0bbIIQ2bdM2bdM2bdM2bdM2bdM2bdM2bdM2bdN+ztrXAiH07//9nwAA/MCGF4k0sGkRMw4OfCRECYjMWpdKDdP1RJm5i+J65tfu2EgTZiQY/iqpwDDLE2TMRB0QCbPLbL4/6uIu5dlj8pc4jFp+66MP8fArU5N8dG4y5x99YLJ+aZrg5LWJQM+JBFm/NJ+pXBt3PjTZ7vffbThDAFP7OyOb/FdUJKgXnmbAUzC6CkEB1Vovu1wm7D9GpJMF1qVkBcxt0piR79CFxQxIzgxMXjVZtYIR9UIUuoi6KbJUUzUSLc2Y/Sj9Nl5Qbr4gn8ad+wZx9Yd/9Ie4f9dwvUhkUyQXB50ufGbAllJjHzbR4KUgMTgHastBwcjuyYWJ2p9TxWcRJ+hRxckJr8te9nohEr6/8G8sKQXe7ffwgx8avocp+QskG+q4FvL8eka7Q7RSVdWYnJtnEO6hJftnNZ0jYy2ssO6nfL/ZaoWqFlUyIhikr2sLtmSFGRGur8ixispBKPWkzPy0um1UzKq7fOdlvNbMqKBspEV+EynOq+t8AuvFUrM/A64FQWOtkhXqSurCzfvUjE5fTM61Hvf/+P3/23yP4/3m/Fgle0FUiSASirzULE5L0AxMxbb9EC6RCm3yFYSRcFIAp8xsiCx7ICpLlqdzbHRgeDuiZK0ZUUFfffa3PwEAXE7OYYeSvbyuTuF4Pv6bf/ZPzWWZaXj+zNT+fvfP/gQO52OXUqEBEX1B4GPKfhOJ+dEWkR2rNXa2zb/ff8+sDZnHTlgBotRA+ezLGZVeyhLp0lwrZxaphHnudrv9U5wQ8v9urwPHvi4H2up0kRJtlHNuD/pU00gSXDILvH+DihzMmrTaXVg0KnFs5rggQRzbUzssarLK33aF3+GqlDEgCJUGqQMAvR55JlptrYOfiuoXs11ZnMDmXiBcJTlvPBxuqd1PyP+gvDdZrtk+2RM810VAlMH2FiWNyUPV8gLN2glCcGtoPrPV6WKxvi5lLMpzeZVrRkj4JhIqoxwcHCBwG+lSoEHZDPs9zRzOqXixw7UYhiHmtANdqp2InXNsS6+lihzMPkdBAD+6fj/Jtud5rmtD7Fwjk5xc4WYzYxsEgV7/bc6huq6Vo0hQIg0nWal/UxW5JSWOPU/lWiOO6YRZxjzJcEklJPle1BZuoADnZ2P2kUE6nJ+Y/TZZLjEkp52D6+iQLKtxSOSZKKIIWse2G3SBjMk79wx/2Pnxa0U5iarT8dkJhiPy/zHnaDNTWlRlI2HM7OTOnkEP5GWJxZK+CPshpqKQZbtwuXaEr+OI3GpVZZC+QIOsvRybd/YcGz2qPXpUC7pxxyA2vU4LU94nF+yp3XBtyP4s/CeClvFDD6UlCDWzBnP6YnYNxEQICUeBF/iKBJCsbqUCkjkcQcRw7ijazHER0c6LIqlwxyWJKEZB5XoVVYhcr0EAq87Hqmr2mkYK2XymzEsMBDVLJFKnJc/kIooETWxsYc510/e6iLkvu7Ygr+kbrdaKzMyvKNMU5HsUtIVydNgOMqpMxeRDE1nsIAhQkUNT3iuRsamBXaJzY3JQLIlkjVodzKgCJYwSgvhzHEtt0v62ydALl9jpw0dwiAKSd1UUJ2pURE8ID6Nmn11X1RAt33xmj5wqlycXil4WDrIpOROHox2k5NdSuwVH/UxBkl1XjqLqGlWgvvWtb5nnqyq9hqzx2Wymfzs8NGuuTV9BVOjmpzOkRIY++soo+UZcd4HnN4qi4s9RbWk2m8Gj/RYFMzl7tNtd7SvZ484ujI2Jogi7I7M+Xe5ZolxWVZX6PqPtXf7N17F7/dpULPz4hz8CYLjgRIHwzakgFsj99/K52rzhFjlY6GsHvqeIilSQsuzjdLmG0VEGHFGF4hkpdDxYAsnIyPtDvym1oL65NOV7KypYVJ+raQuXRIukea426O3vWXVjGwS5kE2Lhk+vvo4qrmursTe43nzXxvYWEZZ5du17lmXB5n3a9HMc2txOx4NDefYGRUtemCS5ghoSDhvKoJeWcoSGtGktnkuqynCAAUBFmym8dGVdq58fUXFum77G0dER4tSsHUHD5lWJXXIpCXcQyAm7u7MN25W+JUqa4z3PY7icHwPu9fJ+o9Go4YIUxCUN+iqJOTua6oILImXLskBM/0/8CfneVd7S3R2zFi/GZ/rOeh/ruhroer2G5ch7CfrH0p+1yrjLhuPo32Sc7ZLfywvl0pVWk2foKrJMuFMd7uFuVenaLkTFmeu6dl0RJUQlqpkc9zRPsKa/53Mt5bn4oqme1eR9xH5VVaX9Ib6XoDqLLEPJM5sEb9R2Wpb2kSCf5HnTOFWu5p/VvhYBoZu37wEAKqvAioSL4pR0aWyTOEclPU9oshAsJ1UCr9XAQ4GGgM2xHP2dy0GxC5IaZhm6LCvo0EBKUGCazrFHefuLM+NsrUkO3R/u4RadrBWvfURCVquuVBq3Jkw4ovMWr9em9grAl0+NXLdsRn4rQsAD45AHs92dAwSEZo8X5hkeffmZeYcaGDEAkvGaEmgYDofoOddhn6UlRNIVVoSeymYgRqJyKnPSRlOWF5OIra5L+CEJuEhG5hKCOl/HePzyOQBgWRpD/8d/+h8BAK1uB7UtZInGwL14Ycr0xpcTJVmMSM54dGL68ezivDl0MAjzzjvvYHxKyfsBpULn4jBm+MZHxsEXOP1iasYSkaNQQSk3kRKfXq+rhMppJhsMjUxWw3GuGwwxdIv5XKGPbxtPG44aZyFelM8E7UiNlwYthQjP8eEyGJXQuPiyiThAzbkSakmccYr6O9v4yQ9NcGOP5IV5GiPTcgzBVXJzdT2FicoBLaQjslwvNCj6Due4kBr/5NNPFRq+S6dpNRXpzzVyzsMJZbCH3KiGnZbCTG0e5MTZLspaIbgSrKv5f8cu4VCKe2fXjK1I2LbbkUpRC9G1zaDCZL7C9773PfNeIwNnbUetBt5LByRmQGS8mOF0bOZdyENzxDK0/RuHeP7SzFc5UD96bko/2t0uXDolJR1AuzbPsFglaDFI5PgCQacjs45x655xxr/9S0bS+K+//58AAFvDNvZvGXv45Ll59uXaHG467QHWJKQMGPydrQmvdxydfwHLGASCWhWllh4IKWGWV2o3JNghdqDOC5xTvl2CIvt7Bsrrh20lERT5eX/A97Oh5aHicAu6+82bN+qoy0FQ1qLvN463BB3EZrd8DwGdeAmsdVk6VhSVjr3H/pAy3aRIkQk5qZQMM0DhdkMt0/QZnHJdWwm+M86xY5YT33twHwe7Ehw19kac5t6wh5dHJkAoh1BxvD3P06BLRgi6Y5tn3xvtNHaGjqnA/6Moasior/xO+m5B2xXSGZdyuVYYaZ++Db/3PFdLKYTEVA4a4/FYnUEl99fxq7DP8gchT42iSIONAnUXZ286nervJFAu431ycqKlBTK+Miaj0Qhn/N7ujZvXnv2qg6T90m6cdD0gZNcPz3fu3NE1t0356F7f9EGn09I5d3xh5vqTJybJI86o6Rsz/2+x5KbTbatt7wzNNWerOT76yASMLMeMU8pS8LAVaWDg5r6xzS9ZtvX5Fw/xG9/5DgATaDUvRCLSQV/h9zKHpETIsXwEPLh0WGpas8Q1CkJd1z/6xJQ0b5OY/N3331Mn+ZI2PWDQrdvtaj/K/SS4DddCQVu5WHA+M7ha5QXOLlgyyiC6faUUXOahtLzMYdFBt0uZo1IaGyNm4EfKUHdZ9tbptjU4J2UQ4gjntYuUfTzk/PVom9rtttIJJJQfTunw94MW9g92+V5mf5b9cLVKtJRwjyXMtRyF0hrFytxbglMObeiw09NDx+XS+CHdXgesVkNbSoNZhtLv9hAnK31WoLGBjuM0QircX6Rkp8hrrOKmXApo/InBYAvZ/Pzac8lnFnGKwBYhCSYsWcLk+z5q9o0tdVp2c6BzOD98zlGXPnMUhlpWV9hS8p7xmq6WuIu9Chh0qvIMdI01eO/ZjtIWyPiG7DM40LIHsQ1jksT7fqhzbTIxdkr8wE6npYeuVAIRDLxub41wyFIbDSbo4b4p43XfKnX94Q8/0UTsjIG8rQGDbnA0EVJzbCU4Vdy4oclWKRMXe7KcL7Rspc6bvVz8IVnrt+/dNf0eRSjof0jZnxxcPaun+54Q2rrsu8l0pnbwm9/8BgDg8olJYpfpCmCpjcegSGk1fhOnDoSftgkY1EqfIXuDJOWyvIIjZXi8VizlMnWlc6whgm6Ifz3ubXpgt+srpebmV6V+XMMCTfmvIyUzngapPfpjOW3YeDLVMlRJXsgaSVdzgH5+LvLl/KzjOIgkof9W2XGapfC4FnwGXiTI5Pt+E6xMmiQLAFiho+WJbSY/hwMz7utVqGu205Fy2QI2A/922QhPACYAVVlypmESY2Hmc7KOsUPqDo/l/ROul6OjY8ymFHHh3BlyftUVMJcgBxNoAUtdfa+jdl9KTosrgd4FS8Arm8CAtdjoqCHx5l4Qi72LmhJXTZJrALDWAKOliQ2YPqhK9YNlTZRlCeetkkKZq3WR63WDoHWtP6uihiNBti0pvWWfoynZv33b+AAxhTPqXoF9lsA59K92CopUwMV6KsTg1210Xddqy66KiUiThKoEj8UmWXA0eK7BTo5HXpUqtvSz2qZkbNM2bdM2bdM2bdM2bdM2bdM2bdM2bdM27eesfS0QQgkjgO1WiMih5COzQRkzFmfjCy1VkgglkxDoDAIw6aQZUsmCRkFbpQ5BZBCYwR22uwrlFWK5YZ/ksnWB18wQt5hhKxlp/MG0Tb0AACAASURBVMu/+j5ahGG2mXWdTaWso0LUJSw1Y5ZgZaKQ260uHjwwpVR/88LIZdYkC3RSCydvKGlOSO32u134Hcq93jZZluPXJhsduSHu3jQIDsmSCMRwXRTYJswxZZZgzcxImdUK2a05/C4jjrCdJrrJztX+zFN0mFUQOcoB391zOhrRXXOMBG2QxTHO35gMrEigKmbbdhEQQVNyjJTgOc40A+ARCTNdr/CTLwyySrKtEaO58eoNTt8YlMfxicnACvFjWSQKzZfs54ASkOt1ohl23xNiTvMM7VYLBaHy6VvIhU6noxDoViRkrQ3Z2tPHZnxFvnhJqOfdgz0sCblW2LgnhHsOCqJeMksi5A2JX2lTvpzIOJEcnydrpCxfmLPM6OLsHJ21kM6a91lJCV2nqxnHsxOS3fZNhno6mai0+xdffWH6hZnq+XKJ9z740Lwr0WEh19n7e/t49dIggwRRNCTSCKg1IyLS4wmlvVHb2v9KHMsMYit0YcGMyZvXpiQxEbK/flezF5KelMyb41oYbpts3YRZ2nQ9RyXykCyhE1SEb1uwuQaknCHh/C+KDGeEY8csz3AlMx0n+OibBrLeJ6rg+VOi3yYTvPeRyb5JhvSLhwbdV1/WSiCaZpSg7nCyOwVKn7BS24zfxcrMIbvVwgoiAStEwsy225aWEGXM5ruC+prNFWEiMrGLVaxrdElETEqb69kWAoHtM/Mes2RnWVVo3zUIJoE516UQna4xILw5Z4YiXlEWux1p1qNPUmQtX2u1mhLCt5AgcZpjd9egBEYc04qym51WF8enZ/w8UQZrGbcEFTOAPpFjBc1Ou9XCkOgOyaJalqUExwkz5juUcXZdG7XIhQZN+RMA2E6JnZH5nJSdTFhWYzlGlhsAHIv9z5KDxWKlGWIp4ZD+iWfThkSTa0iRU1WpY9mUfgkSCljQtkv2XggV9/f38Yoy6fJ9QVwkSaLXl+yTtGfPnimSSMgjwzBUJNGjR4Yo87d+67cAmEy4rPVdlqSodO16DctqsqtAs+/atq17jay9FbOnRV2ipRK3UhpLaLhdax+/TX7darW0lFMkfBNBtqxrzcreYTnYoydmb6lQNugkzsOLSzOmOzs7uJyYOXfjtiGaPjk+BgFBSEj2bvlEY2Wlklf7lK5VedqyxI1bBg21pE8Tc+7ZrqM+TCM5S2RjniMRdAhtZcw1HIa+lrBIvwvZ/s07d6+RqANASRs6nU4b1BbnlUfbUheWzkcpc3RIEpula1T0B0S6uqoLzcxLk7Xu1rXK5Mozy7gblJkg2q6XlRZ1QzwvKF3xeyo3QsUMvQhsxKvn4MPoni9+h2Sc9/e2UNK+CcGs9PlstsCae9T77xp7J6XdszzGq4lBA/YGxu7LWvrg/rtKIyBzpp0l8AgRcg9MxvjJE4MyHfb7+v7vvWdI6VdEN7158wZ7d8z8mNH+HhKtV3sWLs7NOhtTNMKlbblMFpr5lrkmiKRRr6V2bbkw9335yqzroN1FzmeR0iNZI3mZN4hmXM9oF0Wh8s9KHE1UxeH+gdpHmY+3bpl1U9Q18px7j5Dgu65eXwnXy6bER0ou0sLc5yXpGaIo+qn5K+98cHCgJe6hc31ezedLfS/x1UYs6ep12opeEUSz2FfL8bCz22MfmT7eJyF6Eq/h8Xt9Xkt8ve3tHZxSkKZFxNRiZu7ru47291QQbt2OIpBGHLffvGfOEEmS6DO/+8AgjY9fm7H89NNPG3+KZjHhHhl4Djo+ESNnZk8IyVWwM+ggJqKlEAl17nlOXes4+4LoSKVss6GzEFSI9HGW5XAh5TFmzoRSfoVavydNyoZs8wV+TxBeuf7d+jt+it3RUjNB7OQlyplZJ753XXyjSlL43BPlaFIT4TKf5T8lAS6+RqsVwIJQH1wvx3E9G45UWXDuiY3Y3t5WagwV1aEtjOcV2nyWSymXXUn5YQ1Upt+nFzwDVyUcx6yP0ZDiJayaODs7USSt3G/G851ju4jT6+XlCVFz7U6IPSJiM/rMbfq3q9UKIcde7HibaJlOp6VnJ7EfUuljWTaq0uU1zX3ED6ntBqktQidzvkPPH6BMZX40IiTm/vU1gmn5HWCQTFJeK/fLsgyhiDT5IrZEiohugxjO9XtyZs50PolvI2XpdVUosvOIwiuOgCtR6dkpZxxDUOu+7Wl5uLyP2CHf9xUdHGqZYYOWlr1RbKyUiQG2qU+GoW+42h95nqsI1c9qG4TQpm3apm3apm3apm3apm3apm3apm3apm3az1n7WiCE4JEDJKxhMQoo0ptC3OiGDvZHBhUiGZ48YQS8jPHws88BAGPKQnqsqXccR7Oykh2cW+aaXlKgf9NkMeUzFbOT7W4LNpE+U0Zva0ZqvW5fryUInEjksW2oDO4uJR2Lvok4vre9h96OyayefmmyW9k5sxKdbYxJ+CjcEn/25/8Poj7RRpSYa1PCNvI6ePjQoFBEfttemmutswIZOVtyZkaFiLRneVhfmmxFSQJom9xMF8tL2Izw3iJnyC9+62Pz/WSNOSPdC0ZJ10TdzNcL+OQ02CJZrkjFewB8qbNnH40X5vt12ZA4Cnlkn1wUZVliyoi+1El/8fgJLs/N+ApK45CywL7j4eiIxHqBkLiZa/vtFlqM+kb8m/BijCdTrNci30feB+FgcV1YrMnMWeN+OZNnr+Cy3lg5cIQE0fUxJOLm1n3yY9mNHHGbGVyLEr7TGcnSwxaomogOM9tdojDy1VJ5EqYylpT1dKsKEaPGl2OTfYrCAPffuad9CQBfkTTx+GiGPrO5dyUryf6vHUvzNe2ueQaVrJ3OMGWm63XOGnBmCVeOi2POX0FmzY5M9q4qSiVmF3pr4V4IgxaE+lKIvnOipEZbPZOlA9AlGq3rm36NoggrZkkciZhD+BYaQk+b16yqChGz2xGzUzkzNkFRoy3kx5IxZvbj/PgEF1IDLnxBmZA3e3j68BH72zznnATDFUo8fsT1yUyUoLiqDHj22PCV3L9l1tnBgcmalrUNNyDPCclhpQb69PwCRSH1/STQZsbhnXfegeuavnlC1IbIWvpuoNKWx6+O2WeuckE4guYT/pnQxw7n3y75M16+NHLJp/OJ4RkAsEN52Q75vyorRbI2/eYzazQcmud8/Xqi2QrJBouUepZlmi2S+VQyox7nK7w+NvPoYIdoGUpFu4OGz2LN+vSC/Gae46i9keyK8BtZVYkJM/xF2WSV928aLgmPyJ0+54sXuHjy3IyX17ouLzuZXGBCqd/LqUFiSFZ+uV5o1ufdeyaDm5ADqt3uwBfp6bSR7AVMdkey6RHREAn7LMtzlX8W2V1X+BngqNCAIpiI8nD9QPtWmiJLo0g/LwgJkQyez+eKBpIxqqqqIaxnP1ytdZdnlybjDVSKWmtTJnl+2axPIWkV2WPhikmSRJ9Psn0CBrJtC7UQZRIdKRm0q/w1Cfvs9l1BJxTKiSQoj0Ak6eNYr+G9RVC7884dvDk24yVrqshSlIXwsBB5xgzdOivgcG8UIkrw5/7uSFEyQvSv3E1lrkiRtzlYiiJHzX19i8Iae7sj9tVas7SaSScC88mz58qRIVw5Mt6r1UrHLSBXYEYka207SoArnLIJ+Qhm8xXm5FLp+cI/kymXx9vjVhUFhNtSspFCzB95bsNfxPuJIIJVWRgTWSFcLcIPlS8WalvrEZFTi6X2lSt8U/STBHkS+UEjFsH9RdBw0+mi6Q+ViDdz1fO8Zh7SZ5CxcvzAZPJxhZNwkaDDdbg1JKo4FPJqH6uVWYc5UQYhkcrpOlPON/G1HPojVV2qL9PlWio53ufnJ+qTqMCAENHf2FO+L0EmiTS6HbQVSS54B5uotryulGtORDty8rUVdYGaXCWCxpcxTlfLRrY7u84r5QUhRlvkyaLf7ZZlgxDiOlkK32PU1fnaJd/mNnkKwzDEmoIVghgRVM54PEZRiqCAeT5ZU0VeXZH+NuNw1aYJ74ygOD0KnHT7HgLxL73rKIC9vR31dyLb2BZBtbWiNixM9P1N/1P8YXeka1ZEFpKswP4Nw/VikYPmyyemSsDzPEUSfEbfTpC8l7M5Qu7hJa/VJjLXW12iWJnP5TV9bFZPeH4LE67nOCMKXDhVUCiPVink/hH7MalhE0HX4X1G3EOyslRkoaJ5CiHgbZBFDe9Pg/hUG/EW4gfA34kUUpToW7xEeZoBJHwWf0XIy+vaQS3VCESOrGIzdwbd0U+hlgVJY9v2T+1/Im6RZlO1fW+TUbuuq2tPkZe8znKVaNWDcMH5V3mUuKUpeqWuhHZO+cYK+rCuHyAksk3sXG/LjMmN3X1MeA5rcS8fcO32+/0GhcLulPnb67evoE7MvB2QS6jVCrWqo821kWfSx4BtmX5vdc3nHz02FQjz5QwJz5ZpRv+gJXyHUzj1ddn5+grHWElL9TZXXV3X6PrsR6JmlmWlSglFLoTi5EXzLJTCm8ZzmRQW2aGFliPzwuwrl/QfPdtBu2Pe5+zY/E724vVqDVtQQJzj4nP1oi5cQZcJ5xP7PAhDCFmX2BTp8yzLkJLLynNljvPda0th8LXz1pqyS0V0/ay2QQht2qZt2qZt2qZt2qZt2qZt2qZt2qZt2qb9nLWvBULo4ZNPAZiaTKmLkyyBRUzB9vYOej0TbT86MnWvTkWlorrSWlqJxoascV8tYzhEfEjUsxDlrVWiPBi3Dw2PwOKKok0YmvuFRDA5lok4rqpK1c+kFvHy1NTueig0GntONEuH2Ygbt25gCRNVLX1mc+asg08yTFmz3mZUNy8r5CpLa7IKvS5RA/EEHSIJ5lNhbSfXiOWhDBlxZfBQOD0cv40VJTRrcmZY5DiqPA9g1r4emne4tW9q2C/iBeqLc/YxpWEzk2H9zz74GM9fmKzFnKosIyJk7KJCzezpklk0UQnKsgp5LBlB04+xZPbKUrPrUp/rOB7u3zbZEsk4ZMwY9bs9RWLcfMfUWD+kkls77KAdMpvLsG8aiyrJoMlKcuwHlCoej8dIGXH1ibiq+C5wS5Uhd/mcIhFf1xYmrIEVRZMW+8ONnSaDziwGA/RwvBBWIkzwEgVvOE5E/cbjWA6YHYutChbnmGTu/TBALPXujKwfUu64KkrNkI2ZvRNun7vv3FMEgtQ8H5838pAXHN/uW3Kxz758hm3yah0SXTahOp8bOOgMyH0QCP+AMOPbGsUe8flcm7KPdYmEmZBuaebMKVXoHrx7Hzdvmrl5RnWnY9b1ul6kY+qS96cVtlFwrhXkmXEdqf/OUMSUTJaMKqPpJyen+NVvf9v0s8jIc55t7+xiwrW3Wor6GflS0gKvXzw372wZu9Mjv0Ur7GBBqWD5OSO6oYQNO2TW2jVjud8zyMi0BCzJ2DJ7KgpffuBpxnyLyJ3JhRnbNE60P0ajXX4/RcL5cYfS8qdHhgPKry14TI3GREbInBv225hTwWZABcSa9irwgTjle9RmXcbM7lqWretZ5qNkqrMsa2qkr8hTA8Bgawt5KjLrnP9cPqdnJxDyFsnKt5gxrfMCGcdQ6raFr8xxLUzZ35JZcl0bM9ranBnbMbmjvvGtb+KE6yRKmZUnX9PLFy9Q0ZaIfHNAhMug6Gs2URQypA+OXr3ROSbvLnZhf39X14TU0jfP6WJ5LkqJ5jkFFdRqdRTR1SaaRPoxz3O1DZJll/63LKvJ2nvX98rBYKDZTMkSJkkznwRpouoiV9BDigyybb1mUUjdvHm+qwgSmReCKJBr2q4FmoQGOUVEjWc7KInWEJTHmGplZVmqNHFIdNOYfoIfeop4bQsfGpEPk/VUryUcR3MiBMqyVFQIOOd67Q7OyWV1+76xyS+OjC1q97ZRE+2WMbM37Jh+39naxrOvzB61Q/6HyZh7LCJ0yE1Scg6VIe/X68ESVRzh27giYWsL/4sgiji/Xr9+rSgvn2gZQVE4jgWf/oAgRlqq/NJwfAiKpa5lPvr6tyX708tTeMF1dF7BNViXlcqy95kFFuWtk7NTtOnjCVfONvulrmtNV/vkj8poz7d6QxxRDVEQk4fkG9va2kJBFPeYqqMJ7fjZ2Rn8FnnkOPYe/Y/PHz7ENjmwZP5L+juezBBfkJuOyPKS6mvWukCLijQRVdeQ1YoeFuSUzG0LjiJMBCHncEyztAIoOy/XcolwQVXj3RuGQ7LL9S+qsecXE3zypUGJCr+EzI+r6ARZ810q8CV5Bp/oRkFR5Vw/qyxRiWyXP+X/ju3AoYKpKAdtcX1eTudYrKiMRPsh99/vbYlKtLYsTlQZScZNuEJH/W2d72KfBCHkeQGCgJx79AcERVtVhdrdjNUFHtfW1rCtSNWbwm1ENFFeFsjoC8n9BCEeRj19r6oy7/OLH/+C6YPZBOA7tIhwE2TXarXStSHIeVHJyopCOe0GkbERtuug4KxZLURV2Vx7cnkplCEN0o3jPb6cwCGKryIqM2K/b0UOukRftsjZWtMGup0QpzwPyLpvUYmwrnLkRMlkBcc0MuszWxRY0761uIYFGelktSoxCQbBFeiVZTU2QtSSrshv12+hbKqyQUvI90oIT03DIfc2Usi1PVi2+APGvinPlh8hJrqsz/PgYmHsiaCTgQZVJs+SpjnK6XUEpKzrdq+rPq6guK4+tyCSBBmUkGfOtUuMZ2b+BlSKFpTgar5SHjlBgFRVpZyuAefYkH74Ok4R9cjzKhw99HmTOFHEZED17S7a7IOuovNs9m2cLPR52/RLBaW0xUoCx7HUt/DppNlS8VNZiur1eNYYEZm7mJ8hI7K7F5h5n8ZUwfQjVet+mzOqLHNVdKyr63xIALAkAko4vnzbQi0YGEH70wdLswQpuTdLX1QbWTVQVKr81mfFzt13jO21LQsBbfv7739g+oHPN7NsDMmfJipj/d6Q776NJ18+xtXWIMks9ZvV39F929V3LEqizIhyquqG/0jU1uSzZV5cQWr//e1rERAaH5OwNPJRViQM7cuB0wxSnCYIzwX+xi+6lEBN5mgJ3HvewJwBwLZddep8Hka3A7PAzuoEH04pRz4y14pcdrzr4bQkUePSfMa2zMHMrVawMrOAQRicLLQ6boiqkol59l8j8ay13cUbElXXYwlMmAG7iFcIuLArKQ+wC/iluX6HC2V9aSZBnuewGTRIeCAZdMzkq9McSzpLvSEDV+J416kaZ5ebY49lXrlbI7/kwiK88q9/8H0AwMl8hjaJWFc8pHiERGfJBLt73BhYEveGRNKh62DFQ3MQSskGidHWOYq5eYYOnVefwboodPV0OD8TueMOIISSMzM2smCW07nCvYOJcfwEOjk+PYHHnVOge4mUia1SOGD5DR3OkwvzfSd04VdCQEcYPUsRLXhagiJwdSEktgMHOUl/VdaQfpxTu1AVeAZ7DvaNAS/rCo+/MsFOmwGKggevw4M9DA/NwV2I4cZ0wAM/RDgwa+PqwaAhwSORH6GoQRBcgbiKvLc4a2v0KMk96Jv5lBWNxLQcErb4NyF3zdZxU7JFw+rRyUZeYkkItV1Y8vLmR1Fgzc93euYdlkszXw62d3BA2HFLnLttswY/+fHnqHJxIFhyyoPaOl+iopFsM5BXlpU6hXIILen4RYF7hYCcZQ8sWzvY2VLC+oIHLQkAZkkKq6ZcrlhS8zUMEGJnaAJj4lhxqeN4fIobt98HAFwwCLE/MHNgksQoGFBYJ0IkxwmTATGDIz0SmoMBxHS5xmjPzP+8MrZTpKXDlg+fMGk34DXbPqK1GYsnz8zGNOckbTkthEseRPhaE9rOfFaj1zHXr2q+LKXUx9NMieoHPNz0XNre/ZHKAGdcE12u19r3GtI+HkQkINVzKgQ8nF++NjZlStjuKsths+M7PBRZJEp1oj0MSfq5vc/AKfvux3/7YyX4HbLkpi5LpCyDXNI53N4zwefT01PsDM0zyEFpRjvcbR0g3BbYOJ1Xm+SrXg6HBNgyr1bLJhgTcB4uWWq5mhm7c+mW6hzLoVRITZ8+eaKBXhFcsHTTTwDun+JAdBiciXwLDp1Ijz8DHgKSdQxfZOrpLL95bkhvd3sdvH7+iM8pZSsB1izndekwnnJfK0tLnd1LJgzEJlVljojOcUpJ4lbb9NXF5QU8BhgT2rcuHbl0sdISAynbTBjoDaM+aIaxLvh8PGTGy1VTJkh70NviOK5X6HLuZHSsItqrMs21VGmqjpm5zvl4ghsHDETPzPsN+l28OTZ2+94Ds663eZ+syBHSsUzXZmysIQPKg23EDMjkPBBss9S6ti2dR1IKEPGAV7ZS8IwMiwdAi0uxym0tuZPysO7KvGdve1cPuDVLmMbSr46HNUt6bB7mM/7NtSyUDOQJib3Y0Nl8jYol5zYDZJNFjG32n/het28bB/r09FxLfO//onGg/8Gv/woA4MO9+/iLv/gLAMBXXxgKgH0Gt8/GExyQkJlbI3zawAf3P1RS8wc3zf7w4cdG/ODos5/gkNL1tS0HCZIOxxUWTHBYJDCfMyD37j/6VbhdCnMw6HVCX/HWfgstj8ED9tHOHXOP5flL2IWZv7sdOaBWAPfNl09Nac/hyNimKPLQCeg7sUxlTX/i7oPbiEtjM502S//pkwIuTs6MPby4NHa+w8PG/oMP8E+ZXPn8K1O2vOZ6C6IItpSRrFnCJWUeFpBxDDtM+glR/qjd15JFtTvcluqqhCslKQwCdBgQsdsR+i79Gx5WDhjkywugxRKkYZ8E+5NTPfD16a9Y3PuLPMWS5cInTFJJKZfrOHCZeIkZ+IhU5rujyZK0a55LSubzLEGvT/+PfqCUcS4Wl1fI11nyxxJ2oETIAKrH5Jg897A/0LH0eS7pcM7HyxjH4zmfL+Kzsyx1mWHAAPsF7duoP8QZxVK2GeRs8zMYbatt9bjvHj0ziWnfjZQo+ZLz6RfeM4mtX/7mt2HRx0roK3/y8C9ND9iAzSR8wlLGlPLnRZnqQbPfMc+SaeDQRk726jkTvwVFCGzbQryWoL05x1SZeb/SbvYv8QsirjfPcTXILMEl33GvBIJYFiOl747TBAj4oCIgklalBmuE+FwCGlkQg24sLjg2Ska9XGsiSpLdxZXggyRGpORRWponOjclkSJBozAM1RddrUhkLAmVsilF3uozYUb/eLVa6buLhH273UYu9Zks9RUxgFU8hhsJjQUTfCpzb2M1pXgDBSUkEdAfDuEyCHkxMbYl5vsd3L6JOKd/zzERX2UVZ2i1zTNLvG9KsaXFYoEHd0zZvAgZzIV6ZL2G32bAlaCGViTCCzXy3PxOxluC3BWakk6bXeDbzXncdmmDGICyUCFNJLnBcx0BHXlR6FzWMmCOreM4SJmRmozN7yYT2s66QFeCbrY5N6nkO4Bnz+XsauyGlFqnyRpJzQCNd/2n5Vdo+VLCyQCqxaRQWWryvmaeYk7xAtSuBr+krC6rZb7YCNtN8ubva5uSsU3btE3btE3btE3btE3btE3btE3btE3btJ+z9rVACHnM1nqeh4KRSCH6rSXi6wOJwCz445d+/dfM38oY47GJop8cGbhdGJiInOW4Ci3uEPkg8H3YlkrbClnUvUOTWeoPejglcqnVNt9z+L2bN+8iHJqo7xFLWCRSWVgWfGYT/8F3TOarMzRRu1WywpCyzFskXjshcmWr19NMoJB1hX5LI8cSdRTCtm6rq+9xkZsI4ZBS6r5t4c2pgTpKdFvkBz3Pw+XYfH7O6G1AcrvRaIhw10QmT56Z8pH775uM2y/8yi8rGfSQkFA7JQEmaty5b8q0JPP4b//t7wMATo/eYDAQ2Ld53rWQc3uOSlYL0keiudP5TIlHh4QFH70+xvDA/DvqkOA0lygwcHxsMhL/H3tvGmNrdl2HrW8e7nxrrldv7JnNqSmSkkhKdCwZiWQNtmPJkpFEiS1YhoH8EQzHMDIgDoIgCGLQMAzYSBBYMewoPyIooxUZ1BA2R4lks5vN7sfu12+uue48fPOXH3vtc6uahvSXAO/5U+9V3fsNZ9hnn73XXuvi4uTKuwdRYBApKnGupQuB46IkyqNiFshS0vI0M2UBMedTTqhsVRZAQTlgjmFJVNByVoBJUzN/Q86JRZKZTIGWbLRZQjccj/HCCy9c6Q9LI7z2KsOg2YWmtZLYTJnt0Pvp7+UfWqLGTExZGeSSq5DmS8R1SmZXKrpBYdaea2CzX3/tawCAG/uSte222zgjYVqXWV0lfDs/P0WfZXhxID8zRsqzMjeElC6h1EGl2YECj5/K+kpJPqsY6SRNDIzTrlWmVLNCHmq+a1gqnNCBrwThOmeIKkEN2Jp1YJZPydvzokDFUs4lkW6WwjID38DLFUGmMt8oS1xjVrxi+umMWdrFdIJvfvmPAQAPvy1S9P/B3/gV+dtyjhmRPZWWHjC7EDoGiQ6PwxxxPGx7hUYzstEqV23ZmBHKO5lItqXyHYSZkhIzq6JQ/bJEyoxqpXLkDWbV7Bw2iRM1028zm1zbJUoiwHLapoDoiNk0RcHfRURBDEYrGHJG+xsQnRBwbOdphpTr82BfsvLDp0SsDU5XUp1EWOVES7740i5298V22Uqe74kdOh4+wpKEuaOpZJrDMITDa0VEMs4mLK2tS7gkGtTsYkhy0aSuBJmD1XqzzD5VGTn3hD+1P6qqQsSMu8dsnyI3Hz9+jKghfTQmEkdL/SzYK0Jn2ob4UonWqlxZkA5DJXTd3kFaKIxkJWEKSGZW/53YV+Xn67qGzfWvwgthEJv3MqVVfPZO3DREknmpsqiU3HZj8+y6nw1GQ16nbfZQXZYKP7dt22RZNYupzxdFDdhc147FeWhxz4QDi/NoyfK6p0+kX/Z3N1FX0rdqY9pEi7n+Kc5Izq/Za333yWSGZ27cAgA8eEIU1eYucu5bb31HECDbB3f4vZYhpFby64aWZEURGszqJkOZhwnnUuk7qNOrhKMeS05hAzHRE52YJQ4kk53MJuh58h5bzDA/5t6Q5znGhP43GXvVCQAAIABJREFUWAbSpZT1aDRCx7taUrgqR3PgR96V3+k7DQYD1Co7TwROHEfGpwtCoiaIQt67to+E+8vxiewXv/mbvwUAuLW3azLz55RSf+vLr0p/wDIkuUO+j8e19Ntf+TwO9mR9/JgtPtf//uV/DQB456030aTtarLMU+XB46Bt5JF/6if+bQBAGsoa/vM//+eAhClY2goQVYLF3MxpLS9VP20+WxpErq6R6XQO2xDSJryECojU5vPqHy0TeS/XDzHlfDBrlSiu2vKwhPy7FbEsrOK4DVNTSuU/J8IS33lb9pnk5BAh/Q5wDDEVG+E6PmrugzX72FdC1vEYiXvVTilSKAgCnOk7EzUU7cqzZHltstXa/yOW5flxC+W5/G18LP2BIMSUyFMFGFtEMZd1iVCrNYniKYloLB0bBe2vIo4VDV7VNRyiSDISYQ9SmXujwbnxtbTMxV1VHhm03ZiIYcdSlKWLgGS6ijnI6WPGoYftnqBL5wutYqCf4HtoEnlGVxmB3tf3sCByp0s/H6jQ73fZNbR57MdFmiBiuaVHH6OxIXvc5rVr2L0uiPIZ55xW0wwn59ik6EPYUTQhKxwsx5R0qvBKyPcsq9xUBxhf1Eiy59jYVISylhvxDaoK27QzQyJO1Her8tIgy+OApWbs0MV0Dou+hu/JexZFtiLR1fHVKsqyNDbWNUh5IrzKAnmmkDaWZJqzlWP6ZiXvLZ/tdDZMCbPuWQFRrqht4+8EPonnueYxK9G25XdeSdQcfYbFbAErUF+LqCVeM7MKpJwr40psupaElWVpENGXpdRNKb4d8if9fcv5nvI6S2XZ61UZqyGQnq/ImiPOAUWDuzwvOY6DiGcFLTfW/anTaCLNriKlfO4JrShGkmo52VVUlG3bpjzL0vMI37nIKyxYNqsIIVMOeKneVClmTCkGbMShvp9SetSoNXhgr2IOAJAu56ZkzCr1XLe6fsY15HlXhYfyosYFy4cb3OvUD/R93whgKRot1/OZXSPiunKWq/UPyFgpukyRa1p2CNQrmXqWEWvFVF0WyFiioPQpWs5WoTLosD+prRFC67Zu67Zu67Zu67Zu67Zu67Zu67Zu67ZuP2Dt+wIh5JPsczafmux2k5LvFh8xXRZoMEr24ksv8adIZv/2b/0mjo+lnnp/X7I/SvyRppnJLg6ZRbNI6Oxf2zXR3tskiZoxW9LrNJCX8m+HGXqbvGjNXgtaxe8pWoPEfH4vwJKIpJzRvQeHItk8mk1NyM9npuHlZ5+R7zcaODmRTOWjQ7lvmWcmgqk8BNo/UrfJyDEjp/qeF4Mjk6FrbxCRwcjtcrk015iQh0dJm48uBrBIsNcgl9IZa4zTd+7iaCDZVZU4Dy0lJWvj9bt3+W+S6jLj3243YXMsTs9JSEw57bpcIFCib9ZvKrnjxsamqV/X8H3YaiBgbalKDKdEktn2Sn7VM4Rm8p7T0bmRclQkR5JTStJ2UKQqGUw51Vrrogu0Gfm3HZUrlCh34DmwiQZRng6VhrZqCxWzED1KZaeaZSwLHJAMUsd2wTrpqi5hEUWhaI+KEW/fcZFRTlgzc1W5GveAGV+NupdliYLZEkPGZqQ4bThELJVa72okMX0jx6nNNpliy/TNTpsIrSVlS1HgWp/IMSIymi3JbHnpDGHAjA3HRIkpG1GAjJKnsaXks8yQ1pap266YGVVb0fBDkwxQzi4N/ju2g4xpI+WNcLAiziUwZiV1XpWmJttkBXjxsq5MBlGzriW5QKosNUTRmm3yWPubz+fIuc4S/m1E5I8fxWhk0ld98nLlvP9wscDR2YocHljxVAS2B4/ZGc28qJx25AUrxI7Wv5ssUm5kYgOShtqOhzRTsmD5XayZS9eDz77U7FjJ/g/8CCmzsjNb0Xkkxp4uDdoorGScCtZsDwajFUk8MzFqk8piZDKcRuqZ1/FCGz4RjEv2Y1Nl0IdDM4ZK3nz9QFBEYdvF2Vjs6dGp/JynMlerOkfKbLrDeZnVc9iaWSa8T+14p9Ux63BBLgTP0zViG+6wnFmkdEFSdMcxRL1leVUGdDAYIGbGd5NcKJpp870Q10lwOiGfzpQ/0zQ12U+dX7rOLy4uTH9opnKzL3N2Y2MTR6dP+Pn3EWGWhZlXIefCtT3JcOd5bq4VGJRkYeZ5RZRCRlSQF7iwub/GJKlU/p7ReGDEHzpEyj58INw7cGx0iNC5caDk95n5vr7XhHxLrr9CUfihoocUXUy+m90IfXKOFczUKWosTxNsbsh9AqJ9b10Xuzwc/HPT7yGRtXmiBLIFBtx7miR+nY4WCCmzq4iuP/tTHwEAbG1fM/2se91HXxBU5XZvAxaRFb/0y/8uAOCUwg2L5RKnJ4J0vfeOIJHu3xPU7/h8aLgVFx45HmiXi7JEksu6Ph4Q6UrEz2g0wkZX5oPJztKH2N/cNpx7PZWrNoy1tSEENSTlREn6vm+QnZqNni4WqInq2NmWPvoaEaXLRYZeV8bg6ERQWDr/zyYXRo5dpeU3OCa27eKdd4TL6s2335K/8XuWW+HRd4Rz6PWvfFX6hRnWyeAMDZKmptyrrl2TcbfDwCAPlKj31/76X+P3HiMgH1egcvWHIpzhNJpwaYO8Ln0OjmOcxisSK65nOA5Q05fh5xQNjjwDlJibfoDuY2jEMDrTfL6aSFkrDAzSQXkGdU1kWYb5mATm5JuZ0o/LssysZ+XZdH7hp6FNZZHVH1DfYTRayWjX7yN/L4oCgS/74CLVbL4iGXLMuWZ17mRN9l3URJv8Of1S5n1hZZhxLj8gd6SOTdRoYK6E+p7KpUt/ukFoZNnn3J/UB1iUvkEWN2tF3iiPYGlsn0EKKaK6qg0KX9/dUnRDmaPm3kuACzLaq8X5GE841wySkfO6GbcMcrpS1DqhQlEYGOS68tFV9crWnlHqevhIOP9G04lBcGh1wOmF9E9ndwvb5M6aPxR+N+U5m04usN2hv7gQ+5FxzxueDTClHd3cknVqEKmeg4QcfUqin7NaIAwjuLb6iSpIIGt4OBzCIYJjg/6Oa5NAPfCxTJWDk/4ff272+sa2DOg7RVHPIFu02ZdECxQhVNG/V7CXfQn2pWgLRW24rvs95Pza6rLCFtG5+jf1FS9z+qhvo2srtC9xHb0PsX358++Xny+sAgX3QX1OfbasLMy7F7VWk6wQ264iaKpV1YWl3LbKJ8q/1UmNhAT8KnpkZN0voRYVqaLa9q7rIp/L/GjpPqH+RJKuzgr8/Ij7veM48IkSU66+yyinklxKKgKh+1KZlrDoByu6SQt8cIlEvCZCNyuVX6pARd4di/ua44WG11TPUkMVGkgW5tn13dVWu55nhBqUQ2sxX1VkhNxfsuUKRaX9qXNA0dJqI5J0bvotUo4p2sciTw1PldqWlGItlmUZX0jRRuo32nBW/KhqR7gCKqtCkf/ppNJrhNC6rdu6rdu6rdu6rdu6rdu6rdu6rdu6rdsPWPu+QAiZKL/rm8xrlqrCBTPGbgCfyjUeSQbOKQvsuYHhDNIabY2sWXCMnGdF5IHFbMHZcIT8hqotMVtC5RWrrnB6IlmL03clGv5jP/RpuZ/fwiIRtFG3L1H446U8S2drA31eS1nENetn27Zhwb/ODKy3Ic9ydPQU2+Re0cj+t999z7yPRhZLRr6bzaaRybWZqT8nD4HllgblcXQsWeHBhTxvFEXY35IM2R6zpZkqFVkWmh2J+tqVPNeSmXd3sTAcLcqVk1HGcTEcqNiaiVzXYPTdsdAiz4Sq98yUL8TLMKYqnMrIo+L9nVDJ5NFgzXTQaOKNN78t12Atc4/KGrHvoc+ss/I0TWcSVe20m+gyk6LS8EWp9ce5cAUB2GSt9iyd87MWYlcRFRr1XUn/OuyPVizPN9eMg+cZRIDyMRQco0bkwbE0BUhkDCO+QeAhp5xvpYz3lqJtGqtoM2uh9f9h3DAZBiNna1krNFmhHEDkfnGc78mEaJS5EbdWRDVs+s5Fma64Q27fkt8xy4CyMhkhlR8FP3tza2dVF86F6XIt+o5rZO5VErJUqVBYWBK9FpH3QNFwRVkjL66i9DzW9UbNBmbM6JeuMv5bqBWNxmi91aKGVp4bOU/thyHXrB+FWDCbuLElGWlFew1PTtBW6UxmVs8oV+rUFeJCFTKolsSa5sp20QjlWjZRbQ+eCP/V4dMTPD2SezusCZ+N5X5RECJhNk05qnKu8+FsiPGMKhgcb507jcZqftgciDwtULJmOWW9vL5XWZZGwlRtZUxFlPH5BD2+82goGdyzU6qZNVrIiKSbEUWxTYTcrRsH6JJHykgHq0JdlqHiXFUlMs30WU5tkI+avtilvLtlwazdC2YsU9apnw7PTJZ1kYm9OTkXG91sxYYzrsdnSpZLI3ere8nGBmWx47ap61e1PM0u+r6PWLmAOJ/0/dIkX2VzmJFStZR+vw+HyhXbOzIX5uRNiaIIDrN+miXUvSjPS3gq/6yIK64zy7JW43xJbRCQzJ6iQzSz34hXnDGOosqY4V+yX9966y28+Lzwmmm28PGTJ7hxU5S27j8Uzpy0lP4oytrwWty5+Zzcm1n1ZTbHmIoYERUvNjaZMXZ9tJgZ1T3OKKpcVkzMVTWDPGd1jZj7S0bVy1/5jwTl8eEXXzRcSprZV0nrKPDw7ncFcaIINJWNvX7rNr76JVHdUTRmTXscBwEmM3nX27cF3fvd776NbV7jlAqVT58IKjjJUrSpTFQza/rVh/LzeTvGPlGHB3ekjw94P1guQH4awwEX8v+zAhf3HgAAvvWO9P/dR/flI05gVIlUIntyLOt01uiY/cExqDsih7MEG9z/TGbal36tquKSYiX7gWuy02ubPe7BA3kmOw7Qpbrpybk8S0GETKvbxgYVsCKqoi7oF8yzAhMq6C1T8njRjrj2ipvxlQ8L+kq5VeanJ2gRdaLP9eSh9P/jWYYX7wiPzmQgNuIW0XfTvDQ2fXQo9vdv/o1/X97BzQ3/wj7VBhWt/ubbd9Ek75/yrETkZGm324ZbQ6WsDw4OMJ4IuqNNXsM8VW6ZxPBw6dzeIS9fEASwk6vyw0vyGm1vb5t31f1ZOQkBIOrJnHs6EjRJm5yLPT9Ek2pQS+4TyiMFy8K+JWvQoJw0Cx81AOUHoS0z6fJmEyByBPSzjPNWlUb6G6VCepUc6NLxo/wl/qwBcud97u/9VwCA/+MPhUcqbHaNUqfx86G3SwHu64pg1Yx7njqGZ0x5KT3O8Va3g5hrQVEbjqJv5wss6Ed4ygvCPasoc1j0ZVT2WRVXp/MZzlmxYBHdc/PmLXmmLMGCSmmb9FMb3D9cp0ZD1YjY165toaRNGI9lf7//RNa67XsGLaGIS7chv5guLjCeyHVj8pQ9Jsrm+MljLLgvF/R1lb8nzQqDOFG/bPxoyGepzblK/W71l9rdrtkfFFnUbcraasd9VOSjVLdT+bLmy4XxdSMikdRXrIoCea6cTZbpP4sVB+rnqJ/r2o4ZJz3/6D64TOcrhHKhe7KeO91LqrvOlWsWeWZ4MwdDlTEPzPe0OmDJ+RgQ0ZRUJQLlaXJXiCL9/lx9tKby/lBNKs3gUVXv/aq4VgrYrlaM0D/Lc1SK6Fdur0wl1S2DwllxCMlHbW/l/+v19TN1XZsKBd0/Xc7RzmYfTiEX8VmVkPFMmxX5Cq3C99E54XkepvRvHHuFNAYA23JQ0t9LM+VT4/iVFkJVzlIJe8PaVRn+UcWC6f8rrMxUxfOqZzlwiDxXbh9F7liWZZTK5zwPr5TfSmN/df7rvKyLenUOJ6qyyJSvtjAAT8N7xP/PlhPkvE/BsciIbC4uIbAVvbgg6rwqCniuqiu/j2PQdQ3CUBHbyoVVVeVKrfhPaN8XAaEtSkufnp6biaiHBYVTFUmJlB04m1I++HeFmNV1XXg8uM9SMbZKfIy6RkrHSuXktKVZgQkJ7tTh34llc9zd6KEmbCtk2dDTQzmsfPDjn8DGrmzaD1kOplKLP/qjzyJhWdGXviob2YQS7FEUoEOYXZsbUsBJdFatDEBEQrkg9HDBMi0toXC5mabZwjhnOQ9BwwsaXb9alccYAkYl4qoMAfEWnSHdhConRMYDfmxIrKQNJkMj1ak6pSrPGTZcpISwjyZ62FDJ3BrLhFKCLZluLY8BoskxdkkS3QiuwhZ7W12kDCxckHg0ajSMbKVN+Xct16hryxB/BjSoUxrKoq7MpqOlXK5K65YwARBDXJop+ZmHPOUc0PeBkuhZSEmqHZLhN1HIYFFhSdlQlZs28NEqQ0KHBY58Xo18t9czAaHVxrQiXlMnqNGiA1msyGHzK6WE4tTb7lWZ9MubncK+E4U+VlpOtoI5rgJDq9IzNZIqe65k2UWWmw2vTTnxGclT/SCEw36vGFzRA6hr2cgTHqB5OHF0w60sWBrk4RzVA0xRZ4aEVoMWGhAKowgJjZ9zKbilDtv7g2FFVa4CyDwEqFxpVdVmTmp5lsqjVqhNf6jTG3AOJvMxlql+ns4/+9XzfFi2zluZo2++JqSfw+EYKUn03FCdJpYilSVSXmuSaZBI+mU2H2MyuerUeN4qQKGOwWyuBywPLgNkxqHifLKrGimDvTEPsw77YGuzi4D93b12k/1CktjBBWI6QU2WKjG+jvnoHAvKqus7L3j4azabBuJtNl62vEgxGjKgzrHRgEGWJMbx04PZkg7ug++ewSFMXGHSHh3dwcXIkINOeZBZLpfwKIGsDki7Le8+HIxQqAOgZLKFlugkqLh3tGi3NfA6GAwukS7rgVrusbe3hylJ+vXzWsI7mUzgnsrh1act62iZXBSh4jXUgVZp3q2tLTP2Oi9jOm2u65o5oE6aKZksCrPmbEOiLOM4n8/N8xWU087zHDEPmEsls2dwq7Jr2J7ClOV+ZwOWfdYFJkv595t3ZS/t9yW4F8UxEh6UHBJq61woKiDjO2oJqK7qNE1NGXCDdu7tu7KWvvLFP0Cn02IfybMPxiTqtFd76juPJDD0L3/jXwCQkgUtXdJyDpf9WJU5jhhg6bHsAg5M+bDDAPSrX/x9ACJsUHMfs2jLKxI/urZliMv/0//yPwMAfPqzPwYA+MMv/H/41jdfAwDs74ivMSBJ9M98/DO48wEJyPVt+f7zW/IuH3/xwyhY3hl2ZT426BzWVYXRRPrx8RF9Eh7gZ/M5vv3WGwCA3/u93wMAlDmh8EVuDqU6JirRe7kpAWaSJKvyID3Y+krU6ZrEQZbKs9tMkIR2E2cMnHQ5N198Xsi582yGp4+kZCuiL9Mh4a+7vW2CSi88I0G6r33z6/LuvS4OnhW548MnJEXmoa0VNLDZkYOxpeT5icwT261x96GU6J2dyfM9++IPSV8tPByREPvd9+R5lfD32rVrxp7ee1cO7h/9qI1H70hJ/cGBjNuYa2I5X6wOfkymHRzItQfjEYZTFUnRQ6jMx52dLVN2rQTfUaAJQhv9m3tX7qMS7KhXAfZdltcOmFw7OjmGw8CJBpf02mIHmDSi3bl5QwJts9kMsSt7XYOCAWqvhudnJhjlMvBS0Q7bQQPzQsYt4oJ2ljl2+vLsT1nSnFPWuXCBEQ+fDVMMxNcqqtWhl8lkn1kTp7K04kXEQLAKbCbJEsv0KtlqxOdEWZn5G7McT08QnmcjCJWsln4F95lus4HebembhyxXXtI3ytPM+BNK1jybi93LssQwJIcauPJdIzJzfi57gtpvP45Mgm4xlWvMS/ELWlGM0amUiLmOjPcmEwL9dh9tlgY3OGc2tmRNTucJPJ9lsnyfARPNF6enZm7ntDGtjviBmxu7RnxHA6dffFXOZ6EfIIy4F+cz9rv0xyxdGhoALSvLuZZRr47+tpL45vn3lKiuSNlrsHLIBD70HFnaq9J/Y8PUNrmuIebV/U997XazYcZLA72mrD0I/o3JNwAYzCffI6Cg3w/D0Hxe92u9jlPZ5vPm/UySfRX4a/VWCdIyUeEavl+1IrVPGRzSZ9ZyrcBblVqrKICebWzbNfc5OGDpEX2+RqMFO5Tn0qClrUmubsec3/W99vYJfPA8dJhI1CCHKZFPQ2SWvrMGp5jItR1k3D+1P8zu79gmGL462nMtOpYJOir5dJIkhuTd5fOZZGNVmpJpTf5Hl3xRI8LDvgpY6lqiNL5hxCBOxiBTkmVwNPBGn0j3yDLNzdl8ofbKV5Gb2pRkeyxHU39kuVxiNqMoBf17IzjgOGZP0GtVBHYUyRJ1cZUK5N/U1iVj67Zu67Zu67Zu67Zu67Zu67Zu67Zu67ZuP2Dt+wIhdH1PMs17W9cMRO3ePSFStCqJou3v7GKT5Vkq0ffhD38UAHByfGZgphWjc2OWM1ycnxkUSUgCwZIohSBuAJZE+s5PJZMy+TYzs3WNLWYXW4Hc97mbkmk6PT0FgqtZbpWOfPr0KRaZRIIV0nyzLVH0LMuwSaimQmrPmcGxbdsgXBxb0S8umvHVbKmSuyazBAGfQbMxpjTIX0VHFfapmRvXsjFnhHE8kAydIq9KyzVkpsX7kC2+768kjc+lbw1BZzJHTSLA2UyumaUrgrgolAyPQ3nqqSHcKzEcybX2XhKJzGv7B7zHqZEBHI0lS/buve9ipy9/v3NTyCZLRnWLLDeZkwkRRUGoGeYSCTPElsLhiWypUK/Kx7RkiZHU0iqRzUgWSXRUoGiqEigYxXY8lY+Wz87TxPS/Ihc0kD0dj9AnAaCqYE6I0pkvM5SM6CqkUUmfG80IqaISGCCvDOTSNRmN2YzleEGwKs0xEs0sXUpX5GJzypxatsyhVrNjyrIMxLDS7LBjyjXfJWw5JXqlGUYGufSYcOk2M3uHJ0foMBN9OpHsZ+Svyj+V/HhKFIBmIlt+iAnnVcT3U2nXqqrM+2nJgpYP5WWBMSXeI2YMLAA2MxIOJe9LW75XJYkhXa58IhSJ+pinS3RZojQcyHvlfGcHNYZLmb9KWqmY6GKeobFPIngiwpSo2YONKeVXWyxZGibMEi5z+LRlHokXLYvvjgqWf1X6VzMcUeyjEa2kwgGgrogWKVeIwZTIoiLLEXdkLse0N00QoRhFOGemvOb9chJp12UGhyR/PpE0BjHUaaLJkghDCqiZDhcICTvW51M56Cov4CoStLgKa201e4g4rxQxaLJ4s7mxvyrh6RLJt2d18OCh7CGaYXWcFWlipqThtI/z+RLNpnxX7YgSq06mU7iOPIPCnJeU77Z9oND9RDOdKqlbrVB8qywhJUobkTEKSrSs714U1ZXST2CFVKnq0oy5kTtWaHNVrUQZiEy6nA29XD52ueV5bmy7/k0J8qM4xlQz2IWSUDYMAjLn71KiYr0wMPKyF2PZy5+wfMrzbSSF/E2Rifrsy9kcWxuyzmpFfVqr7OmUe5ZtBAOI2ksSzGnzVGr1jW9/EwBw59oBKj5fpytz3SBZL85xdsJ3YNZwd1P2+cD18OiRPHPDeR9xZpoa8t4BEV6NVgvDsSBjt7flGhOiLjrbfcyIiupwbk5T8Qd2tzdxbywlX6eP5P0+/399mX3cx/xU7vm1bwtS6N1MfJPsO/fxF3/25wAA/+gff07enQjPf1V6sGjnxk3pvw0S30dR4xIChAhPojXacQOf+dSPAgBe/UNBCJ2wRMWyLCPh7RCJrei2LMtM6czWhlwTroecYg8OkbxnxxT0sJd4+SXpP127WkqaZxbcDek/JVZecNw9xzYo0UBpBei3VG4Ej9LrikCdcV/buLZnSscVWXDG0o/JcoDujvhoz+7LPu2TIL8V+9jvia/h+OLnPHNdxEzK0wHyDcrNc34pMf/Gxiaa7G9/Lu/+7OZthImMxa1bgngadumjeP4KXUoURIsCJffuv4utviBN7PcJhwS+a4jOhxPphwnRs7YNxJ6MyeSYpa1E9N66fQPnZzJXk1O57+t/LAi5e/feQ9Xc5nPK8y6Jrr937yF6RP7GAdHPqfhsb775DpqB3NsIlTD7beWZQSUcnoo92Lgu/v7ujTuYE5HeacmcGA3P8ITle4d8Tpcl1948gUdfRAFqITPhvu3BJwzIYfmTIdmtV+iThHsAiH4p0wVSzbArqSsRio0oNkjJgOtEgVaW7WBJu5jQH1M0im0BtY4FfcsTlnG2ez34gdpY2skLlrVaUvYBABb9l7rXNWgeLacO6TuhXu0rptSaYjdJWRjRDduSPvaJKLh+8Aya9LEe3JOSwqMT6fM0LwzSR4n41ReFY+PGnox5n8igOUuMHc81SBWLa35D0Z9BCNuSuarE/y7XcNyKjZDBnNUairiKosigshX57jVis08qQsUjQiWOY4M+0X5RIuKszAxiR30h9Rsv75umxJu22vdds0fduCloF0XCl2VpSvh3uHeob93zAlP9MSF6q6VE7VmBFv0kfZc5/c3C8cwebMRgjDhJZvztuKWlxZa5RlpkV66JEihr9SlWJOry/8z4JjlRc3qGOD4+RkabbCobeM2TwyPTH5cRxgBgna/+7fsq3kM74Fo4oUiCxXOT9uN8mRq/1lBz0G+vq8oIepj+sHRsK4MoUmdIQUF1aaGkeozOY0G6yucvE5EDUj2wQmbJNRR5HHo+Aq2EUDRapUicwKApE+75ikgCbNPHxeIqcqosyxXhuR4doKV+tSG4dpTMmtA3u7aN8NCCtjnkvuHUldEq8LCSsAeAsrDEWf1T2hohtG7rtm7rtm7rtm7rtm7rtm7rtm7rtm7r9gPWvi8QQnPyOIRhiGuU5N4i8XFJor3ZeII5OUk0g6vy1u1mH1EoWZnpTLIKi+WIV68Mlx3BF8ggkbWLwRDFdbnG0yeSffvYc5LBuR5X+NhHXgYAOCW5OMC61+UUVqWRTLl4m8TH73z3bXRIqDhnnaxnSVbHCwJUWtPKSOHGntRL93Z2kLNW9JjykqPzqalPXGQrrgsA6LTasKFRbJVVlfeaDVMjL608InVOcrWiMjXSAYl9mzHrc6vCZEQ0+7mlXCCeYwjmcnL2AAAgAElEQVQNd8mf9PRIsh4opityL1tlOuW/ebpEdimLAAApo7oN30VNsuVDcjHp/ZfLuYm22+yX7e1NzJm1tIkcUeSN67qGu1CRKToOx8eHhntJJatVnjCIGlq2bQo8NUOSFjkyEgNr03coi9JEdpUfIajl2dOyMESnir5SwjILNs6HMjcDIqcs1qU+evIUYUAOGiVNZLB5193DbE652PKqPG2z2TR8P0qYjGzFnVPVJF1mjWtZ5SZSbSLsbK5nw6oVScHPkLzMdW0Tgh+QQ2HKuujNfs9kiO+d3OP3mHlbLFARTVKRk0nLlq0a8Hgfj+aIyU24sA3n1vBc1rV1CYnjK/qB897xWXNtpwAzIkaisqpRvo9DSBFFl5vWoGtGsRU3DOlkrhw7jN77tgWrVhI7rlMiHyzHNeOVkFBxxIxuUBVYaJaJstY2s37WsjJztcFa/jggCsMq4KrsKHlBFDFo2RWqQvmtmM0nGsC1bdj2il8CEPSXoow8IvfqMe2V7SJhhrIq5bki5blwU4ymMn8VaRnGKiFrwSGtSsX5lzKDHkausUnKS6HZvMdHh4ZHzsicstmFjQ5JT5+cEnlGXqN0OjfZtFMSZrZ3BIGZLjNDYKy160b+OwiQcF3qmmg1uqihmUdmVYYyx70wMnMtGXEsOL/iMMCC1+py/trkpml1ushpw2rOE62bR10iZIZN54xKndd1bdaZZjUVLVnXJXzabUXUtduSoVoul4YUWtEUNhdalhYmG7azc1VOOI7j7yGj1szSzZs3DfrKV2LJbhd5rWgh6dvknNm++dzwJSmawTfIsAIeURpxLOvaIxouCAL0ejJ2y1zRUIqO8pGmYvdDzn+VbK6KAlNmYPs9mUOtS2S7W9syd1xm5Vvcp99aLnB6KBnLmn2WcZ0maQ6byLaSGWB4zqrPmOEcTWVMdrb7hh9ijyjfpdb5u67hPZocSRb+YlOQJ4ePvwsuPfzeN74EAHjttW8AAP7u3/lPYPlKxi33aXBxHT8+RMU+Pn8se/AzLXn33qJG4so8vrsQ5MH9pspA9zGd0CYRFRXSRuTzBX7kY0LW/Ku/+qsAgF//u/+59OvWNix+ruDGbrhLljNj53WPi4MIm3u35PmG4sv0OuLP5aWNFhE0H/vIhwEAFo2uV9cII5lPC8JnbXZQEATwXSH9dui/lbQteeXh9nXxSWqij1+6Jtn8Gwf7iIjuePllIsoviJCxCvz5nxXJ9Q7Rfde2f16uuZgi5xh2SI57dixz8FMf+qvGduo8VKRQGMRoEU38M5/9cQDA7u4+7p+KuEezKX9TJHwYRLigneltkB+RvBN3nttH070qlrKh/EtJapCZKgGOSyi/raaspZOzU/YjeWtaDTwMhFvmzh1BvF8jb+Zbm3cRbQsCpEv+KbUHkw+/YvglFT13wD2rdecZOC36oJxXZ0QD7PQ7KJUDJH+L/XFL7rF5DfPH8u57sTzvqPSwPJG1VMVEhRCVVqaJyY4jkPWo9jRwPZPFj4JV1h8QaWgVLVBuwUyJeOMYHknw51zryqkS+h58X/mEyPGiVExVblCBuRJoczzsGhhfyLz36eMZ/pOyMghv3Y8U9Ry4rkFIKKLDtpyVPDfngBL8FmVpxke5TWpLn9dFwTnZoD/c7JPjx6uxINdqGCmKWHlyFjiiWI/yKXaMVLyDe/cFdXuvEin77X2ZL55nIcnFJsQ+Ca6V68cCZkTUOo6i2uVvk8XccCTquUQJ2tNlgjS9ylPTDmNDlr9URJHy9Vo2PPrdkfLTXJKDb0YUrFH5eFzmzSQvH/dktW9FXX2PQIPua3EQ4iZ5uBTxo4INTTcy+6sh8ufYTqdT47esuF7Iy1pX5gykKFj9/sVwgJT/bnCuZ2UBi30DM0/oo6S5IUjWaoZIbXQcIVkqQodIKfbjxdk5Hlfv50wk0siqYXG+7+3tmPcBgPlkuiLcpg1TvkdAzkUA0GAf675RljUcXCVI1r/lWQaHi07nuKNzHaXhntQ1hFz5lipYNRHoynVkOyshDr6PosQcx0Ez1uoPJTCX7/mOa/jZSlf5iFRi3jbjvBxPr/RZWVfIlQOWwQfPVmEbmPObz0oWw21q11ek6wHAKleIIZu+yILoOUXqW3Ztxr7ifbUSxnI9eNafHu5ZI4TWbd3Wbd3Wbd3Wbd3Wbd3Wbd3Wbd3Wbd1+wNr3BUIoZX3dYrpApVFAlQ9km8ymhodEI3Ca5bWQGWRKVV1lb+90OgYalC2Uz4G8FmEDC/KJtMixc/NA6ptPnz7Ce/ekvv/2NZGEhU1kkutiNJLodFlotFkiiBenZ7h16xYA4PXXX5f7DiXa/JnPfgYVI365ytAxTHh2doouERZtZkqv7d9Cg9kLkwlQRZVGA7OxRPk1qq2RZc/xDWJBmeT9S5FRjcIuyL2g37f90nBH+NFVpIpdVyaTN6C0tkaWy3S64qAwijQaGbXgkWF9wSx3wlpXVA5CX/r9mGoMlcmeJHB91exbjZtRduH7bbN2dz6dGS6O/qZcUxVf3MA2taOaJR+S68jzApNVn07kfRSFkmUFPEZjVXFBZf3qsoLFZ1C2eM0aWo5k5AGgwWyH8pEUNRAze6/S68pK3+9tGrnpiCgIzVw4jrPic+I7qKSp5wWmjtQyia/YfP79ykGZl5l+8N6n0FVVFZL0Kn+JQc04lqmJvUZUQu5qNgNYHku277lNZoNVMaPVRzmVebUfSYZTszNlUhiEVEQ1P63FdwpgTrlFj9F75U2pbQsNKv8khFHp//0oRJgxU3ZJnUJRHmo/NNviOg6UWkDRXtVA1lZnewMLzT6Q88apmHVKFtggr8KICJXhUGVqbTygzHlrS/rKItKiDl14NeWHfc12cM7VNTxcHa8447sXS1NnXDCDHjQ0I2PB5zpbUj4+ycmRUpZwvFU/AJKJUVRDQHKjeiLzsLu7j54lnztayHspJ0LpJYZ7SBUaR0RjNrots64GzKbvbxENcXqGjiKymH1a1Kpg4aJW5QkiwpQLK/YjzJaaJaQsKNdWnpXYYDZ+kcg17z8RO9LwVlkqS22TqoUMR4iZLTQqPFHDZI3bXaoU8t3bDQcFpetTrZe/hKTxmKFRxM6QCEDf9VZ8SURHxbS1k8nE1JrrmGimrdlsGXtllA8pd++6tslcqb3bpUz1fD439lr3CUVhhWFoUB7vV1JxXXeVNeX817mxtb2F02NBBDiqrFHXqDh2qiqne8hiNESjIe91/4GiPqlsErqYkTeGX8PegWSYr+/dwmKmaCr5m9qFII5Mtvr9CiyO45gspEGLUaFtZ2MD71Ge3XKvKgUePT3GT/7kT0of8VonT0RVKg5CfPddUYV687tvy2dA5CqWK0ljtb9+jIpZ09NTQQYoouBjH/oIfvQjfwUAcPxAkJMXp7Q7toMjIke2DmQMHSJHssMj/BgRNL/1tjxLl2pBUeEhXcr1O/QZjg9lLtw8eAHvPJXs/etzQWl8sKKMuQuUBZFSRGQsiYqIOiHepirW5p6sWVVRarcbmCnPHaWDa9qhvb09M7c7lGdG7aPRot/CrKQi6hwnMjLuaq8U/TafPEbckM+1iDKARfXYxRJN8lpERHuplHVuOeiSIyeqxDZduybvvL+3jSdPxSZcvym+3c/+wi8AAAZP30OHqqULIi3feUhFpbMn2GzJWhhRZSmjTfJmkXlmlZ9Xpahup4/hxWP2h/Txe++cY05bknCO5rTfnc1NVGMZJ7+j6E95lo2oRgnOJyKffJ+o22SKmM++rXwpqpRplSipCNgjj52l3Fuhj+e37/Bz0v83I9mv73zwOlxHx0meZTCQPR3YRE2b9Kkfls+rbTo+dNAJpP9VnbagbSmzhUHw/dBznONtytxHbYyuyf7Z4lzLJiE+9ZLwJn3xa8Kd9cY7gkpJbAce9/im+kDWKpuvCpxWroq83FvLytiLwCUSV/eEIDRzZ0rfV+1wXQsvBwAUdIJUkcx2atDlN0h7RXY1wggRs/6jcxmHbl/u4XkepiPpD+X1VJ7JosjhO8ofIz+bzSYuiLZS5aHa4R4eR5d8QnmGJKVvU66UI3VfO7wQf8QPA/QUvUO0nXK+tNtNTImOsdh/6tc6lo0lfcOA8/jkTGymZVfCiwegEcs6HRPNv7ezjYyVDmf8fLcpNqDb7RvJdsObwn3K82p4RGil9IPrwka7IfM+jmlv1E91VzwwNnkOA/qUodcw5xejZE0+zF6vZ3hpFGE/JidTbtVGqVP3W+XnS5LEnKsUabJPVa2isg0vpavcglxTvXbLfD7itRSRtO17K99Y+W5oX5thYBTHfI7tbDHHkhCpUPkriUSfjOY4PJH9JOcZeJfPd+vGgVHe0/OOIriLZQqLCB1F2el9Ld+FlV1Vs1V0pOd5Btmj5yTdky/OzmBTbdDnNfWM9K1vv4Fzciu6OcfNU0Q7zJlIn8WoBddAViv6UGMBemZxUWulAedThQoVf6nPHlgrpS5F9CuXr/b/bDZBotUjig7jzyRLcXgi+4tyPSnXq1WvUFfKX6ln7yotkbOP1A9TLqLaARLGRAoqtarKWF3WSDh3lLNSkXh5ulwp5/H5FL2U5ZVRwf6T2vdFQCgjnDBwPVha10UiKIVFNWMPJSULdcEEXNBWsTAn4UYg39vlIexJOkOeqQahyvOR3PHCAnZkEvhNGZRTwrOvH+zgA9sMbnBUOzQyrbCDYUlHRzGkXEz9vW0jNffjnxEJWdthyUlZIrNUYpRy8wOWSDhN0OcypV+3X7wFX514leGzVnA0l5LTSqysB7sqO0ehE6OmwzJVcrECYyXZIsRTa5/qukZFR0VLe/RwGUQhPBr8RxeyABYsHfFrx5CO2SSLUyNTVBUYc4NFAm9L5dKLGDkPRREl6ScJZVyDAMviaplco9HAbK4kzyQwprRmYPt49s4tXpeGVGF2tWscojmJv2z2T7pYINANiFLbBUtGwhrIFEaYcEOiYfCyEjk3/YgEXseUBd3t9GETFl+SFF0JIBezIVIeLjsdCWYZCObuPtLs6uEm4UHc931EeyST5fv1eBieTqfIHLnPZkOc0Lqq4PG95lpKyGdodDqYs09tHmY1oDpbLAxRozYNoOZ5aaJDG5QwzTsk2S0y40BoyZMaF8sL8ejxI/bVVRis7/vIWf6npI41+8CrVqTQZa2EuAwWOQ4WavF5ujwnSeP2RgvLQt6v5KHNdSw0GVC+IMRe16kb+5gRaplrGRTXfLWs0NQyxgWJUWncbds2DmmtBNwOyYOXSzNOcUuec4ey5JZdYcr1oWVX9YTk3L5rrpVDS1lI0Oo76FIO9CGJUiuWhzxz5zmcHYmztVBivUid2BQB6/AcEtGjqtD1ZG43IrnmhPb1xR//LM6+8qo8Hw8EFgmr0zmQkYzb5uHBpwy0Vec45+d9lsItLFlvhTfDhQZ5eF+fh+xnbt5Bl0TrCrsf0Pna3urh8WNZ413KwDuXguMLLV2hLWNBFoqkgm/T7iYalBGbUdk1kpF8fqtHcsz5HFWsgWvKz7OUonJsxCrxnop9qrRUrw7QVFlYlkuokxc2m8aZUQfENbqjFXLnKpFzg/PjydOn2GMJTE5J7intSLbI0aLT2tmWz2sQzvECxNxfpiMG60mo3W21zVx5+FACBjvbco/T01P0+2I3zs/l/T7yESkfevWLX8Tutozle5Rn39nbhc2D1XAkh4xOnwGXtMJiwJJWlY3mQS0bVQDX/4SHhcOSZc9WaA41ti+HRN23h0dD5GMGJClvX2oJ47SAmxFeTUf/9r4cKBfDIYpMSxXohBYqUrGBH/nkZwEAAW3f7/zGv5S+e/QYlhKW0kbEtG25bWHEYGmUyvfms5khNZ5zrdoss/3223fxt379r0l/1FLylMZSuhTUHlBLP9Rz6fed3xHZdDdZ4pP7Qsr6ob/wywCA5i73vMePsT8UR/9v/9ifAQA81LIh10OjJ3Pu37KFBPnRXOZHHMcYcz8f8pCeaum1YxvS4Gwg17rZ1UDnBONMS2KlX0LuWUfDR7hxR/rb2ZV5kiWlIQwdsGSJFW44H8/x3Mtaik/oOwmgk7Ft9rtGU76fMNg8Hk8RMh0QdqUfFgsV7+jDIll+yjKIl25JP84nQ0Rcn9/64hcAALMLOWAf+T5eeVGu+cHbFNOg8fzq730Dyw1ZEwGDAA36XmejhSlxfYs2d5tB2Qd330OHCb17UymR8n0fFeetSdgwuH18/ztoMbny8G2xnSX3l1mSokG6gYTRnscssep2Onh8j4Ek+lAqzV3YAKutMGOQWon5a9syvtNKrnuVRPVZinVKO6DlHd1mG1MmSVTaOKVP1G22YKX01TTJoKVc3RbCTQkQdDWxwkOjb1s4YLVLzWBz1toEc5HwvecAAG+/JYlVhD3M6KOpXdT+LAEs6D80KdWsdA6BZxvC19KZ851Z1uo08eitNwAAPQZttOQ1y2uTqCk497Q0aLEcm3Jo9ZH7ntqDCWy+a8YzQ8EHXYynaEb0Ayr6j4HMl9wtjN/c3pH55JUpHn1d5Nsdl8G6QMmzLRRL+tv0k+JI+mA+T2AXcu8GZO8aHYrN+M7hAHsHEvRVEvCEfVUWBRoMtGhQIGbS7+LiDBETshqLsW1Zr2dnU42Bo9+V97rGPWwwPDXB/f7WjSvPC8sydseIg3Beuo3IJMVeuCaJg/GjezifyBj6TKxGLG90AwtnLHcDD9vLhbzXeZ6Ycp1N+q7I5W9Pnp7g6aHsiTHJzV0GyJZlZZL3StY/nGupdhPT4VUBlZhlvednM5M81QDUZLqaewH9bpV3Pz+jn1XVqDlX+ps98ztA/GItt58uVbI8RcOWMe9vU+SHvuvFyQVC0g6kpAA4eSQJiCePHsFx6CNrsIdBGFQ1Qq5Hne+a8LQ93whKaLANlgqdlEbIZ0kRH/WB0yQHuC7bPFPdJEH5YjyHXyhAQv1nljS2Gojyq6T7dDsR+gFK7kMe/VMN4riuiwsGL3MmLm3LRs0xL94n2uG7jgFBLGs9y2rQFAaoonNBEyK+75tSx5SiQlWgcyeHw/XsKsiAayqMOyhBsEYqzzejzxZ6NmK+V8Dxc7dkbBenA0RTBj0ZqHz4UPzj7d0dNDcIsKCwT39P1ls2mWI4OcOf1tYlY+u2buu2buu2buu2buu2buu2buu2buu2bj9g7fsCIaTlK6hX5FoGLs6orOXY0LoOlxkNLb8qytygGFxG6V1HYaMuMiJoNBOikMid3gbGRKHcZ2T5Q6FkM16/+zp2mPVZklRao24eYkMWpRHGKcnEbNcDDCyVL1iyLMF1MTxR2WGJkjYvSSB6GTNsjEAH3Z4CDjAgFD3SzHuyxDblsMeMrNfMWmd2bUqvxswEmj62bZO9S5k9Vi4ry3KMpPZGW6LTmglLswwpQ7M9RrUnJJmuE8tcQzMqNjMARV4gY/bOJ8TNZqR+uUxQllfLmjRz43kBHMpu1xUlwGdLOCRIHg3lvTTz24pinDDK7iljMbPxyyQz13UD3o+IocBTTMGljmDmfjFbwGJUulTJZmZn/MrHaCmR3TlJD5VgdTAdo0VkQBgRpTNjxjkMTFlewIi5R+zx6fkZAhJhj0eDK33mVxUODyUbqRKX770jqJvdrW0sKG0+OpNo8ebGBvKFzntmHIiGOHzy1Kwzzd7rXBoOhwYC+n6Z6qpaEd4NmGVtMDszTedIxsmV7ylJbrPbw1Oip1xGvqtihVirnatx6ZrZtf3epoFasuLDkJWXZW7uo/PZJnro4uzUzPfsWLOuDk4phZxxYS6Y6cByDkeJcInmGxI4dZGM0HBkrc6JzFDpz6oqsOTcVJi5R8LHRbI0Y1fw2g85fhu9LhJFQTG7UGr2w/LNvJ8yW37B9wtbEVzaskqlwwkRv//eQ5OJslQil2iMtuMaWWpjGGzLkOhpKVHI8rMvfOn3MZyIzeqxPGyPJS1PnxwZIuaamZTpTD6bpwkavq5xrmfaJBSWQXZWzNy4RHaVxQLnZ9KnrY7YFodk89l8gi5tnqJX1LZ3treNbVFp44rE7qUX4Oz4hK9MBI+iT5se3JglESRor8IKDkuK/ZglVWfyXouqQtmS50uGzDRzTKfTHE+IKLp+8xYAYKfFDKTrY06kSYNk8Qr1Pjo6gl0oXJ8Em2NKXre6cEgCfkbp5emIcyCOMR/LnB6yNKpHMuWD3etmHvquvEvCtK3n2xgwG6zEgxZJtF2nRsKSlC7HW//fbASArbaM4zAYo6aoQqHE5Cw1u337BUwH7D9m+3pKSuq6ptzV4x7p0f7mWYXOhvRbn/DoJqHQ59027rAM+4Akpls7Yq9ef/11bPB7NTOWTWaO33v3LgbnR3xHonxZYlW7ISwikYbvCZLj+A2RYN4OQ/zwh6Vc64KI4cMRSWJRo6HoQSKuRqMBtndlDO7fk7Krgx1BnHzxq1/FP/nH/xQA8Df/9q8BAO698VUAwOnjESzKMcct6YdP/Iiglrr9Dhav/q48T0f2Fczksy888wLyM/FFXtoTdMGHPyD3G84mqCClURvcu/Mzoh/LwuxxSvSdcH3WdW0QIz2iCD/4siCMfvfVrwKBZMAvKB2+3SV6xguwuyf3fvxU1puTVQiZaW+QKPnadUER1bmNiOt3OiOxNUtMdvoHKFgGpiUHHe63W9v7hhB8OJJ5P11K1vrWxm2DhOsTGT4jYjCvcyyIHD1+V/pMy7ue+cRn8YmXPg4AaLdZw0jb+Qt/qYE3/kiQGR96RZAqd78jSJJPPb9pEIyLec7+kFKM4cldhD2WhXIN7e8c4N7b3wYAvHBHrvWHf/AH0scvvITH78m861DAo7MpfZvMc3iuPPPjJ5LZPyAK+tE7b+JgW0qNBixRPSU6c//GdTw4Fbuxf03G5t59uUe708GSqHtF6Zq9//wCbZb9LegXPBpJH1/b38eE19cSGiXzTbMMGZHDSmOgJRU7+3uGiXnKfUx92tgLUHJ91RZLhRcJWh36G0TX7N6Q+fxHT04xZYl1i46AowgBz4VNAu4Lln589BVZw/PhEEvumzGz/ooksBzfEPDnuQpekEC9SE0Jhkf7Hbq0gZNjI9SiCIH5mMTinm+EObSPtdyo190w937vPSmFM3QQvo+Ue/6UvuViPMScyG4tl9NS92Q8hU87mhERnVN2fm931yDr9Rk6JJVOkxJPDoXgXlFRHVYbWJUFbr0GVaJiC7vX+kh4nxosZ5jLOx/0+5jP5HODpw/kT7RXnXYTFfc63dehFABVpaBgs4dPiJZOswxB6PDfMncy1IBKgMvXMCbyuJxkcDm+nne19G7T9039jp3rOUbsf5Gl8LUSgI7mhP506MdY0I9dyZ5TWGUwgOspbYM803jE0m7Y2N7euvK7kkTEQRSbknXd+22SxSfLpRHosYurPjqKErOBrHUl4o7bbbQ3ZP1Ol1eRSGHUQEC0vqKMSlJfJOkSvbbM5THHOXBWwgkdoqkb3IsvLljymiWmRM9I2fPak8kEG0QD6vupuELoe/AaPEuxpOrh4wfSV1Fg1tLClCvyXDxdoLfBZ2EZsvoQoR+YtaRNS8Ity0KbKKxNloQ6Vm38I10TTVJzzJYLs0YDg+p2zWcd/yqZtI5bs91CyvOVX2sJdMH/14gjFYjhONRammgh4t+mCdFH9CmrusR2S/pxyUqd57h/xrdfwrNtGe/zMSt1eP73fN+Unena1/NxmiQIf3gtO79u67Zu67Zu67Zu67Zu67Zu67Zu67Zu67Zu72vfFwghI3WbV0YeUgnGrEvyaxYf12L9nsfaf892AWadYJFjgNn8Mq9Q5leJ8ipyvgwWY8zIuZAzovxf/P3/FgAwnlzgU5+U7NHFQLJ+DkkNx/ncZAQPDyWDs0HOmCTNESr5FaOcLEdFFEUINELOpiRyYeWb6HnQJPmsb2HB2uetvlzfITKg22tisVBJdBJ5MUvmNJrIE5W/Jkku+yovU/NATxihVWQAqtpIf2sWohWvJAkVETRntF9J9KplZWQNlStHSUYdxzPyeh4zPhk5Uix8LzJICbbqur4kyaj8SZaRK88YZVYitbqyDBpqgzX8WjfvL2YoiBJwWLuPS/K5GmEFeSL02fM6R05ekP6uZABHU4mUn58MYFPOeklS08JR5JqHVkfurRmOvCJXVZWZOtIZo/w5s0Jf+sofISOKReWZNWP38Y9/HK994cv8PAm7ibi4fv06bnxI+Cn+8IsiX9yKYmyRl0Wz62Wb9dSnZyajoZmXlSR9aerYRyTWaxP5FAQBSiLxUs6ngvwKo+XCZAU3memsXPJBNWJES7nGfEA5ZsoL+1ihajTavySBbtRow691fOWdFQVT1sUKwaSym2zHx8dmPrXDlXzuYMC+JbndlM/uuq6ReC+4lubkMUimS4woqa3y1HW1Is5T8l3lVir5LM1eH4dPn7JvWZfuUdI0r1CQP2NCsm2XmazxeGyyOipZC2ahs7TClCgsm5mKipK+g9kSM/ZRyfkR0s555SVZTma05rMl8jaRM7SZWv99Oj41JMa6FqbMFKfLOWwy9Xi+cm/RxpSFWeuFklCT68G1AkPqDa5/JbJ0HBtzkuh5XP+aGWk4jrmmYyTK5ZlOzs7x3n2xzS2VllcZ0kkGi4TkYL8sKUJQokSliEHWnnd6XTRodzVz+3M/9zMAgFu37hiiYkW2KDdQkqRobe7zPswUcS+KwtCg1ww/AvelwLLQbiu5vGbHJFP38OF9REu5xt6LIg2dpDIeo9EAoRbRMyNVcw5s9juw6qv7S5f9cnp2iFZHspDPPScoBeWj63a7iBtKfq9cQPK8L770LL7w6lfknbnMkjRD4MvcdGzlTJDsWuxt4plnhSNmRrJ9JegsqpUMrRMTBTdXIu4xLoiGmhM10yQi6fTsBG8dC8eDkqr/g3/4Ofnba1/C//TP/lcAwLWGICZ+8Zd/CQDw7Asv4utTueYff034Y54eCSC4bSMAACAASURBVIrll3/lb6GrSIxDebGtm8KrsR23ETBjW5DrL2Cm1EMN9318fkmSGBJ0i/v5aKbcLV38j7/xvwAAXHJt/YW/LJxA/+r//B3cfEEyf3tbguT4R7/xPwMA/upP/Bhe+/y/BgB8lv3pc23kDx6guyk2dsz9yGFG8Oj8yKzZ+akgOjIlnvV9nA+lP7Qpd1Rd1BiRYH1CXowf+cQnpe/evIugLxwGPaLfQl/uEfi+uf7mlqyD+dEhTolw3b4tiK47HxK0kTWvMb8gBwg5BeOm+FWhF8LjvJrR/ihhtWU58IgWGkzk2mcngoa9ffsmvvXa1wEAP/wx4b766I+K7zYfXeCYaKqlQdEKyuns/BCDVPpvpmiZUPy67RdfxE+8JP2uUO/mKx+Q5/VibDIT/TLRJdpezjI49Fty3cujCC9yPXlE2L74S/+eXDoIMTunVPWWZLLPiVaPOy3EJOP9GCXOY/IaDU9O0NuR+Q5DhizrLAhDJOwjRSQqN1MYhjgnWlfR952O9P9gMEBAvhhFASkqM/IDTMhfYnj86DP7UYjyXPbBjD6NksIWVWUEDRSBNiWiqemHmFbkYGnIPJmPZ/BImnz/oeyf5w9ItD5P4GxIf6vfgVo5Akv0iFpT3sG9tvTnKAHaO/I8J0S+dnr0aaIYs7nYe92n9TmDwDPIgJJIOj2XhH4ET+W9CYItuccOhgN0KI7gUeZ7e3eHH7IMP82M6InXXxPy7Ft3bhuf6/mXxO5ns5khWS6JuFTOScdeER4r6jaOxW4tl0uDjFUUppI3V7VlRBGUB0mvGQQB0kRsWUgbXfIzaVZgRk6voKIMfEsQCaPZDK0NCobQz6+Jsl6mS0TcXxQNlNJ/6fe7OObeqpLoXmCv+occWo8eyxi5tocL2ryc+9+A/Dae5yKgf9OmTemRwDyfTc2cdJry8/hQEBZ+6JuzRq1QdJ4vyiRbiatwA7TVjymBTlP5j8SmPSVKcjvuIyNSTRFx2/SLXde/JEohc0bny1a7aZB3KgxkVyTT93xYtAePyKsYOS5qipyor7EkUqgRNY3fofumSpb32j34RPk2G+Sp5Xou0wolEcYheeQ2iCRLq8KcLVNKvHuKSHd8xESahZzjc6KjbNuFrQTtRBUnPJdHUYQkVYSbfGarI2u52WyhxbnTU/Q4z/FGph0wHKhenpm/+ay80PMSAGzt7LCPiETSYpLh0Jxf7JxoINrVzEkNOfSQfqpSQc7T1Ng3i2dFPf83g8icT0H7oXxNYdhAuyvjutkXxE/ck747PTtEocS7JIK++6YIS7z8wov4gzfElzmeie3c2ZN9YDqdripmOJ9yIiPLsjSiXH9SWyOE1m3d1m3d1m3d1m3d1m3d1m3d1m3d1m3dfsDa9wdCSJE0jm8kN11GbNNSs8m+UY9yGcdaErHi2N4KfUKUgUbHHduDo2ouzlUmcjjA8VgyIIOZROm+9ZrIzP74p34EEaPF9+9L7XjVkgziVqcPixKQTeUMmSlSwMN8dlUOfkjVKns+hcsM81wlTJl1jYMmRmRD16j/YDKAzbrCNqP1YBR3OJ0YqdSKKfQtRqAnw5kJxWsEVFVFKpQ4PBUukya5HRR1kWeJkdJcUvIsZTTXdV2MZhLtrRjyNhmi2DNZlTxX6WBle/dQ1ypfSXQJM2ewKhPRD5mtUolzYMVQr/WvjmObzLVGQimwgWW6QM3s89YWOTx4sQI1ZuQraLRUfl6/7xnZaI/P4Ks8e1EhVq4FRpQ1iux2ayTMZLjKQ8QM5snhGWrpYnTI/xCEqjQ3Q0pG/cePJTOytcN5tbUHV8eU81e/F1o+nr0ucrGKGtIo9/7+PmxPIsKf/sSnAQCtRoyAtdbKAaRR7Vu3bhgEUkmuBs2COJ5rMoAdzg99Z6sGhkNy8hCIEGlGoALOL8h3whrwFuuyw7ABzyO3SaT3WSkaqPKQhqdVvaAqbSxZw6+ZOpvjYdW+UT5QPi7tD6/RMggohzw+1SKFTbWpmnPBoW3JfQvDWr57WpH3iHwErUYTiwvJiiXM+Gi/el5o1oKiqnJyAdW2jSUzE/NU1U8UlVUbaW1FtGiNvVVamHJ9+InM1Q1mS5594VkEHJsnDwQxkR6L/br5wReRsUZbpaQ3A5knXl4ZtEbIGuNkmqCmYktM5ZDhRHnQLGRE0zy5LzxVqvBVVWcIiVya0/6eUykxSRbG5mk2QrNVlheCr4qa6ZYJOa6C2EdE9KVFBbKKGXgv6iKpyB/gsK80I5aXKDy5j9dm5oeIrY2Ghz65dZQXLeCcb7UbhqNkSj6R2XKBEefA7p2b/CnojVGyxIjPE2nmm//v9rv45hPhoFFbrQodFlY2TFGcNTNZbtPBYCjZRK1x/+grgkiYzs4xm8lYVAXRiyrzHrlYjHRPpCw219tXvvZ1HByIqsTGlsyZyVwVDfsYcR5rNlMRAru723j6VDKOKqm7vS3P9N6998x+NFtQpaLIkBXkZyMP13PPS59dP7iJkChMh3P66FTeM0sSk61bcn5oRtx3fMy4h7Q43xs9uU4/s7FFlY37lG7//L/43wAAv/4f/iqO35A9e87a/XZLFTyP8OChKKNtbbOWvylzYjZ5im988/fle8xivvDzfxaAZNV0f9l5T2zz4Te+xuv0MKMqiEv7ETVCs8cpv9WYXAXtawc4PpHN4L/5B/8EAPBdrtm7dx+h+JZk+3ZuSJbvP/61vw0A2Ox38elf+ksAgC5VgrJA5sLn/rvP4RM//CkAwE/9deElAv2fTgVU9CPypaLRyEFhWbjBDKoiOTS7azkuStodRxX1nlBSugK2iC47PRIumn2iU/obWxiQY2PBDOnBzX2Mj2Wcbn9YkA4Pz8WO3P+ju/jAgaCFpuRRu3sofxs8fc/s3XNVMOVcev75FzE4l/lnEUmtSNSzp0f46AeFL0b3zdMxbfZsgU985jMAgF2iqnIiO958+gB/77/+OwCAFz8h37+xL/P4F/+dv4ivvSpo22+/I/w/z77yQQBA17ZXHA2qqMk9ttPpmT1VfZmyLDFX9LejylKyL0V+gJx73OLd7wIQZBAA+LGLHORuImzg0URQM27XxzCXeeQp30asrCoztHdkni8U8adZ/TRF64bYiAnf4Zz+X2//Gto9GVdVumwQPeT7Ptrce3V/UZ6WwA0wpPxzg0hXhygdkYHmM1ApcceOzT02bFUOlvfcgw2L/DSTLwsycfL/fF7u50eYJuSOpMug6AHkteGa7NNvOTmSDPro/AIN+t3KwakoDM/30eGemBFpWajKkAssqHakCN6Mstg1Miy5d0REo4zJmbO12UdGGzsaqZS1PGe31YPPfSgMxBb+4l/5ywCATrNhFDVtqtKlYwcTRXfwHRrs9yRZYkkk4kaf70AbaMMyqB9VKI6I/rJsF3WD/hTtnEckg+fYGHKuqN9XEC0zHM2QKccRlb5UGbkoCkzpu+Z8hpeel3WOyoLD/mtwL1mOxT4OBqOVT00fT/fKZivGmOu4Ty6y4WAClypOL78o6/Eb3/gGAOBieI42+TVffln+dkaUZJX4aDTFHwoUsU3bN55O0CVn6uNj4eqKiSIvkoU5SylCKyTXUVKteKcKqjBucy+/tn3N7HUWVSn1/0+ePFkh2OnL677b8ENzH0UcBxzvTrOFmwfXrvRV0GgiW1IVlVUkS1Z8BEGACc9jei52ea1Wt4cOuU8bVBIrabCyZYKUynlz7mNZvuIdixvy/jtERzXZdxcXF6bSIKT/3SJHjwUX50PZTww6h7ZpkSzNutzk+PWJ0N3f2ceMKF9dQ+YcmaaouJC1z5SDq7aA5w7EZ1DbN55OMKU/pMh+7f8ojA3iPXCIUuUZ3bZtlDwH6xyqWU1yPr5AI5b+624RrcexrcoStXJRqVoplaFzeCjoP1yjElih1U29PnZeEM7ONhXxBuTMffrwKRb0u7vk9dtgFUQ1XWJ8LuuFYoMG0Yi8wHT6p6uMfV8EhGZT6bR23DBEVRZh8Wbg7A4yDqyr5Fc8fNSVhYJrSAMTHh33VquFhAYqowOikp/D5RyzgKVUS7nfR54Xp9zLC6SE4N16RuDcbz1gMKJdY3hBcsVNGbiLkUBR07oyhFoFD7i5SvQOh98j6R2pQ1bWmJdXZQqtIDQBkHIiP8czuW97o4dGl47biKUGhMN/4PazGPKwrMZzQGni+48eGGOnZIQenZmygpHfjmn0tHxtPp+jpsHwaVCVWDF2A+MQBSHfx1aCrMJArvV3Pp/JDxykWiKylPfT67RabXNgMSTYSW6IaQ0Zm6Pk1SlsGgc1OAqPny1nSHm4PmKJnwYMPN8199TDqxLGpmluxkuJ4dAkqfd4hjmDeRalN1NuHOPx1MhDKqxRDZ6Qmq3K3ACAfio++sFXAO8qGaFe58lggGss9bjFg6oarCCOcH5PAgR7bTlMwbaMEdNg1nRB417kWLC0T7VCdc4t0wRTwvU10KKbV11WAAN9Xi59dnRCSHnchMcSgAXJcecXMqaHT8+RUd54c4sOpyFwrFGqw8Px04DccDxCxncs6PQqAabjOOhty+bYpzOkG0Wz2VyVIjLosZxM0HSk3zyOk5aOLasKSy2p4oF9zLl9Y3/PyGS2wvhKf1SOhZSb9jsPBNL8la8JYex0MsNHPijlCxs06lOS+gZBYNaCzr0eJVq3+xuG9FfnqBLlub6Pks54RVLv1oib82YPZVeer2QpS5sE2cUiQY+bnU+Y6k5zExWf4Y+/JeUWM97HKT3cviFzrdWQTVVJjXubu1CF0Fkq47Z97Zbpf1NCxwOPo7Dkft9szLal5YnyUUQ9NEiUl1Mqt0kZ75m1RPua9I3Tp7Q0v7jTaOA6HRV1qBQa3ez7SJRskgG5SstZQ9cERd5+W4IJqGo0tNyP1/rSV7/C96rR4O8WhJlndH4d20PBwJY6cBHLaaqiRNM41bLOTi8kCNzf7MGmg373HXFo9TDsei0027KWTk5ZKjbgHLCBBWVY59zPFgzUP//cNq7feV76keu6RafhbDDA3W+9KX3KflGy58FojLt3BZK8yWfQgNCrr76KT7wiMukh12DYaOHWDXH29/akTEjju1lewmcwteBa+uRHfgiA2DJdOwrff/RIggGNRsNI3FYs41ZC1+2NbXRZtnrzuhzYx3SQDh8d4s9Qel2rSA4fieN5PliiGYlteHBP+vhgX/byk8cP8ehIbOaXviqy1krK245jhCzT6LCMYXObgYpkbnyLstTSksgQqLoMECSJjMlkNoUfaTmjzP/f+Oe/Jd9zA+zu0Tc4kmDA339PDiSR5+Ozn5GSra22PPNrh/Kcs91d/P4TmYdv/Q//DACwQ/Lg0AuxQ7LUmM+SRrIm+hsbAOfmjJLhKsAQOC7mNIH7PODefU9KlCvY5iD33O1bAIAmCUk77djsJSpx/uT4HOcT2Re++cYfAQB2CYs/PLqP9Fz6Zptj+ebd78j/gwwRA8KPzmRsXvmwzJ1+I8SDuzI+CeXm91/5EABgfjbAZ376pwEADco/v/GmlOF87h/+9+jwd598Ra41HMiznT5+CoelKw9el/n///7mbwMAfvnTfw7Hb8pzfeXz/zcAYHQqz3Ty5MT4H5/4pATmvvAFCexVJZDyEPDp/5+994y29LzOw56vnd5vb3PvnT7ADDgABgBBgihsYpdES5RJWVZxJFm242XHUbJW4sSJIjmWI1nL9pKlyKZjFUJi1EXCYgE7QVSiDzC93l5Or9/5Sn7sZ79nhlqWf4Zr8bx/7sy953zlLfvd797Pfp63S3Km02rgpVckqRgQwv8WkpZ7jouzZ2VdavLIpe/1iU98Anccl3X2m7/0v0s/0Fk4sHoAX/6KBDSVSDSXlTFamJ3DV198jdeUsdTSgZmZGeyzREfX17333ANAiO4//olPyHMyuPf1L8s9bM8GuP4tJtNM4nJnF+u7sh+ZwBztneM4JtGma+P7HpXA6wvPPofPvypz+uXXZf/MlYuIAvF50iyv6GlwpNkCcx4YMrkS5FgikcrBYsZrYkX2ixIDy4WFSVNu8toLQhSuZUqdQR8pBpVqFKBQIYtUKoUgZCIwrWVX4jO7XozpGblmi4FX/X+7VkcwULEDeYbq3j4fPMZeIPeemSIZMM8OSc/CsaMSQL1xWZIMkxNFBPRN9hg8bzHge2BxCXmuQy1JC+jDrq+tYY8E8A5LVSuT8nyZbN4E+jqcF+2O9HmxXMbcggTBVYRHfdBcYcIkUjWRm2aQsF6vw9cACCtTtq+Lr+3Cg8uy8gYDQymWbfX9obq8qHe0vEj+XyrnkORBX8+Iw+EQGU/eOVJ/gnOtlMqa8ix99z0ekIEIbSYsZygsUGESqtvto88ytKmM9FGXyT9/ECCdk3fU4MPufo39E+MQSeK1REoT4r2hb+a7xwRiP6D/HYUmuJFgSaz+bLSbhhZEzz/klkat2TCJihLFXALEyPEDKfrBmgRtNNtw6XdvMjCWoj+ytLqKFMvy9d5dCnRMVkqoUcToBmlF9AwWWDEaFOvolOhP0f9otVqm9HyRZUyTTOQ0ak3Ur4rd7mjilufI2ALKfJ/YUA6QUHs4RIPBw2Awsina/0o3oXQEpvQvjrFGIn71vfr+AA59PC0B03GI3NAEqMD+O7go68COImPPQgactndlDdf3dtHU0mzaJD03pZIeOEVN+fCQvmvsJJCkmMIer3XpiiSvgtjH/pzMsQcffAcAoL0t8zloN1Fm4E8z/M3GqCwvx4AVYlWKuoVuxdWA63+5jUvGxm3cxm3cxm3cxm3cxm3cxm3cxm3cxm3cvsfadwVCyJS0eK4hbNSSMZVHdTzPlHi4jIyFWndhOXBsLTthFOwWYi4DBSWi1uE9bDuBmGiQrQ2Jei7PrgAAJkseTlJudHBDMkPnLkk2eWt9C6vLUr7TJTw4w8z7RKFoIpoOs+S7W5KxHPT76DKKPkNyqwRJV3vtmsnwaPOSGfRJwKih7y7LM1YPrBqi2B7fIWAU/fLly0ZiUmHLS0uSQaw2qgYNUmDmUvt/EA4QKykpv6eRYd/3jWJ1n9HOiCiPoR0iZLRZURqKcPE8z0R0NeqrUpnRcAiPEOokJaj1Hr1OH32S6eo1HSdhStFiZslV5t6CB5fXahBN1SdsLLBGWfsuoZABJYP7ljWCmfM+Cnu0LAsdoq8SnFeNLWaWtvYAErY2CM/zWWI4kSshm72dlHGHMrBxHMMinFrh8OfekGzQgYUDWDggqBfN0JUo/zgcDjHkBD5/Vj6vKID1zQ1MMVunWfaJyUmA0tXxQMZwn89wc33NlBl2tUyR0M7VgwcxRRSOomyUIHiiUEKaEXXXl3etsTyyWKpgoHLiHBNFWoRWjIgoMc0yprwR8a5mQurMRCmCbNjpIUUE3YA/9f0uXbpk5q3Cii0iT+rNtvlbleUarVYDnpZzEe6ZpOxjbb+OkJm1MtF92XmBrn7pS18ysNKDlBXvcc07yRQmKH+9R0TeodVDAICrl66hUZXf+ZzHWk4VhpFZO11irhtDIUe+cu4CbrAcTEmXO0TPdbtdZLlOlOx8lXK4lz7/BLa7Dfa3dLVDmfXJiQmcIspjOhJER7taNVBozeqcnBaYtR9E5iIrfJ+AJbJu+iAuXJSykU1m3IokVk3YNnqK/GIfuymi9oY+BqYkiDLkzBp62RJCll1qibBm+puNDkJKSOfKkgFLcw4NBgPs7sk7V+tKaizX+ZO/+CL6hNOvzOuakn5MOrbJJK1vShZpfmkRd5yQbPzzLwjKS21fJpPBfiz7g2abFAXa7fYAolB0XQ+ZkS1UJjG1LCgIRXtlSOhaqVSQjuUZiiWSyJIEf+7AUVPWoqVp83PHAACObWOzKugQRfpo+U46mTHk/qGiJVXqNpPFOx5+GABM5mx+XuZCKp2AH9wuxbtAVFZlYhoZzpOJac2AJZFyZQyjUMdXnmVqcgprN0jauy+/e+SRxwAAb7xxzmTh7iBhb3ZKkAF7tX3c99a3AgDWr8laqFZlfi3NzuLBB+677XeK8KpMlIxc7I989GcAAM+9KaU+sdPE3KTAsVs7gphwA1lTJ+96K77y3DcBAG+7QxASlkK4K0VceF2QCxdIXFy1ZQ0nEkl4LD11uD59f2iQTzqvfI4bEJusbNCUPi5wzbrDAGdWxcdo1+Q+Fkkry9MVIJT+rhCBd/ZPpEyuUqxgZlr6//VvCeLhQvIVec7dDZy5U1Bi514VlMxL16TPfuLv/JQp61JEi4panLrzJKp7Ndza2kS6ZAsVDFhCemhV5vMuUTa16gaa+5IhzbGUtDqMMaklZpevAQBSHd1DMkhxzkxV5PMl2rSDS1OwWBrpgX4SZYz9bgtFImfmJ8SXSfPZ55emMDNFkY9tKal6/WXpD0Sx8bV6RG0MtASmEaI8JfN8dkFsRNiUcfz6V5/H+9/3AwCANy8IeuvghMylbjuDayyl7Q5kLI+dlLX1zDPPYG5anq/ZY+lGeh5WiuVdnuyD+10Z2ztP3IHkuqyTiL6ulk6+dqmK44ekj46tyBw9f172/qzXw8KUzJ13vetdAIBnnxJEV28/xqAm77hyaAXAqPRjIlXGVaLRDEqBqMx+tYPNm2KL7jwk1w4aWm7Rwk/83Z8GAPzRn8s83GnKZy+8/gZaLNeaLUtfKyLp5voGllcFmas+6OtvCBorlc5hsUbC3pz87cLaBm5uS6acFflIhPKcc5kiUiTAbRCh0qV/1rT6xq/d35X5qEgXK7YRzpPQmntpiv4ObAsXL8t+ZvzUkOXVmRRK9KNbRJBoeZ7rAEur0rdVPkMY6byeQIr7H91N5CgjPT09Y55zikiVARETlUrFVEFoSdvU9ASOnhAy8w79+4vnWQY7NYUB/eEW/Yijy9LXs9PzqBIFpegkRZKtHjmCPEtymnzAvbasfc9L4g6ivAZEEbbolyGKDUIir4h3ElBns1mD+JiYIPE8fdLtzR3s78r8V1Rap0EE9soSXj0rJZk3N64BADJErPR7HYRcC22K65TKBTSrcs/XXhQbrSIag34fHdIBsCIWWSLmK9NTmKUd8OkXrJEA+vCBA3iZlQNpUnM02J+FyoTZXya4z975lrsBiLiRzpmcUkIQLdkbBHDoJ+r5wiLq9MjR46aUR6sZFE0UOQlTchTQTjo8jPV7HVOupuj/WquBeaJeXPJ9TJXEFmYTSRRZotTmmU1R2nu7Vbich4pI2msQIZvPI1QBAwpyOCx5CoKh2fe0X3aCEWKq25f+azZJHE8/qV5rIlAhD5bUp0nVUSyUjbiBijRpKd7OXhWVCfGxY6JrRiJUAaBnX6W+UOGoMESJJPMqQJSNY6Q4t/QaeYpB7e/sGvGLApGF00Ttb1y+hh36TC7Rg01Wn8xWKuYc1gpGthIAWrZt4hnTXBPaH51eAE+FbIzeC8vas3nc+8ADAIA335CYQ2tD1k+/38UuBQYcEkdr2X2pUECC9kntvfqdYRQY3+Sva2OE0LiN27iN27iN27iN27iN27iN27iN27iN2/dY+65CCPV7AwMRUbn0dErJTCM4jsrOS9NInG15I5lzW0kSJezW9wdGblG5b1SePZmdhOMys0c+h4VZiZyXChaaPbn+pW2JtA9Zq2knXNhKcMXn3Canx0plEucZwVcStwolLmcmJlFnVr1HIkaVJmx1OwYN1WPktlKZNpLayq+iBKSbm+uG1LnGrOnqygHTn2tbkkWepRSv1rU/cOY+XLokWdl2Td4vJpKmmCsK5w9GGVhF1LiuC5f1iANG+cs5Zsa9xEh2l6MTMYId2a6pZ1TJ4TwJ4mx7xOtkQaU0iVrIpcx4qTxns9k0yIpBJH1keCcCG2DUfciseMgMfOTaaFEGVzMUivoYDAawVdaTCKFWQ3moPFjkJGnxmgGluSvFKXSUXJvIGM38Bt0+vKLM2ymOl2ZNcrmCuY8h0yP6aGZ62nA17DMTM8XMZzqdNnWvHWYhfEamc6kEusx+WowCX9tewzplzzWLoT+Hft9k6qfJ6ZMvSn/ceecpU0O/uSHomn5L5kLKTWBArgxFgKgcfBD20SRSJ2WQT5LBqXcaZq7WlROM6CHXdjA7K8+wQfLZXF76bqJQwuEVyXit7cv3dP4Hvo88OYB2tpmGY+t3uoaPJSJHRqkyYQhwNTug6KNiOg2XaKoCsxYLM5Ixbm5uYWlW/j1H/oa+r7XMHkoJmX/pWSJBiPJ4+MF3oMu+SnkkECUHUb3VRCp1O8nldXIQ2VGIvKUINZlfWfZHqVQymaUa647n5pjF2NjAYc7tVHok+QvI2q2wv5PkO2hHPWSz8uwnjxwHALzIrHo+n8fx4/I7lS3VrGZletbUyXfJZ+QrKsiycODAIp9PxvLmhiAfvGx2JN2thN/sFydpIXQ4r1LM9CTl3TNFoFqXedwLKKVMu7C3s4sblyS7a9G2hMxMr0xMwCrLeB0kv0qatjSVShhb2+oLKmhydgYdvtfDjwnHxQyJWff3dw3XmeHVUl0Cx0HQlX5WKfAOM5YbO7tod+SZFQWXS8s6aFV3sEMUkBEAMNmuAAHHUvc6G0pUCEzNyB61uCQZz05d1sbW5g2srwm6Rnk+lKYpkUrjKtFJOv9339yBNkW49HblPfdJMH7kyBH4zEwnmd3a3d+ECyW5JoJD+cq2NjE5K8ijmON0aU0Ihuu9BvrMOu8+LyieWlVshuM5+OyTn5drJKRz9zmHplubeKMqiIylRbn2uXPC7/LePJA5KBnEf/4r/xsAYOawjMPaRoibl2VdpUgYPpmQMX3vOz+CNsewxPl4ksia7/vge3H2C/8ZAPDvfveTAICnrgiqYW1zzyB3BhSL8IddzJPDY+ATzUauuXargalJ+dvaDZnHea7noR+gVBJ79vP/4Oekr96Qzzz17NPwVM6d3DKKhvWDED6zpUurglTe25f9fuXwMlJFmWtH7hDbmVoQrp252SVDxTPabwAAIABJREFUCPzhjwj6RfeIYqmCfFmeRffy1qbMjyPH74BD36fJbOT8rGRR84UclrhOOi25dudmjE1Khj9wWtANOfpnUytThutpnfxzj54RrqRyOYlqVdBlh1dlLA4sil11YMPjvnT6LYJkbLUkM33n6dMYWuTIIZn6mbspP3/qBECyzgL7sUW/8dnCG9imJH1tU55lY03m3Ge/8CTe8+GPSN9UZH/a26Hkdr+JbESOmJAIau6/dqeOgivzzxvKugz9EJ0dmb/qByRCckN2qwi75OtYkwz9gRV555tXX8dGU5BxK3cKYuels2KjAxdYJ4/ls68KB9z6nsyB1fkloC9zZe2KvF9J+VIaQ7T3uBZWlHeGmfs4jauXZcwfOv2oPG9R9r7LF5/H//JP/zmAEbrmBFFEVv+iea9eg5wvbbFtnVaEKKKdd6T/X35V1uQH3vt9eMEXwugr5wS1lC6XUUmLL7K0JPeeJCLsa099C3XuQ5OhfObIvCAn4ygwMt0tTrDda9IfAz/C5kXJ8McJ+cyRI/K9SnkSaZKyKkn/zTVB6NZrLQQhSdjpF+eIMBgOBuiFMl6Hj8p8fOklGZsf/ZGPIyDxq2bqX32FKJgb15El6vuN14U7Sv2fV155xaCFSpT7rl2sG2TVFNdngSiR1l4VVe4heSKV00Q35O3RPjlDnrcEEQgbW9s4TzTZu979XgAAbLEZmzs1RByLJFH1MW07Ah91oo42SQS/z70/tiyDaPFYAVCZkuedmZzCwqL4KTeIAMkQvXx9fRsufS43KfOjTLL4wB/g4puCkFhcEFtWKOaRS0qfKtpC597TzzxjzmWrHJMu7daNa5exsy7jpcIcs+TQW9/cxpFj4u+s8Ay1Tr/lqRdfRpk+8sKS/C3HtRQOfZznPpTiGVZRpsXJaYPcGBCupHtsPp9Hn36Vfkb9g1ImizoFgLTKQlGcdhSixHNFnyi2XtBHriRjf/2KjMll2u3QD4x/X+Ka7fEc2W3UAUrYjzi+5Kc/DE21he5dVSLk4kEXPqsDskT5p7heXdc1CKa2ihjRAVlaPmw45mo8A3tENHmRDb9FwRZHUbey7vaq+2gR1epz71JC/jgYGgJ/Fe9QtHsYhtjb4324Dvw4NGh75VY7ckj2Gc9LGrR+Z11sxTR5x8LAN0JRugcNyLeUzmUNEq48VF+IXMCIjD9gE0XvEbGVSdnIEb0WkovzHNfdwaUD8CkSEcTSH1HIs1tooTIvY+L4PItOUrgo4Zo5BlYlKIdnq9OFHY05hMZt3MZt3MZt3MZt3MZt3MZt3MZt3MZt3MbtO9p3BUJIs7WdRhuu1gcytKjIhU6/YxAONqNgGr0cDIdIaLaZb+S4EjG0XRc9X+Uk5ZoFohX2Gy3MLpJ/gRmsQlEyeydPLmEXEn2/simZmA55RnKlMmrkl1Dm/12y2VfrbVSoKoQuaxcZwZ1fWES2IJFW5ZZRxMPBUhFtIj80gzW0BiiRNVyRI03KscJ1DFKkvyfvt92QyPCpIyeRoUz0gPxJc4zQNxoNo7LmFqWvz9wjWai//PITo4g1UQaaTe71ekaiVqEqfaKH7JQDn1FO7UeL0V/LsuAw7phiZuTgqmRn1tZuIF1QaXTyudSkf+q1LiYmZCyWD4jyQj6fR7shkVrlAZhg/w36fbgx67tVAnZCIrC9wEci1CgzOUqY6W82m1hgrfkiWeWVJ2Q4DJFjTXGWUeabdRm3qVQOiSmJIA92JFPZZybSiy2Ezu3y8Y6ryDUbQ/KxGJgB60z9fh+Tq5IN+r5Dwt3ymc98BgDQHtRRIE9VjSibI3dIVgPFBJpk+u9qfXRtG3nyL0yzVl0lSvPZtImyk/rJ8Ct885vfNDwdiuiarggioZIrGGURi2qvLvlSOp0mNsnHcvfp03w9yUzl2hnsUl1pluOVJTqq3+lifk7WR4TbVQQKxZzJtBeJ0Br2KPlsR3Ap5VvOU12LyKtw0EWCCIk85/3BmTnUKaNM5XvDxxPNxaa2WnljeqzZvWvpEN7+kCjJqMy91vKn3RQiogTKDqUfpyVbnnSTyCZyt72PQyRaJp81iBtFADoq5TkxhZWjkvVXIq826/RhWbCYfVhYpvIWs2SWl0DEDIdFO5fg+wEROlxXyZCoQquPFWbFGuRcmCWvAACsXxPkjR9IfySoshLttPAKM9I3bkoG1vE0w+QhTf6nbfKmOeRIG/YHRp5T+Rg6fenjeruGkBwtsU0VNapjxH2gSO6gvT2ZA0bJLAhhMYO7QERYMS0/B3YSFWZ4lHvL4ZzoBV10iRjMkiPiSmMLuaz8u8B1XQ9ljOrRAAkqSHTJvWV7mksJUJlk1g0yxzdY4912hmhZRFhSlW+R/FxRe4iUdTsyMZmmml/goN8nfwATPj7nwMGVQ/izz3wWADA3Lfvfxz/2I/JMMfAbv/Eb8s7kl5vgZ3rdGta3bpeW793CXaGZJeVaGzIr/KWvfAmHF8VeF6dkPu1UGwiInu335JrLVOLcWd9COke1NJvIPWY6O61NvPKyZMUPH5VrrhPJGsWx4TS5dkEQBJNlsd8lP8IzTwuv05m/J0iaAjn8GhcuY5mKJpuLMkavXRL00daOi/WrYpMOzYiNnl8U/odUJokukQQJou4GVHbr213c+YCgau5/UdbIcy+LSpkbhPA4j4fKWwAH6+vyHkeOSibbobphEAQGZado1oEta2pqfgFfel44gFYOi72/Y0nu+8GP/A3stwVl02zKvFpmJtxxkuiQ7yc1zSy+L3N7r96CQ46jAm2Do0hZ38LlC4J+UGSi65FH0MsCjtiNoMHnJXdDs9OHRR+qSElji+vg+JEV3Lwmc6BBPFshO4FD5GI8tSAZ2PUbMqbb1TXs7DB7DBmvI9zfbTuDhXnlbOGeyv36+tVr8PuqDChoi2MnaCdTNjZ2yfFgOCElW37wwCK++IUnAYiyHwBkaBcnkxEefFRsu0f/48OPCBdQvdMDiCj90R/8KABgg7LwQwfocL9YXJLxTnNOrMwt4dDhg3wfeTzPc/C2Q6JwpvLNqpZZLpdxekE+P6QdUCWxfr8Pqy73ecedRFF9v2s+c3JJ5swc5797NxX8IgunjwlCQjn/4kBVEYH7T8gzJ8mpp/b1bfecQI9IWTsl8+k9DwmXxXQxjTSR3Y2u2KK5Cdnr5pKO4eW7605BZm1tCRKkOxwgSUTcAw/KtSIi0nNTU3jXO0WJbW5J5kBkJ5Cg8q/6Mk8/KyiMM8fPoGtJf0xWxI947zuFP+nF55/H+SsyPgn6WrpGDp84NuLqq8ted/6izMd+7xyWybvzElUYcxkibEPf8PilybdZU666VBprRHSFkLlm2fKef/m5r2CKfD17W4KOXCBKdXevivPkIzlFrh71PZaWluASvVKvyZkjOeijWpN9cu+m7KmlDBEZiexIGZdnhm3yurRqVcMzmCICVVERp+++F13yefbIddbrUZ2zUDEcnJ4nf2sRLZq0QiRpI9bXxD+YXxYE4NT0HNpEJWTz8u7KVbfd8TFFRP6p42L3z70uY3X5+g1jixwiVPT7D9x7L5bnVwAAzz0j9v/wkUWsLPDz3D8PHZT1s7B0EC+dFURRgwi1mFUah5cPY32NipZExBSI9EklPdBFwza50Va5P7XhYIroqxrPRutUeJyfmzF7aIcoIPU7d6st4bjB6IynqJfabtXst4qmH1K1tB92EdHmKRpczx5xMDTcXD73rmTag1eU55spy3pUhdBuswGb9jDUyg3uRTv7u3BtmdMB14aqTw993yBvPBqxWp3Kf7msQfBnuCY8qoo2O21MKScbUcLbRCslvDQOHxbE30ZK9rV52o/6XhUbrEaYINrUJzre8VxYaRlv5Zst0155to0BUUd92uos/VTf9xErysb4V0kUySuk9neaKmj2RIwyee/2iOoOuTYmS0VcY3/UmrwffcOhE2FlUezH9FCes8o+3qjuY3tL1nGX63TIM2qnO4Rd5z5LtbHlgzLn3jh3Eeu8xvEV+V3al3cZbq/BJtJ+clb6SvlYu70ObMWU89zaI5IptgKkidj769p3RUCoWJAHLZansbUjiy1maU4qL5PUSyWRIhlVzIXWoFOazWRMQKHdI3zelMskMFHSBSnf63Ewy8kEUgz6DPi9REjC2dQk6nti0DKROFYKkc1NTGDITefCZZnI00UGp3p99FjipERZHg97dtrCgM7W5FyRn6c8bWOAEg1hvSPPkrdsJBPyzC+/KYewySVZaH5ngD1C6l2SGe9sy/PenJkzDsCliwJ1P3xESiN6wxBV6ssaeB4XTBRFBn6pxkEJpLcaG8aRyrE0amlZnmVmfhXXL0qZXJUQ7BN3CWloNwyxR3n7/U1CNluyqRxcPGgCTkp4tTzPsrShjzwDINvb8p5h1MfEIXF+WgwM1UiEZsVAgSRbNUre3tznRm05SHXlGWqEfT9yRhyRqQdPoUuj9cgp2dxec8Th+ezv/iHezuDG1VDG6cWvCcQ5Xczj0Q+/DwDwycf/vbwrYcVeMoHLLFXIsoRRnaf9Wg08k2PlICWUd8VoVHI5TBblcz/99+Xg0ybkcLu6h82vfR0A4DRJCh5I323GXbzwbdkwF1fU2UtjjzDH3r48e6Yr8yTwHWzxQFskCV+Km/GRpSUMJkiMR4dMpUVzhbzZyDyH5XEMPjiejewd8uz5vMz7eCj3OLJyxASXaiyvyRL6mswlsbMta76Q6/Pa8r2dzT10OyyroXPz9rc9BAD49vMvYnNDxi2TYlklg2/zTgbYl3fIM2CVnYwxdUj65sUX5XCXbDPAGQRGDjhL+HKLh1g7DJEkDHvrkpTjNFlCimAIj/fWUgzPlU0vnfTMOyvxojrShXLFEOSFdMy0fKUf9rG9LvO2zUNRiptcq9nGJA/4GlyJuHaD3gAVBnQ0CDlkkNB1XWRLsnbX6NSEoY9aU9bVNTpK+4YcNo1STq6vh9nVQ+KwOz0fEctdPb6fz5LJfmShxfnRbyv5JOVRbRfDHglEGTyOGFhLRRaigCTKDN7MEEZf9etw9+T6i5SEXa9xzqbSyLMc12WwusuylZ3uJq5epZQxAyBaStds1o1zpoeiKIpGJOp8hiTXhAS57Vv+DTRo0+r1Ooa8hpJ2bqzJ3uMlHBN4dbhRX3fFYY3jGDYdWp+EvaakczAwQb35ORnv5WU5UNxx5CD+dCCfq/LQNU3I8PE73oLPfVXswE6NpSwMTMRuH+CadRNiV/MJmS+t1gBEQhs5+EpZxi2fKWJ5lrLWCo0+fBTbm3Lvfk/6Ks1nny5MwufhpM01YciD0yVM52UvLrssGc3JOOdyOWzRDh+uSGBeFRGOzK0i4nx67J1iczdZMvmVb3wDvV2xc3fd+6i885Dr5eozKLO0p+DJ/M2UBPb/3BtfwB98+t/J/RhcXThIwxwdxaU9WevnKTU+QanXrWodAYmPlfDbS6ZMYFfLrbSvfL+PJGH0fkD/pcUAVLsJkGR4ZlEONQ999EMAgKe/+AXY9HdaDHacuVsOkEG3a8p4s3Qml0jCPhj24HqUaPbFfkzRlwrjLiozsucMVY8j5qHPbxoIfz4n9m7/uvgOiXwBLdqUNFSQg0IUwwBTPOxuNcQHsJwO7nvHo/yuzM0F+keWZeFgcPuBRyWUo9jCJP0Pl/fJc83ecfCQ6W8tJaowQNFzM3BzFDQIxO7PvEvmiZdM49gJCaYYolLuNzcuXULkye9OnZb50WAZ242tOhpMuJVIgF5gGZsTNVHbl/U/w5K2iGu4sDBnShQ0MDEc9JBgeUohL2vO9Sg3nU4jc0T6Tw88jqWSwS72KcVtUcDi1KMSVIHnYPZelvVyneXoA1tBjEkmAiMmUnzOl9LCPAIG9wbcO/TAiUTalOwNWFoxcZcEeB45fAROjp/TOhCWtZ+87340WB5XyYsPcJQlHI1BFxkGET0Gm3YZ2Mh5Hg6clOvHCQkQVFYXTeLwzWdeAgBcvM5917Owc03mdMQ1+Gf/+Y+kD2o1FCjkkebh3p6nqEV/A5MFlk/RRvsMktb7CUQuAywVBptYkuuGLXQb0h+pjPgrR1dknaZzWVN2//zzzwMAygwwtGubCHmodOnLXGG5aAgH+QmxNz6DFWAAqu37QETlm77MnZxbRqcrfaulShkGULqdPqK+zMOKI39791sluWtKRzDyrTXAlk6m8La7JfD85SfFn42nuU8PI7gku83RB7hyRfbRKBjg2FFZJ0NX+vrcVRm3a5s3DA1Dpi3fz3POhmEXz39bfFc9E2mpd687QIqJmExB+v2r35SzzlefehkFlo/VmSA59/TT+Fs//MMAgCUGWrQUt9Nuod9jYp4JsDb3+WJxClB5dQaz65xD5VQSNourU0yI+By3O46u4oknngAA3Lwpge8kZclvXskbfyDg79K0d06thZCl/0naAdDnCwYBEtxn9bylghTbG1cR8My1PEmwAv0SN5NBl4kaO0Eb4bhw0vI+B1dWAABNBi2nZicxpI/2+sXL7BeeLXNl9Pl8swyODAcjIYu1G+ITahmlgja61QGG3FPjmOAENs/z0KndLkygfpbv+8huSt9ME8ixQ190f7+KkLah3aFfzKBxBAeWz4Qo/YG22qbJSdx1l8zjXdpth/3Y7/ZQtFUgSd7huYtnsRWLPZ3knnPfaTnDfuZ3n8Dmvly3mmSwjT5fPpnFNsvcEhSvshm4DXoDvPGSUGNcpi/k0uerdXoAP58iXQTY57msgxTPDuWS2KkgkO+l8gnkCFgoMXbQnaZYkDeLJsvss1Piy2jQentrE+fOiX/50ENyTtI1GcMyZvuva+OSsXEbt3Ebt3Ebt3Ebt3Ebt3Ebt3Ebt3Ebt++xZt0aSf7/q917z9tiADh46BCSzEI0mRnRzP3m+ib6hLbdeVzQLuk8P9vumOzP7h4zlwOWFSA0soQ9ogw06+3GWaxOS3RuKSv98N63CWT45JkTuO7LNR7/nEDQTy4IgsRJp3HxpkSlb/BnhhHA4WCAHLMJGp1L5yQS2Gg0cImw42WiQ5RMrNvtYpLwNY2qBlYMpy9hvYsvCpz12BwRIIkEtquEr05JZH1mRTJNKS+FmDKgSg5dJoxwv9lGgxn+iBk3mxHz/MykyVJbjMZqX12+cBEDEtI6LBkrMIPQC0NMMMvkkeTOZslC5LoY8pqpgnxes5pO0TPXXz6wIt9j5rHZbKNNBMEuJStTqQwazdvlK6cIAWw1myazr6UHJUoo7+/vI8e5sjQh2cUMYXfPXj2LKgnkHqOc5I/8vb8DAPjVX/s1tIgE6GmpCOfZ2++9HxuE0io09vCsZLar8QDBHlEUJHGbJpzTTbroETY+uyAZgB4J2PY2NnDymES87zgpWUmPJVPfevnbGFBuk8lQpJjFv+Nt92GPxLL/8f99HADQ3W0hpNTv0LtdBh5+gD0i0zSzvUpJ9e//wIewtSHvs7cj2bFZlnRtbGwYWepplpEoSVs6mzIylK12g+9FqdBCHjsbkpUts4xneoFzPRriyS98DgCweVPum8+OSlq6moUnhP3kCcmSt9td1AnvTbFMSRE5UQS06zLvC4uEpTYbphxselJ+t7Mpz5RLpXGUCJgBszN2geWAwz6u3RCUgEqKeswM2mGMo8cFNThN2P4Xn/yyPMugb9Y2kxYoTHA+VuuGsHu6wnJSZpoRBdjeEptis4TuEJFNV65cA7g+NOPQJnQ47PsGOakklQ2Wl87MzBiJ22e++RQA4MEH7odNZMv2nozzBcrvriwsYZN9M78o6+WRd4p0+DNPfRMe0Q8LLNu5xnG7ev2GWb8thRgzW+K6CdiOkl3LqyoKYHl+FjFRm7ofTRNy/OrF11HiWKyytOTsWZFeXl48ANu9vezq3EXJkHSb+yOp94yiNWQ91BtVgwYqFEZlfUxCGgi/NsdxDLGj2haV7x4MBoYsvEh732yOZHr1PkrMqfYVAALvFvnUW96hUCgYEkMlFj9Iue/HHnsMn/q9PwcA1GoyT87cKzbj+J2n8dyLguqoEVHTH5IQOmXDN+gwDkCofVAway5mGeuQyMROt4VDJHI+fuKIPrnJjve7JOwlKnAwGBqy/L5FgQiSjVqxA5pRg4wLlAwykzFjHxItpvbKtoETdwoaIkfU6AZLzc5fuYQ073fxgrz7gXnJwPcaDXzr618BIOTHAOBwTuTLRbz+hpSi3H1E5nGeWcNTdx3DPtFXE5x7LZYg/MK//FV4zGQ7JEbt9gdmXuQJ1S4ws9du1JEjcq/PrOfejuwblh3g8EHZs0+dkPdbnJf//7c/93eRod/QrpNsf5IlUp4NxJr9lPvEzE5a6QTAssvGlmT2N2nHX3rpKj7/ZUEzdIiWvusMS4uSjinB6DSYKYbOnZxBV0e+2NBMSsb06KEVDPler70h9iPyCibzraXuSqI/UakY0QIlTU2yzMJyLUxwfBVtkeH3kp6LsgoFUCDD5t4VWElTPgyWmn7hz/8CAHDk6AmsnpB3NClSReDEHfzhf5ASy3kionst2WM//eRX8Yn//p8AAB5+6zsAAE5X1hQyCcT0hfZJXDq5KOMWDgMj/uARBYAwMuhtRQ+VaPeDIDDExQmiEhyVBw5ihET99In2zHKdhkMfQyKSk0qySp/LCiIMaTdi7p83WCJVnpjEJNdzl/uZ+kJRp48gSfQhpcpn6ZMCQMRrqh1llRhcL4FL24LiOTzFOcr13U1HGBK1Nbwqa+qJz/4lAODDP/NT6BJ98sSfik1bufMIurS/OxfFL2uzhCY5P4cnPyf7a40lOlpCjSBAjnMlS0RSe0BiZ7+HUMvEk+xPEsPDLcBNMovOEpNOU9497QaYnpC/7bGsWpHAURSZ/UXLXRT169o2Vldlz25zD+4RQdbuDTE1J3vq9CzLvjkfr127gn3uxSdXaWvt2KChQqL+F1jCtL2xKaItAAos658h/UEikTBle+r3aMlqPp0xMvD67NOcE47jGfGc9XX5m5bbTU1W4NCmtygrPiT9RhAEhqpB97Fbz5b6O7WTAUtoEukManX6okTNTUzJ+w2GAXz69FpWliykMEfbUuA6sXhGrO3t4+x5OV85XBMJ7iHT03PYJjJQUVsL9EErpYKxp1qOvkfESbNeM2cURQOpf+CHgUEF299BdWIPQxS5dyhCq3YLeubECTnDahmf3vfCxTdR35F738fzyARpU/Z398w81vV9fWMNe0RFFXhWCFh+lk4k4RA57RCBo+iv9fUtrFBMRG21o8DEKMb6uqw9Q4pMpEtkAT7HTtut42xoXb4Dbe37PiJWsqhwkPoCdmxjdlrWwhTtjSKbBn6As28Iot9JyLVDvvvKoYOYn5d5m2HVyjrFDOwYKLC6qFmV8Xv5tVfRj8S+l/KskCC6ZmOjjhRRrG3uS4roSlgOKtyPlN7D9bSyxTFnqKAt4+ApSXomA3DsIy3lYj96nmfQ2FrdZARL4tjMNfXz1Ud0HMf0bZYlewOeJ+v1KnZZbXLggKCHTAWH7cGmeNEv/+I/Uq2Sv9LGCKFxG7dxG7dxG7dxG7dxG7dxG7dxG7dxG7fvsfZdgRC674wghKZmppBlNFSJVJXkuF1vo8tIdymvJEwSBQsjGCJc21EpWEpMWyHWb0i002aqvq1y0HYBB8nlM5uW773zXqk3n12dxialav/8KakR9LokALNiDFlD2Ff+jC4JqIoVExX1GH2MNYsfByiRxLTDLFeX0b1cIY9tSjoGHJNioYyppESZuzfJ78E6w8CPkGJWICap7hrJTL3QR5r1iUVytYAoika3h5Ch2SSjnHHAiHecQKPBzD4znTo/isUisswkffXLX4PpeABhMsaUK8/wyEmJajdIsukkE+gowSazM9Pkwxg4/REXAfvRYtZ6ojKDFhFChZz0WRQCMUkFcyQa0+fb2do2EVYlcRsMFSXVxZCR0pQj/bd8UDI4mzevY2lZMhLr5wV5cIgZnKvtKppnBR2iBKd99t20k0HPlXvXGUWfJnFsp5BAjvfRDJaiqyLESFIaUYmci8xe91otxBWJ+hbJD7TLjGwykTZExJqdL1CK8J73PIyZE4ISePa8ZL1f/tq3kCRXizXNrHpb5tDa+QvYJRFoiuSMMzPMBEYx0kSOtCgjb6TH/b55hu5QMwday2yb7Ihm9hXlkEokcZAIpIg8MgMiEV5541XMM6Of5Fy4ckmyhvOLS4j5rsOISCFG1R07adAdfWYCNUN348ZNQ9x95Ihk2vb29kwW5sw99/A+wnmxs7WF0yR4VLl1l1Ka3eEAn/uCZDSZmDDcW0nXQYnE5UpgfuWqZMmH3T5cZgOKXKegzdjc3MRxyt52m5Idi4by2TgKsLkl2eQKbeHBQ9J3ly5eMeSAS4ti+85dkfshCFGkpOVBkixWSZw8OzuL6SlBRX2NCKbHHnsn9mgvdCx0nK0oNHLUGdoWRRieP/8mykQbLXBM33hTkBm7O1VM8T5tosPSJE0MAx/JFBFq5NWqMDu2sjRvJMZ1jFJEH25V9wwqTW1sgpnI+blFvP66EMw++NCD8gzMkFw7d9YgEDRrZRBbGGWudP4mk0k0uR51juvfLMtCmjxOmiHSvgrDUJAvGGU/NVMaDAZmTYTBKMOjrTwttl2RpJqtDcPQZHWVXyXHcQiCIWxb+rFJfot6Tfoukc4htCWzlC1I1i+VZSY8DpDjfAwUohWzRj5bMIjQNtf8kPtSFAcok1NmYoIcGf0WIqYqJyckQ3eQGe1kMj0i0bQ0W8j+SaTRZDY4zb1Em2XHUE+kQL4PzXCfOHYIAdEQigzaI9/VCy+9aJAOPsdkn/xC6VQCpbz01QsvCHlzjaSyiUQCCwti57//EUG/2bTn/UEHOSVPZh816DP8+995HGnOW49/84ehQUV57OPDq5Kp393aRFcztsoRWCNvYHMPp+4UO7AwJzbwCRKG/82P/RB+7dd+GQBw9arYKd83z1Y4AAAgAElEQVSTtXXXnceg6dWYpKHKDxXBQkBeoJ0t2bte/5aQKl9fq+GN8zLHvv2aII5tEvhWGzVMTsgekHRkbJwseawWl+ETnRDQb+k1Jds96LSxfOAQ7y3jHDspg57QNVek5HulUjG8XZo5176Lo66xYSqE4BJN5LouSkQP6Z5fpi/l+0DAPXhqUubqBdqFmYVl5Ll3K3Tn7NnX5L1m5jFBglRwfEG001qrjdyqzI8sSfM9ImP6QQt/8PinAQDveee7AQALy9IHcCyDsHAJobHCCCHHSRGNFtfEMBgaG5Fkf/i0P45rocs5pnwWCc3hOg4iIoI0tW+r7HqzCZv+n8v9QueJ5dhwyZMSkK9G912EAYY0TyHnrMd3ty0bHaLNlbNC7fAwGMLVdUJEUp68MA4iI57Ro0/vcm54Tgo6j4dERXj0VQCgd1Xm6m/99qcAAN1UCt9+RtACXYtjQns6VaogCtXGyjurfwXPQYJcVGne2yeCKUYCw2CElARGogfJhI18QeZmgughFe8IgsDMX/V3IqIdB4MBcpzTzZa8l6KPwgjI0J8NdO2yfzrNFiIiSSfIOel4Nob0Y3vkElqijPzO1jYKnNuKiNG9qz/0zf6nLc/KhVKhaJ5PUSsBSXPT6Sy6RPkrOkFl5KPAR4b7oJIp+5yOURQZ/1ubooISiYRZs7rvat/ZtotOh2uP+4Xajn5/gJhOlyImQguI2R/9jsynBNdZ0ktgQCRjgucDPZ+l0hm0WXViyN75vVsRwSqaovt0GMaGF07fz7oFDaSf0/1a0XPZdM70v/rrKomunwFGAkJ6nWI5Z2Tq9bzV475mx0AireJJsvZc10Uvojw9/Ut1VOMwMv2tCCE9b1VrDXg8E+rYKFKtWMiZMdBnt2hjhsMhbEX88m/6/TiORwIqt/g50o8h2sM2+1jeSwm1u622mQM6FmovM5kMuh1Zj0MiKWe4V7Y6bdN/Af19neO5TB4+eaG69BvdQYgKf+eTewt52txUFh0aP+VDzFHQyYZl7KHuR7oOtjc2AV6zSF+tR8RaOpdHUvm+BqMxB4BUKmHOz60ufSLO8TgMTd8qz6TuEZ7rjtBoyj/Kc5bjOAbh7RC1pJVTnuch5v73Mz/9N8YIoXEbt3Ebt3Ebt3Ebt3Ebt3Ebt3Ebt3Ebt3GT9l2BELr//rfGgEQ9m4z6as2oyotHUWSifxqdHg4UiZPEkNmOdIaR/JZkAsv5DBrk2mk35Xua6bejDObLEpU7QAn2B8hRMrsyhU5RonPPXpQM7N5lyah3hkN0mEGNmenxWV+eT2dMRNlmfa1mYJIpD2mNEjPa2Wdmy3IdtPg95adYTE2gRIUjn5mrbJH1nok0Wowkd5VPh2zxdhzDJ4dSRLl1ja666SQiRjSHfYlMKt9B1EuYiPKNm9cAAA8+KJn3y5cvmwhjinXwFiP6UdrBzmX5/MklQSf0yMpu2zaqlArvUUP5ONVSMpkMmozwakTYY6aj3uiYelKtv+z1eshRuUAj8pqBnJ2bNugmzdDvM/tRLBZRZi3t1Zo8V8mSdz4wPYPl44JYuvKiqFrUVL3n6CEkmdm5SFUEzSJlYwdN8p6kc8wcUNlnFwOjiKcIt3JZxm1ndxcZZp0007BPZY9SIY+tkCiFPfnee8lfcOzBe/GLv/orAIDTVDtRFIdn2ZheFn6Vrz33NABgKTeJu45KnfJgRvrsMpEnO29eRp+Z+gTXlNaodjotHD0q11dZds3UV6t72GGNe4VcAynys7S6HbjMJmrmXdXh/P4AH/7gB+VzDJRr5PyZF54xWa1eS9ZCOk0FsmQadfIr5Vi77hP1lMkWsHJI5tqAdf02a5SvXLpqkERFqrvkcjmz7hWFoqoKrUbTZPnuID9ZmVk8Pw6xtimZyoAZn4DrLZVIwCVvQT9QYid5vpef/7aRN+0QwdDmnM3lclil1O2lc4KueeRhkc89duwYGkT2eOTWUIUHy7KMzKyqjGkWJBz4hhvJZIWIAHSTCdjRKGsEALE14h148TWZ92+8IaiBbDIFi9nn69cFZaCowr7fM1K1WWbze31V7EqjSPSmqgAqagbREI4quLVVTY4yoqUc1qlq0WQfqVKD7dkIaOcXFwV1ofM+DiM4WpfOrI4i3XpBB42GzHHNyOnazeeKZg9RW+04jsmmKxeVZslihGZ+9BUhQcUWAHA4XzWLo/dpt9tGJlbHRFscxyhRfUo/v0eUazqdRa1GJAsz/ZqdjOIA2bzYkh75KSJmCCenpxFT/aWn78As6HDQwdzUSOkJGGWYPC9pbCY47rYiBcIhIsoi9ijtmsmlUSdn2d33iGz3/Lwg1qr7NWOTE8ywDbojxNWA8zdHPosurxnbsfleMJAxmSLqLp1O4l//m38FAJibl3c4dFjs3a//+q/joYfFRqpam0ckarGYN0iu1197BcBINTMe+oiZ0f+BD74fAPD2t8teF4R91Cn1rD5HwL3vX/2bf4sa3z2bpWxvEMIhD4BDlMbqAclixsEQ19cUbSfrrd7gnLNjlLgXzJOnTTOezXoVP//zwmHz4z/14wCARlfWRDGfxKd+V5QtwTX0wx//CemDyDZ8VxGRQinK13/yk7+H188J18szL74MANjcF3vuBxHa5ApxqX5UmCVH4NDHkOgLVR+87y2iDjVdnMSA/opm5cNI5g0wmmuGUyL0DXJAxzul/CBObD7fM9l8+d7c3Jz53tKS7NcGAQjhQAIAi88Zkrtv5dBxnKWNfe1VQXqff1NQtKXiNH7u5/87AMA9d58BAGRiRdQBdSJaUkRLu+R0cxHgl//PfwEA+JEf/pu8DzlfLAuRKrFhlIit1agwWVBFNblWMIxMZlhbSP5Hx7bQ5zU8CgJb9APtWzLwuv4DRQp4HmLuCyosYzH3G8OBYvEC9lWK1wqGPkIiqd1E0nwTAHb399Gius1hIlYVVSgWklwlfE6fmekhfKSYVde1cZFI7MzsNGbKwne3c0Xm5fR8xeyp668KH9xnnvwqACA5M4dIUc9psYFqXxOeZ66ve05sj9ANinDTPVX3wSgIDWJS/ZAcuS5t1zF8Rj6Vg9VO2q5j0AyKdvEM8sk1qIlEUvmymLl3PcPHBYMwlOukkyk4dFLaPE84CQ89Vjsk6G8XiO5ptzsYcA2EXDeq0JpMJs2a1XWjfDX9bs8gXWP+LT8ttrbZbsG2FMGXuu373W7X8NOU6Vcpb6Ft2yNeuf6IZ+nWfgFGe3GS6KPBYGDsgPZtm6hpy7IMEkPHxktkzBh2ea7Q76XTaWM/9aySpM9Ra1QNh5Lyv2iL4xg97lHfiXKPotEz6zhrfwRBYN5Nv2d+wkJEtIbaMJ2XlhWba6g/oXZh0OuZNat+n5kvGNmUmHYg4Xpw6ad36Mv4viKeRjxXyiWkZ7ZEIoEhUf7qzxpUTxSb99CxdLwRUqVA9J9+5tYYwnf2g/7Ntm3jU2h/ar84lm36Rp9XxxsYccwZFVae9YdhMNpDPCLkOI7JZBoNxhKUx2c+V8DeK+LjvvLiMwCAow+LKt/UsaNot+jX0qc0SDBrNE55+mGKpnLiCFnue3tVWad18tA5roskz0ejtaBo6YSxXS4Rl7p+giAw/eay04zPFkWj+ch5f+v8tLSPvgMdb9u28YU++P53/BcRQt8VsvN6kC9XKmah6E/dc8IgNAv/MJ3B11+UDePQwVXENITnLwhUuMTDTrveQL9PwzhUKTsemOLQBJyyOTlM9Wngms0m9nvyN5VjHpBAc2nlANYJXVdp9CEN+LWrVzE1Kc6dBrVCyrmm0gkzIRw1ZnRAXNdFhc/cS8tCvW/1TuwTTtblphpz8ftRbCBqLg9f22tCqJYuVNCjZLVnK2yTkqZuDCpxI8XNf4FEhe1wiJgL//73vwcA8OabsnkfmCwhkVC4qCycGoMdE6l5VFhq09cN4/CK9HUQIibMNs1Ffu2CBFfcKGEMTcAFk6B06P0PvhVvkoB7/oA4gL2dtjmg6kTXgMuLL72ADg95alx3WIIXRkPMEubYnJW/zVFyfHtpAa9fl3l096zcZ5fGttgHznzgEQDAp18UYvH950TW+cDyMr78ggRfPva+DwMALt+4BgDY2tvF0Rm51jkSnR6/Q4gtn3vheZw6JeVd1ymnurgqh6mvv/wyllmuVu2Lcdl6Vvpg6a63YGFR+nQWsrHlHpDyxuxmC6sPSKneAQYFprw8hjxwtig5fvZlGUu3AawsSKlCqy3zJFeWOZDJZJCkA6xGJZOmTPWBCooFBh0at28ildmsCYzpZjo7IcEVOwaySXnHOClzoaUGtjiBMtd1n7B9n5DldqeLPMss8rQHy3dIsOrwkRMItMiERqJJosOEl8cuSSCHnoz7fm+AEonhYsp03s3y0JWVFfMeuukrwZ4fhViOhOBbgwc7DHTtbG4hUJI/X/7RqYpdsKxR2Y1uvKk0IboIcYnzok9S7mMnhbB+6cABTPbESXZ5sNVgQi6Xw5COrG4QCvm+duUqJmb4PcJFG7rushm4PCCpQ9dDH5UlOVwHb8rfnn5e5vahhUUk6TjU9+VgrG1yegIpV51/+V6F5R2AjTY3wwSHJkNHJFNIIcEgVip1+zjbcYx8TqVqWY5DB2HYH5iSEpVAXZymre73zWFKnaHatqz5bCWLEucjY0wY0Km3bonNOCq/HcaIAj2kSR9pwGAYDGCHSqhKSHMwChr5DPhpkMnl4cvv983GrsGvW2HW7aY8WKtOSDgdkFI+iWxK+koJam0euObnFtBk4DQc0KnnOOxtNxBaDLDzYJGkfUUwxJWWBN20XEufxXFcs8+Ca0odsyAI0FVCVEK3S6WSIfscMKgU6WGlPIksCXpVOrZ8CyGj8voqaeXULEuDEgns7Ergecg9b/LICgBgc2MdRc6H0yTjvJM29MV778XitMz7jpbl8ibdTg8Rxy1iKbIKUyRsCw5L/fIzMp/eIMHtpx//HZw5LQGPH6bEcY0Jjv/pn/w8fvEX/g8AgG0cVReB9gPd+Z1NmYfLywuj8gMe2HMF6f9Ws4k252SnIz89EmcmM2l841uyv/zYT/43AIAi9+mgVcWf/sEfAAAeeexReR+OpWvZJgpgM+mhk+gPf//38Q//8f8AAPjQh0Te/uN/+28DALKFCjKUeO77PCAz0FOZqmCoBPcr4nuVKyxpgYcSZXPVMfXc2JCfatnU6FCYNsZV+0XXSNJxRoTkdM4DJQ2OE4Z0OZOUtbGxL4H60yePmGDASy9KSZGWbR06egqba2KTn/gzIZr+hX/2TwEAn/3cH+OP/vQ/AQDupQx3mwStuUwGr74ihOS/8q//JQBgdVGCfP/il34T//gfSbBOSwp1YncHfTz55S8CAJ5+6psAgA+//wO4563vlM9zoXksBbDtyFAh6OHmCksEf/P//rd46GEpZ/z+j/yg3Id90G63keH+YPHdaR7xjW88ic9TKvsdj8n33/N9kpAJgwCR+ti0cz4Jub/+lS/jk//pPwIAfvQnfwIA8P4P/AAAYKJcQInBaYvz+Pplec7f+uRvIZWTufPjP/lTAID5hRV5vyDG/paM0+c/I/3/+B/L3D390NvwP/6v0rdzK0KaX93bxKWXRHb8U7/1/wAA8nOL7P8D6HH9+kz4avCz2amZvaBG+2Fsr+eZwE7EA5aWGbmIkFY/icHp0OUe6yVhc/+yaX/U7llxDIf30ySGbRJFyVGpcEeDI5zjqRRi7sV62OszweIHA+NzeQzsDzp9k4wJ1ZRzX096CbMPJVTsgOUjvcC/reQZkKQdIPtU2gT85Lku0Xctlyvw6ZtFHS2Vony8NSovr2of0+bmcqMSKf2ph/zItkZky5ykeyzLSyQS6LMUTs98Oh6u7aBOol4N4AWtDjIkC9ZgWEx71Rx2DVGx3s/n80ZOiFZf7tOvyzPr/Mjlcoh4wNdgQ8C5MOx1jf+l61PtVhAECLtyfT2bmvLXGEhwf9Bn2d7d4Du75hoaNGq0ed4NYxPI9Hn+VCJiK7aNTxIymBAnU+gy6a/+qZbkhuEQzZ4m/XOmvwGg1enCwu1Bm0xS6UiGowAQmIy8JdDQYenXd5bdJxIJs/8p0bG2VCJlgsX6eS3d84cDDDuaVGQf26NEX1/BDMZmyvfyuZw5mw8Tum7k/XbrOyYJmsrx2b0AgyTnYYklkz2ZE159DxHnUe07SNgjKzTBIfTlmqOyvjTOU4wlwX1Jz/iWHaPPZOZIrEMu0+taJqhEDICZQzpGABB8R1AxjmNAid27jds+74d9xPRzdC15JrMHwPuvg3/GJWPjNm7jNm7jNm7jNm7jNm7jNm7jNm7jNm7fY+27AiE0S5KoVCpjSmsCRvc061qulEzUS6FWaU+ifKVsyWhgzjBLnkoza7q5jW5HopVFQt06lJOP/BgdwqiGjLxqliuTmYZTlsjbQleibDcoFxsHMSaZIdPob7UmGcFKvoR7TglaQyO7r16RUp2N7SoOLQtSYm6Z78xI7LDXRcD30rIHf9DHZI5ojVjRTfKz0Whg49Jl+TdLTCZIMBf3ugjYb6UCJfDYn62tqoFV5wizDmJBGXQGAwPVW7ekPypE7KxvbSDF0giH2YWAUeBerY4CiUSrRFxtMItR291DjrBN/fweJU2zbs4QP/tKgMfo6tr6pvn3+Yt8z1YTyUB+d/qe0wCA/ISgsS5ebuG+44LCUWjsWk7QRK+99homiWLYIBqtQ4LwAwdX4VPm+NTd9wIAypRx/ov/8DgqhPl/9N2SUf3impBJnj5yCn1mzleKMufufLcgSar7+ygwO3vmTnlOJf179IGHMMlMrJKBTczKO7T7baQpnwhmXqwd+UyyD/yDH5XsW/WyZNdnliVzlk+mUSPqZZplFtWNXSwflWzu05cFZaRwWi+ZBEg2HlVZWsnSxHe/9z2m1M5AjTmver0eCrlRtkh/BwjZs0axNUOkkesoisznZtPymY0dQdn0ggDXiJTyWT5Z25f7264Dm4SPNymdPH1ASq16cWSgmZGSljO7Y2czAP8WsvSg2+2isy/ZpjyRKhvXWEK3sWOyHZrV2h+wHMcaEfldOi9IMiNNHwPX1wR1MUGUgb8vdiCfThnpTCW51CxLr9cDue9R4Fzd4Tg0+90RGXpfy0qZQRwM/wrZX0Jh6raDbzwtiAKF/ev68dwk+rRz80RTtKM6tmrSp1ssA0yoRHQ6ayDymnEbspRof7dusltdotgsi+TUvm/I4QtEhTQbLAFLWUgpSZ9zOzFlK26gR6RUTGLEiOVeThgbCVglFtY+SDgumoSXK5RaESitbVk3wO3wYwAIfMf0n8ufqVQaduF2+LeWowVBgCRRUTlLEGumJCCRQETUVT9Fwvssy0KykVkTmr12b5Wdh6KOgtuuaTseSiRrzhE51R9qOY+LclLW7swMpYKZOev2ekjmKH9OQnKLCJmU62CfKBQD0Wf2yHUTZt/TMbmVHFIRSIrQ6nRbhuywyQz4WRKLW1ZsUBBK/G9IOyPLoDcVvryxKevHcizzjg7txzZLNTPZNJaWiCDgGn72WyKffvr0GXRJSJkgukxLXINBjJUD8r1LZ4nII5IpnUyg2RA7s0kS6tdfljKqG9d38d53i03OMkuoZQyTxSIeOiP7xGe++XUZh9lFRMxiqoiAlvjlMtkRpJvlZ4rys20bYawZPS0BIHLBtbG7IzZhd0feeWZasrzDXhcTlDR+4B4pdYLKwkfRqPyPdgexjHdlYhoPPviA9A39AY85wUImhwZLObVkUgn9d7d3cMdbBJm1dGCB76nZbwcdIjtz9BWGoT8qrTGZzRERqatwe0vJWSmXDhcpzu35OUHY2u4IMq8owpibz8qBg/yeY4jnf/CjHwMA3FyXOfDSC6+guiv2aaoon9m+ISV0Wzc28KH3CfImTRny118QhNGltYv49d/+dQDAxz8qn9l8VRC2v//4p/FTRMLsbgj6SNHITz/7LXzq8d8BAPz9n/0ZAMDjv/1JHDsl/e4V5R2eJ7r40MohWESJ/8Wf/DEA4LNPyM9HH3sQr31bUJtKVv7BD/0g+8W4l1i7Jsi2Z579BgDg03/4B/iJT3xCnuebIgBy730yT8qVaSQc2ZfXiAp5nmXmn3r8t/FjP/Zj8o6/+9sAgLuIlFuaPyB+A4BXnxG09O9/+vcAAPnJIiaIVvr0Z/8IAPCzP/sPAQDJZhtXX5CS5K89JYird39I0Oft7Zopxy5SanzY6+LrX/4SAODmTbENl1+Wcs+Pzh/AbFlsnofRGgKAVreJHEULPC31CNS++ugSKeHRDg8jmVeDwEejI3ZA16zK1btRiAzRMQDRVIEiRGPjZ9qECTtcL348QEvpC4jWUORIZMVI8Hc+7aKbHJEU6z6RpAR71OsiR7/bEOgORyVuKisd030uT06Yz3oJlbV2b/u+ZVnIkGxfqRdmeZ6pFEujkmwlgqafGiPGHEnbldTfIWoskU0iInoipvmxiXbsDfp/Raghw7WcyaYQ8MzQ0/0prWhC26Bg1ZfMltOjkniiw2ha4Pu+Qaer71qv6thmzO/yJUVysNy/3zeIFB3viL5kIV82/aak4RlWfjhO5jZxCQCwiLZLJbwRQo3VGQX63JlMyvTHgCWQWmKb8BIjCIytvglLIGPLILwDzn/XswwJepolZsm0+t0uEmnd6+WSWo7qeg4CImIUdaTjEIRDeLQRab0W+zjEEBbRx5aSUdNP8pIOYu5nsXU7siWdTgK8H2j3tezVdm2DntX+04qRwaBnzrwTUxX+jhUE7TryFAUwZYp8F9dzkEto6bS8815tA8VVmeePHpcy8QtX5Gw59HuwuX6zOdknMkRVdbpt1FkuOzcn/sv8nPjR62sbiCzSGyRIZ8Fncm4R2nHo82pfx3GMJM+EVZagK42M41qjEmuVudd9NIoAY/vAa5PqIB4hkdQH0/kZWZFBPP11bYwQGrdxG7dxG7dxG7dxG7dxG7dxG7dxG7dx+x5r3xUIofvvfysAYHd3z8hFp5iN0LrIhYUFg6jQzMFb7xVCqN6gDzclr3JwWTKC33pG5JV3drZRZjZNkQuBIbyKRrWRzFIxICdRdWaFm4z2Z0mYWm92MDktkXhDksZo38zkrJGd22XW3yKp9MTEFFqsy1Wp7DIJ7FxrlGH49quSqUxZAXzWKuaIalApvGGvhwKRC5NlyZRpJLnTHcBvkAuJaAvznOmMkW/ud4lu2pXoZwtDzM9LBubcpat8L9Ya2w4aXbmWSkLvMrIZ9SIj2ZwqSl9HEetKnbSRGbQZvZ8gIsy2IiwukND5mqB5tsi9kEikTP1lpSjononsFDLMHvTIbzBDdM1bTp0aRYtZ/3v/GZkfB+YWMGjJ77KMnE6z5t1xk5gggskjt8MM0TIf+dAHjFzp0aOCTHn7//VL8uyw8O4fEtSQPVT0hCAZrhRSePgeuXfIMO4u59Bw4BteFUMcxndaLuTwddbPx5z3j31QOAD61hAvvCFkmCskbd5kLe83br4Jry3j24SM7VNf/waqvy/j8+aa9O10ViLfVifE5iUZ80xb1sQ9U4Jk2t7bxkAJF5l6qa0JIsm2HWxrza4n97FukRNVlIbOGUOkZjujiHeDNbiMhh86ehhf+eoXAABVooZmKV1e3a0aHq6YHF1Xr0tEH14CHV+RNMzouaPMc5L2IAcZ2yNLC0aacsSdohwWIWKi5hLMytyxKuiqfjDEkAiyb12Tfkgyw7G0OI9Nonhckgz7zF7v79VRZkS+TnRByExHOpNBi/OxSf6Ym5z/x44dM9LLg770dS4j6yZZScGmtLNmrdodosxKZUNcWSDvksOs7TAMjDyvkt/aPQf1ffnuhXPSp522vGe13gKYyagTBbe6KvNfM2EAkCDiUiVJI4xIHNPMrih6w3GBkOiMDL+XypJrIGEjzUyjZ4hpuTbggK9s5IrDaFS7rnLKSVdlwuWz+WzOZBJ1nal9TKVSZm5qSyaTaA3J5aPoK2YlLcsyWVljD6NRhlqzUob3zh7lWfzBX5Wb189QOdlce8RNEJl76zsod1EqnQCYRVOkBAJNFdnwmSpWAmgdx5nyNJL91m3X1J+ZTMaMm76D9tlwGCrPNJIpzqHa/i3kj/JHgz5MWAbp1xuoiAPtguvh2g2Z57O028m0EpcG8MiTlOY+ocTkfmeAKe4TSgA74lWwkEnkzXsAYtMBoNmoo0UC51UiC59eE1RgFA5NJvX8WdrVeUFcfuDRd+E97xMUw9aOIEBmyB/jBSEefJvY9s8/J0iJbrcNnaTRdxAm+34wQtQxW5cj4nJzc9vwHzX5nKUlsX22DTT4/h1yRmGGkzuRwA98TJAw973tYfkds93Cs8AsKxFXSoj9sb/14ygvyHvsEkGzvLoCAFjb2EUyI3bDqGGr5LAN7JJLrEyfZmlavhd0QjOGSmJrJ7y/wl9iWCxjG7ZBppGLLKn/d9Emr5hZs46SIQMeSW675OxShEbsONirSV9dJfonomtbLE8aH+MM9+QBkQj/88//Mzz6wY/yuYhKiGhHkIRP/+jD7/shAMDXup+Vvmp3EHMeTtGXuXRB0KO9dgPLJD5/72NCdv7Uk39p9iYlMIsjclBkPUR9ks7yM62q+EBHDy6gelP2RN1LQmaAhXCWBKXsR0VAeVGICfaN8i4ZxBZs6GjMT8tzkmYPCcdGhz7dysoKACBN1EwYhXDD2znBlBet3+3CnZJ/rxBpkleC/nTS7EPpnjz7K1+VdXPqoQcwVxHfziLi0BkMERGB2+Cce9djshanp6cxXZ7hPcWfUkLyQil/i1T4iAQZEKGZ6Un6pcyBKyIhigLUm3ItnU9K9uwlswblqzw+5ZIiSCLD5WHxbzb392wuixx5GNXGmr7q9xH6tyOHU6mRv6R71BrnsZNKoFBRRIuMb5qCGYE/NCgBPVeowMbE1IQRzxhQUr4yJT4voshwvBR4/tD5vOd3MHQAACAASURBVLOzg+kpGRM9MyihbiqTRqMhaLuAgirqq1h2BBAV4iXs0e8A2E5s0DG6r6koQ7veNX57gtxAI1l229h0tSfdQc+Ma4rXUnRg17IMElp9koUlRTQGBkWigiMe140dj+TjU2kdixFP0MzM7WIdyh8DRIZcW89uesYcDIfmmhZRXArYRBSPfLzE7bxLk5MVwwupazZLZOnezo5BQOU5n1uNJlZL8o4672sUYrEtC+UihQ+ILFIC85TnIbBv90lyJEDudmPjb9BlMzLm+XwaCft2biRtcRyPxDOc2+2/HYXoEAWuCKMi/VTbS5tnV04wRY9almXOc8qZliePLjBCBiXJF6m8hYEToMb5n1ZevoSNAtevQp5y3FNmJibQtaSPkjHP2NyLytk0ylyjWfbR1oacBQb+ADO09653u/9nIYLNs7+Opfp6YRiaPtIx7fEcE1kRLPVvjC96O3odGIluaAvDyLy/XlvRY2EUIOX+18M9Y4TQuI3buI3buI3buI3buI3buI3buI3buI3b91j7rkAIqcJGv+fj5k2JjC/MCVLFJ9rmzbPnTETdI8IiYAQvk0lhwBrMjS2J3Gk9pD/sI+DfNHJnonWByVsZqfdbJVGLFYnAJRlRvnBdkEn5cgmta/JvVVRxmDG9ub4GkMNgjhlH8B26gy4qVOS4SVn3G6xNLuRzJjKsPA5TvQ7KzKB0mH1r9JSTI4uEImIYRdyoMouXzMAmU3+P7O1tZpZmFhexQw6fmJn2FCOv3WpVqWuQyrJWmJLolushU1BkELlDEqwDzqXRZCalzQxTpSgZ4FwhgV0q/7jMAHQa5EQ4UMTrN6Qu/+YNqSWPqdZ039E7sTi3Iv04I/1oxzbO70g2d5/y8duUXh4MBmgFzEwzWzLFbOjE4hzsQN7xwSXJFG8ww5S3LSwTzfDmDUFKTK9KxP2uU8fRYPanx1rrgBmE9voOSssyR594Rur0j1FCda2+i6987nMAgJP33QMA+OZTkhWbLE/8f+y9abAtWVYe9uWceeY7vqle1aupa+rqbnpA0GqJhmAyQoAFCIeRHWDsQLZQyLJwhHDgcNgO+Yf9T/IQYCscDodlyRYgtSTEbORuGmgaeqiuHmt4Nb3h3neHM+ec/rG/tXbmOdXY/1wRffaf+96952Tu3Ln32muv9a3vQ0OelRPOkw992CDk/o9f+CV810eM/PgXYdbE//i3/3MAwPf9/F/DL/+K4Rb49rEZl4f/yveY7/3i38fjicliXP/zhr/q9huv486LJms5OTZzaDY3a2PoJVhOzXudkl5F1BXWRYoLqTX3upKVlw/OLTKAGQStS60c5JwDpSeSmuYzo9HI8lqEXRb8N956HXMqSSwX5p2UY64pp0BVmnHImUGvyU31geeeUSSTIEhOH5h5loSRqlJklLuaXlzCZXF7EprrTyZE0oQBFqLyIxLvrHceJkNcUJWix0z4Mb83TGI886RBEp1RployAHujA0SsYb45MZk5QQjNFnMc3TJzcz0zz/WVL7xg/nZ+oRL2jSuoHiLKHFe5TITvwGX99te++GWE/FxMSdeE9mG+XMLjIInqgIsCjvTvqllf73nmPabvg5FmaITPSLg8kqhHRERbitS8y8V6rSounmv6InwpSeDjgu8wYsZLMm0+akWfiFz0gn/zHA8NM0qZyCqTZ6EsS82gVJTKTojUzONQs5GCNlhwPs4yi9rQ7y9TRYlpVqawWaolkU6DQVex04WDFCJpzPdERI3neViSc6Ih4kmuHQQuItoB4RxyRHVmPtc6+5TjVzF3k64K1I2MDdUoqJblhxHmK+H0IufRgFxO60KfP+beEFKq1fEDZJVw0Ni+m0s3cOgmLPlOhpM95bqYMMsnHAie22BNJFJATo85+bxG/QFW5JsR23C4R76lukYQyZwxfZgcG+PkwdH7Cd9SQbRCWTdWGpdr4ZHHzZq8c/s21rRlkyephEXFvy9+9fOYMXv2LDk6/qOfMbwnZZYrZ9RVZv8GzEAOQx9PUXb71//wUwCA3/vkpzAmqlRQlefnZn8KPFfRdV952XC9qLz1YGCldImmWjFz3uvHGCUm8z0emZ8ls6fhZIzv+os/AABwqEaUM+NZO1blribiJB6avv3Qj/1l1MwQHz1iUDP/3k//+wCAn/tP/jMrgcI5Knxge4cT5XH49GcMx07vW8x7vza+ipLP4wmXm+MYmR1YZFAcijywVVCpObeF8qxpLOeWoJPmzCoPerFyiEWct3Pyw03XDgKRNqfd6SVEjfUnSGnbn/7eWwCADz1vFB3jXoO75waxlhI5WR+Z60y/dheozLz9r/+uQQU7RD3/mz/5NxTFsLg0Ptv+hO8oL3CbvBS/+N8bDqLv+LaPoMes9nxu9olv+iZja/PVCid3zTX2yOvyKLkBP/bL/xjf8t6PAgCeedKoawpivqwdeES8ypwpuD6v7h/hl37J+Arf/UNGJcwngmy+XqPmGry4a3xt5enc28PnPmf4en7kJ37CjDURm3Xj4AF5/Ba8n/B9vn76Bp6lqtgz32XU1M5oA/YWFa7EZo1/7w+YOfvmpdmnv/PbvwNjIhMhCkkN8J0f+QgA4LnHjBLqJfdrb7yvfFD9gVkLU3LEpGWh60p4ykqikFFUqhK2om8o3Dl7B/uY8PNr2k7h8xrCU/tBkAE4BZHnufKqRJx7gsr0Uene43PfjVRueqD7pyCD1kRU+r6vKJuEtjDu91CojLhpgsp0nUbfq9xb0P9FlSuSxpV9usXBJ3ylgv5p6OONxyOkRBiucn6fHEezYoEp+VcD+sFTIgZd19U9R5BWimaoXCSRXfcyRgCwWs2R+kRW0acRpcW6AkoiXEUtMkpCjInySImyFvXQ0PPgR1y/HOOiMO/db2q1QbKHCGomCSNF6MyJ0kj4fGHc18+nqy6/XhCEKIkAzjfQMk7TqIqfjMsR0XNN01i/gyiz/tCss8VqjTGRfvNLsz9NeS6Mw2hLXRaeixnfgeyJCVEsUd9yHEmfrxEVuF6u4PYC/TdgeZN834Xrcv7yXeTiHzQlCA7rIKEBoCxKEa1DQg6sNpfc4JgVLBtIoaapUBLF1tD3VWSR42DEeSF7qnDJvfDCC3jf+97Hv5kzxB75WZsgwJhVPBFR/Eno4k2eET736c8DAN792JMAgJvXHsIrc3Me6/Fs4pO/t+f7qtb6qU+ZPV/4On/6Z/46Zhw/zydHoCoFuooU1uok+h913Sia+Mp1csjSBoZhqHNHbJooz7m+q76JIJhUwc/x4EBQRzybK7Ko2kJ0vV17RwSEvvw5c8i/du0ann3KbHzS+YKGcb32sM8AzYyHwkJmX+Ci4EAEPNwnHssZshBlQYNNR3pESGjarLF0zUs8WwlhNQMbp0v4+8bpObpBorIvme9N06m+qKs3DQGxGIsvvfw1PWg+e2wcj7PUyNKll5d49dIcyi+5oPcmhH01DkIa95qlAEXq4MrDZlO8SxjxAyFIvO7jTZIL92m87t81E/qNJsCEcLJr101wo88FffvN1yxxtpDI0tnN3AovvmLIN4+vmCBC1GNgabVAtWQ5HxdrQifq7tmZSo6nXNjpmRmfKOnhoiTB67kxcCK3/MrtLyEkvD9fETK5T0LtNMP9cwOXzqkN3TjQUoG+lJbQgKTrtZKN91hiI0Sne3tjDHwG4jLKrHMDPKsrvHFsNpjf/pe/DgB4763HAQBXnriFf/IPjESqkC1+klLAZw8u8BM/aSR7/7df+PsAgL/1s38TAHD6yiv48ivG2Tq+bg7+/+Jj/xQA8NM/8x/gj19mEOwN8/6e/pBxDu/duYsDzt+7XzNEjNcJSS1nCww4P/ZYbvHWG6b84YNPvQeXnpnb//B/MWSQb776GoY8kM3eNBtGTffcSWr994rO4Esknt6/coARN6eaBl9giFeu7uvmOHC5gS1k4y0U7izEjSFL8KqqQu10CXQ9x4x/Os+RhGa9BIfm+wvOMw8TRCz56l8zz5zxvf32Jz+uZUmNyhibv61WK0vmRhs4m83UqIqzJuTLYRwrNFkM94pztGka3dCffd4Qc7bLIa4dkyTe3AY3WAIyHI/UYZRSJ9mc5/M5Bv2u9Lc45dPpVNenLTOyMNMJA8S1BnPH2qdC5DmjsHNttzfQZ5BxqWsLk5bDdsW/vXZ6rjKbsqHLoXtdvqWlgdKUYNn3dQMMgm5wZTq/tMH2vulfb2h+pnmOB5xH0pewzxKY2tXxSHgQ9OVg7AatMi0GCEgg6KQZmpLOCINZ/AHHd9A4XVLSIPRQFX73eVh2GAUhxhvS2upMJgl6Amfn+81YZlc7DkIGdiRKlNDmjkZ9NCwfkQOC7NdB6GvgQlDdjTjpq5WuM1ecBN4iCGr0SLovn5Ekiht7mAQD/o5y6wzmOr6n71kOSrI2qspRuzpm8MZvvWeBoAdSnliW8Gh3OMQYMgAYBAGOrvQ7YywtjmPt82y97HymzAs95EnfxTmsixIN51PDgNCEJeKu7+DTf2BIeQvO34MrZqU+slyhYADkL/2IOTT/xv/9m3zmSg+TMh9nl0IqeU3Lql1P5NJ9uCRZzhkcXfPhp/UKPZDomwHlmu8oCIFZyQMn99KSLlkY1JiembLtj//WPwMA3HzKrPWL6RwO59WMhyKBphdVowdaCTBKmd35+aWWwxzSjnixkIZWcAmZl89HLEUsZ4VwViOjx/jJL38aAPDMY+/C41dvmc+t6BeUjdoZLc91hai9RTTtCRE812Jp53LD+SdzqEhr5JQDD8X+SpA1rhHwACLlaEvSCxzv7+Pdzz0NwPpJK34mbxwMGFTN7pn5VFyY93H96qP42b9p5OldHlhvMRD43NPPAZwflyT8F/LaZ971HD7wn/43AKwf8sH3fwDgvB0Pu1QDQa+P85VJLl4W5l3+tZ/9OTOOSYK9A3NPCXTbKg8HYAJmfmHm5s2nDAH0f/WdfxHxXp+f4wuT8lLPAZjIO2GJ+7ve92EAwPf/6L+LZMQAZSmHGkkAZRiQ1FWCdH/1r/8t07fhCPA4txl0ULvlZlgemL78hT//b7Hr8t4ruKJaEJt3u+5lePZbDYH5k5U5C/zBp180/T1/C31KO09ZXi5BhOvjEU5PGfi7Kbuxaev1Gl/4whcAAI8+Zigl4oflMy76THR+9avGB3J4+KvyOQISxYYcM2mDwUDtlTTZI9M0VT9C/NXlOtO/qe3joRu0HYvlGumyK8iRLpZIH5jrio8iPhAAxD3uKwxqSzLI9321XZaEuVUiRHs/oG1eUhxgfnHRSpaQmHYuxMkFEt674h6XMig5Go0Q8JpnD8y8kjOS4zi4oL2WcalFQMF1Uc1JCMyEnfgql5fnuLh8wA6zRPPKQ0ApY2t+yF7uBK4S4otYj0irB2Gspd1iY+Szs2Wm70lKo9bsX1La5JHQF6ggheuh4TMrqbQEerJVVyocQEpi8zDylRB7NGL5GvsbFQ2KQpJP8p6t78bLaxLJ9324e93yIvFDmqbCPn156YMEXOIo0ARM6JHAnOPhBpHdezl3xA43VY1kzKQbA9DyvTj00PjdZ/a4Rya+p2T2DYP2Oa8dVZVSPDQMPoY8HwyR4AFpMB55l1m7X/uYSbb/7Z//j/Fz/+t/CwD4kT/33QCAszdZYhuEqKWMm77kvM7w2C3jrz/FUt8/esWs+UVc4anSBOxEREb2l36c4PSOOX8PKjPujx0bG/WZP34Jn/gTkyTJym4A0PHawcNu6ZiDGnRZUTrdxPtilepcizkfH3/UJHAODoZaKra3Tz+rEdDCAj0mVjOK4mj5peugrwT5X7/tSsZ2bdd2bdd2bdd2bdd2bdd2bdd2bdd2bde+wdo7AiHUoxTnq2++qtm3B4wyC1FhHMe4c2JQF0cknRV5w7tv3IObmP88oPxnWRHePkq0hEiyziGj6G6eacQ6FSk7lgakmf2bRJJzknUVRaHZgC+SAFqISCfDHhqS0P7B7xlZ2jAwUfjpxSVu3jBwYJ/Zt9N7LMlKK43uY01Io+/ij75ooG1uJFqOJqx4Nj2DL2VhzHQeXTfohCxtrBwnIa9S+pFlmWbkJBusz1k6cJi1yAuSujLCGUZ9NIxqS7nQakZIadjHkuUgc16zIRuZH0RKyj1k+YJP6ObB4JaWV0yuU96XSIHeeIi7900ZWTgUctwCEaPMGu1nxnPUH2Eg0p58vlXNbHfeoAKzJFVbvg9A0+DBXYOgOb9vfn7yTTP3vM+9gAf83b/42K8CAF4m2fa1a9fw8lfMv9/9jEGJVZSQrHPg2WcNlPHFLxl02Afeb8rCXrt9BynRaE8+bhBkv/9Jk2195OEn8Lt/aP49PTEZlT1mFH/5H/1TzC/N3Pz4iybbdf5l80xXBxM8WJtxe+55U6L2zR/8sxgzixATWSGRa9cPVKpTsglLIlT++E9ewIKkna7fRZUEXmhLxlrkwoDJTsick6i4/L+ua800rIggm5BUL/AdPPKYyQQKAZ5IXvaiWMluBbIpczZvKp3TBRFMQgg9HO/ZbBij9pOjA1u2xn7JvM/LQrMrEpm//pBB1mVZZuXANzI+7X8roR/LGC7SJaZzK30O2KwYmgaTYrI1fgBQeQ48ZrIlkyX9DsPQZpsI9fbdWP8mhKNKrimZ99xT2Li8h0HSsyT0fN8VswmHV6/o54SsMiIE9bB/qNlLmReShYjD0BK9SpkW1/PDDz+kNlOzp2xxP8Y1lubIOMhny6LG0Bt1/taGvm7+TpEJVa3EkpulwkEQqJiAvLeiKLbsh2RYkyjeIluWMuKjoyM0rhmrS6JIpGwlCAL9nSICWDowm12iJkJozrJBhzZzNBohJNmhSrwSyRBFUUvKtLvOPM9TsspN4mjP83CFpaPyLLasbKCIICkDbMvPt8m1zQA1OjbyLuVvTdhY9BAziPL/siw1a7zZ9/V6rX+Tsk21GauVEqRKyZH0PQisvO9iafaZVykjPxoMcTk19vvzzOJNiFp8+qkncZ8lM//DL/5C55kX00udK35g+idIhLosdfyvXjFzNssKEKWv4yDoo7IskQzjTt9X3LvC0MdaZIQ5/tKHoh8jikSm3lyb0wR37lxqGd46k3J4ru+qUfTQgCIYDqFx81WF68yMLnMp6zVj/Bd+4C/hn/zKxzrPMCAK98GDE/SOzLX2r5k5tCBq4NWvvYQ9+jfXWDKNuoLfWgMAdL3lTgO37top8Qd819PSNJdj7EtZThjBYxbZCQRhwTVZ+ihSIiVop67TFzrcP9D0tpToCILbDQVFA2Q5ibHzhN9/F67dNL4oiBCSch6D1jN9n9w0yNVDx8zdJybPYTIWsk8hxHbVWVWxDRkX18URUab9xHzvCZZKOYGHDJwPMPveckWfpijVfo/oOyUsj6n7NVa5ICvM/XL6JqYsiVn/mCW7tF+FO0XAz98/M5l2eUee5yHTkluzpkLSERwEK/g9Y3eWLMOUstQHpyf4zOc+DgA4mxok1M2HTKY/GfTR6xn/RoiZ5+scL792GwBwQbTWMjOT4OD4IQSxmYd7lKlv7wUDIuOmROMousFxcPyQGWM/Mf1s+Fz37t1DQrqIwZ45V4htm85nViadyCSxUWVZoqgEoUn7S5RaCF/7FdMwyDjmea7KNZviG8O6Rl/sKsuj20S9YpNEqCDPcyV0Fxso+4zjOF+3RMTzvBZKSYRAKDbhOi3/RtaN7EV27xGhjREpM9o+kaA52/6BLZUmKoXPXlWVIhI3S5CGw+FWGVlWNPD9bvmZCEl4rocgEPS2EB+bvy0WCy2p3xR48IIAPpEYghCS/ubrvLMGpM/yfJul54qMiZMthLc013VVNEMQtpkgj0v7WX+DBLisKkUICe0EskLPp3aftePpOdI/GSxL2eCLXysIavGPWiIw8jyCdnccB42gqoV6heutQqNIYVs+T78sKzAlQqpiKZagHserAm98zpxzmx6RUiyj6t16BL1zc41j2uvPTs3ec/XJG1jSTv36L/7PAIBnvu87zbUXHrwbXF8UHHhicojbnzIlsW9+2SCDnvkuU55a5BW+dmHO4oX5ob7bW2+8ifP7PBMSxXOfZ9qvvDjFnMjVg57Zc8pWqaDMi0zEEuQM5lrf2I1k7ZCewfO1Suj+ibG1co7vxT5C+mgx39v1ayxrT2KNQ0gFjaDA46iPi5WQtX/9tkMI7dqu7dqu7dqu7dqu7dqu7dqu7dqu7dqufYM1Z7OW//+P9uM//tMNYCJyEp2XSHzRisZKX0Wm7WBiPvvy67cVLSRkaRGzw+vFHK/cfpnXMpG7vm+iZ/OWNOCjzGy/n1mhb/3ge3D0tKnb++xbhgzyH/zSZ7TPEjWX6H3Nn1euXNHs9j2SBkvmoaoqJZiUmnMhwo3jWCO0wtkQO41y85w8MJHQCjbTE7Jec00kgkQFXQSKRghjG4kHTLR+Mzsr963hKWJE6ifls3VdKwmWIisY1a7DEBUzncJfkLAvQRAo2dnhEWVOGUUOk4lGNCUTuCY/1NVrRzi7MGNzzKxk4zYIKouIaPelLEvUheXZaPd9OBzC4efW5DKQqLrvuFhQqvnkjpEY9ok4cXsRxpyPgkwRlJPneQiJ2lIOFiVmrUGeWRTMhpbMfAaRJbuViL6QuvXjBA0zIQPWlZ4zahxXjmY2V3yGMSd9HrrokZByJLw6RYl8XXTu04iM/PQSc2YahZfr/n0zv84vLuD43XpXRRa5LnxmGIRgXOZAURS6PlVquJU10YyLGRa9tufabPp4YK6p6ATHUzvgcz1Lf/PS3i8iL4t8rypKjYxPL02EvSxLJbOTNRu25F41m8g5k5UWzSLrRNa89t3ztlAyTW55icpakABEWDCiXxeWDFmyBIOelbMVAlLJjsm1l8ulvgshs5c577qujpXUZktzPEv4KNmjMs80K6ZcQy17IIiFdpYVAC5Pzzrzod1c10XYGpt2a5qqgxgDLNdRlmUtme688zPuJS0RgK6MfBiGFrXCJmN9MJpsoYfELmdZpgTf7exfP+7K3ubM6qxWK+2Dys637ifcDioRX1gSUHcDjaaZ4zhGVXWzgiLDHYahoow2kSp1XSMjoYs8q7QsyzDiGpKMktj9Xq+H+5RQl7XR3hPEZsrf8paNbxM8AsYeyDMLd55yD+WWS0z2LuVpCgJdo4IAlnfZ6/W0P4IGkj2lrustThrNxLqu9vmllw3XiKybiwdnuM89eMFrCiGp29SK1loQ1SB+xf7+ns7xXtxFRyVJpHNmn3LHH//d38fpvTM+IzlYuAH4QY0bV0zmcHlu3skdcukNJwlWzK6GnhnPAXkd9ieuoiO/93uM7LmIJgRBgIB8cpLxFUTZcLync60gtCgnIuTk/gPt+5zcQ5LBfeqpp/GxjxmE0J8QTeWRPfThxx7BlCTBK85tsf8XZxcIOFff+7xBxbZ9Sg9dZIDv+xZRsSFNn2VZB/kFQBHRqBv45BySfULe1+E4VkLxQ5KKPvm48bP2xiPURE4KcamsYS8c4urVq7wNeSVpI8IwVLTc+aXZG18jciUa2/UsSFshNQ2iSBFagjYvyxr7RLCLvRcbU1WV+oIybrLPZ1mGJOlyWclnsrRoyYKnnWs6joO75yL6YPyVhLxenu+o4MqSaFaPiNzDg2McTsx4nFP4Q9Aejz/+uNqUM5JL59xvkiTRgRCBEiVVdls+GsEPJ0Txh1GMPn3jFf2+Oi/gsz8eCdNdcp+tcwcXFDTxki4y13Eci6Bp+STSLHk7EbK0GU1jkc0PKFQi+yha1+xvkOQGQWCR6LSd8h5c15LJrsgLJXLaRWHfm6JtWv9XVGpLbr1tP83zWfSnoCxkDbURQ7KHqw/V2sPk3upHrwTBGrTQRhTaacTHaO1H/Jmt7ffkmjp+bL7v69641AoC89lRa5++pCCNvNP9/X1EtIdKuBv3VOJdUbC5RakK2kUR14rotWgNQWjJ94ui0GcU/0/G2nOcFmcf0XbCy1I3W36OzDkzrl0fqj0fhWtL+dPUJtp+qrBDi8+njRaX56xLu+7b14zj2CL6+U7aPp6gj6UpiKjZ5vhT3kzP1WqaPudoTNRMWuRa0eOKX8WrNnmpiP6GfveAa+L881/EJ3/tXwIAljwhXH3eVA382E/+JNIzYxt+9X/63wEA/9enPgEA+NTJi/ipv/FTAICPHhghidGHvtmMwRspXnjVnNdz7nX7Bwc4e8v4AyU5QisK2DyoMoREmZ4Jgs/CflFxnxRBA/HZKs9DSH+jWfKMJ6JXRaHPL3xyYgOzIkfI/T3NSRoe0acqCtRCrZbQnsq5JF1B5lWvz7609hKXf5NYgIhlOY6rdurv/p2f7zrNrbZDCO3aru3aru3aru3aru3aru3aru3aru3arn2DtXcEh1BZmYzD8bU+Usr+DcaSvSdzeppqRuiSUp9nMxv5LhnBk+xCySjpOi9UmlGyngX5ZOA2lgOEGWPlJalKVIzsBsz4DI8e0z5rHTojpxL5dqMIg5H53cMTgzASycrVeo05I+oN+3Tt4Vvm/lWl8qGL3ERE/cEAMRVT5m8YvoPZzGRufNfT7KVEvGtm9ALftZwaRJEIOsELepq92EQGOK6P8YQqRhrZpIR1kmjtrmSNFFkUeqo85NXd+tKqthkGj/edkpckifcwojT2ipwIPlnpp9MLeImJtJ5SbSxNV2gyImg4F+T9JUnfZlfYT1v77MCTjK/XVZpKkj5cIiXe954PmL5D5L59FGU3myNR1jzPt/hcHEEwoEIYECHFaLjE28vSolckQ6pR9KbBggoDI5fIrj5VYdwYU9atZkSGpQszny+yHHNG7U9OTaY6XVmuKHmHomxQVpVmUk5ZJwuRxrx2TcdWuKJkvriui8ATWWCX42dRFWdnlL/kOpPruK6ryABX1A44J9rIIkEtyVpKkgQpM9Kki8CD26a/WZZtZW7a2ZPbr98GAFy5ajLGfuBjPwawygAAIABJREFUGJr+aGautog3UVORawwjZiXXa83wbDbHcbbQMm5EladeYpF0VTdD5DgOesw+yDycT2d6f8lAbWZ14jjWa9xhpmMyskg8AeyIxK2sedfxLVcax7hBhQZdBJMgQPIi3UK0SJtQaardLxmzprH2VOTt5dplmdsxCrpZruVyqVnBIe3BYGD5WRTJ4nTV14Ig0Lki70ikZV955SWd9zJP2jwLilKSvvs+zpillvUpKKyqtnNUkDpyvzRb6bpUdCtlhB3HQZ+cVCl5mgT90uv11G5I/wYDM7ZV5elzqcx0abOLSc+q9wEtZFxVYUKkq/aPWeuqqvRZ5ZqaBXQc/dyMKID2nFNERSs7LNeXPkgGPBglitBZEJHXll6VMXrXu7oy2rPZTMfhKvlBilYtvqKvMovakmcX3qpv+8i36eflbznl3EdEtGiGurHS0CWRlKKM1O/3dQ2GXEMrcrRFkVVguXLFoBumFyl+5au/DMDKvfpEj5Z5pTx5MreDqRnjKPB13OUzIrPsegluPmz8jSFRFCXfcxRFFmXLjGUqaLZ1iXXW5RwBbTYc36qS0X6vOD4vvPgFjPeMr/H0s0ZB5c7rBhk9my2UnywmV5FLnsOHr9wA+LsvfNEgtI4Pj7YyzDI/fN/fynwrT0XZKOo2kAw4EUJ5ulbeP5Eh3hub/p6GOcqCqATOxz/eJ69OEmE0pkos/ya+oeMCh4fG3xmLMh37W5YlHO6zGcdoQeWyR65ex4w+jCjeLpiJnc4XiJmdFR7FwWCEF+nftFEk0mQ8JlScrS6sWlXSTDpjJFnlMAwVKXJJm2I5IUuA3DzLhZkf6xX9QMcRgSk4jrlflprxeP31Bm9R+VSuVTtmn3n1tanaGZVCZp8ODg4Qe110jjxTWVYtRAw6bTj0cP7qC+aZiXTd29tHQE6O5YKqtLxv2QB92sjiXBSjBCEXqv3YVukEaqLuZR4ulyL/XOv+rM8snD6jka4hQX8rn2iZtTiwKC3vyF5XIhP1RUGaiPx24G+pk7X3WkUB8X5lVSoCzyffT9vvES47aWlu0TmCXtvkOWxXW2zyE5VlqfNcxnFA2xlFkV7DKogJ51ym5wlRM2rzJknlgPxNxqyua733FXKyKQKncYDGfK5PjqraaRDwusrBFFX6fMIbKM+3zqw0t16XzXJqeoocc8Rutfy6TZS0+kR1szW2ykO3WLwt56S5tKdqiJt+Z1nWZv2iyyck922UJ1IQzi5cX/if6JvIHpnmcDJbQWE+L4joSpHMbdss1xHwkNrmFkejnJ+lYmQJi9iS8ReOqJJn0yLNMCGy8+5dg1T+1V81qs5vvPgCCpeo6gnRxTOz55187suIrpt/x4wFXH+vUY389tcDnL9meMmWt4w/8VhtxvP3P/cCfv03/k8ARvEXAE6aFCWRmg8fGC69iy8R2en5uC7KyVxDOdF2le9afl/uENnK8h3OiMStezwzE2nuNUBN5OkyJ3+g2E4nQHBIVTOqey6nxtbGcQ99VnqUwjHF+/X7PezR35kt6Uv2Rdk3QC3xC85nlIJ29OD9f6gGe0cEhL78JVPS5bq2tEGMsxieMI46kH8AiPtmQ0vXuQ6ALMy4ZwZpOEoAX4wsYZK8r+/7qFkeVPKlptxkl+sVCjpZPS7eXmRJR1PKNIpxlr+t12s1VP3YOC4enYZRMtLSHiHdE8d2sZzhuYcMzFlg3GVsSWQffdQQDUYhN/bGwHEBS7AsC9sJwg4paPtnEARaqrFZnhH2rPy2wj4bW6ogBJibAaXzxSUK9sFh3yuZe40Dh33O+LdMyrXCFSpuoj1C94T4rTcawvMN+eBNDRTM9bnEeVIJSN9KOW4SwC7mK5SE7kqwJ2Y5Q1lXtjyC30tpRIt1it6we6hcccGl2drKNlNWOGlJUA4mNhgCAC7/tlqtkNaW2Be8AjuMoz3jyAXcC0ZEzpdFjSNKpborM58SQTRWhUolyrwM48gSu8lBmJuCWzVohKyW4+FImVwAOCTRrFjOMCeBaAN7qMyLLjFtEAQQNVQp85SSP6C14RUStKQRcxx9/JIBTZ8lcb3YR+ib55rz8Hx0lU5slmkg1AZl7L2C0Dj6IpkK2BIWCx+3pT22pIrlOI30IUIYDjvPIA5clmVqp+TngKSVSRQpbDOqhcA11XtIaaus3YO9sV7H47sZT0jC7tvSKnGGhHyyrmzQbrO0Stau7/soaSzk+6Hv6cFd1pSUuB0O99UuSbBD7EK7TKlUaXIbWGof/tvj0jQVRGE45PuW7/m+b//Ng6pUivhotFzQ25DuDIIAFQPEKQMmTmVLdzfLydrlBfJv+ZmmKXobRMntzysZJp9fxqPX68Fx7doGgJwEtXVdo2SZspCVi3PiOA5GQyMw0NQSmLfbcZts2YyfJbqWoIEEf66x7MVxnK2AzriVuNDy5o1yAc+zJJJrlay1ROq5ltowCOHbAI38zovtniBBdAnCyLiEYYiQwfcVbbq8ooODfRsA4n7RCOS+KpRMV0okbt40Y1e2ZIGFxJpDhfF4D75PAQo60mFkA6gyNw/2zRjJvFou5+jRlknQ7sq1x9mX3GQ5ACwIMX/8qafhcyxFetbx5Z3astIxA+RyiGtgD0N13nW8q8bBcMJgNh1Mh++9N5ro+F8yMDEnBH4wGGiwX+D7cqA8OjhGLzHzQ9b1wZ6xp3fuvIWG8/397zOlX+9777t1XAoRGtgony2dBi4Nv8h113lh16gnZMq2fGKz/K9dirgpSy2lYKgroO4eXmUujPoTlHTiF/NLfl+C8BnyjITRfG9S8uDUFVIm4WaUW5eAZTtwJXNmyCDL3TfWaByR8KW9F8qCYIWQe5sQfl+eFkj65ruTCf2bnvUX5Zk9jzLCIOlqDMxyU8Y0W7DcmT/NO5YyDpbGXzf+wTjpo9KyeR6aKyGxzdDwQBexHCF2pPy+QsK97uhKwN9J3zz0eyx/5bwSHzGKe8iZyELIIDr7VtYVoiH9kLpbSh71+zjuGxuWZ5Is8SxROknVxyKNvpqDj4NB0A18u66r/rokcjnt0ev1bKLC75YpnpycYKU+5aRzzWy5QBN2S+DapX7tYJSMkemLv0XG29lLNkrxZc4ZWXEmB1Mb5Jf7tAM6er96o+zHtXteLH4RPyIJsHaAQteili45nQTP13suaUW2TbC8KS7ieZ4ll9egCtdnltvzC10LOf80DVCL/+xIMjPToK0GY1rJCc+xQQ2gFRRvGiv5Leu69d76G2OrSXLHaQW/uslkF3asNxMkcFzdxzbPYL7v6/NvBpvgNq0SvW6poCHgloAk10hdoce9V/w5uc96vW6VOieda6Zpasv/Nua453mapG77R4ApTxrSV3WUfoA2uwIaKcWvpHzK3KPXCzSYeosE779HX+/RJx7F7VMT2FnTxixoo37tE7+LH/23/woA4F0f/ZDp0+tGqOd0OsNLVFr44j0DmDgOTUD7U7/9OxjdNGeA9MQASIrVAl5lbOwrtw25tCTs/UGC8yETjw/M51ecaCeXl5o4uMZk1SkBGnt7e5ASrvmFsQ2XpRXcse+cZNsh6QVqB3de/RI/w0CSHOgat5Voo4BLq8RwszQ2DK1f3CfdhJ7fxebCQ7Uh5vJ2bVcytmu7tmu7tmu7tmu7tmu7tmu7tmu7tmu79g3W3hEIoSdufSsAk43QKL2Q8TIiOh6PNdMo0c5gQjLCxRqTviF4FJhAPGT50CjEb/zmPwMAfOJ3fhMAEBH274WJQsI1GttCz5QpiU2Zuf3L3/dRACaSmm1E25RkM023YHYxJSsbB7hkBkuy3ZJRv7y81HKHmpHU88XKktamItfLbJrv2/IDydKKHLTrwYu2SaEBYF2Umg1Q8kmO9WI2xZQEx1oC14rQt5EA7TY+nCj0f02EitMikG0ocSvSipLtztI5ZlOJfIK/E6LEXDOAHflyRmNFKlvJivNcs7OaQRQiRh/wCPnz+NoqjmOGBomgXBgtjvaYwSkb5I2N9gLArYdMpvj8/Fyh5MdHZu4JgVccx7j/4E6n79L6/T5SwpXP58ywCVFt3WA+MxlByfKKdPhsuVBJ3tmlye7ujUykfjgY4M79OzoO0odN4vM2qaBk5ARVFhNhFAUhHL4fge+7rnnfVVUhI8KnYYZjNjXvPQgCXbvjkYnCc0qYkiyWMY0Slvo5NmsSct6rrGcj8NTKIm9IBJ/zvffCqEXmTXQJ75EWOe7dN7DUPku/2uS4kknpRZa8Wa4lNibYQJsBQEMUld9IWdgIB6N99lUy4LYUSSDbQjAuyIA4jBSpozLVRCtdu3JVbYm8S0GJxGG0JVVbN3bsZK7ZMiP73gXGOhnxXZalzXRtEB83VQ1PpKuJapAs1duRh7cJYxXh0MqImp++9n0z2xrHsWZCLESfAgL9USdD1n6uIAiU5H0zm7kq1rY0UJ6r1W9530p+6vtIV9vEi9Lk80oWynmY5SWWlDuXJkve8zyLAGlBrgGgbmqcnggRsRA/kvh+nepz9JRc1JaVCt+urA1Be/T7fYXcbKK/ptMpvI3suEN7sszzrfsIbG+xWGg2UvagNrG27FlShpOt1yrXGvOaUmK8XC4VLt5GIMn47pGkWcp7g7ch0xQidFlbnufpWPWHxv5ImVGaphiTVFcynDFlbe/du2ez466Zhz5LVRrX15LHhtnPUxJBl1WuKNYwNj8nh1dwcMUgQIRwV+Rpy7JUG3lMJFJCotR0vdJ1mUliWfazskDNPSqvRVLdrMXz6aXOaSmXkxKr1WqFhGVFR1dMVlNQX3EcwzXbi477hO/mXe96UsdIxrNdfiL2UBB8904N+vPB+RnGRF7uHZgxqHMr1rFZZtFU9duWIQBAU1YoPWbOJUvuW1SJzMzA7dqdxbpAwHkxIgw/EgL/plK7IQihCd/NMJpgTburxPNKExAgdoW4lQgt+h7B0YHarohoIEHF7k0seWcyYD/LEiHXmsiQK/l4U6MhArISNEljS54cl0TnzMorsW1R2ax9IyVWpu8X56cYROa9DvluIimbRYo0M/MxywQVRULzQYKqIbIoFqER81yr1dKW4NOOuKQHWGYrRQgdcvwF5ZPmKVyOkZZ5CmK+GgG1ZNC55sNQgK2AJ6gJQZJ6+l5lL5V9yvd9ZKD95kxZk/Deaawtv6QEtaC7J5MJ9rkupYnNBHyL3CAJssife7WVdS8pDlDVlgzY+s0sncvkMzUabwPp0/JvZa6Kf+A2FjWv+1nLJ5HSHitJLz5200Gw8Obm+7BnFy1rov/d9oX0fFBZNIu8p4TosjKwKF9pmwjbMAwtCbUSThPBOrYICzvupg2HI0X9i0/TLnfb3ENc190ilY5adkhLqgRlwzXsuq5Fx6g/RjL3fm8LIaS2rUW+vCnA0uv1tkpjbalZpaiaTd8kDGOd02Lx5B07jZ3v+tP1sCbNipSqt4mtpVRR/tZuLufrpkhQWZb2uZyuEEiZFwj5OfmbPqdjKVeUBFzmSxDqHnLJfeUHf/zHAACvf+bTuPtrBtlzemrQOTe+11TEPPTIE7j9svnby0sKK33lNgBgVRX4/u8zggs3HzHnsgGMLbtydYKz0vRhFMj+tIe1kPJHFMcRFP/BWMt/ITaG+/RRcIAwIYWHwNxpk9bZAiXRO35trrWU0u5eqH+b8R0d7NFeeTEubhtfaP+q+V7B8q5sndpyTfpQIqYzWy3wBoUyHrpyAwAwvxQBkQLhdSOK5QtJCZ93NbfUBn9a2yGEdm3Xdm3Xdm3Xdm3Xdm3Xdm3Xdm3Xdm3XvsHaOwIh9NHv/TAAZoEksht1swq1083WA0aeDQCcvMGQKJyM0ew1yKUy9DE6MNkVRN2Iuee6oCKr1tdJtDPLMhRCihlTnjYj2Vrhat2lknbWJiqYl1ZWUqKs9+Ym6gnf08zybE3kA0nW8nWqJF1KFFc0OGbt4sJr1Q0DODjYQ5+8O96GfHSaFYgb87f+BooiTVMIVZkgToR8drFYaDRcsrTSnFZtseN1x3E+sxlL4ZsQWUmTqWAGjBHQWKQuqwJNRVQOZXMlo7tcrWymoi2ryGeUbIIQo+V5jkYRC8JnIdF7DyX/7TLCK4SntesgZM1nIXXNJbPWlQNX62vNeJyc3OOA1Khq88xzkh9KnfP9+3fhCgJpLfKvRIlVtY34CwfTgzP9TI/jcMHfIRH+GEuue/U6I8OsdS0jSz4nyIA4jnVuLkhYLGvKd1zNtDVEYkgk3/e8LXSYJxnxIIbHWtjcMe9yMt7XeyvfAMfq5kOGA6osS1xcmL7muZBrLnTMBiSbM9kioBdbHjElYMzMMx4eEJFTWbLKAQnYpFjegYtv/dC3mD5lFk0iKAjJdoiMbpZlmrFRXqLYol42pa7bWSGLaLEE1YBZW7JKhIhYuDba5MsSkT842tfvK/cHkX9wbT23rFnl03EswtFK1nI85P6+rxKy2vcw0XEQ0kjp++XlpaITEvKgtbNk8qxii2LhRHEtObHcRxBGeZ5vZQmVnLTclqSXTNvlbLrFodDui8yPTd6D2XKxxWmAytokQeP4PrNdrqcIIc2EE064WCxs5pbvpM01EEVdqdrVymbcNtGU0r84jjWbFhCpFnB/qupmi5dC5url9AK3KESwicaqqkrJHNvk9wCztF5XNrf9N+FtKDb2wf3JnhJFKp+fH+izyvttc3ncuGnEFO4LGoj96yeJEiLK9dvcBtLnNrklYDgf9kXKns/XnoPy+bfuGtSKkKIeHOzjnHuvw3XtFuazh8dHmr0Xng8ZY991ELIWf0hy4vNzA61JVw7KWjLsfG9Vhas3TGburTfeNM9K8ls0rmZLZRwPiKR5/c4l+olZ90sSTXuuIMpKBERo9ifmWmVt+SBkrFTSu5X1lrE9OTUZVdK9IVis9PNXr5k9ZLUimrCu1afRecF5WRQFhCBOhCEOjg1v1d7hMRJmT1POZy+Mt/hOhLA7SPytTL2u57JWcQ+xebLfV2gsItHtrrdoWCjXkKBba5E/blysl8zC83tC+ryuKyASDjyOeyHPHkFQcgXNR8GBPLm8A7Hcgz45hBJjJ8MwgFt0kQRx4qFYCcEsRQRWghDwlTssF9gb16LbOOh5XSGElCjkssgwIreOoMSUM3GxQBXSN3ZJkEoUC/wSPZK41UQirbk3pIUHl3t9JVlvEQdIQpTyfl0h4yWCLS1wdGD81AuiA7OUfJO9RN/39SuGf6M8JBn4/RNcPzZ8IlNm57M8VbRhQRst7zYJB4oan82nnTGuEajPKf2S/8+Xsxb3Fd99am2fcHDIGm/LdguZ/HhkfBMh/C2KYhsxwte3Xq10TguJcltyXHm1yi7CBbC2sqgt2kP2AuXvIYQqiiJUrhClk2SbCOUszxR237RQ9Ob/24IyQ/pQy+VyCwEsa9F1Xb2G8sIR/TWdTnXcNjlG288eqf2u9X7Cp6PIVfpxDx6c6JrTs02VYch5r7x+4p+GoSLYpX9iT3q9nhWn2LCZrusizGkHXIv+kTHbPHfKu81TS6S9iWJerVZbfovw2G1WDQBtsYocvt9F3lhbWrZQ/hbd7cKeWQFL5Bz5gZ07ZXcc4zjeEi0RIvM2l5V8X5DALhw0RMnJNQVeFscx+uTIlXGvuM6qLEfBz11yz5E9/dHnnsXsn/8SAODDH/0OAEAvobBHOMDJy4Zf6OSN18xYvfgVAMCNH/wIrsVmH/o7/+F/afo35nmtyXHzCSOOEBM5OK9KjK6Ydfzw88Zeff5TfwQAGDoxbl0zv7tDv0W4bwdxgoszYxs8x9iPR598DgBw+/XbGBLZvJeaPsfXzWfOFme4XBlf5BrFbUpWy6ynczzB81ERmfk4Pb/QsT4+Nn6BCPVkrCrpRzEOKKbw4OQBACAiH/FwOFJUoGyfYmvcXoLllAJCf0rbIYR2bdd2bdd2bdd2bdd2bdd2bdd2bdd2bde+wdo7AiH05n3D2O26bisr2FVZiaJoq95SIuWxE+HyxETwQkaP1x4RQq6PeEjOg6abfQYsQ3oqGUdma6IgxiHr9gYHJiL38lvmvicnd9EbdDkaFCnQNBa5wOypQzTKYrXE/rGRVvR435ML8+zj4RAJo7BgFsLxPbxK6VeRhF1TNvbB9AQxlWskK6NZ8srRLFOyIXFZVZUqeAjnjmQX+r2hzRBrHSUjw46zlTnQLENdKY+AKIkJ947b2OxDU3SloavGqGe1x0okcofjIdZEUQkfUVFmCD1zH5cZ3NHIZJ2apsEqFdlVyb5R0cpzkQxNJi9aUUVuyvkSRXBV4tN8TzL8KB34JWvvK5u5BYD1etmSs6YkfUZEWeBp2rnfJ0cEFbvq0EGAbr23KNQFbqDqXU/dehIA8Hpq3u14MNZo/QkRSdf3zLPngYPDfhf9UpYlImZOhteM+oiw2DuOo/O+UcUtzrkKiCllLIiaGTPceV4qh4fjePo7QBA4XXnf+/cNn4brWtnGKBYJZCtLO6dyynwhkrzgGK+RUC0wDkz03a1Yw+/4YAmz8s/MiWy6f3piJW4ri6w7e/BAxwaw2Q/HsXwAviAEC5HKteirTUSG67pbWTBBaDmOo8pvYQvhA5hMjlX2Mmvq4u5b2ifpl9y6LaMriB3lymkhBRRVIhlV5cnxtX5YZY+bVvZoQ542iiJ4zJQtyBsjzxe3UJybSBPH97aQPoKIDPxIs8bSB6uKEejnLy5MP9Weunb8VdEuttkx4d+oFbVofu7t7XXU4NotCIItpZCqqrZkb2UPGo1GFjnKeSEoOoMg6yKXxC4kSYI1173cT95JO/MoWSB5EYPBCCGRggtde+a++3sHKoHseZL1jrQvA0qbKyKvkEx1HysqKglCRbL/eZ4rOqa3gbhyHQc156iohMAPthTtzi7OdWyFM0iUOVT1DtjikZL3nmWZVfGrbOYWMMidr6d257sWgZNxPA+PD3ntErOpGQeZc8dEttx5/Y4iOoVrp6ayycVsgZrQ4VVm+rB3OGZfXNR8nnXObGs/xnPvNopcf/JHnzZj1UKUCX+IzA/ZDwGLHL04E8VDMx7TxRz3iPC5Tz60B9O1fqcfd3nXVDmxKLYUEwWZGPV6GAoKSDLZqaAR+1tjLHOi3++rryDcMIo2jXyUwmPiWJl74YXbkrxuGigIiP0KI+F9jHSep9yLRdMoDEK1O1Zphxn/Va1Z0kA+k1u/wqVSZU2SnrKgnxWWev1aUBuC3o0SRRJkfMCC1zwYXlNEULomwrCkT1m7uH5gEHLif5RZjmlABU1m9gu+Zw+VIgcEqSP8QpEfQSxXRO6hZEC7lWVCD4F5QbSz7E9ejSQ0a7CpRe2L93Nq+L7YPEH0CmqgBxkRQQxG5LiKwwgndw062iMitM8+hUEBl7breGJ8jbmoFcJVTszVgpwt7Pjh3jGWK0rLU0LZ9Tz4gkwj2sV1Yo5VhNP7RDsP6XsSWbHO11vS2hF9hzzPrQx5YfmITJ+WijoKiRxrIxTldyjM9wWh2E/cbVRwbZVJh31RLuyiRN6OV06RPE2jzzDpW961Nj8PAPih5T4sWqgOAOgRwT2bzex5QMF2ls9rU8lKEWgtDlRdb/XX77v4kY7j6DyeE73VVslS5O5MkE8WVSLyow26HKVxEiIIun2JvUi5a8SHSnlOSJJEzzZiRxP6YMvlUveATZ49x3HUjxJ/UbndCsunY7n+zKpczhcdziCgzZ3YKPLPU9/Eqo6JalTVUlQDjD8se4L4EWoT07TDeWjGsTBOO6xytXCbnp7e31Kyk+8vFjPLyabXlLlq53bJ6oyU77YuK1SZuXdI/8FHS32Qe6JUnQR8V2Ec44Ln4MOJQdQ43BfRC3D9A88DAB5+/3sAAM88ZGTkT/7kZdx5y+yDN9asDmHVRLHXQ3GPqGIivB69YRCHo/cc4CN/5l8334N59tPpFAVjAVepBvzsc99kxtgrMeQZ+2uf/gIAYJ/+S5TEePlrRtnsuadMv8TP+pV//sv4vh/6fgCAe8f4IX/vF/87AEB80Me7v8kgiV573aCcnnjEoIKeuHEL/+o3f8eMA+Xuv+e7/ywA4PbLL+Gtt8x54DFyI43Iz/rSSy9hNDTj9/xzHzSfv33bjEu+QE475dFvPrs0/k8SRegJavlPae+IgJCUeri+o85uvuqWXa1WRQt6Z363R+hUVaeo+4QKiryszwNk7uIaYangnh3U5rHTpkQ8psHnwfbNNw3k+6H9HqYMGsSHlLAPjOE5KdaYXwqcm2ScNGbpLEUqh23HbF57lNZMvADnJAROpLSHh+i8LHD3zBygxdge7B0gZqBECBEjkvLKpgcAExLbiuG4cnysBxYxCFJislhbKOMNIaTkZy4uzxR+KU3ln31fYYACsRfjUuQZLi5M4EKMrs9Duuv7WPDAr7BKbsqDYYi0Nn2+Nzvp9NN1XRSE1xUtorx+wsMoAzvnc3OYKusK8w0pY5Ehvfe1exglZq4I8bHMpR6AbONQf3VvT/9fFbLxScmdHJAbrCn5KxuTjMf169fVKMuG7RGa7vm+knp7G+PY6/WQ8VoXOUs+zswccoIAEYMoe5SJdVmq4pelJf3kRcNkoOVFQsC9oDRx7AVI6NRVDKooKa9jiWVFcnnQs+Urcq2j4bbzZOWE+buxmePtsY1H5tB99aqV6lZS0RYpI9A9JJZulwhwPp+3HBxzj7xhcDZycTk188J15ODk2JIQvysHOhoNdD7YdWOdST3cqby9daJUmlhIXTMboDm7fda5jwQ94yBELNBpcYI8ButqB1MSX2pgwrEOSVVY4j8A8GIhrHdR8DRT86Xml7IeHEigpMz4s1pgNt9woNmnssis4+HJuxd4drdECYCSCEdRpAdueTfiPK3LVJ0zIeZU2+I6CHiAlFLGS0J094+OtmTSJVCcr9YIaUdDIceUkjjXRyPS05VA83lfOCgLIekX8tkal3QKXZE752E5SRJ1cuXwUEjwJxzC4RyMJaTxAAAgAElEQVQTKHVR2wPCmmSOZcnDnuy4VaW23FI0g2Ps69oTYsOisHZZ5pEc4CXwUlUVFjMzxwcCq6ddWC1X6gCrg8lx3dvbw2Tf2DxZI9Jc19XDYZ+OahAEWCkBcVcaNwxDDdDIvYXQ1hxEzLu4pCCCzONer4dSiJLpmElQqx0klTkq8+pyNtN7x3RoIymNq4ABx1iSLUJIfMTnBQCPhyOfL+divUIRWZsHGFJG8/0ApSuEpaa/vaiHb/7QRwAA//gf/iOOhzjUawQkeb5/YT7/7GMMXlQhYpa1XD0y9zk9oTGrYqQM/AcMfBxQhj4IPA0aDEngryWaw74mteRdys/KA7LCCh8AQD8xY5enC/RIJHywZ97zGffmXi/GRPfurh1ep2sUG9LJXhjo9aUUSw6LZVmh3kgoScvqEo2UqoYbpe5ZZt8zr5nxoBrvD+3hUPYxBswWVYloQEL49iEKQJqWuiakrFpL3JpWgEVL61keEiU6f0PeR+gI7p2d4u7U+DJCkr5cL+F5tOF8Vj+QQLere82I60uDZ1WLRF3ELYR81vO3Du5aQhe5WJO4Vd6TBIGXyyVy2sN+v2tH/KaEK0EzCbqxNLzIcjRyYGxkn7flJ/fudUU0JCgcJrEe4KW0RUp+FosFGs45j7YTjqfflWSmBJRKOLgaGP8hdKU8d8Vn6au9sH6IDYDIv1csTZP5ORqNtkpVtZS5afRwLvOr7QNskrBLwcV6vVYbLaTl7cN921a279cmoxab64e9LSEJaY7jaD2olG5JlqzXH26Ng8d5VhSFvt+6FYwCbOlj+29hK8giQZu6kbIrR8duMzitgZ041vuJf9VjsM4k1bpS9jK/yrJUSghbyhxtBeBkfJra04Se1KhmDEJWZaFS6iF9XwloOI4HTxKVG8/uuw6iduksDH0GAAwGPX2Xss6sUEGBJRPLFxQkEL/44OCgFbSXADuDTUWJnGcp2W9lfk6nU+zz/DKhoIzbuACfeUES9SAy73A0OdxK3ikhuRuilIAm/RxJgPl+jJD2TMZTysOKvEIS2rMnYM98QRCgER+UpWIIrZ/fH5hzqs8kec338dbrb+Lm5Jb5/ML04faLL5v73rsPJzeB6EP6RJ/4kpFrfyb5UZQwz/rlL74IALj3uBnHH07ei5zns9uNWfN1leEQZtzygsI8N+n3LBcapHv3n/9mMx6OvOcBnnm/CVgtZhSX4Hz+4Z/6dzAgKf39z/8+AOC1r5rStrOnxvir/8Z/AQBYvmCIsa8+bgI8ruPg5IFZ98l104c/9xHjQ9z40lfw0pe/CgD40Af/DACgT1qHX/+N39Kk5Ld/x7cDAF55xYBGXvjs5/De501A7REGnj7+e58AYIQoHnvsMfy/tV3J2K7t2q7t2q7t2q7t2q7t2q7t2q7t2q7t2jdYe0cghDKSDS/TpUblNiFu6/UaZdbNaJ+emRKQvb0xEpbMLBdd0rMoCS25KLM5ZSY60JZAd3/fZFAICEFWVkgZFf30Zz8LAFjAZC6ujW+i8My//T6zVokZyuM9C48U6XHJ+KzXa8yXVj4RAHrMJPiutyXHvF4sNVsyIHS1DZtWsjYpi2HE9XJ6rjBAScMLEiEIPCylhO1uFzFRlY3Ka0qGQvoCWMlUiedrdqbX04jpZkR6Op3rs1riRdO3y8tCo/uCpLk8IToiK3UO9FulTieUJRRoqJD/Op6HmNLk8r5LjwSYexUmRGlJRlClyn3fwnlFwpSZAy90UaILkR/27T2kf/Ls8j6iKMJy3Y2it7N4goLakuRtGvgtCU0AuEay0kGvb8lu+T3JHq7Xa4V2Rp5Fv4gc7bBVsgEYyfYBIbEiaSzrIMsy2x/XEuDKfTVbSthyIwShTaORa0Xyte4rfVlQlnnGDOtqtVZSTEWcFQI9bqGANgid87Itf37J79mSUi3nq8xzRVGkpRqbpMiz6dSSE6tUqMCKh1vE0e2MnsyngUpCn/N+Cfb3DXJDMqTSsizXkjuZV33fEq02jblmv0fCec3GLxFFbblWIF2mOnZaviZyqkLQH0aa2Rfi1+Ggr/fW9dKC3EtmVOahvC84jWa35NnHY5vZlky2QKEFxtwP+3aOMZPVlnJXskS+B836uY5FOWom1lx7PLSlXGKLRJ4zz0tdE5ty9WEYaimi3CdNU0RxlwxTkC5VVWlfxTZELXJjeWb5visVp02NPWZ2KsnItsgd5RrrxbJzzTAMLSkpoei9VqnZ6elp57lkXNsk29JfaW3ScYW883tpmur83ZzraZrC4fwVdGWaprh//z6v20UU9Ho9RSPI+LX7p+SgIsbQKrtQAm2idHXvm/StxDbvo0ih9VrnppSaCiJGvm/6JeTXtvxyk/Bbnn08Hut4yH3bBJxtYnDAIJlCT8bIzL85USJN06AiOkyQbfv7pqSoP0iU7F3u98brxme4cXwDd940pWKTPfMcqTvkuM6UyFlsQxsRHAYOn7krWXt+fq73ERsvazjLMh1/yTj2hvazigqUsnsiqMI4woroCRFqKLMCvi/luA3Hvdafm2UP0i4uLvRv8p4FmdHr9RQBYpGdZlyqvFBie2ny/bIotIy93CBp9XxHEWMjIq1kneZZqeWuMo7JwCJbNvdu2XsOJntqMxVxslorKlK+NxyMtZ8yX0V+XOb29HKOnAgC6YM8V9M0WyXMcr80TbX0XmynECYvFgtLy8C+tEmpZc9ol/u0n1Ov3+qL4zjWPrbWEGBQDQuVUOZ+5lsRlExRmGZsJ4cHikZYE5Un95svl/CEQJXXkrEry3KrZEz9JN9vIeLizvO17bA0GevFYqHzT77f9jE2ye/Fb2mvl03Rg6Kwfou0dl+0z61SHxVQ4TjKvG+LWsg6nl/akutNpI4g+/M875TxmjETu2z/Ju/UzoESZdXdU1dLi4DaLCdrl4sq4mYDpWdaVzDHIlV8uK4gDUXkI96qFFHJd8/ak0303Hq91GsIIGk5X/DT7tb8zegv+WGktl/u27ZRmygsaVVlfdAbNwb6OzM+jSKDpFnp91THP20J5gBAWVmfRsQAmqpS1I+i/DmOaWqv5W7YlnaFiUUPlfpcUk4aiq/H80Lg+VvIOC3lD+w6sqTl5v911GBGygYszOfHPEs9+vhjePrZpwEAJ0uWuk/N801nS3z4CUM07X/mcwCAV1li/1wcIxHEe8M++fTRyyVqIrcvLsx9r0wmuCQtxXRq/JeYdB2O5+ozp5Xxr8SG3Ts9h8P9K9R5aObJ6d1z9Obm+nfvmf06YjnZtYduYEWE4Mmlsb/NA9qt6QyPPmtoQQ5umTPeK68bZNGtJx/HM8+ZUjOp6ljSv/3Of+17tATmAUvKbzxs/ImHHnkY+0Slyvj/4I/+MADju907NT7Jn9Z2CKFd27Vd27Vd27Vd27Vd27Vd27Vd27Vd27VvsPaOQAidnJjIVZZlG5FjG/Vs84lo5o9cGffu3dEI6MGBqTEejoRYskYu0U5mcLM5ay3DAUoiAWYkaxZJ9FVV49Of/bzp38JEDK8emkjc6MoAo+smC5qyllbIQ/NFjYikvAeUUF+06rgle5rm3ahzUVW4dsXwb2iWe5V2IrmAzcokbxORl0j2vZN7Guq7JPeFkt4OB1tZRa2Dr1wMmUEU1JYkhsbjMVz2RRBGQnya57nyv2idfz/k92zGTLKEkvFoihIuo+hSAy0Ey0lo67c9x9ahDjaQC4JYKcvShqOFtLIhqXIyVm4kIZENWqSXSk7MzyhSKPDhRJbAr93qut7KMLfnLgEgW3LYaZoi3UCJtWUopfY/CkRK0DxvjQr3TkwEWrK0cv+0yBHyhQsapcwLlMI9E3czYGdZDpfZCiEclGuFYWgRe5wfbRLlWhBFq65kped5+ow1tjNgWhtfWrlXAJhMxlqjLZl+K/saWkRX3pVodV1hesFW9rqd/RfCwSAItrIjwsOxXC6xScQaBjYjK+ulEqnhseVPknc+Jxny0eGRuW+awqktxwJgydSzLLOZXtZcS+ayDmpFeSl5OzkX6nqsyCL5/ii23CqKYKq7pNmD8bCF8rqufRBUjYz3iESWQRBsSZ6OmM1xQwdTSmQLCbiM63gy0kybw7FqE3e3+Z/a13YcR9N2Up8vLzddLVCV3Sz1ASWOXc/DnHZ7QCTOJWu818tUSRI3s7ur1QpV3ZVXdxxHuZCU4J6tqgq1CWK7JHuU57lKxhZ52vle4LrIhaxd1xk5gbIMxweGFP6ECAblsvA85c94wKya9G0yGiNsvXOgi/hRHqiWDLm5b2ileDcQBVmWKZpn085FUYTl0nxPEBrj8VgJuOfzWedak8lE7ym/a6N+ZbzFrknLsky/t/kMy3Kpz2izkebn/v4+rl69yr6YeSVkvo7jWNlWfn9CCXfP87aeuY0w2JR6lmcJw7CFCGBGcbHEsGeue4t1+n/4ScMZcHx0YJC3sO93NjM/H3roBs4vjO/z7KOPAgC+/EVDPum4FfbJkfjKS4bQ8tqT79G+SN8ne2MdB8DYHYvONWvh+PhYv7eJaJHxmUwmODoytkv8K0H8VFWFiuOnPof4ZWmqcsWybmazxRZHSWdv3OAQsvZnoEjSTXRT0zTwNojMlZcuTDqIPQBYKwdLZMn2Ff1p+j5dnINbnKJzBGnkRdbv0j2c7zGM7TUFeZmRWzBJEuXx0udEa+0QGbSYm/ee57k+qyJhZhYxKGgJaWJ/2kg15UPsWRR5s4Hck2cX/7P9N7lvkiSKnpdnbv+MZI/a8Fs8z8PsUkjsu5LeTdNYpDcRZ+s1EXOLJUgrhMsz8/3LxVJJ2Efc90ZDsw4WqxQLIgeEwLzZQA63x6P9vhXpXltfBjDjv4nukHEMAkuev4lOayPL5TMinNHe67zG1/sAXTnyzbNOW6QiW1vkjbyft0PeSB+UEL7FdyPXEh+5vae334+5tyAfXHheFymraLsyV56VNr+K/H8TsSbN930dU0WBzq1fZlGptKf0gcuyVGJmOWtUVWMrPjbQ9E1Vd30K2Hdz/fr1Dgpe7m36FHT8X8Cio5bL+RbiTBE7ZamoWWmyT7ue5cQU7jHh26uq9pyreR9ja0eT8db6qmL7THJG0fdXVYjZ1037tlqtOvt/+2/tuSffU963KASa7jsMRb4cDvKK85FniILv7cGDc6xWXZ9QkKxRFFneUvpApzMzVovyEiPO27hvfl6uzTqPjyYIKOzwiV/9LQBAzmumZYbLO3c7Y5yEZp6ND65i1ONZrzDXvHm8hzOiq13eR/TZ03WGAZ9/ST/ukmsqiWOtFCnJsTYj5xwqB15pxu9iYZ5nzWqcw/FAuSDvPDA8SKOHjf9doESfJM8ueTOl4uP1O2/h/NTsy9Z3ogDL7FKrjGT+Cl9kHIaKRJL3LnPorQcnem7/MN6Hr9d2CKFd27Vd27Vd27Vd27Vd27Vd27Vd27Vd27VvsPaOQAi95z0mYtXOuEs2rS1tLJFdrX9l9P3Jq09qDf9sSlllRlKPrx6h8k0UV7K61bmJ9gdBoPLKem1m8VdFicWKfDaiZiFy2NMLXHlUMkmsXxVVCzfD6V0ThTzYM9HzCSXP01Y2TRAggz2rMqbSy8za5a163rbyAWCyGBL1lci31Ik3MJkxAC35V9P3Iiuw4OcloxoxI3O4f6TjLhH9diZc3o2geaRPSdzfql+12WGb9ZA65ZDM8/v7NxTVkcm1R1TKSGL0qRCjCKMsxzlVIlTtAVZ1YzNCrlHwuI8k7qqntbPQm1kFq+7koJKa8Q1lpbIsOyoK7e+3ET8LjrFk9uq61ixkFFipYMBkW4WbQZRvHJEVLRuhBdA5K5nwPM+wZObsOlFmewd7mhliUFs5fiLHQV8ySRsZM9d1LbKC/RQEVYc/g2tPIuxtSVJpbaSQjgnRFO31LeO3P+nyPNV1bed93kUdeUGgY6XXFgnyutZMZT8yEfmqqqyaSouvBwD2qNjQbu4GzxOwnY0sigJpyqwdJAMr3BB9FLlVZQLaHElFh0tGfmf65m9xUORUQFytVlrzLGPWi2xtuF5rQ4o2Kwq9pmT0Ts/OsH9os8WAtQer1QoBETuivFQ1Mk8cRVlsci+YRmiPIn7Mf6cXl2+TcbT9LDeUa+S913mKy0vz79HI2PFT1kIvlitITsPy75A7qmk6yDagK1nuuF0bMZvNUFaCNuzKhHuOu5VhE5u7Xq8RRl0VOckSZllmeav4LhZEFIyHQ7z5xhvdvgeWE0z5mbh3CKebeQYqwsRdjgcXdq4KoqmNekx6XSl7keoe9PpbdlvRgXWFY6Le2mhH6c/BnkVwAADqxiLhGovUke9vZrvlOdvSxHsH+50+eI7b4c1o/22xWGzNQ+EyqOsaDz30EABrbyTjHMeJ5f3ivtnmTRLkjLzLusUJInPh2g2rqCcZ5eeffy8A4OP/6rdNf4MAlcwnPvvpibnfZLyPr37ti+bf5B569DGjiPrqa7dxdPR+AMBrr37NjMvNJ/gMriI9ZA3FVDIbjya4d89kI0VVpM2ptKmYqOpkg0GH68lcW+Zcv5N17ox1nACFoKTNHrc32rNoUeEO4Z7fRh8q0oEZVS9w4InNIs9YFZm/nZ2d6RxwN5BFdVmikb1H+AqjLkcYgC2EwNHhoUVop925OhoNMWSGX3ggFBESBmpLRAFV7EjgeYg2eCYcx0F/Qk4qIgFkvhwcHFjUJ9Vp185a+yuoms2Mb5IkW/5KG5nhEHkq95O2WCzUp7t582bn+03TaEZb2nze2sP9Llq9zVEj44ENFGJd13p9WUtg39zAR040ckj0ERwHGbl4zoi6pYuCQdJT2yDQZp2PjaNrYBN5lqYpCu6h8xllmDn+bZ9m09duq4huqozVdb3lD4iyoOu6FpkVdFH8bd9w0+9so5zaPDW617e4jeRv8jvp36pa6TPInuqvuuu6qipFhlsfw3IRCv/X5vP5vg/HoR/tdJ+nLEuA94viLudf0zSqFlauBWFk7Y9Fs3L+pyJNX3QQxoDhdLTniS5ypy6rrTHVPlQFKlHzjbuof9fxW0ipLuqo14t1/sq80L3Idbc4toY3bpgxbqHS2rxTcs22Ui1g5+NwOFSfWlpbZVnnWksZdpMnSD7TTxIMhP+sNd/1Wdr/bo+Z41qoCPd5Rac5jo5pTrsoe2oUJcrVJ2PU9vEOyG/zYG3WNYTvtHUO93hYEWW2R556DMGrZi8uaJtKVp/MlwuMY4N+zUVpeMU9pYyxWht7U4rybzpEuiZfG32UivM4jFwUmVknbiD8o2YQFjOrcD4iEt3h95yiwMA19vTsgvyhfXNt3/fR0O489dRTAKBo5rfWGVwqfIo9lDGrywoTqS4SX01ViRtVYR2Ouvylq8VSOcSGlKYXpOhsMUdPUFF/SntHBIRef80QKbadwk3iwPEo0pISceBEbrdIU+RVV7p6Sojn3ft3MDggtIoDdy81MLM6CFBz5ic03FOSdb1x7x6ee9pIt+UFyccEXlZmWr5zXppJ5DMgFDo9lZpMC5K5zSy0NGmVWQFAzc0vz6wcZRx2idSA7UNUG9YeRBJEMX2II0uEq/DqiJKJjoPR0Gyq5WH34OiiRI+H8k0jNh6P1ZkXIjk5DKC25Gqb8sB1XWsfxF+RYFW2XmrZjyvGQR27RjepOLBEqUe9rgGWBZPnGTyXwRQx6qElPZNDijrQ3BTa5HpiLPUg4nnoj7vEvjIGjuNpYEtIZbPMbhxRr3tY08OoZx0Q6CZky+0ELr25Ea5WK3V6F9wwpyyPqZoaYyG95n1Gg4EGe1JCOmsS2dWug4DOnet0TYBT11sOSLsvIjsqBH5tg68OftMtE2gT78phrR1MU4NG506u6fu+HnzcuupcMy9LVCQ4LHhwkdKMNE11Xsw9+wwyn4SosE3I6PndDVqg7FEU6WYqNsnKFqdbDsiS78ZxgSAQskghVGw5NRzvTfJOx3HUzomTJ5Kko9GRBnbFua5qyu/mtgxN7Q/n6mKxQFl3ywrgBpbwr+6WcpZlqcECeWZxGiI/0M/Js7/dgaRm2Zo819Xj6/pO1mmXSNePQi25kH5Kn8a9npay3L1rSPSk3Ojhhx/WYJnMp8XCzKE4jLQP8m7aZSVF2S3Z9TwPh0csRdOBsYfadsAOsMGbw4MDZAVLYPlyBEbuNN25DHRJ7fdaASDAlsfEcay/W5ZmzGQM9vf3tUxLWtv5VWncjWcuikLJqOUzcjBsByHlfauz7Lpa4tEmkZR51DTdMoGyLN82yCnfl/kgc7NNLi19kGfXJE1eaDB30y627bc93Jj5eHFxofbD31jfeZ7j/n2RtbfBCrmHlqZtkGwniQ0kybpchWs09CPezYCQyk6vMy0rErt6cmLW7vGVa1pb/MqrhlDy4NC8k5deKXBCAn4VNOC8n0wmukfJmEnQ5/LyUu/TPjjK3+Qd2vJLMz7tAKrYnxH399lsptfaPGilaar28QYPQ+vFWt+dtHbZ1tcrLSmKQqXa5X51KxCqtovt1q1bAICzk7Ot+SDjEobh1yVWTZJEhTjOuO9eMlmYp4W+G9mXHO6ZXm4P9RKEVVL8KLKBK97v/PxcxUOEMFnGZ71eaxC7cygEy5m8bnmRXLO998hclffWDhRsCzYUunfI/eSzw+EQRyzHbZc46f3dbqlfe++SYJaMbbukSPonpMsyZlmW2cAkr53luc7JmMFsGTN4rs5zea6izPWnrDktByvs3zYP4G1i+M0Ss7Y4iLQr18xBTukOWsIVun/qAdlVkZvNAEMYhlvzvx0Q2jz/rFarTnANsDazKAq105sHfg2wtb6nZ4i60mfTgBKT31VZoa5FRtwebKW/RdGVV5fv51mqYyOtXYblebSnQttRWF+lnYAFbMI5ieKtoE87IL1Jjo7ajt+mnfKcRp9ZvqdJyazcIkjWgLZTq01SQQR+rwnDrbLtlIFsJ7CE31r6JfbOqbFcdn0oeY+np6dbwhAS/PE8z96nFSDeTLK09/LNOd329eINonptda3+ug122iB3xERsWcj8lXtY8vBNAIPjNLh/bsrfGybC98dmf6myFL3QPOuC9lcFJfYn8F+n+AXPKmVtvn88Psb0rvl84/C8Co6L1wDc859/nym1xjLHa6+ZfVZoGXLxzV1XBVAW9Oc8h+WDcQz45p3M12ad7NHHi/oNfB1TJgm5Rp49OoQjoAmezR0JALpWkOZ4z9i7e3dNTGG1WmmZd59iUvL+1lmKmgnfN183CcXZxaV+VvYOsccyB3tBBC/sgiLeru1KxnZt13Zt13Zt13Zt13Zt13Zt13Zt13Zt177B2jsCIdQnGVuWZVuSdh5jVqm3tn+DIDlIwuk2iIjw6fFaScQyrXoNJ+pGtaW0xPM8Rew0RE8oSifwccgShUdvGkm821MDXTvs3UIem79d489SMqQ5MLxmshdJwlKCPqN2ZamZvU1ixKqqNBNSSuYltqTFAtGXn1XdoCbMTrLV0YiR27RBLeSACjHu6zPLM6p0pJSJ9QOVpRbUYs5I/mJ6iYiQ3D2iZiQaXjdW8lPIyKS8yXEcOMQB+SwZCxi5dRsPQdSVe5QyIwPXlag00Sxo0JDEu2Q/c8L8yqKy1yXk1VWyvwhuvZFNYHNdd6tcQprv+4DDDD+j0/3Qljhskqb+P+y9Z6xt23Ue9q2+1u6n3P4a+QrbE5soiqJEMSp2ACGRk7gkQYAggEtgW4ARwHEQxHGCIEhD/DcGJFuWbRVHUZzENmQZlmTJFmlapMMulsf3+Mqt595zzj67rL33qvkxxzfmXGs/KT9NgHv8Ofees/bas4xZxze+z438kFDYwo8lShmFSLNupILpQ01T7cnF0k5PTzGaCgxTUBQrkgKjVZnGgRMpilqJFg1J6ihErlWtN+TbphsdqOtaI3i+IFyK2iKfFJlVSuTQQa7tSXA6hJgqF//wUef/vu9jNO5GflVyfNtimZuI4DDpIgNKh+yZ6V02sjJVKfTV0krEx1mXBNySyZaoBWXE6Gkgj7oysX25ZBf9pqkRTroKx7ZG82Ejq/2oDqNQg8FgD7ruogIZtdO6hzaC5kad3TYO4khJ1Ck9W5flXtR/OLQy8E2PVFBTJDYFYinDwCHXZnk1NVI+b+XMI4wl6jOU36k0emDHIKOsipBZ5giF8PL555+X7zF1N/LPQlIpsHhFkMBzItMW0cX23BUbbVOWZbdlWlGXEDsIfTQSlSL60E3huLgyUSoig3SdaUxqkvv8lZARhnGkY4cSwyTqffz4sZZrMLLzDd/TTxPQdIE830MIsQ7u5/guF4nAyLuuf04aLKN1RBgBjjT21bzzfJqme8gU159d4lvAIkE2m41N3Wi6yIcssRD7PrQ/CAJ9Fwm4iRy+deuWg0SyaZf8m0VyQN/F70ik3SiyQOj8brezKA9Z15Ik0f4dyxx9JGl2i/k5MlnjYlmXLpciZV8WSFLTv/fumjSvkxsWcl/sZE66MohooiHu3b+LTPY5jDSzHVl3YB9V4nneHsEy+91Nz330yMzRd+8a5PbR0ZESCvf71Pd9Tbe6fGL2R3Eca4TZLrdEZkAll9lvlih8oHOlKyYCmJTt+/fv6/tN3YWY1o/20mzdevJ7iMBT1Fyz0z3GSAg+KQ2dZQMrQhCTxJYpDs66VneREtuiQK4oYtkDlCU8GaOKai9teqIinwSRaKXUG4RxN02LZVoul1pnogc519Z1rX7ujn8AeOqpp/QdbKu3+1wf8Z1lmW4K347IOYStK2DX0TzPdZxxD5oMbNS7VdSERRVGmhYqY1d+Ht+4honMsVtpT5ec3k3/BboE9Jb0/hhds0IZXEvalqjTdA+V4yIg+ih69qmLLOrPW1mW2fWh94xrWcK9SqMZEauVqZdFb0JT/NjGLENZlvvE0aFFmrDsSnYu6SSbcgNP4NGuQAlg2pXIWtaRqbWe53UQlv2691FOzFkL/BCDQRehGfpWOIOobpWa92xb9cd84DDo04gAACAASURBVJBy9xGvaRp30rzddml9h4S97aZIAY22ET/HlLMwDJVAnvOvrpWPnmA46qYwkjLDR4BC+o1+xXParthoOnvE51UQp0VbkwielAtbHVf9urvk5i5lBWD8I+ytBYnT/rudpO3JXF20Nv2tKLrzAJE1o5EVLGK/kdaibVsEQncwkzl3rfuJFpehZGVIauFA6n7/jdcQ3Td+fybn/qAwvndnege/83v/zLwhknNhY5DE6/yBUqFkO9MnZ2/cRfHgNfOc+PvpTYPESTwfr/3eNwAAX3zAVH7Tf+//wIfQCBr41jNG/KEURN1wPIYnIhFXgtwu5Mz4gfe/H6G40UbuKo5qqqWUuBDEVC2fJ+oxgIeH98xaR39SMY6RFf2opW9O5HPXT073UL7ck2ajoaKs/yA7IIQOdrCDHexgBzvYwQ52sIMd7GAHO9jBvsvsOwIhdHJMItZKbx15A+1GeauqGy3dFcLNE9hITSmRwFpux0fDAWa3TATlhRcMKeODz78KQCTHScwnUS5I0DqJQxQSqbl19F4AwEp4YS7Wseb3j46ZyyzEXE0EL+xGBVZLKyOqktqO7DnQza1XWcXAs7nYfQJRJ6pgOXokKj+OOrwNQDdnvRLJZUZXbpwadEIYeRpFoHzzfLHS72NAqJaIQTYlEiq2iK7e9y2XS42q87aSEak0zZT4eNcjbms8IAm7MuFoWgxgCYcB4ORIeD9awJObZ6IMKLOXbwuciIQvSf70Fr2xUpWVyF02LSP8NQL/7cmyAYcHqt9vvu9IkabyN3urTvQEf5c4kXuSqZE8m1xYo9EII4kKkByaN9Ln5+eKPkmUsDrS0HcbdCMHwyRTSVekXR6T+XyOcmcJBt36rVcrJdyN0h6Bo7cv0ax8Ag5vUiq/06hkU+7l0g8GlhiYvp33OAqKusGaRKAFJSEteoB9sRLkiEtKz+hY5sjoEq2iEY7WkojzeZbFzU/v57GvyWGTphrJK7dl591uxIzPPBLZ3SffeEX/dvv2bX0XAMzPn2i9pkLsvFtstc3ZX4ORRBdrG4lUQklBNwTwceP0RqdNGRUy0rhdDhoG5qanxxqxnM+7CKMsSzAe27kcUJ5JFEWF9PfhVWjbVqMxjFEU0mbXrh1bIsQtUZWU2l1q/fhuRvNOpyfqc4y401xCfleeXakImi6BbhzHmCnxeHduPzs7QyQkgrmgNYjYDKJAERx8Of1+VxbYVV0yXlpd1zo3sz70ncFgoJwCfQ4Q+ompQzfSmSTJHurC5fjpIydpy+VSUU6cv4uiUD8kSSL9ZLvd7iEPXM6LPscI6+B5nkUSlRZ9BZiIZX+OoPm+Jfy+cV1InsVXs8zKkbMdGJV78OCBw5HWJX7dbrcQNXJFlZBrZr1eK6IzHZuyp+kAm40pcyqRxDtPGXLoswf3MRaEsMp7C1/C3YcPUMt7K0oUc26JIxSyv1mpaIQp+507T6MQBLByeDi8SUdHz3XaTxGXbatRQuUbIxG0IBTdOpc7QQHB0/qn0qfKabBa6zRDAvUks9wffWltl/uwP3d6LfB4ZerYRxsEQYBMoqOuHwLAG6+/pe3AegSRlWfuz9sF/x8AdUW+lKj7+SBCLOOJSONc+OGOZ0da9oWs08o/uMn3uDyy4QCVRPafPDFzJ/0+DENF03BPyDl0s9kgSrqIJ5cHrc9r6I4zmnJKOHvEPmeOru/rte6D++MzCAIUlUWzu1bXNXyZM1OuQSJuwX2k+zn6nB8G2O5kDREEd5JGmEyIykOnfMt8CW9rxgL3w0QnGHEQfhM5+3x9RtF/YRc1utvtukS7zs/1em2FCcLu/O22qUVRWZ/tz7UuKkj9vYeQc+c2jlOXV4jtxrpUVdXleIL1HXePQQLpSGDPURRZLhrxmcghie6j010eGfpfKmhrcrD6vr/H00RS+qZxeeSIsN3Z7wvjzt/KwiKr+1w0RGDys257+PD2yOz5//v3nyhqgs+Tq2o4HP6+fIhJss9rq3upstL9G7le+L2z2QxhxLaBtJ89I3HvTw5EXTOnI7vWNV0fqgrL40Wp8SiJFdn2dmTbfTSay7W27u07iFKKgtDhyRM0YWy5lfg3tid9MIoiRZnznNCCiLoNfMmMOBK09XMDQeccDfGNbxhxhVIyAlpBFt0+uY2nf+glAMB73/+XAQB//6d/GQBw/to9PFrK+TQwc/V4ZPp0FB9hvTL1e/zQoNhunlxH+h7z3P/zT34NAFTC/T3PvgOXImF/Szh9vvSlLwEAvvqFr2Apk9Ff+Z/+ZwBAxnk1b7C5NO3w6MLcCRzdNvvq0xvXgIXp36efNeIWqbTPa1dXaIdmrF4bSJaRzCfj8Vj3s/RH/i0LQ213zu3u/OUSxwPAa68ZRBQC3yLy/wA7IIQOdrCDHexgBzvYwQ52sIMd7GAHO9jBvsvsOwIh9OjsHgCqEVn2fwAdzgHeeu0K3vybSFaeL8E77clIpKsl4rAuVmjKrtS7e5NKHpyZoF0aQWG0VY1juY1u5JnhsbnJQzzB8EhuaOWm/Eok57J0gu1W5L0HEiWT6NBoNNqL3LqKVqLUrDd5u02+z5bv26iOcotI3SNByARhiyCwObAAMBbUwPVrVmKb6ieBLxGjMFbVGEbEGY3blTVKqRdzfNlHfuA5ufvdaGtVVXt51DYCs1KEENU0VA2jqRWx4MpHlzUlJruoKM/zAMnxZ3sXFaMEnkaq9qKSTmS6r0YSBAECv8uLEzoy7a2UPehJlO+KArHcqFONSy7xUVYF6l4Uo8iJ/NliRVlOIuV4Uz8aYSnP8UZ+LIpwUZah8WRsSK5quStU0jVfdVV72qKyyh2DbvQ/yxJElCdkxEcRYUMMBgZtNwm6aD3A4cNquxFSl09nLJKJjJYsl0uViN+KPORmZ5UkNAJeUqKYahMbXM3NTblV6bBy3/S1iSDD4jhGRDlORjiWotJW11pmRny2G6LFWhQloxxdnrEkTffU/I6PrmvZKYM7Gs46n0PTaM45Px8Kl9npiaPGJaoFF+uFlKVRSdjFlfkbo/lt22I86PYJ37PNNxY9JFH8KMoUfbKWKMtAFerGqJtuZN8jkqFsMBkL0m846XwP0ConR9syAiY54ePRHseRqiRGkVWP6yuWtb7KDnt+V2b5+PhY5yByCFGdYXmxwLHM1/RtV2GwqovOu0y+vSnfyYl5Pgy76DnAztfk7YjjGOta1GW0jWT+2VVaL+WfEhRANhzh4SuGN4bzwK1bt8x7WkfxjZFKqed2t0OYdccsx5mLDCAqiu04m830nf0yHR8f69/4OVfNK4q6iI40TbUv7983azeRFePxWNuUSDJXzazPacB3jkY2MupyZgEGvct2Z8RSlYjGjqJm2VVGCQJf50r+7rnnjBT7arXQcnGccd3N8xxLicIRDds4krzjqRlDlBL3vACVoGk4f1Bm9nOf+dQeqmknc9muqjEVTpNvfdOglk9uGo6C09NTvPmGiWwmA1NnKqRcu3YDvk81OBMlpIT4xcWFtjHb7+7du/oM243cH/Q5d51mHU5um2eLqlTUlvI6zUTW9rRVWWQip+I41AgxOSTsGj5UxRX2CVEKbQ3cvG6iq9wP9PnbAGAj45S/e/HFF/f4RFw/6yvlKN+bF2G1JupHkLzy+fn8An4uexn5PH2w3BXabn2UTuMBTz/zNFwry1I5Pxq/6XwujmNt792mq+w1m83MZOCU2eX44XcSYaTrUpJoWcm7RC4KF73Cd7mqY321Nn7HZrPRaLrlnLNKhORS0XVexlZRFKq4Sh5GF+04G5sxdL6QtXtbYym+PRLUxUjklTdFqRx4RP+8+ebrAAxagX0yHlueDfN9W5038rXlFDTvCXUN5TrGuc2Vjy9ri+7lT0XLyNzM/UFRWFWz/pxWluUeGuXtzEX1cZ5iuVx1Oa4BHAt5btHdqqTbOHtk+ZwqgMkz5Xoun0vheV3EFI0+BNi9WpJYReQ+gobmIlVYdqIbyrK0KJa6i8QZjUaKRuHvuE97O6ureg9RwXdHSYSjk+POu4icWuVr/V2cJu4r0daVcufxnUdUuNtZ1DkRQm6GC/cYRBwTRer2Ozkh1U/aBhHPV9K2rez52rZVhJAirqJQz5t9DiG3ruxv138j4akiKlA5FosScdhVWCQ68urqStuB4599dHJyovP9Zss5jJx4J1hsjY999dOfAwBcC0zdv3X2BpaFef6F22YMt9KuHnYIbpr16+Of+GEAwPuu/0UAwC/87pdRhWZuyRKTKfL6q2ZP/6XZG/hsZN75fS+adpzUS6Syxl9lz5kG8sw6VjyosRsbJNIb3/y8+d3CtOcHXv4IVtKm//ff+yemfLLVujM4xoOvfMu0x5Hxi/uN2Ve/df8ektD8bitr1uriXNpjhOHTBjV0XXiIV5LRstrkOBGuOfYJ/awoS0Q9Xjjl1NtuuFzYTIJjU78kTTt+8fvZd8SF0J3b79B/0zn7F0N1XatTksouJvnneOJsimWyFIhn4ocKzQ+EwLGQv8WBJSFb0IFJeFf7aGqB0Anp7QlJi48yBJ5x9IYSypJ6sy5zeDFJkwXWLYfftlhp+hSJsWdDK4FqFwEZ2NcsjNsl8GN76Eaq11Zp7O8Rp1lnaOzmKes61tVijoppeJryNNDvo/ww27O6kIuk2lMyaMKx3Yn77aCdANAEHtLIyqG65TXkf9xAkBwTeknhcVInkbHv68aeBJgD324AeTDN8+5msmkanWQ1nc+p85O5mWAI38y4AIahJUGVyZx1TgYZ7r75dWlt2dA5sNOgl1LIS8ltvsFoZBZKhXrLYn55ceGkEpqf55K2uNvtUJXdNK/BaIgsMJPf6RFTAGSTnOf6u1EvBWC1yeFJG/OCgOmXTVkhY6qY9I3cz6GsS8Sy0a/kIm8nh4LlYoFK0tAenp2ZOsh3nFw7xVr8iocbXlSWuwJnQuTOtDcSnhop0256Ihfl66fXtG0XMmf4UYzTqRlPhFFynMVhjKsLM/7XC6amCYwbwFAWnWdumAmch+bNZoPjm6f6b8BuVKMosnBsWVxjSuuWjb14krG33oqUeNMiEahxGpq+0ZSCwVSlj5kOScLNyWyGVm4dNd1NhnztAzshD798ZC4hwnSocPtAoOQbSZ8oqlYJbEl0yjS+vKr0d9ywz6Yk564QBkxTYUqEbC7zjb3oFn+/eCRpt6ORtgdJuScn8s5dgYhpguJXjYy30A+UCDqRdmG6I9AotJbzAX+WRYknj01qyrVjc+g9OjqCnNNtimDNS8UjO//KBa8SQscB4ty0H+ffTSMHR1Q4lnHMDfTjh+YCZTIcIROCeqYHbJckEbcbTY7PWzdsatajR/c7n6MFQaBtzLK4aYpM5yi5uZBLz9XV0tZP6iy8qrhxelP94qFAqn3ft5KpsnFR0tCqwlhg0bx8sGMjtIeU3oFzW+x0M94f11EU4ZlnTApWzYtD+Vvo+3qQixKbMgcATdmo3LeucUrYW2s6dELy+4aiB6GS/+rhpib5+9QGE+R7NtutTQmWX37Pyx+SOpTweIDz5cJc1oS2zBAkssGPJF0tN2U5mkwR+WaujGDa5ezumwAA/yPfh51cvhQLXmpL+l8QoJCD93rHy0CZ/8sazMPhQeTsTIgt61p9LZIAB/tmla91bRvIAT4i+WpZdQ4SALAqrRS9L+v7fGkuB8MkRb0y40ODSNJvZVWhlABWIam39IE8z/V7hhMzpiZTpsd46mPsZ6ZUZFmmxMW6xokPnJyc4Pb1a1JXuWiRvj06mirBNGXhb52aFN7Fco4NxUGkzlzDsyzD6lL2DM6lVlFZInzAEGGb8lZWDpuRPQnu3L3/hl7a66V2y9RHux+gxDmHcOhHewfiByJpXBSFXhy5JOqA2XPkuy4hNsUxQt9KWDPQRMGH8XiowTFfSLC3hb183p53hVT6Fy+AvYjLsgxXIj2tace+vWBgW116pg6aBhR6eolrJcpX+jmViJd2HyQ2XZz1YWCEe6jtdotYylVKgGQhAgKe5yFLeZHX3YfTbwDAT3i4N++sd1td8xuZM/ju0PPVn2JNU9zq2FOS7NASjQckgOb+Xu4zGq9GI3MC98gsVZ5vtU1ZVx4cPc/DRtYFMuJy3ESwwT9eJAU1L4EjpZvY9VILQ8/XtCl7McSgUOgEkbp7ZRvqtrZZ7PZSTq0wT6n7ttGom55el6UNUMq73Ms6ir7UdTeNr3DSnOlDsSOgwPlmtc07zwRtCUayuJ9OJVW4bW1qG9dRbSuMUKzNPL8qzZxZRKZvZuNTDCvZA8Xm8w/WG0zlHBdLQHwrQWE/ijTYCllfrsR/kzjGVPY+w9w8s2zEL8MQP/yMWb/+s7/4XwMA/u7nfxcA8L5P/BD+9B81FzPPbk25/ve//Q8BAP/XNz+Lf/MnfhwA8I7a1Otzv/sVAMD8xnUgNuPzPXI+TgUB8fCyxUYu3/+VBEGi1swtz+MS8bNm//s7/+zbAID7ianfD37gB3Hj2AQQLgUk8ol3mH3Cj//oD+B7Egn0S6rxq2/eR37fnB+el/VpsbRBlzo1731Kzgm8bB6+4xT/xm0jaPLVN83ZIZP90S4r8MGPm/uLd16Z9WF475sAgHfduI2boVmr0lOzBseZ+X+7zlG2llYFcNL8ywoXcrZTMR7n8kcDGzIca1lbkjBCIuteJaOd9Cmt32og5g+yQ8rYwQ52sIMd7GAHO9jBDnawgx3sYAc72HeZfUcghNZrc0OWJammHPka7WA6iL9/M7yzhIAV5bPl+rfibTMqTAXqyHQCl4RpKIgH3rC7sCre3GmkX25zIz9AILDKOGHEwXzHyE+sbDEYnTXvC4JAo902CirkdmWhqU6KHLlcvK08PctpZbaFLIoogKvdHvGfpqaEIQKFBUs0Tn4ez060rhalIQTN642SClImeSAR9+12pyklbBdGn9fbjRJSMgI5kah5Mhw4BHldec/1dm2jVCQ/bFvsdt0UACsRnSHzSETeTeEKggBl1b0dVYnLMFQ4nyvfDJhbWSWkFFTEthUIddM4EU6J8LHuRYGp3DKTwLt2UgVt9M70EftvOp6opLYlVH0bAuMePH4wGCCVNqJ/VVWlkSWVapeyDJIBeBec9+CmZdWgkSiyF0j0Q+Dg+Wqt8GWitjSC7jspHkUXXp1EMY5PDcJhIdFBtvFytcZW0hE8QV8QQRKFIVJBKRGF5Xs2ZTKQiMhsYtqPvnN5OVd0wlRSSOu6Vhg960AkWFWUCofupz6WZallZRT55MT07WaT4uzMIG5Uwl4I87J4gJmMT0KOleA3G6AkATwRDGuL8IqlHShVyXbN8xxhQtJwm0IBGPJxRr52RPowdcTzFc1D5MNys7WkmJIaFRBRV1YIpW2jhNFkGVNVrQTJjF4TNef7vkZsGS3UcQoP68r0PclTU5mPm6bZI6W3yJFC0QiafjawcvdMfXTHLACcHF+zkRRGS2R8122DgfRXIKmdi/UK212XuJyL0HJZ7EXcKWKwWM7hSyQzk3KxT7PhQOfDTNJ+SongrBdL3LplolvPv+M5ADZy3jQWQQZBe1FyeLWyPso1wSIbLUJGyd4douSrHkH17Vt3tM0fPjR+TCQCEYqvvvoqGq9Lmr/dbrEV5B0RQpzDyrLcS0lhWTabjRVJkDbSqG3dYCBr8UV+0SnDYDDQFOGl9CH7aDgcanuQ1NuizYI9qWAiDIPAw1RSSxShSaRLXWhbTphiSfTXZqN+T/JwPwj2iD1Pbxjfvn79piXv1pRmpk6X2t6e/I3vTpKkgzYEusiKVGTSuX/gdywXC01DG48FzepkcPRRxey3KIr2UGWd+UegTxzfTAcMPV/7LZXUoCbwOqISph1Me8xmM92bEPnopt0SzVA5hOeASWvUPU3QJa8td5UiKzxnr8Wf3An5vfSz5fJK53Smp7tiHEzFIJKUctxN66J6zO+YMtk0jabjKapqMNA9LtcgSkUTfWT+Hcr3CSr48lLryjXSTQvl2CGq3kXdLc4NSoljiOXNskznT76L/bBZ55jK2kZEHP3La500JpkPLi9NPS8unmA2Nj7H9Cv60Hg80fWFv3v6qWe1fiTepaiIm17UTz1arFd7af1sdzf1X0nmb9ySTzbqh7NpN314u93qHOG2EdBNZQ7qbpnaZl/S26YmJk4amaBtJHMhdSL83O/TJ+IgxCDukuCXZan77n57RFGk60vJ84QgxYMgQPfpLqEwUTisX16TeD3QtukL2LRtu0dgbvffsVInKA0E9+/OecmWwbTHcrnsIEEBu455Tsoef05GFq3Ld/H5trX91RdqiILAkjP3/CsMQ91zsV00BbRpMRP0FOkfOA/VdY1M1hoiutgeq9XKrtPiO1Vj5xOm/BJNxqy5Kizhyd7u8syMr/HNmZRpgfWlQXSyTNeObwIyJWwoGLCVsZGN4RExLb8bNLKmIsNC0uwDOToX8qLdZoN/+imTGvWe95j01z/1PklvetdL+Nj3fg8AYHRh2u/jn/xeAMDddIE//pN/GADwzsSU+Ytf/oKpZ/4Yp9fN3PCX/vx/asonaKxf+bXfwV/7lf8TAPCx7/8+AMCf/RN/DgBwe9Xif/jvfgUA8LOf/5cAgD/5V/4kAOCp9ik8fuVTpu4z02Yf+UMG2fQjH3wOG0mX/Xv//NcBAB/98EexS4jwNr4wLcxYX25XuHNi1qj1wKz5n7xlyrvebvH5z5s0skCyhZaS/vf1197CcUTku2QlXJn59Jd+/pcwERjPppXzmcz7422DD//kj5qGH8pZRc4lNVpsNlbQBLAoyU2x05RppsYS3VbuKhSSlsfsAK5PURoj7BGMv50dEEIHO9jBDnawgx3sYAc72MEOdrCDHexg32Xm9blp/nXYX/3f/kYLmMix3sBXFgkDmBxhX6P3Eh2vbNmJxFBpRZEsz+sc0dR87l980dwm/s2/+tcAAGkFTIWzZSu3Z0O5pX5xeoT33DE3hB//gQ8AAK49b2Trn7QzXJbmVjqU+FMmHEJBNrA3yMILMsiEjM33lKiUxMLM8avrWiUcNXe6LPZI6ezNdaPRHzc6CwDhYLAXRVaC2e22gzICbFQszy0ZHnPwyTMC2Eg7c+NpZWOjM4wOuBE6zRsuujnN26baK4OVOLfRZDfiPBhlnecS8YXJZGLLUO63lR905UYZMXLlVBmhYPtkWaYkhoyYkVC7ciLhvIVVMuUohKeE1vJMaKMRfVJB8kolSdJBNbnl3O12liNKybzNM5vNBrNBN+K+zNdvI7lsorqT4UgjeQvhXcp7JK9sb/fzQRAonxARKuTMSJJEkWN8B6O8WZZhNDC+ek84VBStgbZDkghYbpQstmg7InhKh3SY9eHzd26ZHN4bN25oJNvzrVwv/YP55ZTSvLi4cKJMEuULbNS6P154k+/7/h6PC2VRz87OOu8AoH42GIyUa4vj/+YNw2URer4lUBQ/Zt13ZWHz7KXdCyHJ3JUFWiU17/al53nIhR+BPEvpaNxBpMg/zDuLQp8j0orjc1MWGEmEmcgn1nM0Gmm/Mhp3MSeKKNbyMELN/q6qSv9N5Igl27ZRP/Y3ESAuSqrocWgV21KRe+QIc+WWdY6V9lwtlgiEe4UIOSs5bOcw8p8lMt83Va1tROJ6N3pK1FUqZUlCO8fsiAiV72ObXbt2TdEZuUTxXAJutlEfwZokibZNnx/EjaArlxMjplW9F9HWqLVD5sv1ZrvdKA+RcnM4pKGUjb4QZKFGxHcWaZVE3fm4LEtFjrWCWyZKOE3TPdJx1jONE0fOulumtm3VR9lHRF8kSWKJejerTll837f9LWUgN0VRFBYFx7k6DBXhejQzY/VU5te/+j/+93jl618FANwQ9AW5feI4xAsvGG6Cb3zty6b9hA/ghRde0O/JpF28gZl3fuwn/i0cnwr5pqxHJOctix0ieb6WqCSRw26d+/3seZ76hZJ6S1R/u92qvDrXBOUemU6137Q9txv10T5BeNM0HfQDYFEeRV3p3EB8g0tUq4g4mfvo26EXWhSn+Icr667P9QQAQt/OkUQK0ZrGRX50yVqbxvIwKneWfM7leOD3RVGEq4WZB9PE1I/t//j8wo4x6S7LAxYrSTDb0+0/XavrLlF127ZdjkQ4EsXw9ojWd1LP1WqFSnhtyDGoc3RR21oqp4/d7+TLfK/OgPE5zpnKzeagPogQ4l5lNB5ruVw0FGB8gQTV88eXne+JIsubxOi/i2zRua7sokN83/KI9jnMXFJprnF9pL77Pb5vUfhsh7XMLRTmaKpaeSHzoksSnSUpZnIe4b6qcdqG+wdFXrYO0b8Szdq1xyUQdn9uy8Jy4HGdDbpCMe7z+dpmYvD73LVb29PvY5Js+3Au0c+Ln623G3tucdB5gEHV9yXfk2iwh9a1KCXb7jSdk1arPUl6orSbpumghdzvC+PEtj/5ggStGwUhxhMi/brjbbPa6Jjrt79LCN8Xc/CyBM8KL+W9twyZ8l3hWpyMB7g2Nv5fV4Io3YR47wfNGkIkaC48QTeuH6sYQFnIOUQW192qwDKU85JwngUiPJSXa2xeM+iaZi3ce9Jfl00JlAaFMmlN/UJfuHnTAFtBgSeFiBMdmcyAIsqUKLr1TV0H4hNVkWInnHbjqfGrm0eWC/H+Vwwq6pWVqdeHPmHO498/voNf+MV/BAD4Zmue+ch7DaKp3OzwoqABP/WakbSflC3uPTG8fLn0YSMIyFfX53hW+H4ey55+dCqozyhA8lj4yOQ8zHXzbLfCRFCeO4qrpHJGHCf4qf/kz5p3zUyfxolkEjy+xNH3GO6hmWe5uQDje/RD99wOOOh12HM09aOqwoqYcOmPZE6LfQ+e7Dc/9kM/+PYDFQeE0MEOdrCDHexgBzvYwQ52sIMd7GAHO9h3nX1HcAgRhZLGseZi0tzbeipplBLxGlCyDzWGsYmgMOeZqi67ZoMQJipDVARv2WK/tVH0TBKyDwAAIABJREFUtpvLaaIswhgv6BLesA0GKTyRXm6Fr6NtqJJVazQxTsbyTskN9yxvzIZSf4wSoNXbXN4K+vCsmssdExFUrpKqsrfRcst8JbeY+ZONRt144z8UlFQ6qvV5lSGWKOj09FQRGEVDlJNEWwLAE5RLXXSjjLvSqmH1b/R937d5/ehG2qq23ov86t/qWpVQFPnTNiiKbt41P//o4WMbDTdf18m5jmKrVmc+b/kRmPtMBZbW8YWtIA9WVxIVlrePRyONZhKnthPOAbQNjsddqVBGd2rUVh2ESiyuXGRLdI1ELyQQlaVDfV55QUQxbZNvcfHYoCeeesrk+ibpwOG+ER+V+9+rdY4zUfJgVEcj9wMrPavqXQ4i4zIXqfJ1Vymmbhpg043MafS59QEYlZO2N+MUhUXBceyx38ezIxsRlZxdjt0oaHHjmok+3Lll6mzRDD5mRwYRUDpy0JueggplkgeDVFE4ihyRdp9Op9oO9AtXMpvjjNF8SkdOJhON+pBbYyp51avcyuAyKnn3jTel7hHGwlvCZzgflGWp/eVGqwHTN5xLNN9e6jQZjlCKgptGyZsaVcyc+C5KNI0TVQljzIDPZFmm0cGJRDPZjoHna9npq+yT0TBTP2J0UvkZgsBGXESFjuUciZSna/SXbb3dk5mNY/NzNp3q3+gDqvrje6rWQwRINhxYxcNe+cI40rWHCmfKZxd7uHv3LdNuqeVSAqQvGSUUfyLfR5qmmK9NJIrqXYGULw5D9SflekmISCrwRNqI/XAiPpgkifrmqifTPhqNLBJMzPILbTroGLcuaZoioJLgjmpSFkn6dhHz83MzR5yJxCrnyaPpzCKKpI3JP9O2raoLzSbTzrvTNEYkSp1jQfexXsvlUn1nNurKCkdRhEb8hxFBcvYMswzzuZkz6ass53g81vmezwCW3yLucV3UTYOFKJYtRR2L0sTvffllfPH//SwAwBNFK8+3HEKcm8kpM58b/2iaBqMRf2ciskfkDcrXmAoSqanJaSII1jDElTxPXiKuv+RK4nNAF/mwlDr35/bheISbN292fhc6aG3Op0Q0tk+eWNRQaFGYbGt+J5+h+qXne4qgU5SAoxDG8awqWeKDw2xgUQJFd52o23aP047oCxd1OxpZSW1A0Cthd37k5y8uLKpH24jvmUysDLC043w+x/HsSL8TsL5zNJ3ovs+PLCcg68z9wG7bHZ/T0XQPuUBesyAIlEtmqBwnVsnppvA50YhybaoauSDUdM1z1PVYLs4xRIwfHU3RSkiaaxXbxfM8qz6n/IFbbYOqovKpcO20vkVYxfu+UO26PEE03/ftfmrXnZvCIEA8ogKvIDnkc+4ekeun8mjWtap6Eo3qPq8cOQ6vIWD6XZFFwlnSOqg49lfNPbasRYvFAguZA72IKr+pIpL1e2U8x3G8Px4d7j533+xa63u2jeVnLEjWzWaj/tBHq6dxrISoVHskn2BZljpWrdqaReAo4objhQg+L0ScdvvEokYrB31lVWYVRaj8o4LALvy9McG6zCYTK6He47aLkkTfxTFLH5jOTtC0RKBSydCiNYguJf+XIjjaWFUs95Bu8PT7hoKcDyWzZVmvsRBUzmhkUCUvn5i5dzLNlP/1la8Z9NDP/fQvIDPAePz7/+4fAQAcT037/PZnfgOx8Lx+8EPfb8oiR5QqbzTBpl6JoilRfXGG6TOiqPuWQQp988tfBAB86/F9PHliePhOT+RMmkqmShpjW8i+Q/a89euvmHcPj7AjekXk4I8qwaNUGZYTopZlDyB7SoQx/CPxcUEU/avfMIifz+98YGz2+e+fmLV/+y2jAnZ5GuDVf2x4f77w8FVThuUWwalpm4G0/1vfegMA8Cf+y7+AlwpRRnvLfO7f+WN/1LxzscILUzNn/vRf/xnzuXOzB/uRD34SsYz/U1E8ywUp/plXv46X3vcuAECRU1Fa1ArzHOVD047nMvfduGE+7/KaERnK+a4sS4tuJDpSeEWHw0DHOjOQ6LvldoNt3kXBvp19R1wIuZKhCi2UvxHi7UI7C7mY2YVCgljtdKO3Fugq4eM77JBj3fkeTtxRFDsb2a4UeNu2upnn5HkuUsX5eIBqSBJlyM9G32PfIbBsOSS2vodYDrtc7GJ5wbbYoSlIwiZy602NuUDe78lPFwbLBVBTxmRi3NXAvUfGYXmw4AQ5GAw6pKzuz4cXF3pgKQuST8qBM7WHG/F/bOWA4LcxIifFw20zwDk0yIaxkv57ePZIn+FmIZGNUhQlaNu8U4cgjhB5XVhvJqkthLwCDpTUgSrbhaWb9lPXtR7KuRiwrbaOnDDbmIeVOI7tgdORBATMQXwhcvUkDtRnm9ou3v2Uoq2VYdXnpe0mk8ke6bUuKsMhBgKPvCv97m5c+ilqbdvqRQYPStyg5iu7maGfXIjfP378WN91JNK/bLuqqrBYruEaU3Cm06luSBF1UwSTMNojCLepLJWWmaSMQykT0ygA7KXGrTa5psDNJkfaBpRh3ZT76XxMLdE0ucrKgvYvhFyYOstM/3ggsu5JkqivcFN+tZQU0jTFUMbS2RPzvTevm8WgLMu99Ck3BUnh9HKIyiQFIRsOUAuZnS/nvjXJwJcrPXzxMOs5MO6qNvXjxjYbDDRNgodE2tX6Sr+baTi8BHJJg9m/SuTv2dTAuRDnsl0n0+le6hHreffuXe1rhbxzszaeWOLy3gHh6uJSiVhJFBn0yObdf6dp6hzOTV+6flXIOsSUSY7v8/NzJbHn/MhLu8vLuW5IJ0JuTnLvwPPx9B1D6syftN1mgzu3bsl3V522iuMYT56YwzXnA5XjdgiW2S68TDg7O1MJdtaZ4zvLMr28RE8AoG1b5Jvu5VIY+s5hoQt3btvWwt+54SxtHXiQ4CERmXlokKTafm+8YTZp7NOrMFSyZdbv6dumzaIo0ou8puU8KkSaXqbv5Cb+/NyMtydPzuy4JnG/1H032+jzKvdKGeK61vZmG2SDgV6Y2I2c+d6PfvRj+OVf/AUAwJoXybL5r+saS17cyYVQK5vJzW6LYyEOffLEzL+t+PNb334N7/vAR8zvZN8xIvl1EODy0vT5+sqsqbdum7mF7WXK101ByvNc/z2UtOxQ9iqG4FoujnZdwun1er23D7lx48ZeWsct8Wf3gt1NFwTMWsnPuZLkgFnr+heu7gG8Xz+9EKrrznru/kxjX8mk+Yxde5o9Ul3OX+9733u0n/skx75vU5aYDnV8MkMjaw73Heo7WYahpPspMb7sGY6Pj1XaXANgUr6joyPd39A/Gtk0Z3GCWFJaeQkYDmyAjuOT6xJ9YT6faxBBU0g3NrWFdbx2zVxsXsp68/rrb2IyMfsP7oG4Z5s4F2Tsk+eff17bk6l6/SAZYPdOLsk039EXVaiqSvuVe3kKewA2JYe+5opbMFDMeZtzRl3XSIdmjmw0zdOmVNH6qUhN01gZ8Vj2pXLAbWubbujLXlkFGOpGU8uYTubuXTX4I20wGAz0uzVdbsfAtF1v4XcvdkIv1OBuImurBmIrS5je77fhcGgvmTSd2u5h+2Tgbhpx/3LJTQWleXoui/T73QthwPgey8Wy8OzgBiXo2zblsdAzIvcknnOZpak2FHMQKxsPoQQjuN/hmaCtG9QN0926+8BNXmt5NCgjaz/L7f5tI2O/WhYoNgxWmXeey+XDl7/+BKmkDfulqdd/9B/8h1hdmT3np/7+PwMAvOslk4q03exwXpt549aJEMfLtDqdnMCrC/m3Gc+NXM4WRYGyoIiL8fcHrxqqh8fzh1jVcrHgmYBPdkfqfLXBl+Xi6Ogpsx6Wkkr9Qx//BD73md8BAHzsD5m168MvvQwAiOsBfv1zhjD6Ex96HwDgm5/5HADglW+/gcF1s9YnJ6bON66ZfjgqPTy4MOv5WztJzRRZ+WYFXLttxvUf++gPAwCWr34blZBPv/zOFwEAf+dnf8608fxVPJkbX15sTIAPgWmzGCu88k1z0fT4ra8AAG6/6zlThw8+i0rObbVn5s5jGfOD+xHyWvaqQzMXhY1pu0dNieMJRUhMnzJo0ratir9Y8SkbbLfnadNfD65MQHFxfqXAA5Kdz45MmYZpqnQ1f5AdUsYOdrCDHexgBzvYwQ52sIMd7GAHO9jBvsvsOwIhNF+ZW8y2ruG1XVnfloSzDkJFIbkC18t3W5UP5o3aWCIdmVejSXsE1Uxn8nxNdyAha02CpqpBI1DhrVyrjm+am8k6jdDyFltuhkMGArxIy96IBPXk2NwAXi6usJbIoyekYCRrvLi4QCkQ0iS2Mq51Q8STifS4RMsbSVFK8m7E8mppUR59mdmyLPfexZ+rXa2pCSqrKkDOsmowvyKJqSkTCWNvn9xx0sD4fDe64/67caCljE4Fk27k0gt8mx4m7Z+lGYbxsFM+Rrl3ZWEjvpL6lTIVwPfh9YhiaS6qoS872jSNvt8SITLNYqV+OJR0nOXa+OPdu/exkLYhgobQ47pq4XmCVCEppMrnFoqwChUVIQiybYEoomwuUVSRPrOUFMlNbqP5fo+Yk3VZLBaKiDmaClGsRBLrssK4FzkkxD8KQoUnTgiRFxLlwXC8l3Zix5mHQiKNi60lRAQAL7YpGIoQkmhtGIYaPSNp6vzc3KJ7aBwi3G3nc3ESIxWZ76VE3Iui6KSEABZRANgo00jqTp84Pz/XqCn7WwkOk0QjzC0odWui8cV2p/5IREysCIsY994yUYhIfjc7NtGr+Xyun+vDmLMsU8g2o8lkWPV9XwnPWQbWYXm10PQKKwNvUXWlzgPiXw5qq6n4fuMDp8dHGv0dDQWiDysLTyTBgnOz/BymmSVz7YkDvB1BJ9FUwyRWlCJlxRXpWdVah346ZRrFlkiV9aTE7nZj5XqJQKstgaWmr4Y2iu+mIQGWXL5tG1yKzDTRQIqGeG6m40pTzMTvHz9+hOnJsZYVgA6K1Wploe8SfboSosjpdOoQJHdT/Zqm2ZOip89PJhOdtxkVJplsFEUqH886EBU4nU4xHKT6bwAOmghIBJK+Xtt0JPofydstce8WlY7tQOpHP640Un/Hf0rLxc9PRxbRCVg022az0/5iKoqm8ra1zp/PPPNMp+xVVWmqOtO9aOb35h2e/o6S7Dsn9YWRUk/TXZXoW1BV48lYJYJpRPX4gYvuYpkpXbuyad8Cv28FLXb+5DFSWacfSarwckGi0wDHEgl/5zvfCaCbhtknG9eU5qJQH+W4YfpbWZa6Btg1z0bSmYKlqAF4ztrUdp530yf4bx0btaqsqyw154o0talpTLFWFEaQavpSn2gWgK5nQW8eqOud/pv+xHkuiiJtf0b/1fdam4LF8rnI7ZCojdam1s+mIscuqVxs4wFqnd9V9ID+23g6t2SJSHGHFvmjqUdSLr4zCALEMpcwJdMlr9VUZPFfN81LUbaCLuUaOcwSnecfPjSIBDveRkqTkGVEcZm2L4ot0BBZS98j6mOHkCIYUob5fN4h/3d/NmjVryh6QIRi23q6N2MamkWXVIqUvHnzWqett9utJUYOumtkXddoSivH7v6taeo9snK2q+d5mmLsMSVT2qpqSrREIhIxL3sjHx7SsIsa8mtLMRD0UDYuYTpRQEPxkwZ2bSCKR8d+XXdSzU37G184PjrFqchvcx/NtlutVvoORZCSkDuOtXy+v9+OTGFRpJBz9ugL51hBidhJazZtVJbVHgqZyB8z3/8+kvRJovvs/hyRpqndP1TdsqCsEMhazzFIP67rGl4bd+q6FQLu4WhiU+JZV1n868qKK0BQcFwXr6djvPXIjPlzWfNffPdLAIBj71kgkzXy2Pjxj37oDuIn5jvfev1188rGzOnLzRqtjMeJINDPHkvmQtHg7mODpmG6W1Qb/33rwT3UMu4Hkko4SpkRsMJxOpX2l72a7G+fvXEbUyFPbsieIXuoXVPgWZkDb5yasvvXzXs2T3Y4lfH17qnZ23xG5r03n5zjhWPzuxtCqP3sMyZH7l3pLfzea98CADyQs3pzYupyNAlxdEvO3XLWeeWVV3BTkD2vfMkIPdyXtfHDfoBE5pJ8acrypS98GwCQrUpMItOOxxOzj6hg2uPRvTXmguy5+aJZZ3J5ZxJGaEIrigU4yEYfWK4le2dj/kY/3mw2ihbqiwtdXV3ZPZ7MERTGmh0fqd+v5Uy0zO0ejzQaf5AdEEIHO9jBDnawgx3sYAc72MEOdrCDHexg32X2HYEQGgnXRlWUirhRWWu5QQ1CT297eZNfMUqw2yi6RlK1Ndf6anmB7MR87vGZuXXUPO71Dr7fjWRRwi0IAoewVCLUgsJYeitsGslRJymeRHnWRYM4IIpCyhmYG9QnT55oNIgS826EjugQ3la35/a+TuUQfRvl2uSS977tEu2t8lyfn8ito5tTr5G1HodQ41m56URujbUNdoVGtwOPRHRCCrnZ6vfMJNK5kRzo1Xq9R6rG2EXkB/CFZ4bsyeR+KooCqfAqjIV4Lc5S5ZIpGQVqxE+a2iIJBCWzKazsfIhuNMeVuWf0zJXGBUzfkCtEox4SDd1sNh0OH8DhV9hslNxLuRQq+04lHIy6ucWj8VQjiHxnzQhH3TgcHl0+h6v5FYpQIinio21Z4ESkY8m7QzsaDRWtQnTOOSORcaI31ex73kAng0x9Z5hmnXcu8zXytbnF5jOM1Putw4UkXBca3SxKbCRCr3wMcvNdhpY3gjwJjC5sNxvlyGF5p/Jztcl1jnjmOSNDuV6v96LbuSATmwZIJALAMjNC0jSNRl5ZPvKZ1HWtN/nK8SLvaepaeUX6vBFVXVv0FvPYBQkyGAz0eY4TNzJOHziSOVMV4+FwG/Uiq9HxsSKLOIYR+Hsokj7/hqnzUNsBAILIx7EQ9Sp5uPTDaDRQ9IpG9kr2c70vQS1jqSgKfZ59RLTItelU638hnBVbIiOTxPK1NV1p4/PzcyU2ZL0Kh7PkRN4/lrGXpqn24dWKqDLLAaAoBsrOD83/r50c48V3vmC+U1CBxJW0daMiAo3kdkcRURgjJUm8d+9ep82yLMOc5KIagRWOr3yrkWU3Ig2Y/ujzrDCyWlUVjoRnbB8p0Spi6vZNg27KJ2Yemc/nSCLTRvMr4+uPHj5Wn5lhJu8v5N2RExkVol+pcxLFTn0ok2y50tiXTz/9LAA7pjarNQZHXd475XxwEKjLtfEPIpTc+hPVtpY5yrybHF9dzijP8ywSQIlRLZk4EYPKnTMYKkqU/Crk4JtkAzz/vOEr+NIXDFnl7EQI7BvLmeNJW3miPb7abLEU1NVgIKhFqetyMVfE5XPPmrZSHo2y1DFO/heOpaZpLGk+ubccvhD2DddDStb6LRTlYQlcC20rokaJ0vZbr8PP4ZavaSyy092TAAZ1owIPPRSAKwFO+AlReg3aPXSfyxfJdxAto6S8ieVUGAxG8moSSVvuofnclJ3+GPrBHjFtNrRk50pGTT6Y5QqxjCHlKhLui7Ztrd8JAudSULB5vsVsLL427gps5LutFR/p8SMWRYHlsksMrG2epZbjRVAlYfY2fHyBICVqO7e4yFhjsldPM/hKlL7rPFNVFTy/y0ume4jhUDMA9J2+r23LvTL7Nk0SRdtRKED/lqYOGXfXR5PEIr2TrEvEOjs+smiXusuBUwahzl2eZ/3CvLtVFJDLCcNniBwOBFUSsh+yTMeJCnoo5yWUoDZuLN8j+f/YVmzjum11n8kybyvLIUSUUCjPhLWQMBeF5fMMpY1kbPi+76D6uxkEpm1FtEfm1VjWWLRdDiWWQcvSI3QPZZz5geWjc7Mf2I59kYPJJN0jr3ZFYPp8lByLeZ4j6SHRWc6qqnRdVxEMZi5scj3bFYput5yGRE6SF3UpvG1FUek6oeNTvmM6nlgUssxFA/GTL/7Wb+JXf8dwAX35W18HAPyZn/rzAIDxnZdw/02zxr30b5s9R9nWiGemL55+l1mLz183HHxvvPEtfOVVg6AJUuP/z77DoEbz9Ra3nzFcQ/TDrDZ1v/vtFXLxv0IIpynIcrFaYBAKz5isCTeumfXl/GqueyCiUbbiq4ttjp1M7Q+//joA4GufN3w8x0e3MJW5+Kd/2fDt3bsUQQovQyFra3Ff9n/XzFr+5XaDbCL9Ju2+rM3fkpMpnnnJIKtOYkFOftTHY0Fdvf4N00Z+atrsg+/9CPwvGmGXF18ysvY/9od/EgDwym9+BreeMjxGX/ysIbT2hMT6qWffhcXVlwEAZxemfFffNvs5fxqhkj5/fGbm9EiQU6PRCCM5r0zGXV7cqcO7puhN+dvxkUUBMS2plnNgsdlhW3Duk3VXzsJvvvYmSuEy/v6PmPq9nR0QQgc72MEOdrCDHexgBzvYwQ52sIMd7GDfZfYdgRDSyOzVld5cp3EXOeL7vqo28GaX3ENFscVKcmBvngh/gaBKLuZzDHxzi/3KN4wEHm/fludXiuYpeUvvWfb7ncPYD0DVIO7N13j9gSipKO+P8GH4CbZbQagsRJ5Q1A4a+PDltp/IgIFIy8ZxqrfviiKqrNJZX05xs95iJzeDRDxQyaZEYyXhGWWViNlisdDbfeXDoERoE2JFFAplheU7Qh+YSk79QCSpPaERiBCiEFnwpShNMYcRvovsIhrCPPvU03dsvRQ9YMq0WC1x5EQ2AZG2LEVWWW5Ol8tLfYbRxVCQWZSz9DwPxbJbZzcPmzxO/J2bb39dECn6fGwlh/uoBkZyZrMZkqwrU0pEWFEU+n7K2TI6MBgMNG+77kWDoiDci/qzXY+Pj5GLop3KOBeVKlaQh0S5kuIES0F17GScUclpmA3gMVJGQi1GHjdbRX7lwifFSFtVVXsqUJ4TvWI0dyUoD0bXJ7MJdnlXsYXIjtV6jXIryjySt31+YZBCoyTDDZVxFh4CuQ1fr1fYCmLhq18zucJVUSKWOhK5w0hxkmTwJNqxkTml9q06DvuLPxlxb9tWETHKNVVYPoetKPEo+pD+tSs1AqUy8tLfWZZZyU7xL6IOit3OInzErqQfmqaxCi8SHeZ8Mp1MLPpHnhkMBrhcdJ9jVPPystT+YTmZLz6/muNUcrpj8nuQv83zUUnUlPOUopDSoUYsVRVIUFyz2azDIQHYcfYwzx3lDtOeRNahbvQ5yjOrctYg6UinA7Yf8jxXVB5RFFkU62djGbv04yAMHSXArbxjLZ/fYDQ0824kyj6KBHMUWyLl04LUeYKrhWnvPv/RcDDAo0fM7zfvpM/lea7yyH2urtrhm6AvsN/TNEUm8yPb3yP3xSDTtqHcOus7Hgyt7LDwZJ1eO0YgyFq2Efvh6OhYEXSEr7lcRZzXiALg2pVlGS6l7xcS7eP3rlYrXaPYVuTEiuPY+rkg61imzWajaCHKxxNNeHFxsRdpdhWOqJrZqMqHXT9clUHAIM+IFFR1TeFJadNMOaX+1Wd/17S7Z/n8qh6nXaDcO5W20WwiKlTCq7XFDm++aaKZ3y9oKo6bgaO+SIQPuWLSNFW/cDkFaf0IPefLAJ4ihPhTUQS+p2hMjbi3++pb9NnBYIBK9kzc++jndts9yWrXFN3ECD9VRGHRs8OeSlBVlPp+zklEi41nmbMXknEa+Z1nze9MnyhStCh1DLF+lm8o7PBUAV2/UuUhh2Op/9w14QdZLXNksofZFnb/xnYcj4edslKRsEGNG7Jm0J/4HZvdVuXtlfNJfHw2m6kaViR7ksCzPGBW0YocLIK8Go4Ar8sfyPaZTEaK4lvnXXWztq0VIU6uosiPVB1sNjvulNMgSaH1B9BRe+Mc0Ue6+r6vCNK8EjVbzse+7yCsugjxKIoUlUNv5DvTNNV2L8S3XWSMotKodkVU7K7EStpGwFd2f+x5WMsaTHWyqqpQCnow6KHmAs+D11NiC+VddV2jlvWWara6TgQBmoh+KEjN2s4HnBNc/jmaiwwErCpwWzeqIMkzHHFFfhAowoEIoQgWoU/jnOsif+qGnEB276vnFQe5xHZx1dIA6x+73U77knOgrimJVY5z124AOJ5NcNlDU44Esbndbvf4iDiG00Gi4/PJmUED3hfkX5ZZ+XiWhWj+z/zWb+KN178l5TPt+X/8yi8CAH70j/5peJPnAACf/uy/AAA8nb0TA/mev/PzfxsA8OCe4fgqqxa5nAFmp6b9vymy7ABQ+WZdeFYQrK0goJrdEmeyXr504ympj5w1bpwgl/7dCNoWJ2a8VmgwEERjH7GZjIeIc9NWr79iuHkexuY9txDgezKzp3zja9807z4177yodziRM3oknLw//4u/DACYTK4jqsxYevF7P2bqOTVr7ac+9Wn8w9/6TQDAncp8ft6UirRuZL30ZBv+d3/pb2B6z8ytF8emn1+5/yXzPYsKJzcNr1aTmDb6xpuihBqsEYqnn6TvNT9nwlM29hFlI2k/QVzK+XVblDgmYnVJlKTsJ3aFZgxkMr7GoqrqImUb4SIMRF6yqRoUcmarqdYu4zvyPfhN7/DwNvYdcSHEzVrbtkjk4Ed4Xd2SzM1HVUmalSxIw9gQWHnlFd7xvIHCDWXzROnOaBLi2699DQDwxU/9cwDAjdQMvmSYouYEsDKOG0mD7lACoUyogqPaCXwu8CKVHrxx8wWpg3GwaBDhSmRfhU8VqS8bcM9HvjIbEJXRFWf3fV83qyRUPD091YXy0dl9/R0ATKZDxIHZABMelsuhajqYoAoJ1RaYozjD4OimpuQIOl3JeEfDFJlMemOB6YWeheizzhVhqSJbOEwnetJJRIbxKDJ18aNQJ9nIt4scYEgX37pvyHXHcpnVOnDiJw/uy++gbRQI5LdpefAhNLnWS6nJcCJlh7TPFumoS0iu5Fv5EpGUPZaN0vFUSHlvXUcmJNYuBN28PEZZdi9aotgeCNknTS89r95uMZNBriR6lIv1oYt/zMMyCSZrK2V6XfyEB+yzszMtSx3agxZh8MOMEsOySQ+AyVQmKiliwP0vAAAgAElEQVQLyWSfnJ/romaJQc33rhcLrUdF8r6ylTYfoShkYylS6Nz4NbAbJG6eCJtuPaCUiyO/krEudY8ze0l6JAsNx83d+VwPe9y4sD2GwyFCwacmNw0BnbngFdJxeedINvinpydaVl40FnIRdTQaoiVkdWzmDcJnMwdqH0gnbnaWrDIWQj9+D31nFgb2UC6+wxSQwWSsY+n83Fx+3X1kZCXdAx3bZbOyBPGZpByQyNJXUvwCxdrUfS3z2zz17YZP0h18j+Srvl6u2s24pDJmQ52fNDWIErKJh0o2C5xbRmMh560DR65boOwyH9RFiVguGK7LYYgbivP5ORaykeLGlHN7W5UI+W95/vr4unx9penGHEu5k9J5Lgf4I4E2t2ixEElyHno1paWsdMNHwleST944ObWSv9IeS/HRJElw/bqZr2l6yRQEGI/M+6cT0+4cI4DxScCKFtjL1hy5NDEvDKKEG9vEIajtbtwvnjzGsaQqubK+LDfLxektdi7TSjlEVRR6aDy9JE4Sm2oAAPlqCV/G4WBm/J7jzpW3D1sSyG+0fiReZNkzCepEdQAmUAYJCYVlnNU1ItlIqVQ5DxEtcC7k2JqmKHuHNEyxy83zQ7lIWsna7Mok09TX81L9cLmVzdeuAFohkW1NX549MZD3Ylfhxi2zmWPaZi1zZhREVhJdys5Lh4cP7uq6dDIzm2NPtmtt1eLum2bdfPnD0t9M2QlqXAjR9HRg1onFxszt1XmlKQ6c79wUMvo7U68D2XCi9TSYs6tICm4vlvUCTi7AfdjUVl8v1GwquF7M7roy8ONhrBt2XS95iMsGaDzKikuASEmlLRmy3yP+b2DpA5i+yrSaauFjILLurRw22B+hH+glJ8cL561tvsFYxBS4jm0qygtPMY0kRVj8sNjtNHUgis26Wwm3QVNVDvmx+e51JWkPSagX8Ulr2mMqIhCAFaqggIJNXWqwkbRXTdFhqrznYb0045mXdKzfctEgE0ETBgcYtGI6FWCFLjTFcLlEE/ASkXMKyXlz53LElH0oQYayLJV8PR3Z1EWuwTr/yr4ljICMgTYJPERD2UfAt4IfjmQ70A0mQ9ZntlQQBBDdBPXLyLlA0VSnij4u5PmBr38rq65oTRxH8IRWAeLb68qS/VO4Ar3Ls8pp00D2t34QouEY7ZFKe4Be4DFQUTkE0rw0Z/sxuNu2rU0vlLG0K+x+JJS1v6wobsG0QSBOuhcmuQQWB4OBkhNXSgzPy6oCgQTaU0035FxYKfk6L30CEmu3nh5QmUpbN6VTnu7ayPIDhpAasEGrOI735hS2j+d5mt5s50AJXl2tEfO8JG2Vb20AOU3kHBKaeevasex5fU/PDLdumbX8eMJ9eKvBMA6ryanZW/6Rj/8YvvANkypWDc07F+fm+377Z34WT/2hP2w+cPMnAAC/9uoR/sxHzGf/4z/9lwAA3/6skXD//Fe+gCUDE4mI9QjVySyZ4bf/6d8CYKTdAeDlD3wfAGB4/Sn4Z6Zgn/492Xu9ZKTbP17W2EVmTS0b0miYn+PdFsOpuUBqeGaQYO9uHmBwS/ZC0mY3ZXw+d3SKc5FnT19+tylfafrqZpPjplxGXT4080Iml0eDaIiVL5d7csYZ1+b7JkUAb23m6PtyEE+GI2xk7fUSM0dfL8y7t6+c47EEwxZz40P3v2723agbYGK+58d+/JMAgA9C5v+rCr6sWfMvmnuGf/npzwEAXvj3fgwn8jd/ZdpsKYH7yfEYm7lQbKzMOGFgfL1e67x/547ZO/Asu1xcqt+XsucYy3kXXoPIN78byj1KLPNQsPHxv/w3/ysA4Kf/0c/g97NDytjBDnawgx3sYAc72MEOdrCDHexgBzvYd5l9RyCEeJMf+gHmC4OuqSWCQqh8FEVWVlpu8FuFQDXYCkLi4i0Tnc0EjoywUUncXFKVgjE/X+OKEroNU3so871FmBB2LyTRkjK2fXiJViT6vv3qq+ZzJFFtC+SSNnVNEAUu0Seji0QDaUTAj/ZusNFUGBAy9vQz2g58xso0Rp12DEIfSWTqP5WIY1lYOUVPrhspic5Uh9V8jpmQ1UIiQyQ3DcMQs2OSycoNvUQSijLHTNJIWs9EmBil2RWF3mh6EkXS9LX1Sv8WCUS2qS0hLmHYjDpVZQNElDA1N8pPzk35BoOh1odEWpn032q5RpRJJEUiNQq7DWNMJl1icStt7CmZ8bAnxd62LYqiGzl0UzeYwoJeilrbtthJGUgMrGkGABqJfBNiS0hvuSv25Gkp+TwcZggjU65BRqRLjo1E35j2RluJ3KH591rawbTH7du391JY3BQ1/o1lYDSv9T2b2iRwx7Mnj7U9mFpFc4LPiihgfzH1w4eHOWG6kuLTVpawmpE8oglJiL4rtwiF8NwTWOZ4PNZUMaIUWPbAD7AUQjhNOZpZMkLKUqvCa0CkW6Hk5hzHRyLZWte1tu1r3zYQWfp9GIbapkRmMWo4GA5VZprPkPB7vV7rXMJx+sILL0nZWoU+s982LC88Jc7l5zbbpfqyPr8h2giIQAJsSeeQ+aN1Ugf6aWFN46QeSds+EbTCtZMTHXMnEg1TUuOqUQlwRXFJ2SeTidaZf+O7r66utMx9xEMS+JiIH3HclA7yp9Fyiry9N9ExSvEAN5WDbUWUI78Xno+doAHp4xMhbz6/eIKHAhfnuLHkqQFaz7TplaQCeRKfGQ4GOi9uNkylFVLSOMIg7qaBNCTa90t9v6YwKql9rTLRk0k3ncz3Wgem341CN42VKGYdymKl7+WYp/+vVgtFlzGd0RJ9hgj9/ZRdwIwzppQo6acsg3Fk03M3eTcFejgcYihIotbvpSu2rabo8fuIXEv9TNNdOU7ZZrvdTsden7i7aRqbJse0ssIDEySUPF/W+eFwiKefNsT2/dRF3/fV0d00FQCIwgQ7metsmpDMC6sdzs5MWsCAa97GikAMhEBU+0tkiJvICii4xPgsE32cY2mTWwRPP8WsKCyRLtuN6GUfnvoHRRmYlh2GofU7pncIQiA5mmkakheR8J7olwZRyLQuokJEFvtyoeVjvdz+47jUdUW+98nFpcrH+4r2FOTadqf7CJuCZFOROEdzD6T+ud4qWpf71CgMNT1ZUzgji5bmvNb3gTTJtB11D1lZGWKumzPZJ/Fz2+0W60vT3vTDnaCPdrsdTo4NspZ9r/UMIwelyDRAQb75LvmvpVUAgNDzMV9zrAtSPCHSaKf7b1Iw8GeSJLpPWi1zKdNyb22kXw0GAxRE4Ob0J8o426NMf25pmgaVoNm5l2ycZ4miUuEEbz9OzrFEVGZRFGgdGgBTd0EfpHZMbAtBzfn2nSob3fsOFyFKn6gqi0519/7u9wLWP3JpO/dvoYOS4f9dGga3fkVRaPsrYkoRpSE4zzWkO5D0Kd/31e/5Tpcs2hVqMWbRPUw5Zb9ZNGfdQRnxXaw/3+nOYaGDkHLLEAWBprD1xTratlUBmqrpor3COHob8nCbJlaVcl5sZe/ElNqgVZScpqRp+psHLkQcX2yX7/2RT+L0F37OPCdn4Ct5ZhsUgJAi/+rP/nUAwMWHfxj3zg2K5I//wA8BAN778odN2ZsAZW3e+5U3zDk1kH3tk8sHeMd1g8YZDswc8Q6RtG9OWvi3TX2+8NisM81UxnU2wuzKrPmbopvWW1cFbouew0jOJpB57mqdY/LsewAAAsDR88GgDFCXZg56/tnnAQBTIe5ePl0AsSnLjZuCchqbeXySZLhSegruy0x9R6MZ0mPzu9uyZ4vTCKOZiLHInu1Izgdf+9rX8Lr0VxFIBkFqMwC2gilcnZtzwlrQxMWmwU7mee6FnnmPqcNgNEIlGT43nzZ/y8+NJH20K/Ch730Z0pAAunshm7YpFCCyDw8GJsMDAILIzPsJhbHqGq18X0yEUG38+NUvvoJ8fYX/PzsghA52sIMd7GAHO9jBDnawgx3sYAc72MG+y8xz8y//ddmf+8//qxYwt9zKd1JRhtwSUg6GlAbljbIgK7xa89c1H11yvBvU8GPzrp//xb8JALgS0q3hcIgLQcBEcss2FHLBp6cTfPID5gb1ztTc0j3/fnPD+Sgc4kt3TdRuMjKIAN6+I/E0CnqUmvLOJZoRRZGNVgvXhhsJuH7dvIvP7NYbvcU+FuKutUjH5XmOs8fmhnUiUX9+3o8CzcclGx5JL9M4wck1E3G5ktvmszNDYDoajTSvtpLbUhIlH50cW7li5m/LDWq13Sl6ay7R7qK0EUTNIRdOG809Lwvl2FFZybGJrm82G0XnkOA6TVN4kgvOiBflfnfb0iJ75HvpO7PJFOut5aow9bPErBpx5C2sIKjG4zECvxshJr9FWZYW+UQemNJGZ/rRTNYPsJEvot9cuftc8l2JgmCScZpE6mOM5pDXIc9z5bMhIePlxVx5rZhjqoSP+VL7JEltVJztY/kzugihMAw1Yn52Zm66FcUVRYre6UeWEPhWQpayiA5SK5Ec4USitWz/JIqVZ4aRG3c+qBwpaMBG5fI8t7LKIgOvCAEAb771lrY3YCKQrDOjyFdrkf1GgDTtSl6z7Ofn5/o5RmJbGfuXl5c2Ait9yfz5OI6tvC8jdRJtWW9yRZyxLIrYaiuNXpIEPBZUlOd5SkJNRBHbajKZYChtQ6nzoG2UdNLlteHz5GhRaWNyyjqRTvX72sr8sk/4zuVCCPLrQtuP6EhGMaezseZK69wn/ug5IFaOG37vbDbTduzLW7dVbbleBBG2UTLmUMk+lTOqKHAphOfPPfccAIs4u3fvnhKj3r5tOKnIl7DLN0rkx+9xy8e+5FikHR0dKZ+WLyFql+RficjJzZZYZE0uY8jrEfzWdW0/F3albtu2RZ6buroCA4BZezgXcQ3hexaLBaZH3XYcDEbIBIlIX+PfsixRf2X03so/J4rI0D6RshRFgaFE66aCRHWj2H3uN74zCAL1KyJbGMUvy7LDIcG6AsbP+N2cN/huXcud5zk2oijS3+24XrQVhjKH7bZC0izzltc2aISP5b/9y/+FeSfXnjDQOYI8YySRPDt7hFrWmhOJao5HppxRPEATGL9/53vMnuSNtwzJ9I/86I/j3S8ZcsutoMt2W+EWnE4VDdFfu9Z5rv8mUohja7FY7ElJWx9a6d90vkpS7QvL5bMvJa1k5VLn8/NziyLZOigSAOfzOZXoLbG4IkH8DoEw0CWrpfXlrevWolOjHqok9AOV9Oaaz34fjSb2e9Btz6LcWj+SyHGaplishYeLEsEyhl0xBkXcyNgN/aBD3u1+j+d5iqziWCgVTdGglcgw50D6WVVVHZQz0F3fs2HaeZ7PhH7goC7sWgoY0vFdY+sDoIMkVpRG1OX6iuNY299FwbA+3LO6SFZ+djQRKWlFXnsWmaJoLPkbaoviqcQPHaEHjgmdO0nI6vvKaUn+RUXeBD78oIvUtJyQgZ5NKI7Dcu52OxVQ6cu0A87+xtkT9oUCaC56iP5fOXNmn9jdRea5/gAAQ4cPkSivuurKunuep+iEPmLQvHsfscOf6kc9BE/btmjFn4gsVTRmVVuiavL+BMHeOO7PTe7f3g6lxHexPd4O9an74yhGKeiyPtG979uzBsvJ9ojTxHAFwQq90AI/svNoTxho5vv4jb/xSwCAn/nFnwUAzE9M32SjKT74PsPz8+kvGrGUW+96N7ayn3r+yMy/P/pegxq/lgA/+LEPmXqI64wFrZQEIR6eG197kpu97inPmGdzvPmmOSO/ImdMn+eXbYkm7JH6E80SxyiFX+mrX/48AODuQ3O2bPwA40QQ1HcMUv+5970fAHC8aFBJWz2uRGhjK+jpAVCJ+MZ1QR2uYdos9UPd021K4Re6NHvE0CuwTMw7jn2iMkOcy3MUvDmGqdfv/d438ebS7GVqQRpncoZrQ1+JXr/nRUPAffWWOf88uVrjgfjObmhQQKfCO3s1ihDIfvia/M4fyzk3inXf/PSx2XO55PJ9tCj3f6PRSHkG/db0USR7gSge4JHwMSeC3DuVd3/rq69hIrxJP/UX/lSXINGxA0LoYAc72MEOdrCDHexgBzvYwQ52sIMd7LvMviM4hN7x7HMAjHoDc4/d22Vaq5FYc8s3EF6MOI0QCw8Oo89yAYgaNU7vmOjnu1/+AADg11810ntBEGjEnFbKjV7ZNsjltn4l3DdnctuZvvM9SBPmeZurV5WKLyyi5ezbBolQh/ZCjspIiSj7DCVHME1TvYHmO8dRipXk3jPaFAqP0cnRTG8KKbvNCF0UhRpNJ0/CtRMrlb2R6GwuP8llw7YD9iOjcRRqlI9RFt6q+6iQSa5/NhBOCYmQrNdrLBZdGVby4gS+r9wpenvv5NTyxtS9yd8JvwflLi9EhjzfFhqVYeQ8kv9fLZbww25UklwFTVOpTKblDjLlS8IEd++bW1iVj45stMbyfIj0qUQS0jRFHHTVTqje1nqeVZYpmE8N/Rz9cSaqWkQiNE2DS7n9vZh3VZcmkxGWgkJrJZKYZglmMP1Zyvcx4uO3Vl763qMzbW/WcyBRhMmEkuM20qlRTKoBimvv8twqwmiuunlPNhpqdDeVdlFOhDBQSWdGxMcyNsbDoc09ZxRCUGlFUWC9odqdqFTIVDEaDG2kRxB/8/kcbykySNTIJMq43m6tFG4vmtY2HvzetTmjn+PxeE9udCX5wL7v6zuvS26xooIc9JxG7Rw0BZFmnPk0HzgI4aGLCql2VmKbiL3jE6oVjvRvF/NzLRcATDLbRo10YiOT5mq7sbn+dZfLbX55qX7H+ZERxcFghIcil/5A/Iroi/FwojxG/B1RM+vVRqXG+9HM9WqrvvLUU0bBguiStm3V59Y9Hhg4qnwjQYvOpL6bzcZGVmXdWK6u9Hvu3r0LaRAtE9/11a+ayBx9/PT0FEsqOfYka+/dv6/9xPxyReB49h1c4hgnLstafY7z1Fzm0MvLc1ycX3XayI0mHQtij33kKTLOU4Qm5ZyvXbuh7UkVOYv0yfQ9a+EEIxqtLGvM56LAIca1qK5rJ9pqynVBxa2i6KwxgEWsZXHi+IXIqztcFm/H5WPKUlqUUWWlzQGD8Orz1bjzsUUedHlaLFcEMBh0VRiXyyv9nMsLw74ksoVlWi8XuHODfW8io2+9afYfcRzrdxXyudhBiRElXUsklhH08/klLi5NG33tm98wbSWoxU/80CcxnRxJWURtzFHY1Og9eVMcpAQ5E5UXR1W8AgQ9qWb62VN3PFXMYtu6qJd+5N3zPatUWXVR0oNBusd3MhJ062A8sqjPJOq8MwiCPV4zWhAE+n4ix6wSUYTQtxLebpnqtlA11j4fSVNb/kruV6hq1NRQVSIXhTEVTsucyDFPVG2jaA+tZfe+Na6knxX5KH9TjkynXiznaDzGYNSdozkO6LuA5UNLRnbs9vlw/LdBhBL1qKpaYYiWXCqilDMX1Uzft2qWgcj1kvsPTavyOeSR2RY7RR9zPHKfFAQBBlLvpfASWURYq+grqmop+sgBNCVhF0lT17VuYrTdfaff+9xesp9oYJHCFlkoHH5VrfPoorWch33rI6fCMOzyUDrPuGVWtGKSqL/OBe2p6HHP66CiTRs1+rc+cp38bXGUqnIvOT8rB9GkgDVVVLOoSqrR0odcrh+2LcvAtqrrGhVV9uouosmH56AABZFblvClTfiTi2XbNBYt1EMNJUGoz5N61kUW+fL+SNYZncvq0kHu9eXuG/s9HBssb5CoLDiVxJQzsS7035wfuX8Jb9/GBz72UQDAzV/9BwCAVWHWz7Ja4x//AyO5fnrdIJUvXv3y/8fem8Xakp3nYV/Ntecznzv37blJkbRI2rREyRItyXEMy1YAG0YQILAR2EBgCEkekicHAvKQhwQSAsiJkCCOgcSCYyWxItkOZdqSGFGDKdGk1aSa3Wyy+/Z0+95zz7TnmqvysL7/X1V7t5i8pQHu9bK779m7atWqNf7/N+DGsUGEzs7M9/7Rqy8DAG4+fYxf/cMvAgA+9xlzzYjj86VbT8HPTbsdPmXWp6fohvbuaqXun8maTlahnB8rVBXR/mzHg7H529m7b+If/6NfBAB859vGPj4eyhlggl7f3O/qN8269Cf+6l8EAPzEpz6HiFK+4zGdig+p2bV4jNGeWTcbcdKjTo4fxrhzQm2eVM6Y3A8uLo1jL4BBRnQaKjTUAurzHD2oTP1Cx9Mzq9/IeYnvq3FUZynidzxqDnt+hILv+WpKzaeF2QOf5yt1iX5lbp5574Zp40MnwOuvfZMPbfZEikBrADkwuRtrT1WWANczd9+g6h26KzZFjps3qRG6MufW4yH1v4oMAa//0//x38QfVz4UAaGEoq1lU1uhVzbEiAJSTdPoIBL61MFEbFUXusFZkTpWUHyyNxjg3XceAgCefdFQwL5KS990vkQsMMzGijICwDJJseDg+cTTRhRSKSDzOZ65Z2zuF8su/LA3sgKk2SGF8rimOo6jG0sRGmuLfsomS4QRs2WiG22hL8imt3aAA9rRqoBzIjSNRAU95VAaRzbwFPiyKRYRX24IYAXbZKOuG62qVKFjESCWtnJcB1EkkHyhGdGmvSi1DnJICUnjuZhd6iQrE6Iu8E2phwDZHFZVhYjwUhFUFltU3/eV2iNipGIxXNc1xnxGuaYcgKTNTN0ZBHME3l7g1i0zGcmmsn1I6RNeLZamV1fmO9PpFKeE6smmXt77er3WxUc2cnLf0WSMAfuRvOeyNN8dDocaTNwU8y2KSiHo67WBePqOD5+TVxR2xZrjINT3c8jVKlmu2AoWPpsmYj1rrh34nj0IU0RcgkdBEGCP11eBWaGJuY4VLKWtrYry1taanBrYuiG8QN0KHHUDbOv12uJ1WWQT1g7WrQqOt14PRzdN+0mwWAIoxoaVGw62h4hXDgYD3WRJuxRiXdnadInIorR1fHS0dShaLyx1w1dKialLyf7oBz4c0hSljYqUC2DPRSDCk6ynr/PkSClg54T5yphSIWOgcziSuSURetfafl8kGDP+7TEt2ff6Qwv553VHGrwMlYJ4dGLaWgN6qwxDUl70UEqr58VqiSQxlBeZD0SgcjQaKXWgLZoq95P+IeNL6YORpWlsCjrneb4lRn18fKzvXPpv2ypbBS83rG7TNG0doghRZvDs3r07llqzMMGvx2fvQ4oI/gu9V54l8i2kXETL5Vw2Ho+xNzFjrzMW+Kkbh40DRRiGevhPsy4FJohC9IddGo6UpqmwNzHPI+tSU1W2rjycuxTJTNN0i8Ila91sNtNxWVHU0Y/sWiLjXqDUesBtHWKTjQPuYDCwVLF01WlH13XVanlTGLtqGjhyKOS+QuaP9piX/Yg8082bN+3cQgHLpshVPDlXu205DNk1TUTt33zjW1oXsVPWwFAmh6+mE4AAgIzXXq9SPdX4HPMjrodXT87gkN8pFPsBtzhJntm1g7/LxYjC87YCjMnSBnNs8LI7p7WDCFLffmwFu8Oma5SxmC20H2pweiy2uQ0cVw6MYjZB+uXRkb4foXBVtVBTQn1fUqSftAPz1hKdNJcoVEq8s0GFq8tCn6Ft+gAAl9cLuz9Clx6TrNdYabDBUuv7UZfyaAMM/a2AUFtc9+jopPM3Nb5oUbs3x3xZlqhJJ/em3e/E/UgTKP7GXJYXqe6rNmlQaZFu0YyU8heGSsGS5I7QUV3XxWLRFZJXgf4gsH2u9cyy15W/tU0g5DnSsju/BUGolNOAovueCMr7dp12alWQ1vpJQEipW+2AAf9tueQem2vCKklUtkCIFhL0Cf1Ar7Vey77KlLZwtPTDtnC4yjK0hJk3gylyjTZ9tU2Fld9t9hkpjuNsUbdU8L52towdNpM07boEvvS9Amm6sa6oGHWATVqXiIjXdY2iFpHtrjlL6AdK+bUBqHDrWh9EvZNnb1NI29IMAFBkNvDvbB68ZY+CRgOnW2OwaDS52KYUA+YdbdPW7P1t24Sdz0eXl3j+o4aW9JnP/SgA4PG//FUAwPVqhd4h98prsycqp+cYOpIsMXW5uDTBgCdv9+H1zH2+8fvfAAD85E/+JACgd8vHHgfIjYkJqvRjs87v15fYL5gQYvDHrbinz0rEQ/O9JcdlxvX63W89RFya5/jIXfMMLudXN45wcEThcs+c2//17/w+AODjH/sMngmMjEnAw/KU68tJM8FDGk0FHG8OTTFcr0Zd8vxG23pQfHyVzTHxzXnkiNItV08eAZy3zyhoPRhSYB8VQl9ov+ZSKkfQ2MBfxmSuxz3NjfEp5pSd+bOf/kEAQMxz54OH7+Ajz5g4wTffNJb0N++bpOZh7sD9cz8EAHi8MO/t5s2bAID7959BxPVMzvGSPF2nia63lzPSFNmP33rzDSSZ+d6VY9axp+/RJCqZApk96/5xZUcZ25Vd2ZVd2ZVd2ZVd2ZVd2ZVd2ZVd2ZVd+R4rHwqEkIr49kfwQ5P9FLiuxKzm8zkasT5mJvb60kRJ/UGo2XFBQRzcMuiZsi7w4MnbAIBbdwzEqk+0yPJqikCsWRksFuiqH4WYMcsxJWz32Tsm6vkor/DkoUEdZbmppwpnDkMV2m3WhEQTwpqmqUaGBaHSzly+9ZaBkkt2oMlrhdiXjKJLubq60ki8UDZ6pNpcX06xT4rYwb5pT8myXF9fW8QAoRL1NbNyg57NPFJkzmVEtcxy1Dkhp4T8aga9ytEsmJlg9FysgCeTXitbyqwJhZNH/YGlKvF9C7w9DkKkhWQcGBmGh2RJIbQL8+4l95Flmc38kZYkduH7kz0kFLt2a5MtFKRFOOht2XlK9jPLErgiMCYZ8cCKA2brbkb61qnJ5hVFAc/pZjakbkXm4uiYCLW0K2wbhgEODqyoNmDRG+v1GprcKuS92+zEaDjh96ytcMXxIhk3ycrHcWSzpUL7Y2h4NBzjiH1H7iOw+CAIFPE0JW3NJeWm3+vDZbY0I8UkTZjRrbSmzKIAACAASURBVCulYq6FPlhbsUDJLooA44QIwF4cWjofkz+ayXIA1+9mHiXrlBfWfnFOO9wsy9BnZlIojA77VRz1cX75fqdNXRUtH2xRF0WU/WD/sGXZbp5ZhdB9X9+5ZEvlM89zFddVoWUK6RatDIAKwLYyc/I3/Q7nx9lspnOgUEhlDPq+by3fDziuvdbYqQSCy/ZsiXkLhVdQHvOrS61LSMSCICnLslTUkMwx1lLZUTSfzOmHhyYzdewed6gJgJ2v/MCiOuTdCLKpLEttB0HlKEqh38c+kaBSBEkzHA4xo4C0jrPEIqZUtM+d6G836S1t6+XTU7dTZ80IosaEtMsj0vjUarsslWIqNAbNDuclUgoRi8CpT5qt7zmYMmskdswH+7fsNTfqKRn7uigVQp225hQAaFC30IZdGmsQBCgL886V/nZyhDWpzO+/Z2iYR2z/MIhxwXX5Ouz2cdd1NYvvUvRQ+kkb4SGoi3aWXOoqc4Rm41sZ48Pjg8796rLSjLn0E0UoVVawVAT5VRi+afT7Sh1l31uv1y0KLcXevQaoOXftk1LSWMTUeGSe9aWPGgHo3/vt39RrbmadhTZR17XOi4rYJBI48ALEsfyOayoRn1/+V7+LH/ncnzPP2HCv0bKiFqFk6WsJ2zXNMkUWCtouWaXaBjK+Bh+AJFMaE9/hdDrdovaFsaAJR1sIPBmDjlsrwlgs71VYeLKH8/Ozzvdrrik3b9xT1Imsa51xVnaRf+13qn9rBP1CBFVZ6fxrqS+kG/Qtvd9puui+Xi+y77JlYR0z+1yUch9r2KCIZkE4OMrL0TEg7S39MkkS7YcyX7WFi30iA4T2rWYaZQmXiBihXlihZKhVtqBDRBg+iNwtIWE1j/B9jDmGnD3Sc1t080bRQ11KZ9M0qIlqSIjuC4IA44n5rYzdwyOzH5jsjfRaMUXtLVLNWo6LkL5FkhVWGHkDVuy6rlIxlbIhnKLWMypzgHu1oqoQxSKe36UStVFpg1EXFZtl2RZ6Rf6/KAp9njZabxM5ZtFHa72G9L+2WLOUTaRKm2rdFvw334H2D7lE9/eC7iNSi/OO53lbSKJ2u2wKp/f7FrUs+1oZb/q8jkUsKAqog2HoClW3n3vzs6oyvYHMsdKP23UuN/brjdsgF7oxNpB4uaWTCbLO1/OdNdGoN2j+juMoKm3zfXmOi7cWBnHyp37cIIR++/e+BABYZVeofPM9Ibb0nBoPL75tnoHnspr040985EXMeS57+x1D8f6Hf/+XAAD/9/3fR3RsEKvf/6nvAwB86kVzvt0vPaxoVz8P2AkSIpLSBIvC/LecK+CStnj1Nu7dMXumJZ/hglIFg94RFpem3e6fmvsevkMmzGoB98SczfNHRPnvc38wX2IQiQEOz2zHZn6Ia6AWJCjn5h6/Mx73kfDcfuWZv73yzdfx9fO3TL0WZo27zz3oQW+IilINS1LFRj32qxYFumZdRjzrDeNDvHNu5oQv/Mavm3pxb1+gwGxKSRPWYVSa3+07PZxT/uTuDfYF15xBLp9coce2lXnu2adoOtE70fFyyvfsBaY9fu7v/i4eXBt02Lhn2vq9R+/wOzVKYWV8l7JDCO3KruzKruzKruzKruzKruzKruzKruzKrnyPlQ8FQmifdnlFXaFsTNTLZRRWrB2NIKmJsK6YiXUrcmmdGldzE20cRhR8TMx356s51pWJxCVXROqIqG+Rowol2ssINDMWju+pJeDjxybqdv+jz5nvoMEZkQCH+4YTKBHbGjbqHoreDSOGe8djZMz8CpJJos1VXWA06GqViMYGYC2TDw9N1rs3HLSyn13ObuT6yJmJEsSCxwzzaLKn3GrlHzNi/t6DB5p1rgXtkViur+g5ybU0Ut7UGlH3+cxise3AxYKIIImQe8wcTx89Vh0XtbilFkPTNBhQ+Et526sVYmrQiDDqmlHWo6MDtSK9OGdWkVno6XSuWeeKSKRRz2Ts+6O+3rtStId5R2maGhEvAFHYzQL5ngPftzbdQAupslyqlhUayfCbz+PDfYyJ5OqzHQK+jyiKkCfdDFEgdslNK0vFZ074/quqQk7EjQjnhUEPg1gyZt3sjO/7cDiWxNa6JBInXa0Q7RGl1ZOsrmnrKAhRUNvoYN+iNOQZ8pTZZhHZJtrAC3wcUHNIdJ4k0wbPxYjtp8KBoteUJMgh7c+MGZ+lhINMdaeoQ0JUT5YXNns6EFTQXLNoN45NlN4jvzlNUwx65nuSae7z98NRX/vtgiizd94xUfciL1VkUTKpVWH1NKRfHB+aMSVjvm3DKpnz6bXps3VdKzpSEFBq4V6slM8uz5LTpjkva6w4VmVekD50dHSkabiMqK3Z9bkKk04O9vk90y5JkuDyiUELCDQrdqmNNBgq11k0ZQQtdnh4aDUn2Bf0u9M5ev0uX17G2Xqdboksqn1r4G1lQXs9secs9N3L39qoEnl+QZdY4fRQ6yXfz7IMYJYpVBMAq7/jupybk671b13XKrA+6HWRZL7vanuooKRkHusSPQpryrher4n6qoA4pqiuCB8zm7+cpar5VlOjRNq/LMst0XzR+HJdVxFq0sY1URFtjSkpVgCzUSFV8PtNVWNANMgeOfKCZEqSDPfuGs29Feeptu6azINSTxEzrZtSM/uSKZY5oq6tQHi0gUBri64WRN2GAbPz4wFCZvSmRC2peKfvb+nOCMKr3+9v9Z22RoSOL8kwNxUiyTCL9XRg0Yvy/CfMyLbto0WbwGlpXQAGDes4zOKLZovoa+UZGs4zYxHH3DfrWV3kipwJ+O7zlMLd/eGWZsjJsb2v6rOIeCfvm7Qyi4pu4NakjeyT9szTbAs1IcikMAw7KL5228IFMuqQrKO08zfXD3FybJBwm7o9g/7ICovL3quFFOpk5ttt7Llwud+TeVV+l5ZJS6usO8fklUXwiYGF2BgPh0Pj2gCoYHXTNFizL2eZrFGiX+LCc7tIV0eElrGtXyR1kE/A7sPa81xIDZCm7CIR2oLfUkRTCIAiZTff0WQ07GhztYvvWmShRWGJGLDXQrh0kTtFUWBI8euaKAOnbhTJLJ/gftOpGxU6z1ZmLhPtvqo2uk/tOkgJgghBSMTeympfAVxvOV4s6gvaZoJ4l32xIH8cz0N/2N0DKeKyquFz7c4pUIvW/WTukmv5LY2azbHnuu6Wrpv0cTV+aD1ze67dFENuj8nNNrLzYrg1di0yrFJ2hqxHYnZjnsmi8trtWeSVjjkVjO6ggZrO93V8NjWaqvtuXD/c0sf7IN2eTV2hIAi22sOula7WS1E9/F1eF6ojpdpSpbWf32oj9qVRr69sE9Gaagu1a/1EiJuo+sFgjOvczBV37t8HAPz4jxnE58//4v+IyanZRwfspLNsgcGA2ohsF49GCF//9muIaUk+jAWxTfbD2WP0azO2v/wbvwsA+Kf/56+w7iOc3jKInQk1VI+JYD8Z9hE17PfS1oKW9gd441uvAQD2xjQQGZi95fuPLtTw5hN/+gUAwF8Ymzb41u/9Bp7+K2Y/Nhlx3XU4DuIaxwHZPmvTLotEzlbAkOi+fo8GMVfn2uYBNxJvXRiG0Nde+SYeTI1OIYY881XmbH/r+z+J6tLc87zomiehqOD5RDaPqUdXU2PJ20Ofukff9/zHAQB/+NofmTogx9nXjXB0FZs2upiZun8n9/DyY3OO6NGSXtaBxnVUm6puuvpTjmtDNiduzt+bOTRZl/BD01bzC4OmrRn7GEc1Bv8foj0fioAQFFJX4ZVXTQM+pkvNrVtmEzAYDLBadp1ojvvmAPPo0SOMZEPUglMCBk7b42Ev73cPYUEQ6EbA5QZ8zQW7KCsMOQju3n0KAFQAEp6Lj37kYwCANGn0WgBwsbjE5YU5KB0NTZ2inojX2olE6icH7CAIdIKSg6TrxzrBy6b/7OyRtoHAjoXWIRPxZDJR8asLCl5dTc/1dwIxFqi8uCXcvHPb0hZ42FCnjcbTA6BQxgRGni9rJCJovTQLdcGD6mQy0cCMbPQTbtyfe+65rcOrCIqWRd1x+QGA/f19LK5MH5AJVLYmb7zxRot+N+hc8/bNU0wo6mjhouY9P3k0x5qTpON1N0FRFOn3yrILk14sFlr3ESkBsmkIw1CDRZYSZ+oy2hujEBFwoZ+w/49HQ1WXF7crEWaeXk+VEiVlPDL9azgc4ooQzTXFoY+OjtAXJzYKqMnh8vT0BAsRaiM0MSPtp0GlLim6cRNaZJbo9zbhumEco8i5QWfAa4/Ur7ws9B1eUpz4ii5n09kMq1TEq6vONV3XtfQ4vts+x3J/ONBJUsXKhda0XqNg3xnzkHLjmWdVUO5yatrq8RtvAQBWyVoDOmePzALhVHYsCU1C6Jv7DEZ6XqB7Ba1XZIOCIdt/c3OyStZ68JBNoduY35d1ract2abL2FqvU7iOtLdp4+FYAr0THaubQpOu6+pYEjed48kQpahr83cawB6M0L/FQ2vRPTB5g1D7vTyDzNFFUSiFxeNm6D1Simo0uCZ81op0m/4fx7EGeFerrstKU9cayJC2SlsUGBmrUicJZKxWNojQFiAGDCWmLUwNmCYX+ocKfbP/l2jQOCI4mHV+F4YhEgY1dKPq2nlrc3MsrlXj8RgrOuVIIEOorvEg1t9dc7z4vhUuvaY4udCdR2Mb2BAnQdlItwMZQgPuF91ASNs9UOakNpw+9Diu+B729/fB6qirkHX2GuqhXqjgR6Ry9aN4a32Wg+SgFysNYa00BPP/g8GgRYEzn+Iu2TRNyyHQfP/y8lKffcyAsMzNklBpt82aG01530myajklCsVMxlStwToJbqfJyo5tttFo3JrDSAGSe8vB2mnaASDo8wAmWOE7IrAu70IoGS5CrmdLrrcxKRind+9aWlcg1BzrdFTKYXujf7QFWeONeavX620dlKSPe57XOqCZ3/eieOtw12xQU8z3LVUGMJvfiuKlsqcRx7S0yDUhZV3KLI1H9jSa3OH929QqWYN0TQ5DXQfFoUoDjkEIVwVz7bUAoHbtM4u4tAQ28yJFJkEpCRBHIUYcq0MGEWQer+GoM11WdmUBpP+327H9njYdmCTh4AKouFc9o+ujtHXHZWzDgW86neL2bVI3NtzGHMfZqoMEBWo4Oo43xdvbFCkZU/KdOI41aSQJt6ZpMOc7kXEt32kHQITy2xfqadxHw6RHsuE01zQNnI2gG1qB+ioXAw9xJ7OBhoDft4LmDOS5viaMbfCcLqt+qHSrohEXOprOtNpU1iwJyLktiqBQJc35xTqHyffa7dOuQ/v9bgX+Wv/fFi5vX7Oqmi2aoowJ33f1vCKBTZvUWWtiWtpI18gg3rpfxz1N3kWLLguYRKa38cztNtqcY0wdreB2+z5ZlmgwUdzaxFAoCIKtswZ8cWHrKXfOUXsySwETuQ5JVMr6O11PtwJCjm9F1cV12OV+QOblsoIaIVDTH//2XzBC0EfP38dr3zTn4//tl/4hAKB3eohyxbmLCU6hpTou4NEpbrG84nOZ+i6KGlFm+s/tm/cAAE8fGcrYUXiI9cpc4+1/YxxXHyRGFPmg30d4ZNbzUyYSn7lhhJmfe+n78Po3vgoAOGNSPpMzbxjiHTo2v7E2+++/cuM+AOAL//sv4teOTODpr33SOI9JMHgVeujLXk2Mm5jk6TcOIgmQOCLQbvd8w4YO4Ny7zlcpjvZMzOAsNcCDS8p+ZCiU9uqhm0DwGhcB27Zk91hwzrgR+Tily9pbr5t301tQyiZs0KvN95YL846eic0z5+eX6OdMNrE/x5FZr13fa/Urnge59ju1p2PoPTpVhiX3g9FAjQyE5hhx71ZEJcp8RxnblV3ZlV3ZlV3ZlV3ZlV3ZlV3ZlV3ZlV3ZlY3yoUAIZSJgF/Vw/1ljDX9wamDnDkTkq8JgYpAAQgMpaTX31OmzWFFsWDL8yZhio30XTkq7RmZNDila96Ru4BKOmq0t/BsAQtfVjEPOqK/PbLyzcJAsmN0e08rbo4Cv10NBWFlWMwocEA4LByGzwGuiPYTi0x8NFGYuooKeU2mWSFAvVW3F0kqKYHkwz3pyLILcNeIes/c3Dc1NMhxV42ApVpjM8t28ayLEXhNrxvHy0mRghf4ThiFCUhwEpjt9YrI0bs/DzRMTeU3HkiniLfwAPVLhBBkTk9aX1w6u2Q7LdZcq4nkeBkMrigsATZYDPXPvy2tz79lUaHMFJL45iJmRJaKj8HNcim1g1s0GNU2DpmYfI6ppvZrZOvR5DUbtBQ3U8130KKDYY7b2+sy0cRSGmCfM+jEKvE9xQd/zUBSmTSVz4BL2+PjqCdZsB8kYNIx4Hx4c4PCmEUCTTIOIAcfDCLdvGOqGUMGapkFC2pq3Z+5dw1zz4vyRzcagCw8+2NvbyiJfUJxtsVhgwCytwzaLIiInklxpBCnb+NHbBh1yfTVXNI/k7gqBelc1krWqapprN6YNIs/D6YnJ7Mf9rk0nGosAEbTYCTMXo/5NzRgvLykePJ+rvf1tCvw+dWq+n6apXlfaYUXKk0FPdOkcZSUWxRkajqHY5/uW30UehgMzXqTPiWZl5Fn4vWRi6daJOOqpGLgCCoU+GNvMuWQV64LohiwHCodtZfqoCMYGYawi59cUjN3rORgNuki6lH21LEtEkn3uizWxqcxqtVTkgs28m2o2ACTHLaJ4h8emz47DQMVqFbnDjLbneYgpTn4yMe9bEDxwQzi3aL3sCnRdLOlPFCVZFoR4M0sZeyGeCDKO64ZQQiM/QJ9Z1zXRNl7tovEFLYHO53K51jYass0UEbJcKMIKGwLQcRxv0W5YTSQ5UJHOIfOhK5ms+VLpQUFMKDRRLGmZKori6trMN6vVYz57jkHfWjoDtj/v7+9bJIdQSPlO13liKapCL+CcVtYNagpMTvZEILVAzj6aEF15LaL9rttC15jPxdzM0UmQaAZWstV9Up6S0opwSkZc6lQUhY5nQSLJOjEYDDRj9vDCoIoToiQD37fC+II447iJevGWlbr2nTjGdDrv3Kdt63zCtW7FeWfY6yMmOnE2JYLVNZ/DYR8VxcNPbptM6ump+Xz46LFFILFfiIi+67pI2caCRBIh/qZxwKUKbsi/mf/F0cmptUBfmTrEpO+UWYKq6qI8BH3hupa6pDSGyqLhBLW1mf2v6xwcgvreXNSKIpYxKw/4+Oxcx7b0BUEghKOBRaRwn5Ndmb5dFAWKtItCEarNeH+o9EkRwlZ6BgDUdg8JALWOSVfHXuGS3tyigPhif81nSUhVSKtU91NKbeXeyHV8RVz7ibnPYDBA0ed49tjuufn9oBdZ+iP3AbLvnC9XKLlXsCiN1nrNfaJS6ASlAMDh3nCvJ1oGRNi2xH+nC7M2giiCOzePdc2oxBhC3lUVwkNXuFgATI5j0YoWQSY0334LtUmanaDOHRc50YQy/1dVBT/gtSjMLtlx17Nzwn4ktGEiSBxH+0zTdOmbbbSj9gGlEXqIOE/FYdP5XVmW+lsxMRGUcL8foOR8UbNvC4IhDD04Ls8RFcXbV2IcEqKqZR4WxPBS21X2BYp6DsMtQWaxoE5WK0XpCzJIbL7rum4947aA8WbbeOzjrlMrRVWkJBrCIiIngAM+I9GYhW/6ie/7GI67AvyKGKorXSOFpp9nLRrxBqWtfQYINqi4ZV2peY4UK8beoGrEVEEE00kRzEpFuKqJSd+KgstcIggJ2bNd5kv0KFvSJ/1K5hg3DNAT+jbHoMyL83yFW5F5h+tzs+/JaQRwvZjjgDRsQfX1Tg3a5iRpUJNm1UtNPa9orf7jz/953C/N7/7g9LcAAN/KrjAiui4IuXelwUlvPEZKKZQxaUlXNFRB6OLB+2Z/dLE0Y14QrPktFwcnBrHzkXvm38Ts55uvfgPvnZnf5RRmfvjNr5n2aVJM9nifK0pLTGj00OthzLOC85qZ078QGKRQeOM+BmOzT3wSkHq7JFq4bpAHfG+UUBmUNJTwfeSlqfuE54OI7+M4DOFxDjwhze7k5iHevDT3Hg7Nezoiuuf+3g08ufiOua7sfWJT31HjYs2d7SQzdbjmXFs0CcoJ10me+X5ozyCtfufqLTz/gx8FAMxIZbtzzzznvj/AfkLUEZHpNeubLObwKVifidSD7ENW1wh4PvN98+x9yhk4hYfHjEuMTs0+OuEZIq8bDJz/d/zPDiG0K7uyK7uyK7uyK7uyK7uyK7uyK7uyK7vyPVY+FAghT1LncDFiJsQTt7tExOqsYKZP8U+fUerZ1QUCcgnvkM/oUUQOdYMlI/DFNTNMjXjM16plEFA0ikAGFEWh0XoVK2bG2QmPsWJ09PzafN6+QWvMYYwxLcCvGK0TDbJ+3BNQDuKA/HJGxdfrNS4YRZSo8fX1tbVUdEU7xNQzDmOEYpdJ5I1EwOFWePLEIHwiijCPxyazEYahogUScgq//ZqxLRxEA9W4OGBWvXdCEeuWAKegsQTNEo8iy3nc4Ntfnl9jMRdhRIteAQD4IQZ9Ebc07SdCorPZDBWzaL5DYbTBAHkVsH7meUYD89nUVgMkZNZVBJObskDJiK5k2iTzkGUZHLHkZoRcxf58H71eN0PRMLszmzV6H8nSlLkVCg8uzXNMRqY/BsyQBJ6LmNpXObNVM3LJywaoGuoBEI0mFoa+6ykn+YoZoveoJ3V5dYV+0LWgHQ+GKtwnpc/n6vVihGpRy+wZx5QR9e7qAMSxFdUUHa2U7z5j5ng+SzQ7I+13/45B+T1111ORtJy6D4JkOD+/VO0E0VkS+8zD/Yl5sQC82tr0AkC/F+nYkL52QJHkpmk0AxXEVifBZ/amKAUdZYrje6o/IuimiPXLiwrrtRXvBgBfkHKOq9m+RDKP5Gg/fvw+qvcfarsBts9dXV1bLQKWmBkqY3Nv6jkRO2FB4HiF6nTYzLv5CN0GNZ8o4dxUCL9/ucDRgckY3L9rxPu8prBW41VLawhA3B9qXdt2uab4KCUTW4itctfiFbB2tPK7PLI6AmqnzYxxEAR2fpO7EPnguvb9SiZcMvWLxUKt7IV/Le06zTLNmvp8Jw2/W1YllssZG1Wyiy6ioKvFJqioMPRVE0zEsvW58lwVMm1W17TdajHXuktfjSKiXrJEr6kC9Cye51sx45Z2kLlvotcQDTjJAF9dXeGcKBl5F+ORRWFJiZkBiwKrH7NU0Vs+lyCv6kaz2/J8g8FgK6vb1ncSrTnX3hIAEPiRZn+lXdrjQK5xh0KW8re6rrcsmqUkSaL/Jr8XREcYBIgFAdx+X7ym/Fv7uQDTH1WXSOxmWwjTLe2bloaFaBX5qnnhiAwXRntmjXvhJWM///a771mhU87fgS96g83WvVVHA27LFlz0xqx4qs20i8W5RcMqUkrkMFooAhHAVWQ0EUIGLUPUV9xFlNZ1rcK7mtl3mi29E8mcj0Yj7cuborBV69Vuair5jqvvVTQ81PI57G8JHbf1RWRvIp+Cum6qHCH3RyHbXfTD4Ng+J4LhImZfA7hzg/OoaDBxHl4la92zynvo94Zq8S7rrE8nich34fDfKq5tHu+3N4zQ1F2kX/s51ZCE66euZ44Dh3NSxMx5zfFc1AU8bnIFMavrRrpWZOKQa498tq3DbR+y72ETOSYLVdkS4JX3LrbJ7T229MOqqqwYsRiViE5hS5C8yLs6N67va9ts6mLVrbrK/WR+TdNUvy/zqXynXZf7FPi1IsLbgt+iq1gUGTLurX1qeITU8zLmHaJrw/1Sn6Y6ZaZjVtF2rqtz1mY7xmHY+Z55WIu21p7CZxdEk+M4qp8zIUJzQXvyOI4xkHV6w5o+TzMLZdQn5lqepii3BPjZnmGkGjsixi57vKqqtjSB2vpmm7pVvu/DdWQtYH/kmM3zAjlRFm2dQQCoC6tFKPtAOdd5nqf9SbR9VHPOO8I+UWHJnGiUiZnHV+s1pmQTSJG92t74FkIiBPeefhoAsKRJAAIHhzH3l9TgS+a0Ty8qFCWNhwamX4w4X03nc3ziB/6UadP/gWwI2H6fUWtS5snACzEem/73/utvAgAiIn3i8QgjwtLnRG6fcd46e3yFAVFKct68ffumtvFHXzSaus/eMf+2OjP73G9+9fewXJozWyysEGpozWeJolwi3i8nWuzGeIwfPr1v7ifo+0DWvgZJyT0G5+oFn3PUq3HnjjlLFWtzJppQLHq6nuGCyNyY4+0jxzfxF3/sR0xbhaY//eSf/7MAgE8enODn/7O/AwD41psGRfRgwH7s+zg5YLsRaRVzTL3/aIFR35xzykOX9QMAwE/2cMn/+bFP/mnzb0vzu4uoRHhk9NqG7BcRx0hQ12q8lHDedkTrsizQ475qXRBtTQ2pfJbjaS6iZ4wJvPrAiHwPjgY46pt9zncrO4TQruzKruzKruzKruzKruzKruzKruzKruzK91j5UCCERuKSVVfKnTsYCzeTGTTXqmurLoBw6msHPjnFkt0SnZxllmD/0ETG7t43kc3X3vh98+m8qpzgBSO0BbMtZeDinYdGYf1Tzz8LAHj0trHkzIMlXvqMsQKcJqKQT6TEusZ753QxumIklDo5dVOp5bXo/UDRA55mtCXbcvP0RN1txJHgmg5JF3muDheSlZHMxmg0wnBgnllQMz22cdXUCJk1ndCZ7ejA8Brz5VwtViWjIrbzRWGtZCWLJHzRrMyUVysK92rfPRhgf99Em9XVhXbEdVm2MnnUYCLn/ejgnj6XcKZXqxVQM1vEjGWo7gh1ywK8a22ZVwUKZjbmS3MtdSlzXc0GJ0TLlIWJcjuOg6ruZrKFhtmPe/B6XUcTiaa7LvDSkWlT0axYUWMjyzIsFuZaM2prXPGzhovr60ten/ayYo2+Wmv7S9ZDssPH+0eK7OpxHERRhJpoHGm/xxcX/J2PmJmlm0SADftWm0Yy+6rdxLhx7AWKJhEdEskw9U+PdDxK9k1sYH3fUY72bXJbr+dmbNzYH+r3Ig+HXAAAIABJREFUpf/q+1gtNAMm73YUWt63IB3ACPslnfiyLNNxPZzQJtZx9HnWK2arCosW8PieMtEKOaMrA7DlutFzRWOjtE4hzD65vlivbqMaBJ02mUy2sn7lgpz6slbUj2T7lPrb5Kj4TgUtU+eSDfLhimsMnyveN3PodDpFQptej6iXrEg1g6eWxIIYWmfap6UztB16FHEgjg6Kfok086fzh+hJXc8VSSfXqhvRhvCRUqPrgvbg0j6DONA5SZAchTh+5anaFst8c03EZn8ywIh984qONGql3AstAse1Vshl3tVaEHdI13Vt9lKQN0OTpQ2CiequMemvluBuXanWgqBZffWuLZBX0q+4Zi3tOOj1u/pTkg0dj4eqxdGe7wHg7t3bmgUVDZy2VbRPLQRBOYpWRByG2rai+5Oy/dM0xaB3n+29bXccblj4eo6j6BhB5lqkxKCjY2GuWep9JOO+4PNJP+n1enAkay19z5E+2yCma2hRWcdO+dxEVmw6TgFdBA1gkGeb47Nt9y19R+aYZLXGktoM6iBGVJrr+8i4RxDHyJdeMjqJn//85619uNrMyvxqs+NSF3G9ctFo1j/y6SJSiZZNqNfMmI2X99197i7SquNIRMRfX1xPXFfn9kqALh/k7OOJRW7rUm73vr4ffSDCxFyzsv1I3FWkXq4Dx+m6Ygmap64d1XlTBIc+F9DU4h5FXSFPdJtKOLBtA0CRNU4DRT5tarA4RYWK1xRtQcg+K4gwpOaTOL0m66U4pyOS/xBL+qpAQ4ROFEhfNV8xSJMukrRtsS2LYr2BNvV9H7Ws4ar3Zv6/LG3dfUFc8TnX66XaN8szy2cbLSOl7WAor1w1t9R5ztrOK0qvsuMtq7u6UHEct/qvnWcAMw4EyVi3XO4AgwaU/bDqyulc4bT6XPddGge9D0YWFUXR0q7pulD5vq9DSK5t0Y41Ih3XXSSlQUqzzqHog3ItKaMWKqrVthv9z7oUWhShnU+tO1z7OQCLmsmybAu5p+yLLNzSqwo8WaetXbrsa0UL0nEcuKJLpqgeec+e9jH5nei/+r6v7S/Wla5rdZCk7jIW67LWASLrptTPiwKdg3phFz07HI7t3LDRH9frNWruMQR4LWt6L4jw/sxoYQ4GZmxkRCHlRYqxrK/sDMIamK3WcGpB9pg9SUJAWdjvYZqLCy6d4ohUdiIXQ0EmE5HoEUBcODUSvpPPfsoghd754q/B4TlM0KWV7uNSJI55B8KoWBI1c/v+HWTvm/Uhlz2suGjDwZLIpXRuxtuj8yese4B33zCOYyueiwfcDyfrhTpq5kTIPb4059WiAXwiEo+IjiqopzYsC1SvGQTTV6b/GgBQ3zfomVt7x6g9Ogke0PltSGQMcrz62tcBWA2hgSCqRyEe84z36tlbpk7HIQ6/z6B5xqQgOTm1aOHhP/jZ/wgA8Jdmph2/9Pc/DwD4hV/9Z7i4xzMD+/SA2lTlvocB918v9g3y9+qaKL/vNPjRn/g0AOAeUX3lmfn9//pbX8J3qMX06eeNztA+zzPDINB97MXMtN8gMmix4dEIp4wdHByYd+pThLmHGGcX5nm++FXTjpdkCzz37G3UibnWdysfioCQUnsaRw+RAjEUDJ4La6UrC5pMTmEYYUarSllA44FAIWd4+ZW3AQCvvPotAAZ6B5hJ8Erg7WgttADWWYohD8uysO8fmpdysa5RpBRmbmRyNy/z9NYpDrh43KJo6MM3zYTiOQ1u3jRw+F4strFWUM4jbFw29fP5XDf7xxRnFYtn13XVulSKQsydWoMwQh0TGkMURSp6ePHkjG1kNrMH45GF3QZCURN7VCCvRZiz7LRVXgERD/FDX4S0zf0ur67w7iNDbVqsuzSSOzdOWwcE8wxJYias9SpTC2uhF3iep5bLQhHpkSbkhYGq9srhwdHDWISah1dZ7IYU3t3f39c2lsVnykPpfD5X8VShj0gbl0MfC0If54t3sFma0vQP2dydHptAyPHxsW76XU9obKa+08USewcUbeZ1Yh7Qjg/3ccIgk1DH2pSChILAao3ZWNj+TdIo5ftplilNq867EN7eKNLfyQYn5TvJykwDlEIpTBncS1aF9rFY6UZcmPJMr+HVpBnKhskDBj0zscmG5frSLD5+FCIVW1qKxcl4uTo7U+i0UCzD2Hym8zkStu3lme3jQo9L8q5g5jqxQQ7Z3E0oNjcajSxtk0X6r+/7ugl1SL+J+f83Tg5UqFHmKel7h/3xFkx6/Iy5X1UXyDIRWKcFOP8fjgdXNuESeKrtxl1evVARAy4we3sHrYCGzJkx3I3DsSOCs1WBrJQNpgSEbX09oa7wfiX/1qYAyIbW4zsaDff0eeRA3Q5kCzUqISUrX5nvXl+c6cHbWtZawVLdHDtdW9vacfH43Mx9T56Y/iRz4vHxQIV65ft5nsNtBJbeFdJO09RSVzYsr9N0jT43bjoe2dZR4CMOu5QPpfrBRRRYC+727x3H06C7UNRkg9rv97Ve0+lV5/cnJzd0TEiRQE9Z1irqLWNdDiarJFGbY7VedsXiPEC5ERxp/12K9K80TTVI6gbdg1ZbJNTzpF0opJikSFMRX6dNL9ssz/OtYFTbClyuP6JZhPZBz1MB203b9DwvNeg1arqHS8+zyZlNulZVVVqHh+++p/WUA8GaQTAVIR+PLGWUwfqTU9KNfHv4krmoTOz8qIFavhPPkY0wrBWy0w2cNE3TCrjyXbYO99vfhz7n5jutW1SwzcCarrGt6I88g++7W5bX7WtvUuHada8l8LdRT8dxWv3W6dQvzbIt+2wRXG4HMaSqSo2N463Adfs55W9ita0UwcrsOYFWMJ3fSQt7MLOi2QEct+5+X/YRhaUs+aRM6zp4fY3G6dpo22Z1WoGCzW28g/lsyee3gRaAQT5Z36tuvx/0x5okaVMCzX2bzpwAQNei2gF8bH8fMPvOzXfjNXavLQYKmnBr2dt/kDW6JiY25gjXcTRA2AmawVLpTH26QvJVZanT0ge0z7kNoliMXkwdLOW12GoPSVy0g2cSoMhzG/iSZ5V3Iu87iqJWYCfTZ98M9uha0liLeGvLDn46rYCd0NGt7MNmIKk977si7cBgadDi/kobM64Gnwm7Nr1LaVr8TRDYoKAma2tLC5PvbybeAJtIkfGWl0WLUtidY9pta+9n3snZ2VkrkcoEBZMugeehpTsOwFL2nKy06wnlI86vzb5iMhhqQCjlvtQnxfPpW0fox+aMsSR9Ppfkk+dgNTVnLnn3sve6WE9xwCTXYwZhRgyEXxcJoltm7fjcjxmq0z/4578Mp2IAjeM5JxUxGPSwoBlFyPOPPxDJDA+jY7PnLDiWHvO54qinc8qI8ifLlTlfh46D2xNSt54Yqtg0oWFRmWPAJJqYBI14PgujGLVn9iZTUuIK0vZPqwDfwTcAAN/h2Xd1acAX36iAKDDn8NMj0rY887LeeOMVXF2Y/b0YAf1bn/tRAMDx3ghf+UNjA//cbWOatB+F+PrvfRkAcPN5045l31zrweId/JlPm+DNxz/7CfP5Ez8MAPj+X/gR/MI/+F8AAA8fv2GeyzX7so8FA8yXb5m2fd4ATm5HTGhHKfZKs686f2LOiI/eNXujg36MTx+ZxNCLd027NEyEHfYC3CNI48YpTUW4Dlwt57hamPf8gPIAp2Ozr3WSCikDjdPM3Ofm03cAAP/u3/rrmF+ac/h3KzvK2K7syq7syq7syq7syq7syq7syq7syq7syvdYcTYzOf9/lP/p7/3PDQD0Bn2UggpJLSwPMGgNsdGWCK/NRoe4ZPZTEEJ7ByaquFyv8eTC0HBENPjl138DAPDaH7yCesnMudi4igBpvsafeMpEFl84MdG6Zz9m0Dm3nv04zmmLF+2ZSKOIdV0vp7iemSjnFWH7AcWfxpMhxsw8er5pd8lEjkYjawXeslVVW2qBo7o2EyMZjRkF4QQ9Efcj3DgxqBCBLUpmJM9z+MzcKq2LUL48qxVFITazudruVoqskB4jUfSqstaRkh1eE9Uwmy8tquO6K8BWpzbzLPWTDPfR0ZEidyRjMBwOUTpd21DXtdkxtVWW7EBos5hF0qUTSHsuFgsUmaWPATZrUhQFhhR2k0yuZG6CINDMq2aDeO0kSRALJLlPmtBYhKqBUsTOaJ8pVqGL9Qp5tpFJlKxQWWvbFiLyx88oijBg5F9QRGEYIll1xZAFEeI4jmZ4UqK2pE5RFBkLcwCFCCbzPT1+cq5t+/Tdu51ndl0XSdq933i0p98RFFpGtJegehynUQi5olA4B1Q1VKBWkkGCFkuSZCu7e8rsycnJibbfkhTL2WyG1drClM2zkn4Whi0BRdpZE/Vx79497YcrotM0q+M4mhWUsSDaoWVZbgkKt8Uq5W/SngmNoz3PUyTApuV1XTf6rPIZambQtXasbjebOSIaTq4PAIHvqDW0joWVFdoUlJHMByn7fZlnGHHeGJAi6bRysDKOB6xLxHb0GytKKm0l89B8PlfUkPxtOBTBU3dLvLfdjjK3bGZdm8bR8aEUMI7hNu1N5psgCFDTwnQTMt8uW0gEAFeXzKwJZdSx1E6lk7JdglAQkTUCwsUVueMLVN6I05r7gN+3gptj9sf2WABMNk/6hfybtOd8vtT1b7OYzCqpBlV3fm2aSikYsl4URaHjUtpDrNjblKrFmqYAIjzdt2g7m9G2WXWZSwQVJe/I9/2WvXd3nZjPW8LdUZdagdrRuWxzvXA8S0EX+p/UO4qiLVF1tzXOlEpbWfSQ61i6Wae4jqK95JoO58mf+c//DtKEhgKlRdkBhkYi81VG1IbMNS48NHye0Yhi3oSw/+Cf+WH8jb/5HwIALq9MXSJZB+FtIT8sXW67b+u8g2YLrSGlaeUULRqnKzwLdJE3f5xAeFkUW9dvI3wEIbS5TjeORa9YFGEXAQFYdJ4VYc23UA3Sv6LIImU3RcRdL7AoBdmzVfbaKp4vNvBVpfQ7GZ9WJNdFHHURRbLHKLIcFT6Y2tO0EMCbbVaWpaLzhIYqZhBR1NPnUMREi9YXht130/7cfG9tqpT0R+3j+o4cbKLLZI9fFAWasvue0jTd2k+17yvtkLVoZ4AZi57WtXumKRuLHOn3urIMRVFsoQAFENMWYRct5Taipi10bNrWIj4rRWusOnVp28HLHKYGAOOxpWI29pnbjAHA9oVeFOm7l2sJLbIt9L2JfGqjeeRvKvTuuZ21zTyLMANq3ZvoGGwhBTdR1krnE6FxtNGO0O9sCv5vipcDtm3jON7aF7X365sIUul7vcGwM+YAK00Qx3FnvWvXPWg85CK6vmEtn01n2Ofe6hZFqM/PDWLlD7/6ZXzpy/8KgEUIDUjbeuETH8dnfuAHTP1oC+9zHDi9WOnDGcdJLzWfT4oVYp4jTi7MfP9Lv/LL+OLLXwUATLifuHvXnFs/9QOfAbj+/fc/99+YuhPdc16v8eLdl/gSTHtcTs0+ZLkukCbca5Hiur835OcI7spQnUquOSv2y34UatvofprPcnb+BB5M/aaxaY8x38NeWiMhUvCSbJz+gdlP+IM+nr71HK9vqvtbX/zn5h5NgQHPEQnPm8dHBi1zOBnic58yiB+PY/GtNx9gXZrvXWbmWf2+7N89fP/HDOXrL/3kZwEAd268AAB4rrqN/+Kn/1MAwMtjM5599pf9xMcDruH+obnWUzB0t9UkwmHftH+VGoTVx176k+Z38wn+r39j3luZGLv7E1IS+36IswuzdpcUnH/u058x12yAOYW7PYpEZ/z/YTRASVHpN2losyaN/vkX7qrA+H/1Mz/bXXhbZYcQ2pVd2ZVd2ZVd2ZVd2ZVd2ZVd2ZVd2ZVd+R4rHwoNIUe4t2gwY7RxtuxmF/0wxjGFksUSsGwJ2O1TY2dJDv7FlYnIrdep6ijI74IHJvOwXC+wF5m/SRQX5HGPxiPVMSoYqRWNgtViiu98y/DxDm4zC02u5XS10uzIISPCB0SZuK6DuCdZPkFBCFJlplpFYhgZeI5mj7MNFIvneXCpLxNSVHPMaO5gGKmIsogSB7HNXkjWWDRGJNtbNYFaIItwaaHaIxaBtGpp+gDAqD/SrMqCqBThXt+4cYLR6HkANmvtMEvrVVYUT7OuGu0vFZGkmaimwZLRWMmWrJeSYS2V3yzIIM1MuA6OaRUZUKCsTw0n3x0hT8NOHaR9wjDEYNRFUQhKp23vu5lhSlMPJRXk9ihq7LkiipfCY0R+xT6ek2fqBYGKgK/5tyfUwEnTVPWB9sYWXWPqliLNTBu9+cBEm/PEirSKBovEf+fLJUqif27RPleyLkEQqO7GwCfKIzb3Wa4ytWcf8pqqgQVgQt6r/Ntb77yr19ZsDLMYjy4Nam88HGlWpjcQXSeDZLiaTuEEJqpdzky7j08MJzYe9BVlJPcT5I7rePruJ+TX3moa1SjZzEgNBoOOXgkAxKFky4HLy3P+zfSB0UDszH0dO++8Y/REPGaMyrJEzgxKuz8BJmsldbU6FVYjpeB9xL5Y9HEMwoKCudRgyQhJml5eAhemH8l7F8TDcDhEn3U4PaF9aGF1cXLNllpx0r4idYbolGZb+FWELYzFs4i3m/5bXLNvO/Y9yTUFVeJ6QMT0j4hyr1U3KdB5Y0kkZLsdFT0kQoxE6WRZrnOnv5FBD4JABa5Fh+R6OsN4IPPhjO1hbY83kRVt1NGKKLs5zQPawqWaYU+svop8+oLCSmmOEFrNmLboKQDtS2HYwHOpTzOgLhyF1suy1DqLRtKYc4Xvh6rhJMgwRVN4rup6qEaVZ5EJ0w3B5CiKOjbR7XYpikL71cmheb+queVCbbclg+4GnDvRqECvIK3aWjOblsTtrHd7zTB1F9t5H86G1osk4LMWMkDGsPSzfr9vNSQ+QEtFEW4q8p+osLofWpF3AEiyTIWwUyItb1In7u5T9/Hy174CwCISvdb9ttDbjmT/Gzicy8WMQZL6SZJsZcf1Ok6lGjTSR9toAFmXXNUMaWvMEBXSdG2gm6ZSRJzMO3VdfqC2kT7GBvpEStvGW67f/o4iR6tNnRqvhaDrrsWO41gB/qqLPPBbGl6e390DOA2Qsz+qJhP7TgU7PuMg7lwzyTJFmVrkSKhoIRFDFw2mrChRcQ7XPUkg2mo9rDcs7NsIlU2h9HZbeaKnA9Gas+NTNYO43ioyrAHKat25Vrv/b+reSXFdV1HLikLROnnb9uUtdI/0HUVABQG8wOpdtn+XpKk+B9yNvl03LSQSOn8rysKKxHer3kEBich/G/lkRaXRqZPn27VH0BR5SmOP3kDfiSBdBYEznU6tVuVU1muLXJV9wd7kQNtFEMpyjlGjAFhEojyf1D2KIniyV0AX0eW6roo0h47sCRfaHnrGEN1R3yK1am1v88xL1jdorbEW0SzzntfS/zIlb+375b1tokvqut5iSMgcIPUBrDi6HwZ6phFUaxsJJe0oukSCji/y1N5bBLE5n/iVA4do5zVF+sVoJyoqDPm7f/Ir/wcA4Of/u//WXHt+hSXn5iHfm+wr4i98Hn/jp/82AODf+cs/BQC4esdYne8FEZYUaRakkDzz3t4ermkOEt00Z9m/9Z/8NP4yTWNEzL7i3B72e+jxdX35C78JAHj9gdHRPRqOcPbY7M8PqW96RPMl1DO1Ns95DoxKajyez7GgXm8UcQ8q+n55gao0f5M+KvusfhQjW5l+v5L9BNfRRQ2c07TAD6nrRFSs1wSYPnoAACgG5j1/6mMG2fTKt16H23CODc27BefxvZMjPHzbCFV/8y3z+2A4xK1js/994fCUbUYh7tDB+ZVp9+O7LwIA7jemPWezN/GZnzK28U/BXH+embb65W+8gaZPNFNuzgCXmVnfvxPk+PSpuf6NnILOrO8Xf+3zuBqa/z6NTfuHPG/FYYxbfL95z9ynrqi92Li4RRRUOTfXvPu8QTbBc/Er/+zXAABf+6M/Mr/jQvD6y3+A5541Gkf4GfyxZYcQ2pVd2ZVd2ZVd2ZVd2ZVd2ZVd2ZVd2ZVd+R4rHwqEUOhJZsjFkFo8kiWUzEqe5yiY4X/82KAmxCnq6upKuYrijhUzcnvj+ES1Xvqeidb1GK33fX9Le0LcgvIs02izRFwlg/jyV/4IZ9cmgrl/Yviah4yE7h0eqT6LOMyUCdXz/QZHtIqrmCU5IwIkyXL4zGZKlNQP+zg4Mv8m2SOJZK+SBDNqmsxoDXhN1Mx7j3Oc01lHbEclQh+HgbrtjOncJJH8MI6sDbBmxxiB7Q2wT8STICTkd9fXC0x4rdND0w5xT7L4QCD6KFE3kzWMI8spZmRXsrzLdYmqFqs/EyFP0xXGVK0f0SZdTHt814NH/SflsfM+aZYh8Lt2x5JMC/oR+mE3+xaGYu8ZoCFyIyG6QTRwnDgGKvM7yVaX7expY97bdE7r1IYRcM+Bz5tPmd0RjZ7+cACHY0Gy0E/dexpa6m7WQ5zTjEMdUUdEMDmurxmRPSJuJPuUo4ueAixyx/cCm5nn2FP9mKZGxr788quvmZ8zS5MkqdqQn56a6PuNuyYivVzM8fCh4bQ+vWf6nugn5eka8Ybj3tm5QQ8Nh2MsV+bf7jKLIciRN974NiZEfYk2ioy7i+sLlERfPJpZ+23R/BCtowU50/2+dasSjnzpMUPk+5rxlWyV1bKw2S3JHEo/6fV6CDmOC9ZlvTbZk/F4rFlZ6TuTkckqrNwVELv8N8nQi4tJqO9GECA9ugCORiMEYTfL3dZYWrNfeEQZ9CZ7ml0UhwvJ5kwmE4QbDmmqOdJyZWk2svGe52l/EtRRKdb0gb+VxazV7aZuaU7A1Il/W84XNkvNd7NKxOklsS4wRFw+fmLQXHt7e4oIjdguqmOVZdYJSxx+ohAJ9ZKeXFiXQcBk0gVlp9oyzEBmxVrXhzmvL9nJwWCg/aIoRMum1OuUl0Qwce7Lsiu2T40BUVRTZv9kbPi+i+PDI/63uKWY7/Z6A802SxZ6wTViuVwiZlstxAaX6IMwDBV1OKITjWRRm6rGPsdX22r8iOiuTWtjAOiz7lVh3xMAxHHYQoGI5og4QlpE24JWq23tCem3sm7KPBRHkboUJnlXs6suqy2kiaxnYRjqelvn1OTgnLtcWt27cGNMtd2/BMUchj4CJ+h8PyMirMhyBJwHepFFtgHACy+8gK98+XfNdUNxkWqhgDYQNJrh91wZZlavg+tbmqZb2iYqqdRCHYlTn9fSALG6Ql2Njk49Nua7oiiQMGMOLnVBFGyjk+TncHTe2NQKiYJwy6WqXep6498k05/XW3oiiixvrWfbaLbEfk90rlxBqrgI+Azxhn5PGzkiGkLiMrc/Guu1JLOf5zkCoizEDlv2tfPZUnUyxZGq4RyRN42uaWXRfT600DUiXCfoqLLI0PPpKsZ9mCBrqrpBTSvqPO+6SXmeh1A0KtWm3SK8NjVibBt71h1O0GnqsGa1mDZdnnq9nqIjpbQRa5vacb7v699FL0/7Zt1Ws+pqWAWVRU4KQqitHbWpWdZ2D7NOYESqEGWZrKz+lGo48dp5S5sqq6iXQpTIarWyyEK6QXG4IQgCbY80Mn1iuVx2HA7lewDQj+MP1K+U77aRtO3nK8tS36tcW9aQoii0rwgSNRCtRdfVPadcS+aBwPNa2mPiGsY3Urcc6ri3BufLPM/R1Fb3CIA62YZxD3FL/9BcM+vouQEW7WjQTYIO7WId2o6CUnfZxzlOo2t9RWSQoJhd19U1qk90SEyU3+2TCf7lr/4TAMDP//zPmfrRTXA43ofTyJg19bt3w2jLTJMV/vGv/CoA4DOf/SEAwM0js8YWD2eInjFr6+I9c4brB6b901WCEzJRHi7oCnqWYjwy+2Bhc7zH8/H7Tx7jiPvEj33EWJt/7Q/MenM4uImU42R6btBJwtwoKkdddidHxhU7XRqdxOnVGZqQ6GruS3nUwTpZI+G653BNENfCqsxxyXlun2GHfcc8VxI06hotmmIr7vOdqzX8Ifdenmn/u7eNlu8LTz+H194054r7zz4DADg6Mdf8yEv38A5dxj5605yhPvbi9+H80uynqqCr01ktEkVxf+lfGO2n07/91801n7+Fz75u1vp/7+/+PdP+MG1+tmgQhOY91aG5dnrFPeKNQzw9MfGBH/zkDwIAntkzbderTvGI25vvf8l853hk+lxQ5XDE5Zv7Tp/Oc2meo8exfpyb890//RefBwD8lz/7X+NdOuuGY+6JqEM4Gcf4o5cNaui7lQ9FQEgmGbg1wg3LSBE1DXsh4okZNHrQJ13o6XtPK81FLNXlYF2khU4AJ0dmoH39XfMy5/M5jm+YzrKc2YkUAPq9WCeJZ49p280Z/+hgD5czc2iVA+SEA/VysUCfm+pGxCAZCOlFHpbcRFaEp964YepUNsDVtblf04JxLniglUVLoNG+H2LIAM2YAZ7eSCg0a0zGhr7T5wZENqiB56uNKvcyOCFtaDLuKSx6nXa/0+v1WqKfYhVs2v/Ze89o2xQU7coIr5xPrxFiU0SPAZ7FpQYdop5M/NzQeQUmY9OO+5yoGgdYp0KJIA2HG5giy1VAVBYYoTfVWYYFbaxlopfDc1mWCg+Vjepsag4Dj9Zr+ISEuxsH1mS9tuJ+rHvs202RExKGzHYIXAvBlgCD9JmDfdJc8gJXhBGv5ittd/O8sW5OloSNyjY9DB2ktNkNuYAWVYNrUlhKdAVBF+sMcwZdjoakD7ZEVFW4lcFZoSbuHxxixjH3mIuIwM6juK+T65Mrc6B7cnnFtrN0sCkPo+magaum0f+WMb/PA+/l5TkuL821HtHiUg75Td/H44U5/L/6rrGClEDn3t4eEo75/QPz/bqqdEMqYtcC94/jWA/A0g4FJ+s8LwHf/G6yZ64lgbaqbJCn3U3u0chSTTapaTJejsf7+i5VTLYhzdGJFIorNE/ZI+d5jhUDZCGpNhKEG/V7CEk7kWAnpEmYAAAgAElEQVSH0o2yBHVrYQaAJxdL3TzKxkg2d9eXFzr3CY1HLeL7gS7MssGSw/NstlAB3KhHqDwDuO89fKSbYmmP62vTB6uq0vazY0oobicg8lqDHX322aKqEcXyDlknoYfUlS6qWSa2vqQRTqx4p4gR1nWFIcfxs88aimtbwHXzcNIW2b1inz65IZvIlk3vFvXCbqj7e93gTZ9jdzgcqnC//E2CnvP5HAtS5yRYJH3p8PBQEyrWFpsH3cEAOQMmQjFWCkxZ6xwofUIoTMNBD7N1l7pb1/WWUGz74Ch1djYO4sbK2/y39FG5r3l/FNFsWRlLG8gzboqhtgWg3UDa2Nx3nWa6f5A6CWUHcBHxe0P2HZk/2gK6Upf2exeKmc92dBwHyxWFHrl2Cx0hiqxIqAp9h6aeTz31lBUglrblfBBEsY6FQgOvNgAr9GitF4MDaZpaAwp0rZfbArpwhDtjD5uyrsunzHNZlrXEss01x3tCRfS3+nhVVVuHNmuBbanW1j6aezw/UCqzlA8KWG32vSIvWxQ687swEHFoa7WtB+KCc6dX6PtqJIjAsdvU9vtr2Z9yVLpN6yDcovsAQDJf6q5a+qXnushFAJ7fk/1HHPpwGYQScf6CfSAOI3hel7Io7d8Wr5Y5RYTFqyzFPCe1ncFINxBR+wAp5985rZpFSH48HiMMu6LLjmv7UFu8F2jRwxxX93Y6Z7K2eV52qLcAdK8OWHMJTbYE1qZe+qGUILBi3pv9BGiJHzddymT73g0sDVWK1F3mmDZdVGi2dS3jpP3sEkAVKiPbww3MuAewpqi0PNNkfKj3kc/ZzCYebNLEGofonqTYDtQGG/Q6oQ8XRaH7YBW1F/XwxgpHSx3kTADXRbghVq4B1Na4lrmpIc20KiwtT/YrPbaB4ziom65hjiRv67reuuYHUUrbBioS9CmVimv6SVnUW/sI+f+yyi0FkfIgkoDwA9cmKtit5Jph0MeS7weBzNVMtqQlfvvXf908B/f0wwOzx2lqX9feivNNmZiL70+O8B7pY+8zgXXjWbO+x7WDV0hxenZESj2DJH0vhCvggr5p2/FwBKeUgBiD0kzKj0+OETPo+NKhOeN9/Ssm2PHm2bvwQwkUsq/VQuOO8f5DU4eYxkrXUxP0iGMXRWzmiCXbb84zwXqxQI97Ltk/LOX84wAVKV9Pe2b/Le1ztrhGxGuqSD/rFtXGbAcAIv7bgwdvmzY7vYtn7ph3+i7lKZrS9P/V9C28dMNQv4Ty94VXv6bGRgWpcAIW8WIPGQPlr/+SoZpFN8zv/uozfxJXD8yz3nzmkwCAISlqz+UzOLXZD8xr8w73nzMxhfffvsQ3v2SEo9//umn3iEH4/vgmVpxbP/873wAA3Dkyc2GTLeC4NDDg3kQSMYEDnHCfcvV1c+75wpcMHfC6SOCOTb0uEkrucH26TFKVAfhuZUcZ25Vd2ZVd2ZVd2ZVd2ZVd2ZVd2ZVd2ZVd+R4rHwqEUMqIbRiGqCkuJdlxjwJbvuMgY3ZcrWOJikjWC7VJl6yF/L6qKo2Ql42JNA5ooedHLq6vTIQ2jpgNo791mpSA0LMojDp90/z/8MDDMy8aK7yDUwo1NSbCfjR24TKr0yONSmBzgLWltogMwiXrBofMyGlGoCrhnR7ymSUT26VwmHaQSDozrPdOLT1ow3o5DKNOJq/9+fjqiWbxpd2LgigTN0WRiTggM+6EQWfp4w8QiDSf4/0hEmaY0w3rT7fyFLEjtKSU2bh1kiOv0077jUYTgKKHi9RadQLAMl8qqkGoL73QZNyDXojItWK/AJQeUpaliuMKjF5QZmVdaeZE3onvSUamh1ipZoSPs108z8MoMvdLJCHbyrrkDpFMIqYXE5I6jFARxbBYmkj2mgiNLE0wJRID7AOaNXcdNIQGNkQmeU0Nl2OpKSTbYdr6cDzGkNmS/kb2KW8qLK6n/J3QKU2dTkYj3D4wffTp2wY1J1kW13X1eeQ9SNY0iCxVRDIILrMYru+10Bfm8QQ9UGYrHBAFFzAj0s4AuQ3bY0SoMtFj77/1niKXhkOK/8YRQJqhWoEfmPYYDweIZDwWIl5o4fGWDmPuM1uYjMBisVDqlusSXbOwqCBBPwiioM85yZ82mIzNvSVDveTYalAjZx0WyynbhWLWoxEGPclIm/luthT0S4SA/bEg6uXRQyNy1+v1lB4qorduVStMN83MO3z73XdMO5aljsu+tBlbIC8itTCWcsTs09HhzS0re4Gbn+4ftKxI+Xn7BttnigtmyqQ9Dol2jH0fXmTRBQDUGnlvdGyFsVV8UjLAdi2ImK2SdlmtFmo17nHsPXnyBF5h5hKZUyqlztSWqst15pymBQBQe13kgtCW26jKSlAGzMIN4hj9gXmXaWqpfaaNV5rNHA67dJXRMEbB8Xx02BWFzfMcWcq+Jhl7sdp2XEThBuWU4612G8hWQC22iUq5mi1RqSW0RcMJFdCiNrQ5MBCELPvt0dGB1lP6sjyfZvVrB0PCoosF1zH2nTKokLCPSteT9zZP5qiWRCAJ0LiFTun1uqLSMn+YezMbX5jKv/3gLXO/sgQcoRWZcTAUe/cs1fcWEXWBpoLnE1XADGcsHrmoIKPHaWQtNte6eecmBoSJl2J7TpRYkiQIwi46iUBFeH0fOe1kBXkaEMW5uL5SpGxMqH1TtfYATRcqn5PS4rqeItQmnHPzmqiBvMFoMu60o2R3y6pB5WygxFIHritIFlkTzHi5ns8UeSTrtFC/lisr7iqfZW6pPdIOsu5JH58MPKxTQZzJvM11Pp2jT+RfyLkvScza6riOUq1Rm+e6phB90zTohV1hcaGze76DpLEmDADQCAqrrrX9c6JLQj9Alst+jai3ylqk11WXwuLR6CSrKjgilk+kjyf0oaJU9LbnibkIN11uhBXnCIITMCCCrMpLBEp5MdQL2V+5rqtIIr9FpwGIsC26e0kfljJfh0T4OJFeCwAat9Yxq/2jJSwvMhCK/GshnzbRh1WRWfoT+YlKm3Md6drbSJrGUmAdtlXK9vEDHwHHrkxi69SiWWxd+Awb7QIADoja4Dh3mxwl6bhe3N0j+r4P1xdKvvndYGSNA0q2jS/vNLTGBCPOQYFrkTs6/7I6s6RlvCAUULafSGX0PU/bXamWRECFnq/oHfuMQuO0+AH5iyJwyhKuUtXN71eZRQy16ZYAEBKhGESRmmbIOuOJKHJkRd8F1ZeXOepy3amXIB8Cz0clqFehpXJdynNgKmPb6VJq68ZFj/v6QWDaSJDRi7rEHvd2PueduUMEbNDHv/9Tfw0AcE161/yY/T8F9rieX7jmncyI0D2qSkwaMZYxe+XeoZkTi8USz6RmTYh5FrhIKBodDeFyrs2eGGpQ1O+hHpv226fpEjLz/8O9CWqasUxor/7ZHzBW7N/+pVdRBUQT8zyWkL0Sux6euWXobanOhzQqqVzkK8MOEEOfnLT02A0QillBbuo5IPKnKhv0M3P91Q3uezifBKGDJDFnbLG5rxL2hSjWcT2bmveeE2G4evgQz52aveejpfn+jRcNdQwPHuHlrxnkTURjn2m+xNFt8/3hU6esM/ebvQFi3qjcM+/pxXvmO+M7+3hjYur6Ut+02SuCsj68g1sZTXtic1ZfE9E0W72DF+9+HIBdQ6ZkAfX7Q92ziplUosYjPRSVaYerR1P+jcjtaIDLS3Otl1/5QwDAmdCWwxAlz2PjnhiNmHv0gj5mM0H5/vFlhxDalV3ZlV3ZlV3ZlV3ZlV3ZlV3ZlV3ZlV35HisfCoSQFbhttjK+bd0Cifo6EmWmEKPneZo1kuKr/WCpVolyTRF5bWrDqZbrt4vnOyomJpoeTz1l0EDFIENUMnPITLoKZw58jbI9fvyYv+9mTfiw5lmEDw+nlY1gZLxqZ3O7Ym5te1+bOTevc7FYbIksWr5+y0qztJkQwAgPXhLNMGAkdI8aLE3TaPYtjAe8Jn+flQgiaxnbaU+vRuGKboSJmK+obTMeDuCSe1tv6B1EcY2+ZhUEFeKgFxpUgbS3SzvEQTCCx8ytR7HnYm3tTvuTrminRUy5LYQQxa5hIu2j0QgeedCCHBHrctQlGuq+iK11qda3hQp+q+invNvK9mPVYLm4ZPussCRCTTQb1EY6ijCkLov0xzV1KmazOcINK9lef4DBaNy5hoiPV1WtfUaQN+Oe4aWa9pb+YZFtAJAUJZLCZvcAI+YtvxH+b59jccAodVYUauErMrHy/tIiR5F3hRFDsWwd9dEXC2pmgWS8lWUJl3mq/UnXjrWqGm0/0VJpW6KLmLHofKTrFUZstzE1WETEMwxDm52V7HhL52wTeSACwd541LILNfWThFuerjVTL/oDYqm+WM5bWltdgdSiKBT5J9+5c8tkJfI814yN9NXDfXLJx2Odg0TvJwpDqwVRk7tPsXjHcVo6CqbOcl8nXCKKmVkKROPFtHUQBAhUq0Kej1xmP0DAd+Gyn3i00hwN9/D0/WfNv21ojwC1zqNPLkRsXNBLfYsmY5+Vv40GfUXcSLuIHkcYhipOLu1y7+lnkK4ohMhMnqwXYRii4vuqmQUe75v7HBwcqJijvG+de11Hr+87XWRGXVaYz8y7sPbq5omrskJZCHIj67SH67ooq7TzuwHnKB+Ojivh9UvfGQ+GWKy7mhxlSyBUULeaNRXR1azUviafrutixbaS/i9IsjRNdewd0DpZ1tuyrFvIWHP9NetUFAWCVRc1O89M+5R1rSYFgr4QM4E8y/Sa5eZc67o6/0o/scYBoe47xDK+P+iiEAGrv+EJKjlwrUU238liMYfLOg84nwoyrigyrUNM5NiC847vhSoePrsyGVJfBW1DOwZETDmw+iyK8i03xZdd7X891nk2u9L7SV1kbEhbieYJYFGfjmfXytDv9g9BA+R5btHBnKPDgRXelncjWjFH+ckW8kPmpKapdIwWRVcANkkSrfN83t0rjPojZKKPxT3QgFoKoesqGkeQe6P9I1bXao5Y/ROKIQeBRS9z7ru8NPMPKlfXANkTCXoITaMIOUXzVBV8QeNxzRIpq7qurP7Qhp07qhqOGGVwnVUseF3r2quGCIFFy1jRbxGQNs+XJon+t8gwi9bicDhEnsia3zUMaJqqhbjZQO5UDWbU+hMNM0FZRlEkQHs7v7X29CV1O1TsGRZVJsLbZWXRuqofSrZAIoYXVaN6TtoeomdUl8hVA6yrUxYEAVbLROvT/n2a/j/svUmPbVl6HbZOf/voX5svm8qsltWxUYmSLKo3KEoCBBieaGTYmhjWyAMb8D/wxDY0sGFYkG0ZkCBCkGTZsiiRIlFsiiy2VSx2VZWVmS+b18WL5vanPx7stb59bkSR9jCBunsS70Xce87u97e/b31rbQzde3zqODk98XEguinEHEwT4ShK46lZPlvstLnPl6c1IcGCKIrQcq9cEpn//fjaot7/b/IeZZS1TpIIBBvZ/NI+0HWdrWfNkzrnmHQVlquNtR/w62wymfX2b/JrcW0MBoMeKber39nZibVZe4rx+JRC/U9vcQFJ6KGsNrdEf7oyQcZzSJyM4llNkswEVHS+nHNepnGAKff3lGeJcZAVWzSVUPtE1Lquw2Q4QcR+vLp0aJlm6PpgHazxjd/5Xddv4mhkSsB8s8Zh5N53OnX1PaaMeZS3CGgfSTb9QyKMZnWAk3uvAADe+9DxZs5IRLxczhFxjr39juOPef7RhxiSg/T6mUMNPXjV3VPbLMUJ7wynJPH+lW/8JgCgHsR2jrXiZdfeFHRIyTdVa78vuS/XDUqe2UJyiVOy7Trk5Fdq2IEEp6LY5jg6dvM9p42y5L5ftR1Snpcte17oyq6u0XGuiPOzT1J/TeTwm+Q6euWQ4/+ww5PQ2SGvE4n+E6f3AN2BmLXScX99cfEMK/57u3Hv++a3HALniyfH+OGvOF7Jb7zn5tNv/PzXXbsmhzhhY5up4zZaLB2qJwwDYOLGTui+w1ccgmk+X2BMIvETzjll3Gy3Wzx94YjBxU/7iAI9J5MDy9z4zu8N+RmigdrKbFCtF3HoTccze9afVPYIoX3Zl33Zl33Zl33Zl33Zl33Zl33Zl33Zlx+w8rFACMWSgKtrNK0knhlFZm5nhMCz0He7ii2AjyQZEqb1UQghhFSUR5wkGYaUW8w3u2iIrutuRegVSbz/6hlwtcvQnkykvOM5R6SIdM0oQ9hTUFDoXfm2VQ9hIU9tVdXmbb+ZV+14HHaRHN2NfHO1H/Ceeec5ZJTF1MKcx/bu6ZHP1WX/v6RiVJkX9gzVZbNx3tZRmln0R0FNixwnQ4yO6aU+kuIIuYe6xiIw4swZ06OdpjEiqqpUDHVslls8mztPetJDaahdialXkHdK8uBZfEse2aOjGkMIeZlej7bZ0AM9JCpHOa5VVQFSJ+NPIVYQhNgKhSapS5uP1S2Fh4iRraYLkLFvDhk9UoksNuiRGZ7boELCZ0p5aLG89lxK9CAr376qapt3mgPql81mc0shRoik+XxukZ4ZFdImJjFdY0OPfxh7dSa9Q/0fNh65BzgJ5QnzccUtoXFwCB5Xh6trF/0Iyd0QBjEOqWAVRGq7ixSVdW1zesto13A4xJYcT0+fPgXgI9PjwRDjkWRsXZ21dpPEK98IISFv/9nJkUVXVZpSikoBis1uFF4KJ3XrFdwMpcD876I4tvG6vL7a6cfhcGTrxRA7jF4NBgOERFPNxl5CFnBjE4gbgkiVum2QcZxG5KzoSz6Lj8g4AoigGgxDlIaS07x3Jeg6XJy79Rky2iH+nqD1aAsfsWTErfXRdMur5mfG4+EtPjTjoxoOe3uRG2dF8cdZYvXz+6JXkxHP1TXzqg8PDxFG2m92uY6K7QZJfUMph8QYl4u5jZdQKFLF6PNPmaKJVCkPDvDWJ1zUSfNQbe667pbik+beyckJtvkuF5Dk47fbra1ZrSWtg++99xh3KBWvOZdlXo74ptKLiuMlqljPBd+7qyoGOJSMe1Zpz7i8FGrA81FV5Y1oMKWNJ5OhPet8QXVCRt66ukGcapzd98ZST2oCm3/i7tOKDIMA8Q0UUF9uWfPoJqKgz4dnLBo9mWqv8kOERjZGQMjSiugf0A4Zj8cgaNCjlDgHpgcznFL59JxSwYmQNWGEQP823hK/PrWX11T1DInaCDqgIMrjmN+TqmIfbaD5oXbluVcSM7SM1LKi9JYtJERR3TaGUkqIyq7CAgLMFOWu/TIcZtbfmnMH5Deriu0NFLUfr+PDmf1NdZatdzVfm3pcLdum8Pxf6ncRimTkSorIn9Rvl+q5rUtsyKkhPqkslSJZaOgQ1cmQO12DptlVBmubxiNVpVQmLr0wNMWxjp/v2zZCvUkNRyQpTdOYOq/2qzVRpkVdYTQSyoX8gZwLw9EE09muypt40eq6xsULt3965EnGn0Pj+BKiQCjXKIpwGDiEsfpa6qB1XRtXnfYwj0LsPOKmh2S/Zcv3FB41Pna29s4ZzT/bEQLNy8zb8NEuUhPo7P4hm0jS6JNohIy8TGFAVEnslQ8NGcp9R1xYiCMMeL4fHE6tH/RTe7LuBS+eu7U/m01xduaQSEvOvdFoZKirYbqLrtlut7YnC83T0PbdrP1aNzQ8ufiGaXZL0cv4DeMYbTez5wP+XEIXGvpY9r7dWdr21v1A5xrg14n6QXZqnud2fg7Juyb059XVtb27bcm5mAzxnP0lHsXDmbNFp9ODW3uDUCl1vkGkJIZ2tw1pEpnyXRzs3gWW2wJX566uycztGxsiytLjO3j06JH7G+fAlvZHNwwx5t1SZ39Em2EcDFFwrj35wNlL47HjqwnnS5TfcQpfQo5tZFOiRUzU5oMHDvVyenJg3HThA4eSSaRe19Y4JXr+W7/8awCA986d7Ts4PcSafJfilRzTrh0Nx4amL2RHU0a+CQI0/J5QQJ14CuMIm0Ko+8reAwAPz+7jzYeuftdEdH0Eh/y+mC9gapd8L7iu2zAy3qSW62sY+MyMS/ISvTp273nyrd8HAHzw4kP83f/MycY/+8C1+YPHH2JBTqXLS1eHCyKGBpOp8bWKC+zsNTcOf+PPAtfk47x/QFuGPLCPP/w2ltxTnn/PPVuZRF/60hfwi7/4iwD8HPD7qkfrTGR7sc0hAtSEVmleyl69GE8R8m4553ktpdyua4wzq38vBhzq7vsIM94qHwuH0HorsugQiTYVXpI99LVCbBdaSbP6DUjGk0ne9hwoOmy0+SkdKkoyf6Db5qmJWezIOwJ+A9quUgwGlKC+4zbwbcVLQNt4okG+VwZxX6J1h4WT9dbBbudZ7Tc2fc/g6lGAqtq9RJkkZ10adDqORzv9cnV1ZekSTb0LC962lRFLznlYXZw7stfRcGKQNh20M7bLpbvtHj6BCP7qHGteypUeY3KsowRpygv4sTModAg9/uB9M2bs81GC0yO3wT14cJ+/80aiDg/9jBNvmL28Xu/0oxZKHCdGkWvOOpayLDFgm9XHOuSapsGMDgkZ17oUtG2LON9NQVT/b7dbI6utsNtnSRhhwQNefe3rGfdk5pni0DOwjHRspMteZs/V4btaCbY7tNSykAR5ln4ZDqz/ahJqa1pOxkPMeGgETGMY0sgYjac4OOTfvg9puRnalr4juHtm817w9oLktW3bmTPqM286EvdLplbM59cIOQ9FzKrPZlmCYx6mLS8rcU/u+BOvvc7nc4y6Dj2OSFfPwpNlewl2OiZ66ZE30y5H5oztIJXXkFDUqvQpRec0HLSnySgfDofeSXRDDn4ymdjvYo7XTspeq5QZOvQgotMGpYhLafiVm+2tdFK1pSjzP1YKti4GiJUuwZtBS9nTMBrg5Ijzip+R4yTfzhHyoB1JWp5Ot37/yXdvTsk4tLmq+vVTuVTWMy+RDTg559GNNGJ5q6MowpBSpEHuLwgKCpgscM+xpumRDnfT+a6vr7GgUfGEqW0yWKum9hLb1e5ZMh2P8SENlXv3nHEnB24QBEh54Z4w7VPfmy+WmE68lDbgL2HrlR9TQbBHY9cvHUKsmKrLbWPHgL/pMJdjNAg6S/9Q/drWGzgaE/+sYCeVxP10477abmxPF7m09rBtUdg+lQ59qgfgnBVrysQq/e/+XRI+TqZG7rzaOANJ+1XVc96o2MUwSez5SVLstL3rOhMYCM2OiO2ndxjx3CxyS+tSGp6lK/ZS2lQsFStNMKV8u11Q6dQNwsAuRib/3HmLzkhaq13nAwCsF64fRNDe74ObTi/9TJIYM64hfUbOZ8A55QCgpkNCaTJ125ggRMnzYjAY2XmkuWlE/r1UvZuS9LPZ5Fagx/aynqNIfaW5dHJ68H1THQGgiRsMUk9O655JB1sTmROsZur66MDbnVW1GyQ0mfDF0lJEOl0kudkXRbdzEVabq9o7FAGfphWG4Y7DQ+/W98xZpI1RKe8IvANCc5VOpiQMbO3p4mF2RBB6hwLHTXMgyzI8esVdZuQkMWLhyEuViwC90toPh+jYPgmGyMau6/pW+0x6PIogxulAkt5h4/tIZOoK4A78Gf7w1K1/CYBs8gJ5KZZt9ruCIHFowWM5zy11OsvMEeHpKnxK7pABja6Wo4D1bWpl1VngsraUs9TSwYqVCMZd8ANBgDt33T766mtv8H3uM5vNBlW9m7oIeFvrJq1FGPoxsTtH4wPVciBbyhmHsugKa7/23GNKWXdd16NTkDPdCwAo2GxpKrRKN5vVLboPo9EYDOwM0Z45mfh9r+Q8/OhDd26ul66v7t+/i4cPncN8Pnd7UVEUZg8oMCfH6LNnT3BFZ4POADlHTk+PkLEdCswpuDsajazuEobRThslMY7vc641Wp+yMzP80A9/ydXrH7u5d4cCCuuutjMhm0qwSDbOGG++5uzZ06kjb74/c86SMn1pNAkStNGtZDKKMWa625P3XX88fvdtPHnunBX5gkEx7v+Dk0PcYQrdt3/jt93zWzkMS3PoaqaJdDxNUlT5bmDO7qFJgpj7aU1hA/VLEAIB74Ej1vOVe67vHt1/gIZ3sDNSKGht4f3HWJJQuW1274huPjKIzLrnDLiFwxjU48AjOuumPJfO7p1hRPLlX/85l9512VVopxTiGbpWj6bu/n40GOMBeAfKXN++/gn3t5PXHmJ07ur87/75z7h+mbt6ns3uIm/dWvoUnb/LuQte/9YvfxUDntkTpg0qxTKfF7Z2z3WWiog+6AlLESDz+EOXjjaIEwwZYdLep8+GcWrjJYf8y5fuvjQZz/D/JyFsnzK2L/uyL/uyL/uyL/uyL/uyL/uyL/uyL/vyA1Y+Fgghla7rLLJRMBovVEnbthjsBtrMy52m6S2kT/+ZN0mUR4TyRVHi4ZBCCCnqUkcG7xVR7L0z563OZgmuF847pwhTAUmOdwiDXWyW4L51XZunVZHHfjQo7HY9+lmc3SKH7qd1qMhD2CfbsjSVbrcuXRj0pLt9+pn7CQwpMZ7ecZ7le0TuDIfDW4SUQxHVbtaI0l10TWvR1xjHJwesyy45XhA0KOndF+RbxIiPBmN0bMOS0tqPHz/GxdUH7nf5bgpHFEW9SOpu9DlJEkwZkVBKovp1Pp9bBEbEqH0pzbpS6orzQB8dsS2Nl/xcMzLdJ6wOeqkJABBnguaOb81VP5YpzghxVXSlM4RYL/1Ec+/KeaKbpraop5IcmtHIojgTjqlSs9oW2BKVN6NnXfjZJAzRsn6HjBr10xUNkhvcgHOXxa11ZiTRUWxRRaUEmsR26aOwsUWrXX0LNLYfCFatqNWdO3csQjy/clEuwSXrukaWMnpJIrq6Kiz6cxMxGAWhj5gr0s+o1WQywYQS5bZ24aPyntCdxHoWVe4s0mMwc8rNZlmGgikNirxPRiKBS9EySqjfKS54eXlpdR/w56LykRwjfGXkQONWdS3Onz/bacPBeGpzzBOf+2ijUBcZo0H9vSVkxKbSnOmR8Fq6XymUF2a8yXIAACAASURBVEk8E0/wq1RLRX7DOPLzgXUPGRdLwxQN0Vqt+l9zsKjt+Qkj2RNCeu1d/X7g7/I8tzHpk223fOcV4cR98lshHQxlOpaUdYrR0EWGPvtZh3h64w0X/Xt5ceHh4jciuWkUI4lFDEzSd6YIjsdjnw5zebHThtlshu16F2WnuXfv3j2fKsmfWp8umiyErdIahdqIEVgasU8fce3tcFW6iK2i/1EU2bpU8WnLkcGd1bcF110QAqOxq+uGaKzV2q3dwWCA0zPKnRMFJMTD9HiCbaFoGBHDfP0i36AmqhKcc33S8Zvotz7ZsRHUKv2yZzsIUVDYPAb7Kr2ValYUjQkKaA9S+opQOq56RAswrWw6GXmyVEN2MTLaesSkzjNFSOPAn5NRJXZinj1BgIuXbq1Lbl52TJqmVofri2v0y+HhoUWI1WcPHjhZ8jAMLVXpRvY98qo0yLpsqDiOLQ1M/a33rtdrnz4ZCy3ASH/XoBXZPudXlfvz+mYKTExUSbFeotP+q/2b8z4KE7NTPArOI4kN+ci+EgK1aRpDLtxEmaXZ4Jagh+a6ex6RT0JxZt6OU7pV17PjTNRDe5YQ322Lotw9U4Vk6hMs21wQwjkKMVQqVUqy4dQjjYW+D+Pd9O2maQy9drOvGnjkiNmk0J5WY8D2a9/RGBdFcQsZ1EcOn9HONPL9JEFHfIYQ1yaA0UPaLVeU4iby5HAwwzXTVrf57pqNIo+67RKPoFafiZBcdVfpE2mj4HjBC5UovcXQootr65/pgVLqHUJiNHL/D0OHhgSAFW2wTDb3aIqQ7/vkvdfsWTf7T3DVMOh8yrpI74kQmoxHGAx22by9XRb1kIHsd06dzcaTtysVVH3XNB79pp9CCA0GA0NWycYWKiJAZCha2QfbjezvK09sz/rOpkQrtZGdVUni5nG53SITIkKc+6zDwcEBpgeHO3UveLZeXs93RBEA4EXh6TDW610RF0tLHw9wxJRbzVvtW2d37+Nnv/qzAIA1eL4QSZaGGVZMa9zynnVOlOuXP/EZvP6pLwMA3n/bpYylW0cSff/1MdalO/9mJMHert28fv5yju9855sAgH/1f/5z97ftEg3TOo+IBp4cuv4rVyHOaMMrFfGjDxyaKBqliAaeDLpf6qZEUyml24scAC5tqxny/kh7SQTqTVPhgHbRIyJ475AQu1wssbxytkyyZYo97/bLxQIV11KaaX0SWZ7nqLhpemEOnqNdgVqpZhybN193qLvXTwb4Lu9H999yRM4/dDhBvmVqe8n5mAhlWaHreC7Ebk/6tV9xaXbf+Ct/Ho8+4jnB/fB67tpSNEMEU/e3qwv3bJ3pZVH3kLzuNVnMO0HqkZqBITt5boSh3dt1ToREfAddiw3HJmLbWxodXdMZgs9nNfmz7mYGzPcre4TQvuzLvuzLvuzLvuzLvuzLvuzLvuzLvuzLD1j5WCCEUkbx28B79RUhaloSiUaxlzUNhX5xn42i6BYyqOl5PW/+Tfmsw+EQ25cu+pmRcFR52V3XWR4qHZm4vHB8Op9661OYMd+wzkSKKZ6LHMtrSfLy/b13W2TpRm54EHbo6EY0j20X99Aku5EbySoCPYRP4smK5ek2PpjO52aKA8Q+I0nSTY7tSlwmY/s84CLH4iuRZOU2d57sbORJ++RpX649emtM0jfjExFPRVNhQV4b8UEhYjSvA67Zj5ut8noToHNe8KdPyGHByN5wOLScYpXVUjnMLdD5+gA3SLaVA0sP9IoR6qLIcYeedc+ZQeKxKMKYXB5CU3StH+koEDE4+Q56Mr96d3sD9RXHMcrSvUded895lCHLXPtu8gk0TWWRREOc9Yi0RRTukT6BjauQD31paXmuRdA7n3vPtz4nuVi9bzwee4lmtrmPohCSLqEUZ1H5XHnN24gEs7WIarcLk88ec+3HkQiFR4hFwnYjQvr8+XPMFy76I8LBNE0tsnB5xXx+RpOPjo4wJppKyABFt7Is8RF67CK62rY1CdjO8suZX13XKCX5y7EUj88wHaKRrCafNRt5rgcvCS3UEthXnszViKbJfzAYepSkyJoJPsJoNMJJfLRTv8mghxBqJRfrETGBRft30Wxx1qIil1rBuZoNub7jFg1Jv2vKjw4kR77ObxHjK5LY1Y1Fgw3lJEnYqvKcEjei8n1+OBX1XTrw87i58Zmu6wy5kQ4U/WzQNqwfUQoiZp1Op/ZcRUb17CovELS7fCyq+9npqfW3SaMXHtE1HOzyuXikpj+Wq2pq9VMR0s8kuQuPfFM/Dm5wkNVNg8FgtFN3I7iNe+hKIQxbL3usNnj+nxbT6cFOuxQd7tdffaW9s2mqG+IGfXSr3wcPSKKqc6JBaCjd0cR/HgDGk6FxqmWMbwkZmabpreh6nytJ/TAcTPhMj+Q1wn7xatmaDA39ul55dJMi7Z4EVXwime3TJSN7Nsc7YDrSnq4ziP3ftWgDIfdYlxuoLPesXVRsGDR4/uKJq9/aoV3G0xNrn844EZb2uQn7PHcAsDWZ6978uME11QU9XsPe/O2LX+j5ADAaDm8hbgzxk8a37JY+mvMm75HKYDDwvDRqz7BPYCxuE0Zda9kFI9s/jSuN9c7z3KP7JJBhiOPsFv9a/wxXlLbTfGo9AXQfZeeelfjzBL6tgLOHy+0uek2UPjvky5w7ZU+eXcj683N3hosHD2FnnIcH5CezM72uEZugBFi/3Yize4/4Oon0qWqsi12+x6MD7Vsj2zdMgKWHsJuT1F+k12ESGhLAiCh5thbV1ubHmmicOVFBVd0gr27wmYWur+fX11iSl6Yqd/u4j5q7yfk5GAzMNmmFHiBhe1mWvT4hwb7qWRQI2N955dp6ILtiMkGUijxZnKE865oWBRFg5+cv2Wf1bXEbIaWiyJON4/bPKhcH2y45dJrFZrMGPOsW7MeuC9C0tIOJZIxLj8a3M4eS70HkCeK17yLwnEPuZ237jo5i2RWvvvqq5/3i/ij+zCgKURaeTxJwYyt+U505M5FuD4dmV1aCZLC8+clPGWeQiTGQi2a1WmFK8mXPbal2AUfkqATX1wFRLP/qn/40funXHGnwlmMp2flhFGIwdM/86NzV9wt/5icAAJ95/UtYbci5lbm6f+dbfwAAWKwP8Igy6ZuX2jPcuD9/+pEJevydv/N33O/On+C9lw5lFHA9nt11/KqbtsGL5+7OGnI/mNEevq5KgJL3MNENohzrBrnmOce74tyuggC1RHs4HydjkoKnM8xEEM6+3hIZtlxcI+IZviGCb8k9o2g7FDrrxSkdSlAhQsn9A9pXlY3SBRjxmWv2yzXt6vKjBb77/H3X75/+rKv7ujThGb3oguTtm7rEksTZ7YpcqAfub//LP/yH+C+/9FMAgHnuEFb1wLUr6Cpcnouzz433koTa2XCEkv0otKgh2pME4H0npI9Dd8awbTxnE/nJtCYChIa+H1KIpmqFpt0iSLqd/jMhhq62s/FPKnuE0L7sy77sy77sy77sy77sy77sy77sy77syw9Y+VgghBSRSgZDRMyFnd7M2wwjVJIkltSqlCV60VOLpPaUDGJ6fSOp0tDhe3AwxdPnlNm8oYwUdIFx3kiCPaFXe7NZIYxcxF1cAXNGBJLYR9+k0qQIRD+H7ybfTVv4iI+8enmxRZJIxWJ3qIIg8BF9iNuH0flqezvvuvV525LLNblHopuassIungnYsO1dU9vnJUndmqJBC2M6CcR4zjzKFthe0gtrzPXkwFmvDKEiiUZF9tCFpgghpMkbj15BUGsMFHVmjnEa+ZxpFiFCkiRBUzf4fiWOY4sCKbKkdgZBgADiBfIRA/2Ut3chuVeLIiemQGZcCI2P/Er+MpQXV1HUwcArM7A/JKkOAHmzG8kynqgo9vw5RCuUm2JHNcf9Q2iPyObTk2cvrK2uTrGP2EqqmW3flB3KlhHwiNHrwv08fzm3KKT6YUqVptls5nPH18yZ5hw6OT00/qgVPfTq5NHsAIdUE8mvXbQ7NdVB70W39qWS5Lxvc6bKfcRe+fZSqCsZ+So2W1OBUjR+ICWtujbUkDgrhGRqWxiiUJwqayLs4jSxiK8hFnpKO8NsN1q1FP9AVdhYBrX+wQj1IEXEftM8iWuP4Gk43lPuc301H8XkFQFerZc2P/rRavWnUBDGh6E9NPYKddu1G4t17jltJDGstkt6PAoTxNoi2ptR2gRBLM4VfobIh7SLgFARZqI1KvGKFD4qyTK/dpHg4bjxKINGZ4GPCkfk74nZvulkBAZqjHPLlF6aEqPM9fvh9M7O34pia/uaeIn6EX6LqHKfM+RIliHknllwjlY9fiLjbMo9F4feW5JTQJF+8b51XYfmhlLRgGpqeZ5jvdlVxNO6yXPPOeeRSH5eDYhImk4T1rey+VFVu0qVURzY3ix1mocPTuwzK9bd701C3fkocKszj/tjVTS29gIhMhjVXFxdI76hcFk886phWgOaJ+qXvkKP1LQMudY0XsHuhl3Qtq3n4uDnF4s5suGu+o4hGTYrQ6NNuW6EjBukCWYz7vOhR3sCTuWGy95kgdvCo7A875eQO0Jcdjh/4aKYi2sXHb6i0lFVVbYPTxn1VjvzPO/xuQkF5P5X17UhCtRH4vpK09QQxw2/NxhmePHC7fM35Xan44mNgc7+ly89GqKveAX4s3/QUwzUvO04Xzbb0lAoFuE37rPKJOJlE0qN8erFU7+Psl+OyLkxGPeQHNoHiGJZbTcetUJbZrt27czLwhC5QuyUdWW2ie15hoKIbK6II0pA46ZpDK04JDI0vYEsdxUUEslzxRQLIjhos+rZSZJgufTICMCf03EYmoKSmixOq75CbqT+5x5f5FvbgyqiUXLxvYWB2d+mTkmbN98s7XsBhGYuHIEWYCqiOn/bpjE0w6EUGXlfKIoKtWSYN7KDydGTRBgkREAPd9XoJpOZ5/+7wXFU17XZTGNyLcYZ+TMnnh9E58rJfce55ZCaQprt7rlt7bn+DF/EeZImCWZjt1eWPQ4mv1dqXhFh3/o7gLSoDon6aprG7F/bAyXrjsCQiHYniT2iQMivNed0o7kTJRhIeYyErkks3jHPj5oXrt+1r6bJCCcnDmlvaFgiGdqusOcPqUwltPZ6vTalLaHYiqoyVU71e1l4RUMh5YXSS7lvrK4vkbOvjB9RSLXDA1tDhhK1tdSipF16yH34V//t/wMA+Jl/8dM4p807IMJIqI+rcoPlM2ez/um/+rcAAJ/74o+7en40R0J7JyNP0N1Pvw4AOL94jvPI9dsr91yf3bvjbOBf/dov44MnjmsoiHjOjzyivOV96Tcf/7rrqyDA6YFbJ9GWc7oj7w+ApJYdsGsP1HXdQwMLvehRbBKbTtlnYyJsj6YTQIqilHdfBR5RumEdpuQ4mtxxnIuvHN3Fc3JcbplhsiZ3XBb7OV4QLVm2fu+b0v5dsA9esN5HdYf5Czc23yy+4z6z2eCDj9x7MtZ5OnHjtt3WuHvmlN7u3OX6YtLK3/5b/yG+dMepyf2bf+M4nD79Iz8CAHj1+HUsS/II0y+R0x68uLwy3kZlJUjl9/LyJSIdTEQ1xcYF1KASFxt57MT3lGWJU5iE59YNA/GYdobCFD+Z1sj9hw/w6PXX8P9VPhYOITlcqnZjkEkZihVhvoNhilYwQDl7En+oGimd4Ni9S+1NGfiKl1kHNebFhxO35cUujjwJp37Xh7kvrgixbEhAmvkLQr529TykDJ3gYoCcLuila6hNndVdqQq7cOddCdQkSexSIyi0jK/JZGIkodqkBaGsmtrL8vacRABQVr6PZKxpMc4mU1xSzlDQTqXJTQdjrDe7aTGC/m3yAhdMOZKzQpDcbZciHhNeGmvhuHfEYWCy0cNEBvEaCQ+NQ17QxmOSRTe1N2J4aNWNJ/xtKn1Ohkpo/drJCDcCZxHuJQbRlhyonAhZlnhYqS6vIhEOQ4NaZnKiWUpSa5e2ax4m6x5J7JLEc97YkMHvocN6b99xtVmKqJPpU3GwkzoBwGCgRVEi5pyeHp1ZvdQHMzpjb5JIzudLIzhuO8Ec5QCMcHK2a7wrPcbJqdKxSCuoanSh9OkDumRUdN6V7dbmzEzt4lLarlY9eXb3u5pzsKkKLClTag7DJDGHkAysGB6CTZQ0tkxRq1qf4peQ/DijUaL1k+claNuiNOMrtj7IF3N7vvu8WzfL5dIMfDkrdOnebDb+8srfBT34Z1F5xwAAFD1Hw830PxnnV1dXNia68JxfLm4RJWtdpmlqpHY3U22u5v4yZM5qzpPpdIpLyl0q9bfh/rVYPTeHoS5PE9bz8PjEiFFLXY6UQlN1NoZ9smCARPLKNDUCbRp0ixVGhDJHJCiUE246nRpx96JHBHr58mqnj+RwqevaEwAasa1Pr8vrXaN/04Po61xSne1iHbWWGjGiDKvW2Xq9tHV82Eu9AFw6YKj9O1cqLY37JO4Z5dyPOV/yskSa+VRp1V0/vXNVTrOB9Wua7KZKRlHkDXQaYrrUI2h9eguUwuINOJNXZ+k6L0ltKZ8XT9hnNNY2BQbcW5VacsqgS75d2sVRDrV+aqH6X7/TJaWqKnvflKl06pc4ivz6koRy7h0bES/Zw4F/tuwNrRutjcFgZmkZIgRWymXXdRjpnQr4SGa9bY20t2p2557r012CUzlH4hBYMD1GhN1vfPrTri1F4dMom12bAdiVU3bfz+2zCkxk3EeCkg7DKMCK6SOSsH53s/Hzl+OkPXCxWOycr4B3SAzHo+/rnAbcGSmbSfXT+mzCgQWgbqZtBl2DMfcBOWVEcn582u2kyQKwtKO6rs3xbw4XzuMkzhBxTcyYwhjdEQlqiTX7tKUxPhyMzH4wIvjYpxBYWr/sMV3M2gZJt3tRVQrZYrEw+01pudpPgyjC5I4LepjtyfNtxD0UAPINiVV5vgRdZynrN6Xiy3zric912WYbuqTFNQnxbwarhqOsJ2Hvnb/2WXo9NbZF4eWYzXFiKbWRnZN5ITEBOhGmI4x6UuYA0IqUvW7QyXKJfVqoij/jtE/5etZkr9+mPI8iOUIy+17RaE9jegaAmk6Vu8f+DFE7lW6lov6M0aEVmW+gQGdgjjRzWMPTEYgyQfFOScs3TWN3C62liE6j9WZltohsqPHslG3ubB8YMZ3VC9S0KCxd3LVdazAMA2xz0gK07vsPHz60fily2dgct1TCEIXtRYula8sV+380GiHgvezFC3eRr1vYvUD1bGovc681anOode07mo0s3SqUsEzeI2zXfsF9TuM1GEwQ0HHy27/5ywCAf/aP/ncAwPF4iAHvQG8/pwOczxyeTfDn/+Jfcp87cw6s+UfOQRQtW1zktJPWrq2vP3LOxKPDOwgY4L/iXSjn5f5LX/4xHFCg53LpztsgDZF/zzk8Li/d/lvSMVQnoQ+i0fbfat9vGtwZMMVp5eqi9M02z1HwexOmJZlwSFFjnHJfo81mFC55Zfuc7WVMedo0JdqBc05d0mHSVe6cms0OcXr/kXsPj7iLZ84GOH/6Ic7uOHtsXTGFl47NpA1Q8b70gimdR2euf149fIBN54J30+SE78nxl177pHtBxdSxyq3Fg9EhYjrZqtR97/LKve/tP3qC33rXzfOf/flfBQB86577/x/+zq/g4Ij9Ebizbkyn7GGcIiIZ+t1XCXChXfuVz30ZIfeiu6fOUXMyc58JEeD9D5140tvvPQYAXFE4Iy8KbHQ3Z8AiVgChzm3/2G7dWGr8q6rA+bkT5vmTyj5lbF/2ZV/2ZV/2ZV/2ZV/2ZV/2ZV/2ZV/25QesfCwQQnHai+IR+tzRw3h44KPWN9EdMd3hQZxgS+/vkNEmwdU3m43BPgt6xePOeZY3IZC2lBhfM2UkEUQ2xDZ0dYn5vbymNzF9HXcPXF0+IFxsvXRe62a5MrKzp9eMwAh91PqIahzskqcG8OSd85XzYB9MMpOUUzRCqRRdEKPqlCrm6r6id/r5orDIcN2KEE4EkV7urqAsYiWi0yhCS+RGxkjsTCkHZYCQqUMarzWRSS8vnsKKIIae8xon9DJPKIuoiM8n7418BKoTSomSiU3jI1KRkBwz1PkuMWGoNJQm8CTKRHeobPMNqlBILEXtnGd/MsqQ54Tr10JhKRpXIBkI7kxkACO/ZVVgccVUMYu6MrLXtUDNcWJ0URHkvCwM+ihy2BMicuIwwij10HjAp95EUYQ43SX2tPSOKMDBwW50bDIeWtqa0G/HR4SGxyFySq1221055jQbeklRRu9HRCmcHo0QcCyaYjfC3HWNkbUL4RYJ5dG1jhQewItrL+sLAG0QWFRmAJHFMZraNQgZXTw9FhRa8OoYee7q2WkOMNq4nueQr9sQGUlncHNLF+LPrm3QkSR7MHV1H1CuN04S61NJEpdcb+PpxMb8+NTN2zmRgy9fXls/VpX7nojkR+MBOqJDAs6n53x2kiTYLBi1X7rva8+oqsqI0y2VgpDXPM+RBErd2N0zktEBWr5vTdLD06M7eHjfE8a75xfsxwAdocUab0UazgbDntSy66tLoi5ezOd+DdY98jwA4+EYR0fHO79TPLaqKtubJ2zXkeRji9LamljKgVIWWksf0xgdH7t1sFgsMV95KDng5Wm7psaM/bicu6jag3t3cXz3jO1iFJph17wsDTkjsIv6qqoqi5auGMVZrFzka7FZYsMo/Alh0g/u3e/V1/Wx2nd2csq/wSJfa0KiBRMOoxEOGQVT6kfMvw2HXmY9Z/3W7NdtWWMWu3k11h4jYtwWKELNGcGR3WcXVwtDAlxefAQAePvt38OaUcWvfOUrAIDPfc5BqPMCSBPNTfcskV7XbYsDphSG7EhLM62BlPvGowMXLdScOxiPerL2rn3XcxeRHQwG2Fi02z17zL19NBoZykBn3pQogs1mg2fPnu3URWU6nd4iU/dRck+4fj13c244yjBjiq8QZ5LhDhFY6rPW8XartMECB4duzCUP3nHOBQgRa6/VGUB7pG0qO0/aQGnHTFVrIxymrv3lS1eX7EuuX1brLTpLFdlFa7Rti5cvXPq8JIqrXqr7drMrRW1rseoQE0l9OHZrNjt94GXWhb7g8RsgxZZIzjlTTW3OPvkIeb7Z+d6dey5dYjKZmCT6YMT9g3t2vbhEEmsNiMTdyxavhEzcpjt1r9e5vUcpf2Pu0WVTo4l20S523gYJOiLLAxHycw0mqDBJ+TehCLFCSxRJSrSpEYmGISYHN1Pdhc7xYilC9x5xv79/99DGTn0thO1oNEJVEzFMOOGa6O755ZOe/bAr/pAkCTZrTxzv2i40V4/AmKTcLcc9CoCD45E9A+wRwKWXboj+EeJeaL/BIEPT5myzRCMG1lbZpVoTq/XCk9DfSM08mBwasfuaBMkBx+3k7BgpkeVXL5Zsu6vldpv3qBdY8x5CSXMgENqIn9o2jZGGG4rbovGVIX8vsZvxUFdA3Uj8Jdt55mJ9jSHn4XrhCfklyiL0rPaRoAuxoY0hBPAqYcrMyTFappyXvEuFQhzWrRFOExSBbXhhdfep3LvoyqIobtFaFEzxCS0v09+9hKINI6BjVkZD1GhX9NLtiMIaYhcJdXU+x4ZzYEQk0p0Tf2cQUlsos2wwwlBzspc9AgDLprbU4oApnbr8tuhM1EaIKfUxwhLZ1P3tS192Z9w/Yy7R+dUWlxkpBpgu+F/9538PAPDr//znUNVE0c6JFnvo1u6T1YdID7U/kXR56c7T0yFQN24M16aaxLoNp/jCZ78AAHjnu78HAPj3P/d/Y6F9mgjoTul8SYwlEV2Hd53t9fljhxa9urjGKHP7dTskupqpcVePn6AcuWdO7rjPLC/ceftaVIEAMFSWckYUZ12h4sJa8swTpUrVpRgQzbYWgkmoxSw0u+r4nkOVffLNtwAAX//a13H5wqFkDkkePqIEe1mWuAYzKjhczx87O/rZ8Rt4zR2t2HRubn94fo7rrevLt+46e0zE7i+u1lgwpa1qKBzClNOf/ic/gyefd2dpw/T3CVFcmB7i5db9LZ87+yh74uyKzdUaMeuaEKV6eOrshOvrZzji+k+4nzZr97e6KRDwfHjlxI3Jp+4J2TlDU7k6/NKvfMN9jzZOHUZY8GydTV37aiOL92Ipf1LZI4T2ZV/2ZV/2ZV/2ZV/2ZV/2ZV/2ZV/2ZV9+wMrHAiGk3H2XM7rLfdMnuvJ58ySSq4R02eIl8yefU+JPaAEAOCSBorguLt93iJbZbIaPiOaRHKh52+ocSea8bGd3XZRKkpXL5Qplq9xzoiEYPJkdHyMicmkluejW5xhbRIoRwUxyp0HXy5v36ItOSAq67lpG6Nb58lbuuaIQi6srQ2mkRJWUzFuuus5y9h8cu1xJoSGWbeXzronEECFmBB9FFYdCyEhAenLoOZFYXxG2pWmKkGio/li6toeGlAjlLmbfJV1kMvX6fL5d4YBjqYhvafJ8gZH0VYYkc/9PssSIBrdE58yvXX9k2QCToZeTdW1nJKZuEFaMaN/IMY7CDDXRYYv5Li9U27Zgei3qHpEwAEymY5ycOQ/vmJGy8UiolMyi6dmNiEUY+rkj1IE+GwSBEZ4XW6Gdcrx45pAK14w2qWTDgXnkhSLyfCJze5bW2fLKedj7XCpjzqFU66VrcXHpPOOXF5oDJDNMM+P5ODgUkokkd20DiKsr1drw3AFaE0KzST52s9mgandlZo0wNhoY30xN4vOibVCUu/KrfS6LNNmVwQ7pvW+6Dg3REyIVTRhRKSqfey6kiiL306NDHJ66cTYUXC0yZL+X6W/T2ZG9/6b0MgwlFmMwYJ4yoyXGVZUNkcSeEwboST4niaEk9d5tkRuXlTiwpgMv32pyldzDVJd1j9hWfxyJ8PQ0Md6N2sJ3nEuBR0iZTDLnUhynyBhJ2ZDD6elTl+88Gk6wYgTG0B7Mk95st7cIQZ8/d9+LI79j6T3XjJI3VYl7RAMJIfTOHxGquwAAIABJREFUO2/j3r0H7I/d/arr+lL3u4gpt9bdXDkgqunkxI17Xm5xybUjVOCKZK9d1xm67iUJeD94/NieOcz0TLdejCOpqvD8OdGoPAuGOksyjy5MyJcymbkxHc+mSBWdFSEu61Q1DWoRoBPpoojWo7tnOH/qzst//28dWeXFyw/Q8Uz7P/7BPwAAPHjF5cH/+f/gr+Azn/08AGC+4LmXSNIeWJP4NeG8EDdBW9VGAN9FrGfnyV21Vo/Zx0s+Z7lc9oicKavMOSHEVr//NG5RFOEzn/0UAGDA+a/9oyxLe77W3sHMc4HM524POjqa8T1LXLwk950k4smLuFzkNmdU98uWyK4oxGuPXgGAW7wpVdXc4mNR3YMgQMrfmUK3pK/RGDr4JeWIxe3TdY3N4+16F32YpukOJx3g53HbtkaWr9LnU1S99J6gzD1Pm0ise9LQth9K7ttkxUvbG8RVolLkRY8PTjx+shkSOwtXGyGwiahLYgzIhaJ50RDF3AQ1WqJnSyLjaqKey7IxvruU9kvAvW2zWhtCqNEZl3n5bfWf1vdms8HB8RH7kmgeiXV0gSGxNAdESt22rdl9KkIklXlpyFjbAzuRbG+NDycZExWVUWShKywKLOR6x7l0vc1t/Yv4Oey9X/NRaJCYxLhdG6Ah583VxZzto50wmhoyHDd4LfPNFoMR96mJzvwIx0e7e6z6cTQe2HPbVHNVwi01ipwoFO6LaeIJpzcbNz8SEdWL/6fIb3EzDsZeqKAgUlJcTHnpRSpunhMSbBiNRoaILkqiU8U/lcTWf57rjGu+DozjLOIonZ6eojZUKjmB+N7ZbIY333htp48i2bBtiy7bRclov0/TFKPT4c7vcu69SZLYnvDRR47HRftdHMdmV5nAjOpe1T27fpe7su1qm9sj8l1JcCfpYuOGbftpBQDKaoNG52zp2vC97z01JLqQ2uKjPDw8REJE+apXZ9cvMSZExuouoGcHWYILIrSfktxYd6TjezMch+4O8MZ9xwX09/7TvwsA+G//+//OOB3/8k/9NQDAX/mbPwkA+NV/9q/x4tzx/HzuoSMrzrnnToYjFOTqml/u8kzO4iGqiqgXocpIVpzEER7ePWU/OHLpUTbGInB9ennh2iAE8Nng2EiJX7zj7KL3vudQLHcfPcTBwYD9TpQ17y+TQYCMCOqqITKUe8V6u0JbeZsfcHYpAGy2BbZ8n8i9p5wfWZIYYj6W6AnX2/zqGiP298WFs5daogL/9n/8H+Hf/st/CgD44N33XB+Rm+qzX/gsnr10wgnX5AzdEg0aosJJ6M6vS9ovk+QQa2bt/Mq7v+/qx/vPpm6AVPdAZaa4Nrx1cooBk0deLFz94qmzy+IgQEdk/oQcQAFRUQ8+80nLaNlcuLNx8dyNUb7eIPmcsz++/a4bkw2R5ZcvPsIbrzq74OE9N97DsTJ2JgjI4fQTP+ZQVF/7rd8CAJweHuMLb7jvDWhz5Cv2fxoZv+mfVPYIoX3Zl33Zl33Zl33Zl33Zl33Zl33Zl33Zlx+w8rFACAlxUlbFDvs/0ENKZAkKKguIyVyIjjAe4AFl5Nob/Blt21qyqTzyUr5Is9hUk6TkoUhA0zTGb/D+E+dh/OxnnfdttViioxd8ckCZ9Jq5zV1rnvFD5uCiUe5/5zmEGEkMFeZBa/w2xuMzHPWiaETCKErTAi2kPkL+F9b9zUdTk4WU0k5frlSRBsk/CwU0SSfWb5YT31PTUQT2pie/qBpk/FxM72jGCHXTNMjzXTUj5UmvVgtD3PQlWgFKWEvFSzLODbDeOE+rPOoJEVBN2/p+CAc7z7perbFZS3GByk2xJFNrbNaXbPMu70mWpZhfK+dcCiDMP05CDKjqoShvH82zFb+NcUKQNyLNMKCnNifSYb1ybVqiAx3sJgvaRyJ4BRX3GUWhhsMhRqOQbRfiLEI7UjRH3n6iZcrK+EfOO1eHYuP+P7++wgGjA4dTKiNJkvdwhsnQ9U1lnE9UbkhiHJKjSCpU4m6Zr3JDjJUviX4R90vTWqShJGrIS162Fk27vKQyB+f/ZDKyaBUYwb2eEzmy2WA0pGwr+yiOYwwUMaNUqiJT+bZFy+hNbRKvHn0kjgVT5OC4LxYLm5tLfl/y9V3e2fwrqt39J4giQxLZmJLLrKwrW/MhI9LDMfOB6xoLSkg/e/EBn73hZ0N0NyPvrec4UP+bYksWW8RAvEySlB0MUkMNqu0mYz5IvboKJZf17LrtbOykKidOiiiKEInPpvBKOaqvSaE3u3vLIi1MwUZ1NwnqrjPloGPKEL/5plORaJvK6qA2aA3P53OkRK289pqL3h0dTLG6yU+G3UhuvyjimWUZVlQD0V52QM6tZJDg4sJFkoSmmjGylG+3Fn0z6V+WIAhM3VB1UUS2aRqL3hvXA3lJmtqjjqRApP/HZYyrldvnxAkkxcuirnBw4uZYsXFtkdTte29/G//T//j3AQBXjMZFYWBrKeR4vf+dbwMAfvZ6ju21Q6Z84c/+RQAexYkgMPVLSd0m3K/aeo3l0kXfMqqIPFVkbziy/p5furkgdZuzszOMie7N2Z/ac/vqaTrzNF+6rsNLRoOTdJfPLwpCPybc5+bkPgM8omh55frDjQ3330SqSZRsLrYIhGjOXdsPqELS1RUyntMN99Emkez6wLiH6nZ3/jVNc+sMFk8c0KEiJ8Gz505+XmO1KLYouCcJtcRlDSDEaCRpW/dLIVW2263NOSHW9P4X58/sjNJcnUxGNu+0F605r+qm9Cp0JkFNdEiTYsjzXGeWRwXlXgWU63K74vqJB6YRHMW7NmJX1GjgPrchX96WNlgI9DhRiBo1Vc/W5kNa7dZ3mGUA1YvE7VX2UA6GiuIcHY/HxoWneaJnVVXj0aK2TPjeNMGKqFZ9vuU83ua5V+sSgpWl7VoUW9lq7ndSWKrr2s4xqVgqYj9KMiRCQktzOehJ2UveWtxFEMq1Q0Ao0/Gh2zdMOWqxwCVRQ/3+AISSXO18viwrq5/qrLU0HA5t/q5W9c73sji1fpdc+uMP3fxvusbW/T2idjVX09hzaurs0t/iODaCQu0fOTk6EAbIlM0g3jwhSpvWnnn+0u1hQs2cnd2150txq2XfVVWDly/dHtiI4yd6YaqVqoN4ssqTE2yoZCp0Xpx4lKMU0W6qSg4GAxxxHQcJeUhpv1xeXFufDsj3NnlASXX4s9fUxbhNtm1ryCwV1Xe5LFBSzUn9YmPc5thQRUsI0R2+Nu61Ut569MprtvcMTiUtT86/PDeUvhC2pszYhtiKH4s22pr92hQ5wPdIuSkg8i+uS7Sle8ZXf+mrAIB64+yWT37ukyjOHYrq29/8HQDAL/zrfwEA+Pzn38Lqt36X7ae6U+TOqTBITRbu4V2HOnpBjp7DwcBQc0JJSiVxvd5gQg6xH//RP+3qtJzj/ps/xv5zc+HDd//I1T2pkZDLdEIb8tF9Z+9Mjs5wmrn6bM7JAcu5fpkvMCJaPNJa56JfdJ0hq7ReutDztWktiHtSc64sS0ME2f2T9uNmvkTLf7c31C9PPnqMn/zrfwMA8C9+2iGF3v+I59owNST0vbtuXT8t3DzbLK7w6L5DEpV87/eeP0XFe87kyO1TyuBoog6TI5cxE3dc442rw1tvfAZfGL4BAPhH3dcAAAlVNjeLAuNT972E7pRLqnsWzQpXUl8lL9od8vMdnpzgm994x32vdbbFMbMnBsMIhwdU0hxxjynd2F6tLvD0I2f7Y/Cm67POjePDV17FYODG5sf/lPtbs3Vz73AW4PDYIx//uPKxcAj1NwJdsCy9qPBwWsE9RTK8MdhthzjclSnttMDDxAgprfBw/t6772I4paHdifzQp5O0fJ8MWl3W753dQ8vL8ndIAL3ZkqAvTrElBG9FGNs4E2lgaItJ5kGry0cQIAxleNAgGAzMIaMUjP7loax3pdRVxklk8LzLSzchdVjGcWzSlHqW0taKTWmbkC4LXvIzNifWzTIaHyDPBe+n4UHnHaLQoP9VpXHgYd51Jlk4ZspO1smhEZhBVrE/AzQIbqRuaXOZL1aoBaEeaEzd/zebAjHoPKTzoBXcMQ7NQBGBYhD6lJs203xQCo0O8RopoeupkT27DWS52WKYMKUqI7ySc7xYXvsxT0Ssx7S5oEUUubrrYFuRXLYstmaoj5XiNvBpZXIiijgwCgOA79bhKzLwpguwppG1ZacJtn7n7BRTHkCZ4MCl0oQClDRiOl4qk6Fgpy1Wa0mi06gnsVkUReYokbMojeloiEKfUsK+zXk4SKYZAI7vkxhVhNd1jcYcZNwXxjQQxgd2cVlsfdrVcCBSURocfF8chH6uyeDmBSaKIvudoLueHHZrTikjn5Ujr/OOVpHfb7mXbddra6MZSLFPe1Pqp9an/Qxj76zkHJcMKVqfcpreICavqsqseJODPp5am7cbkV+LzLOwlBn1t2DBwyQyiXiN15ApAcMsgewvyZKPhyQU7hH36vual2maeiO58sScAJAXDd56yx1uWpdyGg2zQc/go5HIMR5Oxta3RiLLc+CVh/cR83zIC15ctznunLq+1PkS9YmFWy/12y/DNMNYsHQRWLI/6+UGhzdSMlOS306Pj0HFZDOC5ECNIk8mqwun2rXdblEp5YX9UJKEMgxjDEieGvMyL0Litm4Q0nEU0Bkw5DovV9c2lnISvv/YQdL/6T/5x3jvne+6uvB76Dq85FxWCu+Y+8Dy6iW+9TsutexzP/rjAID7DxyR9ovLK9uUDyaHbLx3BJ6dOqfeKY2mhyTgTtPUnIe6kOiie3FxicvAGWD1DUfZaDQy41/zWZejLMvM6GzXrv+VIpVliRm7IjvvyzkX211p7iu4VC0A+NSbnwAAHDLNfLlcYk3i1ZLGaM49YphFGNFZozQIrUVd7ADYHqOLYdMTXNDZavnSaO13SldUO7fbjY2z3pdQFnez2Vi/aX8rmD41GAyc0wW99FeureFwaGOi9bxcLq06PiX+ttz3Tcn3umnR5Lv2X9+hJ/OjqXbX4vVqi1ip7RIKaPx7ZCtovs94jrZ1bfNCaTF6ZhiGt1KR5SwK2tj29vZGOl9dlCazrv4AWmyZcqd9USmyaQRsdTGis2GbK9AXoIt3+yo1p/3Q5mR9Y8904iwko262O/2ZpqnVT3Nc+04QhkaGrv3RCwCE6Ez0wf2w1LumsxTJgs5IE24YDBBof6NtuSl9qvtkLGeFe+ZomNl8yiM6aBRkzFIkhfv32X23VxQb7yxRXY+OmFY9I6lvUWDEffj+mfteP6jhxR+4lpSWt62xkbADHXMiHz47PrNnylarezZzyrNj/Non7HeA26MXRnK+e+FP0gh37pDGgYfDy8sLI1aXzLrZc3GMDR05r7/qLvrrjU8B9XcGsgArJbFusWIKneatbJPxdGb9L1tBqf+uf9ud78lRnCQJuMx6qeAMJB4d7djUgA9WtV1ttkWZ8h6o1MK2sjm6oEO+KBv73empS6dRMKgqSgtkDydu7L1DojZej4q/01lwdHqCFZ1SBzw/h9x/f+8X/x1+6Td+DQDwvffcmThV6lm5RcR74OK5cwz9D//1fwMA+OEvfQFvfcqlBAW006/nnP/rDQIGACdjZ3NMzemzxGh6stNX7CqsVit89IFzhnz5iz8MAHj7j34ff/CHLmXo7kPnXMrG7n2LzRYvr5k2zYd8/tNu3x9EId794D0AwJhpg4uXDHqEDTrd0ehEn1IYaJUOcP3C3SnXvPPGpHpoA7/Xar+y/btsgGSXgF+AhOPZFGvOx1zpoXSO/Ppvfh3Zjzjhiq/8ub8AAPjlr/4CAODdDz40x/8bbzghis9/3qWrv//ee3h27c6/Ywa37r9xjPXbzlbQulYdZuMpUJAgndvcZODG9if/8k+i+jmXYnb3E68DANLWrf3hwzHeP3fOvK52/ffZh24Nnz95H+OJe372wJ3hq9x9ZjoY4cc/4eycy5X7zGuvOgfWII6Q0BGfk+Yg5qabBKEBATaBmwtvfMaNzfXmfcxCN48urlw775+6d7zz3vdwtt6njO3LvuzLvuzLvuzLvuzLvuzLvuzLvuzLvuzLjfKxQAgpXSiNfIRCxSQ1mxpSejMytlAkoxUqSo0bWoYeyiRJLHomT/kHHzio+PPn5xgwfeGAcrQhkQ9xF5iHfSUUypKRwbxAPKKEJuVex4zgVmWJkt72GUkkA7UvTYGOctby/sKnBkmu3tpcFLfSFVrB2qPID16zGyG63HSIYiEJnCdTaTlV54n1OsGdN5JYBEL2UTLZjfoFQWCRbNVT71u9eOYj9Sx55aPzFj1rle7mowUar5upgn0YdJ9gPKYEo6RJFdmoK2DLyMR8QXJnoWWixENjGf20iFkWW/TdyPd65MOjgdLHdqMzTVNZutRNAswsjE0yNcpExAjrD0UcFbEQuqdGYHLdes/RkUPZBMGxkS3flMEty9pI2ESqV9UNNltPwOy+yDWRpgYFj+G84DkjpWVdGQw5ZgqioMNBEFi9FFFVXfK8NEJEQbYtKhYlHoEgQkqlX00nGDC1DRxzRYDyzdbm2PNrl56hiG6Ve3lURdCFnAJ89Ee/y4uNyeyGPdSE67PEiJF9pNL9/+DgwNqoVEfNoagn+6o6x2zfZDbFKSNXKSMNNREdeVUbVFWljwbSs0Q8qohbXdeW6qGcon4EXZHlNGXkpjdPGsHnWM6vnluUuyECb0qk3HQ69TDzZjcaPxgk9gxJURt0uPBRz4YdadHTukOk1BkRVG99uluflNU9jAMRdohD1w+GcmQk+GA2Rsd9Q/Nxu3Tvu7g4t/nRMaqpNZkNU4vU63VRFKBlmmcZ7ka5siS1iLfkQPWsdRFiTEJw7QMHU8nuRkZ2qP4XMm65urL02j56U+8VMlbpLeqzPM9tHSuyXYosNE6sXQn701L9RiM0AQndtzrzCPsfDFAS2XnA/eZ/+4f/KwDg61//Ok6P3Tl2zWhjHKdIuR9WFANoO5Gr13hMcux3v/sdAH4Pm00muL5y4/T+++8D8Ov05PTIyEH7hNEqiqoLMq+xub6+9qmYXBrao6qqtD6dMA3WCMbnG4+WYxqfEAZV09o+LERpxFQulDAhhNNTF5Edj8e4uHD707uPHfy+pHz6aOQJ7oUEzXm+NG2IutpFYlSRlyAXYlUpnTfPWKCXhhp4tI32WvXZe++8w/qeGtJNZLJKLVos5maLaD2PR4wmDwa3IvwqbdsgJ3JJfTubzbw8uKVGCWXZ9J4h1JCQLf53OpNjIpO6XiqWiGztvG46Q4IJwRD07CqdK6H2O/4tQID00K2BUxKE99ei+rGftg248fBIYdlnTLEfDJBxXSeR2lfaujSCcK7vqmosVUzzVsTOg9HQE6tXu4jStmmNNH9ABIfWc5IkuCShreA8Me3BKE08cXm8S/KcZZkhOYXa0LwKghAdhJLmm60/GkRErKpfglBE7d7eUVt0JuR5jufPznd+N5n4uVMwpV713Wy3du6t1s6GV/pU2zVYU5BAdotQF8NRZmfPJcmDtW7atm977pK4T6cjHHJe6HuypZ4+fdo7p4mWJCr59PjYS6fzXBppTAeJIdI9Ss+f0xlFI15+9ML6SAgpjZPqUte1pjYePXS0Bf0U78pS0rQmRAbeGUo34JieEZUZdr49SuWSeE8QBLYnqfQl4L1QBvh5y4OwdLKGab2CvpVlie3Wi+4Abt4DwDhNMR7tnn/JcGTIIL1PRMRZltncWRFZoXk1mA5tfdy/7xAjz0gFcvXyudEkiPz+f/77Lk36m7/2VYQH7m/HItknEmyQhqhpRz+661AhZev6+KMn7+D9c0cWfPbIocTe+qSTrQ+DAItrN4+EHpVAweN3H6OBe/4ppeJ1zh9OprikeNLDO+5vP/WTfwPH33Pn7IsXbr+///qXAACj2RlGQ9ee6yvOJ1JuXJw/QUWC5CHRuu+xz9q0Rlm6OTaBm3uvEu2UpgOsLn/Vfa51c1s2aRwGdpfvizcAQBzFyInmGTBToeAZOZnMMCGia6XUVqbjNFWFX/mtbwIA/uyPudS4V9/8NADg97/1DYyIAnx6fsG+cs/87CffwuXC2V6//50/AAB88ctfwltvutSv3/5Nl+KnOZGlMWJmVJwTKbXJ3dg+ufgI+bNvsz7ObpkOXP8fDQ5RHbh++6E3nD3w5isO2byap5ge8VxnKvPFwj37bHaCs4mzi5rEoZt0V5lMJiYRb+d7p+yhyKhR2tKNTU5k4wdPXyCNXXuKLVPvXhJdfPkQHy69bfHHlT1CaF/2ZV/2ZV/2ZV/2ZV/2ZV/2ZV/2ZV/25QesfCwQQuIm6FofSTWZb8kWR+GtaJMiboNB5gk2jdiXyIWyNgk8yZE/uOekGv/aX/0p/PK//FcAgJJoAZAnJO4CNMyPzcckUKRkXNB2GJBta0tURN05b9/J8SkaEQGyXSIvC8PQvPTdjWBf13WWt622lNvNLsEdPKIIXeP5fSIf8QKAJB6jZD/m9GZLVnJb5MbTYbLbjAaNBqlF9vvy1ICLFuh74thILFI/7UU7iCAw4sgQXURkxo3c3c22sMiXUArKHw4TPzVVpxbAJq/suQCQSSowbpE1+twuCWQXBIih+SGUgfeFKgK7Wnt+Gn3/uhBnAoljGeWK4xiNOFfqXaK86XRqfDNbomXk6c2SBGuR2jFKK0Lisq5NylWRHvVVmqY2nxpxMjWeQ0ty323oOZ8ykq1mJOiM2OY4CT0XEwM2WRLZ94wEWTwJnKznl5eoGHW26Gzh0RsP7j/aaeuGcy4vPZFoTi6FUuiy8ys4Fg4vf64Iwmg0wuGUUt4kjetzSyjK7deBCOUbL0HP8GuIzMZe0WfJt8ZRahxkKsotToeDHhn0LlHeaDy2d2u9KBLcti1q9uOasulbkSqXBZpGkX3uaY3Pu78pG29yxHWDsnRzRZHKyezA3msoA/atEFBxHENBedUzHc6MC+WSqI2XJA0eDYY2/w6IyNDc3pQemdWRJ0HRxsFggBHREOK3URQVTduLHrmo/+xgYp/RWGZEU907cxE3JyZwA/1JFMB6ee2juRNF4ClPHiRGZKs6HE4klRt54tzOR7kbckdEhk5klGW7NESQ6uIJ8oeYkz9AXBBlj+vFeJrECdYjSNW5oDXu11Rh6A5xbnmes8jWjog9RyRKHY9GO8gZwEuP13WNLddxtdltQxgFOCa3xjtfd3xBv00p0+PDmefW4FnctYGhE7TXbsT117U4PHJz8qs//+8BAM9euDp84jOfwxnz+XX+nV84JMNoOsSAnBzaV1vuc3fO7uLewweub8vdCOThyTHAaFrAKJwIuaMo8jwY8S5ipa48P4XI7LcbodlKf+4a2ohrqgsxJqKoZCh8dXFh6/keSTu116xWK0PADDm3R8fumev5NYpSeyTPnMhzL6ju4re4yRXo2rPLQ9c0nY3XmtFSRfoPDt60eZURQaa5NxwOEca78cEP3nfzuigK41UQX4dQMHmeG0og5vcXi+UtzqA+N89QKDbx0wjxPUgNnQDKmEckRa3LyjjAtAaFIoyzgc37odA5seeEs/M8FOqF9kHjiaON1L7zKJE+QhAAJqw34jEq8fbUfv9QewVuNCGKpjEeEJGpC+0eIgKH0Nq+JNdUs7hGwjWncVIJEdmYiD9JKKn5coXpbLTzedVltV3bv8MbhMlFGCHme4Ra6nqCHupHrd0++e9q4fYb7atNo31vbXuy5/8S8XdhXEqyv4Mw7vHNeTS867LGc+jFkqsnhx88F5J+LogwLOoGKTkqtY9qbY1GmY15IXRrI4Lw2vp9MvJnh9rQ3uiHyVCo09DsvsPDY/sd4PqwvxaAXfRn23KP4F54L00MKdXn3NPnRbz9h3/k9u075F2LoshLw5OfcLulDRtHmBJJsCWHXtsj4r+NTncNXS8XuKRMuvZOcWSi80T8QkP5uVD5NmuucwmXZY3N1iOQACAhAfdoMsSYBPxCNs6XC9vftB/YvOw6G0sviiO+w8ZzqYnwnutlMh2h4JwBz8380iFOrpsCd8fOFlmw7RkBZekwRZuSqJsIo4aIn2pRGifjb/721wEAK/Kj/cjn/yKS0H0eAbl6eP+5e3KKlqgjcesK0YggsPH6kITp9+8c4s4JEWcZufDIWzMMz9E1zqb7oHI/FxI66XJ88RVnW//BHzgE6TnPhqJrMOa4nlE06btPHSLvjS/+CD71hpM7Fw9PXinDpOtxZe1ecMMwxJBr0FCOrezjlc1R4zfjnSgIAjSR649v/aHj8XlEfr7RbIY5hR1mB26erHk//KPvvoMf/pzjWQort2d861d/Fwc8v2JytTZwf3t6cY63Pvk6AOCQSOpvf9chnX/6Z/4J/gvyGP0nP+54ET/1wPH9XLz7BPHUIcB+4xdc/X7h5/4NAODBa3fwt77yVwEAx+R1km2zqgqU2j9yZzMI3bQpUiw5H2kWmMBDVdcmuFAuabd07pmj6StY8/lR4ubABQnQD189QV6LT/WPL3uE0L7sy77sy77sy77sy77sy77sy77sy77syw9Y+VgghMTb03Wd8cxIEtpk2uPYIgwRIzYLKnK0bWsea/EErYlEeHb+EptC8tL0RNOTenh0ZhFpRUtMraysLTqo3GnlCKNrIADLEVnml2vK0G02aFjnjJwjQs8EYWhSzYowWWSpa5C0u2pLQGQR4s5kZWF91fXQKoBXF/roxUvMKSsujh1FEsvKqyp0N5RlTg8PLKogSXVJOk6nU+McGZoEtdSMGssrXzC6JY9mkg0sqrvocUKoiDNKUQkhoJqytKjChrL1RVVikIiPYRdp5SKq5HphdGUw6PFRsR+LknLAtaKanisghCRo9aUA23xXPld/iqLA0CBBR0UDIheaqsaM0ftcubACdkWJqXPEQ1ffEyoNhAgwnohjale9pCgKLLYcU/u5tc80reSbGTUpK0MuL2luAAAgAElEQVQzqR+MuyHwEvZNb30Bbq4KeaC5oM/eOZz6PHEhrvprV3nDXBx3zshZ0nRYcV6cfdJ50xX1+H7S0JVJ5FYWBW5ZP88NNLYIg9A8QaiwZmNt1jwJQ48wvKlgU5aVRUQ9UkjR08KQS1JzaSXvkkSIFLWTXDHnVVFVJpFraBLOywwj6yuTeE0dymMwGNxSF+tzi/URYwAwXy3ZpsCi/5dE+midp6nnMTGlumyK02MXAXzl0YOd90VReGt/MjW/+fwWr9kFeRLatrXInNaLvj/OBoZsUWR0RIWq2WSKLVeW+viayEvEobW5ZZ9drVw7kyTB8aFrQ851/ZIyn2//0bcxm7nnv/6aQ66dHbuozvX8Eh0jcjpvXrx8ibMjFz0SSsOUaMoKBdtTGjqVSoF1a0i6xVJ7nxCRrY9eSgmPyIzNtsJoxIi55IBNQWhg0Vkvpcyo92KBLNX+Rn43ztXlcmkqb1obI3JsdMgwkdw0o3EaqzAMceeu42X42f/rX7o2cM+Ns4HJTOtMToaRR5AS8RRTUjeJE2yoNpglLtr61usOkfvqq6/hu+SzEe+AeKR+/he/ijX3NSFRdW6ORmM7FxRJFAIqihKckr8hSzw6EgAmo7HZBZKrl5LYKw8f2jyeTNzv7tw54DM9j5dxZw39fqUxzSlxOxsf4uDAjZfUAg2ZNJ6hJfJM5+U1ZWmnR8cmYX9CDo8n5FaKosDWqngOpCDU38tUhDwuywpFTklnoltfPnfR3cGP/CjON+LHY6RSPFRlaWep9k5xdQRBYGg78cRVPV6RPgcKACRRZmtI+5SQGU7lKrF29H/WXWt2hKHGpQYaRUjTyU4fWwkDm8sRxzQnQqMqSuvHiSEE3f/X+dZz+midCimO2+gmO/vCCKVQRxCqO7LPrisptWrPyDGeTa2/3MNV9QChbBm+11AwSYycvHAlz/Kuh/oUekgox1RS2VmKxcaj0gE/Dskgw0C8QHzfltw7Dr8uZBcl5tm+JM6QDMlDdEMlMooizwsnpAX319l0Yn2svxnfXhShY7+NR57/yNTZbB8WGquw+SAlzhdEH9ZVZeOsPWI8pr2KCBOqJEkFzVA5ZYXY1H3ZI/wxn8/t+Yvr+U6bh+ORn9PxLtItDiND0q1yzSGvlqxyE/EahqHNuTc/4bhOLi8v8fS5k44W6l/7yOHxBIFxYbq6PH3uzv7D6Qwx+cho3hunYF03WBDRJbtMV47Dw0Orj5CdNe2PJIpNTbEmonmaunEry9Ls/LbdRTT20Xaao6Mh98dhiilV4TznkCs1gIYcZ3rfYDAwpI/QZQcHRHEWpb1nMt1FIV5eXxnn4YLzXeN1cdUiIMrlmHPomFLiVdMakigMxCVJ1cE4RBN7FCAAHJE39uq6xrIgmp771tkdZ38UTYCX166P7t1z+34QkZeyjDBfuj32kmswG7n3zedzJAe794qvfe1ryGp3pn7xsw5pco/zfpTk2C5dHQ4GVNJLyCk7HeBr33AcR58h981P/PW/CQB4/nKNk2O3Xl5763UAwNfJubPdlPgzf+rPAXA8WgDw3kfvAXDKzREVlyHUIqd4E7RopEDN/XQyc31Vtw1WtDfEo5PRXmobIK/deOdbNy/ff+w+e//eGYLQ/e6aSl8zIsqePTvHN+C4g956w/X7O+9eYcW+zcZuTGLd7dMOw86dcZ9+3fXjw1M3R3/o859Get/ZBp9o3HyaX7hsodVig3rjGvnmZ34UAHD2huM4KroKv/O7bj0OuY/qnLrerBBRFXkYkONVHFrDMfJS91Pyh+lcjBLkFTNFqO6mtd+kAQL6EGKug7MjIpkuzlHesNu/X/lYOITEd3pxcWkGioodjllmm6WMwZIHhSC9AHByQnI0OkeGs2MckBTXeFU7N6FGDw7wew8cGdvjd5yk4DiTxHNoRs054YOeNK3BmJvyoOEBOuemkYVIWB8Rsm54cY+ipCdb7NOZ3Pt6ZFEQyd/QX5IpARv1YN1dF+48S0SAZyenuHvXQUcDXlx0aanruncZJZkjnSWj1G/cMsC3grRvSy9RyfYIrtrF3ni6IOnZhjKCQRhjw8NXbRDkbTQaIGV6UMu+EiFxXmyNHHBM0rPDJDE4tmD4MjbGk+EteWkZD0kSoa4pqctDS86bIAztQiXDdpLJKZNiO3FzRYdwEvn+lyEwNEJiQo83W2xpjB+OXFtDfn+V50ZalvOAqph2VWxyNM2adWGKA+HWSZLYpT5h/ZRW1zQNFivXHykvi8NxbGkSQ8mIGkFkYE7V4bhHvu4ehiH7vSMBYMm0wbODsaVZrYvdCz+a1pw2coSsFyQNjiMckKR8tRLZpSth6KHGmaRaZzTswqEnRg49TBoAqvXK5tyQBLcZD6O6qQHCipeUlE7S1Msb2/zneBUlulZwZx5IA0mIL8xpvCCMVW3fPHtmaXuav+oPd1HaJSLvQ+yN6FGpruHAvq/1JeLvvlNQc9TWqaUidCbxLIfeXRkbiJBvdw3hx48vLcVjy8NY70mSeIekFgAKQnHXNA4BT04vQuf/l70365Usy87DvjOfODHciDvmWJlZc1WT7IkzRUmkTMskmqYlvRh6kB4MAwYM2IBtGPAfsAEb8IsNg7BAgoJtmSZMgxToFs1ZNk2KTbJZXV1dc1XWkNMdY4448/HD+tY6EVli+7WAjv1yM++NYe999l577bW+9X1lXqA/4PrjnrA1Wzc4OJb+6GV5QvLybL1qSaU5npkGhDoRSs6Vfk9Fm3hycoKHp+IIvPmmHP5Pnsjld/bkMc7O5d/f/+qrAIC/9/d/gXPclp/Q9KHI1jgfX3FuPWy2IIyRKGErbe2CAgPn04VdYvsMcOmxWqNGyL2ngaeSD8zxQjvrlDxZ1/bV1ZWt+xs3bmzPYxB8JiBn5WjziV2K+oSwW6lZ4GOlUuom1SyDv5qM8dF9IU38zhuvyeu5jof9BFdTGWtMe5KtV6hoWzVwlZBUdjFdIOBFRIMw3/iGyNBfu/esBXmupiSoZonP0ckxHMqkJ7ywWwlBFNocKzmpXrD7/b02sJtp1F3L5mZ2eUiXnKOZBFwuzi55+QHy4lMA7Z6Kux3bQ08T+Iehb88CJOV9fPYEn5KgVNfFZomPXtr0IhHRFh7s7aETy3zssbTk4/sfAQAGgyEWG4HPzf55ntcGt7l+9YJclvUGgby8/v79+wDksqfzN+e8qMPe6/Vs85nkOAmd1+u1raMTwvU14DifT9vSKi0RyZ0NIQmVHWY50+QSq3Q7WGGJFb8tcxmNOP/cb1m62kiUqb/T2kIll8+NhJ0luC4Qs1RBjaUSzjaBh5JBG91D+h2dODbbqgIADn0hxwswoS3KeXZracv+cITBQC6THQ2U1QXG0/lWnx1NUGWZrVFLptEf8aIQPs/z9Kmy2b04gsPPL3Itt2dwII4wZXB1RduiSQ3f9xHpWc+x5kxS9pMuKl681RZpYs/xMvMJ7TlYIKXBIFYRgu0EwpOrsZ059YaYCCDnYtzXkiqet/NFG9hhwFsDQp0kEtZjAIuxjM/TZEvc3QjuSfd4NKOGg5SBmYrvrzfEEtJGy8oZdA7bhIzLEhZd/xoQPT0/MzugwR/dk9k6bRMhexJs1v/3mBSSOV5tzYfjOBZUee21bwIA1usMh5SiV3+4Ml+jQU0/5/ZtucSe3JQL4N5ggBmlp+czJf/lHaDKbd8PWXIe8SJeVYWVj2lyMvJZttVUFtRT/1vXQhRF8P1tH8jKt1zXnqG+X32OqgY82vvgKT/adV3zUTTIV1WVzbuen0rMnMSdllB/LmNXv73KK/j8Hl3TkdIy9BK4HE/CNf2zf+/fAQB85+3XMWUwr8ezruT8rNIlElJWeLS5I1/LIkOMp7KHRtck4FLTLygrDycnkqSaz0nezKDWoNvDhMTDJohStsFWXwPDpA547VvfxNljKe/++h+SNqJm+XCewaMIxvEeg7n8/0//ra/gp7/4BQDAJ5Gcg7gmfbr3/MjOv2kpc/vVH5GSqcPeCDiXub12JOO6mkh/l+UStSZSSxVeae/v3lO0DHoOeGGAgEmItvxPXlMVJVzOack9rOXO62WGkM/r4FD24JK2fX84xJNc7hrTD8VH/MqPvYoXnpF94nOO2tK4AElIOy/Dw0VDMY0sxx//5evyupIJtqEkS3znWcwy3tVisQ1dBpviytO4Pxre2SZc/534GB73cRMqsEVeu6wLOLTXHuMRBe1xHIdoSGXT5/m0zGR/HwyPMBvLZ8W8L7ncZ/3CwSjo4/+v7UrGdm3Xdm3Xdm3Xdm3Xdm3Xdm3Xdm3Xdm3Xvsfa5wIhBEaPb12/YVD8p6PMVVVZlPnWDYlMlpXCoxwjEYwYWYPKdR7VqDT0RgSNmzN7d3yA6DmSoL4uUbY9T6KPT/wl9muJzF+tBPa1Wgp0M8MrqJm5iZhZYoAXk3VqJHq9mPDlUiGltWX4n8765XkOhxnYEbOFy2KJ2GGkj0gkJTct8hyOjovzonK2XpIg0wwPUTyKZgFcFErKyL4owfBqUX8Gip4TRVSVlaFDFHJtiUgntYzxHjMiQ5XbqxwjzFXpds3uJig3MpuMeiqsOwoNwdSWIrloCl0P21nJPM+xzrdLUtbrFmKfFyQYJHmtZj8C1zFZTX0m3VCRGQtElF0EET+lElLWDRYVM3uNEi3LS8PIt5JCLdtqmJXvNi4Gh5KVaYks24znlUq+EyoYMbvpe0GbIeYjUvSx4zZGJq1jzvN8g8SRQ8ja+VD0VEJW6ZwlAWVZo1ypXPe27HGar239+gHL3Ajtr926hWMzO9gYfAVIS5XNle/TvjV1i9ZShJtC7cumsUwFSJzW1Mw8ODVqqOQ4s93yStRwLPuwZJnoepUZAs/IGb02U6lkduMJ5UqV9RBAEDKqz+x1Qph04CetJHyhsP3WXgW+kkAS/cL5TLqxQcg125XOW1LTIim2vk9LAubzufVd0YtpSbi/2xL5Kaxby6I6iYeEkPw0lTUedzqG1NN1r6i+qsgNPeEx66elWG7dQqi9UG1YW9akWc9QUQZeK/d79YGsbSsH2SAe7HYlGzZkmdfggJLoYYv863I+XO6Xi/Nz/M7v/M7WXD15KES4z957GcM9QTMEPEPO7wsS5N69O1gqSTAz093+vpVSKQpQkYOrLEXNrIzHbJXLbPcgSizD/PghZWYPJHt0dHSEhjbMe1qGu8oxmXBPMI00W5EQMOqaTPJ4wWcCLR+KsWR20OD+pdorDw6fSd0Qakxk1/pqioZ/W/N9CjuvvQZJR5737VtSqrB48ojj8+EPZa+vSVS7ztfISIC470m/OprFizJcEp1wSKTQu28K2eKjjx/i+p0XAADDA0E+xT15//npQyQdImuJYhmxDOtgtI9+T/qga1bX3Hy5wIRoo5gEny59iLxsM9pGBE80ShJHcMk+reWrurfWq5UhfVbMQutc+VFoJM3kA0WWl1ZCpOU0WuIdRS1p7Zw2VhHNi9kcMY9ll8IQTaXn+8oQoE6k5a8tiiUgBL1S1K6en92OQcO7RGNdPJZ1/xff+DN0RwccKz+T5M2J7xvqR5G/eaAlKh7qWvoynYgvFNCmnV1cYk5U78k1eaZOVdo8dxLN7HMsbtOSIeveY6b+9OLcYB2n5fbZn+d5W87OsyMtVMa4Y2dczsxyQYRj0TQoeRYgbUnDAeBw/xg+fZIgkfWvxJ6z5QoeFFHLrDAP+KAJMKCfo+jFnMS/jy6e4ME50eIbEvYOUTi6r3Vcs+XK0EZXU0lNK2muHwToRT3Oo5Iid+z9iixRdJMhSyH+IdD6QjrHTdMg5jzu74udihLZW0EQoCJprU9/s3Kl31EUtaVcbC2huYeGyJuUiKSIiKFR3KLc1fcypG6em10Nrcx8iKbW0n36Rb70fb5c2BmVEFGgpYy1EyLimta952upWbZGzZKgmDQOQUQ0oNJAoLUDRlWwWqHb5Vm/x1IikuJnWWZ2QO8XWgqTlukGolw+W8udUZfmkGk23sTZqwohn6kXK3VDz/ztlGtTkd++7+J0KvvxYiZo2IjIgAdFab5TRd9EETWO11Y/KEJu8Ujs/eH+vj3nRkVLPNqY5QozokHU7yn68rfRaGRI7YCEva7TrnW3IZpP7xdEeURxgoz+xGyj/B0Qio6CayYhAm+RLwB+1ohEwqfn8ppFmmJvX55nteRZQD+kcQMcDeX1dSHzkNWyX65OL7AayxlSnMl8/sXv/5HMVeDhNn2nss/PWtAuOj2AY9yjU/5g+RH7fhPPUvL9kGIrNw+FjHk+LeCTmVpLGdWXrWMfPvng53NBzA67ct/Nqwop7xyffucNef38EsHgiK+X9Tebiv1I83bPLnKW6EUyV//kD9/E3kJQaD/6sz8FAPhhkkUv51OcLknuT6TxPufuo9dfQ+CzFP+WoG3+/FsiQx90A2DJtU1/IOP6X69WCGi7dO25fO7NIm3l6ZXWQmkZfAA50fD83arREkbPyofXGSsB6Gel9QoekW0/8UMy/3/rSy+gmstzmi/EHuyNeEdyKiwzlh4mJOBn1c9hPcTx80J1sVptVzA1zRLEVxmSThGfq2xhiGiPJYFqkxyvtj2UcD+HUSuEUBNBqhijmPYATYX1UtZqpbQunOPZxZnZ1hyKtKIdbwrUVp3017cdQmjXdm3Xdm3Xdm3Xdm3Xdm3Xdm3Xdm3Xdu17rH0uEEKKBEmzVcuP0GyT4fmBA+2u46jMur6/REHkiMP3aaSrrFtiTyXFTBi2b+oaXa1fZyYlUNLVsEHILGtq2W6JJl5djOF98L787kRqdjUT7LotT1Bh0U5Fb6yM7FDHoOMry9oyXorMmK0XWDEroGQX+33Jmu4fDI23wdvIRAHAk/OxBVi1pr4TtrKbHseoGeYnZ5L9Pzk4tMyG1u56DmtP/QDQzL7JgRJVMZ9Z5lblPbX1XBe+r9kp5Xogd0aatRwXzrZMdZ4u7PMVSbOe5xZJNkLrul07Ws+vqCHlQpDPYNZC5dlJDtbUCTxmsmdEh0zIg9TpdBCH6VYfdHm6rm9IHU+ROwrZqZtWprjelqX1/dDI8LRVzAhWBXBwKM/XSA+Z4V5VraSmoo4KZr2Koq05NZ6nLG1fDyXDk+8dDAaGVBgz4q0EhEEQGFJHs4uOo8+0MfTJfDreGoPjNAiY5etzT/l+WzOskeo4Hm7NR1kWtm41up8pwivPDKmzZDYu6ahMbQ2l4exRvlz5fIqyxpCZIodZ7jzPjVRe+6XR+9D3NninyJ2Vtc9N50/XnGadqqrZ4LWQVpBQdDjcxzn5bXpPSbdXRQaPCDXNOET9dg7yXOdmyf7J326M2mymZvuCSmV0XQTKHqnk5lWLmDNyVtqWvUHPMsor1uCrbLHjOFgtZA+o9Pdq0fIEKd9A4CrXC0mR49B4UnRvKCPx0fGJkYQaeTURVJ04NISQjrXkc//08RMsjMCYHDjcGw8+/gRHJONVu3rzmHXt52e4fixZ/I/fewsAcEyixP3DEWb8rAtKp37hB76IggiJ7lOE5M18hkLJWpjhCch947ue9VW5HhRJeXl5aXNsPEFEcQ4GA4SBkmLKnlBi4SzLDNlS2FqQ712tVoCiSp6SUq/ruhUM4F7oK2m/62HF86XPbOuQ/ZwuV8bL98orrwAAFk8EVbJYzmzdRyq9nAKpZnGZhdZ9M0hiLHPyTJB0GZXsz/vvvY0LZmIjZl1VqKGscgwTQcaqiIDa0MVqaRwSut9UprosS4Sss1eSZ99TvqbK+mV2mHNQBb7JrOucKarF8zzsH5C3jkg5PSvzqtzoQ27v21fuMSIQVKih2+2azdLmKWIzXyEKt3Ny2t8ir1AagqlFduq8GIGz8p0octVxzM4MiMR9eCm2Oi9S9Dfkr4GWxyRdLQ2JoPYtW0iGOsvWhhB8+FDWhSIb3SBESHtQpMrr4hpvybhUtAeRKp0EQ9rdp8d8586d9pkvWsEQQHjRDLnQPIXoWq/huNvPWW3EbDYzn8I4qTSLnBbIldeGvlREyes4DM2HUkJbheQGQWU8IkWmNpTndJbbedbt0b6FXfQH25L3ShJ6cNCgbEg2SxSRjS+KUOdKWlts/XRd18bf55rLsoRz0M6D7uspJbOLqkIYSp9nE+5P447yMdzrbvUz5Gc3AGb0RdWWtXwypWXFdV1p35I4Nn9M/VWd/6IokPDfgWayqwrL1WLr8/UuUDWAQ5usXFu6fms0Jtete/b6sfBo+q5nyGlFsuv+GY/HNlZFvG76mGr7VKTGkDEbJNZqN3SNTyaTz5B5274uis+sX10vm3xSOnbH88x3qtnnBfvQ7cQ21oZHf+Qqv8qqPWdpR5TrKOrExu2l83DMs2s+n7e8QOyf7iXP8zA6lDNVhT8a+orKMbj5+pRjqarK5s24uuhrREnH7lyKtNLnf3nxACuihpQk+uToLmYTsUvdiBxrioJZT/D+B4JGVZ/mDjn4OuEeTh+J7XpyJpUeiuQ93BvBDaRfv/z1/0P6UhAVfnCMhiigQSx7ow4oYBQ26JCbdI96OVko/kd8fR9HfH1GRNOY9jQMukgr2ZfKw9hwPjK3h4RIt8WVoLFmDs+1MMBAyeuJlFsWhaFjMvqsNYmWvY4LL1TuWVaDlLwTpT5en8q8vf87gjbqf/HnAAA3b92EdynPoJdKP/+v3/wVAMDbH76BG8/JHWX2SGzK7WdlTXz04Ap+vb0eiohoKr8l/i+f4kD0At8Ei3R/Knl+jQae9zQ/J89MzwMU9a93Khrtskrh8LNuXBee4PlsiWKm/Fh6z8rt9YuJ8t5SJIj3/zdev4/qr+S+bwIF5MrcJExPOjJX+v9+v282PeOdV+/A3W7X1t+EXItNQZsbxqho9+tq+8xy0WB0IHt1tpQzWat/HLR3G/3ekuPrdDtmm79b2yGEdm3Xdm3Xdm3Xdm3Xdm3Xdm3Xdm3Xdm3Xvsfa5wIhFEVtbXJJPgTjwTAZzFa6USOFCRWcmiJHwcibIoQ2FX00O67qXQDrBssKR/sS3dTCdlMNqmtkWpvN6PbFqUQAm2qj/p0s5WHASFwUYp3K3y4Z+XvC7FoYhggYqVX+Eq0XL5vaxqoZg/3REQbdbenqhIppjgeMlbFf5Xo5L1EvsahhN9mWm67hIlcFDqKGbt6WCKpXNRvZDmzNR9M0KKptlSatBU06e4YOCQOt+yafSVWhyrclfDXTEXghUmZeHctwaqavsO9TxIODGpWqFZgEL+XIk54pZqm8ZLnxLEuyyvcpU60tK1qEikqI52simaoGGes1VaFLM9Wu69iagbMd8W4ax7gkULWRbnlfq5yg4JKyaudnmkokWddvQD6M0Hc30EaUi1ZJWM/5jFR80zQb0qqa4WmRP1NKlKtqhimRee1ejJmp0Cy+1u0DQETUl6KUGtQ2Lo2QK/dQWZY2Vs9VtMeG6tVTsq+K1HDgtepnRJxcsb9N0xi/Us1Mvb7IcRrM55K9UIRQt9uHluFqtmpGCcpstba9o1Llg8G+vdYUcyhtXq1bJRXl9zHVL83MNhUy7kvNIutYfN9vlVM478pBoXK/AJBy39QpuWzWXos+5M8kYqYjLSx7rAoPrTJS2KKblLNstba+BrSZKlpY1CUKyl5GJDnZH7U8EwNyuyyZdX1I3p6qqkyqukfUQM01MF4u0IlbhRWgRQPO5kvMuB41o1HwmV6cXSBReV/WoO8zS/nmm29iSf6YiHshW6r8/DkyopxuMktYcw+/8ebb+PGf+EmZB8p8P/70IYKe1qNvI4QmswXWmdatM+NLfizPcbDi/jKFDK3/dhybd4/2OGV/V+mlzYPt60alRn3EtPNqt01JbLFATLlSneNY5aaDwDjflJch4JjdMMCtm1LzP2aGVdUvR8OhITwHVNSrmZn98z/+A6ypeuKS9yHudBBRUU25t9QEOqscz92ULKlXk1vmtiAgXn7hFk5uSS3+fCX922PWuiwLXJ5L5laz3qq0lqYp3r//nnwmf6ev6XQ6yNPtMxJOq/SnvHUtwpM8BIuloUp03ie0LbPZzPaOqnIpJ1mF1ta76msEAWLyHylaV78vDAIknDfNgqp6INDg+Ej2kqoeGbIiSbAulUtNVTA1+5fZ5yviVaGreZ4jI3FDXTNDTRRFP+ng5k1BTZwRFbxa6FptbK+rIp7NQdVYNvMm+Rt7PG8W65XJOOvZPxru2zwr0sHU3vK18ca0KFGiiILAxqh+iLY4jlvlK75ez6V+v29ImDWRRaoMeWtvZN+jqCPdS6dPzlEpioJKL/r9B4MhRvz8iutKkUJpWdo67JEjqdfVrDIsa618mK7rwnG2ERmbUvaGiq/aNQMA3TBA2KO/p2dj0doaU5ojgiTa4PhRVKsiuZZU2auqyvjuUqI8dH3laY4PL0+tz0DriwZ+ZHOjY9dnVJY1BpSQfhoZ4/u+2faLybbaVa/XQ0oUhD6TbqfTnsF76qsRGVdVZk/3BzyDiARpnNZ3bOdYfesKCf3gNTP1myqAamMVwaprdT6fm+y87jedz7gT2/yfPjnf+t6maQy5qmMdPIWKk/61XEX6Ph2f/ux0++ZLqv0I6XeuVgss6ZPEvAuREhJ+EBliXSsjNlFKemdYrldbfUiSxP42m8h6N/8vaTkT4z2ihGnbJpOJqUhV6s9Wihbz4StqP2af1uxLXaFItxUJVZ25E8eIOHbfU3RJihs3ZW4VpXR6Kmu29hy88tLLAICICM2C40sXJaa0RRWvves50ef5FX7lF38RAHDJc9nty/qdL6aIiGrPz2StvvB94k/8na/9GH746C4A4F/90z8AAPzTPxfFL+e4Rof7ZD6RtR1G5LEaDo2DryERXVfVWTsxokzW5imRP9F1+dtkOkaX3DBdzvtpXSKlvS+Ud03vS0EBn3yoyq3bg5JadTGLZJ6fORHbPjv7CADw/Mu3UBHJ+8//518FALxJ5FV9LUFEJM0rLwmaeEgrTzUAACAASURBVPbmJxxfBPC8jGgDS0qqZ+sMDu8MilKvXVWyrVBh249u+P6mqsyvVZ/ZYdVK4frGFWz8to6sy3yVoaFc/dW5PLe7L99GoIrStM0F93WROzg+krOxLLbtWxDEyOj/6V1qvST/YtOYLVkt5Hu0ZWlhe1xV9RQl1TRNi6ZXZbpGkX+JqQ1WZYsakrE3hpZLePbMltK3fr+PXBUI+ZwT2uU8z1E72+q5/7r2uQgIqYPgOI45cwqB1qof13W3LqvyO/nZBA5QbQeC9K5eV46RPKsBv+Qh5FU97PWV1EuaOmGN35YHeQxYrZYkaY17OOAFqeYhlBC6fTVbAC5ly2nUX+LGCcMWGud628GfqqxtsamspOtuyIFr/9YqGVygR2dhf18eupYNNWVph5MGX5SQ1fM8rFLddJw/lXksK3SibYdWLzeN55kkqcvAWuTLZ6ZFCfBCWzCglzUttFbHowdGqLDs7h5mM3HkSgb7NJCEup1/dQjKskapJJg84Fcs/ZheXJl8ZaildDxMLq8ukdOhb9zWEQMEcqwbUx0xP2bAJaiQxOJ4uFYOJj+yLEM6byW4ZY51oyZWJtQGPgizztZYqEyssx1EcFzXSmwUyphzHuM4tvI6XcdW8rNaYcGLpulowzH5dz1oNwNCOp4DkiQaGdk6baXdnyIhy/IWRj+bKPm1BuRiI7zUSdqUJlWS7CxTyWF5Zb/ftRIFIwatVEK4tL6cXV5u9anX7SKPWM5Eg6hBt06nY+NzC0K+53OsSOytYx1RctX3+3BZx1FTVvLBJwIvDoLgM7LxJrMcBgieKgns8TPX6zVGe60xlrFTtrgBJlOxQTWdLdfR0tECoZYskUjYU4Ss66Hipu3zwuOpoasquAYRZ2kag2+e69lzcxjICH0PjULxnyLw7yYddLrbpZ/6HNI8xYTEknObT5mDwWBg9uqKNlYv2X4nwYKXktlMnHKVkUfdIGQZr16eE5KoPn/veZR8rgqL3yOZ5Pn5GZYs74x5ACoJZZYujLB3byiX17v3hDD59MkjvPZXf8n5bgnlqz6DXiyT6PH5RZ0eOiRedY1ovb2A73Xp7NK+qZRvt9NBWTwljgC92BX4+GORA5/OGSTauBg8HfhTp6PX7SJjwM8uK7QnURxjeCD2qscLSEeJiesGZxeyhzo8z+JYpcsdjPYkMHPAOXj2hjhH7731JgrazIQEq9PlCkR0Y8L5vsaSg6RI0GdwKc3FRr/zlhCePnz8ddy4I8/AY8DrS1/9CgDg1VdfRue2vH7OC0lbLhTg4ECCgO15SDtZ5PBpb9YrJa1tkwVGVmnBe/5wWgGFinOs9nF/f9/8gphlclqOXFSlXaDt/ZUmmlqJciX1derG1owFcTy9JPZxciLzphdIJc1F3ahLgz5JvdOVBr4/2zb7YoFWXsQdOtTzxQQ57a9d1mhrXDjwVPqXAbbjG0wUOa7Zct3XK85Hrz9C2JHnpvZtkeatzSSUXy8IbtM+w2QjQA6IjdF9omPQ8qTJZILLyzaICqANPu8NNsqhZc/qWem6rgVv9w9lrrVv48tL8+0SltdpX8p0jdmVnjkyruG+7K29ILBnqfLduibqurY9q2WsWVEgZcmzBgaMdNvvtAEMMLBJH6BOM6S88LeS6lrql7Z90MRSsVHWHmjQRcbVTULrr76+x/NaLxur1QrDES9ttGU6r91uty3TZ9NLy2bSRF+vZ3HTNBgdbMuy63lY17UR+G+eQe1Zs00BsOkLBww+HtDedTodS1DaXNEubpbujigdrs95tVptBD7bBKLOlQbWNJBqhNhlaRkU7Z/6BfP5EuMxS6x5vuhZd3Z2ZgGW69evb83ZYDCwdbFmidp4NrX12voTqc3H7eM70neWCS3Gsl663R58FZ7Q+WTytajyVqiBdxWV8nZdoE/Z7BUv89r3OIzsGZ6fSxDMZ5ndYrGwsjO1mQOvLWnWPdTvy9/69C9m8yXUKCvdhPqrrutaMEBtS44VCjBRz8DTHiXH/Ti2IGzoK6m3zFntTrGvPhNkX//kV38QAPDPfvmf4DFLpH0GuuYz3nUa4Dxh4pvn5YfvfwAAuPfRTfyjn/kaAOC1X/99AMCbb70NAPjK/k14BdeHq+XwLDkrgH5P+qzJz7rWRFOGjHZby8JSzkFe5Qi5rxYEG6zKHDVJ70v1TcBWAy7Pxj0KLtzpy7zseQme6cn3/KN//G8CAP7kDSmL+vB3M/zhHwnh9IdvfihjP5A1e/Fwhifviy1760/kXO+dyB2idgAv0EQKe1Hz/EXXkn0tpYeWSlbmQ2qptQr01HVtZfpKleG47R2VW7YluKe/4/ohquV2Kfgyq1Dr3cZrk0YAcHx03crCHQbp1F/tJR6OT+R8tqQf/YiyLO3fvYj7k2dlEAS2fpt6I8YBYDGbYEm6guYpevmiKm0cSi6tifv5fIqKNClRh2eJliZmj+zc03msNvqyWPCO+F3armRs13Zt13Zt13Zt13Zt13Zt13Zt13Zt13bte6x9LhBCRh6HVopNo/vamqaBr3DUQCULN0hrTS59O+OwiXIIWdJiculZhRHlbF1Cy5Vk0HU9Q2s0fN+EEdvpfIHzc5I9WQRQs04z5I1EQDUbocR3ee5axE6RHBolrR2gUCKtWom4XFTMDrQlKYyeOw08ltroZ1aEtcWBb6VpGoGumFX2vQhdztuC837BaH8SxXCwTQKpmUS4jmUOLcPDv3X80CDzJaOXY0ogZmWFgIigiiRdl0R0OMvKSLYUMdVkLZx+s/xJ+jvHOdEJTcMIOSO+QRRtwPtJpM3IqRv4cNmvyWy+Nb4k6cD1dY6VHLeF7VYqffoU0bLn+HAJWaiVYJLkc1nj4uqxQPL1WVpkuWkQEF3gONswySAIMOi22TpApFYBYLWeY7lqSUwBoENE0kEygLeRoQQks7rgZxjxsWa0Bh0MuO5DQo1VfjQNV5Yh0pIKK3WoapPZ7ROlYcTVrrOBcNN10kbFw1Cl5Z2tMedlhZLPq+bebbOSkUXK77GEa3N8ug5VAtjIGoMWUeCxxG+5XCLraTnW9lxl69yyYJpVv3GD6JAkQszsWdNC1mxcamfWzCasV22WMY7l8xXtYqURro8+Se2U6HpEtF/VlCb7rHvP5O7jGDAyU5bPem2f2gzqdjZZ5n+7ZGx8eWXIOH1fThsxX84wI4m0EW4yZZNXJVZE5SyIDvFcRTdVBt/WLHefEPuiKTXRY99386aUEo32hvY7lXrXMijPcQ3F0DAL+lv/5z8HADz45EPcuymSokecz5x9+uTBffzID31VxkMEzh/84e8BAL7/lZdRr1iixpKDa9dOMCdJth49k5k80zhyEHBv65lgiAynwsUTkZvXsqkuERPDwQCJlmIRGRB3FYbs4Utf+qJ8D8tdlkQUuK5r823oQUXM+iEC2u+Ycxb7bVlURqRloeXDhsaIkGb6+dLPZq12dWnIlBWzVkeUmF/lhZ23DVE26So1Um2Xn3+ppXDLHOeXj2VuvbsAgFdf+dsAgIOjQ0NfqUx1VVKoYZKZbfDDbWnusixRMsPvcc/7QStTqxn0ExKKa3Mcx2CmJn3NTHPTNG05hzvY+r50tTbbYsjJspUNV+JGnUfHaSxDaaXZWiqIlkxTz4maKNc8z/HkyRn/TdvAzzk4OMLlbLucRu23lcahXR/qR4S+b4hCHU+P9ue9d97FksSVjb9dDpyt15iqgAUzsGdcl0WWtwIDWgpGCHye52Y3Aitv/KxQgNqw4XCIgSKEI5WmT9iXxuZdS3PUhzo+PrZnaGcO5yFKOhvnrPxUJFNVVYbuUrSR/izzHLlCfIg4GfRkv944PjEbpDblgpLUizo0SgMrZ9dH4re/U9+mqesWSatIPy2prWsELGeP6YMamtNxkDfb4hSKOHFdWJmnoW83/DO/o+Vd0jG1LXleim+L7XMWENTNutwusVQfIE1TmzfdQ4aUjToIgraMSV8PiA+lZ3gYasm5ogA8LOiP6Rr3fd/2kNpAbb7vt6XxoYxLbef5xZMt8mOZq/a561q5uDrfGnuS9NqSXaIvdL1cXV215OTzifVB35/Qzrck7kP7zJZcW16v67jb7W6IdUif1PfY7KdSQ3QHXbM9n3zykcwxCdvX6zViyoIPiF67eXyLs9XYvWpKAZD5VMaQpqmdpfp9N2/IObpcLG2MTyOfHMdBFLTiIwCQE6WzXDWG7FFyeofqB6enZ1gRffHcc88BAB491jEvDO0/YhlwQVs4X61trdY1y2pCB/2EJcWO3u2I2mo20HlaPk9fyh9GuMYS2oS/+7X/6Zfk52/8M+ydiH/ZVKROSNWvra0scc2zv9cR3/I3f/X3kV/Ivvqp75P5+y//m78PAPjwnT6++W1BEkWhPJt+rNUhud2TXI8iRvT779w8xOFtKUl76z1B7Ez53JLIw4Rnq5ZHep6HjHWCiqSuub+CxjPb0uOZcNKX5319z8dtolP/79/9DelnR7730dsXiIlsThPegSHP6zgqMKLfBlKjTOjXnc9mIIe0+ZZ5SqSs4xppuNpv3adhGG4gpfQ1rOqBB9AGtlzSKgrV3jcjlpEl9I18L8ZP/OhPAADuPiNl6svZOXL6QLePZQ3N52J3Lq8mJvhxyLuNosDhulZu3N45xA4Bjd2PHjx4AADYG7Fix2msPFwrI5ToejgYYMBzGSF9f67Z2Xxqdw63SwQr18e9Z2/auTJj32Mi3+I4Nhsd6H2VJvTq6gIHh9rnv77tEEK7tmu7tmu7tmu7tmu7tmu7tmu7tmu7tmvfY+1zgRDSqG4QBBbtQlhtvcapHYtUK4IjXbeymcrfAE+HpJn6NpNn0e1IZcID9AKJGqtcbM0y/ch124whI9ErRvBKF3h8KpmG5RPJiDd9iczfOL6ByVpRPBINvLyQbFUYBVCKHOOkgSInPIRGoMYop1OjIG9PrlwUG/Ky2j8l6dL62cWilZw00l/lsKiqDULK7UzF1dWVRfmVn0jnJYoi6KPRLJxlAR3y3wBwiY46CJgBcz3UfDYroiFKsDZ5soAbbJNsw9W69BKzK8k6q4TeapWi2ydHBjOIB00rxavZBJVH1sxNus6VmsqQNy1Bc4HLc2Y7i7F9lr62dtp6baCV+NO53Jxbja+6DbBPbh7NsihBc9wJ4bhaW61ZCK199zGfS1+0FlR5C6qqskySZcXOW04FzWxqP4MgxMnJ3tb3tDL0jiHwioIcTopecRp0OX/K56KtaZT2HKhNel3noOXksMw+SFy4XGFCvqUBZW01Mx2GoXH75CrRvWzli7f4ldBmHrOssEx9y8Ml3zsdT0zy3Q02ZB6PhHNC7cecqIbSS3FwIGMejZg5J2llGIYbmUbWdjMjHnq+9U8zUWOSWQO1ZTMvL8VWaBb0+s1bSIgUGe1Ln9xaxpxWBTJmIUBkYmEIuRSNktV2VDJckQsuSBlkWU3NFlRVZVnkTYnyK2Y9lAxciaMPT67Zc1V+JiWe8EMXo31mL/wWGQQAaZpbBkVJDBUhdP3GDYyZ2bygDLYSnU6nU0NmKceJZXmzHIfkCHj8RDIwb7wm/D/XDkboxbrHqfvKLN6N69fw7W99S8ZPjrrAV1LOGj4lyiMSH+/VC2SKJuHYJ5d8JvkFShLVW8aR2Xwn8hD58nolOlaC8PG4sGd+NZa9qui+pNfDgoT13Vilrlsk2ZrIQEO4asbM9xBp9o3rv1CeIMfBmgihCdfq5JIok9kKvT3aTD7nPm38crkw0v0O7dQT8g3t7R/h/bdl3v1aPvNoNLSadp2zmpwNVelhuC+8Fn/na/8uAOCll16Q+RkN4PMAjLl+9/b77O8VXn9dnpdysyn3SJIkxoemtmzPk/d5ro9Gn9dE+qf1+kKeqsgImRdFKQxHI/SZ0ccmhyHEVrfnM+ed6NuqrqHEBYpIRdPAUXJtExhQFKJjn6FcAy0awvsM9024QQxs3H565usZ67pb5zkgHH+A2H/3KZGDgKij6WSC/KOPpA/su3KALOcLhJFmGlW4Qv62t7eHiHtd96meRY4LzLiO17TNTd2eBSZfrhLpSdf+rT91Dm6eHNpzehpR43nehk9DTiTat48+/WSLMBfYQFy5rn3G04iHKi9QMGNbcg3MZ/QBlkvzFxUdfESOlKjXNUSMInCU1yHLV+Zzmcx96KHrk1SY/solOWYW05m9V3muNpExPXJ6KVKnaVoeOx2Hnk9q97MsQ11tE3Cr31NkpZ1Z+j3TpEW/xCYGQI4N9UUj33y1ulS+KvpQRYZ0tY3Osfc3AXL+zVdeIV2ztYcR/YFAkcOBb8gbbWonV4sZqkL2zuM1faFxy4uhPGgJx6BooMVihm5PJe+3ZeAdp2n9YZ5Z6nvVdW3j0X2ma2l/f3+bT2jjNXHcEk4r34+u9V6vZ2t083fa1L/ZJ2/YusiNILkh6qvOSHy+PzTe0If0dwK0yLyK61DX1bXrsn5d17V9b0hXJQOOos+gI/XMStPUEPb6t96ejOH5F5+Hw+9+8kjuROq3h2FofFVTPpOKUupR3MDzZG2uV4J+Uf6pOHKR6BmlAj/LALMLWa8Vz3AVk3HDCiikf1y26HS43y4muHksSJH7DwS585u/8esAgNG1A8yJAgwq7sFSxhInDQ5Cctt1uH5d+ub+CN/4logdfPUVQQi9+rwgoLqOh7fffQsAsKhkrX58/zUAwGQ2xqPHcqZ6RCV3umL/X75zF198Qc7LlJfRI57v3cBBXpD8ngi54jJt7xEyZLsL+65vVRwLInUu6Z96jYuuSrXznSPyO52dP8C8EaLomz8q/fuFfyAiHPcGEb58W5DXv/Tf/e8AgF/+NeEbGp3cQHpF3jQ++4TcZU6d2r1H94vZ+F4XwVN2Sm1L5AfGIaQDbLQipqlVjwaqS9VN5PkNvC5iouD+9E//TObxYA8HezzvUpmH6VTWUAMXg0N5ztOFzJX6703TtDw/PFqvLufWX+Mlow+wIqLs/GJhSKk+7Y+idZPQxdWFfHdGHyWmL+shQ8JnoyJKq5Io4dpFUcjry5rIsY7s6+Xiyqqg1KZURJTeutG1c+a7tR1CaNd2bdd2bdd2bdd2bdd2bdd2bdd2bdd27XusfT4QQpQ4buryM0reWk9dFIUQ7QDIKVMYRipN17TKH/ypBYe+F2xkq/h6tJ8dau090RBl0dZHu/ybH0i0bkn1kidXF2guJJP0yRNhWj96Vr733vffxqBHBMJCXvMio8ZNU1nmRBOQhm6oGqTM8Cjli+OHCJnR0IngFKBIM2TMvKwyVaegDPTZk8+gV1TKs9/vm6KA8lpEDK/6rt9KY4dPqbmUJSpmL3JVAslUNjY3DgqNDGs6tIazoVql46OE596RqYppZDMg70+vv2dcQEUu37N/FFnW42kJ2eVy+RneKW1hGCLpsO442FaG8P0IHc5HSIW1TcncBm3NPgC4aLNCxn9EzgXfbTP1S65R5Y1YFxoNXiI1Jn0imrgG0OkYgkzrlpdryYC5Xsuz1Buo5PuGlC3Xx5JqJtlV2qr3ETWhz3a4N2jlYQNVmQH73hjfUllsZ5plrPLK87E8B1Wcuri4sIytrl/XUd6l1sys35V50Oh74Hkt0k2j/Xw2vV6vVWzqqoytvGbU6VkmSZE+Wst7fDhsuTViwzQhZ0YpW0pkXZ9zJ2p5C5T/ZcqM0TpftXw9tWavqYjV6bRy7rRbnau5jWE4YlaYk6uZNzSOcYd4obz+kHXHpeNhVW5LFKuS1tXFpdkr5XlyA8rgxjESIrs6VIPSn57noeYaWFMqvoJjyCADQzDdMpmvseL+0nr+OGq5mHyuI+WU0Yyn74Umiam2VtVIJpOx9UFVIhSxUwaBZWUVtaJ28eDwEOdnkjn83d/5bRkXSVIC38P0Uri65kQu3LsrtnaQ9HF2JrbZ5aZ66YWXAAAfvfcmmonM/9d+8kcBALcPuqiJhjymwtRQM+C1B69iRo52riQqqPQaNE9xVzR8fv1uz9a5Zfg51Xm+ggfl52DGh7at1+nhgPX9Hufa5E7XGRbc4/o3leZ13AaOr0qOki0/2he+A9SNIaWUb8zh9+/v75uij9oUrYP/D//j/whf/91/AQD4sz8QJZXx5MqyxsoRUJAf7vqdl/B3f+bflmdwTV4zXknGeJGeoxPreSs/x1PapP09vPKSPLv5hGcw91kQt4gdPUtatc2loS+VX0+5BXtJYtLhysOhvFx5VeLxuawBcN4VESKIOtp0P9h6f4P2/Opy7xdVac+8dLdVBzeRzYpeU5uxWi2NQ0yRN2o7q+qz6ATtn+u6cNCihYBtXiFtpkapEsdlK3GuctMp1XTKskSH35Mbyk5Qrp7vIGam82D/LvupRr59FiF5BEf9ns2fNVUrbRrrw9NcEoqm2Gyb8uc6nk2UBgA8/9LLxr+gZ8kmt5La9qfVIn04uJgQEUROoGOi0obJAI8fPgIAXFHR59qxqEK5XvtM8kL3paqJZi3STJF/cWzqpIpi1jUgtnl7zei57XkeavLBKV+ku/GYM6655ULVG4l4LSvE9O086Jl/ZPPSPCXVvMlhqGfbmudfTrvT1GWrDsdx6Thd10VKX0HtXVW0qJmCflmf2XH13R4//NS4yPaooOXBwcNHovik6+P4QFC03dEeClOjJcKYSPTlfIHFQtUJiayDrqsS44dyhrz4wivYbHIma8VAOw+ArC9F1qrdUWRkWbYouD55fMpyyZ+ljVHPRl2D/X7fxqVrRn+6rmtr9OpKzjW4jqEMVIk30D1f16iI1te9qqp0cEIUPJcKVTYiOmQ8Hts8KmpRERl5nuP4SLjY9H6gXERxnJjdV44uCpri6vKBcQcpKrgTU9Z9PoZPFdee+n24aWMOuCdKotpUzTgtVwjUztXiJ90+nqBDpJpTk8PQJ5okFs4fAGg4vopz+0O3b+C11/8KAPDLv/Q/yOt7eifooKGMuBPK+776I18CAFyOP0X6HUHzhFQc/oA8iS/uH6HJpV+/9S/+JQDgb/7ID8j/f+s1LApyivJ5PT4Vf2Q6fYxhTysOFPYiczw5f4zfe/IxAGD/2jMyBqJTZ9MJukQGPTmTdTjsdrHmGnO5rzPlIPNqqC7l2ULPP/n5YAwMPeWXJX8g0VGX1Sn+/f/gHwAAPpwLUujjN/8CAPDST/8YHrzzbQDA+KHs0z6pwR5/8gjP3ror4yACZ8k1GNY1KlMJo99HP8RHA59rp0NFN70nA4Crtr1uufcAuQt3eV9MiATrJ2pfPbz+LUGST+cyIbduX8Ptm7I2370Qv3R8KvN+9/kX8K13ZIz7e7L+FfHdlJWh61RJ2qM0fS8ZGn/ceq2cn7xL9A+MQ6iuiN5krMOrfXRYqdTwfhtQzbUTAL7HZ8r13+U9Jolr4+u9cU1QabmiscKBIfp1/avNTdOV2Y/v1j4XASG9MMrhofBtOk+BSuQmCH2FGNMAEzpVlDUqczL0xKRz7pRoaoUTM5CkFyc/RpeETlpmlE3pnOcFvJK3vIiXAfbpnY8+xJ3r0ofRSByIO7fvyvuyEiWNn0pqPnggm6oTx3b5UuJYNbbL9ao1qCpNOjxEzoWkh/0VL+BlXiLylbiSjiNlR4eHJ3YwP03MV9etJP3llITFhOmVZb0BfyUEmotosVoZDFuxe0oSVrueHb7KtdiQkKvbiXF0KBvr+jFlYrlIL6c5vJAOCEkqr8aE8HkeasKjMwZX8rJGEmpJlPw8PhRHZ510TTZ0wMuyvqbX7WJJqLHJAHK9+L5vJQZKfGdlL7GPDqHeKl095M9OFHwGtm/czU2DrFQJcM6YksK6LQFlTmOpcqCbzpoaEvUAfb8N1qm8qsY+67qtlFR4cNO0cscaRGsUhhlFBsOMCIfVy1Td5NYHJbDUS6JdAgAUhI13SAh442bH/qZOlJYwVmVtZYYKsdfPSrO1EaBpKYsGdoqisL8VhEyecA1Fncjm/WBf3reiQ5it1ubcORUvdHmKK8oI62GjFwo/CsAqMBQmd9mO2SFxsxK72SUMFXLaIJca0Z1E9nCarvFbX/8jAMCI49ID5vTi0koHtBzhY5/E2K6DZUoHMdqGdTvBPmKSgFfm5IlDcjm+svlwuWbUeev1+iZJrOWGmeuhw72ke2/Gy5FbN4hIJKyBmhFL6PrdxA5m32MJLglqsywzexYwqKJw2n6ng1xJYZWwno6L7y8RcaxW+sE9OFnO0e3LGvvaz/5bAIDf+N9+VfqNZmsfA8A3WU7m5CVGLBFUud7pmOVTV+fo59LPb33jjwEA137sh9HpiyS6T5jzkjY6r4AhSzIjyq86LHnKnNzkZWueL+o0JN3YSrZUKn5AuzxbzG09FSQx9bnuO1FoetZznkdKnl+jQZ9roEeybQ0IVVVlTqEGKGva4bppbH0MOJ+XdIocx0XYkX4tl+LIZSx7c6oCP/kzPwcA+AIDar/4X/9XRlhfMEPxlR//MQDAF7/6U3hMwtB3HwjZ9ssvvih9cF189L4411oGoXup2x0j1ov+EYMxDYNhRW6Eufq89UIdBoFdZqZ6WdMy6zz7TDmI2tM0L7BmMKtPqLYGlqqq2iK8ZEetv7UGVXgmp2lq/keoEtl8NnGcICbputrtWglPi6It+ym2y7CrqrLvVsd5s7lPXV7tbHAc2/8WoNw4p/R1N27c2Oqn53mWFOsykBwr/DzpGSm9kt3qno+iCFdTXg4Z9MwXc0ROmxwBJDEk3+Nbks9KnBic6vS6Zm+0bYoxPJ0E0rLjq/OLDTugYhEMTDc1xgwa62VeiY/ztMD+sfgPIYOIl7y07SU9eJRMnl6x1PWcJNvOzNZjmm6fkZ4bWsmdiloEQYj9/cTmC2jXr+/7VgpR6QXJ11KiBOlak28MRFda+hiiwwB2t88L8kYAMa+4jrj+10zereYLBPS5hkP6vLQt89kSp2ePtvqnz6PX68F1NbC2vXcdxzE7cMkgq67jdFO+qAAAIABJREFUG9evo2ZQ6fTxNvn+nZs34PCzZnM9uxdGuK1lDybKUOSWZFqsdV3IZ7lOAFf9fFI2lPz/8fEJDo5eBbBNSAvImni63FAvVUEQoa7VJyy2fh4eytkCtL6MBkmCILDEo+dJ358wcTwej21OdY42BQQsEE1b9O4H71iJsJaRdXghrOsKjx/LfE9ZYnx4LH7EwegQIc9U9adWHF9VlVsBKqBNLtR1jV53wO9pyzWl71Pbe7q/QibLndixvrue2htZq3t7HpIeCdpT2UNu9JBzPQeXAOIOA9gr8dM+PX0fRxD/4eia/EzqTzDsaVBJxrWkXahTBwF9EmhumHeW6tMH+F/+x/9VPr/QzCNLbSapJdrcUF4/aSTo8+P/xg9iXct3//bH9+Vt9AGSyEUvZNIvlfn87//bX5PPdA/w8vf9CACgoQ967zkJNHz8YYWEF/71Qvy3W9fu2nxesn8XFBU4GcoaOhj0cXkhQZiEJUhuk8Dneq0rBv4ZUK68BiXX9pyRuzwjPUjm4iGf6wmf5fgTsZMH1/fxxv8rhNZ39yTQeDv+2wCA935zio4ve+JvfOXn5TUvy9y9f36F9959GwBw/9sfAQAC+hp56qHypJ96Z9gMjKrAgAYhlRphtVohYNBXSZ9z3ksaOIiYYEo6Kl5AqoLExQ9+9csAgLfeFpt2+5m7ODnkveyAgetbDCD1+hhelzlNeVc8JI3EbDpFwbK/+1wDHfoML7/wIs7OVBiCfi0DbMNgb+M8Ymm4Ci8s5ihIjzBi8k3vfJ2OA0+DpKrnw5LOdD1HRZLtfKlxEN6tnAAV32AlwmQTH+wNUPxrEvxPt13J2K7t2q7t2q7t2q7t2q7t2q7t2q7t2q7t2vdY+1wghA5ILNftdjcIUZlpZuS62+1uZLoYyWfWxPMCdEnSqiRrHWZpVunaZFQ1gp1phm6V42hEeOQtiUxePpZoYhfHWIYSweswWzpnxjKoB/jCbck4fOHLQqC5SCTi+Pvf/ARpxQxHl6SfmgF2q5bEjVPfo2T26PCaZVyUOLrO1igY6QuZ6X32RKCWURRYxteyaK7KsI4saqnKeQ0RPOfjCTJGFLvss37fKAlwcCIR4ZARUM1Mz2ZzI4EtmB3T+rXRXtySpUaabZXxDbt9y4QrAufyUjIA+8MEKTMuHjNSfZVDj1s5eM0Al1WFLJMMQ0dLfJi5DPwD1EomqmUBivJwUhwMFMXAris5bxgiCGQeinzIfhK5s87hkeR6riVcJDiMw9Ay80YGqeRuDTAcKvKGEFaVYl/nSAnH1r/1O22JlPKU6ppRMvHKcVAx41UqtJ4oCrcsURF+WPuaPRJE1eb31IRvOenKsoTzC5LMEblTVoXBowMifHSdeE7YEgWSb9pKGJsaMbPNfULKFZIewDXEx7RccR5ZOua3GY0lS0VU5jrcmON8wWdCCc6miOAxCzcjWfF8If2Oog5WXFfuSjJzcS/BjZuS1WvYrxXLp6bruSGKaodE8lUrzauZss1SQgBINzL8Wg6yWvI1Rw3e+1OROT/J5XdHf/cXZMxhH1NmDKIl10fUEhPf7MoebKXGWxlY3evahwum28MiNnnO2MqGWPbZOApIsgyM58ZoWMdwOBCkVOfO8zbvulZ0T2lbLUssmPEtaA80ux4EgaHQHpI8Ugnvw8SBUyvslSgAZmkjPzDkXxRvl3Usx2tcEdV0/RmB+3/tH/57AIB333oDkwspBbh49BEAYL0myslNcEWklfeB2MJ/+PM/DQCY7Ad4975k2hSYvAprLIiSmT1h37mXXC/AoyvJaJYcnyICTi/OUWfbdtjksKPgM+UqmnmP4xjJXrI1VpWIX08WVj5s6BC3RWUuee49SNtyH0BLMbbXjJZxOvCQMYt8ck0yoyPKq3q+g5C2crgn/csy+d6z8QVO3xISzldffRkA8I//k/8Cv/zLvwIAuH4ka/XwhpR7PTy7gsvsdsC5+r0/+joA4N4z9+BwIWZn26W/cZzg4EDOwkqmGvfuCIrl+Xs3kGXyDJckD+8w4xxGDlLuhZPrcpavFU1Rlsh5FmjZcRK3Igla1hJHWloi87letWU/iurRErdOJ7bzJVaE3Wol5Jdo5V5N/jjPsGKpqqfZ/0Z+3jq5gX2KBhwcyk+H503SDXB+IWN19YwLNj471Np6opeJDIv91mYq+Waq6EzHxw+88gUAwCX9q4MTKYPqJn2kRFiOmNUtK9k/vb3BBpGz7MWFlil4np3naif7vX2kPCcKnlVqJ5Mkga+y4io+wJ/jxw8MtblHNKVmYvOygMdsa07bPKO9950QNfSzxB5eXc3ss7UUTQGu+yNZs43bsTL20JO9eHhCMs6qQsU1c+32kPNPexq4Vmqt51pLnh1Y+budqWVupefqs2npbp6vzXdSBI4iSPI0g+vqWlPkKVFAJTCfUSCDZ1aXxL2+H8In4ehiTpQ0ZZKrawPUpaynq7HYuSen4vNGwSHiUPbQ6EBsw9GJ/MyylYkdnD6QvdjraqlUiSFRiw1LIq64JiZXM0M+NbT/itx5+/4DQ8/qXMWdyGxXyn1TnymCpzK72zAbr/tlb7+LkxuylnOW0imyfLFYoFQZbZb8tuXttdEJ1CQSVp8yjALr15J+RMT/Xz56F48fCdrx2pHY0wMKiZRVAYcy2Es64PfuiX1cLufmjw1YHt0lAurDd97F7JTlvDyTw+4QMX3qgneGCe14EPh4+cvfB6CVogcR30VWIiQ6KSJqpku59OeefRk+S6vOL+gfxeIbBX6JupEz9Oz0TQBAJxDbNDgcYDq7zz4rsXPE73+MA+6dqwuZl+VCEC6L+SVu3BQknvooYUJhA7/Gci6v64T8HRG6J8/1MJ3KWuvzuTlBF+MreZbTsfQBK5b+o4fLc9n33/yOIFUiVnD8qz/J8Pp35OVRX37nEbVbOw3AkvCQa+D0LTmELo6O8AM/IcjhY87H+ZiI1FGIaSif8XM/L2iZ52i/XpvegOrPuJk8r0ePWWLViTEZy+8U8ZaRjmCZV8hSomYp+X54IM+2Wk3trlGAd6p8ipS4DkVh1lVbreGoXay0zIqv8TqIiVK65NnR0fvWozmmp9K/d/2PZF7ok0dBgLu35DlNXHlueyzpfHHoYpnw3nJdfpdzredhgRXa8iUAiL09/r9GRT/2/Eyen1bsLFc1lpX8zkmlTy89Izbq5HoPbsJznYTfdw6J5M5LFK6M+YVnpA+v3PXNxwpZXaNVLx99/BBhKO/1qM6S0z5+8dl7CEmBsOhoiavsiTxdo6++Qk8+6+hQ7EASRsh41wt5/g1YQVBkczhEDteZoluVdNsxIZmCPtT4Ss7iNCvQJ+JdgzdaFdW4tZ2zngphLcUOz6YPkXRbwYq/ru0QQru2a7u2a7u2a7u2a7u2a7u2a7u2a7u2a99j7XOBENKa6fl82fK3MIukWaeiKIzTRP+mmZUgaLMKSkRclQv7f/MU4eKaxGvdwoN3IJE/zUx9bJQ7G/V2zBI6WvO4WGJBItuIKIqcGdw7t25jslYOD5I371H2OGh5Z3JG/jbJII0QkVmFXq9n/dIsckuy6G6QMTK6zExp0TS4fnKw9ZnK4XKy30OcPCXrySzt9HSM9FKi7qnbyhwDwFEywDMHkoEJKB+o3E+JX7b1w74iTciBkS5wTgK0WiEBrPcMXAcl57lWtkRmk84vL42QdkFkEQDsD0db8xEz8x4FfitVzWy3IsiybA2n0uiokru2WX1FnOk61AxdN+mjz1pT5QexLHKWtwRe9TaRZdNUmGZEdrF/WhNbFAUGrIE+3JCUlzFfwVUuiFrJvMkBUBRY5SpnSwLHWJFFe3A82Sda431xeYUFyUQVXaBrrSgyy5w77FeWr+1vKqnbVbnRnoyz3+8j0bVTKhGaEsQVJh+vMuSK4or8ACnRGiqXecSxC+pley8oiqKua8ugBKyDV8TKcj5FxWepPAlh0HJmaGZ6tiYZ7XSBtJyw63yfcja4no21z6yMz3rei4sLW9tGII2WTFPXSkIi+X1yTD3xPkVJ7rHegZADvvhV4Vm5CIDBgWQV/RmzTSQdbza4XlpuKvItpall6g1dpqTFVa1mCl1mFC3z7Hk2Vzr2unYNCaYZ9BadObUMqtoUI/KrXfR6Kp1OPiOSbmfLVUumq8SGioi8WtteuCgv+ZlENgIYUBJdSSeV/8BtfARd1l1zP1+/IRmY73/1BXznm38OAPjt3xAiRuUXyNdXmJEo8uxC9sGv/z8i+/qlez08N5D52zuSZ3PVex6DkWQxG/KjaP8CP0R/JPZUbYRKbA/2D4zAz5B13MOj0ci4kYxQnC2KIqzIY7Qgl9uaZ0Pg+WbDFInQ4TpzPBczIqaUG0JRloHXEugagTxJ2QGgp6g0I21nhrqTGGJQ+cN0fV2/eRsvCQWQoU5v3LqN//Q/+88BAL/3B/9ya+xVU+GQpKQV5ZGVLOrb33wN164J6kf3ktbNT5dj9EccI9FJl5TmPRqEGHF9HB3JcxhTYn6czTBIWnsBtD5DGIYoOI+6jhWx1ulE8DgfOW21iit4nreBZuB65vEUBKGtY90vnbhrNmJJWWYjig18s2GKbtAz9fzqEm6wLa+u4gyLxcKeU+Ntc424rrtF6AsADW1ZlmUm7x24ilCRdTxbzHF4RAQOkV0ux1IXtcneGhcKj8zZcmFzqzZF7V6aZTZWsxGVtyGTTltLtM1qtTLkoo55QA6F4+vX7Tnp2dUjB4IfRPBcFawgL5TNiwO3aeW2pX9EK/X7Nm/aTz3v87KVi69y5TNqZbUV4VopAo9Ihrxpybnns5ZIGBBktHLsJIbYcXFA/hfliVQiXdeHnaWKoFTi+tVyCS9SRJAKGhCRkS2NGFj3sYpqrJcp5pRTRiP7ZrKQvRt2gMWcnIIeM9o3+vzsGAntW0nEwv0P3wAADIZD+OQh2d9n9j8lAX13hLQgioFrz49a3qwklj7oelbi+6NrR2hqmStdX1EUGmpL/YIVOe7m8yX2R+QQTJSTiqIpw575/uvVNiIMnos5eaO6idiP4+uyD6o6M37OgOe1CwpzlKlxvMATZP7xjbsAZI88dySon1wFKJRH09sgLnd5D1moRHTHBBeUd3M+kfcfHtw0m76mLPvNw+uYkVezJhKgS17LQW+Iek6upoFIqk9X5HAKA5Ql7yhEUX38saBm3p09wu07Mo9XY3m9ovyuHR3B8aXPDx68AwB49o6Ms85DDIYB50ZQZeuUvpu/xsMnitBWVKxMxzO3r2FMER7jOovlOTx5+ABxR8bzyUdE7BSKnivN33k/F8TO2298C89/QZDC730sZ343lvX7/c99EacPyOs251rjFffh6SeIyD2rPG+KEC+bGnHAdUT59+vXpX8fv/Eekju8H3RkD8+JTGrCBdIpyYmfkJux4RnmO3j4ULhjz84fbs1HU6zsvFA+VbWdqyrAeiXPyyXKrMzJbZenxpe0XMprsjrHmufrmjY2L1sBi0rvXLUKt/Au6xWoA0WXyku0miQrG+Mi1PPZISmT67p4cCFrZbmSuU56FBw4OECP3JE//jeEb7DXJ/9XmWE/Fv9KfcSV8kROl7jkfXrBO1VmSGwXZUd8hpB3tjskeB7udwEioNSOJw7l2lHBJ0LoK18W4ZDQD0wsRasJlGvH9WJ45MPSKghFBy9WBSISl+dNe6+V+QDCnjzL5w/vbc3xepka6bUi5xUZli0Lq+iZEwkZbnCK6Rypn3RM7tTFco2AXH91qffWFg2ud6Dbd5/huOR7Hzz4xPh6v1vbIYR2bdd2bdd2bdd2bdd2bdd2bdd2bdd2bde+x9rnAiGktY/D/QP4jHCtWc8+nUpkeTGfbyiSSNRNI+bL5RqpZmkZ9Y2owOA4raynoTuIhqjqEOFSooKjoWQMVAknD3Lj0TDp9Ux5D1bQAKqnNZnM8tRVgdmMcvCMPnaZ3VysF1iuVVqRKIoNJQqNhmfKG9PUaIiO2ZQ1BYBu3MFwKNHYMFA1J/J9uI4pySivjY69qWqTx9RM2YjM7Mf3bsBXtR+O3FAvjmNRTqXwV9lox3UwVW4BRmCVv6dqahTM6JXKsaOoknJq9Y8qe5ymbXZA07J7rH/3AxfrKSWJmVnNlGunzC1LrXwumsnthBHip6TlVRow7gSW0VPkjWb7iqLAhIipVoaYmcgwghcraoU166oalqZ4/8ET+wwAJl0OtFkBVdEI/VbhoeZ3a5ZFlZxWRYbxXKLoFtVWFAFc3LhJXhCqOj1z57op6+gcKx/UcjW39dDxE86xZjA9LBbMsnCfGPKqLDGZUe4ykHU7oEzv9es3De31kDxc79+/z+9vWqng7rbaynqdtbLz/J7JTLMgS1t/h33p34hcG/1h37g8psziTafyM8vX9pl7+4JqC7MMSW2wFQDtHkzXaxTMECtSxwMlwHs9U6nqPSWJHvqBqTnp90VcE8X5FDMqkN19Ubh5zjivf3nxCL23RcUhm23LaFdV1cpx6vds8NCYSooq1TGLWmQZatrA0NtW2ul0OvC5/l2+PwiCDUSbooZaFJs+H+Uc0kzFIOmaCprtddqrOOmaNLbZXJUJXxWoyC8Rq2Q553qxXOCUMrtxougc8mM57ZpWdMeS++DDusCjD4XfRhFhCfd8L+7DqylFyozqu6eyl5ezj/EVKko89+W/CQC4GtzFG99+w+ZmsznwtlWB0CroJUkCz+XcYLA19nW2xphKIWobNmWxD/c1G0l5b8e116ht8DifpupVFDYfuqd0rsIwNOTIdKaKSvK+8XiMU+5L7Z/JSCex/VvPWK3h7/USQ5x1B0TDhT18+IFkZ9964y0ALX9G3QAffyzP5Bny09y5JfKoH+X3MZuSP250V37elPcVZYmjI8kgnhBN0VBWGOkKF1Q/69PelEWbsVTeGJCLTFWNsiwzNI6qfaiSW1GmpiKn55giefwwMM4Etd+KXHn86NTmdvNM1r2miKz2+QU2p3uGdpbnNR5P8OCRZI8LpmddX89f15CWeq4rP1Fd1y0vnP6kLUzLwhBCPhGkytOyWMwwm8keqMkvEtTSb1SN+RGqbIqcyMhu8pn1of/vD4aIou11+PjJqdkNQykqz4LjGBeS2V/aiOlkYWe22vary4ecRx9D+mgHwwP2k3wyVWpqU1mmaBJ5Ro8e1WYzg3AbaXV8eIAZeXgyQ4nR1vrBhqqq8mvJMx3sD+3ZX4ZXW2OJ4tCURdXXcJzG0D8hnyFB1iid0tRhdV4UM+57HlLOqfY5pcx1GPjoUU3VUGVzGUOe53Aj8Zm6sSKHxVYfHAxw49otAMD5pfCvLQ3dU6Pgv5Xf4ha5warS27AzXAseUcXVBXzyb3S5LmK6ikVRGMK+cRXt1J73IdeoIeWdGiCCRpW6BntiF246122971Hq/eEjQWGMx2OTX87I6ZZx7+4fDHHzpiB8ZoqwIOrIczwM92RdNVToqUpVhK0R97jWFLG9lvdFUST8pAB6tFddcumVxRI5/6aoqsJR9NYEA/KfpUSjZbRti/nYpKFPbsgzcvIJyjW5ns5kL+wdkfPSjzC/lM+4ekK+qyPZd+t0CZ/oqSnRlLPzDzmfPRQrOW/f+843AYjiGwA4WY675GK6e12e/eRKUETvffAATXGDcyrzf3ZJbpP1EsOR9Gs+ld8p8uncyYwTs6Z8/IfvfEM+Z38Pe+Ste/cd8Ynee0/Olheef9HO3ZL+83M3fxzZTMb13D3pywHRjlfnM1y7IbLvLz8va+bXfvXXZQzzFXwqSivHlK7L2Pfh1ER70v+4d0+4YesHId75SLh/FlSS9cg/c7Uc47BPnsGV/O2TM3nOk3qKsLONXN1seq4s5tu+Xub4ODiU+R8SHZmSi+n84acoMvLDOcohVCCn/VCFKb2mCVqPCB86jLWhgRp6uC3XX163Ssh6ATTVbjWwqBDQn2rICURRLayeFPAgayV5KOd8N2E1iF/h8Vp8hcGI91bul6PRAIdUbY068ixVnt33Q6u+GdFfUpRwtlzh6lS+58mprLlVQL8gTuDTyN6/f2pzrLyByql0sC/rxAnncIneDF1y9HDos7RChwiygOfTulIZOxMgxSX5LPWMzNYZRkOt1GlV0wDhbfTom2Q5kVo8Q1DkKIiGj7Jt1Hle5PB85Qwi+pm2rNOLDNX6hKg0jRHs7ffNX/9u7XMRECpJ6JWXlTkH6vTr4XhycmITbcENaPmUjw4vUTmJnbScabMky1F5dkfen+QB4p5MmB4YeogHgWeEuRXlz10GEVZ5gQUJVU9JQOrfYglI6CEZiGHs8bMrOqrwI4Qk1Vww4HWuJVp50ULK2edO7LbOOw9TNZCO75nkaUVnUgMLWTbFjBBjXa0md9pJEJoU7LZkc7FeIW/4mYTgGbmm69oBq+TSKneXBqE52j6d3i5JEDtRjMDIoQlp5PPLvRIdwjjt2WaFvdYCEUoivlqh/yyJyHhwKsFqXpftZUYva2FbsgS/hazLGOb8nhS+L88rMfimzMtiscbhgVy+npYmLYocjrMdoOz3W5LFA0fKJi4u5ODVdd3v9ox8WS/L+jfP8YyMWsnjlITV931zlusRjTydZpQ1fE8vD3pZL+xgUGOiQdLkYGAXpXKl5UhiLMbjNfJSJZ65AVxdlx48BoLGnMePKae6XK82yqdkDagD3+v12ssrJYbXGjybLy240eV+0S1beb5dQuun5Gaz6dxKCfWoahqFbPsIPMJsV+Js1XlmJMYaYEnYp2R0iOGAzj7LoBZ0GoqiMOO8XtLhJsm872+UKXLOgq706YO/ehO8auEHvyCOy3qPBOpXQJjTUYxlPLcOxAFMksQcCXtu3PNBFH7mgqpBsKqq4HImoo1AKCC2wko3NkoEm1r3YxsA1c+yYKoGgnjB298fYqDy8bi+1c+6rq3UY8HSGSufOjiw59VhUDDZkzUwTFMLkmpgZ077ny6mmFMufkLZ6O+8/i0AwMfvv4+Yh2PMy8OC5VRx0kG3J8/mKy9KQMIhYeQ0X+JTvm8cit0OOwFeeOEFeR0XlCUQ1us2WJ+utv42m7o277r+1UZHgY+9vjgEWtqnNqooClyci6NyecFyDs6567oWKJhwPJvCAT1C5I1kl32J49iCllY6Q1uTXDtBTierLTMiEXqa2gW3oKM0Y/nn2fklEpZSXDuhVPmwg3ffFedd93jKvbHOM6x5aXr0qQSg7jwjazsIPWRrEj3eEUizXvqyLMMJLxR36PAdHMr/o9jHORNDK3q0+5SEzYoK07E4hQqh1rLlpmlQlduBOMeCra4F4DT40KGDGsexETzqmbUpbqGBq7JWO9lYwEO/R1tZlrhgOYYGBXPaE9fz7Xxf0i72B0P2IcRqKWNWG6j9bJrGkjP2fVZG5dge1/Id/Wwn6rSE53ErUw8AbugZsf7de/JsijWTY1Vp36Nj0HLuVbq29aQ24tVXX7U1aaWEnEdvo9T96XUY+mEbMGW/ujxLfC+0fbZaa2JEPrubhG0pW6Lky/Qjl0sbYy/R5Bsd8XSFIZNNAEvDnwqWAlJOt9nPi7Nz+7delPQsCQLfbEXOS7DjNmhY7lZDy3RbQviMe03PyE05d016VloqTP+s3+9a6f5HDIp8+MEH7WfH8r79vlywBj3KFs9cnBxJgiLgObMXk8A43keZyXNV8Q2153nZ+pBjlqMNGPwsixAFA2pJh0kuznHgN1aernM1n8uZ7Ps+cl9LHdc2x1o+rM9AS7rqurbn/J03P7DPkPH1sdcnOXSXF2kNwucpZkxYBgzQWBCzWKHOaVsYUAsj9UUXSDM5x26SnFjL7a4ux6jpo63oWLzPS9gLz95Ejxfcj84lKZb8f+y9WY9t23Ue9q1+rd3v6qtOf8/tL6nLRiQVSmwsGzISwxGivBgIEBjIS5KHPAT5HzGSIC0QGEEcCIniWJFj2aRFkRQtSuKl2N2e9/SnTvW733v1a+VhfGOuqnMlCsnTBbjnS51TtfdqZjPmmGN84/tY9mw5FR4/FLu4ty++5TImOfX+AGGgyV15lvlkaURE8kR8rY/eVWETF5GjZLXyPm99R/bGzc1NQ2uhZbo2aQV+9PY7CAImWVwpRSnnMkYffvAIP/szKS37+X159r/1WzJfxqcrPHhfDvVa+jjYYBBoMUbX579H8ryhS3qB2sHOjsyVjMH6mMGLOF5iUsvn/60viejDl78kY+S6IY6PZI+M6B+E1hAJA9YZzwXpTH722tfQbsn7fP/PJbnz1k8fyrV6bVhcL1paqO/gui4Kzt+U++CCgfNwOsf9QwkIuR3xd0L6L0+On+IzL9Nn4PuMSHCdOJkpcz44kP7PUy0frIBagQvymZ0dOS8s68iIVKy4pwZtBptdDzWDB4k6va4LaJm+ktkzrlDXlbF9GqTWwJBVWVClEQN70EBSbUOLh8z5mTbbsiwk3FMD7t16SHcKCz5IkWHGtzb3D1iitpjxjDmSPu7OFvAYvOy3SGJPP8F3Azg2qQyO+bw35az+0q27uPuylKa9U0qw871nMl+mq4b6Qkvdh1tDBJGMXcm9f87Yw6KoUfCMqGV5Le6RZZwjK+RaSsxszmyO3QjL1I0NAyQwvSgagSKg2V9KNNQQ85iJKD1uWSX69CVtlsTNKaaTpxkmUwbuSL2ysyM+rG1bWDARooIxJWTNVyiNUMAvauuSsXVbt3Vbt3Vbt3Vbt3Vbt3Vbt3Vbt3Vbt1+y9olACI2Z+X3yeGogmUoWrHKs7XbbZPZdRnZNlgYwxHyBSl7zM7ZtG21szTiMF5KxswtgySisIoqUnLGsUgOv0wz6qCYcud3DnBE/RVr0iDCop3N8dF+ilUuVI05VajTGbKHlWswqk6S31xugR+hpp005YCe/lLGS+/jM+hdlhYRZlRbxuf6AyIz2lilpUKjrGUndnv78MZZE4Sg01yAay8QQdzeEs01k06Bj+OwO4c9hr2P6AQkXAAAgAElEQVTQCB1GvBWq7LsBbJVSz6/KM5fWytxbh/IyJN1ck6iNVquFOWUoVWZXSUNhW+adlZ+64LhVKTAjFFpbc18LM0rwrpgRbbKhGUYnDwFcJSoFpGSn5DNkCj3RqGxZwg1IgsoMemXg8TFW46npUwDoKqFxtwtySJoMsJJZh66DviJFSCyp5MZWWRk0j0obZ1mGBWHic5LrTi5krqvsKwDEqSCYtDTNdQOTlcxI7ppkSjTbrBM4Mj/6m4Rxhvtoax8xu64Z3bosMSeSpVZSQb6D3/bNsxPAgHOSmh4dHTVE05yrSsRWVZVZzyo3rbLVnu0Y4sbBNmU687whPFbUG7uhKDMjpxwTUTReyVw4Pz0xa0DLQTRrbVkWPJbT9IkKXDB7NT0c4zrJNMNEMkw7Q4nk35nvoFfJM08D6ZceKMV8iQBaZX41Q1pbzdxUhFyLGY8kSYzEpWZrU18zYR5cLfng/Op2uybb77paztiQZut9plPpD82yPH32GItlU74ENASirVYHIUucGuJMZrSK0vRVbsp95K5WWcDh2Hz7m/9K+v9M7udZ9ccImWsjsW0ZItyK2UItS+0UAeZTEjUShflbX/s6AOCN3/p3cEjOVYfPsrntYVnJGBiId5uQ727bIAsvZ/b1/5ptrgxxo3w98Fwzx85O5H203Gi5XJrSL523U47zycmJsTMqKmBIogcDLObxlefUMmvLskwprH5fn9PzPEOIrf1/RuTK6empQSLpoFxGKyUkn10umD3djg3yMWO2VUlJyzI393QpnazIlnbgYcUSumFP+myfks1VkeNgd4/dLu+wvUO0TNtDnzDz7//4pwCAWjOIYYguy0m1rxXlVBQFfM5HQ75eKyJyZZ7dUXTZqkHdlbS/KhygfdZuNwTSimx0Xdf87mM2xrIMCa8+g0u0yHyRYE5/4PDJQ3lm7mdFXv61JZ2O45jr1+Z3um+6Zg81n2eJz2y5MON0k2UyOaHsoR/h5EQg71PNBjN7WlmXylc5LxQNpMSbl9/5rbfeMv2gqLmdXUFV+UH7Y+tZ52xdVShLLfEjYT2X4nw+x4olNlqOt8Ey6clkYsrw9J0bgYKmjFJL5CcTmYNO3aD50kJLOFb8bEMc/Tzqy3Zd9LpaRqz+W1MumiSjK9+zbQtuR54hVTvVcA6gZmafFSmGCDbwAzhE6rBKBdubgvj5xjf+Nb7xh//6yrtatpZTRQiJSPlgKkT6L96VteVZFf7i4i0AwK9/5csAgPaQtqI8RKcliAdFkHkswYhaKfJa/AmL6yvNWYJnt9Hq656vNA6NEIjaxVZby46J9gh9BBznlAT0fuDi5OSEf5d32OHaPzo6QkX4wyaJyLV0dHx2jEFf1tmAdqfFEqEHj49R5izHze7xfYgAqwtz/rh+IMiDE8qDL5ZjOOzTeS3z12Up6Wsv7hlqBy3F+OKnpMSqSKboRlrKKf1guzKQJ8dn8EqiLx/Jfc9PZU0+/OlHhhh8RtLgBx8dgVMbb35OyJQnLD3vtELk7OeXSCL75utCoHtxdoqX33xd+ptnE+3XzWEHMc8OL71INOBK0Ed7u9vG1t15Ufan196Qz9RvtHD/52L3t7ekr8pS5rqgU0mNscUyR0v+b5W+QZpFfbH7C0VsWrUhTI8XWl4k4zaPS/QiQZcm9G1OsUDHJwKX8z4hQfCz8RF+MpHx/eM/kTmecA7ldW1I9nUfNOiXqkCXz65zVMd0GHlwWWqqROsg2fPOVgc3rsnznZwLsm7Escn8CM+Icu5zLRY8h8bLBGWp/rq862Ip185dB6VBFqr90DJ/HxmnrVdzvTiV8X1QacmYQoQaGfKA7+DzWnVlGZ9Lu6GpDqtR0vbp/qIiN5ZlATXPEVrZQ9SXXdsIPaL7eJ6wuPZhVYiIbE7ZD3rGSeYFukpVQkJxr9AzZg6PBPIufb0JSa3fHk/w4g1Br+0RgTZnNcPowUOsUvn83g2Zq0mRYjRnySj9h5+QumFrs4stlltyO4JPlJ8XeMgZX1iyemS2INrfcWGReFuR/Wr7kjhDwXo6LRnT/TBXehMABVGfLb5fmRcIWVY6JPVL0eZ4+35DTUI7HJLyZTwe42Ii81D3I4+orNUqR5IoFuyvb2uE0Lqt27qt27qt27qt27qt27qt27qt27qt2y9Z+0QghI6OJDPVigLcvilkXnv7jMirnOtqhRkz04kS9Gq2PMsxI7GsIVnV7LXlGqKlASXxNq8PeWfX8OAo38/urmRggtSHwwioSu6ljBBXrosJ63M1E7bUumjbMc8ckfgrGipPTpNJ7LSf5xmqTDbSSMUjRcUs5oxZ4bNUkQwrrCilqYTFhucmXWLB+nrNbTnMbNhehIC19LZ/lTjTqVwTSa5rJcBs6vxdX7IyXfajRiG3w+bejqfy85TCixOcXDB6Tl4RRfX0rcogb9okSHRY51tXlvmcEk2XZYleXyLqziUCLgBYrhLMKAepBN+eSn+6LlwisRThokH0OM6wWrIfn+Nwsm0bkSfXV/4dRTkImWlhngtoiB89zzVRYoNiM4TcwIAEzlqTf5lM1lWIELMZKhVoey5qZmM1i1wR6VUVpYk8W6ZY2EJESVeo5C85p4osM5nUAVEK/b789NwQc841jaIPnIbTRsETKaVZlSDy/PwcJ6lmhuQzKjvfjpRNB0hNZohEanlp1q7pT/ZZp9NtZLQtze64pu9UNr7kmn92KHZkETfIs9xImGZmnDRD1GMteLfXMfxRhriVWater4cu56YSb+t1ZquFmdPlRNa/ZojG4zFaJMc+eiCIwSmzynZ8A4dj4Q3IuuSlIPriSjZaub74MkVVNrbFcDIxs5TnJuNiMuLsnzjJTHa8ZsV4nCRIWC+v/DEGcWI7Db/HJS4lAPDbEfb2WIvM+WgyIkmChNntFkmsFXnWjUIjU2/WFyEdOxsDfPtf/KH06alkKoeGDHWJyLWufk/RnJZl5p+S8bbIpdAvbdQbktk85nz+p7//BwCAj+YLfPZv/zYAoE3OlslihHOSrCp3nCJ/XNe9NC5XOdlc1zUIN/282oGqqi6Rjcvz7bf3+Q5N7blK2A+3BCl04/Ytw4+iyB1FVYxnU2TKMbBq0AyAoCjKsjDXBxqkZxRF2CX3jUuUh6I9rl+/jps3b155L0VOeJ5nyOsnRO4tZktT4/7wo4fyDHnTP3coUxyzHw/2ZE/d3ehhPpDvOeTV65HzZbUokBKh0wrJy6XcGW7Z8LsZYYcl+99vyLE5ZxSSmiQJJmeyHpWjRInJ+4Metjakv0PDO0NuOztHGIpt2N4hF+ElBKtyiF0mdlaydm26pk5OjjHlGOp++cHbkr0+uZjgK1/9Tb6HStMTQeI5hqvieaSQZVkfQ684zFrXdd3wJbEfFAUUZ6mZTy9xnB8/EdvkeQFm5GnSdbY9lPnS6nYaonS+l86Tfr9v5kqDWPMNak3XgvZfmRcGhWx8p6USJTsGraGobO0Xy7IMl1pRkkSZKIpWGDWcReTj0TnhuJZ5BsP3xj5+enxs0LZRpBxE4peFkWvedXXJxgLAcNgy62o+Vy5IWRu+71/iVJJ9o9vtYjQdXe0jI1LhGLt4WbwBAGBbxkE/eiI8Qf/9fyMkuQ8fPkYrkmfNuMeBCPbjk3P82puC/nlI6et+KH/7zGdfx8WJkPbOpx8CAMZEXW9tbeG1z0s2/Q/+4LsAgC984e9Kvyo0AcCA3F4XFAIoMYUmn6v8qm2ybdvIKU/OpT8VyZTMUzhuw4sFCKfYznaf7yh8OPc+kr574e5NLIk0afUoPkM01tOjDzGZKk+bCo7IuN1/eB+7+4Ie6vhy78ND2X+nowmuX7sNAPjJn/8RAOBgX2zhYNDD9rb4RRcL+fw77wqHzmKZY4tE+kogPboQBE5dLBH45H4sxcbqHrmxuW1slwXpU0UIvXz3JXRCsRE3DmRf+lu/9hWMidI94fVDnh1effU2BuQ0GdA/PZ1I/2zvvASbvCzKL/nSK4Jm+eznX4XrX+VIy1f7fPYuOtwT44x8UoWsz+XKwuc+96vSbxP6Pal81nFrlJwEPlFfBfXMHdu9JNzSiO8AgONYxjdpc33H3AcCr4U2ETujsbx74lfYIPJryPPI4lTe+a0f/AwfPJU9fMZzXURBkHi2RF2pTZE+C/WsUxaGo1Vtn+6N167fRJvozZR+iMq758sSb/3wZwCArb6sBcuR+6Hy0KUv0qbQyOhIxrHOCvQ73Sv3mRMBBTdHJ2SlSET0s03BnjhHmfNME7I6AxXK7Oq5hUc2OHXD29PhPAlVwKaoMCX3kiHhVL8RteGgqehDKlKoBhBYMkdd3fJc3acKcPkjKVWQRv4fhA7OiOwJQvUJG6RWpZym9F9a3F8Cx0FCX96mve8p+XPkY5WJj63Ioja50169sYeFJc+15GeKuoTr0T8ln1a/kPEabLbRH1A0hv6OouiKqjB+h/LhrhKevcqG97iopG/VjzDcQmj8t+WSXJ55hs2B2JZBX95ryAqEskjgWPK7VTxlP5LDqNdHQJuSjWR9KnG06wIReYW2KVPvs2pmNp8gIv/rL2prhNC6rdu6rdu6rdu6rdu6rdu6rdu6rdu6rdsvWftEIIS++IUvAAA820JIlS+N5mrGaDpbYDyXqPSYfCSFUa1pm/pHJsNMRD7yI7QZJe4T2aI8K7brGERLz5PPHFAC+OL+GVLllNFsFeuOi6JExgghiNKYUnUjL7rYIW9MTk4Km9F4L2iyRym/9/iJMPmfnp0hjhv1IkDQTo0qlnIPMQofuAYlEDAbryohtuOj1b8qsWo5jaqIcjqoFG9EKd9sVRp1q1L5MMgrcll6tiASIaeiVTcaNMoajPZrFhVlie0NiXzevi0Zii65kqokNdlFBdJoxm0+nxvJ1QllF5MkQUx+FK21vhwd12i7Zncd5U1xHbjL8sp9DIs+HNNHftRkFaV/XJN1jvksp0+IQpnOTGRYx1RrqFutFlx+T+exyTw4LlZ8doeoEFW2C4LAzLWSSjRKZOV4jkEIKYJG/+bZHmZEN2SqFFXlCDk/FNnSpiLEapkY1NWKtcunZ5IBS9McHnk3NGtiEGtFbrLONd+nxfHb6HWxs7WNy80orKUrkxntMKupSji+68C1r0auL6MvVIWu5py9LJ2o8qGaXQyp6rDvbDbywUTDlWWJVBEAKqHC5vu+kfxVfhpVvbJhGfTDhPKt5yPJdMzjBHNmEZR/ZHMoqAOEOQacD25PeBk+ekYkXzfBKJY5beXhlfdzHMegvRRRoPMzz3ODHNO5rs9U1zXi5Grm3dV57bsNBxnHNmq1ECqCS+/HrFW73UKgXE/WVf6SyaxRhdP7FJeUmCLW7BtJaWbsy6wyXCYdjpNmihbTEX7yU0FNZFTiyJghqbyGA075KQqiS5I4MbbcV7TSjJwsWYWSte3XKS2asu7++3/2PVPz/+avyN5z57O/gv6GZGw0wzPnfpPlZWP7uL/o+imKAgFRg/oZtQsnp0dmvkbPrcUg8FDSDiSXUI7a14oaUHSdroN21MImebuUX0jHJk3TS+pKRDdcytSr3Xh+07csC6CdSqnil1FNDQAil8+uKj5ZZbLii4nsz6+8+im5jxUYyfY37gra4Bo5vva3BxhRmvXklPaK9rzfHyDsqnIY3yGTMV3lFbxC+kHRPL7T43u5BnGiKCDNfnueZ3iaAPmZKc9WluPiXDKvLaJO1Y47l9CfOn4FM5DdbteMt87tdreD/DnOLR3vN954w+zLij6MwIz2j9/G1776GwCA99+Rvv3+t4UXZjWfN0pXSsKn3At13SDjyNVV8h5FWqHkmnWIflGkbVHW+OgjQQS9+flfY79Lv1iWZZSh1NdYkYOuduoGmck1q7w/Z+fnWCyuouaqCvBoB/QdDArZsuFxz3hedXQymRjVxDCgEiH3iziOcXEmNnM+u4qSDIPGN1F/QvfisixFyhyNPdV3Obh2rfG16EdMeH8AuL4tWVblrdOxda2Go1LfS1GCeVkipgzw+VjeazQdmWxwTmU6lZ+vq9pw5Rh1sctoKqKU/vH//D8CANKUCl+dFk5P6HNS3cbx5dlf//R1JPFDAMCv/qr4XIUj+8Rfvv1tvP0jQQ196rPyTF/6kqgw+naF+w/+awDAT3/0HemDXXmm4fANWLSfx+fSDzo/PN9HZ6B7j6K2FKUTw6Xi5zn5GG/eErtQVRk8yjm/++67AIRP7ckD8RuOj0WNa4uqg9/7zp/iww9FAWt0IeP7yku3AQAb/QCzSvblkxNVSpNnevj0xCghvfHiHQBAh7YGeYDvfkv2nhs3hJPqwb1vyd+sGj1yI92gstHTR4KuOti/jogcLzt8vl0qxEYt3+zP27T3apf7w56RNk/Vx6PPEfkBHEd5WaRvHdtCa0bkTCl9+uIN4RLK4gQWURAKMAnbKjufYdAWm5KuyAE3pm/ed4zNXNFv6XblPZeLCebvi1ra5rZcO6e/PxnlOKzFfmzvqhoU+SmnFwDVmx2uF+VHreoSHcqzjyYyRnmmfeWhoL23DFJT5qrn1phOVWFOfu50t5FzTzs6l+/96N2fAwDuHV5glsu9u0Q3nhF91eu0mrWqaGmSdtVVZdCAp0d6buR+f/tFkdgDUOaqkCvj0LI6GM/InxPJ9zfb5LKZzRD1ZV4kCypJr8Z8lxyWTVQl/RzHa5SlYyImFWHlbyoqqG0UKhU9azkNolwBKZ6riMgSrZY8a7cj7+Py3JPFheHTU3+u4nmiqi3UBjbEa1/6v1XLvFBFttqoVXuNujeUC0+Va3N0aK+1KZVtWtTIaDNbRBQVFH/07RoFFYY7VP/rFeR5Wi2RswZG1TkdRekcHgGbMgemPNt43RAb5M6rqQF8jQpdrpMCrHpYzKVvj6mg3B/2oQYkCNhHfGfbtUwMoWRMoN9t9r4+/TgdE0VsBa6Dfd77fC7Pp75ep7WJMlef5upZZbmcG+R/hwi0Z8/k+1ErRLsjfXP4WHjQdI8cDAYG/fqL2iciIBSzZCS1apQ55RmtqzLfjuOgz8NWRMd0QOfE9X1TRuNdgvkDQCtoIZ5fLa1aciNz4wAVSxo8kjapw35aHjfBF27eKe+xmM1h0Ui6UOIpOqoFsMXJ+exU3mvGyT6bzXBBCWV1gpxLcn562LMU8ueFxvDepTOjjg7QkDw/D3O0ahs3SJCnzpDK1rkOYCkZMp167at2t20mkKt1Pzwo1JZlNob5qiHNAoBFXqDiQvb4jwFJlTcGAwQ8hI5YcnD8UDbVi7IwDvdypWUrcp26tpoDzCVCy81dgfO2ScTc40HJcxpJ7oQHrVx1FG0LF+eyyanhV0i6bV8mUGTJF42mCxuTMy6RSyVfABD1hugMGgnpy9eeno2Qc/7qmGhgKQxDOFZT0gAAc256i8UC45lAY1WKFnSw/MiHQ+I1JfVWvUjXdlDFWoKVmmdSstQ2IcaRHjJbfeMc37gmUOHtHXEKQz8wQRFbyS0JeW21A9NHRaxSwSyxqioTmFmqcafTtbG/ZeRiwefTw7Zt24Y8XMsFtdV1bfpP53EnVNnYAEVxldSx0BKysoRFx9uhc9GJ2nBIaJjEjdQ1IOR9Nr9bsVOnhF4ncdaUIHpKdisH3B3fxYpjP2X5Zr2S60zTc9xkoK+MpAz2lI5MJ4yxuS+GezWWZ1qWDOxUJSYzlp/xsK1B2larZYI3WmbomxLPABauyjmbuRcFhnxZD32OHyBmCe6CZW5aEuS6LtrR1QOdcsc6Vgu1dVW+3OUfqzzHlIdJU/agdjl3kXF9HDKgdv222KjvfffbuOCmps61lg8tqtIEJms6sS43Zz8ImnI6HujUISn6XeRLkuTSDseB2PbaivD2W98HALz3/X8DABi++jpe/9JXADSb6JxrMF6lCNSZsbQMVfpza2vLEMVO6cjpuobjotO/egjVjrQ9H4upzJmnR3LwmWuw07YMCaF+b2PQiCtssNRpMJB9xkiit7pw+FxxQvvmXA5SaR/ZV75Xl7lx9zTolpMcPY5jTFayd5yynG9z2EOnLf1x/bpI6mqy5WD/DnyfRLu+PMPnXpEDzM3b+/jhD/8cAPDh++8AaMh1N/d3cUrC7YpSrZajMPUCFu2G7pcNtrkyB2jdG7Wst6oqYxNqBlMypyHWDml3UpYgWbrxWraZVzqWMdd3WRZmXXnsW9d24DGZoPu6+g7nZ2dmnTXjJGv/0f17ePJQEkInR9K3Wt7earU+VipmyKLruiE+x9VStaIozMFAP28ELNptPH0q6+zZE/npM3kSp4kZw5YeBFWeOQwMqbz+1DVSlKXpdzNXN7aaQCiTVRosmq8W5hpajqrft+BgZ5vSzgwI6d4ahW3cvHFb3oOk/jPaySxOTd9qQGLOEt6joyNDyj8gObGO0dOzp8ZmBgHL6F31BQKzR0YsW1Gb1g4C+IEGoFhCQN9ytVggYTC1y7U/GPRRpSzx0/L+S3tXxHIf9TN1ziVJgv/uv/ov5X0W3At4sEjczAQP2l159u39Np83x0GbpOiZjHPYlWvffOkFI57x05/9EADQ743ZZ2cYPZV3fPVASoP+n9/7XwEAN+5ex+2XZa0H7I+bN14AALz77hP87u9Jye/enozfZz/7WXley8aKCbZv//E/BwDsH8hB7WJ0Crc8uNK3Dx48wGQqdndjQ+ZMt6eCHg56oQTpejco4lIyOLUqEPGwtbslfTRkyfDrr98y54Otravz0bYP8Hd+S8rrapbY+5x7jmOjx+SFW4gNrL/ORJblYZuJhvNz2Td1rnpRaEozF4mS+nPvqTt4+njM68szZPSlsmTZHKjp27WiEGlJageSDSdLJi7iGQYaMGXy48MnQrr/wu0XMWWpdMiS/2FfPrtczo3GeL+jpdksg58sEdJXPT2asI94/8TDfCH9vZzJHq4BkX6/a/Ys1KSLcOW9xhfzS6IsDBJZTEjOK7NmJ+diF8JAnvNoPDJBfl3zJw8eAhXt50Pp9z/81p9KH2/tw6rlb5MxEw2R9nvS0AEo7QGjaN12YGgcFvML9pH4H7//L7+JkOW4Lg/gxyMG0ZwOSs65UxJIDwkssC0LE5a5hQzov/lp2QePj59hxNLHVcwkI30Bq0jRbsu86lGoRM8zaZYbcRAV7VimCxSqEWTrfqQ68K6xT4GvZdiasI/NXmqoF9gvRdUIGpgyfRWwsSwgkD6qck2I83aODzBozKpQ+LRRjhNAq8r1PkqH4fkhUvqUFgPEF1yvebJEWCvbPomWSbicZQnSWt6nxb0r5D5fTpd4dC79/9KnJCE7SRdIKEwwmcncVvLrg/0+6A4gIHG/urC3b9805wJNsOnPdrvdlN5vXk3gnp2dGNveZ6AmYH/YdQHflWu4gcwZPR8XXnPWbLOszuOaPL8YNaWBSo9Dv9hOa3QisQ0bG3LNGWkrnjx5gn5viL+prUvG1m3d1m3d1m3d1m3d1m3d1m3d1m3d1m3dfsnaJwIh1O43ErlLwvIU5mz5Cj9sISAUXTNR6aIpsdKMXMVwqULcZtnEyENq6VbEMqhFNUOVMmNLiTlQNj2tEgxI6jhmpjhXFEa/REaitvGp/O3uK5ItmflnGB0JZDXzSVDI6Gq328ULt28DgEHNaHQxCLwma8FSrtF02shMM2u0Iqoni3MQOwGXmU2HEMPNYQt+RQIzlSwE0TzThYku6/0sRsAnyznOKMmqEVAtR/PcCMsVCXqJYlP58qRqyguS7CpB7cWPf2KyF1omp0SzGRyDftB30P8HjmsI0IaM3m8ON3BtT/49IZnvfMWIb15gFWtmjWUqnBM1StgM/7YIY+21G2lextMbhM+5RO1dz0OeaJkK0QKZojwilEpcR6hfixKBThSgNhLe8pkZ0R6Hh89MZtPX8hp9ANtGpy3Zf9siYWyupKE2lF0tYEheUTdBFOHg1QM+CzMxgWvkXhXeP2TU2Hc9AxkecK410PnMIGd0fkQK8c9KZFxztUM0VqrykF4jY06Ip000xPnRMY64LrXsUyPXqG3URmKyIeMFAN/zTOZ3yDJMLQfJbSmBABr4ppIqV2UJq1B0AdFwcYwhyddMGZRmqHtdLDl3FGJ8zvKdwHPRVUJx9yoCp9VpY8Tsc6qIM2Y6sqNz7O1IhjLRDDNLU9rLOaKCBIWczytCv1uFj6AgzLdHudKQ5VNOhY22RPkjwnanzCSmaXoJ/UYZV/5/Mpp8bEytyjZor50dyUjNcpmjnu2gLuRvSkSuaBTHLkzphH5f0UpVVTUIJIURK5lk18eS2fEOM4BLZse++61vIIieI9VlVqhdVyalVD+Xv6jr+pK8d62/BACUyxE8mxK3fD6bc96PAY8EeyVlSs/e/wB2Ks/+H//n/4X0IzOrWVWjJklit0dp0ZKIEc+Byz0E9dVy4DhOkCYKmVZCc6Ix220c7Asq7/U3Py/vzMTQaHyOp48FOaJ2w+a711mNnIidSS6ZypD74tI+N/2h5ZFKmA/bxmyqRKyClixZhlWWJVp9WZdRi2ukknnZH0RIctp0omAejcZobwrcef+G3OfmntifQb8HAkswIgmkQq9/7VdfQ/kZkUL+9veFtFb322ejMYY9GZO57uVzed4waCOkLWnRH/jokcjF2kEHZcW9lFt4wqz8dDJHGGiZs6znDZYvb2/vQtk0K2a5L5gdrmEbInlFL0YsqbDqCi7lfaeF2Ojj+2dGGEORdUrSXVVAmyiDzaHYsLm4B7ArFxbXaptE2gnnVVKlpqQhclhSS3vu2TYSlmtr+UlhCOFdMweWC+67BJtWVW7KKPtEC7S7Q/6twpRZ/4pztt1lyUOcYWdDsqZbA7EVMUsWbKdCbcoeWJYKFwXtfUIC3RX3vJ3NLThDedbdbUFaqpDHyXxuyoyHHKeKfuBsNkPGMXQo/bsiEtWyXczpD4zHch9FRe3u3UB/q0GOAkBP0XrdNmqDapC+vnYg874tYbUAACAASURBVNiqLEzGLF0h4mFrqFK+HkKiXs5PJQutZfhllhgfdD4S5N+z+z8B3Si8/NIb0m+W2KZeawsrItifPZU5/cYbktH+o299B7/99z4DANjcktKviv6EGzVkpg9/LlLbX/iilEMF4Tksh2TynB+HD2XSnR2f4z/5h18DADx4IvfzXJmf/9f/+R0sFvKgX/iczLm7t+Sa9979Kfq2zJ0XX5G5/eBnUt54fnIfX/2ClIz+q29IqdkP/0TIdre3hgaZNWUpxnQs/dLr9dDukWyZe+Pd2583yBv1kTMiY1zHMb5Bj+T0Bs05nZlyQbWxC/qGrU4bNss68sXVEgwHDnxIH834+WIp43d4dm7mpkOfRu15VZW4b8lcVbR5g/ovDJqYOjGIiHBJUhuOI+9nhG9IytsaBGbvWCoiLGwbG67osMOH9/n9GHkizzwcynq5e/1z7OMJMpJBRxFJ1M8u+HyuKU+5dyi2WUUMBoOeEcjR3dauWRZlVRiwrHw6lfl//0xQPTvwscMKjop+8YJr3mt5hgQ5UwEE+iZd20ZERNYpn+XOHemfqqyNCEmaEgm8CpGSNfknnNNt2qY0L2Bz7Xmsg1LCatuykRVNtQkAWERi5HWCTijz6c27sj/16I/8wR//BTZY2rNPiXnbk3HPlzHOeFZQ+vdRX55p0x3inH1z45VX5ZpE6GcbG6Y8vaK/8voL4guUNUwJnU2Y2JK+sm0Bc1a3KJLRslqwI5aBGeEEuWbL9xERxWoX9IFYwWFbDQWFlsuqDZOLKFpWhZUaoR39WODJutY1WRaVIZH2nEaMSH425P62raXrPJdXhama0FKTNFdBBBcZz+89+u8PiNDaHboY8mvuUnyiGffyW9c38c57FDMayd/2bxxgFst3D/bVJsnfNrZdODwLtT1Wc1gyXtXqBLevs6yW/jpoh6fTMRascLKIsqvYZwFiOJy/q5n4CjUrWg6fHMJnSXfmsfSUPtVivkSP5V0pbd98qpVFMdqMS5SsQNBqHtuN8OHPxbZe293j94heT1OULToCv6CtEULrtm7rtm7rtm7rtm7rtm7rtm7rtm7rtm6/ZO0TgRCaUI4OtmUKEpWjwWZ2t8wrLBnd08y+HyoHwiV+Fy03RBPttJgFVtIs/cz+7j7mpUTuzkjyqoicVquFdPY8+Wwjr5rxc5UiWywljrRRM1Pgk8MlIp+O77oGAWOkocmNMIuXyKtLEVoA7d7Q8HNEzCDWOmRlQ9QbeiqjyGzJfGLqeVVCOWPmIQxbRkJdZe6V4HNZN3LTWkMbZw3nkcl2azCX9w9K36AF9PuZct+4LWwMJYI/IKGXz+xEaC0bqWC2iv0aBSF6zNLaRqI8x/lUszKKNpL7bUQRNjdUWt6+0sd1XaGsrsqWG8Jdxze/izm/RuRbqqoKK9azF1YjkwkAm4OhySooD0GhZJ6Bi7ykVC0zstYtyQh0otDQPmhEPlF2NcfGhAgczRpqza/vuOa9GtRYgwRZTiQa3oukhj9yXcOLoJwG588kery7u204mM5m8+eumZjMleGsuMSnoUiTnKgB/UwUhibLarhJOPdmy4XhKFHS+J0t9ke7a9a6rq9m3GqDckqXcl+t5XXKytzAoAMNUbWLgBwnLdZQH+ztQ6P6yh1iMunxCh5TuPVr8rtD2qTzk2Mjg6oypTpfOp02DphRdnnNc47Dn64SZHzrTUbrb/B+YZnDqmTsQvZjrNwveYVTokKcObMzzGRF3RCzCbORC/ZD1PCL6LhpPyjf0J2ta+Z3+pnACwySLuzIfTZ25V263bZBASkSLyZyoUgsFKzFV9JaJRivqtIQzisJ35Aki5HbcJusuCYOmRE8Oz01aDerUoQL572n8Llf3OqGLd70h46vNu2DLMs+ViPvui4ePRRi0x/8xbcBANt3RD59HieGM055u1QmtrJcRMzwKMmzcnZ5tY2AiDaHaCMlZbdQoGZmTgvuPSI3h+0uQJ6qBftlaexBhifHMje3SOKu3GBb27vYYTZzQDLlmKise/fuYUhJ1naHXBmufKYVDTFPBTWkkvaKVFysjvEXDwVJcPhI0BBRK0BB0ureJU47AEiKHDERf7alKCWZS2dnh9gekvujkP4bHQvHSZ32sd0V5MFSkaS6N5RAnsq/o77sJZ0+13JvAwkziKOLh/I+5BPYbHWgKCDlajhmnf7DZ0dIKZOsnHg6B/uDDczzq1K+SrIe+l6TOec/OlEXd27K82kWNKD0sm3bsGinzs+I9uK1fd/HkuvrwT1BhJ0dy2eGwyHyTPcT+kK+2rvKoBHUbitSwkZDrq38EWoDK6tBDT56JKSTWztK0J4Zux2xHybkxQg834hF6DX7LRUCqIw/oajTJL6E4uaz6FKcxVPsXqOcPdErj44E8fD4+JkhZ54vz/l9sRVR2DZ7zfvvC7GwrvksLRGSa0H5iJR/o993EdB3mk6lbwOug8HgFbTJC1JwPub82Yoc+J6idIlm6JDkPJ7iyROxXUqequiUfmjjEWWq44WsU9sqkRXyPg8est83iAjILJTKPePL/X74wx8AAL72tdcQhTJf3yLnmcoWpxcpbtyW9fsf/keC4vnen3xT+m40w4PHcs2vf03Qh5/5jPCX/E//7f+Gb33zbQDA3p70S29AXp6+izZRepVFcm8iQnb2dzCeST/cJzJjY0/s3Ut3txASWfgP/4N/X/qW69SyfeMzPW+Pl0lqSFYVmVsWhUEEqf8QMSP++PFjXNuXPWpGtNH9B2I/drf3kOVKTi7IDBUcmK9qgyL2VXY7vyx3L+tf5/9Y0WaLGMMdoj75O0X+uI4Lh/tgl/6stixr/CRFuqk/nZS5qX5QrnitEkjjDBZ9KIvnhNF4hlQFJ7jfBvQ7bceD0tUd0W64pSJqEtS8QavDOUOUTbycIFnK/H36VOz+NgUATlcZXE/PXPRLlYqzdrEgL5GKv+QqwDKJMZ/LNVV7XP06185RE/FuEKyKIExSlDyD6d7z4QNZI1mem/OL8enLCmdEiCjaXE9pcZ4Zn1r7VBGGWZI1ogG+8q5Z5qeeSRz6gROisrwowmuf/tSVZ9/flT12dHSKxUps5IziCB75ljZ3h7h2h+gy7u+zVNZN2AV87m3GZyMXjmuVhgDarslxx72rhmWQQbWlfGM5ljxr6DlJbaHv+8ZGJlfmO2C5HqrqKget8fet5lraLDT/v+yfX/mMVcO2FYF0FWtiwzJ7gE58m1xHDixTSWFbV/nyPNuBR86943OZ43w9ZOkSxRZFZ4jaDW0NaVjYow80PxPbe/PaHiLuz22u9T6RzWm2MHyX47l8XtHuddGs34zI2NFIPrNcLg2KUFFiKtzy0psvG34m9adXFBK6c+tFM/aLQs8XMge9rqc882hxb+VRGEWZYUIE35Ck8YtSqxl8DIfyu3v3BDVqzipRBwGRkL+orRFC67Zu67Zu67Zu67Zu67Zu67Zu67Zu67Zuv2TtE4EQMuz8nmdUc1xHH62pZdQMmU3VoyUZw/MivVSfqCgKVQ2yUOJqPalyEV2WdCsYnVbZ07KCQQ1YlFZUJZtlkeDsQqKVR88ka7p1IlmvOsxRkkfHIc9KqfWQZQEwMumzHlgDr5bVyHxb/NtqlVziyFC29ob/p1GiaWR2ASAKB0aSu1IlMq0XLXKspin7g3LdVKlA5qFFZIXyHaiiUF4khleokXiVSOjAcwx3kGbKNGPZHfQRK/cQs5k63nnuwWKdrGY6LYccIF6KKbOgl5EPLWasHGaDyrrpFx3755EteZqhKBR5wAwHr23btnlmEz2vFHFSYnPYMu8hn5euWs7ncKBoMvm+ZpqnozFWC62rpUoKkQKlX5h3PScSRKPIbuBjlVFinOgf5UTJneJj7xVTLWSxSJBPJDtTMGW0tbWFgxtS87zN2lS9b5YUGF8wwxmQY4pjGkSRUczT32nWtd1um4hzrkpAnJdFmhllDeWU6Q9krFzPMxmbhLK5WrN+fnbRyNOrog8RJKvVyqB/oucUyFphaJ4rYF2uykHDtgwqSmWxT49PsLm5eaUfdLwd10fM+dDpskZ+0WQNW/yeItY0g3YxnuLw8JD3lN+dTQRtF2RAf0/6f0q7k3DdhbWHlJxeFrPwHSraTJIcJdeckZBlJmxy+hQWM3IRubD67Bff901GQ9WSLN4jSwszlt0OM5yo0WaWqmSmaE6li5Ojx5dQE50rfQVEhh9kyXWpY9vpdC4pIKht5RrMMqOmV/IzZyeSyV0t5nCYCUlXKu9NWduEHDj/H9vlbLRZ14rKLEszB3Q+e54HFzL29z6g7O41qf1P4wyRTzVDruMDzrlOp49JJVnqCbOmI5LEDIcbhotmmapMPe1BFCFgbrMiKkHRn/3+EMOu8oGQW4b8FnG8BCLpmxXnaEbOgTiZ4C9/9ID3kffb2t427z5ZMBNNZEwUMKN+cY7ZfMRnkfeLe0RTFTMjGx0SwefWgMWMZkLEiUPls7hIsCpUSUN+jsjB9+DpET41lD4NWCNfKp9d1EGa0Q4y20fBFqTJAhnXQJjKvjQmv8vq/MLwyKGS/tzd4VzvdtHlfjYkIiPLlF+uhlXLvU/GiuRo1Cx1b9XMnsrOB5GPTSLBWhybsiwN90dGAqUx1UQ7nY5BzuiY3Lku9jjqeFjFcv1uWxHRzK7bDtJMxtemEpguQdu2L2XAya2h/Bg24JIL4dZteef33xFZ5na3Z/K8ZyfH7Kt9XrtGyj315JGsy1ZXnvPGtQ14Ede1yh4TBRbHKUZnVGTkntXfCAxXmR8ov4UijlPDm7FSqeczyqdnsVmPFX2TSBXjyhzxin4i3+/ll2Qu+V5u+lbn8XQi6+D0eILZlHsdZcKHG1T/6h3gZ49F8XTIbLLySDx9+hhd7gWGM66l2doKx8eSgZ1ekMeEaMz9m9dx60DefXJBu31xih/+QD5/+5Z8/iT6MQDg7os30SaP1jd/X5S63v+ZzKE/ufFN/Mavyxgqf8Y7HwqK8cG9BeiSYLsjnEAvHdyWa9cLg7R8fF/6ocN9/nf+vb+P40MZC92vq1rG7Stf/opBL8+nRCEy43z37iZ8T54z49ivOBdm8znmU0EfK3/XkrY+z0tkz6nheq4ialyUliqhMtNfOzi/OL/y+R2iJUezBfyWqobKGkwzorszy6xf16f6IudOt9s3z5pyX/LZ59PRGEdHYu/3rwla4PBI5tB8ucDBLfpHVKvyfFU4Koy9r+ure5RtN1x96vuq0l272zVKYirXXZErLIlTdLjfLmlHTi/OUGplA314RcG4rgunuKokqJJTqzQ3iHfLIsLE8LnUhkfUpyrR2YVyDHrY4FxbxVSqo9/v2kBNWe8++RH1XBFFkbF9Oe0weM5aZZnpf/W1nZL8fnHDYWPb0rcLKrzOFwv4UaMKCchpcEI/aqrnMqKqsro0CPK8UsQNOQwt62OIFrWdq7jA6bnYYVWiVr/xV77wefQ2ZexnM6rJEbXe6vl45VXh+3rnfbGxCZFr5X5hlIYdIrx9SsQXRYaiks8pMsmg5K0KFX3e5ULRWGJHlssUq/TqOSnJMjjk9jMoG+PTBOa8opUGcaxK2ytkpaqusV+M32gpsNb0meK+bdsy91blX1v71bKM4qetYqCX/DC6rmbeK5eQjbqRvCeSSd/Bqi1TeZFwjge29NkiizFayO8W5FVUBNCgXRiO3d19QfC8fvcFnFzIGM54/oiIFqtrF136JNNC1rPyZi7mU2Mj+t2r3GXdbhdd8vFlI1YG0U86Ojw10FjdQwLavvF4jIBj3vZlvc0nsl5d10WL/LIB5/94Qn5Du+HLLLkfjoiAuvXiC9jcGVx9Tk+5y4qrHFF/TftEBIT2Dnb5LxsxHSvdrJQQKolTBIRMqlEZbMlCLYsaLg/VCk9VSWpYDiwelHSSPzuWQ1sdZ3AZ1ChYNtHiRvEky9CyVJryKuGxF0QAA1YqN6oH1/3NTRDZBtsizstuSLNNsIGHRHVk0iwzcGk1/NNFbKDhhmyV3y/zwvzO0Y3W0VIAG/vXBAKqk3pFY71KErNRUtXXGPydboAWD4wOV69nJNldI5WqB0gjsVvUCDgmfqAy2JQPvLjABTukZNmFkkpXbr8JUuhw0SgVZWaeSydyEARYzg+v9Lc6DaHnm3mh5VAKSSzSDNCAgqUOdAMtNQEC+QRK/itJc9R0ZBckc9PFXmQJYksJkmWOWuz/wA/AigvkOUmzeW3bqdFn6dy1G/Izr3VjSg1k0qHr7mgQqCjNO2vgQ9/XsixEneYwA0iQYzymnKw6LpQBrVGh1Zf+WHEDOyUB3mw2M/NK+8oEi4LA/JvT0MhPt1othPxbi86dfZngvabDPiHMN1Oy3RrdLsuKWGrT1bEJAzN/bd5QA5VbgyEylhUdkbRWCdHH8xkmPCyDczaKIhy9/4G8lpFxl/UZZyksOmkeHQMtVbOt2pRkHh6K06vzpShLhISzD0gS+OihOP5OAuTc7I8Vbs6AQ7Q1wGIm/V3ScQ4uVHbaQ4vXrHKZcz1f5sDu3h56mzykkPTQp40qisKsRxPo4uaY57kJHizpvNZ5buaPkWxWie7MQuTIfWoSJuvBuKrHDfSa189Itr8YnTVy82wjnY9BCLCPTV89ecL7FegMWbrBjTrnurvcni85uNz+Kmfv+X64HFDVtaTPa9s2ikrG4uREDontFm1fbxMOSTFdW4PM8nylmyIbszSK0uQqQ/zo0SNzqGyRbFjnVVVV2OZhS6Xh1al/enqOCyYc1BUrGTxy3Ap1oXscZbAZTIuCEEWp5cq0DVVDJu57lCvOGYAt5edofI5+j0FPBnFHLH103Br37kuJzoiywBZKgOT6M5ZgqYM02L1tShxWTLK0uU7PFilOWBJ7RnLXh09lTb2UAUdnJH/scM+idPAivjClGndflrWhQZWj0TFcV+5zbVvKYtQ2xasET0j6e36kwRvdM12AAaGKQZghAz1uWWJyLu+/5EGuRwfLzjOcHVKynZLPq9UKw4H0X0qS/V5XAnFZlsGyGEijI/3BfSnBe3j4AOcTITG9cUcOox6DzUm6QEibXlQs601J1n8piaHNJFTcJiC0wXWm0tmLZWyEICIGCG7dkud88OghlkyWtHvymU5X+n80eRcFCbS7bcLOeZ0sLrGzLb6Gyh+PZo9R5izXITlu1JL/7+9s48c//pfsK5kDWuYYL4/x+PFjAE2ARuXnkyTBNfo0niv98d3v/olc2xngYE+CBlMGMs7OxE/Y2xni5JGUSFU8hD18T35u7/SMTbk4vVp2fHx8jK9//TcBAP/kn/wzAMDtW/RTnRl+82u/IZ9nQI8K8/jON34Xb3zqRQDAS3fk0PzpF+9gNZG19vqn5F2Hm5rgiDGfy7r6z/7T3wYA3PtA5mzg9bGYia/6woty7zffkGtubdxBzLLh+eyY/XnB+7bw61/5Vfab/G10ccprtoBNGftruxrgkYE7PjoHArFrQaC1OjJuZQEw1oP5XGxMqqTIvgunlPGarBjM4fwYbGyafenwUN5rOpZ17od9pPQl1U6Ox2MzL3ROP3wka7GuLWyw3957JGWGmuRpdzZwn2WXQ5bSxgu59nsfvo1XXnkFALBasQQskeebLyscn8p8WqxkzixjTW5GJviQ8HCuZaWSgGTJtArY0A9MksQED/E8kXFWmb1U/awhid3ztMA5/QBNCk3mCRz6H0XZ7B0AEAS2kQP3mbCISwaRPdvIzWu7nDD1eJba2pVywcjl4XcxQ87+90v16bmXwDGB75x965Ise7lITUJPKT1WmSb/YjOmYaBEws3+a/wIBt192rZlnMPh+i+MYEhuxCUqnpd0fllOQ1ysgQVcKjl/vmxefWXXcTAm6W+iwig8g42n55hwfW6RyFxFYazKQrsjvuv1fVnX9x/IHHocuBhuyOc/86YEjc7OxN8pixwbO7Kez7nnhSxjDVu2KcsbXYg/Ea/4Lo4Pm2CBQk8rgWfWiRIzG9+mKuEwWJYXWjomf4uT3JRkajP/1ygSLgWEjPz8x0vGTOGdZUH9FQ046merqhEf0OaYsjLr46VKVZPE03mvQeNYSxLhI2ZAtObZS4VfEAaweWbb25Y5XhaZOa+ou5hzXrWjCD7PpWGgZaHcI6/fwN7OlnkPAJivlOjdNdfSfVaDv0meYcDgqu6Nuq/lSQunp2LXOptXiarjeInxmH/rshTdV4EN39jknL7yq6/J/Hrv/ocYMZD5ygsvyzvT32lHPRSLvzkgtC4ZW7d1W7d1W7d1W7d1W7d1W7d1W7d1W7d1+yVrnwiE0ISybYBtoNqKClGIph+EJqOvUXqNjqNOYWtEEgp74xUdx0ARLU9CeQc3JNPUhWeyHEWPRKlf/SoA4NlH97Bg9FYjgAy8IqtKrBh5Uxm7KTP90dzDciZRunNCqLsbB7xABY0CqlSclsg5rmtK2/QdSsuCbcs7axbfJ6LJsewG2aJoKD5oFLZM1Fxl6hV9ZNu2gXZqtFmjmP12hBUj5B/dl0zdeCTR8DBsYWNAEtLguZI7xzWk0kmmpMOpud/mpmRsGsSCPG8S54iYeXeY0Xb9Bv2ic0CRQkmeoXX7lvQNYfiGULwqDFxR+wqmtCo2WVPPU7iuom0C81ylQmoZuZ4vV7AIH3QI9dugJGoUuEg0I0S0QK4kx3ARL+RvHZJv9lneVJU5HGaBi1qhpCzRcmyD5FC8kpJSZ3HSQHIZwa6YhbIcG0eSmDOw/el0iixXFBRhukQixGlq5Hlb7lVy3eFw06yv0VguekZIfxCUhjh3wLmgrSiBmnM5Iax1dCHZzbIqjHyz15FrByxzbG9uo9snUoKZCY2wF1WDitKsjkpYnz47Muvf4Zhq9L0z6GOT8y9U2U3bNSURSsynZWtlVWFIyVTNcvEjSFYxtlkOo+tLIbxFWSLlPAxJThrpui6BkAiQgKiLcioXvVgUyEk0f+dlKSsrx7LeFk6NmoipOJb7DUNZn24l0vMAcMZM+Hwsz9vv903/6TtoBkeyGAotJ7Ku9nCkkqmaxeGcy8oCDhF1WnqaU1o39HBpLYXsW943TwxWWGHxc/ZxpxU12Sb+7fCpZIy9oGWyRbbOe5LR2t7fTIR3+V0vt+eJo58nm7/8mbIs0e7LvZQw8L13BBkTw8MZS1HsUImpiexybGyGO+wP+X6LUOASLri84BKNovuZ69lIKxK4lldLacuiwjJVNJN8v6BtqVEAJa9hyc8o0vIrH27AUjte64N70seu48OyZT1ubcm83NmRz+b1HMfnYncepfKecSr2Kwg8PDsUZN3jR4KcaoURIqIvNRN7/5585nM714FS1p6iKy9OpD+f9Sq88pLsvS3awNlI5vG414PLfei+llaOWE7m10gyPk9fSpxCIgpOLyYoVAJ2TgLRrMnK1+BeRb9C55nlhEhYzqEbxzPOxxq2mReaET+cUl48TUw55Mm59Mfm5jY2NiSD+Nqrb/Ca0i+3b+8aMuMdZuP/0T/6v6WPVwX+9//jnwIQdBcA2I6iWyMsY9n3OiozyzVoWZZ5PkOkT0EJ13WwJIn3M4oIaLI8ClsGieHw+U5PZNzu338LK665PaJtRhfSny/evYX33v5LAMAP/+xDAE35LEoX8R1BX7QiwtV7fYyJSHl8T9bQimV931vNsVjInqolXwv24/n5Q3Pv733j9+Q5Xd2Xeviz7xBl87p8b8oSjvb2C/juH/0LAEBKsvfzU5l7m5sOXnlZEDsv3H0BAPDWX/y5/PzTp3j9DZlPcSJ9trMre8jv/LtfxpOn7wEAvvwlsccHB/K3weAOQk/W/+/8tiCF9J1+8+tvotWWsXzyQFAsyfwI/+AffBEAsCRCrq64dsNryLocC5YlfOp1eaa8iBF68sxKWJqS1PT05B04tvrBRLLTHvhhjif3pLSsIOFom2WmVWIZ5GlIFFBdqlDGDuJM/qZIxmxJny2z4BKp4rv0J4isq+0aR8eyJkZT2v8ZCVpnC7MfTReKrpTn7rk9pAn3evo540mGgD6uInCWFD9ptzs4PmZ5FtfxiIjjxXsfGrqHByT81nbzxm3cvyf762qldAlEBQ6HuHn9Jf5txfcjCtcBHnwk39NSKdvWUriGpkLRQOpzJ0kC32dJSkve5ehI0CGL1QqdjoyF7lnvLeUege+jR0qE03PujZ5nSLWz4ir6oq4XSNg3WqIGp0G+qx/8PKK3LDOzf+leOPDVb8+RkCBZETRavpPncYPAUbQN15uU6NPGsqxaiYhbkY+OQVbJtUKv2Xf9QGzXZMI9lf+Pogg+/UZ9liJZ4d4jWSd1rsgYRdEXxl6oD1/RfteOa8pR9cygSKYgCAyytuA4b7FMsdsJDCpy0OVeQuBVmubQ6qlPf0psYEyJ+GVSodsSe/GXP3gHAODxneu6wE9/THRjSNQ49/AgcJqySyLxlloqG4SGdiOrm4oRfX+H421xjFZJilzPDDxz6RwqqqZkS/2+Ws+fVWXQQupjG5/JtlFp/ynzMe/nWpY5Qznqe+mcqypY9dWqjprRB8eyzfea+yjxN+DgasWCCiP0O13VsTHVD236EBeTCwRE1Yxom72Lc+zsiW29oK04I5q7vzVAoc4WLzqfyJ4QL+aGjmFrS84CEVHZWVGada/lqC6rZG7tbn6MnmJJdNnGziZaXdlDE0Xf9mSeTRczOAGfhRMsYEVAmseoeG7sE1n0bCR+3Y0Xb+B6LQjS8yM5s+1vcS+fXBhf5he1NUJo3dZt3dZt3dZt3dZt3dZt3dZt3dZt3dbtl6x9IhBCMTN6dt1Es5WMc9gnusd2TJRNOV4sZjZ8xzdyvto8R0mbDY7IlJMq8iQra5P1V5TN5qYgIDZ2tmBlV+W9lZsgL1PEilx4LjFtW8AWa/fTQqKV45V837VsU56pEVjl7cjKysguala+0++Y/tCmWYw0bqQttV5bCbx63YZEtlTZRUYxgXTZUQAAIABJREFUHcfBoCd9qxnpyUSleFeXZF+ZSWEdcpIC508om0jUhXKr9Hsetkle2lWCarY4WaJF0jON0Cv6oNqsDPrkGespNfPQ7XYN19FlUtjpGWukr/LswakrlESAGKrwS3LpfdZ3OtBrMlpd2chVtpzv7pPE008TuCQG95itii8kc4PARcGsYjvSCL1kNtI8x80bQgapJHVgxs1CDZv3m5FsdUyE3GIZY5Zczfx6RJyEfoB2KO/Qpcy0yZoUBQYBUQacV+m0wNaQyBJfM1jymVv7u8ivkWCd3Cbax2EYGtLka9f3ef0m86XcXlqbqmO6WsUmE67oN50fnXbLfA6JzDWL9bq17WCyvErqqlH1Vidq7lcpEXlD3qqZMuWvUqSQFwbYZBYu14xnUaFLBEdKEt+IKDE/9GGlkn3ucEK1SBq8qmqcPZGsuGZilMzaRYUlpYWHm4L0mZOLya5KbLDmvNsm5wtRIuOqRkLbtZzJfNJyapGCp4z2SJ59RKLfurIw3CIKqq9khCTVjBeYM1v6vOx8nteGT0p/tltDk1VU3hi1GU6SiSEDUMUNnxMg6EBdv3pvzWBuuE0WTnMNRr5+fIxYEYVEkJw+k0yfa1sGFaat3yORI9cI8FejgP665jjOx1AUmhW+fK3LNe4rynxbtMnLUyI7bt1FpcTAJKYtLmUbF1x0AbNayu/itzxDqHp2KvM4yxpOH+SSNa4oO8rlgjBsoxOSQ4Vr3iECJK9WKHJmjGnT55QhPTpaGBScZgQt2urtnV20PfJAJSQxJKqw5V03NfgOzvl5QXs8evgMR0+fNs8MmYfnzJ7lrKXvkQD9/PAefvK2oE48Zr7siiS2H3bQJe/asw+EVDclivCoTFAz+5ZvCOLKWki/9rs9zBdyv+259OMmkaU7BzfQ4r0VIaBEjq12C1RyRUZUhK+Em45rsosbRPIpOhWwDNK1VhQnM4Sr+QxHxzJvf/yjbwAAHn70Nnzalt/9x/8DABi+mzDy8fLLgkDY2hLf4vvf+Q77OkWfPFUJ557vyHivFhW6gz32tyKeSO7q+iiXsi6UjFOXRl1Zhq/g4pwIZxWmcD3jM7z/jiB+ikLQCacXH2E8lbGfnkt/fOePZRy/9pUXTLZ1lxyDowtB9cxnK+ySj+5/+b1vAwBeeblr9o7btwUd/e67gpZpe8BnvyQIn3/z3T9mv4jvcPfFfUynct3f+OJt+Txt2GDYEWlgNJnUfl/22Pn8BK+9JkiaOzfle2miRMaO4Z1TFM+n3/i7AIDhzoEZV1jy+YuRculNsL/zIvtbeJ4UOQvLRx4r+u2Yn5F5NU0zzIhsa1EefNhuYzUjunRKfg+u0ywqkJDoNfSkH8YX9IcdG+cLckkEQjI/W1CwYdgBXEXPKgeZ8JJMJit4RCnORyRRT+WZHNtDnkv/nV1M2VfyWq7dR1GQv4uiAgEz4UVZ4ckTmU8pfRrlu4qTJc7nRGJUVyW9qyo2/oPnEyXNv8WpDceVPtI9v9fdMvZa0Shdckw5jmf6SlGqiqJLsworimxsENEbqIT4bGZQBR7RVAmREz8/e2D8ho8hafKiEdGom70DkPkxpyCE+mE6P4IggEeC/BErBxxb7HcYeSjKqxwsUVv2usDzMCH6OCAqOAxDIx9v2w2Xpn5fycP1GZSLs8hrlOSvtHifgMgFywkMf5o6HolDbpksxVgJemt55gAyv/Iih8PvqR0ZEnnm2BW8QP0CkjzT37QdB+1IeVmIOuL9bNcxKCDlK014xmm1WliwP5TwdHdnA++8LajDnGi5kP03W8SwyIquCHQls7bt2oylqZ7gfaqqMufHUsn65a7Y3tkE3XvkRE413KaVOX+syP00GMp8Hh1NcXYq9rRLf/GYCMA4XqLTE9sOg9yW5316PDPjW6q0PP3Vys4Nh6wioBwHCOgb6PmvyBuupPI58QzlwS2q+mPE0aUiZGzLrAU9a2uHCDk3uWSJFFL+zRIWSuXepDCEQk6KqjZIIkPWqtRIjqXHI6S18o42fDcR+ZUM8p1nnKzOURBxmXny+ZfvCkJmdH6M7qbsPfeeip+VOxUCImRdothu3ZF9wws9HJ0IolYRZLvkHkrTGBdEvJoKAvX/YJvKpZ1dsVMJfdaT81Phr0VTvaDr+/HhMywoZb+5L3/zAiX5rgxn5empzCtnJn23XMYGnZtwsR+RJzGaz818iuj36fkpjmPDd/qL2hohtG7rtm7rtm7rtm7rtm7rtm7rtm7rtm7r9kvWPhEIoT4jbFZdGx4Y5Ttp6l5LxMz6pMze+ZrVdByg1pguswuqKFNXJhujiAJVVZhMU8OHMyPiIfeJGoh8UxNYElEUUYEsLmIk/Pw5I3DHxxJBdIIcBajqsZLswIJ12H7gwdPMO+tEDTqlsg2TPgPrRtoeaJARKSWGPcc1Ckw7zDwqqur49Bj16qrCjq+IoSLHxblEKZ9XJeoEPkIiI3a22P9UI0jTFPFcMmzKd7DBLL6TV43cILOTnqf1w22UifIZsV5cJWJz4cYBAI8oCM2ydPwOQNUdIzlp2wgrZuSIQmkTueA7Furyao1kIzPtoCioBsKaXZ9ZDN+3DAJMs+pMVGBizw1zvCogPHwiUf7tm/voDZosGCCyzwAQ2CGsuTynr2z5UL4gFyu+c8B5ucFVuLXjGc4LBTNQYAqRD7iuPIPnX2WLz/McrTYz2Mxe7EYlHIeoC1si0crPBXsKty03vZhczYohB6ZELOk4KffF3kYHcxVrI/BpuNnhZ0OThVOODY202/USCTOqUUt5XHi/ysbFqaBsYvaLzsvbt2/BXTALp+g3zuOlnRjJ02FHUV/kMTg/wtnThPeRZ9i/dh3vvyvSoIqEOTiQDMLp01N4vJaiO8Z8hsDzcfpIEBJDch3l7Kv5coaLKTlXjiXK/+yBqIy93O8iXYpNePefSzZ+NWbW0HbgQtccsx8c0uGgjV6Hmc1Tubbyu0TdIUbMNrUDzVzKNReLhqtBs4X6M45jMy+0jfIabSqEnPNzu3vkx4qiJpNXXkXlFAsLAbP2um5i2jTXdc0zaNZI+3PTs9EiYk3/5hRUc3EAW9UwmBUbU4XK+f+Zsqiq6mOqZJp5Bv5qVbKMSIyNlty75cu4//pnbiPhc61oIyJf1oRdeDglTLRFvjDNUmZlBZcZcFgyP3SNrFYrVJQZ9aj4tLHB7GSRoS6oxkcUotab205p+NMCZmQvyPOWJLHhhVPUTKdHmfbJFPvbMhYd7mMqGZ8WS6TMKo7Gkm3qdSTLflFVeOG68CNMyVeRpDkOXhQut9VMEAwx97r3f/znsGmv1Q70fKrBrGb42Y/eAgAcPnnKflfuPxcgetMhxtMjWsYpLdw9kP7ut2RPtAqxr+PDBPdGRPxxPisS7KwqsKTKlarCdYiudCwPLtGvxw9VKZQonTBAyH2oojpixr5K0hUy8vK9flPsR5YVhrfIZ9a6jKU/puMV3iXyZrGU3+1tlvzZReCpvK48+4f3iIjMXeRUFVPhFJ3NjuOYua12u64adVX1nWpm80uiX5LVHENmSBNy+kxHlKmenqDDzeazb9wFAPzbXxVkzAfvv4vbtwQBGXNv2PiVTwMADg6uG3nkz77xOwAAL8iwtS3zLkmk37/8a39b+mC+MnxmL96W33muvLvlprAs6VPldMvIe1BWBSxy1qxi5ZyUOTvo3sCMSo5Hz2Q+Ngq7LsYXMiYrPmevJ2vj8ORnBukQEcGgXHet1gDzM6JKiLY50/2tTuDR4fEc8oDRtmSVBZoIhKo66jkYqdIkYYCbW/IMx6dHRhHQ92g/UkXiuXA98bFGE16fSJ/ZMsOStsElOm0xJ4eF30adEh2dKlpA9iLP6jbcjCrbTR8ljh+jrlThicqktO2wbMymsofovh6E5BdKF+iSW9DhvlHminyoUBm1R0Uy0d8qEpREEujeH7U89Dk+RoGW+8zFdALNYYfk1Kg4x+fzlUE1jKfip2axrLswDM3amVA9Ue3kaDJCSciBotF0paWrGK2QNoX7rM6d3e0tbG8O+D5X0UNlWaNQ7hqFQ5ALpNdplO2MMqBWCaRL1DybRIpwLnNkHCflnszZf1YNhORb0mu2fF37QJsIteZMRZSY5yIrqSbMDXaVU0Wt20LYVh+UXJ+p+g61UbjdJA9dsSTS3PUNciTj8+k715VlFNgUaHWxWJk+aBN1qAjKggjillcjaGmFg9jxyTQ3PEkOzxgF9+3aglEgS5RXlb5GbTX9rWdDy9VyjRIe56320ZSI5Z/+ZIQ+kRUdnjWWVK/K6hI2ffLJmTzf7lB8qN5mC5MleUSJFL9NzrSTk1NzBlU/ZEL/rHAceIqyJs+brYp/rmVQV1rN4Hm+qepQV82oxVaVQc+rkq/yllbjmeGkq/UMqgphtm3mk/GhFChU1QY9pe6Uyz63LRt5ofcmspx/S7PCnLkM/xTnh2tXpuKgNryPMP0zIRrW9yg3T/TiPFnAdWWubfepUsu1u7Gzj5AKcH0qw7Z7fcOdqlxurr/Qt8EpUUAx15uia6LARZfcp6GvCEO572Q6wSKQ51P7pv6B50XY25N9U9GpAdUAe30fNX3KEXmFujxrBkGABefD8ZmcAe7cEiRTu7WBjCjJOdfQbEq+TauNex/IGefOvqBno+tEj8HGcqn75l/fPhEBoUzLAqrawBufb5ZloeLkChw1jBxUJ4A6B0a2W8nF8tLA/WseQhd0UnqWZxakHmR6m0ocOMBPWF6xNRRndMKNxvYBy1Ui4mYRAVJOstETp2Z0TkdMCYx9zxjNONYSGNVbB2w6HoGj0L/aGKgwoLNKJxtVjYyw1Kck0cvo3OwdbBnYoULbLisKDrpXiapVXi+PZ+iSLHhGaNvZBQmFywobg5DPJ8+8nDxifxQG7tai0axo1MOw2Sg6LGtaLrX/HWzui+MXcqHo3/L02MDXKjKzlnmBvHWVlLsTMrBjW8hTJQlmAEk96do2jl9bg0RavpJUqLj4bBrWDqHbtpVgQIcgoaH69s9+AAA4/8jF3VvSVzduCVQw5AK3vbYxto5aNkI9W2GIkHMzoNyoIWLzPGQ0DraSExPSblUxUq6TatkQBwIyxk9/Lt/TIOLe3h5mJKhWSeg9EuU9OXwKjwee66ES3RG+HAUA/SI1ninlppNRjZLrZKMlxraWig8UeY4WnQU1+PFS+rXf7aK/JQeRGXeWKeWqHcfD3pBlotfk+dQQB/YCD58IOeadAzGsW1sCi58nFk4uJJBUcS0NGKDsbQRArYS78vPpswe4uy1zs8uN4pxlD1susMMDzIIlTi4hq3VSYtOTd76pkrfvCdlop9vC574oUtdL2pR/RsPfH2wAqayhdiJj8plPyWefpSWWJMe+dltsyxGDtEF+gWgp/XewK312RulJK/Qx5aYVrViW12aQKgJiqOyo9LunjpJfI9ZgMwNd3b0NZDy8XrBsp9umLVtYOD2lxDihyV0GOxIUyGhTVCK65IFia2vLbNYKW1ZZ4DS34DEYW9L5WpwJRLfb7SPjnKkpVw3Obats5Od/UcnY88GfsiyvyOxebpcl6S//LHOZfyX7ZXIk8uDpuIu4lkDwk6cyZ3otWfNu3sK12/8ve28WI9mWXYetO8eNecg5a656Y72pm69nihRNm4Q8AZZpm4YIGzD0acCA4R//iIAMGP6wYMCAZUmgLQMSLVgtSrZMssWm2d3s5tADe3z96s01ZlZmZETGPNwbd/LHXvtEZj2Ssj4MNMA4P1mVGXGHM+yzz95rryXPPDshpFxle4OKkdDVfndNENhF7ClBpzifJQPjLxAyIDOh45iTYHw0OccNBmEzlmlUWLb44s1rsHkIHU7lWTplWS/WuIuMBN8FpASyyLT88wiHhwK1JrIZy6nYwK1aC9f2JSCUuVq+bePsTGDYLTpK50/lmslyhIwHiMyTuTngnFgkMSbvPpS/+XLgae3IXnlw9QZaXL9Xd2Sd7nXkmdLFEjdvKuHiN6UfA3nnTqvANa6PmAIACtF3XRuORYg4RQF8lhKcPe2bAPKMwSI9ABUAMgb51JZt8dDSvFJFpczSNqwJO2cM7rXbN+Sdad+azbop+eqRKDZayRjdvLZvDmvLhfTtf/e3fh0AUK9uI8eUz6Vklyy1ztdJklJJS0d5EE9XyHkymNNhrFFefDkfr9+R17x1TUrbcL2KGcUwbPok6VRs4Cu37poyDptBqk6FMu/didmzzfq0a3jwrtiGvNDgJcsg0gIeg6SuI+OtpW12CabEoSjEPqoD3ev11yVENU1CsJQ/T9BqyTwaU7I5z0h875YwnWowUMrY7r/Pd7GbqHL+nsUydyybksbOHA69pitXON485E/mCYqcZV4MoumhLEoLlCuyiI45B0qug+mKpVG0i/d+xBLlascQ8Kfsd9tW8tQxUEi/jaYiqb6iPbTQgO3I3E7yHn9K32X5GC6kb7NM1gnNAtI4RpawxJqJvYQJLcd1MJ+J3a/UWJaqSTbLQbnFki8VGuA6twMPU9qgdlPGIaC8cxytTCJXffSEpaRFUZgEmM2/3b9/36wXPcB36H8Xydpf1sNUovPFWh/wda7l/NtoNMPhvjyXlou7lnzm+q2bCLiGlhfk5gEgDMrod7nflulHc58JAs+U1utzajnbRfLWMuevHgizODbrRAP6GgBbzoGcBMYWA4FREqHCgESzTZ8rX5c8BfTh9dnDKsULbA8NBn9TlYFfaL8n8Bhs0DKtCgnhl/MFHF8TqZyPSghtWySPBlwGoC36fEme4pyS1+rvK42EVbh4yqSayqx3J/IO3e456FrgznNi7ztbpEYoe9hm4HQ8lWufPO2izLkZVkm5wECl43umLGk6kXXSoK/nBy4i7kMqK67J5DiOzf6sJVl9+paL2Ia1JXv9jPexOe5OpYQ5v1fhxllwPMpNF+lI9ocnJ/LuGhyob7XNecTQHDAAO18sYdGPcznHK3WW7nkldE/7/JzMsXJYRwHdC5TsnQmYaGHI4ZP8sjhLvFqauflsYNJybOMzGZuer30vLVM2/CcKcgCQZOpPqRrGmuphpRLx3Htclrz6vm/O/TY0Qb322VyO1zWWd51T7GZru4E2KWU6dXl3pYrZrdZRb+p62WZ/zGCzZO7aFQmY6BkpjmPs7/BswXXcbjGgVCvDZfBKzzT7+7KX3LzxnBnL057MGQ1uZWlqAk/6+fFYba2PTkfuZycqYiT7veX5qNZVoEHOJQHLbVfLFMdP5P3be/K7WzekJN1BgJd+XhI1VfrPj44e8JoWKs/QufxpbVMytmmbtmmbtmmbtmmbtmmbtmmbtmmbtmmb9hes/UQghIaUuAXW0WxDmJuorGrJROdLhNgWjkSPZ1m2ZnzWlqzLA5CR9EollwlR9nwXK6ZOikh+tzihJGfJg9OU3+ULkiiTeCxOC8wVFszoeZOwCjcrYxJLxjceSDY5qIkU7XQ6xYJEdBVKVaYkSuz3emjUJCqo2fj96vodKpS1jpdrKfZ1iZQ8V42Igr36CgAltj0leGOZkueYaGxGCc5mndKH22UcnfwQAHCX5IwDRkbtPEeakJCWEfp8iyScUXEhoksIOkmpS5llshcaf9wilLXc8nHOEgVQsryUKZrINRlBlaOs+IGZFzqWObNUcZKYCGikhJIKbQ4DWMwmJkouyEh+FCdG8lGzwT0iYqbTKUYkMd3dlWjuv/mLIh+bJIkhwp0TdTij1LznzWEz8q8S5SqXvr+/f4F8MLz0LMPhEHVfy0cUPivXdn0HOZfrdEaUB2Huie2hsU84cEWu1WzWsXsgmc0//MYfSB8tJIJ958ZNE9VeETXQJJl6nKxQ5nxoUgL1yRNBA1iWhX1mYi2WaSkp+MPHT3HlimR4FP32zr2H/J5j+m/EbMSTB/K3T7/xJt59S2Q5v/CFz0m/kCSwudNBTqnrY2ZsCiVURIYF0VT9U0G49ImOchzP9GnOLFC72UJFSSYpIa2yuXE0h8WsrGZnW03JnP/u730Zc47rWV9QInscm2Q+QTTRvxGSPya66dY+9riOt25Kv0yJyFie9xBzHna8GwCAG7vyvF9/e4weYbc7ZP197ZZklr76Jz/AzdffAAAcnUj/9YhajOcxWiy9PTwg2onjvUoSuERGjM655ssROh2xnz94S8p4HFfG5rXX78Km9K4S+Cm68tXDq+gxE2KtxF6VAsofuy7Qke/p/Eppo+aTM4zOCefdkkyPbxFim+TImd2NMukXLfVzgY+heS62Z5FB2jy7gEubMmGpkyIwLcsxmaSMUttB4CHn+BQsVTodyOe/+MX30KzKfXa2ZCx6kMzSzg7w9/6BIInKJNiscf4mUYRKSfrhxhXpq9VyyPtliJipv3VD1sbjHwiqIk1dPHgiKLTOjmSyAiIhB+dLTDrMeNnMOlty7S997RF22vLsn3xDxiQZSQljKa2hQXLWb/5QZLc7u7KW63YDc5a0dI9lDnQXRHPVO7j5gmToM8r8jgZ97L8g/fDD7/0hAGA2EzSEjQJgZjPPSJZPeHX78GXYRId1iEy8zrKrrU4ZV4gWPdgRu9ighOydW28gW0kff/97Ym98oiJWeWwIOT1H1nqaKLrMg8esab6S8QtLRCFdKZuM+RbXxnQk/2+3tnHK0qOzM8ozF9Lnv/uNP8Cc9tchou6F52/i1k3pjwWRElrzOzpf4d13xG58dF/2gGu7zwMABk/HKFj+MJ2SqDNhGYrlG/h7RpvkE71S5JYhcp8TTq8VuOVSiITlzQo/Kha6V66RcctcPvPH35X5YWcOfJZgTCbSDw9SklWW6lgttXxbfh6dSv94tgOfmeYJbVG5VsCyZK4pWmnG7GcQeKhUZG4t5rpPi70qJTXEK3kfl8ibaEnEjntobNGIBM1hSe6RxcCwz71xfhkl4pSrcIhGy4kKaW1xD0nHa5JWX0U4iGLJC2Nb7h8T8cd58vj4MTyW0mt5gSJX8jxHlj3iM6t7ncMuyzOoXdRMvTM6XyO1+XklxHVdFxOStmcg8XlAtExRYEG0oU35+ThWhEsJru5nuSAXtJSj0iqbOaD+1cKocAANX9ZjmurYaPmmCyuV76lPbvG+WZHDSsQeLjkfVz7td1CCw34fzRRJtkYfOCRKLlhm26zvmnJy9ftAJFlSxPAp4x5puTGRwCXXM2OuAgjpBULcgKUeN64LUfgaAbUyFQQl+uQ6Z5fziSF3XerWQ9qD3c6OIej93g/FNl1hWUdmAyGJ1usVmdsujziVUgl08dBs0O8kke6qUjbr2A+IjJk5mLMEJeW5Yqch/bETOtjfo19FdN90IWt2a6uO6VRsclyoeEbKa85gUwBlPCGC7MJRUMv3VLlFKSLspEBEf1HrhsqNtaBHiezLpUD2IBWwWCwipERjtrblvmdEt96+cwMexyvhvBiM5Vnuvf0YFY5370Teq1Fv48WXpKS1XpP+aNSkj8/6U9SJitrdok2ayvuVy1vwA7n+7q5cc5v+T7/Xw05H1lW/Lz5XqyUIyGWcYkz0Z0AZ+ZTlwYG9QMwxWdJRLxEFFDhrAaGAwgFxInZ1upiacistSSyz7NMqLQESn5eJblquVCxhid0r1LxfybNnCx8ghYSnNo++1GLpGFGPsCZ/syOes1YxtvfFx+2dSt9q6VeSJIZ+RMsFV0pHELjY64jdHXPOsZgBJc/GIStMdrflM3p2cLequMq9Xs/AW0TwdJoNtFguVecZrkKE4Xg8RInvp8+01RA/od1oYsBzwdOngsKytQwQOaYU6+gQ/Z9EAWKif9RvL9Mu7LR3DKpXOVvK3HDctDAE2CX6GDntfvfszFRglOsyzjWuXRcFziZyzY/G4tv5RDJNTvpo8xzcbMjzbfO9zvsnhmLj9m3x/0YD+X//fIwa6QCaKce0oJ+7muOU6MEWS+MHUxnblQPYeYh/WdsghDZt0zZt0zZt0zZt0zZt0zZt0zZt0zZt0/6CNetfRcr3/6/2q3/jvy0AyYjUGOGtlGuXPjOfL01mYy35JtHP2WxiZMiV9M1jdjiOY8xJhqwZwRJl15uwYDODGDEb16hKlO+D03fxT//vLwEA7JF8Zky0R+4BHSJAfv45iVb/9F2p43v+k89jzEzeB48eyv18ieJH8xlarEtU3p4qkT/VsGreT+VK7bqD83OJWK+ekbvz3TUfkUasNdN0PhyZ2lSNXjrKS7SKDc9Sxu9pzWopLGMWKdEguY6YXc9XsSHVnVACeM5s3o0rVw3vixLz2Ux1LJdLU6OqdandrmQXtzpNLCIleqxe+sxF3g+VEw68wGTtFCmktdnD4cC8s9bSasbo9PQUr7/+OgDgEREnyhnVbDZNH2kdsWbvms0m+owaN5iV0ecKggAhr6/PoNm/ra0tM1dUolyvPRqNPo6CY/88ffoUbaJKrt24yvvKOjg/75lMimaFNZ3kOI4h5Lt37x6fvW36yiMKTtE85XIZt27cAAAsVxJ5PqK09Msvv4wZrz/mmCqXQpGteXoiXJadn0xmOGe0XjkRIkbRK5UKTpjhmXHuPX0i/DFlPzDzVxFX3Z5k0q/cvIoRfzcay/eGI2a0vHVGsNlm5oZZ0NFwjO1tWXNzZnBWywgz1pUrGboSMpYCD2Eoa0d5p0pEjcVpYggRx+cyF3bb5BQbTTCnrOSUWbV+T8b9c2/cxYQ8Kx3yJ2VMDS5WGZYjmTNbzAJ5dbnOcLJATKLNp48FWXDnQLJVnXoFR8xg1cn5ct6V/jg9PsVV8iRUy8yQUu67XA1x7x2pJd7ZlXnV2S6bmvgSyfp07rz40h2cj+RdlYdk74Ay2oGLJbOEmuVSDoY4TtY8BTTIKrf5J/ceoKB87YTZwZjyu3lRfIxk0az/vPhzEULankUKWchhK7mukWglh9xsZrJaZo24NkLKt5aJvOtw7RXpHEdPZP7+4i98AgDwwos3AEiG9Td+V0jDlbdA0Ue7Y4ZwAAAgAElEQVRFlqNKsvfXX5bssda81+s+3n5bbNHejsyBV14W5Mi3vv0DvP2OoN5uPSf7Sr0hduGDD++h1lQuJrHDL7wsz/RP/8l3UOfYX5fpAYecc5965fPYP5T7/N2/J/vaNeGFxssvX8XNW/KFd96Tud4fyly6eecXcTo+Yh/Juq6WAjz4UFB977/zPf6tMH1rSC6JDquSK+3Ktdso1+Xftaqif0RO+/BgDzU++60bMtdWRF1Uqh6Onghy9aMP/oi/457i2xizrj8gJ4EhT7VsQ/asxJkBeZs8z1kjhnPpl+GAtsVKjHzwjMSgAffpHAVOya/19a9J3y4XE7PWEqI2bt8WJMJ0kaLbFZugBL917k8vvHQNbY79E86vb31TJJULu4ISs3wJ0TyK0ArDMlI+u2bhdR7XqhWsiBRReVrNtp51x6iSb2l3X9asEjSXvDJC+g8hkU8VV77f7w8MybzKRzsc71IpQKtB2Vwir/vnp6iSxLTZlH1JuXrSbGW4ViwiBB2b5N69E0ynMqeN3DnRG7VazfBgEJhh/J2dVsegkJXTsdXssP/nePqUew37U23SbDg3+3KsiBhrTZCve5vHGxpZcjczn5uRq8uQveYwvB1rHo7MSEirDdO9vxQExm9QNLG+V6fZgkeRD90bDRdTlhk/7KLwCiD+S4m2bEVfZrFQkYX1vFf0uK5Ty7JQ9sk7w3WjYhqu7RgEtUq9K2IrhyC6AWBJuFHMuVpr1FEm0ljFWfR9o8XM2DLSbCKKVgYdbXwt+mfT+cz0rfqZTSLlVvM5fHLyWORoVAGWMCghJULe4Tickc+r1qxhRmn4V+++AgB49Ej27fE8gu0pR4484FZD7tcIbOMLdcmBNeK7h5USViRp3t8TX0HX4ng0QM4xUY4SRe+v0tj4Ybdu35B3Xi4MIXmTPuguxWSmo9F6fTR0rZILy7KQ0W9TOzriOSjJgIx7Ym8g9iPnXlyvV02/69irdPZyvjTorTbPAqti7QsoOblyKKlgz2w6xdWrYtPPTsS3U5L0KFkZv12ZhBU5ddY9wfVD4V5ZEl2WWZHhGsq5t334vqAcd3cPDPnu7q6s/+/8iewXud1EAXnW55+TZ+H0x+OHD9GhT6f+eol27uikj6GKP3Bt5JysXpAjjsgraym6T+ZqYVvGT+JSQJln2uF4CoeIOIOk5npzAwuez/1oxPVNDtUbt3aR0M88fiC+fLt+oHSLxgYqyixNU2NLFMWdJGtS6jhSsvDLfEFytpGxUx9Z15YDCzaR9SWuy5AIKB8WaiVFrciV+0PxI69cvQrXJyqS/V4NSdTsuUh5lux2xe9RG1WrVWBBibHJQzqS9XB+fo7ttozzWsSA4g/RAmPOGZ3PW622iQUoEkzPfmGlsrbXzmVusMFgAIc8SSrkVCaacDKZGBQW6CN0WGHxw+99F92h+BR+S37X4FnlUy+/gSZRek/PZU00yI03m08NMXti6JoU4T9F2S/xGWj7eGZfrVYYEX2V08GNuIaDIMDt2xKr+Ov/5d//02H12CCENm3TNm3TNm3TNm3TNm3TNm3TNm3TNm3T/sK1nwiE0N//23+rAEQaXTMnmmnWempAJKCBC3LpuXxmOV9LTmpk92KmxzfXYIaDEXp3NkVAVn+UJDrne9If756+j7/z678BAMiGlGFkpHeFHD4zAJ8iN8q/9/OfBwC89MkXYNUlcmfxeR988L55lsMrZBtnZF4jxFlWYEE0jiJc8iL9GGJG/5ZliUGmaDMKCGHNZHNiZiw1Urycz9BhhkyjwIqWgeXCZi19j9nSEt/Bs2zs7giPwhmRJqqs5Hj+BZ4gXH7eIPyYhKG+cykMYGOdmZD3yswzaYRWkR/Veg3n5C9RJNi6Tr8waCNF+GgmsNfrYotKAYZln8+Q57m5T82oMqTm2n54GYmk91sto7XkL9eQfj9eRoZ5X+eqRnOLojDcQTrHdayCIMDD+0RykO9DEWWz+eTSNS4+U6VSwWqpSguBubaqv7z08iumTwHgm9/8Nl59VdjoPUrH6ns9ePDAIMcOKcuekzOgEpZNJkNr+XXcZ7OZ6WMTyWdWczabmb6qMhOj2cXTkzMzltqWnFdRtDSKBA/7cp8p3+nRo0fmGsrjcPRUsgtpmpusmGb4AdsgpTQ7o/2zWq3QaCtqSNeEZkFtlCuqgEdlCNa6W/CwZGa4z+xujUijl+7cwJMHwi0wpDS9vnua5igpZwLX4JO5ZCy9fIUdonHGS3nBIbOGv/yvfxIZOR1+/UuC0Ng/JLeE4xvEzva2ZE3mlALP8tjUKU+Z0U7SGVbkI2u3xIbpfPRKnuGQePxE+tQmsqLerJlss45prJxdjm3sjAJ8NJPbn1kmy5qlqoamqk4ZVspboLq0qmSh6S78q3EJ5bDMsygaKGXWZbVKUOIa1KxmURTwuT+oCkmb2Zx6vYqj44cAAJ8S8WFF96k5Msi4mmwfs7YoUmRUtVEVHd1Drh42AO5fBQvUP/VTgmK8dvUWvvr7ktnc3xMYj2b6f/jWD7C/xxr+iJmltuwp/a6NgPunbYmNTimR+/LtT6BVpe2bEonqy9x53D1C5Eu/P6KKSboUe/ypV38O7dvMyCrK8ewUx49F3nQ8lvmhvDWW72O+kM+FzGrdeV6QT36pilpV+lRVN5q0+ztbHWxTyniPSoL9sSBx3FKKel0m1O9/TdBNnW3aQj9DQLl5n+p6uq+Vy+X1XkPEsGbjbatY7y+U8naIlD09fWoyh0ZOnHZoq7MLm/5EbyQ/j46f4uhIUCgP7h/xe/IMT08nmM2JCgnIcTcjN0/FNkikhIqhWaboPge2crwsyQPBZ4rjxNhytYFq2zzPwZz7wg554ZSX4fj43GQ4O3vyu5MTQSE6RdmoHi0jmQN+QYnceh0hM6IJeRkqRL7V61XDKbYiAipPUqN65nEsDriXuJ5luN7G5BzqnpLnoxqY/TkkMkYRCXDWSJaAvC7Vqly7XgnM3phe8B8AYDiZmr2txn1GM7qNUtsgoZ9Fo5RKJcO1qNeKaaOarcpaVYxop4vIRrWL6ssWRbGWHaZt0Z+2ba/5EJ+RL7dtG1F0WR2yTElw27Yx5TX1PrBUYj5BHCmnEficsXmWFY2Q4dlT/k0rRy2omWsAoh6qz1tYKk+vSASV77ZgZ5eR2zovLccxfevyp8e9pMhTTMjPt1aksj6Gqk6JCirswvSbPp9N39ApUuwQJe0TiTslr5nrWKgRcTafUk2Y+z3sAkvyNL3y0ssAgA/og7lh1ahfqs/mkqMjmg7MmrPIaao2wrIs1LjmplOxjzo/XccxXCXa/7pHzuZrNNvhHrmfsD5H6B6u1QJWAXR7YiNj9ofnyPdOTs+g+X6LzxkRHbLKCyzpx8W0gQHXWxrFRsVZJbZt+jtxmhguqoh+j6ofh9XKBYS4dO21Q+HTWcVz+ERev/3WjwEAn/7UFwDI8yuKRO3xz/60/O2dez/GLlVKG9wnzmenuHObeyJVXBX1cfPabURENCdUjqyTK+pRbw66dtjZkrFXdE8Sp8aHNGuRcy6odUB3xaCllWN0f3/XfD7mnrecEcVZdbFKdc+XxaD8eUmcGhS9lauaJe+xjGCRQyji/M9y5e8pcO1A9k3ldItmOcr0SRQtelWVI3FB3ZQIJod7cpIkqNF+6nrLyJ3VbDaNorHaFFUNWy6X2CMap9WRvaROdNq4f46M/VimHU34YuP5zBCirs+ia9W3rVbz0vOesxIgDEOjqBZzzr37kfgeHz14gFu3BBH24vOCyNXTYLPegOO4l67p2s6FShtWAnBSnI+G5jzglC9zu0ZR9LEzbMT+qdVqZn8555kh4Vms9/QEU86xLaL8DfqzWkeddsOmvVLFwPlsiXNWJSwvIC0BoFquYIuIXD1x18hn56YFTgeyd58TJeyoCuB8iTo/99f/5j/8MxFCPxEBod/69f+mAMSR0EPJaKjkm/ISjWrNEHAZxztZD5JuwiseTuYkMSyVSsYYg4vcZ1mONRvBIm5wppBfHri6yRR/95+J8xlPeMDtEcJXctGi8/T5m2KcPntXJuQLr9+Cw0PhPkk733sihFJFURipSp2kPifFfLkwpUR6wAodZ13OlF2GIdu2vZa8t5QMkqRz9cqa1JiwQ5WnjaIIjVrdPA+wNiT1RgsprzmghKQG4aqlEIFKJHKSgYtqPJ4aJ0EdaP1ZFIVxEnTj0+ctrDVU0igY0v+zbdt8Tv0V23UMcahCA9UgVyoV8++LY6/Pok6dKRG5QORonAt7TaoGiHPjcLz0+3pN7WsACPk7LSs7OztDnf825Qv8GUURfF/h8Omld/A8z5SDvf/uewCAO3dIglgJsZxPL31eDXmv18P21mWYtR+EBj54dCxOySd+6lMAgA8//BBPScT8U6+/Jteiwel2u0gIQSw9I5mdrhIT7NHSC+2XKIqMU6dN+3E+n5o1mDG4VCX5XJpnOCKpnfbf3pY4AePRAFU6YnMjs8xDQKuJZMXNm/dbkrz2/PwcE5aRnI7X5XwBDeLDh7Ie5zPp65OzMwNXVin17RZJvVHghPDSfk/WhBKK2rZrSsUSfn+La//OrRumRG+hMs4sYarXmkjmJErnGlqlPNS6wJiEcgUdCIeHMGfWx3/2n/5bAIDvvy0Bof/zaw+l71pN2HTg9FDkEDq/ihdGYlXnXF6kpsTjfEjYM+fOzt62CUCok1DjJjSZjsy4aiBP18LFoIwSnPqqd5w75ndKsjjkwcyybHh0VDRAnDLAoGVfwJ8fEHq25bYH5RdUm67BRc9zhQAbaycDtguPAQGdhyaoHi/R4Po4YzmjxqF930eN0OcxCcXLDBbt7LZg8RC1TcdWnew0zeFwS19Lwq4h+moXp0wS1HgYcPwA1UyeL7WZECGkv1LZN7LZW9vSj9cOJFg1HcX48L44Uq/d/SwA4N7bUhrx/uNjpA4PFCQGtRg42W60sH1L7EaXdiSajDAZyZq1ncuytsNFhICHyisqKX8oZYpFYaHMvVcD3iqNvLvdQYOJFO0jtb2Njo/BUMoC3vrRVwAAYVXuV24EiLmvl3LC2y+sT5XyVj9CydyllJk2JbgMqx8OJ1jMFN6u5M0yjot5Bp8llnNIMGWV5EgpXJHn8n73SKi/iNcSwbke0CjZPJudI6DEs+7ztaqM8/n5cF3KQ2EH9W222HfyOXGcTblyu23ktjXJApZKPHl0js6uOKblmjzLZCJrfzpOTbBoNJFxrnCfarUbiEkumpMcdmtHrt1oNHDWJaG4pQS/vpF91+vrOvBDHwUPGSqSsIo5lqUAjq1l1PLkGrgdDoeGWN1l0m57R2yShWJdzkSnXkvWXd8zfoeu6xkPdk6WYUYp6jnnhTr31WrV7HVl7jlKIpwjv2AbLgeEsiz7GLVBmqbwaMvVxqodjuP4Yz6J7u+LxcLscfo3IxFtFzhnYEGfU33LcrmM+UznnHXpexrIkue7LBtdFDn2tmQP1wCS7u9hGIJVRiYRuN5519fVa2qwKAxD87tcfVjles4SI+KwvUU7NZnDsi+XDa9YGmQ5lrGRBZ/Z433btRpmPESpwIkGmH2nwAHXzIgUDAETfePpCA5fTEvVHj2WoG6p0UKdvki3K3a/qiUxeWICaZo8zTmO4+EE169f5zswcMs5sbe3hxFLwPo9GT/1jbygtKaNcGTeH3fPkNF3rFA04pzS7UVhYUBhDT/Usm2Zj+PR1Pj8JX4v0eBFlpvSeF/9Wcq623mBEks5lXBX6TtmiznGnA/qYzRq6zOY2tNKlaTttMuNSmj80wcfyh70/J0XAMjc/c53RdRCE/fPM4Gw22njlJLtOQ/GThDAJfl9gXVZEQBEy4URR5ixXw72xN659RpiEoI7/F6Jye9GtYmHjzWQL/dpMtgxiyxzPtKAuRIZh2Foyu11T9CEm+evyyc1KJ7Q16hW64aKo8a+1XdPYatSu0mEa3Iyjsao0+evhkqt0sAykndW/1uv1e12scWSKi1j12D47s4+UgbNNAg5ZinWzZs314FXrj21j1mWoU0yaLUDWj7VrjewYqAkZxRNfdIoWWE6lP5Xe2WiYFgHmdX2ZcXaFxsMZL5PaasrFJuoNOoXSm/pJ7JcMVoscc7EgyafXNc1h0k9j9mOlrdnxo99zICfnq8qpRAaXFWbpPtMuVQxe8E4UXoVeYbnbt7AItbE8mWbFk1m2OcZaJFoEI39Urg47soz5LqH5HLNWjnA2amMV0Iy6SpjCNFsjirL1QomwNok93YKGynPln/1v/ofNiVjm7Zpm7Zpm7Zpm7Zpm7Zpm7Zpm7Zpm7ZpmybtJ0J2HnNKqc9mWDI73qJ0oaMw01WBlGVaZc32uQrRz5ERWpsRzRBStrRa9lHyLpOrRSTj6/gOqh0iOZg1QkoJ27CKbWYVPmIGvcnI5Hgxw4SEVRGzzh8QBVQ/bGLBTPicUnGamQKAKGbGgFHgkKgbx6siY2YkIkTQ9ddlD8kz0GYv8OEzI2GQQsyMDKczdJiRVqllh7KGoeMZCOqSaCy463votULC8E2WK8swpQxolcgAc1/YuHZNygrWJM8SnQ3D0KA6FFmg1xxPpoasS7P3YPY0xzqrYjGbkcQpYkJ2TaZMszLjMXz/MpG2Zk0rlYq5pyLQLiKtNBJ8sYxMr63/1ixjovOrVDL30WsqUdl2u2Pk6jVqr7K0FoAFkT4a3baYaYsWS4O6qDc1m0lYfbeLrbZkLTSa3maEWRFfwJqYcnT0FMuVvM86Q1+Yd1EZeI1mD08lIt2s1rBiRqPCaLOSSPb6fdR3ZF4tlUScn9na3UGfkXWDfuP77R7smz5KmKXpPpX1snNwiGQlf3v8SPpvTMLHSuDDIiGlloeedWVN9Y4dQ+TZYVZTx+PaThWuwxKpVFFKS/P3F69JNrLNrKTt+rAI4f3wQynzyhZcB/UqhhwvnR/a/6vVSqCwAB6z9GJ1JO9QbdaAJckmWQ3VYwamPxuhygzlMUkgO6E878SvYkXyZT+7oAcMoGju4n/6R4Ja/JVf+gUAwC93XgIAfOVrX4ejWXiWFehc2O804SgJONd+vMqQEubfuU3JbGatnjx5rMrh4JLFnNnXrVYFi1TGwuV7Bam8QxiGazgwNAkh836vVkecE9q8YFazKc/Z7UdAoHYtuPTO2TNlFH9WexY1ZNu2QSQpZ2VAZGOWJLCYIWoS7jwajbEgKsGN1kSq0geZgfNe3RPUy96OjJeFAjtbKX8nJQerRIkmU5P9VaGAMcnEp9MInscSQkLyXWbT5rMx2rXSpfeKE2bLixK2a7J2zweStQ65/seTyKDCHFcGrj+Udzl52sOHD5itKkuG6QdPBIV41usj5HC9+arMp9ATW3N6fIzUZckz+yzPlkbQYMZMWUH7Wqk2UG/JeixTRnUwVTnsMgoiHgZj7o0t+UzhBZix5MgrS38eXhN05CpeGrL2vT0i91ZiI6a9AXJLxnBBVBMylpdOl6ZED8ymKVQ/S9flJ0UuYztjaZbvBfA8GS8dEy1bGU37GBEd9d47grBaxgmWEZ/5hiADupSrr7aqWFGS2yB+bOnbMGzCJfonS5UkWoUyZubzHrOfFc0g5pZBzoWldVYRAKIogc19uUribpV5H/SX6/In7kcO7YFl24iI1FG7oCjjKM0Qkd0yUIg959UyniHLxK5OxjrOEeoVeccqUUdaclCtlRAxk3pKcQmbqN9Wqw6b6IQFfcJymaUVVhkhy5RtJ+O7y3y8feM5zIkIPSPhtxHf8H2DBNWsvGbQLRSoKXqIWWuVLPd93yBH1AYparJaqxu/wFPi+kLLqeK1H6Ey7XFhbKYpxVKkZp5fKFPJP/bsWgbvPkN6vbXVNv6GRwSOzeeNFguEHHstz01oP5IsM9lxLZvQ+1lWgSXHZkU/K2Ffl8M1Wt1LOQ5Yl/yazD5/pgX9M29lyIkX9JlrJIJuNOqwuEBToiBm474RQFEfpVGXMQqrZYMQyhQ1R0SCXeSYR4qYlHfISH7bqdXgMIte5ZpXsZbWVsvIYDu0abs7Ypc/Oj7GiGXXpZBlbkbroEBjmzQTTSLm6cs2RnUkRCtXiQBRn+jeB49QYnkWaGNGnOt+lhnJd4/9Ml1aiIjSePSECCglKIeDwVSeuU1045LzxKvWVT0buZajsjxpEUVIiDhIeJ9WTa5ZDkPYtNELJdT3tTR8iiJTOgZZ1yVH+tz3fXhE3CS0wx2W5RVpAteS3716V0QEHrHsp/bcc/jLn3sTwBpp3OuKb7gYHOGQCJ8SUaej0QLnfZbg13VPJYIndOHRXlRsGZujntjq0mKOSonIJxIlR0TRDc6OzT7RpPy8Itk9y0OnKu9aITG+myqBfxMr9m1BNM8nSM+wXM6M/6VoNsVfNBstnJ2InTJEzlyL81WEFs8j+7uCbNHSxDxJsSRKJiLaI44XpuQxz+Q59axY9j2UOSa6dudj6avVcoIFRTC2SPy/T/RoHMcYDVW8hee/eG2vukSolXlGPOc5N45ypESx6j6zINLQC8u4fV36VksmFaxYrzUR8XtJoch0zsFBjGZIIQqedx6dCpqrOzxHm2cirWpISGfQqFSx4NnmjPQsnU7LXLd7/IR9Sz+w2cSSa1TJrpU6Y5Fb5hxWpeR7lSTxySozgkp0I8y+cXx0YkrudP13uG4828GjIzkzgOj43W2WmU5GeJciMFP6sO0teffPf+7TBk1ftqX/Zzw/LWoV7G1LHw8pNnP+QPpquljA5V7357UNQmjTNm3TNm3TNm3TNm3TNm3TNm3TNm3TNu0vWPuJ4BD6F3/nVwtAooJa/66ZVYMqsdfkdIb815Go4Gy2wGKmFEuXye0AwGLmRflpSippv5gAjBoPZpRnY38sygX+19/+GgDgnXcp7UpCukm0QMD73N2XLMYnX5FsZoIFTo8lwv0zr31arlmRzx4e7qPNCJ7Kq0ZEcbheYBAPikaJ5iPzHprJM4ihNP0Y2kUJGPuThamH1sipZoGKojD/1mipSuouZlOU2O+KEPCIOlotloYATVFHIyInmpUGPvhAIv4aiVayr/F4bDh99G+amY3iFD4j0Ilm85Wo0LJMBDmkXGxeFGCCwmS5DCJhOTckkPo3jUTX63WT0TY1p8x09vt9k3V7Vr51Pp8bBEz2DFllvV43kWRtOldrtZqpr9X1pbW7pVL5UnYQuIB2gmOyTCppauTaZzM0iQRQsmyt7w3DEBazYWF1/bzKYdPuSMZe58np6Rm+//3vAwD2SHRsOD2aTYPIUunfR0Tz1Op1uERZLPq9S8+S57m5viKuNKqeZZkZ8xI/o5nPwnGhcemLPEsA4NuWIRdWEmuLGdnBYGBQVzqfFbmzvbMDj1ktiygb27LWMo0qNcyftudDCRIScubkcxmb9s62QQHpmlCemyhaoFTR2m9yBDjkP1pMoHqgJ2cSrR/N5L1OTs+wWMn8ePhQEEm9rqA25k4dPdbBj4jayomsGaxKyCnPWeHc+YVPCzLv2rVDw71gyMcpX/+ZNz9l6u4nU/IWVOsYnBOpRrLbITlw3rr3FhqUfs2YLVQp2c984nXce0f4i3RNbBFpVavVTJZP6+W1fnsxi7EgwXLEDPAf/+AdAMAffvM9pCThVZLXLCcpnvXxnMX/lz3Ltl1DIKx2R7M1i1mM5+8I8eJzd8RO/f5Xv4HYucxxppxzJd9DyPlUobzyTkfmfafVgG9xHZaUuFGzrhVERLwqD1pA9F2ROxiS3PmTn/lLAACPwgY//OG30CrLe4+Z0au2pB9zt4knvYcAgIcP5eeC6JQoylGpEI2qsthEvlbKdcxGwg2zdVUygX2ivo4ePEGZc+XWtrzXdkvW68GVBv74LRLdb0ufnZ704XAPWNGurviCV67fhOuTr255GfkQhhUElMPeJtJQeWvq9SpeeflF6dMt8qys5LPjwRy9EyEj7T75NgDA92g/sjLmc/JvVbg/GcLfNU9WSqSEcvZZlmP22elS90jONceCF1y2Ke+9L3O1f9Y1++4bLwsXU15YePfD+zIWS2YVua/7oY85EXHKIzCbE2UalIztsi0lF13zyhnECLO8yrfebrcNovH4mFlGtka9CitXBI2MoRfI/XrdhZExXxGxMiWyJkt8Q/Zbb5LLhoiherOJVXyZiBjWWuJcJauVTHx334ba9BK5UCrkmdjf30bGFzk6EZvnc034zgRl8p0oqbRyCnm2Y5AmyuWhCKpq2DbzSLPyauMtyzK2y3AXKj9FuWk4E7NcSb2JcElTs58X6j/S7CQXQIuKZtF+iePE7Oc611arFXqDEf60dpF3Tffdi/yGHJILfof0z4svvoi3f/wjAMAOxT50j+31u7B5b5V/N75Gkq+5inCZTwNWbuaocm8lF7j7FK2vEtFFof2Tmr7xiNJWBEoQeEaivEGJeeUIixZL1MkRpVyVtu2aNaGS1yeUok6zlUGXObSPg64gfQb9MQJy5uTcM6ZEjjQqZZM5rzo6zuSJLPkYM8Pv8ntXbwqKZRwnqNC/KSzxAZaTBcehif5Y9vUJkYUgkqNY2Ugi2iIi3gh8x9OTrnm/7R2xw7pWhsMhXK5rRSvBc8x7nZJr0SqUJ7KCEW1RpKRbRGYlUYIGef9a9CUVUbZKImzvSX8ot9fOlsyX4dkAvs4PIiR0vTi+h529bV6D5zJWMyyXC9Q5vuVQib/J/ZTlhhMPrATokBOv1+sZ7hrly9Q1WAo8LBT9wv4IwxCx8o7SLo5mMg7VRmj4oxz2+3wsz3DamxhkGl0h2FqNkFlwWUHh8TyYkYtvMV4gJ+qqwz1/Tp4gFJ7pY6U6tIlcsz3XVLuMyGekhPXlctnsIcO+zFE9l2zttY3oSZm8RKup2LJWtWXQjYY0fzhEzr1KkYkh15fruhgRHbPLSoWqnqXyFGen4gvqPhjSb4ziBYoLHGwA0KMPu1zFaLV3+TfXXAsADgirK3IAACAASURBVHb3ENPfiIiQWxDxVm7UUGbHG543RRxbQJdoUbWd5pybJFhN5fOKOgRR0M12Czl/Vyg/E8/2i+kMFdod7Zd5NDf3vn716qV+Pzl+iqv8nfqgRrQnLBv7qQhjIyiUrhGXuuerv2nba5EPvVaL58lavWI4yPJCxqteUR7XM/SGYpMeHQvCJyQB+IvPP29QlR5tzP2uoJ0yH2jy7OVwvw5d+d77j54AFND61b/92xsOoU3btE3btE3btE3btE3btE3btE3btE3btE2T9hPBIWRBooqlwEVBYE+aMSrISH4Bz9RrL2LJQi9Ym2/BMRFFn9FRRYDEcYwVMxohEQ/K45NFMXJGlFNmFcvKuYPEKAp4mrW2VBGkgoD/fkjllV3KRN4/eh8TZi3uXhE+hqYqAFgWBkTLOL7KKTPK6loaPEdMGeKwUjbZrZRRTs18FUUBm5FkFXsImFEJw8Iggzz3ckQzyzIk6erStRJGeJNlbLJMC2ZGI6IjPMtGnegTReKUWbc8mUwMV45GeE9PT/n/1cdkxY2Eu1cyGdgR5fKqRCu12g3DV1BidnE8mcFm/aQiETRS7jgWSozGKoJM54Bmgy4+n0Z8q9XqOhOonEUc206nY3gU9DkVEVMphSbKr/fR73/4/gfY3RfEjUp0F8xQz8aTtXJe/XIEe7lcYpeZG/2eyYJWq3j73XcBAK+/8iq/R1b7DKZ2uk+J8jCsoEzuiP6ZRN91vA8PD/Ef/we/BAD4f776dQDAPSK8rl65ghvkwUioWPH8dcnceIGLR0esvSXcrsTM3mQyMSgjk4GklPVgMEBMxYWMChQtSnqfPO0aiWLlm9C5W8A2WYgh5X3LVKhqbR2YaL/OgdY2uTLGI2xTXQHkvkiLDI51WS5dZX6T1QwR11mJKkgNRuSno1O4HF+H7zw5H/D7CfySIvFkfjxdSf/MFlPDWbNHxbJ9ZtD+yl/6LE76sj7mq88AEC4OAMhQ4PETyfpPZvLOj44lyzOdW4Yjpk/U1uCRIBfsZIw7N4XfpiD68PiR3OPr3SO4zKrfuC1ZEKtWQUJU5Yx18Iqo+4VPv4TRSP6t2UhVGzv54Ie4SfUWNTyDsWQuz86PUCF3haqCfPCWzEfYPla0a1vXKBvLOvj5LIZP26wwiBLV2lZLJYHBx6Q/L7ZnUUNWtjLZH5WWB1FchbXm3bh5ReZs/84evv2erBNdnzAqN8nHJKEfHUnf9vpD2IXYQeUBiIkIee3ui7h+KIop50Trjc7lnZuNKtpE41y/I3LzUyJ9Pv35Gt767lcBAI1tyca990C+/8ff/2NkDhUuQulrVSa0XQeDqaDKwopcO6I87SSeoUQk025bvueT92DuVXBlV9A/1w7ke3VX3uHujS0MmKF7yrp033cxI3qkxrldo518/OipQbqqcuFa3jo12cQoUv41KiNVA/i0/drV7Q6VgYYPcP+h7LPbrRsAgITKKoVVRkN5H1JZJ2rjw4pt5KL1d5q1ToocE1XCK5OPhH+7/9EjjMiXp2jYAaXVq9UGbuwIqqw7uCfPubWHWlv6ctqV7+XczOeLpVGNURnckqcyMjF0ThqZzUIReZHhubIz5eaQ5x2PBwYB3GiKvdLM3mw2NVxirZZmfuUdnjwagEsdFjmmrl2jEtz+bczn5C+aU0GO/sV0ssR0onLzcs0WuRcXy5VBcrz6+m35TMOCRU4jj3xompW3rBwxOZ6uUAFPs67d3sggZF2dM8yG5pZt1nFgy/okzQ2Gw7HZL3WPc5x1htn4G4qSJjJgPonXClt8PrUjaZqaf2tWXlEscRKb+aQIoVJBf3MVr8VzmGWPkxQN8k8qitaokl1QYb2YFQeEu69da166j35/OhubDPOMipWOS4RGuQSbY1LMVdJe+YIs43vGzCLDUh87Q8bJqnO1UGrHbGXQHYGjKGZ5p9D3kNF2HVwVPrqQ+3ScrFAo4kP9JNqd1Xhm0B1FqvbVwnQs7+Mr1wuVemzPxpJ7/eE1QdccbgtysubXDN+Xx/meFrvmeSP6i9Wa2DflyFwsZmZOK4r7waOHcl/Y2KZda1N9cTSY8Hk9nFM1KSZSqlRV5FOK5Yz2gCjMEm1brd7Bkr5G71y+XyZCbrlYYXeHymhUjBsMh3BpL7S/Z0TBzWcjYz60CiKkBPnW3g6OHsne+4TKmJ/7nPgayBPcfyC+5OEVWf9lKvfNrAi73N8VqaNI83Jg42BH9jpVNwxInLK/dYDZjLaPY+QZm+YgIaqnQV65mPw91aCB+r78Tjl9QiJ5RsPJer3wRb/3w3vobMm4KsdUGIgtPH7wALfoWzhUnLz/Q+G/u/LSCxj2ZG/M6NfudeT77UYHfSL41Ke+fkfmsdt2sEVkkKJtzkFEWO7B4/yYzOX8MpqRR9OyEZRlPvTH8reCiOBSOTAokokjtsmnCmEeJQbVcUQuJSdWaLqLBffiIX3QK9cPUKKqZkt5w4yvYqPdVA4x5V5VRc0AW0Tp6vmjP5JxrtXra97QUxln5Ze8dec5sz6Vp6lEPszRaICUfnCbCDA/UF7bMXgJ8+5L+nazxRy1kNw6nqJh14jNIf09v1BEI8/hng+bCNKCCC1ztmq0sLcn/sO9d8VH3tvZQaEoUZ7HVDG409k2aHhVIATPAtWwvK64oW3W82ASp+Y8cfWK2KQJxyiDteYOIv+RLolHx0do8Ay0RdTW4Fz23Wg5xdaW9K0XXuE7k3+zd4o5+79elT14f1fO3tsHW2b+zrnnbbE6xO9UEYT/cg6hn4iSsd/7tf+6AGQTVvlEhwd/x8jlTaGPqgeCGqHNjuViMiahHA+Ouvkgy5ARvqryeHPCuhvlAAUH/eETkupqucx2iN/6tpTVfPl3vgsAsC0O3GwCZWzbZtDo1nWZDLtXO3j/B28BAPY8GdRf/mt/VT6708GjJw/lb5ys2v9RslrLT3JRHJ2dmj56Vra7KIp1iQ3f2UiOu2tpxIoGVfjTtm3YnpbHpOZaALAYztEioR4oB6qGp1WrGtyrfk8dHq8UmDHRhWIgdUVmHB41VD4hjkliocbFEBNeqYcqIEesMpaE2S3jCMj0cKHy9muIYbpS+WW5ZsrA13Q8MRvyRcJoQJxEde6M1OIF2fkMlyVkVUZ7sViYoJQJRtJxXCwWZlPTz2ggaT6fG2dV76cOaxzH6GzxXRlAKSwNdJaQJGupWgB47z0hhb1+/TqqHfmeltjYto0lSRxVenLOQIFVXJBhpjNyRLn17vEJXnpODrERn0vn3mw2w9WrcnCsN+R3CkOuVCpoNeU+KmOpAbPnnnsOxwycpo6MSbPOA+s8MiSJ+kxjkjwG5cBA+VXaVcv0er1zHO6LMxNy461xjAfDvtmobchcWEZzIzOa5ZehnUmWYkGIq5FjLeT/tWbLlJToO+yQNLcals1YqBXVvbvVriHhnC7xwLMYUj6+8FCqcVPks8Qxg2KYoER5ZVTkPmMe6st2ZqRWp4oQ50Hk9ORDZAtZq++9JfbHohT2cDzHuyRxbHXEafhr//6/i+/STpUJVdWyC9e1jFS7SmSn3CznSbqet0uVH5aWFzDw9pgBgxFlp31rhZSvNSCB4CwT+/jPv/QdWDzMJCxlsUhWmabrnMWfFgj6s5qHHBbX5ZRrqcJAcRLNEDCY9R/+Oz8n/VL18cWvSH8cPb1cypLn6/VvShi9NSGro0F3np4GPVkTt6/u4pCbtQbWHb5DuVzG1qE4r7Ud+dnek/K/ZsPBYvQIADAcyh7wf/2LbwIAejMHFe6NMx7Sdf5n+QI5Z6IFhWNLpy/jGZoM6L/BcrlFT9a8XwkwzS4HBTssCfo3PvkKUpYE/ujHDwEAHzwaYrSUz7/46hvyzJw78/kcjx/Ks4+mlyXRt7d2L5QuSz984s2fAgC8fPeuCeyeklw+ZwAxzebYoV10uV601HiZJMhVeppwaT2sFMihQUvbVWEJkvR66+CQC1l76jgeHZ9gRrLaPZKIt1la68Ax11dlCMf1MWVQToPH+v0sy1AOKV/Lfcln6eLgfGL8Fd2X6tzrEqSIuebGI0LswTnkBeYwFNPRV4GB8XiIhP2tcunPPS+Bmu9+5x3UuRcMxjKvrl+XAPH1a8+jIPy+25Pyt1Zd5lX3dGhIXbV5oUrApyggzxBp37p1hD4Jd7l/sZIC00kfeaHlOixxUhnn6rbxFXRPNJQBlmMItKt0hBMGNFy7MJ+LacfXQcjc2I3ggoQ9AKTZch2MKa3LLIDLAebA1T1Y/ZjEzGkt09XgfxyvTMBJ77dYRHAcPZix3IW+WrlWNdfVw4PSCOR5Dk+JaLm+NCFQrVYMmawexH2TEKsg46FJ33XJPWwRRx8T2NBSSdu2USdxrh6UVPo6S1JTQluvyHvduCLBmO12A93H4j+rPPKEgZDVhWSc0jnYTDIsl0vcvHHIZ5ax7TQ76HblYKRJgpAlSGGljOFE3ufxg4eXvue6PqZLzhmLJZks527WSqjRRi6lq1Ci8EKn1cARBWGu0J84eir7/OlojIJ7XY3vZVsk/l/EcHhwPJ0w0MDgWTloYzGib835MWdfp3lmDpdKG2F8S8/DoE/bRZtWb9aMn6KlSxowgO1gzJLPRMu6bFmDt2/eQZulYscP5f0adRnb69f3MRwyScg1azssVS0kIADIXJHn43khXaEW6vqUjtypyhyI4xhdioEMB/JeN25I4HyxiFD2xfZNWB4WKFFydqFkiWcBHf9VmsDhWUX9/BV8Ywe0HBq5Srh7eOXOc3ItBiuePuResg14SlZOIYNhX55z0BthQrLmwiM1BIPW2602mrTJ+lyeyxL7wQyTucxHJ5BnmEXSr+91+zjg+liSHqDHs1RYD1CpXD6PaFB8GU3RZtByty42vubI2l9NClQptqT2ptWuIZ0wsW+CFevgeM5Aq5L677Lkb7mcw2Np98FVmfePHz+Ud1/FGE1lLSkBesQgWqlSRUH/zWOJvJLTl1wHTZb8KoH8nIH6rf1dDEiBUOE+rRQFaZri7t278j2uk+6J7E++58Gjv63S8hp0mk8n2OJa0lL0Ec8OtXoTLn0hnVe2bZtS69IzZ6/VPDZntCEDeHqGnU6n5uyqwVids9VqdU2gPdegM4P/foABA42aoNb7HfVOsMXk/3Yo69Ss73IJXQZxZ3zX/X2eu8KqWTtPGMB2eS4cnp2iN2LwkQCBWy+wDL/WQoXBs5/+lb+5KRnbtE3btE3btE3btE3btE3btE3btE3btE3bNGk/EQih3/wf/4sCkKibZklU3s0gaKLIZCuUtFaZ7ObR0kTnwYiolrs4jgco6oQ/U5akrJwYh6EgdWa5ygHLNQ+uHuCL3/0tAMCX/rnADhUhM1+uTClFkzG1Nynd/OlX72D3mkQtzxcSPb59/fn1OzCKq8RhimIJwxLKzFpMZkpimxtEkGZ8Bz2JMjebTUNQHSmUfCbRy3q5YQifTTaC0cT3fvRjvMAoukbflTgsCEMj0aeZxzIz/tcODg3M7lvfEcRUlxDMf+1nPw2fnztjRFQjsffv38ft65L51io5BuqR2b7JlM34vI2GREsH/XNTaqby0XmaosJMXsoSlpKSpMEyUVxLcc6cC65jGVivZg71ZxRFhkxQMzWKGKpVq1hhTcYIAAmj79Vy5UL5mdzOctaZtjHhxErap+/pBT5W/J5mBUaEAGZZgVvPS8Z2yfl7/4EQunq2gxKJIhUGmLMjf/zuO/AT6dznX5CsTKtVR/9cosxKaqdrapUCK0J4lWRR5TbtMMT5iCVHjNI7JJyNp3PUiNC58fwe+29lnk/fWbMeeweS4SjXqnjw4CMAQCmQZ1gTiy8MjFKb77O0c7kykXglb2Z3onfaQ7lEGVwSAh6TpDQrCuwdSERdM7mtRh1PWU63zXdVtEeOAlsk5nzKUscepS1brRY6REiovKRB62UZApa06XyaMFuwimKT1TXlBdDMcWCyD4ogG7Jkcv/gCmacY4qSnHBd+75vCK0XzIYGrMWolGtG3lT77KOPpM9dLzDZ4z/6oz+S/qzXccT+2KW8qa71o6MjQ0KoiCTNUu7s+Cbr9PbbQi69x7KmyWyF2YyIPV/G+eEjGZPd7Ry5Jc/Qncj3H/dk7rz35ByR1rKkRBlwva4KRQyu19DFlpq1ZF/6meUJXEdLMWgzlCQzS7BLuPTVfRnbn/7C5/H+UBAiX/tn0m8m82ONoNW5VsRyK48lSFigWuFYpkQZsAx4v2wjJPmpTSRqwTLHa5UAeUQUGw2jW5Wf1WoVn3hDpHgHc3nmL/7OVwAA09QCK78M0k3JzgskZi/QDL/LbHKyyrDfZNaImakayzqmcYwf3Zcs8klX5hVVlnF7fwf/9mdlrc8W8svf+M5jnCxIoJ/I/P3EbZHbbd18DXZD7umyTOXxY0EMffjgw7WUun8Z3Rq4gUFU+A3ZL/e35Xm/8OYNrGayPmqch7p+ZqsB3EDmTimV+2p20/VKcIjy0n55TMEHxwEePJRxnpzLM9VrMid652sxhwqRnoWliFTgyWOZ0yq7G/g+KkokyTIej4iQl+8+hzmlq1cJCTc51/2gjMcsPZwTUaTCEDevXsE2M5a9rnzmiGTzllWgRnLh8zPJWh8yG92orUugtdxeIfZJkq0JYtl/ip4plwKUfJWul+dcWbq2LGhKURGoRjCgyE3G18Do0zm0FN61pP8bdenbUqlskLRKYq2oNNt2DQI3XSnyl2VAtmNQzuPZmJ9XRLRr1vrJyQnvsyZ0XpcRiG+hNrdSLq1R1kQR6jXPz86Nn2mItNkWyQwu36HA5RIw23YN8kzLTF3XhRcqaTL3UhLeJ0l8AU19uSysXmvAYV2clmZrWZNtFYbEV5HU6rOFlRBWRhQO36tG+zOZzIxdUxJkLc33PA8Z6Rtq9BU0G10rlTEi2kjLORQlUq6ESG0t7aW9o69dLpeNbHaDqNh6TdbUYj7FVkX+puUutXLN+A8f3Jf1qUiLainEihlzLUkc0OdIUqDWErv24SP5Xm8k/mm9WUZYY+nnmcyPPcqZW0UBV2WcK4o8IZI3KBnC4/7s8vwYTyfrcwib2rJybY0aWNFP7ZFGot3sICSa4YxEuttbsjZc1zYoFEsl41GgRcnplHPGVkS77xjZ7RrLgNWnt2GhzfmhEuI2UTZbnRaaRAvNaFN03HzHxYDIje65/NRSZtvy0FB/jEi1rY76lAkcX31zGb+nT8VuVcsVNNm3+g5ZJPe73z8xc1PPRL1IbMudw2vYpUjCR7Tbrlc1/W4oA+I1dcMazXcZCeKVEky57g/3ZD7puJ92ewhIfzFgfyREZx7PzxCWdD/L2P9Ec4YVfP1b35Jn4f4wIbJ6eL7E1WsUgiDavEfUx9Vrh2Z+6LmiTvqI2biH14i61fdTouTd9g7OOWduHMo5YT6ZwqUDMCO6RlHC9XodVfZthe/nFVoylWFOZOeDR7I/q3BRWCmbeTgeim2+fVtQpmHJx4PHQmng0k9XSfrtVsfQdihabBrLO+zfvIIaP6elvx2eY+bzJQ4OBKWkJOJKyL2MFljMpU9VvERtbavVQsE9bkmk3N6WXLPXPUOTa2KoNC3lMjzuXw2uQZvr86h7Co+orfGE4gUlLV+2zD23WGY4GypdTY6zM7Ez80hsc6MuZ+7FKoNlX7bbirYLw8D48LlPdDX31Ea5jMCVuXbtUOITO7tyzadHJ6gTQaZVSipk8Sff/zG+/YMfyOevS3+++XmZS42qgzLL9/6j//wfbBBCm7Zpm7Zpm7Zpm7Zpm7Zpm7Zpm7Zpm7ZpmybtJwIh9JX/5W8UgCALnpWd0yip4zjIiFDRuui9Q4nyL+PI1BunjJapRHpYKqEg8kMlcivMOg2WfQRzZpQDJXOU+77+ydfxv339HwMAvvi/fw/AWvZ1GaeGcLFJotlPk6z1pz/5Em6+IGihZSFR4BLlhB8/fowmI8HKhaLZqjAsGS6ICWsRK5XQZE3nSurIaHFRFJiy7nXCjFmtyVrEOMec9b+aqakzqp4tIvQY0XzlNanbHClKYTxByuxMzCyjcggdbO/jlNwaz7/4slyTpFhXD1t4+EjIdH//G4JA6GxJZPPqlSt4yhrtErMXVw4kch7UaoawTqOsnY5kS8phYDJFmkFzHAcBM6jKLQNmBJIsNyiGOetIfa19jldwWYeu811/xnFsSP0UWaGZzjiO4bBOdsW62ZiZXNd2LsijMuPF7JXr+8iZYVCEkJFGjyNkvHdYkTGZkmclSlbYJYFdnWOpWbJ+t49Pvirks7omCr5fpVHH0QNBe/T70o8Hh/tGJlN5fjRrOpktzL/bRFhNec1yo2bqht8liXWFc/W5m7eNFOlo1r387tMZFjPl3ZB1qXwCXilYR/wX0i9GGh2ZQQ1oZluzwvPZ0vTbkNlIvfZ4MMQVooCUM0Prj8vVqvl3rUleotHAIHT0b4rgCasVg6JSFNAnX/4UAEG47eyRnJLr8zH5lvIkxZVDmcs6R8/4/XgZXUISAWuOhzReXcgoM2NQWqNZfK75iBnf0VgyHFmSGg4JneuevZbyVGlbvY9mxueLyLyr3u+td++hxVr6L/327wCAQeQ9evDQZOF2diUr853vSCass3sbAflbDg93eX3lsHANZ4KiB9Um2UWEoEzeqEzm16/9w98GACRWYGSmV7R9PlMVcb5GMRgZbs6JLMs+JiWt42A76+zbs5l32ypQZg1+kcrc2dlpY5HK546JMjjYFzv+/HPP4d67IvFs2dLvFrmN8vkE3UhspNJl/OJnZO588tohvv4H35A+5bwvsT8Ce4VaRbI+P/0zX5B+yGS8T/qn+KlPfY7/pjR8T/a8L//e15D25X46XsMxuQnKwRolSZ4yffcszbFayrW+8KZkjZ6/Kevn+2/9GG/dfyjPkJMPgP3qJAn+ymduAABeuyvow1/7zW/ieCHzo5zJPhPT5rb2ruH1N+XZt5lNW3N1rYydf5a3bdA/N+jg1177PADg9buyp5acEb7/7T8AANjc123ldHJW8KtEXajMPd/ZDyo4p3yrSsl+9FD4cTzPMRm6aKH8GyR3XSZrafJGjfeTa9679y4K9lFQIk9TnmO7I2Opogglkrvu7W8jYsY7IddFQD6N0WQGz1fEk3epX5AmuEkZ3HZL5sx7738IQOb69VvCO6WZ3FMiH+6+dhePHj249M467x3HAROi63liKUFwzdj5iH7FjFnsIsvh8hq6RxoZ8xyo1GQuaGZ6OOiZbP+KaKiSK9e+mJrcaolNevhIxsT2yiZrrEikJfeUPM/NnqV9pEihWq1h3lH3EMMPdYEj8MmTJ/y8jKnnBWavV04mtQtRvDC2xDW8htKCytoXXaWXJYpdxzdIb0Ww5kmKAuSHCJU4nQjI1Xov0H1J97+DgytYLsX2m72bmebJaIomiVuNeAbH9uI11Y/Wsc2T1Iz9nEj5hP3j+z6WkYy9YqJUgtzOMzi07fu7sm8oun25XKDG/aJOFJZex/V9tLiG8kwFUuR7s+kY7ZbYMJWmt7M1Eevjp4JE0LFttxprcnJyXKqsdRDW4LK/F0ShDMgF4ld843/0zoiA5DhERBYAQJkca2mmROO2QQgtuZb02Rq12sfQ5hdRv7pHzYlWDEvKY+IioS+pa0/FOFzXNs8VODKm/UEfJfod6iO0anKtZqNmkI8nXP8xqxKCIDDI9y361IrsffjRfdR4DUWx1Wuyz+9udTAjt9E5kese9+ZrN67jA/JWekTEVMnLWlgOIvqGqYrcKDKvUkWXY9nk/JgT+RoHNqpE902ITH9CfqPtRgu7FEdIeHZQ8viL/a3+8NnJU3PPbaJJpuR8SdIUN2/cACCCMAAQ0ZcfjSaYkQNuEeuakJc4j05Rp5+uBPc79Jv2trYxUHEbzkuHPt/+3lXDXRtwL3jn3R/LZ+OFsTt7u4qMWfLZfPO3KcmJd4l6iZcxrhDdpGNp5RbAahXDschKhXK5hJs3bwIAhuQ17FKwZDmbw2/Ie/XoT5zSZwvDiuGJTVVKneiv6zeuGju6dyD+kXJuLWcRvGeQ7zbPT3deegGHRAbatoq6gP0TYEKORd1fFH1YIEfBCgUVANAxzpIVdjjO+7tyLtDKoCJLDdLV5RzNbcf4D44aS/7/8dGRIeL3uY8p1268Wpo+1u25TsTbcDgwVQIJUbF6Fi4sD8OpovtpK9gf164dYMXKl8BVkSHO9dUKDZ7LJvTtcj7neBJhcC7X/N47Urk0J3pxFiUYsh/3b0lVTm8gqDTPLQzn2xd/8+0/EyH0ExEQ+uJ/LyVjtVrNLAY1jBcP6brw9aCln+md9+HrpkiHoMxN1fcDU0oxZmflNFz13ToWR2JAK9tyn6MjcSAr9Qp+PBUi1v/jH0mJVLJiiVqcGIenQefzUyTH+9k37+LuJwTyXoQkZM3WyhX9M4Fvnh5LcGV/Xwap3WliPJZn2eYBdDw4R5kkXc2mbP6nT8UBzPLcbPo5jfPSqKdlcFlaZROW3aATFC2WRtFh71CM0ZIHT2QFBtwMqiRSmwxl8g17fVO+1qAjpyoo9Zpn1FiU0OzpiTznc3fuIONzTXlY7hDm6oSecWi13SE0cT6fmsO5zoE4jlHxZMzL/N2U9zs96+IpofW3b4gRjGkQ9rZ3wD0eSzpdF0tMnl0DGvSwLMvc25BW6oYRr0ypnR5mlRzTcRzD/L+ic6LlZEE5MKSTg5GMt/ZduVxBWcn2+LxaLjSfzvAG1cV6vTXpHgBUOy1TIjKng/OVr3wFP/MzfxkAjAqM9meeJga6GK300EAFp6IwhHeqJqDv3Gw2UakqGV7KZyb0eDwz/agOqlFhq5TMNfQwpP1fCn0DE9XPmODABQZ/DbRcvSbrbDaZoncq/XCwv3vp+3t7O6YcStfsYrHArVtyoNUAmR5gfN/HjKoKesguuEFX6zXMGegrGBHW0oPRaIQFHb67L0uQ9JhjiKNn5AAAIABJREFUM5tMDcQ1fkYhMM9z8+yvvCKlNoUq/SWZCQgtuRl/i7Dkz372syZ4qZBoh3Pw9PR0XbamiiqE4a+SBD8glFTvV6lUTKmGsS0sl3t8fGQOUQcHstF++ctfBgCk7r4pFZtOtEQEAIBr18s4vEZVAx6WZ3Ou+VYLjs9y0IXMta/+gQZZqkaZyiWxrcLbkwsg1meTBUmSXFJPBC4HhHSdKRDW42EPeQHbUpJXqjE6Nsq2PPsikHUZMbnwxvMvoX8uhNNuVa7xK7/0n8gzDfsYnYvNjAiV39ohQWqrhCHZv7/+HTmkf/SBzMuKncEmOa4GehskI2ztbGHMOaclT2+/JYpWf/iNb5hyOsv0jToruQmYapB/ykOz53lIqc70+kuiGtimkt5XvvaHAJ3WkKpGFt8ltFx84TVxLn7uMxKguHc8wf/8j78DANhvy9rLUllT55MFKiVxbD79GQl0qZpio90xZas691IGlFzbMsGX3bb0y/UrMi9PHr+Np48laFClPdbxXiZzE2gZkzhXReUW8crsvUpurnPH9WwTrB9PSGQOVVbyEC21hEs+o6UZ3W7PEE3rHI+XEbZZghjoQXAhdjuO51jy0KbBUpW0SvPMlMdq8FLLWaulwAQrE+6fFY5fvV43pLhPaW/mPIC3dzvoE+avTn1e6FzPjE3REitThpnn8N3La6nJgGUURYh5iNWDrqqfSr8+UyYUepiMpb8PeOCp0KZZRWEU/kqe9jfVZjLblCAvppdLl5JkTeTsstzQ7CGVsjkkaDB8zjIB9REB4IMP3jP9BwCD4dwEvjVg0GrLHNw73De/WyUMjNH+1xuh2UP0XLHkQTVaLE0N+Q4Pco5jwyVJu+6JGhAKgmBd0poo74E0zwswoaKu7u9N2gzXdlEm0bcq4hk753kmuGxK45XkfBUb2oJnA+x+EMDnffRgp4HOOzevYkn1xMcPxS/eoUpis1UDYvoPjibxWHo36Jrr636kiaN6pYYuSaIHLIW5cnDV9NHVmzcArJOFrr0uYRvQV1bVsShZmflepu+qZwLH83DWl+tHS1XiXasP6r6sAiIO+yCDZYRTIhIDhxdEWny+l/aVlrO5lm3mlUUf7fRM5st0OkWtJmNoRGQ4Z/M8R4u2DzETl46NlCVHGf10S/dIKzNJt7IG3aZUc80KQzMxZFB8wjKXXu/c+OkaVHQCufbn33wTh7sSoDqmD/W0y+ROq45WW95LCarJSY3xeIyhluo+S8xeZDhgSb4GyBLOj9gu4FMJVqsOp9zfOq02QHGKYSrPGzqOMoWsk8e0hdPp1AQ+dd6//74cmts7+wDnR8jSqIvlrwsGRx0FCzBB2GyU0BvI3FHfYn+H/uYswoIBiBU3ncwEkV0TMNVS4YJzISj5RuilRZVOtVslLzT+m95PRQxarZaZc6p4VqvVDLn2swIsaboypcwHDCRFPE8M+ufoElihlAE6XzTwAqwpGrQsb6ezhYwJjgGvFdBvcRzP2G0tx1OC/Fang2aohPhVfl7V0yxji/Td41QpXxw02UfaD7p2O52OkN1jvfbWoh+eKdPaIv1B77yPFce5zABXyH3pfDwySfgey7BVAMr5f9l7sx5bsuw87Is54szn5HByvHnzDnVrrq7u6m52k01SIkUKJiFRNm0DBm3AhP+B3+wHG5ChF7/JNmzLNiyItC0LBAfAkkCJFskmm032VN3VNdy6c+bNOc88n4gTEX5Y39rn3mqIfDLQAM9+uVWZJ+NE7Fh77bXX+tb3uT7WNqgg1ieBOc/TpYJv3q/vyjwodctsHptzXJmKq4UiC9T9rjmXgYU6iwIMzY0tE+tek2S+25FrLlIXwwHP2HzvChJxfB9tJtLaTEQNeQbO48wQ1j8+HaxaxlZjNVZjNVZjNVZjNVZjNVZjNVZjNVZjNVZDxo8FQuj3/8f/0sjO69AMXLJYtoOYSoa/rPQCwFWnbRAYSmCn5LfxbI4yIdddwtqV6HDhx+g8ErjbAcl8HWb5BqMhjl3JDP8P//BfAgDynHK/sxjkWkWFRZ3P70sG8WfeewPvfuE1uU+Sa6rc8oMHDxCQGFgRO0vZQdugSRJm/SeDIXZ3SB42fhlGuLe/j2fPnsm1WFXQ+Rt0+ti/IVVdm1J9CvMb9gd49VWRohsRGn5ycc75BHb3BQb4yX3JrCspWRYnRnpaK+01kuE5TvJCK5tcSpFeWbKAo+SPiva4lO+799Ybphp0RBnRre1NMy8KgdT37fs+Lgh5LBNirATNbhiZKqG2AizJom1o7lPJ3F68psIMdf7UzjzPQ6+zlFUHltlp3w9N65d+XuXSp9MpFlMSUxNOP2OVsVAoIIiWFQ0AqLEKNxyMMed77jG7HbAFYaNeRyl8ue2tMyQBeLpAQHtXVMTpyQXabfn9V778E/L5rjzLfDpCmQSPPUJ3r68oQ7y7iwq/UyvGZYWWdtpa3MacFSZtO8qyzFQ4tQ0QRK5Vq9UlyZ+nsuREDAUeVBraSLjzS8KwYGxgQWJURRbE04kxNkU7KTTdcRxjf64tP+v0e2iQdDLNX/4+y8rN90SElyo57Hw+R8xqq1ZgqiRrDDwfI1bC1Y5LrASUSiWDyHqxGglIhVrnSH+mVbmr62u4RIUYaD+RkcViUZ4bS4K9Rb4k2lfEWLsl7313V9BUZ2dnpqVBq071WsWsF0Xn6PM1m008eSKIjKMTIRxUJOMos/DksfwsI5Q3Y5m80+kZiWCPPV/63pENMMvkuf7oW0Le/OEDsbliqYaZtkvQj1u0CW3/ke8jwf4LbWKfJZo2sOk8hUPkXW5pq5iiG+ZLsuzgRaSEvOdf+MV/DwDQasu8P7z/AfJE1uOE/uDnf/pLAIC/8dNfRqEiFagWIdce2Mo7OMPWgewFeSB+9dmpVGn+xT//XfzE135Ovnsmz/Xh9wTF1R+PcectQZx98StCLv07/+z/BgB88v738dY7goDcIWR7jaiSdqtrbHNKMkdLy65Oit//E2kBBVFRIeduOhtjg5LLw5FKurLq5bjwcvnZ335PbOCnf/Hv4O//TyK4cH4hz8OiKyazGRwlti7IWrhzRxCzw+EcBwdy74qy80l06NgpJtSEXiuqfDZJr3vn8FiqTGlrWuEex2PMGSMoKaRBmUznZp+4oOStIqjqlSq6Pbl3RciWimxbsQLTStEl2kZRk5ViBQu2C5WqsidcXV2ZqnVI8uAFq/i5lRkUmrYUK6TfcRxckWxW9yP1A1EYoN+W35V0vyASrdlsGjSDIgQUETwcDxCSCNvI5nI/zDMYwnptYTHk/rMZrFznmKgDRz6D3DaCHlrx1La5OI4NIsvleqtVSwaVsM7qboVxROC6iAjFHxuxA5nby9HUtGZfXYl/XG/I2hoOhwbRacQf+I6iQmDQAstW8GVrifog3RMUcbK7f3NJIs11PWUlvFQqocJ2pkePpFVPK7q3b+0Y/6itBGqX4/HYtHMc7Iv/Df3AtPrqPqHxhOd5pvKtbWjahh3HCQgKM21WEclhR8MhCkRLK+K7zDYG27YNkaoS6W9SXOTgxg2cn0gsaNFo794VkZFyuYyTU5l3j3O2z7bsUa+NnLZWYpV7Ou7zfgMM20uaAwAoEklpObmp+uu6AePoTnuAxxRvONwXv5AlC0y4fhXxo/Fm4Lu4JlFxBiKNnSUyqViRn41p41OiS2C5qNfEPi7ZOhMadI6DIffG62t5Dxn3i8l0ZmK1IkncFdWWpanpedF4CUS61atV06bV515sBEe8JdVAhwiNGVELs9nM7F+NgK0z8wmq6+KXbuyJ7xoT9Zkkc4MEI4c4dirio8fjsaF98BlzZYyB+/2R2RsVrWFTNv3Rhx9ij/vJWpWtgdyDz64v0B/JHB3clL1nqyHnjEWW4vjk5Ra/mOgQK0+NX1PejafnJCYuRdgtyfekRHh+yJbXeqW6FABhDBokM+PX1LY1LvA8x0iH1xlT6++uRm1cncq5o8nfKTl6spgbQnFDu0EbyAYLnPC8ojQcij5qlOvIaNMqKa8tpMVyETMiDLXzQ4UGoijCLvdbRZcpAjMIiiae1ZhL26k69FF6DUCQk9pGp0g3aCtic908f0gkeTxZ0mG88ZagxQ2BNM+Wtu2a/Vm7JbT96vz8HLe32apKGoIB37NfKKLGPbQYyftSio1Op4d0QRQrfdGCc3X//n1jh7pHbr6A+g95tinwLGU6D8IQuZ55eV5SWoi1euOF9uGEz2UbhN814wEdGYDnp+KL8mC5rwNAnGaoVOV+np+wPY57yd1XbqHVlm6fm0R9lYhAbXW6sN2X20oVKfTw0wfmmUsF7mva5r/IceNgj/Mm683XDpxJiumE54gNRYsqinGKCd/XMc/0uSW+eq26jpRx5t//335rhRBajdVYjdVYjdVYjdVYjdVYjdVYjdVYjdVYDRk/FgihP/ifBSGUpqmppukwFV8sq2ia8dNe0NyxjeSmDo9Zs1F/gCZJjK9YcVMUUHfWRnnBzN06JSQtybzGizk+mgih7z/9DeFLmMwlw9YfT4XMC0CFmeTP70kG8Wc+9xq+8GXJvBYakmm8JLndfJYYDps0fplotliMTBZRK56z0XgpZUnCu8Bf9sQbTpL8ZW4Nz/IMIe0keZm/pFGrL8kVyQXUZzUpcHyTwXzyVFAAFX7vzmYTV+Q90R7ThOiEeq1ssqla+Vpn/3y/00WRFZQqr3VGcurGxoZBQWgmW8lvj46emeeskbw6SRJDaKtIH60A9wZDww2QsrLUIyFXuVxGliyRWC+O7AUuJs26GwROp4MiK0Ra1TGSrbZjeAQczrtWKefzGEiUOJp/7ykB6cK8c0M2zO+YTuZot5UElRUD/q5RqSJjJt7I5yo/SCFc8kkRsRaFJfzJ178p12eW+nPvil1mi8QQ3aWe2P0P3/8OACC0HbzzhiDIzs8kY75OfowvffUr+J3f+10AQKkg92Bki6OSmVv9mSJk5vHUkHYqd4XOo2XnqNWWPBEAcE7+qY31pkHxzNnDr9cZj8emsqrPrpWHJEmwtyeVK5VcfvTksSHa1UpzmZV0z7GwUAJn+ohykSR1WQrYihaQSk9jTWwuDAqw+ayzMas0rFhkWWY4ApRTachKj23bpr9ZkZAWK33tXh8V8ri82Mst3+cZv6b8FqBdNZtNnJNfTN+D3q/cx8vyyPc//CFe5XvWnylibXd/z6xHRRaNJkremWF/T6q5fkCiekIgh4Op6TlX3g1dbpNhC429ewCA3/5Xfw4A+If/y28CEI4ZfZ45Kxw+iTStRfYjhNHpC3LOOj7LW/Ai4XSmVLa2zoFl+EAiJShMUwxTqWT/1L2fBwD8u//hvw8AOJ9f4+F3BL0TtoUk+j/7TwW586cfPcFv/RtWsFmN+cqXvwAAKG946JDTa70u/FUH+4KWuf/pRxgNxD9Z9BX9K3lfpVIJETkaLq7EVz5/JojNzVoNd28RHUmfPuA6K5UK6HTJtxFS2pV8Rge3bqAzEZ/87W+IXxgSxbFWqxoS8C75SMKCShaPkBNldOjJWty/fRsfXUuF7MmRVOwXuo/WythqKOmvfP7OXbGz737nB8jJU3BwU9an8pDASpCSJ2I4lPtS8tpiIUCRyDO1AfWhJ+dn6BDps7UlVWhFa1iOhyQhGSw5bxzaxJuvv7EUNOgTFUHi4yTOYdNmFEVh0H3FEk5PZJ154RJdo+S0ihAas4JYLJcMt476txmRfACMwMMaK9PKI2VbORzur4qaq7OqfHp6asQDGrX6S/MRRdFSGps+RjlI1tbW0CeX0pS24xOx7NpLv6F8Mx59dRgVzbwrd5BlvfAeiCDQ9VYsRCYOOD2WWGOdz1crl1Ag0kYJS6f0i1YYwLaJ/qHUtaJlxuMxekrQS1tQfgo3z82eqHuCcmf5vm9QAyqyoLEKHNugY37wA+EIU06a9fVNg7jscE9WVGa5tJTUDonY0e8Iwwjccgzy+OjoCAv7ZbRzxH09jmODoFHKbb0mYMPJlshiAHjljvgRZKkhuZ0QadXgPrpWb+DZqdzf5999V+aKc9zvdBHQHmvVl1Hq8/kUCRH2Klevlfc8s4xtRkSdj0eKCiqi11YeDFlvt24L6flkPjHP5/O5up0xH8FCQiTpGuO40IvwjDbzjEimg4MDMzvKw9IbMvYk+n82H6FH/ktdqxubEvfbVoiU8frVpfhTm/YcFQIk3E+Ur9EgvvtDOLRHi2SwylmyvbVp7mWLSHlFv5w8PzbXiMkFVFNuIDuHy/nT9aJ7ep4vJcbXSXy8wAJXXfE3JcaSTfpM13UNun/A/XNvm6Te0xkiEtPqvShRsu066HTkfSni2uc6e/XgFlontG1b/r5LYu1JnCC3FXUltr3TkA6G3LaQWi9zSE6JZt7b3UaLQg3NTVlDV0Q+VDbXcGeDaBnCnB5dnPHvpwap2iV6d79eNXu2+qkXubA0rlTfoCI53cXIIC6LRGDfvnnAfw/xnR+8DwD48L6gl3cZPy66CRKer/a4Z6mf7LV6KBJpqfFVn2hu5AvcuCEoj5Dn1Da5iF5/9R6mY0URkzOH5zwlNAeA9XWJZxUh1GpdmXNuhYi18XiMcshzD14+U04mExweyjOaeJN+uVAooEXybuUws5Uvaxabz0/NPib2cXJygror71n5aiL61fXNJirkOCsTlXZFJI7vh5jxXBvzHenZcmNj3cSq50QraVwM2zaIfp0H9fXlctnErEXanMbjoeeb96RIsEG/b+IAXROKSKrUqstcA6XifdrZk6NjKFSzp3yDjgpL2EgY624opyD3+XiRotcd8Zo820/kd9PJHEOKxRSb8jyK1NpurmFjXX722ivy/hRVe33ZQsC5HU7pm7kPJPHcnN9ff0f8/vWVrHOkAUIS1f/d//y/XSGEVmM1VmM1VmM1VmM1VmM1VmM1VmM1VmM1VkOG+1d/5P//odXaYjHCZPJyRU5VtjqdDgaDlyUjVdq70qhjzGqMVo8DcoGUa1VErK5YnZdZzv2qC3+c8fOSFRyO5HtLxSLK0Aw7kQ9ECFlZbrhytPislekkXZgqgpW9/CxzcisAyyynIiB6vYHJ+ipipD/vYJtVwRGz4V1Wg6IowpTZzUJBM/Ly/3Eem2vYzLpr5cyxbFOpVIb7nBXIZDQ1/ZmvvS7V/NFgyPvrIGLVfoMymZ0+q0J5Ztjvt/i7pdxjDM+TrOWQnEUT7cUdjJdVU2aNDQqj0TByqEwQoxAV0R3IdwbM8mvvbhBERm1DVavMtaexkQNR29EMcRAExlYCZpk1U1xOM8TsR7cU4cOKUblawVQzwXOqPtgqHTxDRHWUZyciUV4ukeMnX6BJ1nutKqq6k+e4aKzLz5TfSTPR4+kECSsuamu5rt50gYw8MMo1FWyE+NpPCXeQZp6//e1vAQDu3b2L2YwKLSV51nffEaUjBwssWPm6dUeQIIpU+f73P8A2VWP+6I++DmApm/n5976Abrf/0nMVWKFK5jH6VBOplGWdPX0qfeJvv/Omef7PqpQdHR0ZHpw9ql19+lAUTuIkx8EdUUsKWKX5kEpMjx8/xeXlH/AeZE3t7O0aecwFOTIsZtbbl2f4j35VeGOqlF/t9HSdhSiRmyDJpMJ0RWSYndumiq7cXq+8IvfU6/XMvDdYQVTbe/bsmeEy0SrGkH3BO7v75lrDkfyrdpyniVHoubEr1adrrofvvv8DI92rn1cbt23XrGsdb7z9lvncmNVFRTKcnJwYtF2vK2tKkVn9dguXREjEsfAAGE43AHParSp/KCJjPBhiysrjsyNBnpXYt79YxOa5lNsgNUi+zKATdF3qv+rrgaXt6O8cx3lBdpuIRt0bggDAy9f0PA8+e8fL9HPnZyITO69XUKrIXP29r8l9/dyvy3Puvh/iW59yjgqyFp4/FH4R69pBndWfMJXPdx6LQtt+OUS0WTf3CgCNilT/jx89xtVzQafWF1TfeVNsaDAb4aOHgt5UKfDbhzfl2q1zDKby+bfuCXdRd0BliWefIAjFxqol8VPTvjyTZ6UosepXYGXK4VxMhhMUqUK3URR7um6NDLqmuU7Fz4LYh18K0VyTvfh2pDLOcu1337tr+GYM34GtPi1BuSI2s7UnqI2USiNnFxcYsvp/gxx3N8iR50QRGr4gAVRiXpEcjuMgp2KNolNHjCG6rTYWM+UsexkhNx7NDUeaqhqqYszF9ZXhAFJur+3NJrK6bFIGuUSFnul4jpx1N+URAbmsFosF6mWZW1WRUqWRQhSiQbTsp5+IX0NJ7r1UW8Occu5Q3gGGcp32yMyNTa7EmKow9coG0oSIh4wca0SEpGlq+BXKkdjAcEDpcNtDQdV7nJeR2OViZNR6LBMDpfCJrKqRq64QUZrbzc1cKhJaOY+SNDYqRIrA7nVVWaaI6Uh98jrvi2s3TzEjn82C3Fm6V1aLBUREFbsM1u5QhdRxAJ/3/sYdUTftUL2nUqthqqp1jL3qjCOnvRYa5MhQXroy47nRaGQ4hCqMXT//5tvoT15Wp+n1JG6ZTCZ49VDsXfmcdA93HAcU5sEVOf5sxrybzTVsrBENMiFHD9dUMQrQ3BQOMqS8FmOF8nrVcJsElG53idJZTGPElI3y6Rdv7Mi6Pj05x5RI8kpJ1psC3QpRBT2fKmZEU/wZEZWO46BBtHjO9cYiOUbDCRDI9zwnd5GdwqyXUk3m9in3jZ3tbcymRJQ73LsH8nelcgEVKmApr9NwrPaSYH1N4tJySd698mT5obfk6puRT4cx3qxRNXyGKedK90zPyQyabE6FP+Wlq1QKJuYPGPdl/PvcWoDbLHqMnz3uRdvNPWwRFRJA/v7Rs2N0evKMWSZ7cvtKfEu/30dIv7ZGn3dF5doPPvgAG4wzNe7QDok4jk088MorgljtdQTJ8dzz4Cnahc+14D6zubdnON8yqkN+788FWW47HprbEtNo/BYQCdXptBCEVHfkPt9tyzOlbo6Pr2UeJozfEyII5/MEcyopuURldzodE9dUGGMrMnw6neKKPFBPGXerBHhQr+HL731Rrk/U88mxzNWkn+LoKedmJvfZvpbna5ZLiLg+FM2zxnPT7nYTZUrSdxj7NsnzVAoCw1GnHRyK1L9///4SJcM4S+2lN5ya75nMXz7HbG83Tbyo/FobGxtGeU9jGf18r9PCJZFZilqZ84zY7/fNmUbnT3nfer0BLjmP2qGw68m+e/O1V9Fl7F6jMnfK2MHzfYM4G1nkQKWa1yeffow6peFfYdyuCNHRaIwqY1eXc6RI5WKpZNBeGw2i5oj6isKiQV/pGlYVMcuyjbJ0SCTvprth9viMdjjhXmQ5nuGctWj3s7HYxGbFRs6ugibPNuojer0OSkRIXZNvMObaDUsRGuRuajTE//TItWbnLsZU6X1GbtcvfeWnAADnp8cA5+GMimc1qjvHeYLWudj22ZhzRI61ca8HSEgOj2jONarAxuM5Lqgy9peNH4uWsX/5j/6LHJDNtMhDgh4WNHDsdvtGtlIDaIVUB8WCkQnUFgcNAmzLWrZQEFrvMFAqrkfwhjwkVAkT7clCdXwb/Zq8sP/9f/1jAECrJc6pN5oYslNtGXt7SwL2n3jjNr70E28DABpNMZTnHR54Mws2VKpWrq3Q3DAM4QcMFGnwxdDFgIfDxroY1AXJFnXRAzBtTbooCq4vGHCYziUDk7ZhmWRKyAO7Rwj29ekZDg5lw9QWAoXbNSpVQ16oxLkKV61xUwKWTkkdnOO5yyQdAx6FBe5sbpn3awiheaCEbf0IybPruugSNnj0jI6esoF5mmGbDkf/zhDHRgHGTGzp95gDpOuYpJw6YkNQ7nmmfUcdsM5jWCwtnRAPDxoYZ4vcSDIqae3eHklb+z0TSenhRgkj19bWUGAb36Mnj3kvbMnyQ0zpJDUxp+Rlo/kUJQbsahfDYf9HNhtdW6PJDCckALyxI/elSUU/sE3ArlBcTdY1Gpsol15OZL7/vkBtJ7M5VDRXkzh6YA0817xDTXTp/1tWbt6TrtPrK3Fcd+68Yr772VNJBJW5GVtugO5Y7uHf/NE3OWdiE73+2JCmTmZy7TTPTKCiCegbJK5rFHxkJF79pV/8BQDAxqbM3XQ+QammsOCXZXrTNEVKWXuFf9ZJRBoEARz6IE14vdgqqGThek8ffShQ5Rs3bqDCg6BKpqqtFsIQzQ3xMwrX1+jStm2TENb5D3jITJJk2frFf0vVijmUmFY/zsv29jZa1/I7DSb1cOTavnIWosBNSqXls2yBg305jLaZMJiylSba2MDv/YG03v7j3/5/5dkpaz0f9uFxbi3C57sMBMPAN2tWbVv/fzgcmlYKE6izfdP3Q0MQq883mi4JXTVZr8n7KIrQZnLt9oasy1/6e78IAFgEdfz+b/x3AID/4x/I7956XXxfJ7uLvZ/6jwEA578tfu0P/pEchtI5YAUMxFSGmfdn72/h9m2xvxt3Zc6abHn67//BfwNroETiJJgtyrxMSi4mltjHhAe0n/yKEFx//NEP0etJcFHlweKS/qpUrWBwSSlqW97b0wfPZK5sCxV+j54cTy7Fvvqz1LQBvrNLYuXNHfz+n38PAPAqW0NeeUUO1Ce9LoZjkvZuSjuB7iWWs/RFeiDR5I3rWMZWXJLl6sG43+9jPpf/1paD7W057CSzxLQnKfm7xgf9QQ99HvADwuJrDJTCIECdQehRS95llrK1K87NOtE2NLWlTqeHwGdyxFUCy7qxPz0saNtafzDElDB1JW93GCR2u10DcdfDk+4haZYYQmZd15WKz2ffNj5Sk566Th8+fGxalzuMd0K2AlTqNVxS/lrnSPfy8XhoJIpLPMT67pIMX3+medqcrQuea8Ohf9LWFzvwTLuwtoArFcBsMjdBuX5Gk+O+75p5SzlXF+diO9VqHWAQroWGMQP3gu+Yvdi0GvBQG0WRaRtcChnwgNCoo93WtgkDl3F3AAAgAElEQVSPc8w9cjww70Kf/fRU9szPvfaGaV+b0C8q4elgODIxzPQF8tMa9161VW1pm07HaDT0MPQy+bXr+sgoTjFWoQzaznQ2ModrTaYPmAxIkwTrPECXuc80WahL5jHGTK7qM2tx0nIt0+LgM66aMD5rdwZmTnXP0vhiNpvhnC2MGh9ojDmbxobkucMYbMBCR7FcMoTuEakQHMs1be8DEtkGJCTvd9ogLy+CwnIN6XhRtEGezzHPqXuv7okac4TFaBnHUdRFD4alQtnM0cXlyUvXjuPY2HSBRNqaRN7ebhr/4bIgFbMYPE3GKLKtVGPrJhNfpaCMzqU8j6+t4LtNzChr3eeB0+K5ZzaZm4O+ksmekyahUCjAYbJ4l5LjrZb87uqyZZ6jwFaxCpP9nusimajktdzLMZN1rmvjb/5NObSuMyaaMWZ2/QA27Vftw7RxXl+aOLPBFj9N1KShiz6FCZ5+KjFvwvlpXV1j2BJbtatyn9WoaBKu6m+0ytDu9ozQS5WH9ET9Y7mMw33Zjzwmm6svnJtittP5BW3HERvd2qiavWpKf1Pi8y2m82WrOv3pGhOoQWYt21fp73Q+JpPJC23wYmualLGjEB0SnyvxtIq7nJ+eGT/fpi0AyzZj9ac6XN83+5Ihatci4HiMIxJ7FxgrKy1JFBZN65sSamscsX+wj5trsvfajG/7E/r6ahk1JsEDe1mMlHvx8IxxsJb6btImFrOp2ft17YeR3FO71zVrV4EcKn5iO8ti9x7J77Xwc3R0BJcFprs3WNiexThlgkwFKNQnzWYzQ0Ez7VBAJJbf7ew1kfEcxnARCx6sr6+vjb0/Z/FUBQRcF9Dgqch1Vg5JjD2aI2IcUeSepYmvJEmwyLlXeS+L6YwGUzTXZd6O+pIYWmfBruT7JlbTPW6Ha99a5MhJsv9r//U/WbWMrcZqrMZqrMZqrMZqrMZqrMZqrMZqrMZqrIaMH4uWseckkU2SBIOnhFqz8qKooOlkZsivtLqVGbm7CAsmi7W6YqpWzhKdsLlFKTtK6w7Oe9gvSrZTM3CLhBda5Bi5hIIqioqZ6DzPYREhpFAyrTKkafoS+gRYomayPMeM7VJDylVrxci2bVOVV7nTeRybCrgSHGoFrNvvG+l6RXRYKr8Y+CbbCGbR9/aksl2MCvB9lWcnzJdpwd3tHZMNDz5D3GZZFkbMGk957QorZxcXFyaLq9+jczCZTc3za6uNfnY06JrsKFixsZjidFzHVBc18x0EAfxc5uvLX/1JADDZ9KOnx3hKIux33nnLzCkAXF+em+y8Vkb0GabT6VJ+3FGp7KUkfZmZeyVZm7E6YSepgeSq7F/CClExKmCbJqNtgiqPuLOzY5BthhSXVdH5dA4nlPlXSKlWvWrNInK+k4Kv0P6CeSZtM1K57u2tTVPByvh92kpn2z62tiTLPCZsud8V+ypWIqyzCq/zfn4q63OR2HhCVEGZ8tYKOT67uMTkM/esFaPJZGLaGBTKqy1IjmNjRjvUapxKKluuY9oL6xtsRZyoXyjhG3/4FwCA734kSJramjzT3RtvoU0Ib9FbmHs6v5QKxR6hzQGrTrByPH0gMNiI7TQRERNHz59hAak27e8vZdwBacOsseVDpb8HnM80zc0ajKKlxDAAjCYTPD+W6oy2162zgjuLY4xICFqrS6XfZXWiWq6YKqHKQOMF2Xldc0dHsg7W2c4wnU7N79RWh6MJHFZEykamWr7n4uICeabtWayyKhn4IjFEu4/vS0Xv3Xff4fc+x2/+X78DAMgS+ftf+HlB2QwWM3zCd18h6mLQp9+yLeRshYgzkiYWtWoyXZJisrqlPnM6nS7X81TbZ0lCO50ZAlx9Lp2DciGCo6ghVog818brhwJlVilZbfv8/ve+ha9+Tux1i20a938oUtQL6/uY2uKD/vifyt7V/SEr7lkN/bn8LqNUc/V1IXb/8P0P8L6AbFBgW1l3KJXYzqNHuEW7+qlfEYLr/+d7QsT96PoaWwdESM3k3n/7X/yefJ9lmWc865HM0JFK2PVwgDqJEWPuVfr+XdimZalPZFaLJLFhtQmPpnbZluqav1bHZlOQQV0iTnrrRDDFc7iBvLuHj2RNldkKPRwMjX+vcY9TVGY8S8z+XmJVNyf6bq28gbzM/ZX+dNghksF1cc2WCr8qflGr883mBha0AYX2G/JaP0CZyINzQrYN6Xy6QEhSV5X0VbRfuVzFhMilbSIMXcvGhIhQRTJVKvI9s9nMoFi1lcWipHexUDD7ra5BrTamSWL2nHWig2uW+JO010dVEdC89gWh5Xu7TWS0gcMD8VfsDsPa2pppxdLqrrZ/z2cT00aTsJUrYyv0sN+DS59cq8qcOYratTJ4bN1S5IMdeJgPiN7m+84ZmwSeZdBye6yAW4YINMSCpMQW0UnvvCNV/Xi+QG8otjIYKBpTfOc8HmLCVoFyRdaN+oHRaGKoBgLum62W2Eu/PcQGkXRdIslU5rdY8FGtic1cnIsdV0n03um2zLvsUTCkRRvqDwcmftB90AtsRHyuZCjfUyVJ8Ua5bvxpxLY6rybfM5vNsAi0nZ3oK8aUfuAaAQOVrq7VAzMv3Wu5Z/WLy3U2X8p2c11PGf/UC3VsrhHBRBsv8Z16zQgu9y9F4imJ9fPnXexTJlnls6cjIjaDAmxW04sFymETGTBPYmTcj5SMdjaeGTRDSMJ03UfL1YYh7J5N5fqFgtiv67pGcMWmzZXLRAlPR8b+9N4V8Z3auYmT9F9TZfd9gySqlmRuQ19uoLm+gT6RH9oOlbE90vEB25OLxAkR75Z832hwDQvyftcY+06GFJ3pdVEhQfApY5XRrAeb31kg2rzBuCAOYqRceyOipdOI6BnPQ5too+cnYgu6J1dLZZRD+W71N48/EgR2c3NziV4hkvTwUFqZX3/9Hups+S+FlEZnHPjBDz8yNBXbjAm/9Rd/BgDwHRdddhMM+0oezFa/rU0c7soa39yRd+kRMdeo1WHdkOeZedp6Poan/Y88jymqobG2iYh7TWOTKBZXaRx6iOlrt6oNMw8AcHJ0DJc+KCa585T+/DweGfn3BpGJU+4v21tNg2AfEl2myOh0MFWufQSekugny/tm3Ka++vJc3nd72scWY91N0kecUAQoDDycHUtsVyAqqFQqGQEPtUON3WazOSo1bSuljc21s8SG55IKYaGxntj4p4+PEPH6O3vy7hMSLV/1Rzh5LO+yVFVUpMSktVIJC85xiW1aNw4EndMdDdAiqn2f8XdRBT3msdmX1XfOiRDd2tgyCCv1YXp2mM3nhoBfkcbXFD7KsgynjNNz+iQ/A444l11FXCrCfB6jT/qRwVB8Up0tcd9++BBVxivlqqy9w5ti408uH6PdlncySHnmZgx783AXNtf95bXcy+felLNpo1SCSwPJRiRY57OMsxT3GTtN2T2yuSX+9fnpJT78SJ7x9jtiHxp7OEGEEVGiexTSOj8S+gLEMQqMr/6ysUIIrcZqrMZqrMZqrMZqrMZqrMZqrMZqrMZq/DUbPxYIoe8fS2YuSVJT3VP0RauvMvQWJqy+K/IjcFjhH02Rs6rokMArIjHZzmYDFn83HbMqwKde9DP0XMmu+kXtm1eOgxmKiWQi7ZpkDOO2ZPQid9mbOWSP9YxkqLZbAAtssNg/ubkhGdHT03ODPFA5VZWADwIP23uS1TPk05aLmIiUlARjmkGtFwtGbk5lFEcJ+9onI5P5V4ltvanhaG4qPYbQlvwnvu2YTLlKOxb4mVarhSIz6iq3G7I//eaebyQ751P5VyuQxYKPBSvS6y4RP6z0dQYjWMyeL9ETRNRYlpHCTAhhOjm7Nhn8/tXLpGeWnSAKVAK8z/ljBtbJAVbohsxSe3xfvuOiSMLBtdoa/2xJBu4uyF2hPcz3bpn3oBw7xhZUkj7Pscnq84QVnDmz9otsCk95WUgKHvNf28lRjOVzO9tEJrF6N15M4bIiopwQ+v6uBm0jk6w/u7y8NhUlw7PE53J9HxZJAWuHUp15TqLfP/3m+3j7TUEx3CH5W6UuleZ5EqNNxA65qA2HhRd6eIs8IsfHggRRfqh8kaLDzPWtWzJ/rUvpSS5EARyWKiNyaN1gP/DZ8xMsWEnZ5898cgZ4G2VMSHC6RpSIoge8YI7FQNaVVh4ux1NUWeGxWV20fXJ7BEC1Lv6mf05EnC/2dXh426Dknj9lxYHVv83tHQy6UhF5+ECqBAjF/jfQRDyjnC99ysKR/69Uyqg1KIXckMpGi/3V3X7bVLKLBcrZcg0XSz5yVhdH9GWVkL3ySYaAKB6rxIoU565YKWKhxOeWkjtmGNPXEqAG35F13VjbMe9OK8w2uStq0VLi9oDExf/sXwvU5bd+5w/RZR91yDu/8/YbAICjiWUqo1ZfbM0ZyrWT1MIsVcSl2EBGNKLrWaYqrhW3Vpuy0XmG7U2pgCVTyp7zHR9dTxHwnneUByaWd1WohKiti82cHcvP3njlNpIJJU9JqpsMhQuoHhyjUJRJ+id/TPuziThpePjmf0VCyCn9yFekKjaZzoEFOSeIOmrwex93+wYdYtNvKP/PK40N/Np/8h8AAAZl+buP/pzIBXcDTx9TylW5zmxKX1eqhl9swHfbp6+1PB8h96Oj5yq9TP9quXg+kHu4IG/EkEYx6rRB8BCeX8pnxukZbmzJemkTUWtD9gY/B2yScN44FCTAjOSHpai4JASnDytsiJ9DlmLBeVDgZERi8igsGnscUm6afw4ncFHhM1+TuL5clvd3fd01KKPNG+SXo//KFgkG5NvYrZGfj1xH9cgzKI0599gZSZxneWz2nAVttFgtY39XYgVdU2Pl8vA9eEQeKZfNFSWf67UKSqx2a7xi0MW+h+1bYkcqV5zyRURBZBC8PX5Ps0ZSb9836Jgs45qvKvFPii/cE2SX8shMJ3Lt9VIFGfSa8qzfefAx57OIvVfFt9saZylR+2RmBCXGAyIt7Ckm45jzIX5NOTJCt4DChqydBedDq+zPW1fYacp+FBLZ5lCowbEXqNIelC/PJyLE2ygZMtHtprxLRQ/sNdcwJXK3zXmvFBlz5BbA6vrOhvjhS3JmTIbzpWQvOXpOlH/JHhtCcZdoa+XzsW0LU+5DG+Txmc/nGEay1yjxv1b1y+XqCxxAbf5M4yzP8IRVKfusBLWHN24gIf+Ovm+X0sSWlaP6zk35byKZlOS8UW2YtbQk86aASynCsCXft7amyGhZu9N5YhbdkEIsZ6eyXuK5jRnJfXzuXTk3gMxKsbmtqK2Qz0leGDvAVVt84Jw/q1VKcMmZU2vI/ndBNEmxGBrhjyGReIqu8hzXcMooafZ6Xd5zJ7cVgI7YVuJpynxPYtzY45rgXIeMm0qR7mJAxrisQHTq8flzbO2KT1Fev4DIq0mvhyLvr0e0U2Vd3rvjpAjpryLGoBOiOWfJ1DzX6Vx8Wet5C3ubcn+1guwdl+cS2xQLPhp1rqVY5m+vJj4XHhAyBu9xrevZ4fzkCrW6vPuDG3LmUHTCcDwyNnaDaP8ykSflcgHX3Hs7XUUryrv67nc/xWgutvan33omc8azVbHko0W00oyiQb4r87IX1BDHJD4mKq1RknXw1huvY8ruhyv6wGatghHj5QZRISYGdWxDHB9TUj1TlHDowGEcpeTayjub5AtERE+k5LdKSbB8as1h2TxT8pzlkWPqm9/91KxVi3F3n+eg0IuMWI1Hbjr1X+VyaFAvepbKKTTQjBqGE/bDD0XUQrnIqtUqarQ1Zf4dz6ZL0SKiSXKu0/F4iv5Y3oGeURTZmeYLrJE/TjtZqoyjO+1rzDjH2Ux8bI/vbzgY48YN8bE7exKTn54SudaoGTSU7gl6LixW6oa3R9HtitjfWG+gRaTl7duyzyw4P4tshox7+JQ+vsdrWukCZe6fH33rA3lmxlmTLEN1Q9ac5VGkaTjB9+5LnO5VJH558D1BxhXCIurkgRsRUnt5Tv7BsIh4LN9zzI6U3lT8wKdP++j1NFYlGkv5hQaXKBR57ovJs0n+4sPtLYzo71vMKxSqy06CKRWDNIZvE8Faa1aR8Zr9tvxu6JB8vGFjkpCr8qHEsm+9IWe5/uwKi8JS/OXfNlYIodVYjdVYjdVYjdVYjdVYjdVYjdVYjdVYjb9m48cCIZSnrCqkCSJm2XqtpUIDIJKa9apkJrXKmMeUcHMslJm5L6laEln6XStDpupbBXnceaZqHy5scq5kzDJr1aVer2NOxnStYCnXjG85BuHjeUQbMEPsOI5RpVCkyYsKGNoHqVlSVdhwHGuplMNKRwbL/O2SlZ7932lq7scgplg1jGdTc8+mj5W/y7LMcLZoVtvwiwS+uX6PHBIq210qlUx2WedfuVRKtmt6yD/5RNSSXntT0ANXV1eYzV/m/vCZsbWLFibMyJtnCFV+18J4qAzwcr/VagXVXcliayZeqwNfePfzsInYUUUxzXz7UWAUlJQTRd/3ZDJBGBKhopL0tJ3JJDUoIK0IvjiPOlf6HrRakKbpkouJ16wWJFvdHQ3M55VzSOcTWFaRPVZlavy+UW+EkFVkVU7wWBkoFAqYkVtHK5VBEACf4U5RW1rMZsYOj6iecfe29MQ219aNJPw3vvENAMDNW5K1v//oAfbIBZQmRL/w3bauO5hNX1aM0/7eSTJCh8gDh4p2VfbBZ4sYrlZnWNV1WVEPw4KRtVE1AVWxOp/PjCzyjFU4xxf7evDgEWqsVKoyTZpnyHKunc+o0Nm2Z1R+lEejUhDkzbDfM73mjTX5t9WVeymGHjYPxe4vz4hYI+9MvVRHpz3ifSn/jtxnGs+Xmr3sPfeIjrp166ax0RFtqMg5mwxHsHjPqvg3GFEBxAsNB0qRXBTJQq9tG7W7K6oU1sIiQvJDZKxWD1gxcxwHRarteGXlw5B7H8QL5FoFZgXy69/8EwBAbzLAvTfEVuY9qfr90Tfkd3EY4eMf/gDAkm/pzTcFLZYuLDx6Ir3O6m+GVJ2olH1T+d7aEinl8VB80+ZGHSfPhMvnp39GJGVLRC1+ob+JTaJM66yEFavkO/jkh8hYC3mtKdXrxTRBO6cKHNd1d0jp4OgAli3PfHku66xWFqloFxHKe1R+JOLJYd8+nDGm5O2qE6V0yXV33L3AflE+R1EKNClxXktH+NpPyPN860QqWCq/u7kZYkFpaLVL/Y50NsWc9junf/S15pOmaNOehuTQS4kMmMxjJFwTYyPFrrwCQKct19rmextPFzi9lErZXaqMzRZUkwsdeAFRLpbcS0jkZuiHBgGsnGoNIoS6rTaam3wXRIspV8x4PAbow2oVmTOtYsfzJSfK7rqgZNQ3rW2WMSTCIeEzWy/s9+rbA66vIu16nswMKqTCfd5KiWQAELKCq7xLo+EC04msxy6RkCqJHoYFg95Uv6McJfNJjgoRbcXAeen+PM+BbasvohQ9EW+WlaLXleqqUVzlv9NxHyntQ1FilZIqm4wA7oMaJ5Xor7qdIVwibhZzud+33hC11NlkhIf3xQ6rVVWi4Rx4Hhb02yqV2xnPsXDkO5WHrjuTebl586aJG1j8XMpIFwqGF3KWMxbieq5UAmi4qupJHtE5eTKEzXcX5eLLUlU5zR2E3Pf2GkXOsczVYJaYfdAm0uQmEbNxkiDnO1QOG92n4tEIgKyFdSKSNE61bcDXqNqgW7dF+RWiSiOfp1TwZIgW+Z92yVWyThRAv9/H/qbEh6pYpKGCCyDNFD0uz3x2LNdZW1vDjL5I98YFK+dx7mBI5OogJr8NHdAPHzzBYKzIJaqtjVT9J0eZPIzHZ+SDUR9draLEdzMeL3na9BnU/lV5aMx9O0sTdEbkkaNddid9o5Kn3F4292YvW35njXxhsJSbyjcImLykCmeM2dwUBaIzFlwnVqaoPR/jnqBelLMyprpZGmeGryYkgj1hLForlXHFebg4EcROhajwg+0dNKob/B4qG5O/J1jYGF6QBzB4WR240VjHOZHCTSKHq2FFwUx4+kg4+1SRaX9vC4VI7FW5VmeqeLuwkU3oP4KXFe6qB1W8/Zqs7fWG3OdsU56r3e1gzthE40AQPXfRusanT+Ue3nnv8wCAyJI5f/OLnwM88k9x3lOXaP88wQY5K8NAbKhel/c2Go2MepRDZEWFSkwX7Ut8732JGd77sihpBq6HmHZ/cSY8jBqbr62tIXBUMVWudeeOxCMPPr1vOiN8PWPI0yGOY4Rc2xHfYdagWlnFN5/TLgbtyNjb2UXKNb7OWMOcAeIEE6qSKUomUG5Sy4NN1FBOhK0qPKZujg6V/k6On3OuyCV0fmWUKrfIEZOkKVzDL6u8fOQPTBfw6Be7fcZ9tPH97S2MjQS93F9CVa3NjZrhrilX5L7ee+eefCZJUeZ+qYqHlUOJybMsw2uvyefGn+n08V0PvRH3AnL1KM9NMp+iR25MRU61yDdrOy6au3KN+ZXYveXqmTs3Z+3nFzJXWzeobNrtwlkQIUfU4/2PP0UQyPvN6UhLREnePthFRGTzN79HNCURnoHtIidCeErE9fFTiVddG6iTS2lsi13uNsRnP3n2ANWm3Ptbd78q30u10g8fHSF0lJMN8uxtrsHONX71V35FnpncsCdU5fOCYKnkyzkrMj5AFuP2jZsAgAsqAyZEHMZxjGJROw7+7ePHIiEU8QBe8ByTGNANRf8/nk/NQVNhaVvrG+YaCrNTAlH6BUwmY2QLhdZS1jPRzcQxZMNeWa59cSrGkCRzzOdyX6lKVLL1xs3spUT5C+1FgBiyQvz2KnJYHHaYAMjSJVSQDmiDQXa2iJe/0w3QC5bJg0Sla3UDzZfk2gxsjdS74wH2y8pyeshEBqRKjMy3r8H/PI7hM8idqRw8N6sMucbIpo1E79cNizhhG4LCl5U017Is44TabQkKCyTZLRQ9k9wwEvPOErTmkkiuRKhbIfIw4oJUyLrKTM6nE0N8GXL+XDr+ZNBfEiLSoeqICqEJJBLaSc5DlOvCkIcrIZyRls8yM++acClV5LnSNDU2p5BmTTTk6cLIhirpoQbXxTBCwgRBc1ts5w++/nUAwNX5BX7haz8LABjzkKMbTa25gYQtQZ73svQnABOQKZQ0zy1jHz4DHoXch76Pw4ObnCudjyWp8TUTM3U6njHldwPP+5HE05x2EvqB+Z0eOCPC/qfTOdbZTqBBr09C17BQNGTvSsR9cSHB26JagsuNWRMhmpyK8xxztgg6fG9JmptEn0on63sPIwc2JdTHE0mKBA6Tz6XQvEOHa31rXRxrt99HmwnNKpMAPKNgOBxid0+CoB436EFL3luWLbDOz9s82JYox+q6rpk/z+f74rvq9XrY2JTv3tzUjX7pTwLOaacjm6lu5rN5ggXbGLa39nkvvaV8M9+TEjrnyDHjRjRnsqjNVr3eaIYyD4XPL98HAKyty/9/be8GPEKf7/2kQFUDyDVPOs/xs18TQr1X2WrmuvIM7VYPB/vy/AeHN1+as16vZw7/vZ7YQlSXv5sOrvBzf+PLMu82N2y2wJQjH8lIbCUP5HdXA/ndfNjHxrZsqsdteSdIgaAg+8nFucxfQDblyCtjQGnViH5kwXX28OQZykxqtJlQO+48k0umKSIaRIFu5+hY/GTkl9DgwUWJGO+oROg4xW/+xj8GALzzt34WAHBzVxIveergcF98Q40Q5w4Pma5tA0wozHlo8NUP2DaOmeSfU4FBE/WD/gRJ9rJfdJm0tywL1ZI835DEpXbow6G/OTqV51nbknuplgL4nsqucqGFSpIbY4OHPdMaxVbm9XrZJB1twqoL9AN5bmHOts0Cn2dGaW/b8ZZytAzaEgazuzsHGDLJEfOg5DF2yNIEKTVkn57KIUeJ75P5FE8fSKJRiwTVuuxh66WCiUM6PSbUpiOETOC7bHHSxGSlWlruL4kSXLPFezBEgzDxnPOvPte1fCT0XYXAfukZer3ejySCVBDBsiyT2DUHQCZEF4s51hqSNNOWKpeJioYXYsgkwkZTDiAjJgea6xs4fv5Mrs+kz4h+3Eszk7jSmKhSreOVe1JguOABV4lZz8/PcefwprnXF0fBjQ0Bf84EhrElL8eCe+MiWx4IAMAPC5gx0NYCk7aVuY6L4WT40lx53DfGoz6KjC1c52WCZseyzOeLPERcsnXpcKuOeuNl4YWyp201ZUPuahIhWCDR9kKi9jVp71sFFHdJ/k0/d/ZcWq7r9TpmU85pmYVOHvBc18VoKO9cY8QJk57ZogVQSlqTb5eU9nY8z1AZ9Lh3axvUfJGgxEJA4tA3k7z95OTU0B2YwJHxRG/aQeSIb9aWj9lCE5sBMlUtYdv8kO1hQRCYll/d5yu1Ijyff0tb00TLbDiFQ4LYMteUys4XKxVUuC4nJI5u7sg7chzHJA1Uil7j1IuzM+S5rNXNdV0n8rsnT54gYmyyRpL4nq6tSglFvmcls1bbyRNgQoLqWkAZbh5cG5WqKVRqTKjS1/3pGFu74tuZ50JppwwX8qyV9+RQWW/InI36PVxeSZzdYBLR47qxXQfdIYnt6Xf0cG5lFlqM1zsnktSy+YVJluHBE/GH60xG8rUhTjMcsHAIT5756ZkIeywyG6/dk/29wEPow6fSctrudrCzLe2vV4Nnck1HfMzx8TFSFgDe5t8782XS/u23pOVc/UiSxthuyn3dpL9WYRTHsbFDwnI9s6U8pH/x818wz3FJUnlNfBVLZeMPZ9yLN7gXB/UQIe2qSoCA7q2VUtkkTOskqtak0VXnCBHjq0ZD9u4Wi3Hj8RQOW4Iefiz7zEyLtb6DTZ4JiyV5f0oY3uuPMeWeMFnIe3/1tddQph/UIvQmhQJs2zaJlR0KIOjvOp02Mu69W5vyLroDbS2MsFuUeyjxfKzrpVAoIO4zYZopJYH4jKdPn6JH8mTdW4/74nc21tZMvF5gXHB5JkkO13XNHn5OWfgh/SsFlowAACAASURBVPjGxgamLGw6LJok2i+X5rh4Jr7SClTQQOb15u0DXN6XswpDIjQa64jUCLhmb92+CQBYrxdweSJJnq9+UZKdGo8Mx0Ps0kfPYrGvVkueK88sU6BoTWX+KnW5h1rprim6ffhdiZVL9N9r5SpcnrUnFmkmtuXdNDZquHlTbHvAwtvxp2IncZKizThgyNY23yFlxnyGzuJlOoGMvrAQ+qYY95eNVcvYaqzGaqzGaqzGaqzGaqzGaqzGaqzGaqzGX7PxY4EQeuWAMr2WZSplRRJQOQrYy31TUVI4YOAs26gcIgImzIIpuWYxdA0CJmaV3NcKh2fDIqpEWzAMGfMiw4hkmGNmTsNQMtPdy7bJNhdZNbkkIeV14CzbTxQlwmxdq9PGaCrZ1QYzhVpVS5O5yapmzFwHxWW+Tp9dq0GOZZlKpaKotE3M91+Az34GtZEkybK9jWgXJa72i0VzDSUabLU65v+1ypcrTJKkbnF/ZGR6K3WZo7MzQVoFQYD2NQk2Kf1roPOWZcirYyWDIyIhz1NEhLo6JPHNk9jI8/bHRKaEKisZI1ZoN1vuoO/bcxGzGp5qWx4RWp4fGJTXdC5Z6TyTv4+iyFS3tFJsyNkcxyCl1Gb03biuayBqWvGN+XdhGJprhEQPGFLqLEPICvaHH0p15fDmTQDA9mYTUyV0JrqEhRW0221TzdSWM70mAOh/GSlxxzWVjGAh/+q6SZylS1D0XEy73FzfMHahlTaf+Pjm9qa5plbthkNKthJRAgAuIeza4jnodHB9fclrKtKH1WfHRY0w2D5JBV3abFCpIiZKISNxpkJ/AteDp0gzomts1zFrQUlGY1acy14FAdFaagM6f2luY87nn7KdlOAQxHEMi/OlRdCcxM5u0cJZW+55MpXnKZBIOpnPMWGlZ0H5y+tYUTA9I5GrqAi1kzDyMeF9XV3R35CQshBVDKmgQ19x+45Upr773W+jyIrxxoas4dPWlfEpU675wKAsHQwGPfOML87HK4c3cXkiVSCtrt8lYe98YaPEKtqoI/fX3JPf5d66eZ5PfiDVkjpJ3L0wRMg1/vGH8jtFHy7ykmkXXPBeFHnWqJdx/JiteoqWZNV1ij5sEiM7T+U9qARtHkS4fiLXHI25b4znGI/Ezyv56RqJN+fTPnz64nKV7aQj+ftwLUf7Qqo4mzW2PNGfDIdD1GtSlVXi53xIKLpfxBc/L7D9GivGLLri1v7b+N7X/1jm/TvflR/SduN4buSEs5itafy+UqmAkJUvXUMG/ZkkGJDod06IcsoqZW55cLSixMqqQZ5YnvGP2n9suQ48VuTGXEvRgKS6mGOHBMshfcPTI6lmbjZ3sCCKx6efS7nX2Y5t0DU513OF+0UYFtCiiIBBXRDBVK4Ul9XF5/JO0lgRijOz1+i+lqbLttEh37ci8BZEaLi+jy1WVHXf7JCI1HU8A+EvsCXWyhwEbGt0I20lJ4w7SeFxHhI+65ztvf3eACzumufS9zabuUvZcu7lDn3LZBqb/UirtLrugiCAT0lpReZeX0vVtb62iQVbc2yufa0KW9aSDLl1IvNYW5O1O5oPERLask6Zdpe+NkkSVNi6mOesqmczbNXk+fergnjQ2GG8V0ef5N+K1DQS8ZMxypQfV7SLtvXGs7HZ67U1WddE7oXIWDbOucZjm23IlgPLl7lJUrVtsZ1qNIXPivImSZQVpRMFoUGVKEn5G4eCcsgWA7M3zigZDLbpxeMM495yrweA017P2INeU+2qVC4b4Q+tOttsobPdAI+fPAAAsw9ub0ul+vz8Agu2Rm5uS9W602clfbLAaCHvVakJnILc73weI2PLXH8q9zDO5O/29/dBNXG0r+Qdgfb1yuFdpLxPmxVxpSNIkhQp0ZRave93KaVcKKJH8tg4JtpmfZPP28LwsVTlQ/rcYWeOWk3eV5No2MVI7uXGRgVrRLQFRNS+dksq6Q8ePEDAM4DHToDIVxQcUCC6PyI6YUR09Zc+9yoIUjdrb0h07Ffe+5xpZ5xQjCTyZU1NkwXWGNcoaivWPT1OcOtQ9t4FBSz+7C/+HADw5ttvmPjbppT3xOI+X1/HgvNf43ru94YAUbZugUiQvpJfB1hbk/0lo68oEY2ZOw5m/O61uviybkdsYjYZoV4Te5ozjlbC3swG3nhDUDmn5+IHvvlNoQ7Y2tnF8YXs/XNFePLeC6Ui/uSb/6d8N9FGX/6JdwAAZ8dHuP+BCE988b2vyM9OZU/Ybu5gTDLpZ0Qm3d2TddYo13FFIneLSIdKpYLAU+QeO0UYI08mI/Rod3O2nB6QmP/y5BLPiLxTYvJNoomuWtdq5gZVqQivq+fXaNDfLxjHpTOZ63q5gozo46NnzwAAn9y/DwB4/bXXcHolZyAV2NGzbCEIUSK6d2df/OJEY7By1RDA6xlOkT+VSgU7JKVXqoxut4szIqd1VCry+dFoZJCSZXYvPHgk/iTLFjg5l+9p0n9ofBynCwwpDuSQnLtBlGkURSiQMV4l3Ie0oXuvvWpQb1X6K41NWp22aZFSH6h/l6YJKnXxDdqN0OnJeiuWq7jiO1Wk1DV9U57mBrnzQxJwf4Fxxd17d3D3pjxXn/MHz0KB4jHPz8S2A/qD6bCLWk2eayuSzyjC6+Lq3Phki8ilw0M5+/p+aPbpr64LpYGe6R88fmyI8b/6iiDrJkO5l9l4ZOzBoo8usaPFKxfwmO8p4JlNCeyzFBgTgVdm3HPIFrJRuwebSLeNDcZgjBP8kocrdg78ZWOFEFqN1ViN1ViN1ViN1ViN1ViN1ViN1ViN1fhrNn4sEEI7rMTatm0QByNWiFUqejIdGdm/vvaM2pKFDBwXuZIGp6yIsAriOQ5ykslqBSxOJdPYKNYMz0GXveDKW+M6DkpEmqyxst86keyn7/sYaGadCvF7a/KZra0t2KxwKjGzx/7vKIoMSV0pkuyecrdYlvUCKTGr3NPpkviSSBpDDBzHptKgmeQXP6OfM9Lo+neLxRJppeTczAxrZhNY8jQpebZlWaaSp9nsBVED1eqyL1qr+S9yHOh/l1Syk5nlern8I9xIDq8DWCbzCkO06S25m/gxJWRN89QgniqcR5tV22k8x2SsksZKTEnCyNnEzIPOp1bzsmyBBUs2S9LIJVeDVqZ1aBXa8zxDhKvcRS8Sho+ZfS/Qng16yLbR7g7N9QGR9ZXntNEbKHeQ2J7ai/3CfSk/VJZl5mcm88/5tLPcoCV2WR3QKs10OjXVXOX0cfjZtVodMRFCx0+lEqJywkk2g6NVcY4aJVHTdG6qHEWSNV+fL1Emen8qk9xjVaJWbSwrnER09ZRo/BQYT7SaLrZTKsu1U2/JMZBgifxTRJFW5VWWvFIsgZyPRsb5mvLpXlTEFSUfz0/Fbh3aY6NaQUbbHsciZznj+4oKIR4+liz/elPm2CbpdevyCiX6mXVyiGzfFpLiB48fGxSUcqgcHMgcX15d4YQ8Fn1WMcuslD59+Knhu1J+rQVJfR3PNRUvlVr1igH6ffnvSSLXiipij0HooZQS8UfS1DVWyRqlokFxdiibuU650t5ouuRoYb93TtRS6BVNxeXyVObY5SK2UwsJ3+WYiKdaWSph5705dnYEZaDvuUnyYBe24dOKoZwjYnODeYqUFd+9LZm/Sl3u87rfx4WStPJ+3SDET94TkuuOqaSQHy61EJNgOrZV0KDM3+UolOW+RhMSp3Md1Ot1VKtSIfroA6l+9joqwQ5MxjIPX3znXQDA574sHArzfIxXXhEujn/1p1JZVg6FN974Ik6fydpTBKvui91BH8UiUSWsNimKII1j9PrynpVgVpGQluPCo1/UKrkOy7KQkajXZcU9t2YIlE/Pkn+vL+VZ1hq3gJjowyJ5Umz5zKCfodulvDQRAQGJHKOSi0KR+x8rZ4oSi+MMk4n6VqI8LPV3lql8O8rXxD22M+wbv5vT31u8thsFiCyxhzIrzT3KTo+HA4OuMWTNRGe6tmP22Rp5B616aFCp5QKfgT5mMplhrSFraMxnODqTSnWauZjH8hxV9eWOkgAvkJGPazIj8oGxSnP7wOzv+u90Qj6kJ0/MvvT8WJBBQUiS+tHMxBZRQVFHRFF4FvyQHHxEV48TiV+CwEOpoggw2SdU3vnW4QFc+p1LIgoO92o4PSUhLceoTQGKUhERCZXr9F3X12LbvW7PoHpn5IpRgmEnX3LU9cnFFpNrpNRw4ZFDzPOJtCJvULVaxa03pTqr3FJqE25aREK/uLYu93JyIu/GDx3YvM8hEay1PfJX5bkhh1bOEofotslkggmRkIrNLVfqmPG5LKJJ1DfFuQOX9ueSaKxPnsQHz05QqbESzbXx/kdC7n112YLLffmqzxiS+0t3PIXD9R/wPdc2iNwcT8EtFbs3xN9pTFUqF1ApEjVI7pUxK9qWB0xJBq2kvBrvxEmKq5Gs/5KS8hJV+2m3Y+TVP/hIqvj7JBhOkgQR/fU55Zzh5iiWicwnImBrrcLPXMElOj3hGWBIEvdKqYiUCN5WS9CpcU3mbDIZ4fDwJgBgQbtKWWVvXZwZBG+RiMSMMdRssoBLVF5G3iqlvVokUzTqFF5grDGfyd7Val9jMJT78ikckBOJdtHpGC4a5VLSs8tkOETONX7xWOKJUqliOGUsol7GtI9+d4EO50FRZinPKp1OF62erAFFhmsMWyiGuH5A5BmRQh2+21KtijG5PhUt8JMkdI4KId54XeIUJY6+7jj8vhbu7cs+GxBmZk9lXn7hK7+KWlX27GdHgkpRztaiX8buOuW2+Y1lcqusVcrY35K/GxJVcj2emvlT0RmPNhH6Po47Mm8DEoO//QXZW281D4yEd4eS8iYmd1wz71NDtCzvptwomjhaOSozIsHave4LKE6553v3XpV5bWzjoiNr9eGnsl8rUn5vcxOlfZn3N94VrsXnJ3LfySzHLYpL6NnN9+X/YVlokUi4EKkAQgpH/QfX2ZRrbzSbI+de2Drtc/4Fkbezsw2fe9UFu0BuHAiqreB4mFCoItGuFe5vnf4AVwOJnRRZBKJnf/DRx6grRyC/9znPeqVSCTcO5PPBvvBYHhGxFacLXNGfZlxgt0gGfnl5jSbjt8lQ9rN33xYEW2bovoFf/uW/zTmStXT09AHqvIfcVQn3KS6viMSijT386CMAwOFhE7Op2P3aBkUOKvLv3nYD59yjYImvCMjr5/kzTGcyt/GU8QB9xc2dTTQ3ZO/XOCItyTva3b6HBw8+lWvOyONKTqd5nOKcyOuZii4xnxFnOZq7Mo/FkPLzfH+1Sh0nz8SOnj6Vf2/fFR9/1Wrh8O5t/FVjhRBajdVYjdVYjdVYjdVYjdVYjdVYjdVYjdX4azZ+LBBC1oJVV+RYkAOlQAnDjIooi7mDGTP3ni2/04y367qIY1aIWYHRvuBJHJvKklZEw1CVmAqwXVVeUiUhctLEMQo1+Z5X7twEAIzaUinqd7oGoeKwP1SrJbPZzGSe/UQyhlfnkl10bQc2K3LK+K28QYXQN1l+7c2Oomgp06vS8Mx24gWVK0XXaCbbdV3MeQ/LLLNv5nuJvMFL85ikmemhHagSBKtQKXL0mVk3Eom8ZrbIcMk+ca04aBV6MpmYe1b1kzWiSvI8M/211me4abIsWyrRWIp0sVAhGmHCiptWSDbX1g067LotFaIyPzueTVEgJ5VWzBWB4/uhQYnpPGbZUjkuoV29iAwC5D1rJVar6lo5dhwHNqFL+jOtOIzH45eQRC9+xrJt+Kw0aI97oUruonSBkMo1+o6Uq6dQKIBAmKXSnL1EKelaSKkileYwHCDK6r+3d8M8pz6Pqoockck/SRJUWYF67XVWNlgByOcJqkSTKb+NqloUggjPyTtTjuTv1SaCMMKY8pxNylBeX0sFYrqIUWQVY8pnjekfFlmOmJwc4WcUCYPANT31CjgLHNvIB2e0NVMxXsyXXCP82THVN6wgwCn5SzrkBArtpU2MaDOXrFTmRKL98t/9JWztS3Xrd//57wMA1psyx3A8JKxWff69L8jz0S6/9M7nDGeQvpt7d+/wz1yjSOLT9jJb1sE7797BGe95gzwfO1tSWSwXD4xdDagQ4Vgp+iOqXPHZ1Q+kcYqEYK9aSdWjiHIsF3FO5RqwQp/TwLxFjmSuFQ2uL6JXrrrDpeoR5S8zoiPSJINLWfebe1LF0GrePG8bedNSRMQEkTXVchVhRASfIhr5nFFqoaoKUcp19Ar5BDrXeHREdcJUEZgRMir63NmXCozyapX39+CSd+TpEdEdrObsbu/jckalC395LQAYTWc4+lSqQKesgK9TQnUxmaBFRY6N6tcAADuvCpLh7OIZvvkD4RC77ss9bG+KLQ06l0blzvD80NdcXbVMX74q31hELnR7PcPHooqTyj9jWTkcVtOUQyXLlop8Iec9ZwV+o17Gwa7Ylq3kC7S1QlBESuO5eC7rZUa1zk8+fmAka27tyDzc4HVCrwCLnBoq2T4aKp/cHAnXR8Zqes77nC+6xm+3uT+pbLdr2UulM/5dh5XmQqFgfJCqreh+Y7uuUd5Tn6K2m6U5OtwbZ2PdDwsG8ZsGiiTlvpnmxt8bjjl+puQulcQGVKvRe5jNZiY2URRQr6/qd/4SSU0b9fksm+sN1Mn3YJ5P52o+RatFlIfaMzlqtnfW8eu//msAgD/+w38t39sm8iqNUaGsdXON1XkivZx4aOKCCveU3Pfxt3753wEAHFM6+ZjIgCcnZ9jj+uqTx+HGgSAAr6+vkHOPUhSRchYlSYxYUTVEebiUvK4UFvA5/4rs6hMdMRqNUALR3Asq79G3XHZ7RiZZkUWeK9Vr27aRkCtEES7KCTlfpIavLQxUzUxrqxboUuCwqn7dGYBhLDpEm+o7nVy0DFrX8AyxOlwsFjEj+gddsUeHcfHuYQ0eq9xTxnpql5bnY0RkTxTRnhKN+WwTozWI0OpcU747neDjR8dm3mSu5V62N3cMb5VKSqvs8draBtaIdFUJ69uHUpler1UwUVnwhrybep3cQPMYdkQOFYfo8dNjHFON6ZOHYjMZOY7euncXI3LbHW7JtS7OJO68ffs2TslLo8pqd6kgtLZeNXupcvMoumo2X2BBn/SQqBxFwme5ZeI9x/2sGq5jEH8F1tWL5IU5KEToMt6b88xy9zVBjkynU1xyvexT8emEMVSjVkWJKNYR11uWZdgjf4iu5/v35ZlzCwY9NGVs49Hvb2xHKFYn/Bnje1UbW183z/XxDz/g5+U7oijCiOi6CpXRKkQqz2cTIx1uMW7fIrJurVLBZCy2puqXirbx3QSRJXvra7ck/rPGsq5HszHqRI2rWtgiX3YNXF6e896JnHQjDGnLtw4knlIulkdPn+DmTbG7Em3tu98W7iI7XvKYbTOm0fXWbDYNgl07HA6o5Hk9aBl+2bF2MXB9z7Lc7D1jcsOqAl921TEqbT2iS58+FZRwc3sHV3zWT+l/G1QqLTkFlIikPmKsoXvQ5uYmmtzbLhhjZ7AMT41y89j0OxvNLRwdyzXmPC+tMY5odfuG+61FhM9VWxB8F5fXRuZul3aBXM/OPiKie8+JpH5G+13bWEeP8/iUa1G51oqVOh4+eswpIgcUUbGWDVQZ06jvXPCdelGIgPHHrTuClBrpWSqewWfs36W0fMhrh4GDhGjYa96nG5bg8f28flfW44z+pBBECIlo7hBheEoVtPr6BlqM7z3y2HXplz3XMkqTLkkgpzwDOLaLY3JiqkKozX2m9DQ0vE6eI+ss4h5WLBdN901OXz3jNaNyBTfIizXi2lCusEcPnxkfUaYq6gU5uAqFEj763if4q8aPRUJIZdvyPEevLxOv7TADlbUuREgYfCocfs7FmGbZ0vlokkK5qG3LLJDwM5Lck/HUEKrm3sttQ65jIWVrSEAj67KVpVwumfYblZXTQ8vZ6QU+/kgW+c//nZ8DALSu2H5iO5iDh1El/c2UVdI2LIs2N5ggDI2jWrbVsIXGcYxD001Kf+f6IRwlALWW8p861yoTq39vmQTIzEDxy4T+ei+0AbVJoLgkxWNrSyHCnLKvGvzq4aNer5uEhN7fmIcWL/BNMkTHLF4+ix7yLCWwjHMkJG8MmawIk+V8MteGCttNamxzGV2cmIWif69kq57nmd+pJH2W6sHJg81AQMmU9ZkXWWoSDIpc9PksWZZhwU3EdTWptUx0KUml/kzbtcbjMYoFcQCVCgmdVS459OAwkdkya0QciJ3bL9hJap5LocIZ9ODnvHi7AGAIUjt0eLVaDcfcRCoMrpWY7/z83LSRaTKsWpeg5rrdwnjCQy9JIxdkvb646uLwtjhgJVRd0PW4YcG859OW3MMzboRnZxcGmn/v4CYA4ItfFJnxSamAIaH5ayWS/hELH88nCHmodLiR+a5nDrQZW8c6PKS7UWaCnj3CYFXWutNpY8rk5toGodvc/O/evWtIIP8/9t6rZ9ctuxIaT3hzzl9OO56zT/SpU+VyVTuIplWiWzQ3qBEyqFviGpBoCcQtfwAhuEECJCykbquF1RZIiLbbdtmmXK54wt5n5/3l8OYcnsjFHHN9Z1dj31JSvetmh+/9nnc9K8w115xjjjGnY3V3V/oZjAd4cF/+fmdPnqnlIZbjIEevIlaSeJaO7lRLykeOFMRJyHIL2rGHMJB3LpK0T4OeqUQGEUuqfPZvSMfaz06wWiq1OOHjlo2IGzNFR3/Ccqabm46xSze82CqMPk4D8zHJ61kKNKJc9SoOkc3L/+kaV8nn6WxmiN216d5fBT56dEqyhNhraWIhlTE2RUt3bRI7LxYz+JHYkvGYMr+EI29WK4i4J+7tcRwh/94uJ9AoSuDpZ18IZDhlO8hRXt1lSZzN0uK0kwXASw15nOmHIAovkNNgFMs6spSpniwtXJ5LQIhbFzsb8rNmsYmdqpxDnUtxnkAy5Gy1id/7F/8SAHC0J3svwzMy6TrI8jKTysj3aileyk2Zi58faCJAxvgytpBSNnRecOd8Pz8IDZmjaVoaZ0WmHClBO+LEEbyZOGW7dBgrDPL/9c8/x5CX0U8+kEDmMw2iRUljY69YYlYtiR0/Pj+DowSIvMBoyVhs2cZea7JEz7wgCACtMuY5o+vMs2LkGWjUQJIpiYss4ytkWeLgLWUdh74Pn5+zaTO1hLpYKKFJR/+c0s0rPwSnBO2ujIueKdPF0jjOHr3drSblny0L80jFDt72Xwq5DFz+XR3oEuHm8+kMGyylmE4og52Reb57dAef/UyIyFcsTVaZ98VigRQJarXU6c6WPCeZiNE7lff5h39X4Pd6DpRKBUNCvaBt6dMHKxVKRkZYz5IXl8d4/IRE/HMtUZPPN5o1s5/TlF5XAnvXTcBlmRzvBbhpS58cN4LNhFm1LnOaYwIiEa+MU95X+0iZ8Hy2hjyTQTHHuEfyZstxcHYlQYDRWNZji+PRqLeMDQLLAy5ZthnBgcMg7M2NXB70PAzD0AQUMrxw9QcjLPlCXQZtNHBVquWNNLTNvbpNGwYAA5bQ6tm95JlXKCRNYOaC5dcu58R2HSTVt+PaGTIIkUy5yPNA8ZcsQSzL+NRrFezdk0tXkgHiMS9f7esbM79VXkDH3J+LxQJZlkT1SZMQ0R/MuBY2eXGv15ocM7l02/kCJr587uE7cgH/rd/6FJck1dXEkiadgigFn4S2x0wwfchy2/F4akqyP/2myLMvVxw7J4XXx/KMFkllU1w7q9UKC+71WlV+1mbyaTZfmsBJfyBzVKWgR6vRgEcxgX5f1o5eFpPpFAoUqUhQtOPqNUuZVgtD+DogUXWNwQDHsdHjma0JkZ3NLeNLXzKxnEypH+eYsmEthz95Le+5e7CLnR1KZFPgYTyRfqaSNpJJef/DQykBy+Y0w20jSZ/VNlLssuYajSYGTEi9eSW2YZOkyMVcFnXe2eocx4DJVM/3MdYAcpIJ/jHPukIOI579PgOuLsuh2t0u0jzjlkz+JaxbgmO15ZoYheVgQR9oymBzl3O0WEVYUNBAbZHadMeKb5Oy9P2vuObsfNLcgXiEYMFyudXKM/QZMe9NeV7yR4M2nrySkrHGnuznOu3+qzcnAP2pDIPo0wWD9rMpqmXZZzme6yqScH5xhSTHZso5sSzLlDXmmGiYM1idctNosrxewQYZ+meLxQJXr6Wsd5OUBiOWDRaLnkmA6B2qxzLCTCp5Cxpg0trnnXY4nZi9oPfVKwIFksk0mhlZHysmYJ6RUD6yI1g8QPU+rvYumUxi+FzWToX3nYkKnsQeLl+Kf2WoK3iX2qw1jCBQIsNxhIuACZjXLNe6w3vFeecSWdrw6Uj8sSsmpi+vJpjwrhfQNyyU5LP1WglRRIGNORMjebHtVuRgzPPMZSC6sdHg2A2R5nh43Gchz+a0nTElxtt12V9OnXcHN2HKtUcM9tgsu93dO0SOASRN/p8fyxxvtbYw62op89/c1iVj67Zu67Zu67Zu67Zu67Zu67Zu67Zu67Zuv2LtlwIhNF1K9LFSqaCUkKiZQlZdyp3my3mDyDimxF+WkdFMJmMQQvn823Bp62ulIgrvM7LWkWvQSSoNq1LU2WQSNiX70r9ADOxa7r9RpqVwxA3bxYQkfcevJDrXvCMZxevrtoEKK7n00r+Fk2skWuVpA0IAgVukjn4votvyLs0qatbUD0Pz/r+ILIqiyGR/FA2kZNuVSsUgUrQcYclxzeVyhhRaSZBdZlZvbq5MVmzJzJBmVjzPM2VeY5Y/aP4viiKDvNHyH43aW5Zl5ICVqNZxHEyZ9dTssb57Ip1GrNB8IkFuupJtQWybz+t7xbgl0lb4sEovZxnNjeMYATO4puyQfxYKBbNG9d2/LnGs82UQU/w9RLFB9ugYvVX6uIrf+pn+/sJboFl4WwrZlDk6zr+B2kqlUgbloV/tsBwPcWw+5xBCmSKKbjweo8W1rO+npJOtVsvIG+TB/gAAIABJREFUPLaZ4c8yi1Eq1gwZpmY1NRq/Wq0wp+R1SLZVm/vt9OYGPUb8b5gR1MxsEERIMtOrJXff//M/BwD0Xfd23RN1oMSufhjAZfnJQjOqrQLcDXmvArOmVnhL6P51+DAATKbSl527d/CUBG1dZjuazCTUK2Uc30imcsryEYfohq3KAVIs1fiYZQk/+0IyIg/vPUTOlp998aO/BgAcMYvU3NnBjOS2lbxK8TILnUnipit91lIPJccb9SdIO/J3RSAkibjonvVQpiR6tyO21nVdEMCFAkn92wN59kVvgTz3bE4lmwnjTtoz1EgKGq64hiiHO5xP4bI8K8ssoaKwsrmMWefjMaXGWdJYyORM+VJa4frMVVieizSJ/PwpbQQz9r2bNobM3O5S6jzhkEQ85xi0zHJFInIiXUbjMWxC6z98X6Tfz8+uUSSpds4gfZj17vThqTQox8X35JmwbDQ55xWiom6YncTKxiHH4eAb35B+2fIuWcdHzP5pqaXfkz2V3G5i/46smTlLX5Q4cGujjkRMFCyzoZ6i4GwHc54nQ2ZkG01Z866dQBxqOSlRGMx2hUGAKKElsdJ1+2uponv3pDz0+LlAyhGEGLEst0q0QYaZ3IODHfz+v5Q9+vF7RxxvnuWpKhpNWYfLvmRgtZSjsdnEiOe0ym+rXbXdpEE8aYkxAa+Yzidm7xZ4Xk9ph3r9LopEkmq5ipLephNpY2MjT86sFMcll0ubs96UKxNt6S08g0RqbQkCatjrwXK0zE3sQKYgc5ItFhDwzNJssBXLvOXSWaRdSrYTPaRnuu065hzTfoa+jGMpn0ApRzJqZhlj9ne3mcfqiPLvWu5N6HxhYws7LUE5KlpDEZ/1ahl/9qd/BAC4fCl+y7sssfSHY4NA9Sn/vMFy4CCIbgUemP3fbhZhk9DeShBlwzWarxcNQnh3T/qpxM6zbMn4b4rwHk1kfxdKSYB7O4Z8/oqQedcumDJv3VNTX8a83W4jzxIHlUL26TfGcYghs9ytXTnXXr0Ru3p8foZmk+SdKZYCkZRzMpia9aHoI49r9+ZmAIvr6OJa7IBlu1hw7x1Qul7PM9u2TWm1Ztfbna8LbaS/9vdb0ZTpdAZql5gSk6QioRxb+ayN/6Blg6VCBYWckvayJIiS8S+fPkGmLHZ0TjJTpQfIBRZaaVlrZaJLSgV5z6vpEllF31NcxWc/lwsPZfZvwX2p6IjpcoHNTXmJrQ3p08XpV0gQ4bdB33/jXSmlXYUhxgW1CUQr0b4VygFyeRm/994V+ediQfp+dXGCCksobs5pm7W5aezRPx/TTiXpT8RuxtigQ5aY6D6djofIpeW9Nrbkz60tec7zNy+RJtL7g6YQ9f7sqSBRnWTCkF6rj36wI2svjENT/hsQMdLttjGb6nqVNb6xQZL62QIuy0V2d2QNPXpXEFNX12cYUo68VJT1q8jj5XIBn0T1S37PjL55ZFmIDHpeRWrknV+/OkGS9ubOPRnjTJb+o2ObtWkFKijBUqvxCiH7vrPDPZ8Ve/LZ4y9RZFmouadp+RWA0bn4K9W62K2HmztGIOaYSDNFqthOwhAVO7wz6PyNZhP0iaYM+/LOKltfLuaNr6x3t6GijrwEfKK3l0S3K6K9XK0hw0ENOWZKX1B2LAQQZNyQe1eRPwk7iTZtw/6e2JTnz4V4+uDOFnI8x16/Pn7r+46Ojgy6DvRvV56HDPsXEhk0IUpmEA0w5j7WMfKCW4SoUncU6GPXqjIGH3/yqblj6F1txnMtmXDgsuwpzf1VU6J3L0Q6R5TSREmpZX1Op2N0TgV5o/dNn5ArP/LR7ss5pPfocyIAoxDY3HoHAPDsK0Vb855WSmN7T372hD/bJALw/HSGC6JLK00Z962NJkLuFz+SMXvymezL6nYT+ZZ87oP3PpVx/OGPAQAXnTYOH4g/dsFy1MlI3mEyvkGNtmW7xjOfCJ7OTQ//9m/8NgCg35fxOybqtpatIZySBD+Qn+kclScjVHgHbZPMukD045vXp9g/PJD/Y4nZ+Y38/ty3YHH9KRpQRY28RYA8bdLf1tYIoXVbt3Vbt3Vbt3Vbt3Vbt3Vbt3Vbt3Vbt1+xZhnkwv+P7X/8b/6TGAAq2QLyzCIrMsVh5jKVyRhkj82opRJ9BkFgkB+K0tCa7vl8bqKUGi22KKnZH49QIAFoyChnoigRcHcVIs1s8B988QMAwD//55L5tBZAgtl4lsmizhr+375/hEf7kin4O78lWeHskURi2xfXuDhW+VWSIzPLG1sRyuSNUam5hR8YJIdHtIVG/NLptOFFAGs4Q6JZUsmcIYU2ZJiKEAqCWxLpr8kuAlJnesuRI0/+OvLELBVmAgxJpuugSS6JDuuBFzPKjKczhgxypXxL/H4LCcSBRPvLrD8eMEIf4ha9ZZPrJGnHhrNF5/nrqC/lFVJyNc34NJtNLKaynsokP1RSUseyjGSsyRjbyg/lI2bdsXJaqfywbdu3KKBfyOTGcQzHlvWgxIsqeR1FkZEVTyjCTckhZ0u4SY4p+QQKRVk74+kCY0qNK4JG5zGTTKBDssoMs1a+H2JIEu/bfqkEe9JwLiw8eaZrybPKxQqyJPS8aUstP8ivtQocLDz5e5Z7SOdoPJ0g4BrLcKy0brnd7wFE70QLZsK5/i9vbhByPHTtKKLMdW0UuR+vyCVkMy0aBTGSzIBr9nPCDNDmTgOplPzs9KnUcW+3mthoyVgqIevL10Jy12xumPdPMfMyWQiaZ2dvH2cXEqVXudNNSkk2KiU4zJaMmEHZIhLHjn3kmS0JSFR/QyK6aRBjxTr0rZZkNAbMXM59YMbM2njK+VNIQhjhhhwlE6Jl0iTO2tjZRJKZX63FD7lWM66LRpNEu0S9uD7gZmScX59J5kb3VL1WBogIqtTK7Iv0dzhyTOZ10BdbdufolohVMzW5goyDZhut5QzVurzrJeWpVTZ0OBwb23x0IBlV5S1YeRZmC9nHU3K8aL35cuFhzHW82ZB+tpgNevzqS0y5/t+9T54E2uizi3OUSMZ5fEGy81IZEedHz4L7zArPZjO8ORWUWI0Z8GxeiUcDxLaiMImAmskaffL0NRJEjjwin1SVCKp6sYQ6uTs+eCDj15vI9//mf/C7+Kf/1X8OAHjxuaA1ygVZc1bWR74g7//0MSV8HZnbbDoD1yLnWIpZO45xdzjFly9lHS+N/DZ5BdI5LOZidwNKMH/rG0QojQf4+P2PpC8vZG63tluYM5ulBP5KHpnPF41dskOZr4iIif5ohbpKTvOAUZ6a3/j2pxjwWSvW5I/Ildbp32BGGdwiUVg2n9npDg1HoG0x202ei2C1xD4RjSXyuWjrj4bmbMuQDFzXYC5XgMPsvZ4lyvvhuq5BcuySSHsymRjC4a3WBp9FP2a5vOXBUJRHTN4v14HDPifI9ZdMUPo9lzboVOU2mM+YtY5iNOuy3jdoi2JFB/uBOddvyIFgJ6QvjmOhXNLxk3dXud/50keeGewvnwj55EaNqDvXxrhP+8t+HpAXrdVqolaX39O5nGJsxBhSJNoskOdtPBgaZESX860k4qtUGimeqTZRJIoyjQLPoAyuSe6foC3LFCvwObYu4Uo58nitpp6oKACwI/l8tUSbthoIXxqALFG7I66dUrVizvf517hyAEHolphdVxSiyrOn7OStsAmkrXwPV0PKehPhkiRKdTSaweH5bxCbVVkfo9kQSYVKKa8kbZ+/8pFKKmqcUspcZ5VKyZwBCSIYlBAa0S0XU8AxU9TLcr7Axqas6RXPDl/J83d28Oq1nKW6dh48ELva7naQI7LFJRffZMrfq2+jVZY1qlLIITP8KzvGJXktMkqmPuqjUJTxSFNQY6a8j1GEHjlhiuTyueFZ4i181Mh3UqB8vCKIV6sFapTDVm4S5SXq9QawyfOh+79CeXDXveVm1L2uJNExbvdlrSZr4eJCnpnNpc2aTifV34zNM3+Rj1L91FZz05CVe/SLC/m8OZeVNFv9OdtyDZRT7xH1hoxB2klgQZupHDuxom7tBMaKPB3IZ+wy7Y6ThhUr6kXWRYW2JpfLYUWeGSskt1paxuDx48fIE9FcJL+KolniOMb3vie8ZEr4fX4qtunLZ48RcD265GDa3ibqK5tDgXupey22orlVhELjkkT5KhFxfzyBRx/X450oRcREFlz/ANLpt3lLO52OWSvaZ937C0QIlkQ0bskaqhNRHYW+8bdnRHto0Y2D2Nik8BfuB66bxPWNrON0nsTuA/LlRS6KFfrw0dx8HgAuztvY2RbUUSbD+643QZu+Z5XP6l73OVa7GNHP7NFPKnF/NgplzCmi0WclwICf3dvbw5Scog+PBOWr2gxPn36FEvdXg8i2Cz1DggVaGzJG7UvxTVoV2Tez4RR2QuxAjryBl9dEDM3HaBKxekFhFJffMfcDlHNEVhGBekZOwgghDo7EX1RE4/6m9CkRW3j8+Cv2S8b/3qNH5s5w+kJ8fz3Lq5stuORX+uaOPDPPe8yb0xME5CP66rn4Yx0itr/1G9/GX/9E4gMuya9//ZMPAQBZhKgpXyxFOh4/fwkAmAQrhPQNVRhpyP2azWdR5x2owvNzqypncSaTRYd35HKFZO+0SYNhDyHn8GhP1klIe3fTHaM3FDv63/2zP/0FwsjbtkYIrdu6rdu6rdu6rdu6rdu6rdu6rdu6rdu6/Yq1XwoOoa0NyaqtJjN0GW1UhI/DOtbZYmkyG6o8pNK65XLZZMU0Eq/oleVyabIjmuFRCb50Losknx+odLKiPsZzzJiJSpJ7RZ+TSt5yvWhEX2UHB+MRPE8yBip9mo1UZt0ySCYjD1e45SfylhoRZjYIjqnlTLHeVZEqjuNgSpWCiJH8LKPVnXbvVkKav59mBNzHLarGqK0xCxJZkemf/l4Y3CLIdGw0Yg1GOFNJ19QrWxYzg8xWRVFgeAealNlckbtiOlkA/HxvKBkDRZIUijkzX3MqPtm5DIJQ0TuUmGftaRiGcGYqsynfXWVW6PziyvBL9AfSlzIlV203AY8oIFVumVC5AVYIOy/zNGJUVlFS2VQaSyJiYqJK0lwfQRBgOWO9MWXTu+T0CBEZhZceUT1tZj+2NjYxIwpowhrc6Uxqi70gRpsZX8PPtJLxSaWScJkp0vU/ncxuVZy4Fyrk0chkUuiSe0UVLuZTmb+z00vsss57oYgMla9Hwqg5TfizEutns8UirphdWf0C6NCOLCPhrTrw7XNB4Lhu2vR9k/M1YTaj0qyinJbPexzHATPVTrTAw3vCLfC9vydqfr//z35f3uHsDbYfSo373jckWn9xfoJmiSouzEDVq/Lvne19nL+RbIUihCo7vwUAWK58hL6sgft3BTXh0g7NZxN0r6U/VdbBv7pa8f2O0SS6xiHa682ljPlkNjeyplevP5PvI59DZDuIbFWskP/L891XiyUcZhxrVIbwKFc9HA/w6B2pp+5wHixVyYlDw7Gh0pjJRISviPgoVSWLc/LmWMallEEmKX2ok//B4/zNZh28finZn3ceSvZotRQ7Vyjm0GzKvlLVqlxBsjvJKI0cEXwrj5nfhezF7Z0jY3c8ytbHDnlPijYsV/6+uSNjvPIp2Tx34TrkZ8kTRZiRzz58+BBj2gblD2twjhKOi1O+qxerwmKAYk3GuUe1u7Nrec9mYws1Ij+mKg+uKnFxjIiEHYMxpWs7rOEfjlAk8kCztBmL6JBm3dgpoxLmyxisJjPDgaDZn2PKdx+8s4XrG7ElU0oXjUcy38lkCgWqxQz7ggba2qdikZVSU4sUv7dWzfOzfTSq0gf4Mu/f+eavybtcnSGflWce7suaKxUyWDJDWS4QWcgs+evXr/Htb8nvBkuZy1fkQkg1U9jZExu7Ik/H9oasj0oxA29KJNGNzFuaGcFWtYw2M+alvJ7B5G6ZL1ApU/WI56eieWLfg0+kw1KRw1T/2S3vmTU3JdItzTO2USkYbhjHEVSbqni5to0apbwrPEOalTzqRIxcMwO+xUz9P/gH/w5+/GPhInhGRRTl2nHjGBlV69L3ooKQ49rwea6rHY9teS8bkVFMPCXSYYPcZ+Va1aif7WmWUDkNOp1bZVae75oZv+50MeL5eUhVqA75xlKuY2RsQdTSDdGc+VIRS6I09PeRsLCk8iCI4rm5kIxswk0ZnsEcx/hSJcE3GkYBzKdftbkp79Wsb8Cmz6SIK1XCQ+zimgqJSWaWPfJo2LaDLs/ZDa6LJRVYEtk0jsjv1uP+3CYHRa1WMxLqYaQIavqi8yV6A/IV0g+xY/qixSpWRNmNiKKLLGCPakzdtuzLVIZqVbUyiiVZvx32YTiQMahU86DIKQo5qsdQcWgUjtT9QpYcMWdnVLIaDdAkauuGHFE210Axlzfjp9nyLFEDbt4xfrSeF6oQO5/NcLAv9v7Zc8m8/+hHombXarXQofLk/l1yWrkyx9fDE4wnsj7+6kciwdweymfvf/AJ/sl//I8AAA3yO/1ff/gHWNJudIn4VUn1dDIDhxlvm6iQo11Zq7lMztwj1J9d0kdpt5fGliiC5J33BN00my6Q5vsrGrBEVPJw2EemIGOlqGz1w+eLFcZEoCqaynA7hhGm5Juxcm/ze97cXBl5ekXtl4pE5n352HA09ulPj8djzNkvVabT30skEkZSvt0Vf0pRSoNOB0XeFYwqFLm7/DDCxx98IuNoydxcTcT/Kdby8GmbFeWobbaYGvS2T/82I13H+5/eheILBn0ZlwyRVraVxL/+/g+ln1RGGvL3e8MZ9g5kDsv0l6547zo+v8YWffJc4Vb1ShFVJ2fH0k+izW0ngSIVpWb0jbO0r/e2D824KV+P3sHqtYpRtKsTAaz3z+lijmRKbPv2ltiG4VA+i9gyHGxJVoz0yffopjMo8f2n5h4j67JWa2CfiOGLc6pwEV0yn3mw+SyLm69A5NV7j+4btc0JFZ+jeIUC986C/lSJaqxh5Bvkrt71qhyrV199ZZD/Jb0LsTLABtBry/NfEI3VoNz9YDbHcEQ59yl5roiqXPhznBHpfrgn6xgQn6/TbWO0FH42j/fB+w+Eg69YSmNITtJDrgUlpluFEeb0uTapblgj8j5EiAnv3XmO32xMLqJaA3fuyrOevxJUzuXpa1gcP4dr9ZAqY53RCCPO06uEvN+2K/akvJlCTITP9+58GwAMJ+r5+Tl+5zv0lUbyDlEg6zNfK2HhkZOKe6JUY3XMKsL+HbGn3Uv5jE8HbbqaIiIyNk/U4tNXRN4XC4hYUbH0Zb70Pt6+6WNBP2kVix1IcBw73RGuOU9/W/ulKBn7w//hv4wBoJjJITRy7jyJeIHs9nvIk+yzSGLIJRfWarE0Ru+ckpVKnNxoNEwARB2rBQ+c0AJySpxG52tCiKy9WKFAecMvFmKg/uf/9Y8BANYywmIu/YtYMlMg1uqjZgPf/UAOmw8+FGdj832BoP3gBz8w0OL9ffm/wNdSLs8c2gauuJwhnWKZG4MAX5cbNDBgkjoqBDX0/K+VmtH50cBXEJggj8KkDRl1sDBGUv9PiYXDMESeTok6keo8pF0Hvl42WEphSJ8tx8hmKnFs5OimTJqgT4HzpzDc0WhsxkHnbT6fGwi0EqM6SS0bskzgwxAa0mm2LRcZXrCWhH8qFHq19BGT8HI0VCheiX3oweI6WpH0WgmWoygyB7S+g5IiJ5NJWAGJQAkcbw8oJxqtzPPVIVtxTlvNJtqEjr4kIfnWplzoIrjmQFvx+1osN5rPJmjxQqYHwHQ6NSTZutb6lD+eL25JQmsNcVTHAxqllY/XLwUa/nd+81tvjf9gNMPllbxjgrHkHPfIIvDMBXdMCKs6moNOFy2WNkxt2XtKZhh6FuDLWKmsZJrw8yiY4Xt/73cAAD/4qRhELVOq1zM4YmnmZkue/eKpGP5hbw6fpW0ZXnSLubR5bjLFfUbnzkkksBhT5pGBlp+/EoMahrEhQ28Trq7yu64FTEkArUSR00APcx8Hu/IsXTPqvNXrdZy9kb5uspRLL66bB5vGaX1zLLaMSxW5dBELXqYWLMfZqPAiWC0hzcuXowSiDMCkUhk8o8RozP1dK2VgqVxpQdbx8QuZ9/2tpt7jUGSwQvfB04sbBCQuLpOEWctI4jhGRAfCC/RPzvNygDTJk8sVWasBPzscjUzpoe4lQ3oee8YJ0j24ZEldfzDE/r6QtGqSQO1BsdgwAdN8WgOjLEN2HFxQLlpLx/woNgSsGSUnpiPirSLYlvZPJkNJr9PpNBZLBmh4Bz49F6dkOJ6jnKfcNssgHh7Imv3mRx9gQVv0iFLPtYY4MHsff4r/7L/+pwCAN1/JGui1Zd9sHlaxQSLVH/5ISJ6dtMzfYDCCv5K1lmEp4yefiON/dnqJp8+PAdxexPcP5Hur5RwGPXFMD0mM2mLweDGfIsW52OCF6wd/9ZfYZ9DgiJDyK8K/U8kMHj16BAD47Oc/5VgxgOr55gxY8uxoNuSZi9nMOE03vNy/wwBnHIeGtNcIQtAmFQtVHB8LhDzLchxdQ6Ne11wo6EOhSTuZSqWwty9BWY8Jh70dlj5aEXptcWxLDMpa3FPZTAoNrpkvnnI80ymzXhNM5gRcqzFCU9KjhKMZFYNwbSPscEJ5WF2j9XodPZbMqU9ju2Jrq9UqIhJFLwkz17Lv3e0dcy5dkrg0oC+1Wq1MEEzH6JJE+ZlMDgHPzSHJf1UW992HD3DNEoAqS59rPMPOzk6MfVMfLJVMgO4UxgySagCvWq2a71Y6AC35nU5H5kLs8zKkPsZkMsFmk5dDOsn6LnZoG/ln/bwG4fujIWzaFi0f0ctfEIXmnZcsudEAdhjGSNHR9kjaaoQ9/AUmFN/IsRRxzhKp0PORoc8GlpKOZlNUWAaiCbOQRNobjW2cnsjeYVUCMiz9nS7HKDIBoP5UmcGD0Whk7FSKwUoNaLipJHIqQc912efF9e7dIxMYduhvvnwpZ1GpVMJgyGQr91eBpVmJhGvKzAuU1h6SGPv07Bhbe+LzlusyHko2HPtLQ4EQqzwzxQhWkY2PHsp87zKg+fzJE5PU0nNwxrHe2ztCxORdm0mPDOfZsWxzZ5jNZayUfNlJWIZRWctH1E+yYsucHdrUZ06mEuYiXaK0c5+lNIv56mtCLyqCwRK8KEKa/TpnubkmQ9Pp1C39APfBVIl/JzPTrySDdv5qhcPDA/k77YiWXbWam6bs3ZS78Wztd3vGv9R7jJZcX15fYYN7acKk9WRJ8YdkHgmWmJ7S7sRMDu0d7GNCP7F7Jeup2OBdbLnE7q7YzyH9aC3lnc+WmDBArP3c2pJzYzi5Ld1tNsWuzhnYb3euTaDWoe/2/uGumecpy9cmDBCl01kzdzmeBXWSUVt+aEh7j9/IeVFtyM8q1bqxYbssWX/1SsbVsixTVjSnOMXWjvh1thWioYmQ5xLsUDLlOJlBnb7dZVsC3jbvsrlMFvssSzp+TXl7+hONjRYc2g0t062x7KpUqqHP8RixxNuPliiWZC6HPdmfKc5fKpM21B96Z9uoS5+mg5EhXVY/eEEy94SdwGJyKyYEAF0GKFeIMGNS/QWTaooZ2NrdQLcjtmyDAaQs+5K0Eugu5RlaCr3LcWxWK3DZz826+ANa7jyZzfGNj96Xdx7I+6m9KlTKqDDxsuB9JqStHnV6QFLpLAh4sG3kSMQ8n8hearGE3c1kcMVkRGRxzZEWo9koIadlwLxXVItMFCXzpvx040AD5l8AAHx/hiITFBokteh/7x0emLsDJiRh5xo/u7lAk+IPj94R2oJXL+SdF6ulOSe6XdmzdzaFmPzlyRluGBDa3xPfZsGA42KxwIC/93/838/XJWPrtm7rtm7rtm7rtm7rtm7rtm7rtm7rtm7rJu2XomRM5XAH/S5qjHBrNmLOzKCbSBrUimZCluGtRPqKWesi4bPaojiAy0yUIoNU2nzqLW+zAcyoWGT2y2YySJOYtmRLtLlgMjfzWwSOSu6xOmOx8tClfPAJI4cFljpUSlUUShLRjJRIkeVr1VLZkPtptDqby5psiU3oV55Zrji2bjN/87ezpi5iDCnlnUopFF3JuXNfQ//IeCiyI5UtYtjX0i+WfBBiGAQBPCXUIxFmjoRZ/nSGNJFBoWafSJSYcN1bSXrOkUbqV97K9FnRAhfMqAyHfQPbVDLllR8AJBjUjGCWaLHxZIY+ibQVMjklbHw4HBrJxzLLQi6v5Xv6wxEyaXnHx18KFPqAiJpWo4ITks4qckpRWePx2KxDfT/9WbFYNNmZLqP8Ru5xu2nWu0btFd2zv7uNxLVEqTN8JqtjEAQRsqlfKAPk+tzaqMHhOgy5J2rVAtKEgOZYRrKYyuTUKg0DdX3GzIaO9WQ4Qp3R6SSzY0o8GsexIe2NAyXg5p/+CjOiXCwiRjptyRYkYwvvkvwtsyl90qzoqDdHlnK2E669Gsl2bSTgxiQGPqBs9PuSQfD9Ja6vZH+dMUuuktJJJ4eQCJVJLNmBIPJhu7ekwgAQMDMNADGzfDeX8syFI30KvBBLZhgqhJL3CGn2fR8JZsHuHAr8deNQ0BvD4dAQuzc2JHOmpYJffPY5vve93wZwS9a3IPw2X8kY6fCtlvxeNin7LJPKY0Xp6eMrmbcFieJqpRRyzMo+/lzW8fYjyay0WiX0+rQbmvWejPHehyJR+4wEe0kSVN872sObF/IMtyLr4slTQaNUNveQ4Xx1mfkyNgmx2Vce00ZJkrum3NsM7itmePoso5zMF8iQsFsRIA8fSmbESeTx+EtBhynKscqM2dH+u+iNxFb+9ItjAECZP4via6SZIdJyH5WWhW0Z9OB4Rfi37WI21zJjooeYjff9EPM5ZVeTb8PoPT/CjJk121UySFkTyyDCnBmriasS77I3As9HQcltad8O77HEMJ25tfs8ZxT5MJ9PUSApuqKA8hWSy2dtzCfye1WegzuEub8T1n6LAAAgAElEQVR59hXuMmsUk3BzW7P5aeCjBx9IX2gXjvYa/LcLh/UZrbq8352DTexQ5tgKpO93WBIz6PVQzUn/7u4q2kP6fnJ2AjctPyuVWZpTkUziYl4G+Pkx4dhTlu6mUimsKDkeEN13964gEmrVBh5QvjxayXoyZdW2jTrLBFWIIsk10b65wnwu9iZcyXh8/iMRjUgkEqaM9ezNzIy79CWBmCgnj/LK9WoNBSLprq/lvFBK4eVyaZAwWm6xvSOZxF6naxABi7mKJLAsPp5hReRkuSzztiKqxHEcFHjeMplsZMxvOl0sWG6liLc+y7H39/fNmaVnz86OPHs2mxnSWR0jm+7h2ZtjwCIqryNZSWVyLRXKqJRYIs8Sq3KuYM7EQk5sp5KA37TbBmFJ02kIcQ92Do1BnBKlVKQ/GDsJeDxzMFXCZHmm74dYeUqkLXu3QcTJxeWNQRuZRpu7u3Ng0BZDwunDQAl7LRSIWCiwZFfl68vlJq5Y5q0lYxtE1JVyeYMc0Wx+Jt/CxaUgWrJE5+Wzsv5n4xnefSDrYT6X+ZqS7LWQKeHitZSB1euCMNnjfh5kkrf0A/w+h0i8wWiIEm1ejrbZCaV/veu2kR9XW1tmacujR+/g5ISCI78gXDEcDg1iSolft7blLL5/9wFC2rfeDaWeaTvr1RaWPAuUbHifZ7kXehixdP3minLMlRIy7LuW3E1pX0/PXyPgOZ1A8q13zmQyRiikXKUQRVf66UdLQ77c4Rqt0R8MguBWcpwoLH1nx3EM2q63kjNf9+tw2kejTvShS9QXkat7u7voE23w7nuCltT9ads2rnlGfvSRkPVfsGTy1ckprrm/Dg4l67+3t4srEhCnSF1BNxwvXr0y50TA/1SaikwmZ9D9x19JiVRIO16qlPHqVBCJWppi00bPoxW0OENFQma8Nz396hmKJIe3XBmjq3NF/qRxeSHrvUhUpU1/4t1H9wyCVAU9dhpEDBZiIwKQYqlNrydrKGeHSFQp+MKqgqtO2yCNs7S5LSJXvdXKIDKuOKY3JOKOwxgu7dr7HwmNwMmJ+HrtXt/ciX74IynvLdBWt88v0SVSM8Vnd74UJPXu7jYqVXmfPMVEXN4tZ4sIr17K3j1l6fndB0Jx0B/PELEEXNdhloiyzqiHSllFVeTZU97l5tMpUkR9Z1k29ez5KfyA9By+CgsQJZm4xXtMuO4n3G/bm1vY25fz9uyM6E/e9arFCl6PjwEAFxyjAYU97GwWG03xYytE9+o9NJV2DPl6kf1bEL1VzhVxtyx2zlIDRPtvBRESRMpe0q6qHbddB12idRc8u9TntqPYnMVqc/O8IyHwUaSPoQi58Whq5N8Vmaso7fm4b0pMq0QWZkhXcXF5YqoLDrZlX37/L/4KgCCElDbmK4p2RNxL08UQybT4GL6viFeZo/5wiTt3BN1+ckzEE8+QhOua9asItGpNy0VDY6dCVl10eGd8c3KJ04ixANqpg7LMR/vmKaz627GR/6+2Rgit27qt27qt27qt27qt27qt27qt27qt27r9irVfCoRQFGhGa2WQCyNKseWYRS1VykaKWDPNmiFBFCMK3iZJjPmZKIpM9NAQohHJUCgUMGe9a7SUaHaeUed8bJmosfIOgNmI5XIOWPK5OfsE1mPDdhEz+qicL8GcBIkb2+iSX8VjFDdJBM/5+aWJ6LconRdGtxFFJXM0XAWJlHl/Jd62tWY4mwfIeaFkeF6g5IehIWSGylcyezeZrgyBYpqR/CU5MzzPM2M7X0pf+iNGRJdLLMlpMGNGXMcuWHqI+bNtEo29eiOIhMjz0WVNcpXZloCIsOmwhzQzRRckois1NnHQkMhnhTwOS35+d3fXZGMKZZUpZG3sYoWf/FwIEJWXIUFuj3phCyfHlM8mZ0WLmcGkY6Phv43mUSRNvlAwkVojVx8pWixAQOLKTRIYjxnlDzwfeSJ37jI7W2NGe7NeR541++fn0iffu+U4MKTeROWodG3grwwJZMB5TjoeKqyjnpN0NpchWXHaRnZD1nQm+zYPUuHegeEXmugezEjU3Vst4NoyHqMVuZgUIeetkMkpMTIzBkT6ZBwHnifPatryfmUWDRfreVPnnbrPsSJpX6VSx2hyyT6TJ0GzNekyLi/lmR3yLin67v7DBxgQbXROvohSLo0TSjJmSfToMivjeyvDzVBilqRIJEi8CDDlXtjgvhxyrCLEyHP/1minyiWZk9dYYYc15BGlg5ckdbTiEM9eCMJHM4eltIzL7tEeBsyOBysZo1qBtmkri2+RE+Z7TSG9fvWV1CsPRz3DVZJ4V9ATdaJ7VvMBNoiMK3KsO29sdCg3X+bYhglKebevcHDnAADQ47ptD2UPPn1zhQTX3+EBkQskiKzVakjRFlm0lfWaZGmm0ylC8qX55JCIaWvKxYZZtyqlHBAdsVr5qNVlXXgL2nRK3pbKVWzuScbmyUvK2V5TYjeeGc6bKyKZNBNcrVZRb0q/HGaUTk5OsFzKd6uc7ZQ8cQgDlLi2NMPZYyZ3MZvCY6ortmUc1AauvABzjlXxFowGQPbziKiT7UeSKao1Ka4wnSBL2/fhI0FKNQpEGtpTlJkE+7vfFYTXV+SH2m4WUczLM4YkryWoB+8/3EIhL/MVRsqjId8x7F9hzNr/Mnm8upeSsd9qbaFBm6sIyPnBjsmiK8JEJXZrxSxuzo/l80R/Rgux/3ubdeSLbxOdFlRGHg6WJMUck2ev05Hfc10XS5J4q41dkOvkpNc2pNKffflzPlO+o15r4qc/lkze1oasIYJKcHiwgxQzsfkkUcI2+WpmC0NY7BAVUd8g2ikI0CdJcVOlrG0bDs/eZku+Z5dnXTKZRI+26JYMWf7sdrvo0N9RWewF0UqnZ5cG6aoohVJC+hvHMUJCcbe3d98az8l4bEQplP9LOaPiMDKoIeUuPDg4ACB2SJE76lcES1n/l1cXhqBaSZ6vrmS99HoDY7+VO2c6Hpr/q5M7Rb93Z2cDXZK9K6ddk7wWhXzV2MUXb4Qkd/dI9kY2m8WE2dwJuSQ8+l5uPm/GKFWQc21Cjrp6bQsDkjRrllXHZzld4mhXnq+IdEW9lMtFTMYyz4oMV068k5dPcUPkcIkIpiLJQrvnxwapGpNHau5PkVXUFX2vLpFk3iqEEjAqZ9aMJP3DUQ8OfS3lAVTunEI6BY++yYJI7y6RJ4eHhwbRtiRqRpE0rY264Z5obL1Nwvz65XMkaXeUW+MJpZujKDL7/4hErDkSob958waVhqzNPdrqNhFR7ZNTsx/T9F0XXPNB6AGOPOOGJLaj6QSbuyT/JpdMsSw26eTkBODaXA5lbd6/I8hcL/YNqkDJiUFbnXTypq+hLfOkktJJ1zacSknudbVRjps0/nbsyvpVfpudwz3j9zkLfoZcmafnJ8b/ndAuZjl2uUIWO7QNV0RTPX4qY9zabGLOu8kJ99ci8IyYzor+i5KJW5ZlCGUVra9j9vrNuUEPpclzNeK6GgyHmBPxUaaNqBdln86mS3j0O5SrJJzKmPthAAt6RtIupuR78/ksOkRkXRGBrbZiMZubvoS8x0z68v21asPwIGnTe5oX+7hsy7NKtE2N+rYRKFEbM5iQ8wUW+lwDDudNeeG6wzFc+iaeEp46shZm8xkWHHfl0UllKY5TKOK9A7ERmyQw1/5GUYCffC7o5UaVMuEVGcfdVgWTlvRz/+AOv0/6lMlksEnEX47+xw++/xcAgK27u/CJmJyMyFNG/2w668Gmv5ikva+U6ljO5HOK6E+l6UMFnrHpyk3X7sm+nCwnqClakc9fzsQGlre28OBIxm1OnptK6wAAYKWSRqBH75RqFyajPoZE9VW45t7ZF1TU6ZtTJH35vSHvfDVyoE68GboDOSNVwClJZKObSeG6LWe97tMa+a8GwzFe/0yQ60d3ZYzPSerdKFcRRzIOZ7zfPbh3DxYrURQ1msiQg3YWokQ/IoQSfJObKpFEimihAud5a0/sYxTa2CC3cZsIuYi/3x3doEiS8jR5MNVm9Ac95OmX1vdkLSj31u7enuFFPCMHUIJQ4Nj3sFSBHvK/OuRpuu6MMCc6++UrQacViebKZrMIskTK/y1tjRBat3Vbt3Vbt3Vbt3Vbt3Vbt3Vbt3Vbt3X7FWu/FCpj/+d//1/EgGSwkkRB2MxWT1lXHUQh0szCuYxjMdGG+XxuVBGM8gQjxK7rmoyI1mTmGV31YcGb3j4fACyiNwqRBZt1g7OGROD+p9//IwDA458+hUPFjzEzbQlma96vVPBNyt19/Ilkd/cPJEpX3Gzgs9eSARsQfeEyDJ+IbRSKktlQidfldAKHmcopuTK0PtR1XcNYrkpsS0a3Xz59hlZLsyxEDXBcI+uWg0n/T1EKk/kKGTLOa135hKoKq9XKsPLPiHg6v5QsxlarictfUO1RmeVWpYaIPEkq06tZKAcBji8kYt3aOpDvY6Q4n4jx0UfCa/EV61hfnbdRYB20InVUwrPRaBgFCF0zyg/lJhNGBSrFTNE1s5OX7a7hDUgnJYKtjPJWGCBN3gAdR32vhOOarJ1mZxVlk8lkcHDA6C3XoypZJB0HAWtVNWOWYAaoUSkDBWayCOJioglXV1fm+0Yco0ARIY6NKZVyXK4Fy45NJimhMse6XsLIZA6SGeXskmcOxkOyXwAU8btFrtXKCCMq5uRLb73zdD4z/bukIo3KW6fchInuYyXjNyNqoFSpot9jxp0Zg2ZL1n+lXEA+K88Yso4YjsxRf+Th+Fz6PCH/hq+qJMsJuj2Z76s2uV9sy2TDYiIkYnZqf38Xr57JvvzNb38HAFAmKmI+GCFPHpwiZS/bzBz/5LPPUaLMaZkcOFX2N1duokslgwHr+n2i2e4c7uOcGdSYkIUEOTpSuRI6HaKGmCk73JK11OtcYklk1h7H+t6RjFm+XMJPfyqqTj1mVDIJtXcZZLhPVPK6fzE29mZAZQzNgozGXTx4RzI7aXLR/PDngr549uUZLKLCPnr/XQDAkmtvNruVS49t2mqisNqdseGsaNTeRiIUCgVccX6VS0gzrJ49Q7CQ76uXJJNyQXnljz99H1dEXxyfy5xc9yVDakWhUaxU7q0okn9nMhlMyNujku+5fB7TMbMwUCQSpXUzSYNiLXE8qqz3fv3yFc578p1jIpgu28qRFsCFPKNVlPH4jQ8Fxfje3UODmOxfSg35v/+P/iMZs7t38U/+w38oY7SQMXrvvtjCwfwSKaI+tyjR+uy1/D4sCw0iFibkZ6o3blW1bq6lL3nK2I6ZWR0N29ggKjJPlJ9PlFO73cH774lapp4X88Xqa/LUMjfNhqzDszevjcJOheeFoonyhaxReNJMmXI+FAoFNPgMzY5VKeWezWYxoaLMVGVm81oPb5vz/fkZ+eeYgYwAowCictFTSll/8tGHuE/uFntBhCGz7Bc3bXOWZrkHk0SHtLt9w4GQpKXc2drCDZEbzbq8g6qWTqdTLLju1cdYrZSPKmVQpkfkIIuCWx6IMt//8krsaasqiI4wDHFB7pudnT0+U+xwv9fDwd7uW7+nvtB8toRDm1Ajck/nptmsGzUtlYU/pdJirV43HCDKxaKoEtd1cf+O2IocM6U//PEPzJmhCCRVIKrUymZO+iqvzrNoOvaxIrdXhygiRcEAtll/PlFUevZ4doj93QOOqbxPh4qQm62GUX5T/0M5OZJuyrx/kln1HNe/69q4IfdHrPxTPFvL6TLGM3n/IdE5NjPAsRcYJNEpx3++XCCk3VVuqRbXiecFWPIsrJBP6+BQUHd/9mf/2mS033koY6xIpnQiiYTyXloqRS19SmezsPA2b5La07OzM/j8uyLDdfwBwHEUNa58Ure8iEv6Ux36Uge0P0+fPsHG5gEAoE40z2qhSPiZkQCPyM9ZSMu6dqIEXpAHcOLJeunPByiVZQ4a9IMz9CO6g77haCmQV099vDCM0KjJ+aBoD/XfV76PJftgg0gd5XvM5zCm/6tqrzquhWLZ+FVHB+JPv3kjaEw/vEVsK0JCEbBhGKOg3Ja6T8h7EgU+puTMUZ4mrSiILRsR15Hy0qWTKaS4ZxVxlk7cqvwqOlLnV+9zg97A8GIFrF5w+V6lcg5Nnl+qsGgT5TqfLJBOURlpLOOypL9Ua9QRruT5/pwqira8X7VaxYDIPbWjaoctK4EhUdyKguj58mzXTeDs9ILvQ564SKs7AhTLRM9viu27u7dpUFAVcsOErECwY+Duodj0UVf8AlXXW0SusU8BEUKqPtgb9I1vrOiyBb/D8wODNG6zUkHRnL1eB3OOX43jub9P3kffxpDjp0q8Pd5Plt7KcExVisoPSc4db2q4qZTzdrXSvsyRJ3fcjKjs0XAOizL1HucpspRXx4a/uLXTAODyPlPfbKHJ9aDoHFWQq5cr8IhMjHg/3b4r43rZ7WKmlQNpWSeqsmfFPjK0o23aTsdSG1PD65fkdSLH7kBVtmzbjOO9e4L0aWyJ7xDaEVLkAz0l6kUVIS+v21hx7g/vHnIc5fsPd3fRaspcvKFC13I2x5AVOiXypKri9WAwMH7Lkn6fQ1605WpifMctqhAXWVEQx/GtGrZF7jLlTk05hktPlb1veXtts25VffH8hMi6jV2j6qtxgk2qpY6ur2DT93o+kX42s/IuP/nyCS6XMoff/bZUEuyxgqHfucaE3L9/8Eev/0aVsV+KkjFdrN1+xwQylID1JeWZU66NHIM1Pi/8Sva31Wri9FScyCQ/o5eNle+htUGCTJI1K/lcBAslBj4iGs0lywrslQ9wEcwYjNGLwmIxM+TJVqbI71NJ0gCsEMGKRn1vSw7OiRWg3bmFxgLAJSUQL0+usbG1zT7wMPd8I7OufVEIZDqbQpsby+YBYdFhefbkBQaU0R6yxEZbOp00B9gGL+Dm8M8kMaYRyxdkc2xsquPiwaczY7Hcp7Ulc9WoVxGQWE9L/Bw6QPvb27B5WPW64jQf7slltlzMwCaUes7DIMHn3L+3j0yGm7Uvv+dHSyRJaKtSvJvbsk6m0wmePhXyvD1KR+pl5ezpGZpV2TQNEoZVKoQFWzZiHoYKY31wJPOFwDckbuq472yScCyVMs9XCHqJhHuL2RzHTz6X8Sho4ErWSSqZwNlNn8+gc0MI5ufPnyKvsEUuMI/jYtsOfv4zeWaxkONnxCCkEg5sQi3bhEwWc1lzScjQ8Jd5UK9WK7M2E7EED5qc52Q1YwiBz05l3FUSdjrpo0bCxirLoUYMkCURItC1w/2sztPO3r4Zq/mAspyxljzkzGFopI156N2MrtDnmn7vA7lIa7niy1c/wS4JNssV+dPn3j0+PsY3WGpzweDKqN9DjQezkoOOpipv/QAHlLtUcrs8LwpxYo4yjXq9yvIWlt699+gBApZUZmI6FJzn68EUcx6mU8rmlnkRX8zHaNblWVqyZGVlH7x41cVVT95xwUDqJ5/I+H/y6UP85V8ItPjzL2Stf/bTHwIAfvcf/2P8/X/vdwEAv/d7/xsAoM1L1TIKUYgYKKHFHy8m8HiRUILlS5JXbrTq6DDAu0vob5F2bn/vDoa8yClRapmX5tliiTT3Uo2yoyq1Ol6ECBj4vGK5lQbmHty9hxQP8iUv53oBP9zfQ/dS9suSNvMOSYSzboTtlnz3ik7eik65C8tcWrUM1ZyCYYBHD6WsToPIUeTjiJLwevh3WHbl2D7CWPrauxZ7HVGCtl7MwwOJV99QPlrlh2MLAde5QvvVeTo/v8KTJ1Iq8PBAxur4lZTJ3K1W8OmHQgjefU3JWl++r1VOYzHke7GE4GMG75bzBdJ0Dp2aSvjKZ9KFNDyeCVz+uLt7n33JGulvJb3W861Sq5mL9zED8+lcHmmeIVnaLtdhGeyd+7B4TswpAGDKuH0LrZqccRme022OcXfZRopnhpbUXjHwl0qlcMX1qGfV/fvS9+l0ijHLx0osBd3bkrlNpnL47q//OoDbRAp4tgbeHLEGfUoyVp8/fgxA/AQtR5jSZwhp59KpFDIcQC09H/b7WHGPq51TKeTZfG4c7iZLAiPadC9cIuJ67VzJOOg502zUMGHSIkWbtMEyx/n8VtRCbXyFJVObzYZJFLzDMVLCWcuy0OZFKc99rYmK2WSMEff10eEBACFNBYDZYooGfagU7eLtRTzGi2NZt0uSshdLVdRoT3skMVUhic3NTWhQZEXbuUyxXGM5NZLy9xlsCBj8geMa4miPF2Ndc+l8wpyXI14460VZnwhWJkit60QlrOM4wOdfSMmtEtavuD9d1zWJnpBBKS3FQHSDIX1CLcfJcx8sZ0vEsayVMhMI9WwKHZayqG3R4HgYxiYhN6MIxuMnnwEAarUysmnpg5JYVxnUyqaTprRG18wWS3Q8zwNU+IPvcMogXLlcNMTISsqrSa5UMoMiE1LqH2jyz3EcQ7SuwSyld2i26hjMmFScynwXMwwwrMZY0B42mhLoylPE4+r4Gnlewor0K9KzFJZKnm7LzyZDlsTYeUSx89Zc+AFFZ2LnlsybxLEqPhBhia9eCBFwWUVduF+Wo6kJ8s85z2mWPt5c3ZjSURWi8JklS2WSmGugi2X9DZK8dm/amPD/emPZbxqASroOUgU5hG2eWSmSJOdKZVxcih1oNSVotphN4Xu04RzTYllLMydYBLIOB30NMsvPYtcygeiFJ/u/ponLpWcIlR1eSnMM5FmWjeGENqLIeuOljMvF+Rs0ykqnIH0J0/IO170zZJkc6wzkHRjvQrFQR5b0BUoarNnKKLaQZJl+mvtZbe9iNkeF97Mq52s2HSLkHnpF2fMyaRb+rd/5LZydyPl8zaDlEcu1rm+6JpGle2JI0Y/9nW3jr3z5WHxsDYyOJ3OkUzJfeuY7gbzLO/u7mDCQoYI2Tx9LYrGezZg9ruWbWqZUKBQAJRDWYIKWNPYWeP1S/Ga6lMgWaOeyCYTc1xqc3m418fip2F8vZpK1Tl9t/xDXF3KGKghAk98zf4E3LPHTwGmyLPtt4I9Q4ZhqXPn0grLnvocRJdSdGoPHDGJWKmUElD3X82nAAHYqncfWrtiNrTtiB3q0e9l8EXZ0e7cGAOXDXgRLtDYlGFUqcMyYbPS8wNwf27xb6t0o6TrwYxn3jW2Z20k/gXJDk7oyl9cdWetRaOEzlp9t3pXPZFIslQw8cw/v9umfJlS4AujRtudIYj2ay3sd1XeNT9dk6Zba6uG4jzppOyKuob0dOW+9VYQB+/eUogIh3zlaRQhZUn99zb3vih361ne+g8fP5B329exhmfrl2RtstMQ+/W1tXTK2buu2buu2buu2buu2buu2buu2buu2buv2K9Z+KUrG/vf/9j+NAaCQzWFFqN6CJTqxrZCuHn74538JAHhAIjmNDN97cN8gDvTzStocW/iaNCPl1gkbd9JZhCQOnRACnCHCxZ3MTWR33pTo7V98dQwAePnlCZgAxIhR/jQzVO8WC/jkjqB4XEcixAdEldz99EP81QvJQobMlk+7EuW7PrvB9o4gW/rMbM+GA9QJm43Z9xYjnHCAG0IYHWZU58y4reYhMiSQUsJBA7Msl9BkeYASMSpR6vWgi4Cf26R8oGZUR6MRGAjGcCTP1FKTd+4fYR4ooZ5kyTdIrpl3E0gRiRD4LNNghrWUd1BsyFjVN+Xdf/KDH8iXLIY4uiP/d8OyvtPuBB8cSfmCQr1fv5YIbCLhGuJA/ZlGYx3Hws2x9MtllmXjUDKQF+0Oliz1YMURCoSdJx0Lc5LgKfRPIaiz2cxAtVX6VzPhlmUhxYy+orfKSpxcKOApiTP18wq9DsMQ0z4J5Qj/tiwi3QLffJ+/YvkEs66L+RTvvCtRdJUjXi7nYODfZAQLOckc/Kt/9ccG0vwBxziIiTYq5tAkLPL8QtbXZCTzXMqlsLEhayZk6ZbL7PWLpy9wh3DdbZYxnN9IBmI4n6JP9JCr2SqWDU0mI3z4iOgfQkiHHclY2lGMMmG+M+7hjR3Jgs4WQ5SJ8tL93W1LtH8+9pDLsqQzotzs1SXSRC7pvtb1sr29jWsSLOv7zEmePbxq45P3RaZ0xsw7iPR5eXaCWp6wdn4PWHZ1fNNHbyxjOiYhc4UooIx7S3SfZFbNpcT8n/7JEzg2yWqZbXn/oXzHv/v3v4t0QjJ55eIuP0NDVK5gSRjxi1fHAIAz7o3J4BqIZc14LDlrt69RLNU4HrLWlPR6s1mDzWza0X3Zb8ckOb8erDDoSqZd7eiA5XnVahU1ohg2NmX/T/js0Mmhx99LENKskprpTNJIXfeIelQUwGLpIRkTRcWs61ZLfq83OEeRtsxzZFza3D/efGyQkFoS0CFxqeu6RvJaSZG73S6WofTVkKDSLq4WS1hEn+WzSkbKemXYCAjZ/dO/EMnap8ey7oPIRj4rz3iHZcPblOQdXV+hwqzsb35T1n+dZIkPv/kpXp8LemjIMqhgxnK3coTQo6ABM5aptKy9cOWr9gAmtM0TZtzsjI0ay6wmihRieV6hWDHn5VJJ9JmBt20bmQSJEImKmExn+OY3vwkAePmScGyeM++98w4GHS3PlTHKEOVweXYNm2lPlZQfjmRONrfqmE5JaslMoJaXzedzvM89qHB9taFRFBnC4z6JenPMOC9XgSErz3CfKcomDFaGWPV0RCQwEYOVYgk5hVFxH2i5RqmYN6IWY56VURCbbLCWLC20bDkIcXIq6IIkyfnTzO5WKhV88J68l5YCvXwmyL97d49wdSHZbj2vBxyf49Nzgw5pkixbkT7L+QxFPQMWb6/n5WJloOhKwB/z0JtPpyiTTHdIZKGWu+SKeaSJlHWSmp2V8VgtfVQpO39JdEM+VzIS44qMUUGQVDphUE1aRmOQLuMubOL4skRpLDmOxUIZK6Kvpiy5VpLYejkDi7nNBBESSSJ2prMZeIRiQVJdP1Lp8hAB/bYhz7jJTN8rwJBoVi1l0fKOVC6V+5wAACAASURBVNFHRpGTHEfNwCethHEkdg7lbE1lkpjdcK+SxFRlhROpLDIZ2hT6A0uSZ6czCYS0eTb9WiWbn03GyKZv/Q0A4FJHHMfweXbozxQFZDuuQWBE9EGvidrwPA9b9FUnY5l7zRkPhhNjT6skO79haUS1WsbznqAaFPWSIKFrEMVo7ojtU4T+kufGo7v3kbbkmUs6Ub35yMiDK52Dlmx7QQw7oftLvltt2HQ4g7/SMk2eObTZyWTalDqW6/RfuH4j3zekuopqUOT9aDxBiuS2w6l8X5OlG51+G6nMLTk8cCt2U6830SBCM83fn9EeW2Fwizwzct2yVq86HaTol0UJIsNjIEskeUh56yZL46bjmUEGaWUFYiKREln0uvKzS557Wi56cLCDERE0G0SqLCF7ajaYmTIfLXv1KGUNANZK+uWCqJWmrIWrqysUiM7Qe9ZtSV3OlFuprSbgFv3xGAR9w+U5UWI5jjefocozq0eEqGet0Okr0k/mV++Bo/EAS97j7t8VvyWlMvDDibHNyYSMd4blyovFAkP6aBbXoUUfZWE5Br3S4vdERBxnUlmck1y+S/uxz/LZ/s0pmtxnik5VIn8vDBBxP5frLPEmSmS/WMeffF/uucWGjMPeHUGOpDMxPNqGLEWNUk4eHvfam0tBk1h0TYqlHJL8ntPTYwCATZ83SNjI8kxTJJJPtFgmnUajLP3q3shYK7rJDyJkLLHXLlEr7RtZX8V8FnOe6+pX6fm7ubEDKyVjq/QiBZaqunbCoPevz+XM63blLMlkk0hmKM6iZOAUHFksVoYKoUSy5ynpHBaTMTwSwddYudG7HuCmK+N3zflKFlmRsQKePRaC8Lgo47C7IyjXUiGHV0/F32hWZe99RAGj2XiAp18JovOaJddVnrcZ10FOEXsREZM8u7KlHLIlinR0BN1UL8seXnk2hiwj9nhn6XB97jVaiBkbaY9oM1kCfe/eHWyz5HHSlXVZKep9cIkxaQT+lz9+9jeWjK0RQuu2buu2buu2buu2buu2buu2buu2buu2br9i7ZeCQ0gJezPJhKmx1lrOLOt7s4U83vnoPQBAklku5UcYj4cmEnl5LdG2IaNmd+7dNRHlSGUh+xLFnM1mJrs1YfbzzqGQU+WzOZwzU7CxkMhdmRmcWWghyxpCaynfM40lMj1AFhNmslJp+fOzE4ncPj5/g8Z9iZyO5/J9R1V59oPNJjKs/QSjiE9PEobnR4mECwWJylaqJXihPGObcojKDzAfzbC5KVkZzbgZFEsqgTF5MyYDyVzeJWHY5uYdjEkurG3Jvuzsb2NBrpEx/9wnquesc4IHezJuBXLRaAhyFfnIUv46TZ4bJyAKqVTEfCyZsnJDIt6fPpJ3efJ0acj9mvy9fMqCM5TP792TSLxHstbpcoVoKhHabFaivitL+u6vfFTr8rksM/ubDIWW6xV0OfcgafmwI3NazJUM8XOe2d2QJIu2H6NKiUnl2OgxS1splWE5MkZ1chclyDGVjGwccs6bzOooH1KlUoGVYZReScBJxDjqz7FgFuHX3hd+nNFU5mo6G6HZkmj2hBl3GyH2SFp7zrrqb39TiMam4yGSLokTiVp5/lwi4IU4gdcDQZYoH0aehO1h5OPySiLVB0S0aPYon0tie1f20mwi71MryNiNx1M45AXIce+mSA4+DZZ49uRnAIB3H8h7BXlyig0G8IlY2CH/RoFoiEK6DJ+ZZiOlzPEp5nLIse7aH1JqPnWLgvICydY+oIy8Cw97e0piBwDAyQ2zJ8UFLkiEnWTWiAkEtNwMSswyKbloglmCXOxj65AoGY8ZftZez0ZTpJKs2acEe+dafhYmIwymYsNyrPeenhBV8XKOwj3JUsUZeXerLvPw9P/5Pv7wD/4FgFsi3VfH8mcYO/jmJ7/GPsj85fPA+UD67DPD1CIh8dyzsN0iBxCLudMFWf/FlY057WCTUp+VT78BQOq/NSOd5prJEK13PZrC5pwrwWeS+7tYquLVK8kwD8gBkmc2PowAj6dUirbvfEaiyCBEl4SUaaIFKkTDTV0XQ2ZuNSOb3ZP9VsjlMJuTk6dK2wQfFzcyd3UiVCLmS1bTJSrk2whpu9yUvOf+nbv4+Zlkmzq0nco/EIa+QR32SX4d8HtLmRxSTVl/XaInKhbr7+MlkrG864R7fEF+lq8eX8FhZvkFeTG2t+U5MUKDttjelrUXsva9mKtgi3xrk6L0QTlYyuWKIUUuMKOtRLq+76NSEJueYWZ1nJ6gTSRojZxZHskLf/b556av2ZzMvdqWDz/8EAd7so+/+LMv+d0y1tfdCSzyTyVT8t1l5fWaLvD8mATJp5L5Mnw8cYyzK+XFkX7++PETAGIXWqybr3FdZogSiSMfNaIFkkXpb60m5+DV5Q3GRHwombWiNy5ubvl7tohynC7m8H3yhfH3HEUAOq4RR1CkGn8dw9EEf/KXfw4AaPPs0Ez/Xz9+oirF+IJcbDnajNhyMLqQM+pnXxwDAFZED0RRZEh4ldA5BO1jEMEiKiSCjANBJojjAD2Swmaz5JjhfP/s8Wd4/+OPAAD1ssz3z7/8awDA7sYOZgv57mvut0Svg11miDfrMkbLOXmXgggjkpc2NsTG3Hkoft3F6SvcXMs45GvkCauSXD5YoEK0gEXkToLkt9FqZMjhA/LqXV6T5+ymi9FU9tfd+yLrHpNYuNvtifQ5hO8LAKrko1t6K5SJhLGJmFBSWi8YGZ4Z9XMiypkf7u/BJ5J0SORNZX8HM457ghnfX/v4YwDA2dkJJhOKnpCLTLO8g8EACT7X5bkJjnUydjCnvzMkKiJJZNjcX8EjetAmyrFLNNxsNkPTl/75hGZc3kg2/jvf/S66RIdNaaMVSRbmkzgbyPusMrI/R5F85uZiYNZKj0g3m7yK6XQWtaKs6TsNEqBPSbabSmBG+zgmt2Ypm0UpJe/fGRFZm+Y8uI5BJM6J6n7VF19lPF8YCekGUU4Ln0jguosZ0cce+wz27+q6A4QyRooi3N6VtesWHFyR4+zBh98GcIsQz2VtgP7A3o6s4+u+2MQgAaAsz7w8E3ulHHnZXBqDFSsU6Is6vEv4lgOLHEl9RZA5LuY8Q0dEWJ0SteG6LnKu/Ew5jipco5l0wUihex65mIj6mnSGWFCo4av+Jb9bxicRhMgq5xvRJVDEVmQB9H12mzLG6Yys60qhApdroDsi+uhA5m13s4Es1/+SiKYB/cHJaIIiEXIlS76nmpAv8fM2gljeubpJEvD0JpLkMA3IQbi5zftZJYs00VYz7o0qEV2lfNmgKHns4boj797vDxGRvF3tvUKIF3YAm/slc5/oSt4FcuUytomULxGVVsxQ5ObBI4P21DNuuVLS7TzclEqbkySe6/Gk0zW2dkW0zfRK1lx+swFLEWCKvi04yC25jvZk3WrlxmKyQpGI2v1tQUwluUc6g74hTVb0oPKcZpDBNQVbCI5ElsjoSimHYlJspCH5p4++t7eLdkf2S28qz1R+1TdXzw0Srk5ury9++hnHo4ghEUnqyxey8h3ZZAp/9fgnAG4Rde+9I2ImrhNjPJJ9sprT96IPEfvAaib7+axPX9nKwE5wz27J2hwoP7DrYpNE1udT2Us93rnfeXAXICpMUfEvTgRN1On2kSlI391YfqZrp9Zq3iIzyUfkEY5VKZVN1UnhQNbMmEInyVIKJd7L5kQyeSqYM+2jQv7WUiTPzhK52b16jYNdQW7nKCbice0Wy2XYsQob/M1tjRD6f9l7r5jZsuw87KuqU1Wncv5zuvneDtPdE3o4Aw6jRFMkZRgWDFu2AFEWZMsQHOEXPRCC4QcDpmwBhmHRDwIo2SRNMQACaREiTcJDcmZ6umc6z719459D5Vx18vHD+tb+5w5g+cnAAKz98t9b4dQ5O6y99lrf+r5VW7VVW7VVW7VVW7VVW7VVW7VVW7VVW7W/YO2HgkPol/72j8WASI7/1E/8OIBr1IVmqKMoMmpMM0oYVqrXNaen5ABJq2IRlWzSeRuPKfU+IafMrTuSKXry5InJyKnaxxUj81/5ypcxGkgETjk2njJC/83vHiPhSlTOIurISZHjwQfePpCs/ca23O/nX5F6w16/jU1FwDyRCGOemY4kEkgw6nuDamPpQgk9Zig0OqqKF1bGMtFUVSzb3ZdrjzsjE31U7ovtbbmn0bB/zRtARIVKdH/htbdwRt6C2zcOAFxL/3729AlqRLuckdtBa3BPB224vAcr0Pkk0citvX1MGOmeDyVCX2aU+s1X7uPhQ4miP3hFsoQJonT6g6HhXFAJ28VyiQRD1uUqM1ecv+PpxNRID5ilUtnGSqWMaoVqabxWj8+wsbVj1JZOzmUOJYnKyadzWNuUfrSZAT9lzfDN23dN/frz588BABHHZjweI+Rvf/nLXwYATJhB8H3fSMrvUW3t40+EV8pxHBQ5p5UTQSEriRh4/kLmzF/6y7JGdvckSzOeDPDkUN5LUWXo0w+/h2JBMhP37grnVp79vrZew+mZZJ3rVZkX/Z7M9UePHqHCrKBKBefIW/DxJx+aeug9ZnAVxYVUEl2qFjRqkmnQueMGCUwpU1rh+pyzf3qDLmqsAy5xPmn9cWcwxGOy9INzvUzE4P7etrkvtWE6HkvXg0tpStchf1gixnCsih+s6yXSrZgvoMmou/IEdZghqVaKmBN11SLaYMQskuOG6FHi3WZWbcAMbjJtocIM8wnnjN57MgZafG/OjEN/RNWNQgMXQ/I3qMR8Reb1z/7MT2D7nnCOJKnW9ME7/ycA4I/+5e8ZWQ/lehhSjeDs4gp77FNVp0inYgynqu4jEzLPrNWN9ToqRenbCbO5ITPbqWTW2Ob9/QMA1+szjhLY2BAUytOnsiZ0bbTHAyPrGTI7qEqSxULZSI2r/dZMX6c9NlKrU47fiHakmM9ji6qBikyqElk0nS+MEpJm4xTlkclYRp60STWG2XiCCZE6gxEzyyXlC7JQoW2uEyGhaEy7WMI//xPJdD15Ic+AxLWaS4l8U4U0ueaYVX79wSsm03hzTf62GjI/3vjKV43c8dlTQdKUiYCwSw2j/qcqfl/8oiC0XHdpuEx0Taii0vn5ObKW2E5V39EMX6/XR5mcKIoM0sxes9k0XBQTztUwDI3ajo7hYk7Uge8jR0SWcsQcHr4w96lz54wotn6fqMpazXC+tYi8WWMW2rIsY+fPyGWlKJ1CqWjQTZsbMscVJVUqlWBxvisyTiXHR8OeQcaGVFibkBgwl8shQ66SLv2ILNEDs9nMIEWshKJygAT7UnltxuOp6WO9V+Wz0Hk5XziYcrzGfHbD9+M4Zq2qYmocqqx1hBkVqVRZsUxUWzKZNDa6TERNsUp1rBBQd+/qUlGpKsXsmUxvg0qXKVXeCq+5GnSMFlRByaZtw5+k9wREuKcqcOSOq3J+NZtNo5zp8d47RBYl49AgvSPe6Na2IMGeP39sEIVKekGXCFHofZ9ykIxvi1yL+XwR5xcX5t8AMCeippgvmLGZTsVHsehvpiwL6+RBvLi45O9Rrc26lj5WW1vgtcfDAVot8TWUDzORiLF0VEqbPCKqfJhIGD49VSdUpctmswmHKlr6e8qP2O/3jWy5IrfnRHOXqxUcP5M1VyP/1ILXXl/bhEc7Nxmpyg/7bjrBJlF5ak8dKl3ahbxB2Rw+E5teo+pmq9HEYs5755zZ3pa16DkukuRpWm8Rhctnvzw9gUd7o+OeTqdRJWpfuXl0PadSlnlmP3yZ+9MLAliEu2Xz8lcR1OkMMCUyM0cfUdfg1tqO4WnscC8vE9HhBR6S/Lz2e7mgPGwTcw2PPkOSqNHeqI8M+YUSSxlLnZf1ehU98vcoH5r6CbPFEgGfK0dEubt0sKSNyHKuqWqb74UoUo2zTt60kByNvhdgTrSyot+UJy6dTpvx9VSlLUWUTi5nOHNURW1BrhkvTABEQsb0q+yKVkMU4Xu00ecy97a2ZBxLRcvsyzH5X8b0TXwAHu3aiGeIBdHWa82mQeZnuU79OIk1Kh5etmVdK19qr9e75nGqE9VKXqJiumDORMqFqlxHw/EU86WqY/EMQb6VqR+Y88pt8le2yVWXyRcxJc+VqkxrRUanc2Hs8E1yhKY5l2bODA5tpqov5sjtMxvPsEW0f55r6TMiXm8cHKBOxKXO/1KlCHfMvZfj1uO6LpXKWOr6oM81Jk/k3s09XJyLX3vtP5N/LZNBwDmma0/tZBAEINAba5RuHxJJEyE09lMrfdQuvzg6RI4cUwvdZ1XN76qLGeeqolqhVTapNCL6ItqfqjiXTWfg0S4uOJ9qtB3NZhMPX7wvn+Mc6o56Zp+tN4mA53j3ewNjY7MV8a2X5BpNxYE5oyhQUxWeHd8xtsGHnivkvX77Cj3yod64KdUPp5dt3u8SKa7BBrkklQ9wMp4aHi+f50a1tXPXwZi2bH9D+mrI80i3PzAowre/LAjUnS3xoYZXHYz7Mi/+x9/88x9u2fkAMnlaWxt4xECJz82txoEDgM8ulZSUwYZd6cBHj5/DozOz5KDGHLmDO7dQbchCfvjiCADwuddk4F6/98AcWNQ43zKkZwHmdHrUAWw2KC9e7WHaoeHlwvQT3PAXvpEWzhM+uEE5ys+9ehenC3kGDcqonF21WoXLa1wxKLO5u4kypXTbl5S2I3QvkUjCoqGq1eT5fJVCrtmGxE43mOWcxHfezDyrlktkk7z3YIHbDGZdUPI34qZQyWaQZvCrpnLpNGLp7K5ZBA5JTHXDWboL5AiNnSuh6FQ+e9UeYp2lR6cnEviysteytjluMCk6SPAiuHTej87k/lRS1vE8NAiTS9EoOSM6zb6Di3PKq/MAGHMO5Qp5xJw76qwpdLhYqiBFotlyQQzxDqVd/cUEEx7Y8zzsqaRhq1FEkaSsr78mEsh/9o1vyr1ZafiUdD0+oVwk5ZlPTo+xEcoBa9QXB8KnoXzl/h2zMV2eCGzad2VMO90zJNnHVRIO7q7fw2hI2dGhbCx7G9LXh08+Qb7A52dQRQ3OgwcPUCGR+dWVjImWWm5ubhonTQ+MasBH0wmGlC0ejyiDu8GSmH4PU8L2ba7nIglM566DZwwG3L8lffXsuRwWZ84SdT5PyIBQitDj7mCENz4n0tyffirlpemMGDzHcU2gqlGU3/PCwMxJHWeVSQ6D2ARtSiRZnNPxrNtNhHOZ589JAN9leca33/8ILg3wl+6/wv6Uz0Yx8IL2JuDaaNWUxDcPHyRyBjcdHv58z0ZEcsybu1KGWUnJZ1zfQYrEiMjIeOnm3x2NUa/IvO0TBntJSc1aswUuJXz6scgs3799Dz2WsKV5zRzL8b753Xex3lCYPskjubGXSpYpe/jgQykbUXlg34vw6JGUMbV4sCiXxWnY3ttFkQdULWnLMkCDKGHmkUpkD1g6tr7exJIHESXXPdiXNVItFozE+RUh8xa9lS++9cAcpD2SJca89nA4NOTrBc7/fLKEL3xeYNUxnQRzYL26NATTMZ2uzEz+bqxXjMOdz4nzxGmFGEkDA/YYjNkhqfHMcVFjn56fy4Hz3j0J9D74iZ+Bx7XXPpb96fJU1ki2MIFlyfP8+I9JEN33CVWOPVgpBn0YjNWAzePHCbx4Jr+j0HkN6CWTSdR1vGlq1SHs97so08m6e09KBKfTKWwevsY8SOu8B4AUySYt9v9rr/4cAOD27dsmoPCY8tl9EioWi0UTALqiw63BkWQyiQwDhPqa2ibXdbHLUjh16AIekMfjkXF2Qzr/un/6rgfXUTlx2jIG6FOplCES1gCWBlJKhQKqDArOCVN3Fw5yWv5Ip7fGzwRBgCkPg7duiv1VG1qtHpiyjDOWlvg8HOWytjmAa8DVccVmxEhjY0vWV9eQsDNhFAfo9ElEHsu4TRg4+e67H+JHfkRKX9744pek/0neOR0PTNCmyLLL23sH8nupJAKylSsZ8oyJm16njxztR4tlt2k7Z4ijr+Xp5Sa63bYJzIzGL+9xrrs0yQiVYO+SgNSyLPiuBkXoLFOSPk5lkWIp9+6e2O/2ldiDMPDw1S+Jczzj3FO/LogtEzB9cFds7XhMiH8yCdAOKDGnkhVHVtLYlt1dmTM5BrvjMESb5Pk6ztPpFOUS5c6j61JMfa4JD8BVHr7yPOR3eiPMfQ0YspSWZN3j6QRZluVq7kjHKJtNY4t7/ZjUBEsmNZe5GQq8fo7rTROL6WQKDpMEGkRP8zkblTLKXPO7DJSVuJY++OAD5ChYYanfxz2okM0gxf1iylJBLdPI5CoYD2T+qU/qeR6WnOdWRn00lkzN5uYQn6Ch0hKY2AcWjhLpkxg/k2T3pIwPuljIe2USF7d7fdRYolduyV8tFckViqgyieORcH7CMqV8NotUrH1EuW6Wh8ROgBITlgjlvdfeljKg48MjzJPSx5kEy+yumOiYDNFgYmpvW+bj2ck59AQ+p0hFif7ZzJ8hy73XU7JnPmeoeuG4Fggo1cQ2ra214DIQpDLfqUjuN3AceJpIZWIPDCosB2OkIiZ1Qk1+yu+4nSsjXqLJ51pDxm1jvYouBUY0SeB7Mlaj5RJpSstXtlkq+EzWTWD5pswozXVz2eugtfNyIE0TG1ubO8YfGHKPm07FJ22u14wt97m3nTMoPlsuDDm8tnJF+nG7uo9JX/YoLftOU3CgUC5hRCEfj7V0agc2D7bM+n94KD6RCewjRI3785LzadSX/shmQyxYJmdn5TlvPJCELkKgzeSMBiEGkzY2GFBwWBYa816Gow42GVyazykMMZV+6fYukeb5VMv61T+ejsaGAD7wXhbVyVhZVFtF9p/4cTkKDhy+eIEaiaI1WHTCPnYDD7WyPHOW81HL6Tf2NxDyjBfQv9Lkoe96sBIUIeLerzY0jmMMWKY/IHgjTZv47DuHCCwZG5+l0O1hH6BAxtkTGROliAnjyOy3E5d7saP0HRZqLKdbcC3U15TyIQRS8rkuz+0Z2qbWWhV5zpWALOp37shY7mztGCoPFYzSREUmk8Fmi+XyEe0pg1Pr6+u44FoKPPl8luTqO9trKJVl3vb7JMFvia2fLgeICFr517VVydiqrdqqrdqqrdqqrdqqrdqqrdqqrdqqrdpfsPZDUTL23/2XvxADQDqRgE+p1CbRFu2+RFkHo5GRnUswaqZZSdu2TcZQI6dKMuj6DkqUtBspLJjR9Pv372P0AyTKRg46kzEwQI16/tkzge798beeIpxJJNJh1HlBlE0xTOHNTYnu7d+UiHeNUev/4G/8exhnJWr54WdSCuCTELNcKqGrErzMnlp2AgUSPCYo8zj8PkLsGknj5o5kiD1PodAlU+amGYMxCS3LhRIqjHZq7ZJmXc+nfdhE3Dz9TJBaDWY6qtUqMoza5gmfe0Yi2MU0wM4uCVvL0v+OwpjnDk5PJKK51pSMVMSosx95JnOo2V0lvy6Xyxgy+3nz1gEAoNe+wuVEXtskkarCnR8/e26g13uEK1skdz07O0WN0oP67GlmnxLJJFzeq5aBaPmKZWVwmyWFGxsypudX8iyZjG2yoJoZ0Uzb2toa7tyR7w2I9FGSUjtXMsiMMaGTbcqlV+o1zBViz2tqZmtrrYWElie6JMFOyfj1+13Uici4upRI+dbmHXTbRDDllChW5kQqtYTjyG82SaysGdn+cGzmSkiot/Zro9EwZLwzllJskUx8Nl8aGfeA6L4Ex3I4miBNeKidU4Jqufd2r2ukxm2ut1JJnn2xdFFgpr3ETJHHPn/1tfsIXIf9Ic9VZdal0+mhS7uRI+miH8cIiS5yGflXyGYcRgauvLkr/XFMxFrajxC68r1DolYszr1ZEMFjFmidJSb96REAoFatolFmJm90LQsJAOP5EFn2wxWzTyOWFwxnWXzrG0Qtkmzyi3cl4/ZTP/omvvCjkuHfoly1l5ao/6/8z/8T+leSFXBYtqbynv3hxBBgdpjh/3t/6+8YwkyFXj+4L9mLXucSu1xDE2bTley12Uob4j6F21ZJqlcoVIzUr8psJoh8sjOWKXHSLK/uPcmEZWD7E0KabxxIX9klCxHRDzHnf44ZpmwyiZCkeSWij87PT/l7eWPLkVAEDyWA53OUiQIM1U5NZ8iVFLUmrykc/MWLF0gQxapZvi0S+XsR8I9/5x0AwILIrt5QvgcrDStJFAqzvA9uHwAQ6doks7PNguRlfvE//JsAgL/8i/8RHr4n0rPf+oPfAQB8jtKzrh8gjOTzirTSsudSqWQI6hWBoPtAuVw2qFktnVP0xo2DA1PGoMjVGzcF+XD84gWKRVmPivAK4sBcX22f/t9zA1PWkSZ7p6IJx8OhQbvonqPv5bK2gad/41uCplQEhOw9ci2VLdaM5cPHn5mM4QXXrM4h287DIjrBlNApqTJRYwCQSrxcatlYa5l9SdGizYYgu14cHZo91Ujdzmao0mYp0bdmo6vlijGENe7r+nc8HpsSyyJLPnSNjEYjbPI9hesr4e9wPMXZpTxrgn1sU646l8u9VFYEAPVtuffnz4/gEr7m0Ubvk3y8Us6b99TvWePYZvI5UyKoZW8lotvalx24RERniDy56Fyaso9bNw8AAHmieqrlikFlmzIaokYd34PHNbS/z8wtx2m+GCOmDHatKtlnz1MZ5xC5nKIcWbpLu9q9usLBvsxl9W20X7Z29vH0+bOX+liRb9VqFcekIdAyTJ1zhUoJE5J9XpdSUKZ6ucSMJVlawt5sNpEhosrI0xNRZtu2ec1k4zl+7tJBdyR2N0EkuZbuTZZTsz7qRNtWiWQvFgqYkUhVie7LRFMEQQCPe806ketaFpxMW5gPFbkkv3fvFbHDlUoB5xdiW3Isn3/x5AgAsL25awitux3pd4cIg1qpiCVRFErurSUOzY1tJIL5S/04moxMmdaCa0htbi6Xw61bcj9PX8gcWrCv4wRga0mVlnDRZkexbzL17UuWkJLiAQAAIABJREFU6LCsrNoswSGyqEfxmZBlP1k7j5jIhRLloxWt3qzXsaAtaxBVXCipxPMCAwq3NKtiryZETWeztkHEHVNiu7Em33cjF1PSK7RUHGEyNyWcWta0sSN2AcmkKSebs7+bRBacnp4ae2ZxH1TbGyLECSsAFAW0wSqDTCYNmwS4Ee3ioC/PEgRAtSRrT0u/QhK0T6dT41NrXz/6TJDbhWIaB/viTygKw5QWpSzMuIenWcqv/kEmmYDHUqAN2ojzzhBzlieHpKfI0MfL21lDdVFmGdl8KjZmMbXg8HeePpWzjRKf58t5ZLiWdL9Q5GWtsoUNIseGRLOdk4R9OJ8jRx/P4543oF/8Uz/1Va16wjFFhXSde4GPJuXmlyzJ1LLn1z5/D30S4qeJ/kqGPJMm0uiQ6uLuPfHVZrMJGkTQqXS9ou+XyyVeuS/0KLqvqJiRH0fIsvxR57RB+QaxOYeFRMpqxclisUCLe4axZRnd5y0k6e9pqXafayqRSCBTkOcIlmpj5HsPP3sKm/fSIII6S3tZLpZQtuW9R58KtYba6ASubYMiyXTfHU/n6HVk7uSJQBvMxpgsZf7tUHCnTBGNcq5gEOQgGlzPganIwj73EPUvk2n2iz/GnPZDy0kVJezOZ3B4dmg0Zc2mLPZxFKPIdRKzYiRPH2A+X2KNlRHOWJ5hzBLGdrdjSrMPDmStB6Q4CRPAFUm95xS72iLB/kZrzdig//6fvLOSnV+1VVu1VVu1VVu1VVu1VVu1VVu1VVu1VVs1aT8UCKFf/i/+WgwA2bSFiBHTw7MjAMAeiR9PLi4Nj0DA2vEekRWJOESOEd6bNySbPmDGc3OjZWSAm4w++guta/dRqUr0ULNGGnGs1WoYseY5y4zIU0rp/ebvfxtWIJFhrbUcUX67lSngflWi+xubEgHcZ+TwP/l7fwfPZ5J1+vjpIwDA/JjIpmwW+ao8HxZK5JdBp837SpIolpkp3/fx6mtCVPXi8DPe8zWZpmY2F8wEmqzYxhby5Om5PJff1v/7pbRB72iEVus83aWDNLmDlFz3CSVAi5GN9V3p27lHWV/GIBOw8OF3JbJ7g/X2b1Fy9fD8yJBvan2ukrJVymXD1eAstL42e00wRvSKw+jqi+Mjk/FW5NPBvsyFRBzC5v3cviM8NY8+k3tqtVomy63f39iSbEav1zeEbXuUdNSs5nw+N1KimtHSDGIQBEiltZ6f8tust3/6/BADzqsMuRcWjJjHcQyLkecCr6XkgnYqbbgFNIukmZRUJgkk5BoqlX12foUfjPdWmTnI22lDeAlK3VocWyudRZuolyaj1BnyCUR+YMYpDigdTsnQdn+ItabMAYtolA3yAvT7Q/jM5vgBiYyJzEhls2aOadm71qAHcYQps1oW0Si6zsvlEnrkH1HeByXiLRaLcDiWylmSyeUNIxwTzSYj7nsONik1S2ALjkm0nnQD/NVf+GsAgD9+/0MAQOGm2KRcrYaA/fHknfekjykHb8UpeAtmKhnBD0hcv/RcjIkO0cx7gpmKXLGGtbpkL2qWzMs0kSpbjRLe+JLw3Gzfk89gSzJA//R//RW8+42vA7gmISyR+6XaaJrM+WQqfbXfaOHmTcle9gfyrDazan4YGbTbiGTjrzLT5C4GJpuriBsljMzlcpgwk6ScF7ombMuGzke9tpLUFwoFPH4sxP/37t176XtPTg/NnKsx09O+FERTMW8bZIDNtag8Vplk2qBCErTpiixdLpcGUVTjvRfzBUxG3BdI6BcTfbFcLlFmBlz3EpuombPLDv7hr/8L6ZtAfqc7YCbQD5GjfCjIE9Ykz1M+k0aaa+hHvySy5L/03/yS9OP6Lp49kyym25ZMeMa7tjvzafRSP25uSsbu6dOn1+ID4csktpVKBXlyWGmmSPt4Mplgn7ZyQa6MXSLlFos5mk3JHn+DEumtVtNcXzObytkVx8CcSICYn9EM4nQ6RZ3ZdEVcKnJiOp2aa+o6Vq6jUqlkxlKRRco3lEgkUDKEx0TZkcsjY+dNHxlOOyJXKuWiQdJ0iZDTfaDaqBpiYEX8HB9LZjCTzRs0lBIuXV5e4s3XhSNEm/LVpFIJw8dkOK3IC5NKJHD/vuzhT57I/N8k79r+/j6ePJH91ZDzc887v2wbrkQVUGhTttvOFUymV9GcJfKSfO/Tx8ha5IyrqODAAQAh2I8Nz4/0+yZRUXPXMVuJrvWF4Vcsosp13FFUZj6Ltaa8dk4UxA3Or0zGQob75ePPnvAa5F1aa2IyfhnZfM21cy0SoQIIxaI8QzoVGySREdPgeHe7XTM3PfqNC9rlbDZr+lbnsXJYhGFo5preg87PbC5jxD30Woqem06nSHCvUgLXarWGIu2vZvF98sotXQe57MscHptE/CGKjRjGFZE3ibTMoYXrqLoxKuRkUtGVOAww6ss6TvJ3FQGbzqSQIJqpTx9ZEdLbu7uIPFmPxYpc69lzsUPFcs4Qaam9sYhaHwxGGBDZ/Oo9IROfkIdnMuibscxzPzqn326XSkjQP40SKiUegGba+E6K2vCWrkEl2OQtUfsWRIr/BRbcW5Uf0XEcxEpym6a9px22c0mM6Nd74ctEutOZY/al2JfPZ9n/+VwagSH8lr5+9VXZIzPZ5PdxLapgBjnFlr5Bk6lwyIj+XGO9YQRibtwm0bgTYDqW5ylX6E+Rg8mLfPiKUmd/DDjuYRijwjWur6nvWqrUDFooT3TJGn3t6WKMbI7CE5mXyexTyCJWUmlKWEec6/VqzYyF2o81Sr5Pp1PQ9OH8QuxonkIKuWzeIDOWRP4osg4poEtUpCJppn3/mnCXCMGtdUFflIp50w8F+gNTVhRk0mtGnMKgiPnsdi5jeHB0HQ/ZV+PxHLUyuRmJEFqSFL09msDiOatFG6sVFe9++0+xf0P8RF3XCyLaF4uFKYVIkkNuQNGDyloNmRQR/SGR2JHyLnlYJ0+qT38ia1uIuMcV6N/rvpaMI0M6rmPvEa06cxZmnPTcU6EwQSqRNAgVRfl//xhNOedKPOfmiU4tFoumuiDgevN8see5XA5TilJ4cxJI7wnar93pYkSfQecliDSvFEsol0jcT19PxZ7SmZQRoJkp+opz1XFdVCldv8mz24uTYwyJhiwUyH9LW1PIZrCk7zoJiTDOyHNNR0vs7shY6nli5nBvrRfRHYod1bk34j7oOgssyVPY55mvSCRxNpVEkZ9v0jbofuGHQCbFMzqra1IJsaFBCCy497NABFUVRlk4qDZkDuh6G0/oC1lZtLhP//I/+/oKIbRqq7Zqq7Zqq7Zqq7Zqq7Zqq7Zqq7Zqq7Zq0n4oEEL/zlf3Y0Dq2X/6x38UAPDRpyIZ16CaRm8yMnw6KjE/n0j08cG9exhR2UHrX1PMOKy1Giazs3C0hvNa6aFUZjT2B3gtgATarOVsMdsVkjn9V/7pHyBYyPccZoomISP1iQxebUgUd2dXos1vkgvgaz/5VeRuSjTvV3/z1wAAh9+ROtuDvX2UGd0rMGocA6g1VHlFopC7VP6w7ZzJjH708QcAAIvfa601cJtS46oKopKTvW4XFY1SJiXqqJwggRWaf4/Jp9Mk70+z2TSKKNpD5+Qs2a6uYUweo5ErkWhFnAw6Y5Qy8ly7jOZu7UiGaTAboduWaOo15wXRA7Wa4ffQTNh0OjXR9pixzFNyKThBaLK5inhKMso8Ggyxxb796Z/9SwCACbMyKSSMypgy22sfX/W7cFm3rdlC5dO5ODtDmlwmLWbQFYF20b5CtzN86Xtt8rmkrIzhizjhPNYMaSKRRLUm8+qMqkIgP0OzUkOGmTbNoCjXSSptGf4BU/ybSpj5rplpi+o/yThl0GGKcrp9V5BTS9fHkNnf+0SFaNT/ow8+QEbllSm57JLPZDCcwGUGpMDM0td+5EcAAJcX1xKciuhIEjWQzGTRY8Q/nyYKjhn+peca/hflLTDZWt839fNaN3z7rmQcJvMZqpSmTFJpZDieYszaW1VHUXSCu1hiwYxLSL6vB1Qw/OoX38aTp5KpH7Ce+oK/d9Hpos7sc5X9cjEmYiKZRiohrx2/EASCligjkUKLEpN1zpn9qnBmlJsZ7N6gEluGddRZWa/LuYeIybP6tnzG2hKEwR/89m/g+ffElox6Yrc0S/mFL72N6UzWpcpuW4GPOevJ79JWTJlpOr/sGU6pnCp4sOY98JdIE6XXYmZiOlEOM8dk2FXm9wbRVPmEbbK6Frk8uuQm6PeHZp0oukHHb7IM8fihoCk186VZmp2dLUyIDov4u8oxEYdTo3ZVpiqLQWh4vkERana+UqnBWchz+IQ3jogmCYLASMTXeK0B+b+K1Qb+wf/yuwCA9z+U+wxBRIIfIkXuoGKOdfrfl5EqZeQ/f/+/+rsAgJ/6t/5NAMDDJy+wR0RFkUotH/7ur8t7Dx8a3rTdXapWUU1uNpsZjh3lElJ0TjabNejSMnkPFDUaBAEs5cpgNlR5bra2No20q/bfZDpGlbx6ml1U6fH5fGFs//mZ7D1ZzqF6vW6ytB999BGAa+6QRq1+LQHOTJk+Xz6fN1xIWYMQFBuas7Mm07nk3lGu1sy1rzovSzw7vM5iNsWIc6dJFdILcqQkUwljF+e8ZsS9xLbzRhY7qQhRJK65+mjLxlQ2KZby8OlbbG7J76TJn7FcLIwEtz677gWe5xkkwWefCQI4Tx48JBJwaUxKXCczoqOiKDJS7Yp6OerIOPhuhAUVPhVJUypQjbFeNv5DEi/fS7lawYRrXG1mRJ8qhSTA15ST4ujFE2SI6mgyo61qRvlc1oydIlzn5JTx4wADciSSksPMq1IxZ7gjajUZyy5RUQU7YWyD/tUMfwggQT/nxfMjAEDMPaVcypt78CnXrXZoMpsaG6RI5RcvKOVer2Bni9LEVIaZco/M5nLwKNM9IdK7UquiznmvvGYloh09zzM+p0/EiSJtu+0OxkQtGjQgVQMvLy8NYko/rzYtCkNUeO/n5Imbkptza2sDM6LaPXLZGETMcoEFVcaUbzOkqmsynTRrr8J7Vy6c4XCIGzfE7g6JdHCorlgsFNCjnbeIVrCpZldrNpGg7zUaiw1L2ZbhLzJ+O9EDBbtgeMmUqy4g3Hc0GWPOOaYoojIVwlKplEF0OTwzqGJlb9BFnXO0SvTiNX9Yzij/eL7c8/Onshaj2MMavwf6QhVylZbKBcyItFROLFVySidTGLI/9HfUj5s7Lko1ymbvyHvbrW2cPBc/0aaKpaKxkukkGtyPJuQfXcyJNo8SBmVwrXAntzsaTRDStnhcX8qpd3R5DFLXYHNL5tXOmtgTeEl02rKuUmkiasrcEyYjo6bcpdR2FJMzJooNN12fnFiDUMZ7f2sHaaKNJjxz7NDuD2YTPLs4AgD4hKpsl/fMmnVZHaC8ZskYaOleT1uWIaILyBsEqEqiqx9xfn5qlELV1zXXzlnYoT1tNuT7KfplXpTAmHOtyTOiKi8PR91rBC7XwpL+cZSIsCCXjUGxufKZ3nBh1Os+eyToYNu+Rh9+7g3xySOeNx1vgTpRm8m0rhvKs5fLyCTV55E5lqYfEyQB35HPz8YvVxxcXVwaVF7eVnVZmf9xGCLgfNf5lNVKB2dpFNy0j9P0cUqlovHhi/S/p7T19+6+YtSiv/5//xlfE6RhJpFCeyj71xYV07SkoNu+QprVC+aQyH00QoxKrsE+lXnVHw4MF5C2508FAZjLZq6V1Kpy73fuCFp9OfXRa7+s5qn21PFcc3bIJGQsE7RJlVLZ8OpFRPjYyoW6mGJOlc0cKxeGtBnpnI0UX6uV5BlOTs7ZxwHqRO5aLrnmeE+PP3uKG+TbrdD2qV/3+NEzWLy/X//zD/5fEUI/FAGh/+zf/ZEYAJKBjy+wDGprQx66sX4tD5kjMe14QNlQJR/Lps2CPDk5AgBckPw3a+cRKrktiZm43hDHEbj3IEPsmBpp28obWdMuicIy+3KI+99+98+wGIlRUPJqPyvfT0yWeIMlVbv7Mii38mKkfuKnv4biXXnvWx9/BwDwnAGhbCqFDJ0zW4NTVhIDHo71wL7F0pZ3vvWukRTepwE9ORaH9v6NfaRoCL/H0iglbjw/PcM6AxglLvYOHcd8pWiCKUseCNdIUtcbDNEdST/YfGaVbfzK/c+hSYKrpzTgp5RSfuXmfQQzEllyEZVYulCtVk3ASo3La69L+cTTp08xIFy0ygDb5eU5HtyVA+aAY9khFK9crxuZ72Zd5k6ORiaXtXFB6HqeB546g3sfvPcuLJby/OSPieyzwlTTubyR21YytwwPRfmCbaDreuDs8iAeRDEihXvSQKmTks1mDSxVyw11Y7IsC546qAxE1UhQlkkmjFNTozOjJXz98QSxK/Mxxdd6gz5cBk/UmJ9xfpSKRZQZlFp68gxavuPHwJSO7ITw5RkDRPVK1RzSkgElXnlgyuRKOCF5XosymEnC4uMoMN9L09CxggxOHKOhpQn8HT10JFLXhy6Xc0f7KmtlTYARDOKcUnrcD0PjvLY4zle9vpH3Vcjx9pqsZ8QRHj+Xw7zK7f7012QuvP/+e0YGFzxcdyj17vohEqESIMpHygWZq+vrm/A4r3ieNoSPn3/ri2jTpigxc9knPDvto9rips0+DhiIara2ka/JPS+Iqy/uyMHkO3/yhzjmWi/l5D4//lAk5l957QFiShKXCJXtjydwefBW0vyPHooDcjWcmQD0qEsSdRJ0rq9V8dYbUh6j87HM+ZzP5zHnISXJdXLMwObdnT1zUK/Q6dUD5PeXOqlD1mrIuFmprDlEjQnp3SBEvFar4eNPpIyvWH7ZcfEXI3OoidlXGkhNJFJm71DS9ygMsd4icT8dOJciBqViHg4dOC2X0FLVKJnGd45lnH/v9/9I+iVmCYiVhkdo8WZTbESdZKbd8wvcOZCx+9Vf+UfSf/skCy1UwMpZLB7JPvG994VoOYE0+qOXpdA1SP5SmQsPuPpeEARwQulbhfZrSWgcx8YAqz+gwZFqtYqNNdkvNCA0nozMwbv2AyU36XTGBOaTDJJoWU4Yhjg/F8dml8SUWg5oWRa2Sdb+g875ixcvzHNsU7Dh+w9t+jwaSFZfIIhCk2DQQH6W9jHwXRNI1rIOJV4PQx9F7nEaFJnzQD4ZLzBhMHBJcv/t7V3UGCTSEkQVSeh0rkyJwb0H4uTOGACJAg9DwsszJOHUgPd0OjWQfF3/+rthHKHCoIjDa+t8LpVKSJOAWAMaxyyJQ5zGck5yaNrHVEKuXSnnTGBN15smEJBMmANdMa/7kdzvYDDCJW3ZmMG2L731eXz6qdiedZJaTgnVz2Vt47d0OvI9DVy7voNWc5PPLPNQfZNSKY+IwYmDPXHqzaEtm8Y255OWHKjscaFQwJPHShxNx49tMhmZgJMmvvTwNhqNTMnMgwdyMDg6kf3NzuRwsMeyZhIYP3ootndrawsjHrCSPDgWSmXMOM913ua5r5cqZVM+tiDhrpLEu4s5NrYkUfDdd6UkWUvq6/W6ITE9vRAbrWuwudbCiD6Mkj0rsarv+1gyKNJkQF+D+JPZ1Ox/JQaNFwxWHJ+coUTJ+5D0CGMGcdY3WhgzOKfS0koF4Hk+0tw31U9Sv2KxmGNzY433wERPHGBKigAuR9RJqlzMljDjGrB5FhgxODJbLlBvyhpXmgS1LblcztjFDPeCp6Q7cIMQa+syd3QuaIl8GPqmXMpnab2OVblSMOTJKS3D4dxrtVrGdmkSGzETntMBGnUS8auvR5s7mMwxo9343OflnvpXA1wcyV7conBIjuN+1b3C7q6spXJRxTM4xyMY4nOl2LAzsveHfmj2fE0KeAuWMOUSmIeyBkBS6RbLo+Em0WP5WUD/tlZVMt8pxiTSrrK0bbGkXR5PUabkfYV/l6QOGPSGJiCpdj9if8yduSFf1/mRCGwjgtFqMQjDvf/p06fYYqLNoo+nvmupXNMt1exB2/xsEAQm0Fhi0F39zEQ2QJPjVaRf1Wewf+H5mLHMrcFgU57364RLs9a1xA8MUlVrJSMNX2DZms4PZxbDWbIcuyPf4xLEaDxBpyv3/hbLzO2chdFY7LbOP8Zy0KrVUGGgT0WX8gxu792+iXFPxlmDzRoo9zwPYfTy2UEDSsPhEGWWOFmks+gyCWLbtinF6tBv3NuT+TldjE2QqEEbMemO+Rs11GsM0HJPaPH/89EEC9p9XWcqaNBq1s3e3x/K91I8v1512ggjJhDKNX6vhItTmTtvvy3J6idPJMALAD6fOUnahwSDzok4iT73RugeQhGUi27blEY6M5bHEVhQKBRhZehbkPLllMIXu1ubqNJvXg7l2j734om3QMh/q73R54yTCSTox25XxF+yKWjjezG6PKdqoqnKZ4+9BN75hviSf/BZe1UytmqrtmqrtmqrtmqrtmqrtmqrtmqrtmqrtmrSfigQQr/1j//rGAAiZ4GL5wLhurFNuW+WVHzwwQdoMlNgE6JWUXn2zDUpWLsj0bZHRIvElo05I+RXbXkvmbrOBK6TDCwONYNOdEOcRJIlHxtEEjydS/TtX/xfH+HoCcmaCMGL8hK1cztDfPWWZLB2DxhZJjHx3/1P/2M4DYnBvf9YMmhXn0o2tVmt4fhCMlA+M513Xn2AMYm41gk3DxhJPj2/NFDr2zck8zgYSOR6LW8bkj4tIdpmRuvF02dIcchzLNHxKcNd218z5QcaK5wRVTFdzA36RIl3+yw/+dL6Puw1edaPCKltEd5XSRfhE86eZ4lVvkw5RT822dwzZrlUErxQKhoSNo1ON1p1gIScx+fMElAee7b0DWw/ZJlViYTHo8EAORJQOrFc69NHn8qzLxdo5SXifUAy6SwJfo+uzoGEXENJzhJEo3ieZ8oQtPTrxi0pvfHD2JDEaYZZMzHZdMpkYhUardeJoggLh4PD7NHOhryXjAIDv3RCif7WmCFxfQ8DopWqNVkvjhdjyEygopPWWPaTTQERCRRHJCxOEE01ns1hMcKtEpLPnglUvpzPGdjtrE3J1HWJUk/mLlxCtjeZqasQDRH5DtZI0lrg2rWU/DOdMVnIBREgKjNeb9YM4qRFadbxQNaD4zhYJ7Gswp8VATIejzBk5lDh7cVKFb4iCdhXrOZBIZtBtSFzoMtMQ8WWzMbcn8KLtcxC5kWNWaRMJmPI049OjwAA/lg+O1s6SDBV88rrQqL+9o9IOWypXMF3vyPRes1SVYssM5h4iEKZFypf6RMplEpnkMqSSJVwWGtDnu+dP/5D/Pkf/UsAwD4Je3cpT3t6emwQQjtb0mfteYg1ZgmrlHF+/xMhEP36tz5CkUiH1+5JhrpIsvOUFZisqcvsmGYhGo0GXO/lcgklLM3BNWVoKi+bUrLRYglDyiMrxHXBLPHBTstI3uc1W0V73ul10SccWD9/h6Tx3fO2IZPOs0QhR/JVN/DNnDFzIZmEM5N73dqWsqsxywWSiOARJaYlpIpwCwEELVkD3/qWlO4+OxTbZKWyqBRlLd3nnvD5VwUBe3FyjP11WRM/+RXJ9n304bsAgPc+e27QHX/7r/9VAMDlkewT/cEEDRL4a3mYxezms2fPDIJJEV5Kvtzv9w0yQ+HIirrxlp6xEY8fk0SWyIIbN26Yst5PPpEyr3KlZODVirK5yVK9TCZrZKaV4F7LhSzLMllTzd5XOMdTqWvUVor70tFzsTt2LmNQE1oeoONdLBZNmaHajz4z1XECODg4AACERPIpUiXwXIMESCVrL93nzu4GFq7KAbPUl2iZILi2N+mc/J3NZqhV5BotolMVGeqHIcpE+nSJBlprUE4+AYyJ5GhfnZn7AoQgtM8SCkWCamlmoVgyGf4BYefqx2UyaVM6dIcltJdErp6cnOPGnqwPRTCUK9J3s+nIlGQZsQOiZaIEMGKfjijHvE4EXyFfRIpoMd1DwuC6vyMWmLtKflupGCSGyVpzXRbKOdgsRXEdeQZFl82mQ1MWqt9X13Xh+UgTEa1w/SpJgzPpFNr0LZQQW9FtGTsDheJpeZ76GuVq1aAEtDRL0WXtztAgAQos+Yj9a7SSltZHRFG1O11TFqPr8YplfI7vmvIlvVaCmXDPdWGXWIZEm6noHnfpmPXb4fgWOc+am+vI6NpT8QZeO5WycPJcJKiHREeqpH2xXMJFWwh3i0owSyTO+UUbRe6lB7fElzw8El8vRoAGy1r29wXBfUkxlKt2x/gMiugYcI8dDDpIE1qr8udIXYtmjLjX59MsqQ8ScGmjVaJZ174beWb/V7l6RT4Ui0WDvFsSnbS+oWS0DipEtCjCUL9nZ1NmPi0jz1wLEHS3luPqWlKS40KhZPzYMVE6s4n0x85WDY06yzVJV+HRtxwvXINOvXmLZV6LGMGSTgZLsCLO2VKliJDCBEqVMSaJbTKZNDYp5J5scT7OZzM0qlpaJf3fvpS54KY9nLTlHFIsyPPd3T2Q+5z6GA1JAk576E213HGBIsl/05SBLxWJHu0PsKAowh36E0lfvv/Jo4ewiRBKck2p72bFwDarLZT+Ye7HCDgWH3woqLldIm3L5bKhvJiM5uz/Kd8rmmoEFZe4fVPso+94iAIVnKD95XxGPkSa61FRwiFLTqq1hkEWavXDjHMoziQQKdKVc07PqK2NJqw0kWf0K6YzFfvYx1Vbnv/JE9kTNrZvso+B4VTm0YQUAHfv3QQsGRMVbNB97PL8BCWiR3S/7nNtWdksIkVd8YyYJmrUtm20KV+uKFVF90RRBG8qn9N9QvftxWKhwBnERPXo/uIHS4QWS0CHRHrXKZvuRojpULWIWs5zDnUvrvDhY/EDtJrntQfiQ8WBj5jnMRUOmBD1HiWAIyM+InZrs7EOm2WUJZ75HJa4XXU7yFBUpXvFyg+ihJv1qkHqdChko6VgsFKm3C2XU/J1ItITWXiBvFet0hc/EX/irdfexAURpx+99w25zybLlb0JrkadiONCAAAgAElEQVRy75tbYjMXlLZf2143TNi31/QcwrNUnESFzzpgrKNVVdn5PXznHfFP/8Gv/tkKIbRqq7Zqq7Zqq7Zqq7Zqq7Zqq7Zqq7Zqq7Zq0qz/74/8/9/OHkq2vGiXMSU/0KTATLgl0c4wSOKchGYZIhgWjOo2N+p4/lSklvduSiZ2M2JmdLxEjpFyizJyiZRkAKxkCgXWHHrMXo9Yw5gIIxNBTiqx5FyyY/U1G599jxLFWYmE5haSZSjZGZzx86+nJVtbI8pjNp8jQ8SIrRLYJBsdXJ7gr3z1CwCAD54Iemhnp4HsgITPjKDmGdm8tZ2F25DostYbdnokkM7Wr7kumCF650NBXo2mfeSJ/Cgye7dHlE7BSsFnxrZP9FGPMqkZO4sGM9pLEsZWWLecb1VM1P3+5oE8KzMja7sVXDDTrqSCFrNO3aszZBnRTPK1KCmR2MNe19RGwhAXuybr8cbrMs4qMVovJNFnLesVswPLhURsj8+6KNRknFQqe60m368cVKAcND1yIERtSrT6BYxcSscyg6WEgK++dt+gH9a35J7W1uVZTs4v4Exlrswp820zS9YbjbFkv2+zht8j6qB7cYXWnmTCt1l7q5nm0XCGaC73qfwP7UuJNheLRRxsKj+L/O20+6jajIyv33vpWmvNOrptyQBEHJMhs1zj8Rh+qFkfErN/QRAM86WDErM5qV3yiii3RC6DnW2ZHyVmQgJmdar1Jh4+kmxiwLWhWaBma91ktLVeWXkqAs9Bi1wGDdbib1RkrjqLJZok0zxnJiBgZP7WnftwifjrEpnV7g6us7+sgTZyoJ6DJLk1trcOAABpR95zr4Yo0t7Y5IYpEXmSsS2QKw7bnAP1t98GAHzwzntYEr3y5be+BACY8V7e+8afG16EZ5SIDzyZe7adwa17zAhx7mmWyy6WsbYrWZUp53puIveSRdKQo8dgDTqzVY+enaBEQj8sJTN34+4mmiSec5id+vmvyji/eXMdJ6eSndolx4NKks5CB4ul3NcxOaOKJZLrpgK45MxJEglSY2aqUCxj/FQyPRkljGVWDcsIaxxXrb8ubsu9jb0+QvI3XF7J5/uUTR/P5ihXpD9iytK+OCEXkVfAhAjGclkGKU6Q+2Iyw5yEqEvyPNnZHGJmeL97JmNyjZ5NGl6KmCkwRYb5YYDZx+TP4V6SzV8TLk/ka/jGt8Wmn5xK5iaXTuK9j4S36h/9k/8DgPCkAEA2nUBMksl1zpN1ks1XC1nUStJXh0+1/+V7oetgRsRJ/0LWRI08ccVUFoNz4c0YjSRL6HNPGU9nCJTvinMIOofaV4ab5IHKKmeyRo621yd6iwiE8bBr1tXBDUGjKLfbfDbHjRtid6u858ND4a0qFirwyRsTk8NijSjhxWyOObP+iu4oFMRWXF21zZypcn+qEpmXTCbx8UcylgnykrWI0MjnK3hMWfdqjSSc5Lg6PL7ElH2kPEiLpfx/spyjzD2ozszg/XuvGnn1HFGwikqxLAtnRETYXIMpjzLTjQYGVzKXXa4pRZek02mT1VUiSuVwSsTXkrNl8oJopjRXLBikyID8GU8PBRFyc+8GMiQetVPyOzfIUXN8eoQ2Ua1BJPdXs0nOHSex2RRf6NUHYstOTuWasZVEhqgez5U5E4Vj2OTbUO4h5U1ZLJdIMTt+cEMy+wXuKf32ELoXF4tEWPm0c3YKMflRfHIHzehXDEd9w+OgXE8ff+9T9mPKoEjaRJx02jI2P/9z/4ZBdyg/hc6B9tXpNecQ97GsQT7Yxr9JE9GoWg5hKmkEKyxyCMVRiCJ5M+bMNNd2yBPkLg1Xoh8W+VfR3RamvSMAwBZRnzlecxa76HE/WaNoSUDesOPDY6SI+lGeoQTnAuKE2Qvs9az5HUBoxNay5NOhLVwuKezhJ4E5paQH5E3JyWcvOlfIl8VOPX4i99udEcngzDCKiGwjMsYQ1qaK6JDnbc59I3QdVLm272yIvRmQYypfyOFyImN3cilIJkXP2cWc4ZnZ2CSHJPlZrFSI9FLey2zTDxsLqjsIPVR4jUxBPrO1r3LpI8OjUwjkmlMi8rzZArmyXP/WtiD0XxzJvZ2fTNHlXCvQHlx16APnq2hsUDocKs0t9u7o6AVCct8owfVOvYJUTj43JheNZct72XwWVaKVB12KCDDFP5oOMSeKROd/iyjtaOFjwd9M0lanKhTjWQa4RcL0GffI91jFcPv2HdR3yX1FRMa8L+tgOI0Q895pwrC+KX0dxnMgvcFnJeo/Rd/oxz6Hywv593go793ZEuRrp3eFi548V4FI28XcQczqhwdvSb87es2kizUiCmsNIlM82pbBEusHMq57N+T6LlHMxSCPpC/3fnIo8+KUPqVViQwvTplIY3emc3aOPBE+uvZcqn5k4yyaisAu6TiTsP1qgCSPNpu79Hs4l6aTPiob8nuvNWSvHCsJ+cLHbVadnPGMgsQYeSK01e4kyU+0udUwqFmXqKo61+li7uHwUj5v2zLOu3uyN/b6E4ym8nm7QhQR7aPvhEhkuE/zrFY0fss1502K6LlSWfqs03fQpN8yJ7dPRBSonSqiTfs0mcucVZSTHzjoLl7mQ1x8cgQAKGfSqNIevk7kdS4m0tBKo5k/eOn+huMeJlO5vu6tWzz7ZqIErii0U6A9qFG0yUoBA47BHjk7VaDGcUOUyBEcuTKPI0/Wkuv46PblmoMyzyGs4nFdF85CnqdHEZLnxx32q4N7t14FAFRow4pp6Z9msYpsSa7fLMsYxbRRs9ECDvdP5TQ8O5bfn/UClHmf/7q2Qgit2qqt2qqt2qqt2qqt2qqt2qqt2qqt2qr9BWs/FBxCv/s//GIMAL4fmyzYiLKh9+5cy6erXOZaQzITPtNkJ6dHcBmp1czUciGRyv0btzEaEmUUkoPFZcY9fS01p7wlWpgeB6GpfTZ1ygX5vV/9w3fwztclQ+ElyIsBIpqCuZFa/eqeRG7vMoPz87/wM0jUKJc+kWjgu9+U+sFqlITPLMnnvyYog6uTc8MDc/Mma/+JDMikLVXYQ8gs1ZgZ92qphIefEnmg0utT1v4mErDJDl/g3y3yHmTKSaRUVpb1xqdnEimvthrX8uXWy3HE6XBg+EF2yb+h6j3pdNogPypV8vEwirkc9uCRa2DG7y+J4njwyivfp1RByVDHQVEzE1TkyLBufDqdIsexrDBLkNM6UdfHCSWQY45zjTXU5UoFE0ajtYZ8RMWMVr2BNarazImKevJIsvoFO2s4WjTjpfdZKpWQIcpFs3gdZrlu3LqDFLOzHXIoVFXSvloxNbDK6aHXjqLA8BG5zJYrUsjzHBRsGZtyVZ691x3CIhdHlhlBlW52nQWa5BPSumatm58tHPOarqExs3jFShkpZhOz7PdhTxACm+st5KlOM9OxIb+W63lGHrnEbL6iKRzPNdxXytlQYWTedx2DFlqy1veQ2e5SsWyyn5rlVSUzRREAQGjLfX766UOsU+XLyOzy9xDFmJKrS9UVIoInw8AzWeANcj1p3X2tVkNeVYg4Xr/3ntyfP5nib/2Nfx8AkCBf0wcffxcAsLO/Y+r6p8z6vfHqK/JZK4GQ9drKd6A10MPpHAdURzh6LIi/gzdeAwA8/ObX8af/6vcBAFVmZe49kGtOZgv0rgTlYXP86us1HJMbKkO0oqr5lerVa56eS5m3e5uSGSk1iuZZta5cZbEzmQwODgTddPhC0CvK6zVzlihwvDZ5rRHX9+HhMS3CdT26rsXAzmFGKd2rLrM7RCtFCWBJ/qmsKqgo+mg2Mwgfx1HJZVmvg8HIcB1lqcIxmy0wIx9DlXb/iqp1m+sbcB1dczLHVUK2Vq/AC4gs5DzR7PB4soBPxI3yTQRcZ8vlHLk8s4Ncbx7vyc5ksKA8rzeXfvjC64Ly+7d/4WfhjqRv1f6rClihmDNouzPa7YDXXFvbgOvINZVDSPkSgihpuIAy6Zf5w2w7i3X2W4PoiV5vYOxFm2iLMtdsoZAz/a0onsn4ms9B1TJeeUUyeoowKuTL5n5uPZC97tNPBeXx/PmhQW6o/VYulgSuFTiU4075Qi4uLozCiMpTH5/Ivr2xuW1QiilL3lNZWzudMco8ynnUoz9SrdfwhApFJfoFe7u72DBIOplP77//vrmX9U1VwKLkPbO2y+XS8BhlMvI3Tbvquu41XwnX2/6O2DfP8wwfztmZzNGISBwrncZrbwjS77d+57flnqnqd/v2bSRimTSfPRSuKEWNNpt186yagd+kvSyUKri4bPP68vktIlzG4xH63C8PDsRXW29UYXH+HJInYUSOsDv37iKR1D2b2Wsifg5fHKNC2zwc9k0/AMDO1rbh7vB9VUYjZ4bvGD8ORMbM2GeHR8+xQzVKVbhUNE+5bJv9RTn/RuS2SqfThqemR94klbsPIt+sOY9cMWcnJ7wmM9QAglh5lCJYVGXyOQ+n5CNJpZLoq8oa5/8eOVvKtTq6Q1nHc+5Pqgw2nc5RoMx5RHS28qFVSlWUiIrUtWEr8jJXNH7AgggJ3adTVgZJ2vQ8n+OK/ESFQgkBESOKmFIbMJ1PjEqscgGN6WN7oYdN8l8u6YPOhuRUydiIcumXrplEAkvuyypj7gdUYVsszP4wJ3LJVv4w30GWvj9dWCM9Puz3TOa7ynXqElHqeR5y3DtSSfm+rrulM4FFws3Qk9ds2n/fcZEnulpVUh8/EbTjZXdo+OpqDXn2wUj68ej4GW4ciD3Y3RH73ye3mBNEOLmQdba+Rd6lasmgPqtEi152xBYlUxnk+cwZogQSKbmnubM0aoEzovzVf08lkvA5liH7c2tH7qlUtI1fNKW90g6Nghh1KnqlofZG+njmuGadbG0QRcGzStrOolSTveC0Iz6vTX7JilXAwpfRUeWyiPv9efsKVom+HM8F08kcMW2W8ttcc4rFSPM9m/Z0k7xVo46LZ1SgXgT08Thnm/UaHpBvzaMPMCW6qtlYx8mJ2NgK+bzUDrz7zfewToW6w+dy7ddff0MumvCM/5HmvNI9tlDOYTyTMZ8uxP9rkg8mjiL0yNe2tsnzBfmaXGeBkqo8UpH604efwqVd3NmiahrndiFvm3Ot3qfy4tbrTfRU5XgizzeeyPjFyBuexyCWuTOjsq4zj83ZQZueZ6IoMnxEHvdg3S+m0ykSPDeWyXmjqKzRcImjEypVzmUs9YxZyGWNanQ+R7Qjf+P1e/cw577Spap4Mq1ocAuhy37Zk/mYyVq4uBAEWJ9+hyqXVwpFc7YZ06dUW+O7nvl8gXMgQ9TucDRBknv2TaK9xj0qIabzBrWlCmJD8ul6ThKNutxXrsSzwEL6OJ1OYXtL9l4ua4yp8F2ql1AkWrx91OZ9kletkEVIftk9nk0DPst0NIXN8+B//st//MMtO/87//BvCql0MoWQG+YVSa3u3RNHuH1+AZeEa3qAORlzEx+NDNnnDhdRVg855Qpcbh5KxKiOzmK5xAWh9VMazVsMQE0mE3MoefCKHKzORjLpfu2P38UH35LBiGwZ6GQoxikbLY1z8vauvPbWvjhyn3vjAW6+Kc7uhSMT+Z//2m8AANbtEm5QQnKZkPu1kEDO5uFVJSTpNLz15muGaGqdB+EjTvbeaIwx5dhLPEzahgQrjSEPLBEPCwsGnfK5hAneaNBGnetcsYAlP394LM5PjxtVIVdGlodwI5XLDX6xmAF04FRitEyj5ro93L4h/W0OgDRgr7zyGgZ0wk9JIJ3P5+FwDijRo8q/WumUOaAq8M2icWg0GijQmKjTdE4Dsrm7gzmf8ckL2dBHhAcOOl2USVyeZoCsTiesnM+ZwFqFpTcaECoUCqhvXZc7ANckquqAA8BscS2/CghJbJaL9sULOXToerBt2xwqVa6+xENYu93Guspak+RuvnBhkUBNybnV0claabw4lOsnWFql5J3lchkFBhS0VEShmuVyGQ43X+2/DPvAnTnGqTMy07GWMeQwIwmskktrPyQSCeNYmtdIHtrrtg1RXrEm/fn+RyIzns/njbM7Y1ClRgfeimMjW74khnoymWA80oCTPJ8G1IIgMAcyLSvbZQA2DDwMSSRX0gMaAxmuB0xm0h9Pnx3J50uyhovpNEoM0u3S2eoPxKY9ePUu5oQdh74acIEHA5GRvFZZeA2gdPsjbLAMx2Ffl7akf04efYoPvv4n8lxKpszntAtFLLlufAaNC/UmHF7fYqmdSljPHBcZBhEtkjS2Sci4t79pZMVv3pTgz4MHAu1/+vSpCRadMwDbIzHu5t4BrihPrcFfdcjOLi4M8a5u3nqw8pIlvPddITMmX6bZ9N3AN/L2CcLVNZAVLRdI8JCsUs1K3u47rllLZ5cyJrdv30ZrV37zkGTGWxsyhyLPw/078qwWnZEcCQjzeRug48yKGaxvSlD88PQC3aHY2rNz6Y/vl0u/fUs+94hEzjYPEc3mGhYMwk4HlByuy71Vinn4hFWrw6LzJW/nzFpNM5B8wd/N5XIol6yXvrdNQv7FwjH74AZLU9TJSCaThiBW+6xarZo9TonkFVIu/5a/SqZZYGB+PJ4a531vT35bA7DLhW+k2meOPLuOZeD7RpJ+MXuZMNayLLOONXiuAeFqqYx292VyfyXMT1kW8hp41oAeg3eB6xliSA0inrGEwPU97HC/ffTJE/PsGsTV/lMZ3P5ghAuW5xYZILNYZzoc9tFskTyW39f+aNYb10S4PEhoUGxjYwM5PrMe7ns9mScL18GXWLaqcu5j7qOO68Lj/qr9of5f3s6hyNKIOgOi0yX9hDhlDnsB15QfOebZdd/bZbntYrg0pWJaxqO2PZvNot2Ve9VgsSbeAi80foAGcTQxWC6WjKzyc5Iiq/2pNap4jyT9B7fkNQ3YBkGAMgPIGjzb5pgmIxc17i8jHl7VfuUKeSS452vSKm1Jn2dtyxDUBgxmqWw1AORYPqnzcjId4+aG2PcOA11KYBpFQIGHrypt3oJk/cvAQ38ucyfHvlJC/0KpjAR/M2CiM0lbPR2MUW5mX+r3gPtaKpE0gQFdi+bg4/tIkPi1Upe5pmWlO5u7SDgsi+Ea1O8vl0vk2Mfj2cuJlSAKTH90GThs1CkGEYboDl8u4aiWrwNW56fiZ4Y653wf29wfYh4mNQDoBS6mLOuMGKwoMNAVeI4py1vTJJ6WTtoZ46MleFjW9RZGS7CrkCHxvBJ/Fwt5k33IMbnFczkcz0fM5IAXkriewep0NoNPP/mQ35OLaynX0otN+c/lQGxL6CzRKGuJO/dikg431zcwoa/qMQiv+wtSyetDOe2iKckvFo2fE+smTBn42XRsbJhSBui+lstksU0RHpuBssMLWYtz1zN0AnqfDgOOuUIeEYMGU5LLFxnQWHTHmHENVejjrRXEf5zOXbgM6k2YmEraEUoaCF5QwIJrtl6pmmDiYq5E3/JcP/djfwV/+i0hoT7vyXunF2KH8gUbb3/5TQDAcCq+XjYvv7uWXzNJHWfJsmotP8zYZq/R+XvCoNN59xh3b0lJW8Q1dMEy4lqtavofSRIts2S4P52hTpJ5JQ+u0T5aSWAypv1gKVHSSpigdD73csLYSqZM4kSDgxMmQSLEyDPYNhrKuTHDzwB5JLlPxmDgkCXlm419LJdyD6OJzD2fJWrlagkZ9seQiXAtpUtEMfo876wzoJazZL+ZDn1MZixR59io3SqX8qjR9qUpdDHm2OYLOVg86/k8/0+YZMiXisgELzPiFMsFFDWpTtulczQOQmMHA9oBTeD0egNTUq97VoqlakfHx7C4tm9sv0yvsNlcg12U9y56TMhSmCaBDK7OZU5XyzLOBZa2ZXI2aD4xG8s9xOQomHsLXLEMbbslsZHTyyP57KKLt7/8OgDgzm3ZP0+OZX3ms3kT6P37v/ztFan0qq3aqq3aqq3aqq3aqq3aqq3aqq3aqq3aqkn7oUAI/fp/+9djAFh4C8wYqXYJ61PESCqIsVGXyGnI7KDPCGfnqm0QMffvSFR2zkhhxkqiThjrlCSjGUhmcDqdYlOleDMq6Sj9MRgMTEZKo6yFNYmw/6tPjvDb//s35R7ShJctJfJXySWMjOdP3JVsxoNN+f4br93Hm18RSOH7h1J6NGImeHE5wJDQ0TajwFtrLfiMTGYJBa1rZi/ysbsv9/70TGDZ3yPCpVTeRoKR2TIhtYqAaK7VkKck69m5fE+zCqnAQkSUwP17gtzpaXazUjYyiguOiZZbHB9dYnNb7mVCtMuYiBBnPjNEiM2SRLoVrtrczMNhNJuBcjQY/X/x4gjlmvw7R5jewlkizVIelRNXyH0+l0HArCATe0gSNTAYDIxE4hqJiC+IcAkAbFGaeMwI6umRlBXs7+wiUvJYZhqUWK6QzeAZy3Y0mq3ZZN/3kWAGqsp7H5CsPG/byBDm6xEKrVmTtJ1GNJPx1nmoaJ1avW4ivFoipRnI8/Nz7BEuenEh45XO5ZBi+UerRcg2+6fb7ZrsGfhcmpVLJpMolV7OHt++LXNhNL4mZvZVVpJkmR+9/wmuziXjUmGWa2NHMk2PHn+KAlFUkUu5b2aCf/zHv2ae5w6zu4o2ePzZQzQacq10WuZOnRDgADFOz09e6o9Ysy5ugBxRJOs3BYXRvuqYLI6u60uWQZTLZYN+mBG5s16VPiuWbPhEUa1tSx+HhJYOJj6uRjJ2770vpMGf//wXAQDT7gD7RIkp0eyHLBnbu7GPN14X0rgZ4cEq2QxECHy5Zq8j/XnzQODM+we3ERLeHnE+5zZlfn39t34DESG/aaKHnr2Q7ECuVDWIirs3hUR2OJ9i0BEY7Dqh+ZpBzORzBkkxIapKJcD3D7bx3nuSadM+e/11yUoMh0OT7bwi4bnJ/qfzJvOqJSI6vwqFAu7cufXSPWiZwPn5ObRS4fxKURAyzo1WHafnkt3S7HOKiLVcKY0Moe6bvCdFSlgJ4BWimrS0Z39/H3WilI6O5FlvEn047PcQEjGzJKpKEYq2nYUdUYKXdnFjZ4/320OOZXJK3Kq2Yjab4fOvEkXIB5yxRLNcaeCcz9Vguc8J76mYzcJmhl2zXIp4c5xrKWpbESREY4VhiCWRNwofV9nv2Wxhssg6/w+I4LEsC4fHci+69nd2dgzaQuHiiqzQ9QoA20S8Xl7KPOu0e6iydFRttBJ2IraQZabd5fxXlNhyuTRoMi19uWIJZD6fN1naBG2mQb3kCqbs+NYtmV89Er0/O3xhUDW73Lvu3hXfod9pGxSy3kPKkuf0ggB9Zj8LRO96nounLwQ1p/uglvN8+713ceOWoPoGXOuLqazFN998E2e0YdkfyOIP+0OzvpTceEz/oFarGpSori/9G0b+NXqKGV9Ft84Wc0yZSVVybUsz9+MhatxrFNE7WkhfZbIFpIiOGfF3ldU6lUpcI01ICl7IXKM81Obu7ct8GgwGBrmkaBfNXuftgkHxVbj3f/Sh2NXReGBQYlo29ejR9+R2Axfqxer6nxCddvfOfWRVpIAZYEXDDTvnuLEnyB2dQ2q/csWCKffOcq/Ucu5SwUaVpJ0qcOJxDY/HY/M8HtGf0+kUG0Q/xPQN05z3KSuBCdHD5yTyL1XEZqTSSVgsqZqRdDWbVN8hxGWHqJdN6VuXwgjf++QhNrZJPEw7YJCJtm1KP5V4Wm2t4zjY35f+aHOuzThf7HQGB1tE9RHJpyS5lVoNva6WX5NAlyW2S2eBbaK9+rS/HufJZDo3ZPSKBLMzWQRECGtpikpg54p5U5KWtRTFLH6cGwXGbjikj6hW5bP1etXY/lOiNNJpWRPpTNLYzNtEdGh/TKdDpIkwHvZo71kyttFaM8giLQFV2z6cTM36zJfkGcb8bKlaMfdVIspDbX13OMUWywXjJEvu3KUpnVN05Jyo5NdefQNTIn+1BP2iw/Kf8RhT+jmK+FHbl0gC9+9Lya7uyRPOpUw6hSppBzpd+fyIFQTZbNoggw42ZT7bRZlnnX4PFaLpnz9/zv6X9/K5AkY8D8zZt+5Efq+5uY4MnewB11tjTWxouVCBM5O5piIQfnqMJBGWSZLMWynudbM5kpw7d26Ln/P0mZyz7CiDfZ4NRyxJ6/Slj/u9kdkzIvq1FyxtWy/nUOFavyJKCTxXWNkMDg7ER3A9knQTlOJhgSLPCosxlSX4nOVCCS5RPdoOj6TP/ISFXImE2ISJrK/TViNhSm+VTD2RtlCmv+47isaUm5gMJ2Z85+z/So0o5FbdyM5bFqHX3GMtq4y5pyg5ziFWHrRPxhgNZd8sEbWcLtLexZ5BhlaJklljuVfg+Ljg+XtGe6dVPFYqj519OWMsiL7SdT2fTRBNiZRlOdTElbW1u7sLf0GUHisrXrl3n89gYUgkaodnL9d1zf6qqExtCUTGB+qOKTvPs2i3M0Ac0V6YkjT5XrfbRY77g+5LByzt7veujE0Kk1rSKTYiCmLkWa426MjvaWlnOpNBuShzzpkTlfn/sPdmsbZlWXbQ2N3p++b27eujj8jIzMrKolxVFrbJsmzBB0JGgAEhgeDL/MCXRRVClpElxAeyZITLlGWQTLlKlsE4sYuiXJWRkZmRkRlRL1689r5777vduafv995nn735mGOuG6+g+EQp+ayfG/HuuWevvZq55ppzzDEIjx9ORwgTknlXZd53KPDTap9iGSs9gux5vfdOxgEueD/77/7O4xVCaNVWbdVWbdVWbdVWbdVWbdVWbdVWbdVWbdWk/UwghP6H//wvJgBQbpTRGRMl0yFxGgm1nNhGyCzJ229Q5pEcJ+Vy1WR4FBWiGd1iKYvJRMkj5V0zlkTyTk9PUSWRqhIOKs9KOV8wKCONvk8ykhH50Vkfv/G3/hkAwMrzJZbklklbJrt+n9KpP/e2oAG++f7bWNuR50Vl+cwTZt4///hHKJC3Y/+eRLe///3vIZNhf7ISfe+3Jdv64N4BauSN+YNPfgAA8BkdX6/vIYhDuKoAACAASURBVMtxmzMreetAstaVZhUdEtzZ7g2/CgBM+yNskUR51JPn/Pw3PgQgmb0BMw5VcjBdXslnmrUtzFQ2m3wWS9Zxn5+fI8NMvce1tkGUjlu00SFK4ZqZm1JWIrg7OzuYMsukpNn1ZgNt1v1qJFqzVDvbG8iTcNonr0WDkoKzyRQjRrw31yX7YOZ07sMnGk1rhZXI69bOHs76ssYU1aQonXq9bpAmivJymHWazmZIM9Za5XrKcU1kMylUybGTI8pJJS574yEO1mSe2szQvSIvVJwkJuOia3STmZ/JZGKIMvWd/SjCOREwN4TRrOnOpg3ZbI7oLc3+ZzIZk9lUBEFi0HoR7t6VbHe6LPutxyxSsVBDQI6BEeWAvbRm2QuGS+LFo2MzfgBwsLeHyyt5xzSJSFtXMh63bh0aniCQIK7ALFTkWoaHIU8ukAEzYIvpHDmiDeaUuR+NRgbp5y/kXV+9EiRYOp01WWDNdm9W5LO2I/KnANAkOezOLclm7L31IWBJlP/xY0HjvHwhfDcvHj81616zEYdEQJWqJYNgOmTWCjaRYUmEmXKvZLTeWMalN5wioJ3KMxuRSUl//+nv/A4i8iQsmRUakotiGgRosi69TuRCq3eFHBFkdaILzBrIZc1e16xkg0iVVD5rxk1trmahx+Mx1tcp/0zEiGYqjltdOOQW8EjMcHosCEXPsg1B55DZI0XkFStpVJhlShORodw0WzvrBqGiRLNbzGKX1iv48qEgCELl0OIzZpOx4WAJ+Zw4jnF9IX0uGWLbvpmHOZGTmhmKuC7L5SrcjPyj8or0eU7lsnmz94xcOtdZrVZDxpbx9pjF6QxkzDvdATyeBX1yELmWkv82kOPz9OxWe5DJ5Mz4K9m7Innm8zny5Yp5VwC4JgItk8ognZH1qGi7baLhFosA9brKMCsJ+Ev8qV/+RQA3NsLU5Pu+4epTtNGEiJjFIjJIhSnJNJXLxoKDa2apK8x8rRmJ+Bz8QIlApe+KnikWS/iSRP9rFHNQu1wpVRHy7zbJl6TnxrMXz0zt/3yk6CbZU/1eF4WsSr1br41xoZgz76p9T+fSeM6s+CW5ApQofDSZoko+sxTX3JzomUajYbLGY/ZhrlwIvQn29wXV9PlngpIpU3q5XCsbNILuRR3zXC5juGhOSXR8uH8g/cxmDIJXCbWX5EGpFPIocm0mtI/guydwUCUq7ekL4Z5zPM0lxgYZq2is0WBosq2KvlD7kcvl0CCKTLPXukZtOMgSdRxxnSyUEwWJ2au3b8n7dMjt9ujRF4ZPqE9Omgyzr1EUoch1nyj5Mvf1WrWAtTUSiX9FiAAASpWyQQzr4arZ3flsavaCT05HtRm2baNYUrEDeU7r6gJTohJq5FEEfYXxZAAQNVHIy3rc3KH8c7+NOBI74BNhsl4nR6Af4MUJ9zaRAftEl4SzBUo1iloQjaWogUqxZCSdlbeqTGRYFIW4OpU+P3kp63mXY91o1NHrynhPyOGhfG/5Yg6QLWdQlio9/ujRIwRcT+mM9EntUCqTQ0jUgOamgyAwSDAllg0Teb/nR88NonBzV5BMx8fH8neLAG+99QbHTc4QJSafTEdo8rxs0r9SdHwunzJ7oNUSe6gcS+vrDXNnUOQTEiUtzmI+VbumstjyO9/3jR32p+oXyxgPpxOkMspVJOOoAinZYsmIbSiJcrlSM/yTivRRZG+lWEMYij20iYwbBzJ/SZIYJGOh8McI1ycTVMgRpWOUtYjgjmP0eiP2j+iE5c0dsUwyaCumyMJS+ru+vYEq7e+SiOopz8FsIQ/S6GBIn97m2IW1DA6JxJgS5Tsm153fHSE4l3dvvi18Kd3xBZKIyDaSG+VS7HsUo82KBuWeLJIEexklKJDYujOW71Ti+1KujOtzijj4Ft+d419yES/leQn9F+WBKZRzeEUOpQw5hxq8k03iOcYD8kTSnGaIsnStlOF2VTEeXXPZdM7cRxKD3pTvqVVqWLLyw02TDH8wQDrvcrxlX0ccl0wmY1CRaa45n0imQjEPl2vm7EzuoEVy/Vl2GpFNvyVPufoiObScKtqXMk8J92fkUBjIn6HL+6byxK6zqiec+pgSghQo/+uOii2MUOT54Cg/JPlxrXiJ2UieM1sSkbeQn83mJoqOzL29kLGKzD1tgGJNieD1XhjC57pTnzVLu++6thEMObkUtO/2ttiY8WRm0K9qp77Kp6h+czCTz+g5nct7GJPvUZGeGfoF15dXaNBW+hOZv0v6UrEFbDRlbHyeG4oEHM7HSOXJ9cQ5KpbI9+klhlD/4ly+y1ZuuyQDz5V3/S/++h+uEEKrtmqrtmqrtmqrtmqrtmqrtmqrtmqrtmqrJu1nAiH0v/03/1ECAJEdInaYGWJAfjxk9G2+BBWkYbOGbgGJfnW7XRNhtZl50YLI9fWmkXNTaemrM4m2pdNpg7rQzKhmYqOZb5QdvvmNb8hnXIn2fXTcxt//e98HAEwtjcpK5M9NAhxQOas8kajz1ygp/Svf+ABdstg/+LZwCX32WOThf/yHP0CFSlhOjqgNf4679+VvVXliyYH58P03cd2WjLSici6Z8a1W8zin7HONNb42MxuwHfSoWJbLKkeDfLdjT7EgOkTVFJRzZz6dGI4Mzdwq83ngxyixLlo5d3wiclqtllFhSJgmUB6IVMExGd85kQiK1NrY2DC11TNm4QrlkkF+aEZP1X56vQ6S+IbRHgC2iXYKgwAha8HrzE5pvX4+n4fDeR4pNw95inKpNLq+ZDkqZYmQa8bTclz4jAz75L3qM9sVRgt0juX7q5QHfosS4J12C3lmm3eZudWs63Q2xzbrxTXDnGamujPsI0s+qNOXxwBglLRy6ZyRoFbuLTflYcAMxeiPcWENej1Ts6+IIo14Z7NZk+HRMZ4SkVEsFo2C2MsT6bNmx4rlqlET6vZVFlX24vX1teH+uTiWNfrwjwRJk3JtxMxivHFf0Ec7VAKZzWY33CTKLcM5tjMpdJiBnTLjrlla17FNrXDCmmTLspAwbal7PuRYDUYTI5GdJjKjTbTNcNBDzM/dPhTk3ngka9VCCumcrIsHbwiPTnsmkfmU5Ri0m6pLXLGm+YOvfQ2bnGdVSMuvy7gOhj2A77gkV06e2SR4WRSITgLX7KN//o8BAJ9//D1sEP3zxeeCKFjf2+N3jgxC7Ra5YVrDa/h8doOcAVvbsl+8VApPX0rma0CEyi2u1fpa0+xL5ZbSvEK/3zX8HBHXkGZ7f/TwC7QoqasKWPMRkRyFItLMVtWJAPsmkYkvrl5hRKnOt975QMaYyIC5P8Y2OVvGlEXNcr8sEeOc6oQxUSLKneE6tuH5UJ6KTvcaZ1REM2oT/On7AXJcHzYRdZrJTWwL3fPX+ZJU1SyJY4NGULn0bOZGjWj/UObims/tMjNbX99Eu6P7U9aJjnmSJIh9yfprxlizjMPh0OwBRSap+szt27fRJHeQZrSi6EZJ0vCe8HfPj1RJ7sCoFynqo9ttG1uk/oPK1AM3EvaKQCgWKP0eLY39oJm/QWP1hgYhtEXOPYtGrdvtGq445cVQhEYYhkY220sRLUbExHAwNuqSat/W1ilDPOgZlUfPkndRvptauWTmTbmOlFsmSRIztrNAbFOukMU5UZ7bVCAbc9x9PzSZv5CozGZD0Aqe7WCN/G7KgaX8IPHSMdn/LCVuX548BiD7ThFBvaGsnWzqRmltf1f2/fPnPFdUWTCfN2Pla6ae73f7YB/Pn4na3YjvXqzImg+DBC95nqns0nvvvw1AeFp07/V6smZn4RgxUQWKzDDSyfO54fbSs0dRwsPhGFVmlBUZZNPOHdzaR/ta7OiEXGl5nv3jyQwlVY4k2k7VZE6OX2Fv70DGmKhbXZ93Dw/wknaukOfZRVTFcrk0XH9L8oo01ohstmzjtyjPkKJgbNtCtcH1Hqq8+wx5njlTohUtShVPZ2MkhBAU8ppFJsodEeyAyqDcn6qGtn9wCz7tU4f/tk0uoZSbxo9/IKpryoOhHCyZTMagV9QfHnMN5XIZdM+oUJQjv02HyO1qCXWiH0ZjqgxN5bl+5GN+Lrbr618XDr19Kuu+OjtBj35ITB90Tj6MfLGMKpU4FQVTr9Qwp7+n6LctcpFFcQiXduAJ522PZ5zruuYOoGe/8xX/Q+1a+1L6wu2C2Foik1E1WplfVfcLgjm2yYNoO+SQ4/7M5QpAomptVOIlAmoRBcaWWFwXXaLnEs9BQsRZQFW0hCpNx2fncHiuFCzp4MbuLo7o79Vrcr48uCPIwXFvgCePBd2RK5F7qyl9qBRLZo0qUu1mXCzj3yhK9O6h7OePv/9DKEAwtqQvY/pQfhhgb5PIZNrvLfoctmsjSuQPVTmxqDwtlo2jUzm/yvRZLXKjXNtzHFZk3Nuc71GOaNhsEYOX8nctIpIam/u4oo+hvDGKpLbhYkAuWaNuSsTwaD7FVVe+a+e2PK9WlTHeXWvAH4gt717Lnrh3T/z10TQ0KFhFBtWptLZMFnBcGePLa0FjBuQDTVeKZj8G5AZczGR8ZpM5svQz9Xd6/qYsDyUiKLus5FDF1o3mJiZD+fzJOatjKg0k5LFVrjhV+w39AHX6N/r9uhYyuSw2N6R/F+SxOzmR8bedFA4fyLrPFcnXSDviDy1cXsjYtnhmKWp6bWPTcBwNuL7S9JcKuQxKa+SdIoplb1fO+avWGTxWd6hKtdrEKJghtuQdji9krW9syxqa9GZon8j7bNS3+O6qNL3ENJY91yTC3E5cLHkuFegXaJzADyYGMebkFJUqbbFcGkTRbPY6D+b+9r6JHUQB75EDmZvd3QaW3OvXHJcy75+ubWPYlb2XL8h6mpH7bDgcGNXcxZQIXvroQexjNJNztpjlXuRmDMIpCuSfUt9LeX+73T62dsQX/yv/2e//bMvO/4+/9m9KQAhzRMsZ/1WMkUJmF0GCYokGinLzr85I1JvPwKcDpgZcy4cGgwH07dUg1uviGB8dHZkLpjpNupkQLRHTiS/SIStsSl9+3Brjd/+JOGczm3KPkRjGaD7Czq5cHPNDMVwfvilwx194520c3pL+pTdlQT59dSx/N56hSgLikJep+XCIdz+UYNTjp+Kgq5EPZxMkhBrnCgqxl+/2kzlaZ/LsMiHYMd8rmy9jQUezTEf98kwWZmMzg9HkxokBbi45Cz9AjZc1ldtM08lOpfMmIKHGU50iy7Lg0xFWGWGfQa2MlRjp3xTny6Hh6vf7sOLX4frpVMrAt7XMbZncEIn67LNFufMyCV3LxRJsQl2vWNahQSM3nTIBBb3wzAh1taMYdXVGuBYUvtnt9k1QpUYHXw+KUqWE7pUYRCUv1AvJYrEwQaUWSepilfXMZhFTAtmi0+xyXC5bVyjyfU5figGvEIZcKhRRqcl/b/CCPBgM4NDpzFHO2sjLBuFN8ISwT7UDvu8bp9gmZPWrEsX6zkp0uCSdZ7fbNcSeEb/rnATX9XoTLRK7jUeEWRP+X6+Vsc7xs2icVfI5tmxDcpnJyjjq+ioWSmY/K8GeOmFffvmledf9LV6OXj43F2i9vGpJXRgtMZ6obCsh2FwLrm1hT+W5VW5+diN5HfJSrRf3GfdB77qN7XUxwErmfe+BBLwWiwBX53LBeucNkuARtn99fWVg90pUqodDYDm4+6f+tIwxD6bv/ubfAgCMum0UDCxY/q7KA/iHP/wEa7yQfPtDCbQcXV/g6kwcpP1NLQ+i3QnmqDF4qCVLGgC4d/eWKclZp715+PAhALncz2mHP/nkhwBu5MV747lx0rQsRAPDuXTGHLhqU9bX5RBfOhZch+V/DFwnur9LOZQKDEzQLqr06iIBnj0WmznV8jcG1ZNoiaWlsGp5XrVeQ3cia1Ql1x/cE7sdLRY3AVTucQ1UFopFLCevEytHDOSNx1NzSbjmpVdlS+fzOdY35XdazuF4LHt2PFN2VizIPtWymlQqhRKJIdXJ0/fq9wcmUFCkXe10b4JVnhLkc561vDpfKmLE4JzaV91blUoJQ0qOazAnk0mZsj29XOt5ES0TFNjnHC+VGph+9vQId3iZqfJyo2IHgb+Ex/cfdmWsJjyTHccxJUpaBvXu+++Y9/ryqZSMuVkGrBhYKuUr8PiOKtiwQ3teLpewwfKdPstedVwc1zUlWRp40vWZz+fx7JmsqxFLb8bTEfQAVGLIt96V/vXaPfQZKFH7o0mJk5MTY2u//nMiFa9kxdPp3IybytW3WL68ublpAmPWHzsPU65nyocdJrfOWMbmOo75Ny2ZzhDKfv/N++iRLLTFgGutIXM0Gs7x5RO5gJd5MQCJ/2uVggmAqq1IpWxTPqYl2qWcfNeg0zflyrcOxbarT3P26goeL3ctzknEU6XeqGJBhznNwF+R0tWh7Zpkjs/E0rd+7uekm3Fi+lWqvF4yOZ/PjY3QsjdWFGIyHSHNgJAJbNIupxIXL17K5SRfkX3WpIDAZesCCUucGk2ea1aCyVDWtApkXLR4YcrkYTH4laEM+e6B9KXdu4Y/5HexpEKFUcqVCs45TzpmmlRr1htIL6XvZzyDp7SdluNiSftZJ11CneSw/UEXXkKya56pj56LcMbMn6NR17I/JuUYkAviEBiy1JHrss7yoUwuiwFti5fK8e9lPY8GY2ysMQFzTbJcJ4U4pKQ5gz5vvCln5HQ6RotrucLzSc8S27Yx5D7TJKZP8mvP84ytXAYMbqeMt2IIdxf08XTtRlGIjQ2SgVvyftctmcdUKnOTSOTe04RKLpfGZCr/lmaZ0Yg2ehwsjE+X5UWux8vi6dkZEkbKt3kBPG+30WD5bpo28NaOvF8ulYLFwFGLBMkaMFwsFljjfAUMYJ/T59jZ2TJUGRrcjhfy3ccvTuG4sqavumLLrvtTjlWMOv3MD1jCZXFPjqZ9LHmJd1j+vr2l9AxLDCfynF/5c38WAPCSZM+vTs5MudCc/k7Acq1UKmWSdgEJ3Z1cBRmuzQWl2kOu7ZSbNgIUL47kXQ9vSUn+yB8giOVzuSLLjUmGnE9ZyNAuxoyG6Xm2uX3HlNe2SFGyfyg+5WgyMQTm6mvrZ0IbONyTe6Am9lQgAtESPgNBWkLd6zJQk8kAnOduv3PzbxBC+RlLiD79qYwf7BR2D8XPzLAPmlDxp0Nj1/R9NGAWhhFC3uO0vK5KgZjPH32JQl3W7f4dvdfKc69OJ4gZCNUEsGPLGiwVKlgwIOazLMzxaDuLWSx5Fm80Wc5X4Jh1WrAYvCVBBBxNvC8jxBRUmgUDDh8pANIVOEsSM5P0XQVgMiUPsEjcTUJoRBZc9tWjzxXxDm15CUr0wS0KJgwZ8I6ThTkLXJZgKZVCys5iwXKyJGKA2Jd+Om5kKC6moYzDnIGktOfA5Rmq98HhWP7uutNGiYnmcVcJ3WUNVJo1lOtMEFOgR5OwxXzBfJeWCBbox7189dLc4379b3y2KhlbtVVbtVVbtVVbtVVbtVVbtVVbtVVbtVVbNWk/Ewih//I//pUEACwnxOY6odCM+E3HErPKZiq4aktGO5uXAFezKRmmXCYDfy7RNYVVgpHl/nUPJWYslTTyUqXUK2VDrnZNiJtG2LLZLFzFFhFNkq9KVPGHZwP85m/8nvzdTLJqSIiIieZwCWXcdiT6+C6lCX/lG+/h6x8K8V2esqDPL6W0a9TqmEy9woqTycRIXEcsqVBCy17nCg4zGiobrXC2l50BpsyYbbJUZzyRaGc6lzFlTDEzdTVm/YrNJjJEkygZ1YDZkvlshoVG6QmhrlEeNXI9zEis3CQMXKParetr1JkZVTjshCUOZds2JWaTQAmkOZxJggURRSlCITNeCmNKJ2sGV+HIGxsbOGGZXIrZoCkj+67twHZfl2PWjH+1VkOREFdF8ajkcNpx4ZGQ7yVhuy+fSmZwrb5myIKVqDRfuCk9GDA7pRkDJVUOw9BAky/P5Hc2X/rWrTuYB5ItmhHSH7AkazKbmvIbjyVtHuO5nuua8pOzyzPzXJWuLxeVJFfGI15EBra/vaPlf1zjxYJZYyrLfvueIFuurq4M9LrgSbR5yixNJpcyGQdFxNgk0auUG7AteV6JEFaCj9BpXSFDlIBK1SqBmud52KGE98OXUlqpZK/xIjJksJoR13G9uLgwyDND7uj7poxRUYHh4uZnxIWokNKIYzWbTPGd73xH/ptZlnWWIj4+fo5XFwKzZVUN0rbYmM21dTSJqLiibG6O6+Pd997A8Qspz3DV/pIkMJNJmSxLiQgwzUTOYgt73xYy37/3t/82ACA4lgx3PuUgryUORH35fJdebwCPmagHBwfymWLZkE/3OM+K1piGPkAUlZbSLZjlWvhDsw41A/vVcqYDktdnWXKqCIZcYc3A0xdEYRWKYocm85mxu4p8GLCMoVHMG2ShR7uaJQw5n/eQJ8KhT/iy2rYgnTElLEWWgyjMdzaboUCyyQWzmr1eD+OuPKfADPg6becy8GFb8Wu/09Lajc119CHrosnyH4dk3WevLtHri/3s9FjiRJsdLRM4c+lzjWUyY9pX10vfINVo5w44b7ZtI2Zm6dEXX3IcbmTnFSWnCJ8u0TbLOEI6JvEzSzIaRJDZlmv2hLLBlsuaLbNRYBmOro9er2syUA2ixE5OxPYmiZRqAUBMYYGdHel7GEQG6QSOp8V3WV/bxiuW8dqxou5kXNK5tEFvuikSk7N0pNPpYEKkTrbCEgLCwTNOGjWW+nZIGNtm2ebPf/sbODsTtJHLfaLE4cPh0Ei2K+pWEZUPHz40/50h0eZ4NjHj9uiR2Cm1O0gs3KWc9aM/kt/tsJTAD+dGXlcRzkUddzvBkiXWasv6JOmOlktjizRzWWafvvb+B3j+RFAdBi1AsswkjmHRRms5fLcne//86gKblK/1KdQQQH5ubx1gPiOSmiS5KrV7eXGKTa6jFO39fDpFmbLDs7Gi8mR8bNhoEv2QI+pQ13g6U8CnP5FS4i2WefXpf7w6P8X2Nku2bOlLtSJrfeamkKJ/pOIUVaKwPMc1yC8tFc5klPB7jALRKinKuevZZwE3ZNJEE2pZQzieG8LpqZLSs09BHMKhNHe2KGOUy7hYTmSvOyS7dlJif4qFG6EMLeFSgvdULm1QfftE/iqRtpPxcMYyRUXpKgJt6S+wVRJfckCk4dmVPGOZWKYcXUvDdV/nCxmDFrokolEJoGfTwCAdpizhVd/Z9mxEQ5bcjGTNael6vlIyNu/Fc7ERLn2BTCoLOLKmb98SxEk5V0avI99RI1pOiY8d10JE37PNEp8SbV8hlzeoCy1j32FJTKVSNf6evRT7MWH2f32rCY82Rcnvp0QAJ1iaZ1u2lg/LGFiugwzX74ylXypRDytEmvaJ1WCYc/0fX3TQ7sh3KvpZkdi90QgJfbvpUHyGvXsPEPOrliQEXqMgzdX5KRxX+rCxLcjLNFVurCTGGdGUOa7bhD5AHN+gQzYo+mCxamA6DdDpyDl01ZVxeEh04MGtQ1TK8rzzU/E7Bi2KEOxuYm1LfJ87vO+kSWLtzyOkuO43dvjOBVmP0yeXGPHMGis85ETmqh/6cDdlDeyxrPfV+AobrD5Y0p9rNnSNhghDeeZwomXfPKeaLi7bgrgpVlheR//j+uISae4zTzl4OXHFYtmsAa0EUFSV7abQZ5l3pyvjoGhur5RXlxA17iFFMg36XVNKW2QfFGW2sbMJn/LqHSI2FbFcL1eRcDG0O/K80Sww95bZRMZtneOR8hLjtykVgpIjW/Aw8sXH0nL2wwNB4vXHU4xCeXZsUSKe6F1/6MEnubNnE913QdRirmwQSVmW/fmhlqi5GNJGFAu8W/LsSWwLW5RqnxId5sTcS8sYCW2rl+V5yNLaUX8GO6LITCz/1qYolVN0USECfUEkTdrJIeOxlDPRM5/l37ORoZZJ+OwikdizoI8x0TsqXlAty/x1rkZI8S4UEAUXhDKXuZKLNtF/aq92iRrz57MbMQEScEex0uXE2GiIj/2THwj9w+OHYjsrzQLuvy3ztFjI3WO9IefvfLKAZ8t35UhXoff3UrVi7j+/9t+uSKVXbdVWbdVWbdVWbdVWbdVWbdVWbdVWbdVWje1nAiH0n/zFewkgWXWVSFXiUY1+InHQYZ1lllwqISNqZy9eYI+cE1v7EqFUufB5GMAiyWTJUTJjZkTyRVMTr/wWiqLwPM9krkzLS8x3AQ9//e/+NgDg8Tmjtw7JsJIpUjl+fyxRurfWJCr5r3zzQ5QYOX3vF4VUehrL3x9/+fiG4wEkxootk93SyKuSWx4dPTdZda1DVZ4PK+0ZrhwmaU1ddRTFRsJXOS9U6s+OfIMUOWR9v8NMx3w+NyR9DtElyn8w8yNDLJnLkweDWav5fG54LWrsg9bwT/pjPGENv5JtNZmVPzjcwhXRLprZ298/hMW69c8+/QkA4PRUoqRWYhselwnrtS+Jzmk219GgFKDKUCrHRozE8M1cMos8Y4ao2+0a4uwCM6L7lFpdX2sYZJASkCriwfM8OEbGWSK0j78URIidyqJBJIGijjQDf2fvAKNAa8Dlu5VbaTKeocG6fOW1ULLjSqmI6USJTikdns3eZDRDraWX+Ut7DqZEIzTXd/hdlGI/fYkas4SaxXdICFqqVI088mImz1YkQTrlGIJvlYF//lLmZhz4cMmlkmUk//JaMmC9Xg+7uzKmRpKamdJkGRspyIQ13lPW7tq2ayR1NavbbKq0bN1ww5TqJI+zHVgktQyYqVFk0TyIgLR87nf/ULhvfviJrL1tJ8R/9et/RfpKAvkuMyJOO8S9dyT7f2xLn+/UJVP37NkzvDo9BgDcuyfky0cvheS10aihwQzRhJKzKSLRelfXuHco32Hz3YtEVVXfegPHH30MAPiD3xWE4lus5R91eobjqEqJeYucFHEMhORIsom2SYopHL2k9C7J5V8R6dNuXeP998U+Kc/HCWWIkXioco/+qjc4DwAAIABJREFU5CeyB7/2rnzWS2IEzFLtEXl22ZYsdm8UmBrmg1vMkixuOB60Bl9RRBWiq56+HGKNmZosDaTy4sSAQcFtbohtKZH4cdwboMnsp5LJBjwHDvb3cUWixqWSsS8jnJ7KGVAscq1yDbWvLxExk7RBngi1k61WC197W9aArkNFPU4ncziU+hxx3U6mmsWOkKZNuSKqQRExtVrVID6Ur0nJx0ulAoZEaSgvxg6za1G0wIC2SPew2uNwPsd4KuN9QV6RgvIthUvU6mJjB2MS1O7Ke161L3DNvZqlcEC7e23Onu118makbvg0suS0s5Yyp4qWCaMYH31f1u+H5LLS1u91DWJhlzZJZYttz8GACFQ9G2NLufSmcDgXe7uCZEyI+NExA24IoCOL4++ECBYyJwUi8TrX8u6Rn2A2pm0lCujOLdmTrasLDHjuVXiu3757x5DiKj/eWo3cI4sEU2YHVVTBWZexvrw8N+tIOYoCcg0sFgkuW7JuF5QaDslP1FzfMGjnn/zkRwCAHMl5P3z7LVCXAwmz/j8kd0g2laBaInKPY725IYi+j374KSaUK/aZMd66LfNXzFYxVble+gqKQHvy5Al++Zf+ZQAwZ9CPvv+x4XLUzHeRHELTwQxp8jg06Res0W431qqw6KOpr6dnZCadgk154+oGEQEL2Qf1Rt4gtE65lwLOQy6Xu+F9oL+jCK97a4cGhdnm3A+6NwiXgOdmhsjOLPdS7M8MF1hnoKgeytzHSxwfC4r41u0DAIDrWpgogTMRBQck+KzVS2ZtWrT3Z1xfneEEHnlP3rora9v7CmfikpnsEfeJkmw3a1U8eyJ7dmHJGC24J6rlLSNdPZhS7KAn62zv9hvIpGR/pGmPlaR10O9hm3x8/lx5T8Q2LSMbJVfGcUkWEPXZoig0HJJ370tm++EX4gvl83n4RPcpavzq6hoWEQFr5BqpcPzjcIExBVGslKwdRaw5joc5EUJpIp5m5AbK5jJGej1Dzp0Zn5tKuXCI0CmRRP3xC/FJa/UmCiRNvprLGN3fkrOrd36FVxdyXubXpJ+NTTmDRu020vQ1Ysjft4hMDJMFhvS98qxcUDSo57gYE115OlQuzx4ePBB76BC1ogTy+WwBM3K1qO+fJTq4UW8ahHeXfFw6J+m0B1tRbETyJhRPyebqmE3JezSSuQ+4BoaTLnIlGavJnOiSUPb13J9gf09QaXf4c8h1NfOnCBUuw7nJ0TdZRolBFiofj3KUWomL4UD+jeBNJJUStnjWn7wQHtcyuWWjKDRo+DfvSiXG44dfmnc/PpH17rnksuI5dXpyhIM9uV8pCfKLZ+LveF4ROVZCTGg/KvR5i5mC4S5b3+A785wqVRoG/amI+Qztx9n5uTkTY1ahGNGDUhUTIkEnXL953nfjGNggGrNL1Hm5lDM8ZsfHguQKDbdsGbwOGHGQJn1D17Yw68kvJ1xDfSJkFs4ChZKiN2UuykQVlvM5RCQ6buTEbg+upZ+RY2GRI2F6wioQXV9TH2dX5OAkT/DWtvh4Vpygyu9yQCQYfZRFAgQ5cmLy3m5xrTYLZcwGRA85Mu96fixt4LpL4SDy+LhZDx7PAtunGAZkvl+dX8NiVUt/KveWNSJZ7fkCDZ63nQF9BFacZMtFpGl3XVarOEUKxIx8BCTxtmzyYnEtrVX3cf2C98YGOVeJ+CxWSyhVZa92idS0ItnXtXwVEf3YRy/FjipKdWNnx3D+bpG7b8lz2wmX8HiH+g//xj9eIYRWbdVWbdVWbdVWbdVWbdVWbdVWbdVWbdVWTdrPBELo1/6Db0snljFKzM7evytZV1Usubq6NgJgmkk19cqDIXLM2Dhk7rcY0YxtBy8eSiTZJlP69i3JXlcqFSPpqvwSX1XVUSSLcp9HLhVYGmv42//H9wAAv/sRI8k+ZeIWQzisSU6F0qc3GhL1/DPf+BqKafmuN78lsoZOgRnc4QgJM+YxWc7n0+D/IQes6BDbFrUR4Ca6rAiLBWKDpNBaSeWMSRLLZL5CZiPHzDw6UWQyv/m89NnLKJfCmuEF0jiiqjWFUWw4NULW1+rfDwYDw4Oj0rpHjGQvYwu7rCluXUoGxWOG41vf/AAX5xKp9ZkFmk1Dk4XRjMiYGchyIW+QPk0qQ2h2Mp1NIZyqFLKMbZXM+vP5FFGi80zFLf5vu93FNdWxKsyO3yW6wXVsI+GoyACtL6/UqsiRP2BCZEa1KhHmXn+EMdVzckTNpIhCWgYLRI7KDDJjz+zfaDQy/6ZIrc51i99dNSpBisaq1hqGF0T5JhQ1k017pg7as5ktYfYi7VpYMsOQEF42I+dIGEYYTTTLWmO/yN1VLSHF9aAylCeU/Z6FocmkhuPX5cgtxzL8IAkzUaqoMp9NzO9KNZWuvqnl12y6ZqtdqjMcHR0ZPobpKRFNUYKAmbgaa9BnynWUzeNyLOvj9z+WjPs1s5+/+v4b+At/5lsyVhsS3f/xJ58DAL5++Dbe++BdAMAwK3M/upD5+qOHnxlUn0pxbm0SKRGGuLqSsVGZ3ieUfM7YLta4VkIi0L7xCz8PAPj0889wfiK8ALuUEF9AnhuMp6YW/JLroskxfnV2hhTXtqLRCmslXLeVy4BqGEQwHB0doUHp0zWik06oqtPqjlDiGvOIavjsx5/Kd6Y9fPCO2DVQgrZNNE+9vm1si545yo1i27aRbb6RUpfxH3aGN6o2VBdR/p8XL14YlGO1LOv4wV1BY80DwGaWe8hMm9LkzCazG46dPuvf6zWouR/y3zpExmxtbRkkgdq8l+QrK5fLRgFPbbTuzyiJ8fM//20AwI8/lTXTYbZ2Z2cXgz5RJdyL+u6ZTMogpXS9KwKzUChgY1POP0Uf6h6ZT2c3PFCaHWeGbjIaYJ1y5FOi/F68kDnd2do16IcREWt52q9wMcdszj1HROIkmBtkoK5HRaDato0q0UZ5jr+O8cbWNh49lnWuPEuHe2L/vZRjFMuylqyL6XhifndF7pRzrieVBC8Uc0blKorIC1ciemA8M5KzU75XRITRdf/ayImHkbxrk1x80cLGeDQz7wPcICEn0yEKHJtaRt6zVq8YJJcqXOpZeX56blC96luAv2u32+aMunNL1q1yQLXbXTMeuj5ohtForKFDRNK770km/Mlj4d7Jp2zkucf7XaqykCenUStiQsnwdUqoZ8i31LruYcpz7MlzQUi8/f5bMlbXHaMMViFaT3kc/PnY+CibG2JvfvzxEzRoY1NpcnpwrS4XMWKid9YVycg5ubW7j3ZH+vz0ufgIlYp8Jl5EhsujvkVpev7d9nbZjJWeCVfkC2k0Glinrbx8Rc4dlbRfRDcKPrRl6h+kUinznWrHFeUxGA7R4Hr3qICo6NPFIjDIgO0dee5sNjbrKM8z//lzQYveubMLL0cOG/IjqjLNcOKjYHGvE20xIOr57t37KBTpU9A2KCrbtYElx9jNy/t0yac2HS7RpG3PFKnmdSa2rLl5C6Mp1dBU9VVVRDvXNzLalI3v98SO5PM3EueKvNazyLVdw9mkaPUq53Q6nmESkjOR6MUgXBp1totz6VeRqM/D3ds4p3z5gHxoRy/kM56bRSEv46Z+mCqQ6d4HgHyKiH5m/Lc2m1jQ3ykxu35+JfM3nc+Mb/2yI3P/4ZuiHjhq9XDyShFCVOyifTzY2caCNjbkmTohR9hgMjYIvO0tsX31mtj9zvXA+O1uiZxR/SNMRvKO+wcybv2urIFlZCGbIV9aRpV4yWPiL1Amz8kp5cSVh86ybzjwVM3TWsh8w4oMX5Ii4HW/tK+HqNeIPCAX05IItnTGQUKeTO1fhoi345NXGPMOUCAyupJX9d2M8ZFV7UqRucsoAel00GnL3pgHMf78nxNE4uef/QAA0OrIOb1/d89wEKpaYEzUY4IlyiXaDRZgvHghd7etrTVkWAmxVAVOnimt6z5soob0LrSglFkhkzXKvyfkvyvTN3JcoEc0yRrtXG8oezBGgpDP0QqEF7wTFey8QZw012n76JfMZ8FXVC/l73Z2NpCnep8i3NSvbbW6RoVuQGTiLd5fMpkUfPq4FisHzsmtO10EKPO+kxCtVS+rohiQob2fUIVOUXcLO8b5WL4jw4qMLe6zeOqjTYVh6B2nQbVU20PrROzbZCj789GX4pvYqTSKTdmXb9wTlG5Cjs1U4sGKiUAi7+uY/D1RMkO1KnajRN/QD6dwPa2qkfHX+1IYRmavt/usthjcKAUqov9SOfdU4TGdMrZvI80qIao+7uzsoZJi1Q79gisisB07i96pvHOKwp0V2uXBdGzs0/WljKdHJNOd3TuoUh38ybGcIXrfmoZzs66MAjgV4UpeBiERlP/+X/uHfyJCyP2TfvH/Z9vbIVzPSyEk/OlcyVrZPM8zEr7ta5KY8jJUKabRUMeBDnuPAYD2VRsBHdq72ySEqpKU7eoagDgZb70lhl4DUEBsnHE1jCMa9XA+QY6Bpznha6mEZNaJbTawOkoKI+/2h9h78wAAYJEEUZ295WKBgAfXJaU1tzZ2jMOiARCFO4/HQ+NMaimG/s5LgHhBSH14I+Gon1GJbb24FEnm60YWMkpSycOxO5DxabXa5mDQ5ylR2dKK4VhKYKuXHPkMHNtcAJXMsESiz2EQ4Yqw/kcvxCA2KO362aOn6NBAZbmpgmAJcltiTGP74D7LBBLLlG7VSapWq5CsK4kwn8oe0NIUhWr2hiM4SljKnwFxlvNghoNDvSzLHJ6fX3J8ikbC0eY4rrF8Ao5tytWUGFuDb14qjWbjdclPxv/Q2KhhML+BOQMwErG5XA4p7/W1oBFS13Vx+7ZefAiFTrtwIXOpxNb9PmH0mZQZK5clXEse5s16DWmubQ281qvy8/ziEmnCe/u8rE1JAN3v93GbcpxqIJWA7emLF6iR7Hf9DiXcZzdlL2PCbKsVWRcqo4gkMU7MmDKuLcq+JkliYOZGelalP9sdfP/7ErD1B9LfcrWKa5KsKnzcoq3IlMt4xcvTjHNpE07cGU7wzW/8gnxumw4jD9Lf/mf/BL/1v/8jAMCv/ut/QX7HC/K3vvFNPH4sgWjdgz4vXLZl4e7d+6+9T4aXuHA2x3PK7b7/tQ8AAJ/88BMAwEcff98EK3pb8ncLlpxurW/A1UAhCQ57Yy0XiIz8dmGjxDEoAFpCx365LA28c/sQjMtiyP1f1ICBH6LTkWfHLNPQ0l07VcCSsNQ+y2RilVkPEiS8wO3syBoYEm5dLOVNSaBFmd6IjnS89DFmEHEaylx+/lQcubOzMzy4J2vs+Ytj+U7O8cb6NjZYSjVnQHnEtWrDNiWZaRL6XbXaqJblv2MGs5Tw2LUts6bHlODd4OW32+2i3WPpxFdIO7X9X7/3zwHclDlr4LXb7cDhuClJ9uNHEjS6ffs2fAZ09CI5o10olXMAyzvDGSVkcdNy9C4CGkqb+/q6dYGZXupJ1qp2fDabwScmX51ylU9f39xCxNKji46W8zVR4aU16ypJozx/gRgXLZkD+nsgRS8c18Ymg6IqBvDsSOZyMpmY/tzZe8A+yP6+s3GIHH+3c1vme+jLeJyenZjESLSUse2QlDaXyqE34VjNpQ8vT8W2bGyswWXZhJbLLuby/17aQ4EStAnrSmOPJTcbVYCyyg8fMhnk2tjblTXduhS/5fbhgYxPKYc+fQR2DyfH/DsnhayS5PMM0OTH199/Fz/+0fflHwk3T1LSJ8/zDFn7d7/7XRnrNUrRWi6GE5YxaaCmoXM6RJVOZ5fnp8ezf+/2AT7+gdjMO7flHLtuaYlFGhGTYgd7LE9cyt+Fec/YX4/iD/fu3UN3QHJPJsfWWUK6WCyw5CUj4soNAwZ/513ELglO78lZMqCt7XUGyLMcJEVyeZUH/uKLL40vo7YlT6c8nAZgBZKB2utn05kiXp7yIsa9r8HMeRAiR9vwnIFTmzSxgyBBRDngaCz+jpYXLPwA9Txlz0fyvDh2UOVF+uhY1ofryD746ect0L3BdCbfVWYJyP7+PgLaCC2/VFLZ1lUPly3ZQxaTfZpICRYBUrQbDm8Luj/n0ynaDEjUY5WzZtnEi5fwabsCllR97QPxi8uFJWZMmljswwbJTGdzH6mCjE2rK765EvkXswV4PCdOWELevZDzI/RD1Hflcztbsj6Ojk7x+KEQqZYZXFKC8qdPn5tytTsfHEhf+GLTSYhowcAwx6xGkYl5uDB+0eFdFZ6Qfh5fPkWTEuyNQPyckMES10kw4fyqpHeLF7RSOm/Kfga8F1g8NOOlBXBMHZfWmWfKzvYajs/k2RekRHApq12vrcPnhXrYlv7ubx9gyitJymPgpEoi7UIdV5eyj3s88w9vy4d39/ZwciLPqdHfDKObEm2XZ85z2t9iTmzoMpnCD2TvrjdviNkBoJBzMWFQe8EasKVF0uBCCjF9kXxOvnvO0vy33noHZ22xgUcMos36LAOq1Mwdp9umD8ZzrVZroFaVMX77vQMAQNQJcPSZlIHVGZC3K7yfdYdGlEZ9eaVcSJIlYrwu1HCfdwfXszCdUlqed68ig+hu1kUmL9+hoiB6l0g5DoKIQREGyGNX9s/mZhORLe84Y5Bi/5bYgPOLK9Sbulel7w0GfwqpnLlvdmhDbb5LppzGrTcliDgcyR7yFzP0ldibt3m9bwVhiHVN1PCqv8XgOJIY/bEmS0kNwcF77977aDOgPhiJ/9dm4jebSWFIYERIe5BjCWMqn8I+k5DTUOz2Swb218sNOCzzvmAyNLYpQOTmjHhDNiU285sFsQeOlzIllhlH7MGIwI7+0DfBXJ8lalaOie58jCrPVpvntT8emn4FFAq4Igl1o1RFek5KFBIzZ7JcQ66Hacy9tyF9nvNMdmDdCBW9YvK/LD7vdDKEPxX7sd6UuQ9nNMTOFOO57HX1fa9H4l9l8xl4TKSEFD1Jcx0fnRxh0CatCInqU+xnfb2GeSzjMOe5XqjIOF63O4bg/v+rrUrGVm3VVm3VVm3VVm3VVm3VVm3VVm3VVm3V/gVrPxMlY3/31/+NBBCJZ0UlKApCkSdxHBs5as0kaoY0l8maLILlKnRMIrWlas1khiaMoteYJQ+DCFNCDPW5ilyYTscGarpgZL1BSHl/OsL/+UxgnH/zf/p9AMB6RiV8F1gy81i0JMp8uymRyjeaDfzpX/waAGDrnkQMP38sWeFbW5vIMZumpXBRGJtor5ZbaAbM92cmCq5Ik6srEo2V6ybKrKTUClsvFovo9CTqq5FyzWxvrG0iIBxPIaX6nZYNlEiarNnydpuZnjC8+X5m1RS2mCQWzkkQphKmFiOiPoAJyfP0HbS0zQ8msIiAiRjBnox9g2RRiH6diCIbESIiMfZILK7Z6J989lNDnKvjqP217MTAxbVpOQkAzJSUi1nFIeVLG40awBKkFMlhVYb1yZMnyBUkml1gplLh57N5gPUt6deECK8ZkRKba2vIsIRwzmz+hBnjXm9g+qJoL1MeVqmgRxL1Zaxk3gGGiojgPOs7p9NpjFnuBkJ/FUW01qjJu0FITwEgy3KZ/nCAAjN/SyI5PKJsPNvCiN+ppWn375PweDLGfK6Eu6/L+7opD6enJ3y29E+JcfOZNPIsoZgEMlYx3y+JLTPeLstPlETR8zyDFnJrRLp1hwaOvcY14zIeXiwWERJaqfKw/+D3BJXz6W9/F//WnxeE0Ld/9ZcAAN/9+A8AAJuNdbzBcivdN21mmMbjIUpEhdy+LVBXRQp1u11j33yuCy2HOD09xb/3l/9dADdou9OzV3z3GLu7kqFXSemEGYRcLgfbZrksM3oKWQ6CBdpE2z24J6W4EeZmXaisu5a01KtlFAjTnzEz9OAN+bvPv3xhUEApfqZPREa1VEaX5WpKgO4QpZByMmbP6lgpLDYIAsxJbKhE4SrJC9tFh3Kl7aGsmRcnklmdz0Z4cFs+V8pIn7TkOOpe4c/+6nfkc7RvT55IKcxoOMXmpmSytIwwtoAZ7X2Oe1bnKJjNb0o4iYzrUoTAcly8/97bAG72kCKE2u0WLNphRW8q8nI0GGMZLl77vBKYDodDY7dt137t78IwhMeMl9okRc6ORiMDKS8UZd+srcmZdXp6jFRG/lvPOrWFL18eC2IMQI2/05K1bC6DBUtc00Q3JY6FHu1ggftT++Jm0vj8yy9kDsZyPmxsij2uNpoGnTojubbalmw2j1dnkjm8YBZ+s86M/XSCe3du8/tJdEwbNfPnN6VVgaK3JNvY6w4N8kbRtBbX/FdRNvORrDktbdnfOzRlDMOJjKeWB29ur6Gm0O4+bfR4iDWiG1u0mVpivLQFgg8AV5Sb3qQctmO5hsyYLgPOXokt3N1sIpjJGN+9I2t8NCU58jzENYnS3/+6oAjbHRLCt69MCeg1pcaLLLV8cvQUJRUM4Dm7JIJkfaMJf0bUKDuztinr5eq6i2sSXOu+MXLclmPQK9dEhm1uboAq7lgmMkZKtmo5riHPPGNJ0IMHUi53WNlAjuvhvfe/CQDocnxGvaE5MxKSANtEM4+G58Z/UN9Q13a/PzSlTkrQqSStnlNBjxD+BUtfAtaoOK6FfRIIP3soZY6KoMw21hCxxCmrMtMkeN5oNkxpSJplE9W1Bmz6kJ/8+CEAIOSYedkMBkPp194us/ksZTw9fokS1/k6S/Bs1l3FFpBX4nmieUKi5jKeDTeSZ2eLRLwSkZ716pixnKnX5/lO5PZkLiSpAHBMEYEq0Urbm2umDC1g6cEl/br9W7cR2LIH1W6ovxX6IYoZ+e96hSXTlJaeT33YGZZbNAW5MOiPjI/n80zQc9N107BYOlFu0lemHc5lK0aK26PE+QVLvxzHMWV/R2dim/QcnI5H6JHEW8/rGgldfd/HjPa+w/nOUyTgYGcXU2bhW0PpQ4qo7kq5jDIRxtW8jOfZuSCHIywN+jIgomkRyLs0aruocozaHbE7fthFvihzHsXyfup/FwolLELZq2O9t8zlDFpf3zQk73pval3JOnv77bcxooy2CoAsKGnvOB5++omIRagNfHBPylI9L40pqQn0PJsTIeOlbJQrcl7ubRGFQqDsMnKxSGSvhjFRdt0LPjcyfmxMW6GS74sownQi/51JyziWkcGY96Mmybw9otNOW6+QYrnV3t4BAODFM0H3jSZjWND7jhI5yx7c3FozCKElydcVQby21jAl5yMiL2v0Hy3cCJOkeHZcEUXbvmwbn6Gx9rqYTq8/xBpJqD36rqbyIG0jIPrSJnExXRSEcYIz2s76mvRhY6OByaUi0F8Xm1hGQL0iZ+G4L7/bIKpt4QdY8HwZ9OkzsKRuY3PHnJs9osHztLUX56+QZdmwStGXS0TuTHtAWgZES6xHvHMvpqGZmynXWroo35nxUggn8sBsStZVRBQjAExok33e9x3CWMr5Ki5bsvdOLmR/1da5hzcKWNBebeTpu11do0fC7oB3vmuu5zf27mO/KmP1qvvstXHMZFLmzpwiSkx/V8hkDdH/oENkXCzfebizAWsofZhN5X2uaaur61lkLJamETnVou1c31gzvrJHAYYcpe0LXh6Dtozp6ZnMTW/AMt9KBnB13crZXa+Ij312dIFxX77zv/9Hr1ak0qu2aqu2aqu2aqu2aqu2aqu2aqu2aqu2aqsm7WeCQ6hGaddCOg+HdejXJBnWzOPm5iZKhdcRLZpxmE4meEUZYS9NwkYSduZLV9hkVHQWSmRteKRcIgVDGnxxLtmOKhEFURTCYzaxsaboH+lLtVLCgweSParXhNgME3LEpFOGuDKItIaUqJflAlNG8k0Gi9mJzK3bWARKriZZoSiMTe2z8hlp5ND3ZwY9pBF25Vvpd/ooMYqtXDH6s93tmIyqRjk1Ut5qxVgjyZzHwvZCUX5qhvqr31VkZLiULSAk0ZoSRrqMMNcbTUOkqMgCRXnMZlNs7xE15ChBtUS7p/OJeeeXRATU1+qIWPu5ZsmclIjmGXR7hlMnRQnDZ8cyp2ubtzBlhlLXjspANxoN+Kx1njFzUy7J3xcKBTieZohJtqrIn2LZZABevRIEx+cPH8m7uy7GExmPR38k2UVFv4TRwnBKKeGromcAx2T4U+SUcbkfviqfq+OiGdPxZGLmss3serlax5u7klkejJhZYtTdsiwz3hZlhG+xnvr6+god9qHOrNpQURGjMda2iVCbyTwdsQa92ajBY+heJX9fkgtH+wsA5cYm+07urGRpiJUVnac/E9tCipH8elb2hI75+fk5bM73gNnyNNFKjXrD7I29LRn348hHhllWlSa/pkRx2c4gZBrm+z/5sXSU7/AL/9K7KBK58cE7ggT5zl/6V+UzuQL+19/4TQDAR/9UUENVZq3WG02D8vje94SbQ21Zs9HAJXmSBkRa6Fr4t//yv4M5Ce8+/lRQSk0iafb399EmamhM5EKZqMLxbIoeET6t1jU/LzZqY61pCL8jw7a4RId14pmcrGlFjVnJEpWSrB2Vde/STm2vrWHCddfiuNc4Prl0GrU7d177vBKFp0sJNrYla6GEqgtmgoulPJ48FV6AvT3hDtFMc319A/u3JZv78Hc/AgCUaiSHDH30+A6/8KtCNKmkpsOTrEHELclho3u4kBljGb9O/tsfDVGpyHpXbojWldgdP4rR6sq4eyndN+C4D3HNrN2dOzLePu3Iwh/DJgdYntnjOdED+XQK5fU19kvmzQ+UGNc36E9FK1615Bl37941a0aJRIc8I754/NSQEu8cCNeOS543J1XAfK5ZYBIKExmQLaQQMTt7+oqcKrRzQRBgnYSKXfb96OTYjFGGiL/ONccnn0U5y8zolqDZdM9OxjMjJZ/LynlC5XAMegNMBiQbZoZ5PlbEg2dQA8GIfHfKV3P3LlpEsRbJQ9Ll2Z+2I+TItxGE0vf+hESftabhK6xtlvkOsmZfnDw1PDV6ds054WfPTlEml8/mOon1Xcs4UlkiMRIvG5cgAAAgAElEQVRm/23HQZ/fu7Uma3vJT3/00Q8MAkbRF19//z0AQHNtE0lETkCShuZ92buZfAaVwgEAwCFXYO+aa8Ir4kcfC8G09qpelj7du/cAfWbClYRTeZp+8ulnODyQ9e/x8D4hcXqcOIb8VLPWGdrQOEoMQnNzUzKs6YyDFyeyx9XmPXkqWdet7X14joxpsy7jcXwi44NBglxB0StiT2Nm6kv5PHxytbhq8y4pd+xFRiY9JkqmSoRWOu/gqidIGG1DyitnnRty7Al9ogKz2NVKBVN+bpP8Rz4RupNxBw0Sl+d5fqYVnekGsEhcfEa0aJyLkVqIPTu4Q18okZ+TaYiDAzl7nz4R/6Fakvn+9jc/xENKZXfIV/jWm2Jf2902nhyJb5Fl1nqL63Ey6mCdtmUSEAXAvZSyHThEBS8iikCQX6ta3cHUp4x2Q5BgG02SWg+6yKYVeaDnvJwNJ8fn2Nwn+rAqa0jJwZeJa9CElz1y/tG3bLXaRhL9cF/er1Is4dEXglDZJHl4hT75s9Pn2NkVG5smP3spK3txOBzh1p70OVQi3Ao5xXptFHLkHrorZ3ifHGZRZKO5diB9VcTmUvqXLBNU6ONmiyqXTh7HfAFDrodNckdeXYuNHllLdNuy5tILRZvSd0siYzuPjgS94rpE2naPEdFO7SoXyyyNNglslQx5a0v2Tb/fRZZ8PTv0bZ48E7TAk58+w9YWScDJwVenX+uPQ4w4F0X6VVvVPMdxjg/f+UUZN5IGe0RFDUY9ALI2H5Eb5t4DQYGn0w5m9KN7Hd5xuHfLuTLmRNdcEGk4HCjRt4X5iDyI9A/SHONnL5+hROL0NFHngyDAnBxRRfomI/KQTsYz1MlndkH+pDQRfCkviw7RZBFt5vsfyFrI5lKmGqRMX0YRrI6dMshyR3mhuOeD6Rw1Vj2MaD/qFDEoZvPo87xUniu9ZzUaNVyei29RJQq2Sk6+2fUV/AUrAUhirXyz2VQaHm16SG68dquHHGQOpxN5P9dV5LEFf0L5dl/e+RXHxYoT8AgwSLBclih8y0GRPJs18kj1e2K/Ymdpzo4572pz+qSpjI08/diAd41IhXrSWYS+op5Z2UI/cjKdIuQ5tFYltyPfPQjnmJLfsLIm322Tv2c6O0e6IGP79lvi72hlwNHxC9h5macGfY1MroBbG2KfJrzbz4/ljLtudVGMZR8qgnrCdZVKZVAkIu6Ce7HOO7eby6FL5F0Y0d+kb5gpFTDmnMSe8k5Jn16cHeP2DrlkubELFa2umRnuoF3et3pEQi29FCJLxnF9m9VUNXngZefS2Jl8jecL+fm272zj7Pj1c/D/ra0QQqu2aqu2aqu2aqu2aqu2aqu2aqu2aqu2av+CtZ8JDqFP/+e/mgBSt6mygVrDqOiC0A+QpjyeZp0s1oLOAx/nzLg7ZM8H6/JKpYKpZxySD2OhtbuLhVHKqpEDQFVg4kUEV6V2yE5PEn349gyXjGD+13/zdwAAvWOJ6rqui7klHyw5EmXdKMszHjSq+PZ7oqCSq8lztF703fv3MadUdrEh/fXHc4OW0PpTRYI0m3WTRVe0kXJeuI6DNDPfp4xEa03sMrlRT7uRCpXntS4vkGGEV2Xjdazr9apRLlM+BkVAFHNVBKG8R5rRc81Uw7YQJzeKXsCNvHXGAQrK18OMb3NDMgKJvcQ5kQ4XRIutr+2gRQlvRSIlRHZkvIxRKBow+6NjVavVYFGm9JCqYco3Ey8is1bmKgfPbH6hUMBswfrrS5WB1s8GBmml79ogIuHV+RkeUwmpwXX1i9/6thm7E3LCaMagVpYsVL1UMRlLVUfYP5TId65QxB98T1RnjNob5+r09NRwPmlm3/HSmFMtTdeQtmw6ZdaKzflKad3xcmm+38iNktelXKvCIgpIORRSlOuslMqImDFIMQOg6j+d65ZBM23vy/tcszY5wU09+h9HsxWLRcyIfkjL1BrEXLFYNOu2SG4r5btaa27g9FTW/XaJ+9ly8YIKF/q8NWa0O4M+vveJIIPOmbF//+d+GQDwxuY2XCIJslQPyLN++6cPP8d8QFQf65SVj+Gjj/4QB+TBUd4f3XfNZh0d8lnofFm0w+V6DRNyEyjKRtWCOtdtjIg+2dyTzEGOUrd2ysM1ldLGzAZ/4733pU/pDI6ZsVeEXaNSMiptOzvSzyMqdaXTaWxtc2zaMqY58pDkUjkMaaMHzKqpra6WK5gzm6ZrR1Eodtoy61C5izTzViwWTTZTeXgePZJseTGbwsah2Mzf+55kjkvkZ/hL/9p38N1/+PcBAPsH8vc7+wcyBq0reOT28jKvr3VECZ4/l+zsOhUuHz19hq9/+Msy7j2ZG5tj9fLlC0SsY9+ilPSQKLrpdIr08nX1kogIr/X1psk661o9ZUYqCBbw+TlFRSn3jW3bX+F3ozISM1+9Xg9dIsEcZuoUOTgeT1FnRl+5mHS/7O5uY0NVY4i6U7RHu9My3DqKuFQb3esN0CzLOys3yvnlhZFhLVC2fDaR5xSqZXz5lNLyJJLRtbC7uwubZ5WeCVfkuRmOp3jrLZE5789l/Iuct3u39/Hk8RfsT+e1sSpmCrgggrRDZFCO5/3Un8MjwnVElcLBWDngGgbt1ZvKPqtWmY12swiYge1R0eP2vnAYTUdTw0eyXiN3wmCICm3K5YWsX1WqLJarOCNqc/dAzp7f/F9+W8bftnFwS773imjFZl1sy1sP7sElKqpKOWGVsm5ddVChwlOOamg8+tAdDdChxLjSMFy15CzaP9xBlgg6VZx79EiUEB3HwZ1be/w7WevtSxmzerOBF8e6XySr2aSqWamYQ8y1sqAc+WyeGMSOnrenzEzbVhZL8rWpfVMFyUYujxT9MeVsuqY9qBYK2Cb3Xqsv43l6LudocaNh1PFSmomlr5hOp80e2KSNMXlQf2r2iaJKFGWdyqRN/5R7RPfkVqNpeGfGHOuTo2MAwLvvvouQyFVVpR3PpiiR92tJLpUkkfe8ag0NSvHePUH/KHLVsz0MiOaxqVLlkN/pyfMnSPP8b26I35GjMg2iwHDXBFTfyZL7r9+OMKWscr1OhOFIVRiXWGvI52KiMO7dlfXZ73eNL6h+jyKHJ5MJtii9PiPfzyXHNbFTGPC8UISm2jvHcZAjmjChP5dPu4iX0r/dHbFlQUT+xSBEQsRHjbblRvE2g54qp3qKNifqYL1uOK96gfrPMmaf/OhTOLRJijBXjpJb+weGL2ZJhKCiW/OZzA1q36ckOtFpuWIOMXmgNgty9qvPnCvmsEMk0+dfCH/ohOdoghtfcmdD/MbLqxZmRHf4VCjK5+i/rDdQr5HnjkpWxWrTPK9D9JUqJm6Qt+bV+bnxgbTiAPMbHrsB58klR4xLhOJwPMH5GVXWKqqSJesfiOHP5O/u3zkAAITkZ0m7njk7xjwn7j14FwAwGAzNOFq2KurK3PZ6PcTk2gopH//o7CWaVAIsZGSe9/d4bnq2udMsaYtCIn3sYtZwgOla2NmVvzs7OzEotGpVfXh5z0l/iIAXvx36XHpnrhTyN+csfbZNKpvmskssiPLQ91OfezyaGlVPRV6qdnguyRoOTvWZ54FyKiVwqOg1nctYB9EMazXZT6SFg0s0uO8HyHiq9kcE2ZdyNmMZY5P2Xm3Zkn3qddu4dSC/G3LeAqK/CoUCEiKWVJ1sRJ7TKA5gO7ybELVYJpIymIdIcW4uaL9jTz7rpi2UiOzyaav1HHS9BH4i32FZMsYDcp+5toeI+3lKSlQk8v+e68Kt8SwgQuvi+Ai7B0QRUqHSoV/bv+rgkFxziS19HxAVnvZSKFSVR5X/RiROIZtDSD+606a6GxFaW9tNFLjmJuTxSri284UMxuSb8qgSliYCM532zJmlZ+uMfGApJ4VcWu/M8n4eYxGzcAZ/QW47xjHGQ9mDtXINPXIY/tW/9sM/kUPoZyIg9IO/858mADCezTGfyYtX6HBmSRDX7/eR8PJTIHGxkqjOAx9Er6JS0QUokzQeDE2wIl+iPCovCkmUGMlUhWyrE1ApFUxgQAMfE0qgDhZdjGoyKb/x21IOcvRTcVwsx8YcsrGaOcqfF2UT3y4W8K13BR48mMsKfusduezUckVcnvGyQPh+o1I2BMJKrKdGolwuGkfCyJfzwHYAU+aiMM4gVqOUvznQWaqQIyw+Wi4xYVBKS1hM8ChZwo5lHekhrM9bWi58JTacK7lj1vStQFilOmIjXqYalaIZf5XKTRh8C4KpObTyHMd0Oo8J5+LJEzFsJZJQJnFsAmMVwvkMiW+/hxlLxu7fkfE/Pj6WHtmuIQDVso4ZSeQy2TRC9ifNEq5HD6W0ZT4PsEYJRx0jhdHnigX0KA8+GVACmEbUdV0slhqQE4O4RdK5QiaNBYMPGtDr8Hscz0WJpRca6FJp6iAIjKy9IRwsVZAr3MhKS58VAmzfENLSuVCDH0WROfA8GsuA47ixvYXHvOzl2QclBi4Wi3hFyXWFRM+nN6SQGxuUkeQ4tEhInsllTYnHks/VC+Tm5qY5cK950dI9efv2IZ4/kzIEJa9u0ZGu15sICE9dUPJ5/+59PHl+LOPGsdo/lEPvDz/+AZ4xWHT/HXFUSpS23NnaxDc/lDIOlYt+dvwUAJDOZeFTkrtZkTlEIjbpt37rt/DGG0LGmONe0r4niE2AIMsDvv3qgv28jcdH4nQq2fkabeFsNkO5SZI4QldvkXQ0V6kY0toJD4ES//7+4SFGE1lH7QGhv3GMXk/2xDodxYsrGetSqWTKUENeaiYk49usN1AgvPmcRNVK3rf0Q3g8ANUm2Xw/1/tKIJ9rQC8ItuNhyGDxzo7MSYulvKNhF+NQ7MYfPZbx3yPZ7X4ti7fuyyGuRJ2XDJaULAszBknvviE29s4dKT17/uS5eb8xnZlcuYguiTw1uGrkyBeBWdMvSYAO2ppUJo2M97oDd59lc5PJxAQpVXq9VJI9fPzyFA0GaO7elc/rZ3d2dsw+VqJrHc/FYoEZS0wzDJhcEwp/dnaBHMv/9FTXUqt8Pm/OuJClzCkSNPvRzPR9c0McJl1DjuOaC646vZZloVmRy1rs0+llAKRQqeKqK3t7qaSiDD64joUUAwVKtK5r4o8efoG7d+/yHQmX3uO6vHwFNy9/p2tObb0TWUYuF3TkLviZVC6LkOdexPF8TmLxX/6lX0LKIRl99xjAzUU3m8lji+NwRqLrUkHsZJIkpu9zvud0OjUE1RrgnZjEVow7LHnUMwskzswW8njyTAIyGTqY11eyr/PpNHJMbmmw7R0S4Y7HU1y25Nn1jRp/siwvmCJNcly1gRcsX6nWSmhRSl5LkqckCF5GlvE1lOAdkXxmfXMNDknbBxOxH3BJCp52EYUsD6AtnE0SWDzb3n9byjIsBoFePj9HgcFzvTQU6Jc9e/Ic9lcoAoAbsu1cykORweVTynVPeAFtzSYmIJRJU2adwdxSuWCcZJ9kxupIN/I25iz7SVTCl6WCQbQ0kYE25Z+1NCuZT4xtLlCeOtGgWLD4yiVP/j6bzcMmWW2RZTv5nMzbeDRHj5cMvQSoXxsGC0QeE17MiOTpY3ipNGY8o9SPs+gblfNZU8Z7ei2lPXMmBO2kDA/yXR9+7U0ZRybCPvrop0ZMQO3jNc8Z3/cR8TtukeD9hGemm0qjklPKBBkXi2tpPJsZ4nIdM02EIY7Rp09Sok9///AWpiR5D0mhoMHOib/AkEHRvZrYfU0ejUYTE8RWQn1N6GaynvF9prSHd2ijHz95ZnxQLde36PM9uHcXQ/3+sax3TcJu7+5hvSkBheeP5bw25PaRj4ilwlXOc6stZ3MUxUbsYYMBzjqDcEE0R7EsfckyKDUPZsama52yS7uFZIkxZcH1Ir69z4BELmeSK1qio37jYhmh0ZAzNOFe8LsMnDjA5aUEWhv0NTQYs4xcnLyU99/alCRSOOfz054Jeuk6/vA98aVmswlsBiv79EMskrBHi9jcJxLOjZYL5TJZnJ/J8yYc//NhH3cP5dk0mTjjvalcK6PCQNXZS3mHNP2RKLU0pUAKLIgTJlGLGbSZaEjzLuSQxLf/f7P3ZjGyZdl12IobN+Z5zszIOd/8XlW9qq6eqru6KVHNQRIsGqYt2x8yJMA29GHYMATDtP2hDxsGCNoSDNjyAAv6kQlagA1RkNikyJbYTbK7q7treK/elJkv54zImOeIG9O9/thrn8f68H9/xPnJqhcRdzjDPvvsvfZa1RaWTBhbQQ0yyzUPtndxRB9US+s9zjnPnZj5rvZU/fBoLGaCywpIGKkfYsfM9xO04yq8E46k0GeQzR/g72NBOLQDEQYbggyUARZsn9jwLAmjj45krjrjCcYMlBzQDw4ySDIZDGCT3iPC56tW6FuOx2acckXZIycECgTsIIK0v+cXkkDQs9GtO7dxw6SCw5KniU/6f/fWLhyWrU1YXuoymJZMhnF2IvYtnYvxsznvF8PNtcy5foek75xLhWIWxU2xAwOeaYejLrIZFe6QNbhZ1n3+CjHacnchv9MyYGc4QJxiJX4mwD3OiVAoZGzX3JF+7zOAuLRc5BPS/33Kz788lz64vXcPSR9/55fxNlQALAMFgCn9viJtTa3agB96dpNnmXOvtQNhDLn24vRXIgwu+jzPnIf/8//mT1ak0qu2aqu2aqu2aqu2aqu2aqu2aqu2aqu2aqsm7ecCIdT4wd/3AOD161O0We6jsaosYdyziYOjQ8nMe0QQFAkb9wWCCDJKV69JJLPFbPdavmAyDDYRD3MieILBMKZjJRNM8N8o7Trs45D3KxGGX6KEH6JzzAhn/YdECP3kj6TEIRAKY0ApxqQtEeLbO5LNKFjAfkHuM17Kdx6+JVma9x48RIuZzSlJqRfOyKCTlGRUIc21WtVE/JXUWCPSfp+LA2bDFeVxar4TMdFpBhYRUWLV7gARoqdCzOJoBma5nGPJTKDeR9Ehjs81UNcI76fVdtVq1UQm48yOV1je5/NbJhOl2esu5dMXzgi725IJCAekz/wIYkmURY8ygPoum+V1HB4TNUSIbH8k0XTL9uEWIf/nRLFoNPbu3bsYEJ2gBNqKEPCWc/QJPVMpdDDTGQiETAZ26Sl0Uu5XLOaRZnblhlm0dIxy0/2+gZLquyvybdzvIcjSuQUzijpnF0sPDAwb1EyW0Fmf68JiJiSoMN9Q0KDElKRc5a3r9Ru0SQismRqTsQhGzFgqIkATnlvb24b8d52oJpN1WixECxdv5odmBypXVwbNtmS2VvusuLFuUBp1jqkihPx+v0EuaEZLSytu3d5Fh4gYRR0sCWGF6zPw3JEj41Za28KEma4Xn0tW/vaezImtrR3sMov/vT/+E7kmyTUrzTrKhHjvrktGr9WSZxgvFpgyK3lDyeVYUMbko48+wv0HglxSBNo5pXwTiThKJG9XhETvQub9v/HXfx0/JrF1tSr3KRfXTF+POHd8jPyvMRt6cVNBn7ZMs9V9og3u7O2gRHLtqxuWWSTSuKTU6YOHkslrUCL08vLaPNdtlgw0CW9NxSMYE1qsGfQJM1+paBwLoq+0JFNRapZlwSLcv8Zxm/O7lj8Ei3hnJXy0uA6qwyHGC5vPx5LfAWG78w7efSglKS7t41VTxuFOLo4Iszobm2J/tdTn9PAEZWaGFEWUL5RQpXSplhAoCsWyLMxp+zw+V4jonP5wbOC5mhnVJhlB6SNFQ015nWAwiM0NogWY1QoSEXJzXTGIoDxtrCI2t7e3DSJAJWtncy1FLJkygSltiq6f6XSKgE1JeZLdXlbPAACl9bxBtHTbHb4LESDBCCJr8lxamrlYLHCLsr7dGxnLIkswXp+coMns21pB+tvHtb6+ljcSta+5t+q+ZgUCppRkPpA53uzInKu2bzAjvDxNdFiDCMONwjpmhFP7KWENZva6vT7yJHwdsrQzQATf1tomXNqD2pn0h0oO94cD098x7iWakR0O+6ac10fJZ5/tw+m5IDFCzNr9wi8IMevV5blBsSpiMhCQvpovZzg8lX7Y2iKUnXtl0G9jMZVxveE6DXHurZU38OkTIY526Efs3ZM5Pp4PkCux7JqOTygq+0y3XcNyIX1VIPnpYi7vVa21TRmf7jkgkswORzCZs2yHksETliycnZ3hV37xVwEAl2fynI1GC4uZ9Pdtls0SsINYOAlv8cXy91xebGazMzSkoIqkc1kuMR2N0SNatsRS3wn3ksZoaNB2TN5jznKhdqdhEBKvTyU7u3sg6JDAcoIcM+cLVlUPuhTcCIXx2UuRiPfC0i/rLE2ZDvoGfbFF4vQH98SPi9hRzCm4EA3Kug4Hg6i2ZZy1BCbEtbuzs4NXR+K3qPhDeYvk466Fi6ogEHIsXZpzTqQzBdzUKZVNcxzieC+cCWJh+b7jyjhVud/f2n8Lc5LXh0JyLV13jgN0SF6t76f7dn84RJeE8GOiAvU5i6V1+Oayr2iZeoflZLB8Zi9JJjSTLnPQBxdVR+xVjijyXqOFAO1FjkhULecbOUt0e/Ls+2XZWxUZfXl5jSXXo/o7Ke67p6evzboqbREto0gmv21kvRWVqWeAQb+LdFJLaGVilRQ5eHmDCEs3Nouy9vosvbOCIUML0O1Jv78iujWZyGHAfdpW+od1ed566xpJ0ksMWYqYSkfBqYJMVlGKMh+nEwf3bovfosj+eld8642NDWOnPApJKGppPJmYc4We/27tyHX63Q489ref8ylBRN+kN4cNuWY6RbJxysd3B2NEtYzM0fGS97yuXuL2I1lzU0rKqxBIwHqDLMpnxU45U9pJy4blyf0Ctvg5rmWjkCMRNknUXx2LT9rudGCTkNpWdB4rTCZeDyH6GGmisBwS7nZ7DYBlVrEEUfhBCubYCSM7rzZ+g+IFs/HEzO3B5I0QCgC0WwPz2ZSof6UeKRaLhsJDEaU2UdOdfg22X66Riuu5h8hy14ZtU8SBZe1B5VKACFwAQCIpcyiRSqLXJrooxEoY+h/uYokGZdzTRGhGiQINWsBYEcIW0XM9Gbfjowsko4qGIsF6XPabdCaHSFjPAzL2ikb2bB86dUF2WkTA9Gh07VAQxRwrCLgHeyTWrlYukY3JvNA9eErKl0SihFZTS9lkbcCS+1VvTmD7iX5lfx7cvQOXaOXLV4Iq2yzJ2p1MPfjDMle2d2SNJ2n7uvUm+qQqGUykX3xEi+Xz2Tf0CDOxLc+v5D33H92BOyfh+VjsaW+mFBNlJJck7J7LGTHM9Vpv1kw5u57FEnGKRLW6xjeOBVlq6Vdk7wLgOdCgVLmWbT8wo4LH3/0fjlYIoVVbtVVbtVVbtVVbtVVbtVVbtVVbtVVbtVWT9nOBEPo//6tf9wCRak4nJRJ2dS3Z2jYl7cqbm+gN3xB5An9OMrjZwZJh/omS0zFzHgjY2KKMrcvMdo3okGF/hCFrMjdJKKVZ4fOLUxSIDFJOmVu3hUtkMe4ivyPX/GefCDHcP/ku5eenEYSYhZwzGxexpY8/fPcR0pTMG/vks689luz8++++h4+OngIAQjWJ9KbWk4Yo2SGKQrPc9VbbRCbnRGJk+bzddhMJRjv1d5oxzpYK6BKJEWLW7gGJfmfhKZaMNvYn0p+plJKRPUOEmbYBI/qKMNq6tYd0SqL1NqPiC777yfkxMsxyF5itrVbl/t1uF37yWHQpB3xNjo69zV1E+Q4Xl5J5bPcHeIvReYvZ9O27knlo9wY4PpE5s1aUfmhRBjQRDSClmUAS181JMu30++hRCvPOnnBY7FNK8/DsDOmwEpLLnItyfsIXwPWNPJfWjoeZ3YzGwgbVMTKEgTJG88UUDrNVKUpOatbp+ctXGPUlRnt6IdmIwVjmqud52OAcVVnaGSWE87kkEmG5lnJRTKdTUzurNaaaodjb3zVZmQbJspXTJlsoosL3UiRCjJ95PhfZHLlXhsoPxAi0bf85dAcRaIxD27Zt1lW4Q/TctmSoK6M22uSnuWDdd5DEm/F4FFGu420Ssp4cS8bU7wO216XfFOVXoBx5MBA13B9Lco+cP3uNL33pqwCAMWuXO5Qjvl1cx3SkyBvybyh/Ur2DPolo68yKD9kvDzf3sEakT5AEnZ++lmu6rR4KGckAREkSf84MUyYcwy3ykHRoW5pH8u6lzTWDyAIJUjWb2e/0DVItHScvF+vFi/kstjelPxpE86h9bLfbGNIO6FxwZ47hyFE51TgJAOvtnqmJHxKNUyZXScQOG0LOBrlrlCx+c2sbZyTzDjBT1yeZHnwxY0fTnL8/+ViQUJFEEm1m5qstzUKTS2xhI5FQCVK5j8/VTPgSZaJEv/oVWbvf+9e/CwDIBTaxSQLcUonEu7SlfitqpI9T7L/1fAwT1oUrT8SQZLeFQs4QkCvyLED0Z7VaRSovNvn6+pr9zexnIATNuSh/yYxrJBKJwUcy44IiwDjnhhPHkO1PiZDw/TnUkpJiKhn+mM+ZTEYNarPdVrtBdMPGFlq0RZ988pTPItf+8FvfMLwRQ/JohEOamcoinZJ3vToX2xwO2ghybilvmqNIjskUMY79YEpS05g803wG3L4ltrXHjNvlpVzz1u19NCjPrvw2yvUQjYZxVpHn81NKtkdOkFgsgdNjyb4vppSRj1JmNhAyvBvnRDxEiVBKRuPYIjHzp58IWfmUcz6zUcKE9u39d94CAFg+sXMnV0fYub0LAOieEU1iA50mSaGJdlF7en52jSD7sk8k03vvCrfY+eUlZtyXbe5nPhJ9TyYjQzZrSPf7YrcWi5lBPftIsuMPaoY7aoQqFPVlR0gYG0+ip4TkROtZzKQn4wnD21O5lDWsyKLzq2ucX5BjTZEwisDsDzAi8WiK2e5QOo1cRtaejkmHNtRZjM1vo8yItkmwH7RDhsdB0VRp2qRhf2j2F0XPKRolmnjDydMjl8RnnwgJ+dbWJibkkVNy/4sL8RMyYaBU3vrCu+p6TXjVNbwAACAASURBVEQj8PnJj2WJ7aw2pQ8K5U3MuvIMmwXZkwdt+qShKGrkAotwHazlc2i2yFlBItuzS3L7OHNEiR4ekJQ4GBMbHYgs4HWJnKSvN6O/PhiOMCPSKknCaOVbGg2GcGaUqiavyFpR3s9dzjEl54iSMCs/WjgSwE1T3iNPUlcl+nVnPuRzMj8UJfnshaDUcmtJBG15dvUdNsgFd3l9ZbhTFA1RKhBhMXHQh/RjgFrN84mLly8EyaU2N0wOzvLmGuo1GYP4vMg+Ik/Lcowl0bMDchImEyr5HsF1VfyBKYn4s+QjCQZ8GHXl2ddyYsdbTfI+JkPojaWPcmnZWwfcz2LhKC7OxCYVaUcSREBVKhXsbcu8Uv+0z7OL53lIhGW8Xj0T1Nj1ufRZJJHGJisAqieCGhs4A8OfpoISatNt+PDVt9/hO4vtrDli54LBoBFqadDfV4R4qVSATa4X5U1aK0p/wuca/i8Vixj1xZ5sbGxiMVOuSRIQX8u6jidTaLSl3zbKggpUAuRqtYoYeZD0PKckSelE1sjOK+ntHnmQmq2bL/BwAUAw7hoklqIm1M9pNesG/Rb0kwuMc30REjQHAFhQXlTdWz3DbdkhCk6fdzatYE7i4lBU9jHln7GDS+RT8gyTAQV6ouIbDeoNnJD/KJSU+zbJjZXIh1BURCPn44jvFEuGUWWliFYQkOoH1col3n5bONkGE+lrK2QjFOEaldfBdCT/71v6EQ7I3qNnRT0DpFIpjIiOHnFPCJGofdAbwCaX0pSInQHnQCwcw2Ag9jpFPp5QVMZ5OO4jzX+7OBE/fZc+KVwPk5E8SzhCJDSR9heVGnw2qzP4fHOP/ZEO4mv3Zc+pX7I/lUsrFEGPCOgIfcosz2fDTgcDci8pSjoQDBvusIvLM3kHiixEwwGE6Wc7ExXtofDTZGK41RptWV8zEt/nywU45Cj2j+jreeSlC1oYcD4qSlV58+bTobFPE6IpZzO5v+uGDU+SRSLtOa+5cH1I5sUuxumT+EDi6vEcHn1jw59LnsNsJoY+RbV+8385WyGEVm3VVm3VVm3VVm3VVm3VVm3VVm3VVm3VVk3azwVC6O/97b/oARI9nzGap1lorQfe2NiAn1G2al2idBrV7Xe6CDIabdAMrBv82c9+Bh9D3ZZGvvm7WCyGDnkpVClGmegTiQQc1jEq4qFUkmjnqFNDcl2ixX/GzNn//tvflftOonBZQ8vgL/yM9j3aL+M+617DWarvsKbx3/31fws/eCWZyjYzB7v3bxkeo4zK9xmVsZSJ+sbJFbPke15cXCNLvoc7VNXqE03y4uiVqd0PEE21R4SKF5hjSf6FY3LfaL1+MZuCj/dT5MhdRm6fPzt+w7fBzKWqn/iDS0wpwanyoydEgsw9P2rM3FjMzmqt/N7GJoZE5ai6hQsf3nlbsqvPXkm/WKz3rtW7qFwzc07enzuUTvRhYeqNCd7CnIX3y6mDIFV0tsvy/SxRB/VeD32qQ2gWjQFz4f3QemFmPQxXUjyO6ysq7RCJpNnQUDj4RvKT2aYw+a8y6QJaVENrM6vjqcz7YgmH0qDgmp0w6xqPhbGWk+fT+ve9vT00WxJRV3UbzWBtbW2Z/44k5FmOXktWrtFooMX3UT6HQknWxHK5xJjKH3s70leqytdqttEkYiTAiV/mvPJ5lpGPTzITEKNk83X3Bh+/khpwizXJyymVVWw/EswqBlg/fEGUTXmthH1yH0SZiW00iPQ6uGMQDhctmccRN4zhgCpCVPWIRil/XrlBgMi4FMfZ4djWXp9DSQYWESIWFB2xcJFihiiQpgJCQJ53ctM0vAgNZnPGimAIx1C5kHmfpdLI9QtRf7j/9iMjczoafxHdt1gsTK31zKHqCTm30sm4ybyfnghiIkVVuiXeIHV0jlpLPxyi8u7e3uZ95JqxZAaBoCq1MPvALFJn4GCXaC291smZZNyHkzGyzP5OiRxTDjLANYibbUqzat/BDuGqQsUmcmQ0eL/RxI/ZXLJ2UWYVux2xIwvHA0Ek+OYHkjnzPPns+FkViSglzl3pxyTRjsVC2SA4qhfS77ubOdy+LbLnZ5xjFxdnAICt7TLS7EvlBVCUwmg4QY5qg4osUu4sy7IQYmZT5dUvryQrtr29a2TLFR3ip12uVG7Q5PpX9NZcx82yMOHYK1dakAousWjIcKOpJH2OXDGAhUZPrjmhSpmqGb3//nsYcC48eSKI12JWbGA6nUOVc8zjMyxmDjzyUZRpY6+4X+zvHpg6/RoRmttbMl963ZGRnfcx7ZkkYqdSqcDHzJ/yVg2JPGs1b3D3EVE1Nbnm5q5koeu1mvmeckxZ3C9s2LC4r3vs4wV9gEI6iz0iRj47FDTJyZXMYzsaNaqLSSIuO1RbuvvgFiIZcqGQ73A6GyGRFJsw7IsNLDLj7vP5cVOlSlVE1lSQaM7xcIQxVVUV5ZFKvFFBaZEnSX2SGeVsq9UqYuSXSJF3Q212q/sG3XfAbGgkLr87fX1i1NBUYlv3i2goangYNDs+Hst8sYNBTLmnBois67RlTR7s3YJNxMIlEWQLLJGiQk6LaEVVEhvNJgD9KVVRG5AbKJYqGk6TOfeACNFis6lrEAufPpHxUvvz7oN9hKlEulxY7A+5ZrGQw4QKpuvc15W3yu8CG+R3/LMf/VDuF1H1sBhOT+V9HjwSuzAgErjvDuHnw6xr/3Os4FmIU0q91xZbFA8ncdOUeRsl2sWDzMN2t4ORqgSRiyydIw9K2MOyKbZka1vWUJUcmUvXZ/hE1PcK0BiORyNEyIXih3KWyeO58wUKBdlzuuQLqt7I2vUH/QgTUbROzrk2M8y2P4x0Sv7tuiIIhslc3m99Mw84X5Rlt2xVlPRjSj9aFWTpmiMcCaK4KWPSb8izWEvLIGCCzMoPiZzKpBPwEd2RiYjd6dKvXdss4TVRCRNyOSpiKhgJG994TNvZp051JBpAmLyLPpeoqpDM3YvKmeH3cRdvzgwARF2K1ywQIaTo+OfPnxsfb2ND3i9PzsVkMgmLvI2k00GUfDW90QQ1Sl1vbor9ODx+gQWRoC7XRoi+zXzsgIBChNiprnwEv99GULk+eW+dJzeNikELTIlgKK/TB5gMDMK1TPTcmApfw+EQG2XuC0RkuEMqBw8HqNL/UoTz+uY6nzdk9gtFGM0JZ4mEE2iTC0vX5emZjGMqmzb8eFHaxWwqietrojTIAafcpEHbMtxyIFIiHZf12Zs0ESbBWDJFvlPa9vFkaBDzyqMWI9JkOqhhvpDvhVgt0CFqOpUIYJP94ZD3DkuibFoTuFT4sji/JlwHdmRpFM4GVJxTJbl+d4CriqzHBw/kfKUKvYNOG0nyH/XJszRzZ4hmw7y3/Inwfu5iCZ+iybge1T/wsMRiKf1QZQXGQXkXgKCLu1T90moG9Zd2dnYQCch7dFldY6s/HI4aRFe7pn1EH8UKAjbPcRb9Wc6BuefDk2cy5i2izDJENN6+swM/K3t6RBDn0nL2RsAPh6i3JNH4M/r4UX/AcFx2+M6RSAw+9sN6kUrIqtQ8HsPPxWT5ZN1cEylbzBeQoGr2Df2A9bKMyfH5a8TSMnZBorEUMbRcLrGxIfuLno3StB+dTg0uOZTiRPzcsFIllSyY58rRjpxfiE8/ns2NbU/zrAGiOMMRCw7Xs1atqCJhMVc2KLTf+K3/f5Wxn4uA0O/+1n/oAeJkp9gBahx6JLnUwQVgpEkP7gj8fDIcYUaiu1ss9zmjc3d4eGigiAEaxAjhxf1BF7mMTFgldNKASzyZwpQyqlmWolSuxUE4O3yOx19/DwDwcV2e7x/9v98DAIzbFjw1VCk6/NwIc0ELX96XCfLW+7LYz54IfP8/+Bv/Pp405SDio2O78NtoEk6tpQCGADkeN4eSAaHlGkS7aQwN7DvPEp8QjZ8VsFCvy4Ze4AEpysNDPGpjg5DVOvu9RWLPaMg2EGgtFekNeNCKpOCjTGGrL7/z/LLYNw7WsKScYZ8w/z7LV3oDFyGOa79BOWyO4355y0i1p2gAPM8zUphdheDS4XdmNmo1lpuMVTZaxuH89AS3MvK9DRKGHdyRQ0fIb2HOxaPS0H4lpgtHzMYZo6M44n0zqZg5JHZIGHtyQeLucAQXp9LHO1sy3tskDV0sZsilpd+1rENl0DOZLIJxlltxzsw0yHdZxbPPJQgWYFAlSAcwFg4hnSJZOYMIy/kCaZIy6oGpyFK67e1t/OSjjwAAcwZaxiQAXF8rG3JsPwNCJRK51us3JgjpkjRON9CFu8SEZJoW2a/HhGJvljbhMCDRZSmGQidTmQSOechrcj53evKd+vkVvsSg43wh11Kyc9vnIs6N6EvvyVo8pwNfyJcMxFjrIs+fn8IHmQPJNdkM6iRYXvQ6uEtyxhRLElWOedYbwk9nSyVrlayyU6mZAMGcm4nKfEYWb0o9rnjYVrLduC+EIZ3xPCXfXz8XUvpvfvghaiQMrBMGntDrXF1hjeVFJhAVFvvtum+k7NXxc6F/bfQJS/3ud/8AABD2RbG3JXZwqyh2yl1S+jSZQTQhc3RjTQ4iRd736FLGCgCe8ZmVSDGZTuH5SyHsLq6LLbp/Xw7y89kEp6e0b0slo6Zj4L2ZR5ck0o7wQDme+LC+wVJAjv2Ch9PXh6fmwPk2yaW1TGzS9yEW03I66Zc4A3S/991/iTRt+haJnYu5OBwSJ965IwfpSkVs6GQ0xJJBfSUetdi3sVhCz9bmsy6DuplMBocsZ3K1dIz2bWtrCy49OJ0naULEbxp1rLMc0qMN0yDT1cUl+gwiFGgLIyTaPz19jRQDvW+9JaVONxVxYEajETZJjn7JJIYGQZfu1PStOvNhHoq63QFGDOhn6XQdvXhuiNaLXEvPORe++uWvoElC8WaFEsV0ipYLFyk6T8+eS7nJGg/po/EMc+63Ee7FFg8Rg34X5S0p51CCyA3a1ZubGyPpPXCkrzQY0ev0TBDw6Ut5PpeHq1Q8jgj3jmdNOcTOGCzq9PpIsRxhwIA32P8b5TVM6PAVkzLHR84I5V15vuFERTFYzhNPIB5hcEQPTCwNDPhs+F2WbjHWH6Uz74wcU9r04ME9uY8l+0Wr1cKApN/7JMPX///oJx8LuSSAEku0ud3g1u19ONM++43rTEtuJjMTINDEV9SW/58vfUaO+ZrBra1NWW/ewgebWZZGTcbbs6YmiD7nAVwDZNPlAus8RKmkbljLNaYOYjFZq5fn6r8wEbP0mWCUY4LNcnh4vL2JNSZzmvRbNJnnt4Eg7aE+pxIzOzMfXFv+O0H/IEJi1ZNXr/H8c0mShBjA67BswksvEOCa2NuS9yoVZW3s7h3g2RM53IR8JHYeLnHdkbG8/0ASdK3uG0lw9QMs7iEqbuF6DuIeD1skMK7dtNkvG6b89IiBkGBYy6GSptQ/YMjQuZdEYwhYXyTBz2SVLH6G3rDBftNgukqOA23u5x59PYtUCFYAyED6//VrsXdqA/dv7SHMQNeYJdcqUBAKBbDP8kslAbaWnkmwlRhQSKQZvL++NH5Op60HYnk+X8BniJLHQxWKYZLGA6ZLLelh+XVf+9yDzeSHEmrPOD/u3j7AkAEn9e2U/D0Q8CNICWpNkGoQ2faHTamvzbJsPb+EQ1ETqFKhER+FSsbTGcIM9Grwvj/uomgEPFgu2yWx+3ACH+2MJvgm3KcSiYQ5/JsAKAOwk8kIYe4ZGiD2zenHzKeGvNqhD9Sj/3j71l0jKqLzarckgfnJdGrEUiY8lNrsn2Ipi3ZH5lWxKPuzCgGEQzFYPLRqMlmv8/ri1IhNhFjKn0vE0KL4RZdCAWOWjqbTaROQ8Ph3zvKfVDyMXk/mVYzvHo3JfZvthgEeaBmmEWRJhNHWuUaxji4DQp5vjAjf0Z3KZ7EIS8i6IWQyYjf8gS+Se9/Uz2EF6Edwnak/545cc7YJsWRJAQnO5I0vuuCZKpKMod4XWxKwlUib+4sNE/g3a50+Rr/fRzQp6+v0lazZOIm7s5miKU+8bogdDrIk+WB/Fz4G9ZoM5PsZmA5FY3AY3N4oyX5oc24PegP4WdqWJb3ChAIFdjCE4dhl34j9njGpk8vlEGS/jUi6P2OSvbRexJLBEC13i1lvBB9GLEe9YQlvPJU2iTkNCOkZ7uT4CDsUQNByQ91/o+GISVTo2fn+20wSjPpo0F8PcC4oV8awNzQJihgTFtyKMJh0UGMJcoak9CH+PhD0GR9m3JN3ffapCBykMmmkCzzTs8y23uZ5vpA1wU5thl5hCnSZxPmffmdFKr1qq7Zqq7Zqq7Zqq7Zqq7Zqq7Zqq7Zqq7ZqbD8XCKHv/W//qQcA9Zuagc4pHLhr4F4Rk4HV5iPk3e+zcH4i2ec8paF9RDeUt7YwoIRelqmyQ5ZnhMM2ciwf0fIaJUC27ACOjgiBJsQzTzTRctTFiFHKzyjz+/RC7tGpLdBpSCQzQniwj9HO7VwKd3Jyv/sPdwEAPcqg/3t//dfRZyZvTEjuwvMZqdUcocmaTe61Owb+psRRmq199uLSZEDCzPwe3JGMnjOdGLh+QKWdmQmP+QUVAwAdEv3OGA323AWSjFrOmOG/um6Yfu125Z3bjOKuEVIXigfQGUofbe3LOx8eSUR6MJnj8V1BgIQU3su/fstCghB2zZYPh2P4iF4pk3juklnXz16eoMGMwZJR4ouqRGBjsQh2GVHPUbJy/0Cexefz4Ge2X0t6BoRLBmMRTChlunDk3zJxubYfcyMJWFiXaPjTF1Ka1Wz3sUXJ5SizY0Xe13EcLAnpV3hrQGHxSxfL+YjPIlkWLRdKpLJgJQbSaZWRV/leP9qU225Qanx/fx+fUr58h/KwmmkYDfrY3xci8Swh0U1m5TqdnilhMbLzcluk0gkjWZ1jBvI5S/dC8Sj8ASXcVelPuU6jUodFOGuzJb/fYanU3YN9vOI41Uw2TX736Q9+hIdbuwCAcFKe/ZrIvy89fgcO52g2o2gW6fNup29k6h2St3uDpZEuPaQkrELl729to7wpzzNw5Z2bV7J+DvZ2cXkp388VxTYoWs/zPLwkIibBUsfmmczHh7dumezPgJP7glDgcauLJDPuea7Z1pX0y8HtfSMP26fE5TqJ5J+/fIkgM2a7uzJ+U2ZBRqORgYlqBl2Jf8eTOV4eSrb7z34oZan5qB/f/vqXAAARH1ErLBNIpNIIEtWgda8616PppLHJCulXueRwLIzhWIlw5fdaJvDq5Qlyabn+5amgE+JEYbhLCwNmpLZ3ZV0rOs0ZzFDnnFFS2B/9+KcAAMsLIEqEQzpBBOCazIWxM4dFBI5CxZUQ94//9Me4vFbyTZlXj+7fweXrU/6bXOvtt0lmeFNBl9DuA66bSkWQUoFAAIW8kuXLe6W4PsdTx6A4t3fE/taIhJzN5rjiPPzwQ5Eo17V+dHRkMpaKkH34UDJStuXHBfeHJeeJ2nNBL1I6mdnCfFbm6nK+QCYnWapXL2TNaj5ouZwjSTRriHOm2SRKdQlYhKkPWP63W96CxX5rdWS+q7xsPp2FQxTgoEWyfV5zOBzgFssTZ5Qx1z2r3erh8NUZACBboK3kddZLRTO3s0Q5KvlyNpU2BNBNkkjGWZp1eHhoMoBbLDF7yQy3bfmQZX9cLeQdekR6hoIR7BBxEgvJHK9XZGybtTcoyY2ioN+evvgcYWbmitz3VExgPOzj3YeC1hoQCTK2xNbOpy7ycZZWL2QvPj8hyjQUMmsum5cxbI5kn45G4ijz+a6rsodoOWy+sIE+/Z3vf/9PAACZLEskogHs35F+CBEJc/Ra5sLUWeKaaDKb9vvBnszr3YN9I2hweS3zXqHo0XACfSJ1roge3NwqGVh/ixnZFH2vRrtlSq3nlI3vcA8PZwNIkFT6jVsqz9lsd7G5Jc9usxRGiZa343Hc1OUaWkNabch4JeIRNFiOOhuy/Gpd1uKTo2PcsKS4vCnz6pd/+S/I/WdLXNBOxUiK/lNmaZ+dPsHXvyK2c04bnedcWnouggFZLxt56bNuo4/GVNZOntndISWip9OJYY3V/VxRW36fhSD7CkSSBUAZ7eESbfpcWuqbzMp9A9EA5kRfjlgu52cm/c7ebQzp36gYwwbRd/XGDXosA1FBCN3rYvEkPvnkU3kf2nstcS2WCsjZYsu3OEan9Gtfn56ZfcglqsrzEbWRSmGN1AvUT0AyEUOjpUgzeeYtlvXNJ2MM+c4xliC2SGje6rRxj/Z9MiJRNUs/KrWaIThWtGizyTLOaBxD+jmbezIvzo5P+SxxJLnHNelPq3RzOBYy2Xiz1oncjEVTWBDZGSSaVW11IBBAnwiXMNeQnk8GjoP1DfF17RTL590pFnT8VAghZJPwdzo3iE7HIeE0EcuxWAwtvqOKzqgdnswdA1VQVI5/Lp8lk1EjhqP2p0/ENmAZhIqO5TqRf+12G7OlIu+kP9VGR2NB9IekN6BIyLgufZZIJBGijTXIMFIUROMx1OnX+ogAyaU9eCTRDRI11GAFSG84MAS7YX52Q/toT0NGhKFYUP9Znrc/7L1BWE0V5UTqknQcDhFjSdqwEMsqF8sxhpwXWh4aILokFFiDn+dGP/3ufRKNj8cDcyYakfB+qfPfTiCRFVvy/FDOE4oeunP7wBBaf/ZM1mJ5fxcxliufncu5SoUNOs2GoW8YEb2Wy8p+C1jwJ+Sm5bzMuR9+/0fy7HYQCaIG17fpR0/lOa+uTpCJyHoc0mcLsvokmU7ghmj/JKke1A+xLAuzMdH9QRVLAftgpOromHG9qAhQ9aaJCM8D4ynJlz2ZX3fu3zJzWhFnMZ/0/9XrSwSKRErRTjnODBOi3rSpr7eYLgzlQtAvc1SR9p7nGf+jQ3vTJV3H/Qf3DDJoTv8owDL/8WiOekP67dWJ7N0e544X9WG8lP7bTMmYZIty/3jSjxCrGSwSTcdDMh7tdh0DEt2vE5XqUADKhyAm48UX3i/HcQyHLDhjOS/9nd/6aIUQWrVVW7VVW7VVW7VVW7VVW7VVW7VVW7VVWzVpPxcIoX/0X/+bHiAROa13VT6MLkmmer2eyfxpdH+T5KaTQd/InPZZp1zhdxLJDIKsy9dIfCKi8ne2yZS1WQcYI1Gn61kYT1Wij4gARv3fu38LF6ytrJEI87//n38bAHB93kcqTpldyhq6JEY7WC/ggOSzd+5Jhi/H7r93ax+RB5KRe/qnktl78OABnn4mJJ9lco0oT9DJyYnpP0WcKHkwfEHs7kumJsfMletJ5LtyfWU4mG6YrU4TfeRNeyiSILVPfqA3EdSZIZJcshY/U6BMbf0ce7tSG398JNmVHjMC6XQKU6Jr7t+TLPdrEhjfvX8b95mVaTLrrWPlDwfhKAcQSWtrlRq2meFRjp3tvV35XSSO65sm/1u+o1mk4aSPdWYelbcgyjrb8XhsatSnzBjUSe4FANEkpbwpd5pjNN7GEkt+P8961AYzzAvPwnqMBKKMHuu89lkWCLAyXD06545en2LUJJqNmUTNvAdCQVPfHCGKTkl6Z7Mxui0SFhNZMJ9PEVACXGYClRCxcnVt3i9CDppH74h8aafdMwTTSWYmDGHbzDEyycOB/L0iF0V2rWj4gXycMxdn8lkoFMOEWcm1uKyXx3dkvmSTcVwS4RYpy3w6oXx39fgM95R/hFwPTZK6HezuGP6pIfkKfIxvBwIRvMP3+fFTyaQcrG2jT5LyJvtjRK6BjXQGO/uSJf38VBA/w4p8NhgOzdrZIRmycr/0xxNDyq2cGSdPpO/+s//oP8ZHP/sx30v674a8EdNODzuU49Sa/2VH7pdIp/Dul94FAHzyRHhWVB58f3/fZGU/oVT2DeviO90ufEQPXbH/5kslFAxhQZ4V5XOxFy1sMrtXIPLPYWY1WygiSTsVjJKMsCdjlI5m0aKsb5TEi2pz+8Oe4WKLEUk3p7TpTb0HcmXjwX15v8aNZPaOj88M8eiEPGPKm/DOvTsma7dLgnzlzqhc32DBvuk2xX5EKWk6mg6xZGY1yDTo5qage2LpPC6IGtB+fPedxzg6lLmiqJ4ouQbW1/Lm3zSL1CCBdCqVguepXKg8lyKF1jc20SKyaIMoLyVSLBaL8HPOPHxLCLGfPXtDlrvkZ4qUVXRrIhbHgNlLzaim00o8eGZ4d8ZcEznKwK6vrRk+sjgJjF8+k7m+vbOJItFvijr99DPh3Mnminh+KhwlGUq07u3sGs6gAjm31E5NxiPD6aO8J0dH/H0mhcfvyh7gI2TwmnN1NJzCZnY1QnRZkNlNy/UMMjbCZ9A+rt/UUCL68u5dQeyoxPPR69eo1eV7X33/y7wvyU29BZbMcj85l+dT4m53usBaQfZi5WQ72JO54/OA2wfCP9cnIunZyxcIMcOsIgBMCsMZDuDOVMCAogMZoony65iSd00z28qP5bouPPJ76F6nSEUAZn5kMjJumxtimzrtHtboK5yT5HlJdMnxySucnIqPcOuuvI9DBPBkNsPtW9J/p5TTxkLm0C/8wrcQY2b/nETrb0hzN9Amp+B8Rq6HyRgRooxiREJOiJ7oDvuIMns8nYxNnwKA3z9HiOgflcPWdTedzwyX2oycErqn7q6XjKy7yotnScS9tl5EsyLPN6Y0tPKlPL96hTBREG0i3b79jW8AAHKpJH76U8mYbxGNrGT/mVQOFm1rnTbM8pEHxplgyXS3rpdsMoY2M/SvjmTNffmrXwUAVKqXyBIREGamXe1WOBjCgHu8S7RByJY1dX1Rg0OemnBM5s4m+RGj8RA++aGszxyvXSYPYK/ZwXAgz6Ik236S2nhYoEpEuu4TNSKvlp5rEMYTzucMUWA+n42gK3769pbMqxYRhp9+9hz5HEU6WiRNpT8Rj0fx+kwQe8p1mUol4Sc/jS8gz6XcL4V0yiCmYkReD+hDBcIh42OEiARWsujjswuMyd02h3ynmKVsfSCENjmAQkRCKjl3KZKyigAAIABJREFULBg26P5FQP6encn+ns6n8O6X3gYgwjXAm2qGYn4dIZuiMZ5yQkqLxmPweB4oZGSPmxK10OoMjK81mHbZtx6W9LmUe69UkLFxJnPDbbfgX62MyGRSGPC93nCoyHwslkoGDa97eRhEgQ67BvmkghARkil7rmUQBLo3VupiK+xAwCDX9dqKpGy0mkaivEfEWsIv18xmckZ2O8n1Ap6bFtMFEnHpj6kjc8GZV+CSozJCwmhF83RHA8OVM3XUlyeaeRkxSJv9PRWbkVGJxSKok9fKR/utiJBQYGGIyKHcWexjy/IhqPxHRMN7tAujkYcMOQsHPIsqn38qlTJz3FLemCj5ESdLOER0XdO3Vu4ndzFHihxaPpLET5cLUFPCcIUqYX0iFoWfz3dF4uhQWMbh1ctj3P3qLgDg8QOZxxeHRMYl0+iNpd+GM7lmmIIe2UwSQXKj9bjPjoiSDIVChv9Jx179uXa7jQBJrJXLtEzidA9zdLq00SMigTPy2XAwhUVezvGCY0sU/3wxQYlckCme+Zw+K4saPQSK7FvuE8lkEmdnZwCAzU1ZQ4qItCzrjVBIh/aNlQeBcMiIFCR4przQqoF8Bpv05UvkRZ05Mn/rlSauK7KGbHJLOYy3dOYDbOwTDci9wRfUCpUm8ow5pMhfp+jWXD6JwVjmk9omh5zFw8Ec8Zg8s/qpEyL2PXeCZFz68T/57/50hRBatVVbtVVbtVVbtVVbtVVbtVVbtVVbtVVbNWk/Fwih/+M3/qoHSJQuRiSHZlsvTgUd0mw0DAt4NimR6Bgj+r1mE+++J4iABCOoz18LT1CnNzH1iSpXuM5saCDgh8cPA2GJwmpkMxZPGplRZSY/Pyd/SswP1896aCJqfudfSDapUXEwI+N8nIoqaSI6ogEPW2mJkm5tSXagRLRNPhXF47/2bQDAZz/4vjzLwjE13GMidnS8CoUCHGYaxqqSpfXmCw9hRit7VDjqMQMWDQUNR8yU15oxUu46XZSpHvL6WLKLs5FEXNeKBaPc1COSoEPEhRVwjULD3rYo9MSZBdwoFjBllHhKJRTlF3CWHaQoodnvDvgs0g4vL1Ch5PuwI5+tZ/NIluT7qhSQohzgozsP0KwQ2eMp47+E0edTBy75SmJUEHOYgWy0mti/K8iDDrPq58wCrRWKmLmUyx2oAoW8SzQcMNlM5f5QLqB4MolCUp4rTGWIS/LHuK6LaJwKWMzGp6kWdH19jYkEypEhH0C1TrTHfCqyjniD3JlT6Ws4HMCZydibyHevZ/hHVOFF+agikYhRxoiQtGlM1Eyt1sDOnvSHj8gKRQWVy2W8IAqtTfb7HvslGo+gSITPT8g1UCTvxI9/9inizO6+T+WQAlFZ0UgQj957DAC4otzpM/ISFZJJOH1KjnOtu8yE2T5gNCTHDhFacYMCW2KPWfwzZseWg6FBSqX4nM+fiY3oVut4i8iFz88lg5tyyQlhAwPWDUeYIbp7V5QMnxwfIUAliDBTNlo33q/c4G/+rb8BAKgOxW5cNmQs7cUCpUKe/ybPl/bLevnpzz7CL/3yLwMQOVQAaFDpqFAoGW6dm6rYpGNy2cwWc9hEvcUTzEaQS+j6uoo0ESY2laWycQe5pPzb+2+9DwB4fXwGAAhFovC4EpeejO/OptirRT+MCTNYyhd2SX6V/qhv+BS2qCSU4jinc28UQFIpsd+tGrMndshkNBxH+Qdk7j3cL2PCTMjFlbzzIdGRi4VrUAaP7ova0tPPZO6lSnGsk9unwd8FqSZjh6I4OiF6YiHrplKp4lvfFuSSquBo9vXBw/tvUKmU4lU1nVarhVRWMj2K5lH0xOXlteFUUoSQZlZd10WeCCvNap6cSYYuHI29uRbRPIqYXS9t4PhcvlchSubOHXn3w6OXuL23CwC4T6W0w+eiYrm9uY4eM2Bz7hsTIuQatRt8+G3hMVJEk6p9TOcuEkSLXZ9RccQO4O4j4esYzaT/f/IzUS3c29lFhPuLRfvbIs+ez+dDnPsSwWwG8VcsbKBKVbII0TYgItJa+gzP0uGxoHmMYs6gjzz5zDTz+89///fk5/AM/4ZylGySL8WyAZd8GJfkcisQZTwbvVHEy5Zk3hdLYmOikQhcIjOiMb5fp2eUMB3eT+87nznocu4siayIhVPmu65P1vMF+Xs2yTOxs7OFi0t5hhQRUxMq5oyGwzd8B+zIIpFC9Wod6+ti39LcJ/rcw3d2dgxn0BV529bL0h/nF5eGvzHEfd4XlLXf7bUQjjMr7qcSGxUQ/X6fUWHVZxoMpwYh5Jupf0k0pzMxqI4s+RSVq2E5XBhuO+XoUoSW53k4vZQ1axOBqmvKmU3w6MFbvJbYDeXHcaZjuMzaLxdqOzkf4zOk0vLsysEUIQ/eRimP8wvZC2od7t1+Gb/wPGF4XIZ9+jSUHl/Aw3gidjFAeeWgf4nyhvhVw5H824tDsR/pXBp+W+a5opAf3Kd67sjBR58KX9pbj8Q2xSk5XK3WkKQiVYAKQKSDQaNZQ9Av7+URxbzFcZ4OHbQbsp8p2jzF9d1q1RHJEu2oyBaS1MxmM2Tyss6qRPUp8qdYKKNaEaRPl37c1pqMY6PeQ5M8MBdXMqbf+PADeV6/H3/wr78LANjfE3uVy+VwdiHzvrwj89gh31K3UUUx80VFRkUdWbbfoAXWqExnFBanc4OCf3Uj9sNyyOtS3sOEXFaHl7I2SkQubBbXkKES2PNjQVwpF1B30MP+LfEx1NdVlb1IKPQG9UbltzbRNj6fH1HydGbor3Q7sh7a3T4SRHgno1Te67SMT9e4ETs14JwrZIoIEv2ge9Ya+2w2cxAmb5JNNIqeqQYTBwP6TsqtpNxdg0EHE3KN6J6jnCrLBTCkz6+qlAEqeYZDUXNOmtH2qRrxEp5Bhd3UZExU/WsxnSNGP3p/R+z37rbM1R987/uGS2ZO/9axZmiSg0ltbYF8lKGIbRCnfaLVS0XpD0w9RKIqJy7ojg++9hUAQHljDa9pF3Us212xb8V8yijwLslBFOT4OZM5FrRviQjVsckfMxo5KPO5XM6vGv2Q4WCCGRGeipxaEBXl81kGJRagr9DnWI0GfVNtkSMP0gIeIpxrinBRP3excDGm7U/Q11MFyk6nDztNHj5ytarqpm354dLOtwf0A6bkzgraxl7pfqGIrV53hDmV2BS1Eo+pgqGLfF7mYZr+35Lo8WgkCL+PeyoRm9XrpukrPSet7ci8uCHH2PXFJcoF6eMM0WKK2nVmcywD8u63bxNZPptiQITarVtinz6i2nI4GsEV95zggihVciR5lg8BjnmQcYI87UkoHESbHHj3HovdnlJFu5jOon5FBB6RnUYhcDY1KLF4Sq59eSP7WyASMuhNz5G+1nuMxn1kClTNJVIrSp+qUrk2Cqh5ImQT3C/cuWtQbH/nN//w51t2/p/+vb/lAWI0dUEuCTtXKce5M0ecJS8aENrYZqfNZxgTWqsbxZTBkdPzCs4uqryGTFILCmkPod+X36kTr1DB60oFLr9XppPW7rNM5vAp9m+J8erSOfmDH4nzMOr64ExUClMGPMmDfDCwRJzkxiqjXaIs3y9985v45r/zHQDAq5/KJI3EQ/BbujFQTpXwxWarA5cHVCV3VUivO5ohmaP8IYm4PJYqHL14jjSdx8yGGMsnJC+bDVrI5+Rw43NJ4sa+3t1cRzBEWXZucleU4KscXyPJ733tq7LZ90m65cMMkwHL/rh5f+V9gUt70Rk++ehjADAQ/QQd7x8//Rw1HiTyhNvdWdvBSU8MfpRO0JILYG9jG5Vz+SxN46cSzOlMAv6sOD+Hn7MUgkYwGArBF/miLGeUQaNOo46razE+eQZVVGZ8tpgZCKqS6Cl52Ww2Q4Wkli7L3kYMQB3s7JnSrQkPgrrx+gMBTCYk3GSwp9uR+8fjcSNl2mMZZYzBsHgiignvM+TmEQgEDOGaBjnrLHNRolUASKcUAsk1MncRo+S4QtLnXIu2HUCbB8Yl5572ccBnmcPWi1eyFpSgvNHto83AzuNNWTdrlBL/kx99HzkGxKYsqchSrjOTTZryLA0EhbhptRt1JNlvGnRLJeU6Gxtb+MH3/1TuwzKvVNCP+Uz6ZknSzgbL87zxEhnKoZ41ZVMoBkiqd2vbBBluUXpdN5ih9cax71fFgdjYlzV1/fkL3NqWAHaGDuYxSf+idhBTwpzf/pqshXMG2s7Pz/H221JCpHTeuq6FqJLQXZVJ9csYdwddPPlcDv+pjDz7PZbQHB0dGUdFiXN90yEStBv3GEwZsawjEo2bcsTWjQQB3n8sBy6/F8NNnST2HMtzliA2O20hScWbAKoGbktraUP0raWtWkayvb2LGYNXWnaihOtfenwfz0mIXWkwuM2AdLffwZwBibWcrO8Yy4EX9hwuyfZyUVnrCqHfv3WAGIP1HR5ghoMx/HN55q994wP2mzxnMBg0wXbjzECaZVnwbOlHLT9rMpC9sbFhxk4diNu35RAxGPYQYFnBOcmlL3lg8vksTBkE035ZMDhi+yxkctKPus6S3PS3dzZxQ9L1Pa7FOZ37eCyEIEUOxkMtm5W+ikcjCJB4WwNxxyQ3DkbjGHLt9rgHpZIZRFkqecQ5rWU8AcuHNCV7ZwEeiNk/9VoLBZZ35bk/nVEye2dnxwSqkrSPNg+jy9kCt2+Ls/U5S9UUur1eLCFMu6uQ6GcsUQtGIxhy74mSwdJmECiajKAzlvU/nMhavHdb1sFsPDaBvJdMDkR4aHYcxwSg+pxP3WYfwwGlcAuyfy6YFGrWb4ydXqfghU0p8X/2e9/F21+RQ8lzzjUl7f8rv/wdrOc1iSHvd86AeafZNmShd/bFFsWYjIgFo+Y9/vh7/woAMCIh6O7OPq4ZdLN4kCyz1Oz12SnmTI5puVZ1Kg5qJpuExQTY/oF8/+WhjEM4HIafhK9bLOE/PX4NlwePtYy8s8XAl+v3TFkBYw5IxmUeOgPHJLzmdIRVWj6XL6HBIOUpJcADPMyO52O4LO//t3/t1wEAn/5MAimFfBIhlhfVmSAJK5m9z0GSQVmP8rwh9msiEjZEqsMFSxAvJIHQvhhiZ1tsX4Gk7U3ui4FoGCcssTzgd6ajIUosDxpzf3/KZMTWzh4CYbXzLFkkqW8mlUWt1TbXBaTkXL7sIcN9PELZ53GXNjcUxtCVORfjXqeE8HABl0Hwo5cy5/b35TnfefwIZ1UZ115f7KqWgDnzGcIsU2nyoN+44TWXQfiCMt6joayDgz1Zr3PHNT6rlvgd3JZ5cnF5ZoRhKkzWWnYYLw+l/x5/WZK8v/KdbwEAuvUL/PEfSLA3uSHPnGTC4fLqAre2d+U+DHx0W+KDOos5PNrvFmQs72/eAwDMGhNM6b/NWLLhBklmXS7DpYx2m/6symrPXA8jJjhL9Fld7ukbpRz6nA92lqVEPIJZlgU/ff4Zg4MugwrOwoOr5c0sv3ry+WcIkIh20Jfn9FsyF77x5a/Dz33h86ey96QpjuEDEKQtX7AstFqXPh6OJsZ/0+Csn/70w0d3Mezz2blme5RdTyfyCJPYWv0XLWMtr5VNyVyUAeUmy9HscNCsdZ2/ZQrFHL44xBqTY2Me1ncYELIsGz/+ofhe/Z68+9iysclx1tLF3kied+E5yBY0yMxAFc9evVobF0ykpLh33b8tc/tgbxelNVnHT59Kmb766KPeCEvQ94zzTMTDdm84QbdJ+0SfVYV3PNdFi2IYQdrvCMu1nJGLQY/E5+z3iSPjMBlP0aVYSpL3KZelr/w+oHIt/a0S9uPZFFGOswa/Opz3y6WHEYNDem5R8ZPheIQQ6RtCDMgnGNBvd+pG7GFBT0fPHgG/jflErl+nn6NBH58VhKV7PX21GH2TQqGEVk/8FSXdD1ryXW/ponole83jt8XPDDEICS+IxkQ+KzABcExKi25rgCnXhI8TrMd5Ofd5Zl9Rkn7PnRv6hzQForSEH5Zl3rl6IuN2fim+1MxdYpfB32MGDtf43e3NDVwwkeeRfmaNpOixQAA5Bqowk7WUjslnL5+8NGf6GUvX20xKdAYTc7ZMEVhQIIG0FfDQ5/5cisqc1SBVs9M05zNNDheYqIgEoygRvPK3f+P/XpWMrdqqrdqqrdqqrdqqrdqqrdqqrdqqrdqqrZq0nwuE0P/6X/6qBwChYBgWo50KR/Mzkj1z5rhzILDSKIkKL84kWue3fAbpECGZ7xXREM8OT1AsSVarWpGI5vW5ZL39fj/ihPppbMyQks0d3HsgmTaFX/3+D34IAPjgnfsorEnE7jUzzP/XP5cyr+uzHty5XGvMTM8aoaywpsiTbDVNcuS7MbnOr/3iX4S1yWzHc5E3ddyZIQ61mEmxmemcLV1DpDglAmRAqFrn7BoIST8GwowMEy3yrQ++jg4zvldtlmS58vvQbGHQTc6IhGuBN7LOF1eSUUoXZGzmlkQ2H669bSCJmjlQaUZ4Iywpr3ewJ4iJsxNGVONviGxVqvySGbFItgBWpmBC+OCj8gHyO5R4rsgYzucaAQ8gSSKyKdEyl0Q3RBMxLEn0tVBJeUaUg+EQdlheoZF5h3MgFYpi56D8hb5VIt3haGJk2V+/loxWhH2+sb6GzynBa1HSPsSMxXo2jwHHzWP2P0SUGVwPbc4ZHyPzCt+NhqKwiQYJ8q+SnnVaDTgWiUt1LgwGaBCRpUihN/LPQ8xJxqsijGmSmPntMKYzQouZJbypy5jMFgtzz0OSLz8kWuar77yNqUrcMhsxJqQ3ns3iz34iGdv725I57E/lu8PFAD5mOy5eybzYZWZ1bWsN9YHce8Joeobog3g0gvtEwNQIDYenWe9tvHwptkFh09uFJKZEgESJBkokZS7ZMz/mLJv8+FDW3q2ijPvm/QN8/qmQRk5JNm7KGsYj9AYyV8aE4c9CMife37+HOslZI0TsLFyxtSF/GAWijepEKfQ68g7b5bJB/1xcnsnvQ4r08sz4xmMk0WO28fLy0pCjjylBqxnZWq1mYMRzroNUMAPPJ3Nth6imKKGrqXQWnSZLOIkmCRAKHE7auEVy+Bsi+NTWup4PW9vSb8+eChn+nIih9VweYdpmbaZUxPXQ51wbKCSdWe8lpmgTBjwibLxF9FEyk4KfEsbkRUWVstib+7soJKQfPZJ2Xp5KVj4QAda3pP/feixZ6IvTcxz/VNCKH3zwNd5bLrpwl7hFhEqb5IIqqxyPx2GxBFSJRBUpeHlxYRCnAZJVvvW29N1kMsbxucyPFEuVjih7X1pfN5lzJRtu0WZMJhODRqnWBEWl5Z/37t3DH/2+lGDcp7w4Kw/w9oN7+PS5rMH1koyRlqNZ8Axxbn+ikGZ59+pNA5uKBGP5djwSN4i/JTOcURLj39xUMOH4bNwVe3x6IgiyzbUdvPVQ0G/djjx7S0smUwl4nGP5hMx3RUKW8kXsbAuq4KOP5R1mzHrfv3PHIB22KU9tE02BUAB3SUT+GaHhNZbu3nnrPsZEHTb4LhFmr5PRKCL0J37wqRDDP/qylLU2m020mWm/y7LUeCSNl09kbvmXMgeyaUGE9Ls9dPpiNxQhNO3J3H55coL3Pvw6AOCMKLGPfiI+xnYpgw/flzKhEOf4HzyRsvTb+wemtEkzkJ6SUY/nhkz3jPNpfUO+67dDCNiyd4eJulW0hwvP7GPn52fynFlZb3fu7CNmsuMyzh2Wi4ZCEYDCGvDU17BN5hazL0qqLzwXBOMgQgL4MPfNuTeC32LJEhO3zYYinIPodsUOJNm3uhYvK5cIE6X0wXuCuMzSz+r163BtyQo/O+GeQFTrdnkTw8kXxTPStKu+pWtk7RN52Rt7LIetXDSxSdRnhGSw1bqsjVavgy1SGzAZjUgghhjf/+RU5n29IfO3sL6BXEnGxPKRyJZryp0vMCaqvdYWGxumjR47A3gsO3mL+2D1WJ4hBBvXRNvaRMYU89Jn3WYHjarMR5v79MHBrnzWa8KyxbZEIuKXhogizJfymFEi+5QZ+jbHZnvjNkaQ90kSfaVlaYPeANtE6fZZfjJ2SC+wv4VhnxL0LA3q9R188lTI9Zu0Ee+9I374X/nOB3j2sazHj1l+c4fl/s5kgBTLTVxWFShyuN3rGoQQiiy7X8hff2+JLukU+h5Lw1k253kedtdJQKwCFtxL8vlN1EmcrXuqxbIfb+EgQ/sxCMg6UZl3AAhyjrcogoKl+upxI8iRplCBs5iiSR8oTOLcQpZl96kculWxn1MifdpLeXd4S9i8Z5TIjxHLL322jQjLDV9zrTf70p/FQgZ5SlWHiPYIWdJXJ0dXyLM0VhE0U5Ldzqczs5/r3/1bYrM7/Q46RPEomuEWqSUq1WtkiVpxWM3Qpx+eKxbhI5ry8EjmthO0TTmYlscU1ygoNGrCRySj7g8xrudBfYogycKj7GOb+3upkEeXokLZnJZkyj4xG9uwQ/I9FdiIkfaj3x8agvY0fQD1q4uZAuAjkTn3GQ/yLtn0JrJJ2QvGHLcWRTHcpQVnIb+r83ymZ/SA30ace5uWCF5cXxiaFN3XldpjPlsiSSJxJaePEiU8d2cIUf59neVnk77Ms8Gwg02W7/V5XlL/LJPOIjineMmVnK+0RHm+nMFheVydCO8p/z8ciyKWZenhjPvFnOcZXwippHy2SRTLsyeypzdv+mi48t/c4lBaE3tye+sOrk9ZMUJf5hV9ZisRASYzPjtRXNk4SusstTNiPWK/JwvHoHK8sfTLguWhSwtwaT9evBDbtLlF1Ja3MCI9Bzu7AACbBPR2wDPVHzfXFACJix0O2Qlz9u1WxY70ZFojV9oziGEQ0bggaf/+nX1kWBK7W5BxXi7ENlVqXXz2VPY4FaJ58FDso2XPEeH8/bu/+cMVQmjVVm3VVm3VVm3VVm3VVm3VVm3VVm3VVm3VpP1cIIT++P/5LzwAePnZSxRjEsGLkrRPQ1mvDg/hp2SsSlSe1FjD6A8iwOx7htl4KotiOJ9jxCjnjDW/lbZ8N5q2MSWy4skziawNexJhdocL/OIHXwIAlPMSvVxnjWb53Uf4J//gHwAAPvxLUt/82+Rz+N1/+mMUXBKTBcgTFJGo+qONTViMNq9nJXL4LrkvvvKN91HzJBqoGdarxjVOP5Hn6jBTv3tPap/3tjbx9ESiiA2iSNaYEW/UeyZjMGW94SOSgEYiEbRJCDlnFimi5HNWECCvSH8p/XL4UsgCt7NpvPtQsttHrMWdcHQKSctwdxTIr3BDgq7RaICHj+V3ta5EjWOMWn/08gglIj4s1plbY+mft+48hp8Z94vqmbzfdt6gTuw6kTQc75eNK5RJTpciqmnIwu1e18EkTZlHkp1pBjMSicEid9DCQJKkX3LRhJGuNxLP5AIpb28ZNFmVEWJD4mfbCKk8Pd9BZQv9Vgi1pvRDbk2yYl3WbLfbTYTtN2SCAJBi5r3faRs+kn3yRqgc6PHJmZE2Vq6L0+tzJMnNo2SfJ0fSj+V8GQNyyaiceK5ACfLyOsLMHk0YZb6klC8svyG7dYfyPjlKa2LmIkKeGs1QK0GcHQ6b+nWHWRlFER0dHeHBA5mbOodOjhXdE8SCfBM37DNiqfD4/n1DvnfNLLef9530Z0iGpB/iBflFKZNBjlweYM1ufk3W3sXVpak5J1gJ60Xpg7PzS9wiiXSXSAzl4SmXtzAi2btKPC88lbG0kWNW1kjeMpPot324TbSjytj6iG6azRa4uRY+J+WrWaNkcLGYw3Ak/e4xe4EkZUWvLuByPc6Zgm+w9n/QnyLAr6c4F8KlPE5eis2KkAHz618We1csZZFIKApT3ksROMFA1JCeKv/aOVE5sXQen9NeKIn9koi3XDKPfk/m+daWZHgWtD+ZfA59Iq2aXGdJcihc1ttokVR7nwhDrQl3F3P4SfTog4ypSjc7w8WbDA83EYdzN53I4PL0DAAw7MqYfvi1b+K6IXaNVDYGqRWwfEgRzaB7kF4zlcsjQF4aPzkX4uQn+/zlS4PG9PmVo0TWcC6VxnBAiVTaewSJUp0NUSyTpJiE7q9PBUEym3pIkCD8/Ez6vXIl/freu1/HH/3LPwQAbJOA8esfCGF4IZ/Ci+d/AgDY3JTPri4k65qIplEqyb8pz5jNZ8nlU6hXpEN65LfZWV/DHiVTK+RJqFM2drr0jMzxt/7CL8l3yPkyn87MulcksKLfXn7+zKAzDrYks/z0maDMLi4u8Ct/+S8DAI6JYlEupvFgaBBSIfIANmtnAIBiNoRySdZOhRk6j9n58t4Omj2ZVw2KEWySkNWzXBRISH5N/sEIESedyQ2GC1mDmRilq30pOEPywJA3cDCVazvTCXIJsQOJkIzp9pZk+Pr9ITJEu9Suanx2eZZIJISzG+m3JMl8eyR49/ss9JRbhoSeS3L2zCdL/OIv/CUAwMc/FV6RdkPG+Tvf+Y4xcGcXYneUG6s9HGO2kGs5M6Ky/bJHxuIuNrfF1tYaMj+6iuzI5JBOE+HgJ+FpIIswM+ZVEmrPuKeWMiXEI0QYky/s1ZHY+1Q6i/w6yetpowdcn/FoyvDMhEiaH02S+L/dM/NpSZSZ+moRO4jjY0FvTWiLKjWZn1YsiCK5GRIxm/1O/2c8NaTLPpe+BuWOvWUQe/tiiy6qkiU/vlA58hwiROJmQzKfm5UbJFIyV5S7qVJVAl7XIKtG9NkUuflrf+1X8fpHgk79wz8VZNzUn+TzeVgjd02RvkYxLXN0NGzioir2NEzin/39XfndcGT2FSXlLRIx+73v/xFq5KD4xgeCXPNx/43HIob8t3Ip/VcjQvHRw3ewdOV7ihCPkGvGGc0wIupzf1/2vNdEGJ2dXiC3K0iES3IYFvJFRJQviWiPtQ2Z/8PJwKARzonQLChn6HAMm76Twzkwpk/k83nwcT4VyuJjzDry3aAXxMJP8vS5vHuXqfpcMosseUgCRGyHQ5RPbzoYkNdmh0I0G73EAAAgAElEQVQsM5Lk+u0lhkRLT4Pyu0xe1sPhyTPM2Vf37gpaslGT+08mc2wTjTmayL7R6XQMt6Xyqi7JlzVsDZGJi025sycoqk9eChIyGgojRptlc09sEqkYSYQQIIpnRuJcHwUXbmoDEEiEjTWx8ftE/TqdLsb0KRZj8jQRzWIHLLjci995hzLm3F/Oz8+xRvSKcpz0KfedSEfgC8jY7FCIYsGzwHI+Q5jE/X0StcfsBIZDuecFKweUGywUCWNIFJrygIboE+Y3d+AQIV7c4D5Bn9K3sI2frvvegiTHvrGIDwFAgE5QgLbG7/djyXOnkhkrn+J04cdaWdZVrqhcl5Q/D8Zg0UebkL8uRHGS3PY+8i5RUS/Il7crdmhpLzBVom6ihPvODNkN2QvbDbFFAUWoBP2okO+xRKRbn+I/7VoDcXk8ZMkZurUuPkCjUofFZ53SZ3I8RTstsZHz8dnl72BI9Od0iSxJspUza0KOq0gqj3Zd7Hyf38/l5Zmy6SRq1+TXpB224yQh740N12XnkvafZ6J8OIbZXPq9dEt8ytBcft9wxrDmsoZU1MKdLw2hu0MkXXZN9pLr5gXWykQpcm8t5WXOTnpz1KvSbwP6bEnanVAsjAbRZXo+S/B8Ox4PECNPU5Q8dgGKurRvhlgrCKJ5j6jFRk38inS6iE8/FySSS8f99h2pSug2WvBYAdMhl1MgoEIdMdic73qU1TOq6y5Mhch/+/e/v0IIrdqqrdqqrdqqrdqqrdqqrdqqrdqqrdqqrZq0nwuE0O/8j39TEEJPX8CmbLMqm8QoqRYI+BBhvd+INYHjAFVSbBtBl7KElOVLFpmVy+ZwwQx2n4oV0PrhKLCxJ1HRKiWQnQnRQwhhRF6PX/zwy3It1gN+fH6KCBVy1vckMvld3uMf/+N/hdxSIoQ1ZhLTzJZsxhMolsgezvrNb5OP5O7ju7BKqnwj2cXz7jX6ZxIFd4mNGLPu8J3bd+Bn7f0h6yYflOVaF8M+5uS8iTDSO2H9e7vXNqz1tqqUUVkjHg0jTAWyF5RVVgnh3WIBDlMHDfIm+Ri5HdW7JmOuimdTIozOrs+xYBZBEQLhsETj/b4J0qxXVqUGlU4M+iOwlvJ8G1RuqVVrGFAp6nZO+l3+D6j1Wygw+7y9RaUnPufe1j5OazI+YfIdLIkU8vkttKhEYxN5EGCcNB4MosVsYoQZT2Wir1QqRhJXs94ZomWc0RjZDdbSk/E/qBmz2QIdqoHMSZQwY1+lUkkEWTteKEgEuss64mDAb5RlckSVzRkp7vfG6LNWt3EjmbZQ2I+Ndcm4bJQlEl2hLKg/EDbS5JElpaeZya3WbgxiSiXDVdr10aNHRtazWtVsKVXzZo5RWLhmbbEihNY3NvD0KRWwqMrCn6H5/7H3JrGSZFeW2HEzc3PzeR7+PMWPechMZjLJTBaZZLEgURKru9CQADUENCABWkirhiBBG2knSDsN0ABJu95IgqTuVldXdZHFarJIJpPMTGZmzBE/fvzZ//d5Nnc3czc3Le55FuKipJ1AoP1tIjPC3dzedN999557Tq+PPSJwwhz/kzP5/nwxCzK2SgVGydVr3gyDgYztgNk4Vb+dtpJ4i9m3zZuSmc3FE5gQieWQY0cpXkQT8UDhTGWy1yg37XpzjMlJUOJ4KnWFTCYHeygrUKnhpNLk7zg9xyolyhuNFsdDoc3aqLP2X9mUXiDjHEaain11SrRWKEFpWSaarDW/d09Qdw1m8549fRwg3Ezyop1fso7bBVxyCN2gvdlcD6NEhNUGOTKOA+lVCznWlefJb6NUmgbOAnNmXtX7dYnuiSSyGIxot4nGSsble+fnDfQ5/htcj4WCPPvg4BBbO9vyDKqCfPlQ0CE7N68FqkcvyBWTYL14LpPFFVU31imrrKSoPWcEm/MW5xhXVsWOtJqdQLHSpT11p07AXVAjmk3xJFhhDQtmokp85wx54U6rl7hOCVPFX6S4TTZ3d/Gcijkqc57kHtH9BdIpWWOvqVIRS1IhLTTDaNrnO4stM3jWOVMvUD3xF7JPufVh2w4aV/LuH3wgXCoJ8vE8fvIV7JGsse98JBLuSsGz3x8iyYycSbWqepOor4SJc3JdbJRlHb568QQ75BjwyZdUJ8dXtdZEifxYCm1XY/b/9o1bgXzrn/25cB29866obD3+8hE8IunuU/UuxZr+WrMBm1wv51WxDR9+8wM+u4oSkRxbOzwn6jKeemiCKBE7SrZYqZ6cnJ/Bom+h+NOS5P2IxS1MyRWi+D5CBv0LDNF3JIM4kOGENw2j2yHHHOfQ06lYNB9jvSRrci0vY2ZoVMxxXTDpjy+/EHWbmzcFLTlbeDivy7jFMkqamHLtuVyA1OxQ0lshhbLpAqonlC8n11eXCM/haIQ0Ea7JnDyzTZSUM0eAEDo+lt8dcn2slKPIpWQcs/QPhkOuQU3DfCbrfXubEtH2BBOiTxQ3W44Kg7GwhTRRxB9/8isAb2Tuc0Yy0E4vEtl1dCh+yPbaNtpU9BkrjjvuKcftBQjXOqW5K/y9lWIZOpGTDcUVQ54saHFEDFmksYTK+hMhAA2HrwTJss4zf5eou1a7iwTn+doNQb3843/6pwAAM2YF516cHCyjTg+1oYyz+jeHKmqpeB6jnvSjStTLLn3KWzf34PF8ec0z9fWVzE1ldRNT+nhhpuPT5GiLxyw4CyJOZ9LXIu152NQDFc9nVOxTPBr2eIg590ufClFbG2KrQ/4CYV2h+qjeYyX4Gx4G/Lyyv0pJspAtBCiNo9en/IzsA8+do0e7GmLmvNsbYJ38bivkaapdyfdarVaA2nKnRGUSBTqfTREhgkkjR4zNebaiBgyq+07oi757R3jizo5PcNWSec6vye+Nx+TUNDWEqDCXScq/dZry/63GCE8fynl555YgYj744AOO6xMcnQgqzeV63tiS76eyJiZTxbWouLdk/H3vDcqgR8RJMhlHiAhehY5SKlJzZ44xke4+18DmaoXv2Qp81tfkziutyfcdx4FBNbMJkTou2SQrlW28PpK1ppC86yuyXib9Lu7Tr2rXZD2/JILSCGv43ve+AwDo0x+b0M8a9EeBSubBAbnWqJQGbY7uUPZsjD5DjL7yfD6H48t7xbPSl3HdRpxVJD0ihZ7xjDVNEzrRunduCq9WqyHveVY/QWmdkBhTvqfxs/du3gvOKF+jWia5VzU3giFRTcpHU2ekaURg6LLmolQyndE2RaIWokmqtI2kfwmi9RYzDyEiOMJ8Vqcv++ejf/VPkODaVorIz6syZjs3dxFTVQ/k6OoPJwiT0yiblXFTnK3dfgcWFcCGRLKr+2M2nUSjIWM7oY2xuAf1kBGsw0v6Qib9iGK5hEiMXEM8Axzul7VyBZaiByK3a7PLSoJyBTFlh8kBlcjLunK9OQylmkb0fT4n+6XbG6ntAYtcOT0i8SzPx5CIsNKu2B2bar92SAvWbYgortFoEnBpNshP1qeaaDQZQZhIui2iVB2baKB4HqYuY3vKszVMGzjzF/Bog9TaU+phqXQSEa4j0pkFfGrNqw5c8sOV04pniHf9RRhj9nFORHqCczzq9pEhv3KWNl0p8no+ECYyzp0rlU4ZD8uykCZ66z/7rz/+/Zad/+/+/g99AHj6+DHu3hYnRkknP3wmsoO7e1t4j6VHHcLaW7yYzMdTjPtKwpsXcV4iap0OGoSAPbgvJI3kzUM4qmE0l++t8JBySCjl2g7yvMxsrIoTdEAZupcXl0gxADWYyCXiSzqQ//xnr5DxxOnsg4SinNTVRBw5knpt8WJ7m+VvH37/m4huyQQ3+rIw/vmvfor1qGwMtUHU5dIfjLB7S0qHijT0Vy/EEZ7Gw0EgaIOXSUWyNl94waV+tbLC95NdfHL6ErRnWPDvSizdaF5cwCQkV+dB06fTN25oqBI2d1SXZzdJYhYKh2DzouWoAM2mOBl/5/sPMKTUbXMshkOjYxZPRHBxIOMddWVOS/F1DOZy2MwJmwUh2Dtrq0jR+Rx6YvwWhB77Iwcpkm826TCW6UAuNB1TwoFnLDsZ9uVdDO1NiZIixVROx9x7s28UOaMyBKVSCetbsmbGNECXvFQlEilE6RT2WHaVpkFotVrARPpDvxR5BsxWSsUgUHh5wZIiwhZjsQS6jLBk+Gx9MYfnyJwrImKTAYlHL19gRshwJSuG9IwO52AwwCoDaoqQ8pQlgsN+H/RNsLYmMFV1MalsrAWXNiWX/uiRXOq//e1vB459muWTymnrTxzYHMspyc3Vuw1Go4DMMQxKDCt5X7hIk+jUX1DWVpPn3N3fR55lOy7J2DRvjh06OHGW/zxjyVQml8doNOK/Ee7Zl/+/fuMGTrjvDVNJyMp81+v1oKxIweJV8EY3w7jk5XzMIGepKP/22WefBcTDO9vbAIBNlqsMB3YA+1SBBRVMiMViODiQd/7ou1Kq+vBQ1sLTr74KpJDHhCFfNaTvnh4OAjQTklj/wYMNmDyg44REf18988mXaDYpA6xgsLxs2I4TlA6ouW8ThqxFkigSdqxkUaskrdzcv4UE3+HgQBxpVZamyjEBIMkyEFVGtQhNA2LIMaHkWUo4x8w4Pv9USioUjHaPRL87m1nEEjKXLULKWzwHMpncG1JMBgDPT88wD7TkZVxU4LrdukSa8HsVlL22Lw7nRa0FfixYA2rPm1YMhYrYGUXACB7Uug9sUD5XlT6q0pliOYexI89S468g7QkrBbDMWZV+dOl0aZoREImrS0SIe7FaPYcRUQIK8g4+7d75WRXFIgmPSXQ/oD3WwyGs3+Ka5j49PjhEpUJ4OYPb5yyBicVTQclclwHDBYPvesjE7ftyefonf/4jeWeKCWABjDh+t3fl9xRp/9T3YTFoQDMcBJtmowmiDFbCG/FR8r3NjRwMwvzVPCsy9+FwiOu8zHcYzFWk7GbEeONPMPioc1w7oyZclhMcH9LeF7bw6KFcgFMMuOzfZFnkfIAI39UKib3SVBngxQmyLAedvll8AID+YIQG90WCJYKbG7wo+YtgbEIsX3A5b7f3b+Cvf/IzAMDX3pZywUxY9sHDp89QIxnpZCGfV0mllcoGrIicNcrB//Kh2P23b2/CteXc/NY35SL9q49l341dB5oh/bl2Tca2UMyiyUSGIu4OUwY3rBvBpV6JHtSYjEh6ccS4L5VfqtPWavM35JmeksPmJac/b8NnckvRAyRJwDvtDwN5dvVsJcmLeQRRS/b69q6s/zpJzjsDGy7X7UqBMtg8+2b+GD4X4tfflTLbZyyV7dpD2LS/Sgik3+4AWXlWKS/zfVWVeTD9OMAyB5vlONmkzNfe1kaQaDu6kPNzQn81Eg4FEuCqfKfWkD07sl3cvSsX91Zb/LF0lpc9402gW/kFGZZgtNoNaAySZumTNJkQm81m0Bmti9CPmM9YDhJPwAqpM0HOOpW4cKdOYCtcJeFOwYJkPIUh5bY9nkWxdBxPXoqwQ2VFfIUNlrgePn+Fck7myTDF9uUYxK1enAa/HWFS8q13xdaEtDm6bXmf18cy7sW8rNW11RyOzmTuorw7FFjKUj17Cc+XdR+NyH6OmmIP7L4LnwTyNYrVgOty5nmoMZirknYFlqZsbhdgj8Vu8P4YlLHsbG0ENA4wKIvtuehyn0R4Tqsk0ny+wIRy4EqEpxJjWd/lJWIqQccS9ATP1lQ8HSQxVZlzlwGJXneEtY1tAIDDS+kxBUTK+TTuM2A94Jl61ZX33d5aR6fDEq4my/9IWRCJRAM/qd9nyVJL1vFoOsSIJONqf6ZJfWGPp6i2+O55jl82jkZT7LU9VcFc0ioMekHAtU+fpMX7krMIobAudnSus1SyLL8zGzmYM8Gfpk/C4cFgOEeK950xbUuIZ7jjeIHAQyEvazTHgHm/18Z0QZJsW9ZHVol+9G1oDOxUmMiasjRzDBN/9O63AADDmsxJYUPs5a8++xUSdDaUiMBFtQZHl++GKPIDnk/FYh7DkZwTCwItVILVtofIpiXxYjOh12S5sj/3YHE9uTwnBixhbPd72LnBJCbP6wgJ/ePhMFZLJCRnqZNKpOiRMExHzvreXOZmzHOja9vQPXlGLiJrdG9N9mCn2cKYic7WBW0KA2uFRBwhEnfXmdhYtOU9U6Uy5vRzVBAolVL+D1BryeevaN/mCCFJ+pLVjPRdJYmL+VIQBFc23aY/8fLwNXJM0GcZtOnTd/h/knp3exReYdny9uYONNrTTkMlquV9Dd0K+lxclaDWQN2nPVmvAFBZFVuoEsGa7mPGdWQyuKVKqZ2ZJ/dLAP/tP3i4LBlbtmVbtmVbtmVbtmVbtmVbtmVbtmVbtmVbNmm/Fwih/+bf+5d8ADAMPcjCK4LNMOXLozEzyDjqzGwgwey1lcCcEbtul3LJhIlt7u1hTsTBiARLM2b/Y8koDEp8Gsy2eHOJAI66wwCO2mlIFHHI3/W0CNKMFHokADxgJuCvfn4Ap8U0JvFzHjH9xVQMN4m+2GFm42u7Egm9/+5NZPYouXcumeJPf/ExUsxCDBgVXN2Qz0973QCGXOYzqs8F+qqbOhIkkFLZphmzJ7F0HPmcRDRrLCEqE32UyEVRI1F3g9Fii/3MJKI4PZOM4Rqz8A9JpP3s6QBjRiav3ZIMwiURQxeX5wHSJMcMwDV+fzE4RV+VdZDcsbhNRMxqIshsDplcH1zM4cdlXl+cCYw4yWeWjAhWc/KMEMkVh3OJDFcPjoIMlpKX3dnhO2ghjAgb1CIyX0OWkFlWBA9I4q0y7zt7Uh7yySefBIigDDOQpUI5+KzKzCmiw5Et7+K48wDxoeQ5o8zqeJ4HjTKbAyJhYswIhA0Nm0SAKPSFIvX1PA8zhUmcyb9NJza2KI3YbstcKHLj49OTICOtMdNmxd6UtN27dw/Am5IgJZ0dj8dxeiRrIKXI0pISVe/YQ0RZ4vGQkuMqG7RSKAUQZW0hY6XkKMeuG8hJLpjlDbPPs9ksyIb5HLMUUV+ZZAS6r7JjhFwTfn5teysgm3z6TIhVJyMbGyWZnzUi6qo1mSPPf5Mpy7A/Lsdqc3MTU/62ynqreXt9fIIPP/zwd8ZKzdHQnqB6KXYjm6es+CuB/kbMKBJEr6gsbTgqv39yfIoCyx1cRR5J4scXLw4C5Ici4k4QSu3YQ9y+LohBm2utwbLbzmiMNUpyHxE+fvz8DD6ziyYzKW+TDNKDC5Pley1mUEL8TDljwWT6TJUszJjtsuIZmLE0x0Mye6pU9Vvf/T56JB5W6Kgzlrrm8/kAtdhnWekqSxVu7+9A49xUazLGjiMZkVy2EGRZwoTfN0hoXCzGApi6kkBdU3MzGgdIE4UMGw5sjIny3N4RktAICS1Pjp4jQ+JWj0S40bis44vLNuJR6X82JX2fc289fvocX/u6lG4pYtscy0pzqSTA7I1C1Cg0XL1+ha+/L8iDv/zRjwEAcZZn9Hs2YhF5+WvXBOFi037puo4Y94daV8pGzOdzRNPMcpPAckx0q+vOcXIqpXcms9UjluY6szFmpoxLnhnm2djBkHP/rY++CwD46S+EsLrb6SFDVGmKttYlmfr+3jV0ePa+ZunXHAqNFcMuyxm7V3KuxDienq7jkpneOksVCLrFje0b6JHcVmM5iBWRccwV4nDYRyVprPH38vl8UA5Z5VotlSQbp2laIFmtzkZVkvHi6BADohpsSi6nkyV4vnxeIR1sR343FTOxTtSQxfwbeeHx4uw1bt6XfaxkZiP0Qww9EmRgFToqmZV9fdWqQ7NkT8RyROsOSTAZ8rG9Lna/wbLUrZKcdU9fHkJXxMAsBVBZ08lwjP0NOevmExLGjmV95hMaaqfyfjvbsoYmPGdcAL0hJatJ/quHNdSJalClaQpZOxkOcfO6lAir/a9QTiEtAXusyhiJ2rIU0meKPn27jU3pjzo3ZothQLSusqUqi5qLpxFndrdP1OFEITbzeeSY1c1Qwncwlt89rV4iFpX1Z2n0Q7oy7+NZD9cpCrCmED8cz7/4yU+QoB+yvSvvmU6n8epSSELV2aFsPFwDviP9H7NMqEuy2w++8R5++1AQ8lWSV9+5K1n9dqMeiKRoRHPnK2/mpkHEXqEo4wdN1lKvX8f2jtjBrCKX5vkymUxgxOVZEfotqrVaLVhEnESJEOqx1CSRSGDWe5OlBgDNkD/d2TRA862S2Hauymr0KHpEwysC6PXdbdRJ1dAgSW7UkL1lhiJgdT2sGNHR9NlilhGcz6q02yZ9hKHN4ZNE9vlBk+8l/3bv/jUMJ/R5mzIOD+4JCs73++gNaIssys+zTDebLOPwlbzfV18Iwsi0ZK0PR2OUWTqnJMcdV9ZVvXGG731PECAKcWbSNxr0uojTD0uT8iJihjElqveCPm+Z0tzd3gA2z/Dda7IukhSmGYzsAB0QsyjQQ7hZMV/CxYWUkSo/LEZkBkJaQHTcpn9bZBWD60wD9HyZKL8pJcRr9SoSCUs9AsAbQvP9/X1cnMn5niCaZ8g+TWY2LPq/Si597hAxPpnBN3jHI0KocXEAcL1PieAr0u925w5GrAxRfsHTJzJ/RjgPlyTlPZLxFkmmHg+HsUkU24x3xODc1IzgXM8QNRc3FQlwCwuiZisV8VfaLOGtXV6g3pFzRY9qnAeZ23gkCYuE86o0SNmm1tTFn3z3BwCAJ59+Js9mCVO2nESrQ+T2QiGAdVxesQTRZPkkFGJzF30iZlwlxkN05Hn1HPGEQvNQAIfjqmlaIPKh3k8hFTOFDHQikRRC3J0qInkdISJgipwvVUFjO2NkWOrn+ETtx+R93QXgjmSMLMi4FHmXODk6gEkxhhD94VBePrOY2EEpVq4sazTrSx/avT5mpAep8/6uGaGAoqTPdQJNCSnMkaJvpnGsmnWxoSurZWhQdk2eqUr9ao02QrwjIyTPZLUcQjCDEkKFDlMlZIu5F1BcxCLqji6frTe7OD5neS3L4BWir5QrYjYmCojvoCpM+oM2zqvyvQntaYbCNtFYMnjP//J//sUSIbRsy7Zsy7Zsy7Zsy7Zsy7Zsy7Zsy7Zsy7Zs0n4vEEL/yd99xweAbDaLPImSPnsomf0oo1/j8TTgMBgzU/Mv/1CkMQ9evEKY5F4Gs8mK58OIRJAoSOTvjFnJBWuZNT0En+gVk1mkFWZZWvVOQFigyIInrMN88fQQK6w3TBYkRDkk4ucf/K9/hcYZMycZiUbO5qyjxBzrrNF+e12i2g+ui3zpvXv7WLkuUeYaeXL+9E//GdoHEsnPs3b6BqXfW+dneHkuiKAwax4TzFKulPMIkY+lyIjo5SVhNkYYE6If2k2SvK5KnzuTHkokwBx2JLKriKBXitkgAm2RUPGrJ1LrfdwLIcpQ7c09yf4NiQ4xjTe8FkNmsiOsbzw4eo3alWSIipyj3d0c58HErT1BPFiQKOfRiw7+9r/xtwEArzsyLv/Tf/8/yDxMFyhTRtxkBNkjUV4ulUSc0dQk5+SMxMVra2tBRs51yMtAFEYkaqGUl+h0mpkNhe45ODiATj6RW7eET6TXk8xDyNCxWpQsjiJBPWYGPmxaAaJIhWkV6ZnjTIKMeZhjpgi/fd/HgsiDEvs5noyCf+srHhFyQujRCDokX1dIJiUvefv6DfQ491PF8cDxeXF4iDnXeYi8JRPW0pZKqwHBb5IEZQq10RgOMOX+tJkxMElSl4ln0GYWP8pxVNHxyXiMCNdOnOuilFPZPwNJStFvsZZWkaQh5KFPYrh1ypXmObfeYhZIX/dYS76zuYE4M40bRE6NHMkSVqtVgHawR4lKxUUTj8exvi77csHPvDwUEsOp6+DGbZl7xZe0TU4gezwOUFeK36NBydDxeIo0USFKUv2KWYx0KoMSub2OTrhnfSILJhPkuR6VfGs+7XN8KpgQJaA4aWbMkp3Uakgxa63WzPMXA2wqkmzKguf5vfJqEROOTZQoxzAzKTHTx1SReBPdl+da1804muQ9uryUjGCOvxtPJQPZSyVHq7L5GxtrAYHlyekRvy/7++98/we4IHoIRBYdn0oWBNACrgtF7m3qau2+yXDPaQs31sXWXtZrAerCJBptMpniNUlkJ8yaRsg1ZZo+CMhAJiVrs866719+/Clu39wGAOxTVlnJA69tbOGIHEqKQ2iDxNaVQhELpogVWqnGPVJvXOHOLUFrbBPV9OuPJVu4vrYFeyCf+4NvCzrtRz8SgmbbHiLNzJoa6zs8Lw4ODlHrynxtbclY91h3v7a2hlNmjDvkBNJJLut4U3z1TOx8idxNt2/ewhGJsL/z/Y8AAK/Zz2G/G0jJK14nlbXdWFtFiaSn5C/GK/KT5Yu5wLZORrIeDWZUZ34InuJAiMi82ZR8nvRt+ERtjbuKS0w+u5iPoZMvolKSeVO8SZGwiRlRLiHy7akx67U7GDMTvkeeJ7WeLptdVIk4u3VL1lMykcfFRYP9kP4poslup407RIp0L8VWWjE5y8MxC2c1WRdOgIDkutSjuMas/1cPhZD/8ZEQT29e28Fnj34DAMhUeG7ui42aTgdYZV8dcrpxK8M0k7gi8lcROyuJ51IiB9iL3xnbEeXQM3Ed3/mWINaeHwjShRzdCFkWFmFZxyf0RxqNVoCKVOT0bSJo1otFJMlj4TETW+R5Vh+5qHNsVfZza1XGeNwdwyXJ54jIGDJmwNf6Af+DOlvDzA4nwhEsJvJJJZM+mrwh7N3eFH9nQF4X9WfINODNiDQjoXDEl7Po9OoYJaJr0uQqStNHfPzyOXxyvah1lclk8OjpFwAQoCQVYrNRb2ODsuU5zsmrQ0E13Ni/hrYaD2bxr6rk5ZosUCIpq+KTmlNu2sqkcGtLOITqbVlfjdYJAGAy7SNLfs4oUVvKds5nLiJEMyjeNiUPHoKOKsnJPaKcrt2Q9emHgMaRvKdC2SXTFHxJJwLUpkIKpql3PZ26gZT38bm8Z63TwjrHRifKyCF3y829W9VhBlAAACAASURBVKhfyO8UyL2lUPxYeLhqytre4b5Roh/9XhuYy5ppkfOmQz7G4loSOfqLnRa5x9bk+7GEB92S+To9lmfn09K/+RTIpRTvGu8FL2SMW70BFtw71+8K6jFiyWe6nQYUh5tJ1HqWhP66psEnv9jEkDM2m0ljQN+ux7NV8b15noHzao3vQPL2GNdsqRhw4SkRiJihuLNmcMgNU1YcNiNZO47jBOfXjLAQJTjQ7w/h8sxX57pOxFS73YJJlHOM3KvqfG+324jSj1sQaRnPy/fmcKFzDcQpnx1ilYbv+MH5rPg9XU/HlOtoRrsf8I0ZfsCPNegJwqXTlnEcDOaY0NddkNembxOFGAnD5HuVKBahUFIjz8YKBW903gv4dejwA2LqN9w8RNvZCzi+jJW6B7ZIBB0z07BJiP3qWHyhGu8g+c1VWFw7W5znXkP23db1FcxDiutI7nxhDzA8Wb+jmUIwKmRcGCDPlz8nsvwBhQZG3UA8Q/HrmWYkGNcu728Gx18hi+69dQ8W+UqfPxdkXIRo8vnURZLn1zn9v2zljYjBWV38tpkje6+QljEzQ2EkeCYeH8s7hSm61Ok3kYvKGaCQMT0ilHZWVwByCBXXxC82+zI+vqbDB/2QkLxvu99CkbxkYx6KEfZL043AF1ecbn36ADN3igLPVIVcVaIiV/VGwLeUy8vvDHqyzqxwBoYm45EhErdBP38ysVGmrzCm2IrC55xVLwPBoQjHSCF5vamHuCm/pzgGc7wvzZwJdFZYKYTX2YWM5+FRDXHe2z99av+NCCHjb/qH/z+bS2jjcDbFlGSmhRVZICcXsrBm8wX6VJlIkdF9NBQDrptxXLA8Q8Fur9MJK5Wz+ILBpQxZx8skCYxF4zg6EiemSOivKm2ZzWaB8sHLI3FQn7+Wz8a1OHZ4Cdqk8sQ5idEMw0OcMOQJYfUhKpUsTGBgy6YtlKQ8I6JUhvpDOAdCHBjbkr6PwjqmVIA4ZknKKx6c+XAE23uE4BLnlSTxbrGyAoMQt25THNUMnfmrZgMrG7LBNF3e84qXm0g2hvNz+Z0ZyzIUyV3HHiOlywZrnomB2lnfBgDcfH8dEQbgDp9KuduAijs7W9twSEgW8XkBJBx2vNjDg/tCfHnK8rMKna/EwofdFKN82hS4eqc1QZIw1EFb3nONsM/bm7tIEx6XL8gGOaFjlSwnkUzJJjrkHI592TBPXj7GLlXXNumYnV2IkV74M/zylz8HgOCilWKpT687wNaWjKNS61COWSwWg07oolL0iPFQnUyngVF5zu/duEZy8HwJC5KxTRm0czkPvgfUqeThcm73b8j3nLmDclF+J85SqaE/Q5djajIwMOc6bPUnKBakzy/Y1yOWtHQHdhAIUgemIn69HF0hTALXMS+Acx6SmhkPiDYTJK8d0ZlsDUcI64R5+krNQt43GqkgSyLAbEL+3OCcRkJ+oCjoz0jESEWbfKmIbEzme4WKYD4P/Gq9gzCd8j4VDV4fneHuTQlWnjDAcEnluVwuhwQPdAWVr1GtTYKZ0scyAzWq3Ktb7eHgpYxbhmSCp1UZl8ePH+O7f/iHAIADKvap8eh1B0gnfrfEQSecc+o6QTBLXVCnhIX3hwO8Opb1qy5MQ4vvli5h4ckzrkhGWGWZTde2sQNVDklVtHYNE5Z13mbpkVLTefHqGe7TcVByGEMGFf1YHvmS9H/EAKgidg8ZOnhvh0lyxjIJQZvNZuDcxenMDOjot7u9QBFsRiL0978uwf5Osw+HF7qDpxKIi3CdVFYL6NCRCvOIU0G7o+OrIBiSYsDx6VNC+6NWEADtcn0NhkMYdGibVNhaia+yfyZmLEd69UrmcoO272/98IcYsMSh21bqG0rlY4ibd+Ri1iQJ56tDmT/PnaHFgMzujlxALBKaR6w4DukoFnjOtDjW5coGTEKM1QXEjBLS7xmo1uR3TFNsUaEitrfe7kNnMEWVMaigTqFUxK9//WsAwLXrUsLk0P589vln+PZ3/gjAG3LGcn4d9lie8flnEqTY4iVurVAIFIfmDMzs7oidNDUNAzo/p1dypmYrLFm1gCFh82oylVOYjCTg0vnf2pRnffzzj/mes6A87uSKajV0bG9e20WBiZpBX+bIY2lzf+ggRIj8hCUEqozH802YtGGdHsvLGci+rPdQrcuY9vqfAgA++MY3odH5XziynoosQei1a7AZhNViLHXQ2U/dQzTOyzWV6hJcl7l0Bk9JVPyb3/4zAIBPgQl7NMX6ipxVibT09ZQCDJlcFM0QEwC8tCkRg/kMQfRRqeqUabea1TqylqyjKO3/gns/n03iirayxnPXovLZqDdAjmePwQvnaOhga0+eq5TBEizx6Tb7MFidokoOJra8e6pUQZnnbJNKjuCam3a6geKbwfU7n8n7pVZC6A/Eps8XFGpg6e+476LA0sCMvBI6J7JOrHgaVy3pT4zUBK+OZH+n8klYVDwMswZmyNKuuGnBZpDTpAutFKA++OCb+Mlf/xSAiEsAQNIykbEYhOFFOMWAdOl2AReXMhdKoTJflgFq9q9gMSjisT4ux9I4LWFiEZA6U6TClXm/bBwjQd+u1aOwgUMfLBbFjLI9C5KYqmDrZDrDGslclb+zviJjPp266FEUROclatiTuV3f2oReJi0Dy896tKuTQ+dNAozBG3VBS6UtHByKb6fOhIqxAp+8yiGlvsXo8eVlAzHayCqFHlZJ2h8KhYJ3VmtHKZ45kynSeaU4JLZJlWCEAPhUZFOlM/2WrCF3qmPiKWViis3wUpqIJhBnaapJ3/c6yzvuxe/i5FT8dLoKGNMHTictWLTDgzZLQlmC1LiqoUti5viKSgzqQZmhxXJgFeiaz4DbDH4pddTTc/H3j066WN2U98nR958ysBTyNGRo6+q0byMS6/cHXezvi19ZPZY9dXKmlJUSgQDEbQZgEyxPiqei4DAEwaYck7wuFpipEmauoUMmujPZBBg3DUoDV7MUMQgbcFi6rMjHjWgSOSrwKpLcvT2xhf1eO1CeTdIulityXkzHGtoswW1x3aa4b8LaIlDn7TOgU+VZUtrIYswaII/lUzoTOelkFD7vNGP6yqoc3tOBKBOCyq9Qvn1r1MXZiezLKf2/6/ckAfTk4BkmVAJL8gLfZo3x4LCPndvb8ju0gXHHhx+Sf7eoatag/+eOHZQzvGuxdP/zL+Q+40V8lLIyNoouIc6S0MnYQUKVyZEM/DX9zkdfPkKK9doWzzOL53RrYKPKRLvPgHxnJP8/cm2UdqjmaTEow/U/13zEuR/Vnh1RVOPOxirsNsmWk/IZpf5dv7pCqSjrsE//KmKzDCudQ5fB9InLM8RzYFP5zqUfoIKXpmkgRN8xnymwX7IeE8kYUiz52mZZ+6Mn4vcMRn3MFjJfr09lTRdzsu/yxRw8Zk5qFCtotRsc6zguGevIJX9XXW9/fyegkNDiVF3nujw9PEWfFCA7DMgrsaf65Qg6ZZB2SC2zsS7vu73VRCL5hlT7b2rLkrFlW7ZlW7ZlW7ZlW7ZlW7ZlW7ZlW7ZlW7Z/wdrvRcnYv/t33/UBYDoY4N4tiXqFSSq9IOzx8PgkIPBSZWG6J1GzrZ1dfPWlwNrLJEzukhBqZ3cD+w/kmY9eC9x5Iy0R6EwmF8gSjpSEaV8iqZlsAV1Cpx8/lixGeVO+950H38JXn0pGdaJLZLhwTaKtP/70AA8fSrS5kJaskSqPmRoOSuzP+8yavv++EPj2q6fYYynLzgfyd//jP/0/MavK83MJiVpGGLWMTF1cvyOIhwUjtGPKjramI7RJBru7I+UBWZaDNHpdjKcSlX7xXNANFqGa97+2HyBbqoTAn5ycAADSmQTOjiRDv0EkwY1tySS89933cHws2WZFVjchQeti4cOjtHyZ0Eslzd2eLQLY+MFLyYa+99Y7AICQtwgy7iq6XSzmsUqJv7HB7CAhikbYwq8fyvwazF5vU1ayOm9hwWyiSXTBORFMrXoLa4RmJliiEGbp0ucPv8TWCrMshISrLBQWITQajPay/EdJa55fXsFkFkFn+qPEjPF4OgkINhdEfrCKAflMFnODWXiW7tkj+czZaRUGSzH6RJkpAnZ7MkKW2Y444amXzRaaXZWJlu85nJPjVydIxiTi3aB05woj8+eXV4gSAjlnKZt6X8MwA3hulmSOIfaz0agF5Ol7JDmP8v2ssBGQY2ezvwsn9txZUK7lM2OcTanMzZuSOUU0/ZsvRe54a2cbPqGrTcqqRlgutLKxjhwzh0OSix6+eIEPPxCyyJdcax6zO3vXtuERVj0bq0y/jMt7770XlHx98ZUgDZXE8WBkw2QWyGdsPRRhXzQ/KK9ThLQT2piZs0CCmU5F9vfWO+/y3V7ihOSR5ETGNglMQ0YYHWb5VPlDiuVkETOEItEWAxI2KkRjrrSCdRJRvqLkuxuO4OJQsuFv3RYUyxr35/MXj7FDu7HKco7zsxMZ9xu3Uef77fEzt1je9NuvHuHkSrKJaq87rvS51+pjjeiOKEkulVRzKpFC40rm8MF9eRe1t2KejwfvSLnKnOOuCHiHo1FQjqQIj9dJGF5rd4Nsh0J9ukQfjUYjbJActztiaWvUhEM0QpVlNSNm6pzJFOtEoR1z7Sj00YMHb+PoQP7ujKgeVepTqKzg4ETmYMaytTrRAOViKZAk9YngA+VbR6MRLkl4vrku73lIZFGnPUCU5Vz7+2L/nz+X8XBn88DOD1nWF40q1GgR5OfEdZKPn1+Iza6UVwNRheGIpL5E1i3g4Wtff8BxlL14enSGJhFjOhGseWaDHXuAMM84l5lEkzPhjm0MiSCoNpWcsHxvNB4GtiWRlXEZ8x2217aQpr26fk1KNP/sz6VMzorH4bL0y+afR89lXd/d2ceNbbFrE5YCqVLIs3oTdUL4PaK/lKRsPpVBJkl0HuVeM0QQf/boFXxd3m+jIp8Jax4+eO9tAMAlkQs+7VYoEkFIsawSafj0hZxTDx68DZ/oDEUQvEr07GzhYGZIfxp97oWYoLBs2w7OozbJmx2P5c66j9FE5j5fkkxslrLijXoPzkjmpMDSlxpLM2sXNaRiFHbIy96NxqV/WmiOgS1ncWlN3k+VXxydn+PWNdn/ymbO/AWaLI2KsUS+x1Lf+WSCFBFL6uyIEyU9XUxgEak54jqM0TfJxjKoseRunQjnGjOlL0+/wsrm+u+8c5/rM2ro2CYNgCpdPzkR+xWLFwPxDIXmmS7oB9pt5FluX0pInxckC40a8TflT5QqjxDNef3OLfzi418AALZIjD/qdZGgoMGjx1I61h/K+733jffx8a8+kWdEpK/btJO1+iVK9Fcm9NmS9AOrF7Xg8wS6oc01PtNmMF0SFXP9xoiUi0RjOD+TvaeQdbubYjPH9gDljHxOIZN3dihUMJrApR+nkMPxlHx2aA+Qi5AegT56h36054dQpu3sEZGqiLzDOnBAYvFkVM6uUd9FIStzoVCZSs68VCkjydKJYe93CY+TyQS+eChZe5dXmq+9K3vSMjR0maF3h/LMMOe0MejDozw4eM7bLD29c/8efvtYkIgzokMU6v/ejVu4sUvZbp4T9brYkzkMtDoyRp2JICTi9M8qlQoihrIt8nuxsCorSeL4tfjYY8hiM0wdYSIxxjzXs8z0mzCQJpG1okS46LEUJp1AmOIBDv1Fnf6m3RvAJlI7vyH2MUzU1tSxkeSaqxNpYpks4Y9nMCYhe7FCeXWKjOimjpV18evVmjs6ln3mLaSEBwBsnrEWpdyzhSRypB/oEe274Fo3NCMoRwLLUg0zFIhFWPQvTf5bMmUF1Ak27ceAJUSFdAW6KXamxn4pgZlUMo4WCckvWC0x4h0iX4nhnPYiS99aldY77gRJ+qoR+sNN2uObG3dQa8s+cyl+0iUaazb1MRrKe12ygmP3+jV+po05oVZqXXSI5owmwogV5R3eIZJ3L1HCcCzjNuAZ4GgsHdUj8Aay3nVSUajKiHkECE0pSU50mUIIzVw/KDdUKMlOTyGvUtA0CsIQWatEKhYzH9Oh/Ha3wzJUnaIpMQM1lnQW4mLD+g3ZI4ZhYOrLu999ID6NInROmBbCEYUcIxqQ1R79q0tc2xdbWaesuzUhEs1xg371aIusdCqgrBjTF1Tz125eYZX32hQJ1tVcdtpdVFhaqaga0ll5TrfXhElqAY1nwOHLEwDAxuo2RkSlhbgG1in4MHamsGi/HVY6Kf9nPnfR4xleWmdZMFHGxsKAxz7qvA8q+gnHdjCiaEciLn8X5T6DoUEneu3f/0//tyWp9LIt27It27It27It27It27It27It27It27JJ+71ACP3xD275ALBwxkgy+2mQaKw9kMjtnXt3g0zgFdEdZZIpXV41sGBsKxUmOTQzB/GECSsrkTTbl+h5/YVEtaNmHKGFki2WKKRCXeiGBQIqsLK2Ld9jnbox0FAmaaJjSPTxgDX2j06GePmaUXrIu6g62EghjuhEIoZvkSPprXckIjq5vMB1ZpTe+Vc+kmedvgBIdOdSirHOiHIibCCSk+f/8qvP5Z0JOJl5cyQZtU2Qc6e8Kr/XGQwxVpwkHXnPHGsm0+kZnj55xfGXyOJ77wuXx4sXL5AiSmPBiOZ6Vp7pLzqo9yXqvk5eG4t14/V2J5CnjhEts7Uq/ax3GrBYP6lk0F9SynO9soseJRzL7Gcpn0CM6bDBjBkokvDV+iO0WVO/wwjvBsm2+5kF3MGE35NJVdxNGnTUz2VeFdfAKVEOeiKCDfZRkX6quvm1lXUkGZl99ERIPxNEKVQqFQz65KBhdHvG7Foml0WcBHtKGnNCbhkDOqJp+btuZ8g/mVWYhwIuH5Pf7xMFEE8moEUk6Kui4o49R7tLCU2FfiORXTiewIjS5ElyGehE5FmWBZuILPWeYUXU6yMgjesMZV/evCFcXdlUFJ5r879lneSZbUynLDx+LPK5OVMyS4qHx7CiqBOlp7iVCiQitW07yIrpJuUiuX/Wt7fgjOQ9+5eyJ2YTZp/iFoprMm9Djsd8OsEW0Ra/+kTqqL/5rQ8AAAvfhcM+t0iSnSXHz/7+PnokJmwSsaZIti9qdUQ4Hgr5YJEHIpGIBwSHV5R/npMHRffDWCut8d/k9zJESuTzeTSYKdPJ1/Ts5QE/U8Bk+obUHADubktmqd1u4N7XJGuUpO37X/73fwgASKWzyJHD6t5N+czPnrxGiqiQFG3FJYnPUwkLWcpFHx/J76zRfuRyKaSZEVFSvilmbLqDIQ6OBZ2RZnZ9h0jI05NaIEc+JHfQBuucF4sFMqxZPyfSUK2FjWIGWUrcdrlmGyTXTKYywR6KcawM1qIj7OPiVNAa11n3HSZEJhqNwDDlcxfkDpiHFnBIJHnjtiBinj4TBOWoNwSIdJpxPVbIc3Xzzm28eixcYMevTwAA770vSDQzFscJyfxtZqS2yTuWiScDriglaJCiHe72ezi/lLlQXBeK/6hYqGBjTTJlL1+KrXbIBbK9dS3gMcsQZXNyIvORy6cwZx92KTtdZpZ3Opljj9nuKc+ZBXNI/eEAobBkfC/J01evtWBa8vzzmpx7C/JGfP2dBwDRf10iFpSsci6eCZBSDe6l1lDmORKxsCC/zYTZ6jq56vY2tlAioqVEieEWESAnF1WUiAp7di7j0SKPxo3VbZTJZWVock68OiaaSgsjmZOMtkXkyMOvhBh+f3cPKXLKWLSrEyImPv7tc1gp+d5qhmfXsIk71ySD6KvMe1ned+S6SJIIvk+BBg4P7NE0QAncuynIOJvCC61hE4lVEqbTb4mHKA8MA70mSWd55oxGsi4zhTy+JL/Bg3cFbTvsybyV8mtoXMi4aySDVYjXbKGIQ67fEPnGfPJ6be9sworLGH31VOy44iXyPR8VjuOC50u9d4U00Ukx2oYTIkE0LGAoItakQqLKGESy4QCVOibqwmbGM6ZbqNNWauQEs135jDY3cXwhz0/T9pnkoUpFotCIKKgQpfuKnG4T28YtjvsJ19q73xRUySef/QzppNiU27uCSpt2ZG99/f438cVvBamqOAJtElW/+433MSD6Z0HU6Xw6wSG5ijZ3xfc5JHcOoGF1Rc6lo1fSh401+f/5zIVHNA7Is6nOnvligQT9jy7JTxcmSawLBfQuxfbN6N+3aE8rlU1kiQB7/lSQagva8Z3tVSTJ86N4eAr87MHhayTVWuFZ5ZE/Jl/KQqMk9IR/JhJEpxXLcDlPF9yfQyLeMqkoPPLi9Boyfto8glJG9vNr8hrqREDtXN9Fh8IJEXJljMhJtrOzhSk16Z+SOzHKPRyLhJAjEkCnTPp5U5HRbsEPyW+XmPV/9oXMTTKVgx6Tce/wHlIkOsSADyvgXpHvTYi2dmchRJNE8A0pFU+k7dnZGba35DyqXYrf45MQend7C/UrOS+uKJxTXinB5N6rE3ESJT/i/to6Pv2poNH2NsgptiVnw/FVFRsUxhjxvNRpw9yxAxAF77C6wCeibOYM4ZPQdkLfqZiX8yIWT8MjnN0k+bhDPkbDMOAR8dfnHL0RBfDhkgMLRGG2a+Iv2c4AmzuyL7tEZNgkNC4Wi4iRVNcmsnM8sIMzUZFlFwv0JXUvQP0kiXZ02OfVchIXlzKH2bT8Xoso6/FkCJ3vqhGJ2iSCJ4QxSJMZ/K66z2iaBo08eVOi44tFWfd+f47zmuzBbOl3+SJzuSKuX5fz9kd/9WMAQDgmtsYKm5jRl+k1ZA2kjRjH3MJzijAYPDe29CzGPlGARA9t3pD11Wv2MSNi7JLCIVpC3nf7zjWUMtKfNu9Z6u4QCoWR5rmi7sNKMGexWECjkIduUAae6Jd8Io+5TbRWTN5F7RvfWKBbl/katGUNLTzpp4s5aiMZq/feFf8UHP9Zb4Ih5MzayoldiHKMuxfnKOaJjiEqao1+e2s8gUZUlEP7f1ZtIckKkzrRShWiggbdJvI8O2zavgzvg34ojCp9H8W6nKI9MSKLAB0dy8g+6RMdaGgGLoimnzEGsLVNgRp48Ej0nYgoInM5X2q1K0x8+Xw0Jet5PKUQgpWEQfEok37LmP503EwgRMctHpf1rERk0oUkYuSk+nv/0f+xRAgt27It27It27It27It27It27It27It27Itm7TfC5WxOxsStdtdy+EnD4WbZ+uucGrMfi3ol0H3EjOieIYXRHn4ErWsNjWMWQ+5nqfEuynRzkF7iPZDRvcZbdugSstqZRsLytWPyBc0YxbabgzwBx8IOqZCPpLpQKJuXzTqGDAqOm9KZDJtSNSyZZ9jTnn2+FSG17PY0fEEJlWgplTOCbGmOXVtHXXWOoZteWa+lIRzKdHG85lETLNEXUztHk5Zq3uTqJzakUQxG5qLIRn7lULDnbREL2uNF1iQwbxIDoQSMx3h0hompmTKrIn04eVrydIkMwmQHgWHRKaMxjKuGkLQyFOjouBD1oDGdAtrGxKxPiIH0WQuEU0rlw3U3V49kQz4FjNuD/a30M5RdYfR806vi1OqyKls8um5RL7ffvstjJldaVHx5fySke/jCfY3WFNJPpyT15IFKuZLmKvsgyNjFo3JeEQTEdSP5JkmURQpZoPs3gAMkOMWFe3UZyYzF/miIEB8KkQcMisZixVgE2UwJqokxZptw1hg3GRmM0+VmrH0LxEOI8LsSpx9+Pb7H8nvOS4aRKg1dbLRj7uYkH1+znrSWIx1wXoYKdaWRg1KY7K2Nh6PY9iXOLHKhsWJcpqMBoEk/JhZ5Cjn8n5pFaMBpSLJy+IRGaBH0sgazCIzuv3yUDJL7tzB7XvCx6LUKZqsA8+ksghTkUNlua5RHaN3VQ+4jZS8+4BRdGe+QONQIvMVSspqIR9n5zIHBcruguPfvLrCzo5k2BZJ2dcauT3GQwdxygEPiPCZaWJ/rOgEM9a75xPyXh2XHD9HVVyey368ySz0qC+/F42GcXRKBTAiwF6Sq+vetX1oRMTVp8zSrhLtmE3AYwbmuiFIk61NchM4q9jdkzEeDmQc/vAD4d65umxi/4YgiWrMWm1ux9FkhjJsSeYlRxXGVwfHaNMOhsOydvI5sR/DUB2ZZIVzIHP4my//GgCwvrGNtCXrNkNerFXKxXprWZyTJyjEeXv2ShAZUSOGI2ZXh9zfK0QRdiY2Zg1KwhIdYkPsJLw5Rty7XkI242sqNbY7A8SY3VLy4t/7g/cBAD6mOKNEtqrrN2MJbFKNZYtZJG1fxvqf/MVfBJnAYoHoNUryvj46wwW527JrREORhyBfNLG2ui2fHzPTz2x5vd/GdEw51JbYyk5HbE06l0SlpLhXZM3cvi42JpcqYcq6/PyKkh8m8izmIark1sjfc/e+8NEBQCZDPgamgYYc835vgPFjQQtkiPxYJwKqnA7jZ48E3TCHUhOZopSStZKgeo/uy7MuLqvIERUyJh/XGnnYUr4Gh9lnlZkuUC790ZPHyNP250xBSNQcyQz6oSSevpKzI5Ew+XuyPk0thDj5R1on5D8aSN9P9A5OL2QP5smjg0Uq+J6uZOc96fPuioyx0x/DjMvayRLBEIuILXtwfQvVJlGKzAy2/CkScXkHl+oo/Y78WVopBVk7gzYlFhFfYTJsQgvL+f+EXFMx8ml8+eoEZz+VdbW3L3tqJSl9zpZLgerI/p6suaEn6/iwc4aNa+Sd+lxQLPGcnL/WzEGUdtuy5E9DJzfKuAE9JGt0l4i66gm5maZ9dOgfrZALR6k4Nhot2OQ7SFPVrBCpQKNy08UL8mhQnck3Hfhh+U0/In/nMQUfQQnuWM6OnRWiiBey1y8uunj4hCibPelfiNCR+XwBDzJu3ErI85waeD5WyjL3xVWx0RPKM7+8OENtTG4kKrueXcge/ODdb6PNLLI3l73oEtl0ORhhh/b05LWgA5VK1uOXj7G7K5xKcaIUMJsiQS6TpCZ/t0l7elY9xgZV2hQHXpWI0pW1TAkmYAAAIABJREFUDWh8bl9JE1M+fTjuQeMe2iQfWqMufXn15XPcemtbnn9KBAclzi3DgkeUkfJrQTT+wgjhilwq+3uigts6lzVYyu7DzFG2nGaEIltYOFFU8nKmVqlC15rIn5dHZwGHZpa8V85IvjiLJmDoMi56RPqVTMUC392yDHaZXGRuGOMqEcN5mZMEfafTi1PEyK2TIT+QUjd03T76PfGPUj79QPqIveop5ro8/+Cl8JBubsl5GDYc9KmKFVaS6KxgsB0bEyopuTMZlzt3iW7AGJoh/XFecIg5b5an4+d/KaiezeuyTkyidaKpBDITokni5NGJe3CIhLl/XfykJhXMTh4dYj0vZ/GcyNDdIs/NehtVctqFyVXXm7NSIa4jk6BvRiRez5G1rmkJDPuy769tSvVCgf5Vt17D+Yn4owX6p06YfDW+hwU5IHVPnq1UwwqJMBY8o/pN2d8afcpSNo9qVWxfkv5mriB22J0sMOyTi1RhGkJpDEfS1+FQxmgwkTW7d60AZya2KEbFOEXfdla7xIzv16gJ6lmp6xlxAx752mauPGslJ37ncbWJhCXvNR7Ku2hUv5tOHYS4dtQdRcnONy7OobOaIUTusRl54k5rL1Ch//0ulaKV+qYTjSBGRcJJmKpf7G86l8B8TvVLqvjZloMm/ztHFa7uXOYyZAHlHbFr29dlL5xciF/sjidYxGT/z1ll0ejJ9yLhOcKGjJVBXq0Z74jDxQzJqeyJGFHIyt90dR1J8uL0mqzCoYrlLGzi7/3xDwEAXz2RffaMCMCQGcb9nPiql0Sclag2PUvEcHkuSK4ZlePqj2Rdt0ct9Ml1VCB/5nPabNMBQltUl6Ti4o14Gq26zO9Wmf460Wius8CwLf1IQfqlUSFs5A9RWJd9mS7wLkVE2aDTxePfyt01UZB1rBCYmqYjmhbbmjJkTocjxSMVwkpJ5mRMLrwh7/M7Wyu46soeL27Is3pUYLWnPuo9+oRb4lcl5/Lsq+NLJKPyfpcdsb8L+t+L8QKhxP93uOf3omTs3/nWlg8Ad27vIcNSj/O6LPK/9a/9AACQqUTx418KwdvpoUzqoC6DdNLp4fotMcYzQsm//M1nAADHW2Cbh/1GQRyDW3sykMfV80Cmu8dSoBIX8Eq+iAkJS5W8YY6Hz/NeK5Cs3SSUfZMScP/XV4/wZ38pv73g5V6TtQPLjAQHylspWay3NuX3CvtF6JQr/sEffR8AcKW18foXcmn67Fw2cobEVZuZNGKbcvC1WzIODZJ0jb0FZiSzurEjxvXPf/wjAEA6m0GapVTf+YNvAXhD2nd4foUpnTODxm+NBMH2uIsVloGd9GUBXxyeAAD2N+4inZDxiPAikmUJyFm1gVRO+tignHOa0LVYNoNuS+YySoIsj85DIp7C6haJXwl3rrebeMWgUorkgnle3nQtEhgm8hri2VM5jWOxBJpqg9EQqJKpsW0H5X9Kur1QljXx+uQI2ySNLK/IwTvjhaaQzSFEeLu6kGW4lgbTMSwS2Cko/ykhm8VKOYAGKghqjs7rzJnAZaBwbZvlNGBwMRrDVBG9kiBbEWP3hwPYLgMYJCke2A6SfB9F/rgg6Vw8oqNEB65Omeq33npLnnl6gVfPZNy2SNRZ4biMhn3scE5UUPHzv5Y9eXtzO7jYFlnCoZMg9PysCo+BwoQiECU54XDUx/e//z2Oo7z7V4+kBC8SieDsjNB3jlWS31clfAAwZZ8Dgmddw+dfCHnnAwbr4M1x/7ZA/x0G5JQEs6GFgueVSb48orxqOpfHCkubvnos79UgpNSZenjva+8BALq0Oz3Kyx6fnCDM0gaTZawLeidzzw/szdqa2KJWTdbnR9/8JgaQd/nsiZRnREhKnS0WcEapyijLQ7/9DSlvajRagfczZEnnFSHRkYiFFINaE5ZFjY0JVklcnonLvj56zbGe+0HAdT4hISL36cIcY3tdxiPMyMIJiXQvmz1MJ7InHvDClLDk+48uasE6HChSdL5vLpHCcCA2bMJ1keaeMOdeQJi5ty1zeUZi5v5kEpBC7+1KUPw1A696NIIIA6IuA9j/9r/1bwIAHKeP18fijKhSoG5vhBUGDXb2xBEGA1e9/ghRruU0//z8EyGC7bdb+NZH3wGAgIz9+FRs9QIadF4i87zo11lipek+ej2Zi25X7O8GD/9QyA8CUOWS7MHLK/nMwjMwZmmEsh/K/lSKpcDeqHNdBU1930eawVFFaLuzKw5Jo9EAVVjhMYij8S8uL86xQvGAXZJR/+I3n6DHEsvdazLPn/9WzrxYOIIVEooXGBhSstODXj8QhAgTFv/4QALzeiyGKte2xSD6rft0Eq/qiLNM8YSEzDna5eLqLs7q0p8Xx2J/2m1Zq7GYBZNnQZ8lO3dvyfu2WjXkCOX3WJ4bZgmlO51gY1XOrGhY1scKSz7C4TieH5CUOCPrspDLoF4T+55n2YnP0hszHg3G7ZIXfeUDHB2fwOb6m7LceW1L3u/Tz7+EzlLC3pBlUyxtyZeLAaG+Ej1QpbTVbgeVnJxxZZ5BVcpoDzpt3GVgcXNL9v6U5UKJdBRDlh616e94PkskZtNA+tpk2XiS4z/oj9BTJZwst9BDBs7PZZ0bTBTN5iTONGbYuSX2o8tgTJcXmkg4hDTt+0pR+hAxxGa0GjbGLJd1WZYX5aW2fulgxrKbKf+Mk8w65E+RYiIwzjNLEc83ew08eihnXTZJP25zGwAw7A8COd8sfUIwCNypN/HRhxJcPnop69HlmYxIHBHKiOcpzQ3XwZgl8SPaoggDcgh5wSUhwmDsgvZ+ZXUdNn2zq7rYvBgDROOpDZOXL5+XojR9yqOjIySj0p8oCYlNlj7O5mM4DIQqQvOAANbTsckyxUiI5+yQF5pcFq2RnCfdkcxbgSS7mGtoX8ieG1OCeRqSOdre28JwJP92QBGBXE6VnPlBqa8aW1OPI8cy/X5X9qXDOfV9Hxb93xnP2cqK9Pm4ehyUIucZ2Dc4LvlcDK7Li1/P5G+TKiIaRZHl0KeXcqGbU/c+lUqhzzuASiTaqoQPCILAR6Q5UJ/J5xJIZ1SSj5LjLMVdq2zgjImiwioTOPRHStkUYjwTL1onAAA9EsGEtmFGIvM+idDd0QgLSqKvkfS2TAL1iTfDMRMwqjxM+b7TsQ2X5+w6S2baHZX4LcOlLzkdK6ENsS1hTcPh0TnHSMb27FLe/eb1XaRI6K6CIypYYkZCCNGPtXQ1jpRy1z0ki7KmlajClEIqmWQODx+JHxcnDcFosEDEFJtQb0j/NjalX4VSFBna5B5lyJVNCs0cDFl+2mLgNEci+Wg6jSHLY9U9JMxk5lybYTyU9/Ec+tgM7LTb7YAqwGfEKkvbO3SmcDjGag0kub+tqAaD2eSRLe+pEgnZQh5h2sxEVPaJPaBwRj4rSitAQPqsRyLQaFtM2hSXvuy4N8bclt8JM0CmMUk2mAwQYdA+ZKhEtvQzGY2iUpSzcUT/qkZ/OJJOI8I7UIMk8amEfH+1WITLYMWMQVz+gdzaOmL0WZstJnrovxxfnAYlTmmWWg6ZqJvaHhqenC8Jg2VQtJdT30GsRNAA/QPlK6eNKOJcV1tMzk9aA8RY6u6QwqPHsc1kMhi0ZS6uqtLnEOPfU38aiA0MmbBQ++bejRtwbEXJwfOJgcf+cBAk5gq8b6m1oCEMfyZz2aFvvcI97C4cjKdiZ6ZMXhTpf8y9UEBYX2YyzmWp38IFLCbQ1e+4IRn09qCFBd/lv/ivvliWjC3bsi3bsi3bsi3bsi3bsi3bsi3bsi3bsi2btN+LkrFtlhQdn9Ux/K3A+f6Dv/8fAwBubAvyx/Y6+PoNyYaXixLVs6sSGXsrYePgSKC7T48ki6+ykpGoiRQjp2kS6CaY8bGGfSRInposSQRPSR82J01kCAk/eCaZgxsxyRznKiVsbEgkvkPZRYPQSXc6AJG/0FlCpNieF7M5QpQUVOSfigxbX2jYJplgnIS7fr+JzduCPPK3JJvwm5/+DACQgo8ESbD6hMkp4tJCtIiEIhhjBvEb3xY00HRsI0fitRoJT39zKGNn+iZSlL1NkjisWCbMt9qFQ3nO9z8UssoKCb1S4VWQkxgtlm11SJQcTaaQKcgzj4iS6Q8kk+udXqLAEgwlDa0QMX5ECyRJ+4zATtwZ4oQdpziHLjNLk9EwQGRcENZqUeqz2+9CM5i5DcnYXtuXrG2rVsM6CUBbzN7rzO7urK6DwKcgk7pKefZo2IDNEj+NZHoq2p8r5jFsy+eVHHbunvyZSKUxIBpCoUqGjJgnEgmELZKHVyULff2mrLloxMKXv/mVjBszRmpc46kk8mkizViC583n0ClzPCIiwAjJeLjOCKOWjFGYJqBfk99L60AqzOwzM+ghkrzqiQhevZKM6oAkegrKOwv52LslCJwBs2p9roGrVgM5IlR6lKpMEz1w9/67GJCQvMUywyznLZZMIJWQZ1a5Bl6+FERBqVQKUD0LoilMwjjPqxfIk8g1m5E/2806BswgxghXVln2SCQMmxkij3MfI6Q3pPlwXNmrHUopTwnhTqXSODw65O+wPIDSy5ulVRS4lxqE4Su5+ng6BXcu86TWQjKh5JLbGJKsE4TyRolcSJgmIsy4xJm91kkOe3F8gT3KnUdYarm2SnTfxMEJ956SKjbcNlQF0UqOJZkNEoKm0+gPCG8+EnsIZnw/fO8bWF+Vfn3yyW9kHEgC2h6H4Zkyjp+/knExKb991u1hPpeM3jah/BplhReLESZEONx6IPN9eCY21xrq2P+alNw1u5IZefCulBO/fH2E3bR8/pLy2UUiCxzDx4QoSYNZk8ck5762u4ZYQuzj9jV5d3vkwB3I/PZIillMCDonnUmiQbJxg/Dc1bLY481yGSCh4RVRIsoeYDHDGQnr2x0Z9wyRQoViEZ4p+6TRlYyxyupMR33kcmKTmlfSr/096efTZy8x5ZpRxJRD7rery7PAPhks6VHlGolEEvUu5XaJFqg15NmdTgfloozDl08FYZXPyH5dWHH0mfVrMjO9s76Kq5a888vHQmBshhQJcxZ37glZ++ULQQ2djGQMaoMBum15d2shay4WEbt8UmvhdV2eeW9P+vCrT6VcvN22EeOZ5RJFqxHWjY6No2MSd0+lXx4zza2Bi5gpvxMhEfTzY2bNtQkwlX/zFrIOXZZqX5w34BEBs8byzfPHsnbsfg8pZjH7PVnrW+Wv4wZLThUx5YiZ40QqGYzNCjOVhyckal9Zx2RKyV/aR48IqvffewdHRJq987aU/b0iyfkiBKwxK94mClAhR1bKG+jUZJ+89Zb4S0Sw4+XTJ7hkeWOTaKARM5GZbBJZPiPMzPH2HbEfF1eXGHLcPWYZH78Q2L/mh3GDdmfKvlSyJYRJhHr4mshmlgOGYyGMmGEPWwpBJvNQSKfRJMn7cV/WTNySvbu7sx+gFs+vTgAALrPXjYsm0hy/G7uCsLJJaJ5I5vDoiazDfZ75CkFWySex/r2PAABffSHolRdPxBeqVCpIcA8ohHiavkfECuP0gohf+lfPnwlyeTF0UV6VvitC90zcCtCycyLv5kQzV1bKwf6dsnxHlcLtbe/hBUlZK0TdNZipN8NRtEnMnsyQXJrSy+mshTHLHxRpOVjmfPfuLbSbsmYaRNatksz+4rwRIAoSLC+IEJkxW3iYED3okcR+NhY7t7O6inVmq3/+a/FR8iX5f88BopqcwRlL7OmAvlGvP8RqSUmHy/nn6zp++VRQIZkc/Rv2bzyZwunJnBs8r111XscyiLE0XpX6KQRrNh5FKSO/naLM+udfiN1a+AuMKK5CEBtAmzFxQ8jm+T1LnrUg2t2deahdyfiViizZu6JseiqO6ZC2nKjsnY1tAMDh6wv06Bcp5LwiUXYtE2PeC2YDsduhRARbROQ+efY0GCMAuHHvPqpXsg4X9Ff6LM3UwxFUcuv8O5apdGUttOpdLIgqWci2RoHlQ89+fYoZURYZonSVb2TF4php4q83iN5SpZYH1WNsrMsZUqQdsYmmKCeLAbF4cY0lcaTaaA06IOsAcpQVb3bkHLDMCNaJmp0Q4TXoT6DAeG+/LQTwSgTl4qyGiS3nicu7UGgu68rSAJ92bWVD/I8eEfe9Zgc6y9tmLEHfIAG9PW6DQCfM6PeVcjIG2+sbgXx8qy3POqdEPOJx3L3zFv9NlYTL+ljbXIdLURxFzj+bEzE/tpGk/zYYsFyzwLKr2RhTokEUfCWXjWI9w7OgK78zIeXC1voWJqQkefRE/OY5fWV34SFtyu80SFZeqsj8xXTgOc8an+hjg+i3iT9FZVX6H+U+CRmyXtqtHiK830YpV79KtN5FrY70BkUciCTTSIGxv737BjHJv1v43LuFBNbSYrdPeZZEY7LPR84wIB1XaLQEaWUuXp9hzZC1E1mRZ3cnk0CifaTuAkQW2aMu5tyrWjbM8RQ7EkslkDSlz0roJaHK+oZzELANbc5y+7Ksr9nkFWIpCjQs5NkGhU10uBizdNekrR1TlGc0tJEjoijBO9uIiDdd1wESRetzGdsBEZ6pWDooLxxS5GBG3zRkacF9+P+tLRFCy7Zsy7Zsy7Zsy7Zsy7Zsy7Zsy7Zsy7Zs/4K13wsOof/8P/wTHwAOT07RengCAPjX/+iPAQA3PhByu4urI6yRFPAx0RMuMw2rbxXw8lyybk5LIoXeRKKWvVETCda9+z3pq0sIjxEzEM2zJttRUnGUJg4bARlplVnam9tC6tabTaD7JNti9r+8KtHV+iKCf/QXktnsX8nvKUJRw9NgpUlwRY6dD+9I9LOynsIDShIXt0kobEzwj//hPwIAXDJaeYs8Gnd2dvHrTz4FIHXQAOCS0+bG5g1c1iTDdkTEzoP3JJo+bLZwe3uH/ZAo4lFfIvIRNwSDPCzJhUR6P/zetwEAv/3iE/Qok7x2VzKCr57ImPfbLmLk+VDkdir62+h0g+i74qnJEH307P9m7z1iJcmyLLFj5ubmWqvvX8vQEZmRIjIqs6q6VFd1cRoNDodqQQFwMRgCBLngEARnQS7IDTcEBiABgiTALWeIQSs2OFNdWaKrqyurMiMzMtSP+Fq61trNzdy4uOdZTi2Gsy2g/W0i83//5s/eu+++9+4995znr3CTsuW9vmS74uTxqDbqeEqi03RexnY6c1CvSmR9RnRBigTBp2eHWCmKfayvy/gtk4+q0Wgg4ZPnlpntCzCCvbq0jD4z55fM3L77nticP2B4Y6oQXYpLIWz6gZkioiMhIjkDhtMRhsw6+VmTPCGaJRyOejwf6t84o9bb29tIU7a83hG7Go7kOa1WC0GOseKBiSQkal1p1JFbIwE0+QsMV0ciJGPTrrU4VuRlCQU9ZMoyiYivTmVNwXExn0p2ShHkDUbkPAqYnlRwhVKVl6dnAIC37tzDmH3tkPNpZU0yVGY47D2r0pNsRITjEg+GEKStgIidEccqEo0iSFn3ESWXVe15q9VCn4SPiktF1SHP5o43T+ULydJ+9Pgx2uRjcJgFKrIOeGVlBZ/8UjKbBWZit9j34WSKKNE/n5CX7Izy7Ldv30WjJs8cE8G0vi7ZvJljI8vMTjxJXgaurUAggDEzWGXW+Vvkx7B7Q2zeEZ/QJzKpeiIcPYWVVfQVKooZZpO+bHd3F76grLNLEqPOibor12reeLzeFxt//PgOTKJIDsntoAh/w8kUWm2VrWD9vCXrwJ2OsLFFFA6RanNNssGdwRiWS3TYgHNDRN5ptYw0M2thyiNrJNr0aw7y5O3yJ/x8dxKEn/axsyUIUiVfXiDnQ28ywoySojGiKstXMp5Dd46TA8lypelr/8N//+8BAEJ+DXVyytSIenHmOiyS44a5HitEEsw13VsvCZLKt2ry9ze2dzCeyjpWKKU7dwTRNHeAJdaFV6vy+ctrmRvHcTAPyDhcngoaaomyzvlEDGvLknU+JxfLW+8LZ0mz00eF7xgkV4MiRP+TP/9T5Ih2jFFufYtcZJ9++im0gIxxiqgX0jmg0+lgb1cITv/6F2LjAZ/YarfbxVqB5IVEC/ydP/w+fvTxP5exikifmZjCYKIjRJLnqzc/AQD0mWWcB0OeXP2Icrhn5K36u//uv439Q7HN188FpcGSfPiDMcSJtlVy2t/4ULi7htMpvqAggcOMpeWQG6U7QoyogSH9aY5koZvbecw1Zr7pf9L5TQDArz55gTFJHN+/I7a3kSW6ctBChPP2+B3hOJpMR9jfl71qnbxrimQYmh8NZmzb5A0rrsqep/l01OuS1VVIgt5Yvve9r72PA0qTK6nnUETW3XA+Riwq7xrneaVPlMFE12FC+mq3FU+brGG/34cSUcHpgszRjKST3XYTBe4nGdp/y6Xc7/aWJxahTos2yUb7/T7m9AMT7hPv3noIm2IWZ0TrhiIy7p1hEzt3xV6fPBNbs4n8iQZzGJBPqMJMu+LhKGQLHgmyrZCFPIM1mkPPv21uy7O7iudmKQ4/eTf2iS7VyB+TNB2skzg3GBA7Pr+U8RmMBtD5d0bgt7m6kmbCQzQrvo9ySdZ3PpNFJqv4laQPw34bKe4FCg1kEXWxvr4OHxFBbSKOFRdifqmIRERGfMz9Yf9QfMXa1g66fRmrPs+XSgY6EDZQjMtYHbwRHwiXfEjJLJp16df6qtiqInu+uDiDTR4/h9Bf5QuT+TyOTmQvxVzG4eaW7FOuZWOJwgeHFEcYcy8pl6p4/FjQ6WoPefFS5sEfCiOVlnF89VLWfiQSR5cE3D6iho6Iwu+PJ54Mdp57XYYkr8GQD6tr5MjhWUPxgT689xa6RFM1iU67Pi/x+xLeOewmz7XPyQt1cHnm8Qfm+L1RIgdtZ478kozfjIipNrm63Lnt8ddd1eUcvkOi9lQiiWfPhBtwk77Z1YgU0F3EiJ64JOqx0x9gj2TViqNuxPcb9rrokpPKR5+Ujsne+PLlSxQowKHOTJGQ/L3Pp3ln1WuS+MaJSi5uraPcVEgzSnoTTdTpDREiEt0mgkMPyFydX5TR64uPffsdsYspRT46jQFU2cTUJqk0kf2hWBxNoo1SUXm2j9xWyWjIQ7NeUQwjHM7ignOXIoKsRQ4zTXMRI3dYmlxuAyIhl7NJz4d1ybHY4xls6jqw6M+Ws7J/FpLkh7o6wGQoY7OyJPugThxFJBLx0CrVinzPiCjTZmfgnbfHE5kvi2eaWDyC0YjcNQnxi22iz92QiTB5sjIk2S5fif3HokG19LzvceY+LBMduXdbbGwylWdXSiXMyVMTi8lY0VTx4vU+8kRk0f16aPrZbIYREZbK580oqJJMZzCZyJlpk/yy+0fkag0FUCCyWRFoB4jWLRaWMSBqJUxeJ5B8fDQaoUNS7Rk3mCHvIH4z7PEr+YieVZw5RsiHelf87grvehaRP+7ERpRIJov2X8zk4dKW1b57+x4R5uUrD009lW55d7fxdIYTopOKebGB5RzvTWclJGgDMfKGXfH8vbe3jWaPnJEkmVdE+bPZzIsvDDmXGgV3GvUubKKH0qxg0kn8v7Sy5Nn7lJMZYuVTvz3AdKo4g4h2J4p2eWsZBtFQ//h/XXAILdqiLdqiLdqiLdqiLdqiLdqiLdqiLdqiLRrb7wRC6B/9199zAcAw4jj7hXAA7WQp/bgqEbLJZAAwM3QxkKhgklnJB99+G08PKEtrSLQ0y8jaceUNhpQn3CsK980VUTODSR9zypAYJMFJUYHLmozApBPWGd1vlSUyV25UkCfKZZOZtmvyHwx9cfw/P5F3mFuqzpAqEo4PAypkvLUi2YW7Renv/VubeIsR18iaRHPHcR3/x//+vwEA1uLyuQfkBTi5ukK1JDWpMWZdfZTXevjoHfzky0/kHSlXv8aI91oq4ynEpAoSEa43JeIbnM9hMnPQOJAx+ui73wIAfPLJ30BXiBhmMXxTDlDYwHQg35MiN0ebfB9rW5vw+SUq2mOG1KB6ieYLQKNc8ZsDZlhZ4x0IBGBS6jPCrPxoaOHL15KlCjJDtEU+klq9jJnNgmhmDgsFKpAZGlyiSaa0dyMgY1avtXFJJasixyO3JFHu8XSEOjkvAsxQh1nza2q6l3WKEsUSoXpas9fxlMsU/8BH3/gGAMnG7++/+a1nNWuUzdzaRjYlP9M5ZjZTAqVSCQlmp5R6WqYoa2Tn9m08fSLZ+BXyVEQCYeg2+VuYUQkwQxSKxxEjP8JVXfqiVBmS0ZiXAVEZFZt21e73sHtDMqqqrlqhPjRNw9mJRNGVT1F8PMlsBiNmYybk0zolx8xSOo31ZSptEa2h5Doj8QS6RGbUWWceZpbsiy++9LLCKuqfzyv+JMvjpBkxg3D39k0kadtB1jynyE3gODZOTyULo9AWSra01enCJQ+OQpXZRI3NJjOA76oUOapt+b5MNo1vf1vUp2rkptp/KbwbO9vb2NkRlMA1uX1+8jeCUNrILSFINaiTC/JvEK2xsbOLS/I/rJH3wZ5RNno6wjpVo/bfiH0o1OLu3h4++UTQhCuUc+4Pe/g1+R6WqGCQpv0fnl8iEGYmdCZr9epUMok3d5YQisr4TciX5CdHRLlcxsMPJDt4cUnVpaz0qdyuIUUEwsmRZINNZqEjgQDWN8UGLplRbQzEHneSqwgYlFMlGlOppfgCJsbMtO/tSHZX2eXQdnFGFUSXpAO/99HXpG/nx+gPxJfP+LtBf/KVIk+I80yFMMfnQ462daEQPlS2m04mOGPG+w9/+G8AEFQNANSqVbz9UFAkx2diXz3uBZVaA+G0zGuTe0eSGan7N/YQpP0lyTPx8lD83s6N27iivxoRPREiMi4YDeKcvGvK9ymU2snxMcascQ+RDyNMafWzszM8uC/9/MVfi50o5crvfe97iNLXHh4Icsd2xtC5hlpdWZi6X9aSL5BGLCnZuv0XP5cx4trw+YNwyPNWVzxjXZnnm9tbiPN3T/fFdhKUNraglOqYAAAgAElEQVRsB0Nyqby9J8ipDx8Jr86PfvwvcEwZ5nhabKjN7GS9OcCcMut+blXZlIyVzzf11GkScyLVAmKfn3z2BiPyS9zdlHf5xrvi9967ewPtuszXNVVuksk4fBwPpSo3ZoYvGI5iZouPGJFjZ3tHMv5XVxeIsK5/a1f2/l/9RtZkupDBjGeFV1zPc0qHh7JRTGcyP1+/TwQ1kRn1bg93bgsS1yEP2IhSL71eD5ubmwCAA6I9Jo5SzcyiyfNEgbwPwaz4vb/8+MeeFP3ODhE45LeY2zNvD48QBeR2HI+vI00CI5McFJfVcxRWxXddl2WtW1R8cX0RDPvkPpgQnU0OrmwqjQnVOd95KBxiio+x2+2jyawpyEFoQ8a6Wr/Gg4cyRj3O6ZBwtrBueVxHa+vyfupsaEb8mBO5cXZBnkhyfNkDDRH6R4MZcIcZ9O31dcyICGiQZyscCXoqYRH6liQl0m3bwRaVYC0qzqksbygSxeaafO43n34BAIhS1faLL595ikFrRJTH4uSshIM5iVl65Ksx+J697hSDnrz/5qrMJYVlMRoMsETOyBJtfMj95dHjD1AnX9XVhczJCjlHquUKIiFZS5t78sxWXz7bbJXBbmJMVEQmx8y9O0OjR1VDorbSiTwcm+o7Taqfcf0kE1lEiFgatWU89w/l3OjzATrn6xYVLieKb8wG1sjPqTgPk3HxixenJU9BsksFsTCRMdf1GiY8W1iUS18hn0+5WvfQSrk8eYWIIrdGY4/3L0DUZ49Inh98/3t48Uz4i0JE9Sg0hqYJ5ykA3NgRlOmPf/5TvCay7Vvf+hYA4D4VlU+ODr0z2uGZnKc2qKp6enyG+3fF7oe0AXX+jkajWNsg8ikna/31bwStpwc0xIi6areJUidUMxSKokRkuFLE8wVlfHz+OOqsYlCorwj5Vo4P6mhTNco2ZIxu3BS/UK408d67wjl3d1fea8p5+/LpE/TJf+kSIdEZjxHg2HaoGqZUAC3LxoCqUer+N3dlLaWiQej0F0pZqkrkZjyd9FSLFQLYIsK51qt4PGbqvBlVSPTJ2EO9pLjnBcmJ50yFfxIQNDsAODwrRiIRb42rppA4nfH0K54x7u9Jyq2785nHgRMn4ufyooqzS0ENbtAPvPe+3BFN3Ycmea7UmppwLbk+n6dkatPvKJRUrVbBXOO8Kn4g3lF010CbiESCxBAIyDqYTsfIsGKjURIEzj2ey96+/QCujwgt2oLGKhSf6YdLnlKD1TylWp2/CyJA9JqPkOYB7aPeqiKWlLmZ0Lf0mtK3SCCKIQ+DimvRbxiefahKlh7Pgasbq3B4b3QnPNOz8gOGH1NypoZ511M2WK1WPRRrlOi0r+5NFoIhHx8hfekQqTyZzFAgwlBx9qVTgjqqVXqoUQmvzvN+XKmUmX70iXhaJmJWqWGGgxEcH/B+zDv6+qb4vWq9gjUiEv+z/+6f/isRQr8TpNJBDpZmWYiypKp4RwZnEJDJuS41PSnR5aJcMr5WEOd32R9g1GHQYSgDPlBEbX4/jq7lknZ2Jk7v5pY8+7pawXQuk6eTLDFHkuPV5QI4zt5FZEwI5MP79/DWfXFeCs73lPK5kUwCa7yInZ+J4QZCJN8aOtBZPtWbUvZ8IgY27g/gI5mbzYu03xfEu48EGg8usOWcOJ6ffv4F5oTg+gj7vsfNwI1o0GMk4WzLOLhcVG9ODuCPyTieP5PDT4ILLZgKwx6R1ItlSfVr2Xiz8TTGhDRfXYmRLidlHM2gDz3C0+eQQMOqKp2xJ7i8kgtxOsayECUfOBt5xGSb6zKnE5beOBMHNolscwwy6bqLjWUxdAVzDOgs/8nGEAzK2KjypAuWWwSDQcRI1mwz2PD5M5Ji+gJY2pDDYImlgSU6jVDUxLvcVF9zftUCDZoBzHjov8EgSZdy2oFIDC2WJ62syyWlxgOWYRpYKmZ/q597N9TBzPXKcF58+QTAV2Ve8VgMPW5gSnq1VqPMcr8Pm+fhOecdMDHkwTTBkiV1gGwPmghQinedBHuqHC2bzXqS8CpgdcVxHNZqHhH2D75BGDht9tnLV1hiKU+nSQgvN5z79+55FxCLBK53fvAHAIDDw0Nck0w6zYCBQWI4TdOR4M/Gptj4yZlchnMrRcy4gd28JQ7/6vwMAOBO5+gwkPfBAymVzGZSqLGM0iFsU23swWAAb7311m+N7ZTkjPmlECIxddmV352xvE7TNG+MciTVPKtL/27fveWVZ/y/f/Fn8ix+5vLszCvLU2Pl5+FQ83118FB9CSrZetdFqyPjP6VUtiLE/d73v4saLyBdbnIrflmfXz59ApAUvVqSvnfGc/j9UY6fvPvhiQTV33rwHsa84Pzzv/gxAOD9dx8BAB6+tYFf/Vok1zu8WEwHYuu5VAzukKTBOktqRzK3uagLnyZ93lqRcUiynKd0eoUR7UqVxL4+lvU2GXWRX5M1lCExaIVw3721G3hBEliLJT4uS8jS+RUkbksZlILKP/lMSnl3djbQI3G0SofMHBsW32crRzlPzsNFpYRkVtbh6bkcvkySHOvQUCCU/NlL6UuSBJ+FXB7Vktic8tsVEo9mU1lP1vSyww1+Qw62g+EEM+57KV44VZD08M0b73JeY7C5xXdJLWUQYIChzDGKJ1VJ1wgu955jBojXuF631nc8YYfzFfm7QV/G//PPP8cKA4VZvsPx2SmI6keZgaPRVP7tDI5hUi7dz6B21CdruFnpoDMRGwBJ1OcsKWzWBzApOZ7jeA4IDa8069iiDHy7K32fz8RvvfvgBjYpoW7N5F2fsIRsHNaQSkufJ4SSjwmhDpoG6iXxj+99XS6Qde5hgSBw46bYjkG52V89V+XZjkfIengqPm1jYx3b25sAgC6h7xaTV8loBAYDyErqdmLR32WDSDMwf07/2G1J/y4uah5h7pwJlHqL8HgzBJ3BIXdKqWteyBOxIlYIa39xLhfPnbsytwevO+hR0CFB/78Uk787Pb/05KKNsPyuyVLqfH4JBm3nkIFJVbaVSiYQi8jfqbI1/1TH/usjfg9LGBmMzy2lYXJf0HluUQH65HIY9owlyVfyru8V5Zw1HgwRMMUGDFN8Z5tBoG5/5B3ClSR0kBfrbHwZL5+KLd+9J3t5syoBRzNkYki7ePL0zwEAW7tygF6NFzFiCbQSEDFYjn32+hIl7r1b22In5QpJ8AM+PLgjPzs4FH/a6o2wwTL9EkvSAtzPdHx1KY/xnKNIr8PhKLY3ZPymDJAdU/b78aOvoUWfOWZpcTxEIYV0HJ/+RoiZw9y7thlwjCVjqFTEB5XO5XuskYzV3I7i6Er2kFRe1mI8LPb59MVnmPRlbFeL8qwxiXDtINCZyLMqTVnznaHYTq/XhMkSjDjPnU3asc8wEOLFxc+yuZnTQ4xlqGvLDC41lQhHD5cMfO5ui43/wda3AQDVRhW1OgO0aZak8C7hTGbQeJZv8Iw34lktGAnBZnmbn+eCU5KBB4JhbJBcOFwQG1Cl8qlsEm2WBAYjYs85BrqSkQR+9deyR2Z49tcZdfv5L36GNJPJ6mLbaSny2hgumDyCK3+XSqc9MvShohpgWWUxl4ePZY2bW+ve5wEgFgggR1EcgxfHCyYSNBc4PXnDZ8r7bJIY3nItvGZyVglz7HHd1GstBAzZ20zO28SWZ/aGY2yTALo3kDOGzTFeiieBmazx9Ruyv4dC0m9n5MewIePw65KIVBRJzO+bzTFmUNunhEAsHywGTJTMvcv5w3yOlWLht8bImsnauLo4x/a62NNNBp4CTLi1RwMvEO0y6a3KzON6yit1nCoi8wmTxEHNS7yoBOSEf287mhdoQYg/Y0LgrFT1ggERlhtVKuJP/D4/prx7RRn86TFpcv/ODU/cosxSpLXiOhIM4J1SiOPkVOYkHo6gXZO+zliLVaXE+WAyxtKSnA9H9IHnTOj2hj3cfVvoUZRYyqTExGOvgxFkbNI8WwRpg6Zho0MBit0bErRfJ5n64VEF47H0OcFgLIcKrg/oDcWXRwjISKTEjucuMCCJMiDj709SoCOU8daxybN8gHuz6xpYYknh2QXX/GjkJYZ0Jtp3d6R/2WweI/qzxiHLrXgu0/0TrJAQuzOS31m8cN2+t4JGR/zpgETwYZYRBwIJlEhvs8ZEvSpL02DDB3X/oHPinTYZTyDDfTMaUeI2LA2fTFD0AtBylgryLBUw/Hj/fUnsdTvSP0VYHzZMXByd4F/XFiVji7Zoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zof8va7wRCaGpJlC85DyK/QghjlTKezB7NbD8aJB/bNiRq6TKTdbpfgl8RRxPF02LU2Y0lEE2QBJWlUi8PhdTt9r37yBclA/DxX0rJjUHyvfu37uHoWDK+BvPIa0XKvDXq+Dnl34PMeviYFY4lE7AtEkkxHDkeS1RQt2yEKNXO2DE0v3zfaDD0oocWoWr+5hz+qGQ5NGbvxszM5XIFhCeU+o1KxuG7f/RvAgD+8osfYcISKRDSf9FXsH0ddpWQPWad4JfeZGIJlC4kAh1yKUfels/GEgnv88uECqcJm27POl402+/BRCXLEoqZSJMoOkDyTlPJgS4lMSH8LR6WyH7XJ5HNarWKEaHoRUb5X715hrUdibQqZMv5pUTDh6MJmiSKnRB9kWC0ud1uIxoSm/ETun7nnkTAG/WOF+kukAwyyzkKhHW4jCRvsHTAYqlIPBrz4IlDZgyCJPqstKtI8XvijPCenEh0VvcBqyRz1AjXN1lmkEwmUa/32XeJEI+YlZiOm7Atmfv19U15T8KkuuMhVlaFHE1JXE6nHbiEQCqpa4Oohmqrhk5LPndnSbIl58y8nV5eIMYMVp0ZsClh40v5HN5/W8ouP/nZXwEA5swOBGMJDzq5TTiykgnuNOqYERE3Y5bLxzG/s7eHfZZ7Kji3R2bdG+AGIaf5VenTDUbqP/v8GdJF+W+HWa77u2IT9myMrz0U1JafsuzWZOBl4z94JO9weS7ovkwm7cleHhxIOdPaqjyrVK3iDdEqBlFUCqKs+ec4IvHrB48EQfPD3/+efCYRQ4XZvvfeFZRSktmWs5MLKPhhgqVIOZOZqWjSK1VSWfVMRPqt+01EuNbvbsq4VKryHS/fHKBA5FmYvqhBZON8OsPOBolcfy1Ihy/PrhFjCdwbEk1fs1zinfcf44LEeEnatEsC+rPzQ09iNUlSwr1t6V+rfIFmlSSXLI2oNiR7kogG4ELeq0byTa0oWVf4/QgRlm2PxWZjlEQ9PjnykHCbG/L5DZJMa76Alz2uRYhSogTrixcvYLGkTcGzzznfqaU8qkQUfUmkYDqTx0qOcGeisFSZYy6TwjkJqreIXgkFFcH7GI26vI+lSLJZPhWPBnF0JH+nECTvsnzl5f5rPNiT0h6bZROJuIyB5ehemdWvn0imP0s7efbp5x7a5aPfkzLUc5axVUpVj3BQIdcaJJANmmG8IZpVjeOIJMC1Uh2rRelfnH93l2T/w8kQMZKhl0hAas80T9rdpfy7n4imuO7HnKjbDx8J8qx8LfN3eXWOmzvipxzuqYGgIucFpkSXdVoKpcASUBjY2ZAxvb9NifO+oAG+9cEjHBzJHnBwJrb24fuy9n/z/DVcnf6TmcQiS2K0uQ2XZM0DlnPcvSdlaK3xACbRAgQ3wPSJT2tpM4Tpu9b36GNMAydEgq3SPxW51ue2gwCforEPI2Y8O+0eHCJCD4kmefxIyhqPTi9wRSTH7g1BCFyfyjv//NPfoN2VNbgdk7mMkVj/pHKGm3tSbhIwZO02W/J+8XjCIzPuEIWiyrU0fYYhy8vLXVkvSo54dW3VKyfgVoBIVN7v6rqKKO1DoUc3lov45g++L3PwKe2XKNWeNUaM+15uRc4RFYpVnJwcebD5ENHgFv0O/FNoJJi1HJZ8NAQto5thDxkHyDv3iPINh0PeWjpmmbJCg15WTzEmkfzGnqzrbFHeqzdsocn36ZHk+Dvf/SEAIJ9cwk9/KQiQ89IZAGB9W86RxUwCfp77EpTtbnWHXomOEhFQ5Ub3793B9fUl+0q5dJ4Ju5UGqldyBvVxv/Absm6Wl9KY8VxkkgX1aJ8y98kkLMqeN1mGHSXqptpowFLkqSyVrBKxAzeIIW3zoi1z8u3vPpZnphOoXxMdzbKJS5bB6dEQ/GEZo8Nr8afFZZb+WyZKLDGLR2S9jPokSQ9HkaCIhiLpNjQdGlGvwYDYnM9HioOshizLhd0Z1xLPpMGQH8UVsZ0Z0fBJEsi3qiOMiPQDUZkdSqKHoz70iUpX1AarRITN5xpOj0843mK/vB7AhY7kEhGuLRIeDygxXVjDt/7guwCAT//mmXwtUXSTyQiaKlUnClfZ/HhuI08/rMrd8vksHtyXs2q1JOPdrMt8WZOpRyx++4Gs+QFLe+zhGLWZfD5OX6TOuYGgH+EA5a9Z1lTvytoYWQOEkzJ+wThR2ZdCgTGdTLC5J3Zs62I7/RMS3FpBTHuqJJClcPUuB0vD0rKM39QVe5zWWTVha5gRjR3mmrg4lb44zgy5POeUKEm330d/KM9YoiiIIvF15z6EWCKt0R8EgvK9c92H4Uhs9MUXMieqxDUbS+LoTOZZCX/U6JMczLC6sgkAOKdohJ8UAqahYToU/x3k/XPIvo0GFrIkqPZIkVn6vpTJweIZt8zzi8kScc1wkc3IfKnqiSWikyuVCgxdfEuOd5VK7Rwmket7vBf4lZjIVR0jVe5HImclCtO1B2jzDMTiGNx9cJ9j5aJBgvByk4hmrmct4+C4PuH4y9xE6K+mkwFuf/QRAMDm2cnPu55pGlhd4R2lQxoInutWttYQZAVHlX4nxLXrNwz4uX+esTQ8TUJnuC4SLMlUVAERysFXyg1YQ5bc8W6qaTpyRB65qqSbtCRnR+dwGA3ROU8P3pWzmmVNvGoLVdraI/Lm6qrkScmrkuIpBQCcmYOtVTmrdnmm3FqWc5XPF8LRISkhVmR+ByxltoZTzPiMqJ+0DBzj5WzWE/Jot+VnqqLDN5/BR99nU6RizLtpYbmAoPWvD/csEEKLtmiLtmiLtmiLtmiLtmiLtmiLtmiLtmh/y9rvBKn0f/pf3HYBQC/riGVY99eUjFmMWa5608LGskTb7pJroJ+XsODn+0fIk8R3g1J9fk2igi/Pm+hTCtkfkCidbyqRtZt3HmBEScE3+xKVLjCavraSR5f19iARrpKGngwtQMmqk4jOCAf5GQOvXlOKeibRxwmz3lrXQtcnEb8UJZ93mTn4YHsV3/1A6v/iDyWKGJg4ONRZm/1M6noVt0elPUaG/EO7jGRWKbX6yf5fYSMo7xEjUsWkrPNx6RKDqoxDhtHzDGstdcvCkNmjuyvSh2BaIsr7r5/BT1spkLSvwDrH8riBgxcSYY8F5XsSnKNEMoDrK4mE7m3IM9dzkmU4un6N8UTG5uCNZK/NAJECjoXbtyUz2mTGeGoNsUWZWJVpGxN5kkjHoCm5wBAzFIzqTiYjGBPyWVBasTNQv5tid1Wyg85kwL6Tt8Nw0GAGVUmvKz6BVy9eYcS641xWMhUZ1mqfXl4hRGKydRJ5qQLR6XSMENFeDWZzfEROPHrvA7w+YRaIGdxTSlKvrhS9aLvGCLZCCLX7PVh9JW2peI120SaBmZJE32QN9Wg49aTrlfSyRfK9aCqOMslSlYR6gtwGmNjoN8V2gqzVLTDzeXx95WWR4yQ5V2SN59dXaFNOOUqOl8KqZBwcQ4efn1NoPUUuHdIDqJclG+bO5P0irO125rpHXq2QSCkS2r3ef45vfVMyFc9PxHbmtoX335X19YTErXXyOiVTcdy8KXalkFxhEoS32l0vm6vWfJH9LJfLHqG7IrQekjS4P+iiwGxmlxwvSnI8l11CgsStT54IWWh2jYTmwZgnRw5motYzkkkfWTP8+oXwgqymxOYUGaFmaLA4RqpufoN2Xb28RoH8RSobNw6a6JFfTPHbPH8lPmbvzi2PNFjxgkyJSvGFbbiavNdgaPOdJXOWDJuYjmROcmuCgrtsSvbUHXQwZwYlHBNfXSvLPEfMEEYkGU4zO5ZRPAJ+A3lm/Z5/KcjOEBFQ0HSY5CNrXDLzSEnZsW55EufX5EFL8zmJbBytvvjKGe14bX0TBrMpSoZYoWz8fr+XhQmSb01xDFyeX6BE4vNdEvhZRKKuLy0hQP+bJKrMT4nQq+sy1vIUKyAS7/WBcN+sb67hkmgcRVCtUGmD7gCn/NzXv/lNAMC9dwRp1B508aOfCOeTS3TlBmWTV5aX8emRZO9XFPKGHAfnB+f49jd/HwDwhgSmAyIncrk0NsmfsX8k9ntwWkKd9fVZEtsPp+R+mw+V2eL3P5SsdZT+8fCshJPXYg/ffSx9f/1MsrWW4cDiPuuQL8lPIv/BsINCSv77wYasm35Z1u7dG/exd0P2vycH8qw2UTC2GcbTF3KOmAwpVatA0Y6L4UjmeSsgv3vnA/EZRxcXsDWiE4ieTRA1eXR8gd09yaT2mT11XcfjG1RoozC5QzY31jAmQkX50Zdf8GwTDaOQk7mIR8RWZ0pX2ADqJG7WKNnsdOWZR60SAuTUiDvi+5TIxc8/+Rlu3hNftrosz65x7fY7HYD7e3E5x7ESW633W+iSSPXWLeHAyZCroFKpYUKUs+KiiPLs0G52UCxIxrZFNFpxPY1YVNZ4h0im83M5A0ymffgMGdNbtwXtdXlJXrNWHzoRPlGOlSK8n046HveM6WNmn34xmUvgi08FSWPqRFUmye0xGoggCb4SfdCYBvUlNETIPaGQtZGoyfecYtAXu7B4dui0ZB7TOROFNdlLK1XpU5Q+2z+bYoX+tN2Wz59e1PHwLfm8Iva9In/Jt3/vW3j1Quw2S/7KDSL45q6Dk33xySfkrXvnPUGinp1fIUD/FiBnl0muuVAghLNTWR8Voqava+Kjo5koHn0kz+j21PmA/Guug4klcw6/rO/hWGzcdOYwyWtjk7KlrvjAIj7kicBW5Kt18tkVMhn4FK8b+RAVR16z1cKDW4JQ7nPdDIc9JMmJYipuDUIYhtYIIa4rh+vZcYhOsP0A0S6+gHx+PJL1Ew1F4drynWmeWcPkr6u3hrB5/lI8VEdvSNC8vIEMZdYbA3JIkjOtP7E94tZInGTvzOJrtg8Rv9jvHlERp+TuiyWiHnrCIWLFVeIK6TQCRAXniTKbORZ6I3INcZ9w2U/XdTEnmjhK9M/sSuZUM/ye/ygTUaR4PYOmgSTP5xl+3xV5ta6rV4hSsj3ONXR1Kn8/nU6RW5V57gxJGk47M6JrmMswYGWVwi1E6BrOqueTe5B9LWeS981yMCe/UIF7PxzxV9u7O+iO5Rm1Nonay02kMrJG/UEZB0dTvCwmWjV5Z7Vfr62Kb6oPJwjR9ysxi6M3goy2NBc2SasVqbHaP8NBA7f2ZH+pcGxN3klb7QYcooIVOlgRLKNjY4n3gVZbfEuQd73V9TUPraRELWIUjLkaVBEj+tIlP+eMHGHZdBwmEfnqnDmfu3CHvJetyJgeKcEAy/H4dhX5dXsma3bkjKGN5VkDVt4MejLGsWwCqWVZJ+pORUpI+OcGdKJyutyDFXftZNjHjPxOc1YnKEbTWxvb4DaIV6wIGJMjL5yJYjLn+9hEPfJ+rLlAmiieGpGuM85fLp1DkNw6c96Le7STmzfveHuOxjnJLxcx4T3O4pmtQ568uW7DJVrZFyZ34ZXMW6GwjCVyAPV55+tzrAB4KKX+WNbJjOeqTDz9legLfWwqIT6wWmkjnSYykPZrsVpGt23kiCIMMl4w4VnFbwRgEk0/saS/ihy81m6izP1oZVv2EI0Ebrpf99DA/+X/8KOF7PyiLdqiLdqiLdqiLdqiLdqiLdqiLdqiLdqiSfudQAj9w//4PRcAprCQoprIaEpmcWZwAwggbDKqnZHIa48KNqftJtCR6Nejm1If/uW5RPmPKmd4955kvJbIAVSihDJ8Bo6ZeYmGqExAiVZ97sJQzPbs53JKMjKfXV8g5ki0t0B0wzDOP/cFcXYokcyzGvkLWvIuw7EFnVp9EWbg76xLlDATsPEf/Xv/lvw3I47RcBwhRsF/cigy8teU/rSNOAavJEKrpLyVctTu9jpeMkocIRdHmNHEleIyguTIeU3JziAzJKGpjTizgmHWrerMglQqFUSpSjQlk3yRaJthwoBJGYdWTSKuqp7yunSJ1SX53LgjWfy1gmTQZn4DtZr8zGbmMc+a4eFwiOVl1ooyQ1SuXGJCe02xFjZA7oRg2O9lS1RGZcQofLfbhWXL2Jisrx20VcQ2iTkzLhdUhUrlJVM3cyzEIvIeedYDq4z/bGJjxoirqkWe89kXFxcYdZUcJRWqWONayGRgsM9R8kPduCUZ3U8//RRTPqPGSO8GuWzKVzU0KXMfpjRmioiH8XiMKdE5ZtjgWF2hT4TQ1z8SWc8qlYcagz5CzCinyYg/57sYmu5xa6h3rXCOAsEQ+hzbHLOtStbWdoAEUStXZfmebEEyFp8+eerx4WxF5DOuLVmJ3//2Iw8BBmZZd+9IBv7o+MRDd5Rr8nmVuSkWC4jGIhwbqocRLePzGYiTa+fnPxE00Adfe4S33pZs5J/8+T8DAA95Uq/V8Bb5Q27uia/4xRcimb25vYMRUWyviNJbJY/M7vaOh6xqUCmkQsTgqDtAIS5rKMqIflKhSq6vsLUnKLsGFSTarKdeyuY83pIgYQcFrolOr486M8wdZoiUakLx9g18QTnbsEN5U/qv61YFM0qFvr0imerCRganNfnuGrP/AaoVZOJhFDm/+88lM7pJdF/Il/D4FzpEkdg07vHMRi4tWfvatYzHoCtZj7k+wpxIyc1VedYJ53bozqHxRUYcP4VGXEutepwB0RiRI8zENhoDJKiqUiJf09iSviVjG7iuUDaXHEKbRLNpzgR+srjlMl+pcJ2dS5aUrn0iYw0AACAASURBVMhTlbPtOebkDjO4NiaWQiYmkPIpXjj53e37gnwIGy4inMM+uX1MopssXcd6Qebi4PiI/ZTMlC8YxOsjyaIZhDMotNLqygrOT88AALEwpZoz4gsfvvMe/ukf/wUAYErkSJLrfGtjGc0Wx4h7XJd+PBoOo0hUQ4jcC599IWiiQCyBNWZur8hhMdf82N4Vn9Wk/XaZ4X/8+AO8eCmIhxhVIsPcI1eKqx7i6fKS/C+Umtd8PqwQVVYnwkpxR/UHPcSJqkxH5V1vkS9Bn0zxB9/5jnye+/rHv/ylfN/OJuLM0iruvf2XIuEOZ44i0TmaJmvJom+Z6z4cvyayqMds9Y7Y7P7ZKfJEgkUNJW1swWFmdELETTxBCXZn7iFu1Duf18UuE4E44lSueodKPof7wtcxm0+hcZ8wOX4hSiPXGh1UiKKcTCizTpsNBPyYEaWxRo6eU8UL6Deg8x1v7sn7KH/e7nWxT3Rkinvde7fEPo/Pj9Hhe+WpTOejXWq2iwgzuK2qvFfAsT0EpOqXWoOtQQ9hjo1BJTAlOXxZ66FDlFGMZz0/uUMSoQim7EOPSm4pcn7NNA0ZIiS4fXrKQ7lCFhOiZg+5zqLci+awsbOxKePIzK9Gtatub4QaUbofPRbkmI/Sei8OniJGRNdcqcnRt7dbDU8dUiffR7VaxXJK9vEj8p0oe/zg3XcQJweFbsl5RfmDTH4Zx+Q9G5F3KU7lKGc28VBhCpE7GFB2/d5buKBs8TGVZZRSVX/QQjBM5AG5ehTnlxkMoe8QlUYUj4+Kn/bIBchVdHkuZ9DTM/EnZigOh/3b3JK1ESKSodtpIUaf4nJfmjGbPxyOsbZORV5m8wOBr1RYp7bYpuGnVLY+g0HuGzMge+uYSO94NIagKecHpfjUpXJRoZjHmLycU/LfRXMyjvVBGyYhjXGeb+fi2mHNdLQVaoLcIyPy5MzGQyQoB2458pk559v2BaARufp4V9a+S1TJwckZQhD72MrKvh4gAlALGiDgCWH6SUfTUeHZYmiJjWbzMj6OPUerIvtdhAity7F8Vps7yJDDqteh7LkjY6CbGmzyR03IG0ZaLoRSPlQ64qfiPN+7E3mXfmfq+ZTBUNZgpaRk5+cIE12XUJyVvAPouo6pJWOUYSVHSBPbbbQGSGXknNim4mrl8gwAcPvmDtpEcPSJIO5ZDkwqlE0defcPvyGKVtXaNTo81xu6jHGPdy975sM6739JcprWqkTGTHRc0Z9GktLnGdeBqQWQoxLYIddSgaqNqVQK5kShvMT+ywPxGdFQAkmqmw7IpaQqCiYzC7tUW+v3FaJU+uv4LUyI3m8Tna1zLYVME1F+rk+V2c21TYzpWyeKU4z+p9XponQtZ+sgz6AJnkkNw4TBz8XI99ig/57bFkCbHJGLZkDbCyciiPnF/ubcZ3SqaK7msogr38czYYOKt3NDh8NKhQDfodaUMc8v5Txkkdq7JrZ81rImCLHaRylHKtVu27YxJqrG1OUdhjzThMwo/KbiJyS3z3SMFO/WMyJ3BvQVs9nUU+ybmnxnKlZObRfRuNitQusqxcRBp4UU97M2EXEjnvWc4dC7Q6mKlDDjDKZtANy7Y1RFVHxozU4XU0vmV6MzKnLd+G0dN9YFdTikj54SnVbt1GDwvhpJ8K7O99QMHxyeCf/z//Zf/G7LzmfSlCRdKaA5lsFUF4MxLz7ZfAJpbuQzknQ1CQU2LAc+XiCekQjaJOnWHW0DaQYD5pwA3RFDGY0nWMmLMxrwYpvJsawgGoPGjRqEr+UJ9y/P50hoYoBbLG0YhuT7n59docmyGj9koi1FQhbQMKPWXpBQZY2b2NrWOl4Swnh1IYeAveI6PvihkEy2uNgNjyh1giChtV0SgN3iBXw5nfPghj2SS/s0JYc4RZrBHuOc8rIMPhSjGbhzeddsVD7zyW+EPPGDr33gybD2eZma+gmHazTQpUSwwZIKi7eqG7ubeMEyl1Sc0GYaq2G4mJTlmeskve10lHxxBzlK2E8n8u7rW2vo8/BSuZCDVZYQvP1Xh4gw+BIjHNblAttZXkOdxK2KgM6nCMBmlgfTVwHAc14Mw9GQR/I852HQx0O6Y83QI0noBi8npYpcmGKJOBIJ+ZwikK5cymXl6OgYAV6Cbt2WS9WTz6Rs6PD4FJG4bOzbG7Lp1FlWZpg+r/xMlSyp8qagP4CVFQlSHJwe8jNF3NyVMVWBEkXcuLW6ih5J7WIkWr48kwOZYejY3pK/uy7JZjLi3MIIwmfIHL68kHefTGQzOrsoIRwRuzcYZHrNoGyzPkXrlQQWmssyDgke7KKfuVhblYtZiqVEr5894Vjr2FuTzSfPshN12HNd1wt4qUvYhx8Jye7x8SmePZOL1TIDrteVCzz7v6TkaI0y5gpCbdu2ZwNTrrNCepNjFsPL55/Kf/u5/hmUndpTrK7L/IYTXM88uORyBU9m9Loq4+jjBpVbysGyFFm42P/cJ2upXL/ACgOmnb6MZ4QljOVGBU0eEsYMYj5Yls3h8ugISQYw2iynTJB08e6dWx4pcjQm9hWeh5Ci9PGEkvRKBrTd6ePVKylLUhUscV7sEhigOiBMnBDbGQ9DM3eGwxMZ9yHLLcYsUdvbuoM2pYk//5wBA67dZDzqlVumGIg6eiUX93gs522qI+4NGfrseExHkwfAjTUevIltTkajuHNHAoAv38iaeE5Z+HQyDos+5bohB07btmHxohLm5W5CudnxYAKTF+lcTvocCjMYOxnAncv43b7LUhtC7kfdtgeHjzJAqQI16WQKQ9rtlMGlJEsqDk5OUeA+FOKcjggZTsYjCOyIbwBkbJfyvAxcnGGLwQpQJrzJssjTkyMUioSwdxlMoA0Nm00v+HAjLYFK78A6nGLA/dbPEg4zEEGdZOZ7eyQ3r4g/tTptPNyVZ1TL4p/KVyQ3DURRLMiF4uS1/O7mPbmodnodD86tkQzSIkQ8FQ5jxlLAckXmay8j69qdWvjFxz8FAPgiMrbxgtjLVe0EvQux41xR1un6pvx7++Zt/OzjnwEAeizF8LPse2bbGBL2nc8zgM2zwI07N9Dl5WbiyN8lojEkKFxxeSlzouks1TYDsEjmmMlQrpu+19CAIuckzkNhuSdz4+iu55PiPvGLladyPvD5A0gx4OJnsLrbZbnXjRvqLI8mCd3fouT4jd1tvHou63OJB9pMWObUsKdwmYAZ8fKsOfKg5fwq3Ir47TnLRKMMTmm6jjZL0PssI7zsNlFgic72ttjCnH7ON+rBz/Jph4HlJIM582wUq1w7MxJshuhzMXORXhNbUSTUPOJgMh9hQL97+6aseRVMHLRncHhhWUrL3Le64kfg6KidUbqd5aIqOBXLpLy9/uU+ZetvSQlkJJiAy/NNIS9jG6BgQ6s+QLct9tsikaimaTgcnwEA9h5I/yp1JhC6Dcx5Qd1eE7+WZ7nFyJpimaX7Qx7sOwyWbm3cRIbnqR7PmzbH9bLURJqXlNQ7kiCdMijmamGPLHyoSqoYZB31R7B08eVzpkENQ94lm8ggwgCUInlVZY7WXMPyKvdS7mtREk9PswVoTIL2WD5YYalwOpFDlMmtaIS+cDpFryPfPWLZfZsBQMPQvTLsYFTsI2iKHffafbRZy6Z8tF+TOXnx5Susb0pAbjSQvv/mqSRYE/kkdnbFZ5oB+V0gLP3t9LpYyTBhpqJELE9PLq3j8lzWhME+ZJZknVZbdcTjJHk/ljX76G0p04utb8PH5KlqSsjCsiYIs2RJpyT1m5MjOLTzYEx+12XybymfRn5Dzkd9nvPXOJ65XAEnx2cAgBCFXvzcg96cHCBXoPQ313qbazFi+BAkAbHGe1KCJLYxXcOIazzC+R1PxQduLa95gbt2W+Z3hT63WCxgn+XocQrRqET/dekcmZz4slicgWXeiZzZzAs8x+lv8okYJix70gyxv9c8q0CbezQFYVP65zCRk11aQZfBJb8tz49l5TPBuQE9JvO6xIC3CkZiYMNU1AQ+saEw6TeqtTIKDLSog9LuhnwmHd9AlMGRw4n07yaTKOelM0+qXBHQX9VYDhuMY8zEUIUJO59PEUmnYBoUs+BddjCaQePvqxQhSDJ4kMlkoGvyrqpUSd0/Q6EIdN5pVkgYvb0q62Bm23jy5ROOu4xxRJdxtecz5EJiF2MG35OkaZnPRl5pqqJcUPfbqW15Z12b/lj5g7mjYU5QQpt7cYpnIdcN4rIp/j7De2vrWO7HGxtrSCSZvOd+bTGwZI162MjIOb9ckrH1+0y0uirQJJ9L8/7eH/bQY4llj/dck4GyTrOLOddJjD63wX4W82n4meyflMUfqESsL5FGm/7aJFn5lGeuVDyFg2NJuoVGMn6RmPTl9Owca1syJzmCX6YMyFmujk8OxZ7CJoNbLgNzQR193k1CXM+prBqfkReg/f9ri5KxRVu0RVu0RVu0RVu0RVu0RVu0RVu0RVu0v2Xtd6Jk7B//o++7gMCaLI3ZAUbpZiRETMUTCDNbquSbD5ilTKVSCDKMbqhIHCOTtZcnuM8yiT6fXWLGvtnpIEg464CEil1Kjd67dwsXZUGKKGKmDGGqRjaLBJESaRJWPTsnFD2Wxsc/lixcb0j4MeGfsVQaPZJZOWMiEQiDu7NRgEFpzF1GB+/v3kZiQ9615sozTn4jz351UUHcYGbprkSeZzWJDmI2QZBomXxBou9U8MTO3i6SGxI5/fQLQUx8/qvPAAARmPj64w/k79ISffzic4kU37i1AxDVcUqZ1AkzMmvxDMqMcBuUSoynJQNhzR2cXp3J+DEaq0gdZ/MhYpQE7PcVSZf8MhFLosF58kiD0wn0+J3XZ4L0WVKZUp+JEhEtk4mM492bkr3W53PMGa1XEHlXETT7DKRZdtJV6CSWQbjO3EOvqcyDkgfe2NhAiTKUmaxEcZXs7mgyRoHR7H5bos5KCjIcCHpywApaHmaGrtPrejLES8uSSa8y09kbDrzyLBBlMCVRXzQSQZBlaz0iCe6+dRsO4cADZiqV/KLP58OQfxtKSYapSdvp9/teCZzKxPoVaWUghEqFEtJdMSgFVx1NbXTaKssnP1MIrUQkiiIz7YkICa5X5P+PXn6J9yjF3W0REUPyvU6rjk2SAuqE35qEm8ZiMVRZynZdUeTQYnNTy0aIaL5QSvpeqzXg57jHYjIOisi42aoiRVnkZSJVLs8oRf3WA/z6c1kfCtUT5pptdcq4+xZLZ0jQ6c6kf/l83vMliiBclbG5DtBh+d8W0VhDQl9dZ47KtdhVjqUbyk4+e/olEhl5x1SaJYyEDn/te99EhwiaGtfnLSJkKq0GKoQOR5k17dlDzAgQbdDH5gryTH3uoOUR5qpSLpZGFOIwSGquYOCKzHuu6XhNCfsk4eYKLVYvl7C7Ke86ph2rcsqV1VVMObYKPp8iyaIRzuH1G/F5QfqfCMtFDw9OkGX5npIKfbkv2ZNE4CvyvSFRbO+9L6UfByenqNDeH1CC+unTZ2jwZybtvaHQQ1MLadrdCmXL4zH5vlDYwIxIoh6JAxUMPJ9OQeWC19hPg9namQvEWbbQJrrDZknFVbkEmz7vBtE2fZYKbqyvgqhqOBx/HwkxZ46N58w2ayRAb5BQNJdOYYvlkKWGrJumQsVmUwiyDM/lw1dXNthzHZfXJLR9R0jZ//SP/wxhrsftdfmc8kUvnn6Bf+fv/V0AwOmlICsUFH11ZRNxEmYen5xJP1k63Rv3PQTkTz7+BQDg9h1BZHz22ed4+20hzh5RKnfUlmf65w7u3RLkyMAU+x/Qnqu1pifPnsmKP93bFUTd/qsDROkPK2Uhur7B50xnEwx7Mt4P7snnFUG/BRtd7i8p2oRPNz1EbIaiGIostNlsemUSCtodIYl1pdwCmMFdJgH81ZWsXSNgwq/K6WhFc40lPo7r7QVp+oF6Vfzy97/zbZwck7ST5OgmWL5mO94ZSs1Xhv7EDAZQo/+1me1OUVb44vQEfqKkdcLUFdI2FIwgRBi8Rb+vhX0AiT/HPOdkMypDPfb2pRQFK1yift9+9B6+5JmkxqyuPVHk6NvQWQYyJmI1TFs6PH/plWPEYxn2U8ZO13WsE8VZqkmfzy7OpL99DWtrsg/l8mIL1Y74yfF8igARMH6e9eCocn8TTa6hDSIC1F45Hk29sgIlYZ1KpTCuy3crJGSZ5duObeE2UVRM9Hv7hT8cxE1SIJS4jg9IBZBIRtFrib9SPjNGAmTLcRFWxKv0O/snh3xOCavrREEQJRkJSj/bzS5CYXnnalOhnSn4kElgNlZlszIPfko862YQLSKeUjw3hojqnE4mGHFv89P3KRS65vNhwj0rwZIsx9bQpq9TIhXqjNHp9Dzi540tsfsUUUvDXg9J7gshot4u1DnVGnvryyAx9rPXghZd3lhDi1QGqhxYSbIHIj74VCkzkQXBgLzfsD/HiCgNhSQbjrneTNM7Uwa5dp2ejEHKH8eQ4jQ17hcKkTfq9ACWqSS3xVc/f/EK7QGz/Vwv9x7IPrq3lYfpk/FQJecKSVOpNmHZrIQYkRg/rQRVOnCIJltlyZ5CZ+rGDOEgETojloPxTAPH8NDm2SLvQhGZh0qthFZbbHTI/i7T9qKhIHJEKnSJHsqwVNuaG2h1KQ/OtduocM8i8gQAZiQmrw3L0HiPU3u+yfNYJBLG6amsD0XxoErNIuEYLKIpbaKGwkH6VwMe/YMq9dNIh5wMhOHSH9Z7st66FGJJJuNIaBwb2uqVErsZxD25+ExS5o3Vl+iMWrisiW1GWUKu3sl2TcSJ2nf4zsss06uXr1ArCQolzFJjQzNQXBL/VqM/UDbgug4a9FPqzhwimk1zgagu390kMmbKMYimk7BdIsf4O1bSImAa0Jpi5zOisf1EsEfjEU+QQyFWOywTM4IRrMQpPsCSKp1IuUq54d2BQjyzKVqAeDKB4UjOYRb3DZto7dlsjCUKZLi8E6mKmEAghAIpIWpEsPr1ENy5PHdIlG6C5/45Zp4Yi26TLF4RfZeuYdHvOCy1VGvEng6wuirzo8QEWrTx4XSEEVHciqJD3f161Ta2lgSRNeBZz2Ip/2AwgE5bUShyJfLiOIAZkPldjapKAtkv4AfaLFnk9odMXmyp2Wl695f//n96uiCVXrRFW7RFW7RFW7RFW7RFW7RFW7RFW7RFWzRpvxMIoT/7n/8TFwD64wmmjED3uxJpdMnDEQuHsLOzCQAolSXbMmWE/mLa8eTndEbfBwx1XV2VYXblHe/tSLbvsCIZwWAs4kl4p9KUaY8qQq4G+opIi2TSpkUuj34fDWbcC6wprJM86+ajD/HP/lh4d67OiXphzWQkHvOk3xwSh22TIG41FcFaXqKIf/SHQpJ53aggQmm+SVDGRdVDHpYqeLwmCIDde/Lv518K18mw3sDKroxVglwBHRK4djodaEklk0cdQGYQMBrjww+FsyjD8WgSKWCYPpQbRKsQZXPBmslCJI84I/5+1kA3mVEdjgfojSTKqTgswnGJAs/mU4+8cMp5VtKHkWAEGWZZVB1xs1b35kLJh0ZJmt3vj3FFqWaD5GAJEmKnEwmPVLBP5JnGDFY6X/Cy1a0GM3PMrk9HY9wjIaiKKE+nrCWNxXBxIXYUCiuUmdhsKBRCj1FtldnPJhXBcNRDgyhpTJUJr9ZrWCVXzpDR3NUNif7PfRpOT6V2VsnIry5JZLpda3l1rylGhDVTEDYAYDACnWZGPGSGvHrSElFROdZxdzodD+mkk4ckwGzEfA5UWKO6dlPGZcB3OCuVUCefi0J05Vnfu5ROexKrq0Q8/PqvBQUwG4xxh7wPP/n4YwDAMnkCMqkoWjWZU5dZdcUnUywWPRShQt3lKH8cCoU8ksrVPSIYLAcXF4K80RgHX1Lk5rMJ0hnyI7DMdtYjyfnaGoZTma9Gi4TpzJakMzG0urImupzvAMnmE+m0J01J4AemIxKrVts4fCX9+953vg0A+PCDxwCAzz97gha/Z4dEtipjPxxP4WMHrxVfFccgEA/g7fcFRaEy4QPabKawDB9TDtWSzF951IauK9JH8YFdotni0QiqZfEzt2/I3JxfyjOr4zHqZRnHEeujd7c2AQCFfB4xykUrBJriCXKdNnRmJcdjlW2VgVxdW8MZM3tqnemsKXd0Exp9YMBkJpxk4v3BBBbrt7NcCzWu4Xl3glxGZQllXe6Q4LM3miJOZJz6PheOR6LUJwKhQmLW2nUVQ66JP/rhdwEAz1/+BgBQb5WxtiI23et3+EwS8KbTWF+TfsXJuaCI8s8uL7Cytc7vlnf99FNBYy6vreI5uV5W+F4KTVWtVrGzcfO3xj9JvhDHdXBVk33pzaFwRj1+X7gr0qkkVkhQ/+S58DnMiCxodVrwMfO1Tj6BKXl82u0+DGbrNjc3AQD/5J/83/gHf/8fAAA+fyKIjoRC2vZ6aBPhkClSOrwgmeLNtW0cHQh30MEh55sohVgqjjL5B4JxeWeFtnnxfB85Zt/qdbHfLMfDnvYxYWbeMchjpxaoM/dQCQFySiiS3NPza2TS8vzNDfmd4iiwrSkm3OP2yNd0QZTU+fUVgszq3qSktOEPoEeyWcUN6DdlTo/P9rGzKz5cJ8fGkNlGDX5Ua78tZaykZ8OGiWUStCfJ59d1eSZyHYTZB+UrEkRHJFMJHLwWlFyZ++FwIM+u1Wr46LEIDFxzPRfoAx+++w5+9ou/4rhLZjVLNFylVEaD/mabvGtJrutGowM/s7oDogxGsxHGXP9BIoYVwlabzz1J4l5X3l0Rx37w+CFMnsc++Rs5Qyk+wHgijctr8UkBojR0jlmjVUYqKeu53RryncnRNp3g7/zhDwAIIhkADo9kfAwzDYPnxiDRAn1KqZ9cHnvZ1bfuy/6sUJN+K+yhg5U9KtRpKp3F/oEg47K02Z0be/ATXaCk0RU3x2A0BIiY6XTIbUQ/mUglMdbEV+pM0Y8Uz5Y+h050iEIZTHj2TabzKFLW+8W+INcdotxdvwaHSDPFmbG1Kjau2Rq++JzCCRvS97Ut+bfXrWDIfVbx/gyGig8zDv9cnYHkM4Wc7P3hkN/LvJvcu8tEUZihICLMdiv+Rms2R548Y42G9M/+l3hP1JmrRPTPSlE+u766hOvzM29sgK+4hza2NlFtiO006rKOc0RVmKEkjog2N4gKUQiXaCzkoZWD9JUr5IScObOvuPNm8u+UPCad+hTdlrxzZknsMkROsXwig3PySRoUFUkTPX1+eoZOnSITimfMDHmCITolqIsFme+lnIkgBWVyJHKuk4x30LcQjsg6LleIXKdPd2DDZ8gYKVqRt7k3jicDBHjGyPOM3ajIPFxdVjEjF2mL/Ibbe5vsp4Z1Vh4ofj6Fdj/cf40i7WFZEWLzflZc3cH+gcwJXLHx+7fkzPHi6RcIE1VtmORHnFbQJRrQxzVh8JwaiUYx5F1jSA5aJTziH828+6LiZWk1ZQ3fvLGKg0NZJz6/rPk2EfDhcBhXRJBmV6TvMXL2DYY9rGdlnTkUzJgTKar5sqjznhqiWIqjy7rrTnqI5eL8HhnbCNGEwaiJNlHfQSL3lH3Z0zG2yDNm8YwyG89wfUkhDqI4czw/G4YPCfoZujkkOB7VcgWXr+hP1c+4XprDDta2yQlGInJ1l8pl0/CRO3VIniWb+1pv3PfuNEs56WepLD5+qbiGtbTYh0OlgQF54ga94b90biTxNp9zVbqGnwiwLvu3va3O9EMETCXBIk2R/Pv9fgwHimdMxmA8nCPol3Efq7M496BozEQgJPa0kuV+TfTu2Jp6SDONd/m+8p0bqx6vqRL0WSGydjjoocw7skIMgsTzs5njVSzY9C0J7qmxcAh+7oNlVsmMpjIeRiCABCsh0OGdWdn8ZIgZJezVz0zyPkbiEe+e+vf/qz9ZIIQWbdEWbdEWbdEWbdEWbdEWbdEWbdEWbdEWTdrvBELof/mHP3ABwAxH4GPd/JsXUuP76O13AABLhRzOziWrGKQE7XZRIoV//MnHKDDy7Cd7/WFbMvdTG4gT2ZMKEwVERY90IYcDcsKoetQhkT6O7uLh44cAgMuqRPkal8zymAEPqWBTbnc4lahpTw/iT38kqlpJn0RZ52PK0c3G0BnRZKAdWfJVbGSTuHdDMgV7O6y1HHYBZmWOyMOjkBzZnQ0MDyXCbfFh7z16H4CoMzXHEk1VdbinlMxeKS7jmNxLy8wMBSntWjl9gxt3JOvZmRJ9wWjw68NDr/40R94egxHUUnuErpKiphxlLCJjHgv4kaXqg5LN9Ifld4WlVbx+rSLzzID1VYajj41lqbHUlRC3A0QpObm2ImPb7ZBXR/N73A5VKuv0yAUUCQawzHFTahtru4K+OL+69mqr64qln9FqazxBhhkKxXqvs158f3//K/UuplnUZ1KpFL54Jdn+FGt3Z8zembrpRYLDrNfvkGOqUqshxeybQfRElrwuY3uCN28k81igXPIGx8A0/LgiAkxJhx+dvEGWctFL7IPGOtulfMHLFmWYYVNIraOjIy8aneP3WMx8OY6GCPscUfL25A6IZLJeVqtBhICu6qJHYwS5vupUchrxs5P+FC7r+t8cKaUzhdDqIk0OA1JfeMiRRDKGLOdNcWUYPsVbMcTu9qaMDRFlPt1Eh7YyGSveI7GXdCaBIN9nwnW8rOZN01FjXXKPSJMBeUzGkz7ALK3NbGGKvikcimBIha1QkNxNVA7xzQP44jNRGNgokg9gR2xuNBp56lgDroXPn4oKXTafw9c+Eh6cH//4xwCAYpzS2a6DGcTGLI2ZSvoO19WRzUp2ZkAknjOeIkxVpRHfecrabAQCOCcfl9Jx1pli6jUG2CJKoEneEpU18Wk67t69yzkQG/qMHGS67iLEteSnIk+cvslx517tf5F8H6OJ2Meg3cYmeTpukxNsQGTMeDLBz38pSIKxJXOvxnw+7OMhzmok+AAAIABJREFUVb/yVC7KMrPS6g3hZ8axXJIsXsDnIkruLJoohsz2VcpNxILip/P0Ze8+kr3h0yefwNCpSkb/83pffO0Pvv9dlMpnAABDl4emmckNB00MmEk9OZcMpMoira6seSpJq+QSG9L2rq8vkUyIL3v73n0ZfyIK7t6/i5/+8ufyjsw8pigza2oGqkT3xekLR8ymlSrXKJArLaW4yKhcdPv2XeSYMbtWqIhWG2miSF5z/4wRoWJZFoZEy2zsiM3NmSmejKYIB+T5I9phh1wDtXYTfqIE9m4KAurqSmzw6PAYPcq25skZoJAIIvcrz5+7soYVsq7damKJnC1KbaZJxOZoNEKa9nDnjvjY6pW8XywU9BQZFRKn3pS/g6Z7qJryqfSvuLziqWNOiCAdkd8pnYui1yeKJC6+IZknkjESR4eqPYpn0CX30I2NHQwomTzqyFrKr0t/m806AlHK3hIdNSKyxefzwU/+RKU82ahIXzqdDoIBmSeFoHyfCDK/38CLl6L+l+K+oQiwrPEIUc7NbKSQP/L99UbbU2QLE4FqOI4SY0KUin0KqXVdquCCHGlhZizzVEO0ek0UluQd5zzTKH9cXFlGjZxePfINKjtp1bs4P5d1DPJSKHXEcCyMOWSMV9bF7g8OiZALhOBz5b3GA/mDGJVeXM1GJCI+ZXNV1uVkKN8/aOhYYhZYoVO/fCb+PJvNY2NHEINPn8t45gtLSEfIXcHzouJdG44m+PI5UTwcs61tOfd0uk1sUmWzdCXZ/DC58aKxCFwifRQPl+Lt2drcgV0X+72gf0uyv5FkGEdU/1Sfj0d4PrB1NGvk73GJ3MsE+F5hTGlrcc6zTRWqVneAhC7rLEKkoE1UQy6d8VA9V0SWqnXnD5hoECGXzcvPook4JkRzq7a+tgkAuLy8xDIRk5dXYr8TpZYaNj102Yi8GwrpdnBy/NX5zRT7ffZCuNaq1QGyeXl+r0+OEiL4djY2kGAFwJSIE5d8bYNRFw732xzRL0UiIXOJFfSoNKcQRooHaDibIqIkwKnepVSJx46DKdXdfLzHNBo91CnHPlf7syPzsL6cACzyuXDvGUVl7YdDMSSTVChScthETIRNH7bIHZSjGnPlhCq4J4ce79e7D8U3mH5Z62dX19B4Tupyf+6UZZ/KF9Loc4zWyStXof++OjuHTcTl/Tuyh2/eJFr16Uts7cqZoVgUu6diOQ7f7MPgWUEn6rM5baJJ5FivK/2ckmdsbM28PdjlGejth8LBFZ25ePFS5twm4jhD1bZCPgw/UVHRGNEd5JEZ1FvQeOe4qBKVRpROPB71UEoBOkuLYxyIxHF2TBVgU37Xpyr2zOei1JRxU4hhrxkzzJWsHGFUCi0TMn2eCp06D6wsLUN3xUbV3hPh3jCfO55yW5/ngSiRPrFICI1zqkkSHfmSPGOdaR/ZJfGVOzxHj/n3tdI1VuijB1Sn7dFHB0ImpuTvC/jke3c2hZdv7rgYk4cyl1YKd4oTD5jwLK4QrwrZMpqO0BvIfIV4tiYQCuGwiR6RasqfuuQBm0xGHrdXkCjE8cBBJCC+zqXCWpP8qmbQwIwcf2HOd4R3sdFogg7XaDSilM6554VCMKkifjFWVUPia3JmBDeWqTJ9Lr5vSr68kT5HmNyC6u414V273+vAoB/QePdV+6fm0+Gn31Z7cp08Ua1WBwGeU9Udbs5zu+POoWI9/+P/+a/mEPqdCAj9N//BOy7+P/beLFaSbMsSWmY+mJvPs1/3O88xR2RGZr4c3jxUFUVDqYUEagk++UI0NAKJ/kSNGqT+Qaj5Q8APiB9apaKa6prrVb2cMzIiY447z359niczdzM+9joW9Uqif3lS+/lJZVx382Nn2GefvddeC1IakaQzEuYAKKj9bGqhpiC7NBJrq2JAWp0mUgmWOM3E8LToUIyuu8gRJtbmv4Uc5cBEMORFXW26Jgf33fcfYuLKofbVd0Iqm2WgYH1tGwYNfZfygXkeOMf9Mf7p//gvAAAZnxwQQZK7zXwzjAjBU5KY/omM/3ouhRubslGWF+N8dhPn52KEQEnMh6U1AMDC7U3vwm2pUowzcRo++MX3cXAph7DyjCZVRdgbwQGdBJu1LDESqZnGDANeEkIk25qyXGvqAp26GHyTF/ilvBwq351cIprhRiEZmc73tDtt/OSHcok95u8+3SNkWwtjhZfLGWHkLZKF9rsDLBPWm2cAqnxZ9khdHTrOOg25o+kAgyhjWxkayvZGY+gQeqccuRE/4/MHPPJYwzA5ZrJf4rEErkjiqIIUsaRycH0eNFM5PJvrDLCFQni2LwHNlIKSc322Gm1srsmFxceDQpWshaMRXF9JYEdB0kN0JKPRMCqUEU6z3EVJSJ6fXyJHctEwSwQtewiHZWcrfOciLzLdbhcVkhpnKUMeImS1Xq8jzMudOuxsQpaFrFXWQ4FSuX0S9nZGY7h+ZcTHfAeZ743VJWggQSRLH5WDZlkO3hzIGrcIGb5gOaaGGVYoA5oIyziWeZlYWFjAhI6LR5zHPnV7bURYovDwPbm4+wMG+n1VMiAOsUv4ZjDkR7nOICkP+3FL+pBeKOGQEO9aVcYsz1Kkeq2KJEmrfTylXFs+Ew5FYLOeKRQgYR5lIvPZZdSuxWm1CTVORrhW43E8eSrlQsrO7eyIE9Xr9RBgQFkFIxNxsRnjbhc+Xgx8MUrlcu/GAmF06ah0Z5yvZgeZtArQSD8vKrL2tm/dwcGJzIkiQ7+5Iwe7X3dgcc5bLM8aD2Wvr62uwqUj+/SpXIZoonDvwW3UGVirNMU5rrXkADw7PYdBgt6lHXEURyQQLKTjCHLvqcuogg5n8hmUade+fiT7LRIXWwFbw1aRpS+USc2R2Hb/+MIb2ykdkds7q97+fcYyi2tFch5NIk7brxIBQ6690uoiDvZkfdykBPvnn/4KANAfdVFaERtxei52JMvyrhvb6+jwYqtIFpVjkElnYVFmWl1OVGBjNOghS8h7khe5XkfGMxwNo9qWM+CMpL9+njPF/DIsOssJBlUuyrLGrckAfgY0VxZVUkLepVqpo8OAxJAlxr3xEA73TpV7b8YIQDQR99bmrW0JdsZ4hgf8Opq83NRqsnYiXL8vXu0hTFu0lpU+q9LV1fU1b48P2AdFOplIpb0y5ceP5VzZ2JLfffnqGfyGCo7IWXXvnhDYv3r1yktoTKdch/viwAc1IEtS0q1tksYzcJWKZWAyiVO+Fgd6NtU8gsyVVbHtlyyLSSQSqDFAPqPfYeoynro/hAkvJ+m8rNse4eevXr7B0tIaAODkWNZXiGd5biELw/SzX3U+S+YjEgt7krqqnwu8wJcWl72Syr03YgPVGWRPR0gycDqlD2BxjK8vLrDFvVFnEHhIx39z4yainDcV6DEd3ZMyn/EsTdBmNls9PFHBE67DWFy+X7s680rtDM7bhP7cT3/xY698/eRMSqeVw//g7gfosT97vIQpOeKD4wNofvp9dPTD9N0q4w60GQUCqjIn7RYvzeEQlktiL3wuy3dmisQ95/kDBstVVGCo0ep4gQhF0Pz81Uu8f1cI2avXEsjIszzRmjko10guzDKQn/38hwCAly+fYDqVflkD2Z+DNkvqIxEvsamkmrP83fcffoBxQwlkyLpVwedYMgIfx1iVQYDiAtFIClESRV+yVPuqLr7NjZsbsOkjz2iTTO7r08sr3FoXX00lu5TdGXYttFvss0rIMug2mtjg3RWn5ycAAEdzcPehBLovWRZWoDDKQj6P589l7QQp8NIjNUE8HEarUfHGBnh7uWz8rYtSUsnWB1SJq43nr2UvhOk/BOlHxgwfwn6WFMaUQAzXgB70AiexqPK55G+FXAYTnpETXiR9cZ5v2xvoNqXP9XOK29Skn65pQmcAKkFJ9V7fgs7LdZRkweORzG02GcWMgZkhSfAnJPqF7kOK54QSAgGDdIVMGl3upTs3xL6pUsn+eIyritiUPs/bLAmhU/ksbEZr+rbst8hI5vTF6+deOaQq88pTbOWLv/kU7z8Quztk0GhxVd7l4roBX5BloSTxNUj5YIb82DsQG5tj0HiizaBxvTbqpCiYMZk3nniB7iTP+q0dscfHrw8xou9TYhm3zvudbg1RZ+lnOEkRHt45Wi+OkObnOwzm1llOH09EPfGeApMshYSMVTQR9Ogvvvn6Mb/P9bGyAt1U5foyZlEm58bjMVxH/nbKIJ1KQJghAx2uHSU44szgBdFVUkHdpUajIfoDlqHy34IkTH/3/j20KqQhoZiGKiuNpmJocO8kKR9vqITx61dIcj8qgR4V7Eyn00jS5h3vyZ66SzoTezr17llJJgnUmMWjMS8BOCQ9g0qwZgtZXJyTWJnOpI8lVqbhg0FyZ8cR21lrybssLCx4JO8qEBrwR2CRKN1m1i/MkuRQyECFRP8jm2XpHOOg30CApeYzfi8Zk3n+9utvvcT+NEe5eZKX7xaWoDVkn5ic02vlMxs+OAx6lUh10enJvkun4p7fvf9G1j9c3rvyWUwprBRmcE/9TfP5wD+h2ZK14KOf2+31vLvyf/s/fzMvGZu3eZu3eZu3eZu3eZu3eZu3eZu3eZu3eZs3ab8RCKF/+B/ecwEhQtphRBdEmEyJNvDpAYwI+1YEon6FAhqNUCAMtUr5+Dbks76RjdBU4l4jRkd1RvJbna4X3VOEchtrkgkbWxM8eSnZCIuRyd28oAfMQAh9Zm50EoXtEOly5QTxj/6b/w0AkJ5KnxQJqhNw0WaE3KAccwgkmwuHEAmyXKVIOHirgzTJiIPMgq6HpL+xzTziCcJzSQwXHUo/F1aLeHN+xjGSSKOPhGrFlSVc9CRSe0oUUZ8ZisW1ZQ9WpqKrKiOeTKaxSSj03mtKmBLJYZpRhEgUPSWpVYCopVIigxEj5cckphwzu+x0p/CRKDbDUgyXsNhetwsfM7hJZpbajTbSJK/eYfZZSelNnAl0ZnYsImfCzAJN+mOsr0kG69lzQS70WdZw//59nJNg01CSjIQAXl5dI5+VDIAiSs6p7PrVhZfRL5GINZl4WzpyyfIMJgCwScntb7/+1htTVYajk3hzYWEBVRL7GoT9T/l+mutgaUUQcUcnMm/VpqwlR9NQPZNsgiLRW10u4upEMqlrzHDcvSuZt9cnJ155UONA3n1tQ/qn+X2YEJ2hslyK3M4IBD1C7AalFfMkj4Me8DJRaq0mKLXozCzozCzNgvK7VZLfzlwNCyx9e3VImdiJ7Cl3YuH2DVlzDmHPFqU4NfixdyTZYD9Jr6dEJpxdXuCI7/4+URuxRNQj1tu+RVJekl1eVq4xoy3RmTHvM6tvw4dKQ5EySr+ifL9bO1t4+lSI3ItEVmi6rPVQIAiT2WNVamkGZG7CZgKhMOHiRMsowu90Oo0qy/CqDZbjMeO5XFrEFWVHVbakSiLSmBbA2rKMo020kkIXxv0GDCKmhpyHid/nSadbY1lHbcrgRpNpBIgC7FelDwWu7ZE2RJBIunPu/xmRhusr67hg+VOOJXfr7NNCyYfvXgtKZmTLuLTbRLidXHlZ4PgCpT4p0xwIBXFIe3O4J+vrB5/IOg6afrQJ81fP7BBe/OzZGe5vkCC5KciFe3cFmn5WrsHHNbOQk99rVS+wTplcjTaJiUdEIhH4XNnrLWa5lJz5+x99D6enstZiLOcgahmWO8OAa+zFGzlLiiTr31lbw2VV7KGyIzPWjMRiMY+gvsvM7wIJ+R1nis3NdfZZ5ssaqaywi2xJMldnlC93bHmXfLKEcyL2VCmhnyhcM+hHhqgykxkli6mmZqsLP9etkuatt5pI5yR7ptCYivS5Xq97RMBT/s7d27LfNNjY23vN9xFblkhJduwP/vAvPDnVtQVZX3HasulsjBXavv1DyTyeXRJyv7iCJktBf/enP+K4S9/PKhcAkTNDIiRaRChFzaiXwX2xL6UEy3yHZCjoSaHrhNMrpEU+W8Tmiox/syPzd3Z2hjXa9zjJjZWk/cH+sUcuPKXtipvi20wmU9y5L0Twr0hq6pDsNZXLo8q+vtmT9dWjHTLMAK5IdFqi3VGlLflM1pPeVedS71rW7NHxKa65fnduSub24krWieZz4bj0aTTpw+6ulI/n83kM6VcFdLGPPp6RqXgeGb7z61cyNwG/jijXgOsqJBmRHPE4DFP6qkg/VRnf+ekhSiTtVeUB5yyV13wObt8RW95qy9yvEiGuTX0eUW+bpRSK+T+eTuOCNlPtKeXj3Hi4hSbLT0bMHKsylMFwDJNzP6KNzRKhMXX0t2c3z3dVzZNIJDDh+aXs73A8ggl55yBliG/urgEQsYMxbW2LPsm9ezyfhg10JrKHIj75fsiVfVosLqJNVEeLKLEA58QZTVBiOYIi804o+z3uwc894bAsYUYfcX//DBP6kB988j15ByKFJpOJV5Kl3lkRwjs60GrJ3GeIzAqzLCqXKsBlCYwSWVC2AtAxYZlEhiVjg0kfR0RTKpLcIlHCU8tGkojrwzPZC6oUPRTwI+XJbUsHD4kWyxeKSLJM5YqoEkXenIwl0CZyY3Fd0KnjiYzrdNCDS5LbEfdlUJfxvLF5E1f0G13eVaL0d2qNukfgekyC8TF9qs3NNdQp9lFkub5Nu19ptPCjn/0cANA5FRRKs9tHi5L1HZaqW0S6WOM+/CyhzyYVmoGIk0Qc5ao844RoUUVOfGNrF8Mu/XsiH7J5IgnGEzj8N+VPm0Rh9HpttEmpkWcpc94vY/bpl59756YqM1dy6KlYAjOLaKqIQlpVOC4a6lz3qkojn5Mz0qdNoZGwWKFRdC0Ik/6HS6oLJZRhhuMIs6SnxXNT7dN6pQ4QKUzAlSd64ptoHgH2RUtseoTE0cH6CK4SCvDJPn3vppSh/c2nf40u7zuFZTmfA0TTW9OeVzZVLcuYvSDa48Mffh/pBXn+gKgQP9eXOwNGA3lGyE//mSV0vV7HO1uDPKfPzi4QJj3HjOveoy9IJ71SYp3IMY1+rt+nwTeVPdrm+Kd4pscScTTog9pc/0oUp99p44RnryqtX+e9cGY7WCKSa0a/p6soRDIx+IhkmXCer+k/JhNpGKxQUJUbOu8/tmPDN6MN496PmDIPf/zHf+TRkaxQoENVmEydKXILMr+KZsI0wujwHB8QUaru//FEyjujLJA0m9QE4ZDpkXGrypQgRVpWVtaw91r2eJeiBaoCpFmuI0sBhBxL1eqsMkAwiIYqwTVZ2TCUsZrqYyyvyb6qVuTfLJYD7m5v4fhIfIXb2+LPKqRsq9XxfPEJ93osIWPWH/Ux4dj+F//dX8wRQvM2b/M2b/M2b/M2b/M2b/M2b/M2b/M2b/Mm7TcCIfRf/8NPXACoNatYXpEIo48IHxXdu7wsI8r65CAjagGSzy3du4Uq6+zzrIHu8vuvjvaQZiZ0eUkyZlpYnlmvNXFFGffbO5J1rjL6Wa03kClKXxqskUxN5HtL2SKWb0lG8PLqBADgJ3rpcDjFP/8//goAEB9J1ttMMttuDzyCshlRSxESgq1mEl7mMBhgbXfQj/z0LXoEALodiYZ//Pd/gj65a1QWU9VMb2QLGJCbwKbEn0kk0gQOoqtEudRkzExGGLVwAgcvhYsjxMj6KrOMCwt5XJGHR/EM1Zk5C/XGsJl1qlHGTyEDtOEUGiFMrYmMkcYa44Trx4S1yKRg8Agtt7a2cEyJ4i7r4TOpLBpDifyfHUlG6uYtiU4nkhHsH0qmt5CXTL/dl35m4llMSACoJOwLrLdv1xse50WhKBkNxa/juBp6A2bfGJFvU1o6nkx42Qcwo6qI3lzXxYM7whlwzCyVqruHPUOSmQlFZN4jMaIv4MeI/RsxG5RkbXEw4EePmawmI/qZRenvq/19hJkt6fF7yws55FjfnGZWYczM0nW3hQGz/VuUeDajCgVgeRxAKoOl5N11V0eNJJAueUc1ErlenZUx7MsYZ7lPV1alfxfnx9jdlXm6JnfOm9dC7LlzYxthoknOWZvtkIfG6vWRJJ/R2oqMWYYovaPTSzS68nutAXmrSFirGQa+fSLE7suco1w+g8MTydBs3xI+nDozzRs3d/HqULKSbWYFUiHZu4+evUCXBNwbRAEcE1GQCAeRychvFhbknce2jH9Q1xHnvsySIyObIjmp7SBEjo2X5C+4sSPIxKBhoEVOmBoRAWEyamu6qwAP6HHvrW7LOmseXqBxLWt1gQgSta5DkylyJCf/1TMheY5FYghHZWxWtsUetEhQeXhVBYjOS5vSz5DKKVgWbJJIa7SrUxL0Dbp9TMiLc4/Ssf22klWtI0nyzkqDJOUjciMYSZwR0ZUl8asvLM98cXwMdyo/rjHTrNBmljXGtxQf8BvyftZMxqra6iBJwu+bq+TcYpalOXSgkf8iSf6Hca8BgyTSmQIlcsn/Ew758JIEseOeZL6Wye+yu3sT4RTPKMoX370pqA8tZOLbF7LOT0mCGvPECAaIU3pW7S/FAaBpPiii5P5Q5uQdkmM2O23YJHHcWBEkgZ+EloFQAEfnSiRB5m/QJhpl4KJJBJhOfo8+11nl6ho3N8kBQqSJkrkeTCyUVtb4TCLKZhZ0nRwqfB+VfR72hh7nwZi8P1misEIhQCOXQZOoEEDW18FRDYuLklkLUv51xOzm9dUp1shRYhCF9eylvOfI0pHNyd+yLlEoadmLj/deIEpEFq0vhoqItG/j4lgQHOGirI8VcgYYro0+kbxlilMsEwGciERRzMpZbDvST01zYYaZ+e7K2XGDhOaAjm++EQ7CMPdSe6zQozochRoiV4OSP3/n4bt4+Ure8ZLkrBkKIZydnyBPMljNoa0mcvXJ14+QI49FvyPr5PRC9uRluYJcUc7Ga2aA0wU5B4OGhu0decdaXbLkd+n/FAqFt9xZXKtBf4jjkcL+KznjFomw06OmxxHXqMv4KS6sTCYFixlile0ekzunPxkhRN4jhY5U68qy+4iRhyXB/waJSOg0mugy4xvh7yok1NjWECXfwzWRUi9eCNeUGbU9aI/iYAoQvQSfAdfhPmnImllIyXi0enXMmCl2mb5PkyPQmc7wO7/1WwCATz/9VPrX7mFhY4N9FRsRJvIhnsjgqiHz0yWfZflKbOHv/PwTGIb8bdImj19VESbnMKW8epqkxoqIdNQZwE/bH1FoCo6jPRkgkZFxv3VPfN4JfcThxMGwRwJ4vleFPGoXFxeoXitUnvgFykaZ8Sh6RHtVr+UMj5HfYrmYxwLt6YSiIgRvIZGIwO7Kfv72iZxLWze38Oi7bzkH0q/bRLNdnp17YiKZZZ4T7Enl8gwaObYWF4kcI/Kp3mijTBTJlHx5fp4pxUIJq5viD9QoQqJQVRhNEOJZ95Ly0XkS/0/6LeTI4aTIYKHLO7e7LlIZ2tNlWY8nFE9JhIKoNmWM/OTsapDLyR7a+MnHgnL88l/+ofyt04PLc1ZxmsbI79lttxAlwmGZAjvtJhEuiRhm5Ipa2RLfq0zU+mTsoEEp+jF9m2lX/BAnEEKInGB+ktbCIW+bb4Yw7VOA/zVssUO9cR8REvuek5tuyvN0eXERtWtZ9626vPvGkqD9zq+r8HHPr1FOvFKRs7JaOcMahQla9Gu1sYblZZnfEf1NjeefbTuY0keYuco35woZ2Ni7FCRHMitrLkW7oA18qPAeOAnIM428jHkmEMdQoQEJGV5Lig96cXWJC0vOHI0E9IvK7lWu4JA71tBlvq7oOzs+F6U1OWtSSZLgU8I94J+hpXhngkl+Rj47GPY8lKNCadv2DE0iQkNct42W2LnVjTWPlL/bk99+8M4dGdvqNaZdsSk+Vig0iIovLpQw4l2jwzNc4zn1wfsPMSPhdodrZ8B7VjyexPnpiTyL6Nk813+2lEZQI+8uUToO92Ct2oRLXiiTKCDlhwfDJmI8axQfpUEunFar5VXJhGhvlL/U6/UQNLn+iTp1MYNLNJ46Vyqck1g0hQarM/pEN+m0b7rmeCjAZaIVlQJANBr1OBx12szCsvye5WiIh2QOA2PKzbPKAK4fI8YvLFbvWK7M+zRoYUCUV5h8ayDyp9NuIJnkeHTl35aWxCZWryvI5X5dlGFKm+EzNHS78n7/6J98OkcIzdu8zdu8zdu8zdu8zdu8zdu8zdu8zdu8zZu03wiE0D/7Z/+BC4gqkaodHxI5olAY494Aa0R++MmkfT6kTGwkijElmpeYpZpQ9nhm2Z5aiVImUUpmVmeIlYJE8xyiZFxyiBycHHvS2NvkFUowixezfajNJIpYIWfGlLwijy6a+PN/IdnkQFr64qPsqREMetFEpawRYp1uJhLEfWY93t2SSPmV3kT1RKLtTk8ii0rWc313Cf0xFQw4hzkq4cwaTWzelPrCjlKr4Xtdd5pwKFdqTfh+m2sAgGefP4PGyPp7D0XCXqFKxq6Ly7+T7Zsw425rPQ+NE2aNtcs62FQii1hCshgBk9kFys43alWEk/K3wxNBaKhobtANwrXkvbKU8zw4OcP+8QkAIEmUkUHG+eFwCJ1KbCHOxYSqOre2t4EZo+7MMCvW+PF4iCFrxi1mr/0hqjS5PjSp7DXpUIqdnBeVasPLRpYYNbbJ6G86GorL0melToMAs0ftpqcUpRBdM/IspNI5+KnSpjg8bGa9DMNAJi4ZDTiUcOf3Xc2HAdd4iuoKpeUcak2Zry5RTUG+c8gIe5KPYWZBfYy6m9EIesze39iVzFmLmbOxawPk4TL8Kisj/TQjMU/WWiFaFELFdRyEVBaCCLcRM6yOpmM0VpwafHfyFL3/zjuoVphNI+Kv6cjf0k4ALcXzQfTblGpI4dU8bCo3zQwOWdCPy6eC7Mkyg/6MdeyVegc++Sp6zAp0+/J7/d4IBnm7An7Zex1KVRbzMeQzMheZtPxQ55KonqCLuzuC1InS7gQpdW5GUzglGmrIMcqnE/xM0OM/yq9JJuyCyhLw6WieS+blzoZzSpA/AAAgAElEQVTMzZIpGYGLShku53edvElX5beKdfrf4bu6blyCiWElqofiEpU1enVEmYVQmYp9xYdxMsBDKjUtMCt+dUEuHN3AJlWZknyf128EweN0+6BKMfxEvXXJQdFsD2GPZNzrZVnTkZBkilaXzLfqaVxXiktuYXENnz9+zjGlFCrRBo7lIJuXvWCEKcFMbopg0O8hHjQi6wK+ID66KXZeKV0YtFf1Vh0HRB9GyKe1Rh4ZM2jAz7XGhBtefSeZyHw0gfSS9Ou76gn7Iu+FnoPricyrmpsI18dwYHmcJErudZ025uG9Gzg/FkTGyrrM84R20tJsHF0wE00OkTaRBS9fnaHZlXU4I8mRyvTPhn0UmHW2icDMF5Q0tI6QmjhK+caiEXz44XsAgOcvBW0RIGfIi5d7mHFM+8yeOlSh2yotIkpljZ0b0vf9Y1nrrm4iEacKItVcvvpWUAO64UMsHed7yZqj+cBwMMPlhWQxJ0P5r5Lanc1sJDkOM2IJllYFufOnf/kZZoSlrtOsKl9jMBigRERugudtmKpXkVAIsaj8W4roxVpzgAZ5PkZUrSuVyBExbGOkeBB5BjSv5Ny1AIw4d0qxL03kYCgEFNaomEdE871lGbPBqI9Tokj0AJE3C5JFPj8vwx4H+Ezpk48cZs50hvXVNQBAn9nCiSV9KRbzsIg2mhE9urssHEKvXr7x+GlazCJbzJhG4kEk89LnCZEEpUAaAXIkBsm7oXgOctmUpxamMv1PHj2R95rqCEXkcwNbzk2TY2xE4njxQtQXfYq3kdLVCyEDYdpm3ZHfvSZHyuVogBjRkSMieBtUQ3t9MfX42RpEbQQNIqpNDSUq+50Rfb6zKbatWzfwr/76T+Rd12WOtui7ab0+dlblc19+LXujPQG2FqUPit8jSYQXtBESKfoiwy7HmEjvtW3EfDL3e+T6q1CxywibKJbI+UH57Az9P9jAPpHePvrRt6kS16o2sbAie3t1W86CBs/NWsVBkKqZFa6LApHeUV8I12fSh2xR3jVMBIk2HMPxyZxcXcm87R/KmbC5s4JsSebk6lJsqJ/8G9lEEetJopzJw9YYDRGgjajx/FokZ6geDGNIfsheX/qySVSJGdI8hObWmuyTNBUnry7K8BN5fUqkxPaaoGZq11WckJPRoC++TSReMmoiTB6julJR5LqGDRweye9dXlOViEioWBBIcB1rfllffn7/7OIUz8/Ffrv0ixW/0Y38Gs54drR9iqtoDeenMm4KnR6lQp0/oCNP3qQZERWtsazffDgKjQgyMyu/o9SMDp+9xFdUAc3dlncdDsktOBi9VSyjP5bOSv+mlo0AxzFKfs4eFfgSqaTHCTakClqY57zPF/AqIjCjEi3Pe5/PhxD99mxC3rl8JeNjmkFcU0Fv546g2QLTKcCqEaV2atIvMAzD47lTdljdBYL+pIduvLyU/VwikmM6nXoKmuqdFW9PJuIgnRVf/tUrWaMdIpP8puH5OVOi85SP9+z1UyyTk+cGbe7+vvgftU4PmZzYFoUer5PnbOwOEea6yBblb7OZ7K3JcOQpi0aiSb6njRnvaC55guoVGf9uw0aYaNvJTM5Gk2rQATOFCe1hjsj1I3IcaSEfDPoDTar4FVlRUcrmYRPlRTAWQqaMwbDrwp3I71WqcvdI5Mk/GtNg8gyJ8u5m8e7QGw88lbEEFZTbbfJ1xtIYO9LPq3OiD1kFNOj1keJZXyCX0Ih39qltw/KTX5Nj1u+NQeFf716llLqsUQ9T+qO9INW3g7J340ET1lj6MKVjlkjKeNTqXfg1eUZ6iWieptiF5XQeZ3WqdtM/1Xl/CUCHQ/Vzx5ZnK9Vox9XQG8p+jielD5Gount0ESNP1vGh2J2zc1mXyysLyJB/Syc6UK1naLqHnPxP/qvf/82Wnf8H/2DTBYDFpaInKXdNCTgFc1rI5gBKb37y3ocAgPO+LNbTkxPvElnMilOpJMfz+TwalNVTcqwpQvUXkjmc7stgBghbXFwXR//LR994B9GMJIEjHpIp3cQRieFaJG77+b/zewCAT/dO8If/p8gOGxl5FmZ97zkqUBWiDKafspbFZAS3SQj68W050OyMhvNDgU8mQoqkUeYrENExowMXoBHUCesL6cBvsz/P9wUK+jUPAEdzYBESt0SiUhXoOr9oYH1VHABVtjMgqXGj14KP5HLKkesSmnh0fopkRDZpmPC+QlKe3ag2PAn15VU5vJXE9MSZIMBgyAIPHRWYOL2+xrePpc8refneze0bcON0nghpzKV4Ka1ce4EnTVPwOrmcb62u47Ism7RGSc337r8LAJhNba80wUe5yzMSVE4d3QsW5RLyPtpMPmNPNagzbkKHP8CLVi4WRY8OaYRy8ENFNHl1id3tXy/pefFKnICbt+5C0+RZiphZwQp90DAbyzOCJCsecC3F0xk0LhiMITns6voSHj2SUoXFJVnHJZbE7b3eg8HLSZEQ6AYJYMeWBR8vqAHuxS5LJnvWAIGofC/JwJpaxwAwJTGfKm8sk8Q2oOneOjdJbnlwIuu60+vDjMi8RWnoLhh8sEZD5ChXHCDpXm0oa+5n73yAEe+pQwYfKyeE/UYCiLDkSZWCdcZDxFniUSrJODRC8oBffvYVWhWZ5w4v4jHag0AggBolSVNxMfgZljpYwzru35b9MuhJ4HbYFRt1c2sFQ5ab3NoVp6vblDUxsl3odGICdJpKCXnPyWiMFC+xe5Ti1aKyFlbX1tBgIAkMfJxwHIuLy1hfl99RBLjfficXrUaj4ZGKvvtQypnenO7BRyi6CkwuKTgsJjApzXzFILCPEGVzpiHCYEqAtkjJd2/t3kGZ/TNNRbzN8rxqDadcD5YKujFgm82XUL6S3zmn1OpSkReRUBgOA38zBgXfkID3xz//GS7ripySzxwwMVAsqao32LST9YZcMAKBAGzuR5seQrvRxm9/7/sA4MFula0Nh8P4/IuvALwll09RxKBeb8JWc1GWdVK7ZtlsoYjL2ok884a8zwHPm43cCnIsSSuwVKlHe3p2esERens5XCnJngr6HMS599ZZCqD2XbVeQastn1fE+DdvyHyHjDi+fCzyt20mJUIMdqTiccRCKnIqG+CahNf2zPIIcQsLcsE6PDzEyorsoRpLezoc93gi5dm6/ojlpRyfsObDB4Ss6ywHUYHhVmuEdEbeUcnI7x/I2ZXKZ72SHiX9qxIqg/4U5St554kl4zJh2ebM6ePWHTk7lngJPr+UZ87cKQxeaO3ur0uHG0ETJpMCUZ5VEf7/R++/B5oyPKac8NHJJaa69HnG4rSFAh0zn4Mmz6GQcoR7LA2PxNFlCeL1ldgPtbfGky5uvyulMifXsmamE3l2MpnEwalcmhgDRo4BoVu3bsGaMMhEexPzyzP9egA+BsZG9Jd8vFzZtg2TTmeaQbdZTdaAq/k8SfQpP19hAmKia9B4kVYk/csRE352LKJIXrlOlldKnsRyXclts+S0Ue/BZYBrNJVnZShd/fWjJ14ZGqcE0ais8U53iCWW8ekWScR5tlZ6bXz0/Q9k3EmM7ajS657rBZcvWQ6vxAU6wy6CJDoNkIDex0CG29IwYZl4Q5UIbopPlDMNZGJKjl2eWe8PUOMaHXMOdfpJsbiJZEp+J5sx+TvsQ6uBNJ+VTDHRacn8jcdjNFriV6VTcn76SBNgjSc4uJK5W+OeLaoywuYQCyVSL8jXkMuLbRr1A0jR7h+ybFDVd33v9gO8esrgO5N4ylbokykcBi37vOgnWOZycLqPsaVK8BkYpWEuZIsYXIqNGLI0K5pOY50++IB2SkmJ+40oXpBGYGiJHSgVZV+vrZZwQHLXoOIfYEnK5to67lJQIM6k2rffyN7VNQNRyp13GTBQ+/XG9rYXHDlsSNDozqb45qsLS/jySwlY7x9J/27elgDqYNCAxkDcxKbdYrluMBqGTb+lN5Q1bjKI4NouMry8DviheNhAkpfes7L4RR2WxYzHY6yQxNdh32f0pbKJBGp1WX8WL7FKJCQQNOEjSXCPvp7JIMeo10ed4i+q9NdhEDNfyHqBiw7PpT2WFMWiSUQpJ658h+lM5ts0TcRpr6tlCmVQxCdgBD2BlzTLf4YkWg7HQpjRJ6+zrC6djHlltkGeccOh/E4qlcKEyZwOfVYViE4Zaezekvn51a/kfnbBwPDu7i4SvEdMeClXpM1RQ/Peq0shiWaLpOOOz0u6qQt4hgnZwWiIkLpaMymvyMP9RhhHZzKXiwWxb0WSqpdrFZRr4tMk8r8utFPMF9BhCacqkU2mUwgb8v5L62IjxrSByfgivvriS+nrVMajz+/FY0WvBHyR/rrNsZthhnJD+hCnbVG0ItPhEFNXbPqEd8Mi38Ea2TANJjTC9L9PxAdd3S4iMpbzT9nx45MTeT/dRZR30MMj2Wcpgik0NwRbk3Hvd6TvCZb6BX06muxnkqJGipqj2+kjTYGSAcnsI+EE+rxDabxnzggCGI06mI7k/W2e9X5SnTSrbe+c1A2SuOf5Lom4V3qvMZAdlz/BmAIGpK/HLPuO0sYXc3mEIH0+YIKvT3uwvLIC25V+9ggyiPJsjphR+Jhk6bGUWa25Sv0KeQbrKzVZ28ruZLJZLwn6j//JL+clY/M2b/M2b/M2b/M2b/M2b/M2b/M2b/M2b/Mmzf//dwcAIEtSvPFwhBjJbZMRCbMpiefpyEae0MprRqmvCafPJrI4rAjczYoSUsqyl5A/jBCjlq4iR2MJx+HJ8duynalEXnUiA4r5klc+ZVPyOkhYfG9oI00kUqfF6CDlWAedAbp9+Xw0TAJGHwmeplP4SfapJBYVmgWJmPd71kQiycNmB1kSZPaZvYwSItoddJCOy3tZlPZTCJKPf+fHeHUo4/FyT+CmqixkMBjh9i3JctRIlOdjxP3nf+/fQp2kgJ9/9jcAgAWFGtBnHmwtYpKwtCvvtZBbxJgZisKiwJAVqgohYJUlaUryr8dscjAWRECVPxEZEE7J+4UjUWSI0FpQkOHJFI+IelglSqlck2xJOpPAmBHrKUmvWaWEeuPKy2hGmRl5+lKySSG/Dw4hmW1m1xOEULbqLQRjMq/7HMcllhgWSis4r0h2oE9pRoswv0Gn6xEQg6V6NrMY2VwOM9bqhJmhv3//HQDAd0+fIRSTNaC48JR8byaZQK0lc7OzLqiL3AKhvNDQqcqcLJAI+tGjxxgRQbS4IGN1QrSZ5vo9ot4WSeoCRIBY7lu4p0IEzIgUag2HmDBjdsFMZ5zrMZ8veIino88+k2dxXMbDPra3BLIeJiJAoRz8vgAGzFwpGWhFvjodT/HkO5JP78r3F5ck4//dixfQGPEm8A+nhNUHMmm0lYTkjDLajuZJEh9cSqZzRDTV2VUHC3mZr26bBN8cf9dxkE4o+UrOGyUxp/0ZxlzTKyRrPbMFJXKwf+xBoM9OJVqvJHm319YQYlbyz3/5VwCA4CLLqFptVJsyxvfeldKsE+7TXquDASGxBrNjQdrOoTbDX38h4766vCZ9IhFuLBH3EBanas9HTY+MMcNMoMlxH3UtBKbyt9U0s8ect2Grh0tmn7d3BJGkMo/fvXiFZp3ldMzsH76QfdPo1BBk5jBPxJpKR1Qbl2j3W/yb7M9QXP7YHzThZyZrRFjxj39LEKKDSQ3DIWHECVWCIX0pFqK4qsi4u1PWA7JsNBpP4eXRCceK+zQZxv6elHg4lvzbIdGVhWwOD0lsWqEU+N98/gUAsVNGjqUXU9k3l7RvvatL+Ggru0QLaFw7qXwab16Q5PIdolGYlbyxueJJUFuU6+2SHBJw4JLw+ZhE1arUZGt925N2ff1KkHGV4z8DACwuFrFBlNGvKFuqz2SsW8M+zklArLLCUULSm90OMswANnkGBUJRPHstz18k7F4hBj/+0Sf40z/9YwCA4aP8vM7x1+CJHeiUQFVncSwZgksS8Amx6L/7e/82AODg4AAdlkvcviklX4pM9fS4gjHLeRt+GfccEXb+YAJ9i/P1KyGZNyjLvLu+ihgRrzVKjn/vPSlLgKvjjIINySRLxoimc90ZmszSTnnGbayvAiyhsIjWjRNJeXZ2jItzZqK5hta3xFbU6m2kmDFcX5MSPJfjclm+wLePBeEZiMq62KEdr9aauL0kz1LEzJ2BnF2VNxeYMSO9uSn7s0YUixGKoVaRNTobyztniORNpjOwFHqFBJgnJDmfziwkE8zA8qz88GMpi2p3OmiTOD5PG7qQ38Sf/dmfS98D0vckSxXOLk6xuMj3b/H5pACIRqM4J/msQt70qG3sMyNokcTUZTmNz5Znh1NTnFVlvhJhORM1v3xmYuv4kz+TvdoicewP3hf7kcsZOL+kiABlwgP0cWYVCwEiny6v5dkPHgiq2EhNPQGDN4csRWdpUcgI4Oxc9qVJOegHG5uos4T8lHLih/uyJvyBMHSu+2KRaKqm2OjTyys0iHq+lZS9G1dlupUqOm0lxU1yaZJF+8I6br4nZ0f7Wp5VJWJiOtWRoMy98j+qbfnbqK+h1Z/y8zKnBAHhO2uKKctqziqCbmj2ZB+MOj3E0uIrBJklD4ZZXjNoo0AfslqW+TaZZZ9qNgIp2YOTOjPhWgzffCFk3BGeRzWe5QO7jgHLyy2KsZxeyTMPjs+xVFQIRvlt5YPeunsL59fSZ1m1wEpebMSgb6PVVnLTYjt3dsQ/jsRiuCBafMx3V/57JhVHISNnjmXJ+6nSp9FIxyVLKw0iqM/b4hdgEMT9+yIQ4K9RUpqiGFNNw9cvxTfx8/yt16/wi1/8BACQImnzBf3One0bsNS9IEzEgkES30weu4viK1lEhfxfv/9/S983b8Bw5HMf3BbfM2PKWC1kCmgRuTfmOfboW0HH+mYOtimp3abdmRAZORpaMFi6H/GzLJrl1Y47xoD7OUtCZ53rrDfoK+5waPRJHVvmIRlLQyNZv0Y7MtVcDImQ6k5VpYOcvydHx57fnORY7e7KjA8umwjxrvWzHwkS+MuvxL6GQgGkE/I+55dif3pteXZ54uLOXZnDAdHpfjqHk5GFVfp9LpH9LlgtEDTQYWmsSTRKjNBGIxLG+orsZ3Wv8wdkPTrOzKNhAKsR1PpPxjMAxRwCLAVL5ZMYEM13RXS2zWt9LLGMDSLahhT7uTgSP8Tth4CEPP/R1+Jj51mKGItHEKVcep+CPlOWUyfMCAoFGe8hEZcGbcTacslD2TY7cs5kifyJBALo8Vkh0sIYtBU+n4skBXA219fknSti2wb9IVjxjCzvbmOWdtmuBiMk/fLzv2OWyDsAOjWW/vNsfvzVCyRytFO08xqFCWqVDqa8kyNIJB1tTKelezQpjl/2iUHxgXh0hn6fqOyJ2OYSkfp2r4eYJuO4JMeshzRMpVIYErGeYGlrOMJKh0oFRkTmMJvlXZT315kNaCCJN5G25St5ZjyZUFX90Fi+cntHUHH1ZhVtoun/dW2OEJq3eZu3eZu3eZu3eZu3eZu3eZu3eZu3efs3rP1GcAj95//ZBy4gXCNj1tGpLFKHcoP2xPL4ZiLkULn9jkguV6pluOS8WWS0uMOIWjAYRJ/ojDrrXRGUCJsZNDGiDJzDrGSYmceAHkCLZKF5RhVbJMSt1VpI5iXCq+o7u6xL/1e//AKBkGTAL2uKoEzCduP+wJM9V7E4Nf6lZBQPluV772xJVn55N4vFFRmHFwckC2WU9OjoCE3KO6YZaS0kJMr64S9+gqcvhFTwnBxHTLThqnKN2zckaq67Si6dPE27Wx6x1tWFZEYK5BmaulOPP0NlXgxGq4e2BhA9odBex4eSXQ+HQggyo1e5kL6kyJcSjZuIkGvAYf2mnySPQ20Gi9nuLlEHfhuIrZIUjePYIarH59Nwxoy5kg11iJhaKhTRJIpE1aF2icJYX15GNktit4ZEtRU/he4PebLz1yQszLIGPZpMoDGUOfczyw3OZdww0KpJRN4mNCtJnhDXdb2Mnsa1Y5J/qdntYjCUuaixL2lGiAvZDBrkYrp7S+rhlfTk6703eOe+ZD3BZ/f7fWSJqFOEzorfpZBdwCFr8RFgto7LcmRPUSafQiFPbhPWb4/sMfyK8PGS68pSZOcRrK1IRursROY+w/V4enSA9x/KXlWIuOVVySZ3en34mf2pMkuo6s2vK1UvWxJiXa9NHoLm9QXu3X3A95eMoOIgeXF47JECdi3p79nphUc4pxEl8OAjyfj+6S//BpGYrPMZZExjkM/mC1lkmAk8PBDUHWk7EDeDMJgRenBHMjFvSDTp0yz86EN5vkXbMOG+KZZW0KWdqzM7afN3fT4fFsgLMBhJJqzZkTXY7rY9VI7iVhuxXjxqRFA+lSyoTluW5ZpbXl/G630SU1JOO1XIYMDM5Kgv73B+JKiP9YUi1lYFjeDQ5jaJ3gr4ErCJYgiakik7ZS2+3wjj9XNB2bhEp6Ui8l73Hm5ixvVuED2RLeX5DhMMiCZzuV8UAWR0QUeA6MsK7V2W/D2AHwnyOjWI3NHIibCxdBfPnwsHWYJ18N2evLsRMhGNytp8/YoooMICutfyHu/el3V1tCcInkl/6J1HL/bl33xcj5lCHpHcGgDgyXOxuc8pa333wX1MiVhqkR9oxjW4mi9gNJS/LZIIMEHC9rBpwIgowmKxfW1m44LBIMJECAWZmgvze64N+FxZq5XrNp8V4DsDp+ckXyaXQZooLr/fgEMeoik5VRRhZGGxiJBOm0letPsP3sNf/lIQpAYFAi7LkqX6wU8+xgZFEf6n/+V/B/CWwD8eCsEhOePmkqzxWySXDhimhxgZEiFrTWz+bhe75KJqt2S/7O/JWo2ZSQR4vlgxnvnmW+lyjZw5Suo8E6bwwmiCIvndJpyjMNHJrVYLJtHAUUU6TO4513E8zoC/9/PfBgD0JjZevpF1BJ0k8+SksGc2yuTy8Jsqmy7PSiayaLfUvCrRAXl2oVTwst1h8iPoJBhu1ofotrl2SBQ5sRTSc4oXL0W2u1CQvZEjUWcwEEAmIb7M+QltBRG6s7EDnTwJORKentTFb3n69DGWVuR7JkmXGzX6HrEYdO7ZBOWqt3dWPXlkg/tkShLqvYNXf8vHovwzUUrlehlN7tEcCc+fcC+1+wNMyTPj6vLfjz76nrzXuIwAEUGKP2lAf+ei3oTL7LZBBN6Y6zhpGPAxezwhYuqaqMJUOopknGTNae5PoggiMR1vDolI4nhORzIfmVjC41c5PJbPxJIZBGMkVw3GORfy7k+/e45bN2RtF4qyNlvkp6xWr3FNpE4uI+Nh+uT7lyeXYHIb0ThJeXPyzGDYh/UVQdTaRA82r2RvDHuAQz9lZVvGPUaeovJ5E6cdef8+eWNuUyTAHDvwc300KUZicz2eHR0jbMszEkS8KR+7P+7jxi1BK83I16aRa3E06mFpVd7r+pIciIEIvvlcECl3bgliL0af67NvnqJOHpfWiIiAEVFHYeCD9wR5o/wqhYCHM8bNXeFby5ITKUYC7o2NHY/Y/pIS1D/8qSBymvU6KpeSxTdJItsggm1zZdFDTEVoU66JCjqvlDGxxd7YROYGuL9HozGKRFgoEuw6OU6/9+HHmBAu1yJR9WDch05/RfFJKk6rAPyweW5ukPuz9VLmr40RTNr+tZTM80uiOv/g82eokms2SyTN6g2Zv3F7hLs7Ml8//fHP5Nlb4qtpsPHyhfAmGXwvnXuy3Rvj9b74lGXyv/pNErSHgXBE+hLioo2Gxf7v750gwD3x3gPxEa8uTmQ8a5doc36zRLcSAAHgrT8WJRF/NBqGS5R6KERkYlL2bjESRZWiJ2mSPTdZReJqmmdrFUJI8dz0+0Gks+TNpD3o0GYnogUkIgrBIWttNOFdJZjyeG0O6XspLiLDDOOa3Ix9cuLduy+Iz1qljmBYvjfg+XdAxNvDdx/AoLjB8YWcN9lixhOb6Y1afCZ9p0AWq0U5HzoNsdcnJ/K79eYMhWVZhy9eCEIoSfsGOIgQfTIgUjlCzsuYGUFhU/qneIX89B1ajabnx+k86yJElppR00O5qDNONce2vDk0gtKH67J8tpBdRp1jpbFSQRFCR6MhhIikUZU+ijcvGc8gSZI0VWHy+Wdf45wiNTrRm9uslgkaPgx49kz4PiCH0HA0QzYvdr7ckHPzvfcEqXzy5hlccsi2J0S4EUX0yXvvw+nQxya6dUCevRs7m54fN7T8HBd5h0wugwl5h0n16vm13c4YM/r8NlHjQ94lQsEIqhWxQYqreIfCQKvLRVyRw/M//ad/MecQmrd5m7d5m7d5m7d5m7d5m7d5m7d5m7d5mzdpvxEIoX/8X37sAkC32fJ4dJQqjlKdSidT6NSJBmGXVU1tIhlDlIoQXfIKKW6Cvf0Dr95PPXPkSkRtMrGxvLQmD3Pld65OJYqWjL2VKfQxkueSNd/xGagzI71/JJFaFeEsFtdR7khk9/f/4K8AAMGwRKT7vQ5m5OKIMEPtEsrgn07w/qZkVh8yIn/jdgl3Hkrk+NmBZJ8vyPGTSabxJ3/wLwEABao+fPBAMipu0IcUs3wTPl9x6LT6XQ91odAaFiOcZjzkcS8pedoYUR4HRyfoMjK+XpL+rTD6PAvp+O6pZA7u3hUVGcX+XqtU4NckWpxLSpRVyebBskCBETQ4b8trMgazmYsxI+RnRMY4rgaCYtBiFkJlflOZJPyMns9UDS4zzTHDRDgj0fC9N5IlmRIylUzG4WeGXcnW1zkGZ5eXWCHKps8IdJHohHwxjyY5TXQiXLap+tNtdNCg+kCctfWX7O/56amnUhMkN0qamep8aQlm8G2tNACUFolcGY9xStUdP1EbaSqETV0HN+8JquE7KgmlUilEuO4vziR70Wiw5r8/RIhcWRoHtEnZ0rFtecorGrM/q0SL9IY9vHojSLUx0VeKp+b8/Bw9zqFJ7oVbjE7rjo04a3a3FhUqQebKFzDgeoySAL4AACAASURBVCgUqmeQsysaS3gZ9x7VeKYW+ZpcC5fk5lhYkD74yOtwsH+EIJEpfb9kHJ49eeWpn1UqknH5/seSCTs82kOCKjWH+0SCpWSSlpZKHjLulAicCNEDCdNEkjxEyyWZ52OO9dQa4B7VxaLkOlLKPtlcERNyjShZz9fHsj6mzhT33rnHd5Z12CJabHVtEZ2OzGGJnBTH/J6h+/HePUEkdYi06pADRA8GMKME7RH5LfL5tKe4ckV+G6VSmE0m0CfnxPK6rOkvvpb9vXdQ9eqaleKCUsyA34CjJOHJdbFMlFmzce7Z3zhVjBQCSjd8KJJX6ICqcKoWvWLVEWZ2SnFKVJlNLuQKMIl+U6gyJSO9U9rx7LfNbPeTp8IjE08mEY0RaUmVNlfzIeQjuo4qM3meL9PJGAvkp+hStUFnBtKMRVEid4rFPTEjoumkWscf/dlfAgDu3BZUn0N7Nex2YNmyv9aKMkYZcsU41gQ5ctSV6/Ku6pz2+XTkk0phS+Z+bZU1/QMbU0upVAEAMJkSnWZ3sXvjIxm/piBUnj6XzGA4aCDIFOkq93ODSJx0NgtYPDvatP+b26gRAWlNyX8B7s/ZCCvr0p9vX8leSGVk7LLZLKyuPMtHxEiCmbqr6wp8nGfFgbCQlzURiyY85RuH31OImnAwhrBJtNeJvE+NaKxAOOhJZCvurYqSHM7mkVbqQkTIqmzyycmJlyFWfVB8GkulJfzkhz8CAJwdyFp9uf/Gy1wXS7SHtAPRWBhtojTOiKKKkkur3+mD4GFEyJmocz37AwFvranmEv35W7/4Pawvcz3NZN4++/yXAIDLi9e4/45kPStVIhDKVHtzXfhpT20iOyfMJn/0/gcA7b1CQFlD6Usyl8ApeXRGat6o/thpD3H4Rv4WphLqnSXTQzOcXYhtyJDvLhaL4IIcZAYludcpE3786jkW6Xf0ifhOEi356aNHyBM1dE3Us0ITJuHDX1M56O474gN9/JNPAACnlROEqIBVq9C2E/H57Itr+H1UeyXKrEr+PNueIB6VfwuSH0iNSzxtIkO+DbqGSFAJKhkLY9iTPa5rlNEutzBxZLzVmR0jCmA07iNGxKRaA8cH5ISIZXBC7hWb/E7DjlI0DcBP5FM0JnuoQ6XLQBC4syHjmE6QtySgJL0HqNAPsH2Uek4qpNYC+jxfm7T/FjPNn+zeQ4jomq/3ZJ/5yA/y0Ycf4ugLQYecUVr+nQ9lfR6dHWM0lr5vbMhZck3kjq7N0GUmfHNREE2RQBhVoo+VjxIkouyq2sQF1VQvmoSAaZR1DmvQqUKp5rfTlOcMOg3EYuSvI6IrR7/Onkw9Rdcpz3luEeSSaU9l7MH7PwAAPH8m6LuAPsOI/kqxsMh+yjjunZygRs6WGpU071Lm3m6P0KMfcIeo6cU12txyGR0iGPy0bxfXZyiTO9LkOMTJ75ZLZj3OT6UO/Ed/JapSCzurMKn0ZFAhsEVU/CBkYET+QEOX31lIUG2pPcTeC/GPzID83jl5lDY21vDeQ/FNum1WW/Du0BvZODiSvR4k4iyekmebEaCwkGQfZFzMEPlPbR2thozRJn0Nlzbm7PwYTSLkrsjH8+69dzyuyj45O9/ysg49TrUYEbZKLcw3HXioxQBRi2meS7VG3VMniyheVtrHXseEq8n3Pvye+NjXrJ5IRDNYKcp95Ql5lnTyxV61+kiSD7VBhcUxD+VgIIQMEScRoo4aXflM67KMFM/+IP3ZMNeVDg0horJPrmS/tftNGCmqQ8blveJEkBm2gSB9a25/1Ihqq3cB15KxUba6UJA+hYJBHFEN1WIFDUF3GPVHSN2UtbNERP6E/IOWZSFH1PdQ2RHeCVLxBDq021FWRESpqHd+fokAN5161+Ul8Z3HoxnK53JevCaPK4iW0YIaNnbWZGz0v8O/O7BRIjLcJWdrNBKHEZN/++KJ8Edt3pQ7SqVaRppIUB/tfYV8e9FoBA7n1aGPmKFvWDu/xnJeeBSr5Lby847vOraHho+oCeB4RqMmzLT8zRnK7zmssplMLU9dLJGR/qrzxg8D7RZVy31UTHPIU9nowx6Q55QI1OM9uTNub214lSL/8X//B/+fCKHfCFLpRW6OpXQOPR66eZZknRLC1+/3vYUbZ/BHkYxq7SnKV2KMlnlRUhtcDwAmJ6VLA6KIqBKpOE5IyDSi7KKfRF6dVgsGS4+UtO6DLVk8HQ0YsIxmm+VXXZaj2d0mCiQeLhUI763Ls00jBFuXQ0uRVEHnhtX9nsRnnyStg+4IhweyMTe3xVgqctd6uY5Vlt2sr4ij1KDjOeu5XllAmf3q0XEvLJXQq8uCCvLiHqCDGnQ1+GaUkiY8OMAlkowkMRvIvxWzMsapqMzbn3/z5xjQYZ6OpH+5uBiG1dyKB+cb8CBUl4iN0iJ6JMA8IvnhgESu7szxSKgdBnrKzQbubIjjkGBgpkaIt+b3eZcEnTBwVdLSa7TQs+hE0rAZDIQYholvnogxX7shY7xFIrqvnz6Dw7KOW5RCzRBW2eq0UGKgJMRD5JQy4YdvDvG9d4UkdIHk10MGAAqFPBIRSo4zMqQCX+1eH1eEdptxeYdH35C4ut3CHZIdNmlYTyjPfu/+fXz39DHfmeVX1Souzy9+7V3jhPS3O11kSOKWzJA0XK1fzcU1ZdYvTmVvHBNSnc9nsUaiQj/JEg8YpFosrcClrH0+J8bLIMQzHgvj+VMhA3e5Z3/wA3GwXj5/jn5P1s7N2+JsXLI8I5FIoEMiaOUcmgyk7lcvsbwuTufpmTjC3zwn1HxtA5sbMof/8pEEMj788EM8fy0HSpMHWIze/L2NEibch8YKywd7KrDTQoCsdgFeyOIk44yHQh4Z4WhAIz3j3h30vKCGn06Kcsj8/giyJOpV8N4NOpVmPIJmlc4WGeK2SRKdMk2sJWTdXxL+uVGQvT8cjvHHf/j/AACWl2WOlMxkt93yLsl5XrB6tTqOGRzd3ZFxDDGw2WlWEWOAW5HfKzs3cXUEGUCNc90vrkqfRvYUGZanhLm2Xz4RB9qn/63AX41lEJRarTfr+OpE5ilXkO+nY/I3x1rynDuVCFh/R2Skk1ET3Y68V7cn/w3TCTjb38MSy21NjsMGpdLH0xmyLKc5OBfHKhQ2YVCuWMkQD0maas9muKYjO5nIfKUN6aczteCnA3v4SghBAyEG0U/PEaJUdfuCQQoexeOp5ZHXq/8evJbvLy+VcM7yTlWu6bLMIhaJYBSQdaRKQNVB7/eHMGIQbHld7OR1VeavNbAxZGBnoShr3NHF6WpUawix/HdlTd5LC1A22WmhxzLbM0qnnl1WsL4l360z8H9+IeOYSocRZKDwgwdSwlFl2dvJ6RH6HMddzo065wcTCwMG8lMJSiKP1J6aecEQk5dQi871TJ/h5Ers03hIieeW2JjFRBId7rkey3vBcvNaZ4QLltXFCW/vsjzY1Q00KRYxZFllIiZzOp5M8Id/JPvsmhel0uoS0hR/OKcfUm+xZC8Rw+GZjE2mSBLvivQpn8ljwuc3uD9VoEUL+HFFKfqVJQmwGUyW7X/zGMWQ2I+vvpB9U6vLOXh1foQNBhgDlK4OMuhRb1Th57784Q8+BgAcs5zh4OgNHJaCsiILOZacPnnx2PNbdFO+HyT0vbSUxa1tmecGSXLHvRHGltgujbbymrawZo0w5iFcSoldrLIs5Kc/+RGevaKNbsi/xXlx/8H7D9Fm0kJn6W23K+vyzv27MJLyLFVO9piJEdPQsBAWG6mx3ApNGfP3djYRVRLGpsxNvU3hgZNTbJGQ85IBisKKrJ1cOI56Veb++FD2bDZHX2pjASWWQ/Z78jufbDzEERONHn3AmEH7ehX9tvQ5l5Z5Xl6QvdFujRGksz9gySgomAG/7gWQwiR0n/LsgWbhkOWvrZH0eWlJbN/1oIlgVsZhwDKGOss4t5YXkLpkwoGBk9fPnwEAGqeXXnlQlD5hNiXvefHyEA7/trFFElP6vLYd8Na2n758hAGYV69ewTVZsj5kWZ/j886t86qs/xhpEsJmwgsIp1jeqTPxm8rG0GqJ36Iu87s35Vwzgxo6DKqULxggp8BAKhpHzJQ1kOJl0eEYHx8eIskgTKvy67Z27/CVVyJfJal6j0Fgx+ciEJS9t0AfeZEJp3L7Ao9fSSnzFRMPt96RJOrg+hoZ+jkOk4WpdBwjEhYPSedQYvnUky8fYZN3gAnFB/7+96XM6/Grp4jOSJR8IEmP1WUJGgWjYbT7Sspb+jDwydrZ2dzGzqqcHZoj7/roW0mkfPP4W7hMLKU8v0f6dnxWxpgBWncsez3LS3rKF8b1K/Fj0/Q76w1Zn6FQHPGM7HF1LwjxXqLrOvJZ2btrW+J/3F5dx3VVnsWqOjg8G02fzwvOHR2JzY2q5LM+gEVJ+QnXEI95+PQAZqQimPHepKhKavWmV/p1REl0neH/SePCE7Hos3ynx5LGvm+GOveSIlofcV1tFhe9c73W5r3ET5tYXESAiZGZy1JQrv/nT54iytLTNMsU9ZmGIW3KxakEMOI++X7QCqHHxE4iL98L8a4dMONe6XKKPlOjeyLPTqbw059LsLLblH6+eSPleRPbD03RntiyPpoMSnb6DTT68ntLGwvsu9ifoDOGRuCCEjPRCRQoFtZRZgL9jMk+i4kOx3ExG6kgCoN8pqy9Zq+Ffp82j85hkom+hWIKrs0SMN5Nc6UkzmiDSovyOd0vayGXj2LQU6Th0j9/UBGE66jyfC2uiI1uNmnLYlF0ee+Oszyueih2IGD4MZ2q8lhZF1nIeiyXqxiQuDxvyvrvMOG2tLwGRxMbe6qS+QR01KoNj0A+nZXvlS9l3vPpAlYYXE4w2Dbiu7fafWTTVFn617R5ydi8zdu8zdu8zdu8zdu8zdu8zdu8zdu8zdu/Ye03AiHUZ/QrZkYx6UkkrelKpPD2LsmbLs49SFuQEMHrsnymXG3CsiUS5iEyWP5g6T589kiy1BlCBIskEe432nDJ2tQkEaDJLHEiHIVBmcCFJUqGnkuEuO0PeMR8PZIhK7ioz3JRJmolkaTsdEUif+Gw+TZrz8idxii65vejz6xnm/D2fs9CMfAWyQIAU2Y/apU6MkTJ2CSH7s4I25848M3YH0r8RUmFu7a2ilZLIsN3mPXf25MyoH6thw8orapKFK5IqpdIJDGJSMT00TcCS02aLKkIzLDIrFbjihKJ0RjH3/IiykqK/aLCCKoZRIrR8//o3/v3AQDnhGyfV8o4L8s4NljCtbq46MmcG5RvzrH8pDfsoUeIfZHZmNoFy2kCBvyEUabTMg77ryXibQYD+N3f/h0AQuYFAHvMLnxw76EHS7+6kj7nSRa6ubkJg3BPEFnhIyJm9+YO2iyN6OwRFUWE0HQ2QSZNWCrXzumpZHBS2ZwHMTxgKWIiTSK6aAhXJCdVpWZrhGB3RgNvHU0mzKClUh4RWdBPckySyIZiEQ9ieXQq48C8I2AEUOXvqDKZFsPG8UjYQ8cMCWFfpsx9Opf1ypmSnKMB4b6j0Qh37kgWTKFswkS6VNt9FAiRvSKp45iZnL2jEzRINGsReZZkVnS/14ahyT4JMluiEUrdaPXQIOlyNC7rq1op4w2JtE2iqPokoBtbTQx1GYF8QsboqqeIHF3kiKqpXMrvjVgSupFdRJjZ7hlJXRNEN2SSCWi0JZYt8zAg2mxWbqBSk7VaYqnUsC9j3rfGiBYkY/no0dcyDpYQqyYDBm4TJba7I3Nfox3xh0L4PrP+rwkT9RnynicXFyguSmYjRTRK2xlheZl7iXtQY3ZssVjy9n+VJUsOESQbSwVEaFOuLk/5fZaWxOM4PJCMuZJa/YBS3muLu/jqK0FwpVmOo6t0hGPj3XcEZdAi4ifCbFBrGPKk7B9Qrtchou7Fs8dYLCnEE0tUmVFZLyxA4xmgbHuzJnYvls3i8WM5ExSi4OL6ApsLkkUvlMSW+XVZCwf7b1Bmxn15UT4TJvlhp97Gl1eUNCeyc0QZ82KuiFRKssjjlsyTQxu9sJzH2SGzbpQ2V5D2XLGE8wv5vS7JSWeWrL3NrUWYLFe7eEQi1pCMZzIBGJRSPz8VpIXGs3JlcQV+S5518kbGKFMQO9nVfR560yEKJUQkXqNyjSd70hfQvlUqZbSY+bpzRxABS0RxRsKGtx9LhMynCa8OGEG8IBLRYcmuzt/L5/P49qWUopxxrBfySt6275U3Oyxx0Ih0yZQy+Pq5jP+IxMAOSVjb/SamzMoGWaLg0wgZ/+o5FktrfH9m+Ih4iyfCcF35XkwRMtPm9oYjBH3yrts7YtO+e/kEi7TvsajMRXOPhMKZgXeuOzzHZpb0fXlxC5/9lZR6zQgXVwX8/eEAa2vSvwQRcq2unMW+hA///H/9HwC8RQF+8n0pB4wvmzghakshbLPMkmuahgRLwF++FpsSoY2oNWtYZ/m8xTNED8q7rG6EvbKVmS7v2WLmWJ866EwkW3pvV9bCme6iy1IZsGRjRinpUDiMgSrHoGx0kgSmT/Zf4uZdoso+E9v35FvZpzNnjPssR3+wIX6EAstM7D7iRH33WOK0TzLqd+7cR7cs/1a7kj2kEIe9WdsjL90iefLH35Pf/3d/7xeo0F647wrSZEi4/8VpFX2e76OerLmWpsiDu7gqqzIvllvcCqDVpR0lYWkhJ3vvwe37uLoUVJkqdwmyVCcSGWMjJO9aa8nc80hBpdaAjyV3M4uCHpSuhmtjFpffWVmnMARLmPxRPwIhWcshkqYq0vFerY4ESy4UgfYybYTTG+AWz/AyS5m7ROw3qzW0e6pEkNLGJJL3mxqiGVIuBClpT7TY2u422kTX+Ej+nspmYM9k3HykcfBFZG4DfgfLJVmTbbKGq9Kbo/NDryxMUTycEf25s7GCe/fkHIqExLaoUslwyEA6I3uixfIwm3Y8Fk8iTn/jyVfi86apH90bjdGiWM2AfkSP77W6uYIhUc9JyrKfkcT9vHyJBWbxnQBRtCxljgZ98HPcuw1Zq93ZAMGUzHOO4jY6ETFbG+swWf6kSnwvSGo81qaoUWb60pW5efpUymSy0Sg43DCIuPSfS393imuoEq2RpGDDj38oaPd2t4ydben7Bx8ISveIKOPN7S1EU9K/T7/8QsZlIjYik1rCCQm3WxNWgBTEV5s5OgxWRjgzJactY/DRex/glPePGRETgekINvfeMgVsKqRjGNoW/JD1q/ykp0/FH9m5u40e75Zv3sjZ2OOaffjeO2gSuaqQxvduylp/EzqDyzNqwnL4aJTlTaaLJy+lVNUMEu1M9Hnf10GcqNEp0T9GWN6zXq96okfbtwRpW+vKOxzsnyHJslKLPnbQZbljPOchgHtcX4GgiVvLFFPpyPhdl2Xce+0+wrTvVpM2gsi1RCYNM8TyKj4zk5Tfta3/l7036ZEsy87EPntm9szs2TwPPo/h4TFnZORUmVnJLhaLpEqk1BIkSNRCP0AL7XqjhSCggRYE7QQIaqAFQmii0QLVolpsssiqYk05VsYcGeEe4ZO5ubvN8zw+0+J891XXQrUuoOxuIjPC7Nl9dzj33HO+830DHLwUhKWipwizvKlQrVqIlsPH8hk/Jep9nhDaXYV8lHkOknw8E4ojD/m3JhHDJSLehkMNF6QFSbHkrMczKL2cRH/O+MBU1mqdVQOhcBR23m0KFfm+Kvnd2trC9rb4hraZ9K/ZqmBsyhrw+H6dBkZ32eF0EPnPMi076Q6a1TYy1+RerJEU3ZWW3y0XishlxZ/yduR7oai8w3A2QI1lym76LZW6jIsRNCykvY2+tZd3oifPX8NGH0PR4pRL8r1ypQXvJtHzbZnboJOo4qkd2WwWgNy5ZGxlHBPxFA6vKHzxG9oCIbRoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zoi/Y71n4rEEIasyuDXh8BZm8Uf8/L51LDHEzEMGeWsMX6ywnrqxOZVUxY015hDaeKFPZGY1y7dQ+AEFcCAOYSHYxGQnh1KvwDCkWhiEtbrbbF1zNjlNNk3e00EsGERFVl1vkvkWh13B2hxCxVkZFrv1ci5+PRwJJEdxNpoQiyWo06Lq6IUmCd6GRsokLUxNM3Eo19zpru3dXrACPXqrbeSXQJnDZMOFYK3WQnoqbfbcPQ5f2PDoSo2s1s/v233oOLhFp1ZpRMZoE67TpWiBhJUj5xmRncqm2MRlGiuPO+vEMtL9m12WyGm3clwzYcqXpGSgT2Ojh7Keiks28ozZiU6GdvOkSJ0WYfOR7u7lzHl48kU+NktlplxAETHnLzlCmJq4hB4/4w8qzX9LNw2LkvY7CxsmxlBfIngnhoF+Xdt5ZW8NWB9C/MOl5FWjsajfDsl8zskIzM8ChCuhmGlAIMhim5HJR/Oz16A4fGjAGzzjdvyfiUqw0YRKytbpKrh5n3Zr0Kjf+tsv4N1jl3Bj0kifyIMTqtzTWLUM8iZyTibTu8ZcmQZ4iwuCQvUb1dt0gm18nHtbtK8vDJCPOZyiDKOCp+qFopjxWi806PZMwcfL8b13csYs4ESfhyBVkfK2s7qJC8LZZgJps10eVKAUFmLZoT+Z0OsxlDTUOXiJseM+JNIkfCuoZ0Rtbm33zxQxnrcQjf/5P/CADwDz/6AQAgeyaR/VAMaM/lu/2mZBqchvzu05cnODqibPRU1uGIUX73aIqb24Lw+fBdqbn+xYlkBGCO4CW/geJi8jgl82WDjut7krFUvFMzcpvURl1LutNU5IxuyQBMG21UyLE24r5JpGW+vf4AOiQCVeSrSj5z9/oebt4Ufqbnz4W/YGqbIUQiZlXP3uNchrnGASBOVNkBpU8Nj9+qxQ86ZE17uL4wsWM3IxmvDG1FixLKunuO1VV5Vpuor02SsE/HffSZ9cueSRZDZct70w5srCePh4Rg/4tPBWnk9zhRyEmGUhEtK2n58QDogpwEHPdeR7K0Y7RRJcLBYZCTZqrB4ZDxqpAfJE8iY6ddR5ry3n6iIuMhGWPd9KLMrJOfssqKp8yc2XFRkLU9YIYnRKLNeDSMP3z/v2Cfxc6B3AbmfI4Az5NqX+ZkTCLNuT5DICF9SNC29/m7xmSOQV9sVyBocGzl32bDKeJh6XNrLv2ddWSf+fQINCKRTk9lvv72h7Jv1q/tILEh464QgEG7C0O7fPeMMqcGUWKY2BGy/zrvxojytDZzhnv35Cyek3Pr+I2gpGyGC15mhhWyrUry69FghGUiER08u87Jn9bs9jCzEZFFcmLFZ9QfjpFMcF0QLdNuS79XV1eFYBC/Ins3SVw/mkxhJxJGrcM5z8hhf4QICdPPyf+gOdzQ7ETNcb6fPhO00/LmNtY3hHvwvCx7KB4QlNl4OMMN8qYp4vMB7VCrVMT1azLuY2a2tYmsk3m3iQfMLBtE1/jJZeGNBpE9fA0ACDNjbydiant7A+acfBOX5E4kJ8rm5raFalBcQCYldpPra+jX5fNVcjGZtG3n+SG2iCwqEBHjnM/hIkm5x674Ioi8dgdhJ3eHSyG9STxtuJ1wl8X+7r8lSJ0zSku7nSaqBfnc6VOxYQGv+Ar2uYEwSavdLll7Hz/4GAAwHA9wep4FAKzyHIuQoLNUHOPp1+JPJMi7Np3J+7ntbWynneyf9OkRibvH3ggclFXf3Fjl78j4ry3vwiByeE4J8dlkahHsFq/Idca1fnFZx6Ane9Q0ZQ86nIojqY2ALrbYQdJ2xUeSTPtQqTDbzWdNiZAz3D50XfLfiuPPRZ/jnfsfgK4yOk0Zqy7RG+agA41ostensoacyufQgRH3RGUg71AgyiETT8OIyjs0ia7p8jdsOjCY0k8ZkbOL/pLP8MHPs8YkAngymeCC5OtBcp3RNYfusiHjopBGXuxUpyW2emcrjSzXkY8oPZP3g3zhAi5d+re1Ls+cE21wcnSKR48FTbZ7Tfywy5o8025q6HblXRWK7Yqk9JrutMQU3ORCsTPTX6lUMCPnFsFY0EhQHsikFHc7kuToqpH8PZmKwG0QsZcXZJw/7MWcNq+pZLHb4rv16x0sLckZoNZ0PytnljYcIsWze8ozLrkindkIxfDNC3JsBWTtbPjkOc8ePoFJcYQ65+brh19w7Faxtir+4utD8X01clu5ocE5JdrTL2N9RlJvPzL4x9/9PQBAtSTjOdNlECazOapE58SIxFaiDi6bHVxymJHw2zYawE+ff9CT8Qjy3pM7P8GMnKT333oXALDOM7JUKOPWLbG16kzMkderdFXBYCDv4efd5pRo8uG8A58hdjSRkPE0IWdXb5RHcln2S7cl7+Ml15w35ISTa6bL80Ujz419ZiLOPdCkpLpJcnSn7sfxkax/xVEVI3LkopJFiXyZCXJHTgc2dApyrlzfknctJ2Xt5ctDOBXr/UT24Pmp+Evx+hjkc4c7IutjQl7baCwMg3deOxHUZ6yOMbwGZl55lich62lKcYzo0gq8uvTrIkexJfKKXlZGGGnyg1Wih2YzWbOY++Fxy3iUVJWAKWOXXElibV2eGU2IDTt8I/PmMHyokdx8iT6vQZEK29xEk+vD4BmXWVtCX8UJFCk638/v98Lg3dznptBOXr7vMQOYtWUch/QpazOilkIu7OzIuE/qMmZ9onb93gDqJfqcRGe76IM1Bh14mvJ37ZKMizmT36jVBnASJdpsST+XV2Rv7F5LY32FJNYvZaxs3Bvd4RCVJvc/0UdtruuZ24aTqtxvf1NbIIQWbdEWbdEWbdEWbdEWbdEWbdEWbdEWbdF+x9pvhez8//7P/nQOAFeXJXTaEilNEn3iIjrEpk0QCMp/N1uSHfD7KH3a7sLOKHqHtcEVZqaH0yFc5GzpTMkaPpMIoNfjRplKBl1myZUsqx12+MiDMyTLecbOGl6fA01VC7vCrCsj5aVKEwHK8T1/JVn8Z19IpDweW7ZY6YdzyZbYmF2YdtxwMQu8lpIo5x+8cw1xWgGnQQAAIABJREFUKoycUgHlpz/6MQDgzz75Lkpk2++7JEK4Rn6LSNiwpLI3t9bl/chPkb+8woz8DWGL70Qi0M8fP0cyxnckcskXkoj5xJzh8QsiYsiXlGF9+e72nqViVCaXR4iR8lIxb2VZlzYk4n3IrN+01oMvIv/WHEo09uCNRLCXMutIpOTde2SLn6CPaYNqcORWcir0i+5Ah9kcpW61mpbI7XQ8w4hs9COijjJ3JcPam87w6qeSLdndEb6qAaP17+/uosuM7Y9/ItlBg8gYt8ONFpEOXiJvvGTRH88nuL4v4654CFxUAmnW6pgxm6i4UHpU+UhkkqhTVtZtp+oVM0y1Rt2Ss3YSRQQ+JxkMQSdHwwYV0i7LZfT6VFMg/5T6PZhzzIjCCZI3yU9upIPXh3ApTiSSvCiGfMPvQ4MolABVxoJRydhli1cWl9XhgWTc3r4hddipQBhTqlEkWIP/6S8+AwDEEkkcUWo8liK6iXu+3mpiSNREfyDvMGem0+52wKBCzMkbydqpevbvfPQt7GzLXvibzyW7NW5P4I1LlL3SkIzv25THPs0eoEpk4f6q9DlMiXpN0yyegykz4Hevyxjr0ynqrBG+sSMogDZVuXKFHMZcO14qawyInnPbnbDT7npYk3zSVTXsTYS452JqL5qsAx/NoPQiG+QM2N+mmkMwilaNmZcpUYeUxa13WhYaLcT1W6tV4KO6xJQIAoePXECRMF4+lwxx6Up+J04bYU57cHJ+akQrKR6T2XAMr1P+2yBJQYiouf3768hSreeyTG6pqeIV8eDNa0ErZpIKbUfOgKAfe0rdkcg9H1UENThwlpXxD0aIhuDa/dnPnlqS0JvXZa8/eiHIolv37+CMCnyKIykcjWPWl37pRJB6yf+wnE7BT0nXIZEtKc6Nx2Xg6yfk+/IrqVv5bCKzjEpLbMT/+3f/DgBw/21ZX4NBA5tUpYjGZR0eU53C4XBjiQgrG6WUyw3JJGouJ9a95NYhx8BIIeqqRQuJAHJ1LadFMcajB3FVkfNCzVsiLc85Oc+hx3WrZKYVz8qtGzeQyYiNPj6X71+V2rBzDhRXFJNraHbzmM7lDE77ZD/XiJTttTvY3BJOFJVtPc0RldntwFDjR4TFgIidSqGMnXV5j+y5ZG4H7O/q5jYuiUYtNuQsz6zIZ588follvmNc2TfapngqjRm57UbkklBn5PrqEjSH7FWDqlo+rsdoOGJxEZbLMo7z7gwDIlCd5CaYeajKpc+xtyZIuFmLtozr7O7td1GtUDGOssqvTgUBHE4EkcmIDbugnLOPvsJoMkCuKONm537rct7292/Aw/MonRZ756XRqDbqOMrK+NUazOyTK2owBEpF6Ys5ky84TCIB3TbEKG2c3JA+1Ygc/uLxY9y9Rl6zdfm39nCECVGEc1qsJtFlhldHqSTrSKlk3bv7QN5l2LXkjpfi8qxRj2t8OIVBlcxLcgv22Qe/YbN8mABRj59+KudLrzuAl4guhZA1qFD14P7b+PznghDSebbO6SPa7X0sL5Fzj+dtsSD2R0+koRENonj5qoo/L+LHA3IOXRzLWpt0Wihwbdl5oHsoU98fDS1/YESl25ifCoaDGS5PZe94ojL+lTalmz0OaBwbjWpQWfIhXVQb8BI14YvJv0VSsrc0O/D6G7G192/d4rvKXDl1E92OzEmX57yXSMP+sAOdHChK7Qfka+l2+zB4xikkWK8t+yF3loOfqCM/x39ONcFQ0I+5ScRvj6jnysRSeNrbJ5ebIXMSiPgspc4h/SMb0RcJfwhdnq8+8jNdlGRPOUwTAfLUlI/Fxs6cMnZbN67h64eC5vN7Od9EVrdadYzI+ebVY+w7eYP6HYBqWCaRbkola9JqIE7kjYdrLZ2QvRgLRlAoiZ3KE5Wt/INsNovvfCJImlMqg0ViOrpE5YWj6zKmRfm+25jAx/Vk2OV36E6gMxhhRk6USyomunkP6g6quLknvrhC3WlVmUt7JIWDQzkbb62InzMlz0p8JYEYkegB8k99841w9Jxd5BAi58rShtyJ1P7pN5vo8cxPhsR2Vml/2qMu5oRR+ak6WuS42G0aKH4GH/0Ie2+MXEnQKkpuXd113LobqbSgAIvkIgyzD5lAAHnKxXfI5aYT5RhPJHDEc6hJfhrFRVaaNBGk/1Ul9+pba+vyLskwbOSozB2KXbWRP9M+19HW5J0VF9BySL6Xz+cx81CC3i52YFaTfbp7bw/1MtFvFXmHEf0429yGFpUVNQeVyGw2xPwyF1v7crbqYemTy2O3uPoUMtFDhN3G+h4SEfneX/9AEPPdiayzxFoMUxtVDelbaHMqddfHcEVokwdKlVIW3cRmh59VGQY5xNJUi62dXQKskqjRj3NxzWJuQ4NVGl7yeSku1KVUGsWi+FdJnuVuQz5zfnSBaIDoLarRHfL+2OkPEEzI+/lod5YySdipEDfgOL4+yco4ulwA7XwwLv7wkHcCn91Aj3xhDdpxF3lIDbtuKbTOo/L9QUM+i4kTm+R57ZJTr9FR93474nHuD/Iq1oic8vsiGPDuFidSfEa7M5/PsMTqh7gp43lCv/rV5TmKbbENnqDsKScNgtfwY0h+uH/+589/u2XnWX0Fw+9Db8hLvVe61uLGTiTD6LA0KkxpVpOO+8gcY8gJ7pLYTRExJv1xEDkKW5vwe0IiYdegc2iWSNgWYomK3xfEfKoOPC6CIS8pPic2k2J4lBxumJeirXgGXkJWVQlB7o0YIM1uWpNi54ZR5QXTyRAOBmEcHkXuNceQF+lOTRbSjJd6zZzj7g050ItDQixZFjYajbBEslolG/2I8tt+vx9DLrYRx+Hg1Wv+RssiXr1544b1LAAYTSfYJVHbEUnZ3ryRP7PFK4RJ7nxKifJVlg9l0mmEWM5xyRKMCzqEQbgRJPwz6JHPxDm3w24fI8rwxQj79wbiqDsIV2QgSJFnjSdDrMTlu20SCtcJhdadbqts6vZ1cT5tMfn/n3z+Ge7dEyjpmOVQYcJvK50GZgwuXb8tFzmdwZVarYkED/kyD/gOL1rxeBRXF+LI5QsMONJxGfT7cLO84P4d+V0VvCzlGzg9kwM6TLK/nW0x8nfuvYsDwj2/YaAxQocacxvmfH6xRxj3yjqmDEg0GQxTpMHT6RRJlorZXbL5ZqSV3tpeQ/ZMDtweHdQ+n/36NGtdongGW86109DRG9FxIKn3BeUQHXOnFZS6InlclHP17gfvWkFHH52ok8ssAOCt+3dQoHMQ9YkRzJM8LhANwqR8ZZKXgFRAxuyD927BxfKfCA+97nQEp52khSQgDXC/7C6vIOSVPXpnh4SlDEJu7e4AmpQFqL4sp3lxdWq4eYcODssUPZT0DTmDqDJI6grIXi+yBGaie39F3NqnlD0dnkDUbcFYo5SOzOdlHFudPlIMmiUpW39K+fnzqzzeufeAfZF13+vIfDQnPYQohe7ixThqs4GcnejxgvqC+3l7dwdLLANpVMT2XfDQ+b3vvIc2bewblljGCH13OXTY6Hh7fTKn65sSjFlb38TJuazN2UTGw0/y4Xavh9VlCfo4HXJge72yR3S7HfkzStTywH59JvvA5w1Z8pxdynfXm4TR+n3okmT++FT6qfNCXb0s4a1dKcf5RUVIIVfDUZRY1thnOZnB4FIqvYQKg+8vnogDXGA5zmp6FTrfWZFbxikLfFG5gofB8CBLhP28MF1b3kWlkgUAfP21EOhG4nIBarSqGI5k/X3/j4XwvvaZrJ1CroiDqtjycETWgJLHjmVSqFaUPDJLhHs8N8YtOBikS1Cie0pZ93QkZO09DyVvx3RgDLuJ2UDG9t6+XBDeuulEiaVR84Gs3x7PyMPneZCHH9EHMg6+oKwP2Jy4uKI9ZFlkuSK2T3PYkeBFs9YkweZrcbINVwBZlu1M6XzGmIxoNBoI8ULVHFD+uSV/3rlzC1k6fHNKqjtJRq3bNaucb++arD2vWy5JmmZibU3Oz1xObK3JS9Fg2Eanxcso3afD0zcW4XCYDm16TdZH6bJoBXIyEQZvmTAql+t49VKSI7pP1kde2Uy3C2WbjM2Azy64xZ4kk3H4fNK/QVf6tRGUfdYdAmNeuE+PpOykXmVJEExEU+JbREmcroQUsuc5vPOh+BMTfj/CUgmPNsfrF/KsC54NVfohe1vXrAD2lCUHpycHuHP7LgAgEFYE+TJ/zVYLqbT8po1lZV8/lODNUmwJJRLAj+kn3b8npbgHr47gZhnONgO8Kvn3pnKCDksr3WP5Ow/fM+jQEYn8OvlsiP7EceEMpsGg4FQRrMpadzpmqFDcw+0mgfG6nEEX+RpCDE6psvskk0+769v483/xLwEAW5QoXk0n0GEwts7SWJ20AEbYb/mXYQbrW5QxPj/LwuxI/4qXYptVgMLncSMTZJCB+zmWkmc2phpgk7nocO2knHJO2zQT6RR9VwqNzEigbrc74eC7bi/LXri4YmnheAAHL+4OBkBUqXGn0wH/Cm4mijaZbAkG3NBod/IXsgZ8LIXujEZI0987p7hFqdjG9773RwCA7kDGqknf3hty4YTl1zovhU6uVfSn0D3y2ypR2maQLhWOwJxTsn1n5dfGuD8Z4cH79MMK8ncvXsj5EoxFMHPI80sVFaSQszmZjKPIfdWfdn9tXDwePzqsmWsxOTMxWRJ9XkWRAZ0ey0jSM5njueaxZLepOI5CqWsFPIo1CVxlKB/fabfhi8he2qa9umL5mVs30aHv7/aSJNuQvidiSWSPJFi5wbNqwn7C58bGpgRjZypAkCaRd7+CgwNJnt7fEltRJxG33a2jxOSKjz7QlDbbcOm4YhnYm0t5d1U2vrKxjrAhe0klCV6/lvvIfDzFPZZ5tWvy+atGA00Ggr1DEh6zzCsRjgEkrO/X5LysV2UtxN591xKkKV5Jv/xM+r149RJh3v8uWObsZgI3bgvA1pbfW0+syxhNZJ7fPDvFWx+Jz7URlSTEzx8Lcffq+jWMxzKJPZ6fqmTSY2rw8o7Xp/12qdrCegMrTKb7mRA3eHfpDUcY0MY+O5AxMgJBjEo8S4uy5qIs0fYYBsqUR7cx6Jtelrn82Rd/BycJh3dviP+gkhLD7tAiHVfJgUpexqXbGsM/knNaJZgVcKJcr8Gc8axjQpbxF7hcXtgohqHoSyIUiRpOZ+iyBHeiKbsl831y3IHbK/NbKMg+8/tVPMNhif0MGDRt9WXec/krONvSr+UlmWc4nXCoYLTBZChLVecTO+p9vj/DAwaDbmOHiYCPd3JuTCeDiSupDA5fSBKnRPqYbSakAgEfLrNMdmdI6cH7SL1Rg4/+ryckY2aOKQBluJHhXugTaOKkL2roBppMRDV6cp59yRJqRyhgUdCsEhyi7iXlyzwi9Fl/U1uUjC3aoi3aoi3aoi3aoi3aoi3aoi3aoi3aov2Otd+KkrH/7X/+T+aARN0UQqXH7IxCgAQCPgSZCVQlSGVG6Kfm3JLRno9JjEWYsGG4EWU2vViTiFq1XLWeo95fke+pjHaj3rKk3tWz7UN5djKdQJ8EyTbKzY5I/JiOZZBnNn2dMPc//1JKrf6ff/M5Mkl5vo2h0/6AJKi9MVJJyfi0meV+dyOOu7ckk/30XLIDLaKc7mfWsUmSRCMl7zoaMaMVciFP6UiVHVPZnOFwbJForjILdH2XZUa5K4Bywqp0bjhmRioUtMhu+0Rq+Vk61pj3USUhrY/R+iEJKl2absk9Okg86uL32sUSllYYBVcEbA35bNAIwsZs8AZLzR4+ewgPSaFrJA1PE2nicWmYTEmWytK7UFjGs1CswA5F/CfzfZ2lG+dXFxacdWtX5iZXy7MvVYyZhXCzz0pCOH9VssjKwkSjhVnyUCnkMWME2amyVsyEL6UzuDjKAoBV/vP2A8kydEc9mMw6X1Ey9f2PhBzvm4NXcDETqzKjCg3g9+gwiIgJR0hAPDUxIVR1fUkySm1Ch/vjEWIsJ2gzm1Zvyr/F4gk0Ccs2mFVsUUqzUKnCTUTQiCmEPiPzhXIJSWabR1wnCcpuarY5kuzX2or8qVAlhXwRdWaPnMwgnDKqnlnJgKqX+ODutwAAR6eSKW3123Bz3BV5spKuLRUvEVPQU0qE9koDXFZk3aqymH0ivD759vv48qGgLjyEB2MsezCRWYKpSSc6RE24SV7eGbRRq8v4KQJtfUpiOp8BG6P1U0JxvIS3urwBTAgNbPelTyHCu4vFvLUXRiS1H4/ks+PZFOGIvFedaCPz3yMoD3FulmkXTO7h3es3cXYsyLNf/OwXMn7VOkDZWzez1WMS2zbbHZwS/TPoSh8ChP3nq5e4e0fQgz6u7Tn7eff2HctuKBSRKrczO0MQvYzkiqCw3hC98fj5MxBkg6UlWUNO9i3mc2OJ71Mh8inCkp1Od4DzCxl/n1fW2pDpnfPWGIOurON9ErRvrFBG/vAlTJYiXmP533g8xt9TonaJ5WcXREJuLmdwm3Z4xvOleCH7uV5pI8q9dHou6/b2u5I9/fzhL+FnKYt9KGv0/X0hy3UMx2jOZP+aXDt9ruNypYh3H1Dql2UPx8diD+7cvo9c4/TXxnZrU0pdx0MnipckXydR5/6eZI53thI4fCXZwsyyZI+mZDddXlmxzts0EWgdoixr5QqWaUdvvSXZ2l98+QsU2a9AWLJvP/yJlN48enWGNcqxx+KUi2baaTbqw0NI7vaWzEmxJBm+VqdpyayPmJlW6IZqtYswS/RqhO1Ho2ILu53Or844ou5OT+U9680uMswCJzkPI+7hSDSJfEHG30bkiSohsGGGLSIc7KxZ8Hvl31rNOpZIMF4pih2ptjvozehHEYXpUKiLmYntNfEDfvaTnwMAdjdkzbVbAwwG8vwLlkH1eIbde+cugsy86prYpIlPfi8YCVriBpf0NVpNnre6gV6fJJWE+9/ekP3q0DU8fCIZ7Dkzxuss4ev0uqjSn5oTubOWknWSPzrBtZV1AICPdqBB9MHpZQ4fffweACAaEjvyf/2bH2CPhNgFImSVHe8Nu6A7BZ3+gI1Iz431ZRy8lGxrgnOa4d4K+PxoNeW9iiyp6BGFke2XscExnhOVPaGNX8ssw8NzZYMkrec5QTFflYoI+WVdGbqspyCz0TNzgBmRHwGWdipftFJrw++mSARRbHfuyn49PD5CLpcFAMSiYpOWU0mcsARoSgL0IaXDt27u4tVreWeHUwbpvQeCrtIddtia8nfZguyTMH3EgMePV0/EJ6wWZa+GSeDaHk9QIzp6RjLqSIyIh1gY0YBCc4t9c7IUzBtyIcR1PiNsv8iylVK5ZlEnKPlzhbZzOBxI8+xX+/MaUXfFShk9+psejlmrKWt8PgU2eRZYsEJoePHiiN+VZ4VIKxBJeHHzrpRg5LOy7tv0gcJeD8b0xXNEbEeS4mPsbW/AQTL1JhEBVYuQ24779+SZZywn+7//SvYIXAaCGTnjfEPZE8W8nDc7u3s4OJI7Q5sl9YoE2Od0ok/bfEUKDFWxYJuZ1mYYsMTMRgJfmFOrrG51Xdb/+XHRKv00AjInJuTPTnOMO/vS9xt7Yrd/8qUgeDSnA2mi9J98IwgCdV5EAgFsr0tfu0RTOlht4YlE0WLZ1FTJkcdkvdhcdmj0T+NEuJRLFPiZzhAn0fHrY0FVRuMkby8VUKUQh4/I4RIFPSaTiSW4ot4zyTV+mbuwyvfU/aU7s2NMtCa1B5COytrTxjOEia7xsWSyM5Tv6T4fNlnqlaOQh7rXHb45RopnYk6hhygi43I5cf5K9niOeyFJdJpHt8Phl058fEuQjF9T9Oe80ICDKEJ/QPaeRoSQ3+9ElKiVIPu7zvPt6qqAFEuJmiRtV5UcLo+BABFhbp4NRiCAr3/6ubzHmaCGQAqRZCaKLu9hlxcUXiHayeHwImHIXrhxW871zohlz4YTc4UwVCjAOs/NcBJtfk7RrVy/LgijqTnBORHrCuHi59l1eXQK3wrvUCzRVmzxq9ubOM9n5X1UeRfntHh6CQdRpm6iZJpVmYeIN4ghz4BUhgg53rMuy0V4IlxHnO97N68jyDO+QDGpIOdobAI9ooeiS7IeVfWOz+VFrSZzUeVdRaHZ1pdXcJUV1KKdMQeTPqLbZke5Iut87pRnL6/LvOevighyL7xza5fjx/LDbAkEglliGHH6pLrDgxxLyFfXlL2X9TG2wyrr7bNSxMn77nJmBfkrsV3/5H/84f9vydgCIbRoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zoi/Y71n4rEEL/9L/7zhwQDhwXZS6ZaLb4Z/L5POzMJOVyEoVcokxhs9OG2035VWYhOuTvmM/nePt9QVkcUGp7wKinruvW9xRxm07JuU6nhylJFRX/zJhkZC6PG2NG5aaKN4UZakxnFjLjrfuC/PiG/Cz/0z/7PwBTIotzEqoabsVT1ITLxUwUv397OYS3dtcBACdViVIHoxIZHufK2N+Q7MDeA8kAzjQWP7psFneQ4mpQWZrRaIR0QqKU6aRkQR8THREIh2Aw7H52THJnRiFDkaBV6+4gYkJF47WgCxdEJKlMdjgg2RknnPBTFtVPEuu8yuo4HNjf37f6BcDi13FqToxI5FVjFsPpceOQMsUKjdOjfGCleI61VYkSK/nK8UzWUrszxJS1sB1Kd/aYZVhfX0WdaKOb9ySzf3h2yHnoIR6RLMuUJJm5HOuiPT606kQ/EDW0vy7ZruVEAuW+QpPI2vEyot9pddEhN1KaPBivDg+s8S+QM0unbGCzI+8XjkatMVY15RozRclEFCPKSqo52lzdsKLnNyinen4ukeVIKmHx2VRPJLq9TZRYq9O2kFkjVd/MSH6730OAJNIFZmunlPl0OZyIhIPsl0KZyZjPZjPM+XdBZgACRJJNJhNUSarWZb2sktb1GE6LrN1vIzfBtqC4BtOhRcoYS0j0fM6Qfn/QQ5G1xUvbzPSPdVyWO+wzAAC3liRLNhh20GBWPUm+E8Wh4nDoFln26RlRM0TNRaJBJJkFU0iHc5Ich4J+pIiCsrE+f/8GJbftOspNmecGSc7Hirg+HUevLf1cY1ZecW0UrvJwOGXOb92WrEyY6I1vnj1Gi3shyUzbdZKGnuWK+OrLx3x36UtvMoKfmbk2uWRmXE9z2FCgrK+DxMoZkjWOZi2L56tBLgQ7URGj0Qi+oNiEv/nB38r4rwk6Le2JYZXoS53124pob3VzA29OyGNGcm3DK3bYMR2iQZLRMREtE55ZpWrDIhKNkqzymHZr7IjARX63t/cFQfPuPdkHT77+HCfH8nsB8m9oDhvaGonLmZGLcc/ev3sDI2bFun2SjHpkDJ49PrBk0ueUJN27IWiFVr9tcYJ1GuTkUbx3zQ5u3CUPDM8ZjUgGX9CAl5ksRcL56JdPAQDXdm9iws/1mXHsDVk/35ji68/Elii+pJ1tOW/+5E8+RJUZIoXeCvJsbTab8JAVOshs5ttvCUrhyZNHv8oCE0F2VcxbJNTekDz/8assACDXGFiZuVUS0haYoQsHXHhwj/aeHAEzcqI4HJo1xj4i1mIJWXOvjnOwkXMvlxd7tbMl6yoWDuLwJXlclmXc3TxvchcFi9soQ1SDQo543H68eEkutris2TVyc/gMHUeUVd7dlfPP65PfPz46woO3xZ+o1ygHH/DB4POfP5PvecayRrdSGRRpt1s9WQMlcpWsLa9Zdf2KOyeekfEslAqYMmPZVNwcRCzH41GsbsvYPGf2X63Btc0VhOMyh0ny6Fzl5Pftdjs67EOP55PhkzVQrTXR6fz6Gv/uJ+8AAMpnVwg6yBUHCijQfjeHbQzG9Atom9yuKGLkkXv2QtajIim22U34aHcU55ASXuh12vj2Rx8AAC4uZB8vczwcmg175Mp68UKQMV32V/M4MaCv4CCnmBLHmI0n0GgHXOQIU30JJ4Ool6XvPrfY8UFPxjyRiCEYkvXbbJE/g2sWPi8c9C3K5HwadEmwqrtRpZ+oOEsuz3MYcj2EiaTp8NystKuIEeEdI3F0i6jT1eUE3AP6Q0RjanaSRXtCOHj2hu9DBE5A3uGyXESNmeUUyenjPKca1QKSJP8tV+W9ig3y9C0nYR/LWlteWgcAcJujXuvg6lI+p9oH778v/1av4oxnY51iB/6AjPXqWhpbW2L3nfTfC0RXziZzTMe/+hwAeL1evHj+hr8tfogi+LXpQ8RTsg6vb4m/4uCZP+530CCios2zVMl+j/o9VHg+x0mKHg7I+/XaNYSD8vzJUObos8/kPUudKZxhef4ukZCVqvzGdG6Hhyi9Cn0pg/xra+llnB9nAQAX5LbTeYY77bqF7lMk1EqS2uN2wk5fY3eP/F0OA0MiXSdErEXJcWmHjgjt0u6O2Ny/+mtBN63vbKDP59pJ/F8lsq7VaCCjBFcUFIHPHs8n8IWlfzEis2b03QLBMCaUGHcQ9TyibxOPpmASyfz6QCGExP54vAbKdRm3zW2ZNxtBIm6XC136O0ckBO52xa6enp3j2nU5L96cCBqr1h9hfVX6Xic/yial5R0zYMoFS/A+ohSpiIX82Ngg6TLJwBVXWq/fh51IJMX1eX4u63ns0eAmoXKWPEYmES61ahmhuNjBKPm4qkTWFppdVAdEjZMzZztDlF4igEJVPre8JH3fSPJ+MOig1eC5Ti7DJd7TDKcLQVZIKBRVpVKBgy87msm675EYX/e4cXUu9xVuIfSIVO70x9jbkGc5idQcm/L9dq+FZY6xEpgpEE2bTmQs1FGffqNJXi2X241LnnVTIl/TPAfGvRH8K3J+nR/nrGcBwNePHyLAe+3WjpzhQfqIzWoDZljWWCoq79wjGjboDKBekDm5uBQ7fEJewGZvgDvvClJZcetOh30kArw38rzV6VcMJlMESOBsp8iKupeEw2GMyJek7l5qz9rmQIh2vs3qmkhAnlO8yKNM3ly1p3auyRk2G5sgTzRmY7ElNYKCAAAgAElEQVR3cRJjz+YOZIhCVmt00lcE3jqc9Mn96v5NkZeDs2P0JzI2efpJYVZTpVIpqyrjv/5v/+UCIbRoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zo0n4rEEL/wz/5vTkAdHtNpFISGU+lJZpVJzdNpVLD3JT4lZ8qXhqzQkbAZ0WxFReCkoguFEpILkkkUski+pVEfaeDMOsTFTt3hdwLk8nMiuRrrPFt5aUvsXgcl6yNN4nm2d+WOsDDl6/QYJQ4sSQRv2lEnvPzzw7wxReSMY5TQlInB8ho1EKHmQ1fSL6X9M6x6ZfodHyN6kVkia+9zOH73/4OAMBJadECGeVb/a41Dj5GCItFifCura1ZqjhTopzUGqj2WhYnkkGk1HwmEcruoGtxohiMpjsZT3QaOjRGqfOM0HrdEn122ZyWzH2fmfcbtyRyi34HDaoNxIlaKrFG22HToJO3J0u0R7PfR4nZTtXPGOv7r+0sAeRtGKl3oHxrMrWCS2bHT/My/k3y6YQCPoRijKJmZO0Vyasz0kxUyC+UYaTb75F5MKdztCkHGacyndtUcoUtGMzidFhf3ibvTyQSw4Tj4GUm++kL4S7pj0fwr5LvihmtJf6/4XZbvExtZlsiVIzSdQ2JFYmeD4hY63W6CJPvIUxp4deseV/fvYbPH4kEd4YS1mNmimza3OLhGhM1pzL373zwLjr8uxp5ABTbvtMG9Jh5zVAWcUB0ms/ng5PIvz45otTeqlQqljqH4r7wUXHH4dSsCLuNiLo4+cDSS0t48Uqy44GIrDU3M4Jur4FLSoz6nLL2DC2AcEYQQUfMZqIh77W9t40+OTV6XI9HVFVYXVnB4UvJcmfisi8nQ/meOehjb0dQeiEiK/7ymYxrr1nDjXXJjG5QPtqrJEZnM+So3gLaLT0sa2E+mcJFGcsxM1EJVT9ss6FAydVtIiSuKlSv67XRbBBJwL7cui+IpD//V/8a45GszQ/f+xgAUOt3sLaxLr9DO3BG6dVYNA4b1bAuuN+2Kf3+gx/8O9zj/k1SYvTRF1LD/skn38aD94VP5OELQbQckpstqnvhIVpOqasohMrK5po1HvWmvIOXa8Dv96JKm3xyKfYtwHXf6g0sJTzFyKW4L+rlCTZXZdwVf0SnTrW2Rhn+kIzRux8IirPRaWPUoZoQs3AbzDya5hiXVAn6h89ElWydvCzm3IlnzGgvM1u4S/6Z2WRs8Uf9+OcyRkyiwuHS8d51qmUSDquUZWLxEALMNn/4jmTh//5vfypjnlhBn3uhO5I1qlN1w+sN4/JCsorNmozxaEglsVQAtp78t+JzuU0lqFwuhx554dSftznHr18f4JL2VKHY2o0+6pS4Tkc5xkQR+qJRXFVlnGea4piSNTrr1/HgnqC1ikSAJJiFDkcCFjfg4+eiumaS9y2cWLWUSPtUkXKSi+XerRuWeuKnXwk6R6di32RmQy4n68pPFdBrW7Jfi4UK7Eq6l3swRt6aVCKO4tUpx0PW3u07ktmLRqMWQjl3wbNkMkAwLXbpkv+W8crcbiQyllKTn/xflRpRAw4HImH5nqIg8tMfqdZrOD854rvKv9mYrXXYbfAyK7hEKV7FPVKt5DEYypm1uiJ2o0407GQyga7L57Z2xF8xiIIrFWsYkwtMSaobbunU2asTRHTp18662AGF6DXtJr7iWRJSdjjkgu6UuU/QZpaIRmn3mnAT4eYnJ16NqIv5yG2dNcs8z548+hIA8P3/4I/w5lBQfdevEc1KXpJyoYwJ+263K8llsbW9fgd8HSvzqyTZp/MhDBf5IqbyvW6T/Iqr6ygWyW9F5RqXWyYimFmCTvsYJQfFl18IArPZ7mGfykgpZsCPj4/xky9kbQbIq6d8w8PjA+wTUQjyRzmJNIwGfRgRhZZMiS0y6H/4jbDFxZEjCqBApJXX70OfvplCzdaoILSSSSKdkrV5VZC1WiBn5VQD7u/K75SJYJiR18Jh92DQF19Qcbl5ieK0zQE9JM98STW6bSJzXY6ZhXZMkP9ka0n24OX5JRpteWY4IutlOOpDd8mabDTk3wpF6Xso4YWNakRd2pi9XXIV5a9QKsr7B4kkGNGPMU0TfiqbvbmQszwSkO9VSnns7ck6XKM60I9+KOusMbBhossau0W07sEbOSNdvjDmVFwtFGUclT9shwMa0VAV8lkqG+WAbqEMTCLXdA+l43UHxvSZwkF51u9/8gAmbfjzJ4KEvHtbkL8OxxBBr+wlJf1dupJnpteWcXIpNlZx89jZ35/+9OfQWBkRod9tG4vfk0lH0ezI2Op+sTEaEUaT4QQO+jDKl/fSfr/31nv45mvxxzy8C/SoQLaysoJ8WdZMnX53OESF3nIFbUrQB4jOrrdlzb8+ycLkfvYQ2eFyORHkuqtSsTZCfrjpeIwW0VThCFVbg/KezlYdaSqqrZNTUCm2jkYjy54p5M0JfeXT5iUq5OUTKwWkabPNyRgxytqvUiVZQZMOsm/wzZGs2w2i2Tw2mcdet4aVbTlfn1L9eZUcX/V+xUKq9unvT3juzicTiwetT7Ti1uY6wgHxM49OxA+Z084Zhm6p4k2JPFPKeJpTh4eckxcXsqaD9EEn05GFalpepi9PNPdg0IObfF8DKncq/yyeTEKjE9NpypkTDsh4tppddG1tvqv4DMMaEfvDAcZzGf9vjgT9uby5DgCIpdIoduWuPSCq1atLv5fCaSRDMqdvjuQs7tO/KtVbcNiJ2N6Ts67daVjlAYpH7c2xnLHVTgcJvqtJdV6laj6dm5jxHhziOvSryg+Px9rPbSL+VtIyR61qE+m02PkK0ftvjtUZdhM2qgtiIuNn5/nu1HX4iSgyDHKSEiFqn+sYkVuxU5T+9akmXWxWEEzIOa34+RTXaLFShMcnz/pf/8XBb7fsvLqwlq4u0SL0WZWiqIBQKBjFFYMwNkUQTJK/dreD22/J5UdBrHLZLABx4JREtkaCq6dPxHAlk0mw8ghVwrI9dLSm0wk0Qvm7dV78o7L5w5EYGiTZytekT4dHsqmWNrfxDuGvj5/LpWhKwt7vfPQ2Lq5kIzfpzGhuJQfqgEFCvwY/b5sBccj7eNuysctvxLHqXNUw5oGXYACp1JN/O3l+igCdLWWAdR4ATrcLEwbLSjxUNQZ6GuMubJRfNQl/U+UF3W4bScLiTU0+46STHXUHMOT3PDb5HQ8PRIzniLHsYWlFDMFTXhaTkSC6DJScZymlzEui2+1AyC+LW/PwcC11oHHTxGm43fSWp5qOCt+nx4uPn/DvueaAj4E1g8TkLRrb2VzDMzqaihDxzr4Y6+ByCq+OJFgz78tYZ1gydZ69tNbHkATj47H8XtDrxsW5XCjUntcNygoXzxHke7VIcpxckfmrthoYEUcYSTKgRFI8w3Ahxt++RWd+NpV5vCrkMCSk0MYyrUAgiBohjxoDfmtrAkPsd5uI81J9/Joywnz3RDKOKUtQvvt7n8hnjgQC3Bu0EUkQek5o+Cod22w2BxOyBlokSlaH7FWpZBFRurkHR1xf5VodfUVeSCI5B8sGm40GlDa6bsjfOXihefzkOdIkJB+MZI3WSJroGZvQnISGsgRvaydtXVSbddnr33kgRNWvjo6xTZLxKSVNCwMGVyZ+jByKbE76txKTtffpj/4eL56JA/xn//l/BgD4x57vyZhjBtBQXzKQ0THlPYu1EloD6fOdB3J5MGjAL86yaNRk/ZbOZT9PN0h8f/gaKwwQ1gmz9pHIMfvsCnY6I3XawK9eiQP5wR/8PrKU60VIxv/73/0efvzjH+Pfb0GW6nz5+S8QYPlBKiPOycuXYjNL+TLay9J3O53dIW31Vw+/QqUj492ljRnP1GFVQESTNafsQIelYAcHj6yyPBUIsjFQ3p+MLFLvjz+WcpKjI0p1O+3wsAwkwsvG0YmQBf5X/+n3sH9Ngg/Pnsscff2Q0P5gEvt3BIpuknRPmwJ+pbVKieJPP/sJAODWrRvQ6GT9oz/8BABwVZb3HE1mWN+TS9SAAacQD/OnDw+RWRNn7a33JPhSoG13B30Ix+S9FATbTrlYn9+LXRLpf/VQLsSJZR70HgdavPj1h/JnbEWcomojC512zssgx50NWdfFYhEDypoqMs3PHj21fl/ZebVPv3gqe348nsBF25VISZ9yvRJsDIIHGXQvkPR2ZFbRbVHmnFK6EwbF3ZrNIiVWcGcvyVaddpdFLOtmsNjHsuPuYIgy3/nGTZlTv0fWcfHyyrqA7G3Khe6bN+LMT8w5EiRENem8qsvR1VUBXj5DEXV++pOfyRiEQ/jW+0IS3G0z0MUEx8lxDkGWPs/tMt9r8ZjFnD30yOeuMYDq9/jhi8o7lqviK/RH8i6ra7tWMHxEMuRjOvWBQAB//EdiS8CyOjsDEpeXl6hWxNZ1GZx+/lK+l15KY8Ty2hc5mYftTdnDmjnEgCVjI5Y4vXwmku+dXgdRlt66OC7zvtiD7a1dnL8R+5EnkagqAW60qtjdkwvPS5KwR1w+9Afy74+eyPkZJwlnKh1DhePQYlmT7uSa1V1WQKjTJsEpAzY/+fkXmNPhVv7cPh39y8s8wjwbVblWkcTCht9Aj5dEj4/nCmXMNR2YsoR8Z0vWVY8XrTelnOVPtLtyFty8JYGMUrWEIcvVYg+krO7uA7mkZ8+vUGvJ98Z8l2tbG6ixZCzIc/eSCZ/33n8LFzm5uHvojccTsh6j4RCuGnLpzV/J+yylxRc6P72An4HraIJJKhvLCyYT2Ene3uDlen1Dzn6nAzjhGnOrM5UR4tkYsJmy9xwMSuVJdr66tg4nnxnjnlJJncPDNzAZLFpjedgVSWJXUjH4+c59XhIje/J+bXcLPYqqXF3IeOi6jtFE/s5PEYYAZZk7nQ58IVkrQ5JDvzqQsyCZTMLpkueXinJ115Vc92CAAEurV+NSNtTtMSmKAD77Wtb26QXLQCimkfJ5cUyKgKZX+qR8cwxsVimWxrKTGZNqbsMHp845YfmfSf8YDsBGO69KoEf03WazGQKGIouXC66JCZz0cRUBcSQY4XtdYkp/MUCS59WU9OXRk88xJjGwm8T4SnTi+9/7GL8k+fGEl/RlEpIHfboVTPnqGzk37SxR8WgulEiSC5b1JynO8Nmnn6Kal3FPMYHV45kynQDgO5eYML7gZ09PsojG5fyqy3ZBk3cCh8+PPH3YAJPY15YisNG/UcnIBikYfKEorhocb54r7739kfzuo69QY9n2xecMwqyK//jeOw8wV3uAwg6xsJw9mq5hWZf3qbKkCgQBxFbS6LM8K8wAwUsGkjYTcQwH8qxEQMZv0mEpo8OP0nkWALDDpJVOv7M3saOQlb3ucssz+3zfRqsOb0b2TjAte/9sUMGAQAkjLnN/eChndywUxc09SWQ8fChzqZIKmsuBLPeol4G/7lDmy2HT0OkogQbZGxMmdXavbcBFv77GElLDK3Mb8DoBU/7bpABTr0+KjtHQKlc+b/KuzP3dafWQXJX9ubMlNj1AG3h8lkOlTqGhoASsprRz9mEFGikQVlfk8yq5du3aMuoFOf/6NTkr3boDGsEMCrhQKLPMdDKGvc0kGsvQJrznRlMRzLnOyapg7Re7BlzRRgaYhJgwqBsNhixKiQDl5t96IGfIq4PncHNPTNvS9wHvkXPNhibLJsE+5EnWPepOkIgyid9iyaldxno2m1gloJ2aPEvXpU+7e9exyiTyb2qLkrFFW7RFW7RFW7RFW7RFW7RFW7RFW7RFW7TfsfZbgRCKEb711s3biDEyeEVyqqHO6LY/ghkJzJxOiWjWGIGezU2LqNjGqLsqb4pF4qizDKRVlIjhXUqEul0eNIlAMkcSiZszYxx2+63SKK9fInmaJhHpdr2NACXjzKj8zp0bkoktVwpwEeFwfU+yTh5V6raawEuWq/zr/1Mitn6vKkHSMGeW3KZkeqcaMoS/pqKSGanYJHIYXfdjTpx5nNHstimZ6vXNJlxE75iM+Sn0y+HrI9y8QZlikpBpRHbYJnYYukSlqwX5/DaJYIfBIVaXmNlvKDJl+f6gOYSH2Qs3ydWmzBDOJjN0bBLB/+mbfwAAGISupe7dRo3wvFBMMogrhHUOhy08fChQ9NiqZHVisRhyVcmmVRvMgBGpMpyvIxmXsbIxsm5jGUmtWbckC0EopJdZFodNg5OZrjAzGyob+tHWdQvhYGME/4CkobPhDDfuCSqtRGRAm/Ls4YAXPsJaWd0Bjb/bHkyxzGzdP/xEJMCrzJRqugvRmIzj6rJ8plCVfTCZTFAoylrtURa+TqjmYNqHj9KznoC8+/6dGxZZn50lktWK9HNrewc6I+nvv/uPAAisHQAMw40SpTfLRM1d2xM0hc0Oq8yiw3edcQ3anE4rA9BimV2TpNs2mx0twj6XKQevSM6//x/+x/iLv/gLAMCYhIUqq3l+dmURZ5sa4bOEXjudzl9JmhNFp1AmnX4DNpuMv5OlAI1WD5Fo6Nc+3+a89Vs19DmWikh4izKWzpmJd+6IVHj+VPaug0iweDwOO9Erx5eCTAlrkhF4fXwAg6VbDsJ2/ZSlXN5ch040zitCZN8wY7exug6nSQTkMmXkKVPtCcRQrkmmoM8SnSDRHqlUBjdvig0y7fJ7B1nJnvbHI+zsC2IhSAK8v/yrf2UhRVTGcSkh72zX7mLEDI+LpaOTqYz/e9/6AEMiyGrMvL/9oZQ1jcdD9IhaKzZkbJMpwnBHdngIx6y3mMG1y+/v7GxZUtJKfrvPcmCXx40oM8yvHwvaQGU6Q5EQ2rqs8VCY6LIlmZty7iW69SyAX5WoZVblM7O5iV98KrbowQMpGZuNRqgROTZsydq8uSPorczSCuosw/nZV1IypnFPRWJxRGl/C1nZE5pTxmBtd9l613UiRlrfyB52O4ZoEOE5oo3x0/bO53MMSeyrsv79mYx5qVZHr8dMHrO05zkSxDfqFnriNuXtl1geUsxl8eJQ1qgq6XRTxMCmOxAi1Fghu/ok55zO5gh2pZ/ZA0GcwemBi0iYc0rATpglb3ancAZlHSkUhcInh0IhS+xghWfJkGWm2ZOsRXoIZrUcRLX2qgWk4rIGFIoiRUnvs2YLaWaYVbn3zeuCFArF4njJPttoG8ZTWbOpdAhPn8ne292XLGqGWcpurQY/s7qjofS+WhP/wu/3okgydbdX+jQfz3B9RdBTExJw10rSF1vGiQH3y8yUM+Tdd+Tc8LgcFhL31cExx0M+e+PaDvyGvGw+K3tpmWfjrNnFVkbOhxJh9wGeXa3RFMGkrPMxkW7NhqzLWCyGBEtEnvPdFSLPYZ+izXIpD9FKsaSsiU6vh51bvy7+cHoiWejllQxMEjl7aN/8oSjsGuWDh/SheMaWClUEgtLX+yxpPTgQhO50PLGg7o+eiT1UZa12uw35C0Hd2mgrFMIls7yECEmkvRGi7fIyR1Nzgne+JSTgF5fy/VJZbIzL68fILuNdpQiJwRJ0be5GShd/KjyWtV7hWPvdfth5hjx6JDLf1/elhLTeqlsl8sssLXn97DlGRAHOSXa7xHPw2TePEWapndq8qky31+yjxzLWTotE1Sy1CUXCcBlKAEXmV8mXB40gQgn5t1yewigkH3Y67Rj05exX5aQxlXmfOtCqyu/U6CvEw2Kzk8kA2rT3GufW8MhYBcM3UWJ5kG6XtZCISiY8GYmgVpD5ypFomdsAGysbGE9lzdVZNpReXoHPQzl7Q/reIlJrNDExZhnZJ9/+rowfS9TCkTg8BlETFMpY3xDftVgrod6R/ZjyyN44PZPPDOZulHoyNr0rGY/lJEsaPVP4ifyLsdxT41nucDuhs0xzTsESl0PGNV+oWmeAnfeJuUK8DjpwsaR1xvnu0j7oLr8liBKNyvfevD6FwdKyKKknCoUz9skPPxGh4Bni4Vxu7a7DzjL9KM/8OH2b4+Nj3NsTn/qMaL1am4jX9hyaLvY+nRIbM2dFxqDSQyYu66rNc+m7H8o8PH30HCOi6FsOVXIjf7bHNYxY0jnhWOVZdRFKr6JICoT6hax7VWbj9ujYuyZ9UKVcrf4FEiztXUnK/D57Ioi349MjuLknFMH4v/23fw0A2IvHoTnk3xQ9wjFpAcLnOQyIFLTTn1WiNTPY4OM52RspX5Q+Rj6HEc+j4huZkxFL3FrtKvwRsYdHx4KuDrul38+eP8PtD4hIHBENzvKfZDKF0wtZm14KQ3hYlhozDFRoBzwRnpFOG14WxAa56autXJezNaAHfoWK51ms+h7z+CzkXSioymZpT+o9DDmXBsds0CcqqNzDNtGesbCs8QlFk5rNJiIUKbAEZTg+e7f2UD2X+T16LX6pIp5udBs4eil+7fZ1sRttVnnomg1LYXmfaFJsfJtCOzOMrc+tsNzr+Ehs/NScIeiTzyvk9cVV3oodDPrcey3pu8PjxXyiENSyX8plQemNbA6sLIlvMuXvXbBMdzabwKA/uslqiflEXvrhV8/R5/32q8fiN4ZYGri1s2zZ665sPbi9spdbrQ5aV2IzN7dk/UdZndS3j0FQGsKsDsg36TOPhgBjIn6frLURBY8ODx+jT/v9m9oCIbRoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zoi7Zoi/Y71n4rSKX/+X//p3MAWF5btaJ5HcrF5kkq6zI86JMzaEySSw+J4gLhgCW3PWIEz85objwYhYeIEQ8j5IpM6+c/+xkcSt+eyIM+Mx1np0f45NvCMeJkJvuC2ZOlzCo0G2sRL1nbTXTKzBxD0+VZdnIoXFuWSPbX2ac4rEnk+m9/JEiXToVEzQ4HJqzLV3LpIa8THyUk63HnvkQK68w8LHmS2AnL36W2JevXc8n4fPbVL60xCgbl+23Kd9frVYRIHlZjdkDxJo26Douw+PqmZLQVkVfA68OUGe1rzLzkKWM+mXXh4e+wvBatDmVPYynYlDQo6zDDRGrk6mVcXkiUPmBIVNbFDMxyJoYeuScaQ4kkt0YmNCK5TNb8Hp9JRDgU8iGWkPeyk7slSgnsZr0FPxE7fa6Bbx5LVkEz57j+tmQ/3UQBzcokU3bpeHQpZLApFxFhlKnd27mBACPWr47lWQZRWa16DXGSgzpIdBxiVH06Ny1ep3qLJJKsd642W9ijDOWEJMfwkDcil4PBjIEisZ6Asr3NKuwteWc3kSczhwm7LusvyPrmIfeI4Tbw4Ycfyngwo/GXf/mXAIBbN2+jTr4ObULJVaIbgqEIDviuTZKUp1ZJblyuoc1otOJoUCTwhtODo0PJCmT8Mr/bu5IhSWXSePaMGRSSFL8kyfaw18PHHwsJciQue6LDOt9Bf4wRkR99ZlmmRLpF42soV1lXrksfqrkz/MGf/j4A4NUryT5rJJU2vD64wrJWaswsb90VbojhcIwqySpdJPqdEiG0vbOFJuXjo0QsPCkKaWUsEsacHDtOZtg6rIX2ubxIMaM9IQm4OyRz5dCcKLO+fjblOxOhUW3WEUvL3NfakkFI2WVudzY2Lf6Xn38uvCAas8KesAdOEle2mQkv10sWUiTIvdfvEMkwm1ty1Iq7RZHROvUEGkTSrK7J9xUnyNQcQWOWtMVn2Wg72+UGIiFZ5y324eYtyaoXi3llfuEnWqnVUeSTAYvk3EE0hYfcDV6P20IeFJgJU/K20HqWnOpU8RcQTeg2vGiTa4QJXCTicXzFbPPOsmSp3rklyAKn5sDrU0Ex9Ej8aiH+el3AJKcACWNHtFutZg9j7gHFw7C5KjZ7POjiVVWyitqMCFQyJ6e9IegkTU6miPbg/788OYbXKzZsPJS9O+gTgeoNwaaI9cnjpexQp97FyZWgnNKUHHYSiTMZzy2S0BbRBoPRr7J+KjPnoh3J5QvwEB3QI7m835DfGY9mFh/D2JTfm5MI+v7tXYxJWOowSRhBRJlTsyOVknPMjMtYZU+yAIBMOm1lhask+v34g29xHGfQnfIeB6+FE2lObolwOo3cpaD6hjz/pkRvaDaXhUiMkqi+QOGBTDSOIDPvM8q42pnNK1Vq0GgzHT7J0N1YX4GTvAFfk6thhagjPezD518IF5WXsux7uzc57n30iOI0eea3O1xL0QTG5Blr16Rfr47E5m5sbGCJHAZhotMC5DV69PQhJiQvdftoU7j37ZoTd28Jl9UP/478YeRou33nLfRov33kpnn1RPpt+CMwyXVkGGJTlpZkTWiahqsrkhLP5d/arXMLzb23o8jJZf+Eg17LXmyuC0rBQ+6FcrkNg8jrKUlyFZl4IhHDJbl2IpSDN2n/04GYZae6SnKc9ur8/Bxzcn/cYPZZd1HOfG6HhyisPoVAZrQ1QV/cIvy+IPqzWKIgSCyNYEj24BW5XnwB+f9uv48YZYcDJOBtl2qAR/69Tb47k3xQ0DWLs2mFUu+KmDx7dI4Rs/ZLFFsZkqPNMFyIxBSfHzl3vDJ2t/Zv49WBoI/bzMJH02J3Lq5y0O1ibP0kKvXo8j2fLwYvCb975BO5KhL5YPYQJh/GkBLMDmbb+/0RNPLUNMmxk4zJ2TDsj+Gxe9l3WV8hL+XIXQaGc7Ef/Z7sy7npQG9IeW+H7ClPgHLV5xV4fTInSTdtIPm1as2ahTAskm9JIU1sOnB4Jutv1SP+ymsKxJzVR2hyznW+Q4ao/9tbKzgmwvDOvuybXz4XPyZbaeHaDfEb2+R5cvCs63an6PPs9nG/2FldUCrW4SASdEJft9uTted1OZHiGMcj8l6OsQM+HlKRgMZxk2dlUltgEQNcTiLdarLXC9Ua3qHAQ59VECdE3SUiYTQnYiNcFFRxUxzAPR7gm5diR4d8eL8vvx+yBywelyYR2x9dF5Tf2fkVzrJimxUxvpfnffb83OJLUojymVM+0+62YKPP4OX5Yif6KBWPoHiVlXGQ14R3yYYOkfI6OfEmI9qdkQ3LG+KrZrNEUXGf9hotxJMk+ienmkG72G1VkSRKb0KUu8QFiVUAACAASURBVCIInzvcmFKg5/mF+L5398SOOAwNp+RUixP9M+C9sFspI7lFoRdKvcc8sifOLo4RXiOXD+e2cibv1O/acHIqdxqFerRxnTh1G27flPfr8hydTnqwKe5NIjU3SErt1nRcEjHjoe9aqomdhNMGp4tkyPTHFFF+pdjBSlr8cy8RdV9+Jfcgu24iSfGHEYmglzcpLT8ZwEHUm+Lr1WmTGv0xbi/JvbEzELv9nATLlWED7iDRnjyLg+SVs/VN9MifWyeKLRiTZ7aaTUyJJE8ovkGiu4fDMRzcew7yErsNL15+I8jWJQrMqLtYpd0FWEXiZnVBjz7KSe4I+/tyVnk5FzMSToeCfnj8sm5XovTx1uTsr5YmyF0Sdc47qc0hdi6RDFn3MidXt413ld60j2pTznrwjrfKs8Grh1DNi70IUySr0pbPvj49sBBQYZ5BWVY13Lx1De2ezP0//V++XsjOL9qiLdqiLdqiLdqiLdqiLdqiLdqiLdqiLZq03woOoakukcaj3CEqVG8ZUlpNZYCNgRu1OtEkzMbMmEFDv4MGmcRN6vouUwnIMweeffkFAMDHaHGjLwGykO5Gj/W7HcpTTxix/eBbnyCRoCoTkTtLhkTk+v0hDENiaZpLosxnlPBMxKLQxlR6ooLYCf9txVjFCmVi8yWJ8P7gR3wn5yo8DonaVyhnni9MUPRKxG+F8qMb+xLZHJtz5Cgzf/z3wmtjc1JycnsTZlMy5Sd5yar1qNQVDkdRPpOMRpVyrxnyMzhtAaSCEu3tEL1SIRJkGAla6mRKkaZDFJBmBPCAUdtIgrwP58yEDepwMqswUCgAxSrhcCJMRZ4RpU/zJcmGegI3MGfGJRqVz7h7AyyTYf2XVMjx8509DhtmzK6qSO/XzOaM5jM82H8LAHD2OgsAKFKO+L/5L/8MCSp6fc7MSJ7Zk85FC7d3BSlydiLjqOmS9ehijjaZ36NElyytEzFR96NDxMItSjs3mvLMVrcHX1D6Vx+SC4HqJ2uRMKrMjiWUpGtYxjOmxdCljKWXNehdyglnfGFUXUSRtGVOfS4PXMavK6sVyM+yd+06qlx/r74ULokPmempD4YosbbaJI/J9Q2Jjp+cvcJ5ScYvxvrcUk3268HxJZweGcfeiLxVlLptNsvYXJcMlBGWqPjhpWRWXxyfW2iDIZX75nZ5v1g0AvuUmQJmUTvkDbJ7ncCcYzSQPaiyOtrUhh7rjH/8RjI4a8EQPv07yZClyWGlke9A97iwuiaoiR4VTlaW5Xc1WwQrlBatU81C8S01ehX0xvJ31TP5HedM1r0rGMLqlmQKfvRzydb6qCASCzmxuS3PTHDtjJkFGUyHcDItnkjKmCkJ24PDI9iY1bp37xrHQ/pbbbdxfCBrekq56NUMZaebDezuSGbps3PJ4i37VuDXZM+up9YBAM8rYkcmkzF0sBaZKQO3g9xN3i5i5EbSNLERBvkR1ndu4wkzMHaHfKZFvpuJHSgwk+2iHPnzp1RSGZkW6upyKuv3j/74OzLWh6/RY4ZufV36WanJ2p3Agx7VcDJUpzjKi1JUemUdb45lz845z34/12yhiDkVAjMJyjm7opi0ZC6Qlj8PX8s871/ftSR7wz7JmLWIVl1Kb6FNTgclyZsjEs+m+xFSHC3keFDIk5P8JbSBnCfVmoyD2ytj7DLmSJJT7RGzaCvkYlpJLuHNOdGA5FCZe5hptk8tDqFcRcaxwz2iOzx4623Zx6fMljf7lIc37Wi05BkTykzbiJyNri3BVZJ9/Id/LAi7H/zgb1BmZvT+22I3Hn8pa+fO/h384pGgZP7gI7Gdzx7K+Rvxe/HwjGoqUyo+NeX/U15gxmz/hiG/fYNZsUKxiv5M3mM+JMfMV2K3Qj4PSuzLSU3GJRGVz56dnSHM7J2daiQe8vslUnHLn+j2ZJ63lmXMox4fLsnLNGXWNcxsbbtSRorZ5AgVNV88/Qr+JDn+/j/23ivGsiy7ElvPe+9duIyIjPS2urxhsbuaZBuymxxyJGgkERLmczT8EiABEjSCJGD0IQMJEiAIoEhxKI5aBE2rDVns6u7yVVlZacP7eN57/+7Vx17n9VCA5rsBvfMTmRHvXXPMPvvsvfZaVnLalWTvWXNu4BKRMDs7wvVwWqLy4doSsvy3WSMHCDOrrVIJU8XxtSRr915I9n6vz4k++V8sftmTnx5LH/sTXtSq0qf7u2IPzJBns7kA0J56fXLNHhHAtVYVYWY4z05l/O7eFASfxeXC031Z1wMiyQp5zlWLBVaiWUN+WfMrV15EiNwrpCXCtRcEbZe0m/HJI0GAPjmVefXWPbmPzdZAgyit9RWxnTr3tUL5OQIRmZt2ZmnzWco4V1s4q4lt6RJNmSFCZXn9Kp5l5dnPiR5/KSHX7lWy2H0uYw+iFNbI73d4ugN3iZxxVJ1KZsTGDHtV9Ory/lMqF6VSMjaGSBhZckxN6VMagh6A6GMr9zGNylEDrYcGs/YhO/l3mCl2xsPQ0mIjDCBn34i+b98+Rwp2OtKPnZ703dGpAxdF2esV55NSjkqFPahyPccViofv0K8cYkL01ZSZ6WhEMs2V5hQO+rPDrtiW8xOxey67EyNmvpkIx7gmA2/XzNDJPxfyyroplOTZXG43YgnxPYdEVTZ7fRwSIbGxJVxgimvE77Bg2qcEPdUC1V6SCbqRJ7o3QqRxqSq+Qq3TBulssF+RZz4k0nviimCsy7t6qVZIlxKTroaAQ/r//S9lPa+vin+8tR7HiOheC9XMxvRrM1f9GI6kjyxe6RenTtXfTgsmI9X7iPRu9GQ+O0IakhG5Ri4n4zastRF0Sh8NBvK3SFLWgdfchoc+coRKvreICtrbPcD+jsx7h1vGfoN/M8xmQF7mit6V+9y6TtsXiWPcIW/dUJ79YiD7hMFkgcFGbsyGjJfq82g0ijb5z+otng+oshdLpefqek+ey9rPkDbLkYigP5S+unFN/J0REaWnJ+dYS9GPoO+l2XWYAuR4csvYKFU58xhw++Wer78kNrdPn8Mfu4IS91m0Zf7Fo4Lw+rtPnqHr51ha5R1cLqKU4kmYiRi7ExSbWSqLjTd37biWFj9MpwqXsoHHhjZGPAN4yIVXLoi6md2sI/tcnnlCbqVl+jbpKz44QuSfmhGJSr8iVygix/42Ew3vsLjg4HxNR2QMxyWuz2oNeSp+xhLyN53r2j0E+pwXHg9986n0q83bg42ce5ubVFjrCNrOFwhgZ0fsop2+TZ3I0mQ8hQk5zybk6Dk+FH9zqg3xmDbBSIS3j35/rVSEy8R9iWM7Y1WI3+eBQ1dqefTPiECOLK1gzJIUl+L0LciePmm0Ua1R7ZRKXf5QEsOxXH9AZT8vHdzhQEOVCH4tqNRz5Rli8QzyBSKh3fKcQaqrjowGGKlMVydBXJRoOKN1iJU1Iur6rCy5LmqUjXINPcVtTB7Mzgk5TccD2FLyPk6eGWxEzc0GBZh98uzHdZkLWXIzmU0OmHR5vkZV1tBNnnvHvRFMQ/bfv6b9UpSM/Y//7Os6AHS7PdRrYkyubomTUFESu/0eHHSAD1m2okijHE73HIpYpZzcfR7E9ZmGGSf3/ols/kkSWnocTih1dDeDIqqG4Hh3HxYeJHyUpVWHRovFggefPQCA+aZs52ZitVqxnJbPHTGIoAInyXASgYx8/oePxTD+5Q9lg7JOXOi2ZICNNCA2swteGprf/BqJWyGTaG9vG06WQiSTstjbDBD02xMknfJivrS8V4/OhtbWoPekPwZ0UlRpld/vnUtkb22K82MmWej29jbWKOvrpAxxiwRn5XYNU5ImGoxynyEdR6fNilBSFk+LcOkZpTJr3R4Cbrm3g++iSEfD8QjydBxMdIxdDjtubAnR6/HJKQDg5EI+EwlF0SU584hlCBvXxVj7wwE8fSqlPDuUmFfy51upDNwk85pQWlpJi37j17+JDx9LkOjzT4Xg2k3iNpfNCh8Xq51BQVV9eP/+PbS5yfW6spDP+ZyxeBx9jqmSKh7zFNcfjGClwdIoUb6SUXDMCXRC659sy7s0SaS+spxCnyRw5bLM//WNS3AyIGSnZOcF5+PG8vLcWVhbUvBXGdO9k2O8+JKUk334oRwyFKHfQJvifZYjfecb3wAADFnGc3ieg40wxb39UwBCAgkA48EUBpYAKLLW0oVszi6zFQlCH5eXxRnvEMLaLFewRJJFg5OeJsswDQ4XLkhWOSXxa5+Hy9lkCifHNM8Sok6lgiSd4mRcHMbLmwKtnWkj5E7lmd94Xd5dwfgPjs6RSLN8kkGLHOdlq9dAMEwJWAYtLrNs0eXzY0QZ7VO+q4fzJeYPwMN14mQphptQ+P6oBwul1EMhsW9PHrEMLRaFkUSRHcr1Hl5QwjOewi2SXzvooB4eHbNflzGk7PaTp3Jwtw41dOmE8zGFlA6Aw2Wf/7tOuLmS6zb47ehwIzuhxKqLQdK33/wquj0Zg26fRJksrYqtZHB6Ks/j5Dwe0PEcDSbokEh8QqJ7ZWs7hQI0kkCG6UAbGczZunJ5PserLGM7OBbHfXXzCiZ0HL54JO/s4zw7PNmHxjV3maWLH3zwCTauyJ4DOmknh1IusJJOIhCUcS2TuNhBos6XXnsVn30ghIFK7nyFEtYmk2leiqKk2G9el3vs7u5jxFIng1Hupw5Hp2eHGHQpgUp5cD/t5I1r17B3Kg6AIs68flPKco6Pj9Ej5D3CEuYig7tLmRWU6NQUWOaiSrS6nTH6XbF9YwYj54S9qQhyDKwFafu+9nu/he/99Z8DAF7ZlDn32SMJdhh8PrRzsme/83UhrP+TP/pDAMDXv/aruKCz+nhfnsHMdeDSx0j4ZCyXL8k6GTDo1u0MYeeeqBJE6gC5ubaMCg/gWTH/OL+QcdjaWIKTa8HOMg2NgSFoY9i5ljz0geokgl5dXoeJ5b8P92UONLh3vfHGazg7lv4w6DJeBtsItR7LVCgjrLF0YNSbIcBDtoklKW2SE3dHHZhZoqNTfaDfkPWzHFuDjcTxiry21JA5brOYUK2w7IpSsurgf7B/MneOrXZZl+MZD40hPzpdeUcVfHMSYv7wwSO88847AIAey5IOnomPsnHtCkqca0oiXklsm0wmmEzSp7EES/5mRhjGMu+V/PbSspQLNMonqFalL90BOWScHcp9unYDxmTMVGNvsbrm13FYmNCjTcqRfPUbX3sbVR4Ys+cyrwJMQh1f7OOF14Q4/oSBfBvL2TZW12Bk2eSX9MfcPCRlUnGcnMg4r7JEvsdytP6kDnJKIx5kIoHk6NV6CQmSf4coljIbT5Avy7qMs4zpwWNZL8GwDx4miKYcQy/nqhlmeIKyL3T6DDCQVb1ab2FgkOexeuR3OvcNzWhB2infU4Tdyi/IpJbQ4IHWS2L4HpNVXrcPJvpAOomMJyxVPS9kkUjLs6vSblUGaLVaESVhf8QrP530G4eNHmoVlhjz0Ht0LvvAYDLESkbWi43BpotCGXbuDw7KYVdLYsNsFhuMtBfZvIy98l0dNie4PWNidPOZa+y70dxXjXDv7wylr6q1IRycAyOur6ifJVmpEIZjmYfnZ7LeVpn0c3scKDNZupwRv6VCvwU2DUP6b0E/k2QM0uY7E4CHXzd9Jz8Jgq0+M3rsqwaFJaoXFVxOS78ZWQ48Hct7XV1P4s6GzM0pS2W6DNh4PB7Y6NN98VB82MyKBC2P9g9goc0bcb/IrMk8tlqdMBqUnDsT1Ey2m+0ONFlSPOEeHGbC0mazzcuM1RqaMMgdjYbhZxD9Eybn10hJkc9e4CJ7KteiL+VQ/vhkBhvfxwAZr7Dfh8mAtocH8BltZ6PRwcYl8WdtFBX6nHtzx+HFnbuy99ZoO3M862BmRoTl3uOxrOPr16RfdbOObJaJNp4jmzxnRMJJJLiH7u6K/Vhfkz7W9PG8jxpMYtboCwRDoXmZnPKvlBiSL5HGJoV8OkzYu+jj1BplTGgfh9yPoE8x02TNgYG0Hm18rVtDbH0FAJBIys/jJ9LXhp4Oa0SeIRGUdT1iwKbf6kGjTxJkmd1oKvM3GIki5Zf5fnwuvlaPtr5Rb8HjEJvnp3DABX0No82AOG3ZOZPxiuLAZrcjwrHPnYs/63apclYb+qR2GE0pHjOleEyvh3hMlU5LHzx8KMmMeCyJMtczq6LR7k1xcCABKn9ACbawJH8yw+l5g+8h46bEeIajMaZM6Jkp2LK+JON++9pllPMMNNJ2Jlm6uhSJwsBkaa4s97Xy/DLo99DiWndExVe7SWqOk+wJTpoyR5Mp7iX0Fdv1BjosY7cwONXh3re1eRVtJtVdNlmDdjOD1aMRsqR4+c//+NNFydiiLdqiLdqiLdqiLdqiLdqiLdqiLdqiLdqiSfulQAj9d//RV3UAqFRqgC4RtJUVQaNMCEeLxSLoEXalWr1C0sVOD2Zm9C5Omf1kdn0ymUBjBivBbEQtywzroI2ZQSKhly6t8jMSkfvi6ZeIEia+sbQCAOiSaG97exsuZjRClHu1kMjSACCfFwSBn6U6XhKOdRpNWIOSOXlEMuU//4GUsbgsEejMjk816YNms40wM6OXmZmwsbwOkwYyKYluFrsSoX3MMpyMNYEbEWZsMvIMTn62fFZFxE2ZXhJob61LVP3qlQxOiZRIJqWv9vclCnxydoEMpXVBtId6T5Ntghqj5kfPJcL7lbuCaNo9O0QqIxHTa9ekjwtN6f/3fvIB3n5LSkO6hJk2eJ1YOo7iPAtEaHSzgZu3pUShRzIxhSCzmm3oMXsx73dmJYq1EjRmg7WuRGxnhDSazWb4WJ6lCK3v3BOY3c7xIU4qLAmi1K1CFrSazTm585SwezCCHfR6YY+72UeSmauyhGOqzeAk8bPKmgRC0j/D8Qw2Xb53eCSokLe2BG1w9eoWnnZlzrz/kZQgBRjPNTtMeOFFIVk9PZU+MxiM2NuWDJ7PKddcpTTjvRs38PlnnwAAooSSqrKkQX8MEwkbvZQhf/hcnsXj96NSlH64fk0y0wp58tEXDzAjcaLGUopqVcaoXGjDZZMxqbLc0Eg0m10bI0jU0K++/ToAwMUs6P72c1i5ri6O5b2uvSzIv6NaGS1COkcCFkC/LmujeJrDzWuCmohHlMTrEB6fXLdMmcYgJXUxHiPNjMYmof+eqGSMvv+jd7G6Lkizk5xE+bsTuWEw4kO5KvNvQhL3JNFsg/FoDo1V5JYD4tZj3gDCbhIQK/Llubz7EGNefzyWvr17W+bj5w8eYGlVns/tk88fM6MSC8ewsS5zpdOSdaDmwu3bd/HsuZTYqCzXUB/jxg2Br1b4DoWyzK9QKAQDM2wJZi8UYed5+QIXp7wn5+0RkUKtRhtBkv+q/giR8LBUb2FI8m8Hyf5qlCzXDTYYOHcczAyFmZnqVuqo5OS51kiky0oM+N0u5JnlUwSrMChSQ6BPAmEP7XiRpNIjTNEhCbCJSEGD0QpfRObop0TBJZi1Cod8SHGdnBHtVWM2OByL4pQSoepa2QvJ7vzGV7+KFpFLRpP0x5AE1FPoOC7Jsy8ty3s1WToyHI7R78jYJ5gBa5RZVhIOQNdIQk+bpGSrp8MptrcF0XKTCNkh0U6j4QTnJbnGgFlhI7OuK+lLqBO1ofyBIUupDcYJ3nhNbMvPPvo5AMBuNuCdt34FALC9I5nR3/nt3wUA/Nn3/k/44yR+ZRnDX/2loIneeefr+L/++scAgDHLQo0sydpIJ7BOouIvnwm0vlJRBLqOOfFwLCLzIsiy8Wa9gqWMZLdPaJvctK/buzswE6kTCVF4gfuMFRqcdplz/qjMOQOZzS0wwssSh64qe0gyizoYotNgeTrkGTTzEM2+9K3KYo6ZtTabLTBxToYpNX51WdC3R/lTlKg522d2PRNlxtPqRoQIky7ROTmicAM+D1y0Lb0Ws85B6ZdOb4AhhRdUOW8mKbbaBDNefkXs4hFLzDqUI54OfSiRiH8wlvuNhpwLswlCMem/zz79nPeV97x//9rcpoSiMn4/e/fnSKxIv7/2gvgBVtrF5rCEQVu+m4mJXW2PxA85zhVxRpSWhXvWmHtr1OfCFhHKJWbOtyljno6uYIV7VZ5ksg6WHiSWI3AzK1vnfHpAxOCLW5fR5TvWFLqUVec3rl/B4Z587tU3Xma/SF/96ff/CgRYIcPMe50la067CT4SVd/ckL85jMDUxiw+bcRjEvzG4iEYWXKXSJA4mu/cqLcwIF2BkSjfWEpskmbookBybhez44q4N5GKwzOVuZOKy9pQaAh9rMNCKel8TnwT5XNf5PIY9pS8vdiUMeez0+NGlWX9kbg8Q5RE1/lSAQ6ipA1E6q/Sf6yWyphRXEGhjob0cw0WI5xE/MwIU601+/PMfKMl40X3Hal4Cn3l91FUYUb7Nh5PMdblewVmydvcb5eXV+e+WYfrTSe6ZDgwwMzv+UmroGTvI1Ev6nzn2VCeM88ylM5wiIhPzgBRIhtNJiLI9CGmZrnGekL260ZFvndcK8IVFjsQJorKxrNLqdrCgKUeI5tCwwIe+q49lgYm6VteXU3iMsd3SNThKUvqXG4PGiSsH7CP3ESZerxeTOhb2DivekSgzWY6nA75nJHQCoX61TQdFiJ2TonEU4T1QZ8fOsuY4jGZj6rc8GDvOYwceyVgs3cke7rZbEaM5dCq/OzsQubqpUsbiHFd7+9LVYjf54CBtrVVph2mlP3qxuacAkRVIViJmsuPh9DpD1zbELtDzRUc7z/HOlE529tynwD3LqNzAppajAYUZaFtGk2Ag0OSNhP9vcJzpM9unJeh2ojkGBBl0ht0EaE/pcie20QPjTCEm/NCG7C8mhULdqcLGufVhOusUCrCwLLJlaT4atWK9J8r4kGV1Slm+t9Tlif2q31sbEjfHu3TZtpIaRAMwWaX+5yciW/nIRLPZrPBOZb3j3G/PjyXPrNYLLATmTJkmefdr0ip8PbR7rwapMSS1XqV5WyxBIIBsTcXLBddZbXAaDjAIcvA/H5FRC7Gd2//GFevXOfnZLwV+shiNCDCyh6zVeZHo9nFeEa6jo7cuzmSs2g0Gcft268AAD57V3zl/SN599VLa3MkeihIsRWixqIhP4bsYwMRQh6ukdVMGv2+jJ2JQgY6a2rddhsmRMEOSHHip9hVq99Cn7ZEiVmomleX0zkvxVTo+4CPVUDtHhJR2aeDrJ7aXBVfo5CtYMB++63/8H9eIIQWbdEWbdEWbdEWbdEWbdEWbdEWbdEWbdEWTdovBULoD//Lf6ADgD7TcX4m0U0Vha0xIh+PROH1ye/8fvL9kGjJYnMAJP1UpJoXZxLBs1qtsJJYskqi5eK23CNxdQ2dISOoLEDOkLvipJGb18ZbSVRtJ5IkHomhUJDIopJ52yBp68H+0TyarSTfV5OCwPnpT38CE7l9MleFC+fpkUSGHz0+Ro0ZOiWj63Lb5yR9LWZ3X7olWbKttBvrmxJZf16Sd/2I9eKjrAm//44gb0ZTZpFXJGKoDzVoE4lcB8gNo+Tvep0c7Hb5t86MTYfEwhazHU+fSfbZxkjylDwCwZAb5aFkNgZleYdUTKLPY6cFuXOJQKcCCq0k0fFGqQQPJW5HzGIoQrqz3Bk2yI+wuSw/LfoMNUZj+0N5Lhvrv7vNNn71TclaP38qNdMKZTAzzHBck+wD6tKhEe8vUFIHe/Jer12TGuMl8kDsF8+wfvW+/Jv8AxXOx6kGaHzmAmszb1wRhIbTaoMjLnN0lxkHFXvd2trCOftDg6pVlTnU7QyQb1BCfcRaUGZwr21uwBqTa+7tSQQ75pH+jMTDqE6l31xOogUmBhSZAVSZb41onhublzBk5HqfvC43N4Rgr5atIMTs3ozykn/1rkgU373zArLHpwCA5ArlvolcKNRaaLQko2GjBHW1Ju/QbAww7ku/Gzl3gsyahJwmvHxfovxNSmnmSYgZCoXmPCl2k7y7IyjfzzcrGNF2tUmK2SjKWsokl6GxPp8c1rDadXiYNXZQjlnj4or4AjApxNiYkXy5DSZTYEy0TLUp72pi9iQWCwuqEUCZaA2vJjd88+03VeIVBv6scA0f7R5j3JO5EwkJmsLuE7sQDrhQIEnx22+9zu/L307Pz9AhIsDt+fuSlfpUh4cZGDPRNor8HQYNSaLD6vx+PBSFldnBGpEpFtrQsD+ANnkDVjgXfvoTIWsehV2YkhvKSln3IDOCJrsJXzyRGm4Dr61q5TE0wUGyYDPrqjWSBTZaA2iaIn6W+asIv/PVFgpEfSpU1XUSSKejcXz5hXC5eYgiNDPtt5YJoFyXdwgTWfRkV5BuvkgQWdpvlf1cyqxiPyd2YDaR3yWIRmk3aoiF5R1VTffx2SkAwOt1o0Xyx8lA3sthkH6J+wNYIiF5oSr3m1DyuTUezclVuyShtlLm1wQbUkQejImQVcSz/oAbxYL828f17+M61SczDPn5X/v1XwcAfE6C573DA0xAkYMDmV8xiib43BE8+ERQOdduCmrs6Fjslo4J1jMyd7xJ6f9pvYkMETQ+ogXSlIsNenz4YEfsrx2K/0HatVu38dc//AkAoNySjFenLfatUSrglRcE/fk37z/9e/1hNVuQJzovrngHmLWdjkcwcp92eUiEeVlIg3cOD+ciFUY+xYt3iZxqVhAlsmoWELv4MomPs+fnSCfJZ9Yml1ZZkBP1Shd5onv9YekDk01DgPLNXSLPhkT8dNs9JMkpY+B8r5zLO1d6VQSTMobNtozpSkr2zVtXN9CoyjtXKTs/M4lvMh0OECcSr5wlQa9doU6j8BC5ZyEXzZPPBNUT8HmwdZXITvKQ7J2I7zAz+HB4Lntksyc29+3XBRnjcLlRpu1aIVo6T5T1w4cPESHPW2pFrl2vN+FIEX1GZJaTCKHD2iFyR2IrX9x8AwDw4FDGW+8O4SWiokZRkaMd+du1ZBghyo/H0vKZLzmP3//kEf7gT9YVzAAAIABJREFUH/1jAMAn74k88k5W9uuXXr2HNJFZT8mfNiJi6EoqiAMSeZbLMs7aTP6WiYXgJDl/OCrr5pvf+joA4A+/9z0osHqvK59vk5x3NGjDYxM7cJ0ExH6bGc2xIBe2NmV9DcgtmM1msbkp/uHJhbyPkf6A2+dHF+QgI9/a0rL0tTeg4emzJwAAm1nWxIzIcrtDx0aY0tr0T13k0jJpJlQrck3FZTNSUuAGI6wUMVGcO4ovJRgOoUREUpHzERb2TyyITFTut78jdsPOPctusc59coWea7DzovEIqkSFjMZELoxmuMiRF5Io9zgRh5hhjkA4OZFncFEgotKow8018ZyZ/QCz+alECCsp6berzKD/9Y/Ep/FGUhgRYWLkHO0QMbuaSuLzj2U+NUlmPyIh9NRkg51E2ikfCestMgfcYR869LkSas8jR2au14TZS8QIkaWKNNjmiMBB0veLnKzBcCSAwVTGazASW5ZeEjucDPnhJMJS9aMlYOG17LDSlw/6pB9Pjk4BiG8/o+9kI/Jy44qM32AwwOMvZZ3YjPIsAb98fzSaIhSUfjwkr5w/JDbJbDBiRgTMgOO7Rs6iTquOFHlOFc+mhYgym9UJq13W5+mZjHtZCSJ0ujCTY1HxVp1VLzCiT3wpLWicXfKC2jwuNLoyb++/fA8A0KM8+GwyRK9OdFlW1vybr4hvX6+fztF/kxlFT3akP1989coc3TQlwlkhMnTNjG3aIMXB6SayzjJso9LkeK3IXpJYIlqvkMeAHHgjnpvA+WL1muDjWvUR3dogksbh9KJClKjZ5WY/uhBOSF926uR8ysvnYbQiliZRN/nJ1LliNhyjU5N+HnN9zuj/LS2nsLwi+5DiW1K8UI1WB7aJzIupJjY6QHS2wagj6JM12OQ5psHzms3lhI1of3VmO+O5SdMwH2cX+fK6SoxkMsIpOQLDRKsfkKM0HIrPOW/vsnJE8SNqswl4vMLlrRUAsi/16OdbPeQ400k8jSky5AptleSZG0QHur2e+fxT3I7amMjE3ghb6+KPbu8JWnopLeNt0OSeAGDnOyvUUqVYwngg1+pZKDjC+AYsOmqUiJ/RZ3ASRehze1BU/RFU/Emcj5PpnIsqHhQ7V+d5fCm5hhH31H/4n/1PC4TQoi3aoi3aoi3aoi3aoi3aoi3aoi3aoi3aokn7pZCdD1GZoF5rYoPs80q961FL1Bj02S+UOAqM8M50KqpEYxgyYqfUfgxMy0+nU2D89+XSbTaJVDqdDhTLEpWekX8mGJIIr9flRrsj0b0+ORfcTnmmfncEJ3lWVLSu35HoYK1Wh59qSyvLgubpVOTa3/7Od+dcAUdZRvR1iUyPJ22YmRmyEyHUadcwI7dL4P/F9ZI7zePSqkRMb6xJTWyO/fKDXA5Pt4X757u/LUihhweSKU5EY7AwvTpi//WJjmr3OqiSG0Opi7Xb8pl6o40+6xif71CCk9xD/mkIrZbi0SASxE7uCgwxZs344ZH0tc5sazLqxA7VrpRaleJ5un3nDk6oGOSgtOZqMgkLI8irVIr72c+E1+LO9ZsoF5htYuT18ZeS9U4spWBjakIhK5rkV5gZNNxhVjxOvpNRX6KqJl3DyRO5hpKdXouT22Q0mkv23v7m1wAAUyozHR8cwjKVebjM5+wysn+ytwdQuUPNQ5vK8jTaeP1FKqI8l37pu1gb224gwih/0CZzL0w5+HF/iOFYPpdakt8ZYIXJLH3VI0/NqCP9flYsYMwsjplzTaFXov4A4sysNRid/rW3pba2WKrAbpHfWZkNy6RlDiZTl/DFl5I1IkgBCWaW/PYemHybIx8umMW7eXkJ8ZisuR/8zV8DAC7fEBtg9XlQVbw9Tem/VRtVSUxWLBG1UdXk2ZNEH9k9NhiZgXK7ZB4no2E4mNGbDKUfNi7LfQq5AqptmQ9VZkYTIXmvfquDM6rZhGNBvoPct9/tIEy+kohL+j3lk8yK02zEmCobTgf5pKjUkF5dhotSsu2WvNfxsfTHdOzHZcrV374l3EF/+7P35P7xOEzkXIgy01DIy7rrdrtzxGSZigYuN2V0bSZ0p5S2HEnWo/2sgggRdLou/VJnNieXP0PuXK7RvCprI5gR+5Pr9lBiZslPJMLu52JbQgk/gszmFpW0MdEDZs0OTneMyBli47rGzILJQDI3QadkCxU/2fpSEl7WStvJTeWPSLbmi+3H6FN1AzP5jGksyY/vf/gIqRTXB5WpJlT1OyvmMKbNUyini4sLjGkPFdrokOg5l9OOc6J5nOSNUEik4XiEIdVEnJRRVWpVxdwZDvbleSpNsc1LV2TO2fw+jPnOfkqYOony0DQjuk0iUqhCcvOWzAm3xwYnkWAf/ly4xNZWhP8gFgpjRN6IP/rf/1juw8zSnTt3sEfk6mwsn/n6VwWh8Xz7GF2uiWWiMvNEAUxmU2hOeQcLURA2rx3Zhtja2xvSxyZyI7kjTrxlFaSlqtf/4Y9+BADY2trEs8eC6Lp9TySQPSuSNX30RRsOKmF2uF8nqNAT8HsxZR8r7gsmbeEN+uFidtZqZIaUtsxhdYGgAbzxylcAAG9Rcerjn7+LREiuf1dxShABvP/xl/AkZO+uUalkNqLKiNkMd4hoRXKd9PsdjIl4UmjMDSoDaXFtzo+QJcef3StzJ+WxYNCXfl/yybpZ8grawzCc4PETQYDEMvI7LzlOjE6zoqfA7TvS11PK7xbLNYxq8qwuzpOuQe4RDljxL7//pwCAG9elP+JELxeqBdz7isyjw1PJup7tC8+Qx+OFxyXvvJUSNCfq5Ah7cQsaeTraQ+n3iM0+z36eEVUcpD+nG0ZYJ3olRDWocUn8slbzAqGUrPFYWNaqaVne4ebKbTz8UJBn2TPxta6/QiXayRlmFiLoEjIX1j2yzvbPTrC+JvtXMCXvUJ8QiR1JY+d9QdvOZjL2SpXutNSEi5L3o5nM+88eCFrpjVdexh/+4ffkWm2uDdqtxFIaGlV3RrRbhW4THre8TzYne+SNq5LR1qbDOdrc7RU7kKX9qLQa6FPxbXWFqq8zuc/nH70Ph0f6qk/VJZeTfmOvgWxXfKelpKyvGjkvgu7QXNL58EyQFW7yT7m8PhRLMkcV34ySty4XL9Bh1h7c1zxEDfhcTvRasicYiRowE903nWqwMDtu1JmVJ+o8e9HBeEjEJFWyjCYbjBa5d4HZ7QKRv26HGx4X9con0retunzPbfeilBXk3q0tmaNFIo0b2TIarBiI3pJ58dZt6U/NZMOIPvUueQrDXN8GswlhJW+vyWeylKvXdB3J5RUAwKBxCgDocf/0xMOY0ofskjvOTFvY7Y3QIPfpsk+y+PmsjHejf46ILczry/fOi1m4Q/KuobTYgQ65bNzBIO6RB7BWEnv8xaHM5/ZsCi/3qC797wCNoT4zYEx7qjgdP38mqKClpSUkiWgBUURurolKuYFzopfXr4jd6NTlPafTKTpEunbobxYq8r3JZAIL7aOPe6tJl3k9HHeQLck+2yDCJUVUi9MZmyM7FXdn0LeJPhVJv/31bwIA/qgle53T78XKZVGJzRXFf4lExa42qnlEiWK9w/NSqSTvsrycwIz8L7sHVOO7RKRio4MwuSZHVE62BjkXSuU5YpgANziJ8tD0CTIpsXNT7h1ffCJITYfDgQw5cs7PhI+xQV66TtGKdaJGHUTRv/KSnC92DnbQzLEyhfPDOO5CI2dNPCS+Z4TKh9pEx9GB9O31q2IrAx7xUbafPUFqU3yKIpVTFc+qbjHg+Y7Mo0vkN1RcmYfHJ9iiT+gmx92Ebly9XIFJ+XTcoM+oKGizu7C6LH2q1PmstAtPn+9Cg3TgGlXzcgX6f5uXcIPvV6Bi6ptU051OdKRY3ZLwsnpIEzTzxdk5pkQAe/zyzn6/fY749RPVNCYfY7/XgZMKYpmrMk96fflboVxCLCp+VJ+VA50G1d46PZzSV24R7Xx8IWM0HI6xyvMYqIym0P6tdhtRIiBrFfn+wZHY42gqjFCY6GVWJ6iz5myqwc3qKRd5FAfcax12G4JEr31JdWyd1UAzGNDnu//r2i9Fydh/9Y9f1wHAZLTMHWBVFpbkhjabTOewNyVX16Qs4v7R4bw0wU7jZeAhR9d1OAPScUMeHhxGQkTt1rnMeYyQMCW36/S5MeEBrs0Fo4/ke26fd26gLkgyGiEU1el0Y0znrM/nDbtl4L1RL47PZPDPCY/sGGSg3/tyD60GIfYsE3O5zSg3xGD76FBt8CA4rOawdVWMipXQ5huUiv8v/uzHSPO9/uk/+X3po7IsTH06m8OBuyyXWyJ5qslsmZN513joUwTSFxdZjIYzvr+MjYelCvu7Z3BQftJNCdRKVd5vaX0NBW54DoOspgoNf2dYhp9lD2++/ab0C4mku4MeBiQ4NLOvw17ffHNT03bQ5yEFRhTPWQrIUo9Lm/IuZpsJZzQmVhIITki8OTMArxJeWi+SeJGlVfV2CyZuSBNC8aaEV1udLjS5AV66ROdzn8Gt4RAWs7yrxu+16vIuL9y7ixKDkEEGXiYTwqUHY/i5yBuEfercYC4qebgZTExviWHNaSRtzpVwe4uEuywTaHcHcDMQcXosm87FicyBrfVLiEUoZ8sSvVFTlU5O5zKXl67IxvmXP/4BACCSTiPIoJlmIuycxKpWSwR5ylofHcmaSLJcZqb14XaLQ6SCpD/6vgR/fvs3vz0PTNRZbvH+p59KX2tGvPSilC18+BGJZstibNPxCHokF7VSJlXnuh6bxqiTJDFMJ8ChA1aWOC5TPldBxPujMbwBRSAsny9V5R0m0OawzTQJ51UJh3kGhOjE+3hgGjZIMj2dzoPFGZY8FnhQHc2M6HHehiMsrSTZ3/WNDJxkTT6lfHYoLTbw2c4h+gwgba2JQ9YieeJ0NJ0T25tt0tcer8yFzqCBozOWsdK5GVbHMDNC4w7KGjTxQN7odWAlSaLNIdcYMMrnnFhgtsjvjk+kj0pl+bl6KQm/T/5WJhxeBb4GMKHNspiIX54hzZJVw9SA4z2Zo2uUeLYQQgzbADPKRF+cy7op8sCkz3QsM4Bs5th73XL/7KADI4PaM67hPA9eBrMBGssXoMkcMGpm9CDj5fPKXtDtSl/XyhWkUjIGLvZtl8FVg0GHOyxj2OAaV2t9Nhri1g0pI7VTXvzTL8Qp9EcTuLQkTtqjLyVIkmZpUSazjOOjE/a7PIOTh7/7L9zBaCj/VkTfH7wvJNiNRgu5C+mjONe32t/7vQ6WVnjw45h0WIZSLNRQJuH2r3/jWwCAx0/FljXbffgcvyBCBIBUMg4HHb4WA7ZvvCoBnngmNpe4/uwzOWSskxi/1Rvi5EI+H+V+qcjLp6Mp9pgc+OxI7PDVq+KwmmFAluTpm5QVfu11OeQ/ffxoDhvfYID8g48+n98jFhNf4cZVmVeVovSr02rCgEmg7afSZ0oOO5oKw8a1cH1LbLub60E3GJHmem7ycNjr9eYlWxoDYx7OQ7fbNd8nVfnrGddBp12HkfuDi0S/ZRKo7xwcwBMVp/DqPTngajxwRiIR9Obzj+VyTFbpRhPqPFz4CCU/7cu8Pzh8CDdl4DGSa08Iod9cj6BOu/byV6RU9S9o93vdATRFyLoqY+mZS9praLPcIlejZHahgBffkoBH0Clz7vkjcXZnljEmHbnn5bQcLDRC5vezD2FyMQFGP+fZQ7GBN1avYG1FxuI4J+PkjVCqfLKLzaTMlTde/DUAwH/yz/45ADlgOG2Umd+Ucfvhj38IAPjOt/8A//3/8L8AAOy0433OiW6jggwlyi1GVa4l/ta/9W98FX/2L/9Snm9XbJ+T4iKRVBQ7LOW6tiE2I+xyI819XZXcKLtTrzfhcMtYHFLa/CQr/Vip9+ELiH1TQhlBipJEYyZMGXhShMmMdyMa9qHPOhBFPD+iLfO7fVhekj34hIG/OA/goUgC1qnMxxwD8nmW2rv8rvn+oEQInJQH73a7cJKIWdlMv4clPsdZeBnsbLNMOsuS11qri0hI5lGA++7pWQ4J7nchHtpKebEZo8EYQ5Y9bLBvx6QOMJkNcx+tw/J0K/epdrWORkVs0sqyPGeC/r7HaMVSRuzGF9sSrHvEsvjlG1uAVT4/ptSz3S5r6vMH23Opa5XoSFDAIhH2YsbkYLZI0loPBWB6YzzdEVv2b/6GBDSe78l9nxydIeSUz+VaMjaBsBOJlDzrlXWZf0YeDpfCIcTou4/GMm8fMvBnsVrnwfMABXDczP/brQ40mTifMnHWHci5C7oGJ/e4aEiubSB9BHQT+n25ZpJl2L2W9KvRYAIZFDAYyGdKLP0yGS0MK/6idMlvlc8YzRaMeVj2+uQ9oywN6ncHc0LldlveL53IIE8/X5UnKQGAWqc1lxpvsTR+fYPiRN02bGZZg8qfUIACu0tDl2dDm136P5uX+z398iHeekt80MskjB705G8ulwdHDDTqJC52cE30mt1flHQzWKH2xWazDjP9gUaHIisMqvdhhIvjlN2XeaEEkmxuJy7Ksh5r9NuXLq0iHKQ/lZJgSJC+QsDlngd/GyTgDgfkMzu7BxgZZD8pUlxoRjvpctkQZ+mbsi0NJrSHMx2+GIMpTKqpM1LA5YaFZYOK7H3AMyNMDmhcE02eaa9dlfLt07PsHBDQaPPcSTJlf8iP6zH6zwWxA2rPyxcLcyGhWFjmjJF9t7OzhyETxlcvyc/NTAR05ef3O8sezG/ns7Kcqyv94vbKuvEFfTgi2fWQSTyNpZ0moxttBoe2rsh+9vi5JCx64y6MkP4Is0w0FWWpvduCIG2fAdLX9SH37bAHI4JRBjwTORjc0qYaJgw8pS+JPTg7OZW+8nthpc/7r4otAcB4pM39j//mT3YWJWOLtmiLtmiLtmiLtmiLtmiLtmiLtmiLtmiLJu2XomQsRQLH7e1tXCOxryrvUhK5s9kMBwcSzTsjzM5ilQibx+NDoSSZtfRliVwniAxotRtwkIywRGJOJbEYNtvnktBTQtYU3HzD7kGMUtR1SNQtFiD8sNGYyxtuLEnmsU6If3fUgZ3Zwikh9maWbuzs7CKfl8ifhbE4t4eRZYcBlap83kMyt1a1AzNhfC1mBA+JLDADGGflWg1KxUdZUvH1d+6i8UAgY81mkdeXv50enc2jhqq04TJlmQPJFA6OT/g9kmERcRWJROZR+ghLNhQiwWSZwsUMvcqkK3nlVrmN9VWJ0l8w+xkgWmT18r15xv3kSMbWTingTqM8R4k1iQSxRV1wOZih+UJKCW/fkChzrVRFLCLRb4UaiJBYa+9oH50KybEJ+bOyP7wOG37ycynJ6Q1kzm0wC33lynUUGGntd2W8qyz1sw8lYwUAu08l+6lQFMFgCE5m/Xz8WXbKuz99+hwOjqmTpQ5Ksvbxk204WYbgoExkgJDXGWaomRTiTN5BJwTQ5jRgQuj1zjMZ9zv37s+Jt8MReYblJYHTlrJ5dHvyNzVOtpBkTyadLnQSIKqSnpduSLZ37dYN/PhnQgrrUZlcvyJ/ryLGEo/wLek/r0KLlLKIJyQDa7XKM//Bf/DvyfM+30VlR/oGRIWYVbagmMfep58AAF69wvLLFYniT2HAI5b9tQcS0c9kxI6M0cdrb0lpSLEumc7KRQFblynBqMrQKGEbi8Xmmf0ciT1dzA74XU7kK3KNcxLWHR5JGUQsGAWIOHvvJ0I+ubIszzcZTpAllLTd4X2isjZ0bYYTIgvDXhnfFKHp1WIOo55kuSaQ+ZV/Trn0mRk9og9/9COZs2tbkj25d+8+9klwaBjKZ45PxE46vHYsLwva6/BEMu5aU4PdRTJCEtgll8UW6kOg02V5gJfy2SxVm4w7SFAKtkrZ+PFExr1SLqDVEDuwFJds2t5zyaxcvn0Da2mir9iPO0+E5Pm73/ptLPPzz55JdkWRLo7RR5tztU57qrJODpsTRa6dDWbvS5QoHRoAmGVNNJlF8ngUFNuK7W2BRKdJALi6vIbPP6f8KjNyr3/tHQDAB+9/jCYzlTqRhVMZGlRKJXwlKnNTsxIZapHxXltbQ5f7lyrpWYrIeI0GM2RIvD/akHkZIlIr6PPAfInQadqbECV8nz3am8vlFrKyvgJEZx6dbMNNcnKvX36uJMUmnh0dY43oTzPk819mBcnwztsvY4/E+im/2OPwSyJ68N57H2PKd+gwC1qrlLHFPfuC6+bZtqB73rh7HyXuxXbq9V4lcvW99z/EOtF5Y0qnOkh6GwiFYdME+fjt3/ktAMDnnwvSZzYa4lfuyVhsbgpK5O/+9l3phGEfr78gCJogbWWfGU+LyQYLM40+PnueKBujLwgTSa+vvykZ4HhL1ojDZQeIvAu5icyltH0hn4WZyOFKRbL/t2/cxrOnUk4UZGb15RcEdZovnM9LTpcJma9wfet2KwzcQ08OZU2E/PIOseQMM0jfNovSVxmWK6I/m9v7bEmeweOlLLDDDiPLo5U9rByK/TBNXSgxs7xBtEGlIGv4vY8+nyNx84W/AADUFLM+LAgRLdBhyVKFz9QbDOAg2uKUZZXTmRHPdlmu05L3Mhikr4MOGxoU94ivMVOvSkg9fiSj0kdet+zz40syH2u9BsZ5WbMulp0UKWjQbIzw0x0pW40EZAzTRIYd7j2Hg5/v9aU/b19iuaJmwu99U8jX/+Z9KT3/9u8JcfR7P/khfERYxiivfkKkbX/QwT/8ve8CAH78rpDtR5e49+gTrKblfmOS3w5bQ5RL8o537r7CZ5F5eFHL45yovuQyyaWJZnH5ndivi03yTmUtJQ3S1w+e5/EbvyL7udkotlJ6GGiXetAp0dxjadVMlzU/s7rxeFdsciolzxyKyNz+mx+/i7ub0n8tEovb3DI/zypniCbEn4ql5aciKbaZ7ehNJBM+oV97SkR2rdtDfUAbRgJiA8tY0ysR9NsyN0skfx9OdRweST8fHgtCwk4Ijt/tR5/I9/2c9MsSyYrDUd9c5KS6L/1nZ4nlpc1lDPm585Y8c5KIsMO9bdiJeG8zQ3/ljtiY2riNONEqNmbemyXpl+trIbT7cmYYkJR691D2+6VQBD6KB3xGf//qK4IeWNr0IkZxEK9L9jGjoct3cMxR7kvrYicDYRdsJCk3zOSnn7LwnUYHhbz0lVXx0RK5YDYb4aMtCpFcOkwUYb1ag2aQDWxMKgSXQrTPRrByDC3EERRp56wWJ5y8xtGhII5jUblxp99Fh/QSbqKllzhP2q3+HKkWIcK8RX98OOwjvSJ7VDwmn1f7h9NiQ5ok8/kZSey3H+HyJZmjGp+9xPNcvlaBiTC5SJykuvQPvE4ntvd5xmBpdzhCVHdfQygin1eVGwp1/vavvIl6VZ69GRTbqfz2p8+2sbK+AgAwmoi2peS72RdCjmj2kkxHuC0yNg6rAxrR6UHOrwLRlbEVGxp5WQuBpIxfi/tvwOhGelXmpoM21GK1Q5/IQD15JPt5hCXKqWgIY/pOHofYjd1T6YNqr4WhJtdw0353pvLZQCAAK/fu5xTFuXZX9rPTUhWziXxuRLTYbCLz0uuwYcQzlELfO4n0PM9WEGF5lxKLOOW5dTycqipUgOWv126IyE06k8LHH4gfYHfIMw2JhjO7LNBIEdClQIyDFRIWdwCnBRn7V1gmOh2PUS5S/IVVJOG42KROtY0Hn4k/msyIv3JEpGYwNsSU9lch18f0+09PCwjwnPkX3/9bAECDfZC+lJmLgQRD4t/6SEkTibhQZqkYiAJKkfy6N+qgwUmjKE4UpKfT7CBokTV3vC3P2+H86DU787mpCLhH9LNGWh+rl1kK+q9pC4TQoi3aoi3aoi3aoi3aoi3aoi3aoi3aoi3a/8/aLwWH0Pf++b+vA1KLvEoegDNGDxVJdKPenMu4+5n9MJAk1mQ2Y0KStBnrGjXWhOYvLuDykweDvzOTj8etWdCoSgS/p0tEX0kKhox2dNus6WOke8poayqVwu6BZEQ9PonWD0k4psEMExE4e7sSjW1V5B1m7hlmM/lbhPLKgTX5/ycnBXz4MeW2rSS1m1lQ6kqkUPF9TFkfDaMd7oDkhC4RZfQWI+36rQCuEGEyYv91zIymF9uIst4yQ9SGj3PgZ9tP59H5e3eE0FZxJZVKJdQpsZ1mzbndLhHvodaHk6R5F0Qy1Uh0lfFEoBOHdk6ukQzf3R5Zws4zQfr4KQMYIm+C3WVFmDwTrTpJJ6tNTEnIrKSvh6yRNU4AG4nE792S7LZCVdWaNThsMmdy3Qb7U/qsVchBN8izbt6TqDRYM904KSFImd1KXsZB4/3MUwPWSYCuIt4KkZMvFecSjs+fSRTXzcit02FRQBi0WauqeAVCkQTydYlK14nGitlZy2ueoOyWfjCRc+FuhvXRkxlyx/J8Oqu1rU4bltcEjeAh4aWShPV7/biglPfNlwT9owgYXTY7LEShtRkhjxER4k0n0SQi4DalFtX6PD09nRPY1smjowjOTAYjelw78U25lpnS3MPhBEckc2xQolFdM+zxIsi54jTLMzVJ0GcNhXFSlLE0kKwySERZqXwOs1XmSWZZxt3lcKPfUQgTci4QnVYultAnIs5PAuIAOSE++uxjLG+uyN+i5NohZ0A+X5zzinVblJnW5NkD7gBWEvI9B7Pjfjft1syIoyPJfk5pdyZmmQvDRhEbS0QJLMn8H1llnT07vMDRtozb1157GwCQCMhkisfSeMK51mjKM9goTTrBFG1mLcZEN8zsbuhMy5RLkqnYWJVsXPb0DDr7+bvfErTGMXkOgq4JJsw25yqyvipdmSeBhBsXzFgm4nKtVEzm6Fo8hnf/5scAgAhruzVmlk6PLqDr0o9WB+Ev1AxNbN1Etix259FzQeKllgU906m3Uc9J9vnX3xCC5DYzKzOtjwr56Ao1+f7yxgoAoNouYcDMSYgZy1gkgaN94a5KkwzSYJQ5ajRY5/KhiuRSoVDTH3lSAAAgAElEQVRuXLuCZEDm9HlV7l1k9i4UDyN/fgoAMNFe3b8pdrWcLaNjkjmTSshci5NLazqdYW9XMuAdztnVdbE12zs7GCtxBI6vX6HLai3U+K6XKM3tYL293WiGnUjBCrNkYSL6kskg9rbFDkeJssldcA37U5iSmP3sUPazsXGGFJG1VtopJwl0Q9EQ1uLyNxfJWU+IgBhOp3N53RlJ8FU/xuNxvPXWV6VvutIvf/on/wIA4HW750IBY/IQKBlYnz+AETnfhmZyBJInp15oYNyXtRAKyp6vMmbRSGaO2rh+WbKE0SUZ96eH+zCBsulxyarFSMj6f//o+7CSH+gkK2OUSS0hRN6pCnmqItzHotHIPBNqp21R2cZKt4UvDwSZtUROsPu3BNnYa7TQ5l4wJImspUtUzmiIEpEVY3I5eYNy/3K5jHRC/IAIOaYsE5lD1dYAdaJxxlaKAhAN++xZdi6Jm8/LONdrYpNeuHMfRmah9a7Y/4efCqdbMp2BlUiEM8qSpy9torQvSMQvdikL/IKgWXrZQ5is8sxjk6zxy0npT81pxasvCGHohPx8Xz6S65hscXz4+WP2h6yvlYyMibUXQJ2k+ZlVefdbW2J3sqULPNyTawybct/rceEb+r1vvwB3TN75j/9cyLYv35F9zeIyYMw52iRJrsEk9uBKJAwTN4F8QUmxU0LcboPDKuPsNBKZVO8jSQTu8dkRr08um0x4nlH2B+XZi0VmfHsGuAPS7/W22OjxSPqsVhkhyex2LCz9NyS3jNsWQ4y+cp+/u3VTUHSlQhEdotkn5CS0KFGLSh06UQLllrzXVC6N2qgx5xBaJll/v0GkXL6CE5Iap8gtEyPa4/DgGN2O3GdArsTZTNbW2voGPPRrPyOiPZFcwkWOHGLXZCzWSfC7v3uAHDk4/cvSV6++LPOl38yjyzWh9lQlUW/UDfMM+08/IbKFvvNXbm6gTUGJBjP1FpLLwzCFkcgDb5jVAeSxm7Y1XFRkfhyVZH4YiaL9zmv3ENKkH09pR5aIDPi7D3+AW5xjV5fkvX74U+GA65hcGPelPyIeefZqs4AERSyW2KeDBvkejTNYA3L9xkTePayRO85qhJUIKYUcaynRlekEE6f8TfPJXz19krlHAugPKGhQI9qWPHZ+XxgzoqGaLXJtERU/GoyxTi5Nh1kmjZLa9rm9CHKv6ZC/tFKXa2qGMTpE+LTpRyiRi5V0BhaSeQ+J5i5UG3POTrMi6B1IX1+9dQMa99kZfdgKSZizjRrMRGOq+RGiqMB4OJxzEKZYWdKlOIPRZMOEfpLya5V/GomEkc+Sl/OqrIk8SbcH7gj6DXlXE9HBKzHZSzq9Lj5+JPbTRftj85Ns2zKCzy7714BCKmbKz2smHZllEkbrIz5TGbaxzLseEdQ6WZ4joTjs7KMC0ShGB/lzHUa4SdbcGP79agG33QonSZ90coo1iNzxJdPQ+yRRbkk/aJwTfp8TQVYFNMjh1ODaDydX4OY5c0ifBlN5tqg/ihbJycecTzn6kffu3cUpzzadrqzTDXKo2h0mdIj4rVVlPlpN0ncWsxM/2TsFAHzn18RGFE4eokafcPmKVCIV6zKm064Ow0Ds+2wg/al4tQazFgxE6V2nANHhidioQqEFv0/8r13eT6GVfJEAUkQmBsgRlQxI/4wHTSytyXyon4pNMYcVIncKDxFWQ6J/yiP5ORnOkPHK/TT2Z51ram//GEHaKV+APlBT7EIsFcVlEsH//j/9swWH0KIt2qIt2qIt2qIt2qIt2qIt2qIt2qIt2qJJ+6VACP2L//p3BSHU6WPKDEg8LhmAEDP1z3efw0NVJpXV7dYlcliolNEkW/sSs7sk90b2rIhYXH7nItpgpyBZmk8+/gIuMvBfoZqInypZg04TFlIsra9IzWptRnWEUh1+FzNyrCn2Bsg31G6hz9pxJYXlDSgp1QyOmS2tV08BALdvSCbrQe4CP/hInsvUpPqUrsFgJPJJsbfPFCrKACcVEDxESr1JtSyPXcMWM4bhFYkYDjxUtTjOIUVuJMVwX61JPzq8GeyQId1skRCxiTXknrB/joSpsM42Q5nDXKuI69clA6UY4JeYbW206nhGScspKd5N5HVwRqIImCUTcshrmlirfSWximJJorDOABWPJjMcU6GrT9UTxcFgt9rQYYZyMyP3VgiVVDyFQ9Ywf/ZQIvMBZuMfPv4C/+6//Y8AAG1m+YZUaUlHo3NljCbriG9cl8hyNBFHl/Xsn1NNp5SXuTAdTNFzyvNFifYK+eV+pqALna5cv3UuaI/LnHsHpQLKZ3Kfdlii97uHMicyfSteuSOR7llU+ui4fAoAsI6GaNfkOe1U21tfXYaBKKWdHaKU3NKP925fRYwZqwqjy6WqRJJv3rqL0zN5riqzfnGqOHzja29hiUpIJ+SByZMXyuUM4Lwgz753Rrlu1r9GAj6YmJVR/EBWl0TjPX4f8lWZh80ex5SZNqfXhmZLrl8i2ivgYd13q4spswnXrknG18AM5kX5AgNygMSo3lMqlWFm/buXiAqF6MjlcliNir3hsMETUTX1QJHS7jXySV2j+tp4PEapINdYY233qkuubYJhnkkacN7vk9NnODGgSnSNyiomMvJs6yvLcNpk3I4PiIghL1c0FkeeCi/xlGRiXURGNnsd6MyDFsijMyVqcTab4fIVyUqectycLg8qRPz5qdbRpArB+fk5vvtdQQYp5NgxuYc6Zw0MiEoojiQ7s3xdrj0YdOBmZsNCDqB1Ikmqti6SDqqCOOVnSckfX2TRycl7pWnvr98Se/LFzjbe/VB4pMxEYbhc8pnsUQ52ZiP7REHUmJkyjI1wMgscIzpkhcjS49MDDOzSN9eurgAAInYLDGYZu509sYFhcqWZTTa0mmLTreRnMZupvOU2IeCU9QjaosyaIJgarS4ePxbb4CJXzhrHzWI0YI/vbDbTLpKHwGAALEa5ppmkZ0oRyO/3Y8TNLU0OojPKmV++dRO7p3+fH8TAOv8nn3yOZc4BB2v5fVSQjPh8qBnk/UpFeaYys3KJlXVcKIQPUXSXNlcw4Sun49Jn2SfCoZOO+HDrBbGRM2ZuFdeDbnGgSlRkicjcDNEsnUIFHrP0n0LrTonU7PYH6PZkPVuY1VQomGIhN+eamzLDqTK6jUZjrgrp91Ppk5xunVYNXtqna+RW0gzy/b9974dIc96+9oogW/a2pV812BFLrQAAcueyPscGHcuUy21wDxkRVWIxGdEl0kQnB8KMaz4VT6DLDKeL6CGlZtJptmBS6jvkADEReXJeK833YjuzxzMTFRZtQJLqfbltQfoYqZwaCgTxfEcQScqfuHVXMp7lZhEB7glPnwsXhdYOsu+86ND+rhCplqN0cKPRQJzKKcmo7HGnzSYOngjfz9uviHqo4rL67MHnaI6ozkb1LtLcwOfswcb3KBfJA+OU/ebThxdwOmU9ppkFDbtlLph9Ohxm8bEUIkDNy9/9nX+An/5MEBjVhmRZHz+Wcbt5J45lIsBqOaUWy742j+Fgv/mplPb0S+nPod+FX/2K8E598lBsU4dKa2GH51/Zv6Q/L57sYfWOqLNNFKcDFV53dp4jTH4wN/3TEZE0g+EIFp08b0TIKVQgzEa0e7KG1laouDWWawYDXmy4hFtRcV0eHcs7p1NJrC8LmqGcl75SHBQOrxvHOXnHDx/Jep7ZZM5FQmH4yCuSiUt/lOviH/gTUVTL5DHh3Fm7LOvBZDGiS4WiCPmxjg9P5V2MdtjtMsfbBSroLUeRh6AsfEHp/1tpQVX+5f/xMwSTgg4wc+x1KkXdvbmK4wOx237yfvVpM5rNJl548SsAgE/eEySS2yv9mlgKwO6Rf8+VsIhGMU8NaJALaRYVu20Ys68MRlTq0rftkdi3Xkfsx53LV9HnHhy4J/PLRTTR8cUJ4JPP36K8e7Mu+24DZrTqsiYUT6rf68CL92WN5gqyBxxnxR6nEmuwUq1OKQ9pVnKuGozoEB025nxy8KyjTTXoRNK4qSKq/J1Ot4kh16dSwVUqUqViA2buSxGi2pst8p66bFiln3JxIuOXJsLfNJvARDSgRgRakLyl44GGLv2+QkkQGQbatqk+nSu4OcgNpFvtODyiyiDPOEpNUdd1FClXHqIkulJBPym159UOfqInvPSHHRYjLi2JffJQdU0p2xVbfZg0qq9WxE+1UL3XYDehOZSxm0H6IZ2RfjnLVZGKyL+dZnmfCVHyRpMFAyKgK3255pTPmbB7EeD5dndP1qyJ/mB6KY69ffryLrEV06mGLG1xJiN7lo+ctdCnsHFPDdKvKuRlf/d6goAuay5LRWgLv2fQjfA75Po2Vrs0h/KclVYNWxlZ2+dHgux3ENGkmazz9yqSP+nt18hzNp1gyPWoj7gne8TutZvtuVLojVviO+ycSAXOeDbEG7e/BgB4/wPhawtTWc9mN+P4WPzSMNFeKjawt7eLLpUdX74uFSP9chV1IoIeH8r1wxkZ90tLKzByv23V5Dk7RGgVJ11kWNngm8kYdnr0Y3pTYCR7j05E+Yz8fgaLBU6qRP/G67Jf7HwgXKPPj/dgS8uYeFnhUJ6KPd9cjsPCOESnTD4txjMMIQ86rJgxkPfLRcVPj8uPMVGORaL+m1Qrm8ymuHtfbOA/+Y//4v8TIfRLERD6X//Tb+kAMBpO0CSc1eOWAVbkxh6/D1bK3j7bEcM/bpBMz2XHnXuyAa6tiXNXyEqH9LojuFgy0+OmekKnzW7zoEPHLUQjkUyKIcmfn2DUlc8vcQGoSbf77DkilNJU8rkfP/hMvp9Ow+2mlCbJDHWSKGdPS5jSWdjdFqKsSIhytdEI/tv/TeRQzXTExvpoDnF3OORzdqf87PUGMNGps87EGMUDcr9Xb6dxd136Yeu+lEE95Gbp1E3oEO7ZZ/nVsP2LCaaTmE/jtWNLJD7VZ6iR3LlLeJ8KCHVMY2zSUXzKA9DBnkBzLWYjXn31RQBAiqSETsqaHxWz2EyIc6KzLOY8KwvgcjyDFidzrUuJ3V4XNTrXNhplIwnitrefwUim1w4P7vdZ9nb75h3sk3zTxjK3YkWcGavZNJeQNrHc8KssP5n2h/hiVwxOhCUKoyGd2XEf59w0ajWSZIZlQ/S6fWhxg2idy/uMSUZrDjhRq/MdeThUwTPNYUGVQZUqA3KP6dSbSl14GCwLrJAInSUn92/ewkd/K4YmnZS/raws4dETGQu7R/pIzaX11TQSEcqb7svmGqPDrsOMg0P5XYQb7g06d6lICMWcbAJjrsV7d8TIdLoDPHgk99s7kSCWS5G5F7LQOEevbcp8NHKjt9gtSKRlDnz2hXw/nhKnL1c6h4GHZSvLlJTs/e7hIYz0Frp9GRMDXbpeu4U0nZOKR75fOc5hlRLvqryjQ/nQcCyCUk42RUXqbSLcWpsZUWDQy6AkoumMrq9dwpSbyIhExz1d7Mn+wz3cXpaNqFmS+9QoR+5LhmAleW+TJYy/+dKvSJ8HAnj0mdiSFRIlnx7JHNSNJiRJmH58IQ6jjQGY44uzOVzUwTVRosO/sryM67fFqXz4RBziYrkKA0ssl5ZXAABTOpcGkxmffCr2qcRAaImSqb7Mypxs0sMSQcWw/MIL11GuycZ+ciHPDKP0T9Rjg40ywPGwKmmRn+FkEj9+Vwj5DCQnffG62PPPnu/BxRKzowuSj3PjbVS66FM6Pcvxm7E/es0urt+RuTYcsVyOsNtIPIgNzmmzLs9unY7RZDmSIha/wsCf3xtAlpLQqqTKQMLv8aQHO6Vm6zXuVbQVs6mGBonPFUnjhHtQt9lAikTaDs65Z5Sr3tq6jgjLenf2xSkcTVkO4nWiy3KaeFAOZgHuby++/BK+3BWH8Zjyw1evSGCt12rjOcmN45rM3w5L2zSLjne+KiWIj+ls91iabKzPUKS8te6Uvp3Ohkhyf7bzIN6lXfzKnVvzQHejLXM0zflVrrVgI7H3mOWCRpZUNEqVebBNCTyMKV3rdHvmh2Qj56zqg1gkgmXKwJ/w2fs8BI+mE5jpjDsdsjfaLfJ/j9sJl1ue3UgS/cfP5BB8nr2YB2GV7PG3vikS0Udnxzg9l/f6rV/7TQDAJw8fzCXrgywts/DwMZvNoBEuHiDR8QWdtc21S+g1KafMMuwDEunWajX0SAD94t37AIBSS/bd43wWAcp6B9j/Cjr/0ttvYkKn8N2/+D4AYIsBOrfbiyoDyvss6VqjLPNwPMDTbXl/L8uNrl2T8jXTdIoKkwQRzjkVoLB4XejRSZ5CfndtZRPFoqyXLx+ITTfyYBIKR1VFNizcQ8pM7AVjRoR9cn3GuXHBgHswHkOlLPN1lUSzhpH0a6F6jq/cl2fdJrGqzjLCN958DY8eS3BKBYRaFGPweHRcZsmczr0xX5S1P9Y6c1/LaWPAccBymdkE/853fhsA8OEXUmZapq9iGE7gZoAySGLb3LNDNHnwW1mVuaozSWC1Geel1iUG6JV4h9FongdofQxmt7lnOd0uHDLIs74hdiQak8+srGTgH9v4zvL9AW2b0WjEdCBrO0Kp9wKDbyabbS5C8vMHUkLqoRBCwOuHmTLmYcpod7jOIqkEaiXZJxwsp6yy3PHG7RvzpO5EJQB2ZO4FfEEcnskeFQ/LPHx29BzlySkAIL0sc2H/kYzJ7Suvo8oyxvFEfsYT8s52p44xg3JtjoWHwV+LxQKPR9Z/TAX3ngkJuT9mx4h7QImHxURc1mK32pmXjhoZjAxwrFwmEzQ6MXsnJCLmwSwdCmPCRJsvwdK9IhNAbiv8THDqTMg6WF56Wm8gzuezWcROnl8c/aJUZlP2LFUaZNAsc99Eo610UAyjP+yhzuSIi+eRdFrmntVsQZ2E7FmW4KkAjd1uhdvzi5I5ABiy/Oruzbs4Y3mL2y3P1+7JO7g9dvR5jrDQjqsEWr/XQYqCEp65Tyh7Uac9xHAgiz3Mkqo8fQ63zwPQttCEYn3jBg7oDw3HFGpYIfF0Po8gCZUjXC+1ivRPa6zPhYq8DBa1KH9uNpoQYImeKmEe8lw4clphpe2ymuTZVVI+GA3CwhL3i5zsCbomc+nSygZ07nGRgDwLecbRbDZxRgn1CEUWOjzXmMYTmLhHjVm2f3wm8395LQ2/CjKfy7rp90fw8dznoJT9kLbG5bTNxTmcTnk/K4mtg8EQPvpIzgxZjtPrb8s57fjgBDYV3GC5m5ey577o/8Pee/VYmmXZYev673rv4t6IuOEjvaksb7trusmZoTiQZtQSSEkgBEFvIxCQIfkqQQNIoATOC/kigSAgkAAxIjUjznRXdU93VVdXV1VW+szIyPA3Iq733hs97HW+Ev9BPdzzkt0VEZ85Z5999rf32mv5UKLNXd2WJLcSKer2BjAyllGJmTDnet7r48WB+GElVBQjmf3BqyN9fw5JafD8pZwbbp8TXhd9JpNNWSbvE4k4bDSMPotPXfrH2WyO9dck5nWzJdZrsqDZlDOkygSXKqT02j24+A06YJvbyz2xs4thB7/7h5KUck0psNEi9Yjdi3Fd5mjE1rkqv6+jsTimihCcdt9sy1yvrKVQqcu+LuXTAABbWPZIwOZGl8n9ZdJU9Fiwb5mt0IzKVpmM5f42Gg0YT2S9XRREmDLReJHLw0l/+Cf/5ItFy9hiLMZiLMZiLMZiLMZiLMZiLMZiLMZiLMZiyPheIIT+9O9/PAeA2QwYMiuaIUzdRdjX0nISg6FkxJTcOXlm4fN7kCTZVpaVc5UZzWbzuH5Tqs2qqljpSgawkK+gQ1jljERQ5Ypkbm/dvIZ6me1glEBdS0o1qZItoEjiOyXt2maF1O7UMGP1x0M45mVTsoKzkQk+ysFZjHyGnFQE57E4Pnkoz55+SIJCp1FvwRry3Q2EbBoNZtjZHuQh5DpOyPf7u0H88KN3eG/JJL8kqmojtoIOkRRGElxlKMcYCAT0imhMkatRbv08n4eLVcn5hESlJFkc22a4PJeMv5KkxlTmIBmNw8/sr4uoC0XcXe6X4SCxWJBomfuPpcKXOzzDkNcw8O9WQmGAcNIxK8UOVgLqzQbarIbZrUrWnZBQzYkztpotL6mstEDn33njTTSZIZ/0ZY57JJSza1bkWSlPsMUsElbtCUNdVrJD4lKNkodL4ShslJDukzxvzApOupCBmfDZO1ckw14j0ZnBZtMRSBoJPk+IfKhmq4hSotzD9wqQkK5ULsPQkX2sqoRzwww1SodfECliMJDUrdvAx++/CwCYshKSZUU3kUzhJVvMpiOZDz+rTte3NzEhweBUkzUNB2Vevv7mIa7dFkSMapEoN6TyOBn34NAU8bbMg6oUhQJePKVk8/WbbImjzadzGVTrshd+eE2QSK2JrMfcacMLVtMPWTHyEhkTcXqh0T60ZVmvTr2JMZ8rSNvuEr48GnRgZLUpyXnvsq1xOJjoJKGXlIp3sQp6sH+IFNtN6D50ifqlYALjrlzDZqDMKUleTcYZnJr87AZRKHaiZz792c9g4v5aY+vjlLYzN3zXDrO2kQIA1IlKmRmMqFNeXVWwfE5WF3o9dIiOsdilCtLt92Cxyf8+ZbWpzznIlxqod+T3VYtCmzY+t9px74as84pPrn/wWNBEf+v3PoLBJs/35IhtJxaxuWQkhm+fS9VnxDamNZIxX79zC+ctEl5yjU6/lQq1JxKCk9Upl1/WxqjaQ/I1FPNSObFaxR6DJN785lf38V/+vb8DQIg5AeDZidj1zvWr+PQvBI1594qgiOyaDZ0xkYis8gXot6bjGcoF2aPq7HnzHZGubrfreJkWv21n9TjCynulXEaJJMM21eLDfeDz+RC0i22eHksF0ECp25XlDbQ5//mSnEfDiZxFP/qbH+LrX0oLjIeknS3OgWluRIzV0oKSXmflczyfocfKl5OiCl2WLH1eJ0D5catV3mHCNsKjkwsUiCC4854gLo3zAaxEC9wmHPvrRw/kHeZzXV52SPLJIZEBW1s7KNNel9jSZqd9FfIljHhNG/23kYSYJpMZFZJ6q/12eHjIOTPATX/YrhNZQQGFcCSIDmH6ioxXje3tFF5/XQj1n7+Qa2UpqKA5PCgS8eBjW8GVHblvo1NGoSbvFXTJWXKWuUCXLYuq9avD6vx4PNbbGWckLq2zZXhjeVVv+1OEwH3u9WyxAB8RmqoqXGRLZ6nRwAZRlDGbrGWRLdfeSAgxtm65aU9tm+ypBw8e4OJMnn1tTdAGCoESjccwZEVbtWB4fbLP/A4HfLQLRQp+lpXr3H/+BG8y1rA5ZN0aR2eIEHGaZutMjki5ZquH61elctuiLTy8L/6ja+5hKiaAZFzsw8Z2HpdPUF0AMOvx7DfIc1o0G+7ekhjvhIi62UjFikY82WNlmq0NGzuCmKpXS5jR562nxNe2OxU+dxo2IoQiJHv2usT/RCIx+NkCes52iy8fSuuYFUaUlCAEY8OIzY0Dyje/+Y6cY4+fSut6qV7GGsn8x6zGj9kKHfAFUSECY3NN7K9PVM5sNsGQIikKnedgC6Tm0KDx7+JElF6SUNjr9cPLSnGBaOQ+Y+6LXB5GElzHSNxvIRKwkCsie6xagcS/3bor+ydfrWIykudSLcYWTWwvuZLQ97WdyAATW1XbzY6O9D68EPvwxYIYTcXfLJE0/LffyFlyc/ce3rgh63x5KjHrV/e/AgBMTdDFS9S6KQnm+WwGM5U8rC2ZY4W4nBhHmFlUnCm+2cI2ucJFCTaj7AGbWfaZl+iNkNOl+9ESY+Z0jt8smoZtEmGPK/Je4P7JVEtYJhonSqRQ20rC3lgYKEjseX5O9ECnDCeJnxNsk52zxcpudOCS861kyx2ksvD43eizpVu1JDrplw1zE2oU5FBkzS62RxqMc+zuyHdOh/HjhP7r3XfeR/qEbe9sR6+RHsPtdsDPc/rrrwThfHyWBiCo7gFJtpNszXIYZN1NRivKJTmrGvw+O2GcZTAB60TUaaTHCPitsFi/Q6QAgJPfiLVaTUdKXbsqsbUiKd47OkGI57KH7VaZS9kTnVYPZhPXmQiaGr8J3MtBXUgiQpGEGdGHiaUYPvpIugn2X8r31S9+8QsAgD8YgImt0i52dyjUZLfbRTgsNtAjci9IGfR6s4LBSHzsEr9Vxrxfq93AlFQZYyLSbWYXeiNZyxs3xK9Wy2X9Pqo9Tq2lajvsdruot0iYTkLrTl/s5ea1m5iz7eklhUoG9Kfb17YwZkt3m+fRSlLW9PT4BDdvSjzw7htyJnzxyV/LHFg0OKNEMim0ElsYW+0+zhUClZ0LnbZcuz/oodCQeysRI7OCi2GmtzOq1nyF2A8EArj7jnxPLLFd/OWDR9jeFts2sDWwQ2qJXCYLC+E0MSJ5B32JXY9bdQzYzv8G44BD0gp4vEEkXLJ26vy08bwe9IaY8Syo81zp2OSay8vLMDTk3sWunJFj9jeOqy0MaMcRrzz7iC2hQ48dNhNbzoMUjuJa5TJ1FMtypprpf00utufF7AhF5ff/+3/41wuE0GIsxmIsxmIsxmIsxmIsxmIsxmIsxmIsxmLI+F4ghP6X//qjOQCEwmGMySGhKpX+oGS3zy8vEWb110bukDT74L0eN7ys5KnKYZyZ6Ml0jlxGqrSquptjNTSeSOjS06ckwlX8NqupZf1Z8qzymigB3q7UsZqQ66tq/Iz9jW6fGz1mYxUSpsisbKfRh4scAwn2PucyaQBAyWTBZ8+l6japSEa50i7pcuVd9kiOmQG1Wq1wcR4SPvmd1ZhcM6mNcecdqebWyJ+RfimZ3oQjgNCuZEnPKC1qZRU1Eomgzsx4hBlrO1EE48kUDSJn4qxOVtjzbndoMDC3aOMch4i0GA9HpOYGpsyGV9jX64440SehsJF9kHVK6847I6mYANgnP0Pu6Awx8okweYsqES798QiblFnvER3mJReC5nCiwYr5EYnabhAZ4HU64GTvaJzNRVgAACAASURBVIk8KYqPwe32osnqiI0VpgirINl8BjMmqmOshjpJWjzqjzA1yQM2WAX9TlbbDCZ44SFaoMyMvsvlQcArz+JjFaPLiuzEakPmWFBY4yorkCQPvayV4CfR7oDVwtt3b6PFeTgnmk3JshtHQ8zImbB+QxAqqiLe7nT15xn05Hd2NoSjwO9xwEB/QTAVHj14xLn2Y6xcCSv8ZiK7Bt0eutwTdq5JhRxOfrcGG8mgVdVkzgrA0Vka2ZL83u1YCgBQJ1+Qf3kJPfJ0eGlrBy9lbW9fuQaw2nrJKpWmWWGyynVN5PRqkTzRZJjCzCqMqvzmdBsK6hVUDxE3ipB1Np3CQCOwEuHjGMv/f++99xAg+V2VXC1ZVtVnvRGi5CQopuW/PWvIc477IwTd8j6K1D7IHnSnyw6rJtefEuXXIJF5oVqGRoSU8p1mIgTK5TIU8Y+LqB63N4gieQSmtM0c+5Tr7R7cRIiUSHhJ9VFMGw0skVfs2nXhDbvMiM9tNgrQSIAYIJnexloKAPDg5ByXJHxUPfXDukI3zaCFZe9U67IHI7RVy9SIBNGfDlYCFTl9tzvUK2Ybm7Kf02nZI+XzApZYEd3elEr9flGqUL3hBH4iirokPD0tZGHidVdYmRvQ360lV9EnIeKY+1GRGxdKRXTo+zbIZaPQCfVSBSaiQpRMOoFrKJRLCATlPCPtEVaJCKsUKzCx773RkLNqOJC50uwmPCGJtJuVbCtL42azBVXyzkT5LBP6rXA8jvbzNABgGlPktWLz67E4MqdS/c++4jnoERs86rdh6FBue0WedynixItjqcgFfLLOP3pHKqWvXu3D5GG1lcS5NaJ7EvElHZ3gYpVQiQM8ffVKRwYGyHmm5qyYLyAelzXZ3hZi7PvfCiJpNvvOXyciss5ThU5dTeLyUvZXn5XiJZ0rbQQHie39NtkTCg1XadZ13ghVjfOSN6w36uNrohIGnNvU+hoyBYkRzORQUDyC6csLnVdoxHjCxmo8hhMYubEUqWabSOBwIo4K/cb7Hwm/WJtoyXQhizEr866eGI9CTkyMRr3KWiU6588+/XMAgM8XQIJS5Q5WZ5fJK9JotXUOLI9CCJE43ed3o0xOmDbPjToJ8wvFon7utuviPzAFhgrVyHjAQX+SLRR0FFWB6FeFMvWEkjpn5DnPfLssDbx+K1o1uX7cL/frNgknmlpw97agVaxEh1UpMDGe9PTqNohodtO28/ksekRVbhJZZ6dNwATE6Acu6aPj5DzrT0bwEOUyMcv6nVIAIxmK4QVj0LVtETuop3O6rHpiVRA7a+T9+fbJQz3OXKLYgUI5eF1udI3idzLkrVpfERtPra3gBVEJvbGSjZb50FxORPiuQxKtz8jrojnsSHOvb2zKXipx3ZxeD5KMoy+IHBtNiawxWTGlD3RaFZeKvHul2oSBaPg6rzXj+RRLxHRxhQaR0FvkbfK6fWhUBOlTIbO4RbPDp5Fc30LOkIbsb6vZhpidKEyfnEFffStIK084ihNypCkenaDHo8+n4m7zWUiITbLWpZUI2uRIg40xGuPBXrOHoFtikuGYQij0ywlfCBVyapZ5jvUZF1g0m47GXueZVeBensAAL88ljSjkJhEeV3e3YCFfz9Gx2G90M4EqUVtrjLt7OYll8+kskuQAqzDOV1yjxXIeTsY5MyO52IjQ6rVHcNDnzUYUprEr1FYDJj67yynX2t6Wc95iseLZ0xecI7m2nZLbV3a3kSFvneJty9OGxvM5KrSLOePiaVfm0eVwoVSknPuQMRj91+nlGRzkGbuyI7bq801wfCL2GybSu8K4ZTAaIUb0mpPzMCAy2mp16VxIik/HSDLfvRcHuPuaIPdqJOw9Jvp8bjBgfUP8jYeky8YpkXn1KgIkgs9cUOyGgbE55ML2jsTWpSrngeftaDTS/VSTtuOiT6x12/q3gvo0V6iN5UQUHqKWSxnxx7MJ0BvLO1aJwgyHyDE6Bhw8hxotuU+tKb9jd2pw0LkGiDQxct3HgzEKlxKHMSRClKiZmWGOWl/sz0aUjSIMNxmmsBFB5zARzdklF+HUgCGFKybk2jFqskad3kjnFdokB7CV3S9/9Rd/Dt+K2P0mhRuaPAeMhjkmExULyrVjUfHZMJjQ6ckc3dwVtBiGI3RJBn2cltwBHwWRUFTnFxwaxV+ZSBZvD8WQ53fxkD6sTUL/wWyGKQVHrt8U5KmBPqKSLem8w4WuPHOuL3s9EojAC7GnA6I3k1ty7jqGLTTbsha5NPeEXfaEbdmMXlf2mSJtrxTlWVaWd9BuynzU6rJG/Sm/cZxjbO3KPP43/8NPFwihxViMxViMxViMxViMxViMxViMxViMxViMxZDxvUAI/Z//6D+ZA4DH49EVPNRTmYngqTcbcLDfWFXfSk2pymyvruvSfq/Ih6O4JFrdAYbMDF+kpbJ6kxLpVqsZY0hGrc0qRiRGmeRKBZfkI1J9mCNmLRvlKky6nC1Z1NkvGggEUCpIZrJWURUted615VUUmSk3U+ErSFb8p9UG/q+fSh+6zyhZzt6wh/mc2Veq1MxVFnc8RITIoFvrUrmKuGUOUrEg7EHOVVQylEdU+wiMrAisSZXqoiNZ4CCroNOJCU5WBTSiPMZE9SwnlnRWdBsrHHXyJNgnZl3JQCm56UoiFgt85LFQCAkf1ddmljls1FtUsuLy10DA7kGtL9nNJjPrPpsDs678XnJNqkyf3xeVj/5wgHhcstgshukyos/3XuKjd98HAOQUQz3VuEb9AY6OxGai/G8BViyH0xnKVANYZ5baTMsslqtokfdhOlNrL/fvtnrwBiTjf8BqhuIJ+v3f/TFKrCZaNPk7K3mhWtUmNCpemMi1c/W29MEWJwNcpNMAgE2/2HaUvegX7SqaA+Z2SWazsbaiI328ZPq3qwx4q404M9dP81IJSahKcaOlq56oKpBCdiXiMXSItDq+oAw2q3B7z17BTHlkN+ehQEWbWrmmyz2fZuV+fiJV4tEwSkWprrTZN3zrnlS7vYEgHjwVLhmNiB87K5iapuGcz3CN/BFHRANVGg2srqUAAJ8+kr8PWx1Y53PFeO9CQda2N+ogQORSiiiNPteh3uzofcmKC8FNjgLNZsMSlcsc5FTy0EeZZoCb860QDJ9/+Rv5naAfDSI5rtMXDahaVa/U4bLJNXY2pMJ0Qp+WXI6h1mQFlqWbelGu7fL5YWWVdsgqleIecdrtOn+JUoF4dXAON9+5N2B/MvdZbzTSOYNsXFPFZZZc8qBQl+qq0U2Jea/YS7vb1KssUyL9Oqy4tWGDnf3eFvqbIStt1VoREdqj4sMwUX1xnDbi3XdFslOjbPeLl8Ip0e61dY65FqvIV3ekGvTg+QMkiPaaUs3l5pui1lSutZE5E0THk5dpAEDiehIWVuan5EeIU/UBo4mOYpgTEWak4p/d5caDl8JRsrUuVf88zzCH2aT799FEqcCI7Z1fXMDolH25tSVV/wk5jErZC11GWzOQL4nyx5rVjjtvSX/+4YnsJY0yy7O5AWP2sZ8SBaD69TXNAQ8VK1KUnVZngtvjQsvOyi3PtZtJqcgaYMMxEbYVVtXcDgOCCZkPhYBUfnwKA04KUsGKUpVM9f5vb2zoPf+XpzJHAXLqDYwmlIgiTBJR0+eZ3G23sEIVLiPhVDai4RrNjs47GIvK75xR5dDlduhILlV1DRGFWG9V0OuJzRjJ2VAiOi2RiiBGfrbVJfEHv/pUJG9XVq9gn8qPtpjsjUQiDgP7/xWqx8Dz2maz6VW3g32RuvXSlq5v7eAx1fw0PqeJ6KGJ2Yyf/uJTAMAHHwpC6IPb4g/++qsvoRGlsUmkhIscGN88e4wNVtMVEu+UCnkBr0+XXj49kXe4ckVQLI1GQ0c7q9hrxDN5bX0FYXLTnfH8bA64d+s11IjidJG3oNyfw0c1OYYrupJmu9tCl9LdHVa2S1R32l3+AI2erEFvJGeXOs967RGGVAe7c0vQz5Wi3Nep+TEj0mpIZOd1Il/LlRys5Ia5TzTJ2+9ILODzujCin6vyWoqrcXl1DYWs/Ld17pf8mdh1YGsZc/qbMv1xhPLFLoMFBvKQrKxKtbtzWcb9Q1n7GhFQNvIhwWzQIYIEDYCmg363hw7ff8S4qqCUI1dWYNKUypo85zJ5a3LlAhJuiRGarIg3qTyUSMRRJJ+iQrEYyM+iue0wEQHWppKVkf7H7fDqMa+TXIkZqj72+2OYreLLFAJvSG6OSq2sxyEeokaVHLzT6YRLI8eXRn4WGDBpyu83+K93iZLvmCBCVSeFWHW6ZS81BzO92l9jjC2rDthmJjSJ5skWZD7dRAPG4kGMKb8dWRJfrTi4uo0+ypfyDB0GlRGf/KxV7iBP+XczEUVunmGX+YL+PRB0kleE6sLD4RAu/p6ZZ/I98prMug30ZrInLi/oi3bWcFmRed5eEaRO/Vz+f73UgD1EtAs5GecWWedXB3uI8D3UGVQhv0jAG4MFXIsO+Z2oKmUxmmCiPU6ouqt89rVr19ChHapODCfRJUGvCymiUvvkOwkRATudG3F4LOjtvX05K5eX5RsnGonofvHuvdcAfKeQ2R+OcYOoi2ePn/CaHjSJ3B2ST3JtTfZnJpfFgLFMn7G5m2hTi9kFP+OhPnldFGeX2+3BOdGKHqLNWx25R706wIxKt3PItbc3xB6t0zksOppH5ixLXq6h06I/35Q+105Vy06npX8bKo66PtWoDBYHokR+nKWpABwWe4kGvQp8j6Wo7LOTwzRWiKq5pKLx8YnExUuxNdSJJD2l8uf7VFCOryQxH8g3SY7KwT0qZw37A0SpmJwneivok2col4s4YVeLh9+1oaUAn9OLLuftxq7wGQ3JWxgMRlBnvK2Q6TXy05YqDYDzNyVa0sOY5vb1GzgryLMrf3/ntsTMdptVP8+UOvmMqHrN7sTcKHvPQMRwvVyGmapiLqXGR/Rovz/UOy4qM7GFCDnqNIsHnTY7RRjnXPLMyncbWOJ3hVImfmdbvtlqhQr+6svPAAAFzrVSQ7t363U42HHw+EDs/85b4gd8GGBiIE9WizFsV96zaavCDPENIZ+ccSYTebwcc1jZXTEZsRsqR+U/qx9uj6zT3/8f/+X3W3b+n/23fyiy86MRTBZOOCW9HdwwBpMJeUKglaRdZSgGMu8NcW1dJmdnWxzIQ0rIun1BPUHjI0TOyl3V7TVhp2SwIoQa0AkuLSXx5Im0wzhU6wvht+P+ABphYRoPfyWt6/MG8PSRfLAkCTn2+2VRTXNgbCAhV0MMSkHs0jPgn/2ZSC87RjwwbFZMKOk8ZWJoTON2u6xIBmXT3EzJh8smg/RQJIgJ7zN2iIE8vy8fxpZiDzskM9YisqE7PHC6rYne5kK1TezvyTxaDHPECdEcM9XSGcqG3g6uwMBkGZG1KLGdDBaTDhFXkn/qMHaHfEjwgD1nUPOSUM2gw40+Q9MQYdLjVhdWsk5Ooe4nNzRZjLDSdmb8mFeknFbNgRrhqyoIUiRrnW5LD4TXN+TA7fOD8PT8AlPKE24ty6Fj5rqbTVa0SWo3mYt9zGfys2g4Bo9fDmi12ZWssNNiwOmZHI5GPu/pkTj+hC+MjSQd8ZF80C0Tjp9ulNGnLSip4V1CIbPNBk6qMrdGyrvvplbQo8N1MUmRIJxy0GnDyjU8obN18UO10+7pbTc7hLyq1rGru7so0ykXKW9Y5gHoc/twg4H6i30JFvIlsYFisYr1lMA9Qwk5lD/5RMj3et0Brl2X+xwcS7CwTUnwpeQy2m1FosnDlbDiWCCAAA/4HvdsoSH3Cy4n0KDjrfVlv0RdPmSZ+HtJee833xNi4GwxhwYP6AhJys0MxL2eICZsExoxMWbhoWIzGZGkRO29W5JsMMwk2Pjtlw8xm8k6PabMbp6BarNTRzQsPuHv/h2RL56WSWpfLuMaWw2MdM1jHlqNRg27V+Rjb0CJXZtbDq3ecIRXfD92DmA+YRurw40p91KcMqczgwMFtu3V+BGbZ2KuVC3hjbdEgvScLTcz/v143sCMcrTtXp3XlIP08PQYCX4ENZryfOm0BCeYmHBjXZ5dSSFXRzJXAb8bazxUq5TYzVJ2887SLXQIf53S/qNxCZg0t4Yqr5VnEOljINF3eNHjeaExoZdaTsn71VqoUXJ2RD9p8WhwsaWwyL9TLZbJWEyHMKvE5tkR2zdHRqxcEd/Qo+Spmx+g5ydHcDPw2FgX33LO1iCny4MSWz8HfbGLEG1iOuqiXSH5cpCJWsoWf/jeRzp8ee9I/MjKpgQSuUIJ6WPxJVsptgWfpHk/F97/UGDx5SPZ85dsmzA5bfh7f1cIuJ8yYH/8REj3oxYvXAn5AMwxEdjptGGfiK9LJmSv/r+/kISJw+ZFiK2zV68QLl0VO7M7bPDwvFMfl9xamDoccBOWbuPPDDzzzJgiyRbtDiVd7YTOHxydIcTEXact+3PIvdFs1hEOi22a+RGr2jfHsyF2r4k9fvZrIekuk9jcHbKhyeRQ2CfPVMzIesSjm4iE5MO71ZP9YrdpmDI2SG2I/WfK8uHebrexzUShme+qPqqcmh0mFhOMtK99EuTXWm0ESELvo09yD+TZuxYjy1iAxgDVzxYwbziIHAlmVbJj0BU/5HG6YKJTUXM0ZALAaDSC3zbIkRx3yP5G23SCt25LgD+jTzGzdSdTK6PakedShPomawRenvFHFCjYZotUrVGBN8TWW3ZnpTOSHPROU3D6GVfJNoOf7V3msQOaSa55eS6/H08qiW4HyjzfgyFFxC3vfHL6Ck5+iB0eS2xx85Z8eIZCHlyQnDjB2Ea1LvT6Y2QvZQ09TGiusFg4D9owo/84vkwDAMa01UG5ibff/0j+zivPMiq28Jy+VSVHC2Wxr2vXr2PK4uDRkczViIlyq9WM6UTe+Zj7ulaV+wbCIZQYt/3o935H/m5OgtTiJfp1JnTY1qFahkvlPDbX5cwaMjFnc0j8AvMcBRbAPExWT0csfDq9eg+Lammeq3Yyow31Att/+YFmYTv88ekJgiQ1XllLAQCePpPzNxAI4LIo81Cqy7PH4wlY5mKTSvjDxxaYH3z4Nl48EansIsVSbt+VYkGmUNHvPZ8xqcdzwmV04PRA/PVXz8V2fvDehwCAbrsNs5XyzQHZS15KNjuMTpQzch7luU/ybNWZj02wsghtZGKnTf/fHQ7h5gd0KEDJcu67K6k1GGiPWcZS/8WPfg8AcPj4W5SNco0RW2Cq7SbCcdod27tKTFIlIjGASXcwUVbpSOwFAB5djlp+liWJcsAd1aXenRQ2uHlVzqdGrQk7k81V7qmWElmxO2HkOytfodpsTfMJluivLEyGnR/JWefyevWC9oAUDEeM82vlCowkKVbJaUWU7fB40Wky+dNn3G8c6y2Wu7vSJr57VeLHTz75RE9SWEmnob6fYpE1PTHg5/eIKvicHB7ARHvt8toDJnMaLcDilWvVe2ILDs71SiSKAWNsjTF2sSnndCaTg8bk/vVbQrQ85rfLq1f7emLCQcBDh0XDQdekAx6UCJKbhf7tjSQaLLI2SVYe9Icxm5NGgGdBlUI2w7ERh0wONSmklFPJWX8AH74uZ3eSgjmrbFm9uLjAq2PZJ1NW9hUpu9ft0c+vAf2UycHfsRgwZJHFQRsacN1u3ryFMVvalGDGhNe+cfO23sb3ySfyDXzzunzHRyIRPHshhaurFGBpMJ7AfKw/l2qVfPxEWhqjsSVMZrLnUixGlEpF5HIZPqvMVSQmPuLkIo0GbS2yxTOnQpGheh8e+rxtCku9YmEEfidcLA6WSBy94pCYYTqYoDyTOVoiCKN6Ic/ebvXQ4Hfnyob8vsMrdolaC9WB2JGH4kStikpMD2GjEMKMe3F7W2Kjbjunfx/ZLeJza2VV5LFhzm/X/+mf/nLRMrYYi7EYi7EYi7EYi7EYi7EYi7EYi7EYi7EYMr4XCKE//pvX5gAwmY3hZkZMkT23mCXt9ntwk8BLPbODMnaG/gg+QqZ9zMyrdiOr3aFLi+bPJOu5vKWgVnMYSApYKkkGzkmy11q1AZNRMtdKyrujZEH7PYyY5fQotIsi+ypV9Gy2ja1AgYhk4d2ahkd7gtR5/a5kja/uSib0X/zyc/zzv5RK5bIvBQDoDXowmZj9ZTaxRyjkylIcfkpXL3vlPq9fleylzWeBnRl5x6pkD79ga1X+6318+NrrAID4liBGWl3J3FYvSzBTunREBInKxvfrLZiYyY+QuK1OlM3OSgpTZmNfkLTZTWLKk9O0Lrl8hRleJ1tb0pkLuAmltfgkO2oieWfI6UGXGXXVTnbwZA+3WEVweGSdqoQKWq1mGIgMWiMJbYZk3ttXrqJRluxvjhWwKZE0MMzQJox7iwihk0OpXtSrDWxRZnBKFIqNkMT5zIAxyyQrrP7nc1LtqtUaiMZljs6JCjm7EMRP0GbDrZsyD6d5scdeS9Y0bg/opKJrbHlUme9Sp4mDszP9+gAQIQKt3GzAxhY1EysiPqsZP2SbXIe26nDJ3vr005/psOgYqyRt7rNms6sThEcUaRmrEYVcHhspqfRetOW9+oSI/tEf/Ie6rOb5pcx7gX/XbfXgVoTblEAu19gOMpjDyhasZlvmb2VVKmJ2ixl2Vr5VK8ucbU2m+QxzVnxWFCEoCb9fnpzgOauL97YEXnp+doq33pFqooFki74lqYg823upE9TbifYaUrLV6fLinBVpH9uTfucHArv1O53In8m7VsvyrsuKlH77OrJNeden5/I7VqJFRv0GNELDgyTUHtZYmer3dFJYPysGScKrHQ6HDjlV/1pJOp9IruCI7Yl9EiVP6IfCvoBObNhk+5TR5tJRQ7Flme862wEvSwW94q3ISBWhfKVWgo1+MRwQ3/cGfdmvfv0F3AGxp0ZP1uaIbX27wYheCVfrNCepptNmxVJI/q5G2LKZfqDdLsI0kWewsF2rSRi0PxpGhWS3RVZ+Pv5Q1sYV3sSrA7GBMWXup3ymcrWJ14mAMhlIYNrvot6T92+2FNxc1ttomsFP/6Sg9cap2JDN4MZFVXxKk4SN5CNGcjmuVygvOA9Bzs+tm6/h5z//JQCgQLLrq9fEj/Q6beQu2UZZUyg/WefxYIgVt/i+114XVFqrRUnZs0ushMVWFPRa2YfJZYefKMCffib3LRM1MJ4MccUuZ9xqQipZL+gnq40OVpbFV7zMCEpBszuQ+//B0gHAwArp42+f4a3r9NFsB1kmIuHz3/wGS2GZxw/vCTrPwura/Zcv4U3Q57GSpcjsTw5eYo0S3uoseUVUZTiexGv33uTvSXVciU602y102Z5i1UgYzbbnfCWHVZKF2ryyJl8+EPRBa9CAj2TZUaJzei3xy7VSCxp92YTtCRurq7pghSL4bdN/TGcTuNhScpdCBqfZtDxLoaL/XYhIJgv9/XQOHL4ieoXIlDc2ZA4+fXYffdb4oqzwN9hSuLy+ord0t/mu2Yw8ezwa1gnMHz0UUm4lVezx+vDgkbRlNFnlthPhEnJpiLHdZ8pWsTwr1fagDzHGGJk8yc61uE6c/duvhIA7GBGf5vY4dBLTKduKlF/tlcdokoh8wgq6QvUlQkmUic5Qo0lERrNV1+OiP/hb/wEA4OxUkLk2K1AnihCMpfqkEIgGQ2AHLTJsm7h+Q8ipzy7ymCjSZBKG//7vChLn4dNv4SFCuUFhgkBMbGjeH2NM1NeE8cGw0ECdLb6KnH6NMvIBnx/nGfHbZiIPqi05Wy12EyIe2c92Inb29uV3Xx2fYoexEIvx2D8QRPvHP3oPl0SRKLJzq50tqFYzJgOZq1abFXu2k0ViQcw47x4726f89MvVqh4DbZOMetSWd7IYNB1JpNqjRzw/0hfneiuXy0P0Bs/7i4sLaGx7r16y5WMywtKK2Mprt9lCtJcGABgxxtgovzcxil8dDuXik5kJ3S7RUyG7fi0AMEyswFjs0cn7FWhL0/EEsYTs8YlBnn1O/F2z3EWzLPvZxz1I88dkZgaPL3gpC1+mHx9OxlhKyF41qbmeybW3k0u6oMm//tlfAQCC9J3X11N4lpEza4dtUOVCGXEiu90ufoewDadYLiEckz2q2uvKXVlvo9Gox9m1Kqkr2N7e748RCrAFmt9Sy2tKAKcCDzspShn5uxGJ/80mK2wO8d9d7rcGkV1ba6tYpf+eEDm/90K+BTTNAZeP7VokefaFUwCA08MDGGdy/RH9gVJeiMSSGPTF5ppsTbY45/ASuasIhRXasdGuI0y0izqDX7BT5OatN3QE0zrj5gjPw0cPHqA3Itqfrb8z9m/a5i6UidZwRVVbozxT9iytI3ts9GFTfre6YNRj97FCt5N0vD/qIsz4e0D7uKTwy7D1Hem11Sw2a2NLp8dlR53IQqdN7ud3B2DgWT+iqouN9mSxe3BwKn6tM5Q5frInfrFUbeDutqzXW2zBsjIe7Pb7mPHeeSKV60RpD/tdmC0kqqavtlMIJxaJYHtFzvoc6S1Uu6jFoeHKquyJJpE4br6nXXPq4g8RovrU90i5VtXFpCxEc3vZHjketLHKa6pOInWGeX1+zHhIKkLtN995EwdH8v7FouwTtUYGm00XsPnwA/HzTYoJfPXN17Dw+h6r/DsmolfzODDn+9T68m+PHRWJZAoh2pymxD2IhC/VWtg7ke+KSER+NqEIjGdqwnFZ4huzR945TpqV+No2DHVZyxeX8q4Bv5PzH0aWbYOTkayfj2d4vnqJYFR+73/+3/YWCKHFWIzFWIzFWIzFWIzFWIzFWIzFWIzFWIzFkPG9QAj9oz96cw4Aq2sraJA0WCFMquTagcGkZxavkDj0mD3r/UIVvZJUOxQ/0Ab5ARrdlujvAdgiQeS3r9IAgNG0jXCEsvbkugiQsHc8nKLbkWfZXCfihMRSxskU/Y5kHaOUZzeo3vzhEG5WIxU/UDIs962V8shWhiMwEwAAIABJREFU5D63rss71Jn5/umLV/jtqWQo4y5BPPQHbV2yUyElBgN5l4DLgxh7kXcoN/8GK8yusBFeVmHqJC5+ei6VpaNffAMvM8m/+0e/L+9elcpGv1zF1Eapa3I2NFpyf5/NBdNYoYZURp+kc+apLjMbjsvfKQ6oVrOj8wkoUrfUsmSRM/kcbCSRKHakGtFhz6U2NSC+KvNwxmzuqN5Gg+iHELPGNSKE5tMJLCbyK5HDY/eqVNDmBgNarKo8JUnxjZtSpcllz7FGQmXFa+GyyNx5nV7U2GefP7/U5x0AwqEockVycJAXwEkE24unLxAgimpMNEqGFfdeIYdbN2Ttrczslogs2vInMfHK9dtEnFhZQR7N5tC8sid+fZ+Sy5SWtbvcGJCE08pqxLVUCgZWUPwkMa0SHTIzGpBi9d1Ngl9F0DyfG+Ei2VmDxJkTrnun1dWJwRXvjIsVe81shIO8Er6QPOeY8+lxupBakfvl+rKWl1mx+6nJjQoRDqWyzPE6SdK7zQYqBZljm4kZcqJ65vMJnOSJOKcMt5L0NlptWCPR3nlJsvCGVhdBs6zrj37wQ/kZCToPLs5h4/5SiL3TlvxsOJqgwmq4m0SgG0TU5NJpncy+T8LTmFXmeimeRK4sz+4l4mf7plTjP//yC51UcEykz7CtEEk2bG2K3edJzqt4MYbDoc5vRboErKhKH4xos589TpSIidw5nUZHr6ZNyaF1lstihftLyeUekg8Dmg3PiJiKkyiyQnJok+ZEJEz7KIstTEgEvb6+DgsJsTN5sd8myUn/8MoOQtekZ/3ffPLvZK6I8tPMVhyTRyRAf5oiN1NpkgH6srCjpvgdf0B+NjVbYaa/Sp9INTLIqtNyJAD4lYSy+NV6Tmx2Z+cKrpGs8tGvpGf9h7tXcUlElZKOVcTAxVIGYb7zakwqUuVzeb/N5BWkq+xLJ1+HNyB2UqoUkM+LHc3YL28zy88ikTievBC+HoXCVMiOWHwZ396Xar/iE/BRQMBqniNJhFWb+0YR0NfO87i7JX7tm2/vy++Tv2Pn3m1okP3ydVaqT33aRL1QhoNcett8v/WUnCX/8tOfotOQ5wuzompz21EiMsJLYvI1+pheu4KLYlrWArLfAuS2i8QDuDwVFM+Kn6TNCbGvF6cnCKVSAIAp5//ajjzDbNjDISW2V0nwaydKBxZNr4iqiu+nn34i8xiL6dK9o6msjZLhzpZyiC1Jte7lIdFQJHo/z2eRWpPnukEetSYRdqNhD2ZWj6dznsmdLlxEBtbJgdUayF4MhQNY5Vk1oMSwkZwLHpcX9+/LOiWJvPSpiqLNgSkRybUCxR/Ie1AyjVFjRdvE31lj9X88HenItiERNDaH/EzTNMy41w1EuPRYzfcHQnCQH6tMrocgia7bzQoaPJ+fv5J1SFBkYTKZQOM+MasKeGeOrroPYxSbV/GZAC9JEHtrS2I0r5UEmp06+lM571yh70QfAEAzTHF5Kna7vi7nZ38k7/D42Vfw0Zco5Gs0JvblsBsRJTrpGxJ493nN1268ARtR1qfn5G4iZ11y8wpGhLasLMuZk+HvzLtdeIlsNvJ8rxKRbpoaYOX5eUCRA/fUAm9MbM1ChLGFh9V4OEKd3Ip2n8y3LybnZ7vfRJocjlYiwexuiSsOTjNwEsUWIuqu2ZJ42OkwYuOKoObMJNY/PpO5y11msElOjTD5Ik/O5L2K5SI0VqI9Vrm2l+TtTpsGB23cq3jr6nJ2vXi0j4FJnl3xBA0pwNDqd2FkjFBri/2PiOz94Acfod0nLxkRQmfHB6gPxdbevCucZ5a2rEMhn4HJL2eaySj3y2Xld+1OD8YzIvZ2FJpH1rndnKDfVXtW1skM8afd7hAWUneY+W+Ze91u9WHYknt3idSc03+3egOsEIluNkiM6HURgTbowueVuZ0X2OFAas7zVwe4Sr6qmU/mOklZd5fFiK+fCKJui2ezZWJAJcvYzip25Y9IfJQrF1GvEelglefUuKa9Th9GkkHvcp9VGI8AM11afn+fiKTb0l1w88otNMvyroUs5eCbQ86dES3GGG7GqaOB/CwS8MBI3sWdTTnnG3Vy4Pj8OKDfjyRkLxUU4scA2PgsCv2phBDa3SE0syJdFnsaTCZoMPZU6AsXuxpcHjv6Iz47vw2Xlvg9NwGGRLNf4xnip2BGqVDEhAfFZUl8rZ9IsuZhFoW+nAvhTdk3RiJ2LtNnOqH1KQmT20QPRVwunJEPbm1d1rLNTgeb3aZzQCo+xeVViWkivhU0GnI/HR3Fc8PrdOgIwzhJn6/t3kbuUHwyTIrYWt49X64gSDRUICb/Wp3i44PhCNzs+PiKiOEQEXxGswlFfmd5KPpQyMu8YDJFu8duFX5rFGvyM81sxlX6ljUifUZE0aaz5+j0ZK+uxCXGaPM9J6MxEkTU6d+U6jxtNuAJqrNAfj8aEV8/GbbgJXJ7yL/r9tkN4XRDG8o1cuRvq3caSKzIPCjRg/1DiRvNDg0torU6OSLUiFKfOWa4bJN7if5Q3Se1nECC3xXntLk5EbCRxAqyzwSRpM7S3duCQHWHYpiOZH8evRKOpHRevtH9ViPg43cFu16M5Pxy+0OIGMVu+0QdTecyL8NBDS4KCBXI6TZi3OmPutHqiq39r/94f4EQWozFWIzFWIzFWIzFWIzFWIzFWIzFWIzFWAwZ3wuE0P/+D35/DgA+hwsVJWHKntNz9kzWu11o7GvcSKYAAJmX7Fn3ujGnHJ+FmUWFMKq1qzg4lipLkOoWs4FUoTqdJsBeZ6dLso5Ou1STTo6P8fY9qei7qdTVmEs2vF5rYUi5RicrMKp30mo0oKOqbswyG4eSdxtbgbFRqiMDyqNOmPnT3EHYmcX9x//8/wAAHJ9V4Z1Q1n4iSb2eWa7tsAB3I5KNTQXknZdSkjlM3diBgWzvg7m8s8cs9/uHn/45zAeSffzj96RH+2Fd1C2CsRBK7OVUEsphyjCaNRsMRJNUyLe0yv7+SrWOUoOIFlYClQzvZDzUEVpmqvgoCXZTboQA+Sl2b0nV7+e/EbWakUUD1YRRL1HBwmjARkjsYpdViKdPJbu6klpGakv9N0EBDYmuwniq97Srvuo+eXWsJjNiYaoWEemj1CocDgeq7L8OUsLXz17hequpp1OVkpCbFYdIMIC/eCEVfh8RPoa2XHM4ACx+/jcb5TmptOY1RfHbY/m7C0pKJwOSod9c3sbjx6LO4eazGCjDXW5XcT0mVYjkttjEWf4cBarNRLxih+2qVCicVk3vr9dYnQWVMjA3YkQUGlhhIqAMJqeGDlEFo9G/r9hwcZzGdkrW8OgwLX/A3vC15QBu3xDETm0i77xHvg93NIZMRapcgymlytk/7NCcMEHmystqnIecNvmjM8xYITZYKN/4UqpdsagXybjs59fflerY/cdnOHginBz3yGmikbMhtH0Vh+QXixCRMXHIszdabfTIQRPivPd78nwbq8swkufKAPn9YpY8Y50W7l6T+ahV5f2UKl+l08LqNm31mVR3rHJJeP1hNPn+FresTTAm73eSPoJGFJaHaAgXK52xWETfZ3aq3akKrkXz4piyu18+l/vZBjbMLfLz1K5UZ54fCiLp8KQGo03WdXlDqplGq+wX5ywAg0H84MWZVD98RE5tbWzCZpM9tL8na6GqeEuTCebkJQtfF1t4Se6d2kEaP3ntXXmvJdlf/y79tcx/ZoQP3hbZ7XPalZUbLxGLYkQkQpwcM1UimYbmnl7JC7HiGDOwAu/x4nP6CB+RfAGnD7WyUlST53SxClopltBry5pEWGHTiB548/Zd9GnnJSpBKOVDs2bXUScN/qvkzwf9EVZWZB5K5J4wmuRdhsMhGk36LlbCN7bFll6+eoVqjZK/DjkbrGZ5pgffPsVqQvyAhxLgXv5rxhQjk6zTMs+q7yqWVoxox9mcnL83X5NK1sHRIY4P5AyOrpArppyHiXxQCXKnTIhYufHaDTTJE9G8FFSCjT7w7s3bOOa+L7Li6HRSKfPmTZxR3TDhFxt6833hQPvy0UM8eS5n+DtvvAMACNnl7zrZCiasZg7Jp5HPXfJdzrEUl3ftdsVmh+TDaLe6WGXltkb+Ow8RrJVCDSH2+htZvW6SC253d0vf/5O2VNwq7R72T4jmC8hZog4vs2GKpWW5Vn9K9Al5Ep1OD4aUvFcxg9cvPyuWMrAz7nA4Zc8/IKIstbyOeET84d4zObsV12AqldI5lHSZb3JitdtdlJXyI1GPYa6f22lDhPLUYaK+ui2iN/oOWN1Uu6PSYrMrqLj+qIwOz/PpQObWp4VxXpFqqS0g66TUWyOeIKY6clXuU6J63fiyCY1+zUCkd4Xnr13TEA3KO3/xa0FVzeYyL9n2BDNyNt0gL2KAvv3qbhJupThGLrgx/aNhaEKPlfo798Te8yX6gPkIDj6D4kYpEa2aL2R1BUIzeTcCPqI3glF8/Y2c4W+9+xEAIJevAGOxGcX3pVBtuWIJRp7BGiWyp4qrMR7BOTk8zGbZny6eCZnLIjpN8W9zzsOz5xILXbm+hZ0kFdkOxWYsRpnrbL0Jm13WYp2+z0Nk88zVR7ZCzgrFYUh/Xi6XEaEvb/AdpkQIeDwejKmMa+LZ4KFqk82iIUeEi51V+YGBnC/dGvxUzipTVevF6Tl8St2Rz9eheqjJYgapivAGY72v9wRt5l+JoUoOt6hfnsXNWG+KqY76MZEDzkquwFZtAm4PhNYk1j3OPuV1llC5JCKdPnafUvO+WBDr5Do6eiC/72YcYvJbMXQyvm/IunmdEscZAczNck11zoyG8lJv37uOnW2JMfb2xQ77/TqG5Ea6SMs8zCmHnVzbwuMD2QtvfCTfKlpf5v3o8JluK8tEYdotRIcEw9h/Jf40Rx5LN5Gkm7EYTERbxKlu1mY8eHyWw6Avz2qkLLbDz++SRh/ZC1nnjQ2JuUJhomyaZZgo2e6ib0pRKdpot8BLBJ/ijank5T0noxmsVvEfoZDMXzFTgckh7+imrygQ3Z4MRnBOvsYW430HOTKH3RKMnOdhi6q0lD93+t24dlNQWnf576NvhM91NHWjTu7CbDUt96Eal3Me1FErsS3ZG494DkTMFuwdiV3MNfK+bpFDp9VH+lzmKknFymmN6lwBG5Ipqudy3vtEzNYrWQQccu+EV57z+s4G9h4K8vGMatpv/+BHkIsa4aS9KwS6UjB0eO1IOsVXPnkqHQfZgszdG++8hQrRXdmCxI3NrrzD3ABcuS3oagd9mJko+XK5iL2XYlf64Ie5YWZBn0hyE/mdIkToNqsl/TvC7ybqkb7m6fNnCPI7t8mOHYdTrum0G9FoSBzhId9mgWgls90CU1Nip4mNCrnVCrwpmb8Z+YhC5GIyezw4/ka+r5pz2QuZrOyNjZ1V9MlvlWHcEqM62ag/wArl6kM+ec7jfVn39c0EJlQh14hyCnjlvT64cQU9Xn/vROLoKb8huv0OClW5T4+xhpHkfx6vS/9+9roZOzD+7GIKP7tIEoxfTolS80T8KJD39Z/86fn3W3b+T//BfzQHpEXExsCmR8M9JgHjndfvoVKUDXy0LxMYDsgGm80miMRkURT5siLQNRiniETlgDi/kMkxD+SgMPqNsAbEmK1swQjNxSC1iQ1Rwvj6TOLsvyJRoc2OHhMK7FJBim0kDocDBsqC/+oLcSoWOiKD3QwDJT6vEMb5iDBms9WBHhf9Ie9zUhnCO5NnbzbEoc7slNYcdfH+lhwCATPJ/q6I87NYDXr7UyAumz4clHf+p5/9Ej/9V2Kwf/yxkJKuvUYiUtMc5bx8EJiZIDDy4ya1sa63RJ3x4zmfkUPLZLAhkpQNclmSA3NCEsX11RTAgFu1G+VIdLhsCmH7TXmHAtvCjigNW5hM0ChLMJRiC4xvJQonZStnDCqNDMjcXg/6E/lZibDPC5LsXtnahsUqv7e0RMlVwsaNMGE+kZ9ZCVm94Hvt7OzA4RbnkLsUR9/vyjOtrC/BTSekpOwfPXwIAAh6fOjQMBxkrQz7ZG0cmh/HZ/Kx7A3Leq/ymaZ9G8b84D8nCXW3I+vu8fjRJynuybH87DUGse1WHdVLCVZf/1Cg4hMb8JyS91227SgJ5oePH2FulH2SZCtKNCr267Q7MKF88wo/OpaYMBsOx0hfyNprJJZThJHPjo+Q5sdGmH8XY5BsH3WxydbMHPfSy31Jziyvb2JmIrE75SQNtDlMDdhYk4NvTBs/fSTB9u988AEatNHPz8RmjtmmuBEIIsVA9toVcdJdgxf1jrzX0SMhj00wuecPxFFXpO0MaJecYguTyQT9nty71RLnrGly0KSWV/D8uRwiG2xRe3ZfAvDV5QQCDLJabN04JnGp3elClsTbPa7vW3duAQAarR6eUfp7bSMFAHCzlSASD6BelwPaSTK8nVVpi+x2WqiyPSnMlgUPA9RIbAV9EtwXKUV9XsnjyVN59hZJmm2EtZpMJphILrq+LlDyLz7/NQAAoSA8HiufXdbbYWeCc2ZAh3NcJzHt9rb4ub3TV4hZxNZChLUqQl1TpoSf3BGi++t35H6jZdkH//aLZzCMKVlLn+tlgqdaK8Dtl2uG43IAdvnBmjnJ4PZd2R8gAWCbH38vXu7jOok91QH6Z//m/9YDm7WtTc672Ef64hwBJgvKJHuO+ySIvX31OmqUpx6MKF/MYkYmm8eQidO3P3hP5owtfyaTCU+/lnWeUy66Q5JXn8eFPgsOURYJVOvZYDBAka0RLc51l/ap2T1YSUqAeckWkQSl3MejHg6eiz9YIWw6EJCzZTabIM6PXiUpO6NfNZpMCHrFPx3RJ40x1pO9y7z+m2/cBQBUWzX0Sdi//0w+1mxk+HQ7XZiztVglNNVH9p279/DZZ58BAJaYAA2R1D4ajaJO2PyAkPkxg+TpaIo4g7NLBlE3bwpJ5mn6BDMS/NooO//ypczBnVs30WJCLM5CkZltPC6nD6mU7GeV2Ozwo2xnZws/Ixmsge0ILoMVY7Yka2xdsrCdx6qZcMq2HxvPmTqh8zCZYWMRYUiyfNUC2ek24eCHj58tgvuHsqYBbwAffyhJ0nZTfEubQgNOu0MninUyyN47lHdYX13HhAmxx09k78dIWL++noKBScESZefHTN4320YYWSi7dlv2zRjygVGvFfDtN7LObo0tHI4JcjVZ1x7P5Dnvu7G0hvUlidtUPBYIy/t9vvcC27tiRy/3xNY6TXmm+RBYZjt6mh99XdqAFklgNSn+KhYUW8sdy/3tVifsTpkPEwn8+/yoPd5PI8jCoYVnzo9/KHLkb711F199LcWpKu3q9EySmdvrKUQZk9TLLb6zvIPJaEGTbYMetqzaXRZos+/IwgHgknFWq9uDUSXw+CyKJmA6ncLpFx/ETgUUcnL+zqYmzFnsOzqWBKiPrQqDcQdnJ/Jc925KIvniTJ5paNZgsci8jVggmjDLYvbOUJnIczl4vtjpEw3TGbbYavPhm5KUVdLonUYTVrf4gZMLJqspODCaTXCRkbVQVAoD0i2YTFO0WSRR7e8mm1Nv/0syafBqT+LVpWQSORaMHZTa7vA7oW+cYsaYU2McHaRvn4yGMPIMmDooCjKQONpuCeLsjEXnnnxf+CPyLJaZGZcHsleHQ3kWZ1DmJV89xRLP905hyveSuZpa+li/Je9Qzsp5PedeCgX9ejvdcKL8tjyvz21FjD55PuI573eiTNETC+WsJyOJbfaeHiPOVt0ttjNFOXe/+uWXqLL1c3NLzoQ33xW/ePvOFaTTYkdpimKoVtK9zBk0JkKtXdUqLO8+0Gxokz7Awvl/643bAICTV8fQ2GaoWq7NJFiuVIpwsJ3azHNjwiTc0cUJkmuyl+JsM1pnG9akP8SE1cjxVBGYG7HCgsYehUMmjL9hMOoxWtTHc4/FvMGgCbsme8lEn3ucUa3CDVy/Imf+EpOyfRa9R1oYDSZlbS6xJ4NZ3mE8LKLFhNBzihxoTAAYnElYvEycTuRaNrPsT3cwCjsT1idPJQYIeSX+zlcr6JL+IRYVP6kEWSrlc1zbkjixW5X56LVqwFjsp8ez1cQC3RuvvYUZ2+A7FBhosm3T5XXDyvjZwOLOhO1JLo8Pe6/of9m2NuI33MbONpbDEpMrsZU7dyTO6rSq+OxzacF3OWWf+BkndVodJCi08+S5nBdWja3C3bb+jRiLiY8vlmUven0+BA3y/o+fS+xvpBDU1aubmLL4ZOG52WWMlynkEQjItaJsI/ZFPDg+Zu6A5OMBUi50y20MWZQp9yh24mLS06HhsiBnYiYva3P7tnw7H+7to3BJOpGArGEwKus8MQzhJBH82orE6RPGbH7NgkffyPmSpFjEdy1/GVy7Jkm3Z4yhEjz75tMxxpSWjy2T1qUrZ7HL60GXhUslKuUmAbc/5EO+LDHkf/cnv120jC3GYizGYizGYizGYizGYizGYizGYizGYiyGjO8FQui/+hs7cwBYX1lGkHDqFUrCdidKXtmGS2YkFSGrWSO51HSEBmFUqsoXpFym0QgsLZFUtyqVCk9IMswW2wTttlQchj22JfXZLmDxInsmGbUEYfhVEqrNZjOYSehkpvakkgwtVOrIFOX5Ts/l2nfWBN5a67V1OdU4YWYTXnM8GGI4k8yfn1C63541kWOFwuWQTGulx8x1r4ttwtBWSGLqdsnfJ7xOrKwJGWZ/KlWgCqsM7sgy/sVfSoVzxyzZy5/8gVTFaqOa3q6jWqXsRKwsJVfQIbogRInobF7ms56tYXVbsvoVwtKUrLsBgMmgpNoJcSaxonEyg5+QxickOm2xTck71UBFUeS7srZJmwND2kOfEEZVfV7f2oRGIm0lyW0k6mM2GMFAZIpCJThYJZhO5xgQ3m8n+XWXf69pDpywelTMScb8zm1BPPzOj9/HX/1cyHEdlHaOENY67A2wRZK0g3OpmPVYWdndvo0Sr2UzkRTWLD8L+BKosCI6oi1EU3Kd/aNTPH5EYlWSoysZzdloiCWfVFdiKbGJX/32c73t0srquMEo797otJBIyR4IGCmFPlctUk2YuD4zVgdKrGbarHYd8p5ckmqJxkqizelCneiCGpEcTiJpasVLeNne1mCJdP8l56U3wBXK5zYUgTzH6toWCiRUtUGueYfE6XduXEGeVY80ya+7tJfheAC/R94595W0BsWXryJ1VarbF1m5t4XEcJuxTYQSMm/VueyXFskaL9PnWCJKo837OB0e/fm+fSzVyyes9Lx7Q+7xwx//GD/9TCoA37KV7YDoqoA/hG1WW1+yUn/9nuzX0WiCIaHCdko3L8UVcfRYb/FTazLqil3fvH5dJ/i2cI7HOsokiQqRFV89kfnwhjyIEU1iYHW2WJP5L1TzKHMtrnLOSmWpWEKzo8TWqqBf9ouqzJ6mL+Fl+2OTEGVlL61mH0uUs1UIsEtKksatdsRoM9dWxc/97f/8bwMAfl09xsWprEUkIHbvIsHqtw8f6CTBirjbTfTRWfoVfDOZj81VQXuU2abb6fewzMpQgu2Dn734FhfPBcG1uiG/r4gDPX6fToivJF7dRFNNBn14CIuOEp6+SyLifD6PKs8l1Q7iJzx+Mp2ikRVfO6JUeZbElFarFSZev8iW6SIRF3deu4l+h21MrIYmVlPybEYzTkm8W2aFTdmLz+fD6XMhULSxjUxVg0wmE5aWlv69dx6xem3V7DBBrjFhK20sEcbBK7F3hRBKEEbvcNt1QusJb7C9KXv2YH8fa3xW1ZLyy198CgD4yU9+gn/7F38OADCzio+J+KTt1Do2l2VN0pSzHdNfbV25ig6JKw/YPnzvTanenV1msEc/46LtxdjGvbW2ikJW/PDUKfNRIdHp1uYO9vdlrqo1kudz/SKRCA4Ppcrod7PN3O1GnsTlS2yzaFHsIBwI6vD3Puc2nZWKYjQaRakueyBHBJiT7cdenxsjIgkUKlC1Ns8mU4SJJlFouS5bEm0WK6L8vWKRkr9sUd5YWcfRK5kPVS0/ZXudwWhEqSzvUC6KzYWJ0Nrcuo7Pfi2oyhnkXN+9LjENDDNUS/KcAR8R1aYyzomGzBTFpq/uiB9xmDU0iSSNEjVqYMXXF/UhTzLM2URsLhqUa46HMxwdif+0e2Q+n70ShJHP48EHH7FaWhH7r2XkmtXiAA6P+JsQ24izWVkbu9ODMluAVOu/nb4pHvHivXelpb4zYLvt158BAJLhHYxZiXaTNHt3U+xzNBrpIgSq/fLG7dsAK/UhypefZgSZMZ7PYCGSYpltr3lKUM8mE2RY1Y3FxW8o39vtDWFhC5CSpE8TTX/z1lWkSXAaYpU7VxLbe3Z6gSDjRb9B/l2iX52au4ixpaHFWC9fEr/z/MULXCGq0ucS29ldk1joaP8VPJRl9hONX+/LOibWVpHhWZW9kGf3kQTYYTXDRPl4LxHUjx7u69c1syXtnITEd1+/hR5RmJctmf+jC0EHdwZ9gK12USKT11bYQTAZo0nEao3tWlajQqy4kc8R+csYXrOTzD1TQcwr63r/VPZSmGjC5aQfo5Gccecn8u/LZ7JvrlzfRrunJKXFLjWb3NeuAT3KYfeJZt29KvHFwd4eNhJydphAMnWnAw6+z9GJzIMiy37t1l20KyTCP04DAD7+GzcBAE8eHaLTEh8JUj1MKdgyHHbx9tvv8j3k/T7/f+SbIKdNsLohaJneS3kHF+PGecSFBuN6J2NEv8YY02yBn2dvie21M35b2hx25C7Ezwx5zpdIXL1xdRNWp0Jmyn389LXjQR/g+W5jnOlyaDCxm+PoVNbeHRObNbkdcBMVaenI381rsrZNJ2DjOeZizBslkr3d7+PwWBDr5QrjKkXa7w8gSjnwSlPWzcQWVMxPkSGqd26VfV14Lms0jaYQcss1DDxLW3U5EJ0OG5ZX5FlOD9JyzYnYSXBNg8Egc6OZZZ/NSBcyHtRhNpBt/A+2AAAgAElEQVTY3ck4q1mA1y3voUQwHCSGb1QrIPgeGknw1Xed0+2GZpKzo8I2KwLr4PWHUWC8ZyXCpUdUyrWb1/Cbv/4NgO++s7bp+zTLDEajvLNCrD14IO1oXpcXsahcS8UWR+zgcHk8iCbEz18QbV2nmJE/GMDFU1mbIFE9S+zGyWbOYed3uEIcK/LyYDwKF78/bEROuVJh2PlNmCMK1kX0UGRuxx5R1SaKMqToP0ajAfI8S91Ey8Wi8ryG8RQNUgWUKDvvoQBJb9yD1yb749qyoPMql0SGtisIJ8RXfv1UuoQUCtxiNOP2LUGCeehrM+zKadcbcDnEfo0kAU+wNbNaLKHREr8bIRprOCaCyjiHkf7xT/7V4QIhtBiLsRiLsRiLsRiLsRiLsRiLsRiLsRiLsRgyvhcIoT/+g2tzQDKNbpJKtdizf56TSpPVatU5UG7cEN6ZmUHyWflsBvmcVFWCJOE1KDTKfA6NFdFSTbJzTfbr+zQL7BbJmvV6lBakJORoOEMixJ51kpnGViRzfnGWhocIhOu3pGpymJZqlcXpwhdfShX+6FiykFb2zQ7nUxiIUIkRIdQjN0LE64SP/X5D9t3//LQGtqNDY6ax2qT87WCIIKt8QUpOvnZbKv4Ruwn7RDd1BvLsja48w3/2o/8Y99kn/vAv5Tn/07ekb98SNUFZgyK4UhV+q9Wqy83vXJd3zhO9kT/KY4V9kIo4rViS+1cLJZgMUgErFORlnKys9oZNzMpSuUndk2d4cixZ2tKzM1y7J/3JLSMl/mwO2ClprrhXOqyM+nw+tIgAmDIjHPLKfVq1OsycI4WAUtK8FotNRzq99ubbAIADSm5fZLIwW+T3Ysz6hoOS2V9ZjWL/WAg9m8xmGyDvGYssY5aV90/dFJs5ovThdGQBOpRP9MkzJVclw9vqDQFyDXRIkhkgoqA7HMNuY28qe/7jRCR8/qtf4vRMKjCqN3ZtfRmQaUC/TvTFFelLLVcraHB/3boq1XsjSSDPS3mUWBX3MjttmMtems1m6PXkWmbuPWXHuXwRaVZE80R++IKyVsNuByFWuUys9ChS1NFgjG+++QYA4HJL9cMfkneGyYweUUpzVrduXJVqhM9uQplcVKMeK8z0D2a3Hel8GgAQsosNZJ5ndC4ga1Luc8nqdcjsw50d4VqILJHMviO23W7UMe0piUmpekSCUjmfG8xw+WXtKuRjiLnl/S4uc1giMiVN8sJTViWOz8/w2h3xYSaSDDeHsjdG/QkuuJZbrLwoUsmgz63LvFpYtbuyKegqp13T+b+C5CE42BMkQ6PbRTSpiKOlgrKUWEe+JBWa1G4KAFCjLOvJRQFWViFGU9kvViWVPfUhRdSanUiOSk2qTq3OEGYr7ZdosQmrvK1SD17Kc0ZZCX9BREIg5IebFTk7q/GDgVyzYx9gPhbb7LZlnd94Xfh48sUSKqyIwCJVRqtd1tbqs+DZF5T0dsk+2SQfktumoU7/1CQn2NLuKjJHsk7xhPj9gyOZq48//hgNVtYMRG00yFN2bWcXWRLRxomcdHPuJsMRvOR/OSK5n+LGiyxFMSC3XJR7SKFfTtNZXL0i9lGjxO5gLD6m1a7DQ3GFu3fFP377UN7TH/ShP1TVSFk3hcrMXubg9Yv9qmpag+tms9ngJeFrgYiAAe2sPxwjQnJXRXY7m45wSLniLRIzjyizfuP6VcAo71VoiT0lErLXq+UKRpR2VZwB/x977xUj63pdia3KOaeurqquzn365HQzyXtFUuSMREVbY8mWRoLHY8nwYAzDejEMAzbgGRjzYBiwJWCCLM9AsjyyNApDMYhBJO/lTefek0N3n865qivnXH5Y6y9CD34n4Ppe+t7T1X/9X9rf/vZee62+5v3u3dv4q68Rcdmd8JbQgMUCoYktN/7OJTtisdnR1ecsyriXDfuayUxIm9viknjlGjPo404DDqVPHUJ/GQTU9UYHDySNbrNzPc1oTZzlTpGVJPQ3vvpXfJewH6995nUAP5Kn37xP5F+/1UNAGbwzCS8ExEXh8btxeEz/xuLkPHnF/zAYARaxbxo+moHesFhMOJNfdF0IPoOId2NjE5sbzKhWqxz/7DozqgGXD16RtA5l0w+FbGoNejg4pt1ZWiL655bGqnxRm3DkNMT/sndA2+L1e9CVfK5FEtFxVwgtEYi2xPnXHXIeOt0eOvK/IGRQTXtwKejC8hq/++h0HwCwuEwk5XmuiPMy/+77H/K7fRH+Lr+1CauN9ndtmbal3zGQwCHsHnA8/CLs9vk4jh3YJufZQLwnfYlx+BwW3LlOpIrHpXUi4Fp37MbWJtG6cUm+G75bOjkLv0/kxC85nv0eYBOKsiTeqpD2fCKTwrFkoiPalzWdv267DVWJKjSa/DerVeskEIRf0uY//JBZ+N19Iiau3LiMy1c5Du99nwg8d0DndLMDk0idZ51clzeXyQFSqJ2ge2K8H3/XEK8U7FZEJUGdF4+GzyHp9moNY/GqmEWUbOzT5aXFieS9sU/L8m2K1RJGIttenuc51q6NMNaedUoUoKUzwR/yoS+nJp8TT404GgfjwUQC/FDjMDJEFtw2OITCumhxDxpS5WZ4UC7qczo7zGPZqO4Y6HBen1Rov9fS4tBq1fHGW7TROSFiNjeNcYli/yXfwSy/c1170OEY4qU4IP3asxCHi6k3gFv+ptnJdbX1bBNLGUmSx/i7Ypl3HV/Ij+McxyZf5DjG+FF4vd4Jf1qjLPLZCn82qy2srdGGZeV7brxHRGTDZcGMOC3T8je78qsvTA14InyHNb1TL893Hw76ONUdrNkT15/8kEqthbC4OAfy5+w+zofT50axxrM0ID7AVIz70zoeoKozKqx/O8+VUS1wDg3S66reLz6XQls+jFWcZaMW18l+t4RBnf+9GOU5GBXv1f7eMc50D1tY554v6gzxz7iQFrLoW/fEF3mb834jFcE/+Z1/yc/Jt4iJ7/DQlsNnskR7lQqSDrfRLhSPNmCysQ8OH33Jpx/Jfr3igcvJebKPOdazMc5Vs3GBF8+5Di+tcc8OTS3025znxCz3p1eoxc0Xz9ET0m9dCM287mx2ux0ZVTRsims0qPlKpuZQKkmIQP7Elu5lvXEfXXEW+cRVGZH4ht/nhF3k96UC/QiPKkyGwzHmVzjuJflSuhaj1ekgr/FfXOWYNeU/Oh1uNOVbF1UR0BLBs8Nmh9Mmrk9xNRp+4PlFHgPt9UuLPFNqwyYyOosNe1+XPzLjiQIOvmtNz/eIG3M07CMvH74uP8Kus/n21Ws43OP5ctDiXlwR4b3FYoFHKC/TgO/1cpPzNzYNYBN32EhiSwafotftg11VNdEA10zIIOSvN9ERMqtl4nvOGjxNFyUUq5Kbt4kHSUIRfqcTp7I7//J7+SlCaNqmbdqmbdqmbdqmbdqmbdqmbdqmbdqmbdrYfiwQQv/qf/iFMcCI2tNnL/7W75JSyRoOAIvqvMtChdxZZzTxQrLVACYolorUNwYYI6zaRVj4O5eRUfR7cSw+ovIpo3MzXkbUspkkfJKxLY8Zkdu6x+jeqN3F3eus8XN6GaGEshnf/sF7GPalmDBmxNsm+dhWr4+QUB1PJTfdVDbolZtXMJLiwpxqJL+5ncOjR8wy2aQY1FAEfDgcwqbsUVgM/tcW+eyQ14TNHDN/66vsw8sdZi9ejSyjuc7MxO/+L8xw/rc/RZnC5VfT2DlgJNivaLGhrhIJ+BEJMUpvZHruP2XE/FJqDfOqGX2qzJlT75SZzeCR5NJbUslyioPFa+9jr8XobxDMxKykiVh5cpFDU3Wbv/bTPw0AuHfwGEXVthoy0BnVnjbbLeyoztJQV6kr6ry2sIRkVpnDviE5yfmen59HrcX53dhkVqcnrgybw4WRuH9WFlVP2uR6KZXP0VY9qjfAcX/wkOsjd17C28tcm0t3mDl4sE20wd7uKcIWrhm/ZOdfucv60mKlMRnvpjKxd8XgX6/W4XBy3A6FxClrDMLxCByK1s8LqXV2doJ6gWPbVWbEryxvJB5DTyg0s0ncSlIFOCvk8UQSz5eXOBfLc/N8h1INTqdTn2fk28iOlbptjCWPXJL8q7GnPD0zLqf4jMgl7ucjoWC83gDsLo7HG29RIW3ncB8A8Bdf+zrsykI6lB3vS2ZybXEW+2di959jBsVQzLDX21gKc8+bNW+t4wYOJE86CAs9KBWfQb6LxSg/3+1JaSTL9Rz2+/HB33wHAPDqDWbMDeWVSqWK23eojmUT58vuluqd/RFsPKEtu3SVtqKgTLjV68CZpJZTc8wQvS80i8vmQVYysfUy5y8hpFUyHsG55t7gM4qHxC2Wv8DcHP9uLEnNLalCeSOhSSYkokzbRr4w4a7aVwZ9fo4Z1lFvjFWtX0NWdUNSmv22GVevMptrklJdvU675XIHJ7KccWXv+lIvaVgBc1XqNiWpcYnrxB+NAsrm2sz83cER90s910WvKwU4yYsn03x2u1fG2jW+84sdjvWdV4g0NI3deCqOgXNxlrgkN+2z29EVB4hViJ+7a2toSQWtLv6jkezA2fEJVsUr1KtzPNalKmcaDlAXD0ZUa+5wl+iN1dVVHAsBcixJ19ks13+1VZ/wFKyscJ/1OtyL50dF1Gt85v4ubdpA79uoF+FIEEXbHyiLJn6u1EwCFhEB+Nx8l4qya732ENW+oZrBfh4oc+92OpEUT1ZZa24kLrelpSU09T33P2Gt+xffeRvH4ojxSCnErT18eX0NQWWzvvo3VBwxbIah6AYALzc5X/Nas3OZFDb1b0UhIA2+g4DPi/k0328k+XIzf8Busk24SeYWieJxCWXcHvWxf0j0RUA8KyMhn3wOM9aXaDca8gEKRZ4tN27cQlnoGgOhlZGNefri+eQM6UqefS4cg0OcRoac7fMd7qm5pSWc7PL9Qjr3Tg1EXbOGsdAB4SjP27aQVi6nB14ppRrqfxPp4F4HI3FreAxEkdBbs5k0LqQuZJPS4rNnnDfz0ITrskUZ7fWvf4e2bWA2T0gkDEn1eXH8NKoDFHJc9wZqtNvlOskuJnEo6Wq/VPmqRyNUhIaakax9RJxROwfHsAiNWtFZ2hWiIGL7kTKXR35VIMg+DAcDtLv0KXaPpHBW59hFAz6cHvEsNCSaU5JujsV9+P732X+/kEFNoYCczjAOxNfj0DiGo/y7cv4UQWVZr8l3WJaq3LPiC9iF0suIz69W5rxZYMbS4oLGiHsWGCF/xL6eiRNsKDVdh9eNiDimVhbmAWCC9Dw62EdBGekbOns2pEB59cp1PH5KX6ssfsj/7Df/IQDgz//9n8Jq8KTE2K9371Ghx59OIZliHwMj9gFNvosv4kBD9r4s/8PtMZCiPYR8XBf9Kvs10M+g24dzoX4MpVZD7cZsNk/SziGtp7zet9nt4JXb9H3amsu9nWM0dJ5YHfzDeld8Jk4Lzi94bs6FaDPXxE+Wz+cnfodVanIbknO+cfsGusr698t8z458+lK9i0Gf+8sjGe221DNNA8Bt5/n6npR/X7lCuz/utmB38RlDG/dido7IkWZ5hBXtr7/+Hrl5lnVe/NRPvYPv/YB+tyHl7bZJ7bE3QizM8T475zuEvGGkZ2n7ukLdHZ/R9p5eFBGZpX/Z6fEu8PKI66NereK2UMjRkCSyqxzj6+uraDW4xmwW7pftY+7n5WQW5/LN+rIpTt2DHm0/xkicJD/7xS+CnRX6//QE0Rg/V6jy3fNCS/qDEWDIOQl61Feh/rf2thGQf1MUT96ykNh+jxcm2VWDr+bl3j7isiUG2n82yv+vXZQmioxdsyo/TLTVnUppoviZkH9lNnH9m8dWDKQ82JYir1t+Z6WZg118RJtt2rm+nv3lhbfxQH7HhV9Iwwp/PsmV8Ov/4d8FAAzFUZUr8T7TsnWxIO4ar2TP98WNdbb1DHMZoljKOfGkmqS6GQugoX1ilWLZyNSa3HdMgtzsCPFz9fKlSSWEsR81nCgXyugIPeX3G1LvtNW5fBFHp5xDsxDfA9WOVGpFuMUXNpcWb6wQg5GAFwmDI1G8iA8esArF6wnCn/7bvHc2oePPz89hkfr2WBxun3mTSoYbz5+jpwoHQ2nOuK+FQhHMRDhW20IwGX8fnYljf58oZrOP85wMhmC2SYFQitmtAm1LddiHS3Pel2qdVVUyVosZNiGPBuLn9AfYh6DXhU6Ne2dLfFLGM70mB8oVjvuhwSWp820+PYsLcQmOhAYcDbiuqoUK5pL0NxYz8wB4/gFA7vgUa2tEsVmEMKqccPxdZgdy8pl2ctxLbvmk6AMj8Yj+m+9t/3jLzv/Z7/ynYwA4PjvHUAuvJKk/60gEmGYbKjUujKCkFr0iOOt2O4joIPcHOQBlSXea7TaciSC2pUVqlFj1hlZ4XLoISnLOPDQkE0cIpwX9FVlfPcfJvbK4BpcgXcciZUwv0sicFEoo5XnoXFniId4XSZ0vEMLTpzQgLU2OUwfoycERrOr78jwXw7ndjv/rT77NPkv6d6QgRKPZnsinzorc1amyk1uX5zEMSFJwyAN+KctyoZQthM0enZJ/9W/psP/y6mcBAIF4exLYcodpSNyC0Y07Hcg/QkLw+X1BRK8vXcGGHOC+ATEWdNhhscKi8iIjGOaRo1s+P8TaZ3ihHsu52H7CS5xnfg6ngt0u6xLbco9hEQl1VU5vt8v5KpXLiIsYeKgxctsMgw+cyOm3C2JsE0zPbDbDI+PwVOS/65fppDx68mJCtOYVfLAgafM333wdDl1wLip8h+NzjnWp2ERcl8tqTxK+IrV+8WwfWUm8dysiERNL3mde/yy29hiMSepwfeNVElt+8N4HCPhVWqZL7HmF33eaz+F2ljDFeCaudyrCpdKVoeCKfoMY12JBoSpZU0HrIymVhWCMihw2h+CYQV18LBYLHjxkKUVjzD7v7u5zrL0e3HmVwSu/5G+jQfahnqtgKPLjQp2GygjO3L37CsoNET5WaNgMaP/h8QleE/nhH/3eXwIA0rPck/NzCXz6mE7uK5/9HACgp7KEw8NtRAPs+8zMPADg5LyMguCzM4tyIIrcpw+//xA3r9DILq4LKi8iv5cvXiArwlybTlO/h3ObiEVwJMnJw0M6aV4Xx+rS4jI8KuuyisDSIMj3J0MoNtln44Bqy1nrtvso5yUhrXIVi2DypcIFZhPcez6t2Y4CPS6Hc1LaY9bl/NFLrmeL1wWnm8/qa+1sne1iqFKsaETkiqpPXVmbxc72psaooz6IPNLZx2yK41EWCbVfF4W93WNk57gODdn5iBwfa3eIrhz9gA7Ao1MF36JJmEV6uCNZUJMkUHdOK1he4twMB/w3o1StVr9AXRLerYn8KPuU8s/AJOljj5c/h1V+/3g8RuYqA15/9Ad/CAB4/dJVRGe5Lz2SeTUImo+Pjyf70CFnwa6fDpsVTkG0m01DDl6OvseLUk2lHrI3RgldqVrCkl9nj3G5D3G/mO0+5FWGeiBpXMPJtthNuLLM8sZzSaEmRKrZatYRj3Bu7Fp7ewpODXtjODyGI8ez5EJOw8zMDLwe46LP9xvpFlet1vHRww8AAGkR4q4vr+L+g08B/Kik1bDD6ZkEhgrafPSAF/HXXntt8qyAbElfgZmxxsNsGiIa5jraznHttFo8b/1+L1Kam1KOa6atuVxKzePpY16C2iM+c+UyL0kWm2VyKRkpyORWMPfS0gJ2d7jGbQrUDOR0OV2eycV2SUHxUyVYbDYbTo5pt50irawUChOiUynHYnl1ReOTmBBOrwoOv3/Ac21jY2NC2usUgfzyMt/dDBMeq2zNIBuGLjczM7OTErGDI9rR5yK6vnn39oTA/Ezv7JW8cMAXnJRIjSXG4Fd57le/+XWsiEzWrHIyx5BjPJ9ew/1P6LdEBE+3yYcqlk/g8nE/rkhYYuO4AJv8SrdsX0oBx+9+/wc417q7c5PnbEtJkIHfhZwSPuYR/25Bgh6zkTDGImZ2KNB1qvIHc8SD54/Zr0dPVVJxk+PjCwHjIcejqpKzmkpUgpbERNY9EOfee/yCySsrxripYENpj3vIoQvMfuslZlM8Q1wibU/EeW5YzVZkZjlfJZGUx+MzgBJLeZW7nMtx73S7k7Ing4w6puREIBBAR/bDoEm49yH3lN1mQ9kg75X9+KVf+iUAwM7eLn74gLbrrdfp9z2SbxPMpnGe5xh5BhKP6PBneikBvxKipyK4bytx5HQ7UBd9gHGhG7T5ve16G2ER/Zdl+2aXeQ5sHewip4StQdJqUYnhsDNCX2diSETh1UoDm/v0JcMiAXeqlLxUKODubdrhpkilAxICqVfqk71e60moRSWJyVQCc1navKD8nZ0c92CuXkfuoqf+iKh+gZ8d9wewWzkeH0g84pJKGv1eE9xuycartOpP/u27AIDbt9cxN899sn9AG7PxjL7Nr/zKF1Gpcz3tKoH5E5/7BfYvd4HTY86TtakxNo8mvqPhk4/khxcvKoiI5Pb2DfrR5Q7n7dGDTYT8tJlh+alBL/vuMA8QV4Jt/+U+xzOoUn6rB3ZOOY5ET3GsO8RZ8Rx+SdKnQsZFk+vf53YhLwJ0r/ZUSWVX84vLGHY4tucn3Ht+JQn65iE2ROh85w7n9mCHa3d2JjkREDLO9WjcC7vOquNDfp/PbAQ2uxNfcuCj/SiLnNva7cLr599ZdS/wKAAQdkeQiNCv2tunP2eIaAT9ITz65KHGiM82/J9kO41Sk7asNy/fv8Q9v/3Qg5tf5pz8Q1FR/O7/+s8AAK53buHvvfoFAMAP/2/6H43PcDw9R0N0+waxPufEqmS522NDuch1v5AVefijjxCI8fdQwMsFARAaRkAaSChB11SiI+wLoWvm3xkiH0ZJVu6ignqdeyKvQIZPgbbV9SXMS1a9XuVeP9mnHxL0BRDR5wzfqa8gh93pQnCOa3VphvtrS+d2sViEM6DyUxEz37xKu2UaDVHRPSchH6AjsYnBYDgRLDoRablFttTldiOkBHV+oBLQSh09HdSXMjyr+vI1PjzewbrK/gZGea/O5kQkNCFyN5IZhthSvXIBYR/QNBI3eYEcrD40FOgLLuv+7uJYH+y8RFC+iC/KfjkVfDvaP0FPZ13QTxtozNF4PEZOVAte+dFu/Z1jbEVH5bZ1kYBvbtPWlEp1vPEK/bDf+/qH05KxaZu2aZu2aZu2aZu2aZu2aZu2aZu2aZu2aWP7sUAI/Y//xRtjADjKnWFW8NzZmGT8BPlOxmYxlnziB58yOzmsMAKaSifwxmssFYjNMHr7QHLOrqAHp0IInQq2X1Up0Wdvv4XzLUYWr16R3CMDlWgOG4hLonbrmaRawyJ2ytcwEklzVyURLcHLyq0mKgVmNhdnCRNNX2Vf8mfnOFJ/rl/j+z5SlLTWaKIhlExc0L2Wc4BtSROfnjPrERa6Jl+sYyQUSl9Q2ddE6PzFW5fR9is6nyRyCV2VI5SqCAmh8t//3r8AAASO+cxf/PIlpNcYOTVQVRFJGHpNZoSUETXKHrZV2tNvdZAUaW1LEsB5ZXLj4RDCIrayCHbn0ziWS1UElWGzCtHxsTIx9q4ZLWWaQ7PKmFms2FNJyoIg/IUi/7/d78EnKd4nzzj3r9ziGNcrVaxeYjbyTJmvTpvjen5+OiF1DQhdtrHF7FEqM4ecygiSQqUZpS3ZbBY7x4zUPnrJ0hy3pFNHsOPxp+zHaMjMUDTEjMjuzgnWRAScEKrtcINZpJXUCq7dXtY7EA5+UeY8ROMxjMeMKDdEjlxW2ZDT7YVf425XpP2iUYTNxf82ZKPtIvceDodYXlU2WH9nZKoHoz7WJJttVtnIQBnIS9evTmSKd4447l4L5612cobVJUFxlbDZO+dnt/aPEIpyzRhy3zt7+wCA3EURCyo3NIgzB0K9eN1e/OLPMHv24d9wTg9zzKoFgm5Y9X4zIc6fSxm0YciEg5bGrc1M273nW9gTRHMuw3fp1BlNPz2uYDbOuUhGJd0ZMmQ9q5iZYTY4IgRHVn/f73Um8sN7e1wzNsmxZpIzcCtbcXDMd24KxRKOxiZS0uLfhUPIooPdQ0TVH0N2uyPSxGQyMSG3i0YMNBA/6nG4cXCgsZEEu1tlKJu7W7gQifIX3n4HAMlxjwUTrygb1Bdxt8nRQ1TywU0uMWRmuWbDMTO++52/nvQRAEYDkcOWmkglaQcMtJLxTguWAM6K3C8mycsastYupx+n5+zX0gL39Y4InV82KrAob9FXtqVR59jNpRMIh9lXY8waTfal0SnC6+bgFEXoPhb6ZXFxETUR2zaVVRsMxzhRufJrgit3tddHJqAhRJchJrAwz4zi+ekJAsrQVIRKNRnE2v0e9oXgKKr0KKvzrVC4wJxFtjXIPWTs3dqgD1dEGUPZtHKLZ9Zp7hRvJYnEe/iICDkDzRIM+qEqNzwXIsDIhoZCYfhUuhwWjNggkr506RI+/JAli08ecwwMWL7T40WuwH1zU8jJRrmOhiTUBxbuQSM7b4UJ1QtmdQ1SeZfKjJwO9wTmXxZCIq01hGEPJyI1LqpELa19t76yjCMhEQ0kWUFZw5PzAlyCxVdUYpxKc+05nGZ4vJwLY43GtG8cVttE3rs/5LgbvpDD4ZgQbxsIXqN8cDweT1L0+RNJS2fSyAktNychir5K1re3t9BTSV9Z+2suxnExmcxIp1UeKkSYUa64fml5sn6HWqsPXpA8eHFhBVWRhhckymBX6V6n10ZIwgszSdqw8kl50oe9I2YMOyptyyxzv7VaLWRE9G/Y1fwp+5dOz6Mj/+HsVFL2Esfo9/voigB00kZVuAy7JtSmUWLVbHWRN5Awyib//Fe+DAD4408/xMhs1zhw7/WELAg6nBiqbCQrhPJQ8P2LYRWFAm3EucqkY2mOnd01RkPoy0SEfs9gwLEu5oaoqzSq0+V4pkXMGvD5cSBk0MvntO3rq7SBlU4Jsynuof5ISG4jdVkAACAASURBVFv5jV6fC+nkPH/XEtLK7p+gu6oqyRwLbZS/KP5IEl2lB6tX+D2tThtBSXLn5Pd47LQVsWAYByJPDggRZiCM2u02zEEiWVxmjplB6h3MzCA6w8+PGxyXTz7Qnl+MI6JMtEH07ReJsC8cgE8lgbvKxi+q1HVv9wBhDh8goZOeUOFd8xgtlegZ56FHcveVQgO5Hr9nRuXDNqcNFzWh+kSuuyy00Up6Hmcqv+yOOVYnO5yjdDQ1sYM9ZezLHX5vo1XFskp+++eyPwOVhXnc6PZFHB/QeSSy+Y/f/xAeld4eaN0nRKZscgxhEzn3UoJ76OHHPLPMTjuKHZ45XZWFzUR4TlnMY/RH3M+ZOb5TTWjHSDCAseGPHfDfErMBNAYiHL5Mv8zgZO82Osgd7wMAinnOye3XWBLa7gCXL/Gc+Dd/8K8BAJeFnDSPhmgU+HyP/I6AkLzj0QgWETKbrBzIosrEzvvNiW2IWlRKpD3osFvhlr0aafysskm9wRB9IRZGsiPFI87jzVfv4jhPv8AgoW6KeN0MEzwi/zaI6AedFkZDvsOFUDwu3YMsFhsqdZUjChUSTNAW1pvNCaqvLB8qJR8v5g2hXOBa2Vf5U1Bo2A7qCAvpfq67RrfDdTnjnYFJa7Tv59//8GOeyfkjP8pdnlH/+CffAQC8/8EPAQBv/OYv4Ld+kuVk//y/+5/4Owvf9+3sJZxdiBRdJW1BrZ3BsI4XT4i8ubZO1NFw0EDNJELsOM+Sw+f8TCyYQFPjHtTdxpANigfDKAhdHRYy9/iUPmK91oEvyLuvQ0TrB6eGD1tDzMX38TpVcTBQ6VK5PEEtLq/TNnTkcx0en8IT4PrwCr1slJz2hgOMxdh/VuIY375ORKTf5kBe7zkS2tQpf+L07BxOIV2Dqn5oq0zX7fPibIPjYJfdsphNyMzw7LAJsZk75j49LF1gTu/sESIvJwT2YiY7QX7lhVjziUg7m0oip7jCyQX3VFtlg6axFdEZjmMbQgrFeR+ZiYYmcvWHukcm4ny3UqmGmvwHiyp0AiGjGqWIkzPunXn5R1Yrx67dbCIg+3t9hSTiVp2nhyc5XOic+f1vvJgihKZt2qZt2qZt2qZt2qZt2qZt2qZt2qZt2qaN7ccCIfRbv7A2BgCHz4NokhG1GUlcPhUZntfjgVP1wgeK3AWczPZl52ZQrzFy9+GH7wP4UYb67huvwaP62JoQCPldRiHzpTI6TUbiVtP83tUZRj/77Q4GVkaeTSJCOzlj5HbGG0f5lM+6rprmnQu+U63fQb9r6OmJnDjCKG3h/ALXhFQxiVj44wdEgrh9UbSajKaGQ+Jzsdfw8pzZt4/uie9A6nqdgQktI5smroDXF5m9+MqNdSRviAywpXSJOJLaYTfcYHTzn/zFHwAAivf4vr/1c7dgl/RjU1m0eREpzrg9CNiUna0akon8jlQqiaGyps83mSUxZLEjwQCG4hUaqp7Xr8wxejZ0lVUfi+C2r+W4vrwOl7gdLiqM6J8dHOCios8LtZITH0EsGYfJZnBD8XdjcUKE/AG0lMU0stDXhAhbXVnCgwdcYy2RlVltkpBNzaGnTEhdiI6Y5I83Xm7BYhBsai5PxRlgsjlwrsyty8yxggg0ZxJZHKqOulTku1+bZ+YmbA0jJsLYYoPR41xbHCmjPgJaF5tPyAHQE1n0bDwJe5Tj3RG3jyvonWQFikLzFPTTPjQjIV4mT5JRaQM1h15vIk9o63DeUkILpDIZzKaFjNAaWF3iOJ6cHGHk4HgbWZPHIh0Ph5LoNfhvRc1fUxwi3X4PbpE7L2b57J/5EgkLn3/6EA/vkZTOouh5XNxKNrsZj0SwGZzl7wyyRbupjcU5cQGJjD0USsKp/dJWpDwnlN+DvROExQnzSlKoBtXnmmz2yV7tipvq5IyZlPW1JZiEDDQJ6hP38+9arRpOVetrZMyOlI1w2dyIe2hnriyQD6arsauWmjCLLN5ADxUKnLdMJgOr+DMM4s294j4AwDoCfOK+8am+3+A+6wy78CjbFxWaYrvcREmoBpcht1kRiXU4gXqJdjF/yu/+0he+BADwZjw4FAeKSeOREqnj/ssjnB2xz++8/TYAwC7OgerAhEpD6CahAc0i3MvvncEZZIbHHDKId7k3zk86E2SQ2cT1GAnxfa9cylLmHMCzR+RQOdjn3Bz263BrTlPzRD40RVjdKFdxOUok5HqG419utSdz5xHnwqZIl4PxCB4+I+Kv2eEYvfU267G3tzfRKTE76BWCrKrsUwcmkvXiR2T2BgmzzWrFxRH33FDIkbzQb5fXlpEUkm7CxaJMWDIWxmhAGx0VGe39R+L1ajYxMNAdyj55hc602e2w2ST1rqyhcfZXKhW0dD5cu86s8pn4Wc7P83A4lVBSdncpO4/7j8l15k8ISadMeLNaQ9TP8XOL3NnIO1VqTXTEnTcQH1RLJN2pZBxN/ffjQ54hMf395197CxatNWNtHwiR9PjwCIdl/ne4zTFKTPbwCENxoBiytMbZOoZ5wjVXEGeaX2SZn/vc5yZZ4e2XPHdzyp522z3k8xybo3PawJ94/TOQAuwE+ZGTfbT5Xag3eHY8f057tXSZ2efs3BI64i1pi+/BK76reCSIoDKqBsfOrnhP+j0TZmNC/oqsNSSugZ3tDdgdfAeD66IjhEyn151wlyWF5jQJtVssXiAjlEZNGXQDodtsN2BRBjcrCd+i+NfyZzWcH7KvtRJt0qWsDVH1IyAEcEPnb9dixtwykRF//P/8KQBgbZV+y3I4DreQcQNxNeTFnQUAJSECnHb21ZB3L5dPkRTPUlQk1i8PeBYUChUsiRvCQGvUNTcfvTxDVOSgLvHDLWZ4lowHQEey3aUGx2FgFadbvwCHg2vNJ7l5g5jfZB4A4NqeF5qk223D7eFczIhHESJF397exSUh7wyBhj2hcLvDHkpCQ8YjQqn6tX47HWTEqfH4IRHzGXGDFQtluIQOTUfkvzQ4/o/3trGwRnsYtHOs/+rfk/vGNxtARPwsnRbXnNfDPi9mUxNU2P6uZNPFHed0uifI6aNDIZkc/F0kFJxwCC2KH8sgTB4MbQgEactiEXE4PbmP4Cz/++ic9nB+jv0aFNroFdgP2zXu1Svz5Jfbf7aNhrLqTdmW+Jy4GnstOHX+tSWnXRJCKDI3i6Mj7QkfP7+mv6tdFLG1IcL0lHg6wzw34pkFnO7Td+8VmLFfmOHeOC7W8ViI4WVVCYzB72jWazCZaJMdTj6zUOBaDfncMA/EK5Lh72qFY3i49eAG/23rCf0Ii8mLSFLkuH2u6b0X3IOhhBer17jHHz+nrZ6VnHwg4EM6xTFuSZp7/wc86zJ3L0+I9Oe8+ozOrkbACeNqEx0KbW6lXT3c25/wmhn+jlmIsOzCPA7EKWMQpqeE5u5iBIeQsS6f4M4iR3Y4rWiIh8gmH3HUsgFC6Bwd0kcwy+/JXlmb8L/0dZa4DW43iw0nEiSp67w1iIKvZJbhVn8+fV8In5yQIKs9XF6jj3Bz4R0AwPs/IPp28/QF6kKkpEQuH02yz4+G21g0871qQnu5xKOWKz7GrRT3Va+qio8eOXN6xSN4Inzp+VWubZ98ok8f/wDJKL8nd8j+tRtFFM1cW//gl38DABAA18S9Dx+hKeLonu5CI3FINiplOIQGNPhpDK6eaGwG8Rl+T1JiDhvbXOulah5diQMFvJzvZp3jOR6P0RF/l83JcTDQ6u1Ob2J3r2S4J1oX4iSLRHEiTtPdM6JtQjr7I74ALCPu2et37wIgsTgANJst+MSTKpo8BFTRsrX9EsGQeAdV/RBczsAs9M7OI57FDXEYzqeyqHBKEHf29A7iUzs+RjQsm6DqjIN9iYQkE0jKB7KM+d0NoaJOC6UJi3etqIoFVYp4gm7Mzom7s8xxePqcSM3UbBZdIe9KNZ6zTfEfm+xmZBY4N16rnA4rz6mZZBxVCWyEwPWcifE8q7QaaAv+84//5+//eJNK/9N/9HfHAA/TWpuLJpjkQjrUpWp36wCrSRrckEWGMb8PALh89Sb+3Te+CQBwyan7D770eQCAuVwEdGkb+bhI86eCdtWrWFP5h0UwdYs+6/X5Js8yqVStqDKjg70jLGUILzMuYScnnIhKsw7YOOluOcb2DA3e84/vY0WGo6zL11COz2muCKuCKsYht9c6xsYpF8TuM26Y5kAbze5CTpempp0GZL3P7/nKm1eRvKGSA13qBxaOZ2fcRSDOZ/zvX/sbAMBH36Wh/G++fAeXr/HQLtYMWJpKpdpduF002G15M1XBMhcSCZQUWBtZ2PeCiHGX0os4kyKNXfcDm5cr0zLyTsZtoABBRKSGqcQMzs5l8OWUtEZ9lC44Pz2VLLRV0/K5n/gcHjyiY1RXecUlEXuW8hcQqhHzSzzQPVL5aOYLKAhmigi/e0bErBdb++j02OexlIRiIjXuDVuIivzxk3u8kC1kRSQaiqEy4DNzUhUxSFoLhdIkWOaTqoWpK2j00hKOD+mAtFRacVjmxW7c6eGNqww+fnRMZ+NjEXlfCaXgMSDvVzl/JocNVRlZn4J6J1W+02JgBrO6PJmWOb+f3iNZZf6siFmV/53rYhDQGj/bPUA2IXJGC9dAW0pTs4tZpKXM9VBqRBaNsc3jQFWlVV6VZ8wuzQMAzBagKtLVz7zKEr+hAgBbB4co6yCbQHoPuF5qjTbsHjlPUqIZSTGtUq/AphKFn/4yg0tRtw+fvP8hv1MErokVOXD5AipntDt+wXRHUvOzhnw4rfF3RqDRr/29kEqjJjWdsaI3p+c0/H6XBUM5C+U6f86mdcD7gjApsHBlmWNdy9MmOZwuvJSy1JXLhM3m81wDL54+w41rUjpTYGFLBITjZhchqciVpHYQzEqBrFlFSWqGByKZi8eSE5Jng/Tdqn3mDgRhErFsTxc/p86euTkfQio5soh03FBk2t7Zg18lrYa6hUGCfZY/Q0OkpPMq9yyWRMRYayI+Ud3g9w7lWLzzM1/GN777PQBA7pzjYOpw7a2lUxMC4+988hEAICzVqkKzgjMRRL79xmcA/Kis5vnOC8zPcx1/6W3+7uLwAMePGWgNqYxpKCLL3dMTlGQjPAZptciUk7EYnkn5DU2u23qOe/7tz34OdQUkYiK831GQqdVo44mUa96Uul4+x7WTP8vjbRGlFxXs76v8weE0IWhR2ZPscUWlJr6wf6Kw9UgBLOgi1Gi3Jko0ZZV0WYc/sqVGAiUnFR+T2JHtbhfmFxn0PdAzAz4/qj2Vw4hIMSgCx62XOwinaYNyO1zHVtm5Zzu7E8L4TILjVxeRfLteQzbLdQEFKBsqJTB1GljQOe1UKdeh1NPytR5quiCgyXX/2XdIRP/4yQPEYgwMG4GnksZzeT6NGZVudaRMtTjH93baPBhoXRV0OQqrlMjk8mBL5WsLCri83NlDW05yQgSRe4KrW80WRLVWcgrSGaptLpcD6YyCqbuy5UpU1KolHKnU1CCadoiQ294doqeSsZMzzld8cR4AcF4vT5QHTVJcPXrKC+H8pQXYlRxb0feMVUq09XwToz7n9OZN2uGH+7wcNaod9JsKHub5vUnt62aziYGEOFLqCzpl7GxwjGYDHH+DjPb8ZB83XiHZqjXE3733kM55tttEcFkqfCqTNWtPrSysYKzSyvce8r2cCjYlPd4J+WlDl9ehLqMWpwtu2aKaHG9DlWc4HGIo8YDRiP0ri1h1bXUeLhHB9xUszUr9aHfnbJKMiMmpr2mfd4ddhIzSZYOQ2W6blEmcHnL9pXWObjzawKDPz127S3sfyErdDR0Ujsvq/zyfL38n6HUhrsuWQah6pkTF8vIqWiLlzh3QFp2qzMAZdeJc4gpuD+fQrBKhfjmHmk1jI5uSUanCGAPYRe58oeRRVOfNjdVraMgnOVAioVylv2Qbj5GrM4DhlUjLjRWeYfsfbuBCQjEmJURCM37jvg+LyuOCCo4fnWxgRoS2ryxzrI7PpX45MqFQ4trO66yLJfgO7XIFjhH36oGCq6Y4xyMQ8WKYk0jChYjuA+x7OBBGYsxggH+W82cEZ3/9V/8jFGTn//zPGNgMqwSpUKxgoGBix82xzktA4XT/Ap97lSIuLpXObW/y3Gn2Wtg54hxevsnvs48d8NpUzp6YBwDUW5yvZ88OcKxglsfJsTVLvcvstCOm0tleW+Tmmr9uo4qRAtdjlRT7dJYHAj64pPjUUfnahYLvS0vLaGgvbG7SXnlF/+B2ugCVoc6lVb4tH+fs7Aw97bOwSrvDbu63s1IJPV1+r99iuVtHvlfxPA+PksI2JWlHQSu6OYlzjHmuHEvNdugaY+kqfbqOQT+gJG9uowNLSsHHsM5BJV39Yyfqx/zcd7d50b+oKYEw34PyC3hjkeeKXftlL3eGJ6IhcHrUd90xPTNW9Fu0b4bwyEyG7/bg0QGW12mnrl7j3HoHtIlwuDAeSQX0nIkRr5754buP8V/+1m8CAJ4/ZPlw6byHB3n62y7tl//qP+ZnovDh3XffAwA0VcN7esH+uawmVPrsY0tk+x4BBawDEyJhrpVglM/c2uW7wGoBGnxnd1gJi6GS3y4TYJAvK6kZ0Hn47OU2Ek72yytfw1D1WorEJyT2Dw65F/q6C8/HMpN7xKDPvVTTOW9zOmDWGVBVSVdSqmONUhnOoALCKjfs9Xq4fpV243CfQe2h7vuBeAD7p/SNR2hrjKQgPgCkcYKggkT9vuHv5yfqdW4Fp5akgjts12HS/rqoG6qG7IPXY8dMjM9yyXdyBtnPWr8ExVTRF61FrcL1UqzXYVMZ5WrCEDPhM+0WO0xDPsMtmhCbktlmqw2Qn//b/9v/d0BoWjI2bdM2bdM2bdM2bdM2bdM2bdM2bdM2bdP2/7P2Y4EQeufW7BgAwkEPLq0wC+MLi3hOkMF7H97HijKP+y9EVHpH0eD2EG2RZbmDjBq3cozKvrawgLksI3Yv84wAbj9nNDLs9yAhQi2bottjRRNTs0mcCCZuEHUGJdFaLlQxl5r/W30oCrbuCvnhMuDbgi1bnIIxdvo4O1K5m8hQTcqGmmzWCbmxkXl4tPEIfRPf7yzPl+gNGBWs50vo29nnpmBv6zZ+79VsAG//nZvsj7I5fS8jp/VeE7kzwuB3lOX94z9ktPnXbyzjiz/FzHS5zkxWyG9kEKzwq0RqKNItp0o/2rsFjAQpLwjZ4hKcsNPq4kgIoSt3SJZ4dMH/j8VSCKk8w6RMpSrHsPXsxYRA1Ks5avQ6CM4QUbElIuawyMSK+Qs0BQnvKCuQCDMS7XV7sDTHbNjqDb7DX37vG3z3bmtCoreSmQcA2BTVbXS7OJTs81iwSrMy7pFIBOkM19W9e0QnGJm+z775Bg6U6XnwiGVTHhG4RmJxPHrIjG1cGVKn5MVL+SJO8mW9M6PvFqE2Ru027CLYnL3NMpmnR/sAgGAHsMxwvzx9xszltZVLmBfSqSiiUqdIPI+3dvDKHDNfjwrcSwbc3Ga2YKi9VBOiw4BvzsVm8PQ+0VA5oY9uzRJxFRhZkZZ0+18+ZNlmWSiPtcgsHIJqR0VabpC2NotFLC1yz4+ZVIBTfX/+dAMBkcAWunyn77xLePv8ygo8Ia6/RktZMsH4h+0eakIQ3BSRYL1eQ6sjOLXKCi7UhztvvYk9ZXEsmvv9Hf5/fzxE3Ci9UObKyEIsLGSxKWj4J485zz4T10fQ48GRYNKZDJEPS8vMyhfyRWRizAKXVf4aXOC4NJtt+FQ+VRaE1yri3l67g5FI7KOyRSWt+V5vMMmkANxEl4UCaLQbKInY04DbBsZe2EQY+FLoBIM4dz6VRbcuOXsRJF9Z55oLhnyoGmgjEUbbhKD0uN0TgsKyyPf29/l9vZEZHulzOvUzEBRJf3+Esci4c0I8lC+YvbtzfR3Nwd8uJ7UL6j1qNSblO54E52Qg4ntf1Ie61sBCeh4AcHFhkPgdIZPmuliUHOu408K3hJJbWb+kL+I7be7vYEFlLlsq+0lLvMDv8qA5FKFtkZkrQ6I04QvCaZQEqUzxuMD+uWMBrItUNJbkejTIZT/86GPMiPzQKGPyq3zIahmiB9rYSFjZMJVhjUc2jEd8Z7MQCUWR7BYKJQST3ONHOpecLmaP+v0+XG4Rcwqx+mJrU98bwKpB/i1ixGgoiu9+wFLOptA1b79K9OKz55s4LfF9ysowNxrK1PeGGKu0JKM+D/r87HA8QF1k8r/wM18BADxRFnTYq2Muq3ImIUc2t7i3rl+7A7eD++XkSGW5IvztjBrwCVVitXA8jFLIbqsNCPF3dkg4fFzZ66X5ZThVHu0QIuzuXaJmnm1sIl8QkXCU52F/NITbxe85P+G56XPINtWa6Op7DCJKk56dy+XgV8mSQ+eK4Y+dn+bwipA0W1vMzkaVtd3d3MSsypFcEiYoalxarRbiIopNuPh9D5/tAwC8QTfsIhZPzHCMukJuPnn8DCOVN0ZVIuwwce02a10sLXL/b4pguS55cYvTju6Ia6AtWHvAPkavY1K/+C51nWujdh1JIVuuXGGZxOMn3FOjZh0JIbFsGqOYPlsvFbEmcuGWykLOhNw8b3cxUJlJToguQ1yk1myhKdj963dfBQDs73A8nEEHzlQSG5FNtwkZ5/O6JmVQfaGXYyrRHJrM2Ffpud3NOYkkuJ4fPXiIlQXu6zdvcf4+/ejepPxjS/5EQ/McisdhVxmOT/atLf+vX6/iUCU9xvrwemgH+u3mxN8IyC8z7LfV4ZygbLNpopad2tcX9TwSsjvf+wH38JuvEPnwycffQ0clyV7Zhp5Q8f1+B2ER9PZVrm8gpxwWKyIOztO2yjPSs/TTFgIxfPox/aOXOaIT5i9z3kdDC14UuJ6cJvYr45+dnIlRodmMRP388jIqKq00jenXGiT1MFswI4J2g/Ta66VNs5nM6EgiOyeEVTjN9TU2DzBs87/tdu4Xi6Shd7d34NX6rTbpz61k+e7NRhlvvEFf8v4D2qndPa692flF5Cvs62hMf2IoHzMeiCHqiarP/N35Cc+lgWmMqnyMkPZBOVdASeVLcSENfbrjVBpNnJ4ZCDKeWU6VPjV6bcRmuSbrdT7fCc6bw2JCU7LWp0IfryzyHCxXSjCPuY5SQjoYNunsNIeRUAYWld8b6IhqpYI3X3sdAGBVaXKhyHksVgqwahL98vdtVo7xUS6HaotzaPgOszFD0r4PmxRKaip59IYd6FS4tu1SAOqqLKfvMqEjIme7BDlWdeY9erwNh2xtS/5RUyWuAUsA29t818wS5/dAFRzr80k8eUl/tqWS6br82uxKFJEkfTuPn993uk/7/8F3zjHucl+GhfzwBTku474Zd3R3bRZ5Fte4vNAemXH9Fufyrc+yROr//Nf/HADwxpvX0BWxfl2+YcDvw6fbtJ9t2a5/9J//1wCAn1x/HX/0O78PAOiopsqboZ3b2NtGKsI9uv1CJNQqS+13unj2jHcUw+cqiAC60zejJyL4pGhWQjGedSGPYyIm0ChyTitliSHFUni5yU5eCGV3eZ3+aTYUxEhUF5Uh96lT6CG/xQurEFPnOY6VV2VXLw934RBh9IX8zbAQjZXCBRy6gy7Ld+t22ggJwW7WebGxyfK/6EwMVknJw8o9OFR9ZKvaglPE5QaKNhqhfxYIRjBUmX6lwTXT0bk07jXhEaLIKqRbWefm6soiuhqHrQ36H5lV+XxxDwZdrvem7k0NIS5HYxPGQq46hMxtC81st9vRljiFx6pSYQkP2d2eiUjK7/7ZxhQhNG3TNm3TNm3TNm3TNm3TNm3TNm3TNm3TNm1sPxYIocV5xxgAQm473n6dmcZBl5GtnkiwbDYv3CKq66uGNjdkhK2cL6HeZbTM6lIGzKtMaekCb79Nib6ayMSaBYPA0Y28aheNLPxSdh4A4HI4cSo0j1VIlYpq0H2+AEIBRrGvXVPGQO/bHHbhEzHiN75Njp7PixulOxxh+4DZC6N23SAbtXscGInUSmWAOHi5g8wMsxBf/5R8E+fH/J5uuYOuU4SlImwMC2JxbTGK9SWihe5cZoboQjXC9W4dG58w4p29wYj+7/w++ZdumUz45b//iwAAh4/vt7PD6PPa0hLSQjD5RQK3scPM5Sc/fIGly4p4K0v4bIMEWbFoEleFLqi1GUFtD/mZK9kFLC7x77761a9xHCQd2ml1AUW8DZTMwfkp7G7ORSLKd+mL5KtWamAgEu+gSMAN4sZquYZIkNHfkjh5rn2O0feO14xD1XB7JXUbDTITs1+6wLwy2WGhebZfMkptNlswVsYlleR8G5HzRqOEeJIZwaEyKkUhtfaOjuA1osYTvqWCnmlGTjWjPaGcfKrRPtnZwZKyHWcNRsPnJB2/9/AZlt/hGptIgJ8V8eh9yRTrc2HxHzVLJawnyX2wVVJWXYgTl9OOE8kxJw2ZTnFfrC8uYucF16FDyKDD51wDw0YTSUlqRxf57GicY9bMl1A/ZubwqCRCZmXjs9kMLCLmrIgH4qX2JGxOJMbiBRAn0MsD1f7azEgvcDwakshdEhJn4/4GhuL0GHnFk2O3YEH8MkFxr9wQ2ejmiw3YlU00iV/oz99j9tRqGSMilIHPxrmcNfrV7SGvDOX2Kd/ZZ2fGwjw0I+zhuvgRASnn224ZI+wxiNVFQCzeq1wuh6RQK4YEdVCIoXgsgo5QFCfHnKPumMYiNpea8GSFxZnx7je+DQC4c+M6EkJhbUsi82pkFT1l8mzKOu9IqnxlJo1KTjxEqve2Kbu2u38y4dGpilugJJROJByETZmouPaNQUR6eFJBW+t2dYH9C4hrY3vvED2R45qVNYUQQ5Z6AVZldXbFz3SssR6PehAga4IQNbuFZlsOIyyk5tHuod6Fzy4UCnjtLrP3pwf7AAC31Yq2VdwdytxYhWTKzs8imeR6/eRDooh8+p61lVUcC4lUKnAOW82eeBf3NgAAIABJREFU3vMMfRHqF4Qg9Yn3anVlCXEHn5GXnK1h7zq9EeYWmdXaEQJtd4/77tL6Cio9ZoVNOrsNSe9oKIKR1tNsjGPsEWKqWq7Bqiz1V37+5wAAf/SnfwIAODw9we3XeRa0RTj9+CmzVv1+H3lxAf363+Pf1VtNfPyU75Weox2oaM9ms4v44AERZ/d3mHkM+LVfGj1UhaK6c5O1/Du7tL0jqxkNneFZ7cWRUG1vvnID66vc2wZxvMEiaTE7UC5yPIw1c+MqzzyHtY/FuYz6w/ELiADdF45P0HL2EW3ErGyi3enCvXvkG3N6hZTo8YzNZLLYlxz5QFnupdUlnGlfjURoW9FaiIWjaItk3CD8rimT2Ov1kJJN94rM90i8V9Vqc0IkXC4bHA2c7267gWCEe2JB5+7X/uavAXCNz0gqe8awP7F5AMCL549hs/Bdul09U/wFvnAQPWVNQ+JdCpoN3pQ+YiLVHMrI9MRP1O524JL9Ps5xDaTNNjx4wbGtSAPcpgzp7s4Wluf5rC+K6+npE6JOT/I1hMV5cCY+h/QKx2c1m0FaY9TWOtmr8Uxpu1zwy0ZWtWYG4p7b3XmJV0SUnpSQQkPZ/52jI7h0FrjFc3V0xHlcW1lFWCjWupCyURGa5ypn2NrjuvUI9WX4qS6HC/svuV8WZ7g3csfnyFzj2rp1lcjtgw3uEfvIgvVl+goGQu7bH5L34/7mU7z1hZ/keOhMjigDDtNowoNhcEwZvrzJZMLlIL9PnLLwijD/xeYDLIlv6pN79NHGQlxmVpJI650PhdJJisD004efIib+r6ZI0meSHM+9o11c9nGMcyaO/8jPdZLyRVHbp+17vsH3DYq83OF1T3i14glJjnfrONT+gs62sLg4l5cy6LQ5F5tHRDAEnLQLbqcd7SHXg9VF+9Fuss/xaBKFAv2Uk0OeQVeviceufI5tiT1E5N/OpdivdqeFttCpL55yTufiXLuJaBC+oPqqc3d3n4PtS8RxVBDHlIdjbQhzWPsjmOWXGj5KS6S8uYvShPTe4Gi7efkqbBJHqNU5jt0x7ZUv5MWpyP+j4g0b8JVQaTThFtfIWGIMEBowEY9ObEpTfHzdIf+wVikjLaERk87UscYgGAxjIFvWE6LfIJKvV5tIp7hWukJij8W0b7GN4HAadpTvYvhNpVodNlUXuJxahxL2aVeqEzGASFgo7VoLc1mu0S2t/9MGz5RwKjQR+aiLq2suyPEMrM2h+IJ7qCDUrtcq/6BUwSc6h372JlGEId3hXG7gm++xmsAmYvhLq/z+XquNR+I/C8xyPDxC4ce8d3AktFBOiL+hzqehyYaW7FQsQhu7tsz1eHhcx9Nn3Jdf+AkihQIB2uhWfwde+ULFIucykw1Ozv9tEXb/2i//OgDgRnQFz35AvjWHj/3ZEJeYNxjC/gPxlQrRHIry2c9fPoNFpNBdLe52n/O3v59HZlbILHHhpYQUclrNyB/ILxURf6PO+Q5HE2gO+PxjCTDBxP0ScpqQENo2Ocd9YuqJ8Lo1glXonE5HHL4SS4HFDKcQZ6JDglVnFgZ9XOi+6VBfUrMJhCV2kBABeln+yEWxhIrQeTfvSLBBPEbmoRX1Cvsa9HJtWvRODx4+RVaIULNL56U4PF32EdxCXjc7qqoRkXwxfwGzqmEKupPmKhwXZ9AGq4iis6qIaUmMZ3YmNRHDGGmt23RnLlUr8Mr2DRRXMAjDzwsXGAoX9H/8yc4UITRt0zZt0zZt0zZt0zZt0zZt0zZt0zZt0zZtbD8WCKHf/u2fHQNAvZSHVxnfxgWj4X5lB9LZVXz3B8zOVhV97CujaoUFbnEtGBLzY8l1R00DJJRNywotUz2TPLjVhEKJ0TkjktcSY3e71kJCqJCelC5cyrzPpdIYqUbVriyygrQ4vzhHV9Fzt4EEUTQ8ksngSAovxTyjiIZEr81mRnnECGVZYX6/MwDPmO/++z9gxvJgm38/G0xhv8b/9kpRwq8+JD0mvLXMKP/1a8wgjhV53T/bx8lD8p04Jdv60TbH+o1YAill7xUAxVAwm6ePn0x4fq6IYyOqmt+m3YftHUa1TUp/GBw49rEFCylG1Ds1QyqRY3xrdRkNZQy2pK7VUR18vd3CWJmDppA4jX4X2TAjrEaG0qgjbje66DfELSBljV7PyGIOcesukVYxcT20O0IBRN14/z5r3GOSUY0LYbQ8v4T33vsYADAzw/Ez5JlN4xEaNWXqlYEJae2VCjnMzDLi31PdpiHhG5mZnah43dc8+LROrl29jr/+IZXSDAWVgJRlTIMRguIKaIo7YFfohmQ0PuGXSEoJ5c+/+nU4tV5T8/y+k1OOcfX8CHeWuRc+2OIzFsRJ4bbbsCWpZUNFzSOehMsrayhIUalZ5N6LzauWNh7Ew085Vnlx57ytmvLzwjne+DxlyL/1PWY/G4rM37y6jr5q1tuKfGeWlPEcmfDiCZEKO3vM/hmoo96wB7uL6+NQHFVGlrdUrMMsToKk5FujwQB8Qv8spFLqK/fNvYefoiCU0ed/ihnZb3+faJlWswrTiNmYcIjP9Pu4hoqVBgaKqRtKSsMx9/rq4jXUylLCE8eR3ck1EAi60ahKfSTH342FzOj3+3BLjSGrdWLY6GazOeEJMpQrRkIavvLaXXz4McffoUzUSOi5z775BtrK5hyKx8Ft9sGszPDiMvfnUHwwln4ffhfXzpMtIituvkYlrJHJhKb2cVGInZFkNmeScThlgw7FsXH/sdSuxjYsSilqSTLwT8UdcnJRxGfephqcWVwvBlLw+cOP0ZLdORHKrt41FNmsWFmlfXu+xYy7T9LvbpsHuTPaAauF897vCWHRH6AmZbSqnrm8sgSLpB1GQjBeFfri/GQXXvEtvf0ZruM//5O/AACsLK/DHaHdeCi1wa3NfQBAamkVlpDs04DPvKEzyD7sIaisrsHX01Cm7vgsD4+P/RCtC5ot2ppUOj5R04OUkbxS+qpWKojoPDFUG1+7TSTk3t4ekuIhaYon78UOUT42XwBWJ/tXk3Szkk+olMu4fYkIhn6D50Q8lUB5yHnKCZ1z6yrPhGa3j1KLL/09nddmqfItrV3Fuz94T2OsjJneyecLwCx0iFXolYUIM/XL4TBCOgs6Q47RueS4TyslzK3RXkTEqTef5fqyWi148IBzYjFzjAzVmmq1iqtXKfftVybcUDCr9bv4d9/8Kv9N6MCAMsZPnr5AMsl92RYfUSDsRiwuNSbN81jnUzgcxalktk9y9DWWVplRNJlMOJPK5p1bRHj6xHuw8WxrwlfQkVJovslno9uCU2s1m2HWeiyeFavThUdPaLs8Tu5hk2yEyTzCygr3ellog6rkxWOxGMxSFoxp3AfiJhwPzUhIwjcmXron4sHb3HyBhKFiKclgq9OFhpAESSlvDoQyGDTaiArxWy9wfy4tcDw7vgh6Qu/ElGlfvcK/b5SKKAq9c3rKTGpe/k8gFUKjRVvbH3GsFhZ4/ubOz7A2Pw8A2BLqzSkbk85envCfOSfrn7bl4vwCc2k+oyDlPWMvjs19FJQF7sk/Mt7FbLHB5dQ6qnB9VIt1xGfEm6bz3FCWWltemfDL+MUr6RQSB3YrZrxEODwWR92E1y8UwokUjtYucYwMtZlQKIjzbaGVNfdWO3++9uoy2lKnPTrhz5Mm16Uv7cOSlXPxUmp8PfGo5fMFBKwco9fv3NBYc8yfvnyG/jEX4NKrXF/VMfdnKBDCfIjvd/CCa70qdclOvw2/g311hTi4rpgLRa3znR2eL0tztMO9ZhH1Kuf+jXeI+n/xkJ+plYoo12jPltbp6733Q46xxeJGLMF+HG1yfV1eoF2OJEJ4/xn9h2BMHJxrklvvtvDRc3F+yndwQag70wjRqBBZurMcnojLzeVAz8Lxd0sROSrb4rFYUMlx7xlo7qrsyKWVq+i2aRcLZX7v3Rt3JqqB2/tbek/aiFwxN0FROaUwVZJqY6PTRVIcQDtSiDIUDVv1GjxC28UlS/3QQKyZTbBKgayr8yggm7m4uIxz2bCw1qqBUDo9zMFs8AoJwWcRIajTZcZAfrDB4xdJ0waWalU4hawQkAMLs+I7LV2gdMF9trTINTRs2SdI2pqUdTsWcVrZeijJprjEpTcfo19r8rvh6us+IeSHwWv0pZ/7Cnry4T/+Ku1+2+CD6cdw0uD6+NVfJPLmVHvxgw+e4rvPaZP6Uk6u5Nj3S0s2/J2fZWXEfSFmHz8S95EvAIu7rbHi9wT8fJdb19/Gtev04e/fYyWHVfcfb2CIHaGiXF7aaJvLBI+D9sMh/i93j/3MeuMQlRgqIgNyCaEyH0lD4nMwS9n1KLcPAPjkyT3MZrkuXD6emyFxeebyBVhbUnAUYt6tipHesIuc5rcp37MlycAhgLUkfYSuqkg8quKp104R8XF/lcVBdinLz1q6FvScP6roAYBDoSV7JhsuauxgoSw+M+0Du8WOaExnvXiIZ1Nx5HRXaMv/W5DirS8UnvBpuc1CX6VoC/udIU4OaWfc2guRKMfjPF/A3h7nJCzeOpM47qzWIVziYitqHa8ss1+7Wzuwy98JSNW2LKSb2QGYhd6PBOQHCikXcPpwpnNw7OFnekIDOX0ueKV0NpYD59Y9vN5toyPut3/6zz7+8Zad/5VfXB4DgN/tQbvKyeuKPM6pA8PkCGD3jIa0Z5JzKIittTueHLQ9TWZQAY3b82kE3Bz4HV2Iw04a9VgiihfbNJYePw2kVZepoNeHlgia7CaD7ZaLdjwcwSSHdiASs/VLPLQKhQIuSy66oFKCelHSumagIgJQs4ySGzTo8WgIQwc3T2KRm/G9Tx/CJ5nu+zLOX/8WL31hSxR1AyIsgrhWl+NxKRLC50QSd/kyjcvMLTr1O8f7ePruD/m5ZZa7fbDNBX01PYeOCNMePiXUsK9+BoJhiM8WHqNEwSC/rdYQl4ReMskFbNfF1To2w+fg2A7q3LyJIMc/Op/ArkgWn+1IetM4TBzOiUy0088xMDtsWBLhXFFBtqYuPhiZsS3CS8PQL63RMYgkE3j3OyzfgzarX3D/heQMPCpdKYkcMy8HvlOsYa8sKKg2ttPBv7M5Rrh1gxeKF8/ogO+pBMHvC+KaysjCER6cbgVzbr/6Kp6KELugy9SqSuoOD44w1Nou6neGbHcwEsfTTf6dAdst6zJ7cnGBG0v8HqNcpdHp4+orvGScCMrvkVNYK+bgUInkC5F9DhQMyMwkkBbBtCFj+3KL47q+cgkDQR+HJpGmCvLtdVhwXuT3BBNcCwYEtXx8hrTKEAYd/u7zv/HzAID7734fH3yLc5O+RTj9oMp3aZwX0IhyDYTN/L5YkAehy2pBZpZj8/69DwAAS9c4Hyf1Fp6JODSiQ6tycIwFEYcuicC53OH4VXtNuOSNGIGxF5JzbfcbmFvmXtrY5TxXJUGenVuCRSUUhiylz891eeP6q/jrb7JfgyH3lD/IuU3NxdDQOHr9PAz8Lq6XZq2OclHlJkFBcVVatL93hL6CAMtL3M8dBXqalSKcCk4FA1wL1QbH8cGDB1hZ5MEX1V6q200oqCT2+hoPqQtdfFr9LtJZ2o0XzxgQmo3xsIvPBFBSaZ9ZcrEGGf7B8TGiM7yM76r0pSh77nYAX3ibwbaHnzJI9MP3eVnvDUwIal9fFLiG4nGuHb/bhRXZ06fbDDSW6pwbt9c1IV0+UYDMgNz7LU50dSm3iBg7ofKceqOFew/o1GVUPnFweASbl3Nis8qu9hXcW86gUeGcWHVpttvdk7F2wpCh5V4oa24HPWBeZT8eObHHJ+zDpWuXsK1kRF6ldxbZLbc3CJPOocNd2maHld/xE++8BZefi82toN2jB7zoupx+9FRO3dHPapHjD5jhsfN8cHi4nkZWrvmzQhU2Xfag5EJR5MgBvx8/ucz15AD/PjmXQmXM92uoJO76VRHKf+NbcEQ4zs6R1m2B/etY7dg6ZH+6TY7tyizX2Vp2dRK4OKvT4XEqiPDW1Vv44z/8A46jzullSRQ/2HoOsxzhTFTQcEG4d89yiM/OAwB+41f/AQCgovKQ860XCHskBf6SgUmI3P/h5hbKPfa1rPIHo8xlc2N/UtbYznMdJmbCiMb5XlBAKK7A5Gg4Rk2XPJcC/wHJ9bZbDSTlDO4oCG84dFaTFQ6tsYECBqcqaV6aSyF3tKfn8/vWr/Csu/fgAfw6X5eNUiSR7CbTKTRF4n1UoG3vSUzDNAAWYwrMqF++JPs0O5tGUf9mkDAbgYneeIh2n2Nl1xnZNTtwphLCV3QGWVXCZeqNcbzH94npbJsQQu+eIpairQ2HOFZXFulXHR4e4mvf/w4A4Nf+k7/P8ZCNf3z8fLKHkhk+0yPBi26vg7akfgsKYEOO8VLmKloN2VqdT42aLmqdEaK6BA0UhLwoSD7d60VvzLnYOeZ+fvWzLDUZjceo6/sqIn6NhWPoSzK5ZxDaajwWsosIaJ4THvbZ3uc+2Hj6DJYk12QuR9vcEClpJB6b2Gt/wCCV5l6MxSMY9/h3LV0OfbpYdFtnaCkgFE/QVp+oBCyyHEP5Gfuzqf1fVrC/cN7Gmi482RjHZaiL6kwmiQORl4ZcqoNocp2EfCGkZ7k2+13utxOtoUqziqGPnw8rORkPxJA7lZS3ArxWUSNs7R7AriDnrMqKLOJXODs6RKHM51qUlOirDHkEC+zy9xwmJdUu9gGQ2HzzlPPUMi6qDslbd1roSc7d8HVHCs7GAgGU81wPIQmbLK6SJPfdDz5Eap42sKhxWNPF02+1IafSnqjOhn2dlSFPFKcHfGYHfKf59AKKefqjDrchQMMhrjbqk3Mi4GaQKCSZ8MPDI/RkP40qmpjGrNNtoVrkfBnk321FtQbtLmoSjZiTsEFHdYdmkwVulUqP5Wu0lMT+4If3/1/23iNY0uy8EjvpvTcvzfOuXvnqMo1GNxpooAECJEgNRVJjKDJGIUUoQrNQjEKLidBiZCaG2mlFLahQyMyEYkTFaMihAUiAABroBhrdXaarXrln8vn38qX33mlxzp8IbqTlIEJ5N9Vdlfnn/a/57ne/73znIJmgLU8vqOxQoidutxOXKkE0BHpGE503ZjM8Xv735Tn9zOUk+2kbTWAT1cXqCm1ZNl9BpWIkR2Qr5kTePBfETz7+iR7Ll95YpW8zOK9jINoIt3ygpzsMam+k51HYoT01y278zm/RP13rOPHRLu9L61EGpXY+oL95PjTjf3zIs/e6CKBNJ9wTT05r2LxOm/nO20ym/d9/zGTIwmICDifX49LcssaYc/w3P3iEdVEZ/Nq3GPTM7JG+YDScoNbi55q6dyZTK3B4aZ+KOmevKNjmGVrR0/41eTwaeP5ROckhneLnjJKjC9mYZGoee6IDMamuKZnimTIcdXF0xPE35jcqsvO5dApDlTca5XEeL/dGq9VDVOXDPd3bT2U70/OxqejPmeahmRdx9/wazDF+byQS5Z6C8M3xBDmRjTv87F+7o+CI0wlblfvZEMoYm4bw+70aUwbpDF82lkoDSpCmFVSaKDDaajSnwjAdiTcYazAQ8mNJvrKqxXGscSlVS/BrPZmV7IaCwB6nD4eHikeI0sMobev02ohGaa9rVZVDav7sYwuaCqgfShAoNU87brJZptQ3I13WDdGKncN9rFzh+v2n/91PZiVjszZrszZrszZrszZrszZrszZrszZrszZrs8Zm/XfdAQDwThjF6laHKAuRMRkri9ZlzMobcGAwMIiuGG2LSXJ1WGmhWmD2JyrJWZ/KXPr9PoIiScOpIUepqGWzPyVLHAs2bpCgenzBKeR3oAyRU5nIWq06zbjbLYzk7ewxoppOpnB4ILKuFqN0p8qWlRs13LxxCwBwoBKHVFTIieEQ6xvMhg1VIhFxB/Dtr30bAJD/wff5b4qOWp02mEV0ZRcsbKSoc2c0REMZ4pJQTuXPGGW+/uA2LhOMwhpZ/5sinqoUS3DaOd7ffP99jplVJM6JefzkQ0bGLUIGjAQDvb22gStbjDZ3ukJFCa10dHaBJxccG0PW3WfjHP1s5wXiKUY3VagAl8q2bBMLBsrWxRXVtY6ssLQk++fmuNmtHINyuYrFBWY7lpckM6js519877sIj4Q2mhM6SeiVVr6EnpA2ExE495SRdUQiuCYSUlVwoVziGnr7nQc4PmZGw4BHvvfer/EdPAHEwMxJR5hNq9BOZ6eXU1iwQUyeU9a6kMvhnS++DQA41JzkBQ9s1JvYP2FE3dzjey0qux5fW8DTPRJIn2xzrO9fvY3tT1l+1heB3ZLk1rc2NgEf57XtZ8ZhT3Lar/czsArKaEDKQxHukSfPXsFllRykyCZzktG+vrKM6w+YJfnJC6LYFhLKWq2voVhkVHsoaGcrw9/9B7//D3FkkDp2+LsuvyTVvX6ERDbXEHH6zjOWJiaDcfhVWvXVL70HAHhxxnW9f7A/lVBuGaWkSynU6wzhP9beK4ug0uawwqJMin/Mz3/0gw8AAO6ACx4hDO02oZQStB+FUh2WnuDHQjd0O8wy/Mmf/Bgdoa6CIh7NZyVlem0dpTrf+cknzDD5lNmwmS1wKgU48HDeEkLdOHtmmJVhaF+y71Wts7DDjiXJg3uFEMicEoFy/fYbCCj70FUW1er1IylZWkPa2SB9z5XrqBiEgSona0r6tlLtGij1Kfn9yyOiiPqDCfpFZj9PcionU0Zwc+vKFOK9I2L9saRFvaEwdk+IEHxwl9nkWpk2czgcIHfOsUoFiA4zUFnZ83MUevzc1gazs3NCcth6BaxuEQH5wUfM8L1+zHl/8823EHiLqLwFZWc++rQ9tf0GhP1Qcq5epwtbd2lTmsqEl/O/IDLvqiTQpNKorjKz47EN2XOOw4HKXIIqB9nbP56iT5KSrD5TOSZ6Flzf4jmx5KOdOz/l+Bw8eYGRyIWbQl/Np5cBABFPBOWuke1mnyJCKHq9XgS8QmKI8Hgnw/3Sao9gk0ypV3LpAZGeD1s9mHoiElbpdLF3Al+cGXC7SpfuqQ/NW3fw6esjPksk6oUTjrt/Po31Rc5hr8V/2xAhZel8Fwevad/WREpvUjlPDz0ERYq+d8SzdSg0psfiRVCogXCSY2tIv//Ol38FNzY4jr4abe76A5ax9pNB/PSTHwIAnpdpY/2yx76FOOrKlppVXlcvCg0UnEP+QiTWKkX0h2LYEXpza439bNa5n1OpNCZWznNXScKCnrW4kMLuPsfGIBm9fp3r+PmzF/C6ON7RMMfohlC4Byc7yHeYDS7WVI72kO88F4oineTndp+xzNwhNFd6Po75pMZbBO8urflyvoKJYOkGCmIkmx1IWNGUT+KSPzDWmeyK+GB2K7MphEopk0dMZNXNS85po2SIFySxts5z+v6XvwoA+N6HPwYALC/Ekdjg+PmElDo753ybXTbMXydKYEdlrz2hlAZDO27d4blpEPw+/Jz+jsfvg0fIm5sPaA/aDdqhSqGElt417qEtcujs61Z7qAitnEhzzCpDnRehMFZUltgSSfeBkB1XN68gJxlxv9bHYjSObJt9Ngkl7ZaNH58WkRMafhSXffPyeyfdHFJmjsdApZWuqM7UdApB9askUnqHykFgN8Pv4DPrp7TjQ5Gw5y/OkcuyrycSKEmuMXOceVyExcw1sKGyq7pQac4rHiSFvnwmX7IrlLbF7IbJzXEoHnPfbMzzs73+ED/8KffZG1eIFltLS5zEfwX7IohtS96+OjpDS7arPVI5tg6aYDwMizLgtaL2oFDdbo8Nb6xwDTzaJvLD5mSfFhbSyOzyzPHF+PlGlTbjo09fYk42aGWJfTYkpuvdLsoSCjEgwFWd2y5HHxH5kmVJXtdqfPeVhSgmYrkVuAmZV/Jb/NEp7cD5Oc+Xgdhe6+UORiJ5rqjkvbabg0W+zBdvEin70ccfAABWN1awLvGBF0/pR+x9xrPYOrEgkaDfVm9w3RYn/N0RxvC7+W/9Op+9d8HzZX1lFQsisrXIXnnk99TrdTT6XDMO3Tlcosz43b/320hKaOT1Ln3Q7KVKz/ptrAr1UhBlRiHH+Xa67YhJxMWjub2qu1W7UkFX43F0IF80GUCpStvXlFy9p6Py5fMzXFlbZt+FCsmXRbod9kJHHGJC6RmCOxeFEmry/9xjfu//+EMKLpRyp1j/Ap85dvDMOxOy9kWlgITKu977Juem9Yj2rmppoiM7E5E/++1f1fp89hCDlsqfJJBhsrCf4XgcH3/CudgX0fU3v0UBjEarD6uLZ9zGGm3FaODAgsQDqmXu+TOVvYZhR8SQHxdFw0CI+aPaKXaFyPr6V38FANDpCR3ZniAVoq29kE/y4V9zffkDLsT0znMqCZyX8Ein08HZqcQAhDyLhYRq61ZRl6hHTEjDdaFXtndfoi+BHYeNtjorAuntR09xqqqa1SWJHfg4R0vLC4gLnTeWo+mSPbizsjoVCHgtCoux2QS3fM47t2mLPnlIuhCzZQKrqj8qQhZVhQofD8ZIqEzcKH1O6BwFhrCoZLGsM86vapDBxIySqoO21rim51R6HgqFIAASsrIxw5GhKgXYhESf1x52S8glEZyDwRFv2pVQg8oI55fmkdnjmjGEEKw6N5KLKVyIbub/rc0QQrM2a7M2a7M2a7M2a7M2a7M2a7M2a7M2a/8/a78UHEL/+D94ewIAhXoTBWVLcpeMaMaVHVtdXkJOde8OScEGxDNx/vIQt64x+zNU1LJcZiR///FT/Mp77wLAVHZt+wmzeatry1iUFLURDTTk3jqd1lQS3qf6YYeZEbxet41gkNHNc9VBBkWs1Wy2YTZLaq7JUJ4hae20WafcLddvMAOTU+27yWJGWVmPWlGE09EoHJJFLQui8j/9NaWkB0UT/CNGIg8kv2oTQihocWJeXDnXbzKyuSiCrfhiGhXVr49VR9y1KDru9uNcUel1SYinF5kJO7q4gF0IqTWuFLwYAAAgAElEQVRF8A9EhjoemmBVrWlAKJuyJPxa/RHcHv5dVnKiaZHkuUNuXKqGsyO+D4MvpVGsISQCaLeymDCNMVGa1R6SvLdXSLJOD1Vx/7z33nsAgB99QkRTsdNAwspnpVcZ5c9rfVQvLmBX9jMhEsmA5IdP80W0tQ6DkinMiWxxfj4Fs+rS5xf/Nkl0pVZFSFk+gwR4acmQR3VN+QrCYUaLn4vsslarYaC+BITyMOpFE8vLOKox2px5zAzYb3/71wEAx/0a/rc/JcltXNmtpVgcdpe4LpQhMikz1Rj3UQT3WbvDd5iIo+rB7Vsoi9jNINI2iLHr1RbWV4hisyp798EPyZPz/je+hqgyf3/+fUogn0mOHGMLmiJxc044D18Wsep/9E/+E3z4mIiiJ//6AwDA8hVmyT2rUXRVZ3siUl2XnWvp9OQSJe2XhSXOqVWcILV2E194h5LGJ2dHAICnnz3BsmRRay2i11TSjCvXrmJP5Mk9EXPOS6Zz68oGmsqEZsTb4Z1TLf5wBIdq3E/3+DttIYyG6MIqYrj7Iq8NuWlH6vU6KpKZN4tMzy6OCZfNDp9Igg2OHoNDxOPxwiT+rrFi+eaWxmAxAZNBzq098vEnzECub16BxUxblJjj+Jndjin5o8E9dnjG/Xnr7j1sP+OaNClLqNJwWDBAQRlpi0i5jRr0VGp+SuQsICMe3CMi4/qtDfzkQ5IkvlSdeGKeGTqLw4OfC334698gamAoUsef/fz72Npk9m0+xbX34cfM6oRiYYRFMn4inpRHn5H7zG81w+TgWHmCIsWUXG32NIsH91mfv3NI+7299xqJINfPotBvMc1zs1Wdng9ri8sAgMf6nZXFJXiVLco84xrqSkY4nEqipDMkW5EUqVBE5t4QQ2VbY8oCDYwxtrlxsMd+XQrVurLEvXh1cwmvM8wqhnUu2UW4PBgMcH2Le8esCSsUOB+TyQQWiMB/j+O/sMSsbb9vwp7I5c1mPsvY+ydHx0iLY20i1NbW4jzcQtk6tQa++AXO0dV7d/FH/9efAvjF/vcJpTS0WOBwcIxD4j3pd0XCPDEhX+BaPn7FcTSLU8np8aGvfrXEZ2HwdlzsHeL2Js/SXWXo+zWeqXdXV7H/mPOUFkp45Rozn5+9foKuBBCiy7TNbwo9VK3WUZPNNGSSu5IqPj8tIp8TYa4QHR6PE3azJk+E/5VLnhPpdBpeyZX3xF2zd8DM7OaVVVQMPhcR2hqSz8eHJ4hHjMyrkI1C/Gzduom8MoBdIVDnRARtHQNWZUuL6kNJPAsb6UW8e5/Z5oNT2tWx5m+ICVrimxo2aA/CGuPFhWW8fEmEg022aU18KY1BG2ankFwioc7WKsheHAEA1sXp4xpz3t1jN+bjHO+qRBlsbn4/lJ6HS5n9jFCc5+L9qfXaWBJ5clcZY5/QqiGXC1dvEgn245+SpyMrJILXF8CgJ66bKM+nSyEOJxYLPB6+f1Ok0Aapd+6yhmbH4OuhT3L/Ae349qunCIc43heydxfixQGAfpu2fKDvh4NBOIOcp/UNcprsH/K9Ou3hlHNo/QrH5bIqpKAD2LQt6fn8u6wkrEORMMIR8Wbq7DBpf1vNEwzM/L1OlfPVqkqi2GPCwMbxyBXEw9gW8Wt/gPQmbdlAxM5vyX4vLi7jv/1nfwAA+N3f+10AwNOH3FtBr2fKceYX/4ZB0FyftFHOs+9fvSGkQ07E3fUWssccd1uI3y+biujLRgzBvgd0bg67Vfg84oXrePTu7OfL13uot+nLzEm8oNbm7yaiYYS87I8vyjWTzXKtHu+d4mvvEC1gCKJ8+pxr3eKwT7nsmnXab8Nm7u/vw+ni7xk2IhKQv9TqISkenEZXa1wy2iuJJezI1g7l03d1RrRqLWwt0y8qCQWAwQQd+StuD/eG2cb5qtVKWBDfY1W2MxbnfsseXaCp6oCw+EhGVs57oVzAWOjliIEuEUbf63DhzjWuc7sgDAan4XA4BGTnDJSHT8iFcDSG7/+IvuAbb94DALzaEYm724V6je8wEdlzqcjfszkc8Ad5FniEYjbLphVyuSlfW0sVD26PE0XZvp1D2lGLYFh2xwihENfFXFR3DPFJuT0uuHWelE+5LsJpzldkcWlKzn24w2eevuA9qOd24s0v0O6EZG8Kp+SSNNnsGArRcpHl97zQeglbUatzrxoCJ2YrP9sY9lCo0b+x2Yz7Af/t7HCCoubSJ1GYlpBQgYAPbt3x3C6Oh9fjQlJj2pAdbsrexew+pMUnuXPIu5rZw88MJ33kzuXTyC9qV+iPDDvmKQeZT+TVFfW3UM4htsVnJsL8t5b4dHweN8qy6QafkUN+6q3Nq3gun7crdLtPyM1AMo5T+Z4LuvdU63znfLuF9ALPmol4/Wp5In8skzE8EnPJvBQHn1BIqVgUjgTneyTY6EWuPOWYNIQCrgolVqrk4PBx/bmGnEPDB/roo5/BIzn3GxJUsohnspDPTnmFYpKkr7Q5/oe5Ekq636ZkDydDvtdoNEJS3D+WGPu5kRaX0NA29YsM4ZxWi3ODgQmn+3x/j1CYfd3n4/E47Gaup4HuMXahrB1eN1odPvM/+2f/54xDaNZmbdZmbdZmbdZmbdZmbdZmbdZmbdZmbdbYfikQQn//nYUJAMRW1uCUgsGz58wSvi051vL5EcZjRdcm4qcp88/A2IGKZNy37pIbotsT+iVXxNe+9GUAgFeIjO99h2oVoVAII9X6OpSZGooqfGllESFF1j9/xprp4iEjyxaLCUtSEVDCF5fiMNq8cm1ag5i9ZFQ1KQZ1tDsIKJvbnzAKXpKE8sBkwkvxoyxG+ezlcBjX7lF5aUeZoX/+b5h9jVqScKuM+tgkBh7VT1osNmyqJv7NN5kRfXONKBarx4WsOJgOpfYzt8BIb6PcR1LRerfQExXJWLZGXXjEf7G1xYiyR5mcR08zCIlTwIhI1hWhdDoDSIqraE/IlpTQNq64DzuG3Lwim0F91ul0T5U1jJp3kxUId9UxySHWhXIY9IZYEB+Uz813OJEC1sRth0PcSB7VJBuqaPaEHzY/swqH2+KWEfdKuz/C/GpAYyv1HWXVYvEUhkJkPHpG/h5f2JBf7AItZc7fYobtXAiG0bAPu2qQNyWvXpdqlclkwrn4JRIpKSjYhESr5uFb5N5YlNLDivbK93ceYtiRNOkLokL2TzNYvcfMebnMLJxHPDduvx/jMN/5dIfRZq+keIetGlYW+dswc53cvk1Fn9PTU8SUte7qWSfi8uj0e6j1ObavVLt7KmWaaqWDxSRRFxFl0FPXOM8LNhM8QoCdajwjyhzMjSeoDjk2ESmESUQNrzOnsIrvxCUOi4mePel3EJX8ttnPeXd6fegKadMTt1dUPCl3btycooCKVW4qk4fvF3X50CywD0Wpb2SlTNOYDDAWotDIODhtfKbHB7jEYWAX51PQzP0T9EWwcZOZwOcH3BMuK+evcH4Oi1BibrvkPRW2X11fQ0aIqQWhGhLKMOWaBZiV4cgZHCddZkt67T6qfUk0z3PtxCZuBCUt+lBcOd0eB7dRqqB4zgzPCByHN98l4qpVbUxrzSfKVAZUC26FCbe3mF2MSHp5IHTDd3/0I4wlcSGwACyS8OyPrWiV+EyrkBhv3eKa2z79FD4psNmkNPex0DmeSBDBJNejoXpiV9Y17HVNEUJGpj+kfbP77BX8yiy1lJ09LeTgFh+ZgToaSEWnOxqgp3eOBDi/BorTYbHCJNWuqDJKAT/PjU6thcMXzBwG3CH9Ht+v0euhbWSwlSnKCA3UH5kRVvZtccGoIeeY1et52ITYM9I8kYD2RqONuTD3laGWtCTU49nJGZ6dktthKCiSzy901WkeSSnDHGh9DWBkoWuw+6SIouxiGKMp2q0r+fevvSt+kFs38G8/IJIlKFWbWkVqS6EI7lyjTbKJ3+2Dn1IVJrayCLv2rF8y5mXJM2ePs4hFOQ4lcSEYMsa9RgNH4uHJHLN/IzvPntR6AKvX+b2dHWZ1rbLjqWACy+I5aFwcajz5b0cnx1jd4P48EV+NgfJJL29i74DzFBXKqdVowqbJSOhss8ivODs/nWYCt3fZB59saNDvwQ1xBj17+gvEGQC0G11kz3jm379HBauTBhFa7VYXNot4A5U5Xxdv3vPn2+gMud5tQqkOhQLyD4BlqbEMxVnUkM/gT0awK+RSVZwo/gjtg81khU9KWF4bf9evvXS4t4umspeGLLsrGcCGFE0MLrdRV3PTMuP6VdqIZpvnrNlOG1EuduAXjNvnomO1neXcLF1ZxVjosLLkgZ3yOcbNwlT72y7uoaGQjYPBBE7xaJTFg2YVGqA5dqMvvhqvJJC98h3m59dxcsK5d2j9X+Y5PjZzDw3xAJbk65k0PvlKHW35dGbxdUSjIYRd/O+I1nE8ynl+8vAFmmWeJyMpvF69tsz+daoYiNNkWciRY/WpVK2g3RbHkyGpO5Fc8vwcMmdSI8obyqL8N7O7P82cz0kd51iIlbg/gHpLConiIenqzNu8fgUZqZW++S7RlS1xmI1bXZwIPfvu/a8AAF5L8XVs6uNGmrbsvqTDP35OO7RdKWMoyWuLSQqZfi/MUnBr1TnP0TD31GAwwEgcen2p3/Z7QiZF5lARx5lL+9JAaLisY1iFmK8JPeETb9vW0jV4+5LdPhNPW41/doZ9hO1SPxNK8tl2bvobnoiQQfLDMq84Vl6HB1e3aFtfbdMWRpy0l5OuBacXnJuRk2v8jS9Qherg9S78Qhw7pNbmtLpRL3N/jaRUVKioqmA8wbK42xyyBzffox1ulmt4vc37REbck/feJsejzWZCX2p1p7u094F57vV+qzvlCJ0Tkr8rhBIspinvSUqoeAlc4fTiDGcFqZbe5x3MIfRdrliAVRwxjTq/UGtqg1rM6AqhfEO8VaWc/P2hGT1xNBY0Bt5xEOc1jnNGSOWekE/LywlcZGhjb21yrXnE3ekfjZCVsmU9wblMSCn3/Y3r6EhR9KPPPgQAtBekUlhzw6/+2Zt8h/gcz/JGoYpRg2vg5jdox//8+WcAgEj6LkxmvkdHFRwOcafC1EcoKmS+FAULJ1zzjoAVTp2zJ0dC8TelRB3zwKzzq1Vif0MRwOfhWhmIEtgkB8s6smMkLtLqmO/gVXVN+/ICQxvnPB4SuZKUy0yDCZoVvpcvSHt1JA5Em8uJuI/7eUv8VX/1F38OAJibTyC8QP/DorthVXewSqEIi9BkfnHlHO4TFeSZi8EmlJdbXL5d3cM9S0lcCUjyXkp1blWFTBwufPiQd1iPFPsaqjAqFAq4vsTfW1mRyuHYhsxr2injgOgJtTSXDsEiRL/NSr80JLSSyWTDoaFaLD+gq7VktUyQllJoTqq9Zo3P4WUNpxeMC1h13x/29HuJyFTtdek6bcW8j/Nx/voECXEL23QR94mHtJSvopbnPO3V2adr17jfrCYzoupfUrxcIylSw2yCV4izd3//D365Zef/4W9tTADgS29+EUdFbXJJ+N5eY5nA893XuH+Lxm5XENSHT7n5KwU7wk5OcMLJhR+d5/fy4z425RRmX9LQF/I0SoF4BBURfxnEdSHB+uwmKyotDnwL/Pz6Cif6/CyPrsrB1mQYuy0a6eW1RRwJ5tyVg2kXBNKbTGE0FGzxmO9pF0zysHQOsy7E63E+M7AQx9EOL2sWDzfRwxM+uznyo55jHwzSuUqbzrLV7UZU0/qbW1wsq1cVaAlbMZYk3fNDbo43Njiu+VwObl3a4sscs5d5OgEnpznEVDJ37xZhc8/2uRnvrF7DoQI7YUE147ocXV5e4jAjaKcOfYPoFMMuCjluYLfg6aG0iHsXF3B8xM2zJ7JRv3eMM0EXK7mJnslNNZg0EVY5zIUCOuJHhGnQwVaMTsWtX+FF8+VTlqiYz6pYWmK5Yeg+x+HxDoORIZMVDkX8wnJKrHLyGo0aOj2uC4sCGjsi9IrHE7DJqbn3DonkMoJEBtx+HAneWBb56aZgiN3xEGmVXZ0eczyN8qFes41wWLBnlcsZkPshRvh4j3159Yx7Ih4MIiWyN4eD62qowGEg4cFEh2jtjGu8p5Kx+eQCbJLsbIhs+5236ACWLi+nco09kQ6v330DAPDJk22Uy3z+jbcZPDhQIOrhwycImHkwX7tHo2fVOrO1OghN+Hs2lVb6RA5ezlfhHaicQAfZ0MvxP89n4RTxWlHlaAMFdTeXVtA8pON84006BqOhFR98n+TCV9aX+a4Jrhev04aLS+5fu0oWHSpR6XSbU8e+qVKFgSRTs+U29kQabCC8twTZ9gQt09LWui4NL54wsBzyW7C1qdIVkUoHFJQ92MmiK3j/lQU67A9u87PtbgEjE9dVzZC/TXN9JOcXUJcTP9SlqqsAXbFcwv4x95DdLjlQmwvXtI+rLc7z7Qecy2y+gIJIk48zHMdokGupcJoDrHxuThLxhuM4aPYR9nJMN68wwHCRO2I/VzdxJnldQ1p7UeVXxXINDgX3Lk/pPEVVkhGJeVFW6UtUgZqqCD7NsQAuVd4Y0MUJRa4FV6sHZ0QBWjfnr6ZAW7bUhEmkrsazv/n+V3CiPddTsLMn77fWamKkMyAiGdFRX4T34SjcCkIlFCwyAnmZ/WP4YlwPDV1knrygk94ZdmEXSfaRbKdX8HhLrYVbt3hpvhBJdzLEZ/fOcqha2JeELiJBkRi6XV78+CM6tB0FJFLLXEPXbt/E2RnPh50d9sEpYtBwNIqC4OIZnS99STbXmm1sqBwpqMD5aNBCs8Z+vf2A9vTeHdrQ3ZfbiIo4sSOS4oU0ExyriwuwieQwozPBJ7vvCwbQ0Zqej0k8oi1y48EALcGwb97j5enRK9ra5wcnOBD5vUklXEcHKjc6P8a//3dYVvvq888BAC6V8J7sH0ydu8gc93NGgWyzzYW4bGdWJJm9Ae3kxuYiXCLLPt7hGLxx6zYgP+rlS9rfWJjfT6XmcSY7fyGn2ufhmggHrEgn2OfdPf7OxlXavlw5g+MMP//r36TM+qtDnrfDUXcqnzvRJcChUtpMJgOrjfszmeR5EY0qyfboKW5d4XxVlHjIqgTa4XJgMOSaLsuJf/NrDET1GyM4FczefrI3HSMAgNWCns5BI+my6AkhKsLdXc2FR8FfvzeEgEqZv/l1zmVLF6ZHmXO8VLnO+hL9t+Il/83ndiEQFpl/RxfCS46ZfWRBNMS9vrhkXAxE3FuoYesqyxnPL0RQrdK93rCHQZf/HZijv/P5KwYrXGYHwjqzKioXdaVp56r1LJwOvk9d5OG9rkqCVjdwccF1tLTMee71W6iLdDq4rDNZwbNCtgx4RU6s8vR+jb+3fH0ZPrukw1UCenHG/RnyhWFX0P1MxLThGO1AKBpEz8T3evGM+6ykMq3Nras4K3DcVjb5zg47P2szjXFR43oqXGhPjQ3BkQpcoi8oVEWAqzkOxcMImejjerz0X8aSmC/nLnFDhPD5LMclKf82kznH1fss9UvKxvz5n/0Z+rqYxdc4ficaz1Gjibub9Gf39vjO8UWjHKqPkEpviypHspjkM3RbGKlUo9sxSvJFHt4tY3me/ZmozwYhfObsHG4lSH02/s6FfJrDy3NcvcYLsVXl91EFgydmB/y6SK9JFGNvVwT+3S56Cmb3FaRaWqR99Nm9uDjgeyWSKicbDXCpssQNyYmfyS44bH7syvZlslwXV9dFIL8SAEaiX9D7RMIqoek7MFBJUKnCtTB2aw24nSirdNRnYt8X/XyXy+NLJFSmklLSMHdBW7GXzyKkRIizq7JeBRqakzbSSa6LtgjXc7JXVrMF5jHn20giDZX1i4Xj2JeIQKnB8+nnr44QTCpB5Od82Z38/vXNRXQVXF2e45xeHNG+7T3PIKkSMa/oPRoi5o9EvdNyvHNd6hNzDOJHHHMIBvg7/SbnoS0i/4g/iqJ8rrze2aEgwsDSRlWUBoYoQF13zbHDivaQn693/nYwvVJtoqOkt9vNPRGLSlK9kYdZvo/FKiSCyQWLRUI+FT2/p7K+UBh+nyHORHvokh22Ot0YSsa9r/Fvili7Xm/DovtwwMM5teguW683YFPSf3ODdtWhUrXnzz7E7ev0KYMKiuTKCgg1L9FRcMrYlwElu91mG5ISA9lWufhQ1BeZixy8LkPsgOsyJVGX2FwUBxKksWk9bS0yMJc/vsDrHm25WaIY1xauo3ip5K5K+O1BjqM75MBE5NB2g8xeSXKHzYmJ/DcjktLvczw9Hhf8CkAXBFwIqkxvYX4dT58qCadAXkdJ6WK5gqJIqLfe4Fi1KxIXaIym977cOffn4qpIrceDKYWNU8kLs3yPSbcLu/yBsQLfBu2Nw+tAu8/18V/9z5/MSsZmbdZmbdZmbdZmbdZmbdZmbdZmbdZmbdZmje2XAiH0j3/v/QkA+J1OhBYZTXVIkrcoWcqJfYxUjJG0vQKjvo0Wo2GVah+xACNq60lG6csqWSqPe+h1BdOSZF9Mct0vnm3DrayHXRHJkqCJjVYPY8mo+vz8TEoRTavLO5UpDEm+3CPIcbdRQVIogYiy6icnjPLtn2Xx+JGkjJURX15hdsDitGBuju8+UdbKGnBhKEIxpzLTT84YcX2ZqaAh5rlOy5AWZfRx2O9jSYiW24Y097eYjbP6gbpIwLqKgIbNjDRWuk3Y9c7HBWZl7HFGpz9/+gKbcWWURCDtifHZEUcEDUXwDSI0p3633W7DLtLZrojsjNbs1TDsqxZCmeORlc+ZW0hjMGT0tlhgNDcR9+Igy6j+975DiL1ViK5Ov4muCAYtym7HA4xu20Zd/Jf/6W+xDyKO/eF3/goAcNXkxkTlQl/47W8DAC6UXdvLnMAqRNeBJOY7IuaKREIwC97oEjxyUWixeCyGV0KjmVQikppjBHz39ADJdYPcmL8bV2Yp7Pbh8IDj3qorc6CYdCwaxWTCZ+3vM+ocivD9rt+4imPJYZskdXt4fIGm4JOHe+z7zQ0iTTqVEvyCK7fsbT1fCDK7A2dar+Egn78maLnb5cTDTwiB/upXvgYAgOCbj7a3EYsyq9IWcuc7HxOR0++PETDx95ZEWvlYhN+pUBDffI8w84DWTETIifWrN/A//OEfcqzOJa0b5u8tXl3BWY6Ik09UQvT2A2a0F/0R9LPcJxsrywCA18f7+O3f/z0AJI0FgJ//mCTHLrsTIZFAfvyEBNf33v0SAODz7/0IWwnuZ4fQDFmhm3oOF5pam64u90Q0xM+aXENYJVdfrnF/Vuu0Q9GkDyatmT/7zgf8vGRIW70eeirpiQr2uahyuUr2EOsL/LtvfIMorBUR6v6v//u/QEvlWQ4R/irRAZPNikNB+g0i6FGvgrSIIQNCigyFZGy2O9Na2PMy91upyrVqNrlQk2R1el5EttoHHqsLIR9tZCTGP51evmf2pASr2cg68d/WhP7c3n6BsTIwqTjXkEvQebffh92MiO7X+fm6srR+twduj4j1dSa8EjzeMhpObatZ83UuItdkdBEjIU5iQt3lzo/hj3Ns14WqfPiMJPGJ+eS0JMIg/+0JRj8aDLEW4b5/tU0Uilcw9Xq9iYkyPcbaaarU4cYbN/HznxExdl7gWl3Y5PtVS2WMmzy/bMqqDSWJWhq2YGNCEMk5zp9H5TWZvV0sCPo/UPmkSciC1fUVXJb5zAPJsN67R/LPdrszJXS+eoMIkoH28M8//QxJkWYvpflst82EYpZ7zy/Sw83VZb6n5RcSsCEnzywDFWQ1WzBQ6Z1V8uVelYn97OcfYz/Dfp22ObZv3DTI2J2wqcw5pUxzX3a1bzJNkU4nKpMxym0fPXuCgGS6PSprzBxyLd28fQs/kdy5xcE5CQqmXSjVURWZ/3xs/m+N9ccf/xS/8Zt/BwDwFz+lwMPq4gIcKll68YQonsU5fm8pkcal0BY+iStklDkO+z3oKBv+8FOWjm5eJ7Lu1v1FDFQOXcrSfmwu8wwZjvrIFflMgyTTELDodvtYUIlOrSHJZe1zDCbIKqNql//h8XJcStUK/EJoHme41jfvEJFw8OoI6HO+Ugn+XU0+h93pQKOh0viUpMaLdTRVZnGpsu+kSlvyFyUspbiObl3ls4Y9fn8unERB5SBPhLSa6PyrVitYXOY+gxAkOxmeg6nlTfhVZtFSifvGGs86q9U9hc17hI4s52kHJhEr5O7gyXP+Xr6mLHt3iAdbzIA7fSopL3HsnI4AnC7aNYNU3Sib87q96Km0zUDCpFNLeP6MNAVjF+1Vr8Y1l55L4OkxfcJ7IsbOHfF3GkMTQhH+zkDlvCats1KuitUVZsNfvWBW3bB33V4L5wUiTd56i+fYuZBFLq8d7Q7H+9VLrlUDIWMaThBT+YPJsP8iFD44PkNm/wgAYNGcXFMJmM/pgMwUanX28/SE9tjjtOGr75L2IGaUqQuFOBpbYJZIQk3I74vsKRxCNSxIqjkoG72z/QILIl0f6Gw7O+d7rqyt4kJodp8QfDYzO+WyuzBWZr5ZU/m20KPF+iXkVsFpNcoNhZDudXAhKohWRfZnRdLXc270RB5uV4mOIQedTEVRqoosPsK+eHT3ODo+w0C2bC7N/ZIXCs7r8AJykU1DlVuVitO5cOuOYocQLs0hHr4QCkICLOYw5yvus8EtgYu5EM/U/oQ2rVZqIwCedX2h8zJ9+p298QTXVLoVUUnxqMa7TrfRgSfIZ1pUylK40DM7Hayvc8/5JEVf1Bq0e2xTmgQDfWG2qrwfQEj+b73ENWDcZ1y+AHxRkRrrPoeWB2cSF4LQ4gHNpcc5gVd3jSOVQXab/J2WxY5DVURsiZze7f1FP5dEfm8WEsasslL0xhiqBDets98rcuqdlwcoqQSuLzLjnhCoTisQ9HHOK3kRLMu+dic9dCb83lhE5gsiQm+3+7hQyb/Hy/URF3quUc3CJiEbgwS7PzDDZjFsglFiyXlz263w+rh2cirDM51v3Q4AACAASURBVErIkul53JI9PTjnWV5WCWgymYZLfkteiC6fSsTL1dpUjMRAEf9CfOMCEOptMpH4iUSUggkPuqogsGg9r65zHoadHl5uEykv1wGJBO8cf/7dv0EizXMiIZ9ydVnoo+IpukLbuUQzERFK7Wh3Hy9LvPes6fuWvgU2cO6M8jXjLLE5TVNy/qBxNnr4Z7XeRCjE5zo0LsZ9dzIcoSzi6HBUBOgqXfW4g3A6aO8NZKJJ6Kp2t4OuyOg9+ruozvJPHz6BR3e7nXPu74EqMt5/621YhyoRbBuodZXUdno4yXAub2zRj9vLEGFn81jhFO/AP/mjv5khhGZt1mZt1mZt1mZt1mZt1mZt1mZt1mZt1maN7ZcCIfSf//17EwAonp0jtMxI6UDkkQllx09LWdxaZ+b2WFmgtqTZwinPNCpnl8RfMMbo3sA6wsFrZgUX/CJaUnTbPpjAJ/4Hi9AJr0UCFZybw6lIFlslRmzvzjFyXu920dewbd1mzeTCojhzsmdoK8tkkGkaJHK95hABZf0NMtqQZNqT0QCSIrq7EEHt/skJIpJNHIvHJFNlFPPp60uYBsruKYvTUU2oy2SGX3wqD8TXc/+L5HgYmlq4VHY3JiLRq1H+uV8+g4L7OMwe8fOqDzWbzbAIFZVXLX5yhe/sGHmm8uphn+T1FOp1OlwIho0IMvveUr2ty2MHTJKFdBsZG/bN4bGiJfLfhoGAGnXw+oxz8eGPyDVgsjA62ui1YNGYuoVG8Skafn01if/6H/0GAOCFIru7+3wH68E5Vpx66RA/n77P6KrJ4cOuiMTzQkoYct/5YgEeN3/HJdZfj+bI63KgqXr0RaFLWk2uhWqnBosyWPGUst1N9mnc7mHnpTJrQlqENH/b29u4eo0ZwXNxjnSEHhsNelgU9xAsnPfVrRtoinCxI8RZX4Ta0WAIOy8Ykbc4GKVudTjGwaAfbWXo7Xqf3dci6rt2BbdvM4s5avHZl+IxqjTrCKg+2RkgquwHj4jcyZ7lELYxUn79DveQX5kcn9+J5zvMjjtFKHwrzfUYSs/jjz9iZnUoRIFjwP6+e+/uNNNZFSl3QBF9k8mEpmxDaMI+2SY9pOe5Xo9ElPn5Ce3Ip89e4ObmMgBgURkwv9ZsIX8Jj7IJVaEHf/VbvwYAqLUa2D1kBL/V5b+VZCtWN+bRE4dKyM+58frEkRR04OiS2fvv/4gkhNWsSIfHbdSUkXJI5jQp6U97q4a7q/zcf/j3vg4A8IjI/ODkFDvK7FeFYrtxl5xY2XIej18QvWKQQl6+uMTtB0SIDMT98VTS0qcX50hE2GezapENokmLO4Yr14hkefyEKC+b9t1iIgGzsp/hmIE84Hv2u1Zc26KtzInvwCLOtGAgMOVsasp2Grwzh6+P0FTq1R3hXl8WwqB0cAqvMjYG8sbslxR1IoxTScobCDyz6uJLxToef871b9K7d4dtLMf1zuIASorbIRwN48VrrtFFIXD2dzjWdosbY8madiRVHvByDXV7bdhkh9siSYwv8Zl2tx07L/iMsM6sO3eYSR8FPPjo4ScAgJR4bgy018Tvwv4nzOa2tf+b4oBaW04jLJ6fwiXt6JtaAw6LGa8kNT5UtnxtjRm6RrWBivjCjEyYLyiuAasVHaGVlkSO7JiM4JBtbVe5B5fFdzUYDHAk3p2hyC4NfrJSIY95zZ1ZpAlGlqxSb8BloFVaXGvtOp/tsQDXxPtlUQbS4KNrtOoIC9nzk09J7v+V974KAMgXqzg5ZZba2Hsff8o1+5u/+e/hM6EdRxH6ANkC16rN7kRC5L/nQmwOOrSdd268gbx8k6442S7Oz3FXdnHYNzK+RDe8fr2LzD7n2e/nmPa07hfSi4gF+Dvf+x7l0kcWno3v/cpd2MVp0FBmdV28RhPzZDp+E3ET2GQrOs0WcpfiIYlz3Q+VVV6an0dJpPkTC8f4SHvRYXdiIcG12VCm/uic9sBh8aBREAJGnBJBoYVDsdCUcH1NKLFhZ4SO0IYPHxEF5xaBf7/9C6L5g10+/w3xT83Px1HUmj6tcoxPSvSFFpaXUBIK0EZTgXSaffCM3MiecYzXJOBhoGk7nd5U6CI9RzvarIjUN+LAWJnbsny1kuTWo+EYri3Tzhms/h9t8zy7desGnCKRPhUvmlXcDaNeFy2dRzGhx/u9LlwGokK8WD4L/800mWAgCemon2ckROxc6PTRrNPvMFAlBgLbZnJhJELauBC1y0kRqL9+jj1xqw1FRDwU91wo5sG8iF/HmodajvPtghsOyb+/2qXvG1tgdr3VHaHZENmzkIYmiSXEg2E4hZBv6Qx/Lc6c5cU0guLrOT+jPTb265UrN7H7jOgogQ1g89oQE69WIcv5XkhxXVrtbmSOaVvgFC+OpN5fPd9BKCS/z8d+TcYGz1safZETe4SGz+lcGjuAqtDYI3HHpVVBkK8UYJFoQ6Uk8vUof6Pdr8AlVMiSEKL1HNesydSfVgyU2tyLlgn/325zT0nmSyKHHirj74AdvbqQnVqzJpt1yp9jIIDTc+xfpwtcGJwoQj0nxbPlcI1xIj6i1WXaebuF37eanKgWOYeJhLjpxIKYTKdQNwQ2RMJuEcdjIpHAUJ8Lp8S/pn3gMNtxeMI5twqt5xHSy+d04OyYdsYvbrVziWkk0ymMRfBrF/KmL8L2WqOFsaoYljb4DgvOND559ggAkG+xf4Uan+1xA9eu0Ec+3OdacwuZVbBYkBdSJyjOF7f8/smwjaFQL8vz9D13X5D7LJQOTn33ify50pn4Yue3piiZc52fbfGTpQNhBES8PzIIknUGtYZNjMTDePUmhWXOL8TlNB5j0BOSsSROSKEDQ0E3AjqXDWl6TKxwmDnOISFFwvJ3Bv32VFzJqztHRWdqo9nGWNxvbvkMoQTntFjKT/0xw1cwkC7hSAx9iay83DkCAPgjQufMuXB2xvPyUjbFogqcO29dx4qD/coIpWqcPT6/B2HZPouQTzZw3g8yJ+ha+b25qIG24byXypcwyb8qCHX75n1WwhQvcyj1NW+6t96/fXfav5H8Rb/4pKwYTzl8bfLnusZ92uee+m/TShidZ616GzZxP1aG4rMUWikcjGHU5zPbusP25e9Eo1F4VF3REWn4zVXGNzI7GYxd/LuDKvvbFIr2C1dvYKKzuFjh/eWrX3kXALD/KoOxfNzJiONyeMoxviwX0dM7/NFfvZghhGZt1mZt1mZt1mZt1mZt1mZt1mZt1mZt1maNzfrvugMAEJljJPULd9/HB4+ZtQtElKmXOlTS58SpFDG8qlm01hl9c7laaCt6m1Mk/1ySbAvpGLrKcO6If6YipMTd6+vwSr1rXpmlruqOLQ4nxuINGCmzVxMqIpFI4N4dokjcUgn7V//mXwEAYgtJLG8y6tuSbLFb2fVQyIKSIpk+8SGJyB+hmB8Pn/DdG/qeCVbYVKNuqKEF1ZdYtIeDV4z+2SXp2BNb/LjXxdirv1O22yoEjtkE7EhCb+wSW/uYz3T5XCgpSxoX6/vQYqgyeKcR1OuSzPYqspy9KCAhDhqDG6mhjJvb5YVF8s92KaPYxL8RdDuRFXrqXIzrwxH/LeJLolxjJHksTpVqbYhnz9n3oZBFI2UvuhMLvA6pCgkR05CkYCyyBqsitJnHrJs/lTKVvduFR/WxcSlMjcQfEY86sDLPqPl4xM9bxAHQ7LUwGBoIEH7fqvEpViooiUcgU5RykaLNHosVsQbHe05KAedHfKdGr4ONa8zoP5ca0b5Y5qv1CjJ/w8+9cZORZLsi0wupFfz1d4mkMdSZ8ueVqVRtQkikwzLHs+JswSIZutw+o8xbkoO+zGcRjvGdM3tEv0SUsXMEPDjP8xmlI+6lOfEWBL0uKKiNt+4xW96V1OJFdA6uoeqNDQSCeE8K5UuspJlhK0qx66Wi+NZqBQHJJPeb4iWxK0NXtaAORtjd4sv65AfkBPnKm+/AG2C/Xh9yHJcTcfz1R0TjnIrzoqdsqycRgt1rcPjQNgSEOFxaWYZNn2s85bN+8JfkDrHbbCgOuZ9X7jEz5VH2qVgqoan68sIJ7U9DHEJn+SNUlZm2upgxK0qNweK2IeDjejILhTjuMvPodw4w7muMPv+Az5xwzXa6A5iVjbk4Fd+Sh1neH334Ezik7OOTaoolEUDHTls3EJLMqLf3OX1oG6pzS5yb3kAovVwXOc1PRIjLhGxZ9ugY8Rj/7jTDfhrS7fOpBbSNd1YNeVGqDNaxGWHxvuQb/LvTkyMAQLnXmKro3AtRBW2ovlk8DkwEF3CKv+HKwjIAYP9gH72Csv6S5u4LtXh0mYN/SfZKaycYj+Jom7bB5+JasCjDWenX4YtzTnLK/o+0fu3+CHYO2b/zLDNEi+LaWZlPwWblb37xHvlIfvBj7tOl0DpW7xIxdbnNffbTn3H9+uMR/MbX3wcA7LwgOm8k/rVRsY0ra9zXBh+OgT5IL6RQErfMulTsnj8nEmpjZRWVKm3s3TtEhjXFC/XJxz/HYopZa7f2fl8oXDhs8CtLm1JG6+LkFHYhahOSUH8su5o5PsFI6J2OlfvsrbfeAgAk7fOYCNlydEpkRVj8CC6Pf8pnc+/mMgAgK26D86MTBLyGsiLt8EsDDddow3bM9+qLuykrlNlgYgaEnDHOjY01orD+xf/yx4hLlWzc4T6YizE7XKrmcCllsC3xOh0f0g6nlxK4LEn1ThnPtq2Kc0ONT5wXhuRzsVqDVWjI+RW+V0rqcKPBCAsp7q/Ol+hPrG0RWeT2mtHrShJ9XbwFygqfnp1hIMW8ZILz9lpom3gogvv3KS/dEB9PTX8O+n0EQxzjqhCNCfGAddoDnJ7zHYIu7YkwfYB4MIJOkBtsqLPYqYyzN+DFpMk9/uQ50UAxf3yK3r51h+s+oHNp73UGiRj7nJAKWk5IkFqthp09KnCu3SJqaFPr2Of3wH2b+//klNl/A2ERC0fgF8/dt3/9GwCAp+LH+eTJZyiLM7Fu5Z8moeLnTKlp9j4p9JVJ6jN3bl3DeYZzni/xdwJCj55d7MEkxIdNfkhBc+NzexBLEAFzoLPH47UhdylOmDaflZRSaMgXQSDKvj9/Si6gWo7zEEj74ZU09qgjdLW4mdKpBYylJLhuyGj7xM8S82IihMn+Gf2PV+LnCtb9GIFrx6Lsf8DJNZHPl9CUfRuL720itbZ5bxQTreNnWfbTI76O+qCPW8t8Z7uy4/E4n2myWDEWauLOHO3AUynJdcc2uMQxZxUHqDvmh9/DfZw/o817uU00iS8aQ1iI62JOiApl3OfCQXjd4qmRzT0RAhiDKm7dFBpWCm71LsfnslKactMVLvl5r/jabGEvHC6+czTIsa03pBjsCaDb5v7sdyVn3qTtnfRNqBf5vQboT8wZds4SQF9Kbs0S16FPY9Zp9lHJ8/nBeY6B1WbDhdBexp7ySoI6FIrBz79CNMq9+vY8/ZCf7n2OkOZk88Yyvyf0m31sxp6Fe67u4HoKm2n33XYX+mbOuSErbvCB5nI5zCUM5SvO/fkr7teTF3tYWuPvdOVX9IRsGVdGsMpX/kTckbEF9jfb7KHfZr96db2f1VDW7UHAOGRe8ty0mU0otWW7lrgW5nRP6zRLqAq96XLQPk3EHxhxu5G+QltyJH87FqePWCr1YBWXlXF/iQohPrL0sbBMm1zSHo+EaLPtZh8+/oQ2b/kKEUwD3Zes3ckUjV0osk8jcS12Jj2YpB5YzHE9Br36vXEXwRTHuOrhumrUhcizmuDWXMynhGwxWXG4z/209gb9CYPn9PXuSyRTfK6BfjUL+dPrN+Fy6azXnTdb0n4xDdAWsqjd4PoIBTmO5Ysaorp3+IXYKdd5/rZHgF8IpmiMY1YV4jOXzWNFioBuIetui6sxEglgItW1gvi03PKv5hJhQPervR36Mi5Vrbh97qlP2e/RXu1luK5HwwlM8lvcsm+fPfkcNiH+5KJMVaOjbg9s4i8yFNmMc7tSq6PckfKeOA/DUjR02Np4/oQox5qZ7zpJSoGyWUQpJzTZIscjqnuyw2GHVff2L36bfKzXAlxX87YAnj0jot/q5O+55FOOchUEx0Kjyk7+/KOfAADWV9exuMDYw84+fSe7m3Nr75qAyf83/ueXomTsP/67dyYA8PUvPkAmT0evrgWc0kAWujWUD48AAJUBDfFqgAar2tuHw0PjF4rSIP7bPyFp8D/4rbdhGXDzZAVRm4g4DDbArYPIIdKzQUNGrDueSqgbGqG7z3m4OkzALRkXA5p8mBWU1WWHRwZ7JLiuU6VgxYOLqYHzL/LvyhVugNZ5dgq7d4nctHJZQ0SL2biw/8u//C4A4NnuKSY9OejnYhmVsfBMxqiMaESuiRj4G1/igZhKBvDjn37AdxRx6xsxHojOmAdDBTW0l7C5QSd5MO7A6eUCvBAkPS656ca4C5+IYndEcHghYkSXy4OkSAvtKu+oyam3mic4FdnhUzlDQS83wNrKAs5y/LeSggGF6hCX+m6nw75MFFTpozuVgg5oLtfjHI+vvbWBqyplGwb4+bFIlevFFi7V15VFkey6BOdcjMO3Ruf1QjDJrMib22MLTkR0fHjIdRGS5HYkEIQ/KiJtOUNNERBWcgU4BeuLqE9eH9955/AIsXkaGuMQMSQGLy7Pp2V1ppGI1FSm57BbYY5xjJNyvLvlChKCYXpV8jQZ83uVWhFOj8igBfs0yjysNhucHv6OQUyb0HoM+bxoizyzeaYyRTk8W5vrcMiU1BTwO1cJTTAQwahMI+uUgbpxm4Sxjz57PN0nPh3sQ13+vvfdv4FdUO8bV7lffuPdb+ldbFi4wwPlT//yTwAAjQseBnaYp3th7ymJgcuY4I33aXi3nxD6H5Cz8K0vfwkulZtldDk5L9GgrnkimJOkZUEO1YEI71NX1xGR4/Ho+9/jWAeWAQCv9ncQ05wcn/BQaOqQLXQKKAhaDwcNfUyyrw6nH37BnK0DjmPAIgcoOELIx0GeFwk2QnREur0BLhVsfvAWCadPzrhPt19uY1XOXUtE5t/+9pdxITjp9jaDDsaBcXh8gnXZN6eCABVdqrLZHKwKMmd1aY6KvL1ZLiMWox02aw82dbiuxZM4OOalxAgkGwSQHosZUZVGNQVXN9aga2MeGfVvTlLXNl2Ejo6O8M7XWR50LKLYQ5GMVvJFrG/RZlb0znU5nv1OF07ZvtvXGLysl2soDPiOhryyXaU2brcdBcm/uxUQ9ge5VkuFFvZf8b1ckgGNqySiXjjHsMG5/+//+T8FAPzkQ8rC21xuFOQsFXWhKGrPj0sNLAb4jLz+LqAL68XFBWIqGfAGaXMPFTxLpGJ49YrOSVhO1IrKgu/cvIXvfcRAZkDzZRDqHmaOcEvni0GafXLEYGIkHIRfl5ljOdKLi4t4vs3ficoxei657ud7e1hdo1PyjpIsRnmXx+VAWqSweUmbx3XB2z88wtUbtAmfP/whnx3nGJhgQ1mlaUsbPN9HymV98uTZtCTZbae9sulC4fUFce/BFwEAn6p8daQ9f3CUgcsIcEnGudygY7uwtgizHPuVFfofxTz/7eWrPfR1Rs4p+HPn1m0MdVmrqxzVqXNp2BvAq3MhIJLREwk2XNu6guNjXqg6Q9pKs1F27AugW+e6sJkU/BFRcrXewDMFnIzSNIMo3DyaTIl303P8s6ESKafHhr7ITDNZ+lnzCqR0OgNEPCLrPKVPEkxwzdnNNpwf8HyY6JaT0zPtHgccOl9S+rxj7EA+z7PRr/MrqVLB46MDXL1Cu22Uh2b2DqbjP6f1YJCLGsIGk8EQE4k9ZMvsn0FwW71soK+ytZgC0kMLz6XmoAWnznyjTzGt56DDi6Dmya3yjj1JnPeGA4SUYHIooXWhgGqnP0I+T3vw4AEDeRaRp2azOaiqGbkc14LF5sE1BbwPlCCKKEA/aA9QUDliRMkPt0U+ZekYKTfPEK/etanLcCwaRlslzEOV4lslhby5soB8n+voM13YPRI62Tk5hEdBjrAC3hsqxzw9PYZXdsOhcWlKgjzsj2IsnoSCyqLHZoMg2I4rafoiHvmEVfXJ4nBOiVgvRXq7sMSz6PQih6aSVKvXeI5dVgsYqXxnXRf2vAI12WIFsSWu95iV89VTKYXLbcOxyve8HiWMNX+5Uh4tnb2WJs9Us/y/7niEL3yBZSYn+7R5huBGt92eyjYbfp/TzfVYLNTRV8LLr1K1kOzx/qsMFlO0u7BxTto1/m6v0Uc6TZ8yvcQ/jXuJ0+qaksLWpbVy9+4d/PBH3wcA9OUPLClJMxeLw2nmXDr0p8TI0XJa4VM52FA2aZDh+g+arZhb5djuyQ7XNbe3r2/BpSRLucj1vri4DIBnz4ooPbYV/DUpox12eFBQmWfDzjE7u+Tc9gtV2EUZYJE9SC1wD3oD3qn/py0Lj2gPTCOgoxJLIynUM0+Q1/v4RLy7MM+z+ORwF8spBppsZvofRplYq99GSGTLBZV3Gfbf6bYjqzuNUdYbjfA5xdwRQroHtpUk9No5rt3GABZj/BUgbw5o0w52j7AgW+aVz5DTWDc67eldyukwghb8f3cQSEU4xnkl9mxKoFndTlRE0QAlBCL+MFxO/ndNdCLHJ7RhXr8PtSY/bwSJYjGOlc8fgn0i306fOTjlGR4OeRARlYnhJ41k9/vDERwK6Fg1Dkag0u+3IiY/rlXmnE509k+sAwRFil4RxYkRCPQ6bLivsuHPX9BfL8h+dCcm1OR3nym4vax9E4/EkVdp2kRlc10dHBOLFRb5idD5WamVMdQZYgSG6vKvvvbWO4Dmt6wSwRMF5jvoozNR4FmJM5Nq+IetCboKEldN3C/G+eZ3e9Cs0G7YVHackB9Xr9amJc9vf+NX2fcDro/qqyMcyld+fMzxWLvBsz/h8aF9yjV91OSaHeleFwqFEFKCLqTSzNdKBIwtE3iCHP//4g9+NCsZm7VZm7VZm7VZm7VZm7VZm7VZm7VZm7VZmzW2XwqE0O/+3c0JAIRsJjQmjKitpAWj3WGE69lhAe8/YHZwZWsZALD3iBHN+Tk/yhVGFPdFAhxXedLdzau4dpVZnMc7zGq2RdzWtw3RGjGiu/+az5oTDHwhmp7C3eoql5i0GGlMzIURjjHqe15mFLepyOTi0hqe/ZyEoL/xPolf+wn25eWTl+gpfRSIMJJ6IWRR0OGewpAvFNV+48ZNTBqMji4u852fiVA4W+ng0cd8n3qTAb+GSPFc/REGkldfdTJyGlPW6Y3ba1i6wuxFRggcX5ERxvVbV3CmkoOzE2YqPCKf9IddcAsGXBGBnXXM37OGbFhR//KCyLns/N1isQKrovVVlZEZGbeJdQxXIKDPMfthEknbwe4u3Mq45NuMWz5+WUBfqJOR5ODNgkZb7CMMNU9RN7Npt1aZhdhYcOJ4n+tiaU0ZN5WFXB4VMOqKdHOZEf3VNfZpLm7FsrIxE2UCjjQ3hXobJ1lGhHdEUG2QEt64cRPnF0Q1DBTpNiDDTq8Hp4KgF1QG6HeKbLQ1QFIQ/uNjPnOKCjKZAGWmDVlrQ1b1wf27mChjblW21uvz4ccfEY1wdZ1Q0jNlYnPH55hXpqAf5Vg9l8yvxx9AR8Soc0I6VJTZWEnN4yNJNa8nOS4OSfLGwxF0KozWt4Ussob4XrlcDkkRTd8W4qyrqH2pUEZKEs0vJY1ZEoCvXelgfCFJ42W+a8DMbI1jEoAqnVBTSZVFMNx2r42oMtJpIeyy9QEmygIXlAWec0oS0woMRc46ECF5p8y9UbYMsXib6AmTsq6t08q07+EN9v37L1iO1s1zDZosZuypbMpYqxvad5GkD3uSyN7LcA1YzRxju8UH24D7JSDo+401lYmGJwiF+Q5L68yyWgt8Tjgyh67IKc3K7saFEPjwww9xqhKdeUFPTf02GoLb+5TZt6lkZ2KywiIUZV4SwR2hFfreMSLKQhhZtIsj2rDlVGpKgPj8NUkZjVLeB1evYF4ywi8EcR6J1DDs8qKrzG1Y5TQ5ZRvtURdKKicdqVyiLfvjc7mmEudd1deZRPTusTuxcZWIkz/9DlGVZT1zIZWERWWlN1c5tyFHAC8LIqIU6svYZwGvC2nJMTtlW45EktnvTdAVueqeCIi3Veb13lsP4ND5EhcS1SWUyNFZFk5l/fdzkk4WGtbZHsIstNZpQxLlIoytnOWRDIvQ00DuKVNqd9uREwqi0aDduX+X5UO7O/vYO+AZ9/Wv81yyCSb/6tUreGQPHcpkVWSj8pc5RGUHDLTHycU5Qiq3evSY0OZbd4lAHU1M8AtW/ZbKST/TeTjodaclQNtCGPmUUWx0OtOxvdSZuKwzJRqN4tEj7i+D9Nog3S+VStgQ4egHH/+I4yB0msPqQFAEm+uSuDXkrUulEm7fZQnS612iU5ML7G9r0ENPJQ6nKgc8OKA9XlrdQlPlx+/c5TPT6TQ+f8yMeUvZTCPr7/eFMFTJeFsysWOVD9+6fh1PnpFMen6Vc3lxye+lk+voCmmZjPBdG4b8tMePZ8/ZZwNFZZBjXhyfIq5SL7vJrnHnczwe11SSu9ara9yVwbQ5pujcV8+4P++/8xUAQKfRwr6QvwZiYk6Z+JHpFyVpLp3rC/F55CQ3PxB5542bIns92INTQh49lai6dP5FV5enaIGOShWkygwMB9OyWrNqkysqe/P7k6irzLvT5Pjn8rLxqRi6Pe6FuGR93Taus56pjpiHv21k0A9UDugL+TEnkYS+KAcispN75+dTFKFH58u2yppi8SSSIsQ/E7LF4/NjwcOz5nP5s0uxZQBAyOtDVXLgdREdW4QgLvdGcFgkd+w0EEh8L4fdjqHKSAdCHy/IHty/ewtdCVXsXQohIbll2O1Iqiy0pPJvj5/zcZI7wZyN62ks3+KiyO9bna5piVrpnHM7L/t/dW0DJ6dE1zSExJm4aEecbhsiURGKC8FU15qbNfHc0AAAIABJREFUmMbASPLU7B067R5Sc3yuRyUVxp71R0Lwygb1L7k+/PJzq40ixqIPePWKY1SvD9R3YGWdmf1eTSizAs+CTrePmNAQQ/n7IY9oAeqdqSCHO6zyOKEIPT439o85fuLaRldnn8djwdI8x/HBXZ4vr5/xTAh7wlOE0EgE+S0hjp0OL14K+WdP076uLS3A7eRYGqjNaoN9v7Z1BX3Zm47QdmYR8C7Nb2DQJxLDoiqGp49Vxpo7x903eDaGfFzTw7DIec/PEFGJvEMIXoMQt16vT5GkNe3Fc61Bp9UhumngTFUPWze4508zGTgsKgk0KC+EHLG57TgT2s4klF1TxL0Wqw19+TTGeVPIVpEr8r3c8lfSOhtjQR8mWmN5Ib3rsgcWByB+X3T1HzffYDnri91tODxcOy752wb5b8Lqmq65qU9u1/hYnNMyQ5PK5MJzXCf7R8dw6w60mOK5vi+UpdPnQaWq0nOVrK4JqWl2D9GvcT3YRLMQlr91dHGBvkkI8QT993a9jsIJ1yF0nkeFTOp0u/AYFAFGNYF8m7PjLDryvw60d33yR+7cvoaJ7lJGRUDXIN1eX8FowLl79ZooU7OVz35w7xoiQsM+fcjzwkDKOn0WtEQWbhBHGyWr9fwlrl6hP3upUvVCl/v71fEREkLBr6xy3+ztcy+N+z1srnONGfQdn4r4f2lrFZtJInU+fUSxCZPlFxUbRZUGJ0V3YO4N4ZDPdSJkW0Slv7H0HD55zGekkvMaTxHlH10gFhIq3q8Sb/lSsWAUJdlPi4iqw/Ld2s0OnEIirs6pQkefaTc6aILjfzngOgnP8XvmRhdRi+hf7FwL2Sx9FJ/Pi4bulCM969otrvFqo4KxhAX+0X8zk52ftVmbtVmbtVmbtVmbtVmbtVmbtVmbtVmbNbVfCoTQb/9OaooQqoofKCTCq7M8I4XHxSq+qIyvUQf/UvXRK8H0NHu8vMIoYkff+9qdd9AYMmr2r1WLu7zKTL0/4oXZLllORWqN2ulhf4JjZQWjqvtriSQ2lz3C2+8wI1pUdiaQ5O+220NMlNnbVCbgpy+Z3YwkU2gpYr3/Uhm+qIEc8eHlU2Zw/ZL4++KX3sChMgZO1UOXRPwwsruw+5JZ/664hKoM4sLTB7pBRmFDyiC8s84I9Je/cBM95//D3nsFSZpm12En80/vfVZleW/a93T3mN2d3cVi1gC7CywJI5EMUIZiiOZJCkVQCjwrghEKvuiBCiJEEUGBAsAA4XcXwJqZnenZmZ6e9qaquryv9N4bPZzz5xAPZOhJsRHI76VmurL+/D93v/vde+457HNfRLrNLWYlb3/pTbz38T0AQFl1toXjfQDAwtIkOn4GFlMibt17pox6woGesqCm3HdKUqGdThdd1cjvbkrKVFnhpeXZIQorXxKqpC/+mmYdazcZ/f3Lexyr3/2z+3BpudoVJR2YctO2DvzKMDuEtIr7+OFYsA23lxH1I2VuqyJLtHcs8AgR8+2vUr5v5RLnzerpIal3FRgKDklKn+fyQ0m/Rpu/3BNqIJPJIzrGTIFd+8ui9dW19uEUGVtBWcn5WXJuPPn08ZCEd3eTa++myDUvr65gQ7LgJpF5V0ic+flZdFXrX9O/3fzcGzgV6aZT3Ca7r/YBENniUrZ54OT3nUoGvd3po22W3upnTOSRHqsT2Qt+LhJkhDwk9EC1UMF4SNlqIRZ6ARHYdRs43+V3z/n4u7Ur7FcLfXx8nzKiXWWIli8zql3Kl9BU7f1cmFmPPSFwAvElNLsc29QMn1lvMDNVOd7DsohLd5vMYmSO0/Ars7Mn6dpjZYP7GOBin5m/z79GNGFgWiSouxtYXWWGwi2erAePmA12+0KoibviRDXob7zzeQDkVfiLvyBCazzBDJGZNey2itjYYBbz6ICDHEoyuxONxuCW/GpffBFRcT3YLG3ElG31K6t5a5WZlUqjDa/WqsvPn+9+8CEA4Ec/fHcoV2xmthdjPgSF7jIzzTllTQeGHWdZ2uHnQjnNLrMPEyE7NpUZmp6YBQA4lLVank5hXFmVip7ZVNpwfjwKiASvoIzoJ7I1d67cxLps+/feJcrj8Qb3/GQqNJS1vv4aCYELIvh7uPEM09O0ay4TrSjSW6fPjt19U3Kc6+Pb3/42AKIHDg7Yh1aT2cJyoY6ZWdosa4/PGogYPuzzIq5xN5EfaRFMnl5kAWVSXVr/j1/Szn3x869j9zkzVpAEsjfELP6rwzNMi/TTpe9riZsjkRiDS98XFaHleyJM75TqQx4ek/sjHOS6fPn82VCEIK4zK5Fgtv3J8xdYEXeHyYnw4BH3ndVqhUN92BKya1l8NaFQGO2SbLpq94/SpzgWV1OxynNiQhwlfpdnaN8tPutfG7Nup4Mr69z3uwf7HBZxE4TjCWzvck6CBtfl2BjfvdVuIKIMfU3fd3zMPRwJh1AQn82JyDc/9xbJa8v5LKqS2TUREyapZi5bRCjE9+yKg0HTQCJog2v1+mXaA5MDpD+wIKKsorvL73v27BksdtpkE1nRHqIXm7C52Z+MCE9RpA0LBiMoV5gR9cdEFKnM+bNH21gU8fmd2zwfIC6zWqODmjbWxg59hiXtz6vr62gKsnAk2WmTQBq9HiwD/i4iqWDYRTrpsqOm7GJEpPY9qzKR3T5sIkQtqA9RjcH4xARsNr77q036Y06vBzbZMBNR5HHzM3s7m4DELxzguh1L8t2jNjdO0xyPqmTrTf+01Whidob70y0hkJxkd72pBGZSswCADz+gMIdXHGSHJztwCekXDoj364z7zBa3wxCqoyEbc1xn/zxhH3yaQ7+F7+4W38fWRRaDrqGxYT/DQe63QCCAak1cYAZtew8tOIQ42BTi8uoi19Xq4gL+6hNyZrl1rrvFgZWpt/HaTYpMnEhcoisERDQcQ1R8gecSY4hHua/LlSIC4spyClH3UmdeMhnHqmzK5gui9M7F1+mJRJEQh1BLflVMe7BQKsMvNFWvzjnNH/H8dBo2VOrcQ8cZfs+qfLej0z04HUIva62Vy0LntKtDHpJ4kO9+aW4dfSGtP/qYfvP4BN9hIhVHS8iBiviTekK/tPptRGPizzBRh0V+Np/LDQUaCnX2yySnLxaLGJdwzdykiFuFhGr3bNh8xXN6bJxjbehB2WwWB+fsR63PeXPJP6hX87D1+d3zMyKh9fKdFiZnkRVqYPuQogCLIq7v9ixoyF/vevn3frsTQZNnRujX3ZN9dsZmwCk/zkSquMBzfjq2AKed/ZhdIZp750SIIY8Bt9DHZ3tCIIyLF6dcQkCEvia6Mi1EzrOXL+GWnbslsvhsTb97tQdI8ONMZ8OdN3lHmpodR1rVCFcWiTB68R7Pxf3zI0QnOG9tIaa6QnsAQFzIXJOfyNt1IikRkrbO/FcbtDtTqXHYND8mh2Gtwf0Cax9t+Yt58boEI1zrfUsTNjt/FxVZttka2TL6eqbJKWtyWnXqPZTyJreU2JvM73c70Zb/MKW7kCEOxIPDQyQl9GJyzxVNJEnQMfQHgrqLmmiP/YuLzxD5QvlFvW5UM9yPOSHpvJKft/ucODrhPaIj0maH1ksxXx5WgxRVdeERj+bl1VXkxMkYE8p/7RL9gfPcOcqSPT8+49ybojWpVAAWXR4yp0JxmwIpRgvVukRMdCbIbYTLZoFLPL11oTl7Wmc9qw3NnHw7DXFKPmb29BQ1ocWdHo7tRZXrZHJlFl5VppRK/AxsThwJueqXXTQ5NVu1KqZl6+wmiuqUSKHp6dmh3HxZdsPkykxEY3j+nHb01h36V1X5ITYY2BS3ol/CRYYqWhx2F7om+k1VHW6JGhlOF6riJ2tZeG5YbByf/EkGQX1uYZb7YF++bLvZQk33Wq+Ha2BYjWMZoNXme/2vv/1ghBAatVEbtVEbtVEbtVEbtVEbtVEbtVEbtVEbNbafCYTQazc8AwC4Mj2OyDQjWxvPGHWLphh12zw5hkvSmytXiPDpuxh12/7gHEkfo2wra4zk795nZP/L1z6PDx5Tls2rLNrKNUY7T/Y2YVgZgYuMK7Ot8Xi+8Qop1ZxfWSIHyzNlRFZSMRQliTsmZFBRmemjXGHIGL8n2e5+kxHKhetXsbHBrEAQUpnoMMNh9fnx6uU+AODGHLPlrkQPx8+FqlFE0iV1oWpngEcP+PxzSVXWVI/tqLZRVc36qhAtK1KW+MrbryHbZSTZqWftv0/+gy/84ldRUvR25xXfs3nOPl+7sYyHh8zaF5TFiNo5/gtr0yiJK8fq4vfkhDBqNdrot8wia/b15lVmx/r5LDrKVFgMZjbQZh/Gx4Motpj5/d9/508AABeNKLKS7h4IlWMTF43V6CAsGWy3MpsBO7/P667AZ2NE+LjCCGpBsdBevYIbcX7+19+m2oTTxr+bXFvEQNnPRJLj31C29SKXxvEpM2snqtMvVdnPi3QJbnGhFGvMRsbGGdFPH+9jLMxxGxO65kDZk+c725gR38+F6vTbqg2fTCYwmWSt9MsXRKiEo+LHWJpHQ5wvfiFAKoU8HOIDMeV5L5TdPc1mMSMloEtX+H1ZzVe93oVfiIqGUFuPPyZPyMLEDK6sMMOjUmE8eMJ92mv2hnKhPSF97EmuvcWVKWw+lCrFCfuTEgfFQT6NhtZAWHxXl8a5TxdWlvHwkIiFhhTkzop8djS4jLii4AMr13PHybEetHLoavyuv/NN9uHRPdRlP0ylkLrWWt9woisVga7QhA4llgqNAp5t8t3feuN1jQufc7h7hMkF2qLzEsd42sm5LVTr8IQ5X/uHpmxsVt/XQK/HLIQpR/z2TSJdImMBeILMbFycMhNl63JcUtEU1lVrnS8wexHsaP073egqheKVmsamatY/+OhjHEvi3eToeO3SNBweZfFttF1mffXl67dx7yPKNu/t8u/sUnGYmvTA5eTkW5VBn51ghqiU3kcsqiyysuqnUpZZ8gYw8PH9Cm1mP8fEcXR16RK+++dUafvxx+SbiYq7y0jaYJGqQkzcGm5JwNfHPChLDcqrrPKYOKAKgxJmxmYBfMb9VFB2aHvn5bDvsUl+xuOPDNUoElIQ23tB+2p0e0PeDVNNyG5y7rg9eKhM9swaEUynQpJ6PA7sbHLvWKXGF5UqlMUTRKvGsWlKNSUj9N3albVhFujnblEx7t4T7vmDegF+cF/5ZdMNqUwYg84QPbi6yn1t1s+fnJ7BL04OU8kqk+N4TExNIiKkalnZrZkJZq3r9QZsQl6+d5coJX8kiGavoXfgMyPKjofsbvjFZRCeFGeL9vXOzg7aUp2LS1HQVCEp15rY3WemK6F9HdU7uX1e2HWmbm7THnQl12m1Alap4pRPhcgQirbaKqFvk6qI0GkOKS8abj8Gktb2CA2REvdLqVJCRapmb93knLaFZL338V1cvkxugpzOyqePH2NuZhYAcHzM/WIqMhl2N8YkDW8iGP7Vv/5tAMDf/qW/h4ePmSkvN3nWOV0c62ymhDuv0Ub7PFwfQbekvZ1+pJUZdQs9mC/yDLm0uoy+bFcxy7msS9lx0OrAqfFu1Ng/T0CKei4DXilPXohDwWrjWE3EkmhoPEyEqLmGLl+9gobQgBVlYj3JEA726X953VwXYSEajUEbHfkBtRL328UJz9Qr4xPIyEZvi1fEKfSbYRgo6xyrlniOpaTe6hsUMD9DRIpXCNRXB/SbGpY2bMreV4WcCok3pWVYYW3y3U0ejdnXiGDLVXLYFxL02gz3Ql6+0F7ej1aD4/Fr3/kWAKAkFcL7n3yMWdkUuXYwDDv29sV3uSp0g5Br1XwV50J7RUNCSggBOX9zCctCleYkPX4kpd3Pvfk2jg75nebPllBIvmAAPSHRWzLSM6s8p9InBygJweGXxHYwynWfKTeQWuf3Tco2H+/wDHEbTpSEQm6JvC8iCeVMNg+nFISqHVOhh8gEq80Ci7g5zMx5Ur6UYbNgZpX28I1Vokm+93vfRV3zVJddi03Q9o5Fg2hX6Kd0ndy7F2kpuTk8mJimTxEI8Hc9IbCj3igqGaH7xVFqKv/F43GcyMfoSsXLJhWlVt+FgY2f8whB0qlJZe+sgkaX41AFv8ct9IbbcKFwyve8cY0+QEUcV0bPgp54RE1Vw4HOFF8gBK/4i0pV2sKpeBKHqoQICXltSKXT5g+i2ZLNkx9QLNFvT8aWIfFaVIvcNzPjRPX4XHakL/j8Qlby5/Lf/TY7ZuSbtYXk88uX3dk/gkN8MRYT5VHkT+/kFA6OuUaj2vM9GaJ8Ow+/EILzEa6ZF3u0D2fZCyys0s4lEpznlt4ln84MUUomWjRzuo/DU5MfiHt3QcqT9WoZWzucy5T8h3iC39dvtlAVH2tbd7Vuj/Ngd/Rh1/yatuXOba7H3YssGl2ui55VfmpQSr7lLko5ngtJnWenF3y3xbUllISs6gkuaiIpm70WfKrOCMg21YXSGXjdKKt/Hvn0hu0zZWRfhOPRE5JmMhGBtcXf12TLtg45t4bbQCzFMc3qrDeRchdnF5hJ8h4cCJpoGfbTbXNhbYF+Zl/I5pcbVLsq1fKoi9uoIsn35KTWSzuLtt7BbeiCoNYd1NCp0zexmzyMQtXb3Qa8zs8UkwEgL7S6PxiGUzLr5SptpkMqt81CHYbepS9eIltYvLONEmIBrj+r0L7tjgVN2SK70FDmvcKKHmJhju3OFs8CE81z9fI1pM947pnoQXM91mqlIY+lVdyADsPk7utCLjXiMdqBWpVrrlJuwC4Ft7nLtIcxratWqT7k1/RIjbIhv6na7CBXkg0TP9TREW2tYbGhXOAcOlVB01B1TrvdRE18UL//0fF/EiFk+0/94v/P1urRwX9yVMO8Nm1SE+zWoVCp99CWs9/c5+QsCH7/pW/dxqMNOcynIqOSXGF+5wHcgmwH8pK/3OPifvn0BLFxGqOXuyr3EfGxy3DiuqB+Tz+iI3z1TZJxprMX+OF9XhJTE/w7n0oyjg6Oh4eNV9LXX379LT7b1oNNULCa4I5bkna85gninSs02FMKXFU7FVxe5EJy9ySRKDj9i4s9vC+Da3hFCpsWrM1oIWjh50yp5RMLjdLmeRpra3QOqg0aM8u4SEmrTSTG+O7bdo77wTkXW3DPgxkF5wYiNpwSwVbI5UJeBMsvHvLwml7ghaRa7WFPZQhfukUST3+TVumk0cHAJaOnQ9UqA5SuOPD9T/l3LSeNra3eRL+py4+Mq02lDrDYUVRZQNctEnBdgCo1O86KDOCFTdlo6ALpAb7ztTt8Z431eb6o92uhcUiHyDxg/AocZJ1AsyfyN0lAFkW82yzU8NbbPKScgje++4AX3cN8FdYOv3tugvPsl3Gye92YEvnyFV0cnTIa4WgUP/4JS4DGlrhm7ZLFfvDyCVZU2nBc5d7IdYrDS15WMM6AAhT2kA9TOigDCoy1tB69Hg+6MpJeOZO/8HMkpm2XKth4THj75Dyd5GVJS4djUZxoXUDkztGkKWG9ibrW3/oNQuArInVbuXoTPS/n8lykfRvHnKt0qYSgxu/KJAMmf1biBffi8H2cZDhPa1/g4e0TIWCn04djksbdKpLdsGHDxArHraVA2aNnL/S6TlTOdSDXOFar1zjfhwMD8TDnIlbhe7aDCmYFG3jygOt9XhKv+QHtTzIcRBE8wM6r++zDDAO9hWobXR1q1taF+kM7UstkcOMq986kCO47Gs9WI4e77/OwXxKp9DMRCj4vHCEsGdVQnvN35w5LZ6ZWgji3ikRPASvLeAK7z1mCuKRgx+dXWBpnr1UwmOLcTY5zbVrlmIXOK3CKbLUsstWkHOGQMYW4yPJ39zmXVxbZ536tjXGVrSatHOOJaV6U/+LP/xz2ON/9m3/nFwAALw9Zjjpm9yP4FV7SKqZMu2SxS6d19L2cS49KrCJ6T8v5ACd7/NyeSNHTglRHwjE4BEu/FeWeSPh8KJolDQqcDtr8vvXVdbR1QFss/IxTgZfBoIO8RcSmL56BneVn4okE1tfZf6cc4jMd3r1aCeOmLLtIIwtZlQi6k2iJyHNTxKUZkYevTE0hNcE9cXjE39VVwmHAhopK2Wpxzs3FqQQUBkkMBD0PT3GOHHMqoTk+R6hBO3xpgmugZZcd7u1gUOfnf+U3/jYA4PjwBOciwsaAn4tFRcRtscEiqHxBsPO+LvVjCR+yRUkMe+TAyeFpGU1849skMf5EBM2ZupzrwDxOt/l9Fjm/Ia1ZV9iJ7UNetje11ztylm9eXYddSYFuT6U2KglotFtIiiR48xVtvFdk9onxKI4VVHr68oX6qZKwTgDlYz4z3WT/gk4XdIwhHqFP8uQl9+nxyS6uX5MowgMGDldU8pC/2ENestsuiWD4QioPdbTQt/GdKy09PMDvvdh/jqiP39PWhcza5PrafLqDy+t09Afg+l1w8hyITwdxppKsv/opkzvzChz2QwP0VObSNaH8XT67VK9g43AfADAtsQ+3YRL47sGm4JBTpS0nT7fhUWleW+VI5y84xtOTM/DoMjSv9T8V43sWixX4VNJwO8rz06agndPtwscPVQKki1yxz7kMed3YT9N+X2xx3NN5ke1HIvCpdCaqQGXKwb/3jCfQC/P5DQUaogO+W9DjhGuWdjegNWOWGUycNXGW1ZwoKGCIXPry7TeG56BDdtHi9sHd516bnGO/DrQ++lYDX7jO+XLIppxluO6DRggP7nPczYDkuoLOTze20Vb53/gyxzEjQY/JiRS6upiZF5DxMOetWu7h2MM9O3WZyZ2I/Oidux9jTOUnZw3JcCfoL3XbPSiWgpjKUC8UlGyFHENRgLLKDsd1RtoMY5g0NcvS8wqOFCo1RNwKhnuV4Ai5EFBSwaoyEDO5WGnW4VApia3Hdz6TEMW115eGMueuc87bnMiGNzY24FLgFPIbc+ecI4/dgrbuGDsiZ72qknVHfYCo1nQ4rESnh4OQGo8MAwyduhIxKs3PVMuYvS3qioqSQEp6ZYrlofDHpTucy51dBnF6nRbqVa6nUonvdP3yLM5U6uX2sM+mCEm73kBA50pwnP07ecnN26914A1rniSEclbhmepzB1CocE9UTd/QpWCTrQ+nT5QG5zw/W8dKyLgiw0ReIMi1fahyFcNixZKIsLMqfe4qyOI23OgrcXOi5LhzjO8bcAbx0WP6tbeu8X5g0znV6w9wpnuFVeX+7kASwQ73XqbGs/SlqD2cdh+m5ugHd0RxvSUJ9mn3GBoNnsuFloQ8zIRYqz9knHa5OMbfU5I87PTAr9Kypkrdix3RhITDCBhmqTB/jo9xn82Fx4EA119dZTwXEmqolGootrmOzACKRYIxpdw5xrVfagqgOmUvDaODrpKKMQUHf/T+D9EdcD187RukvBiX7T07O0OvpvVhpV9U1J6t5Rv40dOfAgC8IoD/1b/F5GmjVseRJN735ZP7InyHUrMJxWcw0J3NaeHcpC9yQ+qQ8oBj7BeJuC8YQEDlnR2RIScT8m97fYSVYKvJRzPLIyNJP8YsXFe1ipKtEq8JxaLY2+d4nOf4b0ZRJZeWFnoNlWH3VQodC6KrwMrSlO6yPe6z08NdBFQjvDItighTGGn/AgHtidMcbcxOjUEjd8CDal3B3gr7urXHWMT4ZAQplZp6gqbYD/sVDHng1pmR0H2wd8597nN4UG4oSauzziTrt7r6mJzjWNkU9PE5ueay6Rw8ZjBbZYdhCQ04DSvs/x+iPaOSsVEbtVEbtVEbtVEbtVEbtVEbtVEbtVEbtb9h7WeiZOz6lyYGAODpu9BXJtbvZcT0TWXL7fUeZmYYuTtSeUbjnBFUXwDYETnaoUpSJpYZmauXy6icMuO7PM3M6q23RdIID55Kbj7fZpQvZ0b9G8CEspC1AhELTjefWWrUkBOZ2toasyxeEVF1mi2ks/y+usiK1ycYpVu4dQ1PHzCL3BcBdFYIntppBn/v618HAOyfMeqeDIcxK7nM8gUzME+f8H3tEyH8qWQkDw4Z2ewpOtpEBxBqJSFpxjgD2Hjz2jJSKZEEhxlh3BFhYarrw/zrjLDn64y43v8J0VFj8RhyNY5RSWS3FqF1ZqZTOBax4bgyiCFlkWAx8OQhYfFTkl4fE+mZ1+9DXczFTo/KdyThvHl4jj1F1ssqr3l0/yna+k67MkVtjbHlPwLBmXA7hzL+Tpt9SBrndzLqOy7SyfWpIL74OjN0Cwsca5iytHDh+SOWRH35m+8AAE43NObZY7xU+V/pnOMxe5UZjvmxBVx5naia+Boj0VmRRT94/An+8j/8IQCgKWnM6QQzCaFQBFZlq3yC7zcanPez8wzyysI1lZVsCF7vcdiRiCrCrnKcSDKCUp1rdGqKGWmfg595+NP7uPMaUVGFLt/9mdAilVoTHu9fJyDe2yVSa3F2BnaRm437mfE8PmAGJhqND8tp5uZFKK6+tDpdWJVJqWW4d+PCMwcDEcQl97y7tQ8A2HzIPTK7uITQ3JTe+QcAgL6ININuL/InHL+cYJkVleCsz03gg3tEEplIuXAsgmmRsMWTfIYpZ769vYtlleqVVaKX3yQy4HG5AmeXfV4R+mXq9lUAwA+fPEdBUNIv3uG+KVXMjH0Lfu29SpHP/OIbzOBcFPMoCH1iEZFiSZ/ptRoIBjhPhZJIIGVb/MEwbIItH2hO3rhGFFA37EKxxP7HlSHae8WMYHJ1BWFJBOf2JQm7vIIPP2Vm7kzlJt6QEFa1Gnw99nlKRKwJN+fryfkWykKAxMdNeVOuw4lkbEjStyeE0PIloXsOi1hcpN39uuzcT++TVPqP//xP0BRZ6re+SYRQTNDof/MHf4yrIiCvisD/KMN+bh8eDxFJ8QjXbLHE+VieisOnNVYXItGlEmO/N4iSyrROD/meDsPAQJmrurLcAaE+Az4/dlVCG1DGa0bCBI1GDXaVeuxtcbwvr3MtlCvVIXnyxBRtS1OSy48/vQ+vkDOzIukPirTVMsDCmOICAAAgAElEQVSQOLok4tzJKdoIv987RA028ioJKvDnwekBrMpEOfTsalmokqMczoVGufUm7ZRdsBYrLHBZ+N8dzUNJcvd9o4MZH9/l0joz5+VaHfeecX9tn6qkOckzbtDvI6JxbwuplpzmGiqUCrCKSHFKpQPjUZWPuAyc5bk2H6vEsi9i4ddfu4mXz3nuFTI8g37u80TrHr3aR0Gk1aciPrZLYrfdaSKk8gq3eb7IDldqZaxIQjmt8qmAytKurMzjmUQfTs+ELhPJeqtcR0glcC6/SpQ9dnRVrtNTji0jIu7BwAGng//W1Pk5LyTwJw8eoq2Sj7KynhMqpwl5HKiqDGlWZLdxCQwc7R7i7IRzaSIkltaFGnVbIVADfCqTS40TzXmwtwUlaXGiMky74PTdXgO+oJAOQqyGQ1xD3cFnYhGmFPWM0BevNl/AKps0LnLRXqcPQxnzep3rqaGzPOANo6my7ZiIhO2C2FfrNXTkt5yoxHVKZ0NqYgw1EUD/8C6Jo8MiMp9KTeBHf/UXAIA1lUb5hJZs97poq8Spq6xr+kQ2Yi2OsLK0gzLX2niUfdje3UFBpSLRefa1L7n78XAChzp76srw2wwzA+wZ2jmTpLxab2BMpYpmac8Pf0DyfPQGWFhe0XfSppdkQ71eDwyVr1tU6hsQ6sjhsiMncmazXOXaFZ5LTx89hqE0vkcliB6h1UuVEixOPiuo8orDXe7hRCyJfEX98QhF+4SottmpKdh7nNO4zhKvSNktTjsKpyK01dpJiNS+12riTChWswSpq1S14XaheErfU0sWiXAUp/KLfJJoNklvnYYFViH9Mmfa6z6uX2/Ei6tXWMr5+BOisR1WlUe2mrALDQKhqk0EZqvRhEdr5ept2u2QSmief/oCP/hT+h1ffofoxbLOoGa7NSwfMUn0u5qjzaNdLF0x7y1cM13ZJMPtQsDDcb/Q2WBV+WvXakVHfm3A09ezQ3j3B+8CAOZSQthq3dcbFXhCkoZvCSGX5h4JxcPwhbnmsme0p7MqXa2XaijKl0ym+G9hidYUcln0ZSSWF3nfMgl1sxcZvP76mwCA7W35vlqrTrsHbVFDWHU+7Yn4v9FtwOUyJez5rCOh4JweJ2Kiw0hpzfiFFsldZHEhf7Gt+svLi0tDUnm7EOFnKq3vdYGmECB1oYCKQmhZDTequmuZhPjm3dcYDFAQkvE12WaTvNzh8yAS4/ze/egu/03nxPjYxGclQfIBomHatKDTgYCP/sbWJve1ScvgiwVRFPVFW2WKXZ11FaOGK8t8h5pKNIvydzw+OyDEa153MMPpgLXJtVWUlH1UPtHG5gssL9MWWWTnnRIm2Nrcw+J12gubIKE7T+jzrs4sfEb8L6TOaY7jU+3UcSL/aF5l0q+2eHeYm5lCRSV3dp15dif3eqlcwKRXqFadeQGhafdPT9DV2Z2akK1V+ZXbYYffznfud3WWqBQ0Ekwgk+F+PEtrPLV2J2ZTaIrC49U2z/LuoI2SEEiX5JfO605wfnaAlChU3E6OX0M2wjKwDKkWOiovr+juZXMb6AjF47NwHXd0uff4HXBw2FHSXSMopFEkHEdTpcLWCtdAQ0jvTq8Hmwjaj4Vk7IhyIBjzwy16nIhKfiNBjU+ni5LiFxpiHAtNv7K0BLfs7q/95r8dkUqP2qiN2qiN2qiN2qiN2qiN2qiN2qiN2qiNGtvPBELoK2+MDQAgXSzD5WV2MWwI2SL5zAm/AbuJmlCd+V6JUTefDyhnGS2+tEaOEruX9cCvNnYQE8dOQHXKLjszHI1OHxHx52yeMsppiB8jfdHBkmoJL46ZQbFIYjQxPzmUTi6rVj0rEsVf+eZ3sClSr2fKvMRElOWK+CCVewxErJgUkeVcPAa70B2v9slLcmvhChqqf+yKWBYW/vQvjeFff59k2Z88IIIJNUYm+3YHalX+XUxR9BnJB3751mXMiKTPEWL48t4T1stO9eMYX58FAHji/PzHHxAhFAn5kBNiwaJorkVR+3av+1mtv2TuxqfZr2a3O/w3Q7XBl1eZmX3y5AmMgEjVFJGv5fnehWYPZ30+syBS2GcPNwFF+ftat2bNqmHY0DaztMrcuJ2fSd9OBjkHEwlJoxuMuC6Ne3FlkRm2ZFxcARFGqYOJKVhFBHqijMbWTynVnCsVcV9y6deXWQv+j/6Xf8bvDY8BepeSeI1MwtRKpYKPPiQy4+kDEvd6Vffpc3mRLnIdjQvFYhLtdQ0De8qEWETebKJ6MBhgVrKhrS7HZ2Z2CgKKDMnEpoTeKuRy6Kp23DnGPqdF/uvyehBWhiEklMajp0R4ReIhDBSBz+1Ipl7ZV7fdDav2qlmranJKGIYdhlUk6la+y4SyBY6eFR6RzNULnPupJLNW85cv4a8esM75yQ7HOjXB7MnZ4S5WLjGzev85EQKPPiY32D/4ja9h44j78rlIr68sL8KhSL5V2W7R+GBxeQENpXouJGE9qex1MxRDR+SFjqoyleKU+PDRJvzij5mPMxtUNCSTbLegYzERU8wWbu3s8/urLdyY41xYlE3oiqwxn77Ac81rURw9LWVwbty5BYs4rMwxPj8hksMZcmHnlAiV2zeZ+SmcMjsQ94QwFZvQv7F/toEFfQffb/eC9uPa6+RiyuUyWFD2Z+c5s0YNEdmVunWsrfP5ZvbOL34Gv9eFA9m8MSFiYiInzeVKqGc5flEhSA6Ecii0q7DoXaoiT72zzmzv3e1D9FUfPpbivhzEONbtgRVJL/+7Uua7nJT091cWUTiTRLaP69nvEgn5AChpT2QrJpfVOg42lCkfE6JFxK9Hx8col/nuVdnVuVmOz9z0DPaEjmwo0yP6GXR6n/EsWZQhXpzn31WLJSwucA1MTzJLZVFW7uz0dCgPXhcStS/uuUQygliEY7v9glnafpPZZLvPjUtC/zzd5Lpv1/icudTsUKbbKT4cm3j6Wt060iI4NZnCu+Lg89l9iPhFGKv+zSwvIKP3+ou7PwQAhM1aeY8PhvL9wY5QCmOcI2/Ug42H3KPL4re4KZJ6d9iL45L4sE74PVYL+3zz2ipysk9N9cfeER/g9ilSelYrLhsjEs9yuY6NDQkhKDO4sMzxPzk7xo1btNuXJG18/yPa5V/+pV/Ey20ikp7v7gMAHEKGNTptBJWRO9rmGMRjIeRyfL9Zcau1RHJeKFWGSKe3P0c0X1jIh/sPH6Fclzz1An2N7Vc8+6NB/5DXxmXaUUm5B4NhPJFkeEVy6aEkn1moFmERL0JbaIa5KaJmvJ4AFueJWC2Iw21rk2v+PF2ER2iLyWnaPjObH4yE0RLa00Td9lom518HDo8I5MXhMhuMDwlKW0LnJaO0Ax6HH+fiJPGJOH19hTb95dHWUGb6TOO5s8v5W1meR7vDcbQLzXAs+edK0QKrEAFxH8cqEuRn8uUSWlb6GOcF/v2NG0Q5VA4+QUnv6RNq+f4jnsmLi0soaowWJdNeE1KlGrZgIM6Ryyu0UzZxEibjiSER7qMnJJQPBAJoCXnaEKLoQJLGoVgUPRF9n8v2mVw0nXYbiST/uy/OMxMZ5nI5EBunnWqKM6svsuJkNIZzoQVMvsKzA3HUlSuYmae9McUmytobDqsLwXn29SJL/3lrcx8A8J1vvQ2/jed0t8l3CYqEtVQuwyX0sUn2bvoJ4UAQfqGT9o7o25R05rVtBqLy84NCFo3FI3jvvfcBAD5JVqcmafdLhcxw3fWbHBczU792ZQ0Bob4PRLydzwup32ujKA4mu4tjnRISrN/ooCHfs6csfEVr4vQojbhsi1Ok+QMJD9htNqyKx+/8TJyH4hcJpsLIyz5+7c1fBgBsvuQ6djoc6An9MBDPTUnryhuM4FxS9lajPhy/nnysi2P6XCYXXDQegc0lwniR53ssHINiuYSw/JRmhWO0kOK8o2NBXUh7k9S/2ub35rIVRERua6InolHOTa/TQlpn6ukxzwuLhCLqjRYGA77LlHyHhpD6B8eH8AmpaaKyD4XImZ6ZwXnmTN8j/1v3hXq9jo7WUyDItRCyOOASomtigufgyTF9834f6JkEv+IsMpFTpVYHHvGH1ooiD5eDfHF6hgmRBGdzQqdJLCEQjyArn9DQGe4W712tXMOcCOedVtpmmUk0qiX4hOAwyYN39kw+nhDqHa5jn5Crpm3JGjXMj3EPlo5p3wzdcewOKx5vEz3fEvJmenUFAnbjiSpG3A6OVTQSRCzMfqTGOKeWrnhVy10U++KWOqcPOSEkaSQURlVVOONRngUtISgPdk6x0Tj/a+MxEMF4KOgdCtn0JHM/J3tSqZQQFCePrl4YyK89LxbREYJPuiEw9Ey/xcBAzzRM0Q/d01wOL2wSZ7LrLDm/EFG7BQh46V8VRapebVZQF7ro+FjIG92XpqbHMDtDO3O2w/75tecdhmO4ts/Ei+gTujI+HkdNQkPVPPegyRNUrZdRKueGcwEANflQ4+PjQ0S40bVrjGijrFYb0uKCHCIa+6ZggAclkWv3G/zMnHxtmzGAtgsCEc63TdUuB3sHCAqZ+N//8++OEEKjNmqjNmqjNmqjNmqjNmqjNmqjNmqjNmqjxvYzgRD6+7/42gAAgkk3tg4Zbc9fiPNFqlqt5inCY4wGFhTl60g+cH1lesh0nhSi4vSMmZ52w4GUamfn1xl1fP/7jLz2YYPVyWd4pXKzI7WfXLqHfoPhthvrUl5R3WbdDsQmJTMohvsxMX5/+c5b2Nhn1m0nzYzBdXEVdAdtNKRgdeMSs+wVRe/tFiCmmv2zDKOX3UwFF1KN6nQ5Dmvr5KapOLr4N99jdvbwnNFDr0TjCrUaLIr8m7XnU6o9v7UwiavrjNoGxfK+IcnVUNmNmKKkAwGSnm8xo5uaSMKjOmCTybwj+Xk4nLAoMm/K2VoU2YTDhhPVwEbFG2EiQk7T5yhI6czuUI215OTtnjB2ihybtpXj8uLpNlrKZpmtayraN1uAJAVNngOzDQYDLPg5zytmVl5S8zMRJ0IuRnZdiuR7JDs9s3QZ4etUCfq/f/d3AQA7HzLrV6210BIvwq/9MtV3vvhLZOnvt3rojUsiWCiq3/6t3wIA3P3pR3h5wNrqgqRhLaasS6uDeaEfqsrYr10muqRnWLEt7oOzDMesomy5x+lG3ay5FSqq3WogoExKt6u6ciF/Bhbg9FRZGWX2XBqziD8Iq0V7QpFuj7Kt3pAHPqnh9LWeElJpctmdsKtmv9XmvIX02Wq9ibSULhpWvktTEXDnYICmsncVSXaumnXtiQjuvSJjf198V50813inWUBOErdPn/L7fvnnyYsUChv4/of8u7BP6lguB6pSIXpTXBwBZSWWVxZhKOMSVL348/eJiuoHo/CFhCJ8wbl/ssf9Ep1ZgkeSoI/fp/rad36dXFPH1TIqJ3y///Ef/88AgFdC5+w+34JLCLJ0kxk3J5g5aLfb8CszmhViKq3MQbvTGXLkrC0z6//RE2aM/ta3v4nzHLO6954RTTUhboNAvY83rxOd0FAN9IWlhriQMzVJ98Yl953PF1HXmjHVvoJx1Vf73KgJPXJ0JOU+v7hGamWsLDHL5BcPQ1UIiK7RRLumua+Le0E8OS92NzGm/RLRmnWKW6znimHnMfs4L3RNZJLow9N0Dk2pQjak9BCKsw/NYhuRAP976ynRFFNCGLU7DaTmmBEstdn3mcUZ7G/STpnS5jVlOG1OFyJaAw3NRb3MsyDg8Q1lgy9LUczvZObn5eYOAiGpuinLlUyI265UQlxS3GkhHUxetLOTY8Q03l63/u2cNmN5eREnZ8rsKQPeFXIkGgwjKC6TnsbdlLr1hnwoCl22v8lzqSzJ3J61h9kVjodZD9+s0Z4vTq6h01bWTXaq0+mgLzTgVaFsXgjZ0qp30ZEcbVtqQtBZcum1deSl+HamLP6cOGJee/MGKh3ZgSrHqiTp8YjXBbs4afaOaLf2lBX2BaMYWIUuU/b5W99iVv69D+4Nx8pEapq8ZrF4GK2O5lcqK4Z+F3I44bHIHmhPBJWhbnVbQz6MrlC3NgvQE4Ijoozqg8e0FcViEdNadxHxB6Sk6mQ1bKjKDniFGsoLhdts1nEsbr+EUK2XTBWfTBFO8bxZZL8fiwuxXuvALtTi0jQRDFMR9s/lCGNihrY1kOSk/Mv/818DAPyhGdx7TF6opcvcl+eHXF+wWbB6ieeQyYe08ZT2NRr5bByhNX5WPIVPKNZbS+Krakuuum/HntTxIkJHe6Xe1U4X8OAZ/Y2wOKlgcFwHvToGOuOmpMBp8hR17HZkT6R8qn0TFIfWJ8+eIifOs7Ep2qbXPyc1uz/7E+wLqeMWH05NKJtutztUPDMV0xKyAYV2GeviCXrrBv2xu+99AADodwdDxdmf3iO61WoM4A9w/Zh29FxcGxaHDUWhL23qT01+j9/lw3mFNn1KZ6JX/CqFQgEtoVqTQhNvi98wGgxgEOe7WxqyO1I8Sp9kYRGaIaP9Waqwz/VaB7kyx9EpJE0ownmcGh8f8mR6teZMny2dPsfidY6txLSw9YTzOD87B0Nw1jb0S6FhC9UynFKKywklkhpPwJDvapU/1xEyrFUrDzPtdjvnKzXGfrVaDRzs72u8JUstn/Qok0FNzwhFOTchN/fu3sYWJsXBFNCaMZFMmVwVoYi4YcQ70xBnTqVQhN/NcXSLi82jsWqhiWP58KlJ+v7Xx3leWwtVlJo8c3JOIfqFzLPUBqgfCsErfpVKqQCPw5Qo1xkkrpGnL14O4RZjKb6nQCk4Pj7GpM7JhJ4VEIri5dMXWJYKcEu8P40evzefbeJojzZ6IFTqlStECkXCXlTzXCudBsd4J8sxv37zFgyX5NE17sdDFcw63EJvmaghixT/3AEPFsVzsyNU5pG4laYnU/BKWrsnFS5bowuTCM2q9VQVJ5nVZodNsG+/eBgD8mFr9RYsA67DI0neB8QB92p/Fx09szngs+aWaTsTdi88Pg7qjvy+jtArwWAIDvXDVPiz6Q52aWV5qDb6TD7a3AL7ma+W4RHqtik02rOn3C/RuVkkg5zL+QmumYw48mB8ptz2SLxET7e3sHCTe+Gt228DAA42eW6sz88jqLW5L9tQzsrvtrtQjEulMMi+ujocazSaaMsvaklF9CzPe1e9bcPYtFPjz+YS8sfSBaIBrlGTK21bEu6AFVEpFpqfaYsvtlivwyG+q77VRKBynbXKNfgDfL6JOgrJR3S7HGiUm/qd6V9xzeeyBVSb3J/z4ns8PT8Z8izVGnx+Q4jDicnk8N5S1j0rIR7cUr405HQsyG4Xhd6NjEWGvpAp+T4Qx2s4HEJGqGyTv/T8nH7LoN3F7DTPsbD88I7u6q1mZ6gG6tSeCseiet/60JcsNIRcE7KxWsqiqhjFQGeJxeBYhQMR9PVe//y3PvnZlp2PKHgQKrcw2edi84xzkHZecuH/6udfx+e+/joA4H/7o38HAPA2uFHTJzm4vYTbmgevSf55Vush16QR3/mYi3P3jINVLfeGpMTxFAe8K3n3Uq6JWZG3mY7zlVsMDB2mz+DS5feaJMAHaW4Ye6eNsC4I/9N/998CAPIqbdvb3IRtSvB2kTZnT7n43F4PBpJv3RVh4c+9/jlMSf79u98nodkHD3jZc8X9kN2FSxBqk3zMYrUND+GGHM66SoSq9eYQtjYwefYUDMhXClgJso8Wj+ZETm8XXRQkg9hRQMiEL/tD8SFsMGyWgCnQ020BbZGB+cIc248/JlGhbyyCbJNjE5OkZlykwb5ADOd650aZ7+uxAn27SPpETgptxmazOSRQNBmmzTKPYDCIptbAhS4ZM2M0gr1BH4ci3opJpt0f43vaXe4hKezT5yIDVxDM5Q7AK5Jah53jbxKRnh8cITVHR2BxUYTOcjbW3noNZV1aM4/4+fYF18J8cnp4IKXmCUWvq6zj2YstFHSRtogYzmfCAq12NBUZKytY6vX6UVR5XUAX45O05sTSQzDGwFhbB/WsjOaVlRUsz/K/QzpUw7q4DmyAXwd0ws2xbjbMMr0++iJIdujS0DDl6/0BWERa21ApRU7Bn3wxh2d7Iup+RYfghUqderk9xGfpuHwiAuRLCZaJ3Xr7K7i3waDNG1fYl1/9xjcAAP/+j/4dInqHGwoAGrBg8hZJI80SDtP4PXn4HDeu8nfNc47Rs5e84MYnFvD4Keep1ed68oksevP5Jqam+PwVlQ5c0bwXnjxGTOV4W0/Yv4JKIE+OTrAgAla3gmYDOXmlfAE9zXlTB0RbJZC5UgWVEvfgIz2zp4D+7/yr38Y/+ce0N30dckEx2t2cWkRDdm5P8PZkIgi3ST4r+e3DXV4+csUaClrnG/uCqZ/wIFy/Po2CnINDyfT63bwMWPoDtERW+cZrtNUffkSCz04ph7gOwI0CHZbehQiupxZwcsySypik6T/VXkokp9EQCerTHQZ2vqDLedRuw5bsqFdlNZd1yTy1NIYlA3OS1rbqnCmka8OLuOkkHm7twykp73FdmucX2a9MroCsJHiXZkVwLRJDozdAVCUNd26y5O67f/rnAABbvzUkQb39GkuZzRLjTrGBVlUQYwUkxpKCLJ8cQ/4vwiEGA2o1zvvuzhHcKrm7pu87kfNQL1dxcML/jgmOfK6LSblRwlVdXhNybgbWgsalh3iAfY+oTNQwuM8/+vQ5Wk32/dKKiNfPynj7c28BADwqF+yaZ0rHgqMdfqdVzrlDZUN7j7fQEwFxscFx2RB58Nbv7eDmGp8/oQt/Uu/58N5DdLR3SiJinJ3nuRtKhtDqqixvjD7DD3/0fY1LCRPTuqiW+J6Hh1x7vlAYi1r3mb7KUGQXHtz7CCEH92XUrxIREXwaTgvigt9nX9KJd1jssOnMr8shW5bdaTYjuLzMfmVUWlipqjzPYmB3n+8TT9IvMIlpm/Um7A2Rb/a4Ru9+SDLlUCCMYol+0f4x5+bGLe63Mb8Hzx9wn7Q8XDs7GX7GZvXiR+9+DwDw2pt8p18QWW6hbEG3z3H0hrkeg3auAZvbjmhIJXMK3q/OU4jh9PgMTklCN3QWfOmtryKvdWiTkzGl0tFCrogvvc21U2hxzRwIhp+KxvDOO18BADzS2Hbl5EQiIXT13RYFbcYmOH9zc0ncrfFCllGpGeRIW1xeTIi4vN2iPUh4aAvXrl7CgYi7I0qO2VpcAycHp/BJgMIkva3LD2lVu3D1+bsXD2mnXj2nb3mcLsChoE1fZZ9Oh4Edka+HJDhi83JvRFPJ4SU5KLLahC6EXpsLuQrtWyHLNVqx8f0mpiawo8TjK4kxTE/Sp2lWayhl+HdRn3waRQpSVyeGfpw3wJ8XWenJDxyYbCigpjoUi0VB5MM9DOQfDVT+XVSCJRoJ4VQ+7pduMCkzrkTMxx9/PCS4H6jcMKUy/HDPhrySam75buVSFbkLPjcZ4bi7zUCUMwifArQNXW7ufkjahIDbC7fmPJni31WV/GhZB0PyY9MPPsirhK7RxvYOk7qXL62pPzybnf442jqLnR7+nJ6if1xI51HIcS77Ntq3ogJrhWoRsRjPoYMdzlFEMdPmeWaYbO0mRfQrXzt7doGkAlVmgMJu2IZEyaUcbYVfF8j5+XkYujP4dGlOZ/mZ1YU1/PyXvgwAeHCfftLM5CwA7sl4iHNpBjR2Mkz8dLp1TM5Fhv8NAMrRotG1Iqk1VspJUEZUDJlyAfYOx7+ry7x5ka9US8hIcr2jg82nO0u1VkapyHPIEBVFV2WVYX8AhsHnl2VXPcEgMif0O4LyXVMq8T44O0NPycyVlIIvOdq+druNjHxOM7lj3hHH6gnYXApaWkSBoBJG98CBWsH0MVSb5VYZT7uNTI7v7pY/PDnJsys2OQ4IsLCoc0WVXwhFQ2hpHZoJgEuXWTY7lZqHTYHovgKjTZWj7u5uY2Ka94mAizb3zbXbyDd4hmaP2L9wgHP66PGnaErc4NIK7bXVq1JfVx8rDvo5H73Pu83+iUqEE2F4Q/zO1SWeY/2ekpKFBg662vdKOA783Bu9Zhcvn/FciqgENxLkZ6LRKI536FMfHpxojLhH+n2gKvGGoJ9zUhQ9Q99uhU8l+L02B9CmO2atWEVZRN2z0wq4yi54bA482X+iMd4HAMzNzWFMPvnBAceqpD3scTvhcHK8IwqwR1Vy2W63kc0L1KDkjFnyNwDXAfDZevLIXhl9KxZvSzBAlCFWK9fQz3/1beTlU5oE/FtbvP8sLi/j0mXaolc7/Lu6EnyTiTE8eMQYgCfGd/F7eJb0m11YLZz7ipIgjSrHI90oYEziRf+5NioZG7VRG7VRG7VRG7VRG7VRG7VRG7VRG7VR+xvWfiYQQoaf0dJ3Nw6RafCV6nVGz/6Ld0gA+Kvf+Sr+j9//fQDAyTmjc7N9yQj74oBFsHsHI5N2KNJY2kFTENnzEqO4eUXPLFY/OooIH4nIUoATRLxh+FTaFI8yApq5YGQzFQ4PSeLGhQyYXJHUaL+DtVVmEapCo1SbjDBHPE4kpxmlyytLMyt4d7nbxUlZGVtJoW6en8CX4DuERNrZLjPC7g970BPypj+QLLVI/urlGmoizjWj0iZxocvlHRJPmfJ9QpfBHnAjK7K3lTVlYAV/NvxOtJX9P5GMaEGyfPnKMXzKbp2eMZIcUekTXDb4VTZxJFj37CyjzoGJGGzn/HITZjcQISZ6XSSj7E+1xnFJRqPIFjmWGUVXTWlHn8+LpiRue3qGiVgpV4pwWPQ7lU1s7HFNHFvasCmCv2wToblJeuZw48c/YilQVmTXDhEqesJe+JWF3xWiBWZ5TL2JZpORXYdg3W5wDa1OzaGoqO3FPqPT+1mNayEHq9BNLZWB1CTnOnCEYBNEVoljFIqMokdCYSSiHIfDfUbMnYYLEcHTPZqbO7eIUrh69TLmhH5wKAPjlZSypd1BXAgMt7JPNZVbeA0XLEUOTuaIGSWLYN2tVgc2raOWcAaeTycAACAASURBVONdlWC0LzJwaS95ldmYEpKjMz+H2XVG0YvKwBYF2dzb28NJiXP/9lvK6phomXQZxWP29Tf/wT8FAJQEB93Z3MevKtNsFXLN7nTAI+nf//CHfwwAuH2LiImBYcCmlz854h6fvsOSTp8lCKGjERpjdqZ4ntY7fQVPVM5kV4lVv869eH3xNVy+TtRRZ2BmergZAyEDb99hKcXeETMq/+Jf/EsALH1aEVKiIoRVVaU0k3OTONjhOn7ny4QHQxmxyvEZCkLsxc3MvrLr9WoDe8qObZ8TIZR9tYnJKfZn/SrLfuqS3awagFPoMLugscW6SRKbw1eFKihmaftOtkhunJyYxJuvEbViVWngscpRVxbWcCwSaqtQUft6pxfbW0ionPFM5KvnQoIUN17BLQK/2RRRFGWVBoXDcdiEDKory/7TD4ikLNTK6AlaOz7DrJ0/yIxKtllFS7LF5YxKM5dXUFLmy8zC7R9xD6fTafREwumxKSMrolSf043DE/bRLO8yD5FkIjokRN18zmxVIsJ1bB/0YLOZMt/8ubnNDFofAzi0Hx8Ibn54uA8AuH37NmwGP18WOe+akEVPsq9wIRJruxAnjTrHrtNpIyQ51d1XfObYpMgIrX08/pBZp2kRdXoCKje0GXDEmIGNuPksf9KCoz3OebFJG3T9Na71+w9fIKB19Er2flIo04mJCeSUDY6b0u2mRnq+CKPIORlEuNZKKlHLlKtIzjAz3b2gzTSlw5fGk8MyphM7105QBKaw2dFq03alJiQmEFRJotOJqt6ld8JxXLnE9Ry69ibKkm81SaI72mdbp7tAimPzhXVmdR9/+gglZYqrbq0TO+1UMVeGbYH2N+7jPGUkHNDrAytTzNwOVC64uUO7OrewgM1XXFcffko04IbK6W04wFXZzLZF8rQqya1Xa4hP0Vc4EhLvY5W6fv2r76Cl7PHTAz7rQMgnjyuCOc19UXLm3jjH8eGTh2jURa6r95xOco/Y+3bkVdoaCbN/1pM6LDmeqdkB32vrMdfLt37hG7jI8rzMKnsfdqnvuy9QF0IqqH2SFwzf6/UOy8t3DojoyGutlw4PEHbzzOprXaXP+Tu3PYLTXe7jJZXDP/grSr4bLh++/MZtjpsQlB2tudVECgWNzdErzslkiignfzSOotBNEyo3quqsPGnX4RHiellnbCV3ilSA/tTELJ+RzrPvzVYLHpXc7m7vc0xFxm50bfAoS708z/m++zFFPpxuOz73xc8BAJ6pzM709c4vMkgI8eTuSkxDvuGzZ09hOOljfOWLXwAAfHj3HgBg6+UzBJVFvn6FNrPRMMvTzwCtaa+kzotl/v/J6S66GdMpkf3RmVdqNhCUsMn6ks417RW0+8gJOeILc635gkGkzAx7Syh1oRc3X21g75B7xx9jf6am6UtOJhPDcpDdA661hXV+X7znR1hIxvA4/Q4TwfBqcxtQyaMATEgLxbm8tIATldhMqtylpTL/eqMMnygeykLk1oR6jCdSiMnnHXPyZ1pnX3XQxauntPN3PDx3o5L27nvDKMjHtvT5LL/fg6BKm85PuGYGfX4mGo4grxKWrvycnsh/XW4D2/eJFGxccFyeZCkeMz09iw2zjEkI55BENByOCAwHP//4yT4AYO0a19nU5BgefvJY3811PyjQr9092kUsZYr3mGcO38liGaArXzwglFnXLLn2+1AV+tXQvnn9Bn2IF0+fYHaRyA9zTw1gwYqECMwSYRMJHInFcCgqhE8fsaS1pjuVIxgZImO7uifUJOJTKeVwKUXb7zTJpIWq/+in7yOhPb6wwP28u0/702y0h6XEA2ErzPLZn3zwHq5JfCMr8na/nuny+hBLcqzMvw9qzGr5HKoNjn9VNqmnu8vXvvH5YVXG9DjvmB67F92ezqEX3Me+cdGnrK5iS2jd/SzHKjkhkuh+AWIKQKVCP3EmKvL8cgM+2dN6ieOeq7F/XZcf1Qw/n0lL/MHg/DWaFSwt8hlNqJrBFBwZjyJR4fijofJtodu9Hg9qdfY5rbtURsjI6OwETg9N0nEJvVQkagQHAn7axymhz/d2eOZVag3ML/P7mnVzzxbgFpp1QhVIEaEXZ+ankRcK6CJ9ovfjXlpancPBLs/LkxP+DOhuGooEh+T8fqGbLBIcqOSr+PhDovOkiwSnKD2ePN5AVJUGLyUiE9eaePdHP8byKv0CQ35HXN+Xz17AJ0n6z18jGrOrs8gxtzQUVjIFbMYkJLR3eIC27NN/ro0QQqM2aqM2aqM2aqM2aqM2aqM2aqM2aqM2an/D2s8EqfQ/+crsAADaY/P45CHr6L6het5/9g//DgDgj37yA/z4E0Z9RQeDWIRZq0ong6pQHqacakMIo6W1BXQsjAa+2Ga01KOI+/HeOdZXmXlpdZRd2GV2wWvz4s0bfIf1RUa1TYLDoMM7JBoMK2rZNgkp42E8fciooJlx80kmr3J2io4SommRI/tVUFxqdZBWpG9FUfv0yRnqVnZ2PM5/izn5LIurjz/8CbO6795nxLorlE2v2hdFLTAQAiSs/1/2u/H5a4w+ehOMNNqVCTx6cYZJEV9+6W1mBTayfPb9l49xJmRQTjLQcb1nzOvHpuSpJ0WgubTO78gULpBTlD4oZICZnTlLn8Eu0rc7t4nWsKlgeffgHPsiDMsogm30HPhYssU1kbuaiC7LAGiIoM0qfieHiIKbzTqmJGkZVP1p2Kva8HYTk3FxESjKPD7LeZuYn8bv/jH5KA7FczDm5rtPLMwPSdHMjJ5btcytUh1BkR2mxiU3WGdG2+P14d5jZn7/5C9Z/36aFyGd2wZfS0SKyjoJ5IB6owOIMLamGm2T5Bu9LmxW1deKRBb9AW5eZwZqVoiHNXGAREIhGIYpoygJSdUpd5sNhJS1MMmCO0KHeD2e4RhDXCAeSRmW8wX4vMw+e918h7iyeBcXF0O+K4syB4kwM/bRVAp+oShsQl3YeuIg6vWwq6j9p3fJO5U94t7q9uywibhufYJzeqCUx2G6gcIB1+N/8xv/JQCgWCnj6Ix9NWVlr14jquHRo0fw+cQ5JgI/r7J/ob4PA/VrO0PbNKbaXVdwHFWhtoJCRb16sA8AeGP9EiZVG+yWrbBLuvLDpw/RcHPhZkvsX6fFuVy/tDjMnJuZY5eX4/jixdaQ68WUhe9aRCiXz+NM2QureLZcIu8MuvzIi3/jWIgrm9cCizgW1he5V03+n9mFZeyIePH5NjMuJaEB7d0zrIkQf05128e7zKC36zUkNDYm0kGK6Og7A0iLYM8vDq1skVmrYqGA//o3/j4A4GCP3/dSHE7j/iDu3Lym/nAcf/93/h++59IielrHRaE9psXNZPG2UVF/Ohr3oviXctki+kICLM0z01av1jA2JQlkrf/HIs6dnJweEmXuiCQxLBs2lhiHITRUVgTGQXGLdZo9WIQAtQmhGdE6c9kMhESc+8Fd8iyZZ8nAYh/KoxYK7NftO8yaOmwGHjzgGXW6x/V47TLnz+ZyY2KVHEebQoA8eMissIEBej2OwxWhP90OjsGVq5dwrgzii+dEvk7p7Ar63LCI7yAobhRrtw1o3YWSmkuTo6MzgMvFfyuX+W/zE8zUPX34AGWJKHhE4ttVRuvSzBx2NN6z0/y8iXA8zaaRFKrURO7c05hFHA5cFcFvzaXMoYPrZGCz41iIV7eQpD3BKLwu/5ADxC0XaHZFnFGFwlAwoFHk90U135eur2L/jGfiqciAu40BqjoTAzpfQkLiFHIXGA/TDsxITrisfVCsFFGSbHZE6JP/GCnkVUbZIbvYEXdLMhxCSnvoD/6Ae2FpTXL3nQ4GQz0HPqslH+Otz11Bq855fv/H7wIAgl6Oy/rqMipCLTfEVXdSZabUagCQnfMrO+l30GeIhsfQ1CYvV02ujC58kk6/KvTh5ib3c61WQzpPO9yDiSSV75bLo69zqNmWL6P1MRaLDhG/J2fimBNpa9jnQVS8NJOzRJK9EEHtwfkxlhb4b5A8+6L8lkA4MER7loUSW5rgZ20OD7ZFOJ1vC62hDG4xX0RKZ5tTxMrf+wHHc3r9JvIiuncINXpjdQ4b2+xzq0ebdJ7j/vSG3ENOup64tmJB2rDcRQkJrR1fQCShdj5zfCY6XId1IR7WFumvnu6fDX2tXoNjG9d+83psELUcXPKP8uLv2Hi6iaMe5ySV4N7rCKWazhdQbnQ0NlqHIkePh4JICun3pz8h+qohuE0smsC4/O15ITRa4jt8sfsKybFZjQfHx+ayIeoTz8cm+3fzMpHNpUoZeSFWAyEh54U+urS8iKbk488LaX0Px2p+ZhYvHglFpWoBj7hGUxNjw3OyInnxhu4S+xdp1LSZLsv+nEn6vV5voS9/rGNKQwsdkjs7wbgQWSkv7ygvDtmXGowhD6ND41DXmWy3G0PkSFX+4uTE+NBO2Ux+kBK/x+Vww+3m3m5qbTfVl8nE2FBu/lg8bQ5JxNscdqyI4Hh/k2fJpIR3YLWiLmTPxiv+zqUxnhgfR0GILpsQcQMVmexfHMPwcQ28LpRw5oA+RKNcxbbQjVdeJ6qhL46Y80wGHiFPTSJh84x0Wm0wbJ/x9QBAy2FDTGjzsrjz8uKRccXjKAsFYZFtqQk9ND65hClVZ3xyjyjikM8knvaiLg48qzgFW+J0tEddQ/7LmlBNEQkAdDqd4d4z97DZTo/2MSM+oVSC+/nsgu9rdTgRjfJOmUtzzWVOuf7b/RYWF+iThOWH2DQGtXoJSa2PA41tpdbC6UFOz2Wfr96iuMXByR7c2icX8inHxGFrsVkwbUgY6YLr76MH9JnT5QYc4tAzEbzmWFkHHYzJRh4c7XOsdG/tWdoISfzF5daYVcSx5HEhFWSfY/KL97UnLBYLGrJTFlVIZCW+FIyG4dLvEnGi+05EzLy6toSe0HKmvLtTBNetVg9O2UCThDxzdorVZdrIW9eJDN3YoL9zenaEZkscqzqD7Nrfa4tr6OtO8uwZkXUmSbrNbUdN9yNDPFSZI+7dgD0Aq43j35D/aIhXMeHzoawqFzi5ryenaB/vfnQXHXEUra4xPhEz+Zp6fbS0TzqyU0tCSTptBtIiN3foHl8WOmpg6SMnQY/f/L/ujmTnR23URm3URm3URm3URm3URm3URm3URm3URo3tZwIh9I9+5c0BANQLAySDjA7/V3/3FwEAv/eH/x4AsHWcR77MaJffx8jd7WvMVHedVfz4x/sAgLwkuZcvMZqbTHmxf8gI3IstZv97Pf6/y+7B7DyjpH0LI5JXJUndrnWRMuv2LhgpX55ndBGlOkKScEwXGYHOWxmtq6OLtLgTFsYY8TM8/L4xjxvhGUZHzQyWXegXu88PiyKaT+9SrnR1bhmuCCOti3NEd+T2JeXYqSBn41j92z9l7ajJSzSo9NAUKsGU3owINRNv1fF3v/2Oxo3vXJOS2dFWFldTjMLGw1LyWSa65If3PsTJPjOGbo/4mSS1a6mUhyzvfnEOHZ9zDCwOKyqShFZCCh1lRePBIAZidh/K6uU5b5duvYlTvh4+fMyxMnpOZJQNMNEJn/yUyit7O7tIKKMMIR9E24NAIIAZyRu3anwXS0e17p0uJqRmYX5mZlgH28B775N/wZNghNbeYrS5YViG9autAtdlsc+9NBkeR9Ki2lRJ+J5WiHzYOjzE6THfYXeT49mWskmtVkbJzJq2TGp99sXp8KKiaK9HGZueso1uu4G+5jsgBaKxsTFcvsS1PDPJObRImc3AYJiZNwRBMrNrFpsBi+pWO3ovq7Lq1gHgMxVUlAEwVSo8Ngc8ysy7DH7PqXiG9nd24TI5VyLcu2Ep1cWDYYS0z2KSHw6qjtsdDSG2yrmo2BgF33z5XQDA/ftb2L3P6L61xFr52CIzIz+5d4ylAN/9n/4P/xAA8PTxEzj1rnvKTLzcIX+P1eWAO2Iq6vAzW4fknVgLzOCa0BlPd4jI62mNRsZm8XyPfVxKcn30pRAxyBeRr3KtnisDu5pkFn9meQVFyap3tJ5NJb0bNy9j/4DZ9DEhKy7EuVDO1+EYKGOjPWhPcp+2B50hMuipkBZOw5wPB5aldJQrce0V82lEtaa39H2buxzP67dfQyzKbNr+Lm2mVZnVtUQSj8Sb9KWf+6qeRRv4anMDfinSFZQ5a2hPvNzcwedfJwrQlA/d2OP31fqdoaR5/ZxjuyD50K//+i/jueyhuXft4ifL1qqYneDa7ooLriyZ1NnFBF4847qYk7rK4QHnNBAIIKYM+KFQVf5gAPsvWMt99SrPFVOt0O5yw6uMy5GyrRfihJiem4VT6kwWSWtnjiTb27YNx7GcY79OVIu+MDOLhtStHovP4Z2vUSXP7nHh9Nw8qzhWq2tEAb188RR3P+B5dP0Wz6PYGG1N4TSLsHiC3nuPiLoJyefuZ07w/7L3XrG2bNl12KjatXPO4eR0z83xvXvvCx342GSTYnCTsinAtr5sA4YMf9iGbQn+cIAhG4YBw4BhQzAkQdSPIcukzNBUk03ydXrx5hxOPvvsnHOovcsfc6x6fDKkXzfgXT/n3nP2rlq1wlxzzTnmGN/7jX8DAKD3JIM1bUqmbffyeWzelLG5/0ja0szLGlkKePG0TtWfoPT7UjSCckH60h+RjNzmrmQ1P/nsIdxECAVG5ADh2vj8yX2sb0vG60/+5M8BAF1msa9fv47PvhT03xL3qlvXab9W0wociSEz9pGY9OsP/uwvkKOClTmSMdkkAkr3ulEi+jIcl323w7WbTuawS2Rcsy52+Bk5nGZOAx/cFX6VR59Km06p9nH52nn0xlQco/23xpq9fuNEFtZqMs7abIRZX9b6uVVp1yTj4meq8FAZKcWMcYe8cIP2EAbXXJBomxb3aYdpIkI7dUIVTA99h7E1xzJRb8+J9grMmAmPjfGzT38IAHjvLtXFiEBLZIJokb9rNKLyHrPKS0tpDLj2FC/ZSpoo2tQSDo5lrnrZzvEMCFF5M+WnXexL2825BQTl/i85h+CX9/R7LLTowyh7UynK3HNqGpaXZQ+o0t4obra1eBzHtMOkaECSajKnpbzdx0OiHKNUwMp4vPCGpc01KpJWWzI/ZpaOChF/F8/JPAyRM/AvPv0xbl0Ve5rLiO/w4kDWy8wZQZBqhSkiZR39Dl4U5L598kkOZ/IzkYvBIIeS05A+c5EDzu8J4vix7FEz2pguFf/WzmfQHFA1LSlZ/0lfxsaaajgjr0WI6ItaSWzStz54z0bETKkkCaodNosNvJzIPSPktXES2TgcTxGl6u5ZXu5FQAJibi/+5jcE+XHkknH+g6dif7yGB9G+rFmjK7asSsWiWSKICTmHXESKV5pVJIgQ2sxyHU+pzlkq2ln/27cEefbknvi+XoeG3lDGcDQT38ZL9Z2g04MlIsiePBUb3yUC+9ad23jzWvaJ6UjGyMlJdFZvIJyRfcivkCPkkiuWanASwWGSk8lDDrm414N5T94xoIudavbkeyeVJr75i8JveJqnf/RW2pQJBbHMduY7Yj9choER0aIxooMbVXnPZCwND1HiXp4xfDHpx0axjJnioKFPOSaKPL2cgYM+3ZC8S9aUoAFjjqnifyHawkXEq64ZODoWXxzkVykU5D39qTDiVGquFMT3387J3jxo9nBGOzxj317eEb/u6PgUYfqss5Hs4QmiOZ1z4e8DgP6I8ySVhE7fYkJfRnFn1SYTBIn29/M5Q37G0XHA0qU/QglZZ0Wi9JZWcljbEN/s8SNByUTINzj3AVOqnml80Gws/VM4PsUO0alRIs7K9A8m5hg+Iuk8/DkiL2JnMMTampxfzDHVgRtEFabSqB6TC5II0U2ink9P9lEgkm6XqrjVRse2036ftCGZEJsLfYjRSD7voZKpUoSE6UEwJ2Peox3QLOkX0/RiMpR5dHLIM59fvq87JugRTblMZMqM82VkTmHQtgSCVL7m/JqOxoit8KxNDt8REcQevw8NcpXFs7Je8gdHAIBMOAaPQ/r96Fje00W1N1/AC2+Itoz2cc4xmkxMzObyuRHPn+l41EbauIj6mrDcqN4owYK0a6rJ3HFyDzI0N4ZEDCe4Pic8S80dFhpEp5u0fdmw2GOv5UMkKjbziPNYIaCsfh8DIsY+pU1S0vSXLp+Hw8XP0f9z0A5PRiP0iFRbZuxC8U8lw1GUSzI2pkYu5ql839JM5Jbk83/9P/wH/1KE0M9FQOi7tzMWADROu1hKykKJ0oE7asii7ZC8EgB21il5WhPD085XsLomndnXKOF9juVk9RpMwhrzTelIoyv9oTtmuHVHNpZehxC1kGxGfo8Xzx9LMOAyyeneJenkkydPbLnt/lCMWCwlz3v8/DHeeU/uaTlIYLwvB7REJA3DlE0jTbLAs8KR/C2dwmmZksSUmTV8I6yGxFClorKQJ5a837RvIc5Axh99JgfVP74nQYeY5kd3rCYCnRKSIZ9PJfBrvyAStYZHJvkIsphqJx0sZwn3J/QsQOdp9eYOOh75XHVfxsRdJsBsOWLLBT4nQZbDQclhtweXCRv/+Mc/AfCVvF46m8KQ889N+Pe4wIDP5kV41tfl8yTT08e6TT445HwolqT/qtUqDC5kH4klVSmHP+CA3yObVZ1EdKo8xuf12ASAQUJqMzwstttt9AnTVQvZwcCLBcMmoDRpHJyUqfV6/XZZnO6Uzytp2aOTU7Q6Moa1Ws1+DiDGk4+ByWCbWp66rkOtVfXTxeCDz+ezSxpuXJfD7JWLl+yNqENnd0BIs244AAWTZhmNKokZD4b255XEsNoQnU6nLa3oZSBpSjj93BxjTjLv8VD6VkklzqHByVIIz0yeO1bBpvkMfkYK6VfBw4DUaiaB7S2xB+/ckDW4/L4cXDs+F350X+b9T//sYwBAnWVOCX/AJjlf98m9irUuihzzAT3Znk6oK0YAy2JW07LJBZOymdQrVaSSskl99oXYg36fpMHJLM725XB+i8HpvZL8fymzjFxSNoMhSVc1OjxeaNhkIKNVlU2hn5a58+z1MzRb8rurMWlLgJBjuJwwWEbZy5Fo2ZQ5EFtdwoxO/P/5+/8UALDGsp9Oo4U5S5dqLZag+mPInpND6IPXQhRpOOngjqbIRVn6QjLHHOG6mcwqDs/k4Gwa0pb73NDC4WVsrEiQYsLg5SbLL8+aTfzRH0r5pZK/JFIWleoptjckcBEKSpv8/FmoFcCKPaQohazgwbrHi7OCbLQTkn1+cFOgwAeVA4y4cQYZxCySQNoXjkNzyE2PSNwb9npRpfN064YcAAckOnQ6LOgsVzNZdqW75J7DkYkEg0UWSxjDJGQcdkaw5lz/hByX2IbeaAyfLn3s5uE+mWJwb9hCwO1gW6Rc4v59ObSsrm3jeUXsvM51NibR/nhqwcOSvVM6BmG/tM09A7JrMoYnhzJ+cb+SBI+jT3LSnS3ZW7MkUH/y9BmWrjA4yPJBp8ONSxck+Pr0kcyd9TWWRwfd2H8rgYjYqoxpleVog8EE8aTMZRfXZ4kw5tFsiDnrf/ffcM8nUXLM6cOch/ltEnuqg/x+6RQ+SqL7eABappyt7nBCp72usjR2dVX+Fg744fcRXu6Qvn72UPZpr9uPSzeE9P3loQQtmiS5tGYmDJLDb3Cv7A0GGBG+PSC8XSU4zs4KmPAAsUYS99MGEwHDAZIxGfMbV8S3UIENc6ahxOBorS5ze6yL43luZR1B2v5CXmyeTlvvj8QwYun4I5bJpK6IbxLx+/DJj2QPVjatN5A98t1bN+BnecDD+wzAMjC/ks3ASwL0cp79yCDrfD7Hyqrcv9WVsayOurA4Xq6pzPsI99/c0gYmPKCeFKVvHUqEwCXBeQBY495fPJXD0ct7TxBiENwVEB9gysDhSjSJw1PphzJLWvJ5Stmnc9CYGQpQbtrN8ofW8QGynLcTHpaTKbG5vfoQu2ty2HO75W91HiDHDuCM60vXVIJD7hk3XIgzcKrxkP62eIbCqXx+e1vey0Ei9L2jA5tM1BuQz1fq8s5TcwgHg0s+HgjqPFjArYOmBUn6yl4GhMqvDuG4LH1VaVCohP7Y7au3cPZSbN4ayxRbFI1o9FvoVGXcLl+QtdvrSLstS0Ox2PzaO0+ZVOo2a8g6xBa9/4t/DQDw4kTmQns6Q592ykeC2bGSM59OEQvKO4cj0paXrx/ZhzazL59vMenqi3lQYWD9elj60c3SjYFlIsIDz2lR5kIxL++ZCToQD8r7TxzSVxZFO+B2olqWueJh+bHyr8aaBh/bcjkn/aHKQ370yU9h0g7oLOmJRVWpvR9h7gmjMtcB11Kt24GmSsiZsFE+qcfvxpwudYB+1hw9zOeyZxv05b1umRPa3I0iJdhjcfEHfvOjbwMAzo5PsE/J+xb34gr9zkgsDgclvEf0TbJrDDaNhig05HMqkXJ1S/b0v/jTH2DnmgT0LQbbCs+5F7s1OFh76/FLO1XZViAQQ6EiY6g7Seofks8e7+8jwQNtmKXxY87jar0NB+2ai05ArVVEkgEjnZQNzTZLw3UDbvrwTtqULn2pmanDSbEek0kqdWYZdOsIkMhco1/sYwmY3+eGh+v48FT2vwDXd7vdg5ffMyfiR08YuMptrCGgQAMMamNKfgvMUWLZ09o6y515nsTEY8+nCQVwNL4LMMeAfo5KMBm6gSrLa1dWpf/8HrGP/UETli79PtdpAylsUG9V8O4t2eOsqfRHi4TcMZ8HOxvid7u4R/7Bn0sp6JX3P4DVlPadck81WXLWHQ7sZHUmJv7b69qZ/e4qMA8Spm8uy74RC4Tgov0sUDTiwUsJzL3/jQ/hIrn8iyPxM4csEzstndh7qqLKiJMapD/qwWSgy5zJ8zKZDHot+XerIvNelRv6kwE4qEWRcMj4NrlvW4YLEb/0w5D7C48oaPRNRJbWAQB04WGw5Czg9atqNdvGhlPSvlqvgzITbTP65hEG/ro/WgAAIABJREFUsNKpJQzOZN4e9+TnmIkb3etGlgTwE4IalIjS8koWqbj4fycn9NWcym8dIcKz87//P/5wUTK2uBbX4lpci2txLa7FtbgW1+JaXItrcS2uxbW45Pq5QAhdv5azAMBleWENmQkhmazDKe3bPb+BVESijsUjiWROmelPBL3QDcq4X2emMiyRsaefPUG7JZ979xuSvRgRclxvlOFkogCMGntcKivRxozlOhFKMM/6ErFtt9vwkkzXyc+niBB6/vwp7tyVrG4kJn+794wlXeUWVlIkbyOhnCLnTC9nEIpLhmOZmalCZR/DkoQYu3VmvBjJnw5hk7F9Tom5HzyWDJajp6FMUi6lBe5jlszotvHr37oLALh9W6L9+bJ8HzMPghH5/IyEmcMSEVe7G0hfkujyfUoomy1m/3JRVEjwxkQb6ixz2T533pYcv0dER5LkyxdyG3CwVOfHn0jm0kdJZVcgiteMFhfKhFf7EpiRxNFkudSM0ebhsI8pMwYeDqqmSbbWnPTQ7cu/g4zuu9kvo+EQASJ7gswcDlnS5vG44A7InFMIGkUwB80Bi+mcGSPCCrHj9vrgZmQWuvxtQLjoWbGAIYnvFDKoR3hxu9nCnFknJdmp1qeu6wAh8oo0W6F1otEo1imtnaRcbyqZtPtBoXH6I2aaZ1O7XSazA6psrd1uo0qys35f5kA0QgJNt9uGgCoob4Qlgn6fB7Mpyc4Ix1Ztdzrd0JiFb/flnefMYs9nJvS5/DvoUZLGksXLxCNg0g5hjnvSLe+cjKcQ310HAJQN6asnbyTDH55auJ6VTO9nL4VAMJpYwilh8/km5VuJPLGsOdym3L9XYznjEsuSajW4mSWZEml4hSiU169fo1qRbO5HH74HAHhDlEEilsSFC5JZq5alrxycE+FIED3CWI8pobxsybp+OSnh8jVBQ22QOLrIkjNHcwzHWPp0/aYgNF5//0+lr7Y3MWKJwillnZXE5Zu9t9i6JJ//5EtBOaE1xtJ5WYdPHwrKY40lbS6PBxe3JGNbPJEs451vCcz9/oPnePD4EwDA0oZ8/9IVQQC+fH6AZETs4BYJHF2GtLc8GKDF8oA+y0LevSHf295axcOHRF91ZS4UC7L27z2+j3eviazqFtFGhZJknfrTqU0+2OPPCBEx6A7hY9mIiwS8JSIwu/U+BoT+dwkj72lTfOMGybVJkqiQD8srGTx7JhkrJ5ES+3tia1dXNjA+OwIAvEM0z3mibEa9CV69EjRkgOUFTSIn5jrw2U8fsf8EHWIo3eP5AGkiQpt1eS+XV+Z9Or2GR3tifwdE16xSYt4fCqHLalny02LQlPW2llnCcCrvNSIBtG4R4pxK4cULeb9NQufLlFsOBEK4kpX1+Jby3eYMGDPbqWROgyFZI+akj2xOwaqlET0SiOYyaYSI7iBTMV6zf6r1NjIkNnVGCYGmHai3mnZpU7crNsmi/YiFI7jKua3kpg8IN799+y6OmSkbj6Tfd1na1u+2sf9Wnr25Lb9bJdHqo2fPbSnY73z7FwDAJnbduXAOrw5lTUw5RxvtFnomSdeZyc6syfrxhfwwmU7sdlmWxNJrw+nA5uY6AODkVO5pMaU4NTWMx2IPS9z/FNF1IhKGl6SuU/VcH+XF15dR4bw4JNpo97aUF7h0DUNKtTsN+jksE/N4XHjnhvTjm5cyv5TUcCwShoP2+y1JaI8PpY8//OAWQspnYOY2koxC4148pc9Uyss8nswMcFpgCpIUe7iPjur46MNvAgBYUYsZM7/H9SoKLNMcsoQmS2SMORzDSXRkkn5YmVl5j8uLOeeqIk63iLYxwm7kiKjTSZis91lW443D5J49NmXO5VbFF8gPu/Ze8OUD6askkdQfXruOAgl0TT6n1G7CIlqxXJI5k6ToRKmSx/q2tDlIBA1YqjAczG3RAILGMXGQgLvbxnxG35BlhivLktV3un02+tgblrG5RaLZ8uk+vPQjRi36oA5Zb3PNhRd1mYdLJLwfdOkr11ow53IvVXaiytHM0QAe9m2YtkgjcgcuAxOiiCe0uaocxzQBTZe/dQfyLvGsH4rA/BkFGmJEG1y9cR6vXsl+WeT5IEnUx/nsGnp1mctKPOKQ6AQt4IA3LvNjjQiVjR3ZY3/22We4clHs75zlO6ekfCi1ukgTKbFKImiPV+zX6719OLi/qgx9LiPz0cAcK8uU2O5xDnBNDqczmCxrUT7QIcs+c8tLGM9lzq2S/DcUcaJcknWszRWyiEiagVQySLtkfXZoIzya46syVNpMJbyTyS3j6LWMs5v+7HFVntEeD2BwD33vtlQSjFlm++zRY7SJgPnub/8mAODJ5+LT1+pNbJ8Xf+fVC0Ggri4JGnN1bRlfPpASQl9I+i9AegFNn9mo+DnPKPUaKSZcQdvftu2+U8MG94kcKyTqRHk8fPnKHpN0kuXwL8W3SadWMKQdUH6gNRcjEw254SAa5xvf/FDuSdRWq1KxKQ26A5az0q8Oh6IY0xaF/DK/RgNp50TX0GX56RLb6yNKvlor2T65KrNViGXHXLMRdUpm3R+Sz8xnX5UPh8Pi2zx59BRVor5nmjwvQgL5QjmPeMbxtd/VavKelj5F3Cd/02gPPLRpl7bP4Yx+qSrPes5Sd28khfcpxpLnGvRQhKBtjlBtSL8pNFSLvnm7UkOC6PsQkcntKkvAdA25tJxfPLSjqsLl2auXWKWNNinq5CKSstZs4HDvSNpFEuwEx90wdLT6Mi+SROWE/X6YQ7nHsKkk2GlYXTpMh7R1wkqKIcUSXC4PsklZS1P67RkicarNLk7yYmsTPCe46IfnVjIYmzI/DM73NufxzLRsCpD1dbE/9TPa2m4fGulxdt+VyoMGx+PTJw+wxHJtFTvwce+bcz4DwNraFv9FMZSD19jeEVv2n/9Pf7ZACC2uxbW4FtfiWlyLa3EtrsW1uBbX4lpci2txLS65jP+vGwAASyvrAADd0pE/lUhkdlcicKvrJFk7OkA3L9l4P6UWlxmFN3stbJ2XWkLFh3NAAqrcUgqRgATEmhWJ/M+Z+UknYijkjwAA50h2eXQkGcVcOocoeX72KEXfpgRtOJbGiMSoihPCYoTX6/Wjwjr7azsSbT4JUKba9CBIRNG7dwVRkMlKFHhkjtBlhuLFE4lqV1sVeCcSEb50TtrHQCgeH72Cz0vJQ9Ynz8j5Mh447ZrKuc15I5fDcKFP2W2TEdDlmGT6Z3DaZHbRhDxoiRFbzZwjT8nl5bR8vuGU51/b3cFrIqyyJAVTqJf+cAA3UQJbJBXLZCT7EdUd6FGS/pfeFbTAjFmvkW6gxVrzB0+lPzwrLjimEmW3SCKrADtenwcu54zvL+M0Z+2ooWtYWpVnq0zDnBw43qAPLRKb6qxx1d2SxbBcbpszZKrKeIl6cTi+erbS+Z1CkTqOYFpfX1pDZqhN07TRP+pdVZ0poNv3V7wH9shZmo24UfxMfmZyotE4fH5ps8q+NppNOIgkUugmVfOuOXToTMsopE+5TJ4mXUeQxO62PCpr3dvtNg6PjuQelB0Gs36d8QQTZjbUe7lJEuOeO6BxvEbMmiriR4euwcOa5wlJkRt8z/loAk1lQfleRR+zEYUTTH/6FwCABNfeMrOaWsSLf372MzaPBGw7UXSPBOFgkLMompHPt5o9VEgEvL4kkfWOX+ZQIpHEiPW/e08l85VISuYxqDmRIulkzinvcOdvSObs4O0BZh0SdPJ5LmYXQ4E4usyqbJxjtoWopV1rCR/I17B9UbIdX26R5PW0hHGV0tBfCqeMRoL9udtAm0iMHjNma+cF8XD7N38L5ZrcNHbnfQCAL53Gwz1ZV3dvfSC/G4pt8scjyO8J0umXiZBoc0zy5SpuvSeEu2clyVo0m0qyVceVi9tsg7xPi9LyRiAMbS79mCQXUKMs339YO0OYRKJjU9AQbi6K7/zar6JJ0sTv//SnAIC4yup7PGhwwq9dkozvsC/9E02E0KVE8UZ8XfqKc9ARcOMKUSFO8gPk8ye4fV3G4rMvBQE147x//uYABsfO75Y1sb4qa3AptYxVIrkU8d/9TyUbOh6PbTRfgOjDZvmUz/Xg8iXKxZMzIEny5X4HiCUkI+Xlup7NKeM67eMc+fJM7k9BZp9CsThec//y00aEE+QSKexjQjRJnNmtIwoUeD1BhBKylz7ekzFx87mBUBj33pDrhXwJz/ffwGBmWnE7lF/K95zGHL9yUfYqs8ex9EgbjJgXHaKvgvzdjBDAje1zaLdk7NJB2XumRDTGYhFscS4rVGV+T9o06PYwJiG+koL98FuCMpnNZjZfm0JTHhK9kU4lsEry5euXZNy//xPhSYDfjZUt6eMuyVAVobbLAewfCbIoE5c2aU4D9SPJsM9Jjrl7RXiokokInj8VJJgifl2hzShVyphwY1F7jiJ07TT76HMfW92S/owoJIc1sXktOiSu75JTrzWcIUjC7SjJrE9IAryUTqHdHrJviFTj3lWuNOx9YkJyXr9OMuuzKjSKAET9so/2wkSDDhwok38ql5U+qpyegaBPm1TdS5n20nEVA6JxgiTgVonN1fNbKPJebXIYNoj8u377NqLk9RgwI+4jD8ynJ8cYkXNPceM5iKByaBaqCp3RJdEvyVpXwnFMSWx6eUeytI8/ewgA2Cs1YBCVuk8i3d/a/Q0AwOHDfbjJP+KjWMLLN7I3xAJB3LwiaJy9I1kT1lyDm2jMzkRs4IAI50u/eBu1hswdRMWWPXks92rVAXLVIkQU8oUNmZdOjwd1EE2yJm0v7Ml63kwGYJkkoa7JmB6/lnk/ak+QI5n6549lj/SSx8cb8GMwFt+1Upd1E6cvsL6RRITozxaROAZ9FJ8zCERlTh+XiJykv6UZM7TI5xT0iX1TEueH+xUsLVOSWxM/cDjuoTch2jMr9k0JWVSrVQwpBx4KU3iCnDH3Dp7i7ETa7vLJvl4eyLunQik45jJ3bt2SNe8h8uHy9i6ylMP+8BuyD/4f/1Q4+OBu4/RIzgw9rzw3EJJ3SCRSNklwlBx/Q6KfvQGPLbARyMhcq5GLqVmvIkgOnF1yk+ZWxfa+2n8Lkyn68UTmR6+n2+g8L/egGBFWpycVnJwoJAeRSESzpYJheKLkHArLOumU5W/ddh7hqNwzSB9y4KaQSqWEmSb9NiSiqMV99OrNGzgh983PfipiLhur0pZwOonHT4QAvV4RG3P/ix8BADLpMG7cEfRQNCnzackr/VKoFvDmUL4XIqIpSmLobmeM0yOZT236NLHsEt705J33xvK9cEzG22F44OP7FFld8MGH35G+fb6H07z4EakV4YM6OJZ155m7sLkm6JUiZeBVhUrI8KHfk3EOh2XsT/LSpng4hRBFNFotkgbTngaDYZvzyU1utgK5rQb9NqJEHRkkTM7kpB9r5TzW0/LvPoUKgi7pF0DDbCp2rl6T5+WWUjCI8FE8QbpXxjsWCSIaURwyYpAVAXEkEsDYQW4/opbdrHq5d/wGGhE+iq/pwg0RVgnGUrCa0jdRjzyvRpS8HnDj7gdyjiuSb8nPCo5za2mMIPNXnQfjMfl/vVzBgHw4tQKrJniuSCVimLDSY0CE1oQCBR53ANevCZl9o0qkOPcGXbMwpv9mmvSfzyq4Rtl5P4U4XjyXs/18oqHNfWniY8UHlSwMy4kWkdpd7rdOl9idyxcuIh2Wsdjj/LKRg7DQ6Ypdswh5LVXkM8n0EmJxmatnL2QeH9NO1gEkKFcfqsq9L++I3XKnogDPrnXGSjY2ZO3u7e2hQT7JGNGzikdsDhdO2L5/1bVACC2uxbW4FtfiWlyLa3EtrsW1uBbX4lpci2tx/f/s+rlACP3GBxJ97A17sD6QrNv2pXX53USyVY8e6FB0P0ri1U0elEnHhTt3hNfj4wefAwCO9iSKeX0rjlREonkvj4QnYTkqEbXlRAwBKpusMDod0CXzeXByignVlbzkmPGkJKtw4cp5PHsj92oz6+QmCuCd997DmFnxEdFDKUb0tzJr2H8t2Z8cswoqC9qqN1Bn9tPLjLHT6UUmQhm+E4kuw0M1pI1tHBxIhNtJjqMMM8yVyRhzZhVMZnF6rDM3dB1FMv5/wfrf85uS3VlZWcMmo4198k0EyB/RrFbs7F2GsrEzZpw//uGfI0YFjwIzFlFKiM/GI9SZpZ2wvrZwIu9c1z0Y9CWCemGDikpUFTCg430qXcyZHTsqtTAnT42XqChdl+h7s1VHh/w2atyWstLv8VgUKSrCzGaS/dg7ILfE2EI4Kn+jWBiczK5rhhcmo+8KYWVxzmm6Aw5G5C18HYkzn04xZcZSYzTXZMp0jhl6RHIpFNWYCgMz07KVdpSq2VeXbks6+pm9V1nvYDBoc3m4qApl6Q6MmRWwa0vZ9vFgZNdDN5oypipyrWkaul25f4eZoT5RFw5Nx1S9B8dhQp4tp0ODgxF5W5GJ4zabTG10UzhABbiZKhy34KWSm4P9OCGfADwGMhnJno08Er0fEN02Ng04mKWtE+GVZ5bWE/AhTQ6bjZRkQ+v5ui13qbFWeIlotlLtGbyUMj/tSD+8Q/WHZCyOQkEyIKvf/jYAoEnejUcvXyLATHSB8uVrGclArOXSyJA3xsNa3zVy0rR7dQymkjE0yGXl6Ejb/CEHhkH5d7kp0X2Qn2FlpKNLNbSZQlFR6rbQKCOUlPl+LS2cO3NyYVXzeYSo4NEnYmp+VoWfFBLTscyrWEjad9qro0Ak3V/eF7RLnoiugNeDCfm4zm9SdfG52MJGtYHPDPn82gprrsmVNu317fWZWhc7ZXGNGE4D5ZL0cTAgf4vsSr1zKpdFnpmoTEjxtlEeFTo2qaTWV7LYzLyfdBtYWhYbNNUoq6rL8ybjNmZjacvtC7L3NJ4/xovPhcdsZ5n8R8w+5/df4dplQZHE2Y/H5FVw97oo1uWZdXKcKIRcNpW112iPUrkWM4hOONEjWuDGdclyqYxl0OtBkKoWSuHyBz/8MQDg8KyEFNFGSqFqQmnRcNCHS+cE4XZwKHZ4yHXea9eweU6yTOvkqBsOlI1yI5WR8Xr2RrJVF6ne0WzWbP6ci5dkXl2+eROPX4mS2F3yTBzuy/dqpQJOTyTTde2CjI1OO9Co1TGjIsqPPhZevSlRqhcvB6BTWc1HXjm1p8xnUzSJYAwRHZxhTf8kFoOLSEbHkPvMWN45FAnjOnlxFAJym/xOL54/R4Zy9U/JF7SSoyrfZIh37gqSDkR2vn4p7/vik7c2ciS7I9+PxSKIER1zciBr1sd1mYqE0MsoyXuqJUUlow1tjhy5ZGJE0Dx/JW3ZWlmDBhnnBH2AtZTMpUa/ixZRUbOmrK/Kgdy7X+siEpE9oFRittQva/fW5avQuMmpeZgvSQYylc2I+iSAIFVMVjOyD2iGD3/2l6I8Np3L7zLL4icUml3Uy7J2h5z300kNiaz4N1Xa6x4VFt0hv40+9jCT7WVfGdoQxzVBIOhKP568DJXDU6xyL5gTjVyntHokFLOzpidUmvSSK+adK9eRpFJXgdx4aSKU9fAMn34i6K3EkswnV07m0v0ff4ow+cK2yYUSoEr7xZ3z+Mn9B9IGvp87KP2yVzyFkxwX+RPpl8tXrmG/KvvDdfJvdOgTlY4P4QvIflk+FV/o6q6o/5yc1NG3ZAxd5N24T3TC1fNbSFBxqHvCrDrH+2U7jx5lzgnqQyLO7Pq4g2JdbEOYCNnpRPrusy9ewEUpah+RjR1m+NeWkyjkqRjEvbtX77Atl7FPxZwB+XSek2sqEgsgzXXmpW/dpF/x7V/6BjS+Q7v7DABQHZbgpsS1y5D5rxQknQ4/UnHZH3o9GedmQ2xN6twStJzYixNycUSpqBRyBGD0xNbdfyTIWrchYzQfz/DoTHignj0XVGwkpfauIVaI3gm4xcaGgvIMa26gSi4ataaWqXLmclp4SU62TJo+BhXqsrkUxlwLJ6dv2Z3M+HfqcNG2/+hn0pbN9SzWN8Qu1doyPw7JQeYyvIjTr+1SJXlIzpLuTMeLt4IA3lkhdwurC0pne7Ya4rlzYg+Vv+70uTGi799i3wY4n88KBwhRhdlBJaY593CHw4VLl8T3qafl+0rtsVYv4vWBzLlNl9iN9YT8zdX12XZ+zD3ScEn/RNw++LbkDFBrkqMqkYGHSPwheeU6rDKoN1solGVefPRtQYkq1caAx2MjdkpFIoXS0gZrOkSrRW6uWpd9S76fmYkabVJmiXa8J89/9fbEbnupQOQv5cJxkrfV/3xEoyml3a2dDVSqcs8G94Quz0Z+twtdngOz7CMHzyXtVhd5ooy2dmUPzyzn4KKCKVz0Q+ifoftVVYBT8R5REn0y6sHkOTrFdg6IvAl5g0gmZXxn5KwctWV+zTQnDo/oZ/O82ezK2ByfVjHS5dkcQoTiVIacWjglejBNdJSHvvPKahrRkLShRiROh3bV6XJhaMr+FYG0acS/QXchlVTvI22PBYhAy2bw4pTIZiLeUtGkzYE6JndZtS7j0JvMMKLS9cAp93r3vPg7o0Ybb96IPQsR8ff2jKjHUBQBjmt9TD6+qKz5L++9wIh+6eWrRPmGuXd1GsgSuZehrzdNij12OjTkgvQRyCkWpP0/frqH//S/+DsAgMc/EERzmIqr2niIdlDa1+IczxINfufGTZwVZA3+q66fC1Lp/+h37loAkFyJI8sSsfSKvMgh4YTVcgk5kkyGCA0/IlHhrD+CixD2PGU2FZQ3OHfAJInvmCSGYRozczpGQpHwpsTwt1sy2dqDMeaE8015GDLI7OcNeVBuiTPi52D2KGkfdXuwRAc1RTKweyxV2dpcQ5cH8KWUvMvhkTiQ/fEIz9+I0/kLH30EAOg6DJw+kcDF2pIsvjFJBUu1vi11G6Qz83t/JsGwXs2JepsHJPaDcsR98zku8sCzyxKuBA8+Sb8fiU3ZNTIktYuEWB42HOCzFwIxTpD0c8bylacPXiASUQEXEpXysO3z+XBE+ODaxjoAoMnAkjUDtug4XiHh1aefyDPWd3ZxUpV3qFG69rjZwZyOSpPSmPRrMZmMMGZwzU24bpR46+PDI2To6CUYiGtQqrJa7cJBeUhYlEbn/yeTCUwGKex18leIne2gDQMtSuJY0zSYDA4Zzq8Hi+r1Og65OSoySEXqpsOBMQnN1L3V9yxLs2UbU5w7qo8jkYgNS1Xfm06nNnEdaBBV+cpwNEKHxLKHezL/lMz9Xw1ETRh8MCnnrOu6XYo4ZkBIbXIul8uGNqvnqHsZDs3+nYd95KOn2u/3MVROtYuklaqcz+2wN9wPvyNy83ESWT5/8BT3Xopz50rJ2hjxPSuHpwgy1r3DTT+XjWKT5J2xrGwaByx5ent4gF/79X8NAPCTH0lZ0rk1EuOOx6hVxaaMKdVaqUrfJWMpxEmqvbEk9uP5a7FXldM8bl6QA/iUjpzbUOUZJXh40NlnEKDKg+7GTMM5QuXvXpcN6U1RDhaVmYVMkAetUzH4HkqVZza3cECyZVXKOCG5dzAYhI+OXJH2MeYM4KQn67CYl7V0+Eo2ub7Zx+/+vf8BAHDv448BAD/8mQR63rl51Z47UZZu/exTOdxvbG1in6UzXIIIhmScu50RNJbDXCKxb5KB2MP9A5jqoMr1pub6ZDBEmIEPy5C/hUmQWCrXMGMANU9HMBSlfOlZG+vnJThikKw8wfJXszZA6UD69OYdCQjlqwVYmrzXlOVqKkDh9fvw+acCkV+OyxzqV6TPfuH9b8FBUly1XlSZS783BFRAnk5al4TavmAAmxvSfxMGqfsknh0OOtikPPoyoexfUkK80RsgTEJgnTDkTEqcB9Oao837KxsxYtnReNhDjaV9Qc6FBuHVpmnBxfJOnXteKq0OXh0UCMe2GAB3ud12APo8x1KJCThmMzx8IIfl994Xp/XsRObqeAScHHHeUmhAlcQFwl6U6XhvkpT69ruS5KkUi5jRPuUolX1EuXV/IIQQCTZnU8orM7htuJwI8qCu5syc8ywSiqLXk/74wz/6E2kvn2dZM4xncq8mHboRg+q6YdiSrpeYPInHkyiwlKLO/T23wsMGpgiyBE7tjTM63k6nGzNuYCO2fWtDDkDaXMPJodzTIuH9+qbs88flAg7L8rcQD3KKwPSda+/ijCXr9x7JgXCiy5x4/85tFAgzV1XKbpKUtrtdFPLyXj7uJZkVmWeJ9DJOKDnucMoaOqGs+GjYRS4TYpvlOfP2BK0RyVVpi6IRmeujzhDpCMlSGRAaz+R7g0bVlmpXtkVXBMjNji3T7aX/MSARbDffR4qBNVU2+PKJHMjHgzHef1eCliOW5JcIw1/f9WDQlf02FpY5N2YJ38sXrxGnvXAwQaExcVN1WEguScD05bH4dupMpusadJIGW/yZimdgDRgIdss97t6RQGWxWMQP/rmUPvv80i/RuCoX9eAFyzPCYZnH10lA//bZExhjjiL9lickCvYlElhlWcyXn0sg+fIlmavTaRfDkaz7GAOT+pzS3i4faiwlVKUDCfqyg2EbIbZhQGJyg4GheDAKg4mhF/RtVElddmkJfQaAIiTNVsGHrZ0dJIZy/588kD1k6KqjO6UMu0vGpMpSh621dVhTGd/XVTmURrn/7mxvo1gQn3zvsMC+lnnidnpsYYcsS4r8LPlo1xt2wKUzkDneGkh7A5EoOITY5HlE2XOX7kONidUOyzRUkiwY8qHIvXjnvIxDoyX3vnjxMh5RxCHNYJEiwXYGAxjSzjimFNFIRZAn4fyAtijNteH1ehHmAfAVA/SJDUkgaDMTPu4rcZbc+eifRcIx5BnM6jDZN6cfl0wnMKEtStOvOtqTw3Cr1sRgIO+4sib+uqbJmJbqVQxpW70c+x4JrnujERrc20oswXXOFd3EOXzAcqRuRfr98M0RAMAynAimWYbKdTDXNSyzRGzCZPx0wufG4nhD/2N1mQIUDAqfLxkeAAAgAElEQVTsLl/AQUme/eC5+GjJHG30fI5BR/o2zLIpFXQOpuI4OJDPKyEbRfaczxdscYPNLVlfKhHgc0ztPTgSlnu+eSN7+M7WCtoEDYRYvsrlg2xyGQXOd91S55go+7OHJYoVjChzX2tUYY5ltVbbMu9DnB9OvxeuICkduPdjTKqH+RQa7WeFAhKhqIy33+PGjctiZ7r1Kp89sNtbLsvc9sRlbBJe7iHzsX0GKDPhPvPIO6R9YRw0pH3xqMwPFRByG24MGcA2KCqkyiPncxMvWQqaoh+o1pnhcsOinz+fKVEoef6018OQ9kklOobDHgI8rzuYpHrxSua2N5JEpU26AQadHZSdd4xMpBjs1WjDnlB4IR7yw8sjU47UN62OzO1nL/YxoE/oZrtiFLvKZfz2vrlqyngdVChpv5SFqcqi6T+bLGXUEl7sXpW946e//30AwBLP8xaAbEbm4bPnUuZcJiilP+jiu78q5ZP/9t/+hwtS6cW1uBbX4lpci2txLa7FtbgW1+JaXItrcS2uxSXXzwVC6N/97asWAPjCXpiESIUpTxthlH8jswSDCIAqM4mPmbVKBbzIEjp5xExxLinR32G5CweLzQxm6kZVEoaFA3a2okX4XLMp0fG19U3UCEmezSV6WaC8eyKbwKXrEkHdY/T4gEiLbDSObWYxr12Sz1RZNlEtn2F5STLMXpZGvXop33d6vOiTkDLOyPw/+uPv42JSsqxXL0pEfuqRPviLTx7DIKx6+5rA0b7/sSCEXj6uY8zIJIOpsGN/kwnSjFhfWJcsV4Wyye9vbONf/zd/GQBwuicZtrBbslWbV67g9372hwCAzkiijgFNnh+PZmyZRnP89dIlzXCgSgnUSyRb3CdiKu71Ic3+f7UnaI9gRt59MJrAPZd7ODWZCyPLgTbxk26Sc/eHSj5wjkRS7nXMrJ0qxXK7fHA6ZVzJlYZAVCK+hWoLra58Tmd0Wsm+jvpDOJlBVevkq/Wi26gXgyVmCi1jGIZdSqUreXdKPDebTTub0yGqzEnUjMfl/SuIoK/Lz89mM3iJIkkmpe0qU+Hz+ezSlBFLCYbDoY0QUtn8Ceehpll2JF/JOavPNptNjFgm4SG6TLVlNBrZ//Z53fa7AhKdZtNtMmmVnXcaBkw+e8hyPvX8PsvmAGCJZOVRko26dR1eD4lYCcU94trvapotZT/i+M0pKTmezRWq3UYfhbzALZahJuPyXuWKZDo2VldxflfQJCFCtt+2JNMZcHtRKsjcdpLUtdOQ5zUrbQRIbBrm2BRNEh36A8glJXL/8plkM7ssC3N5Y2D1GhzMVD7siv141xtBluUnWl6ymsth6Zfce+/ilGVJHSJwQszm1ft9mxR9zsyPn/MxFo6gTeJARY5cb9SQWxX0yZtTec4/+SeClPj2nZv4j//W3wQA/N3/8r8GACT8Yk9TKyEsr0gWok1STSVHHs2k8eixZD8VenGVz4j5wrj3haD/3rsjxIMe11eIOgdJa4/PSE5MVE8mk8OQ5PIeStYqSWUHdNvu3Lwjku+vDmVtpa0oQpxHCq0xo018++IQbSIor14VWc97D+7h1odC9P/Zl5KtnhFu3el1kU1Itu+XviUk23/8f/0zAMBKNocbVyQru8d+qDKbs7q+hWJRISlYWkno9pXL19DoSHbq9ETm4cVd2S9OjvagE8PhoZzt5q4846RUwqgmYxkkObeSHD44OrKJKD94TwQNjpgxHU8GuHFDfvf8mZQltZnpH09MmIRlrxGZpFks53NocMTFrnq5rh8/fIg1liEoaP2AZZtCeil20ReSORDwyHx2GAGYzHzPLcoJEyrf6tXg4Hx4jyiZREbmfaVdh070oLKHTmbq3ZoblTPJqB7+C3On02sjQwSq7lAEmDJ3qtUm3E7p2wqzcXMSXn/0jQ9QJwnsPv2JJu3qoD9C2C+2dtCiAEV2GVevS5b7hKXdIZJ3ppJxzLgWlK08YXmB2+GCj4T6Q5YRh+j3+Lx+uJ1yj5NjmR/KpnmjAXSJ5GgRqewhqvLyzjk8oChFOEFUlCbvWS0VkeBY6tyz5iyL+uLefZzbFhuYIll5kQTjM1jw8J1XNuQzHtrll68eIZGUOa2Uxs1hEoW2zPsuy8ojLJ/tlOq21PWAJX4npSMAwMTphJtQmxDLhZTs9NbFc+iQtNccyFikmdF2zgPoU9o8QnSNQpY+e/gUFy5IKcuPSErvZNvjvi4mDRmbzfQ6AKDRbLOTDZy7IQjNLZKPPyfq6HWxaCNHerQpJe5LK+urmBFRe3YsGfHZxML5bSkDMy1VKjyw7z0xZe/44r6UTfWnRD87hojGxUcbEhGdIqry9OwM6+elXRWKHrS6ck9rrsFH1GKJ8/EDigkk4zH833/0+9JWEj9nWTqWScTw9lTabLCMaU5UWyTqRYvl1PGo+ERjSiMvJZZg0i87ZrnWeE5fSnNiTETSzCS9Av8000ysQ+xImWU/iU0/CnVZV9cuSZsbRHZMR0MYmszDfbYlRD9Tn8zg0kikT5GUPSL5DK8TM/pfXpMlViQpHnTaWF5TJcIskyNCaNDvI+AioshvfK1fcpkl9IjGVOWXXRKUu70ePHzMUsSkfP/6ddnzhsMh4kTTV7mXqzL83sxEgGs24JKf5mSAEoUI/H55v+WctNccT2zkRqtBtDnXTSIRQ0MhhjkfUxRuCAXC2OO8qLPNSlp9bs1QLsj3vvdbIpDRIKpn0OvboiwxEix3W/Lu7oAbL0i2P5ioMkC5dzCYRLFJtH9T+iy7JM8b1FswWDZ8fpkk/SzdKZQLaE3k2aGEvNfa5iqcRGorBJ/fR9RipYF8RdahRsL1b34g5djVowaevZG9kGAUWPTzk/EUahTdaLF9Q5YgFbttZIlY7fXlM2Gi0pLJFM5OpR8VsjbHsrJxtWDvPSmWA++/FVTJndtX0e+ygoNlbKmknBknAwseIrpiMcrHk7JA1zWMuEd1u2IPnIaOLtFFhYr4E06PtO/y1WvwhmRuDTqkLciJvxMN+zGj7+oiJcrbPZLbY4x6SdbOoCv9XydSKJlOwUWE8pDn8QT3qalm2aTajpE0qjygLetNMKUIkddHZCj31EgwhABJxp30A786lwzh8FJgiqW+0z7tXb8Liz6uIkJfYrnnsFnDSUHGa8jyyHA4bCOE5lxzilajNwU8QZLeJ2R9NYg0btSq0Clcc0y0fph75JWrO/ASIjQ5k756ui9z4mW5iT5RXgEKIqRZDns+E0GCZ5qgR/7WJ+Jz5PVjwvXsoGiMh/vb2KvbVSR9ri8n6WqcTjdcLJWuNxS5tvSPaZkgwBb/7f/60wVCaHEtrsW1uBbX4lpci2txLa7FtbgW1+JaXItrccn1c4EQ+m/+k1+xAOD6zeu4/1D4B17cl2zMblayIDF/BGeFIwBAqSER7ANGq7fSMdy+JTKvRyR3TTKrvhrJIUwUylt+P82620qjDouyt4dHwp9RUXwJlobdHYlYrxDVoyLTnoATF69LxvbZCyF/65BoNhwMo8KI7jvkAHnwWtAvN29cRqkgkdcTyilGKeWZTOXgYJ3hgPX3Z/0e3D2J2TEZjL5TIqHF9hwOp4T8ql2JireYff3hD57CYPS7Q5lZAjQkS0kUVowIjnpR3v17Fy/hb/x7vw4A+N1//L8DAFJOiXhfvnQT04B8b8Ts1jGzz9lY1kY8KS4an08aHE+lMVNky8y4qwx/o9bA4WvJKlyl7Hx6V7LDf/qnf4rVoESGXcwGt4ZD7FLS+ORU8UQQNZNOYk55wmJJIrTZlNQRW3MnXu1JxmbOiPycxLT9CaC5JEKr+AMU30c0EkevL+/8FULoq+DqV8ggN3+SINjhxIT9oNBlKkM1HPWxtyfv3GVWXREQGJoBF6XXbVQPf2qaBieJEBUCRyGG3G63nWlWqCXolp1NV1mdNrMSk8kEmsb3UWPDn9Pp1Ob+0dlXA3IzmaZpjysUgoAIAZfbCw9TxFFyRIQ5v2DNMByS7JAZDiV332134CfRdJZEsepnPBa2+cIUwbW6T6lYQb0qGYAQa5g9RHi1mx0YRARU2e8bmSicE8lWXFiXeXH1omSODw/2sbYq83yJ2dInT4VwPZFKwmSUv0OCvWJL2lButlFR9f/kt9GUXGY6hnBM5lU0Ie2bkIsskUmhTKLe7BI5Yr6U7JGj10F6W7JFKWYSp3n57FxzYHlLkII+jo0ekPfcOzmx5VqP38j8uk4SYY9uQCcS4LQg7R1UqrhOCU5HTrLQ/93/9vcAAP/93/7PcP9QUDLfvyeow7gmWZP+wUvs7Mr3LKYcgswYZ1eW8aNPJAtfox1V8pce08LODslZveSy4lg2Gn24ye1wVBT76CCRtHvqxptjQVht7Eo714j4OTvOY2NV0Eod8iX1SaoZczqxSyRTi6iQVl/+NnF5bEno3XVyPOTP0OTSUWSYTiIwTdPEzob0u+KAy7KvO+0WKgfsU5JiBphBvPnOO3j8VGz/NlEv5aLM+3v37sGIKLSjjM0q50KtVISXMsK33hVkwVuiHp+9eY2tnIzrzkX5+eyFIEK63S4unRMUpuIlu09S8Os3rsIxk3X28pXMDw/RKGubGzYnmEXeiSrndcgfAKcOBm2xIxFfAH0irEZEYQ5p7zr9EXzkkHA7SJZPtGQqGUeEXD5TIiv292UcYvEUcll5f7eXXGAkqo3FosjkZD8/I4JBwWXchg8TZgDL5ABQ+6emzzHmfFA8bwZFCGYm4GDWr0c7qemSoZuMB7YcsJdIyBGz7K3uED0i4y5fFbTMkydPcPcdQTOcECVWYOZ4JZvClCjFJWaMO5D/TwdjRJnljkUFQePknt7udjDkWCiupwsbMt4HxTxCJP2ckDTcSX6cVCyGKjnL6kRfgdyHuVQSLqKwXpFvJhSXNkUSafh9RCvekzlj9JhBT8URIpfMHvlELNp/Sx/j4hVZl6/3BeESdK1jeXcdAHBUFd8in5dxS4ficHB/qbSJdiT3UDS3CpDTp/BG/KMEkT5rmxvwk5+jTkTdhc1NvkMSb46OAACrtI8vicItFyu2sINBnsmzqvgOo9kQ51Ly+cN7sk5vXBC/rlg9weaOvFeW/EcWZcxfWWOcHkv7fvM3vifvfiBryul22n7ACblsEtEUzq0S4c096KxAyWu/D+C+3ibfVzhFBI7Vx/2/FO6yyJrYpI5FGO5ohizJsvOnsobm5E972yggPpZ+21gm51mDhNqpEEyKHCgxjS6JdD0ODwo1mRdeEuGmKVSQzsVRbUq/9+lPTEjEG/ZEMJJbgQrd6PalnX5fGFlynBVO5Z0dhqwzr9cD70jedf9M+iqac2M0FdsyaFNEg5CicMQHLxHrLdqfOTlcfL4A8kQK6rSnSo45GnDh5FDGp9mWdq3lxNbouo4y+2ZGMtkWiX5Xk1m45kRActxMEvjHomHk0tK3FaJLSDmC3nAKJ/tv1Jd9UCHm3U4dW+vSrjG5+IrktDLCfnTp7+XoAx0eHto8p4ocVxHkJ2JJOCy5b5X8OylFPN+qo0dRiU3uZxNymxTPKijzvOKmrLjy6yKhkI16mXGurbCSoN/vIkY7oNN/LJ/Icz1BH7wUt1EVAaYpnxkOplhaF1tZ4F78luT7qWQOL56IzVTVDOmUTKbN9SyyOblnty/7kcdvIBWSOTnvyv17fT5nBjQoCx4g2svD85J3HsEjigcov2WFwgnBYBClitjMWkP6bDSncI7Diwkl0aNR2S9jYSJEBwMb4U53E01yA+3kUpgMB+wH+elzSzvvvnsFA5LKj3i++M5H3wUAPH/6GF1+b6aT747nV7/fD51noaM96bNJf4x5jOcAVo+0iQ6JRuJwumTMnQ5pczggcy9/doSIX+77W3/9d+Seh+J7hfxuTIYyp7///d+T/iM6rT8ZYYl299lbQcOniEpb3tpGm0jGkEv6qkqC8napBndQ2r62LvPx5Us5O+u6juXldQBfVXVY5HEc9rvweJK8p4xJj+vThAYnUeCnZdlnEuQ5m3SaqBF1m2K10NzU4SLi1CAC2+I89kUTMHiOSBJFNCS6tdluIcGKkvPr57/WvlcHzzCYyzt3iAbvktNq/cJVTIiEPiMJtUlUT9LvQ4NtXtnl+qQ9zVfbOJ+V/vDkZNwOn4mPF8hGbd7SvZMj9h/k3uYcOqt2trfER29wPHK5LJotad/f+q9+f4EQWlyLa3EtrsW1uBbX4lpci2txLa7FtbgW1+JaXHL9XCCE/v7f/W0LAGqdBgqMsgXdrKHtSMzqiy8eIL4sv7v0rnAtOH0SfUu7fXjzUDI80CXbspSQDMDFrU3EUxLxO2pKhiLskgx8fzyyM68b5wRh5CZa5Mmjp3YN4QozexNKDUfjQcwoXW1RXoXJPGSzOZvXwklVoTvvC78AtAl+8mORiktGJcPcbklENH9WRZrPuXhJpCCPaiXkH0n7LNaVh9bkM8H0DioNec7LfYm0dqlG8uJVE82aUj6gAg7RDTCctmoaAUkAUSz/1rmruPDXpG9f1iT6e/xEsgXf+/BXUKocAQBSyxK1LFBG0JhYtoS64pRQNZO6w4kWkSmKV2T/QMZh6HRhi/Wad25Lre8Js4aNRg1eZlwUiqtlTZCjRHmUaj9OjtfcMYOP9ZZHx5LFVGPpdgXxky9+JP8mt4Puk3cIxDMok8vHnBOVwOj2cDjEfObE1y/pT4du2Mggg7W4Cp1jzTWbp2BqZ6jJDaRbyJ9JJH5EXpcxUSWz6QxDZt8Uj49SO3C73fb9FQpLKZ65XC6bD0Oheybm1OYTUkghdZmmiTHrvBXKSNmB+Xz+lcIZ+ZYUSmk2m9ltUKieORFTLo8PochXmQwAcJHd3+9128+BJjdVa8RlOGxeiwD7LBWTDFE6lYDB9XVYk8zP0/sP5bmTKSbkI8oT4WYwsu8NBNHsyZzzTzk24y52cpJ1i7LtioPJ4dQQCMk4L9HGfPmFZGaz2Sx6HCenj4oXVCK0dANgDbI5YmZpKM87f3kbc431/eQ7GJvyGbfTsDmfHESZ1Sx5l5TLiUlFbGDfoNIF52C72oR/nfxRDXJmJCTLpQf8tkJPjciKCrlKEokEekxfru2IbalNWgjVZA7kLgiq5MXrIwDANnScdOXf+obYUe1MxjKa8YJiIjg+lc+8e1fs28Qao03J1GdU8vCxNtwY9BCLMvPCunKlsFFrjuANUW4+K3axTg6Sx08Pofss/o211kSLxXxBfOuDbwMAXhJ1V2SG7kZo2eaUsYgOaVX5XCMMH9d4iMiM5/uvEKfkspoXat0M+wNcJHJA8R7VOOeqtTKWI9J/2SUlYy57keEyMOC8OLcj6I43lBUf9vsYM2OmkIZKKSrs88BiZq3ZpSols92twQBZKs1F0tJ/SmFt2B9gfWkdAFA+k4xqsShZ/cvXriDqlD4uE503pU0KxKLYp0x9nPwbit/s/bsfwBGT/n96T9ZeyOuxM21KoWRMuzjSdTSpXONi9r5HHoKVpQg0heIkyuPdG98CAPS7Gho1In2aMncubklWORsII0IEpNpoz2oyzkYojAolcd+8EJRBjFK3y6sZwJDP+8gNFiFPwHRqoUVei2PKpjvDsn4ePL2HSxvSj1nyMXTJLbG0vYPX5BFIKBRut48ppZCblLVVCnU+vxNjKsKsrSkZYXlPr9Nj8568ei7oGsUZ5Qx64ebcLNapDEjbG8kkbb4qL+T72+TAqeUreEKJXDdVWd6/JXv6oNfFW2Z1s0RIDMnXcenKdfQ5bjPa+wdEEUUiIRjcg2NhmXMFIg3LjRI2ifybzMWeXA9lUZ+IP9AlQlMpDk36E5yeyNrxcv7q7PcVMwhfRKG3Zd5fOidtTwSCyJ/IO5tUpC/RRjg7I/jZVwXObcU51RsMEaCtpXuEFjPHznEYu6tiD0fk0zo6FTviTzgQCVEhh36E0ZF+KRgueLnfXqbt/PhjUQrrT4Y4d15QzA7Ie+2/OsDV89LfCmFcU/ufx4lCU+ZTcoUy1UQidAZ97BdlDCZTotPdMh+nAxOZmGTOq0SMOMkbBCdQLsicMVvS70sxyp+XSpiovYYKTltr4ksdvHqGtlP2yHJJ2qc4KCLxEBIcL6fi7muRW/OwgsAWkZmUgZ7NFS+iG25m0/u0zRkqvQ5HE7Sbsi+dleVv7pCJb39buM5+9rGguj30KQ2PBZODH3DLmCofoFyoIkBEOb0jpFLy/2jIib3XgsBdJVJlqnyumQWLXB4T+iZH++Kf3ty5itWI2PRKX8ncU13Y64RB/2ZOf2kwkZ/HhRp0h+x1IfL6lUqKT2qIjSWqw5LLU8nJ96ZjpGgj0lHpv+FgjCaluBUHmZIlHw9mCHiJqub6N4jCPyqewEEVP9VH86G8X9AXxZPXspYya/I9b4Acl/UWLCIckuRsOeP+cu7CNsrkllGIWY2frdQagGpfiyp0REc06y1oLvqudCpd5EjSvX6bw7BBYsUSz10Rn4bv/aooBB69lcqR9Eocs56849tHYoNApSlnIIaZTuTinNUjm/IOce+yrY5q8rzjp609OzvD+rbYgddHcs9CVb7f7wG6g3wzY/ndzWvCwzMaDm1+nwQ5ZZSibyYQsHn5ukRm5NIyZ+MRL0JUma1z3oeI1gkENIxYwTGj/6dsWyyaQr8hfkGD/Llm30SXPEHhIDlp3HIvazJHr89KCl1sSqU4ZR9MUOBepRQgo1QdXFvK4JvviU93eiZ7ydsT2TcMjwtdIsAi5OvRG7L254YTlbL8Lcp9dkhU53ImixLVyfrkgjOIAh+NRkiSo9Fhq7LK3pyMx+13tYgob9LeLa2t4xX7f6A4EJflPi7TxMAjv+t3ZUzM4VcqsTevCIKmyr1/MDFt7iU3uY6GRFKubW5AG1H5eCI2PX9wBAAotE8xIQqrQ9zNBjmEt9PLqFDxs0bfc0afoQ8DTZ4r4JZ5meGaGPbnmFMVrjiTd4/zzDKyxggYX9lWQHiWADkjxon4XcqtAwCODuUM4NA1xKkK9zt/5x//SxFCPxcBob/8R/+BBISqVXS6amMWI+HigaLdGWElK6UeV3allOv1ocCoXj7bx/ktCegMCBsPUR4unggjzzItVTqzuykbtgYLHg9h0ZS0f0Vy6GqvjZVtOWy1SCb29kAcGbfbjSYJA2/SYVGytPu1IlwcsO8RBmga0qbHTx/Csg/1lO8eyv+PDo7hIjFfhpDJTmsKLw2F2pidNKynZ0eoUSrRiMokLzO4VK/3UWQ5zYyO+3zKEqbJGE5CqBUpr5Ih3kgDvxWSd/7GvyOlY//sC3F0wu0ptnbkUPSQRNpHBdkoLl7YwLOXUuI3IOHjclYWRTwStcuDZpQtvnVXgj9n+QKOKVMapJy4g6R9tVoF4RgXKAN/ueUc7t8/kufw/X79o18EIFL2+bosvvOXpZ0jSl0WDo9hkMx1xgDDzjUp59srVnBckwU5pLOh05kx4MCIRF92GRVLDhzG/1tm/a9eOkvZ1KUghqY5sQmcVVDmK+JoE11CChu1r5N0u10umBOSjvH7KjBkzmeAkspmW2azGWYMVqjSr79KVK3+bQdvCGl2Op32GM7/hXKywWBgy4Grd1BOitvrg5tBHz9/KoLUaCSESEjGcmaozZiy024PwKDXjO9q0GmIh0PIkmjaRXLGt5R1f/r0Jdo96b8OSziaPJwGdReyIdnchgzcdutVRHkozBDyG2aJxFImik0GWlb582VRDGm9WrHLW5Zy4gQ9eCSHesCDEfumz82qw3LWb929C0uRyROSnlwVR9AX9OGP/0AI2s+TpHXAw0d4bmDE0pweD1NLXEvzZg9BEgAqXfdUWt5zZWUFv/v3/yEAYFnJYfOnOxFG9UDe52ZW4L6eVAjFp1JW4Q4xKMhSxLM3J5iS+C+2Jfbg1edSRnLhzkVM6jJHA5ab/Sj2qj8d47Pn4sSHMoR109me9zpokERTlYFcYpBk2O0glSLZvlvu9cXnEnxoxydI8pBRZJloj/e5dvEcfvm7HwEA7j2Rz4+4JtqNNtokjI1HpN8P3oqjv7W5igqFBeYUHHC7AqgxgFbryf1TOenbau0M23SAhwzaqADs3HLg1jUhtC7SWW7V5fvJeAYzSsJWCJW3eGhOZyN4lj8CAERi4litEZr/xRefoteV9b9Bhz3MYKRjbiCRkX8r4sUe54vf70MoLutElSSWCBn2+CO4fV7Kzz7//FMAwMUL4gQP+l2bAF4F9EwGEO/evYtqTfa9n30h3/OEfMityV6syLLPDmQ/HLXHuLZ7DQBw3JT+nHNMBp0evLSLcQYAU0m5z9FRHtmM/Hv5Bvezt1IqqPf62NqQPV9BsHW3tPfwzQHSdGTrhqzrelXa4pmN8N5V2Z9r7M/Aiqzv109eIeeR770hfD/Ecgun14Ehy0LffilrxK9Lu7cubMAXYxkr59BUszDmntqhTR9z7Q/aXRiMoDoZfAnQ/gR8LjQop0w/GCc8OKYzOYyHJNDmnjPSviLNz5cYBODp12Mpu29hyDKJ2x99Uz7Pe1qjCT5896702+Hh19pSqlQR8BFuT6EGjQfxUCKGkUkWfAZXuyqgoTuwxECoOhy5rCG6LH9q0EdZWpf10xoNcUYp3DgTYAGWRPjGU8RIXt1lGU6UiaXxcIgBE0qJFXlek4mmYv4IAX/ma/1uqMPAoAM3WIJFNtlgQOzWZjqBJgmELR6ex9znG80BNJawbFFi/oiluBOfE/2SrLlbO+JHXL8lNuB//gf/C9Jb8l5rLE1+cu8+6iRZDTJhOe8yKBjwY+CQf1s++cz6WpJ9UMRwRP+cZKF5JkaSySX41IGAZYNKRtrrc6LeYHl+hQeLgiql8SGRkvnrD8j4Btnnz5+9Qashv1tdl7URDMq82lhOYu+V7L2zmSqnYUB0MsGFDemjcusIAGBanEMzHRfXZI+bsJSzxHc4d/k9O/H16rUERC1McPkCJRHSfrAAACAASURBVK9Z1qRKiwM+D0Zj+d2yIe0b0L99U6hgfUcOeUXSP5y9lrW7kU4iSKL5E5aBJLPy/TlMFBjwuMLntsoy9/TRDKUj2c/vfPcb7D8SvZ8d24kDRQ7tZ7CuXKxiRl9NkeZ3GDxrGjN4I9LmjCHrrdmU8X9TO8PFy7JOtpZkTGaDMTTey+dlSfxA5n0oEUORgbtgQOxpmKVBx3uHWKMMtkn/ys9g7syy4NJV6YzM9zencvAv1psw6YP7wkz6jWV+3bmwixxLzJo86JbGTHZNLKQy4lvkWeL+9LncM7e2gWMGeXosh7rAYJPltOBn4PreQzlDBP30HSZDbG1Inzo0JpX7HrQZ5OwNZB7Wua6b/T7mJJNOkYR6dVnuNal00SAJ+Dkmd05P5Ky3kosjxrL3EYNmR8fyfsd1B8aUmw9Hv176P56Y8NIf87K8XFE++NGBm7nklRxlzOlzG5aGPs+5k6kSWWFQ0qvD7VDnD5Ytkwx40Gvj8iWxN4WS7P37+3ksramyX7Gjpaq0Pez1IxeW95/S/qj987hYwfq6BKkHQ9kvnj+T9b27tY7rV2UeLi9H+Hk5p9VaLXTa0h9uBnQsnlt7ozFeU4ggm2J5f1p8r5g3AA0yTo06S1TdYfsdVHlcMC79WWmpIOQ6rL50wJsTikbQ7uiDOeacx02Oe4Tl6q7JHBa4TlgKhpkGjRutlwEWDwNWkYAXZ8dH0oYwiZxZRunyeOHgGWrMfhyQCP1NPv//sPdmMZJd6ZnYd2/s+x6REZH7nlkLi2SxyGY3yV40giRoNNJIM/bAC2DYfhj4xYAf/GBg7BcbBuwnA/bDWIDt0XisGcuGbaA1rZbUrSGbTbK5VBWrKqty3yIzY9/3uHGvH/7v3CKbsvxkoAHHecmqzIi7nPOf/5zz/9//fZjwzPVgV+yq2RT7v7g8Q4BCKKoWa8gzjv6V86MrID/DLunrxk0PrZL0sSpr5zYVPauP3kT6Lzkv477AhLrV6WHEpEUmvSzPQl/R7t8gkpL7/Mf/1aNZydiszdqszdqszdqszdqszdqszdqszdqszdqsSfuVQAj94T8SUmm/z4ebokQ3/UStpAltvr4qIk8Uz8m+ZOh9lGtLxNIYdiXSendXkEJu1kNNjAH6oyH/LVG9S5alOXXNjtKpDPXphWQ124MOUvOSuZ2AaAiWFJWbdfQJMZ4yizcmlH3icsBvSdRxUJDo/fd/TzJ1P/zRD+EiseHllaBmlnKSpTXGU4wIk0skJKo4bXjhZIbGn5bf7ZFMrFCow0socyQiEdCbsjx73fCgpIgCvYoAjSTFgz58hFOrUgpFumi0qvgPWYKRvCuR3UmQGf5SHTFm8vqEXH5+KuMQbg3w7V8TOeYXjOKqTKLP44OD/X5zKdmWdFL69bpSR5lZvtXbMm6q5OPx44dYSEkGJkr0yq3VNTxhpl0RU69T1nozvwQfs3uhmLzzh88EZnrTa2KD0df7bwo6aZ+ksjedATpTuX6rSxTPSJ5XNy1M8HUUkCoP03QHdP2lbPbXf1qwiOhSv1PNtAwbETSmlKBC+kynBvrMmJWZAe40KYNrWTaqxyD5tUIAmaYJ01Iy999ECDmYYba/bxj2v0MkKFOkgrqu2ygBhQxSc2QwGHwDDaXez+n22NdQaIMo7TIaDiMYkr+5SJarkEa67oRF0llN1V2yP3RzaqPZMiSPCzKrPNB1PHokMPDmlcxni9H762oZCEhkPchsgksHxl15rzjLBhcykj3pN6vY3BSo9u6uZLmmLL86OT7CDcuDtrfERtttuU+53razbk+eSoZzju8e8vhwSSRdjBH8N94VWfOf/NVPMSWqaYsIobVvybXLxRI++uDnAIC7lCGPJ2UehMNhgGU4Tz4V2/7Od78NQCCsXc7LjofSn+zOt3dewaNHgtw5a8n8euvubdycSoZM+QEX5U5XVzbRYebqpx++L/1IotTF9VWk0+KHP/nFZ3IfZrTG4zECJPZO+GXs8ySV/vS6hHBI/ubn86WY0Tp4vodEUrJFdZbBKjL2QDSOAsuLolwTtpeXAQDaZIhonFBoZmWOzsQ/enxe9DmfLUOefWdTMjiRcABzzFAq0r16rYsps3SPngnaqM5yrffe+RbctMMpiaPDQaIORhOsZSSbliOB62efSb8YpoZ6Q7LbB0SeTpklG47ayK+IzXWJyFClvJtbKyiV5Z2HRLW6mCJKJ+cwYBlqqy5ZQjVP33zrdexxbSwRwbF7R8j6RxMTeyxXU358c11QN48fP8Yy5eZVxr7ZqNnX7pGwdHdX7NHh0OAj2fuATKDH55LRG00tZPK8FkshFuclW1gvV5AhGXefEtlNEiZPxgaWSfDtdxEJqaR1YWDEzFqdCOD3vv/r8uyPntgE8qqcJEk4/sX5Eap9eY/X7kpmdYfklY1WF89YLu7hM0WYzVtbmsdVQfzvo8eStVZIqF63ic01WXMiPvl8tzfA7/7+3wUg5QcAUCzJ+n58emIjCY6JyqlUpF92djYwIbnlmITWp0S5am4vikQ8Fi7ld3lmvReyOdS4f/juO1Jyd0Uf1ey2sHtHypg+/VQI4ReIAtKmJsaUqdaYC0yQvLZSqSBCsQ2FFOrRtzd6Haysi41XKevcJOo37PVhc13GTZWcVTBCn3LWPpJkv/1ASj9GgyF+8bGU4ybpR9RaFAl6kEyJfy+xNOeGKKxyqWojpe5wjzcgQvTq/AShiKBxvCzDiWXEBg5O9pEifcAN+2wwlD3H792+hxH7/Rd74h8HREdYHj/SKdkDgc/X5broN3WUz8Q+miX53dqOoM6beh9Tn3w+zZIKy5hg/1DmR5KQ/qd7gjaqtFoIpUkazPJGp0l5Zh1wJeR50iwPKxWkP9yWE7GQ+MwR/cbELd9vT/rwEIEejwiaqk3C0nanjqVFea7ijTzTkHvYZDSLv/hAfMStOzKmLhf3vpqBeEiek9tpVIhm7o4MRLjfHllis+TJRtAVwcaS2I6TWfyza+k7XyiPhYw8S5tlJMNRG5eXso+9f1/2am3aUq1WgpuwizgRgr2+PHux1MLtTfF1Ze4HDg5k/Q0nojgpCHozE5C5GF+Vfpl4AS+pHfykVYizPNWAA18eyTXWiZTwcP+jmyaiESIcSIEAnyKjtdCjX1QS5RUiylraFLlFQYz42tK3TSIu3JkQpqbY5i4JzUe9IYYspfIQsacoGKA5wWUasajYlYsIqGQkATcHYcDqgB59e284QshL9AOvWeB+uN7uocY1J5aS5wwGxJb8Dh0B+o1xh2UqCQqdOLzodgip457ohGtyPp/HUljG+fDnItbxuHIm14z4kF+Vd51yjbu8oA+0plhlX3Xb4lf7TT8++Vi+G+aeMJ0TW312eGqLuTiJOInTH5jdDjz0136i1Uf0A6moDzss8yyw1HrM8j9nOIobUmPMEfVcr8t6aE4dOKJUe4Rkxptbsp+rXZ5hfV38jtOiKAuRVolwFEtLLAHnfmxAAvBys43rgrxrJiHvPkcUuMMBNFn1kKBgzmA4RZnrQ5TnpRZL6v1uL5olmY8K+aSpeWMAmMr7+wIsSYvE+Hx+jLviK7fX5D492txgMEKdSNUM1w5V5hyMhFEsy/t0uc4EeXY7O3iB739Xqjkc+tfH2eMNY8CKgxYRm02WmaezCbtiIcX934jM9dVmC2MosR/5qc4uDocDAaLo59Lix2PhKHysTDhjH1eI5t7d2UKLqFR/mOhWnkXdHieScXVWkL+5SDkwt7iKR3uCROzWpI9WaM+l8jWqrA64d098U5No5KlhIcL+LpdoX0RVpSN5DDoyXh9/Ksh8nQhHw2EgQIWpNEsJt5fEzlo3ZWwsyB7t4EB8+8M92b9s31nCcCzv95/8409mCKFZm7VZm7VZm7VZm7VZm7VZm7VZm7VZm7VZk/YrgRD6L/7hexYAuN1OtNrMMBBJM2H9a71ag4MoiA6zR9958F0AQC43j2hYomVD1sx5WSc+NU2bR6eoCLwY2Tw4OICf8pATSsorEqZmv4nFFcn4Zhfk8wWSLEbycygzO9vvKj4YyVRMNA331iRKl3VKJLrvlcj8+eUZ3MzCX5xT5r4kEdFcJoswM3PDAeu99TimbokU1klo1mRmtdM27NpIJYu4vCKR0MoU+OGPhER5OJJ+0EnG7LA0WFM+K7PwSl457PLgX3v9DXmetRjHQfqlXa9iwvrfNRLfPWKd5+imAi8zDnMJiaTeMGOcCEftqHaX3DCHlMtbXd9AnJ9vkpT6/Eq+5/f7YZLccp4kfNXiDcLkAnpAEuoeJdGzK/P44GNBVqgMZ0RlFFc30SCR549//OcAAE+Q7xdKYuxg1JzcyyOiADwuly39q1A2X0UIvWzkZKKEoaZpmE4V59DX/wYAU/a/zQHE/5umaWfcS8ww31COdTIcwUW+HkUmrb5vWRY0/eW95R7f5BB6ef/pNziEVG28ZVk24bP6jJK5Hw6H9jUVd5D6jNPtsomS4ySFTlAuNRIO2lxdChWlpHktS7MRDoqw16uuPTVsGWyXpVBVzLxFoljfEnRdgVnrTz4VqfTBeGiTz1qUYTTHI4RJWj0lJ0GaGb5UNAidnAehkNzbx7r2+WwWR8eSzc1lJYJ/w6zLzq17NpfHBcl7ldTt1ek5rogIeEDOrM+fCOeZ5nRgyqzf26/JfPMyq3zdaiBA2dxRjRlpSrJr0QCePJRMdoDJuNfflWuXSiW7/3uGkuEmGZ9Dg0b/lotKFim3ksXJpfizJtF2WyRIPSoW8fihPOsya8A3toXvxzN0weGXsfvRB38p11qTd3a73UiQy+DyQDKO7ikRmGvr6A/leTgMMIh2qlSbqFbF3i0ns+MBcpVUTDSJkstmJKurk0Bw1K1je0eygwppoejTfQk/ehXxKRs5QcLMkeiwUa/ZhIinlFevtXuIEMlhkCBYc8o8q1VKyMbkuy7a+4AElSsrKzDq5HZhurbFv+0dnsKg302SH+f5odjS/FIWpYJkF7Pz8rcKycBTczHEyQXUJGKqRlSJ1+nH1m1ZXz6nvS+RUyWby9j16wb9jfKvoUgMLnKGKCLzizNZ614838eUfuc2r+12Sf9fXJ5j/7l87te/JyjQy+NTGCQq3SWyUxEmXNZLaDHb+WBdyDcVp161XLLRocodnp+LnaTTcy/llOmPdZLknlyeIMK5evcW+YnI7dHotvHqA+FvKVLCPkKE6N7pES6rMr5vbMt7aSSE1zwemESXRXtynwZRNl6PG1P6qwF5xuKUbtY1IMK/FZlNrl0V4SSyZIF8Xyec+81+HwEi4UKU+XZYcl/TnOCQKNs2eQpXNyXD3Bn2bV7DIaV4D/h8wamOLlHOr74qvFCPXkgm0HQB331beIKujuUZDq+lX9aXVzEgqmPC8ZtflLk7GAxwQkn5OzsypiZ95wcf/QzprOyFklGiUogi8EBDgBM6RYnyvteDK5KUt+lbbm2J/+h2Wogrrou6vEOLyE2/biJEdEGdaKNdcmZUqg3c3hZ7YoIaB88lM+udT6B4Lfs+g/swr1c+lE3EEHaTK4pE942qrKn5sA+RNH3CQL5/dE2eyGAUd18TYlUbwcs9RLlah5tE/z6NUttEMllODemsvN/xUxnbB3dfxYgCA48OpY81vud5qQq6Lvg5T558IWP527/1Hq5qZ9KPnFN57r0uLy8RIH9JKk6/RZlwX8CP84L4mUhQ5luUnF1ep47hQHxJj9lqr1vGFIYLA8q6DymzHo2Qu1IHJkTj+Ig6H07F5h/tPcN3XpVxag/JiUVy3Uali36DUsuKVDdJkRbLb0s8K/ENE4YtUKJxTvW5H2t3W1hbE3/vIyLp2TPhGXNZHkT83NNpMmcbLbnmWHdgoPhYauTeYpZdD7gR8Yn9uogCzJLT7ax4gyRRnEcUdXERnZ0JhGH1ZEwPyK2UJN/hxOVEkYS9y3n5XbUg73RZb2B7d1k+dy1zo0wOotydFXh5Vghzz9VoduEjYknt1WqcU7rhRJfIr7BP3j0eI1Ki2sTWhvi8zx4K4lXJrd++9yqqtFfF9Tkk90ip0kCfc0ihBxPkVvuLP/8zBHh+mRBxonvk5/b6Nhw8Z43JuTi/KOva/tEhwkTQRIis/Xxfxs0bcGJIkvMR+9ZJQu5WrQ4PCZ01cu+lc9tIpmVM/vf/80fyXhxvtzcON4mBCwWZ42rfvpAJ2hUiCo3Tbst6G/I6cfeW9NUN+c16RM3pAR3gfkohUbWJ/GzWR8jl5FkOz+V9FhbFZ/u0oM0X6ODPoEf6f3F+AXXudxy0Y4tzN5nI2EjIC4p2aJxLTq+OEfs2xb1oIBBHJivzVwlkfMx9gWmY0Ehs3+lLPx4XxL/5oynsLovvf/xEkPZlcgUuzCdAnn9ESWA05hzM5xcwJk+bUp0xaS8erx9tigd4ibAvcI+TSoQwnyJiipUVfp/YVeGqiM0t4f/a2xe/NaHPPTk/w8qO2GjGySoBos9HPjeghGt4xnn0uaCzEyvziLp4nuY8LZVuECXqasqxVBx3yWQaZ4eyXmaXxTePx/K98aSHea7FffqUIRE8zc4YgYj438NTedetDUFEJpJhBIJim0+4948TnZ1MZMBjD9rc94TIfxSJZTAxZB6fnAmycXlVrllrVLFHbrQAee/m4vJO69llPH8i73B5RcQl7wH3BB55FPzJR4UZQmjWZm3WZm3WZm3WZm3WZm3WZm3WZm3WZm3WpDn/3z/y/30bm1TOmTpwdCKRUz/r2Y/OzgAIf8a735I69HFKono//ukHAICV5WUEyRrucErkLpaUKOL51TWYjEcgSDnh5yLT7tB0dNoSBXztnmTqE8wchyZBjJk18hLl4XFLdHzv2QESlM40yc0TYK1kfdC3syM1RqAPX0i2bGxMoGmSOdhYFXRDJibR6k6rDYPoCZ0cRNPgAJbFDEpFIpq3F27xWSz7HQ8YSZ5fk0x49+QAQWbx3URAjQYy1MZ4Cgdrpt1UGJiaKlNfwcMziT5+ecqsDpFW9995E2dErZjMFCUS0lc777yFKmVoa1R4SCrlEI8P268I8uCaigvLb0uW1zuw0OtJxDXBDHyU6B5d123kSIh1wJViCZ6YvM/JkUTkk0RJnVaq0DnQHfJNQCkyAXj0XDhe3FSaiiUlm5nMruHJEaX5nJRuJzJgMOpDcym5dKW4Rb4bDYClAq1Eoyi0nWYBzJ5Z2teRQtBMm5NAfVu3/6XZn3OT58lFDobJcMSbAi8vZfH7jm8ghFTf/XVN07RvcBupZx+Px9/47l93LfU79bwupw4vUQI+ZoyV4oKmWTYqSkWgX8rcT2Ewc6OU+gzF16TrcHIsenxcLxEXB3sHePZExvTOW/fl52uSpXzy8AtbIaZM+WyXE+iSG2pxTlAvvabMb1d3iAjRE+Ox3C9AFFav28XupmS3VbZJycD2OnUkomIfuRwz2QO53+LaPG7tyHxUCkKLRNK4XC60iPhQSjl+Pm+/0QbcYsfRoGQMDo6ZVQ74oTEL52QGZkj+CH8ibHMtjJgRaTPznt1Zt9EC4Y58//JsiDnWuF8RlVdrSObxkyePcGdHMjb3FiQj+9OPxdfGfDH0eN1/9+//PXlmvnO/N0B3QkQjs4olcvTkp0UsZ2RuP2eG4/hUspRe7xw8zExDl/dxM21eG5ZhkCvtgIiMuRQRF/EY2pwDwSiz1opDYGRhnuPcJBfQ44fCu+T3epFpCeLBQyTquF7DFf3ThP4wEhWf1Co3MCzLeL1OBM0W67YNYwwn+d2+fCqZfQ95I+68dg8viFpR4xzkNZ89P0ea6m7K5lwu+d7xSQGGSSUloqMKVD3JpDN49FBk7ZV6TGpO1qKDgxc2P5DND0ffFPDqNh/RiFLBBvl45uMpGwWxRWWavWeS0YpobmRot//XT/4CAPDq7m1sbwrnwiWVObzMfq7ls6gx2/nkZ1L/rvgSep0WDPpTN/3akHwM0Vwe80T8Xe2TD4f2nMjmbBXEZ1TGaxM9p3kc9t/icfHfhzXx5+nNBbhDCqEltvCLG3mmZCiFiFJ3nJMx8Qekz4+f7eH+mviU5TVB7ByTjyTg8iBL/oD5ezLn329Uce+OoFdePJWx2b0lPuPThw+xS9RPheitjbyMUa1WQisgY/hgV65FsUiUel24qmIzE8rTRrj/MFo9O8t6TNVFxau1tLpgj0mbPFB52iqcOipUpksSqVUgF1C1WsY60Rf5Jck+Kznz27c34WIfK1WdATm1Jt0R6uQT0X3y8+zgGF2Oa5Box9K1PMt4MnzpI5ipdxIBcdkqIUUOspWsvE+WCBej3MekTs4LZlI95IrKOQLoUcGqS1WufE7G2zDHeEx/8+c//SkAwB8gL+Prr6PBMXFzzcrnpa+6/TEql2KHtlomM/DHZhGdKhEWzHorHqBy8QYnz+Vdl6mg9/yTL9F2y/OBPFXnBfELlWoTXnJqhMmftLws2ehipwGPJe8KoiJN7g0jqRQiRIzUyduhE41ZuWoiSdWvHhHln5BPajk3hyCVdRIRuc+oL31WKjfhzqj9hvR1k+t8KpFEvy/zMUDFQxf3RK/c2kK3J3blcBAtSYSFMZjCQS6bEvkfp1wQI5E8CteC9PNxv2qZE5vHLEi0R5RIgpg/glGd/UgEjU7lPt3rQM+Q57u9KXa85VXqaYcYUGb+CypGbXjFrub8EVt9eKIQ0eRCHAfDKF7Le31re5X3k88cPH+O3/iN35BrxKQfa+QA1cIBxMZUfvKKrRfJ6RH3ezGmelqfSlOpjIzVyeklVpfJnUp15UrpDCvrROVNiL5wqT2XA0vk9mpR+TMZp1qvBbio8JkkKuKCCMNS+cbmWimSszWWE1vtTvtwcS588kh8pdqDvfrmm7gmMlHx6AQ4bk+Oj5GjdHiDHFFHX8o5q9VqYus18Y8D7gOr53KdTC6OCPf052VBQ4SI3t+cW0K3Jfe5uZT5Mog2oGvy998hN2u9If1+dlJEoy7vuLJEpDIRMalMCi6uUbu35Kw37Mm4f/Lh+xiMxQ/GU/Lu46r0T7XfwvqijI+XPrdTk/2ZGXBD5xzY2lyWvw2J6G120arJO/7mr32X7yDvVyrWMCTfZaUmz2cSwXZdbGBlSWytTjRKmvsdn8cLnfujYoEcO3M+vJITW+sXiUrziu2kM3MYGTy70o/urpOPq91HkohEc0F82BGogBrzI5eiciwrdtz6y4qAeo/8guTBnE/JumlMTFSImF9cE7ucy4tdvfngFTT4zKrKIBgS29ndXbFR+Dr3Xpm47M8iwRQiOVkTfIbYc5FnzXZ5gApR8B7ui71EsNXKFbQgf5sjItUfcKFwLetlZkHWh81N8sRNHegn5D4D6sf3eV6IJ5MoVcTGqkV5h0REnq9QrCM8Ev+9rbjtuB/eOzjExvoyACCbzfLZpX/OTy6xyLUmSLVIN9fKYNAPYyr28Lu/85sAgBCrBG6urrBBtfWfPBE0VI97hqNiBXFy6nrnpD8ePReUqu72oNgievNvaL8SAaERSfS+ePgEDyiLqlHWt0n5W5fDg48+lQNgkKUlt16XAFG9VkEsLI5wwkWhRYnSnmViyo4+54KUj3IAvA6ARHcnR7KZ1yFG6nE68PZ9gaIfHZOok0TN6VTehpctsgTjW6/IBrKDCY7PxegMkmHtEC5drzVhEAqmSBnn6YgnIR+qXNhjhF6Xutc4PhQnYk3lHTok6j0pXSA5JwveDaHXfgZvLHMMp4MHfJZlWAw6GIYBi4RrJstwTB4aXru7jFBM7rPM8owRDXhqmXYQRaOTWUjLZyrHZ5jyEDAhSSOC8pnkQg7XLOkxSdpqtAgBvmlg51UJDlVZ+lVqynjc3NzgVZaw+LmB+NbGFlbuCzHsBQ+xhydS9uJ1uODnBmmZm2ODB/Gi20ClLc7SR+nOIxL+6t4IYiwPKjcpBUmSY7fXCfppO4Chgmewvik3r4Ik0+kUOkMf6jOm9dXAkCof+3oQ56uBGkW67OdmdDQa2cElFUr6quz9X1f6aQdytG8GeH45IKQOhOPx+Bsk2epvX72nTTjN4KLP50WAQQqvR5XTcXM47mOiFiaSGFp4GVByu18SYQNAn7ZgmgY8PIjYz8ISDs9SFjc8DH3wY9no39qWA+XW4joKDbFbBxdVj8ttbzQrDOgMSUpqTS24CKcOEdI8HpBIVBvY8vFhEkZv84B3en6h4n5Y4WFqSFsvXxawRMcdI6RT78qiMtWdmBJS/sorUpZQBQlZiyVbZv0Fy05aJIp899Z9FIviN5TsfJkbinq9jmCSG78hCetJnLzlj2D9dbnPZ5cSSO08eYa38jKfhyyhU3Dwv/d7v4uf/EspBwsTarzAkpHAXAyv3xFC3xAPhf0XAs9eDkfh4mEh6hd/fOiTvxUujlEoyRh2uRma6nK/o7MKNJZU9BmEN0nEONRH9lxYIAT98ko2h8u5OOYZwFhn0O79n4s0esjUUb8Wv7h6W4JbuU2xjw/e/zmSlD/vUsI2BB9a9IdTbvprN2JD+tTEG6/KWpBPi78fMOhWrhTRI2R6xChkkITm5zdX6I1lXNXmolaV9cnl8CIYkU1ajTKsPpJuB4Jx9Bjc9nqkH9c35P12Nm/j4kLWhJUVCSL0uxRJcDoxYTBczZcN+sLnL56ixtKELu0+l5dATbXVQoglo0MeUsIk4ITXB4uHKS8h5vn8HIYcn0Bc5ouLc3fS7iBBqdSV+7I+K5n7UCgEP+2izgP1bW6iXuztYYfEvKBdXZPYOTQXB1rSHzolcm9vqgNGH48/EqJSH8uEmiSKRH8EoyPj+xcHUiqcXZP+yCXTuHosgQJVlh5mSZc7k8A5S82qRZlfXZbRLubn8ehKAk7OZ/K91XgcI5b7rS3Jmqha3OeGn4SZfpLHbt2S/vmT979Aj+XvEULCeyPp68vjEhJZlnMxKRSAfH/kdMFkqc0CCbvfuS+HHGPcnO6CEgAAIABJREFUx9VY1kQl7BCc0J+PDczxsBB1yt6hzXLzbCgF70T6vXsjQRIPNe3T8ZBdwn9yIrbnsFhKMDVgsIz6skEBhLAHO+syX4YMOnoJ6e+Vq7g84iGUJTYRHs77Ew0NEoYOD1gSeMMghNOJWJA2MBEfEWXpY6NQhtY3vnYfRXrbGnWQ5EH12+99R37HA43uDeLv/N7fBgA835f93x//8b+Qz77zXbQbXCcYjKnxcDrttrG5LOPV4fs9+PbbAIC9PQda9O2xjPir43IRpx2xlaSSrub+7N3vvY0ik54FEh+n1mRMj9tVZDQZJ4dbvhfQifsf9uHmGjqibPmUPq3SGaB1xrJXlk5rtKFeT7OJUbscL22qkk8RmJSwDwcVZQOTlKYFS5ETsx9UQDq/NI/jY5kT3bbYczIuY7uxuoEYE4fXVXlPlezqtPvwhZjsK8l4+30eRPzyzopI38VjynQ4xs25+ASL+/0Qg1ohvx+vMihrqsQgA6oLiQjuvCL7zO/GJIhz9UzmSP3sCpvcZ6pgxRLn4u7vbOF/+6f/i3Q3/Zst1BEJ45/8idiKn7UYLpa43dregcclz3feJLE194FDhxsGZeTHEyX+wABlOIMiE0UBUj3sbNxGgUTzUdbxxGJybW0wwnDApIpf+uqKfjXkDeLsUvbIYe4/gn0Z90anCy8Tr9WafEYbyZi6Al6E+LeVZSmHUns2czCCm/taL8U6WqSRWJlfQp3BpSz7T4lcaK0OLAq3JPtix2vrMh5nhWO0+2p/Kn8b8P96wocekzmb9CdnlRNcWPLMJoMjar+vz4XQYZA4kpBn77fEHnvtEra2Sfhckn2Vog7IxSMA938Flv5HKeQRyATQob9Y5BrcpB1PTd0+R6QY5Pew1N0b82B9VZJHw7EioR7xHlXs7gjJsJPz+pKBUcvrsoV1XBRu6LG8NxyNwOI5wmJ5+vGTfVywvF8lllVS6PDZDT74VP7GSmm4OW9qzT4yMennpXUJViysUMQn5AKmpHZgwFUFn3u9LuCWft/gOHeYnGm0u9jclT2W2hMFWU61d3CKOImRMzzfqmD8oNdHLk8al32WGpNsu9sbo8ozoZKYd/Jn8fzK9sl6SAX0xec6hwYMBqJvyrK/ikfCcOgyv8BzuMbkRKfewEKaJc9jsYWbGsuWN9dQpH+K8/xtmhQ6CoURijPxWCnwmcXvjMdjtFiiHSTxti28E/RjnrLxTq6lquT680cPce8VsfcHr4pP++f/8z8DAAw7AwRJz/L9d6SEX50zHn36GFUGzANMSv76b7wjfTzqKhP/G9usZGzWZm3WZm3WZm3WZm3WZm3WZm3WZm3WZu3/Z+1XglT6j/6bf9sCgMMX+3Awe1CpS3ROEYYdH50hEZXs1hyjt3lK0pvGBH1CVucXJOr2+KlkDR0eDz57KNmf+XmJ8N7JUe4UE4RJQKmI27rM4sWjMeQYMex25NoffMns+mgAN7MWywF5pq0ViZZe92toTyVKp2T59l8I2ZTPHUC1yogmETUJwtk6rRYsEhFnKCkYD4Rg+uR387sS3f7D/+mfAgAuz0pYmpNs5OK89McKocbPSy188LEQhV1VJFLrcEjEcDqBLfNtsNzEzcjtd3bSiJFsz8FseYqIqfvfew8/+5lkenejEhGdZqXvfvSnf2wT2t4iOdgKx8brdNuQfpMEaseEfw7rLZusstqUPlYE1zvbt2x0zZ/+8IfS18uLdoZmn3KgcwvSVwtrK7j3mqC0To8l+/PjPxXSuVw6g4eEJOYzcr8oSzf6I8BgtnSiS0S52pIIr4UpNJPle7bEO6Rp35Sdf4kQmsA0vj6v1Lvouv4VomlG+62X0omqbGrErHKVBILlchlDZnh0ZgkcX0Eo/TLiZzwe28geTdUssX0VIaTQF4okejAYfKMcTBE5Ay8RQkMi8ILMVCdiMSQJM1XZRUWYJ/f6uoS9Kn2CrtmlWOp+E5ZOYmrCxWtMfgmZ1Gv34OPvJsxqTkhEupjP21KYZaLumu2m/a6VsmQmxkQ+zMWTSIYZuSfKKRaVz2bSEZhTljYwqxsnKvB73/81nBMGfMhM0RdPZd71a028+23JSK9uSOnY8bVkUevNFio3gih4cFeQOz/fl1KT/mUJ774l2ebDsnw+xjLWuVAMZSILR1559y4RQnA67LIkRejnZMYoFAjimiSGvqSMkbc+wu6rgs6oV6WPJrTxngX82Z/+GADwzusypy5Z4vDf/df/JV6ciz/7w//xD9kf4sMqhQqqlMrWlKwtffZFoYsqM+caSzNHBNtNTQ0DlsW06pJRefMN6Rev0bDJLcNEsegkaQw4DNxlac7DLyUT5iLK6aZ0je0t6fff+S2B3V6cSAbn448eYnNHMi9/9MeSeQnGQtghysvLLGOIiDBjMkGMmZ1LkuWOCd/XHQ6kueYoQsr94zNeM444yQirVbEhF/3wZOTE+ZlkKpXPC0UD7KtDWxpao49OMNPmcfowZTmjyy3z+8ULgQ5vbKzZ4ggRm+Bdfl7dnNjle/cfCJpEkf33egPcuiW2UKQca4+Zrel4grxffKXhIMF72IcJUYcOksMXC5Id1kcm/CwLu/36t6Q/SCD64YcfYoEZPEVi7WI5wmTQw32isJhIxXlB7D+aSuAp0Tx3d4RE+dXbkmF+8uIJjk/Ft1+MZC7sJGVddFkaKoas57FF+Z2TxKVeOFE4pxS1zqzyvHyma01RI/nsOsc2RJ/Wmozh4p7BQ/SKN+RDtalIocWGmixH9Xl8aHHNH5Ec+ukT6WO4dfQnco0Hr8v7xGnjbrcXJ9eCrrsoy7O8dUfeHT43yryfgu0bTbmOwzKBgPiG55ynCfqr0WCMECWGNa5dj/cEdT0/n0OYNl68lOfb3Zb+aI36mMtLxr1FFK0iwMznllAhkuaM4zUXCSFJouNrSr1PWKbYH05QbYndVVi2ohHpk4uFodaJ/lDuk1El/E4n0pyPGsvMX90Wmz0Z1nFTkGtOCbVfXJXvmdoUz55Kya1aX1SpwnxqCVNN1oAmRTv+imi2u/fv28SmE5LKXtN/L+VzNrxf59qaILosGvLBxftMJ0SD1zvY4xiucJ8Iok4dJlArid3eUKJ4Spnv9GIWKMu/a00i6u5IBv74aB/1kvwuHiNxfEze+dHTPbSJNFP3eftNQd5Pul0UzijcQTqBBMn02+0udA+RmSR59oVZxuAPoEnf7mPpYojlb4NJH+mM+AinJk69cCbvsrd3jQz3aCPIeCt6h0ggiYBP7L1clGfaXFvFuCf9fXkidhhi6Ve/1UGdZc2HXPPzLH3MBMK4y1KbBpEqd4kKKnWbNrF7s3AmfUbUWOm6jCR964BoryYFY9wBDx4/lbInnYhJVZ7btSbwUfb86EzmmVpHVxcXcO+u3Ns/lXnZLYktPbm8RleTtcPN8vcS5efjyxlMu/J+BlF6fXOEpZ1lAECCJbFDorDy8RSur6Sf3UTmTkyxRxeccNE2G0SChdlXQ2NiC4WoNjTFT0YDYZQu5Jp+Iv8SXN/XVzfw/gci3FIsE/WSlrFcX1iGSeJhNfcvSNDe740AoqFuLsSn1bl+6E4NCSKbj89kD7VAEZk3X3uAG4oHVLjvaVZqePNdQZ4urot/6tSJmu4bKBWl3w5O5XvpRZlvFoYwSV7dakr/OXneSsYzmLLcsqrK/ri/ahsNdDtEXC+Q5L9NBHFpilBI7CGckH7wBkgfMRkjSzSxqhQB17XyTRP5rNjq4ckZAMBHZPrE20aT8uVelhIplF8unUCDCLIBxYySsTQW78m1lokWvaA4QKPVRmp+GQDwgv1+dCpj4nL7MJmq8mGx41xGfmLQhWPI8w4RuX2Wnjo9bjilG7DMMqgKywgnYxMOp8yTJhGaq6uck7UKDENsxsMzPpd+BNxee10KBei3p+L39p4d4rpCsmuWlCukos9wwMM14aIjz7BB9H73soQh5JovnsgcXsilMBzJHOeRw36Gi+NThLjvi3Est27JtZr9OooV6T+FbhqwNDkQjuOCZWR+ijD0B0Qo9gbQiWZKMmbhZHWONR1C4xk7FpZ3VmuKaZq45hqs0FBR7gvCoQBc/J0nKs/i4Dx3ww0P9791CgaMeQ+nU4fBM9u//5//yYxUetZmbdZmbdZmbdZmbdZmbdZmbdZmbdZmbdak/UoghP77/+z3LADw+b34nLLKDWaBtm9J1vDzLx5hY2UZANAlmqTBSOq3v/UWguQWCZOH4csnghBKplOwiPyoU/6ysCcIkre/8wDrW1Kn/+HHkhlKJiivGkniiESSMWbDdF7746ePEY2QiJJghsoFyfHiIdTbkin4zrclQ2oxGv+zDz7BG69JdLvNd3jypRC3vffOOxgSsaCk6K2hidCcRDkv2hKh7DDz06wPcIe1z//qJ/8SANAjN0QgFsfQknsW65JB7DHS2+sNbK4Qi2gLZQGvpVz4278t/CDHBYkyv/hYnu8P/vV/gNNLyZClWNt9+1syNh89/xTxkDxnOig/GxfkXmi2sEki1ir5Co6ZDYqEg1hQEqQk/03wOvl8Fl8QNVEdyriZHicqJBo1rqSPt6IyXpvr6zgoyxjsV+TnlNmqSqmMpWWJ9m6tC2rgmvXVncEIdek2eKLyLCWSV1oOpy3ZbpqKBBnsM/0bCCGV3TRNEwPWJ/8yH4/D4foa988vtwl5d6ZEGCnCw2KxiHq9/tXb2FLH069w/KhrTqfTlzw/upJ4t+y/qX+rmlaF4ur3+/bf1DN/9V3UsyvpSRshFI8glZCxCxCdo/iFLE0Dg9r29y3Gog3DsOuibeQU0XM6AAd5p4ZE50SJPvDqblyTJLFLFIUi0NV7Q2wQZddibXx/NIROIlu3h5LetLnS5RW21yWTEQtJf8RJIBoOOBEmCemE9qtqtSeTCXqsoVf8J3XWe2+trOEZUSsDclBY5K/qjIYwmTFvMssboAzxzekF3rwn82qbnEhH54IkOT8/tSV5Tep23yEHy9BhQSPyr85sUDQrqRwtEcBPfvITAMB6UrI64UgEi0Sv6OR8Upn7RrVlI22uS3Kt26syf15LZfHwQnzDWV/mSYloknAwigFJ+s9I5uhQkr6OMCYk9eqQlD5E/pnRpIkos8alc7Fxn0v6YyFs2RwBT7+UtWFlXp773/z938GXX1BilbYeYXbSP5/BMjmU4uQ/efwLGY9Ox0CHmdTPDgU9Wh20kGcW+M23HwB4yVXUatYxJvpsZ0t82YRZu08+/Ry5pNjKnfvyvS+fCyKm3G6gSeLcwYgE8A5ZS54/u0CIfWzzhQVknhYuj/BbvyGEmTX2v59ku61aEwP6dt2ppJpl3O6+cgeFS8nuNokGGgzl5+6tNWSYeV1YELTHx59+DADIZOfsNaFaEr/qJwdGr91Hl9nZQET60RfwIUzeNZ38Ev0uiYJrPVyci/9dWRWUqPIf5XIFfo5FmDwVXha2z89lsLIsn6+2xQaUdDkmGgIhyaJ98VTQQJckPPYFfUiSdBMxeZYhuQJd2hSLuzKvLZLuOzgOk4mJGsc0ycxcPCH+36E7sfdcbMXtElvNzYldaQ4XNPrTALm+Uok4zkn8DiI8tyljXLgqoUCizTbniT6VTG5uKQsC6dCvcZ4tytjcnJ3ixYlcs8ss33vf+gEA4Oj0xJYfTlIi+/xc+ioYDMDpFV9eqsp9U+QpLF4XkUzKM1/xmbr0Vx6/B42KjPM6uaIWo+SH8nqg05eP+fmQT8bxlVfuwEG/Wm3IuIWHLlwWZH31hmUe75PoenFzCyUiitrk4VIkvv6RiT4zuDGiArskem+Wy+gSLRsmf8mtdfGP/WkHHrd8/iGJbFe3pR/dIRf69LUaJYonltzX1bRwWSMKZU5sKJyVrOvh2blNvK0ga4pcutZqo0RuxdVF6as7zEw//uQjG+HqJ/rF5QuiTS6xMOe6l88yGg/Q5/rg5dqj1henBtSKYq992kB6RcZyMBmgTWLfZUpQL2Rl/vzohz+CTpn58g0z2zGxX4epY4m8a17315GJ+wd7KNaIPKM4y1RXst+W7a+mzND7iWRqtutwe2SNzBLtMR1ovGYVAwoEeKPyLiGijgLuIDbmlu17A8B8bg4DCrVk56Rvz09l39lqtbBI0tRLRfpLnrdcMo0pUQxj/nz1vvC0VHotpJbE9/2t18RHf/QLERiotXrwkbPo4EjW2RNy94UTEYSjRC545V2fPhVEXTQcwsaK+BaNTPATchR2mi04uW+JUFzBPRVbGHjdKHRJms++LhBt4w/78fZdsen6hfyt3m8jlpexC/rFZkJO6cfHn36Ordvyjjfcy7iJvPa7PYgqwRX60Tz9a6PXQYgcI2p/VeM8uLt7D0f74nf69FeK51TTdBu58Rm5aUqW2MLrr92Dm2vi6RPhXF2kf/QGI3hGbp4ykX+32HfXxeLLeUbU0NKCIH/qlaJtc2pf0etN0RrJ2G8TweGlfS3EMvica3woIeN9RcLpRCKAAIVDYhSp6TaILB/o+PknsrdQvtbJs1486Mb6luw3Wh0ZtxAJmgtnPXSJFtogqfTpudhHJBXC9qr8zqRdTIh8a1Z7qLECxh+WcUixUqLZL8Eg72uf57lBVz67ubGGDHlc95/LGBUui9i5J/PE5OciXM/m5uZsgZ0WuXjjcRnL05MLwCF963Ryfzrm+jQ20LiRPrZYIXJVkXnmiQawsk5EYlD8x5DcmB6fF26/4vIVf6CQ8OFAABNyD3EpeSk6MzVtTsZYiDxGEblHMBhGbyT9N08EVINciNeFG2gUslH7TEWA3ihV4AnIuLV5nnNoGj5/LAh+VZGSmxc7iUXDdgzhwe1lAMCXT8WW/BE/RjyDBmmPqzxPvjg6BXgOc5Cz74rclZozgBaRXKsk2S7dnPK9HHC7xXYUV91cWp7F4w7gmug6xbemhJWa3Zr9zAHIOxsUBVjOr6JPpL0n+PX9ezQUhccl4/X3/9E/mSGEZm3WZm3WZm3WZm3WZm3WZm3WZm3WZm3WZk3ar4TK2Pm+RBFrtYqdbVrKCZJj0JKIbb3UxIkhNf9xRsvSrOGt3RQwpBzqcZPZTKIUVjJzuCmpzJIExnY3RZ3FoXvxdE+4CdxE8bw4kOyuA2coMtu6kJWI9YQyduuba/j5p5JhcI8kyrk2t2i/z/qKRDd7ZYl6DsMStYsnMjg5k2xHiM/33vck63dxcYY6pe1cTonurdxaRbsnUdXCqfzNTU6VdCKEqSlRy9felWzy8YXUTofgRI/Z+HKTPBVUcnKNHXYk3iBniId959cDKLH2+7wgkcVXqRTjcXlgEjlyQyWlDXLtRHxxMAmHY0Y7NUaPKx0T5eeStVNqBSXW08fWVnB0IGitYY/RaaJENs2BHfUFZa79bg+WiXBwMRt2Tc6LZ59/gDVKBCsbyjHTuXB7C3pd7KjC9+pQmc3l9mOONfQGn8/FOvHrUg0TS6FvFN8P+YIsExazCb+MAvqqWtgvI/BM07Bl49W1vsr/o2pGdffXETy+YAAeZrRNZhAceIkQUjw/CoHz1937q8+k/uYiB4hCKZimafMl/fJnvvpePvaxqkV3Op1Qr2GyzzQqFJjaS5W1MbOhbhfHVrcwVUp4rOlWfaBpFjR+L0lKBMW5MQ0GkGBNN8hfMmGWwe314eGJZKnCrBX2h/y2vY97VA0jKsfjWEC7I3abomzrlMofw7EBgypf83OSGVpj5ufFsz37/R8T2TiXEF+xd/M5JuR8SvB7c0vyvZtazY7Wl5ilUVmQnVfuIEDel6On8g4NIiYMzURuSTLfVlMygh7WLU+HIwRZpzxdkv7rUyo93DXwg3vfBgCc9ut8r7EtDR0nT4risvE5XOjTnsZEVjiZqfjZwTPoCUqbM8Pj0qhuU2khSrnczVXJFGu0E81Rhccr43VZkGewdHnnW/c3cExpaEdE7rP3SJAg29+/jVBcMtiluozzzrZkuQ6On2NAuWk193us3zYPbuCPix/2Zog+0mU86pjg+RPxO6mQPG/K5UVjLPbzjGvC4iqz8eOezXtRoQzrMdWWQskUNNrVpx8KKtWkbbs8ATx7KL4vtyL+Kk5uH3cohHSCcsVUsOp05f5erw8TKtGEyS3Q4VheF64QyUkW2e0iX1KVcsLVFgwqVhSpXPadt4UvKJUJ4upYuC6UXY0tZpN0C03yEin7vTqVvvZ5AiiRZ8XbEZuIGgPM54mmscRmLH5GMw28ckc4sKwxZWaZtdLHU5hE880x+58kgmTU6wIdcrbQfxTIz7WztP2SZ4zKK1pE+rGrmRiRm2ppJL+bUsbbH/GgTvWjDXLgTMhXUxn3sb0gewyDqJ40FYtuLq+Rou30ndKPX1yKPa4vrmIhJrY9YGbw0eOnSFEW1kW/0S7ImF4cncPD+fLme7KWmuREaA+LSDA73iOPUYwIC2MCWBHpY402ajrEPubn5xEgYixDTqaxQmO6XJgQtbgVEUVOlXl8besemkRJ55LSH/P0ZU+ePUHmDUE0B+gHeuQecbh0tMmjmCZfzQJ92tGTR4gq1SPKYXemCXiIrJgQPWHLu5+dIkJ0QjLATD33EQ2HgX5X+jtFnqYMuR2bAwNvvSmcbFwmbEUlQx/itV0qQ5EPsdUgX1uhjERInvmtN4WX7MU+0Q2BPnyaPEupLfvGHhFhiXgY2kj8xT2qEx7uyVxej+cQINplnojoAdEeDt2EQSGwaYTb64AL/VMqxFHFT60bV/USpkSfLAXlnf1c68zOEC2imTJUZFskL0mxcGVzToCoredEO+Z3lrCxIIjV0hXV2iryfPl0Dj7u9774QvzV4gqll60eRvQ7PiLANF3mhq7rcNAuVIvGxa5D8QDKRdlXTSZEWGgyT3d3dzEgmrVjyL6vWhX0mM/lhZuoVgdVk47295An70m3J/M6vyLIgOgwCQcVCBNl+Znimnr3wVvYey6I/hH3AR89Ez8X8LmRSMvcbna5V+jIs3TOS2jSHuPr0se1ABUedR0epRYbkXFe3JU55Zq+5MgpXJwBeIlqW7u9hS++fCi/o12kw+JXr8ct3PuBoHqmA0GFbJBbJuj3on8t+/Uk14ZiuwElbOv3ynMOqawUjWVBEAnm5rkmUK203+ig3SYygueeKnkEl9fXUCRv1ZhotglRcN1RB1Gi5aYu2dTXqeqnOVzI8pyT3hD/cSsl8+7q+Bhpnsvy3KPsFWS9CU6jWCNa9IGfey6KWP72D97FJ6xCKFzKu6/dF9TzctQDJ/d/JYeMWyU4hnsitlmuy+cV2mg0aiCdIAcqVY7dPcUD1karyb2BQVQ7/UK1N0CKCNA0Ee8+7sEihm4jsjxUy0un5f2mwya0lLxzi74oQU7ZRDKNGlFs6QiRdUS+RyNBe/63iCBuNGSeZubiGBP96SQaqKiULvttpJ3yDHffEA7ESColRGQAfDxT1oleNPpFmETQdKlyGqES51p+FRcXYg83PDeOx+J737j/Jg7PxEeWeS5r8mxkwED740O+h7zDxqb0h0Nz2bxHDs55i/5g3Omhxv3XHHnhyuRcDPp8WFmWtdg0yFPIc+6jR18gPCdropu8gwMafTqfRpeKxA76rSnVzbwhL3xese00UbTNRs/2dWnITy/3Y9lsxq52ePi5VKYcX8j+JR6PIp0S+2iWxYb+1QvxnU6n264SyK9yzSIy7vKqiS5t7uiAin9EY3Z7TSSIllb8hF0irZwRN3Jc11tEyPa5v3Il/ThpytqWgHwvy7XccE9thJDO/VKEzx3yhXDGPevf1H4lSsb+gz+4bwHA04fPcf++LGTZeenU/liM/LJ4gQxl1n0ucc5OHjivK9e4KUmHg4fLqI8DnZpDoyGGl8nJBFOT0R/yIpUjiZVL+uGf/Yv/FQDwztvfg0kYvEmnonvFINutAUo3JCYjGe88n7fTbaPbESMIkOitZIhjiFkONOk4HvxA5OAUKee41IRJY9Z4OJ943bi4lkl7cSUL7vq6Iq0sIpuW65uUd1QH8c3YJs76cs//9p8LIbPHvwwAmHYnGLOkDTzQWW7pq6DVw29tiTNOJKWP+k4x9m8tr+LLiXzvyz99HwDwb/07/x4A4LzyHD4eTNXGQ8liZxfm0e0rIi9x0vTxaA0NLM3LweCGm/8uD1yRkBcdLnxBbpTOTi/R40H1zo4E9Uw62/NSCQFDLpzPyVg8eibwwM1Mzr6nxoOPn5ui/cNTLK2JMyJPGKqqLMfhQLUhfTqkDOPYlPtPp5a96XGw/GRECUN/MIAepbVdXIxVEEfXnXZ5hpIFnqgAj9sFol+h6sJGXLA7nRZaJOFUZUN9ktWNxwaM0UvJe0AFbxxfu5Yq29J1zS6xCfLAqezY6XIBfGZ1rYkiLp1M7N85CXkPBhXBpA8+bsa5dkDXXwa8HFAlc/K9Kf6f/Y76rP6VANR0Io7frS5uWnbgr0dYrIKa+51emGQsPmaZ40IuhjAh7qOh9JtmKSnIsF1u5eAciicC9t8cLA1MkHg6EWKw9ebM3qgbPKV8fCSBBle9i/tvSunX1m3ZrD2kJHpmdRkd2nFgSPvgRikU1LH/XAISqmynRUnr7qgKN8tXTV0WyZhb+sVyOzHm+KoSnSDhu51eH2UeRPoso3R3nViYk3uuLsnm0xVkcOvgGPWu2N0dlr74OHcvDvZt/9Qek3iUUpyNVhs+2hFY2lq6umGfZTClXQVT8uzddpPXduIZN/N9BjTSSzIndxJhvDg4k3dlGcQrq+Iz3J0qHAxw1QmNXWMZScDjx+235IDbo5/b48Z9aBpwkVD1+Z5sWputAdwkaW605JpOSvmWa1U7ODrPQPQBIduryxswKensDss7D7ixguWzgz6jMaWyGXD59LMTNLvyzKWCvLMiKw/H6tAcMsc7DR5ISEAaDFroMfo+GfDAlJJ14N7dV/AxJdhTDJyrwO3l2SH8QfoBlgg6w/J+7f5LInmeAewDv89hodJTJCufAAAgAElEQVRkWRhJQ/1eN9YJ6w+pNYQklMFgBE0la02f2VCE4d0RpiOxgU6FY8+N4+bqIuIJse0xg0afP5LkTCqziC43XUGWmzR4CO4YPvz8c5kvuyTHjJHIfGoMEfDL/Xo9WXe3d2T97Ha7SJBYNkK7z7H05rpcw9Qj/Vbi956eygb51t01MJYLZ59kjtM4/JocopS0sAGW3riGCPlkXLkko9OTDXsoncCQ5S0JBlUVIff2vTt4+kjKDwyWg0RYYhkIRtHnoaEzkL71sdxxe3sTz19I4GKDhJ7qYHhxcYE33hZy4U8/EztRfljTnegzKH3FOasEN+YzKTg5lg76D39E7ndWuEKE5JaKSHtrfhWLXNdV4P+zL+SAfFOtYW1zi/3BwwbJeOeXFzFkecWQ/vGiKP1/VanZa5SbQWq/S8Z2c3sNJSYFsiwTVaTZP//8U0yYuHnzgRDkK3GGer2O8xtZH8IsO1TLS7/bg5eld4rUHlxPw30nypQCV8TCIAltZ9gH+eYRI4F8rVBB7p7YgMk9SoCHgSmmaLH01kcxgFGLpMO6BxP6XfXuas1rNBpYWhBf1GlyX8CfWxubCLD0vHB4BgDYXmVQ6+gUVzWSJtMPW0y86S4nDE3G5JqHtTbLe/2+AECbiyjRCNpCo11H9VJ8WY7PFGAZ1njQR7PV4LvKuLU4d6OpOSS53k5ZmjKYWrZgxZQy8PM8GCcCIcynxO/4vDLHDc6fQauD3LwcjLwpufcHn8l6u769gScsFWn2pHwqRanoSrGDlQXpGyUyYXA/HYsH7XEu1eU5vQ6x+0QwDg8Dea2RnAWSpHcI+IPgJfDsIxJOQ8Z0ZWMFLpKUuxhoqA7l+96AAw2WwVsuCp0MLLjYN6l0lP1NwuRMCm5dbKxelH5Qcz28AJwX5d5eiJ8LmpRGN6dojUhC71fnJLEB0zLgY+nimInjBgPuqcQcfEwA9Gry/eUtGe/3P/gIo7HYajonflRjwHzQa2PCciQn94RrTNaGXV67vHTkkj6uMlh1Z3EDR6fytwb3yB7HyE6S+Cgr7mfw8vmLA9y+J8T7ByRrVuuGJ+YFGJxep42qBGSl3IDTJeuKi2X9Q07i3OI8qiqJTDLqV16Vs8fZzTVGPDT4uJfslqX/+ykDMTqTPAOOPp6JJhrQYwmXWteTpKu4PLlAhEkBncGVcl3mYjweh8EAyIDl/R6XA9GkEsihEA3ndyKctAnS+/xel+eJVr8Ln0fsqcN9WIvX1DQN+XnpozoD9H7uTRxuD0YMSHgtngHYdxqmiHLfXO+ILxpxTnU6AwwN6Y8GidNX1mQux4JeGLy3S5VjM/FQqFaQ5jnruspgLhPq2eU8tjclINlgefQG9yWlqwKuKMFeo1+cyy5ghdQHLZYSxlmyp01NHL6QfcTDKwkkry+LXwh6wjY9zdnJsT0W0lcOFXJAOi1rwVpC7P/08hqHDGrrJL9ejMhaMq027VLTJudZj3YfD3mxwGSkIr8eDeX+A62D2IrM5+45KV8MZdcLCHgZkOO1eyy1nEsvod+T3/3Bf/o/zErGZm3WZm3WZm3WZm3WZm3WZm3WZm3WZm3WZk3arwRC6B/+Gw8sABi2DSwsSpQ9SsherUWJuZgfY4Nw/QOJ8g9KzPS7gRwROmuEkH72808AAGFfAF5GzXKL8pkvPqcM3cI8dA+zpgH5zDNK5OqmG+0KybwILXfys3PZFJpE2SiZUw/l9uLRFGokNc7zmTwpiegNanV0CCFdXJXI5hEjjqN235ZVjpAAcNhpYMTo9wW/t7YgkfXO2QU2SBJ30SSRKCQam5n40WD24YefyruenDO7C79NHqZQM/CQQNc/xRoh2j6XZOiWVyRy/UomA//rEl39yz/6PwAAv/6eEFD3vB2UCZ91kIQZRJDUWm1EmWksMQI6xyxePrOAUknQDwNmhRzM+pXqRdSI7NrYkEitPxhFjwRhQ0qElvguC+vr8BIh4mFmT2WIdhZX4Y4xs8MyoWFLbCfsC+HqUp6rxwz43IJEeCvVOsYBQgV7EmWuE9bX6Q7hMOU+HqeM0ZAIL+gaTIZ2FfGzNX0pu+4ncsMmgCZhtcPhsKXipyxjUBDsyWRkZ08VQqhD1MdwOMSAJOwKXvzVsjDVNF2VgDltEmnV0oRUe3xeG4mkCK1t9JD+8vk8hKKqrKbf54abZYlfRSIBknHgrb9WHgcAlq59k3hbe0lqzX/CZH8oKXXDGNuEtiP27ZhZF4/DZZM2t1qECU/amEuKDUyJBtJoq35fEBGWoowI0ZwQkZTPLcPBMhMP4RNBF5FT4w7SLC90E3GmMhtHBy+wQFlOi9H6n3z0MwDA9uYOtuZl/vdpx08PpCQlv5jB8dGZfG8sY6Tz3S2rbcP2xyQWDnlZChoOIJQS2/6SyLg0CZbn84v4yc/k3jrnRiS1gIsD8Q3/4O/+FvtKxn3vxSEilOJd4jvMsQTk+PoUPpZ6KBL7ZJbE8JaFMkvvBkTShZm9apRKwIAZZi+RTwO5nzcagEmjSfjlvhazGZXTAkxIBuqiIH4uSNvNRj3QDJkL2Zx8DywpcE/7eOOBlC5dEEI9ZAbt+OIEy7cEpdAi4u3w6hoZJ1EyPsk0v/8hy4l9XqTiYnchIk4CLBXp9jT0KmIz3jCJVbnOFK6vcGtHEFZ/9Vd/BQCI0j46nSkOS4KKVCW7qjQzFHDZJTp3bkk28uHnkrXyecPI5SiP3mX5X1c+GwpF0GjLmExo4+GA9FWzUUSRaLIIiRezCm0GC1363wR/F6SvmfcE4JongpKor6kxBpjlThIdFgtLf/QGBpq0adMhfduh3HowksbhgWStX+N7VSmt7nNrqBEJ5+aa52A2/vSyiO1tKdWwNLlWn772tNjB2TWRUlkiWkiCen58ghjJQVOUkl1ZVgSpGg4PieabMjtM4vpcPgXdx3WICKErSoJ3hn0EuU6v52UcljKbONmTrKlONKblIspBG2NK9Ga3Q7QFEQyT3gAelreZtMONJfELw2YXQRJvH5Kweod7hnKlhvScvEefaIuuQkoMe9jZ2QAAjJgVvrySLHs+n0eAa8/TPYHFB1l6t7CyjDZJSWsULwhGFUy+DIvllGss3yncyDUz2RwaHaLfojLXl2Npe70rnMvnWlyfvKEwasxge1nypGSV51fmUSF5qRJsGHOTcl1uIJlS5Wrys8HSu86wjaBf7FDtoc4p21vrNqBqmbM58VMRfub0+gaFonxOkQfrRDFurKxgwD49JclwjFlhaCaqRGKkMmID6HMf2Z7gjKWZrZF833A78J1donm4ZocpU+8J+dAxWEbGko8uS0eNsYkmka6qtPJNSqk3S1XbLrK0i8/2BFF2U6vgdj7GaxARyhqdYDSJOvcPbf40aEOxUBAOEhePlYgGWSV2NnZRI6n36bGsVWOixm7f3cX+C3nnEkUSlAiHNTaQpEDDmKgLB5EPndEEy1wfCD5Cs9NBiuNsEeXk4/rrtpw4P5K9/5QEwd99IKXQnWIVl6dnAAAXKwBWNmS/6vO4cc4x2S8JyiDA8pBcLokQs/dRhXStin32uiNM+GABkujPEbl6Xa5j0ub5gHunvS+ERsIfjiCgSr6I4v7gQ0HVv/H2fbjpk0d98VsB7qVOzg/t+0S4J/c5w3aWX1UCtAfSxzu3ttBryNjtP5H369IOA3MORDN8n7KsT/dWBT1TOL5AnWXoAQoijLiWzC/k7f3e+eUZ30eu4/H4kE/JHFKE1QqdNr+0jPc/EJEChbwcsu/i0SCGA7G1eELs2C//xRcHexgTsXrvdVkTnj6R8vtoKIIrljVFWG0xdnjg4L45RP84IUL84OgIQc6ToUJNUKocqAPck+gsSxoQubq2tGnbuU4UT7EidrK8vgaN5YxXBekPt1ftOw0EWSI24HmiS+TUMKojT384YaVDj/srQ7OQJ7F7iH5rRCRer9lEg5LhhaI8g592+e6779oIeYtzdtDrY0D0sdpDuVmBsLS4goNnsofpkW5iheT3jU4LBsfwCekOlnOqFG4MH5Gqz5/LXAcrK7r9ATqkKlFrlTpTVasVaE4Z8zmujWensse5KZRQI3JSleeZlqwJr+5uwlJ0D0RhtoniXNvZgZ+QoB/9xV8CALKLcu3BaIgW0YdvPhA0vo9njmqlhGRW0DiK7uDF0RE83OcrInPVL616CysULzGmRJCy7L7XHdny8X5WRCRIM3J0eAyHg/EFltCekpw+mslijuf1RkOeUzm604tjjHgu6J/JfHZwvvrCXnjoD0/O5FoOlkJmMzFk/TI2DZK+t0kfMer2cXtb1n4lijMgqfV04oTGffR/9I//bIYQmrVZm7VZm7VZm7VZm7VZm7VZm7VZm7VZmzVpvxKk0iFG3bLxMHqsr3+2J5GxSEIyK0GnFx5mNpXc5cItiYaFYiH0KAv34lQimms7EhmOh0MYsEa73pZI3J07IteeyiRR7wgKxcm66G1mpi4vikjGWK9Mjp0q0SzhxAjrd6Q+f/+FZBlNyufC7cbqFiWsI/K8Y2Z+959dIkKdWQ/RGyr70XMB1015ln1mM+5tziPESK2LxLYtkgQGdB1NSgQrCeq6TTQ7hi8v0elUXKK3h0Qd6J4gLEbYDdZ0WwqNogHn5PBZzkq0c53yer7pxEYcTJlJHbIOcxQd2bwiA0bmT4l8AoBERr6XsyPQ8i77+/vYP5Islz8kWbsUs3hnhSsbWaSIMGvVLurM3gQUySjJxxaSKYxL8rdL8knlWU/aGfewoUs/uPoSMVXIG3/UAQcJnHuMuI6dlNbNBtGsy+/crJePMZvk0JwYMKqvSIcdCunicGDEDLphMy0rEiPTro3XdRIrc/ympgHTUtdkNt5Gs2g2qkdxYA1IoAsALv5NoWwM4yU5tGoTZjo0c2pzizh0xSVECWy/DwMS3SkJR8Wl4Pf6bJ4qNzMpLiLBXC4HNP3r91NE3Jpp2YxBKjRt/9+EzVmkKSI61WeWBdNSnEiKCJ38KdOX5Nya4ldgTbNlAQqSpLJdwxFsqeYgM7Ea0SSl0hU8RF3EoiSr7cr3KvUaXOTBSJA3I8lMQP26jg45ioLMMGeYGb3u1uFvy/dUVuc7v/lrAIDqWRHVK/FzDY7hBmuhB8YI18ygjzryfSUZbAwteNySSfFRorzfI5+GR8fZOckEWed/RPnvienADrnBipTr3szMI0gOoC65PCZEWI1GE9SZ0Riwdjn2hhBhRuMRGzlmcbwePZIMk+V0YsJ+D5FIV2WPStencBC+trK8AwB4/FT8V7NWRYjk6c0qiTDJ3xaKJnF0LEiAdFx8wz5lbUcBD7ZJEtglh8IuST/d7gn2nosP2r8Uf5AjolIPBnFydgYAqBCVEo7GcH5M/oy2ZKHbE7FDr+mF15DnWyP6okmi+6vSOXSyyH7xqXzvXZLsGl4THzz5EAAwT+6aEVFSjmkNSyRPXVoWRNH1jXy/Wmkg6JUM1uELWbOK1/J+uWwKXWaMfR6xVYNo0KOzCnpEBHiC5MciMsb0evDr3xbxgSoRD92yXPvmpon5DbF/N9GOBufkVaeLPHkY5um/S9UK5tLy+TF9hU4ekqlhwPKJ/TkdRIRxvvkCXuy+IsgsnWjCxkgy04nFFWy/KWNXKROh2CfB7aCLoa44xAAASDKTPtSAhVV5LoWWyybl2W4OD1A6l/m5nhX+mOaV2FyleGNn+RqWzOsvv/wFAMAbdGCJWW7PQJ5hPk1Z4I4BDfJ+NcoWj7rP7YzjsydC7NskUfJcPmujKafMUMfIIxWJOmAwu63GUCF/2uMrrJPv4O6b8uxf/kLIVy2XExq5m+5uCWJkj6TsG/F1OEgwXaMMvINZ0UqziUtylCjSYCVGcH55gTaz+COuOT2ignqdLlaJmn22L/urCjkbIskY8vNKllne+ap4ZfMajPjOd8m7UapWbY4oL7knSoqc9+rURs0u8H6tlox7OORBuyEIlQglnk/25f+HVye4sy3+rcX9yw05xbbu3kKlLp9zkfNFrQ0nhQt4iHDVFL8H/bFLd+PZviBuVHa8UZRMf2dSRUiXOd4nGfXZjdz38fkZfvMPfhcAcPSF2FO30UCBKEI3ualOmLkPJoLQPIp/SOwpR1RmPpNDWn6Fdk+uf3Ii/W9oBnwpsdurKlFOQfl/NpvFflEQYHFy0WQo4zwXymB4I34xRPRzzZJrRxNRVIkCiifk8yZ5VsoXVyiobDX3XHkSn5oODUHuDTtDkpCZL8Un8iQ2LROdE4nK2hA1DIy5R4kk5XdD07L3MIrfsEWhkr7lwOL2Dp9P3vUJ0SRhVwBBkr1PuS84IjfN2dkZlpYkU59Kix92uRTyrAKtJn4wS960Hrmwsqk0XCCpOUnRn/2loBT0qRMVEu5ekChW4z4/63YjS05Ataf//R98HwBQbVf/b/beq0my7ToT+85J712lLW+62pvb3dcDuAAtREoKajiSKIZCIUVonhShn6IHvY0eNCEphpJG5Aw5HIoYkHAELoDr2pvqsllZlVlZ6b09Rg/r2+feK81Ar4hA7pc2VXnynL3XXnudtb71fRgQKTImmjagyz4o6BG82RdERfi2+MLnL58gsyTzPByKDQXI/VZrN9Egl+naVfGBpRNZy8FwCIYK8NI3N8ndMpzPMVOx6pSIe4u8p7Eo/C65fpJ8JxZRjwGfH0cnEq9/8I6IFcRDcqZ+/svPYTO+bFHkIBSSeTk7u8TKivg8FVNOGOMEE3Fo9Nun5En1kTM0s7OFDs/L9jEFc1YLcNP3WToFCtbEVoOxAMb0p4pX0ob8uZzLoEn7U8hJxddWPDhBgjxVNmPCIFElF6UKXAqpRt7BVpecT+EQQuSfa9fFD+RXxNYrgx6aTdot7SnNPQXYYHiPRk3sQwkB7GysY3dXUGiN7/0tAOAmOdeO3+w754yXSJhgwIcRxR5cjNMrRHaZ5hRrG0Rvk2vOmolDyUYCWOW73R1K3isewX6niy8Uamhd7rlGLqKpZTrcaCoOVFymZ+UzeEJ8t2T8oNDMmUQWrykmpJDQk4HctwYfRkTmz/j+s0WicJcNuMm3tkEkpELtpeJpjPryXJfnRGGNiHo0DKQ8Mu/L2xvyPfk8OuT0SQfJR2cS+ZOKYMaYLpOXZx4QAdi1pvD6ZI/qimSRsU044nNiugu+C6hulJOLIjJ8V5hQ/EgFMC4/nPe+K9dkvZtEelcuG7DI42UTLenjnrw4amMUEvstrJL4fFV8DUwLZQpoqe6J0ZCdN3oQUwqw/KqxQAgtxmIsxmIsxmIsxmIsxmIsxmIsxmIsxmL8ho1fC4TQlFUJCwPk8lL581Gir9uj7PqrIpbykjX7g+/8IQBgwv64z558DlCNweERYUZP01xIZ6WCVW9J5vrmVclaBoN+wCXXPzqXzHxuVcnHu/Ho56KMsbksmcmdK5K1bPTKqDfIP8KqzJB936elPaxQtvGC0rM2VUYyy1n4WW3a3xOuos1tuaY2CeP5S6nqBMhnhHgEHiKWNqLs5yfj/9zvwVFPnkdRkSeZkfeZGlaYmY2XqBhCBJTX5cFgSiQG53hO1aVULIqZLhna0oVUTY5eSEXg6vv3sEy1DaVUVGVlz+MOoMXqVp9SrVNWflaXl9EhN4FNDg83eWi6moWOIb/vdknFTXEJ/eHvfxc1svsr1Y3ZaI5NZrV1chJkVqV6cvL8NR5ekepRjEo0v9gXLpVut40pe7JD5AEIEfXRbrYcXpzVDalKulkBq7WaKCyxek8JSZ0oFNOvw81KseL2MYleMew5dIV2sZVil8LEuGGyF9xNRSW1DoZhOFw5Chn0JVLI/P/I1Ktqqm3bTtVEqXhpmuHIjX4pN6/k5C3nZwnKLjrKYJqNaFTWwsNrunlNr9cNXaF+iCxSqB5olsNZRDCPElHjID8Qc9Amf9eG7ag/2LaC/PACtv3lPfP/NM6VplvQWelVtFXq++3Z3Lm/JSrxVc+bSJN7QifCrT+UCoIn7EeD+2pKdTilXOHz+xw0WafX53PJPflDcZjsmQ6xwtkz5HeuXFuHayyfO6Z6wdWwVPN1w8SY+4z0JWiyWmh5AJvfXT4VmxuOZV6iPgMpLpTq3feFKLU9MzGjzTSI3Ouz6jS/aMBlfd0OdyYTh+fjL/7V9wEA+TUqS4Xjzh6YUR3F8MoztyZdtKhQohTPbKqVdVsdbBDhcPRcfJldk+fSvHPUiP6ppMS3nJ1KVc2eGxgTmTIfyrzkclLxLA/KaLMCdXqhKtnsQTcNHBWpbkMkzNNHnwMArt24jvyK+Ir9qvifT5+Iktnm5jrKZ4LCdHEPzmBj2JXnWV6VCv1KjDwakwmshsxDrSz3XiR/SXvex+aq+IidHbG1EnvCv/UH9/GTn/8YANCkv2rRrwb9JtbXNgAAH/9U+J1cVFlZW88iFRf//b1/I+pWWcr7rq4l8dd//VMAwHe+IzLaPvIudXqXMDXybVAt5fVzQRTcurECV0nmIR+UquE4L/s8oIcQJF9VhFXQKjnT7n77m4hAbGH/SObP6/HjnNV3hdrtkHcmlc2BLe6YkjtolYol5Yu6U+FME1GglP/KFyXMKXGdIifVgGpXO9e3EArIcwWJtvF71fkbwMqqrPMvnwhv4NG5nK357QIuqLR63pZ5n7FKFo9H4aJqV6ckdvn2B+9wHWYoUzXJoo9pEp0SjqaRpdLcaEJp+eN9hFZljnIZOV96RLbk0+sOyi5G1NaYKKVELAWNCo4pcsrojGkwmeNzqhKu7JBXiLjK/myGzqGsRYs+SVVrLdgOL0KpxJhmU/bS6UkRafJaJJZk/uNEWlWbNZRZQY+Sv9EYyN5dXVtHPCW28vyF7NkPPxCEQOn8BI2OfE+tLRXgd65/S4FqUCMicfCKKofzOdbJPaGUIAusQpfKJbjo3y8vyVnBCqdpfInM/OIzmZckUZkfrL/nxEUR8m1kNNmTh8VjB9Gi92T+Zqz+v/vOAzx6IjFej59P0KcNh0MHxZrg94Yi8n3zoQ/tudjqfk3mo0+t+N/6w29jjbLKrw0qhHlj+ISIpQf3BCl15YrEGkcHew5yV6FyWkTE+FxebMTlWlXGH/vkv0wupxHnHnJT9s4gJ1un20EuLD/TqOzzf39POGx02wM/0ZjLrIRHiJwKDC2EA+SH6ww470RHuL2IEAXen4qttYi8uuzVkSXf1Oqy3O+EZ18hk0VUKaoxlplMlVrvwFE9UpxFNoBGk0gCHxEV5CIrn9eQvCu2PCT/kVKjq1+2EY8xtmNM4w3Jel29ex99onkqJYlnk0SEeINRJ45qtORaS1SzNE0DYcqXB2fk3urLuZHKFRBnbDggr+USkXgYDTEcyVnloVR2uSL+8qxRwoz8ZDx6UJtTRTCdQW8q9qti0gcP7zucqUOF4CMv5cyeIkkkbrUu19/clr1V7w4w5DptbhDxQH4bj8+LoEaE20zsN8H3rlb3EiaRmUHyTvV5du3vH6BQkLPxZ78QtKJrJvZ1ZWsbSaLiFV9Kk8irbCHvIG++eC7nWWxMlKU9Q5Q8jGUqBRJ0joCvjDjn9JyxWs69Aj9Rdj3GTIjL7zSqpw6KPhkRf3p0IGjiVPQauuTy2eJ5FKUSdaveg8XY2s1rF6lI6gvEHTuPJWXOlHivOTdg0g8o5N9BUc5bU/dA57np94r9+xmrwTIRVkrD9E1WQD5fOj+HzfVdI0pGIXDK1Qvn7yqmTyZi+INvfgQA+OmnwuHUbosfsHUXwlzD9bzEZeWzU+d3El7yVBFB1iCidDab4hvvCyrVw/tU+7NcraJOFIpSlbQZ02s+G9O53F+CZ8htdur86Hs/wk3Gl+reG5eyN+bdFnaoOlchp2CDXHInp0U0WrJu7z6UM6fHDouAx4s1KoO+OZB9rfFdZTydITSRz52SQ8/l1xFLy/46pF0oXsRcdhl9okt7Y9k3qqtmNDNw557E7ooHrXwq8xgLRuAnymgI8uSxy8CEjcefyFkV4Hv8gN0o6dUc1pg78FhE8xRl3R4+uIM9+kCNKFpXW7437tZRbsgcrWpUiePZPxlPobMjoq/4Hrlu8UgYMd7Xrxq/Fgkhk0SRHt2PoyOBpUZ5CB/sC9QqVcggEpeJ/5//6Z8BACwa38rGKjYJs1vNimHxnQ3NywscH4hjWluXTeEiLK10UsKUC3xlXQ6aOQ3KrwO3b4gx9+pi+Kcl2ezJ9BIGLbnG9o583/6BvAAlMgEsL4vTHPdkw5VJjhyJhGCQ+HjIlzVFDDXFDCahmYUVkhr3B5iTvDqo3nrpjLxRHwo5Me4TBn5LCUraVXuOJKgiFQUJAC3NcN7UXbyYCnxm3QF8dAC762x7I5nbYDBAgL+4Qzjr6WdFAMDd3bvoNeRg6VF2V7U1mdM5ZgwEOpyH9SShuYMu/os/+RMAQIBEg92mOBttbsLraMXLfd69dxt9r/yfhzC9UFQ21fnhAYp88dbC8syq1SYZDKM3lrkdMdhSAcXNmzehkxRsg6RiKsETm7vx5uQNJ1w2XzorB24wrDny3ooA1iF97o/g4wuLShJpzrN8Cc208WWCBgDmswnmhNOrMZ+rNp6pIyOvAhjVyjWb+TBjYKWkPjXdchI5CqKpkiS2bTstX36+aCkr0DTbgfr6GQCqhA1gQ1OkqbQF+yuJHUUkqZI/6me6rju/b/47SOx150/ra/+G7tzWl89FqLfH1pyWL50tEm7Hnm34XXIIxxPyZySwjP/6v/lTAMDevtjJ//Hnfw4ACEdS6Ldk/tTLVIAEvKtry192+7EttN2RvXvz1hUM5jx8mWDrkHBvxx+Fl/MXvS3EwuuEYPu3dnDMw/fwU3mJVeSC4+kYBvPBU/7ZY/tFOBqClyS5bSbRDZKcT8wpkkxk+EkcqxJLli+Io33xq30mV1e3lnFO2fLAsuxxm2TRbo8fbiZ0A3wBnzbkfv3RIHqU/1Sk4ZctmYON1XWMKTMaIpA5EdgAACAASURBVKkduF6Dvol4TAK/85L4ge1dIblst7p4/EjaMzSTUuhsNXGHDRgkEPawzcikyc6DXsceK5z3kLK5dgXfuCEJ4jcvX/EDlI6/9RDFnrRNJNbF1zZGQ0x5jc20+LefPZLD3IUx3rku+95mwlARS26v3EOnKFDhtRVpJ8usENY90JEPSTGiRfLPaVPsa2UtielMya/KXKUzTFB02yhXxe/cuC1zptGfn5x9gTTJk30k5/78M3mp9Qdj8PnZ8shWuNtXZQ62snF8/DOxtd3bcq41VJLD5UWIsuWzAeV2l7hWxTO4bbn3IG20dFp2Xpxv3b0DAHj5XJLvRnAEgy9pqaS8MH360085Z36M2dZ86ZY5u31H7m91+W3sFSWoi8VkbsNMal30q4hG+JLG1oYRX1Ia1QsYJO/VKJuepYSvnvDhu78lrRo//vHfAwDe/l0JnoulU3z2SKTXr16X9b66KTFEaX8PGqVuY2xlLpVlrtbXoihkZK6+eCx76u7NWwgrfx9joj0isUa90UWA8PS5CuZJ/Ho06GMlK/c653lU7chZcnJx5vjoi4+l7TDAthd/IISRRUldvnSpttZnz54hyXY3lSx6yBeg89Nz/Ogncq3f/o7Mg5e+/tGTJ4hnvx6we0lc32308fFPZA1V68GrF7J/jNkIJpPFISabjqpFBEl+H2Cits3kUn84QOO17H+XRXJYJiHa/TZivJ9mR57LdMiGJwiwqJXifVbKEvfoQ7fTHv2KksE37kgAP++3RTIdcAjn50yKj5tNxAnN75DINZWUOLBWv4Dt4llHP3xWk5eApM+HTJayw3RzXSYaczkNewfyYhbji2qzPkCc0H/VQpctKOLkGfb3xe7dXEMf7f/1/ht0V8X3DUhG/eD++/KFsxmKJFFOZsSGwnzBPa6X8Z23RfDje3/zdwCA3ZsyH5FEGo+fSjvIKV8cN5bFXr7/yedYXZMzRMVvfb7c+z1eKaAC8POle8BCSXZ1FTZfUvJM5LtYIAkGw6iwbVMlvly063a3hSDbsPuUqb5+ZQfDPl9MWaiMcI3yqy64fUqqnfPC5J6WiMJwy32NGGMvkerh8M0hpixGhvPyDHP6kW69h1xOku8myYa9fvFztj3HOQsAtbb4m7c/kLbb8mUVDOOwdVUS0nUmsK35BK+Pxf9WmBhuU/wgmoo5BdEP778LALioy/OWL2q4EZdzZkaxlPJ5BassepoltuCSCNrEzIlBs1m2TQVJ5Gx5YcuUYsLEiZdz5YoEkOO55KefOmlITGRpM8y4TjG27KmC+nA6w4QE+W22LWfZxl1uNVCgHWoetj4ui78q1yrwsWDsIZm3Oq8xsjBhC12c35dPyd6atpoI8P8KuyRl742RZvtuiUWJ7uCAzx6ExtYaJWyQTcvaPn32BG+RKkS9QLcuZd6v797GmIn5Sp0FJhIXmxMvEgmx5fOqfF8qI//OZNI42JP3PmVf6n3k6pUrKLP446JwgLJ/czaFl74l4FdxuNhlsz9E6VT8Wp/nhY8JpUgsigaTMDduypl1fn6OH/5AfHqFZ8HVXXnO7d0raFYkVhuzPcvNc81t2fj7T+RzqnV6Qon5ynkZ13YlRlDJrDTjgdDUQl+TvXfjithqlVQqmmuGVp/vRBRhePmU7cEuG5sJ8b9LSfrCgvjxk+I5XJY86yqT1Kqlf62Qx8a22JFtq+SN3O+gO8D21gYAOK2MflJfRBIxtHxiA4oOYzjqAKTKaA/Yus8zoQsTYe6h02Mmezgvcc2DMUmvOw2Z4yipX9LZJLQA49K4vCMOWPDXAgF4luT/eqRnKJHOJJlfgnFBURAWxbwFsfVa/xIp+ggvW9QmadqQYeDBuvibFbaSPn0mNjgzLaxSUCPGpPjRgSSwg34DnabYwq8ai5axxViMxViMxViMxViMxViMxViMxViMxViM37Dxa4EQ6hiSRRz367j/llTT6yQdu2BlxOgPUXkhWUevh1nYFclablzdQYMtFK/PJVvcrkr2LbuUgIcZTYMVYgWbGw6HMInS8BA54iJkPuTzwi0JO6xTWrTWkuzgdGJgd1cqmyWS/CnJvkIhg+MjyQKOR1/vnSkdn8JLSL+f2cTnB5KZ98UiCLDCUWP1fyWTQ5cS1222UmWXJPN38/qOQwgZZta9zSx1NBqFiy02W+uURbWlKmQYBlyElVksv7mJbvAHQ5iwQjwdyJxZzOpWapewepLVv0bEw7/8O6mw/lF+FWeEp/aqUiUxKeFu6BP4svKsNzcIuyPENmXOUXkj62XOKQVJktJsLgWNbYBajC1grx/Dxyp1gL9/wdaN5UIWGUJ902zheH5RBAA8ffkaNzfkngskQlTolU6rjSrlHRVyR8F1E7E4tigbGMlINtyb3AAAvDw4RcuBVZNIlwiLUCCM0YQSk4agVuYK1WB8KaFukqDZIVq2TcD6OoJGyREb8zkAWRMFi/WwXSMU9MPn/bqSoGHoDqJIESv7SV4JWAiwIqHm4atoIqc1zbkaET+W6cyNg9ihnem29rVPfPXatm0DimtSXUt97isy9E4rHP/LrQGartrqZB5Uy5kL+peVbKJYdMJuPRaQpC0UCIVOpe7i1i5J6NlS+HJfUCnlahs+zkeDtulJiB/oDobws7LWaikSSPl3tzVAhMSekymlg6MkcK3XkYixxYAIhuN9QX1UxwN0o3Kvtb74lGROKrOnZ2UMTLErd4gE4Yq8XPNiBvnuGVsXLbZoum0LU7atKkh0l2iz7nCCO3elVeG8RPisMUOaJJyaL8t5lGepn9cRC8r3eIhIalEa+f073wJo77pHnvXWmvif4/Mzh5h2455UmM5YxZ9pEczbJE5MS8Xx4dsCAf7xq+fw9WRuW+cy/yBZ4I1kAPGkVKvXCoLmUbLVbs0NH1Fv2aj8zjffEcTK8s01PH8lvmWHhJtBVtDDqYBTof/45z+X39/YQa1FmdcT8ckPiTCKBEM43icUnGdVilD9aFLD+b7836Nn0q72Jzf/GAAw7LSwwWqO5pZre1gZNGBh/7nYw+om27QuxZcNu370OvTlcXlWF8UHosECYruEwx8JSiOoSNJ1F8ZsjVDkuAO2mjQwxe//x78LALh3SyqIP/6BoGb8wQAePRMov8WzIB+ihHgsD7NPkmIiwWZTG4GU+GHV9recFz85Nyx0WfEu8Sz2sl3XMk00SPyfZovUwUt5htz6Mi6JwpmTKH2oKtXxiEPqqiS9bxHVUygUUCM61cMWpFqRFb5oCgdPBT0VYyU2TJ85Hw6RCpJwl22l//b7fw0ASIaWsEsizwO2yUVJynv33m28eiFzleL+ScSX0OU91EjyWigI2ujzL55g96oiw5TnmhNx5UvGMAmwBZkEljHlM3o+xFmxNCFxj0JPP7z/loPQqrPNS7XS+oIBJ34IM8YoHsgcry6vQSPjbpVkpgPKF4eCMZhEXRSycn6evRRkR8djwyCpephIrXt3viG/c3aKvVPZG5dEJEWiQyQoVa2x5S5BxIge9GA+p1AACYtbJCxNZrIYk5h6TuJXBYneuLKFPQpQTImGVe158XAAPcYm4Yh879FpEQAwnExg8pywGC/FQ7w3AH0i1jysHJ+RGHQ1V3DO6RoRkdvXxKdd2djB8UuJp5L0vxtEDTRmDazfEX8Tb5Dg/aSFLcqru0hRcEa7TKRSuLIm/rpJcv80W2gGkz6eP5E1V2jgWFjsZNjvACSzH53KnN25JfHV9c17OCZaZYv7ckbi2fDUwvWC/N+bkuylwpr4KF8khs9/Lu2ra6vyDAGfxDS6R0OT55/OOKJOsZZgPI7rWTm/3uyLrfnpP3av30A4LNcY8GzIkTTbgO2Qhx8SJTXs9QHGFqr9XcVA4aAPvTbJhT2U+/bKHLiDXocM2aYfgFv2xPL2Cso1QX6M2A4VT4g/jSc17FyRfdZgK8zFhdhAJJBEpSy21qffD1O4IahpuFDoPMYOa28LsfmbF0+QjQj6QW8TyU5y40wmg8uXMu/PfyI2UKPNwwTGE7bM+OX3o4GgExvnc3JuRihacNm4xIz7t8l4e8Q40JhryLI91yYBbrvNlndYmFMm3TNUiHISeYeiSBKxULuU52uen/F3PGhRiOD+fWlXjrE16OMf/wwKX6D8h0JbpzM51CnoEyIBd2FF5jyjh3HGljglPRJlz7E9j8LPtpoEhRcanQ7qA1kDO87YWMXRcT+CEfYrG3wedjh46yba3OsRIgan/L5ffPYIWaLEynzmdXZ+FJIF/OILaY/rEEHjpdy9Ma878EEfhRRcbrEFyzAx4VkcY2vnUoxUImclvCIVR4jot+yK7In0ch4aW0CHRKWsrsi9dLtdh75gNJT19nr8mBBBbvJ5nrNdfzYzHCRL15R777Tkz2rtEibJmeMkJw5x3YLBMD5/KmdcnGgtz5nEcdlcDjsbMldPHkv7qkIq51MpB/E0GcnaW3xv9Xt1nJ+I3U8YR2j07WuFDMpEoFfrci6trUqHSi6dQ4Vnq+rAaFHQYDab4ZysKd/6SJCTRQqIWCZgUDCgQNLsSuUczx9LbBf0y89C3LuuhAaN3RyqQ0I1M7g0N8b0zRaRZA3upXa1jd0tiRM1l5wFiq6mX68jvyLvmyAC8hpbmgupPIZFufkO0YrLKfndwZsWEiHxb6oVOk5E6tTjxiFb4jGV/ZZk+7cxt+Dhcyk7aXK//sG3/wOMiYr6VWOBEFqMxViMxViMxViMxViMxViMxViMxViMxfgNG78WCKEuiRRHfRPFU/Y8kg/gzh2p+MJjO+SqkYBkz3JZyf7W61X88hHJAdnDGPFSxtxOIkfJQ9Vn3h/I76ytrDtSjhOSUY/YvzydGA4Pic6k82BGie6gD31mvJVcYMAr2b0f//Az6KzmKKm+VFzupZDKQSPbYpfVT4u/64aGBDk/NLKqhTw6vG72v1M+NMRqsG0aKBclaxtLSeXA5ZbsYG4r7vSh95k1TiUkM9kb6DAMRTBLDhv21JfadeSCkolsNtjzn5Y5vvf2jkMumqSUvZs95fPhFMO+zIPKSG6QsCziDjhZX4vWVj+VLC50G2HKUivkRwRyTY+m4d6D+wCAMTO3L94cosYK8zv8WfF7cq1wMo4g5So7rEwo7qHl5VVYRJpVj6XiNiEx2WA2wda2VMjcJFdT2fdALIIOicG75I0xJ/L9jeYQGrePhxl9i7La88ncIb6zWU0z+PDjien0K6v595Cbxu12OSglhf5Sf5qm6SBiHPl4/tvj0R0S5K/meBVCCET82LCcz6nqm+3Iun8d3fPvGpqmOfxF+ApSR75CcxBBah7UsK0vCactRTzN79ddOvhXh6DahS/RQ24+g0U7ditEk65BEQwFOX8eIqG8tgtpkhEmyL11c2Ud7rHKxEul5uFbgpQ4/cu/cfZOmpXeC0qgam4XtskvNiFZs8H7G3SHWCYh6pgIxSWv7J9xaIgxK51NkqpHFZF5b4zpkIgs2lytKZ8PhoO4mpNrRP3ipyoHJE/UTcxZDZqx0m+S2DwUjIJUbHCR5ybHPR/yujEj58r6HUE+JHUXWqwwPCL6JRRnpbTWAliRSBZIPkkS7F987wfQ3UqCkyTWA6ng+F0amuxxn1N2e4l8RoF4FjcfUA78pfB8ePi5j+5voDOQfWzQt2gdscfN5QimrK7kybFDKiE8+uIlLk5lnZbIWVIj59yVexsYQSEy5AN9Te53r3KKIQkw798VlJJpuOHbIDcAfeesXuQ9ZZDLSiV7Sl/p0qRipE3byO2K7+/X5LtX1lk1/ew1Ah7yA23I/51Uhc/IHmu4SaRli6S8g67cZyZxHdpM/GirLlW13Stiq4N5EM/eyFmXY8V4ieiDw+MzxLiGs7HMn+KqW1u/jhCRFX//V38BALh9V655cFJEjgiO//y/FJ6tENF2/+Zv/gpLfkpKt+jfZmO022IPNitRtQarz14PTumjnx/K2rz1lpzh7VZdUUohQtJPayhGe/zJIzSJErt7VewrSa6AaruGZy+kGnlzV+YsGJX7LVfO0COP1pzI4VOSk75/ZQ0dIk6bM7mnF6yuVftNBHn9irIh8mG4XT4cU2TCJnJkhUTIe69OMB1RtrxaBAAUT4/gol1c3ZH9VSUp5Gg+wPPXsuaK72BtR/48ODpCnwiuMIl335BvJZnJIkKy8KOBVNC3NiiZ7fIiwPuakkNE8cTFQjGMJoJ0uHpN1ndKUtRkNIFTQ64/4rmS4zrM5gZa5J2aL8nPIhGiesIB+MnpuLUp1erHL2U9Tk9PcUl+oKV1qXIHA37cvSPn895rQWH2e4zLYlG4ierrE40ZYoU5nFxy0EMXlE6+cYN2P5vASy4IzRLfOSK6u1k8wXgk+3lzU87yAUk1JzZQZyxz75agJM8OJQbojYcOHHWb/IE6z8N2uwuNf//oww9kHSi7Xi6XUSV/pcYqtJIcXk9lkSEy3CaiAHEPQJT6eUkQZ3kiV2dGHx0iJyNEoQV4ls/mc7x9Xe45wMryx48EreD2+7DGuDbAePGiLHZijXqYnknccu2KINXGRFE8PzqETVsxDPqDv/jXAIAry5uIxGUN94+leq3Ow+XsEoK8vy7lmWcjJSIRwI8/FlRCl3FgNCE/q7UfQyf3h59cGwbjl177Ei8ZL0Ijd1xjCIv3msuL/c+JgnF7gGSUfHddVrt7YkPZ7W0MyMM1tGTPqziir03RdcvPkm4V34o/WM4lYDMeUxLRKaKLj44rIC84/ETuFUkWbZhjPLgliCCjTnnqx4KOSukeTMh/NCdPU5p8JNNGB2d1khQTMfJf/ff/HQDg8PQIP/iRoDYTc0H3xCMBnJPA9vpVWcsOOYHW8qtouynIQyRGnQgQ25rCIpLX6yZR+Eh+d3klBhcURxR51xKyr2vlClxunpuMkcNJPz8/RaYgc/PosdjhrYfij7OrSyidy30OFBKJc+cNuxHjO5A3JLaq+GrabgMekjUrH3jZET80aNYRt2Te3UTDtacdRIkymuvyDDrl430+LwZd+WyDAhSKG89lxfH0C0G93Lste8plk8x3ChyV5Mxo8758Cfnz4uQ5GnyPM/hAL16Tr0bXsZIT9Mmc0vQ2BUsuL+vo0TYVv1CB3G5r+WVEiNyZMfat0EdrugfZvKz9mO+pSYq86JbpdDOEiZzKpFMYq/iI3HmKRD8a8GFC1Kb6nWyeYkPZJBp8f3zzVHy04vPKrK/gm7//HQCASWGZI9rgo9Ib7G5LLJQkF2yM+6XWqDvxepBxn3pOM2Th88fkuo3KXF2loNLh6Sn8RAx/67eFo6tGtHCn2XB4G+0Zyc5Jih+MhJ13N4WWdtmyl6eTyZdEzmWxibQ7jEmQXFvk96yRM6rlukSLa5guyJra3D/G1HLyEeucP8Vfun9UwkFFnmud8u+TjviDTCKBywtZr1BY7DhJxNr54Rl6SmSJOQCTpPZxdwzHFfGLKfrcg88kRl/dyuPWNRG/6LMLqkphg1xhzUGSKwGG//Q/+SMAwNnREdYLG/j/GwuE0GIsxmIsxmIsxmIsxmIsxmIsxmIsxmIsxm/Y+LVACD24JVXa8+NLnLFafe2GcARYzGQXz48QjEgGL0wkyOGJVJpXt7exSgWxEHvcB23J0s2nU0yZ6Z4RIdAnIunZqz28+64w/XeZdX/8S1GICPgjcLOyXKZ0e5vImjs31qC75RoJZrerRJLsXn+I9lgydyCvRZCZ6ElvBD9VvDKrkmlUykClpy8RIFQiTGWT4ayPIWWbr7LypRMhcX55AYMcO4ov6cG2VGIr3QamRMQUj6XCrFAohm3BIN+AqioqSbZZ2IPRhOpCVAarsWpb77bRJzfE+k0y/rNPtNdswyZyIehnj3BasqxeE0hS6ezFvlTHYszc9sMafEoqlXM8Jov7n/7jP0aPXAiHJapUGDbe+6b0ih4RZZBbE/RGa9BBnez8cfZUPi3K980GI9y7Ln2ea5tS6b0kaulmoYAgKxMT8hCkiYDYe/ICM7+syZRrajLjPZnM4aYymjEmzwfXxuv1OVLoLs67pfF3zQlGY4WkIWLH+V0XLPaTTli1Un2zwJfqYgbnWqk8ebwu6KyWKjl5ny8AF7lulBoGvoIQcr6bnFYKwTOfzx3FGsU9ZHL/ABp8lD2fKR4ZR1ted66vVNPwFVSQgxAiasat+IJ03akkKR4MNTQbTpXWo5TxlDyo9iUSyaat2kRczeeA4WelknK49eMLxAJSZZ145feTrDiv5JdQLpHziRwv65T8bLUv8ZT91Csp9rHTdhKRILqsPisllHldvnfo1zCj0oWbKjqvWI0IuXVY5OpS1YgA5D5HxhwhchxpebHLEWXTe+02Jqw62aySe1gNMkwXfOT0ibKCYnLukqkY2qyc9GqyR16MZ5jTf6yyyuL3yfxM2zNMKBG//Z5U027ekwrij/7FX6M+kH0JckME2SNfKZ1hhyoHWVueIcB1e3NUws8/FWTLH39XlJ/mI6lsjA972LZl3sZUtznoiT84HHnR68t8P98XJatrD98DAFQDwHBd5m9tW/ihfvhKql37P+4iQj6R9U3xVyZ5eVwGcH4k3720Jut8etlCkv5w0mKFOibPFYsEMCIaSuNajmryXEueVXjDMld6lPLIe/KcYcPAyQupnJ80igCAnR3xV7mtTXQsWYsQZeVyS/IMHlcEXq/cayS6AQBIJaRSlIyEcVSUuV0lQsDHaldvNEaeyi5VH2W7m4Jaag372C5IhXKQpxqGTuWzaQ/vr0v12XwsPnPg4/MNTZhEhnpd8uxba1kEOQ8nx1It7VOmXg/4cOehICqiGzIvn5OD4c7NayiQk+D4UHr5l1gZbPZGWN2Se1CV/qVlKnCZBnZ2JR7wck33iPSMxyNYThFtAXLDsEoZjUZRplpVgEqEXvrOGxvbKJcExbO8It/7VPENJQPI8Vyu8jzSaBvnxxVoRAoHyBc26U/Q53k7Iceahxxc73/0Lhp1OR8CVC/qkENhNZSCwaqnTl+Uy8ia7uY3UDoj2m1HqvAj7v2Xz547CCGFOjo5kfmYYwaDaknq7CiSn+Xx4AlyVDC9zip5+UL2mWFYeP9dWbf6hfgpHuXwJ73w8/l1qhjt3JI9ZXmnuJYWFI8Wk2fwNPsYkgNMqdtcfoWHx+1SKnKy9sqH1uotnJMDYntVrp8m/8PBSQnRlPz+cCa+QqFbQ6EQQuQWdCSQee5ev34TnA4E+b0ZVnkTHg/SadnjB28ExRUnYjPgDcDLKrBSjyqRi6nluUAyTU4g8q4ZHqnIzoIxvCbHxSYVtxLdMU54xq+/LXHIoCNrW22cIxyWZ1TqR4qn7MbuVfTbSnVU9upH7wrySsOX57Jhy1xTzAhGwkSWSon7RC65iJacGH24iKi4/75ca39fYsTTfhebm7KWYVamLSKbPn/6Gr/9kfBGrXGfHjG2bF724A3Ls9rTDp9L0A0r3jh2t8TmQG68Ku2r32ujX6fq5aqcQePhCMWSoBGUymkmLfF+v9PAiHvHS/4vP2Wkjy+OkSLaXOd8gKj/TCKCm9flzCgTOdWsynxcnDfhNeV7lsi5k8rKWibSAcyJLOpPxBbWo+JDc7EQ3IqPi3HIoZIqD2pwueW7fURYeYkCMPoGPnxPYthXn0tcES7JnN2eBTCIi22OQ2L/41ENb92VdXr65BEAUWcDBPkept0vk+f09FzWJB73okkE3pzzbjLQrLUvYJGX6IC8rFubYpf+gA8aY8lgmKh2xg4mZuhQHbVAJeUhEfOabiGVFD88IZp+VfF1ei2YLrFf0jEiMBV/PPHoGOqyXg0iOWLk4+m7ZrBNsaPMnOpQ5Q521wWFeXYi+zISI4LGdiGqs+MgJPvYo1PpNbsGr4sS9iXZe+mE7Lt2p4+Lunx3iO9zU77r+Vw6YjyjXtHec3y3iQdCKBONqhG2HGWc67P9DkpG+eEJ9/fZ0SlaRI5ltuS8tqh0XDw9Qofqnz4ioNoXcm+pVApZotvrvF+PW4OffJRKKTHqpS+0JlhblzN0SFW4WlP2XvmyhgDvz52iMikVEBtHe4jG5RpKxnyVHGEBlwsVovqW0nLtEfnJJsM5huSajFJlliLJGE0mWL0u79pDcvH+4BM5b5dXluAnKjBck/kMkIfHrXkRDcm+7DDGODmTs2776hUHTZig7YX4uYZZRzKtuAvZzaB7ceuucF9d1sR2kvSZM3vs8EDFycNTq8oc1y+asNmlMma8bfL8DS/F4KYvmlDJ2+R6z10mkkk5J9yEgplE5NWqTUxz5Ddtyt6oUTlUcwehj2WjDFWniC7f8fLFOQpV7ueI/CzNtanX62jwnXlEX9atMn4ZWWify/N8F//+8WuREKocyWE80txY2pBA5fhQDjJfQCYpkV5DKsrWr55szPCEL1GXM2RTanPzZWUqk3Z1ewu2RzZpmzDONBfwxvo2eky4fO8ffgAA2GWgFNFC6A1loZ7ygLpO6b3+yMQFpfbyTBB43CSfG2rQCB8PNeQwjS/RuazHYbZkw1wSEn1WIWFWowM3yZNvkphr6J4jf00OyieUed1clsP13t2baLVlsQ86cq2np/Iy1BxY6BsMhPlC7GFyIOBxYTYnYSMTUFPCMRNjA32+qBtD2TAqWPFMQjiqyGFw64o4es+WGHSzNMHqXRIVmvK95MGFHo3ANGWT+5aY8GILwkYyjpM9eQFZvSkvZuZAnq83tmGS2DdNUs7QZRsByt76g7J5l31yrSeffo73viUBy2eHYk+JZQlSVuNLqPXl5atzIi+V3TaJeyM+jC9JzMe2nTbJE5eWMtimhONZVRzCZZeSgh4DBqHvc86fhwkRvz5EnC9bmkcCqwcfSBAw1of42x/+RL6vxSQMne5w1gEVtjEmEfFoJPbv9vjhVq06qs2LRHFJvw++hMzHeCgOUjdd8NNpaV6+IPBwNTUdbjftllDtyUjmw2WZ8FlsgWMyceRFLgAAIABJREFUFnSolnuKqa3gwISd8wA0TdNJMulMgOpM/FmW5fzM61YuZ8ZnMKCT6FWRZOtQpNYWbM6xenGymGDQ3DpMc8qfye+4mEyw3UCXCYZSR+x4zQsM2MbhYtC/FhdbyydWMB2S+J3JxwxbziJrBbyidOMhgyEfXzgrnT60ffn9zTXxDSbXKDjzIUB53cFM7Gl5SexxOu2jMZF78fCgjpDkuHLUwWhIKV0woOCUNS3AoysScPoyBg1urwlfkC2dKRJasvWg3q6jUhPbjpBQ9aN3rmMypm/Nye+P2HrQnTYQ8cn9vHNPiEpf/8OP5J6WMpgbElRHk+KPR4TTZhIZJNi2c8lgrUl4vD8ac4KmC5IEoifXuRjUMeQL9CEDnXhGAu/ZVEMqztYVJrAUYd7D3RWYE1n70VAC01xS1s2Yh9BmD513wCC2INf57OkjXGGCeHlZ/I5l7GPruvi60z22hvDwHusW5iQMzG5wbSKyDk/P9mHy0PXwhaR0ynbnnW2YLQk+d9akvaBSFl84uDhE2yNzpMiJH35bfOijnz9CgL5Eyd+qUWoUcW1T1nCTMqU9tg1tLHkB+rmPboptVyoyZ2cnRazE5e+NC1n3bku+w9IDeNETO94fyuc9fA/dyK5gxmRKgudfMpHFs6ev+X9MwpDM9OTkCG2+LISD8n/XtmXev/tbD3H4pggACLKNoXYue2N7vYAVyvv2KdXcYOAe9gaxfVsIfestmbOzI0m0raR2YDP5EA2QjNonnz+rvMTpiTxXISovWFHK2pZOT5Dg+VK5kGduUBp6O7ONZlESEwG2Xb5+Lmerz+VBmwWU5FRsIRFNOK1zNmHnYQaX0+kE46HYtJdFJL9Hrmn4dSwVZE5rRbGLCv88/vw1WiRW9jDZ3B/Ld9ze3kS9Is9VZWuELyq2q1smMhESzc/E7s/H8mJYvexj+5YExJ2OPJ9fl9/VXG6cvxE/t7LCFg62IESDPtQJS69zbzxlsHz/7Yegq8TlmZzl0ONoVKQ1T7NlTt/5QJJN8UIePZUIZnvLo0dF+VkshWvrsh/nPPee/kLk7jWPF16f+DMXZX299FcHRw3kMmJzUwbQk758byq7hXBSfNJBUe6p3pRn311ZwZT3GZ6x9YYFEiMYRJkvbTm/7CU/E98hTUflQmyuxJfYlW0lRa3BxaLHJ3yBD/rdSEbks+0a5zukklNb8PNlUPPLc2X4ojYddHBekvUNxyRmNZjogubClLHQ7pbEiHF+rllvIFqQvZC8tgEAeHPIlgqYyPNljzkKbG1QXjmbxkZGnvXZC4lTWyQifeu923hyLHvAcyrXzrOt2DOfoT6Xeb9zX/yqTmL4QaeJSyYppnzhTCbFVpPRbQQjJD/l90TTWXxjM817pW17ZS17AHpjuel0Umyhx3h/PB+iM2HSl3tPo73MRxOM2LqBofi83//mtwEAs8kUe3uSnB5RGCVqyOcK6Szy9M1fHIj4QK8t53vn3I/qqfiuNMl4MZM1LTXr2L0hSTP3pdy7Ly/rliuswM0CpHZPEr0/2PsFACHXnbkpNe6XvbuWXEefba/BnMTdc69suJOXLdy/JTHFKRNdb78vtlA8KmJG0uDSodj4Flt9Jk0LPlJq/M5HUkR+0xI7bndKyMTF1irHbDfeoP0v+RFiTJGkXD0MvsSGp/BnKEpR4b6sFOW+Qy7MmFgMhGWfTD3y74hlIkKbjmV5lqhY0ZdAnwnlSUy1i/sxHylyeDlvDb6sNy+aCJNawz1X7xokw/fbDoF/mqTcyRRbpNayMJ4rGXLV0sYinN6H2y/r+kf/0e/J97Aw3m11scxkZ5HtVzO2QGtxHywKykRj4kerTZmr9GoOORayRvTpU9KhzOIZzHiGuP1izw22k5kDGz7SUwTjTJh4TMzb8vxBFoFGLFC7fSFEGPv3pnJe9AZiJ8n8KsI8/06PKpwjzmfHgmKQUHFVqyVz4POG0eM9l+tCrH9Rkc8/uPXbGJ2IHQ1J4B1i0mM8GQEu0nTM5TxaviEFsHr1ApuMWVsENyh6C6/XizW+24/mLLyz7fC8XESOLfIRFnvdTNTPbQ1nTPqsZOTaiYALOhNWlz25VpDvI/vlFvJcy9NDuYcOEzwzv44Qf6/HdyimBpBMZKHTd00ZJ0VCYoM6TATcEiu02GarM7m4FtpGh/PoDss+s0hCDm2CFYIumhR/eHtDkqDHpRIG9Nu3bspeN5vcp+ldTKcyN/sn9HchiaOt8AhnNsnrf8VYtIwtxmIsxmIsxmIsxmIsxmIsxmIsxmIsxmL8ho1fC4RQyEf4pycMkET5yuZbAID9I6lKnJ6VcTCUSsPta5SbZ7UvEPcBfkoeJuRaXUpI1k7P8eCBVLl9rOIZzBSfVE7hIfnvN74hFSwwWz1ujhAldOzG5oZ8D2GWm5tbqF5Khvb0laBRQqpMNp447VkukuFFk1K9mvt0tNlm1efPlinBGVlZh4dZX7srlYdEKIpGiyTXnKsOyeMOqxUUiTLyECZXJDz46ctzJHOsjuiCRNBZVbCsGbwu1QqkkCaSCR1OpvCyTSJBydoeW8b29vawknvAR5TfXyYktPjmAtGWZGHX2NpgU3J1r3wCV0T+HiMJWXAi93tyfok0YYch4tMHc8ksH5+XEON9HZ9LdSs5tZDelgztpiIA1UmO/OHbGOqSqQ1SkjsTl+9rjPp485pIBcKQNwnVfHV05rQzVZjpjkb57JM5Ds7k/+amZJ4nnDNL0zEkmamSRfWQiBW26cieb10VhNHastzLXqmOEJEOHkp2NhqS4fVqPkwJd1ZSrW72Uw1GIwci7tIUWSCfcykFz7KsdyIs81I8qmA6YjWSkOYcoaHT0dwRiDddqs2NlVFomBBZMSRBe4jtIfPxFLr2dZSGIqO2LDhy9f/v8SURtbSBfXVoug2dcHZY9td+SXcBPo9quaPNsgJsGIZjvwblZb38ftOyMKIMa5ttnwFvy/nOZJrwfj5zKOhFnMR986ys05yVHo87BA8Ji4esVlkkcu4MRkhSlr1J4kFwTWzdhcSS7IV4iEgfwpD7gxbmlL+d8lkvKc8OY44Eq/2aJtUVF6RyYE/nMLlyM1bFFOrO7fI6pKs2EYYaCc33918jQUTdb70rhHTmoI0hZXp7ZzI3z54J6uI//MY38O679wAAf/nX/ycA4KQsbSeb167h1kOpAn/xRFAiSyHxGdmlZbx4Iv7QIFLt7ZtCEnjlyjJeEAlarhNlRjyxGUzijDLR+YRURt5aF59dHzSxdyDf8/B9gRwrksZ+p4fLkVRQVHvYvQfio3756VP0SXQc8pMslLDi9Z1VdGZSsfEQwRPw2sjFZM/OVklIS1/bHfXQoG+ospJ6h/NYnzTwqizXuE6E2rgi93R+cIyta+JnjDnXnoT1esyHpC7VXX0g6/R3//IfAAAb+Tw2dlmtOhZ06nAia5SPhTHlHleEr5Nz3me3hyjRr28oUV4sypmZWEqhyVbVk4ogJDZWxc5uXrmCz38hbV3rRE5djOTceFkpYpfI3BFbD872jjCl3/WFZd6DHtkH72y/Dw8r9N2yIKeusk13NhnjcF/WckoE0g4RsJNpD0c8v/yUZ3d7xMbd7hnOKBqhyElvLcvnvHYQv/ziCwCAJyz7OcJ58QT98LINUkkFe1idbzQ7Dju5l2fP7737IQBg72DfkT3/7YdiT5915NmL1QoiJD7HXNYykUpAN+Q737yRavyYVf14cgkdEnsm2A6mxWWfxuGBi6TfKVZrDVYUA+4EbJJzX9+QSupYphjDTguxhNzzS5I2T8l+e/XqdRj87mXKiX/4QJ75M+MF8mnxScsrgsR5/FjmLhyPOC1LQ8rBm205+z/92Qt8i+1CnaGSzxbbSaWyePZUbCfEFurjyzN4LLmfMZG4ZldIqN+674HFtnQf9+WNt8TXTMYDFCty1oc8CgkivsXvDzhUAedEcR6/IgoptQQ9ImtfrYrdvk1Z7MOD1/DRHwaIQnx7V2y80qpDIwIsmJdrt9uydwMzGzoruKMqfQxtaNgfYjy1ON+CXPvpp4IwH0/XkMuucI7El01mXVy2VauT3MOY5PtriSQ8RAi9fC4IK5PIZt00ofvk9xstuQc/95uu607seH4p/mdEH1g5O8NGgIg4Es5H2M73VvYeXjFm9TMOi7Jq3apdAERJ+xkzrDKePitf4FqB98UzyEfCWUsz4ddkPuI8p0eWXKfXvPxSFplr2qJAQfBKAjs0ag+pFB4/fowC27XbRGgqFFwqt4Y6W4mVHHMsKve3Gi2g2ZQ5UmSwfcrcR0M+NGnLbkv8R4n+sdfpw8UW+RkRTK9fC2LoytYaMmxTLJBUfkYxiLPjBqJrspcu+3J/+U3ZE9WjDgo7svcuj4UC45h0BuPHb3CV+3KF7dUn/DymNjJ5WbciEZDBtAF/SPaJf0QECM/tpa0UxlGZy8uu+NpltyCTUss7+IT0F7nNq7w84/1gCDUiOKLc6wOidX0eHVkiKgZdOfNOinL23337OqZT8Yszkqrr7DaYz/voEXHcIal6YUeepXp5Cout+GdsWU9mGGPPZ3ARtRxinJUggffr13sYDElsHZN7KhS20CRp75zhYoDtYWtrK3j1VGx71pfYYh4mEsmIfwWlTjJp2sdkOMQqKTyUOEuPIiFubwBh2uZwRFoBvpfYmMJDFPiDhxLnB0j2jHEP47nc58yUuXX7xVbh8jtE8u22rH21Ln4rv76KKvfz3XtyTYVWanXaDs1HkqTB034TQ7b3Z9kKPhzKv9sXTZROZO1W1mQPeiNsh2qVEQtvAJCWSgCoslvFZdhwzYg8n8maDOp8J11yoc6W7mye90CS+P5k4LxPmETRvzooAgD8gTAwkbVYpd2PKXKxs7EJH98jRj151hhtwAMPTFPWUlFEhBhfhAJBDNlGNqSISYXtdbGlNOZEyFWJZl26sg2LSMYAycYHPPMS8Tj8Xsb17CSYEF0VW0rCxc9pUCggIqbcHgeRNSZKetykCEzMjw22LLohv59dFR+6Ekjjbz/5WL6H8/fuW5LzaFzWMeCzhil2Y3MO8pkMnhFxOR+I7aUZM/sMD8a0hb0jORt3btMfpAMI8vl+1VgghBZjMRZjMRZjMRZjMRZjMRZjMRZjMRZjMX7Dxq8FQsii7GjlqAq3RzJjkYhk0qJhqXCUx3Xs3pD/M6OSbWsZkomLTv0O4XEmK9VTf4R9wMFlh4wxwUrAgCTHYZcXLvKWFDKSQe1RNtN0h2Cz2tE8kCx/m1l172kQR2dSuXWzuhOi9Ds8GgYdyRimSaw1JgJiOBjimEiASFp+v9eV313Z3EGpIZlhm1XDsBVGl5VJi/fZYBb+0Y9eIshs9MqyZAiHlmQoP/z27yBEtqwf/kyqzr6gzIdpjxxUzWTIfmU+gycQQY8IDBB1EWZVLJZMOLKEM1ZGc5RcfP3TR/iDhPQip6Iy/74cSYRbF3hJlNeU2dyVuWTKhxENdk2QOzdvStXunCRh3UEHF6+lmhwmkqY27KB9LBXEKSs+6yTJ7A172C8WAQAECiGbl0z03t4bDEbkUGJFqUJpzLWtLA4OpRoz0snLQmK/qT2DbosNzEl4PCeKxQJAOheHKM+ck7cgGcTN61KVWd6Ue/j4U+FgeXn8FF72b1umrJGfRGXzmQcEMGEyJkKFP/MbPoDrq/gqEiTTe/utt2DqYuMDVjULqRD6ilOG5GqqAhyIhqBZRN6Qf2NGZMvEDdRITucm+gUgqgE2bGb5Dd7L1+XqKUHPLLpCXn1Vrl4RdWrkF3LrGrz8Gc0SbMWF16PDT9SVTYTFjD3elm1gyrUYEg00J7LA7/HBxcXpjkbOvTWarORNKCvJ6o4LGoJECqbiMlcXfbHLgC/mkE02ybsxIVJrPp0hQT+jbCBMHrBed4g+ie1DUdk3gbCst9cOwZrJWhTJRaYqIo3WAC+ffw4AGFE63JgrvqcwPFynMKtiLkoH65YGm2vhDsjNDKZiEyurGXzrgVQfSq/F1t8cVZCk7G2KPfUPNmTvbi8B/+r/+p8AAD94JNXS3BXhO5iMbXz8U6k8jlnpNxpyDyetPRisvt24Kv7ATfRFs17H+0T4/Nlf/nMAX/LP5AurCLXlXr08koIKiaYbCHLe8ksyR4o0++++9/eOrP0/+W9Fuvef/+//QuZFn+PmHdmDqbisUZNV1FwhA4094XGeL6FrQew/ExRDllLNFUrQfvL4l9jMSjWYFCM4b5ILSB/DS7nRMtEFt/l5GHNUKVOcMOTe37olaIifPfoCeVbONzfFh5VYif3k6UucV8T+5pTTNrinYnEbbo08NSGxNYXWe/eD91E6lDPktCSV0t2bsg7RVAz7Dbm/WI5nJGWgNRjIrwqS6edPxFePDdk3N+5cgU4bN4lusr06OkQs6CPyY5HcP5+JO/LDPZKvG0OZR2M6wr23xI56XbnWy9dytjbbLeSJeLgoy1m+kpe1ubW7hlfcEzQL+IgWsb1RrFwX0lWdpIwTcsxMJyYCjC00OpcWkRmRRBINVj11v8xx9UBQA8FcErv3ZN5qRUGjzFgVXV4rIEb+tT75GOoXF5iS2N7D6n2yQE6Efhff/s7vAABeEHnQJlH1NJ1Cuy5/D7rlc3euy/cO+lP4FdqC/Bu31sROzrUZkkS4pjKylvtH4ptiiSQuujIP3/++nP3RrNjnvRvXQY0EXFYEaaFk3l/uv4CPfBsVclKAqMeVlQK+IPKvMySX1uoGAODnv/wMT76QtdlcFxsoNo7h8Yptp4lIapriC08r54j55Xxtk9es2xcf6EtGECDRtBIRGI/E1seDARoVWa/LslRwszH5PiMZxIayP8ZV9aZc8+S8iK0Nmas6K+AzyqD7QnGHyPbOLfEVuiZ2dfLmAAZZmruGPPPpmczZariAGvkbsyuyzu+/JzbYaffw7Ins4zjjlkDMRIwkzXEiu+bksqiWKwgTIV8gIfwP/kE4ZVayeWTIpXbluuyb5y9kHW7evIlmh8ilAP0AyYC1gAsDotJcRF1c1GTOcoU8Mnm5pkKKKw44GCZa5IRJkM9LIRhu3rmKek325YCCCORsxslJGQbjjm2uN4G9yC9n0Sd6sNWUz3/nuvCp3V3JwsNY6ITxdNBtoE7EpSK9jYTl8/3BBIms+NF8WmyoR4LlxkXdQR8vkTezS1RWMBSDx8PKvsmzgLHAdAb4iZ6PE0FarsqePzs9gcuQ/aVDketK7JBLLOPgWGL5QIrce0NZj/tv3cG7bwlC7ROeCSAfl3vX58iJ/y9/+RcAgI8+FMLrXnOA4yOxnfvfkHi4NeijQs4rly3zcXkqZ0MwE8ThhcTDQZ88g+JeHY+m6DTEZ8XDsqfyRIQ2Gg0YJL9vcJ+AYiaJVNyZx7u3BaEyIon78fGhg7LZvC322K0Sid3uI853rxhRLJWWnJGJTBzQ2SWQlv2STZMAHB4MuE4meWMOjuRMCIaDCPPs9pLHp1EbIs64IcpY7fyCpOilA4TIg7NDDqcBkTijwRAba7K/XnbkjOuSE3Ku2ZgTkaHi2Sw5bWrdrsOMbBCZrMjs3R4bo4lC7snnokRJdqtDWF7Z474wEWtECtXLHcSDsgd9RKqo9ZiNxnhwV2KEMTlvNR56M8NCfIkoI9qzPxLChEinbl+eZ9KX+wxHsggmZO8cNeVs6xM95NFieE1uKSVEU2+LLSQiOWi6zPuY+7pHJHZ/3IPJGC+7LPaYW5V3nLPjC5i2PP8ZZer9fNeeaBNE6NMvKrK/k0viYyLBAOYDedattQ0AQIiosePiCUqMoQZ8L44T4Z/OxNCl+Rpjmb9bN8Qunz1/hTFtTgm3mIaNEkUL4kSczeiTUokUkuwoia3IM3z+hpxp/giWKBvf4Nnd6MtZsLO1iY0VsftzcrqVGYPNtRi2lii+0Ccqku/sxSfPYHTk5ldvCyLy2i4FTuBCl+dslDyu5kQRSC+BLgy3rokfff1EYvr6eQ06BQNyRJnWGvJ8lh3A1qrc568aC4TQYizGYizGYizGYizGYizGYizGYizGYizGb9j4tUAIvbmUTPLGUga/803JjJ9eSKY8xEzeu//oBkIrcrs/22eGl73dVzY3EOlIJjSdkyrjEeVUNc2NLqWb1ikx6iWvSKveQDYnFY02kSouljb8IR8OjqhmxiznRp68BZEwbt77Q7l3SrqWKaU5mw5x7walRZmBNhWHiqljk8gRJY3b5DM0mjXMiGZYW5fqRywQgtstP59R6enJc1HJCoZDyLE6OB9LRnhOtMc00kWLVcIokUGq39bn1dBhb7aSDraYnR4Oh/AEJBPq8yiJc/bimoYja5jRpHJbYPXqiTlDxC3fU+vLWn7Kant9NsLvfUfY+Z8cSEbaPJPrpNJxuKno1WP/t4sIksOzQywvSeY5ST6j/dIR3t8RFbj9qsz3p5SZXsnkAHI89VnieEY0Ue+yi607Unl99Fy4KLapvnFeG6A3l6zvnXekIrL3sggAWM5sAl5Wp9ibaZqUph6PYJNjRHeTLZ7ymVe3riCZF1vbPxZp0RdUkujPuiisCyfEjD3GISIe6qOJUmrHmMpBqtLm83owZhbdZt+2UggLej04Z9W0TuRUqrAC8L7UNTRWzgO6G032LKcJeQgRLTJza4gqtSmqObRZVbcMOBxbqqdVDWXPwJfIoC8RQrZTeVGSumDVW7MtgFV1pc7iUjL0lu30/Poo72vyGTRLc7h9FHWRxvy2YVvoUgbaDqrPDWHQD8wpP6yzymLpGnT20gdY4fH5FZLJxBKlPl8fSnVB7SVzbqFJnqUAkVwT8hDMp5azLxt1KgyQD8LQTIdDrEdegHpN5vOyMYLNio3GibB4zVhkBhAp2B/LNX2sPC8lU9iiAmEqJ/vzlJXO5Xu3ECA/2XFdKjcll+VUIz+8JnYfT8ge/qf/+s/w3T/9xwCAjygx/PSRfK7V8gJ+VkmzRF+ci6+9fe8qpuTy0Li+51VB+WVTYehUtcpflftbvyoVkb3DI4TS8t0xrtfjsihYmC4N165tcY6pulGTvb+xsQqXR/bOP/tf/xkA4DNK23/jP/tDXCuIH33y85/Js5+Qi2x7BSbntkYUS3juAqjyU23KvA0ot768u4VSSarBsZDc594j2df+WAAFIsg2t6UqUz2X+/ts7xCbO7I+birQHH8qPCEfffgeHr36BADgCUrlMkqOr/F4hBSrrTbVUkZE5iWW0jBHYof/8NMfAgB08u692juCrsk6W7ShapOoqK0VeAxWzN1SfeuOxVf84tVTgPOeuSbPMCB/jdmaYxgTg1/dEVtoPHkCsNp3bVc4VFr8nueffQY/5Ytu3BDpaxty761uE+dlsaPdK/KzYESqob/85JGjwJZZE76OBKvIb8plhMi/o7iinr4UtI03aCJVELv/yd+Jj/2d3/0tAMDctOCOyL2kqEZy/EZsYD6bYEpkYe1S5mVGBGXO60Kd6lZJl8yn4obo9kY4q8ozKN7AQCAADyGCE6qbdsiHdP/+faeynCYK0GvKOhdLRezcENt20TeVT6WSe3XrOo7eyN+XqWDVPiZCsdcDBUxh0q+myKfoi4UwJ6pyc1N4uOZjiUPSmRjevJFqokWUmZvn2+7uLnQihQ94bs4pfXtS7oFHDYbkEBm5Zc8PRkO4Q0S9sHIcXorCR16aIb/HRwSDLxKCRs6EHOXE6wOqU048SAT5HLpSzZQzr3xx7qj7aORL2mY1uT+rIeYTX/TFK1H2ShCJ4IlFcVCXe/USUXfCOCQ68SBNTp6mW/a36RZb//6nnyO7RVVbxhXXlyS21ENJpFgNbnfFZq9sSyVYt9wYktuhR+VOO2DBJE/MzJJzaYfIYbdtIeCVeSgXpWK8Sc6ofL6ARlWub5N/zs15GQ0muCSK0EWVyD7PCd3jxmlRqtQRVpq9RBEdn5zCTzSaiquUylksHceY8V6f/IjxiNgejDluku/r4Ejs30Ok3NraGl4cyH60ieTY2ZV4K97vwSKa79YNqWhrlK3+yZ//BabkIRoRkbiUTgKsil9W5R5O6ReS+QQuOd8+jX74hcTD7z18iCvkHnr5Qu7FpNqvbekYEnENIuDDRIpbkzkOiGZY2xY/HCM6JBTUUanKPMbC8n/xmNjexDaQoKph3K94T+VcLB9VoJOLSfEslchBt7a9i7Epz/qP/smfAgCOXos9XtYbiGclpn58JPGZFZzjPhEj7i5RwZbM8aVxibFJxVNyZDZ1saFvfPABHv7RHwEAXu8R9Uk0YtilIb29AQDwMK7SiDCP+CNOvFKZkt+GKleJTBy1usy/Qo7FEzIv++U3mFBRVHeJXSmk0dlxEe++Iwi6wyPZZzUqOi4t5TCeyJ4Yjb5qA0Cr1cGAsaeuyVlVSGyi25e/ky4PuWUqTLc98NAu5vSLM12hYIFQQMV2sj93CrLeM9tEt6e+h4qa5PDsjtqIU+Esl5O1OWeXh6brSIQlzp8SRaX8QTKSxLPDIgAgmpdrKa4u3T9FlMgdzZSfmXM587qzAdpUSBxwD6rY2usLOfvassWHRqIBR+58nehIg4img+MK3CnyH1Ed2E3kvD4dYs4FSmfk+VLb4ntrlQ74eoDRgLw9RL43p014puQ84z7LF+R7A2EfzomizCzLvKxTuev84hxWT+5ZoY9HVOlsHVQRpSJgiyhmN33Ug3ffw5worCPyIQ05P08vn8Kryb0oxTmd701hfwAmEUI92tAgHHLek+aMq2YM5msnJbzcl324FKRk+1Q+3zg8RYpIY4XK9tOnzWYzdLq8Z9qeRq40j+XGBdFviid1/4XsxYg5Q5QNGB7a3CG/v1/rIsrn8fDdsE0UeKN+AZtn8OefSPz33gNB3p9FgyjyTAiZ8vmYV/ZG8Xgf3bpiIv73j1+LhJBFaFw8G8QPfywPGcuJkQ4IW76/W0CvKcZ2LyOHaequOJlqqYS1FTm8L1swgymoAAAgAElEQVSyABO+SOouF6aGTMSAfw4JpdR0zQlCwwxq1OgbY6RWZIMkSYZcYaA07o4xYttUtUXSYQa9iUQUIMHeYCKG2D2Te0mlYhjwQKqdS3BnkKQxFgoiSsh2ldLLfd2FVkuucf2GJEK8dyXIOysVESKye4VEdOsRWfyBNoFrwFY4QqFT+Q0AwP/wP/5viBMa16IMokZpb9u2obENR8lLfknq6HXIvdSG3FmT752aE8zZTlBqy4Y75Lx86/1vokYIndcl1zyfsW2uryHEFrFffiyynlfvSyDR6vXh18Xx95kIiSSTqBLmP+BmDZC8ObO8hhad+h7bYm7dkLmyXT7Y1FHe2RXHW7mQezKrQ2h0HIeHEoD7PLKZ3J4UGi15nuGABOEB+T5MRpgNKE+fFqf0gC0qV3c28LIozvLP/+rPAADb12Sukr4lvCQUMZ2VAEa14oUmbkxbMo8+Ep5Gw4oMHOiT6M7HdheDbQr7B3voq7aHKRNsdQNLXOcxk24bUfl3vViBdiHBtZvw8SBfMIx2H9tM9LVIZR4gvL2hGSAyEzpJjWErkKGFXwU4VO1gJh2bzj2i6TbczOj4CS33qkSNNoNpMsHCA1fngaa5XbB4DZOS9Cp5GfCG4Ob8TUksN5rZDtwZXZlHlYDyeVwAX6B1JsgCYbHV6XiEIF9CFYm3QcJUw2WjSfi3SryGSEQ/msxQ54umOtwU7LkzGuD/Ye+9mizNriuxdb33Nv1Nn2W7uqqrPYAGGgRBkBQxJkTIDIOSQqGZB0VMzJ/QgyImRg8aUQqOBhGj4MxoSAIggR6wYbrZaDSqq7u8yUp/093M6713eljrS5APwjMicM9LdWfe/O75ztlnn3P2XnutlsbPIIceGL7aEbooyezo0uETIXS900JNBINhlXm1dLnqttpoaTN1qew2JJLjUq2I93/+Az5LEszl9h5S67yUH8i2n9zlunn5nVfxH79PklTDr9qsOpClbqHXo09+poNmRAfcxqiJ/WOuqyWVTZkc7O/AF8DTAz6/a9LBT39XbtYRizMQsaU1GJuiLwt1TejrAPGD9/4KALCh8ol8vY7+kH0xqxz4y1//EgCgf3CMvMrqnvXZ946fa6l6bwszOsgmX6a/2W+VMOPl/Bow26Kg192eCTUOKcrymRurXLvLc7NoqIy3c87N+PSE73Dz9hq82vWbInJ26LJ+dn6M2ysc/6gC8/kG/WLc7b4ohcuLJNFgYTa1HDArwmtX4MUissC51BTKBpG2V6UbCkJubT1HzE9bc9ro0zMVyUAHfZiRDzLsOCKZ088/28KwxM/96O7fsH9zSXiVOLh7/6G+h2MXiobgjfD5R2cMihhEpF6fCwEdrg2p94rKKq9d2kAhT/9dytGGVl7i+JxagarIJo9P+X5hL+1jbXEWCZW0vXaLF6e6guKAGVaNza4Cf12RRd++cRvvv/c+n3lCH+8SHH/RF0TmkMmg8Dp94ZE+43N6kVxmEOxIl5vDzDG++QdMEN298zMAgEk+bX/rxUXJ0owCh492eI742hffRVslJW0Rxw9FON2xmbCuYFtXCRyHytpnI3F8psSQTX5qrHIh87iLKV2otnVJ37jEPS9bPcf8CtfZ88f8XVRyvRazFZkzvqPXLpJhBblzjRZUOQ2fzkJnOpecnB7iq++SjLsqv5pIziNboL1Paw+K6uJzYyaFcY/veiJJ43e/zgBeca+Aree7ei/2a3mZfXf4bJhWEGXY49hui4i+jxoOVfplkr0/3GLg9cZrr+Bsl//dUHlAaplrt1Cvw69yasc8gz3lKtfwW994B00F8npV+h+XomKlegWxOMfYauH+cnbO9+0OTRdlErUa143d6YJN+9GMAndNXWqtGCGuUqVmmbaZTEpivg9srHENGNLop2l+T7VQw7zK5KaTvHQZJSOhsB8tlSjtpemLOlVJPjebWF5IAQAyxxyzqs7MCwuLMF3ka0TdEJVAQa2LzogBheWgiKqNfdHuxBvxLwAAjuQDDz5l2U/c6cHbV3n2qXxK//+vv8Oy3t12DUGHIaNNu/ebLBgYZdAKXHvc/L7xwAqrSlJampOUCJof3n8Iu4hbV7U+HylZWK03kZjmuA/rtNtdEbM6/WEUmpyL7gn941KKdlZrFmHXvuKXjz4+SAMAguEgrCb2sybxjlKF95Or61eRVfnv1ZeuqU/sp9fthVu+PSd7fKR5G9jM6OiZK3Pc4/quCqQZA6fO30MTfUZy2gebiNlRZz9DXs7X/XsPUVYgJxbVZVlr2OywALK1qWnOzfQUfdTe5j76XaO8RaXqds5Dv2vH+hp97JMnLF188zYvo4nEIg4VKElEuJZmZzmOFZsdtRO+Y3aT9gsxbDTqvYuAh5E07Mr2nG43nG5+d1t78dDURkfUDIZNjyTEEgnHLoQ/KiprtGveLFYrejpLGmc7WPg9lVLx4rsNQaBeV8T8iTiOVc5oMfHzY9FHNJtNNFUO7VDA1a7zn83mQq/Ln21vSmZ9kb+Lhq2oNHnv7NboFw6POFe2qAmZgnyZAmotaZybRha4FPjOKWgX9Acv9v9ciXN6sqfS2JEHRQUIYimRt+v83K81sbhOv1FW3ZXf59a4Oy/EIloq1zIS1MFoCAfPOM/zC5znni4FDicwM0N7HI44p/UKv99h7WFOtCDb2/RlMyl+v89uR1MUHobtlUWV8sOf/hgxnVnPBPIw6BWWVlZREYHzcV5lZUX2zW6yoWvXuyr5Z7E60NTz8wJMuFUKtn14CH+Q71zrStTCzXXT7XZhVtnlmRJfDh/tJRoNoShCa5POOUaJ/ZQvhqHKJ3sSwFlQKe7A0oVZpO1t+aSG2bCPCKI+7vWPdJfN1/h+NpcdSSWo3UElElRi2+r2L8r/XDaDhoP/ri6voS+b/lVtUjI2aZM2aZM2aZM2aZM2aZM2aZM2aZM2aZP2G9Z+LRBCa2FG78/PDtAZMhpayTBK97KIzaZSYfiyzAqsLwkZJHj2yF1Bq8r/tivGFbIw2vn4+TMsp5hJaaqEw6GMPVx2jCWh7hJkLaPytaHXAXHHop9nBLQsUuRauYZjQXkXlK0OSVo67PBg5wWhX4YEtVMR8L6jhx1Bwi2KVEYUAe/0uoCIo45KjITaxqaLsq6HnzMTe/MaUS/e1DJMKnMxCCmvXGdfDhvHaL1gtNIowXr0mOUJHq/tomTMor+zKZJqcTpRqbFfwy77HAkzOxP0ujElEum6yrumrzG7MHSOkTlkNtijMpKECCafPHmCJQdRBtZZRmM7Zs7DKJfDUCUYTWUF0ruMmnZHVmSNchwLx8UCB06VgfrW7/8eAFxkyX74yQf4UDD/hAHxjNCuDuMhPNli6UpqjnDs0UDQP7sdy4J7n+f0XsrOWCxR5ItELlkkZd8FbcE0MEGczZgS5HJdZWiNRhVbZ8wqdEXObVXJQea0iO092qrby397VsGgXQF4ReQ8HvPdoaxEMBjCqUpzjJIqiwiWu90uYgvMxJr1fbV6BU0hF3KSf665+P+HW3swC06drTLC7lDpjWlsQiTHiLVHJL4tEapa7U70BL02qiANKcjRcPR3ZOclvT42MipmwGQgcAR9F8rJbjXDZjNkJDlGbiE7BqMO2h2uuUZd5S5ug2TQCbfGyiZC6KZBCD/oot8RWkiljL3RECZlfyqC8ls0BlazBQ6haYZCyw0U0e/3+ggp2h4WOXRdZKi9wQA1EStLZR0dkRjarB4MVI7REkqpIeRJq9OGQ/6irz6MrOxTq92FQySXbqEWx0oVOewu2MU6Ptbab4J2Uq6Vsb/fVt+ZuQnM0SZa/Q7syn70lKme90/DF+H8FpUWfvmrJLf0uYBojb+rHEs6fJWIEbu3iec/Z2nOtGSI53zM9Jym9xGN87tH6l9cEqOVvgljlRjEBclvNpTNsERgkvzwbILrM5yirzk5e46M4OmBBZHYKyPyxmtv4tMPWQ4WUJnB//zH/yMA4E9+9n385K//GgBwfUnPvEnf+Z3OT5ERWuhUpa0xOBD6HRJvP35BMt5kgu81KDcRCTOlub0tAstdZqK+fPM2trPMvOQ7XEtrKfqTcq6AkJ/PWFXJ6tPn/L6+aQCMZE912tDGJWaTC/kz/PxzIk1csm27pFbNwy7KeWW5hIQJCnny9a//Ht7/AVE8I+0vbZFIDjo9+IQsMlBio7iklM+OEZNfm0tw7eaVgYTbg2ci63/5tVcAEH1bFPLAKVRryE97mY1PIavy6VducjxPhIZtNBoYqfQ5JMEFg1SzVKyjWMmrf3zm7jFRBg6fD16hY45zEpJQ2jxXOIdpzJ+tXyIyIK8y7PPzAmySbX38lCV+hnR582cfo6Is5NoKbTuQ4hz77HZ0hc770Ye09X/2R3/Edy/V8aN7lFm/tMJ94uH9z/H+jygaMDfLZ4w6fHa33bgoyenWREwt1EzxvICiEKjhAPuVjBuEy2WMhIRpi9R45OHY7Z2eYPkKkSMrQrvsCg1h6g9h1bkjsERUwqmyiw77CCYpLqyo/K9Ro7+amUlc2LbPpzXsoF222j00hGQu9ISuBG3+0qU1NFsiMNY5opsuYE1ky8a+acjhvv/DH+GNW1yPQSFBwx35QN8Yr+p3NZUX2IR8jc24cKRsdyZLO+lIytoXjRt6C3Cp9DGncotWs40bQlenRQGwEKAf+fLb76AjpM6+stYG6Xa32bhA4h1lue+uXedzBlsnqAlV6XDyixdWOG+DIVDVevbLpzW7OVy+xPc6Pea66WkPmklO4eCAP/P7eV4ZDLQHDVpwOOm3Nzb49wEf7SQcCKOhEhtDJn2gkrFyzQOXneMwNW+Qi3LsmrU6OjpXQUINViG3z85LcMiPHmfSAADvOd9hZjaGYY7vbNEeuTBDO6mWykis0uflVdKd7tDmjhsl7H3A92sIieNb4p7wrS/+N0jfJSG5cS72hZKoax/PHNKPOOzawzt93LhMH1Qr0QaCEtZYW1rEg8/vAQAORBx79Rrnq1Qr4OSE/tpsY9+zTaIirizO4Y3Z2wB+Sdh9ovl2jEcI6Xx6Z4coM6vKcaZHfdRG7GdtzH/DUa79zqiPq1f43TEh8I7bfJfToxOo4hF9ldRP6YxtdbrgVBlqr8mzwnTEh/wR0UaHO/w3IdRuuZPHQHcg3wyRCEEv56TS7sKvMla/qDKMc9Lmzi76QuY/OmCJ5fKsziMtM9pC+6+pRPLYEOxxhhHw8R37PZ6nD1Tm2Oq1cHWd6KGlKSJB6vLV9a4VXaFcZuMpAEDJSRuslHpwas0aKi133qcvm5t1XPhms1no6mr+onTL5+fcGES/pWIdbtWRGZUVtRrP1rbZWRRF4mu0higbKvXqhXCIQ/tKUCgul9uNVkOk1/3B3/tMp11ETcITi4vcS48P+R3ZXgVOj845edpjPiP0rt2DfJn26HWKbFxItHa7jGWhySpCzbhV4lYqNi/I6Rsaz1Kpg5JQ4ga6t9rn56v1Lswu2pNlTDssizi6Ue7C4xDpt6gCxjrEWkZj+PlxhJZpa3t7RLW2znsICgGWTvPv3e4U//9wB4OBhF0kKmDR2dnuHKDVpP/dO+C7G9eFK6uriIr+pKkS1zPtzcnUIhy6A0Hn7qMTocKdVZgM4n+VHRro/Rdbm1he55xY3BKTarXgDnCtPXrO9ZyU8NB8ahU7+/xZUAichiowMrkyWvL3ox77F9V5x+r2oCfUvlV73fI1nvX2fvEIoSvcnzsqOz47oh9IXl/AXIr9aypmMdJ9qVqvwSP/XWvz2T3tlYlEBB4NXLNp3LM4xwG/FS/f4L7+5CmRRTanSmu3n8MlcvNf1SYIoUmbtEmbtEmbtEmbtEmbtEmbtEmbtEmbtN+w9muBEKrVGElOLiSR3mF2byXBDNi7t5m1fulLl/DTv2Qk+M/+/C8BAD0Rl2ZKeThVVxiQ5KR5zIjy0vQC3CKKtape+VBRuq4VyJ8zQ/fWNUa3jVRTNpuFTyRgHdVYl5U1NA3HeP0VZhUGit4+f84szXGli5jIvUaqHyyoPnQ2HsdbX/giAODj98lf4FPGp9lsYl51gwVFpMPBKKolSQqqVrKh2ulOq4eiSKvrDETjOM+IcraTxZlI7TrKzuYa7IPJZsdwaCA3xIkizpJmtw+XK6TxYzSxojrnQasJC5gFsol7xSkp5KmbKXRE4hsU98dIkeJyo4bDIqOjdpfqgPvs29r6K7i7zUyDwQPjFDJpYPKjIpRBuchov7PTvUAXVMRXcPgLySl263CK8M8u3o6/+P532L+lhQs+irND2tfsHKOzey+eYdQ3yIxpJ56AyEx3zlFr0z4MafS+UGIeRLEoucFghFHYxy+I4iqXGjgXoiUrIl2zlc/sdbsXfDF2ZY5nZ/ico3QRDvE39ERGWNdzQsHIBUnzUDXMVmWAApEwMmnJPEpKOuh34Fy2nRXXUUEIOe/qDDqKdB9v0S7sQvr4fAHklF1xdFQfLU4Ej9OOsI997hhk4yJl6/fGGGrOof6NxEc1xhAQ+eFQ2U+rxXA9ZpjEQ2QR2bNLJI1DmDGQ5P2KMkttSeTmSkUMleWyKwviF6+OzWLDqG9Adugjmv3xBYkehgZhOn9nGQNt8RYM1Pfh34mVm8RHFgsIRSjJ2jaG6IvEtyzklEsIrTHqqKru2pivtvrrCfsuSC23t/eMYeC7OFzoiyvAIFl0GygRWOAUR0YsRF8xbnH+BpYRBg6ul6pq6s/FgzJ2W7EiIvP0Cb/v8uW38MEn5GtbXGGWJD7F8cmfnMGlNbQQ49+dZ8VP5Bnha29RUvfpDm2uNeD4eyNR+DR3IRffL+ijX9769BHmp+lbVleYQfzoLpEW47Yd+TRt9dZtoj8dkiN+MW5dkGTntXbfeP1tAMDhWRY5ccyF/Hx2+inXoHXcRkISxc9eMFviKDE7ZnN44FImcTykPT+4+xjPRFTvkzT0g6fsn9drx9ICMy9vXydi9eSYY/uLzz5BMM41eyIJX49swtwbwqI1cXRCBKVR/748k8LMLHkbnu/QB46ENshVq2iJE2ZJPDItrcVip452T3MjnqZmk3vET376fZjE+2VIgBv8A/l8EWNlz2zKtlbl20OuMJ7IDg1C0Y4IOK/efh2nKWXV9phB67RbcAld6hXC1SeErXtoxZSDc5GKcy+w9DgGg7gFe8p2P3hEpNT6FWaAj/LHgNBvHfk+tyCYFucAN6/TZhammZ0snLPv7WYJj3aI/pkVynTYlp8cmXCUYRZzQwitvLiE2p0xYgnuZ16hxbzySfmzE4yFmPrn//0/AfDL/exv796FX2jC8+M032FlCc0Wxy2qbH/mxOA8c2JrW+TQU8y2JkMcs4Gpf4HYqyjLO9LZxDIc41CcCctXiERKBjlvEX8KtZYyxMecN6f8ZKfXgkuZYreyycMK/240qqIn5BLGtPGi0L6lSgFe9Wtf3EgD8ULV2i3kqvRlffEkzAkJvDq/ilwure+WzHIDqBywf3bxkCSSfPdnjx/DvMnnQ9Loy9PaZxZC2NkWubPOJr4412m5W4LVxZ+F47Svc/mDteUlnAnZHTGEQ2wkMO52uxjo3DavPpzss7/Ravdi7YYl19sQenngtqEsboiWOPimRN4eW0jh5JQ/O8lwjqziBts9egGbjeM4t8DscMSVwJG41bySU46IZ+vsLIdyVhwUY55vV0SO3O03sHXAdbIgX+ESz5nNZYZH6Ndik3vA/NqC/q4Ln76nLalmv3hTYuvrgGy5KoSEL8nxPD7LwSq0wILW26kIuXcrFTQbXHO/89V3AABNiYy4ov4LKfW4uEcuf+VNPvM0i4JsrF8QV0xLfEYPXiCis1YzRn91sHUKm+b56jUiJXri3bz/8Bke3Of3rIhI+GCXfhimFDxae4ciiU7McL4TyciFgEd5wHlbWOFYDUZNRHzsw9paip/RfC8vLOHhp0QwhabYP6fOpKVaGd4I/64vMlmvzu/lUh0ffvi3/J3Q1pmyCJdDbvhEAu72cQ11xIWY3dqFTfePlpnzVq9Mw6F7y0yKe0FaxPwby5cwEFntUTbN9wHX29xyGH4P3ycjol+TxSAIdsEfph/1CYnX68r2Vq/gwx+z79in3zLZjSqDTSwscNyuXef6yhxwrN76wtt4/Ihoo6277wEAbEKgvfHWTZR1Bh1L+Ob0BcejUq0hGufnHG76/5S4whqVEs6ORYgvtL/X40A2w7722rRbAzVTLR1hrD3UII6OiZx3bBphc4eI06Dk6heW+C4xp+WCDyvqk/9O0w9N2a3w6HyZlR2HhYIOBGPYfCGJ8S7395aECjxuKyISyjCIlQNe8Tx5bKhJtCE8xbP/2io/k6/sw6Vz8IkQQhYrfWCh0MDT55yTWaGlj9IZA5CPboL21xbivtRqIGyTn89wHJs6P0/NLmM44nnFbOb7tYSyiQRcqIvjyGri7wYirnY7Q8jo3hkXKffmHvt07co8LEIY5s64f4aFyDk4fIxqQ0hIrfmC/N798hOk5rgXG3yxGXGcdsdOWIRgd7iFOmryO37ww0+RFE+VgaKCuEbXrl5FKc85mfbyM9u7e+jrHhLXem7rnjAbjMJk4+fLunNbw7QJk8UOkwi41jZ4Xtk/5jnm/sOnuCl/vZXm3m1VaZHP5cSxqjourdKXnTyhP++6HViXTT9+wsoWn2IXTrMTxRztY1vo7K64wcLxEEy6a+wIKb+2xmc77WY8fcBnhWTHA50jbaYRoiKs/1VtghCatEmbtEmbtEmbtEmbtEmbtEmbtEmbtEn7DWu/FgihxZvMuuYLp2gXGbEzg1HzyyFGY9//7p/j29/9zwCASIjRvYBUME6aNbhU/J0Tv4JB6jGfnEZ0mtHUwxwjgB2pDRVqDfSF/MgITeETt4fL4WZBOHCh/pUTx4/f7UC1zOhmUyilqNS4YBui2RAqocU+BBTCPTs8xdwSa4tnZhi1DPoZLT06OUZdmYm332B2pdUG8g5Gpd029stQA7h77y6CMUaX2+IoOlTEcPt0DzYno8pTC4wKXpYk8rMnxzjPNDSOzFRks8z8mi0uuKQuZjV1/954TE1F/o4kI59tl3TU1OVFbP3fPwcA/O7brPE+VrarVW+ineV4HB/ye5ZjUpo5Pce26kEXpFxxesr/L/YHsCpjEFCUPxYN4vVrzFwZKl4Zzemo18aC5A99U7SZjVsc6/x+BoUW+5OWFPebr3KMh90KHjxiFuj1t98AAJgkP13r5TFWxnAoexwJpWNzjLG0muK4WTlHf/3e9wAA87PrKAiBI3AaAn7a4NbmgwulsmyGf5eW6kw4NAOPkA7RKD/TU8TbYrFcZAJseqhNigYOlwfz4klJH5G/ajzqISEZ1GCSc1+t0n5nFpaQTjOzZgK/pzuwaHwKsKpeNa/M0m+/Q0RI/igDq9A1YWWKOkIddUwjDCxC4AwMuXSpeikDBvxSScIiDiGr1QKLEAs2IeoMG4TFiqHJqJUWZ0CY45OcikLJugvEm8Fv5HY6L1Ty+uJIsvV6aIqvx6zsc0/S2V6nC11D9cvgRFJ/O90+mjVmKwzmfp84jsZWO2xClRl8Lu4RMylDkwVW1aGHhDRs55lB6AxbiDg57k5l33oDPXPguJBodnqFwtBwJKNuBN0cN7fQQJHpJQ1sHzYptdTEJVFXxn7Q+aXE/CWpYzV9Y5id4qpo8f08DiHVLGZ09fywMtLtDNdl5riMkLK5thH70lHG2WS2QQApFKWq9fMXREIuzm2gUmDW6dkzceCIK21cseAbV6hSY61x3dx8hWt3/8CFQZFrYG6OGVIDMfjhvXuYXeRaP6pwLf3ph0SPVnI5vPLOOwCAhPg7fvIR+cBm4in4le3fU9Y7vD6PlSCfv3VGP/XNb/1DAMCP3/shXtwnv8Q74tHxLDGjVR1W4R5K/WmOa8KueY84vXC4DLUNzsk7kgjNPdzGX338bQBAUpnsQUPKYodHmIpyLoZV2mOnJuXKxgABIaY88sMNycfbrUBI2beUMogNcVqZhkMcC9VgUn1+V8iiR+Uibr5CZFZskXO7/5Com2BvgDNJf09p7RUKQxweM4PqFyLA4yXisjbooyD+Fqc4Pb745a8DADZ39/HOF6go9YtHnItzqWy5IwGEYxwHR5trwuPoa1zyaB3Q37s6UlYTSq9WzKIv3juDkyOepE3c++wR/PKjOwfKql9iZnvvySFykklODuh3ahmhUzwW3LpFvqm//R6V7axCvm1cu4znn3FsXn6TKOFWqwG3Q3xk8mtejQuGgFt1/CapJu3k03yvdg+z4tgyuJR6QjF2u12sSrVyeZrj0tom+s3ui8At7oQDzYNF63Vkc2Agm2sJKXhtjXtmMGLG5jPyc1jAufRIZSV9ug+HuGSSQ/bltMBnv/r6bRSqPMvsi8dkI0X7fPbwGQIBjp9HZ4VewgKfTWeLNPdbp402tLSyjozsMO7nPD3KcG4CwzaOjrgefdoHh3b5dpMJdo0RhKYKeLjf5zZ3MDtHu82LX8jvl7pQwINTqadl9buBFPv+vHCIyA77fmWB67ljlsqNqY/5y2t6ls5om/QL9WoT+0JGGBu8K0S0zNTMNHryh8firVlOLaIsP2MgmWIR2qXD5YdDpjKScmpfu0932IPFxZ+VG+q76DTqbRPaXc6JP8IHnEkSvN3vICBep4HGaqB9eioUxfWrtO0zcTJ1hNCNTM2iJ1+SFQrAKhS41eHCuE9b+ejH5G27dZl+1eGwYXnA8W62JCFeo53YO1RPBYBChZ2fXeNYH1VOUdvi+r9+k0p10fAAJzmeTayae5O4om7eegm9Ps+EFn1PQOieF1s7uP3mqwAAs53f9+QZlfjOzoNIzdMfunQ2bEplaH11GZtPeWaySOW0J9WwbrOP198gor9soKt1/hl5IsiWhQ6RepUz9stKBLOQyY/M9FNj8fUFg354hZptCr3lUpXA9IYXu1IxmxXqoL2Qja4AACAASURBVLDfRlGqw+u3iATAgLbjagVRLMkPa+N1SIFsOACKUv0KyHcZaIgvv/kSNne5P3viBv8I/WLQPYWEfNLYLMRIlPubyxXCvXv06a+9yv4Fpb6UOTrBN3/n9wEAdz4iUujsiPZYazYwFuqzKrS1w6pKDphQKHCsktN81pQQVIuzSziTfzva59kpFHRdIMbMQqNsPiPyp91tIBzjM9xCc1uFcmwOTEjoHnhuIIHFkzi7MI9mUzwwQmN5hBg6OkgjMaPzs5QujXPm6solNCTLbrFISVY2VG+dwu2nb1mW6l3pjOut3+7D56FP72jN7x9yvfTqWfiWtZ6P2U+TXejMsQ2uAMetLjVAXyyJGfmSulA8Zak4+qNhjAyEoLisDIS9yWLHSPx6J7p3Nhu0VbvPiv6Iz/AYiCuh7yvV/oW6lYF4v36T94NuO3+BPqxpTy4X6HtHJhdc4gKbihMNVRefkc3iQK3Efv7iU6Kyp8TJ9PTJNqJx2q9pwDHe3+MzbbYAakIDN3TfCmofNDsdiMeJwDFQkjaXB31hYKxCI+8c8u5l93qxLL6qH36fd1nfNMfV6/OiK3/TEeewgfB0mU34/DPuyz0Pf3Zyn2eV+dkE2kL5X5UNzKToM9Hp4dNf0D95w+yLce+xmJzY304DAAqqnppZ5N9Hkwm4dH5bEcKwpbvtrZdfR0Fctbub9KE1xUG89jBKOfblV7Vfi4DQqMrNxzawIy55vPWbfNm/efohAOC7P/sxvvjObwEAohYO/CePuTHZHU4EtTGEooJJK3jT7XVxJAdQ0aXNIdJFd8yHtmQsd0V09doioeXtswb8czxw1Gw0urOsDtJjK9Dh4C7r0DAQxNztd2As6beP79O4Z5cZaKj3S2gKKuh3cOLsYxr5UnAeXpGVhcc0DKfXgWGMG9fCKr/npKxyN/sATRuNNCNZ4KxIKD3RJRR1+bIWuXgWV3nBctlHMI1UyqKLtEMQf7NphG5XB3TJuZ+I1GrGGkFXcL6q5I5dglS/Ggnh31m4Yfa7/F1DFzSHzQ2LNvaUlYem1pDP3jzYgbNGJy1eLDjn+X21+yMkJLdptel3ljGOS3yvXovzdmWJh3Kb3QybLrgOP53RWOOT8xbgUJnb7VVe2iw6TNkCM1h/m6UXbkGAWwXOc+04DZ+XdmiPGZB5dsbaHuLJJ9wcq3b2qe8Q4WDCj+17fP8pEQOb3QpsoIFlD7+vJMllV4AbYKfVxqAj2e6X6Jy8DjrPbmsISLbcY2WfvFVdnNo2hOc5pqVz2nHYN418TrDLGucppqBlZu8FqtqcpqMpAMBpiX3p2odYEtz8n/3j/5bv5aV9bVoaSO/xgG9qKuAY5fzlig1on0TPCNRosxt1e7AKi2jMgyFjbG32YBdxo1UHv5Rkk03TQZzucK26LrOfVcnFOsZ9VARZN+RHuyr5GAzbaBxynVk1VjdvvoL9Q47NwQnH2C3o8NgywrCrS69KeowLYNY0+OUmqsO4T+U0g+EAOkujLhsfyz76Nis8krAfiUz2apwHuqPqPsZ29sEmGVEpGmMEE2CiHSUCXABJdgkRvxUxSQzbBAm1DDgGdocZDRF5RgV97+a44RYOThG+wgOfz0Gf9tmDj5Gcpa+s6HK9vadDoj2AkIKHllPavTMucujeKVoivHQlaU/FM/qo7kkRRQXpO16Vyak0bqt4iHdfZdDh4SYPjP5ZXgwcVgtygsHfVGnnX/34zwAAz46zWN/g5dOQTn34KTfxt66+BLePc/6gxeDSSBfVSqkIc5bvcEnkfadfoB8u1Kuoi/xxKc7xOHSPUATf6x984V0AvxQK+DRiRU/fU4fKH2oqV3QG4R+LzDXLd1i/yvf80uoNbGyw3OdMgejPfvYhAGDXUsHMOhMhlSHH/+MnvJgsJhfg1yGv11QA2slDaTw6wqgjwswq58tto4FMBWfQr5xczAUAhLQmXrqZxAdlzskDEez/3h//dwCAe/cf43CH66qsQHFzrFKHnRxQoXO+scbDzNzGFCxBrtlWj37tWAmVccSM5JIIi1WGUKrr4lRJ49kzkjTPar8enousOJNDd0prx6cAcZRrsNoZICOp4XxJpYs6GIc9S0jnaKM97etDlXT5gzHsPeOcGLK5JTef4/QGUajTBk5zXLsRkei7PSaUB/xcf1Ek9rr4N/tVRATzd6r8od+oIWilD1oI0kYrOf792GRBt8/fXU9xXKwXCYHcBZlpf6CyIe0hz55tIexmGXtaxK0m8Xy3zo7gElGmoXxhjEffYkb2lLamal7s53QoHwEuD9fj7ILKW3QRGs7MwyGS1lde53ngwc84nqV6Gesqt/KMeKGeU/lnI9BHRdLCM9MpAIDPbUNBPvaSvsc4l429novAfFiBzXiA6/Ozz3fgXeT3VCQm0B1zTTZyBdxQOVh1JN8epvNtdLsoiFjcneC4lOs8J6HvR+ac76jKMXhVWj6bXMFAEsPbIoQPSqhgULPi8Qf0U/N6h5LIvSOXVmAbci9JzbPvVnD+Dve34HbQtoMiD9/e28b0skrTRGgNBSF3d0+xdoPk6/ks53msJIPfEkBee8/JCecwGuX8vbSxdEGU7FC55rDKtejxhNDXvA5U4p6QpHTSPMazH/0YALC2QV97WJAgiN8Ol0rGznRZm9W5ot0fIhwT0amCRSVdzJvDMV7kuK5fU8B7pGRoZ9SBKtuwskZfa1YS9eb8Kl7oAt7oKUgYncH8NC+YIwv35L00/dXsVBRjScunn9K3xLQHpXOH+OwzCqfEYrS1t94k3UR/MMBHv9AlzyNp+Xnuo9m93YtL5Fj7rk2k9jcvp/D4Pi9tbb2PV8IIzUoVJpXST01xXY/NRlC2g75PRL8l+t/ffve3AQB3PrmL7gn3S6/kqSNTOn+7TAiI+PXDQ/rx1XgMI/muXdECJC9z37yz8wDNPa6zqWu8H8TnVW7VraM0EPm6DimdrlV96mNOtBa5PP/ep6T3yqoLkQTP1J/e4f3FrHOZ3WbF9raSMR9xD55b5d+Fh3Hs/C0Tq2El3B7nabOmgBNf/xoTAZt/+n8BADx++YXYFJqdkfrHMd7cZGnR3HQCbp1zRkPaXLViwTjC74zN8BnXtIfvPk7jPK87mig2DMGXvr16Qfmxdpn7c0gl1Kefv0BfiTavStWdVvqBUqOJWdmoVwEeVeRi0D7B8iz7UhaVh9drXO7NGBjBA5MoEAb8zO7uc9x4jUHZ+1ssZ2+KViDhCuHFDv1pe8D9qCZRl8hUEAEf+1w17swWB1plg9yZvsGvROwo2EV4jePnFmm1WXe3XDqDoMbRLv/RlMDA4e4AHSUAXDba6uoafcVB/hnqwlyEdbbryg8XC8DYxGfcfpPnzZL2nkbFjP1d9rM6VLnxlRQAYPvRY7RVFrcmO444VUrab8Oj/TKueTbImD+9dw/hMN/HIPNuNFQu5+jgrNTR+4no2tVEUPtkU0Ffr1fl8I+f4cu/xaTkG2/R/neURG0UiohP8buLCsa4RdfhDgbgXeY8vdhXqeky59YX9uLRfd4Rn23zfOpRbOD8dB9zOn8PVH5fEGl/t1/HeVtxgijnzavkTvUsh4LOhHYHffxyVNQDHz9Hs8e5d4ng3pnk93VqDSS15n5Vm5SMTdqkTdqkTdqkTdqkTdqkTdqkTdqkTdqk/Ya1XwuEUEuprE6rgW/+AeHlb0gi+P/8N/8aALC2tAqIyPYwxwhySrKXPo8bI5V+9SqMrCVjzMQcHxbhEEojEWAkPiuorMliQkJyr541leok+Hcr165i54TfcyjUhdct8q18BX7JAOcFec9kmZG6vLiCfJ6R+SURAcYDguadNYEOo7GPPmc0/ZLISmeic3AKxtasMwpZ7FcAZS8yghofnbLvXr8HVUHzTZpGo8yu0xqhL4JNqzIafWUpm83mBTStUmGU2Sui4F6vB7vgtmZJmudEmlgKe2AWaZldZHEmkYUGl1fhcAnOU2GU1Cs0xX69gewpv+ft15mVqfUMUl8zLA5GPitFjuf0osotogXKlYNwQwCo1QeAg/DQ2TmWbrTr7MuljTX8/M4PAAAuv6LoknZttAYYjBjRjYow9lv/xT/i39ui+PZf/pnGiFHmUp3v3By0kJhlpNrsp+1lJHvf75ngH7Ovj7eEbniTmeNMy4aqSK/feYdZ3qogyh6P62IuhsruDkWA3u52YBkL1ZTjfBcO2ZeXX7qFYJGR6E5JZHMqsyu1KxiLuDsvMrJ+a4ApZXXdAY5xV/Ln7XYdjSY/l1UfLE4+++WFBCySk3w3SlTCe5tE4s2HY7CGOR6Hj1hC4xTRqdk6RkgZikaTk19XJthmc18QJJtMxlpnhN4xsmAgpJlXa8ql2rjpcAxjJ/vy+mu/BwD4f4d/we/f/RRRn8pxNGZfukabOHy6j8YKM0PDIn93kj9GoaISJUFwZ1ZSAICf3/35hZRp28e+G6gqT9iPdp59tlglnTonQuLjLBptjl9MiIeRPtO3mVFRadNgqGyEi/aSmJ6CI8if5dSXgSL7JrMdJmWLzMr0eLW2opEAoiFJ0gf5LKhsM1c4R00lYthnxtcn+cxOt4t3V4gQNEuyMh5+C4cidz/3SpK4IbK6UhY+kQmaz1QaYeXf9UwNeMOc8ykRmHfBv9seFjArAj9rhTY3rnHslmensRykP2wEaXvVispjmmOEXmZm6LtPSYr3+TEzs27rNJ7cZYbYJrsaK0O1nU5flGdZO/Tbz99jxjN004aDHv327iP68dWXWTqTjAXRT/Ad7PKdnlYbaPFZ4jLGz37+IQBm/bwirrSqvNHv5djWSk3UPud6nJoigubLKWa4LeU69n5K+XjDHzwWefPC9CwGbq7HkdbCa9eJJhr3LGjIN88IXl0T2f7h2T7GIkBfv0rE2fplolq/85++h+mwShNU3uXwsL93dvfgvU0bXVAGfFhnBuz61SQeefjMp5tc18uzIrk8SCMsePShylDauQyckuR+aZl9Hs6LTL3bR0Jla9u73Bv/7b//PgCgXC9hbFHZpZ+++TzL9zw5rWJuRN/89qspfk+edhJzBrDiou30Ohwro7x09uoSIDn2WZUYbx/Q/t0eC37rH3wZALB3oDIUpcC+/tZX8Fd/+dfse5c/nFJphMMxhke5svkZ+li70sInh+cIT/NzP9ojVDwZ9uO2iJ/jc/S5TqHFhhYLRiJ23G/wZxb56r2dNCLy890hfUU4zr3Y5Bii3BJadIFz+uCU5wpv0I9qhXPRbHDebgvufnx8hGiKfrss5M5BWcIIVhuuSNZ+SyXXhmhELBSGTWoHjSz/LiIEQzjsw2eP+PmxEJ5D2ePLVy5hR5LtJ7tcZ+5wBCOdDdxCtOyIgDsSjyE1ReSMVwiL4zRLsULRIDJFjsfSCktSjGe7vUGUVLLhForTIKwunB3ApLLkokjDC0WOVWrRi7U19mFvX8TfQitVWjUkVF5oCCKMJbBhc1hhVXliWmiNqYTKeHJF/NEf8fzw+AlL8MYj2onZ7YFZZbYdG/1idWiBXUISTc3bgo/+6kpsAZaySK89XLOlHPtuGQ4vkKprl2hfI83RnU/vYmnJkDRWGY4QjaeZc4yPaU/QGKW3+A6R/hmgc8CByjTmlrnWe4URrvr4fZEk/bFNZ+bTWgUDK8doFBd5rZArrVYH0Qj9/taLNADA5xTFQWIK2/v0eYmkQWrK5zzcfIi4EGE2oVCKxR1Ewv6/97OwUFu2UQtmITfMcyphEWp9aiWFvoRJWtonjh7znTEyYZCTVPMU19fOPu2kO2XF1BTtwTibAHyvu3d+gY5QEzVVF/SF3BwEHTCJvPeyyKQjOmP650N47z79/so61+KZyi/9IScGKnt7rPJNf5HvMreagklnn2+tETV2eHAChxBB4zbt13zC9Xnt0gbSQtnNREQPIHh2rzrAnIt+2CTE2WGO55+w3YqwznFQiaBLZ9mte1u4cpl3kiWhDEIi6y5WqvAIJl2UHR8f8t6zdnMWL46Falqi7QRkJ4PBAA8eEG03t0x0x8cPuc9cunYDdZVTloXqKUgQp1SoX5x9vEKRO0cD1ETQPa1a+nCCY7wci8OaEQLsKs87H935EAAwjASwus41dCCS/7zOToVCFg4h47o6ezk1t3BY8WCTfj4e5VoYi1Jh/3APqZkUACAiYQKHELnz8SXsC7Xyiwcf6x045s64Cxkhs5IaW5tgdOXGMeza28JCiEdVBneaPcecxCIaNQkBNfqAyuMcJa7rmsrEbVM+HKns7I1VInbcIicu26vIZPi7We1Zbt1vO/Um5iXeYJDFN+qc79TCAqxCGxlzYlKlScDjRFJIGg0RTKJiSMQScFhU3pnlWjjXHvva7ddxqLLI9Db3aWfgl6IVwYCQiUXNe4x9+9prb6MkOfh2g2u3rcqgw0oTY1GptFUi3B4PUGnzGa0mD5EOIeMsngDuPiBa69Isz1Vhzc2oUoXFwj4URF3hsHKsi9UW7EIGWt3cl07Ouc6SlggSGscHj3menRLKKRmfwbnG1KYxbqs032Y1YWqW8+xVObZXCE+HzY5Zlb8WS6p8ksDE5mEaOe1/XfD9Nq7yzNvv9ZAXYu8P8f/fJgihSZu0SZu0SZu0SZu0SZu0SZu0SZu0SZu037D2a4EQylcYGZsNhdAoMwL3ww9+CACIJhkNc9jtF1wXfWXcy6rna3c7cIrEsS7584C4X8amMXWlAQQC/FmuoFq/eAzJJFEd1SafeW+TUcKQ04uc6h8hSVEIdZOIR42EOXaO0gCAeEJSkm43NsSh4AkxotwSKe0XXrl+wQ+y9l//V/ydsvKWsQ3nIvG1WBlpbLVaWFlKAQAeSpp4rHrW9nB4waGiACEcVqPetoeOuH/icWaRjGzOeDy+IL4MCmVgoEWGY8At4q+mMrFJ1Ym6HS6YhC4ICHU0u8q60k9ffIaWIrMuN2OMIfHQlLJ7gMgqC5Xu3xvPSHz6gsBv7yEj3t40+/3GG4v4/l8zg+2bYwan1Oog7OXnP32gzHuW77Cf3sHQxCjz1SlmBzIZ2tX6xkswm1XP/yklNX/yn3/C8Vl6Bcc7QpytGLK2qrd1DdFXdrdekDy7k++1uXWKmzf5/ovrynK5GUUudcc4OWI/3SLCHIlszuN0oSsJ35FswS1yWH/QfyFv3JaMcbHKd+qNO3DaJU/a5XzZ/ByXxHQEJrtByMzxMJssKAtl4BdaYKws6ur6PBKS2x2oD70u5/bawiqqs/z8X935GwDAoV8SwoUunOKgmJ7nODRbzOiOxzbYDbsSF4XHRTvOlxuwucQLIjJUQw5xOLZckJOP+uxfRWN93byGikgto/rer736JQDAe64WHt0nL8D4lGOU8zITuZPdQU5omZUYs2ub6dML4sANZfM3d7mm1q5tIK/If0XytG+8SWnzZw/TF1wcFqEbvvAGsy33HjxEIc/xy59zjEyShe/YBnArs+kWEWWhyui91WSB2ywInUiY7XpPl90Gu54REN9BRISUM9MxeDx8pkN10S2rpGRDbogqA9ltIiSwbqDionAIWdh0si+H+Sy+Jz6XsJARSyI+v75wCTuSXc1oLhxCbzWKZ5hSX8eSrDWyCq9ev4xSjlmPmjgXynW+g8Vmxp54QQ5lxyf7/I75ths3vsLMyct/SCLn9HeYQZtpruPOiw84jouq0/cbSLky1lIiIla28F6Ag1Atl/AEtIvzTb7DV0Vc9cZXXsOLHsfIyLyY6jXMip/p8RYzt04/x/pSbAVdIQd6HZGLyk92K3Ucuzh+/+VXfod9ePQRx91rxu2X6Is+/5zokN/91rcAAH/+n76HV68ze+QO8t8BxN9xXIDTIo4ig99CxIjjUQuLiyl+fsj3ufMZ5/HKrTXYR/RdNSEJHG5tVJEkiuLhqQhpUdC6ydZO0VJGdGmd/Ap+E989spxA38a+nJfkA2EGhGo4fM6M3oLQSsf5Ewy0T5+lmTHOiVjyC1/5IuKzQnLt0rfPCEXr9R/BJH6QTJ/ohk7XQPeVcSwC/p6IsJeW6DO+9+0/xfQCbaC9xnXd7dIGh802Zrzc//yrIumWsfbGJdgCtMPpAMe/VaJ9bkyv44Wywqthor4skgKeW5pFTpns6Riz3TG3C5eTnOdHPyPvhqOufXo8wFBrffeQ6NaWfODpSQ5uyVKLdxrNIefP47Gg0OT3eGriH5gRb099gK74BiF+ip0jzmWn3obDyvG3CEmWmOW7+0NhfHSPtmLIR9vi3C9KvSZErYOqzgM9kUUUGxmEhSKeV4a6VqW/RKeJ6Qj31hsbHIPnewcodTgHDYN3Z8qQhi7DJKe3J26kpvjbQiHLRQY8k+G+4nHzneuVKgZDjkdZ5Kdju/astXn0xWVVL3E83WaOi2XUhVMo5LVL7MNJhnZldw9QbYmfSbyKM5LmbZVsiOm9PB7aWjRM//Dd/+e7uG9+X303SEY5ntdTl9G3CL0lF1/Kj2Af02dZhMhtCLk6H0viKENfNxD64sbLRIecnpygqr2nUecYGWjTaHIKQfntn/2cBO0WZe5ffvkWsnauvW1lnZsW9q94eHYhh20K872ysq+T+/vYfMyfrYiIPDEnvqeAG/tprlmLzho9nfks/RHcXvZlrMx5Q1wu551zRP0cx4rIayMh7uXD/gD1MufQ7RL/o82Ck0MDkc8xc4hnr5CpIVekv5kWB1A4wvn65P4jWCVxLaoo7IlH7axQwZnEOb5xizxyfZ2PC6UmhuKkaoubbag59btdmBfKOqR/Hzxhpt8zDKGmZ3iEEm5rTwjU/QjorHB4xDVv8Lv02iNUxDdjEeGxRWu51WqhKmSyucuzmzPgg1MVDsf7HP9joeim56axHKXvevqESLqk5q1fH2AgXrNYQshX0Y7Vajm4DbEISY0X+rQBhwXYO+I5yiDzTovweGyxoi80sEsIjsiY58jdoxOMRDLc1OfnUrS5arWMUonvc/UG+1dp8Tn373yOhPaAK+s8T//iLkn7RwMbSmWua5ebazddO0Fqmvbk1x2j3OR661vHqIjfymUnavbKDaLGT86zcJjE19in/ZaHfHY8tYCQkNfHWotlCYhcWljAwSFt7ly8PREzv9fuceBcQiGzEdpH4Yz/Xyr04Re/Zl3o+GBAfmQphROhWA1uqq7Q2rOLYQx6/JxTfHKR6JyeWURe8vF1IbB9oShyQrG5mxxTk3hpYXJiYZ7roy60rXEvC/o9cPmMsyTHI+TjGDtHQ4xFih4UcfFYyKlAMIyxyMldsvG2/Lff7cJQl9G2zvJ9zfP2/ikcHq7nZJjzV87RH9zN37sgZgqIu3YoJKppbEZJyCCnieedzD7PUo16H1WhAnva2EttfS8AtxHd0Bk7V60gqKqKYpX+Kekx5NldaAilX6lxrAqypd7QgkyWfQ2EhC6tsk9+nxtj+W3xpqMuJJ8l18OCkNZNVUEYHD+7pwU4xefklh8o6PxxeXUetUZFXed73bhC1J25Y0ZBlUsGEjoc5/6+cv0K4nX+3bHIsr26a5f6LQjU+yvbBCE0aZM2aZM2aZM2aZM2aZM2aZM2aZM2aZP2G9Z+LRBCVSGE3r68AfQY+dw7TQMA0qpRnfJHIYodmBVptQuJ0Kr00Wwz4peYZcTcCA/WBi1Yhvz8+R6j6KMRI3MxXxBNZfv2sowMF5TF7nV68Coyb8hk5j5l5jhzfILFFUa1L4n3oaBM+u7uLmYlGWwRD8/cAiOi9WoNVXHsGHwucdW/PnvxApEQsywjsY6f5w6Ru8vnHqom3h0wlIGKiMWYPbN2lIHcZlbI7nXj8tV5vSsjk8kpyUSGvMgcCWGi2mK3OIXqjTYqFX5+Qe8QUcR2bDUjr4zI879hFvSowrFaWr4Gn4sZEOgfLxgNvnn1GvaeMQtnVTYTNkaRw3EnjvbFli/VtfQuvz8+dYa1NfFm7Eqafs0Gt9AS0yuSMi0KJTbq4K0vUeFi6znn8viEmRRfyAGbm5mu5Dzn7YmUdl68/wFmVzkHbXE3rV9j5jd1nkHjTPLxyhCHhAYIbIRhVxZ/LGl0nzJEz54/RzjKzNf8POdhX0oATx6W8M13X9H7M/p+XGRGLFvIIZ7kz0aKwq9fZn+bnTKiUvZpZgy+A9WJzi2jGOK6KUlZrFTM43d/j3xcrT7fwajVtlttGI35s7llZvaTZo5rtDjGRzvkdPjHf/BNAMDZKufmgz/5D5hepD0cS7HBJ+RO9rgAh8ExMDI4hNi/qbgP9Q7fpy5FPKtFvFWjEVySv6wLxfZsh2pL8bgX5QLncvfeTwEAa9eJzhm88hUUC6pjPyLvjCHDu1csIrnCPo8djMhfe/UqTvaZBXuxqQyYbK5bdiDuZgQ+rezDwxERAol4Crlzjtv6RgoAYBHisFwpIBRmZsgs2dFaTip53RLsUn/yCTnWbatm2GqB1Uz7tZqVcXPymT6fGbPTXOMhZeGCftrS0DRGR/KaAuvhpEL/6HR4YJFUWR/89+Y6+at2Hj+5QOPcfoPSvIe1AlaVTTw747g8l8qV3XoMj7KzvSrnOaDMuc3bg0v+aUe8aTNB+orp4zacdmagMi6pMUTEqzUuo7dN/xlUtnRLWdusc4xv/+Q/AgDii/z7kJcvuFl9AgcfgZDQdpldzsdXv/z7cEuVpVCknbx9je9cyXbx2S79/fQGbfbgKf1jyh6C1U3brIjLplKp4mSXiDOzfMyZlPg8YwuuXmb2smiTAmHJ8H2zOJb/PX+P6MPf+hr78GDvPo4fEnH6kleqD0JJBsLBC5WgyzeJQrn7gD7JbHIilJTi4Yj2NLfETJMjPEJHmflTSXQnxTGzcWkBjaK4MmrcL7ojjnGh1oZtILWrl4hAGCur9spqAkNl9HI5vnNC318ol5CQpHxvmf2sVuroSYY1k6ft7B5wTXkcZhRq9PcR7cVFZdy8FjsefkCESk/cAn1xLphHA9SUrY7SVeI86fGsUgAAIABJREFUw+eMrGb04pJaljTxA8kt25IBNOWHs1XurZelqHd2dIiTA2bop2e4pjJC/cZjYVzZoG/NaC6Lbf6u51jF6k1mmMMxSewOJDvbqMImaXJvUPwDNhu+/Rf/ge8jW7DIVuvDHqwevuPqdfIiPrzLdZacv4zdfWb7HMpshqWQNGy2YBKKslBhhnpGnIljkwW9EdflbErqf+IOAAaoKBtcPOeaWJ5NcayGbiStXF+B8d/nEQz7g3D0aQ/xBPcs1zrH+m8/+RyLC7SBiBQgz064R+ztNy5khDNSwnL22mgJDdk1ixdBKm3omXFwxM8ZGct8i+Npd9eQ26fdHkuy+vpV8nGV+xXsyd94xYFlF5/XznYRX/8aEZ2HoB3WdaZsVQYXSLAzqWk1m+xLv9O4UP0y+PwKBX6/x5rESGRlT8X/YLcS+TCfmEJPctN9wUfvvqDC1RhDJKVOey6elVyph9uXpPIqfsOakPD10QBLV+hb7t8nr+Sh9qCw24eOUAw97fX9Lv/fYh2iJP7KeJzny7E2BZvZhGWpK509lypiS2dmtwuXNzi/V19KAQAG4rD46LQCiLfks8M0AOCKZIw750ewihikX9Y+pj3v0uplHJ5rrSod39A5fn/rDAPNcyRB297cpz2vrC6hnpca1yznpNWqXiBkR9ojvUHO0d3Hz3DlGrmyOjb5b51tli6v41R+0SyOylduvAkA+OnHn8ApJPRCTJLqI372ww9+jEGf52i7kx2tqVpgdn4BWxqHN7/Es1BE9lnLV+EPC7kkLk+zi+/SMrdwfEw7DKjv2TOpLTX68Hjprw0ETa1R1e8asIubrSNetPTxEWbC7PM7t4luSq1JrTBXQU7y8X4H5/ue1CKbgwbyVfrmqw4+y6bzY6aSA8QtZ9UdB+JhNJutsAjltbrCNZ85lvqd1YnyFp/v9wthLjW645MzRKdp9wWhJ5ziZ4l5AyhrP/rsM57V3rrJc0jMH0SpJsS8Wfw/Ov+0+yOYhArptDnGvlgMK5IkN9Rez8THs7qyDquN3/nZoycAgKLWcCgcwPNn9FkeOz8TXeC4FksZ9HRXmFVFytx1rsnCThpx3YVOsvQfA3HmrK+toJwRp4y4jnySqz9rVeEQt9e07mkRnQO9Ziueap89k4+Ip3gOL1bqmJ/j/J6cCJk1ou3EwjEciBPI4KYZjIGEFLk8QY5VV+j4s3wW3ineV6zyv+cFzlc06Edda8BAV/pUXdAql+GRYqdpSFsw6/8x7qMpFJuOABgKGYb6GLPTHNN92eFQinMOTwDdMcdtNsK+N3XuPzg4QFDf3REi0kCNDkeATVyyxp5aLNCnZYs1mMVV1hVfUGdgKIZG4fcaPoVnNrPdfoHCcfu4rx8c8xxhMplgd/LzFa1/h86prVoHraYQ4n3dV+ULh/02IARwT/eP1Azt5eWrly6UI69K0TEjzsSD41PY7LSZ2Wn6g0SCfzcedWEVB9CM0FQu7QNXL1/FnrjOShqH4Zi/y1fPEQnSFlYWuSfXxV0ZDoaRFjfdr2q/FgGhhbkUACDoDePxvQ8BAEkR3Xk9NGhLs4+YZGEHOsxvi8TspZXLF0S9BnyqIgdSDYRwdYObyPPHhCK2R0bA5fyCRNm4YLUkFZiIhhFRydeLXTqSsRb46koKThEuntY4KXYRJHbbLbzyOg8x+9pMnuzzYtJpdBAStNYsp3SY5WIPz02hoXK3LRGwVZsthOdoEHYFXF7s0IA7neZFOYFTiz2V4pi5Qr+Uyr5xi4djq+DVtXoRIQWeaiUaslnwPOvYdEH6mZZ07VURQJo8bpQM2KCIKf/VvycZ84J5Hv/0j/8pACC0oEWo8+lOoYL9cx56zVY6i1sz/Nc078XjB3R6Q8nxjTqCgRbGWLnE8c9nuNht7hYKVRFvD7jQ2oK1Ll67hKou3PvHgpuHNCcoYVdQ0FuL3IjSIt6FrwlvWGUMD3l58IfoZHqDIqqScr15g2O7+4yLajCyo6VDZyjO/g1UVlJttnD1Gh3AQAfNfZVIfPndRdy6Sbnpj+8yyLF4mZtBKxhEAzwMJpyco9UFPufzz55gTuVPbRFFujRvK7Fp+FN8nZokzq9duwaT5jKvi+0v2whmORVbljZw0uJaGrj8OBtwHPcO2Ocz3i/wlVdfR1alDWMRbn72KQ/CEV8AsSAd9pnKOwIi0RuZrRhpHHrCVQ4E9eyaTWh0+buRLokjOdiPHtyBV8TFC4Ixl8P8joVQGF/fYGDtX37OAGVWJRJvvXwdHxzxED+y8t3HIyt6CrLZBK2fitIOk5EpeHWg+sY/+iIA4NM9rkELRnj9FgMnOcm47+5yzfb7JvgVPGiBdjGtgKG920OzKiLELt9rJqYShIQfjb5KKrx02Mk4P9tvtTGV5H+//BLXbrPFi+7+0Q5GI76DIa3blN/b3nmGq4u8dIwEMS4c8d1jZh/uPOdBrJfnhvSNf/4/4MX//i8BAL91hRfVKu/7+P6dX6B1wHdcCvKQF1DwuGIHnH6uD4fgzsUy+5BP72J6kcHVt77EdfY4zctvOLKI/g7XZemUz65L9jU2NwOzCA0N8t+S5E4PsybEtekfPeHf/4s//BfqkwdBHYKqvhQA4H/93/4Vv+/WNZhtOgCf0rZDOhxmygV4zPQjeZUXHTdKqIqsPSxW6ZTgvq29c0SHIqF20g87oyr7OT2HTfze/STtKSup7CP38IJod9HGvyva6eN704A7z+/J5PleN24xUAOLE2dZrr1Kg31Kb9KeU0tJRPy01bzII0MB/n96dwfNgkrM+vRbbp/KUi0uLC0y6PlY+9lBhj5wJphH/hnX10AkjS+9zqDW9FoKkZaCRZKWX1tOYVNz3u/w+xplOonbL7+GZksS3AXOTVYX9+988hn6CiS/9TYlXo1yzPO9A4TmFEDa53zNKDDRt/QQlDRzvcv14lM56kpq8UKEISdp12qB/29xWDCAZNzlpwwSz/VWH7fnaPA/bfAi7pLtvth/ikCInzuvcd+IqsxlVG/gNY1jsaNAl9+HuWnaw4EOgDZBy+1eH77/Hsmru2mRB8+n9O4mtPqcw8QUL4era1zDTx5+ipGg6Cu6ABqA7ma3DnFc4qDAPSscDOmdnSjnOEYdB9dU3WqUNzYx0rg92+U+E/FJ2rjaxYt7PB8tTnPcF1/he/qCATwRAa5T0P5p4+C/tYNWm/NVKmkecidommlHo5KCTHHusbOhOBwj2sysDs4mkYzOzMYxUGBsIck+5FVWMD83h6l5njteKLHX6fL7+jUPvv1tBuTeeIWBvJHKKY/OynAGaGRmvftYRUWteh+lM/qbK1cU9NQZIpvewdUN2sfqkr5Xa8Q9fQ3VLm36ucpjjNKb126/grzs70RJQpffhU8+ILHs1cs8ixqXtsHICXuf87SqQOa5hDye7W5jQQkls5ItXo8SKYMO/B7+XUaXmqCfNnC4/QJXRAh8SQnEhydc67awFy6JPTgOtT/oYrYSCMOi8sthir7pjoJUwVgQYZWZJ3UhD0pIodDowpOgQfYU3FudpX3MdoZ4+IzzFVYyLjDPdebyuGHt0Ke0mvx3aG7CoqBIU+eCQY025Ag74JsyApo6P2+n+exYEu98gQGgF0+5Jpq6dP9P//APcPcFAwTWHue3qnKQly+voj2kPeQUyPDqfJw+z8KuJF/6KUmQvartzJZyGPW45sMiT+6oNOj8JIdgirZzJcxE7LGTF/lssYKQSM2T0xzrfIHzvX+wg40NrnWzst/WkRND0TZcf4Vy2L5Drok//3ffR0+k0l0752vr6FR9CsIZ4prrm1SG6hL5bbOLUtEoVeXmtRhjX7qNFrp5jtHBmYRQ9JmRuYPXVpgAf/CE4/m1t0ja//joBcwqsR5LDCMvH2NqtLC8ngIA1LTfPrhzBwAwn1qCTYJAJ7qwrizQjgdDwKSEQUHlhtlCGwURl9sj7NfhGdegqQOsyKfvS4ylo/Lo3doBrkxzLg5UahMYcZ1GQ274RcVhUFicC4iAVh9eBSKSIk4vqZznMJ25SJjvHHPc40Ge8V7buIrtLdpMV3ex4xLHwzyywSOaiKSCDu0e75aZ83NklFR0qVTySPt0MBxDNM6xqaps1uF1Iqag2VDJzy999S0AQOXkHJ9/znPf7Xe4Npo1rlnHaICukiNulS7ZFeSY31jFuXxKQgm9vEr+2p0mBkoeQfPW6XGdJhKxi4CTXYHGQpl2FgwE4L0oU+S4N5UAjk7PYDASBYKoSmISdzrePUDAy/WSK3NcukpI9jwutEf8brPujwHVRfbKRWQlzx5WUCUyMwOL7kyGKMVQd4bR2IKh/OJxnvM7N69yc4sXrQzHaqR5iki8Yzrph1mS8LWWSO0VqK9kMnALSHCqgLBF5eI+t+dCRKpdV0DZJnLueBSzClxPKSEVVMDRbR3Bpb31xlX6091znh/DyRDaCmaFdJ4OSZTgrFpHLMRx+FVtUjI2aZM2aZM2aZM2aZM2aZM2aZM2aZM2aZP2G9Z+LRBCUWW3Pvj4EzQkG99XFt9hlDMVy3CKANQg9nR2GHFsZaowK9P4eJMRUQNhNBdKoCByy7pINAMiMRuZTagITuZUxPC1a8wwRfxRbCrCa0SEv/gWo6yNfAnhBCPDATujl09FRHrl1hXk2yKza/D7CoKZBex+FCWTGVTUMr7ISOh++gAVRWFnBANHroBAlN9zIhjxuUqCVpfnsLTKCGZFZJ9mMBoYiAwwkIRvS7KS6Er+eXUJ937OfgUVzS4rojkej9EXHNIgLOwqS5M+P0db2ZVbyhJ0lMl5+GQTl+4wa/T7idcBAKY6I6nehBOJG3yH001GSaNPOMfrqVexuJACANxRiaDXLnjmQQ0rlzg3HkH/RsM2tnbZh5deYcR0ocV/D0534XXxHW++yjnMnDGL4fJYsZgUyaSV0e2naY7nS28k4IsxapsvcGx3JEs5tRBEucTIvy3ATPO114kWKTcG+PDjewAAZ1x/r0y1Mz6FRo/jfl5gpuHaDWb/rpkD+D/+l28DAN79Bvt5cipi1tgCHr/gXLinRaAmmPzZ2THWpplZm5Ps8aLgrYlQAH/yF/8GABAMMqJcrZYxNUWUhpFhqFREuO73Y6RykcO0YPgifR5OxZGQBPjHOY5Du8Lo++FZA04h1rJtSWarzGDQH8OltWqVtnMlT7ucXdjAVJxrbhNCiQi11DdbUde4wSe5zQizGPtHm1hY4ztvKkIPC7NBPUsXP8twft1+zntPUOdyoQ9rm2M0vcoMa+E8C7MQKQIsoNVhRursuAinstZrMfoUt9AlDscYJpV1ra+lAAAvduhP5ubmYBZKz+UT0lBErG7HEC0R1wXczGq++RptxxuP4Pk+kTM+i5Hx4TNjIT+SIdp7UATtHskRD62LyAl6vXueBgC8dIskwMP2GEVlpONrzJKlN2lXtnoTHcHGb94mqqpQLyH3gPP7T66x3CL5BhEq//YHP4DZK9nPOSGe/PLHFRv+P/beK0jSLL0OO+m9qcysNOV9VVdXezN+dmbdzC4ALogFJAYkQgQhiiKpEPQiKRSiXhQKvYgRCoWCDEqIUIgAJAGEX2Cxs7szu+OnZ7qnfXlvMyu990YP52Qp+LJ63YjN+1LdVWnuf+93v3vv953vnCdbRJhMh/k9bRHl1Ze82MvSt1xWxicscsZOyYxiVSgbZbInhcw73T1AVuU+0/KHDmVp35qfQsegEgWV0Lz/IWXM02vbePElopseCmVwZGCGZGlyAZtHgvfnBekF27u7e5hs08+UVFJlCw6h2GYfwiLB9zrYz0rjHE/vEaETlORncIZ2aV/woVGiTdcMzBSt7XPfGPMEkYvQHj6IH7BfQrxOtCwwRbl3mEQeeSxCymbbgGKfOF4E/n3RhEQ6jYlx+X0RCH75iH4r7Atg2MPnMoqgeWNVBJ8TE9jOcA25hoke+Ooy5/sH3/shVne4p75yi2v/TKhAc7yLvcck9XYJSXDl9lV8/9H7AICOSrNjgjvX63W0DSot0TNcv8v9Ijoxhg8/ExF8jPblE3rlum8WZ0d8/vAd2rTTzs9effYQiQ2iM37977J044c/+gEA4Mn+faxcoQ0cpDjfv/oKicm3t3cxNUL0ZeqUftVuE+p3+wD1Ekv0+qUAU9O054WJCTSEaLxylWv2cJPjaIEDuw+IQGir7Mc8HkVF5bglycgX2lx71++8hMsOIp+CKqOO5w8AAN5QCDfvEBFQE7LzRMTTDosRDq2TeJK+4Z9+93cAAB9/8SGckkle2+MzNFSW12kAhSLXSzAgImehQU29DqqC4rsitMOqMvAnyTTqEfrdcoT9PBd6uVyp4TjOdT07x/6uaj5qlRJqIk+OSCr3tNvGuOSHq0LxeJSxPE/n0JTIho5zmA9ybx6JRJA9U7lJXwJZ41IvFxERAnqkRjRDv73/4+eYmWKf+5D8Z084/n5vEENQaX2fUbzLZw75QnALYdHXDQkNMys/4ffAqjKjiTHuIXatqVTViN0d2qoZgv3H+OwelwfFLG3n9lWi7HwhCwpZ2t9JlnPpE+qgXGzBYeO6Os/Tnjo2SUNbOljW3hFW6cc77/0tAGByZvSi1HcsxvHoizIYGx38/kdEJP3Gm3+X75cceQZNxNv0hP4cfaVBqNucw4qVIY6jU+Srz9ZY8np2koBHhLQVlWq726JesLvhUGlZtirCbxHdLi1cwZDQkY/XeUaenOQ8OmFBTs9aFAO3yWZDLkeE25DQCTsHBxxbv/+C3PW5kEs1nVedZ2lkRSJdyNAGhGNAu1mAS+t5WWjsSlUE7Y0aakK0JO7RxrMigDUbzJgc59znmlzXGSFPxiKjMEuCPqeSJzSFujYOAxWOVb5Jey4ItTcxNg6rxurxQ6KQF+Z5dp4ZG4NXZSTNFMcvm81j3MMz1/EHjwAATz4muqYzOoSwEGfRNm3b7ef8JbMZ1CQscLglEuoobSgWmMDmOv3Z0TH38q7QhMMBP2q671iEroyqTHRmdhpOu0rv5jg3falscxfwCD01NTUFANht0+ba1fqFoIdJSLfYBO845VoRXp9KZi7uLELLpJJwShxoJMjxHBuJIr7LtV1WLaK/Pw+1IvZVJtdzc6220xyDxdkxjITpD+tCsDYanLe5qUs4O+BZ6OkT+rUl3ScDFifS2jfDQrDqaINUKoW20LN1lV02dc9aiMwh5KY/PdBZrStEjdvtQkvVBS0hOnxC/Trn5tARujej8/NQhLbq93nQ6vJvc3M8h+QLGZgk8BBvcd6cOk+4fTYciAD+0Ze8F999gT7JXWuhKTl3X4j9PM/Q/h+tPsC0fF7pAs0jQZZLy0gZacv7CaGIxmkL5UYZIyP8d0Pk0v4o94G9s1PkTunfliM8vyTOOVevf+1raGvtbBwIqayznsUANPtiM7I1g5A1dUMXTa1yv7NfskefVDhLoizxk2qpX+afg0P+pi2E5tUVrp/zRBbnKrUui9T7TAhDm8WOusrqHKq0SatawGlpoyeE4dg0fcvUCu96z+8/RFbCVyWthaDucNVqBQb5pJoI5C0qQXU7XCjlaReFFO+pVgmObK69g7bs9prO8tcCPLN1jHYcCNFcUWnxkO7xQaMFbeP/f7hngBAatEEbtEEbtEEbtEEbtEEbtEEbtEEbtEH7BWs/FwihR48Y7c/n83j1RRIDm0R4VRVnQHBmFJ8+79cBs9tDinAeb69iRNxBZ5K4tfiYlXD4HVh7xizH3LiIOU2qdXVaUFf2f9jACLlLBNSFxDk6ilJGReDVrxE2Groo5Bk93FNEuiVip3QlhZQyxmWzyJ5EOmcyO+AQ8atJNYEVkeK5gg4EJWuaFZIpMh1FWRwDMIlPYG6K4xH0XJAEN0U8enLCaOJ3r7+Ndz9gVtdXZuT58hVmacNhK0wmZlJqfdlMSUrn2110RGaVEtmkdZQRzUhsBJ8/YNap6GMkemmZCJe/+ugd+MWfkThR3byyaV5rG5PzjOBviVeo4xHvydMDTPfrykMc//ffYYS41XSiB47bxAz71+lFYeiT1Tr5N4hwzGUPotFk1u7giBl6r7fPjTSMAIcB5yI0XFpmVNzpdeI0wTnsiEcqpEyp1VtHtc3scVWk5Q6T5GlnxvGGm9FlKBNeENosEPGhpuyq0crIt3+ItvrRJw+xsMTPv32Hme3PnjHz00MHoiGCUZwlcRF1BqNhdIRmsDOhgsMsszs/eP8vAGUzXxB/lclgRFqIs37cN6g10q41cR5ntN4+xAh+VHXijXgCt/W6J5LULBQ535HxWeyn+e/9Y47Z4gKj4kaLAU83yBXgcfD5AkFmMU6O0picEGG6oHz9Wt58rQazsgMNZYbiyvCZDT04QlzHH2wf8Hv3+TPTzMN6nZ8/EmIGzaTPsUaGsezkcxmNHBd/IHRRN/xM9e85cfO8fP0GTsVz0JJM8lma31MuFdBVRjUgwrbZeSIJyo0iqpqwK8vkhdre2NTntOFQXf+In8YX83PNOwJ2rG4rQyQZ4eEAs0CWRhtFocK6Y8z8ekWoe5qto2sSN0xAqLQ4fc3l5SVsbyjb15fddtBnPHq0in/4m9/luIHfu/7kPuwR9ieR5FwGTujnfvP1N7AryeucslRP9+lDV8LTKAkZYYjJRpNcU41uEy9c4zj0CW2PdpmJNDZ9SOzyueZVH/3Lb30dADD6q7+C3/urPwQAbIh8fcnAZ842nsIgVMPVWWZeDEKGNkecGH2TKDhDhn+zrgpN8fZ3kd1lHw5F3BseZdbL2qlfSN0uSw47kUnhphAzJmVbP/wR5ePvLF/F4Rp9ikU8QydFZc67VTiETPOYucaDQX7mk08+w9wb7N/KK98AANSF2sqfnuPFN74CADg+ISokLxLlYq0Fi0i1E/IjsdAUAOD2jWv44jFtNVuhD7SZuXaTJ2dYkq+c0F5XlcRuvlVDO8W58EtqtZTjHNs9Dlx/k76saRbJsxBix8kDHMWZCfx7v/HrfOZCCgEr/z69wPdVtDc+e7YBt1eCArPcO9xB2vbo7BRurvA5+siD8xOul6OdPYQDktF+l4TuN27QlyWOzy/q7b98TBuPp7l3Tc5OwBvh+n/xq+Sz+Ot3fszxOExiZYEcbFKURtKmjF2jckHmuhBhn3pCjuxv7+LFV3kOefqEvvn0lOtgd/0I6Tj9wUyI437dGcQLL5ETqfuMfa+1xbdndGBlghm8lkgnT4T4qVuLCGtt53Xe6cjWp8cnERIRdjzPNfHwAXmQzo/iaB8LGSG/3W7x/8V8EUM++ql6nZ8Vs9P/mDpAzMe5GBI/4tkZ12QwGsHordt6VmXZdfZqt5qYFO/RiZCaPZFap5JJrFwRKk280YFhJ6JCUFvN9GFPRK6eyxXQUNY5WKIv60vZf/rBR7g6LxSl+O98yuhaDcDJEdeO2Si5bol23Lwzc8Fn00c7X3uBfujxo+doCuHq6XAczFZ+9tXLU7ALvbK7wz2hXeFnBhxO7AuZ4jTwGSKSZW43ihh2crxHJE1fF5L6yadf4IWXyfUSENLq0dpDrNziWem+RELadtpHMBDBOz8ml+DVq+xzt0dbGHK5cKrxLuis5hZyp2s0wydOiKQ4h4a9QoBEx/GtMdrc8yeremau13YhC5+XvsGj97/7Kc91TY8Fdu0Paw9FwCui1Wt3vokvv6T9nYhA/vYi56phtCAU5WfNxYh2iYuI+LN337/wUwGhj8o6V7RtZpiFALEb+P5EIgmTlc8R0nnYIL6gYrGEpWVybPWEUv/gKdenLzgMa4Xz3JANeCVPnutWEFLFgVnoxb6Uda5YhFFcTEtz9BUhcfvs7+zCrqx6S32fEWq0XW7AKOLRoDjcghKpuDl/6YJv0KXzYlMcU7nkKYwlvi4mxIpfyCu30QgraH93L/OMYfQfwFnj77z6W32Er28Puy9QLuVzjrFFqMpex4y6+JwS51zHSf2MhwsoC8FvkU2fVrmeM+dZOOw6u4urMiPkfMVuRXGV6+Rbb30TALArlLHFYULqSEh0oSQr4u+5e+cOdreJZLRp7caFCPb5fCg2xJkjifNghD87PQPs4kBpiXMyELWgKal7ow7sMcm7n6WTOM5zz3cIgXdN6KtXXryM3QP278YN2u2zh/TVG6urcIg3dHyZNgfNe7ZWvBBlccqO+4IvQy7PxZk1ooqPPg/N82dPYHbxWWMxnqvsLn7Hvc++hFU+eVLiRPEk+10s52GRf5JKOxwSzrBYm1ieF7euEP1P4tvoCUU8Id6w1CnvgWP2IfyX/8U/AwD8wff+jK8RF97SSgSfP+RcJgqcX6PumKFoEF3xCQ2JI9Mgrl2jxYyxEY5318bXZ2pCuJh6MJo5Huksx/pEtmP2emFXpccbr7GKpK1Kn3q5gpwqX5xCNHWEao34vTgt0TY74u/pq6d7TFY4VI3Q0mflRb5sctjR0HzValwHNrMFZ7ojywwv+A7bLQM6bT6jQ2To/TNNpppDSHeMssbKKiTfaboKSESnZ+eaPxOK0GPzoCmO27L4h0/FJRsO+tGUT2+IAyoY5T64thMHxKXU6/YFM4iudLmMWLnMO1dZ54mG9tFWs4kjIVehOz2EtM1nc8hnBfv+Ge3nIiBkVTlDaGICX65xI7o0RqMzqLzjweYqairHMItkrip2c7/HD/FHoSvYYl8Ra3wiBPswNxi4OYnXRgWLN1ZwvM7JM4gyPeDnZaXWamNMEOi4IIPZnCBk7e7FpSGR4AT4xulYF1cWcXh8AAAolqWo1BUhlMeLRoq/a+qS6RPzvKULrD+lg7oyI0byehkOkQg6nJzgepXvGx0J48Gj9wAAc4t0ErdUPtFqGlEp8HWXLvNvKcHhPO5hGLTY2lKdMZilmIEuIDZ5qy6z/fK8Zr2BpIIIH+7yAPPt/+53AQBPfvIcbRuf8UwEWY0eD33LMxEURN72cVkqHUvccDc/P8DUoi4zFTrpBfZtAAAgAElEQVSQ6QVdBA+N6LW5gfmD7EPyyI5mj5uNXQcqm4XfGx0eRULkrJUKF63fTWe7vZbDTZG/tdQ/f5COcWJ8GqvPeCFbXOZ8Zwv8HK/PCYlxoaeDYk8Bns21VXQMHA+d+2BT6dLa/gGGtcG4PFLtEGSz2erhza+Q9K0tlaW2IKKtXgs+XQSdRtpFnxx96eoyLDpI+9T3zXVeDB/vPoBtiU7iXJtxLBZDUw50QyU+ly/zMGXsdi+UDFyCi5/s8LDtng7i8JiX345UH65M8vBVs5rQ0kVxUWVMlSrnaHx6FtEwN76H9+m8KmU68lBwColjHgCKBj6DVcGZYi6DCR1kOy3+bX+fh4y7CzGEY/QDmyKr+/EmN7G3X/sGrCKZz/bk6KTAcCkyjD9ap+KTRxBljzcAi4PO8fJNXjT7qjhHyQompxgMyJd0ADRz3NOFDNpVfsbcDMf4+JSX2P3dp7h6k5f6uiCe6YKgpP4OxgUxDpmkJidy+Yh1FkaDiLTB9+nuhtnxcbS1iW4+JsmrIyziV2sbNpEoWxRY29uUqk6hiLMcn6ddYdDCK9WPkWvjqIjs9pNdzu300hIWFUx5IKWozAYP18ndffhUenhVhNP1RR5Qm50gGgbadFYHuX5ZX7TUwqRU52bf5IX6scpxot4hzH+T4/6H/5Iku6a//H8AAL9j/DWMaPMNKQiWOKA9FqsVeHTx/kzqiMV9zvOv3X4djjPa+LcWGZgoPuL73v2f/gfYjLSnqSXa6lyUP8eMFphl2z/8wZ8CAHyRYZgrhDI/ukc/XNFBIrfQhVHqIXv7PFCYRWh56eUb2HiXazs/Rfs6PqT9eqeGUBNRoGuTBz63SHwTsQY+F9GxTb6sL4yQSCQxIUi4U5eFskpn/u0f/SlsQxzvSUHz46ccu+jYLPbO+N27Iu4eUXnG1Mootj/g2tmVMt2v/Pp/yGd35y/K1Y6P+P4Dqcn4nBaE5vgZH+7zUF+Kx3F3luNdPaS/6fg5HieJDK57OYcff8pET58APX6YwpUr9EFXxrjHmRrcR588XcPCTfrrIZU8fXpA++/4XBf7WWWd/ZuP8dmng2Gs6kJ93u4Hljme7kAIJ9rXm0ZF+6VCkk5nMD/NzwhNCjZ+LBJUkwWrG3xWT5j2nBc0fea119CUmueIAl4Grw3PT7i23/gmy8oLUvc4PDpDs8kLqk1nk5U5roOGtYdtzVdOQfD5EY6BqWtBrUB7cFhpM8/PpNxiNaDVUrl8ls83PUffFPSHL8rEz/Jc18k4///SnTso61BYPOBZZkkl2+lECvaylIakXhIY455pdwSxF+fzvPQySfe/uM/SP5fbAquNfdkX8b/FXkP1TBdhI8evT66ZtnpgkEDG+0/57AYnvycWjl4cSGtKQlwRye5ZsYgjqX21dBF36LJoc5Zxds41UKvRBrxKMH3126/g7Ih9z2bYp4AIv7vNOrYUNA6rLKZa5Gt3Nvfh1mWjkuc+ZtI+HR0dRrnDsTIrQHB+zrmyt6r4ZFUBHpeCW6NuVBTonpWiaVKXnPaQA3ffegMAkJES5JAIbjuFOrakHtdUwjE8JkWxQhUOF/valZLPuS5VbV8PgTB99OIllX6IMDkSjKChi/qTda75l6XQlq1m4VNpj0XB0kUl7A7PjhEQ6bpbgioFBSOsPhdyCs4NKdHpm+VFvJJMwKSbrUXKZ3MLUqU7PUJI5Spf3uf68YX8yGVEuXBCe5pSScpY0IuIi989IYk6c5hn+Vq7g5pI7+eH6H+2ldSoGVso6UyY1YWurou80Wq+KNcM6qLbJ1rPOd0XJT2LCwzyuXWhbhvbsOqyZpXCmkGB1PTGffSGVcIitc6XXmBAcPckjnMlFYLD/L5n2ueHXC5YlLzrai4tbeBY5/Scfi4ucy8ORaM4P+cZtKAyvq0z+qZy04CsLooeqQC6ROqbKGRg1KV3SApaV3SesNktF8qDVtl/rcRnef5oHy+L2Hr9AX3lo2cU9HjzK6/izvJ1jTHH3bHIsTpKnKGuvhv1PS3542KzBp9U184SfBa31NoCQxGcn9MWUhoPr7uGYS/XR1dlOEsqmXSZDKhLtMSqsp+oaDT2Hz9EtS+kYhLBvUQ+9s8OYdIZw6XfGUWJAEsbSwpSF+U74yccY1PPAJfOlP2yJIv4CM67VVhVo95T2dqSVF3nomOoKSFaaHGdGUVbUd09hk90BQGtjaYOh2dnWdTtoioRMfurt65j9Yx/d4piwKRSJKPNhM1NrqtJiSQgz/Hshl1YmeN55/n3eS6wKxFeaVQu5sumwEdIe3ijUUdFieaIyo7dXYm0xILInEiEQXeUmgr1fbEwllYY8C4oGFuXYEa71cTxrvYCr+qIHZw/V6kBg4JzwyJYtkhFN36eRkak1XYlQZryQ8ViGV0BOrw6k5oMhguy6svLM3oGnpHvf/EYCannmO3/bsLB43Oj3e3fkRV7EHDEaXMinaXdVtp8do9K6/0uICWRjlRNAiBKXrXNPUzM0DdXlMjeOtQZ0QQ4ZUeVCsdv4jL7O7c0itPMAQDAmpYit4fP1Ci2YDbQjgsS7/hCvuXFOzfhcSqY8jPaoGRs0AZt0AZt0AZt0AZt0AZt0AZt0AZt0AbtF6z9XCCE0iqb8Juc8PkZIUxJFtJjZ8zq7Zuv4fvv/QgA0PIxOjcpgk836miK4HhiilHYblbIBZcNJiFSLGW+JtNiFuT52jaSGUbwaj5Js7aZyfH5HMgqElptsg+zItxMH8YxrGzCS1cZqX2+wUjcR5++jzFJhXYEv3cL5nTW2IHPMQUAOE+JANfEDHo5t4erV4hSyDQEk3Z2cXyiTKUi0Un1PVuqY3SanwUDo4ghl6TAjz+Hf5xIp6Yyic2GSpiMNfh8jGA2Ksx6NgRT79UaKFgkkS2ocFFlXk5TDkdhyQg/E5zwjyk7j6YRzeSqxpERZdcYI68bjw5g9/HffZI/cUji1GlBpyXyVBHENc2MtI9eM+I4z6hvLMp5MJhziAxx3Jo1IRGU4Ug0NgCV9CwqW30oUll78Cp6VsEiPfyessr49p9sYEjEplBw+rTA7EevNARP3x4FqbVUmaUN+KaxtaNywYiI6xZpZ69dXUEyT9TRubLxRtn4ytwiHq5xTkc1R/2I+8tfv4aahd99csr3h7y0s4XYCLZ2iExJHCoTNk1bR7uEjx4yY/vNZcL+rWYLPhKJY7Mv5RuirebcWVSVPWupLK/a41iFux60RAiaVxYn1+G4DrndWHHxdzVlJS0eleUVjagpk902MZuwk+JY2XN1TIT5rO1jrkuHm+9bnlvC7iH7GVNZnalN28s20zCuq4ToGteUrcAsqiHYgV1lGW7Bn7eyyrq20rAFaB8zDn5vvpACelw7o7P8XaPCdWkzmxGYYOT+kUpZ9qvM+i0vrAAiyNvd45wYbf0MzhWEh5it+/E73wMADKuEw+BxoWzn++6tEXHy77/FsqFv//Jv4kf/1T9n3yVZ3ssxA5GqlC9gpltP6VNCgu2GosOw1DkXLZUOVJrMVqWepVCtqzzxKp/PZVV2vmzA0wpt9bzEcb89uYCSNL/f+ROWRqVv0g8kPWkUhZYICuW4MEK76kSK6CpLfSy0oktE7S9fGsfuT1mqCvV5XNBoQ7mJuojcb82KMPrXfwsA8Hvv/RVyKiOYMNDec1nalzE8j3Flxd66qhKiYdpANrONtoEorN/7P34fAJAX8tLotuLmItEFo5gCANTPaPPuyXF8esB53uXwYcxXwkiMY2u/KT+/ShvYWL+P4dv8rJyRz37dpPRfbh2BSZFHShBhyMz/1xJt3D+ibZ8JXRocoe380q0Xse2kPcbzXINuEQAuxEZwsHsAABhRKa5Dcvc/uXeARSPt/D/5x/8AAPDF/Y/4OfEURt1c44/v03ZWloV4jZ9heY5ZanOF33PZwazTwfn/jTPJ3PeczCY3VcJhNPbgCYk4Xoiw0fk5PE5yLkdG+VlhyZFPTE7Bo7KRPWUCn4tA/uHqBjwq2YhN01fWzfzMhRfmkc1yXn/rO78MANgR+vYnHz+HuUU7WlQpTPyIaJFivoBkVvuZi2tofI4+2+vwoFNnprNc4FqcGiUyqZisoNEVmjL0VQDA9iOO2d0bKxfSuMlD2kBISN5oxA67ifZ4dEL/Njy0hCE/x/29dz7lZwY5LlOxCIr7koK2cH+eu0T/ms7XEPRwTkwide2oFKvXqcFk5Hr0CvZdc/Yu3ufXntoTFL0oUYbp0XE83xU6zMK1VFa28PDhCYbkcBx22uhP3icqcGlhDjnJ5jolVX7/Ccff4+hhVPvgUItz+fo1okb/4M+fAxb2MzzRL4OfhtFBm7lxg1nhxn36QFfLi/ER2sdPf8DDhVFS7+v7Zxi6S7vbXOf8PlojAuHW3VuY0P6Q58thMimjXfVjNixUgc42p8dc2AuXF+C28W/r4Flrcpxz5W03EVNZTDHF/fnwhPvT5PQs9vaJrG2VOG/jIn71dAqwV0WGLOqAhgjp79yZxm6K/ax3RNrabKCeEbJ7vC9GQrssHR/A1OK45YV8nL5KxNrVF64C21yXZklQpzL0MfVq7YIkuN6g/1jfJaJmdHYGZRFO5+VbvCrjjHrDKPREQm/j83zxLpGK8Xgcu6M8T9mFths3iuzf0IXZoz1H9RandT5TytTAmJG2c/Cc82bQ1cJrNeGZkMzlJL9v6xNJ2btcyAREWBwZvnhOfx9R5KMN7SY4BsXjIuKbnHynZK1PU0KgTobhm5DojJBLw02hlUYmUBTJqkfE4jmhdKrVGtxCAgSEODOWaM/jnjC+/+EP2XdVJ/zq11kq5TdZUBfZ7XtCmJ+dCy1ZaWBpgja+O8zxmwr3y0zH0cryeexiJ76mUs3PHz2CKo6Q77JPnU4LftFfLKq8yyXUxu79h+iaeYZpWvhGs0one406ehIduDXLtdonDd4tFfDyS5Kwl9BGpkx/vjy7hHaNn390xDXUVrnu2Ng1WGz8vp0t2trKDfZ9+/AQyQLPkEOS5PaIiPfk+AwGlYBahL6KGWgLx/FjVIVYW1nheXZrgyhmt8sCh4t2NDZFpJq9bcfeAddlR+XzGZ2Hx7wjqIhg+ZHKr2uSI3eGWjDqvLhk4xh5fbS9eYcHJZXZW+Rz3T6u+WKyho7WV1vy6hYJgQQ8TmTTQnjKd0ajHOtGpwCTqia0lNAT8XFowoyMUHAVkVFfnqS9jC14ka9yfdpEPr6VPODzdgwXJURTOv8ZCjkkRFuQOaRvMImkfu20gCGt+65QRrlTobd6HVyZpp8Z6683P9e+rduAUXvV6pe8qwRtEsMITaLVldCO7jSJMu2+5y3ANkT7GBri/FqGuSazpQpSW/Rv94X+Ngmxli3nUdIa9Fr4vlqF++9e7hyxIM/YEA1JX+beanHCJ/so1zm3NUOfeBpoVWlzWd11fC4brGa+d1c0HbUkzyFRtxdVK7+zGxDyr0WfW68aLqo4vF5+39wUz53nJxmE7LSHeofjbxcZfqbcREX3b5vK+lry33Mz44g5ua6H5zn+5uvclzLpEqolGk3Ayz4c7ZOA3m0swCOE7bGqfpI22md0fAS+CZ7DlsY5Zk6n9o2zcxiE1v1ZbYAQGrRBG7RBG7RBG7RBG7RBG7RBG7RBG7RB+wVrPxcIodE+sWI6h7Dqao/WmS0MThIZ0Gt08O2vMMNu9TBy2s+mdXI5xFUbfHbI6PsLIp+7dvUWCo8/AwC4HYycnhwzoplIJ+H2MLoXCQf1emZNHU4zHoiQzyqySU+HUdP143OYNHROySFOipz3tLKLiCL6aZG1Wr2M7J8flFGRfKXTxOjeiMjIGjUncqrfDsXYp1rNgskpRg0Lkq+LxUQIWs5hSN9tNXMcKkKxtKpdZFVnaBTyY2aW4/jwy31UhCSw9wny6pKURg9ekVEVJK/X8IpnCFb4zvm3t77DmvN/80MSlY23g8g12b+xSUbyDU5+5vb+AUwa23/y25RAXd1iRttocVxkRnsiwHNJxtjuaqKq+ktDT5H1qBvGUUaJkxvMANwR31Kp9hAVyRp6hJw5XKMs/NtLPhwm+sTN/KyhECPD2fMMqh2hkib5uxEbx2ptbQ+dOr+vq5rzacmuu5x2XFbNdCLHzHI/6n/jshkuP6Pvx/uMhn/l1bcAAI8fPUNC2dyoxqVRZnahnGmhIKJXv9BOZmWK1jaeYWuHWZmgSFoTGSGgTF3clL0nRTQ+HvFhSOSA8y8zi9PqMHo+OT6OJ49ohwcHXGfL88xs1Ot1DIts0hdhtHnnGV+TyWXh8vN3I6NTHJcqbTB+VEZRcuznNWYQnC7OX69VRk4Z+uGgZLRtjOiHJvywOxltP1XmoCui943DPEZM/O6VFaLnCjO0r1arBaf4tfZECh4Vsml0bhGOM9rq84+JqpqeGcXsIud1Rxkmf4CvL5cq+PBLZvZP98UPssS/uTxWQHxaeydcs1cXiVLweSbx+AEzorVan2xc9exRK2r77Nebd8jV842XXgIAnBxs45WviAzZJd6OuGqT63WcKpNnFmqxT2g3PTuHZI9zOCay93pcCMNID/Uk7S9d4fjnNjnHj/aPEBJXVEBZia1P38X1N7/F34nU9fsfM9u1cjsAj5/PvN0WR0GL4/hCNwSjyPLDk/ysT378LgAgf+8LzApt9+g5s3wjV+hP956uonTAMfpv/v5/zjESeeI79RJef5ME01XxTvVCHJduzYyNz5jBHla9+IqkwJseL7732fsAAIvqtgs5ZvpKsXm0TyQ/rDErC+ESSPnwlQC5kRxv0TdbLW5MVvjdL0/Svx1O0Yb+8Hv/G/L3uK/kxHnzMET79eQNEI8iluaIhtgpEWWQL8bhFQeNW37LnGKf1nbXYB/lWE2rljwaI5rnvXd/hElx6FVKfH1cRJEvvTqC3B4zbd//kz8BABg0jpVSHjZxeCxcox2+/jbRL+vPn2HnKTP1w22uN1OZc3N3eQG9CMfvL35KX1YSDGM2MIqlKWabKkKLpVI5LIqsOVtkdnZf3Eomkxsbq0RF1YX88ExyLRlGg3jnYxLYLu3xWYfm5XMdQWSKtNcH9/iaSlGcgakc/Fba73aGe/JBiWtkwuXGsNB9R0ll/ctcpxORKSRTnFNPTwIUq+R7uhzzYizCrOwPfkyk621lu8uGGuau0Masp5y/L+/xfZNDUbwQpg8SVQN2N7eQd/SJPUVkK26182dn8EpfPSgSWXuDe+s3X3gVf/xXlBFvi3x2epqf/f2//R7mRNjfKnJ/qAmpMjk8hcePOA6ZJNdnVHwOhdw23CLFPRYXkDhokWieoWnjXEwKWXvNz7mt5/OAmbbT8gslbJVUdNAJi+xjv0jbqyT58FcWb2CjQJ+ZERL1zZEIdg6ICDKBtjY5Q1v//ME2itu06eU7RPwdHfL9wVgM7z/jHpA+52d5nRzP3b9+isuLM3pGcbaAe2VgYgE7T+iHR26QsLQo5N8n33sOq0s2INLf9cfMehtbLZit7JdBXIStHsc6mTnDnAhch+SHfV6+f/NsH06zEKgz3AuGm5zvg9MjOEx8nafGMWvmyhdk3LvH9Nd9kmin1w+fkDddScynK8xQe4s9mIVcSAltZxLCotxs4fmu+DY1LnMznMuDzV2MSkXDJiGDklAzxdNDuMRPeCgU4q7GfygYRF5zaBM/Zy7OPcFs7+LlW7TNPXESmm0SQjC7UWj1ETf8aTKK87LdQUkErqMTHM/iiXzo7h7CXvavLCi6t1lExEub2X1Inzt/jejMkrsGUTzBEOI58YbOYLlMAm0hJ+tW/tw7Yd8nTC04Nc8O8RVCKPLwcPCC4+z8jPtmn0PnxtVbqAsdXJOMeUbzUWy1UZCfX3rhJgBgWmi9w61deMRZeSpDTOf5WqfFgd1DjvuMSzan97169y7afYntBPuQTJ8jEuojwYQYHObZMNwdvSDtdYvzb0F8NQ27BQnxWNY77PNRguMxNzmKpnxDW77ILMLfB/ceIyi+GZP8wcYm9367y41Sjna7fEl7sNaLZ8iHrHiypqfofwoFfkc44ILHx74MiZw7L3SDw2eBwcw+nOss0xcJycbTgNBz/X09Xi0jKKGRsO5GUnNHt9SGADcwTXGM2uKpGZ4fhU8Ik6BQy6WUUPKFAvI19md4jn4xJwGXZsuIrW1xiupMHp0UEr5ahc3JNWAw0d5PxfnVaaQRE4djV4IBrQZ/9no9GLp8ZpMokpPyD+VeESYDxzTipF2WRZAdz2ZhdPNvW5rLTqWIeEG8puLh8QT4PpvdhbTuVX2RpkKS5+l0JYcvN/jvc3FO1sU5CpcNfhF898Tve16m/zHarXAYaKPZEz5rT1Lve5vHMIn829zjWLvE7TM+M4uQkN45VU1kVAWUSxVwXQI7mRTPeEPgGC/MRWBqsQ9nCdrecZzru9hooGvi87XFg2uSGE+rYoW1IG6lIZV+dExoCUkHcbUmJSwzPebH7Dz9jk1nyVy+oWcvotblM0/M0IfdvM3+/m3qh9BwwG2SbctJmSw2WPziBNP4uyWEdVo/hcvCv/XWJIRl57MMj4RhEHevQ1xKI7p3nqXzuH2T372ve6dL69RZq2BPokm1OsdoOEgbdFqcKLf5mT+rDRBCgzZogzZogzZogzZogzZogzZogzZogzZov2Dt5wIh9PJVohPuffY5mhVGahclx+c0M/r49P59RCTfF1KmLdPma7v5BqKjjEo7JFM6oazh1sYO8jlGCrsupfSELLh++xYqQn4EhLbpq4O04URJPAL9Wr2ilHocI2GsiWnecMJMysyi+GDMXTw+YIa0L686OsFoeuC8h3SN0WaDjZFJYyesUQjgg4esrZ4aYzZjZuYmqlJ7KEl1w2pjdLxZb+JMnBh9GcUNKQ4NDcfgZ/ITpSqzHukCnzkUG7tg7M9JGanT5+/p9mCuM3JaFyeTN6QMTteAdopR2JM0I/kZRe//45dfwCvfoKpQxyLpcMkl17sdrH7MSOavz3JOb77A+uUHn/4A1gqf1SaehJNjRq3Dxh7aVX7+3g6jna+/5kPGocxJgmHZwwYj11MvRlBUFs0TGNE48LNOzu6j5eA89eUoTxOcU2O3B7dY5at1yXV3JTuYMUGJQ5SVOWuJT+r8PI4eJP/Z6kegJcWbWcPJMcevWeb7Pv4p59Zo6l3IerqFxmopio+6BX5lAM0dvv9UrP2jtyZxxc3IcD8K3FTGqFWr4rVxTvh9ZcU6xx3MhLhOjOKDguqpEyfHCOtvjYLC28pqps5TcBk5DuWCMkuSxDQYjRi3Erlw/lhqFhnaZ2Q4hGyBiJhqh7/zildkd+8UE1F+96RU5XLiXvjgwz/Fy68wM2/qMpvglBJQz+KBwcoxzhyJ20sqEMlnpzjb4/dllbX+zktUbzPHYvjiPWbeTeL7abR6eP9dIg9ik1yrw0KJbW5/DKtTKilTHH9TT/Z1uA8omxObYnYgqQziaaKGkmqdA+IMcYREkIUWAlIVGpOkfE5rotBqYnWT6DXXGG3nBz/k/yd8HnT7yoIj7EuzJDWXRhdXJ5kNNqSlaibZY5e9BXdIWVnV0Q/72N+2w3Ohs1mWjPyT1QLuvkL7uXXVptex56VSG2nxOA0HaYe/8QZ9TKJRx7PnVHB7ZYqKQ69/ncinzH4ar32D/C9PxCVRrnF8zDYzfCZ+gbMoJSAXn+HrL7+IolSINnYOAAA9F1+TL7dx9XXax+5zZip9WY7n7Ms38flPiE5KHzOb/LUrRAjUbGUU01zjV6aItDiJ8/v+5kc/wq/dIWLv5VHaQsDmQdXBDFRPyMShYfrmXKsBizgrmmd8HltfiaZmhv2Mr99ZJ4rELb4Zh9kMt9QiXMoGuya4h8UP9+GRdPqMh/OUS3LdXF6YRiFN3+51cJ223GZ9bw+1SSEJelyfk5KkD48PY/0pa81vr9BXnDzmmJ0+PoJnhP1KrNJHrD4h79j4zTCyKSHjRlUPLw4Bs6kBn60vDc05+eCDT1Cu05fbVMPf3zdN3RZu3uA6PE7xmS1ShZoZGcV7W/QlP5X6yUtTQmiZDDgWUmF0nKiLhp/9NU/70e1wX8mfc82/dINZ+dn5GewJFeyTqsjSFfLydJs5nJW598yPSUVQKlTZThdRoYPnV8TH0+W6jnnHYJcK1FKEz9m9wv+fFZKwhSUbLen2sNOJIUlXu+x9NVDaSXg8gt1VouUuhS4DADpF2kvi8AgucWu4AvR9XXGdTI9NY0so6bq4vi5rX8ucnsPU5FqqCLF2LEnpyZEo2rLRgLLl5Sh9Wb3chFUZ3NyJ1DqVwbVZrPCJT6RzRh8xMklbrVQLKB/Qx958iaqKP03zmZ4d7OClNzjfJik6PvjkKUpt2vb/9effBwAYxDEz5AjB2uUzls521Gf6zlsvvoQv7veVxGg7fh/9m8XUw71ntG1Dl/tlQJw+IzDBJeXDtV2eTVYLHANjvoy//y32efcZbcFpkoRywIqTM3HQCEYVUl+s5h6MUgjM5ojCMkgZ0owODlOcm4JUe27f5hk2H7Rjf1e8gVYhaKw9+IW27coHhtxE8+weHcMoBbGGjZ+/fkQETnQ8iplJ7rdGKcocZTiu0ekJlMQ1ct5XhRN/XadSRcIszhAh/VxuZuddNj+qZY7NZpL9NOvsO3XzKtppzv2p/IHNzs/0eqw4kxLeiJR6rPrMVKkOv5fPujgjRcE497pGr4uI0Kw+oeHnxF3ypd0PR5jvcxk5Bi6PFeVKX4mXvnN1jf6q0i4jFOR3byfp22uys/nxEfz4p+T7MUiSOzpK+0W9DZvIeZzaI00CCmSyeXjlm63K2A+Jh2d7aw31BseqISKYkJRGn93/EmZJZNu19j0Ozu3Nq9ewvynuJ+1r+RLnwe/xwhXk5xeEprILXRg/PEREe05T66XaqWFfyo+XhUy2tnmWXR4ZR0Rol4rQJwdOKWuOhHFpVOgAobUmdK7ItjdwUTcAACAASURBVBooqj92PcPcHO13Z38PDSEJimXakMPN75gYn8Ip6L/7MvAucYuNxEYw2uLaqegc7db3mQ1GnBzzboI2bSBs1tnGYERXEB+b9pyGfKDRaoPdw9c/3iLiMDo1AbMQHyc6y4SEmB/1+jEirpZxjfHpKfu7v3GEjuzvs6fkTFwQKv44XoRbr09JHXFYyN6SoYu8+IHMOpOc7FOVKxYMwSDUy+E+/alZZ/vf/u6voSgOp/v3iXA+0f7W6/VQUTVIQxL2zSZ/VtpVzKvKYv/JAQAgIFSbfyqGkyTvoFst/myjhUaDz2wLcW0UZOvZ8xQa4tCbUDXCyk3e0wqZE+xIOdImXrnMOefN5mzBpvtHwKMqBqHCq90G2kbaQ06+wtfjPE+NzaEj/q0h8TPde0AOuFS6gCkhVy26jgSlnmsqVmASD53FwvFfmqetB31erN3j2dgkriSHS6gbhxNNYx99xf61tR86jDY0vNz3+hUV+WwOdim42ft+WOe6b776Jj79gOfa5zvcEwwGvsbYM8BqEgJdd6I/+7M/17M4UBfCb2qMPv30RPt2swCnzkcri5zTOzdYyZE+O4K1x3kqtWgndfEXO/xd3Jzn6+OqWLBKPW9hYgGf/yXtN3hX5xapUja6Xdy4TtStUWuqrqqhQjkPg85hP6v9XASETrd4wZ0ZHQNEOtmTUTdKXDhLV5YQlYNLZgThExS7Y/FclGz82q9+GwDwve+R5DVX6SEoaOG2nFJNQY9QMApTjwN30C9XEUGzxWWCRRKXhoIk20VqGFmcxCc6nIQUYLDKqUenpnF4TJK5K/OCY+tg4XP4EYeI2kRw++Qxod/D0WnUBb+sVWTUTisefsnDgVfEX9kcjW14dBIRj6I+BhGgSbo59ewAb3yThneqA0S5oQPjzHU8eyxWRkkfmkTE1fU54FLgo6TgyoigoYV2GWeCRTe3uEF1k9LK7lTh06U3EZfj1yJZvHYLhcY9AMDWYzr1m28R5nv55jTOdUmW2j3GJwjZ9A+VkKwZ9W+VueyvIzYjsrIYndhzjd9K0Iy3v/VLAIAf/hlL0hYkWV6oHyKbkmSnpDAPDzmuDosFuayguCK3C0cJUbaanHB5dRE547i4HNqwm9mLErHlFW4s6QIDgaenp7C46fzO4/zMkyMR2npMGA5rIeuCNTLO/xerSdgUgUrps402vmZ99wmcNjlUkQpaRR7XMZhwqkOoVzKKzVYDTZHcBmfpnM9VctMpN9GTMxofZbnK/XXas7HTgU9EZH0Z5nyDB8FgLIaTBDfYpogpQyoB2z37/CKAuniNl9GkILm9ng3BwBQAYOOQF0G3DmGeoO2i7KTV5DzcfYHlVD/6+APsKmB75w0GGtbXuTm0KjX0tJE5dfB+dJ8X8pH5FVi1VpOCahePc6hLVvmrb7FU6t0PuQEEolEYRDbnk62VJElvDfkuJCfX9mgzX/8aiSXz6RZO9o80jjxs9Es3bE4LzhRs6weQ2kbJmj95BLc2Coec+dVL9FFhgxOBy/QbT9ZZ2rC5St80sTgHgy5TyOc1/gxomAxlFE20GZOC0/c/YTDA2w1gdpbjb1fwPTV7iqoCig+/5JrtKa759qt3kBGJplekq4efsg+fHD/H1AgP2rWs1q6CpKM2HywZHmx+6e5rAIAHh5yvg2e7MHXod9+/9x7HWnLrtZARTV2k50Q2/MlzHiQKpy10tQkPK0DQhxqXPv0cWyqDWpjgMzs7CvwmmmipPO697/0YADB1m36neXsJ/+qdPwYA3LrF8agamnhbJUNXxzlW25r3m5enER5RMNAjvzjMvWGzXUXFy7V0Llvrpjmus4sziOjSdLzHflaqtNnbkyvwj/Lf+9tcl2Vd1EZibryqMsP/+V/8W46xyknswSqkHQCzbjX3P2W544sv3sZ1HfiORbRZEFn3zvoBxo3cQ2yS/s2Z+b2WQhfpM74uKt+X1qWsbWzgs0+5rlraK9swoCMSyLZB5PLyGXubh3CrXCLs4+F46wn9YsJzgpZKB6pK/BT2ebBdXJnHtW/w4u7QZc83wnWQazbR7F9+NzgnEzrcu1o9tLS/9on7M09oz7EJNywSYdjbZTBgbIIHza4pg5ad87Tk4+Gr3aQ97h3vYLXNPdwr+L3DxX6//fJbKPTLNERWvPb8KZbmubafPuUeZ5Bvn5y9hZj25S9XGezoKBEzt3wJgRGOlVm+fFMkwvNXl2HUxfTeQ47fFyrNCsWiGJnj2BQEmZ+IcN/0uJx49JC+PKYD+FSU54TQtAdGEcGvKyC3nuVZKhqN4slHXHN5kckOe7h+lq5dQq1Ho/vBT7m3zl/hWnrw7DFMOhf0RW1Hp0fRMugy6hNps4Kz3VILUxH26959Buj7AgfZZALncdrhV7/2Bt+vRJHT40WrSf/2dE1nqCb9/nGmAqfKf5u6bEy/xKDD1994E0//msmBVJH2XndxXMd6BszMqKT1hON+9Tr9QaGYwfYhfd5YlP5udUcX87EwTltcX5GoEisi7bd6Whid5jozSLTDPx6CVeWPdZ3tqlX2xWnuYCQswm4F52IxnrmOzw4RdausSEd1j4/7i6VrxVuvkUJhZ522nS2xT51OC0aVEaQUsILODO2u6eKMYfDxM6NjfL6ioQi3W+W1Kj0yuGJ6bQAViZ0M66ybyXCubC474kmNrUoV3No3bCYjarrI2Ywq7wB99MJkBDYrnzmoErBM5hynea7Z8UWWpXqGRNWw/SVcHr5ub53nkOwhx71WrGJmhnNnUTLTpAuQ3WDBwgQDOcW25OeL3FMq5SK2d9j3u3cYND4/42eWymXMTIuyQXtQWefi4SE/Okoane7TLqxKPnncbnh1NrHrItmQ7LTN5YJNlBcWqxKCOisbjUAwRJv2qmzf67Qjr4Dk2R6/xzmi5HPhHCWRhvuVGAqEuU94LV0ElAQ6XOc54PYsx2Dt5Ai2UZ6NOxqjmsqFYpEg8joXGCp8/5gSaNVyGYuLU3weXcBbvbreX4JiIehKRrum56qVK/CpLC+hc21byVOT1YKK/GM/MNYnWrbZfRif5L77j28y0XO8d4CPHtBv/OovMamTOqYd/uhHP70IYNQVaPHG+P/l6WXU6/2yHY5VV4mOrt2D0yR90LgCwtW8yrssJrhUbn+cpN+xKIhvdQ5hRxd2s4Jf4VE+5+cPv8C87n+jUzxjN5t85nKlcjH3bt0fddSAqRpA/ITPX86wT9UK1/XClSs4jfOM4JA8e2x09EIUyCYRo0ZDpZOlGlySrg9KwGb6MpMS609rqBzTps0dycYrqNusVlCPc6165H+c8id7R6cwqnzSKhJ2vyhRJsIzSDZO/53xm4hNaawNaMrGIhJk2tzf1efYkE7TxutFPvv5Ec/V3un5i+CSzaH3K2G8eZBAQzQuZp2zDCq3Gwq4sVsROXyI/f3arbtI7PC8YeryfftHDP78t//8X8AjQuy733gDAHByLH9aaaOnMa0oSDo9xnNSp91Fosa+l1SqHhiiHwi6A5iRuNJEjDa3oLNRrW7A1lPu6+Fh7t2FKm22mEihKyDBmChz6n1S8FQNrnGRtvv5M+Dimi+fp5BP0j/1Yz9uN/fwVrODdkvAg5/RBiVjgzZogzZogzZogzZogzZogzZogzZogzZov2Dt5wIh1K4zqtdoFGFTRLIPw1wUaZzNaUGtLdjs0QEAwFKXpFq8hktTjNj91d8QopyrihDRGUJHVTEVZY+DMSEtzGbklbkyGPmiM8EQPfAgFWf0d0wR3oayA8ljE2KKvIYUZY4rej88G4NnipkKuyLlfZLdfLFzAZ0ORxj5Tmf5mTA34FaGKJ1kZP44fnaREQorI+p087lqzRpaLUZ2z1UC5tH7UW3h+SNGPl9/m3DujCK33//rHyKf52c5LHx905DS+JvhEzlaWPPQyQnuPxpG088I4+Epo6VvX+KYT8yO4ESZTauIi6cU9fz8YO8im1xTBDV/KPj57Uncuyf0RIdza+8xghrweVGyMSLcl9jNt3JwNCUjGRTcLqwxzmRwuMaM3pt3WU7wx+/RFjoO4NI0+9pqMgJtsPBZOoYuSgy6wy4sY6mo8oB4CV+5yjFq5Gg7nRaXjM/nQVGEdR5F6z/9nBH91169jNM67cGlDLNRkPbj400MRaYAAAWVFdglZfhk80PcekmZEGXOnX5l8UaCqOT477SQN6Gwyu1cXtgcjBafK/M+EQyiokz2lRFmuc7zRLgcHu1iWEgiq7LrwQhtolWroSNJ7XERvj444/sa3TLKIkudvcbswN4TPmcssIigSqRMgl77I7SlQHABG0+JdqkYOM9zs0J6+aw4SdD+XH2p2xHaTrVcx5TKQw+VOX4u+WL/6CSGZe9mBz/rywfMlPbghEXZJnOAY2zqGDE9z3HfU+bXIbj1kDuAzR0izcoVjpm5Tpu12rqwCim4skK7cnn4vs8+/gjxDNfV8nURAytqX691MD7OjNzBLr/vv/9X/xIA8Obrr+BSmM9oVdnD7hrRWPVqCU8eElWTVUT/O/8e0VFdA/B8i6Ua/+BrvwIAeHrMbOYf/Ju/wOyysiRCM/RLfYZNPuw9J8zZcsosyMJKGF/scbyMyh6Vs/Qtn/3kPqIi+i94OA7768zm2PxDqGW4PnKSszabONbDznFkj9if7//R/wkA+Np33+AY2yN4eETkglkszDNu+sBusw0z+N0uydpOhJlFso1P4PM1EsIbA8x2jKmM4qN7n6CrCj3LCG3hj79kCZn/zIXgKF9fLtPmZnMFjf/L2NjleMdmiGaD24sHj98HAIwnuL7iRq63K4uL2FXpVbctmd6SSBo3z9D1cH1djxB9UhY54Mb5Ccwx9qE2zPH0yH998vlTXLvGcZ8cYR/65Yd7+0+QT3Y1ViqRTLDv9mYDE1GuXU+Ez1wqccw/+ugBFmaJDF19JMTDMjOCd18exVqS4zgU5KA9r3FNHb9zjMvL7PtxvC+Hq3UTdKAktBAkcTy9OI96j89olyTxtass0woNhWFWmVyxzT5nVA5ydXEcFmXYIkKUOlS68cV7H+G3/tF/BAA46dHGq2nJ2VbqcEuC+sBEm/twnX7A1jUhIAh5Oc3x2Nwh+mUy7cXIBNdsvSFS6kOu89EpNx4/5d77aU4+TOg+t8sOh0hWCzVltNvs9/aTpzBrDu9JGr1araJQoM2cnUs6fG5K37cPi7J1rn6Jmc4fm3vbsAtJcPsOkV0joy8DALZ29hGUvX/z628AAK5coz85PDnGpoh9r87T79xY4TlpY3UDt6/z33YJO/jw/5X1nQtZaJBkeSEr4tegGzASDZJzq/xkjxnSYY8Ts5NEayQrnNO/vE/Y+m4lh9ki/bdJmf4Xrl/BvkqdoyMi7DWyL0fVc+yrXHsnIYJ8+eVSMoFp7VWnKjn99jeJGrM6zTgK0M+kVJ6b0J68VTnDN1do5waVSAREhP4P/87fwe8Lsf1XJc7z7b/Hz3SuH8Hm5h7VNYrE3sjPDISHcZ5jP8OTPDsYnfx/JptCu8s9oU9ia0hzvXSC5ouSql6Zn/V48zkMKm0YG+Y6c6nMomc0YqjDPdgqct1+Vr4c8uJYdAcnKicNerhvPPn0IVZUEhVS+eoX27TtnskMT5jjePUy/cGRSgTdDiOaKuNdkYBKXnPaNQIVoQ2Gha62uUJ6zjomRGZc0RmgXxq7d3yCrov+zSsC7oN0XM/iQE2f2bGI3Ft789i0F6tbtJ2mmf6uWMjAYeWaiPm4NxrNHMebl1dgk02/OMvSiESNfUmUzmHTOfOKJLwPNzjf/sgw4kXu69ub3AddGrNOF3C62ed0Vpn+GveLUCgAh5P2UaqoXEhldmNjYygUOY5uE8clJx9QytQQ0Fof1hkjOkMbSmZTsKq0zGKhnzwVOtNqtWJVIiTGNMfKG/DCphJHgwhm1w64Fz978hyXLgvh2mI/HUJx2c0dHCVFLaBSp6KRxvrCnZu49xFR+30EQbLG8an1OrC62C+P6Dr6Z8SmoQm7aA6qIimvCoURbxfRbQsJJqRUWgIKNqcLQZ2tWxKRKUhePBAKwtdl/8qS5LZo//AYzeioTPFMBMbp7QO0hfR7/B6RQhb5t4rdjnKV/TLq7rUuP/SPvrKMku5JmTjHsZ7iHenqzCQebXMvKLR57ra5+0JHBRgstD+/6AD8Ep05PszAF6StRse5Fs8S7Of+aRJGoZ77rMMCjQJmEzqqzugIGjQ6xrOAp9C7GL+jQ/Z9REjNZCGHUYkO1IRYK5zmUGxwPPqot7BQJZGQHw3drTOy7Y8+IsLT4zLiktDie9tE4zQFPneYbOipTDmXFYpbyPRK3YacEJ0O3c9+9VcoCPLXf/bH6Anl0kdH9f1IrV3BySm/p0/gbzNx7Mq1OtIqBZ+Kse/JBOfjcOMQt+6wn0ZJv/cJ8yOeWbiGuBdkVAJ5KNLs6LgT35n7DsfoXGv/8SZ8EhVKqGSvrsqAu2/fRLrA/W5smHNjEOF6pdhFqaQ1UOPvorrz9XpGDKvszOTl+yyq9IkNeXH9MvfN0/0D9m+NexEqbZRFGXCYE+p8gj7DbA7g8JhjfCxamNlF+nOr14ylmxyjvX3a7wtvcO/L25wXKKzNbX7PlEjS9w8PYOlTh/yMNkAIDdqgDdqgDdqgDdqgDdqgDdqgDdqgDdqg/YK1nwuEkFlynn6fHWlJF1pVs5hVdnft0eoF50JaZNK/LBn6P/2Lv8XHT0naG/Yx8q9AOXr1NMaVBcoqo104Y+ZgfHgEDUU++3wsUREfb+1tIycpe6+y/ukEs8qp7d2L2vG2iHOzFfbz5FkCI1FGe0OqF8wn+UyVlgHNFiPqbaGbYlPM9mayRdhF1jUqGdHT9BYycUUKRap7/QZr48/zCaQkRX9ZWaAeJJN3FMfyOH+3u82op2uYkd7pqXHsPGFGwiQZVouKfz0VA6RyjoQirg8lTXrHMoMpIaUeH/EzV0T8PbowApuy6bEhZks6IvZLnaTgkVzjJfGYNIWsMXp6mBpnBLPY4GfuPGNU3O+OwSuJvqoyNuGREbQlHT0kqeWiJHLrDhMevEsbWL7MaKp/SlKXZjcmo4xU758ya7K0JCLXTBV2cXHUJP9+IvnRYBhoi8CvLpnYM9nOVxaWcHjCLG0mq6yReGQmx65hf4NZ6pQkxNsV9mV6bhQ2ZaQKiugnJe3q9QKtulAyE8wopSrM+hlsHRxLsjaX5vtiY7STeDwLk5sT54qIHDqVQ0+EYmen7ENB2WGnz4N037a9zD70uWzMhi62T4ku8Cv6PjPDsWt3TXCL+K8u1IBoRTAy5kNBXFuWPpGzaqZr1Rxmljg2xTaj4NN92epWHKvP+Fz/wdvkncnmaAtLC4s4l+SpcZPz7FPtuyUcQE8y2POzzAgWCAbAzpMDnKYV7V9i5sbSM8BgEpfXMNdzVxlFu92OGXEMlAr8W/JEtbhdE6LK6AfFG9NptDQuU1gW3w8sXHv3vyCfy3h0CV7JxRrEozGi+u1MpQi7Mg2tHvswuyLS5o19XL3EsTHIJ209ZrS/VinjxVeIIDgWysk/RP9x5+3X0ejSRh09rsX7z4iiSPnLCIi09mibNlvq5uEJaBxUW98nxrc0jKhKbtfq5TPnxC80Fo6iJDLMSq7PS8YxPs7m4IrIh4nj5PMPPgMAPN/pwBwSmrJOOzQVxKHzfAd2ZdDvXOP7gm72bd43itB12txhmuvSJ5JLM0wYMotgUMiAxev0e7E3nLg7S1+5leP63LxH5Ent/hN0Wxz3lAgE/8ff/a/xv5ZpMx/ucu1+95/8LgDgn//r/wUJcWN4gvRXXckPm5w+eDMibhW66Z/+s98GAHyy8QAmySqPjHPedtY5/lM3byOzyQxnysCMb16IwVqngbVHRPOUayKu7/L7FxcX0eyI+03jcPtVonMK6RJ2d/isArSgJHLqqNeJV158AwBQrHPtrz0jN1BkPIBzyRwH3bQnh/gE4pUkeuL1MwlFu7m2esFJ1ecgO5LkrctmxUiUY5Q855zefol8SJV8CSllyK7cIV9TSdl/Y82Dpz+hrViW6ZvLTmZ7t5MJtAviJtGeOiV+nVK6AKOd/XP7ub6Xp7h+EufHaNj5vtFpcaHo+RYXxyFlbbSb9B9jEe7pR9vr2F4lkmByWtlgZTDXN/fw2mtEmITFpeVyOi94S154lfOclP9JZDIoCYUcUDa+Kb/jsNoxGuGaO9s7AADcElm2YWQUefH9BKL0P9kvaBP18xOMiYPtxssc27QQKpXzFFrKpJpENnUU5Lw5LHY43dxfRqfY9842bfc8tY9xcdtN+Yliqb5Cuzr8ch3xPfrh0AT31hHxivRsZty8RlTrO//7HwIA3qtWMSn0yrCXffjR998BAPiCUfzOf/qfAQAyQrYZdehohEZhFFeWXySaW0/5msmJGO5qz3BaidxOivjbMW5FR3vP4/tEojrWuQ7mgxPoCMXw1deJxEsXaYPVTAaVuKSJJUNe6pP/BkOIxGiH6yIIrkn8IZ/OIBzj3ruvcZ8USen5k1M0K/RTd6/Rxm9O3cF+hn0WRzFWprnfDJncODk9AADYAkK8n/BFVrcb/iBtppDld9v6KL3YCHIJ7XEiZu+jXgwO+8V5rNPm3Af0t2qpjMsax13NaSzIv1kcVrTBdWXQ/r4wx+c8O9tBqcnvOxNpqt3K/S02uoITcVtWRObdKrO/VpcXnQ7nMnvKM+zSixQVefjF54CXtl2zCKFfzwJ9lEdKCAsJq4yEwtg75r63J6R9WvyX2XYJ33mbnDIH27SB/nrb2FlFTUgfm1NoHnGTBocDiIsbxmwVUkVno/BQED0IcSY01Mw4beL0LA67eJkCPp5JIGJgg8GAmuxobZfj0peazxazcAhRsCgURUycWvlq9WLPP2/Qnr98vI2ZhSkAwMSkkN4ibZ68vgyrj346LY6ignhnZoJhNIQ47VjpHzPilGxW20ikeCbsCJVTt9JnNE3A4jTPIgZxzR1uc3+KBENICM1Q1dkuK37EQNALj5vP1ecA6qNgbE7nBY8i9Jlh+ZFEMg2/l3Y/PsX7RFqCPYd7xzAa+H0WIYpaliauvCi/9Iz79Nkhn8V3aQE+N9dC/2x4/SrX2Z2FZXSnaWNxycivbYus3GRGWGu9JD6YunjeAgE/TCI6bjQ4v60GP8didMDvpW1viMfL7qLNuYd8ODjlfdFho53MTE0BAJxwIKm12xEK3G7ls1e6KViEsHeJ06YtZNfscgwQN9LQeFDPcoJQWL62JoSJnYc1v92Dusi/h4N9NLHug24bIEJxqwQrjo50V2kALvEDGbT3l3XmtXo8cIBzaOjQdr64T7RZZCR64cvj4nlsC12VKyUxPkU7f/KI63Nhnmc1n82JrlOk7eJ3sgUkdNJoI19lvyqak0ZFJN9owS00q93Mv/kcnJtGuYmte/Tzs1o3NpsNu0L4VUT2PnOJY3dpYRzdJr/bnOP33bgxBQBYWr6GP/sLVptsaT1PxVQNYXPgQHxHqLFfL94mevFobwc/fOev+RxCwb3wIs8HiXgKvitc/271+eSI68zlcMOo6p2CqpJ8EpM6OtrCSJTP49VdbELng40vn+DKNa6NRpvz1UedX71+HQ3xLP+sNkAIDdqgDdqgDdqgDdqgDdqgDdqgDdqgDdqg/YK1nwuEULonCXbbEKqKhkY9jNwZlXnzuIfgUSQ5YmeE7CcfS35tJoJOQEgOcd5sJhhlDvqtaJ9K+crBzypXpX5k6SKRZsRwOMbPjJ8xglpqteELiFNA2YFkgNlJt62BqpBFO3lmmO1CxLgsFrSkplCUAoixxT4Njw3DW1NaUipjbUm1bu8k4JVa0uVl/m71YRXpFCPx88oCBVU/n8zmkBNCaGuXGaximZFvi8OCVJHPn0rzpzslPoxrr2L9Ace4KJUwq5nRx3Y3D4+UeTr92mwhH/7mez/B8S6j5wEbI5qFCv82uzCO9AGjquvHjLpDrPuGagdNpauPVA/fVqTSUjKga2MktNri9xmUGUwkyrApOm0Tj0ki3oRB2T636i1NUrnJ5QqISNWm52Cc85JQCvX9BKolZu0c+ny3eInOjjbhGeorXbDPx6e0AVPbhJNDft/iIj/LhJzGqoh5ZW6aUlOoVpi52VjbhNcu1IRQB7MTnLeeMYNSkfMUCfEzu8q2DAWr2Nk84N/GmLV2Sq2j2iygpWyCR/xafSWPoYAJ56pfH1ZW3mjvoazXn6dY17wjtQnnkBP+Ia6lrN7XzwJNT0+iJ1vekRLbsiL5dosXW3scx5NzjsONS0InZPYvpNsvKfP18WfkwmnXGli+TnSMR5xAMh1srd2HL8z5ypb4mdb/l733CrI0Sa/DzvXeu/J1y1d7M9M93uzsYnexC0coCChAUQEGQiKDEiU8SaFHvSgkKvggKaRgSCDIoBMggDDcxZrZnZ3p6fHd09O2qrrsLXOrrvfe6uGcWwhEiHjVRuDmS3VXXfNn5pdfZn7f+c5p8nPK9TL8MSF2hG6y87/onZzC4uJnVcVLMlpn2WYT01JOcWMkKWtCUwiMZxtEzly4RK6NQqGAHfGOOORv7E5+1qVrq8hK5ePBl8wCvSa+j8zpKS69wP7fvf8+AMAlCeH43BVAmRSzxnN+hdkqYzWP2AxRFHuqEb4yyfk2puvoZmlHyVICAFCqqe9OOzpaew3VA9dynLcXb13D40324dHnRBKYxYMSicdgMLBfqzeYvfD4h8gr47IWJiKg2KBdRlZ82JOM6vw0s8mNMjN02c2HgBTcTPIVhTYzI2ePd5Bv0xf9g//67wMAfu8v/q1e00Vbtuns8eebv8KsWOLsEA759lCUv3vvM6FnPGXMKiu7vswx+uIZ5wF+H6alLrS6SjTQhx8ToTXZTSB0SdLoKo7v+tmHrWoBC3PM6q7P0YYSmx/gl194GwDwpajY7nyu/WU4xOzrRG4YxZmT7p4d+QAAIABJREFUfUwESWoxhM4ux6pqom//7MdUuOz6zLj7Q6Jw/vZLrLN/U/wu7XIH7zVVSx9lpudUKJT9ZBEmI/82vcjsT188I263G1ahtxL3iR4c7TehyUn4o3z4fINrKWelzZ8ePID/kPawLgTlfETKNJ4QDFLGaIoDICK1Pc/Ag8xIuUIcfK+8+iIyST5r/oQ/fULK1koFpMTTZlTGzB1hX0whL8wh/rtU4pg5xSkxPTWJHSHPpreZcbv8BjPVQ1MWh1Jim5L/9gtR9rhyAIuVc/jKBY7txlOuibmlW7BoTpJPyElTlujS+/fvYf0C3/fiKr8ns5EAABgMTmQanJMTqRktLxEpdPPCNMzKvi1dpM3tbW3DKz6EowSf3SK0WMAbRtjDc4NRWcIJKQI9erIJn4Q/guKsePqMPrrVrGJijt95IH6zdx8wExvyuREw0Ja/kOqXxco16Ql7sCnujriyny3tQauTUaQ22cfcPscoPst1UChkkZQs+4zQLq+8Qz/3oduCnuyjLY7ASIP/vzSzgp6Qrjk3fWa/2IGWM559wSzthQXyQHy6+RD/7F/+b3yeOMfBIH4We3wZj+7Rd124SRTJrnianh7vYn6Bz+oRh8SlGaFe6i08yfEcVhH/ncVOf7d7cnie9uwXuRZyzzgGLncUoUnawLYUb8LiPkyenGFykigot7jYikJfzCxPoltiX9N1Kdspg24zuOAX8vKTr4hItJp6CMU4F6EJnluK4vrrWgfoi1Mnlde5SGtwLTKF5SX6vi0rxzuf4zmphCqyde5LqWc843V1FjLBBpP4iE61Ppvy0R53EG4pAholLT1S3GmV2+dS44vT3Kue3ucePjQ14YnQprtSyDSK62X/OAWnfFCzzTGyDPi3z35yH1Pai61O/m5ng3Nls/hRUEVAR5xKE3NzWJ3nnprZ5+uSeSItHv5sA+YBbaZh4/m5L1SPI2BHQWqxD5/Q5lo6Z3YtFtRF4BJzcX5HWf2JWh1TMzybNUcS4EKF339wDy+9wDUQj7APVVUZZM/OYJKcu0X8OEOpH52lUrA56Oc6Qtwf7BPZNDUziaEkgHxSDq7pHJ9MnMAovsKb14kuM+w58HibiIr8iAtvVkihozO0pcK6IxTbQGgvX7WNkLgVG6ocaDb5c9gC/FIl3MvQN3gd9PcnqSMM9FlXhCS7tHSZ41lvYa/K+aroe1tCoPX6hnP1W3+QPx1S0Or02ri0zr17f19zI+5Fp9mC9BHvDCPe0pgUmZrdHhLHvFdYDLSvVDuLb0/SPl65TR9xLB7FjcfPUW/SF4X1Ga/Ocf08e/9LPJcaXKUm1Oiy/H6ljqN93gPXtd+6xCWWKxTPuYNaVa43r9D0Aa/tXFHt6jr3npM0+zUbncWxUGEdoYvLQs24nU7MTtKehgN+T/qY9mgyAlXx/TjB7ymLD9cOL/pCrG1u8Pxh7hkxO8VqghkhVltSik5nimhrPRe0Jw+EqDsqn8Eh+12f49zEQvSvO/unsOqc09RZw6uqhu5gCJt4ZsN6/VmKzx7wu8/5V6tSC7ML6TO3NAObVGWXV3ku80sda9A1nStP+oTUHIqHp9cboClEUMhj0TNxDIqlFE7FM2gUcjKoO8HGkxPsdMXNJoT+zPIsqiY+g1H2XpEy7Ec/+CGWJ3h2mpjkXnz3ww8AAJ989Cluv/ImAGB+Pg4AeLbF9QxrFW7dNyeFbsrInqejIayv0Y8+2eHZvNShf6wMKshlEgCAV5ZoO27xJ5WqFQSjQntKNa0k1O/k2iqqdfb/ptBAWxtUKzNaDHgsJdOgeCZHd2hTtwtjS5euv6b9XASETCERlZmaWLhA49x7zA3ap8NeJDaBVosLw2oSQW+KzsVtCZ/L3LUKNEBXeLQJteDUhFkjHKTsGY02lTqDXdBraAMtimyq3m6fk+9tVmk0XsmPmvsDdCVDF1Gg5lSLYioQgk1/qxbozFyCO9Z7fdgkWdjr8XvyNToJizkIn1UlZrpUlettxJe42EsqSzqQkzGaAL9KsQaSEDeZ2IdvfOtNPH3ES69RBK7ZUzqCTWyj22d/+gNJkQ65ITomTCgdCN7ulBS6lQ7rMN1BtcGxdahsa2Geh7x2u4mBLr91HcC7YvK2DkyI6EBc7HHxFkTwN2nyo23h85l5TjrfXFsNMwIhkb8JmvjmO9/Go/d5wTouihy6RkcVcQJ1i4j/FFi4rMNDrprFsEOH4ZCMqt1Ix2MyOnAsiKrHoyCWIP2Ncgt5kbumE7S1b31LUvbVU5TLfF10gpuOzc7/f3j3S0ytcG5iAQWGDvn+9Ss+2LRRZxR8FD8rosFJHO9xjDwqfXz4kGM9PwvMz/MCkj/lrWZRENti/hi7DRGMrfJyf1ZMoS0oaFsBq3qZ824NWlDvcOMsqPzs2irnsjvoYah+OH2SH5UjqeSycOtgevs2X1OVPOVeoorF5aW/MraxsCTci3WkT7gWPAE+U6tBRxeOetASxNisgK1FJZ4OrwtlbfYVA3/n1PuCbSdqHs73h49IVhw2Sk54dh5OvyDiRT77wtwMTs94sDzRoWYkmZpI7iFxzDF98YZI+7Qu7z+4C6OZfZ2ICLaZ2NVnziKoILFBr3FIGt3msKIsmLRPgbuk5FGdaOHu57SHbIN22RKJnzEWRr/COb96gUG0qiQnk4cH58Tbz78SRNnJ8T893ofFwUV0U5LlP/kZ5dbXZufOg7euCC85xsNtbBXYD28wznGz85C4ErqIzgbXseWMa+mFF7lh70+a4RNR5qeffg4AKPY4dhVrH2cqP/vv/+U/BwCYdXmbXp/B0TbH/43LtNHXdei98+VduPw85J6KTBMqTyoZynDn6DOHLc7vwSZ97de++Q5mVQJ0dYr2e+27XCPPEh/jD9+jD/yFUaCgqsu6143rIY5D8bnKMAPTqHa4kSdFxjm5xAPxy1dWYFcZXlBE5sUFrvlLRTP+IMH3LYhAPuOhPW/sP4cvzjn56DkvVv0C58ETiuHyMgn/S0OO32uvf539vP8Ufj8//94ntG2LoPbVWh6XFMgwyl9FQzxIWGxm+CPcA+bjfE6TDgStegc+yFfaRuWKtOe5+UU8ECG/J8zxbIs0tFUp41SXhhtXeDHoNRrwqCzUoIvwaYrreyE+hVSaY3rs5/NZFBSIeL0IL9OOugqO4Iw+yVl1oClJ6dQz/q6ZYbBjcjmMV9Ze4TgqYNCFpIon/JiYod/57M7P+H0eJQ18KzhL0Y4LLV6m4pf52mE/juOkhCB6XJeNPp8pdVzDpCMOAMhKJrmvfSCHM/Ql1x30c+33YUBDhMqVIg98o8RNeDqEWpnffXzCcXmu/SYSnILJzz3quS4EkwrEnWYzOKkyEDep8gq39hJHwImBlf6mpov+CyJV/vSLz7EoieeQSja6Ra7JXC+DloIGWyo5NWY5t9Pzk2gFuFbzHvrvfl1rsduHV+WenVHZRIf9fXqYRCHB33lW5QtPm/CIfL16yL7XRJ4ajawjMRLymOJrjLI1+9FjzM/Qrg6yCnb6JcU7bMGzKil7K+2qWFDSqthAWGX2NcnUe4b0w//+T/4N3HZB+V+lvwl6eaYyB00oq7THocu5S2VGgUDovLzz5lWWmvklFW0I1FGUj7bMcp0NdWGtlotwyOdNilDe0mtiOUS7awxoH30bx/Ykf4qyQTZa5d+aSZV2WwYwKNBl1uW6rXLPaq+MvoHjENPlvqDLs21gRUalEX2dy4YDrnmH1QEoEGQ18TOHbZXstYco1Pi+zGfcdxdiEuMw2XGa5e/m1lTeUtGlbdBDUzLkPsmlF9Kc91Sli0qS8718QaToItu+fu0aemn6hr1d/rx+YQZnj3h5aos82BfR4bBtQMGgi47sr65LtqlhwVf3RyTSfOa8zspmsxNlicV0yiqfl213z1II6iIIzYNlVD7fH+JEa7WodV1RQrLnspwnj0MqY53W/aBVzGBf1ALzS9zrPBIVqDYbsKuc75M7H3P8dX6vlEoISRDCJFLZC95JnHU5NgEzbftApU7pdPr8fjC5RPtfXaQf+Mmf/SnWLjMxd9ymD7t4mXtkv9iGXyW3y7rcdxWMmTGbsaf9zKR7yI01vu/LL57A7B+tDz6n28+56XebqKgMr1HlvDlFqlytFHFq0J1BJPMDXT07nS7qafb1SMnkQYvzUM3XMenjmPYUYFx54TaiFfrihJIyQZ2Dr01OY18UDaYwbfrLD1mq6vK6MDNP2/SWaav7SQZewjYzmjbOgV+UC0YzX9PfNSDkpv91mrje+m3a0np8Ed//cyagfAV+ti/KOUqfpc733pjunSERjReyWaUpgVmV1qZ1N+r3jHA7Jbhg4VgvKjhjdAC5KucyFOL7mo0+jOB47D2nnfgVmOiUa5jSmaQnugSLyN/tXjOGRwxe1Qpcnx4J01y/tIpJkfv/6Eccv1Hi3Wo0IiOqgDUl6KameC7Ipk+xdpm+8tFDBikCEieptcpo9jnPNmV1T44T7N/MKk5kc3tKOEzNjGgq+nj5Es9Jz3a491d6nLeFa4uoy5c8ucdEylyI/TW1gMCk9jPdnS0DLwIq3+tbJFAgmgXfzAJMEtt4kqYNucM8g0VCMWyqnO6yzpJLy3x/rV5Bo8U4wYyeeXuDAdxitoO61oJF+19bIlIRhxkDK+ftMyU4A7qzRCNBOFU6uu6lbTck3GA227HgjQMAoGDRcMjPGRoMuKwz2r7Gs9vW3n90Ard1ZHX/4TYuGRu3cRu3cRu3cRu3cRu3cRu3cRu3cRu3cfsb1n4uEELoSob0qzrcbyi7coVZz+YxHzFZT6FiYLS+l1YEf4LRwWHdiKBLEUWnJJElcW4whdDoM7psE9Tq6hqjmI+fPUBUMK1Gj9HLqWlG3RoV73kGZPmCZMUf8PtrlQ5GAm4LZkbKnW1G3FOtJix+RpdtypAaekIIFA8R8DHqaxbK5pIixK6eFZkso4keRYbXF9wolhk5NgdNGge+ZnY2gqFJxKZdRv5mfcwWnD07wkSIUWaDIqFmp4irTk9haPLzIdi30SlIYnWImrLHHZW7WfuCRlsC8FoYGb4pKb1bcUG2c3VU+5LgVKZtOJKC7Jcw42aUs9YYyYhyfDZOKnCFmeER8he2IcfT7itg7Soj3ffuCgHVGqCjcehvsD8uRYSNgw6qhyo7izDSGxPSpxqKIHNGW7ngpi2ktxjxffHyGr56rmyRUDPi8UJg1oW20BnGBmOnJ8f87NnlFkyK8G7vMIvXFXx2KhRGQETp69eZufmTO8wAl086ePNt2t9m9j0AgCXH72idRXHpGqPuRcFGxacG+3QQvR5Rc3ZlXgYDzqPdb4HtmYjeVE6W36/ikpAYwTDnuaPwb3xyERmVkdmHKh/ssu9tQwNhZTK6gnEbRYyby6cw7HH84naRzgqi27CYMZRM8pMEofJ1ZQTmV+dg7PK5lHPGUYblOL6gDy//6ncAEKYPADvKBJjbXYSVWfWIvbpnYhbE5wvi4iLLeL4nwjf/IrMRt65M4MkeMxQhLzP1xUwOpSLXzqULjKLXBaettxqYXafddmmiCAcIJx50zZiVdGYhxzk8OWZm8MrFOaS3+cyDE47jpQVCS20WM5rmEVyTc5M7YsZtOj6D+Dqz1X1lxQpnfH+hngIMXB8pZTiTIkj99V//2yiItHNbUPtvvvI2AODZx49gV0nPlctcn80uX2sYNHF1hdmOjx6ReL3Rr+P2LY5fSQSTL7xFctgPP7mLvSoRBEH5NVee33ft9ksotPisM0K/TCgzaum+jKJKU2+/wdK00xoz/dt/8QmmljiOl1YFTd68BwC4tbCIVJ3P/oXIGS/e5rrZe7QFe5Son6aDc7lwi1mal2+/gtO7zBK+98m/Y9+v0AeuvGTF4++zX3ebfPbXfoMiBH/++7+PipCh08vM3u0mH2P1Ot9bOKTdPn/E7J/bG0FL6K43V5l1PRHcd+2Na7jxJudy8ws++2UDbfZyeB1LK/zMSkXQ5Ie07YlhGHMm+q4zlX5M2ZiVe+vCDFwiwU+W6UcOz2hnM9PzaDhpt2E/1+KFRb6v1RlgQaWI799nZi9+id/hNXTQ7ovgeoo+5qv79PvunglXRfy8I9LxgAjlpyLTcIiIdSooCdPCDnrKSnVVYtm3c+3f/XgbDiP9jT9O/9FUBj3Xb2AopGB8nZnsnafM7JUTZ+iq7OzFRZE7igj9wePnmIxwn7i8IlTkQAS/mQy8kjuev0B/92xf5dK7OURVWjlj42dWhZS9/eYFLM6qxLhJf7pyha8JBI9RKnJsZoV6/OoB/UmhbMDsHMchtihvZjchkadvuazXO1QmgGJllMhDQETEhgrn0mLpAwPahd3O/vSsKjc39JA85kZkn2GfvZKrDrjcCPg4l1UR7n71jGi9e5tfwualLwvW+ZxBP+doL3OMyRnakyNLf/DkGVF0kxN2oMXfwctn/zef0VfYnUPgiFB0o1FoPeW4QxPzWBPKaShxhpwtgRORrp+22Ie20ANeiwdOH+dkdonPhwLH7tGTR3AJnTQw0neGpvQaSxQOnbXaQhTkt4g4XLp2AX5B5ENdfvbOl/S1Qa8HVXETP5MYxnqYa6JZr6Eqgt45Ibr8A/av3O9jQWXiDc1tQ6U9/mEI8Wl+Bnoj2WiuwR+X61hcJvqwL1GHQjKLp0naj9Wmc9Exv9dqtcIp9J9VCNQb33ibz1Cqoyp0mT8kFJEIT/2BZViEfOqKhiAq/9iutWD26KzV4fsntXZtJh+ePaNPXrswokmg/yknO1icFyEqEgCAinmUfW4hFFC55zH9v8nA+TDbz9DWGTvV5LnbO821+6s3fw1ffEQ/OhXgGaCZ4fd5Sg0s2ujn9lpEK/QyA+x9QZ/eCfD8tixky80p5zlp8P/5h++yr0KinwyGSPo5ppHw6Lk4LsFQF9du0OcZ9riG3ldW3uR1oNHgGC1KDKYvqe7VVxaRStM/lXR3CMY4xl6vF9Pz3PcGBva9rL3Z7vfBpVLCkxzPWT4P19309DzyQiuNyvOcQkxEXUFUZGMJt6oL4EJFhPqlFNdQV/Zis7pRK/F3Rp2P7EU+5ysvvI5Mnd+9EOAdp5/luaLV7iAvSojjY/pKkwiGjUYj5kSnUCpxXD5/Ttt1L02irvNwT+zoLtXETU8vYV9lqE6DUNJCdbcsA8idwlLj608PhJa22LAW53n46IB+sSPEvDcwib7sT8BrNHI5nAb4go6QhcUDrvXY1CSu6Kz78DFtvC/UJ8xGFMq0u/NyJAf7UusDMaGHDKICcaqU68L8LNy6nGQS9B8era2tnRPMrXBsy/JvdjvtPptNoauxMds5jgOVZjZyQ1xWGbxDFRwLC7S9/f0C9iRWExVdwvLsoj4zi5Z8il/25LAakK9IBt7P9WUS3YHRBTR1pwn5+MwBF+1kfiqIdJq+6JG+L3CbzzAfm0BV+/KL83yGmWvcr7P7SRw1OE9RA+fGzqlEeaeGp0WOu1nnaJ+H35feSSGqUsd9Efnvp3UHiaVwUhK6z07bK5Y4dp3hEE/3iRgcDlR1IhxLt95EV8biElp6aoZn0moujXxRVRAqt3d5vbCLXN6p++3KWhwAcOfjz1BVidmgrL/NsSTL7fTgwSYR3okBxyp5yr3H7XUCQmhtDDmXaaEJB+0upibpdydmJQwhhGKv38aqRJmqql5pqrLF77LA3Oc8HyY4uEaJbBlQQSorUSiZ9i1RWMyYTWgK+Rjy6d5T5GfOrcyirAqRv66NEULjNm7jNm7jNm7jNm7jNm7jNm7jNm7jNm5/w9rPBUKo2WMG0TljwsZDZqaXlBWzeRi123y2A6uXYUdXTBlIi+pyWwMcZpi9D6kO0GLh+yrFE0xfYLbfJL6a/RNGHE1mN9IpRm9D+sxQJA4A+HTvCebmiCRIqaZ7KcJIbcPfRCbPKN1jEYq+/TqzSYZ+B4eHjB6+JDK2jw6ZCXda+ohKTjmvmlg4GZmOhrKwWxjZ3T0R+Vunj2VJ6B4c8TMnVYtcr5aQEtnnwgyf2a4o6f7WEWzK0OWr/MzUsUPvn8cAfGabj9HEXlc/e2LAA9BShtM4HEVqS2iJS2bEl9AQUVm39pcZhoCi58dnjKZHwjFk84xMDlx8jdkoBM90C7WWZALr/Nnv8bUO+wAqWUd8hlmrQr58nuE822YfGi1G/UMhJ4xmRumPT/jd2QyjszOzV3GWIBfHva0EACCxz758d/Y1zKquuSpOpbKyXLbBEH5ljZopZrCMIv4unJ5hcobx1KYkP7O7HLPl5QmYxbUTrvE1r19ltHnj7mP4nfwej10kjcpglls1xOyM7D7dYiTarexupzfAULwbI6L1vgiMt/c3URb52527HwAAAgEfVPYLg3irllcZoR+gj8FIc1kka12hS9K5HMpV9meUXZkMsi++cBA74lWwHjND1BMKy2jv4eSUfxtF6a3iLHn2cAMvXmMUuyqOLrPkwrOpHC4vsz9OcdPURciaSmWxGufce8VDdXDKjJbf5cGVi8xaFM+4DqJRZmsCUxbkSjSevaf0J0MMkJdkqkM8HSOeK5fNjrULQocoA3usbNftm2/i+SZRSQDXlFHj+Sff/zPMLcQBAGeqFf693/s+AODl168jrvF+96f83UJcpKg+J5ojSVjV+PrA/6/fuISWshd//AMinw41N8ZpE44+Ivrqso+2fS3IzINproLnu/QRX75PgusLV+n3zlJJvPcJ0WgVretXr93Gg3uU+b5+g37qTDwmicMkbt56HQCQPOTvVteJ0qmXkwhZuAb/81/6Fb5G6Jk10ySmYxzbuuqqT0XW/6SyC0NL242QgveFOgiuraMpAvS1KY7n1ybEd2BoISDixnqNa/07l9mv/Q8+QEfcPsko7crlFsrsjz/B8iqRRKdJ2sDhgHZ9ZXYRR0/4u/XX+XzrV+dwR7bdMNKmr4sA+tr6VdTEQ9fy0Z9mvsc5vTr0YlZks9ZpZv1Oa7SzQqOOwh6fayCundnr3CfShSzSynwFprkHJFN8drPZitop/c2tNaJeQqp9D3p98IdEwl7ifAc8QqLZXLiwzn3z/U/5O9HQIJOuYnqBvkVuG74A11Q4FkFD/Hwum3jbJL1cKFQQCNA/TogEMnbjBh49ox12hRQqCPGaqOVxYZW+bjbO7zO0lLF3m2FTDqopzq7YBJ9hK3uG61Ncv9tt7gGmI74mHPQjI66XjlAh18Wvtdev4qMviWSZcnBNXPHRZw/iLtSEvGmL5HKmSZ+U+nIDU3Pci92S+z5T3f3kRBBmaJ4zXNe/+R//GsezVsbmc2ZBmyU+X9DnOSdpLYpse0QU67NY4RV6xCi/NjPDZz85PsLjZ8y+N+U/Ll7kWnTbXLixTgTC0SZ9UbNDXxFxBpETUjiV5l4XlADGpUuXkDih7cRFZPz8jHYdjiyg0db5yM8xDipr+9VGA9EJftZrr3Htlf+I6+6H736Fa6/y2afmmcG1mzh2pVITC1MikZUUeHfgR87Az1++ynHpiNDZZrHAJA4mh7L/+2nu5faJKNo6W8QlPgAR4abPMvg8T38VDtHnpYS8PP78c1y/TZReU4T1TXFmuKZC5/Pr1vkoJ1vK5E7x6mW+L30kguaaSGkDYUTEl5I0c09wiC+yNGhi7wnH9Mo09416kq+ZdfphEI/a1i5fky8VYRdieGGR63N7m2s/EgognRInRpz2f3SUAABUKrVzkveYzldXb3I/fP+nXyIY5riL7xWDDm3WabPDbNA5sUYbNYDrud8rwaQ9O5vhG0/FpWK1+FDUXJh17jsRQWo46jiXFTdbhCYBx9hm8yEgktzl+RGfF1FB3mAf33yJUst5SZwfZPlz4y9+jMlJ2pG3z0NQtlxBf0o8ZoJqF7a4puq9Ie7XuR/1bDxj2PUsg0EPdqEBuy2hB3M5/d+O1TjP7oct2rjDKb7C4fCck+r0jHNoFFdPrNzGQCiPieBfzbifFkrnPCt2TUBThN/T/iBmXHy+QyE0luc47/1aE40Ox7srctwZ+f+TTB5DkeYfak/1uQOYnie6qam9u6NzYK5SgVucTWZxAf3gzk8BANGZCRymiFy3ihNyhLDodruIRDnGASFRE3Xah99ggV9cIy5xVDndXD8GgxldzYlXxNgZ7RuPUg/gFGolIInzljaaXCaHGZE7NyQsE4vSR/t8HnTFtRPRvLs1/sM+kRQAENBazDfPkMsJPeXgc5U7gh8Zs/Brnmbj/PxMbmT3fUTV50ePifZwCgVrNTlwfEobe1FCAR4796n7D5+h0+f8uvUMbd2DfN4wRLGKhnhjO22J0FxYRU72ns4RbTMhFGPXZMJ+UiixTe5nwQDnwet1Y2mRZ4Tnu+KiqdJ3zi/EMT3P1x3s8312u+O84qXXpz2NxGeMBitS+h6H1u79LylMkDwNYH6Bd8ugiWP1ZIf7xpuLr+F//bN/DQC48Ar3qiWaMZzdNhqqOimXOe45cZSetbuoHNFuv/4NrvlqXly7/nW4+7QLr4l2GY+Ju6sB3LrG8+bxscQBxEtZ63ZgTgglOcHzh3eS8wbDED5xzlpC9Hc9VSx4Qg64u+xztsQxSGzvoKA7wDfeIl/jsXh+zUEPTEKnXhTCsyCE3eb+c9SF2DlIs39F7RNdmwkhiXN0Knz9vPauQW+Isvb1nS3+NOp85fa6sSOBnobEcAJe2tlJMoXcGT9rKc6zbipNn2FwmDExw+eD7g5Hh7wTOZ1O5ER87hSXXlf2mDjcx7UrPJf9dW2MEBq3cRu3cRu3cRu3cRu3cRu3cRu3cRu3cfsb1gwjhvv/P9sv/craEAAWb86iuMsInknIEdcCY1bJw1NEPIwyv/hdcl3c/cmfAgDKNSMMFimoBJXFUDbNYXOgLb6BoWqKk5KGLWdtMCjDPCs+nAFUB5gpotZgNK/bZ+RwVhFwT8yFjx4yynz5Jp+pokij1+qAUQobK3FGenezjOBejTBGAAAgAElEQVTNRr0w2Rixe7LDCK/LxMzKC+tXUWkzO3PnIeubKydAODKK2fGn28XoaGwqALuLUctRxNztFPN8oYnjHCOZdqkJJCVvjS5g7UnWsaiMiiK87XYbRim4DZRNaNT5PqPJDEOf47HiYjbin/zu3+NrvSb0hDRxSjawodrOu5/fw8dfEZ2TVLbq5lvkq1hbWMCHn3+Pz66s4f4Wo+GWAWBVBvHFm4w2ewJGfLLBbLBbymiJFF9/ZTGC55vM+nsnGKGdCohjyevA6Q6j+uF5Rlc3nzJ7F7La4Z3gnHTcjEAfJVTfXwMWVhhFrwvZcvaU3/fyW3GEZ1XsrMj3B3epgOZxenF4SDu+IZ6P3/zd3wEA/NP/59/imrIy05eZ6fzgPvtky1vxi7/xDgDgw0/J7dASR4Hd0sPhNqPnc5KXjM0wo/jZw3uQ+AgC4v8xmbp4+aUbHKNEAgBQq/HZoxMxbG2y7toipM7CArNQ9+5tYkURcrcUV6JRPu+TJ09gFOzoxnVmkZ9Lrr7d6aEvXxL0cfx9UszYf76D69eYxfjygLbtdHOMDcMhDB1+/vUbklHcJ79QrdGFx8EI+ZF4muIETGDQcWFW3DI/+dGPAQAO8Zi8/Z2XMCL5GmVpD3YPsLrCZ26odt+qrBoMA8TnVd8sWdQXL1PZ4CxZwtMNoiEGRq63ubikTJ/vncsIt3pSO8lwbb3znXfw/Z/SP1mlYLG2xDEeDgY4VibFLL8Vl4RyMVPCfJxZ1oFU17akWDI9MYndz8mvELPQ5iTohnShgqIyNtOarxde5Jj/7O77qCrLtDjNTOXqhYt4+pTr8tu/SJv72YdUdXL5wzhVhiImjq45KSjMTwex8SVt0xUR4mmWdrh19zP4xe9xfYWZpfAS5+jLdgVbH/P78sdcp4tXlTUNulDq0DcXlSEaCn2RM1kQMHCMfuFV+n2nFMgarSH2s1XNBcfl7Vu0l2SnimRKKn7y6TMhKaT03agdcqyWhZCITtpw7wGzhLs5vu+dt94GACyZ/Cg8T3DcxLu0XaHP/elnHyOmPccjKXuLVHF2Tg7hlkJIXVwgVSlWLq4soiXk07MnHJd51ZknTk/gkSzq2iz3kEVluPcT+6iLr2BWfHSxOFEYXzy9j8uy8TufEZW6cok+oJNpIG+m/V5eEdpLymB2wwB1KRyli+KduMT56xrM+OorIrlGiiU2owEtyQ7XlI3vSLmoUGhgKN6560v83dAp3plOExapVGWk3mWVrba7RlhVJ9+v8/WlIudh+yiJmUX6e2ebn+1ucuxefPs1GBf5XP/8f/kXAIDXLhD18QuvvYp79z8CAMRHKFojx+y9+x/DPCeVNrlxm7jZvEEfAurrXkJ8CTH6xIOTBM7OmNkLz4lHxx1BwCFFS6Efyg3Oc7lWwlBnkUtS6znROi3kswh6xQNn4j7RFyImFoyccw/VhdgsCeF8fHx8zuVjMnCMe5LKvv3CbWxuSqZaCAkE+T67LQabk36mLa6MiFDP6UwBBgtf3x9Ktj5AhFy12cGmVKCmZpmJbdRpO9dWllE+Yl8ffEK009riMjpRogP6TFRiysXvqZyWkMpr753gs8TCtPumoYV+k/NbKwnhXeGzV+oNBIT0mdb7QiIWOd5L4tJ17h33v6Ddm7rcp4qVBqxST7t5jeMfD4ubcOchAhWOY63Pnx884P70y7ffREWcDpt1nt/yPfrQt268gJqU0oxNqcc02N/IxOQ5kmOkTmkxmZFqSV1MWfxomOMIDNHSew2Gkd3zewa9IXyT3DPyJe6zRnH1lDMtLK9w3deVyS6Kg9LpsKLZE6cmRqgXPpPD7kUwQKRCXmuwWOD3dVoW2Ey0NZfkm00OoTM7KfjEBTYUGeHyAv2+w2oBTLRDc1c8H8p6V/Nl9MFxSOn8sZXiOTddreBlcdutil/r2ekpjDpnX3WzD/vPaHufHezAL9Sb1K3h8XIfTKczmJ6kHTXKI7UrKf8165iREtXZETncAlMjZL4PU5KzhtAeBivtzIohwg72J2vgF7oc4mmBAWmtr6H6bhmpvgXCqBd0vvHTVqeFXJlweM9tpy6eobrOTbupFNpCywQ89AsuuxO1Kr+7PeBaGJrFaVPMwiC00EWpxBaEZO8PhzCKw3CEDBrqLBmNRrGf5OsKZdplZEr20unCrTuARVxKuVPOV61RRU/KtRPL8fP+AMDAAGwLVT0vu0ioqsHtdqEpdTajgc/r0NkyXy+eI5h+4fW32JdZHvL+5P/+dwhP0lZz4slx2w2YERK3lB/ZO+crXyqiP0K+j3iWhMI/PM5jclLcmEIdZYX2sroC6Apd5xMHZ2KPNlcfNHHjOlGbh+IqGtmZzxvE06c8G377298GAHz1Fe+FLrcTee31VjfHPyhut2raAJkTMie8i0YjPIdUS2n4gnxOp08Il4KQsrEgfAF+RkdVHZFICKU0fYrMEDYhUQO+IHI6jy3Oi5Nxl8hEu9WEthbRltCO+z1+zrcuvoYf3KFC7YlBqBfxLpkiZrx6XZxPZ1xnlTa/zxMNwSB+2Fya/Vqa4fcmDzLYPeB+fv02+zo9owqfTB8t8fZ4vVI8lBK22x+AWxUl735GFdGVK3EAwMtXr8GQ1TOccc+q6XzRNgOWMP1jNkvUDAwDmHvixD3QHi6l0FBkEk0hO2fXuRayGdoHhiY4HfQzdXH0lCuqkBj0YZGNzYkz2K+1e3J4im3dvSq6Yys0gFgsBrNZZyaddUecWEvzc7CoeKuQ5/fUm/zecDSMiFDVPfk5i1CFnXYDHqkFWoQCXVriXaKQK55X9vzOP/5zDsL/R/u5KBmLiGRw2Bwg1aCT9Uu6E4LKDz1WVJo07ofvP9T7OHG7x08wsPPSkDzlIPkl7fjaiy/hSISoe3JQNuiUYnDAbqfjmJA04JZkiAd9y/lC7GmhtDOCHx52YZbsa7PMhTI3yYP7aWIXDTlwr5dGFxCucHMvg4VlOjO/ic5sbYkH73Qri+f7vNR4rey7aaJ1fskeSQJ6vXR+zUoDw4HKdUTilhHZlNtqwcI8n6ctsrnZWUnWmizYf0bD2FApnMNGA2sbz9DR4XV04BxJcAYDIZjbIj8cilhOZGsWixn9Pt9nk1faksy62+eGVWShqiiCW7LRz75IwK5F1KDdo5zlZy7OTyEW47/NCqJ5fRMoFjm2sUk+n1XEio+e7J7Poe4jKJToiPOpPKQKi4GIzCZjcgTuICw+XrgdnjgAoHZGiKdlMMByVIRhSZHnOfnz6uVZfLZD519WScp3fpuEwt/7g48wpcBMTmUqH3z/AwDA+uI6GiUF8Ix0JGe6rE/WXPhQl/LCyIaCPLi0msPzYIzHJ5JWHUjmF+aBWY5xPq1NzgZYnfz8jqD1bV26C4UsJgXxTkpisSWHFYnZ0B/qeaa5sRt0UnK47MhkuQaebyYA/KXcrsU2QFSw10qJry/rILKxm0VzwIOYISCyxRP2byIUwKTk6dMprs+OCLxvXHsZJutfSs4CQL/PubJYbOfSiq++yUBBo84+lKslVLPsQ1oEjmarEweHXB8xbRQ7e+zDlUtrSJ7QRwxF/PfsuUrjzAGYdUAs6FJf0UHT7nChUpNsZYg+5fXv8mLS6mfOZV8bghgnVZ7qd/lw/SoDTgVJfvbNKiEz2vHoKx48RlDXb3z3awCADz+9i1aVYwqXbEGE33O3LsNbUGlrmHN7LPLOfAeI6cBdV2nmUfkUM+t8vk++4kVuIBJIm8mMecFgZyRTn0lk1Ic0apLAtNT4fTftfO3bL74N8Rri6Dn7sKrLhzGVx8J1bk6pGsf6o0e8uEYmp+GVVOpUjIevlVd4EXJ5p/HZFglzT5/TPv7Oa5Qg/9nJKTZ3RLDXos98oOd0hR0w65K+oUCxRUS15XYZ3/7WLwMAfLpQfP/dP0PeqocX0fTzrohOrU2ErvGzPivz0JsUQV9oaQ4HBdrhTSfXi0vrrXuaw7oIYp/s8CDWU+All0qj5eDcvfkm/cZZMgEAcAc8WJ7jWK2EFYjY4ByFpyOoZLR2VjlGBUnRnuZy8Lq5f3kVpMqVOT5+pwtTXvbLIPL7si6E9X4btQrtcFE2YZXM9cBoxu2XON5ngqlbjEa0OnyGRo4Xn1xJZaYDC3q6DGZ04HGFuXf1uwa0dJnZVYD9ty8z6JDfPcYTBVoCYY5jMcvvWH/1AlxKNFT2GaCJiHC6epLGqnztf/P3/xEA4M4ntJc//sH3sBBgP+IKwv/oYwYMzB4bWmkFcVVekBUBrMPvxc4e7beg0i+DiX1qNWso6lzQUBLENmnHgw8Z1KuJ4Nc/TR/T7LcwLYGHisbYVBBBrcmJxjnxJX830H4/dDiQlk/6xje+yT53+b3bO88R0hofkZlaJYn+07sfYEok72uXeEk0DPg5JmMEA4eg+A3OzZJKdtDbwPwsX7+/o9dYOD5TE3OoV0RYrLKYkMqeK5kKmjrlTqpEvmMAUlnaoSdGm3vvE671VqmPN7/NOa/UeUYoVyQJ3jWhIz/vk9z0jMpsDRYzShVeKCqy6bNTzlGxAURErj1U2b1HQQUjAIeSadUtld7N0h/3DiuoWNiPw4Z82Su87EzOT+KLP6Qs+MxInMHIuT18vouVRa7PosRI+qZRfbYBmQx90NQUL46HiQSaNj7DtSs87zXqfN5oNIpdEbmP9nefn75s+/ku+m4FYVTCVS5wvlcWV2FTaXHHxP2zWlcpXCl3LoiyJlL5RpNzaXT4sP2c/rsq4RWvgpITUzEEVdb/SCW1wUmez4rVGgwG2krAyd85RJDdLA2RleDCyhx9UkmiGEcHB/AqAHekcsqhle976523MC9m1KJKbsrdOibbXOu5Bp/PHKHtvRKfxpxKjb78gms8pnUWcVtQ02VtVGbUVUCu1DWicMA18LUX6N9yIzLm7gC1CsfNKUoKsxIxlVIRYfXVIBGCsvye02ZHU8EHi2lEyMy+lBtVNFUyPWul/XcU9DP5QjA6uFaNIrvt67I3MRFFucnX7ekiabM6oWMUTnPsw/wa/SPsJvR1pjtKM2g5r8RNKpU6vwAGPRKYkb+q19qw2yTe4uYzTysgcffHP8UFJQzK6k9FIi3l+hABldcNcxy/epB9d/t9mJjk3Jxm6D+mZ2kL7UYdfreCMeprSyXzk8EgnEHuu6dn3F9SO7Q9k3kAs0q3TAPOic1sPT9PhSdUOirKgHqrg1aX4+cPcS5tKtW0Ouo4PqPfyBa5zyyuMHlUqzdxqH1lFGBblxjEYXobpyd8Lp+Xz5nL0g9ZjSYszrLPBgkbOOV/qtUsikpoTPppA9U27dkZDMAq8vvWqJxa99D5hUU8esK9fnaBZzaY+L2lagd9E8fNaOba30nsIL9P+52Y1BlKwT2TsYdImO99991/DwD4+jvcS06PT3HS4Hl56ib9sXNUDpnfx61f5pn671zjGBXuMSl0b+shKg3266pK6j+/x7NNP3eEKyKqT6sENClS9okL67AoIReM8pkaormw2W042ucZKl9iv0JRzt+wWsbTE9rDpRv0zQ7dwe58cBeTIvGe0d2jrH2nPmjDqQCPXUFWr9cHq4freEZ3gLYC2J1eH2cq3e0+ov1GI7SvSrmC/IDjXq3RvlIZjkF8aRHP7jOJ8NzI/sxOz6h/LVhUujUvaoem7lks6RLBt0vADJWAvXD9RWRVCo4hf7oFSvEF/HAq1pFXIN9iUZTJaMCUyskyadpzKiXG74EB5bICXH9NG5eMjdu4jdu4jdu4jdu4jdu4jdu4jdu4jdu4/Q1rPxcIoclVZkGSh0ewSKaur8h1V9DrhcU5bD5lZDf1hFHHW2+wnMdis6CrzKNFZT/llORYH25iQhDGbluEkmeMqManw1ha5nePoH+723zfysVlXLjI6PLjp8x+NIQeCPgjOBFx45MRKudFRvAurV3FgycsHWqa+T1nG4K/zQThFcx58wsRf/UYAXz9l5awdcgs46gEaf1FK7YfK8tf5mfVVHZlMxvhLDPqmBUEdWaR0c/VlQU0u8rYKnvx5efMIKDuPYe2GiGZdkm925wO9IrKngkVZbLTRMrlMowi5nzxJqPGDUXF8/snmBC0s1pixqwjsjmDcYiY4LlBSVyOpMstrgzKeWYO0pqTN99iiYPb0TmXXO0K/h2Mxs5lxA2SvU2eMEq6PDGFrDLejihfn0/z+eJX/OgoK57cYDbhjVeZzZxanMejLWYqV5Rd6QQ5BrVWE2fKymRSHE+Lvjfg9MFhkdTyc9rCfyQ0y/LiHJ5/nAAArIuUbVPotFC5jNl52txpg2OUYuISfqsRrbqIOYXUGhFA7h9nEY9Kthij7EJFY2xBX1mxEdLH7Tfi4JgZ1ECQczOK/w5NVbSVkXMKBj6SrowvTcHvp53ff0gST5/KDmF0oaGoe+qYWdALV5gptbtMSEiO/eiEdtUWDNcd9aAuiUqLSM5iYSLlmpUeDpsJ9Yc2MDPDzFKnZcL2BskpC5wG+CZEMpo8QCzEdX2aYTR8bY1j3Te38KnkvcNe2p7R2EdEpQleH+19d1tym+EI8lpDOUlG9pVVL2T30e/RtywtTug5mZW7fuk6dlRitjbHrEAqzzH/6uk2guE4AMAged5kkt9RczRQl19zuTnWBaGVDA4/WlbO0xvfZilXWCVIy7Nz+PI+szGxSxy/kPze1sYWrr3AUpmBShZ+evcOAGBibQYm9bkrmPCLNy/i6ZM99Yfft7ZOdNNx4jmKylC0a+xrYpf/f+PrtxB6gd/9PEGfuZVg9qg+nIRRUrUvvczSqqGw0V/8yffx8ttERYVFOvmtXycp9Y/e/SkGDc79q1dvAQBeXmNfzEMTvvUK//2v/vH/AAD43//ZHwEAsrEosh3a1ZrKrUpG+phOooB0hn9bnKA99cWwHF+NYjvBLNxpRoTwZWBtimuh46fddmV0vWwR0dtEyeRjIqbdoN+fbgGrylTeFGz/VISg1UIZ7//sAwDAwmXuVVMiGD47PkFA8O90UgSiKrH0TURwtMc9Z5DhuHhF1p8vl9AY8HcPNmjjxSHHODARh1UlxTUh+ZIn3Ctfu3ET//AXON5/8P0/49gqW1ZotjAQkmY3qYV2wLGymp1YmOJ8d864NnqDPqZVJvjKy0Q3/eB9IhtbfRNSyhA/SnBsQwcj3XUH4rc4RsugDUgVG327D4s+2uGJiCGtHq6NWDQMk7LpB3nuh3OXOObdIfDxD38EADB4iIZ44Y03AAClWhERt1CYPZHZK0tZO8xDyWd89JSI45Gfs9TqiIbpMwdmzvdILjzkD6ARYXaxlEsAANyXQxjMcryPHnHeXp4jqiSxvY2zMz6zb4Gol80tIoFtdjusQonNz7OkZTqmMpmN5wjaOSc79znPi9c45tcvXEFGOs6RUfZe5QkdwxBn2gfLygAHBeO3mvtoqdyy2uZcxiboO9OZHezLH4a8nKP5CNEUmw8+wVSUZ6fdDDOwARdfc7hXh8VFHxSY53yVS1747Bwjp50Z0mpV5YmBKGyCvJuzInsWSWllsIzeYOR3OJ7lNt8XmgiiLiRcRzLf/vgVAEBhfwMViWhMXxC6TGju2Zk4rKIfyBxxn86J6LdfNyIT4bO7olyL33yFpM2//09/D8sXuWYbKlsLTLBPZfsAJaEvjIIjm0T+fLK1h3BMJOzaEwrDJirHXFdVSZM3hJBo1Orwevjdz5+PyL/5PXanC36hq606x0XitPt6qYVnz3leHJhoA6UWbSk8FUGlLlR8QrLiJtrxk8d7GIKf5VS5catL28nky3jnda7napv7mr0zQpivIKV9PSWZ6qGIVhcmr6IvXt+cytZqQgDOXVlHW2fJFR/nOSy0XmxyCpsPiPRJiuj7wvQMEl9xX6r6uR6bgsj8xlu/iq2nXDuLs0SxPBPB+5WbV7AlFGZfSLrFhTgAYP3aNI6zI3QZn3lUknv1ygUY2nz94y1+lk9ou8X1OAxCs7oNI8ERznu9XERcgiN2m5ClcnN2WOCO0mdW61x7lQrH7mG5jL5KAxtCy3Qld20x2TEUYmFVSM3TZBoWofEuz3HNdYVEaBarsOp83pQM/PYu/b3VYodTE9wWEsNt5//r9SY6Kh8LCpnb1LP8xu/8Nkp5rp31WZ7tdp9xXDKZDDKikJhcVwmoEOapXBYdzbNf8zYqY3U7PXA6aMctQ/2vjLF/yg+ZL1oa26GZz1ZoZGHvq8x7WtQXTw5gt0gwJ8f5fuMNlpqFYlE82+JZ5EQCDSsi6jVbLYgF6X/NXkmhi+bDZvLCInLybpN9yAp9PzsVQUxoaZd89amQ5sVMAQPdK46tI8EcfnavaYRLFAbdJs/wDZE9V1qnCDjpA2sqG4zH4xwrq/e8vDGteXBqPOu1BtoDzpP40xGO+NB0ibw7n9YzjASVMhjIJn0i1H/yiHvJcGDCiVAu33qd5yv7x0S6pNDBnfdIw9A9Ek2FEDXWTB17upcNOiptk8iN12rA8QbHf1YE/M2W7uXDKr5+i2viy89po+Gw/GMjg6VrnJvTAj+7KFRPrVZAMMrvsZlV0m2jwRiiM0idsg/FBp/TofFvlxpwy6/aJfBj6PZwcEDftbBM3352KgTN0Iglnc368u113XcBI5JCl40+c26BfekPWrCoUgZukakLGd0xmzAwcA306jz7e4TG9HudyKgU0+Lg3KyIPD5zenZOUWIWgvLCurgyANhs7E9JyLhRVU98YQHHR7R7u1CIWd1nbBYrLLLfv66NEULjNm7jNm7jNm7jNm7jNm7jNm7jNm7jNm5/w9rPBan0P/qf3hwCgLVewfsfMhodUqZsLsCoqis8iSdHjNgdbzLC6JGEW2Dai6Iy7n1FY+tZvm9+KgKLau42dpi9u3mNEVGDoY5Wi1G2apGRvEqB0T2r24hrLzLbtH/M+sbkGb+/0+ij02Do1SHeDXQZ0Vu7Mo1Xv85M+8EZI9iJXaFlOjWYbIwon4x4IILMMlxensHELCOhdx4wymoyAza93ipunnufMOMzFfYiPseoql0kYulT9qVZr8If43P1rRyXfIqfk9rxwm4YkRFKDl4ZmEKzAceQ2Y7WiEvIYdC4lLEW5Jz8d//g7wIAnC5GH+uVJlaWWHPbETLJLjKR9776FJ9vcL7e/zQBAJgTga8tUIRbNfLVCrMYZcl+t8tmoMfPWL7MOZ1ZCuHTTxkBdQpxklZtdsgyhe6AmR7vLP/mGSqiPG2DkiRwq2Y3L0JV16wNDq8IQLfZ1+kAo8fxy3FUlI3JJ4nw+fGHzF79t7/5a9jIs18/vEN+hN9aInLqhb/7Fv6v3yehsE1om6KkMVeaFnzrb5ET5t0kyaR33yeqpF/14uotZiNUNoy5VWbCdjbPYFDA2ihy40aXmZJ6r4eoiLSPD4Qgs5cxMcGIdcDB/uTEpVVrHsPt5FwaLRyPVIY27g+aAXAOHaoX39kWL5HJglCQc2eoc6zmFmm/7W4TO/viLwkwi+kUoWKzU8U9kbCvLdBWTw5EDFwBgiGR7klG1+dhdv5rX/s6nm1yjHJZfl+jz/Fv1B3Ip5lNmJJ8pcnIdT41H4VV2bHth5L+zZURDnOMgsoGD4aynVDgnGdmYZ5jlVc2OntWRVbkxN/8BuuqT5Ps57e+/qv46MMPAABb28zWGiz0p9GJKZxlRhLGHI+heEIWFqIwWjnGuQozD+0K/Y8nFILHzeczjFBiQmVdunIVJyKzu686+2khJ9KVMi5dvqJnZib8WLXvwYkoWuI2mRZK6sJCDIen7OP3f8ws7Te+SenPbqeGrx4RNTEc0gaC4hjolE/xn/2n3wUAVAbMYH26KzRLyQHTIdfSd98hGigjsu3H+zlcElLz3qeUPrVFOB89rxEeJ+fXIPJ7R5a5iqWIE8s3ifj4UjXe/+RfECE0MTUBKGtkN9FG1yf4OcflHnoD2lFRvsUk0txQoIu3Xmdfmx0ib/71H/0QYWXdREeE+Sn+Y6ZlRBDM7FimRVyq543EgrjlZyb1YyFNnOIV+N6PfoxlkXjbxLMyI34Xh8OG6BL/dpygjabSnDdXwHfOaZU9YZ9NI+JSpw0DowjX2lw3Pskf+wJRRMxu9ZnzYA/RltYWFlD6knsrxK+yK3Sr0WKEW1xxSa3hcoFrY239CqraJx6KsDfs955L+C7KPxnE9XeazMEqovSquJQ2RTy//3wXN5b5rEtxrtmhm3YZjkRQ0f61lRZPjdZ16ugIV5Q9G9o4SZVRhtTvQzfD952OOESynO83b7+Mz+4TuRT0cJ9xWLjHtu0D5G20me0t7tPLYqyvZsu4Lvn3qLiKNp8RDWcyWTA9RTuulrgXhRdnkVd2+/HnRJ4FLRx3K4ywiScPLtrAYEReazDAKMLdkJ/P1RHh/dN7D7Ag2ylluU6vvsRnGg5tyAiJYRARdjrPudw/PoR4vuF2cy+ZnqCPqDUseCJRBHeU749N0a43HqcgM8J/8hv/kGN0zHl476M/g11KvyZl+hMJjt1EeBIOu9DERnGsDddgNCtbPaAdzfiJeNg6OoJtgnuATxAtn5Atk9Mvn5ORNoVw6RvoQ7cSScyKPH3ko61mPsuE2YKzM9pttk5baIiRu98GZsQ16ZSNfvA5ka9eUwTWVX6mTVnhmyGuz+2DBHriFEunhfQWMe4bt5fOkbIjDrgRUqiaL2JCog8zi8wiG+wW3P+U+19IfFeRsMQIWg20OrTXtpCoI44Uh8uDgwPuy06R068vk7ejVqni8Sb3nEprxPMjguEX4pgSIW21ItLVCv+2s5WFVdlj8abD5RE/UbmJnvYJj0+vCXGMTWYjbCIbRl/8OEJGdhp2OIQWMuss6hKHWeJoH92epMNFgh/ycU15XO7zecqccb7XI3FsiOupL/nmly6SOwTlEnpB+sEP7tAXjVAo5VIJLsmlW0S0DgnG9ExGZDVPHaHmQrNCPbaquH2ZaBef0PsPt3kea7QqiMdoA4MOH2YAACAASURBVBYbF8AI1bm7vQ3rSDBAaz/o52v8DjdMIq/t657Q0qLPN2qwuPg9dvG0He5xn3aabLALkdgfEfHDiKbswxXkXGTLXFt+vxeiksFQBNB2oQpr1QZC4u/LCPXpkJy7wWBCuSlCWhc/8xUJkASGNtz5HhGXXY2tXXybxV4dIcl7d4VsiS0KGZ1KnZMZO+QjrELd9DtduCVdnxWiqFGmTzOZ+wjpPOYRinlfaPqVi+vI6D6wssb7ReLJCY6EWpmZon90u2hXxXIJXQkIBYRCNon7bW/rEHYP7aEovjCTREU81hC6EimYE6rv+DTB8THVkBYfy4jAPy7kVLFQR0Pj4HBzbIsl7telygCxWaGaUhJQ0NwuxWOwmzhGXSHwzBbu5bFgGA5VuXz6Bde3XZ/dG/YAiQG4HFyzTrvlnBPwHOEqhEry6PCcTy6jPTLgF7+QwYl8kXfJhSvcV5Lvce/Ke+2o2cQD+Ji/m7nF8be02gg0ua8kdZ/YF2r01q11TIXEXSg+KIOQjd5wCJ453UH5JzgstKXNxFewSVQoFOE+bzPRFsJ+D/Z3tB5VxTAZpc+enJlGXufhji54MRfHrppOw6b7tFFrxNQxnBORD7U+R+eqVrWOoMb9VFUMNqFbjRYrDOIk7siX9cSZa7Ka4PPxfUXtIVPiZ03s7pwLNkUnRdouniArTFiQKFBil/5uRPafyeQwHEp8IEaf0hJqenZ6DlmhXy0W8e9KiGI4HCIizr50jnZ47RqrJnb3dzEUhPF3/+cf/AdJpccIoXEbt3Ebt3Ebt3Ebt3Ebt3Ebt3Ebt3Ebt79h7eeCQ+jJY2boLG0TDEKFhCWrXJGiTd/SAMTcv7DODFH6SNHmegcuZSU7FkZ9e8o8lrsdDMQM/pb4DkbypYnkY+SVCW3XGaXuSl7PYTKfq/0MpBTVqjES1x8AQXHmjH7nsTObcXKSw3s/IIfQm2+Td8JwkdnFuz/IwKxa5Mll9s/rlWJJ3YInXzICGpUqSdQ7j48+/wEA4MZtRmgvXGJUvFHrAA72ccQXlJLkbcDpw8P7HJuLLzDCeOsVRi0/rZ2hnlcNs/g3LIpcD4d1QCz7Rv3sSD7eZjFjSgiLVJKR5cM00RuXF28gnxKP0YzqdKXU4HF64POxPw5bAgCQTjLCOeHso2ZmdHn9CjPN25v8nMQZcOkifzcxz4xIMrsHlUHDK6UKj3g/nn54Bq8ysUMplQ0HnNPjdAahCO3q8RYzkLOTHJfMXh5SZIQ7JkneHj+nWixgboHj7nZyrN/qcuy8YStKGal8qH7+qeq3o0cx/Nbf+zUAwJ//6R8DAJY0p91yHfU2o9RX5/nZxzH2eWcrj1svMUMxNHJOnz9nXa9x4EfikON+/TrHxWukLSVOU6iUmM01KcZrMgKFArMCR1lG+W/cZCagcnyItuqZXVZGrEfShdVaDxYhg0Z8AA6plc1PxeFTDe3TZwzz1/Q5p6c5WFSre57tCHOyvvfjDxAT/GJhiln4kVLdxsYxTDYpUUkmdf+Anz0ZewiPlfOU6omDQmg/I/wIrzLLl00xi5qVusvC8jyS4jj66BPa19fevoRMlhmNrx4xIv9f/le/CIDZ//VFZibWV4myebTzQOPjxKxkjut1qWsp5VAqpnF2xuxRSDxNo9rf2zdfQ69POzw+pM1dvsi+P916hKIY/9MlrVm/arStVgzFhzFUtrYrefI7H32BsriiPFGpz2jM12ensZ8gamj/UKoYUoypp/K4fomcPkcnHJfHxSpiqvNevyJfFKDvvPdpCrEwbWz1CjP7FqFmPr5Twv/xP9Kmf+ltZqtnxH1TcxgwfZu/+1c/pd+KSuUmbanDI+6lhoF2VU6xLzPRdVSkKDM/xezb0hrRdq3mp7AqW/3sPuc5ukabyG+msC7UUVZyo+mmsn6REFriHzisJNgXZTNrxQNsSDHr4hWivqYXQggHOKZhZbczRaIunrVqsIqv61qJ6zgudYtlfwDvfv4eACAn2VC/m2swOhNBTxmllhQh6mnxyXg8uH+f/nPhEv3AlNQprGYbSlba0dOykHuSR3U0hugJhTbh49hGVd9fKVZgdnDOF0LMSM1e4l65+WQTH3xGPq5XXuM+OOGhrz55vo2AED9ra7STZUntPjs+wtNj+ojb33gVAOA0G2FUZj+Z4R4ZElfXUiyAmQg/91AcDaFprqnL8UlUJN27nyQq6uot2pm120Jslv1waQ02pGIXDUdwJo6tS6u0iylxq9z59A5W12kzLknxdjUGe8ljOKRwdCwJ2XZRErTOIdZfo20Hb78EADjTeWJyYhq7+1xLbg+/ry4Oonq1C4OkpwNSP0odHuNIWb55qfKVhY7KN1pYk4R3O8vP9wnZEp2MoVCjXXmEvugE+fPqGy+fSy2HF/kM2zscA7vVdy6D61SW+6l4q+YnppHWmeY0yT0hp3F86fUr8BRG3IB8X1X8Ok73HG7epK38xQ/eZV+lnvnGO79wrn6UzBHFFozxfS6fEdkzjq1B0u1L6x4cCJWQz/G7r83S///yW7exn6U/fLLJc9/0Gnl7Eo9/jK64I154jQi+P/8Jn+W1b3wTjx7wXHVTyk/VNPv87ruP8c1v3QYAlPScGhZYzTYM9bv2iMpKaqT+cAxd+djHn7KvxiX2q9UzY/dEUu9SmPKGxCXSMsAuVMKJuLpis1y7rlgM+xtEDGeL3H8vXb6IsNaqRUpiJqGXl+Oz2D+mL8olxXPTkB3DgfULXIfVSkVjxjMGDF1ML3H/i7S5P23vccyPj/ZhnhllljmeO9u0+2BoFl1wf4nN8TUHCe6R1ZIRDnHSWCS3bpLSlLlvwcwkbS6R4PccHvJ9hqETTqnwzIiHwybePK/ddS5hXy5J6czDz0wnT3D5Ouc+MLSc9/Pqbf5uIK6jhnxms13AmTLfEanM9sRH6fdFMNA5rJhi/6LiYtra2YddqJzlJe7zz4VUqbbKSAiNaZeSbPJYKOuAE493+LfZGY7LQPNndriQKvJ1ZhG6NKRiOex1YBXPVUvyuU4hF7qtPnpCU6EjpeIa/+8Ius/XmVPotGqtgqb2kKJ4tUITtL1GpQqHxijm5RiXpLSayRZxkhwhYmm3HfEEnZ2dwRMQz4/QTbhHW//h3hasC9w7cuD3nkm5aXlqGl4nxzEvtcGRGqPf40YsQHuyiCPJqXGp1WqwC7UyKcTDsYnrzthqwScuzpZ4uIbyhalCAR75qeQm71B2dxDxZfpYkxAPbj+/r1itwyHUln/EVyU0iaUxRFp3SWeMyLaYFDz3Ng8RdOlc+4BIPn+E9jsxNXOuXntywrOe0cB5OE7mYDLz+zxtqTLbeT6IzRvREE/bS7eJlt5+xrFq1ltwhzn+W7vc3w12KeSWS4hJ1j4YFYrWSDtptRpYWSCPFLqcm6ODfTSlNtiXYuJIka0ztKGgu69fnHijSgyjwYC+h2NkltqxdYrPnimlMTcV5zNcYrXLhtSq6sUSIC7BK1LsWwlzjkq16jlazi+U/4hL1my24klCSoSz3GdOD7m2Lly8ipIQzQ7xjq4IlTbnj+DCC7SPXSHey9rndw4PYDaqCsQq1J3mP7iyhJSU9yy6J9mNdjS0MfS1h9il2m2L+jEU4dKSVHNHqmOlchFOScmbhHhyaT2Xy0UYxS28vMjx62tuZmci0BUAh8fcN2vikAu6ArCKCzaT5nhabXx/4vAMyyv0UyNuq5aqQ1KpFHziKkrqHm6RDVocXuzs8nenUhguCTVt85ixvc196XfxH24/FwGh0WbwcK8L14iUCzTyzzbouF97LYR5Gd7H9zi4ZvsIhljACzd4qH78JAEAsEnm0OwwYkIb2ekRDbLd4QCaTV2ciJS43+HisDm4MLvFFlwOOXEt9u+8w4P00/09zK5Islkw+m3Bqy/FJ7C/IWLOCi+Vr/4tHnp/679Ywwc/Y3lGfp9OZe4GHeRpPoNqhdMxq9vXSaqEhSjfe7zD13uiXDAbuxmcqYzGJbi/zS84W6eJoBxA8liks5t0+FZ40DHICTk5xrUKg0zAX24aFjnznkgonS4XrNoMtySBHL/ITej/Ze+9niS/rjSxL733pkyWr+py7Q0apgkQJAiQBEmQQ8zujFYKbewqNlbaUEgveto/QG961NtoQzs7RmPJIQd0IAnfQDfad1VXVZd3WZmV3vtMPXxfVmhfqFdGMO9LA5WZv98155x77znf+c7O9hFqeUpsTMbMIwNbrdYxLANcr5DQTPsmlq95sPqYc/ur93gge+mW0jySxwiPcIMo1zifLo8Z1W322Ss5aQof73aF4PNSjhoq1ZdSWeHx88NQhhkqXR6sTgWhfvONKdgc/O/Hzykfz+8z9WN5woCwiJ9LU5wPT4b9TDXsMAa5TteXeSExuPj7j+58iPOCD165Qched40yu+o34MM7JPu9LmfTqZPvN/SAmg4E07PclDMqDWsxBvHJr7kphoO6rIgItlqtwi6j5HLRkOZLxxgZFomgDidVwaWHhoawuU6DMTZK3TBb+L5HKxtIpTnvoZAgwCIuLpcK6DWpL+euUi7v3eFchTxDMAqea7eK1PuUBrxeBs4vUR8PJPc5lRruoAmng+/26zJUKHHzKVcyeP6Qm3BRm8DMBcH4j3OYnqCsvHCdl4HHj57pvRnYzJyb5csqW2w1Y2iE8npukb/L5/vE0UdwGDlXp3LeNEqc95A/Coug1gf7TLmxKW3l4GAHQ4JorojMcGZyCgCQPj1BUbDoWZEEfvrRr/necg3RGa59MKhDlErZf3pnDa+/yQ2zLWh/XaSS5XwDc3Ja9C+XK2ke1oKWNGZUKnVPlzCPnLPWBvDbh7x8ZUUm2TrJ4bxBzokY7U7LTNlLxgv4ui5kj+REmL9EOYk4h/BZleP6+1XO3/VrdMocPl9FRYTsQ9F+CXaR/KVN2NPBfu4iHQsNyWy1kkVYG63oy5FR2d1rE3MYCShdyHhb80E59lxwot2lrJ0bJxn1bx/x0vhHrwVx9QWSTR4f/ScAQOFE6baBAJCnQdj8nE6ZK+NDCIapx/sJlalXadg3LlzDZ79hmttOi/MeHqM8/5e//StEJjlWB1QIQRelG9OzOFFZcMsYbeCTRyRFzZYbeOUCnS8QsXhcDsuF2BzGhgVdH+flpi2y0WoqD58IS/1KCbJ1bepvEw5bU9/jWEsJyvX2yiO4nYJQC67e63Ft9naOMeFUmqKDcvj+3zAtr9xr4aW3Waq21+WzG40qsgWu5ezyFOdFB9t06/SsMMHmU9q8iIWHcofVjZT67jNS7rce0lZ32z04pLOzs0qXEMn8zPXzOFZJ3Ie7XJtxkaEOjY7jWBeXsEpCB2bYJ5PFjXpcpJ0x/u3ERft1mj1ARo5niOC0DxkfjkQwPky9PjjYAwAUlJJusDiwdJWX9IJIOePZDJbl7D0RoXZblwhb14TELp8RcislRQ5sy+nBGSnpNRvTYvr72ezYOEZs7E9T40qPUCZMHRu6chI/uMPzR0QBh2qpitQRbevYMO3IVlxO4MermFGKVD6vdCs5AkcDBgR0aU45+L6L3/8GAGBlde2MyNPR5dpUitTXvUQGEKluR/b/yWefYOYiHcOOjtLgf8Z+vvPm67CX+dupECMxp0rnyTbSGI+p2IBIqC/P8f/NtRL6uXCffMoz1EiYtt0368NOjvtE1Ur7VjCI4NbhQkElxjslpQiLjiCTT2K4ynGN6bz0SGm+i3PLiNmoE03w99cv0TG3++Q+aj2uW1kpWRcX5VSPuJHY5vpWlFbQ8zrQ3GJ/zA7OX00H9Q8++AQ9OcFaukifKrCXrzTPiHr7F5G+M6tSSyM6TB1afcq5cthpC+eXz6HeFFm+0g2nZrgHZfMl+EJ8X7snRnflCcyfu4rkKcmkrXY5pfScWqGBhL7v1X4Uj3OuLNYWLpyXs12Oq/VHCvQtnD+7pFmUEmdU6o3Z7kBL6aFVpf53/G6kRYre2eezKsqLyphLcChd3mTwqi9yzGWSKBdoB7pyxvbMXKM//tfv4ihNXV3/9Inmge+du7iAqs5YqQTPJkcH1J+huQkYlHqe3JCtGOL7X7n1Ej79gqmHdqsCIkmtW62BsSjtvUOEwKU85+pg5wAvq0BBWmvTU2C2Wm+iK1mAyKhrlTo6cr63TEoPP5Fj2eVERamLhSblo6h9wmyzoaYz06HoEaIBnn9m5hdQU+GJZodnvV+2aastU0FkCpwHq+4AU/3y7okEerof1FQEwqwrZMNkQuZEqWxKGWsZ++Wwe6hJF/r3Crsc9b1MAaU8n7ktW+uepIw73V4ElFbalKPB7HcgfsT+9VNgqnJIN1tN2PT9uki2DaCOuCw2XFSQKau+Hx/SZlw6dxFdBb5LSvlyuTiuUqWOC5fooPQGuD8/X6dsh4ZmMKfS9c82edken6C9Mtr2YChyLeIp3r2WlmnvTk5WUaxq/1yiXq4fUi6N1ghOdA4wyIlmllMgl8/g+tUfAgDufvIZAOBw9xi+c6IAkRyFvJS5yVAM2ZTW1cm9xNSgvarWurC3FQDfEtVGgOsWszlR2OF5cWaE+9pwg86zR6kKqnbql000EyM+vnck5EOlRHnK6tzT6PHfsDsKV5JrXhBB89I5PvMk0YKhq3QtOXHykoUjdwg35jhHhVP+rqZiGF1TB8Ny0ldTPPPe/Zw66Q57MH2BfU8eUC4NtRIMcqDWRepv1t056PWiIWJ7u+TqwuwUAGBnP46wAj11kY4ndZY11gx4sMq9zRLlM195ifrtdViRVLEaj1IzPbJXrUoXLaUZR0ROb9b5x+EJoNPlZ8lUP9WPe2yj0jojCvd6+MzKWSGiNrqdfmCf9uP+L5kyPzYdxNIF3g9+VxukjA3aoA3aoA3aoA3aoA3aoA3aoA3aoA3aoP2Btd8LhFBDkXCHp4i6kBVw0LN7/SLh40/uJ2BVGhis/LeRp7cvGB1CsU6PPywiQlO029aOYGudHtdQkF42t0j0nu0W0RSDqEGQ8FJbkT3vFGLTjMYXE0S2OFt839KsC23x6739HXp9H/6W78jFTzF7gR7uqqI7f/d/fgwAuPXaRXz3KktJ748RPVRp9wl7o2hk6CmPLDCCVTQCLcGHv/c1wtpX1unFDHpPUW9yjhxBEWsV6H1MJG0oKZI9JNhsu0yvcTqVRcDH33WUwmF1MhJjajrQVCn5piJDFsEVG706WkobOWkIdnggArtyEfMzjK4/UinU4iOR3KGDnlKkhE7HmMjFImMuZD6iB9QJRi/6MDr/kBvlPa7JlMr17puLMEe4zqkTfs+h6ILdX0G5zfF3s4zY1M1cy/2VNM6/Sm/4NZHTbT8lfPP0oIfxy0KTyYs7vaxS4IkKfvaYEbPZLudsr8lxTXUjiJT3AADD8/x9vsY+ZXtrePQJkQff/D5TUr4wcz5LyQomJ9m/p0oHiZQ5xwgaYPXQ0+02TfF9VXrMx0IdLL/O+e4pwtqSDBqMRZgbnD9zkD5eawvIqJxpw6gynSoXG7s8gWi4TwSqErQiOLRaQnAGRAjqUjqYgmPTY24kBBd9+yIjIeG3qFPlUh3NtAgijxl5mb7GMbzz7i1srXCssIj8XSSvEacdtbSInwtCyI0zWv5oYws2C2VAyHc4TfT6G6xrOC1zbTplrs3sLOWkaq4h5KK87+zzO9XjbZgV9fnev/n3AIC//rM/BwCs3O8A19ivZIYoo15FUYWSEVtF6ZKYvss1/v+J1Yh6Qsgst0ypUvBKnTy2k+z70x0iwg73aZPGx7yYdqkUrIn9DMpmPDncRjzPebcI2j85wrl2Oipn6JVmi2M2uRgtqyOGjKLVbrAP4yH2d2cvi+klIrqMIsS2W3p44RLJ9V1eKvazNY45NjuGL9Z+DgCYnmK/oiFGgJ+nvsSlW4JTC9HlFHF6xe7G0wLt4JCiF6+NE3Jcbu3C1BBJX4/9y2eIxnJ43PDq+fFDjufWRUKjD558hqTQCecvEZkx56Ct2X2yAnOF72mDduDyPG1Ap1nCxjYRRV/9BveQO/cYyTG6XfAqipZVBOf8/GU822c0be8eo2NXZtmHZqGJyfNEKW1v8DO3CPanrn4DlQPqRNNEO1wUkfaQw4SYyNvLWer/t0RmHY66EBCaZ/eAffjpx7QZz/x7GBZCZWGKMm00U083rHuYGOcYd75kVDGicuuJSgE7JuqetUfb7osLQTYxDGeIe0FJZZnPqXTwlTev4WiPfU7tcB59o4wKj88O4SsxIk2+fMT5bFQz8KvMrqNBGRh1ECUVCDjOUovbBSptPkf7bzbUcDmm9REiL65U1Uajh2ORnKce0h7U8/z/fDyHGZXbtk7xd/unXCu/14XscxWZUAQ4k+Ge4va0cCRyS7eTsjczx3WMDk+irjlqNJWaJZl4/GAVgWnK45HSbi8vERHstdtRqzJSGRsW6iKZxeN16qUnQrtWsXOPM5jMqLc0DyK+fPESEXJP1k9wWqW+JCoc68Z9RprXnmThFIlsYIL2YHmYxK/hcSd+8h4Ra+/f5u+/8lXqxvkXPciZlBan9PTxGZXwbU6gUaKuLgyrRHya653MJNFT2s/Ni0SEbW1+wPk5zSDl4hr6xymPtRzXLRs3Yfka57Qdpb3Kb+TQEhrk7R+SEP4f/47j2iquYntDiAzB4Bsi+nabOqh2aRNWntMWnZ8nwjaZ2D0jxc2UOB/rca77rRencaTUu3YfIq8CA6mTPHJp9mtUaCif0D1TQ8MwtFX+Wuhng4ijPd4a5uapu2YR9/tUXvipwQ4j+IzRIdrOY6GCCk4nQgHqWS7P+SzFuzjNcn/xCsFbKggxhAjSBe4nc7IxwypBXW5V8fSe0tZAuY+NUWaHh7043BbhPPjs2CjXu5hsYe48+9C3219+SVSVoRVFW0h0O6cD1g7/39A9wYhQhNWUaBJEbmo19NAsc05zp9QXr4jQh2LDONzm3/pFOG4u0OY20MTksFJxlYp+/5hjevXGy9jepi61pSOeRh3BKOf9sRA3U8scyxC8yKTZB7uBY/Yodc9sNGByinvch7KjHZ1tbEYrTp7Rphjt7N9XLnEPOTk9RanG9TXYhRCcVr6hyYhalXNzfp66dyJ7evv2ozMi4qKQgj6lYWWyDRTyewCAWaXe9HpKhZlfQkIownRWaXxKj0lUMzCIamFujHLfyyfRVIqZTc84jXNeKvkmrEINNdt9JDnH7Lba4Y9SZvxCU64941kon8kjrDSyhpBIZrdI2M0dBETu3Efg2JTK7xwaRrFIXbJ5eZ4OR7g3Tw6PYl9ZCAXRTdR0l5qamUZD5MuZHe5PcyqokHR2kapT1loinu6jECMtMyol6vWhCogshs/BaaXcdYVY7RfFKdXzZymj/SIwzp7QSqYswh6u77hfKbwqcT4/OYSyUqvuryhrgtOJqbEAPv2IaJzl89wDvvs2bVriOIt8Zg8A8J0XeVboiL6jXJrCfIQ6sPqcZ4bnh0SYxyaj6IDvc5n4/RvTfHYCwHiVa/hcpcr9KjNeqTYxqcIHn/8Zi9ZYYINDSOuw9jivkzLk8zmwsMislv/nb/+G8yEUaLPTRqfMdaoHKQudFOVscnQcxtAU5zHHc8HNi7QjN8/H8MUWdcmhTICIUCkn24fIKZvAq5S4SoX2ai9ZwPw4zwiJg34hFa53o5BEVnu2U0imfI5yVsjE4fRSHtyyEc+FHrIOh1HLKs1WBVuSfo496AsjbOR8WEeF9j2NwycaiyEVbslLF4uVOmxKPzs+Yv9COgMZ3QYcnHB/b9coa+2O0r0qNdhjtNvGAO24yc3zenUvgVJC2UITfJ/bTdtmzRkwNqzzmO5LuyfUH9+QEx07dc8sRJPfw/N+z9JFXIjfftpnTefNdruGuvalQJQ6ci3A8/fi4iJyQl7+rjZACA3aoA3aoA3aoA3aoA3aoA3aoA3aoA3aoP2Btd8LhNCQj6iN1kgTq08ZVdneoPfWHWDUu4sy2jWVi67/15E9p8eGTEpcQA2VkRMZXrGSgwmMnMyeo3fuYJ8euZHheTwVmZpRJZ4jQ/TG1pt5mJTf/0ffJCnYz35N8lC7xYR2lR7J0Tn26SuvMYLwyb1PsC4Pnkv54sMz9MB+cPsRNvaJTPnuD8lv8cUDep9Xnh4i4qNHcnWTKIXNzRrOz/Fvf/Hn/F1A3BxOawRZkdr1Sxk32pyDVreEyAg9i7Wyyl3mOI9WqxkdRe+8bo61IO99pVJCtyViN+UpGm30/tpsNjwXr4c4U5FM0js7MzaOTIERm3aZnt0xebz3to9RU710gY0wLELnw6N9TE5yvUbFI9PsMELYMbbxdIvRmNMMoxj+iz64xJtTf6Y+K0IUHHLCVNCai9Q4L09vy+hGW0TaQfFQGV+gF3dj7xCFckFzw99dWmLk/E5xC8si93J46Z0OVkQonM+jWOmTntIbC8lEwOXBqlBRW0/Jt/TVl14HAKzsp1AXf45Rzypk6OEdvRACxNmUE3+G00hP9K8/3cL8IuetZud7S1lFx5yAW2UoKwptHOWAKUW8piYUFWgoAr9dxcwcIw31Up8zhxEz9NqYneJ49pXDPzNPGbd4zDDl6XX/yS+JIHn5FUXO9g9hFRnbc0VnLrwoYuB8Hdv7XIvXXqFcZA6og+VyAX5xgfmCzKXd2me0teWo4Mo1RnPufsZo2q7IZd1+L8xmoaFUGt0rWShVy3CrVOf8eY7r7sE6/u0PmX/9+a+J2Ns7pff+6+/eQPWIcxOMco6/vE8EwvHRDkwRylVcZIIt6cjirBXFKN/jAsdVbXH+7TbfGQeTV6SHNltev1vA/Az7+uiBCKB9lMfp8TGYhGwzii9hbZO8M+ViBXNCKni8IrUHI1Nb60fYj3OOzBYh4w5Umn7kHOxuztHWHhFnw9FRHPS5EkYo22YrZS4QMmB6mpw8deXbf/jx+wAAg9mCdEF8AJLNqRlGKpam59BSCUyn9rwD5gAAIABJREFUIp2JPMdca7ZgUPwhnuS89wycR4OxhUpN5Potfj9TFwqra8baz38KAHj9e28AAE6z/H2z3sKGbHlT6Ib5C+LJMZqQExFiQNHMazOcO7idCIps0iCC32f3VrG2S13ol1JfTdC+jdnMuCKE1YsidayJu2FpegE/f8YyvW5Fp4wqUTo2HMOTu4wKTkTF/yJUys7zDZQU/QwNTwEAps/xHXNz86jLJllkD6oqleu19lDLcszegAgtA9TzbKcKq8gOZbYRU7nT+GECTZWpn5jk3+YC/DfR2EQ3Rt0tCQ0xF6LeTeeBv/npPwMAVp6QcPPiwgw8Tq55U/Z+S/xdnXYL8+IrsRn5rIDKfUdHRtETmVuf3yIotGRoKIYH94l4qhT4O7+4euKpozOkjknE8UbxUxyn4+iKVLGhEucOL+Vy52ATY9PKz9f3UxnJVSZ5Rqzs15w57eLkc9RxmuS5YFkE1x1xnJTrBbQ6XJvGkSLUJiA4y33aIA4Qtzg63njtq3guDqFUlev9mYoPHB+n4fMK4abCDtMi1N3dSqFh4PNHFdH+5NeUM5PfhpbI/a+9zbNTskyd8CQCGApQ1o53iAioKZK+eHEZ9Rrt2rBKgEes4gHcyaKhwglHJ+ThWtO+e1yvIn1EG3hLBQaKIsKtuLqYuUzbtfWUctL1AIdHjIo//IjzNiF0WrLQgiUiHU2oaEFD5PslN4ZMKpeeE+ruAaPRIbsNPkVebX4hPwKc19W9I/iEcO3WqXtOce5MTs3BbqZdzOpcNr5I9MWQw4vHz3iuGhnjPh8Wse1EeBg1cRuZLHzm06dCpHZsyEqOlhdoU/IZoWInx+EVMhxGPisctKGndz55SqRUPEl5npidglNEu0cp9rOg9779rbfx6DZt/4iQWQbp1PbOKSpl9mtJZdmzee4luwdbcPu5vzvF5bi0TDnZ3SlgaJjyPr9AnU/HKR+bzw4QVPEHu4v7Wq1C/Q75AqjKXntVDr4uHb7/eBVdIVXe/d47nFuRdW/ubcJzgTZzY2ePYxEioXycgKhlsCGE3b/7n/7VGQGrU2e9tPjKepYWWkKX1sVZabfQfoyPjMKlsuff/ebX+O5tPnN37QmiXo6nJ+6hzW3ug9VGHWPidxsWx2CrzWeX6gV0tIapU+r8qTiPGvXuGeFrrkqblqrxs1bHCJOZtuUgzvfEhI6wO13IiQ+tIZ6QokqCd6yGs/LsjSbHbLM20dWemk1pLYQI7TQsZ9xL+bSKv4S4DiGf/azkd6bEfkUlQ502EBffj1OoEpMIiXvoIi4y3WiYyCVPSAV3Wm00VLyhp777RO4/OTp2puO3bxNJ2ulwXtKnKZiFburPWQ98jsUMJIW6cguhHxYf2u7uLqz98uois975YgVviMt1Q8ibtPZRY9eCU91DIFk9TFKWYkEPHtxjpsf8AhGvy5e43z57/gC9Hm3QV1UoY+eIZ4F8K4N3/+R77IM4Aps5zrnF64BL+1mhobOhuMvMVgtOVJTCKYLrllHo2F4PTXHAFYWCdYqrKmr1wT3FPWsxp/OslOTVf/1DOBriydPN3fn6HC4qe+FI952CsiaStQMcecS5J5lJCZUWCvhRM1NWJoZ0FlJGgaEHBPw+9Y+yWsyyn5FQGOfFEfX0dI/zHtRZIGxFKCD0Vpl99tpVoCOVgdfCeegXMdre5h7r8fsQjdIm9JtNXLST0RGcE1LWphLxq09017SaUNG9/VRIpgn9bjo4jJp4iFPixIpNxFCQrMTjnKuwkDe1bAVGPV91YeBpUGb9njBGQiqYA8rj/XuPNVcV9IQKGzVw3y1I5x29OiIqFrN9Qt0Nz/AdLqcJiYwQgn4h13Qeeb6/i7AKoNiEwjpOc3+JhUcwNkE97p+jfbJtyVwBVvEhGgziRhKSvVUrY297Hf9/bYAQGrRBG7RBG7RBG7RBG7RBG7RBG7RBG7RB+wNrvxcIoY2njKLOLw3B56Kn8NkzenbnFOGs1FKoFMQ0D3pO/SFFWIfsWF9V6dOu8tHluV7bPsDVy/T67h8zGjQ6qmjQxChqTXr6ospdNLbpFTS1DNh7qiiCvPuX3yIa4qOfPwTyfHfxHj3RJhP//82Xvo5Glx79h48Z8cyJp2JyPoJqnp9ZTPTA9tp8bzDoQjDKv/nCKicPIN9H75QU+e1XpLLY4BZUJ3ksvp8uve8GexGxSXo+73/GfrlUNs9uNKJd5RhTKUYJTGZ6eO12J/IVelMDKiHZjwKVGnX05HXsKAe/J09xppDCZ3f5rJu3GFG1yqMPiwE9RQqGhlSNRJFcty2AjIlzUy4xuhUY5xjG/D6khVApK+A27fIhNsHPT1UJKzDFCF2nU8SzO1zLpUVGljuKjJRtPtSqlKcr1/j9ZJZe6uODQySPOadWVZ+zi7fJZ3LDpXzcXEZVKeYYVcgWjs4qeTx9yvc6NB/LExH4bnH+Kiq/Wy6Jld7cwFOVbB+J8Psz84rOGDsoiQsrqKj3zCQ9xevxBvJxPmvmFmU89YTe47bVg1qbEVWDKiNZ3cDECJFOk2Nct3trlOfiWhGuoLhvxItQFVqpWasDbUaGAj7KicHCdWi0Arj+Enlnkori7T+nThmMRmRVLj2tnPx1RUMvX74Cg7FfPpFrP6nKFd1hD9yKGFS0RgtTHHs278D+DvVl4RzH0qJ44mD/ALExet+P5Sk32lR+tJHGwxPOY0gVwf6P//F/xXY/Uh+nrL55nci/qsmA5TeYF/7+j4hGSZY59hfnlzE0wyjE3V3q8zdukAPkYHcdqRI7tLnJsV+8wSj29s4+oHEZVWLVor6k0glMV7h21xcZua12OObzs1NomNm/gqoeuLqMHpmsFgSVm93pqvTmCiMHnXYXhn7ER1XN9rfFBYUc7EKcuG1CB4ZCZxG89TVGoT0+VcApl3FxibnH68+59kMjfObc0DjiJUaLMhXpZ5khFVOrdRYR6ihiXBJSwuCwolakvngVzT9WJOXSyDIgvoJCnfNZAuX5eauC6SWhIDYYoR+eYYTky2IGeyrtfPOmSrZbKS9z0TEcqZrFcyHO7Hban0X/BYw5uc+0VbFsM7+PCeWaVw3s8/ILtCPjoTDqsiWr4hDqibfg3e/+EcJC19hUsnnlU35nI/ocr1wh99uje0TX7B1zDL6gDedfoMyZbFzfxMfkX7twwY6SUFDffIOVnj799EMAgMXoxvVl2qC//zFRekYHZfXSxWWcCjV6/hJRA/vPxW1j9mH9hO+eG+E4awWumyfgx4g4V4IG2aQZ6ttfvPcPWFO587deZWS2W2ugUhH/gNAQw/NEJPiiPmwLBbssZEs/4pw4jqMmjrrrL9KO/M3f/zUA4LvvfB/hKOVi75DrlRUvxoVrS5gYYdT0zoesmnG4T9mZWZrDK9LdrSNyRD1RFczrL984K/MaV8ngWIzPKbWraBk4hnaLMhBWTr4B3bPqOQ5xJdkUO6sVirC4qLt7OVV3cjgQ1H5X7ZfZHVKJ7tQBClXKaFBcEDkfvxubNGJSVaM6bdqIqvbWwIQPYY9QBjucj7yqRTYKFURVLrcnRJ1DY4kf5HD5Ks8pd59wvScC3PMatVOUVS769hr//ZP/5gcAgJdvJfFAaNZ8nqjKt99mtZSJ8Vk8e8A9bnuFNt2q6pSBiBknm7Tb0w4hQks57ArJ9esk+zA6xP5OjUzB7aRdqzZpP6wulf4t2vHH77wOAEgc7gEA7mtvLRoscAgV6RahnKdK+1ay+XH1AvUscUL78cmnRCkE3AGMiIfL5xcyWZXE/CEvbKqu98Ujjn1uhGe9rfQWSqpcUxUyYE32yhkZg8/FvuzsUE9tqmh1FE/C7dEebGE/j462APAc20cfGjyUgUKrCruT/XKKPzGxy2c+/OwRrl/h2qUy4m6xU4ZO0z3EE9xLcwXO8bl5rskb33gNhTznf2eXn/VLdY9PRNASX+bWFj+7cpkInlzqNgziDbT7FR0XktJoNKIpvpnNY6Inrl7hudpsd8Egroxj2ZuKmTri8vmxu8L3PLxNbh+7qgtNvvM9hHb4/StujnPreB0HJ3xWUkgMj2xTs9LB3BT72g4W9TfKsc1hQU7IyT6KxWXlGCZmYjg5ZJ8rPc67UegZT8B5xjuz/+U9jYvvKBYKmBSXWF51n6PaI3omI0YnuddnV4iObOru4Hb5MSJ7ZchTZtZ3hPCPRM8qxpl0XvSaeTYvViswdqjzJ+JT8zisiOgsbqrzs3KRa9Rq9NDTmb8gDpxh3Q9cPRs6RXEJ9ivTqZJppVvDrPhL+vY7V+1XlavDIDvTxwvsbHGNXE4nJiaoH/FDVXxSBcSNnQ2YhRqanJsCAESUufD46SryRerSiCoOx0+4Hv7RGMZH+b2WOIdOZTv8bi/2DnhWNgj9VSkZ8Od/9xMAQNBHeU+KP8w3M4bAOe45j+5S/6tNoXlqo7BIJp89U7VeVTpz2szol25Kp4laGRH/0rJ/DKaexrhJtGJRnHgOuxdGnfGaqurU1TnGGQijIH4as77j8kr2jIBN+6xV53y/ODUvLVzA4Sbne22V/XzxO+Sd/R+uv4Evf8SxWycoM992+bGv7JiIqifatM+0Oz1kC5x3n5BnRvH1Ot1uDAupeUHce0ZlxHzy0ccoiIsmKH7J/X2tQ6eNfKmPJOIzsyfSU7sPyR3KmFFIGp/41HxRNxpl7oPREa53tiEET+IEwUhY88A1zdX5joePnmJX59o3bnE/WtQdrGDtINzrj0cVwTX27e1NgFMKk6pfZneyyKS4PwQ13z6z0MEOz1kVPh3p0dOZvN0wwiTIdZ/T6uWXL+vhRjgCnIdHP9/j38TJe+7iLI52+bA5L+1IH3VqMPaQrVNmdne5RuOqLHtt6QKSeZ3rVaExrMp721vbGA/TtviFDB+f4Nl8rFZHW3XuV9eJQi6qap7TZsfEKOXjd7XfC4fQ5AQHtvIgjhfkUJicoZA1BWNzeYzICbaJBhfdN0Sh3d8/REtOA79PG6ZgmOMTUdhdNGxF6g1CgsOZzF2IqxMOXWSKgpe12mY4BeH68T+RwPFP/rc3AQBf+UYDH37Ig1ElwQc8e0DDf+/9JCy6WI1eoCEwyZh5PGGgyTFUSjqoHvJQU286sLbBg9WNEJXCF47g4TMRrwrq6vJywafnPcAxhSuxzTmy9ii03mEb2j0Km90jqKw2k9PjEwQdNMpOB/vXhxhWqwWMj1HY0mnOQx8u7fEH0WpQ2buqG+/0sk83XjwHm6CME7PcJJ+s8CBnQhfiiENNv2+3+cz97TbMcsJ0mux7V/86A23MXKXBWv+cB2Jjs4buETeLB3IOfk0Ey6NjIdRu8uJ48Akv7l4LX5w2pCEeNFyYpvJNhmgQfrILVKUGr96kozBf4KYwEQ0jJUdQW5DSUV2mjpJP4ZZRwZnsse+mTgn2OvsenOalcn2PCpo8yuLWBRq29x+wRKKP+oxxpxk2rUmfOHCvzIPEKze+ho9/VlTfaRB3a58CAHqVAOoGHhjf+eZ3AQA///R9fH6HcgtwTZIHIohEHrMTvPA/WeMl9CTL9Wq2DdjcEKHnFcphXcbZbrKjXFS6ioM62yhTTswWI+ymPlyRb+1o3U9TGbx6k5e2gFvk7SoNO7d4Db/57a/4zAg/65n5zEq1i8wh3z18UZBNlRW1W50Qjxxmp7nuRyrnbI9az4jTX1ziRXX7/U/xVIcLOChzuSznzOPx4FBEdTe+yotF5SH1bvdg54woOiSD+mxDEOdWE3mVr1xc4CUsFKU8Hjy5C3+AelYVEbndSfk42ttHXaVFv36LKSLFktKMJkP4dIWX3uFxbqZfrvG7DqsHwSA3rQ9EdHi0R5n45rfeQPyYYzjco+3rdjnOZrMFk0oaX79I2StWbdjcItHogtJSDVqwuxvHCAR4OJ49R/n9u7/lhcnWsSOeZn8u32DZ9KCXsn7n8QbCAepEUiSBV1/i+AyVBBoioy+JRNLpoP2wmuxotmjvnSrT+3CNh6Fyt42enc8/3qZO5HQINdm7eO0Nrr1dh2y7QYfE0zJu36XOXb3OA77LQt3K7MbREYH/sMjwX3nlFioV2l+z9oKW0mI2Vu9j74Rz27RQbkfs7HvhaAdh2ZJ0mrbp7Te/CQCoZUtYUZl5sw6ji+cJV7//9A4sG3zmSIwHAYuJz+50Grh8mfvgrtJ0j+LsWwcd/NVfEvoemaS9KpZo0xZGJ9FU2edTpUPuHFLmR+0h/PDbtA1P16jrH35MsvOI2Yl8lTIekzOhoJTVC+cWkNX+ddykfjoNJgREwGxVqkE5zYNHw9CDXR586xzncdzH/fbDTz/H0kUePrcT1C/fOA+CxW4Z0xc1HpWljqvE/LDbjoqI3dNygATmeahsGhpoFCgXaaXtGAXjv3P3M1hlk8I67K2vU648QT+auuRZrJT7L+9xrVYfp3DuBlN8TKAx29pk0MpuNGNiijbiJEc7cBJPnB2kyirra/JyXtwhLypynDaUKrI4RptUcxew/phrsbDI9bZIRqPjAaRO+HyPRcUtlJnsxCjWVQbbLmLy0VGO7+ighqERytilmyq3rnTprimOmpz1QRe//+EnvwYAtAPAcUqpW0rvPa1w7/E1owh6lHp7l+s2e0VEv2439p5T1o4sWstcHLNz/L5fqQBffvIIAGCoVjESU2qenDFHIhA1Zy34p//8MwCATY5rZ4TzaHYA+RrnI75L23Jxgt85HwzBkqEdHrPyma/JWeGOOmBzK7UyNgUAeFSmXdjL5ZFQwO36SyR7f3iHjltUmqgpBbyh1NiGnECNXO2MqLda4fxX5fgubsdhFbHqq1+hnDRqFTx5phTpKtfknFJ/wyNhbDynbAV9XOC0dCuezGBKJLCLCzyX2XS+jY1PY3ub9vSXv+BeYNY+5fbY8MJNjtUqJ/XWLtd0emYBqXQ/vZ9zVnVQt7y+FqK68NeVitTt6LLnDcHp4jw8XqEteiNIG+i0WhDPqOS6iKAbcqKVa2XkZUsmlX4/OUtns6trRUHFAIrSkcTzBIblaBmyU+5PTvg+r9dz5ixoGzj2poh3Z+djZyWuT5WG43HzDLC2uQ6bqV9eGvxXadzHmSxSOaXnKn14XfJsMFhx984eAODyMvtUkdOn1qpi+4BytCC7VZFTIOj1Y3uTuuPQ3h+VAyZVLKKfUWiSw8UgR0i93kRDaaXNvjMmG8dIlONamqdO2S3cP/OlKpoqwhOM8Zw/HuKYS5k8bDqIWVSKPi+nncvnPQs8duWAHomokE0pB5+czROj7HNZ+0syfgLFHjA3zjPD6Aj1rVwswaUgfEMX/eIp94SZ4RHcOeAaBkWxsSOS3sOHTxHQuSqvO8dIhGPwBqPwFigf6Sw/2zcY4LZwLfspnOeUzr2yfohLSjN+9+1/AQCoKvXrZG8bHt37Lt3kuSWjYFKxUkSjLgJ4pR5ZVexgYriDU+1tHp1tVNoITXQR1TNTScrlpoi7L162ICTagnbPqPFQvyvVErJZvm8oSr12KUi2fbCD+Wnq4IXL3CvTOiv+7//xP2JCzrMJrXPtJAuX0n/HBVzoOxxbzQ7cBdqSfqqUQ6mg1XodHVEf/OpnDCz5lDrtcDhQKPQdrVzTsQnKfyGXh3OcYx5r8D0R6U0mXcJBjneH0UleatYf8n6x6FrG4gzXqV+wKCCQgstmRkVBxYlz3LsMddoPRIGmiLdXFYS2qlhKyGSFT6mZbe0Xed05/FNDKNTZl67S4NvN1lkg2iQi7maJ8uXxeXEsJ2dXe35FBQZ2d3cxoXTLZlNOBKUGGi1AKCJnm4Hvub9KGXCEAjiRDrjlvOxTAURHY6grvcutlEyDCLjrxSJ0zDwjni+2uH5TC9PolmhnIFv0ZIX3XbPVBo9Syd2641iVojk9MY1En+D6d7RBytigDdqgDdqgDdqgDdqgDdqgDdqgDdqgDdofWPu9QAhlM/R+He+1sfqMxM2LlxiNcbnlRXOH4bAxWpHK0Wt2UWRwG2s1uD30ltkFR44rjapcrCIUpbe3pHKbcaV3rKfvwuagt7EgOGyvw+d0TR1kRKhqkQfv0T8xgvjSO4t4v8lIo1HlGkstPjtdMmNZiIWFJUaRfvJjlm9tuuowdugFTArpUi/x/yPRERT2hICq0+O4tn4Al5Oe2VdV1vDOHUbaTPYGPEL/HKssZU1k26amAU1FJgIhep4ffUHvp99hR6fbh5wyOhAb6c91BKcn9Jo7hI4yiCC1UqwgHBARpd470i8tWE+dRYxLNUYTjpLytrbyCMyofJ+87qV+iVibAaYyo071Itc2NstoudGcw2GSiA+ryFpP4wVMKiLxyo0pAMCTD7gOQ6OXsfZUZdy77ItX5JOXZr0orFMe0iITm1cpe1fAgLII+baOiAy6dYPr5guZcP92XP1hH/wbRIa1W2UYbIwYWCwitOyKQLeaxuEW579nZLTq4lcVectnYVTU7Z23lA6y9lsAQKcJGAUJdyqVyyuiZb+hi8go5erxB0LpCE3hMc6iK+TNxyqtnd4tYIRThLig78085z841UW9yH517Zz/XIvPtLuGYVFaR1bROLvKjgbGTagoyp3eFwKhwEj1xPwYTHXK09XzHGtG6YeZzQKCNkaBNkTi+a23XgcA3Pv0fbQa/TLkIv5WhCjkKWHoAtfiNM0xtHJ8RygSPoucnNS4zstCX3gnnLD2KKO316gvZqsDY8v8vK/ra0eMONdXD+BRVMU6obUPcv6HHEMIqeSyRems7/2Uz/z2SyFcWqJ+nm7QJrWOWxqDCRYRUuZylIVRpVmMBq7gdE3lLhUVO4hzfGNzLyAqKG/foz8iKOn+dgrpUyEQDBzf8jnKoMNsxblZor42RVqbUll3u7N9RhJfziu1IXOAdq+f7krZ7goFt7joRU1RXZ+PUaOQSIaf7OzBKDhm/oSy0EfePN2Jwx+kHL184yW+T2T2Dr8Zx4KJDwUo03ml8+zvnKDZ4pqMqNTnzj7Trl64uIS9PUYT8yJqvznNVL/S/WOYSpSdhlCLDpEdv3fvEXpUeTjb1M/DXdqHpYtLSIu0tq65WpiYhXGCkettRTM//4wIKrvBAI/SwoqCj7dVovTz1afoCinYTxlNn1AWYtER2BXN/OizDzn2aerB8Nw85qf5vt19js+p9NTRsQjMsrv/9FNCxJ1KMzpMJDEe4Rxduc45Pljl703VFi5OTAEAjjsVPYu2PXuUxNG61ndWJW6FIjot5gGRM1528b0PvuDYK+kivnOFcmUQDPw0k0NIhKNfPmEEcGKCMup3eWAWoecTpVLUlcIxPjmBhfPcx27fJQpubIKykM2nUVT0zCHEjt1GoU2e7mPxChE7l1+gDn/5gDrYc7jw8w9IZjyzwLkdjymtIZk8Q3n0ib6Niv6V8gVYZPuadY5rTAUb7I444ko92k+IhFnR3nqridufE9UT0blgZGwcq7uUI5OJff/OTSJODvd3EfJzro6ztKexAOVkv5ZDUPamJH3zG0QQvhM/q8Kwsk69WVxm1PW3Hz+Hxc8zQnCee7JHRSS8jR5+/uMfAwBCQf7t0abSlR0VDAnFeu0qx/75berExl0L8iXandiEyPpt7IvZPYYffUSi6cAo+xsW8icWsuHuA5XLjfH34fFZrD+hTM5OUeZ6DUYsP3u8g2GqMa5c43pdUlpO3FxAsU67mFW67EKT8+7PZ1DT34YnpwAADwqc83iljEqK7wsJxXPhIuUkcbKHUSEQdrdoUzo52pqG24R3f/BHAIC1x4QQm4zsZ7xZQ0fpB10T/z05Yd9c6OLRM77b56ZeOoWiM/Z6GFPRjB2d59LJBOqKxk8qlcius1dp7wgBIeJ2hF4bGqMcHidyyKQ55rllztFnX7Iggj8Yw5wIz5+usM91ITMy+QqOjim3fZ2yKW1lf2cVfpG0BkSoGgjy9wZjB26dZdIp2vGLS9pTve4zZOcPvksUYTYlYlZ/ADWn0pmEAjfqzODxu7EwLIJrxZ9nrJSd/Q8fwaH0sbp00OL2ICPkrt1FvTGqSIjZVkOuSFnLCWXutfE9P/rVT2HR/uXVuFpCLuTbbSijDyM2ztGI7JfN5cXcHG3Ssc7k2TzHED9JwC9UR7PD95WECKs1avDIJvuU8niiogT2ThtDHr474uc58yChFNeZGRQKHF8/Bc+ktJdCvoKubGzXpDQjvxFlpbQdJ3g2DwT5Pk/IiZLsPJRWk1BqVrvbhFFpqB2l0vU8HEPPbUSvf54VZMonpGc0MIxUgt9vVTmGq0LgFIfCODrgmWlU+3R2l+PK5/MYHqbuRcNEFvXRCiajGdsqHFISmfryeabcNLvA9j7nrV+k4kg0C9Vq4yzzI5ORXLl98IiA2Sl6iXaFY4/Y/cg+p0zW2/ysm+Ycuz1+lIQG/OTjLzR/ykDYXMebX2daVjqllF+lzT1PnsAvJHMsQvvbyqkwxMYBSmn2yxXh+eickIY3Z6axd6RzxH2iDgtCsroDbpglt5VqVmPeAwBMTUyjo2I1mTblZEfFAQJX52F1cOxFpSJt2LtwZ2lQj4TodKu4SjqdPUsfCwlFlBfaK2xzoqr7osvDeegoZWw0OorKMdf5/mPeeW/d4pkrHHGcpdxVhZKBUq09o0G8+zqLkcwLaZh/gevcqFdgt1IvNzaIiFwQ0X2tWkRFd8Kc7p9o6/4aCCA0S6RULUX7m5TdqXeaKErPTCIkj4zyHanjE9iFyj4WgbrP6YWxn66i55tUmKZeq2F4mPvEwxXuE9kD7ptfffV1NIVKT2m9k6ecf6fLio1n3DvqKvbzdJe/Wz3+JSZG2a8l2e+YUuNqtQaGJ0Y1HspJQ+TUJrsDJaUzGeXXqCgVPV8pYDxAPTsU2X4gxGdWKhUcHlMfx8d5L+mf++OJxBly73e1AUJo0AZt0AZt0AZt0AZt0AZt0AZt0AZt0AbtD6z9XiCEtnboCS3XbPC66f1KH9BbXPXys9j6/TP+AAAgAElEQVS1BWyW6MGfXeBnWaEc2k0LGspF7naVUzxHb+725j5CIjR8tkpP4aRbxJY3LmHvkPl3TjO9/Xblz54WazAaVTJPEf41E9+/9V928S9/yBLIP/oJI5UukaUFo36UEozKvP8X9Ay3FfVbWT/C7Cw9mF0hGLY3+6UZT2DQcmw9UblTmxFHh/QCfgHmiVeKioS7DMiIMMoi9IXbT4/o5lYeFpFghaJ8ptIVYeia4BcJVrVIL7jZyvkID4ewskYv55AQAeUSvYpuhxtBcQYV0/Rojvn7kY0SgiJjCoqgemSEUYL93QSsTvZ5bp5ey+P4HgAgFjEjX2P/2pb/urznzdlpOEz0dhoVgUkcF9FW2dVvjjJq4XhFef49C8wF5bgf0Hs7XKW/02Eq4KvXGWGeiHIt7FF6bqPzBnjH+LdeitGfY+UDY8qKushxC0mVXN1m9CIwbIFDeb8bq/Sm9yOrEbsNFj+97hMq+1qMc1zDU5NYe0SZez32Nvtg51wfp5OYsHD+hvkPDsSV5Kgnsb1Hz/WwIre2Ic55x1SAT9xDxyLSdfbM8AUoW2uriri1GBUrt7JnBIOBiFBfQs81Cl141efVx5SFuWnOVbmahN/LPPZnKUbJY4rwVRs1TCrH2iHESKqmPPjtQ3hMIjX30pv9i/c+BAAsLM4hdplRtPU9zktXaDtT2410lrJ5KpSTUwTL1o4VLg+jWiURtS0rknWQ38ftz0gqOD3C95ldfuTLHLO9w3n7wS1GhT64cxu7Qo/MNqifVgvnLFOqwaik/6+99G0AgO9PaaN21+9j0sp1XvgGUReP14nQ8jkDqAq9cl6kfTbxf22v7qJSUEniYc7nbkYRn40NGMV54bRRCJx2zk/YNY5shvNgEUn3dMyn/6/AJAReToSirS6jCu6ADwWVrLXaOLfJ9DasRtqgeEKRCfFQGSxAMU+57zb5nq+9Ri6g39x5iLbIiA3KYe6Txs/NB+EUomhEXGSrX5J7KGQyIhzk3D5bJ6osrHLw6FnRUygwFOLc2qyMGm6t7MMS4lz5Z6lvdSv7Fg5MIdSX1Wey4+f6udotvPwio1UV8R6YVWL3/Pnz+MV75CrxKLqSzqWReCrOg6vkH/mT/5Z8BCuPn6DbET+E0C9QGd3jfAn3/5l2/j/8B6IN3rv7HgBg7fkG3v3B9wEA124RzfPFY3Iz3Vx4BRvPiDbMFBkVm5wSl5OxjV99QH6f2DT5KRweRaFLBThE9HqiiHFEpKEb21vIFvg3a5jz4hCK8OatF86IIT/6iM++eY1rOrq8gJYidI0S1/buXcrxtde/jiOVvO9zE3Q8FiRqiuQ5VHa3zLlrZtMYD1Je3bO0uQ8e0lbMjE+iIn6PgFCmBwfUu6mpaWRPqcd2ET1WxEt3UE4j/5T9QYNr/5WrjDweJOJohcUbKN61npUyPjuzhPV1Rle3t/meURGdtjpt7CuaNuwjUisiWXjnna/j8zXa2kCY6IaOZN5l7WD7kOtmd1F3LYY2UKOuT85wvbIFzufm/i7S4qpYEjrq0y8/5FyZnCj0o7QWRq+rNY5hZmweSdn+jOxPv0z1a2+eQ1EksOUa7UZVUeL5eSs2u/zb+hr7XCz1UYRBbB2yL7Fh7hNji7RfRSsw5xVC7jk5qlaecG1f/+NFvOQjKqR1zPfU97mfFZs+fOPWTQBA7lTIwUILFqMInBO0KUlxUsyem0XPxD48ecy9dGyM0dBa1wxvmGt37SVGpE8eEokTdUURdYnLKsVnzYcZYU5UC7j2Gvu3fp/R53CUZ6LP732OpFCKJpH3TixzTUcmrPjit+8DAJ4+pHyYHZyPRgvIqXCIU8QpXivtTw9NtIW+UC0G1GS/CvkM/CHqcX5PHI9HCUSGqY+nQsMnRYhtNXUxvcCzqsVE3RibZHS93O3h+JkKAwjR8c03WFJ9Z+8Ifi9t0o3rUwCAzW3afZvThqMEx7y2xrPJ99/m3vW1ry3hr/76/wIA7O3w2TMztNWxyQkUUuzzVaFmJsS9ZbIYz9DBIaHDkuUTje8IiyOMgKeFSDr/OrnqPvzZz7ErHZwQcuTjNdoho8MCl5Fr77BxztKZOJoG7edCGnus7MPOxvpZYYDDPN+zoKIbsdg4eor6u92Ud6uDti9dqMFoFSpPUX+TnlMv1eAW0mdpjvK/vkUU0sLSTaSE6jDqvH5ugqisTPYEPqH0jHWuzfwE5Sp1kkJSSBirhbbMJHL6TrcJl7iYhoUWeHSfeyTMNgSESHLxCABT1wFDv3y70Iq1Ntc9FApgOMLxF/vnRPHQOTxelIQW6olTLaY9udNtwCOUolEoCoMQCMFgEH6XCjQIObwujhKf24OK9DglfqJTcTiVaiVkkkKWg7YwIJ6i7e19tMXnWa5QxhtxIehhhEuolz5PjS/IOXvyZAUjKok+HOPaPFhdQ0d8P04fJymps1MynoZV95DM6aHmj3p648YimjVBhpUF0tH5JTo8gdM87ZRJSKak0CjhkeAZYu3zDNEy0yLxHTFZURZyJKf9oS0i9E8OkjCIvyUUVOEAwdQqlQp6Ip8WPScmxjj2TCaFU6HA30tzn/n3//K/4/tKRrRVYtwQ5Li27n4Kk499LghVkhaSMpfJn5Uhn/fzbFzVntoy9FAQ71ksIq4o6dTu/gGGRPw8N0dZ6yONcrkcDF3xeYqoev4SzxHmRgeZXdqgjO7MdXEa1hwdHK1zPCXxGSWF3JmZnoBH814RiutYhV/K9QZOxR+4tESb1CmKA83gwJFIw5viNr2hrJXM7h58EdrcRRUecjs9eHhfXHZCCl29xOIWRqPpDLX1x3/Kc9zDL3jmGB0ZwkPd2dLiu7M7qcPesAcJ3RcNHurerTeIEtvbT6Haoq0rg2MOT3Adks9T6OksZOyIo1R3FVPXBKjojEG622vyX7/Hj2Mhu71Bfr/PzekN+WEWaXizx/3IozN2o1A6Qyb+rjZACA3aoA3aoA3aoA3aoA3aoA3aoA3aoA3aoP2Btd8LhFBL+bKjkyMwQSUgN+jttCp/e2NtBftHYtt/idGftKLYzWYLvS6HIocaejb+x/lLE2dex7rytm1Cz8zMxRCMMhpwsqHyofLGB0KjOFblhK5CzP5XGPWyWbzYPaQHenJS1UtWGBGoFQ9QTtBT+PINftZRXvzC0iRiyrPPKpKilGEAdviFuLGa1E+/A4dt5aEnVNZQ7Ojxw84ZX0FR45udp2d9bcWAyVHyPniinMegyozXsyYYxIZeUlnKUp1Rk8WbMUwt0qN4vEevu9sR0Xu7qFXoYVyap4fcZVH1JLMHDgs913b9m0zSG9zptWC183teFeXyheRhdhlwqLz0UIwf5uQ1jZpv4fI0I7B//WOu0fe/cgEIMCrw3o/IG/GD/+WHAIAvNx4jq2hFXZUTDpVe/c7cefznv2Te8FdfoLf+re8zYj86Molf/jOjg29dV2TJr7qDRhOMAa5l5Zjzl0lyzk22JibGKbcdseCbHZy7QsUIe4Ayk8nT050vcI2C1iC8IUYqn6wyEmuwi61/2Iy2KmWsbTK6U2oo6jdmg0154o4o+5KVx7eENKKzisr3IySNKnycDoQptshWGVVI7AD7Jq65tcMx+OXtT5XyODkUn4WX6zQcY9R7e28bFjPzhzM1RhfOCdGRKyXQEr/Kc0WULlwm/4D7/BxOVSK3WOXa9Dlw0OniOMEImcvHeWyVqcudZhTlqqriRDk+e1uooGoKZrOi8iO0B/1KYS1bB0aVY135gs/2u3ywiduho9KMh6vM/X3xO2/CLGSLq8T+ORUBMA410K1Qt0+fiNfmFUZEfvTer3DrKqNuqQbfnRI6x9gegd/D+bar+kurxnXPFfJwaW5P81xnt5/zkStWUUpxbbwBVapTpKNRb8OqKHlB6MiIoqGVVhFOH5/h9asEZ58ro2VGdJRRvx0hoYI+M4aGuK6RMKOzn3zKaLnbaYVNqMaEKrzUa7QjXrMLKfEWZFWlZtrJSO7S9CTMNspmv3BtnxMI3RRiUeVMJykL05OM8DeKBbz5jdcBAE9WxPlk4JwFY1M4VGTOKITEymPK16h/FkeqotVTsnRR1Speu3wJsUm+b32Xtigs29vuNHCwc4D/b9tJJTHsoa37/IMPAQDnr3Cdr1+7hi8+J+eNWRxwEUXQjBYrxmc5R+9/Sn4Pq8qB1msVnKrU6ud3yckzPsUxr6/soicdKgidM7XM9ShVipia4pwatTeuq0zi5OQ4StL7iEq07p0S6fIsvgm7kzM/ZqA+55TrHgkMoxVhJDxygX1IJilnvdMsJi8zivahKuec8rWoJEsIXGG/pib47+7+DpauEvXmPuQznz3n756lUvgszr3xq/Os5uKVAXqy9hQOv6qS1cWToGihPZNHQ7r+ZJ+IgpFZ6vVp8QR5yVpA0eRLIX722w8+Oqta1EcT1lS1J1fpINevYCV+ivAQ121zZxMBVYebHua6ex2qxtXpYHyYn3WM4hITD57L2kV0XLwF4sjYW93EqOxHQCi0vKLjJbRx6RrRK23JZqLIPmUzRVy5zDmKDHPdqop+HhVO0W6yz9956y0AwG9UWdBoKePbbxKhfO9j7i/L57geT7c+gle8IHNXVOXwnrhvvGbMXiGKwSr9Wl/fAwBc+8o4vAHaXaeB62x2UHbT8Wdwq3qX2cHvHB1SruwuE6w6hzjEE1QzHMFgpY73+QPNYX7H1XSglqfcuoVia/Q49v2nabz1FiPlafEefX6fkd/J2ShGQuI2UhWvr88QrfG4kUS5wPPbKzc4vvOSHcNbb8CgSl2/lH527KpGlYwjJ4SVNco5SwrR1K534DDozKTIex8h1zS0oC0EjSYjsTXxvIyPTuBU3GghRfNzlToKcVV9tfL5o+IcadSqqO1xjH3bcF/ovEarhwtCwm1u8gw0LrvTqwFdocmsQpfFximzCxdmcBKnTZg9x/lsqoLNb37xU8T6+0mFa1lrsE/RWBi5Q+pjVJHljmxNIp1ESciMPh9XMsH3VwplHBgphzEr9eCVUZ4RX/3v/x3+77/5CwCAJcZ1X1CFtfd/+UsEwuI9Elqp264iOMR5K6Woz0c7QqXVnGhWeWAeFzJmfowR98OtdbhV5cpjZ98TGcp9vVmFXWjiuuxau8Wx1FtVGEuU83xSyHehDGz2Luw22ptzQtcUcnymwe1Cs0Y5yukM25eJjskCd5jrW9eFJKezM2wGlErU8WpNsjBDhMZpsYo9lamfn+Tfjg9SsJvZB7ub4xJdFUxdI0LiiwnZ2edUgvvhwdEWbNLVnioO9c8M0UgIJVVTDgnVXTHRHmw8X4FDpa4nRsY1R/zM08MZ52pRNiwyw30qaOxiaIw6VxWKaER8WddeeRkPVonc2z7g+FzqdyFbQF1IlV5WslCkLPgj3rPKxEbde5Ymh2HX/aFm1PcfUX++c+t1BMUf9Vz7ZanCNdpNxuFXaXKjqn51VKbW7rai0tZ7HJSvao865bJY0BGHVbJfjU73jHKriF6nfweijbH2OOeFTh17WzxrNbsUDLtHZ+2ADwHpVzbJdehV+GynzYi4gfMRFALk4a9ZTfjpQRajOlNfWCJ6edroxK6Jut3T81tCj7pHgsjrfJlV9cueeApP85mzefcKVdIRj6PH7YZDd+SgUHAC3cJhMcKp+0QwRDnpFYk29RqdOD4ml1tX9+8G+N4CGlhc7FdYJWJ4Y5vfPUmcwqvqZ01xGXZ1L3aG3JiZmQIApHK0A7fv8vzXNBngHeaZzqaqg1VV7754cQnxOM/iR3uUuejIKCbP0cZmtZZ9hJvT5kROlQ63dLcP6D537949dFXB26VqYYentBVdCzASpa6HhWwr7HE+riyPod3l32omvm/jgHLphgWWtlB6Qjn1dHbYOjlCWWi2WIx2oKFK4GtPniEnv8T0DMfS6qPrwwHUuqrkK7vdH1M6nYFP1YB/V/u9cAh5on2y5zgMHaVLmahgNREdXnk5CvcQDUWpzs3f6eRnJnMO3TYns2ugkKZTVDRHw4jh/oFP89FqcFF/9Ys7aLUled0+JIvKYUIWy+dUVv0q4ctWFzfleC591geXgxfi1HNOvNXeg00Q/mctCsGt6xSK1YfHKKm8sttEQ+yNUCiatTosUtahEb53/XQPiwvc8J7e42XUJ6eKxTAKm1MwfWUxlNsURFPbiXtf0ABPXtDFKqAyqR332cawtMiUisgEhTtbP8X8RW7W29tMbTA1KWBOiwMGg8oiS+icIhK0mNyoitgtKIhxH6qYyVbg9vG/i1vsn0Wlcj1dLwAasXyDm7JH63Hvg0eYE3H0O+9yTS+PT+Ivf8R0DN33sS0I4PiFEbSjnKMXz1NRvv+vvsN5v/s5HFMqT6hD64f/yJSiS9+8ip1lGY4TfsdXpnxUTXkI8YtWh5/lMuzf8sUYPBqXy0vZMUiUXJ4xBFTmNGfl4hiK/fLMLUxoLU7loKwoHcVQbfcrCaKYp7B63dSHqq2Gq+d5AJ5/jZvwZ7/gJTOebOI0vwcACChtqOzzwCCY4ciQyGCzOpSWvXj4hJvoTY8uoSKbs5qB1Am//9JXuOk0e/zd4aEJziC/ZxaJuFFEdLl4Emmr0ki0NnY5Houl0lmpcLPKwVdVyvPZZg7RRRGXC9HbyogIL+rCjVd1UGd3kdykDDUNDTic3Ng3t0RgOjcFAFi8eBFPPyPBXsImp6mhiYhKtC5e4rje/4QXhOO//Qd8+2vfAgD84yM6RWIy/Bh2A3JKe1W6PSqH3LVXz+GxCGZ7c4LTixB75YsTLM7JueSiDMydY9lvb+AZnNr0jxJyGKqs5GmqAt15sbTIA9mdT3kRPDc/A5vI85pyFO8/55zH8+uYvURbcfkCHRnHJ1yrJyt38J1v8eJp6nLeM4U2PG7NTVkEyzXKaKfVw+WLHMfqKi8n3f5nHSt2NmlbR+VwOdAGerS1gn/xR+8CAO4+phMmWZTjcMwD6IL1zveYRrWjA9O9z+9jSpeFouDBLdnOjx/fx4hSIz06dBWT3PQL7Ti8Sos0Kz0gLnLukVE3Tg+5+Tpb7MP2OmWiWs/DN8/Da2CI/47Cgt0cDw6jIuLvqDR6xtKAXWWKN5/Q8XFTZcl7B2XEI3JmF/hub4AHidmZEZS12QfD1Oe9bR7Yb736dXx2mzaoqb3H6uQcGy1GuHRAOjoS2bsOaM1qGXsiofbIxvTTyXxLE2c2ulKm7vk1Z35XACE35+hxmnZyvUrZObJbUTvaAwDYRKr5p9/+AQDg0fYhynv87LnWy2EAKlnKba1HuX3hAnWqMW/Ghx9y7/h8hc7YRIbfdfitqO3yoHJJKbzHZerNsGMBMZWbfpLg+maVWui29/BQpO3feIu/2z6hnATcYZjkCJpX6fZ+2vftL+9gYpoOgqJSP0Ja78PTBCam+L5rOnR15Ayu5urw6nJ5mOLY/RaubTpROiO4P3+BTphcMQ2nUvNyKqowEeKzR90eHB1wHKW29n6RkI8FpuHrKJXlGdMOr92inmazdvz8H1iMQvcfRJWKu3sAPHmgogBKG/rH37AYh70bQk1Otqsv8vuWlyhXO/E4jGY5CCN0LKROOFcPvzjC6Djn0Wenza3JsRSxeXEuxnk8FMF+MUtdanf2sccYwVnp5OmRKIKSSXOZti8gR/ZJKY14nmtxZYyX+m9/41UAwN9u/zWuXqBMP3zI88vEpNIHKzXsdTi3sSHun0cOBbQaWfg9lPepKY75wac/ZacaJsyfZ3Dsf/4+06buPCJB9v3kLrpepcYrFdEq8ngkGyimONZhpa3EFazqdc2wKgWopcteUCWUrW4nUnIsuN2UtasvXUFdTq+jY+pzSA68RHwf21l+Py0HwZVFfnZyEEc6RzmPhPn7u7d5GZocHUNJMlkrs19TM7T7dqcBZifH00/hcE0zhatc2EdSRVliQ1zTepPP6VqtsCi1p6oLavlARTUMBngVNHKJZNduY39DkyMoKHDVUBnnf/o1A3YXgzFcGRJxvC7nJ0na5dj8JPySgZ7SHtCtoZijvny8xdRWb4Dz6HTbUVXJ6maG3zeKFsDatKJW59/iNfbZoQua2dRDuSgC1gna4cM9zrnTa8aknHOZOO1hWwcRg8WMoGx5OsH9rF8Aw+uyn+lx/2ILOUvqnRbsQTmLFbBwyrZnClmYZKPTCh4FVPLd6DQhHFT5d11+fd4Quk0dLBX06OgO0evUkBH9gsNCI2H1UwfHQnNodtnXsUnOf1xFHWrFU7jlBJi9IsJeyfbk9DT2tvnMnT1+f06BWbvVAbfOlP302laLa5pMJxHPihZBAYCe6B+iQ9MY0T506StMeXTpInPnzh00GhqPmbZiP6lgTd14VvimqguxtVhCk9sJLBHO6dULDAgkN57C4OJahhRIOU1xjsMTEQT0To/SBQ9OeG6Jp08xOccz9egk7275IuWjbG/BIILpQJdjaKg0vc3aQ16BspqNfZ+eZVpTcruKugiL7aK8GJWsO21WuExypogeIKBU1ZHlITz8Fe3+5XNTAICMgiexG+eQ11nm/VUGuHv1CuwG9vncIu89VxVkqJeq+M2vuC9Udfa1qlBBrQ24gtyz3nyD1Al//mf/iRMbDsOuokJbSvOKSEdGhoZxTul7/aIkTqVM7pwcY6/I9ywN815R2KVuzHsn0ZDu9B2H407KldfhQuqE9+mwW+T3p9Thaq+O2+u840VVYGdxnnP88ScPIPwGrqvYxFGqfy8HZsY4f8b/l733irF0O6/E1sk5hzqVc1dX53D79k28geQlKUoURXk0JjSYhxkDxsAG7AdDNjCAA2DYsF8GMDwBMMYajMJQ1lgjXIkaMd5L3uYN3bdzV3XlXHVyzvn4Ya1Tgl/4TIBnv1SHU//Z4dvf3v/3rW8tkaTXmk1U5Gcsej+1KFDcaDVhEhH+wgT94obOoJAvgrOkCOcVWJue57O3N9dQKtMeLCrzqpq5d4Mh/3lCu+ejDRl05zYNeiiXVD6p4GwqqxIylxfFuuxql983qfkv1UrwSaSi3OD3tGUTloYJaQkB9URpAJ0ph6enGIvJx/6SNioZG7VRG7VRG7VRG7VRG7VRG7VRG7VRG7VR+zVrvxIIIZOwliZj41xSsWVl5G5YFjKwNWD3KjvQUhZjwEyTzRREShKfY4q2Rxck59reRCHNjMa4hxntRzvMzH7la2/CbWYE7YGge2VBgLtFFy6tMNLqcjO78Kf/hj/f/PoA7SajczkhP2ZvMEpbKuZRzit7d8Cob+eWJIDnDCiccTxQVHVWZUeTsVXE48y6nZwK7rUDLH2T/TlOMTrdSTPiaGkXERCxWCDKn7YmI/OWqQY8MUZAMydC6mSZ/YhFq2gc8VkzysqvXuF3/Nm9e1hcZJTZbmMU3aIocG9QRkeopiFLq3uSzyn16jhWeZLpQ0ZVr00xYtsYbKPa43f3uxxrpaSwLtowBwTFE+y8Bc7Zzx7u4n/7Dcqyxxtc27WNj4UnAt57l6itzJFKnzYz+Kf/gJ+PiqBt/4B9yafWsSgp416UkdrjPc6/5eMtRF1cn0yfWZ1UgZml2ysDNAwiL7zGdYvHGY2122+cZxAzOWVNxJGbbm2jqKTRwETURlqQ6CvzEXRjnO/IPPty8DNGdaf9AeREqGj2nGhC2ZfMZhoTVyRx/oy2uv+C0fBIeAa1OJ85PS9oc/8YRcFgnUZmjyZCihp7fHAKLeBXZlvocfRsgCcruGeHs23o0B6dvhqsNv5bLT/cl5yPet+EqIf2MDfFNfzBD5kJvHJtAt/9LSJwfv6CmfCNNO3KN+FGB7KHNvdnocz18AcTcNb5fddniVzbUanDwYsOyvINBj9/RiVT67b10TDQLiYW5zhkqwPJfe5LszJul6Y4/4VKCfd32C+7kBmlMtdhbqyPqQsi5JM8cOaUSJK3llfPiQb9A8G5JbF9+e0SBiJlLKtcrllltnD1wgr24ywXPCkOy3/oP4LBKvJ19r0vv1MUfHnnJIs3pjgPRRE4hhZEPtdx4mCH9vCOstw2+YCI/RtInAqqqkyFqx5A6lAw27bksGOca5stgK6IvX0iAm3W+KyHa+uIzdDPXLlA8mWri/07yZ7gsfZjT8TAbit9u7XjwIlQKJcvcgxP80ScXL0xi9P0kGhQSMMg5/zGrAulHhEtcHOdT7YkZR8cINRhtioxzJSKrLhbqaIgPxMWomDlIpECiUwadjP9tluZ1eThGZomZqXSRtphUwTcF11BBISguyw7zCe4zpcWPfhijWNemZsDQBg8AAStDhhEAGoTmvL9b77P70hXYHZxrOa2stwiRc2e7aGicgSjletrks3aTeOIznFcRypHnZJggKvXRFRo2IKRv5+T7HrVZkRrm3PUzbHvbklsW01NbEo2vVvn/L1ME31YbuZxcYWZvDGhKdwWG+4/4f+3JA2/k6MdB6whNNP0O4c6p0NRrtGgXYddstbxY/bPp5LkdqGCuQX5D69QPDXaeDyRwptv3wUANJoc+0cffcZnB0xwC6F5us1Ss6/+zrfYt85NrD3h/qyq5OxP//QvAACvXBrHJZFIPviP3PuhGO3E7vdA1R/oKOu3pvKT2GQM195iGU8qR3sJjI0hfqRSiDERDze4KMdbaQRF3D8t9GxFWfadnQf4xju0h1yVYz4+5HrdvL4C/7dJTunp8PP3nezLwGNHXkgMxxj9RtfC8UV9i/joJREwjlkdSF3aZy0FeISgSed4vhhdtPH56BRkqjCAd7ColfNz9cIlmFXqO+fkGN79x/8zAOCjLz7DxvZfAQCu3OQYrJ0e/EK0lfM8+5tCdMx4ZxGR9PfqMj9TK9P2XvvyDTx7wIPzrlA9Mz7uu5/d30c4SDu8pnLKQYpzbjkzoGHj8zP6uXYkktiQDUhIRvi+srxCEURiK4hKCnkxQL+4I7h/t2dEV2XsZ1Wh/Nr0FQazAz07bdtj5M9uc2O58tAAACAASURBVFiS0YdXWeeOBEF6wSCaLu6JZZUbNhNEm1+bncOxfOTmhlAJQjO/8dVLuPeU2fFCn+t246sk8M6dneLZPs+owBxtIJ4lVCuzY8Xb770BAHjyU/7bQY5z4A9M4mBfxK8d/myrfKrT7+LCGFEMQ1iazc07R9jlRVxS9Ie63ARkX/ZuG3N+Iveeinz1hcqr05EiTk85p8O7hkso10I6B7Pm6LXXeNf2eEL46EOek22RqWeF7gu7ophcoC/Zfkb04dMdIhj2j48QG5fsuUr1Mqf0j1F/GFUR7VZEJ2AXIiOTzMDh4P85IuxfR77CYDUiLKRPS2Iue2meWal8FVNT/J5QkHNWEOm2x2hDJs65WrlA5OT+Ptfb4/KjrxKs8THacUdCKbD00deZY1CZWLtQOz8DenYhhURE73d50REr8bFQd1Nujstus8PtVlVAlufR3RWWrj58+ADZLO11RwTfs0Y+M16OY/YC75ACtqAiyouTbAcZCRl8eYx7aHaan/X4bdhVyX5LyPATleLt/u0H5yV+FpVJHxw9BgAYi3l4zLR3s0R8Bk6WG8JhhN3L+TCrBOno6Ahl0H6KIn13iX7AYLVhrcq1HJvjWgZFSG4z9tHocw+uPeFdyGQckjFbkJZQRUXoJK+bfiFfN2BGpW+w8fuKKht0uR0YU5n+jPwwOvRXhvFpeFRd0BA67dYi5+DkKI+Wyi/bKtVLS2LedNLExUtcp4b2ZygsxGbLAXOLdtiVeES1Y4anQp83ELr3yQc/AQD0MUBPyKAZka/v7fEd09BtI2ClfXz4458BAIwqjyzUezAVJNJkoN8pDeijirkk3B3dSWSXzRD3SNNrwriVPjMgpE9exNrZRBk2VUvYhYiuDvfIjbvYeaw7nhD2YbveNU092CRQUBG68uoqfVIkdhWToosYCp04dEcxBV0oHR3yu42cA0sDMHkl2CKajqbQmbGLy/AOVMlyxvUaIrue7x1g+4R2YfNyXKt3eQf4UuwdPPqUaK2y7DcyLaRgvwJjU2XH2yJtF2F7tplGWgTrPknL+70cZ7c5QFS+stGUfegMOk2Usbwkf3PGM9zi5vcZLUFEx7g3+rKvluIUr7xy6Ryh+cvaCCE0aqM2aqM2aqM2aqM2aqM2aqM2aqM2aqP2a9Z+JRBCdhujWZNjF+F2MsqWbCliKPLbXN6NepPxK6ePEUaPidmCg0wWnQ4jvPUmI6dmEyNs0bEQ1r9ghmJhgvWy1+8yOhjPnOHoWEgFP7PCdpeItuwB/Phj1l86mHDD5BVGF188skKJZYTnGeXzu5hJPz1twSwiypjqvn/xt/z79IwB46rTX1xmdHZmkdHn3YNdGEVaWBfZV6Ntwv1PmFlYXmXN/4bIE+tNFwIiEatI8tcyz4zKa285cXbAf9ve4ne//97bAIByfQ+pATMneyfMngbE+xoO2dFWZNGtjHZf3ECDTh9x8VmMK+PebjEyPBb1oWFklPNH98l3sjDF7Icj7DrnJLCD3zsIMrKcPO3CrZpKs13YH6FRvKEO7m+Qv8QS5non80Bkmt95sCdiWXV++eplnAhx8MmnPwMAfOmtd9jPvgG5BO0q02ZW8WSLkde22Yu+eJxsysCMT3Cctq4HKknF1Tmub3+YeS+WUFbmRFy5CAp91Ld6kDUou6XIstnOdfDFvBCIAR3DkPyUn7W1CogscXyJrCS97ZLGnBzD+hbXyznkeBDYzOUxoC+EVUK1rp324Jxbql5jBsHvY0fTZ03MhxllNg0REtwimFq24OIlRqoLyuwZRBYdDDhxcqoa4SUiJaqyj263f05gt6mMxbVr/MzMQhA//glReSaT9rqyJ+6uA4Yux1xW1qor/oLUSR1oM0OKE47r5uvMnrj6FjzeV5ZJ0qLFIpFT8c9OEBtnlqldF+9VpwGrMl5PJRvdEheWwx/C7s4hACAgorGSpJvtbmB5gvP9x/+K/uDrQnlg0IZhyKFUUsZBpHjhSTP6ZXG88NPoiA9i/OIiEg2hjTTW1QlmOioHexBvMZKS3nznHWZPbYZJpHL0B6GYEIld2vz8hStYf0RbSyVoC15lHryeFuqah0ahqrkqIp9hXx1u8WEMSTG7HSQTnI/VVWblE6dCSLjDMCmT8uAj7vXf+y7nY3liBhERNzr93C9bL4mEOqvU4fOLK6EtVMIE68yPcwnU5PNayuJFxN3QBpAU4mQ2xjU9dDAzYoAVA7Avsejs/29eKp00TCowd4gPJpngvu7Wm7CLRDavzP5eLolLV1g7fmmC6CSTuA0qpQJ6IgZvurlXzW5+78bawTmh9fIFnke59JBjwIZmXQTCafrj4F36oSePnkHUYQh6aHOrF5llf7H2GXra3IksM78JyX0H/ZMIRPl5R1DS3iIKD0eC8Pvpm8sl/v5ElJ/55OOPYFFmOqCMZ1cJo1SpiIYy6JNRrl9Pc2c3e3FwxLUv+WmrzVoOzgA30+uvkpclk6Vd7Wy8RDzH/piCIs7tiz/M1IZD54rNQWM7PmWGzu+y4MVL8nF9LlJ6i0iex5fCaBnZh5yQOjMrnOtGpY3NI/qdkmTk5x/xOdV6FUcZPt8rAukby8zY97stfCg+p/V1Ih6ui1C0ljmBT/NmUUZw5eIcAKBQyODZc3IkLa1Ons9RNs75sNk4/7kMF9dkdJ4jI5xeg8ZO2zveO0Bd4hKLkqAuiVS6mU1idl78hCJ2X7CLc262iYkZ/tmls+DWJOVzG50OTE72dWODyJtyhX642y8jfsr1HYjoLTbBfdaoGWAEn7UtZO3MCe3soOyAS2TlDqlgBMc55//gxnXcnRYv31NyvrTsJpjFYXD9MlGE688OOX/p5jk5ebXIcRkMtL352VfwPE3fvHvE/7NY2L+l+SVUa7SZ0xPu416d+yyXMmBmnn19/pAosagOZfOEHw+3eYZELByfZaDzPp7Hfk5cMg6tt+S785Uy/GHO/7xIeQfLtKHV6yvYE4+XQwTLdiFnk+UcahLwKInL6aRThbPFvreGvC5COT1de4CAEAChGNfm5Rb7W+kYsDhFn3AokuhhxtflAbwhcRhKRGDgpA0eHibxyUPuoS+9TaSQSejWxM4RfFr7sHiPHm9wPucuRZAUkrwxRNKYOS5rx4h8TudyTZxDen2IunwISrDBcci58kVE2Nk3wSliWoPYkGu6q/dNTkxd4D3kuXh4HFYTnH6ueVcn5/IK9+xnX3yG6RmeIW7ty0ZbnB4zk3B7OZ6cCGOHPDw2mwv1ppD/qgCYnuZ5USvWYRA3oEFS6m2hfHvWAcwa/ycviLQanpH1dgcnGfoZt7izLLKBUr1yzvWX0JlTHXK4WG1o1cSh6eT3mMU1MxGNoNSiHx3e+3q2DjAkbRd6yCXBi3a3g4E4EqNjnH+jlf9XbbYBi4RohNj56BMiR5aWFhGXPe2dyEdI9jtRziE6y/3pMfHfinXOZ9jnRV8VA5UB92zqmGdksVGFUwiTvFDP4UnxlOWz8Nr5ew9Pub+TW9x3Nrv97wi3jbSPru5UvUH3nKfTKzGZMWcAJ1uHHJcqS4wi9H9t9Tqe7fPCXjihHU+N0Q6LgzJ8AaFPxrgWwRD99/7xCcxOPqsgXpeh/HzU4oVRCJ+a/HBMvtrqMJ1L2dcr/L0xfcdhIgGTifMfjdKPbG4RmWSAE3ML/O4TrUNQ55PdbEIrTxsYIvJaBfYtEpiAR++INnG0tTs9tCSisC9OJI/Qz4VcHkOK3NxL2q9dxMVmjxMFEXabhLqdFi9UY9DHkyPaRWCKfTfIRmvZHCoi0nZqf47rrhcZC8BpE4dvjn3P6PxFb4CWmKIvCnFvF6I/W6oA4vT5+HO+J1y6ykoVq9OE5RjtyD9Pn767xbHcXF1EUBxMU0Ju7+pMv//xUwSn2K+ZMPe6rWdBWmIPDjP70tGd6Oc/uoer4hv0ODjHVQkOuD0mzE3rnVeccYdbRFpdWlxB1Mv+mSWYER7nHLf6YRxUOA+WMPflhw/5DgGTEUEhCztCKheGZOo+FyCi6LbOvJbu7812DYkszzibKm5O97nu+UIFb71JXmCD5vOnD3lXmb2wdC7u8cvaCCE0aqM2aqM2aqM2aqM2aqM2aqM2aqM2aqP2a9Z+JRBCd65QZSKdruLpc9YG72+Lu+WmajnzeVgl+VRIiF1baKBBu4/52TkAwIm4JIp51Z42u8hnGV2LehnZXFD2ot+z4PhY2SZF/qrK6Ho8TozPMPLXqIi/QRFVz2oI28fsQ3jAKN2U0ED1FT82njFa7HVL6UFKaWcHZbgczGisq85/Sgz+LrcZGxl+3iC0jTcQQ2fAaHFWDOSXb3GcxWoKzaEUvcJ6j14wc/PlL4fgkLKAs88o7ovnzNSPzzjPFcASqsEPBJnFcxbTqEqGLxRmBDpeIqrBYXHCouivxSpFNzH65DN5OLpcm6iY8Z1aq5kJJ5oVZhHuLBGhtS81qsTRIYyCsYSFmAqqzjQRb2H9lGiIt66TVf6pyYJiU3KZ4gG4cYvKMul2CQMzI93zE+xDTZH22EQEOz4xuStzO3OBa3LtPRviZUZjC1tCMA1lHI0GtPKc3MEks9w9cH5yJTPGlb2Ym5V0p/gS2m0bLA6OJ+SjPc1dYaR952QHzjOu8/zrzMS4I5JZPapjrE07mptk9LgsBEmjm0ehymi2TwirmJBJvX4BLiHrTBZlAtMVqDIdlbIyy/2i+mdCQQoXvmlG4tEVp5LRhUaPGZFUlk+YVaYnV2pgZkr8Km3OQznJfVMq9+DxsV8Z2WVsjBm7w/ghdqR2YhFSamyc/V07zGBxXvwZBUbdw1HOZ99iRcmmbGSGaKXPP+dzbsy9hjcjzBw+u8+MqluZs0QpjXUpj61OEElQq2Vx8RJ5rTzKxjzbUB1324KTNG2gPhCf0ySzlK5ZHz7eI1Lt81P2pfgx/37nzSlE5/mszz/l92WOuF5fWwRO81J8EsJlTOPcPt1COKhsaYr2NcwGNboN+CVB7xDfSkTSsJHQMgpPuWeHGZWzJDMwPlcTeWUYymVmRsbC9AEv19ZQK9Gmh1kPOCwwK5s4Ns41PTgissNsdMKs2va9fUmnNrm2Xk8MmQR9UVIIsiEybHtjE9/fZUbiW79BzpdJZVb+7KeP4RAXysZT+vh336ff9wb8SB5y/lwau8dDv/WHf/pTLFzmv5n6RFrIBLG6MoGnXzATPjPDsSpJjEq8DYOYYHrKoC2If+L07AxzQjB2lTn2GCy4u8A+ryp79+/++N8AAOy+MQSW+OBiinv8IE1b3X25D/8YfV1SMDtjj3MVHZsApOQxEOLmYJeZt6WZKOqSqh4qQ2yLAyedzyJVYKbTLslsi4tnwsx8DDYplCTTXG+7nXOVzRWRzRE1d+cmM0U+SRX77E4kBXfsmenLuvp+q9MDp5BdDj/tyiApZYvVhSXJvm6LB6Pj9MIim0yLR2dSqKNKdfxcttXSEz+WeElmJifwyWeU0PWJF2B8jj6w0TVgS8o/Fy7xTDw9E0rE7DxXOBqIFy0lBbiNzTzaXe4ln0fqoZ9yHlPpLO6+T0RhUvvLLW6OUHgSeanCjS8LKRjimVAvdlGXuuaM7KQh1Y9WqYxZodDKBe7d54+f4OIiM5RjU5zH3Ze0Z3/Ajb74uJxmKYUUaUO3bt3G3jHH4bFwTSfEoTAfncRxnHuvpUz48hwNP9szwOzg3o14+OzPPidaL9u1wGClrQaUHU9pHd740quolzgPQ+nl0AzHEvBM43t/9Jd8ZoQ2d6jP2Bx9vH6Ta2Io8Fl/9FffAwA0y3m88iXeH8oZcS9EYmhI6alR53j8koM2toAnD4j+np6n3Yaj3N+59AHsbn73+h5tdXaS61Wr1CGVaMwLdXRwJBRL1YQX64ec0+EeP+YcW0wDTFwSwlKIvMIJz64Lr1xHRjZtAL939gKzyfZODTMx2q1NZ2NSd8N3ry9iSZLLz7VnPYL9mopFnAllFJWaTiqXhlX8Uc+fk2+iq2dOL8yhJGXFgLg4UinuS28wgn5PPGHiBhv06Ae2tpMYGPn5fpEDC4bZhxtvXcTxOte5oLX3B2kLxydxjAlhmNF6LczTR/vdXsSFEDK2hFqWOtTe7jH6QgmMC9mZzAmNXBsAdtp7sc77bSAgFR+HFY4B7bHTZt9LRc5Po9GFX4ha/8Qw+78Nv0s+S0iMovj8rl6+hJRQgEO1saA4/xxuB7x+jqtQ5F616k7UGhhQ6/D8KxW45ye0tm63By0pDw2RKVah1YvlKh49IQroOMPfG9rnxStXcSgVrrjQn5cvSU2t2UKpwWfOCXVhq/P3ipUWejoHTxOa6z7n2mAwoAf+uSlCr/2TY1y+zjtuSVKrHtlJvlU+f9bFC7zb7Etu3WAwYNw/XGe+q9ildNbH4PwuHwoJDV7hfHZsLtSkjOSRqpbHxO/owIiBkO9dK386hODbPz5DV9rkw/vVxhrPOn/If87h1hUvlFMEO3VDF5UGv29qgvNnlLJYpZCHSbSXNSlUeRx23LrC94GXL8VTleAe6dR7iPk5npYQtrUi91LPZURK9j4+Ll7EDsf8zlfu4jTOM8u+OERR8PenTX50JMfeEUfm9AT34s7RAWoNjmciRl6bn3/C8/fa7esoSBnNYOJ9YFYquM+ebCLW5Vl19TpRKRWpc7XaA3jCnMelAP9vS0p1FtcA9bL4rYRqq6MMU0d7NM/xXb9G/jVj1wFDTcqWUlbbPpRSaNAHgfRhACf5KE57rhl68ETpd41C6Y5JabHfbMAxzjWvircqoXcJU6eDCaGCu5qXkPjz+t0BToRIf7HDu9NQuSy9d4hYjL7Sciw1vop4wxwePH7E+7Zb3HYZ3cGePd3AeFRKc3M8f4NSImskDbi3zd9bvMk7QKfeQkvVO5unHOui7r5BZwzpJH1Xf5zrNawgcHv8WFzh8yGVtlyad+Ds2QlSx7rvdDlXySTteSw2BZ8upM5Z+pToEtcqfZzCa7d4/600ub7xxCEAwGAywiI0YESchHYbn+MP29EU31TMST+cF39SNp3HulRwHeK4W5jnO/fJWRJlKX7+svYrERD64HskqHzjfS+i0xxssUADbLf1YuYDSllB3ER05RTJos3ex9ExL49dXQ4zp1yU1asXEI1w8dc26SwdPh76vpAJAwNfkHqSFa8p+HO6d4Q5BVUGBjqo1B6f/aU7Idx8mwfKD75PicBum4u6PDWHfFKk0hERuNYkBVcNYP2hIK5L3Nh2L51Zs9tHV1LU9bICEl2gKXJRl00XqiCNNBTxIRBRecabKtVZ4/eertthMvFZd+7QqSxfoEE9eboNsy6mtSrHfnoqmHCjhbYkDm16IWsKUmc2OeAVTDEn4ra4yk+M/TbGe+znwio3mEXBi3IlDaOPZrYcodPs6EX0eDYFkwhOkzpwQ4KWvvn6VTgnGIhAjd9z8/oENrbZ56uXKaP640/5AlrGIRZE2rYi+LxbLxG5fhG2Vb6oz5RoV4sRSbBby8gd0a680o2/fZOb/8FRHJ0hgba0wOstOl2XsweTiLf3j3l5dWu/DUx2JFTa4CzTIbsEg66dlpHkHRTTNwRN1uFjtgJJXWSXl7nZi5pHg3EAl4gvy7KJeUEom/U2mpK3NvT4mYnxGRwpoCZUK8wQ+bjFCp+kmdN57o1YhBdpt2WAy7f5Z0tfktci5nN4zCjqspvL0WE73LSTmbko8vm0xsE+nJzx2WOzRoRi/L6zl7SdoMomXr02BotRBOaLHHNPJSa9rhMOwaULDpG4FziGR/kHGFvmIeILsA+NLn9v5eYVfPKSB1++x3mpdUtoah9WclzDqsqggtFFvP+N1zi3IlgOK8CZOYijJ7j95Ay/53e+8xvqy9PzMr6Vcc6ZU3PdaZVQURBmeo5Bt0KH37e2ncaVy5JSFwR4R4SMTocBy7O83NkVDA7Kjo93doA+7fGTTzi+8Yhklr94jKs36MOCEdpHU2VYuVQXm5sc1xS7iW6vi3n1y6qLnEMw6bN4DgWV3wkBjUldoPcOkmiKGHJ2ifZXVklhGR1E57mWHz2gT//db7NU9b2vvIdPf0Jfmauwf188YSBvbD6KqSjX0iSCyLVd2u784hzcdu7VIxGzRwX1TiXzaOrFwC0i17QkRjvVJt59+10AwPMHJA1On+kFu9bAYYVnwfQ0fdLS7CLaCdr0kzJfOuIllWf03Qi3aa9R+a5TyaJ23eZzklSPlz9DXpWtlcrnMsVLsu31ddpLoVTEmEhFWw3u+UPt106/gZIucka9MHl8/P6rN67jVMGG8Qi/pyMZ43gyDfOAPuz4gHtP8WFcvLCI4wR9YCKlC6YC+15LFD4RKHYa3BuqbkK30oKT3US1TXssVJswiFDy9jVeOI52WGa0fOEy+gpQ9VRa8vrtOxr7y3Pi0KxIKz0B9uHgLIVOlf92+wb9jdesi2cGuDLDM9uv0oG4LlHzE17YVArbM9AWHFqHqas+mJq0h7Akss9yDEYenwEz+j2vLsteCVkcZvLo9nl/iDj5crOgMopmuoLJAPfC423u5+Wlm7CrxCOe5OWwa+RYrly7iITkrI26bnV1j2k4nTBpg6XznKtanPs0/cGP4dSL6bUbvOCnzDrDXFGUyiI4VcJmdpbr8OrMNfStnOO//v5/4P/ZuKYz1mVUAsMzR2XECgRm8me4/S79QbnOsSz1OB8XwyGYT1RmrtKesgKcyZ4RqYe8hN66yQvu0dERegpiDcnU603Ov9HugsnK71x/qRJOke/H/HbY3Vz7fFmJgyh///LleexuslQgr7L5voV9MFgt6Nr5jIdHDNAMyZGDbj9yWUl4O2gXbtlHw2DEwgLPeruBY7UN+JzfevUWXCp7y+3SH/ztj1hy8yKfwe1Xef/ITHAPJvWi5vd4cKQXnrTKUbPJBOoi0p6d5stsUaWBN29cw6OH3KvFPO3C5uDLYr1hgkGE4PefcI6vXrmqeVnCy032q1nl97jd9ItmjwkXlVB6ss0SnVMFIQ1RHywiBG6rzMWh+a8lyqipVMGv8rOugoIw2FBT6XniWEIeCo50YMLamuxe+hCpLMcUHouiLnJ4q4XPNCkYYYUNA51/BZVRufwR5IvDhC/nyGrmZ+xWCxwKhIZ8vMcZ9IJWydVht+purD21q2DfYAAEAiIZDjNAU1Cppt3jRCHL8XQUIJ+a4Nz1CmWcich2+RLnfSg6kc4XcPsu7wyba/Tp+8f0SRcurpxLmnd0VnqctPl4LQ+vl31pa91MKkNL57KwqzTTKrLyTreLREriITYR1EpK2hl0nu9Hv0RngjHOrd8fRFbB+rbKvGJTvE9vbu/CYWd/LFUumE0JpsjYHLZ3+MxIj2O1Dl8TTYbz4P6W3qVqds5j12yAtjxsShRbVRreOC3CL9GMowOuScuisjdTDybZ37D8Mp+lr6mVS4iGeT7neiqtjYWRV3ApMMGAV7ep8lyPAx1FjZ0+7vG8iL6NBgM6uh9eXlTCQWtULaVhNHCOaloTp/xJodlBWYIS4Un2JaGgeMdoh1sBkIzuemFJlrfrfRjlS2oi893bYzDYAAtOE4f8XEc0CUoEN1pN2CVkU29wQsslCQbE3Lh+m0nQPfmk08Q+LkpevRPnHto6ov+Junzn5ezlPO9zwzth0DMNKAjoCPOM207TfqOTY+dJPpMCeJtntKXQWBRhBdT8ft5JSimeQVtbL5GRbfclnDBMSIWCYbg9w0Arx3dwTBvq9YGGxHrefJdCCmvrvLO1Oh2Udf/OqQy+3lZQpVwEPCpl2+LdLp2gv4su3EBYgZ0DJRdyxSJKsp1LKzzr4vmK5rqFCe17o4PzcZJmsnE6ZsPmBvu6sMh7iMdj0fwMMDnNMWdMEnxSIGnc6cPaFvuTSvH/You8c3jCfiSG9A9BBcoLEs2q2+D1cn96laAbDBSs6tVh6tK2a2X2fWGOl/peb4BSSQCCGu3xioKn7Z4ZJ/Jlv6yNSsZGbdRGbdRGbdRGbdRGbdRGbdRGbdRGbdR+zdqvBEKoppKitScdeMdEMFtjlHhCEm7FfAsmgdym5xg5ffEFMwnpeAtOZWltynbnJYP74skWli4yqpcrMhOw9ohooslZO0JDaFxHKdE2/272WTEQu+tQIlBK8fjs3+/iv/xvKaP93X9I0r4PPiAZVqfbx82bjD7+8BkjtQI3IOjooN9mNNFtZZR1XGRwjzc+hXitYThjtjxXKqNeZIT29hVmKCs1ZmK/+LyM5WXO28pN/uI2mAX69MMzLK3we7KpnwEAbr7KSKHTEUBOUrp2KyPsDaFgao02hPRFE4xIGvT3gdGEmrLxA0WZjYq+WzsGzPqHpG+MAt9/RFng6IodvSLjjj5lqaIiLc5WGxjvM2vhtHOShnPe7ZZxfMjntxQhv/nqAqzzJFksiZyxbObng9YIxpx81kNBSY+Fuvjud97B136TtvNAMqwpZWkPDgCBPPDWK1z7SIwR4sZuAUYD56FeYcR7eZ7R2JPdYygYi5ak0TMHgm5P9c4hlu06+1CuceyuiA3lbX1OZLKdIfuzARgbF5Q3Inhklbbu8nvQEKn6qUhU5yZp15nkIfwTXO+U0GndbvccGbSwJIis7NDvHkNHSLBSQxKaFkW+LT4ciLTv8mXazA9+wHKE2bkY7E5GuktFzkd0mn2qlLrniCy/MrAplXZubbUQGuccxfl1qLQZtR+fX8LpHtfL0hG8UvziZnMPyzPMjj/Z5rr57Vx3d9eL+yJRXl0IaiyS6u61YBJ6cAh59bl8aCobs7vNSHlAUfhKpoCrFykj6Q4xS/Bimz5iYIyc74lrSxz7Jz/6M/79zjW0hCzc2GRmemmB2afJhT5sgzkAQEuy86hyQS4uR9Ay0Bc1hHZ0OdnPlaVpRPzMOhmVGbGq9CkVP8W4yM1fu8OftTz3rtnURChMOxpXmVa9qPKYiSV4c5yPOjhnoUAEJ4JJD0mlU8rMpVIt8mJTCQAAIABJREFUTCj7duUG56MhovDVq+P4TCVzN5StcrloszOzs2hInrpqp70PodjFghXf/DaRVccqkymkmeUytFq4OE//li1zjh/m6DtfvbGE3V1maqaiJCN0SDI0k3EhHGVWvC6fVshwHv0G03mm8pU3SS7/L/71/wUAGBiAqDKwYyoZi7jc+NHP/hoA8M5v06cfxGm/szNT+MXHRCJOzUnWXuSf4aALHpU4FEQAnT3l2k7FZpBXZu5AmdHVKzwbTGngTOTLLj/7Um1zTzY6FUQn6CMPDkXmHVZJ6MCKl2vMOs1c5JwNywbTqRyssqdZoSstVj57fecZdlOHAAAl3GEycE3PTg+RFNGsQ0SYV5Y51153BNsbzBjPL/BcmrcPUEhyXFWVfwS9PIMq1TwcTs7bmI9j2FojjL7b6CAa4V4NzRJ29HCN+9rstOPdd9/j/AnWfpoQCqtrRKVI2/ztrzODeFinfbicfhwIoTkd4Fp2WhzLxSuraJXYz0Mh3qoi2Xznna/Do1KWnRcsp/b7uF/GxiKIJ7iGT59vaG7pO05PzvBkjfOxolJmt9t/XooyLM+YEULjw48/wauvcp9ExtmvgxPOWaXVwJRK5hYX+PnDl0SCZPNFBEzcg4822IeuSKXnxsqYF9Jh7QWf1WvxTP7y5XEkhIi5p8P7SGfI7uYpBkb2c3aGv7/3Cz47Fo5gPEq/05TNnB5wzh99/AvMy56KOT7bKIb+9977Er7/w78BAHz+Oe8mMPUQHBMCANwnFh/t6nD3CNNCv/pFur90UTZhHyCZlZ9ZJcy/lOch20cfUfmk6BTPuhkvbejxx9sIydb6kvC26z7YKKcRUOlMTwjZ2Yu042qpjzkP52Fibg4AcKYyRXOygMePueeL6oO/zr55Ynbkc/RrZju/ry3UtN1rgX2W50pLxOlf/o3fwvYDzvvBCc+JYdmK3TbAt7/1NQDApz+nnzuI01YLpQYaQoXAxrUZIpFiFyLw+2nM86s8I0sV7pdyowqTUAk+leOVhnc1tw9vvPsuAOCLeyzfTEm+OOhz45buiU8l6+63ScbcbEdX51BTKIyeSsC6vQ56DdqDS0jNhi7L9TrQEGltX2gDbUG0Wm2kTrVn25xHh8MFo0qlJ0Qmm1cpvtPnPS/fqcQ5DyGVlY2Fx3GiktNgmH22CHliMPZhtAi+IjqGHRH8hsIBGIzsX6PLc/owQaTEoGtAWwiLYTY+OyTd7raRPOW8+YW2Ngw4luOjXXiH5aeFYSUAfU2tXkGtqfuAxDCCAdpL22yA2yekj87+WCRyLlPfVT8DKl2KF7KYmydqJS85eHdfpc2lM1QkFOAUkXD5lH7L43OcI0WcXu51q5DfiaMMXE6RequscX+P82qtuuCTkMywYmHjhL663myjo7I82x7PuulJ7s9B24JUTij84PR53wEgHAyiKdRGS6TlfjvHl0qlUNPYi7KddCaHjsrplpckSnFCux8YjYgNJdD1rmJRmdz8WAxTMZUgqsQpqnM7nkijoRLhaze5l7Jp3hFP4km4RDLsUJlbQghzAPAOyxKr7F/Y41M/8+d0Dx4RoF+5ShTn7v4+DCL6LqmCoCs5eZfLgk6D8xjw0i/Gc6rasNnOq1V6QjQ1+y2cnvIeZbRwLY2qs8tkUpjyC0UspJpT6N1MMgWoVHIgifirN4iCMxgGmJPIycPPifS+e5eIyHyhgLUHRHZfWOZdd3yM+7SSKyDsU2mqyinrOhedwQh8nJrzeSyJS9xscSOh95Z8kT/ndR/EoInTgqpkVOo6kF8di9lhEQ2MXWjarFA3lUYeqrZCRQhFRzCK1RssHy4WuIaeAJ/p7fXx6AVfBO/4ifyLzRLttbu5j8ur/PODh7wrRHSPWZyeRVnloQhzHpZENxGwmfDtd3j3/GKD95y2ZOgbzRy28uzrDfCd9tZlUhb0ulZs7/KdqFrmWbAoqfloMIBiXtUtQkc2m7SFqYlZvJSfMopYfH+f6+D3eDEzpgDDL2kjhNCojdqojdqojdqojdqojdqojdqojdqojdqvWfuVQAg5fYzAFrMhZDKMqk4IKeTxSwa0UIVH0IGhRHH6mL/nH3OgVGXmKhQTh0qY0WmPx4dqhxnEBcmGNiURnc0VUK4y2jYpIuKG0EqDLlBT7Wg0xMhf28zom7s7hv/jfyJK4H/43/9TAMA/+j0++5//2fdhFsnXdUnXbuaUNe/3AZGBNTuq659h1PjhCyAQYmQ36JV0XzqPhQVl5grMLJ0oA+n2WHG8rcipkz8j45IeD/Wxtcm+r1xgBv2jHzPiaDK74fEy2piJMxOyx0fDPedHSPWkGRFXibIEZrMZRjP/4gkwCxHPqAa9b0VBRKdXPJKUlaay1+hCJcU+x7yMZEYlW202AruHzBRMMomKQETExKUTdMTpEwqp5ncvi+sXyUny4RYRWdk6sxfVYxPmxhhprfcZMTf5GD1++ugA09fZr8kYbehQ3LoWtwmeSWYoykLqVFvMjFq6ZVglCVgpi6ROUs31UhdV1bR2VNPaE99Hp9PC0f4hn8HEHC47lGnrlmFycG7yWcl5CqFR73YAkZ41RGBeUCTb6Q3BaeP3GAdcW7NJ/D2zS0gU+X1tZU8qlSZEd4Kush9r6/y9m5dc6IjPxizuC/TFV+NcQEW8FH2wD80O91upXEMwyLU3mEX2PBCvl82HTFLPENeRV7wk4dgUmqoBv/iqyPDqyiQarYjOEp308Ce00aCPn/n6d95Ecygpn+XcOn0c1MlpEUFlDE4T7Mu8VxnMZhMF8RDUhVKyugG/ZHq9k8y2WocSrcdFHLxgdvvNO+SUaLQT+r4xwMnxF03iSFL2L7Z8FZbnkkC3DAlw+X0WkwHp/CEA4OyQe2lqmfbsdLYhzmbcWXkVAFDoCUFmN2BnjegHj4f222rR6Ky2ALqyMbdQMvbwkKCzh1ySz/8kwWyEychnFssDDDzK0AlJ1mn10VZGc2Fa3AL79Ad2B7BykXtubppz9UIZ45npGaxz6dAUGWopS5958HIbhgGzkTGtjUc2Oz43hgf3hRpUNt7q5mefPtnBlQtEzrhF5D+3xI1jMA7gUsZwIkqfPiT+r5TryEnWsyHEjqFDmztplfDP//jfcr6VFXvzHXKcrF5ZxcOHJPW3iH/qycsNzNyij/DKBw5akvSNRpEVvC4p/qOVJc7P/vYuPDH6+VKLTsXrdZ73acj/4PaKqFcy691BE+ki94TASugITeHwOtGSNHNUZ8jKAjP38dM0+soA1uUj6vLVZmsPr92+pYfx99d3mM0b2Lqwi/x6VuSdvabQh74Beqr5TxzRBvbPxNfk6eHabXJaGSUbfXRyDJuRRlAVz0c+Q1ubW5xD4URjDtPvhPW9va4V9iCvHC0rv/u7f//rHNfxKZSoBESG/Pp7HMvWwQ7cNtp226hzxUc72Xh6jAtXiOg6PSbaxZLhWP7i/gMYffyeO28Q8TTjktxsPIeinXZrkyz2gRBr5V4LRh/XMHXEeTjZ5LPn5xaRPDzkeIbksKcnaA4FJ4K8t5hcnKupxSk8fkbkR71J+7JPcO56hRL8Fu7nr77+JgDgL8+YNUz1AbfQ0XVlx/d0Xvf6fmzucI9PzdMu0uKe29lZx48/4tn4Yov/NnOZWXnXnBXpNOf/MCOUgnhuUmtFmEVc2THS1huSBbZF/TAqy33hEvn5Uju040r6DKvLXItklevucbgRi9HP7B9xPD5lu02uJsKTHPPNu/S1X3xBuXrzzAXcFAFoTdwm9z4mOvXw7AjBMPdqVXvdIyTJ1btLOBDhrsHL8Zl0R5wc+M6Rqgclrq8/xGcbeyHsbikTK/QQhDD/87/9D3DpnjPkgjTZRKDbzWFtg35/VfstpHV8+7XbSKRpKx7JyTtcPRhMEsOI0v42tmkToUADnSbtYW6edh+Zos+Fp3GOWEoJ6WYdnn+JNPo9Oo7N57zARYUu9kRcqDk5RmeHc3Z7ltnhWGQGh5JQfyl0nleImpbHg8ePieYLRIhgGvr2cm0AX4j/Vhc5fSzEfe23OnAmgvZyhX7HaBTCOZM/R3TaRJBvt1g1diuKQv8MybIbrT7qskmP7hpDlE6l1cDBHhEpF8d4b97c5t+9Z5lz5M1AJLmhMFEOu0c7KIoU3mbmfFgd4oyyWRAIiN9RnCpn8n12ixt+n+7Dcfk0oc269Tq21nkmBuQ/XOIXslkMcDm4zhbdDYdUTBPjIXTAtXGL00qJfvT7bViFaHGKC24mGgYkkR0Ur2evy/kZgx8OSb33dKesFPnZdDqLFVVGNFtDoRwRfVsNgIlj7RuK+j1+ptAowan5OEpLwl1omwn3BE4E8bb2NYYAESgXZmcQjxOpMyX0p0H393yuiGMhF+Kax1WR9afPsqgLFRIJiidV9rFw6Qp+/Dl5B7/1e7/D76u2cSw/fyDeUaP4KGutBhxCSzjVvyuLRHbMhCNoCbHjs3Cdtna4bzKlIrp6Lzvc4r4uFLjf8okE7r5GDrx6nneSiHhx3D4vIFtLZel/shKIqFq857xwFjv3zZCj8faN2zg9I1qrIo7Qep1zVSoVYdH6mk1CGupOv7V9gLOD4X2Ha9I1D5AUB9aExF8iQukcpnKoSsTIInl2h/ZSo1FDXW//NqEHV7VumzsvkRwIDSmBlyFKr54qoiDy6h0Rz8/NiQux2UZHd3CnV3fkCPdgr9OD1yPOXxfH1ZeQC0xWFHay+jPXbWKM31vMZ/D+u0TsJMUhmS/+HfeRTX4XIrP/7u9/k2M4yyF3xLW8fZPo/4rBAIcIvge6J/Y0B4vTsziWX6vpXWVMth2K+jCQRE84xrX0CPH3bHMPbtlfRkiurO7RiyE75sXXFnETnfbzJ/T7hkAPEyt8Vk88ySeHXNtcoXDOw2oQt91j8fTZ7Ca8LnscD3B/f/JzclMN2nVMC9lVbQ73t/bBpcvoCOH6y9oIITRqozZqozZqozZqozZqozZqozZqozZqo/Zr1n4lEEJwMrLfrxjQU33yMHpuVx281w04lCl+8ImISCyK9tn6uHGJ2aYh50tW0epSoYyl64zAtSTp2PAxqmt3zSEr6cyiavWaysZ1ep1zhYy4sryXViXz1kyjon79r/81pVr/yX9PTqH/5r/4x/jen/0IAJB5otpOIRF83hhadUYPuypptopt3+uKoabs88SMUgauLvpGRivdXmZ1+0LlNJtmpJKMAgbC/MzUK8zKzV6s4nRH0XlJ47Z7/B6HcwCIRyDg53xajYxGBp1OUr4DuLzKjOrTj5ihanVb5zWLZs1LOaMaZbcPxgCf/4uHzDCdqQ52zOhHxCHJTfEcLElyPOKehU2qcuaOMhQVMevX+vAG2JeskfOffF5ENcW6/lvfYNZ6YZnR6f/n//wZTpPKuDsYzXUqG7x7doZsis/132Q0t1plbeX15WVcusS+PvlC0uEVzmNkyoWqmP5zyg40NVcuix2VtOxW2Wuj1H5WVucx6NF+7/+Ikd3jPWYpC70KBuJoqBcZ3R6XAsNW+QwFKXlUlAkbSOmk0+yjXefzJeaF7S0iSW7cvIa6geMqiNgoHPHDYOS4zFJjGefXwDgAouGhSoc4bGz8zA//5vtYvc691O4yav+bv8vs9Yc/3EClwij99CyzLL0B5yAYcsGn7Gz6hBmEZmWY8jfg4ETqaZJlXrozrzEcwmflXP2j330fAM45UvwWBz56wGz3nau3AQDPRPjk80aRroqTSmpB5Tz3z5R1BvOS6W2rdrpUr6AhzgO3ovvZLG07EPKjouzR1o7qeoPi2DjKwRiiPa1IYaCQpw2c7R7g7etEpWWk/OS2sk+9thneKD8XCNNWU2WuaTRihKfD/2ueMXOZtyk7YGii2ee8daU0NbBJda1lgqcvdTj1vd6mTYT9IRTLzCZMTdHf2V18zsuDF4iJM8SsDHit2sbcMseTLfFZEpbBzNQkpiSt2lY2vqOMlMHaxCuvER0TmeA83vv0YwCAPxRCuUA72hQHk1nKL9/+6ltY26TvG/OxL8kDZufDkybc+4KZwItXmA1anWO2a29zH3Vxde2scc8urdJ2q7UyrB766+lZ9uVkl348srKC37koZZgS+zA1XPdUFgUpeiXLnI+yq4WvCP3wwd/8gP8m1IzFa0C5QLsfynjuibfmNFGH18N1mpPkdVpKU7AOYFNmzhfl5ktkOC/rm0lILA0z08zqDFEmpVrlXIa101QGUiof1y6Mw+mjfxpyukXFeZbpJWAVh8rOMcdnl9rKfvoMkOpJxcpx1cTdYLUa4Qnymcas4F9SAiq0iig0OKezPknkJtvIZoecOZwXf0h2WbBjdYk8DI/WuKYxZdouLV9Cau+QzxXy0WfnWdLOpuAbpy9P5XnGzczTBgbGKIppcWSY+Pm7lyg93quZMTnLzPLsJfqIXWXTshUbrr3G/Tk9x7XPHRIxFXG0kDriM1suyUWLY7DeaSAYpY11xGnn8clvLc/glVsc39ku/VSllsXlazwvn78kauCZfl69dBltcVm93KbNdC006NlAFCb59C+EhJmQsmnPY0FHaKhqTfwv4kk4LTZQF6/Cnbfo+8zidznMVvD3/rN/wjn+3p9w/pSd7xQKmIxy74luDbYAz2LbhQg8USKJigPOy1iatlTv1dDt0QeldnnmBD20l2I5ieM0M+3f/C79d2G/jJdSIao3aGsuq/hmWkAmRz8gQADGxONYrFeQEM+VUb56apKZ5ng2jbbUVy8IhTVEbXRsNuQO6H+X7xBqXOqL+6/dOZcvD0it8MkWs7SOZgheJ8dhkj8dG+e+e7b/EjfuMLMcCfMze/IBmXwN9QbP884afVhUvuVf/Ot/hViEdjth5wDX7z3BkXjFYjNEZ996jTbUaJdxesb99VIqsaJhw9yNGK5fVbY/O1Qi5R5+970v4/knRPh8/AnvRAOpeH3j63fwYov7P3/Mc6Uo5H3mNI2JKPvgF2oxKsRs2wQYxW1SV4Y5MsF9EN/PYF9opapQu3VJS5fjWTRatD+7yaZ+cp86XcFzzrK6pMDLBfbFNBjAL3SSVfeQg8M4/Frfk1P6vKGCX7/VxbTULktCbA7VcHtGIyo665+/pO2NTXIts6Um7B32b3FcCob6vVa7io5kz7vik3MKeW0z2OGUOphJ6GKj1BXN5r9TlxxyAbXkv11OJ9pSvnL7xe2YEwLYaYFBKHCzUAfDVmu2YBFSZUkS5Y1na3BKBTGRpv2FtWdjnhB6fT7LPc3Px8VXaLUYYRYaYvhzeoq+ZWdnCxNCiZ6c8KwqiePHGvViVxxKAbuQMEKCPX/5Ah7te4teYDaO2adK24JVcdp1hWjptbkHb127jKoUJM1C1CQTtPXZiSicej8q6q5RlhrY+sMv8I23iepdNKvaIrWPVy4TVb1zSL+TElL/7/3ud7D+4B4A4LG4XgLi/qwnk/CJ49Ip9MVEhPefdteORo9rkRE31bCSw++dwMcfcn/Z7TwTLUPlxHYLY0KAWMVN5ZCPtnhNcIoTs6T7x+MH7G8k6sPiCs+6odJqU/xV81OzGIJeDk/oW/whnu/7x2dY0r27bhg6cCv6ssmexpBP0i/OTk+i3havVYp7yWzRe7XFeq4WbZaNH0rZtVGuIlmRMqbO577szO9x480lnrMZKSlXKtzPAwNQFU/m0I5jfvre+NEZNqVeZx3y/aqCYO3FI/SlNmhxijfTNbwX1NET15F3WElR1XtXvYWQ7vl+j2TZQ3q/qxVwc4Z3yezZEClfxLwQY1Wp+ZW6XO/d588wo7UM6+w3STZvciKM65d5j3ip9w+Lg/2rtuzIi1PKK0TtWYpz8OnGGXZOeed//Sb78tor5GI6yByhWaAP24hzXoZ8aM0uUKvwzJ+e4d0pFmCfnA4zcmXxb7noO3/7N78KAPjZTz+FX/xiQx7cTalM7m29RNjL5/+y9isREFq4RIPc+KIEr0eBhJjI3zbpsKyWCHbWOfHyv7jz9pClqo60pHTrRRpPRyVBXpf776T2Jvg9z9a50ZyGGGKC5yYSPESMZk5kq93F9Bydpklw38qAC2hqmFDRgdkRjPhf/jNefv/zf/gK/uDv/xYA4H+xUva1+ohv8D5vA2Mxfr5e4dQf73PDLi3P4nv/jgRer9/9CgAgOpOFXVDX4wNudk+AF8BUKo7FBb6cGFWusrdLQwmF/Shm+HynmUaTEhme1+tGS0GXTFKXlMAcAGDQHOD0hPP4jdfeBQDYTR9yzlsddJryVCIOnJmhU5sPuvHsgJcTq5vrZvBxXnOFHjwKgFx+nS9omRbnI3eWhj/IvrRzOij0QmnqAldjNO6KXdBQtwO7h7zsNz7WARvmQRGZ8SAgMi+rSkpSSX7PwGrBgcrrVq9yXuwKJv78j/fw+jf4gvv228MXdzqcjsODhspAfBpzPMm+fOX2WyiWOe9xOU+fT9vJ0EKjptKvpgiWT/lMe9SAZJZjHPS1RiKCLTcA8YaiIOjpsuD4xXwepSxtQHcMDEQyuLu/g56ZduLUC2iv04dfUOZWW3BMVYe53W54BN88PZIkZpaH1bXLF+EakhamKP144RKdkdXmQ15BQJ9IQt+4dUfPtuPHP+JhHPPxxSKpeUzGs2hVOA97e/z96Uu0vUq7hHqdc7yX4+fjZe6tcHEKdR1gz1TicyaZyFYhjbHLkpuWP2gLJhz2BLAoQsNkR4G43Qrc2qvWtqCr0shNFZJw6P8sLZEvq6LiyfY+Xn2F9mEfsC8mkSe+2HgIj8hTvZf5f7ErmrtcFtEQ/61wrCCdhf6nXsoOK3qQy4igcIp7y2Tqnb/o+1VD0zbSRwyMHeQUuC4UaF8TIrGuVkvIJRR0u0IDCYS0764uwGzj83N5+sJCtQhnSfKdgoZHYhy7w2RBVGU7J0e8SPzud74NADiopTA2x729vsZLl0ms25Pzi6hKSnpWv1+ThOqDL57g1mWWV7gkof4fj3ihuPvODIwmzun9Bz8DALz7GqG/45NeGDrcc4YBx7CpQ3l2ZhX7InedVtlho0T7f88zC1OBi/jTzxmw2jjmZz1hEyK6OLw6RVstpxKI2DlvdpdIa6/xLNiIryEk4udxScq/XOd+mb19DdMqk30uEtqB5M/tLjv6A65TVz9bCrjPLvthcXFDnujFdkovMI1ODYoDo1Kh/bokD24YdNDW5cxiGj6b8zIxGcHLjWcAgLZIWhNpkTrGLDBlud6zXvbX5OfvH2c2YFL54PBitThLu9p48guYrAwIT0xwv1nNn6NU4tqN64IVGqefQr+F+1/wzDCo5MPmp/+4d/8pOoL0zy8q6Fzi996cvwrohWxKiZ/gsNxw5Tq2O7w0tZMix4zwOZcnHHjx4gMAwPWvMhhg0h4cGGKopLm/1iWx/ZVv/TYA4It7n6KX5eX6/bf5gvHBXzO5g0HnvCTi3bsMPBVU3tvKJhEM0T42GjyLXG4b2kok3bjBF5hLq9xL9+7dg0UEoOPj3BOGMvu3/+gYRpHy921KYogweWA3Y6D6ZLsiBP4uf887ZUJXcrSf/iXvHeO6TG7mNlDscqxzIfrAmp+/P724jN19JmzCKt3rnok09+AYX73B+8SD54ecxwj30vHjTazOMti5eoPzkdBLfWNQxrdvvAsA6OhFJJusoaLSNLeInwct9r1eTGFlkXaez4iY2SQxgskY0OaevbzKUrhTlaj4JqI4StC3WERo3VMZd8XUwFdeZdlgR6WIaUnT76equH3tywCAgALL9QTPVmvAg54IgeMKUHrD/PvlN2/g6VO+rN3Ui7styDEcfXaC6BLtfT/O82jtgMm/ms+Oy3cYWDYU+Xtf+86rqH6fxNvZEvfz4jgDG69eX8XWM5I7Lyzopfd4WBrRQVNkv195l3twKPJxcPIAvgj744uIxDTIPv3wp4/Rysb1ea5hWeUdLrcFz3d5V3P5af8RBTs7vQ5MkmfvW/h7p0nOeaXawPQ892x2n0HB9PCOMjaFtR0GQINOng0p7btIZOw8EDQQQbBtWKU+6COplymPh78XjEbRVom7KqVgHOqZ97vngaOGSp5soldw2Fww2zieYFhlzhu851vdAQyMQzoGznFeZS/5XAJ1kdw2KirZtfLv+XIBA7/sWNmSlMpzrC47slmexXbdbyMiLW71GoCF39fu0waGpRsBnxdWyVPnElxnj16MU7kc4vLtn4nUuJYtwiV5b1dU5foGPttvcyKZUqBcgdOlZT6rVrPDZBY5/Clt7ucfMujs94WR6tOP1kuaTw+fmW/WEPb7NO/8t4FeuFweJ+wq8+4OCdaDcwCAjY0jHO/TR495+bK9Os+9H4EBPnFPdHS23mtz7o5P42jopbyoeQkt8Pf+q3/6B/jOTYoI/Mv/7n8EAPzhh0/xjbd5frmDHOuh5uBHP/sFJjxar6DoQWQ6L549Q0iJFLv2hDcwLOXyo6ugQ7XKfpltKj01+BBTwsZg4nwmlZHN1go4eMn3Ja+ENQaiVxi76MO4youqcYEMKiq53ksimWWgwKp7uMnJ+Tk4PsGUn+N3iKC6a+H8D4w2bGqOI9NKyjf7cJn556wCNNMqGSsUckiolLOtZL5xSLbdNyNkpf8YBhpbAgj4PHbMOtn34wT9aEtiMr2oH90u93FItABx/f7U7DSc8htHmxxfQuTSPosLPZNKknXv7sgv+KNRtJV8GwY7enrXnpqNoTugjZ7F+czVi7xzZMp5OM20gUKc+zInsYtWt4ZSXfcrJc5bTht6uk91yyWNgX1aunkDn6zz7lQr00dMBulHOk4jUkmu8/S0gnNx3v/mLgSxYuNcHWzQLibeUCCqVEU2xTtCvMq1sSt42W+acHzItfGHuDbFCm0nk+mgp8SeycV5afY5LsvAD1Ui4qSjpKbu6F6nHQ758mxNAXMlEAb9LnoKWP+yNioZG7VRG7VRG7VRG7VRG7VRG7VRG7VRG7VR+zVrvxIIodRiWXHeAAAgAElEQVQZI6D+aA/T84xRNUSE1hswIzsw97F6dwjNF4lnnn8/240gE2eUUuAQhEQI6A16cPSS0V+fyGFvX2KE+fHDBHbSkt12MBIXdDICGJ4eoN1ndC88TrhY/nRHfTJjSpDOqsisWiZGuf/VBw/wrqDWf/Ct/wQA8JOLzFR8/08+wo05Saf6iD7yRjn2xekxXPyM0b1uWcRrF2z47CeMZDpdHJjLJNl6J2ALMgp4jnx6qHKmiTCsdkYw2yJG7XdF3J3vwtBR9NvF/9s95XekrT0gLeldZd9mXmX0efejHsJeImiSA44noAjvW196H//ewrl6+Dnn8ZV3iI4w2Pawfcr1utPls//wz/8IAGDuWfHl95mx/bf/9/8LAFgJiHjTUoZBmc5Aj5HyZMdwLhtqzEiqco/zFwmMweWUDQyRTAZmzKq9LE4GjKIurTPz+Pu/SZK6P3H/De4nGMWeivNZV2+wv3vVMgoqC5uO8fccZtrS0/Vf4NoS52NQFXlhms+J7xoQHzCa3cizDy5wHg3uHOpCtGw85zpfuMKounkATE1JIrfFeSzWmSky2wGbsm89SZ13ekJOmE2wdBhl9grpkjZW0BfypmNlVqcocu8nrRe4fZ19/vQRn39zkf0MRceRbim7ygA0gm5mcl+5EsJf/YDjiglBMyPyyB+9fAi/nX3oGvm9Lw45VxMO4LU3l9RX7penT5npWLoUxVyUyJGdR5Qzv7BA1EZ66wm+++XfAwD88UfMsHYOhI4Y2NAdyswHOKF9O+26lKydQ5PtKkFdHJ/CoM8/+/z86XAx673z0034A7Sn2WVlPQT1vPPKTSTKREr5plXjICRJu9fA/AyzFSYzswMFQdktZhMSSf65JuJAY1dlKPksQuINtU4JwTHGPXi250QqLdI8lSJ5TczchKNBtKu0ld//1rf4AANt/oc/3Mc7yky7lD3dfU57nJ+9jvVNZgdt4LrPLI8hecb/97qY2fQoKxawBZEQGSx6zHxvPRZZbvAAa4fMxqRz9Kez40STxNcOYW/T785cFOIvxb5nGhnsP+MzwyLmvDAjREzSArODNrq0wn/bStKfFGpdvFinjY65OFdvv0N7sbndKDc4p2dHhMaurrIvHz74ITbXuE+KLfY3NMPvnVldQUnEr34fv690dIq/vvfnAIDIBMt/TJKSfbmXwdw093pcBJji18eFqBstycW3BZM2CwnWMTYwcYE2kysOM3Vc546hhXpZBMfi+qtkaccz45dRlWRwIcOxTwrtdf/5PTQaQjkqq2tVBtFgiiLRO+Tnx9nf/i5tbyn6Chqy0TGVFOaz3Ofz09MQhymOVdK1sSGbtU5j0ik4todzdZKpwxehAbvsKtE+I5qiXGmjINTPN28wU7Z1xO/1xlbgEEIqn6BPiUa5l9LJY6TXVWLd5tnRFTrC5jZgSYS0VtWfxAvse8PtgHWJ+zj7Cc+8HoTWs7tw/4hn1d0vEQX04guiuJI7ewiK6P8v/4JI3iEJaiQ0hf0N7o2aSngvXOA+X3t2hJRQNQWhSdpHwL0P6Lve+Ar30sKdOQDAK++sIr/PfVIRIq6hbHA3uIr7ae6JWZWJ206ViW0M8K1hCe0Wx3UgCeB6IYBBX5Lyx7TtuWvcE+FuG8U92ti7b/De0mvze1uNA9j9zLb2RSB9b1cklx4DfvSUpO95oa0jPZ4JfQNQrNHGHnzGda5Jir1SL2BQo60e6vzzjc8hVaM/bUrwwmnkPc7UDSB/RFvO6/7gCUqQYmEBHz/+WwDArZtExLzcI9l/qVbB5ctE1YR8HPtZRkSse0dYE2p2ZoUolksznI8fbP8UD7fY5+Vl3rkyadqQ2ZTDwQvaXEx7/mWa94RstoiJBZaMmQNc05hb8r4Xg4jN8guzFf6b10JbCtlCKK8ROTyumripUglvvMr+7O/yXHnxlKXQWw8/hV0l9UPSdl+E/sPj9GJ9nf35za9xPqOS1f7LT5I4yXGfWTV/bQv3y2kyAV9Q5MkdlTp1JEP8LA+Hm/1y+/lvW+vsb7ORgSM43AMcT1hl++PBa6hKcGVcKPCbl4neqNcbGOzwbuwUwepQRt5iNKBQpu/rqXxlWK7ebrZhEFq3LXSI3dGH2co/G4Qq8UgevFaqo9XV52Iclw/sZ24vi4bKWFeEclqOch7Xz9JYuUVJ54Mt2naxxs+GY2GYHPxc+lRkvkbaZaFcgkdlOOMgAtWm0sBM6hgum8RR5O+rWQk3+OwoZOnzeg7+7DZVZn6cwcwi+9cUKvbkSOfb+BwCIa6JqkVhjblwkuP/x6p8RkPCEp5JDypNzt+4nb45fkKk1nxgAtk09+XWM/5+IEbkSdTjR1+oUouDczw7GNf8mxAQsnNnn37HpHvVhDOCM539do3ZMsb7mNVixdI892dAJUztFu3lwcP7sItVuyaEt0kk7rl4HvY3uGd/QyWar47RPubLRqz/sz8EAHxc5z1n9bdXUVcJ1eYO/U2rwTH89N7/x957BUmanVdiJ733tiory7v202MxBoOZAeFILBYEKVpxGVKIK3FXCpkI6Xkj9CA9KKgHRehFKwVXWi5FBxKEIcz4HtveVVd1eZ+V3nunh3OyEHrBMyKQ96VnujP/vOa7373/953vnKeolVhqEwxybV54gfeKTtCHvsigM4INPdRZd5xOI+DleCIiPG6UeIeNzQITQfqN3R3aTlgiQ25rACaTkJZDlWEL1ekqmzDocMyuEcBN71uHtSF6A+7BtuDtdlXGdEsnOMioamLIsRRTo3v+HDKiNjCrbMrQcWOod9djIZecQs0Nun2Ua+xPSKi5jsR+srUMunXORzwqShWV+NkcXrhVXpdv0u/0wTOsV89gR+XKfreQuJ2RQIETZhvnLxjVBVeYk/qwgb7O11F5XEx+dXZuEk8eqsJE5Q89Ea87A1FUZIeNBtFDdx5zPoqFGhYn+V5RECKxb1Ilht+EsIdr743SFjzuEI5UljidUBm7St0rpTqi8jP7Kfpoc0ck5EYjDg55hmZ1/0sszXJ4Btv53SmpS323MxJ0msSGQUi8HufhxjrPs3g8ilCCdtUQWq7R5O8ZzA2Eo/LpQ37fJtR07qSEhFclzHpvDURVRVJrIKS7fyjAdVhy8zcOD3PIC6X+i9oYITRu4zZu4zZu4zZu4zZu4zZu4zZu4zZu4/Yr1n4pEEJGg/gzLDbk0ozgDbqMVVmsksp+OQyH+Bv+7t8zEhoKMDJcLxfRVdQ8GmP0zKZ6z2z+ELlTRvldDkUkrzKSd+XKNaw9YoauJ4K8ck8y3y4bWnV+fuCTfKCJmcjQ5BlORBLYaTFDEZtnhNdmKeLtj8mhsLHNrOG/+COiYBb+uz/Gdz/7fwAA6Q8Z/XWX+efsn34ZZkmiN8QPYuyFEYpwrJcuMWI4ynDEHTYMlUFJiAfpgQjKDg/6mJxitH5fNZUhSYYODXlkjpRZMjBj4AoWNHcBHJ8xI7W7zYj8V36dmdWn738Eg5WZhkZNqKMqI7Df/+sNvPYtZossUUZzCw8YvewEPWiJ6Ph/f+8vAACLQcntLbvRGUWX1TJZ9mVudojrLzH6+/aHqlk/BiYinKOWou+np4yIVvNV9FWbahQRz4wIOjePU3juS8x67N5hJHrxjGv729/4Gr77fWZGKyn25fYDReQ9Rlx9XoRrkqhEX+iQrh1PnojUUrWZMckEF6vHcNiJVOgMaV+BKCO8bavtnL+hXqddiuKK5ItDkRgaFVkWx52t/3OCaYOIH212EaUWKsjsE43ypS8zk1XL1tEUObFdfc41+JnXriRQEI/RSPJ0aFCmo2NDq8vxiLYEafEmXb4wjckJPvPS7BsAAKdXqIGj9zAbZ1bS4hAS7wVmQTy9DkxOPqMtid2IjbZnKhrRchKtsXvKzyTfILrh4MY+7t5ihn/Kp89f537ZPzmDUbXgltEzw+zwZ/c+wNCnrGtPpLB2M8plZqfcLu6NiriA3AEzHMqcP3q6zz6EhAYyD+D1jGqtmR0YtEc8TSY8ecpMr1mQEZtTkuONCsRxiaEQXcUS7cXpNGLQk2y3JEZ7VRHYnzWRmKWf80tS/uCYRuBdasGkGu13P6DNZtPM+BgcQwwd3CenadlhiWOKxYwolZld9PiZZamctpAQd1gwJN4jSV6nan24lIU7Vl166oA8PFNLZhwK0SmwHvZP+cxasQPvSM5XxI1p1aCHnN5zme6n2/Rhy0vcI4ViFlbQEJ9fIg/MusjwZqPT2NZ+9CSVmZOPP95cx7wIXHcLRBRVjPSBsA1gUnY7LORau0XbOXz8CEnVjpcb4toZGpDTXO6fkqPo0rPMWC4vziCb5jyYtJZh1c+vb62jWWSW6eJlzufBEX2M0TzEwSHXpFiiMUxOEQG5traHurLPS9P03zZlYi3uIXJpZu3FGY5Sledbq2ZErSlUK/8JA2Xei9kyOvIXbvGpXV/lHIdCWbiT9HmFMn1zUZk+l8eDDfGKBMTJdnlZqL2NA7id4nf65G32oV3GUIT1RfGkeCTP3Bs20OmrTt7MMSe99AtTkQhOxN1REf+RS+Sz1XYTJWXyTGdch9NtnkUrU0mciQ/OOyWSNZF3rk4vI5zlWD89kijDlDhq8ke4eonrNOzuAwDaOjPzgx5iUzxfumf07fU2+z1zfREDkbUciFPPomxoYmkGn39KVM28SHm/8fu/g1sXaa/9tpB+n4qs9SiH1ddIJLm7wnHd+iGzhH7XNIyWkawv13R5njbwhV/7Iu4IsXOapQ2FnieKpZQuYCAUbGiO81CvE+myMOHDwRrRGn/5v/0lAODqFfrTSrF4zgMYjtGfVu5zXu0RP4w6owIR2mhKRMSO0AzWxaFUkx2O5HubrS4qWWWhY7wfOcKTmIpwHFcv8jy6eZvoppN8DSH5t1aNa2EW0rter+Lll8gFdCaeH8eIW+MkhTv3aTsvPkPbtEjOvDG0Ythi/x7+A9Fe164TLflbX/st/N0P/1r9o29yhZWZrR/BJYLedI7rvBplf1eW4oiLfPnmJ+TGMxq5ueYurgAG7jmTCFVDIjz12ds4FlLqcY9rcuNJGosL1wEAbjPn70//5A84f70+7j2mvxkhweYXiZDd399HqcTfee8G+S2++AX6JKfXj5hu76cFcSuZOZaFmWmktV7lOn9vapJrag5GsLHHDPhxlvvTpzMl4Z2Ay8nP379HG51d5BycpXdQ1j0uluAPG0y054ePbyGi87JYpB07JArT7bURFGF9W7wzbSFH7DYH0BeCTIS9lVoDZgmtDITyDQh1kK2lYBWKKlTQPdAmpETQieMM95zllPNuFXrDY7bhqy/yHvvv7hDtu7BKW2126xgIdTQhIn+r5nH1wiXsHgnp3ZGAip6ZmEmgLh6SgtAkXqGHaukiDGaum8fJZ9olJ58vpNARV4hP3EFd8c+4jD5s3iNyOiz+V18ocP7cEalxUbxLhWIGPXEUNdsl9Z334pNCGiZxsIWkJtIUFLVnNmAomPqEiHCDJv5p99iRkdCOX/fLhMQSyrUWWr3RvufvXhYhcWJhBW2hSWySjb+zxrneKLexLL7TTkeorTJ92pf/89/B//Sb/wkA4Lv/5n8GADzs8Az/v0520IpxIq4u0/f5/GHs7nNNApJxT1V4rx06LLA7aIebZ/uc0zzfY6Z9ITTFX7e3T/vwxciJ5w8azu02HqVvmArwTG71qnj8hHvhnK9J/ES5WhEbm/S1JjPtIiRkzGDoQUGcmIYB590lhFK330dN+9Mgfxrsip9yYMJwwDU0mvl3Q/AeWKk2ERCvzQDiBR0aMZSUfEwE7cMOx9nr9eEaIW50H+t2RkIzDtjj4hoSMgVd7kVr3oKMRHQKIqwJT3Bea6UifH7euaqyewNG/D8FnBUkQDFFPxr1c67u374DV4Djrwr9bCvRlqrlMhYXeRb32iKsF4/Ywd4+DDnOY3vAubIKyX7h4ioEoMHcIv1btizi9bgP6VP6xZ72wdxsEj1VTaxIXGFTQg9n5RxCU+I5fSwuvDT3gTkQQFucnzOqCtk94j5tGVpoiQPL2BbZuFBi5W4LeXEWdSUiM5/kvAx7fZh6QgrXeF836T49lYigM0JktTjWqTD9yMplB3xm+oG1XT47Jb44o9WGoZl7Ni309+oC+5vP1lAWSv0XtTFCaNzGbdzGbdzGbdzGbdzGbdzGbdzGbdzG7Ves/VIghIqS0a6XOnAoku5wMHro9jDqlj4p4PATRs2MHUb3RtnFob2DgF/R1L4i8VlF94x9iJQfsTCjzPki62xvvLsOKdEjqmyQwyleh04D5RzjZaVSSn3hh0/Xi+h3VH8pMpU3vsrI7ec3SkjOMNrpizDy+tPPqcYzY/XgG69SbvSem/1b/x6jia/tdzAnhFBBSgblYgszSUb386qRHMkaVkpVNMBId7PN6OPUFCPed947QVvwBCk6ot9l3+0uOwJiNc8d8zMWSajanHa0VfP4wYeco6tz4joKGWATj1E7LSWwASPMt7ZSqP0tI6//8t+Q2+QnQY557e0dzK1wbjuSgk1nGFFdecGPp5v8Haudc92pMHNcH1ZQl+TypSXV8jeOUe6N0FqMPJtsjCTnyyUEG4yO9mr6jLJ2Aa8DMPG3zxj4xqcfM8s7mfYipizh07xqd8XHYy83cDrgmO1SmeiOymXL/XNp1UCYa2R1MpIfiNhx9zb/W0IB6Am95HZagMFI2pJR94Ekg81m4KOPmV2MC8EQENdUKnWCSFhRfgvXstUccZbYMT3JH/rwU2YSL837UZIqVnqNgxbADqGwDffucj76ilJ3JCP66OEhIpcUDte+yZ8xMn9g30VQ+yw+z/n40fvk9pmdMyAgqWqbMlnZzCgT6EW2IJ6CIl3OgiLleztbiCalkpIQWqPIOTC5vLi7wSx5SMiKvTSzQyvXVnC8wSh9ucF5mPoiUWpbt99BK8dMgbPP/nq8dsQiyuwo8723L1Wui7NIKaPkkKRoWzW/jzYeYHaB/sZi4HhaUpKoNZsYVkfKbxxzqci19XgAqY3CJE6TlVXuz7W1DXSFXArH+TuZNJ85lbiIQpHZ6mpzVP8tpQCTAS7ZtEtZw5lZzuPOyUcoi+/EJO4sX4Tr1jFmcSb0S1dqJ9OhGIZCkJUKzKy2O6pXdtrQtXFuevIfoXmOoVBpYmmV2feR5HhZ2a4Xnn8Tj+8z213IM2vRlyJFpdzAVIJreCp+oFSOdmm29jG/xHHcEZLgqhTJSt0mfG7O8bd/jWpBt+6QZ6RYP0FU2cv8rqA0De671aUJbOsMsEkhI6k90j0yIn/Iz91tM8N31mvCbWAWxmJTv8Tx0G91kE9xjNEY9+zQwmdWmnWsLvFcaUjNbyBVokoBgJHZn+npkfww59rhMsJqYH88Pj7TMKSvyRVPURSHmz8gWWVIu9nogjvC7E84zHX+4J19AJTTFUUAjveIWIlKEcsYMuCz+/y7lRlyNlwViuLe/SfoiWurLJWPkZJbcnIVT+7THk+y/Lff/Ge/jQeb9DM7+3zmjNBemwf3cfl5Kg/eWuPenXYR8bD17hY8yhi++BKla5tSzZucj+Oow0xsYoJreusdKgueNgfwSz3jtECb80nF7vTkEIdbPJ+jc3x2pcV9c1yqoC+FPq8Qw34p9rz5lRfhHCEP+lz3jOxxbyd1jswySOUwuUDbrdYaaN7k2ZHVGZvfXccLkma+d1ccJVJNadqGuCjuKucW+56eoT+pVg0olXnW5HN8Vr1K+1iaK8ImXpqVBapzdoROC8asKFRko8p+fiquHbfTAZe4aypSsdx3cQ7qA6Bm4n9nTDwjm+JiafQr6PXp3wIubnqjnf3cO82gLGRRTUjsrs6+aDCKC+IO6YnA0WZ1wGbkGswmaKPpDBFJ04tenEm+fXGZvHwjJZr79+7gyhUpkWb22ZcA+zIzF0U+z+efSLLaJhmqDswwiJdiYZF+JHfMz9zIfoSLF3gfu/eYiIXL14lCMvb8CES4vo0G71IOjSvid2HtHn0RlOGXABTKrRKKmRN9judgQNLovW4ZXfE49LQOrsgEjqV6FnFzTm+/R3U4r8kFpLkHEiO+n4yUBXsm5Abiz5FiTrNJP1DIV+GL0m4TNt2FtIezpxm0+pwbj2y8U+V6P//cZTz/Em35xz97BwDQLnNghVoDNaFRlxaJ6ChXhcaPBLGwxPNrf593uw9v8PsWkx0O8erVquyDQUhRvzeAg719AEBXUuxWM9e0XChBQHZ4xHtSrtXPpdS64lc5zooDyzhESHwnZ3tS1HWz789dv46KkA5PxSNTEs/kf/07v4XeJs+cgS782QLXw2gyodDg561SuyuLZ6Rab8AsBaemzvx8hr+RiIXR0wXJKf6ZgqSizWYDwuL5MIi340zI+0a1gojUR0fS74M6x9dr1GDV+Vxr0tarx1UEdb90Cn1slKIgTEM4XdzjZymieyNS8MwPGrAKQf3lt14HAGyLA6dQKCDg4FgXhDh5usXvT1oTcGrMJqEAW2XuZYfVhq+8QRXFEYpr59E/cq4bmXMkmN1Jn3TtS1zcWqULk1mcMEM++xsrfM5/+bVv44f/4/8CAHioPRh5kefT1y7OoiJE5/7jfQDA4419VKTwPOLs8wW4z55s7+L6c1RBnF/lbzdb4u7rDhEIcR6TQmuUBfuYSM4gEpcy7hl9U0VIvplZP+xay2ZvxOtJ+wgnoviiFMiq4pI9SXGda80BXPaf7wEAcIgHpoUaclJk7EqJF0P2aTIURD5DW2v1aANdI8dZLJUR0bq1xNPntrpRa0kdT+itXlH3OJcHJfFm9qWCGQ2yL51qCwO9R4x4+ap5zkcPRjQqfGZikvMyei+uNkuoq5omL8S91cL+uY1D9AUxubdONcvXXiIXnNPvgcVi1rNoO5ubRERev3YdLz7Hu8gPv/9j9kH9nohH0RXSuKGXry64Nqn0MV56lujbA9l2aaTiuLyAzAnH4J5U5U3nEEkHbeDgCd8jNlP04z23BfWSEIlu7s8RmsdudaHa4ucHRv5bW8RBO3vHSExqr3s4j0YjbbzQzMOhSoW2eEHnpmjb5UIRXSGSRTt6jtTvdX9+ru9usX9fuMo9nN5dg9XLefBIKbcmn2ZzO9EWsmjEA+Zysb9hvweZI6LyflH7pQgIDVVKYDeH4LTRooJhGmetxoncXW/B7mB3X3qRC7W980SfGSJ1yonwOGnwoxf3zsAIn4+TsrsnmOJvc1G2Hh6jpkMqV6YzKuVUWuF2nEv+WgRRq+ry6guasXKRl9aTlIgs69yoX3nzm9h8yj7ff/Q+AKDV4+HVtk7CGBU8TGOv6KXj0c4BFq7x8vTOJokvixk32m1tBgVKppOzAICAp43kvAxC8udWiyRTr3kxGeehf/EaL+r/+F2Wtuxt1fDyazzsuz0GYxqSoQ9FnDBIE3RUonCwIwlsfxRtOVe3HF1NUE13ArCa+Ht/+2c/AgC8/qdvAACmbFZspxjMah1zbou6vHnPtgGHXsJ08TaqbGL2ggd7JyJgLfB3Li8t4d1HnwEA6jpEfTFFXAxtONyc1Xicc1Ru6mKQ7yAMHqKXrrNc7Z4CQv6gD5PTIpsL06lXRAJs6HQh9DHcLq5DXFK8TUMPFgPHkyvTLpySCrT7nLA76fCrWTqjx3doZzOWARbm2JedOxyX2cELnRdetDz8O4O5qnFyPzRqJngXaDtmkUe2FRDa2TlCk+cYAgn2aXFlBbf/kRfaDN95sUyOV1gcLRTyKhfRRSwYplPLn3Uw4+XvGBQ887m53473NhGd4sX+MM+XtaeHtO3Vy0YcHuhiZdQzJfFaLbXh9NJJ+nvcn5X2PgAgeS2EzT06tGuXeLEa1Dkf/qVpDBx8Zlx2/+Q/8IXp0c0NvP4ML/olvRx98pQBomJzCGNTa+GhbZdrOcxLrvL2LR4eA8nZnqUOzwOnBr1E2VQO0qlmkRdUfj7MPlT00rxycQ61Ji8AdZH9GSV7WiwYsP2Ufbi4yD1V1qU8PhlFq8vDyimJVnOff9aLNawsc44zOb5sDw2Stnf5sLbGgzZfod9YvMQLXfFhF4EI91Jskn0YlQa1e51zlkqXg/ZfrTTh9IgwdJJrYhOhosHagsnG51dpJpiYUGDN4sfSLP1n+mSffyfo++OH+0inubfjS5xbuy4SPlMYNZVIDnQpnFYwp1DcRUXEo8k4L8n1EvubqW/j6kU+32agH4mrrKk2KGBgUjB8gqVm+RwDFSfZLDwe9rmpC2RdJSpnZx3YzRzr628wQH/26F2cnrHv11/kJWioi0er3oJPL3I5BevdMf5ua9DDkV4O/QHum0ORQJ6dAi+JTD0xQ5veVwmdNwzkT/h7LgVSLRbO1XFqd1QRBZPG1x5JN/d+/kJ1oJeiqxd4uTaggYbIJtsd9ikjuLUrYIR4oFGpKfAh4vB6qwi/SnFtNs71VIR7/mw/B5OCUy43fcTdB/exdcAXrEuXaQs7euGqFDpw64Jo0AX8s3Weu/2KCV6VF1Vz/NOkS9TQMMCEl3N04Yssryl2aS+PHz1BUMHLFZWDJVU+uL6/g2NIZt3MsTZ1YbUYnLi4wgujXyS7xdI++1KvnJdZqGoQsaTIQ/dO4JeNjdbELynmh4/v4pXXBG+3q5TCMcCTu7wwvv9TBqciEdqC0zjAxz9isMaotfAkuDf+5X/zFv78z/lC1WnS5gJB2v3tRx9jaZbBkbZIxPMVHkYbT48QT9Aehzq7f/ef/T4AYHtj5/zFNgP5Gy/PrkuvvICzHG3m8bZI/VXmWMjlz2XIy0WeZ34312PBEUd5U6W7IgF3qgTB0e/DbRPJti7jq8FVnPnYr7/5/r8HACSnVU5mAKbkI10eEWfOsr+ulhuH+zzI4vJJTjGy+ntO5LKcv7TIct98gwdaNNHGxhOe5zsH9HkuO9fLYbHj8VOuzcIi70I+EZRvPynALXufSdDn9pv8vaLiSDYAACAASURBVL2tY6RUCjswKhEjGe7ZmBtOK/vnsiqo6+XmSqVS5+UFyVmeN+99dANTRvqboYvPOK5xDKVGEYEoAy27IheunkoMoj+ETWs5EBl7qcg5sJgdmE3ypXdooq3mThi8iSTiSMzSPj7/mOeFReUWW+++j7feZMD2v/rWlwEAn67TD7x3dx3lrEjUV+hP+yprnUpO4uFDzvGgZ1Uf+Kfd4kFOZNweBSSKBc5dMODBlF6anz6hjxiVYC+vzKPd4veKFc7HYDg8Dx549OI4BPu0tHgRR8f7AIDQNOfMJHnlfOYAb36BQebPNzie/pTWcm8dJQV0phaUnFTwLJGcxP6uyOx7IzJkBWUaVQSVAPBIrKNZ5zpu7e6gr5LzaUW1vH76u1g0CL3Dw2TjWi776K9OjhzY3aW/KhU4LouVv+dwATNzKrUzch77PQOKKsMp6FCwqywsEAqiVuP4m02ecY+3OJaJhXkYjHqvUuA6qABWbTCAUSTPvRY/8/Jb3EvlchVmrUVUZOdBvwKbLhfaDdrfU9lCT2dCvV5BTQE4q0org4GA/t8AdGl/DRHQX5jlO0jq3Y/QNrF/V1a4Pw/1vtDu2hGSAI5xkb6543QiqkDaex/xnWZigntvIh5FyMP1CetFvFrUe4URMJo4f34fx9wWwfXmxtPzf4uEuG+MCkTdfbSOZJK+GTp3cyqnGhhNMGqhR+JCLRGvmx0ONMpNfT6rPmjdogmEYrSHhhJYIzqDgMeCWpPfy6pcyxem7XV6XVR0vltF3N0zAD3dY1XFirkQz+6ByYBSVSTgeq+yqw+xiUkU9IWe6Dc8Me7TSrmBybgCalqvqt6l+sMG9rU+naZK8nXWuYNeDFXDZfdxrj69zTWKBmJIRPjMRzdEfK7gvdFkwfe+z8Ty0hzP1uNT3tHTqVMU8rQnl0iiozGeS+12G7fuk5ZldCfqKbm+tr6Naol9T+u99eUXr6Ete3+4wz5MXaTNbZ2ewmTh3dht575Mp3kWDQZGZPUOeuMuE2XBCPfn3GwcUODa2KXNREQubQLQV2mlVaIg2VPehQKBAHoW9sUfFHXABdr1h59+jMTMLABgQYmoY5WuziWTyJ3wv09qvOMNdUex+6wo5kXfIPLrdol2GXbHcFGCK7+ojUvGxm3cxm3cxm3cxm3cxm3cxm3cxm3cxm3cfsXaLwVCCILwOawONCRXml1nhMswVGTTFkBLZFTtCKOVLjczFMcHZfg8jO45Jd/XETwtezpAS1Ht1iiKW2Y2KJF0YOOpSO0kCdgUFK/ZsMEfFLRVf2caMkLpMPXRaDAT1RKp5k/+lpmOdP4uLGL7DAqO6bSw3x2HGQ8eMws0iIqATbKNDx89woKJn2+INCqXssCuUqpWg5HXgUjkHt7fg8HIiOLMiiRGTYzCf+d3X4JRZSoPH+6zXxlmpt2uSXx8g1mj3/5dZooe3OZY0scNGCyMfPqUHSipnM9oGqKUUXmRIIoWkeElEiakTxjpXtslUuLqDxi5vfaHV3H8E2Y4nh4x4jo/zcio1WqDV3DYG6eMdkZ9KntxWOBQpu3OfUZEI88H8ZvfYdnIT39E+HfqSCgbqwWP1zhGv5+ZuXyG63Z00MdKiNHR+yplaTs5Pw8eHWNRWZbnXyIB10f3OT8T08nzsh2Lgeu0vSGoctCKtpE2+vqXmF3ZEuz2wc0s5q9IzrDHaPaJIOyFozouXhJhr5N2caB5OS4OEQnQbueXOf/rItTtdwGbsgIVwTlPTkZlX4DLJeiqoKg/eudz/O6vfxUA8N0fs3TDIzt0hSwwDLgnXnmVfW9I3tpkNZ5LZIuTDRYZgdUwwPPPERFwUGeEPSNuO/feAMtLGpeQiVVFsI1OC7p1rkU6zWcvPMvsUbpQRbesDKdK04IO+oOgz4uWym/8yiqvSP4ZTSv295lxnJgRYqTDdSyUqnhmXsgZlVHZXC7cfcjo/uQU19lmpa3eu7eJb3yFGatmjet8oiy51wf0h+z7g7tcJ0+AezES66KREcn4gM/yigiwXBzCZuY69foqIahx/eaXJlEZlZuUVIoUEjnsIIXFeSI+hl3aTE1Q1qAvCigD4rBzrjbWuA4Xr0yg1uLaj0oXy/v8/dypGa98cRYAcHzEteyYHPD7OTePNrknHGbOWSBWR0rlpB6VUdXz9LmevhO7Qim9+gXKWn/6EREQT9c3cfky7anWJILMKFnbYraBrW36mSvPMDM0Kr1LzsygkmXWyaNsXyEjv1w3wR7jvH33/b9hP030Gf1GB1WR+7WFYpmYZhmKK9DHP37O9bZI7vSkwbketCNo1jlH5k8+AgB88xtfxC0b5/v0jMhTu4fZwk6nh6DIGRtdPqMtgs4+Bii2uK/Kp9xT4utEIAGUBTV+sMbxuTy01YmJCFAh2u3Zi7THe/dYipFPNZGYYQZxVGpqF+Og0TTE0MB9Vu9yLQ+ynH+LvQGvU+TrTvZ9f4+/YUt04FWG2SRk41lO5NK1DCySrrU6OM479+i/DU0/nrvEZ+4d8VkHZydYUJlcUWVWyTAzWV9++RI+usG170kCdj5JOxn0rHhyk6i3mkovHHX2aTIUgNdGn3DvDuehJ8LZZ1++gLII3ctCDT3c2gcAeKIe+Ax81sQs1x497oPrs0kklYk9OCSSt1XjusXmVlAU+vjSS8wqbzym4/JHfLh8mWsyBJEtaaGpnr10EWtrnO+JHv1BcnoJq79BX9uycn1Hn/nGt7+Fjz4nUnNLNj5d5lx/+P7bsNppO6+9wnLX3S0icQ52j1DOcn1Sae7PC5clYBENoyPkaFUiDA3JQburVTiEIO126Zwf7fDPdCaP0rHKh90856cFi89nD2Fb4Jk1e4E+fmOX+2BgBi5epN89O+WYL85zvQ/3NnGU53wnZ3kfKZ1l8fxllnlXJHv8xZco+/107QluPuBz4zMqhZHU9sBoRVsZ1REa7fSU9tLstDE3x371Whx76pQ2gU4BQ5X4BkUUOyEUbrleQC3Le+KtT+gPvv1t/u7K7DTaQlY4VfJ1uE9/MjUzjeaA69SXmINFBOCNXPm8dCiv8q57j7luE5Mx1FUGMhDK+ne/8h3sQOgdIdgdKl8+eHSIflulCiae+UeSLL/83PM42uYZd/kSSypmhYzb2djFvTvcoyurswCAln6vfFKDVyi9puBvZ1XaibVRxyc/4v4UZzAsS/Tx0QUPulbecbe3uV8uXeHvvv3uO+clSyYJX4zIg7OZKiaS3McGCcR4/fz9vYM1JCSvPJJSnkpwbX0BE+7c5hkVi3PfWO0dpAVpvnxpVc8USqTXgFNlTG2h+9oW/l52UMKshX7qP36OqMCdHe7ZrdIedp0id61JKcPIc7TaqsEtRLQFHEOzqbPI70Vd9RyZA673kvye1xeDRX1ptEcIOfapXG3AK9Rs20DEjtvJ8aVzVXTa/J5NCDy3h98/PF5Hr0f/dOUKx7Awt4Cbn/D+VhVKoaKyEGb8aaNxSU93rZz3SqWCVy+zrxGVzZ/kOK8+sx0OreWpEK9dUS/YLVb0hVAxmbi+NpEW1zJpHOxxTo9E7OxZ4G9E3XNoqGRuKHqKlsocJyPzqEvAYKC76wef8dxtrKwgeYF79uQT3it6NX7miRG4HCY6Jy3UkcfphFl3wplJfg8qh1peWkR6n8iSvu4WhhK/VzEPYFGZW1RnuUs+sI8haiI8b4gofChEnicQxfEZ/UxLd4YRIrpdbaPTVrmOyq4SQoJV+l0EfLzLGMALwWmKvrN6mkJXPmWEbIFKPI9PS3A6iAjqiVwaKlOyWICBqmm8kmxHq4uB/EYowvk4OGJ/3R4nYiO7EPWFVaTNJ/sHGIZFGq79abPydz7fvwOfm34qGNHdmkNHtlCGRyisoYN7qC80Va3aRljIztQZ7b6nEuN6vYqtTdqMyylRkBMJnNRaP0dWiZy7KhRztVFGXXQWdaGKRyVnbq8H60/pd0elbX1FNI5SpwgLER12cnw7m3kYPDxLXRcd+m3dv51xnIis3efUHKvU79HjLZR1qXvhBfrDpugVep0mJiOMPZR1FozK71OFM3hc3GdBSdq3K/xepXIKFSMgSreF3RO+08aTYRhV2m3Tvtw9INF6+tSBeJDr7NPa3LxHtJ6ncIZYlGebYWQ7srPT01N4RHvyi9oYITRu4zZu4zZu4zZu4zZu4zZu4zZu4zZu4/Yr1n4pEEKiyUG2dACbk1HLQZ8RNZuN0UCnw4F8TvXCadVyr4iDIrOHsxNGXyNJRkunlSHKnqXgcTHSN+KdOdthxPHll5O4c1MktZeZ8So5mQVt5AfnMoUhEQ8WVCNbybsxPcWo4MDEbNexMm8OWxy9hjhzhEBwxQQZ8nbx5B4jtf/Ff89653tOZmI271ew+3ZFY2AdZdDfQ1612F4Pw50OF39nacWK6RlG/I52+YyqZPycvit4/933AQDv/ZjPnJvj93sdwDhkhPHuTWZEp0R6ubt5hrz4cxajjMIXyvw9m8uITk0ZM8l0ejUut3+IlDKHS5OjtBPn7Mf/8D6qVfZzTjw1WWVgVmBHq845VkAfi8/zM/1+CQYjo51uD+f94xv38HqcvAqLs1yv9fuMnIY8MVjMzNDUlXVOpyT96XHixtusZZ15lUiQton2cXLvCF9fYAa7fMa1OdtjVLbTyMDpFdtTU4RfDUWWexYEp/hve0fMYh7sS+o1Y8Cu0CGRoDIB4lIpHjlx4uT4vapzblSYXYiHajg95nwkknzW6gVG40+sVqROxE8hwumUODMw8KJW539fmGcm1xZrYv8pbfPV15k5L58yU10pt8/tYWOdWdNRBuzixZlzdJkSPeiLZ8vjc6MsieDkjFA2bvbJajTCYOO+MoqYc9AUQWdoGZ+9L/LwKL8/1dGeOmgiHGVG9P5jztXV63xmY3cdhjrn7Vkhg/7wO78BAPg//uK7OCowbeEQUjCq7ETGMMBRURkeF7+fSMzjww9pK5OTIs9T1iQWAVLyH8uqbTcosn+UyqItgthsuqHxcZybe9vY5dJjTsClYoHrXkgP8fJLF/U9oXJE4N3qlFES98FImr4hJNPcfBBHQj4ZBuKMUq33w3sP0VcmfGDSHlR9/8SMEYWK6tnr3Add1d873Q7U20QbdFQvbrCasXMqf+Pnfqnmufd7nR7cFv72gThfromvafP+FoxKahl3+H2nnWv58msT+OhDZp+vv6l9Y2Y/O4M+opP8u6kZ7nGLVUT3Fg+engppIsTkR5/QVhcXpmG3cm68WsuSyGU9Li86yhw2hZYp7RCt8NbXfw2GLm3bJOTT5AT3zcZODmbx0SWnmKXdW3uMt778TQDAj35Gv7i5xbPANHAiLz84VFa421UuxWbEKFl3csDzJTnDjPtwWEVbZLweyfs6RlLD7SbMyhzWlFluKwtVzgOTCXFuidfl/m0a2txUGDaHIEjKlOaFZvvy1y7iqeTjxWF6jtAaWiw4O9E8urnXR9wIzX4ZVs3HKHtXq+0DAK4sXsHuAed084CIP2/YjZQQhQeygd/7NjfARDwEhzKHuzv8vWCRn7nywsuoXOJeOj5mB2cnmL0r5jK4dcBz7OtvEgWaOuQ6m4pVVJRB9CV45jd0YIQDbnitktnN0p5GKAyP04zjU+4li3huopLmDTm8aIlM12qXOECEd46LFxOIihD46Qb91msvE/n2k3/6MVLbtIveFOegvHGCBRE//8kfkLD47Z/SLuPOJt66wD7PGbiWlSZt4IOf7cPpoR09sdA3tevsUynjw0mW8/7SG0S3nuqcr3eApUuc7+GQ87kuLsNy6RDeKG3UNcvfSzrpq91mL7oOZm4tIr486wsJGfecEwivPWCmfkScefnyZaw94jyMyHK98k0vLD+HH7xNkvfXvvAmAODH3/8ZarqrJae5F27fIC/i0OlFYIp296IIYM/2OZ+fHO7AqoxmSLwUZhHjGodD1JS9DylDmhcfUvrwAeLT1/R3PFsfbYkjYiaMgIjVreJiy+gcDQXtmE7yHvep/I1FnECtQRuuIOdo75Bnwysvs7/G0jHaIjnPFNknq9QjCpUqFkSw/s6PfwoA+G//1b+CV0jr/afsl19E8kfmBvpmnflzfGaxrvPM1sblrzMjvfmIqLm9J7STYCiGapf7Y1uojYw4/DolO3JClTV1jlnED1LMlrFR5l6YE2fX8WOeDZPPzePBE/rMF57n/fTRY/bX4gDsLvbrcJt+zungfFrsTtjFXzlC5cwvEs2FoQ3NBm10ZZl8bUcH++z35gGa4j9xibNvJhSGX3M1leBeLYhDZ3/3KebmuJd2ciKS9/Mzi7EE8ke07eMMfeWIjxK2PjpCBFnEkTnic+l0m7Bp/4ckC+4UQtkT8CEl+WaviJLbInu2ms3Iyf4iQntVhe7pVtsYDke8gUI0BUTqOzmFnW2iWIpV2s7sCs/Ww7NTBMS7mDoS2rdvxuwEuVdaXb3/VCSVnT2DUbxWxQzX0OrmfjEPgLNd/k62poucWrNWx0AspnHxH7XFgxdNJjHUfaMpdF9KAhhOrwfhafrrgkhaI2Gd84YGKkLa53RWjoiST0tlNGq0uabOOOcsbf0nD29jOU3b7EoRoSFBoengJMrg2rSE5DN2mzDa2b+ASJoL8gv59CmOUxyzV4gMn4tnfr1Wg1VzVW/SwEZ8apH4JC5dEn/cFhGC4SAP9Zmledy9S384skOzkXbVa/fgFmn7CH2Uz/EzHUsHBVWP6JhGtTEibR6gK24uq1n8PyKsb/f78Pk5ZrOILftCyBgNA/SFuDH3uM5OuxM9oQEfPeHec5jYp8PMCa44ZgEAr77yIgDec/hsF4zi+iwIZTpCQsbicRTE6diTLVQaQmMaDIhF6E9NEswYca0Vck0YxS23KJ7Jh7c5d4FEEMUM/UA6LXSweP2MzR7mxe9WbfLcHAkPXL62goYQlGfaEyXxLQ0GPUzPc3xp+f22Kmk8Pj/CEyIDz9N2stUa8kV+94rOz1hAQiMFI0wm7oViVzGALr83mYzAkBmdPUL6tDj29cd3z7kPPeC/HQml5o8FzkU9Ugd85lDrHpuKo6o9lJzXvaAqcu+6+ZzjK5/eBwBMJ/gunM3VsLbH59uj3Eur4jMuFXNwiNfN59M5pvdrp9uNvWMR4v6CNkYIjdu4jdu4jdu4jdu4jdu4jdu4jdu4jdu4/Yq1XwqEkNnGqN6zL84ik2ck0i75+ZH04enxAUTqj+MTRkI7JknNm5vnIymPJBIdqnMcdlBVdNOm+uhP3mHd4dWXX8Y3fpPR+X/6LrPCL7/BTN9adQN9yVm3xWXgUV1lq9XA021+flY1tO0uMxY28xBGk2pTJSvnMDBKWDEUcPWLjEIWNhgd/I9e+h0AwK1Lebx9gxmlxi1GE70zAXj7UiuxKTMXYOR0bu4aTo/53y4LswqJRUYRq2Ujzk4ZjU0kmbkRjQYa9SasUtYo5hj9lVokLl9aQOY+VXpaUtvoGNnPTt0Mk1S0vC5Gz5tSCzPbjXBKLc1mZn8f9KXgsjNAQeiVK4vs58wMI5q9ZgtdIZKcinJbrYr+9vs4PuDvWWyMeuarbWw9ZCbj+guUpfZIVq9QyGNpiRmDoRSVLIre25xD9MWrUpVs8UDju3wtiXfFI5JTENyiLP50KIIn20SQBRStH0VgTUYXipIvD4T5u14PI9ndMtDrSJpRakk2cTK1qh30pBh0eDSyVUaYV6fDOFJtfEuZlFCcht1p1eB1cn3DfmZUvG5mZPZ2GhhtjnqTkeEvXX4FP/nZXwEADHlGhi9fZGa2mh6g21PtvuqOrYID9YdVbGywD1bVkK9c5h4xdTv4+7/nXH3nj5kJH3EdGXIWHJ+wPydZrptJnDkG1xBfk2R4bsgMZKPCsccmAIgHIDLJZ+WlmpdMRHCyycj/O9+nTO8XvvIGAGAu4cNQ/DZQBuV4lxmEgNOP9R0u5tKUJEY9LTjEwbSt7LHdocxgxI+8lAzyQaHM2CUUs0NIPAcTk0SaDIdSG/SaMTFL3xWLs1b7yZoylyELtjdZsx8L0t4PT56eP3vlGvfQdpnZlUZNSkLmAgw92rQJ/L3ukLY7hBFVyfoqIYt8jWvbP7Oc89p4xX1jtHJM9fYZ8kJDGOQovW4DlCyFx819Wc4zc+bzTCIk5FFZvAPpFCfB6rEgosztzs5IbUIcIMMjxJOc42SShdEnyjyW0UJc6E2DJaffEVLIMECvKwSdOAb6JnE8WLuoag85rex7IsnfX9vPoC8+gLnlWQDAu3/P/m7PbGF6mr52MOA8xhP0Ta1GGVcuEL21sEI/+dN/+gDuOBUMn32WvuWnQlmGw04U81zzszP2ZfUK92ypUUBRteMG6VKXcxxDKBDF6oWRXDm/P0KsuDwGGKVeNEL3jSRrl1cnUW/TD249YjYoFqdNONwO9IXyKJ9KgSnIeU2fVZCYkpqWFK18PilzBMLYk3KHyytOiVNmlSv1Jnol9mUjvw8AmBT/ncU6wHDA88xikaKjyYaTLG1l4QL34LuffcB5P93B2gGzrLkcffrpCcdi8fjOFWG6Dc7ZvFR/7pdP8K3f/zb7Jx6ZOx/Tdhq1NizKBg/M4vcIKgu6c4StxzyzJkPcgyPVpkZvgKFQizuC8tW1DvO1DMJeokMMXUHe2srwN6owhyRrf8g525ACnxkO/OY3/wAAcPcGfVK/YUDJSpvea9CGkjbOcWuvCJuy3HPz3JdPjjmuSUcSRsm/+73cjHapmR0d7GFylvNXaPG+M3WJfmTr8BiTl5hRHbY41laWz/zdr/0Rbty8wT5v7AMAFoQIWztYh1nceQuviAsB4vZIt1EScjcY53qX0x2Nr42GsrLBWWUjlS1v26x47hKRM5+9RxTu0pWLeO9H5D35yjUid/78//07AIDV6cFv/POvAQAaTSEdjmlLgXgIfXG2VOpSS0mxf26PB5CUvc2sszXD/joCUWSL9PcjCeV2S4qmZ1X4hOYZqcUWhFbY3rmLmXnajFkIkKMjjmtocyMglaRMhn9XFVq7mjpCVfeH6TkiSnOSOt/e2TpHbUzN87y9+fABOpKiP9Ces5xxzDNT07h1l+ifK9c5j4sGnvPFyj6iOoMdnhGPDv9/aDKirTvG0M6+xBdpCzsPC3Am+d9rkut+Zo7+ai55AT/87vucfyF5XZKiHlaB+WX6070jIg1FWwNf2AyXeDZfEFKq2eB87O4cYCJBP9dRVr3RpF1PTcbOeUEmpYxXyPOMfO6FN1Bp8RkjWfZKzSaWPODROu/pI74fW8CBjtZ+YJBin4VzdbS2j6a4b1r6zIh7aDo6AX+D61uWamNQmXuz0YSzI/F6Wui3lzR3PfMAEFraLHTfSJ11OjmJ23c4RpdQfj75TI/bh6H0t80W7pfjE55LFusAHvnfzoBzldb9bGBswyl/bWmI9+5gH6+8xPPIaOUYXDrLW506XFZeBHJCpnjBcU2FYkBXZ6iJZxzCfLat8HN1qydCdLnaemb/DP0+9327w/m0iyt0JRJDr8N7ZrPN8d3b2QcAuN0u1MWx44vzTHCo2mPr6eG5TzY0tHfl/82TCRRViVHX3abnltpssYWnzRGXI8du6/VxsLah+eB4Vi/yHewsm4LZzb/r651hT4pgdrsTQ6HRRlxAsRj9f73TOlfJNOt+f3TEfdqoFdETj+hUTFBgvUzt7h+hIEU2s0XILCmGOixmlPMcY1X8mXWp1/VNBhgMQqrpT4tstdvroi9eqFF1zIiHx242wO/jPu6K1McedAOmEdJVaojyGcOeCzt7UqCWwhwMfHauZ4axwmf0DVLkdfJ7veEATs1jS/xMqyu8L2XShzhL8/10ZZ52WZJ8fe7sFNEYn2Ec8M8L8/SP2ZM0ikVxdPGYhd3AzxycpuHycs0nJ4WkbnOPbB5swa73OMP5EKSQ1wOyJZ1DUiJ0Bvi9Qb+Dcp1jLbbEj+q2wlLn+hzv0u9Y45y71EEKXjNRYj3drRtSDo4GQue+5WCL94hwiPbsMrvQbwkRLeSwLcDfyJcaOBIa9UVx6u1t7vP3TmowuTkfZwe0IYPencPeOC4sEdV7skffvLW1pTGb0FdMxKGzvCB+z8n4BJojh+3kPWIo5cTds2PMLfE97he1X4qAkFPBm2KpC68uRMGIJCN3eVD0O8FzGF+jTXi0VfJwL15Zwic3+LmeXg4HXZUuGc8griy05AQx4Io/uHmA62/wQvCTn6pMYJ0H9UuvXMfuJhdzZ4cL7TLrhR9d9Dv8771DfsYZ4qYtZCrwOmnUJkGU06dc8Fcm/Dis8wXy337KA/feOzTof/GvX8fcH5N48S8NhNmd3D2DQ+UYkCziYMgx/+Dv7qKU5ViTM9wgb/5nDGZ9fusA6w94uF17QaTGhzKU3iyMJgWS9IL86BYvVn/yP0Rw0uOhdvwzfr7o5KFnN0zC5eTvlPP8O++kSNq8fTR9dK53dniBfl4vibnKAE4bn5kTsd9ygwfGdi6LI5UO2EWM5nQoyGSyot+THHyeLxiDgQvzCQXsHop4VEGpWMKGhcuCqB4wiON08bKwf3yCsCQLHUbBTItc59d/7yv44U9U4idIo8/MTdir5dBRuVs/wHl3uGlD+WwVTr1QPHnIy6jPNSpvtKEkqUOzgdDJeoeX35lpByIqQaxJNv74gAfi3U8r6HZFRq3SI4eZm73dBNKCcY7IXa9dv6DvHwMijE4VOK5mtonYHF/qUWb/NvZoq/mdHpavzQIASnmOKzHJF3hv2IiTNTlZXQw+P+QL14uXlhCLsc9rd3jQtB08LNNbXvT8PFh0JiIW4TyWW0WciijSYQnod3XJCwNBQWTtBj6rnpX0deIi1jb5ojmscFw33+aLRmTKj3aC6/3Zfc6HlLYxFTFCVRpYnaVjNZv8aFd5kRgRXpYUfDD0rYgrgLErWdu9bck1TtiwOMODIpWlHY5eLPb3WlhktSHOjiWJ63mqkwAAIABJREFULjhtMNzFqZ7x6Q1eNhLT/N2lpTBO9gSrHiiY0uO8DAwpmEXWuacDRvdMLE8vYm2dl8fZOD/TUNC5UOpiVEjUkfRntiwJUJMBvTZ9p93NgzCX7iCqtWzrhLbb+QSrOYCcgp31Ludoe4t/JpftKLVFnidp3HpLsrZRYGmZB2VFgS6Tgb8RinqRL9L+YnF+fkRaiaEfWbGT9/o8cN0BSc2bqziQPKydUwSviXM9Pz0DiyTsi4IFRxYFlW024VGgZGNr9AJCKeXaSQe1M9rCnsZcNwOPRZb98mtfBwA8c5X++OHabZhtHIc/IIlWJQeqzQJKp/QDZgvXO6oX6lI2j12RUE/Psy92C/1VLQ84rDx73nmXNm5XcHv16hIebLA0RJzSMJjpq1u94fnesStIYdM87u2cIhLVZbJPv39pkZflXPb2z097A9d7JGE7EZ87D2T2O/wdr8ivW+0KWiJQ9Idpq5VuHbMLJCO22SQ+UOM8JpeexdK1Vzmu97mWaQUrQouTSD3iuRfQ7a5e475evbyA5TgN/f/+t/8nAGArTb9ldITQyXMe51TecWGZv9/puzGwjM4O+t8R3HrvYA95EWcvX+bL+ZmJ+67r6eNwwN9e6nFvTEguNp3KoFcV6bjKpm7f4npks3Xcv88zrini0ReuryAggvZ0gX6uPeRati0hzCR5DrWKtDmnzpKth9vnUPCVOZ5r64/2AQDzc3GEl2hjJyXujfBQgatIFJMKkC0u8Az44B84lr/+sx/AH+JY2/scz+c+zl3QPom4h2fv2ge873TBvTw5E4FLZU9OpwIoVj6zgipe+tbr/O+KylYKXJu4PQxfiN/7fIOkmCvPrGDpGZVh2zmPV15n4M/dm4aty31SEAl+bJU+o5DOnicHRoGaDYli2K12nOV4rkMS6iNi+ExjgKmEqAb0EjwV4wV85+ARxPONkMrDnjyhDayuvohiRYIOklJOZWnrRnMZkwnO8bNX+OJzKsEMly+GVJr7ZVQ+ZdEls9cywe1l34c2lbjCgskm92NilXZ7d51EoLXOEJEZzk2mwnUOBHlXyeYPUZDP9CsAOmjR/k/SefRVOjM6b916AS93j2F3cBx/9J9SoOD2ewwSttsNvPoiz8Q1lcRmq1yPbqeA5EXdYVTuE1RyIjFjw6VLXNPDHZEGt3kmFGtGlGtM9CxcoD0fHHI+bW4zqhKDOZGwSSShNzvLEQJOOiWzmX3PnJXREkmzVeWrPSX4PEEH8kqYBeUX08d6ZmweDtvId9EGvCqDze+coH7KtTMHuCc6CsJXWw10G+xfUX9XUfmJwdqHL6x5HwWiBiPy5QBefJHnSU7lLQ4lmIzGISwKLPT7I4oHrn+52EEoJHLyluTkS1yHL7y2hLxoKTAKUpvNODlk0AZWrmm9zz0Yjk/ApgDXaG1aWstmoQqTkuplyVs7fVzLyUAUJZVo+xUUufOQvilXKMAmAQODSuAWo/yMw+zA1poEF1Qy6vFz3QqFDPoKuEAlwk0FHMJeKwqS23arhCtf4LN9XRfaKnPL2Livu3qHMzS6qJ+qXNDFvTGZiKCtgElHghyHHzJJ6XZaEI/L17a4XmYFzErVBqzGEdEun99WErXZbEKuHHXZwKjMP3tYRyjC86HX4tw2VELXHQ5hsHH+Sypp8zk4L91KHQ2V6/R7tEObaDSagw56Ko9rNdnPjoRL+sYaBgN+z+vVfi4q8Oi24OpFBmaePtD7T7MBgxKBEyJmv7dFe4yF4zC12a+bd7m+M0v0P4W+GVYJFI2CbdmyguHoo6dSZKuJfQ+oVLiUy2J6iv7NYlF5mPgS/IEoNnZ4TtZ0Zi3P8F5tM1pQVzAwPsHzKa+AciQWw+EJ77W1Nu0yPsl993Ath+mEkuK6Y5gVpGoPehhqrgYqG/cEud7NagMdvSsbldyslnKYUMDVIUqEjN6heyYnLBb68p4SOD4FjVudJuKjYCBoqyXdN6cTUygVaIeb+7znvDhzHQCwe7qFlQW+iJhUxjcR4/+fZvNoiQ6jU+azWl2u26k5jYOHfGcISyzL0vNqXAXkayIN194N6d6Cbu88CHygmMVMkr7e6w9hU6XFv6iNS8bGbdzGbdzGbdzGbdzGbdzGbdzGbdzGbdx+xdovBUKoVGakLBitoymo2P17jNKbhOh46zeWkMkyC/H4c2akvCLePS2UkT9l9Da5wAxYYoZRxN0doFdjVM9uZdRTqo/Yun2EN18i0d3qNcbGThhYw3R8CKeZEcaeQ1mPJ4wa2+2Wc+Ius+S77QMROZrCsKkkyxrYBwC06hzTfmUCj28yuxgUeuKwwe//2f/6M/z6d5iR/oMvfwUAcHd2A9/7HstbDiUlOHDzWS5/DZ0qswhf+jIJw96/9TEA4Giri7k5zsPeBueh2+bvmeybWFpg1HbnkaKjRmahnnzew/UVRh3/asD5N6p0ZmI6jp19MZUKUjeriHTtrI3INNfCviXS1Ybksa1xdJQR7IoE+IMnfE691YMFXF+j5tqs8qvTShUbjxg1dvTYp+S0G14XP9c28e8qIlRNGH5O2ltXhtjQYPbIBOB1kaqtb1Kq9eSQz3nydANCcsLi52CHglUebvcRkiSpw8kIdFWwR4PJBZORn3MpQ28wsO+ZUglKtsLhksyg5I77hgAe3JFWe4fPmp0Q0aypC4jwUUAylEwciz1ix+lT/mWjwe890B4JBM3INfiMKJNP2Endx9QU0Uk/fkTbufQsn+Xy2mBSSUOlyCx+RwRxEbd1lKRFdp/uIZgkQuZB+gBTyppWWrTjgI0IhMr8GQaKntsFf5VKL9rHp3jC4De+9Dr3km/IrFynA1RTtM19lQCEJOv++btPEBMxpFFQ1JsbzDQtWhz4+hvcJ4Ycv/+DA2ZdB14L0jucj2URrPYcGZgdHH+uptLCrrKNRiuGNUG8lXk3e2izi+FlzCwKESQEUuGMdh/x1RASVLjuY/+6Ne6lUt6AjMZsF6pk+hn2KVMpoF5ln91OTrahLgixYxWqIITDxd8dqgRhfeMYQ8FKix1lAov0Q25XG7FJPmtUdlUVKDC+OIXcsSTOZ5lJ3M6sw6jMoQA+EDAAy0sTuHuXazGC9/qSzFBEYn3c/YwPdo0UfEVQ22oDhRZtrSJyv6FQDYWdDiDYclv+2CgS31SqBrdfcPgg1yubpX2suKZwLIRQRASprrag1L0eTJJtDfuZCekPiPKx9Q147QVlMY30gT/7ATOJNqcBkRjntl1SJrvtw7bIDi9Mc9DPXOfZ8Pj4c6SLfMazkis1DEUMWjFgIOJtt4/PPEvRJ03MOSEADW4+4fwrmQcj7HCAGbZrL7P88tFTltys7zxCxKPyD8miGuIisE8fIxEWZNrOBTjK0J/G/DZUhB5yhPns2XmRO/7Vp/BzG8MTkK3W2bl6qoq+HM7VF9mXYopzfu/DR7BG+Ll4lHP93JUkzLIdqzJtpiZ/r3Kwhee++CX24XlmEnczIylxG25mquoXM27rKWYUv/n61/DoAyIRrWHO9XSfvqJbM56zbvqFDhmqzMjdNCMgUuJqiP0bCm2a29/GpAi+uzmJTVhpJ1cDSSzPMMNfNtKe9/dVJhAwIi3kUmHAtW1ZOP+RSzPIHPHfJkQun6um4FJZoiptkPwCs6YHWxs4kc967S2ibAJTI3LjTzCh7PP3/ur7AIDlefZvJRmGxyMS34ciStY+iPqi2P2IGcSPj34AAPD5uD/tfh9cYdrocyLgHhh57s7NzCMgZNvGJvtw41PuiaUrFzE9wzN17THXpCIEw8TEAham+KxigWfyVoVonb2jbbS6tMOuneuVz1UwI1LozG2iKlei/L7RasTuE/a9MeA6X36Otj55+Sre/ofvck73ONZHt2nbU8smzF7hftxQyYhhwDWdSoZREmH6oM0+z6qUdMY9h43bawAAZ4jPDPnYl9s37+D5Z4kk6vmF+N6ULXn92D3mHk9McB2uLjOz/WBtHZMqfTwTmiQldPHMxGVMqAZ/qIyvs97Ce/dJqv3lt34bALAjZJH17AhvvMV73/ERn/XkJscXiXnR3FcJYYXzGNQebhRcGLR5Dl0kCAiZEjPANkMAQQPvPvUc7eurXyc1wQcfvIfYBNd+usvvD40SYCkeYqYrhL5ERVxB7o1+24L3f/gO5+HSCwCApyI5N9k6sFiIPDh4wjmzCr00Me2FKUP7s6lUfirG0oVMtQWz7iE2nc3uYBF+P89Jc5+2OiIkrqVLCOrMsCX1DmAXuS4GOHy6o9+mXTgi9D/oWRCYngUADKo6b7sSTSmUEAjwcydCNRkks15t5BGWoMZqkuNLCLlqaObg1uvTQGiUisp4utYBhnrX2HpI/22y83fDMTd6QjXEY7T7ZoV7Jb3exOoqS++2cvSF5WYdtSMhUgQXtQjZ36gUsTQq8UvzchIzcE2NRhfSKaItcjpDhhYh4WOT8Bnor+1C88wmaM/5TBZ2lSoVhXxYP6Qt3d87Qk3UAt4Af9coIQuj0Qhpb6CqsrKsytjCkSlkVcJp0x3A4uG6p3J19HqC8NUk3a53o8awBtcs95xdd/phr4pSQWIxulcN5EeaQw9OCnrXEOF6LqX3Cr8XTZUxWlVG7OirhKnZhs2ue75D53uIdtVrR1CXmMVgKHSvUNlWsx8dlbu5hXTrdTnn+VoHBtFgdPr8XltVA0ajCVaV/RhE7m3UPX8AM4wD9sEn264P+L1KtoKzU549tabubg0DelMizRd5e0BkxWFLHQURWpfMEkE65u+sTk8idchzqSLElVnCRf1iA8WsSpkj9H01XSZ9DTcqOpe2tunHPSp9sttciKr8tKFy1jPZly/mRF0XYpP2V8CrO0OhBJMIrs8OOLfHB3xH8ocTMEp8JJZgmXReyGFTP4OeSh2jTp632W3ev4PBBjxO7utqlmdIu9GDc1KS7Tb6j6buEb6A55ycO6Sy3FqGc2Drm2BWdUxVZ6JD6MNCJQez7v6vf5PI6AMhcZYTc3hhRci9Cu0i3eAc1BpFoCtRphDfx7sisPc5XWjLp+T6fPZTrdXM0gTCbtrcZIBnfhd6x6zWERVqqCFk6RDar34rTCrF/EVtjBAat3Ebt3Ebt3Ebt3Ebt3Ebt3Ebt3Ebt3H7FWu/FAihURRrfjGBtQ1mHWbnmApxOhg9zmczqJT5ueQsMxq5Y0aw9zczgKQAj7YYZUtMMrLssFpRUT2/V1Fpu50Z9HbZgNu3WPO/ssooXU7ycndubiOZZMT0q19hlG/wJUbmbn/2CFvrzCaGJKXukvR1pdaCzTH4//WzsMf/377zCE7xzBjM/LuKpMMt7gD+5m9+AgB4dpN9+eoffhVhRW1v3WWWK6MI8dKFGcRfZe35qKb46Rajq8szF7C3z4hivc55MRk1drMZx5LvM6jO2elmpPKzz24hsMcYYUwEgFWRFKfSOxhI6jAc458NZYVNrjSWpjlHDh/n8/hYtdam0nlNfFWoAa+4gM1VA7x21XdWGTntiqCrnCYBMwB4PKPa4hqcVn7ZJzlEw0D1xE37OfmYzVpQvzjXL8wv42yf8zcVZVa4+SKzoNXuQxQO+LkJkZLuCQoyMTGB6CSfnxeHRUUSnuGwHx1J9lqcHKtbiA6T2YOhuCesplEGhc/JZHIYKpUy7PB7pRL/f+nSEgphyROKEK21z8/MPjOJQ9Vhi9YF0TCj3R1TDYMjRt27Q9pCJt9BfIrR4pkE13AkWTk778LWI47Hp2zMfpYR7KNUA8ERJ4ed2ZbVZT7n8eMj9CSvuTTLLN+o7v5Lr17A7j6zxk8fcC09yv6Fww7YVP8+IgaNR5jt/fTTzxGb4L+1xI9wKkL0eLSFSlmkwcpqmk0iqLV68aEkzl9/6/cAAJ9vM7NaqddQrXCv3vqQCKhnv7WCgZvPnQ+JMK8s2e9BDz/9hH7ni1eva8z87NnJGfZPODfiFUcJHN/EVBjtKtc3KkTHrQfMXszPJeDxiHh3lZlzq51931yvwKKo/uSkeFmU0d0+OYWjTDtuVEVgbhLaxpCDL8jPi94JgwGf6fJYYBe5vNVEm5me7Whec5ia4BqepoRytC5gU0i9YITzcGmedmI0ONARCacogOCL8HfyKRfiMWYmerI1o3iQTIYO7EJNuJ3MpOaz7EO13UZIkvJdITLa4jXKFDbhC7MPRwfcUyO0zb21E2xtM/PqDDG7OLDwMzazDzYRlh4eEJoRn5L88WEJ/SNmeE5HmUEhr3pDA6ySK04J9ZWvtbC8Ij8Q5jOb4i9I+n1oqN7d4hjxzND/T0dXcWxkRsgstIxDZInZ3SKMIvRMjuR6a9y84VgCAUHohkP+nehxEPR6zol24+IOcY58m7GMiiCNhirtMqzsZnzGicMd9vML18gt47ZwXi02K0IR2uHmfWaKe1rbYjOHuQv6nQ4z/BUhcZ7/1hv47KP3AQCBKP1jqdxC+jSreWffn79GZMvUxBQ++ZCcSE7BFYeSS67kM/BPiBehT6TJxXn6EdvQi//wo78AAMwuk7S2IG6OkC8Os3jdnHFl082cH5PNgIx4LOx1ybGKwD9kcCMtFKXXRj/VkOzrBx/dR+9Foi6mv/QaAMA44Hn26y+9ifsPiEb22zkGq0GotnoPz/9rkl//8HvfAwBcee467t0hp4N3knvo9RfoR9Zufwan7h1pcbkdfMrs3eK8CXOr3Jf+xD/nPNjFb9Z8iv1t2l+zQcNwJ4jGPMnWUBZnHGSrsaTuOBYPjje5FxJx8ZZIKtu1kEAlTx8dFYn1m6+Ru+g0vYWAztnL4rn56b2yfr+Gz9/+MZ8hJNKzL78BAHj/5gfoZ8Q1EqcPLLVayAu5m63wz+8/YDY5NmE/J34ul9gvb4J21Ts+RECcCRDJqEH0CC1HCxOTRE9kDkTQvMH1C8Ri2DnOacy0k9tr9G25fBsuP595f4171ufn/NutAbzzLhGFl6/y2ckkfVIXDZRL9LtP13kmt4Vu20+n4PZz3p0i8DeL26Y+2MT9R7JR+Z/V1UnMa38cZWij158hF8jHn36KEyHTal2ufV/72T+3gHd+KNnmIOeoW9YZWU8jpz1YeJdr4hbPx8DWQqfFPbC2ToR5S1wir37xGdx/SPTJ/8fem8RIlmVXYsfmeR7cfJ49POaIjMiIyMxKZlVlDWSxqovdJNBAC63uhraCoI2gvQAtBKgFCNBC6EZDECGpiSaL6mYVWezKzBpynmP28PDZ3dzmeZ6/aXGOOQEtyG0BZW/jEe5m/7//3n33vX/vuedE406NB5/BcW5GQ3wlIZcEPThVaFTGCEh8wCrE5d1XeOb7/NEzNEQOXRpxzFbla+rFFqx99jmrM0YpRyTvwswivOLGPDzm3j274INFbHhlOWfbgOPhHYyx7ue5viXUS+GM9+24XHBonjsjfr9aoP8IBAIIym5zhhCs2jc7pjbcQivPL3K9OIR4dTnnYbJOePXEcyq7tlgsaLXo+4pFkcKKFy0c8WPQkPy1OE0sDvlhjwNekcFWq/RJ+bJ8abGLnVOeV65uS4Z7XEdXfj4kQuGeuH3aLQPpJJ+x2+Xa3RvzHDEaDBGJCHljYR8K4v2ydduAk7/b3+O5eG6ZSNJwOIbihDBelEBDIZNGVgcWxQmTLXDcBzqMOl0WDAcTWXf+bnaWe16/N4RLSG+7CNCbIqzumwZwh2ir/RFt1BD61Bf1XKCPi2Whazoj+H2aJ4dsVK+xJrMbzZYk1EXYPUGF210mzArl1ZBPCkpyvNno/N07ke63tMTxON4/Q7+n9xz5Las4lkajERwicu6LZ2/ynmCyOmATabvLwnVmmqCADANm8RmZhMmY2I5p4MWjJ7SB29fokxaE3Nw72MUQHLeR1sigb8At8aN5cceZajoXhL3IH/O5nOrDBK2ULufgECegIRsPulSp0BrAHeDc9cxCWjV5bj89OYNrhmvbIyh7T74lGg2jJO7Ifk88lhLcODwo49W3aNO5pIjF03Iuoxkcp+jLRxb2z6t3xvReGvUKbebqJu8XdPN77bYHhtlyMaYAMB5N+DdLOHJwH3QIzeML+nA0IUGXIJXfyme+ceXWBYn37g59UUs8SiaXDbvnfD+AOKOur/D9ZWt7GyEffcvK+h0AwC/e4V6ZzZxg9+UTfU3E8zFVFgX9yGr/W5ujb39R0nuTx4OAn8/x7ofkTA2IX7HbG8HnFbfRQJy3YdpxMOJHU/Y3eX+fcJ61q10EZnQA/nvaFCE0bdM2bdM2bdM2bdM2bdM2bdM2bdM2bdP2O9Z+KxBCraqkKtttbEqaPJcVq7/QL71uEeeKLM4mxP2huuXLG2OklI1xqZYzn2P0zeEMXihRGaqV7HQZwfbZ4nj8jNndB6rdnahclUsNxCOMBv7yF6yd9irT12r34fWKzbzLa7aUhe73zWi1JUceYpTu8ae8x6ALBBWp9aqG1yc54XZ3CAmXIZlkhPff/Ov/E3fuPwAAzAQZMa1LGn39sg/lIrOkX37FrJ/Tp9riAbBxhdm6jOQTQ6qpt1mHiAoVgllGhg9fiivJMGPY5d9e/w6zkb/+gDK6hdwAkZikiMWbkstxbnaygAFmKPxBRkCrFUaUbZEeXMosNXqMpHqkwtYenaDd5zUsUhgI+sQbEZnBS4vqouN89p0nKeTO2b+FbY5bLMhxjEXDcHukMCf4xECKOeXTIlwF2s4f/Ct9JsD7fPJREvGI6pNV474YZzav2NnFnMG+ryyTd8Bp59y0GgN0akKcKMudyzHC22o1YUh21CRVGwlHod0awA7V3Cq70JQyxO7zLBL3xBL/nH1RyToGG3lcf4V9+JufMJpeboo7ZMELd0i12RwqWK1uNNocv4UFIsl+9QGzQUOjA4uJ8zQvtaCHh2L3DyZQEPJubp79XF3hGLx45AI6vEHxlFnWlS1mOmcCAXx2zufflgLQJHNQaXQQikhFpMAMafpcnAiBdZiEkAhG9fkCnyWVbWB5gWsol1U9r57v7KQA15iD89En7wAAzBahpHoWxGeVcREfVL/ZxN1LmxpvqdGJ08psauG732Tmq9PhRBmqlTdZ3Oj1GIEfjJT5WuB6icScF7KObvEnzUtBwe1wY2aGPsgblHqJVCeMgR1mIaY8XklLq/57bt2LWl2qEVH2pZTSPewL8ESk8CLliitCtaQODfz6PTqHt3/APjx8wsy2121Co017sva5hnvtFFwOjq3NKRnzOq/57Nkz5EuSoA5znU2kSWt5K/wR2ujMjGQzk/zZ7ABShkenyXUd8/Me7jd9OD/l9/JV2ns6+S4AwG4EYUhJpingw9Iq1+dxsQLfAte8I8JM4mGK3792yYulNa6JX0qp6/ihOGYMICq0xp17tP+DF5zvo5dljJVlNQ3F4zUfwvIax/uTD38JAHjrVXLhbEXnsSeZ0nCUa+HwJf1qKXWAyDXO66zkRotnvE+9DZgl/fatt6hUZDJPJF4dFxnmhztEAUjEEr1OBzXJ5SZitMuKsrtOuxWemLgr8vyCS0ijwagO0VhgJR7X36QCWOjDNcNrVpv8XlWIzWuvxmA0xZumrHx8gftHqpTB7KK4FsQFl87VERUh0WyCN6wKvfvlF08w0F64qiyXXWuwVC7DHWbfWyJTWl9YAQD8x5+8i+Xr/LdTHEI2Sb9EZ72YjXKtP3pIVGBACkT9RgfpU+6DNQefx61Mc21gRmPA+735CvezgnjwyucddI64L6+Iq+HqKu//cm8PDSGkwlrfo8m8LYbw+d/+DQBgzcu11DhKodvj2MytcdyKGaltvnodFSvt/s0fUG7d4iNvz9KcG42slFOEHMseMTs5wAAra+S3sZo4J80Sn69hdLGxSbs/LdJXmyUpPYILZY13WGjOYym4xAolLES4Hl+eMAsdEMK5b3Pg3U9phz/40Q8BAA/uvQ0A8Jg6SO4T4XMg9ZIZyeLaLUE4HFzr/gDnqNGqXCjQXLpMpJrDJ7l1mw3xKMftcJ+I10l2PhJ2QgJTeHRIpPHKVY7n9toGfvU3XJcuocUcUg512rsIRjjPXWXAJ8pW+VIdb2yTo2dmnpn2pPgpym0TTCJEPDkR4kxKQCvLi3D7xfMo9MoFX0hsDg4n11BF6I3VDX62VEmj3+XeWm7Qdk5zXQwr7Jc7xAcsiOto8dUFfJbi2MQCQsOKN8VrWBBd4u8CXq7nrz7luMRibnz7u0ShtSp2PQPPgaGQAY8UVh0O+vGJI8pkcojJBsp12pVbyk9uhwGLOB3LRdqMX0gonyMGt0eIzgL3fpef9hULxmFt83lu36Bd9rR/FpM5hHUOrgy4ptbXeNZ2OgM4e8k+tDpCWNgBnxA0NfGPbMxzPJ4/PsSzCjP1t24QbWDx8NlzzRqKWkNOqSZZbJwjp9eDRpP9S1fZ9/XNFQCAJ+ZCQ6q5M7LLbplz1Wk0EIvT35+e0f+mxNfmiwQRiQqRpM9XpZQZDSXw3d/7FgDgF59Q3S0kbsxcOYNlnRFqQpbbzBxHm92MpM5FeXGIwOuDy0k7sghxIlcEj88Dk7hXOvLlVRv/2O/1MKzTJ/WH/N0EwZorVDFw0R7cIe4dB/u0QbvTCZdPHItCRKfTfOZGq4N6b6KcJZVYjX9vCLTFjzca8HcRnbHL5TLMHnXaxO+PzfQPbqsdfflOk1AvvQ7n3WR3ICRVqMASURRGz3bB0TfZP4NCfDeabTQkFe4W74/FxX52jDLMQqy7feL7GXLMItEgskJON8W/eD7guhmjj7SQeBDy1yPyxMGgDYu4gCbnfZebfqDZrmMsNbOJlP0EgdLtdi9kzCd+0uPhmI8MOyD1r2c7tPVrV2kv80uzyIhnpjeifYTCEYSteoes0+dZ9F5RrpcxJ55Xh4192Tngug7NrMAQCrBa47k9Ie4up92FcoFjvPEK11lbRJPlQRf2HMfojTfJy/rkKXlZm50RhoZQ4OLo8gnNFTTZUchwXtvil+0KEWkkD5X8AAAgAElEQVQe2+B00Uf0QVuoN7jeZhadiInHtSHkU1BQ78KpCU69h/c8tJ18hXMVjUUwEpKr0+e42J1ubIg/ty01P9HrYefhS7Q0ft0BfzmeREdcZixd0zioqsOsMd7Y2Ma8FLa//JKIpHu3iHD+37/4NQIBfm5xiWfQnnioKu0O7r1JzqF+csLxx70uNhPFSUpzssBzllm2kz0vYDjiGF2+zM+bzBxPi3WIuJDvZjef3WGibaQyWdhNIk/+e9pvRUBoUupTyg6xuEYDrpfoXJfWRHBWacJpEzRcTJ0nL7k4FlZmEAjyEDQhE+v3bfr/GJGE5BcFQR+JlC1fzyIuWffUKY3VCsE5uzk8ecYXaL/I33af0DHabFY4RMRnHvFaVjvv4XGbLsqf/B5ujtEtOrOs0YDLzAnr6oAflIQ1LAbceoEZeTmZmccjvFvkwW1ug4b4+n0eAmDqIV/i4WBjncbqD9PxfPzxCeblqK5cZ4lVap9GZLc7YNHhoCPYZrXExR8LxtHRwn+5y4Pf9nUuuFq5BrONfxsIktsd8P92sxvHezRqvyDsQ5X/zM3a0ZUkYyWnF/YhF7YjDABymhYRXOtAd/SiiDfu8iXKYuX3d56kYLRpK05JRi4vTpx0Ccm05LC7dDSFrMrPhnm4mzyMHB1xw718jS8Ip4dplNKShQxoExYM1m0JY9SfHJB47YjIMlOpE3QaKgVI0tYqKv0ywQKHIIlen2CRCjY1uyZYFFDTFMEs2HQm1UDgXIvbKZlwkTqWjuuYnV8BAPyX/+IPAQB/+85fAwBivTE6eoEJ6oB7dl7F/d/TwftQcHb+gM/pRHvIdTLocZ2tr6ik0BTFIEdbuX5VgTEFHu0uN1oqEzx4qcNGjxvMwvIahHjE5grnpKUXhVITCMXoNKtibb8medqPfn2ExUVueH0R0U2IFdOpLGxO2szRCW1mZYuDVmnbEQzSuXsld2/VZ7ttK5xhHVh8tIUXj4/w5jf4YuDW4awS4GeOd6uYVfAmrQ0lKBLIg4MallZoK+0W16BfjOHnqQraOnRducR1HUvwOsa4DKteTNMqn2y3ReRodyEU5drJisg4HKPfOzsrYaLGbvPSR4TCtI/zfStccvSb6ysAAF+A988YI2ysiyizwvkK+/i9erWHxCLtwjZk3xuDBo6PeQhM8PFQLUtC1dhHYklloTpMHu5zbiLmMBoixnPpwDMw8Zl9QTMcIus7eMF1vDzPMbCEx3Dqxdsh6fWuymZ9Dh8W5mlr2+sK6KiszO1JwLGlMjQf+3Bjg4GapcUEvvqaAQLLkH7Bb1OpqquJlsie50K0bR+4dn/v5h3kS/zenErAorMjlCVBXRCJ6W6aMtql2hj/9A/5Mv94ny/zrTLH1mLtYUFBykaFa2kig953WdFRcO+pSqF9gop3yhWca+3ZRBIoxDZ6gz4cbl6/1uCitQpqb3KY0dHLk0elJbks1+BKOIbFKP3GJ5/x+exOvbCuBlFv81qKJWJxjvP9B3e+jy8e0S8+rbKf57vc65b9AYSitG2XVWWEAfOFT/D5uY77PT5npVGDW1LIj79kWe6S1vdZ7hyxNb7ghlQq/OVDHqIK1RoaZ/S7m4Kwl87kUCpm2JXgOE9n1U/+7d69exj6VCIiBs22DtRmpw09j4Juafodp4s2aNtcQ0rwdFQ5fntPGMTxOh3oixjVrLPJxirts9tuI6dSyZRklqMODywKBpYkmxurcQzGjSa6krW2Cb7/oyCDJCZrE3+9R3s6yXFON2+pLKp2jHSRc+JwcMwuX+M54jcfP0IpyT67dVAsJrmHf+ON2ygrGZYs8eX32v3X+CxWBw40fsEEr1Vr8bNjhxeqkMSXzxhYWFRQoR/po6uXQ5fKiz57j3Mbm/HCpLKMcp9jbXOb8flvWKr05n2WaN9X4uD0NImOyGonfn8SRf74wy8xpxe/y1t0Sp0a/bGrY8KDaxy3hy9po7e/Tdi+f9iETYISkwRfoyE/YLchdc6xGqmMwSlifpfHCZeTZ8pKkXM/YX2v5HaxuMo5nbz4D8S637VaUZds9voGF223o/mwzeDpC9qmXWs4nR0gp70g0mA/r1zj3rW5NYOeCEeX53XA18H9NHMKn2p2fbK5f/5jCinEYjH89GcsTSirTCAqAQUbrKiJBNail6KzE23+Rgd+BdK6CtrUtc94fSMkj+gzPQrmVnRumY05kRR5e1l75PZ1JlhG/REyk+SPzthevTDVhx1EVUbsVxAAJQXthgaqPQW1gzwrZypFNPv012VFt2vPGDhstwbYuMzzb7FMOx6qNP/S6jZeCfNF8eT8hNcqcE4G7QGykhGPBujLevKhqfQZFrQWfKJ9yBzw+9VyE8+eKUBroZ0k5mgLrU4HFo2xoTOsW0nXr3ae4qlKsb73gGeOXpfPbGkO0EpxvMtlft+p82A8EMdYUZVqV2eZIeBRLfFA9zGJYPm8WEHEz2e2KUCePWPgNhyMoaKEV72iMuWwEroWKzoNkfSr/DusgPtZKo2+giJjBaCsXpUk2j0oKZkjjn/YB7TxVquJoRKxbr1THR8peNFrA2aJ7/BR4VQJns1ig9ESNYQCG1c2mWAt5rNwTMr/9I5itwcRDqjMXkEwTIJTFjMuX6KfHqp0MZPlHvuDf/QasinuaScidM8oCBTwhaGqIuREWJzr8373Xt+Gzc5+5bN89qZ8S69fR0jk2h35FmOk986+gb7Iky1KVE7WtWGYYFfg2WqVQIQCKMX8GWZn6WNDfvqDrPy5y++Czc7z1VilXCOTGd0219BJlj7MrfOi22KCX6VK0HuVcgVodce4pXegD99XgFdiMM5QAIdJBmqLIga/tMU5OXh0CIcCi++8+z4AYGmV/Xx5eAyzklo2+Z3JPhoIJPAr7Rnf+iZLTbMZKTCMK3jwOonqs1kJDinwEk94kc0oYS46gZubtNVgJIqu9uCG1nOrrzHv1NHVPCVmedZo1ds4G9An2BS09Jg4nulkFh7RHCRWuQefixagUCohqgTAP/oxy8VPDybjU4epy7lUvPvima/dvHZRKtpWyV3ynGPtcAeQPeP1h0XOjUv7RaNbh9NHG58kIM2ixJkNh9CW7HxB54l+hfcPhd3Y05lJ0wCHnc8UDsWRy1fwD7Vpydi0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0TdvvWPutQAhNUCHF7AjDoSQLla5a2WDUbGHZhfSJMgwKQ9YlP9gO93DlGmXnvvqasGezssEGTBgLpjUWtGrYUdQy5EE4wixYq8rwXlKlMNYxcEmlQGaxycajKslq1VFRVsaqbKhFWt3hcAiBiGBbigwHowzLVmKEXQJ/R1zXLDOy2Ru2EVkTWVqI92n0K7hyh/1bu8TPp84YaRxbGlhVWcVLZTbvXf8B+/DDJfynvyRB9fffZhT48Bmz3dZ2BG4P+1xIcYx9Ko3rtNswBNk72Gd24RsrjORfvR5GJs3n+eb3mPX74jOilxqWBgzB2MIi78uDn/U7/DBEOBgUkeXVm4yAZ4p2ZJRt2uQlYbaoJKbdgWFwbB59yWyj1exCrcHxmx1PNK+ZzYhv+nAiNff8GefLLmh5LFaEy87s9v4e594yYrT0T/7obfyHnzILFJnlNd/7GbM7IbMLDmUQT2UXhZKkCWNhHCprMejTrnpCgHh8DvTaEzQJszKtrhBaNhtcLt6nkE/qGXhNh9OHcVES9pBcps/Qdbz42z8jQdnv/wnt5PffZjZk/+VLXL7OTMWozp9WjHBwqEyljX24+woj32sL88jmSYI67AkNJVjr6cEz9AWj3N9nFNws2ON5oYDV2Jr6IwhjhvPxYvcMc7OMVJeqXBsVZT4DYRP2johmazH5jISfGdL/9r/+ffy7f0Ok0y7NCfE/ZPYjHLGiWacNTNBU9Srtyuv1IhITCkvICvHLIRQF/F5ld0U0+cnBGV4qev7mLWY2D/qMsLtnzCi0mK1bjUwQVipB6DdQbjKS7xEpdbPMn4ViCwmV7zzXGmw7+MyXr1hhtgoJprKkqiDwhmmAmMo2j3Ykg+tk1N/n8aEg8tLlO0T37Sirb0YPIcGw06d8lkm2djCywafsZ6OqDFZ3AkkHOi2NkV/lJ0Urtq8wO4Ix56uU59rbuGzDWPZnHnHdrG1IAnunAIdkWycJ37DICS12AzYb++DzK9PZ4XicP2pjieaHVl6Q5hTHcftBCOfHXAsWoYccLpUC7NYQTPAZrQnOTSVH314+PUCuwOepZehb3CL483m9GKlkdEJsmBcK46jQxvImx/3hM2aM/tmrV1ATwtCW4Hw9lx3Xz4AbkvzulziQsTDnxhYaY8bHfn39GdEJ4bm2xsWPOe1LbmXfzkSE2yzUMFYW3i9yRrP2kPNsFXYT+xBR5rYtCPvA3EdAtWXJIu9z6SrnKGz248E9wrj/7b//9wCAG7dpLzAqEMIe1gl5tZdr48nne7A5eM0f/YB7yIsn9Lm9wjF8QuZOsuozsQAGKsMe21XSLcRmdGEWpYxQTcqYff2M9rt2bQbt+gn7tf5NAMDHymhH16NIy4EXRSLrWmJHw5EILCLXXlfp11AooLH9HH0H58TnJbz6pRAkc0sBbG0Rqp3P0Q7jkqtutTKYFfLX6xNaT1KtrkgA23NE3Tq9/MzHH3wGAFiYmcHVuySPrKisYysUx8EJ5/4oTQf3+AX3kM5oAJuLz9PKc7/4i//wV+xTaYit2/RFN19dAQCYRFrpbHihCgcEJI7w06dEfZUaXVySAML1dSJvPvuEZe0vv3oIp4lzE/BrAlSC4fQH0e1zrIpCEUZmaMdWsxnfeZ3oi0mGc3ePc3PvjUuQK8H8Ivv5N//vrwEAvd4iohItaLR5n/uv3YRfRN9PvuY+syeRi1AoAqtLZZpzvNZ77/Naf/D268iIYLct5FL2lD9TOyncfZVjdfWG0JRZZpgH1giCyqTa3Py+R0bearWQEbrXrXL9syL9+Tjgh1/oh/gM0UpWoTC69f4FAekll8oAC7y2zevA6Sn9hiEEsV3lJPVqD3dFGL13RFtoNm2IzNMOxyIw7gy5dj/8zSdoCWXqsSqDq+qafnuMiFOIlrrQHibe94P3fo5neyJ+f03iEUKRP/m8jFGPz7opZFs2w+/ZrQY6ks32uLleMud8rqVVP3oD7kMxO521WeuskC2g3hIKApzvL0SFcP3WMqIDnjHcOltHVOpTNleRrPHeE0Lyqozps+MzLEdpf5OzfK7Yh9NGR3XrMhFgmTP6zMSCFzqOoaRzQUciH6OTI6ypxmMpwGsmfKo26JYR2OC56HSffe4KsTMXWEC7zPvlDfqftXX67Ef1F4jO8HtOoSSjkaDGI4O4yFwhdGRe6EDPXBChOD9frdIXZgWfjsxEMZCoSLPBOXWqvOkkeY6izpRula+Neh00VFJl1nuPV6T5fZMdyRLvOSHVDYrkvN8HhuKgsDnYl1ZLSCanFTUdCIryxz6RTNt8PrTbHA9V/8A0YRYfAy6J00wQQkOVShlj8wUawSqIc0kIJdO4D7OIyGNC29VKKimPBVEQcsHv5/c//vxLAMBCIo5Gj59ziSvA7RzC4+fz1w/Zz5pI7R1eJwyhNU7PiDTxq3zw8defwFA9alT3+cad7/F7dhdSIqV/8phr1q6ypF6/eoGy66p6YoK/sNocsKnCY6gS1UmFxXg4viCKFpAahgQNrFYrvELpjYUiqtV41njwjct48ZR+t9+0a4xoz9lUAx6/U+OuM0DqFHGdceeEZD9J8aw9ghsRIZh8AdFUSHCnVmjg7vZdAEBQ5Mm/+hURP8vbl7GsUt9qheerCcXJ3auXkC7xgcoql62I8sIXCiMmahOnlTa984Jr12Z3YW6O4356xr1nRu8SZ8lDFIt8B7u0SbqJcZfP/Jd/8SnMkrJfmBGyqCofHzzHWJUllZYQV6qsaHUbWI9zXDw6n7lcTuTS9G9Dof0nsJ5me4iaIZL3JG3ALeSq3eJA+oQ+7Iv3SfL87W+ySiN5UkBaa7BSEzK3fKq+uJDOnwAALAbXyfVLKvNtdWDIn3pEoN1XqVqz1YDXz/U/J7RYRnQcLocDNsdEsIjOsJHh/SN2H1wW2rv0MdCSr7d1evD6p6TS0zZt0zZt0zZt0zZt0zZt0zZt0zZt0zZt0/b/a78VCCGHg9Hj8diCvrQOl7cU1e4yMra0EkG5wAhaXtLwFjMjf+nzKmBVxlAcKv3+hHDQjHSeUU6HuB3CIuCtFduICB0TVIbUI1Jqi8WBoxNGIkNC7JgwqetzXUTkR6pdr9eEZEi0cOsVZog6bUapreKmCPpNyIqYVsFECESEmDuOksh8by0zO3PnW12EQ+zfzi6jvgNllta3FrEjVEK/zP5VU+K5WQrgzfvk32m3GBH1BBj7S8QTKFX4vQkPScjPqHGj3kevwyiiRfWu7arIs7YBlfOj12W0NOBVvWzDg+NjXsNkFTGXovGlvAnBsFBDRfbl6cMTXceAT6irQET8JyL2SiwkUBH6YUJSNxiY4QiK7FdSmOGYeFKGDeQZeMagwejq2MLszO1rAfRM7EP6EW3h43c5BibnAFfv0Fb++j1mdcPq7+V5L5bXGHm22Bix/uRDZru3r/qwcU1ypUrCF4vsW6c5RFdZo2KezyzFd1itVjRFfthqijtExHxDUxOlCu3eIyLioXh1zmsWqNwe7/7FrwEAf/QvmBHwR2eROeVYmRVl9rkcaJaZwQqJnHh9i1n2X773MZwOjt+r94iCe6Q6/WDIgdAEiSGyxDt3KU18lkuhJFbzYZVroTniGL8ZsOJ2giSov/oFSebGguwk5kIIiUz2IM3+ffAxM+7F6j6sJmYx5hJd9Ze22m6ZMBD5ctDB31nGzGomEl5kJV9rcnG9KbkLX7CHoINjk69yvrw2H2oZXqu+pbUqPp6QKYy1JdpMtUR0QSHNsVrZWILdy2u0KsqsSt7d7XPCJd6AZ5/yM4l1ITq6I2TO9XlloZUwwkzMA/Hao1Hk5xe2+XzZRh2bW7zmw08Z+a/Lrh0uIJUVF4LkKy3K2BWKHXRH4g7SfPcbkrr0+S/IobsDztfYcMGk9WFX9s1sF+pj5IV4Q/HoMW08yCQ5ErM+jEb8XUCZtvFEz900xsE+M0J7IqpfYrIVS/NehDzsT6lBu//W28xC5dM5DCRN3BkJdWcow281LurefeJ3E3cn9tMpbDGJDOsCJz+inycvuvjGGyTkb3boO60h+c6FORTrRMC44+Jde/oSr9wgTLEBroVMns83tDXx+AmRp6OxkKdm9tNmD+LJPtfEWPPrVHpmPR7H4T5RDDY3v9ezK2uYCCJzxPFf3lImu8d9YnYugbxQNrmiyAVXRJTqGCG1y/GLL7B/ZkmqD+odlETaHtF8HSUlt14ewC5ej1UhY3xuokw6DjPMI2bFCk+5Z43K7Gd2OEJaqCaXg2NVdVYxMIlPrsfJCSnzu7q4iuQJhQiiNkH3RMycqpxjI05/7XdpzUvmO5k7w2gsbjon+/Dt73MPK50OcXZOhKshmjezZLVruSx+9OY3AQB/+iHn6NXfp1EMBzVkCkSDGCNx/AitMBwV0ehyv2yO2JdLs0QtpkpZPDugDXTPuCdEZpm59MbiMDdp25uSLd758EO4/fx3ws253HcK6RaKIpHg3lGSz01ucmwDa6/jsEa/gRr9zkKY93H2vfCKmyemM4onxp9+fxSdNrOy3RH7cv0OM4/tbv9CdnukrL9T3DeHJymYRNQ9GnM8fvXXHNdX71/GUHtWRxwiazd5DqmVeii8VGbZxe8nVtjPk0odEPfBYox+bpStwC4C5utXuHdkyxMi2A6urnPdHzznM2+Hic7Z+eI54qszuj5/VyjyXHBeyOCXXxBtdHmDY70Y4l5wUCpieXJvEaUmlhJ6dhO++pzfs0o2OrImUY1eFCNtGu0JikriFotrywiLfH0gXoq+SFC75Q5uXGc/Y5JBP3zBs5vNWkO9rnOYMvf37m5jd4/jbHIL6dOjjXtjIaxtEcUzaHDcj56JONqfwMjDedo9JLJla4V9twfsuHyX67hjkwCLh/O3dfsanj+lAz+VLHu3K1+z4v87ieyK/PGErLzTwUhOrN/hfQfifzQ7bZhb5EHn+TPuM0UhZbudAZoOjn/yjP3cdHHNh2f9MOus5u9JoMDgWWB9ZhZW8Xi5nFyDQWcEdRHqe+RPtyS73WpXcHrKPb+l8/qmNpj8aR7vvkuRgniEcx+doW8ZWQ28/d1v85oj+uGf/pyo5MhcAusbK7xmXah/oVmC8Xl05d+Ggm3lchzXUb+DjhBx44lTEnl8eCaC1oDPdSL5eZN4dXbOMxC4Az75j6LQOmarDYbIhVuSpHc43Oh0xfNlU+WAOO68/gA6QnTZJOueiGmO9g6Rk7CLR/xCJkNzAhOGsntDfWkJzW1zOi44Qg2dJQPiBfT43aiI23UiqW4Vjx3FEoSuE7ePUxDHYX+MTpvX6nVocyMR3j8/OkF0hvP7Mk3fHBHKJNusIiCC8f4Eye4eICKBEpOZay8nHkZ/0IuYCLsn81Zvsb+1fOXi+Uci3k7ZuDcszs/i+ID7dEecWR2JfHSsVUTln+IzHIe65tQwhrA6uE6CUXEitcQzVPs7pKVdaEWnk8/c6/VQlD+ckGyPNOY+nwU/+H0Skr/3cyJ2ikWRlc/Mwyq7z2aFaF9ex+OnXHM6MmGs9ZVO1yEqHvhUFRLVM5QLQ/znn/8CAPDP/ohr49Fn3LefPd9FREjQiFvr+oAIbrfJCsPQmVAIuQNxUT64fwt1kbbbQ5JiF1HyZ1/t4sHr5NOqVtn3WlWHYMONbIp28Uy8g3dvEOn84PYDPBUKvDcQslwo7c0ZNwwxrKcyk7XLvt28PY/tWSJKj8/Zv/NcCeMJdEbcY00NUG3YhtMilJ04u3pa16GAD1Yz/5YSUujdd1iB43GEcXmLe1xZ5OOZc/a3jSoiqhayidy5LDLrWqmImzeIeCqMhNAvcJ4Hwx5Ksg+XhCs64ps0mSywqnLGJDzPzat873J5nDhIau9I6DymSoS9F0fwebXv/T1tihCatmmbtmmbtmmbtmmbtmmbtmmbtmmbtmn7HWu/FQghi3h4YJhREVfDGzcZ+U+mGFn+6tMCQoo6lvPS8pUMbjDghUkkI07xs0jJHqVSClYpZETDzBiUJB0ZjvhwdnYCABgLPTQzw6xTs1NGIcnIncPOa1rt7GejWbmg8fapTrnd5WeHwxGKBf671WPEcCHAzOM//uGbqEqNoaS6y5fK5Hz9yQG8bmVc6owG9kct7D5ndNOkzEZ0caKoZMLZKadvXGdn3quI52A9hEKZ13/rbbK333kgnovPzzAcMkp55aq4jSrMMsyteRF2E22RE9qoqzrndiuNq1eZsT09ULZ2qCx+13SRHXco8xtJlHTtHuKzkom1M/qeyzNb9eDqFryL7Ge7x+d0q0i83TeQPGNUeqQMrt0xRm0oBMGA0Wmn0DUd2wijoZSRlKGIzPB+2WwdDin5TDIiPWOiCmBC1M1o6tuvc2z3vmL/vP4QnA6Om8UiFTpJhO7vneEP/4S8AOfHjAhPpIfX1sKAwe/lZAtOt2o7zaOLCLlbv3Moeu/wGBgrct1RhmlD3AjH6Tbmtpj5ijgY1d6RokrHZIXNxGsNzez76VEfvmvMbJhF5vFcJD3dLmA2xPdT47hIvRjjfg8m1Te32swalUt8vplIFPuSCJ6J0KZHylSPjB5iAc6JDcoeiRl/MbaGJ8+Z2bNJqnZ7kzaxv1NGMa2Mkn8iU68adsMOj5/2UJfSglOqCelUDhtSpHn69DHHZZb9zqTbyBdYhx1YIHol4rJgIB6GZ4dc/0tX6WOefbiDxrnqw5VNd/s4D2NXGgHJcZ4/4zj6l6R05+3hcKLaM0fbMymLZ3WPYBKCsdFgpkFLCf1kG9mx6q4dzLSnz4nsWNoCbA5+sPhYNtvhmC1f9aDRkf1D4yFpKq/fBYdQZd0u+1nJcaxmwx44hcKsVHitdruM+Ar7kxPyMiwZ+X7fiTMhzjxK0y4nlI0eNS5q4hNhZiNPntOPNLo1bEnO95U7RGg92qUcfH/QRK8hVTgv+2cR35LDa4XP4Dz35WPLZX5m7o4PVnC80zWp4YTFTWVywS3UxEKMz3ok1E23C1QnqpLillDiB7HZIPKai5GyumarGwFl46G5mQ3RPgyjAbOTa+7whOMfXpI0cqmAjBCat18lwujwBevhz09TcIh/wSQeAq+kuf2RGTSzT3k/ZS5DyuD4AiGMpdI4lhTvRCHFHQLG4i2wiWujIxSn1bDj8f7EN/PSbhfXpHvFB6+QBD7Zc1EqVN1SHQ6puqEhZKeSd/Erqyi0pJQhNGuxUMeMUISTLLJPKJHZSADfuMuM1bE4WLbFQ9cwtWEWX8a77/8MALCxKQnqSgsLV+jrXhfPT/+ISISTRzWIig1mSf5+IeTa/ftXsJ+V+oiQH1ub9Of7ux1EHfTRy/PkK0hliHaqN7sYCMXy9BOOw/ANztErm1tI7fJ3OaXz9z4h+uitkYG21vpIqps5WxfpF3zWb71FFaieMuBuuwNtySTbV7mPzXT5MHZHERWbkClh2tpZh89VHuWxYKZ/++t3iLjcvCZ+hbEXz54R4VbviIdKXBlzM3GYrXzmhUWOQy7J9VIo1rEwz/Gu1Okryh9zD7G6owgG+Vw//+wdAMA3fkDU0ee/2EG3xb/FEhzjt35Epb//40//Ew736QObRa5ZV38TBSEO7PLfE5nq/edJxKRe1q5w/OZifM68pYqkEC2VJn3KgtAhHSMKt1CiA2VbrSYhE40yjo+Y4V/RnuDzaA/rFXFli74rFOC4OOyc98rpCMdp2thQ2eAJz+Tx2THmpIwpYBH6A342FvEiK79/ss+N0xiyTw6nAVOPNr4kjqSDl0/QFLJqdZY2UD3jnnpj7RbmhD577xMiXBI3eOvfGYAAACAASURBVAY9yhSx5uaZ9dYD2ofPKjhyvYWeic8o4BLsdi7Q0XiIvjLfdp0VklnOzcq6GWbxVxpy5E3xnsy4F9EQj85Xj2hft6/Qj7daI1Tb9L9u8UPNiwsqk6lgNspnzQn40CzTRyXzRfQEoFleZEebNd7DFwpjQXPfrPNvxUwLAyFm9nfpH2Nx3q9QrKIrha0JB04lw4t7nF4MpSB4VOI85cSFEw0FcXLIzH5LvF8Wnem7fQte7nEuAn6haKWCWyzW0epwTN06Z07k691eFwY17jU9KR25hMgz94BBjeurprNQJEJbrRcr8AV579gMn32Yoy1ZrXZYhSCtiZMGGAJj8YpZef16TSizchYuoW7vS83Mr+ezwYz9DMc5rTOoWRBli91yIZfeEaeYU5xHnWYDZos4LSeH+rF4SCxWWGVjIUm9t9tcSzarFWOhqMzixLTrHmObGxP0UFmKZ37xUi4vzMPt4fxO1Bj9XiGWR214xKkWlnqm2zdGpiz1uB7tb8Lj0zMaeLEvBFlH/LJ9cccF42gKMWXVu14mzfEJBXzw+Pi7WTvvs7NLm2gZFSR09hFYBjUhhBrtHqIm7lmLy5zLcnVSjWJDSdwyZkGcXW6Oh9liwVjS62Pwbxa9iR/sHGP2NV7rn/8XfwIA+M0HVKJ8+Owl1jZmdU2pB55mMT9Pn3JlnX7Er/eKd375IbriyBlJObknJbhoOI6D4gkA4OMvPgYA3LvD97tQro1zvT9mhFQxNG+RaAgD2UxHnFR2KXG+3DvGxiLvXZWKtt3OQfP7PEhqnxiC4zfpW7nSRqfDa4wt9NGPXvI87PGYYehsUpdStFkqZ2dnY+SFJgzq7LQQlxJqp4mPP6QPM+tMmc6X4RT/1gS1a3HzGeqtBpz8EyyyY4cQXTtHB3DaON4z4nM6POL7hRtuhDV5Ltm9S+q5swtLcEi57HhP/kcHq3u3r+PomOfExSs8m5iE5jGMAToD9uv5Hj/jC9F/RBeCcLvYl7LUoz9/zrPJ2vI6ukLTJzMc64IUyVbXl2DI5v6+9lsREOo0JMc3HiEg8siXuxwIY8QBbZbt8AtO6bTTIGweOv5QzI2RiU6yI4fo8elweHkdnQ4/15IsbdDDF5NG7xBukXRVBE22u7mBrlwLQSg+NHKcgLBrAl+swKYDX0+Ewm69SDpsQaTO6Njcfv6uNuaC/en/8w5Wl3kNe0CHesFTAwkblhbZZ6uTizD1pR2QHKHNxHFYUTlJrTpAUAecclPP1+LfKpkmZmZpuGfHNJpbtwnTfvb0BOMu791sM+CyvkonOHT3cPiMQYOdL1SWZ2X/7nmdaFUJvQ4FWWbkC0pO23QG/wwdY9DHA0suS0M0DX2w6CDn1AvrXFCy37kC2jY65aV7HON8lp9tNIYQ0hg2kUFma1n4gwxYpVN82RtKZrrUHKJS01uMDnU+jfGoG0FNQQabXuxee5swv1h0CZ++w0Dazft6ebJz/l7s9LGr8gyTSpcCfjn+RgENsbTaJaFpSFp2bTOKdp19OD3nODhdIh+3AB6vJKtVflIRSdrdK2sXEpPiIYZh5lh1yiU4gzxcdCN8rlaWfXNUh3jjH/8eH93Ma/27/+UxemsiuJMseFNBn9NjA1ev0A6zInOcVHe0C25UK+z79pWEno+OuF1r4cZVEWyKqNepl+1qpY2TY5YAaH++gKlWczWcPBd5uktw9Srnu9echVny9MHAhPyQ31/dCOD5Q5WRaPOPrQoye9TAUKRxMyIBLo1EMNkGliUPu/ec3x+YCojQzOHMi5h9QTDdeS8+eJclM68sk0w2Pi/I5twQe0908CvxAjmzDgSBEoKzIjM281ruAP8WnbNg5yH/bRZBcCLB+T7fB2wquTFMfNG/epcbpzHuI52e/I39dUdos6lCD6GgiKM7OnAWJRHrADxe2uHeE873wjw3kfGog92nKgtV3MvvCQHgZjoa8EYT0srzZAELRNvC66PN7T7lQafRBr7zHa6T82Nu2vuP6KNuPvCh2+aL9NKy5KYffAMA0K1XUFRNZ11r8SzJ8Wi08hiOVEIhidfYIsfTazMjc0x7vHSVf3N6OFdxrxlmSa4mTxThydHm5qIDGA2uhdmYAn+nfAb3ghNzkgmviyDb77Zgd4cv3kJlY3OVL2a7xVMciFR+9jL3jglZqH3QwbVLvP6LHR4SXCIRzjQacErP1q7Snlsql3n++AVGPZWMWvmZtkpT8qkcXDpkxeO0Oa9ekD1hA8cOjt9shN9/9Evavds3xMA0IUvkD5tIumOrG6ik6ecOMhwXn4Krrr4FtTYdQFlw8Ng8x7F5coyB5mQCs55dXMLsDG3r+c4j9ZnjczB4jL6CSg4RWlbKkm529PCVePTvX+Nz3b7KMU4m91EysV8/+4i+fZHnJIyCQZhV/rG+yfIad5xjkEp+AEub9ugbyi5f8IBUL3YQjvKQ/OVTHqoH2kc7vTCcAY6pL0HH2K3y/7/86CukzjhubkHMTWJQ/2jvKX586Zt89i85nkgksHqN/TJbuNYdFtrA6c4eWoL8L1yhbzFLwraYO8K4Jej6AZ95bkmE2t4ZFAp6wXdynqpp2uzDp1/DG9XLnebXGIjI/ySFBe1RJzsMDp6pDGJ+bhELepEbSuTgh99l0McyaOLrzz8AAFxd5Bgld3mgdvkiOEvxPs1jBtTO8+xv+kUGl0ScbrhoA//X+0/xX/1Lkm52RJyZVynp29//HgoV+p3nRfqDU5WqJRYiGBS1T+jlrWSolKNdgcXGBIc3zDVUUDJiObCBQkmlgXIDEQ9tr11K4eQR/+a9wrNJTwFfl9OAacwveD30q06VOnWqtQsi8tUt2p7dyb6dnJRx68YbAIAX+7R/t842LkcUww6v8c6vaHOXt+bwxhV+vtlkKeLIzz3v0d4LvPspPxdcUGmJgoQjSxNtPePqEq//+H2+vMFiRYjHP8wndH4TEXfxsA69Y2P7Gm3BE+BztloWHO5z7lZW+bcbr/A8l81mERHJ87DDF4rukPfPF2pwi9x1qDEzK3hhggvnItkeqoToIKUXf3MQ0ndAvsTv+bWnWDs1PNzj7yZB/0a5B4dIbv0qsx2ZdQHPLCDC+YRDgVMFZVr9Nmwe+SlF/vtKvtbO6sik+DzhOfqI6AIHL50vYxKsWN/i+qyK2Pz69evYPeAe11MCdzRS2VZ3gIbKL20Sj5gNyr4qdUQMvRhrLznePwEAxGJxBJRwK6tPkxdJjAC7XsmqCqb0jRGseuE0ROA8oyBaq1GHYdBWqgW9bKvUuFFvwK+SrQkpr0lnNQwHsJp4rUnwp6Ogj9PvRVtjGlMQy6QgQrtZhVOHu1xepOq6psfjhMfNcfAqaJ9NKShs8yCgZGFFaz8c4fjcv/cAXz9hWa5XB55ZHz9rHTvh197oHHBDK1ZbMFvpLyyyi6ECLk4HkD7hWneIXDgQUeKmNoAx4LWiS/QjA5VRfvTZlwhFREqvBMr8Iu1jbvU6aqJJGIjuY3VtQWPQQibLtWRR2bbXz3lzmCMY6TzakO30leQNhf1Y3+A1OuIOODmlf5yNrOAv/4IB+dfv0x5fe53vPKEZJ47PaJuxGPubOs0jEuQzzoXot3t1js9sKATHLMd5VfutoaxOcb+BsvaVc5VVmytcL11zFAOVJU4Cva0Gn/O02MB8SBQeChINdX7cvnQNQaeIkUVG3VKQLxqNoia6CLON89ZX4DwYjV/4GcPEa9aVhOr3ffDoPLW0wbPCi12+Z3SaFsxofzeUjB+0JNJiDsDAJEnIQ4cvFIZD12pI9KcryovN7Rn0zNxDwwIBlKq01ftv3MaJysD0qOiW+dl4wI1P32P52PwCky0WBbxOjg7gU9LUoWBxYFFk5cMBAiGuk6dfcZ+en2OiKBQOwDGUgNO3tM8cc9+oG3UMFQy8+hoTb06Vq+8+38fMHP23L8Ixzhd5rrYBcIz+4XDPtGRs2qZt2qZt2qZt2qZt2qZt2qZt2qZt2qbtd6z9ViCEIgsiBq6N0BRJmmMkdILIyyKJLnpCCYRmVTLgUfbDZUUhy+hrQ2SohsGIJnoWfPv7rwEAzhWRfyxS42YL8DkYzbt6mYiRx49ZfjJXW0RY0plewRaLNV7TZBgYC0oKQ+Spa4zELm4Bn33G6HmjzUi085IizFbgNzv8fKPOzMMETRFwzaEvSBxMIjG02WATgXb4JlM+DpuyybUSnDP8XEBZTK/Iea2eMYJLjLT++Z8ymrp1iYiJjYQP2ZSixSaOR1Gw/9jYjVCQYzr2MHLqCwuW7fRA1TAYCzYbFIFmZB4on/AZnx/x56ybEIORrYTTmmQGhWB4LALGdM2Fu+TVgqXPKGctyZu47XaYrCrjm+M4WkJtnIvtdvWyZMGrjMLX9s1wCVIUX1eZUILZE7N9iIJIbmPr7NdE1vajX3yGmeuM4r7zDqP017b4f9+CCflDkdOJBHLex/vlHw1hdYngeIXPvL3NOUodphGYo+0silQtmlAU9+kJXBaOw7AvJJnkDQ3jDCvb/HwrL6Tc17Sl+fAGtpY5Hj/7ijYKK++3Fasjq/K6ybWDPisabUa1SxnOacDPZ1mam4F1LPLCFNfXm7duAQB2XuzhZJ/96hkch2CI0fq5pQE8Dqbt95PMaqKjroxs2DuhzWxvMWOcEUz78GQPFi8zIi5JvHrDtJ1qq4DQzASaTDvuCvF2tGPH0R7ncKAywFXJSDdb5/jiEyJF3voj9r23T7nSjYgf/ksiTXzB73tDZswp0xvw0hb2HnLe7r96Gw99nN/PGiyheHBHKLpCFHZB7K0Jfu+syrV7+6oXgbjIsoXEu7TG5zp5WoK45PHqmxzrgcp/zm19OCRhf/SUP2/ZOG+FXB1Fkbc77ZMFJ5Sg2QeLR2gGYUMrQn2tzAeQ+ZKfM0q0f4dQe6VOEkrmoKOf23e7ONkTSW2INj0QBDgQtMPrkzxsizakJCiubcfg8KpUJi8C18sigr10A3/zc2a3Bm1Ct7/7BjMcT08bqKZpo0EP10JPxKXLy5to9ZWBkczxBHlp9gyxuM0BaTZoJ0c7HM9P32/h1fscownkOzxLg2y1gS8/Zz+//2MOUrHETOnHj57CLqhwQ/KjJ8/7WNnm3yMe2tjuzq8BAGfZMVY3VbNkYTYtIGLxaj2C1Al9rEUlGOUMr+nym7G+zj3LkFjCrGDBzUU78kJrtUb0w50KkST5pAV9E/1ceYn3W5lhFj86NNAU4tU/Flqpxs/MbrZQ7QpKvsv7bggq0Dr+Eh2RjC8oQzpBmaW6RThd6rPIODtl/t8MIKZMnm+e93MF+2iUmNWd7AlDD227aqnAaeczFlt8Bq9H5NytMcI+2twHH0li/oiZr8W1O4BI0YdCVS2oBNRh9SJXoG9+9CHl1YciiR4MrTCJ7PrZ7oRsUYicRBxffMB1bQhxmVgSzN0WQfqAiC6zstBGgrb3m192sLDAZ128zWeYCdLvtVsF9EcSKFCpzY3AGCsJZnMLQkpdX2epzeGTA1jn2L/ztMoJ3DzbFIZlJHPMbO5k+b35fT7X/JyB7asrvGeXvsGufcLvt2MsxyvUPRpCkgwGQ3z8qdCzsq/FVdlcpoB332c28oHK+kzKvIeiW6gNicbxx5lxzkj0ID5fRmzI/pnGfJaNVQlnwAqXBCvWJKlr9B/j/Z8RbXf3Np/hD99gOcInnx6gN+b57dpN2kWrx++PB4MLVHBkhn3e3OBYrTf7FyIT+WP6+U8/4v9XL8URl0x35pw+ZlJKsLKyhtUtXn83yfk22YS0rbXgcU7IobnXpbP0/6ORgbGTxl1W5tw8YJ9CMTeKTV5r+xp9rV2o9eRpGYbKmgQgwaVrl2B0VfovZNaVzTsAgK+e7KKjM+CayhjGmtQVrwWZJBHbiQTtseOR6Indi75QaDXJ1k9oDEyRMYIO2nJnTId/fqAyuZM81i/zPPD2298BADxSSUqw0Md4nnu/L8Bz0ssdkSgPxhdIyIoQEyPJNF/evon33iUB7qgjwl+bEPueEUZD+ma/SrJsY52hSmP0hIqs14TIdXngmJSsa11O5Jxddj/MFn7Xrvodp1D8rVYHNkldRyQVX6zSn4zcZliCnKe9I55Jlhe4nj22AGZUuvX8IW0nmeTP6zf9mE/wgPqkSLRcscx93uEzwyUE9FgTfZzhWDVrPegRYXcKLSm0TqXaBCTY4lI5SU0k5BaHDQUpJliFzAcs6MmnuwMi/JYUe/LsJWIqpUqd8HfPD+lPw/EQElGV8Et6WtXRsBh2dFRS2BbK3So0lsvehysglIvQXiWhNWxBG4KSfB+ea59oTdZIF4lZ+sC+UCEmg+PhsztgkTy4W4iVS8sc/73HnyMq9LxJZWQBlSKabD6cFei/01mS5ZrGY0S09zrivL5biKmxyQxHgvcJipogolEsLFRQyaqMscO9pCmp91p3jKGoE1w9obZUIniYKeEbKyyP7aa5htsqYTKHTPDaeb94gGu3NeTZ3BfrYO+AY+wL8j0hX+A5YXnZArdbZfNW3ndjTehHUxd6FcKHQmWPfOz3pfAsnu+UdB/a8Z/8kx/hi5+RHDobEmLVJkUJkws3RNj9x3d/AAD45i2u+X/7v/0P+J//inup8YqI8fV+O+w70OvTXlsD+th+h3bvdroQ0P5lF7p6Q8JAnUYB2SOVaQbpj2t1ft/i6sOkkvy+0EB+iZ84OhaMG7Q1SDgkNsf1ncmmURSFSk+1p0EhYi5tLCOf4Vn8KMfPrC5xvWZqTZisOk/IT5ocTjSHKlfVmcTpZJ9arQLMdtp038xnmF/gM6TPk4g4ec+R9tRQgj7GFQ+h7uEaf9ni3mEWutsbmr8op+sLdReeEfo2m4QhRL5vnudNX3RSPltFuSrEsFcE8i760+HYDvQlN1/n2uiLPsLstKGqUtNZlYtXwPEvN1rw+4P4h9oUITRt0zZt0zZt0zZt0zZt0zZt0zZt0zZt0/Y71n4rEELhCKNmwZAbu3uMBJeKjNROOIVWVmdQKjA8l5HMYCLKqH+11kFrQiQcE7mreDuqtQb+7z/9NQBAABXcukU0kMdnxocfMKIr/ih02ozcvnhxiOVlZoRGQ/7OLcKxdrs/ScYgPMPoqD+iVKnZjvv3GP3OphgVtFolL1nJw2JhJioa4c8JP0uj1IZH5KLeADsTcDlRdSt7r2xdMcmo5+X7l+GWpOJeihwNBfEn+RxzON3j56/MM2r/5HNmOCwBE158eQIAmO0xfr68zD589kkZSwusybx8ldHRfFZZ17GB6JzqjUuMpL7/KbNkd//gCs5bnDezkDfeZdXBo4rV+RX2vcSI8mDIvy1e9sIuXqaMOIfMNo55cr+IhNBKa0tCKZ33sb3FTHm9yyh/WKTeT0tFzIvXYthQZqlEO2k2AaPP6OjglH1vS+632+zi6hvkf7AMmV05UR3y9358A7kV2uaf/2tmjfrr/P7ilgfBKp/ni+fMVMSXeI/zzBAjZe1qIvIKm2iX/ogThjIuXj/7mxKnh909i+Md9mvcY7ZkY4PR/vP8GTK7HCuPnVmWXk3k1Js+pJW9yJyIrNgfvCCzK5YkZ51gJLpUOsTKIjOOT54wk7u0yuzf1UvrsCorEPSJZFg10OGABxZlHFVei415RqKbnSriM26NH220oprhxaUYssrGDEXyUDxRHbi3g7kFzakysScE/sAfbMEdZITcP0tbaHcEu7EOUa1xbDOntJ0lobFe7JSQeSxugYb4pGIeJM/Yr8A2MxzPHooQ2lfC69+gTzgpibtMiI7jl0XEfOzfrPi7whGO1f7BIdwuZjkWZ3W/Jv9/dgAIWIhKkWvi7HSgzwARAU7uPOD4Ddv8WyEJhCIipLXR37TazAQEfEHMSU5yZ5+2OuG8qJSHqMjmxuI7sTtpX7lcG35Jz0ajk2xkB/EIsynNLrPQE19kd4yRydHeu+LkmOGQYW3WjXaD15igk5bidKw/fPM7uCnyoZ/+FQlSP/6c2fzwTAJWJYGsdn6x3eczl6smzNHtwK7xFM89mo0W+g2unYOnkmjt8EIRvw2NMv/dbHH8g6qdTh5akNyTL3LRT10RX83XjzLoSKNVdDe4c3cW1S7HuSlp3GSSNrB1zYN2g752WJc8coBzk26+RCTEyawL8tMs8Ps2A7AOODZFSYp+WWEWaTj0YmWd17dKlrZVV2bWEcNoLM6LOMfo8JyInJcvxxciCSenXPPf/h59fKsTwJMn5M+5eYcTVhWhaMg3gh3sp0dwr65494Y9A50x+76yzInoCjmVOs9hfl2cEBkiSNx1L2Dm7wYjkWyPRfJvuPBS+5E3wV2y2RPfjcuLOcl0z3lpoyHJ9r5y/SrOSlz4La35aovXydeOYExSc2M+vFms2W6bCx470TgHdtXiJ7hev3iUht3DMY4ucW1YZP9BWxi7h1wfITPn/dI614Prj5fx8JB2G1/jc+4L9eswVTFeYFby+isi1u/2UNL+5RW5dld8cts3t/F4V2jKMfu8tKwsYQGIJ1YAALkkbdUjHhRLwIr//DH5aYbiI/GscDwsi27YtHe/eMx5aopjqtnv4Js/JCK6JfGI9inJeSOhKLAgYQLJJE+4lWyhDl55S5yEVY5VQvzFD78+xtw8145ZqMW2zkQnyVOsmOibq1lx6K1uoC7+oz//yecAgP/+v+OZaG52Fe9/zrPCjWU++0hZ4UopD/PkPCBy6ZGIgVut5gX3ntfDZ99YZaa02iiiXJbtSDjA7xdJtCmNSp1ja4hDwSxOFrPdgYGywS/36E8DwYmASA1NIR1q8nd+P+ctGo0hrcy0R8iKtu7hc3uRqdIWYjGu61KxirmQeKBEdpva53ms0ChiKS6i/xR97twm16DVb0KnxnF0Wrh2g14RCndqsI6FNO7SBkziwnGarajmub7iVt43FmFfXD47brzKfTL3kj5l9wmfpdQaYSMoP7DJ+z5+XtD3vHhySvszGbyPSTLQZ6n3cOcmEWAff/KF5kFcerYohiaOd037+0B8H6OBCx1BVq9c4f6bOs/Aov2r1xNXX5O+feQeX/DxVYXCrMtW3S4f2uL16Bnax5y8zqA/gF0IXLtIZZ++oC9bX7uEXJlninyF5wOnSPc/+vxTzM9xjVuFznMIUeDzetEfEiHVER/J6ir9cCRgYH+ffr4kHiqvEJh+f+BCOKdaF6GwXh267RZakmcP+ES03BnA65LggkBD9RqvGY0vYjCkT/F4uSauXqUPK9dLaDZ5rQmCySmRD7vdDo/8lKYSJhv/Meq3kAhxb6s3eR+reOlMoy7qFa6FiUBPTXxXIasD1RrP5BaRUc/Nck9w2VwYCvrXEILhXHyCR8dHuLRN3xARF1ZLxPqZZB6hOH30xib3jfKoi+hYXKvi+jTEQeYyLBg1+e+iKk0MIZu7xRYWgzznpwqaGylLFNNWjOZo50t6Fy3mhPjpnyOZ4Dtix0kfOO5xLd27HEQqL66cPt8BIhF+xjCVMTcvvlihga7epn1kzlKo1DgXEx7S1Q2hbwfhCxJ7c5+G8fUjIotsq90L7r7jl9x3zdYGrtyl33Z6eb8zVZWcpk4xM8OL/dlP/hwA8PAjcpCNu0N4Nc+prMbPzetYgj48f0l/6BIpukm8NUZvhBdCofW0x8FCG5ydiaMokv6e5iQgrqhQ1AfLqKVn5tfefpPIq5/9/F20hAI024SGVyXMoNeFXeichviBCkXapdNuxaI2qd6EU7PM+3c7AyxviTi+MyG/r2JugWPUFlHpnLiGi2UzAj75xSavtX/Cc8GNG9fRaPFZ56KqHhF6rt6qICQ0VMjLuXGHOW/FZg51oaRX5/jOcLgnFJHNQEjn5tHkfKT3NavVihcvebY7yvDn2mXyE/W6A3jki0LiYgqtcv0kvDMwFJk40/dC4cnYtZAvqPTg72lThNC0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0TdvvWPutQAglk4pcGS7MzU6UcRiZtCly3WrU4BOiQrQDyItsIxxcuFCdmtQ1jg1lgRCAHYxWGso4PPqKkeyZRBAWRUAffsUs8kSdZWN1CwVF1HqSp928zOhxGmmUlXm1K+M5ifanMgWYFXavZBn1XL4suUGY0FEGwCluJLdDGe5OEeMKI6CbrzBS2xpm4Q4xItmqqtZX0cGT3Rxef4uZ0VROmUNlqM2OIs6T7F+/wgho5Wv24V/9Nz8CHKwdvXqD0cNUinwfxZwXzQYzV1euK7paZ3R1dWkexQozlj3zRM1BNZa7AyyvMysbczKqOnYok+v0oVFltvtY8taG+GvWb4Txcu+E4+jj+I2VHei2xhirfvLF008BAJG5LhzK7pVOlf1QHaYVJhwoat6t8ufyJse4mh/AIYWuRprR30nUeQgrjp4xAn9PyLG2wYzZ2fN9fPvN7/Pf/5KZlw//jGN9ZXUES1jqASVGakuSA7x8w49sln+rKmN5ekz79Drd8M/w82cpZp174hw4SQ5haTHT0xHXgFXZltWtFeyWmI3JP+N4XlZGpdgtYSBxoWicUW6b2YLVFf5795hZu7198UCYTHjyiFnaBUE/zvb4t43La5ibkUSJ6sxbZcmq+l1wSCXML7WrtpQ80rkizD4+l6EsfkBa0V6vHyZlWZ3iB3AEJF+asMAuFbp2XzwHVa8+04RJaIaqNNsNqT0FYlbUJLd9fkqbW5MCWn3YQzYpBRDxReQyDbiDqncv8G+Gwb58/PEOvvNPqFxjq9MBNLNCPIzHF2ihZOGEP49FDmFyXCjlvPYtrolcis+VPbciIu6lUZvPIMVruMN9LK2yX6cHzISf7otLoQhcvSxZagvn5vkTZqGWEm6kjsUfI/utilOhlB/Aoyy0S76lIa4Zr9eFVJJZ1re+p/G3eXBQZIZYVAYIKoucPzcQCrDvQ2VZ51eUbW0lUanx+UXXgV6FD/a//o//E/xCJa2tMCPyyQ4RCC8y+3DLS2IymgAAIABJREFUbZhlqxO1Gpt5ovMCKLmObo3+J+raRr5KX2Yynagv/HQxZ0YmQ3u0Otknu0t8JCYn7OJhKKX4mdBr/N6VjZv45An7rMQN6uYsTE1+9/iYc7KxIbRH24SylPfc2jEP97l+4nEHUufcJzqS5nZAa6NSRCnNcXzlzqsAgP/4E2boBiMr3ELhhP3KFkoBpFPvQRQ5CIrPpVLs6OcILqFzaq2/U/AAgNSXdaxLHWVs4jMEo+LSMgPVIm1gbZUIhFaHfjIStiGg9WzSfvj55/SBG+sLOFI2XrQkcFrGOCvxuyFJlbu0lgL2Rfgj4syTYllrPOF1sGLUpx8MiDeiL0WO47MKKgXaa7omFSih37r1EUxjcZOMiNAamvjZjcsOpE84/hN54OMT+seRxYSFbd7bE+T9bJIq73Qz+PEfk9/qpz/hWL3zHlFBb3/3Jq5duQEAyGd57UVl2twBJ/IaN6dXCjODIKxCNeztEnkwv875Oi3U4BCP0+4O99lX3yB/z/JiAGWh+l77Pfqfgfg+zjNJJFaZ0T46pv178joDOACMpWqlRWUy2L+5SAL+FvviE9/Ph+cTyco6YhHJnte4Nlzivtl5coqYlEkLKc7fmuxqdfESRuJR80Ukqatz2mAEDJUNruW4nuNLFjgiZ/o3b/31Y+6xvUEBQ/EWtdO0Y5OD82UejDErtZ+bV6TcJ+nx3Z0y4nE6Krektq02oX67FswKwTSQImNS6DmrtY3/r70vi5H0Oq87te9rV1Xv+zr7DGc4lEgtpE1J0GLHMSwndmwkQPLkwECAJEiCvOcheTBgIAjyEgQwEkSG4tgOZVkLJNKkOOSQHHL27unprbq7qrq6a9/3PJzztyEgsV+FzD0vM91d9f93+e537/2W83kUFTKyiw+wWldffOjLa12vqOpSXRwnFxdRlG4fKZrQ5rDOJkVMylPsF8+hOySv8FkeTjfnJiXOhnqjhmdlVRBUOWZ/mPt6IhlA3cE5D8b5+cwhzwVhTxhN7YVhP/cEu40eZi8CGPYUrZHUuzOU9bFIGONxqxIY+3ekKIVh14UH96i7Yjbpkaoq3iYnEVQkQNTGz79xlfM8GE3guKTqt2eU+4CHbSpmGzid4zpZvsD5s6qmt+t+1Kr8XqOpEtbi/ek0OphV1Z3lRZ4fjw8zaLetaH/23S2izUarg4DOGBbH1+4ON6HCWQWhEPtzkud7IhrPdquDrqI/Z+eoIyKK6ux3uojG9R4/z/KWfDjdPgwV3eRTNWK7SlDaHXZEFKnWkTx3FQVTLpcQ0DnHqi7rdvn0viFKdWvtct56imhaWJqHw05dNKsqaNVKE1lFXYRUzXYgTsFGuwWneJbSKjPdVvSVJ+DCoM+2tpX14HCzD6flEvpWJbcYdZNL8x0IBqAgPfTE75SI8jPVegv9Dn/X0zl9TVU3S4UihgP+zalrZUecqOVyESE/9VMySp1y/zH1ZDIVRU73uKEqnS0vMxriV179CjJ5zq9TZb8d0RCcigzfPeD5ZW+Pa+LS2ipWlqg3DlXe24q2mew74dU5u3TItXHzBiuhZseb2N3jmjsRp2ZM+0XMnUC6xjY81J0qXFFkaDOD8Dz7bEXZeFRpLRr0YXaefe4NxQOb4rOblTAqRY7R9Cw/c1qjvoq6XVi/wPHunzIaPKxqsbn0Q8THFgAA33zzFQBAsb4PZ5Bz8d6HPBv7VMXZHwd6kt+69pVTVYH93nfuw3uNEUvFks5QCg+xuQrwRjne1brkSXpu1O9hRvuEd8jvWRk+Z6d5RMcsflo+67QgTiyvDXGtCbvk/X/98f8EAGxcuolOax8AUFGEVy7D9ZpKxZHOqrS89i6Lry19lD+PlkkqYs2KQBt5HCg2xHXm5PcmZsMY2agzQz62eaSqgWOhaaR3VHVXhzyHqo2dlpvnHErdjqrJiTxx48Iqnj6jHK4t6g6hrJ7TSv08+m2gCKZGV3yzqxfh8eveU2CfywqLj8eS+M3f+m3+rkXZOTzm+Hzh9g08us87+tt/8Q4A4Fd/9Vsc7MgQZWWNjKuC2Zaq1wWiUVy5wvvt34RfCINQOKKSk+UWRiIVbSmtZmODh6LM2QN4HBTKjctcKA2FSZdKBYQiXER1kSs2KlqgLud5GNSsyJu2NnnBalbz56RS1pVkQWWn+y0nMvuln2tnapxPevULc9jdp5L46CP+Oz6+AAAol7toF7hQfCNuOmPKDwkOXEhviwjKrcONm8I6PT2Nlo1hz/WGSLOn7KgoZHd8XEYiL4Xt6fvbeP1zFEDPHPseUH7H2ASwI6NUzi4jiQhF7/zkffzat1kKOtOmoaWoDRp2JxJKuXM6OY6lUy6wWtUPh0fla9d4ofjsMS8NtkIFUYVOH54qpafGOV3ZmET2hIfBmQm20x3lpry/e4DcsdKKVOba4aAiDgTtqDWpTIJ+znu9DmQzVPQOcPPe2qQiH/VjmBJhV8kq6a1Dcygwg/qAbUWPC7nv4jPtrvD5nCwtc4x7CtVsn4bwoz/n4ekrr7A/07/Hi9073/8Qj5+yfeF1zmFhh5tyKRo73yjLCint1FViPlfHq98i+ebLr5CY8y9+TDK49EEGCZcVms9J2dFlJ9KI4dYNKuKCFOvOQyqQ6YgXZZGqQ+smFBigoXS1bsciIxQ5snuIoi7JNoWwX32Zcr+3c4jTM8pMUocFKxR6ZnoSz/eZXuTwUFF5lfIRiYdQH4osUgfGpJeXzG63Db/msCvi4o3LVD25XBMVHZJ7LZEKRkXIvWDHs/sWQS/7EFGo88htw2mNfb6V5IYZT+jQ5rMjqVKr2gtQKDtRKVB+2zOcX6dyupLjfqT3eagYi3PePrvPNVivNOEQGbQVgh2UvSwy1sGVDR4Kt5+yDw7l8wx7fkxNKtXmqPRzfb/+WhgDXYQHoL4bqaRsszpEtaYy9w3+LZXketna3EZW6Zprl9lX6+Btd7oRinGMBj0ZQES0WqpWEIvrUmPnGORzbch2iG5VpexLfK/L6cdQoejRmAywMmwkpmw44h0N1y5SfsdEBlk7S+OjR7TW95/yX1XAxsyqEw8/YtuDMjKtXFBqlROI+3gIbOlQ+TTPg4sjksfaIi/Q2TRl4UAE9n6/F6sihG/JaJecpA5w+po4fMJxcDh1CH3CdX56WkFilvptY51rqtrMYfEiN+/XblJoTsvULZ/ercPn4xiNFKZe00Utl61CNv3zPSDf5qbcavbQFwFgQ8amwyz7t3E5BZebcjFUeVqHjFloes5TUQ73qO/y8pkMRnaUlT48r1SuTz+ibgsGoggFKNuFOr+nMw3OCk7EYpSZnEoGd3rSFf4YaiLnf/yQ+u7NL1PP1aptPDnkhC9MWofLEZRBh3qOc1rRxbPa2ENQqUbNJufZHrVKvdbgFbloQoTn+QrX8MPtbTRP+KzYrELze2xvp3OGhQWO//NN6qaRnd/LnLTQ6fL580l+b+tT9s8f6cIth1IzS/3j6iU0/l5sXGSb7z3kmh+2KVfFs/tIP+C7l2WU6Qf5zEweiExwLUF9bjYCGJ+moJ8V+blSS8b78hmO9/m7iQRvWpubdCr0ByOERaAalUMkqzRwx6iDi6vUu6kI1+xPv/c2n7PggM1HOZy+wP5k0xyXs0IR20dcxwuzXP9Pd7lunMEuJucpM8+ec6862KZOW16Zhkey6utQLh6JkL9RKyM1qcukyJoTKi39z37/t/DeOzRyTk7wXHCUu4fEJGU5llIZ8n3uCdNzNoSCXF8tpVxH4sPzsU3XeFEMpLlOCrrYraxew+ERn3GkPfVrX+OFrvqkiWqT8hDwykCmcPqTQhFuXYTDUV6+dBdGLB5AVXJvV3p0UwS//c4Y3NLlQRmUzgmFfX2sLMkZl+V8WSk+tdoJVtaoWEcDfv/59jGaBY6DXwaNkAiJ26MRdtOUo0vXqX9Otygf5edDdLocx60dpoJevEIZf+dHB6gq3SR5bpziZbh2VsXaKvvxvEw5WVqnwcW/6UH+mAaGQ5V4Hl9W2nK7ib4uzcWnHJc3L/KMmckVMBpybAdKs/CE+HPMexVPNmnsfOW1BQBAOi1S/BMvYgn2NZHgGE1Psp2tZgdFXfjvfcLUwna7AZ+Mq03LYJ6kjA+GFVSVb1IoUk8FQ5SlUH+IdJr63UrBCoQoQ/V6E/2enG5ai2srPHA2RiUUdf6w0gXPS3r3ymireIzlvBjoLNCsd9Dpithe3riWDPSFcuHcSO/qU1f0e0rpbHbODTXDpuVY4s+9rh0Ou9JJD/cBAOFwDN2edGuNemBMhQn6/S4iEf5/d4cGha7OfFG7CzaVs3fadKYOUI69LjequmhaaT+OLtu5sLGAQZeyk82eaNy5tpxuIBTheO9sU4bcMTEg92wIeq3CGFwTywuUZ8e0EydH1DfNGmXuyiWun8WlGchWhrwIpHd2eF+YnIgiGWGbc0phv7E4BV9cKfVxOmnLKsKznT3EwMY+11ucp7FJ6navo4+m0k8ddgrIxx/zYr1+dRn+PMcxeyyDRI573i9//QqaRY77P/ndLwEA7v5vbsaNegWlvIxY0yxs8voXuZYOtnLYekhdFptSkaAOdcBZvoNuXWd5FYSYu6i08UEWL73Mv/30O5TVsmg7vnDjIs6OlG6p/MGdzDaWVTTAMv74RbvhD1VRUqpTQw7V61dfBQDMruXxgYzmU9o3C2cq+pG0YVEk1zvblIV2S+mo3iFO5CSJ6H7QkCNrdmYJ1y9yXu+8R5L5VpVyFgpF0Mj8vEG422N7n+2k4ZHBcFgXfYacmu1eH4kYZa7ZkBy6RF3SbJ+T0sfl0Gqrekq7M0AjQ7n3uSlgw1ELAxlt9/a5VmM6uwZDDnRV1MYf5ruDUd1Ryoe4tE45simoJCaC7GLtGGF9rqT0sJN9yv/I4cCHH1AGZkTyPL7Eu0qtW4HNJyP/SAWPZGjLnVTw1g85fouiSLF0RDnfRswv3bK2AOCvC7E4nXYMRxYtBcfhtdtMH09n84gGtVb/BpiUMQMDAwMDAwMDAwMDAwMDA4MXDL8QEUJNRfoMes7z1BeXwlmtErajgQ2ZY6WIxeiNcyrUtj+sIBqjJdny+LjdtF7a7V3AQWtZpydivYA86YMB+kNaXCNRWhHjSVoJt5/tnJvLAvLmZPdpJZ2ZGcKj58/O8XvtlrzfQT8cslp2FHpaOlMUkrd6Xhq0Ky9cLkvvxNT0OFIqTV5WisTMfBAeRRUEFcl0mKWV1e914c779Bp94bffAAB8950/AwCMj4/BLSuqMjeQiNIienh/Ew/v0QMSWGIbZERGatyOeFxEVSK2Xlnm+DscdjTlrQqrluP4Aj8T8g8RF2P3lpOegCA4rtOpMWQyKperVJ3UEgfh7p0uppQ+lYpZHjrNQ6qHkUggaxXLAurGs216B0dKW7OppODpaREB5b4MlEYDD625PXTRV5jp2pyspEH25dHjKqIiq2xoToYau0yxhLjc/3uP+ZlLa/QuXP+3v49/86/+EAAwL6LlxDplcGuzi/lFtt1Kv3peoHex1ejg+39K6+/qZUYkLS3SAu4KdZAXmeaMQudhE/Hu8SkmQpSLxcuc1A83GVHmyozOS7ref5LXewOwu60wYqs2NPtQrRThctKreFbg5ze3+KzpFRcuv8S/eRWa2VPqWDZ/iqLIM7/4ZUZ07D6iV2F/v4lgiutkLMnvBVVeNX+awZnKnc+qlHT1TFbxUBROJ+ft6SPO7fwG+5LP1BBXudiwor/6FslmKIh4UuljShOo1bmWZucmkVa5y5LSGF66eQVnecrd0gqff/icZIEuTwgZpf3ciFO3pCaV9llsYKA0GsuTNSWy+VLtGB0Ra/Y7HNuC5nkskUJV0Q/VEtt86Tot+5ev+fGznzEiyWEpGYV+L69Mo92np6wgz2VA6WHpoyEuXKOMDQcqm16ifCwvxuHzUT6yGY5HqSzvdTyCZpXtyhyKpDvmRKWs1K8dkcGuc3xarQ4GIkN+9pjzvUpHMY6rQCCoyIOHe3q+wmgTDjimOOcxlYEP+pV22PSgo3K5Fzb4bI+HQtEqxXBSVchvVfrDzzF//qgB55DjEI1yneTz7Es4HkJQqW31Ez6rXlW4tL+Pm1+kB7GvkG13TB74g11cU3RUWanGMX8MqzP87sEe31fNKm1zEIAcVmh1OWbbm+zLzPw8KjWlFstj32wyimJyIoZ2i3P31ltMtx2f4c8npQKmlVbrVjRQqcOxPitUsBDj7xQ8hL7KwZ+etbF8ySJGVJqyIhMvXW2grYiAxITWukKwS3k3bAmlvKgAg92mKMmuE7kD9mFeUZwbC4z2KJ6dwuamd+xIKdSO0QBeUO7LBb6noohS11gfNqXHeD2UhWqV8uFweHGUZht6df7bUtTv3s4QM2F6FwsVemdtPbYhHJzFWVkk5yKXtokttN5sY6jQdSjabqA9dnzcj2FP3vemUu+KjFxbXrqEZ5t85qUFEbNX5f3v9FBosV1NRUQmJ/iOpenbmI9zUtJ1pXlWu8jkKUceP2U7PqUCFN3QefRVckJnlCbld3e7gGiE6z4RpH4b177bKQ8xqlI3f/M1RvReFrHqg/t3kIwxcunZkz19nu9odXrArNKfxqkjviyyaL/PjpHScj2amzcUZTNytPH4McPLo3HOg1sE7fFkAX4v5941oO57/y8pz698yYuOjXP51k+4r9mawMw0IzCaSn22PI8ffVBDSOmdoUWVca6rHHSmdL7Gq02ur6YiO4alNlp99mdiljp6U3OTzeYwoZLt3S7PhN0+xyOeGPtrQtQh3+u3iLvRwaAjagF5WcfGlEL36XNMKNpw0K3p8/xeuVjEJx+RYqBap1xYaaPF8immZ9mHWJRe4W5niJGiZ5sjEb7yGIelmXm8urAAACg94fi3VZZ5t+rDeIrv3jlgvyB9OjZjg9umSIIfK/pCRTQc9iGOcnzft//R1wEAiShl57u7P0QoxTZ7FD3n1hl4JhVCNs1n7WQpJz2lqkbiI/iVNhjRXvXkOSMT7Z4xTM/yvDLSnj9QpGciGcL0LMexWqNe7Q9a+mwbi0uMTnCqZLzX78DeHp87ktQMVVa50WghqtTNiI5x29scs69+5VuYmmFERa1O2Rn2ld7U68GjSGaXjX0+zbCfbo8dLaXFOJVS6HJbkRw5jCfn1BbdIaSjA54AUimuhaqiyqw0mRFC8Hgl8UqzbSmyIDWRgEtp8Ccn1BlWlsKzzQMElMpSU3rd6UntPApopFSxuGgkut32eTqZ06FzsyIvS9Wz83Q1KzKoL93p8zvhj/GZRWUjlCqUrwcPn2ByimcuryIKbIoW9ri9qNcoV3NzPIP2bUot73ewusz9Na7S9FErdcnvx8yEiisoOu3jD6g/FhfGgD7HyqGshqsXuG877S64R4rSOODZcN8GDNSesqIAV1epaz45+QSVqgoASY88eipyXZcNE3PUyR4VLXhlhZH6p2eAw0mdvHGJfc9n2Pdc5wRfunKT/VHacnKS8rk+dQ3vvfNA7SMtQyL6sr6fQUcZGylFuHmU+tjrdtCo654a1R1KUf9HZ1m4/VzHswscvx1Fhj19/hSpCPdEizA9GAjD7WNfkzp7FYoWdUgEzS5l+7nONE47z7zxsSQSWh/hCMd9eZmHoompyPn5tNfmvAXcbGexdGhxSKOi8HSvKjId2dLnd9+B/i0qHco+cp8TM3eUpWFTGlqjbUNB0WF1RbaEwxxrp8uDgFtrSYUMnHqfbRDAUCaMss4YORVwGcKBfl/PFw+Mw9WFtRN1+lwvB7p/z3gTCE1QR/aH7HujqQjquAedIfsxUvR9+5Tjura6jIrOfe2WKEQU7T/oOdDqsA0PNlUkRGmwLmcDRekpV43jkT7meX0sNgWfIh+t33ncnO+7lU8xFhbNjOTr5GMW01henEdbc9rvcU7zispy290o5sXH8DfARAgZGBgYGBgYGBgYGBgYGBgYvGD4hYgQssosn2Tz5zl+JREoDvqMRFhcTiEvr0dL0TjHIpidXw6h26NVtS0r5Dmpm9cFpyzyWUXXuJVT6HR6YVP+6WBAa+DhIS3KnfYQUIpvV0RqTnnVPvirbcwt0RJ//cYCny3r22cfp+FTzvpQPAe5E773wvUYFhZpcc0fifNF1u7D/TSWQ+L50Hvnxm0YXlQO/rvMqx0U5YaYCOBZmh6Rv4NfAwCklmlV/cnPdjAtz0buIS3fzWk+dGFtDg8OfgYAuDkvAuMsx8OOKhzKwa2WaJGsKue3XPXhIMtnXLjB74Wj7PPxQQVBJ62dN1+ll8DVYDsP93YQiih/Xkypz8Tl0Sk70AvS8ppPq4xwSKWKm2WsrdB7uTfk53vDAIYjvifgFz+NxnjkBGwuPssibByb4Hwd7B8hOKCX9CjDMRtXlFI45MaY+B/uPaTXz8rn9U8OkNvZBwBkT9m+7VNaiN9sevEH//ofAwD+8Dv/hWO1TzlpOtv4yz+jl/tXf4NeiGyWsjrsO2Ef0Avxwbv0Vn/j2+SK+Hvffg3/6T+zXPfBIT8/vUzvwtzSJKAImLYsy9/4NXqO33vrXQSSnBu7Euh7fRsG8pz3OlYZVxHOwHZORji1xr5vXKV1/Ky2hb4EP5riPD+4zzzYueAKJufZnn2VoL390ucAAK1eD//tz2ipXlpQ6d6RSJzLI6SUHxuOse1HT2k5v3Ipfu6RajToefH4OaeHD4H1VfahIn1QzNFLsOhJYCxEWe3Kw7n3XNF+3stwexU9JGJJh/cEaXmnmn1Fnoksvv58gLi4crYsgvE3VSL0uIyAVx72ENtykOG8eZw+7G7K06PotHiYlvmYL4yDfXovXYowDKSoByr1Ggp0GOD2TXq3br9Mwrf37zzDkSJ8knF6mMsnfEevDfg8lM1jlbP1e+jJOjvN4MZVevabTcrxWILr7tHjAkTdgolxjnv+qAM5zOGR1zm9z58Lp0NMTymnXZFdc4uc90cHZ7DEyMrpTi7w50p/AKciLQdOjlV3wPlqVvsQlQFCImBNb/MzUa8DC+LDOTlh20sihl+cuwCnmzp9fpn64+5nfO8wdwx/hLKTVERMv8M1ks+4kDnhPK1eE7+HIkTtfiAa13y/J2/jsI20yPq8MUVf1diXo0wDE162QcGpuPGG2pvuICLZsTxYPekk+Ls4FJH+qSLiXvo897Na6xShIOeiooiao2PLk+WAy8vPD3rivWpxzPy+EGIJ6q4nH3NyUtJz1U4NovdAb2SVCqY32eMcwB9Vu4b8/OEu9wbHsIzVVXp8R3rf6SnX4knuCP2WFQ0k4n9bB3HthS6fSlAHOLb1bhNdRcRNLfEzQZU79gcSeKzIoGpF46kIUbfPhWyd62WNWwhGKjObyRaQVB/Hp7gWC3mOld89REn8Md9/+1P+boqy4Brr4bN7HIdfepkRNeMv8b2lYg0IcjL3MoxscYpV8967wEvXRT4GevE7Lc732x+9j0uKYl6/wLa0gz5sZqkPz8Sf8c1FRmaE3HVMJEUO/4ztG0txzU6Mp7A0rbLW0tX1Etf1qN3DmJ/jPqE9sXLM+aruHKDjo9w/uaeIOnF79OHFuJc65ad/Qm6fb7xOHd3rtxGS57Wq6MN0jntre9RDxytyYvW5tktdvboYR117Tq+siNnn4miMbsElAuOVi9RXnZMAPvmIHC+RKMd0bZ2T6nFFMVLER1t8LGGR825cieP+J4oE61KuXOIl2Xu+hWmdbyZnuIbufUxdHYuEURJ5aSBAPZxKUV+NRrZzolGndKcVRVSv2dHX3uj1KVIrKWLn9ClsdpFCK1pp1A3q5wAyGY7RioppBPXeTKaIg11xyQRHek8P9iRl9Au3ODdBh0rZD2Lo1tnHj3fYh8RFzvdseBId6bDDXYWnqkDKxnUHfA6u7asj/msTOVLmaB/XXmbU+HSYf/vxX/w5AKDSq2BMPBY98Yo4q9ZadmIuQlm1LfAzj9Kcj5jbhmiAa64vPeX3UY7t40OE5LXOHqsMeYxzGvR7YLex7y5xt9TK4udrV9Cqi0NLxMWhcBCJFCNQIU6PoWQhFAxjIN4zZ0A8MuKMKhaLGCqCplYTia/4bSanUggH+P9BRzyHKnveaYwQkB5uiZOw1KNedTtdsDuscyI/Ew9zroI+LyKKik+OqQCLnr1/3Dnnqelae4IiDByeIdpdtm+kPfJEZPpeTxB97ZdhEbF1W13YVGDHIfd9o6TCKB4ncirI4/cp6tapqNt2BX5FaXgV2txqsQ2nZ3W4RGhrkWSfFXh28kdiODphNE4yzPWcSlAmpsYjqCgi7uhIUbfiukwFQ/Bq37MPpSuU7lEo9BBXFOy1i+zXWPA2x79Xh8/Nto8UybGzQ52U3ncjlaAcXlhjlE6hmMFIEesPdxgd+ev/4DfZ3tsh/Mn33+YgORSFOElZsrVamFmivm6KD6opcm8M+hgpssIt4u72gPo8FA7g7bfIb/X5y2zzqXhV+8FTTCnj4NFd6uadbc6HbVDC9BT/1lLEsEecmuGQF9UiP5cQUX56j3p8ZSMOr6JM7S6OY1T8ZoXjBrxhvuc3vvLLAADvAydq4ixMjIvMWFHq/bYTXnEKLm8sAAAeb3JsnThEQpypU1Nsw7wKh2w+fQY7+D2fyr/bFAn20rWN86hUK/oYOucPhzZ8dJeRX7/zO78BANj9H98FABwdnuL1L7HM/Hf/9AcAgMOsooUdLkzNM1IwID7h7LFI4Gen4FAkTEARb1XxBLe6nXNurrLWrCXjI4cLQZ/Fz8kzRjASRlg8YdviqZqeY//iiQYO9jgnX3yZ4fA58QGe5fNIRqnPCnm+eyibwNbWh/jSGyT4tgpbzc/pAAPg6TOeo3Z3dd5JLmrsjhFfsHg9Of6NM8pJuV3E1BzXnFNRVBmRbHdHXdx/zLulP2HpQP57/+kWlqf57tkZ7ksPHpLw2hf0YQiLLPj/DRMhZGBgYGBgYGBgYGBgYGDQ/ImSAAATc0lEQVRgYPCC4RciQmhmkRbG9YvL5xTk779NK6Tbq8o34RpK2/QaO120vMZVWs3ncWNpRV4SO7/XULlvryeGopUHrUpknTativ2+Az2VjFyZo7ewUFDFo24fXg+t0yNVBGrI+7yxMYONi8y3PJRn8PpNVsI5Pd1DvyVenBStfMe5ffbFk0SlRitiWdwfs5N8TqXcO/fc2odWKbwIdhq0DOZU6nk5xGe2A3X4ZM3+0R8xquT667RU7j/6KxQtK3GH1se9R7Q0Zg/T+Oqvc9ovrDN65bOPaNVtNxvwqWRe7pjW22CQYzAxOYXDDPvvUU7nJ3c4H56eE5iTB1ucN2Wx0u8c5DG3xDZUOrTiHis6KhiIoiPPd1m8FjclC1tPgaMDWs2vXqd19uOPn6MpTiW3m5bguWl6AifGbHhyn54Np42WV8urY0MbgyHf3XWwLQWR2ownAojP8fNnmxyXQZ1tuHjlCt57RnnaPlN1ilu0Hj85KOLsJ/8dAPAv//k/BQB89x6rts19tos9cWW0BvSkTC2wnXeOTuCThzQoeTw+Zrtt9/r45ptX1VfOe7VFr+FWdYi5JOcbJxy/y1au95eX8dlTWqIHbVquh0PA5uKY9vuSp6S4ksp9BB18t8NFeTw82gcAVBp1HKl0eiHP748UlFZtFuBUdbw8A+nwvE0P4ld/5Wu4vst+PH5Ij5dL1vtk0o+xlCpundAK321zTu9+sIvXVQHr4g3l+bu5ltcve1BRNRFVsIZDnGIHz4poax1fvUrvU6fFNV88PENIHjafi/qk3c9BQSdYWOQaCgTZTvTGMOjyuzblsb//LktQu10RHKS5xpMTqhCjUs+Nqh12u6J3+vTSeEbUTbVuFVeuMRe+0ua4uKNsQKk8REuRNzOqILb9mO84Oc7CJf6BVlUePZVWH7Qc55E+fjvnb6SS5W6nH5/cYYTF8hV+3xugt3FjbRlQBOSDTyknN29MY2jn7549oQxY1R8+/8YCPru/DwCYV/W/dlt533knFifozXE5KduxCXFz9B3A8Oe5lDyKEBgNBxD1B452OWabTGfH7VsddMWb1FT1k56IYFrtKqoZ9mNiQSWpqeawsBJGp8W/HW6yDR6wwfNLAyys8G+tjsVbwPG5cD2OSkEl71X97jQ3QKNM2booT1tfeq7armJdkSyzK4oI8/LZ1VINF8QNdXxA79HUItfI5HQUj+5Rb8xbUZJeDnK3bcOxqqUNbPx8SNFVLmcXNjvnfCJBL+1AFbu69jpyJ9LDXs793DIjGZzeFppa/yVFJAXEE+Jxt9Cu8f+NBp/9uVeoy+KRFN575z22T9Uzyqq8WCp1cXmdvAWvJDm2TzeP0epyns86Kss+wTZ4XS2sv0z5qCsKaCUpvrJKBesXtPa8/PzcOPt3Vj3BW29zDQQ9nJPxST7n8acfwyqQ4VeVzVdfZbWUbreLdoXRgGsX6A3uSqe5HB787re/BgCwgrh8Xi68YLCOuw/uafwpVy55Or/2xksI+TmXNZVwr6lcrC/hgFtRJP2aVcnqDD1FQTjV9qqqYwWdNvSkW+KTlK+IqiHuPyqg17cqc3G8rcqYvS7wRHuP3yKwGorfotHDzIzmfIE6paC9dX5qDumcVSmLOvcHP6QuC4Z86KrMelyVlOqqfre8dhv7R4rqSfB3V1c4N5njM3isiFyPZMjBPrUGPWws0iu5u6VoXX8Db3yDEadlRTO01D5fYICTHNd4d6C1V6F+jCe8GMoz7RA3oEce6nhyiJT4Y05VYjsg/qlQIIhYxKpoafEiUsabzTZEtwavomKtCKF21wZPUFGDiqwdqFJPIOFDV3wzLfEMteSZDoUnEVS0Xb6sylYejlUwGIFNId6pcc7X5HQUqQ0+31NXBa0TzZGjh3xNlXwUQelQVMpiYohcjWObiFGf+hTtvvnwGJcu8Hfjk/T0507IqXT5VgIXFzk2b33vjwAA4TDXncvnxVmW4786xei0Ro992T89ROOQeuPWEsf6iyusnnRWKKKpCGV7kJ9fGOM74G8hon2ockr9Y9fYtdsZlFT51OPgGLkkzzG3A/WaOMHs4iKrNuDQXDgd4goRB1R/0DqvQNoSP99QvJY7z3awus4ox4MDrpvDQ55N4rEERuKf6qs6k3WmiUQT55EsHY1DVNFNsDdQqrLPVtTGtCIZ0O/gOK31qSp0DvEUDdpDdG18ZlilsgPhyHnfh6rkllD0yokqT966/gqKZ5SFatHiUomgVFB1JkUPDcVDtbJ4ESE/7x35HNvpFidZMB6DR1yYfkVWRAPcp05Oz1BtigtJ1fE+d1tVjHsFhMW/07YqNYuCMhWPY2vrjp6p6p5t6lq/Nw6PCO8G4issi+NucXEe3RLfd3zKtk8uUD8cn5QRH6NsDhwc22ZH/Kdz69gTR9RdRRyurq/hRFFz+4ecrx/+gOW3UawiroixV7/MaJ6Dbe4N9z59el69eUZRGjvPOX8Obx3rK9wLf/ATRnHe+BLlLDoMYFscgd/9MSNbKjXu1/ajp7j1GsfNG9feA57X3/j85xFS5svDbe6txwccl/3d9jlfT1QcQrYyf35+/wQnWc599YwyG0jwfSsvXcKfv8eIjzdf51rPZQ/h8Uf1LPE3JihPx5kG6i2urzHJBVziw/SGkRPv3ViCn1l1LwAAnI4AZrRXlbXEs9K52VwHy2scvy1Fv9ittVspYWGacnHnLrMFIgmupZ10DuNhRmB//vM8wPXvMvPgpFhFschxczmpKxbm+JxwwInjoiqHi+O1rQixYDiETlfl3CtcI1OqOByOxFCss809rflkIobRiGtoYZrztXqRY7u8OonHIX5+VvytLp3tjzM5tMSdFYlQhx0f8VznDfvwqRWxo4jS+5/y51QyiqV5yvb4DOc3e8L9IhoJoKq14BVvmKUz3I42yqrm6dO+Cwf/trKxiMtXlf2j+bMr1H9+YgYV3WsjIvqdmeFn46kQGu2fr5r+f8MvhEFo7xkPFz7vED6RJ/mcPCjtb3MiLt2eR6MiI0dPZGVOfm98fBKra1R2M8tcyD/4Hg97lbwLLjsHpycyu6kFKu6zfAt+PxViTIS4p2UO6Mg+Ql9kiQ4n22BTacbF5RRicRFRVjxqOxfvb/3DL+JP/pgKqnDMXWdljeHqSyvT2N5SuGxQF8keF6/N5oBDRJ0tldZsNDtYn+LCeJKgsHqc3FiC4T56HQr3p/d4kJ5NcHzctQiqTi4w+zgX0Sgv0tppB2wFKs1ekQf80DyVfLA7i2GfCjgxxtvbwT7H4/5nj7CypBSu5yTdsgjzom4vTpVO0BO5V2Ca7fRFHeiLNHIosjlVOUS91ES3LUJlPxeyL8Axv3E9jsd3uciXL3Lh/P3f/Ar+63/8Ib8r4rRIjGN2lC6iU+V7HG4RlW4qBDs6gcGA41dr8+XZI5ENf20WzjFuprdu8gL//D7H892zTWSL/Nw3X2fI6le/QWX2zt3v494djn/n39Eg93v/gsacH4TK2P2OygBboaGT3HCu3fbj2SMdJLR5WYei3L4NtnHO4Rtf4bPuP2AKzUlpgKKPcuHIcU7S7+0DAKa/6MKbM68DAKpZktyVCzVEVZ6zVuG49AcK1e+00ZaRIujn7/xBKo7ymfs8RTKqMOSldZV9HXVQLXM8Rm2ReWc53z/50duYGOcm//QBx8UBHcJ6LewfcEMvl7jevAP2we9JoKgxvnqLZcwrJW6yfdsn6He5dodtPrPEIYC960BAqSiqBgyLx7GQLqHqoly8dJttL9UBt0IzRz1uBr0RlXoq7sfuFg8OqRgVd50ih0goiBObflAZYSscPBhsnae7zi2w7R/8iHMbjbnQFolyqUFZuHqRsn6400JCet4qB/rJXW6Wk7NLSOtwF5/TRqHQZtcwjPwx9YcNKi0qvVMpNtFR6sHUDNt0kKP8Fw7zsNus8F7+0+964bf41UVGvX6VG31neICaUlMTN0Xap4Of/QyYu8pNseVmX1tK17W1+lDUN1ThHA2V1o34/chrnlsyxizMcB7K5Sref5f991mNcrDtrU4ZeV2uPTLwRkJ8f3qnib5SeapFpZy6+T2/o4qpFGUtJ3WljFLYBiWUixSWikgCi0MbIhPsc9umcqp2rpHIDJAU4XS/z89/8FMK4jd+ZR69Ed/pVfj9wQHbWyjWMZ6iHo2IiLujlOa9xzWMVGGgb+ecLlzlfHt9dvicMuyqNKuV/unwleHpU7ZHYQ52RxeaUrOJRo3PDKo06eyKihHsnp4TUV69yHmenZlQe/fRUsi8U8UAAiI5tre6KBX5nokxfuarX/4l3NeB6NrrCwCAp3c+ZD9943B4qRvsTaU3pjmOvqANHaVQuNr828oEL3EP7z7FkgjnlV2B/Mk+AGB5cRwtEaTbIpy39D6tiUfHOQyH7Nf6BZWPt1M+PrvTg5PDgDt3mTKTjLJfU+tJ2OUkuDpDx0h8nB++9vIN/Ps/+A8AgHHlQ86uiHx434aQSrtuPeaF0xYIwhPUXi8975SOyOcziIlcNTbGM02zw/Hwh9woKr0lm6cMrFygAWA09MCu9PmOQwSiuqh5fUFMaA3MfY772UgFNuLjMfz4p0yBS0zyvSsv8VK/+VkWLelvr88qwU7dNKidonVEHTju5rMrNVr9GyUXRh6dTbxs5+w1jlVgzI+ayjKnn6qIgW1wXvijIxJ2i/j7ow9yCIus2q9CIHtpKvBQvg+Pg/q+KyLndotyEvKPoXwi4nI5g9ygnqtXamjKABSSYWhmmmO9s7+Hspx8FrmuXUafTseO2Jj0Z5nPjOoM5gvbMZL13e5mm6ZSKoXc7OLGbaYUb8mq3epzn19YmcPBHhVOIKR0huoxHMxQwHaWMm5LUD5CqQFKBZE8K5U8JINSZn8HBZHbfv3vsl/bj9nOejOBRw85P34PdXNVDoTDvRL8h3r+JM+PGZV/nk7N4GCbe/Fxjhe6iMrW+4Z2eG8q1eA+L8a3ligL4UEImVOdI/ycP6edfb4QW8F+U0US7LqQqXTz3HwUJxnuX0/uK3VGZPozc5PoyDNiFYzx+hyIxyhbZ0qhiKns9GDQg8slPRVQ2pssvcVSBZ9+wrNPdIxj1hOR82g0Onf8NvU+m2Qgf1pAUPQScPB3ZwW+tz8CXEozrFUoe+ld6r2JZATxsNUuGeFLIpfuAR4ZidpKYYkqfc3m6GMsyf2lXFHa4TrHf2gro960yGf57OGgDJfHIu+V0a2sghmVZ/C4RY8gI4BN6emjxgBuy3ukTbmt1Bun041wWMViRMzc73MN3ri+horSerX1wC5C9CcPPsWwKyejnZ8p56k/rs+u4pooHmo5/m5L66DqbWDvMRfA5y69BAB4vstLc3JyCkE5QnoOpQ/LILK9d4yhHEzlKvv3ybvb6NuUVqt0+bqIzC9srOAkzbHJPKEszAbkhbp6A+nnMvZcYTuTIZ4djk56iCW45t74ulKnffzZV/Fj9Sbfff8x9VzxkOOxNjmDUpmDNLEmQ4uDzqePPnyEuVXKcSJOXfT6a0zdvblRxV/+kKlUra4MxE3Kc7vmQXKa519XnG1oypg+Mz2NN77FOTxrce2+fOsKTuS4/eSB9L72sVu33sS2DGJ3PqCzOhKgXHVbdrjlpCrI+PDoke5wbTeebnK+1i5Sz837KaOFfAcV7VkhyXRBBptobOyc1LgscuihXc7JUBSfWPfUecmh6ElcLgda4iFwKS8yHNC5p9vAUCmPfTlWujo3xb1exMLczy+vcIwXF3gGeHD/EY6r7KtbTqR7Hz/DvFLO21XKVUnG0rv7n2FtlbQNA6XguZR2OD8dB7TnLylF2NrPvAE7xpIch/wO+zAxw/t+q17Gsy0a8GbWuE9PzfB99UIVoxF1RNdPuQqJcLpdqcLm4DhaVDgTKlLhcA+QzvLcPVQxrpkJ7deFAhLad3MZzntX63V75xj+2N+eEGZSxgwMDAwMDAwMDAwMDAwMDAxeMNhGo9Hf/ikDAwMDAwMDAwMDAwMDAwMDg/9vYCKEDAwMDAwMDAwMDAwMDAwMDF4wGIOQgYGBgYGBgYGBgYGBgYGBwQsGYxAyMDAwMDAwMDAwMDAwMDAweMFgDEIGBgYGBgYGBgYGBgYGBgYGLxiMQcjAwMDAwMDAwMDAwMDAwMDgBYMxCBkYGBgYGBgYGBgYGBgYGBi8YDAGIQMDAwMDAwMDAwMDAwMDA4MXDMYgZGBgYGBgYGBgYGBgYGBgYPCCwRiEDAwMDAwMDAwMDAwMDAwMDF4wGIOQgYGBgYGBgYGBgYGBgYGBwQsGYxAyMDAwMDAwMDAwMDAwMDAweMFgDEIGBgYGBgYGBgYGBgYGBgYGLxiMQcjAwMDAwMDAwMDAwMDAwMDgBYMxCBkYGBgYGBgYGBgYGBgYGBi8YDAGIQMDAwMDAwMDAwMDAwMDA4MXDMYgZGBgYGBgYGBgYGBgYGBgYPCCwRiEDAwMDAwMDAwMDAwMDAwMDF4wGIOQgYGBgYGBgYGBgYGBgYGBwQsGYxAyMDAwMDAwMDAwMDAwMDAweMFgDEIGBgYGBgYGBgYGBgYGBgYGLxiMQcjAwMDAwMDAwMDAwMDAwMDgBYMxCBkYGBgYGBgYGBgYGBgYGBi8YDAGIQMDAwMDAwMDAwMDAwMDA4MXDP8HFE08qln28IUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# from http://cocodataset.org/#explore?id=345434\n", + "image = load(\"http://farm3.staticflickr.com/2469/3915380994_2e611b1779_z.jpg\")\n", + "imshow(image)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The prediction result is then displayed." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# compute predictions\n", + "predictions = coco_demo.run_on_opencv_image(image)\n", + "imshow(predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusion\n", + "\n", + "In this notebook, we have walked through the complete process of preparing the container and data required for training Mask-RCNN models. We have also investigated various training options, trained and tested Mask-RCNN models with various configurations.\n", + "\n", + "## What's next\n", + "Now it's time to try the MaskR-CNN on your own data. Observe the performance impact of mixed precision training while comparing the final accuracy of the models trained with FP32 and mixed precision.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/requirements.txt b/PyTorch/Segmentation/MaskRCNN/pytorch/requirements.txt index dba313d9..5c634323 100644 --- a/PyTorch/Segmentation/MaskRCNN/pytorch/requirements.txt +++ b/PyTorch/Segmentation/MaskRCNN/pytorch/requirements.txt @@ -1,4 +1,4 @@ mlperf-compliance==0.0.10 opencv-python==3.4.1.15 yacs -git+https://github.com/NVIDIA/cocoapi.git@v0.1#egg=cocoapi&subdirectory=PythonAPI +git+https://github.com/NVIDIA/cocoapi.git@nvidia/master#egg=cocoapi&subdirectory=PythonAPI diff --git a/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker/interactive.sh b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker/interactive.sh index b1bd2db8..58a57988 100755 --- a/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker/interactive.sh +++ b/PyTorch/Segmentation/MaskRCNN/pytorch/scripts/docker/interactive.sh @@ -3,4 +3,4 @@ PATH_TO_COCO=$1 MOUNT_LOCATION='/datasets/data' NAME='maskrcnn_interactive' -docker run --runtime=nvidia -v $PATH_TO_COCO:/$MOUNT_LOCATION --rm --name=$NAME --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --ipc=host -t -i nvidia_joc_maskrcnn_pt bash +docker run --runtime=nvidia -v $PATH_TO_COCO:/$MOUNT_LOCATION --rm --name=$NAME --shm-size=10g --ulimit memlock=-1 --ulimit stack=67108864 --ipc=host -t -i nvidia_joc_maskrcnn_pt bash diff --git a/PyTorch/SpeechRecognition/Jasper/.dockerignore b/PyTorch/SpeechRecognition/Jasper/.dockerignore index 9b92b75f..41263747 100755 --- a/PyTorch/SpeechRecognition/Jasper/.dockerignore +++ b/PyTorch/SpeechRecognition/Jasper/.dockerignore @@ -1,4 +1,4 @@ results/ *__pycache__ checkpoints/ -datasets/ \ No newline at end of file +.git/ diff --git a/PyTorch/SpeechRecognition/Jasper/Dockerfile b/PyTorch/SpeechRecognition/Jasper/Dockerfile index 10d0db89..80c3cb4e 100755 --- a/PyTorch/SpeechRecognition/Jasper/Dockerfile +++ b/PyTorch/SpeechRecognition/Jasper/Dockerfile @@ -12,23 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:19.06-py3 +ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:19.09-py3 FROM ${FROM_IMAGE_NAME} -WORKDIR /tmp/unique_for_apex -RUN pip uninstall -y apex || : -RUN pip uninstall -y apex || : - -RUN SHA=ToUcHMe git clone https://github.com/NVIDIA/apex.git -WORKDIR /tmp/unique_for_apex/apex -RUN pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . - - RUN apt-get update && apt-get install -y libsndfile1 && apt-get install -y sox && rm -rf /var/lib/apt/lists/* WORKDIR /workspace/jasper COPY . . RUN pip install --disable-pip-version-check -U -r requirements.txt - diff --git a/PyTorch/SpeechRecognition/Jasper/README.md b/PyTorch/SpeechRecognition/Jasper/README.md index 97039c34..59b13ed9 100644 --- a/PyTorch/SpeechRecognition/Jasper/README.md +++ b/PyTorch/SpeechRecognition/Jasper/README.md @@ -1,6 +1,6 @@ # Jasper For PyTorch -This repository provides a script and recipe to train the Jasper model to achieve state of the art the paper accuracy of the acoustic model, and is tested and maintained by NVIDIA. +This repository provides scripts to train the Jasper model to achieve near state of the art accuracy and perform high-performance inference using NVIDIA TensorRT. This repository is tested and maintained by NVIDIA. ## Table Of Contents - [Model overview](#model-overview) @@ -23,6 +23,7 @@ This repository provides a script and recipe to train the Jasper model to achiev * [Training process](#training-process) * [Inference process](#inference-process) * [Evaluation process](#evaluation-process) + * [Inference process with TensorRT](#inference-process-with-tensorrt) - [Performance](#performance) * [Benchmarking](#benchmarking) * [Training performance benchmark](#training-performance-benchmark) @@ -50,7 +51,7 @@ This repository provides an implementation of the Jasper model in PyTorch from t The Jasper model is an end-to-end neural acoustic model for automatic speech recognition (ASR) that provides near state-of-the-art results on LibriSpeech among end-to-end ASR models without any external data. The Jasper architecture of convolutional layers was designed to facilitate fast GPU inference, by allowing whole sub-blocks to be fused into a single GPU kernel. This is important for meeting strict real-time requirements of ASR systems in deployment. The results of the acoustic model are combined with the results of external language models to get the top-ranked word sequences -corresponding to a given audio segment. This post-processing step is called decoding. +corresponding to a given audio segment. This post-processing step is called decoding. This repository is a PyTorch implementation of Jasper and provides scripts to train the Jasper 10x5 model with dense residuals from scratch on the [Librispeech](http://www.openslr.org/12) dataset to achieve the greedy decoding results of the original paper. The original reference code provides Jasper as part of a research toolkit in TensorFlow [openseq2seq](https://github.com/NVIDIA/OpenSeq2Seq). @@ -85,7 +86,7 @@ Each sub-block applies the following operations in sequence: 1D-Convolution, Bat Each block input is connected directly to the last subblock of all following blocks via a residual connection, which is referred to as `dense residual` in the paper. Every block differs in kernel size and number of filters, which are increasing in size from the bottom to the top layers. Irrespective of the exact block configuration parameters B and R, every Jasper model has four additional convolutional blocks: -one immediately succeeding the input layer (Prologue) and three at the end of the B blocks (Epilogue). +one immediately succeeding the input layer (Prologue) and three at the end of the B blocks (Epilogue). The Prologue is to decimate the audio signal in time in order to process a shorter time sequence for efficiency. The Epilogue with dilation captures a bigger context around an audio time step, which decreases the model word error rate (WER). @@ -96,7 +97,7 @@ The paper achieves best results with Jasper 10x5 with dense residual connections The following features were implemented in this model: * GPU-supported feature extraction with data augmentation options [SpecAugment](https://arxiv.org/abs/1904.08779) and [Cutout](https://arxiv.org/pdf/1708.04552.pdf) -* offline and online [Speed Perturbation](https://www.danielpovey.com/files/2015_interspeech_augmentation.pdf) +* offline and online [Speed Perturbation](https://www.danielpovey.com/files/2015_interspeech_augmentation.pdf) * data-parallel multi-GPU training and evaluation * AMP with dynamic loss scaling for Tensor Core training * FP16 inference with AMP @@ -153,7 +154,7 @@ For information about: For training, mixed precision can be enabled by setting the flag: `train.py --fp16`. You can change this behavior and execute the training in single precision by removing the `--fp16` flag for the `train.py` training -script. For example, in the bash scripts `scripts/train.sh`, `scripts/inference.sh`, etc. the precision can be specified with the variable `PRECISION` by setting it to either `PRECISION=’fp16’` or `PRECISION=’fp32’`. +script. For example, in the bash scripts `scripts/train.sh`, `scripts/inference.sh`, etc. the precision can be specified with the variable `PRECISION` by setting it to either `PRECISION=’fp16’` or `PRECISION=’fp32’`. Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP) library from [APEX](https://github.com/NVIDIA/apex) that casts variables @@ -169,7 +170,7 @@ value to be used can be For an in-depth walk through on AMP, check out sample usage [here](https://nvidia.github.io/apex/amp.html#). [APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to -leverage tensor cores performance. +leverage Tensor Cores performance. The following steps were needed to enable mixed precision training in Jasper: @@ -178,7 +179,7 @@ The following steps were needed to enable mixed precision training in Jasper: from apex import amp ``` -* Initialize AMP and wrap the model and the optimizer +* Initialize AMP and wrap the model and the optimizer ``` model, optimizer = amp.initialize( min_loss_scale=1.0, @@ -188,7 +189,7 @@ from apex import amp ``` -* Apply `scale_loss` context manager +* Apply `scale_loss` context manager ``` with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() @@ -216,11 +217,11 @@ The following section lists the requirements in order to start training and eval ### Requirements -This repository contains a `Dockerfile` which extends the PyTorch 19.06-py3 NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components: +This repository contains a `Dockerfile` which extends the PyTorch 19.09-py3 NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components: * [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) -* [PyTorch 19.06-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) -* [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) based GPU +* [PyTorch 19.09-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) +* [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) or [Turing](https://www.nvidia.com/en-us/geforce/turing/) based GPU Further required python packages are listed in `requirements.txt`, which are automatically installed with the Docker container built. To manually install them, run ```bash @@ -240,7 +241,7 @@ For those unable to use the PyTorch NGC container, to set up the required enviro ## Quick Start Guide -To train your model using mixed precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the Jasper model on the Librispeech dataset. For details concerning training and inference, see [Advanced](#Advanced). +To train your model using mixed precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the Jasper model on the Librispeech dataset. For details concerning training and inference, see [Advanced](#Advanced) section. 1. Clone the repository. ```bash @@ -265,7 +266,7 @@ and mapped to the corresponding directories ``, ``, `< 4. Download and preprocess the dataset. -No GPU is required for data download and preprocessing. Therefore, if GPU usage is a limited resource, launch the container for this section on a CPU machine by following Steps 2 and 3. +No GPU is required for data download and preprocessing. Therefore, if GPU usage is a limited resource, launch the container for this section on a CPU machine by following Steps 2 and 3. Note: Downloading and preprocessing the dataset requires 500GB of free disk space and can take several hours to complete. @@ -290,7 +291,7 @@ Once the data download is complete, the following folders should exist: * `test-clean/` * `test-other/` -Since `/datasets/` is mounted to `` on the host (see Step 3), once the dataset is downloaded it is accessible from outside of the container at `/LibriSpeech`. +Since `/datasets/` is mounted to `` on the host (see Step 3), once the dataset is downloaded it will be accessible from outside of the container at `/LibriSpeech`. Next, convert the data into WAV files and add speed perturbation with 0.9 and 1.1 to the training files: @@ -317,8 +318,8 @@ Once the data is converted, the following additional files and folders should ex 5. Start training. -Inside the container, use the following script to start training. -Make sure the downloaded and preprocessed dataset is located at `/LibriSpeech` on the host (see Step 3), which corresponds to `/datasets/LibriSpeech` inside the container. +Inside the container, use the following script to start training. +Make sure the downloaded and preprocessed dataset is located at `/LibriSpeech` on the host (see Step 3), which corresponds to `/datasets/LibriSpeech` inside the container. ```bash bash scripts/train.sh [OPTIONS] @@ -330,7 +331,7 @@ More details on available [OPTIONS] can be found in [Parameters](#parameters) an 6. Start validation/evaluation. Inside the container, use the following script to run evaluation. - Make sure the downloaded and preprocessed dataset is located at `/LibriSpeech` on the host (see Step 3), which corresponds to `/datasets/LibriSpeech` inside the container. + Make sure the downloaded and preprocessed dataset is located at `/LibriSpeech` on the host (see Step 3), which corresponds to `/datasets/LibriSpeech` inside the container. ```bash bash scripts/evaluation.sh [OPTIONS] ``` @@ -342,7 +343,9 @@ More details on available [OPTIONS] can be found in [Parameters](#parameters) an 7. Start inference/predictions. Inside the container, use the following script to run inference. - Make sure the downloaded and preprocessed dataset is located at `/LibriSpeech` on the host (see Step 3), which corresponds to `/datasets/LibriSpeech` inside the container. + Make sure the downloaded and preprocessed dataset is located at `/LibriSpeech` on the host (see Step 3), which corresponds to `/datasets/LibriSpeech` inside the container. +A pretrained model checkpoint can be downloaded from `NGC model repository`[https://ngc.nvidia.com/catalog/models/nvidia:jasperpyt_fp16]. + ```bash bash scripts/inference.sh [OPTIONS] ``` @@ -364,7 +367,7 @@ In the `root` directory, the most important files are: * `model.py` - Contains the model architecture * `dataset.py` - Contains the data loader and related functionality * `optimizer.py` - Contains the optimizer -* `inference_benchmark.py` - Serves as inference benchmarking script that measures the latency of pre-processing and the acoustic model +* `inference_benchmark.py` - Serves as inference benchmarking script that measures the latency of pre-processing and the acoustic model * `requirements.py` - Contains the required dependencies that are installed when building the Docker container * `Dockerfile` - Container with the basic set of dependencies to run Jasper @@ -380,9 +383,9 @@ The `scripts/` folder encapsulates all the one-click scripts required for runnin Other folders included in the `root` directory are: +* `notebooks/` - Contains Jupyter notebook * `configs/` - Model configurations * `utils/` - Contains the necessary files for data download and processing - * `parts/` - Contains the necessary files for data pre-processing ### Parameters @@ -438,7 +441,7 @@ SEED: seed for random number generator and useful for ensuring reproducibility. BATCH_SIZE: data batch size.(default: 64) ``` -The `scripts/inference_benchmark.sh` script pads all input to the same length and computes the mean, 90%, 95%, 99% percentile of latency for the specified number of inference steps. Latency is measured in millisecond per batch. The `scripts/inference_benchmark.sh` +The `scripts/inference_benchmark.sh` script pads all input to the same length and computes the mean, 90%, 95%, 99% percentile of latency for the specified number of inference steps. Latency is measured in millisecond per batch. The `scripts/inference_benchmark.sh` measures latency for a single GPU and extends `scripts/inference.sh` by : ```bash MAX_DURATION: filters out input audio data that exceeds a maximum number of seconds. This ensures that when all filtered audio samples are padded to maximum length that length will stay under this specified threshold (default: 36) @@ -538,7 +541,7 @@ Apart from the default arguments as listed in the [Parameters](#parameters) sect ### Evaluation process Evaluation is performed using the `inference.py` script along with parameters defined in `scripts/evaluation.sh`. -The `scripts/evaluation.sh` script runs a job on a a single GPU, taking a pre-trained Jasper model checkpoint and running it on the specified dataset. +The `scripts/evaluation.sh` script runs a job on a single GPU, taking a pre-trained Jasper model checkpoint and running it on the specified dataset. Apart from the default arguments as listed in the [Parameters](#parameters) section, by default the evaluation script: * Uses a batch size of 64 @@ -551,6 +554,9 @@ Apart from the default arguments as listed in the [Parameters](#parameters) sect * Has cudnn benchmark disabled +### Inference Process with TensorRT +NVIDIA TensorRT is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. Jasper’s architecture, which is of deep convolutional nature, is designed to facilitate fast GPU inference. After optimizing the compute-intensive acoustic model with NVIDIA TensorRT, inference throughput increased by up to 1.8x over native PyTorch. +More information on how to perform inference using TensorRT and speed up comparison between TensorRT and native PyTorch can be found in the subfolder [./trt/README.md](trt/README.md) ## Performance @@ -604,12 +610,12 @@ The results for Jasper Large's word error rate from the original paper after gre ##### Training accuracy: NVIDIA DGX-1 (8x V100 32G) -Our results were obtained by running the `scripts/train.sh` training script in the PyTorch 19.06-py3 NGC container with NVIDIA DGX-1 with (8x V100 32G) GPUs. +Our results were obtained by running the `scripts/train.sh` training script in the PyTorch 19.09-py3 NGC container with NVIDIA DGX-1 with (8x V100 32G) GPUs. The following tables report the word error rate(WER) of the acoustic model with greedy decoding on all LibriSpeech dev and test datasets for mixed precision training. FP16 (seed #6) -| **Number of GPUs** | **Batch size per GPU** | **dev-clean WER** | **dev-other WER**| **test-clean WER**| **test-other WER**| **Total time to train with FP16 (Hrs)** | +| **Number of GPUs** | **Batch size per GPU** | **dev-clean WER** | **dev-other WER**| **test-clean WER**| **test-other WER**| **Total time to train with FP16 (Hrs)** | |--- |--- |--- |--- |--- |--- |--- | |8 |64| 3.51|11.14|3.74|11.06|100 @@ -619,7 +625,7 @@ FP32 training matches the results of mixed precision training and takes approxim ##### Training stability test -The following table compares greedy decoding word error rates across 8 different training runs with different seeds for mixed precision training. +The following table compares greedy decoding word error rates across 8 different training runs with different seeds for mixed precision training. | **FP16, 8x GPUs** | **seed #1** | **seed #2** | **seed #3** | **seed #4** | **seed #5** | **seed #6** | **seed #7** | **seed #8** | **mean** | **std** | |:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:| @@ -632,7 +638,7 @@ The following table compares greedy decoding word error rates across 8 different #### Training performance results -Our results were obtained by running the `scripts/train.sh` training script in the PyTorch 19.06-py3 NGC container. Performance (in sequences per second) is the steady-state throughput. +Our results were obtained by running the `scripts/train.sh` training script in the PyTorch 19.09-py3 NGC container. Performance (in sequences per second) is the steady-state throughput. ##### Training performance: NVIDIA DGX-1 (8x V100 16G) @@ -700,7 +706,7 @@ To achieve these same results, follow the [Quick Start Guide](#quick-start-guide #### Inference performance results -Our results were obtained by running the `scripts/inference_benchmark.sh` script in the PyTorch 19.06-py3 NGC container on NVIDIA DGX-1, DGX-2 and T4 on a single GPU. Performance numbers (latency in milliseconds per batch) were averaged over 1000 iterations. +Our results were obtained by running the `scripts/inference_benchmark.sh` script in the PyTorch 19.09-py3 NGC container on NVIDIA DGX-1, DGX-2 and T4 on a single GPU. Performance numbers (latency in milliseconds per batch) were averaged over 1000 iterations. ##### Inference performance: NVIDIA DGX-1 (1x V100 16G) @@ -800,6 +806,9 @@ To achieve these same results, follow the [Quick Start Guide](#quick-start-guide ## Release notes ### Changelog +September 2019 +* Inference support for TRT 6 +* Jupyter notebook for inference July 2019 * Initial release @@ -808,9 +817,3 @@ July 2019 ### Known issues There are no known issues in this release. - - - - - - diff --git a/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr.toml b/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr.toml index 7b7ce228..088cc426 100644 --- a/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr.toml +++ b/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr.toml @@ -55,7 +55,7 @@ dither = 0.00001 feat_type = "logfbank" normalize_transcripts = true trim_silence = true -pad_to = 16 +pad_to = 16 [encoder] diff --git a/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_nomask.toml b/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_nomask.toml new file mode 100644 index 00000000..d532543c --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_nomask.toml @@ -0,0 +1,203 @@ +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +model = "Jasper" + +[input] +normalize = "per_feature" +sample_rate = 16000 +window_size = 0.02 +window_stride = 0.01 +window = "hann" +features = 64 +n_fft = 512 +frame_splicing = 1 +dither = 0.00001 +feat_type = "logfbank" +normalize_transcripts = true +trim_silence = true +pad_to = 16 +max_duration = 16.7 +speed_perturbation = false + + +cutout_rect_regions = 0 +cutout_rect_time = 60 +cutout_rect_freq = 25 + +cutout_x_regions = 0 +cutout_y_regions = 0 +cutout_x_width = 6 +cutout_y_width = 6 + + +[input_eval] +normalize = "per_feature" +sample_rate = 16000 +window_size = 0.02 +window_stride = 0.01 +window = "hann" +features = 64 +n_fft = 512 +frame_splicing = 1 +dither = 0.00001 +feat_type = "logfbank" +normalize_transcripts = true +trim_silence = true +pad_to = 16 + + +[encoder] +activation = "relu" +convmask = false + +[[jasper]] +filters = 256 +repeat = 1 +kernel = [11] +stride = [2] +dilation = [1] +dropout = 0.2 +residual = false + +[[jasper]] +filters = 256 +repeat = 5 +kernel = [11] +stride = [1] +dilation = [1] +dropout = 0.2 +residual = true +residual_dense = true + + +[[jasper]] +filters = 256 +repeat = 5 +kernel = [11] +stride = [1] +dilation = [1] +dropout = 0.2 +residual = true +residual_dense = true + + +[[jasper]] +filters = 384 +repeat = 5 +kernel = [13] +stride = [1] +dilation = [1] +dropout = 0.2 +residual = true +residual_dense = true + + +[[jasper]] +filters = 384 +repeat = 5 +kernel = [13] +stride = [1] +dilation = [1] +dropout = 0.2 +residual = true +residual_dense = true + + +[[jasper]] +filters = 512 +repeat = 5 +kernel = [17] +stride = [1] +dilation = [1] +dropout = 0.2 +residual = true +residual_dense = true + + +[[jasper]] +filters = 512 +repeat = 5 +kernel = [17] +stride = [1] +dilation = [1] +dropout = 0.2 +residual = true +residual_dense = true + + +[[jasper]] +filters = 640 +repeat = 5 +kernel = [21] +stride = [1] +dilation = [1] +dropout = 0.3 +residual = true +residual_dense = true + + +[[jasper]] +filters = 640 +repeat = 5 +kernel = [21] +stride = [1] +dilation = [1] +dropout = 0.3 +residual = true +residual_dense = true + + +[[jasper]] +filters = 768 +repeat = 5 +kernel = [25] +stride = [1] +dilation = [1] +dropout = 0.3 +residual = true +residual_dense = true + + +[[jasper]] +filters = 768 +repeat = 5 +kernel = [25] +stride = [1] +dilation = [1] +dropout = 0.3 +residual = true +residual_dense = true + + +[[jasper]] +filters = 896 +repeat = 1 +kernel = [29] +stride = [1] +dilation = [2] +dropout = 0.4 +residual = false + +[[jasper]] +filters = 1024 +repeat = 1 +kernel = [1] +stride = [1] +dilation = [1] +dropout = 0.4 +residual = false + +[labels] +labels = [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"] diff --git a/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_sp_offline.toml b/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_sp_offline.toml index adb133db..bade525c 100644 --- a/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_sp_offline.toml +++ b/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_sp_offline.toml @@ -56,7 +56,7 @@ dither = 0.00001 feat_type = "logfbank" normalize_transcripts = true trim_silence = true -pad_to = 16 +pad_to = 16 [encoder] diff --git a/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_sp_offline_specaugment.toml b/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_sp_offline_specaugment.toml index 7b842eb2..d01dc51c 100644 --- a/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_sp_offline_specaugment.toml +++ b/PyTorch/SpeechRecognition/Jasper/configs/jasper10x5dr_sp_offline_specaugment.toml @@ -56,7 +56,7 @@ dither = 0.00001 feat_type = "logfbank" normalize_transcripts = true trim_silence = true -pad_to = 16 +pad_to = 16 [encoder] diff --git a/PyTorch/SpeechRecognition/Jasper/dataset.py b/PyTorch/SpeechRecognition/Jasper/dataset.py index f501a4dd..ad88d2f0 100644 --- a/PyTorch/SpeechRecognition/Jasper/dataset.py +++ b/PyTorch/SpeechRecognition/Jasper/dataset.py @@ -13,7 +13,7 @@ # limitations under the License. """ -This file contains classes and functions related to data loading +This file contains classes and functions related to data loading """ import torch import numpy as np @@ -66,7 +66,7 @@ class DistributedBucketBatchSampler(Sampler): bucket_start = self.bucket_size * bucket bucket_end = min(bucket_start + self.bucket_size, self.index_count) indices[bucket_start:bucket_end] = indices[bucket_start:bucket_end][torch.randperm(bucket_end - bucket_start, generator=g)] - + tile_indices = torch.randperm(self.index_count // self.tile_size, generator=g) for tile_index in tile_indices: start_index = self.tile_size * tile_index + self.batch_size * self.rank @@ -93,7 +93,7 @@ class data_prefetcher(): return with torch.cuda.stream(self.stream): self.next_input = [ x.cuda(non_blocking=True) for x in self.next_input] - + def __next__(self): torch.cuda.current_stream().wait_stream(self.stream) input = self.next_input @@ -133,7 +133,7 @@ def seq_collate_fn(batch): return batched_audio_signal, torch.stack(audio_lengths), batched_transcript, \ torch.stack(transcript_lengths) -class AudioToTextDataLayer: +class AudioToTextDataLayer: """Data layer with data loader """ def __init__(self, **kwargs): @@ -205,7 +205,7 @@ class AudioToTextDataLayer: sampler=self.sampler ) else: - raise RuntimeError("Sampler {} not supported".format(sampler_type)) + raise RuntimeError("Sampler {} not supported".format(sampler_type)) def __len__(self): return len(self._dataset) @@ -214,9 +214,9 @@ class AudioToTextDataLayer: def data_iterator(self): return self._dataloader -class AudioDataset(Dataset): +class AudioDataset(Dataset): def __init__(self, dataset_dir, manifest_filepath, labels, featurizer, max_duration=None, pad_to_max=False, - min_duration=None, blank_index=0, max_utts=0, normalize=True, sort_by_duration=False, + min_duration=None, blank_index=0, max_utts=0, normalize=True, sort_by_duration=False, trim=False, speed_perturbation=False): """Dataset that loads tensors via a json file containing paths to audio files, transcripts, and durations (in seconds). Each entry is a different audio sample. @@ -264,6 +264,3 @@ class AudioDataset(Dataset): def __len__(self): return len(self.manifest) - - - diff --git a/PyTorch/SpeechRecognition/Jasper/helpers.py b/PyTorch/SpeechRecognition/Jasper/helpers.py index c611bc48..5a17d4dc 100644 --- a/PyTorch/SpeechRecognition/Jasper/helpers.py +++ b/PyTorch/SpeechRecognition/Jasper/helpers.py @@ -43,7 +43,7 @@ def add_ctc_labels(labels): raise ValueError("labels must be a list of symbols") labels.append("") return labels - + def __ctc_decoder_predictions_tensor(tensor, labels): """ Takes output of greedy ctc decoder and performs ctc decoding algorithm to @@ -136,7 +136,7 @@ def __gather_transcripts(transcript_list: list, transcript_len_list: list, def process_evaluation_batch(tensors: dict, global_vars: dict, labels: list): """ - Processes results of an iteration and saves it in global_vars + Processes results of an iteration and saves it in global_vars Args: tensors: dictionary with results of an evaluation iteration, e.g. loss, predictions, transcript, and output global_vars: dictionary where processes results of iteration are saved @@ -162,11 +162,11 @@ def process_evaluation_batch(tensors: dict, global_vars: dict, labels: list): def process_evaluation_epoch(global_vars: dict, tag=None): """ - Processes results from each worker at the end of evaluation and combine to final result + Processes results from each worker at the end of evaluation and combine to final result Args: global_vars: dictionary containing information of entire evaluation Return: - wer: final word error rate + wer: final word error rate loss: final loss """ if 'EvalLoss' in global_vars: @@ -200,7 +200,7 @@ def process_evaluation_epoch(global_vars: dict, tag=None): def norm(x): - if not isinstance(x, List): + if not isinstance(x, list): if not isinstance(x, tuple): return x return x[0] @@ -220,4 +220,3 @@ def model_multi_gpu(model, multi_gpu=False): model = DDP(model) print('DDP(model)') return model - diff --git a/PyTorch/SpeechRecognition/Jasper/inference.py b/PyTorch/SpeechRecognition/Jasper/inference.py index 581dc148..fb8834e5 100644 --- a/PyTorch/SpeechRecognition/Jasper/inference.py +++ b/PyTorch/SpeechRecognition/Jasper/inference.py @@ -19,14 +19,16 @@ from tqdm import tqdm import math import toml from dataset import AudioToTextDataLayer -from helpers import process_evaluation_batch, process_evaluation_epoch, Optimization, add_ctc_labels, AmpOptimizations, print_dict, model_multi_gpu +from helpers import process_evaluation_batch, process_evaluation_epoch, Optimization, add_ctc_labels, AmpOptimizations, print_dict, model_multi_gpu, __ctc_decoder_predictions_tensor from model import AudioPreprocessing, GreedyCTCDecoder, JasperEncoderDecoder +from parts.features import audio_from_file import torch import apex from apex import amp import random import numpy as np import pickle +import time def parse_args(): parser = argparse.ArgumentParser(description='Jasper') @@ -44,14 +46,15 @@ def parse_args(): parser.add_argument("--save_prediction", type=str, default=None, help="if specified saves predictions in text form at this location") parser.add_argument("--logits_save_to", default=None, type=str, help="if specified will save logits to path") parser.add_argument("--seed", default=42, type=int, help='seed') + parser.add_argument("--wav", type=str, help='absolute path to .wav file (16KHz)') return parser.parse_args() def eval( data_layer, - audio_processor, - encoderdecoder, - greedy_decoder, - labels, + audio_processor, + encoderdecoder, + greedy_decoder, + labels, multi_gpu, args): """performs inference / evaluation @@ -74,6 +77,21 @@ def eval( 'logits' : [], } + + + if args.wav: + features, p_length_e = audio_processor(audio_from_file(args.wav)) + torch.cuda.synchronize() + t0 = time.perf_counter() + t_log_probs_e = encoderdecoder(features) + torch.cuda.synchronize() + t1 = time.perf_counter() + t_predictions_e = greedy_decoder(log_probs=t_log_probs_e) + hypotheses = __ctc_decoder_predictions_tensor(t_predictions_e, labels=labels) + print("INFERENCE TIME\t\t: {} ms".format((t1-t0)*1000.0)) + print("TRANSCRIPT\t\t:", hypotheses[0]) + return + for it, data in enumerate(tqdm(data_layer.data_iterator)): tensors = [] for d in data: @@ -83,8 +101,11 @@ def eval( inp = (t_audio_signal_e, t_a_sig_length_e) - t_processed_signal, p_length_e = audio_processor(x=inp) - t_log_probs_e, _ = encoderdecoder((t_processed_signal, p_length_e)) + t_processed_signal, p_length_e = audio_processor(x=inp) + if args.use_conv_mask: + t_log_probs_e, t_encoded_len_e = encoderdecoder((t_processed_signal, p_length_e)) + else: + t_log_probs_e = encoderdecoder(t_processed_signal) t_predictions_e = greedy_decoder(log_probs=t_log_probs_e) values_dict = dict( @@ -98,7 +119,7 @@ def eval( if args.steps is not None and it + 1 >= args.steps: break wer, _ = process_evaluation_epoch(_global_var_dict) - if (not multi_gpu or (multi_gpu and torch.distributed.get_rank() == 0)): + if (not multi_gpu or (multi_gpu and torch.distributed.get_rank() == 0)): print("==========>>>>>>Evaluation WER: {0}\n".format(wer)) if args.save_prediction is not None: with open(args.save_prediction, 'w') as fp: @@ -122,7 +143,7 @@ def main(args): if args.local_rank is not None: torch.cuda.set_device(args.local_rank) torch.distributed.init_process_group(backend='nccl', init_method='env://') - multi_gpu = args.local_rank is not None + multi_gpu = args.local_rank is not None if multi_gpu: print("DISTRIBUTED with ", torch.distributed.get_world_size()) @@ -135,9 +156,10 @@ def main(args): dataset_vocab = jasper_model_definition['labels']['labels'] ctc_vocab = add_ctc_labels(dataset_vocab) - val_manifest = args.val_manifest + val_manifest = args.val_manifest featurizer_config = jasper_model_definition['input_eval'] featurizer_config["optimization_level"] = optim_level + args.use_conv_mask = jasper_model_definition['encoder'].get('convmask', True) if args.max_duration is not None: featurizer_config['max_duration'] = args.max_duration @@ -148,20 +170,22 @@ def main(args): print_dict(jasper_model_definition) print('feature_config') print_dict(featurizer_config) - - data_layer = AudioToTextDataLayer( - dataset_dir=args.dataset_dir, - featurizer_config=featurizer_config, - manifest_filepath=val_manifest, - labels=dataset_vocab, - batch_size=args.batch_size, - pad_to_max=featurizer_config['pad_to'] == "max", - shuffle=False, - multi_gpu=multi_gpu) + data_layer = None + + if args.wav is None: + data_layer = AudioToTextDataLayer( + dataset_dir=args.dataset_dir, + featurizer_config=featurizer_config, + manifest_filepath=val_manifest, + labels=dataset_vocab, + batch_size=args.batch_size, + pad_to_max=featurizer_config['pad_to'] == "max", + shuffle=False, + multi_gpu=multi_gpu) audio_preprocessor = AudioPreprocessing(**featurizer_config) encoderdecoder = JasperEncoderDecoder(jasper_model_definition=jasper_model_definition, feat_in=1024, num_classes=len(ctc_vocab)) - + if args.ckpt is not None: print("loading model from ", args.ckpt) checkpoint = torch.load(args.ckpt, map_location="cpu") @@ -169,25 +193,28 @@ def main(args): checkpoint['state_dict'][k] = checkpoint['state_dict'].pop("audio_preprocessor." + k) audio_preprocessor.load_state_dict(checkpoint['state_dict'], strict=False) encoderdecoder.load_state_dict(checkpoint['state_dict'], strict=False) - + greedy_decoder = GreedyCTCDecoder() # print("Number of parameters in encoder: {0}".format(model.jasper_encoder.num_weights())) + if args.wav is None: + N = len(data_layer) + step_per_epoch = math.ceil(N / (args.batch_size * (1 if not torch.distributed.is_initialized() else torch.distributed.get_world_size()))) - N = len(data_layer) - step_per_epoch = math.ceil(N / (args.batch_size * (1 if not torch.distributed.is_initialized() else torch.distributed.get_world_size()))) - - if args.steps is not None: - print('-----------------') - print('Have {0} examples to eval on.'.format(args.steps * args.batch_size * (1 if not torch.distributed.is_initialized() else torch.distributed.get_world_size()))) - print('Have {0} steps / (gpu * epoch).'.format(args.steps)) - print('-----------------') + if args.steps is not None: + print('-----------------') + print('Have {0} examples to eval on.'.format(args.steps * args.batch_size * (1 if not torch.distributed.is_initialized() else torch.distributed.get_world_size()))) + print('Have {0} steps / (gpu * epoch).'.format(args.steps)) + print('-----------------') + else: + print('-----------------') + print('Have {0} examples to eval on.'.format(N)) + print('Have {0} steps / (gpu * epoch).'.format(step_per_epoch)) + print('-----------------') else: - print('-----------------') - print('Have {0} examples to eval on.'.format(N)) - print('Have {0} steps / (gpu * epoch).'.format(step_per_epoch)) - print('-----------------') + audio_preprocessor.featurizer.normalize = "per_feature" + print ("audio_preprocessor.normalize: ", audio_preprocessor.featurizer.normalize) audio_preprocessor.cuda() encoderdecoder.cuda() if args.fp16: @@ -197,8 +224,9 @@ def main(args): encoderdecoder = model_multi_gpu(encoderdecoder, multi_gpu) + eval( - data_layer=data_layer, + data_layer=data_layer, audio_processor=audio_preprocessor, encoderdecoder=encoderdecoder, greedy_decoder=greedy_decoder, @@ -208,7 +236,7 @@ def main(args): if __name__=="__main__": args = parse_args() - + print_dict(vars(args)) main(args) diff --git a/PyTorch/SpeechRecognition/Jasper/inference_benchmark.py b/PyTorch/SpeechRecognition/Jasper/inference_benchmark.py index 4e5e7a7a..fcc927ec 100644 --- a/PyTorch/SpeechRecognition/Jasper/inference_benchmark.py +++ b/PyTorch/SpeechRecognition/Jasper/inference_benchmark.py @@ -98,7 +98,11 @@ def eval( t_processed_signal, p_length_e = audio_processor(x=inp) torch.cuda.synchronize() t1 = time.perf_counter() - t_log_probs_e, _ = encoderdecoder((t_processed_signal, p_length_e)) + + if args.use_conv_mask: + t_log_probs_e, t_encoded_len_e = encoderdecoder((t_processed_signal, p_length_e)) + else: + t_log_probs_e = encoderdecoder(t_processed_signal) torch.cuda.synchronize() stop_time = time.perf_counter() @@ -115,13 +119,13 @@ def eval( durations_dnn.append(time_dnn) durations_dnn_and_prep.append(time_prep_and_dnn) seq_lens.append(t_processed_signal.shape[-1]) - + if it >= steps: - + wer, _ = process_evaluation_epoch(_global_var_dict) print("==========>>>>>>Evaluation of all iterations WER: {0}\n".format(wer)) break - + ratios = [0.9, 0.95,0.99, 1.] latencies_dnn = take_durations_and_output_percentile(durations_dnn, ratios) latencies_dnn_and_prep = take_durations_and_output_percentile(durations_dnn_and_prep, ratios) @@ -131,7 +135,7 @@ def eval( def take_durations_and_output_percentile(durations, ratios): durations = np.asarray(durations) * 1000 # in ms - latency = durations + latency = durations latency = latency[5:] mean_latency = np.mean(latency) @@ -167,11 +171,12 @@ def main(args): dataset_vocab = jasper_model_definition['labels']['labels'] ctc_vocab = add_ctc_labels(dataset_vocab) - val_manifest = args.val_manifest + val_manifest = args.val_manifest featurizer_config = jasper_model_definition['input_eval'] featurizer_config["optimization_level"] = optim_level + args.use_conv_mask = jasper_model_definition['encoder'].get('convmask', True) if args.max_duration is not None: - featurizer_config['max_duration'] = args.max_duration + featurizer_config['max_duration'] = args.max_duration if args.pad_to is not None: featurizer_config['pad_to'] = args.pad_to if args.pad_to >= 0 else "max" @@ -181,7 +186,7 @@ def main(args): print_dict(featurizer_config) data_layer = AudioToTextDataLayer( - dataset_dir=args.dataset_dir, + dataset_dir=args.dataset_dir, featurizer_config=featurizer_config, manifest_filepath=val_manifest, labels=dataset_vocab, @@ -226,16 +231,16 @@ def main(args): opt_level=AmpOptimizations[optim_level]) eval( - data_layer=data_layer, + data_layer=data_layer, audio_processor=audio_preprocessor, - encoderdecoder=encoderdecoder, - greedy_decoder=greedy_decoder, + encoderdecoder=encoderdecoder, + greedy_decoder=greedy_decoder, labels=ctc_vocab, args=args) if __name__=="__main__": args = parse_args() - + print_dict(vars(args)) main(args) diff --git a/PyTorch/SpeechRecognition/Jasper/metrics.py b/PyTorch/SpeechRecognition/Jasper/metrics.py index 76fe8ea5..fdf28784 100644 --- a/PyTorch/SpeechRecognition/Jasper/metrics.py +++ b/PyTorch/SpeechRecognition/Jasper/metrics.py @@ -65,4 +65,3 @@ def word_error_rate(hypotheses: List[str], references: List[str]) -> float: else: wer = float('inf') return wer, scores, words - diff --git a/PyTorch/SpeechRecognition/Jasper/model.py b/PyTorch/SpeechRecognition/Jasper/model.py index f0b49d6c..d61d68f2 100644 --- a/PyTorch/SpeechRecognition/Jasper/model.py +++ b/PyTorch/SpeechRecognition/Jasper/model.py @@ -13,7 +13,7 @@ # limitations under the License. from apex import amp -import torch +import torch import torch.nn as nn from parts.features import FeatureFactory from helpers import Optimization @@ -50,7 +50,6 @@ def init_weights(m, mode='xavier_uniform'): def get_same_padding(kernel_size, stride, dilation): if stride > 1 and dilation > 1: raise ValueError("Only stride OR dilation may be greater than 1") - return (kernel_size // 2) * dilation class AudioPreprocessing(nn.Module): @@ -74,7 +73,7 @@ class AudioPreprocessing(nn.Module): return processed_signal, processed_length class SpectrogramAugmentation(nn.Module): - """Spectrogram augmentation + """Spectrogram augmentation """ def __init__(self, **kwargs): nn.Module.__init__(self) @@ -90,11 +89,8 @@ class SpectrogramAugmentation(nn.Module): class SpecAugment(nn.Module): """Spec augment. refer to https://arxiv.org/abs/1904.08779 """ - def __init__(self, cfg, rng=None): + def __init__(self, cfg): super(SpecAugment, self).__init__() - - self._rng = random.Random() if rng is None else rng - self.cutout_x_regions = cfg.get('cutout_x_regions', 0) self.cutout_y_regions = cfg.get('cutout_y_regions', 0) @@ -108,12 +104,12 @@ class SpecAugment(nn.Module): mask = torch.zeros(x.shape).byte() for idx in range(sh[0]): for _ in range(self.cutout_x_regions): - cutout_x_left = int(self._rng.uniform(0, sh[1] - self.cutout_x_width)) + cutout_x_left = int(random.uniform(0, sh[1] - self.cutout_x_width)) mask[idx, cutout_x_left:cutout_x_left + self.cutout_x_width, :] = 1 for _ in range(self.cutout_y_regions): - cutout_y_left = int(self._rng.uniform(0, sh[2] - self.cutout_y_width)) + cutout_y_left = int(random.uniform(0, sh[2] - self.cutout_y_width)) mask[idx, :, cutout_y_left:cutout_y_left + self.cutout_y_width] = 1 @@ -124,11 +120,9 @@ class SpecAugment(nn.Module): class SpecCutoutRegions(nn.Module): """Cutout. refer to https://arxiv.org/pdf/1708.04552.pdf """ - def __init__(self, cfg, rng=None): + def __init__(self, cfg): super(SpecCutoutRegions, self).__init__() - self._rng = random.Random() if rng is None else rng - self.cutout_rect_regions = cfg.get('cutout_rect_regions', 0) self.cutout_rect_time = cfg.get('cutout_rect_time', 5) self.cutout_rect_freq = cfg.get('cutout_rect_freq', 20) @@ -141,9 +135,9 @@ class SpecCutoutRegions(nn.Module): for idx in range(sh[0]): for i in range(self.cutout_rect_regions): - cutout_rect_x = int(self._rng.uniform( + cutout_rect_x = int(random.uniform( 0, sh[1] - self.cutout_rect_freq)) - cutout_rect_y = int(self._rng.uniform( + cutout_rect_y = int(random.uniform( 0, sh[2] - self.cutout_rect_time)) mask[idx, cutout_rect_x:cutout_rect_x + self.cutout_rect_freq, @@ -154,18 +148,19 @@ class SpecCutoutRegions(nn.Module): return x class JasperEncoder(nn.Module): - """Jasper encoder + + """Jasper encoder """ def __init__(self, **kwargs): cfg = {} for key, value in kwargs.items(): cfg[key] = value - nn.Module.__init__(self) + nn.Module.__init__(self) self._cfg = cfg activation = jasper_activations[cfg['encoder']['activation']]() - use_conv_mask = cfg['encoder'].get('convmask', False) + self.use_conv_mask = cfg['encoder'].get('convmask', False) feat_in = cfg['input']['features'] * cfg['input'].get('frame_splicing', 1) init_mode = cfg.get('init_mode', 'xavier_uniform') @@ -183,7 +178,7 @@ class JasperEncoder(nn.Module): kernel_size=lcfg['kernel'], stride=lcfg['stride'], dilation=lcfg['dilation'], dropout=lcfg['dropout'], residual=lcfg['residual'], activation=activation, - residual_panes=dense_res, conv_mask=use_conv_mask)) + residual_panes=dense_res, use_conv_mask=self.use_conv_mask)) feat_in = lcfg['filters'] self.encoder = nn.Sequential(*encoder_layers) @@ -193,106 +188,146 @@ class JasperEncoder(nn.Module): return sum(p.numel() for p in self.parameters() if p.requires_grad) def forward(self, x): - audio_signal, length = x - s_input, length = self.encoder(([audio_signal], length)) - return s_input, length + if self.use_conv_mask: + audio_signal, length = x + return self.encoder(([audio_signal], length)) + else: + return self.encoder([x]) class JasperDecoderForCTC(nn.Module): - """Jasper decoder + """Jasper decoder """ def __init__(self, **kwargs): - nn.Module.__init__(self) + nn.Module.__init__(self) self._feat_in = kwargs.get("feat_in") self._num_classes = kwargs.get("num_classes") init_mode = kwargs.get('init_mode', 'xavier_uniform') self.decoder_layers = nn.Sequential( - nn.Conv1d(self._feat_in, self._num_classes, kernel_size=1, bias=True), - nn.LogSoftmax(dim=1)) + nn.Conv1d(self._feat_in, self._num_classes, kernel_size=1, bias=True),) self.apply(lambda x: init_weights(x, mode=init_mode)) - def num_weights(self): return sum(p.numel() for p in self.parameters() if p.requires_grad) def forward(self, encoder_output): - out = self.decoder_layers(encoder_output[-1]) - return out.transpose(1, 2) + out = self.decoder_layers(encoder_output[-1]).transpose(1, 2) + return nn.functional.log_softmax(out, dim=2) class Jasper(nn.Module): - """Contains data preprocessing, spectrogram augmentation, jasper encoder and decoder + """Contains data preprocessing, spectrogram augmentation, jasper encoder and decoder """ def __init__(self, **kwargs): - nn.Module.__init__(self) - self.audio_preprocessor = AudioPreprocessing(**kwargs.get("feature_config")) + nn.Module.__init__(self) + if kwargs.get("no_featurizer", False): + self.audio_preprocessor = None + else: + self.audio_preprocessor = AudioPreprocessing(**kwargs.get("feature_config")) + self.data_spectr_augmentation = SpectrogramAugmentation(**kwargs.get("feature_config")) self.jasper_encoder = JasperEncoder(**kwargs.get("jasper_model_definition")) self.jasper_decoder = JasperDecoderForCTC(feat_in=kwargs.get("feat_in"), - num_classes=kwargs.get("num_classes")) + num_classes=kwargs.get("num_classes")) + self.acoustic_model = JasperAcousticModel(self.jasper_encoder, self.jasper_decoder) def num_weights(self): return sum(p.numel() for p in self.parameters() if p.requires_grad) def forward(self, x): - input_signal, length = x - t_processed_signal, p_length_t = self.audio_preprocessor(x) + + # Apply optional preprocessing + if self.audio_preprocessor is not None: + t_processed_signal, p_length_t = self.audio_preprocessor(x) + # Apply optional spectral augmentation if self.training: t_processed_signal = self.data_spectr_augmentation(input_spec=t_processed_signal) - t_encoded_t, t_encoded_len_t = self.jasper_encoder((t_processed_signal, p_length_t)) - return self.jasper_decoder(encoder_output=t_encoded_t), t_encoded_len_t + + if (self.jasper_encoder.use_conv_mask): + a_inp = (t_processed_signal, p_length_t) + else: + a_inp = t_processed_signal + # Forward Pass through Encoder-Decoder + return self.acoustic_model.forward(a_inp) + + +class JasperAcousticModel(nn.Module): + def __init__(self, enc, dec, transpose_in=False): + nn.Module.__init__(self) + self.jasper_encoder = enc + self.jasper_decoder = dec + self.transpose_in = transpose_in + def forward(self, x): + if self.jasper_encoder.use_conv_mask: + t_encoded_t, t_encoded_len_t = self.jasper_encoder(x) + else: + if self.transpose_in: + x = x.transpose(1, 2) + t_encoded_t = self.jasper_encoder(x) + + out = self.jasper_decoder(encoder_output=t_encoded_t) + if self.jasper_encoder.use_conv_mask: + return out, t_encoded_len_t + else: + return out class JasperEncoderDecoder(nn.Module): - """Contains jasper encoder and decoder + """Contains jasper encoder and decoder """ def __init__(self, **kwargs): - nn.Module.__init__(self) + nn.Module.__init__(self) self.jasper_encoder = JasperEncoder(**kwargs.get("jasper_model_definition")) self.jasper_decoder = JasperDecoderForCTC(feat_in=kwargs.get("feat_in"), - num_classes=kwargs.get("num_classes")) + num_classes=kwargs.get("num_classes")) + self.acoustic_model = JasperAcousticModel(self.jasper_encoder, + self.jasper_decoder, + kwargs.get("transpose_in", False)) + def num_weights(self): return sum(p.numel() for p in self.parameters() if p.requires_grad) def forward(self, x): - t_processed_signal, p_length_t = x - t_encoded_t, t_encoded_len_t = self.jasper_encoder((t_processed_signal, p_length_t)) - return self.jasper_decoder(encoder_output=t_encoded_t), t_encoded_len_t + return self.acoustic_model.forward(x) class MaskedConv1d(nn.Conv1d): - """1D convolution with sequence masking + """1D convolution with sequence masking """ def __init__(self, in_channels, out_channels, kernel_size, stride=1, - padding=0, dilation=1, groups=1, bias=False, use_mask=True): + padding=0, dilation=1, groups=1, bias=False, use_conv_mask=True): super(MaskedConv1d, self).__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias) - self.use_mask = use_mask + self.use_conv_mask = use_conv_mask def get_seq_len(self, lens): return ((lens + 2 * self.padding[0] - self.dilation[0] * ( self.kernel_size[0] - 1) - 1) / self.stride[0] + 1) def forward(self, inp): - x, lens = inp - if self.use_mask: + if self.use_conv_mask: + x, lens = inp max_len = x.size(2) - mask = torch.arange(max_len).to(lens.dtype).to(lens.device).expand(len(lens), - max_len) >= lens.unsqueeze( - 1) + idxs = torch.arange(max_len).to(lens.dtype).to(lens.device).expand(len(lens), max_len) + mask = idxs >= lens.unsqueeze(1) x = x.masked_fill(mask.unsqueeze(1).to(device=x.device), 0) del mask - + del idxs lens = self.get_seq_len(lens) - + else: + x = inp out = super(MaskedConv1d, self).forward(x) - return out, lens + + if self.use_conv_mask: + return out, lens + else: + return out class JasperBlock(nn.Module): """Jasper Block. See https://arxiv.org/pdf/1904.03288.pdf """ def __init__(self, inplanes, planes, repeat=3, kernel_size=11, stride=1, dilation=1, padding='same', dropout=0.2, activation=None, - residual=True, residual_panes=[], conv_mask=False): + residual=True, residual_panes=[], use_conv_mask=False): super(JasperBlock, self).__init__() if padding != "same": @@ -300,7 +335,7 @@ class JasperBlock(nn.Module): padding_val = get_same_padding(kernel_size[0], stride[0], dilation[0]) - self.conv_mask = conv_mask + self.use_conv_mask = use_conv_mask self.conv = nn.ModuleList() inplanes_loop = inplanes for _ in range(repeat - 1): @@ -334,7 +369,7 @@ class JasperBlock(nn.Module): layers = [ MaskedConv1d(in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, padding=padding, bias=bias, - use_mask=self.conv_mask), + use_conv_mask=self.use_conv_mask), nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.1) ] return layers @@ -352,13 +387,16 @@ class JasperBlock(nn.Module): return sum(p.numel() for p in self.parameters() if p.requires_grad) def forward(self, input_): - - xs, lens_orig = input_ + if self.use_conv_mask: + xs, lens_orig = input_ + else: + xs = input_ + lens_orig = 0 # compute forward convolutions out = xs[-1] lens = lens_orig for i, l in enumerate(self.conv): - if isinstance(l, MaskedConv1d): + if self.use_conv_mask and isinstance(l, MaskedConv1d): out, lens = l((out, lens)) else: out = l(out) @@ -367,7 +405,7 @@ class JasperBlock(nn.Module): for i, layer in enumerate(self.res): res_out = xs[i] for j, res_layer in enumerate(layer): - if j == 0: + if j == 0 and self.use_conv_mask: res_out, _ = res_layer((res_out, lens_orig)) else: res_out = res_layer(res_out) @@ -376,9 +414,14 @@ class JasperBlock(nn.Module): # compute the output out = self.out(out) if self.res is not None and self.dense_residual: - return xs + [out], lens + out = xs + [out] + else: + out = [out] - return [out], lens + if self.use_conv_mask: + return out, lens + else: + return out class GreedyCTCDecoder(nn.Module): """ Greedy CTC Decoder diff --git a/PyTorch/SpeechRecognition/Jasper/notebooks/JasperTRT.ipynb b/PyTorch/SpeechRecognition/Jasper/notebooks/JasperTRT.ipynb new file mode 100644 index 00000000..7d7b5587 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/notebooks/JasperTRT.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Jasper Inference For TensorRT 6\n", + "This Jupyter notebook provides scripts to perform high-performance inference using NVIDIA TensorRT. \n", + "Jasper is a neural acoustic model for speech recognition. Its network architecture is designed to facilitate fast GPU inference. \n", + "NVIDIA TensorRT is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications.\n", + "After optimizing the compute-intensive acoustic model with NVIDIA TensorRT, inference throughput increased by up to 1.8x over native PyTorch." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Overview\n", + "\n", + "The Jasper model is an end-to-end neural acoustic model for automatic speech recognition (ASR) that provides near state-of-the-art results on LibriSpeech among end-to-end ASR models without any external data. The Jasper architecture of convolutional layers was designed to facilitate fast GPU inference, by allowing whole sub-blocks to be fused into a single GPU kernel. This is important for meeting strict real-time requirements of ASR systems in deployment.The results of the acoustic model are combined with the results of external language models to get the top-ranked word sequences corresponding to a given audio segment. This post-processing step is called decoding.\n", + "\n", + "The original paper is Jasper: An End-to-End Convolutional Neural Acoustic Model https://arxiv.org/pdf/1904.03288.pdf.\n", + "\n", + "### 1.1 Model architecture\n", + "By default the model configuration is Jasper 10x5 with dense residuals. A Jasper BxR model has B blocks, each consisting of R repeating sub-blocks.\n", + "Each sub-block applies the following operations in sequence: 1D-Convolution, Batch Normalization, ReLU activation, and Dropout. \n", + "In the original paper Jasper is trained with masked convolutions, which masks out the padded part of an input sequence in a batch before the 1D-Convolution.\n", + "For inference masking is not used. The reason for this is that in inference, the original mask operation does not achieve better accuracy than without the mask operation on the test and development dataset. However, no masking achieves better inference performance especially after TensorRT optimization.\n", + "More information on the model architecture can be found in the [root folder](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechRecognition/Jasper)\n", + "\n", + "### 1.2 TensorRT Inference pipeline\n", + "The Jasper inference pipeline consists of 3 components: data preprocessor, acoustic model and greedy decoder. The acoustic model is the most compute intensive, taking more than 90% of the entire end-to-end pipeline. The acoustic model is the only component with learnable parameters and also what differentiates Jasper from the competition. So, we focus on the acoustic model for the most part.\n", + "For the non-TRT Jasper inference pipeline, all 3 components are implemented and run with native PyTorch. For the TensorRT inference pipeline, we show the speedup of running the acoustic model with TensorRT, while preprocessing and decoding are reused from the native PyTorch pipeline.\n", + "To run a model with TensorRT, we first construct the model in PyTorch, which is then exported into an ONNX file. Finally, a TensorRT engine is constructed from the ONNX file, serialized to TRT plan file, and also launched to do inference.\n", + "Note that TensorRT engine is being runtime optimized before serialization. TRT tries a vast set of options to find the strategy that performs best on user’s GPU - so it takes a few minutes. After the TRT plan file is created, it can be reused." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Learning objectives\n", + "\n", + "This notebook demonstrates:\n", + "- Speed up Jasper Inference with TensorRT\n", + "- The use/download of fine-tuned NVIDIA Jasper models\n", + "- Use of Mixed Precision for Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Requirements\n", + "\n", + "Please refer to README.md" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Jasper Inference\n", + "### 3.1 Start a detached session in the NGC container" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-docker run -it -d --rm --name \"JasperTRT\" \\\n", + " --runtime=nvidia \\\n", + " --shm-size=4g \\\n", + " --ulimit memlock=-1 \\\n", + " --ulimit stack=67108864 \\\n", + " -v $PWD/data:/datasets \\\n", + " -v $PWD/checkpoint:/checkpoints/ \\\n", + " -v $PWD/result:/results/ \\\n", + " -v $PWD:/workspace/jasper/ \\\n", + " jasper:trt6 bash" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also specify single or multiple GPUs to run the container by adding \"NV_GPU\" before the \"nvidia-docker run\" command. For example, to specify GPU ID 2 to run the container, add \"NV_GPU=2\" before the \"nvidia-docker run\" command. You can use the command \"nvidia-smi\" to check your GPU IDs and utilization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#check the container that you just started\n", + "!docker ps -a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Download and preprocess the dataset.\n", + "You will not need to download the dataset if you directly go to Section 5 to play with audio examples.\n", + "\n", + "If LibriSpeech http://www.openslr.org/12 has already been downloaded and preprocessed, no further steps in this subsection need to be taken.\n", + "If LibriSpeech has not been downloaded already, note that only a subset of LibriSpeech is typically used for inference (dev-* and test-*). LibriSpeech contains 1000 hours of 16kHz read English speech derived from public domain audiobooks from LibriVox project and has been carefully segmented and aligned. For more information, see paper [LIBRISPEECH: AN ASR CORPUS BASED ON PUBLIC DOMAIN AUDIO BOOKS paper](http://www.danielpovey.com/files/2015_icassp_librispeech.pdf).\n", + "To acquire the inference subset of LibriSpeech run (does not require GPU):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-docker exec -it JasperTRT bash trt/scripts/download_inference_librispeech.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the data download is complete, the following folders should exist:\n", + "* /datasets/LibriSpeech/\n", + " * dev-clean/\n", + " * dev-other/\n", + " * test-clean/\n", + " * test-other/\n", + "\n", + "Since /datasets/ is mounted to on the host, once the dataset is downloaded it is accessible from outside of the container at /LibriSpeech.\n", + "\n", + "Next, preprocessing the data can be performed with the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-docker exec -it JasperTRT bash trt/scripts/preprocess_inference_librispeech.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the data is preprocessed, the following additional files should now exist:\n", + "\n", + "* /datasets/LibriSpeech/\n", + " * librispeech-dev-clean-wav.json\n", + " * librispeech-dev-other-wav.json\n", + " * librispeech-test-clean-wav.json\n", + " * librispeech-test-other-wav.json\n", + " * dev-clean/\n", + " * dev-other/\n", + " * test-clean/\n", + " * test-other/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3. Start TensorRT inference prediction\n", + "\n", + "Inside the container, use the following script to run inference with TensorRT.\n", + "You will need to set the parameters such as: \n", + "\n", + "\n", + "* `CHECKPOINT`: Model checkpoint path\n", + "* `TRT_PRECISION`: \"fp32\" or \"fp16\". Defines which precision kernels will be used for TensorRT engine (default: \"fp32\")\n", + "* `PYTORCH_PRECISION`: \"fp32\" or \"fp16\". Defines which precision will be used for inference in PyTorch (default: \"fp32\")\n", + "* `TRT_PREDICTION_PATH`: file to store inference prediction results generated with TensorRT\n", + "* `PYT_PREDICTION_PATH`: file to store inference prediction results generated with native PyTorch\n", + "* `DATASET`: LibriSpeech dataset (default: dev-clean)\n", + "* `NUM_STEPS`: Number of inference steps (default: -1)\n", + "* `BATCH_SIZE`: Mini batch size (default: 1)\n", + "* `NUM_FRAMES`: cuts/pads all pre-processed feature tensors to this length. 100 frames ~ 1 second of audio (default: 3600)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-docker exec -it -e CHECKPOINT=/checkpoints/jasper_fp16.pt -e TRT_PREDICTION_PATH=/results/result.txt JasperTRT bash trt/scripts/trt_inference.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ### 3.4. Start TensorRT Inference Benchmark\n", + "\n", + "Run the following commmand to run inference benchmark with TensorRT inside the container.\n", + "\n", + "You will need to set the parameters such as:\n", + "\n", + "* `CHECKPOINT`: Model checkpoint path \n", + "* `NUM_STEPS`: number of inference steps. If -1 runs inference on entire dataset. (default: -1)\n", + "* `NUM_FRAMES`: cuts/pads all pre-processed feature tensors to this length. 100 frames ~ 1 second of audio (default: 512)\n", + "* `BATCH_SIZE`: data batch size (default: 64)\n", + "* `TRT_PRECISION`: \"fp32\" or \"fp16\". Defines which precision kernels will be used for TensorRT engine (default: \"fp32\")\n", + "* `PYTORCH_PRECISION`: \"fp32\" or \"fp16\". Defines which precision will be used for inference in PyTorch (default: \"fp32\")\n", + "* `CSV_PATH`: file to store CSV results (default: \"/results/res.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-docker exec -it -e CHECKPOINT=/checkpoints/jasper_fp16.pt -e TRT_PREDICTION_PATH=/results/benchmark.txt JasperTRT bash trt/scripts/trt_inference_benchmark.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Automatic Mixed Precision\n", + "\n", + "Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in the Volta and Turing architecture, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. \n", + "\n", + "Using mixed precision training requires two steps:\n", + "\n", + "* Porting the model to use the FP16 data type where appropriate.\n", + "* Adding loss scaling to preserve small gradient values.\n", + "\n", + "The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.\n", + "For information about:\n", + "\n", + "How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.\n", + "\n", + "Techniques used for mixed precision training, see the blog [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/).\n", + "\n", + "APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/).\n", + "\n", + "To enable mixed precision, we can specify the variables `TRT_PRECISION` and `PYTORCH_PRECISION` by setting them to `TRT_PRECISION=fp16` and `PYTORCH_PRECISION=fp16` when running the inference. To run the TensorRT inference benchmarking using automatic mixed precision:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-docker exec -it -e CHECKPOINT=/checkpoints/jasper_fp16.pt -e TRT_PREDICTION_PATH=/results/benchmark.txt -e TRT_PRECISION=fp16 -e PYTORCH_PRECISION=fp16 -e CSV_PATH=/result/res_fp16.csv JasperTRT bash trt/scripts/trt_inference_benchmark.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the performance metrics (pyt_infer) that you get from res.csv (for fp32) and res_fp16.csv (for automatic mixed precision) files, you can see that automatic mixed precision can speedup the inference efficiently compared to fp32." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Play with audio examples\n", + "\n", + "You can perform inference using pre-trained checkpoints which takes audio file (in .wav format) as input, and produces the corresponding text file. You can customize the content of the text file. For example, there are several examples of input files at \"notebooks\" dirctory and we can listen to example1.wav:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import IPython.display as ipd\n", + "ipd.Audio('notebooks/example1.wav', rate=22050)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can run inference using the trt/perf.py script:\n", + "* the checkpoint is passed as `--ckpt` argument \n", + "* `--model_toml` specifies the path to network configuration file (see examples in \"config\" directory)\n", + "* `--make_onnx` exports to ONNX file at the path if set\n", + "* `--engine_path` saves the engine file (*.plan) \n", + "\n", + "To create a new engine file (jasper.plan) for TensorRT and run it using fp32 (building the engine for the first time can take several minutes):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-docker exec -it JasperTRT python trt/perf.py --ckpt_path /checkpoints/jasper_fp16.pt --wav=notebooks/example1.wav --model_toml=configs/jasper10x5dr_nomask.toml --make_onnx --onnx_path jasper.onnx --engine_path jasper.plan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you already have the engine file (jasper.plan), to run an existing engine file of TensorRT using fp32: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-docker exec -it JasperTRT python trt/perf.py --wav=notebooks/example1.wav --model_toml=configs/jasper10x5dr_nomask.toml --use_existing_engine --engine_path jasper.plan --trt_fp16" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To run inference of the input audio file using automatic mixed precision, add the argument `--trt_fp16`. Using automatic mixed precision, the inference time can be reduced efficiently compared to that of using fp32 (building the engine for the first time can take several minutes):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-docker exec -it JasperTRT python trt/perf.py --ckpt_path /checkpoints/jasper_fp16.pt --wav=notebooks/example1.wav --model_toml=configs/jasper10x5dr_nomask.toml --make_onnx --onnx_path jasper.onnx --engine_path jasper_fp16.plan --trt_fp16" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you already have the engine file (jasper_fp16.plan), to run an existing engine file of TensorRT using automatic mixed precision: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-docker exec -it JasperTRT python trt/perf.py --wav=notebooks/example1.wav --model_toml=configs/jasper10x5dr_nomask.toml --use_existing_engine --engine_path jasper_fp16.plan --trt_fp16" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can play with other examples at \"notebooks\" dirctory. You can also input your own audio files and generate the output text files in this way.\n", + "\n", + "For more information about TensorRT and building an engine file in Python, please see: https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#python_topics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#stop your container in the end\n", + "!docker stop JasperTRT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. What's next" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you are familiar with running Jasper inference with TensorRT, using automatic mixed precision, you may want to play with your own dataset, or train the model using your own dataset. For information on training, please see our Github repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechRecognition/Jasper" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/PyTorch/SpeechRecognition/Jasper/notebooks/README.md b/PyTorch/SpeechRecognition/Jasper/notebooks/README.md new file mode 100644 index 00000000..7c068d47 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/notebooks/README.md @@ -0,0 +1,75 @@ +# Jasper notebook + +## Overview + +This notebook provides scripts for you to run Jasper with TRT for inference step by step. You can run inference using either LibriSpeech dataset or your own audio input in .wav format, to generate the corresponding text file for the audio file. + +## Requirements + +This repository contains a Dockerfile which extends the PyTorch 19.09-py3 NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components: + +* [NVIDIA Turing](https://www.nvidia.com/en-us/geforce/turing/) or [Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) based GPU +* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) +* [PyTorch 19.09-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) +* [Pretrained Jasper Model Checkpoint](https://ngc.nvidia.com/catalog/models/nvidia:jasperpyt_fp16) + +## Quick Start Guide + +Running the following scripts will build and launch the container containing all required dependencies for both TensorRT as well as native PyTorch. This is necessary for using inference with TensorRT and can also be used for data download, processing and training of the model. + +#### 1. Clone the repository. + +``` +git clone https://github.com/NVIDIA/DeepLearningExamples +cd DeepLearningExamples/PyTorch/SpeechRecognition/Jasper +``` + +#### 2. Build the Jasper PyTorch with TRT 6 container: + +``` +bash trt/scripts/docker/trt_build.sh +``` + +#### 3. Create directories +Prepare to start a detached session in the NGC container. +Create three directories on your local machine for dataset, checkpoint, and result, respectively, naming "data" "checkpoint" "result": + +``` +mkdir data checkpoint result +``` + +#### 4. Download the checkpoint + +Download the checkpoint file jasperpyt_fp16 from NGC Model Repository: +- https://ngc.nvidia.com/catalog/models/nvidia:jasperpyt_fp16 + +to the directory: _checkpoint_ + +The Jasper PyTorch container will be launched in the Jupyter notebook. Within the container, the contents of the root repository will be copied to the /workspace/jasper directory. + +The /datasets, /checkpoints, /results directories are mounted as volumes and mapped to the corresponding directories "data" "checkpoint" "result" on the host. + +#### 5. Copy the notebook to the root + +Copy the notebook to the root directory of Jasper: + +``` +cp notebooks/JasperTRT.ipynb . +``` + +#### 6. Run the notebook +For running the notebook on your local machine, run: + +``` +jupyter notebook JasperTRT.ipynb +``` + +For running the notebook on another machine remotely, run: + +``` +jupyter notebook --ip=0.0.0.0 --allow-root +``` + +And navigate a web browser to the IP address or hostname of the host machine at port 8888: `http://[host machine]:8888` + +Use the token listed in the output from running the jupyter command to log in, for example: `http://[host machine]:8888/?token=aae96ae9387cd28151868fee318c3b3581a2d794f3b25c6b` diff --git a/PyTorch/SpeechRecognition/Jasper/notebooks/example1.wav b/PyTorch/SpeechRecognition/Jasper/notebooks/example1.wav new file mode 100644 index 00000000..5cc09018 Binary files /dev/null and b/PyTorch/SpeechRecognition/Jasper/notebooks/example1.wav differ diff --git a/PyTorch/SpeechRecognition/Jasper/notebooks/example2.wav b/PyTorch/SpeechRecognition/Jasper/notebooks/example2.wav new file mode 100644 index 00000000..da403b90 Binary files /dev/null and b/PyTorch/SpeechRecognition/Jasper/notebooks/example2.wav differ diff --git a/PyTorch/SpeechRecognition/Jasper/notebooks/example3.wav b/PyTorch/SpeechRecognition/Jasper/notebooks/example3.wav new file mode 100644 index 00000000..3c1431c8 Binary files /dev/null and b/PyTorch/SpeechRecognition/Jasper/notebooks/example3.wav differ diff --git a/PyTorch/SpeechRecognition/Jasper/notebooks/example4.wav b/PyTorch/SpeechRecognition/Jasper/notebooks/example4.wav new file mode 100644 index 00000000..b0f6e830 Binary files /dev/null and b/PyTorch/SpeechRecognition/Jasper/notebooks/example4.wav differ diff --git a/PyTorch/SpeechRecognition/Jasper/parts/features.py b/PyTorch/SpeechRecognition/Jasper/parts/features.py index 0d77cb32..a7a0a538 100644 --- a/PyTorch/SpeechRecognition/Jasper/parts/features.py +++ b/PyTorch/SpeechRecognition/Jasper/parts/features.py @@ -21,6 +21,15 @@ from .segment import AudioSegment from apex import amp +def audio_from_file(file_path, offset=0, duration=0, trim=False, target_sr=16000): + audio = AudioSegment.from_file(file_path, + target_sr=target_sr, + int_values=False, + offset=offset, duration=duration, trim=trim) + samples=torch.tensor(audio.samples, dtype=torch.float).cuda() + num_samples = torch.tensor(samples.shape[0]).int().cuda() + return (samples.unsqueeze(0), num_samples.unsqueeze(0)) + class WaveformFeaturizer(object): def __init__(self, input_cfg, augmentor=None): self.augmentor = augmentor if augmentor is not None else AudioAugmentor() @@ -51,6 +60,7 @@ class WaveformFeaturizer(object): constant = 1e-5 def normalize_batch(x, seq_len, normalize_type): +# print ("normalize_batch: x, seq_len, shapes: ", x.shape, seq_len, seq_len.shape) if normalize_type == "per_feature": x_mean = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device) @@ -191,10 +201,10 @@ class FilterbankFeatures(nn.Module): preemph=0.97, nfilt=64, lowfreq=0, highfreq=None, log=True, dither=constant, pad_to=8, - max_duration=16.7, + max_duration=16.7, frame_splicing=1): super(FilterbankFeatures, self).__init__() - print("PADDING: {}".format(pad_to)) +# print("PADDING: {}".format(pad_to)) torch_windows = { 'hann': torch.hann_window, @@ -242,10 +252,13 @@ class FilterbankFeatures(nn.Module): @torch.no_grad() def forward(self, inp): x, seq_len = inp + dtype = x.dtype seq_len = self.get_seq_len(seq_len) +# print ("forward: x, seq_len, shapes: ", x.shape, seq_len, seq_len.shape) + # dither if self.dither > 0: x += self.dither * torch.randn_like(x) @@ -282,7 +295,7 @@ class FilterbankFeatures(nn.Module): max_len = x.size(-1) mask = torch.arange(max_len).to(seq_len.dtype).to(x.device).expand(x.size(0), max_len) >= seq_len.unsqueeze(1) - + x = x.masked_fill(mask.unsqueeze(1).to(device=x.device), 0) del mask pad_to = self.pad_to diff --git a/PyTorch/SpeechRecognition/Jasper/parts/manifest.py b/PyTorch/SpeechRecognition/Jasper/parts/manifest.py index be0326f7..08cd7b56 100644 --- a/PyTorch/SpeechRecognition/Jasper/parts/manifest.py +++ b/PyTorch/SpeechRecognition/Jasper/parts/manifest.py @@ -89,7 +89,7 @@ class Manifest(object): else: min_speed = min(x['speed'] for x in files_and_speeds) max_duration = self.max_duration * min_speed - + data['duration'] = data['original_duration'] if min_duration is not None and data['duration'] < min_duration: filtered_duration += data['duration'] @@ -112,7 +112,7 @@ class Manifest(object): filtered_duration += data['duration'] continue data["transcript"] = self.parse_transcript(transcript_text) # convert to vocab indices - + if speed_perturbation: audio_paths = [x['fname'] for x in files_and_speeds] data['audio_duration'] = [x['duration'] for x in files_and_speeds] @@ -122,7 +122,7 @@ class Manifest(object): data['audio_filepath'] = [os.path.join(data_dir, x) for x in audio_paths] data.pop('files') data.pop('original_duration') - + ids.append(data) duration += data['duration'] diff --git a/PyTorch/SpeechRecognition/Jasper/requirements.txt b/PyTorch/SpeechRecognition/Jasper/requirements.txt index 33cbc8ea..87e8b09c 100755 --- a/PyTorch/SpeechRecognition/Jasper/requirements.txt +++ b/PyTorch/SpeechRecognition/Jasper/requirements.txt @@ -6,4 +6,4 @@ librosa toml soundfile ipdb -sox \ No newline at end of file +sox diff --git a/PyTorch/SpeechRecognition/Jasper/scripts/docker/launch.sh b/PyTorch/SpeechRecognition/Jasper/scripts/docker/launch.sh index d750a59f..68a91f87 100755 --- a/PyTorch/SpeechRecognition/Jasper/scripts/docker/launch.sh +++ b/PyTorch/SpeechRecognition/Jasper/scripts/docker/launch.sh @@ -27,4 +27,5 @@ docker run -it --rm \ -v "$DATA_DIR":/datasets \ -v "$CHECKPOINT_DIR":/checkpoints/ \ -v "$RESULT_DIR":/results/ \ + -v $PWD:/code \ jasper bash diff --git a/PyTorch/SpeechRecognition/Jasper/scripts/download_librispeech.sh b/PyTorch/SpeechRecognition/Jasper/scripts/download_librispeech.sh index 5a32ce4c..ee322fe3 100755 --- a/PyTorch/SpeechRecognition/Jasper/scripts/download_librispeech.sh +++ b/PyTorch/SpeechRecognition/Jasper/scripts/download_librispeech.sh @@ -25,4 +25,4 @@ then python utils/download_librispeech.py utils/librispeech.csv $DATA_DIR -e ${DATA_ROOT_DIR}/ else echo "Directory $DATA_DIR already exists." -fi \ No newline at end of file +fi diff --git a/PyTorch/SpeechRecognition/Jasper/scripts/evaluation.sh b/PyTorch/SpeechRecognition/Jasper/scripts/evaluation.sh index 3b540a94..fcd472fd 100755 --- a/PyTorch/SpeechRecognition/Jasper/scripts/evaluation.sh +++ b/PyTorch/SpeechRecognition/Jasper/scripts/evaluation.sh @@ -89,4 +89,4 @@ else $CMD ) |& tee "$LOGFILE" fi -set +x \ No newline at end of file +set +x diff --git a/PyTorch/SpeechRecognition/Jasper/scripts/inference_benchmark.sh b/PyTorch/SpeechRecognition/Jasper/scripts/inference_benchmark.sh index 2e7de186..7aeea84c 100755 --- a/PyTorch/SpeechRecognition/Jasper/scripts/inference_benchmark.sh +++ b/PyTorch/SpeechRecognition/Jasper/scripts/inference_benchmark.sh @@ -82,8 +82,3 @@ else grep 'latency' "$LOGFILE" fi set +x - - - - - diff --git a/PyTorch/SpeechRecognition/Jasper/scripts/train.sh b/PyTorch/SpeechRecognition/Jasper/scripts/train.sh index 062cf586..17c1042f 100755 --- a/PyTorch/SpeechRecognition/Jasper/scripts/train.sh +++ b/PyTorch/SpeechRecognition/Jasper/scripts/train.sh @@ -108,4 +108,3 @@ else ) |& tee $LOGFILE fi set +x - diff --git a/PyTorch/SpeechRecognition/Jasper/scripts/train_benchmark.sh b/PyTorch/SpeechRecognition/Jasper/scripts/train_benchmark.sh index 6f1d1a49..7b5a3370 100755 --- a/PyTorch/SpeechRecognition/Jasper/scripts/train_benchmark.sh +++ b/PyTorch/SpeechRecognition/Jasper/scripts/train_benchmark.sh @@ -128,4 +128,3 @@ else echo "final_eval_loss: $final_eval_loss" | tee -a "$LOGFILE" echo "final_eval_wer: $final_eval_wer" | tee -a "$LOGFILE" fi - diff --git a/PyTorch/SpeechRecognition/Jasper/train.py b/PyTorch/SpeechRecognition/Jasper/train.py index feb55c5b..d5613af4 100644 --- a/PyTorch/SpeechRecognition/Jasper/train.py +++ b/PyTorch/SpeechRecognition/Jasper/train.py @@ -28,10 +28,10 @@ from helpers import monitor_asr_train_progress, process_evaluation_batch, proces from model import AudioPreprocessing, CTCLossNM, GreedyCTCDecoder, Jasper from optimizers import Novograd, AdamW - + def lr_policy(initial_lr, step, N): """ - learning rate decay + learning rate decay Args: initial_lr: base learning rate step: current iteration number @@ -45,7 +45,7 @@ def save(model, optimizer, epoch, output_dir): """ Saves model checkpoint Args: - model: model + model: model optimizer: optimizer epoch: epoch of model training output_dir: path to save model checkpoint @@ -57,8 +57,8 @@ def save(model, optimizer, epoch, output_dir): if (not torch.distributed.is_initialized() or (torch.distributed.is_initialized() and torch.distributed.get_rank() == 0)): model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self save_checkpoint={ - 'epoch': epoch, - 'state_dict': model_to_save.state_dict(), + 'epoch': epoch, + 'state_dict': model_to_save.state_dict(), 'optimizer': optimizer.state_dict() } @@ -69,15 +69,15 @@ def save(model, optimizer, epoch, output_dir): def train( - data_layer, + data_layer, data_layer_eval, model, - ctc_loss, - greedy_decoder, - optimizer, - optim_level, - labels, - multi_gpu, + ctc_loss, + greedy_decoder, + optimizer, + optim_level, + labels, + multi_gpu, args, fn_lr_policy=None): """Trains model @@ -128,10 +128,10 @@ def train( # final aggregation across all workers and minibatches) and logging of results wer, eloss = process_evaluation_epoch(_global_var_dict) - + print_once("==========>>>>>>Evaluation Loss: {0}\n".format(eloss)) print_once("==========>>>>>>Evaluation WER: {0}\n".format(wer)) - + print_once("Starting .....") start_time = time.time() @@ -157,7 +157,7 @@ def train( if batch_counter == 0: if fn_lr_policy is not None: - adjusted_lr = fn_lr_policy(step) + adjusted_lr = fn_lr_policy(step) for param_group in optimizer.param_groups: param_group['lr'] = adjusted_lr optimizer.zero_grad() @@ -165,8 +165,8 @@ def train( t_audio_signal_t, t_a_sig_length_t, t_transcript_t, t_transcript_len_t = tensors model.train() + t_log_probs_t, t_encoded_len_t = model(x=(t_audio_signal_t, t_a_sig_length_t)) - t_loss_t = ctc_loss(log_probs=t_log_probs_t, targets=t_transcript_t, input_length=t_encoded_len_t, target_length=t_transcript_len_t) if args.gradient_accumulation_steps > 1: t_loss_t = t_loss_t / args.gradient_accumulation_steps @@ -238,8 +238,8 @@ def main(args): dataset_vocab = jasper_model_definition['labels']['labels'] ctc_vocab = add_ctc_labels(dataset_vocab) - train_manifest = args.train_manifest - val_manifest = args.val_manifest + train_manifest = args.train_manifest + val_manifest = args.val_manifest featurizer_config = jasper_model_definition['input'] featurizer_config_eval = jasper_model_definition['input_eval'] featurizer_config["optimization_level"] = optim_level @@ -255,7 +255,7 @@ def main(args): featurizer_config_eval['pad_to'] = "max" print_once('model_config') print_dict(jasper_model_definition) - + if args.gradient_accumulation_steps < 1: raise ValueError('Invalid gradient accumulation steps parameter {}'.format(args.gradient_accumulation_steps)) if args.batch_size % args.gradient_accumulation_steps != 0: @@ -282,9 +282,9 @@ def main(args): multi_gpu=multi_gpu, pad_to_max=args.pad_to_max ) - + model = Jasper(feature_config=featurizer_config, jasper_model_definition=jasper_model_definition, feat_in=1024, num_classes=len(ctc_vocab)) - + if args.ckpt is not None: print_once("loading model from {}".format(args.ckpt)) checkpoint = torch.load(args.ckpt, map_location="cpu") @@ -304,13 +304,13 @@ def main(args): args.step_per_epoch = math.ceil(N / (args.batch_size * (1 if not torch.distributed.is_initialized() else torch.distributed.get_world_size()))) elif sampler_type == 'bucket': args.step_per_epoch = int(len(data_layer.sampler) / args.batch_size ) - + print_once('-----------------') print_once('Have {0} examples to train on.'.format(N)) print_once('Have {0} steps / (gpu * epoch).'.format(args.step_per_epoch)) print_once('-----------------') - fn_lr_policy = lambda s: lr_policy(args.lr, s, args.num_epochs * args.step_per_epoch) + fn_lr_policy = lambda s: lr_policy(args.lr, s, args.num_epochs * args.step_per_epoch) model.cuda() @@ -333,7 +333,7 @@ def main(args): models=model, optimizers=optimizer, opt_level=AmpOptimizations[optim_level]) - + if args.ckpt is not None: optimizer.load_state_dict(checkpoint['optimizer']) @@ -341,12 +341,12 @@ def main(args): train( data_layer=data_layer, - data_layer_eval=data_layer_eval, - model=model, - ctc_loss=ctc_loss, + data_layer_eval=data_layer_eval, + model=model, + ctc_loss=ctc_loss, greedy_decoder=greedy_decoder, - optimizer=optimizer, - labels=ctc_vocab, + optimizer=optimizer, + labels=ctc_vocab, optim_level=optim_level, multi_gpu=multi_gpu, fn_lr_policy=fn_lr_policy if args.lr_decay else None, diff --git a/PyTorch/SpeechRecognition/Jasper/trt/Dockerfile b/PyTorch/SpeechRecognition/Jasper/trt/Dockerfile new file mode 100644 index 00000000..dc4fd9ce --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/Dockerfile @@ -0,0 +1,30 @@ +ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:19.09-py3 +FROM ${FROM_IMAGE_NAME} + +RUN apt-get update && apt-get install -y python3 +RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.1.243-1_amd64.deb \ +&& dpkg -i cuda-repo-*.deb \ +&& wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb \ +&& dpkg -i nvidia-machine-learning-repo-*.deb \ +&& apt-get update \ +&& apt-get install -y --no-install-recommends python-libnvinfer python3-libnvinfer + + +RUN cp -r /usr/lib/python3.6/dist-packages/tensorrt /opt/conda/lib/python3.6/site-packages/tensorrt +# Add TensorRT executable to path (trtexec) +ENV PATH=$PATH:/usr/src/tensorrt/bin + + +# Here's a good place to install pip reqs from JoC repo. +# At the same step, also install TRT pip reqs +WORKDIR /tmp/pipReqs +COPY requirements.txt /tmp/pipReqs/jocRequirements.txt +COPY trt/requirements.txt /tmp/pipReqs/trtRequirements.txt +RUN pip install --disable-pip-version-check -U -r jocRequirements.txt -r trtRequirements.txt + +# These packages are required for running preprocessing on the dataset to acquire manifest files and the like +RUN apt-get install -y libsndfile1 && apt-get install -y ffmpeg sox && rm -rf /var/lib/apt/lists/* + +WORKDIR /workspace/jasper +COPY . . + diff --git a/PyTorch/SpeechRecognition/Jasper/trt/README.md b/PyTorch/SpeechRecognition/Jasper/trt/README.md new file mode 100644 index 00000000..a070c8a9 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/README.md @@ -0,0 +1,299 @@ + +# Jasper Inference For TensorRT + +This is subfolder of the Jasper for PyTorch repository, tested and maintained by NVIDIA, and provides scripts to perform high-performance inference using NVIDIA TensorRT. Jasper is a neural acoustic model for speech recognition. Its network architecture is designed to facilitate fast GPU inference. More information about Jasper and its training and be found in the [root directory](../README.md). +NVIDIA TensorRT is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. +After optimizing the compute-intensive acoustic model with NVIDIA TensorRT, inference throughput increased by up to 1.8x over native PyTorch. + + + +## Table Of Contents + +- [Model overview](#model-overview) + * [Model architecture](#model-architecture) + * [TRT Inference pipeline](#trt-inference-pipeline) + * [Version Info](#version-info) +- [Setup](#setup) + * [Requirements](#requirements) +- [Quick Start Guide](#quick-start-guide) +- [Advanced](#advanced) + * [Scripts and sample code](#scripts-and-sample-code) + * [Parameters](#parameters) + * [TRT Inference Process](#trt-inference-process) + * [TRT Inference Benchmark Process](#trt-inference-benchmark-process) +- [Performance](#performance) + * [Results](#results) + * [Inference performance: NVIDIA T4](#inference-performance-nvidia-t4) + + +## Model overview + +### Model architecture +By default the model configuration is Jasper 10x5 with dense residuals. A Jasper BxR model has B blocks, each consisting of R repeating sub-blocks. +Each sub-block applies the following operations in sequence: 1D-Convolution, Batch Normalization, ReLU activation, and Dropout. +In the original paper Jasper is trained with masked convolutions, which masks out the padded part of an input sequence in a batch before the 1D-Convolution. +For inference masking is not used. The reason for this is that in inference, the original mask operation does not achieve better accuracy than without the mask operation on the test and development dataset. However, no masking achieves better inference performance especially after TensorRT optimization. + + +### TRT Inference pipeline + +The Jasper inference pipeline consists of 3 components: data preprocessor, acoustic model and greedy decoder. The acoustic model is the most compute intensive, taking more than 90% of the entire end-to-end pipeline. The acoustic model is the only component with learnable parameters and also what differentiates Jasper from the competition. So, we focus on the acoustic model for the most part. + +For the non-TRT Jasper inference pipeline, all 3 components are implemented and run with native PyTorch. For the TensorRT inference pipeline, we show the speedup of running the acoustic model with TensorRT, while preprocessing and decoding are reused from the native PyTorch pipeline. + +To run a model with TensorRT, we first construct the model in PyTorch, which is then exported into an ONNX file. Finally, a TensorRT engine is constructed from the ONNX file, serialized to TRT plan file, and also launched to do inference. + +Note that TensorRT engine is being runtime optimized before serialization. TRT tries a vast set of options to find the strategy that performs best on user’s GPU - so it takes a few minutes. After the TRT plan file is created, it can be reused. + +### Version Info + +The following software version configuration has been tested and known to work: + +|Software|Version| +|--------|-------| +|Python|3.6.9| +|PyTorch|1.2.0| +|TensorRT|6.0.1.5| +|CUDA|10.1.243| + +## Setup + +The following section lists the requirements in order to start inference on the Jasper model with TRT. + +### Requirements + +This repository contains a `Dockerfile` which extends the PyTorch 19.09-py3 NGC container and encapsulates some dependencies. Ensure you have the following components: + +* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) +* [PyTorch 19.09-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) +* [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) or [Turing](https://www.nvidia.com/en-us/geforce/turing/) based GPU +* [Pretrained Jasper Model Checkpoint](https://ngc.nvidia.com/catalog/models/nvidia:jasperpyt_fp16) + +Required Python packages are listed in `requirements.txt` and `trt/requirements.txt`. These packages are automatically installed when the Docker container is built. To manually install them, run: + + +```bash +pip install -r requirements.txt +pip install -r trt/requirements.txt +``` + + +## Quick Start Guide + + +Running the following scripts will build and launch the container containing all required dependencies for both TensorRT as well as native PyTorch. This is necessary for using inference with TensorRT and can also be used for data download, processing and training of the model. + +1. Clone the repository. + +```bash +git clone https://github.com/NVIDIA/DeepLearningExamples +cd DeepLearningExamples/PyTorch/SpeechRecognition/Jasper +``` +2. Build the Jasper PyTorch with TensorRT container: + +``` +bash trt/scripts/docker/trt_build.sh +``` +3. Start an interactive session in the NGC docker container: + +``` +bash trt/scripts/docker/trt_launch.sh +``` + +Alternatively, to start a script in the docker container: + +``` +bash trt/scripts/docker/trt_launch.sh +``` + +The `/datasets`, `/checkpoints`, `/results` directories will be mounted as volumes and mapped to the corresponding directories ``, ``, `` on the host. **These three paths should be absolute and should already exist.** The contents of this repository will be mounted to the `/workspace/jasper` directory. Note that ``, ``, and `` directly correspond to the same arguments in `scripts/docker/launch.sh` mentioned in the [Quick Start Guide](../README.md). + +Briefly, `` should contain, or be prepared to contain a `LibriSpeech` sub-directory (created in [Acquiring Dataset](#acquiring-dataset)), `` should contain a PyTorch model checkpoint (`*.pt`) file obtained through training described in [Quick Start Guide](../README.md), and `` should be prepared to contain timing results, logs, serialized TRT engines, and ONNX files. + +4. Acquiring dataset + +If LibriSpeech has already been downloaded and preprocessed as defined in the [Quick Start Guide](../README.md), no further steps in this subsection need to be taken. + +If LibriSpeech has not been downloaded already, note that only a subset of LibriSpeech is typically used for inference (`dev-*` and `test-*`). To acquire the inference subset of LibriSpeech run the following commands inside the container (does not require GPU): + +``` +bash trt/scripts/download_inference_librispeech.sh +``` + +Once the data download is complete, the following folders should exist: + +* `/datasets/LibriSpeech/` + * `dev-clean/` + * `dev-other/` + * `test-clean/` + * `test-other/` + +Next, preprocessing the data can be performed with the following command: + +``` +bash trt/scripts/preprocess_inference_librispeech.sh +``` + +Once the data is preprocessed, the following additional files should now exist: +* `/datasets/LibriSpeech/` + * `librispeech-dev-clean-wav.json` + * `librispeech-dev-other-wav.json` + * `librispeech-test-clean-wav.json` + * `librispeech-test-other-wav.json` + * `dev-clean-wav/` + * `dev-other-wav/` + * `test-clean-wav/` + * `test-other-wav/` + +5. Start TRT inference prediction + +Inside the container, use the following script to run inference with TRT. +``` +export CHECKPOINT= +export TRT_PRECISION= +export PYTORCH_PRECISION= +export TRT_PREDICTION_PATH= +bash trt/scripts/trt_inference.sh +``` +A pretrained model checkpoint can be downloaded from [NGC model repository](https://ngc.nvidia.com/catalog/models/nvidia:jasperpyt_fp16). +More details can be found in [Advanced](#advanced) under [Scripts and sample code](#scripts-and-sample-code), [Parameters](#parameters) and [TRT Inference process](#trt-inference). + +6. Start TRT inference benchmark + +Inside the container, use the following script to run inference benchmark with TRT. +``` +export CHECKPOINT= +export NUM_STEPS= +export NUM_FRAMES= +export BATCH_SIZE= +export TRT_PRECISION= +export PYTORCH_PRECISION= +export CSV_PATH= +bash trt/scripts/trt_inference_benchmark.sh +``` +A pretrained model checkpoint can be downloaded from the [NGC model repository](https://ngc.nvidia.com/catalog/models/nvidia:jasperpyt_fp16). +More details can be found in [Advanced](#advanced) under [Scripts and sample code](#scripts-and-sample-code), [Parameters](#parameters) and [TRT Inference Benchmark process](#trt-inference-benchmark). + +7. Start Jupyter notebook to run inference interactively +The Jupyter notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. +The notebook which is located at `notebooks/JasperTRT.ipynb` offers an interactive method to run the Steps 2,3,4,5. In addition, the notebook shows examples how to use TRT to transcribe a single audio file into text. To launch the application please follow the instructions under [../notebooks/README.md](../notebooks/README.md). +A pretrained model checkpoint can be downloaded from [NGC model repository](https://ngc.nvidia.com/catalog/models/nvidia:jasperpyt_fp16). + + +## Advanced +The following sections provide greater details on inference benchmarking with TRT and show inference results + +### Scripts and sample code +In the `trt/` directory, the most important files are: +* `Dockerfile`: Container to run Jasper inference with TRT. +* `requirements.py`: Python package dependencies. Installed when building the Docker container. +* `perf.py`: Entry point for inference pipeline using TRT. +* `perfprocedures.py`: Contains functionality to run inference through both the PyTorch model and TRT Engine, taking runtime measurements of each component of the inference process for comparison. +* `trtutils.py`: Helper functions for TRT components of Jasper inference. +* `perfutils.py`: Helper functions for non-TRT components of Jasper inference. + +The `trt/scripts/` directory has one-click scripts to run supported functionalities, such as: + +* `download_librispeech.sh`: Downloads LibriSpeech inference dataset. +* `preprocess_librispeech.sh`: Preprocess LibriSpeech raw data files to be ready for inference. +* `trt_inference_benchmark.sh`: Benchmarks and compares TRT and PyTorch inference pipelines using the `perf.py` script. +* `trt_inference.sh`: Runs TRT and PyTorch inference using the `trt_inference_benchmark.sh` script. +* `walk_benchmark.sh`: Illustrates an example of using `trt/scripts/trt_inference_benchmark.sh`, which *walks* a variety of values for `BATCH_SIZE` and `NUM_FRAMES`. +* `docker/`: Contains the scripts for building and launching the container. + + +### Parameters + +The list of parameters available for `trt/scripts/trt_inference_benchmark.sh` is: + +``` +Required: +-------- +CHECKPOINT: Model checkpoint path + +Arguments with Defaults: +-------- +DATA_DIR: directory of the dataset (Default: `/datasets/Librispeech`) +DATASET: name of dataset to use (default: `dev-clean`) +RESULT_DIR: directory for results including TRT engines, ONNX files, logs, and CSVs (default: `/results`) +CREATE_LOGFILE: boolean that indicates whether to create log of session to be stored in `$RESULT_DIR` (default: "true") +CSV_PATH: file to store CSV results (default: `/results/res.csv`) +TRT_PREDICTION_PATH: file to store inference prediction results generated with TRT (default: `none`) +PYT_PREDICTION_PATH: file to store inference prediction results generated with native PyTorch (default: `none`) +VERBOSE: boolean that indicates whether to verbosely describe TRT engine building/deserialization and TRT inference (default: "false") +TRT_PRECISION: "fp32" or "fp16". Defines which precision kernels will be used for TRT engine (default: "fp32") +PYTORCH_PRECISION: "fp32" or "fp16". Defines which precision will be used for inference in PyTorch (default: "fp32") +NUM_STEPS: Number of inference steps. If -1 runs inference on entire dataset (default: 100) +BATCH_SIZE: data batch size (default: 64) +NUM_FRAMES: cuts/pads all pre-processed feature tensors to this length. 100 frames ~ 1 second of audio (default: 512) +FORCE_ENGINE_REBUILD: boolean that indicates whether an already-built TRT engine of equivalent precision, batch-size, and number of frames should not be used. + Engines are specific to the GPU, library versions, TRT versions, and CUDA versions they were built in and cannot be used in a different environment. (default: "true") +``` + +The complete list of parameters available for `trt/scripts/trt_inference.sh` is the same as `trt/scripts/trt_inference_benchmark.sh` only with different default input arguments. In the following, only the parameters with different default values are listed: + +``` +TRT_PREDICTION_PATH: file to store inference prediction results generated with TRT (default: `/results/trt_predictions.txt`) +PYT_PREDICTION_PATH: file to store inference prediction results generated with native PyTorch (default: `/results/pyt_predictions.txtone`) +NUM_STEPS: Number of inference steps. If -1 runs inference on entire dataset (default: -1) +BATCH_SIZE: data batch size (default: 1) +NUM_FRAMES: cuts/pads all pre-processed feature tensors to this length. 100 frames ~ 1 second of audio (default: 3600) +``` + +### TRT Inference Benchmark process + +The inference benchmarking is performed on a single GPU by ‘trt/scripts/trt_inference_benchmark.sh’ which delegates to `trt/perf.py`, which takes the following steps: + + +1. Construct Jasper acoustic model in PyTorch. + +2. Construct TRT Engine of Jasper acoustic model + + 1. Perform ONNX export on the PyTorch model, if its ONNX file does not already exist. + + 2. Construct TRT engine from ONNX export, if a saved engine file does not already exist or `FORCE_ENGINE_REBUILD` is `true`. + +3. For each batch in the dataset, run inference through both the PyTorch model and TRT Engine, taking runtime measurements of each component of the inference process. + +4. Compile performance and WER accuracy results in CSV format, written to `CSV_PATH` file. + +`trt/perf.py` utilizes `trt/trtutils.py` and `trt/perfutils.py`, helper functions for TRT and non-TRT components of Jasper inference respectively. + +### TRT Inference process + +The inference is performed by `trt/scripts/trt_inference.sh` which delegates to `trt/scripts/trt_inference_benchmark.sh`. The script runs on a single GPU. To do inference prediction on the entire dataset `NUM_FRAMES` is set to 3600, which roughly corresponds to 36 seconds. This covers the longest sentences in both LibriSpeech dev and test dataset. By default, `BATCH_SET` is set to 1 to simulate the online inference scenario in deployment. Other batch sizes can be tried by setting a different value to this parameter. By default `TRT_PRECISION` is set to full precision and can be changed by setting `export TRT_PRECISION=fp16`. The prediction results are stored at `/results/trt_predictions.txt` and `/results/pyt_predictions.txt`. + + + +## Performance + +To benchmark the inference performance on a specific batch size and audio length refer to [Quick-Start-Guide](#quick-start-guide). To do a sweep over multiple batch sizes and audio durations run: +``` +bash trt/scripts/walk_benchmark.sh +``` +The results are obtained by running inference on LibriSpeech dev-clean dataset on a single T4 GPU using half precision with AMP. We compare the throughput of the acoustic model between TensorRT and native PyTorch. + +### Results + + + +#### Inference performance: NVIDIA T4 + +| Sequence Length (in seconds) | Batch size | PyTorch FP16 Throughput (#sequences/second) Percentiles | | | | TRT FP16 Throughput (#sequences/second) Percentiles | | | | PyT/TRT Speedup | +|---------------|------------|---------------------|---------|---------|---------|-----------------|---------|---------|---------|-----------------| +| | | 90% | 95% | 99% | Avg | 90% | 95% | 99% | Avg | | +|2|1|71.002|70.897|70.535|71.987|42.974|42.932|42.861|43.166|1.668| +||2|136.369|135.915|135.232|139.266|81.398|77.826|57.408|81.254|1.714| +||4|231.528|228.875|220.085|239.686|130.055|117.779|104.529|135.660|1.767| +||8|310.224|308.870|289.132|316.536|215.401|202.902|148.240|228.805|1.383| +||16|389.086|366.839|358.419|401.267|288.353|278.708|230.790|307.070|1.307| +|7|1|61.792|61.273|59.842|63.537|34.098|33.963|33.785|34.639|1.834| +||2|93.869|92.480|91.528|97.082|59.397|59.221|51.050|60.934|1.593| +||4|113.108|112.950|112.531|114.507|66.947|66.479|59.926|67.704|1.691| +||8|118.878|118.542|117.619|120.367|83.208|82.998|82.698|84.187|1.430| +||16|122.909|122.718|121.547|124.190|102.212|102.000|101.187|103.049|1.205| +|16.7|1|38.665|38.404|37.946|39.363|21.267|21.197|21.127|21.456|1.835| +||2|44.960|44.867|44.382|45.583|30.218|30.156|29.970|30.679|1.486| +||4|47.754|47.667|47.541|48.287|29.146|29.079|28.941|29.470|1.639| +||8|51.051|50.969|50.620|51.489|37.565|37.497|37.373|37.834|1.361| +||16|53.316|53.288|53.188|53.773|45.217|45.090|44.946|45.560|1.180| diff --git a/PyTorch/SpeechRecognition/Jasper/trt/perf.py b/PyTorch/SpeechRecognition/Jasper/trt/perf.py new file mode 100755 index 00000000..b08ab3c1 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/perf.py @@ -0,0 +1,140 @@ +#!/usr/bin/env python3 +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +'''Constructs TensorRT engine for JASPER and evaluates inference latency''' +import argparse +import sys, os +# Get local modules in parent directory and current directory (assuming this was called from root of repository) +sys.path.append("./") +sys.path.append("./trt") +import perfutils +import trtutils +import perfprocedures +from model import GreedyCTCDecoder +from helpers import __ctc_decoder_predictions_tensor + +def main(args): + + # Get shared utility across PyTorch and TRT + pyt_components, saved_onnx = perfutils.get_pytorch_components_and_onnx(args) + + # Get a TRT engine. See function for argument parsing logic + engine = get_engine(args) + + if args.wav: + audio_processor = pyt_components['audio_preprocessor'] + audio_processor.eval() + greedy_decoder = GreedyCTCDecoder() + input_wav, seq_len = pyt_components['input_wav'] + features = audio_processor((input_wav, seq_len)) + features = perfutils.adjust_shape(features, args.seq_len) + with engine.create_execution_context() as context: + t_log_probs_e, copyto, inference, copyfrom= perfprocedures.do_inference(context, features[0], 1) + log_probs=perfutils.torchify_trt_out(t_log_probs_e, 1) + + t_predictions_e = greedy_decoder(log_probs=log_probs) + hypotheses = __ctc_decoder_predictions_tensor(t_predictions_e, labels=perfutils.get_vocab()) + print("INTERENCE TIME: {} ms".format(inference*1000.0)) + print("TRANSCRIPT: ", hypotheses[0]) + + return + + + wer, preds, times = perfprocedures.compare_times_trt_pyt_exhaustive(engine, + pyt_components, + num_steps=args.num_steps) + string_header, string_data = perfutils.do_csv_export(wer, times, args.batch_size, args.seq_len) + if args.csv_path is not None: + with open(args.csv_path, 'a+') as f: + # See if header is there, if so, check that it matches + f.seek(0) # Read from start of file + existing_header = f.readline() + if existing_header == "": + f.write(string_header) + f.write("\n") + elif existing_header[:-1] != string_header: + raise Exception(f"Writing to existing CSV with incorrect format\nProduced:\n{string_header}\nFound:\n{existing_header}\nIf you intended to write to a new results csv, please change the csv_path argument") + f.seek(0,2) # Write to end of file + f.write(string_data) + f.write("\n") + else: + print(string_header) + print(string_data) + + if args.trt_prediction_path is not None: + with open(args.trt_prediction_path, 'w') as fp: + fp.write('\n'.join(preds['trt'])) + + if args.pyt_prediction_path is not None: + with open(args.pyt_prediction_path, 'w') as fp: + fp.write('\n'.join(preds['pyt'])) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Performance test of TRT") + parser.add_argument("--engine_path", default=None, type=str, help="Path to serialized TRT engine") + parser.add_argument("--use_existing_engine", action="store_true", default=False, help="If set, will deserialize engine at --engine_path" ) + parser.add_argument("--engine_batch_size", default=16, type=int, help="Maximum batch size for constructed engine; needed when building") + parser.add_argument("--batch_size", default=16, type=int, help="Batch size for data when running inference.") + parser.add_argument("--dataset_dir", type=str, help="Root directory of dataset") + parser.add_argument("--model_toml", type=str, required=True, help="Config toml to use. A selection can be found in configs/") + parser.add_argument("--val_manifest", type=str, help="JSON manifest of dataset.") + parser.add_argument("--onnx_path", default=None, type=str, help="Path to onnx model for engine creation") + parser.add_argument("--seq_len", default=None, type=int, help="Generate an ONNX export with this fixed sequence length, and save to --onnx_path. Requires also using --onnx_path and --ckpt_path.") + parser.add_argument("--ckpt_path", default=None, type=str, help="If provided, will also construct pytorch acoustic model") + parser.add_argument("--max_duration", default=None, type=float, help="Maximum possible length of audio data in seconds") + parser.add_argument("--num_steps", default=-1, type=int, help="Number of inference steps to run") + parser.add_argument("--trt_fp16", action="store_true", default=False, help="If set, will allow TRT engine builder to select fp16 kernels as well as fp32") + parser.add_argument("--pyt_fp16", action="store_true", default=False, help="If set, will construct pytorch model with fp16 weights") + parser.add_argument("--make_onnx", action="store_true", default=False, help="If set, will create an ONNX model and store it at the path specified by --onnx_path") + parser.add_argument("--csv_path", type=str, default=None, help="File to append csv info about inference time") + parser.add_argument("--trt_prediction_path", type=str, default=None, help="File to write predictions inferred with trt") + parser.add_argument("--pyt_prediction_path", type=str, default=None, help="File to write predictions inferred with pytorch") + parser.add_argument("--verbose", action="store_true", default=False, help="If set, will verbosely describe TRT engine building and deserialization as well as TRT inference") + parser.add_argument("--wav", type=str, help='absolute path to .wav file (16KHz)') + parser.add_argument("--max_workspace_size", default=4*1024*1024*1024, type=int, help="Maximum batch size for constructed engine; needed when building") + + return parser.parse_args() + +def get_engine(args): + '''Get a TRT engine + + If --should_serialize is present, always build from ONNX and store result in --engine_path. + Else If an engine is provided as an argument (--engine_path) use that one. + Otherwise, make one from onnx (--onnx_load_path), but don't serialize it. + ''' + engine = None + + if args.engine_path is not None and args.use_existing_engine: + engine = trtutils.deserialize_engine(args.engine_path, args.verbose) + elif args.engine_path is not None and args.onnx_path is not None: + # Build a new engine and serialize it. + engine = trtutils.build_engine_from_parser(args.onnx_path, args.engine_batch_size, args.trt_fp16, args.verbose, args.max_workspace_size) + with open(args.engine_path, 'wb') as f: + f.write(engine.serialize()) + else: + raise Exception("One of the following sets of arguments must be provided:\n"+ + " + --use_existing_engine\n"+ + " + \n"+ + "in order to construct a TRT engine") + if engine is None: + raise Exception("Failed to acquire TRT engine") + + return engine + +if __name__ == "__main__": + args = parse_args() + + main(args) diff --git a/PyTorch/SpeechRecognition/Jasper/trt/perfprocedures.py b/PyTorch/SpeechRecognition/Jasper/trt/perfprocedures.py new file mode 100644 index 00000000..08eaac73 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/perfprocedures.py @@ -0,0 +1,337 @@ +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +'''A collection of accuracy and latency evaluation procedures for JASPER on PyTorch and TRT. +''' + + +import pycuda.driver as cuda +import pycuda.autoinit +import perfutils +import trtutils +import time +import torch +from tqdm import tqdm + +def time_pyt(engine, pyt_components): + '''Times execution of PyTorch inference + ''' + baked_seq_len = engine.get_binding_shape(0)[1] + preprocess_times = [] + pyt_infers = [] + pyt_components['audio_preprocessor'].eval() + pyt_components['acoustic_model'].eval() + with torch.no_grad(): + for data in tqdm(pyt_components['data_layer'].data_iterator): + tensors = [] + for d in data: + tensors.append(d.to(torch.device("cuda"))) + input_tensor = (tensors[0], tensors[1]) + t0 = time.perf_counter() + am_input = pyt_components['audio_preprocessor'](x=input_tensor) + # Pad or cut to the neccessary engine length + am_input = perfutils.adjust_shape(am_input, baked_seq_len) + batch_size = am_input[0].shape[0] + torch.cuda.synchronize() + t1 = time.perf_counter() + # Run PyT inference + pyt_out = pyt_components['acoustic_model'](x=am_input) + torch.cuda.synchronize() + t2 = time.perf_counter() + perfutils.global_process_batch(log_probs=pyt_out, + original_tensors=tensors, + batch_size=batch_size, + is_trt=False) + assemble_times.append(t1-t0) + pyt_infers.append(t2-t1) + + pyt_wer = perfutils.global_process_epoch(is_trt=False) + trt_wer = None + trt_preds = perfutils._global_trt_dict['predictions'] + pyt_preds = perfutils._global_pyt_dict['predictions'] + times = { + 'preprocess': assemble_times, + 'pyt_infers': pyt_infers + } + wer = { + 'trt': trt_wer, + 'pyt': pyt_wer + } + preds = { + 'trt': trt_preds, + 'pyt': pyt_preds + } + return wer, preds, times + +def time_trt(engine, pyt_components): + '''Times execution of TRT inference + ''' + baked_seq_len = engine.get_binding_shape(0)[1] + assemble_times = [] + trt_copytos = [] + trt_copyfroms = [] + trt_infers = [] + decodingandeval = [] + with engine.create_execution_context() as context, torch.no_grad(): + for data in tqdm(pyt_components['data_layer'].data_iterator): + tensors = [] + for d in data: + tensors.append(d.to(torch.device("cuda"))) + input_tensor = (tensors[0], tensors[1]) + t0 = time.perf_counter() + am_input = pyt_components['audio_preprocessor'](x=input_tensor) + # Pad or cut to the neccessary engine length + am_input = perfutils.adjust_shape(am_input, baked_seq_len) + batch_size = am_input[0].shape[0] + torch.cuda.synchronize() + t1 = time.perf_counter() + # Run TRT inference + trt_out, time_to, time_infer, time_from= do_inference( + context=context, + inp=am_input, + batch_size=batch_size) + t3 = time.perf_counter() + trt_out = perfutils.torchify_trt_out(trt_out, batch_size) + perfutils.global_process_batch(log_probs=trt_out, + original_tensors=tensors, + batch_size=batch_size, + is_trt=True) + torch.cuda.synchronize() + t4 = time.perf_counter() + + + assemble_times.append(t1-t0) + trt_copytos.append(time_to) + trt_copyfroms.append(time_from) + trt_infers.append(time_infer) + decodingandeval.append(t4-t3) + + + trt_wer = perfutils.global_process_epoch(is_trt=True) + pyt_wer = perfutils.global_process_epoch(is_trt=False) + trt_preds = perfutils._global_trt_dict['predictions'] + pyt_preds = perfutils._global_pyt_dict['predictions'] + times = { + 'assemble': assemble_times, + 'trt_copyto': trt_copytos, + 'trt_copyfrom': trt_copyfroms, + 'trt_infers': trt_infers, + 'decodingandeval': decodingandeval + } + wer = { + 'trt': trt_wer, + 'pyt': pyt_wer + } + preds = { + 'trt': trt_preds, + 'pyt': pyt_preds + } + return wer, preds, times + +def run_trt(engine, pyt_components): + '''Runs TRT inference for accuracy evaluation + ''' + baked_seq_len = engine.get_binding_shape(0)[1] + wers = [] + preds = [] + with engine.create_execution_context() as context, torch.no_grad(): + for data in tqdm(pyt_components['data_layer'].data_iterator): + tensors = [] + for d in data: + tensors.append(d.to(torch.device("cuda"))) + input_tensor = (tensors[0], tensors[1]) + am_input = pyt_components['audio_preprocessor'](x=input_tensor) + # Pad or cut to the neccessary engine length + am_input = perfutils.adjust_shape(am_input, baked_seq_len) + batch_size = am_input[0].shape[0] + torch.cuda.synchronize() + # Run TRT inference + trt_out, _,_,_= do_inference(context=context, inp=am_input, batch_size=batch_size) + trt_out = perfutils.torchify_trt_out(trt_out, batch_size=batch_size) + wer, pred = perfutils.get_results(log_probs=trt_out, + original_tensors=tensors, + batch_size=batch_size) + wers.append(wer) + preds.append(pred) + + + return wers, preds + +def compare_times_trt_pyt_exhaustive(engine, pyt_components, num_steps): + '''Compares execution times and WER between TRT and PyTorch''' + + # The engine has a fixed-size sequence length, which needs to be known for slicing/padding input + baked_seq_len = engine.get_binding_shape(0)[1] + preprocess_times = [] + inputadjust_times = [] + outputadjust_times = [] + process_batch_times = [] + trt_solo_times = [] + trt_async_times = [] + tohost_sync_times =[] + pyt_infer_times = [] + step_counter = 0 + + with engine.create_execution_context() as context, torch.no_grad(): + for data in tqdm(pyt_components['data_layer'].data_iterator): + if num_steps >= 1: + if step_counter > num_steps: + break + step_counter +=1 + tensors = [] + for d in data: + tensors.append(d.to(torch.device("cuda"))) + + input_tensor = (tensors[0], tensors[1]) + preprocess_start = time.perf_counter() + am_input = pyt_components['audio_preprocessor'](x=input_tensor) + torch.cuda.synchronize() + preprocess_end = time.perf_counter() + + # Pad or cut to the neccessary engine length + inputadjust_start = time.perf_counter() + am_input = perfutils.adjust_shape(am_input, baked_seq_len) + torch.cuda.synchronize() + inputadjust_end = time.perf_counter() + + batch_size = am_input[0].shape[0] + + # Run TRT inference 1: Async copying and inference + trt_out, time_taken= do_inference_overlap( + context=context, + inp=am_input, + batch_size=batch_size) + torch.cuda.synchronize() + outputadjust_start = time.perf_counter() + trt_out = perfutils.torchify_trt_out(trt_out, batch_size) + torch.cuda.synchronize() + outputadjust_end = time.perf_counter() + + process_batch_start = time.perf_counter() + perfutils.global_process_batch(log_probs=trt_out, + original_tensors=tensors, + batch_size=batch_size, + is_trt=True) + torch.cuda.synchronize() + process_batch_end = time.perf_counter() + # Create explicit stream so pytorch doesn't complete asynchronously + pyt_infer_start = time.perf_counter() + pyt_out = pyt_components['acoustic_model'](x=am_input[0]) + torch.cuda.synchronize() + pyt_infer_end = time.perf_counter() + perfutils.global_process_batch(log_probs=pyt_out, + original_tensors=tensors, + batch_size=batch_size, + is_trt=False) + # Run TRT inference 2: Synchronous copying and inference + _, time_to, time_infer, time_from = do_inference( + context=context, + inp=am_input, + batch_size=batch_size) + preprocess_times.append(preprocess_end - preprocess_start) + inputadjust_times.append(inputadjust_end - inputadjust_start) + outputadjust_times.append(outputadjust_end - outputadjust_start) + process_batch_times.append(process_batch_end - process_batch_start) + trt_solo_times.append(time_infer) + trt_async_times.append(time_taken) + tohost_sync_times.append(time_from) + pyt_infer_times.append(pyt_infer_end - pyt_infer_start) + + trt_wer = perfutils.global_process_epoch(is_trt=True) + pyt_wer = perfutils.global_process_epoch(is_trt=False) + trt_preds = perfutils._global_trt_dict['predictions'] + pyt_preds = perfutils._global_pyt_dict['predictions'] + times = { + 'preprocess': preprocess_times, # Time to go through preprocessing + 'pyt_infer': pyt_infer_times, # Time for batch completion through pytorch + 'input_adjust': inputadjust_times, # Time to pad/cut for TRT engine size requirements + 'output_adjust' : outputadjust_times, # Time to reshape output of TRT and copy from host to device + 'post_process': process_batch_times, # Time to run greedy decoding and do CTC conversion + 'trt_solo_infer': trt_solo_times, # Time to execute just TRT acoustic model + 'to_host': tohost_sync_times, # Time to execute device to host copy synchronously + 'trt_async_infer': trt_async_times, # Time to execute combined async TRT acoustic model + device to host copy + + } + wer = { + 'trt': trt_wer, + 'pyt': pyt_wer + } + preds = { + 'trt': trt_preds, + 'pyt': pyt_preds + } + return wer, preds, times + +def do_inference(context, inp, batch_size): + '''Do inference using a TRT engine and time it + Execution and device-to-host copy are completed synchronously + ''' + + + # Typical Python-TRT used in samples would copy input data from host to device. + # Because the PyTorch Tensor is already on the device, such a copy is unneeded. + + # Create input array of device pointers + inputs = [inp[0].data_ptr()] + t0 = time.perf_counter() + # Create output buffers and stream + outputs, bindings, stream = trtutils.allocate_buffers_with_existing_inputs(context.engine, + inputs, + batch_size) + t1 = time.perf_counter() + # Run inference and transfer outputs to host asynchronously + context.execute_async(batch_size=batch_size, + bindings=bindings, + stream_handle=stream.handle) + stream.synchronize() + t2 = time.perf_counter() + [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] + stream.synchronize() + t3 = time.perf_counter() + + + copyto = t1-t0 + inference = t2-t1 + copyfrom = t3-t2 + out = outputs[0].host + return out, copyto, inference, copyfrom + +def do_inference_overlap(context, inp, batch_size): + '''Do inference using a TRT engine and time it + Execution and device-to-host copy are completed asynchronously + ''' + # Typical Python-TRT used in samples would copy input data from host to device. + # Because the PyTorch Tensor is already on the device, such a copy is unneeded. + + # Create input array of device pointers + inputs = [inp[0].data_ptr()] + t0 = time.perf_counter() + # Create output buffers and stream + outputs, bindings, stream = trtutils.allocate_buffers_with_existing_inputs(context.engine, + inputs, + batch_size) + t1 = time.perf_counter() + # Run inference and transfer outputs to host asynchronously + context.execute_async(batch_size=batch_size, + bindings=bindings, + stream_handle=stream.handle) + [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] + stream.synchronize() + t2 = time.perf_counter() + + + copyto = t1-t0 + inference = t2-t1 + out = outputs[0].host + return out, t2-t1 diff --git a/PyTorch/SpeechRecognition/Jasper/trt/perfutils.py b/PyTorch/SpeechRecognition/Jasper/trt/perfutils.py new file mode 100644 index 00000000..875fa0e9 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/perfutils.py @@ -0,0 +1,252 @@ +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +'''Contains helper functions for non-TRT components of JASPER inference +''' + +from model import GreedyCTCDecoder, AudioPreprocessing, Jasper +from dataset import AudioToTextDataLayer +from helpers import Optimization, AmpOptimizations, process_evaluation_batch, process_evaluation_epoch, add_ctc_labels, norm +from apex import amp +import torch +import torch.nn as nn +import toml +from parts.features import audio_from_file + +_global_ctc_labels = None +def get_vocab(): + ''' Gets the CTC vocab + + Requires calling get_pytorch_components_and_onnx() to setup global labels. + ''' + if _global_ctc_labels is None: + raise Exception("Feature labels have not been found. Execute `get_pytorch_components_and_onnx()` first") + + return _global_ctc_labels + +def get_results(log_probs, original_tensors, batch_size): + ''' Returns WER and predictions for the outputs of the acoustic model + + Used for one-off batches. Epoch-wide evaluation should use + global_process_batch and global_process_epoch + ''' + # Used to get WER and predictions for one-off batches + greedy_decoder = GreedyCTCDecoder() + predicts = norm(greedy_decoder(log_probs=log_probs)) + values_dict = dict( + predictions=[predicts], + transcript=[original_tensors[2][0:batch_size,...]], + transcript_length=[original_tensors[3][0:batch_size,...]], + ) + temp_dict = { + 'predictions': [], + 'transcripts': [], + } + process_evaluation_batch(values_dict, temp_dict, labels=get_vocab()) + predictions = temp_dict['predictions'] + wer, _ = process_evaluation_epoch(temp_dict) + return wer, predictions + + +_global_trt_dict = { + 'predictions': [], + 'transcripts': [], +} +_global_pyt_dict = { + 'predictions': [], + 'transcripts': [], +} + +def global_process_batch(log_probs, original_tensors, batch_size, is_trt=True): + '''Accumulates prediction evaluations for batches across an epoch + + is_trt determines which global dictionary will be used. + To get WER at any point, use global_process_epoch. + For one-off WER evaluations, use get_results() + ''' + # State-based approach for full WER comparison across a dataset. + greedy_decoder = GreedyCTCDecoder() + predicts = norm(greedy_decoder(log_probs=log_probs)) + values_dict = dict( + predictions=[predicts], + transcript=[original_tensors[2][0:batch_size,...]], + transcript_length=[original_tensors[3][0:batch_size,...]], + ) + dict_to_process = _global_trt_dict if is_trt else _global_pyt_dict + process_evaluation_batch(values_dict, dict_to_process, labels=get_vocab()) + + +def global_process_epoch(is_trt=True): + '''Returns WER in accumulated global dictionary + ''' + dict_to_process = _global_trt_dict if is_trt else _global_pyt_dict + wer, _ = process_evaluation_epoch(dict_to_process) + return wer + + +def get_onnx(path, acoustic_model, signal_shape, dtype=torch.float): + ''' Get an ONNX model with float weights + + Requires an --onnx_save_path and --ckpt_path (so that an acoustic model could be constructed). + Fixed-length --seq_len must be provided as well. + ''' + with torch.no_grad(): + phony_signal = torch.zeros(signal_shape, dtype=dtype, device=torch.device("cuda")) + torch.onnx.export(acoustic_model, (phony_signal,), path, input_names=["FEATURES"], output_names=["LOGITS"]) + fn=path+".readable" + with open(fn, 'w') as f: + #Write human-readable graph representation to file as well. + import onnx + tempModel = onnx.load(path) + pgraph = onnx.helper.printable_graph(tempModel.graph) + f.write(pgraph) + + return path + + +def get_pytorch_components_and_onnx(args): + '''Returns PyTorch components used for inference + ''' + model_definition = toml.load(args.model_toml) + dataset_vocab = model_definition['labels']['labels'] + # Set up global labels for future vocab calls + global _global_ctc_labels + _global_ctc_labels= add_ctc_labels(dataset_vocab) + featurizer_config = model_definition['input_eval'] + + optim_level = Optimization.mxprO3 if args.pyt_fp16 else Optimization.mxprO0 + + featurizer_config["optimization_level"] = optim_level + acoustic_model = None + audio_preprocessor = None + onnx_path = None + data_layer = None + wav = None + seq_len = None + dtype=torch.float + + if args.max_duration is not None: + featurizer_config['max_duration'] = args.max_duration + if args.dataset_dir is not None: + data_layer = AudioToTextDataLayer(dataset_dir=args.dataset_dir, + featurizer_config=featurizer_config, + manifest_filepath=args.val_manifest, + labels=dataset_vocab, + batch_size=args.batch_size, + shuffle=False) + if args.wav is not None: + args.batch_size=1 + args.engine_batch_size=1 + wav, seq_len = audio_from_file(args.wav) + if args.seq_len is None or args.seq_len == 0: + args.seq_len = seq_len/(featurizer_config['sample_rate']/100) + + + model = Jasper(feature_config=featurizer_config, + jasper_model_definition=model_definition, + feat_in=1024, + num_classes=len(get_vocab())) + + model.cuda() + model.eval() + acoustic_model = model.acoustic_model + audio_preprocessor = model.audio_preprocessor + + if args.ckpt_path is not None: + checkpoint = torch.load(args.ckpt_path, map_location="cpu") + model.load_state_dict(checkpoint['state_dict'], strict=False) + + if args.make_onnx: + if args.onnx_path is None or acoustic_model is None: + raise Exception("--ckpt_path, --onnx_path must be provided when using --make_onnx") + onnx_path = get_onnx(args.onnx_path, acoustic_model, + signal_shape=(args.engine_batch_size, 64, args.seq_len), dtype=torch.float) + + if args.pyt_fp16: + amp.initialize(models=acoustic_model, opt_level=AmpOptimizations[optim_level]) + + return {'data_layer': data_layer, + 'audio_preprocessor': audio_preprocessor, + 'acoustic_model': acoustic_model, + 'input_wav' : (wav, seq_len) }, onnx_path + +def adjust_shape(am_input, baked_length): + '''Pads or cuts acoustic model input tensor to some fixed_length + + ''' + in_seq_len = am_input[0].shape[2] + newSeq=am_input[0] + if in_seq_len > baked_length: + # Cut extra bits off, no inference done + newSeq = am_input[0][...,0:baked_length].contiguous() + elif in_seq_len < baked_length: + # Zero-pad to satisfy length + pad_length = baked_length - in_seq_len + newSeq = nn.functional.pad(am_input[0], (0, pad_length), 'constant', 0) + return (newSeq,) + +def torchify_trt_out(trt_out, batch_size): + '''Reshapes flat data to format for greedy+CTC decoding + + Used to convert numpy array on host to PyT Tensor + ''' + desired_shape = (batch_size,-1,len(get_vocab())) + + # Predictions must be reshaped. + return torch.Tensor(trt_out).reshape(desired_shape) + +def do_csv_export(wers, times, batch_size, num_frames): + '''Produces CSV header and data for input data + + wers: dictionary of WER with keys={'trt', 'pyt'} + times: dictionary of execution times + ''' + def take_durations_and_output_percentile(durations, ratios): + from heapq import nlargest, nsmallest + import numpy as np + import math + durations = np.asarray(durations) * 1000 # in ms + latency = durations + # The first few entries may not be representative due to warm-up effects + # The last entry might not be representative if dataset_size % batch_size != 0 + latency = latency[5:-1] + mean_latency = np.mean(latency) + latency_worst = nlargest(math.ceil( (1 - min(ratios))* len(latency)), latency) + latency_ranges=get_percentile(ratios, latency_worst, len(latency)) + latency_ranges["0.5"] = mean_latency + return latency_ranges + def get_percentile(ratios, arr, nsamples): + res = {} + for a in ratios: + idx = max(int(nsamples * (1 - a)), 0) + res[a] = arr[idx] + return res + + ratios = [0.9, 0.95, 0.99, 1.] + header=[] + data=[] + header.append("BatchSize") + header.append("NumFrames") + data.append(f"{batch_size}") + data.append(f"{num_frames}") + for title, wer in wers.items(): + header.append(title) + data.append(f"{wer}") + for title, durations in times.items(): + ratio_latencies_dict = take_durations_and_output_percentile(durations, ratios) + for ratio, latency in ratio_latencies_dict.items(): + header.append(f"{title}_{ratio}") + data.append(f"{latency}") + string_header = ", ".join(header) + string_data = ", ".join(data) + return string_header, string_data diff --git a/PyTorch/SpeechRecognition/Jasper/trt/requirements.txt b/PyTorch/SpeechRecognition/Jasper/trt/requirements.txt new file mode 100644 index 00000000..a1c1b904 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/requirements.txt @@ -0,0 +1,2 @@ +pycuda +pillow diff --git a/PyTorch/SpeechRecognition/Jasper/trt/scripts/docker/trt_build.sh b/PyTorch/SpeechRecognition/Jasper/trt/scripts/docker/trt_build.sh new file mode 100755 index 00000000..0e44c7fb --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/scripts/docker/trt_build.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +# Constructs a docker image containing dependencies for execution of JASPER through TRT +echo "docker build . -f ./trt/Dockerfile -t jasper:trt6" +docker build . -f ./trt/Dockerfile -t jasper:trt6 diff --git a/PyTorch/SpeechRecognition/Jasper/trt/scripts/docker/trt_launch.sh b/PyTorch/SpeechRecognition/Jasper/trt/scripts/docker/trt_launch.sh new file mode 100755 index 00000000..8c5ea218 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/scripts/docker/trt_launch.sh @@ -0,0 +1,39 @@ +#!/bin/bash + +# Launch TRT JASPER container. + +DATA_DIR=$1 +CHECKPOINT_DIR=$2 +RESULT_DIR=$3 +PROGRAM_PATH=${PROGRAM_PATH} + +if [ $# -lt 3 ]; then + echo "Usage: ./trt_launch.sh ()" + echo "All directory paths must be absolute paths and exist" + exit 1 +fi + +for dir in $DATA_DIR $CHECKPOINT_DIR $RESULT_DIR; do + if [[ $dir != /* ]]; then + echo "All directory paths must be absolute paths!" + echo "${dir} is not an absolute path" + exit 1 + fi + + if [ ! -d $dir ]; then + echo "All directory paths must exist!" + echo "${dir} does not exist" + exit 1 + fi +done + + +nvidia-docker run -it --rm \ + --runtime=nvidia \ + --shm-size=4g \ + --ulimit memlock=-1 \ + --ulimit stack=67108864 \ + -v $DATA_DIR:/datasets \ + -v $CHECKPOINT_DIR:/checkpoints/ \ + -v $RESULT_DIR:/results/ \ + jasper:trt6 bash $PROGRAM_PATH diff --git a/MxNet/Classification/RN50v1.5/examples/INFER_BENCHMARK_FP32.sh b/PyTorch/SpeechRecognition/Jasper/trt/scripts/download_inference_librispeech.sh old mode 100644 new mode 100755 similarity index 60% rename from MxNet/Classification/RN50v1.5/examples/INFER_BENCHMARK_FP32.sh rename to PyTorch/SpeechRecognition/Jasper/trt/scripts/download_inference_librispeech.sh index 1fc38269..47bb2e97 --- a/MxNet/Classification/RN50v1.5/examples/INFER_BENCHMARK_FP32.sh +++ b/PyTorch/SpeechRecognition/Jasper/trt/scripts/download_inference_librispeech.sh @@ -1,3 +1,4 @@ +#!/usr/bin/env bash # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,8 +13,18 @@ # See the License for the specific language governing permissions and # limitations under the License. +#Downloads the inference-subset of the Librispeech corpus. -# This script launches ResNet50 inference benchmark in FP32 on 1 GPU with 1,2,4,32,64,96 batch size -# Usage ./INFER_BENCHMARK_FP32.sh -python benchmark.py -n 1 -b 1,2,4,32,64,96 --only-inference -e 3 -w 1 -i 100 -o report.json $@ + +DATA_SET="LibriSpeech" +DATA_ROOT_DIR="/datasets" +DATA_DIR="${DATA_ROOT_DIR}/${DATA_SET}" +if [ ! -d "$DATA_DIR" ] +then + mkdir -p $DATA_DIR + chmod go+rx $DATA_DIR + python utils/download_librispeech.py utils/inference_librispeech.csv $DATA_DIR -e ${DATA_ROOT_DIR}/ +else + echo "Directory $DATA_DIR already exists." +fi diff --git a/PyTorch/SpeechRecognition/Jasper/trt/scripts/preprocess_inference_librispeech.sh b/PyTorch/SpeechRecognition/Jasper/trt/scripts/preprocess_inference_librispeech.sh new file mode 100755 index 00000000..60695dad --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/scripts/preprocess_inference_librispeech.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Constructs JSON manifests for inference-subset of Librispeech corpus. + +python ./utils/convert_librispeech.py \ + --input_dir /datasets/LibriSpeech/dev-clean \ + --dest_dir /datasets/LibriSpeech/dev-clean-wav \ + --output_json /datasets/LibriSpeech/librispeech-dev-clean-wav.json +python ./utils/convert_librispeech.py \ + --input_dir /datasets/LibriSpeech/dev-other \ + --dest_dir /datasets/LibriSpeech/dev-other-wav \ + --output_json /datasets/LibriSpeech/librispeech-dev-other-wav.json + + +python ./utils/convert_librispeech.py \ + --input_dir /datasets/LibriSpeech/test-clean \ + --dest_dir /datasets/LibriSpeech/test-clean-wav \ + --output_json /datasets/LibriSpeech/librispeech-test-clean-wav.json +python ./utils/convert_librispeech.py \ + --input_dir /datasets/LibriSpeech/test-other \ + --dest_dir /datasets/LibriSpeech/test-other-wav \ + --output_json /datasets/LibriSpeech/librispeech-test-other-wav.json diff --git a/PyTorch/SpeechRecognition/Jasper/trt/scripts/trt_inference.sh b/PyTorch/SpeechRecognition/Jasper/trt/scripts/trt_inference.sh new file mode 100755 index 00000000..0caff525 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/scripts/trt_inference.sh @@ -0,0 +1,56 @@ +#!/bin/bash +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Performs inference and measures latency and accuracy of TRT and PyTorch implementations of JASPER. + +echo "Container nvidia build = " $NVIDIA_BUILD_ID + +# Mandatory Arguments +CHECKPOINT=$CHECKPOINT + +# Arguments with Defaults +DATA_DIR=${DATA_DIR:-"/datasets/LibriSpeech"} +DATASET=${DATASET:-"dev-clean"} +RESULT_DIR=${RESULT_DIR:-"/results"} +CREATE_LOGFILE=${CREATE_LOGFILE:-"true"} +TRT_PRECISION=${TRT_PRECISION:-"fp32"} +PYTORCH_PRECISION=${PYTORCH_PRECISION:-"fp32"} +NUM_STEPS=${NUM_STEPS:-"-1"} +BATCH_SIZE=${BATCH_SIZE:-1} +NUM_FRAMES=${NUM_FRAMES:-3600} +FORCE_ENGINE_REBUILD=${FORCE_ENGINE_REBUILD:-"true"} +CSV_PATH=${CSV_PATH:-"/results/res.csv"} +TRT_PREDICTION_PATH=${TRT_PREDICTION_PATH:-"/results/trt_predictions.txt"} +PYT_PREDICTION_PATH=${PYT_PREDICTION_PATH:-"/results/pyt_predictions.txt"} +VERBOSE=${VERBOSE:-"false"} + + + +export CHECKPOINT="$CHECKPOINT" +export DATA_DIR="$DATA_DIR" +export DATASET="$DATASET" +export RESULT_DIR="$RESULT_DIR" +export CREATE_LOGFILE="$CREATE_LOGFILE" +export TRT_PRECISION="$TRT_PRECISION" +export PYTORCH_PRECISION="$PYTORCH_PRECISION" +export NUM_STEPS="$NUM_STEPS" +export BATCH_SIZE="$BATCH_SIZE" +export NUM_FRAMES="$NUM_FRAMES" +export FORCE_ENGINE_REBUILD="$FORCE_ENGINE_REBUILD" +export CSV_PATH="$CSV_PATH" +export TRT_PREDICTION_PATH="$TRT_PREDICTION_PATH" +export PYT_PREDICTION_PATH="$PYT_PREDICTION_PATH" +export VERBOSE="$VERBOSE" + +bash ./trt/scripts/trt_inference_benchmark.sh $1 $2 $3 $4 $5 $6 $7 diff --git a/PyTorch/SpeechRecognition/Jasper/trt/scripts/trt_inference_benchmark.sh b/PyTorch/SpeechRecognition/Jasper/trt/scripts/trt_inference_benchmark.sh new file mode 100755 index 00000000..8f4eaea9 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/scripts/trt_inference_benchmark.sh @@ -0,0 +1,162 @@ +#!/bin/bash +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Measures latency and accuracy of TRT and PyTorch implementations of JASPER. + +echo "Container nvidia build = " $NVIDIA_BUILD_ID + +# Mandatory Arguments +CHECKPOINT=$CHECKPOINT + +# Arguments with Defaults +DATA_DIR=${DATA_DIR:-"/datasets/LibriSpeech"} +DATASET=${DATASET:-"dev-clean"} +RESULT_DIR=${RESULT_DIR:-"/results"} +CREATE_LOGFILE=${CREATE_LOGFILE:-"true"} +TRT_PRECISION=${TRT_PRECISION:-"fp32"} +PYTORCH_PRECISION=${PYTORCH_PRECISION:-"fp32"} +NUM_STEPS=${NUM_STEPS:-"100"} +BATCH_SIZE=${BATCH_SIZE:-64} +NUM_FRAMES=${NUM_FRAMES:-512} +FORCE_ENGINE_REBUILD=${FORCE_ENGINE_REBUILD:-"true"} +CSV_PATH=${CSV_PATH:-"/results/res.csv"} +TRT_PREDICTION_PATH=${TRT_PREDICTION_PATH:-"none"} +PYT_PREDICTION_PATH=${PYT_PREDICTION_PATH:-"none"} +VERBOSE=${VERBOSE:-"false"} + + +# Set up flag-based arguments +TRT_PREC="" +if [ "$TRT_PRECISION" = "fp16" ] ; then + TRT_PREC="--trt_fp16" +elif [ "$TRT_PRECISION" = "fp32" ] ; then + TRT_PREC="" +else + echo "Unknown argument" + exit -2 +fi + +PYTORCH_PREC="" +if [ "$PYTORCH_PRECISION" = "fp16" ] ; then + PYTORCH_PREC="--pyt_fp16" +elif [ "$PYTORCH_PRECISION" = "fp32" ] ; then + PYTORCH_PREC="" +else + echo "Unknown argument" + exit -2 +fi + +SHOULD_VERBOSE="" +if [ "$VERBOSE" = "true" ] ; then + SHOULD_VERBOSE="--verbose" +fi + + +STEPS="" +if [ "$NUM_STEPS" -gt 0 ] ; then + STEPS=" --num_steps $NUM_STEPS" +fi + +# Making engine and onnx directories in RESULT_DIR if they don't already exist +ONNX_DIR=$RESULT_DIR/onnxs +ENGINE_DIR=$RESULT_DIR/engines +mkdir -p $ONNX_DIR +mkdir -p $ENGINE_DIR + + +PREFIX=BS${BATCH_SIZE}_NF${NUM_FRAMES} + +# Currently, TRT parser for ONNX can't parse half-precision weights, so ONNX +# export will always be FP32. This is also enforced in perf.py +ONNX_FILE=fp32_${PREFIX}.onnx +ENGINE_FILE=${TRT_PRECISION}_${PREFIX}.engine + + + +# If an ONNX with the same precision and number of frames exists, don't recreate it because +# TRT engine construction can be done on an onnx of any batch size +# "%P" only prints filenames (rather than absolute/relative path names) +EXISTING_ONNX=$(find $ONNX_DIR -name "fp32_BS*_NF${NUM_FRAMES}.onnx" -printf "%P\n" | head -n 1) +SHOULD_MAKE_ONNX="" +if [ -z "$EXISTING_ONNX" ] ; then + SHOULD_MAKE_ONNX="--make_onnx" +else + ONNX_FILE=${EXISTING_ONNX} +fi + +# Follow FORCE_ENGINE_REBUILD about reusing existing engines. +# If false, the existing engine must match precision, batch size, and number of frames +SHOULD_MAKE_ENGINE="" +if [ "$FORCE_ENGINE_REBUILD" != "true" ] ; then + EXISTING_ENGINE=$(find $ENGINE_DIR -name "${ENGINE_FILE}") + if [ -n "$EXISTING_ENGINE" ] ; then + SHOULD_MAKE_ENGINE="--use_existing_engine" + fi +fi + + + +if [ "$TRT_PREDICTION_PATH" = "none" ] ; then + TRT_PREDICTION_PATH="" +else + TRT_PREDICTION_PATH=" --trt_prediction_path=${TRT_PREDICTION_PATH}" +fi + + +if [ "$PYT_PREDICTION_PATH" = "none" ] ; then + PYT_PREDICTION_PATH="" +else + PYT_PREDICTION_PATH=" --pyt_prediction_path=${PYT_PREDICTION_PATH}" +fi + +CMD="python trt/perf.py" +CMD+=" --batch_size $BATCH_SIZE" +CMD+=" --engine_batch_size $BATCH_SIZE" +CMD+=" --model_toml configs/jasper10x5dr_nomask.toml" +CMD+=" --dataset_dir $DATA_DIR" +CMD+=" --val_manifest $DATA_DIR/librispeech-${DATASET}-wav.json " +CMD+=" --ckpt $CHECKPOINT" +CMD+=" $SHOULD_VERBOSE" +CMD+=" $TRT_PREC" +CMD+=" $PYTORCH_PREC" +CMD+=" $STEPS" +CMD+=" --engine_path ${RESULT_DIR}/engines/${ENGINE_FILE}" +CMD+=" --onnx_path ${RESULT_DIR}/onnxs/${ONNX_FILE}" +CMD+=" --seq_len $NUM_FRAMES" +CMD+=" $SHOULD_MAKE_ONNX" +CMD+=" $SHOULD_MAKE_ENGINE" +CMD+=" --csv_path $CSV_PATH" +CMD+=" $1 $2 $3 $4 $5 $6 $7 $8 $9" +CMD+=" $TRT_PREDICTION_PATH" +CMD+=" $PYT_PREDICTION_PATH" + + +if [ "$CREATE_LOGFILE" == "true" ] ; then + export GBS=$(expr $BATCH_SIZE ) + printf -v TAG "jasper_trt_inference_benchmark_%s_gbs%d" "$PYTORCH_PRECISION" $GBS + DATESTAMP=`date +'%y%m%d%H%M%S'` + LOGFILE=$RESULT_DIR/$TAG.$DATESTAMP.log + printf "Logs written to %s\n" "$LOGFILE" +fi + +set -x +if [ -z "$LOGFILE" ] ; then + $CMD +else + ( + $CMD + ) |& tee $LOGFILE + grep 'latency' $LOGFILE +fi +set +x diff --git a/PyTorch/SpeechRecognition/Jasper/trt/scripts/walk_benchmark.sh b/PyTorch/SpeechRecognition/Jasper/trt/scripts/walk_benchmark.sh new file mode 100755 index 00000000..6b72d486 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/scripts/walk_benchmark.sh @@ -0,0 +1,38 @@ +#!/bin/bash +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# A usage example of trt_inference_benchmark.sh. + + +export NUM_STEPS=100 +export FORCE_ENGINE_REBUILD="true" +export CHECKPOINT="/checkpoints/jasper.pt" +export CREATE_LOGFILE="false" +for prec in fp16; +do + export TRT_PRECISION=$prec + export PYTORCH_PRECISION=$prec + export CSV_PATH="/results/${prec}.csv" + for nf in 208 304 512 704 1008 1680; + do + export NUM_FRAMES=$nf + for bs in 1 2 4 8 16 32 64; + do + export BATCH_SIZE=$bs + + echo "Doing batch size ${bs}, sequence length ${nf}, precision ${prec}" + bash trt/scripts/trt_inference_benchmark.sh $1 $2 $3 $4 $5 $6 + done + done +done diff --git a/PyTorch/SpeechRecognition/Jasper/trt/trtutils.py b/PyTorch/SpeechRecognition/Jasper/trt/trtutils.py new file mode 100644 index 00000000..0a19ef7c --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/trt/trtutils.py @@ -0,0 +1,92 @@ +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +'''Contains helper functions for TRT components of JASPER inference +''' +import pycuda.driver as cuda +import tensorrt as trt + +# Simple class: more explicit than dealing with 2-tuple +class HostDeviceMem(object): + '''Type for managing host and device buffers + + A simple class which is more explicit that dealing with a 2-tuple. + ''' + def __init__(self, host_mem, device_mem): + self.host = host_mem + self.device = device_mem + + def __str__(self): + return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device) + + def __repr__(self): + return self.__str__() + +def build_engine_from_parser(model_path, batch_size, is_fp16=True, is_verbose=False, max_workspace_size=4*1024*1024*1024): + '''Builds TRT engine from an ONNX file + Note that network output 1 is unmarked so that the engine will not use + vestigial length calculations associated with masked_fill + ''' + TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) if is_verbose else trt.Logger(trt.Logger.WARNING) + with trt.Builder(TRT_LOGGER) as builder: + builder.max_batch_size = batch_size + builder.fp16_mode = is_fp16 + builder.max_workspace_size = max_workspace_size + with builder.create_network() as network: + with trt.OnnxParser(network, TRT_LOGGER) as parser: + with open(model_path, 'rb') as model: + parser.parse(model.read()) + + return builder.build_cuda_engine(network) + +def deserialize_engine(engine_path, is_verbose): + '''Deserializes TRT engine at engine_path + ''' + TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) if is_verbose else trt.Logger(trt.Logger.WARNING) + with open(engine_path, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime: + engine = runtime.deserialize_cuda_engine(f.read()) + return engine + + +def allocate_buffers_with_existing_inputs(engine, inp, batch_size=1): + ''' + allocate_buffers() (see TRT python samples) but uses an existing inputs on device + + inp: List of pointers to device memory. Pointers are in the same order as + would be produced by allocate_buffers(). That is, inputs are in the + order defined by iterating through `engine` + ''' + + # Add input to bindings + bindings = [] + outputs = [] + stream = cuda.Stream() + inp_idx = 0 + + for binding in engine: + if engine.binding_is_input(binding): + bindings.append(inp[inp_idx]) + inp_idx += 1 + else: + # Unchanged from do_inference() + size = trt.volume(engine.get_binding_shape(binding)) * batch_size + dtype = trt.nptype(engine.get_binding_dtype(binding)) + # Allocate host and device buffers + host_mem = cuda.pagelocked_empty(size, dtype) + device_mem = cuda.mem_alloc(host_mem.nbytes*2) + # Append the device buffer to device bindings. + bindings.append(int(device_mem)) + # Append to the appropriate list. + outputs.append(HostDeviceMem(host_mem, device_mem)) + + return outputs, bindings, stream diff --git a/PyTorch/SpeechRecognition/Jasper/utils/inference_librispeech.csv b/PyTorch/SpeechRecognition/Jasper/utils/inference_librispeech.csv new file mode 100644 index 00000000..40dac4e0 --- /dev/null +++ b/PyTorch/SpeechRecognition/Jasper/utils/inference_librispeech.csv @@ -0,0 +1,5 @@ +url,md5 +http://www.openslr.org/resources/12/dev-clean.tar.gz,42e2234ba48799c1f50f24a7926300a1 +http://www.openslr.org/resources/12/dev-other.tar.gz,c8d0bcc9cca99d4f8b62fcc847357931 +http://www.openslr.org/resources/12/test-clean.tar.gz,32fa31d27d2e1cad72775fee3f4849a9 +http://www.openslr.org/resources/12/test-other.tar.gz,fb5a50374b501bb3bac4815ee91d3135 diff --git a/PyTorch/SpeechSynthesis/Tacotron2/Dockerfile b/PyTorch/SpeechSynthesis/Tacotron2/Dockerfile index f35c922f..b5752d77 100644 --- a/PyTorch/SpeechSynthesis/Tacotron2/Dockerfile +++ b/PyTorch/SpeechSynthesis/Tacotron2/Dockerfile @@ -1,4 +1,4 @@ -FROM nvcr.io/nvidia/pytorch:19.07-py3 +FROM nvcr.io/nvidia/pytorch:19.08-py3 ADD . /workspace/tacotron2 WORKDIR /workspace/tacotron2 diff --git a/PyTorch/SpeechSynthesis/Tacotron2/README.md b/PyTorch/SpeechSynthesis/Tacotron2/README.md index f13a4483..236112b2 100644 --- a/PyTorch/SpeechSynthesis/Tacotron2/README.md +++ b/PyTorch/SpeechSynthesis/Tacotron2/README.md @@ -1,4 +1,4 @@ -# Tacotron 2 And WaveGlow v1.6 For PyTorch +# Tacotron 2 And WaveGlow v1.7 For PyTorch This repository provides a script and recipe to train Tacotron 2 and WaveGlow v1.6 models to achieve state of the art accuracy, and is tested and maintained by NVIDIA. @@ -38,7 +38,8 @@ v1.6 models to achieve state of the art accuracy, and is tested and maintained b * [NVIDIA DGX-1 (8x V100 16G)](#nvidia-dgx-1-8x-v100-16g) * [Expected training time](#expected-training-time) * [Inference performance results](#inference-performance-results) - * [NVIDIA DGX-1 (8x V100 16G)](#nvidia-dgx-1-8x-v100-16g) + * [NVIDIA V100 16G](#nvidia-v100-16g) + * [NVIDIA T4](#nvidia-t4) * [Release notes](#release-notes) * [Changelog](#changelog) * [Known issues](#known-issues) @@ -99,7 +100,7 @@ into spherical Gaussian distribution through a series of flows. One step of a flow consists of an invertible convolution, followed by a modified WaveNet architecture that serves as an affine coupling layer. During inference, the network is inverted and audio samples are generated from the Gaussian -distribution. +distribution. Our implementation uses 512 residual channels in the coupling layer. ![](./img/waveglow_arch.png "WaveGlow architecture") @@ -130,16 +131,16 @@ The following features are supported by this model. |[AMP](https://nvidia.github.io/apex/amp.html) | Yes | Yes | |[Apex DistributedDataParallel](https://nvidia.github.io/apex/parallel.html) | Yes | Yes | -#### Features +#### Features -AMP - a tool that enables Tensor Core-accelerated training. For more information, +AMP - a tool that enables Tensor Core-accelerated training. For more information, refer to [Enabling mixed precision](#enabling-mixed-precision). -Apex DistributedDataParallel - a module wrapper that enables easy multiprocess -distributed data parallel training, similar to `torch.nn.parallel.DistributedDataParallel`. -`DistributedDataParallel` is optimized for use with NCCL. It achieves high -performance by overlapping communication with computation during `backward()` -and bucketing smaller gradient transfers to reduce the total number of transfers +Apex DistributedDataParallel - a module wrapper that enables easy multiprocess +distributed data parallel training, similar to `torch.nn.parallel.DistributedDataParallel`. +`DistributedDataParallel` is optimized for use with NCCL. It achieves high +performance by overlapping communication with computation during `backward()` +and bucketing smaller gradient transfers to reduce the total number of transfers required. ## Mixed precision training @@ -267,16 +268,9 @@ this script, issue: bash scripts/prepare_dataset.sh ``` - To preprocess the datasets for Tacotron 2 training, use the - `./scripts/prepare_mels.sh` script: - ```bash - bash scripts/prepare_mels.sh - ``` - Data is downloaded to the `./LJSpeech-1.1` directory (on the host). The -`./LJSpeech-1.1` directory is mounted to the `/workspace/tacotron2/LJSpeech-1.1` -location in the NGC container. The preprocessed mel-spectrograms are stored in the -`./LJSpeech-1.1/mels` directory. + `./LJSpeech-1.1` directory is mounted to the `/workspace/tacotron2/LJSpeech-1.1` + location in the NGC container. 3. Build the Tacotron 2 and WaveGlow PyTorch NGC container. ```bash @@ -290,8 +284,14 @@ After you build the container image, you can start an interactive CLI session wi bash scripts/docker/interactive.sh ``` - The `interactive.sh` script requires that the location on the dataset is specified. - For example, `LJSpeech-1.1`. + The `interactive.sh` script requires that the location on the dataset is specified. + For example, `LJSpeech-1.1`. To preprocess the datasets for Tacotron 2 training, use + the `./scripts/prepare_mels.sh` script: + ```bash + bash scripts/prepare_mels.sh + ``` + + The preprocessed mel-spectrograms are stored in the `./LJSpeech-1.1/mels` directory. 5. Start training. To start Tacotron 2 training, run: @@ -313,8 +313,8 @@ Ensure your loss values are comparable to those listed in the table in the samples in the `./audio` folder. For details about generating audio, see the [Inference process](#inference-process) section below. - The training scripts automatically run the validation after each training - epoch. The results from the validation are printed to the standard output + The training scripts automatically run the validation after each training + epoch. The results from the validation are printed to the standard output (`stdout`) and saved to the log files. 7. Start inference. @@ -327,10 +327,10 @@ and `--waveglow` arguments. ```bash python inference.py --tacotron2 --waveglow -o output/ -i phrases/phrase.txt --amp-run ``` - - The speech is generated from lines of text in the file that is passed with - `-i` argument. The number of lines determines inference batch size. To run - inference in mixed precision, use the `--amp-run` flag. The output audio will + + The speech is generated from lines of text in the file that is passed with + `-i` argument. The number of lines determines inference batch size. To run + inference in mixed precision, use the `--amp-run` flag. The output audio will be stored in the path specified by the `-o` argument. ## Advanced @@ -390,11 +390,12 @@ WaveGlow models. #### WaveGlow parameters * `--segment-length` - segment length of input audio processed by the neural network (8000) +* `--wn-channels` - number of residual channels in the coupling layer networks (512) ### Command-line options -To see the full list of available options and their descriptions, use the `-h` +To see the full list of available options and their descriptions, use the `-h` or `--help` command line option, for example: ```bash python train.py --help @@ -470,8 +471,12 @@ To run inference, issue: ```bash python inference.py --tacotron2 --waveglow -o output/ --include-warmup -i phrases/phrase.txt --amp-run ``` -Here, `Tacotron2_checkpoint` and `WaveGlow_checkpoint` are pre-trained -checkpoints for the respective models, and `phrases/phrase.txt` contains input phrases. The number of text lines determines the inference batch size. Audio will be saved in the output folder. +Here, `Tacotron2_checkpoint` and `WaveGlow_checkpoint` are pre-trained +checkpoints for the respective models, and `phrases/phrase.txt` contains input +phrases. The number of text lines determines the inference batch size. Audio +will be saved in the output folder. The audio files [audio_fp16](./audio/audio_fp16.wav) +and [audio_fp32](./audio/audio_fp32.wav) were generated using checkpoints from +mixed precision and FP32 training, respectively. You can find all the available options by calling `python inference.py --help`. @@ -548,9 +553,9 @@ To benchmark the inference performance on a batch size=1, run: ``` The output log files will contain performance numbers for Tacotron 2 model -(number of output mel-spectrograms per second, reported as `tacotron2_items_per_sec`) -and for WaveGlow (number of output samples per second, reported as `waveglow_items_per_sec`). -The `inference.py` script will run a few warmup iterations before running the benchmark. +(number of output mel-spectrograms per second, reported as `tacotron2_items_per_sec`) +and for WaveGlow (number of output samples per second, reported as `waveglow_items_per_sec`). +The `inference.py` script will run a few warmup iterations before running the benchmark. ### Results @@ -635,31 +640,36 @@ The following table shows the expected training time for convergence for WaveGlo #### Inference performance results -##### NVIDIA DGX-1 (8x V100 16G) +The following tables show inference statistics for the Tacotron2 and WaveGlow +text-to-speech system, gathered from 1000 inference runs, on 1 V100 and 1 T4, +respectively. Latency is measured from the start of Tacotron 2 inference to +the end of WaveGlow inference. The tables include average latency, latency standard +deviation, and latency confidence intervals. Throughput is measured +as the number of generated audio samples per second. RTF is the real-time factor +which tells how many seconds of speech are generated in 1 second of compute. -Our results were obtained by running the `./inference.py` inference script in -the PyTorch-19.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. -Performance numbers (in output mel-spectrograms per second for Tacotron 2 and -output samples per second for WaveGlow) were averaged over 16 runs. +##### NVIDIA V100 16G -The following table shows the inference performance results for Tacotron 2 model. -Results are measured in the number of output mel-spectrograms per second. +|Batch size|Input length|Precision|Avg latency (s)|Latency std (s)|Latency confidence interval 50% (s)|Latency confidence interval 100% (s)|Throughput (samples/sec)|Speed-up with mixed precision|Avg mels generated (81 mels=1 sec of speech)|Avg audio length (s)|Avg RTF| +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +|1| 128| FP16| 1.73| 0.07| 1.72| 2.11| 89,162| 1.09| 601| 6.98| 4.04| +|4| 128| FP16| 4.21| 0.17| 4.19| 4.84| 145,800| 1.16| 600| 6.97| 1.65| +|1| 128| FP32| 1.85| 0.06| 1.84| 2.19| 81,868| 1.00| 590| 6.85| 3.71| +|4| 128| FP32| 4.80| 0.15| 4.79| 5.43| 125,930| 1.00| 590| 6.85| 1.43| -|Number of GPUs|Number of mels used with mixed precision|Number of mels used with FP32|Speed-up with mixed precision| -|---:|---:|---:|---:| -|**1**|625|613|1.02| +##### NVIDIA T4 -The following table shows the inference performance results for WaveGlow model. -Results are measured in the number of output samples per second1. - -|Number of GPUs|Number of samples used with mixed precision|Number of samples used with FP32|Speed-up with mixed precision| -|---:|---:|---:|---:| -|**1**|180474|162282|1.11| - -1With sampling rate equal to 22050, one second of audio is generated from 22050 samples. - -To achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide). +|Batch size|Input length|Precision|Avg latency (s)|Latency std (s)|Latency confidence interval 50% (s)|Latency confidence interval 100% (s)|Throughput (samples/sec)|Speed-up with mixed precision|Avg mels generated (81 mels=1 sec of speech)|Avg audio length (s)|Avg RTF| +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +|1| 128| FP16| 3.16| 0.13| 3.16| 3.81| 48,792| 1.23| 603| 7.00| 2.21| +|4| 128| FP16| 11.45| 0.49| 11.39| 14.38| 53,771| 1.22| 601| 6.98| 0.61| +|1| 128| FP32| 3.82| 0.11| 3.81| 4.24| 39,603| 1.00| 591| 6.86| 1.80| +|4| 128| FP32| 13.80| 0.45| 13.74| 16.09| 43,915| 1.00| 592| 6.87| 0.50| +Our results were obtained by running the `./run_latency_tests.sh` script in +the PyTorch-19.06-py3 NGC container. Please note that to reproduce the results, +you need to provide pretrained checkpoints for Tacotron 2 and WaveGlow. Please +edit the script to provide your checkpoint filenames. ## Release notes @@ -674,7 +684,7 @@ June 2019 * Fixed dropouts on LSTMCells July 2019 -* Changed measurement units for Tacotron 2 training and inference performance +* Changed measurement units for Tacotron 2 training and inference performance benchmarks from input tokes per second to output mel-spectrograms per second * Introduced batched inference * Included warmup in the inference script @@ -683,6 +693,10 @@ August 2019 * Fixed inference results * Fixed initialization of Batch Normalization +September 2019 +* Introduced inference statistics + ### Known issues There are no known issues in this release. + diff --git a/PyTorch/SpeechSynthesis/Tacotron2/common/stft.py b/PyTorch/SpeechSynthesis/Tacotron2/common/stft.py index 9655190d..12582744 100644 --- a/PyTorch/SpeechSynthesis/Tacotron2/common/stft.py +++ b/PyTorch/SpeechSynthesis/Tacotron2/common/stft.py @@ -124,6 +124,7 @@ class STFT(torch.nn.Module): np.where(window_sum > tiny(window_sum))[0]) window_sum = torch.autograd.Variable( torch.from_numpy(window_sum), requires_grad=False) + window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices] # scale by hop ratio diff --git a/PyTorch/SpeechSynthesis/Tacotron2/inference.py b/PyTorch/SpeechSynthesis/Tacotron2/inference.py index 05e4e087..bff8f8d1 100644 --- a/PyTorch/SpeechSynthesis/Tacotron2/inference.py +++ b/PyTorch/SpeechSynthesis/Tacotron2/inference.py @@ -41,6 +41,8 @@ from dllogger.autologging import log_hardware, log_args from apex import amp +from waveglow.denoiser import Denoiser + def parse_args(parser): """ Parse commandline arguments. @@ -53,7 +55,8 @@ def parse_args(parser): help='full path to the Tacotron2 model checkpoint file') parser.add_argument('--waveglow', type=str, help='full path to the WaveGlow model checkpoint file') - parser.add_argument('-s', '--sigma-infer', default=0.6, type=float) + parser.add_argument('-s', '--sigma-infer', default=0.9, type=float) + parser.add_argument('-d', '--denoising-strength', default=0.01, type=float) parser.add_argument('-sr', '--sampling-rate', default=22050, type=int, help='Sampling rate') parser.add_argument('--amp-run', action='store_true', @@ -212,6 +215,7 @@ def main(): args.amp_run) waveglow = load_and_setup_model('WaveGlow', parser, args.waveglow, args.amp_run) + denoiser = Denoiser(waveglow).cuda() texts = [] try: @@ -242,6 +246,7 @@ def main(): with torch.no_grad(), MeasureTime(measurements, "waveglow_time"): audios = waveglow.infer(mel, sigma=args.sigma_infer) audios = audios.float() + audios = denoiser(audios, strength=args.denoising_strength).squeeze(1) tacotron2_infer_perf = mel.size(0)*mel.size(2)/measurements['tacotron2_time'] waveglow_infer_perf = audios.size(0)*audios.size(1)/measurements['waveglow_time'] @@ -254,9 +259,10 @@ def main(): measurements['waveglow_time'])) for i, audio in enumerate(audios): + audio = audio[:mel_lengths[i]*args.stft_hop_length] + audio = audio/torch.max(torch.abs(audio)) audio_path = args.output + "audio_"+str(i)+".wav" - write(audio_path, args.sampling_rate, - audio.data.cpu().numpy()[:mel_lengths[i]*args.stft_hop_length]) + write(audio_path, args.sampling_rate, audio.cpu().numpy()) LOGGER.iteration_stop() LOGGER.finish() diff --git a/PyTorch/SpeechSynthesis/Tacotron2/notebooks/README.md b/PyTorch/SpeechSynthesis/Tacotron2/notebooks/README.md index f049bb6d..d3a338f5 100644 --- a/PyTorch/SpeechSynthesis/Tacotron2/notebooks/README.md +++ b/PyTorch/SpeechSynthesis/Tacotron2/notebooks/README.md @@ -2,10 +2,37 @@ A jupyter notobook based on Quick Start Guide of: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2 + +## Requirements + +Ensure you have the following components: + +NVIDIA Docker (https://github.com/NVIDIA/nvidia-docker) +PyTorch 19.06-py3+ NGC container or newer (https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) +NVIDIA Volta (https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) or Turing (https://www.nvidia.com/en-us/geforce/turing/) based GPU + +Before running the Jupyter notebook, please make sure you already git clone the code from the Github: + +```bash + git clone https://github.com/NVIDIA/DeepLearningExamples.git + + cd DeepLearningExamples/PyTorch/SpeechSynthesis/Tacotron2 +``` + +Copy the Tacotron2.ipynb file into the folder 'Tacotron2': +```bash + cp notebooks/Tacotron2.ipynb . +``` + ### Running the quick start guide as a Jupyter notebook -Navigate to the folder containing the scripts for the Tacotron2 example. -Copy the Tacotron2.ipynb file into the folder and run: +To run the notebook on you local machine: + +```bash +jupyter notebook Tacotron2.ipynb +``` + +To run the notebook on another machine remotely: ```bash jupyter notebook --ip=0.0.0.0 --allow-root @@ -23,4 +50,4 @@ in, for example: ``` http://[host machine]:8888/?token=aae96ae9387cd28151868fee318c3b3581a2d794f3b25c6b -``` \ No newline at end of file +``` diff --git a/PyTorch/SpeechSynthesis/Tacotron2/notebooks/Tacotron2.ipynb b/PyTorch/SpeechSynthesis/Tacotron2/notebooks/Tacotron2.ipynb index 627319f2..aa9485d9 100644 --- a/PyTorch/SpeechSynthesis/Tacotron2/notebooks/Tacotron2.ipynb +++ b/PyTorch/SpeechSynthesis/Tacotron2/notebooks/Tacotron2.ipynb @@ -54,22 +54,7 @@ "source": [ "## Requirements\n", "\n", - "Ensure you have the following components:\n", - "\n", - " NVIDIA Docker (https://github.com/NVIDIA/nvidia-docker)\n", - " PyTorch 19.06-py3+ NGC container or newer (https://ngc.nvidia.com/catalog/containers/nvidia:pytorch)\n", - " NVIDIA Volta (https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) or Turing (https://www.nvidia.com/en-us/geforce/turing/) based GPU\n", - "\n", - "Before running the Jupyter notebook, please make sure you already git clone the code from the Github repositories (https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2#training-performance-results). To do so, follow below steps on the command line:\n", - "\n", - "1. Clone the repository:\n", - "git clone https://github.com/NVIDIA/DeepLearningExamples.git\n", - "cd DeepLearningExamples/PyTorch/SpeechSynthesis/Tacotron2\n", - "\n", - "2. Download this Jupyter notebook and put it at this directory: DeepLearningExamples/PyTorch/SpeechSynthesis/Tacotron2\n", - "\n", - "3. Start running this Jupyter notebook:\n", - "Jupyter notebook Tacotron2.ipynb" + "Please see 'notebooks/README.md'." ] }, { @@ -153,7 +138,7 @@ "outputs": [], "source": [ "#for single GPU, specify your GPU to run the container\n", - "!NV_GPU=1 nvidia-docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -it -d --rm --name \"myTacotron2\" --ipc=host -v $PWD:/workspace/tacotron2/ tacotron2 bash" + "!NV_GPU=2 nvidia-docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -it -d --rm --name \"myTacotron2\" --ipc=host -v $PWD:/workspace/tacotron2/ tacotron2 bash" ] }, { @@ -285,17 +270,6 @@ "The training scripts automatically run the validation after each training epoch. The results from the validation are printed to the standard output (stdout) and saved to the log files." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "

Note

\n", - "

Pre-trained models are available for download from NGC:\n", - "

\n", - "
" - ] - }, { "cell_type": "code", "execution_count": null, @@ -505,7 +479,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/PyTorch/SpeechSynthesis/Tacotron2/run_latency_tests.sh b/PyTorch/SpeechSynthesis/Tacotron2/run_latency_tests.sh new file mode 100644 index 00000000..aadbdd01 --- /dev/null +++ b/PyTorch/SpeechSynthesis/Tacotron2/run_latency_tests.sh @@ -0,0 +1,5 @@ +bash test_infer.sh -bs 1 -il 128 -p amp --num-iters 1003 --tacotron2 checkpoint_Tacotron2_amp --waveglow checkpoint_WaveGlow_amp +bash test_infer.sh -bs 4 -il 128 -p amp --num-iters 1003 --tacotron2 checkpoint_Tacotron2_amp --waveglow checkpoint_WaveGlow_amp +bash test_infer.sh -bs 1 -il 128 -p fp32 --num-iters 1003 --tacotron2 checkpoint_Tacotron2_fp32 --waveglow checkpoint_WaveGlow_fp32 +bash test_infer.sh -bs 4 -il 128 -p fp32 --num-iters 1003 --tacotron2 checkpoint_Tacotron2_fp32 --waveglow checkpoint_WaveGlow_fp32 + diff --git a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/model.py b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/model.py index a2ae1a70..81f574b0 100644 --- a/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/model.py +++ b/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/model.py @@ -491,9 +491,6 @@ class Decoder(nn.Module): decoder_input = self.prenet(decoder_input, inference=True) mel_output, gate_output, alignment = self.decode(decoder_input) - mel_outputs += [mel_output.squeeze(1)] - gate_outputs += [gate_output] - alignments += [alignment] dec = torch.le(torch.sigmoid(gate_output.data), self.gate_threshold).to(torch.int32).squeeze(1) @@ -502,6 +499,11 @@ class Decoder(nn.Module): if self.early_stopping and torch.sum(not_finished) == 0: break + + mel_outputs += [mel_output.squeeze(1)] + gate_outputs += [gate_output] + alignments += [alignment] + if len(mel_outputs) == self.max_decoder_steps: print("Warning! Reached max decoder steps") break diff --git a/PyTorch/SpeechSynthesis/Tacotron2/test_infer.py b/PyTorch/SpeechSynthesis/Tacotron2/test_infer.py new file mode 100644 index 00000000..9f69f799 --- /dev/null +++ b/PyTorch/SpeechSynthesis/Tacotron2/test_infer.py @@ -0,0 +1,316 @@ +# ***************************************************************************** +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of the NVIDIA CORPORATION nor the +# names of its contributors may be used to endorse or promote products +# derived from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY +# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +# ***************************************************************************** + +from tacotron2.text import text_to_sequence +import models +import torch +import argparse +import numpy as np +from scipy.io.wavfile import write + +import sys + +import time +from dllogger.logger import LOGGER +import dllogger.logger as dllg +from dllogger.autologging import log_hardware, log_args + +from apex import amp + +def parse_args(parser): + """ + Parse commandline arguments. + """ + parser.add_argument('--tacotron2', type=str, + help='full path to the Tacotron2 model checkpoint file') + parser.add_argument('--waveglow', type=str, + help='full path to the WaveGlow model checkpoint file') + parser.add_argument('-s', '--sigma-infer', default=0.6, type=float) + parser.add_argument('-sr', '--sampling-rate', default=22050, type=int, + help='Sampling rate') + parser.add_argument('--amp-run', action='store_true', + help='inference with AMP') + parser.add_argument('--log-file', type=str, default='nvlog.json', + help='Filename for logging') + parser.add_argument('--stft-hop-length', type=int, default=256, + help='STFT hop length for estimating audio length from mel size') + parser.add_argument('--num-iters', type=int, default=10, + help='Number of iterations') + parser.add_argument('-il', '--input-length', type=int, default=64, + help='Input length') + parser.add_argument('-bs', '--batch-size', type=int, default=1, + help='Batch size') + + + return parser + + +def checkpoint_from_distributed(state_dict): + """ + Checks whether checkpoint was generated by DistributedDataParallel. DDP + wraps model in additional "module.", it needs to be unwrapped for single + GPU inference. + :param state_dict: model's state dict + """ + ret = False + for key, _ in state_dict.items(): + if key.find('module.') != -1: + ret = True + break + return ret + + +def unwrap_distributed(state_dict): + """ + Unwraps model from DistributedDataParallel. + DDP wraps model in additional "module.", it needs to be removed for single + GPU inference. + :param state_dict: model's state dict + """ + new_state_dict = {} + for key, value in state_dict.items(): + new_key = key.replace('module.', '') + new_state_dict[new_key] = value + return new_state_dict + + +def load_and_setup_model(model_name, parser, checkpoint, amp_run, to_cuda=True): + model_parser = models.parse_model_args(model_name, parser, add_help=False) + model_args, _ = model_parser.parse_known_args() + + model_config = models.get_model_config(model_name, model_args) + model = models.get_model(model_name, model_config, to_cuda=to_cuda) + + if checkpoint is not None: + if to_cuda: + state_dict = torch.load(checkpoint)['state_dict'] + else: + state_dict = torch.load(checkpoint,map_location='cpu')['state_dict'] + if checkpoint_from_distributed(state_dict): + state_dict = unwrap_distributed(state_dict) + + model.load_state_dict(state_dict) + + if model_name == "WaveGlow": + model = model.remove_weightnorm(model) + + model.eval() + + if amp_run: + model, _ = amp.initialize(model, [], opt_level="O3") + + return model + + +# taken from tacotron2/data_function.py:TextMelCollate.__call__ +def pad_sequences(batch): + # Right zero-pad all one-hot text sequences to max input length + input_lengths, ids_sorted_decreasing = torch.sort( + torch.LongTensor([len(x) for x in batch]), + dim=0, descending=True) + max_input_len = input_lengths[0] + + text_padded = torch.LongTensor(len(batch), max_input_len) + text_padded.zero_() + for i in range(len(ids_sorted_decreasing)): + text = batch[ids_sorted_decreasing[i]] + text_padded[i, :text.size(0)] = text + + return text_padded, input_lengths + + +def prepare_input_sequence(texts): + + d = [] + for i,text in enumerate(texts): + d.append(torch.IntTensor( + text_to_sequence(text, ['english_cleaners'])[:])) + + text_padded, input_lengths = pad_sequences(d) + if torch.cuda.is_available(): + text_padded = torch.autograd.Variable(text_padded).cuda().long() + input_lengths = torch.autograd.Variable(input_lengths).cuda().long() + else: + text_padded = torch.autograd.Variable(text_padded).long() + input_lengths = torch.autograd.Variable(input_lengths).long() + + return text_padded, input_lengths + +class MeasureTime(): + def __init__(self, measurements, key): + self.measurements = measurements + self.key = key + + def __enter__(self): + torch.cuda.synchronize() + self.t0 = time.perf_counter() + + def __exit__(self, exc_type, exc_value, exc_traceback): + torch.cuda.synchronize() + self.measurements[self.key] = time.perf_counter() - self.t0 + + +def main(): + """ + Launches text to speech (inference). + Inference is executed on a single GPU. + """ + parser = argparse.ArgumentParser( + description='PyTorch Tacotron 2 Inference') + parser = parse_args(parser) + args, unknown_args = parser.parse_known_args() + + LOGGER.set_model_name("Tacotron2_PyT") + LOGGER.set_backends([ + dllg.JsonBackend(log_file=args.log_file, + logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=1) + ]) + LOGGER.register_metric("pre_processing", metric_scope=dllg.TRAIN_ITER_SCOPE) + LOGGER.register_metric("tacotron2_items_per_sec", metric_scope=dllg.TRAIN_ITER_SCOPE) + LOGGER.register_metric("tacotron2_latency", metric_scope=dllg.TRAIN_ITER_SCOPE) + LOGGER.register_metric("waveglow_items_per_sec", metric_scope=dllg.TRAIN_ITER_SCOPE) + LOGGER.register_metric("waveglow_latency", metric_scope=dllg.TRAIN_ITER_SCOPE) + LOGGER.register_metric("latency", metric_scope=dllg.TRAIN_ITER_SCOPE) + LOGGER.register_metric("type_conversion", metric_scope=dllg.TRAIN_ITER_SCOPE) + LOGGER.register_metric("storage", metric_scope=dllg.TRAIN_ITER_SCOPE) + LOGGER.register_metric("data_transfer", metric_scope=dllg.TRAIN_ITER_SCOPE) + LOGGER.register_metric("num_mels_per_audio", metric_scope=dllg.TRAIN_ITER_SCOPE) + LOGGER.register_metric("throughput", metric_scope=dllg.TRAIN_ITER_SCOPE) + + measurements_all = {"pre_processing": [], + "tacotron2_latency": [], + "waveglow_latency": [], + "latency": [], + "type_conversion": [], + "data_transfer": [], + "storage": [], + "tacotron2_items_per_sec": [], + "waveglow_items_per_sec": [], + "num_mels_per_audio": [], + "throughput": []} + + log_hardware() + log_args(args) + + print("args:", args, unknown_args) + + tacotron2 = load_and_setup_model('Tacotron2', parser, args.tacotron2, args.amp_run) + waveglow = load_and_setup_model('WaveGlow', parser, args.waveglow, args.amp_run) + + texts = ["The forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves. The forms of printed letters should be beautiful, and that their arrangement on the page should be reasonable and a help to the shapeliness of the letters themselves."] + texts = [texts[0][:args.input_length]] + texts = texts*args.batch_size + + warmup_iters = 3 + + for iter in range(args.num_iters): + + if iter >= warmup_iters: + LOGGER.iteration_start() + + measurements = {} + + with MeasureTime(measurements, "pre_processing"): + sequences_padded, input_lengths = prepare_input_sequence(texts) + + with torch.no_grad(): + with MeasureTime(measurements, "latency"): + with MeasureTime(measurements, "tacotron2_latency"): + _, mel, _, _, mel_lengths = tacotron2.infer(sequences_padded, input_lengths) + + with MeasureTime(measurements, "waveglow_latency"): + audios = waveglow.infer(mel, sigma=args.sigma_infer) + + num_mels = mel.size(0)*mel.size(2) + num_samples = audios.size(0)*audios.size(1) + + with MeasureTime(measurements, "type_conversion"): + audios = audios.float() + + with MeasureTime(measurements, "data_transfer"): + audios = audios.cpu() + + with MeasureTime(measurements, "storage"): + audios = audios.numpy() + for i, audio in enumerate(audios): + audio_path = "audio_"+str(i)+".wav" + write(audio_path, args.sampling_rate, + audio[:mel_lengths[i]*args.stft_hop_length]) + + measurements['tacotron2_items_per_sec'] = num_mels/measurements['tacotron2_latency'] + measurements['waveglow_items_per_sec'] = num_samples/measurements['waveglow_latency'] + measurements['num_mels_per_audio'] = mel.size(2) + measurements['throughput'] = num_samples/measurements['latency'] + + if iter >= warmup_iters: + for k,v in measurements.items(): + measurements_all[k].append(v) + LOGGER.log(key=k, value=v) + + LOGGER.iteration_stop() + + LOGGER.finish() + + print(np.mean(measurements_all['latency'][1:]), + np.mean(measurements_all['throughput'][1:]), + np.mean(measurements_all['pre_processing'][1:]), + np.mean(measurements_all['type_conversion'][1:])+ + np.mean(measurements_all['storage'][1:])+ + np.mean(measurements_all['data_transfer'][1:]), + np.mean(measurements_all['num_mels_per_audio'][1:])) + + throughput = measurements_all['throughput'] + preprocessing = measurements_all['pre_processing'] + type_conversion = measurements_all['type_conversion'] + storage = measurements_all['storage'] + data_transfer = measurements_all['data_transfer'] + postprocessing = [sum(p) for p in zip(type_conversion,storage,data_transfer)] + latency = measurements_all['latency'] + num_mels_per_audio = measurements_all['num_mels_per_audio'] + + latency.sort() + + cf_50 = max(latency[:int(len(latency)*0.50)]) + cf_90 = max(latency[:int(len(latency)*0.90)]) + cf_95 = max(latency[:int(len(latency)*0.95)]) + cf_99 = max(latency[:int(len(latency)*0.99)]) + cf_100 = max(latency[:int(len(latency)*1.0)]) + + print("Throughput average (samples/sec) = {:.4f}".format(np.mean(throughput))) + print("Preprocessing average (seconds) = {:.4f}".format(np.mean(preprocessing))) + print("Postprocessing average (seconds) = {:.4f}".format(np.mean(postprocessing))) + print("Number of mels per audio average = {}".format(np.mean(num_mels_per_audio))) + print("Latency average (seconds) = {:.4f}".format(np.mean(latency))) + print("Latency std (seconds) = {:.4f}".format(np.std(latency))) + print("Latency cl 50 (seconds) = {:.4f}".format(cf_50)) + print("Latency cl 90 (seconds) = {:.4f}".format(cf_90)) + print("Latency cl 95 (seconds) = {:.4f}".format(cf_95)) + print("Latency cl 99 (seconds) = {:.4f}".format(cf_99)) + print("Latency cl 100 (seconds) = {:.4f}".format(cf_100)) + +if __name__ == '__main__': + main() diff --git a/PyTorch/SpeechSynthesis/Tacotron2/test_infer.sh b/PyTorch/SpeechSynthesis/Tacotron2/test_infer.sh new file mode 100644 index 00000000..f778e1be --- /dev/null +++ b/PyTorch/SpeechSynthesis/Tacotron2/test_infer.sh @@ -0,0 +1,68 @@ +#!/bin/bash + +BATCH_SIZE=1 +INPUT_LENGTH=128 +PRECISION="fp32" +NUM_ITERS=1003 # extra 3 iterations for warmup +TACOTRON2_CKPT="checkpoint_Tacotron2_1500_fp32" +WAVEGLOW_CKPT="checkpoint_WaveGlow_1000_fp32" + + +while [ -n "$1" ] +do + case "$1" in + -bs|--batch-size) + BATCH_SIZE="$2" + shift + ;; + -il|--input-length) + INPUT_LENGTH="$2" + shift + ;; + -p|--prec) + PRECISION="$2" + shift + ;; + --num-iters) + NUM_ITERS="$2" + shift + ;; + --tacotron2) + TACOTRON2_CKPT="$2" + shift + ;; + --waveglow) + WAVEGLOW_CKPT="$2" + shift + ;; + *) + echo "Option $1 not recognized" + esac + shift +done + +LOG_SUFFIX=bs${BATCH_SIZE}_il${INPUT_LENGTH}_${PRECISION} +NVLOG_FILE=nvlog_${LOG_SUFFIX}.json +TMP_LOGFILE=tmp_log_${LOG_SUFFIX}.log +LOGFILE=log_${LOG_SUFFIX}.log + +set -x +python test_infer.py \ + --tacotron2 $TACOTRON2_CKPT \ + --waveglow $WAVEGLOW_CKPT \ + --batch-size $BATCH_SIZE \ + --input-length $INPUT_LENGTH $AMP_RUN $CPU_RUN \ + --log-file $NVLOG_FILE \ + --num-iters $NUM_ITERS \ + |& tee $TMP_LOGFILE +set +x + + +PERF=$(cat $TMP_LOGFILE | grep -F 'Throughput average (samples/sec)' | awk -F'= ' '{print $2}') +NUM_MELS=$(cat $TMP_LOGFILE | grep -F 'Number of mels per audio average' | awk -F'= ' '{print $2}') +LATENCY=$(cat $TMP_LOGFILE | grep -F 'Latency average (seconds)' | awk -F'= ' '{print $2}') +LATENCYSTD=$(cat $TMP_LOGFILE | grep -F 'Latency std (seconds)' | awk -F'= ' '{print $2}') +LATENCY50=$(cat $TMP_LOGFILE | grep -F 'Latency cl 50 (seconds)' | awk -F'= ' '{print $2}') +LATENCY100=$(cat $TMP_LOGFILE | grep -F 'Latency cl 100 (seconds)' | awk -F'= ' '{print $2}') + +echo "$BATCH_SIZE,$INPUT_LENGTH,$PRECISION,$NUM_ITERS,$LATENCY,$LATENCYSTD,$LATENCY50,$LATENCY100,$PERF,$NUM_MELS" >> $LOGFILE diff --git a/PyTorch/SpeechSynthesis/Tacotron2/waveglow/denoiser.py b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/denoiser.py new file mode 100644 index 00000000..4b5352c9 --- /dev/null +++ b/PyTorch/SpeechSynthesis/Tacotron2/waveglow/denoiser.py @@ -0,0 +1,67 @@ +# ***************************************************************************** +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of the NVIDIA CORPORATION nor the +# names of its contributors may be used to endorse or promote products +# derived from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY +# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +# ***************************************************************************** + +import sys +sys.path.append('tacotron2') +import torch +from common.layers import STFT + + +class Denoiser(torch.nn.Module): + """ Removes model bias from audio produced with waveglow """ + + def __init__(self, waveglow, filter_length=1024, n_overlap=4, + win_length=1024, mode='zeros'): + super(Denoiser, self).__init__() + self.stft = STFT(filter_length=filter_length, + hop_length=int(filter_length/n_overlap), + win_length=win_length).cuda() + if mode == 'zeros': + mel_input = torch.zeros( + (1, 80, 88), + dtype=waveglow.upsample.weight.dtype, + device=waveglow.upsample.weight.device) + elif mode == 'normal': + mel_input = torch.randn( + (1, 80, 88), + dtype=waveglow.upsample.weight.dtype, + device=waveglow.upsample.weight.device) + else: + raise Exception("Mode {} if not supported".format(mode)) + + with torch.no_grad(): + bias_audio = waveglow.infer(mel_input, sigma=0.0).float() + bias_spec, _ = self.stft.transform(bias_audio) + + self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None]) + + def forward(self, audio, strength=0.1): + audio_spec, audio_angles = self.stft.transform(audio.cuda().float()) + audio_spec_denoised = audio_spec - self.bias_spec * strength + audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0) + audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles) + return audio_denoised diff --git a/PyTorch/Translation/GNMT/Dockerfile b/PyTorch/Translation/GNMT/Dockerfile index 89985d1a..674daa4c 100644 --- a/PyTorch/Translation/GNMT/Dockerfile +++ b/PyTorch/Translation/GNMT/Dockerfile @@ -1,4 +1,25 @@ -FROM nvcr.io/nvidia/pytorch:19.05-py3 +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:19.05-py3 +FROM ${FROM_IMAGE_NAME} ENV LANG C.UTF-8 ENV LC_ALL C.UTF-8 diff --git a/PyTorch/Translation/GNMT/LICENSE b/PyTorch/Translation/GNMT/LICENSE index 4343c764..40ed084e 100644 --- a/PyTorch/Translation/GNMT/LICENSE +++ b/PyTorch/Translation/GNMT/LICENSE @@ -1,5 +1,3 @@ -MIT License - Copyright (c) 2017 Elad Hoffer Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. diff --git a/PyTorch/Translation/GNMT/README.md b/PyTorch/Translation/GNMT/README.md index 3ba19139..4c9bed4e 100644 --- a/PyTorch/Translation/GNMT/README.md +++ b/PyTorch/Translation/GNMT/README.md @@ -39,10 +39,11 @@ achieve state of the art accuracy, and is tested and maintained by NVIDIA. * [Training throughput: NVIDIA DGX-2 (16x V100 32G)](#training-throughput-nvidia-dgx-2-16x-v100-32g) * [Inference accuracy results](#inference-accuracy-results) * [Inference accuracy: NVIDIA Tesla V100 16G](#inference-accuracy-nvidia-tesla-v100-16g) + * [Inference accuracy: NVIDIA T4](#inference-accuracy-nvidia-t4) * [Inference throughput results](#inference-throughput-results) - * [Inference throughput: NVIDIA Tesla V100 16G](#inference-throughput-nvidia-tesla-v100-16g) + * [Inference throughput: NVIDIA T4](#inference-throughput-nvidia-t4) * [Inference latency results](#inference-latency-results) - * [Inference latency: NVIDIA Tesla V100 16G](#inference-latency-nvidia-tesla-v100-16g) + * [Inference latency: NVIDIA T4](#inference-latency-nvidia-t4) * [Release notes](#release-notes) * [Changelog](#changelog) * [Known issues](#known-issues) @@ -537,7 +538,7 @@ training setup: maximum sequence length for training (including special BOS and EOS tokens) (default: 50) --train-min-length TRAIN_MIN_LENGTH - minimum sequence length for training (including + minimum sequence length for training (including special BOS and EOS tokens) (default: 0) --train-loader-workers TRAIN_LOADER_WORKERS number of workers for training data loading (default: @@ -1088,6 +1089,21 @@ command to launch the inference accuracy benchmark was provided in the | 128 | 2 | 23.81 | 23.79 | | 128 | 5 | 24.40 | 24.43 | +##### Inference accuracy: NVIDIA T4 +Our results were obtained by running the `translate.py` script in the +pytorch-19.05-py3 NGC Docker container with NVIDIA Tesla T4. Full command to +launch the inference accuracy benchmark was provided in the [Inference +performance benchmark](#inference-performance-benchmark) section. + +| **Batch Size** | **Beam Size** | **Accuracy - FP32 (BLEU)** | **Accuracy - FP16 (BLEU)** | +| -------------: | ------------: | -------------------------: | -------------------------: | +| 128 | 1 | 23.07 | 23.08 | +| 128 | 2 | 23.81 | 23.80 | +| 128 | 5 | 24.40 | 24.39 | + +To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) +outlined above. + #### Inference throughput results Tables presented in this section show the average inference throughput (columns **Avg (tok/s)**) and inference throughput for various confidence intervals @@ -1095,63 +1111,37 @@ Tables presented in this section show the average inference throughput (columns throughput is measured in tokens per second. Speedups reported in FP16 subsections are relative to FP32 numbers for corresponding configuration. -##### Inference throughput: NVIDIA Tesla V100 16G +##### Inference throughput: NVIDIA T4 Our results were obtained by running the `translate.py` script in the -pytorch-19.05-py3 NGC Docker container with NVIDIA Tesla V100 16G GPUs. Full -command to launch the inference throughput benchmark was provided in the +pytorch-19.05-py3 NGC Docker container with NVIDIA T4. +Full command to launch the inference throughput benchmark was provided in the [Inference performance benchmark](#inference-performance-benchmark) section. -**FP32** - -| **Batch Size** | **Beam Size** | **Avg (tok/s)** | **50% (tok/s)** | **90% (tok/s)** | **95% (tok/s)** | **99% (tok/s)** | **100% (tok/s)** | -| -------------: | ------------: | --------------: | --------------: | --------------: | --------------: | --------------: | ---------------: | -| 1 | 1 | 936.7 | 930.9 | 869.9 | 851.5 | 811.5 | 695.7 | -| 1 | 2 | 640.5 | 642.7 | 584.6 | 561.1 | 512.1 | 393.6 | -| 1 | 5 | 595.0 | 600.9 | 531.3 | 503.2 | 456.7 | 268.5 | -| 2 | 1 | 1452.3 | 1455.9 | 1204.7 | 1145.1 | 1060.7 | 969.5 | -| 2 | 2 | 1032.5 | 1038.5 | 851.2 | 808.5 | 746.5 | 642.4 | -| 2 | 5 | 979.5 | 981.9 | 806.5 | 773.1 | 701.5 | 601.4 | -| 4 | 1 | 2425.7 | 2414.3 | 1990.4 | 1876.3 | 1706.3 | 1505.0 | -| 4 | 2 | 1697.6 | 1690.2 | 1401.5 | 1325.1 | 1180.5 | 989.9 | -| 4 | 5 | 1634.4 | 1631.5 | 1344.3 | 1274.0 | 1138.3 | 993.9 | -| 8 | 1 | 4162.5 | 4176.7 | 3462.9 | 3254.8 | 2885.4 | 2575.2 | -| 8 | 2 | 2953.0 | 2961.9 | 2438.4 | 2288.9 | 2073.9 | 1817.2 | -| 8 | 5 | 2723.0 | 2716.6 | 2299.6 | 2138.5 | 1888.9 | 1855.2 | -| 32 | 1 | 12178.6 | 12181.0 | 10445.3 | 9805.9 | 9253.3 | 8584.7 | -| 32 | 2 | 7990.9 | 8067.5 | 6785.3 | 6354.9 | 5912.3 | 5900.5 | -| 32 | 5 | 5640.1 | 5650.5 | 4951.4 | 4771.7 | 4486.0 | 4446.6 | -| 128 | 1 | 25192.7 | 25083.5 | 22951.1 | 20610.8 | 20068.7 | 19750.6 | -| 128 | 2 | 14639.3 | 14662.0 | 13285.7 | 12482.4 | 12100.9 | 12031.9 | -| 128 | 5 | 8610.5 | 8607.0 | 7880.1 | 7589.0 | 7129.6 | 7069.2 | -| 512 | 1 | 33281.8 | 33442.0 | 29899.4 | 29721.3 | 29644.1 | 29568.4 | -| 512 | 2 | 19803.8 | 19867.4 | 17872.7 | 17757.6 | 17693.8 | 17669.8 | -| 512 | 5 | 9236.4 | 9282.9 | 8309.0 | 8272.7 | 8241.2 | 8228.6 | - **FP16** | **Batch Size** | **Beam Size** | **Avg (tok/s)** | **Speedup** | **50% (tok/s)** | **Speedup** | **90% (tok/s)** | **Speedup** | **95% (tok/s)** | **Speedup** | **99% (tok/s)** | **Speedup** | **100% (tok/s)** | **Speedup** | | -------------: | ------------: | --------------: | ----------: | --------------: | ----------: | --------------: | ----------: | --------------: | ----------: | --------------: | ----------: | ---------------: | ----------: | -| 1 | 1 | 920.8 | 0.9831 | 917.6 | 0.9857 | 844.5 | 0.9708 | 820.0 | 0.9630 | 751.8 | 0.9264 | 503.5 | 0.7238 | -| 1 | 2 | 623.7 | 0.9738 | 625.7 | 0.9736 | 563.5 | 0.9639 | 539.1 | 0.9609 | 485.4 | 0.9479 | 389.4 | 0.9894 | -| 1 | 5 | 597.3 | 1.0038 | 603.9 | 1.0049 | 531.1 | 0.9996 | 501.2 | 0.9960 | 450.8 | 0.9869 | 260.0 | 0.9684 | -| 2 | 1 | 1496.5 | 1.0305 | 1499.2 | 1.0298 | 1228.8 | 1.0201 | 1165.6 | 1.0179 | 1074.5 | 1.0130 | 936.7 | 0.9661 | -| 2 | 2 | 1027.5 | 0.9951 | 1033.2 | 0.9949 | 848.6 | 0.9970 | 801.2 | 0.9910 | 737.5 | 0.9879 | 644.7 | 1.0036 | -| 2 | 5 | 994.5 | 1.0153 | 994.1 | 1.0124 | 815.1 | 1.0106 | 777.2 | 1.0052 | 710.4 | 1.0126 | 605.8 | 1.0073 | -| 4 | 1 | 2500.6 | 1.0309 | 2498.7 | 1.0349 | 2038.1 | 1.0240 | 1913.4 | 1.0197 | 1722.5 | 1.0095 | 1534.9 | 1.0199 | -| 4 | 2 | 1707.7 | 1.0060 | 1697.5 | 1.0043 | 1402.2 | 1.0005 | 1330.9 | 1.0043 | 1198.9 | 1.0157 | 986.5 | 0.9965 | -| 4 | 5 | 1668.5 | 1.0209 | 1664.2 | 1.0201 | 1367.9 | 1.0176 | 1287.7 | 1.0107 | 1165.0 | 1.0235 | 969.7 | 0.9757 | -| 8 | 1 | 4489.7 | 1.0786 | 4498.1 | 1.0769 | 3693.0 | 1.0664 | 3451.2 | 1.0603 | 3048.2 | 1.0564 | 2667.5 | 1.0358 | -| 8 | 2 | 3025.4 | 1.0245 | 3026.8 | 1.0219 | 2462.6 | 1.0099 | 2295.7 | 1.0029 | 2108.3 | 1.0166 | 1877.5 | 1.0332 | -| 8 | 5 | 2925.9 | 1.0745 | 2920.0 | 1.0749 | 2433.6 | 1.0583 | 2302.0 | 1.0765 | 1996.1 | 1.0568 | 1890.2 | 1.0189 | -| 32 | 1 | 13953.9 | 1.1458 | 13976.1 | 1.1474 | 11972.1 | 1.1462 | 11480.7 | 1.1708 | 10203.9 | 1.1027 | 9704.5 | 1.1304 | -| 32 | 2 | 9095.4 | 1.1382 | 9206.5 | 1.1412 | 7718.9 | 1.1376 | 7344.8 | 1.1558 | 6721.9 | 1.1369 | 6620.4 | 1.1220 | -| 32 | 5 | 8556.1 | 1.5170 | 8610.8 | 1.5239 | 7098.3 | 1.4336 | 6860.5 | 1.4377 | 6511.3 | 1.4515 | 6371.9 | 1.4330 | -| 128 | 1 | 39354.1 | 1.5621 | 39356.0 | 1.5690 | 35942.3 | 1.5660 | 34173.7 | 1.6580 | 29650.9 | 1.4775 | 29355.0 | 1.4863 | -| 128 | 2 | 24597.0 | 1.6802 | 24458.4 | 1.6682 | 22311.8 | 1.6794 | 21035.5 | 1.6852 | 19444.6 | 1.6069 | 19336.3 | 1.6071 | -| 128 | 5 | 18740.1 | 2.1764 | 18698.6 | 2.1725 | 17010.1 | 2.1586 | 15965.2 | 2.1037 | 15454.6 | 2.1677 | 15219.2 | 2.1529 | -| 512 | 1 | 78936.4 | 2.3718 | 79001.1 | 2.3623 | 73298.5 | 2.4515 | 72552.1 | 2.4411 | 71825.3 | 2.4229 | 71451.9 | 2.4165 | -| 512 | 2 | 47856.9 | 2.4166 | 47924.3 | 2.4122 | 44186.7 | 2.4723 | 44055.8 | 2.4810 | 43574.9 | 2.4627 | 43425.8 | 2.4576 | -| 512 | 5 | 28243.8 | 3.0579 | 28268.4 | 3.0452 | 25709.9 | 3.0942 | 25604.0 | 3.0950 | 25373.4 | 3.0789 | 25196.4 | 3.0621 | +| 1 | 1 | 987.6 | 1.221 | 985.5 | 1.221 | 921.6 | 1.208 | 898.6 | 1.203 | 855.8 | 1.195 | 665.2 | 1.127 | +| 1 | 2 | 664.4 | 1.239 | 667.5 | 1.239 | 608.4 | 1.234 | 582.4 | 1.233 | 529.8 | 1.225 | 412.1 | 1.218 | +| 1 | 5 | 633.1 | 1.373 | 639.7 | 1.371 | 566.0 | 1.369 | 537.0 | 1.371 | 481.0 | 1.356 | 292.2 | 1.344 | +| 2 | 1 | 1530.2 | 1.301 | 1538.6 | 1.304 | 1281.3 | 1.288 | 1225.7 | 1.285 | 1127.6 | 1.261 | 1032.9 | 1.241 | +| 2 | 2 | 1085.3 | 1.325 | 1090.3 | 1.323 | 898.5 | 1.286 | 852.2 | 1.279 | 780.1 | 1.260 | 692.1 | 1.277 | +| 2 | 5 | 1041.4 | 1.381 | 1041.8 | 1.380 | 855.2 | 1.382 | 819.1 | 1.375 | 760.0 | 1.402 | 636.4 | 1.364 | +| 4 | 1 | 2545.2 | 1.392 | 2538.4 | 1.387 | 2104.4 | 1.358 | 1985.7 | 1.347 | 1801.2 | 1.332 | 1607.0 | 1.304 | +| 4 | 2 | 1820.8 | 1.348 | 1815.3 | 1.347 | 1508.7 | 1.328 | 1421.1 | 1.308 | 1278.3 | 1.309 | 1052.9 | 1.273 | +| 4 | 5 | 1702.1 | 1.339 | 1694.6 | 1.336 | 1395.9 | 1.347 | 1314.0 | 1.349 | 1181.0 | 1.333 | 1000.0 | 1.346 | +| 8 | 1 | 4361.0 | 1.453 | 4372.8 | 1.460 | 3636.9 | 1.425 | 3388.2 | 1.401 | 2945.2 | 1.342 | 2650.7 | 1.351 | +| 8 | 2 | 3087.6 | 1.337 | 3094.6 | 1.339 | 2555.4 | 1.322 | 2411.7 | 1.333 | 2173.2 | 1.348 | 1928.6 | 1.329 | +| 8 | 5 | 2927.4 | 1.623 | 2934.8 | 1.619 | 2456.4 | 1.588 | 2304.5 | 1.578 | 2018.4 | 1.512 | 1976.1 | 1.577 | +| 32 | 1 | 12564.5 | 1.621 | 12615.6 | 1.632 | 10924.6 | 1.632 | 10252.5 | 1.602 | 9577.1 | 1.653 | 8987.6 | 1.594 | +| 32 | 2 | 8652.0 | 1.753 | 8765.0 | 1.761 | 7460.1 | 1.762 | 6903.1 | 1.690 | 6531.2 | 1.739 | 6413.8 | 1.746 | +| 32 | 5 | 6750.6 | 2.455 | 6774.2 | 2.455 | 5842.7 | 2.347 | 5640.9 | 2.342 | 5239.2 | 2.325 | 5185.2 | 2.401 | +| 128 | 1 | 29255.3 | 2.602 | 29157.9 | 2.578 | 26514.6 | 2.622 | 23953.9 | 2.540 | 23105.5 | 2.541 | 22825.4 | 2.543 | +| 128 | 2 | 17823.4 | 2.640 | 17788.7 | 2.633 | 16089.4 | 2.641 | 14960.7 | 2.521 | 14573.0 | 2.677 | 14440.7 | 2.671 | +| 128 | 5 | 10106.9 | 3.128 | 10116.9 | 3.109 | 9111.7 | 3.087 | 8798.0 | 3.014 | 8273.3 | 3.133 | 8207.6 | 3.141 | +| 512 | 1 | 40817.8 | 3.381 | 41080.7 | 3.391 | 36490.2 | 3.418 | 36296.2 | 3.416 | 36133.0 | 3.416 | 36066.3 | 3.412 | +| 512 | 2 | 23112.0 | 3.238 | 23174.9 | 3.240 | 20655.0 | 3.262 | 20540.2 | 3.250 | 20430.7 | 3.243 | 20429.4 | 3.245 | +| 512 | 5 | 10836.4 | 3.460 | 10888.2 | 3.467 | 9598.3 | 3.432 | 9573.4 | 3.434 | 9527.9 | 3.424 | 9498.1 | 3.416 | To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. @@ -1163,64 +1153,37 @@ Tables presented in this section show the average inference latency (columns **A measured in milliseconds. Speedups reported in FP16 subsections are relative to FP32 numbers for corresponding configuration. - -##### Inference latency: NVIDIA Tesla V100 16G +##### Inference latency: NVIDIA T4 Our results were obtained by running the `translate.py` script in the -pytorch-19.05-py3 NGC Docker container with NVIDIA Tesla V100 16G GPUs. +pytorch-19.05-py3 NGC Docker container with NVIDIA T4. Full command to launch the inference latency benchmark was provided in the [Inference performance benchmark](#inference-performance-benchmark) section. -**FP32** - -| **Batch Size** | **Beam Size** | **Avg (ms)** | **50% (ms)** | **90% (ms)** | **95% (ms)** | **99% (ms)** | **100% (ms)** | -| -------------: | ------------: | -----------: | -----------: | -----------: | -----------: | -----------: | ------------: | -| 1 | 1 | 61.90 | 56.96 | 103.4 | 117.9 | 139.2 | 171.2 | -| 1 | 2 | 89.38 | 82.67 | 145.9 | 165.1 | 194.8 | 239.3 | -| 1 | 5 | 95.82 | 88.84 | 154.9 | 175.4 | 206.7 | 253.3 | -| 2 | 1 | 80.29 | 76.37 | 121.7 | 132.0 | 152.8 | 179.0 | -| 2 | 2 | 112.48 | 106.88 | 167.3 | 181.1 | 211.5 | 251.3 | -| 2 | 5 | 118.31 | 112.14 | 174.8 | 191.2 | 225.5 | 253.8 | -| 4 | 1 | 96.19 | 94.35 | 132.3 | 142.6 | 164.4 | 183.0 | -| 4 | 2 | 137.06 | 134.90 | 186.2 | 201.1 | 234.8 | 255.8 | -| 4 | 5 | 142.28 | 140.10 | 195.3 | 207.0 | 243.9 | 255.8 | -| 8 | 1 | 111.78 | 110.94 | 144.9 | 153.6 | 173.8 | 191.2 | -| 8 | 2 | 157.16 | 157.23 | 200.9 | 213.3 | 243.0 | 254.3 | -| 8 | 5 | 170.33 | 169.87 | 215.2 | 235.9 | 265.0 | 273.5 | -| 32 | 1 | 152.15 | 152.61 | 183.2 | 193.3 | 201.2 | 210.9 | -| 32 | 2 | 232.17 | 230.32 | 283.0 | 294.7 | 307.4 | 327.8 | -| 32 | 5 | 328.42 | 328.79 | 397.2 | 417.2 | 431.4 | 437.4 | -| 128 | 1 | 292.81 | 294.50 | 322.3 | 328.2 | 360.0 | 361.0 | -| 128 | 2 | 504.35 | 504.82 | 556.9 | 574.8 | 610.8 | 619.2 | -| 128 | 5 | 857.25 | 863.34 | 956.6 | 980.6 | 1045.4 | 1062.1 | -| 512 | 1 | 885.94 | 868.39 | 1002.8 | 1008.1 | 1016.9 | 1017.4 | -| 512 | 2 | 1490.94 | 1476.64 | 1683.1 | 1697.5 | 1705.0 | 1709.8 | -| 512 | 5 | 3195.79 | 3168.53 | 3596.3 | 3641.5 | 3656.5 | 3671.3 | - **FP16** | **Batch Size** | **Beam Size** | **Avg (ms)** | **Speedup** | **50% (ms)** | **Speedup** | **90% (ms)** | **Speedup** | **95% (ms)** | **Speedup** | **99% (ms)** | **Speedup** | **100% (ms)** | **Speedup** | | -------------: | ------------: | -----------: | ----------: | -----------: | ----------: | -----------: | ----------: | -----------: | ----------: | -----------: | ----------: | ------------: | ----------: | -| 1 | 1 | 62.91 | 0.9840 | 58.30 | 0.9770 | 104.4 | 0.9898 | 119.6 | 0.9858 | 143.5 | 0.9701 | 176.9 | 0.9677 | -| 1 | 2 | 91.97 | 0.9718 | 84.61 | 0.9771 | 150.1 | 0.9717 | 169.3 | 0.9756 | 199.9 | 0.9747 | 260.2 | 0.9198 | -| 1 | 5 | 95.55 | 1.0029 | 88.14 | 1.0079 | 155.1 | 0.9989 | 175.5 | 0.9991 | 209.7 | 0.9860 | 252.9 | 1.0015 | -| 2 | 1 | 78.18 | 1.0270 | 74.05 | 1.0313 | 118.7 | 1.0252 | 129.2 | 1.0219 | 151.6 | 1.0079 | 181.9 | 0.9843 | -| 2 | 2 | 113.11 | 0.9944 | 107.32 | 0.9959 | 168.6 | 0.9926 | 181.9 | 0.9953 | 211.6 | 0.9995 | 249.8 | 1.0062 | -| 2 | 5 | 116.67 | 1.0141 | 110.88 | 1.0114 | 174.1 | 1.0040 | 188.3 | 1.0156 | 221.5 | 1.0181 | 246.6 | 1.0290 | -| 4 | 1 | 93.53 | 1.0284 | 90.98 | 1.0370 | 129.0 | 1.0254 | 140.8 | 1.0121 | 159.7 | 1.0289 | 175.4 | 1.0433 | -| 4 | 2 | 136.42 | 1.0047 | 134.75 | 1.0012 | 184.6 | 1.0087 | 200.0 | 1.0052 | 235.1 | 0.9989 | 247.9 | 1.0319 | -| 4 | 5 | 139.49 | 1.0200 | 136.88 | 1.0236 | 191.1 | 1.0219 | 202.3 | 1.0230 | 240.2 | 1.0153 | 257.1 | 0.9948 | -| 8 | 1 | 103.75 | 1.0774 | 103.01 | 1.0769 | 135.6 | 1.0682 | 144.4 | 1.0635 | 162.1 | 1.0724 | 168.6 | 1.1338 | -| 8 | 2 | 153.57 | 1.0234 | 152.57 | 1.0306 | 197.3 | 1.0184 | 209.8 | 1.0167 | 236.7 | 1.0268 | 244.4 | 1.0405 | -| 8 | 5 | 158.68 | 1.0734 | 157.76 | 1.0768 | 200.3 | 1.0745 | 215.8 | 1.0934 | 245.0 | 1.0815 | 246.7 | 1.1087 | -| 32 | 1 | 132.89 | 1.1449 | 133.35 | 1.1444 | 160.5 | 1.1414 | 167.4 | 1.1548 | 173.9 | 1.1570 | 176.9 | 1.1922 | -| 32 | 2 | 203.80 | 1.1392 | 202.76 | 1.1359 | 245.3 | 1.1540 | 254.7 | 1.1572 | 264.9 | 1.1606 | 269.8 | 1.2150 | -| 32 | 5 | 216.72 | 1.5154 | 216.19 | 1.5208 | 261.7 | 1.5182 | 273.6 | 1.5247 | 282.4 | 1.5276 | 292.8 | 1.4940 | -| 128 | 1 | 187.34 | 1.5630 | 188.53 | 1.5621 | 204.9 | 1.5730 | 208.2 | 1.5765 | 213.6 | 1.6858 | 219.7 | 1.6432 | -| 128 | 2 | 300.33 | 1.6793 | 300.59 | 1.6794 | 333.2 | 1.6716 | 340.8 | 1.6864 | 345.2 | 1.7695 | 373.7 | 1.6572 | -| 128 | 5 | 393.62 | 2.1778 | 393.64 | 2.1932 | 435.0 | 2.1993 | 443.0 | 2.2136 | 457.6 | 2.2847 | 468.2 | 2.2686 | -| 512 | 1 | 373.13 | 2.3744 | 367.21 | 2.3648 | 407.2 | 2.4628 | 410.3 | 2.4573 | 414.3 | 2.4548 | 414.7 | 2.4531 | -| 512 | 2 | 616.52 | 2.4183 | 610.26 | 2.4197 | 676.7 | 2.4872 | 682.2 | 2.4882 | 696.0 | 2.4496 | 698.5 | 2.4478 | -| 512 | 5 | 1044.46 | 3.0597 | 1039.28 | 3.0488 | 1169.8 | 3.0743 | 1172.5 | 3.1056 | 1180.9 | 3.0963 | 1183.9 | 3.1011 | +| 1 | 1 | 58.35 | 1.217 | 53.92 | 1.214 | 96.43 | 1.208 | 110.4 | 1.202 | 129.7 | 1.211 | 161.2 | 1.227 | +| 1 | 2 | 86.04 | 1.238 | 79.70 | 1.232 | 139.58 | 1.241 | 158.2 | 1.241 | 187.3 | 1.242 | 231.9 | 1.252 | +| 1 | 5 | 89.92 | 1.373 | 83.20 | 1.369 | 144.67 | 1.379 | 165.1 | 1.372 | 193.9 | 1.387 | 249.0 | 1.307 | +| 2 | 1 | 76.07 | 1.298 | 72.06 | 1.299 | 115.35 | 1.292 | 124.8 | 1.287 | 146.7 | 1.284 | 169.7 | 1.305 | +| 2 | 2 | 107.00 | 1.319 | 101.46 | 1.323 | 159.65 | 1.312 | 171.4 | 1.314 | 199.8 | 1.310 | 236.5 | 1.278 | +| 2 | 5 | 111.24 | 1.383 | 105.79 | 1.384 | 165.88 | 1.379 | 178.8 | 1.392 | 210.3 | 1.410 | 235.0 | 1.465 | +| 4 | 1 | 91.62 | 1.385 | 89.44 | 1.387 | 126.05 | 1.375 | 136.9 | 1.358 | 155.5 | 1.395 | 173.4 | 1.393 | +| 4 | 2 | 127.74 | 1.346 | 125.35 | 1.349 | 173.20 | 1.348 | 186.6 | 1.344 | 216.4 | 1.350 | 237.0 | 1.419 | +| 4 | 5 | 136.62 | 1.349 | 134.64 | 1.335 | 185.34 | 1.386 | 198.6 | 1.396 | 237.0 | 1.405 | 250.0 | 1.492 | +| 8 | 1 | 106.57 | 1.450 | 106.08 | 1.452 | 137.45 | 1.440 | 147.0 | 1.452 | 166.0 | 1.463 | 175.8 | 1.455 | +| 8 | 2 | 150.30 | 1.341 | 150.59 | 1.340 | 190.34 | 1.347 | 203.0 | 1.361 | 232.7 | 1.386 | 245.5 | 1.417 | +| 8 | 5 | 158.51 | 1.628 | 157.91 | 1.614 | 200.90 | 1.665 | 217.3 | 1.683 | 244.2 | 1.706 | 269.5 | 1.633 | +| 32 | 1 | 147.38 | 1.626 | 148.19 | 1.597 | 177.61 | 1.686 | 184.7 | 1.685 | 192.5 | 1.694 | 197.6 | 1.725 | +| 32 | 2 | 214.38 | 1.756 | 211.63 | 1.773 | 259.40 | 1.816 | 273.0 | 1.780 | 286.3 | 1.787 | 293.8 | 1.825 | +| 32 | 5 | 274.72 | 2.455 | 273.77 | 2.461 | 337.34 | 2.443 | 351.2 | 2.498 | 363.2 | 2.518 | 375.0 | 2.530 | +| 128 | 1 | 252.24 | 2.601 | 253.45 | 2.609 | 276.06 | 2.663 | 281.8 | 2.661 | 309.7 | 2.647 | 312.4 | 2.658 | +| 128 | 2 | 414.53 | 2.642 | 415.38 | 2.648 | 458.75 | 2.675 | 474.4 | 2.665 | 501.5 | 2.719 | 509.1 | 2.721 | +| 128 | 5 | 730.79 | 3.129 | 738.87 | 3.118 | 820.11 | 3.141 | 843.1 | 3.137 | 895.4 | 3.150 | 915.7 | 3.141 | +| 512 | 1 | 722.77 | 3.382 | 710.66 | 3.377 | 823.14 | 3.414 | 826.9 | 3.419 | 831.8 | 3.412 | 840.0 | 3.395 | +| 512 | 2 | 1278.33 | 3.239 | 1264.85 | 3.227 | 1453.04 | 3.252 | 1467.6 | 3.251 | 1478.1 | 3.250 | 1485.9 | 3.245 | +| 512 | 5 | 2726.34 | 3.458 | 2700.51 | 3.467 | 3107.75 | 3.432 | 3146.0 | 3.433 | 3172.9 | 3.422 | 3180.1 | 3.443 | To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. @@ -1243,7 +1206,7 @@ outlined above. 4. Jun 25, 2019 * Default container updated to NGC PyTorch 19.05-py3 * Mixed precision training implemented using APEX AMP - * Added inference throughput and latency results on NVIDIA Tesla V100 16G + * Added inference throughput and latency results on NVIDIA T4 and NVIDIA Tesla V100 16G * Added option to run inference on user-provided raw input text from command line diff --git a/PyTorch/Translation/GNMT/img/diagram.png b/PyTorch/Translation/GNMT/img/diagram.png index ee5fc59e..1a406ac1 100644 Binary files a/PyTorch/Translation/GNMT/img/diagram.png and b/PyTorch/Translation/GNMT/img/diagram.png differ diff --git a/PyTorch/Translation/GNMT/launch.py b/PyTorch/Translation/GNMT/launch.py index faa6d03a..ab55b811 100644 --- a/PyTorch/Translation/GNMT/launch.py +++ b/PyTorch/Translation/GNMT/launch.py @@ -1,3 +1,75 @@ +# From PyTorch: +# +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) +# +# From Caffe2: +# +# Copyright (c) 2016-present, Facebook Inc. All rights reserved. +# +# All contributions by Facebook: +# Copyright (c) 2016 Facebook Inc. +# +# All contributions by Google: +# Copyright (c) 2015 Google Inc. +# All rights reserved. +# +# All contributions by Yangqing Jia: +# Copyright (c) 2015 Yangqing Jia +# All rights reserved. +# +# All contributions from Caffe: +# Copyright(c) 2013, 2014, 2015, the respective contributors +# All rights reserved. +# +# All other contributions: +# Copyright(c) 2015, 2016 the respective contributors +# All rights reserved. +# +# Caffe2 uses a copyright model similar to Caffe: each contributor holds +# copyright over their contributions to Caffe2. The project versioning records +# all such contribution and copyright details. If a contributor wants to further +# mark their specific copyright on a particular contribution, they should +# indicate their copyright solely in the commit message of the change when it is +# committed. +# +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# +# 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America +# and IDIAP Research Institute nor the names of its contributors may be +# used to endorse or promote products derived from this software without +# specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + r""" `torch.distributed.launch` is a module that spawns up multiple distributed training processes on each of the training nodes. diff --git a/PyTorch/Translation/GNMT/scripts/docker/build.sh b/PyTorch/Translation/GNMT/scripts/docker/build.sh index e0e15f6c..a761fec9 100755 --- a/PyTorch/Translation/GNMT/scripts/docker/build.sh +++ b/PyTorch/Translation/GNMT/scripts/docker/build.sh @@ -1,3 +1,23 @@ #!/bin/bash +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + docker build . --rm -t gnmt:latest diff --git a/PyTorch/Translation/GNMT/scripts/docker/interactive.sh b/PyTorch/Translation/GNMT/scripts/docker/interactive.sh index ab444151..d19da6dd 100755 --- a/PyTorch/Translation/GNMT/scripts/docker/interactive.sh +++ b/PyTorch/Translation/GNMT/scripts/docker/interactive.sh @@ -1,3 +1,23 @@ #!/bin/bash +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + nvidia-docker run --init -it --rm --ipc=host -v $PWD:/workspace/gnmt/ gnmt bash diff --git a/PyTorch/Translation/GNMT/scripts/filter_dataset.py b/PyTorch/Translation/GNMT/scripts/filter_dataset.py index 96dc1e97..2c1be8d7 100644 --- a/PyTorch/Translation/GNMT/scripts/filter_dataset.py +++ b/PyTorch/Translation/GNMT/scripts/filter_dataset.py @@ -1,3 +1,23 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import argparse from collections import Counter diff --git a/PyTorch/Translation/GNMT/scripts/tests/inference.sh b/PyTorch/Translation/GNMT/scripts/tests/inference.sh index 8cc0b896..a54d1db6 100644 --- a/PyTorch/Translation/GNMT/scripts/tests/inference.sh +++ b/PyTorch/Translation/GNMT/scripts/tests/inference.sh @@ -1,5 +1,26 @@ #!/bin/bash +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + + set -e DATASET_DIR='data/wmt16_de_en' diff --git a/PyTorch/Translation/GNMT/scripts/tests/reference_performance b/PyTorch/Translation/GNMT/scripts/tests/reference_performance deleted file mode 100644 index d5fc6267..00000000 --- a/PyTorch/Translation/GNMT/scripts/tests/reference_performance +++ /dev/null @@ -1,14 +0,0 @@ -fp16,1,Tesla V100-SXM2-16GB,66050 -fp16,4,Tesla V100-SXM2-16GB,196174 -fp16,8,Tesla V100-SXM2-16GB,387282 -fp32,1,Tesla V100-SXM2-16GB,21346 -fp32,4,Tesla V100-SXM2-16GB,76083 -fp32,8,Tesla V100-SXM2-16GB,153697 -fp16,1,Tesla V100-SXM3-32GB,65830 -fp16,4,Tesla V100-SXM3-32GB,200886 -fp16,8,Tesla V100-SXM3-32GB,362612 -fp16,16,Tesla V100-SXM3-32GB,738521 -fp32,1,Tesla V100-SXM3-32GB,22695 -fp32,4,Tesla V100-SXM3-32GB,81224 -fp32,8,Tesla V100-SXM3-32GB,156536 -fp32,16,Tesla V100-SXM3-32GB,314831 diff --git a/PyTorch/Translation/GNMT/scripts/tests/train_1epoch.sh b/PyTorch/Translation/GNMT/scripts/tests/train_1epoch.sh index 018c8673..8528b4bd 100644 --- a/PyTorch/Translation/GNMT/scripts/tests/train_1epoch.sh +++ b/PyTorch/Translation/GNMT/scripts/tests/train_1epoch.sh @@ -1,5 +1,26 @@ #!/bin/bash +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + + set -e DATASET_DIR='data/wmt16_de_en' diff --git a/PyTorch/Translation/GNMT/scripts/tests/train_1epoch_fp16.sh b/PyTorch/Translation/GNMT/scripts/tests/train_1epoch_fp16.sh deleted file mode 100644 index 21b4f347..00000000 --- a/PyTorch/Translation/GNMT/scripts/tests/train_1epoch_fp16.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash - -set -e - -DATASET_DIR='data/wmt16_de_en' -REPO_DIR='/workspace/gnmt' -REFERENCE_FILE=$REPO_DIR/scripts/tests/reference_performance - -MATH='fp16' -PERF_TOLERANCE=0.9 - -GPU_NAME=`nvidia-smi --query-gpu=gpu_name --format=csv,noheader |uniq` -echo 'GPU_NAME:' ${GPU_NAME} -GPU_COUNT=`nvidia-smi --query-gpu=gpu_name --format=csv,noheader |wc -l` -echo 'GPU_COUNT:' ${GPU_COUNT} - -REFERENCE_PERF=`grep "${MATH},${GPU_COUNT},${GPU_NAME}" \ - ${REFERENCE_FILE} | \cut -f 4 -d ','` - -if [ -z "${REFERENCE_PERF}" ]; then - echo "WARNING: COULD NOT FIND REFERENCE PERFORMANCE FOR EXECUTED CONFIG" - TARGET_PERF='' -else - PERF_THRESHOLD=$(awk 'BEGIN {print ('${REFERENCE_PERF}' * '${PERF_TOLERANCE}')}') - TARGET_PERF='--target-perf '${PERF_THRESHOLD} -fi - -cd $REPO_DIR - -python3 -m launch train.py \ - --dataset-dir $DATASET_DIR \ - --seed 1 \ - --epochs 1 \ - --remain-steps 1.0 \ - --target-bleu 17.20 \ - --math ${MATH} \ - ${TARGET_PERF} diff --git a/PyTorch/Translation/GNMT/scripts/tests/train_1epoch_fp32.sh b/PyTorch/Translation/GNMT/scripts/tests/train_1epoch_fp32.sh deleted file mode 100644 index 0030d041..00000000 --- a/PyTorch/Translation/GNMT/scripts/tests/train_1epoch_fp32.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash - -set -e - -DATASET_DIR='data/wmt16_de_en' -REPO_DIR='/workspace/gnmt' -REFERENCE_FILE=$REPO_DIR/scripts/tests/reference_performance - -MATH='fp32' -PERF_TOLERANCE=0.9 - -GPU_NAME=`nvidia-smi --query-gpu=gpu_name --format=csv,noheader |uniq` -echo 'GPU_NAME:' ${GPU_NAME} -GPU_COUNT=`nvidia-smi --query-gpu=gpu_name --format=csv,noheader |wc -l` -echo 'GPU_COUNT:' ${GPU_COUNT} - -REFERENCE_PERF=`grep "${MATH},${GPU_COUNT},${GPU_NAME}" \ - ${REFERENCE_FILE} | \cut -f 4 -d ','` - -if [ -z "${REFERENCE_PERF}" ]; then - echo "WARNING: COULD NOT FIND REFERENCE PERFORMANCE FOR EXECUTED CONFIG" - TARGET_PERF='' -else - PERF_THRESHOLD=$(awk 'BEGIN {print ('${REFERENCE_PERF}' * '${PERF_TOLERANCE}')}') - TARGET_PERF='--target-perf '${PERF_THRESHOLD} -fi - -cd $REPO_DIR - -python3 -m launch train.py \ - --dataset-dir $DATASET_DIR \ - --seed 1 \ - --epochs 1 \ - --remain-steps 1.0 \ - --target-bleu 17.20 \ - --math ${MATH} \ - ${TARGET_PERF} diff --git a/PyTorch/Translation/GNMT/scripts/tests/train_6epoch_fp16.sh b/PyTorch/Translation/GNMT/scripts/tests/train_6epoch_fp16.sh deleted file mode 100644 index c5402145..00000000 --- a/PyTorch/Translation/GNMT/scripts/tests/train_6epoch_fp16.sh +++ /dev/null @@ -1,36 +0,0 @@ -#!/bin/bash - -set -e - -DATASET_DIR='data/wmt16_de_en' -REPO_DIR='/workspace/gnmt' -REFERENCE_FILE=$REPO_DIR/scripts/tests/reference_performance - -MATH='fp16' -PERF_TOLERANCE=0.9 - -GPU_NAME=`nvidia-smi --query-gpu=gpu_name --format=csv,noheader |uniq` -echo 'GPU_NAME:' ${GPU_NAME} -GPU_COUNT=`nvidia-smi --query-gpu=gpu_name --format=csv,noheader |wc -l` -echo 'GPU_COUNT:' ${GPU_COUNT} - -REFERENCE_PERF=`grep "${MATH},${GPU_COUNT},${GPU_NAME}" \ - ${REFERENCE_FILE} | \cut -f 4 -d ','` - -if [ -z "${REFERENCE_PERF}" ]; then - echo "WARNING: COULD NOT FIND REFERENCE PERFORMANCE FOR EXECUTED CONFIG" - TARGET_PERF='' -else - PERF_THRESHOLD=$(awk 'BEGIN {print ('${REFERENCE_PERF}' * '${PERF_TOLERANCE}')}') - TARGET_PERF='--target-perf '${PERF_THRESHOLD} -fi - -cd $REPO_DIR - -python3 -m launch train.py \ - --dataset-dir $DATASET_DIR \ - --seed 1 \ - --epochs 6 \ - --target-bleu 22.00 \ - --math ${MATH} \ - ${TARGET_PERF} diff --git a/PyTorch/Translation/GNMT/scripts/tests/train_6epoch_fp32.sh b/PyTorch/Translation/GNMT/scripts/tests/train_6epoch_fp32.sh deleted file mode 100644 index 1e090a88..00000000 --- a/PyTorch/Translation/GNMT/scripts/tests/train_6epoch_fp32.sh +++ /dev/null @@ -1,36 +0,0 @@ -#!/bin/bash - -set -e - -DATASET_DIR='data/wmt16_de_en' -REPO_DIR='/workspace/gnmt' -REFERENCE_FILE=$REPO_DIR/scripts/tests/reference_performance - -MATH='fp32' -PERF_TOLERANCE=0.9 - -GPU_NAME=`nvidia-smi --query-gpu=gpu_name --format=csv,noheader |uniq` -echo 'GPU_NAME:' ${GPU_NAME} -GPU_COUNT=`nvidia-smi --query-gpu=gpu_name --format=csv,noheader |wc -l` -echo 'GPU_COUNT:' ${GPU_COUNT} - -REFERENCE_PERF=`grep "${MATH},${GPU_COUNT},${GPU_NAME}" \ - ${REFERENCE_FILE} | \cut -f 4 -d ','` - -if [ -z "${REFERENCE_PERF}" ]; then - echo "WARNING: COULD NOT FIND REFERENCE PERFORMANCE FOR EXECUTED CONFIG" - TARGET_PERF='' -else - PERF_THRESHOLD=$(awk 'BEGIN {print ('${REFERENCE_PERF}' * '${PERF_TOLERANCE}')}') - TARGET_PERF='--target-perf '${PERF_THRESHOLD} -fi - -cd $REPO_DIR - -python3 -m launch train.py \ - --dataset-dir $DATASET_DIR \ - --seed 1 \ - --epochs 6 \ - --target-bleu 22.00 \ - --math ${MATH} \ - ${TARGET_PERF} diff --git a/PyTorch/Translation/GNMT/scripts/tests/train_bench.sh b/PyTorch/Translation/GNMT/scripts/tests/train_bench.sh index 001d3e4d..b03c5d8e 100644 --- a/PyTorch/Translation/GNMT/scripts/tests/train_bench.sh +++ b/PyTorch/Translation/GNMT/scripts/tests/train_bench.sh @@ -1,5 +1,26 @@ #!/bin/bash +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + + set -e DATASET_DIR='data/wmt16_de_en' diff --git a/PyTorch/Translation/GNMT/scripts/tests/train_full.sh b/PyTorch/Translation/GNMT/scripts/tests/train_full.sh index 232c0076..3badc2e4 100644 --- a/PyTorch/Translation/GNMT/scripts/tests/train_full.sh +++ b/PyTorch/Translation/GNMT/scripts/tests/train_full.sh @@ -1,5 +1,26 @@ #!/bin/bash +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + + set -e DATASET_DIR='data/wmt16_de_en' diff --git a/PyTorch/Translation/GNMT/scripts/train.sh b/PyTorch/Translation/GNMT/scripts/train.sh index cf16f447..2084a8e4 100755 --- a/PyTorch/Translation/GNMT/scripts/train.sh +++ b/PyTorch/Translation/GNMT/scripts/train.sh @@ -1,5 +1,25 @@ #!/bin/bash +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + set -e python3 -m launch train.py diff --git a/PyTorch/Translation/GNMT/scripts/verify_dataset.sh b/PyTorch/Translation/GNMT/scripts/verify_dataset.sh index 7974fea4..2544671e 100644 --- a/PyTorch/Translation/GNMT/scripts/verify_dataset.sh +++ b/PyTorch/Translation/GNMT/scripts/verify_dataset.sh @@ -1,5 +1,25 @@ #!/bin/bash +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + set -e DATASET_DIR='data/wmt16_de_en' diff --git a/PyTorch/Translation/GNMT/scripts/wmt16_en_de.sh b/PyTorch/Translation/GNMT/scripts/wmt16_en_de.sh index 32d93cf1..ad6ab2ff 100644 --- a/PyTorch/Translation/GNMT/scripts/wmt16_en_de.sh +++ b/PyTorch/Translation/GNMT/scripts/wmt16_en_de.sh @@ -14,6 +14,26 @@ # See the License for the specific language governing permissions and # limitations under the License. +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + set -e export LANG=C.UTF-8 diff --git a/PyTorch/Translation/GNMT/seq2seq/data/config.py b/PyTorch/Translation/GNMT/seq2seq/data/config.py index 3819766f..df530752 100644 --- a/PyTorch/Translation/GNMT/seq2seq/data/config.py +++ b/PyTorch/Translation/GNMT/seq2seq/data/config.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + PAD_TOKEN = '' UNK_TOKEN = '' BOS_TOKEN = '' diff --git a/PyTorch/Translation/GNMT/seq2seq/data/dataset.py b/PyTorch/Translation/GNMT/seq2seq/data/dataset.py index d6903eae..bbec1082 100644 --- a/PyTorch/Translation/GNMT/seq2seq/data/dataset.py +++ b/PyTorch/Translation/GNMT/seq2seq/data/dataset.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import logging from operator import itemgetter diff --git a/PyTorch/Translation/GNMT/seq2seq/data/sampler.py b/PyTorch/Translation/GNMT/seq2seq/data/sampler.py index 01649777..6c6d27f3 100644 --- a/PyTorch/Translation/GNMT/seq2seq/data/sampler.py +++ b/PyTorch/Translation/GNMT/seq2seq/data/sampler.py @@ -1,3 +1,23 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import logging import torch diff --git a/PyTorch/Translation/GNMT/seq2seq/data/tokenizer.py b/PyTorch/Translation/GNMT/seq2seq/data/tokenizer.py index 38117511..bd29db36 100644 --- a/PyTorch/Translation/GNMT/seq2seq/data/tokenizer.py +++ b/PyTorch/Translation/GNMT/seq2seq/data/tokenizer.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import logging from collections import defaultdict from functools import partial diff --git a/PyTorch/Translation/GNMT/seq2seq/inference/beam_search.py b/PyTorch/Translation/GNMT/seq2seq/inference/beam_search.py index 917c792f..5e63ccad 100644 --- a/PyTorch/Translation/GNMT/seq2seq/inference/beam_search.py +++ b/PyTorch/Translation/GNMT/seq2seq/inference/beam_search.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import torch from seq2seq.data.config import BOS diff --git a/PyTorch/Translation/GNMT/seq2seq/inference/inference.py b/PyTorch/Translation/GNMT/seq2seq/inference/inference.py deleted file mode 100644 index 5ec3a4bf..00000000 --- a/PyTorch/Translation/GNMT/seq2seq/inference/inference.py +++ /dev/null @@ -1,289 +0,0 @@ -import contextlib -import logging -import os -import subprocess -import time - -import torch -import torch.distributed as dist - -import seq2seq.data.config as config -from seq2seq.inference.beam_search import SequenceGenerator -from seq2seq.utils import AverageMeter -from seq2seq.utils import barrier -from seq2seq.utils import get_rank -from seq2seq.utils import get_world_size - - -def gather_predictions(preds): - world_size = get_world_size() - if world_size > 1: - all_preds = [preds.new(preds.size(0), preds.size(1)) for i in range(world_size)] - dist.all_gather(all_preds, preds) - preds = torch.cat(all_preds) - return preds - - -class Translator: - """ - Translator runs validation on test dataset, executes inference, optionally - computes BLEU score using sacrebleu. - """ - def __init__(self, - model, - tokenizer, - loader, - beam_size=5, - len_norm_factor=0.6, - len_norm_const=5.0, - cov_penalty_factor=0.1, - max_seq_len=50, - cuda=False, - print_freq=1, - dataset_dir=None, - save_path=None, - target_bleu=None): - - self.model = model - self.tokenizer = tokenizer - self.loader = loader - self.insert_target_start = [config.BOS] - self.insert_src_start = [config.BOS] - self.insert_src_end = [config.EOS] - self.batch_first = model.batch_first - self.cuda = cuda - self.beam_size = beam_size - self.print_freq = print_freq - self.dataset_dir = dataset_dir - self.target_bleu = target_bleu - self.save_path = save_path - - self.distributed = (get_world_size() > 1) - - self.generator = SequenceGenerator( - model=self.model, - beam_size=beam_size, - max_seq_len=max_seq_len, - cuda=cuda, - len_norm_factor=len_norm_factor, - len_norm_const=len_norm_const, - cov_penalty_factor=cov_penalty_factor) - - def build_eval_path(self, epoch, iteration): - """ - Appends index of the current epoch and index of the current iteration - to the name of the file with results. - - :param epoch: index of the current epoch - :param iteration: index of the current iteration - """ - if iteration is not None: - eval_fname = f'eval_epoch_{epoch}_iter_{iteration}' - else: - eval_fname = f'eval_epoch_{epoch}' - eval_path = os.path.join(self.save_path, eval_fname) - return eval_path - - def run(self, calc_bleu=True, epoch=None, iteration=None, eval_path=None, - summary=False, reference_path=None): - """ - Runs translation on test dataset. - - :param calc_bleu: if True compares results with reference and computes - BLEU score - :param epoch: index of the current epoch - :param iteration: index of the current iteration - :param eval_path: path to the file for saving results - :param summary: if True prints summary - :param reference_path: path to the file with reference translation - """ - if self.cuda: - test_bleu = torch.cuda.FloatTensor([0]) - break_training = torch.cuda.LongTensor([0]) - else: - test_bleu = torch.FloatTensor([0]) - break_training = torch.LongTensor([0]) - - if eval_path is None: - eval_path = self.build_eval_path(epoch, iteration) - detok_eval_path = eval_path + '.detok' - - with contextlib.suppress(FileNotFoundError): - os.remove(eval_path) - os.remove(detok_eval_path) - - rank = get_rank() - logging.info(f'Running evaluation on test set') - self.model.eval() - torch.cuda.empty_cache() - - output = self.evaluate(epoch, iteration, summary) - output = output[:len(self.loader.dataset)] - output = self.loader.dataset.unsort(output) - - if rank == 0: - with open(eval_path, 'a') as eval_file: - eval_file.writelines(output) - if calc_bleu: - self.run_detokenizer(eval_path) - test_bleu[0] = self.run_sacrebleu(detok_eval_path, reference_path) - if summary: - logging.info(f'BLEU on test dataset: {test_bleu[0]:.2f}') - - if self.target_bleu and test_bleu[0] >= self.target_bleu: - logging.info(f'Target accuracy reached') - break_training[0] = 1 - - barrier() - torch.cuda.empty_cache() - logging.info(f'Finished evaluation on test set') - - if self.distributed: - dist.broadcast(break_training, 0) - dist.broadcast(test_bleu, 0) - - return test_bleu[0].item(), break_training[0].item() - - def evaluate(self, epoch, iteration, summary): - """ - Runs evaluation on test dataset. - - :param epoch: index of the current epoch - :param iteration: index of the current iteration - :param summary: if True prints summary - """ - batch_time = AverageMeter(False) - tot_tok_per_sec = AverageMeter(False) - iterations = AverageMeter(False) - enc_seq_len = AverageMeter(False) - dec_seq_len = AverageMeter(False) - stats = {} - - output = [] - - for i, (src, indices) in enumerate(self.loader): - translate_timer = time.time() - src, src_length = src - - batch_size = self.loader.batch_size - global_batch_size = batch_size * get_world_size() - beam_size = self.beam_size - - bos = [self.insert_target_start] * (batch_size * beam_size) - bos = torch.LongTensor(bos) - if self.batch_first: - bos = bos.view(-1, 1) - else: - bos = bos.view(1, -1) - - src_length = torch.LongTensor(src_length) - stats['total_enc_len'] = int(src_length.sum()) - - if self.cuda: - src = src.cuda() - src_length = src_length.cuda() - bos = bos.cuda() - - with torch.no_grad(): - context = self.model.encode(src, src_length) - context = [context, src_length, None] - - if beam_size == 1: - generator = self.generator.greedy_search - else: - generator = self.generator.beam_search - preds, lengths, counter = generator(batch_size, bos, context) - - stats['total_dec_len'] = lengths.sum().item() - stats['iters'] = counter - - indices = torch.tensor(indices).to(preds) - preds = preds.scatter(0, indices.unsqueeze(1).expand_as(preds), preds) - - preds = gather_predictions(preds).cpu() - - for pred in preds: - pred = pred.tolist() - detok = self.tokenizer.detokenize(pred) - output.append(detok + '\n') - - elapsed = time.time() - translate_timer - batch_time.update(elapsed, batch_size) - - total_tokens = stats['total_dec_len'] + stats['total_enc_len'] - ttps = total_tokens / elapsed - tot_tok_per_sec.update(ttps, batch_size) - - iterations.update(stats['iters']) - enc_seq_len.update(stats['total_enc_len'] / batch_size, batch_size) - dec_seq_len.update(stats['total_dec_len'] / batch_size, batch_size) - - if i % self.print_freq == 0: - log = [] - log += f'TEST ' - if epoch is not None: - log += f'[{epoch}]' - if iteration is not None: - log += f'[{iteration}]' - log += f'[{i}/{len(self.loader)}]\t' - log += f'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' - log += f'Decoder iters {iterations.val:.1f} ({iterations.avg:.1f})\t' - log += f'Tok/s {tot_tok_per_sec.val:.0f} ({tot_tok_per_sec.avg:.0f})' - log = ''.join(log) - logging.info(log) - - tot_tok_per_sec.reduce('sum') - enc_seq_len.reduce('mean') - dec_seq_len.reduce('mean') - batch_time.reduce('mean') - iterations.reduce('sum') - - if summary and get_rank() == 0: - time_per_sentence = (batch_time.avg / global_batch_size) - log = [] - log += f'TEST SUMMARY:\n' - log += f'Lines translated: {len(self.loader.dataset)}\t' - log += f'Avg total tokens/s: {tot_tok_per_sec.avg:.0f}\n' - log += f'Avg time per batch: {batch_time.avg:.3f} s\t' - log += f'Avg time per sentence: {1000*time_per_sentence:.3f} ms\n' - log += f'Avg encoder seq len: {enc_seq_len.avg:.2f}\t' - log += f'Avg decoder seq len: {dec_seq_len.avg:.2f}\t' - log += f'Total decoder iterations: {int(iterations.sum)}' - log = ''.join(log) - logging.info(log) - - return output - - def run_detokenizer(self, eval_path): - """ - Executes moses detokenizer on eval_path file and saves result to - eval_path + ".detok" file. - - :param eval_path: path to the tokenized input - """ - logging.info('Running detokenizer') - detok_path = os.path.join(self.dataset_dir, config.DETOKENIZER) - detok_eval_path = eval_path + '.detok' - - with open(detok_eval_path, 'w') as detok_eval_file, \ - open(eval_path, 'r') as eval_file: - subprocess.run(['perl', f'{detok_path}'], stdin=eval_file, - stdout=detok_eval_file, stderr=subprocess.DEVNULL) - - def run_sacrebleu(self, detok_eval_path, reference_path): - """ - Executes sacrebleu and returns BLEU score. - - :param detok_eval_path: path to the test file - :param reference_path: path to the reference file - """ - if reference_path is None: - reference_path = os.path.join(self.dataset_dir, - config.TGT_TEST_TARGET_FNAME) - sacrebleu_params = '--score-only -lc --tokenize intl' - logging.info(f'Running sacrebleu (parameters: {sacrebleu_params})') - sacrebleu = subprocess.run([f'sacrebleu --input {detok_eval_path} \ - {reference_path} {sacrebleu_params}'], - stdout=subprocess.PIPE, shell=True) - test_bleu = float(sacrebleu.stdout.strip()) - return test_bleu diff --git a/PyTorch/Translation/GNMT/seq2seq/inference/tables.py b/PyTorch/Translation/GNMT/seq2seq/inference/tables.py index 9af848c0..354c4091 100644 --- a/PyTorch/Translation/GNMT/seq2seq/inference/tables.py +++ b/PyTorch/Translation/GNMT/seq2seq/inference/tables.py @@ -1,3 +1,23 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import collections import itertools diff --git a/PyTorch/Translation/GNMT/seq2seq/inference/translator.py b/PyTorch/Translation/GNMT/seq2seq/inference/translator.py index d103494d..a3998e7d 100644 --- a/PyTorch/Translation/GNMT/seq2seq/inference/translator.py +++ b/PyTorch/Translation/GNMT/seq2seq/inference/translator.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import logging import subprocess import time diff --git a/PyTorch/Translation/GNMT/seq2seq/models/attention.py b/PyTorch/Translation/GNMT/seq2seq/models/attention.py index 76ad1104..d1b608d3 100644 --- a/PyTorch/Translation/GNMT/seq2seq/models/attention.py +++ b/PyTorch/Translation/GNMT/seq2seq/models/attention.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import math import torch diff --git a/PyTorch/Translation/GNMT/seq2seq/models/decoder.py b/PyTorch/Translation/GNMT/seq2seq/models/decoder.py index ac86655a..39049d51 100644 --- a/PyTorch/Translation/GNMT/seq2seq/models/decoder.py +++ b/PyTorch/Translation/GNMT/seq2seq/models/decoder.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import itertools import torch diff --git a/PyTorch/Translation/GNMT/seq2seq/models/encoder.py b/PyTorch/Translation/GNMT/seq2seq/models/encoder.py index 16a7d7ef..4c146253 100644 --- a/PyTorch/Translation/GNMT/seq2seq/models/encoder.py +++ b/PyTorch/Translation/GNMT/seq2seq/models/encoder.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import torch.nn as nn from torch.nn.utils.rnn import pack_padded_sequence from torch.nn.utils.rnn import pad_packed_sequence diff --git a/PyTorch/Translation/GNMT/seq2seq/models/gnmt.py b/PyTorch/Translation/GNMT/seq2seq/models/gnmt.py index c71e2837..f8304b89 100644 --- a/PyTorch/Translation/GNMT/seq2seq/models/gnmt.py +++ b/PyTorch/Translation/GNMT/seq2seq/models/gnmt.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import torch.nn as nn import seq2seq.data.config as config diff --git a/PyTorch/Translation/GNMT/seq2seq/models/seq2seq_base.py b/PyTorch/Translation/GNMT/seq2seq/models/seq2seq_base.py index 4b063171..12b7042f 100644 --- a/PyTorch/Translation/GNMT/seq2seq/models/seq2seq_base.py +++ b/PyTorch/Translation/GNMT/seq2seq/models/seq2seq_base.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import torch.nn as nn from torch.nn.functional import log_softmax diff --git a/PyTorch/Translation/GNMT/seq2seq/train/fp_optimizers.py b/PyTorch/Translation/GNMT/seq2seq/train/fp_optimizers.py index 3402aaab..f86e99fb 100644 --- a/PyTorch/Translation/GNMT/seq2seq/train/fp_optimizers.py +++ b/PyTorch/Translation/GNMT/seq2seq/train/fp_optimizers.py @@ -1,3 +1,23 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import logging import math diff --git a/PyTorch/Translation/GNMT/seq2seq/train/lr_scheduler.py b/PyTorch/Translation/GNMT/seq2seq/train/lr_scheduler.py index 91e665ff..47ec0d9e 100644 --- a/PyTorch/Translation/GNMT/seq2seq/train/lr_scheduler.py +++ b/PyTorch/Translation/GNMT/seq2seq/train/lr_scheduler.py @@ -1,3 +1,23 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import logging import math diff --git a/PyTorch/Translation/GNMT/seq2seq/train/smoothing.py b/PyTorch/Translation/GNMT/seq2seq/train/smoothing.py index d5b60b27..dbac0262 100644 --- a/PyTorch/Translation/GNMT/seq2seq/train/smoothing.py +++ b/PyTorch/Translation/GNMT/seq2seq/train/smoothing.py @@ -1,3 +1,23 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import torch import torch.nn as nn diff --git a/PyTorch/Translation/GNMT/seq2seq/train/table.py b/PyTorch/Translation/GNMT/seq2seq/train/table.py index 064e0b3f..b6c84d4d 100644 --- a/PyTorch/Translation/GNMT/seq2seq/train/table.py +++ b/PyTorch/Translation/GNMT/seq2seq/train/table.py @@ -1,3 +1,23 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + from pytablewriter import MarkdownTableWriter diff --git a/PyTorch/Translation/GNMT/seq2seq/train/trainer.py b/PyTorch/Translation/GNMT/seq2seq/train/trainer.py index a10fcb9b..8acae683 100644 --- a/PyTorch/Translation/GNMT/seq2seq/train/trainer.py +++ b/PyTorch/Translation/GNMT/seq2seq/train/trainer.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import logging import os import time diff --git a/PyTorch/Translation/GNMT/seq2seq/utils.py b/PyTorch/Translation/GNMT/seq2seq/utils.py index e0298812..e1a0290d 100644 --- a/PyTorch/Translation/GNMT/seq2seq/utils.py +++ b/PyTorch/Translation/GNMT/seq2seq/utils.py @@ -1,3 +1,24 @@ +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import logging.config import os import random diff --git a/PyTorch/Translation/GNMT/train.py b/PyTorch/Translation/GNMT/train.py index 1dcf49a6..04d93979 100644 --- a/PyTorch/Translation/GNMT/train.py +++ b/PyTorch/Translation/GNMT/train.py @@ -1,4 +1,26 @@ #!/usr/bin/env python + +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import argparse import logging import os @@ -330,9 +352,7 @@ def set_iter_size(train_iter_size, train_global_batch_size, train_batch_size): def build_criterion(vocab_size, padding_idx, smoothing): if smoothing == 0.: logging.info(f'Building CrossEntropyLoss') - loss_weight = torch.ones(vocab_size) - loss_weight[padding_idx] = 0 - criterion = nn.CrossEntropyLoss(weight=loss_weight, size_average=False) + criterion = nn.CrossEntropyLoss(ignore_index=padding_idx, size_average=False) else: logging.info(f'Building LabelSmoothingLoss (smoothing: {smoothing})') criterion = LabelSmoothing(padding_idx, smoothing) diff --git a/PyTorch/Translation/GNMT/translate.py b/PyTorch/Translation/GNMT/translate.py index 8f49ca73..a8f72851 100644 --- a/PyTorch/Translation/GNMT/translate.py +++ b/PyTorch/Translation/GNMT/translate.py @@ -1,4 +1,26 @@ #!/usr/bin/env python + +# Copyright (c) 2017 Elad Hoffer +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + import argparse import logging import itertools diff --git a/README.md b/README.md index b3324d7e..4ce56016 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# NVIDIA Deep Learning Examples for Volta Tensor Cores +# NVIDIA Deep Learning Examples for Tensor Cores ## Introduction This repository provides the latest deep learning example networks for training. These examples focus on achieving the best performance and convergence from NVIDIA Volta Tensor Cores. diff --git a/TensorFlow/Classification/RN50v1.5/Dockerfile b/TensorFlow/Classification/RN50v1.5/Dockerfile index 131dd01a..0015f8ac 100644 --- a/TensorFlow/Classification/RN50v1.5/Dockerfile +++ b/TensorFlow/Classification/RN50v1.5/Dockerfile @@ -1,4 +1,4 @@ -FROM nvcr.io/nvidia/tensorflow:19.07-py3 +FROM nvcr.io/nvidia/tensorflow:19.08-py3 ## MAINTAINER Paweł Sołtysiak ADD . /workspace/rn50v15_tf diff --git a/TensorFlow/Classification/RN50v1.5/README.md b/TensorFlow/Classification/RN50v1.5/README.md index ef3a7984..d3f3f13d 100644 --- a/TensorFlow/Classification/RN50v1.5/README.md +++ b/TensorFlow/Classification/RN50v1.5/README.md @@ -29,12 +29,14 @@ This repository provides a script and recipe to train the ResNet-50 v1.5 model t * [Results](#results) * [Training accuracy results](#training-accuracy-results) * [NVIDIA DGX-1 (8x V100 16G)](#nvidia-dgx-1-8x-v100-16g) + * [NVIDIA DGX-1 (8x V100 32G)](#nvidia-dgx-1-8x-v100-32g) * [Training performance results](#training-performance-results) * [NVIDIA DGX-1 (8x V100 16G)](#nvidia-dgx-1-8x-v100-16g) * [NVIDIA DGX-2 (16x V100 32G)](#nvidia-dgx-2-16x-v100-32g) * [Inference performance results](#inference-performance-results) * [NVIDIA DGX-1 (8x V100 16G)](#nvidia-dgx-1-8x-v100-16g) * [NVIDIA DGX-2 (16x V100 32G)](#nvidia-dgx-2-16x-v100-32g) + * [NVIDIA T4 (1x T4)](#nvidia-t4-1x-t4-16g) * [Release notes](#release-notes) * [Changelog](#changelog) * [Known issues](#known-issues) @@ -79,6 +81,8 @@ during first 5 epochs according to [Training ImageNet in 1 hour](https://arxiv.o * 50 Epochs -> configuration that reaches 75.9% top1 accuracy * 90 Epochs -> 90 epochs is a standard for ResNet50 + + * 250 Epochs -> best possible accuracy. For 250 epoch training we also use [MixUp regularization](https://arxiv.org/pdf/1710.09412.pdf). ### Data Augmentation @@ -172,7 +176,7 @@ The following section list the requirements that you need to meet in order to us This repository contains Dockerfile which extends the Tensorflow NGC container and encapsulates all dependencies. Aside from these dependencies, ensure you have the following software: * [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) -* [TensorFlow 19.06-py3 NGC container or later](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) +* [TensorFlow 19.08-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) * [NVIDIA Volta based GPU](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) For more information about how to get started with NGC containers, see the @@ -181,7 +185,7 @@ following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning * [Accessing And Pulling From The NGC container registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry) * [Running Tensorflow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running). -For those unable to use the [TensorFlow 19.06-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html). +For those unable to use the [TensorFlow 19.08-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html). ## Quick Start Guide To train your model using mixed precision with tensor cores, perform the following steps using the default parameters of the ResNet-50 v1.5 model on the [ImageNet](http://www.image-net.org/) dataset. For the specifics concerning training and inference, see the [Advanced](#advanced) section. @@ -279,7 +283,7 @@ The script for training end evaluating the ResNet-50 v1.5 model have a variety o ##### Common parameters `--mode` -: allow specification of mode in which the script will run: train, train_and_evaluate, evaluate, training_benchmark or inference_benchmark +: allow specification of mode in which the script will run: train, train_and_evaluate, evaluate, predict, training_benchmark or inference_benchmark `--data_dir` `--data_idx_dir` : allow specification of dataset location @@ -325,6 +329,8 @@ The script for training end evaluating the ResNet-50 v1.5 model have a variety o `--loss_scale` : value of static loss scale. This parameter will have no effect if `--use_auto_loss_scaling` is set. +`--mixup` +: value of alpha parameter for mixup (if 0 then mixup is not applied) (default: 0) ##### Utility parameters `--help` @@ -378,6 +384,17 @@ To run a configuration that is not based on the default parameters, use: * FP16 `mpiexec --allow-run-as-root --bind-to socket -np python ./main.py --batch_size=256 --use_tf_amp --data_dir= --results_dir=` +### Inference process +To run inference on single examples on checkpoint with model script, use: + +`python main.py --mode predict --model_dir --to_predict --results_dir ` + + +To run inference on a SavedModel with dedicated script, use: + +`python scripts/inference/predict.py -m -f ` + + ## Performance ### Benchmarking @@ -428,29 +445,40 @@ The following sections provide details on how we achieved our results in trainin #### Training accuracy results -##### Training accuracy: NVIDIA DGX-1 (8x V100 16G) +##### NVIDIA DGX-1 (8x V100 16G) -Our results were obtained by running the `./scripts/RN50_{FP16, FP32}_{1, 4, 8}GPU.sh` script in +Our results were obtained by running 50 epochs training using the `./scripts/RN50_{FP16, FP32}_{1, 4, 8}GPU.sh` script in the tensorflow-19.07-py3 Docker container on NVIDIA DGX-1 with 8 V100 16G GPUs. | **number of GPUs** | **mixed precision top1** | **mixed precision training time** | **FP32 top1** | **FP32 training time** | |:------------------:|:------------------------:|:---------------------------------:|:-------------:|:----------------------:| -| **1** | 76.18 | 41.3h | 76.38 | 89.4h | -| **4** | 76.30 | 10.5h | 76.30 | 22.4h | -| **8** | 76.18 | 5.6h | 76.26 | 11.5h | +| **1** | 76.21 | 13.3h | 76.14 | 50.42h | +| **4** | 76.13 | 3.58h | 76.11 | 12.65h | +| **8** | 76.08 | 1.87h | 76.12 | 6.38h | +##### NVIDIA DGX-1 (8x V100 32G) + +Our results were obtained by running 50 epochs training using the `./scripts/RN50_{FP16, FP32}_{1, 4, 8}GPU.sh` script in +the tensorflow-19.07-py3 Docker container on NVIDIA DGX-1 with 8 V100 32G GPUs. + + +| **number of GPUs** | **mixed precision top1** | **mixed precision training time** | **FP32 top1** | **FP32 training time** | +|:------------------:|:------------------------:|:---------------------------------:|:-------------:|:----------------------:| +| **1** | 76.14 | 13.79h | 76.06 | 51.38h | +| **4** | 76.07 | 3.72h | 76.17 | 12.7h | +| **8** | 76.04 | 1.91h | 76.02 | 6.43h | #### Training performance results ##### NVIDIA DGX-1 (8x V100 16G) -The results were obtained by running the `./scripts/benchmarking/DGX1V_trainbench_fp16.sh` and `./scripts/benchmarking/DGX1V_trainbench_fp32.sh` scripts in the tensorflow-19.06-py3 Docker container on NVIDIA DGX-1 with 8 V100 16G GPU. +The results were obtained by running the `./scripts/benchmarking/DGX1V_trainbench_fp16.sh` and `./scripts/benchmarking/DGX1V_trainbench_fp32.sh` scripts in the tensorflow-19.07-py3 Docker container on NVIDIA DGX-1 with 8 V100 16G GPU. | **number of GPUs** | **mixed precision img/s** | **FP32 img/s** | **mixed precision speedup** | **mixed precision weak scaling** | **FP32 weak scaling** | |:------------------:|:-------------------------:|:--------------:|:---------------------------:|:--------------------------------:|:---------------------:| -| **1** | 802.1 | 364.9 | 2.20 | 1.00 | 1.00 | +| **1** | 825.2 | 364.9 | 2.20 | 1.00 | 1.00 | | **4** | 3197.4 | 1419.4 | 2.25 | 3.96 | 3.89 | | **8** | 6209.9 | 2778.5 | 2.24 | 7.74 | 7.61 | @@ -458,13 +486,13 @@ The results were obtained by running the `./scripts/benchmarking/DGX1V_trainbenc | **number of GPUs** | **mixed precision img/s** | **mixed precision + XLA img/s** | **XLA speedup** | |:------------------:|:-------------------------:|:-------------------------------:|:---------------:| -| **1** | 802.1 | 1177.9 | 1.47 | -| **4** | 3197.4 | 4654.1 | 1.45 | -| **8** | 6209.9 | 8104.4 | 1.30 | +| **1** | 825.2 | 1335.9 | 1.61 | +| **4** | 3197.4 | 4964.9 | 1.55 | +| **8** | 6209.9 | 9518.8 | 1.53 | ##### NVIDIA DGX-2 (16x V100 32G) -The results were obtained by running the `./scripts/benchmarking/DGX2_trainbench_fp16.sh` and `./scripts/benchmarking/DGX2_trainbench_fp32.sh` scripts in the tensorflow-19.06-py3 Docker container on NVIDIA DGX-2 with 16 V100 32G GPU. +The results were obtained by running the `./scripts/benchmarking/DGX2_trainbench_fp16.sh` and `./scripts/benchmarking/DGX2_trainbench_fp32.sh` scripts in the tensorflow-19.07-py3 Docker container on NVIDIA DGX-2 with 16 V100 32G GPU. | **number of GPUs** | **mixed precision img/s** | **FP32 img/s** | **mixed precision speedup** | **mixed precision weak scaling** | **FP32 weak scaling** | |:------------------:|:-------------------------:|:--------------:|:---------------------------:|:--------------------------------:|:---------------------:| @@ -485,7 +513,7 @@ The results were obtained by running the `./scripts/benchmarking/DGX2_trainbench #### Inference performance results ##### NVIDIA DGX-1 (8x V100 16G) -The results were obtained by running the `./scripts/benchmarking/DGX1V_inferbench_fp16.sh` and `./scripts/benchmarking/DGX1V_inferbench_fp32.sh` scripts in the tensorflow-19.06-py3 Docker container on a single GPU of NVIDIA DGX-1 with 8 V100 16G GPUs. +The results were obtained by running the `./scripts/benchmarking/DGX1V_inferbench_fp16.sh` and `./scripts/benchmarking/DGX1V_inferbench_fp32.sh` scripts in the tensorflow-19.07-py3 Docker container on a single GPU of NVIDIA DGX-1 with 8 V100 16G GPUs. | **batch size** | **mixed precision img/s** | **FP32 img/s** | **mixed precision + XLA img/s** | |:--------------:|:-------------------------:|:--------------:|:-------------------------------:| @@ -501,7 +529,7 @@ The results were obtained by running the `./scripts/benchmarking/DGX1V_inferbenc ##### NVIDIA DGX-2 (16x V100 32G) -The results were obtained by running the `./scripts/benchmarking/DGX2_inferbench_fp16.sh` and `./scripts/benchmarking/DGX2_inferbench_fp32.sh` scripts in the tensorflow-19.05-py3 Docker container on a single GPU of NVIDIA DGX-2 with 16 V100 32G GPUs. +The results were obtained by running the `./scripts/benchmarking/DGX2_inferbench_fp16.sh` and `./scripts/benchmarking/DGX2_inferbench_fp32.sh` scripts in the tensorflow-19.07-py3 Docker container on a single GPU of NVIDIA DGX-2 with 16 V100 32G GPUs. | **batch size** | **mixed precision img/s** | **FP32 img/s** | **mixed precision + XLA img/s** | |:--------------:|:-------------------------:|:--------------:|:-------------------------------:| @@ -515,6 +543,16 @@ The results were obtained by running the `./scripts/benchmarking/DGX2_inferbench | **128** | 2126.5 | 1168.8 | 3469.6 | | **256** | 2203.6 | N/A | 3713.2 | +##### NVIDIA T4 (1x T4 16G) +The results were obtained by running the `./scripts/benchmarking/T4_inferbench_fp16.sh` and `./scripts/benchmarking/T4_inferbench_fp32.sh` scripts in the tensorflow-19.07-py3 Docker container on a single T4 GPU. + +| **batch size** | **mixed precision img/s** | **FP32 img/s** | **mixed precision + XLA img/s** | +|:--------------:|:-------------------------:|:--------------:|:-------------------------------:| +| **1** | 173.2 | 138.7 | 204.2 | +| **2** | 302.1 | 207.6 | 359.8 | +| **4** | 450.3 | 267.4 | 660.0 | +| **8** | 558.7 | 305.9 | 924.7 | + ## Release notes ### Changelog @@ -526,6 +564,11 @@ The results were obtained by running the `./scripts/benchmarking/DGX2_inferbench * Added benchmark results for DGX-2 and XLA-enabled DGX-1 and DGX-2. 3. July 15, 2019 * Added Cosine learning rate schedule +3. August 15, 2019 + * Added mixup regularization + * Added T4 benchmarks + * Improved inference capabilities + * Added SavedModel export ### Known issues There are no known issues with this model. diff --git a/TensorFlow/Classification/RN50v1.5/dllogger/logger.py b/TensorFlow/Classification/RN50v1.5/dllogger/logger.py index 25baaebf..46695200 100644 --- a/TensorFlow/Classification/RN50v1.5/dllogger/logger.py +++ b/TensorFlow/Classification/RN50v1.5/dllogger/logger.py @@ -261,7 +261,7 @@ class _ParentStdOutBackend(object): prefix=self.prefix, token=self.token, ver=self.version, secs=now, model=_data['model'], call_site=call_site, msg=msg) - + self.logger.debug(message) def timed_block_start(self, name): diff --git a/TensorFlow/Classification/RN50v1.5/main.py b/TensorFlow/Classification/RN50v1.5/main.py index 97d306b1..cae091cd 100755 --- a/TensorFlow/Classification/RN50v1.5/main.py +++ b/TensorFlow/Classification/RN50v1.5/main.py @@ -37,14 +37,15 @@ if __name__ == "__main__": RUNNING_CONFIG = tf.contrib.training.HParams( mode=FLAGS.mode, - + # ======= Directory HParams ======= # log_dir=FLAGS.results_dir, - model_dir=FLAGS.results_dir, + model_dir=FLAGS.model_dir if FLAGS.model_dir is not None else FLAGS.results_dir, summaries_dir=FLAGS.results_dir, data_dir=FLAGS.data_dir, data_idx_dir=FLAGS.data_idx_dir, - + export_dir=FLAGS.export_dir, + # ========= Model HParams ========= # n_classes=1001, input_format='NHWC', @@ -53,7 +54,7 @@ if __name__ == "__main__": height=224, width=224, n_channels=3, - + # ======= Training HParams ======== # iter_unit=FLAGS.iter_unit, num_iter=FLAGS.num_iter, @@ -66,6 +67,7 @@ if __name__ == "__main__": momentum=FLAGS.momentum, loss_scale=FLAGS.loss_scale, label_smoothing=FLAGS.label_smoothing, + mixup=FLAGS.mixup, use_cosine_lr=FLAGS.use_cosine_lr, use_static_loss_scaling=FLAGS.use_static_loss_scaling, distort_colors=False, @@ -75,6 +77,7 @@ if __name__ == "__main__": use_tf_amp=FLAGS.use_tf_amp, use_dali=FLAGS.use_dali, gpu_memory_fraction=FLAGS.gpu_memory_fraction, + gpu_id=FLAGS.gpu_id, seed=FLAGS.seed, ) @@ -95,12 +98,14 @@ if __name__ == "__main__": model_dir=RUNNING_CONFIG.model_dir, data_dir=RUNNING_CONFIG.data_dir, data_idx_dir=RUNNING_CONFIG.data_idx_dir, + # ======= Optimization HParams ======== # use_xla=RUNNING_CONFIG.use_xla, use_tf_amp=RUNNING_CONFIG.use_tf_amp, use_dali=RUNNING_CONFIG.use_dali, gpu_memory_fraction=RUNNING_CONFIG.gpu_memory_fraction, + gpu_id=RUNNING_CONFIG.gpu_id, seed=RUNNING_CONFIG.seed ) @@ -118,6 +123,7 @@ if __name__ == "__main__": momentum=RUNNING_CONFIG.momentum, loss_scale=RUNNING_CONFIG.loss_scale, label_smoothing=RUNNING_CONFIG.label_smoothing, + mixup=RUNNING_CONFIG.mixup, use_static_loss_scaling=RUNNING_CONFIG.use_static_loss_scaling, use_cosine_lr=RUNNING_CONFIG.use_cosine_lr, is_benchmark=RUNNING_CONFIG.mode == 'training_benchmark', @@ -137,5 +143,19 @@ if __name__ == "__main__": warmup_steps=RUNNING_CONFIG.warmup_steps, batch_size=RUNNING_CONFIG.batch_size, log_every_n_steps=RUNNING_CONFIG.log_every_n_steps, - is_benchmark=RUNNING_CONFIG.mode == 'inference_benchmark' + is_benchmark=RUNNING_CONFIG.mode == 'inference_benchmark', + export_dir=RUNNING_CONFIG.export_dir ) + + if RUNNING_CONFIG.mode == 'predict': + if FLAGS.to_predict is None: + raise ValueError("No data to predict on.") + + if not os.path.isfile(FLAGS.to_predict): + raise ValueError("Only prediction on single images is supported!") + + if hvd_utils.is_using_hvd(): + raise NotImplementedError("Only single GPU inference is implemented.") + + elif not hvd_utils.is_using_hvd() or hvd.rank() == 0: + runner.predict(FLAGS.to_predict) diff --git a/TensorFlow/Classification/RN50v1.5/model/resnet_v1_5.py b/TensorFlow/Classification/RN50v1.5/model/resnet_v1_5.py index 6efc5239..d912d116 100644 --- a/TensorFlow/Classification/RN50v1.5/model/resnet_v1_5.py +++ b/TensorFlow/Classification/RN50v1.5/model/resnet_v1_5.py @@ -146,12 +146,47 @@ class ResnetModel(object): if not self.model_hparams.use_dali: features = normalized_inputs(features) + mixup = 0 + eta = 0 + + if mode == tf.estimator.ModeKeys.TRAIN: + eta = params['label_smoothing'] + mixup = params['mixup'] + + if mode != tf.estimator.ModeKeys.PREDICT: + one_hot_smoothed_labels = tf.one_hot(labels, 1001, + on_value = 1 - eta + eta/1001, + off_value = eta/1001) + if mixup != 0: + + LOGGER.log("Using mixup training with beta=", params['mixup']) + beta_distribution = tf.distributions.Beta(params['mixup'], params['mixup']) + + feature_coefficients = beta_distribution.sample(sample_shape=[params['batch_size'], 1, 1, 1]) + + reversed_feature_coefficients = tf.subtract(tf.ones(shape=feature_coefficients.shape), feature_coefficients) + + rotated_features = tf.reverse(features, axis=[0]) + + features = feature_coefficients * features + reversed_feature_coefficients * rotated_features + + label_coefficients = tf.squeeze(feature_coefficients, axis=[2, 3]) + + rotated_labels = tf.reverse(one_hot_smoothed_labels, axis=[0]) + + reversed_label_coefficients = tf.subtract(tf.ones(shape=label_coefficients.shape), label_coefficients) + + one_hot_smoothed_labels = label_coefficients * one_hot_smoothed_labels + reversed_label_coefficients * rotated_labels + + # Update Global Step global_step = tf.train.get_or_create_global_step() tf.identity(global_step, name="global_step_ref") tf.identity(features, name="features_ref") - tf.identity(labels, name="labels_ref") + + if mode == tf.estimator.ModeKeys.TRAIN: + tf.identity(labels, name="labels_ref") probs, logits = self.build_model( features, @@ -210,13 +245,9 @@ class ResnetModel(object): 'accuracy_top1': acc_top1, 'accuracy_top5': acc_top5 } - if "label_smoothing" in params.keys() and params['label_smoothing'] != 0.0: - one_hot_labels = tf.one_hot(labels, 1001) - cross_entropy = tf.losses.softmax_cross_entropy( - logits=logits, onehot_labels=one_hot_labels, - label_smoothing=params['label_smoothing']) - else: - cross_entropy = tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels) + + cross_entropy = tf.losses.softmax_cross_entropy( + logits=logits, onehot_labels=one_hot_smoothed_labels) assert (cross_entropy.dtype == tf.float32) tf.identity(cross_entropy, name='cross_entropy_loss_ref') diff --git a/TensorFlow/Classification/RN50v1.5/runtime/runner.py b/TensorFlow/Classification/RN50v1.5/runtime/runner.py index ad236dd8..fa72cbbf 100644 --- a/TensorFlow/Classification/RN50v1.5/runtime/runner.py +++ b/TensorFlow/Classification/RN50v1.5/runtime/runner.py @@ -59,6 +59,7 @@ class Runner(object): use_tf_amp=False, use_dali=False, gpu_memory_fraction=1.0, + gpu_id=0, # ======== Debug Flags ======== # debug_verbosity=0, @@ -145,7 +146,8 @@ class Runner(object): use_tf_amp=use_tf_amp, use_xla=use_xla, use_dali=use_dali, - gpu_memory_fraction=gpu_memory_fraction + gpu_memory_fraction=gpu_memory_fraction, + gpu_id=gpu_id ) run_config_additional = tf.contrib.training.HParams( @@ -204,10 +206,10 @@ class Runner(object): return worker_batch_size @staticmethod - def _get_session_config(mode, use_xla, use_dali, gpu_memory_fraction): + def _get_session_config(mode, use_xla, use_dali, gpu_memory_fraction, gpu_id=0): - if mode not in ["train", 'validation', 'benchmark']: - raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark')" % mode) + if mode not in ["train", 'validation', 'benchmark', 'inference']: + raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark', 'inference')" % mode) # Limit available GPU memory (tune the size) if use_dali: @@ -223,7 +225,8 @@ class Runner(object): config.allow_soft_placement = True config.log_device_placement = False - + + config.gpu_options.visible_device_list = str(gpu_id) if hvd_utils.is_using_hvd(): config.gpu_options.visible_device_list = str(hvd.local_rank()) @@ -245,10 +248,10 @@ class Runner(object): return config @staticmethod - def _get_run_config(mode, model_dir, use_xla, use_dali, gpu_memory_fraction, seed=None): + def _get_run_config(mode, model_dir, use_xla, use_dali, gpu_memory_fraction, gpu_id=0, seed=None): - if mode not in ["train", 'validation', 'benchmark']: - raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark')" % mode) + if mode not in ["train", 'validation', 'benchmark', 'inference']: + raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark', 'inference')" % mode) if seed is not None: if hvd_utils.is_using_hvd(): @@ -264,7 +267,7 @@ class Runner(object): save_summary_steps=100 if mode in ['train', 'validation'] else 1e9, # disabled in benchmark mode save_checkpoints_steps=None, save_checkpoints_secs=None, - session_config=Runner._get_session_config(mode=mode, use_xla=use_xla, use_dali=use_dali, gpu_memory_fraction=gpu_memory_fraction), + session_config=Runner._get_session_config(mode=mode, use_xla=use_xla, use_dali=use_dali, gpu_memory_fraction=gpu_memory_fraction, gpu_id=gpu_id), keep_checkpoint_max=5, keep_checkpoint_every_n_hours=1e6, # disabled log_step_count_steps=1e9, @@ -285,10 +288,10 @@ class Runner(object): return config - def _get_estimator(self, mode, run_params, use_xla, use_dali, gpu_memory_fraction): + def _get_estimator(self, mode, run_params, use_xla, use_dali, gpu_memory_fraction, gpu_id=0): - if mode not in ["train", 'validation', 'benchmark']: - raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark')" % mode) + if mode not in ["train", 'validation', 'benchmark', 'inference']: + raise ValueError("Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark', 'inference')" % mode) run_config = Runner._get_run_config( mode=mode, @@ -296,7 +299,9 @@ class Runner(object): use_xla=use_xla, use_dali=use_dali, gpu_memory_fraction=gpu_memory_fraction, + gpu_id=gpu_id, seed=self.run_hparams.seed + ) return tf.estimator.Estimator( @@ -319,6 +324,7 @@ class Runner(object): log_every_n_steps=1, loss_scale=256, label_smoothing=0.0, + mixup=0.0, use_cosine_lr=False, use_static_loss_scaling=False, is_benchmark=False @@ -424,6 +430,7 @@ class Runner(object): 'loss_scale': loss_scale, 'apply_loss_scaling': use_static_loss_scaling, 'label_smoothing': label_smoothing, + 'mixup': mixup, 'num_decay_steps': num_decay_steps, 'use_cosine_lr': use_cosine_lr } @@ -433,7 +440,8 @@ class Runner(object): run_params=estimator_params, use_xla=self.run_hparams.use_xla, use_dali=self.run_hparams.use_dali, - gpu_memory_fraction=self.run_hparams.gpu_memory_fraction + gpu_memory_fraction=self.run_hparams.gpu_memory_fraction, + gpu_id=self.run_hparams.gpu_id ) def training_data_fn(): @@ -500,7 +508,8 @@ class Runner(object): batch_size, warmup_steps=50, log_every_n_steps=1, - is_benchmark=False + is_benchmark=False, + export_dir=None, ): if iter_unit not in ["epoch", "batch"]: @@ -519,7 +528,8 @@ class Runner(object): run_params=estimator_params, use_xla=self.run_hparams.use_xla, use_dali=self.run_hparams.use_dali, - gpu_memory_fraction=self.run_hparams.gpu_memory_fraction + gpu_memory_fraction=self.run_hparams.gpu_memory_fraction, + gpu_id=self.run_hparams.gpu_id ) if self.run_hparams.data_dir is not None: @@ -614,7 +624,61 @@ class Runner(object): LOGGER.log('Top-1 Accuracy: %.3f' % float(eval_results['top1_accuracy'] * 100)) LOGGER.log('Top-5 Accuracy: %.3f' % float(eval_results['top5_accuracy'] * 100)) + #def get_serving_input_receiver_fn(batch_size, height, width, num_channels, data_format, dtype=tf.float32): + + if export_dir is not None: + LOGGER.log('Exporting to', export_dir) + input_receiver_fn = data_utils.get_serving_input_receiver_fn( + batch_size=None, + height=self.run_hparams.height, + width=self.run_hparams.width, + num_channels=self.run_hparams.n_channels, + data_format=self.run_hparams.input_format, + dtype=self.run_hparams.dtype) + + image_classifier.export_savedmodel(export_dir, input_receiver_fn) + except KeyboardInterrupt: print("Keyboard interrupt") LOGGER.log('Ending Model Evaluation ...') + + def predict(self, to_predict): + + estimator_params = {} + + if to_predict is not None: + filenames = runner_utils.parse_inference_input(to_predict) + + image_classifier = self._get_estimator( + mode='inference', + run_params=estimator_params, + use_xla=self.run_hparams.use_xla, + use_dali=self.run_hparams.use_dali, + gpu_memory_fraction=self.run_hparams.gpu_memory_fraction + ) + + inference_hooks = [] + + def inference_data_fn(): + return data_utils.get_inference_input_fn( + filenames=filenames, + height=self.run_hparams.height, + width=self.run_hparams.width, + num_threads=self.run_hparams.num_preprocessing_threads + ) + try: + inference_results = image_classifier.predict( + input_fn=inference_data_fn, + predict_keys=None, + hooks=inference_hooks, + yield_single_examples=True + ) + + for result in inference_results: + LOGGER.log(result['classes'], str(result['probabilities'][result['classes']])) + + except KeyboardInterrupt: + print("Keyboard interrupt") + + LOGGER.log('Ending Inference ...') diff --git a/TensorFlow/Classification/RN50v1.5/runtime/runner_utils.py b/TensorFlow/Classification/RN50v1.5/runtime/runner_utils.py index 7acb24f6..544467cc 100644 --- a/TensorFlow/Classification/RN50v1.5/runtime/runner_utils.py +++ b/TensorFlow/Classification/RN50v1.5/runtime/runner_utils.py @@ -82,9 +82,25 @@ def parse_tfrecords_dataset(data_dir, mode, iter_unit, num_iter, global_batch_si return filenames, num_samples, num_steps, num_epochs, num_decay_steps +def parse_inference_input(to_predict): + + filenames = [] + + image_formats = ['.jpg', '.jpeg', '.JPEG', '.JPG', '.png', '.PNG'] + + if os.path.isdir(to_predict): + filenames = [f for f in os.listdir(to_predict) + if os.path.isfile(os.path.join(to_predict, f)) + and os.path.splitext(f)[1] in image_formats] + + elif os.path.isfile(to_predict): + filenames.append(to_predict) + + return filenames + def parse_dali_idx_dataset(data_idx_dir, mode): if data_idx_dir is not None: filenames, _ = list_filenames_in_dataset(data_dir=data_idx_dir, mode=mode, count=False) - return filenames \ No newline at end of file + return filenames diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_inferbench_fp16.sh b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_inferbench_fp16.sh index c02ed959..5b2bf3c0 100644 --- a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_inferbench_fp16.sh +++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_inferbench_fp16.sh @@ -4,4 +4,4 @@ mkdir -p /tmp/results python ./scripts/benchmarking/benchmark.py --mode inference --bench-warmup 100 --bench-iterations 200 --ngpus 1 --bs 1 2 4 8 16 32 64 128 256 --baseline ./scripts/benchmarking/baselines/DGX1V_RN50_tensorflow_infer_fp16.json --perf_args "use_tf_amp" --data_dir $1 --results_dir $2 -python ./scripts/benchmarking/benchmark.py --mode inference --bench-warmup 100 --bench-iterations 200 --ngpus 1 --bs 1 2 4 8 16 32 64 128 192 --baseline ./scripts/benchmarking/baselines/DGX1V_RN50_tensorflow_infer_fp16.json --perf_args "use_tf_amp" "use_xla" --data_dir $1 --results_dir $2/xla +python ./scripts/benchmarking/benchmark.py --mode inference --bench-warmup 100 --bench-iterations 200 --ngpus 1 --bs 1 2 4 8 16 32 64 128 256 --baseline ./scripts/benchmarking/baselines/DGX1V_RN50_tensorflow_infer_fp16.json --perf_args "use_tf_amp" "use_xla" --data_dir $1 --results_dir $2/xla diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_trainbench_fp16.sh b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_trainbench_fp16.sh index 72b4fad8..dc6cbf26 100644 --- a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_trainbench_fp16.sh +++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/DGX1V_trainbench_fp16.sh @@ -4,5 +4,4 @@ mkdir -p /tmp/results python ./scripts/benchmarking/benchmark.py --mode training --bench-warmup 200 --bench-iterations 500 --ngpus 1 4 8 --bs 64 128 256 --baseline ./scripts/benchmarking/baselines/DGX1V_RN50_tensorflow_train_fp16.json --data_dir $1 --perf_args "use_tf_amp" --results_dir $2 -python ./scripts/benchmarking/benchmark.py --mode training --bench-warmup 200 --bench-iterations 500 --ngpus 1 4 8 --bs 32 64 128 192 --baseline ./scripts/benchmarking/baselines/DGX1V_RN50_tensorflow_train_fp16.json --perf_args "use_xla" "use_tf_amp" --data_dir $1 --results_dir $2/xla - +python ./scripts/benchmarking/benchmark.py --mode training --bench-warmup 200 --bench-iterations 500 --ngpus 1 4 8 --bs 32 64 128 256 --baseline ./scripts/benchmarking/baselines/DGX1V_RN50_tensorflow_train_fp16.json --perf_args "use_xla" "use_tf_amp" --data_dir $1 --results_dir $2/xla diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/T4_inferbench_fp16.sh b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/T4_inferbench_fp16.sh new file mode 100644 index 00000000..ebddfe14 --- /dev/null +++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/T4_inferbench_fp16.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +mkdir -p /tmp/results + +python ./scripts/benchmarking/benchmark.py --mode inference --bench-warmup 100 --bench-iterations 200 --ngpus 1 --bs 1 2 4 8 --baseline ./scripts/benchmarking/baselines/T4_RN50_tensorflow_infer_fp16.json --perf_args "use_tf_amp" --data_dir $1 --results_dir $2 --gpu_id $3 \ No newline at end of file diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/T4_inferbench_fp32.sh b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/T4_inferbench_fp32.sh new file mode 100644 index 00000000..45722bdf --- /dev/null +++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/T4_inferbench_fp32.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +mkdir -p /tmp/results + +python ./scripts/benchmarking/benchmark.py --mode inference --bench-warmup 100 --bench-iterations 200 --ngpus 1 --bs 1 2 4 8 --baseline ./scripts/benchmarking/baselines/T4_RN50_tensorflow_infer_fp32.json --perf_args --data_dir $1 --results_dir $2 --gpu_id $3 \ No newline at end of file diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/T4_RN50_tensorflow_infer_fp16.json b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/T4_RN50_tensorflow_infer_fp16.json new file mode 100644 index 00000000..ef420e20 --- /dev/null +++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/T4_RN50_tensorflow_infer_fp16.json @@ -0,0 +1,31 @@ +{ + "metric_keys": [ + "total_ips" + ], + "metrics": { + "1": { + "1": { + "total_ips": 90.0 + }, + "2": { + "total_ips": 180.0 + }, + "4": { + "total_ips": 360.0 + }, + "8": { + "total_ips": 500.0 + } + } + }, + "model": "", + "ngpus": [ + 1 + ], + "bs": [ + 1, + 2, + 4, + 8 + ] +} diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/T4_RN50_tensorflow_infer_fp32.json b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/T4_RN50_tensorflow_infer_fp32.json new file mode 100644 index 00000000..80795104 --- /dev/null +++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/baselines/T4_RN50_tensorflow_infer_fp32.json @@ -0,0 +1,31 @@ +{ + "metric_keys": [ + "total_ips" + ], + "metrics": { + "1": { + "1": { + "total_ips": 100.0 + }, + "2": { + "total_ips": 180.0 + }, + "4": { + "total_ips": 250.0 + }, + "8": { + "total_ips": 280.0 + } + } + }, + "model": "", + "ngpus": [ + 1 + ], + "bs": [ + 1, + 2, + 4, + 8 + ] +} diff --git a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/benchmark.py b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/benchmark.py index 7114a018..978a609b 100644 --- a/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/benchmark.py +++ b/TensorFlow/Classification/RN50v1.5/scripts/benchmarking/benchmark.py @@ -28,6 +28,7 @@ parser = argparse.ArgumentParser(description='Tesnorflow Benchmark Tests') parser.add_argument('--bs', default=[1], type=int, nargs='+') parser.add_argument('--ngpus', default=[1], type=int, nargs='+') parser.add_argument('--perf_args', default=[], type=str, nargs='*') +parser.add_argument('--gpu_id', default=0, type=int) parser.add_argument( '--mode', @@ -55,7 +56,7 @@ parser.add_argument('--results_dir', default="/results", type=str, metavar=' 0: diff --git a/TensorFlow/Classification/RN50v1.5/utils/data_utils.py b/TensorFlow/Classification/RN50v1.5/utils/data_utils.py index fcba4b71..566a363b 100644 --- a/TensorFlow/Classification/RN50v1.5/utils/data_utils.py +++ b/TensorFlow/Classification/RN50v1.5/utils/data_utils.py @@ -131,7 +131,30 @@ def get_tfrecords_input_fn(filenames, batch_size, height, width, training, disto return ds +def get_inference_input_fn(filenames, height, width, num_threads): + + ds = tf.data.Dataset.from_tensor_slices(filenames) + counter = tf.data.Dataset.range(sys.maxsize) + ds = tf.data.Dataset.zip((ds, counter)) + + def preproc_func(record, counter_): + return image_processing.preprocess_image_file(record, height, width, _NUM_CHANNELS, is_training=False) + + ds = ds.apply( + tf.data.experimental.map_and_batch( + map_func=preproc_func, + num_parallel_calls=num_threads, + batch_size=1 + ) + ) + + ds = ds.prefetch(buffer_size=tf.contrib.data.AUTOTUNE) + + return ds + + + def get_dali_input_fn(filenames, idx_filenames, batch_size, height, width, training, distort_color, num_threads, deterministic): if idx_filenames is None: @@ -169,3 +192,19 @@ def normalized_inputs(inputs): inputs = tf.subtract(inputs, means_per_channel) return tf.divide(inputs, 255.0) + +def get_serving_input_receiver_fn(batch_size, height, width, num_channels, data_format, dtype=tf.float32): + + if data_format not in ["NHWC", "NCHW"]: + raise ValueError("Unknown data_format: %s" % str(data_format)) + + if data_format == "NHWC": + input_shape = [batch_size] + [height, width, num_channels] + else: + input_shape = [batch_size] + [num_channels, height, width] + + def serving_input_receiver_fn(): + features = tf.placeholder(dtype=dtype, shape=input_shape, name='input_tensor') + return tf.estimator.export.TensorServingInputReceiver(features=features, receiver_tensors=features) + + return serving_input_receiver_fn diff --git a/TensorFlow/Classification/RN50v1.5/utils/image_processing.py b/TensorFlow/Classification/RN50v1.5/utils/image_processing.py index ac1833d1..8902a48f 100644 --- a/TensorFlow/Classification/RN50v1.5/utils/image_processing.py +++ b/TensorFlow/Classification/RN50v1.5/utils/image_processing.py @@ -22,7 +22,7 @@ import tensorflow as tf _RESIZE_MIN = 256 _DEFAULT_IMAGE_SIZE = 224 -__all__ = ['preprocess_image_record'] +__all__ = ['preprocess_image_record', 'preprocess_image_file'] def _deserialize_image_record(record): @@ -136,10 +136,11 @@ def preprocess_image_record(record, height, width, num_channels, is_training=Fal imgdata, label, bbox, text = _deserialize_image_record(record) label -= 1 + try: image = _decode_jpeg(imgdata, channels=3) except: - image = tf.image.decode_png(imgdata, channels=3) + image = tf.image.decode_image(imgdata, channels=3) if is_training: # For training, we want to randomize some of the distortions. @@ -150,3 +151,23 @@ def preprocess_image_record(record, height, width, num_channels, is_training=Fal image = _central_crop(image, height, width) return image, label + + +def preprocess_image_file(filename, height, width, num_channels, is_training=False): + + imgdata = tf.read_file(filename) + + try: + image = _decode_jpeg(imgdata, channels=3) + except: + image = tf.image.decode_image(imgdata, channels=3) + + if is_training: + # For training, we want to randomize some of the distortions. + image = _crop_and_filp(image, bbox, num_channels) + image = _resize_image(image, height, width) + else: + image = _aspect_preserving_resize(image, _RESIZE_MIN) + image = _central_crop(image, height, width) + + return image, filename diff --git a/TensorFlow/LanguageModeling/BERT/.dockerignore b/TensorFlow/LanguageModeling/BERT/.dockerignore index cc912280..6e13e7db 100644 --- a/TensorFlow/LanguageModeling/BERT/.dockerignore +++ b/TensorFlow/LanguageModeling/BERT/.dockerignore @@ -1,6 +1,24 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + .idea/ .git/ __pycache__/ results/ -data/ +data/download +data/extracted +data/formatted_one_article_per_line +data/sharded +data/hdf5* +data/tfrecord* checkpoints/ diff --git a/TensorFlow/LanguageModeling/BERT/.gitignore b/TensorFlow/LanguageModeling/BERT/.gitignore index 28c67509..0185ce8e 100644 --- a/TensorFlow/LanguageModeling/BERT/.gitignore +++ b/TensorFlow/LanguageModeling/BERT/.gitignore @@ -9,7 +9,12 @@ __pycache__/ *.so #Data -data/*/*/ +data/download +data/extracted +data/formatted_one_article_per_line +data/sharded +data/hdf5* +data/tfrecord* data/*/*.zip #Resutls diff --git a/TensorFlow/LanguageModeling/BERT/Dockerfile b/TensorFlow/LanguageModeling/BERT/Dockerfile index dba047f6..046f6553 100644 --- a/TensorFlow/LanguageModeling/BERT/Dockerfile +++ b/TensorFlow/LanguageModeling/BERT/Dockerfile @@ -1,4 +1,4 @@ -ARG FROM_IMAGE_NAME=nvcr.io/nvidia/tensorflow:19.06-py3 +ARG FROM_IMAGE_NAME=nvcr.io/nvidia/tensorflow:19.08-py3 FROM tensorrtserver_client as trt @@ -12,16 +12,19 @@ WORKDIR /workspace RUN git clone https://github.com/openai/gradient-checkpointing.git RUN git clone https://github.com/attardi/wikiextractor.git RUN git clone https://github.com/soskek/bookcorpus.git +RUN git clone https://github.com/titipata/pubmed_parser -# Copy the perf_client over +RUN pip3 install /workspace/pubmed_parser + +#Copy the perf_client over COPY --from=trt /workspace/build/perf_client /workspace/build/perf_client -# Copy the python wheel and install with pip +#Copy the python wheel and install with pip COPY --from=trt /workspace/build/dist/dist/tensorrtserver*.whl /tmp/ RUN pip install /tmp/tensorrtserver*.whl && rm /tmp/tensorrtserver*.whl - WORKDIR /workspace/bert COPY . . -ENV PYTHONPATH=/workspace/bert +ENV PYTHONPATH /workspace/bert +ENV BERT_PREP_WORKING_DIR /workspace/bert/data diff --git a/TensorFlow/LanguageModeling/BERT/README.md b/TensorFlow/LanguageModeling/BERT/README.md index 06bfd93d..a6f2088a 100644 --- a/TensorFlow/LanguageModeling/BERT/README.md +++ b/TensorFlow/LanguageModeling/BERT/README.md @@ -1,21 +1,21 @@ # BERT For TensorFlow -This repository provides a script and recipe to train BERT to achieve state of the art accuracy and is tested and maintained by NVIDIA. +This repository provides a script and recipe to train the BERT model for TensorFlow to achieve state-of-the-art accuracy, and is tested and maintained by NVIDIA. +## Table Of Contents -## Table Of Contents: -* [Model overview](#model-overview) +- [Model overview](#model-overview) * [Model architecture](#model-architecture) * [Default configuration](#default-configuration) * [Feature support matrix](#feature-support-matrix) * [Features](#features) - * [Mixed Precision training](#mixed-precision-training) - * [Enabling Mixed Precision](#enabling-mixed-precision) - * [Glossary](#glossary) -* [Setup](#setup) + * [Mixed precision training](#mixed-precision-training) + * [Enabling mixed precision](#enabling-mixed-precision) + * [Glossary](#glossary) +- [Setup](#setup) * [Requirements](#requirements) -* [Quick Start Guide](#quick-start-guide) -* [Advanced](#advanced) +- [Quick Start Guide](#quick-start-guide) +- [Advanced](#advanced) * [Scripts and sample code](#scripts-and-sample-code) * [Parameters](#parameters) * [Command-line options](#command-line-options) @@ -25,54 +25,78 @@ This repository provides a script and recipe to train BERT to achieve state of t * [Training process](#training-process) * [Pre-training](#pre-training) * [Fine tuning](#fine-tuning) + * [Multi-node](#multi-node) * [Inference process](#inference-process) * [Deploying the BERT model using TensorRT Inference Server](#deploying-the-bert-model-using-tensorrt-inference-server) * [Performance analysis for TensorRT Inference Server](#performance-analysis-for-tensorrt-inference-server) * [Advanced Details](#advanced-details) * [Running the TensorRT Inference Server and client](#running-the-tensorrt-inference-server-and-client) -* [Performance](#performance) +- [Performance](#performance) * [Benchmarking](#benchmarking) * [Training performance benchmark](#training-performance-benchmark) * [Inference performance benchmark](#inference-performance-benchmark) * [Results](#results) * [Training accuracy results](#training-accuracy-results) - * [Training accuracy: NVIDIA DGX-1 (8x V100 32G)](#training-accuracy-nvidia-dgx-1-(8x-v100-32G)) + * [Pre-training accuracy: single-node](#pre-training-accuracy-single-node) + * [Pre-training accuracy: multi-node](#pre-training-accuracy-multi-node) + * [Fine-tuning accuracy for SQuAD: NVIDIA DGX-2 (16x V100 32G)](#fine-tuning-accuracy-for-squad-nvidia-dgx-2-16x-v100-32g) * [Training stability test](#training-stability-test) + * [Pre-training SQuAD stability test: NVIDIA DGX-2 (512x V100 32G)](#fine-tuning-squad-stability-test-nvidia-dgx-2-512x-v100-32g) + * [Fine-tuning SQuAD stability test: NVIDIA DGX-2 (16x V100 32G)](#fine-tuning-squad-stability-test-nvidia-dgx-2-16x-v100-32g) * [Training performance results](#training-performance-results) * [Training performance: NVIDIA DGX-1 (8x V100 16G)](#training-performance-nvidia-dgx-1-8x-v100-16g) + * [Pre-training training performance: single-node on 16G](#pre-training-training-performance-single-node-on-16g) + * [Pre-training training performance: multi-node on 16G](#pre-training-training-performance-multi-node-on-16g) + * [Fine-tuning training performance for SQuAD on 16G](#fine-tuning-training-performance-for-squad-on-16g) * [Training performance: NVIDIA DGX-1 (8x V100 32G)](#training-performance-nvidia-dgx-1-8x-v100-32g) + * [Pre-training training performance: single-node on 32G](#pre-training-training-performance-single-node-on-32g) + * [Fine-tuning training performance for SQuAD on 32G](#fine-tuning-training-performance-for-squad-on-32g) * [Training performance: NVIDIA DGX-2 (16x V100 32G)](#training-performance-nvidia-dgx-2-16x-v100-32g) + * [Pre-training training performance: single-node on DGX-2 32G](#pre-training-training-performance-single-node-on-dgx-2-32g) + * [Pre-training training performance: multi-node on DGX-2 32G](#pre-training-training-performance-multi-node-on-dgx-2-32g) + * [Fine-tuning training performance for SQuAD on DGX-2 32G](#fine-tuning-training-performance-for-squad-on-dgx-2-32g) * [Inference performance results](#inference-performance-results) * [Inference performance: NVIDIA DGX-1 (1x V100 16G)](#inference-performance-nvidia-dgx-1-1x-v100-16g) + * [Pre-training inference performance on 16G](#pre-training-inference-performance-on-16g) + * [Fine-tuning inference performance for SQuAD on 16G](#fine-tuning-inference-performance-for-squad-on-16g) * [Inference performance: NVIDIA DGX-1 (1x V100 32G)](#inference-performance-nvidia-dgx-1-1x-v100-32g) + * [Pre-training inference performance on 32G](#pre-training-inference-performance-on-32g) + * [Fine-tuning inference performance for SQuAD on 32G](#fine-tuning-inference-performance-for-squad-on-32g) * [Inference performance: NVIDIA DGX-2 (1x V100 32G)](#inference-performance-nvidia-dgx-2-1x-v100-32g) -* [Release notes](#release-notes) + * [Pre-training inference performance on DGX-2 32G](#pre-training-inference-performance-on-dgx-2-32g) + * [Fine-tuning inference performance for SQuAD on DGX-2 32G](#fine-tuning-inference-performance-for-squad-on-dgx-2-32g) + * [Inference performance: NVIDIA Tesla T4 (1x T4 16G)](#inference-performance-nvidia-tesla-t4-1x-t4-16g) + * [Fine-tuning inference performance for SQuAD on Tesla T4 16G](#fine-tuning-inference-performance-for-squad-on-tesla-t4-16g) +- [Release notes](#release-notes) * [Changelog](#changelog) * [Known issues](#known-issues) + + + ## Model overview BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) paper. NVIDIA's BERT is an optimized version of [Google's official implementation](https://github.com/google-research/bert), leveraging mixed precision arithmetic and Tensor Cores on V100 GPUs for faster training times while maintaining target accuracy. - Other publicly available implementations of BERT include: -1. [Hugging Face](https://github.com/huggingface/pytorch-pretrained-BERT) -2. [codertimo](https://github.com/codertimo/BERT-pytorch) -3. [gluon-nlp](https://github.com/dmlc/gluon-nlp/tree/master/scripts/bert) +1. [NVIDIA PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT) +2. [Hugging Face](https://github.com/huggingface/pytorch-pretrained-BERT) +3. [codertimo](https://github.com/codertimo/BERT-pytorch) +4. [gluon-nlp](https://github.com/dmlc/gluon-nlp/tree/master/scripts/bert) +5. [Google's official implementation](https://github.com/google-research/bert) - -This model is trained with mixed precision using Tensor Cores on NVIDIA Volta and Turing GPUs. Therefore, researchers can get results faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. +This model is trained with mixed precision using Tensor Cores on NVIDIA Volta and Turing GPUs. Therefore, researchers can get results upto 4x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. ### Model architecture -BERT's model architecture is a multi-layer bidirectional Transformer encoder. Based on the model size, we have the following two default configurations of BERT. +BERT's model architecture is a multi-layer bidirectional Transformer encoder. Based on the model size, we have the following two default configurations of BERT: | **Model** | **Hidden layers** | **Hidden unit size** | **Attention heads** | **Feedforward filter size** | **Max sequence length** | **Parameters** | |:---------:|:----------:|:----:|:---:|:--------:|:---:|:----:| |BERTBASE |12 encoder| 768| 12|4 x 768|512|110M| |BERTLARGE|24 encoder|1024| 16|4 x 1024|512|330M| -BERT training consists of two steps, pre-training the language model in an unsupervised fashion on vast amounts of unannotated datasets, and then using this pre-trained model for fine-tuning for various NLP tasks, such as question and answer, sentence classification, or sentiment analysis. Fine-tuning typically adds an extra layer or two for the specific task and further trains the model using a task-specific annotated dataset, starting from the pre-trained backbone weights. The end-to-end process can be summarized using Figure 1 and the results are covered in the following sections. +BERT training consists of two steps, pre-training the language model in an unsupervised fashion on vast amounts of unannotated datasets, and then using this pre-trained model for fine-tuning for various NLP tasks, such as question and answer, sentence classification, or sentiment analysis. Fine-tuning typically adds an extra layer or two for the specific task and further trains the model using a task-specific annotated dataset, starting from the pre-trained backbone weights. The end-to-end process in depicted in the following image: ![](data/images/bert_pipeline.png?raw=true) @@ -81,18 +105,19 @@ Figure 1: BERT Pipeline ### Default configuration This repository contains scripts to interactively launch data download, training, benchmarking and inference routines in a Docker container for both pre-training and fine tuning for Question Answering. The major differences between the official implementation of the paper and our version of BERT are as follows: + - Mixed precision support with TensorFlow Automatic Mixed Precision (TF-AMP), which enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable. - Scripts to download dataset for: - - Pre-training - [Wikipedia](https://dumps.wikimedia.org/), [Books Corpus](http://yknzhu.wixsite.com/mbweb) - - Fine Tuning - [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) (Stanford Question Answering Dataset), Pretrained Weights from Google + - Pre-training - [Wikipedia](https://dumps.wikimedia.org/), [BookCorpus](http://yknzhu.wixsite.com/mbweb) + - Fine tuning - [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) (Stanford Question Answering Dataset) + - Fine tuning - [GLUE](https://gluebenchmark.com/) (The General Language Understanding Evaluation benchmark) + - Pretrained weights from Google - Custom fused CUDA kernels for faster computations -- Multi-GPU/Multi-Node support using Horovod - +- Multi-GPU/Multi-node support using Horovod The following performance optimizations were implemented in this model: - [XLA](https://www.tensorflow.org/xla) support (experimental). - These techniques and optimizations improve model performance and reduce training time, allowing you to perform various NLP tasks with no additional effort. @@ -102,16 +127,22 @@ The following features are supported by this model. | **Feature** | **BERT** | |:-----------------------:|:--------------------------:| -| Horovod Multi-GPU | Yes | +| Horovod Multi-GPU | Yes | +| Horovod Multi-Node | Yes | +| Automatic mixed precision (AMP) | Yes | +| LAMB | Yes | #### Features -Horovod - Horovod is a distributed training framework for TensorFlow, Keras, PyTorch and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the [Horovod: Official repository](https://github.com/horovod/horovod). +Multi-GPU training with Horovod - Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the [TensorFlow tutorial](https://github.com/horovod/horovod/#usage) + +[LAMB](https://arxiv.org/pdf/1904.00962.pdf) stands for Layerwise Adaptive Moments based optimizer, is a large batch optimization technique that helps accelerate training of deep neural networks using large minibatches. It allows using a global batch size of 65536 and 32768 on sequence lengths 128 and 512 respectively, compared to a batch size of 256 for Adam. The optimized implementation accumulates 1024 gradients batches in phase 1 and 4096 steps in phase 2 before updating weights once. This results in 27% training speedup on a single DGX2 node. On multi-node systems, LAMB allows scaling up to 1024 GPUs resulting in training speedups of up to 17x in comparison to [Adam](https://arxiv.org/pdf/1412.6980.pdf). Adam has limitations on the learning rate that can be used since it is applied globally on all parameters whereas LAMB follows a layerwise learning rate strategy. + ### Mixed precision training Mixed precision is the combined use of different numerical precision in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architecture, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps: -1. Porting the model to use the FP16 data type where appropriate. +1. Porting the model to use the FP16 data type where appropriate. 2. Adding loss scaling to preserve small gradient values. The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in [CUDA 8](https://devblogs.nvidia.com/parallelforall/tag/fp16/) in the NVIDIA Deep Learning SDK. @@ -120,25 +151,26 @@ For information about: - How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/Mixed-Precision-training/index.html) documentation. - Techniques used for mixed precision training, see the [Mixed Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog. - How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide. -- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/). #### Enabling mixed precision -Automatic Mixed Precision (AMP) for TensorFlow to enables the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically. The TensorFlow framework code makes all necessary model changes internally. -In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing `tf.contrib` loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling. +Automatic Mixed Precision (AMP) for TensorFlow enables the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically. The TensorFlow framework code makes all necessary model changes internally. + +In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximizes the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing `tf.contrib` loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling. ### Glossary -Fine-tuning +**Fine-tuning** Training an already pretrained model further using a task specific dataset for subject-specific refinements, by adding task-specific layers on top if required. -Language Model +**Language Model** Assigns a probability distribution over a sequence of words. Given a sequence of words, it assigns a probability to the whole sequence. -Pre-training + +**Pre-training** Training a model on vast amounts of data on the same (or different) task to build general understandings. -Transformer +**Transformer** The paper [Attention Is All You Need](https://arxiv.org/abs/1706.03762) introduces a novel architecture called Transformer that uses an attention mechanism and transforms one sequence into another. @@ -154,7 +186,6 @@ This repository contains `Dockerfile` which extends the TensorFlow NGC container - [TensorFlow 19.06-py3+](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) NGC container - [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) or [Turing](https://www.nvidia.com/en-us/geforce/turing/) based GPU - For more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation: - [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html) - [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#accessing_registry) @@ -162,6 +193,11 @@ For more information about how to get started with NGC containers, see the follo For those unable to use the TensorFlow NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html). +For multi-node, the sample provided in this repository requires [Enroot](https://github.com/NVIDIA/enroot) and [Pyxis](https://github.com/NVIDIA/pyxis) set up on a [SLURM](https://slurm.schedmd.com) cluster. + +More information on how to set up and launch can be found in the [Multi-node Documentation](https://docs.nvidia.com/ngc/multi-node-bert-user-guide). + + ## Quick Start Guide To pretrain or fine tune your model for Question Answering using mixed precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the BERT model. @@ -181,64 +217,71 @@ bash scripts/docker/build.sh 3. Download and preprocess the dataset. -This repository provides scripts to download, verify and extract the SQuaD dataset and pretrained weights for fine tuning as well as Wikipedia and BookCorpus dataset for pre-training. +This repository provides scripts to download, verify and extract the SQuAD dataset, GLUE dataset and pretrained weights for fine tuning as well as Wikipedia and BookCorpus dataset for pre-training. -To download, verify, and extract the required datasets, issue: +To download, verify, and extract the required datasets, run: ```bash -bash scripts/data_download.sh +bash scripts/data_download.sh ``` The script launches a Docker container with the current directory mounted and downloads the datasets to a `data/` folder on the host. -Note : The dataset is 170GB+ and takes 15+ hours to download. Expired dataset links are ignored during data download. +Note: The dataset is 170GB+ and takes 15+ hours to download. Expired dataset links are ignored during data download. +4. Download the pretrained models from NGC. -4. Download pretrained models from NGC. - -We have uploaded checkpoints for both fine tuning and pre-training for various configurations on the NGC Model Registry. You can download them directly from the [NGC model catalog](https://ngc.nvidia.com/catalog/models). Download them to the BERT directory to easily access them in your scripts. - +We have uploaded checkpoints for both fine tuning and pre-training for various configurations on the NGC Model Registry. You can download them directly from the [NGC model catalog](https://ngc.nvidia.com/catalog/models). Download them to the `results/models/` to easily access them in your scripts. 5. Start an interactive session in the NGC container to run training/inference. -After you build the container image and download the data, you can start an interactive CLI session as follows: +After you build the container image and download the data, you can start an interactive CLI session as follows: ```bash bash scripts/docker/launch.sh ``` The `launch.sh` script assumes that the datasets are in the following locations by default after downloading the data. -- SQuAD v1.1 - `data/squad/v1.1` -- SQuaD v2.0 - `data/squad/v2.0` -- BERT Large - `data/pretrained_models_google/uncased_L-24_H-1024_A-16` -- BERT Base - `data/pretrained_models_google/uncased_L-12_H-768_A-12` -- BERT - `data/pretrained_models_google/uncased_L-24_H-1024_A-16` -- Wikipedia - `data/wikipedia_corpus/final_tfrecords_sharded` -- Books Corpus - `data/bookcorpus/final_tfrecords_sharded` + +- SQuAD v1.1 - `data/download/squad/v1.1` +- SQuAD v2.0 - `data/download/squad/v2.0` +- GLUE The Corpus of Linguistic Acceptability (CoLA) - `data/download/CoLA` +- GLUE Microsoft Research Paraphrase Corpus (MRPC) - `data/download/MRPC` +- GLUE The Multi-Genre NLI Corpus (MNLI) - `data/download/MNLI` +- BERT Large - `data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16` +- BERT Base - `data/download/google_pretrained_weights/uncased_L-12_H-768_A-12` +- BERT - `data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16` +- Wikipedia + BookCorpus TFRecords - `data/tfrecords/books_wiki_en_corpus` 6. Start pre-training. -BERT is designed to pre-train deep bidirectional representations for language representations. The following scripts are to replicate pre-training on Wikipedia and Books Corpus from the [paper](https://arxiv.org/pdf/1810.04805.pdf). These scripts are general and can be used for pre-training language representations on any corpus of choice. +BERT is designed to pre-train deep bidirectional representations for language representations. The following scripts are to replicate pre-training on Wikipedia and BookCorpus from the [LAMB paper](https://arxiv.org/pdf/1904.00962.pdf). These scripts are general and can be used for pre-training language representations on any corpus of choice. -From within the container, you can use the following script to run pre-training. +From within the container, you can use the following script to run pre-training using LAMB. ```bash -bash scripts/run_pretraining.sh +bash scripts/run_pretraining_lamb.sh ``` -For FP16 training with XLA using a DGX-1 V100 32G, run: +For BERT Large FP16 training with XLA using a DGX-1 V100 32G, run: ```bash -bash scripts/run_pretraining.sh 14 8 5e-5 fp16 true 8 5000 2285000 5000 true +bash scripts/run_pretraining_lamb.sh 64 8 8 7.5e-4 5e-4 fp16 true 8 2000 200 7820 100 128 512 large ``` -For FP32 training without XLA using a DGX-1 V100 32G, run: +For BERT Large FP32 training without XLA using a DGX-1 V100 32G, run: ```bash -bash scripts/run_pretraining.sh 6 6 2e-5 fp32 false 8 2000 5333333 5000 true +bash scripts/run_pretraining_lamb.sh 64 8 8 7.5e-4 5e-4 fp32 false 8 2000 200 7820 100 128 512 large +``` + +Alternatively, to run pre-training with Adam as in the original [BERT paper](https://arxiv.org/pdf/1810.04805.pdf) from within the container, run: + +```bash +bash scripts/run_pretraining_adam.sh ``` 7. Start fine tuning. -The above pretrained BERT representations can be fine tuned with just one additional output layer for a state-of-the-art Question Answering system. From within the container, you can use the following script to run fine-training for SQuaD. +The above pretrained BERT representations can be fine tuned with just one additional output layer for a state-of-the-art Question Answering system. From within the container, you can use the following script to run fine-training for SQuAD. ```bash bash scripts/run_squad.sh @@ -246,19 +289,25 @@ bash scripts/run_squad.sh +``` + +The GLUE tasks supported include CoLA, MRPC and MNLI. + 8. Start validation/evaluation. -The `run_squad_inference.sh` script runs inference on a checkpoint fine tuned for SQuaD and evaluates the validity of predictions on the basis of exact match and F1 score. +The `run_squad_inference.sh` script runs inference on a checkpoint fine tuned for SQuAD and evaluates the validity of predictions on the basis of exact match and F1 score. ```bash bash scripts/run_squad_inference.sh @@ -274,7 +323,13 @@ For SQuAD 1.1 FP32 inference without XLA using a DGX-1 V100 32G, run: bash scripts/run_squad_inference.sh /results/model.ckpt 8 fp32 false 384 128 large 1.1 ``` +Alternatively, to run inference on GLUE benchmark, run: +```bash +bash scripts/run_glue_inference.sh +``` + ## Advanced + The following sections provide greater details of the dataset, running training and inference, and the training results. ### Scripts and sample code @@ -282,39 +337,46 @@ The following sections provide greater details of the dataset, running training In the root directory, the most important files are: * `run_pretraining.py` - Serves as entry point for pre-training * `run_squad.py` - Serves as entry point for SQuAD training -* Dockerfile - Container with the basic set of dependencies to run BERT +* `run_classifier.py` - Serves as entry point for GLUE training +* `Dockerfile` - Container with the basic set of dependencies to run BERT The `scripts/` folder encapsulates all the one-click scripts required for running various functionalities supported such as: * `run_squad.sh` - Runs SQuAD training and inference using `run_squad.py` file -* `run_pretraining.sh` - Runs pre-training using `run_pretraining.py` file -* `data_download.sh` - Downloads datasets using files in `data/` folder +* `run_glue.sh` - Runs GLUE training and inference using the `run_classifier.py` file +* `run_pretraining_adam.sh` - Runs pre-training with Adam optimizer using the `run_pretraining.py` file +* `run_pretraining_lamb.sh` - Runs pre-training with LAMB optimizer using the `run_pretraining.py` file in two phases. Phase 1 does 90% of training with sequence length = 128. In phase 2, the remaining 10% of the training is done with sequence length = 512. +* `data_download.sh` - Downloads datasets using files in the `data/` folder * `finetune_train_benchmark.sh` - Captures performance metrics of training for multiple configurations * `finetune_inference_benchmark.sh` - Captures performance metrics of inference for multiple configurations Other folders included in the root directory are: -* `data/` - Necessary folders and scripts to download datasets required for fine tuning and pre-training BERT +* `data/` - Necessary folders and scripts to download datasets required for fine tuning and pre-training BERT. * `utils/` - Necessary files for preprocessing data before feeding into BERT and hooks for obtaining performance metrics from BERT. ### Parameters Aside from the options to set hyperparameters, the relevant options to control the behaviour of the `run_pretraining.py` script are: -```bash - --[no]use_fp16: Whether to enable AMP ops.(default: 'false') + +``` --bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture. + --init_checkpoint: Initial checkpoint (usually from a pre-trained BERT model). --[no]do_eval: Whether to run evaluation on the dev set.(default: 'false') --[no]do_train: Whether to run training.(evaluation: 'false') --eval_batch_size: Total batch size for eval.(default: '8')(an integer) --[no]horovod: Whether to use Horovod for multi-gpu runs(default: 'false') - --init_checkpoint: Initial checkpoint (usually from a pre-trained BERT model). - --input_file: Input TF example files (can be a glob or comma separated). - --iterations_per_loop: How many steps to make in each estimator call.(default: '1000') + --[no]use_fp16: Whether to enable AMP ops.(default: 'false') + --input_files_dir: Input TF example files (can be a dir or comma separated). --output_dir: The output directory where the model checkpoints will be written. + --optimizer_type: Optimizer used for training - LAMB or ADAM + --num_accumulation_steps: Number of accumulation steps before gradient update. Global batch size = num_accumulation_steps * train_batch_size + --allreduce_post_accumulation: Whether to all reduce after accumulation of N steps or after each step ``` Aside from the options to set hyperparameters, some relevant options to control the behaviour of the `run_squad.py` script are: -```bash + +``` --bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture. ---output_dir: The output directory where the model checkpoints will be written. + --output_dir: The output directory where the model checkpoints will be written. --[no]do_predict: Whether to run evaluation on the dev set. (default: 'false') --[no]do_train: Whether to run training. (default: 'false') --learning_rate: The initial learning rate for Adam.(default: '5e-06')(a number) @@ -325,43 +387,66 @@ Aside from the options to set hyperparameters, some relevant options to control --train_batch_size: Total batch size for training.(default: '8')(an integer) --[no]use_fp16: Whether to enable AMP ops.(default: 'false') --[no]use_xla: Whether to enable XLA JIT compilation.(default: 'false') - --[no]verbose_logging: If true, all of the warnings related to data processing will be printed. A number of warnings are expected for a normal SQuAD evaluation.(default: 'false') --[no]version_2_with_negative: If true, the SQuAD examples contain some that do not have an answer.(default: 'false') ``` +Aside from the options to set hyperparameters, some relevant options to control the behaviour of the `run_classifier.py` script are: + +``` + --bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture. + --data_dir: The input data dir. Should contain the .tsv files (or other data files) for the task. + --[no]do_eval: Whether to run eval on the dev set. + (default: 'false') + --[no]do_predict: Whether to run the model in inference mode on the test set.(default: 'false') + --[no]do_train: Whether to run training.(default: 'false') + --[no]horovod: Whether to use Horovod for multi-gpu runs(default: 'false') + --init_checkpoint: Initial checkpoint (usually from a pre-trained BERT model). + --max_seq_length: The maximum total input sequence length after WordPiece tokenization. Sequences longer than this will be truncated, and sequences shorter than this will be padded.(default: '128')(an integer) + --num_train_epochs: Total number of training epochs to perform.(default: '3.0')(a number) + --output_dir: The output directory where the model checkpoints will be written. + --task_name: The name of the task to train. + --train_batch_size: Total batch size for training.(default: '32')(an integer) + --[no]use_fp16: Whether to use fp32 or fp16 arithmetic on GPU. + (default: 'false') + --[no]use_xla: Whether to enable XLA JIT compilation. + (default: 'false') + --vocab_file: The vocabulary file that the BERT model was trained on. + --warmup_proportion: Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% of training.(default: '0.1')(a number) +``` + +Note: When initializing from a checkpoint using `--init_checkpoint` and a corpus of your choice, keep in mind that `bert_config_file` and `vocab_file` should remain unchanged. + ### Command-line options -To see the full list of available options and their descriptions, use the `-h` or `--help` command-line option with the python file, for example: +To see the full list of available options and their descriptions, use the `-h` or `--help` command-line option with the Python file, for example: ```bash python run_pretraining.py --help python run_squad.py --help +python run_classifier.py --help ``` ### Getting the data -For pre-training BERT, we use the concatenation of Wikipedia (2500M words) as well as Books Corpus (800M words). For Wikipedia, we extract only the text passages from [here](ftp://ftpmirror.your.org/pub/wikimedia/dumps/enwiki/20190301/enwiki-20190301-pages-articles-multistream.xml.bz2) and ignore headers list and tables. It is structured as a document level corpus rather than a shuffled sentence level corpus because it is critical to extract long contiguous sentences. The next step is to run `create_pretraining_data.py` with the document level corpus as input, which generates input data and labels for the masked language modeling and next sentence prediction tasks. Pre-training can also be performed on any corpus of your choice. The collection of data generation scripts are intended to be modular to allow modifications for additional preprocessing steps or to use additional data. They can hence, easily be modified for an arbitrary corpus. +For pre-training BERT, we use the concatenation of Wikipedia (2500M words) as well as BookCorpus (800M words). For Wikipedia, we extract only the text passages from [here](ftp://ftpmirror.your.org/pub/wikimedia/dumps/enwiki/latest/enwiki-latest-pages-articles-multistream.xml.bz2) and ignore headers list and tables. It is structured as a document level corpus rather than a shuffled sentence level corpus because it is critical to extract long contiguous sentences. -The preparation of an individual pre-training dataset is described in the `run_preprocessing.sh` script found in the `data/bookcorpus` and `data/wikipedia_corpus` folders. The component steps to prepare the datasets are as follows: +The next step is to run `create_pretraining_data.py` with the document level corpus as input, which generates input data and labels for the masked language modeling and next sentence prediction tasks. Pre-training can also be performed on any corpus of your choice. The collection of data generation scripts are intended to be modular to allow modifications for additional preprocessing steps or to use additional data. They can hence easily be modified for an arbitrary corpus. -1. Data download and extract - the dataset is downloaded and extracted +The preparation of an individual pre-training dataset is described in the `create_datasets_from_start.sh` script found in the `data/` folder. The component steps to prepare the datasets are as follows: -2. Clean and format - document tags, etc. are removed from the dataset. The end result of this step is a `{dataset_name}.txt` file that contains the entire corpus. Each line in the text file contains an entire document from the corpus. - -3. Sentence segmentation - the corpus text file is processed into separate sentences. The result of this step is a `{dataset_name}.segmented.nltk.txt` file in a `final_text_file_single` directory that contains the entire corpus, with each sentence having its own line. Documents are separated by a new line between documents. - -4. Sharding - the sentence segmented corpus file is split into a number of smaller text documents. The sharding is configured so that a document will not be split between two shards. - -5. TFRecord file creation - each text file shard is processed by the `create_pretraining_data.py` script to produce a corresponding TFRecord file. The script generates input data and labels for masked language modeling and sentence prediction tasks for the input text shard. +1. Data download and extract - the dataset is downloaded and extracted. +2. Clean and format - document tags, etc. are removed from the dataset. The end result of this step is a `{dataset_name_one_article_per_line}.txt` file that contains the entire corpus. Each line in the text file contains an entire document from the corpus. One file per dataset is created in the `formatted_one_article_per_line` folder. +3. Sharding - the sentence segmented corpus file is split into a number of smaller text documents. The sharding is configured so that a document will not be split between two shards. Sentence segmentation is performed at this time using NLTK. +4. TFRecord file creation - each text file shard is processed by the `create_pretraining_data.py` script to produce a corresponding TFRecord file. The script generates input data and labels for masked language modeling and sentence prediction tasks for the input text shard. -For fine tuning BERT for the task of Question Answering. We use SQuaD for this task. SQuaD v1.1 has 100,000+ question-answer pairs on 500+ articles. SQuaD v2.0 combines v1.1 with an additional 50,000 new unanswerable questions and must not only answer questions but also determine when that is not possible. +For fine tuning BERT for the task of Question Answering, we use SQuAD and GLUE. SQuAD v1.1 has 100,000+ question-answer pairs on 500+ articles. SQuAD v2.0 combines v1.1 with an additional 50,000 new unanswerable questions and must not only answer questions but also determine when that is not possible. GLUE consists of single-sentence tasks, similarity and paraphrase tasks and inference tasks. We support one of each: CoLA, MNLI and MRPC. #### Dataset guidelines The procedure to prepare a text corpus for pre-training is described in the previous section. This section provides additional insight into how exactly raw text is processed so that it is ready for pre-training. -First, raw text is tokenized using [WordPiece tokenization](https://arxiv.org/pdf/1609.08144.pdf). A [CLS] token is inserted at the start of every sequence, and the two sentences in the sequence are separated with a [SEP] token. +First, raw text is tokenized using [WordPiece tokenization](https://arxiv.org/pdf/1609.08144.pdf). A [CLS] token is inserted at the start of every sequence, and the two sentences in the sequence are separated by a [SEP] token. Note: BERT pre-training looks at pairs of sentences at a time. A sentence embedding token [A] is added to the first sentence and token [B] to the next. @@ -373,7 +458,7 @@ The `create_pretraining_data.py` script takes in raw text and creates training i #### Multi-dataset -We are able to combine multiple datasets into a single dataset for pre-training on a diverse text corpus. Once TFRecords have been created for each component dataset, then one can simply create a combined dataset by adding the directory to `SOURCES` in `run_pretraining.sh`. This will feed all matching files to the input pipeline in `run_pretraining.py`. However, note that in the training process only one TFRecord file is consumed at a time, therefore, the training instances of any given training batch will all belong to the same source dataset. +We are able to combine multiple datasets into a single dataset for pre-training on a diverse text corpus. Once TFRecords have been created for each component dataset, you can create a combined dataset by adding the directory to `SOURCES` in `run_pretraining_*.sh`. This will feed all matching files to the input pipeline in `run_pretraining.py`. However, in the training process, only one TFRecord file is consumed at a time, therefore, the training instances of any given training batch will all belong to the same source dataset. ### Training process @@ -381,61 +466,74 @@ The training process consists of two steps: pre-training and fine tuning. #### Pre-training -Pre-training is performed using the `run_pretraining.py` script along with parameters defined in the `scripts/run_pretraining.sh`. +Pre-training is performed using the `run_pretraining.py` script along with parameters defined in the `scripts/run_pretraining_lamb.sh`. - -The `run_pretraining.sh` script runs a job on a single node that trains the BERT-large model from scratch using the Wikipedia and Book corpus datasets as training data. By default, the training script: -- Runs on 8 GPUs with training batch size of 14 and evaluation batch size of 8 per GPU. +The `run_pretraining_lamb.sh` script runs a job on a single node that trains the BERT-large model from scratch using the Wikipedia and BookCorpus datasets as training data. By default, the training script: +- Runs on 8 GPUs. - Has FP16 precision enabled. - Is XLA enabled. -- Runs for 1144000 steps with 10000 warm-up steps. -- Saves a checkpoint every 5000 iterations (keeps only the latest checkpoint) and at the end of training. All checkpoints, evaluation results and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory). - Creates a log file containing all the output. -- Evaluates the model at the end of training. To skip evaluation, modify `--do_eval` to `False`. +- Saves a checkpoint every 100 iterations (keeps only the latest checkpoint) and at the end of training. All checkpoints, evaluation results and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory). +- Evaluates the model at the end of each phase. -These parameters will train Wikipedia and Books Corpus to reasonable accuracy on a DGX1 with 32GB V100 cards. If you want to match Google’s best results from the BERT paper, you should either train for twice as many steps (2,288,000 steps) on a DGX-1, or train on 16 GPUs on a DGX-2. The DGX-2 having 16 GPUs will be able to fit a batch size twice as large as a DGX-1 (224 vs 112), hence the DGX-2 can finish in half as many steps. +- Phase 1 + - Runs 7038 steps with 2000 warmup steps + - Sets Maximum sequence length as 128 + - Sets Global Batch size as 64K +- Phase 2 + - Runs 1564 steps with 200 warm-up steps + - Sets Maximum sequence length as 512 + - Sets Global Batch size as 32K + - Starts from Phase1's final checkpoint + +These parameters train Wikipedia and BookCorpus with reasonable accuracy on a DGX-1 with 32GB V100 cards. For example: ```bash -run_pretraining.sh +scripts/run_pretraining_lamb.sh ``` Where: -- is per-GPU batch size used for training. Batch size varies with precision, larger batch sizes run more efficiently, but require more memory. +- `` is per-GPU batch size used for training in the respective phase. Batch size varies with precision, larger batch sizes run more efficiently, but require more memory. -- is per-GPU batch size used for evaluation after training. +- `` is per-GPU batch size used for evaluation after training. -- is the default rate of 1e-4 is good for global batch size 256. +- `` is the default rate of 1e-4 is good for global batch size 256. -- is the type of math in your model, can be either `fp32` or `amp`. Specifically: +- `` is the default rate of 1e-4 is good for global batch size 256. + +- `` is the type of math in your model, can be either `fp32` or `fp16`. Specifically: - `fp32` is 32-bit IEEE single precision floats. - - `amp` is Automatic rewrite of TensorFlow compute graph to take advantage of 16-bit arithmetic whenever it is safe. + - `fp16` is Automatic rewrite of TensorFlow compute graph to take advantage of 16-bit arithmetic whenever it is safe. -- is the number of GPUs to use for training. Must be equal to or smaller than the number of GPUs attached to your node. +- `` is the number of GPUs to use for training. Must be equal to or smaller than the number of GPUs attached to your node. -- is the number of warm-up steps at the start of training. +- `` is the number of warm-up steps at the start of training in the respective phase. -- is the total number of training steps. +- `` is the total number of training steps in both phases combined. -- controls how often checkpoints are saved. Default is 5000 steps. +- `` controls how often checkpoints are saved. Default is 100 steps. -- is a flag that indicates whether output should be written to a logfile or not (acceptable values are ‘true’ or ‘false’, ‘true’ indicates output should be saved to a logfile.) +- `` is used to mimic higher batch sizes in the respective phase by accumulating gradients N times before weight update. -The following sample code, trains BERT-large from scratch on a single DGX-2 using FP16 arithmetic. This will take around 156 hours / 6.5 days. Checkpoints are written out every 5000 steps and all printouts are saved to a logfile. +- `` is used to indicate whether to pretrain BERT Large or BERT Base model + +The following sample code trains BERT-large from scratch on a single DGX-2 using FP16 arithmetic. This will take around 4.5 days. ```bash -bert_tf/scripts/run_pretraining.sh 14 8 1e-4 fp16_xla 16 10000 1144000 5000 true +bert_tf/scripts/run_pretraining_lamb.sh 32 8 8 3.75e-4 2.5e-4 fp16 true 16 2000 200 7820 100 128 512 256 large ``` #### Fine tuning Fine tuning is performed using the `run_squad.py` script along with parameters defined in `scripts/run_squad.sh`. -The `run_squad.sh` script trains a model and performs evaluation on the SQuaD dataset. By default, the training script: -- Trains for SQuAD v1.1 dataset -- Trains on BERT Large Model +The `run_squad.sh` script trains a model and performs evaluation on the SQuAD dataset. By default, the training script: + +- Trains for SQuAD v1.1 dataset. +- Trains on BERT Large Model. - Uses 8 GPUs and batch size of 10 on each GPU. - Has FP16 precision enabled. - Is XLA enabled. @@ -446,7 +544,7 @@ The `run_squad.sh` script trains a model and performs evaluation on the SQuaD da This script outputs checkpoints to the `/results` directory, by default, inside the container. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file. The training log contains information about: - Loss for the final step - Training and evaluation performance -- F1 and exact match score on the Dev Set of SQuaD after evaluation. +- F1 and exact match score on the Dev Set of SQuAD after evaluation. The summary after training is printed in the following format: ```bash @@ -458,8 +556,9 @@ I0312 23:14:00.550973 140287431493376 run_squad.py:1397] 0 Inference Performance ``` Multi-GPU training is enabled with the Horovod TensorFlow module. The following example runs training on 8 GPUs: + ```bash -BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16 +BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16 mpi_command="mpirun -np 8 -H localhost:8 \ --allow-run-as-root -bind-to none -map-by slot \ @@ -471,21 +570,43 @@ mpi_command="mpirun -np 8 -H localhost:8 \ --output_dir=/results ``` +#### Multi-node + + +Multi-node runs can be launched on a pyxis/enroot Slurm cluster (see [Requirements](#requirements)) with the `run.sub` script with the following command for a 4-node DGX1 example for both phase 1 and phase 2: +``` +BATCHSIZE=16 LEARNING_RATE='1.875e-4' NUM_ACCUMULATION_STEPS=128 PHASE=1 sbatch -N4 --ntasks-per-node=8 run.sub +BATCHSIZE=2 LEARNING_RATE='1.25e-4' NUM_ACCUMULATION_STEPS=512 PHASE=1 sbatch -N4 --ntasks-per-node=8 run.sub +``` + + +Checkpoint after phase 1 will be saved in `checkpointdir` specified in `run.sub`. The checkpoint will be automatically picked up to resume training on phase 2. Note that phase 2 should be run after phase 1. + +Variables to re-run the [Training performance results](#training-performance-results) are available in the `configurations.yml` file. + +The batch variables `BATCHSIZE`, `LEARNING_RATE`, `NUM_ACCUMULATION_STEPS` refer to the Python arguments `train_batch_size`, `learning_rate`, `num_accumulation_steps` respectively. +The variable `PHASE` refers to phase specific arguments available in `run.sub`. + +Note that the `run.sub` script is a starting point that has to be adapted depending on the environment. In particular, variables such as `datadir` handle the location of the files for each phase. + +Refer to the files contents to see the full list of variables to adjust for your system. + ### Inference process Inference on a fine tuned Question Answering system is performed using the `run_squad.py` script along with parameters defined in `scripts/run_squad_inference.sh`. Inference is supported on a single GPU. -The `run_squad_inference.sh` script trains a model and performs evaluation on the SQuaD dataset. By default, the inferencing script: +The `run_squad_inference.sh` script trains a model and performs evaluation on the SQuAD dataset. By default, the inferencing script: + - Uses SQuAD v1.1 dataset - Has FP16 precision enabled - Is XLA enabled -- Evaulates the latest checkpoint present in `/results` with a batch size of 8 +- Evaluates the latest checkpoint present in `/results` with a batch size of 8 -This script outputs predictions file to `/results/predictions.json` and computes F1 score and exact match score using SQuaD's evaluate file. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file. +This script outputs predictions file to `/results/predictions.json` and computes F1 score and exact match score using SQuAD's evaluate file. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file. The output log contains information about: Inference performance -Inference Accuracy (F1 and exact match scores) on the Dev Set of SQuaD after evaluation. +Inference Accuracy (F1 and exact match scores) on the Dev Set of SQuAD after evaluation. The summary after inference is printed in the following format: ```bash @@ -499,14 +620,14 @@ I0312 23:14:00.550973 140287431493376 run_squad.py:1397] 0 Inference Performance The [NVIDIA TensorRT Inference Server](https://github.com/NVIDIA/tensorrt-inference-server) provides a datacenter and cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or gRPC endpoint, allowing remote clients to request inferencing for any number of GPU or CPU models being managed by the server. A typical TensorRT Inference Server pipeline can be broken down into the following 8 steps: -Client serializes the inference request into a message and sends it to the server (Client Send) -Message travels over the network from the client to the server (Network) -Message arrives at server, and is deserialized (Server Receive) -Request is placed on the queue (Server Queue) -Request is removed from the queue and computed (Server Compute) -Completed request is serialized in a message and sent back to the client (Server Send) -Completed message travels over network from the server to the client (Network) -Completed message is deserialized by the client and processed as a completed inference request (Client Receive) +1. Client serializes the inference request into a message and sends it to the server (Client Send) +2. Message travels over the network from the client to the server (Network) +3. Message arrives at server, and is deserialized (Server Receive) +4. Request is placed on the queue (Server Queue) +5. Request is removed from the queue and computed (Server Compute) +6. Completed request is serialized in a message and sent back to the client (Server Send) +7. Completed message travels over network from the server to the client (Network) +8. Completed message is deserialized by the client and processed as a completed inference request (Client Receive) Generally, for local clients, steps 1-4 and 6-8 will only occupy a small fraction of time, compared to steps 5-6. As backend deep learning systems like BERT are rarely exposed directly to end users, but instead only interfacing with local front-end servers, for the sake of BERT, we can consider that all clients are local. In this section, we will go over how to launch TensorRT Inference Server and client and get the best performant solution that fits your specific application needs. @@ -515,33 +636,35 @@ Note: The following instructions are run from outside the container and call `do #### Performance analysis for TensorRT Inference Server -Based on the figures 2 and 3 below, we recommend using the Dynamic Batcher with `max_batch_size = 8`, `max_queue_delay_microseconds` as large as possible to fit within your latency window (The values used below are extremely large to exaggerate their effect), and only 1 instance of the engine. The largest improvements to both throughput and latency come from increasing the batch size due to efficiency gains in the GPU with larger batches. The Dynamic Batcher combines the best of both worlds by efficiently batching together a large number of simultaneous requests, while also keeping latency down for infrequent requests. We recommend only 1 instance of the engine due to the negligible improvement to throughput at the cost of significant increases in latency. Many models can benefit from multiple engine instances but as the figures below show, that is not the case for this model. - +Based on the figures 2 and 3 below, we recommend using the Dynamic Batcher with `max_batch_size = 8`, `max_queue_delay_microseconds` as large as possible to fit within your latency window (the values used below are extremely large to exaggerate their effect), and only 1 instance of the engine. The largest improvements to both throughput and latency come from increasing the batch size due to efficiency gains in the GPU with larger batches. The Dynamic Batcher combines the best of both worlds by efficiently batching together a large number of simultaneous requests, while also keeping latency down for infrequent requests. We recommend only 1 instance of the engine due to the negligible improvement to throughput at the cost of significant increases in latency. Many models can benefit from multiple engine instances but as the figures below show, that is not the case for this model. ![](data/images/trtis_base_summary.png?raw=true) -Figure 2: Latency vs Throughput for BERT Base, fp16, Sequence Length = 128 using Various configurations available in TRTIS +Figure 2: Latency vs Throughput for BERT Base, FP16, Sequence Length = 128 using various configurations available in TensorRT Inference Server ![](data/images/trtis_large_summary.png?raw=true) -Figure 3: Latency vs Throughput for BERT Large, fp16, Sequence Length = 384 using Various configurations available in TRTIS +Figure 3: Latency vs Throughput for BERT Large, FP16, Sequence Length = 384 using various configurations available in TensorRT Inference Server ##### Advanced Details This section digs deeper into the performance numbers and configurations corresponding to running TensorRT Inference Server for BERT fine tuning for Question Answering. It explains the tradeoffs in selecting maximum batch sizes, batching techniques and number of inference engines on the same GPU to understand how we arrived at the optimal configuration specified previously. -Results can be reproduced by running `generate_figures.sh`. It exports the Tensorflow BERT model as a `tensorflow_savedmodel` that TensorRT Inference Server accepts, builds a matching [TensorRT Inference Server model config](https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/model_configuration.html#), starts the server on localhost in a detached state and runs [perf_client](https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/client.html#performance-example-application) for various configurations. +Results can be reproduced by running `generate_figures.sh`. It exports the TensorFlow BERT model as a `tensorflow_savedmodel` that TensorRT Inference Server accepts, builds a matching [TensorRT Inference Server model config](https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/model_configuration.html#), starts the server on localhost in a detached state and runs [perf_client](https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/client.html#performance-example-application) for various configurations. ```bash bash scripts/trtis/generate_figures.sh ``` -All results below are obtained on 1 DGX-1 V100 32 GB GPU for BERT Base, Sequence Length = 128 and FP16 precision running on a local server. Latencies are indicated by bar plots using the left axis. Throughput is indicated by the blue line plot using the right axis. X-axis indicates the concurrency - the maximum number of inference requests that can be in the pipeline at any given time. For example, when the concurrency is set to 1, the client waits for an inference request to be completed (Step 8) before it sends another to the server (Step 1). A high number of concurrent requests can reduce the impact of network latency on overall throughput +All results below are obtained on a single DGX-1 V100 32GB GPU for BERT Base, Sequence Length = 128 and FP16 precision running on a local server. Latencies are indicated by bar plots using the left axis. Throughput is indicated by the blue line plot using the right axis. X-axis indicates the concurrency - the maximum number of inference requests that can be in the pipeline at any given time. For example, when the concurrency is set to 1, the client waits for an inference request to be completed (Step 8) before it sends another to the server (Step 1). A high number of concurrent requests can reduce the impact of network latency on overall throughput. -1. Maximum batch size +###### Maximum batch size -As we can see in figure 4 below, the throughput at BS=1, Client Concurrent Requests = 64 is 119 and in figure 5 below, the throughput at BS=8, Client Concurrent Requests = 8 is 517, respectively giving a speedup of ~4.3x (Note: We compare BS=1, Client Concurrent Requests = 64 to BS=8, Client Concurrent Requests = 8 to keep the Total Number of Outstanding Requests equal between the two different modes. Where Total Number of Outstanding Requests = Batch Size * Client Concurrent Requests. This is also why there are 8 times as many bars on the BS=1 chart than the BS=8 chart). Increasing the batch size from 1 to 8 results in an increase in compute time by 1.8x (8.38ms to 15.46ms) showing that computation is more efficient at higher batch sizes. Hence, an optimal batch size would be the maximum batch size that can both fit in memory and is within the preferred latency threshold. +As we can see in Figure 4, the throughput at BS=1, Client Concurrent Requests = 64 is 119 and in Figure 5, the throughput at BS=8, Client Concurrent Requests = 8 is 517, respectively giving a speedup of ~4.3x +Note: We compare BS=1, Client Concurrent Requests = 64 to BS=8, Client Concurrent Requests = 8 to keep the Total Number of Outstanding Requests equal between the two different modes. Where Total Number of Outstanding Requests = Batch Size * Client Concurrent Requests. This is also why there are 8 times as many bars on the BS=1 chart than the BS=8 chart. + +Increasing the batch size from 1 to 8 results in an increase in compute time by 1.8x (8.38ms to 15.46ms) showing that computation is more efficient at higher batch sizes. Hence, an optimal batch size would be the maximum batch size that can both fit in memory and is within the preferred latency threshold. ![](data/images/trtis_bs_1.png?raw=true) @@ -551,13 +674,13 @@ Figure 4: Latency & Throughput vs Concurrency at Batch size = 1 Figure 5: Latency & Throughput vs Concurrency at Batch size = 8 -2. Batching techniques +###### Batching techniques Static batching is a feature of the inference server that allows inference requests to be served as they are received. It is preferred in scenarios where low latency is desired at the cost of throughput when the GPU is under utilized. -Dynamic batching is a feature of the inference server that allows inference requests to be combined by the server, so that a batch is created dynamically, resulting in an increased throughput. It is preferred in scenarios where we would like to maximize throughput and GPU utilization at the cost of higher latencies. User can set the [Dynamic Batcher parameters](https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-master-branch-guide/docs/model_configuration.html#dynamic-batcher) `max_queue_delay_microseconds` to indicate the maximum amount of time they are willing to wait and ‘preferred_batchsize’ to indicate their optimal batch sizes in the TensorRT Inference Server model config. +Dynamic batching is a feature of the inference server that allows inference requests to be combined by the server, so that a batch is created dynamically, resulting in an increased throughput. It is preferred in scenarios where we would like to maximize throughput and GPU utilization at the cost of higher latencies. You can set the [Dynamic Batcher parameters](https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-master-branch-guide/docs/model_configuration.html#dynamic-batcher) `max_queue_delay_microseconds` to indicate the maximum amount of time you are willing to wait and ‘preferred_batchsize’ to indicate your optimal batch sizes in the TensorRT Inference Server model config. -The figures 6 & 7 below emphasize the increase in overall throughput with dynamic batching. At low numbers of concurrent requests, the increased throughput comes at the cost of increasing latency as the requests are queued up to `max_queue_delay_microseconds`. The effect of `preferred_batchsize` for dynamic batching is visually depicted by the dip in Server Queue time at integer multiples of the preferred batch sizes. At higher numbers of concurrent requests, observe that the throughput approach a maximum limit as we saturate the GPU utilization. +Figures 6 and 7 emphasize the increase in overall throughput with dynamic batching. At low numbers of concurrent requests, the increased throughput comes at the cost of increasing latency as the requests are queued up to `max_queue_delay_microseconds`. The effect of `preferred_batchsize` for dynamic batching is visually depicted by the dip in Server Queue time at integer multiples of the preferred batch sizes. At higher numbers of concurrent requests, observe that the throughput approach a maximum limit as we saturate the GPU utilization. ![](data/images/trtis_static.png?raw=true) @@ -567,12 +690,11 @@ Figure 6: Latency & Throughput vs Concurrency using Static Batching at `Batch si Figure 7: Latency & Throughput vs Concurrency using Dynamic Batching at `Batch size` = 1, `preferred_batchsize` = [4, 8] and `max_queue_delay_microseconds` = 5000 -3. Model execution instance count +###### Model execution instance count TensorRT Inference Server enables us to launch multiple engines in separate CUDA streams by setting the `instance_group_count` parameter to improve both latency and throughput. Multiple engines are useful when the model doesn’t saturate the GPU allowing the GPU to run multiple instances of the model in parallel. -From the figures 8 & 9 below, we can see a drop in queue time as more models are available to serve an inference request. However, this is countered by an increase in compute time as multiple models compete for resources. Since BERT is a large model which utilizes the majority of the GPU, the benefit to running multiple engines is not seen. - +Figures 8 and 9 show a drop in queue time as more models are available to serve an inference request. However, this is countered by an increase in compute time as multiple models compete for resources. Since BERT is a large model which utilizes the majority of the GPU, the benefit to running multiple engines is not seen. ![](data/images/trtis_ec_1.png?raw=true) @@ -584,9 +706,9 @@ Figure 8: Latency & Throughput vs Concurrency at Batch size = 1, Engine Count = Figure 9: Latency & Throughput vs Concurrency at Batch size = 1, Engine count = 4 (Four copies the model loaded in GPU memory) -#### Run the TensorRT Inference Server and client +#### Running the TensorRT Inference Server and client -The `run_trtis.sh` script exports the Tensorflow BERT model as a `tensorflow_savedmodel` that TensorRT Inference Server accepts, builds a matching [TensorRT Inference Server model config](https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/model_configuration.html#), starts the server on local host in a detached state, runs client and then evaluates the validity of predictions on the basis of exact match and F1 score all in one step. +The `run_trtis.sh` script exports the TensorFlow BERT model as a `tensorflow_savedmodel` that TensorRT Inference Server accepts, builds a matching [TensorRT Inference Server model config](https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/model_configuration.html#), starts the server on local host in a detached state, runs client and then evaluates the validity of predictions on the basis of exact match and F1 score all in one step. ```bash bash scripts/trtis/run_trtis.sh @@ -621,67 +743,148 @@ This script runs 1024 eval iterations by default on the SQuAD v1.1 dataset and e ### Results -The following sections provide details on how we achieved our performance and accuracy in training and inference for Question Answering fine tuning. All results are on BERT-large model for a sequence length of 384 on SQuAD v1.1 unless otherwise mentioned. +The following sections provide details on how we achieved our performance and accuracy in training and inference for pre-training using LAMB optimizer as well as fine tuning for Question Answering. All results are on BERT-large model unless otherwise mentioned. All fine tuning results are on SQuAD v1.1 using a sequence length of 384 unless otherwise mentioned. #### Training accuracy results -##### NVIDIA DGX-1 (8x V100 16G) +##### Training accuracy -Our results were obtained by running the `run_squad.py` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. +###### Pre-training accuracy: single-node + +Our results were obtained by running the `scripts/run_pretraining_lamb.sh` training script in the TensorFlow 19.06-py3 NGC container. + +| **DGX System** | **GPUs** | **Batch size / GPU: Phase1, Phase2** | **Accumulation Steps: Phase1, Phase2** | **Time to Train - mixed precision (Hrs)** | **Final Loss - mixed precision** | +|:---:|:---:|:----:|:----:|:---:|:----:| +| DGX1 | 8 | 16, 2 | 512, 2048 | 247.51 | 1.43 | +| DGX2 | 16 | 64, 8 | 64, 256 | 108.16 | 1.58 | + + +###### Pre-training accuracy: multi-node + +Our results were obtained by running the `scripts/run_pretraining_lamb.sh` training script in the TensorFlow 19.08-py3 NGC container. + +| **DGX System** | **Nodes** | **Precision** | **Batch Size/GPU: Phase1, Phase2** | **Accumulation Steps: Phase1, Phase2** | **Time to Train (Hrs)** | **Final Loss**| +|----------------|-----------|---------------|------------------------------------|----------------------------------------|----------------|-------------------------| +| DGX1 | 4 | FP16 | 32, 2 | 32, 128 | 48.66 | 1.48 | +| DGX1 | 16 | FP16 | 32, 2 | 32, 128 | 24.35 | 1.53 | +| DGX1 | 32 | FP16 | 32, 2 | 32, 128 | 12.98 | 1.61 | +| DGX1 | 32 | FP32 | 32, 2 | 32, 128 | 30.92 | 1.49 | +| DGX2H | 4 | FP16 | 64, 8 | 16, 64 | 25.85 | 1.56 | +| DGX2H | 16 | FP16 | 64, 8 | 8, 32 | 7.9 | 1.57 | +| DGX2H | 32 | FP16 | 64, 8 | 4, 16 | 4.77 | 1.61 | +| DGX2H | 32 | FP32 | 32, 4 | 8, 32 | 12.72 | 1.53 | + +Note: Time to train includes upto 16 minutes of start up time for every restart. Experiments were run on clusters with a maximum wall clock time of 8 hours and 2 hours for DGX1 and DGX2H systems respectively. + +###### Fine-tuning accuracy for SQuAD: NVIDIA DGX-2 (16x V100 32G) + +Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-2 with 16x V100 32G GPUs. | **GPUs** | **Batch size / GPU** | **Accuracy - FP32** | **Accuracy - mixed precision** | **Time to Train - FP32 (Hrs)** | **Time to Train - mixed precision (Hrs)** | |:---:|:----:|:----:|:---:|:----:|:----:| -| 8 | 4 |90.84|90.86|0.97|0.64| +| 16 | 4 |90.94|90.84|0.38|0.27| ##### Training stability test +###### Pre-training stability test: NVIDIA DGX-2 (512x V100 32G) + +The following tables compare `Final Loss` scores across 5 different training runs with different seeds, for both FP16. The runs showcase consistent convergence on all 5 seeds with very little deviation. + +| **FP16, 512x GPUs** | **seed 1** | **seed 2** | **seed 3** | **seed 4** | **seed 5** | **mean** | **std** | +|:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:| +|Final Loss |1.57 |1.598 |1.614 |1.583 |1.584 |1.5898|0.017 | + +###### Fine-tuning SQuAD stability test: NVIDIA DGX-2 (16x V100 32G) + The following tables compare `F1` scores across 5 different training runs with different seeds, for both FP16 and FP32 respectively. The runs showcase consistent convergence on all 5 seeds with very little deviation. | **FP16, 8x GPUs** | **seed 1** | **seed 2** | **seed 3** | **seed 4** | **seed 5** | **mean** | **std** | |:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:| -|F1 |90.75|90.82|90.89|91.05|90.79|90.86|0.12| -|Exact match|83.85|83.93|83.95|84.25|83.59|83.91|0.24| +|F1 |90.99|90.67|91.00|90.91|90.61|90.84|0.18| +|Exact match|84.12|83.60|84.02|84.05|83.47|83.85|0.29| | **FP32, 8x GPUs** | **seed 1** | **seed 2** | **seed 3** | **seed 4** | **seed 5** | **mean** | **std** | |:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:| -|F1 |90.70|90.80|90.89|91.08|90.73|90.84|0.15 | -|Exact match|83.82|83.77|84.23|84.19|83.63|83.93|0.27 | +|F1 |90.74|90.82|91.09|91.16|90.89|90.94|0.18 | +|Exact match|83.82|83.64|84.03|84.23|84.03|83.95|0.23 | #### Training performance results -Our results were obtained by running batch sizes up to 3x GPUs on a 16GB V100 and up to 10x GPUs on a 32G V100 with mixed precision. - - ##### Training performance: NVIDIA DGX-1 (8x V100 16G) +###### Pre-training training performance: single-node on 16G + +Our results were obtained by running the `scripts/run_pretraining_lamb.sh` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance (in sentences per second) is the steady state throughput. + + +| **GPUs** | **Sequence Length**| **Batch size / GPU: mixed precision, FP32** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 to mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** | +|:-------:|:-----:|:-------:|:-------:|:-------:|:-------------:|:------:|:------:| +| 1 | 128 | 16, 8 | 80.1 | 23.1 | 3.47 | 1 | 1 | +| 4 | 128 | 16, 8 | 282.1 | 85 | 3.32 | 3.52 | 3.68 | +| 8 | 128 | 16, 8 | 540.4 | 166.1 | 3.25 | 6.75 | 7.19 | +| 1 | 512 | 4, 2 | 10.9 | 5.3 | 2.06 | 1 | 1 | +| 4 | 512 | 4, 2 | 35.6 | 19.5 | 1.83 | 3.27 | 3.68 | +| 8 | 512 | 4, 2 | 61.1 | 37.9 | 1.61 | 5.61 | 7.15 | + +Note: The respective values for FP32 runs that use a batch size of 16, 4 in sequence lengths 128 and 512 respectively are not available due to out of memory errors that arise. + +###### Pre-training training performance: multi-node on 16G + +Our results were obtained by running the `run.sub` training script in the TensorFlow 19.08-py3 NGC container using multiple NVIDIA DGX-1 with 8x V100 16G GPUs. Performance (in sentences per second) is the steady state throughput. + +| **Nodes** | **Sequence Length**| **Batch size / GPU: mixed precision, FP32** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 to mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** | +|:-------:|:-----:|:-------:|:-------:|:-------:|:-------------:|:------:|:------:| +| 1 | 128 | 16,8 | 440.3 | 167.9 | 2.62 | 1.00 | 1.00 | +| 4 | 128 | 16,8 | 1712.3 | 600.7 | 2.85 | 3.89 | 3.58 | +| 16 | 128 | 16,8 | 4833.5 | 2186.2 | 2.21 | 10.98 | 13.02 | +| 32 | 128 | 16,8 | 9742.9 | 4020.9 | 2.42 | 22.13 | 23.95 | +| 1 | 512 | 2,1 | 74.9 | 26 | 2.88 | 0.00 | 0.00 | +| 4 | 512 | 2,1 | 257.5 | 91.2 | 2.82 | 1.00 | 1.00 | +| 16 | 512 | 2,1 | 899.7 | 313 | 2.87 | 3.44 | 3.51 | +| 32 | 512 | 2,1 | 1737.1 | 579.4 | 3.0 | 23.19 | 22.28 | + +Note: The respective values for FP32 runs that use a batch size of 16, 2 in sequence lengths 128 and 512 respectively are not available due to out of memory errors that arise. + +###### Fine-tuning training performance for SQuAD on 16G + Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance (in sentences per second) is the mean throughput from 2 epochs. - | **GPUs** | **Batch size / GPU** | **Throughput - FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **Weak scaling - FP32** | **Weak scaling - mixed precision** | |:---:|:---:|:------:|:-----:|:----:|:----:|:----:| -| 1 | 2 | 7.19 |14.37|2.0 |1.0 |1.0 | -| 4 | 2 |25.61 |40.44|1.58 |3.56 |2.81| -| 8 | 2 |49.79 |74.61|1.5 |6.92 |5.19| - - -| **GPUs** | **Batch size / GPU** | **Throughput - FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **Weak scaling - FP32** | **Weak scaling - mixed precision** | -|:---:|:---:|:-----:|:-----:|:---:|:---:|:----:| -| 1 | 3 | - |17.2| - | - |1.0 | -| 4 | 3 | - |50.71| - | - |2.95 | -| 8 | 3 | - |91.88| - | - |5.34| - +| 1 | 2 | 7.19 |14.37|2.0 |1.0 |1.0 | +| 4 | 2 |25.61 |40.44|1.58|3.56|2.81| +| 8 | 2 |49.79 |74.61|1.5 |6.92|5.19| +| 1 | 3 | - |17.2 | - | - |1.0 | +| 4 | 3 | - |50.71| - | - |2.95| +| 8 | 3 | - |91.88| - | - |5.34| Note: The respective values for FP32 runs that use a batch size of 3 are not available due to out of memory errors that arise. Batch size of 3 is only available on using FP16. To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. - ##### Training performance: NVIDIA DGX-1 (8x V100 32G) +###### Pre-training training performance: single-node on 32G + +Our results were obtained by running the `scripts/run_pretraining_lamb.sh` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 32G GPUs. Performance (in sentences per second) is the steady state throughput. + +| **GPUs** | **Sequence Length**| **Batch size / GPU: mixed precision, FP32** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 to mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** | +|:-------:|:-----:|:-------:|:-------:|:-------:|:-------------:|:------:|:------:| +| 1 | 128 | 48,32 | 130.2 | 33.5 | 3.89 | 1 | 1 | +| 4 | 128 | 48,32 | 462.1 | 127.7 | 3.62 | 3.55 | 3.81 | +| 8 | 128 | 48,32 | 874.8 | 255.4 | 3.43 | 6.72 | 7.62 | +| 1 | 512 | 8, 4 | 22.1 | 6.3 | 3.51 | 1 | 1 | +| 4 | 512 | 8, 4 | 80.4 | 24 | 3.35 | 3.64 | 3.81 | +| 8 | 512 | 8, 4 | 155 | 47.1 | 3.29 | 7.01 | 7.48 | + +Note: The respective values for FP32 runs that use a batch size of 48, 8 in sequence lengths 128 and 512 respectively are not available due to out of memory errors that arise. + +###### Fine-tuning training performance for SQuAD on 32G + Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 32G GPUs. Performance (in sentences per second) is the mean throughput from 2 epochs. @@ -690,14 +893,9 @@ Our results were obtained by running the `scripts/run_squad.sh` training script | 1 | 4 | 8.74|20.55 |2.35|1.0 |1.0 | | 4 | 4 |32.22|57.58 |1.79|3.69|2.81| | 8 | 4 |62.69|100.22|1.60|7.17|4.88| - - -| **GPUs** | **Batch size / GPU** | **Throughput - FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **Weak scaling - FP32** | **Weak scaling - mixed precision** | -|---|---|-----|-------|---|---|----| -| 1 | 10| - | 31.33 | - | - |1.0 | -| 4 | 10| - | 94.19| - | - |3.0| -| 8 | 10| - | 155.53| - | - |4.96| - +| 1 | 10| - |31.33 | - | - |1.0 | +| 4 | 10| - |94.19 | - | - |3.0| +| 8 | 10| - |155.53| - | - |4.96| Note: The respective values for FP32 runs that use a batch size of 10 are not available due to out of memory errors that arise. Batch size of 10 is only available on using FP16. @@ -705,49 +903,88 @@ To achieve these same results, follow the [Quick Start Guide](#quick-start-guide ##### Training performance: NVIDIA DGX-2 (16x V100 32G) +###### Pre-training training performance: single-node on DGX-2 32G + +Our results were obtained by running the `scripts/run_pretraining_lamb.sh` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-2 with 16x V100 32G GPUs. Performance (in sentences per second) is the steady state throughput. + +| **GPUs** | **Sequence Length**| **Batch size / GPU: mixed precision, FP32** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 to mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** | +|:-------:|:-----:|:-------:|:-------:|:-------:|:-------------:|:------:|:------:| +| 1 | 128 | 48,32 | 141.3 | 35.8 | 3.946927374 | 1 | 1 | +| 4 | 128 | 48,32 | 520.4 | 138.8 | 3.749279539 | 3.68 | 3.88 | +| 8 | 128 | 48,32 | 1024 | 275.1 | 3.722282806 | 7.25 | 7.68 | +| 16| 128 | 48,32 | 1907 | 533 | 3.577861163 | 13.5 | 14.89 | +| 1 | 512 | 8, 4 | 23.9 | 6.8 | 3.514705882 | 1 | 1 | +| 4 | 512 | 8, 4 | 89.8 | 25.8 | 3.480620155 | 3.76 | 3.79 | +| 8 | 512 | 8, 4 | 177.2 | 51 | 3.474509804 | 7.41 | 7.5 | +| 16| 512 | 8, 4 | 332.2 | 94.2 | 3.526539278 | 13.9 | 13.85 | + +Note: The respective values for FP32 runs that use a batch size of 48, 8 in sequence lengths 128 and 512 respectively are not available due to out of memory errors that arise. + +###### Pre-training training performance: multi-node on DGX-2 32G + +Our results were obtained by running the `run.sub` training script in the TensorFlow 19.08-py3 NGC container using multiple NVIDIA DGX-2 with 16x V100 32G GPUs. Performance (in sentences per second) is the steady state throughput. + + +| **Nodes** | **Sequence Length**| **Batch size / GPU: mixed precision, FP32** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 to mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** | +|:-------:|:-----:|:-------:|:-------:|:-------:|:-------------:|:------:|:------:| +| 1 | 128 | 32, 32 | 1806.7 | 599.3 | 3.01 | 1 | 1 | +| 4 | 128 | 32, 32 | 4088.7 | 1762.3 | 2.32 | 2.26 | 2.94 | +| 16 | 128 | 32, 32 | 14719.6 | 6400.2 | 2.30 | 8.15 | 10.68| +| 32 | 128 | 32, 32 | 27303.6 | 12203.6| 2.24 | 15.11| 20.36| +| 1 | 512 | 8, 4 | 269.7 | 109.6 | 2.46 | 1 | 1 | +| 4 | 512 | 8, 4 | 960.9 | 268.5 | 3.58 | 3.56 | 2.45 | +| 16 | 512 | 8, 4 | 3726.3 | 965 | 3.86 | 13.82| 8.8 | +| 32 | 512 | 8, 4 | 6192.7 | 1800.3 | 3.44 | 22.96| 16.43| + + +###### Fine-tuning training performance for SQuAD on DGX-2 32G + Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-2 with 16x V100 32G GPUs. Performance (in sentences per second) is the mean throughput from 2 epochs. | **GPUs** | **Batch size / GPU** | **Throughput - FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **Weak scaling - FP32** | **Weak scaling - mixed precision** | |---|---|------|------|----|-----|-----| -| 1| 4 | 9.39 | 20.69 |2.20| 1.0 | 1.0 | -| 4| 4 | 34.63| 62.79|1.81| 3.69| 3.03| -| 8| 4 | 66.95|111.47|1.66| 7.13 | 5.39| -| 16| 4 |126.09|179.09|1.42| 13.43 |8.66| - - - -| **GPUs** | **Batch size / GPU** | **Throughput - FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **Weak scaling - FP32** | **Weak scaling - mixed precision** | -|---|---|---|------|---|---|-----| -| 1| 10| - | 32.72| - | - | 1.0 | -| 4| 10| - |100.73| - | - | 3.07 | -| 8| 10| - |168.92| - | - | 5.16 | -| 16| 10| - |249.54| - | - | 7.63 | +| 1| 4 | 9.39 | 20.69 |2.20| 1.0 | 1.0 | +| 4| 4 | 34.63| 62.79|1.81| 3.69 | 3.03 | +| 8| 4 | 66.95|111.47|1.66| 7.13 | 5.39 | +| 16| 4 |126.09|179.09|1.42| 13.43 |8.66 | +| 1| 10| - | 32.72| - | - | 1.0 | +| 4| 10| - |100.73| - | - | 3.07 | +| 8| 10| - |168.92| - | - | 5.16 | +| 16| 10| - |249.54| - | - | 7.63 | Note: The respective values for FP32 runs that use a batch size of 10 are not available due to out of memory errors that arise. Batch size of 10 is only available on using FP16. - To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. - #### Inference performance results ##### Inference performance: NVIDIA DGX-1 (1x V100 16G) +###### Pre-training inference performance on 16G + +Our results were obtained by running the `scripts/run_pretraining_lamb.sh` script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs. + +| **Sequence Length**| **Batch size / GPU: mixed precision, FP32** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 to mixed precision)** | +|:-----:|:-------:|:-------:|:-------:|:-------------:| +|128 |8, 8 |349.49 | 104.03 | 3.36 | + +###### Fine-tuning inference performance for SQuAD on 16G + Our results were obtained by running the `scripts/finetune_inference_benchmark.sh` script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs. Performance numbers (throughput in sentences per second and latency in milliseconds) were averaged from 1024 iterations. Latency is computed as the time taken for a batch to process as they are fed in one after another in the model ie no pipelining. -BERT LARGE Fp16 +BERT LARGE FP16 -| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | -|-----------------|------------|------------------------------|---------------------|-----------------|-----------------|-----------------| -| 128 | 1 | 89.4 | 11.19 | 11.29 | 11.44 | 11.71 | -| 128 | 2 | 162.29 | 12.32 | 12.5 | 12.57 | 12.74 | -| 128 | 4 | 263.44 | 15.18 | 15.32 | 15.54 | 17 | -| 128 | 8 | 374.33 | 21.37 | 21.56 | 21.72 | 23.23 | -| 384 | 1 | 64.57 | 15.49 | 15.61 | 15.73 | 16.18 | -| 384 | 2 | 94.04 | 21.27 | 21.34 | 21.4 | 21.9 | -| 384 | 4 | 118.81 | 33.67 | 33.89 | 34.37 | 36.18 | -| 384 | 8 | 137.65 | 58.12 | 58.53 | 59.34 | 61.32 | +| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Throughput speedup (FP32 to mixed precision) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | +|-----------------|------------|------------------------------|----------------------------------------------|---------------------|-----------------|-----------------|-----------------| +| 128 | 1 | 89.4 | 1.19 | 11.19 | 11.29 | 11.44 | 11.71 | +| 128 | 2 | 162.29 | 1.56 | 12.32 | 12.5 | 12.57 | 12.74 | +| 128 | 4 | 263.44 | 2.24 | 15.18 | 15.32 | 15.54 | 17 | +| 128 | 8 | 374.33 | 2.98 | 21.37 | 21.56 | 21.72 | 23.23 | +| 384 | 1 | 64.57 | 1.87 | 15.49 | 15.61 | 15.73 | 16.18 | +| 384 | 2 | 94.04 | 2.47 | 21.27 | 21.34 | 21.4 | 21.9 | +| 384 | 4 | 118.81 | 2.96 | 33.67 | 33.89 | 34.37 | 36.18 | +| 384 | 8 | 137.65 | 3.26 | 58.12 | 58.53 | 59.34 | 61.32 | BERT LARGE FP32 @@ -762,19 +999,18 @@ BERT LARGE FP32 | 384 | 4 | 40.16 | 99.6 | 100.76 | 101.62 | 103.4 | | 384 | 8 | 42.2 | 189.57 | 190.82 | 191.47 | 193.27 | -BERT BASE Fp16 - -| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | -|-----------------|------------|------------------------------|---------------------|-----------------|-----------------|-----------------| -| 128 | 1 | 196.58 | 5.09 | 5.18 | 5.23 | 5.42 | -| 128 | 2 | 361.92 | 5.53 | 5.62 | 5.67 | 5.85 | -| 128 | 4 | 605.43 | 6.61 | 6.71 | 6.8 | 7.04 | -| 128 | 8 | 916 | 8.73 | 8.83 | 8.95 | 9.19 | -| 384 | 1 | 154.05 | 6.49 | 6.6 | 6.72 | 7.05 | -| 384 | 2 | 238.89 | 8.37 | 8.42 | 8.47 | 9.1 | -| 384 | 4 | 327.18 | 12.23 | 12.3 | 12.36 | 13.08 | -| 384 | 8 | 390.95 | 20.46 | 20.5 | 20.8 | 21.89 | +BERT BASE FP16 +| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Throughput speedup (FP32 to mixed precision) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | +|-----------------|------------|------------------------------|----------------------------------------------|---------------------|-----------------|-----------------|-----------------| +| 128 | 1 | 196.58 | 1.19 | 5.09 | 5.18 | 5.23 | 5.42 | +| 128 | 2 | 361.92 | 1.41 | 5.53 | 5.62 | 5.67 | 5.85 | +| 128 | 4 | 605.43 | 1.79 | 6.61 | 6.71 | 6.8 | 7.04 | +| 128 | 8 | 916 | 2.18 | 8.73 | 8.83 | 8.95 | 9.19 | +| 384 | 1 | 154.05 | 1.58 | 6.49 | 6.6 | 6.72 | 7.05 | +| 384 | 2 | 238.89 | 1.99 | 8.37 | 8.42 | 8.47 | 9.1 | +| 384 | 4 | 327.18 | 2.47 | 12.23 | 12.3 | 12.36 | 13.08 | +| 384 | 8 | 390.95 | 2.82 | 20.46 | 20.5 | 20.8 | 21.89 | BERT BASE FP32 @@ -794,20 +1030,30 @@ To achieve these same results, follow the [Quick Start Guide](#quick-start-guide ##### Inference performance: NVIDIA DGX-1 (1x V100 32G) +###### Pre-training inference performance on 32G + +Our results were obtained by running the `scripts/run_pretraining_lamb.sh` script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-1 with 1x V100 32G GPUs. + +| **Sequence Length**| **Batch size / GPU: mixed precision, FP32** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 to mixed precision)** | +|:-----:|:-------:|:-------:|:-------:|:-------------:| +|128 |8, 8 |304.88 | 100.88 | 3.02 | + +###### Fine-tuning inference performance for SQuAD on 32G + Our results were obtained by running the `scripts/finetune_inference_benchmark.sh` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-1 with 1x V100 32G GPUs. Performance numbers (throughput in sentences per second and latency in milliseconds) were averaged from 1024 iterations. Latency is computed as the time taken for a batch to process as they are fed in one after another in the model ie no pipelining. BERT LARGE FP16 -| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | -|-----------------|------------|------------------------------|---------------------|-----------------|-----------------|-----------------| -| 128 | 1 | 86.4 | 11.57 | 11.74 | 11.86 | 12.04 | -| 128 | 2 | 155.32 | 12.88 | 12.98 | 13.05 | 13.31 | -| 128 | 4 | 252.18 | 15.86 | 15.78 | 15.89 | 17.01 | -| 128 | 8 | 359.19 | 22.27 | 22.44 | 22.58 | 23.94 | -| 384 | 1 | 62.45 | 16.01 | 16.16 | 16.23 | 16.42 | -| 384 | 2 | 89.34 | 22.39 | 22.45 | 22.53 | 23.13 | -| 384 | 4 | 113.77 | 35.16 | 35.24 | 35.33 | 35.9 | -| 384 | 8 | 131.9 | 60.65 | 61 | 61.49 | 65.3 | +| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Throughput speedup (FP32 to mixed precision) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | +|-----------------|------------|------------------------------|----------------------------------------------|---------------------|-----------------|-----------------|-----------------| +| 128 | 1 | 86.4 | 1.18 | 11.57 | 11.74 | 11.86 | 12.04 | +| 128 | 2 | 155.32 | 1.52 | 12.88 | 12.98 | 13.05 | 13.31 | +| 128 | 4 | 252.18 | 2.18 | 15.86 | 15.78 | 15.89 | 17.01 | +| 128 | 8 | 359.19 | 2.88 | 22.27 | 22.44 | 22.58 | 23.94 | +| 384 | 1 | 62.45 | 1.84 | 16.01 | 16.16 | 16.23 | 16.42 | +| 384 | 2 | 89.34 | 2.37 | 22.39 | 22.45 | 22.53 | 23.13 | +| 384 | 4 | 113.77 | 2.84 | 35.16 | 35.24 | 35.33 | 35.9 | +| 384 | 8 | 131.9 | 3.13 | 60.65 | 61 | 61.49 | 65.3 | BERT LARGE FP32 @@ -824,17 +1070,16 @@ BERT LARGE FP32 BERT BASE FP16 -| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | -|-----------------|------------|------------------------------|---------------------|-----------------|-----------------|-----------------| -| 128 | 1 | 192.89 | 5.18 | 5.29 | 5.35 | 5.55 | -| 128 | 2 | 348.23 | 5.74 | 5.91 | 6.02 | 6.26 | -| 128 | 4 | 592.54 | 6.75 | 6.96 | 7.08 | 7.34 | -| 128 | 8 | 888.58 | 9 | 9.11 | 9.22 | 9.5 | -| 384 | 1 | 148.64 | 6.73 | 6.82 | 6.87 | 7.06 | -| 384 | 2 | 230.74 | 8.67 | 8.75 | 8.87 | 9.44 | -| 384 | 4 | 318.45 | 12.56 | 12.65 | 12.76 | 13.36 | -| 384 | 8 | 380.14 | 21.05 | 21.1 | 21.25 | 21.83 | - +| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Throughput speedup (FP32 to mixed precision) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | +|-----------------|------------|------------------------------|----------------------------------------------|---------------------|-----------------|-----------------|-----------------| +| 128 | 1 | 192.89 | 1.19 | 5.18 | 5.29 | 5.35 | 5.55 | +| 128 | 2 | 348.23 | 1.37 | 5.74 | 5.91 | 6.02 | 6.26 | +| 128 | 4 | 592.54 | 1.79 | 6.75 | 6.96 | 7.08 | 7.34 | +| 128 | 8 | 888.58 | 2.15 | 9 | 9.11 | 9.22 | 9.5 | +| 384 | 1 | 148.64 | 1.57 | 6.73 | 6.82 | 6.87 | 7.06 | +| 384 | 2 | 230.74 | 1.96 | 8.67 | 8.75 | 8.87 | 9.44 | +| 384 | 4 | 318.45 | 2.42 | 12.56 | 12.65 | 12.76 | 13.36 | +| 384 | 8 | 380.14 | 2.72 | 21.05 | 21.1 | 21.25 | 21.83 | BERT BASE FP32 @@ -850,28 +1095,34 @@ BERT BASE FP32 | 384 | 8 | 139.75 | 57.25 | 57.74 | 58.08 | 59.53 | - To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. ##### Inference performance: NVIDIA DGX-2 (1x V100 32G) +###### Pre-training inference performance on DGX-2 32G + +Our results were obtained by running the `scripts/run_pretraining_lamb.sh` script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-2 with 1x V100 32G GPUs. + +| **Sequence Length**| **Batch size / GPU: mixed precision, FP32** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 to mixed precision)** | +|:-----:|:-------:|:-------:|:-------:|:-------------:| +|128 |8, 8 |350.63 | 106.36 | 3.30 | + +###### Fine-tuning inference performance for SQuAD on DGX-2 32G + Our results were obtained by running the `scripts/finetune_inference_benchmark.sh` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-2 with 1x V100 32G GPUs. Performance numbers (throughput in sentences per second and latency in milliseconds) were averaged from 1024 iterations. Latency is computed as the time taken for a batch to process as they are fed in one after another in the model ie no pipelining. - - BERT LARGE FP16 -| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | -|-----------------|------------|------------------------------|---------------------|-----------------|-----------------|-----------------| -| 128 | 1 | 79 | 12.66 | 13.13 | 13.36 | 14.49 | -| 128 | 2 | 151.28 | 13.22 | 13.66 | 13.89 | 14.84 | -| 128 | 4 | 250.41 | 15.97 | 16.13 | 16.3 | 17.81 | -| 128 | 8 | 369.76 | 21.64 | 21.88 | 22.08 | 26.35 | -| 384 | 1 | 61.66 | 16.22 | 16.46 | 16.62 | 17.26 | -| 384 | 2 | 91.54 | 21.85 | 22.11 | 22.3 | 23.44 | -| 384 | 4 | 121.04 | 33.05 | 33.08 | 33.31 | 34.97 | -| 384 | 8 | 142.03 | 56.33 | 56.46 | 57.49 | 59.85 | - +| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Throughput speedup (FP32 to mixed precision) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | +|-----------------|------------|------------------------------|----------------------------------------------|---------------------|-----------------|-----------------|-----------------| +| 128 | 1 | 79 | 1.18 | 12.66 | 13.13 | 13.36 | 14.49 | +| 128 | 2 | 151.28 | 1.52 | 13.22 | 13.66 | 13.89 | 14.84 | +| 128 | 4 | 250.41 | 2.18 | 15.97 | 16.13 | 16.3 | 17.81 | +| 128 | 8 | 369.76 | 2.88 | 21.64 | 21.88 | 22.08 | 26.35 | +| 384 | 1 | 61.66 | 1.84 | 16.22 | 16.46 | 16.62 | 17.26 | +| 384 | 2 | 91.54 | 2.37 | 21.85 | 22.11 | 22.3 | 23.44 | +| 384 | 4 | 121.04 | 2.84 | 33.05 | 33.08 | 33.31 | 34.97 | +| 384 | 8 | 142.03 | 3.13 | 56.33 | 56.46 | 57.49 | 59.85 | BERT LARGE FP32 @@ -886,19 +1137,18 @@ BERT LARGE FP32 | 384 | 4 | 42.79 | 93.48 | 94.73 | 96.52 | 104.37 | | 384 | 8 | 45.91 | 174.24 | 175.34 | 176.59 | 183.76 | - BERT BASE FP16 -| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | -|-----------------|------------|------------------------------|---------------------|-----------------|-----------------|-----------------| -| 128 | 1 | 192.89 | 5.18 | 5.29 | 5.35 | 5.55 | -| 128 | 2 | 348.23 | 5.74 | 5.91 | 6.02 | 6.26 | -| 128 | 4 | 592.54 | 6.75 | 6.96 | 7.08 | 7.34 | -| 128 | 8 | 888.58 | 9 | 9.11 | 9.22 | 9.5 | -| 384 | 1 | 148.64 | 6.73 | 6.82 | 6.87 | 7.06 | -| 384 | 2 | 230.74 | 8.67 | 8.75 | 8.87 | 9.44 | -| 384 | 4 | 318.45 | 12.56 | 12.65 | 12.76 | 13.36 | -| 384 | 8 | 380.14 | 21.05 | 21.1 | 21.25 | 21.83 | +| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Throughput speedup (FP32 to mixed precision) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | +|-----------------|------------|------------------------------|----------------------------------------------|---------------------|-----------------|-----------------|-----------------| +| 128 | 1 | 172.33 | 1.19 | 5.8 | 5.94 | 6 | 6.27 | +| 128 | 2 | 315.17 | 1.37 | 6.35 | 6.64 | 6.78 | 7.07 | +| 128 | 4 | 549.36 | 1.79 | 7.28 | 7.47 | 7.6 | 8.05 | +| 128 | 8 | 872.67 | 2.15 | 9.17 | 9.33 | 9.5 | 9.92 | +| 384 | 1 | 138.52 | 1.57 | 7.22 | 7.45 | 7.52 | 7.84 | +| 384 | 2 | 222.05 | 1.96 | 9.01 | 9.11 | 9.24 | 10.94 | +| 384 | 4 | 314.47 | 2.42 | 12.72 | 12.87 | 13.01 | 14.42 | +| 384 | 8 | 392.32 | 2.72 | 20.39 | 20.44 | 20.67 | 22.16 | BERT BASE FP32 @@ -913,18 +1163,86 @@ BERT BASE FP32 | 384 | 4 | 131.72 | 30.37 | 30.64 | 30.77 | 31.26 | | 384 | 8 | 139.75 | 57.25 | 57.74 | 58.08 | 59.53 | + +##### Inference performance: NVIDIA Tesla T4 (1x T4 16G) + +###### Fine-tuning inference performance for SQuAD on Tesla T4 16G + +Our results were obtained by running the `scripts/finetune_inference_benchmark.sh` training script in the TensorFlow 19.06-py3 NGC container on NVIDIA Tesla T4 with 1x T4 16G GPUs. Performance numbers (throughput in sentences per second and latency in milliseconds) were averaged from 1024 iterations. Latency is computed as the time taken for a batch to process as they are fed in one after another in the model ie no pipelining. + +BERT LARGE FP16 + +| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Throughput speedup (FP32 to mixed precision) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | +|-----------------|------------|------------------------------|----------------------------------------------|---------------------|-----------------|-----------------|-----------------| +| 128 | 1 | 53.56 | 1.18 | 18.67 | 20.22 | 20.31 | 20.49 | +| 128 | 2 | 95.39 | 1.52 | 20.97 | 22.86 | 23.15 | 23.73 | +| 128 | 4 | 137.44 | 2.18 | 29.1 | 30.34 | 30.62 | 31.5 | +| 128 | 8 | 166.19 | 2.88 | 48.14 | 49.38 | 49.73 | 50.86 | +| 384 | 1 | 34.28 | 1.84 | 29.17 | 30.58 | 30.77 | 31.28 | +| 384 | 2 | 41.89 | 2.37 | 47.74 | 49.05 | 49.34 | 50 | +| 384 | 4 | 47.15 | 2.84 | 84.83 | 86.79 | 87.41 | 88.73 | +| 384 | 8 | 50.28 | 3.13 | 159.11 | 161.75 | 162.85 | 165.72 | + +BERT LARGE FP32 + +| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | +|-----------------|------------|------------------------------|---------------------|-----------------|-----------------|-----------------| +| 128 | 1 | 40.34 | 24.79 | 26.97 | 27.38 | 28.21 | +| 128 | 2 | 45.17 | 44.27 | 46.01 | 46.6 | 47.68 | +| 128 | 4 | 47.39 | 84.41 | 86.31 | 86.92 | 88.14 | +| 128 | 8 | 46.98 | 170.29 | 173.35 | 174.15 | 175.48 | +| 384 | 1 | 14.07 | 71.06 | 73 | 73.42 | 73.99 | +| 384 | 2 | 14.91 | 134.17 | 136.72 | 137.51 | 138.66 | +| 384 | 4 | 14.44 | 277.03 | 281.89 | 282.63 | 284.41 | +| 384 | 8 | 14.95 | 534.94 | 540.45 | 542.32 | 544.75 | + +BERT BASE FP16 + +| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Throughput speedup (FP32 to mixed precision) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | +|-----------------|------------|------------------------------|----------------------------------------------|---------------------|-----------------|-----------------|-----------------| +| 128 | 1 | 107.3 | 1.19 | 9.32 | 10.18 | 10.32 | 11.48 | +| 128 | 2 | 185.18 | 1.37 | 10.8 | 11.71 | 12.11 | 12.35 | +| 128 | 4 | 335.47 | 1.79 | 11.92 | 12.58 | 12.72 | 13.36 | +| 128 | 8 | 454.12 | 2.15 | 17.62 | 18.45 | 18.68 | 19.25 | +| 384 | 1 | 83.5 | 1.57 | 11.98 | 12.71 | 12.93 | 13.29 | +| 384 | 2 | 117.75 | 1.96 | 16.99 | 17.62 | 17.83 | 19.48 | +| 384 | 4 | 139.08 | 2.42 | 28.76 | 29.59 | 29.85 | 30.74 | +| 384 | 8 | 149.93 | 2.72 | 53.36 | 54.83 | 55.48 | 56.93 | + +BERT BASE FP32 + +| Sequence Length | Batch Size | Throughput-Average(sent/sec) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) | +|-----------------|------------|------------------------------|---------------------|-----------------|-----------------|-----------------| +| 128 | 1 | 92.82 | 10.77 | 11.06 | 11.11 | 11.24 | +| 128 | 2 | 127.87 | 15.64 | 16.2 | 16.4 | 16.86 | +| 128 | 4 | 151.68 | 26.37 | 27.26 | 27.48 | 27.98 | +| 128 | 8 | 164.51 | 48.63 | 50.36 | 50.72 | 51.52 | +| 384 | 1 | 45.64 | 21.91 | 23.39 | 23.66 | 24.14 | +| 384 | 2 | 48.11 | 41.57 | 42.99 | 43.47 | 44.44 | +| 384 | 4 | 48.64 | 82.24 | 84.35 | 84.97 | 86.2 | +| 384 | 8 | 48.04 | 166.51 | 169.9 | 170.84 | 172.6 | + + To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. ## Release notes + ### Changelog -March 2019 -- Initial release +September 2019 +- Pre-training using LAMB +- Multi Node support +- Fine Tuning support for GLUE (CoLA, MNLI, MRPC) +- Jupyter Notebooks July 2019 - Results obtained using 19.06 - Inference Studies using TensorRT Inference Server +March 2019 +- Initial release + ### Known issues -There are no known issues with this model. \ No newline at end of file + +- There is a known performance regression with the 19.08 release on Tesla V100 boards with 16 GB memory, smaller batch sizes may be a better choice for this model on these GPUs with the 19.08 release. 32 GB GPUs are not affected. diff --git a/TensorFlow/LanguageModeling/BERT/configurations.yml b/TensorFlow/LanguageModeling/BERT/configurations.yml new file mode 100644 index 00000000..675c6d89 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/configurations.yml @@ -0,0 +1,206 @@ +# Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +#1 DGX1 phase1 +bert--DGX1: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "1" + BATCHSIZE: "8" + LEARNING_RATE: "7.5e-4" + NUM_ACCUMULATION_STEPS: "1024" + PHASE: "1" + +#4 DGX1 phase1 +bert--DGX1_n4: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "4" + BATCHSIZE: "8" + LEARNING_RATE: "1.875e-4" + NUM_ACCUMULATION_STEPS: "256" + PHASE: "1" + +#16 DGX1 phase1 +bert--DGX1_n16: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "16" + BATCHSIZE: "8" + LEARNING_RATE: "4.6875e-5" + NUM_ACCUMULATION_STEPS: "64" + PHASE: "1" + +#32 DGX1 phase1 +bert--DGX1_n32: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "32" + BATCHSIZE: "8" + LEARNING_RATE: "2.34375e-5" + NUM_ACCUMULATION_STEPS: "32" + PHASE: "1" + +#1 DGX2 phase1 +bert--DGX2: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "1" + BATCHSIZE: "32" + LEARNING_RATE: "3.75e-4" + NUM_ACCUMULATION_STEPS: "128" + PHASE: "1" + +#4 DGX2 phase1 +bert--DGX2_n4: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "4" + BATCHSIZE: "32" + LEARNING_RATE: "9.375e-5" + NUM_ACCUMULATION_STEPS: "32" + PHASE: "1" + +#16 DGX2 phase1 +bert--DGX2_n16: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "16" + BATCHSIZE: "256" + LEARNING_RATE: "3.75e-4" + NUM_ACCUMULATION_STEPS: "4" + PHASE: "1" + +#32 DGX2 phase1 +bert--DGX2_n32: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "32" + BATCHSIZE: "32" + LEARNING_RATE: "2.34375e-5" + NUM_ACCUMULATION_STEPS: "8" + PHASE: "1" + +#1 DGX1 phase2 +bert--DGX1_n1p2: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "1" + BATCHSIZE: "2" + LEARNING_RATE: "5e-4" + NUM_ACCUMULATION_STEPS: "4096" + PHASE: "2" + +#4 DGX1 phase2 +bert--DGX1_n4p2: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "4" + BATCHSIZE: "2" + LEARNING_RATE: "1.25e-4" + NUM_ACCUMULATION_STEPS: "512" + PHASE: "2" + +#16 DGX1 phase2 +bert--DGX1_n16p2: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "16" + BATCHSIZE: "2" + LEARNING_RATE: "1.5625e-5" + NUM_ACCUMULATION_STEPS: "128" + PHASE: "2" + +#32 DGX1 phase2 +bert--DGX1_n32p2: + <<: *BERT_ON_CLUSTER + <<: *DGX1 + variables: + <<: *DGX1_VARS + NNODES: "32" + BATCHSIZE: "2" + LEARNING_RATE: "1.5625e-5" + NUM_ACCUMULATION_STEPS: "64" + PHASE: "2" + +#1 DGX2 phase2 +bert--DGX2_n1p2: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "1" + BATCHSIZE: "8" + LEARNING_RATE: "2.5e-5" + NUM_ACCUMULATION_STEPS: "256" + PHASE: "2" + +#4 DGX2 phase2 +bert--DGX2_n4p2: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "4" + BATCHSIZE: "8" + LEARNING_RATE: "6.25e-5" + NUM_ACCUMULATION_STEPS: "64" + PHASE: "2" + +#16 DGX2 phase2 +bert--DGX2_n16p2: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "16" + BATCHSIZE: "8" + LEARNING_RATE: "1.5625e-5" + NUM_ACCUMULATION_STEPS: "16" + PHASE: "2" + +#32 DGX2 phase2 +bert--DGX2_n32p2: + <<: *BERT_ON_CLUSTER + <<: *DGX2 + variables: + <<: *DGX2_VARS + NNODES: "32" + BATCHSIZE: "8" + LEARNING_RATE: "7.8125e-6" + NUM_ACCUMULATION_STEPS: "8" + PHASE: "2" + diff --git a/TensorFlow/LanguageModeling/BERT/data/BooksDownloader.py b/TensorFlow/LanguageModeling/BERT/data/BooksDownloader.py new file mode 100644 index 00000000..53ee6c43 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/BooksDownloader.py @@ -0,0 +1,26 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import subprocess + +class BooksDownloader: + def __init__(self, save_path): + self.save_path = save_path + pass + + + def download(self): + bookscorpus_download_command = 'python3 /workspace/bookcorpus/download_files.py --list /workspace/bookcorpus/url_list.jsonl --out' + bookscorpus_download_command += ' ' + self.save_path + '/bookscorpus' + bookscorpus_download_command += ' --trash-bad-count' + bookscorpus_download_process = subprocess.run(bookscorpus_download_command, shell=True, check=True) \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/data/BookscorpusTextFormatting.py b/TensorFlow/LanguageModeling/BERT/data/BookscorpusTextFormatting.py new file mode 100644 index 00000000..22e48d4b --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/BookscorpusTextFormatting.py @@ -0,0 +1,32 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import glob +import os + +class BookscorpusTextFormatting: + def __init__(self, books_path, output_filename, recursive = False): + self.books_path = books_path + self.recursive = recursive + self.output_filename = output_filename + + + # This puts one book per line + def merge(self): + with open(self.output_filename, mode='w', newline='\n') as ofile: + for filename in glob.glob(self.books_path + '/' + '*.txt', recursive=True): + with open(filename, mode='r', encoding='utf-8-sig', newline='\n') as file: + for line in file: + if line.strip() != '': + ofile.write(line.strip() + ' ') + ofile.write("\n\n") \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/data/Downloader.py b/TensorFlow/LanguageModeling/BERT/data/Downloader.py new file mode 100644 index 00000000..20b48c1d --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/Downloader.py @@ -0,0 +1,120 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from GooglePretrainedWeightDownloader import GooglePretrainedWeightDownloader +from NVIDIAPretrainedWeightDownloader import NVIDIAPretrainedWeightDownloader +from WikiDownloader import WikiDownloader +from BooksDownloader import BooksDownloader +from GLUEDownloader import GLUEDownloader +from SquadDownloader import SquadDownloader +from PubMedDownloader import PubMedDownloader + +class Downloader: + def __init__(self, dataset_name, save_path): + self.dataset_name = dataset_name + self.save_path = save_path + + + def download(self): + if self.dataset_name == 'bookscorpus': + self.download_bookscorpus() + + elif self.dataset_name == 'wikicorpus_en': + self.download_wikicorpus('en') + + elif self.dataset_name == 'wikicorpus_zh': + self.download_wikicorpus('zh') + + elif self.dataset_name == 'pubmed_baseline': + self.download_pubmed('baseline') + + elif self.dataset_name == 'pubmed_daily_update': + self.download_pubmed('daily_update') + + elif self.dataset_name == 'pubmed_fulltext': + self.download_pubmed('fulltext') + + elif self.dataset_name == 'pubmed_open_access': + self.download_pubmed('open_access') + + elif self.dataset_name == 'google_pretrained_weights': + self.download_google_pretrained_weights() + + elif self.dataset_name == 'nvidia_pretrained_weights': + self.download_nvidia_pretrained_weights() + + elif self.dataset_name == 'MRPC': + self.download_glue(self.dataset_name) + + elif self.dataset_name == 'MNLI': + self.download_glue(self.dataset_name) + + elif self.dataset_name == 'CoLA': + self.download_glue(self.dataset_name) + + elif self.dataset_name == 'squad': + self.download_squad() + + elif self.dataset_name == 'all': + self.download_bookscorpus() + self.download_wikicorpus('en') + self.download_wikicorpus('zh') + self.download_pubmed('baseline') + self.download_pubmed('daily_update') + self.download_pubmed('fulltext') + self.download_pubmed('open_access') + self.download_google_pretrained_weights() + self.download_nvidia_pretrained_weights() + self.download_glue("CoLA") + self.download_glue("MNLI") + self.download_glue("MRPC") + self.download_squad() + + else: + print(self.dataset_name) + assert False, 'Unknown dataset_name provided to downloader' + + + def download_bookscorpus(self): + downloader = BooksDownloader(self.save_path) + downloader.download() + + + def download_wikicorpus(self, language): + downloader = WikiDownloader(language, self.save_path) + downloader.download() + + + def download_pubmed(self, subset): + downloader = PubMedDownloader(subset, self.save_path) + downloader.download() + + + def download_google_pretrained_weights(self): + downloader = GooglePretrainedWeightDownloader(self.save_path) + downloader.download() + + + def download_nvidia_pretrained_weights(self): + downloader = NVIDIAPretrainedWeightDownloader(self.save_path) + downloader.download() + + + def download_glue(self, glue_task_name): + downloader = GLUEDownloader(glue_task_name, self.save_path) + downloader.download() + + + def download_squad(self): + downloader = SquadDownloader(self.save_path) + downloader.download() diff --git a/TensorFlow/LanguageModeling/BERT/data/GLUEDownloader.py b/TensorFlow/LanguageModeling/BERT/data/GLUEDownloader.py new file mode 100644 index 00000000..e270b371 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/GLUEDownloader.py @@ -0,0 +1,109 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import bz2 +import os +import urllib +import sys +import zipfile +import io + +URLLIB=urllib +if sys.version_info >= (3, 0): + URLLIB=urllib.request + +class GLUEDownloader: + def __init__(self, task, save_path): + + # Documentation - Download link obtained from here: https://github.com/nyu-mll/GLUE-baselines/blob/master/download_glue_data.py + + self.TASK2PATH = {"CoLA":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4', + "SST":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8', + "MRPC":{"mrpc_dev": 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc', + "mrpc_train": 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt', + "mrpc_test": 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'}, + "QQP":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5', + "STS":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5', + "MNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce', + "SNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df', + "QNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLI.zip?alt=media&token=c24cad61-f2df-4f04-9ab6-aa576fa829d0', + "RTE":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb', + "WNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf', + "diagnostic":'https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D'} + + + self.save_path = save_path + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + + self.task = task + + def download(self): + + if self.task == 'MRPC': + self.download_mrpc() + elif self.task == 'diagnostic': + self.download_diagnostic() + else: + self.download_and_extract(self.task) + + def download_and_extract(self, task): + print("Downloading and extracting %s..." % task) + data_file = "%s.zip" % task + URLLIB.urlretrieve(self.TASK2PATH[task], data_file) + print(data_file,"\n\n\n") + with zipfile.ZipFile(data_file) as zip_ref: + zip_ref.extractall(self.save_path) + os.remove(data_file) + print("\tCompleted!") + + def download_mrpc(self): + print("Processing MRPC...") + mrpc_dir = os.path.join(self.save_path, "MRPC") + if not os.path.isdir(mrpc_dir): + os.mkdir(mrpc_dir) + + mrpc_train_file = os.path.join(mrpc_dir, "msr_paraphrase_train.txt") + mrpc_dev_file = os.path.join(mrpc_dir, "dev_ids.tsv") + mrpc_test_file = os.path.join(mrpc_dir, "msr_paraphrase_test.txt") + + URLLIB.urlretrieve(self.TASK2PATH["MRPC"]["mrpc_train"], mrpc_train_file) + URLLIB.urlretrieve(self.TASK2PATH["MRPC"]["mrpc_test"], mrpc_test_file) + URLLIB.urlretrieve(self.TASK2PATH["MRPC"]["mrpc_dev"], mrpc_dev_file) + + dev_ids = [] + with io.open(os.path.join(mrpc_dir, "dev_ids.tsv"), encoding='utf-8') as ids_fh: + for row in ids_fh: + dev_ids.append(row.strip().split('\t')) + + with io.open(mrpc_train_file, encoding='utf-8') as data_fh, \ + io.open(os.path.join(mrpc_dir, "train.tsv"), 'w', encoding='utf-8') as train_fh, \ + io.open(os.path.join(mrpc_dir, "dev.tsv"), 'w', encoding='utf-8') as dev_fh: + header = data_fh.readline() + train_fh.write(header) + dev_fh.write(header) + for row in data_fh: + label, id1, id2, s1, s2 = row.strip().split('\t') + if [id1, id2] in dev_ids: + dev_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) + else: + train_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) + + with io.open(mrpc_test_file, encoding='utf-8') as data_fh, \ + io.open(os.path.join(mrpc_dir, "test.tsv"), 'w', encoding='utf-8') as test_fh: + header = data_fh.readline() + test_fh.write("index\t#1 ID\t#2 ID\t#1 String\t#2 String\n") + for idx, row in enumerate(data_fh): + label, id1, id2, s1, s2 = row.strip().split('\t') + test_fh.write("%d\t%s\t%s\t%s\t%s\n" % (idx, id1, id2, s1, s2)) + print("\tCompleted!") \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/data/GooglePretrainedWeightDownloader.py b/TensorFlow/LanguageModeling/BERT/data/GooglePretrainedWeightDownloader.py new file mode 100644 index 00000000..bb0684d3 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/GooglePretrainedWeightDownloader.py @@ -0,0 +1,158 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import hashlib +import os +import urllib.request +import zipfile + +class GooglePretrainedWeightDownloader: + def __init__(self, save_path): + self.save_path = save_path + '/google_pretrained_weights' + + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + + # Download urls + self.model_urls = { + 'bert_base_uncased': ('https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip', 'uncased_L-12_H-768_A-12.zip'), + 'bert_large_uncased': ('https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-24_H-1024_A-16.zip', 'uncased_L-24_H-1024_A-16.zip'), + 'bert_base_cased': ('https://storage.googleapis.com/bert_models/2018_10_18/cased_L-12_H-768_A-12.zip', 'cased_L-12_H-768_A-12.zip'), + 'bert_large_cased': ('https://storage.googleapis.com/bert_models/2018_10_18/cased_L-24_H-1024_A-16.zip', 'cased_L-24_H-1024_A-16.zip'), + 'bert_base_multilingual_cased': ('https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip', 'multi_cased_L-12_H-768_A-12.zip'), + 'bert_large_multilingual_uncased': ('https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip', 'multilingual_L-12_H-768_A-12.zip'), + 'bert_base_chinese': ('https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip', 'chinese_L-12_H-768_A-12.zip') + } + + # SHA256sum verification for file download integrity (and checking for changes from the download source over time) + self.bert_base_uncased_sha = { + 'bert_config.json': '7b4e5f53efbd058c67cda0aacfafb340113ea1b5797d9ce6ee411704ba21fcbc', + 'bert_model.ckpt.data-00000-of-00001': '58580dc5e0bf0ae0d2efd51d0e8272b2f808857f0a43a88aaf7549da6d7a8a84', + 'bert_model.ckpt.index': '04c1323086e2f1c5b7c0759d8d3e484afbb0ab45f51793daab9f647113a0117b', + 'bert_model.ckpt.meta': 'dd5682170a10c3ea0280c2e9b9a45fee894eb62da649bbdea37b38b0ded5f60e', + 'vocab.txt': '07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3', + } + + self.bert_large_uncased_sha = { + 'bert_config.json': 'bfa42236d269e2aeb3a6d30412a33d15dbe8ea597e2b01dc9518c63cc6efafcb', + 'bert_model.ckpt.data-00000-of-00001': 'bc6b3363e3be458c99ecf64b7f472d2b7c67534fd8f564c0556a678f90f4eea1', + 'bert_model.ckpt.index': '68b52f2205ffc64dc627d1120cf399c1ef1cbc35ea5021d1afc889ffe2ce2093', + 'bert_model.ckpt.meta': '6fcce8ff7628f229a885a593625e3d5ff9687542d5ef128d9beb1b0c05edc4a1', + 'vocab.txt': '07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3', + } + + self.bert_base_cased_sha = { + 'bert_config.json': 'f11dfb757bea16339a33e1bf327b0aade6e57fd9c29dc6b84f7ddb20682f48bc', + 'bert_model.ckpt.data-00000-of-00001': '734d5a1b68bf98d4e9cb6b6692725d00842a1937af73902e51776905d8f760ea', + 'bert_model.ckpt.index': '517d6ef5c41fc2ca1f595276d6fccf5521810d57f5a74e32616151557790f7b1', + 'bert_model.ckpt.meta': '5f8a9771ff25dadd61582abb4e3a748215a10a6b55947cbb66d0f0ba1694be98', + 'vocab.txt': 'eeaa9875b23b04b4c54ef759d03db9d1ba1554838f8fb26c5d96fa551df93d02', + } + + self.bert_large_cased_sha = { + 'bert_config.json': '7adb2125c8225da495656c982fd1c5f64ba8f20ad020838571a3f8a954c2df57', + 'bert_model.ckpt.data-00000-of-00001': '6ff33640f40d472f7a16af0c17b1179ca9dcc0373155fb05335b6a4dd1657ef0', + 'bert_model.ckpt.index': 'ef42a53f577fbe07381f4161b13c7cab4f4fc3b167cec6a9ae382c53d18049cf', + 'bert_model.ckpt.meta': 'd2ddff3ed33b80091eac95171e94149736ea74eb645e575d942ec4a5e01a40a1', + 'vocab.txt': 'eeaa9875b23b04b4c54ef759d03db9d1ba1554838f8fb26c5d96fa551df93d02', + } + + self.bert_base_multilingual_cased_sha = { + 'bert_config.json': 'e76c3964bc14a8bb37a5530cdc802699d2f4a6fddfab0611e153aa2528f234f0', + 'bert_model.ckpt.data-00000-of-00001': '55b8a2df41f69c60c5180e50a7c31b7cdf6238909390c4ddf05fbc0d37aa1ac5', + 'bert_model.ckpt.index': '7d8509c2a62b4e300feb55f8e5f1eef41638f4998dd4d887736f42d4f6a34b37', + 'bert_model.ckpt.meta': '95e5f1997e8831f1c31e5cf530f1a2e99f121e9cd20887f2dce6fe9e3343e3fa', + 'vocab.txt': 'fe0fda7c425b48c516fc8f160d594c8022a0808447475c1a7c6d6479763f310c', + } + + self.bert_large_multilingual_uncased_sha = { + 'bert_config.json': '49063bb061390211d2fdd108cada1ed86faa5f90b80c8f6fdddf406afa4c4624', + 'bert_model.ckpt.data-00000-of-00001': '3cd83912ebeb0efe2abf35c9f1d5a515d8e80295e61c49b75c8853f756658429', + 'bert_model.ckpt.index': '87c372c1a3b1dc7effaaa9103c80a81b3cbab04c7933ced224eec3b8ad2cc8e7', + 'bert_model.ckpt.meta': '27f504f34f02acaa6b0f60d65195ec3e3f9505ac14601c6a32b421d0c8413a29', + 'vocab.txt': '87b44292b452f6c05afa49b2e488e7eedf79ea4f4c39db6f2f4b37764228ef3f', + } + + self.bert_base_chinese_sha = { + 'bert_config.json': '7aaad0335058e2640bcb2c2e9a932b1cd9da200c46ea7b8957d54431f201c015', + 'bert_model.ckpt.data-00000-of-00001': '756699356b78ad0ef1ca9ba6528297bcb3dd1aef5feadd31f4775d7c7fc989ba', + 'bert_model.ckpt.index': '46315546e05ce62327b3e2cd1bed22836adcb2ff29735ec87721396edb21b82e', + 'bert_model.ckpt.meta': 'c0f8d51e1ab986604bc2b25d6ec0af7fd21ff94cf67081996ec3f3bf5d823047', + 'vocab.txt': '45bbac6b341c319adc98a532532882e91a9cefc0329aa57bac9ae761c27b291c', + } + + # Relate SHA to urls for loop below + self.model_sha = { + 'bert_base_uncased': self.bert_base_uncased_sha, + 'bert_large_uncased': self.bert_large_uncased_sha, + 'bert_base_cased': self.bert_base_cased_sha, + 'bert_large_cased': self.bert_large_cased_sha, + 'bert_base_multilingual_cased': self.bert_base_multilingual_cased_sha, + 'bert_large_multilingual_uncased': self.bert_large_multilingual_uncased_sha, + 'bert_base_chinese': self.bert_base_chinese_sha + } + + # Helper to get sha256sum of a file + def sha256sum(self, filename): + h = hashlib.sha256() + b = bytearray(128*1024) + mv = memoryview(b) + with open(filename, 'rb', buffering=0) as f: + for n in iter(lambda : f.readinto(mv), 0): + h.update(mv[:n]) + + return h.hexdigest() + + def download(self): + # Iterate over urls: download, unzip, verify sha256sum + found_mismatch_sha = False + for model in self.model_urls: + url = self.model_urls[model][0] + file = self.save_path + '/' + self.model_urls[model][1] + + print('Downloading', url) + response = urllib.request.urlopen(url) + with open(file, 'wb') as handle: + handle.write(response.read()) + + print('Unzipping', file) + zip = zipfile.ZipFile(file, 'r') + zip.extractall(self.save_path) + zip.close() + + sha_dict = self.model_sha[model] + for extracted_file in sha_dict: + sha = sha_dict[extracted_file] + if sha != self.sha256sum(file[:-4] + '/' + extracted_file): + found_mismatch_sha = True + print('SHA256sum does not match on file:', extracted_file, 'from download url:', url) + else: + print(file[:-4] + '/' + extracted_file, '\t', 'verified') + + if not found_mismatch_sha: + print("All downloads pass sha256sum verification.") + + def serialize(self): + pass + + def deserialize(self): + pass + + def listAvailableWeights(self): + print("Available Weight Datasets") + for item in self.model_urls: + print(item) + + def listLocallyStoredWeights(self): + pass + diff --git a/MxNet/Classification/RN50v1.5/examples/BENCHMARK_FP32.sh b/TensorFlow/LanguageModeling/BERT/data/NVIDIAPretrainedWeightDownloader.py similarity index 56% rename from MxNet/Classification/RN50v1.5/examples/BENCHMARK_FP32.sh rename to TensorFlow/LanguageModeling/BERT/data/NVIDIAPretrainedWeightDownloader.py index d1b70ee6..13c9a320 100644 --- a/MxNet/Classification/RN50v1.5/examples/BENCHMARK_FP32.sh +++ b/TensorFlow/LanguageModeling/BERT/data/NVIDIAPretrainedWeightDownloader.py @@ -1,5 +1,4 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -12,8 +11,17 @@ # See the License for the specific language governing permissions and # limitations under the License. +import os -# This script launches ResNet50 benchmark in FP32 on 1,4,8 GPUs with 32,64,96 batch size -# Usage ./BENCHMARK_FP32.sh +class NVIDIAPretrainedWeightDownloader: + def __init__(self, save_path): + self.save_path = save_path + '/nvidia_pretrained_weights' -python benchmark.py -n 1,4,8 -b 32,64,96 -e 2 -w 1 -i 100 --dtype float32 -o report.json $@ + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + + pass + + + def download(self): + assert False, 'NVIDIAPretrainedWeightDownloader not implemented yet.' \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/data/PubMedDownloader.py b/TensorFlow/LanguageModeling/BERT/data/PubMedDownloader.py new file mode 100644 index 00000000..a2aef07a --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/PubMedDownloader.py @@ -0,0 +1,93 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import bz2 +import glob +import gzip +import os +import urllib.request +import shutil +import sys + +class PubMedDownloader: + def __init__(self, subset, save_path): + self.subset = subset + # Modifying self.save_path in two steps to handle creation of subdirectories + self.save_path = save_path + '/pubmed' + '/' + + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + + self.save_path = self.save_path + '/' + subset + + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + + self.download_urls = { + 'baseline' : 'ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/', + 'daily_update' : 'ftp://ftp.ncbi.nlm.nih.gov/pubmed/updatefiles/', + 'fulltext' : 'ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/', + 'open_access' : 'ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/' + } + + + def download(self): + print('subset:', self.subset) + url = self.download_urls[self.subset] + self.download_files(url) + self.extract_files() + + + def download_files(self, url): + url = self.download_urls[self.subset] + output = os.popen('curl ' + url).read() + + if self.subset == 'fulltext' or self.subset == 'open_access': + line_split = 'comm_use' if self.subset == 'fulltext' else 'non_comm_use' + for line in output.splitlines(): + if line[-10:] == 'xml.tar.gz' and \ + line.split(' ')[-1].split('.')[0] == line_split: + file = os.path.join(self.save_path, line.split(' ')[-1]) + if not os.path.isfile(file): + print('Downloading', file) + response = urllib.request.urlopen(url + line.split(' ')[-1]) + with open(file, "wb") as handle: + handle.write(response.read()) + + elif self.subset == 'baseline' or self.subset == 'daily_update': + for line in output.splitlines(): + if line[-3:] == '.gz': + file = os.path.join(self.save_path, line.split(' ')[-1]) + if not os.path.isfile(file): + print('Downloading', file) + response = urllib.request.urlopen(url + line.split(' ')[-1]) + with open(file, "wb") as handle: + handle.write(response.read()) + else: + assert False, 'Invalid PubMed dataset/subset specified.' + + def extract_files(self): + files = glob.glob(self.save_path + '/*.xml.gz') + + for file in files: + print('file:', file) + input = gzip.GzipFile(file, mode='rb') + s = input.read() + input.close() + + out = open(file[:-3], mode='wb') + out.write(s) + out.close() + + + diff --git a/TensorFlow/LanguageModeling/BERT/data/PubMedTextFormatting.py b/TensorFlow/LanguageModeling/BERT/data/PubMedTextFormatting.py new file mode 100644 index 00000000..6caded2f --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/PubMedTextFormatting.py @@ -0,0 +1,44 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import glob +import os +import pubmed_parser as pmp + +class PubMedTextFormatting: + def __init__(self, pubmed_path, output_filename, recursive = False): + self.pubmed_path = pubmed_path + self.recursive = recursive + self.output_filename = output_filename + + + # This puts one article per line + def merge(self): + print('PubMed path:', self.pubmed_path) + + with open(self.output_filename, mode='w', newline='\n') as ofile: + for filename in glob.glob(self.pubmed_path + '/*.xml', recursive=self.recursive): + print('file:', filename) + dicts_out = pmp.parse_medline_xml(filename) + for dict_out in dicts_out: + if not dict_out['abstract']: + continue + try: + for line in dict_out['abstract'].splitlines(): + if len(line) < 30: + continue + ofile.write(line.strip() + " ") + ofile.write("\n\n") + except: + ofile.write("\n\n") + continue diff --git a/TensorFlow/LanguageModeling/BERT/data/SquadDownloader.py b/TensorFlow/LanguageModeling/BERT/data/SquadDownloader.py new file mode 100644 index 00000000..6d64ffc6 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/SquadDownloader.py @@ -0,0 +1,54 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import bz2 +import os +import urllib.request +import sys + +class SquadDownloader: + def __init__(self, save_path): + self.save_path = save_path + '/squad' + + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + + if not os.path.exists(self.save_path + '/v1.1'): + os.makedirs(self.save_path + '/v1.1') + + if not os.path.exists(self.save_path + '/v2.0'): + os.makedirs(self.save_path + '/v2.0') + + self.download_urls = { + 'https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json' : 'v1.1/train-v1.1.json', + 'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json' : 'v1.1/dev-v1.1.json', + 'https://worksheets.codalab.org/rest/bundles/0xbcd57bee090b421c982906709c8c27e1/contents/blob/' : 'v1.1/evaluate-v1.1.py', + 'https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json' : 'v2.0/train-v2.0.json', + 'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json' : 'v2.0/dev-v2.0.json', + 'https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/' : 'v2.0/evaluate-v2.0.py', + } + + def download(self): + for item in self.download_urls: + url = item + file = self.download_urls[item] + + print('Downloading:', url) + if os.path.isfile(self.save_path + '/' + file): + print('** Download file already exists, skipping download') + else: + response = urllib.request.urlopen(url) + with open(self.save_path + '/' + file, "wb") as handle: + handle.write(response.read()) + + diff --git a/TensorFlow/LanguageModeling/BERT/data/TextSharding.py b/TensorFlow/LanguageModeling/BERT/data/TextSharding.py new file mode 100644 index 00000000..85012a53 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/TextSharding.py @@ -0,0 +1,331 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections import defaultdict +from itertools import islice + +import multiprocessing +import os +import statistics + +class Sharding: + def __init__(self, input_files, output_name_prefix, n_training_shards, n_test_shards, fraction_test_set): + assert len(input_files) > 0, 'The input file list must contain at least one file.' + assert n_training_shards > 0, 'There must be at least one output shard.' + assert n_test_shards > 0, 'There must be at least one output shard.' + + self.n_training_shards = n_training_shards + self.n_test_shards = n_test_shards + self.fraction_test_set = fraction_test_set + + self.input_files = input_files + + self.output_name_prefix = output_name_prefix + self.output_training_identifier = '_training' + self.output_test_identifier = '_test' + self.output_file_extension = '.txt' + + self.articles = {} # key: integer identifier, value: list of articles + self.sentences = {} # key: integer identifier, value: list of sentences + self.output_training_files = {} # key: filename, value: list of articles to go into file + self.output_test_files = {} # key: filename, value: list of articles to go into file + + self.init_output_files() + + + # Remember, the input files contain one article per line (the whitespace check is to skip extraneous blank lines) + def load_articles(self): + print('Start: Loading Articles') + + global_article_count = 0 + for input_file in self.input_files: + print('input file:', input_file) + with open(input_file, mode='r', newline='\n') as f: + for i, line in enumerate(f): + if line.strip(): + self.articles[global_article_count] = line.rstrip() + global_article_count += 1 + + print('End: Loading Articles: There are', len(self.articles), 'articles.') + + + def segment_articles_into_sentences(self, segmenter): + print('Start: Sentence Segmentation') + if len(self.articles) is 0: + self.load_articles() + + assert len(self.articles) is not 0, 'Please check that input files are present and contain data.' + + # TODO: WIP: multiprocessing (create independent ranges and spawn processes) + use_multiprocessing = 'serial' + + def chunks(data, size=len(self.articles)): + it = iter(data) + for i in range(0, len(data), size): + yield {k: data[k] for k in islice(it, size)} + + if use_multiprocessing == 'manager': + manager = multiprocessing.Manager() + return_dict = manager.dict() + jobs = [] + n_processes = 7 # in addition to the main process, total = n_proc+1 + + def work(articles, return_dict): + sentences = {} + for i, article in enumerate(articles): + sentences[i] = segmenter.segment_string(articles[article]) + + if i % 5000 == 0: + print('Segmenting article', i) + + return_dict.update(sentences) + + for item in chunks(self.articles, len(self.articles)): + p = multiprocessing.Process(target=work, args=(item, return_dict)) + + # Busy wait + while len(jobs) >= n_processes: + pass + + jobs.append(p) + p.start() + + for proc in jobs: + proc.join() + + elif use_multiprocessing == 'queue': + work_queue = multiprocessing.Queue() + jobs = [] + + for item in chunks(self.articles, len(self.articles)): + pass + + else: # serial option + for i, article in enumerate(self.articles): + self.sentences[i] = segmenter.segment_string(self.articles[article]) + + if i % 5000 == 0: + print('Segmenting article', i) + + print('End: Sentence Segmentation') + + + def init_output_files(self): + print('Start: Init Output Files') + assert len(self.output_training_files) is 0, 'Internal storage self.output_files already contains data. This function is intended to be used by the constructor only.' + assert len(self.output_test_files) is 0, 'Internal storage self.output_files already contains data. This function is intended to be used by the constructor only.' + + for i in range(self.n_training_shards): + name = self.output_name_prefix + self.output_training_identifier + '_' + str(i) + self.output_file_extension + self.output_training_files[name] = [] + + for i in range(self.n_test_shards): + name = self.output_name_prefix + self.output_test_identifier + '_' + str(i) + self.output_file_extension + self.output_test_files[name] = [] + + print('End: Init Output Files') + + + def get_sentences_per_shard(self, shard): + result = 0 + for article_id in shard: + result += len(self.sentences[article_id]) + + return result + + + def distribute_articles_over_shards(self): + print('Start: Distribute Articles Over Shards') + assert len(self.articles) >= self.n_training_shards + self.n_test_shards, 'There are fewer articles than shards. Please add more data or reduce the number of shards requested.' + + # Create dictionary with - key: sentence count per article, value: article id number + sentence_counts = defaultdict(lambda: []) + + max_sentences = 0 + total_sentences = 0 + + for article_id in self.sentences: + current_length = len(self.sentences[article_id]) + sentence_counts[current_length].append(article_id) + max_sentences = max(max_sentences, current_length) + total_sentences += current_length + + n_sentences_assigned_to_training = int((1 - self.fraction_test_set) * total_sentences) + nominal_sentences_per_training_shard = n_sentences_assigned_to_training // self.n_training_shards + nominal_sentences_per_test_shard = (total_sentences - n_sentences_assigned_to_training) // self.n_test_shards + + consumed_article_set = set({}) + unused_article_set = set(self.articles.keys()) + + # Make first pass and add one article worth of lines per file + for file in self.output_training_files: + current_article_id = sentence_counts[max_sentences][-1] + sentence_counts[max_sentences].pop(-1) + self.output_training_files[file].append(current_article_id) + consumed_article_set.add(current_article_id) + unused_article_set.remove(current_article_id) + + # Maintain the max sentence count + while len(sentence_counts[max_sentences]) == 0 and max_sentences > 0: + max_sentences -= 1 + + if len(self.sentences[current_article_id]) > nominal_sentences_per_training_shard: + nominal_sentences_per_training_shard = len(self.sentences[current_article_id]) + print('Warning: A single article contains more than the nominal number of sentences per training shard.') + + for file in self.output_test_files: + current_article_id = sentence_counts[max_sentences][-1] + sentence_counts[max_sentences].pop(-1) + self.output_test_files[file].append(current_article_id) + consumed_article_set.add(current_article_id) + unused_article_set.remove(current_article_id) + + # Maintain the max sentence count + while len(sentence_counts[max_sentences]) == 0 and max_sentences > 0: + max_sentences -= 1 + + if len(self.sentences[current_article_id]) > nominal_sentences_per_test_shard: + nominal_sentences_per_test_shard = len(self.sentences[current_article_id]) + print('Warning: A single article contains more than the nominal number of sentences per test shard.') + + training_counts = [] + test_counts = [] + + for shard in self.output_training_files: + training_counts.append(self.get_sentences_per_shard(self.output_training_files[shard])) + + for shard in self.output_test_files: + test_counts.append(self.get_sentences_per_shard(self.output_test_files[shard])) + + training_median = statistics.median(training_counts) + test_median = statistics.median(test_counts) + + # Make subsequent passes over files to find articles to add without going over limit + history_remaining = [] + n_history_remaining = 4 + + while len(consumed_article_set) < len(self.articles): + for fidx, file in enumerate(self.output_training_files): + nominal_next_article_size = min(nominal_sentences_per_training_shard - training_counts[fidx], max_sentences) + + # Maintain the max sentence count + while len(sentence_counts[max_sentences]) == 0 and max_sentences > 0: + max_sentences -= 1 + + while len(sentence_counts[nominal_next_article_size]) == 0 and nominal_next_article_size > 0: + nominal_next_article_size -= 1 + + if nominal_next_article_size not in sentence_counts or nominal_next_article_size is 0 or training_counts[fidx] > training_median: + continue # skip adding to this file, will come back later if no file can accept unused articles + + current_article_id = sentence_counts[nominal_next_article_size][-1] + sentence_counts[nominal_next_article_size].pop(-1) + + self.output_training_files[file].append(current_article_id) + consumed_article_set.add(current_article_id) + unused_article_set.remove(current_article_id) + + for fidx, file in enumerate(self.output_test_files): + nominal_next_article_size = min(nominal_sentences_per_test_shard - test_counts[fidx], max_sentences) + + # Maintain the max sentence count + while len(sentence_counts[max_sentences]) == 0 and max_sentences > 0: + max_sentences -= 1 + + while len(sentence_counts[nominal_next_article_size]) == 0 and nominal_next_article_size > 0: + nominal_next_article_size -= 1 + + if nominal_next_article_size not in sentence_counts or nominal_next_article_size is 0 or test_counts[fidx] > test_median: + continue # skip adding to this file, will come back later if no file can accept unused articles + + current_article_id = sentence_counts[nominal_next_article_size][-1] + sentence_counts[nominal_next_article_size].pop(-1) + + self.output_test_files[file].append(current_article_id) + consumed_article_set.add(current_article_id) + unused_article_set.remove(current_article_id) + + # If unable to place articles a few times, bump up nominal sizes by fraction until articles get placed + if len(history_remaining) == n_history_remaining: + history_remaining.pop(0) + history_remaining.append(len(unused_article_set)) + + history_same = True + for i in range(1, len(history_remaining)): + history_same = history_same and (history_remaining[i-1] == history_remaining[i]) + + if history_same: + nominal_sentences_per_training_shard += 1 + # nominal_sentences_per_test_shard += 1 + + training_counts = [] + test_counts = [] + for shard in self.output_training_files: + training_counts.append(self.get_sentences_per_shard(self.output_training_files[shard])) + + for shard in self.output_test_files: + test_counts.append(self.get_sentences_per_shard(self.output_test_files[shard])) + + training_median = statistics.median(training_counts) + test_median = statistics.median(test_counts) + + print('Distributing data over shards:', len(unused_article_set), 'articles remaining.') + + + if len(unused_article_set) != 0: + print('Warning: Some articles did not make it into output files.') + + + for shard in self.output_training_files: + print('Training shard:', self.get_sentences_per_shard(self.output_training_files[shard])) + + for shard in self.output_test_files: + print('Test shard:', self.get_sentences_per_shard(self.output_test_files[shard])) + + print('End: Distribute Articles Over Shards') + + + def write_shards_to_disk(self): + print('Start: Write Shards to Disk') + for shard in self.output_training_files: + self.write_single_shard(shard, self.output_training_files[shard], 'training') + + for shard in self.output_test_files: + self.write_single_shard(shard, self.output_test_files[shard], 'test') + + print('End: Write Shards to Disk') + + + def write_single_shard(self, shard_name, shard, split): + shard_split = os.path.split(shard_name) + shard_name = shard_split[0] + '/' + split + '/' + shard_split[1] + + with open(shard_name, mode='w', newline='\n') as f: + for article_id in shard: + for line in self.sentences[article_id]: + f.write(line + '\n') + + f.write('\n') # Line break between articles + + +import nltk + +nltk.download('punkt') + +class NLTKSegmenter: + def __init(self): + pass + + def segment_string(self, article): + return nltk.tokenize.sent_tokenize(article) + diff --git a/TensorFlow/LanguageModeling/BERT/data/WikiDownloader.py b/TensorFlow/LanguageModeling/BERT/data/WikiDownloader.py new file mode 100644 index 00000000..87f95297 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/WikiDownloader.py @@ -0,0 +1,58 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import bz2 +import os +import urllib.request +import sys +import subprocess + +class WikiDownloader: + def __init__(self, language, save_path): + self.save_path = save_path + '/wikicorpus_' + language + + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + + self.language = language + self.download_urls = { + 'en' : 'https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2', + 'zh' : 'https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2' + } + + self.output_files = { + 'en' : 'wikicorpus_en.xml.bz2', + 'zh' : 'wikicorpus_zh.xml.bz2' + } + + + def download(self): + if self.language in self.download_urls: + url = self.download_urls[self.language] + filename = self.output_files[self.language] + + print('Downloading:', url) + if os.path.isfile(self.save_path + '/' + filename): + print('** Download file already exists, skipping download') + else: + response = urllib.request.urlopen(url) + with open(self.save_path + '/' + filename, "wb") as handle: + handle.write(response.read()) + + # Always unzipping since this is relatively fast and will overwrite + print('Unzipping:', self.output_files[self.language]) + subprocess.run('bzip2 -dk ' + self.save_path + '/' + filename, shell=True, check=True) + + else: + assert False, 'WikiDownloader not implemented for this language yet.' + diff --git a/TensorFlow/LanguageModeling/BERT/data/WikicorpusTextFormatting.py b/TensorFlow/LanguageModeling/BERT/data/WikicorpusTextFormatting.py new file mode 100644 index 00000000..9d356b13 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/WikicorpusTextFormatting.py @@ -0,0 +1,46 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import glob +import os + +class WikicorpusTextFormatting: + def __init__(self, wiki_path, output_filename, recursive = False): + self.wiki_path = wiki_path + self.recursive = recursive + self.output_filename = output_filename + + + # This puts one article per line + def merge(self): + with open(self.output_filename, mode='w', newline='\n') as ofile: + for dirname in glob.glob(self.wiki_path + '/*/', recursive=False): + for filename in glob.glob(dirname + 'wiki_*', recursive=self.recursive): + print(filename) + article_lines = [] + article_open = False + + with open(filename, mode='r', newline='\n') as file: + for line in file: + if '' in line: + article_open = False + for oline in article_lines[1:]: + if oline != '\n': + ofile.write(oline.rstrip() + " ") + ofile.write("\n\n") + article_lines = [] + else: + if article_open: + article_lines.append(line) \ No newline at end of file diff --git a/MxNet/Classification/RN50v1.5/examples/RN50_FP16_4GPU.sh b/TensorFlow/LanguageModeling/BERT/data/__init__.py similarity index 61% rename from MxNet/Classification/RN50v1.5/examples/RN50_FP16_4GPU.sh rename to TensorFlow/LanguageModeling/BERT/data/__init__.py index 7b1eef4e..d49f0d05 100644 --- a/MxNet/Classification/RN50v1.5/examples/RN50_FP16_4GPU.sh +++ b/TensorFlow/LanguageModeling/BERT/data/__init__.py @@ -1,5 +1,4 @@ -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -10,10 +9,4 @@ # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and -# limitations under the License. - - -# This script launches ResNet50 training in FP16 on 4 GPUs using 832 batch size (208 per GPU) -# Usage ./RN50_FP16_4GPU.sh - -"$1/runner" -n 4 -b 208 --model-prefix model ${@:2} +# limitations under the License. \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/data/bertPrep.py b/TensorFlow/LanguageModeling/BERT/data/bertPrep.py new file mode 100644 index 00000000..de6049b0 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/bertPrep.py @@ -0,0 +1,387 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import BookscorpusTextFormatting +import Downloader +import TextSharding +import WikicorpusTextFormatting +import PubMedTextFormatting + +import argparse +import itertools +import multiprocessing +import os +import pprint +import subprocess + + +def main(args): + working_dir = os.environ['BERT_PREP_WORKING_DIR'] + + print('Working Directory:', working_dir) + print('Action:', args.action) + print('Dataset Name:', args.dataset) + + if args.input_files: + args.input_files = args.input_files.split(',') + + hdf5_tfrecord_folder_prefix = "/lower_case_" + str(args.do_lower_case) + "_seq_len_" + str(args.max_seq_length) \ + + "_max_pred_" + str(args.max_predictions_per_seq) + "_masked_lm_prob_" + str(args.masked_lm_prob) \ + + "_random_seed_" + str(args.random_seed) + "_dupe_factor_" + str(args.dupe_factor) \ + + "_shard_" + str(args.n_training_shards) + "_test_split_" + str(int(args.fraction_test_set * 100)) + directory_structure = { + 'download' : working_dir + '/download', # Downloaded and decompressed + 'extracted' : working_dir +'/extracted', # Extracted from whatever the initial format is (e.g., wikiextractor) + 'formatted' : working_dir + '/formatted_one_article_per_line', # This is the level where all sources should look the same + 'sharded' : working_dir + '/sharded', + 'tfrecord' : working_dir + '/tfrecord' + hdf5_tfrecord_folder_prefix, + 'hdf5': working_dir + '/hdf5'+ hdf5_tfrecord_folder_prefix, + } + + print('\nDirectory Structure:') + pp = pprint.PrettyPrinter(indent=2) + pp.pprint(directory_structure) + print('') + + if args.action == 'download': + if not os.path.exists(directory_structure['download']): + os.makedirs(directory_structure['download']) + + downloader = Downloader.Downloader(args.dataset, directory_structure['download']) + downloader.download() + + elif args.action == 'text_formatting': + assert args.dataset != 'google_pretrained_weights' and args.dataset != 'nvidia_pretrained_weights' \ + and args.dataset != 'squad' and args.dataset != 'MRPC' and args.dataset != 'CoLA' and \ + args.dataset != 'MNLI', 'Cannot perform text_formatting on pretrained weights' + + if not os.path.exists(directory_structure['extracted']): + os.makedirs(directory_structure['extracted']) + + if not os.path.exists(directory_structure['formatted']): + os.makedirs(directory_structure['formatted']) + + if args.dataset == 'bookscorpus': + books_path = directory_structure['download'] + '/bookscorpus' + #books_path = directory_structure['download'] + output_filename = directory_structure['formatted'] + '/bookscorpus_one_book_per_line.txt' + books_formatter = BookscorpusTextFormatting.BookscorpusTextFormatting(books_path, output_filename, recursive=True) + books_formatter.merge() + + elif args.dataset == 'wikicorpus_en': + if args.skip_wikiextractor == 0: + path_to_wikiextractor_in_container = '/workspace/wikiextractor/WikiExtractor.py' + wikiextractor_command = path_to_wikiextractor_in_container + ' ' + directory_structure['download'] + '/' + args.dataset + '/wikicorpus_en.xml ' + '-b 100M --processes ' + str(args.n_processes) + ' -o ' + directory_structure['extracted'] + '/' + args.dataset + print('WikiExtractor Command:', wikiextractor_command) + wikiextractor_process = subprocess.run(wikiextractor_command, shell=True, check=True) + + wiki_path = directory_structure['extracted'] + '/wikicorpus_en' + output_filename = directory_structure['formatted'] + '/wikicorpus_en_one_article_per_line.txt' + wiki_formatter = WikicorpusTextFormatting.WikicorpusTextFormatting(wiki_path, output_filename, recursive=True) + wiki_formatter.merge() + + elif args.dataset == 'wikicorpus_zh': + assert False, 'wikicorpus_zh not fully supported at this time. The simplified/tradition Chinese data needs to be translated and properly segmented still, and should work once this step is added.' + if args.skip_wikiextractor == 0: + path_to_wikiextractor_in_container = '/workspace/wikiextractor/WikiExtractor.py' + wikiextractor_command = path_to_wikiextractor_in_container + ' ' + directory_structure['download'] + '/' + args.dataset + '/wikicorpus_zh.xml ' + '-b 100M --processes ' + str(args.n_processes) + ' -o ' + directory_structure['extracted'] + '/' + args.dataset + print('WikiExtractor Command:', wikiextractor_command) + wikiextractor_process = subprocess.run(wikiextractor_command, shell=True, check=True) + + wiki_path = directory_structure['extracted'] + '/wikicorpus_zh' + output_filename = directory_structure['formatted'] + '/wikicorpus_zh_one_article_per_line.txt' + wiki_formatter = WikicorpusTextFormatting.WikicorpusTextFormatting(wiki_path, output_filename, recursive=True) + wiki_formatter.merge() + + elif args.dataset == 'pubmed_baseline': + pubmed_path = directory_structure['download'] + '/pubmed' + '/baseline' + output_filename = directory_structure['formatted'] + '/pubmed_baseline_one_article_per_line.txt' + pubmed_formatter = PubMedTextFormatting.PubMedTextFormatting(pubmed_path, output_filename, recursive=True) + pubmed_formatter.merge() + + elif args.action == 'sharding': + # Note: books+wiki requires user to provide list of input_files (comma-separated with no spaces) + if args.dataset == 'bookscorpus' or 'wikicorpus' in args.dataset or 'books_wiki' in args.dataset or 'pubmed' in args.dataset: + if args.input_files is None: + if args.dataset == 'bookscorpus': + args.input_files = [directory_structure['formatted'] + '/bookscorpus_one_book_per_line.txt'] + elif args.dataset == 'wikicorpus_en': + args.input_files = [directory_structure['formatted'] + '/wikicorpus_en_one_article_per_line.txt'] + elif args.dataset == 'wikicorpus_zh': + args.input_files = [directory_structure['formatted'] + '/wikicorpus_zh_one_article_per_line.txt'] + elif args.dataset == 'books_wiki_en_corpus': + args.input_files = [directory_structure['formatted'] + '/bookscorpus_one_book_per_line.txt', directory_structure['formatted'] + '/wikicorpus_en_one_article_per_line.txt'] + elif args.dataset == 'pubmed_baseline': + args.input_files = [directory_structure['formatted'] + '/pubmed_baseline_one_article_per_line.txt'] + + output_file_prefix = directory_structure['sharded'] + '/' + args.dataset + '/' + args.dataset + + if not os.path.exists(directory_structure['sharded']): + os.makedirs(directory_structure['sharded']) + + if not os.path.exists(directory_structure['sharded'] + '/' + args.dataset): + os.makedirs(directory_structure['sharded'] + '/' + args.dataset) + + if not os.path.exists(directory_structure['sharded'] + '/' + args.dataset + '/training'): + os.makedirs(directory_structure['sharded'] + '/' + args.dataset + '/training') + + if not os.path.exists(directory_structure['sharded'] + '/' + args.dataset + '/test'): + os.makedirs(directory_structure['sharded'] + '/' + args.dataset + '/test') + + # Segmentation is here because all datasets look the same in one article/book/whatever per line format, and + # it seemed unnecessarily complicated to add an additional preprocessing step to call just for this. + # Different languages (e.g., Chinese simplified/traditional) may require translation and + # other packages to be called from here -- just add a conditional branch for those extra steps + segmenter = TextSharding.NLTKSegmenter() + sharding = TextSharding.Sharding(args.input_files, output_file_prefix, args.n_training_shards, args.n_test_shards, args.fraction_test_set) + + sharding.load_articles() + sharding.segment_articles_into_sentences(segmenter) + sharding.distribute_articles_over_shards() + sharding.write_shards_to_disk() + + else: + assert False, 'Unsupported dataset for sharding' + + elif args.action == 'create_tfrecord_files': + if not os.path.exists(directory_structure['tfrecord'] + "/" + args.dataset): + os.makedirs(directory_structure['tfrecord'] + "/" + args.dataset) + + if not os.path.exists(directory_structure['tfrecord'] + "/" + args.dataset + '/training'): + os.makedirs(directory_structure['tfrecord'] + "/" + args.dataset + '/training') + + if not os.path.exists(directory_structure['tfrecord'] + "/" + args.dataset + '/test'): + os.makedirs(directory_structure['tfrecord'] + "/" + args.dataset + '/test') + + last_process = None + + def create_record_worker(filename_prefix, shard_id, output_format='tfrecord', split='training'): + bert_preprocessing_command = 'python /workspace/bert/utils/create_pretraining_data.py' + bert_preprocessing_command += ' --input_file=' + directory_structure['sharded'] + '/' + args.dataset + '/' + split + '/' + filename_prefix + '_' + str(shard_id) + '.txt' + bert_preprocessing_command += ' --output_file=' + directory_structure['tfrecord'] + '/' + args.dataset + '/' + split + '/' + filename_prefix + '_' + str(shard_id) + '.' + output_format + bert_preprocessing_command += ' --vocab_file=' + args.vocab_file + bert_preprocessing_command += ' --do_lower_case' if args.do_lower_case else '' + bert_preprocessing_command += ' --max_seq_length=' + str(args.max_seq_length) + bert_preprocessing_command += ' --max_predictions_per_seq=' + str(args.max_predictions_per_seq) + bert_preprocessing_command += ' --masked_lm_prob=' + str(args.masked_lm_prob) + bert_preprocessing_command += ' --random_seed=' + str(args.random_seed) + bert_preprocessing_command += ' --dupe_factor=' + str(args.dupe_factor) + bert_preprocessing_process = subprocess.Popen(bert_preprocessing_command, shell=True) + + last_process = bert_preprocessing_process + + # This could be better optimized (fine if all take equal time) + if shard_id % args.n_processes == 0 and shard_id > 0: + bert_preprocessing_process.wait() + + return last_process + + output_file_prefix = args.dataset + + for i in range(args.n_training_shards): + last_process = create_record_worker(output_file_prefix + '_training', i, 'tfrecord', 'training') + + last_process.wait() + + for i in range(args.n_test_shards): + last_process = create_record_worker(output_file_prefix + '_test', i, 'tfrecord', 'test') + + last_process.wait() + + + elif args.action == 'create_hdf5_files': + assert False, 'HDF5 format not fully supported in this release.' + + if not os.path.exists(directory_structure['hdf5'] + "/" + args.dataset): + os.makedirs(directory_structure['hdf5'] + "/" + args.dataset) + + last_process = None + + def create_record_worker(filename_prefix, shard_id, output_format='hdf5'): + bert_preprocessing_command = 'python /workspace/bert/utils/create_pretraining_data.py' + bert_preprocessing_command += ' --input_file=' + directory_structure['sharded'] + '/' + args.dataset + '/' + filename_prefix + '_' + str(shard_id) + '.txt' + bert_preprocessing_command += ' --output_file=' + directory_structure['hdf5'] + '/' + args.dataset + '/' + filename_prefix + '_' + str(shard_id) + '.' + output_format + bert_preprocessing_command += ' --vocab_file=' + args.vocab_file + bert_preprocessing_command += ' --do_lower_case' if args.do_lower_case else '' + bert_preprocessing_command += ' --max_seq_length=' + args.max_seq_length + bert_preprocessing_command += ' --max_predictions_per_seq=' + args.max_predictions_per_seq + bert_preprocessing_command += ' --masked_lm_prob=' + args.masked_lm_prob + bert_preprocessing_command += ' --random_seed=' + args.random_seed + bert_preprocessing_command += ' --dupe_factor=' + args.dupe_factor + bert_preprocessing_process = subprocess.Popen(bert_preprocessing_command, shell=True) + + last_process = bert_preprocessing_process + + # This could be better optimized (fine if all take equal time) + if shard_id % args.n_processes == 0 and shard_id > 0: + bert_preprocessing_process.wait() + + for i in range(args.n_training_shards): + create_record_worker(args.output_file_prefix + '_training', i) + + last_process.wait() + + for i in range(args.n_test_shards): + create_record_worker(args.output_file_prefix + '_test', i) + + last_process.wait() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description='Preprocessing Application for Everything BERT-related' + ) + + parser.add_argument( + '--action', + type=str, + help='Specify the action you want the app to take. e.g., generate vocab, segment, create tfrecords', + choices={ + 'download', # Download and verify mdf5/sha sums + 'text_formatting', # Convert into a file that contains one article/book per line + 'sharding', # Convert previous formatted text into shards containing one sentence per line + 'create_tfrecord_files', # Turn each shard into a TFrecord with masking and next sentence prediction info + 'create_hdf5_files' # Turn each shard into a HDF5 file with masking and next sentence prediction info + } + ) + + parser.add_argument( + '--dataset', + type=str, + help='Specify the dataset to perform --action on', + choices={ + 'bookscorpus', + 'wikicorpus_en', + 'wikicorpus_zh', + 'books_wiki_en_corpus', + 'pubmed_baseline', + 'pubmed_daily_update', + 'pubmed_fulltext', + 'pubmed_open_access', + 'google_pretrained_weights', + 'nvidia_pretrained_weights', + 'squad', + 'MRPC', + 'CoLA', + 'MNLI', + 'all' + } + ) + + parser.add_argument( + '--input_files', + type=str, + help='Specify the input files in a comma-separated list (no spaces)' + ) + + parser.add_argument( + '--n_training_shards', + type=int, + help='Specify the number of training shards to generate', + default=1472 + ) + + parser.add_argument( + '--n_test_shards', + type=int, + help='Specify the number of test shards to generate', + default=1472 + ) + + parser.add_argument( + '--fraction_test_set', + type=float, + help='Specify the fraction (0..1) of the data to withhold for the test data split (based on number of sequences)', + default=0.2 + ) + + parser.add_argument( + '--segmentation_method', + type=str, + help='Specify your choice of sentence segmentation', + choices={ + 'nltk' + }, + default='nltk' + ) + + parser.add_argument( + '--n_processes', + type=int, + help='Specify the max number of processes to allow at one time', + default=4 + ) + + parser.add_argument( + '--random_seed', + type=int, + help='Specify the base seed to use for any random number generation', + default=12345 + ) + + parser.add_argument( + '--dupe_factor', + type=int, + help='Specify the duplication factor', + default=5 + ) + + parser.add_argument( + '--masked_lm_prob', + type=float, + help='Specify the probability for masked lm', + default=0.15 + ) + + parser.add_argument( + '--max_seq_length', + type=int, + help='Specify the maximum sequence length', + default=512 + ) + + parser.add_argument( + '--max_predictions_per_seq', + type=int, + help='Specify the maximum number of masked words per sequence', + default=20 + ) + + parser.add_argument( + '--do_lower_case', + type=int, + help='Specify whether it is cased (0) or uncased (1) (any number greater than 0 will be treated as uncased)', + default=1 + ) + + parser.add_argument( + '--vocab_file', + type=str, + help='Specify absolute path to vocab file to use)' + ) + + parser.add_argument( + '--skip_wikiextractor', + type=int, + help='Specify whether to skip wikiextractor step 0=False, 1=True', + default=0 + ) + + parser.add_argument( + '--interactive_json_config_generator', + type=str, + help='Specify the action you want the app to take. e.g., generate vocab, segment, create tfrecords' + ) + + args = parser.parse_args() + main(args) diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/clean_and_merge_text.py b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/clean_and_merge_text.py deleted file mode 100644 index 0b297b1d..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/clean_and_merge_text.py +++ /dev/null @@ -1,15 +0,0 @@ -# NVIDIA - -import glob -import os - -output_file = os.environ['WORKING_DIR'] + '/intermediate_files/bookcorpus.txt' -download_path = os.environ['WORKING_DIR'] + '/download/' - -with open(output_file, "w") as ofile: - for filename in glob.glob(download_path + '*.txt', recursive=True): - with open(filename, mode='r', encoding="utf-8-sig") as file: - for line in file: - if line.strip() != "": - ofile.write(line.strip() + " ") - ofile.write("\n\n ") diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/config.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/config.sh deleted file mode 100644 index e14a1acd..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/config.sh +++ /dev/null @@ -1,27 +0,0 @@ -#! /bin/bash - -set -e - -USE_BERT_LARGE=true -MAX_SEQUENCE_LENGTH=512 -MAX_PREDICTIONS_PER_SEQUENCE=80 -MASKED_LM_PROB=0.15 -SEED=12345 -DUPE_FACTOR=5 -DO_LOWER_CASE="True" -N_LINES_PER_SHARD_APPROX=396000 # Default=396000 creates 256 shards - -N_PROCS_PREPROCESS=4 # Adjust this based on memory requirements and available number of cores -export WORKING_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" - -BERT_BASE_DIR="${WORKING_DIR}/../pretrained_models_google/uncased_L-12_H-768_A-12" -BERT_LARGE_DIR="${WORKING_DIR}/../pretrained_models_google/uncased_L-24_H-1024_A-16" - -if [ "$USE_BERT_LARGE" = true ] ; then - VOCAB_FILE="${BERT_LARGE_DIR}/vocab.txt" -else - VOCAB_FILE="${BERT_BASE_DIR}/vocab.txt" -fi - -OUTPUT_DIR="${WORKING_DIR}/final_tfrecords_sharded/bert_large_bookcorpus_seq_${MAX_SEQUENCE_LENGTH}_pred_${MAX_PREDICTIONS_PER_SEQUENCE}" - diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.py b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.py deleted file mode 100644 index 194f15b7..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.py +++ /dev/null @@ -1,18 +0,0 @@ -# NVIDIA - -import glob -import os -import random -import shutil - -input_dir = os.environ['WORKING_DIR'] + '/final_text_files_sharded/' -output_dir = os.environ['WORKING_DIR'] + '/test_set_text_files/' - -random.seed(13254) -n_shards_to_keep = 3 - -file_glob = glob.glob(input_dir + '/*', recursive=False) -file_glob = random.sample(file_glob, n_shards_to_keep) - -for filename in file_glob: - shutil.copy(filename, output_dir) diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.sh deleted file mode 100755 index 34fa09e9..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/create_pseudo_test_set.sh +++ /dev/null @@ -1,10 +0,0 @@ -#! /bin/bash - -source /workspace/bert/data/bookcorpus/config.sh - -# Convert test set sharded text files into tfrecords that are ready for BERT pretraining -echo "Creating test set tfrecords for each text shard" -mkdir -p ${WORKING_DIR}/test_set_text_files -mkdir -p ${WORKING_DIR}/test_set_tfrecords -python3 ${WORKING_DIR}/create_pseudo_test_set.py -. ${WORKING_DIR}/preprocessing_test_set_xargs_wrapper.sh ${N_PROCS_PREPROCESS} diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing.sh deleted file mode 100755 index 9adbd268..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing.sh +++ /dev/null @@ -1,23 +0,0 @@ -#! /bin/bash - -SHARD_INDEX=${1} -INPUT_FILE="${WORKING_DIR}/final_text_files_sharded/bookcorpus.segmented.part.${SHARD_INDEX}.txt" - -source /workspace/bert/data/bookcorpus/config.sh - -OUTPUT_DIR=${WORKING_DIR}/final_tfrecords_sharded -mkdir -p ${OUTPUT_DIR} - -OUTPUT_FILE="${OUTPUT_DIR}/tf_examples.tfrecord000${SHARD_INDEX}" - -python /workspace/bert/utils/create_pretraining_data.py \ - --input_file=${INPUT_FILE} \ - --output_file=${OUTPUT_FILE} \ - --vocab_file=${VOCAB_FILE} \ - --do_lower_case=${DO_LOWER_CASE} \ - --max_seq_length=${MAX_SEQUENCE_LENGTH} \ - --max_predictions_per_seq=${MAX_PREDICTIONS_PER_SEQUENCE} \ - --masked_lm_prob=${MASKED_LM_PROB} \ - --random_seed=${SEED} \ - --dupe_factor=${DUPE_FACTOR} - diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set.sh deleted file mode 100755 index f1f5ad10..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set.sh +++ /dev/null @@ -1,28 +0,0 @@ -#! /bin/bash - -INPUT_FILE=${1} - -source /workspace/bert/data/bookcorpus/config.sh - -OUTPUT_DIR=${WORKING_DIR}/test_set_tfrecords -mkdir -p ${OUTPUT_DIR} - -#SHARD_INDEX=$(( echo ${INPUT_FILE} | egrep -o [0-9]+ )) -SHARD_INDEX=$( eval echo ${INPUT_FILE} | sed -e s/[^0-9]//g ) -OUTPUT_FILE="${OUTPUT_DIR}/tf_examples.tfrecord000${SHARD_INDEX}" - -SEED=13254 - -echo "Shard index ${SHARD_INDEX}" - -python /workspace/bert/utils/create_pretraining_data.py \ - --input_file=${INPUT_FILE} \ - --output_file=${OUTPUT_FILE} \ - --vocab_file=${VOCAB_FILE} \ - --do_lower_case=${DO_LOWER_CASE} \ - --max_seq_length=${MAX_SEQUENCE_LENGTH} \ - --max_predictions_per_seq=${MAX_PREDICTIONS_PER_SEQUENCE} \ - --masked_lm_prob=${MASKED_LM_PROB} \ - --random_seed=${SEED} \ - --dupe_factor=${DUPE_FACTOR} - diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set_xargs_wrapper.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set_xargs_wrapper.sh deleted file mode 100755 index f41516b7..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_test_set_xargs_wrapper.sh +++ /dev/null @@ -1,12 +0,0 @@ -#! /bin/bash - -source /workspace/bert/data/bookcorpus/config.sh - -SHARD_COUNT=0 -rm -rf /workspace/bert/data/bookcorpus/xarg_list.txt -touch /workspace/bert/data/bookcorpus/xarg_list.txt -for file in /workspace/bert/data/bookcorpus/test_set_text_files/*; do - echo ${file} >> /workspace/bert/data/bookcorpus/xarg_list.txt -done - -xargs -n 1 --max-procs=${N_PROCS_PREPROCESS} --arg-file=/workspace/bert/data/bookcorpus/xarg_list.txt /workspace/bert/data/bookcorpus/preprocessing_test_set.sh diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_xargs_wrapper.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_xargs_wrapper.sh deleted file mode 100755 index 387069ef..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/preprocessing_xargs_wrapper.sh +++ /dev/null @@ -1,13 +0,0 @@ -#! /bin/bash - -source /workspace/bert/data/bookcorpus/config.sh - -SHARD_COUNT=0 -rm -rf /workspace/bert/data/bookcorpus/xarg_list.txt -touch /workspace/bert/data/bookcorpus/xarg_list.txt -for file in /workspace/bert/data/bookcorpus/final_text_files_sharded/*; do - echo ${SHARD_COUNT} >> /workspace/bert/data/bookcorpus/xarg_list.txt - SHARD_COUNT=$((SHARD_COUNT+1)) -done - -xargs -n 1 --max-procs=${N_PROCS_PREPROCESS} --arg-file=/workspace/bert/data/bookcorpus/xarg_list.txt /workspace/bert/data/bookcorpus/preprocessing.sh diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/run_preprocessing.sh b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/run_preprocessing.sh deleted file mode 100755 index f660e22c..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/run_preprocessing.sh +++ /dev/null @@ -1,28 +0,0 @@ -#! /bin/bash - -source /workspace/bert/data/bookcorpus/config.sh - -# Download books -mkdir -p download -python3 /workspace/bookcorpus/download_files.py --list /workspace/bookcorpus/url_list.jsonl --out ${WORKING_DIR}/download --trash-bad-count - -# Clean and prep (one book per line) -mkdir -p ${WORKING_DIR}/intermediate_files -python3 ${WORKING_DIR}/clean_and_merge_text.py - -# Split books into one-sentence-per-line format for use with BERT scripts -echo "Applying sentence segmentation to get one sentence per line" -mkdir -p ${WORKING_DIR}/final_text_file_single -python3 ${WORKING_DIR}/sentence_segmentation_nltk.py -# Note: NLTK can be replaced with Spacy, although it is slower (2 variations provided) - -# Shard finalized text so that it has a chance of fitting in memory when creating pretraining data into tfrecords (choose appropriate number of shards for distributed training) -echo "Shard text files - size is approximate to prevent splitting a book across shards" -mkdir -p ${WORKING_DIR}/final_text_files_sharded -python3 ${WORKING_DIR}/shard_text_input_file.py - -# Convert sharded text files into tfrecords that are ready for BERT pretraining -echo "Creating tfrecords for each text shard" -mkdir -p ${WORKING_DIR}/final_tfrecords_sharded -. ${WORKING_DIR}/preprocessing_xargs_wrapper.sh ${N_PROCS_PREPROCESS} - diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/sentence_segmentation_nltk.py b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/sentence_segmentation_nltk.py deleted file mode 100644 index 038205b6..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/sentence_segmentation_nltk.py +++ /dev/null @@ -1,20 +0,0 @@ -# NVIDIA - -import nltk -import os - -nltk.download('punkt') - -input_file = os.environ['WORKING_DIR'] + '/intermediate_files/bookcorpus.txt' -output_file = os.environ['WORKING_DIR'] + '/final_text_file_single/bookcorpus.segmented.nltk.txt' - -doc_seperator = "\n" - -with open(input_file) as ifile: - with open(output_file, "w") as ofile: - for line in ifile: - if line != "\n": - sent_list = nltk.tokenize.sent_tokenize(line) - for sent in sent_list: - ofile.write(sent + "\n") - ofile.write(doc_seperator) diff --git a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/shard_text_input_file.py b/TensorFlow/LanguageModeling/BERT/data/bookcorpus/shard_text_input_file.py deleted file mode 100644 index 5efe0be4..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/bookcorpus/shard_text_input_file.py +++ /dev/null @@ -1,41 +0,0 @@ -# NVIDIA - -import os - -input_file = os.environ['WORKING_DIR'] + '/final_text_file_single/bookcorpus.segmented.nltk.txt' -output_file = os.environ['WORKING_DIR'] + '/final_text_files_sharded/bookcorpus.segmented.part.' - -doc_seperator = "\n" - -line_buffer = [] -shard_size = 396000 # Approximate, will split at next article break -line_counter = 0 -shard_index = 0 - -ifile_lines = 0 -with open(input_file) as ifile: - for line in ifile: - ifile_lines += 1 - -print("Input file contains", ifile_lines, "lines.") - -iline_counter = 1 -with open(input_file) as ifile: - for line in ifile: - if line_counter < shard_size and iline_counter < ifile_lines: - line_buffer.append(line) - line_counter += 1 - iline_counter += 1 - elif line_counter >= shard_size and line != "\n" and iline_counter < ifile_lines: - line_buffer.append(line) - line_counter += 1 - iline_counter += 1 - else: - with open(output_file + str(shard_index) + ".txt", "w") as ofile: - for oline in line_buffer: - ofile.write(oline) - line_buffer = [] - line_counter = 0 - shard_index += 1 - - diff --git a/TensorFlow/LanguageModeling/BERT/data/create_datasets_from_start.sh b/TensorFlow/LanguageModeling/BERT/data/create_datasets_from_start.sh new file mode 100755 index 00000000..f21914e4 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/data/create_datasets_from_start.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +export BERT_PREP_WORKING_DIR="${BERT_PREP_WORKING_DIR}" + +# Download +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action download --dataset bookscorpus +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action download --dataset wikicorpus_en + +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action download --dataset google_pretrained_weights # Includes vocab + +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action download --dataset squad +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action download --dataset "CoLA" +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action download --dataset "MRPC" +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action download --dataset "MNLI" + + +# Properly format the text files +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action text_formatting --dataset bookscorpus +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action text_formatting --dataset wikicorpus_en + + +# Shard the text files (group wiki+books then shard) +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action sharding --dataset books_wiki_en_corpus + + +# Create TFRecord files Phase 1 +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action create_tfrecord_files --dataset books_wiki_en_corpus --max_seq_length 128 \ + --max_predictions_per_seq 20 --vocab_file ${BERT_PREP_WORKING_DIR}/download/google_pretrained_weights/uncased_L-24_H-1024_A-16/vocab.txt + + +# Create TFRecord files Phase 2 +python3 ${BERT_PREP_WORKING_DIR}/bertPrep.py --action create_tfrecord_files --dataset books_wiki_en_corpus --max_seq_length 512 \ + --max_predictions_per_seq 80 --vocab_file ${BERT_PREP_WORKING_DIR}/download/google_pretrained_weights/uncased_L-24_H-1024_A-16/vocab.txt diff --git a/TensorFlow/LanguageModeling/BERT/data/glue/download_glue_data.py b/TensorFlow/LanguageModeling/BERT/data/glue/download_glue_data.py deleted file mode 100644 index 7f452d6c..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/glue/download_glue_data.py +++ /dev/null @@ -1,153 +0,0 @@ -# -# -# @unpublished{wang2018glue -# title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for -# Natural Language Understanding} -# author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, -# Felix and Levy, Omer and Bowman, Samuel R.} -# note={arXiv preprint 1804.07461} -# year={2018} -# } -# -# Script for downloading all GLUE data. -# Note: for legal reasons, we are unable to host MRPC. -# You can either use the version hosted by the SentEval team, which is already tokenized, -# or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually. -# For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example). -# You should then rename and place specific files in a folder (see below for an example). -# mkdir MRPC -# cabextract MSRParaphraseCorpus.msi -d MRPC -# cat MRPC/_2DEC3DBE877E4DB192D17C0256E90F1D | tr -d $'\r' > MRPC/msr_paraphrase_train.txt -# cat MRPC/_D7B391F9EAFF4B1B8BCE8F21B20B1B61 | tr -d $'\r' > MRPC/msr_paraphrase_test.txt -# rm MRPC/_* -# rm MSRParaphraseCorpus.msi - - -import os -import sys -import shutil -import argparse -import tempfile -import urllib -import io -if sys.version_info >= (3, 0): - import urllib.request -import zipfile - -URLLIB=urllib -if sys.version_info >= (3, 0): - URLLIB=urllib.request - -TASKS = ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"] -TASK2PATH = {"CoLA":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4', - "SST":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8', - "MRPC":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc', - "QQP":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5', - "STS":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5', - "MNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce', - "SNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df', - "QNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLI.zip?alt=media&token=c24cad61-f2df-4f04-9ab6-aa576fa829d0', - "RTE":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb', - "WNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf', - "diagnostic":'https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D'} - -MRPC_TRAIN = 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt' -MRPC_TEST = 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt' - -def download_and_extract(task, data_dir): - print("Downloading and extracting %s..." % task) - data_file = "%s.zip" % task - URLLIB.urlretrieve(TASK2PATH[task], data_file) - with zipfile.ZipFile(data_file) as zip_ref: - zip_ref.extractall(data_dir) - os.remove(data_file) - print("\tCompleted!") - -def format_mrpc(data_dir, path_to_data): - print("Processing MRPC...") - mrpc_dir = os.path.join(data_dir, "MRPC") - if not os.path.isdir(mrpc_dir): - os.mkdir(mrpc_dir) - if path_to_data: - mrpc_train_file = os.path.join(path_to_data, "msr_paraphrase_train.txt") - mrpc_test_file = os.path.join(path_to_data, "msr_paraphrase_test.txt") - else: - mrpc_train_file = os.path.join(mrpc_dir, "msr_paraphrase_train.txt") - mrpc_test_file = os.path.join(mrpc_dir, "msr_paraphrase_test.txt") - URLLIB.urlretrieve(MRPC_TRAIN, mrpc_train_file) - URLLIB.urlretrieve(MRPC_TEST, mrpc_test_file) - assert os.path.isfile(mrpc_train_file), "Train data not found at %s" % mrpc_train_file - assert os.path.isfile(mrpc_test_file), "Test data not found at %s" % mrpc_test_file - URLLIB.urlretrieve(TASK2PATH["MRPC"], os.path.join(mrpc_dir, "dev_ids.tsv")) - - dev_ids = [] - with io.open(os.path.join(mrpc_dir, "dev_ids.tsv"), encoding='utf-8') as ids_fh: - for row in ids_fh: - dev_ids.append(row.strip().split('\t')) - - with io.open(mrpc_train_file, encoding='utf-8') as data_fh, \ - io.open(os.path.join(mrpc_dir, "train.tsv"), 'w', encoding='utf-8') as train_fh, \ - io.open(os.path.join(mrpc_dir, "dev.tsv"), 'w', encoding='utf-8') as dev_fh: - header = data_fh.readline() - train_fh.write(header) - dev_fh.write(header) - for row in data_fh: - label, id1, id2, s1, s2 = row.strip().split('\t') - if [id1, id2] in dev_ids: - dev_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) - else: - train_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) - - with io.open(mrpc_test_file, encoding='utf-8') as data_fh, \ - io.open(os.path.join(mrpc_dir, "test.tsv"), 'w', encoding='utf-8') as test_fh: - header = data_fh.readline() - test_fh.write("index\t#1 ID\t#2 ID\t#1 String\t#2 String\n") - for idx, row in enumerate(data_fh): - label, id1, id2, s1, s2 = row.strip().split('\t') - test_fh.write("%d\t%s\t%s\t%s\t%s\n" % (idx, id1, id2, s1, s2)) - print("\tCompleted!") - -def download_diagnostic(data_dir): - print("Downloading and extracting diagnostic...") - if not os.path.isdir(os.path.join(data_dir, "diagnostic")): - os.mkdir(os.path.join(data_dir, "diagnostic")) - data_file = os.path.join(data_dir, "diagnostic", "diagnostic.tsv") - URLLIB.urlretrieve(TASK2PATH["diagnostic"], data_file) - print("\tCompleted!") - return - -def get_tasks(task_names): - task_names = task_names.split(',') - if "all" in task_names: - tasks = TASKS - else: - tasks = [] - for task_name in task_names: - assert task_name in TASKS, "Task %s not found!" % task_name - tasks.append(task_name) - return tasks - -def main(arguments): - parser = argparse.ArgumentParser() - parser.add_argument('-d', '--data_dir', help='directory to save data to', type=str, default='.') - parser.add_argument('-t', '--tasks', help='tasks to download data for as a comma separated string', - type=str, default='all') - parser.add_argument('--path_to_mrpc', help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt', - type=str, default='') - args = parser.parse_args(arguments) - - if not os.path.isdir(args.data_dir): - os.mkdir(args.data_dir) - tasks = get_tasks(args.tasks) - - for task in tasks: - if task == 'MRPC': - format_mrpc(args.data_dir, args.path_to_mrpc) - elif task == 'diagnostic': - download_diagnostic(args.data_dir) - else: - download_and_extract(task, args.data_dir) - - -if __name__ == '__main__': - sys.exit(main(sys.argv[1:])) \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/data/pretrained_models_google/download_models.py b/TensorFlow/LanguageModeling/BERT/data/pretrained_models_google/download_models.py deleted file mode 100644 index e671c194..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/pretrained_models_google/download_models.py +++ /dev/null @@ -1,123 +0,0 @@ -# NVIDIA - -import hashlib -import urllib.request -import zipfile - -# Download urls -model_urls = { - 'bert_base_uncased' : ('https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip', 'uncased_L-12_H-768_A-12.zip'), - 'bert_large_uncased' : ('https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-24_H-1024_A-16.zip', 'uncased_L-24_H-1024_A-16.zip'), - 'bert_base_cased' : ('https://storage.googleapis.com/bert_models/2018_10_18/cased_L-12_H-768_A-12.zip', 'cased_L-12_H-768_A-12.zip'), - 'bert_large_cased' : ('https://storage.googleapis.com/bert_models/2018_10_18/cased_L-24_H-1024_A-16.zip', 'cased_L-24_H-1024_A-16.zip'), - 'bert_base_multilingual_cased' : ('https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip', 'multi_cased_L-12_H-768_A-12.zip'), - 'bert_large_multilingual_uncased' : ('https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip', 'multilingual_L-12_H-768_A-12.zip'), - 'bert_base_chinese' : ('https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip', 'chinese_L-12_H-768_A-12.zip') -} - -# SHA256sum verification for file download integrity (and checking for changes from the download source over time) -bert_base_uncased_sha = { - 'bert_config.json' : '7b4e5f53efbd058c67cda0aacfafb340113ea1b5797d9ce6ee411704ba21fcbc', - 'bert_model.ckpt.data-00000-of-00001' : '58580dc5e0bf0ae0d2efd51d0e8272b2f808857f0a43a88aaf7549da6d7a8a84', - 'bert_model.ckpt.index' : '04c1323086e2f1c5b7c0759d8d3e484afbb0ab45f51793daab9f647113a0117b', - 'bert_model.ckpt.meta' : 'dd5682170a10c3ea0280c2e9b9a45fee894eb62da649bbdea37b38b0ded5f60e', - 'vocab.txt' : '07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3', -} - -bert_large_uncased_sha = { - 'bert_config.json' : 'bfa42236d269e2aeb3a6d30412a33d15dbe8ea597e2b01dc9518c63cc6efafcb', - 'bert_model.ckpt.data-00000-of-00001' : 'bc6b3363e3be458c99ecf64b7f472d2b7c67534fd8f564c0556a678f90f4eea1', - 'bert_model.ckpt.index' : '68b52f2205ffc64dc627d1120cf399c1ef1cbc35ea5021d1afc889ffe2ce2093', - 'bert_model.ckpt.meta' : '6fcce8ff7628f229a885a593625e3d5ff9687542d5ef128d9beb1b0c05edc4a1', - 'vocab.txt' : '07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3', -} - -bert_base_cased_sha = { - 'bert_config.json' : 'f11dfb757bea16339a33e1bf327b0aade6e57fd9c29dc6b84f7ddb20682f48bc', - 'bert_model.ckpt.data-00000-of-00001' : '734d5a1b68bf98d4e9cb6b6692725d00842a1937af73902e51776905d8f760ea', - 'bert_model.ckpt.index' : '517d6ef5c41fc2ca1f595276d6fccf5521810d57f5a74e32616151557790f7b1', - 'bert_model.ckpt.meta' : '5f8a9771ff25dadd61582abb4e3a748215a10a6b55947cbb66d0f0ba1694be98', - 'vocab.txt' : 'eeaa9875b23b04b4c54ef759d03db9d1ba1554838f8fb26c5d96fa551df93d02', -} - -bert_large_cased_sha = { - 'bert_config.json' : '7adb2125c8225da495656c982fd1c5f64ba8f20ad020838571a3f8a954c2df57', - 'bert_model.ckpt.data-00000-of-00001' : '6ff33640f40d472f7a16af0c17b1179ca9dcc0373155fb05335b6a4dd1657ef0', - 'bert_model.ckpt.index' : 'ef42a53f577fbe07381f4161b13c7cab4f4fc3b167cec6a9ae382c53d18049cf', - 'bert_model.ckpt.meta' : 'd2ddff3ed33b80091eac95171e94149736ea74eb645e575d942ec4a5e01a40a1', - 'vocab.txt' : 'eeaa9875b23b04b4c54ef759d03db9d1ba1554838f8fb26c5d96fa551df93d02', -} - -bert_base_multilingual_cased_sha = { - 'bert_config.json' : 'e76c3964bc14a8bb37a5530cdc802699d2f4a6fddfab0611e153aa2528f234f0', - 'bert_model.ckpt.data-00000-of-00001' : '55b8a2df41f69c60c5180e50a7c31b7cdf6238909390c4ddf05fbc0d37aa1ac5', - 'bert_model.ckpt.index' : '7d8509c2a62b4e300feb55f8e5f1eef41638f4998dd4d887736f42d4f6a34b37', - 'bert_model.ckpt.meta' : '95e5f1997e8831f1c31e5cf530f1a2e99f121e9cd20887f2dce6fe9e3343e3fa', - 'vocab.txt' : 'fe0fda7c425b48c516fc8f160d594c8022a0808447475c1a7c6d6479763f310c', -} - -bert_large_multilingual_uncased_sha = { - 'bert_config.json' : '49063bb061390211d2fdd108cada1ed86faa5f90b80c8f6fdddf406afa4c4624', - 'bert_model.ckpt.data-00000-of-00001' : '3cd83912ebeb0efe2abf35c9f1d5a515d8e80295e61c49b75c8853f756658429', - 'bert_model.ckpt.index' : '87c372c1a3b1dc7effaaa9103c80a81b3cbab04c7933ced224eec3b8ad2cc8e7', - 'bert_model.ckpt.meta' : '27f504f34f02acaa6b0f60d65195ec3e3f9505ac14601c6a32b421d0c8413a29', - 'vocab.txt' : '87b44292b452f6c05afa49b2e488e7eedf79ea4f4c39db6f2f4b37764228ef3f', -} - -bert_base_chinese_sha = { - 'bert_config.json' : '7aaad0335058e2640bcb2c2e9a932b1cd9da200c46ea7b8957d54431f201c015', - 'bert_model.ckpt.data-00000-of-00001' : '756699356b78ad0ef1ca9ba6528297bcb3dd1aef5feadd31f4775d7c7fc989ba', - 'bert_model.ckpt.index' : '46315546e05ce62327b3e2cd1bed22836adcb2ff29735ec87721396edb21b82e', - 'bert_model.ckpt.meta' : 'c0f8d51e1ab986604bc2b25d6ec0af7fd21ff94cf67081996ec3f3bf5d823047', - 'vocab.txt' : '45bbac6b341c319adc98a532532882e91a9cefc0329aa57bac9ae761c27b291c', -} - -# Relate SHA to urls for loop below -model_sha = { - 'bert_base_uncased' : bert_base_uncased_sha, - 'bert_large_uncased' : bert_large_uncased_sha, - 'bert_base_cased' : bert_base_cased_sha, - 'bert_large_cased' : bert_large_cased_sha, - 'bert_base_multilingual_cased' : bert_base_multilingual_cased_sha, - 'bert_large_multilingual_uncased' : bert_large_multilingual_uncased_sha, - 'bert_base_chinese' : bert_base_chinese_sha -} - -# Helper to get sha256sum of a file -def sha256sum(filename): - h = hashlib.sha256() - b = bytearray(128*1024) - mv = memoryview(b) - with open(filename, 'rb', buffering=0) as f: - for n in iter(lambda : f.readinto(mv), 0): - h.update(mv[:n]) - return h.hexdigest() - -# Iterate over urls: download, unzip, verify sha256sum -found_mismatch_sha = False -for model in model_urls: - url = model_urls[model][0] - file = model_urls[model][1] - - print("Downloading", url) - response = urllib.request.urlopen(url) - with open(file, "wb") as handle: - handle.write(response.read()) - - print("Unzipping", file) - zip = zipfile.ZipFile(file, 'r') - zip.extractall() - zip.close() - - sha_dict = model_sha[model] - for extracted_file in sha_dict: - sha = sha_dict[extracted_file] - if sha != sha256sum(file[:-4] + "/" + extracted_file): - found_mismatch_sha = True - print("SHA256sum does not match on file:", extracted_file, "from download url:", url) - else: - print(file[:-4] + "/" + extracted_file, "\t", "verified") - -if not found_mismatch_sha: - print("All downloads pass sha256sum verification.") - diff --git a/TensorFlow/LanguageModeling/BERT/data/squad/squad_download.sh b/TensorFlow/LanguageModeling/BERT/data/squad/squad_download.sh deleted file mode 100755 index 19f80940..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/squad/squad_download.sh +++ /dev/null @@ -1,60 +0,0 @@ -#!/usr/bin/env bash - -echo "Downloading dataset for squad..." - -# Download SQuAD - -v1="v1.1" -mkdir $v1 -wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json -O $v1/train-v1.1.json -wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json -O $v1/dev-v1.1.json -wget https://worksheets.codalab.org/rest/bundles/0xbcd57bee090b421c982906709c8c27e1/contents/blob/ -O $v1/evaluate-v1.1.py - -EXP_TRAIN_v1='981b29407e0affa3b1b156f72073b945 -' -EXP_DEV_v1='3e85deb501d4e538b6bc56f786231552 -' -EXP_EVAL_v1='afb04912d18ff20696f7f88eed49bea9 -' -CALC_TRAIN_v1=`cat ${v1}/train-v1.1.json |md5sum` -CALC_DEV_v1=`cat ${v1}/dev-v1.1.json |md5sum` -CALC_EVAL_v1=`cat ${v1}/evaluate-v1.1.py |md5sum` - -v2="v2.0" -mkdir $v2 -wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json -O $v2/train-v2.0.json -wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json -O $v2/dev-v2.0.json -wget https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/ -O $v2/evaluate-v2.0.py - -EXP_TRAIN_v2='62108c273c268d70893182d5cf8df740 -' -EXP_DEV_v2='246adae8b7002f8679c027697b0b7cf8 -' -EXP_EVAL_v2='ff23213bed5516ea4a6d9edb6cd7d627 -' - -CALC_TRAIN_v2=`cat ${v2}/train-v2.0.json |md5sum` -CALC_DEV_v2=`cat ${v2}/dev-v2.0.json |md5sum` -CALC_EVAL_v2=`cat ${v2}/evaluate-v2.0.py |md5sum` - -echo "Squad data download done!" - -echo "Verifying Dataset...." - -if [ "$EXP_TRAIN_v1" != "$CALC_TRAIN_v1" ]; then - echo "train-v1.1.json is corrupted! md5sum doesn't match" -fi - -if [ "$EXP_DEV_v1" != "$CALC_DEV_v1" ]; then - echo "dev-v1.1.json is corrupted! md5sum doesn't match" -fi -if [ "$EXP_EVAL_v1" != "$CALC_EVAL_v1" ]; then - echo "evaluate-v1.1.py is corrupted! md5sum doesn't match" -fi - - -if [ "$EXP_TRAIN_v2" != "$CALC_TRAIN_v2" ]; then - echo "train-v2.0.json is corrupted! md5sum doesn't match" -fi -if [ "$EXP_DEV_v2" != "$CALC_DEV_v2" ]; then - echo "dev-v2.0.json is corrupted! md5sum doesn't match" -fi -if [ "$EXP_EVAL_v2" != "$CALC_EVAL_v2" ]; then - echo "evaluate-v2.0.py is corrupted! md5sum doesn't match" -fi - -echo "SQuAD download complete!" \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/config.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/config.sh deleted file mode 100644 index 88065ffc..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/config.sh +++ /dev/null @@ -1,28 +0,0 @@ -#! /bin/bash - -set -e - -USE_BERT_LARGE=true -MAX_SEQUENCE_LENGTH=512 -MAX_PREDICTIONS_PER_SEQUENCE=80 -MASKED_LM_PROB=0.15 -SEED=12345 -DUPE_FACTOR=5 -DO_LOWER_CASE="True" -N_LINES_PER_SHARD_APPROX=396000 # Default=396000 creates 256 shards - -N_PROCS_PREPROCESS=4 # Adjust this based on memory requirements and available number of cores -export WORKING_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" - -WIKI_DUMP="ftp://ftpmirror.your.org/pub/wikimedia/dumps/enwiki/20190301/enwiki-20190301-pages-articles-multistream.xml.bz2" -BERT_BASE_DIR="${WORKING_DIR}/../pretrained_models_google/uncased_L-12_H-768_A-12" -BERT_LARGE_DIR="${WORKING_DIR}/../pretrained_models_google/uncased_L-24_H-1024_A-16" - -if [ "$USE_BERT_LARGE" = true ] ; then - VOCAB_FILE="${BERT_LARGE_DIR}/vocab.txt" -else - VOCAB_FILE="${BERT_BASE_DIR}/vocab.txt" -fi - -OUTPUT_DIR="${WORKING_DIR}/final_tfrecords_sharded/bert_large_wikipedia_seq_${MAX_SEQUENCE_LENGTH}_pred_${MAX_PREDICTIONS_PER_SEQUENCE}" - diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.py deleted file mode 100644 index 194f15b7..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.py +++ /dev/null @@ -1,18 +0,0 @@ -# NVIDIA - -import glob -import os -import random -import shutil - -input_dir = os.environ['WORKING_DIR'] + '/final_text_files_sharded/' -output_dir = os.environ['WORKING_DIR'] + '/test_set_text_files/' - -random.seed(13254) -n_shards_to_keep = 3 - -file_glob = glob.glob(input_dir + '/*', recursive=False) -file_glob = random.sample(file_glob, n_shards_to_keep) - -for filename in file_glob: - shutil.copy(filename, output_dir) diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.sh deleted file mode 100755 index ffe355c7..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/create_pseudo_test_set.sh +++ /dev/null @@ -1,10 +0,0 @@ -#! /bin/bash - -source /workspace/bert/data/wikipedia_corpus/config.sh - -# Convert test set sharded text files into tfrecords that are ready for BERT pretraining -echo "Creating test set tfrecords for each text shard" -mkdir -p ${WORKING_DIR}/test_set_text_files -mkdir -p ${WORKING_DIR}/test_set_tfrecords -python3 ${WORKING_DIR}/create_pseudo_test_set.py -. ${WORKING_DIR}/preprocessing_test_set_xargs_wrapper.sh ${N_PROCS_PREPROCESS} diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing.sh deleted file mode 100755 index 35a96b21..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing.sh +++ /dev/null @@ -1,23 +0,0 @@ -#! /bin/bash - -SHARD_INDEX=${1} -INPUT_FILE="${WORKING_DIR}/final_text_files_sharded/wikipedia.segmented.part.${SHARD_INDEX}.txt" - -source /workspace/bert/data/wikipedia_corpus/config.sh - -OUTPUT_DIR=${WORKING_DIR}/final_tfrecords_sharded -mkdir -p ${OUTPUT_DIR} - -OUTPUT_FILE="${OUTPUT_DIR}/tf_examples.tfrecord000${SHARD_INDEX}" - -python /workspace/bert/utils/create_pretraining_data.py \ - --input_file=${INPUT_FILE} \ - --output_file=${OUTPUT_FILE} \ - --vocab_file=${VOCAB_FILE} \ - --do_lower_case=${DO_LOWER_CASE} \ - --max_seq_length=${MAX_SEQUENCE_LENGTH} \ - --max_predictions_per_seq=${MAX_PREDICTIONS_PER_SEQUENCE} \ - --masked_lm_prob=${MASKED_LM_PROB} \ - --random_seed=${SEED} \ - --dupe_factor=${DUPE_FACTOR} - diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set.sh deleted file mode 100755 index 3a12ee63..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set.sh +++ /dev/null @@ -1,28 +0,0 @@ -#! /bin/bash - -INPUT_FILE=${1} - -source /workspace/bert/data/wikipedia_corpus/config.sh - -OUTPUT_DIR=${WORKING_DIR}/test_set_tfrecords -mkdir -p ${OUTPUT_DIR} - -#SHARD_INDEX=$(( echo ${INPUT_FILE} | egrep -o [0-9]+ )) -SHARD_INDEX=$( eval echo ${INPUT_FILE} | sed -e s/[^0-9]//g ) -OUTPUT_FILE="${OUTPUT_DIR}/tf_examples.tfrecord000${SHARD_INDEX}" - -SEED=13254 - -echo "Shard index ${SHARD_INDEX}" - -python /workspace/bert/utils/create_pretraining_data.py \ - --input_file=${INPUT_FILE} \ - --output_file=${OUTPUT_FILE} \ - --vocab_file=${VOCAB_FILE} \ - --do_lower_case=${DO_LOWER_CASE} \ - --max_seq_length=${MAX_SEQUENCE_LENGTH} \ - --max_predictions_per_seq=${MAX_PREDICTIONS_PER_SEQUENCE} \ - --masked_lm_prob=${MASKED_LM_PROB} \ - --random_seed=${SEED} \ - --dupe_factor=${DUPE_FACTOR} - diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set_xargs_wrapper.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set_xargs_wrapper.sh deleted file mode 100755 index 8d61469a..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_test_set_xargs_wrapper.sh +++ /dev/null @@ -1,12 +0,0 @@ -#! /bin/bash - -source /workspace/bert/data/wikipedia_corpus/config.sh - -SHARD_COUNT=0 -rm -rf /workspace/bert/data/wikipedia_corpus/xarg_list.txt -touch /workspace/bert/data/wikipedia_corpus/xarg_list.txt -for file in /workspace/bert/data/wikipedia_corpus/test_set_text_files/*; do - echo ${file} >> /workspace/bert/data/wikipedia_corpus/xarg_list.txt -done - -xargs -n 1 --max-procs=${N_PROCS_PREPROCESS} --arg-file=/workspace/bert/data/wikipedia_corpus/xarg_list.txt /workspace/bert/data/wikipedia_corpus/preprocessing_test_set.sh diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_xargs_wrapper.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_xargs_wrapper.sh deleted file mode 100755 index 6e52bc75..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/preprocessing_xargs_wrapper.sh +++ /dev/null @@ -1,13 +0,0 @@ -#! /bin/bash - -source /workspace/bert/data/wikipedia_corpus/config.sh - -SHARD_COUNT=0 -rm -rf /workspace/bert/data/wikipedia_corpus/xarg_list.txt -touch /workspace/bert/data/wikipedia_corpus/xarg_list.txt -for file in /workspace/bert/data/wikipedia_corpus/final_text_files_sharded/*; do - echo ${SHARD_COUNT} >> /workspace/bert/data/wikipedia_corpus/xarg_list.txt - SHARD_COUNT=$((SHARD_COUNT+1)) -done - -xargs -n 1 --max-procs=${N_PROCS_PREPROCESS} --arg-file=/workspace/bert/data/wikipedia_corpus/xarg_list.txt /workspace/bert/data/wikipedia_corpus/preprocessing.sh diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/remove_tags_and_clean.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/remove_tags_and_clean.py deleted file mode 100644 index 69ec57e2..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/remove_tags_and_clean.py +++ /dev/null @@ -1,30 +0,0 @@ -# NVIDIA - -import glob -import os - -output_file = os.environ['WORKING_DIR'] + '/intermediate_files/wikipedia.txt' - -with open(output_file, "w") as ofile: - for dirname in glob.glob('extracted_articles/*/', recursive=False): - for filename in glob.glob(dirname + 'wiki_*', recursive=True): - print(filename) - article_lines = [] - article_open = False - - with open(filename, "r") as file: - for line in file: - if "" in line: - article_open = False - for oline in article_lines[1:]: - if oline != "\n": - ofile.write(oline.rstrip() + " ") - ofile.write("\n\n") - article_lines = [] - else: - if article_open: - article_lines.append(line) - - diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/run_preprocessing.sh b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/run_preprocessing.sh deleted file mode 100755 index 4736359b..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/run_preprocessing.sh +++ /dev/null @@ -1,49 +0,0 @@ -#! /bin/bash - -source /workspace/bert/data/wikipedia_corpus/config.sh - -# Note: There are several directories created to make it clear what has been performed at each stage of preprocessing. The intermediate files may be useful if you want to further clean/prepare/augment the data for your own applications. -# NLTK was chosen as the default over spaCy simply due to speed of sentence segmentation on the large files. - -# Download Wikipedia dump file -mkdir -p ${WORKING_DIR}/download - -# Not using --noclobber since it emits an error if exists (incompatible with bash 'set -e') -echo "Downloading Wikidump" -if [ ! -f ${WORKING_DIR}/download/wikidump.xml.bz2 ]; then - cd ${WORKING_DIR}/download && wget -O wikidump.xml.bz2 ${WIKI_DUMP} -fi - -# Extract dump -echo "Extracting Wikidump" -mkdir -p ${WORKING_DIR}/raw_data -#cd ${WORKING_DIR}/raw_data && pv ${WORKING_DIR}/download/wikidump.xml.bz2 | pbzip2 -kdc > ${WORKING_DIR}/raw_data/wikidump.xml -cd ${WORKING_DIR}/raw_data && pv ${WORKING_DIR}/download/wikidump.xml.bz2 | bunzip2 -kdc > ${WORKING_DIR}/raw_data/wikidump.xml -#cd ${WORKING_DIR}/raw_data && bunzip2 -kdc ${WORKING_DIR}/download/wikidump.xml.bz2 > ${WORKING_DIR}/raw_data/wikidump.xml - -# Wikiextractor.py - Creates lots of folders/files in "doc format" -echo "Running Wikiextractor" -mkdir -p ${WORKING_DIR}/extracted_articles -/workspace/wikiextractor/WikiExtractor.py ${WORKING_DIR}/raw_data/wikidump.xml -b 1000M --processes ${N_PROCS_PREPROCESS} -o ${WORKING_DIR}/extracted_articles - -# Remove XML Tags and extraneous titles (since they are not sentences) -# Also clean to remove lines between paragraphs within article and use space-separated articles -echo "Cleaning and formatting files (one article per line)" -mkdir -p ${WORKING_DIR}/intermediate_files -python3 ${WORKING_DIR}/remove_tags_and_clean.py - -# Split articles into one-sentence-per-line format for use with BERT scripts -echo "Applying sentence segmentation to get one sentence per line" -mkdir -p ${WORKING_DIR}/final_text_file_single -python3 ${WORKING_DIR}/wiki_sentence_segmentation_nltk.py -# Note: NLTK can be replaced with Spacy, although it is slower (2 variations provided) - -# Shard finalized text so that it has a chance of fitting in memory when creating pretraining data into tfrecords (choose appropriate number of shards for distributed training) -echo "Shard text files - size is approximate to prevent splitting an article across shards" -mkdir -p ${WORKING_DIR}/final_text_files_sharded -python3 ${WORKING_DIR}/shard_text_input_file.py - -# Convert sharded text files into tfrecords that are ready for BERT pretraining -echo "Creating tfrecords for each text shard" -mkdir -p ${WORKING_DIR}/final_tfrecords_sharded -. ${WORKING_DIR}/preprocessing_xargs_wrapper.sh ${N_PROCS_PREPROCESS} diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/shard_text_input_file.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/shard_text_input_file.py deleted file mode 100644 index dad10935..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/shard_text_input_file.py +++ /dev/null @@ -1,39 +0,0 @@ -# NVIDIA - -import os - -input_file = os.environ['WORKING_DIR'] + '/final_text_file_single/wikipedia.segmented.nltk.txt' -output_file = os.environ['WORKING_DIR'] + '/final_text_files_sharded/wikipedia.segmented.part.' - -doc_seperator = "\n" - -line_buffer = [] -shard_size = 396000 # Approximate, will split at next article break -line_counter = 0 -shard_index = 0 - -ifile_lines = 0 -with open(input_file) as ifile: - for line in ifile: - ifile_lines += 1 - -print("Input file contains", ifile_lines, "lines.") - -iline_counter = 1 -with open(input_file) as ifile: - for line in ifile: - if line_counter < shard_size and iline_counter < ifile_lines: - line_buffer.append(line) - line_counter += 1 - iline_counter += 1 - elif line_counter >= shard_size and line != "\n" and iline_counter < ifile_lines: - line_buffer.append(line) - line_counter += 1 - iline_counter += 1 - else: - with open(output_file + str(shard_index) + ".txt", "w") as ofile: - for oline in line_buffer: - ofile.write(oline) - line_buffer = [] - line_counter = 0 - shard_index += 1 diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_nltk.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_nltk.py deleted file mode 100644 index 58381def..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_nltk.py +++ /dev/null @@ -1,20 +0,0 @@ -# NVIDIA - -import nltk -import os - -nltk.download('punkt') - -input_file = os.environ['WORKING_DIR'] + '/intermediate_files/wikipedia.txt' -output_file = os.environ['WORKING_DIR'] + '/final_text_file_single/wikipedia.segmented.nltk.txt' - -doc_seperator = "\n" - -with open(input_file) as ifile: - with open(output_file, "w") as ofile: - for line in ifile: - if line != "\n": - sent_list = nltk.tokenize.sent_tokenize(line) - for sent in sent_list: - ofile.write(sent + "\n") - ofile.write(doc_seperator) diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy.py deleted file mode 100644 index 69a061b4..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy.py +++ /dev/null @@ -1,22 +0,0 @@ -# NVIDIA - -import os -import spacy - -#spacy.prefer_gpu() -spacy.require_gpu() - -input_file = os.environ['WORKING_DIR'] + '/intermediate_files/wikipedia.txt' -output_file = os.environ['WORKING_DIR'] + '/final_test_file_single/wikipedia.segmented.txt' - -nlp = spacy.load('en_core_web_sm') - -doc_seperator = "\n" - -with open(input_file) as ifile: - with open(output_file, "w") as ofile: - for line in ifile: - if line != "\n": - doc = nlp(line) - for sent in doc.sents: - ofile.write(sent.text + "\n") diff --git a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy_pipe.py b/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy_pipe.py deleted file mode 100644 index ca8b83b0..00000000 --- a/TensorFlow/LanguageModeling/BERT/data/wikipedia_corpus/wiki_sentence_segmentation_spacy_pipe.py +++ /dev/null @@ -1,33 +0,0 @@ -# NVIDIA - -import os -import spacy - -#spacy.prefer_gpu() -spacy.require_gpu() - -input_file = os.environ['WORKING_DIR'] + '/intermediate_files/wikipedia.txt' -output_file = os.environ['WORKING_DIR'] + '/final_test_file_single/wikipedia.segmented.txt' - -nlp = spacy.load('en_core_web_sm') - -doc_seperator = "\n" - -file_mem = [] - -print("Reading file into memory.") -with open(input_file) as ifile: - for line in ifile: - if line != "\n": - file_mem.append(line) - -print("File read.") -print("Starting nlp.pipe") -docs = nlp.pipe(file_mem, batch_size=1000) - -print("Starting to write output") -with open(output_file, "w") as ofile: - for item in docs: - for sent in item.sents: - if sent.text != "\n": - ofile.write(sent.text + "\n") diff --git a/TensorFlow/LanguageModeling/BERT/modeling.py b/TensorFlow/LanguageModeling/BERT/modeling.py index 6c163c11..10cb472b 100644 --- a/TensorFlow/LanguageModeling/BERT/modeling.py +++ b/TensorFlow/LanguageModeling/BERT/modeling.py @@ -525,7 +525,7 @@ def embedding_postprocessor(input_tensor, # sequence has positions [0, 1, 2, ... seq_length-1], so we can just # perform a slice. position_embeddings = tf.slice(full_position_embeddings, [0, 0], - [seq_length, -1]) + [seq_length, width]) num_dims = len(output.shape.as_list()) # Only the last two dimensions are relevant (`seq_length` and `width`), so diff --git a/TensorFlow/LanguageModeling/BERT/notebooks/README.md b/TensorFlow/LanguageModeling/BERT/notebooks/README.md new file mode 100644 index 00000000..c87d313a --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/notebooks/README.md @@ -0,0 +1,90 @@ +``` +# Licensed under the Apache License, Version 2.0 (the "License") +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and limitations under the License. +``` + + +# BERT Question Answering Inference/Fine-Tuning with Mixed Precision + +## 1. Overview + +Bidirectional Embedding Representations from Transformers (BERT), is a method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. + +The original paper can be found here: https://arxiv.org/abs/1810.04805. + +NVIDIA's BERT 19.10 is an optimized version of Google's official implementation, leveraging mixed precision arithmetic and tensor cores on V100 GPUS for faster training times while maintaining target accuracy. + +### 1.a Learning objectives + +This repository contains multiple notebooks which demonstrate: +- Inference on QA task with BERT Large model +- The use/download of pretrained NVIDIA BERT models +- Fine-Tuning on SQuaD 2.0 Dataset +- Use of Mixed Precision for Inference and Fine-Tuning + +Here is a short description of each relevant file: + - _bert_squad_tf_inference.ipynb_ : BERT Q&A Inference with TF Checkpoint model + - _bert_squad_tf_finetuning.ipynb_ : BERT Fine-Tuning on SQuaD dataset + +## 2. Quick Start Guide + +### 2.a Build the BERT TensorFlow NGC container: +To run the notebook you first need to build the Bert TensorFlow container using the following command from the main directory of this repository: + +``` bash +docker build . --rm -t bert +``` +### 2.b Dataset + +We need to download the vocabulary and the bert_config files: + +``` python3 +python3 /workspace/bert/data/bertPrep.py --action download --dataset google_pretrained_weights # Includes vocab +``` + +This is only needed during fine-tuning in order to download the Squad dataset: + +``` python3 +python3 /workspace/bert/data/bertPrep.py --action download --dataset squad +``` + +### 2.c Start of the NGC container to run inference: +Once the image is built, you need to run the container with the `--publish +0.0.0.0:8888:8888` option to publish Jupyter's port `8888` to the host machine +at port `8888` over all network interfaces (`0.0.0.0`): + +```bash +nvidia-docker run \ + -v $PWD:/workspace/bert \ + -v $PWD/results:/results \ + --shm-size=1g \ + --ulimit memlock=-1 \ + --ulimit stack=67108864 \ + --publish 0.0.0.0:8888:8888 \ + -it bert:latest bash +``` + +Then you can use the following command within the BERT Tensorflow container under +`/workspace/bert`: + +```bash +jupyter notebook --ip=0.0.0.0 --allow-root +``` + +And navigate a web browser to the IP address or hostname of the host machine +at port `8888`: + +``` +http://[host machine]:8888 +``` + +Use the token listed in the output from running the `jupyter` command to log +in, for example: + +``` +http://[host machine]:8888/?token=aae96ae9387cd28151868fee318c3b3581a2d794f3b25c6b +``` diff --git a/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_finetuning.ipynb b/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_finetuning.ipynb new file mode 100755 index 00000000..25c0b8f5 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_finetuning.ipynb @@ -0,0 +1,624 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# BERT Question Answering Fine-Tuning with Mixed Precision" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Overview\n", + "\n", + "Bidirectional Embedding Representations from Transformers (BERT), is a method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. \n", + "\n", + "The original paper can be found here: https://arxiv.org/abs/1810.04805.\n", + "\n", + "NVIDIA's BERT 19.10 is an optimized version of Google's official implementation, leveraging mixed precision arithmetic and tensor cores on V100 GPUS for faster training times while maintaining target accuracy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.a Learning objectives\n", + "\n", + "This notebook demonstrates:\n", + "- Fine-Tuning on Question Answering (QA) task with BERT Large model\n", + "- The use/download of pretrained NVIDIA BERT models\n", + "- Use of Mixed Precision for Training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Requirements\n", + "\n", + "Please refer to Section 2. of the ReadMe file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. BERT Question Answering Task\n", + "\n", + "Here we run QA fine-tuning on a pre-trained BERT model.\n", + "To fine-tune we will use the [SQuaD 1.1 Dataset](https://rajpurkar.github.io/SQuAD-explorer/) which contains 100,000+ question-answer pairs on 500+ articles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "\n", + "data_dir = '/workspace/bert/data/download'\n", + "\n", + "# SQuAD json for training\n", + "train_file = os.path.join(data_dir, 'squad/v1.1/train-v1.1.json')\n", + "# json for inference\n", + "predict_file = os.path.join(data_dir, 'squad/v1.1/dev-v1.1.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.a Mixed Precision\n", + "\n", + "Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of tensor cores in the Volta and Turing architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures.\n", + "\n", + "For information about:\n", + "- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.\n", + "- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\n", + "- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook we control mixed precision execution with the following flag: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "use_fp16 = True;\n", + "\n", + "import os\n", + "os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\" if use_fp16 else \"0\" \n", + "\n", + "# For detailed debug uncomment the following line:\n", + "#os.environ[\"TF_CPP_VMODULE\"]=\"auto_mixed_precision=2\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Pre-Trained NVIDIA BERT TF Models\n", + "\n", + "Based on the model size, we have the following two default configurations of BERT.\n", + "\n", + "| **Model** | **Hidden layers** | **Hidden unit size** | **Attention heads** | **Feedforward filter size** | **Max sequence length** | **Parameters** |\n", + "|:---------:|:----------:|:----:|:---:|:--------:|:---:|:----:|\n", + "|BERTBASE |12 encoder| 768| 12|4 x 768|512|110M|\n", + "|BERTLARGE|24 encoder|1024| 16|4 x 1024|512|330M|\n", + "\n", + "We will large use pre-trained models avaialble on NGC (NVIDIA GPU Cluster, https://ngc.nvidia.com).\n", + "There are many configuration available, in particular we will download and use the following:\n", + "\n", + "**bert_tf_large_fp16_384**\n", + "\n", + "Which is pre-trained using the Wikipedia and Book corpus datasets as training data. \n", + "We will fine-tune on the SQuaD 1.1 Dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create the folders for the pre-trained models:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# bert_tf_large_fp16_384\n", + "DATA_DIR_FP16 = '/workspace/bert/data/download/pretrained_model_fp16'\n", + "!mkdir -p $DATA_DIR_FP16\n", + "!wget -nc -q --show-progress -O $DATA_DIR_FP16/bert_for_tensorflow.zip \\\n", + "https://api.ngc.nvidia.com/v2/models/nvidia/bert_for_tensorflow/versions/1/zip\n", + "!unzip -n -d $DATA_DIR_FP16/ $DATA_DIR_FP16/bert_for_tensorflow.zip " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code that follows we will refer to this model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "notebooks_dir = '/workspace/bert/notebooks'\n", + "\n", + "working_dir = '/workspace/bert'\n", + "if working_dir not in sys.path:\n", + " sys.path.append(working_dir)\n", + "\n", + "init_checkpoint = os.path.join(data_dir, 'pretrained_model_fp16/model.ckpt-1000000')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Running QA task fine-tuning\n", + "\n", + "In order to run Q-A inference we will follow step-by-step a simplified flow implemented in run_squad.py:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import run_squad\n", + "\n", + "import json\n", + "import tensorflow as tf\n", + "import modeling\n", + "import tokenization\n", + "import time\n", + "import random\n", + "\n", + "import optimization\n", + "\n", + "tf.logging.set_verbosity(tf.logging.INFO)\n", + "\n", + "# Create the output directory where all the results are saved.\n", + "output_dir = os.path.join(working_dir, 'results')\n", + "tf.gfile.MakeDirs(output_dir)\n", + "\n", + "# The config json file corresponding to the pre-trained BERT model.\n", + "# This specifies the model architecture.\n", + "bert_config_file = os.path.join(data_dir, 'google_pretrained_weights/uncased_L-24_H-1024_A-16/bert_config.json')\n", + "\n", + "# The vocabulary file that the BERT model was trained on.\n", + "vocab_file = os.path.join(data_dir, 'google_pretrained_weights/uncased_L-24_H-1024_A-16/vocab.txt')\n", + "\n", + "# Whether to lower case the input text. \n", + "# Should be True for uncased models and False for cased models.\n", + "do_lower_case = True\n", + " \n", + "# Total batch size for predictions\n", + "predict_batch_size = 1\n", + "params = dict([('batch_size', predict_batch_size)])\n", + "\n", + "# The maximum total input sequence length after WordPiece tokenization. \n", + "# Sequences longer than this will be truncated, and sequences shorter than this will be padded.\n", + "max_seq_length = 384\n", + "\n", + "# When splitting up a long document into chunks, how much stride to take between chunks.\n", + "doc_stride = 128\n", + "\n", + "# The maximum number of tokens for the question. \n", + "# Questions longer than this will be truncated to this length.\n", + "max_query_length = 64\n", + "\n", + "# This is a WA to use flags from here:\n", + "flags = tf.flags\n", + "\n", + "if 'f' not in tf.flags.FLAGS: \n", + " tf.app.flags.DEFINE_string('f', '', 'kernel')\n", + "FLAGS = flags.FLAGS\n", + "# FLAGS.verbose_logging = True\n", + "\n", + "# The total number of n-best predictions to generate in the nbest_predictions.json output file.\n", + "n_best_size = 20\n", + "\n", + "# The maximum length of an answer that can be generated. \n", + "# This is needed because the start and end predictions are not conditioned on one another.\n", + "max_answer_length = 30\n", + "\n", + "# The initial learning rate for Adam\n", + "learning_rate = 5e-6\n", + "\n", + "# Total batch size for training\n", + "train_batch_size = 3\n", + "\n", + "# Proportion of training to perform linear learning rate warmup for\n", + "warmup_proportion = 0.1\n", + "\n", + "# # Total number of training epochs to perform (results will improve if trained with epochs)\n", + "num_train_epochs = 2\n", + "\n", + "global_batch_size = train_batch_size\n", + "training_hooks = []\n", + "training_hooks.append(run_squad.LogTrainRunHook(global_batch_size, 0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create the tokenizer and the training tf_record:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Validate the casing config consistency with the checkpoint name.\n", + "tokenization.validate_case_matches_checkpoint(do_lower_case, init_checkpoint)\n", + "\n", + "# Create the tokenizer.\n", + "tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file, do_lower_case=do_lower_case)\n", + " \n", + "# Load the configuration from file\n", + "bert_config = modeling.BertConfig.from_json_file(bert_config_file)\n", + "\n", + "config = tf.ConfigProto(log_device_placement=True) \n", + "\n", + "run_config = tf.estimator.RunConfig(\n", + " model_dir=output_dir,\n", + " session_config=config,\n", + " save_checkpoints_steps=1000,\n", + " keep_checkpoint_max=1)\n", + "\n", + "# Read the training examples from the training file:\n", + "train_examples = run_squad.read_squad_examples(input_file=train_file, is_training=True)\n", + "\n", + "num_train_steps = int(len(train_examples) / global_batch_size * num_train_epochs)\n", + "num_warmup_steps = int(num_train_steps * warmup_proportion)\n", + "\n", + "# Pre-shuffle the input to avoid having to make a very large shuffle\n", + "# buffer in in the `input_fn`.\n", + "rng = random.Random(12345)\n", + "rng.shuffle(train_examples)\n", + "\n", + "start_index = 0 \n", + "end_index = len(train_examples)\n", + "tmp_filenames = os.path.join(output_dir, \"train.tf_record\")\n", + "\n", + "# We write to a temporary file to avoid storing very large constant tensors\n", + "# in memory.\n", + "train_writer = run_squad.FeatureWriter(\n", + " filename=tmp_filenames,\n", + " is_training=True)\n", + "\n", + "run_squad.convert_examples_to_features(\n", + " examples=train_examples[start_index:end_index],\n", + " tokenizer=tokenizer,\n", + " max_seq_length=max_seq_length,\n", + " doc_stride=doc_stride,\n", + " max_query_length=max_query_length,\n", + " is_training=True,\n", + " output_fn=train_writer.process_feature)\n", + "\n", + "train_writer.close()\n", + "\n", + "tf.logging.info(\"***** Running training *****\")\n", + "tf.logging.info(\" Num orig examples = %d\", end_index - start_index)\n", + "tf.logging.info(\" Num split examples = %d\", train_writer.num_features)\n", + "tf.logging.info(\" Batch size = %d\", train_batch_size)\n", + "tf.logging.info(\" Num steps = %d\", num_train_steps)\n", + "tf.logging.info(\" LR = %f\", learning_rate)\n", + "\n", + "del train_examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to create the model for the estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n", + " unique_ids = features[\"unique_ids\"]\n", + " input_ids = features[\"input_ids\"]\n", + " input_mask = features[\"input_mask\"]\n", + " segment_ids = features[\"segment_ids\"]\n", + " \n", + " is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n", + "\n", + " (start_logits, end_logits) = run_squad.create_model(\n", + " bert_config=bert_config,\n", + " is_training=is_training,\n", + " input_ids=input_ids,\n", + " input_mask=input_mask,\n", + " segment_ids=segment_ids,\n", + " use_one_hot_embeddings=False)\n", + "\n", + " tvars = tf.trainable_variables()\n", + "\n", + " initialized_variable_names = {}\n", + " if init_checkpoint:\n", + " (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n", + " tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n", + "\n", + " output_spec = None\n", + " if mode == tf.estimator.ModeKeys.TRAIN:\n", + " seq_length = modeling.get_shape_list(input_ids)[1]\n", + " \n", + " def compute_loss(logits, positions):\n", + " one_hot_positions = tf.one_hot(positions, depth=seq_length, dtype=tf.float32)\n", + " log_probs = tf.nn.log_softmax(logits, axis=-1)\n", + " loss = -tf.reduce_mean(tf.reduce_sum(one_hot_positions * log_probs, axis=-1))\n", + " return loss\n", + "\n", + " start_positions = features[\"start_positions\"]\n", + " end_positions = features[\"end_positions\"]\n", + " start_loss = compute_loss(start_logits, start_positions)\n", + " end_loss = compute_loss(end_logits, end_positions)\n", + " total_loss = (start_loss + end_loss) / 2.0\n", + " \n", + " train_op = optimization.create_optimizer(total_loss, learning_rate, num_train_steps, num_warmup_steps, None, False, use_fp16)\n", + " \n", + " output_spec = tf.estimator.EstimatorSpec(mode=mode, loss=total_loss, train_op=train_op)\n", + " \n", + " elif mode == tf.estimator.ModeKeys.PREDICT:\n", + " predictions = {\n", + " \"unique_ids\": unique_ids,\n", + " \"start_logits\": start_logits,\n", + " \"end_logits\": end_logits,\n", + " }\n", + " output_spec = tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)\n", + "\n", + " return output_spec\n", + "\n", + "estimator = tf.estimator.Estimator(\n", + " model_fn=model_fn,\n", + " config=run_config,\n", + " params=params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.a Fine Tuning\n", + "\n", + "Fine tuning is performed using the run_squad.py.\n", + "\n", + "The run_squad.sh script trains a model and performs evaluation on the SQuaD v1.1 dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "train_input_fn = run_squad.input_fn_builder(\n", + " input_file=tmp_filenames,\n", + " batch_size=train_batch_size,\n", + " seq_length=max_seq_length,\n", + " is_training=True,\n", + " drop_remainder=True,\n", + " hvd=None)\n", + "\n", + "train_start_time = time.time()\n", + "estimator.train(input_fn=train_input_fn, hooks=training_hooks, max_steps=num_train_steps)\n", + "train_time_elapsed = time.time() - train_start_time\n", + "train_time_wo_startup = training_hooks[-1].total_time\n", + "\n", + "avg_sentences_per_second = num_train_steps * global_batch_size * 1.0 / train_time_wo_startup if train_time_wo_startup else 0\n", + "\n", + "tf.logging.info(\"-----------------------------\")\n", + "tf.logging.info(\"Total Training Time = %0.2f Training Time W/O start up overhead = %0.2f \"\n", + " \"Sentences processed = %d\", train_time_elapsed, train_time_wo_startup,\n", + " num_train_steps * global_batch_size)\n", + "tf.logging.info(\"Training Performance = %0.4f sentences/sec\", avg_sentences_per_second)\n", + "tf.logging.info(\"-----------------------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.b Inference\n", + "\n", + "Now we run inference with the fine-tuned model just saved:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eval_examples = run_squad.read_squad_examples(\n", + " input_file=predict_file, is_training=False)\n", + "\n", + "eval_writer = run_squad.FeatureWriter(\n", + " filename=os.path.join(output_dir, \"eval.tf_record\"),\n", + " is_training=False)\n", + "\n", + "eval_features = []\n", + "def append_feature(feature):\n", + " eval_features.append(feature)\n", + " eval_writer.process_feature(feature)\n", + "\n", + "\n", + "# Loads a data file into a list of InputBatch's\n", + "run_squad.convert_examples_to_features(\n", + " examples=eval_examples,\n", + " tokenizer=tokenizer,\n", + " max_seq_length=max_seq_length,\n", + " doc_stride=doc_stride,\n", + " max_query_length=max_query_length,\n", + " is_training=False,\n", + " output_fn=append_feature)\n", + "\n", + "eval_writer.close()\n", + "\n", + "tf.logging.info(\"***** Running predictions *****\")\n", + "tf.logging.info(\" Num orig examples = %d\", len(eval_examples))\n", + "tf.logging.info(\" Num split examples = %d\", len(eval_features))\n", + "tf.logging.info(\" Batch size = %d\", predict_batch_size)\n", + "\n", + "predict_input_fn = run_squad.input_fn_builder(\n", + " input_file=eval_writer.filename,\n", + " batch_size=predict_batch_size,\n", + " seq_length=max_seq_length,\n", + " is_training=False,\n", + " drop_remainder=False)\n", + "\n", + "all_results = []\n", + "eval_hooks = [run_squad.LogEvalRunHook(predict_batch_size)]\n", + "eval_start_time = time.time()\n", + "for result in estimator.predict(\n", + " predict_input_fn, yield_single_examples=True, hooks=eval_hooks, checkpoint_path=None):\n", + " unique_id = int(result[\"unique_ids\"])\n", + " start_logits = [float(x) for x in result[\"start_logits\"].flat]\n", + " end_logits = [float(x) for x in result[\"end_logits\"].flat]\n", + " all_results.append(\n", + " run_squad.RawResult(\n", + " unique_id=unique_id,\n", + " start_logits=start_logits,\n", + " end_logits=end_logits))\n", + "\n", + "eval_time_elapsed = time.time() - eval_start_time\n", + "eval_time_wo_startup = eval_hooks[-1].total_time\n", + "num_sentences = eval_hooks[-1].count * predict_batch_size\n", + "avg_sentences_per_second = num_sentences * 1.0 / eval_time_wo_startup\n", + "\n", + "tf.logging.info(\"-----------------------------\")\n", + "tf.logging.info(\"Total Inference Time = %0.2f Inference Time W/O start up overhead = %0.2f \"\n", + " \"Sentences processed = %d\", eval_time_elapsed, eval_time_wo_startup,\n", + " num_sentences)\n", + "tf.logging.info(\"Inference Performance = %0.4f sentences/sec\", avg_sentences_per_second)\n", + "tf.logging.info(\"-----------------------------\")\n", + "\n", + "output_prediction_file = os.path.join(output_dir, \"predictions.json\")\n", + "output_nbest_file = os.path.join(output_dir, \"nbest_predictions.json\")\n", + "output_null_log_odds_file = os.path.join(output_dir, \"null_odds.json\")\n", + "\n", + "run_squad.write_predictions(eval_examples, eval_features, all_results,\n", + " n_best_size, max_answer_length,\n", + " do_lower_case, output_prediction_file,\n", + " output_nbest_file, output_null_log_odds_file)\n", + "\n", + "tf.logging.info(\"Inference Results:\")\n", + "\n", + "# Here we show only the prediction results, nbest prediction is also available in the output directory\n", + "results = \"\"\n", + "with open(output_prediction_file, 'r') as json_file:\n", + " data = json.load(json_file)\n", + " for question in eval_examples:\n", + " results += \"{}{}{}\".format(question.qas_id, question.question_text, data[question.qas_id])\n", + "\n", + "\n", + "from IPython.display import display, HTML\n", + "display(HTML(\"{}
IdQuestionAnswer
\".format(results))) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.b Evaluation\n", + "\n", + "Let's run evaluation using the script in the SQuaD1.1 folder and our fine-tuned model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python /workspace/bert/data/download/squad/v1.1/evaluate-v1.1.py \\\n", + " $predict_file \\\n", + " $output_dir/predictions.json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. What's next\n", + "\n", + "Now that you have fine-tuned a BERT model you may want to take a look ad the run_squad script which containd more options for fine-tuning." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_inference.ipynb b/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_inference.ipynb new file mode 100755 index 00000000..27e6db11 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_inference.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# BERT Question Answering Inference with Mixed Precision\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Overview\n", + "\n", + "Bidirectional Embedding Representations from Transformers (BERT), is a method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. \n", + "\n", + "The original paper can be found here: https://arxiv.org/abs/1810.04805.\n", + "\n", + "NVIDIA's BERT 19.10 is an optimized version of Google's official implementation, leveraging mixed precision arithmetic and tensor cores on V100 GPUS for faster training times while maintaining target accuracy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.a Learning objectives\n", + "\n", + "This notebook demonstrates:\n", + "- Inference on QA task with BERT Large model\n", + "- The use/download of fine-tuned NVIDIA BERT models\n", + "- Use of Mixed Precision for Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Requirements\n", + "\n", + "Please refer to the ReadMe file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. BERT Inference: Question Answering\n", + "\n", + "We can run inference on a fine-tuned BERT model for tasks like Question Answering.\n", + "\n", + "Here we use a BERT model fine-tuned on a [SQuaD 2.0 Dataset](https://rajpurkar.github.io/SQuAD-explorer/) which contains 100,000+ question-answer pairs on 500+ articles combined with over 50,000 new, unanswerable questions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.a Paragraph and Queries\n", + "\n", + "In this example we will ask our BERT model questions related to the following paragraph:\n", + "\n", + "**The Apollo Program**\n", + "_\"The Apollo program, also known as Project Apollo, was the third United States human spaceflight program carried out by the National Aeronautics and Space Administration (NASA), which accomplished landing the first humans on the Moon from 1969 to 1972. First conceived during Dwight D. Eisenhower's administration as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space, Apollo was later dedicated to President John F. Kennedy's national goal of landing a man on the Moon and returning him safely to the Earth by the end of the 1960s, which he proposed in a May 25, 1961, address to Congress. Project Mercury was followed by the two-man Project Gemini. The first manned flight of Apollo was in 1968. Apollo ran from 1961 to 1972, and was supported by the two-man Gemini program which ran concurrently with it from 1962 to 1966. Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions. Apollo used Saturn family rockets as launch vehicles. Apollo/Saturn vehicles were also used for an Apollo Applications Program, which consisted of Skylab, a space station that supported three manned missions in 1973-74, and the Apollo-Soyuz Test Project, a joint Earth orbit mission with the Soviet Union in 1975.\"_\n", + "\n", + "The questions and relative answers expected are shown below:\n", + "\n", + " - **Q1:** \"What project put the first Americans into space?\" \n", + " - **A1:** \"Project Mercury\"\n", + " - **Q2:** \"What program was created to carry out these projects and missions?\"\n", + " - **A2:** \"The Apollo program\"\n", + " - **Q3:** \"What year did the first manned Apollo flight occur?\"\n", + " - **A3:** \"1968\"\n", + " - **Q4:** \"What President is credited with the original notion of putting Americans in space?\"\n", + " - **A4:** \"John F. Kennedy\"\n", + " - **Q5:** \"Who did the U.S. collaborate with on an Earth orbit mission in 1975?\"\n", + " - **A5:** \"Soviet Union\"\n", + " - **Q6:** \"How long did Project Apollo run?\"\n", + " - **A6:** \"1961 to 1972\"\n", + " - **Q7:** \"What program helped develop space travel techniques that Project Apollo used?\"\n", + " - **A7:** \"Gemini Mission\"\n", + " - **Q8:** \"What space station supported three manned missions in 1973-1974?\"\n", + " - **A8:** \"Skylab\"\n", + " \n", + "---\n", + "\n", + "The paragraph and the questions can be easily customized by changing the code below:\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile input.json\n", + "{\"data\": \n", + " [\n", + " {\"title\": \"Project Apollo\",\n", + " \"paragraphs\": [\n", + " {\"context\":\"The Apollo program, also known as Project Apollo, was the third United States human spaceflight program carried out by the National Aeronautics and Space Administration (NASA), which accomplished landing the first humans on the Moon from 1969 to 1972. First conceived during Dwight D. Eisenhower's administration as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space, Apollo was later dedicated to President John F. Kennedy's national goal of landing a man on the Moon and returning him safely to the Earth by the end of the 1960s, which he proposed in a May 25, 1961, address to Congress. Project Mercury was followed by the two-man Project Gemini. The first manned flight of Apollo was in 1968. Apollo ran from 1961 to 1972, and was supported by the two man Gemini program which ran concurrently with it from 1962 to 1966. Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions. Apollo used Saturn family rockets as launch vehicles. Apollo/Saturn vehicles were also used for an Apollo Applications Program, which consisted of Skylab, a space station that supported three manned missions in 1973-74, and the Apollo-Soyuz Test Project, a joint Earth orbit mission with the Soviet Union in 1975.\", \n", + " \"qas\": [\n", + " { \"question\": \"What project put the first Americans into space?\", \n", + " \"id\": \"Q1\"\n", + " },\n", + " { \"question\": \"What program was created to carry out these projects and missions?\",\n", + " \"id\": \"Q2\"\n", + " },\n", + " { \"question\": \"What year did the first manned Apollo flight occur?\",\n", + " \"id\": \"Q3\"\n", + " }, \n", + " { \"question\": \"What President is credited with the original notion of putting Americans in space?\",\n", + " \"id\": \"Q4\"\n", + " },\n", + " { \"question\": \"Who did the U.S. collaborate with on an Earth orbit mission in 1975?\",\n", + " \"id\": \"Q5\"\n", + " },\n", + " { \"question\": \"How long did Project Apollo run?\",\n", + " \"id\": \"Q6\"\n", + " }, \n", + " { \"question\": \"What program helped develop space travel techniques that Project Apollo used?\",\n", + " \"id\": \"Q7\"\n", + " }, \n", + " {\"question\": \"What space station supported three manned missions in 1973-1974?\",\n", + " \"id\": \"Q8\"\n", + " } \n", + "]}]}]}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "\n", + "notebooks_dir = '/workspace/bert/notebooks'\n", + "data_dir = '/workspace/bert/data/download'\n", + "\n", + "working_dir = '/workspace/bert'\n", + "if working_dir not in sys.path:\n", + " sys.path.append(working_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_file = os.path.join(notebooks_dir, 'input.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.b Mixed Precision\n", + "\n", + "Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of tensor cores in the Volta and Turing architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures.\n", + "\n", + "For information about:\n", + "- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.\n", + "- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\n", + "- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook we control mixed precision execution with the environmental variable:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = \"1\" " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can choose the mixed precision model (which takes much less time to train than the fp32 version) without losing accuracy, with the following flag: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "use_mixed_precision_model = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To effectively evaluate the speedup of mixed precision try a bigger workload by uncommenting the following line:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#input_file = '/workspace/bert/data/download/squad/v2.0/dev-v2.0.json'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Fine-Tuned NVIDIA BERT TF Models\n", + "\n", + "Based on the model size, we have the following two default configurations of BERT.\n", + "\n", + "| **Model** | **Hidden layers** | **Hidden unit size** | **Attention heads** | **Feedforward filter size** | **Max sequence length** | **Parameters** |\n", + "|:---------:|:----------:|:----:|:---:|:--------:|:---:|:----:|\n", + "|BERTBASE |12 encoder| 768| 12|4 x 768|512|110M|\n", + "|BERTLARGE|24 encoder|1024| 16|4 x 1024|512|330M|\n", + "\n", + "We will take advantage of the fine-tuned models available on NGC (NVIDIA GPU Cluster, https://ngc.nvidia.com).\n", + "Among the many configurations available we will download these two:\n", + "\n", + " - **bert_tf_v2_large_fp32_384**\n", + "\n", + " - **bert_tf_v2_large_fp16_384**\n", + "\n", + "Which are trained on the SQuaD 2.0 Dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# bert_tf_v2_large_fp32_384\n", + "DATA_DIR_FP32='/workspace/bert/data/download/finetuned_model_fp32'\n", + "!mkdir -p $DATA_DIR_FP32\n", + "!wget -nc -q --show-progress -O $DATA_DIR_FP32/bert_tf_v2_large_fp32_384.zip \\\n", + "https://api.ngc.nvidia.com/v2/models/nvidia/bert_tf_v2_large_fp32_384/versions/1/zip\n", + "!unzip -n -d $DATA_DIR_FP32/ $DATA_DIR_FP32/bert_tf_v2_large_fp32_384.zip \n", + " \n", + "# bert_tf_v2_large_fp16_384\n", + "DATA_DIR_FP16='/workspace/bert/data/download/finetuned_model_fp16'\n", + "!mkdir -p $DATA_DIR_FP16\n", + "!wget -nc -q --show-progress -O $DATA_DIR_FP16/bert_tf_v2_large_fp16_384.zip \\\n", + "https://api.ngc.nvidia.com/v2/models/nvidia/bert_tf_v2_large_fp16_384/versions/1/zip\n", + "!unzip -n -d $DATA_DIR_FP16/ $DATA_DIR_FP16/bert_tf_v2_large_fp16_384.zip " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code that follows we will refer to these models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Running QA task inference\n", + "\n", + "In order to run QA inference we will follow step-by-step the flow implemented in run_squad.py.\n", + "\n", + "Configuration:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import run_squad\n", + "import json\n", + "import tensorflow as tf\n", + "import modeling\n", + "import tokenization\n", + "import time\n", + "import random\n", + "\n", + "tf.logging.set_verbosity(tf.logging.INFO)\n", + "\n", + "# Create the output directory where all the results are saved.\n", + "output_dir = os.path.join(working_dir, 'results')\n", + "tf.gfile.MakeDirs(output_dir)\n", + "\n", + "# The config json file corresponding to the pre-trained BERT model.\n", + "# This specifies the model architecture.\n", + "bert_config_file = os.path.join(data_dir, 'google_pretrained_weights/uncased_L-24_H-1024_A-16/bert_config.json')\n", + "\n", + "# The vocabulary file that the BERT model was trained on.\n", + "vocab_file = os.path.join(data_dir, 'google_pretrained_weights/uncased_L-24_H-1024_A-16/vocab.txt')\n", + "\n", + "# Depending on the mixed precision flag we use different fine-tuned model\n", + "if use_mixed_precision_model:\n", + " init_checkpoint = os.path.join(data_dir, 'finetuned_model_fp16/model.ckpt-8144')\n", + "else:\n", + " init_checkpoint = os.path.join(data_dir, 'finetuned_model_fp32/model.ckpt-8144')\n", + "\n", + "# Whether to lower case the input text. \n", + "# Should be True for uncased models and False for cased models.\n", + "do_lower_case = True\n", + " \n", + "# Total batch size for predictions\n", + "predict_batch_size = 1\n", + "params = dict([('batch_size', predict_batch_size)])\n", + "\n", + "# The maximum total input sequence length after WordPiece tokenization. \n", + "# Sequences longer than this will be truncated, and sequences shorter than this will be padded.\n", + "max_seq_length = 384\n", + "\n", + "# When splitting up a long document into chunks, how much stride to take between chunks.\n", + "doc_stride = 128\n", + "\n", + "# The maximum number of tokens for the question. \n", + "# Questions longer than this will be truncated to this length.\n", + "max_query_length = 64\n", + "\n", + "# This is a WA to use flags from here:\n", + "flags = tf.flags\n", + "\n", + "if 'f' not in tf.flags.FLAGS: \n", + " tf.app.flags.DEFINE_string('f', '', 'kernel')\n", + "FLAGS = flags.FLAGS\n", + "\n", + "# The total number of n-best predictions to generate in the nbest_predictions.json output file.\n", + "n_best_size = 20\n", + "\n", + "# The maximum length of an answer that can be generated. \n", + "# This is needed because the start and end predictions are not conditioned on one another.\n", + "max_answer_length = 30" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define the tokenizer and create the model for the estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Validate the casing config consistency with the checkpoint name.\n", + "tokenization.validate_case_matches_checkpoint(do_lower_case, init_checkpoint)\n", + "\n", + "# Create the tokenizer.\n", + "tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file, do_lower_case=do_lower_case)\n", + "\n", + "# Load the configuration from file\n", + "bert_config = modeling.BertConfig.from_json_file(bert_config_file)\n", + "\n", + "def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n", + " unique_ids = features[\"unique_ids\"]\n", + " input_ids = features[\"input_ids\"]\n", + " input_mask = features[\"input_mask\"]\n", + " segment_ids = features[\"segment_ids\"]\n", + "\n", + " (start_logits, end_logits) = run_squad.create_model(\n", + " bert_config=bert_config,\n", + " is_training=False,\n", + " input_ids=input_ids,\n", + " input_mask=input_mask,\n", + " segment_ids=segment_ids,\n", + " use_one_hot_embeddings=False)\n", + "\n", + " tvars = tf.trainable_variables()\n", + "\n", + " initialized_variable_names = {}\n", + " (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n", + " tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n", + " output_spec = None\n", + " predictions = {\"unique_ids\": unique_ids,\n", + " \"start_logits\": start_logits,\n", + " \"end_logits\": end_logits}\n", + " output_spec = tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)\n", + " return output_spec\n", + "\n", + "config = tf.ConfigProto(log_device_placement=True) \n", + "\n", + "run_config = tf.estimator.RunConfig(\n", + " model_dir=None,\n", + " session_config=config,\n", + " save_checkpoints_steps=1000,\n", + " keep_checkpoint_max=1)\n", + "\n", + "estimator = tf.estimator.Estimator(\n", + " model_fn=model_fn,\n", + " config=run_config,\n", + " params=params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.a Inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "eval_examples = run_squad.read_squad_examples(\n", + " input_file=input_file, is_training=False)\n", + "\n", + "eval_writer = run_squad.FeatureWriter(\n", + " filename=os.path.join(output_dir, \"eval.tf_record\"),\n", + " is_training=False)\n", + "\n", + "eval_features = []\n", + "def append_feature(feature):\n", + " eval_features.append(feature)\n", + " eval_writer.process_feature(feature)\n", + "\n", + "\n", + "# Loads a data file into a list of InputBatch's\n", + "run_squad.convert_examples_to_features(\n", + " examples=eval_examples,\n", + " tokenizer=tokenizer,\n", + " max_seq_length=max_seq_length,\n", + " doc_stride=doc_stride,\n", + " max_query_length=max_query_length,\n", + " is_training=False,\n", + " output_fn=append_feature)\n", + "\n", + "eval_writer.close()\n", + "\n", + "tf.logging.info(\"***** Running predictions *****\")\n", + "tf.logging.info(\" Num orig examples = %d\", len(eval_examples))\n", + "tf.logging.info(\" Num split examples = %d\", len(eval_features))\n", + "tf.logging.info(\" Batch size = %d\", predict_batch_size)\n", + "\n", + "predict_input_fn = run_squad.input_fn_builder(\n", + " input_file=eval_writer.filename,\n", + " batch_size=predict_batch_size,\n", + " seq_length=max_seq_length,\n", + " is_training=False,\n", + " drop_remainder=False)\n", + "\n", + "all_results = []\n", + "eval_hooks = [run_squad.LogEvalRunHook(predict_batch_size)]\n", + "eval_start_time = time.time()\n", + "for result in estimator.predict(\n", + " predict_input_fn, yield_single_examples=True, hooks=eval_hooks, checkpoint_path=init_checkpoint):\n", + " unique_id = int(result[\"unique_ids\"])\n", + " start_logits = [float(x) for x in result[\"start_logits\"].flat]\n", + " end_logits = [float(x) for x in result[\"end_logits\"].flat]\n", + " all_results.append(\n", + " run_squad.RawResult(\n", + " unique_id=unique_id,\n", + " start_logits=start_logits,\n", + " end_logits=end_logits))\n", + "\n", + "eval_time_elapsed = time.time() - eval_start_time\n", + "\n", + "eval_time_wo_startup = eval_hooks[-1].total_time\n", + "num_sentences = eval_hooks[-1].count * predict_batch_size\n", + "avg_sentences_per_second = num_sentences * 1.0 / eval_time_wo_startup\n", + "\n", + "tf.logging.info(\"-----------------------------\")\n", + "tf.logging.info(\"Total Inference Time = %0.2f Inference Time W/O start up overhead = %0.2f \"\n", + " \"Sentences processed = %d\", eval_time_elapsed, eval_time_wo_startup,\n", + " num_sentences)\n", + "tf.logging.info(\"Inference Performance = %0.4f sentences/sec\", avg_sentences_per_second)\n", + "tf.logging.info(\"-----------------------------\")\n", + "\n", + "output_prediction_file = os.path.join(output_dir, \"predictions.json\")\n", + "output_nbest_file = os.path.join(output_dir, \"nbest_predictions.json\")\n", + "output_null_log_odds_file = os.path.join(output_dir, \"null_odds.json\")\n", + "\n", + "run_squad.write_predictions(eval_examples, eval_features, all_results,\n", + " n_best_size, max_answer_length,\n", + " do_lower_case, output_prediction_file,\n", + " output_nbest_file, output_null_log_odds_file)\n", + "\n", + "tf.logging.info(\"Inference Results:\")\n", + "\n", + "# Here we show only the prediction results, nbest prediction is also available in the output directory\n", + "results = \"\"\n", + "with open(output_prediction_file, 'r') as json_file:\n", + " data = json.load(json_file)\n", + " for question in eval_examples:\n", + " results += \"{}{}{}\".format(question.qas_id, question.question_text, data[question.qas_id])\n", + "\n", + "\n", + "from IPython.display import display, HTML\n", + "display(HTML(\"{}
IdQuestionAnswer
\".format(results))) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. What's next" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you are familiar with running QA Inference on BERT, using mixed precision, you may want to try\n", + "your own paragraphs and queries. \n", + "\n", + "You may also want to take a look to the notebook __bert_squad_tf_finetuning.ipynb__ on how to run fine-tuning on BERT, available in the same directory." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_inference_colab.ipynb b/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_inference_colab.ipynb new file mode 100644 index 00000000..e4bf17e3 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/notebooks/bert_squad_tf_inference_colab.ipynb @@ -0,0 +1,773 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jDXroBuNw60P" + }, + "outputs": [], + "source": [ + "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "k-XnFINow60d" + }, + "source": [ + "\n", + "\n", + "# BERT Question Answering Inference with Mixed Precision\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TfF7V662w60j" + }, + "source": [ + "## 1. Overview\n", + "\n", + "Bidirectional Embedding Representations from Transformers (BERT), is a method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. \n", + "\n", + "The original paper can be found here: https://arxiv.org/abs/1810.04805.\n", + "\n", + "NVIDIA's BERT 19.10 is an optimized version of Google's official implementation, leveraging mixed precision arithmetic and tensor cores on V100 GPUS for faster training times while maintaining target accuracy." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Ah3Lv9zyw60l" + }, + "source": [ + "### 1.a Learning objectives\n", + "\n", + "This notebook demonstrates:\n", + "- Inference on QA task with BERT Large model\n", + "- The use/download of fine-tuned NVIDIA BERT models\n", + "- Use of Mixed Precision for Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hxNJ8HByw60o" + }, + "source": [ + "## 2. Requirements\n", + "\n", + "Please enable the GPU runtime (Runtime->Change Runtime Type)\n", + "\n", + "Download the required files from NVIDIA-Github:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KV_WnOY4zUa_" + }, + "outputs": [], + "source": [ + "!wget -nc -q --show-progress -O ./master.zip \\\n", + "https://github.com/NVIDIA/DeepLearningExamples/archive/master.zip\n", + "!unzip -q -n -d . ./master.zip " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "5D7i7Pao5qoj" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "WORKSPACE_DIR='./DeepLearningExamples-master/TensorFlow/LanguageModeling/BERT/'\n", + "os.chdir(WORKSPACE_DIR)\n", + "print (os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "p560UwaE6lAf" + }, + "outputs": [], + "source": [ + "ls" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mjlZbP0dw60r" + }, + "source": [ + "## 3. BERT Inference: Question Answering\n", + "\n", + "We can run inference on a fine-tuned BERT model for tasks like Question Answering.\n", + "\n", + "Here we use a BERT model fine-tuned on a [SQuaD 2.0 Dataset](https://rajpurkar.github.io/SQuAD-explorer/) which contains 100,000+ question-answer pairs on 500+ articles combined with over 50,000 new, unanswerable questions." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mOc16svBw60t" + }, + "source": [ + "### 3.a Paragraph and Queries\n", + "\n", + "In this example we will ask our BERT model questions related to the following paragraph:\n", + "\n", + "**The Apollo Program**\n", + "_\"The Apollo program, also known as Project Apollo, was the third United States human spaceflight program carried out by the National Aeronautics and Space Administration (NASA), which accomplished landing the first humans on the Moon from 1969 to 1972. First conceived during Dwight D. Eisenhower's administration as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space, Apollo was later dedicated to President John F. Kennedy's national goal of landing a man on the Moon and returning him safely to the Earth by the end of the 1960s, which he proposed in a May 25, 1961, address to Congress. Project Mercury was followed by the two-man Project Gemini. The first manned flight of Apollo was in 1968. Apollo ran from 1961 to 1972, and was supported by the two-man Gemini program which ran concurrently with it from 1962 to 1966. Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions. Apollo used Saturn family rockets as launch vehicles. Apollo/Saturn vehicles were also used for an Apollo Applications Program, which consisted of Skylab, a space station that supported three manned missions in 1973-74, and the Apollo-Soyuz Test Project, a joint Earth orbit mission with the Soviet Union in 1975.\"_\n", + "\n", + "The questions and relative answers expected are shown below:\n", + "\n", + " - **Q1:** \"What project put the first Americans into space?\" \n", + " - **A1:** \"Project Mercury\"\n", + " - **Q2:** \"What program was created to carry out these projects and missions?\"\n", + " - **A2:** \"The Apollo program\"\n", + " - **Q3:** \"What year did the first manned Apollo flight occur?\"\n", + " - **A3:** \"1968\"\n", + " - **Q4:** \"What President is credited with the original notion of putting Americans in space?\"\n", + " - **A4:** \"John F. Kennedy\"\n", + " - **Q5:** \"Who did the U.S. collaborate with on an Earth orbit mission in 1975?\"\n", + " - **A5:** \"Soviet Union\"\n", + " - **Q6:** \"How long did Project Apollo run?\"\n", + " - **A6:** \"1961 to 1972\"\n", + " - **Q7:** \"What program helped develop space travel techniques that Project Apollo used?\"\n", + " - **A7:** \"Gemini Mission\"\n", + " - **Q8:** \"What space station supported three manned missions in 1973-1974?\"\n", + " - **A8:** \"Skylab\"\n", + " \n", + "---\n", + "\n", + "The paragraph and the questions can be easily customized by changing the code below:\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "srU0TT1Iw60v" + }, + "outputs": [], + "source": [ + "%%writefile input.json\n", + "{\"data\": \n", + " [\n", + " {\"title\": \"Project Apollo\",\n", + " \"paragraphs\": [\n", + " {\"context\":\"The Apollo program, also known as Project Apollo, was the third United States human spaceflight program carried out by the National Aeronautics and Space Administration (NASA), which accomplished landing the first humans on the Moon from 1969 to 1972. First conceived during Dwight D. Eisenhower's administration as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space, Apollo was later dedicated to President John F. Kennedy's national goal of landing a man on the Moon and returning him safely to the Earth by the end of the 1960s, which he proposed in a May 25, 1961, address to Congress. Project Mercury was followed by the two-man Project Gemini. The first manned flight of Apollo was in 1968. Apollo ran from 1961 to 1972, and was supported by the two man Gemini program which ran concurrently with it from 1962 to 1966. Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions. Apollo used Saturn family rockets as launch vehicles. Apollo/Saturn vehicles were also used for an Apollo Applications Program, which consisted of Skylab, a space station that supported three manned missions in 1973-74, and the Apollo-Soyuz Test Project, a joint Earth orbit mission with the Soviet Union in 1975.\", \n", + " \"qas\": [\n", + " { \"question\": \"What project put the first Americans into space?\", \n", + " \"id\": \"Q1\"\n", + " },\n", + " { \"question\": \"What program was created to carry out these projects and missions?\",\n", + " \"id\": \"Q2\"\n", + " },\n", + " { \"question\": \"What year did the first manned Apollo flight occur?\",\n", + " \"id\": \"Q3\"\n", + " }, \n", + " { \"question\": \"What President is credited with the original notion of putting Americans in space?\",\n", + " \"id\": \"Q4\"\n", + " },\n", + " { \"question\": \"Who did the U.S. collaborate with on an Earth orbit mission in 1975?\",\n", + " \"id\": \"Q5\"\n", + " },\n", + " { \"question\": \"How long did Project Apollo run?\",\n", + " \"id\": \"Q6\"\n", + " }, \n", + " { \"question\": \"What program helped develop space travel techniques that Project Apollo used?\",\n", + " \"id\": \"Q7\"\n", + " }, \n", + " {\"question\": \"What space station supported three manned missions in 1973-1974?\",\n", + " \"id\": \"Q8\"\n", + " } \n", + "]}]}]}" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ujyka-8Iw603" + }, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "working_dir = os.getcwd();\n", + "data_dir = os.path.join(working_dir, 'data/download');\n", + "if working_dir not in sys.path:\n", + " sys.path.append(working_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "6gA3-6LVw61D" + }, + "outputs": [], + "source": [ + "input_file = os.path.join(working_dir, 'input.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "D9p8XaBnw61N" + }, + "source": [ + "### 3.b Mixed Precision\n", + "\n", + "Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of tensor cores in the Volta and Turing architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures.\n", + "\n", + "For information about:\n", + "- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.\n", + "- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\n", + "- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ceeYPqQcw61P" + }, + "source": [ + "In this notebook we control mixed precision execution with the environmental variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "k4jIJevFw61R" + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = \"1\" " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rt_4-ZA5w61Y" + }, + "source": [ + "We can choose the mixed precision model (which takes much less time to train than the fp32 version) without losing accuracy, with the following flag: " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "BRdclfEaw61Z" + }, + "outputs": [], + "source": [ + "use_mixed_precision_model = True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tUQ1jWFHw61h" + }, + "source": [ + "To effectively evaluate the speedup of mixed precision try a bigger workload by uncommenting the following line:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VpkeBiyPw61j" + }, + "outputs": [], + "source": [ + "#input_file = os.path.join(working_dir, 'data/download/squad/v2.0/dev-v2.0.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iu4Jb5puw61p" + }, + "source": [ + "## 4. Fine-Tuned NVIDIA BERT TF Models\n", + "\n", + "Based on the model size, we have the following two default configurations of BERT.\n", + "\n", + "| **Model** | **Hidden layers** | **Hidden unit size** | **Attention heads** | **Feedforward filter size** | **Max sequence length** | **Parameters** |\n", + "|:---------:|:----------:|:----:|:---:|:--------:|:---:|:----:|\n", + "|BERTBASE |12 encoder| 768| 12|4 x 768|512|110M|\n", + "|BERTLARGE|24 encoder|1024| 16|4 x 1024|512|330M|\n", + "\n", + "We will take advantage of the fine-tuned models available on NGC (NVIDIA GPU Cluster, https://ngc.nvidia.com).\n", + "Among the many configurations available we will download these two:\n", + "\n", + " - **bert_tf_v2_large_fp32_384**\n", + "\n", + " - **bert_tf_v2_large_fp16_384**\n", + "\n", + "Which are trained on the SQuaD 2.0 Dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "5JWKZfP8w61t" + }, + "outputs": [], + "source": [ + "# bert_tf_v2_large_fp32_384\n", + "DATA_DIR_FP32 = os.path.join(data_dir, 'finetuned_model_fp32')\n", + "!mkdir -p $DATA_DIR_FP32\n", + "!wget -nc -q --show-progress -O $DATA_DIR_FP32/bert_tf_v2_large_fp32_384.zip \\\n", + "https://api.ngc.nvidia.com/v2/models/nvidia/bert_tf_v2_large_fp32_384/versions/1/zip\n", + "!unzip -n -d $DATA_DIR_FP32/ $DATA_DIR_FP32/bert_tf_v2_large_fp32_384.zip \n", + " \n", + "# bert_tf_v2_large_fp16_384\n", + "DATA_DIR_FP16 = os.path.join(data_dir, 'finetuned_model_fp16')\n", + "!mkdir -p $DATA_DIR_FP16\n", + "!wget -nc -q --show-progress -O $DATA_DIR_FP16/bert_tf_v2_large_fp16_384.zip \\\n", + "https://api.ngc.nvidia.com/v2/models/nvidia/bert_tf_v2_large_fp16_384/versions/1/zip\n", + "!unzip -n -d $DATA_DIR_FP16/ $DATA_DIR_FP16/bert_tf_v2_large_fp16_384.zip " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GrFrZickw61z" + }, + "source": [ + "In the code that follows we will refer to these models." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "cU8mGJDa1FfX" + }, + "source": [ + "Download the Google pretrained weights and vocab file:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "5hRb96NKE3X0" + }, + "outputs": [], + "source": [ + "os.chdir(\"./data\");\n", + "from GooglePretrainedWeightDownloader import GooglePretrainedWeightDownloader\n", + "gd = GooglePretrainedWeightDownloader(data_dir)\n", + "gd.download()\n", + "os.chdir(\"..\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VY1Dipam15DE" + }, + "source": [ + "We need the horovod package:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jqAJob92C2wA" + }, + "outputs": [], + "source": [ + "try:\n", + " __import__(\"horovod\")\n", + "except ImportError:\n", + " os.system(\"pip install horovod\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5NuuGNsDw611" + }, + "source": [ + "## 5. Running QA task inference\n", + "\n", + "In order to run QA inference we will follow step-by-step the flow implemented in run_squad.py.\n", + "\n", + "Configuration:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "_c2qCQ9-w613" + }, + "outputs": [], + "source": [ + "import run_squad\n", + "import json\n", + "import tensorflow as tf\n", + "import modeling\n", + "import tokenization\n", + "import time\n", + "import random\n", + "\n", + "tf.logging.set_verbosity(tf.logging.INFO)\n", + "\n", + "# Create the output directory where all the results are saved.\n", + "output_dir = os.path.join(working_dir, 'results')\n", + "tf.gfile.MakeDirs(output_dir)\n", + "\n", + "# The config json file corresponding to the pre-trained BERT model.\n", + "# This specifies the model architecture.\n", + "bert_config_file = os.path.join(data_dir, 'google_pretrained_weights/uncased_L-24_H-1024_A-16/bert_config.json')\n", + "\n", + "# The vocabulary file that the BERT model was trained on.\n", + "vocab_file = os.path.join(data_dir, 'google_pretrained_weights/uncased_L-24_H-1024_A-16/vocab.txt')\n", + "\n", + "# Depending on the mixed precision flag we use different fine-tuned model\n", + "if use_mixed_precision_model:\n", + " init_checkpoint = os.path.join(data_dir, 'finetuned_model_fp16/model.ckpt-8144')\n", + "else:\n", + " init_checkpoint = os.path.join(data_dir, 'finetuned_model_fp32/model.ckpt-8144')\n", + "\n", + "# Whether to lower case the input text. \n", + "# Should be True for uncased models and False for cased models.\n", + "do_lower_case = True\n", + " \n", + "# Total batch size for predictions\n", + "predict_batch_size = 1\n", + "params = dict([('batch_size', predict_batch_size)])\n", + "\n", + "# The maximum total input sequence length after WordPiece tokenization. \n", + "# Sequences longer than this will be truncated, and sequences shorter than this will be padded.\n", + "max_seq_length = 384\n", + "\n", + "# When splitting up a long document into chunks, how much stride to take between chunks.\n", + "doc_stride = 128\n", + "\n", + "# The maximum number of tokens for the question. \n", + "# Questions longer than this will be truncated to this length.\n", + "max_query_length = 64\n", + "\n", + "# This is a WA to use flags from here:\n", + "flags = tf.flags\n", + "\n", + "if 'f' not in tf.flags.FLAGS: \n", + " tf.app.flags.DEFINE_string('f', '', 'kernel')\n", + "FLAGS = flags.FLAGS\n", + "\n", + "# The total number of n-best predictions to generate in the nbest_predictions.json output file.\n", + "n_best_size = 20\n", + "\n", + "# The maximum length of an answer that can be generated. \n", + "# This is needed because the start and end predictions are not conditioned on one another.\n", + "max_answer_length = 30" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2h_eLUgPw618" + }, + "source": [ + "Let's define the tokenizer and create the model for the estimator:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "RXHdoUb9w619" + }, + "outputs": [], + "source": [ + "# Validate the casing config consistency with the checkpoint name.\n", + "tokenization.validate_case_matches_checkpoint(do_lower_case, init_checkpoint)\n", + "\n", + "# Create the tokenizer.\n", + "tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file, do_lower_case=do_lower_case)\n", + "\n", + "# Load the configuration from file\n", + "bert_config = modeling.BertConfig.from_json_file(bert_config_file)\n", + "\n", + "def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n", + " unique_ids = features[\"unique_ids\"]\n", + " input_ids = features[\"input_ids\"]\n", + " input_mask = features[\"input_mask\"]\n", + " segment_ids = features[\"segment_ids\"]\n", + "\n", + " (start_logits, end_logits) = run_squad.create_model(\n", + " bert_config=bert_config,\n", + " is_training=False,\n", + " input_ids=input_ids,\n", + " input_mask=input_mask,\n", + " segment_ids=segment_ids,\n", + " use_one_hot_embeddings=False)\n", + "\n", + " tvars = tf.trainable_variables()\n", + "\n", + " initialized_variable_names = {}\n", + " (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n", + " tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n", + " output_spec = None\n", + " predictions = {\"unique_ids\": unique_ids,\n", + " \"start_logits\": start_logits,\n", + " \"end_logits\": end_logits}\n", + " output_spec = tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)\n", + " return output_spec\n", + "\n", + "config = tf.ConfigProto(log_device_placement=True) \n", + "\n", + "run_config = tf.estimator.RunConfig(\n", + " model_dir=None,\n", + " session_config=config,\n", + " save_checkpoints_steps=1000,\n", + " keep_checkpoint_max=1)\n", + "\n", + "estimator = tf.estimator.Estimator(\n", + " model_fn=model_fn,\n", + " config=run_config,\n", + " params=params)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xSKkf4JLw62E" + }, + "source": [ + "### 5.a Inference" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "3OKhc349w62F", + "scrolled": true + }, + "outputs": [], + "source": [ + "eval_examples = run_squad.read_squad_examples(\n", + " input_file=input_file, is_training=False)\n", + "\n", + "eval_writer = run_squad.FeatureWriter(\n", + " filename=os.path.join(output_dir, \"eval.tf_record\"),\n", + " is_training=False)\n", + "\n", + "eval_features = []\n", + "def append_feature(feature):\n", + " eval_features.append(feature)\n", + " eval_writer.process_feature(feature)\n", + "\n", + "\n", + "# Loads a data file into a list of InputBatch's\n", + "run_squad.convert_examples_to_features(\n", + " examples=eval_examples,\n", + " tokenizer=tokenizer,\n", + " max_seq_length=max_seq_length,\n", + " doc_stride=doc_stride,\n", + " max_query_length=max_query_length,\n", + " is_training=False,\n", + " output_fn=append_feature)\n", + "\n", + "eval_writer.close()\n", + "\n", + "tf.logging.info(\"***** Running predictions *****\")\n", + "tf.logging.info(\" Num orig examples = %d\", len(eval_examples))\n", + "tf.logging.info(\" Num split examples = %d\", len(eval_features))\n", + "tf.logging.info(\" Batch size = %d\", predict_batch_size)\n", + "\n", + "predict_input_fn = run_squad.input_fn_builder(\n", + " input_file=eval_writer.filename,\n", + " batch_size=predict_batch_size,\n", + " seq_length=max_seq_length,\n", + " is_training=False,\n", + " drop_remainder=False)\n", + "\n", + "all_results = []\n", + "eval_hooks = [run_squad.LogEvalRunHook(predict_batch_size)]\n", + "eval_start_time = time.time()\n", + "for result in estimator.predict(\n", + " predict_input_fn, yield_single_examples=True, hooks=eval_hooks, checkpoint_path=init_checkpoint):\n", + " unique_id = int(result[\"unique_ids\"])\n", + " start_logits = [float(x) for x in result[\"start_logits\"].flat]\n", + " end_logits = [float(x) for x in result[\"end_logits\"].flat]\n", + " all_results.append(\n", + " run_squad.RawResult(\n", + " unique_id=unique_id,\n", + " start_logits=start_logits,\n", + " end_logits=end_logits))\n", + "\n", + "eval_time_elapsed = time.time() - eval_start_time\n", + "\n", + "eval_time_wo_startup = eval_hooks[-1].total_time\n", + "num_sentences = eval_hooks[-1].count * predict_batch_size\n", + "avg_sentences_per_second = num_sentences * 1.0 / eval_time_wo_startup\n", + "\n", + "tf.logging.info(\"-----------------------------\")\n", + "tf.logging.info(\"Total Inference Time = %0.2f Inference Time W/O start up overhead = %0.2f \"\n", + " \"Sentences processed = %d\", eval_time_elapsed, eval_time_wo_startup,\n", + " num_sentences)\n", + "tf.logging.info(\"Inference Performance = %0.4f sentences/sec\", avg_sentences_per_second)\n", + "tf.logging.info(\"-----------------------------\")\n", + "\n", + "output_prediction_file = os.path.join(output_dir, \"predictions.json\")\n", + "output_nbest_file = os.path.join(output_dir, \"nbest_predictions.json\")\n", + "output_null_log_odds_file = os.path.join(output_dir, \"null_odds.json\")\n", + "\n", + "run_squad.write_predictions(eval_examples, eval_features, all_results,\n", + " n_best_size, max_answer_length,\n", + " do_lower_case, output_prediction_file,\n", + " output_nbest_file, output_null_log_odds_file)\n", + "\n", + "tf.logging.info(\"Inference Results:\")\n", + "\n", + "# Here we show only the prediction results, nbest prediction is also available in the output directory\n", + "results = \"\"\n", + "with open(output_prediction_file, 'r') as json_file:\n", + " data = json.load(json_file)\n", + " for question in eval_examples:\n", + " results += \"{}{}{}\".format(question.qas_id, question.question_text, data[question.qas_id])\n", + "\n", + "\n", + "from IPython.display import display, HTML\n", + "display(HTML(\"{}
IdQuestionAnswer
\".format(results))) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "EMT0sKxHw62L" + }, + "source": [ + "## 6. What's next" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mKBM_UD6w62N" + }, + "source": [ + "Now that you are familiar with running QA Inference on BERT, using mixed precision, you may want to try\n", + "your own paragraphs and queries. \n", + "\n", + "You may also want to take a look to the notebook __bert_squad_tf_finetuning.ipynb__ on how to run fine-tuning on BERT, available in the same directory." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "bert_squad_tf_inference.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/TensorFlow/LanguageModeling/BERT/notebooks/input.json b/TensorFlow/LanguageModeling/BERT/notebooks/input.json new file mode 100644 index 00000000..7910fb83 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/notebooks/input.json @@ -0,0 +1,31 @@ +{"data": + [ + {"title": "Project Apollo", + "paragraphs": [ + {"context":"The Apollo program, also known as Project Apollo, was the third United States human spaceflight program carried out by the National Aeronautics and Space Administration (NASA), which accomplished landing the first humans on the Moon from 1969 to 1972. First conceived during Dwight D. Eisenhower's administration as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space, Apollo was later dedicated to President John F. Kennedy's national goal of landing a man on the Moon and returning him safely to the Earth by the end of the 1960s, which he proposed in a May 25, 1961, address to Congress. Project Mercury was followed by the two-man Project Gemini. The first manned flight of Apollo was in 1968. Apollo ran from 1961 to 1972, and was supported by the two man Gemini program which ran concurrently with it from 1962 to 1966. Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions. Apollo used Saturn family rockets as launch vehicles. Apollo/Saturn vehicles were also used for an Apollo Applications Program, which consisted of Skylab, a space station that supported three manned missions in 1973-74, and the Apollo-Soyuz Test Project, a joint Earth orbit mission with the Soviet Union in 1975.", + "qas": [ + { "question": "What project put the first Americans into space?", + "id": "Q1" + }, + { "question": "What program was created to carry out these projects and missions?", + "id": "Q2" + }, + { "question": "What year did the first manned Apollo flight occur?", + "id": "Q3" + }, + { "question": "What President is credited with the original notion of putting Americans in space?", + "id": "Q4" + }, + { "question": "Who did the U.S. collaborate with on an Earth orbit mission in 1975?", + "id": "Q5" + }, + { "question": "How long did Project Apollo run?", + "id": "Q6" + }, + { "question": "What program helped develop space travel techniques that Project Apollo used?", + "id": "Q7" + }, + {"question": "What space station supported three manned missions in 1973-1974?", + "id": "Q8" + } +]}]}]} diff --git a/TensorFlow/LanguageModeling/BERT/optimization.py b/TensorFlow/LanguageModeling/BERT/optimization.py index c1b94718..e23745e1 100644 --- a/TensorFlow/LanguageModeling/BERT/optimization.py +++ b/TensorFlow/LanguageModeling/BERT/optimization.py @@ -1,4 +1,5 @@ # coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """Functions and classes related to optimization (weight updates).""" from __future__ import absolute_import @@ -20,14 +22,25 @@ from __future__ import print_function import re import tensorflow as tf +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import linalg_ops +from tensorflow.python.ops import math_ops +from horovod.tensorflow.compression import Compression - -def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, hvd=None, manual_fp16=False, use_fp16=False): +def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, hvd=None, manual_fp16=False, use_fp16=False, num_accumulation_steps=1, + optimizer_type="adam", allreduce_post_accumulation=False): """Creates an optimizer training op.""" global_step = tf.train.get_or_create_global_step() - + # avoid step change in learning rate at end of warmup phase - decayed_learning_rate_at_crossover_point = init_lr * (1.0-float(num_warmup_steps)/float(num_train_steps)) + if optimizer_type == "adam": + power = 1.0 + decayed_learning_rate_at_crossover_point = init_lr * ( + (1.0 - float(num_warmup_steps) / float(num_train_steps)) ** power) + else: + power = 0.5 + decayed_learning_rate_at_crossover_point = init_lr + adjusted_init_lr = init_lr * (init_lr / decayed_learning_rate_at_crossover_point) print('decayed_learning_rate_at_crossover_point = %e, adjusted_init_lr = %e' % (decayed_learning_rate_at_crossover_point, adjusted_init_lr)) @@ -39,7 +52,7 @@ def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, hvd=None, global_step, num_train_steps, end_learning_rate=0.0, - power=1.0, + power=power, cycle=False) # Implements linear warmup. I.e., if global_step < num_warmup_steps, the @@ -58,49 +71,109 @@ def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, hvd=None, learning_rate = ( (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate) - # It is recommended that you use this optimizer for fine tuning, since this - # is how the model was trained (note that the Adam m/v variables are NOT - # loaded from init_checkpoint.) - optimizer = AdamWeightDecayOptimizer( - learning_rate=learning_rate, - weight_decay_rate=0.01, - beta_1=0.9, - beta_2=0.999, - epsilon=1e-6, - exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]) + if optimizer_type == "lamb": + print("Initializing LAMB Optimizer") + optimizer = LAMBOptimizer( + learning_rate=learning_rate, + weight_decay_rate=0.01, + beta_1=0.9, + beta_2=0.999, + epsilon=1e-6, + exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]) + else: + print("Initializing ADAM Weight Decay Optimizer") + # It is recommended that you use this optimizer for fine tuning, since this + # is how the model was trained (note that the Adam m/v variables are NOT + # loaded from init_checkpoint.) + optimizer = AdamWeightDecayOptimizer( + learning_rate=learning_rate, + weight_decay_rate=0.01, + beta_1=0.9, + beta_2=0.999, + epsilon=1e-6, + exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]) - if hvd is not None: - from horovod.tensorflow.compression import Compression - optimizer = hvd.DistributedOptimizer(optimizer, sparse_as_dense=True, compression=Compression.none) + if hvd is not None and (num_accumulation_steps == 1 or (not allreduce_post_accumulation)): + optimizer = hvd.DistributedOptimizer(optimizer, sparse_as_dense=True, compression=Compression.fp16 if use_fp16 or manual_fp16 else Compression.none) if manual_fp16 or use_fp16: loss_scale_manager = tf.contrib.mixed_precision.ExponentialUpdateLossScaleManager(init_loss_scale=2**32, incr_every_n_steps=1000, decr_every_n_nan_or_inf=2, decr_ratio=0.5) optimizer = tf.contrib.mixed_precision.LossScaleOptimizer(optimizer, loss_scale_manager) tvars = tf.trainable_variables() - grads_and_vars = optimizer.compute_gradients(loss, tvars) - grads_and_vars = [(g,v) for g,v in grads_and_vars if g is not None] - grads, tvars = list(zip(*grads_and_vars)) - all_are_finite = tf.reduce_all([tf.reduce_all(tf.is_finite(g)) for g in grads]) if manual_fp16 or use_fp16 else tf.constant(True, dtype=tf.bool) + grads_and_vars = optimizer.compute_gradients(loss * 1.0 / num_accumulation_steps, tvars) - # This is how the model was pre-trained. - # ensure global norm is a finite number - # to prevent clip_by_global_norm from having a hizzy fit. - (clipped_grads, _) = tf.clip_by_global_norm( - grads, clip_norm=1.0, - use_norm=tf.cond( - all_are_finite, - lambda: tf.global_norm(grads), - lambda: tf.constant(1.0))) + if num_accumulation_steps > 1: + local_step = tf.get_variable(name="local_step", shape=[], dtype=tf.int32, trainable=False, + initializer=tf.zeros_initializer) + batch_finite = tf.get_variable(name="batch_finite", shape=[], dtype=tf.bool, trainable=False, + initializer=tf.ones_initializer) + accum_vars = [tf.get_variable( + name=tvar.name.split(":")[0] + "/accum", + shape=tvar.shape.as_list(), + dtype=tf.float32, + trainable=False, + initializer=tf.zeros_initializer()) for tvar in tf.trainable_variables()] - train_op = optimizer.apply_gradients( - list(zip(clipped_grads, tvars)), global_step=global_step) + reset_step = tf.cast(tf.math.equal(local_step % num_accumulation_steps, 0), dtype=tf.bool) + local_step = tf.cond(reset_step, lambda:local_step.assign(tf.ones_like(local_step)), lambda:local_step.assign_add(1)) - # Normally the global step update is done inside of `apply_gradients`. - # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use - # a different optimizer, you should probably take this line out. - new_global_step = tf.cond(all_are_finite, lambda: global_step+1, lambda: global_step) - new_global_step = tf.identity(new_global_step, name='step_update') - train_op = tf.group(train_op, [global_step.assign(new_global_step)]) + grads_and_vars_and_accums = [(gv[0],gv[1],accum_vars[i]) for i, gv in enumerate(grads_and_vars) if gv[0] is not None] + grads, tvars, accum_vars = list(zip(*grads_and_vars_and_accums)) + + all_are_finite = tf.reduce_all([tf.reduce_all(tf.is_finite(g)) for g in grads]) if manual_fp16 or use_fp16 else tf.constant(True, dtype=tf.bool) + batch_finite = tf.cond(reset_step, + lambda: batch_finite.assign(tf.math.logical_and(tf.constant(True, dtype=tf.bool), all_are_finite)), + lambda:batch_finite.assign(tf.math.logical_and(batch_finite, all_are_finite))) + + # This is how the model was pre-trained. + # ensure global norm is a finite number + # to prevent clip_by_global_norm from having a hizzy fit. + (clipped_grads, _) = tf.clip_by_global_norm( + grads, clip_norm=1.0, + use_norm=tf.cond( + all_are_finite, + lambda: tf.global_norm(grads), + lambda: tf.constant(1.0))) + + accum_vars = tf.cond(reset_step, + lambda: [accum_vars[i].assign(grad) for i, grad in enumerate(clipped_grads)], + lambda: [accum_vars[i].assign_add(grad) for i, grad in enumerate(clipped_grads)]) + + def update(accum_vars): + if allreduce_post_accumulation and hvd is not None: + accum_vars = [hvd.allreduce(tf.convert_to_tensor(accum_var), compression=Compression.fp16 if use_fp16 or manual_fp16 else Compression.none) if isinstance(accum_var, tf.IndexedSlices) + else hvd.allreduce(accum_var, compression=Compression.fp16 if use_fp16 or manual_fp16 else Compression.none) for accum_var in accum_vars] + return optimizer.apply_gradients(list(zip(accum_vars, tvars)), global_step=global_step) + + update_step = tf.identity(tf.cast(tf.math.equal(local_step % num_accumulation_steps, 0), dtype=tf.bool), name="update_step") + update_op = tf.cond(update_step, + lambda: update(accum_vars), lambda: tf.no_op()) + + new_global_step = tf.cond(tf.math.logical_and(update_step, tf.cast(hvd.allreduce(tf.cast(batch_finite, tf.int32)), tf.bool)), lambda: global_step+1, lambda: global_step) + new_global_step = tf.identity(new_global_step, name='step_update') + train_op = tf.group(update_op, [global_step.assign(new_global_step)]) + else: + grads_and_vars = [(g, v) for g, v in grads_and_vars if g is not None] + grads, tvars = list(zip(*grads_and_vars)) + all_are_finite = tf.reduce_all( + [tf.reduce_all(tf.is_finite(g)) for g in grads]) if use_fp16 or manual_fp16 else tf.constant(True, dtype=tf.bool) + + # This is how the model was pre-trained. + # ensure global norm is a finite number + # to prevent clip_by_global_norm from having a hizzy fit. + (clipped_grads, _) = tf.clip_by_global_norm( + grads, clip_norm=1.0, + use_norm=tf.cond( + all_are_finite, + lambda: tf.global_norm(grads), + lambda: tf.constant(1.0))) + + train_op = optimizer.apply_gradients( + list(zip(clipped_grads, tvars)), global_step=global_step) + + new_global_step = tf.cond(all_are_finite, lambda: global_step + 1, lambda: global_step) + new_global_step = tf.identity(new_global_step, name='step_update') + train_op = tf.group(train_op, [global_step.assign(new_global_step)]) return train_op @@ -206,3 +279,120 @@ class AdamWeightDecayOptimizer(tf.train.Optimizer): if m is not None: param_name = m.group(1) return param_name + + +class LAMBOptimizer(tf.train.Optimizer): + """A LAMB optimizer that includes "correct" L2 weight decay.""" + + def __init__(self, + learning_rate, + weight_decay_rate=0.0, + beta_1=0.9, + beta_2=0.999, + epsilon=1e-6, + exclude_from_weight_decay=None, + name="LAMBOptimizer"): + """Constructs a LAMBOptimizer.""" + super(LAMBOptimizer, self).__init__(False, name) + + self.learning_rate = tf.identity(learning_rate, name='learning_rate') + self.weight_decay_rate = weight_decay_rate + self.beta_1 = beta_1 + self.beta_2 = beta_2 + self.epsilon = epsilon + self.exclude_from_weight_decay = exclude_from_weight_decay + self.steps = 0 + + def apply_gradients(self, grads_and_vars, global_step=None, name=None, + manual_fp16=False): + """See base class.""" + assignments = [] + for (grad, param) in grads_and_vars: + if grad is None or param is None: + continue + + param_name = self._get_variable_name(param.name) + has_shadow = manual_fp16 and param.dtype.base_dtype != tf.float32 + if has_shadow: + # create shadow fp32 weights for fp16 variable + param_fp32 = tf.get_variable( + name=param_name + "/shadow", + dtype=tf.float32, + trainable=False, + initializer=tf.cast(param.initialized_value(),tf.float32)) + else: + param_fp32 = param + + m = tf.get_variable( + name=param_name + "/adam_m", + shape=param.shape.as_list(), + dtype=tf.float32, + trainable=False, + initializer=tf.zeros_initializer()) + v = tf.get_variable( + name=param_name + "/adam_v", + shape=param.shape.as_list(), + dtype=tf.float32, + trainable=False, + initializer=tf.zeros_initializer()) + + # LAMB update + next_m = ( + tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad)) + next_v = ( + tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2, + tf.square(grad))) + + self.steps += 1 + beta1_correction = (1 - self.beta_1 ** self.steps) + beta2_correction = (1 - self.beta_2 ** self.steps) + + next_m_unbiased = next_m / beta1_correction + next_v_unbiased = next_v / beta2_correction + + update = next_m_unbiased / (tf.sqrt(next_v_unbiased) + self.epsilon) + + # Just adding the square of the weights to the loss function is *not* + # the correct way of using L2 regularization/weight decay with Adam, + # since that will interact with the m and v parameters in strange ways. + # + # Instead we want ot decay the weights in a manner that doesn't interact + # with the m/v parameters. This is equivalent to adding the square + # of the weights to the loss with plain (non-momentum) SGD. + if self._do_use_weight_decay(param_name): + update += self.weight_decay_rate * param_fp32 + + w_norm = linalg_ops.norm(param, ord=2) + g_norm = linalg_ops.norm(update, ord=2) + ratio = array_ops.where(math_ops.greater(w_norm, 0), array_ops.where( + math_ops.greater(g_norm, 0), (w_norm / g_norm), 1.0), 1.0) + + update_with_lr = ratio * self.learning_rate * update + + next_param = param_fp32 - update_with_lr + + if has_shadow: + # cast shadow fp32 weights to fp16 and assign to trainable variable + param.assign(tf.cast(next_param, param.dtype.base_dtype)) + assignments.extend( + [param_fp32.assign(next_param), + m.assign(next_m), + v.assign(next_v)]) + return tf.group(*assignments, name=name) + + def _do_use_weight_decay(self, param_name): + """Whether to use L2 weight decay for `param_name`.""" + if not self.weight_decay_rate: + return False + if self.exclude_from_weight_decay: + for r in self.exclude_from_weight_decay: + if re.search(r, param_name) is not None: + return False + return True + + def _get_variable_name(self, param_name): + """Get the variable name from the tensor name.""" + m = re.match("^(.*):\\d+$", param_name) + if m is not None: + param_name = m.group(1) + return param_name diff --git a/TensorFlow/LanguageModeling/BERT/run.sub b/TensorFlow/LanguageModeling/BERT/run.sub new file mode 100644 index 00000000..b743fda5 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/run.sub @@ -0,0 +1,73 @@ +#!/bin/bash +#SBATCH --exclusive +#SBATCH --mem=0 +#SBATCH --overcommit + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -eux + +readonly docker_image="nvcr.io/nvidia/tensorflow:19.08-py3" +readonly datadir="/raid/data/bert" +readonly checkpointdir="$PWD/checkpoints" + +readonly mounts=".:/workspace/bert,${datadir}:/workspace/bert/data,${checkpointdir}:/results" + + +srun --ntasks="${SLURM_JOB_NUM_NODES}" --ntasks-per-node=1 mkdir -p "${checkpointdir}/phase_1" +srun --ntasks="${SLURM_JOB_NUM_NODES}" --ntasks-per-node=1 mkdir -p "${checkpointdir}/phase_2" + +PHASE1="\ + --train_batch_size=${BATCHSIZE:-16} \ + --learning_rate=${LEARNING_RATE:-1.875e-4} \ + --num_accumulation_steps=${NUM_ACCUMULATION_STEPS:-128} \ + --input_files_dir=/workspace/bert/data/tfrecord/lower_case_1_seq_len_128_max_pred_20_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus/training \ + --eval_files_dir=/workspace/bert/data/tfrecord/lower_case_1_seq_len_128_max_pred_20_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus/test \ + --max_seq_length=128 \ + --max_predictions_per_seq=20 \ + --num_train_steps=7038 \ + --num_warmup_steps=2000 \ + --output_dir=/results/phase_1 \ + " + +PHASE2="\ + --train_batch_size=${BATCHSIZE:-2} \ + --learning_rate=${LEARNING_RATE:-1.25e-4} \ + --num_accumulation_steps=${NUM_ACCUMULATION_STEPS:-512} \ + --input_files_dir=/workspace/bert/data/tfrecord/lower_case_1_seq_len_512_max_pred_80_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus/training \ + --eval_files_dir=/workspace/bert/data/tfrecord/lower_case_1_seq_len_512_max_pred_80_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus/test \ + --max_seq_length=512 \ + --max_predictions_per_seq=80 \ + --num_train_steps=1564 \ + --num_warmup_steps=200 \ + --output_dir=/results/phase_2 \ + --init_checkpoint=/results/phase_1/model.ckpt-7038 \ + " + +PHASES=( "$PHASE1" "$PHASE2" ) + +PHASE=${PHASE:-1} + +BERT_CMD="\ + python /workspace/bert/run_pretraining.py \ + ${PHASES[$((PHASE-1))]} \ + --bert_config_file=/workspace/bert/data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16/bert_config.json \ + --do_train=True \ + --do_eval=True \ + --save_checkpoints_steps=100 \ + --horovod --use_fp16 --use_xla \ + --allreduce_post_accumulation=True \ + --eval_batch_size=8" + +srun --mpi=pmi2 -l --container-image="${docker_image}" --container-mounts="${mounts}" bash -c "${BERT_CMD}" diff --git a/TensorFlow/LanguageModeling/BERT/run_classifier.py b/TensorFlow/LanguageModeling/BERT/run_classifier.py index d26466b8..84aa9344 100644 --- a/TensorFlow/LanguageModeling/BERT/run_classifier.py +++ b/TensorFlow/LanguageModeling/BERT/run_classifier.py @@ -1,4 +1,5 @@ # coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """BERT finetuning runner.""" from __future__ import absolute_import @@ -103,7 +105,9 @@ flags.DEFINE_integer("save_checkpoints_steps", 1000, flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.") - +flags.DEFINE_integer("num_accumulation_steps", 1, + "Number of accumulation steps before gradient update" + "Global batch size = num_accumulation_steps * train_batch_size") flags.DEFINE_bool("use_fp16", False, "Whether to use fp32 or fp16 arithmetic on GPU.") flags.DEFINE_bool("use_xla", False, "Whether to enable XLA JIT compilation.") @@ -240,17 +244,17 @@ def get_frozen_tftrt_model(bert_config, shape, num_labels, use_one_hot_embedding num_nodes = len(frozen_graph.node) print('Converting graph using TensorFlow-TensorRT...') - import tensorflow.contrib.tensorrt as trt - frozen_graph = trt.create_inference_graph( + from tensorflow.python.compiler.tensorrt import trt_convert as trt + converter = trt.TrtGraphConverter( input_graph_def=frozen_graph, - outputs=output_node_names, - max_batch_size=FLAGS.predict_batch_size, + nodes_blacklist=output_node_names, max_workspace_size_bytes=(4096 << 20) - 1000, precision_mode = "FP16" if FLAGS.use_fp16 else "FP32", minimum_segment_size=4, is_dynamic_op=True, maximum_cached_engines=1000 ) + frozen_graph = converter.convert() print('Total node count before and after TF-TRT conversion:', num_nodes, '->', len(frozen_graph.node)) @@ -264,7 +268,7 @@ def get_frozen_tftrt_model(bert_config, shape, num_labels, use_one_hot_embedding -def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate, +def model_fn_builder(task_name, bert_config, num_labels, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, use_one_hot_embeddings, hvd=None): """Returns `model_fn` closure for Estimator.""" @@ -272,6 +276,25 @@ def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate, def model_fn(features, labels, mode, params): # pylint: disable=unused-argument """The `model_fn` for Estimator.""" + def metric_fn(per_example_loss, label_ids, logits): + predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) + if task_name == "cola": + FN, FN_op = tf.metrics.false_negatives(labels=label_ids, predictions=predictions) + FP, FP_op = tf.metrics.false_positives(labels=label_ids, predictions=predictions) + TP, TP_op = tf.metrics.true_positives(labels=label_ids, predictions=predictions) + TN, TN_op = tf.metrics.true_negatives(labels=label_ids, predictions=predictions) + + MCC = (TP * TN - FP * FN) / ((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN)) ** 0.5 + MCC_op = tf.group(FN_op, TN_op, TP_op, FP_op, tf.identity(MCC, name="MCC")) + return {"MCC": (MCC, MCC_op)} + else: + accuracy = tf.metrics.accuracy( + labels=label_ids, predictions=predictions) + loss = tf.metrics.mean(values=per_example_loss) + return { + "eval_accuracy": accuracy, + "eval_loss": loss, + } tf.logging.info("*** Features ***") for name in sorted(features.keys()): tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) @@ -294,16 +317,6 @@ def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate, output_spec = tf.estimator.EstimatorSpec( mode=mode, predictions=predictions) elif mode == tf.estimator.ModeKeys.EVAL: - def metric_fn(per_example_loss, label_ids, logits): - predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) - accuracy = tf.metrics.accuracy( - labels=label_ids, predictions=predictions) - loss = tf.metrics.mean(values=per_example_loss) - return { - "eval_accuracy": accuracy, - "eval_loss": loss, - } - eval_metric_ops = metric_fn(per_example_loss, label_ids, logits) output_spec = tf.estimator.EstimatorSpec( mode=mode, @@ -335,23 +348,13 @@ def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate, train_op = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, - hvd, FLAGS.use_fp16) + hvd, False, FLAGS.use_fp16, FLAGS.num_accumulation_steps) output_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, train_op=train_op) elif mode == tf.estimator.ModeKeys.EVAL: - - def metric_fn(per_example_loss, label_ids, logits): - predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) - accuracy = tf.metrics.accuracy(label_ids, predictions) - loss = tf.metrics.mean(per_example_loss) - return { - "eval_accuracy": accuracy, - "eval_loss": loss, - } - eval_metric_ops = metric_fn(per_example_loss, label_ids, logits) output_spec = tf.estimator.EstimatorSpec( mode=mode, @@ -424,7 +427,8 @@ def main(_): if FLAGS.horovod: hvd.init() - + if FLAGS.use_fp16: + os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1" processors = { "cola": ColaProcessor, "mnli": MnliProcessor, @@ -460,7 +464,7 @@ def main(_): master_process = True training_hooks = [] - global_batch_size = FLAGS.train_batch_size + global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps hvd_rank = 0 config = tf.ConfigProto() @@ -468,7 +472,7 @@ def main(_): tf.logging.info("Multi-GPU training with TF Horovod") tf.logging.info("hvd.size() = %d hvd.rank() = %d", hvd.size(), hvd.rank()) - global_batch_size = FLAGS.train_batch_size * hvd.size() + global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps * hvd.size() master_process = (hvd.rank() == 0) hvd_rank = hvd.rank() config.gpu_options.allow_growth = True @@ -517,6 +521,7 @@ def main(_): end_index = start_index + (num_examples_per_rank) model_fn = model_fn_builder( + task_name=task_name, bert_config=bert_config, num_labels=len(label_list), init_checkpoint=FLAGS.init_checkpoint, @@ -700,4 +705,4 @@ if __name__ == "__main__": flags.mark_flag_as_required("vocab_file") flags.mark_flag_as_required("bert_config_file") flags.mark_flag_as_required("output_dir") - tf.app.run() + tf.app.run() \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/run_pretraining.py b/TensorFlow/LanguageModeling/BERT/run_pretraining.py index 450bded6..854dd2e1 100644 --- a/TensorFlow/LanguageModeling/BERT/run_pretraining.py +++ b/TensorFlow/LanguageModeling/BERT/run_pretraining.py @@ -1,4 +1,5 @@ # coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """Run masked LM/next sentence masked_lm pre-training for BERT.""" from __future__ import absolute_import @@ -23,6 +25,7 @@ import time import modeling import optimization import tensorflow as tf +import glob flags = tf.flags @@ -35,8 +38,12 @@ flags.DEFINE_string( "This specifies the model architecture.") flags.DEFINE_string( - "input_file", None, - "Input TF example files (can be a glob or comma separated).") + "input_files_dir", None, + "Directory with input files, comma separated or single directory.") + +flags.DEFINE_string( + "eval_files_dir", None, + "Directory with eval files, comma separated or single directory. ") flags.DEFINE_string( "output_dir", None, @@ -47,6 +54,10 @@ flags.DEFINE_string( "init_checkpoint", None, "Initial checkpoint (usually from a pre-trained BERT model).") +flags.DEFINE_string( + "optimizer_type", "lamb", + "Optimizer used for training - LAMB or ADAM") + flags.DEFINE_integer( "max_seq_length", 512, "The maximum total input sequence length after WordPiece tokenization. " @@ -74,15 +85,27 @@ flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.") flags.DEFINE_integer("save_checkpoints_steps", 1000, "How often to save the model checkpoint.") +flags.DEFINE_integer("display_loss_steps", 10, + "How often to print loss") flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.") flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.") +flags.DEFINE_integer("num_accumulation_steps", 1, + "Number of accumulation steps before gradient update." + "Global batch size = num_accumulation_steps * train_batch_size") + +flags.DEFINE_bool("allreduce_post_accumulation", False, "Whether to all reduce after accumulation of N steps or after each step") + +flags.DEFINE_bool( + "verbose_logging", False, + "If true, all of the trainable parameters are printed") + flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs") -flags.DEFINE_bool("report_loss", False, "Whether to report total loss during training.") +flags.DEFINE_bool("report_loss", True, "Whether to report total loss during training.") flags.DEFINE_bool("manual_fp16", False, "Whether to use fp32 or fp16 arithmetic on GPU. " "Manual casting is done instead of using AMP") @@ -93,52 +116,83 @@ flags.DEFINE_bool("use_fp16", False, "Whether to enable AMP ops.") # report samples/sec, total loss and learning rate during training class _LogSessionRunHook(tf.train.SessionRunHook): - def __init__(self, global_batch_size, display_every=10, hvd_rank=-1): + def __init__(self, global_batch_size, num_accumulation_steps, display_every=10, hvd_rank=-1): self.global_batch_size = global_batch_size self.display_every = display_every self.hvd_rank = hvd_rank + self.num_accumulation_steps = num_accumulation_steps def after_create_session(self, session, coord): self.elapsed_secs = 0. self.count = 0 + self.all_count = 0 + self.avg_loss = 0.0 + def before_run(self, run_context): self.t0 = time.time() - if FLAGS.manual_fp16 or FLAGS.use_fp16: - return tf.train.SessionRunArgs( - fetches=['step_update:0', 'total_loss:0', - 'learning_rate:0', 'nsp_loss:0', - 'mlm_loss:0', 'loss_scale:0']) + if self.num_accumulation_steps <= 1: + if FLAGS.manual_fp16 or FLAGS.use_fp16: + return tf.train.SessionRunArgs( + fetches=['step_update:0', 'total_loss:0', + 'learning_rate:0', 'nsp_loss:0', + 'mlm_loss:0', 'loss_scale:0']) + else: + return tf.train.SessionRunArgs( + fetches=['step_update:0', 'total_loss:0', + 'learning_rate:0', 'nsp_loss:0', + 'mlm_loss:0']) else: - return tf.train.SessionRunArgs( - fetches=['step_update:0', 'total_loss:0', - 'learning_rate:0', 'nsp_loss:0', - 'mlm_loss:0']) + if FLAGS.manual_fp16 or FLAGS.use_fp16: + return tf.train.SessionRunArgs( + fetches=['step_update:0', 'update_step:0', 'total_loss:0', + 'learning_rate:0', 'nsp_loss:0', + 'mlm_loss:0', 'loss_scale:0']) + else: + return tf.train.SessionRunArgs( + fetches=['step_update:0', 'update_step:0', 'total_loss:0', + 'learning_rate:0', 'nsp_loss:0', + 'mlm_loss:0']) def after_run(self, run_context, run_values): self.elapsed_secs += time.time() - self.t0 - self.count += 1 - if FLAGS.manual_fp16 or FLAGS.use_fp16: - global_step, total_loss, lr, nsp_loss, mlm_loss, loss_scaler = run_values.results - else: - global_step, total_loss, lr, nsp_loss, mlm_loss = run_values.results - print_step = global_step + 1 # One-based index for printing. - if print_step == 1 or print_step % self.display_every == 0: - dt = self.elapsed_secs / self.count - img_per_sec = self.global_batch_size / dt - if self.hvd_rank >= 0: - if FLAGS.manual_fp16 or FLAGS.use_fp16: - print('Rank = %2d :: Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f LR = %6.4e Loss scale = %6.4e' % - (self.hvd_rank, print_step, img_per_sec, mlm_loss, nsp_loss, total_loss, lr, loss_scaler)) - else: - print('Rank = %2d :: Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f LR = %6.4e' % - (self.hvd_rank, print_step, img_per_sec, mlm_loss, nsp_loss, total_loss, lr)) + if self.num_accumulation_steps <=1: + if FLAGS.manual_fp16 or FLAGS.use_fp16: + global_step, total_loss, lr, nsp_loss, mlm_loss, loss_scaler = run_values.results else: - if FLAGS.manual_fp16 or FLAGS.use_fp16: - print('Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f LR = %6.4e Loss scale = %6.4e' % - (print_step, img_per_sec, mlm_loss, nsp_loss, total_loss, lr, loss_scaler)) - else: - print('Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f LR = %6.4e' % - (print_step, img_per_sec, mlm_loss, nsp_loss, total_loss, lr)) - self.elapsed_secs = 0. - self.count = 0 + global_step, total_loss, lr, nsp_loss, mlm_loss = run_values. \ + results + update_step = True + else: + if FLAGS.manual_fp16 or FLAGS.use_fp16: + global_step, update_step, total_loss, lr, nsp_loss, mlm_loss, loss_scaler = run_values.results + else: + global_step, update_step, total_loss, lr, nsp_loss, mlm_loss = run_values.\ + results + print_step = global_step + 1 # One-based index for printing. + self.avg_loss += total_loss + self.all_count += 1 + if update_step: + self.count += 1 + if (print_step == 1 or print_step % self.display_every == 0): + dt = self.elapsed_secs / self.count + sent_per_sec = self.global_batch_size / dt + avg_loss_step = self.avg_loss / self.all_count + if self.hvd_rank >= 0: + if FLAGS.manual_fp16 or FLAGS.use_fp16: + print('Rank = %2d :: Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f Average Loss = %6.3f LR = %6.4e Loss scale = %6.4e' % + (self.hvd_rank, print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr, loss_scaler)) + else: + print('Rank = %2d :: Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f Average Loss = %6.3f LR = %6.4e' % + (self.hvd_rank, print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr)) + else: + if FLAGS.manual_fp16 or FLAGS.use_fp16: + print('Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f Average Loss = %6.3f LR = %6.4e Loss scale = %6.4e' % + (print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr, loss_scaler)) + else: + print('Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f Average Loss = %6.3f LR = %6.4e' % + (print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr)) + self.elapsed_secs = 0. + self.count = 0 + self.avg_loss = 0.0 + self.all_count = 0 def model_fn_builder(bert_config, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, @@ -195,19 +249,20 @@ def model_fn_builder(bert_config, init_checkpoint, learning_rate, tf.train.init_from_checkpoint(init_checkpoint, assignment_map) - tf.logging.info("**** Trainable Variables ****") - for var in tvars: - init_string = "" - if var.name in initialized_variable_names: - init_string = ", *INIT_FROM_CKPT*" - tf.logging.info(" %d :: name = %s, shape = %s%s", 0 if hvd is None else hvd.rank(), var.name, var.shape, - init_string) + if FLAGS.verbose_logging: + tf.logging.info("**** Trainable Variables ****") + for var in tvars: + init_string = "" + if var.name in initialized_variable_names: + init_string = ", *INIT_FROM_CKPT*" + tf.logging.info(" %d :: name = %s, shape = %s%s", 0 if hvd is None else hvd.rank(), var.name, var.shape, + init_string) output_spec = None if mode == tf.estimator.ModeKeys.TRAIN: train_op = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, - hvd, FLAGS.manual_fp16, FLAGS.use_fp16) + hvd, FLAGS.manual_fp16, FLAGS.use_fp16, FLAGS.num_accumulation_steps, FLAGS.optimizer_type, FLAGS.allreduce_post_accumulation) output_spec = tf.estimator.EstimatorSpec( mode=mode, @@ -414,7 +469,7 @@ def input_fn_builder(input_files, lambda record: _decode_record(record, name_to_features), batch_size=batch_size, num_parallel_batches=num_cpu_threads, - drop_remainder=True)) + drop_remainder=True if is_training else False)) return d return input_fn @@ -453,27 +508,28 @@ def main(_): tf.gfile.MakeDirs(FLAGS.output_dir) input_files = [] - for input_pattern in FLAGS.input_file.split(","): - input_files.extend(tf.gfile.Glob(input_pattern)) + for input_file_dir in FLAGS.input_files_dir.split(","): + input_files.extend(tf.gfile.Glob(os.path.join(input_file_dir, "*"))) - tf.logging.info("*** Input Files ***") - for input_file in input_files: - tf.logging.info(" %s" % input_file) + if FLAGS.horovod and len(input_files) < hvd.size(): + raise ValueError("Input Files must be sharded") + if FLAGS.use_fp16 and FLAGS.manual_fp16: + raise ValueError("AMP and Manual Mixed Precision Training are both activated! Error") - config = tf.ConfigProto() - if FLAGS.horovod: - config.gpu_options.visible_device_list = str(hvd.local_rank()) - if len(input_files) < hvd.size(): - raise ValueError("Input Files must be sharded") - if FLAGS.use_xla: - config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 config = tf.ConfigProto() if FLAGS.horovod: config.gpu_options.visible_device_list = str(hvd.local_rank()) config.gpu_options.allow_growth = True + if hvd.rank() == 0: + tf.logging.info("***** Configuaration *****") + for key in FLAGS.__flags.keys(): + tf.logging.info(' {}: {}'.format(key, getattr(FLAGS, key))) + tf.logging.info("**************************") + # config.gpu_options.per_process_gpu_memory_fraction = 0.7 - if FLAGS.use_xla: config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 + if FLAGS.use_xla: config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 + run_config = tf.estimator.RunConfig( model_dir=FLAGS.output_dir, session_config=config, @@ -494,18 +550,11 @@ def main(_): use_one_hot_embeddings=False, hvd=None if not FLAGS.horovod else hvd) - training_hooks = [] - if FLAGS.horovod and hvd.size() > 1: - training_hooks.append(hvd.BroadcastGlobalVariablesHook(0)) - if FLAGS.report_loss: - global_batch_size = FLAGS.train_batch_size if not FLAGS.horovod else FLAGS.train_batch_size*hvd.size() - training_hooks.append(_LogSessionRunHook(global_batch_size,1,-1 if not FLAGS.horovod else hvd.rank())) - training_hooks = [] if FLAGS.report_loss and (not FLAGS.horovod or hvd.rank() == 0): - global_batch_size = FLAGS.train_batch_size if not FLAGS.horovod else FLAGS.train_batch_size*hvd.size() - training_hooks.append(_LogSessionRunHook(global_batch_size,100)) - if FLAGS.horovod: + global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps if not FLAGS.horovod else FLAGS.train_batch_size * FLAGS.num_accumulation_steps * hvd.size() + training_hooks.append(_LogSessionRunHook(global_batch_size, FLAGS.num_accumulation_steps, FLAGS.display_loss_steps)) + if FLAGS.horovod and hvd.size() > 1: training_hooks.append(hvd.BroadcastGlobalVariablesHook(0)) estimator = tf.estimator.Estimator( @@ -522,14 +571,19 @@ def main(_): max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=True, hvd=None if not FLAGS.horovod else hvd) + estimator.train(input_fn=train_input_fn, hooks=training_hooks, max_steps=FLAGS.num_train_steps) if FLAGS.do_eval and (not FLAGS.horovod or hvd.rank() == 0): tf.logging.info("***** Running evaluation *****") tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) + eval_files = [] + for eval_file_dir in FLAGS.eval_files_dir.split(","): + eval_files.extend(tf.gfile.Glob(os.path.join(eval_file_dir, "*"))) + eval_input_fn = input_fn_builder( - input_files=input_files, + input_files=eval_files, batch_size=FLAGS.eval_batch_size, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, @@ -548,7 +602,8 @@ def main(_): if __name__ == "__main__": - flags.mark_flag_as_required("input_file") + flags.mark_flag_as_required("input_files_dir") + flags.mark_flag_as_required("eval_files_dir") flags.mark_flag_as_required("bert_config_file") flags.mark_flag_as_required("output_dir") if FLAGS.use_xla and FLAGS.manual_fp16: diff --git a/TensorFlow/LanguageModeling/BERT/run_pretraining.sh b/TensorFlow/LanguageModeling/BERT/run_pretraining.sh deleted file mode 100755 index 41c3de0b..00000000 --- a/TensorFlow/LanguageModeling/BERT/run_pretraining.sh +++ /dev/null @@ -1,19 +0,0 @@ -#! /bin/bash - -mpiexec --allow-run-as-root --bind-to socket -np 8 python3 run_pretraining.py \ - --input_file=/workspace/data/bert_large_wikipedia_seq_512_pred_20/tf_examples.tfrecord* \ - --output_dir=/workspace/checkpoints/pretraining_base_output \ - --do_train=True \ - --do_eval=True \ - --bert_config_file=$BERT_BASE_DIR/bert_config.json \ - --train_batch_size=14 \ - --max_seq_length=512 \ - --max_predictions_per_seq=20 \ - --num_train_steps=250000 \ - --num_warmup_steps=10000 \ - --learning_rate=1e-4 \ - --use_fp16 \ - --use_xla \ - --report_loss \ - --horovod - diff --git a/TensorFlow/LanguageModeling/BERT/run_squad.py b/TensorFlow/LanguageModeling/BERT/run_squad.py index 89200b6e..71e9b0a3 100644 --- a/TensorFlow/LanguageModeling/BERT/run_squad.py +++ b/TensorFlow/LanguageModeling/BERT/run_squad.py @@ -1,4 +1,5 @@ # coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """Run BERT on SQuAD 1.1 and SQuAD 2.0.""" from __future__ import absolute_import, division, print_function @@ -114,6 +116,10 @@ flags.DEFINE_integer("save_checkpoints_steps", 1000, flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.") +flags.DEFINE_integer("num_accumulation_steps", 1, + "Number of accumulation steps before gradient update" + "Global batch size = num_accumulation_steps * train_batch_size") + flags.DEFINE_integer( "n_best_size", 20, "The total number of n-best predictions to generate in the " @@ -231,17 +237,17 @@ def get_frozen_tftrt_model(bert_config, shape, use_one_hot_embeddings, init_chec num_nodes = len(frozen_graph.node) print('Converting graph using TensorFlow-TensorRT...') - import tensorflow.contrib.tensorrt as trt - frozen_graph = trt.create_inference_graph( + from tensorflow.python.compiler.tensorrt import trt_convert as trt + converter = trt.TrtGraphConverter( input_graph_def=frozen_graph, - outputs=output_node_names, - max_batch_size=FLAGS.predict_batch_size, + nodes_blacklist=output_node_names, max_workspace_size_bytes=(4096 << 20) - 1000, precision_mode = "FP16" if FLAGS.use_fp16 else "FP32", minimum_segment_size=4, is_dynamic_op=True, maximum_cached_engines=1000 ) + frozen_graph = converter.convert() print('Total node count before and after TF-TRT conversion:', num_nodes, '->', len(frozen_graph.node)) @@ -336,7 +342,7 @@ def model_fn_builder(bert_config, init_checkpoint, learning_rate, total_loss = (start_loss + end_loss) / 2.0 train_op = optimization.create_optimizer( - total_loss, learning_rate, num_train_steps, num_warmup_steps, hvd, amp=use_fp16) + total_loss, learning_rate, num_train_steps, num_warmup_steps, hvd, False, use_fp16, FLAGS.num_accumulation_steps) output_spec = tf.estimator.EstimatorSpec( mode=mode, @@ -899,6 +905,8 @@ def main(_): if FLAGS.horovod: hvd.init() + if FLAGS.use_fp16: + os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1" bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) @@ -911,7 +919,7 @@ def main(_): master_process = True training_hooks = [] - global_batch_size = FLAGS.train_batch_size + global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps hvd_rank = 0 hvd_local_rank = 0 @@ -921,7 +929,7 @@ def main(_): tf.logging.info("Multi-GPU training with TF Horovod") tf.logging.info("hvd.size() = %d hvd.rank() = %d", hvd.size(), hvd.rank()) - global_batch_size = FLAGS.train_batch_size * hvd.size() + global_batch_size = FLAGS.train_batch_size * hvd.size() * FLAGS.num_accumulation_steps learning_rate = learning_rate * hvd.size() master_process = (hvd.rank() == 0) hvd_rank = hvd.rank() diff --git a/TensorFlow/LanguageModeling/BERT/run_squad_trtis_client.py b/TensorFlow/LanguageModeling/BERT/run_squad_trtis_client.py index 23d7a7c5..83f91b13 100644 --- a/TensorFlow/LanguageModeling/BERT/run_squad_trtis_client.py +++ b/TensorFlow/LanguageModeling/BERT/run_squad_trtis_client.py @@ -1,3 +1,16 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import modeling import tokenization from tensorrtserver.api import ProtocolType, InferContext, ServerStatusContext, grpc_service_pb2_grpc, grpc_service_pb2, model_config_pb2 diff --git a/TensorFlow/LanguageModeling/BERT/scripts/data_download.sh b/TensorFlow/LanguageModeling/BERT/scripts/data_download.sh index a3b28714..79ffc9b8 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/data_download.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/data_download.sh @@ -1,6 +1,19 @@ #!/usr/bin/env bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + docker run --runtime=nvidia -v $PWD:/workspace/bert \ --rm --shm-size=1g --ulimit memlock=-1 \ --ulimit stack=67108864 --ipc=host -t -i \ - bert bash -c "bash scripts/data_download_helper.sh" \ No newline at end of file + bert bash -c "bash data/create_datasets_from_start.sh" \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/scripts/data_download_helper.sh b/TensorFlow/LanguageModeling/BERT/scripts/data_download_helper.sh deleted file mode 100755 index cea0c2b4..00000000 --- a/TensorFlow/LanguageModeling/BERT/scripts/data_download_helper.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/usr/bin/env bash - -# Download pretrained_models -cd /workspace/bert/data/pretrained_models_google && python3 download_models.py - -# Download SQUAD -cd /workspace/bert/data/squad && . squad_download.sh - -# Download GLUE -cd /workspace/bert/data/glue && python3 download_glue_data.py - -# WIKI Download, set config in data_generators/wikipedia_corpus/config.sh -cd /workspace/bert/data/wikipedia_corpus && . run_preprocessing.sh - -cd /workspace/bert/data/bookcorpus && . run_preprocessing.sh - -cd /workspace/bert/data/glue && python3 download_glue_data.py diff --git a/TensorFlow/LanguageModeling/BERT/scripts/finetune_inference_benchmark.sh b/TensorFlow/LanguageModeling/BERT/scripts/finetune_inference_benchmark.sh index 81ae7b39..3ab042d0 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/finetune_inference_benchmark.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/finetune_inference_benchmark.sh @@ -1,13 +1,26 @@ #!/bin/bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + bert_model=${1:-"large"} use_xla=${2:-"true"} task=${3:-"squad"} if [ "$bert_model" = "large" ] ; then - export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16 else - export BERT_DIR=data/pretrained_models_google/uncased_L-12_H-768_A-12 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12 fi echo "BERT directory set as " $BERT_DIR @@ -31,7 +44,7 @@ echo "Results directory set as " $RESULTS_DIR LOGFILE="${RESULTS_DIR}/${task}_inference_benchmark_bert_${bert_model}.log" tmp_file="/tmp/${task}_inference_benchmark.log" if [ "$task" = "squad" ] ; then - export SQUAD_DIR=data/squad/v1.1 + export SQUAD_DIR=data/download/squad/v1.1 echo "Squad directory set as " $SQUAD_DIR @@ -48,11 +61,9 @@ if [ "$task" = "squad" ] ; then if [ "$precision" = "fp16" ] ; then echo "fp16 activated!" - export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1 use_fp16="--use_fp16" else echo "fp32 activated!" - export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=0 use_fp16="" fi diff --git a/TensorFlow/LanguageModeling/BERT/scripts/finetune_train_benchmark.sh b/TensorFlow/LanguageModeling/BERT/scripts/finetune_train_benchmark.sh index 861a7f96..b6c65fd2 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/finetune_train_benchmark.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/finetune_train_benchmark.sh @@ -1,15 +1,27 @@ #!/bin/bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + bert_model=${1:-"large"} -precision=${2:-"fp16"} -use_xla=${3:-"true"} -num_gpu=${4:-"8"} -task=${5:-"squad"} +use_xla=${2:-"true"} +num_gpu=${3:-"8"} +task=${4:-"squad"} if [ "$bert_model" = "large" ] ; then - export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16 else - export BERT_DIR=data/pretrained_models_google/uncased_L-12_H-768_A-12 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12 fi echo "BERT directory set as " $BERT_DIR @@ -25,12 +37,6 @@ if [ ! -d "$RESULTS_DIR" ] ; then fi echo "Results directory set as " $RESULTS_DIR -use_fp16="" -if [ "$precision" = "fp16" ] ; then - export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1 - use_fp16="--use_fp16" -fi - if [ "$use_xla" = "true" ] ; then use_xla_tag="--use_xla" @@ -53,7 +59,7 @@ fi LOGFILE="${RESULTS_DIR}/${task}_training_benchmark_bert_${bert_model}_gpu_${num_gpu}.log" if [ "$task" = "squad" ] ; then - export SQUAD_DIR=data/squad/v1.1 + export SQUAD_DIR=data/download/squad/v1.1 epochs="2.0" echo "Squad directory set as " $SQUAD_DIR @@ -76,11 +82,9 @@ if [ "$task" = "squad" ] ; then if [ "$precision" = "fp16" ] ; then echo "fp16 activated!" - export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1 use_fp16="--use_fp16" else echo "fp32 activated!" - export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=0 use_fp16="" fi diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_glue.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_glue.sh index c8e12265..359113e2 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/run_glue.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/run_glue.sh @@ -1,49 +1,47 @@ #!/usr/bin/env bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + echo "Container nvidia build = " $NVIDIA_BUILD_ID -batch_size=${1:-"32"} -learning_rate=${2:-"2e-5"} -precision=${3:-"fp16"} -use_xla=${4:-"true"} -num_gpu=${5:-"8"} -seq_length=${6:-"128"} -bert_model=${7:-"large"} +task_name=${1:-"MRPC"} +batch_size=${2:-"32"} +learning_rate=${3:-"2e-5"} +precision=${4:-"fp16"} +use_xla=${5:-"true"} +num_gpu=${6:-"8"} +seq_length=${7:-"128"} +doc_stride=${8:-"64"} +bert_model=${9:-"large"} if [ "$bert_model" = "large" ] ; then - export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16 else - export BERT_DIR=data/pretrained_models_google/uncased_L-12_H-768_A-12 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12 fi -export GLUE_DIR=data/glue +export GLUE_DIR=data/download -epochs=${8:-"3.0"} -ws=${9:-"0.1"} -init_checkpoint=${10:-"$BERT_DIR/bert_model.ckpt"} -#Edit to save logs & checkpoints in a different directory -RESULTS_DIR=/results - -if [ ! -d "$BERT_DIR" ] ; then - echo "Error! $BERT_DIR directory missing. Please mount pretrained BERT dataset." - exit -1 -fi -if [ ! -d "$GLUE_DIR" ] ; then - echo "Error! $GLUE_DIR directory missing. Please mount SQuAD dataset." - exit -1 -fi -if [ ! -d "$RESULTS_DIR" ] ; then - echo "Error! $RESULTS_DIR directory missing." - exit -1 -fi +epochs=${10:-"3.0"} +ws=${11:-"0.1"} +init_checkpoint=${12:-"$BERT_DIR/bert_model.ckpt"} echo "GLUE directory set as " $GLUE_DIR " BERT directory set as " $BERT_DIR -echo "Results directory set as " $RESULTS_DIR use_fp16="" if [ "$precision" = "fp16" ] ; then echo "fp16 activated!" - export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1 use_fp16="--use_fp16" fi @@ -60,34 +58,42 @@ if [ $num_gpu -gt 1 ] ; then -x NCCL_DEBUG=INFO \ -x LD_LIBRARY_PATH \ -x PATH -mca pml ob1 -mca btl ^openib" - use_hvd="--horovod" else mpi_command="" - use_hvd="" fi - export GBS=$(expr $batch_size \* $num_gpu) - printf -v TAG "tf_bert_%s_glue_1n_%s_gbs%d" "$bert_model" "$precision" $GBS - DATESTAMP=`date +'%y%m%d%H%M%S'` - RESULTS_DIR=${RESULTS_DIR}/${TAG}_${DATESTAMP} - mkdir $RESULTS_DIR - LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log - printf "Saving checkpoints to %s\n" "$RESULTS_DIR" - printf "Writing logs to %s\n" "$LOGFILE" +export GBS=$(expr $batch_size \* $num_gpu) +printf -v TAG "tf_bert_finetuning_glue_%s_%s_%s_gbs%d" "$task_name" "$bert_model" "$precision" $GBS +DATESTAMP=`date +'%y%m%d%H%M%S'` +#Edit to save logs & checkpoints in a different directory +RESULTS_DIR=/results/${TAG}_${DATESTAMP} +LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log +mkdir -m 777 -p $RESULTS_DIR +printf "Saving checkpoints to %s\n" "$RESULTS_DIR" +printf "Logs written to %s\n" "$LOGFILE" + +#Check if all necessary files are available before training +for DIR_or_file in $GLUE_DIR/${task_name} $RESULTS_DIR $BERT_DIR/vocab.txt $BERT_DIR/bert_config.json; do + echo $DIR_or_file + if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then + echo "Error! $DIR_or_file directory missing. Please mount correctly" + exit -1 + fi +done $mpi_command python run_classifier.py \ - --task_name=MRPC \ + --task_name=$task_name \ --do_train=true \ --do_eval=true \ - --data_dir=$GLUE_DIR/MRPC \ + --data_dir=$GLUE_DIR/$task_name \ --vocab_file=$BERT_DIR/vocab.txt \ --bert_config_file=$BERT_DIR/bert_config.json \ --init_checkpoint=$init_checkpoint \ --max_seq_length=$seq_length \ + --doc_stride=$doc_stride \ --train_batch_size=$batch_size \ --learning_rate=$learning_rate \ --num_train_epochs=$epochs \ --output_dir=$RESULTS_DIR \ - "$use_hvd" \ - "$use_fp16" \ - $use_xla_tag --warmup_proportion=$ws |& tee $LOGFILE \ No newline at end of file + --horovod "$use_fp16" \ + $use_xla_tag --warmup_proportion=$ws |& tee $LOGFILE \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_glue_inference.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_glue_inference.sh new file mode 100644 index 00000000..6e7e0e75 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/scripts/run_glue_inference.sh @@ -0,0 +1,78 @@ +#!/usr/bin/env bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +echo "Container nvidia build = " $NVIDIA_BUILD_ID +task_name=${1:-"MRPC"} +init_checkpoint=${2:-"$BERT_DIR/bert_model.ckpt"} +batch_size=${3:-"32"} +precision=${4:-"fp16"} +use_xla=${5:-"true"} +seq_length=${6:-"128"} +doc_stride=${7:-"64"} +bert_model=${8:-"large"} + +if [ "$bert_model" = "large" ] ; then + BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16 +else + BERT_DIR=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12 +fi +GLUE_DIR=data/download + +echo "GLUE directory set as " $GLUE_DIR " BERT directory set as " $BERT_DIR + +use_fp16="" +if [ "$precision" = "fp16" ] ; then + echo "fp16 activated!" + use_fp16="--use_fp16" +fi + +if [ "$use_xla" = "true" ] ; then + use_xla_tag="--use_xla" + echo "XLA activated" +else + use_xla_tag="" +fi + + +export GBS=$(expr $batch_size \* $num_gpu) +printf -v TAG "tf_bert_finetuning_glue_%s_inf_%s_%s_gbs%d_ckpt_%s" "$task_name" "$bert_model" "$precision" $GBS "$init_checkpoint" +DATESTAMP=`date +'%y%m%d%H%M%S'` +#Edit to save logs & checkpoints in a different directory +RESULTS_DIR=/results +LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log +printf "Logs written to %s\n" "$LOGFILE" + +#Check if all necessary files are available before training +for DIR_or_file in $GLUE_DIR $RESULTS_DIR $BERT_DIR/vocab.txt $BERT_DIR/bert_config.json; do + if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then + echo "Error! $DIR_or_file directory missing. Please mount correctly" + exit -1 + fi +done + +$mpi_command python run_classifier.py \ + --task_name=$task_name \ + --predict_batch_size=$batch_size \ + --eval_batch_size=$batch_size \ + --do_eval=true \ + --data_dir=$GLUE_DIR/$task_name \ + --vocab_file=$BERT_DIR/vocab.txt \ + --bert_config_file=$BERT_DIR/bert_config.json \ + --init_checkpoint=$init_checkpoint \ + --max_seq_length=$seq_length \ + --doc_stride=$doc_stride \ + --output_dir=$RESULTS_DIR \ + --horovod "$use_fp16" \ + $use_xla_tag |& tee $LOGFILE \ No newline at end of file diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining.sh deleted file mode 100755 index f8611613..00000000 --- a/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining.sh +++ /dev/null @@ -1,102 +0,0 @@ -#! /bin/bash - -echo "Container nvidia build = " $NVIDIA_BUILD_ID - -WIKI_DIR=/workspace/bert/data/wikipedia_corpus/final_tfrecords_sharded -BOOKS_DIR=/workspace/bert/data/bookcorpus/final_tfrecords_sharded -BERT_CONFIG=/workspace/bert/data/pretrained_models_google/uncased_L-24_H-1024_A-16/bert_config.json - -#Edit to save logs & checkpoints in a different directory -RESULTS_DIR=/results - -if [ ! -d "$WIKI_DIR" ] ; then - echo "Error! $WIKI_DIR directory missing. Please mount wikipedia dataset." - exit -1 -else - SOURCES="$WIKI_DIR/*" -fi -if [ ! -d "$BOOKS_DIR" ] ; then - echo "Warning! $BOOKS_DIR directory missing. Training will proceed without book corpus." -else - SOURCES+=" $BOOKS_DIR/*" -fi -if [ ! -d "$RESULTS_DIR" ] ; then - echo "Error! $RESULTS_DIR directory missing." - exit -1 -fi - -if [ ! -f "$BERT_CONFIG" ] ; then - echo "Error! BERT large configuration file not found at $BERT_CONFIG" - exit -1 -fi - -train_batch_size=${1:-14} -eval_batch_size=${2:-8} -learning_rate=${3:-"1e-4"} -precision=${4:-"manual_fp16"} -use_xla=${5:-"true"} -num_gpus=${6:-1} -warmup_steps=${7:-"10000"} -train_steps=${8:-1144000} -save_checkpoints_steps=${9:-5000} - -PREC="" -if [ "$precision" = "fp16" ] ; then - PREC="--use_fp16" -elif [ "$precision" = "fp32" ] ; then - PREC="" -elif [ "$precision" = "manual_fp16" ] ; then - PREC="--manual_fp16" -else - echo "Unknown argument" - exit -2 -fi - -if [ "$use_xla" = "true" ] ; then - PREC="$PREC --use_xla" - echo "XLA activated" -fi - -export GBS=$(expr $train_batch_size \* $num_gpus) -printf -v TAG "tf_bert_pretraining_%s_gbs%d" "$precision" $GBS -DATESTAMP=`date +'%y%m%d%H%M%S'` -RESULTS_DIR=${RESULTS_DIR}/${TAG}_${DATESTAMP} -LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log -printf "Saving checkpoints to %s\n" "$RESULTS_DIR" -printf "Logs written to %s\n" "$LOGFILE" - -echo $SOURCES -INPUT_FILES=$(eval ls $SOURCES | tr " " "\n" | awk '{printf "%s,",$1}' | sed s'/.$//') -CMD="python3 /workspace/bert/run_pretraining.py" -CMD+=" --input_file=$INPUT_FILES" -CMD+=" --output_dir=$RESULTS_DIR" -CMD+=" --bert_config_file=$BERT_CONFIG" -CMD+=" --do_train=True" -CMD+=" --do_eval=True" -CMD+=" --train_batch_size=$train_batch_size" -CMD+=" --eval_batch_size=$eval_batch_size" -CMD+=" --max_seq_length=512" -CMD+=" --max_predictions_per_seq=80" -CMD+=" --num_train_steps=$train_steps" -CMD+=" --num_warmup_steps=$warmup_steps" -CMD+=" --save_checkpoints_steps=$save_checkpoints_steps" -CMD+=" --learning_rate=$learning_rate" -CMD+=" --report_loss" -CMD+=" --horovod $PREC" - -if [ $num_gpus -gt 1 ] ; then - CMD="mpiexec --allow-run-as-root -np $num_gpus --bind-to socket $CMD" -fi - - - - -set -x -if [ -z "$LOGFILE" ] ; then - $CMD -else - ( - $CMD - ) |& tee $LOGFILE -fi -set +x diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_adam.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_adam.sh new file mode 100755 index 00000000..8c6a2d4c --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_adam.sh @@ -0,0 +1,111 @@ +#! /bin/bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +echo "Container nvidia build = " $NVIDIA_BUILD_ID + +train_batch_size=${1:-14} +eval_batch_size=${2:-8} +learning_rate=${3:-"1e-4"} +precision=${4:-"manual_fp16"} +use_xla=${5:-"true"} +num_gpus=${6:-8} +warmup_steps=${7:-"10000"} +train_steps=${8:-1144000} +save_checkpoints_steps=${9:-5000} +bert_model=${10:-"large"} +num_accumulation_steps=${11:-1} +seq_len=${12:-512} +max_pred_per_seq=${13:-80} + +DATA_DIR=data/tfrecord/lower_case_1_seq_len_${seq_len}_max_pred_${max_pred_per_seq}_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus + +if [ "$bert_model" = "large" ] ; then + export BERT_CONFIG=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16/bert_config.json +else + export BERT_CONFIG=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12/bert_config.json +fi + +PREC="" +if [ "$precision" = "fp16" ] ; then + PREC="--use_fp16" +elif [ "$precision" = "fp32" ] ; then + PREC="" +elif [ "$precision" = "manual_fp16" ] ; then + PREC="--manual_fp16" +else + echo "Unknown argument" + exit -2 +fi + +if [ "$use_xla" = "true" ] ; then + PREC="$PREC --use_xla" + echo "XLA activated" +fi + +export GBS=$(expr $train_batch_size \* $num_gpus \* $num_accumulation_steps) +printf -v TAG "tf_bert_pretraining_adam_%s_%s_gbs%d" "$bert_model" "$precision" $GBS +DATESTAMP=`date +'%y%m%d%H%M%S'` + +#Edit to save logs & checkpoints in a different directory +RESULTS_DIR=${RESULTS_DIR:-/results/${TAG}_${DATESTAMP}} +LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log +mkdir -m 777 -p $RESULTS_DIR +printf "Saving checkpoints to %s\n" "$RESULTS_DIR" +printf "Logs written to %s\n" "$LOGFILE" + +INPUT_FILES="$DATA_DIR/training" +EVAL_FILES="$DATA_DIR/test" + +CMD="python3 /workspace/bert/run_pretraining.py" +CMD+=" --input_files_dir=$INPUT_FILES" +CMD+=" --eval_files_dir=$EVAL_FILES" +CMD+=" --output_dir=$RESULTS_DIR" +CMD+=" --bert_config_file=$BERT_CONFIG" +CMD+=" --do_train=True" +CMD+=" --do_eval=True" +CMD+=" --train_batch_size=$train_batch_size" +CMD+=" --eval_batch_size=$eval_batch_size" +CMD+=" --max_seq_length=$seq_len" +CMD+=" --max_predictions_per_seq=$max_pred_per_seq" +CMD+=" --num_train_steps=$train_steps" +CMD+=" --num_warmup_steps=$warmup_steps" +CMD+=" --num_accumulation_steps=$num_accumulation_steps" +CMD+=" --save_checkpoints_steps=$save_checkpoints_steps" +CMD+=" --learning_rate=$learning_rate" +CMD+=" --optimizer_type=adam" +CMD+=" --horovod $PREC" +CMD+=" --allreduce_post_accumulation=True" + +#Check if all necessary files are available before training +for DIR_or_file in $DATA_DIR $BERT_CONFIG $RESULTS_DIR; do + if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then + echo "Error! $DIR_or_file directory missing. Please mount correctly" + exit -1 + fi +done + +if [ $num_gpus -gt 1 ] ; then + CMD="mpiexec --allow-run-as-root -np $num_gpus --bind-to socket $CMD" +fi + +set -x +if [ -z "$LOGFILE" ] ; then + $CMD +else + ( + $CMD + ) |& tee $LOGFILE +fi +set +x diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_lamb.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_lamb.sh new file mode 100644 index 00000000..8c8d97a2 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_lamb.sh @@ -0,0 +1,60 @@ +#!/usr/bin/env bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +echo "Container nvidia build = " $NVIDIA_BUILD_ID + +train_batch_size_phase1=${1:-64} +train_batch_size_phase2=${2:-8} +eval_batch_size=${3:-8} +learning_rate_phase1=${4:-"7.5e-4"} +learning_rate_phase2=${5:-"5e-4"} +precision=${6:-"fp16"} +use_xla=${7:-"true"} +num_gpus=${8:-8} +warmup_steps_phase1=${9:-"2000"} +warmup_steps_phase2=${10:-"200"} +train_steps=${11:-7820} +save_checkpoints_steps=${12:-100} +num_accumulation_steps_phase1=${13:-128} +num_accumulation_steps_phase2=${14:-512} +bert_model=${15:-"large"} + +DATA_DIR=data +export DATA_DIR=$DATA_DIR + +GBS1=$(expr $train_batch_size_phase1 \* $num_gpus \* $num_accumulation_steps_phase1) +GBS2=$(expr $train_batch_size_phase2 \* $num_gpus \* $num_accumulation_steps_phase2) +printf -v TAG "tf_bert_pretraining_lamb_%s_%s_gbs1%d_gbs2%d" "$bert_model" "$precision" $GBS1 $GBS2 +DATESTAMP=`date +'%y%m%d%H%M%S'` + +#Edit to save logs & checkpoints in a different directory +RESULTS_DIR=${RESULTS_DIR:-/results/${TAG}_${DATESTAMP}} +LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log +mkdir -m 777 -p $RESULTS_DIR +printf "Saving checkpoints to %s\n" "$RESULTS_DIR" +printf "Logs written to %s\n" "$LOGFILE" +export RESULTS_DIR=$RESULTS_DIR + +printf -v SCRIPT_ARGS "%d %d %d %e %e %s %s %d %d %d %d %d %d %d %s %s" \ + $train_batch_size_phase1 $train_batch_size_phase2 $eval_batch_size $learning_rate_phase1 \ + $learning_rate_phase2 "$precision" "$use_xla" $num_gpus $warmup_steps_phase1 \ + $warmup_steps_phase2 $train_steps $save_checkpoints_steps \ + $num_accumulation_steps_phase1 $num_accumulation_steps_phase2 "$bert_model" + +# RUN PHASE 1 +bash scripts/run_pretraining_lamb_phase1.sh $SCRIPT_ARGS |& tee -a $LOGFILE + +# RUN PHASE 2 +bash scripts/run_pretraining_lamb_phase2.sh $SCRIPT_ARGS |& tee -a $LOGFILE diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_lamb_phase1.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_lamb_phase1.sh new file mode 100755 index 00000000..f9f67c33 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_lamb_phase1.sh @@ -0,0 +1,103 @@ +#! /bin/bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +echo "Container nvidia build = " $NVIDIA_BUILD_ID + +train_batch_size_phase1=${1:-64} +train_batch_size_phase2=${2:-8} +eval_batch_size=${3:-8} +learning_rate_phase1=${4:-"7.5e-4"} +learning_rate_phase2=${5:-"5e-4"} +precision=${6:-"fp16"} +use_xla=${7:-"true"} +num_gpus=${8:-2} +warmup_steps_phase1=${9:-"2000"} +warmup_steps_phase2=${10:-"200"} +train_steps=${11:-7820} +save_checkpoints_steps=${12:-100} +num_accumulation_steps_phase1=${13:-128} +num_accumulation_steps_phase2=${14:-512} +bert_model=${15:-"large"} + +DATA_DIR=${DATA_DIR:-data} +#Edit to save logs & checkpoints in a different directory +RESULTS_DIR=${RESULTS_DIR:-/results} + +if [ "$bert_model" = "large" ] ; then + export BERT_CONFIG=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16/bert_config.json +else + export BERT_CONFIG=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12/bert_config.json +fi + +PREC="" +if [ "$precision" = "fp16" ] ; then + PREC="--use_fp16" +elif [ "$precision" = "fp32" ] ; then + PREC="" +elif [ "$precision" = "manual_fp16" ] ; then + PREC="--manual_fp16" +else + echo "Unknown argument" + exit -2 +fi + +if [ "$use_xla" = "true" ] ; then + PREC="$PREC --use_xla" + echo "XLA activated" +fi + +mpi="" +if [ $num_gpus -gt 1 ] ; then + mpi="mpiexec --allow-run-as-root -np $num_gpus --bind-to socket" +fi + +#PHASE 1 + +train_steps_phase1=$(expr $train_steps \* 9 \/ 10) #Phase 1 is 10% of training +gbs_phase1=$(expr $train_batch_size_phase1 \* $num_accumulation_steps_phase1) +seq_len=128 +max_pred_per_seq=20 +RESULTS_DIR_PHASE1=${RESULTS_DIR}/phase_1 +mkdir -m 777 -p $RESULTS_DIR_PHASE1 + +INPUT_FILES="$DATA_DIR/tfrecord/lower_case_1_seq_len_${seq_len}_max_pred_${max_pred_per_seq}_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus/training" +EVAL_FILES="$DATA_DIR/tfrecord/lower_case_1_seq_len_${seq_len}_max_pred_${max_pred_per_seq}_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus/test" + +#Check if all necessary files are available before training +for DIR_or_file in $DATA_DIR $RESULTS_DIR_PHASE1 $BERT_CONFIG; do + if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then + echo "Error! $DIR_or_file directory missing. Please mount correctly" + exit -1 + fi +done + + $mpi python /workspace/bert/run_pretraining.py \ + --input_files_dir=$INPUT_FILES \ + --eval_files_dir=$EVAL_FILES \ + --output_dir=$RESULTS_DIR_PHASE1 \ + --bert_config_file=$BERT_CONFIG \ + --do_train=True \ + --do_eval=True \ + --train_batch_size=$train_batch_size_phase1 \ + --eval_batch_size=$eval_batch_size \ + --max_seq_length=$seq_len \ + --max_predictions_per_seq=$max_pred_per_seq \ + --num_train_steps=$train_steps_phase1 \ + --num_accumulation_steps=$num_accumulation_steps_phase1 \ + --num_warmup_steps=$warmup_steps_phase1 \ + --save_checkpoints_steps=$save_checkpoints_steps \ + --learning_rate=$learning_rate_phase1 \ + --horovod $PREC \ + --allreduce_post_accumulation=True diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_lamb_phase2.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_lamb_phase2.sh new file mode 100755 index 00000000..b26311c8 --- /dev/null +++ b/TensorFlow/LanguageModeling/BERT/scripts/run_pretraining_lamb_phase2.sh @@ -0,0 +1,115 @@ +#! /bin/bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +echo "Container nvidia build = " $NVIDIA_BUILD_ID + +train_batch_size_phase1=${1:-64} +train_batch_size_phase2=${2:-8} +eval_batch_size=${3:-8} +learning_rate_phase1=${4:-"7.5e-4"} +learning_rate_phase2=${5:-"5e-4"} +precision=${6:-"fp16"} +use_xla=${7:-"true"} +num_gpus=${8:-2} +warmup_steps_phase1=${9:-"2000"} +warmup_steps_phase2=${10:-"200"} +train_steps=${11:-7820} +save_checkpoints_steps=${12:-100} +num_accumulation_steps_phase1=${13:-128} +num_accumulation_steps_phase2=${14:-512} +bert_model=${15:-"large"} + +DATA_DIR=${DATA_DIR:-data} +#Edit to save logs & checkpoints in a different directory +RESULTS_DIR=${RESULTS_DIR:-/results} + +if [ "$bert_model" = "large" ] ; then + export BERT_CONFIG=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16/bert_config.json +else + export BERT_CONFIG=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12/bert_config.json +fi + +echo "Container nvidia build = " $NVIDIA_BUILD_ID + +PREC="" +if [ "$precision" = "fp16" ] ; then + PREC="--use_fp16" +elif [ "$precision" = "fp32" ] ; then + PREC="" +elif [ "$precision" = "manual_fp16" ] ; then + PREC="--manual_fp16" +else + echo "Unknown argument" + exit -2 +fi + +if [ "$use_xla" = "true" ] ; then + PREC="$PREC --use_xla" + echo "XLA activated" +fi + +mpi="" +if [ $num_gpus -gt 1 ] ; then + mpi="mpiexec --allow-run-as-root -np $num_gpus --bind-to socket" +fi + +#PHASE 1 Config + +train_steps_phase1=$(expr $train_steps \* 9 \/ 10) #Phase 1 is 10% of training +gbs_phase1=$(expr $train_batch_size_phase1 \* $num_accumulation_steps_phase1) +PHASE1_CKPT=${RESULTS_DIR}/phase_1/model.ckpt-${train_steps_phase1} + +#PHASE 2 + +seq_len=512 +max_pred_per_seq=80 +train_steps_phase2=$(expr $train_steps \* 1 \/ 10) #Phase 2 is 10% of training +gbs_phase2=$(expr $train_batch_size_phase2 \* $num_accumulation_steps_phase2) +train_steps_phase2=$(expr $train_steps_phase2 \* $gbs_phase1 \/ $gbs_phase2) # Adjust for batch size + +RESULTS_DIR_PHASE2=${RESULTS_DIR}/phase_2 +mkdir -m 777 -p $RESULTS_DIR_PHASE2 + +INPUT_FILES="$DATA_DIR/tfrecord/lower_case_1_seq_len_${seq_len}_max_pred_${max_pred_per_seq}_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus/training" +EVAL_FILES="$DATA_DIR/tfrecord/lower_case_1_seq_len_${seq_len}_max_pred_${max_pred_per_seq}_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/books_wiki_en_corpus/test" + +#Check if all necessary files are available before training +for DIR_or_file in $DATA_DIR $RESULTS_DIR $BERT_CONFIG ${PHASE1_CKPT}.meta; do + if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then + echo "Error! $DIR_or_file directory missing. Please mount correctly" + exit -1 + fi +done + +$mpi python /workspace/bert/run_pretraining.py \ + --input_files_dir=$INPUT_FILES \ + --init_checkpoint=$PHASE1_CKPT \ + --eval_files_dir=$EVAL_FILES \ + --output_dir=$RESULTS_DIR_PHASE2 \ + --bert_config_file=$BERT_CONFIG \ + --do_train=True \ + --do_eval=True \ + --train_batch_size=$train_batch_size_phase2 \ + --eval_batch_size=$eval_batch_size \ + --max_seq_length=$seq_len \ + --max_predictions_per_seq=$max_pred_per_seq \ + --num_train_steps=$train_steps_phase2 \ + --num_accumulation_steps=$num_accumulation_steps_phase2 \ + --num_warmup_steps=$warmup_steps_phase2 \ + --save_checkpoints_steps=$save_checkpoints_steps \ + --learning_rate=$learning_rate_phase2 \ + --horovod $PREC \ + --allreduce_post_accumulation=True + diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_squad.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_squad.sh index 534fe0af..da3c8eb7 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/run_squad.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/run_squad.sh @@ -1,5 +1,18 @@ #!/usr/bin/env bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + echo "Container nvidia build = " $NVIDIA_BUILD_ID batch_size=${1:-"8"} @@ -12,14 +25,14 @@ doc_stride=${7:-"128"} bert_model=${8:-"large"} if [ "$bert_model" = "large" ] ; then - export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16 else - export BERT_DIR=data/pretrained_models_google/uncased_L-12_H-768_A-12 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12 fi squad_version=${9:-"1.1"} -export SQUAD_DIR=data/squad/v${squad_version} +export SQUAD_DIR=data/download/squad/v${squad_version} if [ "$squad_version" = "1.1" ] ; then version_2_with_negative="False" else @@ -29,29 +42,11 @@ fi init_checkpoint=${10:-"$BERT_DIR/bert_model.ckpt"} epochs=${11:-"2.0"} -#Edit to save logs & checkpoints in a different directory -RESULTS_DIR=/results - -if [ ! -d "$SQUAD_DIR" ] ; then - echo "Error! $SQUAD_DIR directory missing. Please mount SQuAD dataset." - exit -1 -fi -if [ ! -d "$BERT_DIR" ] ; then - echo "Error! $BERT_DIR directory missing. Please mount pretrained BERT dataset." - exit -1 -fi -if [ ! -d "$RESULTS_DIR" ] ; then - echo "Error! $RESULTS_DIR directory missing." - exit -1 -fi - echo "Squad directory set as " $SQUAD_DIR " BERT directory set as " $BERT_DIR -echo "Results directory set as " $RESULTS_DIR use_fp16="" if [ "$precision" = "fp16" ] ; then echo "fp16 activated!" - export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1 use_fp16="--use_fp16" fi @@ -68,40 +63,45 @@ if [ $num_gpu -gt 1 ] ; then -x NCCL_DEBUG=INFO \ -x LD_LIBRARY_PATH \ -x PATH -mca pml ob1 -mca btl ^openib" - use_hvd="--horovod" else mpi_command="" - use_hvd="" fi +export GBS=$(expr $batch_size \* $num_gpu) +printf -v TAG "tf_bert_finetuning_squad_%s_%s_gbs%d" "$bert_model" "$precision" $GBS +DATESTAMP=`date +'%y%m%d%H%M%S'` - export GBS=$(expr $batch_size \* $num_gpu) - printf -v TAG "tf_bert_%s_squad_1n_%s_gbs%d" "$bert_model" "$precision" $GBS - DATESTAMP=`date +'%y%m%d%H%M%S'` +#Edit to save logs & checkpoints in a different directory +RESULTS_DIR=/results/${TAG}_${DATESTAMP} +LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log +mkdir -m 777 -p $RESULTS_DIR +printf "Saving checkpoints to %s\n" "$RESULTS_DIR" +printf "Logs written to %s\n" "$LOGFILE" - RESULTS_DIR=${RESULTS_DIR}/${TAG}_${DATESTAMP} - mkdir $RESULTS_DIR - LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log - printf "Saving checkpoints to %s\n" "$RESULTS_DIR" - printf "Writing logs to %s\n" "$LOGFILE" +#Check if all necessary files are available before training +for DIR_or_file in $SQUAD_DIR $RESULTS_DIR $BERT_DIR/bert_config.json $BERT_DIR/vocab.txt; do + if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then + echo "Error! $DIR_or_file directory missing. Please mount correctly" + exit -1 + fi +done - $mpi_command python run_squad.py \ - --vocab_file=$BERT_DIR/vocab.txt \ - --bert_config_file=$BERT_DIR/bert_config.json \ - --init_checkpoint=$init_checkpoint \ - --do_train=True \ - --train_file=$SQUAD_DIR/train-v${squad_version}.json \ - --do_predict=True \ - --predict_file=$SQUAD_DIR/dev-v${squad_version}.json \ - --train_batch_size=$batch_size \ - --learning_rate=$learning_rate \ - --num_train_epochs=$epochs \ - --max_seq_length=$seq_length \ - --doc_stride=$doc_stride \ - --save_checkpoints_steps 1000 \ - --output_dir=$RESULTS_DIR \ - "$use_hvd" \ - "$use_fp16" \ - $use_xla_tag --version_2_with_negative=${version_2_with_negative} |& tee $LOGFILE +$mpi_command python run_squad.py \ +--vocab_file=$BERT_DIR/vocab.txt \ +--bert_config_file=$BERT_DIR/bert_config.json \ +--init_checkpoint=$init_checkpoint \ +--do_train=True \ +--train_file=$SQUAD_DIR/train-v${squad_version}.json \ +--do_predict=True \ +--predict_file=$SQUAD_DIR/dev-v${squad_version}.json \ +--train_batch_size=$batch_size \ +--learning_rate=$learning_rate \ +--num_train_epochs=$epochs \ +--max_seq_length=$seq_length \ +--doc_stride=$doc_stride \ +--save_checkpoints_steps 1000 \ +--output_dir=$RESULTS_DIR \ +--horovod "$use_fp16" \ +$use_xla_tag --version_2_with_negative=${version_2_with_negative} |& tee $LOGFILE python $SQUAD_DIR/evaluate-v${squad_version}.py $SQUAD_DIR/dev-v${squad_version}.json ${RESULTS_DIR}/predictions.json |& tee -a $LOGFILE diff --git a/TensorFlow/LanguageModeling/BERT/scripts/run_squad_inference.sh b/TensorFlow/LanguageModeling/BERT/scripts/run_squad_inference.sh index 195b0661..2a8aa2cc 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/run_squad_inference.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/run_squad_inference.sh @@ -1,5 +1,18 @@ #!/usr/bin/env bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + echo "Container nvidia build = " $NVIDIA_BUILD_ID init_checkpoint=${1:-"/results/model.ckpt"} @@ -12,33 +25,18 @@ bert_model=${7:-"large"} squad_version=${8:-"1.1"} if [ "$bert_model" = "large" ] ; then - export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16 else - export BERT_DIR=data/pretrained_models_google/uncased_L-12_H-768_A-12 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12 fi -export SQUAD_DIR=data/squad/v${squad_version} +export SQUAD_DIR=data/download/squad/v${squad_version} if [ "$squad_version" = "1.1" ] ; then version_2_with_negative="False" else version_2_with_negative="True" fi -#Edit to save logs & checkpoints in a different directory -RESULTS_DIR=/results - -if [ ! -d "$SQUAD_DIR" ] ; then - echo "Error! $SQUAD_DIR directory missing. Please mount SQuAD dataset." - exit -1 -fi -if [ ! -d "$BERT_DIR" ] ; then - echo "Error! $BERT_DIR directory missing. Please mount pretrained BERT dataset." - exit -1 -fi -if [ ! -d "$RESULTS_DIR" ] ; then - echo "Error! $RESULTS_DIR directory missing." - exit -1 -fi echo "Squad directory set as " $SQUAD_DIR " BERT directory set as " $BERT_DIR echo "Results directory set as " $RESULTS_DIR @@ -46,7 +44,6 @@ echo "Results directory set as " $RESULTS_DIR use_fp16="" if [ "$precision" = "fp16" ] ; then echo "fp16 activated!" - export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1 use_fp16="--use_fp16" fi @@ -57,10 +54,20 @@ else use_xla_tag="" fi - printf -v TAG "tf_bert_%s_squad_inf_1n_%s_gbs%d_ckpt_%s" "$bert_model" "$precision" $batch_size "$init_checkpoint" - DATESTAMP=`date +'%y%m%d%H%M%S'` - LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log - printf "Writing logs to %s\n" "$LOGFILE" +printf -v TAG "tf_bert_finetuning_squad_%s_inf_%s_gbs%d_ckpt_%s" "$bert_model" "$precision" $batch_size "$init_checkpoint" +DATESTAMP=`date +'%y%m%d%H%M%S'` +#Edit to save logs & checkpoints in a different directory +RESULTS_DIR=/results +LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log +printf "Logs written to %s\n" "$LOGFILE" + +#Check if all necessary files are available before training +for DIR_or_file in $SQUAD_DIR $RESULTS_DIR $BERT_DIR/vocab.txt $BERT_DIR/bert_config.json; do + if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then + echo "Error! $DIR_or_file directory missing. Please mount correctly" + exit -1 + fi +done python run_squad.py \ --vocab_file=$BERT_DIR/vocab.txt \ @@ -68,6 +75,7 @@ python run_squad.py \ --init_checkpoint=$init_checkpoint \ --do_predict=True \ --predict_file=$SQUAD_DIR/dev-v${squad_version}.json \ +--predict_batch_size=$batch_size \ --max_seq_length=$seq_length \ --doc_stride=$doc_stride \ --predict_batch_size=$batch_size \ diff --git a/TensorFlow/LanguageModeling/BERT/scripts/trtis/export_model.sh b/TensorFlow/LanguageModeling/BERT/scripts/trtis/export_model.sh index 2f729282..d6bf4f4d 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/trtis/export_model.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/trtis/export_model.sh @@ -1,10 +1,25 @@ -init_checkpoint=${1:-"/results/model.ckpt"} +#!/bin/bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +init_checkpoint=${1:-"/results/models/bert_large_fp16_384_v1/model.ckpt-5474"} batch_size=${2:-"8"} precision=${3:-"fp16"} use_xla=${4:-"true"} seq_length=${5:-"384"} doc_stride=${6:-"128"} -BERT_DIR=${7:-"data/pretrained_models_google/uncased_L-24_H-1024_A-16"} +BERT_DIR=${7:-"data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16"} trtis_model_version=${8:-1} trtis_model_name=${9:-"bert"} trtis_dyn_batching_delay=${10:-0} @@ -17,7 +32,6 @@ additional_args="--trtis_model_version=$trtis_model_version --trtis_model_name=$ if [ "$precision" = "fp16" ] ; then echo "fp16 activated!" - export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1 additional_args="$additional_args --use_fp16" fi diff --git a/TensorFlow/LanguageModeling/BERT/scripts/trtis/generate_figures.sh b/TensorFlow/LanguageModeling/BERT/scripts/trtis/generate_figures.sh index dc18e99e..d5bb1cc2 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/trtis/generate_figures.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/trtis/generate_figures.sh @@ -1,3 +1,18 @@ +#!/bin/bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # Set the number of devices to use export NVIDIA_VISIBLE_DEVICES=0 @@ -12,9 +27,9 @@ init_checkpoint=${4:-"/results/models/bert_tf_${bert_model}_${precision}_${seq_l MODEL_NAME="bert_${bert_model}_${seq_length}_${precision}" if [ "$bert_model" = "large" ] ; then - export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16 else - export BERT_DIR=data/pretrained_models_google/uncased_L-12_H-768_A-12 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12 fi doc_stride=128 diff --git a/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_client.sh b/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_client.sh index 726565b0..aa662b48 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_client.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_client.sh @@ -1,12 +1,27 @@ +#!/bin/bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + batch_size=${1:-"8"} seq_length=${2:-"384"} doc_stride=${3:-"128"} trtis_version_name=${4:-"1"} trtis_model_name=${5:-"bert"} -BERT_DIR=${6:-"data/pretrained_models_google/uncased_L-24_H-1024_A-16"} +BERT_DIR=${6:-"data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16"} squad_version=${7:-"1.1"} -export SQUAD_DIR=data/squad/v${squad_version} +export SQUAD_DIR=data/download/squad/v${squad_version} if [ "$squad_version" = "1.1" ] ; then version_2_with_negative="False" else diff --git a/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_perf_client.sh b/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_perf_client.sh index 59e7979d..849d48bf 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_perf_client.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_perf_client.sh @@ -1,6 +1,18 @@ - #!/bin/bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + MODEL_NAME=${1:-"bert"} MODEL_VERSION=${2:-1} precision=${3:-"fp16"} diff --git a/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_trtis.sh b/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_trtis.sh index b0f93c92..eae582e8 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_trtis.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/trtis/run_trtis.sh @@ -1,4 +1,19 @@ -init_checkpoint=${1:-"/results/model.ckpt"} +#!/bin/bash + +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +init_checkpoint=${1:-"/results/models/bert_large_fp16_384_v1/model.ckpt-5474"} batch_size=${2:-"8"} precision=${3:-"fp16"} use_xla=${4:-"true"} @@ -14,9 +29,9 @@ trtis_engine_count=${13:-1} trtis_model_overwrite=${14:-"False"} if [ "$bert_model" = "large" ] ; then - export BERT_DIR=data/pretrained_models_google/uncased_L-24_H-1024_A-16 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16 else - export BERT_DIR=data/pretrained_models_google/uncased_L-12_H-768_A-12 + export BERT_DIR=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12 fi if [ ! -d "$BERT_DIR" ] ; then diff --git a/TensorFlow/LanguageModeling/BERT/scripts/trtis/wait_for_trtis_server.sh b/TensorFlow/LanguageModeling/BERT/scripts/trtis/wait_for_trtis_server.sh index bea54bee..ab73f0f6 100755 --- a/TensorFlow/LanguageModeling/BERT/scripts/trtis/wait_for_trtis_server.sh +++ b/TensorFlow/LanguageModeling/BERT/scripts/trtis/wait_for_trtis_server.sh @@ -1,5 +1,18 @@ #!/bin/bash +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + SERVER_URI=${1:-"localhost"} echo "Waiting for TRTIS Server to be ready at http://$SERVER_URI:8000..." diff --git a/TensorFlow/LanguageModeling/BERT/tokenization.py b/TensorFlow/LanguageModeling/BERT/tokenization.py index 8d4a797a..6e53ce76 100644 --- a/TensorFlow/LanguageModeling/BERT/tokenization.py +++ b/TensorFlow/LanguageModeling/BERT/tokenization.py @@ -1,5 +1,6 @@ # coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors. +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """Tokenization classes.""" from __future__ import absolute_import @@ -23,6 +25,18 @@ import unicodedata import six import tensorflow as tf import re +import os + + +PRETRAINED_VOCAB_ARCHIVE_MAP = { + 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt", + 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt", + 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt", + 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt", + 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt", + 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt", + 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt", +} def validate_case_matches_checkpoint(do_lower_case, init_checkpoint): """Checks whether the casing config is consistent with the checkpoint name.""" @@ -76,61 +90,41 @@ def validate_case_matches_checkpoint(do_lower_case, init_checkpoint): def convert_to_unicode(text): - """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" - if six.PY3: + """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" if isinstance(text, str): - return text + return text elif isinstance(text, bytes): - return text.decode("utf-8", "ignore") + return text.decode("utf-8", "ignore") else: - raise ValueError("Unsupported string type: %s" % (type(text))) - elif six.PY2: - if isinstance(text, str): - return text.decode("utf-8", "ignore") - elif isinstance(text, unicode): - return text - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - else: - raise ValueError("Not running on Python2 or Python 3?") + raise ValueError("Unsupported string type: %s" % (type(text))) def printable_text(text): - """Returns text encoded in a way suitable for print or `tf.logging`.""" + """Returns text encoded in a way suitable for print or `tf.logging`.""" - # These functions want `str` for both Python2 and Python3, but in one case - # it's a Unicode string and in the other it's a byte string. - if six.PY3: + # These functions want `str` for both Python2 and Python3, but in one case + # it's a Unicode string and in the other it's a byte string. if isinstance(text, str): - return text + return text elif isinstance(text, bytes): - return text.decode("utf-8", "ignore") + return text.decode("utf-8", "ignore") else: - raise ValueError("Unsupported string type: %s" % (type(text))) - elif six.PY2: - if isinstance(text, str): - return text - elif isinstance(text, unicode): - return text.encode("utf-8") - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - else: - raise ValueError("Not running on Python2 or Python 3?") + raise ValueError("Unsupported string type: %s" % (type(text))) def load_vocab(vocab_file): - """Loads a vocabulary file into a dictionary.""" - vocab = collections.OrderedDict() - index = 0 - with tf.gfile.GFile(vocab_file, "r") as reader: - while True: - token = convert_to_unicode(reader.readline()) - if not token: - break - token = token.strip() - vocab[token] = index - index += 1 - return vocab + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + index = 0 + with open(vocab_file, "r") as reader: + while True: + token = convert_to_unicode(reader.readline()) + if not token: + break + token = token.strip() + vocab[token] = index + index += 1 + return vocab def convert_by_vocab(vocab, items): @@ -141,21 +135,13 @@ def convert_by_vocab(vocab, items): return output -def convert_tokens_to_ids(vocab, tokens): - return convert_by_vocab(vocab, tokens) - - -def convert_ids_to_tokens(inv_vocab, ids): - return convert_by_vocab(inv_vocab, ids) - - def whitespace_tokenize(text): - """Runs basic whitespace cleaning and splitting on a piece of text.""" - text = text.strip() - if not text: - return [] - tokens = text.split() - return tokens + """Runs basic whitespace cleaning and splitting on a peice of text.""" + text = text.strip() + if not text: + return [] + tokens = text.split() + return tokens class FullTokenizer(object): @@ -182,131 +168,197 @@ class FullTokenizer(object): return convert_by_vocab(self.inv_vocab, ids) -class BasicTokenizer(object): - """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" +class BertTokenizer(object): + """Runs end-to-end tokenization: punctuation splitting + wordpiece""" - def __init__(self, do_lower_case=True): - """Constructs a BasicTokenizer. + def __init__(self, vocab_file, do_lower_case=True): + if not os.path.isfile(vocab_file): + raise ValueError( + "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained " + "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)) + self.vocab = load_vocab(vocab_file) + self.ids_to_tokens = collections.OrderedDict( + [(ids, tok) for tok, ids in self.vocab.items()]) + self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) + + def tokenize(self, text): + split_tokens = [] + for token in self.basic_tokenizer.tokenize(text): + for sub_token in self.wordpiece_tokenizer.tokenize(token): + split_tokens.append(sub_token) + return split_tokens + + def convert_tokens_to_ids(self, tokens): + """Converts a sequence of tokens into ids using the vocab.""" + ids = [] + for token in tokens: + ids.append(self.vocab[token]) + return ids + + def convert_ids_to_tokens(self, ids): + """Converts a sequence of ids in wordpiece tokens using the vocab.""" + tokens = [] + for i in ids: + tokens.append(self.ids_to_tokens[i]) + return tokens + + @classmethod + def from_pretrained(cls, pretrained_model_name, do_lower_case=True): + """ + Instantiate a PreTrainedBertModel from a pre-trained model file. + Download and cache the pre-trained model file if needed. + """ + if pretrained_model_name in PRETRAINED_VOCAB_ARCHIVE_MAP: + vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name] + else: + vocab_file = pretrained_model_name + # redirect to the cache, if necessary + try: + resolved_vocab_file = cached_path(vocab_file) + if resolved_vocab_file == vocab_file: + + logger.info("loading vocabulary file {}".format(vocab_file)) + else: + logger.info("loading vocabulary file {} from cache at {}".format( + vocab_file, resolved_vocab_file)) + # Instantiate tokenizer. + tokenizer = cls(resolved_vocab_file, do_lower_case) + except FileNotFoundError: + logger.error( + "Model name '{}' was not found in model name list ({}). " + "We assumed '{}' was a path or url but couldn't find any file " + "associated to this path or url.".format( + pretrained_model_name, + ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()), + pretrained_model_name)) + tokenizer = None + return tokenizer + + +class BasicTokenizer(object): + """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" + + def __init__(self, do_lower_case=True): + """Constructs a BasicTokenizer. Args: do_lower_case: Whether to lower case the input. """ - self.do_lower_case = do_lower_case + self.do_lower_case = do_lower_case - def tokenize(self, text): - """Tokenizes a piece of text.""" - text = convert_to_unicode(text) - text = self._clean_text(text) + def tokenize(self, text): + """Tokenizes a piece of text.""" + text = convert_to_unicode(text) + text = self._clean_text(text) + # This was added on November 1st, 2018 for the multilingual and Chinese + # models. This is also applied to the English models now, but it doesn't + # matter since the English models were not trained on any Chinese data + # and generally don't have any Chinese data in them (there are Chinese + # characters in the vocabulary because Wikipedia does have some Chinese + # words in the English Wikipedia.). + text = self._tokenize_chinese_chars(text) + orig_tokens = whitespace_tokenize(text) + split_tokens = [] + for token in orig_tokens: + if self.do_lower_case: + token = token.lower() + token = self._run_strip_accents(token) + split_tokens.extend(self._run_split_on_punc(token)) - # This was added on November 1st, 2018 for the multilingual and Chinese - # models. This is also applied to the English models now, but it doesn't - # matter since the English models were not trained on any Chinese data - # and generally don't have any Chinese data in them (there are Chinese - # characters in the vocabulary because Wikipedia does have some Chinese - # words in the English Wikipedia.). - text = self._tokenize_chinese_chars(text) + output_tokens = whitespace_tokenize(" ".join(split_tokens)) + return output_tokens - orig_tokens = whitespace_tokenize(text) - split_tokens = [] - for token in orig_tokens: - if self.do_lower_case: - token = token.lower() - token = self._run_strip_accents(token) - split_tokens.extend(self._run_split_on_punc(token)) + def _run_strip_accents(self, text): + """Strips accents from a piece of text.""" + text = unicodedata.normalize("NFD", text) + output = [] + for char in text: + cat = unicodedata.category(char) + if cat == "Mn": + continue + output.append(char) + return "".join(output) - output_tokens = whitespace_tokenize(" ".join(split_tokens)) - return output_tokens - - def _run_strip_accents(self, text): - """Strips accents from a piece of text.""" - text = unicodedata.normalize("NFD", text) - output = [] - for char in text: - cat = unicodedata.category(char) - if cat == "Mn": - continue - output.append(char) - return "".join(output) - - def _run_split_on_punc(self, text): - """Splits punctuation on a piece of text.""" - chars = list(text) - i = 0 - start_new_word = True - output = [] - while i < len(chars): - char = chars[i] - if _is_punctuation(char): - output.append([char]) + def _run_split_on_punc(self, text): + """Splits punctuation on a piece of text.""" + chars = list(text) + i = 0 start_new_word = True - else: - if start_new_word: - output.append([]) - start_new_word = False - output[-1].append(char) - i += 1 + output = [] + while i < len(chars): + char = chars[i] + if _is_punctuation(char): + output.append([char]) + start_new_word = True + else: + if start_new_word: + output.append([]) + start_new_word = False + output[-1].append(char) + i += 1 - return ["".join(x) for x in output] + return ["".join(x) for x in output] - def _tokenize_chinese_chars(self, text): - """Adds whitespace around any CJK character.""" - output = [] - for char in text: - cp = ord(char) - if self._is_chinese_char(cp): - output.append(" ") - output.append(char) - output.append(" ") - else: - output.append(char) - return "".join(output) + def _tokenize_chinese_chars(self, text): + """Adds whitespace around any CJK character.""" + output = [] + for char in text: + cp = ord(char) + if self._is_chinese_char(cp): + output.append(" ") + output.append(char) + output.append(" ") + else: + output.append(char) + return "".join(output) - def _is_chinese_char(self, cp): - """Checks whether CP is the codepoint of a CJK character.""" - # This defines a "chinese character" as anything in the CJK Unicode block: - # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) - # - # Note that the CJK Unicode block is NOT all Japanese and Korean characters, - # despite its name. The modern Korean Hangul alphabet is a different block, - # as is Japanese Hiragana and Katakana. Those alphabets are used to write - # space-separated words, so they are not treated specially and handled - # like the all of the other languages. - if ((cp >= 0x4E00 and cp <= 0x9FFF) or # - (cp >= 0x3400 and cp <= 0x4DBF) or # - (cp >= 0x20000 and cp <= 0x2A6DF) or # - (cp >= 0x2A700 and cp <= 0x2B73F) or # - (cp >= 0x2B740 and cp <= 0x2B81F) or # - (cp >= 0x2B820 and cp <= 0x2CEAF) or - (cp >= 0xF900 and cp <= 0xFAFF) or # - (cp >= 0x2F800 and cp <= 0x2FA1F)): # - return True + def _is_chinese_char(self, cp): + """Checks whether CP is the codepoint of a CJK character.""" + # This defines a "chinese character" as anything in the CJK Unicode block: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + # + # Note that the CJK Unicode block is NOT all Japanese and Korean characters, + # despite its name. The modern Korean Hangul alphabet is a different block, + # as is Japanese Hiragana and Katakana. Those alphabets are used to write + # space-separated words, so they are not treated specially and handled + # like the all of the other languages. + if ((cp >= 0x4E00 and cp <= 0x9FFF) or # + (cp >= 0x3400 and cp <= 0x4DBF) or # + (cp >= 0x20000 and cp <= 0x2A6DF) or # + (cp >= 0x2A700 and cp <= 0x2B73F) or # + (cp >= 0x2B740 and cp <= 0x2B81F) or # + (cp >= 0x2B820 and cp <= 0x2CEAF) or + (cp >= 0xF900 and cp <= 0xFAFF) or # + (cp >= 0x2F800 and cp <= 0x2FA1F)): # + return True - return False + return False - def _clean_text(self, text): - """Performs invalid character removal and whitespace cleanup on text.""" - output = [] - for char in text: - cp = ord(char) - if cp == 0 or cp == 0xfffd or _is_control(char): - continue - if _is_whitespace(char): - output.append(" ") - else: - output.append(char) - return "".join(output) + def _clean_text(self, text): + """Performs invalid character removal and whitespace cleanup on text.""" + output = [] + for char in text: + cp = ord(char) + if cp == 0 or cp == 0xfffd or _is_control(char): + continue + if _is_whitespace(char): + output.append(" ") + else: + output.append(char) + return "".join(output) class WordpieceTokenizer(object): - """Runs WordPiece tokenziation.""" + """Runs WordPiece tokenization.""" - def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200): - self.vocab = vocab - self.unk_token = unk_token - self.max_input_chars_per_word = max_input_chars_per_word + def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100): + self.vocab = vocab + self.unk_token = unk_token + self.max_input_chars_per_word = max_input_chars_per_word - def tokenize(self, text): - """Tokenizes a piece of text into its word pieces. + def tokenize(self, text): + """Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary. @@ -323,77 +375,77 @@ class WordpieceTokenizer(object): A list of wordpiece tokens. """ - text = convert_to_unicode(text) + text = convert_to_unicode(text) - output_tokens = [] - for token in whitespace_tokenize(text): - chars = list(token) - if len(chars) > self.max_input_chars_per_word: - output_tokens.append(self.unk_token) - continue + output_tokens = [] + for token in whitespace_tokenize(text): + chars = list(token) + if len(chars) > self.max_input_chars_per_word: + output_tokens.append(self.unk_token) + continue - is_bad = False - start = 0 - sub_tokens = [] - while start < len(chars): - end = len(chars) - cur_substr = None - while start < end: - substr = "".join(chars[start:end]) - if start > 0: - substr = "##" + substr - if substr in self.vocab: - cur_substr = substr - break - end -= 1 - if cur_substr is None: - is_bad = True - break - sub_tokens.append(cur_substr) - start = end + is_bad = False + start = 0 + sub_tokens = [] + while start < len(chars): + end = len(chars) + cur_substr = None + while start < end: + substr = "".join(chars[start:end]) + if start > 0: + substr = "##" + substr + if substr in self.vocab: + cur_substr = substr + break + end -= 1 + if cur_substr is None: + is_bad = True + break + sub_tokens.append(cur_substr) + start = end - if is_bad: - output_tokens.append(self.unk_token) - else: - output_tokens.extend(sub_tokens) - return output_tokens + if is_bad: + output_tokens.append(self.unk_token) + else: + output_tokens.extend(sub_tokens) + return output_tokens def _is_whitespace(char): - """Checks whether `chars` is a whitespace character.""" - # \t, \n, and \r are technically contorl characters but we treat them - # as whitespace since they are generally considered as such. - if char == " " or char == "\t" or char == "\n" or char == "\r": - return True - cat = unicodedata.category(char) - if cat == "Zs": - return True - return False + """Checks whether `chars` is a whitespace character.""" + # \t, \n, and \r are technically contorl characters but we treat them + # as whitespace since they are generally considered as such. + if char == " " or char == "\t" or char == "\n" or char == "\r": + return True + cat = unicodedata.category(char) + if cat == "Zs": + return True + return False def _is_control(char): - """Checks whether `chars` is a control character.""" - # These are technically control characters but we count them as whitespace - # characters. - if char == "\t" or char == "\n" or char == "\r": + """Checks whether `chars` is a control character.""" + # These are technically control characters but we count them as whitespace + # characters. + if char == "\t" or char == "\n" or char == "\r": + return False + cat = unicodedata.category(char) + if cat.startswith("C"): + return True return False - cat = unicodedata.category(char) - if cat in ("Cc", "Cf"): - return True - return False def _is_punctuation(char): - """Checks whether `chars` is a punctuation character.""" - cp = ord(char) - # We treat all non-letter/number ASCII as punctuation. - # Characters such as "^", "$", and "`" are not in the Unicode - # Punctuation class but we treat them as punctuation anyways, for - # consistency. - if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or - (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): - return True - cat = unicodedata.category(char) - if cat.startswith("P"): - return True - return False + """Checks whether `chars` is a punctuation character.""" + cp = ord(char) + # We treat all non-letter/number ASCII as punctuation. + # Characters such as "^", "$", and "`" are not in the Unicode + # Punctuation class but we treat them as punctuation anyways, for + # consistency. + if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or + (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): + return True + cat = unicodedata.category(char) + if cat.startswith("P"): + return True + return False diff --git a/TensorFlow/LanguageModeling/BERT/utils/create_glue_data.py b/TensorFlow/LanguageModeling/BERT/utils/create_glue_data.py index 1ce432b0..de21962f 100644 --- a/TensorFlow/LanguageModeling/BERT/utils/create_glue_data.py +++ b/TensorFlow/LanguageModeling/BERT/utils/create_glue_data.py @@ -1,3 +1,16 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/TensorFlow/LanguageModeling/BERT/utils/create_pretraining_data.py b/TensorFlow/LanguageModeling/BERT/utils/create_pretraining_data.py index 8bcbe274..d6280918 100644 --- a/TensorFlow/LanguageModeling/BERT/utils/create_pretraining_data.py +++ b/TensorFlow/LanguageModeling/BERT/utils/create_pretraining_data.py @@ -1,4 +1,5 @@ # coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,54 +13,26 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + """Create masked LM/next sentence masked_lm TF examples for BERT.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function +from __future__ import absolute_import, division, print_function, unicode_literals -import collections +import argparse +import logging +import os import random -import tokenization +from io import open +import h5py import tensorflow as tf +import numpy as np +from tqdm import tqdm, trange -flags = tf.flags - -FLAGS = flags.FLAGS - -flags.DEFINE_string("input_file", None, - "Input raw text file (or comma-separated list of files).") - -flags.DEFINE_string( - "output_file", None, - "Output TF example file (or comma-separated list of files).") - -flags.DEFINE_string("vocab_file", None, - "The vocabulary file that the BERT model was trained on.") - -flags.DEFINE_bool( - "do_lower_case", True, - "Whether to lower case the input text. Should be True for uncased " - "models and False for cased models.") - -flags.DEFINE_integer("max_seq_length", 128, "Maximum sequence length.") - -flags.DEFINE_integer("max_predictions_per_seq", 20, - "Maximum number of masked LM predictions per sequence.") - -flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.") - -flags.DEFINE_integer( - "dupe_factor", 10, - "Number of times to duplicate the input data (with different masks).") - -flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.") - -flags.DEFINE_float( - "short_seq_prob", 0.1, - "Probability of creating sequences which are shorter than the " - "maximum length.") +from tokenization import BertTokenizer +import tokenization as tokenization +import random +import collections class TrainingInstance(object): """A single training instance (sentence pair).""" @@ -90,7 +63,7 @@ class TrainingInstance(object): def write_instance_to_example_files(instances, tokenizer, max_seq_length, - max_predictions_per_seq, output_files): + max_predictions_per_seq, output_files, output_formats="tfrecord"): """Create TF example files from `TrainingInstance`s.""" writers = [] for output_file in output_files: @@ -99,6 +72,16 @@ def write_instance_to_example_files(instances, tokenizer, max_seq_length, writer_index = 0 total_written = 0 + if 'hdf5' in output_formats: + features_hdf5 = collections.OrderedDict() + num_instances = len(instances) + features_hdf5["input_ids"] = np.zeros([num_instances, max_seq_length], dtype="int32") + features_hdf5["input_mask"] = np.zeros([num_instances, max_seq_length], dtype="int32") + features_hdf5["segment_ids"] = np.zeros([num_instances, max_seq_length], dtype="int32") + features_hdf5["masked_lm_positions"] = np.zeros([num_instances, max_predictions_per_seq], dtype="int32") + features_hdf5["masked_lm_ids"] = np.zeros([num_instances, max_predictions_per_seq], dtype="int32") + features_hdf5["next_sentence_labels"] = np.zeros(num_instances, dtype="int32") + for (inst_index, instance) in enumerate(instances): input_ids = tokenizer.convert_tokens_to_ids(instance.tokens) input_mask = [1] * len(input_ids) @@ -134,9 +117,19 @@ def write_instance_to_example_files(instances, tokenizer, max_seq_length, features["masked_lm_weights"] = create_float_feature(masked_lm_weights) features["next_sentence_labels"] = create_int_feature([next_sentence_label]) - tf_example = tf.train.Example(features=tf.train.Features(feature=features)) + if 'tfrecord' in output_formats: + tf_example = tf.train.Example(features=tf.train.Features(feature=features)) + writers[writer_index].write(tf_example.SerializeToString()) + if 'hdf5' in output_formats: + features_hdf5["input_ids"][inst_index] = input_ids + features_hdf5["input_mask"][inst_index] = input_mask + features_hdf5["segment_ids"][inst_index] = segment_ids + features_hdf5["masked_lm_positions"][inst_index] = masked_lm_positions + features_hdf5["masked_lm_ids"][inst_index] = masked_lm_ids + features_hdf5["next_sentence_labels"][inst_index] = next_sentence_label + if 'tfrecord' not in output_formats and 'hdf5' not in output_formats: + assert False, 'Either empty output_formats list or unsupported type specified. Try: tfrecord or hdf5' - writers[writer_index].write(tf_example.SerializeToString()) writer_index = (writer_index + 1) % len(writers) total_written += 1 @@ -159,6 +152,17 @@ def write_instance_to_example_files(instances, tokenizer, max_seq_length, for writer in writers: writer.close() + if 'hdf5' in output_formats: + f = h5py.File(output_file, 'w') + f.create_dataset("input_ids", data=features_hdf5["input_ids"], dtype='i4', compression='gzip') + f.create_dataset("input_mask", data=features_hdf5["input_mask"], dtype='i1', compression='gzip') + f.create_dataset("segment_ids", data=features_hdf5["segment_ids"], dtype='i1', compression='gzip') + f.create_dataset("masked_lm_positions", data=features_hdf5["masked_lm_positions"], dtype='i4', compression='gzip') + f.create_dataset("masked_lm_ids", data=features_hdf5["masked_lm_ids"], dtype='i4', compression='gzip') + f.create_dataset("next_sentence_labels", data=features_hdf5["next_sentence_labels"], dtype='i1', compression='gzip') + f.flush() + f.close() + tf.logging.info("Wrote %d total instances", total_written) @@ -175,160 +179,161 @@ def create_float_feature(values): def create_training_instances(input_files, tokenizer, max_seq_length, dupe_factor, short_seq_prob, masked_lm_prob, max_predictions_per_seq, rng): - """Create `TrainingInstance`s from raw text.""" - all_documents = [[]] + """Create `TrainingInstance`s from raw text.""" + all_documents = [[]] - # Input file format: - # (1) One sentence per line. These should ideally be actual sentences, not - # entire paragraphs or arbitrary spans of text. (Because we use the - # sentence boundaries for the "next sentence prediction" task). - # (2) Blank lines between documents. Document boundaries are needed so - # that the "next sentence prediction" task doesn't span between documents. - for input_file in input_files: - with tf.gfile.GFile(input_file, "r") as reader: - while True: - line = tokenization.convert_to_unicode(reader.readline()) - if not line: - break - line = line.strip() + # Input file format: + # (1) One sentence per line. These should ideally be actual sentences, not + # entire paragraphs or arbitrary spans of text. (Because we use the + # sentence boundaries for the "next sentence prediction" task). + # (2) Blank lines between documents. Document boundaries are needed so + # that the "next sentence prediction" task doesn't span between documents. + for input_file in input_files: + print("creating instance from {}".format(input_file)) + with open(input_file, "r") as reader: + while True: + line = tokenization.convert_to_unicode(reader.readline()) + if not line: + break + line = line.strip() - # Empty lines are used as document delimiters - if not line: - all_documents.append([]) - tokens = tokenizer.tokenize(line) - if tokens: - all_documents[-1].append(tokens) + # Empty lines are used as document delimiters + if not line: + all_documents.append([]) + tokens = tokenizer.tokenize(line) + if tokens: + all_documents[-1].append(tokens) - # Remove empty documents - all_documents = [x for x in all_documents if x] - rng.shuffle(all_documents) + # Remove empty documents + all_documents = [x for x in all_documents if x] + rng.shuffle(all_documents) - vocab_words = list(tokenizer.vocab.keys()) - instances = [] - for _ in range(dupe_factor): - for document_index in range(len(all_documents)): - instances.extend( - create_instances_from_document( - all_documents, document_index, max_seq_length, short_seq_prob, - masked_lm_prob, max_predictions_per_seq, vocab_words, rng)) + vocab_words = list(tokenizer.vocab.keys()) + instances = [] + for _ in range(dupe_factor): + for document_index in range(len(all_documents)): + instances.extend( + create_instances_from_document( + all_documents, document_index, max_seq_length, short_seq_prob, + masked_lm_prob, max_predictions_per_seq, vocab_words, rng)) - rng.shuffle(instances) - return instances + rng.shuffle(instances) + return instances def create_instances_from_document( - all_documents, document_index, max_seq_length, short_seq_prob, - masked_lm_prob, max_predictions_per_seq, vocab_words, rng): - """Creates `TrainingInstance`s for a single document.""" - document = all_documents[document_index] + all_documents, document_index, max_seq_length, short_seq_prob, + masked_lm_prob, max_predictions_per_seq, vocab_words, rng): + """Creates `TrainingInstance`s for a single document.""" + document = all_documents[document_index] - # Account for [CLS], [SEP], [SEP] - max_num_tokens = max_seq_length - 3 + # Account for [CLS], [SEP], [SEP] + max_num_tokens = max_seq_length - 3 - # We *usually* want to fill up the entire sequence since we are padding - # to `max_seq_length` anyways, so short sequences are generally wasted - # computation. However, we *sometimes* - # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter - # sequences to minimize the mismatch between pre-training and fine-tuning. - # The `target_seq_length` is just a rough target however, whereas - # `max_seq_length` is a hard limit. - target_seq_length = max_num_tokens - if rng.random() < short_seq_prob: - target_seq_length = rng.randint(2, max_num_tokens) + # We *usually* want to fill up the entire sequence since we are padding + # to `max_seq_length` anyways, so short sequences are generally wasted + # computation. However, we *sometimes* + # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter + # sequences to minimize the mismatch between pre-training and fine-tuning. + # The `target_seq_length` is just a rough target however, whereas + # `max_seq_length` is a hard limit. + target_seq_length = max_num_tokens + if rng.random() < short_seq_prob: + target_seq_length = rng.randint(2, max_num_tokens) - # We DON'T just concatenate all of the tokens from a document into a long - # sequence and choose an arbitrary split point because this would make the - # next sentence prediction task too easy. Instead, we split the input into - # segments "A" and "B" based on the actual "sentences" provided by the user - # input. - instances = [] - current_chunk = [] - current_length = 0 - i = 0 - while i < len(document): - segment = document[i] - current_chunk.append(segment) - current_length += len(segment) - if i == len(document) - 1 or current_length >= target_seq_length: - if current_chunk: - # `a_end` is how many segments from `current_chunk` go into the `A` - # (first) sentence. - a_end = 1 - if len(current_chunk) >= 2: - a_end = rng.randint(1, len(current_chunk) - 1) + # We DON'T just concatenate all of the tokens from a document into a long + # sequence and choose an arbitrary split point because this would make the + # next sentence prediction task too easy. Instead, we split the input into + # segments "A" and "B" based on the actual "sentences" provided by the user + # input. + instances = [] + current_chunk = [] + current_length = 0 + i = 0 + while i < len(document): + segment = document[i] + current_chunk.append(segment) + current_length += len(segment) + if i == len(document) - 1 or current_length >= target_seq_length: + if current_chunk: + # `a_end` is how many segments from `current_chunk` go into the `A` + # (first) sentence. + a_end = 1 + if len(current_chunk) >= 2: + a_end = rng.randint(1, len(current_chunk) - 1) - tokens_a = [] - for j in range(a_end): - tokens_a.extend(current_chunk[j]) + tokens_a = [] + for j in range(a_end): + tokens_a.extend(current_chunk[j]) - tokens_b = [] - # Random next - is_random_next = False - if len(current_chunk) == 1 or rng.random() < 0.5: - is_random_next = True - target_b_length = target_seq_length - len(tokens_a) + tokens_b = [] + # Random next + is_random_next = False + if len(current_chunk) == 1 or rng.random() < 0.5: + is_random_next = True + target_b_length = target_seq_length - len(tokens_a) - # This should rarely go for more than one iteration for large - # corpora. However, just to be careful, we try to make sure that - # the random document is not the same as the document - # we're processing. - for _ in range(10): - random_document_index = rng.randint(0, len(all_documents) - 1) - if random_document_index != document_index: - break + # This should rarely go for more than one iteration for large + # corpora. However, just to be careful, we try to make sure that + # the random document is not the same as the document + # we're processing. + for _ in range(10): + random_document_index = rng.randint(0, len(all_documents) - 1) + if random_document_index != document_index: + break - random_document = all_documents[random_document_index] - random_start = rng.randint(0, len(random_document) - 1) - for j in range(random_start, len(random_document)): - tokens_b.extend(random_document[j]) - if len(tokens_b) >= target_b_length: - break - # We didn't actually use these segments so we "put them back" so - # they don't go to waste. - num_unused_segments = len(current_chunk) - a_end - i -= num_unused_segments - # Actual next - else: - is_random_next = False - for j in range(a_end, len(current_chunk)): - tokens_b.extend(current_chunk[j]) - truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng) + random_document = all_documents[random_document_index] + random_start = rng.randint(0, len(random_document) - 1) + for j in range(random_start, len(random_document)): + tokens_b.extend(random_document[j]) + if len(tokens_b) >= target_b_length: + break + # We didn't actually use these segments so we "put them back" so + # they don't go to waste. + num_unused_segments = len(current_chunk) - a_end + i -= num_unused_segments + # Actual next + else: + is_random_next = False + for j in range(a_end, len(current_chunk)): + tokens_b.extend(current_chunk[j]) + truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng) - assert len(tokens_a) >= 1 - assert len(tokens_b) >= 1 + assert len(tokens_a) >= 1 + assert len(tokens_b) >= 1 - tokens = [] - segment_ids = [] - tokens.append("[CLS]") - segment_ids.append(0) - for token in tokens_a: - tokens.append(token) - segment_ids.append(0) + tokens = [] + segment_ids = [] + tokens.append("[CLS]") + segment_ids.append(0) + for token in tokens_a: + tokens.append(token) + segment_ids.append(0) - tokens.append("[SEP]") - segment_ids.append(0) + tokens.append("[SEP]") + segment_ids.append(0) - for token in tokens_b: - tokens.append(token) - segment_ids.append(1) - tokens.append("[SEP]") - segment_ids.append(1) + for token in tokens_b: + tokens.append(token) + segment_ids.append(1) + tokens.append("[SEP]") + segment_ids.append(1) - (tokens, masked_lm_positions, - masked_lm_labels) = create_masked_lm_predictions( - tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng) - instance = TrainingInstance( - tokens=tokens, - segment_ids=segment_ids, - is_random_next=is_random_next, - masked_lm_positions=masked_lm_positions, - masked_lm_labels=masked_lm_labels) - instances.append(instance) - current_chunk = [] - current_length = 0 - i += 1 + (tokens, masked_lm_positions, + masked_lm_labels) = create_masked_lm_predictions( + tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng) + instance = TrainingInstance( + tokens=tokens, + segment_ids=segment_ids, + is_random_next=is_random_next, + masked_lm_positions=masked_lm_positions, + masked_lm_labels=masked_lm_labels) + instances.append(instance) + current_chunk = [] + current_length = 0 + i += 1 - return instances + return instances MaskedLmInstance = collections.namedtuple("MaskedLmInstance", @@ -337,106 +342,160 @@ MaskedLmInstance = collections.namedtuple("MaskedLmInstance", def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng): - """Creates the predictions for the masked LM objective.""" + """Creates the predictions for the masked LM objective.""" - cand_indexes = [] - for (i, token) in enumerate(tokens): - if token == "[CLS]" or token == "[SEP]": - continue - cand_indexes.append(i) + cand_indexes = [] + for (i, token) in enumerate(tokens): + if token == "[CLS]" or token == "[SEP]": + continue + cand_indexes.append(i) - rng.shuffle(cand_indexes) + rng.shuffle(cand_indexes) - output_tokens = list(tokens) + output_tokens = list(tokens) - num_to_predict = min(max_predictions_per_seq, - max(1, int(round(len(tokens) * masked_lm_prob)))) + num_to_predict = min(max_predictions_per_seq, + max(1, int(round(len(tokens) * masked_lm_prob)))) - masked_lms = [] - covered_indexes = set() - for index in cand_indexes: - if len(masked_lms) >= num_to_predict: - break - if index in covered_indexes: - continue - covered_indexes.add(index) + masked_lms = [] + covered_indexes = set() + for index in cand_indexes: + if len(masked_lms) >= num_to_predict: + break + if index in covered_indexes: + continue + covered_indexes.add(index) - masked_token = None - # 80% of the time, replace with [MASK] - if rng.random() < 0.8: - masked_token = "[MASK]" - else: - # 10% of the time, keep original - if rng.random() < 0.5: - masked_token = tokens[index] - # 10% of the time, replace with random word - else: - masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)] + masked_token = None + # 80% of the time, replace with [MASK] + if rng.random() < 0.8: + masked_token = "[MASK]" + else: + # 10% of the time, keep original + if rng.random() < 0.5: + masked_token = tokens[index] + # 10% of the time, replace with random word + else: + masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)] - output_tokens[index] = masked_token + output_tokens[index] = masked_token - masked_lms.append(MaskedLmInstance(index=index, label=tokens[index])) + masked_lms.append(MaskedLmInstance(index=index, label=tokens[index])) - masked_lms = sorted(masked_lms, key=lambda x: x.index) + masked_lms = sorted(masked_lms, key=lambda x: x.index) - masked_lm_positions = [] - masked_lm_labels = [] - for p in masked_lms: - masked_lm_positions.append(p.index) - masked_lm_labels.append(p.label) + masked_lm_positions = [] + masked_lm_labels = [] + for p in masked_lms: + masked_lm_positions.append(p.index) + masked_lm_labels.append(p.label) - return (output_tokens, masked_lm_positions, masked_lm_labels) + return (output_tokens, masked_lm_positions, masked_lm_labels) def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng): - """Truncates a pair of sequences to a maximum sequence length.""" - while True: - total_length = len(tokens_a) + len(tokens_b) - if total_length <= max_num_tokens: - break + """Truncates a pair of sequences to a maximum sequence length.""" + while True: + total_length = len(tokens_a) + len(tokens_b) + if total_length <= max_num_tokens: + break - trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b - assert len(trunc_tokens) >= 1 + trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b + assert len(trunc_tokens) >= 1 - # We want to sometimes truncate from the front and sometimes from the - # back to add more randomness and avoid biases. - if rng.random() < 0.5: - del trunc_tokens[0] + # We want to sometimes truncate from the front and sometimes from the + # back to add more randomness and avoid biases. + if rng.random() < 0.5: + del trunc_tokens[0] + else: + trunc_tokens.pop() + + +def main(): + parser = argparse.ArgumentParser() + ## Required parameters + parser.add_argument("--vocab_file", + default=None, + type=str, + required=True, + help="The vocabulary the BERT model will train on.") + parser.add_argument("--input_file", + default=None, + type=str, + required=True, + help="The input train corpus. can be directory with .txt files or a path to a single file") + parser.add_argument("--output_file", + default=None, + type=str, + required=True, + help="The output file where the model checkpoints will be written.") + + ## Other parameters + # int + parser.add_argument("--max_seq_length", + default=128, + type=int, + help="The maximum total input sequence length after WordPiece tokenization. \n" + "Sequences longer than this will be truncated, and sequences shorter \n" + "than this will be padded.") + parser.add_argument("--dupe_factor", + default=10, + type=int, + help="Number of times to duplicate the input data (with different masks).") + parser.add_argument("--max_predictions_per_seq", + default=20, + type=int, + help="Maximum sequence length.") + + # floats + + parser.add_argument("--masked_lm_prob", + default=0.15, + type=float, + help="Masked LM probability.") + + parser.add_argument("--short_seq_prob", + default=0.1, + type=float, + help="Probability to create a sequence shorter than maximum sequence length") + + parser.add_argument("--do_lower_case", + action='store_true', + default=True, + help="Whether to lower case the input text. True for uncased models, False for cased models.") + parser.add_argument('--random_seed', + type=int, + default=12345, + help="random seed for initialization") + + args = parser.parse_args() + + tokenizer = BertTokenizer(args.vocab_file, do_lower_case=args.do_lower_case) + + input_files = [] + if os.path.isfile(args.input_file): + input_files.append(args.input_file) + elif os.path.isdir(args.input_file): + input_files = [os.path.join(args.input_file, f) for f in os.listdir(args.input_file) if + (os.path.isfile(os.path.join(args.input_file, f)) and f.endswith('.txt'))] else: - trunc_tokens.pop() + raise ValueError("{} is not a valid path".format(args.input_file)) + + rng = random.Random(args.random_seed) + instances = create_training_instances( + input_files, tokenizer, args.max_seq_length, args.dupe_factor, + args.short_seq_prob, args.masked_lm_prob, args.max_predictions_per_seq, + rng) + + output_files = args.output_file.split(",") + print("*** Writing to output files ***") + for output_file in output_files: + print(output_file) -def main(_): - tf.logging.set_verbosity(tf.logging.INFO) - - tokenizer = tokenization.FullTokenizer( - vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) - - input_files = [] - for input_pattern in FLAGS.input_file.split(","): - input_files.extend(tf.gfile.Glob(input_pattern)) - - tf.logging.info("*** Reading from input files ***") - for input_file in input_files: - tf.logging.info(" %s", input_file) - - rng = random.Random(FLAGS.random_seed) - instances = create_training_instances( - input_files, tokenizer, FLAGS.max_seq_length, FLAGS.dupe_factor, - FLAGS.short_seq_prob, FLAGS.masked_lm_prob, FLAGS.max_predictions_per_seq, - rng) - - output_files = FLAGS.output_file.split(",") - tf.logging.info("*** Writing to output files ***") - for output_file in output_files: - tf.logging.info(" %s", output_file) - - write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length, - FLAGS.max_predictions_per_seq, output_files) + write_instance_to_example_files(instances, tokenizer, args.max_seq_length, + args.max_predictions_per_seq, output_files) if __name__ == "__main__": - flags.mark_flag_as_required("input_file") - flags.mark_flag_as_required("output_file") - flags.mark_flag_as_required("vocab_file") - tf.app.run() + main() diff --git a/TensorFlow/LanguageModeling/BERT/utils/create_squad_data.py b/TensorFlow/LanguageModeling/BERT/utils/create_squad_data.py index eecb790a..fe376754 100644 --- a/TensorFlow/LanguageModeling/BERT/utils/create_squad_data.py +++ b/TensorFlow/LanguageModeling/BERT/utils/create_squad_data.py @@ -1,3 +1,16 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/TensorFlow/LanguageModeling/BERT/utils/utils.py b/TensorFlow/LanguageModeling/BERT/utils/utils.py index 3ac12ea0..84affeeb 100644 --- a/TensorFlow/LanguageModeling/BERT/utils/utils.py +++ b/TensorFlow/LanguageModeling/BERT/utils/utils.py @@ -1,3 +1,16 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import tensorflow as tf import time diff --git a/TensorFlow/Segmentation/UNet_Industrial/.gitignore b/TensorFlow/Segmentation/UNet_Industrial/.gitignore index 3f60e3ed..49f23c3e 100644 --- a/TensorFlow/Segmentation/UNet_Industrial/.gitignore +++ b/TensorFlow/Segmentation/UNet_Industrial/.gitignore @@ -100,6 +100,10 @@ venv.bak/ # mkdocs documentation /site +# weights +/pretrained_weights +/exported_models + # mypy .mypy_cache/ .idea/ diff --git a/TensorFlow/Segmentation/UNet_Industrial/README.md b/TensorFlow/Segmentation/UNet_Industrial/README.md index dcd74f70..44bc2419 100644 --- a/TensorFlow/Segmentation/UNet_Industrial/README.md +++ b/TensorFlow/Segmentation/UNet_Industrial/README.md @@ -219,6 +219,11 @@ cd scripts/ ./UNet_FP32_EVAL.sh ``` +If you wish to evaluate external checkpoint, make sure to put the TF ckpt files inside a folder named "checkpoints" +and provide its parent path as `` in the example above. +Be aware that the script will not fail if it does not find the checkpoint. +It will randomly initialize the weights and run performance tests. + ## Advanced The following sections provide greater details of the dataset, running training and inference, and the training results. @@ -506,8 +511,11 @@ To achieve these same results, follow the [Quick Start Guide](#quick-start-guide ## Release notes ### Changelog -March 18, 2019 -* Initial release + +* October 2019 + * Jupyter notebooks added +* March,2019 + * Initial release ### Known issues There are no known issues with this model. diff --git a/TensorFlow/Segmentation/UNet_Industrial/download_and_preprocess_dagm2007_public.sh b/TensorFlow/Segmentation/UNet_Industrial/download_and_preprocess_dagm2007_public.sh new file mode 100755 index 00000000..d68c7b5a --- /dev/null +++ b/TensorFlow/Segmentation/UNet_Industrial/download_and_preprocess_dagm2007_public.sh @@ -0,0 +1,92 @@ +#!/bin/bash + +############################################################################## +# Copyright (c) Jonathan Dekhtiar - contact@jonathandekhtiar.eu +# All Rights Reserved. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +############################################################################## + +# Usage: ./download_and_preprocess_dagm2007.sh /path/to/dataset/directory/ + +if [[ ! "$BASH_VERSION" ]] ; then + echo "Please do not use sh to run this script ($0), just execute it directly" 1>&2 + exit 1 +fi + +if [[ -z "$1" ]] + then + echo -e "Error: Argument is missing. No dataset directory received." + echo -e "Usage: '$0 /path/to/dataset/directory/'" + exit 1 +fi + +DATASET_DIR=$(realpath -s $1) + +ZIP_FILES_DIR=${DATASET_DIR}/zip_files +RAW_IMAGES_DIR=${DATASET_DIR}/raw_images + +PUBLIC_ZIP_FILES_DIR=${ZIP_FILES_DIR}/public +PUBLIC_RAW_IMAGES_DIR=${RAW_IMAGES_DIR}/public + +if [[ ! -e ${PUBLIC_ZIP_FILES_DIR} ]]; then + echo "creating ${PUBLIC_ZIP_FILES_DIR} ..." + mkdir -p ${PUBLIC_ZIP_FILES_DIR} +fi + +if [[ ! -e ${PUBLIC_RAW_IMAGES_DIR} ]]; then + echo "creating ${PUBLIC_RAW_IMAGES_DIR} ..." + mkdir -p ${PUBLIC_RAW_IMAGES_DIR} +fi + +PRIVATE_ZIP_FILES_DIR=${ZIP_FILES_DIR}/private +PRIVATE_RAW_IMAGES_DIR=${RAW_IMAGES_DIR}/private + +if [[ ! -e ${PRIVATE_ZIP_FILES_DIR} ]]; then + echo "creating ${PRIVATE_ZIP_FILES_DIR} ..." + mkdir -p ${PRIVATE_ZIP_FILES_DIR} +fi + +if [[ ! -e ${PRIVATE_RAW_IMAGES_DIR} ]]; then + echo "creating ${PRIVATE_RAW_IMAGES_DIR} ..." + mkdir -p ${PRIVATE_RAW_IMAGES_DIR} +fi + +echo -e "\n################################################" +echo -e "Processing Public Dataset" +echo -e "################################################\n" + +sleep 2 + +BASE_PUBLIC_URL="https://resources.mpi-inf.mpg.de/conference/dagm/2007" + +declare -a arr=( + "Class1.zip" + "Class1_def.zip" + "Class2.zip" + "Class2_def.zip" + "Class3.zip" + "Class3_def.zip" + "Class4.zip" + "Class4_def.zip" + "Class5.zip" + "Class5_def.zip" + "Class6.zip" + "Class6_def.zip" +) + +for file in "${arr[@]}" +do + if [[ ! -e ${PUBLIC_ZIP_FILES_DIR}/${file} ]]; then + echo -e "Downloading File: $BASE_PUBLIC_URL/$file ..." + wget -N ${BASE_PUBLIC_URL}/${file} -O ${PUBLIC_ZIP_FILES_DIR}/${file} + fi + + # Unzip without overwriting + unzip -n ${PUBLIC_ZIP_FILES_DIR}/${file} -d ${PUBLIC_RAW_IMAGES_DIR} + +done + +chmod -R 744 ${PUBLIC_ZIP_FILES_DIR} +chmod -R 744 ${PUBLIC_RAW_IMAGES_DIR} \ No newline at end of file diff --git a/TensorFlow/Segmentation/UNet_Industrial/export_saved_model.py b/TensorFlow/Segmentation/UNet_Industrial/export_saved_model.py new file mode 100644 index 00000000..2796fec1 --- /dev/null +++ b/TensorFlow/Segmentation/UNet_Industrial/export_saved_model.py @@ -0,0 +1,221 @@ +# !/usr/bin/env python +# -*- coding: utf-8 -*- + +# ============================================================================== +# +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ============================================================================== + +""" +Usage: + + python export_saved_model.py \ + --activation_fn='relu' \ + --batch_size=16 \ + --data_format='NCHW' \ + --input_dtype="fp32" \ + --export_dir="exported_models" \ + --model_checkpoint_path="path/to/checkpoint/model.ckpt-2500" \ + --unet_variant='tinyUNet' \ + --use_xla \ + --use_tf_amp +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import argparse +import pprint + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import tensorflow as tf + +from dllogger.logger import LOGGER + +from model.unet import UNet_v1 +from model.blocks.activation_blck import authorized_activation_fn + +from utils.cmdline_helper import _add_bool_argument + + +def get_export_flags(): + parser = argparse.ArgumentParser(description="JoC-UNet_v1-TF-ExportFlags") + + parser.add_argument('--export_dir', default=None, required=True, type=str, help='The export directory.') + parser.add_argument('--model_checkpoint_path', default=None, required=True, help='Checkpoint path.') + + parser.add_argument( + '--data_format', + choices=['NHWC', 'NCHW'], + type=str, + default="NCHW", + required=False, + help="""Which Tensor format is used for computation inside the mode""" + ) + + parser.add_argument( + '--input_dtype', + choices=['fp32', 'fp16'], + type=str, + default="fp32", + required=False, + help="""Tensorflow dtype of the input tensor""" + ) + + parser.add_argument( + '--unet_variant', + default="tinyUNet", + choices=UNet_v1.authorized_models_variants, + type=str, + required=False, + help="""Which model size is used. This parameter control directly the size and the number of parameters""" + ) + + parser.add_argument( + '--activation_fn', + choices=authorized_activation_fn, + type=str, + default="relu", + required=False, + help="""Which activation function is used after the convolution layers""" + ) + + _add_bool_argument( + parser=parser, + name="use_tf_amp", + default=False, + required=False, + help="Enable Automatic Mixed Precision Computation to maximise performance." + ) + + _add_bool_argument( + parser=parser, + name="use_xla", + default=False, + required=False, + help="Enable Tensorflow XLA to maximise performance." + ) + + parser.add_argument('--batch_size', default=16, type=int, help='Evaluation batch size.') + + FLAGS, unknown_args = parser.parse_known_args() + + if len(unknown_args) > 0: + + for bad_arg in unknown_args: + print("ERROR: Unknown command line arg: %s" % bad_arg) + + raise ValueError("Invalid command line arg(s)") + + return FLAGS + + +def export_model(RUNNING_CONFIG): + + if RUNNING_CONFIG.use_tf_amp: + os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1" + + model = UNet_v1( + model_name="UNet_v1", + input_format="NHWC", + compute_format=RUNNING_CONFIG.data_format, + n_output_channels=1, + unet_variant=RUNNING_CONFIG.unet_variant, + weight_init_method="he_normal", + activation_fn=RUNNING_CONFIG.activation_fn + ) + + config_proto = tf.ConfigProto() + + config_proto.allow_soft_placement = True + config_proto.log_device_placement = False + + config_proto.gpu_options.allow_growth = True + + if RUNNING_CONFIG.use_xla: # Only working on single GPU + LOGGER.log("XLA is activated - Experimental Feature") + config_proto.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 + + config_proto.gpu_options.force_gpu_compatible = True # Force pinned memory + + run_config = tf.estimator.RunConfig( + model_dir=None, + tf_random_seed=None, + save_summary_steps=1e9, # disabled + save_checkpoints_steps=None, + save_checkpoints_secs=None, + session_config=config_proto, + keep_checkpoint_max=None, + keep_checkpoint_every_n_hours=1e9, # disabled + log_step_count_steps=1e9, + train_distribute=None, + device_fn=None, + protocol=None, + eval_distribute=None, + experimental_distribute=None + ) + + estimator = tf.estimator.Estimator( + model_fn=model, + model_dir=RUNNING_CONFIG.model_checkpoint_path, + config=run_config, + params={'debug_verbosity': 0} + ) + + LOGGER.log('[*] Exporting the model ...') + + input_type = tf.float32 if RUNNING_CONFIG.input_dtype else tf.float16 + + def get_serving_input_receiver_fn(): + + input_shape = [RUNNING_CONFIG.batch_size, 512, 512, 1] + + def serving_input_receiver_fn(): + features = tf.placeholder(dtype=input_type, shape=input_shape, name='input_tensor') + + return tf.estimator.export.TensorServingInputReceiver(features=features, receiver_tensors=features) + + return serving_input_receiver_fn + + export_path = estimator.export_saved_model( + export_dir_base=RUNNING_CONFIG.export_dir, + serving_input_receiver_fn=get_serving_input_receiver_fn(), + checkpoint_path=RUNNING_CONFIG.model_checkpoint_path + ) + + LOGGER.log('[*] Done! path: `%s`' % export_path.decode()) + + +if __name__ == '__main__': + + tf.logging.set_verbosity(tf.logging.ERROR) + tf.disable_eager_execution() + + flags = get_export_flags() + + for endpattern in [".index", ".meta"]: + file_to_check = flags.model_checkpoint_path + endpattern + if not os.path.isfile(file_to_check): + raise FileNotFoundError("The checkpoint file `%s` does not exist" % file_to_check) + + print(" ========================= Export Flags =========================\n") + pprint.pprint(dict(flags._get_kwargs())) + print("\n %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + + export_model(flags) diff --git a/TensorFlow/Segmentation/UNet_Industrial/model/unet.py b/TensorFlow/Segmentation/UNet_Industrial/model/unet.py index 63da11f3..de40f822 100644 --- a/TensorFlow/Segmentation/UNet_Industrial/model/unet.py +++ b/TensorFlow/Segmentation/UNet_Industrial/model/unet.py @@ -157,6 +157,14 @@ class UNet_v1(object): if "loss_fn_name" not in params.keys(): raise RuntimeError("Parameter `loss_fn_name` is missing...") + if mode == tf.estimator.ModeKeys.PREDICT: + y_pred, y_pred_logits = self.build_model( + features, training=False, reuse=False, debug_verbosity=params["debug_verbosity"] + ) + + predictions = {'logits': y_pred} + return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions) + input_image, mask_image = features with tf.device("/gpu:0"): @@ -175,10 +183,6 @@ class UNet_v1(object): all_trainable_vars = tf.reduce_sum([tf.reduce_prod(v.shape) for v in tf.trainable_variables()]) tf.identity(all_trainable_vars, name='trainable_parameters_count_ref') - if mode == tf.estimator.ModeKeys.PREDICT: - predictions = {'logits': y_pred} - return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions) - if mode == tf.estimator.ModeKeys.EVAL: eval_metrics = dict() diff --git a/TensorFlow/Segmentation/UNet_Industrial/notebooks/Colab_UNet_Industrial_TF_TFTRT_inference_demo.ipynb b/TensorFlow/Segmentation/UNet_Industrial/notebooks/Colab_UNet_Industrial_TF_TFTRT_inference_demo.ipynb new file mode 100644 index 00000000..f727241d --- /dev/null +++ b/TensorFlow/Segmentation/UNet_Industrial/notebooks/Colab_UNet_Industrial_TF_TFTRT_inference_demo.ipynb @@ -0,0 +1,1634 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Gwt7z7qdmTbW" + }, + "outputs": [], + "source": [ + "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i4NKCp2VmTbn" + }, + "source": [ + "\n", + "\n", + "# UNet Industrial Inference Demo with TF-TRT" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "fW0OKDzvmTbt" + }, + "source": [ + "## Overview\n", + "\n", + "\n", + "In this notebook, we will demo the process of carrying out inference on new images using a pre-trained UNet model downloaded from the NVIDIA NGC Model registry. We will also optimize the naitive TensorFlow trained model for deployment with TensorFlow-TensorRT (TF-TRT). TensorRT is the NVIDIA high-performance runtime environment for deployment of deep learning applications. TF-TRT is the integration of TensorRT directly into the TensorFlow ecosystem, allowing users to benefit much improved performance using a relatively easy and convenient Python API interfance. \n", + "\n", + "\n", + "### Requirement\n", + "1. Before running this notebook, please set the Colab runtime environment to GPU via the menu *Runtime => Change runtime type => GPU*.\n", + "\n", + "For TF-TRT FP16 and INT8 inference, an NVIDIA Volta, Turing or newer GPU generations is required. " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 316 + }, + "colab_type": "code", + "id": "HVsrGkj4Zn2L", + "outputId": "444fdef7-46bc-4e92-d33e-32a35f9faa34" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fri Sep 27 04:28:18 2019 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 430.40 Driver Version: 418.67 CUDA Version: 10.1 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 69C P0 72W / 149W | 6601MiB / 11441MiB | 0% Default |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: GPU Memory |\n", + "| GPU PID Type Process name Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "chjbvfyJbUrY" + }, + "source": [ + "\n", + "2. Install TensorFlow GPU 1.15.0\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 430 + }, + "colab_type": "code", + "id": "4kxy3SY1UoX3", + "outputId": "546180b2-ed02-4b8a-a877-4e7b078ae229" + }, + "outputs": [], + "source": [ + "!pip install tensorflow-gpu==1.15.0-rc1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pV3rzgO8-tSK" + }, + "source": [ + "The below code check whether a Tensor core GPU is present." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "colab_type": "code", + "id": "Djyvo8mm9poq", + "outputId": "c92131a7-9911-4d6c-a502-a50a7f128baa" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor Core GPU Present: None\n" + ] + } + ], + "source": [ + "from tensorflow.python.client import device_lib\n", + "\n", + "def check_tensor_core_gpu_present():\n", + " local_device_protos = device_lib.list_local_devices()\n", + " for line in local_device_protos:\n", + " if \"compute capability\" in str(line):\n", + " compute_capability = float(line.physical_device_desc.split(\"compute capability: \")[-1])\n", + " if compute_capability>=7.0:\n", + " return True\n", + " \n", + "print(\"Tensor Core GPU Present:\", check_tensor_core_gpu_present())\n", + "tensor_core_gpu = check_tensor_core_gpu_present()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FCEfkBAbbaLI" + }, + "source": [ + "3. Next, we clone the Github UNet_Industrial repository and set up the workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "colab_type": "code", + "id": "y3u_VMjXtAto", + "outputId": "e04d1fb2-24ce-41bb-f13f-006df7916389" + }, + "outputs": [], + "source": [ + "!git clone https://github.com/NVIDIA/DeepLearningExamples" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "colab_type": "code", + "id": "-rE46y-ftAuQ", + "outputId": "fd16441f-0068-4432-c72e-8ecc4eeba491" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "WORKSPACE_DIR='/content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks'\n", + "os.chdir(WORKSPACE_DIR)\n", + "print (os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HqSUGePjmTb9" + }, + "source": [ + "## Data download\n", + "\n", + "We will first download some data, in particular, the [Weakly Supervised Learning for Industrial Optical Inspection (DAGM 2007)](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html) dataset. \n", + "\n", + "> The competition is inspired by problems from industrial image processing. In order to satisfy their customers' needs, companies have to guarantee the quality of their products, which can often be achieved only by inspection of the finished product. Automatic visual defect detection has the potential to reduce the cost of quality assurance significantly.\n", + ">\n", + "> The competitors have to design a stand-alone algorithm which is able to detect miscellaneous defects on various background textures.\n", + ">\n", + "> The particular challenge of this contest is that the algorithm must learn, without human intervention, to discern defects automatically from a weakly labeled (i.e., labels are not exact to the pixel level) training set, the exact characteristics of which are unknown at development time. During the competition, the programs have to be trained on new data without any human guidance.\n", + "\n", + "**Source:** https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 355 + }, + "colab_type": "code", + "id": "S2PR7weWmTcK", + "outputId": "9d5c8000-8ae7-4179-9b6a-5ed8cab4acc8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "################################################\n", + "Processing Public Dataset\n", + "################################################\n", + "\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class1.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class1_def.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class2.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class2_def.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class3.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class3_def.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class4.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class4_def.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class5.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class5_def.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class6.zip\n", + "Archive: /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class6_def.zip\n" + ] + } + ], + "source": [ + "! ./download_and_preprocess_dagm2007_public.sh ./data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "EQAIszkxmTcT" + }, + "source": [ + "The final data directory should look like:\n", + "\n", + "```\n", + "./data\n", + " raw_images\n", + " public\n", + " Class1\t \n", + " Class2\t\n", + " Class3\t \n", + " Class4\t\n", + " Class5\t \n", + " Class6\n", + " Class1_def \n", + " Class2_def\t\n", + " Class3_def \n", + " Class4_def\t\n", + " Class5_def \n", + " Class6_def\n", + " private\n", + " zip_files\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xSztH-mf-6hY" + }, + "source": [ + "Each data directory contains training images corresponding to one of 6 types of defects." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RL8d9IwzmTcV" + }, + "source": [ + "## Model download from NVIDIA NGC model repository\n", + "\n", + "NVIDIA provides pretrained UNet models along with many other deep learning models such as ResNet, BERT, Transformer, SSD... at https://ngc.nvidia.com/catalog/models. Here, we will download and upzip pretrained UNet models. " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "wNA8uFflu7gO", + "outputId": "82d5f16b-69f4-47e0-f352-851e8fa2051c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Archive: ./unet_model.zip\n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+1/checkpoint \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+1/graph.pbtxt \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+1/model.ckpt-2500.data-00000-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+1/model.ckpt-2500.data-00001-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+1/model.ckpt-2500.index \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+1/model.ckpt-2500.meta \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+10/checkpoint \n", + " inflating: 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JoC_UNET_Industrial_FP32_TF_20190522/Class+3/checkpoint \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+3/graph.pbtxt \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+3/model.ckpt-2500.data-00000-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+3/model.ckpt-2500.data-00001-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+3/model.ckpt-2500.index \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+3/model.ckpt-2500.meta \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+4/checkpoint \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+4/graph.pbtxt \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+4/model.ckpt-2500.data-00000-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+4/model.ckpt-2500.data-00001-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+4/model.ckpt-2500.index \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+4/model.ckpt-2500.meta \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+5/checkpoint \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+5/graph.pbtxt \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+5/model.ckpt-2500.data-00000-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+5/model.ckpt-2500.data-00001-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+5/model.ckpt-2500.index \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+5/model.ckpt-2500.meta \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+6/checkpoint \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+6/graph.pbtxt \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+6/model.ckpt-2500.data-00000-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+6/model.ckpt-2500.data-00001-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+6/model.ckpt-2500.index \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+6/model.ckpt-2500.meta \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+7/checkpoint \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+7/graph.pbtxt \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+7/model.ckpt-2500.data-00000-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+7/model.ckpt-2500.data-00001-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+7/model.ckpt-2500.index \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+7/model.ckpt-2500.meta \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+8/checkpoint \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+8/graph.pbtxt \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+8/model.ckpt-2500.data-00000-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+8/model.ckpt-2500.data-00001-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+8/model.ckpt-2500.index \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+8/model.ckpt-2500.meta \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+9/checkpoint \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+9/graph.pbtxt \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+9/model.ckpt-2500.data-00000-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+9/model.ckpt-2500.data-00001-of-00002 \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+9/model.ckpt-2500.index \n", + " inflating: JoC_UNET_Industrial_FP32_TF_20190522/Class+9/model.ckpt-2500.meta \n" + ] + } + ], + "source": [ + "%%bash \n", + "wget -nc -q --show-progress -O unet_model.zip \\\n", + "https://api.ngc.nvidia.com/v2/models/nvidia/unetindustrial_for_tensorflow_32/versions/1/zip\n", + "unzip -o ./unet_model.zip" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i6ADZZfGtAvP" + }, + "source": [ + "Upon completion of the download, the following model directories should exist, containing pre-trained model corresponding to 10 classes of the DAGM 2007 competition data set." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "colab_type": "code", + "id": "jtqhp3X5tAvS", + "outputId": "db51e242-2828-4994-9179-86f459c17db2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class+1 Class+2 Class+4 Class+6 Class+8\n", + "Class+10 Class+3 Class+5 Class+7 Class+9\n" + ] + } + ], + "source": [ + "!ls JoC_UNET_Industrial_FP32_TF_20190522" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dt6oArfSmTc5" + }, + "source": [ + "## Inference with Naitive TensorFlow\n", + "\n", + "We will now launch an interactive testing, where you can load new test images. First, we load some required libraries and define some helper functions to load the pretrained UNet model." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 352 + }, + "colab_type": "code", + "id": "6NktI1GUtAvb", + "outputId": "700b92ec-db41-40aa-ec65-cd5599938550" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing /content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/dllogger\n", + "Building wheels for collected packages: DLLogger\n", + " Building wheel for DLLogger (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for DLLogger: filename=DLLogger-0.3.1-cp36-none-any.whl size=9884 sha256=294b0b226bb82109933049017fcab2268b6a282b616790276b0a2af8763ed245\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-kpseamaa/wheels/23/a4/72/2606d992c53ecdd7969c79ed3fb0c23dacdbdb438a8c17999a\n", + "Successfully built DLLogger\n", + "Installing collected packages: DLLogger\n", + " Found existing installation: DLLogger 0.3.1\n", + " Uninstalling DLLogger-0.3.1:\n", + " Successfully uninstalled DLLogger-0.3.1\n", + "Successfully installed DLLogger-0.3.1\n" + ] + }, + { + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "dllogger" + ] + } + } + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.15.0-rc1\n" + ] + } + ], + "source": [ + "!pip install ../dllogger\n", + "\n", + "import tensorflow as tf\n", + "print(tf.__version__)\n", + "\n", + "try:\n", + " __import__(\"horovod\")\n", + "except ImportError:\n", + " os.system(\"pip install horovod\")\n", + " \n", + "\n", + " \n", + "import horovod.tensorflow\n", + "import sys\n", + "sys.path.insert(0,'/content/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial')\n", + "\n", + "from model.unet import UNet_v1\n", + "\n", + "import numpy as np\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Ct-izSTsv04V" + }, + "source": [ + "We will now load and inspect one defect image from Class 1." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 614 + }, + "colab_type": "code", + "id": "EIOhZBuptAvu", + "outputId": "68def336-33cf-4a25-8893-848da6ddda42" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 89, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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XHg6HpXFUucDpdGK1WllbW5O9Y7fbmZiYEJ6r0WhkfHycvb09QTotFgsDAwOM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s7u7G/Pw8ZmdnFSpNmGF/fz9mZmZw+/Zt9PX1yZJ+cHCAyclJXdDt7e0IhUI4OzvD9PS07Mlc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\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72WxWBQ/J7C9evEBra6uYL1Qu/H4/YrGYLjlUivjMknlEKr3L5VJ69OTkBPv7+zAYDOqd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\nX4fZbBabaXFxUe8rBa+ZTEbri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CEt/6lUSplu5M3U1taisLAQwWBQgsbNzU2Ew2HE43EF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\nlY3FYjg6OlKRwfdYWFiIsrIydS3ZDSEokp8pNaocWxOYyNEZ9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s0oBq/Xi+rqaqyurqqr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FAwTPJIvFonOdtKPdbldYK/B1htPa2hoCgQA2NjZQqVSQTqeV90OH3tnZGfb39zExMYHFxUW8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FKRE8Qi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\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FnJqawtDQkLQ+ZI3QveXxeLCwsKA1Fm2T7PCvrq5wdXWFpaUlpZUzg4c7aYPBINcR1xSv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lx8fF6oAWCxxenozs6OrLj86uvr0/j+6OhIU75UKoXLy0uMjo6iubkZiUQCd+/eVUba+fk5vF6v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BxGIBDAF198gXw+rwKeOVHUTYTDYQmrybNaWlrSVOi9997Dw4cPMTMzA5/PhwcPHuD169fw\neDzSC3ESwufDYrHAbrfDYDAgnU7DbrcLskjNUFtbG5LJJILBoMb1LJYo3GfjQd4Yhe4+n09dNh04\nW1tb4kcBEFKAoEAiTqxWKzY3N4UnoG0buHJInZ2dqUhhKCwvVTqB3W63aNaHh4ciS9frdTJlZDLJ\n5/PCPFDPQifTxsYGfvzjH8PlcuHhw4eCR3Lyw/UGV9Gkp//TP/2TCivmzN27dw9nZ2fSOhkMBszP\nz+vv9ud//uf4u7/7O1SrVWWteTweGRRevXoFr9cLj8eDra0taXXcbjdevHgBAMrxy+fzYnANDw/j\n0aNHCIfDckoysJkbBTY8ADSpsdvtYoU5HA709PRgY2NDzk0WpNQ1ud1umR9qtRoeP36sM4KrsMHB\nQU2ROBQgG4+ONZ6FDodDUNJXr14hFotJB8Qi0ePxIJPJYGRkBA6HA8+ePdP3ClxNUqh7vLy8RHd3\nt9xrLEQI8eTq9datW8jn8yrqGWhMMwgNAO3t7RgZGVHo8rNnzzAzMyPxPp9P4Kph43OZSqXQ0dEh\nQCu1cG63G8ViEX/yJ38ikxM5a7wbrg9PEokEbDYbIpEI+vv731gzs7llU3y9mPb7/QLEsmFKpVJY\nWlr6H14sRXCFDnACeA7gf2m1WmdtbW1dAP4WwDyAIwB/1mq1tv9bP9fhcLTu3LkjYezo6KhgfYQ5\nfvvPoVgswul0qjNMp9O4vLzE4OAgUqmUAjt//OMfIx6Py9bMNQv1K998840SiJvNJsxmM9bW1hAI\nBN4IP2w2m7h586bYNcCVzqdQKGB1dVW7fI5U/X4/JicnFSRJSnQwGMT6+rq+PBZt29vbCAQCWn0R\nqsUOOZVKYXd3F0ajEZOTkwLPfft9IJVKSYQLXB2AY2NjmmJdXFxgdnZWBwkJxiyWeDmRz8PLky81\ncDXy56XRbDYVusiHmfZXWv4JDzs6OtJ6wOl06iWnTqZcLutCDQQCWv/Mz8/j8ePHEuFyLWA0GjE3\nN4eHDx+KhBsIBDTyJaiUEyyOjWu1GlqtFkZGRhAIBPR9PHr0CGazWZMQ4KqLGx8fh9/vx2effQaT\nyaQVLVER/P5psa/X6/j666/xx3/8x/oOKpWKIk/a29vl5GH6e29vL3K5nLhiXIk8fvwYAPCHf/iH\nEp4ThsfVps/nk/Ps/y0yZ7o6cNVckL1jtVq15qlWqwiHwygWi/j444/xD//wD1oplstlRfwAeIPR\ntLa2Bq/Xi4GBARUxNptNWoZ4PI63334bqVQKzWZTa2WDwYDd3V090/w57MDv3bsnUS1tzlyf8OAm\nqJR2drqOCFRkfAgvW3LMiMEAoCLxyZMn6OzsxOzsLI6OjpDJZPDRRx+h0Wjgiy++EAOJAaszMzN4\n8OCBnnXqsXi2sJDmBcRg0EAgoCBiroiBK9BgOBzG0dGRZAcAdDHt7Oyo6CRihBoePqssfpaXl/Vu\n8hKen58XdI9nY6VSgcFgwPPnz3Hz5k0A/xGmy0uQesuTkxPY7XbMz88rfJZnAQCt+4GrAuPevXua\nlhHAyouIz3xHRwfK5TL6+vqQSCRw9+5dTaZv376NlZUVGAwGbG5uIhqNah1GQXYqlcK9e/cELaY8\ng80FXYEUw3OdEw6HUSgUEAgE8M0334gm32q1dO5eN5lcXl4qIoMGDTZLfJbpLGaMDLEtPJP57JvN\nZiwtLYl5x2kq7yrGi/Dvymf9urObIOLBwUH8+te/xp07dzRB4oqcov50Oq07am9vD3NzcxgaGsLq\n6qq+m0gkgmKxKNTF06dPEQ6HhS7hdBG4WsEz4Livrw/r6+ua0AwMDKgYHRoaUlgzZRpkbNFJy+Bh\nbg1cLpdW3FtbW5pgu91u1Ot19PT06E4bHh4WV41cwEwmI/I/WU7pdBrBYFDvJ7W/ADTpi0QiclqH\nw2Gsr6/jv/7X//qfXyz9j/rjdrtbc3NzGhkWi0Vl8rAgAaAH6uTkRA8o7YDsjjlR2d7eBld7uVxO\n6710Oi0BNC3eTDhnZ08xOACxj2jxBa40GHSfFItF6UnsdrsQ+hxLGo1GnJ6eKqWZlff6+jru3bun\nvB0K3WjdZ0Iyc7FevnyJ8/NzzM7OvtGx0xHCyZDNZsPc3BxyuRzW19fV4fJ3L5VKsFgsmsY0Gg2R\nT7k3Z+wHLx5OG5h9RPswR89kmBSLReVHud1u2Z7JvqIAnbRwFjzXP2t26hRntlotrK2t4f79+8jn\n89Iw+Hw++Hw+HB0d6dAlRJGrC3brXOeUy2U5bti57+zswOVy6fDnVInaAx58c3Nz2N/fx/Pnz7XG\n4iHFCRYPTI7dq9UqisUifvCDH6jTZnfJ3b3FYkE0GkWtVkM8HtchA1xZd7lSttvtSlNnt0ZdFTVk\n0WhUMFHgCqxYLpf1DvH/TuEy7cp0eHV3d+sColCU1GDa/wlLpeZjaGhIk5EXL15gfHwcR0dHaGtr\nw9raGgBIa2exWFAulzE6OvpGZhcLUD7HnORaLBa9L5OTk6hUKoqYiMfj0mG1tbXJ9fcHf/AHyGQy\n+OKLL9BsNkU7B66mqF6vF//8z/+smAcS5EdHR1Gv1/HgwQO8/fbbSCaTynVbXFzUKo/MKopNd3Z2\nsL29jffee0+5b3zn29vblZnHCwe4Wn0T5kiXKAG1TF4np4sGDIJk+c6dn5/r3e3t7VXRxvUe3WH8\n7Ds6OrC5uYnh4WFNpsl5YlMBXDVxZrMZ0WgUxWIRJycnyoxjUwRAxYPb7ZYhBIAcTfl8Hu+//z6e\nPn2qHEhOUMifoxTgujuv0WhoVUqdSz6fh9frxe7urgT3rWs5YQBkjjg4OBAtfXV1FY1GQ05o5r4R\nF2CxWN6Yxuzu7qJYLGJ4eFi4Fibc8xkFoPXb0NCQ3I5sxgki5qSUZ5jP59PqiHpXAMJCxGIx/Qya\nIbgRua6lI4D2448/VuJFo9HA+++/j4cPH6o44HrQ6XTi6dOnkjfMz89jbW0NH374IQwGA548eaII\nl0ajgePjY/zkJz/RZ/r06VOdb+VyGW+99ZbML+l0WokLdJdNTEzg/Pxc5+no6ChevXolrAa1xGTK\nLS4u4vnz5wJEPnv2TCw6PlPJZBIDAwMyzHDgQEjn7373OzSbTTV8ZrMZ5XIZs7Ozem8JfiXShokQ\no6Oj+Nu//dv/rmLpfwqC909/+tNPgsEghoaG0NPTg/n5ebhcLsTjcUSjUQwNDcHj8Yiiy+iFpaUl\nrK2tvaHdGB8fV7hiuVxGq9USDZTC8ZOTE0HXstmswHDMhNvb25MTgToo6m+q1aqmXqyQmeFDUFZb\nW5s0OpVKBTvfZtvxIu/o6NDDMD09LVIug1fr9Tqy2azG2AaDAWNjYyoGvvnmGyQSCR06LP6MRiMu\nLi7w5MkTtLe3i/7Mfzdt8D09PYhEItJJkGfDKQQdK4QWHhwcwOfzIZvNClkPXBVr1WoVg4ODuHnz\nJiKRCDY3N3XZNRpXAascedJFwUucxSU1Z0dHRxoD82dEo1H09/djZWVFP5+BmqVSSdojUtBpOz49\nPUUkEpGNvPVtTlGxWMTq6qqw+rx0SFemu2pqakow0FgshuXlZWxsbKCnp0f/LgpXSQlnAUnXJv/O\nRqMRiUQCIyMjePLkCcxmM2KxGPb393H37l3U63V1Zcyiok6JBRdNDS9evNAY3W63IxaLKYOKJNz3\n3nsPkUgEfX19KBQKSKfT8Pv9CAQCqNfrePHiBWw2G0KhkKKBbDYbbDYbNjY2tIriypXOLZJwKdaf\nmJhANptFoVBQwCcFvnR1UoTbbDbF0aHd+uzsTFl2HO8zFJPdKCN3rhdG1C/xOaSOxO/3yw7+6NEj\nrX/y+bwKFq7xiOO4f/8+qtUqXr16JTH/gwcP4PP5FMtweXmJo6Mj1Go1VKtVdHd3i7xPIf7e3p66\nZ8Jj6Qpl2Oh1+zQvVDqcrj87pLP39/ej0WjITj0zMyMX1cnJCfx+P8xmM1wul9aoPB8XFhY0cSa5\nn7lgk5OTmrAyPopxI1xRPHr0CH6/X2YXBiTv7u4inU5jYmIC3d3dMghQ3E84oM/nUwbhzMyMQk0p\nbt7Z2VEEx9jYGAqFwhvTOlr7R0dHsb29LdnE8fGxdGNcc9vtdp2Ta2trKqKYjUjwIc8LOj8Z1Mu8\nPavVilu3bgkozAmS2WzG/Py8fs/j42M1S7y4+/v71dwyroiaJ9rue3p69JyTy0fxOh2rnFA+ePBA\n3zWnMaRw87lkKDtDsXkH8r01GAzY+TakmFonmj2eP3+OTCaDmZkZ6XiorS0UCprc9Pf3Y2NjAzdv\n3pTwnuJyasSoYTSZTBgfH9c9Qvft7u4uLi8v4fF4tBnh5O3f/u3flKnId4hJFmStMZKMshcOTPb3\n95HP5xEIBBAMBhW10tbWBp/Ph/39faytraFQKKC9vV1ynu7ubty4cQNutxuPHj1CNpv97sSd/Pzn\nP/+EvCQe5GTREPdP0S7ziyiaDAQC6OnpkeZocHBQ8R1er1eod66ZmM1Gh5rJZBIsK5PJoFwuY2pq\nSjqIL774QrqC0dFRPdjNZlPxGRSOLi4u6sLiZCSdTqviJQ2Xrig6QvL5vHQOPMAI2aN7p6OjQ7DM\nZrP5RpBle3s7Li4uYLFYZAnf3d1FLBZT93N5eYlEIoFgMIgf/OAH6qq5UuLvwoyzaDSKhw8fYmJi\nApOTk3JadHd3I51Oi4pOYmsulxOcLRKJKLmbgZO0OJtMJjnIyFuiEJmRI5lMRpdquVxGIBDQtIzp\n28fHx9jc3NTkg5cFQaAstMhu+t73vofXr19LtE5CNinlFNlSFMgOmqwXBmXS1s2O0m63o6+vT4Go\nHIu/ePECgUBAQbfpdFqHPenztLy/fv0a+/v7WncZjUYlgdNZyI47EAhIRLqwsKDnP5fL4fnz53pv\nstksXr58iXw+j1gspmdvdnZWEQ4sWDc2NvT3YQPAsTUDdvlumM1mFAoFhMNheDwerK6uwuVySTBu\nt9sVesz1UrVa1ffOC31gYEDr0ePjYxiNRlHae3p6EIvFcHBwgKOjozfIx5xwscuuVquYmJgQw4nP\nMy3VfFfIsGJW2+zsLDKZDN59910cHh6qq6czkO5WFkZsSPL5vAo6h8OhCAYKzPv6+vDs2TPxdWir\nZ6Dz5eWlRPwWi0WXBlfczWYTDodDtHo69+ikpb6RHTxlB7Ozszg+PhYeg7ozXhQEKxqNRl086XRa\nsTbA1ZSIHDVedPPz87i4uNCkjXDYVColt1hfXx82Nze1Duzv70e5XJauZmNjAwcHB5rO0JXIDM7d\n3V0V0HTUERdCkwQ/I7q1urq6JIhn2gINCAaDQRE3ZrNZBdz4+LhkAsxzJFyyu7sbJpMJLpdLMTrU\nkRKESrYcv1uun+v1OjY3NxVD8uTJE+zv72uyx+aCZxTPWU69GKHCXDYGPjMfjmTu6elpfPrpp9IH\nUZ5BeGTr24w25lIyTJwRPNzI0ARCdAJTEvx+v6bQLCgJT93e3tZq8Msvv0RHRwdGRkYEUGawLQ0k\nfPd3dnaU/jAyMqLp6ejoKBKJhHhNvb29cnFyWsoILk7TeV8TKzM5OQmv16ss0mw2Kxg0kULMn3S7\n3ZLjcPL1r//6r/xsvzvF0l/+5V9+0tvbC4fDgbt376JareKLL76QTdPr9aKrqwtzc3OKqgAgJwC7\ngkqlgpWVFbx69Qq/+93vEI/H37CI8+dxFNjW1qYqmNZ96kvOz8/x7NkzRKNRJBIJbG1tIZvNIpFI\nYGlpCefn5wiFQrrEyL/g/pvCwEwmIzcdrfT8nbkKcbvdePfddxGLxTQa5fQpEonAbDbjq6++Qq1W\nw87OjoojvnAmkwmHh4cavVKHRLoqp2EjIyPI5/Pq6qlRmJmZQTQaVagjJzHEN9DmT3bK9d+bwkpO\nxILBoKB2nOpwP81pF1eUAwMDiMViCIVCMBgMgif6/X7cuXNHgMFms4loNAqXyyVwaFdXF27duiUg\n3uXlJUqlknQfFO7youYlZzQaBXmktovrHx7Eg4ODuoj7+vpgt9u1H+e6hN1hrVZDoVDA22+/jZcv\nXyKXy2FxcRF+vx9PnjxBf3+/+ETb29s6PEulEorFItLptFYjPDQBCHp4584dxcbwYCFgkFocMrWy\n2aycjQDE/qIz5vj4GCMjI+ju7tZBD0CNB3VYPFxJKDcajeq6l5aWNFnj2L1SqSjq53oECt85q9UK\nh8OhsXpnZ6cyvSgyZ0I9L43rMT3t7e0IhULK3OOaizqtyclJTYWz2Sw2NzcxNDSEzc1NXYB00JnN\nZvj9fhSLRbGF1tfXRVofGBjA5uYm/H6/dCl7e3vIZrMqeMvlsi7Pcrmsouns7AyHh4cIBAJYX1/X\nuH9ra0uBu8CVu8dkMmmlR1wFbf3EIbhcLmmIiFJg8C4Ltmq1ikgkIr0WEScsxDs7O8WWAq6iI3Z3\ndxV4u76+js7OTjidTvT19WktxKk1sRCcgJPbw1VXe3s7CoUCBgcHYbFYEAgExCZiwO/s7CxarRbe\neustafy4Vurq6pK4eXBwUBmQH330kSZTlGWcnJwgGo0iGAzi6OhIME6uPBlATjAv0xQo7m42m3Ic\nd3Z2Ip1Oq1nlhCKTyUgnyIbv5OREBQkZYDynKFhmZFKhUMDw8LAgkXTxkdHHqTknt+QWdXZ2al3K\nQq67u1umG06TyEQql8tKmTAajcjlcqJ0U3LCXD2urTldYghxe3s79vb28PTpU2EUaDq5vLwKMO/u\n7lYTxsm23W5Hq9XCnTt3tF1wuVzKx+PPomuZE8tGoyEX7fHxsWCf5+fnyOVyIr7z78Cfk8vl0N/f\nj87OTqyvr+tcYD4ddWWFQkEr0unpaRwcHMDr9cLhcChwmIkL1wHC+Xz+v6tYMvx/rG9+/+f3f37/\n5/d/fv/n939+/+f3f/5/8ed/isnSL3/5y0+Gh4eRzWbx5ZdfauzKUTf/51//+tcYHh5WJe/3+zVO\nBIBHjx7BZDLBarVKJc/1l8/ng9VqxcTEBM7OztBqteB0OgUHs1qtikeg2HtqagrHx8e4desWotGo\nJkQTExNwuVzo6OjAwMAAUqmUWCMGg0HZWAwCJTuJwblMdPZ4PAok5LqBbCTgCqB5fHyM1dVVwSv9\nfr/WRO3t7ejv71e8CbUzfX19IkNnMhmt1jiKpwCXq8iJiQklurM7Pz09xdHRkYJp2bWenZ1hfHwc\nH374oXQpJGXTXkykAcGWZ2dnePjwoZK82SVzupLNZtWJMlfu008/RV9fnwKGmdXG/T3XWxTqtVot\nTcQ6Ojr0/drtdlQqFXGCent7xZ0iOdhisWBlZUXdJVchR0dHODs7w+vXrxVdsLS0JKeG3+/HwcEB\n3G43crmcoHiPHz+WA3B7e1vjfMYtcDJ1eXkJl8uFiYkJxONxTE1NSStxeXkp2KjH48H8/Lys87dv\n31aAb61WU6dEuCpXc/x8qOuy2WyIx+OKe+CfyclJCYm5ImCCOtcdXGuXSiVkMhmEQiGEQiEEAgE8\nevQIkUhEOVLswGnV53qPUwBSz6npoPi5Vqthfn4eqVQKFxcXqNfrGBwchNPpxMHBAYxGI0ZGRjQp\nJhwQgDrzbDYr8fjBwYHYLXz/ODXzer2iC5+cnGB4eFii8kAggN3dXYyNjYnEf/PmTUxMTAg1YrPZ\nhIxwOBwCLfJZ5T9De/7q6iq8Xq9WscfHx8rB4oTF5XKhr68Pa2tr0r3QpdbX16cMLgBiU3GKfHR0\npM/T7XZr6sOsLp/Ph0gkIh0luWrUhTBrr9FoyPTidDpxeHgomcHGxobOLE5GOO0AoFUio18IW2Qm\nWyaTwebmpnL+qE0imJUogcvLS8WhDA0NIZfL4dWrV+jv79dUh3pATmcYBEwpBiOKqEUkaZurMI/H\nIygoYY21Wk3mFrpv+VlxBU/JBKnbnOBzIsrPhGvI8/NzZaoRnUBt7sXFhThglEJUKhVUKhVEo1Hk\n83mEw2FsbW0plomsJq6racyhHIFrx3w+j/fee0/2fovFgvb2diwsLGjdy+kkP/ObN29KRsK7hpq1\n7u5u7Ozs6P24ffu2clT39vYwNDQkpzRZiIRFExVA8fn8/Lz0Q8QSEAXEZ5mTfj4nRDNwuki96/7+\nPr7//e/D4XDg008/1efN83F7e1uaxs7OTgGOeRYNDQ0hkUh8d9Zwv/jFLz6ZnZ1Ff38/BgcHsb6+\njkgkgnq9rgOFl3Q8HkexWBTMjcTi09NT+Hw+1Go1ZfFcp2TTOZZIJPRCmM1mWdpNJhO6u7vfcIPx\n5eUInwJzZgi9ePECJycnKJVKovJSZ0UXGGGLFKteXl7qBXc6nYjFYqhUKigUCmg0GgiFQuju7kYw\nGJRAlPbWhYUF0cepS7BarchkMigUCsK57+7uSi81MDCAGzdu6OXk+s7tdivL6vHjx3j58iVMJpNy\nsij8tlqt6OnpQbFYRLVaxdHREfx+P/x+PzY2NqSr4IHHGAym3bvdbvh8PjidTrzzzjvI5XLwer2K\nROBnVCwWYbPZYLVatTN/+vQpQqEQLBYLksmkuE82m02ajXK5jJ6eHkEwKUQvl8twu924c+cOKpWK\nAJQkH9+4cUOuNLpO+BKRjEwtGzULh4eHGB0dlTPtzp07yjAzGo1YXV0Vxp8iUQYbc33MlWZbW5t4\nOEQ6sPhh0UzdCtdBBF2SzNzT0yMH49raGur1upoFFlu5XA7hcBgTExP4zW9+g5/85Cdoa2vD8+fP\nheYg38ZkMiESiSi7q7OzU2se5jednZ0hFAqhq6sLr1+/lr271WpJsJ3JZGQ2aDQaMJvNKsaur5Cp\n/6rX66jVajg5OdFzxoaIOg4+4+3t7RgbGxMx3mq1Yn19XdwqHrputxs3btyQjqS9vR31eh0LCwty\nDdHE4ff7hbvgeo55ZYODg7KhUyS+v78v0TMRHQcHB1rvM/qDukDG9vD5o7WemkeuX/ksDg4OwuPx\nIJ/Po9lsas3ELL5yuYxgMIhkMqkoD4p1CdM9Pz9HKpWSG9TpdIrJRV0J0QEsrtiwMeWAzDK6lzo7\nO7Vep/vu7OxMdOqTkxO89dZbarjsdjtWVlZUiC0uLiIcDou4ToF+PB5HKBRCf38/Dg4O8MEHH2B0\ndBR7e3v46quvkEqlVKgHAgHp2drb2xWmyvfg5s2bWF9fV8HAtTdjSOr1ugp3rmZoAKhWqyos2tra\n9L0SN0A+FlfEyWRSUU+EIFNnRzMEV8/lclmSkutnJLMBaU6iRnBlZQUTExNaG52fn8Pr9aoJ7+zs\nVP4kHcuM9mo0GohGozAYDPD5fDCZTNja2pK5haYQn8+HfD4vE4DL5cLm5qZiZVZWVlCr1VAqlTA1\nNQWPxyOAb61Ww6NHj3B8fKz34PT0VFFay8vLODo6kvuM4d8A9AyWy2Wx7Hg/Eq/AAqrZbCKdTgt5\nQOcrcyjppNze3sbCwoKaesZdmUwm/Zz29nbMz8/LnU3eXaFQ+O4USz/72c8+Ido/l8shEAhgbGwM\nZrMZ8XhcPItqtSrhs8vlkuBxc3MTlUpFwlJOkdjN83L1er3SzXBXys4BgOCLBoMB5XJZ1mcWDJ2d\nnULyk1UzMzOjsNRGo6FK2OFwqPNh/AajVyh+rVQqiMfjEkzzEmH6NYVtTqdTh9ny8rK6N3bMhK4R\n+gVccYuOj491+fCwN5lMmJiYgNFolObLarXC7XaLxwNciU+pYyEQjpcVPxumfJMRdXZ2hp6eHmUF\nbW5uSpfDnKze3l657IrFoqZFtJV3dnaKq8JC8Pj4GAaDAYVCQUn1l5eXOgQBCGvg9Xpht9slEj8+\nPhZEkx38dW2CxWKRE6pUKr2Bf2AAcjgcVsHTarVQKBRwcXGBly9fvtHp3b17F2NjYzg/P5doeHNz\nU+HPdAgyvoIwNo/Hg1AopPBi6r9GRkbecA3R+cnC6fHjx2g0GigWi4hEIhJXEz55dnaGZDKJbDar\nZ2B3dxfZbBbBYFCXJKcQLFb7+vo0yQQgTg9DRznV44FFaCVjDjo6OjA0NIRYLAa/34/PP//1asFz\nAAAgAElEQVQcc3NzmoDevXsXuVxO0wNOW30+n/Lw5ubm0NHRgV/96lfY3t5WBBDF3larVfFI+/v7\naDQa0m4wi42W5euMtXQ6DZ/Ph8PDQ6RSKZhMJvT398sqbzKZ5Bq8c+eOEtt3dnak15uampLugoJg\n4EqnODIyguPjY+Ec6NIaHh6WXdxmsymclFlcnCKQG8cCkhgNq9Uqmz8v6KGhIeEcOjo6YLPZxAna\n2dlBIBDQ98uunWw2CpXpPuMUlM5hj8cjgTZZR4wPYm4XRbc9PT0Kmh4cHMTOzo4mf8ydZIYmdTV9\nfX2Ix+Oyox8cHGBwcBAGgwHJZBI9PT3Y2tpCe3u7JjPUbDFR3mazwWw2C7zJy5SNqMViwdDQkDhX\nhFASY8IoF56FFOHzPWcYdHd3NwYGBvDNN99I20WzEJsCQl75/hB3Q2ArcIWuICnd4/FIL0URf6PR\nUEoDnYadnZ2wWCx6Hrh1YU4l2W+ckF3HzxweHuLs7Ax2ux3BYBCFQgHvvvsulpeXhW0hgofxNoFA\nQOdFJBJBZ2enBg38TAqFgu4awliLxaKy/FqtlprBgYEBJBIJ3L59WxDMoaEhTRuZS0mcw9jYGGKx\nGACIJ8c7w+FwYG1tTbmFvb29CIVC2NraUgPKiSeflVarJX3g8PAw7Ha79HidnZ3UKH53iqWf//zn\nn9Bdxs6MhykR+3QsTUxMoLOzU0JtEknD30L2jEYjQqEQbty4oSwvVv2VSgV7e3sIh8PIZDJotVpi\n43A8TSAbHyCn04mjoyM8fPhQdnMGytbrdSSTSXE0OKL+5ptv1IGUSiVZNzmKpHj69PQUTqdTBQYP\n4Hw+D7/fr2BhotxZTDAGgZycRCKh8bzX68XIyIgslNexAteR/BTmWa1WGI1GZetc7145aTg+PtZB\nT/EdR/bsxlhIMbOHkz+LxYJgMCiM/uXlpey09Xodk5OT8Pv98Hg8SCQSmJmZwcjIiIrOarWqLDMA\nGttzogdccWs4McpkMuo6h4eH4XK5NMnKZrOo1+sKND07O8PKygqazaYOXgByLNJNd3Z2hng8rkL+\nxo0bEl9zzdbX16cRfCgUwsnJCTY2NkTRJfm3q6sLfX19cvfYbDbs7u6q0KaQk2ukzc1N5a8ZDAaB\n1CiIZOwARawk/7JpaDabePfddwFA+IDh4WE5jlh80lDACBK32w273S5rMyexdMRtb28LSdDT04P1\n9XWsra1pkjIwMKDn4+XLl2g0GhqxR6NRdHV1YX9/X5+Zy+XSYUwHi81m06TT6/WqiFpeXtZ7v7a2\nhuHhYdy+fRvxeBw2mw21Wg0TExNauabTaTVahFSymA6Hw3L49PT0SAxOUfnl5SXi8bgmO+zuyera\n2tpShhrdaZzEMn6hVCppSmo2myXY7+jowI9+9CM4HA4xhDj1ZUPG/8ze3h5cLhdevHihSQ6jJWin\nN5lMWiFTmM21D88/8tXobCMygHR9AgAJ4aWTmNgVnl/XJ+Q2mw2FQkEXM6cGbW1tmvqyAQWAly9f\nvsFEy+fzWpkxaqOjo0OIAJ43qVQK9XodrVZLMVJc1VitVhQKBSwuLuLi4kJifDqjKFm4jnQh04wX\n9/b2tqC2bA5arZZiqSwWC6ampiQFIHeJrD/+ztwqJJNJ+Hw+mUQODw/l1nK73TCbzbrAU6mUsCSE\nQ6ZSKTgcDmEK+FlSJE4hOvExRJsAVwaR169f67z0eDxYW1tDNBrVe8ZVYSqV0pp6f39f9yYRDIOD\ng+js7EQ0GlUTRWgnHZ58X54/f67A6nK5LMc6zw06Wsm34trfZDJJvP/ZZ58hmUzqXdn5NgqMK9hC\noaBJKZvVYrGIYrEoVyMLeTbuTHHgvUKX8cjICJ4/f/7dKZZ+9rOffTI0NAS73Y7T01NpHMjnoeuL\n2hOHw4FoNIq5uTnZRvf29nB8fAyn04m9vT1NJEjUZZdzdnamCr9UKqFQKCCRSODg4EC26JWVFeW7\nsTL98Y9/jMnJSYyOjiKVSuHly5fo6+vD9PS0uutcLif7e6vVgs1mw/T0NJxOJ9rb23FwcICdnR3k\ncjmMjY1hcXFRqn+z2YxQKCQuT7lcFsOH6wuTyaTujETT09NTOBwOrK+vi3lyfn6OUqmEiYkJ6VU6\nOjrw6tUrfb4c+5Or8aMf/QjNZlNwMHYOpVIJBwcHSKVSCIVC+Pjjj8UXymaz+rs7nU74/X5sb29j\nYGBAmUEmk0n0VwIFOe2ivokTNrvdjuXlZU1MeHCx0ONkj3lkJI8z5+jly5e4vLzEW2+9pSnDdS1T\nd3c3kskkAoEAnE4nlpeX4Xa7EQwG9R2USiVdmnRm7u7uolKpYGFhQZdzMplEPB5XjhwvrmazKUgf\nnXWk45JhdP0S4j9HijJDUdvb2/Wc3rlzB4ODg28UCQMDA/B6vSoAmXH4/PlzRKNR9Pb24smTJ5q+\nra6uol6vy9VJ1xD/vnyPWMhxCkfXT3t7uwpDXggsDkiWpgaAE4+dnR3Zu+n44YSN7kSuXDmxpWOl\nv79fcQtGo1GcrRcvXmjiRlgmOU5cBTAahE3Be++9h+HhYRQKBf2dc7mcLOgEt15cXGBra0uF69bW\nlpoOrkhKpZKwD8fHx1hbW0M+n5eWihODs7MzrU2bzSasVquwBlarVS7WYrGoENGtrS2MjY0JbUJX\nYDablSsvHA7D6XTqeaFOjtOJt99+W4gNu92O8Ldh28QFhL+N56CbrdVqIZFI6Pfld099DIGFzWYT\nXV1dWFxc1GTXZDLh7bffRr1eRzqdhsPhQCwWw+rqKsbHxzEzMyOQ5vHxMd555x1sbGxgbGwMN2/e\nxOeff67Ug3q9jhs3bmBmZgZGoxGRSAQrKyv6HD744AMBKRcXF/H69WvUajXpf6gVY7PLaTRjmlwu\nlybg4+Pjb7gxWdxFo1ExoRh3dHZ2htHRUZ1lPG84weDWg+dUpVIRAJQFHZ93MphGR0extLSk851F\nKzVcFxcXuj/YjNIRSb5YrVbTf2d4dTQaxc7OjuK4YrGYpjfU8jGWiOBSYhAASGd5PcM0EolgY2MD\nwWBQyBsyAXO5nNbonZ2dePz4saZz1HiyKXnx4oWaOrKXarWahhVsCE9OTsRZIwOLgwGCQnmeVyoV\nBAIBudw5jSsWi8jlcspX5cqUMVeckl5eXiKbzSKZTH53iqW/+Zu/+WRiYgIAlIdEYR4PPY54aX3n\n2md8fBz1eh03b96E1+vF119/jUgkAqfTiba2NnzzzTdIJpNIJpM4ODhQdg+z2qLRKN59913MzMxg\na2sLfX19mJmZ0W4zFAppDPny5UtNC5hjxi+He2SSq6lzooCcKx6HwwG3263xPGMLwt8mxI+MjCAe\nj2Nubk4TNsLGyuWyDnVSqnt6eqQNAq7I4AMDA2hra4PL5cLMzAwajYYO6VarBb/fL41ONptV4jxz\nporFomIJWK1z/Ht+fo7NzU3s7OxgdHRUYY77+/vK2OJqifqMvr4+uN1ucZH40LtcLo3+2fF/73vf\nQ61WU3dFy25nZyf29vZ0SNlsNhU8nLpxApLP5xUSS/AZSa7UbRCE6HQ6pXljMc0RL8XW7Ez4uVut\nVty8eROzs7PY3t7G1NQU3n77bbGjVldXkcvllOOVz+dlu+Z3w1gA4IpgzDUGi8F6va5igmwrTliZ\n8cScJP5Mh8OBr7/+WmJIHizMWzo/PxdfymazYXx8HI1GA0tLSzg6OpJGkMUfieG0xTP0t16vK1qC\nhRCpy9TmUNfCw9PhcGBubg6BQEDg0mAwiMvLS/j9frGnyE9iEPUPf/hD6TiKxSLef/99UZHZHITD\nYWlT+AwcHh5idXUVbW1tKBQK2Nvbk5aEzxEnSLyc/H6/RLcUt+fzeUxMTGBzc1OhxAzq5CqVeBEG\nVKfTaU0nWIQwdoiYDF7e0WgUnZ2dKkTIFVpYWMDBwYGs5UajUVTrvr4+ULbA0Fuuy2u1mv5eh4eH\narZSqZQE0oT+Me/v8vISsVhM+qTrQE5qAgn75DS9Xq9LJzowMCCZAplK5XJZIF9q2tbW1lCr1WA2\nm/HkyROtwVlQZbNZrK6u4vDwUEaKrq4uBRknk0llRQ4MDGhi09nZqSaAz6rD4dBksFQqaV3F9Six\nJ0xbILSTjUqxWBSihpPXUCikLQV1bfV6XZ8hg5nJ1crlcjr7h4aG4PV6sbGxgVarhenpack7qAfk\nvWI2mxEMBtVAEsjKKRr1Nqenp0gmkwiFQsoCHB0dhcPhQDablXSA2kHqAIkTKJfLKoSNRiOSyaSo\n7pSJsKD57W9/i1AopO0PcFVcWa1WDSV6e3uVMkEKObNWnzx5gtHRUf3n+fwxm/Ds7Ewh3YuLi4qN\n4QSW07TOzk7EYjEVeGRfUdvLaePExIRkK8lkUiu5paUlDQEo6cnlct+dYumv//qvP4nFYujsvEoe\nn5ycfOPiYEXKwiMYDCKTyWBtbQ1bW1twOp1i8lAYvbm5qYeYLx0JtmdnZ5ibm1Ne0t7enrgVjUYD\n6XQaBwcHiprg/pt8CjrkDAaDXFBra2va09tsNhFiq9UqlpaWsLOzoy6fHd3W1pY6CoIcuYIDoI6P\nX3Sj0UCpVJJ7wm63S8ROMZvT6cT+/j52d3e1y67Vaujr61OVz67+5ORE7Bnm5XCiwhiS/f19jI2N\nSai8vb2Nvb095PN53Lp1SwcDIxs4EmZWF1cI1/VM/f39ePbsGebn5wH8R75YpVKRDsVsNivLrLe3\nV+4zUndXVlYkCGXxwW6ZQn2v1yt9xPz8vOCXBLGRpUTQ3vUASU6iAoEA3nvvPQmrg8GgJh3n5+do\ntVrY3t6WiBCAQmR5ibGzdzgcyOVyEp8CEM+EcEs6wZh8fnp6ilQqhS+//BImkwmTk5MYGRlRnlI0\nGsX29jbi8Tj29va0fuHPv3fvHtrb2zVRocvR7XajUqkgm81Kr8SGhEJnrlc5waKOpV6vw+v1yrnp\n9Xrf+LtSzM7MJpojfvCDH2Bvb08XKOM+OK0yGAzKF2TA8OHhIba3t3F+fg6Xy4WtrS0VQNRzcera\naDTk3HG5XJoG+Hw+MZoYh0BSvcVikWYom83i4OBA69ZKpQKfz4eTkxM8f/5cGWPJZBLRaFQuuufP\nnyMUCqFWq+HBgwcYGBgQt41nzMHBAWZmZtQsvH79WmteTitrtZp0XGtra28AT9kR86C/HllyfHwM\ns9mMRqOhdTQdZ1zP0ZhSLBa1fiWbzWQySSDM6SbPQLLbuF6l4Nzv94vnVCqV1Njx9/V4PPD5fJrI\nUrNZq9XkTKNT7qOPPpKLk7lv4XBYmiNOEynSpWA8GAxibW1NhT6fPb5vlD4w1ogFHUGS3AQQ/Ht2\ndqYJeqlUQqlU0vqZ/0yz2ZSui7ErdDtzcsTJCYX8drtdxRxDnEl1L5fLYqHRucVzjIUyOYKBQEDy\nCa7IaCIBrqYz6XQa2WwWZrMZiUQCoVAIdrtd00sWMXw+ent71QgVCgWF0/b29sJiscDlcokdxQm0\n1WpVZuHa2pqijWhmIgeMlHrGUVFXlE6nxV3iRojTOq6fx8bGlDZBunu9XlegeF9fH7xer/RPT58+\nhd/v1/aFWmXe/wQzc73MyJmOjo7vVrH005/+9BNWwMvLy1hfXxeJ9vDwUEJghqIeHx8r5+p6fAID\nCD/66CNlWHV2dgpuZrFYkM/nZUPmlzk2NoZnz54JCUDHEjOTotEootEo7ty5g8XFRUQiEaRSKbx6\n9QomkwkPHjxQNc4OiTBH7u5nZ2fVZdNBQuhWs9lEPB5HpVKRM4c2XYq9ueLhF05H3HvvvScC7fHx\nsajes7Oz2N3dVcF1fZ31ve99D729vRgYGMDGxoamKfV6XasaZt4tLCy8Eb1Sr9elzyKsj113Z2cn\nRkdHEY/HYbVasbS0JB0GL52ZmRl0dHTAbrfj8ePHEqHTgbG9vY2JiQnkcjn86Z/+qZwVlUoFTqcT\n77//Pi4vL1GtVnFwcKBEbk5iNjY2JF61Wq2KOTg+PsbW1hbcbjfOzs4AQHRl6jB4Ebjdbk1lKAxd\nX1+X9ox79oODA+lzqtUqtra25LJZXFx8YzUMQCN8Xmp0TfEZ50FM3RhBd11dXQiFQtJ1vH79WtqL\nWCyGsbExZDIZfPjhh8hkMvB6vbBYLDg4OHjD5XdxcYHFxUX09PTg5cuXisk4OjpSsCi7bBY0LpcL\nGxsbGB8fVxHJ4oYXBAuVVCoFj8ej55r6Ixbs+/v7clly0kloJ4N+iUYYGxvD7u6ugJpOpxNLS0ua\nXEWjUQwPDyMWi2FiYkKHNS9wugUZVFqtVkVe53M+OjoqfRmz8xilYzAY4Ha70d/fr0vV4XDo+WBH\nzWw7XgKcIhE0yXR7v9+vi6y7u1vF+v7+vlyRXPO43W6MjY1hb29PRpVMJgO3262unitF6onojr3u\n4CNpnd02Bdr8TLq7u+Xg5VqVhWihUNAan+LiZrMpaCmLCTYY1NZx2s5JaCqV0qopHA6rCeOkkFoq\n6vdo2uno6BA8l6YKNiCEunIt9S1YEMB/5IdSv8hsNbpkudLZ2dmB2WwW6oPxV5FIBPl8Xo0dZRtH\nR0dqIliE011MYTFda0RSMPg5EAjg5OQEQ0NDQg0Q4HpycqJmze12652sVquaelJLyKkW3atTU1My\n6bBR5sTt4OBAkMy+vj44HA7E43E1LpQyEH9DSUexWJRxyOVy4YsvvtC08vqzzvd1enpaq/pbt24h\nkUjIGVwsFhVDcnl5ifv37+P8/FxQVA4BOK28uLhQAXkdbUJNEz9D6l35+fM5oVnoOvCV60puBTjI\nyGazitgqlUrfnWLpF7/4xScGg0FfNPNg3G43AoEAPvjgA8zNzWFxcRHn5+dYW1sT48FkMsm+TDfX\n7u4u1tfXUSgU0N/fLzcTx8yzs7M4PDzUiJIXGvN2dnd3AQDLy8vC8edyOeRyOSSTSeRyOWxvb4sk\nypwvs9msYFH+ngDEiqF2pVKpqIhjkcXA3hcvXqBWq+l/HxwcVDdqMBj0QFxcXODo6AivXr2SGJic\nIjrRSqWSOjJeWHSCkAtlNBpFdO7o6BCCniNrMptarRby+TwikYjWeTs7O8pIo47JZrPhzp07MBgM\ncgNST0DBHiMNyOzweDyy4XIN63A4pJe4uLiQbubJkyfKp2MBSH0GD27aWx0OBxwOh7KvXC6XpjPP\nnj2Ti8dgMChnqlAowGw2w2g06mJ9+fIlLBaL2B3sSkkyJymZ3RTXSRRlc0LS39//hhaot7dXo3oe\nouQGjY6OotlsihBNsno+n8fk5CSOj49htVqxsbEBs9mMfD6PVquFXC6niJlAIIAHDx4IEeFyuUSn\nZ3AsOSqcLpZKJaTTaWntPB6PBOexWAz37t1Dd3e30t89Hg+WlpYwNTWlZ4YauampKQQCAQQCAbRa\nLQWVGo1Ghf7euXMHmUxGGYyjo6PCWHCyQe3R/v4+MpmMJpnDw8P41a9+9UaMCs0KpVIJy8vLMBqN\nmoxSr2a1WoWXYHIAWTpbW1sSFbP5oB7qukuJEypOSpl4zimEwWDAyMiIeG/b29twOp1qFIxGowr8\nRCKhfLWjoyO4XC48fvwY7777rnL5WIRxLU8BfVtbG46PjzE1NSWxLfOwmHnITproAortqXdkUU5m\n1/7+vtYj9XodNpsNuVxODrDrlxaLRDrLSFK+d++ehPXRaBR9fX0KdGWDYrPZ0NPTg+npaZTLZbx4\n8UJC/maziUgkojxICuo3NjYQj8cxOjqKWq2G5eVlHB4eYmFhAX6/H4VCQVofYlxo0d/f35fQmO8p\n0RRsLrg+pnCd00421gw+ZhA4sS6MlwEgwf/09DS6u7txcHCAYDCIg4MDANCZwAKMlGtOvqg75cTn\n4uIC4XBYGA5OfJjxyWf14uICiURCZ4DZbJaAm8UzNyxEMQwPD2sSn8vlsLCwIE0rI0UYdUOXW0dH\nBw4PD9VUHBwcyAhEbRHXbeTVMfOyXC6LvzYwMIC9vT0sLi6KO8gJJ6Ua/N45DeXqnxlztVoNwWAQ\n+XwelUoFsVgMk5OTCvumBnN4eFjZeHzXGfVzcHDw3SmWfvrTn34yNzenCpC5WMT/r6+va80GXFn8\np6enMT09rbBWcnhYqe/v76vLYkFCGy4nBHyRuPtmt8GOkC8F9VFU1//mN79BW1sbBgYG8O6772qK\n1d7ergeKjodoNKrKOZvNYufb9Oa5uTlMTU0p74Z76K2tLeHqGVBKlwUPYnYKLChSqRT8fj+MRqMY\nFByxs9JnpEcoFMLu7i6eP3+O7e1tDA8P69DiJUTNCqdUnD50dnaiXq9jeXlZ432TyYTj42MdHtyl\nc2zMiQOdG4SokeHDKQZF1Ofn51pXUbvC74vTp1/96leIxWISwF6PZ6G+hxcr3WdDQ0PiJXFfvb+/\nr+Kns7NTnJSVlRU55tjxVyoVeDweLCws4Le//a0mbr29vQLUsRjnd0GWTjAYRLPZVOYRd/SpVErT\nz+uBs0ajEYVCAc1mU8/VwcGBVi4sPPv7+2G1WpFIJHB6eirnE4s3ulv+7M/+TONwmgUuLy+xt7eH\nSCSikNL29nYsLi4iGAzi1atXCosl9+zy8lKFE9fDVqtVkQvMC6NQu1AoSGxJEGB/fz+mpqYwNDSE\nQqEgy/jnn3+OVqulgoffB909CwsLuLi4wO3bt1Eul+Hz+fDo0SPcuHEDRqMRy8vLsoi3tbWhVCoh\n/C0gkywrghGvB/tWq1UV8V1dXdje3lY2JfEbzWYTAwMDEoVyhcFCnu4et9uNZDIpJ14qlZI+ido3\nRpQwdDSVSonTdXJygr29PTlkyfmhkJi/M3U6zHHjMxyLxTR9B4DDw0NJC3K5HGw2mzhlAJQVRlcw\nu/NgMKgGiToXPqec9HCCMzY2pgw+RqqwCeVFFQ6HhRNgQ8epx/n5OWZmZhRRs7q6KrkCJ2I2mw2R\nSETB6lyzuFwuuN1uDA4OSttILt/p6akkErwYfT6fGuL29nYFEttsNq3vmcFnMBiQTqdRLBY1bWS+\nWzAYhMlkkjuQ7yz/izBjFhzEO1Bvymk/sQfNZlOaWWIi+NlwBcrGmnmeg4ODwpuwoWG2JOHJdLWx\neKTzmZwuSkvi8bhAn8SlXF5eIpfLaUNCdAXP93g8riloX1+fJkVHR0d4++23tTL+6KOPVLiTM3U9\nFzQSiWhd6na7MTw8DKfTqXVmq9XC5uampp+lUknPUl9fH9LptKbvFHQbjUasra0JNsopJfWNzJHl\n+vk7lQ33y1/+8pMbN26gXq+Lp0OrqMFgwMzMDDwej5wenEJtb2+jr68PY2NjAK40PtQsUHS4vb2t\nzpGTGv7vPHyYq8Vct3K5DKfTKQEqRdAsDvr7+9VhsWrmBUHrMZ0bZOtw5WMwGOBwOKQx4kFD+6rZ\nbJbri10u7cR8SXkYch/Pg4uuCgpY+eAODAxo6vH111/j4OBA0xaulQjoYhETj8cVZsiR8NramlwS\ntDSTxxSJRBSkez2MMhQKYW5uDsViUb8Xu1vmJVFYHQqFpKMh+4WdHl0UBDG+ePECu7u7KlQpXqaT\njcA5/myOr2OxGFqtFuLxOHw+H3Z2dqS96urqwsrKCkKhEACosCNArtVq4e///u8RDoe1AgmFQkil\nUio0mXkHQOPnr7/+Wpozdnt83sbGxpBKpTAxMSFh+fVVCZEYsVgMmUxGug+S4el+4SFDhwwFulwd\n8FkMBAK6uG/cuIHe3l4J6ff395WlSEhgNpvF4OAg0uk0JicnNSFhgDQdkdR3EBvBvMTr65Xx8XEM\nDQ3hs88+w+rqKt566y2tKqPRqH6HXC4noCOdpf39/Xj48CE8Hg8ODw/R3d0Ng+EqwZ5rU7o7eUaU\ny2VNYSgedbvdAshywsvVCgGe1MyR1Eydy+XlJZ4+fQqLxSL+TldXl/htv/71r3FxcSF7M5uXg4MD\nWdB5UQwMDCifjPoUrs+i0ahQHrxkOFWempoS2oSFKfVhrVYLDocDRqNR5x/Xfswxo/C7Wq2+cd4R\nzGi1WhEOh1GpVJDJZN4IxqZ+lIRz2uAHBweliWKDQ6mDw+HA8vKy9DvA1eSF2plGo4HNzU0kEgkV\ndTwX6NTk78zpDd89u92uxnp1dVWAxEajgXw+L1Yf2Xbt7e0qKE5OThAMBtFqXaXdVyoV2Gw2Wdb5\nvjI4PRqNijfHSV2pVNJE6vT0FKenpwgEAnA4HHj9+rUwH16vF5eXl2K4JRIJjI+P4/T0VE0gG5ze\n3l4kEgmdzdFoFJeXlzoPKNSnbIRTKU5sKUZfXl7WVIf3EZ9hruHYWACQUJ0Tw66uLrjdbq3FMpkM\n+vv7cXFxgfPzc4nTr0/f7Hb7G39vPsuc0PIdzefzMv6wGKNjl8aYy8tLvHr1CoeHh/pO7XY79vb2\ntLnhncuJFu9aBnUnk0kVjGxUr+fEBQIBRKNRPH78+LtVLI2MjCAWiwnoRyHZ1NSUdtO/+c1v9AGZ\nzWbcvXtXpNyjoyP09vZKqMbCieGu3IUnEgnpl05OTtQFXeeBMEaBbBPqIyKRCHw+nxKM29raFOq5\nu7urh4sxJS6XC5FIBG63W1ZhHmwmkwkPHz7UbpwsCh7kfLHYQbKDpUOCbJFms4m5uTm43W6tAi8u\nLuQeYJfOWJS5uTncvXtX42N2LAA0lr28vITb7UY+n9eLynGr1+tVd8FJoN1ul0X8+PgY5XJZY/Zq\ntYqVlRU5G5mUfV1gx5ePK5aenh7RXT/66COBxBYWFt548fr7+3UYspihnZUOqlQqhYGBAf2+6XRa\na1YAEomS28UpGfUrkUjkjc8nHA6Lt8MCnGGO6XRaq87Z2VlxhghX4wQhHo9rvbG1taUD5PDwEIlE\nAvv7+1pD9/T0SEPAw45cGk4FCY7jeo9Onmq1ij/6oz9CT08PZmZmtLZ++vSpCjquY16/fi0O07cR\nAMI/cH0DAB6PR+YJg8Gg9QEREjs7O/j444+RTCbl2guHw8jn83LtvHz5Uh0mw3oZYX2tV14AACAA\nSURBVLC7u6v4HUIUrzcs5HHRXk+BMdfMxAfwmQAgQwXfmba2NmxsbGBychKNRkN6FqI9XC6X3u3r\nQng+wzabDYeHh1hbW8Pk5CQcDge+/PJL3L9/H8vLy4o1Ia2ZzRXhh2dnZ9jb2xNJmky3tbU1hMNh\nrKysaAXJaTovVgrpM5mMGjKLxYJWq4Xj42M9s5xwA9D70mg04Pf7EQwGEQgEAEC6PE6vv//9778x\ngSLjyOPx6DImyoX/2WAwCL/fD6vVinQ6LfBvs9nEwsICwuEwvvzyS+Euzs/PMTIyokgeWt6j0ajO\ng2AwiNPTU0xPTwurcHh4qAJgcnJShUlvb68kASzs7XY75ubmBPzlipEOZYZPb2xswGazCaS6uLiI\nrq4uNeQHBweYmJjQGW6329XMtFotRUcxiYBSBoPBIBcttV3ENJjNZg0Guru71aRsbm5KnM3IEDoI\no9GoBObFYhHDw8PY2tqCyWTSswtAyBxOCjltoQOtWq0im81KA+TxeFAul3Hjxg0kk0ncuHFDhgnK\nN2w2G05OTvS8UCdnMpkUU8O1LVfW0WgUfr8fa2treP369RsTTA4z6FrlXcAzeGpqSjEo7e3teq9L\npRJu3bqld4b4Hw4dAKiQZgFMCC+dfBwcMArsWz3qd6dY+ou/+ItPpqenFZ9hs9lUqZI07HQ6USwW\n9RDwsCgWi8r9Ojs70/6dKcWRSETUUIvFgsnJSTx+/FhchouLC0xOTqpb46jYbrcLqJfP53FycoJE\nIoHt7W0cHh6Kh8KVXyQSQaFQUCL3dbFcPp9XojKFlplMBrFYDMViEZlMRp1DW9tVorvdbsf9+/dV\nKGSzWSwvL4vJc3h4qA6RYEyHw6HCo16va/JyeXkpzg0LO05rOOIlI2Rra0vJ0EajUT+XXX4ulxMo\nra+vT8nvPKhCoRDq9Tp2dnYUCzE+Pi7NyNDQkFwOfr9fQsCVlRXFK1C7Q00Cn4v9/X0kk0nMzMyg\nv78fS0tLommfnZ3h6dOnCIfD2NvbE1hxYmICGxsbGsGzK+F/DQwMwO12i/x9dHQksBlpyGNjY5oi\nUDjsdDoxOTmpuBxOQ7na5XTlyZMn6ojoNGKXS7yFzWbTRReLxRAMBoUuSKVSWFxclGiTzwYF5Q8e\nPMDY2Bja29uRTCY1vTSZTDCbzTJHkJS7s7ODZrMpwjCpwWQIBYNBeL1efPrppyiVSrJIk1LNiQZX\nk8xto+bthz/8Ibq6urC5uYmxsTFxpvx+vw5x0qzZBITDYa3wyLShLubs7EzwwkgkgqGhIblRQ6EQ\nnjx5gmazqUKKThyPx6PCl0WTwfAfuY0c45PuzYOednOuQKgdoROLnTAnsplMBuVyGeFwGM+ePUM4\nHEYsFkM8Htf3xQiV/f19rQNJfiZ2g/9uxgHxu5mYmFAXzOKREgKHw4Guri6sr69Lc8YLjoJ3TtAo\njuYalpcPmTynp6e4ceMGQqEQEomEZA/MF+Q6zWw2a1LEz6G9vV3uXGIjuPqiLq+zs1POrnQ6rYYh\nn89jfX1d787JyQnGxsbkLuYUnSJ8asbu378vJxx1g7xsZ2Zm8M477+Dw8BAbGxtvuMHsdrs4cplM\nRvgJrq8AYHd3V4Vqq9XS6o4N9eHhIfb29tDV1YVqtYrx8XFlOHJbwefOarViZmYGOzs7MghQK0NZ\nCLle1zcLX331FRqNBgYHB9+ItOJkmhrdzc1NrXA5CbNYLEin00oroMiZ/CkahQhYZbHJ9TT1RYSP\nUvzP58ZmsyEQCMDtdmN9fR2tVgtzc3Pw+/1oa2vD/fv3hQfhNJCFcKFQUJ4hZS1GoxFHR0cwm824\ndeuWsDvX72KiVYaHh+FwOLSd8Hg86OrqemMaSFkHZRBsKsrlMm7fvo1SqYTnz5/j3r17RKd8d4ql\nv/qrv/qE6zAeFNSqHB0d4auvvtIOknEgPGjS6TTW19elI+jv7xdtltMd7ncvLi5EiKVegoXI2dkZ\njo6OEAgEsL29LbddNBp9owMkb4crPeo2KHCkloHrKArutre30dPTg5GRETidTh3qjGnxer0avyaT\nSdnByc/hasnr9aorCYfDmlhQ3D40NIRyuYyTkxPpUSiAZudcq9WQz+dRLBYxOTkJt9uNVColvtLt\n27eFGKD+g537W2+9hWg0im+++QbDw8NvxAxw5725uQkAmhLys+bhQScLtVJkZPDyPT8/VwFbKBQw\nOzuLZDKpC6LZbCIajWJvb09ak/b2dszMzCAYDKp4TaVS6qiOjo4QDAYVI8MXjBBCp9OJZrOJ4eFh\nwfAIrGPxVKlUlDl0+/ZtCR0fPnwIn8+HwcFBCcYPDw+xubmpIpxNADUss7OzyOfz8Pl8EmTevn1b\nherS0hJSqRSmpqaQTqelz6IjjQcWP2e73S59GC97/l2Ixujq6sKdO3fw6tUrhU2mUikkEglxoDKZ\nDEqlErxer8wR/N1nZ2fF8eJlyKKDVPkHDx68ATHlYcysNXKu6MS5deuWHCxHR0dIpVLCX4yNjemw\n3N/flwOSQNRkMqn/LI0d1IokEgkEg0EJeVloANCkhIfx8fEx5ufn0Wg00Nvbi4cPH2JkZAQGgwGp\nVEouRbra+B0Wi0UsLCwoEJqXLV1s09PTGBoaQqlUQiQSkbibLjDqcq5PxKi/oAOWjCQ6e7iyY/gt\nmxe3263vmQJaWuq7urrkuuWqnSudzs5OaaBoR+dKg/gDBufyXCXPhvweYkg4dWpvb8fIyIgmDxTo\nj46OIp/PqyHgGc4JH6dEdDVTp1epVLT65jro7OwMOzs7WjuzObHZbJiamhI+pVqtikV2fHyMrq4u\nrfyIdaBelLZ0TuTJqCKapNVqCSTJNTALehahXO2yOOOUnoU2J6o7OztiJ1UqFQwMDKiB3d7eht/v\nx+joKDY2NjAzMwOv14t0Oq3ss3w+L7bc8PCwYMAM8wWg6VBHR4d4auSJcZ1KVzgZbpzwcArV19en\nNfXOzo4cjYyi4SS7WCwqIJrfX1dXFzY2NnD79m2t6FjQXFxcyGx0//59/Tto8FlbW9P/n42gzWbD\n5uamzDFGoxGJREI6UGIa2OhzvWmz2eB2uzE0NISdnR0sLy8jFovh1atXdMd/d4qlX/ziF5/cvHlT\nNkyOcQ0Gg9ZlJpMJ9+/f13708vIqGyyVSknpThcMbdV2u10anv39fbnjnjx5olF5LpfT+qdSqaCr\nqwtjY2Nv2K/Pzs4QDAbV1fX29mJ3dxcnJydYWlrC+fm54jj6+/u1RjAajRKNczJSLBY1CaLegm4r\num0oSH/rrbfkepqYmEC1WlV0hs/nQzKZxIcffoiLiws8fvwYRqMRHo8HX375JRqNBiYnJ8XhYaU9\nOjoKn8+nQ+K6g8rn8+mB5NjfbrcrduK6821nZwdra2vY2dnRiJ+ZVgRHGo1G+Hw+xRBsbm7iJz/5\nifQ3zEHipUt3SiaTkbsqFotheXlZCe7NZhO5XA5bW1tIJBLo7++Hx+NRoCoP5usd4cDAgAS6THvn\nv4crLopbY7EYTk9PpTFj0PHXX38tga7JZMLz58+RSqVkrTaZTNjY2HijW6Ve5zrXand3F4FAAF6v\nF2dnZ5oqWK1WDA8Pi15LFhCfC8bN2O125PN5XWDb29uo1Wq6UOn4o5OQ01kiMF68eCGd0nWw29DQ\nkCai5A7V63VNnEgtr1ar6O/vl56HXeDFxQWq1aqs4u+//z4ymQwFlJibm0O5XMYXX3wBAIpVqNVq\nsqq3tbVpAsuVF3Pu+Fkwb49Azba2Nrz99tt49eoVvF4vms0m+vr6MDg4iM3NTTgcDhXllUpFk4i2\ntjbxf9hcsICw2WyCvrJzpUuRFnWHw4GZmRmtKGgYIGOMax0W6hSUAleanXA4rGw9g8GAi4sL3Lx5\nU66tzs5OxWmQzsxoI0ZxkM5PQOX+/j6mp6dhs9mQTqelzQiFQpo6E+LH5oQTVk6Oy+WypgwAZK6g\nsJ8TOK5YqYHhu8mmiCGv/w97b/LcZn5fex+QADgAIEgQBIgZIAnOs0SpKalj2Z2246HSWWSRdfKH\nuFMVx4ldSVX+gmxSWabKWcQuO3biHuSWWhQlzgQJEMREDAQIcALBCXehPifU+27u4i5u32pWpeI4\nVlsEnuf3+w7nfM7r168RCoVEe7++vpbphAUw3ZvpdFpNDM/M6elpHB8fY2BgQMVRuVzWmo+FIyfy\nDNRlERoMBuFyubC1tQWr1SrECkGxfr9fjTGBnTy7fT4fTCaT9DEej0cwx3K5rLOLRiT++Xw+j4WF\nBWVrVqtV7O7uSqxOnAAAmZDurk0ZjcJ3dmBgQAUBoauZTAYXFxeYnZ3VlGh7e1sZnwTBcp3H4rZe\nr2tqmsvlxAY0Go1YW1uTIYB/D2r/ms0mBgYGxHoaHx/X3Uv3K6UOFG23tLToO0smk8LO0JnHzQiF\n3m/evNH6jGtWYnR6enpgsVhweXmJZDKpKCO6efmv6aSLRqMAIL0mmyKz2Yze3l5JGb5aI359iqWf\n//znH1PENTQ0hMnJSXz22WfSDIyPjyMcDmN2dhaTk5M6BGhxZHVOSy5ZGtQZtLa2YmxsDG63W7lR\nl5eXyOVyetHJ46jVatppUnMSCoWkAQKgtYnX69UBkEwm0d7ejvfee09WYq5Z+FLyguLBxuwejk5/\n9KMfIRAIiIFSKBSwsrIi+ydfAI4gTSYTPvvsM+lB+FIzaZ32ZqvVqpgWQhHpPiNGv1wuS7DscDi0\n62ceEpkUqVQKGxsbQjHYbDZZ49nBBINBTE1NoaenB7u7u3KQUIidTqfFTeEOm/t+cqVcLhe6u7vl\neqEWamJiQnBQ6rAoauVY++joSBlhra2tCrNtNBrIZrN48OCBJnLBYBB2u13C/+XlZWl1uHIhIZ1d\nNCNUmBPV19cnB6LRaJSOh8DDhYUFAG8Pn4cPH8rZwYuFLKi7rKd0Oi3OCwDRtKvVKkZGRvDy5Ut9\nJ/39/ejq6sL29jYikQh6e3uFd+DnQK0CHWPVahXz8/PY2dnRKsdut8u6zkvryy+/VMp7LpdTHtTC\nwgL6+/tRrVZlmbZYLBgbG8PAwICAk9VqVdZdrp7o5Dw5OcHu7q4me+Qzzc3NYWNjA1NTU8hkMri8\nvITT6VSB7HA4NCVgCPDx8TF2dnYQj8dhtVqRTCaFluCzcXt7C6fT+U4wKenvbW1tSKVSWosycJVO\nKRLnKVKmK85mswlbQHs531dOFOlWPTw81MSPXbPNZsPJyQlMJhOAt6aNSCSiUFS32y3idKlU0vSU\nz5PFYsH+/j4ajQYWFhZUlJMFxImRwWAQMJI5bsyyJFaDK2RemFx5ud1upNNpmEwmwTtZzLOwiEaj\nKqQNBoMcR4yfonaTYcg9PT1yvbEB9fv9KgCtVivGxsbwu9/9TpomIlUcDof+NXVgtI4zEJYmDqPR\nqDVxX1+fprR0yZJFxuaRzi/qwMif83g8yOfz70yCLRaL9IV0Y11eXmJhYQHb29sqgtl08u+ey+Vg\nMpnw+vVrBINBvW8AdIbyn9/W9jbg982bN9JznZ6eore3FyMjI3qGSR9nSHt3dze8Xi/q9bp4TTc3\nNzpvj4+PpR3l/06n0+jt7VVhwhggg8Hwjmno9vYW8Xhcwd9utxuZTAbAW6Avp7EMvD89PcX9+/eR\nzWbxJ3/yJ0p2YMHJz5rPdK1Ww4MHD+D3+4WnIFKHjl1OX+keHR0dxdXVlQrRlZUVdHd3w+PxqDg6\nPz+Hx+PB6uoqTCYTstksPB7P/3aQbsv/mXLnm59vfr75+ebnm59vfr75+ebn/82f/ysmSz/+8Y8/\n9nq9coq8evUKkUhEe/pSqYRisYjPP/8cq6urqNVqGB4elvuHjqBqtardK8Fn1Opw3N/T04ONjQ0B\n4liNMkmaBG+OVOv1urQH3d3dsjdzesA8HL/fj8vLSzF6KK5bXFzE0dERDg8PBZJrb29Xt0J2CZ1y\nFHlzB26327VGNJvN2NjYgMfjEVdoenoaV1dX6opJr7ZYLNjd3VWX3NbWhsXFReLdBbAjh4piYI7n\nKUo9OTnBs2fP0N3dje3tbfFFQqGQHD6Tk5OaInHHzM6UvCF+lndjGSj8JtyT7pLDw0OUy2Wsrq5q\ndMwO+OTkRCLWRqOB3d1duFwuCd+Hh4clNqat22QyKSSWayWuTzwej+jp5XJZIk6uYyqVisB+LS0t\nGgUPDg4K28+MP65UyuWyJnTU9nCtxXE2P/urqyssLy/D4/HA4/HIkn17e4vFxUW8efMGLpcLyWQS\nH330kSaHFAOPjIyIm8OOEXjrniKKwe/3S+jPNUMoFEJvby/S6bT4JVz/ckpar9fx0Ucf4ebmBrFY\nTCGp1P/w2Uqn00in05iZmcHExATi8TguLi7gdrsVqJlKpWCxWBRoCUCRB/xOP/jgA7Fm+N0AbydW\nw8PDckpy8shJ1eXlJTo6OvD+++/LqcaVMG3IFJRygskYHgZ3JhIJDAwMCNRJlAcArVGJY7i4uADw\nVldJ8jrjaQgkvby8xMjIiLSQ1EdRKH1xcYHp6WmlBFAnMzAwoIkvoYXUN0ajUU3G1tbWZBwwGAyw\nWCxyVSaTSU3eg8GgrNUA0Nvbq1U+Se59fX2IRqPw+XwS1jKfkNwzTi6IXqAombolmkf4WXPaT1cZ\nMRotLS3I5/OCrhqNRoyNjUkDylW61+tFqVTSOZ3P5yVWpgvY6/XiD3/4gxywtIxTPzM+Po56va7M\nS/45amju0r/Pzs7Q29srY1Amk5ExgBN6pg10dnYiEolIP0XUCfVe/f39Cpcm942ibJL7OSViXiQN\nTACkyyQINBAIoLe3V0J8urddLpcm9Pv7+++swjjRYbqF1+vVc8UJIjc09XodmUwGU1NT77icJyYm\ndHc5nU6ZgUZGRpRFynWv2+2W2WVsbEybAeqT3G43tra2cHJyokxFru45XSdbzO124+zsDDs7O2LI\nkV5Pl+P+/j5evHihjRH1pul0Wu8E3Z9tbW3w+/3Y2NhQLEp3d7cCxTOZzNdnDff3f//3H3d2dmJ/\nf1+iZY5Z+YIZjUY8efJEHwgDCV0uF8xms6B6R0dHellvb2/R29sLv98vABj3xkajEYVCQewMviy8\n0PL5PHK5nNAExWJRSIHb21u5F8hS4ciyWCwiGAzqYUylUmhvbxf0kgylmZkZPfynp6eiNBcKBbjd\nbrS1tcnCTjEnRaJ0K5TLZWxvb6tgKRQKcDqduLy8BADxk1jY8eU9OjrSw31xcSHEwfn5ucbaDC30\neDyIRqNC8A8PD6Ovr0/ja+ppWltb9VlwXF+pVBTr0NraqtEpYYW0htKtBUB02fBXaelOp1OiYmZ5\nmc1mEX5PTk4kBGaYJZkhLpdL2qVGo4HDw0PxiFKpFKxWq4I5CYrkmo1rlaGhIa1KmNVEDQtXcHTx\nkIfj9/sVkEpOCyN8Tk5OVDy2tLSIlsy/O19kWtMpYuzr61OcBZ1Yd8Gg9+/fl0GBVPS7zwMPJlrN\nfT4f1tbW4PF4FHJLpwwjdwjp4+dzdHQEn8+HSqXyjh2ZSevkuxD9wQOOQanM8WNmE119dP0kEgmM\njIyIl8L3m+4eXkTMdxwZGUGlUsHFxYXW66RJM/D4LnKBAM+zszMVcgQXdnZ2ClDJdQODdRlnwyaG\nTVZ7ezvOz89F9ycagms9ggypCTIajQo2npychNfrxbNnzxSqTSs53WHDw8NCIzDb7uXLl2pK+Dsl\nk0kRvPmuRaNR6Xt4+QaDQTgcDmWyMTjV6XTqv4NyCAA6Q2kx//DDD6XPYTHJC53rYK5dea4xKzMa\njaLRaCCRSKipffjwoQwrhUIBOzs7aprGxsZkLnj27JnOBjZh1Hp2dHQIiMsEiJubG7kIyeKLRCI4\nODjAyMiI1jJbW1taU3ENR9s+kx0uLy+VWdfd3a3PjOBZruZYCFssFjlfWRzRZPTixQux1pLJpDSC\njEnp7u6G1WrF559/DuB/sA6ElXZ0dOiMZk7o7e2tVvTUKnH1R9guf5dYLAafz4dSqfTOYIFaO5oW\niERhgU0Xr9PpBAAFQDPeqbu7G5FIBM1mExMTE6jX60gkEqhUKkqAoMYsEAjA5/Nha2sLExMT2Nzc\nxL1797SWJmWb7lMAMhW0tbWhXC4rTqfZbEqjyUZ2Y2ND61Dy8ajJOjo6wsDAgKQVjx8/Rl9fH16+\nfPn1KpZGR0cxPj6OWCymL5+29cHBQbhcLnz55ZdYWVlRhT86Oop4PI54PP5O+jMt8WTgEDLHKQ6L\nDcYvtLS0CBzGS2Z6eloo/56eHmHSKeAjWO7o6Ej2Wj6YxOofHR0pCJCANvIgms0mms2m4HaMPCAd\nlQ8ID3uq+yn8o5PC5XJJDMnOlJEnBHYRJUDHEvfCALC6uqoigJqqcrkMq9WK+fl59PT0SMjpdDpR\nLBZRKBRUQNTrdYRCIf2dvF6vJm/syHiIU6dAuiwBfsya4uFIbsf4+Lguak4r+PehgJmaK9JmaQNm\n2COnQ9SrkdpNKyv5Liw8BgcHsba2BrfbrQ61p6cHHR0dmJmZUUYSDQQMMe7v78f+/j6azSaCwaAO\naQpJDw8PpQVg6CcJwalUSqHGnKzNz89rlz8zM4Pd3V0MDAxoUkoRMvVULPpisZggfz6f7x3SObtP\nOpd4UYXDYTSbTRkPWJzRaUSMQz6fRzabRW9vL+x2u/LyKGImCoDTLf5Zr9eLVCqlyR+LJU5sxsfH\nkcvlRFF2Op2KQfD5fOjt7ZVDsaurC93d3Qob9Xq9CjDe3t4WriOZTAroGg6HNXXj5ITvBS8Ggvso\nYibEj45VuozIG+MlxyknnXC8MMiCYtFI0v319bV0Ib/4xS/0HJOq3N7ejv39fRkJ9vf3NYWh1sVo\nNKKvr0+/K7tk6l0ikQgWFhYUHRGPxzE0NKQzhxMzht9eX19jYGAAZ2dnGBsbQ3t7O/7rv/4LFosF\nwWBQxRYAIVHuupp2d3eVhEBxOV2D29vbuLy8FKV+dXUV0WgUHo8H6XRaQF9GVgFv46EGBwexu7uL\n3/72t7Kj091GHEE+n9efYXxMIpHQu0lNSmdnJzKZDEwmE6ampnTeXV5eygHJaTnPJf4Zp9OJlZUV\nRKNRPHr0SJpTktc5Ieb3Qg0Qm2qXy4VPP/1UxT/vLeq0dnZ25PzjBJFaKAJx+Zns7+/rrKKrslar\nibNGlhgdjDabTcHJh4eHCAQCSCaTGBwcRG9vrwpu6vFoWhoaGhLAkdNCnlMsNGlkoWu5VCrBbrdr\nQ1EsFnFzc6PncWJiQkgGup6r1aoYeExK4MQyn8/r/UulUvo+6XozGAw645lwQL0e9Z80kNTrdaTT\naXg8HrEa+fuPjo7iN7/5zdenWPrbv/3bjynGSiaTCg4k0I20WzpiKOZiQcC1GR9OirdpoXQ6nVhc\nXBScktCxm5sbTE1NwWw2Y21tTR0WmUONRgOTk5NyRvFgJDSRacrlclmxJmTKsCAgyXRra0tf5Onp\nqdxQdrsdVqsV9+/flzCcgYf8QpkDx8LN6XTC4XCgvb1dgLvj42NEIhGMjo4KjglAwZYUBnKqwz/P\nVPlYLKYi4K5LaXBwEOFwWONqPmgc9fPvxnXK+Pg4xsbGsLa2pvBaohK2t7eFZWBxWqlU1L3H43HR\n1aPRqCB07DLozDEY3gbNUiz5/e9/H6FQSHlQU1NT6k5sNpumTBcXFxgaGsL19TU2NjZgNBoF/by8\nvNSlabfbkc/ncX19LZgZP+tUKqUoC7orGMjJaQmfg5OTEzk+CGQkYJXBpysrK/j+97+P0dFRlMtl\nTE5OSlCbz+cxPj6uQNLh4WE9E7TQ86BNpVIqJu+CLHlgvXnzRnRmiqEZZptKpVCv12G1WnF+fi7h\nKw9ITmZqtZrWPVwnc1VEQSgDlp88eSLh6d7enphhfB6IhGCRPDIyglqtpskQ7dn5fF7RPczB4nSP\nuXjs1BmV8urVK7hcLgwPD+O9995DW1ubQpq5WrFYLJpck+NCN+zdlTSJ1Ol0WvE82WxWbkS+v2/e\nvIHT6RTCoLOzEz6fTzl3RFRw4pJKpTA4OKgpMH9Hu90uYwP/f/z9+N0Sakj3KLk9bMaurq6ws7Oj\nbDaXy4WNjQ1dVABkjqGDMhKJoFAoCKBIJy/dvTc3N1heXlaTSAPC0NCQ1i9HR0dqitgodnR04ODg\nQJR34i0o0OffNZPJyGpvMBjw4sULTXEGBwclAGa0EV2Mc3NzgrceHR3hyZMnKBaLWFtbw9jYmEwY\nTHLo7OyUWJqYGU7SJiYm5NKq1WqaiBoMb4OM19fXlT03NjYmFAaAd9x+NOIcHh7CbDbD6XRqFUY0\nCqGx5Be1t7crr5DTo0ajgZ6eHuzv7+M73/kODAbDO9milHKweOB3RkI6/25E3TDzkStj4jsoJeGA\ngiL5arWK6elpOYKPj4+xsbGB+/fv4+bmRs/GXUdgOp1GR0cHJicnEQqFtKVhlionzMxstNvtODg4\ngN/vl7mExSCbUaPRCI/HA6fTqYKI0y5OhDnhJsy1o6MDTqcThUJBAwj+8yniJyT5awWl/Id/+IeP\nOeokAZkOkIODA7x69Qrr6+vqCtPpNAYGBtDW1oa5uTlZJLk+oruNe/9QKCQ3D7U/LAq2t7dRKBT0\n8N2l2WazWfh8Pvj9fmUQkQC6vb2tXWsoFBLEjcUIx8N//Md/rL1psVhUACIfbsaL0Gp+dHSkKdHB\nwQFKpZK6Uuo4+DD4/X6tjKid4OTm6OgIhUJBFn1e4Jx2cOJG63ckEkFPTw/Ozs5gsVgwPz8Pu92O\n3/3udwKczc/Py1Hm9XoFhKSWg1qju2wbl8ulyZzdbpfO4/LyUqG7w8PD6O/vR71eF32YfCZScDn6\nLhaLyGazmJ+fF1cmmUxK68DijRfDzc2NUqlp1fb5fOrgGetBtkyhUIDBYBDX5cWLF+jo6FCgcbVa\nRTweRyAQwIMHD7C2tqaxOw/LlZUV9Pf3awJJtxtp3R0dHdITmM1mjI6Owmq1GyFKvQAAIABJREFU\nYnV1VWA7n8+nOBMGiabTaezv78NsNuPBgwe6LMfHx6WdMhgMgvXxc9/e3tYBGIlE5LwiyI22ZDqS\nCAtklE4qldLBNDg4iFwuh9nZWdGbrVarbMuc0HC9yhVgS0uL4oOazSZevXolBlBvb68sxbwEeany\nnQoGgxgaGsLS0hJmZ2dxcHCAvb09VCoVvTNkyczMzGgVxEw85rhVq1XZ381mM2q1moJS2aHT7UQn\nGEnIhG0yYoK/PyOMWLQwG+6LL75QziGbDJPJhO9973u4vr7GixcvBBMk8JRaya/S0GXTp+6ObjNS\nsG9vb+X2pPON8RE84/r7+wX9Ozg40PSSz/Te3p7SA8h44zPLvzNX6yaTCW/evNFUiUU0JyyM/Zib\nm0MsFkNPTw/u37+vrEdq2djcUrvIc+Ti4gKpVEq6w/HxcU3eQqGQJsmcmmSzWWSzWdnpNzc34XQ6\n4fP55O59/vy5ssvImCPb7fr6GtFoVM7SZ8+eoVKpiGH1/vvvK5eTYejUDPH55fTTbrdjdXVVqfcG\ng0G5iCx6yOJjzFWtVkM6nUatVsPQ0BCsVqtWlhaLBfl8HgCQTCYxMDCgAoQNJ13TRAYQmEudUGdn\npzSPLS0tcLvdmoAz/42DBepYOc3i77q9va2sxrOzMzQaDQ0vSqUS/vM//xO1Wg1erxfT09PY2toS\nnsbn8+Gzzz4Tb4pn0fj4OHw+n5r0TCYjndbg4KAYdGwUqtWqEAI818joY1g8G3C32414PC5Hd6PR\nUGQPC106KL/aZH19iqWf/exnH//lX/4lenp6ZH2mLoBJ9lx/VCoVVb88oFgQTE1NacdPOzFzvu7a\n1Xm5OhwOfPDBB4LXkcRLtgQtky9fvtT6j5ER8/Pz7+zCK5WKXgyK+EilNZvNCiukjuNusO3q6qos\n1NSm9PX14Uc/+pGiOVpbW/VCULy2traG29tbXF1d6b97b28PW1tbyOVymJmZkTWafBV2EJubmxpr\nMwCT0EXuljc3N/Wix+NxLC4u6kVkR8SpyuLioiCWlUpF+V3BYBAmk0mFYK1WE6CNhxf1Ei9fvkQq\nlRIHxOPxSPtFcS8vZu7Py+UyxsbGZIkn4wd4K8Dd3NyE2WxGMBiEzWaT3sNsNmN+fh6//OUvlWXF\nC+DJkydIJBKCgFITQ6ssL/KNjQ0FIcdiMYRCIWEqOAXjd14ul/Hw4UOYzWaYTCY8evQI4XAYDocD\ne3t7WFlZUS4U12zMvWPxYDabMT09LZ0F87Xsdrs0Kfw70+rc2tqKcDisVWG1WkUkEnlHFE40QywW\nE4iQrDFyylgAUUNIDhonZWTccHLCwpUBpvfv38fW1hZGR0eVRs5LnAR3FvzJZFIHGgnOa2tr74Sc\nMq+K2i+Xy6WpscPhkH5wZmYG6XT6HZAn2WJcpdjtdp0nvBhbW1sRCoUUmFwsFqUp4wTJ4/GIf0Tx\nPDVvXLWVSiVRiUdGRiRGBd5OvMvlsqIgHA4HFhcXZRenLs1ut8Pn8+lzpnaQ5w5zttgxA//DDGL0\nkMvlki6H1nQWFiwM2tratDIbGhoSsuHLL79UYwJAzzHp7dSY2e12hRCvr6/j6dOnWFpa0hnJ5oWY\nFgBi1NXrdezs7Ci8+ODgAGazGQAk/mYuGhEhzWYTo6Oj+vdYTDidTiwvL0skzGeWeaI0CZydnaFQ\nKGiNzCaL3z1zCBmDZTab9R5ZrVZNeMhYOj4+xr1799BsNlUY8vep1+uSSVBcf3FxITwAJ4o0i/C8\nJ+y3ra0N/f39KhzZdBuNRnEGOREk5ZuE9vHxcck2yHkjBZ4N3eHhIU5PT6V/pbatvb1dmwKn04lw\nOIxisSgSd6PRQLlc1hnKCS6zBV++fIlGo4HBwUGBP0nXL5fLug/6+/vhcrkwMzOjd4MaMmJzBgYG\ntGKj8YH0+EQigUKhAJ/Ph3g8Lqjy+fm5zu5IJILW1lasrq7i9PRUuJidnZ2vT7H013/91x8HAgE0\nGg3s7e0pGHdzcxOlUkn5X9wpFwoFBAIBxGIxvVTMqyJA0efzwWazKYyWwtjNzU0p+dmV8cUKBAIK\n4uRhZTab4fV60dLSonUShZCjo6PqSMmNCYfDePToEVZXV1Eul7G2tgYAokxzrPjkyRMRRIG3LhUG\n4p6fn2sF0NXVhZWVFXWX7BgpIKSeq9lsIhKJIBKJIJvNyiHy5s0b2O12hEIhuQ3ZeXk8HiwsLODy\n8hJffvklOjs7MT4+jsXFRVgsFsRiMVitVrltSP3e2trSNI6Ols8//xw3NzcqFAky49+XTiMe/tz7\ns9Pj1OPBgwc4PDwUTI0XKF1mnHbwJaDI9OjoSCL7QqGgLsjhcMBqtWJ5eVmTDwqqKV4mnRt4S2yn\nSNfn8wlUeXR0hJ2dHSSTSfT39+PP//zPxZgiy4oAOsbemEwmaQG49iRB/uDgAIODgxgaGlK3TKHv\n3eBHdvVnZ2fo7+9HMBhEuVxGPB5XV0qHy/b2Nmq1Gg4ODjSVo4g6k8mo4EqlUtKARaNRrShrtRqe\nPn2q8GHqOcxms/REqVQKDx48wL1793B5eakigispdq+dnZ3Y2dkRUPbFixfSaTDbjER2vm9cA7NI\nc7vdCIVCcmlRc8YpcGtrqwoXro45hWSuITWELIZOTk6QTqcl4B8YGFCQL8GBnLIA0EXocDjQ1dUl\nxlG1WsXw8LCCbFOpFJLJpATSnZ2dKq5IXvZ6vTrzuBoKf0VE5vtKhx5lAE6nE6lUSlTuu5lsFosF\noVAIf/jDH9DX16fPYWVlBWNjY5oyUZrADDOPx6M8utbWVpk+Li4ulM/JojuXy0lU39/fD7vdrtWP\n3W7H+vq6WGXhcFhOUJPJhBcvXsDr9aJQKKjZZRFxVxRNSKrX68Xe3h6urq7g8/nEm7Lb7djY2EAk\nEkFHR4cmm3zHqUlZXFyE1WrFzs6OuG4DAwPSg1HIHA6H8cknn4iPZLPZpDNik8IVMldczWZTEULh\nr4KGmev29OlTnSM0GHV1dcFoNGJnZ0frVE710+m08svI1bJarSrS6dQ7Pz9XhuHw8DCazaamP5w4\nMt6IMEafzyeWEiHHhO86HA4Ui0XpyU5OTuB0OnF2dobOzk6J9L1eryDNu7u7klkwfJkNAVdZjDKi\ncYVTq+vra5yfn+Pp06cIBAL4j//4D4GYs9mszFRcdTJShmu9Tz75BLFYTI3x6OgopqenUSqVNDRh\n0RSJRPCd73wHt7e3SKVSKkDZtFCOQRgts0w/+OAD/PKXv/z6FEs/+clPPr6+vpbIi9qClpYWRKNR\nBAIB9Pf34/b2FuVyGQMDAxLMcTzNYM1CoaBwTD7Q1Dfd3NygVCrJXWO325FMJrXvNxgMePjwIfx+\nv4onroa8Xq9El4lEAnt7e9jc3MTx8bHs9NRa/eu//iumpqZU6DEL524Xzt/19PRUllEKAAmrY7fF\nogYAVlZWcHZ2JrfZ48eP4ff7cXp6ip2dHdRqNXg8Hk3G+vr6JNilSNVmsynCg5qQ/v5+OTLoumCH\nTPorwXBcrTDBfnR0FN/61rfU7bW3t6voY2p1s9lEIBBQUG+hUJBl1mKxYHNzU2nmfHEoJKcglxoP\nHnhcO1LLxs+3p6cHVqtVVnibzYZQKIT//u//xsjIiNYixEVQaMixN51yiURCTpZXr17B6XTi4cOH\n+PTTT+FwOHSp8rCiRZ8aJ47u6UjL5/O4d+8e8vk8/H4/YrEY/H4/bm5u5CgjbZpi+u3tbSEYzs7O\nVFzw+WhpacH4+Lgo5MQz0MlEUfP4+Dh+8IMfCKDY1tYm1yWF4gAkQr6bqTYwMKCgTupS6LixWq3I\nZDJoNptyUJLsTRhfs9nE4OAgqtUqMpmMNAs0IHA1SHH54eGhBN8skghItNls6Orq0qqAWivq0LiK\ncDqdWj05HA6FwzIklBdTb2+vYjiYPG+z2ZRdxslsIBBAe3s70um0phOcPPMsuhv6SWI4bdiPHj1S\nx+3xeKS7WFtbw+LiIubn53FxcYGJiQmBTekS8/l8qNVq8Pv90k+GQiF0d3cjnU6rkaNhgjDMFy9e\nYGpqCl1dXZqC8B2nq5afFSfYya/oyFxzUN9Zr9fR2dkpEC21M3fp3WwaY7GYGhXCUVmgscvnFItS\nAH5mfIbcbrf0VHR9cdVPrExHR4cm3GwE6XRkrh8bqv39fa1kKEi32+24d++e/g68W5gFyIl7S0uL\nCOwsMo6OjpDJZCRU9/v9Cmzm88EcSW4rLi8vMTExAavViqurKwwODuLhw4d4/vy5sDI0AORyOUQi\nEZyfnyvzjnofNtKRSEQg3N3dXVSrVSSTSWQyGaEPDg4OEAgEsLOzo/enVCppgsRzIJ/PC9PA3FGu\nGEnU93g8SKVS2N7eFh6GDej5+bk0SXSX2mw22O12mEwmbG1tCTGwubmJ29tbPHz4UM0km01uWHK5\nHMbGxhS4Szhls9nUgIQrTUblHBwc4IsvvtBZ0dPTI9yGxWLRhJ8r7uHhYeTzebx69errUyz90z/9\n08fsKhgcS60OOUckLjOsr16vSwvU19enDmB+fh7NZhOfffaZSLEjIyOYnJzEwMCAmEyjo6NYWVnB\ngwcPFIjb0dGBjY0NcTP4MBBDT17HXcEaCyHueqenpzE9PS0CMTt7dgLMe+J0hSPd9vZ2hMNhnJ2d\nYXl5WRldHE/Sodbf3//OwfSrX/1KUxHubOlM4cqjWq1idXVVlNiJiQkVMEQpZLNZJUtfX19jd3cX\nfX19sNvtOsjIIQGAm5sbZQFdXFzg2bNn6kBNJpMuyevra3Fl+DIS+0CXErtFfia89MfGxqQrczgc\nEpgmk0lRiVks8WIl3ZwWb3I76vU6ZmZm8OWXX0pvcNeuHQqFYLPZhOp/9eoV3n//fQnAaUc/ODiQ\n0+/s7EyuQbvdLpcQJ12MyyF1nQcynTJWqxWxWExBwNfX18hms2IeNRoNRCIROdsqlYoiK+jYo+CV\nolDiICiA5Difo+/Dw0N88cUX6O3tVYgsp6Pd3d0qYEulkvKbrq+vEYvFlIMVCoXeKfipQ6Izi+sN\nBgJXq1X9fsxGjMfjoqIHg0Fpizi54ftMfU5HRwfcbjdsNhtevnypdPJwOCxtBaM/OFFxOp2IRqNy\nQFJjx8aFq6vDw0O0t7dr2sQGpV6vS3/IM4ej/0KhoKgGrm9ou+a6qbu7G+vr67Lfd3d3o729HUdH\nR9jd3cXl5aWwJWzyqMFhAUZZQvKrCBJqe7hSpo6I6fMjIyO4vLx8x43JIoQYDQrNqUVsa2tDMBgU\n8oAif05TotEo8vm8dDBkV1mtVuzt7WFmZkb8rnq9jmAwiGw2+84UhitDmgX8fj8ePHiAgYEB9PT0\noFaroaOjA/l8HuFwWH9HmnuoQZucnNT3dXp6KsI6ZQ+Dg4PSklLOcHp6inq9rt+DeXRcVX3++edI\npVKi53MizgBaOrNoRwegdTWzP/ncswgkTsLhcGh9xrPLYDCoqY3FYiq8mcnWaDQUxs3Im6urK625\nqBkqFApqnNkUu91uraLpDF9fX5eEg8+vzWZ7Z5We/IqtxkaZU6FGo4H5+XndVW63WwU318V0dafT\naRHSg8GgpkQ0tnBKzjOH2ZEcdiSTSSwuLurMoZMtEomoyOK/T1e0xWLRtJxN5dHREbq6uuRyJG3/\n9vZWmYXhcFjYhK2tra9PsfTTn/704/HxcU2BGFjLS59iLboUyDuhWJg6HxY7fGn55W9vbyObzSKZ\nTOq/k+NMl8ulf02HBkfgnD7MzMwI9OV0OrUe4PSLIrTLy0usrKyIPcKuh2Lj0dFRJBIJHbrUL9jt\ndoWVbmxswOfzyYVFPgQ7NBYXHGHPz88LSkl+EmNZGN1Cm/jp6SmSyaRWQUTeU1/icDjg8/mUxbS0\ntKSL5m5cAp1fjDCg6JDdKLt2AEqw9/l8ClMlo+bk5ESZfezgCUXz+/3SrfEw5N+T+3ratSnUZMfG\nsS7DjKPRKJaWlrReKxaLAIBYLAaXy4Xr62vhKJiRl8lksL+/rxE6eUTkdf3hD39Q3ATXAcPDw3C5\nXIphIcyNsRPUqpCtREPD9fU1isUiwuEwkl9lKbFzJicnHo+/c5D29/fD7/cjHo9rLUJXD1c1NBDQ\nDdbS0oJisQi3263R+dnZGY6Pj7G7u4tIJIKtrS28efNGtn9259VqFRsbG3rX8vm8tBijo6NKJ6e4\nlbiAarWKaDSKFy9ewO12Y2NjQ4wUADr0DQaDmGnn5+dyYrEzJfuGEDw2FiyUqJ8YHx9XQe92u1Gp\nVLC2tqag4OHhYQDAyMiImo54PK5ok9PTU4XVMkrHarWiVCpp3en1emG1WmXvLpfLODw81HTD6/Vq\nEnd5eSlJADUUvByKxaK0KiMjI8o1bGtrg8vlEmSWAmxeLlyTkiMTCoXU9T958gRbW1syAkxPT+Pl\ny5f47ne/q+nd5uamIoH6+vpUcA0NDSEWi6FYLGp6TxG1z+fD6ekpcrkc9vb2NNUpFotCBlQqFYUy\nHxwc6BlnvhqbApobqBPl5P8Pf/iDppY8N9vb2yXOp6P4+PhY0FlOGZeXl+WsIuCQxU46ndbqnWso\nrooZjE3dGFlZY2NjsNvt2N/fR3t7u4o5u92OkZERHB0dIRAIKDqHxgSu46gRo1ic2kH+3yzsiKcp\nlUpa/TLWg5okxnTQ1l8oFGC1WpHP52WEyufzOmsoIYnH44Jsmkwm3XGURlDu0dbWJs5SV1cXfD6f\n/v7b29u4vb0VrJdh9ScnJwqb5nfExigSiajIvbm5kVONxSBNA7VaDbFYTO5T3gHLy8ty/VKfC0Br\ndG6JGA9FUxLvF4PBgEKhIBTB2dmZ7p1isSgEAh2br1+//voUSz/5yU8+ppOtq6tLzgROC/gC8Qtk\np8IHm19EIBDAkydP9KHyAQkEAnj//fcRjUY1muRUhCO+WCymA7i7u1uXMqGKALS+YIgv9/nsVu8S\nboG349ve3l7EYjG0tLSoW7PZbNja2hLJNRQKYXh4GEtLSxgZGZHmB/ifrBx2HtTc2Gw2hSHSdcGp\nBEMM2YWws+3t7RU8kjt1HpqcMoVCIQkBb25uYLFYRC0nPoFjdHa/e3t7WF9fF+WXgtnkVyHCdDeS\nZMvUbep1jEYjent7deEFAgEkEgmNgDl5a29v16iaziEWlC0tLQp5JGWXFPBMJqODmUXc0NAQnE4n\nJicn8fLlS7kqedDwIunt7ZXzg87BcDis6Wc4HNZlc3FxgZOTE/zLv/yLXtq7IbrUKtAKS/synYEU\nJlIDNTc3B6fTKT4YQzSpcUin0zAajbKic1LHNSAAgfOov6tWq5idnRVAcm9vT+yUZDIpJlC9XgcA\noQWmp6cxMzMj/c/s7CxcLpcI58wC47uysrKi7K6trS1porxeL2ZnZ+UGvb29xejoqGjTo6OjWF5e\nlhjW4XCgo6MDhUJBK1qPx4OWlhYMDw9r9cuCksUvJ0HJZBLd3d3ixthsNrx48eIdlx+hmMlkEqVS\nCc1mE8PDw3p2OYXkGos6uu9+97sS6oZCIRXqZAZxQtTS0oJEIiGHF+3RtGP7fD7U63VsbGyo8SAx\n2mazKSCUf46kaPKs6KA8Pz/H0tISDAaDikGTyaQUd2rX2Exx8kvYHxlsBoNBK31ePFNTUwgGg1hd\nXYXT6VToKbMcGVxKmUEoFEI2m8XBwYGaoMvLS3R1dSGXywlnEYvFUK1WZVV3uVwIBoPIZDICbm5v\nb6O7u1u6y2aziaGhIUSjUfzqV79CJpMRd4qF2dTUFABIj0NXLJPp3W63RMvLy8uoVCrSJhJWyxU2\nL/3r62vE43Fx5Mg0IvGdjLezszM4nU7s7OxI4kHJBY0oV1dXsNls2mrQHHNxcaEw9dbWVj2Pt7e3\nCIfD+izJAmTT+vjxY0kXWBzwDuD/9vl8KBQK2NvbE4aDQOW2tjbUajXE43GtDfnZdXd3i7tGpzOb\nuWazie7ubmlMOenZ3NxEKpVSIcyA+87OTty7d09A4cnJSemUj4+PMTs7q9+bU9RKpaKQ5kQiIdkD\n8QoAdLeen5+jra0NT58+RVdXl75bv9+P7e1tNJtNaee4AUin01+fYulv/uZvPna73XJIWSwWXVy3\nt7eYmpqC1+uV8p5fcDqdRjQaRXd3t0SvxWLxnQp0eHgYt7e32NnZEfmb/IyTkxNYrVZ4PB6F0fp8\nPvT396Ovrw9bW1vY3NzE+fm5iNfUYtAO3Wg0cH5+jvv372Nubg6pVAqvXr2Si+IHP/gB/H4/AODV\nq1doNptK+uaYlVwoOv8uLy+xtbUlwTV5EdQV8YGlY4NOM4vFgunpaaysrGB8fBzlchn7+/u4vb2F\nz+fDwMAArFYr2tratNJhIUPUAF+C2dlZOYoIPAOgQNt6vS54GQ97Wp5pQW40GopdcDqdolO/fPlS\nFzSnQEajEdlsFgBU+Lx+/Rq1Wk0rP3b7BoNBB9De3p4uRnY+dABVq1UsLi4K5tnb26vvmkUkhYbk\nHpEXRM3PysqKBIcU7dJ+zoDH/v5+lMtlpFIp5PN5zMzMoFgsivpLOv1dZABddIODg4hEIjg+Pkah\nUBBskQJGCqlfvXqFw8NDiWG7urrw7NkznJ+fY3Z2FplMBouLi7o4+XsA78Y1UIBJQTnfldvbW7lF\n6Iikq4d8J5PJJIhhZ2enXHEUF8/NzeH4+Bijo6PweDzY2tpCrVaTzmZyclJdoMlkUmI4i2SHw4Hn\nz5+js7MTZrNZBT7fGa5EQqGQCjtG5SQSCQwODurSZZhnS0uL1oBcZVCYzM95YmICV1dXWF1dlcOW\nuICDgwP09vais7MTFotF73UwGNR07vnz5wo+ZQHPi4Q6C4rSqb9Kp9OYmJjQvw8AkUgE5XIZGxsb\ncLlcCuQlvZ9TbU54uW58/vw5jo+PNTFhYxIIBHB2dgafzyctSS6X0woqm81icHAQr169Ev+H4Epa\n2Wmfz+fz2N3dBQBdvG63W8UgJxA0XtzV3vh8PhkOyCm6vLyUJoXBvhQw8+9F/ZfX60U2m0U0GsXR\n0ZGmtr/5zW9U3JOR5fF4tAra3NzE4OAgNjc3tebheq1WqyniyOv1wul0Ym9vD8PDw7qIOak+Pj4W\nP4zIjOPjY1QqFWxvb0tPy6aUYmy650hAr9frWFtb0z1zdHQkF+7ExISs7UwzOD8/l7jfZDLBYrFo\nUlMqlTSp5nvL9R65g5xgUZfGlT8naIy52djYkJ6Mf5b3A1fst7e3eP36Nc7OzhQdc3Jygng8LvzH\n8vIyBgcH8fr1a624Ozs70dXVpXeCPCq73f7OGnF7exvDw8PSx01PTyOVSimk22g0Ynp6WvfdX/zF\nX6jZoOmLnw2nbk6nU7T35eVlbSd4TlAKsL+///Upln7+859/TF6JzWYTPj+TySCbzUr3Q3Bco9GA\n1WqF3+/H0tKSLi4WMSsrK6jVarqo7roURkZGNA1iZXp8fCxtAxkjkUhEPIj+/n588cUX6OjoQK1W\nw29+8xu0tbXJUVav11EsFjE2NiZWSyAQQD6f10g1n8/j9vZWDyAdLVNTUxJv3u1+nE4nxsfHMTg4\nKHEcEff5fF7jxLW1NXR0dCASieDs7EzJ80QZVKtVCb6pq7m5ucHQ0JBecv5nOM2h4K5arSIQCKCv\nr08TrqmpKbS1tWF3dxe7u7uIx+PY2toSMHNychLb29sqkui2IfNiaGgIBoNBguKenh517BSmMtPO\nYDDIYTQ1NQWj0YitrS3t2Rl1QqH/5eUldnd3sbi4iGg0qsua/790Oq3Dh9EJdOrcXQ/QbcMMvouL\nCwSDQYRCIa3n3G43enp6YDQasba2pmkgXUd9fX2CPYbDYRVE1BHd3NxgZGQEFxcXuHfvHux2Oz75\n5BPMzc1J5F0oFJDNZrG/v6+4F05B+vr6xEZKp9MYHBxUxtvS0pKmpzabTeJqxn/QdcVJqsVikRaA\nK7bFxUV4PB48fPhQMQ9v3ryRKLpUKsHtdiMcDmtV1NXVpWL65ORExTy1HIVC4R0wJaMn+Lvs7Ozg\n4uIC5XIZ8/Pz74hsb29v5erZ2NiA3W7H9fW19Gsul0tr51wuB6vVikqlIrvy5eWl4nISiQTm5+e1\nriM7iuvq+fl5dcFer1dOIyIjfD4fDAaDeGEEnZLoTaaYxWJRfhvzqTidNRgMKmIKhYKMIlarFVNT\nUxgYGAAAxONxTExMCNBYr9c1OWD8ESnbFP2XSiU8evRI6yq6vgBoNUsNDh16nJrzu3Y4HHJ0dnd3\nq3kli450ZU5Yu7u7MTQ0hNXVVbmuOA1mQ8noHyJFAMBgMKjIY3NF/VhnZ6dcknQuM2XAaDQiHA4L\nH1Gv16UbY3P08OFD5PN57O3tob29Xc5HJgPY7XZN6W02m2z0vHSpFWUDQwd2S0sLBgcH4fP5pOtk\nPBXBohcXFxLxZ7NZfPjhh5p28R7Y2dkRsykcDmN/f1/gXCIJzGazdHmUJVCeQlc3iz8aQLq6usQp\nmpycVFFfr9cxNzcn+QK/11KphOHhYVgsFom3eR4TVcFsR5oWaMhyOp3Y3d3FyckJbDYbjo+PlaRx\ndnamZ7RWq6G/v19TuaurK53dDocD3/72t7GxsSHSOt93goWJBSqVSqjX6/jyyy+RSCTQ2dmJaDSK\nnp4erYdvbm7Q19eHYrGIqakpOJ1OTE1NqUHnOtrtdtP1/fUpln76059+fH5+/s4LGovF0NfXh0gk\nIj1ONpuVjXF4eBjT09NYXFxEs9lEoVCQCJpI+FqthrW1NSwvL+sQq1QqwtSTzUK3RjweR0tLC6am\nptSFUsR7cXGB9fV1HZaNRgOjo6MYGhrCb3/7W2SzWQncyuUy3G43vF4varUalpaWkEgkNDr2er3a\nwxPMRhorR/YcXya/im3g3pxRDNQCnJ+f4+DgQFMjclo4vqUDgcUa2S10ZTCQmN0cgWLkCVWrVekk\nCJ189eoVbm5uZG2nvoDcKeIL+vv7YbPZtB6jYJRTKovFgv39fRVr9+9fq6/UAAAgAElEQVTfV2dA\nSJnT6YTBYMDS0pKErexoQ6EQlpaWpKH69re/rReNmjHCJ/lnqR+7d++ewoD9fr8mYWazWW4oOrI8\nHo94IQcHB5oiMFePBR8PtM7OTtmje3p6cHx8jPfee09aIV6mtMS/fPlSrKnV1VVks1m94BaLRTgC\nXobRaFQaPTrjOM1jJ2s2m3H//n3FkdC0QOBnvV7XRfVHf/RHKrQbjQamp6f1n+V0ZG1tTaNwTtoo\n5EwkEipe6LA8Pz/H0NCQhNn7+/t4+PChxPapVApzc3N6/vv6+lCv1/H69Ws4HA5NZDKZjCbOzLcj\nuysWi8mZREMEi3viRUZGRtQJszniu8K4lpubG7FpHj9+jGq1ipcvX0okyilgR0eH4HrxeBwmk0nx\nL2TGDA8PSwtC1yVDnq+vr3WpsWAnuJEZeA6HQ93y1tYWHA4H0um0mkg6HeluCwQCCqPmc9PW1oad\nnR2taLkCOzs7e8cNx2SB/v5+Mb04LScQkO9SOp3G+Pg4AGh9RH3a2NiYRNcsBok9YSE3Ozur9S51\ndH6/X+wrBluzIJuZmVHTTJoz43IYqpzL5TA5OSlQr9lshs/nk1OXujueU5xMM2PRZDJhf39fqBVa\n1G9ubjAxMYFcLifhdj6fx9HRkc6dq6srjI6OylVHgw+J8GwYqC/c2tpSwDlZQSykGRVErMzGxoYc\nfoTlAtDnQ42SzWaDz+dDsViUZpH/WYPBoEkNGXt0ErI5o/6MsScGg0H5enT7ki1HGKrNZntnBczv\nk6gOag9p9mGYMplzdzEtgUBApqm+vj7BdycnJ+Hz+XB+fg673Y733ntPmkkGg7MxsdvtmJ+fl9mB\nq9jj42NlvVKby2KLkWGPHj2Cx+PB559//vUpln72s599/Fd/9Vdyb+XzeZTLZe2XM5mMLjs+LMTt\nc5dar9f1BedyOV0ktA0ycJMfMg/C73//+zg+Pta65d69e+KGxONx7O/v4+DgQBZg4G1FHo1Gsb6+\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cBoNB0RFMn2dqPPBWe/Lee+9hYWFBYuKenh51l0xc5p6WGUb1el28i/Pzc5TLZSwvL4sv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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "img = mpimg.imread('./data/raw_images/public/Class1_def/1.png')\n", + "\n", + "plt.figure(figsize = (10,10))\n", + "plt.imshow(img, cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Z9zzrzavxLUR" + }, + "source": [ + "As we can see in this figure, there exists a defective area in the top left corner. We will now load the model and carry out inference on the normalized test image." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "iKGu4mpztAv8" + }, + "outputs": [], + "source": [ + "# Image preprocessing\n", + "img = np.expand_dims(img, axis=2)\n", + "img = np.expand_dims(img, axis=0)\n", + "img = (img-0.5)/0.5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "XwsDthGwtAwB", + "outputId": "56172862-e212-4893-9509-981657a41c6d" + }, + "outputs": [], + "source": [ + "config = tf.ConfigProto()\n", + "config.gpu_options.allow_growth = True\n", + "config.allow_soft_placement = True\n", + "\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + " with tf.Session(config=config) as sess:\n", + " network = UNet_v1(\n", + " model_name=\"UNet_v1\",\n", + " input_format='NHWC',\n", + " compute_format='NHWC',\n", + " n_output_channels=1,\n", + " unet_variant='tinyUNet',\n", + " weight_init_method='he_uniform',\n", + " activation_fn='relu'\n", + " )\n", + " \n", + " tf_input = tf.placeholder(tf.float32, [None, 512, 512, 1], name='input')\n", + " \n", + " outputs, logits = network.build_model(tf_input)\n", + " saver = tf.train.Saver()\n", + "\n", + " # Restore variables from disk.\n", + " saver.restore(sess, \"JoC_UNET_Industrial_FP32_TF_20190522/Class+1/model.ckpt-2500\")\n", + " \n", + " \n", + " output = sess.run([outputs, logits], feed_dict={tf_input: img})\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 614 + }, + "colab_type": "code", + "id": "2vGBGRBBtAwG", + "outputId": "853e4c91-b890-4129-9e8a-c729e12fc793" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 92, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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NcaafT8NpuP/WWnuX/TR9hn1y67C4mORPWmu/WVWvyIR/940eSwAA02zs03AA\nAFNNLAEAdIglAIAOsQQA0CGWAAA6xBIAQIdYAgDoEEsAAB3/Dx5DOeSm3wWaAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "# Print out model predicted mask\n", + "plt.figure(figsize = (10,10))\n", + "plt.imshow(np.squeeze(output[0]), cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BPs_nyzcyAxo" + }, + "source": [ + "As expected, the model points out the correct defective area in this image. Please feel free to try out other defective images within `./data/raw_images/public/Class1_def/`" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 335 + }, + "colab_type": "code", + "id": "HRQiqCSMAOZS", + "outputId": "49cf33cd-4699-41ac-baa3-2ff8c41d77ed" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100.png 116.png 131.png 147.png 26.png 41.png 57.png 72.png 88.png\n", + "101.png 117.png 132.png 148.png 27.png 42.png 58.png 73.png 89.png\n", + "102.png 118.png 133.png 149.png 28.png 43.png 59.png 74.png 8.png\n", + "103.png 119.png 134.png 14.png 29.png 44.png 5.png 75.png 90.png\n", + "104.png 11.png 135.png 150.png 2.png 45.png 60.png 76.png 91.png\n", + "105.png 120.png 136.png 15.png 30.png 46.png 61.png 77.png 92.png\n", + "106.png 121.png 137.png 16.png 31.png 47.png 62.png 78.png 93.png\n", + "107.png 122.png 138.png 17.png 32.png 48.png 63.png 79.png 94.png\n", + "108.png 123.png 139.png 18.png 33.png 49.png 64.png 7.png 95.png\n", + "109.png 124.png 13.png 19.png 34.png 4.png 65.png 80.png 96.png\n", + "10.png\t 125.png 140.png 1.png 35.png 50.png 66.png 81.png 97.png\n", + "110.png 126.png 141.png 20.png 36.png 51.png 67.png 82.png 98.png\n", + "111.png 127.png 142.png 21.png 37.png 52.png 68.png 83.png 99.png\n", + "112.png 128.png 143.png 22.png 38.png 53.png 69.png 84.png 9.png\n", + "113.png 129.png 144.png 23.png 39.png 54.png 6.png 85.png labels.txt\n", + "114.png 12.png 145.png 24.png 3.png 55.png 70.png 86.png\n", + "115.png 130.png 146.png 25.png 40.png 56.png 71.png 87.png\n" + ] + } + ], + "source": [ + "!ls ./data/raw_images/public/Class1_def/" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yBeZjO4JtAwL" + }, + "source": [ + "# Optimize model and inference with TF-TRT\n", + "\n", + "In this section, instead of doing inference with the naitive TensorFlow environment, we will first optimize the model with TF-TRT, then doing inference.\n", + "\n", + "We first need to install NVIDIA TensorRT 5.0 runtime environment on Colab." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 598 + }, + "colab_type": "code", + "id": "3WA9N43UTq_c", + "outputId": "ee1eaaef-3eb7-4f5a-d690-66887fb7b429" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Reading database ... 131335 files and directories currently installed.)\n", + "Preparing to unpack nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb ...\n", + "Unpacking nvidia-machine-learning-repo-ubuntu1804 (1.0.0-1) over (1.0.0-1) ...\n", + "Setting up nvidia-machine-learning-repo-ubuntu1804 (1.0.0-1) ...\n", + "Ign:1 http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 InRelease\n", + "Hit:2 http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 Release\n", + "Ign:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease\n", + "Hit:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Release\n", + "Get:6 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n", + "Hit:7 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ InRelease\n", + "Hit:9 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n", + "Hit:10 http://archive.ubuntu.com/ubuntu bionic InRelease\n", + "Hit:11 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic InRelease\n", + "Get:12 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n", + "Get:13 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n", + "Fetched 252 kB in 3s (81.0 kB/s)\n", + "Reading package lists...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "--2019-09-27 04:36:43-- https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb\n", + "Resolving developer.download.nvidia.com (developer.download.nvidia.com)... 192.229.232.112, 2606:2800:247:2063:46e:21d:825:102e\n", + "Connecting to developer.download.nvidia.com (developer.download.nvidia.com)|192.229.232.112|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 2926 (2.9K) [application/x-deb]\n", + "Saving to: ‘nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb.3’\n", + "\n", + " 0K .. 100% 94.3M=0s\n", + "\n", + "2019-09-27 04:36:43 (94.3 MB/s) - ‘nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb.3’ saved [2926/2926]\n", + "\n", + "W: Target Packages (Packages) is configured multiple times in /etc/apt/sources.list.d/nvidia-machine-learning.list:1 and /etc/apt/sources.list.d/nvidia-ml.list:1\n", + "W: Target Packages (Packages) is configured multiple times in /etc/apt/sources.list.d/nvidia-machine-learning.list:1 and /etc/apt/sources.list.d/nvidia-ml.list:1\n" + ] + } + ], + "source": [ + "%%bash\n", + "wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb\n", + "\n", + "dpkg -i nvidia-machine-learning-repo-*.deb\n", + "apt-get update" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 318 + }, + "colab_type": "code", + "id": "BV6JkgDyUD_Q", + "outputId": "cdcb211f-daf4-4d23-ca95-21349b303405" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading package lists... Done\n", + "Building dependency tree \n", + "Reading state information... Done\n", + "libnvinfer5 is already the newest version (5.1.5-1+cuda10.1).\n", + "0 upgraded, 0 newly installed, 0 to remove and 125 not upgraded.\n", + "W: Target Packages (Packages) is configured multiple times in /etc/apt/sources.list.d/nvidia-machine-learning.list:1 and /etc/apt/sources.list.d/nvidia-ml.list:1\n", + "ii libnvinfer-dev 6.0.1-1+cuda10.1 amd64 TensorRT development libraries and headers\n", + "ii libnvinfer-plugin-dev 6.0.1-1+cuda10.1 amd64 TensorRT plugin libraries\n", + "ii libnvinfer-plugin6 6.0.1-1+cuda10.1 amd64 TensorRT plugin libraries\n", + "ii libnvinfer5 5.1.5-1+cuda10.1 amd64 TensorRT runtime libraries\n", + "ii libnvinfer6 6.0.1-1+cuda10.1 amd64 TensorRT runtime libraries\n", + "ii libnvonnxparsers-dev 6.0.1-1+cuda10.1 amd64 TensorRT ONNX libraries\n", + "ii libnvonnxparsers6 6.0.1-1+cuda10.1 amd64 TensorRT ONNX libraries\n", + "ii libnvparsers-dev 6.0.1-1+cuda10.1 amd64 TensorRT parsers libraries\n", + "ii libnvparsers6 6.0.1-1+cuda10.1 amd64 TensorRT parsers libraries\n" + ] + } + ], + "source": [ + "!sudo apt-get install libnvinfer5\n", + "!dpkg -l | grep TensorRT" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XRMZiFjdUPCZ" + }, + "source": [ + "A successful TensorRT installation should look like:\n", + "\n", + "```\n", + "ii libnvinfer5 5.1.5-1+cuda10.1 amd64 TensorRT runtime libraries\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UGYe4yTyUN0x" + }, + "source": [ + "Next, we are ready to optimize the model for inference with TF-TRT. This is carried out in the following steps:\n", + "\n", + "- First, we convert the model checkpoint to a frozen graph that is more relevant for deployment.\n", + "\n", + "- Next, we employ TF-TRT to optimize and convert the frozen graph into a TF-TRT graph. This graph can be employed for inference within the TensorFlow environment, but the underlying runting will be TensorRT. \n", + "\n", + "**Precision mode:** The model that TF-TRT optimizes can have the graph or parameters stored in float32 (FP32) or float16 (FP16). Regardless of the datatype of the model, TensorRT can convert tensors and weights to lower precisions during the optimization. The argument `precision_mode` sets the precision mode; which can be one of FP32, FP16, or INT8\n", + "\n", + "## FP32 Inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "P9T2hwS_tAwM", + "outputId": "99c7d6d3-112d-40a1-bc59-9c18c4dad6a9" + }, + "outputs": [], + "source": [ + "from tensorflow.python.compiler.tensorrt import trt_convert as trt\n", + "\n", + "config = tf.ConfigProto()\n", + "config.gpu_options.allow_growth=True\n", + "\n", + "SAVED_MODEL_DIR = './TR-TRT-model-FP32'\n", + "\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + " with tf.Session(config=config) as sess:\n", + " network = UNet_v1(\n", + " model_name=\"UNet_v1\",\n", + " input_format='NHWC',\n", + " compute_format='NHWC',\n", + " n_output_channels=1,\n", + " unet_variant='tinyUNet',\n", + " weight_init_method='he_uniform',\n", + " activation_fn='relu'\n", + " )\n", + " \n", + " tf_input = tf.placeholder(tf.float32, [None, 512, 512, 1], name='input')\n", + " \n", + " outputs, logits = network.build_model(tf_input)\n", + " \n", + " #print output nodes names\n", + " print(outputs)\n", + " print(logits)\n", + " \n", + " saver = tf.train.Saver()\n", + "\n", + " # Restore variables from disk.\n", + " saver.restore(sess, \"JoC_UNET_Industrial_FP32_TF_20190522/Class+1/model.ckpt-2500\")\n", + " \n", + " # Freeze the graph:\n", + " frozen_graph = tf.graph_util.convert_variables_to_constants(sess,\n", + " tf.get_default_graph().as_graph_def(),\n", + " output_node_names=['UNet_v1/sigmoid', \n", + " 'UNet_v1/ouputs_block/conv2d_2/BiasAdd'])\n", + "\n", + " # Now you can create a TensorRT inference graph from your frozen graph:\n", + " converter = trt.TrtGraphConverter(input_graph_def=frozen_graph,\n", + " nodes_blacklist=['UNet_v1/sigmoid', 'UNet_v1/ouputs_block/conv2d_2/BiasAdd'],\n", + " precision_mode='FP32' ) #output nodes\n", + " trt_graph = converter.convert()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "X7JIKRHNBJ3M" + }, + "source": [ + "After this step, the TF-TRT optimized model is stored in `trt_graph`. Next, we carry out inference using this graph. We will also save the TF-TRT graph into a save model which is ready for deployment later elsewhere." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 111 + }, + "colab_type": "code", + "id": "dPLstDqLBGVp", + "outputId": "64f7dd39-540b-4d30-ad9a-75afa85b19d0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rm: cannot remove './TR-TRT-model-FP32': No such file or directory\n", + "Saving model to ./TR-TRT-model-FP32\n", + "INFO:tensorflow:Assets added to graph.\n", + "INFO:tensorflow:No assets to write.\n", + "INFO:tensorflow:SavedModel written to: ./TR-TRT-model-FP32/saved_model.pb\n" + ] + } + ], + "source": [ + "!rm -r $SAVED_MODEL_DIR\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + " with tf.Session(config=config) as sess:\n", + " # Import the TensorRT graph into a new graph and run:\n", + " output_node = tf.import_graph_def(trt_graph, return_elements=['UNet_v1/sigmoid', 'UNet_v1/ouputs_block/conv2d_2/BiasAdd'], name=\"\")\n", + " \n", + " output = sess.run([\"UNet_v1/sigmoid:0\"], feed_dict={\"input:0\": img})\n", + "\n", + " #Optionally, save model for serving if an ouput directory argument is presented\n", + " if SAVED_MODEL_DIR:\n", + " print('Saving model to %s'%SAVED_MODEL_DIR)\n", + " tf.saved_model.simple_save(\n", + " session=sess,\n", + " export_dir=SAVED_MODEL_DIR,\n", + " inputs={\"input\":tf.get_default_graph().get_tensor_by_name(\"input:0\")},\n", + " outputs={\"mask\":tf.get_default_graph().get_tensor_by_name(\"UNet_v1/sigmoid:0\")},\n", + " legacy_init_op=None\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 614 + }, + "colab_type": "code", + "id": "yKNEbyhktAwW", + "outputId": "e54780a5-5104-4c1e-e286-76ef1147a47f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 109, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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f3z2sAwDYdNbyarhK8qkkD7bWfmvFXbcnuXa4fW2S21asf//wqrgrkxxdcboOAGBTqeUz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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (10,10))\n", + "plt.imshow(np.squeeze(output[0]), cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yaTNBqZEEMUW" + }, + "source": [ + "Next, we load the saved TF-TRT model and carry out inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 93 + }, + "colab_type": "code", + "id": "ZKt61QEFgUaC", + "outputId": "a1e604aa-64e2-4958-d822-1da2842d215b" + }, + "outputs": [], + "source": [ + "# inference with save TF-TRT model\n", + "with tf.Session(graph=tf.Graph(), config=config) as sess:\n", + " tf.saved_model.loader.load(\n", + " sess, [tf.saved_model.tag_constants.SERVING], SAVED_MODEL_DIR)\n", + " nodes = [n.name for n in tf.get_default_graph().as_graph_def().node]\n", + " #print(nodes)\n", + " output = sess.run([\"UNet_v1/sigmoid:0\"], feed_dict={\"input:0\": img})" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 614 + }, + "colab_type": "code", + "id": "ruWzCVXUj2vq", + "outputId": "20478327-8a29-44bd-ab09-bacd2863af58" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 112, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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f3z2sAwDYdNbyarhK8qkkD7bWfmvFXbcnuXa4fW2S21asf//wqrgrkxxdcboOAGBTqeUz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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (10,10))\n", + "plt.imshow(np.squeeze(output[0]), cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "k-WdEzncEnI1" + }, + "source": [ + "Upon inspecting the node list, we can see nodes such as `UNet_v1/TRTEngineOp_0`, `UNet_v1/TRTEngineOp_1`... These are portions of the naitive TensorFlow graph that has been convert and optimized for TensorRT execution. For parts of the graph that are not convertible, execution is carried out by the native TensorFlow runtime. " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "vIPgTM8nhGpK", + "outputId": "b45bdb1a-d3d9-4545-dc0e-7600ca20f1a9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['input',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_positive/Const',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_positive/assert_less/Const',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_positive/assert_less/Assert/Assert/data_0',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/Rank',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_greater_equal/y',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_greater_equal/All',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_greater_equal/Assert/Assert/data_0',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_greater_equal/Assert/Assert/data_1',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_greater_equal/Assert/Assert/data_2',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_greater_equal/Assert/Assert/data_4',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_positive/Const',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_positive/assert_less/Const',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_positive/assert_less/Assert/Assert/data_0',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/Rank',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_greater_equal/y',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_greater_equal/All',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_greater_equal/Assert/Assert/data_0',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_greater_equal/Assert/Assert/data_1',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_greater_equal/Assert/Assert/data_2',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_greater_equal/Assert/Assert/data_4',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_positive/Const',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_positive/assert_less/Const',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_positive/assert_less/Assert/Assert/data_0',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/Rank',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_greater_equal/y',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_greater_equal/All',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_greater_equal/Assert/Assert/data_0',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_greater_equal/Assert/Assert/data_1',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_greater_equal/Assert/Assert/data_2',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_greater_equal/Assert/Assert/data_4',\n", + " 'UNet_v1/bottleneck_block/deconv2d/upsample2d_layer/resize/size',\n", + " 'UNet_v1/upsample_block_1/deconv2d/upsample2d_layer/resize/size',\n", + " 'UNet_v1/upsample_block_2/deconv2d/upsample2d_layer/resize/size',\n", + " 'UNet_v1/upsample_block_3/deconv2d/upsample2d_layer/resize/size',\n", + " 'UNet_v1/ouputs_block/conv2d_2/bias/read',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/Shape',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_greater_equal/Assert/Assert',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_greater_equal/Assert/Assert',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_greater_equal/Assert/Assert',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_positive/assert_less/Less',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_positive/assert_less/All',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/assert_positive/assert_less/Assert/Assert',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/control_dependency',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/Shape',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_positive/assert_less/Less',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_positive/assert_less/All',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/assert_positive/assert_less/Assert/Assert',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/crop_to_bounding_box/TRTEngineOp_2',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/Shape',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_positive/assert_less/Less',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_positive/assert_less/All',\n", + " 'UNet_v1/input_reshape/initial_zero_padding/resize_image_with_crop_or_pad/pad_to_bounding_box/assert_positive/assert_less/Assert/Assert',\n", + " 'UNet_v1/TRTEngineOp_0',\n", + " 'UNet_v1/bottleneck_block/deconv2d/upsample2d_layer/resize/ResizeNearestNeighbor',\n", + " 'UNet_v1/TRTEngineOp_1',\n", + " 'UNet_v1/upsample_block_1/deconv2d/upsample2d_layer/resize/ResizeNearestNeighbor',\n", + " 'UNet_v1/TRTEngineOp_4',\n", + " 'UNet_v1/upsample_block_2/deconv2d/upsample2d_layer/resize/ResizeNearestNeighbor',\n", + " 'UNet_v1/TRTEngineOp_5',\n", + " 'UNet_v1/upsample_block_3/deconv2d/upsample2d_layer/resize/ResizeNearestNeighbor',\n", + " 'UNet_v1/TRTEngineOp_3',\n", + " 'UNet_v1/ouputs_block/conv2d_2/BiasAdd',\n", + " 'UNet_v1/sigmoid']" + ] + }, + "execution_count": 113, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "nodes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kyVrRvVbFFzi" + }, + "source": [ + "## FP16 Inference\n", + "\n", + "Next, we convert the model using FP16 precision." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "j9fbc3xRFFBD", + "outputId": "97e10dca-f2d2-4617-829d-c183a9001bca" + }, + "outputs": [], + "source": [ + "SAVED_MODEL_DIR = './TR-TRT-model-FP16'\n", + "\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + " with tf.Session(config=config) as sess:\n", + " network = UNet_v1(\n", + " model_name=\"UNet_v1\",\n", + " input_format='NHWC',\n", + " compute_format='NHWC',\n", + " n_output_channels=1,\n", + " unet_variant='tinyUNet',\n", + " weight_init_method='he_uniform',\n", + " activation_fn='relu'\n", + " )\n", + " \n", + " tf_input = tf.placeholder(tf.float32, [None, 512, 512, 1], name='input')\n", + " \n", + " outputs, logits = network.build_model(tf_input)\n", + " \n", + " #print output nodes names\n", + " print(outputs)\n", + " print(logits)\n", + " \n", + " saver = tf.train.Saver()\n", + "\n", + " # Restore variables from disk.\n", + " saver.restore(sess, \"JoC_UNET_Industrial_FP32_TF_20190522/Class+1/model.ckpt-2500\")\n", + " \n", + " # Freeze the graph:\n", + " frozen_graph = tf.graph_util.convert_variables_to_constants(sess,\n", + " tf.get_default_graph().as_graph_def(),\n", + " output_node_names=['UNet_v1/sigmoid', \n", + " 'UNet_v1/ouputs_block/conv2d_2/BiasAdd'])\n", + "\n", + " # Now you can create a TensorRT inference graph from your frozen graph:\n", + " converter = trt.TrtGraphConverter(input_graph_def=frozen_graph,\n", + " nodes_blacklist=['UNet_v1/sigmoid', 'UNet_v1/ouputs_block/conv2d_2/BiasAdd'],\n", + " precision_mode='FP16' ) #output nodes\n", + " trt_graph = converter.convert()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 111 + }, + "colab_type": "code", + "id": "twMXj3ANFazk", + "outputId": "202d5243-182f-4f75-bbd7-a8a2d1762605" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rm: cannot remove './TR-TRT-model-FP16': No such file or directory\n", + "Saving model to ./TR-TRT-model-FP16\n", + "INFO:tensorflow:Assets added to graph.\n", + "INFO:tensorflow:No assets to write.\n", + "INFO:tensorflow:SavedModel written to: ./TR-TRT-model-FP16/saved_model.pb\n" + ] + } + ], + "source": [ + "!rm -r $SAVED_MODEL_DIR\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + " with tf.Session(config=config) as sess:\n", + " # Import the TensorRT graph into a new graph and run:\n", + " output_node = tf.import_graph_def(trt_graph, return_elements=['UNet_v1/sigmoid', 'UNet_v1/ouputs_block/conv2d_2/BiasAdd'], name=\"\")\n", + " \n", + " output = sess.run([\"UNet_v1/sigmoid:0\"], feed_dict={\"input:0\": img})\n", + "\n", + " #Optionally, save model for serving if an ouput directory argument is presented\n", + " if SAVED_MODEL_DIR:\n", + " print('Saving model to %s'%SAVED_MODEL_DIR)\n", + " tf.saved_model.simple_save(\n", + " session=sess,\n", + " export_dir=SAVED_MODEL_DIR,\n", + " inputs={\"input\":tf.get_default_graph().get_tensor_by_name(\"input:0\")},\n", + " outputs={\"mask\":tf.get_default_graph().get_tensor_by_name(\"UNet_v1/sigmoid:0\")},\n", + " legacy_init_op=None\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 614 + }, + "colab_type": "code", + "id": "0wFLBT7BFj_8", + "outputId": "0092d9b9-5a2a-4a35-85fb-c6c7d33a67d6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 116, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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NcaafT8NpuP/WWnuX/TR9hn1y67C4mORPWmu/WVWvyIR/940eSwAA02zs03AA\nAFNNLAEAdIglAIAOsQQA0CGWAAA6xBIAQIdYAgDoEEsAAB3/Dx5DOeSm3wWaAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (10,10))\n", + "plt.imshow(np.squeeze(output[0]), cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DTOuQAkaFpGR" + }, + "source": [ + "## INT8 Inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "InnNdsJEFtm-", + "outputId": "a515a46f-74a9-487c-defd-8f483f5a1d72" + }, + "outputs": [], + "source": [ + "SAVED_MODEL_DIR = './TR-TRT-model-INT8'\n", + "\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + " with tf.Session(config=config) as sess:\n", + " network = UNet_v1(\n", + " model_name=\"UNet_v1\",\n", + " input_format='NHWC',\n", + " compute_format='NHWC',\n", + " n_output_channels=1,\n", + " unet_variant='tinyUNet',\n", + " weight_init_method='he_uniform',\n", + " activation_fn='relu'\n", + " )\n", + " \n", + " tf_input = tf.placeholder(tf.float32, [None, 512, 512, 1], name='input')\n", + " \n", + " outputs, logits = network.build_model(tf_input)\n", + " \n", + " #print output nodes names\n", + " print(outputs)\n", + " print(logits)\n", + " \n", + " saver = tf.train.Saver()\n", + "\n", + " # Restore variables from disk.\n", + " saver.restore(sess, \"JoC_UNET_Industrial_FP32_TF_20190522/Class+1/model.ckpt-2500\")\n", + " \n", + " # Freeze the graph:\n", + " frozen_graph = tf.graph_util.convert_variables_to_constants(sess,\n", + " tf.get_default_graph().as_graph_def(),\n", + " output_node_names=['UNet_v1/sigmoid', \n", + " 'UNet_v1/ouputs_block/conv2d_2/BiasAdd'])\n", + "\n", + " # Now you can create a TensorRT inference graph from your frozen graph:\n", + " converter = trt.TrtGraphConverter(input_graph_def=frozen_graph,\n", + " nodes_blacklist=['UNet_v1/sigmoid', 'UNet_v1/ouputs_block/conv2d_2/BiasAdd'],\n", + " precision_mode='INT8' ) #output nodes\n", + " trt_graph = converter.convert()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 111 + }, + "colab_type": "code", + "id": "PN6Duzd7F5Xk", + "outputId": "3c647fb6-79cf-493a-9858-b7c781d608b5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rm: cannot remove './TR-TRT-model-INT8': No such file or directory\n", + "Saving model to ./TR-TRT-model-INT8\n", + "INFO:tensorflow:Assets added to graph.\n", + "INFO:tensorflow:No assets to write.\n", + "INFO:tensorflow:SavedModel written to: ./TR-TRT-model-INT8/saved_model.pb\n" + ] + } + ], + "source": [ + "!rm -r $SAVED_MODEL_DIR\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + " with tf.Session(config=config) as sess:\n", + " # Import the TensorRT graph into a new graph and run:\n", + " output_node = tf.import_graph_def(trt_graph, return_elements=['UNet_v1/sigmoid', 'UNet_v1/ouputs_block/conv2d_2/BiasAdd'], name=\"\")\n", + " \n", + " output = sess.run([\"UNet_v1/sigmoid:0\"], feed_dict={\"input:0\": img})\n", + "\n", + " #Optionally, save model for serving if an ouput directory argument is presented\n", + " if SAVED_MODEL_DIR:\n", + " print('Saving model to %s'%SAVED_MODEL_DIR)\n", + " tf.saved_model.simple_save(\n", + " session=sess,\n", + " export_dir=SAVED_MODEL_DIR,\n", + " inputs={\"input\":tf.get_default_graph().get_tensor_by_name(\"input:0\")},\n", + " outputs={\"mask\":tf.get_default_graph().get_tensor_by_name(\"UNet_v1/sigmoid:0\")},\n", + " legacy_init_op=None\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 614 + }, + "colab_type": "code", + "id": "MHNtVwoWF-lV", + "outputId": "a5ef1660-bf8b-4595-a673-3a6c3aa34ef8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 119, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (10,10))\n", + "plt.imshow(np.squeeze(output[0]), cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "g8MxXY5GmTc8" + }, + "source": [ + "# Conclusion\n", + "\n", + "In this notebook, we have walked through the complete process of carrying out inference using a pretrained UNet-Industrial model.\n", + "## What's next\n", + "Now it's time to try the UNet-Industrial model on your own data. " + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "include_colab_link": true, + "name": "Colab_UNet_Industrial_TF_TFTRT_inference_demo.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/TensorFlow/Segmentation/UNet_Industrial/notebooks/README.md b/TensorFlow/Segmentation/UNet_Industrial/notebooks/README.md new file mode 100644 index 00000000..316a6eaf --- /dev/null +++ b/TensorFlow/Segmentation/UNet_Industrial/notebooks/README.md @@ -0,0 +1,42 @@ +## Jupyter demo notebooks +This folder contains demo notebooks for the TensorFlow UNet Industrial model. + +### 1. TensorFlow_UNet_Industrial_TF_train_and_inference.ipynb: end to end training and inference demo. + +The most convenient way to make use of the NVIDIA Tensorflow UNet model is via a docker container, which provides a self-contained, isolated and re-producible environment for all experiments. Refer to the [Quick Start Guide section](https://github.com/vinhngx/DeepLearningExamples/tree/vinhn_unet_industrial_demo/TensorFlow/Segmentation/UNet_Industrial#requirements) of the Readme documentation for a comprehensive guide. We briefly summarize the steps here. + +First, clone the repository: + +``` +git clone https://github.com/NVIDIA/DeepLearningExamples.git +cd DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial +``` + +Next, build the NVIDIA UNet_Industrial container: + +``` +docker build . --rm -t unet_industrial:latest +``` + +Then launch the container with: + +``` +nvidia-docker run -it --rm \ + --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 \ + -v /path/to/dataset:/data/dagm2007/ \ + -v /path/to/results:/results \ + unet_industrial:latest +``` +where `/path/to/dataset` is the path on the host machine where the data was/is to be downloaded. More on data set preparation in the next section. `/path/to/results` is wher the trained model will be stored. + +Within the docker interactive bash session, start Jupyter with + +``` +jupyter notebook --ip 0.0.0.0 --port 8888 +``` + +Then open the Jupyter GUI interface on your host machine at http://localhost:8888. Within the container, this notebook itself is located at `/workspace/unet_industrial/notebooks`. + +### 2. Colab_UNet_Industrial_TF_TFTRT_inference_demo.ipynb: inference from a pretrained UNet model with TensorFlow-TensorRT (TF-TRT). + +This notebook is designed to run on Google Colab via this [link](https://colab.research.google.com/github/NVIDIA/DeepLearningExamples/blob/master/TensorFlow/Segmentation/UNet_Industrial/notebooks/Colab_UNet_Industrial_TF_TFTRT_inference_demo.ipynb) \ No newline at end of file diff --git a/TensorFlow/Segmentation/UNet_Industrial/notebooks/TensorFlow_UNet_Industrial_TF_train_and_inference.ipynb b/TensorFlow/Segmentation/UNet_Industrial/notebooks/TensorFlow_UNet_Industrial_TF_train_and_inference.ipynb new file mode 100644 index 00000000..c5ddbb85 --- /dev/null +++ b/TensorFlow/Segmentation/UNet_Industrial/notebooks/TensorFlow_UNet_Industrial_TF_train_and_inference.ipynb @@ -0,0 +1,781 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Gwt7z7qdmTbW" + }, + "outputs": [], + "source": [ + "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i4NKCp2VmTbn" + }, + "source": [ + "\n", + "\n", + "# UNet Industrial Training and Inference Demo" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "fW0OKDzvmTbt" + }, + "source": [ + "## Overview\n", + "\n", + "\n", + "This U-Net model is adapted from the original version of the [U-Net model](https://arxiv.org/abs/1505.04597) which is\n", + "a convolutional auto-encoder for 2D image segmentation. U-Net was first introduced by\n", + "Olaf Ronneberger, Philip Fischer, and Thomas Brox in the paper:\n", + "[U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597).\n", + "\n", + "This work proposes a modified version of U-Net, called `TinyUNet` which performs efficiently and with very high accuracy\n", + "on the industrial anomaly dataset [DAGM2007](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html).\n", + "*TinyUNet*, like the original *U-Net* is composed of two parts:\n", + "- an encoding sub-network (left-side)\n", + "- a decoding sub-network (right-side).\n", + "\n", + "It repeatedly applies 3 downsampling blocks composed of two 2D convolutions followed by a 2D max pooling\n", + "layer in the encoding sub-network. In the decoding sub-network, 3 upsampling blocks are composed of a upsample2D\n", + "layer followed by a 2D convolution, a concatenation operation with the residual connection and two 2D convolutions.\n", + "\n", + "`TinyUNet` has been introduced to reduce the model capacity which was leading to a high degree of over-fitting on a\n", + "small dataset like DAGM2007. The complete architecture is presented in the figure below:\n", + "\n", + "![UnetModel](https://github.com/vinhngx/DeepLearningExamples/blob/vinhn_unet_industrial_demo/TensorFlow/Segmentation/UNet_Industrial/images/unet.png?raw=1)\n", + "\n", + "\n", + "\n", + "### Learning objectives\n", + "\n", + "This notebook demonstrates the steps for training a UNet model. We then employ the trained model to make inference on new images.\n", + "\n", + "## Content\n", + "1. [Requirements](#1)\n", + "1. [Data download and preprocessing](#2)\n", + "1. [Training](#3)\n", + "1. [Testing trained model](#4)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aDFrE4eqmTbv" + }, + "source": [ + "\n", + "## 1. Requirements\n", + "\n", + "\n", + "### 1.1 Docker container\n", + "The most convenient way to make use of the NVIDIA Tensorflow UNet model is via a docker container, which provides a self-contained, isolated and re-producible environment for all experiments. Refer to the [Quick Start Guide section](https://github.com/vinhngx/DeepLearningExamples/tree/vinhn_unet_industrial_demo/TensorFlow/Segmentation/UNet_Industrial#requirements) of the Readme documentation for a comprehensive guide. We briefly summarize the steps here.\n", + "\n", + "First, clone the repository:\n", + "\n", + "```\n", + "git clone https://github.com/NVIDIA/DeepLearningExamples.git\n", + "cd DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial\n", + "```\n", + "\n", + "Next, build the NVIDIA UNet_Industrial container:\n", + "\n", + "```\n", + "docker build . --rm -t unet_industrial:latest\n", + "```\n", + "\n", + "Then launch the container with:\n", + "\n", + "```\n", + "nvidia-docker run -it --rm \\\n", + " --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 \\\n", + " -v /path/to/dataset:/data/dagm2007/ \\\n", + " -v /path/to/results:/results \\\n", + " unet_industrial:latest\n", + "```\n", + "where `/path/to/dataset` is the path on the host machine where the data was/is to be downloaded. More on data set preparation in the next section. `/path/to/results` is wher the trained model will be stored.\n", + "\n", + "Within the docker interactive bash session, start Jupyter with\n", + "\n", + "```\n", + "jupyter notebook --ip 0.0.0.0 --port 8888\n", + "```\n", + "\n", + "Then open the Jupyter GUI interface on your host machine at http://localhost:8888. Within the container, this notebook itself is located at `/workspace/unet_industrial/notebooks`.\n", + "\n", + "### 1.2 Hardware\n", + "This notebook can be executed on any CUDA-enabled NVIDIA GPU, although for efficient mixed precision training, a [Tensor Core NVIDIA GPU](https://www.nvidia.com/en-us/data-center/tensorcore/) is desired (Volta, Turing or newer architectures). " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "k7RLEcKhmTb0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mon Sep 30 04:12:27 2019 \r\n", + "+-----------------------------------------------------------------------------+\r\n", + "| NVIDIA-SMI 418.40.04 Driver Version: 418.40.04 CUDA Version: 10.1 |\r\n", + "|-------------------------------+----------------------+----------------------+\r\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n", + "|===============================+======================+======================|\r\n", + "| 0 Tesla V100-SXM2... On | 00000000:86:00.0 Off | 0 |\r\n", + "| N/A 41C P0 60W / 300W | 316MiB / 16130MiB | 0% Default |\r\n", + "+-------------------------------+----------------------+----------------------+\r\n", + " \r\n", + "+-----------------------------------------------------------------------------+\r\n", + "| Processes: GPU Memory |\r\n", + "| GPU PID Type Process name Usage |\r\n", + "|=============================================================================|\r\n", + "+-----------------------------------------------------------------------------+\r\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HqSUGePjmTb9" + }, + "source": [ + "\n", + "## 2. Data download and preprocessing\n", + "\n", + "We will first download some data, in particular, the [Weakly Supervised Learning for Industrial Optical Inspection (DAGM 2007)](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html) dataset. \n", + "\n", + "> The competition is inspired by problems from industrial image processing. In order to satisfy their customers' needs, companies have to guarantee the quality of their products, which can often be achieved only by inspection of the finished product. Automatic visual defect detection has the potential to reduce the cost of quality assurance significantly.\n", + ">\n", + "> The competitors have to design a stand-alone algorithm which is able to detect miscellaneous defects on various background textures.\n", + ">\n", + "> The particular challenge of this contest is that the algorithm must learn, without human intervention, to discern defects automatically from a weakly labeled (i.e., labels are not exact to the pixel level) training set, the exact characteristics of which are unknown at development time. During the competition, the programs have to be trained on new data without any human guidance.\n", + "\n", + "**Source:** https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html\n", + "\n", + "\n", + "**Important Information**: The data download script below will download the *public* DAGM 2007 data set, while the *private* DAGM 2007 requires an account to be downloaded. The script will invite you to download them manually and put them in the correct directory for subsequent pre-processing. We will employ the *private* DAGM data set of 10 classes to train UNnet models." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "S2PR7weWmTcK" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "################################################\n", + "Processing Public Dataset\n", + "################################################\n", + "\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class1.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class1_def.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class2.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class2_def.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class3.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class3_def.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class4.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class4_def.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class5.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class5_def.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class6.zip\n", + "Archive: /data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/public/Class6_def.zip\n", + "\n", + "################################################\n", + "Processing Private Dataset\n", + "################################################\n", + "\n", + "\n", + "Error! File not found: '/data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/private/Class1.zip'\n", + "Instructions:\n", + " 1. Create an account on 'https://hci.iwr.uni-heidelberg.de/node/3616'\n", + " 2. Download the missing file(s) to: '/data/DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial/notebooks/data/zip_files/private'\n", + "\n" + ] + } + ], + "source": [ + "! ../download_and_preprocess_dagm2007.sh ./data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "EQAIszkxmTcT" + }, + "source": [ + "Within the docker container, the final data directory should look like:\n", + "\n", + "```\n", + "./data\n", + " raw_images\n", + " public\n", + " Class1\t \n", + " Class2\t\n", + " Class3\t \n", + " Class4\t\n", + " Class5\t \n", + " Class6\n", + " Class1_def \n", + " Class2_def\t\n", + " Class3_def \n", + " Class4_def\t\n", + " Class5_def \n", + " Class6_def\n", + " private\n", + " Class1 \n", + " Class10 \n", + " Class2 \n", + " Class3 \n", + " Class4 \n", + " Class5 \n", + " Class6 \n", + " Class7 \n", + " Class8 \n", + " Class9\n", + " zip_files\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RL8d9IwzmTcV" + }, + "source": [ + "\n", + "## 3. Training" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "o6wayGf1mTcX" + }, + "source": [ + "The repository provides several training recipes with 1, 4 and 8 GPU with FP32 and automatic mixed precision in `./script`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eQPsExf6mTca" + }, + "source": [ + "### 3.1 Training with 1 GPU\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HapDsY4VmTce" + }, + "source": [ + "#### Training with full precision\n", + "Training on 1 GPU with FP32 with the following syntax:\n", + "\n", + "```\n", + "./UNet_FP32_1GPU.sh \n", + "## 4. Testing trained model\n", + "\n", + "After model training has completed, we can test the trained model against the DAGM 2007 public data set which wasn't used for training. First, we load some required libraries and define some helper functions to load images." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "TURMzc6HmTcy", + "scrolled": false + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0,'..')\n", + "\n", + "from model.unet import UNet_v1\n", + "\n", + "import numpy as np\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "img = mpimg.imread('./data/raw_images/public/Class1_def/1.png')\n", + "\n", + "plt.figure(figsize = (10,10))\n", + "plt.imshow(img, cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see in this figure, there exists a defective area in the top left corner. We will now load the model and carry out inference on the normalized test image." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Image preprocessing\n", + "img = np.expand_dims(img, axis=2)\n", + "img = np.expand_dims(img, axis=0)\n", + "img = (img-0.5)/0.5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we start a TF session, load the trained UNet model and carry out inference on the test image." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint\r\n", + "eval\r\n", + "events.out.tfevents.1569830054.ac4fd7ffba26\r\n", + "graph.pbtxt\r\n", + "model.ckpt-0.data-00000-of-00002\r\n", + "model.ckpt-0.data-00001-of-00002\r\n", + "model.ckpt-0.index\r\n", + "model.ckpt-0.meta\r\n", + "model.ckpt-1000.data-00000-of-00002\r\n", + "model.ckpt-1000.data-00001-of-00002\r\n", + "model.ckpt-1000.index\r\n", + "model.ckpt-1000.meta\r\n", + "model.ckpt-2000.data-00000-of-00002\r\n", + "model.ckpt-2000.data-00001-of-00002\r\n", + "model.ckpt-2000.index\r\n", + "model.ckpt-2000.meta\r\n", + "model.ckpt-2500.data-00000-of-00002\r\n", + "model.ckpt-2500.data-00001-of-00002\r\n", + "model.ckpt-2500.index\r\n", + "model.ckpt-2500.meta\r\n" + ] + } + ], + "source": [ + "!ls ./results/1GPU-FP16/checkpoints " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "config = tf.ConfigProto()\n", + "config.gpu_options.allow_growth = True\n", + "config.allow_soft_placement = True\n", + "\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + " with tf.Session(config=config) as sess:\n", + " network = UNet_v1(\n", + " model_name=\"UNet_v1\",\n", + " input_format='NHWC',\n", + " compute_format='NHWC',\n", + " n_output_channels=1,\n", + " unet_variant='tinyUNet',\n", + " weight_init_method='he_uniform',\n", + " activation_fn='relu'\n", + " )\n", + " \n", + " tf_input = tf.placeholder(tf.float32, [None, 512, 512, 1], name='input')\n", + " \n", + " outputs, logits = network.build_model(tf_input)\n", + " saver = tf.train.Saver()\n", + "\n", + " # Restore variables from disk.\n", + " saver.restore(sess, \"./results/1GPU-FP16/checkpoints/model.ckpt-2500\")\n", + " \n", + " \n", + " output = sess.run([outputs, logits], feed_dict={tf_input: img})\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Print out model predicted mask\n", + "plt.figure(figsize = (10,10))\n", + "plt.imshow(np.squeeze(output[0]), cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the model points out the correct defective area in this image. Please feel free to try out other defective images within `./data/raw_images/public/Class1_def/`" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.png\t 114.png 13.png 145.png 25.png 40.png 56.png 71.png 87.png\r\n", + "10.png\t 115.png 130.png 146.png 26.png 41.png 57.png 72.png 88.png\r\n", + "100.png 116.png 131.png 147.png 27.png 42.png 58.png 73.png 89.png\r\n", + "101.png 117.png 132.png 148.png 28.png 43.png 59.png 74.png 9.png\r\n", + "102.png 118.png 133.png 149.png 29.png 44.png 6.png 75.png 90.png\r\n", + "103.png 119.png 134.png 15.png 3.png 45.png 60.png 76.png 91.png\r\n", + "104.png 12.png 135.png 150.png 30.png 46.png 61.png 77.png 92.png\r\n", + "105.png 120.png 136.png 16.png 31.png 47.png 62.png 78.png 93.png\r\n", + "106.png 121.png 137.png 17.png 32.png 48.png 63.png 79.png 94.png\r\n", + "107.png 122.png 138.png 18.png 33.png 49.png 64.png 8.png 95.png\r\n", + "108.png 123.png 139.png 19.png 34.png 5.png 65.png 80.png 96.png\r\n", + "109.png 124.png 14.png 2.png 35.png 50.png 66.png 81.png 97.png\r\n", + "11.png\t 125.png 140.png 20.png 36.png 51.png 67.png 82.png 98.png\r\n", + "110.png 126.png 141.png 21.png 37.png 52.png 68.png 83.png 99.png\r\n", + "111.png 127.png 142.png 22.png 38.png 53.png 69.png 84.png labels.txt\r\n", + "112.png 128.png 143.png 23.png 39.png 54.png 7.png 85.png\r\n", + "113.png 129.png 144.png 24.png 4.png 55.png 70.png 86.png\r\n" + ] + } + ], + "source": [ + "!ls ./data/raw_images/public/Class1_def/" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "g8MxXY5GmTc8" + }, + "source": [ + "# Conclusion\n", + "\n", + "In this notebook, we have walked through the complete process of preparing the container and data required for training the UNet-Industrial models. We have also investigated various training options with FP32 and automatic mixed precision, trained and tested UNet models with new test images.\n", + "\n", + "## What's next\n", + "Now it's time to try the UNet model on your own data. Observe the performance impact of mixed precision training while comparing the final accuracy of the models trained with FP32 and mixed precision.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "249yGNLmmTc_" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "name": "TensorFlow_UNet_Industrial_Colab_train_and_inference.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/TensorFlow/Segmentation/UNet_Industrial/notebooks/download_and_preprocess_dagm2007_public.sh b/TensorFlow/Segmentation/UNet_Industrial/notebooks/download_and_preprocess_dagm2007_public.sh new file mode 100755 index 00000000..d68c7b5a --- /dev/null +++ b/TensorFlow/Segmentation/UNet_Industrial/notebooks/download_and_preprocess_dagm2007_public.sh @@ -0,0 +1,92 @@ +#!/bin/bash + +############################################################################## +# Copyright (c) Jonathan Dekhtiar - contact@jonathandekhtiar.eu +# All Rights Reserved. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +############################################################################## + +# Usage: ./download_and_preprocess_dagm2007.sh /path/to/dataset/directory/ + +if [[ ! "$BASH_VERSION" ]] ; then + echo "Please do not use sh to run this script ($0), just execute it directly" 1>&2 + exit 1 +fi + +if [[ -z "$1" ]] + then + echo -e "Error: Argument is missing. No dataset directory received." + echo -e "Usage: '$0 /path/to/dataset/directory/'" + exit 1 +fi + +DATASET_DIR=$(realpath -s $1) + +ZIP_FILES_DIR=${DATASET_DIR}/zip_files +RAW_IMAGES_DIR=${DATASET_DIR}/raw_images + +PUBLIC_ZIP_FILES_DIR=${ZIP_FILES_DIR}/public +PUBLIC_RAW_IMAGES_DIR=${RAW_IMAGES_DIR}/public + +if [[ ! -e ${PUBLIC_ZIP_FILES_DIR} ]]; then + echo "creating ${PUBLIC_ZIP_FILES_DIR} ..." + mkdir -p ${PUBLIC_ZIP_FILES_DIR} +fi + +if [[ ! -e ${PUBLIC_RAW_IMAGES_DIR} ]]; then + echo "creating ${PUBLIC_RAW_IMAGES_DIR} ..." + mkdir -p ${PUBLIC_RAW_IMAGES_DIR} +fi + +PRIVATE_ZIP_FILES_DIR=${ZIP_FILES_DIR}/private +PRIVATE_RAW_IMAGES_DIR=${RAW_IMAGES_DIR}/private + +if [[ ! -e ${PRIVATE_ZIP_FILES_DIR} ]]; then + echo "creating ${PRIVATE_ZIP_FILES_DIR} ..." + mkdir -p ${PRIVATE_ZIP_FILES_DIR} +fi + +if [[ ! -e ${PRIVATE_RAW_IMAGES_DIR} ]]; then + echo "creating ${PRIVATE_RAW_IMAGES_DIR} ..." + mkdir -p ${PRIVATE_RAW_IMAGES_DIR} +fi + +echo -e "\n################################################" +echo -e "Processing Public Dataset" +echo -e "################################################\n" + +sleep 2 + +BASE_PUBLIC_URL="https://resources.mpi-inf.mpg.de/conference/dagm/2007" + +declare -a arr=( + "Class1.zip" + "Class1_def.zip" + "Class2.zip" + "Class2_def.zip" + "Class3.zip" + "Class3_def.zip" + "Class4.zip" + "Class4_def.zip" + "Class5.zip" + "Class5_def.zip" + "Class6.zip" + "Class6_def.zip" +) + +for file in "${arr[@]}" +do + if [[ ! -e ${PUBLIC_ZIP_FILES_DIR}/${file} ]]; then + echo -e "Downloading File: $BASE_PUBLIC_URL/$file ..." + wget -N ${BASE_PUBLIC_URL}/${file} -O ${PUBLIC_ZIP_FILES_DIR}/${file} + fi + + # Unzip without overwriting + unzip -n ${PUBLIC_ZIP_FILES_DIR}/${file} -d ${PUBLIC_RAW_IMAGES_DIR} + +done + +chmod -R 744 ${PUBLIC_ZIP_FILES_DIR} +chmod -R 744 ${PUBLIC_RAW_IMAGES_DIR} \ No newline at end of file diff --git a/TensorFlow/Segmentation/UNet_Industrial/runtime/runner.py b/TensorFlow/Segmentation/UNet_Industrial/runtime/runner.py index 659e5e48..7252d855 100644 --- a/TensorFlow/Segmentation/UNet_Industrial/runtime/runner.py +++ b/TensorFlow/Segmentation/UNet_Industrial/runtime/runner.py @@ -148,7 +148,7 @@ class Runner(object): os.environ['TF_SYNC_ON_FINISH'] = '0' os.environ['TF_AUTOTUNE_THRESHOLD'] = '2' - os.environ['TF_DISABLE_NVTX_RANGES'] = '1' + # os.environ['TF_DISABLE_NVTX_RANGES'] = '1' # ================================================= @@ -627,7 +627,7 @@ class Runner(object): LOGGER.log('TP', tps) LOGGER.log('FN', fns) LOGGER.log('TN', tns) - LOGGER.log('FP', tps) + LOGGER.log('FP', fps) LOGGER.log('TPR', tpr) LOGGER.log('TNR', tnr) diff --git a/TensorFlow/Segmentation/UNet_Industrial/utils/hooks/profiler_hook.py b/TensorFlow/Segmentation/UNet_Industrial/utils/hooks/profiler_hook.py index 9ea0fbe5..bd790fd8 100644 --- a/TensorFlow/Segmentation/UNet_Industrial/utils/hooks/profiler_hook.py +++ b/TensorFlow/Segmentation/UNet_Industrial/utils/hooks/profiler_hook.py @@ -210,10 +210,7 @@ class ProfilerHook(tf.train.SessionRunHook): (avg_processing_speed, total_processing_hours, total_processing_minutes, total_processing_seconds) ) - perf_dict = { - 'throughput': str(avg_processing_speed), - 'processing_time': str(total_processing_time) - } + perf_dict = {'throughput': str(avg_processing_speed), 'processing_time': str(total_processing_time)} perf_filename = "performances_%s.json" % ("train" if self._is_training else "eval") diff --git a/TensorFlow/Translation/GNMT/Dockerfile b/TensorFlow/Translation/GNMT/Dockerfile index 01369840..645fdc62 100644 --- a/TensorFlow/Translation/GNMT/Dockerfile +++ b/TensorFlow/Translation/GNMT/Dockerfile @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -FROM nvcr.io/nvidia/tensorflow:19.06-py3 +FROM nvcr.io/nvidia/tensorflow:19.07-py3 COPY . /workspace/gnmt WORKDIR /workspace/gnmt diff --git a/TensorFlow/Translation/GNMT/README.md b/TensorFlow/Translation/GNMT/README.md index 57eb1b5c..e1522879 100644 --- a/TensorFlow/Translation/GNMT/README.md +++ b/TensorFlow/Translation/GNMT/README.md @@ -36,7 +36,7 @@ This repository provides a script and recipe to train the GNMT v2 model to achie * [Training performance results](#training-performance-results) * [Training performance: NVIDIA DGX-1 (8x V100 16G)](#training-performance-nvidia-dgx-1-(8x-v100-16G)) * [Inference performance results](#inference-performance-results) - * [Inference performance: NVIDIA DGX-1 (8x V100 16G)](#inference-performance-nvidia-dgx-1-(8x-v100-16G)) + * [Inference performance: NVIDIA T4](#inference-performance-nvidia-t4) - [Release notes](#release-notes) * [Changelog](#changelog) * [Known issues](#known-issues) @@ -178,7 +178,7 @@ v2 model. This repository contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components: - [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) -- [TensorFlow 19.05-py3 NGC container or later](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) +- [TensorFlow 19.07-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) - [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/) or [Turing](https://www.nvidia.com/en-us/geforce/turing/) based GPU For more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation: @@ -232,13 +232,13 @@ argument. To launch mixed precision training on 1 GPU, run: ``` -python nmt.py --output_dir=results --batch_size=192 --learning_rate=8e-4 +python nmt.py --output_dir=results --batch_size=128 --learning_rate=5e-4 ``` To launch mixed precision training on 8 GPUs, run: ``` -python nmt.py --output_dir=results --batch_size=1536 --num_gpus=8 --learning_rate=2e-3 +python nmt.py --output_dir=results --batch_size=1024 --num_gpus=8 --learning_rate=2e-3 ``` To launch FP32 training on 1 GPU, run: @@ -515,12 +515,12 @@ accuracy in training and inference. ##### Training accuracy: NVIDIA DGX-1 (8x V100 16G) Our results were obtained by running the `nmt.py` script in the -tensorflow-19.06-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. +tensorflow-19.07-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. -| **GPUs** | **Batch size / GPU - mixed precision** | **Batch size / GPU - FP32** |**Accuracy - mixed precision (BLEU)** | **Accuracy - FP32 (BLEU)** | **Time to train - mixed precision** | **Time to train - FP32** | **Time to train speedup (FP32 to mixed precision)** | -| --- | --- | --- | ----- | ----- | -------- | -------- | ---- | -| 1 | 192 | 128 | 24.90 | 24.84 | 610 min | 1237 min | 2.03 | -| 8 | 192 | 128 | 24.33 | 24.34 | 156 min | 237 min | 1.52 | +| **GPUs** | **Batch size / GPU** |**Accuracy - mixed precision (BLEU)** | **Accuracy - FP32 (BLEU)** | **Time to train - mixed precision** | **Time to train - FP32** | **Time to train speedup (FP32 to mixed precision)** | +| --- | --- | ----- | ----- | -------- | -------- | ---- | +| 1 | 128 | 24.90 | 24.84 | 763 min | 1237 min | 1.62 | +| 8 | 128 | 24.33 | 24.34 | 168 min | 237 min | 1.41 | In the following plot, the BLEU scores after each training epoch for different @@ -534,26 +534,26 @@ configurations are displayed. The GNMT v2 model was trained for 6 epochs, starting from 6 different initial random seeds. After each training epoch, the model was evaluated on the test dataset and the BLEU score was recorded. The training was performed in the -tensorflow-19.06-py3 NGC container on NVIDIA DGX-1 with 8 Tesla V100 16G GPUs. +tensorflow-19.07-py3 NGC container on NVIDIA DGX-1 with 8 Tesla V100 16G GPUs. In the following table, the BLEU scores after each training epoch for different initial random seeds are displayed. | **Epoch** | **Average** | **Standard deviation** | **Minimum** | **Maximum** | **Median** | | --- | ------ | ----- | ------ | ------ | ------ | -| 1 | 19.706 | 0.106 | 19.590 | 19.860 | 19.710 | -| 2 | 21.694 | 0.214 | 21.420 | 21.970 | 21.770 | -| 3 | 22.424 | 0.252 | 22.030 | 22.690 | 22.550 | -| 4 | 22.954 | 0.093 | 22.820 | 23.090 | 22.920 | -| 5 | 23.814 | 0.090 | 23.670 | 23.950 | 23.810 | -| 6 | 24.328 | 0.100 | 24.200 | 24.460 | 24.340 | +| 1 | 20.365 | 0.096 | 20.200 | 20.480 | 20.385 | +| 2 | 22.002 | 0.080 | 21.900 | 22.110 | 22.000 | +| 3 | 22.793 | 0.078 | 22.690 | 22.890 | 22.790 | +| 4 | 23.220 | 0.160 | 22.890 | 23.360 | 23.260 | +| 5 | 24.007 | 0.153 | 23.870 | 24.220 | 23.925 | +| 6 | 24.362 | 0.167 | 24.210 | 24.710 | 24.310 | #### Inference accuracy results ##### Inference accuracy: NVIDIA DGX-1 (8x V100 16G) -Our results were obtained by running the `scripts/translate.py` script in the tensorflow-19.06-py3 NGC container on NVIDIA DGX-1 8x V100 16G GPUs. +Our results were obtained by running the `scripts/translate.py` script in the tensorflow-19.07-py3 NGC container on NVIDIA DGX-1 8x V100 16G GPUs. * For mixed precision: `python scripts/translate.py --output_dir=/path/to/trained/model --beam_width 1,2,5 --infer_batch_size 128` @@ -569,14 +569,14 @@ Our results were obtained by running the `scripts/translate.py` script in the te ##### Training performance: NVIDIA DGX-1 (8x V100 16G) -Our results were obtained by running the `nmt.py` script in the tensorflow-19.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. +Our results were obtained by running the `nmt.py` script in the tensorflow-19.07-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance numbers (in tokens per second) were averaged over an entire training epoch. -| **GPUs** | **Batch size / GPU - mixed precision** | **Batch size / GPU - FP32** | **Throughput - mixed precision (tokens/s)** | **Throughput - FP32 (tokens/s)** | **Throughput speedup (FP32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** | -| --- | --- | --- | ------- | ------ | ---- | ---- | ---- | -| 1 | 192 | 128 | 28 959 | 14 106 | 2.05 | 1.00 | 1.00 | -| 8 | 192 | 128 | 165 908 | 93 688 | 1.77 | 5.73 | 6.64 | +| **GPUs** | **Batch size / GPU** | **Throughput - mixed precision (tokens/s)** | **Throughput - FP32 (tokens/s)** | **Throughput speedup (FP32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** | +| --- | --- | ------- | ------ | ---- | ---- | ---- | +| 1 | 128 | 23 011 | 14 106 | 1.63 | 1.00 | 1.00 | +| 8 | 128 | 138 106 | 93 688 | 1.47 | 6.00 | 6.64 | To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. @@ -600,33 +600,38 @@ python scripts/translate.py --output_dir=/path/to/trained/model --beam_width 1,2 To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above. -##### Inference performance: NVIDIA DGX-1 (8x V100 16G) +##### Inference performance: NVIDIA T4 -Our results were obtained by running the `scripts/translate.py` script in the tensorflow-19.06-py3 NGC container on NVIDIA DGX-1 (8x V100 16G) GPUs. +Our results were obtained by running the `scripts/translate.py` script in the tensorflow-19.07-py3 NGC container on NVIDIA T4. -| **Batch size** | **Beam size** | **Mixed precision tokens/s** | **FP32 tokens/s** | **Speedup** | **Mixed precision average latency (ms)** | **FP32 average latency (ms)** | **Average latency speedup** | **Mixed precision latency 50% (ms)** | **FP32 latency 50% (ms)** | **Latency 50% speedup** | **Mixed precision latency 90% (ms)** | **FP32 latency 90% (ms)** | **Latency 90% speedup** | **Mixed precision latency 95% (ms)** | **FP32 latency 95% (ms)** | **Latency 95% speedup** | **Mixed precision latency 99% (ms)** | **FP32 latency 99% (ms)** | **Latency 99% speedup** | **Mixed precision latency 100% (ms)** | **FP32 latency 100% (ms)** | **Latency 100% speedup** | -|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| -|1|1|563|544|1.035|96|99|1.035|89|92|1.035|160|166|1.039|180|188|1.042|215|224|1.045|271|276|1.021| -|1|2|516|521|0.990|104|103|0.990|97|96|0.989|172|172|1.003|193|192|0.997|240|236|0.983|285|275|0.965| -|1|5|471|485|0.972|113|110|0.971|105|102|0.974|187|181|0.970|209|204|0.978|269|259|0.965|296|289|0.979| -|2|1|840|855|0.983|129|126|0.983|122|120|0.982|193|191|0.987|212|209|0.987|250|247|0.989|305|281|0.921| -|2|2|790|806|0.980|136|134|0.980|129|126|0.982|204|200|0.978|226|222|0.983|270|269|0.996|294|289|0.983| -|2|5|747|701|1.066|143|153|1.065|135|143|1.061|213|228|1.071|240|258|1.074|293|315|1.074|310|328|1.057| -|4|1|1403|1400|1.002|154|154|1.002|151|150|0.996|214|215|1.005|228|228|0.998|284|285|1.005|289|290|1.003| -|4|2|1382|1268|1.090|156|170|1.090|152|166|1.094|218|237|1.089|239|258|1.083|281|304|1.082|293|329|1.121| -|4|5|1304|1236|1.055|164|173|1.055|158|167|1.056|233|247|1.059|259|276|1.063|291|308|1.060|296|316|1.065| -|8|1|2482|2294|1.082|174|188|1.082|173|188|1.091|226|244|1.082|243|265|1.090|281|307|1.089|287|315|1.096| -|8|2|2346|2271|1.033|184|190|1.033|183|188|1.027|249|256|1.027|264|270|1.023|291|299|1.027|294|302|1.029| -|8|5|2120|1886|1.124|202|227|1.124|198|223|1.122|279|312|1.116|302|344|1.139|312|351|1.126|314|356|1.133| -|32|1|7581|7203|1.052|228|240|1.052|226|236|1.046|293|302|1.030|299|309|1.035|302|316|1.047|302|320|1.060| -|32|2|6734|5804|1.160|256|297|1.160|254|297|1.168|324|374|1.156|329|377|1.145|333|381|1.142|339|385|1.136| -|32|5|4962|3834|1.294|345|446|1.294|340|445|1.310|431|550|1.277|433|556|1.285|436|560|1.285|442|566|1.280| -|128|1|19533|14782|1.321|354|467|1.321|352|462|1.312|396|528|1.334|398|530|1.331|400|530|1.325|400|530|1.324| -|128|2|14636|10174|1.439|471|677|1.438|488|703|1.441|513|742|1.447|515|743|1.441|518|743|1.435|519|743|1.433| -|128|5|8513|5170|1.647|804|1324|1.647|852|1410|1.656|864|1425|1.650|865|1431|1.654|866|1438|1.660|867|1440|1.662| -|512|1|34401|20544|1.675|803|1345|1.675|818|1372|1.678|830|1396|1.682|831|1396|1.680|832|1396|1.678|832|1396|1.678| -|512|2|21084|12414|1.698|1308|2220|1.698|1343|2284|1.700|1350|2290|1.696|1351|2290|1.694|1352|2290|1.693|1353|2290|1.693| -|512|5|10254|5374|1.908|2669|5093|1.908|2745|5316|1.936|2759|5321|1.929|2761|5322|1.927|2763|5322|1.926|2764|5322|1.926| +Reported mixed precision speedups are relative to FP32 numbers for corresponding configuration. + +| **Batch size** | **Beam size** | **Mixed precision tokens/s** | **Speedup** | **Mixed precision average latency (ms)** | **Average latency speedup** | **Mixed precision latency 50% (ms)** | **Latency 50% speedup** | **Mixed precision latency 90% (ms)** | **Latency 90% speedup** | **Mixed precision latency 95% (ms)** | **Latency 95% speedup** | **Mixed precision latency 99% (ms)** | **Latency 99% speedup** | **Mixed precision latency 100% (ms)** | **Latency 100% speedup** | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| 1 | 1 | 643 | 1.278 | 84 | 1.278 | 78 | 1.279 | 138 | 1.309 | 154 | 1.312 | 180 | 1.304 | 220 | 1.296 | +| 1 | 2 | 584 | 1.693 | 92 | 1.692 | 86 | 1.686 | 150 | 1.743 | 168 | 1.737 | 201 | 1.770 | 236 | 1.742 | +| 1 | 5 | 552 | 1.702 | 97 | 1.701 | 90 | 1.696 | 158 | 1.746 | 176 | 1.738 | 218 | 1.769 | 244 | 1.742 | +| 2 | 1 | 948 | 1.776 | 114 | 1.776 | 108 | 1.769 | 170 | 1.803 | 184 | 1.807 | 218 | 1.783 | 241 | 1.794 | +| 2 | 2 | 912 | 1.761 | 118 | 1.760 | 112 | 1.763 | 175 | 1.776 | 192 | 1.781 | 226 | 1.770 | 246 | 1.776 | +| 2 | 5 | 832 | 1.900 | 128 | 1.900 | 121 | 1.910 | 192 | 1.912 | 214 | 1.922 | 258 | 1.922 | 266 | 1.905 | +| 4 | 1 | 1596 | 1.792 | 135 | 1.792 | 132 | 1.791 | 187 | 1.799 | 197 | 1.815 | 241 | 1.784 | 245 | 1.796 | +| 4 | 2 | 1495 | 1.928 | 144 | 1.927 | 141 | 1.926 | 201 | 1.927 | 216 | 1.936 | 250 | 1.956 | 264 | 1.890 | +| 4 | 5 | 1308 | 1.702 | 164 | 1.702 | 159 | 1.702 | 230 | 1.722 | 251 | 1.742 | 283 | 1.708 | 288 | 1.699 | +| 8 | 1 | 2720 | 1.981 | 159 | 1.981 | 158 | 1.992 | 204 | 1.975 | 219 | 1.986 | 249 | 1.987 | 252 | 1.966 | +| 8 | 2 | 2554 | 1.809 | 169 | 1.808 | 168 | 1.829 | 224 | 1.797 | 237 | 1.783 | 260 | 1.807 | 262 | 1.802 | +| 8 | 5 | 1979 | 1.768 | 216 | 1.768 | 213 | 1.780 | 292 | 1.797 | 319 | 1.793 | 334 | 1.760 | 336 | 1.769 | +| 32 | 1 | 7449 | 1.775 | 232 | 1.774 | 231 | 1.777 | 292 | 1.789 | 300 | 1.760 | 301 | 1.768 | 301 | 1.768 | +| 32 | 2 | 5569 | 1.670 | 309 | 1.669 | 311 | 1.672 | 389 | 1.652 | 392 | 1.665 | 401 | 1.651 | 404 | 1.644 | +| 32 | 5 | 3079 | 1.867 | 556 | 1.867 | 555 | 1.865 | 692 | 1.858 | 695 | 1.860 | 702 | 1.847 | 703 | 1.847 | +| 128 | 1 | 12986 | 1.662 | 532 | 1.662 | 529 | 1.667 | 607 | 1.643 | 608 | 1.645 | 609 | 1.647 | 609 | 1.647 | +| 128 | 2 | 7856 | 1.734 | 878 | 1.734 | 911 | 1.755 | 966 | 1.742 | 967 | 1.741 | 968 | 1.744 | 968 | 1.744 | +| 128 | 5 | 3361 | 1.683 | 2036 | 1.682 | 2186 | 1.678 | 2210 | 1.673 | 2210 | 1.674 | 2211 | 1.674 | 2211 | 1.674 | +| 512 | 1 | 14932 | 1.825 | 1851 | 1.825 | 1889 | 1.808 | 1927 | 1.801 | 1928 | 1.800 | 1929 | 1.800 | 1930 | 1.799 | +| 512 | 2 | 8109 | 1.786 | 3400 | 1.786 | 3505 | 1.783 | 3520 | 1.782 | 3523 | 1.781 | 3525 | 1.781 | 3525 | 1.781 | +| 512 | 5 | 3370 | 1.802 | 8123 | 1.801 | 8376 | 1.798 | 8391 | 1.804 | 8394 | 1.804 | 8396 | 1.805 | 8397 | 1.805 | + + +## Release notes ### Changelog 1. Mar 18, 2019 diff --git a/TensorFlow/Translation/GNMT/img/diagram.png b/TensorFlow/Translation/GNMT/img/diagram.png index ee5fc59e..1a406ac1 100644 Binary files a/TensorFlow/Translation/GNMT/img/diagram.png and b/TensorFlow/Translation/GNMT/img/diagram.png differ