Repository: yifanlu0227/MIT-6.5940
Branch: main
Commit: 869de40e7cdf
Files: 6
Total size: 9.5 MB
Directory structure:
gitextract_b8gsmqh4/
├── .gitmodules
├── Lab1.ipynb
├── Lab2.ipynb
├── Lab3.ipynb
├── Lab4.ipynb
└── README.md
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitmodules
================================================
[submodule "Lab5"]
path = Lab5
url = https://github.com/yifanlu0227/LLaMA2-7B-on-laptop.git
================================================
FILE: Lab1.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RdC9V9xB10Gt"
},
"source": [
"# **MIT 6.5940 EfficientML.ai Fall 2023: Lab 1 Pruning**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3GlPu1PwwmqS"
},
"source": [
"\n",
"This colab notebook provides code and a framework for ***Lab 1 Pruning***. You can work out your solutions here.\n",
"\n"
]
},
{
"cell_type": "markdown",
"source": [
"Please fill out this [feedback form](https://forms.gle/fapEmEUYr3WnXjBU8) when you finished this lab. We would love to hear your thoughts or feedback on how we can improve this lab!"
],
"metadata": {
"id": "6BpLnUHfQ9cP"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "ykXj956Jr5wm"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Goals\n",
"\n",
"In this assignment, you will practice pruning a classical neural network model to reduce both model size and latency. The goals of this assignment are as follows:\n",
"\n",
"- Understand the basic concept of **pruning**\n",
"- Implement and apply **fine-grained pruning**\n",
"- Implement and apply **channel pruning**\n",
"- Get a basic understanding of performance improvement (such as speedup) from pruning\n",
"- Understand the differences and tradeoffs between these pruning approaches"
],
"metadata": {
"id": "2IqUbyC9YaLZ"
}
},
{
"cell_type": "markdown",
"source": [
"## Contents\n",
"\n",
"There are two main sections in this lab: ***Fine-grained Pruning*** and ***Channel Pruning***.\n",
"\n",
"There are ***9*** questions in total:\n",
"- For *Fine-grained Pruning*, there are ***5*** questions (Question 1-5).\n",
"- For *Channel Pruning*, there are ***3*** questions (Question 6-8).\n",
"- Question 9 compares fine-grained pruning and channel pruning."
],
"metadata": {
"id": "VDkb-exkYai1"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "2tFjnZZVlIFL"
},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"source": [
"First, install the required packages and download the datasets and pretrained model. Here we use CIFAR10 dataset and VGG network which is the same as what we used in the Lab 0 tutorial."
],
"metadata": {
"id": "W76-32bi_mHm"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nyngBRTXQG2n",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2c34103b-229f-425c-9594-c207b9cd2dc6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Installing torchprofile...\n",
"All required packages have been successfully installed!\n"
]
}
],
"source": [
"print('Installing torchprofile...')\n",
"!pip install torchprofile 1>/dev/null\n",
"print('All required packages have been successfully installed!')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jQb3_6zlQfib"
},
"outputs": [],
"source": [
"import copy\n",
"import math\n",
"import random\n",
"import time\n",
"from collections import OrderedDict, defaultdict\n",
"from typing import Union, List\n",
"\n",
"import numpy as np\n",
"import torch\n",
"from matplotlib import pyplot as plt\n",
"from torch import nn\n",
"from torch.optim import *\n",
"from torch.optim.lr_scheduler import *\n",
"from torch.utils.data import DataLoader\n",
"from torchprofile import profile_macs\n",
"from torchvision.datasets import *\n",
"from torchvision.transforms import *\n",
"from tqdm.auto import tqdm\n",
"\n",
"from torchprofile import profile_macs\n",
"\n",
"assert torch.cuda.is_available(), \\\n",
"\"The current runtime does not have CUDA support.\" \\\n",
"\"Please go to menu bar (Runtime - Change runtime type) and select GPU\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yCjLyoR10Eib",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a8318ddf-e02f-49e8-fe29-f2deb99379e8"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"random.seed(0)\n",
"np.random.seed(0)\n",
"torch.manual_seed(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PYvLYMPC1Hel"
},
"outputs": [],
"source": [
"def download_url(url, model_dir='.', overwrite=False):\n",
" import os, sys, ssl\n",
" from urllib.request import urlretrieve\n",
" ssl._create_default_https_context = ssl._create_unverified_context\n",
" target_dir = url.split('/')[-1]\n",
" model_dir = os.path.expanduser(model_dir)\n",
" try:\n",
" if not os.path.exists(model_dir):\n",
" os.makedirs(model_dir)\n",
" model_dir = os.path.join(model_dir, target_dir)\n",
" cached_file = model_dir\n",
" if not os.path.exists(cached_file) or overwrite:\n",
" sys.stderr.write('Downloading: \"{}\" to {}\\n'.format(url, cached_file))\n",
" urlretrieve(url, cached_file)\n",
" return cached_file\n",
" except Exception as e:\n",
" # remove lock file so download can be executed next time.\n",
" os.remove(os.path.join(model_dir, 'download.lock'))\n",
" sys.stderr.write('Failed to download from url %s' % url + '\\n' + str(e) + '\\n')\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "54K1dJOx1JYZ"
},
"outputs": [],
"source": [
"class VGG(nn.Module):\n",
" ARCH = [64, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']\n",
"\n",
" def __init__(self) -> None:\n",
" super().__init__()\n",
"\n",
" layers = []\n",
" counts = defaultdict(int)\n",
"\n",
" def add(name: str, layer: nn.Module) -> None:\n",
" layers.append((f\"{name}{counts[name]}\", layer))\n",
" counts[name] += 1\n",
"\n",
" in_channels = 3\n",
" for x in self.ARCH:\n",
" if x != 'M':\n",
" # conv-bn-relu\n",
" add(\"conv\", nn.Conv2d(in_channels, x, 3, padding=1, bias=False))\n",
" add(\"bn\", nn.BatchNorm2d(x))\n",
" add(\"relu\", nn.ReLU(True))\n",
" in_channels = x\n",
" else:\n",
" # maxpool\n",
" add(\"pool\", nn.MaxPool2d(2))\n",
"\n",
" self.backbone = nn.Sequential(OrderedDict(layers))\n",
" self.classifier = nn.Linear(512, 10)\n",
"\n",
" def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
" # backbone: [N, 3, 32, 32] => [N, 512, 2, 2]\n",
" x = self.backbone(x)\n",
"\n",
" # avgpool: [N, 512, 2, 2] => [N, 512]\n",
" x = x.mean([2, 3])\n",
"\n",
" # classifier: [N, 512] => [N, 10]\n",
" x = self.classifier(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FI49lOle1KuL"
},
"outputs": [],
"source": [
"def train(\n",
" model: nn.Module,\n",
" dataloader: DataLoader,\n",
" criterion: nn.Module,\n",
" optimizer: Optimizer,\n",
" scheduler: LambdaLR,\n",
" callbacks = None\n",
") -> None:\n",
" model.train()\n",
"\n",
" for inputs, targets in tqdm(dataloader, desc='train', leave=False):\n",
" # Move the data from CPU to GPU\n",
" inputs = inputs.cuda()\n",
" targets = targets.cuda()\n",
"\n",
" # Reset the gradients (from the last iteration)\n",
" optimizer.zero_grad()\n",
"\n",
" # Forward inference\n",
" outputs = model(inputs)\n",
" loss = criterion(outputs, targets)\n",
"\n",
" # Backward propagation\n",
" loss.backward()\n",
"\n",
" # Update optimizer and LR scheduler\n",
" optimizer.step()\n",
" scheduler.step()\n",
"\n",
" if callbacks is not None:\n",
" for callback in callbacks:\n",
" callback()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bCSnuQVKidfZ"
},
"outputs": [],
"source": [
"@torch.inference_mode()\n",
"def evaluate(\n",
" model: nn.Module,\n",
" dataloader: DataLoader,\n",
" verbose=True,\n",
") -> float:\n",
" model.eval()\n",
"\n",
" num_samples = 0\n",
" num_correct = 0\n",
"\n",
" for inputs, targets in tqdm(dataloader, desc=\"eval\", leave=False,\n",
" disable=not verbose):\n",
" # Move the data from CPU to GPU\n",
" inputs = inputs.cuda()\n",
" targets = targets.cuda()\n",
"\n",
" # Inference\n",
" outputs = model(inputs)\n",
"\n",
" # Convert logits to class indices\n",
" outputs = outputs.argmax(dim=1)\n",
"\n",
" # Update metrics\n",
" num_samples += targets.size(0)\n",
" num_correct += (outputs == targets).sum()\n",
"\n",
" return (num_correct / num_samples * 100).item()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QBBKNhKNlAwE"
},
"source": [
"Helper Functions (Flops, Model Size calculation, etc.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mRdK_ThzlMxL"
},
"outputs": [],
"source": [
"def get_model_macs(model, inputs) -> int:\n",
" return profile_macs(model, inputs)\n",
"\n",
"\n",
"def get_sparsity(tensor: torch.Tensor) -> float:\n",
" \"\"\"\n",
" calculate the sparsity of the given tensor\n",
" sparsity = #zeros / #elements = 1 - #nonzeros / #elements\n",
" \"\"\"\n",
" return 1 - float(tensor.count_nonzero()) / tensor.numel()\n",
"\n",
"\n",
"def get_model_sparsity(model: nn.Module) -> float:\n",
" \"\"\"\n",
" calculate the sparsity of the given model\n",
" sparsity = #zeros / #elements = 1 - #nonzeros / #elements\n",
" \"\"\"\n",
" num_nonzeros, num_elements = 0, 0\n",
" for param in model.parameters():\n",
" num_nonzeros += param.count_nonzero()\n",
" num_elements += param.numel()\n",
" return 1 - float(num_nonzeros) / num_elements\n",
"\n",
"def get_num_parameters(model: nn.Module, count_nonzero_only=False) -> int:\n",
" \"\"\"\n",
" calculate the total number of parameters of model\n",
" :param count_nonzero_only: only count nonzero weights\n",
" \"\"\"\n",
" num_counted_elements = 0\n",
" for param in model.parameters():\n",
" if count_nonzero_only:\n",
" num_counted_elements += param.count_nonzero()\n",
" else:\n",
" num_counted_elements += param.numel()\n",
" return num_counted_elements\n",
"\n",
"\n",
"def get_model_size(model: nn.Module, data_width=32, count_nonzero_only=False) -> int:\n",
" \"\"\"\n",
" calculate the model size in bits\n",
" :param data_width: #bits per element\n",
" :param count_nonzero_only: only count nonzero weights\n",
" \"\"\"\n",
" return get_num_parameters(model, count_nonzero_only) * data_width\n",
"\n",
"Byte = 8\n",
"KiB = 1024 * Byte\n",
"MiB = 1024 * KiB\n",
"GiB = 1024 * MiB"
]
},
{
"cell_type": "markdown",
"source": [
"*Define* misc functions for verification."
],
"metadata": {
"id": "FtPpHs2blEqt"
}
},
{
"cell_type": "code",
"source": [
"def test_fine_grained_prune(\n",
" test_tensor=torch.tensor([[-0.46, -0.40, 0.39, 0.19, 0.37],\n",
" [0.00, 0.40, 0.17, -0.15, 0.16],\n",
" [-0.20, -0.23, 0.36, 0.25, 0.03],\n",
" [0.24, 0.41, 0.07, 0.13, -0.15],\n",
" [0.48, -0.09, -0.36, 0.12, 0.45]]),\n",
" test_mask=torch.tensor([[True, True, False, False, False],\n",
" [False, True, False, False, False],\n",
" [False, False, False, False, False],\n",
" [False, True, False, False, False],\n",
" [True, False, False, False, True]]),\n",
" target_sparsity=0.75, target_nonzeros=None):\n",
" def plot_matrix(tensor, ax, title):\n",
" ax.imshow(tensor.cpu().numpy() == 0, vmin=0, vmax=1, cmap='tab20c')\n",
" ax.set_title(title)\n",
" ax.set_yticklabels([])\n",
" ax.set_xticklabels([])\n",
" for i in range(tensor.shape[1]):\n",
" for j in range(tensor.shape[0]):\n",
" text = ax.text(j, i, f'{tensor[i, j].item():.2f}',\n",
" ha=\"center\", va=\"center\", color=\"k\")\n",
"\n",
" test_tensor = test_tensor.clone()\n",
" fig, axes = plt.subplots(1,2, figsize=(6, 10))\n",
" ax_left, ax_right = axes.ravel()\n",
" plot_matrix(test_tensor, ax_left, 'dense tensor')\n",
"\n",
" sparsity_before_pruning = get_sparsity(test_tensor)\n",
" mask = fine_grained_prune(test_tensor, target_sparsity)\n",
" sparsity_after_pruning = get_sparsity(test_tensor)\n",
" sparsity_of_mask = get_sparsity(mask)\n",
"\n",
" plot_matrix(test_tensor, ax_right, 'sparse tensor')\n",
" fig.tight_layout()\n",
" plt.show()\n",
"\n",
" print('* Test fine_grained_prune()')\n",
" print(f' target sparsity: {target_sparsity:.2f}')\n",
" print(f' sparsity before pruning: {sparsity_before_pruning:.2f}')\n",
" print(f' sparsity after pruning: {sparsity_after_pruning:.2f}')\n",
" print(f' sparsity of pruning mask: {sparsity_of_mask:.2f}')\n",
"\n",
" if target_nonzeros is None:\n",
" if test_mask.equal(mask):\n",
" print('* Test passed.')\n",
" else:\n",
" print('* Test failed.')\n",
" else:\n",
" if mask.count_nonzero() == target_nonzeros:\n",
" print('* Test passed.')\n",
" else:\n",
" print('* Test failed.')"
],
"metadata": {
"id": "NLYqk61hlEyZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Kx48A6hRhXxB"
},
"source": [
"Load the pretrained model and the CIFAR-10 dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "y-C55wY2nHLT",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "332385e2-b2db-4741-8399-dc803517d840"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Downloading: \"https://hanlab18.mit.edu/files/course/labs/vgg.cifar.pretrained.pth\" to ./vgg.cifar.pretrained.pth\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"=> loading checkpoint 'https://hanlab18.mit.edu/files/course/labs/vgg.cifar.pretrained.pth'\n"
]
}
],
"source": [
"checkpoint_url = \"https://hanlab18.mit.edu/files/course/labs/vgg.cifar.pretrained.pth\"\n",
"checkpoint = torch.load(download_url(checkpoint_url), map_location=\"cpu\")\n",
"model = VGG().cuda()\n",
"print(f\"=> loading checkpoint '{checkpoint_url}'\")\n",
"model.load_state_dict(checkpoint['state_dict'])\n",
"recover_model = lambda: model.load_state_dict(checkpoint['state_dict'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZxBWrQIeoSu6",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d4a42ac7-eb69-4b0e-9f2e-4189d07d5040"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar10/cifar-10-python.tar.gz\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 170498071/170498071 [00:05<00:00, 30751597.43it/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Extracting data/cifar10/cifar-10-python.tar.gz to data/cifar10\n",
"Files already downloaded and verified\n"
]
}
],
"source": [
"image_size = 32\n",
"transforms = {\n",
" \"train\": Compose([\n",
" RandomCrop(image_size, padding=4),\n",
" RandomHorizontalFlip(),\n",
" ToTensor(),\n",
" ]),\n",
" \"test\": ToTensor(),\n",
"}\n",
"dataset = {}\n",
"for split in [\"train\", \"test\"]:\n",
" dataset[split] = CIFAR10(\n",
" root=\"data/cifar10\",\n",
" train=(split == \"train\"),\n",
" download=True,\n",
" transform=transforms[split],\n",
" )\n",
"dataloader = {}\n",
"for split in ['train', 'test']:\n",
" dataloader[split] = DataLoader(\n",
" dataset[split],\n",
" batch_size=512,\n",
" shuffle=(split == 'train'),\n",
" num_workers=0,\n",
" pin_memory=True,\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rjESsg5nkdBG"
},
"source": [
"# Let's First Evaluate the Accuracy and Model Size of Dense Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mMjz8MHjZRLg"
},
"source": [
"Neural networks have become ubiquitous in many applications. Here we have loaded a pretrained VGG model for classifying images in CIFAR10 dataset.\n",
"\n",
"Let's first evaluate the accuracy and model size of this model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DTiA8hxMkkbU",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52,
"referenced_widgets": [
"10e602da054f4a868f523d8b3b211751",
"157bed79805944cd9a15dd1125ae319d",
"2d38e867fbd24514a70c85ad0babc223",
"d92f58dad548490aa7d4e28ab461b0ab",
"8e73405283084f66b0c56bbbab4564ce",
"dfca63ad3a334383b82e01a93afb8f3b",
"69732ab72e094c5a928f7c81e29f35b1",
"718f9ce911a94b3a8106043daa52169d",
"db3f67e9b27d46b59746ec6f3505cac8",
"8a57e4bfc5924a25b0e0e7ecbfde8925",
"9849a9d4186e481e94238076c319fb84"
]
},
"outputId": "d5950075-ac85-4a1e-e276-fbf472ce73ff"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "10e602da054f4a868f523d8b3b211751"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"dense model has accuracy=92.95%\n",
"dense model has size=35.20 MiB\n"
]
}
],
"source": [
"dense_model_accuracy = evaluate(model, dataloader['test'])\n",
"dense_model_size = get_model_size(model)\n",
"print(f\"dense model has accuracy={dense_model_accuracy:.2f}%\")\n",
"print(f\"dense model has size={dense_model_size/MiB:.2f} MiB\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G-Oj8IYkxKst"
},
"source": [
"While large neural networks are very powerful, their size consumes considerable storage, memory bandwidth, and computational resources.\n",
"As we can see from the results above, a model for the task as simple as classifying $32\\times32$ images into 10 classes can be as large as 35 MiB.\n",
"For embedded mobile applications, these resource demands become prohibitive."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "05ebw8KW1wDj"
},
"source": [
"Therefore, neural network pruning is exploited to facilitates storage and transmission of mobile applications incorporating DNNs.\n",
"\n",
"The goal of pruning is to reduce the model size while maintaining the accuracy."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9EtFfSfCumT6"
},
"source": [
"# Let's see the distribution of weight values"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0LNx2qCtZFlh"
},
"source": [
"Before we jump into pruning, let's see the distribution of weight values in the dense model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qg2rkEo8umkb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 609
},
"outputId": "487a71c6-7b20-4aa2-d8b8-97dad1424e2f"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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/j5SUFPTt2xd2dnbYtm0btm3bhgkTJhj1sVdUeeHh4YiIiEBISAiysrKwYsUK5OXl4e233zZ1aURERNXGS82JiMhq7dy5E3PnzsXJkyeRn5+PwMBAjBo1Cn/729/0biZGtefNN9/EN998g0uXLkGj0aBTp06YPXs2IiMjTV0aERFRtbHxJiIiIiIiIlIRv+NNREREREREpCI23kREREREREQqYuNNREREREREpCI23kREREREREQqYuNNREREREREpCI23kREREREREQqYuNNREREREREpCI23kREREREREQqYuNNREREREREpCI23kREREREREQqYuNNREREREREpCI23kREREREREQqsjN1AWorLi7G5cuX4ebmBo1GY+pyiMjERAQ3btyAv78/bGz42WMJZiUR3Y9ZaRizkojuV5WsrPON9+XLlxEQEGDqMojIzFy8eBGNGjUydRlmg1lJRIYwK/UxK4nIkMpkZZ1vvN3c3ADc2xnu7u4mroaITC0vLw8BAQFKNtA9zEoiuh+z0jBmJRHdrypZWecb75LLgNzd3RmQRKTgJYL6mJVEZAizUh+zkogMqUxW8ks7RERERERERCpi401ERERERESkIjbeRERERERERCpi40113pw5pq6AiMg8MR+JyJow88iU2HgTERERERERqYiNNxGRmYuNjUXXrl3h5uaGBg0aYOjQoUhPT9eb5vbt24iOjka9evXg6uqK4cOHIysry0QVkyXgmR8iIqLaw8abiMjMJSYmIjo6GgcOHMDOnTtRWFiIAQMG4ObNm8o006ZNw3fffYcNGzYgMTERly9fxrBhw0xYNRERkXmYM+d/HzbyQ0cylTr/HG8iIku3fft2vfdxcXFo0KABUlJS0Lt3b+Tm5mLFihVYu3Yt+vXrBwBYtWoVQkJCcODAAXTv3t0UZZOFuP+AlIiormG+kbngGW8iIguTm5sLAPD29gYApKSkoLCwEJGRkco0wcHBCAwMRFJSksFlFBQUIC8vT+9FRERkDfiBI5kCG2+yagxdsjTFxcWYOnUqevbsibZt2wIAMjMz4eDgAE9PT71pfX19kZmZaXA5sbGx8PDwUF4BAQFql05mgrlHRERU+9h4ExFZkOjoaBw/fhzr16+v0XJiYmKQm5urvC5evGikComIiCwHP4yk2sLveBMRWYiJEydi69at2LNnDxo1aqQM1+l0uHPnDnJycvTOemdlZUGn0xlcllarhVarVbtkMlM80CQiYhZS7bKoM94LFiyARqPB1KlTTV0KmRl+V4fqMhHBxIkTsWnTJuzatQtNmzbVG9+5c2fY29sjPj5eGZaeno4LFy4gPDy8tsslIiIyCzw2JHNiMWe8Dx06hM8++wyhoaGmLoWIqFZFR0dj7dq1+Pbbb+Hm5qZ8b9vDwwNOTk7w8PDAuHHjMH36dHh7e8Pd3R2TJk1CeHg472hOREREZAYs4ox3fn4+Ro4cieXLl8PLy6vcaXmnXiKqaz799FPk5uYiIiICfn5+yuurr75Splm8eDEeffRRDB8+HL1794ZOp8PGjRtNWDURERERlbCIxjs6OhqDBw/We1ROWXinXuJlRVTXiIjB15gxY5RpHB0dsXTpUmRnZ+PmzZvYuHFjmd/vJiIiov/hsSPVBrNvvNevX4+ff/4ZsbGxlZqed+olIiKqGh50EhERqcusv+N98eJFTJkyBTt37oSjo2Ol5uGdeomIiIiIrBs/UCRzY9ZnvFNSUnD16lV06tQJdnZ2sLOzQ2JiIj766CPY2dmhqKjI1CVSLbs/RA3dyZwhS0RUNmYkERGRaZj1Ge+HH34Yx44d0xs2duxYBAcH44033oCtra2JKiMiIiIiInPEDxnJHJl14+3m5oa2bdvqDXNxcUG9evVKDSciIiIiIqoOQ1dSEhmTWV9qTlRTJQHKICUiIiIiIlMx6zPehuzevdvUJRAREVkcfgBJRERkOjzjTURERGzMyeLFxsaia9eucHNzQ4MGDTB06FCkp6frTXP79m1ER0ejXr16cHV1xfDhw5GVlWWiikkNNcky5iCpiY03WRyGIhERET0oMTER0dHROHDgAHbu3InCwkIMGDAAN2/eVKaZNm0avvvuO2zYsAGJiYm4fPkyhg0bZsKqichaWNyl5kRERERED9q+fbve+7i4ODRo0AApKSno3bs3cnNzsWLFCqxduxb9+vUDAKxatQohISE4cOAAunfvXmqZBQUFKCgoUN7n5eWpuxFEVGfxjDcRERER1Tm5ubkAAG9vbwBASkoKCgsLERkZqUwTHByMwMBAJCUlGVxGbGwsPDw8lFdAQID6hRNRncQz3mTxeOk5ERER3a+4uBhTp05Fz549lUfQZmZmwsHBAZ6ennrT+vr6IjMz0+ByYmJiMH36dOV9Xl4em28zxeNBMnc8401ERFTH8YCUrE10dDSOHz+O9evX12g5Wq0W7u7uei+q25iXpBY23kRERASAB5xUN0ycOBFbt25FQkICGjVqpAzX6XS4c+cOcnJy9KbPysqCTqer5SqJyNqw8aY6iQePRERE1kVEMHHiRGzatAm7du1C06ZN9cZ37twZ9vb2iI+PV4alp6fjwoULCA8Pr+1yicjK8DveRERERGTxoqOjsXbtWnz77bdwc3NTvrft4eEBJycneHh4YNy4cZg+fTq8vb3h7u6OSZMmITw83OAdzYmIjImNNxERERFZvE8//RQAEBERoTd81apVGDNmDABg8eLFsLGxwfDhw1FQUICoqCh88skntVwpEVkjNt5EREREZPFEpMJpHB0dsXTpUixdurQWKiIi+h9+x5uIiIiIiIhIRTzjTUREREREFok31CVLwTPeZJGqE7IMZiIiIiKqCI8ZSQ1svImIiIiIiIhUpFrjnZCQoNaiiYgsAnOQiKhizEoisgaqNd4DBw5E8+bNMX/+fFy8eFGt1ZCV4CU/ZImYg2QOmJ9k7piVRGQNVGu8f/vtN0ycOBHffPMNmjVrhqioKHz99de4c+eOWqskIjIrzEEioooxK8kc8UNLMjbVGm8fHx9MmzYNaWlpSE5ORqtWrfDKK6/A398fkydPxpEjR9RaNRGRWWAOEhFVjFlJRNagVm6u1qlTJ8TExGDixInIz8/HypUr0blzZ/Tq1QsnTpyojRLICvGTSjInzEEioooxK4morlK18S4sLMQ333yDRx55BI0bN8aOHTvw8ccfIysrCxkZGWjcuDGeeuopNUsgIjIp5iARUcWYlVQdPMlClsROrQVPmjQJ69atg4hg1KhReP/999G2bVtlvIuLCxYtWgR/f3+1SiAiMinmIBFRxZiVZK7mzGFzT8ajWuN98uRJ/OMf/8CwYcOg1WoNTuPj48NHSBBRncUcJCKqGLOSiKyBapeaz549G0899VSpAL179y727NkDALCzs0OfPn3UKoGIyKSYg2SJeHaHahuzkoisgWqNd9++fZGdnV1qeG5uLvr27avWaomIzAZzkIioYsxKqg5+SEiWRrXGW0Sg0WhKDb927RpcXFzUWi3VMWp8t6ZkeQxsUhtzkEyNOUeWgFlJRNbA6N/xHjZsGABAo9FgzJgxepcNFRUV4ejRo+jRo4exV0tEZDaYg0REFWNWEpE1MXrj7eHhAeDep5dubm5wcnJSxjk4OKB79+4YP368sVdLRGQ2mINERBVjVpIl4J3NyViM3nivWrUKANCkSRPMmDGDlwhRtakRcgxOqg3MQSKiijEriciaqPY4sdmzZ6u1aCIii8AcJCKqGLOSiKyBURvvTp06IT4+Hl5eXujYsaPBG2WU+Pnnn425aiIis8AcJCKqGLOSaoJXMJIlMmrjPWTIEOXGGEOHDjXmoolMgt/roapiDhIRVYxZSZaEx4NkDEZtvO+/VIiXDRGRNWIOEhFVjFlJRNZGted4X7x4EZcuXVLeHzx4EFOnTsXnn3+u1iqJiMwKc5AsFc/sUG1iVlJVMJ/IUqnWeD/33HNISEgAAGRmZiIyMhIHDx7E3/72N8ybN0+t1ZKFYGiSNWAOEhFVjFlJRNZAtcb7+PHj6NatGwDg66+/Rrt27bB//36sWbMGcXFxaq2WiMhsMAeJiCrGrCQia6Ba411YWKjcNOPHH3/E448/DgAIDg7GlStX1FotEZHZYA4SEVWMWUlE1kC1xrtNmzZYtmwZ9u7di507d2LgwIEAgMuXL6NevXpqrZYs2P2Xn5viUnRe/k7GxhwkS8ZMpNrCrCRLwEykmlKt8X7vvffw2WefISIiAs8++yzat28PANiyZYtyORERUV3GHCQiqhizkoisgVEfJ3a/iIgI/PHHH8jLy4OXl5cyfMKECXB2dlZrtUREZoM5SKbEszNkKZiVRGQNVDvjDQC2trZ6AQoATZo0QYMGDdRcLVGVlXeAyoNXqglj5OCePXvw2GOPwd/fHxqNBps3b9YbLyKYNWsW/Pz84OTkhMjISJw9e9YY5RMR1QoeMxJRXada452VlYVRo0bB398fdnZ2sLW11XsREdV1xsrBmzdvon379li6dKnB8e+//z4++ugjLFu2DMnJyXBxcUFUVBRu375trE0hIlINjxnJUvBkDNWEapeajxkzBhcuXMDbb78NPz8/aDQatVZFRGSWjJWDgwYNwqBBgwyOExEsWbIEb731FoYMGQIA+OKLL+Dr64vNmzdjxIgRBucrKChAQUGB8j4vL69atRER1RSPGYnIGqjWeP/000/Yu3cvOnToUO1lxMbGYuPGjTh9+jScnJzQo0cPvPfeewgKCjJeoWQWTHlHc356SWoxRg5W5Pz588jMzERkZKQyzMPDA2FhYUhKSiqz8Y6NjcXcuXNVq4uIqLJqIyupbuAxG1ky1S41DwgIgIjUaBmJiYmIjo7GgQMHsHPnThQWFmLAgAG4efOmkaokIlKPMXKwIpmZmQAAX19fveG+vr7KOENiYmKQm5urvC5evKhqnUREZamNrCQiMjXVGu8lS5Zg5syZ+OWXX6q9jO3bt2PMmDFo06YN2rdvj7i4OFy4cAEpKSllzlNQUIC8vDy9FxGRKRgjB9Wi1Wrh7u6u9yIiMgVzzkoyH+Zytttc6iDLo9ql5s888wz+/PNPNG/eHM7OzrC3t9cbn52dXeVl5ubmAgC8vb3LnIaXT1o2cw2zOXPMtzYyX2rk4IN0Oh2Aezcn8vPzU4ZnZWXxsk0isgi1kZVERKamWuO9ZMkSoy6vuLgYU6dORc+ePdG2bdsyp4uJicH06dOV93l5eQgICDBqLURElWHsHDSkadOm0Ol0iI+PVxrtvLw8JCcn4+WXX1Z9/WSejPVBIT90pNpQG1lJRGRqqjXeo0ePNuryoqOjcfz4cfz000/lTqfVaqHVao26biKi6jBWDubn5yMjI0N5f/78eaSlpcHb2xuBgYGYOnUq5s+fj5YtW6Jp06Z4++234e/vj6FDhxpl/UREajLmMeOePXuwcOFCpKSk4MqVK9i0aZNeFooIZs+ejeXLlyMnJwc9e/bEp59+ipYtWxqtBjI+c/sAkB9KUnWo9h1vADh37hzeeustPPvss7h69SoAYNu2bThx4kSVljNx4kRs3boVCQkJaNSokRqlkgWojYBjkJKxGSMHDx8+jI4dO6Jjx44AgOnTp6Njx46YNWsWAOD111/HpEmTMGHCBHTt2hX5+fnYvn07HB0djb9BREQqMNYx482bN9G+fXssXbrU4Pj3338fH330EZYtW4bk5GS4uLggKioKt2/frvE2EBGVR7XGOzExEe3atUNycjI2btyI/Px8AMCRI0cwe/bsSi1DRDBx4kRs2rQJu3btQtOmTdUql4jI6IyRgwAQEREBESn1iouLAwBoNBrMmzcPmZmZuH37Nn788Ue0atVKjU0iIjI6Y2UlAAwaNAjz58/HE088UWqciGDJkiV46623MGTIEISGhuKLL77A5cuXsXnzZmNsChFRmVRrvGfOnIn58+dj586dcHBwUIb369cPBw4cqNQyoqOjsXr1aqxduxZubm7IzMxEZmYmbt26pVbZRERGY4wcJCKq62orK8+fP4/MzExERkYqwzw8PBAWFoakpCSD8/BpOURkLKo13seOHTP4aWODBg3wxx9/VGoZn376KXJzcxEREQE/Pz/l9dVXXxm7XCI9vNycjMEYOUhEVNfVVlZmZmYCAHx9ffWG+/r6KuMeFBsbCw8PD+XFG/ZSCR4rUlWp1nh7enriypUrpYanpqaiYcOGlVqGoUsrRQRjxowxcrVERMZnjBwkqipjHwzy4JLUZs5ZGRMTg9zcXOV18eJFk9ZDRJZLtcZ7xIgReOONN5CZmQmNRoPi4mLs27cPM2bMwPPPP6/WaomIzAZzkIioYrWVlTqdDgCQlZWlNzwrK0sZ9yCtVgt3d3e9F9UufvhHdYVqjfe7776L4OBgBAQEID8/H61bt0avXr3Qo0cPvPXWW2qtliyQKQKVIU61gTlIRFSx2srKpk2bQqfTIT4+XhmWl5eH5ORkhIeHG209RESGqPYcbwcHByxfvhyzZs3CsWPHkJ+fj44dO/I5iURkNZiDREQVM2ZW5ufnIyMjQ3l//vx5pKWlwdvbG4GBgZg6dSrmz5+Pli1bomnTpnj77bfh7++v96xvosriY2ipKozaeE+fPr3c8fffmfLDDz805qqJiMwCc5CIqGJqZeXhw4fRt2/fUusZPXo04uLi8Prrr+PmzZuYMGECcnJy8NBDD2H79u1wdHSs4hYQEVWNURvv1NRUvfc///wz7t69i6CgIADAmTNnYGtri86dOxtztWRmKvvpn6V9SvhgvZZWP9UO5iCZklqZxLwjY1MrKyMiIiAiZY7XaDSYN28e5s2bV/WiqdYxd6guMWrjnZCQoPz7ww8/hJubG/71r3/By8sLAHD9+nWMHTsWvXr1MuZqiYjMBnOQiKhizEoisjaq3Vztgw8+QGxsrBKgAODl5YX58+fjgw8+UGu1RERmgzlIRFQxZiUZYilnuy2lTjI91RrvvLw8/P7776WG//7777hx44ZaqyUiMhvMQapNPPgjS8WsJCJroFrj/cQTT2Ds2LHYuHEjLl26hEuXLuHf//43xo0bh2HDhqm1WiIis8EcpLqEjT2phVlJRNZAtceJLVu2DDNmzMBzzz2HwsLCeyuzs8O4ceOwcOFCtVZLRGQ2mINERBVjVtKD+EEf1UWqnfF2dnbGJ598gmvXriE1NRWpqanIzs7GJ598AhcXF7VWS2bowTuBWwpDtZZ1Z19L2i6qPcxBIqKKMSuJyBqodsa7hIuLC0JDQ9VeDRGR2WIOEhFVjFlJAE9mUN2l2hlvIiIiIiIiImLjTSqr6DLzuvSpZl3aFiKyLLWVP8w5IqLSmI1UGWy8iYiIiIjI5NjAUl3GxpuIiIiIiKgG+KEBVYSNN9UKS72zeXXU9e0jIiIiIqKqYeNNRERElcYPF4lIDcwWquvYeBMREVkwHqwSEZkH5jGVh403UTVUNVgZxERERERE1ouNNxERkYUy1Yd6/DCRiIypLmVKXdoWMi423kREREREREQqYuNN1WLo07wHh1nDJ35z5pS9nRWNIyIiIrJ2PCYia8HGm4iIyAKZ+mDV1OsnIiKyJGy8iYiILAybXiIi88WMJkPYeFO1MVRK4z4hIiIiIqIHsfEmIiKiauGHjUREhjEf6UFsvImIiIiIqNaxOSVrwsabVMEg/Z/y7vbO/UREVcXcIKK6wBqyzBq2kSqPjTcREZGFMMeDOHOsiYjMmzXlhjVtK5WPjTcRERERERGRith4W6nyPn178FLoiqataBq658H9ZGifcT8SERFRXWaNxzrWuM1UGhtvIiIiqhEeVBJRZVhzVljzttM9bLyJiIgsgLkftJl7fURkWswIsnZsvOuYikKtsqFX3XBkqN5T1f1QmUv6iYjMHbOKiIjIMDtTF0BERERlYzNLRJaOOXZPyX7g/rBOPONNRERkpizx4MwSayYi9TATSuM+sU5svOuo8u6YXdanbeW9v38ehkXtqszPsqzpypueiMybJf+9WnLtRGQ8zIKycd9YHzbeREREZHT8oJbIuvHvv2LcR9aFjTcREZGZ4cEYEVkyZhhRaWy8q8FUd5+uzp2yqzoN76xd+yr7M6jMdJW99Lwy6yMi06hrf5N1bXuIqGy80qXquL+sB+9qTkREZGIlB6t19QCsOh8KEpFl4d929TEjrQPPeBMREZmQtd380Fq2k8ia8O/aeLgv6y6LaLyXLl2KJk2awNHREWFhYTh48KCq6yvvjtFV+WMwxqW+FdVi6CxJRZcgV2UcmY6hs19lnRGr7B3py1pOZe5aX1Y91WHMy+D5+6uvtvOSqo5PibDe7SbzwaysmbKOQck4uF/rJrO/1Pyrr77C9OnTsWzZMoSFhWHJkiWIiopCeno6GjRoYOryiIjMBvPSfBnrQ6u6hPuETIVZWXX8+zQNXoJet5h94/3hhx9i/PjxGDt2LABg2bJl+M9//oOVK1di5syZpaYvKChAQUGB8j43NxcAkJeXV+l1FhQAeXn/+++Dw+4tr/x5H/x3ZdZXUS0l631wWImyhpUspzyG5iXTqcrP48FpDf2+lDV9ef9+kKFxlf0dN7Ssmv5tVLeGkiwQkcrPZCGqkpfGyErSFxt7778xMf/7N1VeTEz1xpE6mJX3WEtW3p9ZzDDzZCgHmY2mV6WsFDNWUFAgtra2smnTJr3hzz//vDz++OMG55k9e7YA4Isvvvgq93Xx4sVaSLHaU9W8ZFbyxRdflXkxK5mVfPHFV8WvymSlWZ/x/uOPP1BUVARfX1+94b6+vjh9+rTBeWJiYjB9+nTlfXFxMbKzs1GvXj1oNBpV661IXl4eAgICcPHiRbi7u5u0lsqytJotrV7A8mq2tHoB/Zrd3Nxw48YN+Pv7m7oso6pqXppzVgKW/3vGmtVhafUCll3zyZMnmZW1mJWW/LtiKTVbWr0Aa64NNalXRCp9XGnWjXd1aLVaaLVavWGenp6mKaYM7u7uFvFLeD9Lq9nS6gUsr2ZLqxf4X80eHh6mLsXkLCErAcv+PbMkllazpdULWGbNDRs2hI2NRdyHVzWmyEpL/F2xtJotrV6ANdeG6tZb2eNKs05THx8f2NraIisrS294VlYWdDqdiaoiIjI/zEsioooxK4nIVMy68XZwcEDnzp0RHx+vDCsuLkZ8fDzCw8NNWBkRkXlhXhIRVYxZSUSmYvaXmk+fPh2jR49Gly5d0K1bNyxZsgQ3b95U7kRpSbRaLWbPnl3qkiVzZmk1W1q9gOXVbGn1ApZZc3UwL02LNavP0uoFWLM5MtestMT9bmk1W1q9AGuuDbVVr0bE/J8T8fHHH2PhwoXIzMxEhw4d8NFHHyEsLMzUZRERmR3mJRFRxZiVRFTbLKLxJiIiIiIiIrJUZv0dbyIiIiIiIiJLx8abiIiIiIiISEVsvImIiIiIiIhUxMabiIiIiIiISEVsvI0sOzsbI0eOhLu7Ozw9PTFu3Djk5+eXO09ERAQ0Go3e66WXXtKb5sKFCxg8eDCcnZ3RoEEDvPbaa7h7926t15udnY1JkyYhKCgITk5OCAwMxOTJk5Gbm6s33YPbo9FosH79+mrVuHTpUjRp0gSOjo4ICwvDwYMHy51+w4YNCA4OhqOjI9q1a4fvv/9eb7yIYNasWfDz84OTkxMiIyNx9uzZatVW03qXL1+OXr16wcvLC15eXoiMjCw1/ZgxY0rty4EDBxqt3qrWHBcXV6oeR0dHvWnMaR8b+vvSaDQYPHiwMk1t7GOqmFp5ZE41A8Dnn3+OiIgIuLu7Q6PRICcnR9UajZ2haqtKvSdOnMDw4cPRpEkTaDQaLFmypPYKvY+xc782VKXmjRs3okuXLvD09ISLiws6dOiAL7/8sharrTvUyozqLFetem/fvo3o6GjUq1cPrq6uGD58OLKysvSmsebjRDVqVvs4Ro1cruo+MHXNc+bMKbWPg4ODq1aUkFENHDhQ2rdvLwcOHJC9e/dKixYt5Nlnny13nj59+sj48ePlypUryis3N1cZf/fuXWnbtq1ERkZKamqqfP/99+Lj4yMxMTG1Xu+xY8dk2LBhsmXLFsnIyJD4+Hhp2bKlDB8+XG86ALJq1Sq9bbp161aV61u/fr04ODjIypUr5cSJEzJ+/Hjx9PSUrKwsg9Pv27dPbG1t5f3335eTJ0/KW2+9Jfb29nLs2DFlmgULFoiHh4ds3rxZjhw5Io8//rg0bdq0WvXVtN7nnntOli5dKqmpqXLq1CkZM2aMeHh4yKVLl5RpRo8eLQMHDtTbl9nZ2TWutbo1r1q1Stzd3fXqyczM1JvGnPbxtWvX9Go9fvy42NrayqpVq5Rp1N7HVDlq5ZE51SwisnjxYomNjZXY2FgBINevX1etPjUyVE1VrffgwYMyY8YMWbduneh0Olm8eHGt1Hk/NXLf3GpOSEiQjRs3ysmTJyUjI0OWLFkitra2sn379lqrua5QKzOqs1y16n3ppZckICBA4uPj5fDhw9K9e3fp0aOH3jTWepyoVs1qHseokctVXaY51Dx79mxp06aN3j7+/fffq1QXG28jOnnypACQQ4cOKcO2bdsmGo1GfvvttzLn69Onj0yZMqXM8d9//73Y2NjoNTeffvqpuLu7S0FBQa3X+6Cvv/5aHBwcpLCwUBkGQDZt2lTt2kp069ZNoqOjlfdFRUXi7+8vsbGxBqd/+umnZfDgwXrDwsLC5K9//auIiBQXF4tOp5OFCxcq43NyckSr1cq6detqvd4H3b17V9zc3ORf//qXMmz06NEyZMiQGtdWlqrWvGrVKvHw8Chzeea+jxcvXixubm6Sn5+vDFN7H1PF1MwjtdS05oSEBNUbb2NnqNpq8vfduHFjkzTeauS+2mpas4hIx44d5a233lKjvDpLrcwwVn4ao96cnByxt7eXDRs2KMNOnTolACQpKUkZZq3HiWrULKLucYwauWyMDKrtmmfPni3t27evUV281NyIkpKS4OnpiS5duijDIiMjYWNjg+Tk5HLnXbNmDXx8fNC2bVvExMTgzz//1Ftuu3bt4OvrqwyLiopCXl4eTpw4YZJ675ebmwt3d3fY2dnpDY+OjoaPjw+6deuGlStXQqr4yPg7d+4gJSUFkZGRyjAbGxtERkYiKSmpzG26f3rg3r4qmf78+fPIzMzUm8bDwwNhYWFlLlPNeh/0559/orCwEN7e3nrDd+/ejQYNGiAoKAgvv/wyrl27VqNaa1pzfn4+GjdujICAAAwZMkTv99Dc9/GKFSswYsQIuLi46A1Xax9T5aidR2owVs1qUSND1WSMv+/apmbuq6WmNYsI4uPjkZ6ejt69e6tZap2jVmaY03JTUlJQWFio9/sVHByMwMDAUr9f1nacqFbNJdQ4jlEjl9XOejWXf/bsWfj7+6NZs2YYOXIkLly4UKX51T8ysSKZmZlo0KCB3jA7Ozt4e3sjMzOzzPmee+45NG7cGP7+/jh69CjeeOMNpKenY+PGjcpy72+6ASjvy1uuWvXe748//sA777yDCRMm6A2fN28e+vXrB2dnZ/zwww945ZVXkJ+fj8mTJ1e6vj/++ANFRUUGt/306dNlbpOh6Uu2p+S/5U1TXdWp90FvvPEG/P399cJi4MCBGDZsGJo2bYpz587hzTffxKBBg5CUlARbW9tarzkoKAgrV65EaGgocnNzsWjRIvTo0QMnTpxAo0aNzHofHzx4EMePH8eKFSv0hqu5j6ly1MwjtRijZjWpkaFqMkaG1ja1cl9N1a05NzcXDRs2REFBAWxtbfHJJ5+gf//+apdbp6iVGea03MzMTDg4OMDT01Nv+IM5Yo3HiWrVDKh3HKNGLqud9WotPywsDHFxcQgKCsKVK1cwd+5c9OrVC8ePH4ebm1ullsHGuxJmzpyJ9957r9xpTp06Ve3l33+Q2K5dO/j5+eHhhx/GuXPn0Lx58yovT+16S+Tl5WHw4MFo3bo15syZozfu7bffVv7dsWNH3Lx5EwsXLqxSoFqbBQsWYP369di9e7fezcpGjBih/Ltdu3YIDQ1F8+bNsXv3bjz88MO1Xmd4eDjCw8OV9z169EBISAg+++wzvPPOO7VeT1WsWLEC7dq1Q7du3fSGm9s+rkvMIY+qqrZqJior982Rm5sb0tLSkJ+fj/j4eEyfPh3NmjVDRESEqUszOUvLDHOol8eJxsXjGPUNGjRI+XdoaCjCwsLQuHFjfP311xg3blyllsHGuxJeffVVjBkzptxpmjVrBp1Oh6tXr+oNv3v3LrKzs6HT6Sq9vrCwMABARkYGmjdvDp1OV+pOfCV3hzS03Nqo98aNGxg4cCDc3NywadMm2NvbV7hN77zzDgoKCqDVasudtoSPjw9sbW1L3QkzKyurzPp0Ol2505f8NysrC35+fnrTdOjQoVJ1GbPeEosWLcKCBQvw448/IjQ0tNxpmzVrBh8fH2RkZNQ4TGtScwl7e3t07NgRGRkZAMx3H9+8eRPr16/HvHnzKlyPMfextTPHPDKHmmuDGhmqJmPkUW2rrdw3purWbGNjgxYtWgAAOnTogFOnTiE2NpaNN0yfGVVdrpr16nQ63LlzBzk5OXpnvSv6/bKG40S1ajbEWMcxauSy2llfW/8v8fT0RKtWrZTj38rgd7wroX79+ggODi735eDggPDwcOTk5CAlJUWZd9euXSguLlaa6cpIS0sDAOUPPjw8HMeOHdMLv507d8Ld3R2tW7eu9Xrz8vIwYMAAODg4YMuWLZX6lD4tLQ1eXl6VDlMAcHBwQOfOnREfH68MKy4uRnx8vN4Z1/uFh4frTQ/c21cl0zdt2hQ6nU5vmry8PCQnJ5e5TDXrBYD3338f77zzDrZv3673PaqyXLp0CdeuXdP7H0Jt13y/oqIiHDt2TKnHHPcxcO9RHAUFBfjLX/5S4XqMuY+tnTnmkalrri1qZKiajJFHta22ct+YjLWfi4uLUVBQoEaJFsfUmVHV5apZb+fOnWFvb6/3+5Weno4LFy6U+/tlDceJatVsiLGOY9TIZbWzvrb+X5Kfn49z585VbR/X6NZsVMrAgQOlY8eOkpycLD/99JO0bNlS77ELly5dkqCgIElOThYRkYyMDJk3b54cPnxYzp8/L99++600a9ZMevfurcxT8jixAQMGSFpammzfvl3q169vtMeJVaXe3NxcCQsLk3bt2klGRobeLfXv3r0rIiJbtmyR5cuXy7Fjx+Ts2bPyySefiLOzs8yaNavK9a1fv160Wq3ExcXJyZMnZcKECeLp6anc4X3UqFEyc+ZMZfp9+/aJnZ2dLFq0SE6dOiWzZ882+JgIT09P+fbbb+Xo0aMyZMgQoz7qqir1LliwQBwcHOSbb77R25c3btwQEZEbN27IjBkzJCkpSc6fPy8//vijdOrUSVq2bCm3b9+ucb3VqXnu3LmyY8cOOXfunKSkpMiIESPE0dFRTpw4obdd5rKPSzz00EPyzDPPlBpeG/uYKkeNPDK3mkVErly5IqmpqbJ8+XIBIHv27JHU1FS5du2a0etTI0PVVNV6CwoKJDU1VVJTU8XPz09mzJghqampcvbs2Vqptzo1V5T75ljzu+++Kz/88IOcO3dOTp48KYsWLRI7OztZvnx5rdVcV6iVGRUttzbrfemllyQwMFB27dolhw8flvDwcAkPD1fGW/Nxoho1q30co0YuV7RMc6z51Vdfld27d8v58+dl3759EhkZKT4+PnL16tVK18XG28iuXbsmzz77rLi6uoq7u7uMHTtW73+m58+fFwCSkJAgIiIXLlyQ3r17i7e3t2i1WmnRooW89tpres/xFhH55ZdfZNCgQeLk5CQ+Pj7y6quvGuVxOVWtt+RRFoZe58+fF5F7j5ro0KGDuLq6iouLi7Rv316WLVsmRUVF1arxH//4hwQGBoqDg4N069ZNDhw4oIzr06ePjB49Wm/6r7/+Wlq1aiUODg7Spk0b+c9//qM3vri4WN5++23x9fUVrVYrDz/8sKSnp1ertprW27hxY4P7cvbs2SIi8ueff8qAAQOkfv36Ym9vL40bN5bx48cbLZiqU/PUqVOVaX19feWRRx6Rn3/+WW955rSPRUROnz4tAOSHH34otaza2sdUMTXyyNxqFrn3SBJDNd//bHljMnaGqq0q9Zbs3wdfffr0MduaK8p9c6z5b3/7m7Ro0UIcHR3Fy8tLwsPDZf369bVab12hVmZUtNzarPfWrVvyyiuviJeXlzg7O8sTTzwhV65cUcZb+3GisWuujeMYNXK5vGWaY83PPPOM+Pn5iYODgzRs2FCeeeYZycjIqFJNGpEq3rufiIiIiIiIiCqN3/EmIiIiIiIiUhEbbyIiIiIiIiIVsfEmIiIiIiIiUhEbbyIiIiIiIiIVsfEmIiIiIiIiUhEbbyIiIiIiIiIVsfEmIiIiIiIiUhEbbyIiIiIiIiIVsfG2EBEREZg6dapqyx8zZgyGDh1a5vg5c+agQ4cOqq2fai4uLg6enp5VmqeinzuRpWFWUkV2794NjUaDnJycSs/DnyvVNcxKqgiPK42PjTeRkRw9ehS9evWCo6MjAgIC8P7779fq+p955hmcOXPG6Mtt0qQJlixZYvTlEpH1uX37NsaMGYN27drBzs7OJAdoPXr0wJUrV+Dh4WHU5ardyBCR9di9ezeGDBkCPz8/uLi4oEOHDlizZk2t1sDjSuOzM3UBRHVBXl4eBgwYgMjISCxbtgzHjh3DCy+8AE9PT0yYMKFWanBycoKTk1OtrIuIqDqKiorg5OSEyZMn49///rdJanBwcIBOpzPJuomIKmP//v0IDQ3FG2+8AV9fX2zduhXPP/88PDw88Oijj9ZKDTyuND6e8bYgd+/excSJE+Hh4QEfHx+8/fbbEBEAwJdffokuXbrAzc0NOp0Ozz33HK5evao3/4kTJ/Doo4/C3d0dbm5u6NWrF86dO2dwXYcOHUL9+vXx3nvv6Q3/7LPPEBAQAGdnZzz99NPIzc1VxhUXF2PevHlo1KgRtFotOnTogO3btyvjf/nlF2g0GmzcuBF9+/aFs7Mz2rdvj6SkJL11/PTTT+jVqxecnJwQEBCAyZMn4+bNm+Xum++++w5du3aFo6MjfHx88MQTTyjjrl+/jueffx5eXl5wdnbGoEGDcPbsWWV8yaU0O3bsQEhICFxdXTFw4EBcuXIFAPDDDz/A0dGx1GWJU6ZMQb9+/QAAa9aswZ07d7By5Uq0adMGI0aMwOTJk/Hhhx+WWXOXLl2waNEi5f3QoUNhb2+P/Px8AMClS5eg0WiQkZEBACgoKMCMGTPQsGFDuLi4ICwsDLt37y61HfebP38+GjRoADc3N7z44ouYOXOmwUu7Fi1aBD8/P9SrVw/R0dEoLCwEcO8Mzq+//opp06ZBo9FAo9GUuT1E5oJZWTZTZ6WLiws+/fRTjB8/vtLN75NPPomJEycq76dOnQqNRoPTp08DAO7cuQMXFxf8+OOPyv6NjY1F06ZN4eTkhPbt2+Obb75R5jd0qfny5cuVn9cTTzyBDz/80OAlll9++SWaNGkCDw8PjBgxAjdu3ABw7/LKxMRE/N///Z+Slb/88kulto/IVJiVZTN1Vr755pt455130KNHDzRv3hxTpkzBwIEDsXHjxjJr5nGlBRCyCH369BFXV1eZMmWKnD59WlavXi3Ozs7y+eefi4jIihUr5Pvvv5dz585JUlKShIeHy6BBg5T5L126JN7e3jJs2DA5dOiQpKeny8qVK+X06dMiIjJ69GgZMmSIiIjEx8eLh4eHfPbZZ8r8s2fPFhcXF+nXr5+kpqZKYmKitGjRQp577jllmg8//FDc3d1l3bp1cvr0aXn99dfF3t5ezpw5IyIi58+fFwASHBwsW7dulfT0dHnyySelcePGUlhYKCIiGRkZ4uLiIosXL5YzZ87Ivn37pGPHjjJmzJgy983WrVvF1tZWZs2aJSdPnpS0tDR59913lfGPP/64hISEyJ49eyQtLU2ioqKkRYsWcufOHRERWbVqldjb20tkZKQcOnRIUlJSJCQkRNm2u3fviq+vr/zzn/9UlvngsFGjRin7r8SuXbsEgGRnZxuse/r06TJ48GARESkuLhZvb2/x8fGRbdu2iYjI6tWrpWHDhsr0L774ovTo0UP27NkjGRkZsnDhQtFqtcr+XbVqlXh4eCjTr169WhwdHWXlypWSnp4uc+fOFXd3d2nfvr0yzejRo8Xd3V1eeuklOXXqlHz33Xd6v1fXrl2TRo0aybx58+TKlSty5cqVMn8OROaAWWneWXm/+/dleT766CNp06aN8r5Dhw7i4+Mjn376qYiI/PTTT2Jvby83b94UEZH58+dLcHCwbN++Xc6dOyerVq0SrVYru3fvFhGRhIQEASDXr19X5rexsZGFCxdKenq6LF26VLy9vfXydPbs2eLq6irDhg2TY8eOyZ49e0Sn08mbb74pIiI5OTkSHh4u48ePV7Ly7t27FW4bkakwKy0nK0v07NlTXn311TLH87jS/LHxthB9+vSRkJAQKS4uVoa98cYbEhISYnD6Q4cOCQC5ceOGiIjExMRI06ZNlVB4UElAbty4UVxdXWX9+vV642fPni22trZy6dIlZdi2bdvExsZG+aPx9/eXv//973rzde3aVV555RUR+V9A3h8qJ06cEABy6tQpEREZN26cTJgwQW8Ze/fuFRsbG7l165bB2sPDw2XkyJEGx505c0YAyL59+5Rhf/zxhzg5OcnXX38tIveCBYBkZGQo0yxdulR8fX2V91OmTJF+/fop73fs2CFarVY5cOvfv3+puku27eTJkwZr27Jli3h4eMjdu3clLS1NdDqdTJkyRd544w0RuReIJSH966+/iq2trfz22296y3j44YclJiZG2Y77AzIsLEyio6P1pu/Zs2epgGzcuLHeAeJTTz0lzzzzjPK+cePGsnjxYoPbQGRumJXmnZX3q2zjffToUdFoNHL16lXJzs4WBwcHeeedd5Scmj9/vvTo0UNERG7fvi3Ozs6yf/9+vWWMGzdOnn32WREp3Xg/88wzysFqiZEjR5ZqvJ2dnSUvL08Z9tprr0lYWJjyvk+fPjJlypQKt4fIHDArLScrRUS++uorcXBwkOPHjxscL8LjSkvAS80tSPfu3fUuyQgPD8fZs2dRVFSElJQUPPbYYwgMDISbmxv69OkDALhw4QIAIC0tDb169YK9vX2Zy09OTsZTTz2FL7/8Es8880yp8YGBgWjYsKHe+ouLi5Geno68vDxcvnwZPXv21JunZ8+eOHXqlN6w0NBQ5d9+fn4AoFy+dOTIEcTFxcHV1VV5RUVFobi4GOfPnzdYd1paGh5++GGD406dOgU7OzuEhYUpw+rVq4egoCC9upydndG8eXO9uu6/pGrkyJHYvXs3Ll++DODepeWDBw+u8t0e79erVy/cuHEDqampSExMRJ8+fRAREaFc5pOYmIiIiAgAwLFjx1BUVIRWrVrp7ZvExMQyL+tKT09Ht27d9IY9+B4A2rRpA1tb2zK3ncjSMCvrVla2bdsW3t7eSExMxN69e9GxY0c8+uijSExMBKCflRkZGfjzzz/Rv39/vX3zxRdf1DgrmzRpAjc3tzK3ncjSMCstIysTEhIwduxYLF++HG3atDFYF8DjSkvAm6vVAbdv30ZUVBSioqKwZs0a1K9fHxcuXEBUVBTu3LkDAJW6OULz5s1Rr149rFy5EoMHDy43TGvi/uWWBH5xcTEAID8/H3/9618xefLkUvMFBgYaXJ4xbvzw4LZqNBrle04A0LVrVzRv3hzr16/Hyy+/jE2bNiEuLk4Zr9PpkJWVpbeMkvdlfY/R09MT7du3x+7du5GUlIT+/fujd+/eyl0kz549q/yPLj8/H7a2tkhJSdELMwBwdXWt9nYDhre95OdBVJcwK02fldWh0WjQu3dv7N69G1qtFhEREQgNDUVBQQGOHz+O/fv3Y8aMGQCgfJfxP//5j94BPQBotdoa1cGsJGvBrDSfrExMTMRjjz2GxYsX4/nnny93nTyuNH88421BkpOT9d4fOHAALVu2xOnTp3Ht2jUsWLAAvXr1QnBwcKlPlkJDQ7F3717l5gaG+Pj4YNeuXcjIyMDTTz9datoLFy4on8yVrN/GxgZBQUFwd3eHv78/9u3bpzfPvn370Lp160pvY6dOnXDy5Em0aNGi1MvBwcHgPKGhoYiPjzc4LiQkBHfv3tXbd9euXUN6enqV6gLufTq5Zs0afPfdd7CxscHgwYOVceHh4dizZ4/ePtu5cyeCgoLg5eVV5jL79OmDhIQE7NmzBxEREfD29kZISAj+/ve/w8/PD61atQIAdOzYEUVFRbh69Wqp/VJWYx8UFIRDhw7pDXvwfWU4ODigqKioyvMRmQqz0nyzsrr69OmD3bt3Y/fu3YiIiICNjQ169+6NhQsXoqCgQDkr1rp1a2i1Wly4cKHUfgkICDC4bGYlWStmpXln5e7duzF48GC89957lX5CDo8rzZyJL3WnSiq5Cca0adPk9OnTsnbtWnFxcZFly5bJ1atXxcHBQV577TU5d+6cfPvtt9KqVSsBIKmpqSJy7/sn9erVU26CcebMGfniiy8M3gTjypUrEhwcLMOHD1duTlFyE4zIyEhJS0uTPXv2SKtWrWTEiBFKjYsXLxZ3d3dZv369nD59Wt544w2DN8EoqUlE5Pr16wJAEhISRETkyJEj4uTkJNHR0ZKamipnzpyRzZs3632nZObMmTJq1CjlfUJCgtjY2Cg3wTh69KgsWLBAGT9kyBBp3bq17N27V9LS0mTgwIGlboJx/3dYREQ2bdokD/55nD17VgBIaGiojBs3Tm9cTk6O+Pr6yqhRo+T48eOyfv16cXZ21ruRyMaNGyUoKEhvvs2bN4utra3odDpl2JQpU8TW1lZv34rc+85hkyZN5N///rf897//leTkZHn33Xdl69atBrdj9erV4uTkJHFxcXLmzBl55513xN3dXTp06KBMY+g7llOmTJE+ffoo7/v37y+PP/64XLp0SX7//XchMmfMSvPOSpF738FMTU2Vxx57TCIiIiQ1NVVvW5OTkyUoKEjvu59paWmi0WhEq9Uq3zFdvHix2NraSvfu3fWW/7e//U3q1asncXFxkpGRISkpKfLRRx9JXFycsh9g4OZqH3zwgZw5c0aWLVsm9erVE09PT2WZs2fP1vseY8n6GzdurLwfP368dO3aVc6fPy+///67FBUVldp2InPBrDTvrNy1a5c4OztLTEyMchOyK1euyLVr15RpeFxpedh4W4g+ffrIK6+8Ii+99JK4u7uLl5eXvPnmm8pNMdauXStNmjQRrVYr4eHhsmXLllJhdOTIERkwYIA4OzuLm5ub9OrVS86dOycipf9QLl++LK1atZKnn35a7t69qxx0fPLJJ+Lv7y+Ojo7y5JNP6t2xu6ioSObMmSMNGzYUe3t7ad++vXInRZHKBaSIyMGDB6V///7i6uoqLi4uEhoaqndzjdGjR+v9AYuI/Pvf/5YOHTqIg4OD+Pj4yLBhw5Rx2dnZMmrUKPHw8BAnJyeJiopSQluk8gEpItKtWzcBILt27So17siRI/LQQw+JVquVhg0b6oV0yXoeXOa1a9dEo9Ho3XSiZN3Lli3Tm/bOnTsya9YsadKkidjb24ufn5888cQTcvTo0TK3Y968eeLj4yOurq7ywgsvyOTJk/UOUisTkElJSRIaGipardbgPiEyJ8xK88/Kxo0bC4BSrxIljfH58+f19pmXl5fezcxSU1MFgMycOVNv+cXFxbJkyRIJCgoSe3t7qV+/vkRFRUliYqLe8u+/idHnn38uDRs2FCcnJxk6dKjMnz9f78C1Mo13enq6dO/eXZycnErVT2RumJXmnZWjR482mJP318njSsujEbnvCwdEVKf1798fOp0OX375palLISIyW+PHj8fp06exd+9eU5dCRGS2eFxZNby5GlEd9eeff2LZsmWIioqCra0t1q1bhx9//BE7d+40dWlERGZl0aJF6N+/P1xcXLBt2zb861//wieffGLqsoiIzAaPK2uOZ7yJ6qhbt27hscceQ2pqKm7fvo2goCC89dZbGDZsmKlLIyIyK08//TR2796NGzduoFmzZpg0aRJeeuklU5dFRGQ2eFxZc2y8iYiIiIiIiFTEx4kRERERERERqYiNNxEREREREZGK2HgTERERERERqYiNNxEREREREZGK2HgTERERERERqYiNNxEREREREZGK2HgTERERERERqYiNNxEREREREZGK2HgTERERERERqYiNNxEREREREZGK2HgTERERERERqYiNNxEREREREZGK7ExdgNqKi4tx+fJluLm5QaPRmLocIjIxEcGNGzfg7+8PGxt+9liCWUlE92NWGsasJKL7VSUr63zjffnyZQQEBJi6DCIyMxcvXkSjRo1MXYbZYFYSkSHMSn3MSiIypDJZWecbbzc3NwD3doa7u7uJqyEiU8vLy0NAQICSDXQPs5KI7sesNIxZSUT3q0pW1vnGu+QyIHd3dwYkESl4iaA+ZiURGcKs1MesJCJDKpOV/NIOERERERERkYrYeBMRERERERGpiI03ERERERERkYrYeJNZmzPH1BUQEZnOnDnMQSKiqirJTeYnmRM23kRERBaAB5BERJXH5pvMTZ2/qzlZngcDksFJRNaO+UdEVH28eojMAc94ExERmSFDB4k8cCQiKh9zkswVG28yKwxLIqLyMSeJiKqO2UmmxsabLAYvEyIiIiIiIkvExpvMBptqIqLKfcjIvCQiIrIsbLyJiMzcp59+itDQULi7u8Pd3R3h4eHYtm2bMv727duIjo5GvXr14OrqiuHDhyMrK8uEFRMREdU+fmhJ5oyNNxGRmWvUqBEWLFiAlJQUHD58GP369cOQIUNw4sQJAMC0adPw3XffYcOGDUhMTMTly5cxbNgwE1dNRERkfth8k6mw8SazwBAkKttjjz2GRx55BC1btkSrVq3w97//Ha6urjhw4AByc3OxYsUKfPjhh+jXrx86d+6MVatWYf/+/Thw4ICpSycVMTeJiIgsBxtvsjg82CRrVlRUhPXr1+PmzZsIDw9HSkoKCgsLERkZqUwTHByMwMBAJCUllbmcgoIC5OXl6b3I9JhvRETVw/wkc8fGm4jIAhw7dgyurq7QarV46aWXsGnTJrRu3RqZmZlwcHCAp6en3vS+vr7IzMwsc3mxsbHw8PBQXgEBASpvARFR7VqwYAE0Gg2mTp2qDOM9MYjIVNh4ExFZgKCgIKSlpSE5ORkvv/wyRo8ejZMnT1Z7eTExMcjNzVVeFy9eNGK1VFt4hofIsEOHDuGzzz5DaGio3nDeE4OITIWNN5lUdQ8aebBJ1sbBwQEtWrRA586dERsbi/bt2+P//u//oNPpcOfOHeTk5OhNn5WVBZ1OV+bytFqtcpf0khcRUV2Qn5+PkSNHYvny5fDy8lKGV+eeGPxajmWo6nEhjyPJFNh4k8mx+SaquuLiYhQUFKBz586wt7dHfHy8Mi49PR0XLlxAeHi4CSskIjKN6OhoDB48WO/eFwCqdU8Mfi2HiIzFztQFEBFR+WJiYjBo0CAEBgbixo0bWLt2LXbv3o0dO3bAw8MD48aNw/Tp0+Ht7Q13d3dMmjQJ4eHh6N69u6lLpyrgh4lENbd+/Xr8/PPPOHToUKlx1bknRkxMDKZPn668z8vLY/NNRNXCxptMhgeZRJVz9epVPP/887hy5Qo8PDwQGhqKHTt2oH///gCAxYsXw8bGBsOHD0dBQQGioqLwySefmLhqqi1z5jBPiQDg4sWLmDJlCnbu3AlHR0ejLFOr1UKr1RplWWRemJ1U29h4ExGZuRUrVpQ73tHREUuXLsXSpUtrqSIyNh78EdVcSkoKrl69ik6dOinDioqKsGfPHnz88cfYsWOHck+M+896V3RPDDJvzE+yFPyON1k0hi0REbOQCAAefvhhHDt2DGlpacqrS5cuGDlypPJv3hODiEyFZ7yJiIiIyOK5ubmhbdu2esNcXFxQr149ZTjviUH34+XmVJt4xpssHgOTiIiIKmPx4sV49NFHMXz4cPTu3Rs6nQ4bN240dVlEZAV4xpuIiMiE+OEhkXp2796t9573xCAiU+EZb6oTeOBKRNaOOUhE1oa5R5aEjTcREREREVklNu9UW9h4ExEREREREamIjTcREZGJ8EwLERGRdWDjTSahxsEmD2CJyNoxB4nIWjDvyNKw8SYiIiIiIiJSERtvqnX8hJKIiIiIzAWPTak2sPGmOoXBSUTWjjlIRERkfth4ExEREREREanIpI13bGwsunbtCjc3NzRo0ABDhw5Fenq63jS3b99GdHQ06tWrB1dXVwwfPhxZWVkmqpiIiIiIiEyJV/aQJTJp452YmIjo6GgcOHAAO3fuRGFhIQYMGICbN28q00ybNg3fffcdNmzYgMTERFy+fBnDhg0zYdVERERERERElWfSxnv79u0YM2YM2rRpg/bt2yMuLg4XLlxASkoKACA3NxcrVqzAhx9+iH79+qFz585YtWoV9u/fjwMHDhhcZkFBAfLy8vReZF34KSgRWQI1s4o5SERUNcxNUptZfcc7NzcXAODt7Q0ASElJQWFhISIjI5VpgoODERgYiKSkJIPLiI2NhYeHh/IKCAhQv3CqNIYaERERERFZm2o13gkJCcauA8XFxZg6dSp69uyJtm3bAgAyMzPh4OAAT09PvWl9fX2RmZlpcDkxMTHIzc1VXhcvXjR6rURElaFGVhIR1TXMSiKyBtVqvAcOHIjmzZtj/vz5Rmtso6Ojcfz4caxfv75Gy9FqtXB3d9d7ERGZghpZSURU1zArqSp49SRZqmo13r/99hsmTpyIb775Bs2aNUNUVBS+/vpr3Llzp1pFTJw4EVu3bkVCQgIaNWqkDNfpdLhz5w5ycnL0ps/KyoJOp6vWush0ajMoGcpkDoydlUREdRGzkswFjx9JTdVqvH18fDBt2jSkpaUhOTkZrVq1wiuvvAJ/f39MnjwZR44cqdRyRAQTJ07Epk2bsGvXLjRt2lRvfOfOnWFvb4/4+HhlWHp6Oi5cuIDw8PDqlE5EVGuMlZVE1cEDSLIUzEoisgY1vrlap06dEBMTg4kTJyI/Px8rV65E586d0atXL5w4caLceaOjo7F69WqsXbsWbm5uyMzMRGZmJm7dugUA8PDwwLhx4zB9+nQkJCQgJSUFY8eORXh4OLp3717T0omIak1NspLqHjbFRIYxK4morqp2411YWIhvvvkGjzzyCBo3bowdO3bg448/RlZWFjIyMtC4cWM89dRT5S7j008/RW5uLiIiIuDn56e8vvrqK2WaxYsX49FHH8Xw4cPRu3dv6HQ6bNy4sbplExHVKmNkJRFRXcesJKK6zq46M02aNAnr1q2DiGDUqFF4//33lTuRA4CLiwsWLVoEf3//cpcjIhWuy9HREUuXLsXSpUurUyqZCZ7dIWtkrKwkIqrLmJVkTubM4XErqaNajffJkyfxj3/8A8OGDYNWqzU4jY+PDx8PQURWjVlJRFQxZiURWYNqXWo+e/ZsPPXUU6XC8e7du9izZw8AwM7ODn369Kl5hUREFopZSYbwTAqRPmYlEVmDajXeffv2RXZ2dqnhubm56Nu3b42LIjIGHtySqRkrK2NjY9G1a1e4ubmhQYMGGDp0KNLT0/WmuX37NqKjo1GvXj24urpi+PDhyMrKqvE2EBGpjceVRGQNqtV4iwg0Gk2p4deuXYOLi0uNi6K6hQ0wWStjZWViYiKio6Nx4MAB7Ny5E4WFhRgwYABu3rypTDNt2jR899132LBhAxITE3H58mUMGzbMKNtBlov5S5aAx5VEZA2q9B3vkoM4jUaDMWPG6F0SVFRUhKNHj6JHjx7GrZCIyMIYOyu3b9+u9z4uLg4NGjRASkoKevfujdzcXKxYsQJr165Fv379AACrVq1CSEgIDhw4YPDxiwUFBSgoKFDe5+XlVWkbiYhqiseVRGRNqtR4e3h4ALj3yaSbmxucnJyUcQ4ODujevTvGjx9v3AqJiCyM2lmZm5sLAPD29gYApKSkoLCwEJGRkco0wcHBCAwMRFJSksHGOzY2FnPnzq12DURENcXjSqoqXsVDlqxKjfeqVasAAE2aNMGMGTN4+Q8RkQFqZmVxcTGmTp2Knj17Ko/byczMhIODAzw9PfWm9fX1RWZmpsHlxMTEYPr06cr7vLw8BAQEGK1OMowHjUT/w+NKIrIm1b6rOcORLAEPcsmU1MjK6OhoHD9+HOvXr6/RcrRaLdzd3fVeVDcxB8nc8biSiKxBpc94d+rUCfHx8fDy8kLHjh0N3gSjxM8//2yU4oiILI2aWTlx4kRs3boVe/bsQaNGjZThOp0Od+7cQU5Ojt5Z76ysLOh0uipvAxGR2nhcSeZszhx+aEnGV+nGe8iQIcpNL4YOHapWPVTHMLTI2qiRlSKCSZMmYdOmTdi9ezeaNm2qN75z586wt7dHfHw8hg8fDgBIT0/HhQsXEB4ebpQaiIiMiceVRGRtNCIipi5CTXl5efDw8EBubi4vpTQBc2m8zaUOMj1LzIRXXnkFa9euxbfffougoCBluIeHh3Izopdffhnff/894uLi4O7ujkmTJgEA9u/fX6l1WOJ+sUSmyiJmIFUVM8Ew7hfTqe0cY25SZVQlE6r1He+LFy/i0qVLyvuDBw9i6tSp+Pzzz6uzOCKiOslYWfnpp58iNzcXERER8PPzU15fffWVMs3ixYvx6KOPYvjw4ejduzd0Oh02btxotG2hmuNBHJFhPK4kImtQrcb7ueeeQ0JCAoB7d9ONjIzEwYMH8be//Q3z5s0zaoFERJbKWFkpIgZfY8aMUaZxdHTE0qVLkZ2djZs3b2Ljxo38fjcp2PSTOeNxJRFZg2o13sePH0e3bt0AAF9//TXatWuH/fv3Y82aNYiLizNmfWTBzOlAz5xqIevBrCQiqhizkswRjx3J2KrVeBcWFio3xPjxxx/x+OOPAwCCg4Nx5coV41VHRGTBmJVERBVjVhKRNahW492mTRssW7YMe/fuxc6dOzFw4EAAwOXLl1GvXj2jFkhEZKmYlVSCZ06IysaspIowQ6kuqFbj/d577+Gzzz5DREQEnn32WbRv3x4AsGXLFuVSISIia8esJCKqGLOSiKxBtR8nVlRUhLy8PHh5eSnDfvnlFzg7O6NBgwZGK7Cm+NgH0zDXTybNtS6qPbWdCcxKAswne8ylDjJ/zErDmJWmYcrsYm5SeaqSCXbVXYmtra1eOAJAkyZNqrs4IqI6iVlJRFQxZiUR1XXVutQ8KysLo0aNgr+/P+zs7GBra6v3IiIiZiURUWUwK4nIGlTrjPeYMWNw4cIFvP322/Dz84NGozF2XUREFo9ZSYB5XaY4Z4551UMEMCuJyDpUq/H+6aefsHfvXnTo0MHI5VBdYM4HdTzopNrErCQiqhizkspj6uM2HjuSsVTrUvOAgABU855sRERWg1lJRFQxY2VlbGwsunbtCjc3NzRo0ABDhw5Fenq63jS3b99GdHQ06tWrB1dXVwwfPhxZWVk1XjcRUUWq1XgvWbIEM2fOxC+//GLkcoiI6g5mJRFRxYyVlYmJiYiOjsaBAwewc+dOFBYWYsCAAbh586YyzbRp0/Ddd99hw4YNSExMxOXLlzFs2LAabgERUcWq9TgxLy8v/Pnnn7h79y6cnZ1hb2+vNz47O9toBdYUH/tQ+yzhchxLqJHUUZuZwKwkc80ac62LzEddyMrff/8dDRo0QGJiInr37o3c3FzUr18fa9euxZNPPgkAOH36NEJCQpCUlITu3btXuExmZe0zl7wylzrIvKj+OLElS5ZUZzYiIqvCrCQiqphaWZmbmwsA8Pb2BgCkpKSgsLAQkZGRyjTBwcEIDAwss/EuKChAQUGB8j4vL0+VWskwNrtUl1Sr8R49erSx66A6wlICkjfKoNrArCQiqpgaWVlcXIypU6eiZ8+eaNu2LQAgMzMTDg4O8PT01JvW19cXmZmZBpcTGxuLuXPnGr0+IrI+1fqONwCcO3cOb731Fp599llcvXoVALBt2zacOHHCaMUREVk6ZiWZI37wSObG2FkZHR2N48ePY/369TWqKyYmBrm5ucrr4sWLNVoeEVmvajXeiYmJaNeuHZKTk7Fx40bk5+cDAI4cOYLZs2cbtUCyHDyQI9LHrLRuzESiyjF2Vk6cOBFbt25FQkICGjVqpAzX6XS4c+cOcnJy9KbPysqCTqczuCytVgt3d3e9FxFRdVSr8Z45cybmz5+PnTt3wsHBQRner18/HDhwwGjFERFZMmYlEVHFjJWVIoKJEydi06ZN2LVrF5o2bao3vnPnzrC3t0d8fLwyLD09HRcuXEB4eHjNN4TqNH6YSjVVrcb72LFjeOKJJ0oNb9CgAf74448aF0VUGxigpDZmJRFRxYyVldHR0Vi9ejXWrl0LNzc3ZGZmIjMzE7du3QIAeHh4YNy4cZg+fToSEhKQkpKCsWPHIjw8vFJ3NKfaxeM0qmuq1Xh7enriypUrpYanpqaiYcOGNS6KiKguYFaSOeNBLZkLY2Xlp59+itzcXERERMDPz095ffXVV8o0ixcvxqOPPorhw4ejd+/e0Ol02Lhxo1G2g4ioPNVqvEeMGIE33ngDmZmZ0Gg0KC4uxr59+zBjxgw8//zzlV7Onj178Nhjj8Hf3x8ajQabN2/WGy8imDVrFvz8/ODk5ITIyEicPXu2OiWTyngAR1SasbKSiKguM1ZWiojB15gxY5RpHB0dsXTpUmRnZ+PmzZvYuHFjmd/vJnoQj3epJqrVeL/77rsIDg5GQEAA8vPz0bp1a/Tq1Qs9evTAW2+9Venl3Lx5E+3bt8fSpUsNjn///ffx0UcfYdmyZUhOToaLiwuioqJw+/bt6pRNVAoDlNRkrKwky8NsIao8ZiURWQONiEh1Z7548SKOHTuG/Px8dOzYES1btqx+IRoNNm3ahKFDhwK496mlv78/Xn31VcyYMQMAkJubC19fX8TFxWHEiBEGl1NQUICCggLlfV5eHgICApCbm8s7UarIkg8yLbl2qrq8vDx4eHjUaiYYMyvVYor9UpdZUq5YUq1Ue5iVhjEra4c555I510a1ryqZYFfZhU6fPr3c8fffdfLDDz+s7GLLdP78eWRmZiIyMlIZ5uHhgbCwMCQlJZXZeMfGxmLu3Lk1Xj8RUXXUdlYSEVkiZiVZqjlz2HxT9VS68U5NTdV7//PPP+Pu3bsICgoCAJw5cwa2trbo3LmzUQrLzMwEAPj6+uoN9/X1VcYZEhMToxfmJWe8icrCACVjqu2sJCKyRMxKIrI2lW68ExISlH9/+OGHcHNzw7/+9S94eXkBAK5fv46xY8eiV69exq+yCrRaLbRarUlrICLrZSlZSerhB3lEFWNWEpG1qdbN1T744APExsYq4QgAXl5emD9/Pj744AOjFFZyh8msrCy94VlZWbz7JBkdD5RJDcbKSj4BgtTE/CNTq43jSrIczCSqq6rVeOfl5eH3338vNfz333/HjRs3alwUADRt2hQ6nQ7x8fF6601OTkZ4eLhR1kFEpCZjZSWfAEFEdVltHFcSGRM/HKDqqPSl5vd74oknMHbsWHzwwQfo1q0bACA5ORmvvfYahg0bVunl5OfnIyMjQ3l//vx5pKWlwdvbG4GBgZg6dSrmz5+Pli1bomnTpnj77bfh7++v3PmczAPDh8gwY2XloEGDMGjQIIPjRARLlizBW2+9hSFDhgAAvvjiC/j6+mLz5s1l3oiS6H681wWZkrGykojInFWr8V62bBlmzJiB5557DoWFhfcWZGeHcePGYeHChZVezuHDh9G3b1/lfclN0UaPHo24uDi8/vrruHnzJiZMmICcnBw89NBD2L59OxwdHatTNlG5eOBJxmasrCxPdZ8AYejRi0REplAbWUlEZGrVarydnZ3xySefYOHChTh37hwAoHnz5nBxcanSciIiIlDeY8Q1Gg3mzZuHefPmVadMIiKTMlZWlqe6T4DgoxfVwQ/viKquNrKSyNh4woaqqlqNdwkXFxeEhoYaqxayQAwcooqZY1by0Yv0IB5EkqmZY1ZS7WIGUV1WrZurERGReajuEyC0Wi3c3d31XlQzPGAkIrIuzH2qCjbeRPdhgJKl4RMgiIiIiMwfG2+qNjapRLUjPz8faWlpSEtLA/C/J0BcuHABGo1GeQLEli1bcOzYMTz//PN8AgRVC3OdiEyF+UN1HRtvogcw+MncHD58GB07dkTHjh0B3HsCRMeOHTFr1iwAwOuvv45JkyZhwoQJ6Nq1K/Lz8/kECCIiolrA40aqrBrdXI2IiNTHJ0AQERERWTae8SYygJ9eEhERERGRsbDxpmphY0pERERExsDjSrIGbLypyhiORET66lIu1qVtISIiMhdsvInKwINPIiIiIqoIjxmpMth4ExERkR4eRBJRbWHekLVg401VYm3haG3bS0RVx5wgIiL+v4AqwsabiIiISuFBJBERkfGw8SaqAA8+iagszAciIirB/ydQedh4U6UxTIiIrAtzn4iIyDjYeFOlWPvBl7VvPxEREZGx1cXjq7q4TWQcbLyJKolBSkT3s5ZMsJbtJCIiUhMbbyIiIiIiqlV1+UO9urxtVH1svKlCDI//4b4gIsD6ssDatpeIqKaYm/QgNt5ULoZGadwnRGSNmH1ERETVx8abqBp4AEpkvfj3T0RUM9aSo9aynVQ5bLypTAwLIiJ91p6L1r79RFRz1pYj1ra9VDY23mQQQ6Ji3EdEZI2YfURERFXHxpuIiKgS2HASEdWMteaotW436WPjTVQDc+YwTInI+jD3iIiqhrlJbLyJiIgqwAOm0rhPiKgqmBncB9aOjTfp4Rnc6uF+I6q7+LddNu4bIqoMZsX/cF9YLzbepGAQ1Bz3IVHdwr/pinEfEVF5mBGlcZ9YJzbeBIABYEw8+01UN/DvuPK4r4jIEGZD2bhvrA8bbyvHJlE93K9EZE2YeUR0P2ZCxbiPrAsbbyvGP3b18YMNIsvEv9vqYeYREcAcqArmpvWwM3UBVPv4x137SvY59z2ReePfqHEw84isF//uq4e5WfdZxBnvpUuXokmTJnB0dERYWBgOHjxo6pIsEj9RM72SnwF/DqQW5mX18O9SHcw8MlfMSnXwb73mmJt1l9mf8f7qq68wffp0LFu2DGFhYViyZAmioqKQnp6OBg0amLo8i8A/XPP04M+FPyeqKeZl1fHvrvbcv6+538mUmJXGxb9n9TA36xaNiIipiyhPWFgYunbtio8//hgAUFxcjICAAEyaNAkzZ86scP68vDx4eHggNzcX7u7uapdrcvyjrHv4MzWuupwJNcnLurxf7se/J/PGn4/5qMuZwKysGf6dmh/+TEynKplg1me879y5g5SUFMTExCjDbGxsEBkZiaSkJIPzFBQUoKCgQHmfm5sL4N5OMXexsaaugMzRfb/+Rl1mbKzhZZc1vK4oyQIz/8yxyqqal5aclSWYmXWPMbOnLudYbWBW3lMXsrI8zNG6wdh5V95xIumrSlaadeP9xx9/oKioCL6+vnrDfX19cfr0aYPzxMbGYu7cuaWGBwQEqFIjkSVasED/v2WNr8tu3LgBDw8PU5dhNFXNS2Yl1XXWkGO1gVnJrCTrU9FxIpVWmaw068a7OmJiYjB9+nTlfXFxMbKzs1GvXj1oNBoTVqauvLw8BAQE4OLFi3X60iduZ91iiu0UEdy4cQP+/v61sj5zZelZaS1/Iw+y1u0GrHfbTbXdzMp7zC0rrfXvwFi4/6qP+86wqmSlWTfePj4+sLW1RVZWlt7wrKws6HQ6g/NotVpotVq9YZ6enmqVaHbc3d2t4o+B21m31PZ21qWzNyWqmpd1JSut5W/kQda63YD1brsptptZab5Zaa1/B8bC/Vd93HelVTYrzfpxYg4ODujcuTPi4+OVYcXFxYiPj0d4eLgJKyMiMi/MSyKiijErichUzPqMNwBMnz4do0ePRpcuXdCtWzcsWbIEN2/exNixY01dGhGRWWFeEhFVjFlJRKZg9o33M888g99//x2zZs1CZmYmOnTogO3bt5e6KYa102q1mD17dqnLoeoabmfdYi3bWVusKS+t9XfHWrcbsN5tt9btVpMlZyV/H2qG+6/6uO9qzuyf401ERERERERkycz6O95ERERERERElo6NNxEREREREZGK2HgTERERERERqYiNNxEREREREZGK2HhbiOzsbIwcORLu7u7w9PTEuHHjkJ+fX+48t2/fRnR0NOrVqwdXV1cMHz4cWVlZetNoNJpSr/Xr16u5KaUsXboUTZo0gaOjI8LCwnDw4MFyp9+wYQOCg4Ph6OiIdu3a4fvvv9cbLyKYNWsW/Pz84OTkhMjISJw9e1bNTagUY2/nmDFjSv3sBg4cqOYmVEpVtvPEiRMYPnw4mjRpAo1GgyVLltR4mVR31OXcu5+1ZOCDrCUTH8SMpPJYS+4Zi7Xmp7FYaw6bjJBFGDhwoLRv314OHDgge/fulRYtWsizzz5b7jwvvfSSBAQESHx8vBw+fFi6d+8uPXr00JsGgKxatUquXLmivG7duqXmpuhZv369ODg4yMqVK+XEiRMyfvx48fT0lKysLIPT79u3T2xtbeX999+XkydPyltvvSX29vZy7NgxZZoFCxaIh4eHbN68WY4cOSKPP/64NG3atFa360FqbOfo0aNl4MCBej+77Ozs2tokg6q6nQcPHpQZM2bIunXrRKfTyeLFi2u8TKo76mru3c9aMvBB1pKJD2JGUkWsIfeMxVrz01isNYdNiY23BTh58qQAkEOHDinDtm3bJhqNRn777TeD8+Tk5Ii9vb1s2LBBGXbq1CkBIElJScowALJp0ybVaq9It27dJDo6WnlfVFQk/v7+Ehsba3D6p59+WgYPHqw3LCwsTP7617+KiEhxcbHodDpZuHChMj4nJ0e0Wq2sW7dOhS2oHGNvp8i9cBsyZIgq9VZXVbfzfo0bNzZ4UFmTZZLlqsu5dz9rycAHWUsmPogZSeWxltwzFmvNT2Ox1hw2JV5qbgGSkpLg6emJLl26KMMiIyNhY2OD5ORkg/OkpKSgsLAQkZGRyrDg4GAEBgYiKSlJb9ro6Gj4+PigW7duWLlyJaSWHu1+584dpKSk6NVoY2ODyMjIUjWWSEpK0pseAKKiopTpz58/j8zMTL1pPDw8EBYWVuYy1abGdpbYvXs3GjRogKCgILz88su4du2a8TegkqqznaZYJlmGupp797OWDHyQtWTig5iRVBFryD1jsdb8NBZrzWFTszN1AVSxzMxMNGjQQG+YnZ0dvL29kZmZWeY8Dg4O8PT01Bvu6+urN8+8efPQr18/ODs744cffsArr7yC/Px8TJ482ejb8aA//vgDRUVF8PX1LVXj6dOnDc6TmZlpcPqSbSr5b3nT1DY1thMABg4ciGHDhqFp06Y4d+4c3nzzTQwaNAhJSUmwtbU1/oZUoDrbaYplkmWoq7l3P2vJwAdZSyY+iBlJFbGG3DMWa81PY7HWHDY1Nt4mNHPmTLz33nvlTnPq1ClVa3j77beVf3fs2BE3b97EwoULLTaIrcmIESOUf7dr1w6hoaFo3rw5du/ejYcfftiElRGVjblHamEmkrli7pG1YA6Xj5eam9Crr76KU6dOlftq1qwZdDodrl69qjfv3bt3kZ2dDZ1OZ3DZOp0Od+7cQU5Ojt7wrKysMucBgLCwMFy6dAkFBQU13r6K+Pj4wNbWttSdN8urUafTlTt9yX+rsky1qbGdhjRr1gw+Pj7IyMioedHVUJ3tNMUyybSsPffuZy0Z+CBrycQHMSOtF3PP+Kw1P43FWnPY1Nh4m1D9+vURHBxc7svBwQHh4eHIyclBSkqKMu+uXbtQXFyMsLAwg8vu3Lkz7O3tER8frwxLT0/HhQsXEB4eXmZNaWlp8PLyglarNd6GlsHBwQGdO3fWq7G4uBjx8fFl1hgeHq43PQDs3LlTmb5p06bQ6XR60+Tl5SE5Obnc7VaTGttpyKVLl3Dt2jX4+fkZp/Aqqs52mmKZZFrWnnv3s5YMfJC1ZOKDmJHWi7lnfNaan8ZirTlscia+uRtV0sCBA6Vjx46SnJwsP/30k7Rs2VLv8RKXLl2SoKAgSU5OVoa99NJLEhgYKLt27ZLDhw9LeHi4hIeHK+O3bNkiy5cvl2PHjsnZs2flk08+EWdnZ5k1a1atbdf69etFq9VKXFycnDx5UiZMmCCenp6SmZkpIiKjRo2SmTNnKtPv27dP7OzsZNGiRXLq1CmZPXu2wUdBeHp6yrfffitHjx6VIUOGmPxREMbezhs3bsiMGTMkKSlJzp8/Lz/++KN06tRJWrZsKbdv3zbJNopUfTsLCgokNTVVUlNTxc/PT2bMmCGpqaly9uzZSi+T6q66mnv3s5YMfJC1ZOKDmJFUEWvIPWOx1vw0FmvNYVNi420hrl27Js8++6y4urqKu7u7jB07Vm7cuKGMP3/+vACQhIQEZditW7fklVdeES8vL3F2dpYnnnhCrly5oozftm2bdOjQQVxdXcXFxUXat28vy5Ytk6KiotrcNPnHP/4hgYGB4uDgIN26dZMDBw4o4/r06SOjR4/Wm/7rr7+WVq1aiYODg7Rp00b+85//6I0vLi6Wt99+W3x9fUWr1crDDz8s6enptbEp5TLmdv75558yYMAAqV+/vtjb20vjxo1l/PjxZnGgVZXtLPm9ffDVp0+fSi+T6q66nHv3s5YMfJC1ZOKDmJFUHmvJPWOx1vw0FmvNYVPRiFjwswSIiIiIiIiIzBy/401ERERERESkIjbeRERERERERCpi401ERERERESkIjbeRERERERERCpi401ERERERESkIjbeRERERERERCpi401ERERERESkIjbeRERERERERCpi420hIiIiMHXqVNWWP2bMGAwdOrTM8XPmzEGHDh1UWz/VXFxcHDw9Pas0T0U/dyJLw6ykijAriZiVVDFmpfGx8SYygvT0dPTt2xe+vr5wdHREs2bN8NZbb6GwsLDWanjmmWdw5swZoy+3SZMmWLJkidGXS0TWLSMjA25ublU+sKspZiURmbtffvkFGo2m1OvAgQO1VgOz0vjsTF0AUV1gb2+P559/Hp06dYKnpyeOHDmC8ePHo7i4GO+++26t1ODk5AQnJ6daWRcRUU0UFhbi2WefRa9evbB///5aXTezkogsxY8//og2bdoo7+vVq1dr62ZWGh/PeFuQu3fvYuLEifDw8ICPjw/efvttiAgA4Msvv0SXLl3g5uYGnU6H5557DlevXtWb/8SJE3j00Ufh7u4ONzc39OrVC+fOnTO4rkOHDqF+/fp477339IZ/9tlnCAgIgLOzM55++mnk5uYq44qLizFv3jw0atQIWq0WHTp0wPbt25XxJZ/ebdy4EX379oWzszPat2+PpKQkvXX89NNP6NWrF5ycnBAQEIDJkyfj5s2b5e6b7777Dl27doWjoyN8fHzwxBNPKOOuX7+O559/Hl5eXnB2dsagQYNw9uxZZXzJpTQ7duxASEgIXF1dMXDgQFy5cgUA8MMPP8DR0RE5OTl665wyZQr69esHAGjWrBnGjh2L9u3bo3Hjxnj88ccxcuRI7N27t8yau3TpgkWLFinvhw4dCnt7e+Tn5wMALl26BI1Gg4yMDABAQUEBZsyYgYYNG8LFxQVhYWHYvXt3qe243/z589GgQQO4ubnhxRdfxMyZMw1e2rVo0SL4+fmhXr16iI6OVs7UR0RE4Ndff8W0adOUT1uJzB2zsmymzsoSb731FoKDg/H000+XWy/ArCRSC7OybOaSlfXq1YNOp1Ne9vb2ZdbMrLQAQhahT58+4urqKlOmTJHTp0/L6tWrxdnZWT7//HMREVmxYoV8//33cu7cOUlKSpLw8HAZNGiQMv+lS5fE29tbhg0bJocOHZL09HRZuXKlnD59WkRERo8eLUOGDBERkfj4ePHw8JDPPvtMmX/27Nni4uIi/fr1k9TUVElMTJQWLVrIc889p0zz4Ycfiru7u6xbt05Onz4tr7/+utjb28uZM2dEROT8+fMCQIKDg2Xr1q2Snp4uTz75pDRu3FgKCwtFRCQjI0NcXFxk8eLFcubMGdm3b5907NhRxowZU+a+2bp1q9ja2sqsWbPk5MmTkpaWJu+++64y/vHHH5eQkBDZs2ePpKWlSVRUlLRo0ULu3LkjIiKrVq0Se3t7iYyMlEOHDklKSoqEhIQo23b37l3x9fWVf/7zn8oyDQ2739mzZyUkJET+9re/lVn39OnTZfDgwSIiUlxcLN7e3uLj4yPbtm0TEZHVq1dLw4YNlelffPFF6dGjh+zZs0cyMjJk4cKFotVqlf27atUq8fDwUKZfvXq1ODo6ysqVKyU9PV3mzp0r7u7u0r59e2Wa0aNHi7u7u7z00kty6tQp+e677/R+r65duyaNGjWSefPmyZUrV+TKlStlbg+ROWBWmn9WxsfHS9OmTSU3N7dUbhnCrCQyPmaleWdlybYFBARI/fr1pWfPnvLtt9+W+zNlVpo/Nt4Wok+fPhISEiLFxcXKsDfeeENCQkIMTn/o0CEBIDdu3BARkZiYGGnatKkSCg8qCciNGzeKq6urrF+/Xm/87NmzxdbWVi5duqQM27Ztm9jY2Ch/NP7+/vL3v/9db76uXbvKK6+8IiL/C5H7g+bEiRMCQE6dOiUiIuPGjZMJEyboLWPv3r1iY2Mjt27dMlh7eHi4jBw50uC4M2fOCADZt2+fMuyPP/4QJycn+frrr0XkXrAAkIyMDGWapUuXiq+vr/J+ypQp0q9fP+X9jh07RKvVyvXr10vVotVqBYBMmDBBioqKDNYlIrJlyxbx8PCQu3fvSlpamuh0OpkyZYq88cYbInIvEEtC+tdffxVbW1v57bff9Jbx8MMPS0xMjLId9wdkWFiYREdH603fs2fPUgHZuHFjuXv3rjLsqaeekmeeeUZ537hxY1m8eHGZ20FkTpiV5p2Vf/zxhwQEBEhiYqKyzIoab2YlkfExK807K3///Xf54IMP5MCBA3Lw4EF54403RKPRlNt8MyvNHy81tyDdu3fXuyQjPDwcZ8+eRVFREVJSUvDYY48hMDAQbm5u6NOnDwDgwoULAIC0tDT06tWr3EtUkpOT8dRTT+HLL7/EM888U2p8YGAgGjZsqLf+4uJipKenIy8vD5cvX0bPnj315unZsydOnTqlNyw0NFT5t5+fHwAoly8dOXIEcXFxcHV1VV5RUVEoLi7G+fPnDdadlpaGhx9+2OC4U6dOwc7ODmFhYcqwevXqISgoSK8uZ2dnNG/eXK+u+y+pGjlyJHbv3o3Lly8DANasWYPBgweXugTnq6++ws8//4y1a9fiP//5j94lPw/q1asXbty4gdTUVCQmJqJPnz6IiIhQLvNJTExEREQEAODYsWMoKipCq1at9PZNYmJimZd1paeno1u3bnrDHnwPAG3atIGtrW2Z205kaZiV5puV48ePx3PPPYfevXsbrMMQZiWROpiV5puVPj4+mD59OsLCwtC1a1csWLAAf/nLX7Bw4UKDdQHMSkvAm6vVAbdv30ZUVBSioqKwZs0a1K9fHxcuXEBUVBTu3LkDAJW6OULz5s1Rr149rFy5EoMHDy43TGvi/uWWBH5xcTEAID8/H3/9618xefLkUvMFBgYaXJ4xbvzw4LZqNBrle04A0LVrVzRv3hzr16/Hyy+/jE2bNiEuLq7UcgICAgAArVu3RlFRESZMmIBXX31VL4BKeHp6on379ti9ezeSkpLQv39/9O7dW7mL5NmzZ5X/0eXn58PW1hYpKSmlluXq6mr0bS/5eRDVJcxK02flrl27sGXLFuVDSRFBcXEx7Ozs8Pnnn+OFF14otU5mJVHtYlaaPisNCQsLw86dO8scz6w0fzzjbUGSk5P13h84cAAtW7bE6dOnce3aNSxYsAC9evVCcHBwqU+WQkNDsXfv3nIfb+Xj44Ndu3YhIyMDTz/9dKlpL1y4oHwyV7J+GxsbBAUFwd3dHf7+/ti3b5/ePPv27UPr1q0rvY2dOnXCyZMn0aJFi1IvBwcHg/OEhoYiPj7e4LiQkBDcvXtXb99du3YN6enpVaoLuPfp5Jo1a/Ddd9/BxsYGgwcPLnf64uJiFBYWlhs2ffr0QUJCAvbs2YOIiAh4e3sjJCQEf//73+Hn54dWrVoBADp27IiioiJcvXq11H7R6XQGlx0UFIRDhw7pDXvwfWU4ODigqKioyvMRmQqz0nyzMikpCWlpacpr3rx5cHNzQ1pamt7Nix7ErCQyPmal+WalIWlpacoZ/bIwK82caa90p8oquQnGtGnT5PTp07J27VpxcXGRZcuWydWrV8XBwUFee+01OXfunHz77bfSqlUrASCpqakicu/7J/Xq1VNugnHmzBn54osvDN4E48qVKxIcHCzDhw9Xbk5RchOMyMhISUtLkz179kirVq1kxIgRSo2LFy8Wd3d3Wb9+vZw+fVreeOMNgzfBKKlJROT69esCQBISEkRE5MiRI+Lk5CTR0dGSmpoqZ86ckc2bN+t9p2TmzJkyatQo5X1CQoLY2NgoN8E4evSoLFiwQBk/ZMgQad26tezdu1fS0tJk4MCBpW6C8eB3DDdt2iQP/nmcPXtWAEhoaKiMGzdOb9zq1avlq6++kpMnT8q5c+fkq6++En9/f73vCG3cuFGCgoL05tu8ebPY2tqKTqdThk2ZMkVsbW319q2IyMiRI6VJkyby73//W/773/9KcnKyvPvuu7J161aD27F69WpxcnKSuLg4OXPmjLzzzjvi7u4uHTp0UKa5/+d+//r79OmjvO/fv788/vjjcunSJfn999+FyJwxK807Kx9kaJnMSiL1MSvNOyvj4uJk7dq1curUKTl16pT8/e9/FxsbG1m5cqUyDbPS8rDxthB9+vSRV155RV566SVxd3cXLy8vefPNN5WbYqxdu1aaNGkiWq1WwsPDZcuWLaXC6MiRIzJgwABxdnYWNzc36dWrl5w7d05ESv+hXL58WVq1aiVPP/203L17V2bPni3t27eXTz75RPz9/cXR0VGefPJJyc7OVuYpKiqSOXPmSMOGDcXe3l7at2+v3ElRpHIBKSJy8OBB6d+/v7i6uoqLi4uEhobq3Vxj9OjRen/AIiL//ve/pUOHDuLg4CA+Pj4ybNgwZVx2draMGjVKPDw8xMnJSaKiopTQFql8QIqIdOvWTQDIrl279IavX79eOnXqpNTcunVreffdd/Vu3FFys437Xbt2TTQajd5NJ0rWvWzZMr1p79y5I7NmzZImTZqIvb29+Pn5yRNPPCFHjx4tczvmzZsnPj4+4urqKi+88IJMnjxZunfvrrcvKwrIpKQkCQ0NVW4aR2TOmJXmnZUPMrRMZiWR+piV5p2VcXFxEhISIs7OzuLu7i7dunWTDRs26E3DrLQ8GpH7vnBARHVa//79odPp8OWXX5q6FCIis8WsJCKqGLOyanhzNaI66s8//8SyZcsQFRUFW1tbrFu3Dj/++GO5N+YgIrI2zEoioooxK2uOZ7yJ6qhbt27hscceQ2pqKm7fvo2goCC89dZbGDZsmKlLIyIyG8xKIqKKMStrjo03ERERERERkYr4ODEiIiIiIiIiFbHxJiIiIiIiIlIRG28iIiIiIiIiFbHxJiIiIiIiIlIRG28iIiIiIiIiFbHxJiIiIiIiIlIRG28iIiIiIiIiFbHxJiIiIiIiIlIRG28iIiIiIiIiFbHxJiIiIiIiIlIRG28iIiIiIiIiFbHxJiIiIiIiIlKRnakLUFtxcTEuX74MNzc3aDQaU5dDRCYmIrhx4wb8/f1hY8PPHkswK4nofsxKw5iVRHS/qmRlnW+8L1++jICAAFOXQURm5uLFi2jUqJGpyzAbzEoiMoRZqY9ZSUSGVCYr63zj7ebmBuDeznB3dzdxNURkanl5eQgICFCyge5hVhLR/ZiVhjErieh+VcnKOt94l1wG5O7uzoAkIgUvEdTHrCQiQ5iV+piVRGRIZbKSX9ohIiIiIiIiUhEbbyIiIiIiIiIVsfEmIiIiIiIiUhEbb7IIc+aYugIiIiIi61ZyPMbjMqKqY+NNFoMhT0TWbM4c5iAREZGlYuNNZu/+A00edBIRERERkaVh401mjY02ERERERFZOpM33r/99hv+8pe/oF69enByckK7du1w+PBhZbyIYNasWfDz84OTkxMiIyNx9uxZE1ZMRERUu3jlDxGZK2YSUeWYtPG+fv06evbsCXt7e2zbtg0nT57EBx98AC8vL2Wa999/Hx999BGWLVuG5ORkuLi4ICoqCrdv3zZh5WRKDHgiIiIiIrIkdqZc+XvvvYeAgACsWrVKGda0aVPl3yKCJUuW4K233sKQIUMAAF988QV8fX2xefNmjBgxotZrJiIiIiIiIqoKk57x3rJlC7p06YKnnnoKDRo0QMeOHbF8+XJl/Pnz55GZmYnIyEhlmIeHB8LCwpCUlGRwmQUFBcjLy9N7kWUq78w2z3oTERFZtyZNmkCj0ZR6RUdHG5w+Li6u1LSOjo61XDURWSuTNt7//e9/8emnn6Jly5bYsWMHXn75ZUyePBn/+te/AACZmZkAAF9fX735fH19lXEPio2NhYeHh/IKCAhQdyOIiIhUxA8aiQw7dOgQrly5orx27twJAHjqqafKnMfd3V1vnl9//bW2yq1zmE1EVWPSS82Li4vRpUsXvPvuuwCAjh074vjx41i2bBlGjx5drWXGxMRg+vTpyvu8vDw23xaIYU5EVDY+05sIqF+/vt77BQsWoHnz5ujTp0+Z82g0Guh0ukqvo6CgAAUFBcp7XklJRNVl0jPefn5+aN26td6wkJAQXLhwAQCUYMzKytKbJisrq8zQ1Gq1cHd313tR3cSDTqor9uzZg8ceewz+/v7QaDTYvHmz3vjKPN0hOzsbI0eOhLu7Ozw9PTFu3Djk5+fX4lYQEZnOnTt3sHr1arzwwgvQaDRlTpefn4/GjRsjICAAQ4YMwYkTJ8pdLq+kJCJjMWnj3bNnT6Snp+sNO3PmDBo3bgzg3o3WdDod4uPjlfF5eXlITk5GeHh4rdZKRKSWmzdvon379li6dKnB8ZV5usPIkSNx4sQJ7Ny5E1u3bsWePXswYcKE2toEIiKT2rx5M3JycjBmzJgypwkKCsLKlSvx7bffYvXq1SguLkaPHj1w6dKlMueJiYlBbm6u8rp48aIK1ZsvnuQgMh6TXmo+bdo09OjRA++++y6efvppHDx4EJ9//jk+//xzAPcuB5o6dSrmz5+Pli1bomnTpnj77bfh7++PoUOHmrJ0IiKjGTRoEAYNGmRwXGWe7nDq1Cls374dhw4dQpcuXQAA//jHP/DII49g0aJF8Pf3L7VcXj5JRHXJihUrMGjQIIN5VyI8PFzvxE2PHj0QEhKCzz77DO+8847BebRaLbRardHrJSLrY9Iz3l27dsWmTZuwbt06tG3bFu+88w6WLFmCkSNHKtO8/vrrmDRpEiZMmICuXbsiPz8f27dv510o6zB+ukr0P5V5ukNSUhI8PT2VphsAIiMjYWNjg+TkZIPL5eWTRFRX/Prrr/jxxx/x4osvVmk+e3t7dOzYERkZGSpVVjfwuIzIOEzaeAPAo48+imPHjuH27ds4deoUxo8frzdeo9Fg3rx5yMzMxO3bt/Hjjz+iVatWJqqWzA3/Z0B1XWWe7pCZmYkGDRrojbezs4O3t3eZT4Cw9ssniajuWLVqFRo0aIDBgwdXab6ioiIcO3YMfn5+KlVW991/HMZjMqLymbzxJiKi2scbUVqG8g5keZBLdO8JOatWrcLo0aNhZ6f/Dcrnn38eMTExyvt58+bhhx9+wH//+1/8/PPP+Mtf/oJff/21ymfKrRUzh6hmTPodbyIiKt/9T3e4/6xMVlYWOnTooExz9epVvfnu3r2L7OzsKj02h4jI0vz444+4cOECXnjhhVLjLly4ABub/51jun79OsaPH4/MzEx4eXmhc+fO2L9/f6kn7BARqYGNNxGRGbv/6Q4ljXbJ0x1efvllAPduGJSTk4OUlBR07twZALBr1y4UFxcjLCzMVKUTEaluwIABEBGD43bv3q33fvHixVi8eHEtVEVEVBovNSeLx0ufyNLl5+cjLS0NaWlpAO7dUC0tLQ0XLlzQe7rDli1bcOzYMTz//PN6T3cICQnBwIEDMX78eBw8eBD79u3DxIkTMWLEiHLv8EtEREREtYNnvMmssIkma3T48GH07dtXeT99+nQAwOjRoxEXF4fXX38dN2/exIQJE5CTk4OHHnqo1NMd1qxZg4kTJ+Lhhx+GjY0Nhg8fjo8++qjWt4WIiOqeio7PePxGVDE23kREJhYREVHmpZLA/57uMG/evDKn8fb2xtq1a9Uoj4iIiIhqiJeaExERmaHKnEHiWSYiqm3MJqLqYeNNdQIDnoiIiIiIzBUbbyIiIiIiIiIVsfEms8Gz1kRERESWjcdzRIax8SYiIiIiIiJSERtvIiIiIiIiIhWx8aY6g5c2ERERERGROWLjTURERERERKQiNt5kFni2moioepifRERE5o+NNxERkZlhM01ERFS3sPEmIiIiIiIiUhEbb6pTeJaIiIiIqOZ4TEVkXGy8iYiIiIiIiFTExptMjp+oEhERERFRXVatxjshIcHYdRARWRTmIBFR9TA/icgaVavxHjhwIJo3b4758+fj4sWLxq6JiMjsMQeJiKqH+Vl38SpGorJVq/H+7bffMHHiRHzzzTdo1qwZoqKi8PXXX+POnTvGro+oyhj6VBuYg2ROmHtkSZif5o15QqSOajXePj4+mDZtGtLS0pCcnIxWrVrhlVdegb+/PyZPnowjR44Yu04iIrPCHCQiqh7mp/lj801kfDW+uVqnTp0QExODiRMnIj8/HytXrkTnzp3Rq1cvnDhxwhg1Uh3GYKe6gDlIxsRcJGvC/CQia1HtxruwsBDffPMNHnnkETRu3Bg7duzAxx9/jKysLGRkZKBx48Z46qmnjFkrEZFZYQ4SEVUP85OIrI1ddWaaNGkS1q1bBxHBqFGj8P7776Nt27bKeBcXFyxatAj+/v5GK5SIyJwwB4mIqof5SUTWqFpnvE+ePIl//OMfuHz5MpYsWaIXliV8fHyq9LiIBQsWQKPRYOrUqcqw27dvIzo6GvXq1YOrqyuGDx+OrKys6pRMVoaXapLa1MhBIiJrwPy0DjwWI9JXrcZ79uzZeOqpp6DVavWG3717F3v27AEA2NnZoU+fPpVa3qFDh/DZZ58hNDRUb/i0adPw3XffYcOGDUhMTMTly5cxbNiw6pRMRGRUxs5BIiJrwfwkImtUrca7b9++yM7OLjU8NzcXffv2rdKy8vPzMXLkSCxfvhxeXl56y1qxYgU+/PBD9OvXD507d8aqVauwf/9+HDhwoDplExEZjTFzkIjImhgrP+fMmQONRqP3Cg4OLneeDRs2IDg4GI6OjmjXrh2+//77KtdPRFQd1Wq8RQQajabU8GvXrsHFxaVKy4qOjsbgwYMRGRmpNzwlJQWFhYV6w4ODgxEYGIikpKQyl1dQUIC8vDy9FxGRsRkzB4mMgZd1kqUwZn62adMGV65cUV4//fRTmdPu378fzz77LMaNG4fU1FQMHToUQ4cOxfHjx6u8DVQ5zCWi/6nSzdVKLvPWaDQYM2aM3iVCRUVFOHr0KHr06FHp5a1fvx4///wzDh06VGpcZmYmHBwc4OnpqTfc19cXmZmZZS4zNjYWc+fOrXQNZDoMY7JExs5BovsxF6kuUyM/7ezsoNPpKjXt//3f/2HgwIF47bXXAADvvPMOdu7ciY8//hjLli0zOE9BQQEKCgqU9zyhQ0TVVaXG28PDA8C9Tyrd3Nzg5OSkjHNwcED37t0xfvz4Si3r4sWLmDJlCnbu3AlHR8eqlFGumJgYTJ8+XXmfl5eHgIAAoy2fiKybMXOQiMiaqJGfZ8+ehb+/PxwdHREeHo7Y2FgEBgYanDYpKUnvGBEAoqKisHnz5jKXzxM6RGQsVWq8V61aBQBo0qQJZsyYUaPLKVNSUnD16lV06tRJGVZUVIQ9e/bg448/xo4dO3Dnzh3k5OTonfXOysoq95NNrVZb6mYdRETGYswcJCKyJsbOz7CwMMTFxSEoKAhXrlzB3Llz0atXLxw/fhxubm6lps/MzISvr6/esIqupOQJHSIylmo9x3v27Nk1XvHDDz+MY8eO6Q0bO3YsgoOD8cYbbyAgIAD29vaIj///9u48LKq6/R/4e0AY9k1ElgDLBcjcMkHsa5Bi6GXlVhqWSw9lPrkVj6b2LTGz0EdLe/xqpqmUS2jl0malKOSC+xImIpBKLmBqgpoiy/37wx/nYWDYhtlg3q/rmkvnnDNn7vvMzJvzmTlzJhlDhgwBAGRmZiI3Nxfh4eENvn8ioobQRw4SEVkifeVnv379lP937NgRYWFhCAwMxIYNGxAbG6uX+7C0D3T4dRciw6nzwPvhhx9GcnIy3N3d0aVLF60nxSh35MiRWtfn7Oxc5XcbHR0d0bx5c2V6bGws4uLi4OHhARcXF0yYMAHh4eHo3r17XcsmItIbfecgEZGlMEZ+urm5oV27dsjOztY639vbG/n5+RrTajuSkvRj5kwO6onqPPAeMGCA8o7fwIEDDVWPhgULFsDKygpDhgxBUVERoqOjsWTJEqPcNxFRZabIQSKipsAY+Xnz5k3k5ORgxIgRWueHh4cjOTkZr732mjJt27ZtPJLSwDjgJrpHJSJi6iIMqbCwEK6urigoKICLi4upy6EKjBHEDHuqjJmgHbeLeWhoZjHzSF8aQyZMnjwZTz31FAIDA3Hx4kXEx8fj2LFjOHnyJFq0aIGRI0fCz88PCQkJAO79nFhERATmzJmD/v37IykpCe+//z6OHDlS5SjM6jSG7dIQhswQ5hM1RfXJBJ1+x/uPP/7A+fPnlesHDhzAa6+9hmXLlumyOiKiRoc5SOaIO7bUGOgrP8+fP4+YmBgEBQVh6NChaN68Ofbt24cWLVoAAHJzc3Hp0iVl+R49emDdunVYtmwZOnXqhK+++gqbN2+u86CbiKghdDq52vDhwzFmzBiMGDECeXl5iIqKwkMPPYS1a9ciLy8PM2bM0HedRERmhTlIRKQbfeVnUlJSjfNTUlKqTHv22Wfx7LPP6lI2EVGD6PSJ94kTJxAaGgoA2LBhAzp06IC9e/di7dq1SExM1Gd9RERmiTlI+sZPq8lSMD8tEzOOLJ1OA+/i4mLlBBnbt2/H008/DQAIDg7WOKSHiKipYg4SEemG+WmeODAmMiydBt7t27fH0qVLsWvXLmzbtg19+/YFAFy8eBHNmzfXa4HUNBkr3PlHhAyFOUhEpBvmJxFZIp0G3nPnzsUnn3yCyMhIxMTEoFOnTgCAb775Rjl0iIioKWMOEhHphvlJRJZIp5OrRUZG4sqVKygsLIS7u7syfcyYMXBwcNBbcURE5oo5SESkG+YnEVkinT7xBgBra2uNsASAVq1awcvLq8FFERE1BsxBMkf8ig01BsxPyzRzJjOKLJdOA+/8/HyMGDECvr6+aNasGaytrTUuRERNHXOQiEg3zE8iskQ6HWo+evRo5Obm4u2334aPjw9UKpW+6yLSG767SobAHCQi0g3zk4gskU4D7927d2PXrl3o3LmznsshS8BBMDUFzEHSJ+YiWRLmJxFZIp0ONff394eI6LsWIqJGw5g5OHPmTKhUKo1LcHCwMv/OnTsYN24cmjdvDicnJwwZMgT5+flGqY2IqL64H0lElkingffChQsxbdo0nD17Vs/lEBE1DsbOwfbt2+PSpUvKZffu3cq8119/Hd9++y2+/PJLpKam4uLFixg8eLBR6iIiqi/uR1I5Hu1DlkSnQ82HDRuGv//+G61bt4aDgwNsbGw05l+7dk0vxRHpC7/nTfpm7Bxs1qwZvL29q0wvKCjAihUrsG7dOvTq1QsAsGrVKoSEhGDfvn3o3r271vUVFRWhqKhIuV5YWKjXeomIqsP9SCKyRDoNvBcuXKjnMshScPBLTYWxczArKwu+vr6ws7NDeHg4EhISEBAQgMOHD6O4uBhRUVHKssHBwQgICEBaWlq1A++EhAS88847xiqfjIxvNpI5434kEVkinQbeo0aN0ncdRESNijFzMCwsDImJiQgKCsKlS5fwzjvvoGfPnjhx4gTy8vJga2sLNzc3jdu0bNkSeXl51a5z+vTpiIuLU64XFhbC39/fUC0QESm4H0lElkingTcA5OTkYNWqVcjJycFHH30ELy8vbN26FQEBAWjfvr0+ayQiMkvGysF+/fop/+/YsSPCwsIQGBiIDRs2wN7eXqd1qtVqqNVqfZVIRFQv3I8kIkuj08nVUlNT0aFDB+zfvx8bN27EzZs3AQDHjx9HfHy8XgskIjJHpsxBNzc3tGvXDtnZ2fD29sbdu3dx/fp1jWXy8/O1fieczA8PCSdLw/1IIrJEOg28p02bhtmzZ2Pbtm2wtbVVpvfq1Qv79u3TW3FE+sSdW9InU+bgzZs3kZOTAx8fH3Tt2hU2NjZITk5W5mdmZiI3Nxfh4eEGrYOISBfcjzQv3D8iMg6dDjVPT0/HunXrqkz38vLClStXGlwUEZG5M2YOTp48GU899RQCAwNx8eJFxMfHw9raGjExMXB1dUVsbCzi4uLg4eEBFxcXTJgwAeHh4dWeWI2IyJS4H0lElkinT7zd3Nxw6dKlKtOPHj0KPz+/BhdFTRPfUaWmxJg5eP78ecTExCAoKAhDhw5F8+bNsW/fPrRo0QIAsGDBAjz55JMYMmQIHnvsMXh7e2Pjxo16rYEaH2YumSvuR1JlzCuyBDp94v3cc89h6tSp+PLLL6FSqVBWVoY9e/Zg8uTJGDlypL5rJCIyO8bMwaSkpBrn29nZYfHixVi8eLFe75eIyBC4H0lElkinT7zff/99BAcHw9/fHzdv3sSDDz6Inj17okePHnjrrbf0XSOR3vAdVdIX5iARkW6Yn+ajfL/IFPtH3CcjS6PTJ962trZYvnw5ZsyYgfT0dNy8eRNdunRB27Zt9V0fEZFZYg4SEemG+UlElqjOA++4uLga51c8C+WHH36oe0XUJPFdTWoKmIOkb8xGshTMT6qI2UeWqM4D76NHj2pcP3LkCEpKShAUFAQAOH36NKytrdG1a1f9VkhEZCaYg0REumF+EpGlq/PAe+fOncr/P/zwQzg7O+Ozzz6Du7s7AOCvv/7Ciy++iJ49e+q/SiI9mjmT77SSbpiDRES6YX5SfXBfjZoinU6u9sEHHyAhIUEJSwBwd3fH7Nmz8cEHH9R5PQkJCejWrRucnZ3h5eWFgQMHIjMzU2OZO3fuYNy4cWjevDmcnJwwZMgQ5Ofn61I2EZHe6CsHiYgsjTH3IytLTEyESqXSuNjZ2encS2PFQS2R8ek08C4sLMSff/5ZZfqff/6JGzdu1Hk9qampGDduHPbt24dt27ahuLgYTzzxBG7duqUs8/rrr+Pbb7/Fl19+idTUVFy8eBGDBw/WpWwiIr3RVw4SEVkaY+5HauPi4oJLly4pl3PnztW7ByKi+tLprOaDBg3Ciy++iA8++AChoaEAgP3792PKlCn1GhT/+OOPGtcTExPh5eWFw4cP47HHHkNBQQFWrFiBdevWoVevXgCAVatWISQkBPv27UP37t2rrLOoqAhFRUXK9cLCQl1aJD3iu6rUFOkrB4kMiYdrkjky1n5kdVQqFby9vXUrngyqusxillFToNMn3kuXLkW/fv0wfPhwBAYGIjAwEMOHD0ffvn2xZMkSnYspKCgAAHh4eAAADh8+jOLiYkRFRSnLBAcHIyAgAGlpaVrXkZCQAFdXV+Xi7++vcz3UdDG8qaEMlYNERE2dsfYjq3Pz5k0EBgbC398fAwYMwG+//VbtskVFRSgsLNS4kGFxH42aKp0G3g4ODliyZAmuXr2Ko0eP4ujRo7h27RqWLFkCR0dHnQopKyvDa6+9hkcffRQPPfQQACAvLw+2trZwc3PTWLZly5bIy8vTup7p06ejoKBAufzxxx861UNEVBND5CARkSUw1n6kNkFBQVi5ciW2bNmCNWvWoKysDD169MD58+e1Ls8PdAyvPgNtDsqpMdPpUPNyjo6O6Nixo14KGTduHE6cOIHdu3c3aD1qtRpqtVovNRER1UafOUhEZElMsR8ZHh6O8PBw5XqPHj0QEhKCTz75BO+++26V5adPn67xG+SFhYUcfBORTnT6xFvfxo8fj++++w47d+7Efffdp0z39vbG3bt3cf36dY3l8/Pz+d2cRsKc35k059qIiIiobqrbj6wLGxsbdOnSBdnZ2Vrnq9VquLi4aFyIiHRh0oG3iGD8+PHYtGkTduzYgfvvv19jfteuXWFjY4Pk5GRlWmZmJnJzczXerSQiIiLt+CYjNVW17UfWRWlpKdLT0+Hj42OAComI/qtBh5o31Lhx47Bu3Tps2bIFzs7Oyve2XV1dYW9vD1dXV8TGxiIuLg4eHh5wcXHBhAkTEB4ervWM5kRERI0FB8REDVPbfiQAjBw5En5+fkhISAAAzJo1C927d0ebNm1w/fp1zJs3D+fOncNLL71ksj7IeHh2dDIlkw68P/74YwBAZGSkxvRVq1Zh9OjRAIAFCxbAysoKQ4YMQVFREaKjo3nGYNIbBjAREVHjVJf9yNzcXFhZ/fcAz7/++gsvv/wy8vLy4O7ujq5du2Lv3r148MEHjVU2EVkokw68RaTWZezs7LB48WIsXrzYCBWRPnFAS0RkHvgmIzVFddmPTElJ0bi+YMECLFiwwEAVNQ7lWdDYMqGu9TLvyFyZxcnViIiIiIiIiJoqDrzJ4vFdUSIyNuYOEVHttGVlxWnMUmpMOPAmg2AQEhEREZEhVDf45v4nmTMOvImIiCwAd0iJyJxzwJC1VV63OW8Haro48CYCA5iIiIjInHDfjJoaDrxJ7xiURETVY0YSkbE1xdypfIh5U+yRmhYOvIn+PwY2ETV1zDkisgTMOjJHHHgTEREREZFF42CdDI0Db9IrhhYRkXljThNZJr72a8dtRIbEgTcREZGRcKeOiMg0mL9kahx4ExERERGRWWrIgNmYP1FGVBsOvElvmkIANYUeiIhqw6wjIiIyLg68iSrhDikRGQKzhYio8WBmk75x4E16wXAiImpcmNtEZIlqyz5mIxkKB95EWjB0iUifzDVTzLUuIiJDK88/5iAZCwfeRERERERNlKUPLOvSf+VlKl6vOEDX12Dd0h8TS8WBN1E1GIpEZAmYdURkqep62DlzkvSBA29qsKYcRk25NyIyDuYIERlbxU9nLZWp+rf07U7V48CbGoThQkTU+DHLiYh0w0/Fqa448CaqBYOUiHTVmPKjMdVKRHXD17Vx1PQd8fpOo6aLA2/SmSWFhSX1SkT60RhzozHWTETUmDBnLRcH3qQTSwwNS+yZiIiIGgfup5hefX4jnI+X5eHAm+rNkoPCknsnorprzFnRmGsnIjI0fWektsPUdbkPZrf548Cb6oUvap4plIhq1hTygTlH1DjxtWtcNW1rff3Wd0M/JefzwXxw4E11xheuJm4PIqqsqeVCU+uHiMjY6vpmiKkG1cx54+HAm+qEL0rtuF2IqFxTzYPyncam2h9RY8afsjJvdX1cqluuuk+963IW9druh88Z42sUA+/FixejVatWsLOzQ1hYGA4cOGDqkiwGd7Zqx51SMifMS9OwlNc/s47MUX1z78svv0RwcDDs7OzQoUMH/PDDD0aqVL846LYstQ3OK16vKav5fDEdsx94r1+/HnFxcYiPj8eRI0fQqVMnREdH4/Lly6YurUnjzpVuOAgnU2JeGpclv94tuXcyL/XNvb179yImJgaxsbE4evQoBg4ciIEDB+LEiRNGrrx2HDhZHkM+tvVZt77q4HNYk0pExNRF1CQsLAzdunXD//3f/wEAysrK4O/vjwkTJmDatGm13r6wsBCurq4oKCiAi4uLoctttCz1BWAK3Nam1ZQzoSF52ZS3i77wtVs/3F6NW2PJhPrm3rBhw3Dr1i189913yrTu3bujc+fOWLp0aa33Z8ztUvkTbb7hRRXV5blQ3TIVn0uVL+XzKy9X3fpqen7WdIK4+r4RYK7P+/pkQjMj1aSTu3fv4vDhw5g+fboyzcrKClFRUUhLS9N6m6KiIhQVFSnXCwoKANzbKJYoIcHUFVBlFZ7OdV4+IaH+tyPtyrPAzN9zrLf65iWz8r+Yk4ahr8xi9plGY8hKXfYT09LSEBcXpzEtOjoamzdv1rq8PrJS17/h5XdbWPjfnOLrgcrV5blQ3TKFhfeeX+XzK/+rbbnp0+/9v/z5WL5/Wvm+ypetOK/ic1hbbeX/1/ZaKb9d+UuuutdT5em1vWbqup7a1CsrxYxduHBBAMjevXs1pk+ZMkVCQ0O13iY+Pl4A8MILL7zUePnjjz+MEWNGU9+8ZFbywgsvdbmYc1bqsp9oY2Mj69at05i2ePFi8fLy0ro8s5IXXnipy6UuWWnWn3jrYvr06RrvZJaVleHatWto3rw5VCpVvdZVWFgIf39//PHHH2Z9mFVdNJVe2Id5aYx9iAhu3LgBX19fU5diUvXNysb4WFfGHsxHU+ijqffArLxHn/uV1WkKz6X6Ys/suamoT1aa9cDb09MT1tbWyM/P15ien58Pb29vrbdRq9VQq9Ua09zc3BpUh4uLS5N5sjSVXtiHeWlsfbi6upq6BL2rb17qmpWN7bHWhj2Yj6bQR1PuwdyzUpf9RG9vb5PvV1anKTyX6os9W4am3nNds9Ksz2pua2uLrl27Ijk5WZlWVlaG5ORkhIeHm7AyIiLzwrwkIkujS+6Fh4drLA8A27ZtY04SkcGZ9SfeABAXF4dRo0bhkUceQWhoKBYuXIhbt27hxRdfNHVpRERmhXlJRJamttwbOXIk/Pz8kPD/z7Q0adIkRERE4IMPPkD//v2RlJSEQ4cOYdmyZaZsg4gsgNkPvIcNG4Y///wTM2bMQF5eHjp37owff/wRLVu2NPh9q9VqxMfHVznEqDFqKr2wD/PSVPpoKgyZl03hsWYP5qMp9MEezENtuZebmwsrq/8e4NmjRw+sW7cOb731Ft588020bdsWmzdvxkMPPWSqFprE41Bf7NkyWGLPNTH73/EmIiIiIiIiaszM+jveRERERERERI0dB95EREREREREBsSBNxEREREREZEBceBNREREREREZEAWNfC+du0ann/+ebi4uMDNzQ2xsbG4efNmjbe5c+cOxo0bh+bNm8PJyQlDhgxBfn6+xjIqlarKJSkpSWOZlJQUPPzww1Cr1WjTpg0SExPNrpfjx48jJiYG/v7+sLe3R0hICD766KMqfWjrNy8vr051L168GK1atYKdnR3CwsJw4MCBGpf/8ssvERwcDDs7O3To0AE//PCDxnwRwYwZM+Dj4wN7e3tERUUhKytLYxldtpUx+yguLsbUqVPRoUMHODo6wtfXFyNHjsTFixc11tGqVasq233OnDkN6kPfvQDA6NGjq9TZt29fjWUM8ZhQwxgqH8tdvXoV9913H1QqFa5fv26ADu4xVTY2hClyUd9MkYnm3ENlY8eOhUqlwsKFC/VctSZD9JCRkYGnn34arq6ucHR0RLdu3ZCbm2uoFixGfbPq2rVrmDBhAoKCgmBvb4+AgABMnDgRBQUFRqy6fgz5mjJX9el5+fLl6NmzJ9zd3eHu7o6oqKhat5E5qu/jXC4pKQkqlQoDBw40bIHmRCxI3759pVOnTrJv3z7ZtWuXtGnTRmJiYmq8zdixY8Xf31+Sk5Pl0KFD0r17d+nRo4fGMgBk1apVcunSJeVy+/ZtZf7vv/8uDg4OEhcXJydPnpRFixaJtbW1/Pjjj2bVy4oVK2TixImSkpIiOTk5snr1arG3t5dFixYpy+zcuVMASGZmpka/paWltdaclJQktra2snLlSvntt9/k5ZdfFjc3N8nPz9e6/J49e8Ta2lr+/e9/y8mTJ+Wtt94SGxsbSU9PV5aZM2eOuLq6yubNm+X48ePy9NNPy/3336+x/XXZVsbs4/r16xIVFSXr16+XU6dOSVpamoSGhkrXrl011hMYGCizZs3S2O43b97UuQ9D9CIiMmrUKOnbt69GndeuXdNYj74fE2o4Q+VjuQEDBki/fv0EgPz1118G6OAeU2WjrkyVi/pkqkw05x4q2rhxo3Tq1El8fX1lwYIFjaqH7Oxs8fDwkClTpsiRI0ckOztbtmzZUu06qe7qm1Xp6ekyePBg+eabbyQ7O1uSk5Olbdu2MmTIECNWXXeGfE2Zq/r2PHz4cFm8eLEcPXpUMjIyZPTo0eLq6irnz583cuW6q2/P5c6cOSN+fn7Ss2dPGTBggHGKNQMWM/A+efKkAJCDBw8q07Zu3SoqlUouXLig9TbXr18XGxsb+fLLL5VpGRkZAkDS0tKUaQBk06ZN1d73G2+8Ie3bt9eYNmzYMImOjja7Xip79dVX5fHHH1eulw+8ddlxDg0NlXHjxinXS0tLxdfXVxISErQuP3ToUOnfv7/GtLCwMHnllVdERKSsrEy8vb1l3rx5yvzr16+LWq2WL774QkR021bG7kObAwcOCAA5d+6cMi0wMFDvO22G6GXUqFE1hqghHhNqGENnypIlSyQiIkKSk5MNOvA2ZTbqyhS5qG+mykR9MlQP58+fFz8/Pzlx4oRBMrwiQ/QwbNgweeGFFwxTsAXT19/BDRs2iK2trRQXFxuizAYxRi6Ym/r2XFlJSYk4OzvLZ599ZqgS9U6XnktKSqRHjx7y6aef1rrP2NRYzKHmaWlpcHNzwyOPPKJMi4qKgpWVFfbv36/1NocPH0ZxcTGioqKUacHBwQgICEBaWprGsuPGjYOnpydCQ0OxcuVKSIWfR09LS9NYBwBER0dXWYe59FJRQUEBPDw8qkzv3LkzfHx80KdPH+zZs6fWmu/evYvDhw9r3L+VlRWioqKqvf/attuZM2eQl5ensYyrqyvCwsKUZXTZVsbuQ5uCggKoVCq4ublpTJ8zZw6aN2+OLl26YN68eSgpKal3D8boJSUlBV5eXggKCsI///lPXL16VWMd+nxMqOEMmSknT57ErFmz8Pnnn8PKyrB/cswhG+vDVLmoT6bORH0wVA9lZWUYMWIEpkyZgvbt2+u97ooM0UNZWRm+//57tGvXDtHR0fDy8kJYWBg2b95ssD4shb7+DhYUFMDFxQXNmjUzRJk6M1YumBNdeq7s77//RnFxcYP/thiLrj3PmjULXl5eiI2NNUaZZsViBt55eXnw8vLSmNasWTN4eHhU+/3kvLw82NraVvlD37JlS43bzJo1Cxs2bMC2bdswZMgQvPrqq1i0aJHGelq2bFllHYWFhbh9+7ZZ9VLR3r17sX79eowZM0aZ5uPjg6VLl+Lrr7/G119/DX9/f0RGRuLIkSM11nzlyhWUlpZq3Q411VzT8uX/1rZMfbeVsfuo7M6dO5g6dSpiYmLg4uKiTJ84cSKSkpKwc+dOvPLKK3j//ffxxhtv1LsHQ/fSt29ffP7550hOTsbcuXORmpqKfv36obS0VFmHPh8TajhDZUpRURFiYmIwb948BAQEGKT2yjWZKht1Yapc1CdTZqK+GKqHuXPnolmzZpg4caLea67MED1cvnwZN2/exJw5c9C3b1/8/PPPGDRoEAYPHozU1FTDNGIh9PF38MqVK3j33XcbnEOGYIxcMDe69FzZ1KlT4evrW+UNCHOlS8+7d+/GihUrsHz5cmOUaHbM6y0yHUybNg1z586tcZmMjAyD1vD2228r/+/SpQtu3bqFefPm1fuPrTn0Uu7EiRMYMGAA4uPj8cQTTyjTg4KCEBQUpFzv0aMHcnJysGDBAqxevdootTVlxcXFGDp0KEQEH3/8sca8uLg45f8dO3aEra0tXnnlFSQkJECtVhu71Go999xzyv87dOiAjh07onXr1khJSUHv3r1NWJnlMXWmTJ8+HSEhIXjhhRcatB5T91FRddlIhlFTJpqzw4cP46OPPsKRI0egUqlMXY5OysrKAAADBgzA66+/DuDe0W579+7F0qVLERERYcryzJKxsqqwsBD9+/fHgw8+iJkzZzZ4fWR6c+bMQVJSElJSUmBnZ2fqcgzixo0bGDFiBJYvXw5PT09Tl2MSjX7g/a9//QujR4+ucZkHHngA3t7euHz5ssb0kpISXLt2Dd7e3lpv5+3tjbt37+L69esan4bk5+dXexsACAsLw7vvvouioiKo1Wp4e3tXOdNvfn4+XFxcYG9vb3a9nDx5Er1798aYMWPw1ltv1VgPAISGhmL37t01LuPp6Qlra2ut26Gmmmtavvzf/Px8+Pj4aCzTuXNnZZn6bitj91GufAfz3Llz2LFjR62f7ISFhaGkpARnz57VeDPEHHqp6IEHHoCnpyeys7PRu3dvvT8mVD1TZ8qOHTuQnp6Or776CgCUr+B4enrif//3f/HOO+80ij7K1Tcba2OqXNQnc8pEXRmih127duHy5csaR3qUlpbiX//6FxYuXIizZ8+afQ+enp5o1qwZHnzwQY1lQkJCav2bb6kMmVXlbty4gb59+8LZ2RmbNm2CjY1NQ8vWO2PtX5gTXXouN3/+fMyZMwfbt29Hx44dDVmmXtW355ycHJw9exZPPfWUMq38Db5mzZohMzMTrVu3NmzRpmbar5gbT/mJLA4dOqRM++mnn+p00p2vvvpKmXbq1KlaT7oze/ZscXd3V66/8cYb8tBDD2ksExMT0+CTqxmilxMnToiXl5dMmTKlzvVERUXJoEGDal0uNDRUxo8fr1wvLS0VPz+/Gk+08eSTT2pMCw8Pr3ISofnz5yvzCwoKtJ5crT7byth9iIjcvXtXBg4cKO3bt5fLly/XqY41a9aIlZVVlTOG14cheqnsjz/+EJVKJVu2bBERwzwm1DCGypTs7GxJT09XLitXrhQAsnfvXoOcFdncsrEuTJGL+mYumdgQ+u7hypUrGs/99PR08fX1lalTp8qpU6caRQ/l1yufXG3gwIH8FYoG0vXvYEFBgXTv3l0iIiLk1q1bxihVZ8bYvzA39e1ZRGTu3Lni4uJS47jCnNWn59u3b1fJxQEDBkivXr0kPT1dioqKjFm6SVjMwFvk3k83dOnSRfbv3y+7d++Wtm3bavzxOH/+vAQFBcn+/fuVaWPHjpWAgADZsWOHHDp0SMLDwyU8PFyZ/80338jy5cslPT1dsrKyZMmSJeLg4CAzZsxQlin/ObEpU6ZIRkaGLF68WC8/J6bvXtLT06VFixbywgsvaPwUVMWdngULFsjmzZslKytL0tPTZdKkSWJlZSXbt2+vteakpCRRq9WSmJgoJ0+elDFjxoibm5vk5eWJiMiIESNk2rRpyvJ79uyRZs2ayfz58yUjI0Pi4+O1/myOm5ubbNmyRX799VcZMGCA1p8Tq2lb1Ze++7h79648/fTTct9998mxY8c0tn15CO3du1cWLFggx44dk5ycHFmzZo20aNFCRo4cqXMfhujlxo0bMnnyZElLS5MzZ87I9u3b5eGHH5a2bdvKnTt3lPXo+zGhhjNEplTWkF9FMGUfdclGXZkqF/XJFJlo7j1oY+izmhuih40bN4qNjY0sW7ZMsrKylJ9D3bVrl8H6sBT1zaqCggIJCwuTDh06SHZ2tsbroqSkxFRtVMsYrylzU9+e58yZI7a2tvLVV19pPJ43btwwVQv1Vt+eK7O0s5pb1MD76tWrEhMTI05OTuLi4iIvvviixpP7zJkzAkB27typTLt9+7a8+uqr4u7uLg4ODjJo0CC5dOmSMn/r1q3SuXNncXJyEkdHR+nUqZMsXbq0yu9a79y5Uzp37iy2trbywAMPyKpVq8yul/j4eAFQ5RIYGKgsM3fuXGndurXY2dmJh4eHREZGyo4dO+pc96JFiyQgIEBsbW0lNDRU9u3bp8yLiIiQUaNGaSy/YcMGadeundja2kr79u3l+++/15hfVlYmb7/9trRs2VLUarX07t1bMjMz67WtdKHPPsofK22X8sfv8OHDEhYWJq6urmJnZychISHy/vvvawxmzaGXv//+W5544glp0aKF2NjYSGBgoLz88stKAJczxGNCDWOITKnMGANvU2VjQ5giF/XN2Jlo7j1oY+iBt4hhelixYoW0adNG7OzspFOnTrJ582aD9mAp6ptV5fmp7XLmzBnTNFELQ7+mzFF9eg4MDNT6eMbHxxu/8Aao7+NckaUNvFUiFX73ioiIiIiIiIj0ymJ+ToyIiIiIiIjIFDjwJiIiIiIiIjIgDryJiIiIiIiIDIgDbyIiIiIiIiID4sCbiIiIiIiIyIA48CYiIiIiIiIyIA68iYiIiIiIiAyIA28iIiIiIiIiA+LAu5GIjIzEa6+9ZrD1jx49GgMHDqx2/syZM9G5c2eD3T81XEpKClQqFa5fv17n2/BxpaaGWUm1SUxMhJubW71uU9vjTkS6OXv2LFQqFY4dO2bw+9L22l+2bBn8/f1hZWWFhQsXNooMZ4Y1Xhx4E+mJiGD+/Plo164d1Go1/Pz88N577xnt/nv06IFLly7B1dVVr+s19ECGiCzHzJkzoVKpqlwcHR2NVsOwYcNw+vRpva+3VatWWLhwod7XS0T6Ufm1X1hYiPHjx2Pq1Km4cOECxowZg8mTJyM5OdmEVdaOGdZ4NTN1AURNxaRJk/Dzzz9j/vz56NChA65du4Zr164Z7f5tbW3h7e1ttPsjIqqvyZMnY+zYsRrTevfujW7duhmtBnt7e9jb2xvt/ojIPFR+7efm5qK4uBj9+/eHj4+PMt3JyalB91NcXAwbG5sGraMmzLDGi594NyIlJSUYP348XF1d4enpibfffhsiAgBYvXo1HnnkETg7O8Pb2xvDhw/H5cuXNW7/22+/4cknn4SLiwucnZ3Rs2dP5OTkaL2vgwcPokWLFpg7d67G9E8++QT+/v5wcHDA0KFDUVBQoMwrKyvDrFmzcN9990GtVqNz58748ccflfnlhxNt3LgRjz/+OBwcHNCpUyekpaVp3Mfu3bvRs2dP2Nvbw9/fHxMnTsStW7dq3DbffvstunXrBjs7O3h6emLQoEHKvL/++gsjR46Eu7s7HBwc0K9fP2RlZSnzyw/Z+emnnxASEgInJyf07dsXly5dAgD8/PPPsLOzq3II96RJk9CrVy8AQEZGBj7++GNs2bIFTz/9NO6//3507doVffr0qbbmZ555BuPHj1euv/baa1CpVDh16hQA4O7du3B0dMT27duV7ZuQkID7778f9vb26NSpE7766ivl9toONV++fLnyeA0aNAgffvih1sOTVq9ejVatWsHV1RXPPfccbty4AeDeoUmpqan46KOPlE+mzp49W8MjQWR6zMrqmTornZyc4O3trVzy8/Nx8uRJxMbGVlvzI488gvnz5yvXBw4cCBsbG9y8eRMAcP78eahUKmRnZwMAioqKMHnyZPj5+cHR0RFhYWFISUmp0kdFs2fPhpeXF5ydnfHSSy9h2rRpWg83nT9/Pnx8fNC8eXOMGzcOxcXFAO4dGXTu3Dm8/vrrSlYS0X+VlZXh3//+N9q0aQO1Wo2AgACtRwWWlpYiNjZW2dcJCgrCRx99pLFMSkoKQkND4ejoCDc3Nzz66KM4d+4cAOD48eN4/PHH4ezsDBcXF3Tt2hWHDh0CoPnaT0xMRIcOHQAADzzwgLJ/o+1Q808//RQhISGws7NDcHAwlixZoswrz+v169cjIiICdnZ2WLt2rcbtmWGkEGoUIiIixMnJSSZNmiSnTp2SNWvWiIODgyxbtkxERFasWCE//PCD5OTkSFpamoSHh0u/fv2U258/f148PDxk8ODBcvDgQcnMzJSVK1fKqVOnRERk1KhRMmDAABERSU5OFldXV/nkk0+U28fHx4ujo6P06tVLjh49KqmpqdKmTRsZPny4ssyHH34oLi4u8sUXX8ipU6fkjTfeEBsbGzl9+rSIiJw5c0YASHBwsHz33XeSmZkpzzzzjAQGBkpxcbGIiGRnZ4ujo6MsWLBATp8+LXv27JEuXbrI6NGjq9023333nVhbW8uMGTPk5MmTcuzYMXn//feV+U8//bSEhITIL7/8IseOHZPo6Ghp06aN3L17V0REVq1aJTY2NhIVFSUHDx6Uw4cPS0hIiNJbSUmJtGzZUj799FNlnZWnzZ07V9q1ayfz58+XVq1aSWBgoMTGxsrVq1errfs///mPtG/fXrneuXNn8fT0lI8//lhERHbv3i02NjZy69YtERGZPXu2BAcHy48//ig5OTmyatUqUavVkpKSIiIiO3fuFADy119/Kbe3srKSefPmSWZmpixevFg8PDzE1dVV43F1cnKSwYMHS3p6uvzyyy/i7e0tb775poiIXL9+XcLDw+Xll1+WS5cuyaVLl6SkpKTanohMjVlp3llZ2fjx46Vdu3Y1PqZxcXHSv39/EREpKysTDw8P8fT0lK1bt4qIyJo1a8TPz09Z/qWXXpIePXrIL7/8ItnZ2TJv3jxRq9XK9l21apVGDq5Zs0bs7Oxk5cqVkpmZKe+88464uLhIp06dlGVGjRolLi4uMnbsWMnIyJBvv/1W43l19epVue+++2TWrFlKVhLRf73xxhvi7u4uiYmJkp2dLbt27ZLly5creXf06FEREbl7967MmDFDDh48KL///ruS4evXrxcRkeLiYnF1dZXJkydLdna2nDx5UhITE+XcuXMiItK+fXt54YUXJCMjQ06fPi0bNmyQY8eOiYjma//vv/+W7du3CwA5cOCAsn8THx+v8dpfs2aN+Pj4yNdffy2///67fP311+Lh4SGJiYki8t+8btWqlbLMxYsXNXpnhlE5DrwbiYiICAkJCZGysjJl2tSpUyUkJETr8gcPHhQAcuPGDRERmT59utx///3KDlRl5TuTGzduFCcnJ0lKStKYHx8fL9bW1nL+/Hll2tatW8XKykp5cfr6+sp7772ncbtu3brJq6++KiL/DaeKO2C//fabAJCMjAwREYmNjZUxY8ZorGPXrl1iZWUlt2/f1lp7eHi4PP/881rnnT59WgDInj17lGlXrlwRe3t72bBhg4jcCzAAkp2drSyzePFiadmypXJ90qRJ0qtXL+X6Tz/9JGq1WhnkvvLKK6JWqyUsLEx++eUX2blzp3Tu3Fkef/xxrXWJiPz666+iUqnk8uXLcu3aNbG1tZV3331Xhg0bJiL3Bto9evQQEZE7d+6Ig4OD7N27V2MdsbGxEhMTIyJVB97Dhg1Tgr7c888/X2Xg7eDgIIWFhcq0KVOmSFhYmHI9IiJCJk2aVG0fROaEWWneWVnR7du3xd3dXebOnau1pnLffPONuLq6SklJiRw7dky8vb1l0qRJMnXqVBG5t5NaPvg/d+6cWFtby4ULFzTW0bt3b5k+fbrSR8UcDAsLk3Hjxmks/+ijj1bZaQ0MDNR44/HZZ59V8lpEJDAwUBYsWFBjL0SWqLCwUNRqtSxfvrzKvMoDb23GjRsnQ4YMEZF7A0QAyocOlTk7OyuD4soqv/aPHj0qAOTMmTPKtMoD79atW8u6des01vPuu+9KeHi4Rv0LFy6stn5mGJXjoeaNSPfu3TUO/QgPD0dWVhZKS0tx+PBhPPXUUwgICICzszMiIiIA3Pv+CgAcO3YMPXv2rPE7J/v378ezzz6L1atXY9iwYVXmBwQEwM/PT+P+y8rKkJmZicLCQly8eBGPPvqoxm0effRRZGRkaEzr2LGj8v/y79SUH+p5/PhxJCYmwsnJSblER0ejrKwMZ86c0Vr3sWPH0Lt3b63zMjIy0KxZM4SFhSnTmjdvjqCgII26HBwc0Lp1a426Kh5++vzzzyMlJQUXL14EAKxduxb9+/dXDvUpKytDUVERPv/8c/Ts2RORkZFYsWIFdu7ciczMTK21PfTQQ/Dw8EBqaip27dqFLl264Mknn0RqaioAIDU1FZGRkQCA7Oxs/P333+jTp4/Gtvn888+rPQQ2MzMToaGhGtMqXwfunUzD2dm52t6JGhtmpflmZUWbNm3CjRs3MGrUKK01levZsydu3LiBo0ePIjU1FREREYiMjFQOvayYlenp6SgtLUW7du00tk1qamqDs7J9+/awtrautnci0i4jIwNFRUXV5k9lixcvRteuXdGiRQs4OTlh2bJlSkZ7eHhg9OjRiI6OxlNPPYWPPvpI+boLAMTFxeGll15CVFQU5syZU+3rvi5u3bqFnJwcxMbGauTJ7Nmzq6z3kUceqXY9zDAqx5OrNQF37txBdHQ0oqOjsXbtWrRo0QK5ubmIjo7G3bt3AaBOJ2Fo3bo1mjdvjpUrV6J///4GOzFExfWW7xyXlZUBAG7evIlXXnkFEydOrHK7gIAArevTxwkmKveqUqmU74QCQLdu3dC6dWskJSXhn//8JzZt2oTExERlvo+PD5o1a4Z27dop00JCQgDc26EPCgqqcp8qlQqPPfYYUlJSoFarERkZiY4dO6KoqAgnTpzA3r17MXnyZABQvgf0/fffa+zQA4BardZ77+WPB1FTwqw0fVZW9Omnn+LJJ59Ey5Yta7xPNzc3dOrUCSkpKUhLS0OfPn3w2GOPKWf2zcrKUt5AuXnzJqytrXH48GGNHUyg4SdMYlYS6aY+2ZOUlITJkyfjgw8+QHh4OJydnTFv3jzs379fWWbVqlWYOHEifvzxR6xfvx5vvfUWtm3bhu7du2PmzJkYPnw4vv/+e2zduhXx8fFISkrSOJ9FXZXvey1fvlzjTUkAVfKlpl9mYIZROX7i3YhUDB0A2LdvH9q2bYtTp07h6tWrmDNnDnr27Ing4OAq72B17NgRu3btUk6ioI2npyd27NiB7OxsDB06tMqyubm5yqcY5fdvZWWFoKAguLi4wNfXF3v27NG4zZ49e/Dggw/WuceHH34YJ0+eRJs2bapcbG1ttd6mY8eO1f70Q0hICEpKSjS23dWrV5GZmVmvuoB7n+SsXbsW3377LaysrNC/f39l3qOPPoqSkhKNdyPLf+ohMDCw2nVGREQgJSUFKSkpiIyMhJWVFR577DHMmzcPRUVFyqdiDz74INRqNXJzc6tsF39/f63rDgoKwsGDBzWmVb5eF7a2tigtLa337YhMhVlpvllZ7syZM9i5c2eNJ1WrKCIiAjt37sQvv/yCyMhIeHh4ICQkBO+99x58fHyUNz27dOmC0tJSXL58ucp2qe5XH5iVRIbVtm1b2Nvb1+lnuvbs2YMePXrg1VdfRZcuXdCmTRutn/R26dIF06dPx969e/HQQw9h3bp1yrx27drh9ddfx88//4zBgwdj1apVOtXdsmVL+Pr64vfff6+SJ/fff3+91sUMI4AD70YlNzcXcXFxyMzMxBdffIFFixZh0qRJCAgIgK2tLRYtWoTff/8d33zzDd59912N244fPx6FhYV47rnncOjQIWRlZWH16tVVDoP28vLCjh07cOrUKcTExKCkpESZZ2dnh1GjRuH48ePYtWsXJk6ciKFDhypBMGXKFMydOxfr169HZmYmpk2bhmPHjmHSpEl17nHq1KnYu3cvxo8fj2PHjiErKwtbtmzROPv39OnTMXLkSOV6fHw8vvjiC8THxyMjIwPp6enKGYbbtm2LAQMG4OWXX8bu3btx/PhxvPDCC/Dz88OAAQPqvvFxb2fyyJEjeO+99/DMM89ofNIcFRWFhx9+GP/4xz9w9OhRHD58GK+88gr69OmjhOmBAwcQHByMCxcuKLeLjIzEyZMn8dtvv+F//ud/lGlr167FI488oryD6uzsjMmTJ+P111/HZ599hpycHBw5cgSLFi3CZ599prXeCRMm4IcffsCHH36IrKwsfPLJJ9i6dWu9z1TZqlUr7N+/H2fPnsWVK1f47iiZPWblPeaYleVWrlwJHx8f9OvXr8q8TZs2ITg4WGNaZGQkfvrpJzRr1kyZV56V5Z8UAfd2uJ9//nmMHDkSGzduxJkzZ3DgwAEkJCTg+++/11rvhAkTsGLFCnz22WfIysrC7Nmz8euvv+qUlb/88gsuXLiAK1eu1Ou2RE2ZnZ0dpk6dijfeeEP5ity+ffuwYsWKKsu2bdsWhw4dwk8//YTTp0/j7bff1hhEnjlzBtOnT0daWhrOnTuHn3/+GVlZWQgJCcHt27cxfvx4pKSk4Ny5c9izZw8OHjyoHIGoi3feeQcJCQn4z3/+g9OnTyM9PR2rVq3Chx9+WO1tmGFULVN/yZzqJiIiQl599VUZO3asuLi4iLu7u7z55pvKCYTWrVsnrVq1ErVaLeHh4fLNN99UOVnF8ePH5YknnhAHBwdxdnaWnj17Sk5OjohonqlXROTixYvSrl07GTp0qMZZHpcsWSK+vr5iZ2cnzzzzjFy7dk25TWlpqcycOVP8/PzExsZGOnXqpJyxUUT7CTT++usvASA7d+5Uph04cED69OkjTk5O4ujoKB07dtQ4EdGoUaMkIiJCY/t8/fXX0rlzZ7G1tRVPT08ZPHiwMu/atWsyYsQIcXV1FXt7e4mOjlbODClS9SQVIiKbNm0SbS+P0NBQASA7duyoMu/ChQsyePBgcXJykpYtW8ro0aM1zmpefvKziifxKC0tFXd3d42TmZWf7GPatGka6y8rK5OFCxdKUFCQ2NjYSIsWLSQ6OlpSU1M11l/xJEbLli0TPz8/sbe3l4EDB8rs2bPF29tbmV/5JCIiIgsWLJDAwEDlemZmpnTv3l3s7e2r1E9kbpiV5p+VpaWlct999ym/nlBZ+UncKrp69aqoVCqNEwGV3/fSpUs1li0/K3KrVq3ExsZGfHx8ZNCgQfLrr79W28esWbPE09NTnJyc5B//+IdMnDhRunfvrsyv/LiL3DuRXMXtm5aWJh07dhS1Wq11mxBZstLSUpk9e7YEBgaKjY2NBAQEyPvvv18l7+7cuSOjR48WV1dXcXNzk3/+858ybdo0ZV8lLy9PBg4cKD4+PmJrayuBgYEyY8YMKS0tlaKiInnuuefE399fbG1txdfXV8aPH6+ccFKXk6uJiKxdu1bJTXd3d3nsscdk48aNIqI9r5lhVB2VSIUvZxFRk/byyy/j1KlT2LVrl6lLISIyW3369IG3tzdWr15t6lKIiOqNGWaeeHI1oiZs/vz56NOnDxwdHbF161Z89tlnWLJkianLIiIyG3///TeWLl2K6OhoWFtb44svvsD27duxbds2U5dGRFQrZljjwU+8iZqwoUOHIiUlBTdu3MADDzyACRMmYOzYsaYui4jIbNy+fRtPPfUUjh49ijt37iAoKAhvvfUWBg8ebOrSiIhqxQxrPDjwJiIiIiIiIjIgntWciIiIiIiIyIA48CYiIiIiIiIyIA68iYiIiIiIiAyIA28iIiIiIiIiA+LAm4iIiIiIiMiAOPAmIiIiIiIiMiAOvImIiIiIiIgMiANvIiIiIiIiIgP6f2vQ0EmnqlLJAAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
],
"source": [
"def plot_weight_distribution(model, bins=256, count_nonzero_only=False):\n",
" fig, axes = plt.subplots(3,3, figsize=(10, 6))\n",
" axes = axes.ravel()\n",
" plot_index = 0\n",
" for name, param in model.named_parameters():\n",
" if param.dim() > 1:\n",
" ax = axes[plot_index]\n",
" if count_nonzero_only:\n",
" param_cpu = param.detach().view(-1).cpu()\n",
" param_cpu = param_cpu[param_cpu != 0].view(-1)\n",
" ax.hist(param_cpu, bins=bins, density=True,\n",
" color = 'blue', alpha = 0.5)\n",
" else:\n",
" ax.hist(param.detach().view(-1).cpu(), bins=bins, density=True,\n",
" color = 'blue', alpha = 0.5)\n",
" ax.set_xlabel(name)\n",
" ax.set_ylabel('density')\n",
" plot_index += 1\n",
" fig.suptitle('Histogram of Weights')\n",
" fig.tight_layout()\n",
" fig.subplots_adjust(top=0.925)\n",
" plt.show()\n",
"\n",
"plot_weight_distribution(model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4gRkueJTY0hL"
},
"source": [
"## Question 1 (10 pts)\n",
"\n",
"Please answer the following questions using the information in the above histograms of weights."
]
},
{
"cell_type": "markdown",
"source": [
"\n",
"### Question 1.1 (5 pts)\n",
"\n",
"What are the common characteristics of the weight distribution in the different layers?"
],
"metadata": {
"id": "LrKtD5x6AGYt"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "klj92pzBcvaI"
},
"source": [
"**Your Answer:** A lot of 0."
]
},
{
"cell_type": "markdown",
"source": [
"### Question 1.2 (5 pts)\n",
"\n",
"How do these characteristics help pruning?"
],
"metadata": {
"id": "g5eIrHzR__ir"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:** We can remove these 0."
],
"metadata": {
"id": "DD5lzMoAABdO"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "USisw8QcnPNP"
},
"source": [
"# Fine-grained Pruning"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hkeZm_NJZ0NM"
},
"source": [
"In this section, we will implement and perform fine-grained pruning.\n",
"\n",
"Fine-grained pruning removes the synapses with lowest importance. The weight tensor $W$ will become sparse after fine-grained pruning, which can be described with **sparsity**:\n",
"\n",
"> $\\mathrm{sparsity} := \\#\\mathrm{Zeros} / \\#W = 1 - \\#\\mathrm{Nonzeros} / \\#W$\n",
"\n",
"where $\\#W$ is the number of elements in $W$.\n",
"\n",
"In practice, given the target sparsity $s$, the weight tensor $W$ is multiplied with a binary mask $M$ to disregard removed weight:\n",
"\n",
"> $v_{\\mathrm{thr}} = \\texttt{kthvalue}(Importance, \\#W \\cdot s)$\n",
">\n",
"> $M = Importance > v_{\\mathrm{thr}}$\n",
">\n",
"> $W = W \\cdot M$\n",
"\n",
"where $Importance$ is importance tensor with the same shape of $W$, $\\texttt{kthvalue}(X, k)$ finds the $k$-th smallest value of tensor $X$, $v_{\\mathrm{thr}}$ is the threshold value."
]
},
{
"cell_type": "markdown",
"source": [
"## Magnitude-based Pruning\n",
"\n",
"For fine-grained pruning, a widely-used importance is the magnitude of weight value, *i.e.*,\n",
"\n",
"$Importance=|W|$\n",
"\n",
"This is known as **Magnitude-based Pruning** (see [Learning both Weights and Connections for Efficient\n",
"Neural Networks](https://arxiv.org/pdf/1506.02626.pdf))."
],
"metadata": {
"id": "Qajrblu0Is3z"
}
},
{
"cell_type": "markdown",
"source": [
""
],
"metadata": {
"id": "r9nTCaD8wxvA"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "rZpAlUYwc7U-"
},
"source": [
"### Question 2 (15 pts)\n",
"\n",
"Please complete the following magnitude-based fine-grained pruning function.\n",
"\n",
"**Hint**:\n",
"* In step 1, we calculate the number of zeros (`num_zeros`) after pruning. Note that `num_zeros` should be an integer. You could use either `round()` or `int()` to convert a floating number into an integer. Here we use `round()`.\n",
"* In step 2, we calculate the `importance` of weight tensor. Pytorch provides [`torch.abs()`](https://pytorch.org/docs/stable/generated/torch.abs.html#torch.abs), [`torch.Tensor.abs()`](https://pytorch.org/docs/stable/generated/torch.Tensor.abs.html#torch.Tensor.abs), [`torch.Tensor.abs_()`](https://pytorch.org/docs/stable/generated/torch.Tensor.abs_.html) APIs.\n",
"* In step 3, we calculate the pruning `threshold` so that all synapses with importance smaller than `threshold` will be removed. Pytorch provides [`torch.kthvalue()`](https://pytorch.org/docs/stable/generated/torch.kthvalue.html), [`torch.Tensor.kthvalue()`](https://pytorch.org/docs/stable/generated/torch.Tensor.kthvalue.html), [`torch.topk()`](https://pytorch.org/docs/stable/generated/torch.topk.html) APIs.\n",
"* In step 4, we calculate the pruning `mask` based on the `threshold`. **1** in the `mask` indicates the synapse will be kept, and **0** in the `mask` indicates the synapse will be removed. `mask = importance > threshold`. Pytorch provides [`torch.gt()`](https://pytorch.org/docs/stable/generated/torch.gt.html?highlight=torch%20gt#torch.gt) API."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3n6g76DPnXFz"
},
"outputs": [],
"source": [
"def fine_grained_prune(tensor: torch.Tensor, sparsity : float) -> torch.Tensor:\n",
" \"\"\"\n",
" magnitude-based pruning for single tensor\n",
" :param tensor: torch.(cuda.)Tensor, weight of conv/fc layer\n",
" :param sparsity: float, pruning sparsity\n",
" sparsity = #zeros / #elements = 1 - #nonzeros / #elements\n",
" :return:\n",
" torch.(cuda.)Tensor, mask for zeros\n",
" \"\"\"\n",
" sparsity = min(max(0.0, sparsity), 1.0)\n",
" if sparsity == 1.0:\n",
" tensor.zero_()\n",
" return torch.zeros_like(tensor)\n",
" elif sparsity == 0.0:\n",
" return torch.ones_like(tensor)\n",
"\n",
" num_elements = tensor.numel()\n",
"\n",
" ##################### YOUR CODE STARTS HERE #####################\n",
" # Step 1: calculate the #zeros (please use round())\n",
" num_zeros = round(num_elements * sparsity)\n",
" # Step 2: calculate the importance of weight\n",
" importance = torch.abs(tensor)\n",
" # Step 3: calculate the pruning threshold\n",
" threshold = torch.kthvalue(importance.flatten(), num_zeros)[0]\n",
" # Step 4: get binary mask (1 for nonzeros, 0 for zeros)\n",
" mask = importance > threshold\n",
" ##################### YOUR CODE ENDS HERE #######################\n",
"\n",
" # Step 5: apply mask to prune the tensor\n",
" tensor.mul_(mask)\n",
"\n",
" return mask"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZQy75p5iB0QC"
},
"source": [
"Let's verify the functionality of defined fine-grained pruning by applying the function above on a dummy tensor."
]
},
{
"cell_type": "code",
"source": [
"test_fine_grained_prune()"
],
"metadata": {
"id": "tqKLOsLdihcX",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 440
},
"outputId": "522e9e78-c7b4-418d-e248-3a37f10a4211"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"* Test fine_grained_prune()\n",
" target sparsity: 0.75\n",
" sparsity before pruning: 0.04\n",
" sparsity after pruning: 0.76\n",
" sparsity of pruning mask: 0.76\n",
"* Test passed.\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9GdyRrweym49"
},
"source": [
"### Question 3 (5 pts)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YxQyShwLylw4"
},
"source": [
"The last cell plots the tensor before and after pruning. Nonzeros are rendered in blue while zeros are rendered in gray. Please modify the value of `target_sparsity` in the following code cell so that there are only 10 nonzeros in the sparse tensor after pruning."
]
},
{
"cell_type": "code",
"source": [
"##################### YOUR CODE STARTS HERE #####################\n",
"target_sparsity = (15/25) # please modify the value of target_sparsity\n",
"##################### YOUR CODE ENDS HERE #####################\n",
"test_fine_grained_prune(target_sparsity=target_sparsity, target_nonzeros=10)"
],
"metadata": {
"id": "E5Q3jj6YArPa",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 440
},
"outputId": "0ccd3060-490f-4785-e563-215e43a6e972"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"* Test fine_grained_prune()\n",
" target sparsity: 0.60\n",
" sparsity before pruning: 0.04\n",
" sparsity after pruning: 0.60\n",
" sparsity of pruning mask: 0.60\n",
"* Test passed.\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Rse5ur_eDj9C"
},
"source": [
"We now wrap the fine-grained pruning function into a class for pruning the whole model. In class `FineGrainedPruner`, we have to keep a record of the pruning masks so that we could apply the masks whenever the model weights change to make sure the model keep sparse all the time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TR0JgPAdpLlW"
},
"outputs": [],
"source": [
"class FineGrainedPruner:\n",
" def __init__(self, model, sparsity_dict):\n",
" self.masks = FineGrainedPruner.prune(model, sparsity_dict)\n",
"\n",
" @torch.no_grad()\n",
" def apply(self, model):\n",
" for name, param in model.named_parameters():\n",
" if name in self.masks:\n",
" param *= self.masks[name]\n",
"\n",
" @staticmethod\n",
" @torch.no_grad()\n",
" def prune(model, sparsity_dict):\n",
" masks = dict()\n",
" for name, param in model.named_parameters():\n",
" if param.dim() > 1: # we only prune conv and fc weights\n",
" masks[name] = fine_grained_prune(param, sparsity_dict[name])\n",
" return masks"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1pH3xlQFs3nE"
},
"source": [
"## Sensitivity Scan\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "O7L8Lm4_bCSY"
},
"source": [
"Different layers contribute differently to the model performance. It is challenging to decide the proper sparsity for each layer. A widely used approach is sensitivity scan.\n",
"\n",
"During the sensitivity scan, at each time, we will only prune one layer to see the accuracy degradation. By scanning different sparsities, we could draw the sensitivity curve (i.e., accuracy vs. sparsity) of the corresponding layer.\n",
"\n",
"Here is an example figure for sensitivity curves. The x-axis is the sparsity or the percentage of #parameters dropped (*i.e.*, sparsity). The y-axis is the validation accuracy. (This is Figure 6 in [Learning both Weights and Connections for Efficient\n",
"Neural Networks](https://arxiv.org/pdf/1506.02626.pdf))\n"
]
},
{
"cell_type": "markdown",
"source": [
""
],
"metadata": {
"id": "OSQ1jR8YE8-d"
}
},
{
"cell_type": "markdown",
"source": [
"The following code cell defines the sensitivity scan function that returns the sparsities scanned, and a list of accuracies for each weight to be pruned."
],
"metadata": {
"id": "6f3YWd7eDpY7"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IhkFEhHDs6r0"
},
"outputs": [],
"source": [
"@torch.no_grad()\n",
"def sensitivity_scan(model, dataloader, scan_step=0.1, scan_start=0.4, scan_end=1.0, verbose=True):\n",
" sparsities = np.arange(start=scan_start, stop=scan_end, step=scan_step)\n",
" accuracies = []\n",
" named_conv_weights = [(name, param) for (name, param) \\\n",
" in model.named_parameters() if param.dim() > 1]\n",
" for i_layer, (name, param) in enumerate(named_conv_weights):\n",
" param_clone = param.detach().clone()\n",
" accuracy = []\n",
" for sparsity in tqdm(sparsities, desc=f'scanning {i_layer}/{len(named_conv_weights)} weight - {name}'):\n",
" fine_grained_prune(param.detach(), sparsity=sparsity)\n",
" acc = evaluate(model, dataloader, verbose=False)\n",
" if verbose:\n",
" print(f'\\r sparsity={sparsity:.2f}: accuracy={acc:.2f}%', end='')\n",
" # restore\n",
" param.copy_(param_clone)\n",
" accuracy.append(acc)\n",
" if verbose:\n",
" print(f'\\r sparsity=[{\",\".join([\"{:.2f}\".format(x) for x in sparsities])}]: accuracy=[{\", \".join([\"{:.2f}%\".format(x) for x in accuracy])}]', end='')\n",
" accuracies.append(accuracy)\n",
" return sparsities, accuracies"
]
},
{
"cell_type": "markdown",
"source": [
"Please run the following cells to plot the sensitivity curves. It should take around 2 minutes to finish."
],
"metadata": {
"id": "pB_YNZ2iDXyj"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tQN88zrsIRjE",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 461,
"referenced_widgets": [
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},
"outputId": "02b7f804-1654-418a-935f-fd4f79ee13ba"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"scanning 0/9 weight - backbone.conv0.weight: 0%| | 0/6 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e7b12c184f0d4aba9bf9ac521d758365"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" sparsity=[0.40,0.50,0.60,0.70,0.80,0.90]: accuracy=[92.42%, 91.19%, 87.55%, 83.39%, 69.41%, 31.81%]"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"scanning 1/9 weight - backbone.conv1.weight: 0%| | 0/6 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "65287307f9e94158bd557cf760a8a06c"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" sparsity=[0.40,0.50,0.60,0.70,0.80,0.90]: accuracy=[92.93%, 92.88%, 92.71%, 92.40%, 91.32%, 84.78%]"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"scanning 2/9 weight - backbone.conv2.weight: 0%| | 0/6 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "08e5b0e9072442799a0a342251f2e18e"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" sparsity=[0.40,0.50,0.60,0.70,0.80,0.90]: accuracy=[92.94%, 92.64%, 92.46%, 91.77%, 89.85%, 78.56%]"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"scanning 3/9 weight - backbone.conv3.weight: 0%| | 0/6 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2eb0a5d6bbdb424b985c789f7c4286f9"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" sparsity=[0.40,0.50,0.60,0.70,0.80,0.90]: accuracy=[92.86%, 92.72%, 92.23%, 91.09%, 85.35%, 51.31%]"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"scanning 4/9 weight - backbone.conv4.weight: 0%| | 0/6 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ba0ee0dd7b144a83ac8a8330745165ee"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" sparsity=[0.40,0.50,0.60,0.70,0.80,0.90]: accuracy=[92.88%, 92.68%, 92.22%, 89.47%, 76.87%, 38.78%]"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"scanning 5/9 weight - backbone.conv5.weight: 0%| | 0/6 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "bd57b15b9b64421c9e7d6594815b2441"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" sparsity=[0.40,0.50,0.60,0.70,0.80,0.90]: accuracy=[92.92%, 92.71%, 92.63%, 91.88%, 89.90%, 82.19%]"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"scanning 6/9 weight - backbone.conv6.weight: 0%| | 0/6 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e3072ea0fdc24f9fa11c29ceba0abc34"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" sparsity=[0.40,0.50,0.60,0.70,0.80,0.90]: accuracy=[92.94%, 92.86%, 92.65%, 92.10%, 90.58%, 83.65%]"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"scanning 7/9 weight - backbone.conv7.weight: 0%| | 0/6 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9f3a582f2d1446a9b115af0d0f0a93aa"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" sparsity=[0.40,0.50,0.60,0.70,0.80,0.90]: accuracy=[92.94%, 92.92%, 92.88%, 92.81%, 92.63%, 91.34%]"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"scanning 8/9 weight - classifier.weight: 0%| | 0/6 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "0f621f57142c4f178243c5318bed1ed4"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" sparsity=[0.40,0.50,0.60,0.70,0.80,0.90]: accuracy=[92.91%, 92.83%, 92.81%, 92.97%, 92.68%, 92.52%]"
]
}
],
"source": [
"sparsities, accuracies = sensitivity_scan(\n",
" model, dataloader['test'], scan_step=0.1, scan_start=0.4, scan_end=1.0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ktK3IyAHICVC",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 805
},
"outputId": "2de1781c-ddb3-4ae9-da5c-b896ca1bcb46"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"def plot_sensitivity_scan(sparsities, accuracies, dense_model_accuracy):\n",
" lower_bound_accuracy = 100 - (100 - dense_model_accuracy) * 1.5\n",
" fig, axes = plt.subplots(3, int(math.ceil(len(accuracies) / 3)),figsize=(15,8))\n",
" axes = axes.ravel()\n",
" plot_index = 0\n",
" for name, param in model.named_parameters():\n",
" if param.dim() > 1:\n",
" ax = axes[plot_index]\n",
" curve = ax.plot(sparsities, accuracies[plot_index])\n",
" line = ax.plot(sparsities, [lower_bound_accuracy] * len(sparsities))\n",
" ax.set_xticks(np.arange(start=0.4, stop=1.0, step=0.1))\n",
" ax.set_ylim(80, 95)\n",
" ax.set_title(name)\n",
" ax.set_xlabel('sparsity')\n",
" ax.set_ylabel('top-1 accuracy')\n",
" ax.legend([\n",
" 'accuracy after pruning',\n",
" f'{lower_bound_accuracy / dense_model_accuracy * 100:.0f}% of dense model accuracy'\n",
" ])\n",
" ax.grid(axis='x')\n",
" plot_index += 1\n",
" fig.suptitle('Sensitivity Curves: Validation Accuracy vs. Pruning Sparsity')\n",
" fig.tight_layout()\n",
" fig.subplots_adjust(top=0.925)\n",
" plt.show()\n",
"\n",
"plot_sensitivity_scan(sparsities, accuracies, dense_model_accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9FXNTS9m2ZQC"
},
"source": [
"### Question 4 (15 pts)\n",
"\n",
"Please answer the following questions using the information in the above sensitivity curves."
]
},
{
"cell_type": "markdown",
"source": [
"#### Question 4.1 (5 pts)\n",
"\n",
"What's the relationship between pruning sparsity and model accuracy? (*i.e.*, does accuracy increase or decrease when sparsity becomes higher?)"
],
"metadata": {
"id": "Up-1jYGvFDfo"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:** sparsity increases, the model accuracy decreases."
],
"metadata": {
"id": "O2kP-8e2FDsE"
}
},
{
"cell_type": "markdown",
"source": [
"#### Question 4.2 (5 pts)\n",
"\n",
"Do all the layers have the same sensitivity?"
],
"metadata": {
"id": "ijzmUoL1FDzl"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "6qGJ3Cra2819"
},
"source": [
"**Your Answer:** No"
]
},
{
"cell_type": "markdown",
"source": [
"#### Question 4.3 (5 pts)\n",
"\n",
"Which layer is the most sensitive to the pruning sparsity?"
],
"metadata": {
"id": "w5V972fVFfPd"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:** backbone.conv0.weight"
],
"metadata": {
"id": "vl0KTLioFfRo"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "w6uBecMG14pq"
},
"source": [
"## \\#Parameters of each layer\n",
"In addition to accuracy, the number of each layer's parameters also affects the decision on sparsity selection. Layers with more #parameters require larger sparsities.\n",
"\n",
"Please run the following code cell to plot the distribution of #parameters in the whole model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "P9eNjKSUGiCk",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 607
},
"outputId": "47f627d7-6635-4e48-8c80-940ca017e7cb"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"def plot_num_parameters_distribution(model):\n",
" num_parameters = dict()\n",
" for name, param in model.named_parameters():\n",
" if param.dim() > 1:\n",
" num_parameters[name] = param.numel()\n",
" fig = plt.figure(figsize=(8, 6))\n",
" plt.grid(axis='y')\n",
" plt.bar(list(num_parameters.keys()), list(num_parameters.values()))\n",
" plt.title('#Parameter Distribution')\n",
" plt.ylabel('Number of Parameters')\n",
" plt.xticks(rotation=60)\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"plot_num_parameters_distribution(model)"
]
},
{
"cell_type": "markdown",
"source": [
"## Select Sparsity Based on Sensitivity Curves and \\#Parameters Distribution\n",
"\n"
],
"metadata": {
"id": "hR7C4hJBMqmb"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "9f-qooLCo1pO"
},
"source": [
"### Question 5 (10 pts)\n",
"\n",
"Based on the sensitivity curves and the distribution of #parameters in the model, please select the sparsity for each layer.\n",
"\n",
"Note that the overall compression ratio of pruned model mostly depends on the layers with larger #parameters, and different layers have different sensitivity to pruning (see Question 4).\n",
"\n",
"Please make sure that after pruning, the sparse model is 25% of the size of the dense model, and validation accuracy is higher than 92.5 after finetuning.\n",
"\n",
"**Hint**:\n",
"* The layer with more #parameters should have larger sparsity. (see *Figure #Parameter Distribution*)\n",
"* The layer that is sensitive to the pruning sparsity (i.e., the accuracy will drop quickly as sparsity becomes higher) should have smaller sparsity. (see *Figure Sensitivity Curves*)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3D2T8n3EHwIz"
},
"outputs": [],
"source": [
"recover_model()\n",
"\n",
"sparsity_dict = {\n",
"##################### YOUR CODE STARTS HERE #####################\n",
" # please modify the sparsity value of each layer\n",
" # please DO NOT modify the key of sparsity_dict\n",
" 'backbone.conv0.weight': 0,\n",
" 'backbone.conv1.weight': 0,\n",
" 'backbone.conv2.weight': 0,\n",
" 'backbone.conv3.weight': 0.6,\n",
" 'backbone.conv4.weight': 0.7,\n",
" 'backbone.conv5.weight': 0.8,\n",
" 'backbone.conv6.weight': 0.8,\n",
" 'backbone.conv7.weight': 0.9,\n",
" 'classifier.weight': 0\n",
"##################### YOUR CODE ENDS HERE #######################\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aoJYtUuMTMwO"
},
"source": [
"Please run the following cell to prune the model according to your defined `sparsity_dict`, and print the information of sparse model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LVRqhiUno65x",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 992,
"referenced_widgets": [
"d800b20ccf3b47f0b899f8fdf55c3790",
"7f5274d0b27c445b9cb2a847d9659726",
"0e09693537df4fc6ad451df8a1c67a4d",
"a816fc592f3049c4abe92eeb6e7d87b9",
"8df47d6d34a74e9e889015d6d3ec0e38",
"5ff5a9c9d87047ad8260b7ce9fcd6c30",
"5609024f386c48a2ac78ec1aafa7fb37",
"de3dbd10a80841c58e9c927f16163646",
"7caabb9e5d8448538e692c77c2b7e000",
"44971b35769a4f8a856d190b2bc52ad6",
"f5cc89c13bdf4b358fa903bc00c9bada"
]
},
"outputId": "6abed1bb-833b-40ef-f24d-3031457db459"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"After pruning with sparsity dictionary\n",
" backbone.conv0.weight: 0.00\n",
" backbone.conv1.weight: 0.00\n",
" backbone.conv2.weight: 0.00\n",
" backbone.conv3.weight: 0.60\n",
" backbone.conv4.weight: 0.70\n",
" backbone.conv5.weight: 0.80\n",
" backbone.conv6.weight: 0.80\n",
" backbone.conv7.weight: 0.90\n",
" classifier.weight: 0.00\n",
"The sparsity of each layer becomes\n",
" backbone.conv0.weight: 0.00\n",
" backbone.conv1.weight: 0.00\n",
" backbone.conv2.weight: 0.00\n",
" backbone.conv3.weight: 0.60\n",
" backbone.conv4.weight: 0.70\n",
" backbone.conv5.weight: 0.80\n",
" backbone.conv6.weight: 0.80\n",
" backbone.conv7.weight: 0.90\n",
" classifier.weight: 0.00\n",
"Sparse model has size=8.20 MiB = 23.30% of dense model size\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d800b20ccf3b47f0b899f8fdf55c3790"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sparse model has accuracy=87.66% before fintuning\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"pruner = FineGrainedPruner(model, sparsity_dict)\n",
"print(f'After pruning with sparsity dictionary')\n",
"for name, sparsity in sparsity_dict.items():\n",
" print(f' {name}: {sparsity:.2f}')\n",
"print(f'The sparsity of each layer becomes')\n",
"for name, param in model.named_parameters():\n",
" if name in sparsity_dict:\n",
" print(f' {name}: {get_sparsity(param):.2f}')\n",
"\n",
"sparse_model_size = get_model_size(model, count_nonzero_only=True)\n",
"print(f\"Sparse model has size={sparse_model_size / MiB:.2f} MiB = {sparse_model_size / dense_model_size * 100:.2f}% of dense model size\")\n",
"sparse_model_accuracy = evaluate(model, dataloader['test'])\n",
"print(f\"Sparse model has accuracy={sparse_model_accuracy:.2f}% before fintuning\")\n",
"\n",
"plot_weight_distribution(model, count_nonzero_only=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RO_VGbmXoVjr"
},
"source": [
"## Finetune the fine-grained pruned model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9hBr3e-8RogD"
},
"source": [
"As we can see from the outputs of previous cell, even though fine-grained pruning reduces the most of model weights, the accuracy of model also dropped. Therefore, we have to finetune the sparse model to recover the accuracy.\n",
"\n",
"Please run the following cell to finetune the sparse model. It should take around 3 minutes to finish."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DJqfHuE6utbf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 121,
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]
},
"outputId": "8caf4811-5f24-46ab-8503-7695cd195520"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Finetuning Fine-grained Pruned Sparse Model\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c74ec8b9ebb64648928d837596ad5fbd"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9b6ef952363f4044a578c147fe18d967"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 1 Accuracy 92.60% / Best Accuracy: 92.60%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ee1d1ce34b2e478f8f9fc92ab49bdca1"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8a1b77f0b9024513a7b8a6060bf59a00"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 2 Accuracy 92.68% / Best Accuracy: 92.68%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "917f044464134d46bb1b5251c14cbd4c"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9d8b9369e3854e3e84b62cddb2ddb6a2"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 3 Accuracy 92.73% / Best Accuracy: 92.73%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "70fea7f1dceb461caea6b97f97135013"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b97c513f7e0a4dab864534afb4f78495"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 4 Accuracy 92.76% / Best Accuracy: 92.76%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "dc410d258a624453b4cad87d66075366"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "5349fb21b1c64c53bc7c3aa88ffc719b"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 5 Accuracy 92.83% / Best Accuracy: 92.83%\n"
]
}
],
"source": [
"num_finetune_epochs = 5\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=1e-4)\n",
"scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, num_finetune_epochs)\n",
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"best_sparse_model_checkpoint = dict()\n",
"best_accuracy = 0\n",
"print(f'Finetuning Fine-grained Pruned Sparse Model')\n",
"for epoch in range(num_finetune_epochs):\n",
" # At the end of each train iteration, we have to apply the pruning mask\n",
" # to keep the model sparse during the training\n",
" train(model, dataloader['train'], criterion, optimizer, scheduler,\n",
" callbacks=[lambda: pruner.apply(model)])\n",
" accuracy = evaluate(model, dataloader['test'])\n",
" is_best = accuracy > best_accuracy\n",
" if is_best:\n",
" best_sparse_model_checkpoint['state_dict'] = copy.deepcopy(model.state_dict())\n",
" best_accuracy = accuracy\n",
" print(f' Epoch {epoch+1} Accuracy {accuracy:.2f}% / Best Accuracy: {best_accuracy:.2f}%')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J9mP8tbNTmzD"
},
"source": [
"Run the following cell to see the information of best finetuned sparse model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-mkDWmfyQ63G",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52,
"referenced_widgets": [
"b8fa7143013e4f77a40edbb08490b477",
"23adc78d78d24c9f8ee1791637f03bb7",
"7cd8207afdec42b39b03931f0e992ea7",
"3f71e39755cb495496645c3ab8e917e0",
"2b6893fc88c04e5f83ca332c80a8b9a9",
"7a8b7e0a75394ae095168bfc8ef58e13",
"5c7e43f403bb4a629d0bfb3eafb08a8d",
"c7715cf6e00646ab88561edc7efaa7d6",
"0467925d6dc54ce2839a48933cd5f567",
"843d9944908e4a6b87b00a09169cb614",
"e2f8a38f2dd84f5b94d0fdbe8f27d1c4"
]
},
"outputId": "89b81aaf-9e54-43f1-c6d2-4ee818447e69"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sparse model has size=8.20 MiB = 23.30% of dense model size\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b8fa7143013e4f77a40edbb08490b477"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sparse model has accuracy=92.83% after fintuning\n"
]
}
],
"source": [
"# load the best sparse model checkpoint to evaluate the final performance\n",
"model.load_state_dict(best_sparse_model_checkpoint['state_dict'])\n",
"sparse_model_size = get_model_size(model, count_nonzero_only=True)\n",
"print(f\"Sparse model has size={sparse_model_size / MiB:.2f} MiB = {sparse_model_size / dense_model_size * 100:.2f}% of dense model size\")\n",
"sparse_model_accuracy = evaluate(model, dataloader['test'])\n",
"print(f\"Sparse model has accuracy={sparse_model_accuracy:.2f}% after fintuning\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yAygqoc_eEax"
},
"source": [
"# Channel Pruning\n",
"\n",
"In this section, we will implement the channel pruning. Channel pruning removes an entire channel, so that it can achieve inference speed up on existing hardware like GPUs. Similarly, we remove the channels whose weights are of smaller magnitudes (measured by Frobenius norm)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sPZDhDWlQc6-",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34,
"referenced_widgets": [
"bc3be40894af4bf084f45dbe0d4cbf4b",
"885225db4f99422d93db9a0e4d109251",
"4db73287c14c4c50a9b61d5c6a2d6dd9",
"03142313602640af8b6e5431b0d98c6d",
"62a116e88d1649d1b40eadde6388abeb",
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},
"outputId": "f6b271a4-3bcd-464e-b03d-ec465fec72f0"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "bc3be40894af4bf084f45dbe0d4cbf4b"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"dense model has accuracy=92.95%\n"
]
}
],
"source": [
"# firstly, let's restore the model weights to the original dense version\n",
"# and check the validation accuracy\n",
"recover_model()\n",
"dense_model_accuracy = evaluate(model, dataloader['test'])\n",
"print(f\"dense model has accuracy={dense_model_accuracy:.2f}%\")"
]
},
{
"cell_type": "markdown",
"source": [
"## Remove Channel Weights\n",
"\n",
"Unlike fine-grained pruning, we can remove the weights entirely from the tensor in channel pruning. That is to say, the number of output channels is reduced:\n",
"\n",
"> $\\#\\mathrm{out\\_channels}_{\\mathrm{new}} = \\#\\mathrm{out\\_channels}_{\\mathrm{origin}} \\cdot (1 - \\mathrm{sparsity})$\n",
"\n",
"The weight tensor $W$ is still dense after channel pruning. Thus, we will refer to *sparsity* as ***prune ratio***.\n",
"\n",
"Like fine-grained pruning, we can use different pruning rates for different layers. However, we use a uniform pruning rate for all the layers for now. We are targeting 2x computation reduction, which is roughly 30% uniform pruning rate (think about why).\n",
"\n",
"Feel free to try out different pruning ratios per layer at the end of this section. You can pass in a list of ratios to the `channel_prune` function."
],
"metadata": {
"id": "q9zH6n8asAy_"
}
},
{
"cell_type": "markdown",
"source": [
"### Question 6 (10 pts)\n",
"\n",
"Please complete the following functions for channel pruning.\n",
"\n",
"Here we naively prune all output channels other than the first $\\#\\mathrm{out\\_channels}_{\\mathrm{new}}$ channels."
],
"metadata": {
"id": "KIGCVkQKHyDH"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aFPMZzWlyvUR"
},
"outputs": [],
"source": [
"def get_num_channels_to_keep(channels: int, prune_ratio: float) -> int:\n",
" \"\"\"A function to calculate the number of layers to PRESERVE after pruning\n",
" Note that preserve_rate = 1. - prune_ratio\n",
" \"\"\"\n",
" ##################### YOUR CODE STARTS HERE #####################\n",
" return int(round((1-prune_ratio)*channels))\n",
" ##################### YOUR CODE ENDS HERE #####################\n",
"\n",
"@torch.no_grad()\n",
"def channel_prune(model: nn.Module,\n",
" prune_ratio: Union[List, float]) -> nn.Module:\n",
" \"\"\"Apply channel pruning to each of the conv layer in the backbone\n",
" Note that for prune_ratio, we can either provide a floating-point number,\n",
" indicating that we use a uniform pruning rate for all layers, or a list of\n",
" numbers to indicate per-layer pruning rate.\n",
" \"\"\"\n",
" # sanity check of provided prune_ratio\n",
" assert isinstance(prune_ratio, (float, list))\n",
" n_conv = len([m for m in model.backbone if isinstance(m, nn.Conv2d)])\n",
" # note that for the ratios, it affects the previous conv output and next\n",
" # conv input, i.e., conv0 - ratio0 - conv1 - ratio1-...\n",
" if isinstance(prune_ratio, list):\n",
" assert len(prune_ratio) == n_conv - 1\n",
" else: # convert float to list\n",
" prune_ratio = [prune_ratio] * (n_conv - 1)\n",
"\n",
" # we prune the convs in the backbone with a uniform ratio\n",
" model = copy.deepcopy(model) # prevent overwrite\n",
" # we only apply pruning to the backbone features\n",
" all_convs = [m for m in model.backbone if isinstance(m, nn.Conv2d)]\n",
" all_bns = [m for m in model.backbone if isinstance(m, nn.BatchNorm2d)]\n",
" # apply pruning. we naively keep the first k channels\n",
" assert len(all_convs) == len(all_bns)\n",
" for i_ratio, p_ratio in enumerate(prune_ratio):\n",
" prev_conv = all_convs[i_ratio]\n",
" prev_bn = all_bns[i_ratio]\n",
" next_conv = all_convs[i_ratio + 1]\n",
" original_channels = prev_conv.out_channels # same as next_conv.in_channels\n",
" n_keep = get_num_channels_to_keep(original_channels, p_ratio)\n",
"\n",
" # prune the output of the previous conv and bn\n",
" prev_conv.weight.set_(prev_conv.weight.detach()[:n_keep])\n",
" prev_bn.weight.set_(prev_bn.weight.detach()[:n_keep])\n",
" prev_bn.bias.set_(prev_bn.bias.detach()[:n_keep])\n",
" prev_bn.running_mean.set_(prev_bn.running_mean.detach()[:n_keep])\n",
" prev_bn.running_var.set_(prev_bn.running_var.detach()[:n_keep])\n",
"\n",
" # prune the input of the next conv (hint: just one line of code)\n",
" ##################### YOUR CODE STARTS HERE #####################\n",
" next_conv.weight.set_(next_conv.weight.detach()[:, :n_keep])\n",
" ##################### YOUR CODE ENDS HERE #####################\n",
"\n",
" print(\"Tensor's shape has been changed, but the print info of model doesn't change.\")\n",
" return model\n"
]
},
{
"cell_type": "markdown",
"source": [
"Run the following cell to perform a sanity check to make sure the implementation is correct."
],
"metadata": {
"id": "0IIVDYLe5Asd"
}
},
{
"cell_type": "code",
"source": [
"dummy_input = torch.randn(1, 3, 32, 32).cuda()\n",
"pruned_model = channel_prune(model, prune_ratio=0.3)\n",
"pruned_macs = get_model_macs(pruned_model, dummy_input)\n",
"assert pruned_macs == 305388064\n",
"print('* Check passed. Right MACs for the pruned model.')"
],
"metadata": {
"id": "9o0cU3B34_w1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c568c703-ae57-4637-f669-75d3509ae650"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Tensor's shape has been changed, but the print info of model doesn't change.\n",
"* Check passed. Right MACs for the pruned model.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Now let's evaluate the performance of the model after uniform channel pruning with 30% pruning rate.\n",
"\n",
"As you may see, directly removing 30% of the channels leads to low accuracy."
],
"metadata": {
"id": "6aKXW5BO6Hrf"
}
},
{
"cell_type": "code",
"source": [
"pruned_model_accuracy = evaluate(pruned_model, dataloader['test'])\n",
"print(f\"pruned model has accuracy={pruned_model_accuracy:.2f}%\")"
],
"metadata": {
"id": "MyZkwnGW6aP0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34,
"referenced_widgets": [
"516e66867c2545f4a272259be3f9df5d",
"0e30fc5a33d74edab5a5bf97660485bf",
"4a78c0d99db44f96b359b05d202a7b1d",
"8fd001a87edd4595a0a60a7241014926",
"e9e5a1bc48734211abd1218c5fc62872",
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"ad0f86ba9a3048b3ae3bb42762441a25",
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"3b32c93a053549e0828208ab53b2ea55"
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},
"outputId": "42958d6d-c68d-49e7-c382-82b4dd9dd839"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "516e66867c2545f4a272259be3f9df5d"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"pruned model has accuracy=28.14%\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Ranking Channels by Importance\n",
"\n",
"As you can see, removing the first 30% of channels in all layers leads to significant accuracy reduction. One potential method to remedy the issue is to find the **less important** channel weights to remove. A popular criterion for importance is to use the Frobenius norm of the weights corresponding to each input channel:\n",
"\n",
"> $importance_{i} = \\|W_{i}\\|_2, \\;\\; i = 0, 1, 2,\\cdots, \\#\\mathrm{in\\_channels}-1$\n",
"\n",
"We can sort the channel weights from more important to less important, and then keep the frst $k$ channels for each layer."
],
"metadata": {
"id": "SoqUbcUN6yS_"
}
},
{
"cell_type": "markdown",
"source": [
"### Question 7 (15 pts)\n",
"Please complete the following functions for sorting the weight tensor based on the Frobenius norm.\n",
"\n",
"**Hint**:\n",
"* To calculate Frobenius norm of a tensor, Pytorch provides [`torch.norm`](https://pytorch.org/docs/master/generated/torch.norm.html?highlight=torch+norm#torch.norm) APIs."
],
"metadata": {
"id": "AUOLUBjbNLbq"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8fpUuOmmsJQZ"
},
"outputs": [],
"source": [
"# function to sort the channels from important to non-important\n",
"def get_input_channel_importance(weight):\n",
" in_channels = weight.shape[1]\n",
" importances = []\n",
" # compute the importance for each input channel\n",
" for i_c in range(weight.shape[1]):\n",
" channel_weight = weight.detach()[:, i_c]\n",
" ##################### YOUR CODE STARTS HERE #####################\n",
" importance = torch.norm(channel_weight, p=2)\n",
" ##################### YOUR CODE ENDS HERE #####################\n",
" importances.append(importance.view(1))\n",
" return torch.cat(importances)\n",
"\n",
"@torch.no_grad()\n",
"def apply_channel_sorting(model):\n",
" model = copy.deepcopy(model) # do not modify the original model\n",
" # fetch all the conv and bn layers from the backbone\n",
" all_convs = [m for m in model.backbone if isinstance(m, nn.Conv2d)]\n",
" all_bns = [m for m in model.backbone if isinstance(m, nn.BatchNorm2d)]\n",
" # iterate through conv layers\n",
" for i_conv in range(len(all_convs) - 1):\n",
" # each channel sorting index, we need to apply it to:\n",
" # - the output dimension of the previous conv\n",
" # - the previous BN layer\n",
" # - the input dimension of the next conv (we compute importance here)\n",
" prev_conv = all_convs[i_conv]\n",
" prev_bn = all_bns[i_conv]\n",
" next_conv = all_convs[i_conv + 1]\n",
" # note that we always compute the importance according to input channels\n",
" importance = get_input_channel_importance(next_conv.weight)\n",
" # sorting from large to small\n",
" sort_idx = torch.argsort(importance, descending=True)\n",
"\n",
" # apply to previous conv and its following bn\n",
" prev_conv.weight.copy_(torch.index_select(\n",
" prev_conv.weight.detach(), 0, sort_idx))\n",
" for tensor_name in ['weight', 'bias', 'running_mean', 'running_var']:\n",
" tensor_to_apply = getattr(prev_bn, tensor_name)\n",
" tensor_to_apply.copy_(\n",
" torch.index_select(tensor_to_apply.detach(), 0, sort_idx)\n",
" )\n",
"\n",
" # apply to the next conv input (hint: one line of code)\n",
" ##################### YOUR CODE STARTS HERE #####################\n",
" next_conv.weight.copy_(torch.index_select(next_conv.weight.detach(), 1, sort_idx))\n",
" ##################### YOUR CODE ENDS HERE #####################\n",
"\n",
" return model"
]
},
{
"cell_type": "markdown",
"source": [
"Now run the following cell to sanity check if the results are correct."
],
"metadata": {
"id": "-fo60_ni-T21"
}
},
{
"cell_type": "code",
"source": [
"print('Before sorting...')\n",
"dense_model_accuracy = evaluate(model, dataloader['test'])\n",
"print(f\"dense model has accuracy={dense_model_accuracy:.2f}%\")\n",
"\n",
"print('After sorting...')\n",
"sorted_model = apply_channel_sorting(model)\n",
"sorted_model_accuracy = evaluate(sorted_model, dataloader['test'])\n",
"print(f\"sorted model has accuracy={sorted_model_accuracy:.2f}%\")\n",
"\n",
"# make sure accuracy does not change after sorting, since it is\n",
"# equivalent transform\n",
"assert abs(sorted_model_accuracy - dense_model_accuracy) < 0.1\n",
"print('* Check passed.')"
],
"metadata": {
"id": "D_oR3Qxd-TOj",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104,
"referenced_widgets": [
"fd2931fd58d24e629173fe401f2636c9",
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]
},
"outputId": "ab4e8c03-a7f3-4c3c-c487-da321363c94b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Before sorting...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "fd2931fd58d24e629173fe401f2636c9"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"dense model has accuracy=92.95%\n",
"After sorting...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c606544eaa1e4ad99ddfb77e2376ff4f"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"sorted model has accuracy=92.95%\n",
"* Check passed.\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aLdnjTU4-m4O"
},
"source": [
"Finally, we compare the pruned models' accuracy with and without sorting."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aVLm3dsh5cnC",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 121,
"referenced_widgets": [
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},
"outputId": "457ca705-4c77-4099-b906-b347acd8f2be"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" * Without sorting...\n",
"Tensor's shape has been changed, but the print info of model doesn't change.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d67eb174f96643a1b87af28ed49487ed"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"pruned model has accuracy=28.14%\n",
" * With sorting...\n",
"Tensor's shape has been changed, but the print info of model doesn't change.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "bd381097d1b14f3b874882fd5efce6e0"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"pruned model has accuracy=36.81%\n"
]
}
],
"source": [
"channel_pruning_ratio = 0.3 # pruned-out ratio\n",
"\n",
"print(\" * Without sorting...\")\n",
"pruned_model = channel_prune(model, channel_pruning_ratio)\n",
"pruned_model_accuracy = evaluate(pruned_model, dataloader['test'])\n",
"print(f\"pruned model has accuracy={pruned_model_accuracy:.2f}%\")\n",
"\n",
"\n",
"print(\" * With sorting...\")\n",
"sorted_model = apply_channel_sorting(model)\n",
"pruned_model = channel_prune(sorted_model, channel_pruning_ratio)\n",
"pruned_model_accuracy = evaluate(pruned_model, dataloader['test'])\n",
"print(f\"pruned model has accuracy={pruned_model_accuracy:.2f}%\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o_5HsoOcAOAJ"
},
"source": [
"As you can see, the channel sorting can slightly improve the pruned model's accuracy, but there is still a huge degrade, which is quite common for channel pruning. But luckily, we can perform fine-tuning to recover the accuracy."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cXkGH-mAAIKc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104,
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},
"outputId": "8dd4e9c1-d2b4-44a5-8847-ac8cba0ad41d"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "78c5b68ca45845fb899b0f6e4aacf15e"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3cf05ed6ddac4b9e956df1ad7b594230"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1 Accuracy 91.52% / Best Accuracy: 91.52%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "be8f06c92ec44e7792cea228fc2fc8fb"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "270ec2909df64576844465aee800a0e4"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 2 Accuracy 91.75% / Best Accuracy: 91.75%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "7f1a7008080143928734646cc30ae692"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8a6f34929e4a4af2b49f8ca9c32ed8a0"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 3 Accuracy 92.04% / Best Accuracy: 92.04%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9de58e0592754126a227e92cfcd5bfc2"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b35e1a8f80a14d22ae3af1620e625ad0"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 4 Accuracy 91.97% / Best Accuracy: 92.04%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "60f3ea4b756a4a4b9013655f175f03d3"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a1ce5ff7b8574adf93db7e98040a5241"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 5 Accuracy 92.22% / Best Accuracy: 92.22%\n"
]
}
],
"source": [
"num_finetune_epochs = 5\n",
"optimizer = torch.optim.SGD(pruned_model.parameters(), lr=0.01, momentum=0.9, weight_decay=1e-4)\n",
"scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, num_finetune_epochs)\n",
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"best_accuracy = 0\n",
"for epoch in range(num_finetune_epochs):\n",
" train(pruned_model, dataloader['train'], criterion, optimizer, scheduler)\n",
" accuracy = evaluate(pruned_model, dataloader['test'])\n",
" is_best = accuracy > best_accuracy\n",
" if is_best:\n",
" best_accuracy = accuracy\n",
" print(f'Epoch {epoch+1} Accuracy {accuracy:.2f}% / Best Accuracy: {best_accuracy:.2f}%')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G8L4ICpzN6pS"
},
"source": [
"## Measure acceleration from pruning\n",
"\n",
"After fine-tuning, the model almost recovers the accuracy. You may have already learned that channel pruning is usually more difficult to recover accuracy compared to fine-grained pruning. However, it directly leads to a smaller model size and smaller computation without specialized model format. It can also run faster on GPUs. Now we compare the model size, computation, and latency of the pruned model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TF8lXvyeIpTk",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "77145fdd-2e07-4fca-debe-b4a80e604b03"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Original Pruned Reduction Ratio\n",
"Latency (ms) 19.1 11.7 1.6 \n",
"MACs (M) 606 305 2.0 \n",
"Param (M) 9.23 5.01 1.8 \n"
]
}
],
"source": [
"# helper functions to measure latency of a regular PyTorch models.\n",
"# Unlike fine-grained pruning, channel pruning\n",
"# can directly leads to model size reduction and speed up.\n",
"@torch.no_grad()\n",
"def measure_latency(model, dummy_input, n_warmup=20, n_test=100):\n",
" model.eval()\n",
" # warmup\n",
" for _ in range(n_warmup):\n",
" _ = model(dummy_input)\n",
" # real test\n",
" t1 = time.time()\n",
" for _ in range(n_test):\n",
" _ = model(dummy_input)\n",
" t2 = time.time()\n",
" return (t2 - t1) / n_test # average latency\n",
"\n",
"table_template = \"{:<15} {:<15} {:<15} {:<15}\"\n",
"print (table_template.format('', 'Original','Pruned','Reduction Ratio'))\n",
"\n",
"# 1. measure the latency of the original model and the pruned model on CPU\n",
"# which simulates inference on an edge device\n",
"dummy_input = torch.randn(1, 3, 32, 32).to('cpu')\n",
"pruned_model = pruned_model.to('cpu')\n",
"model = model.to('cpu')\n",
"\n",
"pruned_latency = measure_latency(pruned_model, dummy_input)\n",
"original_latency = measure_latency(model, dummy_input)\n",
"print(table_template.format('Latency (ms)',\n",
" round(original_latency * 1000, 1),\n",
" round(pruned_latency * 1000, 1),\n",
" round(original_latency / pruned_latency, 1)))\n",
"\n",
"# 2. measure the computation (MACs)\n",
"original_macs = get_model_macs(model, dummy_input)\n",
"pruned_macs = get_model_macs(pruned_model, dummy_input)\n",
"print(table_template.format('MACs (M)',\n",
" round(original_macs / 1e6),\n",
" round(pruned_macs / 1e6),\n",
" round(original_macs / pruned_macs, 1)))\n",
"\n",
"# 3. measure the model size (params)\n",
"original_param = get_num_parameters(model)\n",
"pruned_param = get_num_parameters(pruned_model)\n",
"print(table_template.format('Param (M)',\n",
" round(original_param / 1e6, 2),\n",
" round(pruned_param / 1e6, 2),\n",
" round(original_param / pruned_param, 1)))\n",
"\n",
"# put model back to cuda\n",
"pruned_model = pruned_model.to('cuda')\n",
"model = model.to('cuda')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TDpOt1WCABWy"
},
"source": [
"### Question 8 (10 pts)\n",
"\n",
"Please answer the following questions using the information in the previous code cell.\n"
]
},
{
"cell_type": "markdown",
"source": [
"#### Question 8.1 (5 pts)\n",
"\n",
"Explain why removing 30% of channels roughly leads to 50% computation reduction."
],
"metadata": {
"id": "EZAkttHyZXo1"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:** (1 - 30%)^2 = 0.49"
],
"metadata": {
"id": "qHaBDss2ZYEd"
}
},
{
"cell_type": "markdown",
"source": [
"#### Question 8.2 (5 pts)\n",
"\n",
"Explain why the latency reduction ratio is slightly smaller than computation reduction."
],
"metadata": {
"id": "fqpqToMYZZkX"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:** Computation reduction != latency reduction. We should consider data movement as well."
],
"metadata": {
"id": "UhGuKP9oZa15"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "5gGwXyU44pvg"
},
"source": [
"# Compare Fine-grained Pruning and Channel Pruning\n"
]
},
{
"cell_type": "markdown",
"source": [
"## Question 9 (10 pts)\n",
"\n",
"\n",
"After all experiments in this lab, you may have become familiar with both fine-grained pruning and channel pruning.\n",
"\n",
"Please answer the following questions using what you have learned from the lectures and this lab."
],
"metadata": {
"id": "x_onehCuZc91"
}
},
{
"cell_type": "markdown",
"source": [
"### Question 9.1 (5 pts)\n",
"\n",
"What are the advantages and disadvantages of fine-grained pruning and channel pruning? You can discuss from the perspective of compression ratio, accuracy, latency, hardware support (*i.e.*, requiring specialized hardware accelerator), etc."
],
"metadata": {
"id": "bygFUTmRZeQm"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:**\n",
"\n",
"### Fine-grained pruning\n",
"#### Pros\n",
"- Performance\n",
"- Fine-grained\n",
"\n",
"#### Cons\n",
"- Require specific hardware design\n",
"\n",
"### Channel Pruning\n",
"#### Pros\n",
"- Not require specific hardware\n",
"\n",
"#### Cons\n",
"- Performance is worse than Fine-grained pruning"
],
"metadata": {
"id": "bkCGH6dVZfuU"
}
},
{
"cell_type": "markdown",
"source": [
"### Question 9.2 (5 pts)\n",
"\n",
"If you want to make your model run faster on a smartphone, which pruning method will you use? Why?"
],
"metadata": {
"id": "VPzfEqAyZhEm"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:** Channel Pruning"
],
"metadata": {
"id": "BydLPTgCZihp"
}
},
{
"cell_type": "markdown",
"source": [
"# Feedback\n",
"\n",
"Please fill out this [feedback form](https://forms.gle/fapEmEUYr3WnXjBU8) when you finished this lab. We would love to hear your thoughts or feedback on how we can improve this lab!"
],
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FILE: Lab2.ipynb
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"cells": [
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"metadata": {
"id": "view-in-github",
"colab_type": "text"
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"
"
]
},
{
"cell_type": "markdown",
"source": [
"# **MIT 6.5940 EfficientML.ai Fall 2023 Lab 2: Quantization**"
],
"metadata": {
"id": "pmi4V4VaYR6i"
}
},
{
"cell_type": "markdown",
"source": [
"This colab notebook provides code and a framework for Lab 2 quantization. You can work out your solutions here."
],
"metadata": {
"id": "_PC9ifUhHwRT"
}
},
{
"cell_type": "markdown",
"source": [
"Please fill out this [feedback form](https://forms.gle/ZeCH5anNPrkd5wpp7) when you finished this lab. We would love to hear your thoughts or feedback on how we can improve this lab!"
],
"metadata": {
"id": "A-antxp8SSyb"
}
},
{
"cell_type": "markdown",
"source": [
"## Goals\n",
"\n",
"In this assignment, you will practice quantizing a classical neural network model to reduce both model size and latency. The goals of this assignment are as follows:\n",
"\n",
"- Understand the basic concept of **quantization**\n",
"- Implement and apply **k-means quantization**\n",
"- Implement and apply **quantization-aware training** for k-means quantization\n",
"- Implement and apply **linear quantization**\n",
"- Implement and apply **integer-only inference** for linear quantization\n",
"- Get a basic understanding of performance improvement (such as speedup) from quantization\n",
"- Understand the differences and tradeoffs between these quantization approaches"
],
"metadata": {
"id": "vhVOMmbAaHRd"
}
},
{
"cell_type": "markdown",
"source": [
"## Contents\n",
"\n",
"\n",
"There are 2 main sections: ***K-Means Quantization*** and ***Linear Quantization***.\n",
"\n",
"There are ***10*** questions in total:\n",
"- For *K-Means Quantization*, there are ***3*** questions (Question 1-3).\n",
"- For *Linear Quantization*, there are ***6*** questions (Question 4-9).\n",
"- Question 10 compares k-means quantization and linear quantization."
],
"metadata": {
"id": "W6HPdGZ7aHZo"
}
},
{
"cell_type": "markdown",
"source": [
"# Setup"
],
"metadata": {
"id": "2tFjnZZVlIFL"
}
},
{
"cell_type": "markdown",
"source": [
"First, install the required packages and download the datasets and pretrained model. Here we use CIFAR10 dataset and VGG network which is the same as what we used in the Lab 0 tutorial.\n",
"\n"
],
"metadata": {
"id": "Bz16rxaSH_7f"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nyngBRTXQG2n",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e2925bfa-e693-43d3-fdf9-87b181fe1c20"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Installing torchprofile...\n",
"Installing fast-pytorch-kmeans...\n",
"All required packages have been successfully installed!\n"
]
}
],
"source": [
"print('Installing torchprofile...')\n",
"!pip install torchprofile 1>/dev/null\n",
"print('Installing fast-pytorch-kmeans...')\n",
"! pip install fast-pytorch-kmeans 1>/dev/null\n",
"print('All required packages have been successfully installed!')"
]
},
{
"cell_type": "code",
"source": [
"import copy\n",
"import math\n",
"import random\n",
"from collections import OrderedDict, defaultdict\n",
"\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.colors import ListedColormap\n",
"import numpy as np\n",
"from tqdm.auto import tqdm\n",
"\n",
"import torch\n",
"from torch import nn\n",
"from torch.optim import *\n",
"from torch.optim.lr_scheduler import *\n",
"from torch.utils.data import DataLoader\n",
"from torchprofile import profile_macs\n",
"from torchvision.datasets import *\n",
"from torchvision.transforms import *\n",
"\n",
"from torchprofile import profile_macs\n",
"\n",
"assert torch.cuda.is_available(), \\\n",
"\"The current runtime does not have CUDA support.\" \\\n",
"\"Please go to menu bar (Runtime - Change runtime type) and select GPU\""
],
"metadata": {
"id": "zDWoVhv_wGmA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"random.seed(0)\n",
"np.random.seed(0)\n",
"torch.manual_seed(0)"
],
"metadata": {
"id": "nLcJUofTDKud",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0cad23ff-2fa5-46d6-aab1-438e68638343"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 93
}
]
},
{
"cell_type": "code",
"source": [
"def download_url(url, model_dir='.', overwrite=False):\n",
" import os, sys\n",
" from urllib.request import urlretrieve\n",
" target_dir = url.split('/')[-1]\n",
" model_dir = os.path.expanduser(model_dir)\n",
" try:\n",
" if not os.path.exists(model_dir):\n",
" os.makedirs(model_dir)\n",
" model_dir = os.path.join(model_dir, target_dir)\n",
" cached_file = model_dir\n",
" if not os.path.exists(cached_file) or overwrite:\n",
" sys.stderr.write('Downloading: \"{}\" to {}\\n'.format(url, cached_file))\n",
" urlretrieve(url, cached_file)\n",
" return cached_file\n",
" except Exception as e:\n",
" # remove lock file so download can be executed next time.\n",
" os.remove(os.path.join(model_dir, 'download.lock'))\n",
" sys.stderr.write('Failed to download from url %s' % url + '\\n' + str(e) + '\\n')\n",
" return None"
],
"metadata": {
"id": "6kpu78GyHGA-"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class VGG(nn.Module):\n",
" ARCH = [64, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']\n",
"\n",
" def __init__(self) -> None:\n",
" super().__init__()\n",
"\n",
" layers = []\n",
" counts = defaultdict(int)\n",
"\n",
" def add(name: str, layer: nn.Module) -> None:\n",
" layers.append((f\"{name}{counts[name]}\", layer))\n",
" counts[name] += 1\n",
"\n",
" in_channels = 3\n",
" for x in self.ARCH:\n",
" if x != 'M':\n",
" # conv-bn-relu\n",
" add(\"conv\", nn.Conv2d(in_channels, x, 3, padding=1, bias=False))\n",
" add(\"bn\", nn.BatchNorm2d(x))\n",
" add(\"relu\", nn.ReLU(True))\n",
" in_channels = x\n",
" else:\n",
" # maxpool\n",
" add(\"pool\", nn.MaxPool2d(2))\n",
" add(\"avgpool\", nn.AvgPool2d(2))\n",
" self.backbone = nn.Sequential(OrderedDict(layers))\n",
" self.classifier = nn.Linear(512, 10)\n",
"\n",
" def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
" # backbone: [N, 3, 32, 32] => [N, 512, 2, 2]\n",
" x = self.backbone(x)\n",
"\n",
" # avgpool: [N, 512, 2, 2] => [N, 512]\n",
" # x = x.mean([2, 3])\n",
" x = x.view(x.shape[0], -1)\n",
"\n",
" # classifier: [N, 512] => [N, 10]\n",
" x = self.classifier(x)\n",
" return x"
],
"metadata": {
"id": "qqInscyoifYN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def train(\n",
" model: nn.Module,\n",
" dataloader: DataLoader,\n",
" criterion: nn.Module,\n",
" optimizer: Optimizer,\n",
" scheduler: LambdaLR,\n",
" callbacks = None\n",
") -> None:\n",
" model.train()\n",
"\n",
" for inputs, targets in tqdm(dataloader, desc='train', leave=False):\n",
" # Move the data from CPU to GPU\n",
" inputs = inputs.cuda()\n",
" targets = targets.cuda()\n",
"\n",
" # Reset the gradients (from the last iteration)\n",
" optimizer.zero_grad()\n",
"\n",
" # Forward inference\n",
" outputs = model(inputs)\n",
" loss = criterion(outputs, targets)\n",
"\n",
" # Backward propagation\n",
" loss.backward()\n",
"\n",
" # Update optimizer and LR scheduler\n",
" optimizer.step()\n",
" scheduler.step()\n",
"\n",
" if callbacks is not None:\n",
" for callback in callbacks:\n",
" callback()"
],
"metadata": {
"id": "WqnPt0LUEaWi"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"@torch.inference_mode()\n",
"def evaluate(\n",
" model: nn.Module,\n",
" dataloader: DataLoader,\n",
" extra_preprocess = None\n",
") -> float:\n",
" model.eval()\n",
"\n",
" num_samples = 0\n",
" num_correct = 0\n",
"\n",
" for inputs, targets in tqdm(dataloader, desc=\"eval\", leave=False):\n",
" # Move the data from CPU to GPU\n",
" inputs = inputs.cuda()\n",
" if extra_preprocess is not None:\n",
" for preprocess in extra_preprocess:\n",
" inputs = preprocess(inputs)\n",
"\n",
" targets = targets.cuda()\n",
"\n",
" # Inference\n",
" outputs = model(inputs)\n",
"\n",
" # Convert logits to class indices\n",
" outputs = outputs.argmax(dim=1)\n",
"\n",
" # Update metrics\n",
" num_samples += targets.size(0)\n",
" num_correct += (outputs == targets).sum()\n",
"\n",
" return (num_correct / num_samples * 100).item()"
],
"metadata": {
"id": "wVA1_oeUEUf6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Helpler Functions (Flops, Model Size calculation, etc.)"
],
"metadata": {
"id": "QBBKNhKNlAwE"
}
},
{
"cell_type": "code",
"source": [
"def get_model_flops(model, inputs):\n",
" num_macs = profile_macs(model, inputs)\n",
" return num_macs"
],
"metadata": {
"id": "mRdK_ThzlMxL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def get_model_size(model: nn.Module, data_width=32):\n",
" \"\"\"\n",
" calculate the model size in bits\n",
" :param data_width: #bits per element\n",
" \"\"\"\n",
" num_elements = 0\n",
" for param in model.parameters():\n",
" num_elements += param.numel()\n",
" return num_elements * data_width\n",
"\n",
"Byte = 8\n",
"KiB = 1024 * Byte\n",
"MiB = 1024 * KiB\n",
"GiB = 1024 * MiB"
],
"metadata": {
"id": "cepv4SUalU79"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Define misc funcions for verification."
],
"metadata": {
"id": "CGhomDjsaDB5"
}
},
{
"cell_type": "code",
"source": [
"def test_k_means_quantize(\n",
" test_tensor=torch.tensor([\n",
" [-0.3747, 0.0874, 0.3200, -0.4868, 0.4404],\n",
" [-0.0402, 0.2322, -0.2024, -0.4986, 0.1814],\n",
" [ 0.3102, -0.3942, -0.2030, 0.0883, -0.4741],\n",
" [-0.1592, -0.0777, -0.3946, -0.2128, 0.2675],\n",
" [ 0.0611, -0.1933, -0.4350, 0.2928, -0.1087]]),\n",
" bitwidth=2):\n",
" def plot_matrix(tensor, ax, title, cmap=ListedColormap(['white'])):\n",
" ax.imshow(tensor.cpu().numpy(), vmin=-0.5, vmax=0.5, cmap=cmap)\n",
" ax.set_title(title)\n",
" ax.set_yticklabels([])\n",
" ax.set_xticklabels([])\n",
" for i in range(tensor.shape[1]):\n",
" for j in range(tensor.shape[0]):\n",
" text = ax.text(j, i, f'{tensor[i, j].item():.2f}',\n",
" ha=\"center\", va=\"center\", color=\"k\")\n",
"\n",
" fig, axes = plt.subplots(1,2, figsize=(8, 12))\n",
" ax_left, ax_right = axes.ravel()\n",
"\n",
" print(test_tensor)\n",
" plot_matrix(test_tensor, ax_left, 'original tensor')\n",
"\n",
" num_unique_values_before_quantization = test_tensor.unique().numel()\n",
" k_means_quantize(test_tensor, bitwidth=bitwidth)\n",
" num_unique_values_after_quantization = test_tensor.unique().numel()\n",
" print('* Test k_means_quantize()')\n",
" print(f' target bitwidth: {bitwidth} bits')\n",
" print(f' num unique values before k-means quantization: {num_unique_values_before_quantization}')\n",
" print(f' num unique values after k-means quantization: {num_unique_values_after_quantization}')\n",
" assert num_unique_values_after_quantization == min((1 << bitwidth), num_unique_values_before_quantization)\n",
" print('* Test passed.')\n",
"\n",
" plot_matrix(test_tensor, ax_right, f'{bitwidth}-bit k-means quantized tensor', cmap='tab20c')\n",
" fig.tight_layout()\n",
" plt.show()"
],
"metadata": {
"id": "WOJBXVXQeCXF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def test_linear_quantize(\n",
" test_tensor=torch.tensor([\n",
" [ 0.0523, 0.6364, -0.0968, -0.0020, 0.1940],\n",
" [ 0.7500, 0.5507, 0.6188, -0.1734, 0.4677],\n",
" [-0.0669, 0.3836, 0.4297, 0.6267, -0.0695],\n",
" [ 0.1536, -0.0038, 0.6075, 0.6817, 0.0601],\n",
" [ 0.6446, -0.2500, 0.5376, -0.2226, 0.2333]]),\n",
" quantized_test_tensor=torch.tensor([\n",
" [-1, 1, -1, -1, 0],\n",
" [ 1, 1, 1, -2, 0],\n",
" [-1, 0, 0, 1, -1],\n",
" [-1, -1, 1, 1, -1],\n",
" [ 1, -2, 1, -2, 0]], dtype=torch.int8),\n",
" real_min=-0.25, real_max=0.75, bitwidth=2, scale=1/3, zero_point=-1):\n",
" def plot_matrix(tensor, ax, title, vmin=0, vmax=1, cmap=ListedColormap(['white'])):\n",
" ax.imshow(tensor.cpu().numpy(), vmin=vmin, vmax=vmax, cmap=cmap)\n",
" ax.set_title(title)\n",
" ax.set_yticklabels([])\n",
" ax.set_xticklabels([])\n",
" for i in range(tensor.shape[0]):\n",
" for j in range(tensor.shape[1]):\n",
" datum = tensor[i, j].item()\n",
" if isinstance(datum, float):\n",
" text = ax.text(j, i, f'{datum:.2f}',\n",
" ha=\"center\", va=\"center\", color=\"k\")\n",
" else:\n",
" text = ax.text(j, i, f'{datum}',\n",
" ha=\"center\", va=\"center\", color=\"k\")\n",
" quantized_min, quantized_max = get_quantized_range(bitwidth)\n",
" fig, axes = plt.subplots(1,3, figsize=(10, 32))\n",
" plot_matrix(test_tensor, axes[0], 'original tensor', vmin=real_min, vmax=real_max)\n",
" _quantized_test_tensor = linear_quantize(\n",
" test_tensor, bitwidth=bitwidth, scale=scale, zero_point=zero_point)\n",
" _reconstructed_test_tensor = scale * (_quantized_test_tensor.float() - zero_point)\n",
" print('* Test linear_quantize()')\n",
" print(f' target bitwidth: {bitwidth} bits')\n",
" print(f' scale: {scale}')\n",
" print(f' zero point: {zero_point}')\n",
" assert _quantized_test_tensor.equal(quantized_test_tensor)\n",
" print('* Test passed.')\n",
" plot_matrix(_quantized_test_tensor, axes[1], f'2-bit linear quantized tensor',\n",
" vmin=quantized_min, vmax=quantized_max, cmap='tab20c')\n",
" plot_matrix(_reconstructed_test_tensor, axes[2], f'reconstructed tensor',\n",
" vmin=real_min, vmax=real_max, cmap='tab20c')\n",
" fig.tight_layout()\n",
" plt.show()\n"
],
"metadata": {
"id": "jI92NMafaCk7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def test_quantized_fc(\n",
" input=torch.tensor([\n",
" [0.6118, 0.7288, 0.8511, 0.2849, 0.8427, 0.7435, 0.4014, 0.2794],\n",
" [0.3676, 0.2426, 0.1612, 0.7684, 0.6038, 0.0400, 0.2240, 0.4237],\n",
" [0.6565, 0.6878, 0.4670, 0.3470, 0.2281, 0.8074, 0.0178, 0.3999],\n",
" [0.1863, 0.3567, 0.6104, 0.0497, 0.0577, 0.2990, 0.6687, 0.8626]]),\n",
" weight=torch.tensor([\n",
" [ 1.2626e-01, -1.4752e-01, 8.1910e-02, 2.4982e-01, -1.0495e-01,\n",
" -1.9227e-01, -1.8550e-01, -1.5700e-01],\n",
" [ 2.7624e-01, -4.3835e-01, 5.1010e-02, -1.2020e-01, -2.0344e-01,\n",
" 1.0202e-01, -2.0799e-01, 2.4112e-01],\n",
" [-3.8216e-01, -2.8047e-01, 8.5238e-02, -4.2504e-01, -2.0952e-01,\n",
" 3.2018e-01, -3.3619e-01, 2.0219e-01],\n",
" [ 8.9233e-02, -1.0124e-01, 1.1467e-01, 2.0091e-01, 1.1438e-01,\n",
" -4.2427e-01, 1.0178e-01, -3.0941e-04],\n",
" [-1.8837e-02, -2.1256e-01, -4.5285e-01, 2.0949e-01, -3.8684e-01,\n",
" -1.7100e-01, -4.5331e-01, -2.0433e-01],\n",
" [-2.0038e-01, -5.3757e-02, 1.8997e-01, -3.6866e-01, 5.5484e-02,\n",
" 1.5643e-01, -2.3538e-01, 2.1103e-01],\n",
" [-2.6875e-01, 2.4984e-01, -2.3514e-01, 2.5527e-01, 2.0322e-01,\n",
" 3.7675e-01, 6.1563e-02, 1.7201e-01],\n",
" [ 3.3541e-01, -3.3555e-01, -4.3349e-01, 4.3043e-01, -2.0498e-01,\n",
" -1.8366e-01, -9.1553e-02, -4.1168e-01]]),\n",
" bias=torch.tensor([ 0.1954, -0.2756, 0.3113, 0.1149, 0.4274, 0.2429, -0.1721, -0.2502]),\n",
" quantized_bias=torch.tensor([ 3, -2, 3, 1, 3, 2, -2, -2], dtype=torch.int32),\n",
" shifted_quantized_bias=torch.tensor([-1, 0, -3, -1, -3, 0, 2, -4], dtype=torch.int32),\n",
" calc_quantized_output=torch.tensor([\n",
" [ 0, -1, 0, -1, -1, 0, 1, -2],\n",
" [ 0, 0, -1, 0, 0, 0, 0, -1],\n",
" [ 0, 0, 0, -1, 0, 0, 0, -1],\n",
" [ 0, 0, 0, 0, 0, 1, -1, -2]], dtype=torch.int8),\n",
" bitwidth=2, batch_size=4, in_channels=8, out_channels=8):\n",
" def plot_matrix(tensor, ax, title, vmin=0, vmax=1, cmap=ListedColormap(['white'])):\n",
" ax.imshow(tensor.cpu().numpy(), vmin=vmin, vmax=vmax, cmap=cmap)\n",
" ax.set_title(title)\n",
" ax.set_yticklabels([])\n",
" ax.set_xticklabels([])\n",
" for i in range(tensor.shape[0]):\n",
" for j in range(tensor.shape[1]):\n",
" datum = tensor[i, j].item()\n",
" if isinstance(datum, float):\n",
" text = ax.text(j, i, f'{datum:.2f}',\n",
" ha=\"center\", va=\"center\", color=\"k\")\n",
" else:\n",
" text = ax.text(j, i, f'{datum}',\n",
" ha=\"center\", va=\"center\", color=\"k\")\n",
"\n",
" output = torch.nn.functional.linear(input, weight, bias)\n",
"\n",
" quantized_weight, weight_scale, weight_zero_point = \\\n",
" linear_quantize_weight_per_channel(weight, bitwidth)\n",
" quantized_input, input_scale, input_zero_point = \\\n",
" linear_quantize_feature(input, bitwidth)\n",
" _quantized_bias, bias_scale, bias_zero_point = \\\n",
" linear_quantize_bias_per_output_channel(bias, weight_scale, input_scale)\n",
"\n",
"\n",
" assert _quantized_bias.equal(quantized_bias) # original: _quantized_bias.equal(_quantized_bias)\n",
" _shifted_quantized_bias = \\\n",
" shift_quantized_linear_bias(quantized_bias, quantized_weight, input_zero_point)\n",
" assert _shifted_quantized_bias.equal(shifted_quantized_bias)\n",
" quantized_output, output_scale, output_zero_point = \\\n",
" linear_quantize_feature(output, bitwidth)\n",
"\n",
" _calc_quantized_output = quantized_linear(\n",
" quantized_input, quantized_weight, shifted_quantized_bias,\n",
" bitwidth, bitwidth,\n",
" input_zero_point, output_zero_point,\n",
" input_scale, weight_scale, output_scale)\n",
"\n",
" assert _calc_quantized_output.equal(calc_quantized_output)\n",
"\n",
" reconstructed_weight = weight_scale * (quantized_weight.float() - weight_zero_point)\n",
" reconstructed_input = input_scale * (quantized_input.float() - input_zero_point)\n",
" reconstructed_bias = bias_scale * (quantized_bias.float() - bias_zero_point)\n",
" reconstructed_calc_output = output_scale * (calc_quantized_output.float() - output_zero_point)\n",
"\n",
" fig, axes = plt.subplots(3,3, figsize=(15, 12))\n",
" quantized_min, quantized_max = get_quantized_range(bitwidth)\n",
" plot_matrix(weight, axes[0, 0], 'original weight', vmin=-0.5, vmax=0.5)\n",
" plot_matrix(input.t(), axes[1, 0], 'original input', vmin=0, vmax=1)\n",
" plot_matrix(output.t(), axes[2, 0], 'original output', vmin=-1.5, vmax=1.5)\n",
" plot_matrix(quantized_weight, axes[0, 1], f'{bitwidth}-bit linear quantized weight',\n",
" vmin=quantized_min, vmax=quantized_max, cmap='tab20c')\n",
" plot_matrix(quantized_input.t(), axes[1, 1], f'{bitwidth}-bit linear quantized input',\n",
" vmin=quantized_min, vmax=quantized_max, cmap='tab20c')\n",
" plot_matrix(calc_quantized_output.t(), axes[2, 1], f'quantized output from quantized_linear()',\n",
" vmin=quantized_min, vmax=quantized_max, cmap='tab20c')\n",
" plot_matrix(reconstructed_weight, axes[0, 2], f'reconstructed weight',\n",
" vmin=-0.5, vmax=0.5, cmap='tab20c')\n",
" plot_matrix(reconstructed_input.t(), axes[1, 2], f'reconstructed input',\n",
" vmin=0, vmax=1, cmap='tab20c')\n",
" plot_matrix(reconstructed_calc_output.t(), axes[2, 2], f'reconstructed output',\n",
" vmin=-1.5, vmax=1.5, cmap='tab20c')\n",
"\n",
" print('* Test quantized_fc()')\n",
" print(f' target bitwidth: {bitwidth} bits')\n",
" print(f' batch size: {batch_size}')\n",
" print(f' input channels: {in_channels}')\n",
" print(f' output channels: {out_channels}')\n",
" print('* Test passed.')\n",
" fig.tight_layout()\n",
" plt.show()"
],
"metadata": {
"id": "vK-56lHteduz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Load Pretrained Model"
],
"metadata": {
"id": "mDDp0OWLh6vK"
}
},
{
"cell_type": "code",
"source": [
"checkpoint_url = \"https://hanlab18.mit.edu/files/course/labs/vgg.cifar.pretrained.pth\"\n",
"checkpoint = torch.load(download_url(checkpoint_url), map_location=\"cpu\")\n",
"model = VGG().cuda()\n",
"print(f\"=> loading checkpoint '{checkpoint_url}'\")\n",
"model.load_state_dict(checkpoint['state_dict'])\n",
"recover_model = lambda : model.load_state_dict(checkpoint['state_dict'])"
],
"metadata": {
"id": "oNEYAZ_TQf7d",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7b38bbfe-600d-478e-abe7-6715850ddeb3"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"=> loading checkpoint 'https://hanlab18.mit.edu/files/course/labs/vgg.cifar.pretrained.pth'\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"image_size = 32\n",
"transforms = {\n",
" \"train\": Compose([\n",
" RandomCrop(image_size, padding=4),\n",
" RandomHorizontalFlip(),\n",
" ToTensor(),\n",
" ]),\n",
" \"test\": ToTensor(),\n",
"}\n",
"dataset = {}\n",
"for split in [\"train\", \"test\"]:\n",
" dataset[split] = CIFAR10(\n",
" root=\"data/cifar10\",\n",
" train=(split == \"train\"),\n",
" download=True,\n",
" transform=transforms[split],\n",
" )\n",
"dataloader = {}\n",
"for split in ['train', 'test']:\n",
" dataloader[split] = DataLoader(\n",
" dataset[split],\n",
" batch_size=512,\n",
" shuffle=(split == 'train'),\n",
" num_workers=0,\n",
" pin_memory=True,\n",
" )"
],
"metadata": {
"id": "jQb3_6zlQfib",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4a71f8d0-1964-4d6e-e456-b0f32418b22e"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Let's First Evaluate the Accuracy and Model Size of the FP32 Model"
],
"metadata": {
"id": "rjESsg5nkdBG"
}
},
{
"cell_type": "code",
"source": [
"fp32_model_accuracy = evaluate(model, dataloader['test'])\n",
"fp32_model_size = get_model_size(model)\n",
"print(f\"fp32 model has accuracy={fp32_model_accuracy:.2f}%\")\n",
"print(f\"fp32 model has size={fp32_model_size/MiB:.2f} MiB\")"
],
"metadata": {
"id": "DTiA8hxMkkbU",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52,
"referenced_widgets": [
"c74e72a06b504aabbe8da1a78445f19b",
"3edba56dd1df4c30925dcca7af05a61a",
"cacdb2b818bd4ac39cb04d76f9b27a26",
"fd0c889a4e2242f5a204dea82f78dc7d",
"7a378ea5cbd3438c8b7a20ef9791bdea",
"c3f8246129a7467ab3e154a41df4921a",
"6f383f9296fc4b5ab03d4464e4cec56a",
"a81555f069cd429a8c73788016c44acf",
"9bf853e82bd84b7994dfffaa3eb6d0fc",
"7c68af8955fa4a39a36d4d195ee2b939",
"95382e4fe6494696927e7cfe0ebee840"
]
},
"outputId": "5973caed-db56-4db5-f616-4d8db974b812"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c74e72a06b504aabbe8da1a78445f19b"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"fp32 model has accuracy=92.95%\n",
"fp32 model has size=35.20 MiB\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# K-Means Quantization"
],
"metadata": {
"id": "6QdGiddu87p2"
}
},
{
"cell_type": "markdown",
"source": [
"Network quantization compresses the network by reducing the bits per weight required to represent the deep network. The quantized network can have a faster inference speed with hardware support."
],
"metadata": {
"id": "Gr0MvPoVTirU"
}
},
{
"cell_type": "markdown",
"source": [
"In this section, we will explore the K-means quantization for neural networks as in [Deep Compression: Compressing Deep Neural Networks With Pruning, Trained Quantization\n",
"And Huffman Coding](https://arxiv.org/pdf/1510.00149.pdf).\n"
],
"metadata": {
"id": "lzlKpiNvY3lc"
}
},
{
"cell_type": "markdown",
"source": [
""
],
"metadata": {
"id": "KDoR8iXP0VDG"
}
},
{
"cell_type": "markdown",
"source": [
"A $n$-bit k-means quantization will divide synapses into $2^n$ clusters, and synapses in the same cluster will share the same weight value.\n",
"\n",
"Therefore, k-means quantization will create a codebook, inlcuding\n",
"* `centroids`: $2^n$ fp32 cluster centers.\n",
"* `labels`: a $n$-bit integer tensor with the same #elements of the original fp32 weights tensor. Each integer indicates which cluster it belongs to.\n",
"\n",
"During the inference, a fp32 tensor is generated based on the codebook for inference:\n",
"\n",
"> ***quantized_weight* = *codebook.centroids*\\[*codebook.labels*\\].view_as(weight)**"
],
"metadata": {
"id": "mGkEk4CX0VIe"
}
},
{
"cell_type": "code",
"source": [
"from collections import namedtuple\n",
"\n",
"Codebook = namedtuple('Codebook', ['centroids', 'labels'])"
],
"metadata": {
"id": "Z2x1DbpfT3SD"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Question 1 (10 pts)\n",
"\n",
"Please complete the following K-Means quantization function."
],
"metadata": {
"id": "f3BczeDEN0y6"
}
},
{
"cell_type": "code",
"source": [
"from fast_pytorch_kmeans import KMeans\n",
"\n",
"def k_means_quantize(fp32_tensor: torch.Tensor, bitwidth=4, codebook=None):\n",
" \"\"\"\n",
" quantize tensor using k-means clustering\n",
" :param fp32_tensor:\n",
" :param bitwidth: [int] quantization bit width, default=4\n",
" :param codebook: [Codebook] (the cluster centroids, the cluster label tensor)\n",
" :return:\n",
" [Codebook = (centroids, labels)]\n",
" centroids: [torch.(cuda.)FloatTensor] the cluster centroids\n",
" labels: [torch.(cuda.)LongTensor] cluster label tensor\n",
" \"\"\"\n",
" if codebook is None:\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # get number of clusters based on the quantization precision\n",
" # hint: one line of code\n",
" n_clusters = 2**bitwidth\n",
" ############### YOUR CODE ENDS HERE #################\n",
" # use k-means to get the quantization centroids\n",
" kmeans = KMeans(n_clusters=n_clusters, mode='euclidean', verbose=0)\n",
"\n",
" # flatten all numbers in the weight to [N_ele, 1]\n",
" labels = kmeans.fit_predict(fp32_tensor.view(-1, 1)).to(torch.long)\n",
"\n",
" centroids = kmeans.centroids.to(torch.float).view(-1)\n",
" codebook = Codebook(centroids, labels)\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # decode the codebook into k-means quantized tensor for inference\n",
" # hint: one line of code\n",
" quantized_tensor = codebook.centroids[codebook.labels]\n",
" # print(f\"{fp32_tensor.shape=}, {codebook.centroids.shape=}, {codebook.labels.shape=}\")\n",
" ############### YOUR CODE ENDS HERE #################\n",
" fp32_tensor.set_(quantized_tensor.view_as(fp32_tensor))\n",
" return codebook"
],
"metadata": {
"id": "bnZ2b4hgOnoC"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Let's verify the functionality of defined k-means quantization by applying the function above on a dummy tensor."
],
"metadata": {
"id": "XlVfIFRYPbfc"
}
},
{
"cell_type": "code",
"source": [
"test_k_means_quantize()"
],
"metadata": {
"id": "WZd8z1vGPdG8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 627
},
"outputId": "db90e5e1-29c2-4dae-c129-cbd7e8fbed8a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"tensor([[-0.3518, -0.0213, 0.2921, -0.3518, 0.2921],\n",
" [-0.0213, 0.2921, -0.3518, -0.3518, 0.2921],\n",
" [ 0.2921, -0.3518, -0.3518, -0.0213, -0.3518],\n",
" [-0.0213, -0.0213, -0.3518, -0.3518, 0.2921],\n",
" [-0.0213, -0.3518, -0.3518, 0.2921, -0.0213]])\n",
"torch.Size([5, 5]) torch.Size([4]) torch.Size([25])\n",
"* Test k_means_quantize()\n",
" target bitwidth: 2 bits\n",
" num unique values before k-means quantization: 3\n",
" num unique values after k-means quantization: 3\n",
"* Test passed.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## Question 2 (10 pts)"
],
"metadata": {
"id": "O4FIfKk8JQX5"
}
},
{
"cell_type": "markdown",
"source": [
"The last code cell performs 2-bit k-means quantization and plots the tensor before and after the quantization. Each cluster is rendered with a unique color. There are 4 unique colors rendered in the quantized tensor.\n",
"\n",
"Given this observation, please answer the following questions."
],
"metadata": {
"id": "o5mT6vV_JS5w"
}
},
{
"cell_type": "markdown",
"source": [
"### Question 2.1 (5 pts)\n",
"\n",
"If 4-bit k-means quantization is performed, how many unique colors will be rendered in the quantized tensor?"
],
"metadata": {
"id": "DwERfTxNKFqa"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:** 25. Because 2**4 > 25."
],
"metadata": {
"id": "0PGiy4rGKPbe"
}
},
{
"cell_type": "markdown",
"source": [
"### Question 2.2 (5 pts)\n",
"\n",
"If *n*-bit k-means quantization is performed, how many unique colors will be rendered in the quantized tensor?"
],
"metadata": {
"id": "Dt1ZGParKRWD"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:** min(2^n, num of element in tensor)"
],
"metadata": {
"id": "gK8v80tnKZHa"
}
},
{
"cell_type": "markdown",
"source": [
"## K-Means Quantization on Whole Model\n",
"\n",
"Similar to what we did in lab 1, we now wrap the k-means quantization function into a class for quantizing the whole model. In class `KMeansQuantizer`, we have to keep a record of the codebooks (i.e., `centroids` and `labels`) so that we could apply or update the codebooks whenever the model weights change."
],
"metadata": {
"id": "Aa7KVrLrSlNb"
}
},
{
"cell_type": "code",
"source": [
"from torch.nn import parameter\n",
"class KMeansQuantizer:\n",
" def __init__(self, model : nn.Module, bitwidth=4):\n",
" self.codebook = KMeansQuantizer.quantize(model, bitwidth)\n",
"\n",
" @torch.no_grad()\n",
" def apply(self, model, update_centroids):\n",
" for name, param in model.named_parameters():\n",
" if name in self.codebook:\n",
" if update_centroids:\n",
" update_codebook(param, codebook=self.codebook[name])\n",
" self.codebook[name] = k_means_quantize(\n",
" param, codebook=self.codebook[name])\n",
"\n",
" @staticmethod\n",
" @torch.no_grad()\n",
" def quantize(model: nn.Module, bitwidth=4):\n",
" codebook = dict()\n",
" if isinstance(bitwidth, dict):\n",
" for name, param in model.named_parameters():\n",
" if name in bitwidth:\n",
" codebook[name] = k_means_quantize(param, bitwidth=bitwidth[name])\n",
" else:\n",
" for name, param in model.named_parameters():\n",
" # print(f\"{name=}: {param.shape}\")\n",
" # only quantize weight, not bias\n",
" if param.dim() > 1:\n",
" codebook[name] = k_means_quantize(param, bitwidth=bitwidth)\n",
" return codebook"
],
"metadata": {
"id": "eaMAP465Te-p"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Now let's quantize model into 8 bits, 4 bits and 2 bits using K-Means Quantization. *Note that we ignore the storage for codebooks when calculating the model size.*"
],
"metadata": {
"id": "od8iy9KjVhlT"
}
},
{
"cell_type": "code",
"source": [
"print('Note that the storage for codebooks is ignored when calculating the model size.')\n",
"quantizers = dict()\n",
"for bitwidth in [8, 4, 2]:\n",
" recover_model()\n",
" print(f'k-means quantizing model into {bitwidth} bits')\n",
" quantizer = KMeansQuantizer(model, bitwidth)\n",
" quantized_model_size = get_model_size(model, bitwidth)\n",
" print(f\" {bitwidth}-bit k-means quantized model has size={quantized_model_size/MiB:.2f} MiB\")\n",
" quantized_model_accuracy = evaluate(model, dataloader['test'])\n",
" print(f\" {bitwidth}-bit k-means quantized model has accuracy={quantized_model_accuracy:.2f}%\")\n",
" quantizers[bitwidth] = quantizer"
],
"metadata": {
"id": "CCI9sCUmVre-",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191,
"referenced_widgets": [
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"outputId": "54664d60-93ba-4db1-9fa5-7a3fc659de2a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Note that the storage for codebooks is ignored when calculating the model size.\n",
"k-means quantizing model into 8 bits\n",
" 8-bit k-means quantized model has size=8.80 MiB\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "133f66c1a65f400fbb4d8cf61074270c"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" 8-bit k-means quantized model has accuracy=92.73%\n",
"k-means quantizing model into 4 bits\n",
" 4-bit k-means quantized model has size=4.40 MiB\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "1c8d626fcb0f4f948149b7010d0b9302"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" 4-bit k-means quantized model has accuracy=81.61%\n",
"k-means quantizing model into 2 bits\n",
" 2-bit k-means quantized model has size=2.20 MiB\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c4aef9925f4e452581493b4d8d5d4616"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" 2-bit k-means quantized model has accuracy=16.23%\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Trained K-Means Quantization\n",
"\n",
"As we can see from the results of last cell, the accuracy significantly drops when quantizing the model into lower bits. Therefore, we have to perform quantization-aware training to recover the accuracy.\n",
"\n",
"During the k-means quantization-aware training, the centroids are also updated, which is proposed in [Deep Compression: Compressing Deep Neural Networks With Pruning, Trained Quantization\n",
"And Huffman Coding](https://arxiv.org/pdf/1510.00149.pdf).\n",
"\n",
"The gradient for the centroids is calculated as,\n",
"\n",
"> $\\frac{\\partial \\mathcal{L} }{\\partial C_k} = \\sum_{j} \\frac{\\partial \\mathcal{L} }{\\partial W_{j}} \\frac{\\partial W_{j} }{\\partial C_k} = \\sum_{j} \\frac{\\partial \\mathcal{L} }{\\partial W_{j}} \\mathbf{1}(I_{j}=k)$\n",
"\n",
"where $\\mathcal{L}$ is the loss, $C_k$ is *k*-th centroid, $I_{j}$ is the label for weight $W_{j}$. $\\mathbf{1}()$ is the indicator function, and $\\mathbf{1}(I_{j}=k)$ means $1\\;\\mathrm{if}\\;I_{j}=k\\;\\mathrm{else}\\;0$, *i.e.*, $I_{j}==k$.\n",
"\n",
"Here in the lab, **for simplicity**, we directly update the centroids according to the latest weights:\n",
"\n",
"> $C_k = \\frac{\\sum_{j}W_{j}\\mathbf{1}(I_{j}=k)}{\\sum_{j}\\mathbf{1}(I_{j}=k)}$"
],
"metadata": {
"id": "4jj3FnIIWVq0"
}
},
{
"cell_type": "markdown",
"source": [
"### Question 3 (10 pts)\n",
"\n",
"Please complete the following codebook update function.\n",
"\n",
"**Hint**:\n",
"\n",
"The above equation for updating centroids is indeed using the `mean` of weights in the same cluster to be the updated centroid value."
],
"metadata": {
"id": "Q_hfG16fLVda"
}
},
{
"cell_type": "code",
"source": [
"def update_codebook(fp32_tensor: torch.Tensor, codebook: Codebook):\n",
" \"\"\"\n",
" update the centroids in the codebook using updated fp32_tensor\n",
" :param fp32_tensor: [torch.(cuda.)Tensor]\n",
" :param codebook: [Codebook] (the cluster centroids, the cluster label tensor)\n",
" \"\"\"\n",
" n_clusters = codebook.centroids.numel()\n",
" fp32_tensor = fp32_tensor.view(-1)\n",
" for k in range(n_clusters):\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # hint: one line of code\n",
" codebook.centroids[k] = torch.mean(fp32_tensor[codebook.labels==k])\n",
" ############### YOUR CODE ENDS HERE #################"
],
"metadata": {
"id": "t8_FfiUf_1ZM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Now let's run the following code cell to finetune the k-means quantized model to recover the accuracy. We will stop finetuning if accuracy drop is less than 0.5."
],
"metadata": {
"id": "Ho4RDW0ccw4f"
}
},
{
"cell_type": "code",
"source": [
"accuracy_drop_threshold = 0.5\n",
"quantizers_before_finetune = copy.deepcopy(quantizers)\n",
"quantizers_after_finetune = quantizers\n",
"\n",
"for bitwidth in [8, 4, 2]:\n",
" recover_model()\n",
" quantizer = quantizers[bitwidth]\n",
" print(f'k-means quantizing model into {bitwidth} bits')\n",
" quantizer.apply(model, update_centroids=False)\n",
" quantized_model_size = get_model_size(model, bitwidth)\n",
" print(f\" {bitwidth}-bit k-means quantized model has size={quantized_model_size/MiB:.2f} MiB\")\n",
" quantized_model_accuracy = evaluate(model, dataloader['test'])\n",
" print(f\" {bitwidth}-bit k-means quantized model has accuracy={quantized_model_accuracy:.2f}% before quantization-aware training \")\n",
" accuracy_drop = fp32_model_accuracy - quantized_model_accuracy\n",
" if accuracy_drop > accuracy_drop_threshold:\n",
" print(f\" Quantization-aware training due to accuracy drop={accuracy_drop:.2f}% is larger than threshold={accuracy_drop_threshold:.2f}%\")\n",
" num_finetune_epochs = 5\n",
" optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
" scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, num_finetune_epochs)\n",
" criterion = nn.CrossEntropyLoss()\n",
" best_accuracy = 0\n",
" epoch = num_finetune_epochs\n",
" while accuracy_drop > accuracy_drop_threshold and epoch > 0:\n",
" train(model, dataloader['train'], criterion, optimizer, scheduler,\n",
" callbacks=[lambda: quantizer.apply(model, update_centroids=True)])\n",
" model_accuracy = evaluate(model, dataloader['test'])\n",
" is_best = model_accuracy > best_accuracy\n",
" best_accuracy = max(model_accuracy, best_accuracy)\n",
" print(f' Epoch {num_finetune_epochs-epoch} Accuracy {model_accuracy:.2f}% / Best Accuracy: {best_accuracy:.2f}%')\n",
" accuracy_drop = fp32_model_accuracy - best_accuracy\n",
" epoch -= 1\n",
" else:\n",
" print(f\" No need for quantization-aware training since accuracy drop={accuracy_drop:.2f}% is smaller than threshold={accuracy_drop_threshold:.2f}%\")"
],
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"id": "DR1Fu6Arfgnk",
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"outputId": "b3d4e384-5152-41cd-b688-d3d9ead7bd2a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"k-means quantizing model into 8 bits\n",
" 8-bit k-means quantized model has size=8.80 MiB\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a7b220c5524a4910ab0a48c372f9214e"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" 8-bit k-means quantized model has accuracy=92.73% before quantization-aware training \n",
" No need for quantization-aware training since accuracy drop=0.22% is smaller than threshold=0.50%\n",
"k-means quantizing model into 4 bits\n",
" 4-bit k-means quantized model has size=4.40 MiB\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "67e4c3f50a0746ea96717bd589f1c33b"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" 4-bit k-means quantized model has accuracy=81.61% before quantization-aware training \n",
" Quantization-aware training due to accuracy drop=11.34% is larger than threshold=0.50%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b7d4ccb6dd8a4ed59e090377d03f1ea4"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "724629fc47524eeb91bd70755a2c9018"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 0 Accuracy 92.70% / Best Accuracy: 92.70%\n",
"k-means quantizing model into 2 bits\n",
" 2-bit k-means quantized model has size=2.20 MiB\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d3cd81ccf86d4080921a668c0316a5b2"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" 2-bit k-means quantized model has accuracy=16.23% before quantization-aware training \n",
" Quantization-aware training due to accuracy drop=76.72% is larger than threshold=0.50%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e5d66733430a49118d50dc54ca68fd49"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "4afdbf31372d4e5989204623fffd0d78"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 0 Accuracy 90.60% / Best Accuracy: 90.60%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b2eda5a67ee4481db9611847a9515e9a"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "82abad7d564a483cbe7a4ab333c5114d"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 1 Accuracy 91.15% / Best Accuracy: 91.15%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d8c085f536414d83b20656c945b32c2d"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "56688655f516474db56a6bba3d0cb99a"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 2 Accuracy 91.32% / Best Accuracy: 91.32%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "01340b41395b47d2abdf3cf74223afb6"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c9058ca0028a4303898433b0d60c842c"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 3 Accuracy 91.33% / Best Accuracy: 91.33%\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/98 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2a7c830e17764756b0645373e20c55f9"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f9339a391aae414c8d42b01038910a42"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch 4 Accuracy 91.47% / Best Accuracy: 91.47%\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Linear Quantization"
],
"metadata": {
"id": "USisw8QcnPNP"
}
},
{
"cell_type": "markdown",
"source": [
"In this section, we will implement and perform linear quantization.\n",
"\n",
"Linear quantization directly rounds the floating-point value into the nearest quantized integer after range truncation and scaling.\n",
"\n",
"[Linear quantization](https://arxiv.org/pdf/1712.05877.pdf) can be represented as\n",
"\n",
"$r = S(q-Z)$\n",
"\n",
"where $r$ is a floating point real number, $q$ is a *n*-bit integer, $Z$ is a *n*-bit integer, and $S$ is a floating point real number. $Z$ is quantization zero point and $S$ is quantization scaling factor. Both constant $Z$ and $S$ are quantization parameters."
],
"metadata": {
"id": "sEzwgl9mZ6F2"
}
},
{
"cell_type": "markdown",
"source": [
"## *n*-bit Integer\n",
"\n",
"A *n*-bit signed integer is usually represented in [two's complement](https://en.wikipedia.org/wiki/Two%27s_complement) notation.\n",
"\n",
"A *n*-bit signed integer can enode integers in the range $[-2^{n-1}, 2^{n-1}-1]$. For example, a 8-bit integer falls in the range [-128, 127]."
],
"metadata": {
"id": "c6vO1_VBdTzs"
}
},
{
"cell_type": "code",
"source": [
"def get_quantized_range(bitwidth):\n",
" quantized_max = (1 << (bitwidth - 1)) - 1\n",
" quantized_min = -(1 << (bitwidth - 1))\n",
" return quantized_min, quantized_max"
],
"metadata": {
"id": "uz51HcD8dTIb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## **Question 4** (15 pts)\n",
"\n",
"Please complete the following linear quantization function.\n",
"\n",
"**Hint**:\n",
"* From $r=S(q-Z)$, we have $q = r/S + Z$.\n",
"* Both $r$ and $S$ are floating numbers, and thus we cannot directly add integer $Z$ to $r/S$. Therefore $q = \\mathrm{int}(\\mathrm{round}(r/S)) + Z$.\n",
"* To convert [`torch.FloatTensor`](https://pytorch.org/docs/stable/tensors.html) to [`torch.IntTensor`](https://pytorch.org/docs/stable/tensors.html), we could use [`torch.round()`](https://pytorch.org/docs/stable/generated/torch.round.html#torch.round), [`torch.Tensor.round()`](https://pytorch.org/docs/stable/generated/torch.Tensor.round.html#torch.Tensor.round), [`torch.Tensor.round_()`](https://pytorch.org/docs/stable/generated/torch.Tensor.round_) to first convert all values to floating integer, and then use [`torch.Tensor.to(torch.int8)`](https://pytorch.org/docs/stable/generated/torch.Tensor.to.html#torch.Tensor.to) to convert the data type from [`torch.float`](https://pytorch.org/docs/stable/tensors.html) to [`torch.int8`](https://pytorch.org/docs/stable/tensors.html).\n",
"\n"
],
"metadata": {
"id": "FSRwyEWrnA7o"
}
},
{
"cell_type": "code",
"source": [
"def linear_quantize(fp_tensor, bitwidth, scale, zero_point, dtype=torch.int8) -> torch.Tensor:\n",
" \"\"\"\n",
" linear quantization for single fp_tensor\n",
" from\n",
" fp_tensor = (quantized_tensor - zero_point) * scale\n",
" we have,\n",
" quantized_tensor = int(round(fp_tensor / scale)) + zero_point\n",
" :param tensor: [torch.(cuda.)FloatTensor] floating tensor to be quantized\n",
" :param bitwidth: [int] quantization bit width\n",
" :param scale: [torch.(cuda.)FloatTensor] scaling factor\n",
" :param zero_point: [torch.(cuda.)IntTensor] the desired centroid of tensor values\n",
" :return:\n",
" [torch.(cuda.)FloatTensor] quantized tensor whose values are integers\n",
" \"\"\"\n",
" assert(fp_tensor.dtype == torch.float)\n",
" assert(isinstance(scale, float) or\n",
" (scale.dtype == torch.float and scale.dim() == fp_tensor.dim()))\n",
" assert(isinstance(zero_point, int) or\n",
" (zero_point.dtype == dtype and zero_point.dim() == fp_tensor.dim()))\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # Step 1: scale the fp_tensor\n",
" scaled_tensor = fp_tensor / scale\n",
" # Step 2: round the floating value to integer value\n",
" rounded_tensor = torch.round(scaled_tensor)\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" rounded_tensor = rounded_tensor.to(dtype)\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # Step 3: shift the rounded_tensor to make zero_point 0\n",
" shifted_tensor = rounded_tensor + zero_point\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" # Step 4: clamp the shifted_tensor to lie in bitwidth-bit range\n",
" quantized_min, quantized_max = get_quantized_range(bitwidth)\n",
" quantized_tensor = shifted_tensor.clamp_(quantized_min, quantized_max)\n",
" return quantized_tensor"
],
"metadata": {
"id": "1vg4SfIJYFUq"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Let's verify the functionality of defined linear quantization by applying the function above on a dummy tensor."
],
"metadata": {
"id": "Qn-OU5z9IAtr"
}
},
{
"cell_type": "code",
"source": [
"test_linear_quantize()"
],
"metadata": {
"id": "10sN7WRNAWCc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 415
},
"outputId": "bd9f24de-71cf-41e8-c84a-3a55d94c0f23"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"* Test linear_quantize()\n",
" target bitwidth: 2 bits\n",
" scale: 0.3333333333333333\n",
" zero point: -1\n",
"* Test passed.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## Question 5 (10 pts)\n",
"\n",
"Now we have to determine the scaling factor $S$ and zero point $Z$ for linear quantization.\n",
"\n",
"Recall that [linear quantization](https://arxiv.org/pdf/1712.05877.pdf) can be represented as\n",
"\n",
"$r = S(q-Z)$"
],
"metadata": {
"id": "y9WBvG1jnhEz"
}
},
{
"cell_type": "markdown",
"source": [
"### Scale\n",
"\n",
"Linear quantization projects the floating point range [*fp_min*, *fp_max*] to the quantized range [*quantized_min*, *quantized_max*]. That is to say,\n",
"\n",
"> $r_{\\mathrm{max}} = S(q_{\\mathrm{max}}-Z)$\n",
">\n",
"> $r_{\\mathrm{min}} = S(q_{\\mathrm{min}}-Z)$\n",
"\n",
"Substracting these two equations, we have,\n"
],
"metadata": {
"id": "lLgyax_fgrS1"
}
},
{
"cell_type": "markdown",
"source": [
"#### Question 5.1 (1 pts)\n",
"\n",
"Please select the correct answer and delete the wrong answers in the next text cell."
],
"metadata": {
"id": "PkSalSPzSwN2"
}
},
{
"cell_type": "markdown",
"source": [
"> $S=r_{\\mathrm{max}} / q_{\\mathrm{max}} [\\times] $\n",
"\n",
"> $S=(r_{\\mathrm{max}} + r_{\\mathrm{min}}) / (q_{\\mathrm{max}} + q_{\\mathrm{min}}) [\\times]$\n",
"\n",
"> $S=(r_{\\mathrm{max}} - r_{\\mathrm{min}}) / (q_{\\mathrm{max}} - q_{\\mathrm{min}}) [\\checkmark]$\n",
"\n",
"> $S=r_{\\mathrm{max}} / q_{\\mathrm{max}} - r_{\\mathrm{min}} / q_{\\mathrm{min}} [\\times]$"
],
"metadata": {
"id": "dctWTxHaTnUy"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"There are different approaches to determine the $r_{\\mathrm{min}}$ and $r_{\\mathrm{max}}$ of a floating point tensor `fp_tensor`.\n",
"\n",
"* The most common method is directly using the minimum and maximum value of `fp_tensor`.\n",
"* Another widely used method is minimizing Kullback-Leibler-J divergence to determine the *fp_max*."
],
"metadata": {
"id": "WWfff4eISuES"
}
},
{
"cell_type": "markdown",
"source": [
"### zero point\n",
"\n",
"Once we determine the scaling factor $S$, we can directly use the relationship between $r_{\\mathrm{min}}$ and $q_{\\mathrm{min}}$ to calculate the zero point $Z$."
],
"metadata": {
"id": "SSb42B8Gjlpa"
}
},
{
"cell_type": "markdown",
"source": [
"#### Question 5.2 (1 pts)\n",
"\n",
"Please select the correct answer and delete the wrong answers in the next text cell."
],
"metadata": {
"id": "u13Wmw_8TdDA"
}
},
{
"cell_type": "markdown",
"source": [
"> $Z = \\mathrm{int}(\\mathrm{round}(r_{\\mathrm{min}} / S - q_{\\mathrm{min}}) [\\times]$\n",
"\n",
"> $Z = \\mathrm{int}(\\mathrm{round}(q_{\\mathrm{min}} - r_{\\mathrm{min}} / S)) [\\checkmark]$\n",
"\n",
"> $Z = q_{\\mathrm{min}} - r_{\\mathrm{min}} / S [\\times]$\n",
"\n",
"> $Z = r_{\\mathrm{min}} / S - q_{\\mathrm{min}} [\\times]$"
],
"metadata": {
"id": "uD3Q_FfSTwIL"
}
},
{
"cell_type": "markdown",
"source": [
"### Question 5.3 (8 pts)\n",
"\n",
"Please complete the following function for calculating the scale $S$ and zero point $Z$ from floating point tensor $r$.\n"
],
"metadata": {
"id": "tk4aPTKdAikt"
}
},
{
"cell_type": "code",
"source": [
"def get_quantization_scale_and_zero_point(fp_tensor, bitwidth):\n",
" \"\"\"\n",
" get quantization scale for single tensor\n",
" :param fp_tensor: [torch.(cuda.)Tensor] floating tensor to be quantized\n",
" :param bitwidth: [int] quantization bit width\n",
" :return:\n",
" [float] scale\n",
" [int] zero_point\n",
" \"\"\"\n",
" quantized_min, quantized_max = get_quantized_range(bitwidth)\n",
" fp_max = fp_tensor.max().item()\n",
" fp_min = fp_tensor.min().item()\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # hint: one line of code for calculating scale\n",
" # [VERY IMPORTANT] quantized_max - quantized_min = 2 ** bitwith - 1\n",
" scale = (fp_max - fp_min) / (quantized_max - quantized_min)\n",
" # hint: one line of code for calculating zero_point\n",
" zero_point = round(quantized_min - fp_min / scale)\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" # clip the zero_point to fall in [quantized_min, quantized_max]\n",
" if zero_point < quantized_min:\n",
" zero_point = quantized_min\n",
" elif zero_point > quantized_max:\n",
" zero_point = quantized_max\n",
" else: # convert from float to int using round()\n",
" zero_point = round(zero_point)\n",
" return scale, int(zero_point)"
],
"metadata": {
"id": "LfAjS4KhfwDY"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"We now wrap `linear_quantize()` in Question 4 and `get_quantization_scale_and_zero_point()` in Question 5 into one function."
],
"metadata": {
"id": "OW1AOZboSK0e"
}
},
{
"cell_type": "code",
"source": [
"def linear_quantize_feature(fp_tensor, bitwidth):\n",
" \"\"\"\n",
" linear quantization for feature tensor\n",
" :param fp_tensor: [torch.(cuda.)Tensor] floating feature to be quantized\n",
" :param bitwidth: [int] quantization bit width\n",
" :return:\n",
" [torch.(cuda.)Tensor] quantized tensor\n",
" [float] scale tensor\n",
" [int] zero point\n",
" \"\"\"\n",
" scale, zero_point = get_quantization_scale_and_zero_point(fp_tensor, bitwidth)\n",
" quantized_tensor = linear_quantize(fp_tensor, bitwidth, scale, zero_point)\n",
" return quantized_tensor, scale, zero_point"
],
"metadata": {
"id": "FWeOwwXgoPLc"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Special case: linear quantization on weight tensor\n",
"\n",
"Let's first see the distribution of weight values."
],
"metadata": {
"id": "a0YWp1YtfY-a"
}
},
{
"cell_type": "code",
"source": [
"def plot_weight_distribution(model, bitwidth=32):\n",
" # bins = (1 << bitwidth) if bitwidth <= 8 else 256\n",
" if bitwidth <= 8:\n",
" qmin, qmax = get_quantized_range(bitwidth)\n",
" bins = np.arange(qmin, qmax + 2)\n",
" align = 'left'\n",
" else:\n",
" bins = 256\n",
" align = 'mid'\n",
" fig, axes = plt.subplots(3,3, figsize=(10, 6))\n",
" axes = axes.ravel()\n",
" plot_index = 0\n",
" for name, param in model.named_parameters():\n",
" if param.dim() > 1:\n",
" ax = axes[plot_index]\n",
" ax.hist(param.detach().view(-1).cpu(), bins=bins, density=True,\n",
" align=align, color = 'blue', alpha = 0.5,\n",
" edgecolor='black' if bitwidth <= 4 else None)\n",
" if bitwidth <= 4:\n",
" quantized_min, quantized_max = get_quantized_range(bitwidth)\n",
" ax.set_xticks(np.arange(start=quantized_min, stop=quantized_max+1))\n",
" ax.set_xlabel(name)\n",
" ax.set_ylabel('density')\n",
" plot_index += 1\n",
" fig.suptitle(f'Histogram of Weights (bitwidth={bitwidth} bits)')\n",
" fig.tight_layout()\n",
" fig.subplots_adjust(top=0.925)\n",
" plt.show()\n",
"\n",
"recover_model()\n",
"plot_weight_distribution(model)"
],
"metadata": {
"id": "-HdIc5kKk89j",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 536
},
"outputId": "cd6643f8-4ae3-4d91-a4c4-06426607a5d2"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"As we can see from the histograms above, the distribution of weight values are nearly symmetric about 0 (except for the classifier in this case). Therefore, we usually make zero point $Z=0$ when quantizating the weights.\n",
"\n",
"From $r = S(q-Z)$, we have\n",
"\n",
"> $r_{\\mathrm{max}} = S \\cdot q_{\\mathrm{max}}$\n",
"\n",
"and then\n",
"\n",
"> $S = r_{\\mathrm{max}} / q_{\\mathrm{max}}$\n",
"\n",
"We directly use the maximum magnitude of weight values as $r_{\\mathrm{max}}$."
],
"metadata": {
"id": "WpAmSr7IlDn9"
}
},
{
"cell_type": "code",
"source": [
"def get_quantization_scale_for_weight(weight, bitwidth):\n",
" \"\"\"\n",
" get quantization scale for single tensor of weight\n",
" :param weight: [torch.(cuda.)Tensor] floating weight to be quantized\n",
" :param bitwidth: [integer] quantization bit width\n",
" :return:\n",
" [floating scalar] scale\n",
" \"\"\"\n",
" # we just assume values in weight are symmetric\n",
" # we also always make zero_point 0 for weight\n",
" fp_max = max(weight.abs().max().item(), 5e-7)\n",
" _, quantized_max = get_quantized_range(bitwidth)\n",
" return fp_max / quantized_max"
],
"metadata": {
"id": "v3d-Y-G9enEA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Per-channel Linear Quantization\n",
"\n",
"Recall that for 2D convolution, the weight tensor is a 4-D tensor in the shape of (num_output_channels, num_input_channels, kernel_height, kernel_width).\n",
"\n",
"Intensive experiments show that using the different scaling factors $S$ and zero points $Z$ for different output channels will perform better. Therefore, we have to determine scaling factor $S$ and zero point $Z$ for the subtensor of each output channel independently."
],
"metadata": {
"id": "X71SQhfDnwS7"
}
},
{
"cell_type": "code",
"source": [
"def linear_quantize_weight_per_channel(tensor, bitwidth):\n",
" \"\"\"\n",
" linear quantization for weight tensor\n",
" using different scales and zero_points for different output channels\n",
" :param tensor: [torch.(cuda.)Tensor] floating weight to be quantized\n",
" :param bitwidth: [int] quantization bit width\n",
" :return:\n",
" [torch.(cuda.)Tensor] quantized tensor\n",
" [torch.(cuda.)Tensor] scale tensor\n",
" [int] zero point (which is always 0)\n",
" \"\"\"\n",
" dim_output_channels = 0\n",
" num_output_channels = tensor.shape[dim_output_channels]\n",
" scale = torch.zeros(num_output_channels, device=tensor.device)\n",
" for oc in range(num_output_channels):\n",
" _subtensor = tensor.select(dim_output_channels, oc)\n",
" _scale = get_quantization_scale_for_weight(_subtensor, bitwidth)\n",
" scale[oc] = _scale\n",
" scale_shape = [1] * tensor.dim()\n",
" scale_shape[dim_output_channels] = -1\n",
" scale = scale.view(scale_shape)\n",
" quantized_tensor = linear_quantize(tensor, bitwidth, scale, zero_point=0)\n",
" return quantized_tensor, scale, 0"
],
"metadata": {
"id": "tFkA5JlgoiLs"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### A Quick Peek at Linear Quantization on Weights\n",
"\n",
"Now let's have a peek on the weight distribution and model size when applying linear quantization on weights with different bitwidths."
],
"metadata": {
"id": "bqKnjcUJpq2R"
}
},
{
"cell_type": "code",
"source": [
"@torch.no_grad()\n",
"def peek_linear_quantization():\n",
" for bitwidth in [4, 2]:\n",
" for name, param in model.named_parameters():\n",
" if param.dim() > 1:\n",
" quantized_param, scale, zero_point = \\\n",
" linear_quantize_weight_per_channel(param, bitwidth)\n",
" param.copy_(quantized_param)\n",
" plot_weight_distribution(model, bitwidth)\n",
" recover_model()\n",
"\n",
"peek_linear_quantization()"
],
"metadata": {
"id": "6PzZe2DGvWap",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "8e5368ba-df08-4b6c-cdd8-5c1ec22662d3"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## Quantized Inference"
],
"metadata": {
"id": "5Dw14QcxxItm"
}
},
{
"cell_type": "markdown",
"source": [
"After quantization, the inference of convolution and fully-connected layers also change.\n",
"\n",
"Recall that $r = S(q-Z)$, and we have\n",
"\n",
"> $r_{\\mathrm{input}} = S_{\\mathrm{input}}(q_{\\mathrm{input}}-Z_{\\mathrm{input}})$\n",
">\n",
"> $r_{\\mathrm{weight}} = S_{\\mathrm{weight}}(q_{\\mathrm{weight}}-Z_{\\mathrm{weight}})$\n",
">\n",
"> $r_{\\mathrm{bias}} = S_{\\mathrm{bias}}(q_{\\mathrm{bias}}-Z_{\\mathrm{bias}})$\n",
"\n",
"Since $Z_{\\mathrm{weight}}=0$, $r_{\\mathrm{weight}} = S_{\\mathrm{weight}}q_{\\mathrm{weight}}$.\n",
"\n",
"The floating point convolution can be written as,\n",
"\n",
"> $r_{\\mathrm{output}} = \\mathrm{CONV}[r_{\\mathrm{input}}, r_{\\mathrm{weight}}] + r_{\\mathrm{bias}}\\\\\n",
"\\;\\;\\;\\;\\;\\;\\;\\;= \\mathrm{CONV}[S_{\\mathrm{input}}(q_{\\mathrm{input}}-Z_{\\mathrm{input}}), S_{\\mathrm{weight}}q_{\\mathrm{weight}}] + S_{\\mathrm{bias}}(q_{\\mathrm{bias}}-Z_{\\mathrm{bias}})\\\\\n",
"\\;\\;\\;\\;\\;\\;\\;\\;= \\mathrm{CONV}[q_{\\mathrm{input}}-Z_{\\mathrm{input}}, q_{\\mathrm{weight}}]\\cdot (S_{\\mathrm{input}} \\cdot S_{\\mathrm{weight}}) + S_{\\mathrm{bias}}(q_{\\mathrm{bias}}-Z_{\\mathrm{bias}})$\n",
"\n",
"To further simplify the computation, we could let\n",
"\n",
"> $Z_{\\mathrm{bias}} = 0$\n",
">\n",
"> $S_{\\mathrm{bias}} = S_{\\mathrm{input}} \\cdot S_{\\mathrm{weight}}$\n",
"\n",
"so that\n",
"\n",
"> $r_{\\mathrm{output}} = (\\mathrm{CONV}[q_{\\mathrm{input}}-Z_{\\mathrm{input}}, q_{\\mathrm{weight}}] + q_{\\mathrm{bias}})\\cdot (S_{\\mathrm{input}} \\cdot S_{\\mathrm{weight}})$\n",
"> $\\;\\;\\;\\;\\;\\;\\;\\;= (\\mathrm{CONV}[q_{\\mathrm{input}}, q_{\\mathrm{weight}}] - \\mathrm{CONV}[Z_{\\mathrm{input}}, q_{\\mathrm{weight}}] + q_{\\mathrm{bias}})\\cdot (S_{\\mathrm{input}}S_{\\mathrm{weight}})$\n",
"\n",
"Since\n",
"> $r_{\\mathrm{output}} = S_{\\mathrm{output}}(q_{\\mathrm{output}}-Z_{\\mathrm{output}})$\n",
"\n",
"p.s. activation的量化step_size是大量训练数据的指数移动均值统计出来的\n",
"\n",
"we have\n",
"> $S_{\\mathrm{output}}(q_{\\mathrm{output}}-Z_{\\mathrm{output}}) = (\\mathrm{CONV}[q_{\\mathrm{input}}, q_{\\mathrm{weight}}] - \\mathrm{CONV}[Z_{\\mathrm{input}}, q_{\\mathrm{weight}}] + q_{\\mathrm{bias}})\\cdot (S_{\\mathrm{input}} S_{\\mathrm{weight}})$\n",
"\n",
"and thus\n",
"> $q_{\\mathrm{output}} = (\\mathrm{CONV}[q_{\\mathrm{input}}, q_{\\mathrm{weight}}] - \\mathrm{CONV}[Z_{\\mathrm{input}}, q_{\\mathrm{weight}}] + q_{\\mathrm{bias}})\\cdot (S_{\\mathrm{input}}S_{\\mathrm{weight}} / S_{\\mathrm{output}}) + Z_{\\mathrm{output}}$\n",
"\n",
"Since $Z_{\\mathrm{input}}$, $q_{\\mathrm{weight}}$, $q_{\\mathrm{bias}}$ are determined before inference, let\n",
"\n",
"> $Q_{\\mathrm{bias}} = q_{\\mathrm{bias}} - \\mathrm{CONV}[Z_{\\mathrm{input}}, q_{\\mathrm{weight}}]$\n",
"\n",
"we have\n",
"\n",
"> $q_{\\mathrm{output}} = (\\mathrm{CONV}[q_{\\mathrm{input}}, q_{\\mathrm{weight}}] + Q_{\\mathrm{bias}}) \\cdot (S_{\\mathrm{input}}S_{\\mathrm{weight}} / S_{\\mathrm{output}}) + Z_{\\mathrm{output}}$\n",
"\n",
"Similarily, for fully-connected layer, we have\n",
"\n",
"> $q_{\\mathrm{output}} = (\\mathrm{Linear}[q_{\\mathrm{input}}, q_{\\mathrm{weight}}] + Q_{\\mathrm{bias}})\\cdot (S_{\\mathrm{input}} \\cdot S_{\\mathrm{weight}} / S_{\\mathrm{output}}) + Z_{\\mathrm{output}}$\n",
"\n",
"where\n",
"\n",
"> $Q_{\\mathrm{bias}} = q_{\\mathrm{bias}} - \\mathrm{Linear}[Z_{\\mathrm{input}}, q_{\\mathrm{weight}}]$"
],
"metadata": {
"id": "zsHy-Bx-UfpL"
}
},
{
"cell_type": "markdown",
"source": [
"### Question 6 (5 pts)\n",
"\n",
"Please complete the following function for linear quantizing the bias.\n",
"\n",
"**Hint**:\n",
"\n",
"From the above deduction, we know that\n",
"\n",
"> $Z_{\\mathrm{bias}} = 0$\n",
">\n",
"> $S_{\\mathrm{bias}} = S_{\\mathrm{input}} \\cdot S_{\\mathrm{weight}}$"
],
"metadata": {
"id": "XlH0zg7M2J_L"
}
},
{
"cell_type": "code",
"source": [
"def linear_quantize_bias_per_output_channel(bias, weight_scale, input_scale):\n",
" \"\"\"\n",
" linear quantization for single bias tensor\n",
" quantized_bias = fp_bias / bias_scale\n",
" :param bias: [torch.FloatTensor] bias weight to be quantized\n",
" :param weight_scale: [float or torch.FloatTensor] weight scale tensor\n",
" :param input_scale: [float] input scale\n",
" :return:\n",
" [torch.IntTensor] quantized bias tensor\n",
" \"\"\"\n",
" assert(bias.dim() == 1)\n",
" assert(bias.dtype == torch.float)\n",
" assert(isinstance(input_scale, float))\n",
" if isinstance(weight_scale, torch.Tensor):\n",
" assert(weight_scale.dtype == torch.float)\n",
" weight_scale = weight_scale.view(-1)\n",
" assert(bias.numel() == weight_scale.numel())\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # hint: one line of code\n",
" bias_scale = input_scale * weight_scale\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" quantized_bias = linear_quantize(bias, 32, bias_scale,\n",
" zero_point=0, dtype=torch.int32)\n",
" return quantized_bias, bias_scale, 0"
],
"metadata": {
"id": "0JZiyAms2G1J"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Quantized Fully-Connected Layer"
],
"metadata": {
"id": "mMM7uYX25rFM"
}
},
{
"cell_type": "markdown",
"source": [
"For quantized fully-connected layer, we first precompute $Q_{\\mathrm{bias}}$. Recall that $Q_{\\mathrm{bias}} = q_{\\mathrm{bias}} - \\mathrm{Linear}[Z_{\\mathrm{input}}, q_{\\mathrm{weight}}]$."
],
"metadata": {
"id": "znsT4EWL5tA5"
}
},
{
"cell_type": "code",
"source": [
"def shift_quantized_linear_bias(quantized_bias, quantized_weight, input_zero_point):\n",
" \"\"\"\n",
" shift quantized bias to incorporate input_zero_point for nn.Linear\n",
" shifted_quantized_bias = quantized_bias - Linear(input_zero_point, quantized_weight)\n",
" :param quantized_bias: [torch.IntTensor] quantized bias (torch.int32)\n",
" :param quantized_weight: [torch.CharTensor] quantized weight (torch.int8)\n",
" :param input_zero_point: [int] input zero point\n",
" :return:\n",
" [torch.IntTensor] shifted quantized bias tensor\n",
" \"\"\"\n",
" assert(quantized_bias.dtype == torch.int32)\n",
" assert(isinstance(input_zero_point, int))\n",
" return quantized_bias - quantized_weight.sum(1).to(torch.int32) * input_zero_point"
],
"metadata": {
"id": "4rnNs4MN5tgF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#### Question 7 (15 pts)\n",
"\n",
"Please complete the following quantized fully-connected layer inference function.\n",
"\n",
"**Hint**:\n",
"\n",
"> $q_{\\mathrm{output}} = (\\mathrm{Linear}[q_{\\mathrm{input}}, q_{\\mathrm{weight}}] + Q_{\\mathrm{bias}})\\cdot (S_{\\mathrm{input}} S_{\\mathrm{weight}} / S_{\\mathrm{output}}) + Z_{\\mathrm{output}}$"
],
"metadata": {
"id": "DqNMxMzk3cDz"
}
},
{
"cell_type": "code",
"source": [
"def quantized_linear(input, weight, bias, feature_bitwidth, weight_bitwidth,\n",
" input_zero_point, output_zero_point,\n",
" input_scale, weight_scale, output_scale):\n",
" \"\"\"\n",
" quantized fully-connected layer\n",
" :param input: [torch.CharTensor] quantized input (torch.int8)\n",
" :param weight: [torch.CharTensor] quantized weight (torch.int8)\n",
" :param bias: [torch.IntTensor] shifted quantized bias or None (torch.int32)\n",
" :param feature_bitwidth: [int] quantization bit width of input and output\n",
" :param weight_bitwidth: [int] quantization bit width of weight\n",
" :param input_zero_point: [int] input zero point\n",
" :param output_zero_point: [int] output zero point\n",
" :param input_scale: [float] input feature scale\n",
" :param weight_scale: [torch.FloatTensor] weight per-channel scale\n",
" :param output_scale: [float] output feature scale\n",
" :return:\n",
" [torch.CharIntTensor] quantized output feature (torch.int8)\n",
" \"\"\"\n",
" assert(input.dtype == torch.int8)\n",
" assert(weight.dtype == input.dtype)\n",
" assert(bias is None or bias.dtype == torch.int32)\n",
" assert(isinstance(input_zero_point, int))\n",
" assert(isinstance(output_zero_point, int))\n",
" assert(isinstance(input_scale, float))\n",
" assert(isinstance(output_scale, float))\n",
" assert(weight_scale.dtype == torch.float)\n",
"\n",
" # Step 1: integer-based fully-connected (8-bit multiplication with 32-bit accumulation)\n",
" if 'cpu' in input.device.type:\n",
" # use 32-b MAC for simplicity\n",
" output = torch.nn.functional.linear(input.to(torch.int32), weight.to(torch.int32), bias)\n",
" else:\n",
" # current version pytorch does not yet support integer-based linear() on GPUs\n",
" output = torch.nn.functional.linear(input.float(), weight.float(), bias.float())\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # Step 2: scale the output\n",
" # hint: 1. scales are floating numbers, we need to convert output to float as well\n",
" # 2. the shape of weight scale is [oc, 1, 1, 1] while the shape of output is [batch_size, oc]\n",
" output = output.float()\n",
" output *= (input_scale * weight_scale.flatten().view(1, -1) / output_scale)\n",
"\n",
"\n",
" # Step 3: shift output by output_zero_point\n",
" # hint: one line of code\n",
" output = output + output_zero_point\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" # Make sure all value lies in the bitwidth-bit range\n",
" output = output.round().clamp(*get_quantized_range(feature_bitwidth)).to(torch.int8)\n",
" return output"
],
"metadata": {
"id": "3PVvI7jP3cMo"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Let's verify the functionality of defined quantized fully connected layer."
],
"metadata": {
"id": "115enVamIG_e"
}
},
{
"cell_type": "code",
"source": [
"test_quantized_fc()"
],
"metadata": {
"id": "HWmsLxgHH_B3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 901
},
"outputId": "36ac5875-3bba-46cb-d49b-61b7ef03efc5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"* Test quantized_fc()\n",
" target bitwidth: 2 bits\n",
" batch size: 4\n",
" input channels: 8\n",
" output channels: 8\n",
"* Test passed.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"### Quantized Convolution"
],
"metadata": {
"id": "ATooyRrH50ls"
}
},
{
"cell_type": "markdown",
"source": [
"For quantized convolution layer, we first precompute $Q_{\\mathrm{bias}}$. Recall that $Q_{\\mathrm{bias}} = q_{\\mathrm{bias}} - \\mathrm{CONV}[Z_{\\mathrm{input}}, q_{\\mathrm{weight}}]$."
],
"metadata": {
"id": "8mk1Ziof51JG"
}
},
{
"cell_type": "code",
"source": [
"def shift_quantized_conv2d_bias(quantized_bias, quantized_weight, input_zero_point):\n",
" \"\"\"\n",
" shift quantized bias to incorporate input_zero_point for nn.Conv2d\n",
" shifted_quantized_bias = quantized_bias - Conv(input_zero_point, quantized_weight)\n",
" :param quantized_bias: [torch.IntTensor] quantized bias (torch.int32)\n",
" :param quantized_weight: [torch.CharTensor] quantized weight (torch.int8)\n",
" :param input_zero_point: [int] input zero point\n",
" :return:\n",
" [torch.IntTensor] shifted quantized bias tensor\n",
" \"\"\"\n",
" assert(quantized_bias.dtype == torch.int32)\n",
" assert(isinstance(input_zero_point, int))\n",
" return quantized_bias - quantized_weight.sum((1,2,3)).to(torch.int32) * input_zero_point"
],
"metadata": {
"id": "wEeANE_I53hz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#### Question 8 (15 pts)\n",
"\n",
"Please complete the following quantized convolution function.\n",
"\n",
"**Hint**:\n",
"> $q_{\\mathrm{output}} = (\\mathrm{CONV}[q_{\\mathrm{input}}, q_{\\mathrm{weight}}] + Q_{\\mathrm{bias}}) \\cdot (S_{\\mathrm{input}}S_{\\mathrm{weight}} / S_{\\mathrm{output}}) + Z_{\\mathrm{output}}$\n"
],
"metadata": {
"id": "0x2SqxOp23cw"
}
},
{
"cell_type": "code",
"source": [
"def quantized_conv2d(input, weight, bias, feature_bitwidth, weight_bitwidth,\n",
" input_zero_point, output_zero_point,\n",
" input_scale, weight_scale, output_scale,\n",
" stride, padding, dilation, groups):\n",
" \"\"\"\n",
" quantized 2d convolution\n",
" :param input: [torch.CharTensor] quantized input (torch.int8)\n",
" :param weight: [torch.CharTensor] quantized weight (torch.int8)\n",
" :param bias: [torch.IntTensor] shifted quantized bias or None (torch.int32)\n",
" :param feature_bitwidth: [int] quantization bit width of input and output\n",
" :param weight_bitwidth: [int] quantization bit width of weight\n",
" :param input_zero_point: [int] input zero point\n",
" :param samp: [int] output zero point\n",
" :param input_scale: [float] input feature scale\n",
" :param weight_scale: [torch.FloatTensor] weight per-channel scale\n",
" :param output_scale: [float] output feature scale\n",
" :return:\n",
" [torch.(cuda.)CharTensor] quantized output feature\n",
" \"\"\"\n",
" assert(len(padding) == 4)\n",
" assert(input.dtype == torch.int8)\n",
" assert(weight.dtype == input.dtype)\n",
" assert(bias is None or bias.dtype == torch.int32)\n",
" assert(isinstance(input_zero_point, int))\n",
" assert(isinstance(output_zero_point, int))\n",
" assert(isinstance(input_scale, float))\n",
" assert(isinstance(output_scale, float))\n",
" assert(weight_scale.dtype == torch.float)\n",
"\n",
" # Step 1: calculate integer-based 2d convolution (8-bit multiplication with 32-bit accumulation)\n",
" input = torch.nn.functional.pad(input, padding, 'constant', input_zero_point)\n",
" if 'cpu' in input.device.type:\n",
" # use 32-b MAC for simplicity\n",
" output = torch.nn.functional.conv2d(input.to(torch.int32), weight.to(torch.int32), None, stride, 0, dilation, groups)\n",
" else:\n",
" # current version pytorch does not yet support integer-based conv2d() on GPUs\n",
" output = torch.nn.functional.conv2d(input.float(), weight.float(), None, stride, 0, dilation, groups)\n",
" output = output.round().to(torch.int32)\n",
" if bias is not None:\n",
" output = output + bias.view(1, -1, 1, 1)\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # hint: this code block should be the very similar to quantized_linear()\n",
"\n",
" # Step 2: scale the output\n",
" # hint: 1. scales are floating numbers, we need to convert output to float as well\n",
" # 2. the shape of weight scale is [oc, 1, 1, 1] while the shape of output is [batch_size, oc, height, width]\n",
" output = output.float()\n",
" output *= (input_scale * weight_scale.flatten().view(1, -1, 1, 1) / output_scale)\n",
"\n",
" # Step 3: shift output by output_zero_point\n",
" # hint: one line of code\n",
" output += output_zero_point\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" # Make sure all value lies in the bitwidth-bit range\n",
" output = output.round().clamp(*get_quantized_range(feature_bitwidth)).to(torch.int8)\n",
" return output"
],
"metadata": {
"id": "LVRqhiUno65x"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Question 9 (10 pts)\n",
"\n",
"Finally, we are putting everything together and perform post-training `int8` quantization for the model. We will convert the convolutional and linear layers in the model to a quantized version one-by-one."
],
"metadata": {
"id": "32vvQ4WJHVlA"
}
},
{
"cell_type": "markdown",
"source": [
"1. Firstly, we will fuse a BatchNorm layer into its previous convolutional layer, which is a standard practice before quantization. Fusing batchnorm reduces the extra multiplication during inference.\n",
"\n",
"We will also verify that the fused model `model_fused` has the same accuracy as the original model (BN fusion is an equivalent transform that does not change network functionality)."
],
"metadata": {
"id": "2E5EDR2sINVS"
}
},
{
"cell_type": "code",
"source": [
"def fuse_conv_bn(conv, bn):\n",
" # modified from https://mmcv.readthedocs.io/en/latest/_modules/mmcv/cnn/utils/fuse_conv_bn.html\n",
" assert conv.bias is None\n",
"\n",
" factor = bn.weight.data / torch.sqrt(bn.running_var.data + bn.eps)\n",
" conv.weight.data = conv.weight.data * factor.reshape(-1, 1, 1, 1)\n",
" conv.bias = nn.Parameter(- bn.running_mean.data * factor + bn.bias.data)\n",
"\n",
" return conv\n",
"\n",
"print('Before conv-bn fusion: backbone length', len(model.backbone))\n",
"# fuse the batchnorm into conv layers\n",
"recover_model()\n",
"model_fused = copy.deepcopy(model)\n",
"fused_backbone = []\n",
"ptr = 0\n",
"while ptr < len(model_fused.backbone):\n",
" if isinstance(model_fused.backbone[ptr], nn.Conv2d) and \\\n",
" isinstance(model_fused.backbone[ptr + 1], nn.BatchNorm2d):\n",
" fused_backbone.append(fuse_conv_bn(\n",
" model_fused.backbone[ptr], model_fused.backbone[ptr+ 1]))\n",
" ptr += 2\n",
" else:\n",
" fused_backbone.append(model_fused.backbone[ptr])\n",
" ptr += 1\n",
"model_fused.backbone = nn.Sequential(*fused_backbone)\n",
"\n",
"print('After conv-bn fusion: backbone length', len(model_fused.backbone))\n",
"# sanity check, no BN anymore\n",
"for m in model_fused.modules():\n",
" assert not isinstance(m, nn.BatchNorm2d)\n",
"\n",
"# the accuracy will remain the same after fusion\n",
"fused_acc = evaluate(model_fused, dataloader['test'])\n",
"print(f'Accuracy of the fused model={fused_acc:.2f}%')"
],
"metadata": {
"id": "V2K8KSl7IE4D",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69,
"referenced_widgets": [
"67b2b5de21724b19aa006e388fc293ed",
"c8b5e7626a2242b4b4bd8bcb840dbdd2",
"8a1d0b2bb5d648aa8ba5543f9e415d2e",
"0170f1e6ca364f50a7c585af2e988cee",
"4e1b67c04d43438c9e3852f2bc8426f9",
"bbf1e16ec2fc4c12947fefa228bf80cb",
"ec3a2fd8033d46b3ab51f9f9deff6be5",
"dccd067ec4a447639f99bf71cfa43c52",
"43fa6b4d59574bd1a02826f4678436a6",
"efce5de162704803847d488d06fa1ba2",
"3c44b1b82ff043c6b2f355811a9ac946"
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},
"outputId": "828c1d7b-6600-437e-b78c-075cd6661faa"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Before conv-bn fusion: backbone length 29\n",
"After conv-bn fusion: backbone length 21\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "67b2b5de21724b19aa006e388fc293ed"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the fused model=92.95%\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"2. We will run the model with some sample data to get the range of each feature map, so that we can get the range of the feature maps and compute their corresponding scaling factors and zero points."
],
"metadata": {
"id": "oCOXprerMY5t"
}
},
{
"cell_type": "code",
"source": [
"# add hook to record the min max value of the activation\n",
"input_activation = {}\n",
"output_activation = {}\n",
"\n",
"def add_range_recoder_hook(model):\n",
" import functools\n",
" def _record_range(self, x, y, module_name):\n",
" x = x[0]\n",
" input_activation[module_name] = x.detach()\n",
" output_activation[module_name] = y.detach()\n",
"\n",
" all_hooks = []\n",
" for name, m in model.named_modules():\n",
" if isinstance(m, (nn.Conv2d, nn.Linear, nn.ReLU)):\n",
" all_hooks.append(m.register_forward_hook(\n",
" functools.partial(_record_range, module_name=name)))\n",
" return all_hooks\n",
"\n",
"hooks = add_range_recoder_hook(model_fused)\n",
"# sample from training data. record the scale and zero point for inference usage.\n",
"sample_data = iter(dataloader['train']).__next__()[0]\n",
"model_fused(sample_data.cuda())\n",
"\n",
"# remove hooks\n",
"for h in hooks:\n",
" h.remove()\n"
],
"metadata": {
"id": "CjKrC7L-UrxZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"3. Finally, let's do model quantization. We will convert the model in the following mapping\n",
"```python\n",
"nn.Conv2d: QuantizedConv2d,\n",
"nn.Linear: QuantizedLinear,\n",
"# the following twos are just wrappers, as current\n",
"# torch modules do not support int8 data format;\n",
"# we will temporarily convert them to fp32 for computation\n",
"nn.MaxPool2d: QuantizedMaxPool2d,\n",
"nn.AvgPool2d: QuantizedAvgPool2d,\n",
"```"
],
"metadata": {
"id": "uq3fvyx_6bLW"
}
},
{
"cell_type": "code",
"source": [
"class QuantizedConv2d(nn.Module):\n",
" def __init__(self, weight, bias,\n",
" input_zero_point, output_zero_point,\n",
" input_scale, weight_scale, output_scale,\n",
" stride, padding, dilation, groups,\n",
" feature_bitwidth=8, weight_bitwidth=8):\n",
" super().__init__()\n",
" # current version Pytorch does not support IntTensor as nn.Parameter\n",
" self.register_buffer('weight', weight)\n",
" self.register_buffer('bias', bias)\n",
"\n",
" self.input_zero_point = input_zero_point\n",
" self.output_zero_point = output_zero_point\n",
"\n",
" self.input_scale = input_scale\n",
" self.register_buffer('weight_scale', weight_scale)\n",
" self.output_scale = output_scale\n",
"\n",
" self.stride = stride\n",
" self.padding = (padding[1], padding[1], padding[0], padding[0])\n",
" self.dilation = dilation\n",
" self.groups = groups\n",
"\n",
" self.feature_bitwidth = feature_bitwidth\n",
" self.weight_bitwidth = weight_bitwidth\n",
"\n",
"\n",
" def forward(self, x):\n",
" return quantized_conv2d(\n",
" x, self.weight, self.bias,\n",
" self.feature_bitwidth, self.weight_bitwidth,\n",
" self.input_zero_point, self.output_zero_point,\n",
" self.input_scale, self.weight_scale, self.output_scale,\n",
" self.stride, self.padding, self.dilation, self.groups\n",
" )\n",
"\n",
"class QuantizedLinear(nn.Module):\n",
" def __init__(self, weight, bias,\n",
" input_zero_point, output_zero_point,\n",
" input_scale, weight_scale, output_scale,\n",
" feature_bitwidth=8, weight_bitwidth=8):\n",
" super().__init__()\n",
" # current version Pytorch does not support IntTensor as nn.Parameter\n",
" self.register_buffer('weight', weight)\n",
" self.register_buffer('bias', bias)\n",
"\n",
" self.input_zero_point = input_zero_point\n",
" self.output_zero_point = output_zero_point\n",
"\n",
" self.input_scale = input_scale\n",
" self.register_buffer('weight_scale', weight_scale)\n",
" self.output_scale = output_scale\n",
"\n",
" self.feature_bitwidth = feature_bitwidth\n",
" self.weight_bitwidth = weight_bitwidth\n",
"\n",
" def forward(self, x):\n",
" return quantized_linear(\n",
" x, self.weight, self.bias,\n",
" self.feature_bitwidth, self.weight_bitwidth,\n",
" self.input_zero_point, self.output_zero_point,\n",
" self.input_scale, self.weight_scale, self.output_scale\n",
" )\n",
"\n",
"class QuantizedMaxPool2d(nn.MaxPool2d):\n",
" def forward(self, x):\n",
" # current version PyTorch does not support integer-based MaxPool\n",
" return super().forward(x.float()).to(torch.int8)\n",
"\n",
"class QuantizedAvgPool2d(nn.AvgPool2d):\n",
" def forward(self, x):\n",
" # current version PyTorch does not support integer-based AvgPool\n",
" return super().forward(x.float()).to(torch.int8)\n",
"\n",
"# we use int8 quantization, which is quite popular\n",
"feature_bitwidth = weight_bitwidth = 8\n",
"quantized_model = copy.deepcopy(model_fused)\n",
"quantized_backbone = []\n",
"ptr = 0\n",
"while ptr < len(quantized_model.backbone):\n",
" if isinstance(quantized_model.backbone[ptr], nn.Conv2d) and \\\n",
" isinstance(quantized_model.backbone[ptr + 1], nn.ReLU):\n",
" conv = quantized_model.backbone[ptr]\n",
" conv_name = f'backbone.{ptr}'\n",
" relu = quantized_model.backbone[ptr + 1]\n",
" relu_name = f'backbone.{ptr + 1}'\n",
"\n",
" input_scale, input_zero_point = \\\n",
" get_quantization_scale_and_zero_point(\n",
" input_activation[conv_name], feature_bitwidth)\n",
"\n",
" output_scale, output_zero_point = \\\n",
" get_quantization_scale_and_zero_point(\n",
" output_activation[relu_name], feature_bitwidth)\n",
"\n",
" quantized_weight, weight_scale, weight_zero_point = \\\n",
" linear_quantize_weight_per_channel(conv.weight.data, weight_bitwidth)\n",
" quantized_bias, bias_scale, bias_zero_point = \\\n",
" linear_quantize_bias_per_output_channel(\n",
" conv.bias.data, weight_scale, input_scale)\n",
" shifted_quantized_bias = \\\n",
" shift_quantized_conv2d_bias(quantized_bias, quantized_weight,\n",
" input_zero_point)\n",
"\n",
" quantized_conv = QuantizedConv2d(\n",
" quantized_weight, shifted_quantized_bias,\n",
" input_zero_point, output_zero_point,\n",
" input_scale, weight_scale, output_scale,\n",
" conv.stride, conv.padding, conv.dilation, conv.groups,\n",
" feature_bitwidth=feature_bitwidth, weight_bitwidth=weight_bitwidth\n",
" )\n",
"\n",
" quantized_backbone.append(quantized_conv)\n",
" ptr += 2\n",
" elif isinstance(quantized_model.backbone[ptr], nn.MaxPool2d):\n",
" quantized_backbone.append(QuantizedMaxPool2d(\n",
" kernel_size=quantized_model.backbone[ptr].kernel_size,\n",
" stride=quantized_model.backbone[ptr].stride\n",
" ))\n",
" ptr += 1\n",
" elif isinstance(quantized_model.backbone[ptr], nn.AvgPool2d):\n",
" quantized_backbone.append(QuantizedAvgPool2d(\n",
" kernel_size=quantized_model.backbone[ptr].kernel_size,\n",
" stride=quantized_model.backbone[ptr].stride\n",
" ))\n",
" ptr += 1\n",
" else:\n",
" raise NotImplementedError(type(quantized_model.backbone[ptr])) # should not happen\n",
"quantized_model.backbone = nn.Sequential(*quantized_backbone)\n",
"\n",
"# finally, quantized the classifier\n",
"fc_name = 'classifier'\n",
"fc = model.classifier\n",
"input_scale, input_zero_point = \\\n",
" get_quantization_scale_and_zero_point(\n",
" input_activation[fc_name], feature_bitwidth)\n",
"\n",
"output_scale, output_zero_point = \\\n",
" get_quantization_scale_and_zero_point(\n",
" output_activation[fc_name], feature_bitwidth)\n",
"\n",
"quantized_weight, weight_scale, weight_zero_point = \\\n",
" linear_quantize_weight_per_channel(fc.weight.data, weight_bitwidth)\n",
"quantized_bias, bias_scale, bias_zero_point = \\\n",
" linear_quantize_bias_per_output_channel(\n",
" fc.bias.data, weight_scale, input_scale)\n",
"shifted_quantized_bias = \\\n",
" shift_quantized_linear_bias(quantized_bias, quantized_weight,\n",
" input_zero_point)\n",
"\n",
"quantized_model.classifier = QuantizedLinear(\n",
" quantized_weight, shifted_quantized_bias,\n",
" input_zero_point, output_zero_point,\n",
" input_scale, weight_scale, output_scale,\n",
" feature_bitwidth=feature_bitwidth, weight_bitwidth=weight_bitwidth\n",
")"
],
"metadata": {
"id": "iFD5W0H1Zban"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"The quantization process is done! Let's print and visualize the model architecture and also verify the accuracy of the quantized model.\n"
],
"metadata": {
"id": "YIPgF84IygCy"
}
},
{
"cell_type": "markdown",
"source": [
"### Question 9.1 (5 pts)\n",
"\n",
"To run the quantized model, we need an extra preprocessing to map the input data from range (0, 1) into `int8` range of (-128, 127). Fill in the code below to finish the extra preprocessing.\n",
"\n",
"**Hint**: you should find that the quantized model has roughly the same accuracy as the `fp32` counterpart."
],
"metadata": {
"id": "vGkf1sf27uFj"
}
},
{
"cell_type": "code",
"source": [
"print(quantized_model)\n",
"\n",
"def extra_preprocess(x):\n",
" # hint: you need to convert the original fp32 input of range (0, 1)\n",
" # into int8 format of range (-128, 127)\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" return (x * 255 - 128).clamp(-128, 127).to(torch.int8)\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
"int8_model_accuracy = evaluate(quantized_model, dataloader['test'],\n",
" extra_preprocess=[extra_preprocess])\n",
"print(f\"int8 model has accuracy={int8_model_accuracy:.2f}%\")"
],
"metadata": {
"id": "PEZT47BE6dXe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 347,
"referenced_widgets": [
"081148b7787a491ab5d0b7228f82002a",
"260b2eafaf2f4a4bb91c407ccb4874ef",
"6b3d6741f8a041a6ab1f50e269b3543e",
"19784218ebe0465f866b1b8e074dce13",
"67f488f22ade4edca5545461198d469d",
"8e2e729918dc4948a52f89668475e776",
"332f99389a3d46268f6063ea583d410c",
"bd02637240db4d08b315ba84135458ca",
"ad7f84c678c04cf886db1710f418f738",
"486458adcadb4e849ee9766ebe095a5a",
"0a6e9898231e4ed5a04c65785b03f7a6"
]
},
"outputId": "d646bbf5-d10b-4623-f166-56a04cfb1886"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"VGG(\n",
" (backbone): Sequential(\n",
" (0): QuantizedConv2d()\n",
" (1): QuantizedConv2d()\n",
" (2): QuantizedMaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (3): QuantizedConv2d()\n",
" (4): QuantizedConv2d()\n",
" (5): QuantizedMaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (6): QuantizedConv2d()\n",
" (7): QuantizedConv2d()\n",
" (8): QuantizedMaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (9): QuantizedConv2d()\n",
" (10): QuantizedConv2d()\n",
" (11): QuantizedMaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (12): QuantizedAvgPool2d(kernel_size=2, stride=2, padding=0)\n",
" )\n",
" (classifier): QuantizedLinear()\n",
")\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"eval: 0%| | 0/20 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "081148b7787a491ab5d0b7228f82002a"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"int8 model has accuracy=92.87%\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Question 9.2 (Bonus Question; 5 pts)\n",
"\n",
"Explain why there is no ReLU layer in the linear quantized model."
],
"metadata": {
"id": "61iwKRAA8A7c"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:** It will clamp range [-128, 127] to [0,127]"
],
"metadata": {
"id": "8Ewbt01T8F1b"
}
},
{
"cell_type": "markdown",
"source": [
"# Question 10 (5 pts)\n",
"\n",
"Please compare the advantages and disadvantages of k-means-based quantization and linear quantization. You can discuss from the perspective of accuracy, latency, hardware support, etc."
],
"metadata": {
"id": "HhSnPZpD7ye9"
}
},
{
"cell_type": "markdown",
"source": [
"**Your Answer:**\n",
"\n",
"k-means quantization\n",
"- easy to implement\n",
"- lower accuracy\n",
"- lower latency\n",
"- better hardward support\n",
"\n",
"linear quantization\n",
"- hard to implement\n",
"- higher accuracy\n",
"- higher latency\n",
"- need extra hardware support for integer operation, e.g. maxpooling, avgpooling"
],
"metadata": {
"id": "mhlf55UM73yA"
}
},
{
"cell_type": "markdown",
"source": [
"# Feedback"
],
"metadata": {
"id": "xxOBqoXoSUfE"
}
},
{
"cell_type": "markdown",
"source": [
"Please fill out this [feedback form](https://forms.gle/ZeCH5anNPrkd5wpp7) when you finished this lab. We would love to hear your thoughts or feedback on how we can improve this lab!"
],
"metadata": {
"id": "ajqZJes3SVc-"
}
}
]
}
================================================
FILE: Lab3.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"id": "07b998ce",
"metadata": {
"id": "07b998ce"
},
"source": [
"# **MIT 6.5940 EfficientML.ai Fall 2023 Lab 3: Neural Architecture Search**\n",
"by MIT HAN Lab\n"
]
},
{
"cell_type": "markdown",
"id": "e9df27e7",
"metadata": {
"id": "e9df27e7"
},
"source": [
"## Introduction\n",
"\n",
"This colab notebook provides code and a framework for Lab 3: neural architecture search. In this lab, you will learn how to search for a tiny neural network that can run efficiently on a microcontroller. You can work out your solutions here."
]
},
{
"cell_type": "markdown",
"source": [
"Please fill out this [feedback form](https://forms.gle/bVWGf3DUNL4ShJ6N7) when you finished this lab. We would love to hear your thoughts or feedback on how we can improve this lab!"
],
"metadata": {
"id": "W42o2zoEKV5Q"
},
"id": "W42o2zoEKV5Q"
},
{
"cell_type": "markdown",
"id": "da9c5bc6",
"metadata": {
"id": "da9c5bc6"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "5d14ec1d",
"metadata": {
"id": "5d14ec1d"
},
"source": [
"Over a long period of time, researchers manually design neural network architectures. The design space of NN architectures is very large: it includes #layers, #channel width, #branches, kernel sizes and input resolutions. As a result, tuning these design knobs manually is notoriously hard. **Neural architecture search**, or **NAS**, on the other hand, can help researchers automatically tune these design knobs under various type of efficiency and accuracy constraints. As a result, it greatly saves engineering cost in NN design and helps democratize AI. In this lab, we are going to walk you through neural architecture search from scratch."
]
},
{
"cell_type": "markdown",
"id": "fdf52afa",
"metadata": {
"id": "fdf52afa"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "1d032763",
"metadata": {
"id": "1d032763"
},
"source": [
"Early NAS methods train candidate network in the design space exhaustively and use **RNN-based controllers** with reinforcement learning to optimize the sampling strategy. The representative methods include [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578), [NASNet](https://arxiv.org/abs/1707.07012) and [MNASNet](https://arxiv.org/abs/1807.11626). These methods are usually very computationally expensive because each candidate network has to be trained from scratch such that the RNN-based controller can get the reward signal (which is the accuracy of the candidate network).\n",
"\n",
"Later, researchers develop **differentiable NAS** methods such as [DARTS](https://arxiv.org/abs/1806.09055), [ProxylessNAS](https://arxiv.org/abs/1812.00332) and [FBNet](https://arxiv.org/abs/1812.03443) that greatly reduces the total cost of training candidate networks. DARTS model the output of each layer as a weighted average of outputs from different candidate operations, and ProxylessNAS further reduces the memory cost of DARTS by keeping only two paths instead of all paths in the memory. Later **one-shot** methods such as [Single Path One Shot](https://arxiv.org/abs/1904.00420) further notice that it is possible to keep only one path at a time during the training process.\n",
"\n",
"Albeit being much more efficient than controller-based methods, differentiable NAS and one-shot NAS still requires running the entire training, search and finetune pipeline everytime we design a new neural network. This brings about large cost (typically 200-300 GPU hours for the ImageNet dataset) for model specialization considering the large amount of edge devices (e.g. there are [> 20 billion IoT devices](https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/) till 2018)."
]
},
{
"cell_type": "markdown",
"id": "12a118d2",
"metadata": {
"id": "12a118d2"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "4e8d1e82",
"metadata": {
"id": "4e8d1e82"
},
"source": [
"In this lab, we therefore refer to [Once for All](https://arxiv.org/abs/1908.09791) (OFA), a method that can greatly reduce the cost of specialize NN architectures for different devices. OFA trains a large **super network** that contains all **sub-networks** within the design space. If we directly extract the sub-networks from the super network, they can achieve similar-level of accuracy compared with training from scratch. As such, OFA supports direct deployment with **no retrain**.\n",
"\n",
"Furthermore, OFA introduces **accuracy and efficiency predictors** to further reduce the evaluation cost during architecture search. Intuitively the accuracy of a sub-network requires running inference on the entire holdout validation set, which can take around 1 minute on ImageNet. OFA, instead, collects a large amount of (architecture, accuracy) pairs beforehand and trains a regression model to **predict** the accuracy during search. This greatly reduces the cost to get the accuracy feedback from 1 minute to less than 1 second for each sub-network. Similar idea can also be applied to efficiency predictors, where the evaluation of **latency** are usually very slow since we have to run the forward pass of the candidate network for many times."
]
},
{
"cell_type": "markdown",
"id": "715ac10f",
"metadata": {
"id": "715ac10f"
},
"source": [
"In this lab, you will study how to search for efficient networks that can run on extremely resource-constrained microcontrollers with **OFA** and **predictors**. Microcontrollers are low-cost, low-power hardware. They are widely deployed and have wide applications."
]
},
{
"cell_type": "markdown",
"id": "c0e41800",
"metadata": {
"id": "c0e41800"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "2eefde2b",
"metadata": {
"id": "2eefde2b"
},
"source": [
"But the tight memory budget (50,000x smaller than GPUs) makes deep learning deployment difficult."
]
},
{
"cell_type": "markdown",
"id": "cbabeaa6",
"metadata": {
"id": "cbabeaa6"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "0e3a7475",
"metadata": {
"id": "0e3a7475"
},
"source": [
"There are 2 main sections: **accuracy & efficiency predictors** and **architecture search**.\n",
"\n",
"- For predictors, there are ***4*** questions in total. There is one question (5 pts) in the **Getting Started** section and the other three questions (30 pts) are in the **Predictors** section.\n",
"- For architecture search, there are ***?*** questions in total."
]
},
{
"cell_type": "markdown",
"id": "7e036a4a",
"metadata": {
"id": "7e036a4a"
},
"source": [
"First, install the required packages and download the [**Visual Wake Words** dataset](https://arxiv.org/abs/1906.05721) that will be used in this lab."
]
},
{
"cell_type": "code",
"source": [
"print(\"Cleanning up workspace ...\")\n",
"!rm -rf *\n",
"print(\"Installing graphviz ...\")\n",
"!sudo apt-get install graphviz 1>/dev/null\n",
"print(\"Downloading MCUNet codebase ...\")\n",
"!wget https://www.dropbox.com/s/3y2n2u3mfxczwcb/mcunetv2-dev-main.zip?dl=0 >/dev/null\n",
"!unzip mcunetv2-dev-main.zip* 1>/dev/null\n",
"!mv mcunetv2-dev-main/* . 1>/dev/null\n",
"print(\"Downloading VWW dataset ...\")\n",
"!wget https://www.dropbox.com/s/169okcuuv64d4nn/data.zip?dl=0 >/dev/null\n",
"print(\"Unzipping VWW dataset ...\")\n",
"!unzip data.zip* 1>/dev/null\n",
"print(\"Installing thop and onnx ...\")\n",
"!pip install thop 1>/dev/null\n",
"!pip install onnx 1>/dev/null"
],
"metadata": {
"id": "Dh_Z651tzZLc",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "36011934-c5a9-4f91-d930-820f587540a8"
},
"id": "Dh_Z651tzZLc",
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cleanning up workspace ...\n",
"Installing graphviz ...\n",
"Downloading MCUNet codebase ...\n",
"--2023-11-29 08:52:08-- https://www.dropbox.com/s/3y2n2u3mfxczwcb/mcunetv2-dev-main.zip?dl=0\n",
"Resolving www.dropbox.com (www.dropbox.com)... 162.125.65.18, 2620:100:6022:18::a27d:4212\n",
"Connecting to www.dropbox.com (www.dropbox.com)|162.125.65.18|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: /s/raw/3y2n2u3mfxczwcb/mcunetv2-dev-main.zip [following]\n",
"--2023-11-29 08:52:09-- https://www.dropbox.com/s/raw/3y2n2u3mfxczwcb/mcunetv2-dev-main.zip\n",
"Reusing existing connection to www.dropbox.com:443.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: https://ucc4afd81d5989f6df823f55cbe4.dl.dropboxusercontent.com/cd/0/inline/CIfAt52s16tuFZOA_bEnW0Bn_7U5qhV3dWqC9fCI6ffblhXuvjnTvqlgqLdMIojuSzLzPkFC5J1adHPzt7n2hZG5w1sNc2i_N2OqeBO5cAJv1XDBDsRxFpEhZCOCWhIre0ocAMW8767-NbVL2lDQHQ_H/file# [following]\n",
"--2023-11-29 08:52:09-- https://ucc4afd81d5989f6df823f55cbe4.dl.dropboxusercontent.com/cd/0/inline/CIfAt52s16tuFZOA_bEnW0Bn_7U5qhV3dWqC9fCI6ffblhXuvjnTvqlgqLdMIojuSzLzPkFC5J1adHPzt7n2hZG5w1sNc2i_N2OqeBO5cAJv1XDBDsRxFpEhZCOCWhIre0ocAMW8767-NbVL2lDQHQ_H/file\n",
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"--2023-11-29 08:52:10-- https://ucc4afd81d5989f6df823f55cbe4.dl.dropboxusercontent.com/cd/0/inline2/CIdyDsqg2DhgTMD0hF-WWsn7qZCw-kr25ZWW0xOV6p8_PFwN5O4RfS9x1RRDaV6bwycAGRbFUvFf_iCIkRUFJkybLQORuzJ4giGyA6Y317_znkWGs_OxdNV-9d_2oXBt-WzyKg5p_3rLyCu7woLpTvM5RHM4JAakV1mdYuXduZi1SSevrlcagKA6UJOSO5CUoquE3aCQDDDc_Cnch-0dZXGwSLIXj1_2qmcciMrakE99oH01gUT0BKLCrNG2Ti0Qm_svJjK850lwz3OabJjuR-9bMsCDnK5xnO9Gw1oGgd8njFmGCwfgKsTleENsp3mO26_NzHWKWROce22pOFJ0vBy9Qz8BQbfgsEGSVwnXwo92r1EbQZjSP3v0nyeYPZ2RAkA/file\n",
"Reusing existing connection to ucc4afd81d5989f6df823f55cbe4.dl.dropboxusercontent.com:443.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 31675052 (30M) [application/zip]\n",
"Saving to: ‘mcunetv2-dev-main.zip?dl=0’\n",
"\n",
"mcunetv2-dev-main.z 100%[===================>] 30.21M 15.4MB/s in 2.0s \n",
"\n",
"2023-11-29 08:52:13 (15.4 MB/s) - ‘mcunetv2-dev-main.zip?dl=0’ saved [31675052/31675052]\n",
"\n",
"Downloading VWW dataset ...\n",
"--2023-11-29 08:52:14-- https://www.dropbox.com/s/169okcuuv64d4nn/data.zip?dl=0\n",
"Resolving www.dropbox.com (www.dropbox.com)... 162.125.65.18, 2620:100:6022:18::a27d:4212\n",
"Connecting to www.dropbox.com (www.dropbox.com)|162.125.65.18|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: /s/raw/169okcuuv64d4nn/data.zip [following]\n",
"--2023-11-29 08:52:14-- https://www.dropbox.com/s/raw/169okcuuv64d4nn/data.zip\n",
"Reusing existing connection to www.dropbox.com:443.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: https://ucc014c4ea5f0daaf31da6b87ac0.dl.dropboxusercontent.com/cd/0/inline/CIdlc37eJUo1tGuXYr9mfIGnbyz0LBs43WXwtgPHwjoL9ssOlGKzYGj92IA_2RPxCRbDNNRjMvVhEIMpmPgUjWxsfBGIs2IQHBfTKxHnaXpwJyHdLnsVTRcu7YVnH3rnl7Q4Lj_eZX9rux7Pb3V75RXG/file# [following]\n",
"--2023-11-29 08:52:15-- https://ucc014c4ea5f0daaf31da6b87ac0.dl.dropboxusercontent.com/cd/0/inline/CIdlc37eJUo1tGuXYr9mfIGnbyz0LBs43WXwtgPHwjoL9ssOlGKzYGj92IA_2RPxCRbDNNRjMvVhEIMpmPgUjWxsfBGIs2IQHBfTKxHnaXpwJyHdLnsVTRcu7YVnH3rnl7Q4Lj_eZX9rux7Pb3V75RXG/file\n",
"Resolving ucc014c4ea5f0daaf31da6b87ac0.dl.dropboxusercontent.com (ucc014c4ea5f0daaf31da6b87ac0.dl.dropboxusercontent.com)... 162.125.65.15, 2620:100:6021:15::a27d:410f\n",
"Connecting to ucc014c4ea5f0daaf31da6b87ac0.dl.dropboxusercontent.com (ucc014c4ea5f0daaf31da6b87ac0.dl.dropboxusercontent.com)|162.125.65.15|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: /cd/0/inline2/CIcc5dBc2a1x8mHtrSkUtnoDuq9RMaKFaxsAHFsAnzrSUoujGNSSJ_0-LvV6ZjekXhTH9Tr6sLxoI9n_WmSutLFlPUyTMzYNgqbHeKG09vmLTVoT64ZfreyC1FdEB0RlnwD4BN46pQMGikJFJm8ppFjfFMImuatMaE5BHD_92d4sGBnJRB-918GpR5UaYcsz0UnbTJBgZxokIuti_4Dv9-jrYdh58mrGy3XILOpzgSlYgd7s04Dw0YWBH69498H1vmkfhLFwuIwfPti7g31kZ26pviyoGEq3CUBot8UhYiXpXXmNnAx7cqVVEYE9tzcp5Dy9R3CJnuYtYDHTkYUz3pLH6VPVsKJIZNojHb--JnEMWKGVJsjQkVn7IyJmrRp67PU/file [following]\n",
"--2023-11-29 08:52:15-- https://ucc014c4ea5f0daaf31da6b87ac0.dl.dropboxusercontent.com/cd/0/inline2/CIcc5dBc2a1x8mHtrSkUtnoDuq9RMaKFaxsAHFsAnzrSUoujGNSSJ_0-LvV6ZjekXhTH9Tr6sLxoI9n_WmSutLFlPUyTMzYNgqbHeKG09vmLTVoT64ZfreyC1FdEB0RlnwD4BN46pQMGikJFJm8ppFjfFMImuatMaE5BHD_92d4sGBnJRB-918GpR5UaYcsz0UnbTJBgZxokIuti_4Dv9-jrYdh58mrGy3XILOpzgSlYgd7s04Dw0YWBH69498H1vmkfhLFwuIwfPti7g31kZ26pviyoGEq3CUBot8UhYiXpXXmNnAx7cqVVEYE9tzcp5Dy9R3CJnuYtYDHTkYUz3pLH6VPVsKJIZNojHb--JnEMWKGVJsjQkVn7IyJmrRp67PU/file\n",
"Reusing existing connection to ucc014c4ea5f0daaf31da6b87ac0.dl.dropboxusercontent.com:443.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 375960448 (359M) [application/zip]\n",
"Saving to: ‘data.zip?dl=0’\n",
"\n",
"data.zip?dl=0 100%[===================>] 358.54M 22.2MB/s in 17s \n",
"\n",
"2023-11-29 08:52:33 (21.3 MB/s) - ‘data.zip?dl=0’ saved [375960448/375960448]\n",
"\n",
"Unzipping VWW dataset ...\n",
"Installing thop and onnx ...\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b0b87f54",
"metadata": {
"id": "b0b87f54"
},
"outputs": [],
"source": [
"import argparse\n",
"import json\n",
"from PIL import Image\n",
"from tqdm import tqdm\n",
"import copy\n",
"import math\n",
"import numpy as np\n",
"import os\n",
"import random\n",
"import torch\n",
"from torch import nn\n",
"from torchvision import datasets, transforms\n",
"from mcunet.tinynas.search.accuracy_predictor import (\n",
" AccuracyDataset,\n",
" MCUNetArchEncoder,\n",
")\n",
"\n",
"from mcunet.tinynas.elastic_nn.networks.ofa_mcunets import OFAMCUNets\n",
"from mcunet.utils.mcunet_eval_helper import calib_bn, validate\n",
"from mcunet.utils.arch_visualization_helper import draw_arch\n",
"\n",
"\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"id": "1a7ce7b4",
"metadata": {
"id": "1a7ce7b4"
},
"source": [
"## **Getting Started: Super Network and the VWW dataset (1 Question, 5 pts)**"
]
},
{
"cell_type": "markdown",
"id": "e48b8270",
"metadata": {
"id": "e48b8270"
},
"source": [
"In this lab, we will be using the **[MCUNetV2](https://arxiv.org/abs/2110.15352)** *super network* trained in an **once-for-all (OFA)** manner. Recall that *super network* is a randomized large neural network that contains all candidate subnets within the design space. We can directly extract the subnets from the super network and evaluate their accuracy. The accuracy can be further used as a feedback signal to guide neural network design. The advantage of OFA super network is that the directly extracted subnets can achieve similar (or even better) performance compared with training from scratch.\n",
"\n",
"MCUNetV2 is a family of efficiency neural networks tailored for resource-constrained microntrollers. It utilizes patch-based inference, receptive field redistribution and system-NN co-design and greatly improves the accuracy-efficiency tradeoff of [MCUNet](https://arxiv.org/abs/2007.10319)."
]
},
{
"cell_type": "markdown",
"id": "66373375",
"metadata": {
"id": "66373375"
},
"source": [
"Let's first visualize some samples in the VWW dataset. This is a binary image classficiation (whether people is present in the image) dataset subsampled from [Microsoft COCO](https://arxiv.org/abs/1405.0312). We first define a function to set up a dataloader over the validation set.\n",
"\n",
"Note: The function `build_val_data_loader` has an argument `split`. We use `split = 0` (default value) to represent the validation set (cannot be directly used for architecture search), and `split = 1` will be used as a holdout minival set (used to generate the accuracy dataset and calibrate BN parameters)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "871e0deb",
"metadata": {
"id": "871e0deb"
},
"outputs": [],
"source": [
"def build_val_data_loader(data_dir, resolution, batch_size=128, split=0):\n",
" # split = 0: real val set, split = 1: holdout validation set\n",
" assert split in [0, 1]\n",
" normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
" kwargs = {\"num_workers\": min(8, os.cpu_count()), \"pin_memory\": False}\n",
"\n",
" val_transform = transforms.Compose(\n",
" [\n",
" transforms.Resize(\n",
" (resolution, resolution)\n",
" ), # if center crop, the person might be excluded\n",
" transforms.ToTensor(),\n",
" normalize,\n",
" ]\n",
" )\n",
" val_dataset = datasets.ImageFolder(data_dir, transform=val_transform)\n",
"\n",
" val_dataset = torch.utils.data.Subset(\n",
" val_dataset, list(range(len(val_dataset)))[split::2]\n",
" )\n",
"\n",
" val_loader = torch.utils.data.DataLoader(\n",
" val_dataset, batch_size=batch_size, shuffle=False, **kwargs\n",
" )\n",
" return val_loader"
]
},
{
"cell_type": "markdown",
"id": "73f66245",
"metadata": {
"id": "73f66245"
},
"source": [
"Using that dataloader builder, we are able to navigate through the VWW validation set. You can run the following cell for several times to see different images in the dataset."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7a521b47",
"metadata": {
"id": "7a521b47",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 406
},
"outputId": "392e8720-5fcf-4c1d-c760-bb5a3cadae5d"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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hRDzG+YD3gNBACklKko2R+QRdTNGjXZLpZfRkBnqGC57xdIzSmrpaMj85olydM5tNWS5rVqen5K5GmWO0a1EyRyY5qZijwzlS7yFlShaWBBRWjUBIVHOKFQlNeoWTGx/g2vEpZfYQv328y9s/oNm9dIVve8ODfOZLDUkmUWlCUBpUQusF149XvON3Psiff/2no7RHCYFMFGgJSgAChAB523sQCALQGMf166f81//2m3zJF38O05Fisap58gPXuX79iPmiit+FypjOJjz22FUeuH+XIlUoCYgAov++PCdzy7//ibfx2a99OS9/9IBJ3nXNEiGO48PIfL7g4x5+7DlRhD5f0l/rtZ/2f5MXBUophIAQQEpBlmaMxiPSJCXLMsTActZakWc5OkmQUhJCIHiPc44QQjyXlGityNJsvY9SiizPGI/G6CSJx4WAaVu892itUVojpSTRmiRJyLsuZ0rKbowC5xzWOdIkWfenF0Igu89DCLiOEz2EQNu2tMbEsStFkiSkWRb39X49VgDv45i891hjqJsaa2/fl5Td+Z1fe6XeOZTW9E5qCLf577VSBALG2Atj8t3z8t7TGrN+htba+OPs7bF0+/X3CZFn3jkHQN00WGPimK2lNYa2aeIa4B2mNeuGM957VuWK07NT6rrhxs2b3DyKHPr9LH02X7tHVgsEAnCEF2TufvPf/2eMJlOUUus5p3T3u9umpERKiRDywhwO3uOsxXmP8y4uod7Tmpa2bfDWEoJnuVwgCfjgQUCSZfjWYUNglGUopagbQyAgtESKAHiyLCfPC4o8xTlHuSoJIbCzu8NkMmacp5i2xjqYL88JIcT9i5y2aambBq1TxuMpCIFWmixLKIqUohiR5wlJkuCM5fqNQ1arBc47kkSxszPhmWeuc+2ZQ0TQ5EVBCA7T1rRtCwTaNrBaLSjynNFohFQa4xxt02KtZTTKGeUpV65cYjab0tQN128ecnQ+ZzTb47GXP0FbtbzzN38Dbw2EgPcORCBJE7RMAMnO3h5FUZDlBUmi0VpQFCl7uxOKPKOtHQ5AeEzTcH425/DmMUcn55RlzSjXjKcTnPeE4MA7PAGdpAgJrjVoqUBKpFB471mWK67duIkE6rrBekuRJjzw0AM8/PAj7B3cRwiSs6Mjvvqvftmzzt3nZAz0L2aWp4zyHK0SUBqnBFYHfFiAd8gUEp2ilegWE4d1llQrrGuRQiCDBGR803wgBI8PnT6SEiUFQniECMjgCd4TfEApCRK8BxkCQThsIF7XS4SIi1FUXJIgFFIrhEyQyQhd7JFMdslGM1Q+RugclU3RxS4hSWgdVOdHeFOCbZmfHnF+cpPQLCn2RujGMPaCaZoiWaJki0wFIs1IREIaCtA5AUHmJUFIvNYgExKZ42ROkhTURUqeZ5hshNRjUJo0HbMzGjHJW9JCo/OUoDRCp9RWcrqA2WzGznSC0h6punvTkqAkYb26ifV3FY2BQFUZkqRiMply6WCPcrHgPe/+IItFzWy2w/0PPkCaZlgPZWW4dmOFI+Mlj15iPElIVLQzRBCE4DlZLhlN97jv0j6X9ndJtYTuuUe9EO6YNx9qPv1JSH+tRCekSVxYhtdPkiR+lkZjIOmUd98FLc9ziqJYK3Tv/VoBym4BVkpRFAVSSryPyjPpFHie52sjwXTKsF/A+/FlWbZuedqfrz+X9540TUnT9IKi7MN9dqB4syyjaRoAtNZorUnTdG1YKKXQWq/378UYgy411lpCCGit18e0bbtWrqEzMKLSERfO05+3bdu1sbQ2NqzFe0+SJOt7cs6tf/q/++v30o9leI22bTsjJY6tTRKMMVhrqWW9Pi4ulktOT884m89ZrVZ0ZnN8hs8yb4YlVtHwumik/EnIet0tRuTFKM4LpVBKrg1CrfV6PvXGQP/99N9Bb0A57zrjwCGlimuEdkgp0UqjlIzP1bT4EBBaMSpydiYTlssFykYFmBcZUgaU1mRZhtIavEcIw97BAVmWkaUpWgam0wlVGXBeoDQIKUmSOFcEgtnODkprlEoJUlJkKaM8iwbBaITWMs7hINFpzvvf/z4Ojw7JsoydXcFsZ8b5eY0Iiul0QppoqqpiMZ8jZFz/vLOkWTS4R6MxzgUWyyXlaolWitEoZ7VaMB6nNG1Naw3GWM7Pzzi8fo3LB5fJ84zVMuqwNElQWpIkCVk6xhPXl/Fk2r2XguChqQ2rVcXOdIYS0YgKwVJLcHaEdwGVpRwfnWJbi/PgQ0ArTVrkKB2/zxACTmkIgTTNyPICEKRZjg+wXMxxrcFaT2NayqqiNYYkTShGE3rb8Nnm7j01KgqITvF4VDDITqnbtkZ6kDRIPARJNELjZIyKIsQIQO+zBjorK+AJhNDZ3iEgiQaBxxGCI/hAcAKciJartwQbPRBrLMZ4jBUYL4AEoTOEHiGzMTqfoNIpyXiPdDRDZln0oqyFqkGbM6wPlHXJYnFCQoNuF8zPTyhXC3IdUEqjbYvOdkjFBGgQwYCvCS4hEL0xlOju1XcPzMcFSAiEVHTzhIDABYH1Ah8EiYRMOoZKtXvMOOtpGkeaaqQICMLFhax7ph9qdXPO0TSWNM2xxvIHf/Aezk/nXLrvEvc/cIX9gxlJkmC95/y85AMfPOH6rTky0fDgDgezDCX7xTFwerakyDJGWYJSgvgtc3H1XA8tbvxoyHcKGQ0lIcR64Rwqnn4B7RVtr2yHinezCndoDNxejMX6uF7x9Yq/P8/w+v0+vaLvjY1eIffn7885VLL99Xrl24+/36c/nxpEEnpjpT9fr1D6yMPw8xDCWkH35+vPNbzW8Nn02ze39fc/3DY0eobj659Bv73/THXGyHCMQ8mybH28sYbWtJydn3F6dh6P6efCc5syaxHrfz86qrA3R/Fh369AF9eI0htem99NmmZorRBAYlPqpiEoQVHkaK0uGsFCkGhNlucUowKlFU1VEYKPEbHxGK0UOENZlrRtjTUBoQRaRSOkNS1N05DnOUoKnLc0rUXJQJ5rpExJE40PHiE7w2E6YXdvl/l8Tl3V1HVDmiSMigJrPEorlFZopdE6wXuHd1V874LAGYc1tovIddEv72jqirJcEELLqmxYrlpE53keX3sGFUx0UAnd2q6RQiFRKJ2QpQlJmhCCp2ktontG4HG2YXdnRp7mtE2NtYamaXDWoRPFdBIdjbPTBa2x+GBAJKggwQUE3XxuWkQISCHJshylFUmqmU0nmKbi3FuCd+isQApJ27aUZUmaZTHS/hzk3owB0XmhIQ5SYFFEr1EBIRhCsDgXbfBAIPhuIINFI+CJWs/HLwxwISBDQNIZAwRiYM4DEqwnePDB4Vy09rzzeK+wckTIEqTUCJWisgk6n6GKHVQxRagcKTRBChpjqMsF9WqBaxs0AeEdTVPShkCiLdouKOwKlcCoyBlpR7AGi0PhQcaoePAObxsQhiA8wVl0otFS4IUkdIu1lAIv48IuiLduHRgHPkCiINceJUH1MewQIHic8dR1S742BujWpe7Zil5JxDc/9C+4CIgAznnq2pKlBTevH/L+932QV736ZTz2+CNMp2NkH+UHZpOMvd0p7/i9p7l+44xMScZJwrhQBBFDtUcnC6aTgizRyP6KIRCEIPjQhZAFw/WpV1QvpEhxW9kPPfNesegubD9U/nBbuQ0V2TAy0J9ruG1oUPT3vhlNuJvR0IfD+2P6ffvx9IpzqJyllJguNdDvOzQOemNAqdsL+t0Mnj6C0Yflh+H94X0PjZD++H7f4fb+XoZ/D5/j5nzon2F/3PD44X30xs/wGfZj659P9G4b6qpitVph3e32ss91Fvb7hTiI7h6evU3tR1TERaN/aHg9Fwmd83V7/oguLO1JdYIUt+dzdNwUIUDd1CilSNP4XltrkDKmddI0ZTTO0QraVqG07rz5hOAEt25cI1FEhyRL8D6mhL3z0EVctI5pjrptcN4CASFjdM15h1KS4EEnkgfvvwoh8MzT1xBekOcJ4/GI5aJEEGibhtY03R1LsjxBaY2zMapclmV81xFoqUiUJrhoKC7mS8qqxTtJnuToNMW7isXZMd47RnkRI2Wyf/c1UsJ4HA0i5x3WRaNBS4GSCu9jRHA6HTFfVrSNwbkYDRdCoBPF7t4uIUhOz07xFhDgvKNtDTJIpBSYtgUfo8LWtPjQfW9ZwmhUMBoXIAWz2Q6z6QwpBYvFOSqR2Lb5UFPigtyTMSBFlz2TIipEGVAi4J0F70A6rHfRIx7krkKMf3SZAY9AdtGBeEPOe6wP6KTzDkJnSoQQveQAQcS/HQJHgiclaA3FiGSyTzLeRyU53geE1AipCULhfMw11uUJ0rW0bUWzmmObGuFqEl+RC8c4keR6zP64QOtAI1NaMnSSIULMU8kQUHgUcSIEoTCAVgKcw9oaJXJ0ovFCg4jjkD5aw1KImFon4hmsi83GsiQwSnrDKm4TXSMy7xx108RJHc2ruD1aRgipumPiefvFot9kTGBVtkilecc7fouPe+Rhnnj5SxiNM4Tw/VEIJEoEZhPF//UJD/P2//0+Dm+eMck0H/dxu0gRDZfj0yUHO7ukiewiAjHKE4LHuX7Bvr34ABeU4Qsm8rYn2ofLh950H1YfKumhou+NB+BCuL4/1+Zx/fk299/0pvt9+jB5L2vsQZatjx0q0aF3vun194py6EEPUwRDY2D4uVIKY6LnEnOuXLjvoZEwjIrcLTpxt+hA/xw2ja1e0jSlaZq1wbFpUA3POzxu06ASAuq6Zj6f09xlIXx2dMtt6b3qF9SYHd434o57//ASLhwrpcAP5ktd19ApXmvtGt9VNy0gcMGTqmjo6yRByPiux4iRY29nRpopUh1oWw1E3eBsS/CBqqpQ45wsy5BSUK5W1K0hz3OuXr3Cwf4eWZ4iVcrOToPWmjxLSbSODqJUCKHwwSGkoBgVXLlyBSkk1WoBWEJoMbYidVH5+g4fMyoSdnf3sEZycraI+BRruu9UdOnBhPEoYzwd0TQtShqa2tC2FkdgPBtTZAotLAJFWcbodJZnjPOCoCHLFSEI2trQNAbnLGmi2JnOGI2ymJ7UCmMqWtMSvMB7sNZhrEMqSV5k2CND07TRkZMJUmk08R1LdcLi/DwavCHij5q2YTKdENyM8ajg7HyBUposzXDGMT87WzvOz0XuyRjo41OuU+TCeyACHpw30ePtvFolPDLuFvP+IRBcVP69798ru9YYXAg4L0l05/340OkZCSiQCSQperRDMt4nneyTFDN0mqOSBB+grlbMjw/xTUOwJb41uKahrkvqZolfHaJpkM6TqpQi14wzR+YrhA8YL0gsBFMSTJxQqITWOGwT0GlMZcT1QSFUgpYpiRTgDF4QsQ1CAH3eDpz3oCTBR+Bj8IIgJQGBkIIsCeQqglNC0EjU+oHHF88xHicEb0BI8AHZAwc9xEQLCBXDYYGAEAGCpDWWs7MF5crjqor/5ws/gfEo7bCG8fu7HQaN15yOJK9++QP8/ruf4f1P32T/8oSdscRauHFY8qcevEqS9HnUgEDg3DAcGRjq/k3v+IUQrdU6vzr0aoeh9jRNL3w+VPSbefKhZ54kydpo6I/rc+vA2nMfLty98k86HENd1+uQfH98lsWQ69B4GGIKhh5z70lrrdeKuffqh/eyOY7eeNmMmPQKv8cqDPP33vt17r6/7800waaR0ssmJmCYLtg0lIbnsNaujZXhZ/3Yvffrz5wXzOdzTk5OY7SK26H1ezME4hHB++jkvFAiRIxOiE6NdWlE+RwMlP6+pZTdDYVuPVZ4r0HItfEUnCPPUqbTKfuX0vW6HDojIQIWZbe2BCaTMaMi47779gCL946yrFksVyyXJVVdc7C/j7U1BCKuRitU06KUYrVaMZuO2N2dYnw8X2/AqiShbluC94wpIoiuA/0Wo4KDSwcce8N8cZNEK3CBcrkCEdjZ2+Ghhx5id2fG0898AOcSxrMJPoAxlnJVcXpyyrIsUWqKTnJGkxGSwHKxYjFfsaSiNBVucY4SI1xrSZKIOVA6QSuNd4ZyvqTIckbjKcv5CWfnC4yxjMcjJsWYREmUiobH8ckRp6cnTKe7FNmE1apCa818vsJ5x6jIqauS0+MFOlXs7e4x29vHd+9i1hqWZUljTMRpiJh31knC7u4u051d2qalXJUsz+c44nvjXfucptk9GQMieLxzBAJOBIQH6aOnGvB430e4Y34/CMD7DiUcurD+7cVoiMwOIdA0LcYmBKFAaWRaINIdkvElxgcPUexdIh3P0EkaKwnKClOvWJULqmpBdX7G6ekR3tQIU0FTEpoaYwxpptkVK8baI6TASQAL3uCaOr4gWUoQI3yXmZdCooUiWrsaJyUiSREujXl0nRCERnqHlAleqPj9IBFIghe44KA1oAIuWIxxMQnSPSspBLkWZMp3KRIfn5k1IAPOGkzdMrq8gzcWIVSMBvRRANEtcEJyO4MfP/BI6tZz6/CMW4crvvDPvIrJJEWIfi8BqHUcYr14CMvepRF7pzOu35zzgadPefXLLtO2LYu5ZXc2RivRhU0lUkhC8EgphhDsi6vRC5xvleK28u8VT68cISrs3hgYeqVDPMAwLN4rpD4CkGXZei73RgfcDndLKdepiGHo3Vq7VuC90dAj7UMIFEVBURRrIB9Ehd8DIftx9wq3D7X3oLohKLC/j142UyLDVEn/0zTN2qDojYEeEGiMWUcQeqNpqNA3Uwabz6/f1qcwhniA4T79McDayBpiC9Z+b/d8qrpivliwXC3/aJNlQ/p43AslmwZWFx+I729g7Vr1P5sS+mijiE5EIqJHXBQ5k8mYqq5omwpvicbAZEw2yrl+4wZ5npMVOdY5XAgoLUkTjQCaquLaM0+TpYKd2Qgtu5RnoklmU+472KMocnywrFYRU7AqaxbLkrqpCcFx8+ZNJuMCqTXWtzgfkErj6aNSgrppkEoSRFzhlJBkeUpaZJw/c87O9DL+0gG3Do8pqyVBOGazETuzEQ/c/yAf/OAh+Tjn8pWrpElOWVY8/dRTVFVDmuecL0rOFwseevB+JrMZ2WjKrnXMF+cc3Xqa5XlJphIqU+KQ0WNXCucsi/MliSqoS0O5WNLWdVyfhaBpa5pWEcIOQkg+7uHH0XrEZDwhTXOq6hrz+ZzWGI6PT5iMJuzNpigBq6piPp/jrGW1LGmNYZRnFKMRQYB1DiED1nkmkxnGtCxXKxKtY9pCCs7nS85Oz5hORs9pnt0jgDCG8KMii15lohxSqD5aHL1/H/AIPIB33QLiMW1UZlJEzIDvQsvWQxskTuakxR7p9DLp7IBsdplkcokknxCsIyQZpXW41YLlySHL41vYcoFvVzSrE9pyibEtSnpStyL3K1LpMUoAE4okkGOwweM84BOkjLn6ICRWJSAkSiqyVBF0itIp0qfIxEE6QskEJVSHiJD4IAjeEoIBFS1bHxxBpqBlxAFIgXUeHwzWOQIC01ps26KFJJWASDrMhQNnIMSMvDcttq3JVdqlTlwsrfQRFwCSHi4aglx/U/H5BpZlwzPXF5R1w+OPPdR/K3cuHeJ2agEkUgXuvzylXBqefOqUxx+9xNm8xovAeJJ2QKA4F2zoQt5SgPfxPEKuUx6IQFDqBV1Qh8p8uK2XEGJp3jDEnyTJhXB/L8Nw9TAS0CvLIegPboP4egU+NID7aw/H0f/23rNardb59t5o6MewGaofhvCBtSHQtu1ageZ5fiHC0W8fpjSEiGHjPM/X5xnee19xYa2lrusLUY7NPHYfOenvpx/fZsqgTwcMxzE8ZgjC7A2M/keqGKZ2zpKkCYvFgvn5nKZp77BNX4wi1sZ9HxGA/v0VG/v0EgbmfRARiEyIIG5BIMsSINDULYmUZOMxWksIjqpc0rYlRSqpVsccr2qSpGBnd480iWWGhHiFqm545totTLvHqEhomprFYoW1jqtX9km0Q0jFqJghhcRaH9dCOjxTVTMajQkIUkS3PgYIjraxpGnKqqwJhFjF1rlqAA7JctkyGzvSNGe2uxML1UKgLFcslmfMxjvYtuLc1uxeusRkljGajtGZZlWuOLp2i7KtGWcZx8cnKB1TwEJKskQzyqesVkt0rpHdHLTeYayNUV8EZ2fn5FlGmmlmekSax3JLEyxN2yCA1WqFVgkveeQlKClYLhcIETg+PuLW0RHj0YjR6IBiNCLNMtLFHGNagvOUq4qdnR2qckGWJ2RZgXWe5XKJFIJRniEFnJ+f46yjyHPyPGNvfxfrIpj2ucg9GQPOhpin7hcs0VcLBIyHICJCPniP7MLP1npEiOANh8c5uvCGAqHxyRiyKdnkMnpyQJKPyfIJOi0gyfAypSprTm48hZAa7xrq82PKk5uY1ZxUWER9hmzPmQiHRSK0JhMtuTQIXLy2zzqMgotlMCpEb1qENUBIao1HkogEIUWs9ZfR65ZSoLRCCnXhFYyMzqILuAPWY4UH4bChK5UKfp3rNy7iI6ypSULCRAra1Tnvfn+LfskOB/uSRAWE9nhiisAYQ5Z0L2AI0cDqKjCElAipuhXCR+6BbjF21rJcxFrrBx+6ynRn1BkQd6rlPvQIrOsVppOMyTjjqevnnJ1VnJ6WFJmiyGQEHro4J3wIsX7dOYJ19AjLfuERSoDwWPPCAbCGXi/cVrbDUPew1K0/pk8B9MfA3Ty1i172MNc9TDP0Xm2v3Ptz9kp5E3gXQlgr25hzva3k+xD+0CDYDK33SriPFPTXHKYw+rFsphZ6pd/vM8Q09Ocfpn+GZYGbEZV+29DrH977sFJh+Hf/LIYAzX68m99JUDFy0VrD4eEh5/M5YZCe6OWPYhTcEeT6Exex8f/h/BuAUWH97sNtgyB072GP8Qn4LvQMTV1FQ0EDIsVZx2q5oCxLklSSFQlVXTMa76HTNAL0mgalFHmWdcbXCiUVVZEADmtb2tZwdHzEZblHMRqhpIzrlRCxaqED12VpQpJomtYgpSZNEjxR4TpvaCN0IYKocxFB1dauPZfxeEpdrUCN0SqWSDprqMqWsqpJZCx1Pzw65tLV+9nb2yMvMi6nl3n5x7+cZlWzuLEkhIy6aZFWoJP4frWtJQiJ1CnGB5QUESjdzfsizznYv8Tx0SHni3NkCUJLsi4sP9vdIU1zBDGFvSxjWkAJSV1V4AO7O7sU4zGL83OatkUoBQKK0YhRyBEIrHU468iLEU3dEvwcANsaWlVzdHiETjQPPvRQxKK1BmtanDV4Fy4Y8x9O7jky4JzrNEdMA5gAVgacjy+Lcz4qhS747F0geIlxChMSnMqQyQSZTmPJXz5B5LH0TyQZznpK6wjNfP0QbLXg7Na1mBNvF4RqDvWKxDVIYcl9SR4WaOmpvQJyUjy6M1YkAutMBG4ER++whv6HGNbwXhCCRKgUKQROKoLoFjfdL/B0SNtIlhRkl7EP3YvZl58Ej/cW5x3CGbw0uCAQ3iEJTLXhJTstlwrNfXnJyVHJ7zZHPP7wFa5enpHmEq8S2qbGtTWJ8gjvEC4q/RgxEwgX+th+NArWLz241lKtVtTVkocf2Ecn4jmshqIDbHoSLRmPNHkiOTlacXJWsTfLSZN4n55OCRJNomC77z74+O37OE4ZJM5annzP++9luj2vMsQL9IvnsHStV2q94rsbon/42d0iDUOQ3ybyvY8MDPP7wIXzDRXccDz9+PsIQx+i7xV2b+gMDZuhR94r4KEy7xX9UKnDRU+9f2Z9Tf9Q2ffH9QDHYQniJqhteM6hYh8aQkOcxvD59Mf293038JyUsXInLuCxnLCuy4vfzQXkwItLhq7H+v8bEZjNfQdhPqSIoMGYKoDgA01TI7r13DobI7nBYdqGqq4jiNQE0nyXy5evsntwCQTMz5Yszs8QSHZ3dxFSYKxlsVxRNxKC6UoJLVLuYJ1HSUXwAetcFyEFIaOzqJTs5mVAiliVJIUgUV1ZYZAY35FWWRujAj5ilXyI/795csje/tW4dsc7xjpYLko0kta2LJcl56fnXLnvPpQUJDrl0qV9dnYnnJ7nWGfIkjTqMA9CKrI8RrrGswlCRpCgc9GQEkKQZSmjIqWqYlRAKslqtaKpGrRqYtTUWJwzgMe0FW0dndCqaiK4Umuu7u8xHRV4QCcpbRO5OghxzRmPCo6PT5nt7OCcjXw9QiBTCSHQtpF0a29/H6kSVmGJs3F9qOsS654b3uWejIHoYTsQHkQk1omzK7KTATjbeVYiAstCSAhqhM8yRMcAKJIJIhkh9QiRjvBC4WSCrVqW8zNsu8TWK1xdEkxLaEpMuSTJUjJ/TupqlHB4YTCmJteOQpnofRJfednn1ISIJH0+htYDnhAiOpYQS23iWyTjPSG7vHxH+BDfpi4SLwghht9DuF3i5X1X5teD+ZwDYYEWfOdtEkNd4zzFKci9ZLewOAyXdj337SSsmjnXb1wjkQ2zWR6No7olU4ZMtmBBCB2NAKnj8xWCnh2QEMGFcdUQWGOpywZnLfcdzBDytmcb14uLf9+WsH4keaYZFwnHRytunS7Z352glehQFbHUSHiPsIFgDcHG2hjfW1pSYB2cL5e8913vuZfp9ryKGoT7Nz37zbD+0GN1zq2VLdxG1PfnGYb4N6MEw9B2DxTcxCA0TbNWpL2SHZ67P+dmKH5ICJSm6YXjh9fux9KD64Zh9+H+Hwqtr5RaYwSGUYMh0LG/x36fYQSm9+SH5900yHojZxg9GBpUm9iB3ri5ECHorrtYLFgsFvF+/7iT5qNGwoVfvdzxfW2+x32kSAok3bMlrn3eR14TnSg8AWNamqbzKJ0FAW1rqOuW++67TJpmzFfnLFZzFos53nnatmYynZJmGc57/MrElKl3KCEwxtK2baxYoEPPu5iiFUKiVQQkhhCjBUHJ6Ih064dzLqZYjcF6i29DF4sN3fZ4/vPzBbt7VyNxkpSYYGhbw3JVkyhB09Q0Tcv169fZ3dsly9JIJOZjrX8kuuvSg0nCev0SMNudopOENM1wLqbcrIm6LtUKpQI7uzNciFwN8uiY5WJBUzcszpbgclarksjLZLHGYa1jPi+ZzxdkRc6oyNASllVFXuQEH6irEtNapFSxXF3LaHiLnNA5XKrXPd1aW9cNad47KNFwscZeAPB+OLknY8B6j+npSTsdFBHsHUoecF7gxW0GPeQYme+gR/uo8Q4kOa6zmBprwbS0TUMwBrNacH56hLcrglkh2pLEO5JgSYUgp2BHV6Q6EjDUrcE2MScTRCCIgBeAkHhi7loSOnbjGKvw9BGBmPIIUkScgNK4DvgRRFcnL8TaGIjp+IiS9z4gRPT+8R7nbPyCrEXjsc5GQ0QLZOjSKs4ileZgd8o0SFonsBa08ly+JLn/vhHnlef89IzFHKQYk2QZ0gUuTyFxc2ypkdkIrx1SZNBRkkZgjSR4iwjJ2gtqWktZtQQvmU3H0YjrEYd3hxoN/huNqSxVjIuEDz694vrRkk9+9cMo2ecrA8J7QqfMXNNgmoo0SXEdyYtUktoYrn3waU4Oj+5luj2vknYeNFz0KHvFOaT73VRUw1r9JEnuyPnfjcRlU6GtaWMHP72iG3IMDA2CXuH23vSQY2A4rs3SxaFXP6wQ6M/ZGzn9/ncDDt7NKNj02vtr9cDLPnIyjLQMywQ3oy1DoqXhfW9GSTbxFZtYixBCrPwRcHh4K3IL2N4t6O67H/M9z5yPDrmQKNiIjgzTBMOUxu3tAqREie6dlRJBnH9IgdAa52wkBkKgdcQTeGdZzJfkxRLjPeeLU+bzc+q6wjvLcrlgtVqxu7tHMR7TmhrvPalSFHlK07RUVU0sNe+YY3wfRZadMo1Olw/pem74EGispaxq0iSjaU1nhEejTwkI3XyLKdwUrVMSnVElFaLt3xVo2xgNk1Jy6+YtiiLHWcNkMqatGxaLOVW1YndnL6Y+8owQIh9B1dbMppfIsoTReBTTdo2iNTaWWsq45k5nM+qmieyGxsT07LLi6OiEtimYjATTSSQK6oHyVVnSNg1ZkZIkkqaKzI95MUKIqB/rumI8mqCkYjqboBONEAoTWpzt2CRd6IrCHPOzc7Kx61JC/bv3oWCld8o9GQPGO5S/7RHG/gEeqTVOCIJMcEmGyKao0RSZz/BqQkgyVD5Fq5S6qVmVc5bnxzSrc3QwuHqJa0pMVdJUFakO5MJSCMskU+SpxCLw0jPCITH4EFDaR8tWOFyn43qP1OFR3iFFLIFUKieaBtGKCevQeowExMWwRw67WNHQRTe6ty6GWoWF4JHBgbN4qXDGgDcEHXm+nYuWsZR6jUVIdILXklQnCJXibIwmKK0o8hpaz95IMdIFSgTwLcF4CiV4cF/Tnl9nVWuK2T4iKZCuQCYJaB0rHASE0PUrIIIJy7JhuWgRZIyK8SB02IcMB/nFoYcLiK67daIVaao4PFtytmjZ3Z0ihe4MQIc3Bkw0BkxZspifMp1OI6d9p2AWyxXPPPU0eTG5l+n2vEqe5xe85aFB0GMDNssN+32H4exh3n+oWJumuaBIh7X4w5TCUBkOvWe4iLLfNAj6/Xu8QP95TxK0ieAflupZay9Eg3pPv49y9N59L3czBvrz3i3HD6xTEn0EYhN/sBl92sz7989xGBkYejS98TB8jv3vGImMZWNPP/00q9Uybrs43V+8smFg3mWH25EBsUYHXJhDkeZWoITGEVOiQkWQstIaJaDNUoKPpG5NXTEZjblx8xY3rl8DGaJ37i2I+G57Z2iqihNruaw1SZ5huv4YWglWpaOpxygZ87JJmiJEB4wODmsDQiQkiYqsfKZdz7HWOeqqppUNIFFJ0kUM4vfshCNJUnZ3DzDGraOhWsceBVk2Yjabgjfs7I5YVoHD4xOuX7vG6ekxWits22JMy2o1Z2e2Q+ytYfDeYWyNaWuW8zlhPCLVHS+7t5EBtw8oQyQcqizGNGRZJEGqa8P5oo7kV805D95/ib3dXZAS5yFJM6Y70dlKtGS1WlLXFUoqTGO6SFeMSKdpSpqnuBBYLWra1uC776JtLKnWLKoSnZVkVUOWpSS6i9oVxXMmy7pHzIDBOo2IaBNcxwGQpCPkzj56dh/F7DKqmILKcDZQrmqOD58h00twYJuKcnnKcn5EvTpDuxrRzqPyV5KxTuMDTSSF0iQ4BJ7Wa2rnsSr2JnA+5rmkjqQ7vXKPTrgj4FCC2BxJdt656PRh39AnBAhd6WBXs+lDwAYbFbKICPjGNGAtQRlwDkxLwCNxOGmwbYsLhiSNoByRaGSaoNJYgqKFRklN6w0IS9ZzEYQAOEJTUxuJKCXOO7wKETyZaISKC/tyVdPoBN826NEUUWYIrZE6Qedj0tkO6CTiMlUsz2zqBtta9mazCGQLdIGB/j8fghmwYy8EEXkTpOTarSVVaznYKZBEzghvLN4YhLU4Y7F1hfKOarEgEPOBxjhOD8+oz2py9dxKXD4SMh6PLyjRXjarB3rplc5mnrzPt/cRgj583yP2e8Bgr+B6r3noqcNtZZYkybovQa+0hwq399r7a/YyxBT0107T9ELvheGYh4qk6Zr79NGFuq4piuKOsQ/nxt1y/f324f30aYXh/ffn6o2QYfXDMOqxWU74oa41/F568SFwcnLMyfHJmvwq4NYGsBB9ROzFZxVIKdZ02r3Cv1sE50P5fz3Wy9mAkkS8jw8oJRhPxlRViYGoyJsaIWA0GlPkOauyQkiJ8ZblqqWpG5RW7E6niNGYUV5wdHRMXVZMphOUgMrWVPWS5cKyu7PDdJp3bljA++hVSxRSS1wIeGswTUtINdhuLgRQQuGsAzxaJ7GcvTM4rbVMZglZnjOZTMmLEXk+iVUHTdX1BUkJLkaZ9nd38SFwdnbC+ekZWiv2dnY4ODhgf7ZHCAohNWma4EODR6JlxvnZkpOjM+6//wqz3RkASipUZ8y2dcNiuSAvMpw1tG1DMcq5muYk2ZyyrDg9PWY1X3Bw6YCdnR2SNEEpzXQ2w7Qr/uAP/oDFfMVoNqOtakKAnZ0dxtPIMVKWNVVVMZlNYaJx53MQnmI8IYQVIPA+RIrmNpYaj8cjJpMJo/GY+fz8Oc2zezIGWitBJMhkgir2SCb76J0D0t2riLTAhYBQGuM87XJFvVyxms85vfkU2BK7miNMTXAteMtIBCY5FLkiFcTGRJiOgFggIm3CutOhdYEaR4qPoDVBLPfwHgydgo0KXUhwQOsiRqBHt7sQIqWPjADIvkuX6I0BzxoMKdZWdqwG8M6CkusJKqXH+WjFCiVAKXSiSHREedN5YVJrXFNFSkkl8XXTsRFKCJJAQCUACpA42WLaVexnoFLSJKJ0hbMsTm6RlEuCVPggkDphMttFBQd5jsKBSkGmtMsSjeeRBw7WmA5C6DyNziC46+IBfSFTCJ6qsbz/qVPuvzJjmiuEdwTvomHkYwWBrRpc22KbiKQNQtB6mJ+vuHn9iLbxNPK55a4+EjKdTSOo8i5MecNw+ibYbagch2V7w3LCIYvgcHEepgiG3AObCq7n1B+G1och9WE4ftM79N7TNM26MVIvm+mK3qgY/tR1TQiRJQ5gMpmsxzCU3kjQWnddQu+kKe5lqLCH0Yz+fnq8Qq/8exmCM4e4jOG5h2DIzYhD8IGnnn6Kqi4ZNhWKnfgGxl1fuvYikjXrYB/x7bzg29/xsxwvRBeKt9iO+M1aQ7VasqpWEWzmoa5qTNt09eux50sPSsZ78iRjOp4glaRtGgSCNEuwzjCfn2Fsi/cG51rSVJNIxZNPPsVskpOmEt96lNIolXRr8m3Que/uU2uND3ENTjINAaqqiZVIMmKjrGtYrlbUpiWYhvPFnKQowNTYYAGDMS2Hhyumkxhmb82KNBHszKbAjCJLKUYZVV0jgMVygU4yVDrBWs9y0cQuo8YjheTGzUOsd0ymY5TSQMDaliAcaZawM5li2palDxjr0Ans7U1wzuDsjNX8mMNbpyA1k+mEUZEgpaJcNVy7dhMhIB/HuHeiY9O08XTKwcE+5ariyQ8+FemSy5qyWiFlpDJOO36T8c4MrRU3b90kTaNR41xMV2glP8zsuC33xkA4e4T08qOks8skk31kMSGoBB8U1WrO0c1nkN4gTIOrStrVgnZ5Trk4RQhLIRpG0pKnoDrlK2VgkmmEsx0/gaINHWGRBOjZCC0hJDFMhUcEHzvpCRmBgU5EgFzwKCk6dq7QlwvggkeG+CbFfggOZDQQfBiEI4VEyDhZQ1/YC3jnsV0zIWMsHo+SNuauUBRZGstCkDF8bltC36jIGoJpMVUZTQsVc+kB0YEaJUrFkh+VZGS5hI6pUCmPIhoz1rR4vyRta2LKI6JPRbCxFbROKKY7oDJUMaM+O0Oamvsv7VNVFSHsdyHTsF5A7roshr4hh+jybnDreMUnvuIlFFognMVbhzeOYHqjwMcIjbEYa3Eh8pEfHZ1y6+iEWhTM/XOrd/1ISKLvRPIDdyjIoUEwDFcPS+eGuflNIp9hXr8HCPbRhKHR0Cu7YS6+v9bw8z7a0HMYbBotfWi+H9fwnMCFcQ+jHEOMwNAzH5YhDQ2izdTIpsHQy9AIGqZChop/mArYVPabFR79fQ7xG0NjxPuI0VmVK973vvfFaqY+0SXiu9XnrKUA415chgBwRyQg/vSfrf+5y2G3d+pRPr6L+BXjEZfuuw8hHMv5gnK5osgzgjOUVUkQLUqnXTlhTZKksUyttQgR8NZinOVmW1M3NY1tmcxGJGRYI9BSUpZLzs5PeeDKAbPZmC7c2Dk5CUEpdJJQNyaS2RU5eZaTpllsoSwkwQVmOqdpa5blEu8MbV1RlUtywDYNTz19HeMFo8kI06xwZokUnuAL6rKMrbml4OTkhCLLmU5nKCGoViX5OOf6rZvUrSHxDnNi0ElCnk9JE0tTr7h0cJn5fE5ZVThvSDt20GI0prIx+rSczxHSo1SgrhqWy5LWGkajgkSnVIs5dd2wXCxjynKsIhZgukORj8lyHTsSJhmh66HQBocPnksHB1y9epV3v+s9rBZLjIlGvKlbdnb3OLh8mXJVYk3DZDyO1MdtS+09JycnjCfPLT17T8ZA8einoMf7oDOMVrjWYOoVTbWiPjtkcXoLWy8J1RxlarQ3JL7lQAu8UkxTxUh4dHAE52gdtD4AEi181/svlo2AjBPXOxTEdo4uNi4KfVF/p9yFSAmozmDoFxOF8LHUBaEIQuClit0Pu2tIBFLE+n3nPV0AYh1N7G0EYeOC4xMQQeJ8WFcNSOnRaRaVTWcttqbpiIgi9VKqJdgWV1eEIPCJital6F9OibcWaxuCUCi5ExeyIBBYbGtw3uJ8G6sahER3yk0Gj6tKqhD7NvhqhVA52Z7HVStSFTg4mLAqS4KXCOW6pSPeabzX7jmGvh65M4QQOBto6xZnLQ/ev4PExRRB6/C1IVgD1mLqlraqaZuYJ7MusFrWnJ+eslos8LkipON7mW7Pq/S5/qHnOkTUAxeUdu/FfihlNVR0cFvB9sq3L//r2+72LIVDZdmHyftzCyHWOfd+PL1HPiQ+Girpu6V57pbz768xbEQ0LD9USq3TDZuYhmF+vzcWhsbI8DltEjRt7tufbxgpGAIhh893eP5ehq2OQwgYG4ldbt66xdl8EZ2DcBs7FOd0BM6pREcClheZPdB79gJxmw9kGCnow6SD/aF73n1uW3QmQYj06KK1hMKRZBrVGZqznSn33bdPVZc8+fTTHJ8cduDj6HWmaUbwFtM0lMZibCxLFEJQVxXOGbROybrW3cY0pHnKjcNDkJHdMN6LQyqHUCo2eg2eRCfUTYuxjsw58iBQOo2NhHxMN+UupyoNzjTgLImAxljKZcnZ8THetCgpsTbO3b293ciL0LbUraEqF8hQUmQWLaZY42llizGG/b09Ll+9zM7uDk3Tcnx0wnKxoMhzwJHmGVoprGmoqyV57phOdri0P+Nsfs7Z2QqtAoK+gZGgqUq8tyhVkI/HnJ1UnB4fk2jFKI8VBGW1Ynd/D6UldWOwXpAlKUprvHXMz8+plqtOFwYmkwkhjDpcg+V8vkDpGOVINIgwgw48aJsmPo/2I0BH3IiCelER/IIQLNbWmGVJuzjFLI9o2opEBTJfk2lLLgM6xGY6lYNMBRICynuCjAxSwUXPPBKEdAtQ6Fobd5THobN+4wIgunIK6N8GL1TH2x1Z77wPa+7/EGJHxBBzCjgLwoeu2iDgQ2wnLH3sLIjvOjIKCOhY97pushQQHXthoAvqK0miCqT0EGL7yuAttm0wpsa5Fi1B4/CtRUqNCwlS2c7g6POzkSlKSI2dTLqe3yF2RvSWtq3xIfKD+yAgF10ipSsZI46pcRaRjAgqQ/uW/WnOwe6YG2dLnA0kkttxxRC67os+RlJ6rKQnXjt42qZlfr5C4rmyPyJ4T101BOtwrcGbFm8MTVPR1hVNXWJsi/OC1WrJYn5OuYqUncVk716m2/MqPbd9H7LfBNkNS9X6EsA+x93LsHyuD3cP8/O9wtwMf1traZpm3Sq4v+6wOdKmcTGsqe/H3ef5N42FNE3XYx16j310YojeHwIOhwZF//kQ/LcJ1htes///3SoZhqmVzTENSwg3cQlDI2D4vW1GBYYRih6vcfPWLZqmiY2O+giKjMtb07aRMGwwtheT9Dq/f29FD2sWF7+PzYoJ0RsC/f2K/ug4L89OTklyTVNXZFnClSuXmIwLlqslzjtOz+c88/QNTq1ndzcyDEodGVfjWGLlgbeGNM3wTiAzRZpItI6lhLnOODtfRMDcuCDLImBQ9xgCZ/HWIrsmdbFbX0sIkvFE40PXyVNEELAIlrpMKVniTEOiNXmiaFYrSiGYTmdIldHaFuMMaZ6i05yT93+Q5aJEoVhpaOoGKTOyYpfdnRk6VezuTNndneGDJ1GCa9YgREApGTsrKoWUGc46yrLk5OSE/f0pAsF4PCHRASUli8WKxSLqtbquKfKEnd0ZdbUEHM5a5udzytWS1WrJzs4Maz0uxIh5EJHq3Xc/yWyKMTaOQd/mPKmb2DFXSkGeZQiRkmWxWqJtYtnxaDKhbT8CDITzkyNs3eBtg7crXLtANA3KlKSuQhNIk4wiE+RSkuARPnIRmhAVrOg6FfbzM4SA9Q5NH3rsFpEQcAJEEB3wTXQUf7eJf6I9IPFSYQHdndN3oULfebvOe3xHOewCyBDL8WVnCLggkL3REECGCAQMIsTzE7EF6wVadAusju0xlRKE4AjORo8/eFxHI2xtjfEWFXyMRKgUFQb5YRdLRLSK10mzLGIJuny9DxbnJdHvj0qibdoYGZAyAhlD6MoLo4MgUJhyQaEEV3ZHzHLJU6uSpo5EQrGDlOiMkVgeiZIEFXNyPW9BCIJyVXNyNCfTcHmnIDhLU1expbRpcabBNDVNtaKty1h25B3WwXx+ztnZGU1l0KMW+RwbZnwkpDUtyt02BIZc/UPl9+FK/4Yh76EXO1SEmyVy/Tl6gwBYGxtDwOEQSNdfo1eWw7H0ynDIONgTEm1GMYD15/0CMuQBGOIS+mM20wdD5Xk3MOXmffbXHBoK/bbN/TYNkDtD4bc93B6rMExT9N9B0zRcu3ZtzdaolIotZLtcqXUOpTvDhxddYGAtESpwt9LPCzmD7nfonCg6owFiBVGHuwiRWyAIS9tWjKYzxuOC0Sg2Bbp69QqznRnXnrnR1enXjMdpF0ly5EUeOfoTxSI4MqEJ3bqVpDriuaTCGI+xjrOzyLM/GecURYbIYze/4ONaGYJHSA0o2qalbV3kBlEyUp+rSAecpglZqhES2rZBoBnlSVeubmIfAx0rwVxYoUVBXuySJQmT8YwiVwTvqMyKJImdBr3zLBY1vqs+y/MUsb/DarliPp8jpSTLVKRKDrESoq4rbt4ylOUSnSZMpzuAi70bUkWWRQxPWUZsRTHKI6DR9zwDi6hjOmOnbU1sZ5+mXQSijREwIotp07SR5RUiO26ITsLu7oiiiB0hi6JAdk5DuVohlKLIc+qPRAvjs2vvA9sig0OEEuycnMBEQ5YIgoq0uHmqSQRIF/1V410kpqFrzNsp+54wwfmOztd3JSJe4gVYL5ChC19LEYGEa1xqx/wnZMwtEUkY1guqFPiuu5bzrKMJLhIR0HMOuBBD9Y7O8/cdqDAEhIoRBucFxnhQcVHRqSZZL+QdKM2amJtMo4J11mDbBu+a6NmbljTJEU4gbHefwUcgXvAEnaDSnDwfrSmPgw+44FA+MiB658GFDpNgcSqCEL3v6Y4FUmmksNCUjFTOJJcoX4NpWZ5HHnAZ+07eToWELlISib27aEHAWc/5Wcn1mydM8oSDaYrw3bWbGBWwpu4iAhWmrmmqOkYUbODk5IzT0zNCUBRNTXV2ci/T7XkVax1eXFTgm7X1QwNhsxb+2VDuQ6W7mYsfpgX60sDNqELv4ffXG4behzS9faShvx7crjgYGi5wW3EMIwC9wh8q6k0PfrOEcKis+7EPn0dv0AyJjYbPYTiW/tp3i4ZspjSGP70xMDQQ4rUcy9WK4+Pj9di01h1u6OL4XmwRgU1ZP8/OAIhG3EVDYPN5XjSyuB1m8ANDz8d0pnMtzsUQ92QyZjabMCoKFraMmCOmqM4AKEY5aSZIEoU1NUoVGGvje+YTlJaMxyPKskYgWS5XsWurdwQBSZYhuvSosw3O2QjgUxAQOO+pyzKS7eQpmVJ426eBoxJtmgYpMma7M7wXGOviuusNOgOEobEWKUfMpmNmkwKtBE07x5bnkTlxvsSYltp6ytUSszMiyzR5kbCzM+Xo6Cg6fGmkujadko59ORpWZcl0OkapBGdr8lx35boyAnLRZFmOkoCIXDyma12slWA8ir1CvPMYZ6PDLAfRSh8NO2Paznjo4N2dc1oUGUkajYdawGgyIS8KAkSwKL6LMj+73JMxUB++h1QL8ixnlEGeQ6EUmVIE6/BSUndcHzHq3Hn6zkZ2Pq9AyI70Yq2JcB6cAI+P+wWJJwIDfejyfR2vNUoTUNFw6BL8QUSqyG7pWhsKfd7Qd6U08VzxRQh4hA9rA4LQkRKFEEsTRUDIOL7WBFrj0NqSZjk6y7rSw/gleWcJpunSG55gLNYYmqqmNSVaRRYtKTOCb2NL4+6dXps2Hsb5OCLLQ0B51sZCCB4lE5q6idwESkeKy9YjlURKFceAJKDRuUJLjRYa7Wp8fcoksdy8fsSlKzkCTxCdMojEizGm2OMwulxjU1tuHZ7xwaeP2N3dYzqSCG/QQtAag6mrmA5pG6yNTGN13dK2LY1xHB2dcHp2zrgYUy/nVPPnp4vcH0W0Vh2eNFzIy28CB4dphE3vfOiZDpVLr2x7vMEwv96nJ4algb1HO2w81O/TK8oh8K8/pv89HP/mWIb59GEKYsh1sBlF6Eu1NpV/b3z09zMEBg4jIz24cdh58UPhDjYV8uZ+mwDBYQSgZ4McGhnOeU6OjxHA1atXOTk5ud1pUUVjPU0SWmsQ3doTe3t8KNjdR6dcNKp6T/9DAQdh+JgDETDcPztrTIyWWhdDyF04e7VcEHxkvZNSoZVkd3fG6emctl3SNLtoFYHMIEg6ZrzxeIQQKWbeUtctSipGoyzW+TOoodcaWSuSLGNsoapqTBfq1kkG3iG0IlUpSIX3hhASrLVkacC0kc/FmJayLKnrliJXXH7wYVwbMG1D3YELXVPhTddUqD4lSxKU6pRoSGMDpGULNvZRkVKymJ+xuzMizzRKadJcY5xDtZZgLLIjSJKiK/XNsujN64zz8zPquibRHucMUml2dw+YzXZx1rNcLVkulxAEWVYghaCxhiLP1mF8ayJ9fCOjkRQjrLbrFjsnywrojfueBnoxJ00VeMGTTz3F3v4eV+6LbIwgODs9RcrnZgTfkzFwJVswKkZ44Uh0SpEWpAKUCAQV/c0+lO2FQ4QYApchoITAekhU1963rwF2Hq+jd0+fHwo+sglKHQ2IzmgIAWyQ9Ox4QsQc/20UTc++5BFC08X6I+6gQ+fTYQ76hJpD4r3EuYCXnuBCLJ0TAVwgSIVxEoj5Xq06gybYbrzx5XTddZraoELAmNiusywjelRKhS9bBHTYAGK9qRA40zIqYp2rMQ7hHPgIiPTe4XWHmwgSgUdLibcGZ5vILCa7Zjoqw2FIZQrSInzdMSM2XJ7kvP99T/KSJy4xGSmEFiA6sykERJAxkiPjM8LB6fGCp54+YrFqeNWjl8iEwzYN9XJJU5YEZwjO4qyhaVqa2lCXDe97/wdwKG7ePGK1KimygtY0GPvCeWZFXqy9116x9Hn8LMsuKLpeOQ3L/TY92aHSGub/+xRArxz74+IcuBg16PP3vQGRZdkFNr9hXtwYc0edfX9tY8z63L2hMCTpGYbvhxGJ/jzDsQ7HN/Ty+5TGprGy6X0OlffQwBoqd7gIKtzECQyvPSRGGl5L6xg29qsV169fZzadQmeQVVUVKXjlbTbGxrTR+JYKJwbo4BeBDL8v6LL+4sOYMoMUbO8xSSkZjUZIJWjrhuViQeUtZdmSZglVGcv1fIjzcVQUkWVP9bTClvOzcyQTtO4Nk+7cQqJTSZYn1HVLVbfRsA0e6wxJmjBLdtk/uIRzlqa1nM1XCG+oyxWTccZ0luCVi05N8DGCK0CrMdWqJdUZQkTl3poWY+M8b5qaxXLJfZevEtyIcqnBW1arhtXcsCgrJuOcJJdUbR2jyQSUyEmUICsyqrpitTjj/e9rESI6WOPJhLqqSJOENMuYL5dMp2N0UZBnWYx+GkNVN4zGBbeObpGolKpcsFyeRXZeqTg4OADg7OyU5XJJno9QHc6lXC1YLuaMRgX3XbnCwd4+pnOqAgGt1DoF1tQNaVqQ5xlKJ3gf50VV1QgSRsWYg7195osFaXJKluas5gtMHTlxnovckzFwUCiSRFDbAF6ggiJRHWhOdY1pnIuuphBIBEEKXAhIoDWelKi2e7pIrVOQkf6SYLrJFUGBHrVOCcSeCCoSHQUJLkQWQByoSK/pg4mOrYh8AqLrFyAQKKmQQg9QxhCCwHlJYxReBlAefOh4BDoLG4EhYzzeiQrCWrwzeBcjAVJpEp0hdIozkbnLtjWL8wXn5yVN02K9pCjGrOqSS3u7GO87+EOKVIq2dQhVkOVTWuup6pIiTyB0JWYyI5UpAhlrgpM0Ik87iuMkVUzzEWkxxaLJ8lF8Yb2PBgOemc5Y3jjk/e884WUvv0w+krFZSOdmhD5v4CF4QdNY3vOep3n6mUMefPA+Hr66i3AOU9dgLYmQOERsArJYMD87J1jHclVTNS1IHc8pJV4GLA77Ai7ASmuyNF3Xyg/D8c65NbAPbivN3msfRgP6v4eKaug9Dz/rQXqb5YG9QhyeozcI0m6Mm0q6l2G+fqhoh3TGwxLBYURgE3/Qj3dIszzkWbgbWHKouHujoJd+3EODC7hgDAyf5zDysol/6NMhvSEwTLUMUzhFkTOZTLh16xZJGpHsER9hsc6RrVMkssuz6lhq9iLKGNyBo9gADq73u+O46G8JIcnzAp0kaC2RAqq6RKpYleRdwFhD0zikaJGyjdTFIuX8fE5ZlVjj+eAHn+LkpIjpg9GY/f19CJ48H9GaBoQkLwpECMwXCwSCvEjICsl8vsR5TzEad/NWoMg5O59j5iUexc6uRunYsChJVNdrQCCTLvIZiNTvHWlRjP5IxnlBcA6tE6azXdI0J1+MOT09xTZnnKxOWaUrpIrpZqUleZawvzelcTXt3CGF4vj4CB88bWu4dPkSdWOo64rGWHSaERDs7u2hZQRHN+dnJIniYH8XFyxVZSjLFTopCN5y88YNzs9PGI0mHB0f4l0Easf8fk6eJyzmc3wI3Lxxg/2DfXZ2dwlJbF3ehkiWNl+cIYXq6Ioz6mrF6ekZp2cL8jQBb8iLEbOdHSbTKVonSCHZP9gnuB2uX3/mOc2zezIGdBAkQmCEwNL3sfcROeoswVmC13gb6/RjeD7W5sc8R99UJIahBQIlJMYJLB7pe1RswIkQeag7koxExpntHbFhjwuAB+nxPiBQa5R/14MoRg+8WOddQoj0QYqY1/fOYWX8HZwndD0FTNv3+xEkWqPyKdY7fGvItY/1yq3DmDYCBnOBkpoQHGW5xFYlbVPivcN5aIwjH8VUhpCKYjSJHr3SEcAnM9A56DymBVRC1cZ+BzENUjEWAm8blCBWRISki34kyKTAk9A0nnQcrVZnXLx/GaMPwjc8dinhd9/++5zMH+PRl17l8qWMUS6RscMSQcV79k7wzne+j2vXDimSnIeu7HF1L7IfCh/IdIJwnrpsOD895fDWLVarkoOdPcq6xnTGn+zQrzE05yjGLxwdca+QewU6rM3vve88zy8wBfYkP0Pw3aaXa4xZh/c3+wsMuQLgdoXAMO8+JN+x1q4rDnrjYjjOYfnd0MPWWq+V8hCt37Yt3vu1odOfcxMTsKnkN2v7+32GuIIhRXLblS715Zt9ieXw2P58w/H1spmWGBpf/XcxvOc1X4N37O7u8mmf9mmcn51zfHIcPbkkoe2ByC4C0bqBIOWHZur7qJWNiNXtO+gjMoNd7zx0/YGQEp0kCAlSRYCyULHBzvx0RdtxCAQfKddVsoPzjp3dXUSQnJ+fUtctWjVIqambEmcNu7M9FssKgSLLUwhxbQwhsuJdnl1if/8STWPI8pzpbIaUgsOb12kdFDrj7HyFStIO9KkizgrJaBwH31CSpBlZllOMpgh1zMnpMePcEfwtdvZbrl59gJ2dPfI8wzvPzZtHXH/maZbzkqY1LFZLqqqF4NjZGbH/yH20ZzWXDiIp29l5bMf8rne/l8OjEw4uHUTPvmvKJ1WspAiIWB5YFDjruHnzGiJJmO7MYqXBSmJMg9KaPM0Y5WMeuPIAo3wBQF7kjMcjEp1y69Yh5WpJCIazs1OqqkTrBK0TRqOC0WhMlqcdaFxjnaFpa6QS7O7MaKqGs/M5466KIk1SnPM0tqFtalbLJU3zEagmQAaQHtGR74TQgXRCWAMfEDFH7who0QXtu4561hi8jsonAiBiJydD7AuQ0efDOkteaBAKb33HORi62S4jriBYApFwJ5YVgnddSF2Ijj1LEoLHeof0gIsdFj0WLxxBerw3uBYQHlJF0HvkxRhdzEAnJF5TnRyRBkOCi9zUPnRAP2ibBt3lipxpCCHml1SakAD5eEya5YS2pU/SS5VEhKyWTNMxo/GU2sbFNxtNwFlMG+v7jYuIaduU5ElK7RuCUkidk+QjkmJE00ZksJc1BSmiI1QSyqNSRfAND+yPqKXg1/7X7/GOd17jsccO+PjH93n46pRMSXSqMQHe9fvv5bd+6w+YTvd5yaP38eQzc67MMmxX32tMi7cxv3VydMTR4SF5VnRNiRqOT07QSYpzhkRJUiXZmc5IRrv3NN2eT9EDYCDcZgsc1v/D7RLBYZ69jyb0Cm7o4falbcOIQ68wh+WBQxleq1ecvdLvOxPeDWQ4rGLolemwTLL38oeGxub57wba2wQO9tuH17/gmQ5kaGT0htU6N909m01Q5DBS0Y+zN2b6SM3muYE1w+JwnCRw6dIlPv3TP53/77/+fywWi/g8BlGc0H/XSUJd1XfgFj7aZf0dSLnu99HnAm5/P8P9WYO1e8O3f+5Ka4QIZElCk0agn3MOlSiWqxXlKnrmzlqcjxUw52dnHOxfohjlCCKvfppmFMWYulpxPp9jbcRBOesoRjkP7ezgrKGqSm7evMlLHnucK1evcL6Yc+36M6RpBkIx29mL7YpdSwiCujYEH0i0RiqBd23n3HmyLCPLIodBJEHyLJcr6iZyFtx/v4hYhZ0JqU4ZTSY422Laa0z3Z6TLETeuHbI4n6OTBus9WV5QLSvKtiErMkbjCUqlJGl0aJWK67XrjNOyXBG8RUnJKM9pW8so0xydn3F+vqSuImVzCJ7xaEye56RpwYMP7iJlLH+VSnT4mpJEJ5RliVaBUZKQJhnT2ZQkjc3eAPb29lguVngfqKqapm2QQjKejkn372O+OI5GnojfeVyjIoXzzu4eZfURqCYIuKhg4iUjUU/oPCsXYmvH4GO5ngAvQSAxvmOnswZjNIm+ncsKIRC8IqRd+90AUWP7joEwAr+cc3gh8LguxB8rBMB10QdBsBFb4IPr+AYEwYIIHo/HeolwIXLpy47W1AZME0izBK0K0ullxrsHZKMp1hmq80N8vcKaBuFrrEgIzuF8xyOP7ABzscrC+9ChOAV5PmI6y5jt7kIAFSJHgFQJSieRrCjJUB0pUus9wXlymUUWROtpXYOwPqJJg0MJjVABKTVJMWY82yUfTSjrGm9aFvNzgs/Jsti8SISAtA4lE7T2vPThEecu8IdP3+S33/EUT753xGOP3s9LH32AUZHx5JNP8YH3P8ne3h4P3v8Ah6dxQR8VCY1rsXVLXa5wbYN3sfa1yFOKIuf49Jj5asmqqihCfO7Cx26OUipq+wIyEHYAviG4rt8+JMrZDMkOexf0CrWXYRqhBwoOz9H/v1d4w1bIQ+mPH0YgeulD+P1xm42Jhh760BgA1tfsDZihcTJUiMPxbqZENj8bboc7OwzeLZXSb9+sghimIYZj6e9naAhsMkduRjUeeughXv2qT+Adv/kba0PAd7nnvrud4E4iqReFrHsTAB2kR24YZheePbcNghhJAO8M1jQYRcehH1OyWsXQe5YnXUl2BNStVhXHR0eR2a5taZuKNFFIFVvqSqVpG9N9p5GyOMtzdJKgpKYoRhwczFiu5pz/3pymtYwnE4pRztlZSmsce3sHKAXHh4ecHh9jTaAmMpgmXSg/RglApNGZdEmKlGod7fIu0NQV5WLBan7GcqTR0sFoTJ6lPPzww5ydnTOajEnzMQRJnmq8M8zPz2mbiqqsWFYlOzsFk+mU8XhGlhVkeRY7OAY4Oz/HWLNe79IkkiFJJcizMePWYFuHyFOcs7RN2zmkHRdInjPbnXF6fIqxBmMtxlgSqUkUtCZyNeRFQZ6PSPMMBKRak+gUwrIzsnOUisZ2lkaMUWASqfeNRUhFmqju2QnquiUb5c9pmt1bZAAQIcL/RFfaEDFnHVrf+Z7SIpbs+bh/6FD8UrAuLaSLGPgQQ/aIhJjOd3h85/XHTlWE0HUHGNQYh1gcGHwEBMqgovKXcREQPsROih4EDhtsjB44SXBgfMQ5KJkS0iliPMVle4R8B71zP15rmvkRdbVELU+hLTGuxukRQsiuXNGCcZgO9JIq0VVLaNIkJUkyimLEbLaDtR4tY51pkqckWYpOUqTUOOc7hsA4OUCidIpKHDQNBIf1HoXEE2tukSpGS4QmycZMsjGr8zPa9pDVMnpXSZqSSE3wAtHVqE4L+KQnDphNM9739BGHZ3Pe9c6WmzcXVFWFdC1X79vl8UcfIctyrh0e4oNHJ4HWWs7P5wRbE4xh1cTGGDbEibiqKlpryIrIyOicx3mDaRpWyxXpwQuXJujD770y6BXksBxwk4RnGJrt89Z3k6HiG3rYQyXdX+9uNflwu0lR78kNeQQ2mRKHuIFN0F8v/ZiHXvhwfP247xYV2FSWmwDDXu52f5tjGl53iJW4G4hxaAh8uLFsjtt7z2g04mUvexknpxGoZazpjIGYRuwrN2KPjvCiShXcxgoMDbH4M3wW8cPhkbfXS2sNVdVVP3lH2zQxhy1vt/s1JoK6lRA423J4eIoxDqkE1tRk2Yg0S2/Xspc1SgpG0wLnyw5tHyPEWivyImO58kyns1gBsFyQ5YpRkTIqNJcvXyLNFKatObp1CyMESRqbdjVN2xV/J8Rsqca4JXXraI3F2FiFJmXkiGmaknJ1xvxcIIhlqPlowng6Zndvh/P5HKESZrMcEaZUqwVluWK5WKB0gvMBYyLhUTHypFnCdDqOlWvWopRksWwiB4GI6URrDCF4zucLCJBnGUniEVJQ61iimaQpQgqss11kI4vAyjY6WUmuKIqMIGNxuOqAsX1Kp8gzlJSR10EqnEvIu+9Ziv55S/Cxy6G1BuEFQUkIdFUcz22236MxEPNVEpDE0Lv1omtR2b2cXdogBIEXdJUEUZH3IReIky8SB0Vl35f1eWtxCJwyINKOhTCi/4PwEU4YQleGGMGKznskCtdHDEJfKtgv1I7W2cgkGBSeHCszZDImLaZdr4UdSpdQ2YBYLLCmoT2/QTi/QVIdIe0SfEujJCrJsc5HIJLvCJK8JS1ysnxMyHLSJIkEEjpDqJRUSaSOOegkTci6l8o6R9O0OGL3rtEo7xjUNEmSk+fRSvbB401LkmWoJMULRdsaVmVNPnGMJjPK5RKlUpq2BWEJQqMSAR14MgDGNFw52Gf/8iUevH+fJ5854vpRxfH5it9953t58L5dXvHEYxRZxrJsWa4a0lQhZWC5XHJ0dESRa2zrODxbcXi6oFquSFUde0NImEzHsftYHcB7bNNSrlZcffnle5tuz6MMw/9wu1RvCE4b4gU2S9j6PP5QiW+mAjYV/PD/m3n+Iaag/7xt2zUtcH/uIXZheI3+PJtphCEWYDiG/nzDNMZwfEOjob/OZh5/02jq9x9yB9wNE7EZldgc23DsF1IAg3EM731oePSipGR/f59XvvKVHB4d0rQNVV3FxVEIlNLU9erCM3+xyJ2GwJ2/NyV0gOBh8ydjbYwseBcrmkLAd0Ru1jjKZdWlqRRKCsrKYKwjSRQhOLSELE0IUnferSTNC0bFGGMtOtEoKdE69k1ZVRWLVcl4MsZax9npEVJ6dJKwu3cZqWJn0zzL4vyTgjTN0EpQlrFRkvMmspk2FXWzRMgIuFssKlorUFKQKoXzDY1ZUVUJWncGrxBkuWR/f48nn3oSqRRSgNIOnQjKVYOxjtnuAWkxItiGsmwI4RTw5JkmTcfRs+8iEdZGg0HJzoklcHZ2BiFEhsAkYTqdkuc5bV1T5CPA0zYNAciLFIhVE42Izm+iE0Yd6NVZu26e13vQQUAxHq25X3yHfwuuayMf4vtQZCl1VVM1JqbivadqGoT+SDQq6ghqBB4RIrrTeo12xJaUsUyAEGSH6pcICUp0AEOiR626OH980eMNOwS2zx0KSRCR0c9JgXcOITqWvRBziz4CDhCSCHhB4ELM6doQEN53tI4h8ut7QYbA6xEyn5HkU0S+gyhm5LM9nExp50vKo5scXns/tp6jzYIJK5w9Q4QWgscYh06a6LnL2OM6eIu1HqVSprMdxKAhB0LSGE+aaHRakKQpSgqUVoSuZFIqTZrnOBPIiwJnPcZahJCMRlNm0zFCSaqqJO14RpyJNd1uOSfJcvLRBCEk+XhG2zaoJLJyNa2jCBKEQoiAbSzBtIyKMY8+vM9DD17C+ZTDk4q9nYxrT1/n937vnSwX99H6hJMzy7gYgQs8/eRTzE9PGE1nLMuW4+MVp6c1dVmRJoJER4CmkAJrG6xpcNZE3mYEL3n8Jfc03Z5PEQOU/DD0PvSehymBHhk/TBf0Cm54fJ+PHeIM7kYotLlob4bZew+65yYYeu9DUqGhZ3+3MsFhaP7Z0gLDcQ0V+DCPv2nMbN7XcEyb1+j3GaYKhkbRsARxmEYYKrp+n+HzuJux08vDDz3Eww89xOnZKXVTR5hRR+LStg19rv3FJncz4D6EHdBJZ0CtDUTfMaz23CUBnURQc0JABEHb0TnbVJPlWVfZVDEq8lgSLqPyValGaYFEkSQZUimKccF0MkFLGR0c1+LOLaCQacyvB9tydj7vEsQJQiWMxwVVVZNmWTTMtYoedZLSGkNdthgnWJYrzs8rjHMIEVPEdSPwxjLKLXkeS/2sNbEXQVWjdYaUCdPZFCklp8eHgEMKifdxLu3s7nJw6RJJmrI4P2U+n3Nycha7MLYtB/uXyPKiq2rRmLaNSP+2RADj8ZSiyLhx4yZSSma7O9EYyBIqKTpwsaU1NdZ6hAxoLUi0JE2SOF7rkDq+f1VdMp6O1+vPqqri89DR0FJa0lR1ZDP10cGOEQNDliQ4Y/Gmje8cASEkSfLcesLckzFgrSPTASkinE+gohfvQQuBEBF8QochaPEIHGA6SGEsM/ReIruugMERgYndZ33WS4YOZIgkOA/OoBKBDba7BmsOfS8MBoX1baTZ9BKJA6ExrUSIEcloD5FOme5cJhvt0IiM0nhKZ2nmK+bLa8xPTxBtSXV2kyTUTFOBSwX4EuNaCJZEFySZIE0VWsdySmtNbOtpDEJOYlMh56OlrBKqZglCMxtP0FmKNwZjXGetG6yP+AYhNEmSEoKhqmqC9yRa0baW8XRCmoGWoWs8ESsArKlZnB0ym+3QNA0OyezgfrIso1rNMabGGI+ShkwlmNZg6gaVlHilkElCXqQk9434C//Pp/PkUzf4pV96G29/2zs4WkiS2YO86lWv4Omnj/nVX3kb9129wsgnCBKSdMxkYmLXRCyNb1nVBhliz29JbIXqhSDgKcbZvUy351WSQb5+yDTYK/UQwoUoQR8p2AzDbtII13W9Vrpr9rtBHh8uGiDDkH//e7PscDNi0PMhwEVg3zCdAbcJh4Zo/SG4sMcQbDLy9Up3k9a4P8/wHEN8w9DQGRorQ+mfyTDN0I/tbmmQ4XMbKv3hM+nHPow89D9KKXb3dplMJ5yen66NibZp4r7cibj/aJfNSNPdsALD30MJ6+MlSkLM72u8in9HrIsmSaJH3xpPWS04OrlJWdbkecrBwT55ltNUDUJIijzrGGQFSEFjDOPRlCzNYiTQOaqmplpVKKG4+uAjzHZmmHoFMmG1XFIua0K4wXKecXY6p6nr6PR5FxnzRGw4dHzWcnpeMl9UEZvVtlhrCD5y/Ju2JlGB/b0puzu7TEZTRiPR9VCpkCohG03Z3d3n+NYhVVVhrUMIzeVLl7l0+T6kkuSTjCTdI89Tlssc0zY0dcP5+TFjt0dZNzjrIgOjjmmUui4RQnDpYJc01bHNsWlp6pIszcjznPn8nMlkzHQyZr5YsawqTNPQNJHkSaeKLCto2gaVqjWfhrMxNdyaFikF466yQCmJbAwgECqmIVzbkBYjpFJMxhMSpTAmggZHQiKT57bu3ltkQIjuTfKdVeqxXuClxwWPszaGToIgCEUkufQdH0AsWWmdQKoQexH4EFvhSoP0RewXABGg6KMR4UliKaLzCOnxUsQGRz6AjUrRaIXSYxoD1gcs0QvP8h1G40sko4KQ7uDEiGRnB2RCs6pZzOcsF8dU57eo5scoKZnMZgi7RISWoCTeJyQ6wTuDJPJNG+toTKyjTZQiyzMmkwlpotdhMakCXqgItZQSS2DVVIwUMY8fYkdAiUYrQWscSZpQ1yZOWBdDSUoKlATXgRMTLTGmxZhI9RhEoKxWLObnGOsQKgKA+okrSCOi23qs1QitaKoKhEAXOSpReCxozUgLHjwY80Wf/akcnpzz87/+JP/7/RWHv3ON9zwtOF3soO/bI88OUK6hsmcs6gWtazFtfDF2dw/YGY2pz844qlaYcoGzAdPWnB0d3dN0ez5lGMoeKrEhFmBYGreJZO8X2iHRUB++H3qvm9iC/npDQqLhZ/1xvYKGO7kPNr3hYXRg6OFvRg/WCPKOM2ForMBFL7s/32bIv7/G8Pfw2E2j4m7GQL/A9RUZQ6NlWN0xNDCG49pMXfTSP/fhOUMIFMWInZ0dzs7PWIolprUsFivCizMocEcU57nJ4FkNDFvfMZpGKpiYshR4Lh3sY9qGtq2oGkuSal724Eu7lGbO7u4+8/MF9WqJrVt0liG1xgZD04IxltV8ifeOvMi4fPky1agikSmXL18hm44xdorKM2489SQffP97us6uIhK1daRbO7u7jCdTnLPcuHHC9ZvnrEpD0zbU9ZKyXNI0Fc62kcCoriDArZsj2rpBIMnTHK0UzkYnVCYJo2IU+77EGkt8EDStjayBmWZ5a85olLK7N2NnZ0pVVbiun8hv/dbvsru/z6WDS50h7pEioFTC4a2b7O/sMJmMGS+XaKUo8qxbGxTOeaqqoSgydBJ1SVvHioDWtMx29imKKcfHp7StxZgl3gf2g2B374CiGNG0DT4I2tZG/Jzv32VJliasmjqmFpUC7zC2RmpFlo2pG4cSH4k0ARBEx7oXIojQOh+Z7n0bFbh3EFRkEKQvobB4F7EBBB3Z+kLXi8CDx2C9R1qHtyYWEUpB0AmuAxZKYkmgCwaSGIpybYPA0hhNNtnBJQdk+YhRMYV0SpJPGU8n6CynbGA1X3H9xnWa5Tmr+RxjW6S0tGc3kfUZOp8gbIag92hiGsB5H0NO3lJXFa2HtrEx17O3x2w6ZTwZMcpyTo5PaFpLVhR4BGXdULexvna+WMScnFLkOkd0tataKqx2scTRx+dWjMbRkg+eJFGUyzmrsu5aagbSLEelaURlSoXUCRiLMxXHy3Nmk11AYG1LkihkktGGlsneHlp3wBMkwgloHFpK6rLh+gevc2l3irx8wJX75lw594ik4R1/eI7PXsFu+iCF98j6JlUdOJ8vOTu+wXSU8rLHX8IoH2FrS4vAG4dpGiwgq5Kja9fudbo9b9IrcrjdBbBXZj0eYJOQBy6Gu3svtPdy+woF729TBA/bDfd8+v3fvdIfevLDBb4vXewVeW8Q9DKsRBjm0ftj7ma8bCrq4f1tKtehgh566UOswWb1wt3wEZs4ArhdArlpqPTnb5rmWT3g4fPvqyqG5ET996CkIvjYung0GnFSn3aliy9Sa2BDbhthF1MFd0ZmbnMteN8xRnbbffBYb7umOAZ8QOuMvf3L7F+6TJql5OkI520MZRuDVPH7z5KUKw88CEpRNwvKqo5KyvTRSku5XJJmGXiBsYanPvA+jk8OMfUKV62wzjE/W5BnRSw7TlNAYZ3m+KRiPl9xcnLGalXFiLJtKMs5q+WCti4xbd05RU0suVudU1fnrFbnnM9PecXHP8bVKwdYu6KpFxhTszObgYAsHyGk4uzkhFu3blCMxpRlyf7eGOE94/GYnd0pq7Lld3/vffy3/9//4P77r/Dp//encfnyAc62lOUilsp7y9npMSrNIzjQOdqmxjsbK8JC5CIxxjAajxmPp1RljbEenaQIGdPFWZ5D8JF1sKw58cfkeQFdKlKkKU7GsSmdUK4iC2y9WiCUilF221I3CxarOUlasKuz2BTp/Pg5zat7MgZcsPgQmeW8U4SgcD5EpLuMnQKD99HiEwk+6K63OLQ+InmDTPHodf7KA8bHJkOKqPwgtrZ0WOjr5a1BCI8VFZ6C1mpsKEh0isp30ZPL7Fx5BJ1mGBcoW0cbJKoxtIuS8/mCqqlpzw9xqxNsU6F0is7HBNsSrMG2LbZtUUTaZC1lJPkxBikip7lpK7zQjIoR08mMndks5sN8wLlAUYxpWosNkZGrtY6AREiF94GmNVjhIIuNiExr0InGe8dqtWQ8GgECKTVpqhDBYW1N09ZYa1ktV5F8Y5yi8xE6SRiNxoyKMW1VUa2WMXTdWIKI3RTzLEFNpqRC09ZVrIcnUjCr1iEzh1RjlucVtjYszuccruDkZEloay4XgjBdkV2+j6eun3D9aMLBaMaOuh+XVTTNLcL8JuHKPggwZRObFrWGujW4ENDlivf85m/fy3R7XqVXLkNmvv5FzfP8jhw7XETb3807jQx4BVLG8lJjzFph9rlyuO3pb3LrD3P+vZIdNhnqr9+zA26S7wyV6xCzsKkUhjn34X3024d5/f68w1D+8L43w/qbsmmADO+r375ZJtgr8WEUYHiuYUpgCEjsja2hcdCaNrYVTxKkklgXvb//UwyBodwtNTB89kO5EMEB8CFGF73AtDHtM57MmExjl8eyWnE2n5OmCU1TQV3FUL6xMCIC05RktVyRpYq6U9p11eBKD8KTZznZaETbNuzuHFAUM9p6iauX+PuuUK9KinyE7QCzZWU4PS85PVuyWJaxd4ExBDxVNacuVzTlirapsLbFtDXWma4xXMRuffCp0KVpHVmasncw5fozz/DUB6+T5Tkf9/AjzHZ2qesKLQLBG7wLHOzu41zL4eEJZ+cLZju76KTg+q0ThMh46qlr3H/1Scbjgp3ZBKUkxlpGeYo1NdY3WAfCe5rGUFGT5aOuTDFntSqpq6p7HzQ6SfHEXgdn5yfkoyl5khOco6k8dbni/OSItChIshwpBfN5LG8M3tPWFc7USBlToEmRo5WARpEmYxCa5arENA0nR7ee03y6J2MglurYmAbwRGUue/O07yYV8L5T5ELSMQLjnCd4Q5DhApAlvqMC593tkKj3eCxO2Eip67tWvyEQFKhkQjq+zEjL2L9ZaIzIKS5dwVmDXy4wbU1dlSzairpcUJdzVD4i1HNozpGmRooR0qcoAqZTjs4YVPBoIZHB49oW4R1SKfI8YzzOQKUkyYgszZFCUC7L2CK5ECQ6xdgW3/UN8CEinaVUSBGtPK800pgYhqoqfHAoKTC2JXcpWT7uIhKRcrk1Ld5H4hjTWvaLMcV4Qj6ZkKYJk2JMohKm0wlae6z1kfyibaOC6MCHiAzVZpi2IQjwwUXeA3IaU3F6PGd+vuL6tTMOq4xbh0vqZUW2I/m/XnGVl73yZbzn2pzff++Kw7njWE3RfBxhWiHbhmq5Qks4vHnC+fk5y7Kk7Uo8fGs4fvqpe5luz6v0+eT+/71Catv2Autd//kwbN4ry7uFz4UQF3obDBXskL+gz7X3YfqhkTGk+h02DOoJZobAvv5cm+H54fZ+3EPDYBMfsNlXAW6DBIc//bn68/fPbpiO6I2HTXbEYRSgj5psnm8z4jCs9uhTAMPvaxMrsBkt6LEPWRoBvm1r1q2jX8xytygORCDd3dI4cZ/4u2/lHLyk7/MiRWx2VlUlIcS2ukrH2vmmbaLSQVA3LSLISPcrNUHLbntFXhR446hNHdsgj6d40ogtS1MSpRDCsVwe43VOUUzJJzvIyQRJB3RrLdbUmNYxXx6zXJWRKbBu1k2UmqakWi5pu+oQ7+yFnxA8IkiMjVirw8Mz3vOeZ8jTEY8//gBPX7vJyVnJlatXSbKMnb1diianKhfMFyl12ZKmCW3rcELjrGCxqLGuZlXWaJ1SVUtu3rrFSx59mEv7u6RJynK1IssKdJbihKQ5W1CvVghBFwWYMBqP0DolywtWyyUgaEeGom6gqqJz6Ow6fRMdhvg9taaimBQkiSBNFE0sYYj8LXics3gfyNICZ1raJnI+XL78AElWsFqtOGtrsvwjgBnw3uOdhSAJIfIJQMAF2Sn3yHwkfKT68x6sp+sS2FETh1jl6wCCQ3bhPO8MLogOLGhjqZ2I5ENOJKAnqGyK0FP07D7Gu5cYTSJKtFytqOcLVlVDtTqjPD2mKlfYpsI3C0x1hmtqlLyECgaCJYTYSCkaMVFpB2fx1pJqyJQg0wLpY4RAq4Q8z9BZEsPyKkMKhetKTawLJLrAeRsnsRC3a0aFxJlIRBHweNWVVhpLVTc09Yos13ECtBlKJTRtg8CjcARXx9ZOJlYzrBeBIJDILrwUw93BZ6SzEcuyoSxXOGdQSuDM7bBVcAZvYmQihJTgLYcnS979rvdwdHSEl4EzP8UFuO9gl8c/7hIveeQSL7l/ykHmuIThnddKPnjuODQj2uRxpjPFjeYcszTMl6sYzjNtpIj2EJyjbV+4roVt53luKrlNWtwh8G4TqAZcCMX3ir3HAgwR+D1+YJhe6HPbmwC+zdB8RAJ3SrNb3+821uGYhwbMZrheSrlueTo8Zqi8N8sdN7cPIwHOO5z3nZF722jajCJsYgDuRofcGybDDo5DA+b/T91/NluWpfed2G+57Y65Nl35rgbQDgBhmiRIiuLMiAwSYnDI0AShF6MYfRt+htErTYQYowgNgiGJVkMFBiQoEcOG6W6YttXVZdNdd9x2y+nFWvvck7cKQKWmwW7ujurMvPfYfdbZ63n+z98c+jccPvZ0HL7XGFPmiFFpVBN8oN21n0Ai/lM9Pq0YmP5+d8Tywu8BiUg8AVT2ewmEoLHDSIgJyg/5c03deLp+EZM83IeUiSqNwhhN8A6X1RnjaKnqGUcnp+giyQSrokDKpPAadis2/QVSVZRVQ93U1FWBRjCMNn1O/cBqvWOz7ej7IWv4PRJwY/JGyKsr29wnj5okac/jJp8Id9vtjqdPL6iKkhAdl1fX7NoRU1Ss1xtOz87QhWE2X6CUoe83wA5nxyx3l2xby/Vqs3cTjCGy3e5ou5YQA3VZUvuKpqoIwSERSCHwPlCUJUUeRSSJYKSqS7xPpPjS1iwWaXRs+wFEpCjLHL0tkRKcszjvcW6EITXaSoHRAqNLFvOaEI5Yr1aJq5fPTF03zGYzpCro+h3okeqziQlethiIeQOJBFJIUIwei6JwSe+YEIAIpIUlg0T4kLpD7/HSgTTJkTCnGoaY2JmQSHXBe4KQRCmQGMziIYVpMItzbJSga4rFGdViwTD0jG7D2LY8X13Qrp8zbK5TroGIyNCj7CYXHmluKJguIlPssSS5GVoUnkIpjILSCApVUeiUPSB1idIKpECqW6tjKSW2H3IOwcBoLcak+NQUqRDou5a+26ULc5F9FXzEusAwWnQhEDlK1LqQY1gDpRaUKlKXJVKki+s4DGzXa2IEX1fEocOcnKBkMiFV2jA7qlBlkQg2wWMKqOcLYkzxyiAQXuC8ZQwtz55f896HH3OzWnP/1Uf0NnJ8NOPNB+f83OfvczJXlN2KR7Lj5JHggY788RPLH13Ae+GYNV/kB+4pXX9JCGvGsEpjCpKZZFo3Pz6Nd991yDvz8rvz6wlyPoTf4ZbJftjBAvtQoQmmn7rc6fGmjv8wuOfQSviQqX+3ow8h5DX2yVTAu2OLu897+Ps9zyBLqSa0fLrNoWRwet67I4G7iEHIRT9EVHjRuvkugnH4Hg/Rk8PHmpCXQyvi6TgsLA5/Bi8WZj4GpJUgYy7SUl5Du2vZu5j/J3p8WgH0aSOaw9+9OG5J41gJqSDIzu5aJ43/MIxoqUCkkDiV429jBKMMIjd6SCiyR4pSEm8tRZGigY9PT1gen9LM52glcTmVL3iLHXqG7Y5d9xykpqhr6jpxrMZuQEnJZr3l+nrNbtdhR5cMkdKrJ+RiUSvNKGRuKAGhkEqkbjmj0on017FZSz78SNINO5xz9MOIj5HTszPqpuHk9JjReq6vN3z8+Fli4PuEZguhGW3gZrVOuv7gc+eexopDP1AYQ10VLBczVptkOlQYQ93MqJuGqmnSKMt5QiS5BZZF4rRoRVVXNI1iLEaU0ZSFoTAqqdOszhyk9JzOWrq4oygrtBRUVcliPseYEpCsb26IwVNoTVUXSBHwrsXHLcJsEp/vMxwvOSYQyc1OTNpjR2TEUzNYixDJite7gBeOVDIIZL4QehfyLD0VA8HlTO0YCeNIZRocmiAVUVeIYoGoTjh+5fMsz18DUbBa37DZbtjuOsaxp11dsbr4CN9u6W6eYbsrpB9RqkAWBUY6YhpaIHPsY9g7OuRxRZYyGiWoNTQaVLSIoCiqGqMkSiqCkPiQoiW1UggxzXwl7eDwIdDbtOi0lMmMaBwRUrCzA0Pfo5SmUgovUjZDlBK0RhsDPjD0Ay707HYtMXjqUrGoCzRQFBXee9rdFh88Q99SVyV1odASjk5PcEbjvUPXFbVS2U4zUpUVs9mMm+uLxDxFIpTEjj3rwXKzXTE/PaI5OuKtt3+a7gfPmFUzvvjqCY8aQWiv8KsOaUdmUvK5heS80rx5Evj6Y8/vfFDw4fA5WnePhZKo0hFtIKqc5ZAJpT+uY7Qjxpr9JngIoU/z+IkEOMHOnwbpH26SRVG8IEcss3nK4Xz87sgBbiHwpF023O2gp9ckpYQ7GwG8qCaYjsMN/O7PpJR7Ul2It+qFQ2Tg8JzAi4FHdx8vxpjzQG5HDofFwN33ePc1HyIuh6jBYQLj4X0Pn/vwsQ7fB6MgOI9304ZVopRORjv5u/6f8nH42f5phcCn3zd1jQIIPhkNxWwnb4oUflOWJS77uQiREMi+7yjmGlNoAp6qqmnqKp/bVORWTZ3SBY1Bm/RYfW/ZbtbsNmtKowg+sGjmCBSb3Y7rZ1sugeV8QXBQljXX1yvW601CT2Oq3iS3hWNhCgY1kUwFQqa01qnJFCKmRMMY8G6k65Lr4sXlk7SZR1it1uhs5Xv/wQOePH7MN775hzz9+HFKj7WeKCRKFSlNVyiULpDKEAXM6holJO2uJcbA6emSxaJhs9vhXKAsK6p6RtU0KKPp+z7xsmQyjSpLk1Jn8/6gtUIEg9RLQnDUtcGOI1pJFvMGrRUuBBCCrusyOR+IaewuhEsZEc0M54ZkeiRTnoMPPcg1FNepIfwMx8sRCL0mJrFAsgwOnhB6gi4QISLCBG0EPJaIQyhJtLfVlfMjUiVEYOgT0cPGPHutj5CzmnK+QDdHRFXho6Y4foXi5BH9ep2qvN01V+vn2N0l4+aKMHYsqjnGbzBsESKClAhhkl9iTL6JIaMFZNGjIEC0qOipDCybkuNaURqRZjB9RAkIRqO1oahKunHIlxWdGLnOIrWmqEqss/TDgEyC3vTvrkXr5H7mxxFJspkc2pGARBuNLkui1ChgHHqk1AlCGgbs6BilZUfAlJKh73ISoMPbHrtTjEZhpEAXkqoqMc0MiyaKSFVrjNYEBKaeofsOSbIYFVlyEqKjKjVvvP4K56dnnJ69yg8+XDPDMfMb/NoRhi2lFhAsKEOpDEoKyjPN2VJxf7HjN//4GX+0OkWXv8g8HFPZb1Pa96jEVRoM/RiRgRA+PYbYOUfbti/MwWOM+YvsaZpmrzY43BynzeuwOz0kDt5N3zvU5k8b3aEq4a79MNx27wlpgzjhWHdUCdPzHSIChzLKu5v99ByHzzP9ebcDPyQ0Tu/j00hrn8ZNmAqjQ2OhQ/TgEB04lAhO7OuJLzCdq+m5DrkF03uNAkRMCg5hJQ8ePOC1117j48ePWa/XL3bW/wvW0Y/j+LRze/u7T5oPfbIoiJCtmafHC1kloLXAeot1geCTl4y1No0NvKcoDIhI05Q0s5rlcsHx0RHLoyXz+YwQA13Xs7q5STHzwQEeRMwZA4a6mgGS46NT+mFkt+uxPqCVZLPpeX5xw2q9ZhjGvatfNkgghkhRlBmJSt63St1+X7z3qbEhp8/mUYd3fv8dRkSkTKFy3/3Od3j69ClCSa4vb1iv1jjr8DHH0ZuKsp5R1TOKosKUitnRMcO44+zshFlT4vxI20WO/AIfPNfX15gquTAeHc9o5g2IhOqtV+tsLuSpConRirqskEIBAYoILikwgkv/zWYzjo6WGK24uLrEebDjSAyWpp4TvGe9XmOtZbfdMZvNmM2O8N5meX+HjVv68ZJd/5T1uPtM6+zlTIeiyF7fNs30YyREkT+EiLR98ux3IW21oiSKOsUQB585BR4bwXlJ7xQRCaqiLM+oTl9nfnafcn5MkAXtrmdz/Yybqwtubm7YPH3M5uJ9gtvh+ht8d42wLWVhkEGRQgoTYS0KAVITRcohCIAIDrRAa4FWKRVL4BEKFqdHnM7r5ABJRJQFQaQvxzgkG2CLoreOIgqsHwghOe7VumS0Pf3QIfQ0J45pjiQ1w5i8tPveogCpd0ShkKogBkGMCjsEXMIokAJmTYPVkr7dJELPGKmDxoWQktdahxJQFwWnyxm2m7Fdryir+1jr6JxN8cVS0Q+WoqpwwGy5RJBkjBFJETQLKahNxenJCUYrnq+e0W5WHC0rKmVxbkgOZAJEWSBUgVIGKQwxKk5s4Av3d8S2p/7eH/OHl8c8M69RnLzO8fJj9PZb2N07BPf8ZZbbj/S4O/c/LAqmkKFDot50+ynT4DCsaOpkp8e4q5GfEIVDyHvfwcJ+04O0eR06DB5C7gB3A3enDfXwPhN5bnqd0/uC2w79cHxwlwB5CNsf/vzu5rP/WSR1bpnTcPgchzyI6XHuwv+HXf+h1fEhb2EqIg5f1wvn5WDD28c317fjmocPHvLLv5Skwb/1W79F/xm7o5/U424h+0my4KdxKW5TYBEqjybT2vOTrFVqpCoIIXXkWhcIIXFuYNbUCAnODsyO5hwtZ5yeHnF8fMRs3lCWab0dH824f36SgoSyQ6xzgevrVVIZxJRqWxQFbdux2ezY7jouL5N0cNeOrNc7nBtzly/TWlaK6B11XXN5fZ2+Zyo1UMm6d8S5Ee98sqkPWdGWR9A+xgkWwXtH3/VcPH/O1eVVarB9gv198ESR4t5DJHu1CHxwhGiZzea8/vqbnN17xNn9+xwdLShMwWw2Yxg81nqKCppFjdCRXbsh+MThevDwPnZMKqO6LIg+0vfJAAshODo6YrPdIrL9sdYKHwLr9RYhoG07mmZGXRl0oRAiuToSYiKCR0/ft5Rl8sDpnE95PCIhgdebNRfXN59pjb1cMUBk9Al2D9ne0sccEYoCHzPD0+NiIMgKXR9hQ0SGSG8jfQyYIFHNEfXimKKeUZYn9INk1BVxdsSoK8auY7u+YHvzGN9t2F19DN0G4S1F1WCIRCGJQqBEIgIiC0JIFVfMaIDIWdQxRgiOWVmzWJyiRWS0nq63xFpyMq8oleT65gZjTLKEVJq27+nHnqAKul2LEAoXLEI4gk9FR5SawSYp39HyCOctIUSc8/gAQhV4RkbnKZVECcHDhw9YHp8hVcE4DOy26/ShzjTD2GGMRElPsAo/OoqyRmmD0IJu3bG+ucaPA8u6otYPGMcUPOJCRMVk5+lccl5TSlHXVeIKRA/Ro02BLooEi2lP1AF3/ZhAZHXtkd6yqOYsZiWlVgjvsWNLYUp0MSkqKgqhUYNjcCOvn8/prp4h13/E+6t3eC7f4HL2Gu3x/4YH87dpVl8HfvdlltyP7Ngzqu90u3CbC3C34z7caCc/gcMN9XAjnQqCQ4e/SfqWLrL6BZ/4w1HCJEmcColDAyK4zdpQUt3O3uWtfTB8Ur54yEM4hNsPO+rDwmDaUO8S0YQQSCWzhWty8EMmstTdscddjsBhQTGd84nxf1hg3FUZHBZUh0XNVCwcFjx7pENInLC40SJFypt/cP8+f+mrf5Hdbsdv//Zv5/Pwn9644E+C/+8efxJyoGRCDGOMdF2XzMlQVGWJjx6lSopSMQw9zo4IGTg5P2Y2aygLgxs0z58/xdme4+WMRVNSlhofRmJIj6UVaKnYR0AJybypWd/c8NHHH9F1A00zp6oryrqk3vV03cjVOx8yDCmpsOt2eOcoi4qyKPA576Dr+/T9lcnJ1EeP9w7nxqxIc4SYv9OTk0KczkcilQqRjOrGwSKlyyhX+r6EGEAKtBBomYL3YhixY2Dodgz9jtPTe2xby83GUTVQVxoZYRwdJ2dnKZgtMzPsOOCcpyhSXLQxCm0MPiZjubIqGW1C0HfdACKpmqrK4Lxnu2tRSnN+dsbDRw+ZzSpurouEkJBUYIrIoqlo6uQ7Q44sns0agvcM3iJ1BbHCuc+2fl5uTCAEo3eImHyPQ4zEIMB5AgUxkDYkH7BZruJ8pB0gOtD6mKJeomYnVMtTqsUSaWZAjdtsUkX19AOGoaNfXTGsLvHDjtpUyM1zCt+ClkihEVnmEmzMkaUBqUq8kMiYTg5+JIps4woQA7UxNAbc0BGHHuk9LnqsVZTFHKkLUAZP6uivVxvG3lJjmB8viSiESrGWMbsC7tqeth9o5gt8jAx9yptWMl20h3HYOwYao5g3iYcgQtLjJlOQVJX74Fhv1rf5D85TFCXn984xRcPNZkdRNcyXPnklCHA+JW75CC4ICpM8uUfbMVpLqVSGzTqs7Vk0NQHH6D1CK5TU+DgyDGtEgOfPOwoBjZEURIyQRC3og0IXBq9BS4+UAaEklapYhiPGruPmZMbF848Z148p2g9ZDw/YFW/xUf0QXf114P/yMkvuR3YYbT4R57sn6h10pIdSt6m7P5yLTxvcBMvfNTA67F4PxwaHhcQLEGcuKKaCAw4u6lKkTT934kqm55IqP4cUiHAnV4AIMhMdxe0Y4e4mf/j6Ykx5EsD+PkyFgBCJZ6BU/g6ndNAJbI93OvzD5ziUC949b4eFyOFrOrz99Odd5GF6rOmzVEoloDim9aiMBi9QEc7Pzvgrv/IrXF1d8d3vfTeple4UBHfLg/90xggTL+XFIu/wdwKoqgohJhOs3D0D42gRSuFc8rhfLBpmzQlnZ8fce3CO1omc9od/8E1WqxuGvmO5aDg+nnEsj0BEht6hdQnkIjum5L2iKCAG5osZDx484PLyEoTA+TRmDThW6w1t27HddbTtNo2Q8zUzhEw6D4AQSK1QWu7hfp+l1t5ZBCGt2ziN0gRCKkQgFQuQOAAktr9zucCMCU2IIhUMKfgnxchHmdZZVc2I0XN5ccFgLavtmrdef8Rbrz/i/GRJMytT3HJVsuk6ht3AOPR4b1O2jh1QyjBbLJLhDomgKZXG58AjKSXzWcOsaajrmqqZpbA6bXBeEtE09SL/eyAiUyCUMnifRpq73RYfXI5pt4x+x2hbpEopuJ/leLliAM3oQQWHEEna4YMg2hGkSkl1LunchyAJREoU1ekrFEVNWS0JSHrncdIQRIUQDdZaunZFt7qkveqw7TWxW4PtEVKhzBlKeXTwCCWIMqbwGy0QKsHx3id4JyKT0iF3wKDIuAF4y2gHekGSEWa5lbOWVvQslieYagYCPJLRR0YXGFzAREHvHNaOlAXoHCOMSKZJw+goXEz6WOuoygqhDcGmRSuCpyp0Ytragc16RYiCcrbEBU+IkV23Y+xadrsdxECpNU2ZZCSzpgGRrEFPC8PYVGxX17hxQChN1cwp6wVFNUOoIhUscky/q2vqJnlX31y2rG9WKWa0MBR1iTEVUkt0ZRBecLW9pjINMxNRocfZwMWm5Vvf/x7n56ecnZ7QlBVK90RlCLKgMDVHywXnJ6ecLZZs6it8f0UxrlmGG7buEU/G5mWW24/0mDruT/MLgFvo9RCGP/QJOPQIOBw5TMdhFwufVB7Ai3N8KeV+/j4VG2WWFwG4kAi3h48n+KQ9r3eeYRzY7XaJga1uN14pBM1sxmuvvgqRFwqdw1GAtZab9YrZbEZVVmkOenXN5cUlSiveeusthBB88w++yUcffcTrr7/Oa6+/TtM02aP+xWTCT0NQJvj+0OvhLnIwjQsOcxgORyF31Q7T/Sap4+hcmltP9wMKWXD//n3+2l/7a6w3a54+ToSyTxxCpF31J/C4iwz8SUjB4Zqe3so0JrDOYW3KTAkh7Ut2GHLAmuTkZMnZ2REnJwuOjubM5nVqqMLAw0f3k1Oq0iyPjtJG5lPUvB0d1kYQKcIeIdAhqaVkDmxbLGZUdSrErXVsti0XF9c8u7hktJa22zKMaZxQlsksyoeQiHZaE73YJ9xOZlPjON4WzYj9+kaklL/9eozJrfX83gPG0SaUN4TEIYtJiui9zQq2gPMOHXwa5+5HdQohoe97nj19TnQWPzr8G49YHjcUWuFdIAqJFMmhcQrY887jfaQJqdDw3jMMAyF4qsogJIy9oywMsyZZ8tdNg9QageTq+prrmzVNWVKZpORATmM1TQjQ9knC7XwE4fFxpBtX7MZnuLhBmU+PXr97vJyaAIkLgug8AouPpP/GHlkvsU4QgyRKgyoaTH2MqeY0i0cU1RFS1ckMaHVB3/V4e4VgQ9/d0F4/AeeRsUW018hhl2b3RY0SKUZTBYGUZA8CCTJp+BOBJEOiiPz3BBFFBENIFw3rHYUR2Eoio0sVgkxJitt2YDl6PBKtUq6CiwJUAUrQW4fbdVjrsU7smeRSqpSpoNJG4bxnGC0ImXgTPgABIyP4gHeW7XZD13X01rOIoHXJYEe6rqMymsVimRQARcGsLKmLnHDlwTtHbwdKbTg+PkmwWlURpWb0gdEGZJm6yLJuMIXBlBUhkgJASJBUURaJeBRSdVwUBWI+Y+w8m8FxOhfUJuBty81uw//3D77Nv/vd3+fRg4e89trrNM0SpRI/ojAFrz18xGuv3OP+2T2u712zWa8Yuh1iu6V0A8avuN7NX2a5/UiP/SYT08VD7LvndCERMq+aA6h12rgOyW13Nzkp5b6bvvvz6b53N7fp9RyS6UR+HVKk4Jf3P3ifb3zzm2y229R9583qBdJdHguM47gPTIJb+LwoCt58803+j//Nf0NZVrevL8+SI9C2Lb/17/4dX//G13nrrbf4lV/5FXa7Hb/7O7/LN77xDepZw9//B/+Ae+fn/A+//uu8++67fOUrX+E/+8//c/7Cz//8XlGwV0+ElBY6vfcJtZiOw2JgOieHBdoLaEX+96f5DMQYGceRi8tLvvPtb9Nmf/ZxHNPrySONGFIC6Ga3+wQCMP1bConUCmvdXmz8k3b8aaOCu7ySu4e1jtGOWOdzMeBxPkmMpZScnR1z//4px8dzFosm+egnMwLqquDRo/vUsxkhRJbzOUqb3AimMamUEh88LsTc9Xt8HCh0irQ3pkSbBkEqXu3oGbqBq+s1XdfRdS3WjmkMKiQx5tFYyJ41IskcJ4M6KTWFKfFSEqW85QvIjCJFv0cWgFxkJwi+KJLKxJgyKbO6LcPQM/RtOi9uxPkCHdPjOJ8dPqVGInDWcn2zRkmNkopH4ZTFogLAlDWmMEg0MRisTwTKYbS0bYuSMknvY6Qskm+NlJLBKNrNDUPfokyBCzHb0Bsiyd9ACUFRaIoy8bVMUVLWNUVRI5TG+cAw9FhviWJkDD02bHF++5l52y9VDMQQszFQYo36GHFIPANFU+CKBdooimKGqZbIcoGTBcXsBF0usE7iQp9O/m5N5xy+7xiHa2K/piqXJKTUEoXPizwtCKUNuJSMmAZCAlB7Kq0KydJYZk27I2CxWC/Y9Wm+NDqLC5ZdpSiUoC5KZlWN9IF+17PddQlXk1myEgVSl0Qt6a3FSLCjJ2IJURKixBiZCBsZ3kqdSrpQEWPKzyapGEY3EhG4PiCkYvRgQ6Sqkje2iJGjo6MECQtJaQyFEkTbEyMM1tF2HdvdlvOzE+bL4xRrqTSqnBGlIWTPBKMUdVGkDAmZzD1UTOFFyhToosQUBdpopE7phUYK2qHFxci8VpQq0LYtP/jgfX7jt/89X//uO5w/fsZbq4GqOUZKjSTSaMnV1TXL5Vc5Xh7x2qNX2e229F2Hs5a+21FJx0zal1luP9JDCJG+jAedqpIyeQ9kwxCRN9y7krupa51m+1LKbCY1bfypczkkzR0iAoes/8Mi4DBcJ2bIMsQE+333u9/l13/913n+/Pm+42FSIcTUZUwXvInXAOw3eoSgqRt+/ud/nl/7tV9LSJWUt78nwcZPnj7hn/yTf8L//B/+Zz73uc9hioKry0t+4zd+g6997WvM5nOOjo/50pe+xP/4r/81z54+5fHjxxwdH/P5z3+exfygwItZ73DAy5je/10vhU8zUDrkDByiC4eIwOG4YbPd8P3vf5/f+M3f4Ga1zuFek4oj5LlxIITEbdrttrnDvLM2pKQwRR7l7fmRP3HHJwqC6Y184scxt0RpB03zaXvLmYgJPtdKcHK65NVXEymurk2yQBfp/InsjLpcLDBFRT+kQKEQBS6jCz6SZNZMpD32fjNpTHrryqmkQujUIK3WO1brDbvdNoUChYBUCT3wzhGk35vVRTgoDg1FkZQI46hA+7RLBIfwCTEIueARWZEgpKRtdwxZ5q3UxKlJCK+XioGp6Lf7ggCZRs5KaGRIeTtRpOLqZr1F60tMqanrdB3VWqZ0VAQxKKTXuVAKDONAWSRypikMWmnqukqKLKNo11d0XYv2HpcRFl+VSGC0A85XjHbAR0tRlOjCoI2hns25b0qGYeDpkyd0wwgykMz9Fba32OHPgTMQncUH8D4SfQ4iQuBEpNQN9b0lzfKYYrZMKIAN9Jsbtu1AYSNtNzCunrK9eoIfe1Sw2M0lMrYUAlSs0LIkKklUIm34MW3uQhqiEBnuTwYYCR4KSBdQcUBjCF7Qe+i8Ze0srd8ytj1ETxSB3lrWHczKgtNFQTMvMDOFHDz9MBKlSLwDrfBREKTG4ggIKmVwIo1GnI8Il2b+CMFoHUP2RxdSJstIN6TixQ9Elxy7hDYEn1y9hrZj6AeQCq0M52en+3jQsigpC4MIHkdgDJLWBmyIICW6qCjqOVqlCMzjew+Zn6ao0SDAaJWCMIRESMluvWIcekxVIXWR843SbVRh0oaoCxw9RaGY1RIpAs+vrvnmd7/DN7//Hda9w16tqM+3NL4EqRHRU0TPOHQ8enSfv/zlL/Hw/j2sSza/wQeePH/C6H0awfyYDkna6AWp+54cw1R2iISU/Y14kTh42NlOP4/E/UxdkDYT8SmM/U+bi99lxO8VBt7vndX6vuPZs2d861vf4vrqCsjz/UyKCtMlUoDkgMEvBFKk3Hkpk+vlZrPGH+QxTAVPBIZh4Ifvvcdv/bvf4uryisuLSx4+esT19TV//Ed/xM31Nbvtlv/pN36D58+f8/TpU4a+54MPPuCdd97h6vKS5WJx27UfnO+7qoBDc6bD8cBd0uH0s087f4eFkw+e1WrF99/5Pu/+8D0uLm8TMT/t0nf42m63ydufKqXy/cSdW//4j0/jVhy+x8SHSn+P5IKJBN1H0ubqM0tfKZU67qiYNTVvvvGIk9NjjFEUhURpiRDJR4K9+kZSGI2zCVkYBkuIGkQanxXG5GIg4H0EITEqSbqDj9hoKUyCt621XN2s+eDDp2w2W7pul7IPhMI4T1AWFzNRVYicr5JkkUIkIh5EPGDMbe9m7YgfIjEmIvW+MEVkzpbFWpfXfY8pWiDlzjg3EauTZ4EdLUrbfZ5MJI2lrUuKiMQDs1yv1pgnioePzqmbOYKIUml9hez0KIjUdc1oR5RKc36lDEobCqMhOqIXlE1NwGPdmDJxlAJv0UIk8zyZEJO+2+L9mMiUUuGCYj6bcXx6ymazYvQdHoUWS0p1H98/wY6fzY77pYoBOzqUUNiQwoqkmaHKJc3sDDU74eTBQ2S5wAuD9wHrdow+sltdEnZXdOtr2F0T/UBR14nNWghikoISg0OKGVFpglIIny8KMelEfUhVZ5AOFUIiiFhPOVhMH1CFRGrNxo5cbwduuj6RBrWiqUuCUnRuoLeJ/e78FoRiVs+w0uAzB6AfLdJHghCM2SUwMU4VUfg0mySZMQcpKIyhrKvsMzBilEIJj/Ij0Y74YaD3Du8CpZZEsgNd9MTgMKqkMpJZUzHYpI+NdUTLFI9czpJtptMbMBXWDsyPjqmqGi0ldVlxcnKGmdVEZ5EeZPCoGPOJTXaXIaRQJD9B18EloxEfMEWZLhZlyb3zJajAqh955+MnfO0Pv8VV2xNjQdt33GxvEOUcRYqg7r1FxJE/+s73+dJrr3FUVbzxyivUZcVstkB+7/u8+/Ez1rvlyyy3H+kxuSEKJRHkVMosxVMya+CVTJ0lt93rodRPqkQGjUBVlpRFQZAy0VcOGPyHHe2njR0OmfR7fwMpsgoq4vNYYtrEIc/7yZ1uvq8UqdPfjxmmTSMmIq8PIXfLL+YFiIkwSWS73bLbJRZ3CJ7f+jf/Buscu90OQfIV+MM//EN+8O67jH1KpxuGgZubG25ublBSEg8kmYdox1RQTUWVEOIFk6ZDpcDh3w/P0QtjFG7VCDEkBGW9XmMK8yc1yZ96yDu8j2nk8BN93C0IDt5oPKgGRC56EfEWGcnzdikVIkbQisKUvPLqA87OTqnqMo/RUrc8MfOtd5jC4PtkRKS0TrbqdmR0Ls33hUFKTYwBrRXWJ3kgumC0luBScxQB72G13vL4ySUfP76kz+Fek6OlVjOUEEmGDgil83QsF45ksqRQ6KqmkYnfNQzdrZomJP/zW2QpYm0iJjrv8ggi5DyASHAu3T5bDscslQ/OEaxPyrkQsdg9AgKCGAXOWezYcrKsee2V+5SFQkqBVgWqNkiVCpmhG5K1M4GyKmlmc8qqRgiwfUfX7zg6PyU4z/rmCqJDqlSYJTThlKJMfDOiwtkUyb3tI0JuWB4tKJVhebTAuRYXLFKXlKZgs+7ZbL7/mZbYSxUD3a5jcXwfszxBN3NmZ/cp5mcEUXC9uqYdHToOWNfR7jZsrp+zW11QAePlO5jQokNyKtRCoWWBKjSuS911CAk+D6iMCsS9KgCTEhDTxc8jvaMYA3o1UF2tmYslJ7/wRX4Qep6+9y43u4GZh4emYlmVyLrkyqYxQOqeBdY7Pnj6nHnTUzcN+EBtUliHIqdL6UDdSNpuRz86hNYYkfWoWlPWTerTQs4ocJa6kJQyoMeRcdjh2hGfv8BxgsyMyr72msViwdnxKa++9gY7D9/7znfZbbaEe5GHy1eZH5+gdYGp5pycHLFtN3Rthx8s0ToG13Px9Al6baiNoTAFTknCmIyNglSoQtLMZiitiJDcwoaesW+Tc5sYUcZQF4LXH55xcbXlex885/e/9QF//NFzAolj4HBsVitm83sYnfgjRM9OaH7w4WO+++77/MLbb1BIxcOz+zSLM5YPP0f1nQ/49u9cv8xy+5EeMYlvb5nqSu1n+Urf/j1lZuQuOvMKtNbpohgC/+Jf/ku+/Z3v8Nabb/IP/sE/4P69e0h/WwTcZdBPx91/39X/7+fX6iAu+c57SHdPfa0QSTJGntkLUhT2/r4Hnbk7sFlOby+hCgmyLPbFkBCJsJTGW5KmqnExsFmv2azXtwRGBH3Xs91uk+TSpceeCptPixc+VAgcnoOJNHjIGbhrVzydv7vyzdPTM1555RW+/d3vHvCE/ox1AC/IPOFFA6mfyByDg+LxT+MOpGMPEeTzlwhwWk0FKtSlYTYrWB4tkyQ5b/5laVDZTwTCPsNAKomKhoK0rvthoOv7zBcy2DF1cy6b/ggizo501rJeb6nKBrneMZ+VPHl8yQcfPmPb9hRK0TnHOPRokyRyNucDSJ265mndTB4DSmtkYairEiVhu9m+SOY9IJemf8asoCA7FKZsDeGziVgM+7CjiXSbq/L92C6EiFSKQMQJkciXeWToreb73/8Br752j7c//wbn9x9ydHSKUprRDgRv2a03XN+sc5MhUBKCHxiGVDhV9YxyPsONgV3bYofEbwlREIjMF6eAxQjABYZ+y+B6gtgyjCPvvdtTFyVvv/kmhYHd9oLtastuZxkHEPHPYUwgmhOq87eZnz6iXBwhKpMMfcYBoVZcXj3Fdh3j9oZxe4XvtwghqE4eUlWSOI5EfKq+YmL+SmX28JzMAKgQiigUKezIgx+RhcYL0BG0Hyl2K2Y3I83zDbWP1G/d50kY+Nq7P+D6+ppz63lLlzyoluj797jsNsjnT/F+oDk6ppkvCBJWu5brzY5RSKrZDF021HWDUpkk4z1qGNOiIc2hvLOYoqTIioFx6AnO0u22NKWmUZ55qYmywtuACQYRLeOY7IiVNtRNzXw+Yz6rqeuK5WzByfERx6Zhu9pwffUcFzxjzmevjKZuGuLQE8aRoesYuxERAkYKrp8/Y7asoazoomS1XlMvllSzBWiNimCaBt/3WO+IRQHBp3MeIl07Ml8smBnF2w8XzOeGj578kMG2PLx3StXt2Gxaht5TBJ8NpgQRmaSMZcHlbsfXv/0d3n50n/PjY6rmmFm1ZPaqplt+nv/Xe197meX2Iz1kDhSCdFHVWu+9A6YO3lqbzLPEi8TA6T7OOR4/fcrXv/ENnjx9ylf/0l/iwf0H+y74MIhH3LmA390M7zrqJSVMvq9SBHjhopYIf9MElT3ZcW9mFG+thw+fO4SAddnNT4AmHlxgHZvNBgT7c5NeUypKiqJARk/bZsGWECipIAba3ZbVaoXgRZLfXfLk4YZ++LpeQCoOUJRD1OAQFZg+s8NiYj6fc3p6SlkUL7UWpsLjEM2Zij7v3Z/9AP+Rj2m09f/PcXi+kr+/oCiScc/V1QXbzSZbS0fOz844PT2mqgsEgXFMULpUGoQmBotUgqoqks7fO5SSCUUSAkREK51Im9YxWofzge997z1W6w3HR0tWqy0fP37OOA5EIuOYQomKokryWp14YN6nBL4QklRdZXk0JOl0Wdb0/Y7NbpucWoceZ5O/yzTqSWtM7McXztk8bpDIzBtgjw6lc5TSXCPOO6RPpPBA8s9xMREApUwmZUZrOttxhefb3/4uX/m5rzBfHCUyqveZCAiL+Zx+sGw2G9rdDu9GpBRp3OIDIUpaG1ksjjg+fcTQbVFSsFgcsVgeEYPk+vIJfbehrJPSoOtb2m5Lbzt0bphXV09p3ROuNu/TjSNaL6nrAiU/2/fjpYqBoze+QiwX+KLBSYPrHV2/o1tdsH72EfiWYfccaXdUYUDi8cIgQo8sCgiGEETOJIgEI1CmRkidP41AcDZX+Uk3HIHoXYIsAxTOU/Uj9fWQ/htGxHzGtir5xg/e5eL6kvvW83aUPHQBMzq0qGmagjN1wQyNjxLnLTYqamVwpeRmtaEsaubVAu0VwiQHLOFGpPUYU+Kio2vbRNbKFXJvUwxoGEfmdUGtFSqMCFEhTEk0ET/2icTXblDOcjSfcXp6ytFySVEYpADnA5dX1zi/QUnFg4cPqaukWLh+/pxL5yhlIrGYJttZilQ+BaDbbBm6DW42o5gtCMETixJRlsjoGbqO7cVzRHRYN7JYzJnNkoe2Eyn8IroRIaBQkbO55Be++CrH87/E06s3ee/jZ/y/f/O3kbbjr3zxp2id5OluZOMiNvoEi2nF45trPry85OzeQ8r5EbGYIfrAbhS88ege/5+XWXA/wqPM6o/puLvBTBsRin0SodEGIcVeDnSzXrNarW5tio1htVlzdnLygiNhiDGPtm79Bu7KDF/gImT5V8z3ndjwhx4G6Yj7i5aQgqqquH//AdfX16xuVumiHm7HBpN50ZA7OWUlXbZo1Vqz3W559uwZISblwd4VkDTn74ZbhULakOR+HDERHmV+nkO5H3zS6OhwUz8kDB4qDQ75A58m35xGHNO2WBYF8/k8zXGnju7POARpTixksgXXWudQsMNBw08eZ+DF4vL254dHjJlcnS6f7O3GvUcpiRKgBETv6doRcAxDjvAOjo/6jq7bcu/+GXWpsWOPDzHP6ots7ZvQKKlgt+uYzRpE8HQ5fU/JbA7sBZv1wNX1lvW6B0pW654nT6+4vLqhH3pGOzAMPQB1VWG0urXWlpq6LvdKHpl9MKqqpGlmyc2wa28Lz5i4ClLKFNV8cKSY57z+MrchjSJiPjdpPBwiaUyRORe3nJJ4gFZ5pHQEL3FjIi6OtuXq6oztpqfr+lRMhIAdR4Z2ix8HrE8od9f2tDu/V1aMo+VmvaNenPDam5qjo1Oah6+mcZpOniJ2HDFtjfVdIj1rleSFKmCKAik1hEDvtmzsDaNtCTYysuP59SWr3Z+DHXF59BDnPLtuR7tdMbZruu0FdnOB73uaWYUQDiUsRiQvAi8U0nukKolRowgIkYl3RKLQyRgopMUZvZ1W+sEHkcwyVBCY1lGve5p1jx4sg4CtkjxeXfLxZkXjHa9GyWmIGDxYh912FKcnnJw8Ij77iM1m4GoY6QuNN4amLlkPPTebDafHZwjlET6ChJg7n6KsEH4kViFrzhU+eMZhoO06wjAwKxQCRQjgPEnCUjcUUSJUxF5fsdm1nJzfYzZfUjcLlFSJgKME/ejZrFuEFByfHFEUiqHr6HYtQ5+SxU7Pzzi7d59h9Fxfr1JYkZFEP/L82VPsYHllecLR6RnlfE5R1ygh+ODdD+i3ayqTLC8rI+lEQBmDqWuIntV6A95SmwqU5PxkxuhOWW8vkSHlmc/mJ3zucz/HxfU1F+377HZrvNIMfuThyTGdczy+vOYLIVJ7Txh62q3j4uKGnzpfvMxy+5Eeh5sj5Fk0iQmtdDLoCRkmlCqxoF3wPPnwCf/ha/8h2WHvdvzxt77FervhnR/8gP/zf/ffcXZ6yl/+y3+Zr/7SL1M3NSAyvDiZpgSGrCzp+o5xtHt5l3d5pp5HTGPWTz979oxvfOMb+wtVmGah3G5TMaQL/6SKIMY0UYv+ttMmcnNzwz/9p/+U2WyGEIK2bfcQ5ziOfOtb3wJug4mmjWdvthQCRptbBUB+7KIokkLhgAsxHdMFHF5UUtzNQ7h737uGRIcz/en590YxuYBZLhZUVZXHCLeFyyc+/4O/R17MRjhcIz+Jx20hAFNBeCi/hFtOyR7MihBjLgJMmmVLGVFKoHX6d/Qg6ybzOizW9azXK6QInJ+f5PWl03r1Sd0VQ3I5ndCjvu8plEBpkcmFI91uYLXqGB3cXLfsth2z5QJrR3Zty3bXMlpH1/V7PklZFnu5Y4xJGh3yyCmNwtLnpHWSc1uXUAdTVLS7bSYcJlMidIb4MxdBCJlGBKRR2F58Hjx7BCHenut0rjJCESc+S1ZYiBT2FL1Aq6RGsnbk+mbFZr1Da01T1wzjyNB2rG6ucWPPbHFMPZ+hBsE4dLhhwDuXGkE7sN1cI8RrKCMTH806cMmozg4dw+iIyBSaFAVKF5QiIuWIc5Gu79mON7TjCtsOuC4whpGxH5hXn+26+1LFQN/u8G7ADy2+XeN2l8RxhXQ9BIGJOkVkClD5xCcWt0++z5lniUg6BGJiuAYSw1WKFKDxgtYUUlqTixgb0ZuBYj1i+iRtvDaKj1TgolvTjR0PlGIpNaWPSRtamNQJbFoaWRDHiPIO66DznjZ4ilKznM+5WXfcrNcIoRBKEWN6TUpJlJc4nyRIgsS2tXZkGC2jtcgYUuJWXSB1nWfsmqJMlabSkrKZ07dtloeBkAZd1hR1lSw2bYepEwwrpUYJgdGSui6p6oaIwNR1crSa1Ume2Feo6NFS8vjpE3aDxUfBbHGEKisiMcUui5Sqd3R8ghBgqgoEyYyk7RDSst1uqUuTnO2ICALeOtbrlt12oDA1i/NHPG0r1r2i7cbkJVBVeF9ATBKu9a6lH8dk7GQDu+stN8+vefOsfJnl9qM9ZGZYIwjBY22COUN2pIuksBCboUSfw0B+//d/n29+85vsdikKdb3bJn27fc7XvralLEvee/99vvOd71BV1QukOR/ShjqOqfPs+z7J12JMCYLTBYtkeTH5BrRty+XFRSJ3Hmxjh43vRHq7ubmh7/v9z/YbmriV8Rlj9l7v02hEKbWP+P3BKz/Iv781VZpGH13fUZiCpmnYEwC14stf/jKPHj3MT3WLDtzNabgLUx+OTO52vPuP6uD+h7wC4NbHIEakktR1vX9vLwPxxwwFH3ITJkXBTxYukA5x8MKmYuaF8zf9JpI2xWz4UxYmn/OJZyLQSmC0IUpBH8ZEgC4NXRcYbc92k7ggSkJZ1Wle7m4Rm333jkgjgjo5HMaYCKvbtuP6Zo33ku1mS9vtcNFhR8tms6Xv+0yQTYZRSufie7CZ7KfR3DpzSqGyG6fI3LK456KovKYmQi2TNwjkL0zekIQka8D3I7W43+hfLBXFxFSM6XyGkDgRQqpEzg0+qZF0IhVrWdJ1PT/4wbu8/fYbzOczisLQ1BWFMfRt4tsoraj1DKUkPVl1EAOnJ0dpHzCKcUwNQ2ooExlyHFpurq6Yz+tkMSxcslIex6TeiEk14nyP7y2x98QhfT4lmrL+bCqulyoGVs/eQcWA8I7Qrwm7CwphUSripEJg0TJnASCz/A98sAhR5FMdMm/AIkIKfckD0f1sRuliD9VARPiAtAFjI6azGCcQwtCbyHMDH6pADA7lffJv1wYpIhKJEQLpR4ari5RnMFp08FgE2xi4Dg5bFywWp1yvWi5vbrL+XiEIaCkpMwM9zZoUzqZq0IaQ9LYZLk02lDOKao5WRf6SeqqqQmvJYrFEwl6e46KkLhtklcyYUJH5cY2IHoJn6AcEgaapmJ+cg9TsNlu22w3LoyOaukDiEN6jjeHo3j3azY62H1LUNCmYRGjBYrFEMef8/qPERpeB6C02d6QxJIe9Zj7DFDXWWeJgGV2gHTztGKlnR5yenfP+swu63TXd0KFEwGhFU9WICEamVC4tJCY6xrZn/ewZw/Ulr55/NiesP4/j+++8szeFsuOYw58s/dDvdcmjdQy5YnfOsVqt+OM//mOub24S9J8+0MyMTgFWQghuVjdcXF6kYsB52q6l3bWMdsgbTbJp9SGgpOTk5JSz0zMKY5BKUZYlZVVRFobpwrQuihcuSLfw+t59Hec86zy2ONx0J7a5EILFYsEv/MIvcHx0hD6IS1YyIVs/8zM/w2wxpyyKfTrjtGGHGNluNlRVxXw+34c2SSm5d+8eb7755v45J+LfofpieqxDcuDh7w+5AcALf/+TpJmTji5kkmNVVgliNiYhMNwyz/f3+9QVkQsybkcRWmuslHyqQ+GP9YgHQ+38R5wUKJn0li5QkP+QuWstjAYmmByIIfnEqElWCwgoSsNoNcIqnAu0bYeQkt6mDddkToXI68K5RE7su4HaFGlYKSBGgRCJpNz3O7a7G8DTdY6uHbPev8c7m0m9Wd0hyBp/izEiIxzJ1TAKgcr7SSpG0vdzb6YzrRUpEFEiZBrTkV8TQiJVagLSfbIpVfaiuM3KEBDSCCF4T5CpMQ3eE2VE5QIlhpDOfbb5Nbom+MD3v/993njjFcrScHxyjCBSFnqPNiZzOoUuSqoY8UoxjgNlVdAsjnHOM7gtQmhiSN4w0VuGfsvV1XOq8jUGa7HjwGa9YbO+JARLU9V4MSR12NYShpiiegLU0dDMjz7TKnupYmD35NuUpqSu5hhGkJZSBKJIEaI+OqTMuQHS5S9awLkBqNOHlsUZIXiwAxTJrS9JqlxKFiRpOUUmeAgfkONI7QSVqTBHBQLB4Aaufc+ayLGQLISksxYrKwgCvCN0HUIrdNvBYJHOEwk0FpY+cpFno9U8XdButhvqpqEqC7SAKASmzLI7pbLnQTKSmMgoMVFPU7XmAmXRUFazJMEZdlSFwUg4ns+piyLDXBBQqKJGmhp8RBkomoa6MOwuL9lttggGTk5POT4+xtQz2l3LZr1i1pT4EGi3a4xKqoY33v487737HrtuYLfdsdTJ2jPGyGI+x9oBVZYUZRoLECzGWvSQGcW1p2rq9HkEj9AaVZRgKnqvaOYnKBXYbT6k6zcI6WjqFFxU6pJK1zw8mfPTb77OaVNThIGr66dcf/w+Vbvi3rh5meX2Iz3+2b/8F3RtS9t2DMOQWMtZejd1CkVR5k3RJHjUOUZr00UmtRpAhhLzWtZac3p+xquvvopUit1uRz/0qSBob2d1k8lJOV/w5S9/mb/yK3+F8/PzFIrVNMzm8+xoKXn//ff5zd/8TZ589DE+utTYTDLCAwIhsO/QlL51S7zdFFIx8MUvfpHj42PgQE+f2eZvvPEGX/nKV3LHrzNR7bYLG4eUr77fwKeZfUz/N0kbp+ebCgl1gEK8kCFwUCRMx+FtDmH76TH30sPcNMTcuUkpqaqK5XKJKQrIs9HP2tlPyoTpecuyTKY19ta97ifiOCwEBDmwNxenMe7R2H0RSFKaGJ03+9w5EkFkzb6zGfFUgnEcGMe0AU6GWs6HHMTTMzpLXdcYo5NiRSqQMm04CEJIboSJKW+o65rFYsTaHrAsljMihmG4SaMwOyRULob8GRS54477URACYvY7kCQliZoIhZl8LoRI0b/e30InYiLX3qJNMqtuROa8xJCeO8ZDoiEJ8fU+5R1khQ1C5JGfgOBSIZB5WnaMUJR4HxFInj1+yu/9zu9BDPzUT38OowXj2FGUJWU9YwgB51IgWllVBJUQmwAURcXVepsCk1RBCIJ+6PG2pWu39O2a7XbFZnND2225vr7g+uYCrST37p0jZY+wAbtzBCcRwoCLKAdzXX2mZfZSxYBxa0rZYGSZkp1UYid7RCKmREeQKcs55hkLQeDDiAw+zd+RhJg+1CB8IgcKSZycBb1DhAioTJKLaWH3LTNhOP/8F6nmS3prEZfPGZ99yDDuQGtmRUO3a/F+SN4FPsAYkKqgkhI/jkzRjsYFToTjQfB853rDs/gMYgpiul6vqYsCrEOGgDpaZp1ouhSbosS4gBtz/GUIuHFgMasJWW/bNBVyEPgc/1uowNF8BiyxQVI1C1TRIE2F0gXzmSIGkUgxosFns5Bge/ww4gePLCUPH73Cs4/eJ+SchH7o6EOkH0cevvY60lSsr29wY9L4lmVJ9CNuHNmsNzgU8+MT6rpK+mAlkAVEO9Dvtoxjl0xrlGb0jmHsGYeBrrP4INhsthAdhYHYFPRjyl7YbAbeePAz/K+/+hV+4adfpXY7ws0Nu4vHbG+e8ko5cByfvsxy+5Ee7777LrvtFjva6Zq6PyaY+8033uBLX/oSb77xJsfHx4zW8rXf+Rpf//2vJ9vUvttnCMznc8qyZLlY8qu/+qv84i/9In/wh3/I7/3u7/H82XOU0syaJjk8CpntnyX3793jr/3Vv8p/9b/7r3jw4MF+9l+YAqHSxedbp6d89zvf2W/IkkRwEhlYOaS6TcdhKNLerU0ma+2JVzBxy6ZCIExIxdHxfhPYP6bM3Vad8iScd0mHzS2zfZrp33VsvLvhT78HPsnbOCAK3vUaODz2xMIwBXulbs6UBcfHxykY5865+dMYAHI/G56MjkKCz43GO4ufkKCfgKPQaQ1FkTZ+KSUynyIhE6FwAgaEuJWvKZl4A5DGgSabBxktsXbMHJaRYegJuehUShOiwNpAURjqqkG5NKceR8d23BEjHC2PMEpR1E2ycy7y9TEEykKzXNYU5T2WJwui85RFTdeNRG4h/pAJidoUSJHc+2IuzqdQpRB95vMItEjuqiEZ0qOMJLQJdSNOhMnkOEkmFCaUJBGBVQx4L/EuF7BhIrKmkXAi8IYsPUxcBiaEOiar+/TXycMhIpRB2BFCjbWOd773LtE7+nbN628+ojCKk7MHLE7vMxMCO3aM7RbXtyjtaVRFkJJytkDvdtiYQu3s4Nns1vS7Nb4fIXqefvwuox0gJjfb4C0BxbDtMCagWkVdn9Bbh0ShjGC7WnHx9NlnWmcvVQwUMqBFQARPlBKUhpDsIBP87UAnV0LvPMGO+eSRZk4qeQWEMCUeOrwbKJQmCLJ707SqFVIZFBEtQe82VOUxp5/7HProFDP0zAqBuvmY0Hs8hrOzM+pjuD/2VKsVcrtDhgS5ow26qSmlxA4Dfug58gG84mJwfLzr8EYjhKMdtqx3CRkopGTbtcznc6SSyRIyJ2UJ4bG2xw0DJYEim2Tc3KzwIVBWBXVTsNus2TlLcCnuctYsKJolZTNDmzIFO3U7uu2GED0DaVaUTD4kq82W+PFHPNKae+dnqGAZx4GiqqjrisuLC7bdFu8CDx69SqFL2tWKq+fPWbg5zaykns8wZUW9OMJGi/cWYyqUVIyDJeLZbtc4N3B+PwWTbLdrnj17zM31JcqAigmN6bqOIeeAD9bSjRFVLbgWr7NzD+lXLWL4CHbPWV1csF1v+PxJQRHXL7PcfqTH//4f/hr/6l/9Kz766KNESpoaCSHSaCt4Hn/8MfPZnFdfeZUvfelL/MwXvsDf+y//Hr//e7/Pe++9x//9//n/4KMPP+Qv/Pxf4H/7q7/K25//PPP5nL7v+cf/+B/z73/730OEhw8f8vM/93O8+cbrPLh/n6qqODs7QynFyckJr7zyKsvlMhmyiGy67ZITWRTpQuhc4jO8YDAzsfDzBWoiPon8b7XvZkgWu2VJVdf7gKV9fkfmREyEPanlXh0QyR1+Cre7RRkOWq673Xt6abeMa7hVUEzHxKU4HA0cFgnTGOHQtGh6rdN9Jv+CQ04DMTKfz6mrpFOfTtWfvZEnkyfn3b5w6ruOAy7ZT8wx+V0kdCTmy6Mk14qpkNEaozMJlHwJzYx4pSTOp7HS5MTpfJL/9f2QCKEmpQwqqfJ6kcwXC2I+T7t+xziOWOdQUtN3LX2IqZE5OgKfdE3BJ/OqojA473j91ddYzudcX95gdPJVKYqSse8Yho6QIXlIksH0mSRidpozS3Rh8mZ9670RY2TsB4Kz+yLUaEMMgWnIIw7Wr1ZT7PVIEO4gAjwVESEcjqvS2Nm52++eUnpfXEzfH6VVMjByI8Oww+g5Xdvx3g/fh5hMmX72Z7/MaD3b7RbrI7OmYXF0Tqh7uu01fXvNYnnE/VfeBiTPL9+n221od1vWl5eMfc+iXiJ1gTIaawuGYUAEULUArTiZHxHchk5YqvqI2VHKlnDtDtX1uOLPgTOgCUh8spKSGkyNt0NiZ0aSTWLOLIhRkOs0EJ7gHcScPpWZ1gQHrkeYGQKBzCXexPIUMYB3FF6ith1+F9l9/DH++SU+BsRuQyMNAskILOZLTjtL2Q15UXhc8Chvk+ZfG5wfiQQkkSIIzjH8VKG4sZ7n3mNjxEbHehxQQ0+jDKNLI4+iMMntyll8dDg74MYBKSLzekbT1CzmM8pap6jJfkREWK/XqPwlVNsd95enHJ2c0CwWaV4WUvymt0PiC+jkV12dHCNPj5ktZkmVoALddkWVfRC2mzXb7RYhBa+//hoBWK82SClZHi0xWiKCY7fpuffKI6LSlEVDER0Xz5+yXa8wRmPHEZVCv7Gjp9sNNMuC+eKIB49e5dFVx9o+Yds6Nhc3WB9pZieYwtD2LW51QxQ7Pvjg6/zf7JpnX1jwlaOWWez5cDPSRc/JScH4GaM0/zyOX/yFX0AAT58+TQx5Yyjyf2dnZwx9zw9++C7f//47/A+//ut87Xd+h1/7tV/jb/2tv8Xf+Bt/gydPnvCb/+Y3ub664qc+/3n+6l/9q5ydnfGtb3+bf/SP/hHvvfceX/3qV/nVv/23+cVf/EUePnyYuSJJd31Xh58uQGG/ESmlcNnTfczObGlTn+BNkld7TP7o8nC7EwKl9QucACkTua7OxYA8GA/Ardxv2pDvyv6m2xx2+IdKg7tSwUNPgEPoffJTmH4O7AuRFwiP8ELxcPjaEglzxDm33xSTmVIqHGazGbOmQUn5qajCn3TEGBExhY5N8mWjFYP4s0uJ/5hH8qDwaUQgb3kBSqa4YJGLoH0hlNdYDMlMRwiJFgofHF3f0ZEMhsqyoqxrlBLEMNJmkmwibJbEEGi7ll07UjcFRTFHSYPRmhA8N9fXNDON8y57AKTXp7WhyNB8WRg2qxusHTIyN0KICVUdx5Q46RNfydkpeC6johnVKDKa5K0DnWyQ7ZhQUWtHfAgURu+LiojIcl3ypq8T4hESqTwqT4wBH1N5LKUk2kCMt4VmyORfRMpDCARURu6EkEhJRjBGopB4X+CsxeiCoXc8eXzFd7/9Hg8evI5pKoTaocsUd6x1gTQlVVWzuoqMo+P68ppZs0DwCtfyGX3XAw6lBcdnRxRFQz1bJBTDO4a+Y7vbMPQDtTKs/A1BR4QoqWZHeDuw2z1lsC1Bfbb1/HKmQySDGx8dkQqhSqwViZgX0mYSoyW3K4gYUUIQRCSOA0LfzpuET8HCIlogSTl0TC5Tgcx81RodNWoYKXcdRfRsvvFN+hAJhWYsNbPgkQFabxm2K+SmI2x3eDsiBWnW45Kcsa8MTjTIAGa7QW7W1Nbx+mj4KIxc6phSDEVk2yWFhCtLFlXJrutw3ibyypgCIyYzF289aq4oygKtJc6P2N5CCJRFwcnxMeWkcRcCqRK0JwUpVGkc8zxQgPdsb67wdcnR0RHzXDDs1iuU6xmU5mad4j6DT5IgIWEYR+6dvkJZL9isr1AS3NCxXq1ouy2z5YJivuDZs6dcXV5Ql4bFYk50gW7bUlcFp0enXKMp6yOQBaasOD2FRw973vnwiqouePTq6wQnkMIk2Q1QdAOmnnMyj+zGp/z77z3jj41DRccwBiQl+qnjiJczh/lRHk1Z8Tf/i/+CqeHdE/eyRE4KQdt1/PD99/ja177G7/7e7/Hf/1//ex4+eshXf/mrrFYr1qs1VVlxdn5OWZa88/3v83/6b/9bnj17xv/hv/6v+bt/9+/yxmuv0TSzFFgyQdBCpu8HqUM5ZGXHGPEy57RLkUlfhsViwYMHDwgxJuvjMikxhBBolTzajTEUZZnZ4CVFUVBWFUUmA85nM37mC1+gaZq00R4gCdNjTRf/uwZBh1bKh1kKk+Rw+vfdwKHDGf/07xfUAAcFwFRMvBAAJeULxcV0ng5RhH0h4xMBrM4R3cl3I/yZI4I4vY4D1GE/N5a3Cag/KUdwDkyBVpkxT9K7F6bIaIq9LeIixH1eRW6uYkibkDKokPhOkRS4U89qEIGuHajqCiWTrr3rOpRSXF5eZsKzpqwa6nqGyQXi8ckJ9WzGaG1y6HQuw/syf7fKPdnOeY/1Kc43GWG53BTmUCGRrOR1LNPmGmJmy6dCIUSSvFsmImBCFdzeMyOtrzjZIOyPhDhpIEkqIxEZPYoIwiFI3724X18JIfMxZZUki/Lkmpv8CuREWknrzQeiAu/GRMDMnLiut1xdrXj3hx9xfP+c495S1BprW5pmiTEVwVtGG3ny+COqpkXKxLGLMlLNGubLJX3XoQvN4uiI1976PIVJhWvX7bi5vuTy2VOeP32fXt7w7OpjGnOOEonfELJz72a9/Uzr7CWLgeT1LKLPTFCV7G5zBUf0RD8mNqcUIGKStBCSooBqHzss5RT4kr7oUumUOihByCQBUVGjpUH5ntI6xOhwXYcoKpRs0vzMOiqp2XnHenfNvd0Aw0j0Nnvzq7So1mvW5w8I52+gywp18QzL+4TNDRrJ60LzbnQEWTCIsNdYB5mS7rphQMoU7iJiuuSMNtB1PQTYtD3HR4522DG4Hu8ChS6oy5qqqnBjjzGGsqwptSHYkaFtKYqUUBi0oWkaohW0tqPvd9RNhQszdustTz78IfeOG5bHacyAEKxWa7p+YL5YoExBXTdEEem6LbNZzexogdTgn41cX16yiJLoI9FZiqakyjCXzixjqZKGVxqD0CX4gPWRzXbH5mYN1QKpCurKoGUqBnx0DCEiywVVVVIqsNFzOQaCl0SvkRL+eBVR4bMRWf48jsmtrygKZCayaWOoTLGH0auqYr6Yc+/+fe4/eMA//+f/nH/2z/4ZX/7Sl/n444/ZbjccHR1xdHTExeUl/+Jf/At+8M47/IO///f5h//wH/LqK68mKRcihwalC8Ndu91Dh76pcw4xIGO6iD98+JBf/dVf5Stf+VlMYfZs+UOG/QRzpmyFZNOqlLr9M/MF6qbZ3/eWZ/+i49/0GqZNd9q8p05+2oTv+gnc3bQPxwSH5K3p54dowOGmfzez4a72/25hcWh1PEHko7V7ciUcvtdPOUSeu2dScFmW9F1CGiZL55+sciBdF7VWqbkKYc9kT3LKmEmDk3tfZryLkEKGMjHUOpvC3mUysxFKc319lXoQN7JeD9Rl6ljTughJUioF2+2Oy8tLmqrn+DjlopiiIISUVQEDUsi9/XUi7KXXF6LPaHGat09BWzGGPWFvGIY94dQ6h8gzY6nS/mKdpyir23WUfRUiISNCMb9vbkdZuTKIeWMXIn3mUk4jg5R5I4UiGk2MPkuN81mP+fGkyKgBwEQuTVi2kopCZy6GSrkz1o20PTy/vOSPvvVtzq8f8rNffJtxjPR9S1e3FEWZ3ss4EIJgvb4CenxoccGnxFypWMyOmM+WhBC5vrjCecvQdzg7YIeOm6tLlNGsVyu6sSWMNxRFnaS2CGyMePfZ0LKXKgbYw7weoiMEQYhZvxkjkkj0iYmJklmvHpAiEKNjz+iUKdEqiiz/yLp6GRxKJoLIdLJlUAiXCxFAyzTnGV1kNAY5W1D7kdXmkqux49UYk/wRQTRlYnsGcEHg5qcM83OEMYiFxwwjrqzRQaBcx6MyUtiey9glBynn6IY+VZIxmSiFEFkuljjv6AaL8wFnHTe7HWfDEqU0cejxLlKagDGpYxu6lsKl4kZLges7vA3QRMqqJsbAYrHAyAYVEycAqfBBMIye7baFcUcIkQfNnKKqcH6WFo1KRkchBpzt2W5v0IWknteUTfrSblcbirJhcXzCYlYT3cDQCYSSgGOz3rLbSbpxpFzMUUozjJ7VZsOziwuuVysaUaGMQBc6rQQBRVFytARPRaE1Rf58bEgWoDGCQrJ2khDmn76s/iMde0h86nTzXnHYvdZ1zeuvvkYMgccff8zXfvd3eP/99/nwgw/o+57XXnudcRz55je/ye/83u/x5S9/mf/y7/09PvfmWxSF2ZPzBLyweR9q7Kdi4LDjPoTLZ82Mz7/9Nm+++RZFWSQJopR78lJI7KV9QaCmblYepCimYXKGNe90vxljTr3URIiK+wvh3onwwDL58PUdPs5hEXC4aU+/nySEkwnR3Y19Og9/ksxwKlAOkyP3aYj5ojx5H9w7vwfwQnExveyQ5+HT+S+KFKSzulmloiK/t8mVMHifWOQ/AYcQk1mQRElBCImltY8kJqmzxjHkubZEKXmbDeEtJsPrISZtvxaGojB5zn9ryev24xcQQjGfL1NzlgmE1o6pARKSTbujaRpurq8pqwopJOMUA13nz1YlouYwWobMOUj+G1PBkIKBhqGnKHSSRCqJjgpvdC4mQv4ccyHo3AumXlMREsLhZ/bJ9TqtqbTedJbkpi1QSo2Uma8SArf1aBqziBgT9yyzNBP6FJMPjQRjFHVdgpAMo2MYO65uPO04sGm3/NwX36TQJSIK3NBhhzYFkcWQYoyLgq7fcrO5ph16tKo4mp0ynx/hQ+T68oKLyxXej3g3gnf4YWR1c0Nz1IAviV4zeJvCu1SBCxJTNizmf07FQDqxnhhdtn1UxGgRMREJRVYHIDOd1Sf/exknBrDMuthbCmwgFwNRoAh4MjM5KHTQCC+IpkiWxkLifaSTmracY0/OqexA3N5wFUa22lDqiBAaMZsRmxnj6PBK45tl8gVwnmBK9OlD/Mk9RusZVivuVYHi5iNG17GKydO9bQPRjmiR4KzCVJhqhm23OB+JCKz3DM4zBKiioO9GxsGi5Yj1eV4cRkYlkUIz72YIJEKVKCEpq4Kq1FSFoi4kYViwaxVKFyAL6mbJbHlCd/OE6+tr5senKKNomgZdlClJL6ZuaOgT2z/GyDgmH4GQZ1tD2zKfL6jKgrEfcW7AyAJjFJutZewsQUDX7xAeLtc7PvjoI97/8CNuNitktUwKC6FwUew9/JuyZLACYkKA0vcmpe8h0kUlSIOPL7ncfoRHgskTeVWGCDLuO4jDiwUhUhjNo4cP+Ytf/Sr/5t/+W/7gD/6A9z/4AGsdVV3x7Pkzrq6ucM7xd/7O3+FLX/xikgXmCOTpmDZmeBGa/zT9/CFasLcv1smdUhtz4KiW4Mo4dYNKpRHD/jFvC4DDx9932weEwP0oQKTPxZFHBnc6/cOO/dA34HC0cDhiuGsmdJeLcPi64JabcLdg+DQ+wws8BFK87nK55Od/9uf4qc99Pq+3F/kUMSSGuNFp3j0VQM+ePefb3/42FxcXaRY/MfEzenPYJf44DyHI5LvE+/DB7tfD/tyQ5uhRyGy7HRmHHueSNLSsSsoyWU4naa1FqjnGJHOzidOiZNrmhExy1aIoiNExm83wLjBaRyQyjAO7tqUsC8ZxQGtNbwNt190iPlIhZHKK7PuefkhM+SlF8xAhcpkTcvhzrZO8N3iP1GnMupceAomGleSHIau6bo2EJvtsuf9eTCTBdM7S9yYlLkaEzAT3nJg4/Z/IXC+Y/D5um4gYQibQp5hhYwQIyehCej/B0w8DpydzqlIzn8/zunZYOzDaDgAlNaVR9H2k3Q20/UhdFYi5RpmKru+5vLrCuYiWAaMFwnu6zY7ttqOYzVG+QfoapEoyUenRpmLelITq1ob9Tzte8uqcc8CjR0RLjBohkntgjHn+kg0aEhM0GRFJEZHRMXqHFgcfEgm+GUkbjIykXPjMGtVRU8oCK7YMs5qinDGMntYFttWM7WLJbrakcI7SlGyHgRutaCoD5RxxfEacLxitxzcVQRvE6ppQGEJVYmfnDKrgajtg44zPyw2FfMYVil30dN4TvUcGiRfJDWsxNzgfafuBbhySEYYUiKLACklnYdc62rYjhsC26xjtwFFdUyiJUhsunyfSUlnOMUWJlLBYNPTbG2xMzN7BOBCpIDg6PcZ5ywfdmrbruLq8BKk4Pr/HfDmHLfiY9LDr1TWzuqYyJX3bs1rd0HUdCMHQ99xcX1IUCU42ecZsygrrYdf3BG+xw0jXed7/8Anf+d47/PCDD9j1A1W/w4WYgo9c2jRLCSomcqm1PX2IGGWQMUvQlEQaDcog+PFdXA/nipPE5XBzecE/36dK/c033+To6Ij/8B/+A1dXV0lGCrz/wQc8f/6cn/3Zn+V/9df/OkabfWFxtys+nL3fhdCnscG+6yWiYmL9xxCIPuCEyyTTgHc5aW/anMlz8xAPJHt5Iz54jhcKgX0xAMnHJf1vv4GKRLNJRkwvzug/7X3EmAOeYnwhqnh6vMPRw2HBcHgeDu9zF8E4LJQOFSB7xEVpzs/O+dt/62/hbOo6nQ/JfjbzNFKoTqDICMIwjmw3G76lvsMP3nmHEEL23neMo/1fsMr+fA4pSd+1Iln2+j7HQmcEABIJ1eUCbbQWYsAOA8lLv8D5QClkIlsPKYY4kOR2ZV3TD8OtKVTmtST+FjibNtpmVlP6iAtpYzUmhX2V2YjKuoE+5zwUfZHjd5M51NBb7GgzxB/TuEJIpEzjAucsfZ+vp/k2RLL8MJmZJbTroHieEKb9eCqS5wR74q1S+gUEK33/b90xp++ey8mbZHkiIu65I+m+6XNgwgQi2aDIpefA4/1IFCp1795CgMIIXn90SmESmTIVLal4CN4RSJbP6fsdCV4jI2gqCIphGBn6jnEcUuhdqTFC42NgdGPyJdAlMWh0rNGmRAmNVgVVNWOxqOi6z6aPeflWLV9PQvDE6LOcKRtaCA9e4lwgyjTDkvnkKmJ2w8swlhB7mFISICZ4OQZPNjGmUAV1VRPnI5fDwAWenfLYqmSoC4YijSHmVcG9+YKPdjc8V575YgHzM9TyDGZLYlEjmpLi6gK3vsQWFWa+JM7nXG5HLgfPcVMTXY8r50jXIXyPEDFZuEpF9AMuBIRQWO9SXGcmrAidJB7bbmR30xOdy/7zFt9ZCi0ptQYMg7XcrG8otKFuHLIsORo67Ch58tF7FEpT1xXORyQJakcqitmcxekD1pePWa/XVE3D4vgYFQzD0COk4clHH/Heu+/wubfexuiSrt9ws1qxazecnxwz2hHZtQyjZDabUZsaqQui9yyPT6l8ZHX1nEDEjp6rqzVPnl2x3g3osmLod+z6FsoGqSKVUXglqUTyzLbeM/okNzK6Tuz2QoHWRAxh+PElwkUSaVAKkUibMUnKpjz1aa6+n0dKyXw+54033uCb3/xmzj8PXF1eEoGmrvmbf/Nvcnpyst8ED/kAn3AE5Laz/tOKASkkQaaL4T60aA/jxv0I4u7jpryMXBDETGO6gw4kPu0Ecabj7rxeZK6DExmOPSDuTTP/uwTCvf2y9/swqMNC63DOf8grOByZHN5n/zoOTIGSjDCHQInbqGPye5FlhVMOax1SWoK69VUIweQNP3XMxIgrS2ZNs3dWTIFFPc7u9lDxT8wRI9pITKGQHrwvCaFgzCFDQqT8CGNMSjTtB6J3VJVJ1uVNg8twOrC3oY4CjAiIGOmHEe98MhbSmqpusG4kWMFm09J3W+azBlNWlCZxEOq6TImtOVWwrqoUnKN0QrSkTMWIkMnAa7S4bMc9FayShNjZcUSINo0tM7HajklTnzg4OSBJiGQ+RPIoUFpjhwFBGnFMqYXpeyb36gvv3a2UVkIaeb/IJ0AkgnsaixxwB/Z/piJgzxjIjz9rapRRSXbuI0M/MvRprHx2NOe1hyf07Tqbesm9T0dZloQYmS0WSKnp7ci988jQDggvcJ3laneJ0g6Co5KKxhj8ONJ2LQ4oF036HHWFVg06GqqqYTZf0jQzhBrYrNrPtMxejkAoFSKkOVyiBSZ4JfECBEIEbIbTRUxaZZlnT1JETPR7SDKt8Uj0IYX4hICQBmFdltEEbAysQuDx0PNe3zJKSVSGEAd875FupB56itmc48WcZ5eKJ3ZgdlJi5g2zssApxVZX3LRQFWfos0jfjwzrLSpEjK6oZaTAYl55jZ2C+LhHthYtIk0x495sRhwHFlpTHS2x3ifzmhBQQiBiYL25IXjPWTOjMApTpmAmok8XV+ehrnFeEIJLdstjS7y5QCoYdnPa7ZouCrq2RGpDUQZEFAz9ljE4zl95g9FZNtcfU29XlNc1rNc8e36BQGHHgcdPPiRGTWdB6hSXXBhDM5uhpaAuS5YnZ2BKhITg0rgAbWiWxzx79pR+6HAioQUBRd3MGa3DDh12GGm3W0wRCYMCoylmM+Z1TVnM2QwWVZQo3SSSkgShFUqUaWz0YzomwtG06U1z52lDmuD5ZFEbk/OlELz99tv8q3/1r7LMCD788EPu33/Az371L/LVX/7lPcIwPdZ0wdk/70EBcLdrnja1/b8RKKOIWhPDrXxu2rylkBmdeHHmPj3P/v2k+UTa/O/A8+IOenH4eyllUjsIiRIOJ2TqRD+luz8cDUyFwHReJ3UAkBz97owTpue6Sw48PEeHn9EEhzvvUmEk8nhEJAe6MY82lNb0w0DXdntCqDFmb3KzRx9CpKpqjo+POT8/573338eOKS3VeZ8T7V6Y+PxYD61V8kAIPiF6RuNDZBh6tNE5GMtj7cg4jDjr0RKaWYmSgW63ZnSRdrtN59N6KlOhhOD8/H4aO0hJsJ7FYsZiPqMbBja7jhBAScNscUJhEvEwBE/f98QY6IeBoqgJ3lOWhuogYGjXDdnCWNANPc6HPXEwhrDntAB5NOUTLBWTZbvNQT1KJ41/Yv1PHjWk+8pb7sR+nZKitpU2CbkmJq5E9pQAnZ92GnXl552CjqQn+pjvlzkZSERMJkRSiMzLSa+hHRxiGJPBGwLr0vdjPld84WfepN9u2O62RDRVmeTGU2Gw3a4JwOL0ASf37rM8WtKuW7abXcp0sJbB7Rj9BmED171gdIHRp0jyICM3N8/YXF+hhOTB+Snz2YJm1lDXBdtWcH7y4LOts5dZlOmLHNK8GwdBYKNAYFDRZzKFRB1UTxMsm8DIACHDL4cSjZDcCH1MVWqMHhsGrraO1fYJN+2OQRuCMenDkElnrUQghp7t1lJpyb3Te3z4/Ckf71Y09YxytmCQhudWc9k6mu2O17eXvDLuMNFh1xXr5TEFBWevPMCXEkSkFEnS2BMIqmC2OOMNNePVi8e43cjzWUVzdMJNXbDerSEKeutQwVJgKQqF8BEZQEuTSHU2mcoMPiKFz9phS4wbFIHVxRNEjNw7f0jXD6gCTJU8um3fYmPypL5erVlfrbHDyM3NjigN1kfuP3zIK288orWWxdEJTd1gjKSUlk6ORNvi9eR939PMF0hd4rLk01QzvEgM1LZ3FAtDM59jiiIZgESJUYZSg+t6Kl3d+tlXJeVySVktEbueUef0xpi4wyoIZrXidPnjIxAemtXA7UZ8SOJLf6b/mzbXuqoSJyNvQqsQ+PKXvswv/dIvMWtmHDCNXsgIOHTnO/Tq/zTE4PDYb1iSpD09mIFnUVnmC9zC8LekKLH3q5/GFlM3uIdRD58zExIPj1RMCKKUKB3QXmOt3c+Z7xYF00Y7HXddCacRwu1ruE1IPDwXUzFxOK6Zfj9FzAoliW27/2ysdez6luvra9599126ruP999/nyZMnlGXJgwcPODs7w3vP/fv3OTk5wdpkrauNocmSRGMMox1xPpHr4CenEACoqoqyLGnbNtljZ0a8s47lvCKIZHwzjj3WWrQ2nJ4c49yIB3btsF8XXd8z9gNlaWjUku1qg64NdTNPBG2dRgO6qCgrz/OLK4L3PLh/jtQK7ywhesqqomt3aKWJMdD1FmU0SiYZXwweJTzXlxcMzifeVi4svXf7tXSbX5GJojJt5uFAJZHWwu1oDDHZb6f1LbJpmBCHiJI5KLT3NNlMOvRIeWfkRQpxElrhoszr4JYsSIy3zY0QKCUwWu/9LdJr0YzWEXzk7OyEX/7ln+cXf/ZLbC6fcXR8zNnJfXa7DgRpPGsMN+s1H37wIfrpM+6fn1CYgrKpEUojtIJiJHhBLSP99RX18Qnt6Bm9ZzZvcuaMIZ49wnUtSkcGu2V1s6PdGvrO4j6Fr/Npx8shA0onooUAZMQHy+gFHkMpIMUTCUTwydZHCmJMMx0RAoGQbC+JyHzBUiJd88iXOJd5Ceuu5dlmZDMGotKgDShFtpTIcBGAJxDovaBoaoq65Gp3zYdKYKqaeX1METxljBxFz9HNc452l+jgcKakur6mq+aUjYJ5Q6xLtscnGD8gxg6QhF6gZgtOb97hlXHN6mzJ4+WMD7ThSb1kWJyy2t4k+0g7EKMieo+IkaowFCTYq+0HClMiBcgx8yyyVlVLydXVFWW5wHrP+fKY2XKJlJrr5xes1heYsuL8/D6aiLMDZb2knh9xs90SpGB5dsYr1nK0OKIoDP32mtXlxwTfc/zaG3Teo1XE9xuGrUZXc0KQKW4ZhVKGBw9fx8bHPL1Z89GTJ1xeX+OcoywqqqKgqRegE8/AlJmRLOFmfYO9XBFNg2lqTK1RugQEhZbcOz/m0fnxyyy3H+kxwdRw6453uCHtGe6QZoEkOPWjjz++JZLFxD14+PAhn3vrLWQeKxxuatNxCKeHEPbyxWnjhk8WA4cdO9PtjN4raUIMOWsi3T7ElAkymaO8oDTYQ/kHzxMPom7Fgc1qdrvcvw6ZnlHJfKGFRDj7FA+A6fVOiMjhKOGuymAqjKbzM912KhimoKQXCqr8Op33XK9WfP3rX+fi4oLLy0uePXvGxcUF69UNT589Z3SWxXyRwnD6ngj7vImTkxOWyyVSShaLBXVV0vUdT548yTv/pzsr/iQc4zAwn80oihIpkhS0bXuUlKxXG5yzKAlDPzDaNJe/uVmhlKLvB6qi5PhkzvHJKaYosGPP1eVzPnj/Qy6fXXL64AxTNin/RCdovyhqjC549eGDPTo0jMmyOHjoujbXlhofI8oU6KLAO4d3PVoJ6sqweO0VbIh8+NFzCpNVSCHsa9KJ54JU2QToVtaZWGVxz/2Y5spTIFGSt4sM8adUQYn8hPslJNRfQEYWJiMsmEYF041ETPtb2rvS7yEROJWSRAlVWfLo4UP+ws//PD/10z8NMbK6vuL6+pq+79FKcv/eGT/9U28Rhx2hbFg0M6QQVFWZeC1uxNoxyXdjZNiu8fMKFz1Sp2C706Mlb56csb54n4sf9owusLq6wjQLjpdHzOYz6rKgXff07Zah3SJLgw0BLZOvyNOP3+fJ08efaZ29HGdAKoieGBzgkGgEhigVE4N9quaIMTPOk37TI/FIutGisGnbjwGVZTPS1EhT4rxn1/Zc9z1b63DSIAuNkAqVzSFiViWkuVMgComNHpAcHS0JwXLdrqiefcBbTvFQH1OEihFHN5szG7fUrWU27lh0lqHuiD/soKiR1YwjU3FezzHBUwrFcb3EN0doVXN/+5jTccf5TcFrywU3Dx8xvP4m37u54He/+01am9LvFJFCCqyTdIxY6dFCEaPAyIhWEhWzGgOVxgZSonSBLpOpDCJJ0o6Pjuj6DYujJffuvcrx8XFyPVwsiUJhHz9m0695fnmJLhvK+ZL19oarp49ZP/8IjWM+n7M4f8D6+prFvMpGOCBNQ1mUyfq5rDBVQ1E2XF69z0ePn3KzWmcL7/SaTWESpOhHhm5gGFqEMkSpMcWMup6hFRRGUjczmiaNEBazmt1u9yevrT/nY+oSDolDd7vTCUqeZnrjOPLee+/l36YLxqxpuHd+zvnZWWag327wh4SkTyMLHkLlh5vi3aIk3SH9MXX2QqRcD5llT5GYR3a37P7pIrqXUAmRr4Axz85vA3/S+8zqh5AukvuCSMRPnKtpdn94TLc5VAHcRQrukgKn2x16G0yci32nuD/bScP+zT/8A/6n3/xNHj9+zMcff0zbtgzDkJInrU1e7s4mx0eTPAfaPs2yuy5tmpvtNvtMGGaz6cJcMZ/PmM1qrq+vUgjZS6yp/1jHhPSYfG5CCMkSvB/ou46ySFyR0dqs4BGs4oayKpFa8lM//WVmtUEXJVobmJVI4Xn3++8wdCl7pB0sm92WKvtalMWIkIL5vKaqCsaQInWTdC9gipLoI0obEBGpNTGCIwULxeDY7XrKsqLrU0JoyOOMkKXf+0NMORGJPDvdZloI6TuSEYSYFANSiqRoh2zbHoDkDCilYvoChVx47McSMSEE+TSlAjf4zC+53btux1mKuq44Oz2hKCtMYTg/P+enPv95vvSVLzNfLPDO8corD2l3u4SIKEFpkkKgG7Y0dY0dLO3ukjTQyOqHGJGy4OToCEHEmALnRxQgvKBrW9zQYRQ8eOUVpPBc71qkKYhuZH2zw9Yl6+ueYb1FRkscC6LQUJVEHNvtGjt2n2mdvdyYIMdjiZisglMYRsoVCCIgPPiYJGWD94wxEN2YbCR9ZGSkd5YQRiQeLQVGKowqqeeaolwyxoH14Nlaj5MyBxZMl4dbSUgqBGSClMQE+QSqsuTk6Jhuu+F6e01jA2+LJY9ExXp+xLqZo909aheZ79Zo5ZkfzSlef4TvR9TFCrSiHgMuKAobOYoSZnNsPUfGwLwbqMeOIzvSB0lPRV0qnnrBU2vpSWYfotCoEGmtJQRLERJLuy4S2ccUFfOjE4qqou+2CJW+sKOzXN/c0PU989mCWT1jMZujgMViRlVWCBlRxtANDllW+KHjhx89R0jD1aYHu8XvtsTgsXbgow8+4H6UhH6DYYEuKpwukUEgTaQoFGY+p+x6pJKsVyu2qw1udMnUI3oGN9I5iw+pOtfGoIsCIwVaapq6Zj5f0DQ1zWJJVc6oTUGjFYW3ED4bkeXP45jUBHf/g0/RuUuJj4Fd2/Ls6VNuKUSwXCw5PTmhrmuAF7r9F8iAf8osfPr99OcLbPmDZ/tU5EAmzXPck6Ru4fRPbLxC7B9vskSeioEY4wsF0b54kQevMT+EykXgdEwb9+FM/9BV8XA+f/jf4Tm4ix4cvvYUEJkKi8urS/7Hf/2v+bf/9t+y225TxHT2AIj5djG/98V8gR1HnLWZdJnjo4OnyxIwqRTtrsUYzRtvvMHrb7xB13cvFjB/9nL6j3qE4NnuNmlmHiNd1+ODY3Qjgx3zufQZFUiW1s6NKKM5OT3m6GhBaXLkrhTZ8rqnbdsEr8dAURTpce2YOvcI81lDXWmKQudxmMC6ZDUvhARFGlsoEN5R1xVaFgwxMDiHNgURwWy2pO2nmOspmTCjRvk9RkQ2qmNvHLTntIjEAcg3nPb0vVRQSpldPF/07biLYN0+QNorEn8kjS1URhdeyNeQkvl8xltvvsnnPv8my+WSpplzdHTMvXv3uP/gXspnGQLBBZbLBcGPODumBthBYQoCjnFIss+JGzGOHV3XMZ8dURjDfHZE220ZbYcWlmAD/XaHCklRNF8eIXTBrJHYmIKKRtfhxx5nJYO1GBlodxvKekFd1wgRqOoas/tzkBaGCEIpRHCIXOMoAmPwjMLjbbJX7EbHdnS0NrGMg8v50VJhgeAHJCFF72qNkRErFaaY0TrY2MgQJGR3qMMPUkqdOiSRiYwTygT46FFCc7Q4wijJdnXD090Fx7S8HjXHauRy+Yh1c8RJuyO6AXU8p/7iT7H4wtu4j58SH/8ezbolBofxHi0sFBdcLR6wW56wKhuWLklDShspLm9o2kixrLjpRr4TPO9Jz7ZQWK3QgA+OfnA0UVAqhZaCEKGeLTh/8ApFUXB58SQZfhBpu56u7zBGMywH/HIiB1mEt1RVwWAto3PYGBGmoT4uWW/aRCDa7CgZKGKu2J3k4ulzdt3IUa0pdaScLUH1COtRwVP4IwoRKUpDVSi0SMhGbQpiHLF2YLAjY7aAbZoGUxZpnlk1KFVRV3OqoqEuK2ZFRa0Ec0aOGTkxElP8+BjahxeKuxvzdBx278PQ8/TpUy6vrg5vwdHxMUdHx2ilE/yvXjQT+kSHf+c5Pw2K3l+wxIv+AIe33JfDeZQxQf0iS6wON9SQxxxSiH1ndTtaiHuewyFq8cKI4vC5Mwyb4Mx0HifC4CFp8NBN8QVlwp2Ca9p0Dy/Uh+ctRTKno207fvhesod+8uTJJPT6xGadZt1pfntzfY0b7Qu3Oxz9TGOS09MT7j+4z3K5uFVUfOKT+ck4vPcMw5AJqoFhGFOzIZOpjwuOoR/2s/V0/iXDOKCyP4HWgpCzYew4cHNzw3a7xQeX+Vc57SIEnB1pQ6AqJEMrKZRMOTEHG3iIZJOhnjB6Ci2hMreNGlA1NQJJUTSYdc+k2d//bw/PT0Y/gRhTJHycUAESKqC0TjbK+T4hJLOkiX+WeBQvyng/WQiw/x3ZPj/EpHKTObRo6ti11iwXcz731lv83M/9HK++/pDFYkHTLJKRnDG3fjnBMw4jsiwYhw5r+6SHkyn5luAYx5GyahAioec+Ky0IFnzECCA4hI+JQO8DOu+1m5srTNnQ9ZaqrpK5X3bq9dbS1A39ZkAboEv5HVIpJJHFckk3fDZE9uWKAcidSfoAJ4RgHC1j7Oi3HbvepWJgGNmNuYskRxFrnYoJ0gdtY5Ig2uAYtlsCBc5G+iDwIhUCEkGc5kqAEBIlk+VrJCQOA1MTlGB3rTVHR8cg4fLZBR+GkVm0LOwaKR7ijcbOZri5JL72EPVzX0S/+hoySI4LzenVFjN2FCECmq2Fq/lDLk8e8sHZ69yPI5VOGl3dj5iu5/zDD/nrjDxSmt9Xge8SWWnFoBVjcHTOUpgalMR6TxQpxvjo9JzCFCk8R2mchyBkVuQnm83Vao0ddxjl6XdrqsURm+0GGxWYGbo5pmoW1OcB7zpK5fDb54xXI27cEVBstx0fPX7Ga49OMUZSLU6ookIojYmBbn2NLCqEH5nXBY/Oj3nr1QcINE+vb3h80bIbejwSbUqEKjDljKpeUFYNkEyFnPcIH5hFz7kK3DOOh5Xn0dJA9eNTE0zH4QXicIYNBxthjHsy2s3NzQu/Pzk6YrlcICAlc6pPuuPd1c1P9/20AuTwNYn4ImEwTr/L95cHnVR+0BcUElPHf0gSTPB/Nn+S2XXw4IK5d/e7WxBMzwsgBTLK2/TDg/c22RlPUr8XJH/i1n1xOt/wSTLnXXmhz5Du9fU1f/RHf8Tjx4+ZypPbEcLBaYAkQQ6Btm1fKCg+ca6B4+NjvvCFL/Laq6+y2WyxdmKZ36VT/mQc3iejGTs6xjHJmpVRe+nn0HdYazMXospQuWC13jKfz/ddvUgzBvqu4/LyKtkTK4XICoFEoksb6m63RQpP126ImQNmfRoVIAVFkXz+J0JgVdY4axFYvBsTZ0pIyqJKuQTjkIOEyDwQmLp0RDIO2vNNcvE6delKJfa9H8d0r1w8TAhUnOK2pUJEz2EC4YvHVCBk98L9HCIwAUOTMd58Puftt9/iq7/8S3zhC19AF9MYMLmMjsOId5a6KgjOooTAjgPj0CKVSL4Vo2fXtlSFIlqLKRMJUUpBSUnwDVJCcJax3yZb+KxowiTDMdzI0HbY0RFiRCpFU5RU9QwXBvq2pdQz1pcbtJIUpUUbhbMWN7RoqTD6s9nAvxyBcNqUE5uJ6AJEyzB0rLYrunagHQMBmRLYiDiRZkE+OETeKIyRyYfdBzrGlDhlHe3lJUpWBCGS8cX+K32by5U6o7yYxMEEISYDFUQiRM2qCqPu4fvA9WrFD7TkQWEQJlIqQfnaPczJHP/wPpvZgkoXFKenzB7dR27XlFtQztIHQV/P2Jw94OPmHP+2YVhIlsajvUO2PfXlNec/+Abn66ecC88rRnEkIl/XnsdY1nbERwlaM4ZU9UapCAi6fqRrB4qiQh2dorXhTCmsGynLgqpMVsLtJiDCQNe26Nmc682Ozkua4zl6doyoapazgvv3Fzw4XfD4+9/mw287rnYt27HjprV8/OQZUVp0aagWJ5xIQ1E1hLYliBWz2RzfjZQSPvfaQ44WS37qcssffu892m+MbJ49wxQV2jQEUTKMgl0XCCIZkITBEtWWs9LwVlXwlfsVD+uWMlwTxIZdefoyy+1Hehx2pHfn9oebtMhzdh8CmwxL7wlNUnJ6dsbRcgm8SEq821n/SeOCw+OQWCcy0vVpnfnUye835+n+h7c5+LuYJJypJQbkC9bLh+8zEbKytvsOmnE7nEu3UXy6/fBUEBwWQYfnW2v9grzwcLQwFRWJBa/3XgmjtTx+8pjf//3fp+/7T7znF4ui5JtxeX39QkaB+LTbAm+//fb/j73/fLItS8/7wN9y2x2b7tq6t3xVO7ABEIYUOaRESpBGMdJETARj9M/pE/VxNHLzBZQQFIAhAAqNBrq72pSvW3VtupPHbbfcfFj7ZOa9VU1Ukd1dLcSsiGsyc+c5+5yz9l7vet7H8O3vfAchBJ9++hmz6RwlNc73/DqOvnf0vafrEukshIjJNMvlEtv3rFcXTKZjisKQ5QohIrPZFJPnTEYlxEDfe7SWeNezXi159uyY2WxGNZqgiwKjc2JM/XqlJaVWFGWGiJGnz47RJifLS3rnCSQPmd4nNUlRFGhlaNqW4AcUsWkQgJyJIZfGJ4d6mbRoUoghr8AjGPxoYrxqH8SB5xJ3PgDicpGPIQ78jqvi/RI1iwK45na7k/sOfLcdh4YYcO5yJUmcgmGnn+cFt27e4nd++7f5je98g9l8Dx+SoVXb+CQTj+BdSNbyzqN1jrVtIhhWFXlZUa+35EzQKhJkQtV9jAQfkUKRm5y+TzLYvk8ZN66rBwvpdJplXpBPJpis4MbNW/R9Q55nZHmZAu664f0gst42LNYLxiEQXWRxdkKRa07Pll9qnn3FNkFI6EqIKb86pN5L3zb0MaLyAi0jTWcHeUqWEreUgKDoe4cmQU5GJj/1pu3STkBKiJ5MkpjMDBCjUmkhDQkVkDsOwVBUpr8GaEkNdpoi7YgKbbgx32PR1yxby9p2TJenHO1NGb90B3PjKGUTKIUjMt/fw988It9sUYsL2rpnIQo+vPMWJ0f3OW0F3J2T6xa7PaGwLXq2h7v1BlEpqh/+MfvdGW9hQJQErbkQgnOn8ESE1viYqu/epfCNGFJvdFOvGZcFr7zyOnXbUDc10+mI2XTKcrlku57w+JMPWdYN3WrL1kmiHhPMGJllVJOC2/cPuHv3gFxLuvomrt4yLid02y1OV3z0+AkPnl2kz6maIPKSW+MJo8mEpuvp6y2E5EVwOJ+S5wXVdA89mtMJQ/tXfw0qw3pAGFzUuKgJIiMOKXqCSN2mLPBcaCpTInJFuH2D0c2Xv8p0+4WO6339y9S7awvS7pi0CAqapuHBJ5+knvMwMmM42N9nNptd7pBjvLIGfnG82JLYff0cw36HsAkB4fpCLS4Lgc/t2He7/xdQgKvn3e20U34BUl4WFDJeQeKJSLW7CXJ5zNXPn0cRdqjFi0ZFO7OhHZfgOqFwdzO+Cha6QgN2RcPuuN1j++BZrVd8/MknvPfee3xRa+CSV0H6na7vqOtt2vW9cMz1obTi4PAAozVt27K3t8e9e/d47913OT8/+4Lf+PrHp59+drkjtS7lAygpadsG2zeMRgVFZlivLuiLnPl8RpYpqirn9u0bxJhM0JQybDebFD1cVhweHaFMwabZ0tY9WZ6jddq5CiWQBJaLCy4WC27cvEVRjclVsnNWKnlQjCejoRWQY4ymbWu2m5qLi9XA32jJcoMUKTlRQSpAEZc8Fq1VQiZkMiFKhSeARITUAti5Su5aVf5aQXp93iZjoM8X3ekykKkdwa6gDdc4MqnXnGU587197t+/z+tvvMp0Osb2HXlhMFITLlvk6bKyMTI92EOpjOA6nMtRSqOVIdtTBD9hs7rABpvSGwdFRqbkkIORFAbBwer0HOu3tE2N6wO5zhE+0LtAvdnS+y3K9nR9S4ugrjtOzs4oR2M22w1t7ynKMUoq6nZLWzfICEZ+ObzrKxUD3js6bTBRoWIiX/Q+ULc1UWgikswkkkTb9/gYyI2mdUP1JzVuZ6kaEztdSkPXO4zJrsGFkiglUSZHKDUQX6RUA2mQS+grkiKRETGFYigJMbkhCCkpyxw3qqBfsulqzmIkqo5+IXmpkkz1AW4jaNYbsuUC/e77iLNz2i6wGh9wfOcNPtu/j80KCq3oFJy2LWbjuCEFWVngtcEe3qEf7RPsOUXouO8L7jsYqZgsQ4UgK3KqssAgkFrTdi3nFwuc61DRM5+OqKYVZ8tzVhcXaAnz2ZTxbIIQjj543n/wkFGjmBzcJR/PMOOSo7tzXn3tLtNZzo5nI7UerC4lt196iagVnz7+jE/e/zHvffgZq23H2fma+6cLXnntNe69+RayKBNcFgKmrKhUgRpnuHzG6ONH7B3d4WJdI7DorMAUJSovENogZNL1aimpveMnTx+zN73D6PYtDu8dUNx9iTXyC+fVr2K8uOjvFqXdjnaXmBZjpG4aHj1+zM/effe5x/DB8/DRI54+e8Zrr7+Okorzi3Ostdy5fZtiWBC/iDD3RWTA50h78oWfc1UU7NzwnoPIf24hcA1ZEMNf14997lde+J5I5ODLB4hX7OpLnsFQUF1HALIsu5Qe7nb414uAF9sC142WrrcIdu6Ffd/z/gcf8Fd/9VdcLBafe43XT1kpxXg8ZrvZph3biy/xhfHqK6/ynW9/h1FV0Wy3zCYTjg4P2dvb+7UtBrz3tH1aCJ3taZqa6BPk7K3F+Z62bUCqxIOYzSnKgouLp2RaXrZenPcsV0tWyyUH+/tUZUk1niCURIuGcVUlS2bbMxqPqfKcUTlh/+BgKIqTi6PRBq0VTeMQWU6zblKrVkoiitFkRp5nEBxd1xGCx3vQQy87RpKzKgxwv4O4M+sSQ1sqkm5Gkjwv8dZdTsOEEDAg/OFynobhm7vH2bnkwjCFBwj5ai5eYW1SSeSQAzKajNk72GM0Lthua4IPeG8ICJwD58LguggoibCRcSYR+LT+hIC3DdZZlJAUZUa7uCCGGeNRlTas3uE6T4wCJQ2ri1MIgabrERLG4xyjKkI0zMcV63rNjcMZm5OndNsWF6Bz6XVvlmu2yzOqyT7CObbrDcvlms2mpm4cN2/8EkyH2t4PO3UwQw/KxkDbO6ySaKMJ0aONoBCazlpCjJTG0NgkdfIi4nbMXqlRUuH7ZEmsi3STkGq4N4oE56TPVg0tgbRD2Wmkd22DnRwsxBTuGWLAxeQQqExOLiW2qWkknFtF+/QZi7rhlTs1b95/hUNREP/8L1EfvEejSx7P7vB07yXq2Q1Gtw5ZKUkeA6Ht8ZlB3bxNrh2Z9MTtAjZLQmaIJMZvKTT7puQghwe+JkrB+XJFu14zLnKMyfDB412PjIGjg32M0dTbDUZLjIa+3dLWWw5v3kykns7x5HTNKzcLyEpGexPu3LvJy6/doRqZwQ1yd8GEVDwF2FycI6Pl3u1b0NScL05p1g3Ls3MuyoKT8YTZrbvc2L8FStCul4znc2ZZRes1F/0zlss1ddOilKEqR2RFnrINyoqqGmF0jiAm21Fbc+ZhqV/C37pN9so9KDPE5utTE1yHtl9kG8PVLjYSsc6yXC4HvsDVEtz3lvc/eJ8Hn33K77rfY7Va8f/67/97/vJ7/we/+Zu/yT/+j/4xb731Fgf7BxRF0oR/ITlvt1oFgRL6uQURrkHz6Zee29m/eBzXzn/3HFfPOei0r/3Oc+dz/Tm4tojGmOSk194fXniM3TnvvAG01vRDTzcZAtnn3t/rMsUXWzW77+0+k7brEirw/vv48Pn+fyDtLHcJd33fY+3zpMHrhZOQKWr24OCAP/iDPyDLshT7rRSPnzzBh8B8b++F3/r1GVVVEqWm7zv6jmRE1lnWmzVGAlLStC1CJmfCCFjruXX7Dkbr1GQVgs1mQwiBg4MDimJE27bYvmcymbA330uW8SEkh1XnQEpm8zllXxBigtqlTIFS9SDVNJkhlimgKC+KFOlrMjIl6doa52q0Vld8E0hcr6FvZW2aM0IqvPTpXo5MrdTgETrZs1vvk4vg7k0JKdCOwc1wN392iHEiuvpr8yoZ3sXLgmFAmgc/gZRMCEQYlWWSEmYZ3nm0AhsibZfs930I+OCISmFkRERH06xBBmLoET5CTMi3jND7xOdICY5pWfM2pQ4aZVguztmuL8iKgqZpsM2WVQBpRsyP7pJrQVtvWG8MlkAdPNYLtDbsHUxpNmtu3TgkqIrxbMJm2yFHa8Y3OnKlGVeTLzXPvlIxcLGtyfqOTHoKFTASLOCjpO0shdYIGRERkpeCpO8dyOTu5H1MMZxCYmRisBJS2EjctQWkGEImuKr8SLDUbs9zueGBtHMSYrC6TbIahCCEgfEqJVLnlHlJ3nYsbEvnCnrnWW9rVs+ekY1GFBuPePIY07c0N+5yeus+j/Zewo6nQxBPRAmHNoK9QlFGj7I9ZnWBePSEbNslBCOpRFHZiL3ZHrfHEXm8xlvP8ekJ4zxDMhuSGiNKwaiomIyTE9pmvRyMmALNds3piSJKxYeffMqz8y2jvduY8R7lfMLNu0fcvX9ENTJIGbmK0xLkRUFWlDRS0W63nD59SlWU3Lx5B2UyomuZzWZMpzN0VuBFhtAFfbei3qwoizK5RcbIdFTyG996m2eLCxbLlqoakZdV8iE3qRfXNdshbMPRtxY1LZGTEeZgn1iWSBlAeL6usetVX2ewX0cKLk2BiNi+v4Qvn1cARKpqhNaGBw8e8Md//Mf8v/+H/4EnT5/w7rvv82/+zZ/z9ltv8fY3vsHbb73F66+9xt27dxmPRux2PHC16Gqtn2sH7M4FuIx0knC5WMdrf66P3eKaDn1eavi3jS9UL1ySuNKdNVyzbb686V57rutqCiFE4llcQ0fgylnw+vPu/lwnHQJ89tlnfPTxx5wvFgNR+PlXvLv+M5NRjarksx+fLwSuD6UU3/173+UP/uAP0k1ZSqqqQojkNdD3PQcHB899Dr9Ow/UWmSebFyHT/Gyahu12Q5EbVJQooci0omkaTk5OuHXzBjFC2zYELxFKY/sOOfTEpRyin5VMpEvnkUSMMeg8Q2fJvMZZhxv8C0yWTN+8TRkQZZahpGD/YI/lao1zSd4oiUMvPRHzlDK4ANoolL5KEfTeDS0lQboU3NCKUkSReDulTpHSbjC1Sr8b8NFd+hFcImfi+rVxdc3Ga0qIFEm8sxNOczmhgxFCRAlFWZaMRxUgkyGXMayXNc5BnqW1rNAZRZkThEcrSWtrogy4rkVEiZFZMv6RkuihKItkDiV0aoIHgUBi+44YBEU1R+ZJKm+KMePxHJ2NAM/x08+Sp4ufsVif0fZbpCzJ44TepWTEpncE1VGFAiUje5MR42p/iDL+JbQJrA8EIj2eloCRqRcThaJ3LcJa8kIPfftB8hQUrbVkukAPoUZiCMeIfre0D1WUHPyeEZekkRh2nlS73f+OCZV2B+xsW6UcFq8dyXAglUiVDHV0jtGGcVUR9vYxZcUswq3GUnzwKaKYUt66hbIN/f4B2/mcTZHjZSR3HZkE5VuOqoJJ5hlZS75YYR4/Qi4WmKxEeo9AE4UilBPMeMKosEPwUsRaR6cEaJW8BJaLBK/tzamLjEikKCsgsFqv6buO1gUaL1k1nmp+g+mNe5STCUe3jzi6vc9okg8L7VUXNcGCkSiSXn69WRMRzA+OGM8PuPnKy3SbC0oFk/199m7eppjOsc4Suh4RArazWNfQeMm23iKEZz4b07YeLSUixARRuqSpDT4QBmKNiAqpNH2MdMFf2up+neN6f/o6iz7GSNu25Hk+3BxTbOu9l17i937v9/nDP/xD+r7bPQpCCD748EMePHjAn/7pn/Lg0wfJtGVbc35+zqcPHvA3P/gBd+7c4dVXX+U73/42v/Ebv8FLL91jNpuS5Xly9rvOC/gCfsC1E7+C+V/4+Ytch+vf233/i8Z1NOSLHi88V5yE5xbqHS/gxee77rNwfbd/GY177evd8btxST6UyVXu9OyMxfk5zn4+QXBHDdwRINNj7nxLv3goqbh79y7379/n4uKC+XxOWZZ478nznLZtuXnzZuIruF+jgKLdiBHn7ADVR5ROJMGiyDDaJEm1MWidVFYhpPuetY7T01OMTm3J4FOmgLWOoqgoijxFFUtJ3dXkmU5oZdchtMFkqR3QtQ0+BIRzBJHmRJZlCCGwXYeRyZ1PCJEyZmLEZDllFemsRekMby15nmEyk3bjQIgpByJ4CGrgB4TkG5PcNNOmRilNjC2Q3AOTdXGyjr5Enoa/07oRB3VASlfcyeCJ1/JJLueOx/vdxkxgsozZdMre/h55XiCko3ddep+dpbMdeZ4C9LQx9M5RjSpi7aibDVIaMlOSqWLYFgYKWWK0IlOaTOe4pqWpN3Rtg5KJNxeVRgqw3pLpDCEVru+4OHmMtVuK8QSVKYJ0BO3QOrnGBiLWB+qmp5gki+MqL8CDEoIQFN7/UkyH0o7Fe4ENkQ6PkpEgBC4GsD0mUym2ViqETmSOzloUkBsDeGDoJRIud0BKDTuLYeGPO2tKkSqohK2m3T9w6Z8+mEVfnw7JctJHogalDVIbnNL4KLhRVBzN95jmBZP1lsmmZuI009fvUR4e0tQ1cTJDlCUm0xgR0LbG6MAoj+xXkVHoqeol2ckx+vgMZSQmBnTXQVT4fIKbTnFlQfSWnbGmkAIXI9JoXAysNuu0gAaPEpBlhq631E3DZrPFh0jWQx1zZDFlduMlitmcvcM5N27uM52NUGqQWKZ3hRjBOk/X9wQBMjMonXHz1h3G03kiD2rFxbPHNKtzylHF5OAGxXjCdrkkiwm6Cig667nYtjw7OefkfIFAopWE4LFdk4hzg9Y5TRBPpvNBblhQ1zWr1YoQjkBI3NdI1n4REbjeJrDDohNCIMsztBlcxt54g3/1r/7Vc49zdn7OX33ve2w2Gz799FOss6lfGTx1vaVpak5OT/joo4945513+NGPfsQPf/QjvvWtb/Haa69x+/ZtDg8PmU6n5Hk+kPFSY+CLeAVf9PUXLfgv7tqv/+yLVAw/d+x2X1IO2nxxSbK6Du9/0eNeP8/rbozXv/dia+Y6qiGVpN22fPrppzx79oy+77/4XEVa4InQtu0lV+Dnv6RUjDx9+pS2bZFSsl6vWa/XXFxcsFgsLpGNrw+7+vlDyBT0xrCbpxDYvkcbQ4zJNhuh0CajKEqKosSYjO1mxWpxhtaCajQmywx9n6KCvU9QeVGWKKWpqorMqGQuNvjrI2RagGIcfA0GjEYKlElLh7OWdXcBWmNMhtFpM1hVI0ye0doOqRQ6pnZHnucIMcyDkEgAyadi4Az4hCgTI9JklFU11MH+8tgUT+0J/qoNIIaCWcorAuEOgRA73s2AdMFVIbBDJnak3KIoGE/HVKMKBvdcEcEojVUOHz1C7Pw1wNvBzTAqXBeSLbMuyfMRIlhstyVEQdc5yiqD3tHXW5p6hXcOTMHqYoHMNTJP0cQhONpmRVM3PHv8KWUpMeOcICxRSYQwoDTBDSqpkFbNUVWSaYWMER9DCsUjJqXClxhfUVp4OTuJQmN9pOk7bAhEEQema/JFllIiBsvdLEuZ2FKZy552jODiNeMRZZ7rdTLU+iJt79ktdOmY1CtM1le7QkEMxwyRqYSkJBGJGNJpzdI7yr7jft9zy0cmF2uyixWTl4/Yf/VlWC5pixFZXjGrRtgsR/Y9sdvCRHMwr5hqR3WxpHj6GPPsGdJaxHSPbLUiazYEqfHTffr9PdpCsV04XEivRWmFD2nXbr2jtQJBS1bX5OuM+XzOZrXmYrmidwGhNJnwhC4wnVQYDaYquHHrgPl8TGYUceBUxEGIGcOQP45E64yyHKMP9plNJ1TjOdl4BgToO4LtkTpD6xwZBavTU6ZlynyXOsN5OF8t+fjhM54cX7DZJqc2NdwEQgzkuabMCqTSIFLgSTmakxfZ5c0iuogH2vXXVw1clwA+pwSI8RKm9t7jnb/8WT+0C3ZDAE+fPOHpkyeD7e4XENZixFnLxlq2mw3Hx8f8+Mc/5t69e7z11lu89dZbvP3227w2tBCOjo7QRg+f4Qu+Abvn/TmL7ouL8s8jKX5RgfDiIvvc777AOxBydzO9Mhj6oiLkxTyCF8/1eiG2+94l6iDSOTx++oSf/OQnfPrZp7Rd+3MBpV3wTtd26Rr4OcdBOufj42P+4i/+grIsL42Tuq5js9mwXC4py/LfXSR9jUMIyLOcKCLBp0TYoiiRUrBtOgqd45xNvfosQwqJtZ62s6w3G6oyZzROPWYQOB8SStm15GUJQjAej1EyJotnHyBKuraHIPExYpRGymHhJhG08ZHgAl1To7McESJGm4Sy5RkyKPKiQIRAZiRVVZKZDEi2wmkhSKq0MKRzRtLmUgiByUvyIk+R8c4NssNECgwvIFZRiMFDY/eeDYTzpC/AeTdwYMIl2nUlcR2KVpHg/CxPvi9NXdM7R6YkRkuqMofLdlnA9h7vIquLDTE4vB0yRHySTHrf0fRbnBWELlnUN9uGenOB9S06y4lCsFotGE9HxGggBrztaGzHarOi3q4weY4NDdt2jdQlKmqC17S9RRLIdMFkBONRAcHTNjXeR1RW4qPFh44vM75iNgHXKgJJCJLOQ9vbyxuItY5MGaQZtMsStDE42xGCRQ+LeRAJuolhsAwlv1zs5XDMlX+1QiCJQSD1YKGqUpxmepIEWXh2FqVpUfQhJNaq1sS8YAW8d/wMsdoQheHlLrDvBWJ8jv3RD+iOnxGePWV8+x53ZiNGpkCcbyG0xHlJWRlG9Rr18BHZ40do7xH7cwSB8vwJmevx4zn+9i26o31Wdsui67ERgoRMG6zt2Ww3EBzjckRRjsnGM2ovCJsaZz1tAF1UmKxEZRUyK6mtReqM/YMDppOUMOZdRGbX1RXpxmeM4eDgCNF43MUatGF5ccG27sm3Hu9aVucX9E2HQNBtNmil2ZydEascXeaYkaS1itPFBe9+8DHH56uULRACOsuROASeXEgmucbkRaqKixFowc3bB7z51qvcuX2H6CXtpmf9aPmVptsvcvTWXjLe4Xm4ewd5Jqg6FW6bzYb333//Uia3G7bfFTQ754ufPyKpoOj6juVqyfvvv8+f/MmfcOfOHd5++23+o3/4D/m//Vf/Fbdu3bpM5vuq4+e1Db7sz+HKg+E5A6b0CykdLg72wy5FKjvrnrMR3j32bre1Kwquew3sMgustZeEw+tujZGUR/+9v/or3v/gA9brzRe/XnZrSCSIcBliE1845vqw1vLX3//+4Ksvnj+34R60K2b4Ao7Ci4//qx7OWyb5hEhI0cEhDLv5kk3dMp5O8N5iTIoxPzs7Y7OpKauM+f4BNw72KMoKIdP9WaqMGALjUYH1AaUyrO2JOjm7FlmOMgVN22JtB4hEDo39gEJosqLChUDfdhiTY22aF2VZoIyi7RqSc6VCC003IDJiaDOkzy0SBxJg6oBEGBACrfWQxRDZ1jVNkx4vzbsXWlzDhxNSJZ0IowKIkhiTt0IcCo+dpHD3OFfXR0INUutF0TQ1tu0oRxVow2w6RWuJ9S3OWfreUW+3CZ1abxFKkmmNMYboOjbrDc6tcb7DqDlGBmLXsD47ZltfgIFSz3G+pu1ajoobbFxPv14TBOgyR+eCqtCIDIJwtNua/fkNetexXq6xIZCVJVlVUXVbgvL0A/xaFCUyNzw5fYL/W5Cz3fhqbYIQYYClA5EgJUEpHDsoC4ILWOfhknwliTIiTdKlCr1zD0xtgBhdgoCIIEAqhRJqOGbnGikGyU+hfwABAABJREFUJmvqK171hiT4SFGWiegSPOK6D0FMFpza5Ahj8Jnhot3w7vYcJzMsBW/5DPPgIe6Tj8h8TekVenGWCHHTGfbuHaI/QM8mCG/pHj2j2rRkh0fkSiPqLcUn7zE+f0I0I+Ktl+nu3GQxMjw8OeNhsxlKlGSdmZkswXTO0bSWoHJu33udbDRiuV7SNz1t13I4mjI9PGI0mg8yvor9wxvM5nNcH2m2fdrBK5Mm/i6fgYCQkm1Ts1itOFtcsHryiMXpMSApJ4fszyeEZkWzvqCtFXmmWC/Pefz4MUVV0MfAwa27VPu3UEazXq9ZnC/wIZAZhZCBUWkwKmM2HnGwt4dF0gRJ63pu3Zjx93/nm3z7228zHU/ZrHrqs4Z68eV6V7+MEUKg7/tLZOD6n93N55IkN/QTV6sVzy8D/+4l4XM/jVd0P+8dTZN2YxeLBQ8+/pj3332XPM/5f/43/03yEv+ix/w5C/sXff23fX/3sxd357t/r3MBhoMviwIpJV6kWNfIleHSiyjBdUKgc+45zwE7JOrtzmPHI9gVs5vNhp/8+Mecn59/oYrg2ruCc/ZKLXBJLY6XeoDnCrUYUsrecOyLCovda9VK4X34XLH0dY/RqERrhbWJ5KcERO+SY6lziBgZjUryTJEZjdIGoTTeR/b293E+pJAht2uVKZquY9sEjEl95tbbVOzHmJJVM9BKIJVBqxH1+gIRI7nRKFOgsxynIu22Yd12yVZeK4QUyWCuB2JaYISIxOjI85RzkGyy06Ls46Ay88lhL/kKiEsL4hiha9shmVE/97mlYnK3mUwjfXTJtC4hpY6+87vqdlgbnucWxIFcKKRkf2+P+y/d4aXbt6jXW5TRjMYVeS5YLVcsV5sh7trSNS2ZMfguZV2MRhOEl0M3wqHIyIoCvKCrazaupd8s8X1N30eWbUcrN4z29uhF5PjiKTQrSpMTM5Gca/sW+ikzOWJSTZhXM2zXo0tFbxyy0GzXDXmu8NGhjE7KrihTm2h5jq5GX2qefaViYAf9B1KQhVAgjSI2yTBIDqQ/6xy4qx0XEZTRdKFDEFFCEkgOVD7uyG87yFNeFgLpg1UDlBNBSXRepFzsobpXQmK0QUpN6y3BeoRMgUAuJl6C0RlCCkyVs1wmxzyEp6VnpSJvRM9rfWDsJGUIqPff46gaU+UZ64Mj3GgvsVnbwGy6Ry4NarnAPHlI+eADqpNnZDIj3HmD/qW79HsTPlmf8s75CZ+1m8SMGO5QimSg1PeWrvdgVjw+PaN59AQpk/HE2eKUovyIl166zxuvv82rr7zGaJRjlMB1jvVFk1oy3jELI/JKkeUJIUj9e0E1GTHem7E+mxBPC9ZNxycffUwU8Oor95hXBcJ35EawPI9cbGuCnnB27nn3gw/ZO3rGd3/39xEmQyhBlitAYbRiMq2Yj0vGZc44L8hMziYIvNC89for/PN/9g+4d+cGMShWFz3dyhM3EcWXC8z4ZQwzONtd7xW+iBJordFGY52jblvOn8sl+PLjapH6/DeD99gQWK0cH3zwAf/dv/yXvPnmm/z+7/8+2ph/50L+ixrX2weXaolrbPxLR7d08OW/2pjhuhy03UJcvpe7x7leEDyX0zAssDsewQ4p2IXTrDdr/uW//O/4sz//cy6WF//u8+fnvMdcFQJf+T2JMcnGfPj8z64939czAqvlAggE74gxMJtP8CG1qtqu4c7dQzIjBs+MAh/T4uBcoNluKaoxWaZo25a2S2S8pmmomxYxxCHbtk1EN5MTvMW7jigEve+JAyGtzPMUVRyhHI+wnaW5uMDFQK7MZftGEFOmrTa0vsO59HgQSNNiiNtm5zKYCoQ00hqQZQX9IBlNppqpgIA4SM8FwQ88lhAGhcBVGSilpOuTPfEVp4xLdHvHKUhfRKq85O6tm+xNJ2yWSx48eMDB4T5CB1BjHj5+RrupUQryXFKViqzQuAxGmcF2DXXX4HxSbVTljBTKV2ObFV29ot1cpI1wnrga8+k+ui94/NEnFHsFzCqEF7SdY1k3bLzm7o2XuXMztRXxkcb0YDLqesNqc06eC548+oyimnHz6Dbz0SHb9Ybj06eM8gmvv/HWl5plX9mOWEtSboACEZIBhJGJeCITlRwfItKJRCpRaohIFYMOFqJMKICQgy1lSKoBGRUSPRgYcVW/C/AyVZh9sJdJUyJCrg297UDooZCISOEJwuN8oLeassqQmpTUFwU+eKR0KJUgpdrDFsk3UdxxgWJ9gf6bv0Q/+ozqzj26/QN8OUL3HcXiFH12ijx7SrY8IetqlC7obryOvf8S9saMh7blhyfPeP/igi2JnS2JyJgIHSEOHgtaI6Vmtd2yuFixqddsVkuaZksMkcfPjvnZ+x9wdHDE4Y0jbhzeoSzHKJMRJBzdOOCb33mLv/ebb6MORii52yVFyjJn/8YB3WbL8mzBeP8223c/oF0tkL5H3LvDqNAoBLbdoJCYasRqA6qYEYVms9mybjzOevIipypyxlXOZFQxG1dUWYYUiqAMBweH/INvf4vv/sbb7M9HtLVlu+rp60CsHWKzYcrnb7S/qvG5MJyhPbD7XtJXO4RKmu3T09N/72Lg37VYpZGQorZt+OlPf8p/+9/+t9y4eZNXX3klwYx8HtL/ov7/f8i4/jhSJHnuTuGQELlrJL9rr0MqhTIGISU6xkFOmnwFwkDSCteIhs95DcBAkIoDKVGkXjTw3rvv8j/+T/8jJycnX5At8MXL8SWFifili4AXrYoZHjm1KZI7nBxSclIIzvDzrxEtyDLNdDrCu46+a6mb1HfubCQzhvl8TpYbtEpGbkWeoVSWrt91Awguzs+pRkV6rHGVXE83Ftt3FCZdx9V4QpFnZJlJctIokcNu3IxKiJoQHW2zxQUIvkcJz3RUYp3DA8IHhEj+AGHo1V+cLdCZoapGFHmGEGndkFLi3VAoDnMQAVIrdGYoigLbOYJLXB7v+2HeDIWEDwTvk8utECTjInmpOnO2x9rrVtZXVvZRxKRaiACBvb0p33jrDW4c7hF9jxOOg8M9qnFBYTSh75mNK4r0yJhCUowqTB45Oz8jK+5R6BIhczbrc9p2hQuSvusJ7QW5LBBKYEYTMpNhsgIXZSLjx8j+wRGtbIg6I9MlEzXmyORkbxdU+QhF8pDAe/quoWm3eHoOpmO6vuP2S3eZTo8YFVNC4wl9R6HT/e380dMvNc++YjEgiSEmH+nhJqqEw2hF8JLoXQpaiAwxmoFca6SQKaVQZ/TWDTc1hnZ/ugF5n4xOdvuTHfFTCFJQhTEpzGKQlcSQYPfdhFMyEUhccLiQ2KZCJJtLpWRiumYFUUl6FyCkClVJhdAKpMPi6aXmJS+pmpb80SPM8Sl5YcAolLOY3qJ6Dy4x0GO1hz26S3PzLvHWlGaU8WTreIRkQdotJ13sAGVJSVQ5piioqgn7Bze4eese03nP+x++T2vP6QfoL/YN/iKx1E/OT/i0eIjJRui8BKUpypwPP/6Yi/U5/+Sf/yMmoxKjhh6wFORlxnR/zuzwkPVmg8qLS637ertFyhHIjNh4pgcHWFMRjKOcz5nt79E5wdlqi9CGQkimozGjImM2KhiNR5iixBQj5gdHvPmNN3n1tbvMRyPq85rVosM6TbAQ65qsX5GL1VeZbr/Q4Z+DEuNl8l5RFInApxIRbrFY8M477/Cv//W/Zrm64jh8mYXmWvfx5w4pro70PrDdbvmLv/gL/vAP/5B/8S/+BYeHh5fBPtcXoF9kIXB5vrsCQ+4UOVc7J3mN6R8HKFcIgRICaQxx5w0QI06kVsCuILgkY17PbRCC1WB6kxc59bbh/OyMpkmqlD/6oz/ifHH+OcLm8Oo/f+5/y9f/PiPGlF+f2paJOCllclQNL3BHfpVjb2+OMQbvOtpaDUiLZGJGdH1qAdTbllGVIaWgsx30HUJGLhYXFGXJKB8TnKULPUoq8rwgN4a2bXB9T1Fm6brwAe0DIUY29YYY070zMwmyjy6x3aMA7xtiIEkGc0VIqXQ451ksVhADboD/ubQsNpdo8fX6KhWNICRoIcl0hpSKvrW0bZ0W/RBSmBeJcBi8vww/isEPHLV4uU5Z21+FI/E8KkBMm9ndONqb8VvffIN7R3sUSiXEOjY0655ROabtGrzrUdlgbKcEZVER6SnGB9igCG1S01WjOWU1oo8dLkBVHnEwus3DBx/gVz3OedpmQ9O2VNMRDoE0BZPJnB6LETmFGScZctQokWFtoGkt0bu03olEUqw3m1TcBAl4NvWKft2zujhndXGOpaOOvwTOgBBqgAclQmiiGAoDAUWW0TaOgEj69pAkbj5EZPKUQkuRohsZJoLYsaeTY6CPARc8QyvoUu6hpCQfZHdp1+GJMbUIYox46xFGEP2gex+Yzzv2K4BWBp1lKR/AQxcCG++Q9CiddjwihyAdtdPcjpK9IKm6QNE1CCwiJCZvFBpnCnxVEPZmcOsW9mhGW0rWWc7+a2/w6tE+5z8bYx98TLPdEKMjyzMOD29weHST+Xyf6TSZ/kznU3yITPf3mb434dFnn1BvVgTvgIgLPW2viFFjvCBHInWgd5aPPnpAVJ7RbMw//Ae/g8hkQm1ExGSK2cGcu6+/QmN78umUzfkxQkp8ENio6BvP9mzNHTNHTQLHiwvWTYuue9b9OSeLJYjEdRBCIbVB5gWqHDM9POLG7du8dO8O9166ybjKqU83LD5bEKIBEZHWwXaF7M9R4uvjDMDVbni3WDnviQJGeoQSguVyyV99//v82Z//Oe+8886l5PDf67l+zveHKU3ivaQ5vl6t+N//9b/mzTff5Hd/93fZ29v7nCvhL3K8SCr8XKHxQhHyYttgJ+vdSbYue//XCITXuQhNk8JYnj57yrauGY3HfPLxx3zvr/6KZ0+fEmPgk48/HqKHv94hhEjJqgOHQEp55cj3NY08zyBC7zx9b2manrrpOJwc0nZbgvdkec5oPEo5AFIMC6VD4skzPcx5cF0PMd23N5st2/UWY8qEBCh9WRDEmDZTSmtMlhF8wFpP9FebsLZJWRBFVVBUCfYmihRZT0I/o3NkmUEKyaiaMJ3OLw2f2CFRcZhdww5xxxewNgUzOZfyAFKOh0gFqPd421/jhsRhAzkYZvG86uV5Wmhk2HkOzyfQwHa5INhDcjUBaYjO8OzZGZPpEU3Tpc2utkTZ430HK8l0NKHQc7rO09ueICSZyQjB03ctWZUhVOr9l9UU4TvWizMulku6viWfFgQp6YNlWiggI/Yy2S9Lg84kbbMl2EDfF6mdpTS5MHifMmx0ltO0HdNZoLeRTVtTd21aIzXEL3lZfTXOgNYD23PIjxZDKSegynP6tk2GJSIZXziXLqJcJjhaCIlWDGYRCZ7c2QjHoR/kvEXGK1tdKa7c1JLtpCeGK5mI94I+QIwe51q828WRSkJMDOUQUj9QG4MyBvrUI+59ZB3T82lTIJUiEHAi0ITILaE4JGfqI6oXRBEIShHzElcV+OmYeDBHHUxpS8WDixXHfeDNf/B7fOeVu6jDPYrJlEefPKDrtrx05w73X36Vm7dvs7+/x2w+YzoZkecZ1nn2D/eY7824cXSTR599wvLijOBtyiTPSpQqUVlJUY4weZ7eFwmL5YoPP/yYb3zjLQ73p+RqKLKUoBwVHN6+wWK1oprNONcGrTO0yRAqZ1t3PD5Zw2hN6QsePX1K1/dYDxHJpm6QA8HOxYDIMyaHN7h5+zZ37t7hzt1b3DiaM8ozNudblp8t2T7bkI0q8ixgbIu0S4RdIvXXa+jyInHO+eTeZbKMzXbL97//ff7kT/+En/zkpywWi+e3Ll9xfFGb4Ao5SDc/KSR6WEg/eP99/s2/+TccHR2R5zmj0egLlQC/jPGi9PD6eySHtsFzsPo16P+SvT0gBbvf33EHnHM8efKEtu/49LNP+fCjj1FK8eDBA37wgx9wenrKDtK9zlv4OsaO8xBjREt5SSqNpPuQj1/T/I1D8iRJtmZ7h7NJktf3PWowFBJCpF26MYTg0VLQ93bQ9u+In0nRgdikvALn6boOPWTD7Ii21iWzoOSFoWldTd9bBAGt5KAMSXbqSicUUiuNkmYIjEutHm0kMVi8jSipGVVVKgbkdUl4WjN2KYPaGJTWyZrXJoJ5QqniQDoPeGcTqVAJCPFaC1AgZRh2y+HaNbyDIuIleXEHPUspkDEQQk+IXerzR01pDIfzGYXK2fZrttslZqwQeUJHfBsZ5SVx4Mnt+G4upBhnoiC6SG97uuYppakwmcEriVUSNRqRT6d4YYlWk5c5BJdaKDYgfSTXisZ3KCVoNisEaoiPDimm2Eds22IHvwPnPD4GlJEUZYHXOklFv8T4igTCdBJiWNjTlSsRSIqsQMrNFVNXJIep3lrywly6rilk6tfEgESglETJgBQQxeD5PEiAAKJIXtgpAcsSsRBT2l9A4kIk4PHeEmJ/yZbdJRmmtkIqBswgxwmuw3UdMXr6GFi5gBYCbQrQyRKyi4GVcFyIjJveUPhEmIx5hp6OYVLCdISYT+grw9Nmy19/9ohHeUZ29hu89p1v8/f3f4/53iE//eE7rFZnfPOb3+Tu3Xsc3TpgMi2ZTkZMJxVZpunansm85PDokDu37/DBu0c8fvQpdb0emKozlClAafJqRF4W6b3TgsmkZDIZs9lsmU7GlzGwUgqEFmRVzt7RIePZHkiDVBl5ViGEoe0bVpueYrmFxnH85DEeibUheQcQKTJNVhQUo4q7r7zEm994m5fv3+PG0T7TcUUmBW7Zs/hkyfrJGtE7nGqosIxii6EhqIAVv7zd7pcdl31xAZvths3JllE14unTp/zhv/pXvPf+e6zX68ubyH/o4nSd2X5dCSW4cu0TETbrDe+9+y6fffYZd+/evdw9/aoKgufOeacOGBAAri3+X+RTIHi+0Nr93znHYrHg3Xff5bPHD1mulvzl9/6Kru3Y1jXL5QV9N9DOw9dbKO7GrqiR15QmamdI9CVvqr/wEVNEuCySaqppe6RK90kpBXlmqOstRaHJiwyT5SgFs9lkUHE4lDJ0XUPyiEn28WU1omkagnMoOWyobEJz+96SZSViyNcIMWV2SJlstJXW5HmJJaWwElIhp6RCao2SyV3QiJy2SUZzIYZkIV2W15IvJbv8st2mw5hUWHRtkyzEr3tXhBRAFLwjhLQAp2JAXPb/d52Bq2IgrUoJ4YpD+m5aY6SUKBnJc83ejTGqjHS+RmHIc3jl5bsINaZZXbB1LUKUKC2RQpOLPOUluB7f9lRqTG4MHkuQEak11oaEbiwW6PkhyIx8UjHNjjB5xvjgiK5fYGJBWY6Idp3QNjQqCgwK19XM5jNWywuUKohlQRSWtnXEqOlsTQiavk2fn5YgK0PQJb0C9SWvra9UDKTgn2sXvtilA2ZInaGkIgY3mAIKCMnCOJJUCN57kBItJT5e2ZAmBnyyjYzRpchiEvoQY6R3PXaTkq2kTIhACBEhAkImzkAIAiSDBFEDiSjoBz2p1IZMKo729pkYw3K9YFOn6rgLgYu+R0ZBzAxyMINovWXlNyxiwUxJqirDjDNG8wwzK5GTElsqzvqGv3n0mO89e8TaKLp//b/SK/j2d36L3/7ub3Hz8CbbesHdu7eYzfa4/8oNxpOcLJMYpZAiZXUf3Njjs0+fURQ5ZVFydHSDxflZssd9+RVm+/tEKTGFoahyitJQFobxqGQ6mxB8oO9digodQkEQEaUF0/mU/cND2i7gHCiV4Wxgs65p2452W/P4k49ZbbdkxZhtSG5co8mI/f0ZR3fv8Mobb/Dt77zNrds3qaocIwXCgVt5tg8b+scNYdORFxLRN2glyJXDGIWqDlh2X5/p0I4MhkhugRfLJX/9N3/DT376E4wxPHr4iMePHtF2HVfMlf+Q53u+0y3ElY5/RxAUwxe7VtbtO7cpy+J50t2vqAD43Li+SxdXCYXXlQHXEYMXXQWttZyfn/OjH/2IH/zwB/z0vZ/RtC0ff/wJJssgxmTwNDzXdSTl66LqXUc1dl4IwOcll7/ikWfJTCgoidGa0XjCtmlTBHocJ7dXIl3bs1nXSKEwmSQER5ZlGF2g8xzrPVH0ZLkZFmTB1FmKLGM8HrHZbul7C0IihaRrW4iBsiwv27YxgJCSoqwo8xJvG8AlJRmplx+dTyhETHHLzju0ViAMRZEPPB2DlIpdGmLiaEiU1iitUtDc0CKAK8VBHEiriSie5mRCkYdFf2g5pDXCX6oPuFYM7OaoGDajWgv29/cZz3NkFRG5RGDow4YudkwzyY3DQ6qRxmcOLx1KZUyrA5qmob+o8dsOMRmhEAmpET2beo0wJb1Lr6UPW7JMsH8wZa73sDGSlTO23QW5ydFhnILvVOJZ2KanUy2Pn3zKdPpdbG9RVZneDy8IThO9JlcFF9uOi5MVRVkgRFLfiUKTlTlFln2pefbVsgmsRWU69ddFCnHoQ0ToApGNGM328W2dwoaEJPqQoHqhMEIiUOjUKEUoQVTJCQugtx3a9ghpQKgE3cjkRZD8qHu0kAPRMFxNjjSNUFKTZTmCxH613uJjQIRI7xylzjFCYaJkNJ8yHWWcXWiOT8+ICJoYOPMdIghikDhtYNDSr0WOIcOMK8q9ElmWdFGwsR3nvePDzvKXF6cca4+Skk8+/JC27Tl5dMLv/4N/zJvffJP5fsG0NGzqhskkpywlUkUEDmJ6vbNZSfmNlxmNSjKjKYuSmzdewlrHG2+8zt1X7pNVGVmpyEtFZmSS3AxBHH1vOT1dI4RIBcEQ+CREJC8Me/tHZNUYGyRt7zk/X/Dws4dcrFas1yusdyAFWV5RCMEoz7lz5ya/9fu/xe/9o99j/8YhudakxLGA7wPtuaN50hPOPWU0iExS5gEVWioX0USEDEShIXx9fdckYRP01vLJgwf82Z/9GX/6p3/Kar0eCE3phvHvuuU/t/h90XO8cOz1b+4WGbhSNmhlErSqFPv7e/yzf/6f8ve++5vs7e39YhhxX3HsbrRf5G2wg9BT2BNcGreEay6iw+tyznFycsI7P/4xf/bnf8b7H3zABx9/cEkwvHP7Nra3PHnyhN4/T2762gqB4e8kY76KaYYBwfklcjj+trFcrqiqCiUieZ4xms7ZR9J2PavVmq6zFEWW2PNtwwY4PDpgs+0xA+ch+kBRjVKyrA+XC/Ht27eBSJ7nTKYTlssV27qhLMf01rLZrLm4WCTiuJRIPYTrxEhVlEMbKQUlqQhCpKjkpm0Hma5ls96gEZSjMVKlwCKt9aUUMMYrVEkphZAS6yzWdpfzLcH56rI9LMVl4gACMSiEQMqrcnLnNrgbMQ5rx5DsKGVaQ2bjCbdu36EqpyQ5A5isgCjphcIqKMZj0BFdJCWdjAIRM842C5ZnC86PnyK0oOszskwjTETJiJM9TdwQYk8TWzZ1T+4mTGeHHMyPUFlF0y2pVx2+XdHLFSMzJtcFG7dmsV6wsUtkllOMJsTY43xyHdyb7LOVGx49ep8YFavzC8zhAT72BFpEHtjUW5o6/1Lz7CsVA13fU+YZggQzXWxqlnWN1ppHpwu6dnBiyjPKsmJUjajKijIv6Jqkb3XW4nsLscPKFHvkgiUi6K3DZIZd6IjYhZeEAd4ZKshkh8mu4EvBOTLZ4VoXknuxEEQR6Z2l73uqcpSqVes4Wy14aX/OjRu3kG3Lk9UmIQExsvIhJWgFCDZgg6cvHDHT9LFmu4qIVYfNS+zeIf7OfY7u3Ob1h+/R/+QHLM8XeB958vAR280f8+z4mH/2n/9n/Mf//B9SFJLTsw3OZsQiY5fDMExVADID91+5xWg04rNPnvHo02M26wbnkxTNCkvvBb0VGCMxSqCVJDcSrdJF0TQ9VWEwJhlrSAFGKw5u3ECakuPzC+qmZbVccHGxGFjhYGS6qA9mU27cv8c3vvsdfvP3fpu3vv0mZWWIYiheEHgnWDzbsPlsi14pCgp8rJkUnlneo12LihHhk4NdFC/Qh3/VQwgWiwX/x/e+x7/9t/+WDz/8kK5LNp07XfSXfKDh3+d3sy8ecdmpHAiDl7sRkQpco/TgI1/QtA3WWj777DPefOtNJpMxxnw1c9Bf1Li0CL62S74+EmScpF5fZD3sfepBL5dLHj58yE9/9jOOj4+x1tI0LePxmPFoTCObhA6/8Ptf9wgxJITwGjKTMlO+vlE3LdPpFKMg4um7Gq0z8kxzsD9ls6kZjQuCy+k6S4hqqG0lz05PaduG6XSPLM8QIgXyaK3YbGp6p9isN4xHFTFG6rrBdg4pWqSAyaikb1t65zCmQERomxaamuh7CmNou4bZdEIM7kr3Hz0XF1tCiGidEW2LMQozIAI7iD7Nr8SbKYqCyXRKlhW0XX1lJjSgEj6mQKrLopUB8RvGdd8MH8JV8iZcoge7PISd9VSpFb/59/4eymSYfEygw0ePyTXjcg+d5+AUeWXIRwc02y2h7YgkqePRwQGzUUnfbBmPMrbLjs1qRVQpp6CYZxxUB3T7GhctKisZ7+1xcHDEuJyx3Tbo2KNig3AyoareYfsWa3uabsVkOkHIgCkUy8UZUk1wXeDJw6ccn52R62QnfXh0iFKCziW+FyKgpCcf/xKKgfW2YTIa07YNZ4slp8s1fUgOgKNRyWqTGIxC1Ei1xhhDkRdMx2Nm0ylVNWY6zdAyoQZt37LdrlhdLNhstilwZAge2sm/IAwOVf6KwYwYpI1JuSCUwQtBcJ6qLJFKEKJFtJG+Tf2vGEOK19TJVEa2Pbf29snuv8LFu+9SD4EVfYROSOrBDCUIjwsW6xVLq9E6EMsKZjeYvf4Wr/72b/HGd7/Jd5e/yx/+f6b8+Ic/4OTkJAV4bFe8+9OfUDdbumbNf/oH/5Suj3R9oHSpXYIcLDmHKRuigCiYzab4OxA8nJ9ccHB4wHQ2Q+UKk4M2oFSK0zRSoGXyXxuPxmzrGhcSKrO7lQspGI1GIDW9jyyWK9q6STd/HwjOYn1gf/+A//if/ye89Tvf5f6bL3NwtJ9uIteWvtil1oCuDVnMca6jdUs262fsH0h0t0ErQCYXMBEjwXqC//raBD/80Y/4y+/9JT/72bssFotrUsOvuBBdSvDE5a7jRYLdiyNGLiNZBST7W0Apm6RWCJTS7O/vUeTFgLyJS0jzVwlRCylTHNiw0O+iY3fSQiklDC2o4BNM6/zzhYGUEh9SWubx8THn5+fJGx4YVaOUT2/tcJ1/vp/584qsX8VwziO0uGzd7F7zL1Pd8beNsizIiwwRLFobsiLHukDXbREIppNhh24yoofOOpxtiUMUnM4MUkm6tgFCSg4U4GzHeh3pup62aRKB0JPQ19AihKAsM0ZVwagsLgmGkKEzTd+mFqPte2KoQASs6+k6R2cT6qtVhndJEpdikxVa74yp9GXYkNYytQ+0GeZFKjqJkRAGLxACSkJhDLnO6Xub5h6pFbyLIA/DmsG16+aLxKl5lvHdb30nkfycZ7vtMASyPMeoCi0Mrm0weszFZoUSkq5b4fs2bRT7JGesyil///f+KX1oWZcb2m5D57ZJ4mnhcHSTejZmPC4RQmJ7z+LZGefxFIEmhI7zi2Pu7L2GUIroI1mecfvOlLqrkEtH29Y8e3RK19QoZSiqkmKcM/IFk6JiNKrYO5gTfU8ZDS6CjQFNjfqSy/xXKgbqtmO5bVhv1pwtFmyalojAe5tytkNMEaAiIHyg6y1t3bJZbzhbnGOMITcZeZZTFCVlWVBO9pjOj/DW0/UNbd8SrMdZR987bO8SKxNFFDIJtXcMRa4mjbepV661xjuLdyEFRgDWJW8DyeCiiERWJXt3brKnNB8ulnz05AmRJG/0QtARCHiCiARh8ALWWUE22md6+z4Hr73G0RuvMbt7m2pvzP6tCf9X+V9Sjgveeecdjp+c0NUd1loef/aY//V//d/pveAb33iT+TzFkUbSa9lNVOclrk/SHtt5Vqsty/Mlm9WK05MTfAxkVYHOJdokpm6WaYpcM8o1WsmUYhgjzg8xnur5C6Le1FycneO6Gu96grdEn6RGUiluv3qX3/j93+bVb79JNauGCzfphImevgm0jzvsqSV4j4qBmHtkf8qUU7JQokSAqIAwlOR++OfraxP8T//z/8zJyQlNvb3Sv//7PNC1xevF379+wxnUsVfYT0x/RRKx1oXEaUlhTynWeDoaY5RKRj4h3dB27Y2f95y/6CES1nrZj93FCBudbGR3ZEEpU6tQxJDaL31/5UkwMPGzLKMoiitSMYLZbJb0432PdUmKLNMTX/Z0v46xQ3NSPO6QqgeXSM71BMZf9ZhORoyKDOfSjlYCYoDKJ5MJmTYpbTWCqjKyqNFZSvwrK81mu8VoiRmNkDLNu67tUUpQ5AYRd/138CpghLjs6Z8dP2MyrsiLkjwzeO9oh0jvUTmm79fY1rNYXJAXhrbvaVuLEAZjCmJwGCHoQ6BvG9q6xvY2zTGlLknKWkucC0hpkvNg1xMjeGsRQgOJaFjmhm+8fI/feON1tpuGxnmiSUj14mLNxWrFttlSN3VaB7zgMoUiXM2vLMu5eXTEfDrnk8efcf/Nl5jvzymqgig0q+WGC/+Mxm546c5rdE2SrD87+Qx8vPRt8Dg29Zrz83P2D/fY39sjxj36rsW6Dh+AtsPWHS7PQHoWi3PWqxVZbsi0YdE95cbtO0yrKa1dsl012D7Qq5rHxw8pRgYbLPs3jrg4OabtaorK8NJLt8nKDKMiQlkObhxAn1o7W5tk+Mr1rJvll5pnX41A6AOL1Yb1Zs2maVKPGRB9ZLMepJshJqVITPtdH6FzlsZ1qT8qUq80MwaT5YnMkleDGiGSFyW6UvSdhW1DwOJswPqkKZUxLehGDXBTDPiYEAQbHG3Xscu7DlEQhcL6MPTRNUpppMlRRUW5t8fBfI9vr2rOL9as28EURwiCFDgCbQwJ/pKCcjyivH2bg7e+ye3X3+DopduM5zOapifTJa+8/gb/VEZG0zE//fG7PH18TL1psVbw5PEZf/y//xnGZBwejAn7VZqiYVi0RdK+do2j3nZsVxueffaYs6fPIIKtG9p1jfMREzQmSDLUJSfAKPBut5tLi8kOSNsFOfVti61rurom2D6pMnZyGyW48cpd/pP/+r/g/puvMJ6NUXqIXg5pGetrx8mna/yTDpqIjx1IR4anCBfkqkZZh8yLdMvaEXZE+v1t/fX5DDx8+DCFsfwHQNJf1A64Pl585OtNICmvxyYPaMHwM6UU1lo+/fRTXn/jDYqqxFpLnheUxdezCF2HXHd+HzuiX4RBFSaQQ/aI1vqqxQDsXt3z4UdiMKRZsNlsrkiH8ByJ8Fc9doXAFREtoTj9wHEY0lC+prNLvW2pIJN6YPMrhIRqVFBV5ZC06ZMhlNYgDUIpylxRFSOKLBGqY4yD90vAKInIDVWZU+TJBCpGjzEGk2dkeY53PqUOqlRcmCJH+uQ0GILflXFonWFDpO3STh0hUDpjOp0TQg/OkRmFukymVUiZCgGpfLoPAQiJUgalNHE43xATsXw3H42SxOBYrZecny9poyQvMqqq4t7dO7x67y4CT9e1PDk+5dOHj1mslmyb5hJ/lVoxnU64c/cmD589Ihsbjg6nIDoQBSFEttsV280zTJ6z2j6lXTv6vqXtNyhRpFRZIlILpAh09ZbNtiPQQMiIFkxmKPKSzkfG1ZgiK7FuC9GiREQDXbchCkeVFdTNEh9a+q4Gp1Ciou8tqhJ0fsN0dIOJm9C1K6K35JniYP+Azi5x9IP6J0NKA6HDuxYbWhr35czevlIxEEi8gba3uBAuL+LUJ2wTi1+IS2+AS1FHjDgXkp1iFEjssBOtUVqRmTWFKTBacXi4R24EdWfZtD3BC3wUBJG88UOIyJi4BGaQvRAjUmiEVLh4JQtCeiKKQJIL6iypHkxWILMcXVTsHRzxrbciDz77jPc+2Q5th1SFCiRSa6LW+CyH8Zjs4JDpndvMbt6knE5BSrptRxOhmmS88vrrKK2YTKa8+7MPePjomM3aYjvHkycnPHz4hN/8zbeIURAD2N6x2dT4YBmN59jesjxbcPLoCcePn9Kta8bTObnSZMpgTNL+ZqUiKxKJUCsASdu5hIi4IRUsDvuImDzxm20N3iJDMu8QUqFMRlaVlHsT/ul/+Z/z+//0HzE7mA6FAOkxgsC1losHC5aPtshFi1YG5zdI0VPlgjxsEcIiA4hoQJhLGVMUKT3y6cm/n73vL2J0fXd5O/+6buvPWQAP0POOqS6V5Ec/eof7L7+CkJLJdIKUiqIoXuAz/PLO/kUDos/JBbniPlyH0Z8LHRoW00uzL60vuTHGGNq2vXQpLIvimrHT18sbuFJFcNmiDCH5iiipBina1zVSDz4Em65ZJXA+kmWJBNh1DdZ25FmScEcBUihU9BAFZjBf88PCqqVIC680aCVAKWp2xLzUAnLOQQhURUnv/eD+GolCogdTneg9UiqyvAAf8cGSyqa0BhRFjvcCLyRZloEUaJNhBhtwcRkMNuRaDAqOhMLsyKyD0kAIREzqK+ccT09PeXx8Su0S3D+uKmbTGbNxSWkUKkYO5jOqsqRue05OTllv16w2K5SSzGcTiHBycc63X3mNyVgTQkPf59ig6G0LeIiai8WCbusRoh0M8TydbdE6ooOgKDTtpqW3LW4bwBfImFPEgA0pQtjkGVoYIGc+2mNSTFBSsmwucHZLs9wm1Hsw8pNSEJ2DEGk6RzHqgZ5qnCMxyGjxvkmJkQK8h7ZtycwInedouyHahigtPn659uxXcyAc5H7JY0AO7Ki04IcYiMGzy4beEbR3u4gEjyaoJrlWeqT3yKDpnaepW7RKmdfWBNabmvW2JS9GlFXFJM9Sv2WIyowh4AYTCiU1cpfWJXXS5CJSxSw9IpJUD8oMRJESpMYjyPKCu7dv8cZrr/Lp44/xUaFNTgzpJlXkBSarqGZ7TPYPmBwcko3HRK3onSXWPTo6lOtwvWJ2OOG1115jVJZMZzMm733Mk6cXbFYt64sl54vlEJ6RpNXOelbLNdt6w74ViGhYL8548uAB6/ML8qyiUIZcZRQmS6SzsiCrNCaXGJ3oEzEGmralbyxd3ROmgehTX5cYsb1nu9mQGUlhND4KdGHYv3ODl99+nb07R/zn/4//mruv3kMqLi9QIvjOUz/bcvHBOa6xyK5DFwa6NTJuMVGifQo/kTueoBBEleZJDJGu93zw2ZfzyP67NK7x8i//J2XKljfGPGeL/P777/P9v/oeSklefe01tNKMRlUix/J8GfDL6qv/rZkI15CVncwwctUa2N3Ud8fuPC8QMBqNLhf/LMsYTyas11e7lq+TpHc1riyYw6BxDzI8l4z3NZwR1vf0XU2WGfLcIEQqcEGw3W5xtkcnswCcD8lrYNgL26Ev74ePTgpBGGTiXduilCEZ+6TF19qebt1hlEq+MCG9F27gXCmtEbtWislBi6Ta6sWl0iTGSNe2WGcJPlCVBVInSWJVlakYJq1/PmmgUTpxGwQx2bHHOMTWBhASESNllqyR677nom7YdAElJafnS8bVGWWeJ/k7Ke3x5q3b7O8dMh9NaNsti4szhIK8rFiuNwgpuHlrhskEMkLftbRO4KOnqiZEn9FvG1wXKSqNUuA8qfAJDqIhG1co6fDO40SPFBlGCmy0LOolgkipS6KPCBcoVIUpNUIKRKZxFzXNqmUyn4FU5KMxOgpcZxnnBTZKVJ8ikLOsJEiIweFci8PRUxMJNHWNnk9QmUHqSKRJnLQvWWd/9aAinXanqusSu1MMn+jQ2hxiIJ7zEIkhItVATBIC5O7DHswfYuIaCEzqI2mDMDmmUExmB9y5fYe9vRmCSNMmOU292SZ2fdcSvMMFiZI5UQr8AF8JlSSNINi0XSJ9KYPUGb0PbOqWuuvQWvHa/Zf468mEdRcoygl1vaKzLUjFaDzh1s077N99mZv375NXFa1t8JuOTID2HTGL2C24bsPhrRvcv3uX/fmUGzcP+cl7n3B6XnP85IzG9mybDuuSxAUBVVXStA1PnjzjcO+AzfKM0yefYRtLeVQiYsT3lmZbI7VOaomYQdCQSQY5NE3ds101XJytmE/GuCq9z5B8rbebNZNJgS9LeuGY3Djgt//xP+S//Bf/d8YHE+7cu5vSIYkE0iQKnac5b1h8dEZYBqJbIwuJsDV5c47uLohbD0VyKtuFUEbSfwRJm7zZ9vz408dfZbr9UsbXveAkopMciFTJZa3vk1/8ZrPmL//yL5PxypD4OZvNEqFwOPvr4V0v/OeXcK7xuYwBhLgkGMIVyZBIur6HlkBZlhiTXYYdZVlG6BmMU1aXx+T5l2M5/ypH5Ar9YPh/EP5a++NXP8RAzOv7PrVFS09Z5CzXG0xWYG2SNDvvqeuWpu3I8pzJOLUIhEwIat93dLZPigkpUCJns20oygpne4IfrKV9pKlregllXmK0RiuVeAoEkJLgI1oqMAbvSLC3SamFWkesDZyenuKjJ8bE9ZqXE2az9CfPDNtrslsArQ0xkgqI4Hep7GnEiJAwmYwYj0csVit8TFkZqVhQVFWB95GLbcO2bnCPWvSHn3I4m3Ln6JCX7t7k1ZduoI1i3bS8/8lj8rLk7s0bFHlFJhT1pmXbdUitGE8nSGHQuqJXFpVZtIhEo3AiEtiibU6m9xlPCs6WTygMmFyQS43OFMvWYa2nqzu27QL6iBaGshwxGpcUJmNkZoRxRq4KylFFniXPnhAcZSZpNx3b5WOMGSGkJfYWHzzWR2q35rx9wsHkENt1Q7S3p3M9226Lzw1f9h7xlYsBKa+gHOHELvcm7dAHBqeAaxrStCDtbiEpYVdeEpGiStVkGMwhkiWlIc8VZZVz4+Yt2qbj/fc/IOIpihF7e3vcuHEjeUA7x8nxMZ988oAoPEopum6Dty1aQpknsuKqC4gwsOuloe0DF5ua1bbmYDrh/uENXr9zlw+Ol4xnc1zwbLuWrbUcGsOoKJjPphweHnJ0/x5RCbbbNdvlkrpe04UWHTqqyRjbdRzdOmJ+MOd3fvM3uHfvDt//4XtURcHmYo0cWK/W9VjbUY0Kyuou27rn+PFTfvSDv+aT937KqBgxmUzwbh9ney4WC9abLUVVMdmbMtkbU00KgkmV/nrV0dWOrrXYztO1HudB6JQT0bcNk6qgy3OQGffeeJ1v/+5v8+q33iQrVUqdBGIUyCDp1pbV8ZqLT8/pn65RWUG9WrCfT4nbE8TmGb5ZsBWO6uZ9lMmHXYRLi4aQIDW2dzw+veCnj46/ynT7hY7r/ftf9fOmEQc99e7mJy7h8r7vkUriO8fjR4/46U9/yq3bt7n30ksABOcHW9yr3TqRyx33L+W8h2u3bVvqumY8Hl+hBVKm/rSUBB8uTcZ25MEsy8jy7PI+sX9wwPligXeetm1RKrU/dlbkv27j+sKfXPa/3iaGlAIlBVlmUELirEUrzWQ8xlpHVeYYnXgn/bZl03RkHkajCettndjxxiQOnhKXHFhtDOOxxFqPFNB7hwqeoqyY7o3pug5TXC1MEHC2wfqI9WBkag14B1lm0r1bKAIpHG5/f04QARdI7S4R0VpQFhlFYdihMLuhtSaKpOhwYccjSCRaIWE8KtnfnzOqCk4WC3rniEJdbjqm0zHrTTPIeSUBaL3l8dkpT06f8sP33uHb33iL/fmMEAKvv3qft958g7v35ngb6LdLlmenbPqe8WyPMh+l5EYnkCHQ24DMM/K8ROc5Sgua9Zqu6VksljStoJhmiV8Ve5AVMs+Z5iP2RgWPPnoXrSRlVVLkGh88TdNRd55xEKyePcUc3aCYTBHag6uZqZbt4jHny3PK/SOkypDZBNts2W42kAc29ZK7s5coswyDwAkJ2hCNxguPNr8E0yEhRfLnFoOpUNzpqHfQ4s4NSiAuNdXpmF3REBmABCFAiitSR2qXsDhfklWR6WyfvKh4/OghZydnEF1ayYXg408ERhmqcsTR4Q1efeVV7ty9z49+8hPWqw0xKtre0jZLtBRMJlOq8RwldEIkpKL1gZPVhmeLJWOTM+4Cb9y6x2kH470DAgLnerqYigKEw/ZbtqtzZu0tJgcHFGWJMYbTxy0CjfCavrd02y1nJwrrIwdHc16+c4ubB/v8zY/e5Sc/+Yi22dLUDTJKuq6mqirWm5obB7f58Ec/5Wfv/JhHH33AZDQGASbPUHmOlwrVZWRS4xtLn/WJ12BSTnmztnRtR55lSU5kNNIohIjUmw3L81PKsqDan/KNt9/mH/8X/5zf/ke/Q1Zoriw8IEaFPwusH6xZPDqhXi3ABmwecKHBrjqoT9F2izERZQxW+sGK1IDUqQKJkuhh27T87MEnPN5uv8p0+zs1rnLWk6Qq8QUSDI2C87NzisF98OT4mOOnTzFaY/uen/70pyyXy8vd9GQ6ZW9vj735/JcqO9wVA4vFgjt37nzuuXZcB8Lnl8pbt27xT/7JP2EynfL9v/5rPv30U/I8Z7PZYK29IiX+mo3hTsZ1FObrPktJxPXJL0VEEEEMxWEiCvd9g1YVUkpm86TO6mzP05NjMqOYKokymhgFPkAIcjD9ESit6HvHZDJhMhUpzCgEijxnNCrZrrZ437LZbNibjrF9z3K9IQjDZD6l73psa4Hy8nM1mUm73jxjsVqm1NMiQ0ooyozxJPHDYgxE4iXva/fehxhwMRAliJik5mVe8Mb9l5kUBa5rr9oIQkBQeODjx8dEH1Ib2fukPiOl4YqYIuw3bc/ZJ484Pj7hxuETDvbn3JzllJMxutDcvLHHXpRk+YTSGPouIKSg79aMyilaVcnRUWmiD+zNjvjogw+4WGy4c+8lslFHHxpcDGhpUCjssuVis8W2HlVKVosFC3uOGY8oDuZsmpp2U+PahqKcYOuG1fkxxyePyLMIRcX+m69RlCN0NcYHTdP1+G6FQeM7+OSjj/kH3/02oFldLDldXrC2Nfnoyy/xX6kYiCKmpEKjUZmGTgxb7QE+lDu5XzJ9iMQ0eYcPOY3kCy/kIGFCQUjaUCE167pmXk7ReUEQpMAY/PBHokikKxd6Nt7TNDXPnj3l/r37/O7v/H3e+dGPWS6XeNfStRuWy3Oaes24qbl95x7j0TT1AV3PRef5yYPHGKH5jYNDXr3/Ch8ua/LJhEwJMhXpmpq6qam7NZO+pt5csFmcE4VAFxlaaubzIzaLM6LIkSaj7Sx+tUmWyyEg2GM2Kfnd3/oWk1FFV3eJ0d8n+6TDgyPaxnL29ITv//lfsDx+Cq5jvWz58L0Uefltqdm7dZeyrBAx0DUNNgR025OXBevVmuggusBsPyEGeZHUFsvzc/763/4Fm82K2a0bfPv3foff+b/8I+69/grlpOJSfBMhRkl/bNn87ILN6YZ6vWZdL9jbn7J++h5Zc0ZYtcRgiRqkych0WvgDEqJCoojhqrxwwVPbllJ//dkEv+qxm/WpI/S8Z8BlbzoEHj9+zP7+fiouT055//33ef/995nN5/zpn/4pH37wAXvzOfO9PV5++WW++a1vMZ/NfnnnHSN933N2dsbx8TFvvvnmF8L6qd17TfooBcvlBd///vf53/7oj/jpz35G13WMqoofvvMjNpsNgpQQqdQgYfw1Mh3aoQC/LoUAwGq1ZFSVSCEJ3rNernDW4aOjqDK0Ts6QfR8wumAyHbOXacpcsVlf4L2jazqsc0MiYcR2jnrToHVqR1nnBgl22uiF4Ml0chV0LhBdz+r8jNxoxnnGtk/zI4ZAZhTGqJROKCVFmVMUOadnZ2R5Uo0F78CHwRPAo/VuMxlT/DDgXI/tOryzCB/IpCY3BaOq5O996zsIPM12TVuvsE2XlgQVwQuiBNsPhnNAiG6QXyauGkFwOD+gay3LTU3UOTLPWW/XPH72iHG/hxYWoSQCje89rrfkpsB1jluHt5EyY9XWhH6DEAYRAmdLS56PeeXlfTApSErpEm0qtM4oW01ZGXzf83C7Zrnqmc8PmOzv443grD1nbRv2qn2qqmRdN2yWF2w2Z9jYUzuN2Fry8w3mpZcwosKqQKYyhMtxXUSS4Tyst0vycgIqtYCEyyA6iuyXYDoUiBBTpSS1QqgEH13u9EViru/q6suFn+sxlbt/JSkSWV5WhxEQUjOf76O1Yrm8SAlS4trNFNJzxHhpStGGwIMHH6O04rVXX+H9Dz6k7xvKckRbr+jaLd67FIV8P2M8mRNjibOWjfP89LPH7OUFR0eHHMxnyKIgN4LcQLut2SwXnJ6dsHd4k367YnP+DOdbhEqExel4ynz/CGcbZLSoYJMVs02tibZzmKwnLwwHe1MW/iJZBcf0iiQaLQw/+Jvv8cO/+HOa5RkyWoSUdM2WTz74Ga3t+dZ3f5f7L72GnijAkFIbw+BJvqXIC3xwVOOC3Ch813B2csy77/yYj99/l2/9xrf45re/zeHNQ+YHe5gyI8pwCTvHINksLM17K5oHFzS+pfYtjWuZWkF89j4ybPBSYKUhRkXEJB2w9ihpYWcjPRCS+hBp+pasyPjmqy/zJz/6KjPu787YSW53zOhdOl4KYkkFwWq1oigKvPd8+MGH/NH/9kcorfnk4495/OgRrk87r8PDQ/yvIFJ3VxD0fX/lzbBjBv+cEULkR++8w//yv/wv/PEf/zFKaw6Pjnj46CFNnYJnBCIl5Sn5tz7e1zEulQVf61lcjabe0LV14magMCpD65y63RLZqaeSt4fA0zY1ecgTomB7mrYm01WSquYZSidl1nazRZuCLEtGbG3bIlUKICIGREwhRMuuTT4TQ45AQoQUMfhLfoUgkuWJENt1HVJC0zbkRYa0Ah8sSoJQAi0VZZE89mMMl0TZsijJTYY0OfujMQfzGTf2D+jalovVBc9OnrE3S177USmyLENlhvWmBgQqgEQSEGljMsDSEY/WgtG4ZLG8oOtaxqOKm7cmFPsj8vk+Kq9w3QbXJRv7GBxd0zCqxoS+Zx1BZznoyHicU5iMGAzTSUlmeqL1lGVBG1JyYSFLcpHRRs/i8VNs11NVI/YPX6acTGldz3J1AiYyMhrfWoIUXCwvWG82oCWj+QGZ1ExGB+RZzun5Enm+wEaLsy2KwMH+Pp1vKPYnLLYL9vMMbSSjckTnxyzXT4hfkvv61TxPw44YliyAM6XonL1EB67d7y71yHKQmuyUBzvpSVrUd4LrYYIJRZ6VVGWFt556vUKIRDQUA6k04FJJsItRJqW+9V3L04efcefmzQRv1SVd15DlJTEmB6zlxSl5lmGUZjKdUxZFSlbbbPj+Rx/zD7/7TfYO5lhh0F6iVCTTSeayWi3YLC+YzLfUq1O8T+RCoZIt6Hh6QDGuwHXgWgiebEgQK4p88GxPRivGZORZRrtdsVouGeVjNqs13/v//innjz/Dd9ukJ87yVPW7jscPP6aznsXZCXdfepmDm3cZ7x2RS7B9S71ZQrBs1xfUq5yH7Zrl4pjHn33Cs0ePyDPFm996m1fffp28zFBmkPQwfFAetmc9i0crwtMN3nma2NNiEVrQXDxFt2cYGYhC4rDEkCEjdGhQXSKQZRHlNUJIeuf49PSUv/zpu/zg/Y+IWfWVptvfpTE4LiBIBYBzlhh2eu207Fy58qVWwV/+2/+D4+Njnh0fo6RMpK4uEb12C9Uv2qHwuvZfDOYzIaTWYLzkBz2/UO5kdzFG2r7j/Q8+4L3332e1XnPz1i2Ojg559Oiz3RsxyI2Hbvyvw9b754xfl1NbLdcD+16ilKYsBGOl0CbD9Q5lIPrE53J46qbBup52u0nRyz4SNXTdkClg0n0pBR0libFWUORpgSYGgnXUzg3ywSRDzKVBRk8fAmiB0QqERqOQQuJsTyRiTApW2pvNQIahkJSXu3YlJZlJduxCAFHiXcC2LSJCprNUpOQFj46fcXZ2wmazIc8VSlUIrRA6SSzffuM1PvjkAav19pqHyDWia0h08ul0hI/JCM9ow43DfV66fcCoyinHE3zvaLqett0itKIoR0SV0ThLvVoS+wCFxGQSIcdo6bG94NbdNwgnJ5weP0Ozh3U9JjcQJdvVhnZbY+suLdDjKXmekifrpsF2HttaNptzcjshn04IIuJjRKPJdIVSyW426pLlekmzWKAMaBWRwWEEqK0jqzLquqWtthTFmMl4TOMmPD59iAtfrj371doEPu3GxeBhn5kM2/dXu3UYbhhXXAHE7qud1DBeFQG7g3Y+0gJG4wlaatp6Tde0lwhDCqaIA3Fq9xxyCMpIgPR2u2W5XJDnGXleABKls2RxKQXOWc7PjinygizLmM73MSana3s+PVlw4+kxRVVgu4CMiiLPMVImE4rNmpPTE2aHt9BZRgw9Qhl0XhG9RRpNUeQoURD6guAsUqdUR0hGId4HmrolUxnRe9rNmovTM4TXnB2f8LMf/oC+3iCiTdGxKmBySZnldDFycfaMj4NlsThl9vgRs4NbjGZ7CAHr5QV5nlPXWzaLx/jYsl1f0DZr8szw5ttvce/VlykmFVJeITdEQbQRt/KsP6uxpx39YoVQkc5tca5GBMvFyWNmoUdqQxxaNcFZbAqiJumWhwjYmHwMztYrfvzRR/z5D3/IJ8dnyNH0q0y3/9ONv3XxGOa/dRZIstgQAlLIy3ZKHK6npmk4PjlmtVkjgIODfZqmoW3bVEhcY7tfPv8vqCi4fEyRlA/A58h+zxkSXfu99XrNw4cPOV8siEDfdyyXF7TtleHUznskDF4lw0z8tRq/TufjXUwWySTVRhRiSNcE3wcyEfHCY0xEiuT3sN1u6ZuGLNeU1YgiryDEIYApKaxMnqe5tyN+GoPzHj/EA0dnUQJGZYGzHc45lAhIJciUQQqN0QYiKCPwvicEn7wFvGU0KnG+o209KIEbTJyC94lwCEAc0M0OQkKWRAFWK548e8rZ2QnbZkuRZczy5LnhXCIozycT7ty6Se88H3z0MV1nL4npO04aAaSQzOczNpstvbXMplNu3z5kb3+C0p7WLrFbi7fJ7XYX/R4F2BDpfaBvGowyyYwpetbNhmbrme03KRsm1zT1hrrrqEKJiZHNek237bhYLMgrTTWdY52lc46+aQkBmrqla1ukN1ibWswhSEQ0SGfIixF9ZzFaIJShbi3ae/IMpO1Z1BuC0KiioYsBa1sm4zFKpZC9XE8I/pdRDMRA8CIlNgmZfNVF2jmHIVOay2r/Gvw3tBECVwlnxJ3sLRUYKXhCsLe3TwiBpkmQIgKUkCip8MMualdciKFtEaNHyNR7PF9csHdwRGYyEAKjM4LWiKCQMrlInZw+I8tLsrxiNJlgioJ6pXjv00e8dOeAvkspgEWmqUpDjJF6OuViteL87IQsz5ESTFai8yIl85HimbMsI2qDtz0x9FgXaHvLeJwjhaKtW8qRoV5esF6cY+uWZVjz7k8+4Nmjx+A9athBJkGrp8gyJmVFj8K7jtPjx5wtLjCPHlOMxhgtk++/lEgXWVYZQkeyXLO3P+OV11/mO7/9m8wP9oZCKu1RiRJ6sCtP87Cje9oT65bNZoEuBa45JbYXyNizXZygoiU3OcZohE9+4M5agh8uYiJSG2TucC7yyZOn/OiDD3n/0SOWfaBdf30OhL9OwwdP7ANKyMRfGWT5YviZG/q3xjn6rksFlg90XTdIEe3PTRf8RY0QA845mqbBWYu1lqzI2fEgL2VfLzzvyckJT589S5CzlCwWC9qmpms7Yrw6NqUe/kJP+e/syAfP/hghG3b1XX9VDCidpfvfYJKmlKDtGpTRIDXj8RijU75IHKBA792g/MgHlEqgQ5J6ez8w7nBkJiMrMrbesrUWLTyFziiMxgWd7mmuQxkDQAwe23taJRiPR8iYQomcdTTbhq7r6NqGIjeDP0o6l4GXjpAC6xTb2rNeXeCCJc8zyrJIKG0I9M4Tg+DoYJ/gAwd7+zwunyapNmnTmkaStOdZkvKdnT0kxsDe/oQbt/cpJhpMw3KzIWwgV1OKokLlGoRMNvsxYzQak0tJNauYjkuMEfTeIlSkbWuqzHB44ybL1QWhT68x9D3LiwW2cyxXS0aipJzM6a2l722yG5cCfEB6CJ2lqRva1uI8mCiwNpC7iG1bvOwpTElWjEB2GAMqBjbHp6jxjLipcRm4YJEK8Mm2em9+BLXly4yvHo02bOqlTAFBQl1J0oBhgReDn/1AEEEM1SiX3IAkOQzp+OiBBEnO9/ZYLtfUTTPcLGJ6LJNILUKolOgWI9H7ZIFM8igAQdf3aKMxmUYrjShKgqsTGSQ3FGVF2/U8O36G1Dm3TbK/LGczTtbnbB48wkhNrhQH0xGTgzl6PMJ2c5q25dmzJ0nrOqrIjSY3ybDDdg1SGzQRoxR5ng8e58mru7eeaB3dtsErw8XxM9YXC4psQpWPOHl8TN95zNBEUQgIHtu19F3LfP+A/fEMnWc4BH0QtC7g+g3YhDz03jHRGfPpnNn+jMn+lJsv3eLNb77N0e1bCHUN4I2CaKFfObYPtzQfNUg027Njtu0CEyxh9Qi1fYYSgYKedd2R6Yz9LMdkiuAC1nm6vsU5jw+WICDqnE2A9z59wPuPHrGyljYKuvDrtN/6+sduZ7yjq2mlL2H/Xb9ea02e52hjLhMB0837l7uSeudZr9Y8efKE1WrF+dkZRVkitfpCFcCuyP/gww959uwZIXiM0SyXC9p6m9oMl0U8n0MV/v/j549qNLm8fxptyLIMIVKaYd30jKYTtMmRWiOVRGlNXqTFs+ssoPAu7fhjDAnZVQZk+nkyMUrWv2ZYkENwNLah0ApNMsXqXCTaluACSlkCiqZrqd2WGHJE9EgJUkuCd6ngEIrgoa4buqZjvV5TbxvKwqDV9UTaiPWRjAzrOrablJo4m04p8oLNekXINVLmBB9wMXlwPHjwGSbP0UoynlTY3qZURYYNXVGwP53RNT1d1zOZl+wfTZkdVMiyQxdQL1tkr4jliLzMUZlOKJ13CB85OJgzGd+iLAti6OmbnswLir2KyXwPv2ko8hmqLEAIlucLzi4WXCyOCT5Q5mP29o5QytBZR9v12OARmaAwBetTR6y3aClQxuCio3OWdSuoN1tmhaGNW/RkymQ6RUhHYUA1hm18QlSJ2K9ziYue3nuij0TvmI4nLPsvZ2n+1QiEIRCiTA5PIqVPGZPjbbs7YgfqswPaQozXs3KGMTgW+vSBpV9SlNUEnefUzTFN21zCiXGARYPW6Lzi9p07dNstp8fPiMEjpCIik8OWlCiV/qR+VEAIiTaGshrxjW++zZMnT3jy9JjHTx5hspzZ3j5BgionXKyXiGjJRSB6y7jIuHNjH28dvfWcnZ+wOH/GwcE+ZXkzoQp1S8xbtClonUeVOSZPTocxanrbUNc9zWZDu93SEagvLsBG8qpgfbHi4uSE8XhCsw3EaBF4RAx417FeLdFlyf35EfsHNzBFidCaKBJZRghFUWToXDPKDHsHBxzcucH0cMZ4PmE6nw39ul0hAMFCe2HZPNxSf7TCLyxyX/Lo6YeUpcJuz4nNMcat0uc8yrmwJecXK8osYz6q0JmAGHDO0/Y1vY1Y77BIzrqeR8+ecrq6oA0WLxRkX07v+nd17OBwSdLpX1p1D7u0GGPqtc73yLOM7Xabwr3yPLXjhKDt2ksJ13X5LvC5xfVF18AXl96fhybEGFkul3z26aecPDuma1veeecd5nt7TCaTZGH7BaOua/70T/6ETz/9FO/TzieGq7bAdbZBogrFK74Bv17Q/K/TMEYhZfL1DyGlsBZFRV4oNtuWKBTFaIQ2evAEiFjr6duOPKtYrbcpACt6hCBtlExGCNDZPrWtRMD2FqU0WZZDDNitIQgQvqUo95HFmK7e4LYrus2GRbdkVE2p12tcV2OMYjyqMCYVtc46QnTEKAd74oq+8zgH47FFmwQzJf5ykkn64IjW4UPP3sEh27rh/PwC5zpu35wRVEpZJNMU44In7z7l9s3brNcr7r70Es4Hzk7PaNsGqQRHN2aMizEfffQJLjiObs64dXdKNQYbG6KdMskrRNEjc02MHVFYpJEoJ9jWDQvVofQcaSJ1s6BZtmgq8qKiq2vaeouMsG02rC5WNK2l6x2buqbIM6b7e5ii4tnJGb2NrOqa3vfklWFzvqY+b5kbRdF6sqKE3FC7juXqArVtObp3G3RC1oVOaEK93NIuTqk7j+k85TQlGSpZIGRO22xZb9ZM9vaYVl+uPfsV2wRXF6wQiRWaZYbWt1cHAOCJpAmwUxL4mAIskDub25jkhQwZ4kpzeHSTtutpuyZNChjUC6nMOLp3n1v3Xmac5zx58IB4cjzsUiQM+QNZVnApZoyBpm1ACIrRiH/yT/9TjNY8fXqSPAS6lqdPPkNpjY3gokBXU1yzBS04X9esl+9yY+93uXV4hAsCrWC9WvLs+DHlZM5eNUfqDOscInjwjl54orfJtlMJfIh0XeTTBw/pmy3rfsM4ryhmYzoL7/34e3z8wY/YOzxECIXdLgixReEhRKK3LM8XPCtP8DFjNPHMZhMmVUmWlUR00h630G57wo2Mo5fusn9zL/EDdqSO9MlBJ9k8bVg/afAnjrzL2JiGxenHuP4UWY5x7QnSbjEDWVRIya2jQ54dH3O+WJArwagsKfJkHmJdT2t7urVNVqG9p96ucd4hZPKlV7r4KtPt7+S4XBN35lxxELEJgdaa6XTKK6++QpEne+I8z1ObqmlS0Tv0fZVSVyE6Sg0X5y9uOd1sNjx58oS6rrFdz6OHD7m4uKCqqlRov3C8EIKzszM++ugjVqvVgHDsXvTuoHhVDwwGZV+r5f//SUb7/2PvP59tS/P7PuzzpJV2POnm23FSD2YwAyLMIBCBEEhRom05yFWSq1wu639y6TXfuFysskpliiQkygRIgdQMJmBS93S89/aN556040pP8otn7XNPDwZGNwI5BPrpOn3C3WHtvdd6fukbmo5qZNDGYLQe5Kk1WsDPffkGq/WK3rV0FpRQSZ5dKpqmxqgqgazFEKBdalHvXAX1AJD2+NRRNRlaCqK1aD2mGJf4bkmW5WRIiANqP15Q2p4oOvamFcqY5EJbFmitCS596MvlBYvVFmt7jDTp/koThWc8GXF+vhmSFIHWCuc8WglMZji7uGBbJ139UZlx8+YN6nqD9p75bI6zASUF144O2dRrtvWWzbpms9kQQqTIDPP9kvX5hvVmQzUtuX7zGkWec/JsAdJSipq7t24higK0outa6r4FozByTjHJyKuc2fiAdb3C6DHF/pTQw2ax4OzxIybTCWfHz3j/ww9RuWY0niFzw2zvkDs3bjKaHfLOO+/w+MlTytGEajQmz0tqu2Xb9SBy5GSEmhyihSbYJr3HQbOqO54vFkxVwXR2wHw2YbU45fTinHXd0VnFqE5eQcv1lqPxPrkqCMpjrefRkw+pZn8N1MJ0gQ8s3BgGsMUAGBOJOXDl1i8ogyGk5reSwwYQLjGECcMmUabg1s07PD8/o+vatFEOhhoiCqLK+aVf/TV+6de+zvf++Ns8eP89nHfsOt8xRJCCajSh69Os09qO3lqyTPLVv/OL7B9e5w//1R9wOvir275jvTrn9HTMdO8IULjgQGmEyei7ntPnZ3zjj7/Fb/36rzEfj/Guw/YtZ89P0OYBxfiAg/k1vJC4tqNpt0glKasKkxdECUTHWVfzwbvvc22cExHocoz1gocPH/Ddb/8R68VTpAgcHN5gpaDbnhNcjYoB4wOxaTl7+gTnBH3rKIzmYDTlYLRHJccocmKmuP6Vl/nsFz7HdG86OOXtwIIkuk0t6R61bB/ViAaqWCCURRE5+fH3kMsH2JVCCIeUCqUyVJToKFFacuP6dU6On3K6XBKIzEYjqrIgCuhXHZvtFppE2YzOYoREofFRI4T5JKfb39iVRFDi5efCUDnbAWC1Xq/ZbDZ0XYcaNP/LsuQ3/u6vc+fuXT73uc9xeHh4CcITg0zwn9V23zF3dmOJHciKK46C7P5GAguqwcbYDbTCjzx2/NNdiBgjH9y7x+nJCX3XkuVZMpy5ervUSPpoEvTp+nNXgEFFLrKtt1jr0aZkf/8QqRRZLum6HikMRqcCTamCvdmEetsTbCRGn1wYYyBTeUoohERLg9SS3vbsDJK9T0WI1hmzvX1Wz1a09ZYoDQKJKQqUKCmMIIwq8qyCQfEvxEBT9wQfmc3mlEVP7wLOGbbrLdY5vHCoDPYPZjx6+JxkdChRKkNrhdESQWC5PMcLjSRy/WifUW6wnWQ6LjnYn9GsLbeuH2G7luNnzwlCJhXGPgF0TaY5OJzz4INnhBgZVSOKzKDQBGdQaszh/j46ZByfPKScTei8J5qkmFhWI3SI7O9NcdExqSapa2IDPvQgPSvrWJydsDlfsF1uyKcFxaRgPJ4wLg84O7/g+UXN0+cLvNAs1lsWmzXZOKeajzB5AgVurCOLnr7vkL6jkoHxZMSto+vUF6dMRjMOJhXWWoQ0eJWzcrDpPLIQ9M+OyQ4qDoIn+B4ZA/vjGUejG7z/5Ecf6zz7ZJgBsbMkTfN6IcBohZQ6JQdiOHPho1WK2CE8X1AOd2CWECMBTVlNGI3GbO7dpx8oMGkfEWghKMdTxpNDLs5XPLr/IauzM2RMnGVEIEaFiILRaMR2s2K7TUpnUknu3n2ZL37xK/ybP/oGi9WKtq3p+zZxcKVgubzA5BXV5DDZc5LkNpEGnZf8+L33GZU5v/ALv8jR/iEiRlbrFYvTEx7ff5dyNGN6dJsYHXVTM5/P8S7Q9RuIAaUCD588RFlPYQryskCqjNOTU95/720ePXgPEXo2i+fkxjA7OGAlI/XCE2xLkvPxuHrJ+kyRV1P61rN2G0prmI1KppOC6199nc/93leZ39pH5+oFljMKYhR4K2gftbQfrAi1RwtJjJ7ablitH9MvnpHFDknq+mihUkKhEgZECsFoVBGu3+Di/JSLbY0QgvlozKiqsMGzrnvqbZ38tJ3DqCRJ6waN77/tK5LOeRFeqHLuGjfOOYRIxjNd16WNv++ZTCaMxmNeefVVvvrVr3Ljxo1LeeDUOg6XOAN4gaa+DNhXv4dA2N1mQJHvrtVdhwLSZ62EvKIi+qcpjFcZDX3f881vfpOTkxMEUJXlR+yLd69UiMiVHOjT9XGWUkSROozOp/l6XhRU4/EgapZkobVO8vAhWCIO72Bbb5KMrhRY6wb8VqS3A3hbWnw/dHKlSudkTI6Bea6JNjmualp0aZBaEZUnRkNwDZXJyMcjfICma5MUet8joqTerqjrLUZJgldEoPM11jd4Wvb2xigpsARADQlB6jS2XTdAxSOZElw/2Ge93tD1Fl3kdG3Hu++9z2ycROSIO7O8dC+tk1xz8JLVpkWbnCIv0TKj1BVZVuBxeA9nzy5Y12tcFciLKcoUWAur5Rm3D65jbUQYgfSe1WJJu64pZMbh0Q3e++B9CA1GG774xuehyPBZpGlrLs4u8J0jasfx2XOOrt3AKIV1DS72tFaSjRR1YynLfc7Pz1FEjOrRI830cM7R/k3Gn38dFSWr5QkuwPnpOcdPn3FxfoHUOccnF+ztH1FcM3jr6bstfd9CFBzMb/LO/R9/rNPsE8sRv+AQpu/pgh8AghEQcQj2u51usJ8UiY4WxUdZygKJVFkS7fEB23UDKlQMCUOyzSxGU7Qp+MGffI+HH3xAu90MYMUXvgZKKybjMaenJ2y22+Q8NZrwhS9+iQ/uf8jxs2PW6ws22zXOdhDB+0Db1jT1hqyYpgxcCoJ3SK0pRmPqC8GPfvwW1WjEZ17/LEcHR2itWW03nDx9SDmaUIwmqPGcoqrorEuPn/Jstqszzp4+4cbBHnuzfbKswDrHycUFH9x7n/VqgfA9wloWp8doUzKaHiCQ1BfP8dEiRUSEDt8ssFJhijmtkCyDY78SHH31JT73O19mdneOKuQl3HvnC+5aOH2yJNzraZ8uMYUh4Om7lk234vz0A2Ro02xSBpRUqTpUSbJ0Z0alpGI0ndP7wHa1YLHeooVkNplQlSWZyVnYlu22uRTGiQhMVlCNJp/kdPsbu5LLZ/pZXqHeCkQSdxmq/F2Q3dn+rtdrnEv4Ankl2MchyL8A+F8J2vGjDJ7dtRsZMDtCJklh9cL7IDmLBvygDBdCoG877KA4x5WkY6ct//jpU/7tv/23LJdLtFaXDoZiGIkkP7IXDmo/HcXw6fqpSyicT+9dUVaUZUlZjbCuHd5Pg1b5IGTmUyKnDbazaA1yGBsIkezgkxy2wvsUDEOMZFmB80koTIlkWawk1MslREloG6zQyNyhZEheGkLQrLeocoINEZMVxGFkm9gvPW3bYbKMEANCpe5iUILSFFy7fkheZNRtPeDMEivJD3bJO1vs/emIKtNcXCyQecaoLNHGkFU5vbcJI3bZoZYYLZnNJrz6yss4F4nRUJQ5k8mYqqgwSqFFRBudKO1AVU1AepQeRtkiUJWa6XSO9QoVPF29pG22tNsGp0ZkVZJj7ps1CkU1yujcFo8DmRK2k7MTskmOUioZSGXJ1yAaiY+Bg8N9jq7tY88iUrZcP9jHuQUoy8H+nNkoKUouzi7YbNZ0fU+zXqaua5ajdIEHqvkepixpNy1n8XmSAFAFs+k+lZh/rNPsk40J2Bl3JNMVYhh82cWLWehAXdl1Axh4rGHHMhiq/XSHAEKjlOHw6Dqr9ToJr4RdC3XIXhFIpen7lmePH7FZLvC2v0IzTJtfnmVoo2nbhrZtMMZweHgNqTLeefNNlstzNpsFtm8SkGvgUFvb4WxL3yVXQIQgCEEUCqkLRuMZi5NH/ODNNzEm4+WX7rK/N0cpybpueProHqPpHke3XyOqbKDZQXSOfrtiefYUQ2AymmBMCUJRNw2NA/ISLzXYBgP07Ybl+XP2Dm9SjmfEGGhXZ3hC+rC8T7SzomB8dIPbd27zuTde5+UvfY75a4eIK4kAURID2MaxeLpl8WhDfLLFbluyEOnbFW2zoY8N9fJp0gmQGimTp3YSgpLDZywHASnITM50up/UFbcrltsGYzRaa6qigrimbi1t7/E+IoSiLMdU1aedgd0lksIxaYYqBwbJztwrhCQsNCgTWmvx3vPO2+8wnUwxxvDSSy9RFB/FYFwmATsDo8uGwIuEYIfpT7lBSGprcdCKkDusDYPkd1K4DMM5l+RqXyQCu58D8OjRI+7du0ff9xhTEXxKJuLuAWG3Lex+RIpBP+RnmGN4qZHyHzBxKYsSbRKDIM9zyqJEIFLLWKjhsxRDBzAQXUxeBEZivCLYwVFQCKyPKJ2wT12X5vHGZCgpcM4ni/jgUD5hCPq+R2YVod2A7wjO4UVE+CQfj9Rs1jVeyhS8AalzkAEXarKiRKlEP9SZYVrsI/U+IJhUltFkzGqTxJFSgpvOGx8SpkwLye1rB0gEm7rhcDZhb3+OUpqbt26wWW0wuaYaFWzrHiciZZFzdHjAbDqltg2TyYyD+Yhb148o8pzW9tjYMy1mRAS9HIzARBo1Cy3IlKIqsjQuiRJDYFPX1M05BFDZCIcnLws2K0eWGVb1mla3qEJgMDgr0FlJ1/ZMx2McIqlDYtBaoYRgNB4xyg1CJmG6yahitYw435CrkmbTcrFYcXJyiutbQuxRKjAdV+mcUDlKzpCZSY6VZy39tExS+YVGRCjixwvzn4xNEHnRRiJ5XyupkDLNmS4lhYfbx8v7pQ1IS3mJH0CES4lWY3IODg55+OGjRH9hhzVKoKgoFPP9fcpCJw/p4IciJ22rehg/jCdjrLP0tiPEwGw65fqNGzx5/JTj42dsNhd03QYpklXnzic+eIfrW5xtcb5Aak2M4ANoYZiMZ2yXpzx6ckxmfkCRSW7cuMn+fI7QhvPlgnvvvknbBSb7R1SjEVII2u2G9fkZvqm5cesmZTkiIllfLHjw4SO2reXmq5/j+NmHHL//JniLiI5mdYrRmsn+dUb7RwQEdnuOjAGpC3Q5ZXJ4nbtvfJ4v/8IX+fmvfJbJ/giVq3RCM3QEfKRbdyyfnHP6YI3dQnt6gio1m8UZzfIZtl0hlCc0F2RSDK3CCPJFu1mQRKbU0DKWQjGppmTSsFaG7fqCxXrDbFxR5YZMa7o+pGQgJFBTkRUo9cmZrH+T1tXrYtdcS92jiJJJoninMNj1HT54JOJSqOX+vXsoJSnKgqqquHHjRqIjDgF+lwyIq084gPWu6hJcyk/H9LghSEQICK3ShjyA+wCcdzjvkT+ha/BCmEgQvE9gwyaBdb1PHQ0fPEPN8Kde/+44lPzZTgZ+FtZ0NB1GBSlwx5jsiIVKyn0+eILzaAnWemxIojxmMAeytku6KpkhWkeInhD69OWhKLKkUxItwfeJvRUCSoGPkFUjYmhAOLxz+BgQUZHLDJUXLBcbVJ5jvUVKjc6yhGEB8iJPbrJWIE2JGRRgCRJJQzUaYbIi1YVSDYVmGBJXQZHn3Lp5jdPTBV2ITGZT9vf38CFwi2usRxW5ypjNprTdBUoGqtGI6XSCI2ljXDvc57WXbjAeGXyMbJoWoyTjCIiAF0nbRSuDix6FozQFmTCcHZ+QmYpZmSVr9uhTN00rpFKMxhXL8wIvBNt6jR/V5CEj9oG2juTFGLvZMJ9McEJzfn5O37WovGI8GlFlJVUuKff3aLsudUREjlSatoXF2YKzxTmL5YKqzEB1GKGS+6FRmCynGk3Y+Iazk1PoYFzepqgygodmsyXYjydd/ol2Z3k5gx7+F1K1kRzYwgvlp117PyRd6EtXQwbQsxwybgEgqaoR4/GEi4sF3ocr1X760nnO57/4eW7eOmI6rlA6XRi7/SjNQOHg8GBopQaqaszBwRGTyZTvfOe7NM2Gbb1GxpQF7iyWdztm13VUzqXxwAB8iiEiosJkJbO9A+r6MU8+fMCPZATruHX3ZfZnc5wPPHn0kMWy5ue++lUmxQ02my3LxRJcYH9vn6OjaxidYzvLvXfe5lvf+RZmMuG1n/siv/z3/j5/sLygP3mCIIDvWJ0/AynZv/EyB7de4+SxxPZr8vl1Dl/+Aq++8UXe+PnP89obrzK7vodUfnglYvfR0KwbTt5/zMM/eZ9gx2Qm42L1lPn4kMXFQ9rTBwi7RulILhxKmtQRkAkoKgYA4q6C04P1tIRkwjErKYuCMy1ZnD5FxiVFVlFkmkBKppK+efr0o/+YItl/y5b3AaPStbNer1MLdrA3Vip1XIQQNHXDow8f8cPJD5lOJuzN5+RZltg6QqCHGX0cxgEvemZcjh5253WISfUzgQM90YHyamDAqBf3Cek2IYY/pUIYY7x8/KZpKMuSbZZhbU/TDo6GV1sUH7lz+vbX6br4V7F+FmCOZV4lMaoYEDHJvlvnKUxGUzcQkxq/9+D6JOrTO0flK2Lw1NsaXU3SvhkThZBoiVHgekf0ReoABYcSMTGgnGVbtyAyykzjXQGhx7seGwV5XiGQOA9t11NJgbcdfYyITiONSWqFUuKlRecGMFhrqdc1vksJrxGa3JSJGSM1IfSXngcSwcF8xmg85js/fIdiNmKyNycvcrbbNdPxmOloROgF8/mc5bpGaU2R5/gYaELNwXSfOzfm3LxxyOnFGdtNT1lqijLD+o7Qe7Q2VDpDjwxLV0MUZHKE7CUP3r/PaFwRjw4oy4IgpzTblr53jEh7W5EXnK7O6VWHytokrtcZghOgPVWVoaWhmB+ipKRuN+TGsDeaMtIjDqYZm+Oe+/fuMSor5qMRShpOzlecnC/prUOYglXbUNsVyiu0AxM9+3sZWgkyAevFkul4xnw+5/DgOq2D7XrDpu0+1nn2yTEDYccmINkZx4jSGj/MMndgozDYU17OD8Qw2xe7YkUO/tqa/f0DrLV0bYt3nogHkuOhlIpyNCIYzZ9870066wBxOf5MSUXaeK5d2+diuSJEwY1bdzk83OfZs+dsNiu2mwuC71Aq+YOnoKcYfJFxzuJcj4KEgFcCpyD6iA2KvdkhzXIN7TknTx7zpgu0jeWVz3yea/tH9J3jfLWgWRxzb/GczWaLUjk3rt/h5s07ZLpARHj68D7f/KN/xY/f+j4izwix4Tf+4X9Gs1nxh//v/yeu3SBjxLuWxfkzhMp45bO/wPhLX+fp84fcfe0NXv3Cl/jSVz7P65+5ycFhhZBX5sUBQhT0tePJew/58f/6bZ7++AGvvPFLPH12Hxk6il5im1Pwa7ToUUPHRyFS9S+S73nSJBdIBUonmU7JgDYXAiU11WiGUpLYrdmszsFDbgSZBk1AR4+Jjmh7YvZpMgBXCvYrfwsxIIfsNm3MqSugdoYsKs14F4sFD+7d59bNm7zxhTcYjUapNQsDHudqa5sXI4Mrz35VDtj7MFyXyZ5chSRK44bRxK5K885/xGVxt3bAwq9//eu8/uqrrFcrVl2Hch6zG3t8BCGw+z2+yOY/Xf9/12K5YG8yJc9ypEmCONZa8iwny7LULRUB7yx127LZbFnXNXq5YjqqCAGsbdnWLTFAZiqkVHS9pShTu1nnSbVQkPa/1gfOnj5hfv0a3jW0XYcehsRCZ+i8pFDgbWS+N4O2Zjyp6CNse493AWUU+/MDehqOj09Zr05p6m3a521ESLBdjdGGKJLccui64dwPVIXh8595mYePHnO2XPHZG0cYpam3W5bnF0wnc4qywEXBeDLl1Vc0trdsNjXL5YL9O9e5dfOAa+Mxz56fsu17TK7JjEoJQSERUTKej9E+okcZ0/wIpRWy95w/O8UFS1CWbVOjlcb3grKYMp1fo6hGHF+sOHv4ADlRlBNDsV/hW0l3AUYqhBJ0ncO5nlt3XuX6tSPA0rcb6qYm05L1xZoH7z1mOqqYjkeoGDm/OOXB6RnKVCzXaw4PjtiuaoKQCJ1Tdw63rRlNJRWwWD3nYG+e1BKritnePjNhePLkGZvur0GBUCAHgGCqFHY9QCkEWpvLPWcnJLK7V1oJZ7BzapNCoJWmKEfcvfsypycnqXIZ5qcxJtSs1Ir5tWugBbk2LM4u6Lt+qFpVmmMLiTKSvYMZ73/4IVlZMtvbw1rLB/c/oG3WdPUqjQdkUlCLpGpXKElwHuc6umaDs3OKokpzNBQOKCrDkYqMpWRxLGmaNdvNmqePHyF1ztHNW9y9dZuubfjOH/8R3icnrru3X2F0+w45Adf11NuGP/qD/5G33/ou2+UZgshb/y4pJv6D/9P/kefHT3nzD/+/BNsgYiRYy3p5ztOzE37pd/8L7n716xxd3+fnv/Iad+/uMxoZlIzDaCAxBiKSi8WW4/tPef+7P+Dxe++leX8OQrVU04zN4jGhW6IlKFkOFMR0zFJKpBEoQRKXIuEFlFbpvZNpridEHKjiiqIYc/3mLerNBev1krKYcGNvxMVqyXpraZ3Fbc5R+m/3mGC3frIWjgwmRUiyPAm+BB8+kggopdA6ucIdPz/m7bff5rXXX2c6n2FMkswO3qcEWqnLmf6lRjtJVTAOHYfdl/NXWoieS3fC7WbDerXCOkcIga5rWSyXSZbYvKCIpkQh3abt+5QwhJTMhJhGiMFfSR6uVNrhJxKLT9dPX53zdNEjew99AzFS5YY8U6g+OYPmWUYHSJWRZSMQkbrZUJhEN1ytN4mimhUYpXDek2cZ41GqtNt6leyNnU9Ykd4iY6Jut9stIsTU8el7etljvaTNGsZMmI7nNFhEjFR5STkyhODJTQkRNAodFSOjqCYjaimTd0X07M0Lzs6WmKJCKI0IGdH2ZFnG515/mflkwje+8W2cCxwd7kP0NNstSulk22wtCLh+bU6urvPw0TP6pmdcFuxNp4xLTR8kN29f59WsxGhBpAV6qsxwMJuDD8RMM9m/RlkVNO2Wk9Uz6tYyHc/IjKBrLbVuWSzPkTLHxgrd9FysF8xuHDC6McZlFjHe0HlFVo6pJlOK0QgRHF2zxNULJBXKSPAkm2EMdbvg1u07bDYrjo+P8bZmWS85vlhi1JQnTx+hpOFifUZRSJq+R4SMa3de5u5nXufp8SNkJsm0Is+zlFQNmK+nT55ifor1+E9bn9DCeMD4i+QDEENIVKUQB6XBK2poIQ6bQBoj7OQ0gctgjNCU1ZjDo2t897s/GOiJghjlJcjKI2htz0uv3Ob40RNWFxeXamypUk06BOPRiPF4RNO0HB5co6lrTk+e0bcbttslIfRJOU2oF+MMseNCOkBi+yaxGaqEpFU6iXwUSvDqjeuEZs6HRnDvwTsgE4Dn5Nkzzs/PeemV1znc26fenHO2eo7MMmK7YnP2lCe+R1X73Ht4zJ98/ztstss0e/KB1fOnfPcPfp/pbMx/8X/5v7JZLXn0w+9C21JUU66/9lm+/Du/xeErdzk83ONzn7nGjesj8kwg5a7WYki0BKvFhrd++D7nHz7j+f1H2Kbm4OZdbL3ALx/h+xy7XCBsk2gsgvS5aQWDiYnWg9yziEil0TpDKkOUAqEUQssBU5DavFJlVNPr3LxxxvEH36PtPXdv7FGWObPHx/z4yXOebS9Yh79+293/2NauQ7Cj3obgLwOkHEBZbmBlFGXBarVivV7z/vvv881vfINXXnmZ/YMDlEzunUKAHnA8cUgCduZR1r7wNNjN/Hcjvt3X7trYdQH6rif6kJDX3l/iHRiS+s1mw7e//S3+6T/9/7BeLTE6qeAl8KP/CMP4sns1fKkBoJqM6T9df9bKsoIgJEEk2zMxGFp1waNNDiEl8k70FJmhyHOikEipqaoxk3FF7x1FViBJ5lE+BqQ0dLZDGY3UBhMDPnRpxhw9Jles2yXRBzKdE20/PBcE37LenqPwaF0Qfc9qs0QXJflkRhSSxemG4+MzZrMJtu/JdYb3AUVkVM7JMvjqGzmz6jCxJYTmYrWg7xuO9ua89sotvv+9P2FTd1y7dZ3RrGRVL4k+MirGLFcrZrMZHo9wHiE0s0nF/vwVbt7cZ3ZzTKENN27f4fjkmF6kGBBDTt9uofO0sgfRMJ1fRwRoVg1925LLjMODGzx58AHj0YSmCVz05/jokNLQNi2VVnRB4vuI3azpizUjAbQlXdOw2K7xUYMPlFnHK6+8wmQ2BiFomwaBJQTNjbu3OX77OcW4IjNjLs6esnr+hMfPT8l1ixSSs+fP8PmGwsxplw0mRg5nE27dvM1ye0y7DcgsMp2OKfKK9bpmuVjT9jWL1eJjnWefjE0QkrTvpXoYKQhJnRGtS7KqXEEvEz+y+ciBXgiAVKgsZzI7wGQ56/U6GRZdZhPJWjOvxnzuC5/DSPjg7feoN2ti8IlXGhIfWgg4PErSlbbvefbsGc71bNcXdM0W29VcwgPSkyfMgBxalgIInrZt6LoWZy1KZ0glB7dEyWrbMpWKozuv8fD5MdF3CCHpmy312XO8dcyPjtibzslUpG8bVufPeaeuKaopoTrk5S9/jfHtl9luTpOEs0gmTfXinG/+j/+c/Zu3+Af/h/+Sf+k967MzPvPFn+NXfvf3ePXnf4HWwp1rc64djDFGIGV4MRMOSab06YOnfPNf/69INcY1Fo9Cl2Mm0xnbZ/fwZw+wukRFgRIRhUDGgNIRGWUaE6jUAUjfJUZptDEokyXRKCUQShMlBAJSRKQyKD1mfnATe/J9ltsVHYobe1MmRcHRfMyPTy5463T1SU63v31LJBT/jrK7C8xaJ62Gru0uA/ViseC9997jrbfe4mu/8jWiSvNZESEOs9oEvuUysPuBDbD7PSXUL5KBj4gYDZdF33dEn2hYWuvUsRpEiJbLJd/5znf4b//b/wff//73yYsSxEA3HmYh4kof5Cc7Iuk5P+0M/HlrNBqnMUBM1te+70jeoY58PEl0wyLH256gPFIbOhdQJmPbdoOlbmC72eB9krLW+YjW9uiwU4JN/iq+bfFtYlcJlSFyw7ZtEQMrTBDQMmAUjKt95NazXJ0j6gt8CKANqg8EZbjY1AQlUKaksz1nFwuWi3O2zRaUYFzNETLj7t07aJMc+47aPYiOXEu8tdy5dZuDg9vcunOTbXeWhLiExjeWpt7inePo7jX2JzMyEZmMVfKBCZG+9xyM5/S2g45EmR5JWlo2cU2mRxyMJuSjPcbFnMXpGh8dQiWwU9+0FLkiyzXWJJxaVzcgeoxp0ZmkGO9hu47Gn1KOQEmDC5q67Xn69AxkQd91fOFz11isVtz78DnKaA7297h2dJ396/s8vXib58ePKPOcaXlEpjKUMBg08+mY6WRGsJ7yqMT2hr15xbX5AbduX+fx44dMZhO2fY50MKvmzKYzTDFGmgyEJx+PP9Z59sn6tjFezvwBRBRJPEVrdnP8EIcqY9hYwgB6SW3/FMASTzppVe8dHNA0LX3fkfwmuUQaCiUoxyP2D6/z1g/e5fT4Oc72CPGi2RhEslm9dv2IxWKJc466XhN9msu0zQaCG7wKdoCmHUp+mLEKhfM90Sc/a+d7Sj3GZJroAs5HzruG4BtmN66zd+Ml6sVpGpfYDtusWV8ofIyUk5zppGBUFGkWj8S5lgfv/oji+i1++bd+h2/W55w+eAffbAdzMM/q5Jhv/P4/43f/y/+ar/+n/zlKRF5+7VVe/fzn0AM952hakRsB8qMfiXOR02cn/Nt/9vvcf+ser/zcL1DkBdV0itcy2WlePEGHDu0EWppLdkCiCwo0Eo3ESINWBmVylE6VqdQKIQ1SGVCATniLwM4qNBmk5OWYIlPEZss6dPR6zHg64XaeQVUxmpX8yw/e/USn3N/U9dMYN7sEezdLT/Kx7lJ6uO3agXoYaeqGZ0+f8d3vfJfXXn2No6OjFMhJlq1iMKTZAfS8fzHzv8oKcEOLf/d8u5+tS06FPgSCc/TDCMB5f3mfzWbDgwcPePPNNzk9PWW+t59GEQOgJ8QXnas/a30KG/jzl9ISpQ3OSwgRFyyOQO8tY6kQAurNBuEdRkIYQNJKaXKTI005JIIgRKIOZ3mGjgIfPcYYQpTgPVJ5ohqcSCXkWSA0LUIWCKMRPiKjw9uOEBSib1FK4kJiCwiRoZCURUmZ5wjg/PwY51raZo11a9AbRKFweoTRBbHuaG3LcrPk7PwCEQNVkfP40RP2phNGowm50oxnB0DE6AytNNvNmiA9o7KgkAbbtJSjkonRGJWhiyTC83T5hG7lyWcJJ2HpKMuMo+oG1/dexocNzbrm9OQ5k8MDirzE+ySLHHxH3W2Q2T7FeAJG03U9HkuIDic80/1rODbkpSWswPegs4xiPCcEwWQ6ZX50m81yTV7OGI0q5tMpo6JgszrDe08xKnGrDUG0KCGYTee8/pJhf3+GdZ560zIdz6iKfUo5YlpN0VnBycVTSiXIdEWpC+aTPXKTgxAIKdjWHbb762ATDEDAnVaAIG1MUg7zzOg/2vEbaGnsKg5e8IqVlpTViP2DQ05Ozwgh2RUn8aIkTmSyjPFsStM0/Mm3vsv67DnB2YFyyCUFSkrJweEBDz78EGt7bN8SfI/tG1zfpNtfIrYGv+pdYhAZRgeKEJMAUWcbxjikyglIYgxsvEN0FlX33LjzOucmoz17Sui2CN/h2g1dXdD5jq7vmI8rZqOKMsuom5rF84e8+yf/jt/8R/87vvQrv8ZboePkwfv4vksMAmt5eu99nj56yN/5rd9i/2DOZFwxGlUoKajyjCLXSJGAmXHAcnoXWS22vPeDd/ngW98iUqGsx3YLlLcYo3CbBbQbjNCpI7ATFRIiuYwNYEEtJFoqlNIobRJOQMk0GpA6JQRagRaXmA0hNSgx4BYcmkAhFTZEnAgEqcjynFtTw83px8tQ/7aunTBMGEB7MQScTeCf7XZLlmVIIbC2xzvPZr3mgw8+4PHjx+zv7wPD2EYEREx0wR3t92rA/0mtgF034OqowLsEUvMu0cn6vmez3rBZr9luNrRNw5OnT/nwww9ZLBbDff2L5/ARIZMb6Z+1Ps0DPt4SA7JeS43Mq9TebzY427Pa1OSZJnRdoiaLiNSSMsvwLlKUBaPxhK7rhuInEKODCIpBjlhout7i+6RRIJBok+NkD86jPOB9oph6TwwO71qgQGKpJHTe4b3HoJB9j3Xn+K4Fb1ktLxAi0HcdiIA2EZn3KO3AWzyWtnd4LM634APeSJqu51AIbhzusV2tGE9zRlVJmZfkRlNpjSkUte4JNmBMhikNJleDpbNAqwJT9tTrJSrP0+gTQ1WOuLZ/DSU121XL80eP6eqauTwc9r6MclQhlsl7Z7s+5Wh8Fyc0NnbEaMmFx3o7sOM8SimsV9g+jZhv3kzA+P39WaKr0zKfzZjPJoxHyQ68bTpa59FZDtqm49OGw8PrHNyUhFDTdg2GQFWUzKZzDsb7CC84ubigDz1+G9CiYG/vGtYF+rYlSs9mvaHvLeJPKYH+9PUJ2QQpgMZdRZjCGBGBzjKc64bfSe3CAScQQyC4xBAIw7hAKkM1mjKbzvnwwVuX93mxJCYr2NufkxeKul5Rr5bI6IcYPtxYgMo009mExcUCZy0x+OQhYNvBRvPFTDYO2gW7eXcqYkQKdsHT981gyZv0rZVRuBhxCOoYERcL7n72cyghed5saLYXSALCd8S+JsgxF4sG23uCdbhCU282uHrJ03e+z9P7P8ern/8C/focW2+4ePYYEQNaK6azOeUo5+jWNQ4OD1AM87U8I9fqSss1EIOg7wPrdcfx4wvu/fh91s+fcfO1ryCcZXv6mGBrcq3pusHjQBq03NlPJ7EoJQVKi0unx0uBIUkSwVEKZPoSSiO1TuMCyWWSQAyEfoNfn4BzIAxCakLweN9hBOyPoMo+npXm38a1M2oRiEEm1aVz1ElCjKxWq8EkSOGsg6FrsFwuOT09vZQyhuHzHYL6ThL4qlrhR/QGrvxt1znYrRjSc3jnQArqesvDDx9ycvKc09NTnh0f88Mf/hBrXQI6SpUsrCGpF4aBubB7jf9+3sq/cSsOzq5SKExRkBfxUjCq7/tBREhfYqCkTCO/qsxQOpBpQfAKBsxB8ILeWqRMdsgRkWiJzhOdI/pAVBIfBXHr0S4Zrl0KVdmWut2AryinY5xtaLstrfdI52n6DuUsTeex0dF1LUVZok2BEJ4gHZEeKSH6FqEc3nmUVkxnFQbBpKwQ3OJwNuX2res8PzlNxYk26EwhVEB6QVGUNMEDkvF4jMkNwkQ8jrZ3GFUxnQi2mwVZlaFkjpQVpcmJfeBk8yG2bmm2NUYphHe4rsW6PgEplQTpeL5YMjm4jrODv0cmCHIn89wijAcniaFI45IomJYlsczZ2xuzWq8ZjydMJ+PhcxF4QGiDcAaCxVRjhMnQITIyJbrUnJ9vODiYkbFFVSMm43Gi8K42bLYrokwgyr3RnOl0zma5gajwSM5OL4gR8uzjGcR9cni3SBTDRDJLdpkhhORUFQXucg6YugBSJma6H9pXISTlqiyvGI1nZFnGar1Ihj6ACCFZUcc0fpjPZrz22l0ef+5VvndyQvQ75bMXlMW8zCmKjO22Tlz2YHF9g+tbIM3EpFJXwI3p4hKXSm1J/EDg8a7HWUvvHCFCrhNwSsoMKwRNXaNNydHtl+m3F6xPPkwbefTEbktWTpBmRNdZnm1POXFbfNciXCDYJd//X/6A6zeu89obX6LfrvnRdoNvayb7h/zyb/8Wv/n3fofxqITgyY2mzPTAQR+6GzFtsF3Tc3ay4vnxmovnWzarLUIpZgcHuO2a/vQxsV0jTWJ5ZEoP7IE085dKJUyETInA7udEuxxMqOWQAEoJSpEeYDCcSsbl6TNzNXb5mP7sPs4FWlnQxAwbIBeWeaW4Pinp+/4Tn25/09dV8KAazrUQPS44ontBQNxut6mdWBSX2h5JayN+ZATwERYBXCYDu3U14L/QBfmJYxJiYC9onHUJeAgE53n7xz/mG9/4Bvfu3WO1WvH02VNiiBRVhRAyERekIPjhWv50/aVXGISjlFIYrRBSMhqNcK6nKkpUJqnyCTEGnOuwbUOz3VCNK6yrqTeRiMGFlhiSGZaPkao6AMC6HiUFGIW14HtL7yNdFEib2GIyJjYXeLq2Z7M8wdYZWfVZ1u0Zdb1O+2PTIIXhoCyI1tHFAEpQTaZoqWm2S+qGJHIUFEI6VOYILiC15Pr1I2ZVwUhr7r50hJKKqih5fe8urXVINMoEfNxSb1rcVhILRTWeMpnOkQIiFkePDYLgJFFGstKQlxnjbESpC7Ce548ecbr5kIPpHeaHRzhvcV1H3zc0XcO2rnEikOeSKNJ+nBlBLBXZNMl351mOD5480/jG43uNDBF8wG1XmMLQbhdoKdjbPyKXAhEdIQqkyTCqZCJm1P0aVVWgDCqkrmCzbQjec7B3RLQaOZswmU2xjaNuW4xRgEfpjPFkRiY1fdOxETW9DyyXC4pq9LE7cJ8oGUj511AVygRqCtbR9y3T8QijNd72Q5jeKQJFRExSq3EAqyhtGE2mjCZTmqaladrLAB/DEPSkIWpD4wLPTy8QJAYDMQHXxCBfKZViPB1jnaNtOwiO4Po00/L20mhnt3YoZq1Umnsny6xBICklLH3f0XcW6zxZRrLflRonJJvNgtXynFc+91lC/yqP3/5O6nZEjwg9oTlndn2PfHqTZr1i+eQesavRQiGiZ/n4Ad/8V/8Tv/73/wFf/Nqv0vY9zz+8z6/8zm/zf/5v/htGB4fUbUslJVWm0UogZEg+DMJBEPg+8uG79/jxD95ltei5dfMz3Hz5ddZPHjE9us7qw/fBNahokT5pOUipUoFvNFKkz04rcal8J6RGaINUGqUzlNKXDJA4zJ+UHio/mUZDSkiEbYnrU/zpQ2KzptMlS1dQ+4xRNebafMLBNIe4ol19yib4qSvGnVBg2qgHdg7eI3iBGdgFfiGSJfSOZXD156vGRUKIS6rg7t+AjyQOV7924kZX/y2EcJnENU3Ddru9ZDR0fTfModMoYr3ZkOX5JTBwp7D46frLLdtbjMlwLgXyqqooiop20AloujVSS7q2x/YWHzybtqZtthS5oes7qskcGxrq+gLb1SiVU42OaPuW548eUpUjFBFve5CSqiqRIeCbgOuXtJuYCojo8J3HbT2brWfaRELYsFhdEERFY6HIIJsdIkXNaLqHWC0Yj0ZoUSTAt44EWlxMz5cbRWEi0/keL716h/m0pN6cs27PicEiM4OnZrOqCR5M2YPaUHcSbSpUzLhx8yYyksB8raXzDcooovDY1nE4u0GWG4QNdNsa23S4znJj/2UIUMwm+Bhouw4pI5VRtM5xY/YKZBtu354y39sn3y9wsaGTS9p+i2sdSieRtn4baBct0UmqImdalvQx4qzk2tEBQkSc7VBZQmm1bU/bO7bbNW1rmU4M201NMaoo51Oy6JhODPX5munhIXqWcbE45fzZkmbbUVY51w6OWG1WPH/2nMOyQApF0/aovODg+nU26xXPT08+1nn2yToDUg/0s7SpOJ8Q+H3bEF0PISaq3yUHPQHUUqBNrS2pNCYvmM7njMcjTs/PcM6yk1SVpEy4KEpee+1V/sE/+B1GecY3/6c/IISkliaIQ4tbkZmMvfkey+WS3vaEaLF9g3fJKCghZZPFK4NiWggOazukSqpXUimCVMTgcNbSNTVt21A3Dc45ytygVAAlWazPefzwfV76zGc5uvs6R69+hkffe0qGQwaBtDVuc8Ktz3yR4vNf5uT+IY+++4dgIypoYuh5+tb3+d54zFd/4zf5R/+3/zvnp8/4u7/z2xSTKUjB/nxMrlXK1sULY5gEwhR8+N59/vV//y9Yni7ZP7rDWfc+GMWrX/plci1oL54gXJ26MkIQpSQqhdApeZIyzdRiQnMidYZWGUoZpM5S60qZVFXu8BUCBiNqEAakwvcNfvEU+/wefnVOh2QRR2x94NrBnGt7N5iOpmgV8U6hP6b4xd/GFSPY3tEPYj+C9Fkp9YIKuzMu0lpjBq6/UipJ//oXSp87SiLwER7/VYzA7nY/7e+QdA+6wT10Bxb8p//0n6K1vhxLOOuwfT8o26WRghzMZS6f89PhwF967T6D4ENijQjI8oIjnaOUJPgNq+VzvPWU5ZjR5AgXBaLZEl3LaDTC9z1tE4lOU0iduk9uS73eUq+f0rclhUxBKuqKXiVNChtbIh19F1CqYFTmjKo92ryjc5HFpmFvPmI812TlXuL+95azdYMJPdO8pywKYqgpyiSzK43BRYuXjtAUZIy4+cUSoaCoPC7WeNFD5piMJ4S+Z7U8Z7ntkUKQSYsQW2IoOdjbQxZTjMpZL87RRlCOCrQHozPm8wOW54KskGz6NZt1jyGjrCr28wpioAsNbRAJLGsMJs/IpGISk17CeFxw6/qcqqzwVtI0PRu7xIUtOpsS8ORRMpsfsWgcgozppKJQBt2DGc24fec22+UZ7dbR9R2u7+gdnJ5esNksGZuSIs9plhu6vkU4TQyO7mJJlpe8/srr/PjB93j/vUc0G8+kmrKfz2jrnrPTC/bzCefHJ0SVgN1SKsajCUrnjA8/nkHcJxwTSHZQ9hCTgYq1Pd72bPs+cZ1FCmBhsLzVyjDYlaCVJgiFMjnT+YxyVPDgw7eJL5AGiYoYoBpXTPb2OFlu+Wf/+l9w//17gzIV7MjLEVBKs7c3Z73eEqOn62qs3UK0KMXQzk4JCSEB7wSpq5DEXWxC3WYG2zVpZmst0btk0CIlxgl8jGgp8cLx9P5bPLz3Ep//yi/wpa//Jo/f/gGiO8dIiQqRsDpj+fA9jm7f4dX/5O8j+iWP3/4Roa/x3uKd5dEH97j9+S/xC7/92/z8r/8qrmto2prDvTlGX3p4De+7IAaJdYHHHz7m3/wP/4Ljd95n7+AmldYcv/s9XLPm6M7LrHwN3RIVAkqZZEU80AWFlInPEVMiJUUcAr0mCg0qI8iduJBO750YEoLBhESoHFSGsw32/AndyX269RmdF9QWUIpXbtzgcP8aeTFByIxIQJoxpvh4sph/m9YuVEopEPLF/F6K9LdAHNwDxeVIIcbk6NY0Dc+fP+fRo0e0bZsAhlcwAjsdACEEZVl+pDOwu83ubzuHuKt2yDtAYd/3nJ2dsV6vLx/XWst2u6Xp0vOmx5DDpXnFGEl8dEzx6frkyzrLLDMIkSVtk7MTYgyMJ2OmsxkHezdoui3e9WhdoEzBdDqllx5vM9ptQxSCUTlGVAXOLanbFYvFI/pNoJrsk5sxyneEAE4YeuvptufEbIXJAyFovLNoL8gjjKuK8c3rHK87cu3Qc0kwKRZIJVEup9lsmYaOzAgyHentktF4wt7BXZp+S2NX3Lh7nScfPieqLbqK9OqCTeewjWEyHrFa1NTdE4yo2JtOsKEnao9UY7QtiD1kZU692iBjoCzGmFzjXY5wgiLLUPv7nG+esejW9H3AREfnetym5/mjx9y/9yOEzpnuzTFVwWx/j2vXbzI7OEIoRds/pV1v2XjDeDJnbz7G9BPOLrbk44KsKPFZQ6Xm6JsGTYYiYtuW6cEB84NDqirn9OmW3nqaNrDabqm7nqqsqNdrxvMJVVWyqXKUMRgkTetoWo+ucs5PzrlYntNbR1mOmEwmOCKL03N863GipyYgi4KgBevNhk3TcniwR8jaj3WefUI2QfoveRIkWVpjNKHbcQsSQC9RCpPKoFEmzZhd2mS0ySlHY6pqgkBycX7BpZNJHOb5QjKdjpnPJizOz3lw7x62rUluFglIs/NZV1IxGo344N79ob1fE7wdVPJ21LfdJjVsggyoaZmqXe8dPiTcgFIS5y227yBGiqwgLzRCSnSWI42hX55w/61vcXA04+VXX+fOF77M8+99g5EaqnkE3bOHXLz7Qw4OZ/z6P/rf8/vrBdtHD3BSM9o/5Od/8zf4ha/9IqPpmLbrmWQZ46Ii2yUCA/Mhkuxf+9bz4MFz3vzmd3nwgx9igqIaVUTXYi+eomzN5v4KqyVZjGil0FJjlCZTZkjUkvlHcsmLl/QjpTVSZaBeJAAJOKgTawHQyiT8gUl/882GfnlOs11Tu8jKQpUVvHzrJtPJPllWJaEiIYlRgMiRxfSTnG5/S9Yg/yyT4NMODRNisnOVMg4eAg41OEjuAniWZRRFEiL6x//4H3Pjxg2+9rWvsb+/TwiBi4sLvv3tb1NVFX/37/7dy27BLlD/ZDdhhxWQUmKMwRhzSSlsmiYh0kUa1/XW0ts+XYNKYq1N1abfaXdcZfB8uv4y6+DwiPFkkrBZtke0ke12TbNdEbxjOhrRd0m11IaGEBzjkWYTSuo24/T0ghgaRjOPGRmiLDBVRnAGPe2QyjPSE+qVQyiVdAG2S7zaQOxYdQ0+lOhQIK3DW0uwjsoUHF7LEd053ltE1lNUmmw0YyQO2ZQSreHafMr88JAueKQwA/4BPDV9aOlZU2Y5mVL0scH7lhAUXefpmgYcKc5EjzYKVY6RShC8ZjyZMCrHuNgQtU7UcwJGK1BJGbetLdu6ZtNviD5BdEUQrJYrnpw+po0tWSaYH04Snio6+nqFG08pJ/uERrJcLcFL+r5jeq1iMp3iugZUjs40G2DbdggXidHhQkRFwXg+Jc8VpyfP8UTavqNuW2wIKGPS2EAJ0IrOWlrriC6wXNcsF0uuHR4w2d/n2ck5ZTHjs6/fJDqRrIzrhq7vabseOZszPzjg9Pyc5bomaE1elqzWAZ+ff6zz7BNTC0VMaAApFEgw2tBLOcz5uUIcTq35KCRZZnCDaEomFdPpHGMyttvtYJox3GWYncYY8daiidw6mHP76JCT998bhIPUJS0wzU5zJrMZm82W4Cx91xKCu+Rby8E9axhu7uordsJJOyU1BINBUaLuedcTvRsEXzLy0qCjoJzu0ayfc/70Po/e+RHXb1znl//e7/H7H94nbM/IJCno2jWLh2/xZF7xc7/5e3zlN/8e3/xn/x2z8Yxf/K3f4ed/49fYv3sHvMWYnHGZXXYEdpiuGAXBB7arhvfffJfnFz2b5RbfO+Z7+xitsesFsl+TqYAOCh0zcp2hhzGKUmqgC6rBmjh1OJQaBIa0SdrkWY4kIY4HT8qBdaDTqEXnoDOi0gTX0m7OaZoVdd/ROEdVjLhz4zrT6T5alyALwiD9nLpFGcJ8Kkf8kyv5d4hLbv7lSIgXCH9jzADiC2j0ZRXvnKPrOr7zne+w2Wx49dVXEz9aa5qmoa5rHj58yN7eHr/yK79CPsiS/qTWwO73nbgRcMlE8IPAkHPukrEQQricXyPER4SDUjcAdrojIn6aDfxll+17nPfYvqdparq2xvcdkZLF9gzbbbGuJjM5SuUob1E6Z13XgKGYzFFSEKOlr7cIDcqUSJGSAudP6botUkqq6Yxivs+oGXN+Lmm3K2w4AwWF0hTCoITEZZLe1gRt6boLCp0h8xKRGTKRUSjNeHKD8WiCXZyD9RgjcQS8iwRHOp6whSxQFRW+9YPegUQIRfQGWk2ZGbJM0XYdCo0WKu1QoscTCC7QdBtm+1NGoxEQ6bsGGyx107NYrFhvakyhme3PwAYW5+esmjVyIqnKKfvXp/i8xXUGpXKi0iyWCzqfgL1Sa8qqIssNgUjb9cSgiV1PZ3uKskQqnVhU3iN8YFSMmc/GhOApi5KTszPavqdpW7Ztjw0e4S1GKfLM4JylzDNOL5as65oiK5nvHXB8fkqel1Rmjus9neuSOJJWaJMcds8XSz776uv484tUuCqN7R2nZx16/vHE3j4ZtVCIAXUPqaaOKJkU6ryzH7mdUkmWVCjFeDpjs17RbBukVMymc6SUrJZLCCHpF0iRmAAxojOD9YGnT58PrZKeKCVi13YUCoYWd6pgNNvtluDdEMbSv6WqdEfNGf52SSVMbfPo7KBrAEq9QFd7ZwnWDtoKEqMNhIApx9RCUG+WPPnwfW49/gy/9Ju/zZd/+3d58u0/QrbrRK+L0G1WnNx7l0e3XuaVN77M6dkzXrn7Ej//ta+zd/M6Os/RSjMuc4yWKICdoFJMXO/l2QXv/ODHvPUnb7J/4zNU5Zjp0U3me4coBO3qHBMtmdRopcguRwPyspJMXyIlACLZ5Sop022UTLNpowdNiOEYYuqcCK1TQqBz0AVRS7rtgu1mQd2s8cFTlSVHh0fMZ/sYUyJkmQCggwoeg0tkVD87gSH+xPfdEj/x/d/X+sngnLAtASESNifEmNT/xOApMCQCdV1zcnKC9575fM5bb711+ffkKfBCtfBSGfSnJANXWQW74G+t/Ygy4S4xSH7zAakkxugBpJi6Tumx0+Ok+/57egP/Bi/nA96lWbwfEjKtVDJX846668iyNOJz3hJdn0zkECgRmEynWNuzXi2x3QIhAogKVEtRZpgsIHwkz5IjrA8NITqmozkiJDMhK3oMCoNBKPC6QxiHMI7et0yLKULniYbtI15YlNFM945YtR2b5Qo9KnACrE8GR4SAtcmKOfYC6Q2ZrnBEOm+xTUdXBybjMaNRhnaCPjhiSCqBKpMJY2M8QkaKIievKpyzCGspymxos3lGxRib9aggcNbR2462rRkdloxMwWzfYJuAERlSG2z0tPWKVd1y5+4hR0c3mI7mRAJ9qOnaNFKZjecs1hcYn5PrDITGSYGQkSwzeNcRGeSHRUpyvI90bU/negiO69euU5YVm/UaJQXjUZHG5OMZzvbEEMmLLFE+fXr9BD8w4mA8GUOIVLM9qvEZttliI/TW4kSPjB+PxfUJS7XdppE2DsEuIGd472AQA1IytdWVNpTlmPF0xnK5QJAwBJPJBCKsFstBwCitweeQoqrIipL1pubsdMVmvU0BJYQUxIcRgVQKnZnUxqwbwuUxDFX/ICq08zwQMVVhaXyQOgYhJBEeBviiSNwUvLX0fUfwDh8NwacAqUzSCXd9z+nxE+6//w5f+92/z9f/4X/GHyvYPHqM367xfY9zlq6zPHz3be7+3Ff4pd/7T3nl7i3m+/tEwCjFpCop8yxdoJfvajKVWZ6e88EP3+K7/+bfsbrYMBvfxDvL7PodxlVFf3GK367IJGRSIXViPSRjGzk4EKbhDQPoUg90Ty1ftJx3ksxxkChO/hOJRSCVQugMVAbK4Aks1wvW9YoYPGVZsj/f43DvEJ1VCJkjVEkkWSGnt1WD0gj1s6VB/9Pi1H+Izna88qwfDZ5xoIu5QdYbgkhB2Q1CQLsOgNaaEAJnp6dstlvatr1MBK21rFYruq6jKAqEEAPex1KWJZDGBM45jDEIKVkul5ydnV0aFV3FEOyMyFKHItA0/SXO4JIVFCPpsku3/dlJA//jWyGk4NF3XQoMxiA0bOtk2StNTmD3mbZE7xPqXmui65JynkiIdxcbgmuh7/DRYruS8UihlCAaRe8a3GqBszAtp9hM4mNFZwWVqsiCwTmPMJ48B1lmnMdAsECj6EJL9BZdGVAZQhrIM/pVj9t6nCRR/kKyYiaA9BrXerTKEDKD0OBDjwgCZTT5yJDnOXqkEF2NlR1KJw8b7wPaKMx0hCkM1lt6a1NHOh/RbHtE8Myne1xsL+i3Nd535EYyGZdkR4qsKpF0iNZjyglKGHy0aKPo+4CShsP9awQHbdfiBr0cgWQ236fvuxRwe4fQIrGsZGLyLM/P0mi5bjBKUuQZVVkSoiD3FhEch9dSYRg3a4oypxpVmCzHmIzz03MmVRoRESLBpe9aSlSmcEanolJn6KKgms7oiPi2BenReY8yHy8j/2RGRcEnVL1I0r4yRkIEbQx0qS2ohiRAaUNRVhwcHCJk8mLXSmOynLIoU7BbLlMln3a5y51wNJ4w39tjMpvTNR3NJs08pBqCNSQFLaXIshzbO5ztUzUfw0CQ3wXBK8c/ALMY5p6pa5BusxNI2tEMre3o2pqu7xBZTt/bZNurk7iKi4Ht6oLHH7zLow/e5df+4T+kE5J7f/IO3fk5rqmx3Za+2yKVou8avvwbX2M6ymjqNbkyjIuSItcIeYWPHcGFyHZdc+/Nd/nuv/4jPnzzx9x57Qs058ecHD9lfv0mgkDoNmCTDabRKaMVQzdGy6QumB4yVW7EgU2wm5JAes+DJwQHInULhJQEmRgWUmqEzojKEAXUzZqzixOE7ZmPRhxMJswmc3RWpO6BLBEqGz7LXcUISXfik5xtf7VLDLP5S449DC3un36hfJzL56/q5bzoUuzSYeBSMCglb0ncLxACeC8I3uOd/wjiv+u6pJHRdZcyxjFG2rblBz/4AUVRcPv2bUajEZvNhvV6zUsvvcRms6GqKparVQogwDtvv83b77zNZrMBXsgWXx7flW6BtfbSCyHJkA/X1pAU7D7/T2mGf7HVtz1uoAxmRlNkGaUWtG2DUIbxeMb56Ql1uyGGDqMFmSoQUdJua8xoRFnm+NkUzJK2b9FK0tU90WrcVmILj1NrRGhRsUdTJvnxWFPlJSLCuJqgvaTzjhgMuZZkZZlU/DY1GkFPl6pnnVNNcnwfsDFgioxms8IGj0NiQwQZyXWO9iCDoA+WTrh0e62YjA6RWEZTT7SgTU4hIloB2uKiIgYYjSoYg401m1WPs5EyK4lB0mwbYoQiK2meHtOt1mRFYDwuGZkxYt+hTcXmfEPs2/RYUuAITKYzvDXgI3lWsFgvadueqEEpgwgSrXJm0z0263O6Zo0yEaEMIQo65wjrLTLXjKoy4Rhyg96fsncwB8B7y2RvQt00BCLj8RglJcE6Qt8zGVdYZThZnjCtRnRDhzvPDcaUYAN1a9k7vEbbWZCarCxxItCLLaK0mL8O0aEQHXiVZuuIwbVw4JxLjQBMZlDGMJnOODi8RlGU/Oh73yVYiypGqRKJ0NY17bYeTIdIGgTDLL8ocvIix5Q5Otc07zeIoZWfvLsHC2KlKMqc9SptWME7tEzBNc1gB8KikIkrT0jgQSJJRO+FKuEAHyBGSfAWGzvaLvl4FxOJ80mcZ1wVbI1Gaknwnu7ihLe/88d8/fd+j1e+8Fnu33+OsDCeH5GVOUVVcHg05vNf+QJN21FmgtloTGGShoC8EgBilEQfaLY9j95/xtvffpOHP3qHPAoO9o9YPH1I+/A96n5DNArqJUYGTFaidY4a9AS0VIPEcEq9E5gyEH0kiKQmljLbBCQkJqGmAHiRoU2JzEaQVQRTgknjAdvXHD++R7+54OZkxPXJiHFZIXRBUHmiJQ6gw+jDwJMfPgMZCb75JKfbX+lKSmsMfPwBDyGTEUbw6f2/DHZXcC/xzwhgu9D2l00Ido/uiTjvLufvWimqskqzewEbv8H7F+eqFAkUu6vwY4w8efKEPM8/QkXUWvP8+XP+yT/5J5RFwRfeeINXXnnlslsghOAP//APqcYjnjx5cplcnJ2d8fTpU4qioCxL1psNTdsMHYTBt2CQoN0lBbvX86fzqxf4nE/XJ1+r1SppfpmMzGiUBC3TeSvyPKHLyw14hxAZJpPYviU6xXq9RhVjZtMKKQeKtxCYSYa3TaIUiwJLD2EFfY1xYHLPcrNmJTZU4wP62mN1i85GmGKUFFptQNvIpBhDUJTViEkxIwiPNKCkp6tXiRIbI3W7JboAwuBcwGEp9gXjYkQeM87W50nTwmiqccHNa7eJsSbPGpYnG3yMw74GNnqiUFgbiHiePnsXR4vrFZmcoCdHkEvK8ZSjW9epmyWbxYYnDx9STAX7Nw6QbcYrr91ie+HAZsS+hyxSjkfovCAzmtW2ZtnWHL20x6QaI7XBBYvvFH7TYGeW0WjM4vlTxtUYlecs1jWr1RYpNUppJpnB9pbxZI+m6XDOY7LkDtsEywfvvMl4NsJZy/lpUr4t84zJaI4NAWcbxlWSUq5GFdYpYvC4zhGc5GK14e4rFYvlipOLM/KxRFUB23SItmA2OfhY59knGxOIJI26kxYWQ/tdSk1RjFBSkhcFN27d4uDwkN5afvSDH1BvtogA3gVm0ynBOdq6TtXoUD0QA2EAJ9q+4/bta3zui2/w4YNH/C+LZdIYgKHiVUQEJjNU4xGnZ2eDk1oYkoodVSuNFNyVjSrBHtIHkWiEDkFC1ccoEjBqEHxx1uKtI1hL0zryaUVWZJRaJ01vKVCh48k7P+TJhw85fOV1bt845Bvf/Le4zYo7r3+G13791/jyr/w8iEB3sWRsJoyyZBYi2FEdSbgFB6cnC+6/f8zxwxMuTjfIINjbO2BUjnm2PKeMnnD6FKs0mRLkpsBoNRyPRA3CMQxUtTRQ8UiSoFMcIthuU0BpUDmILIEMEeh8hJocELVJc0ilabqa0/tvYS8e89rBjL3KDO3KHKGKRDlEk15QJOCRyVk7adb7CP7jUVz+OlbXv2hlJ7R8RlkVia8fB9X2K0C5vu+HgPditLFLDK4mAH9VY4WdhkBKSNKz7ICvuyHW7na7n73zSJ0EiXZUv12r3xjzQg/EpaDddR3f+973ePPNNxlVFfsHB6w3a/7l//wvWW+S370fbuuv2BpLKXnjjTd48uQJ69Ua5/rLf7+kBF+J8xH+VAr1Ue2BT9cnWsJTFvkwwrQEF3AoWmcRzRLbjRmPS5SWNG2Ht5bMaKQWmFyiRNJW2bYLiI6qqAihJWqL8C1KFbR+SaYajFYYlxNDz7rxuEKmK1nDaXuG2CyQvSGLAtn3PDt/xGg6QyIYzTKKyT5aF0jRUddPWK0fc7I6QxLpREOmFCYaglA0XYPvHcptcMUYsuSb4OkJIgX9BO7OuXnzBr4P1O2KZXtM11uUGCGU5uGzh1z4p0TjyX2FiFD7jCzkxChZWcfJ6VMm4z3K8R5BNiSAO3gnqJsOGQ2T2SHFKKOcVUTg7NkxwUm80Gx6i1BJMjxaj+0jzaZjuViwvz9mb+8QIpxdnLJuGoLUCJlRNxal4cbBNe7fv0+zXTOZTcjKEb0LrLc9QWjWdU2uC5bLDSE4RlVJiIaiHKFEwPs0Eum7nq5PyoRKGJTRvPrKS0jveHZ2wqpeY3SPnrRUNzwyXGMy3v9Yp9knSgaUKWAwHoJdezkghCLLK+bzPd544wtoo3n85AkffPAB9WZLEvpNwMIyy1mvlyyW5/hgh3b1i7l2QCCznMWq5gc/fIe3f/wW9XaFGkxQkniQRElFmRdUZcnZ6WnyIwh+ENpJxxvDC6fEHcI5sRAS3U0IidLZJbADIVGaBMIZ/Ni9tbRtS9d0jMYZpdHkusCYDBlTVW3PTnj/m3/M+NodDm/f5Kt/9xdRXcPLr73Gq19+I2nN9z2v3Dqi1C9e6wBpSBiBKHj+4RP+x//+fyavrrN//RpHr75EsGvG+ZS+baBtyIzBEMllxCid5sJxUBFMLRa0zhMmQJAshkVAiEj0bhAcUkO3RIPMQBegS4RRaJ3jRI4LCqkyvILNdsHJo3cp6md85nDELDep5aWz5FRmKsBAfCHspJVO44kQUkfGvwgc/2HWi4o/Bk87CEsppTE62fOWZclkNCLLc5ROl0bXd9TbmqZpUoLgbBJ/ieEjr+YvHeAieP9CKtg6R9xuAS7NvV48WepY+JgMieAFI+CqH4EbLIt3SoTOOZqmScH+SkXfNE16fBkx2iT8gU8JRuNSAtd1HUYbyqKgbpLv/Z81YnnBhtm9tKHr9+n6Cy3nHaPqEEGgqRcsmp5bN+9w/e5NLk4vUGisC3Rtmt0bVSC1pGmWZAJiZ7GFBdkiANcLGrtOlaaekEmFVwEjJUVWEnTGeluTzffxYZN8DZQkRokuNEVVopwhNjXNuif0a7SLhBpMsGSiIgs9fb+CvCNgqYopQVXINkAbUSGQ64LmmWNxdp9rX/gsxuREIfExw6CxdkvXrzB6H5llLDcLZGYoR2PauCJaQYgtTScRVUbfbSm1IvSRk7MTlqdrRuMRP/7xe1y/dQ2pK15+/bPIokeqhjxq2u2G6X5Jd+HZG00RUtJjiRL2rt3i/jsPmE4yLk5O8U2HioK8yNk/OOD2nZc4Pbvg0YMzxnsVy8UZ3bbB2o4gLEFHmqbl6OYRzjqmsyn7h3OQMnW2dc61mxPaumbZnBO9ROWgCOTjMaPJjBjBNS3OB7quR2tDVZYorTCqoOsd2ijaZk0mBWNj8F7gWwe5YzZT9M3H03f5RMlAjDsb4NQViENgNVnJq6+9xmc/+xmOj5/z3gfvcXp6Qtu2LzaBS5lT2K5W1Jt1QkUKkQLzwKmXUlAVmpvX5kxmMx68C8JbiA6iHCo1iRQeRGr3N/X2siuQNp5Ur+0UEMMV6tOO8iikGgyT4pAIpGNQSiOFwsWE1O1ch7I9jkAQkmo0pikn9JkB79BCIvueD/7kW/z87/4nNLbj9V/8ZfYnJUWeYYnIENifjSj0DtgYBwpf+jFYx/HDp/zL/9d/x7MHp7z+xTnLp4+wznHw6heQUbJ68j6GiMoyjIookYK6lAn8IweNBCmSSYkYflcSEAGCRxk5OBUqUBplSlReIbOE/kdrYj5G5COCyWido16esTm5z6g959ZexVxrjEzdoCgVCJXAgmIQKPLhRbk8MDrkkPGon4Eu8UdCUox4lxDaUkrapmGlFWpQ+MvzgjzP2d/fJ8/zS6pdXdcsl0uWqyVdmy60n9Y1+KTHcxWJv/s9PfZHxxI7RP9VvQAp5WVwvwocvGpCtJMV3rELdmwBrTXl0E24yhzYGVrlJuPi/JzgA3meD7gcLrsoMabE9tKYjCRKdvXj/ou+P58u0NonR8LB+n1nDpdpiVSe9fqCzXpL37Z4ZzEmGbeN8pLlYoWXLW0T2G5b6mYDImByRxCGspojo0G7OZXMAM/aXbAU58yyDF93nB6fICqNyQbclhFE4egutiiVg5YE0dP6Dc4C0pKR4buEgtEmp7epsjZ5gREarXtUiDQXNfVqlTj2rsYJR5ZpRsWIHIExMwiBZ8cPca2nkBleWVx0tN0FKkb25ze4iAbXQdf0BJsKtrIoeHZ2xnK1ZTJNAdoEQy4Fo7FhXM3pfUdUnulsjLSSetPRuC1RRYpyn9F4yv78gDwv6MyW7XLJerGkrmvG8322Tc960yLHFaP5HtPZFC2TtHdEsF6sGU9Likyzd3CX9WZL3bRYvytTJZ0L9E1gMpuhyBEyJKCvVqyWK8qypF50STJaK5pmS103KJNMiBbn52w3LUWRJ/ovGtdk1F2DrZZ4v/5459knOSmTDXFMfgMioo1mPp/z+Te+yHg85v0PPuDZs2NWqyQNHEknjzQm0ZGip+1b6iY5A4aQTDAEL8RWiJEH9x8QpeDm7dvJrc+7hEoemAG7qizpCAiaNnkbxIFuKAgvWA8xJlobYfg+iOoM4CxgGDsMCnvD7RJNKgyyqxY/fGhZNeX6Zz/PRVzTnjxCCSB4Tp98yLP7H3Dt81+gKBRkhqAEuYBpZcjNjpKZRgMRAUHQ1R3PH3zIN37/n/Pk+99lduMziHbN4sNH2OCYHF4nK0rc8oxcKoSWaLMzE0r0QaU1SoqBTjgoDmo9JAdDv0VIpAwJ6aoEQidZUJnlqLwiDt4E0WREJdlsVzSbc2J9ytQt2R9njPMqgVvii+MXYUgMZUjMEAERiZRJLpeYxhMiktgcP5MrDmMokdT+rKXr2nTBKXXpGDgaJczL0dERd+/exVrLxcUFFxcXnJ+f03XdpY3vlf7PZQD88wLhTzIJwpAt7gS+gGHUEXDOA4Ol7JWOwI4W+OK2L0Yfu8B9VWAoy7JLyeNdYnF5e+/x3mERSWp4J18sSe3SOHTPfFIbTdCA4ZqL6ap+AY78NBH4i65xnqOUx2QGoQq6ztJ2G8pshuWE6Dz1uqGpN0QcmdGI2DIZTbHWE+0W2Uh8D8Sk0yIiGJnhQkr6inKMUQVe9gi9gdDTxmd0vUHIPaSWROVQRqOMoe/WuNAkAGAmccEShKO1S6zf4uyYTOTgM/KyQiuorcOFRJONIXnHZJmgPNpj7+AaTdiwqlfJ5dYHurZJQkMC1oslwoPzCm96nAt0tkPRUM4qViuDCJo8K8lVRehg6xoa5RiNKrreEmUgIyKVBqkS9Txour5GhBzXWjabhiAd1SSnzAryo1v4YDk7fc752UmioodAludkRU7Xt+hConQCs4d6Q/CO3gd65wkSmq5DKEW/2XJ+ccHJ6RlN1zMeTzjcP0gUXV0ikBxeu4bREmt7lss154sFB/ogFSh5jjEmCQ3VLcG2CJGzuFgz3dunmoyS3XjX4VtHqSZkaspi89egMxCCR4vkZjabTbh24xoHR4dE4P79+zx59pTNdoN3bgcbS9r3SiOUwrlkytB1Hd52iS4l1LBLyBcFpVZ0LrDZ1PR9lwL0JT9654o4uBFKSdt26fnkLqF4gV/eVeKXrAGtUIPdZ5qf75gHacXIJQBrF7xFSNt631taH/ncV75KKbc87TfEzQqpFXlV0tQ1RWYoc51oJEpSZpLcSNTlxhgQUeCjoKkdZ08XvP/m+9z/3vfw2w2z/QP69Rn9yUOC6+i7DaooUfUKo5JToB6cBhUCLSRam2QsKARaKozWQyuKy3mz0KkTkroiXCYDwmREbRC6AGXonKXZLtksz5D9hqnqmeeKcV6hdUkcEOpiACASAgQ7mEEl0Q1IEsZSDIlDCBBdAqD+DK8EHiR1DAJ4Ak7KS4R+U9fkec5mvWY0WIkeHBzw8ssv07Ytjx8/5tmzZ2w262T7+4kP4E9N2tM3wZWwurvZFdDjUM0rpZKp1pXgvutm7HADuyTAGHPZTbDWfiRZ2P2caIQRZ93leaO1vjJm+NNU0Z98BT/JIfg0IfgLrODpuwZPwEc7fHdIwIctthcQBUp5QkzaFFJpfAChNNqItCcUY5SV+NijRU6RV/TtBp2PkSh6H4lRoSjIRI4TNZgcFQQmEylayIQhKYoRzmwJTZecB3Wk79PzEyLWRbTKCDYwUgVFkeFDQ9s1RBnIRxm2aclmE4q5YTaa0DdtgncLwCRcQSAlpH3bgvM0XUcwAW8G9loOUUSi9ehQoE2B84GtbWCUEbPA9WsHRCPwMZCNBCJzeCLBB3ob6VXAEGi71KnOdIEiS1W2zFiuVyyW56zWK7RIRZcmQyvBuDSoMqfdLAiNJPZtksEXkm2XFAVHUiJEjvWWdd3SuzAYwhnOzi/QSrLd1FRVNTBzNLutSBf5UFSk+COHfbsaTYhC0/QWaXJGkxnFZIzRks1qidQSZeYU1Zzl8uMBtz8ZZgDBfDbjYH+fvf05o8kYHyMPHz7k2dNjrHfJy5kBqS8T4DDZ5WqctWzWi2Fz6olBIFRMCHQikYQFePnll3nlM59BIPjxD35wqWw6dCMvaUtJf0glLvQgVQz8xA40DOYZFNFkUt1LuIGQ7JhlAo9Frli6inT8Ul4y73G2Z1vXjA5e4fYXvoRfLzi/9x6zg2t84Ve/zv7RIdFZSpUP2AJJZgRSpJB8eUg+sF3WnJysOX5ywXrjcDaQFRWj2R4X995BtEuMs4gLj1WSLER0liOkxJBEg5QcOgMDrzUhzJPFcNyBBGGwlU2bOUKAlghtYEgEojYok+NC5HxxTr8+QfcrxhnMs4JxUaB0gVAFUSXWiIghAUhJCRrBDaMCMbyPaqCg+iEtjCSq4c/m+mlB7BJ+EiPeOZqBvrfebFBnZ4xGI+7cucPNmze5c+cOR0dHPHv2lOPjY05OThN1dtem9392cvDTnnv395/2c/o9XRS7lv9OEGi3dsJEV50LdwZGWuvLLoK1lrquL5OF3X2vihQFIWAYS/gBL7FzJtzZL/9569Mk4C++XB9ocUg8Hktwliofpa5bdHjfII2hVIrgJFJqqqpCoiiLnPEkQypF5gWqjfS9IDc5VV6xak4RMQzmbSmpV8JQignbaCmr1DU0GqwI+MECOVM5Qub4fovwDpVpaGoioHRGqadMshmda1BREWIabSopUCKxwGJco6IgM1lSj7UtQYJSGUJlBKmTiZ2NiCjp2g1dWCByiRlPEm5sLGi7NcJ7MlEQoqCJHVvRJJG1Aq5NDjjfLsmFQVUWb3p8VKnzJhXOSWq3pe0sOmqiE9jOYcaS4Bq221XqoCiFyTPyzJBlGX3XAoE8K1m3W9q+RcnkhyBVUiLs2o6sc2xFhyOilGE8noJUqMxwvjhGExEeYoDlckFZFhR5jslyprM5IrhBfCx165CSsqrQpuT8/ILJfI7SGT5Crg26KMkF5FlGlo8pBi2RP299omTgaH+fl156mWvXjwgx8OT4mIePH7NZb4ZJvYShRZ9a8vFypzPaEFTHZr1MCOkUmZEyiWqkNn1AqpxXXnmJL77xeU6en7BYLIhCJrT9peDRjr88SKbGhH6+nEsO7cokhTpwycXOn2Dn3CeTStdumhBSMqCUvPybGCrpeNld8LSbDSdPj7l74zav/J2vo4qK1z77WX7zf/u/gWJO5xyFEoyyZBG8SwSGsEkM0K22PHr3A548vqC1htHkkP27n6VbXJCZHNuuUdGRaYGRAbzD6Aytk8FSkhpOjAklUrIiEUihEoBSAmJH1dy9IaTXIWTSAzAlURcEnQ0tM1htl5ycPGUaN+yXMK9KiqJE6iLpDKiMKPWQdASIHqIj4ok+IFUkSghxcIkMgUgCHl0ey8/w2oVSSWrLy10yOOAs5NCJioAbRgSQ3t6XXnqZW7ducevWLRaLBQ8fPuTe/Xu0bctmvWG1XBI/gl356HPugr28/H3oSw2qXPIn73WZIO/Q/Mkv4KqF8dUEYdct2CUBbdvS9z193192BK6OEC7FqOBS2+OFNbK8xDek26Xnl1eygp/sCHy6/uIrBIFQJlmZD0lYcArnA1pmoBQ+cykYdkmcTuIwSiDLyHhi8D6inCbY5MA6LibkeY4rCpRMqHSGIkJFg4ozrHOMJjNUNsbbLSG0qZqmx/VDG9x1+N6QlwUyNEQBWVYxr47YK6ds2y2d82y2a4SIZKogk1lic2WwvVghW4csRnSuxmQ5WTYBaRJornUYGSnzks3FMZ3bYpTB+AqTG6qRoFkvyYzBdsm8qxEdrox0fU01yhlNSo4XJ0wnE2zWJFW+UCbzOWPoN55Ns0R6jbcZ3brBOc/8usTaDu09s6LECElR5uS5Jtiei8UZWml85pBa4W1MBnAydZ+L0hCkxpiM1llUljEeT3HW0TqPVxJTjGjXKw4mE3zw1E0NAkyek5cl2hjWizPkoOobQwIaCwmZgNGoTKPbIGi2W4JzBBERukBIQwwwnc4/1nn2iZKBX/nlX0YpxYMPH/Dw8SNW201SlxNpG0sFaNIgIIQUkAQJX6A00WR0TZPaxlIkGhw7aWAzVK+Cbe95870H3HvvfS4Wy4TyV4o4lGoviv4Utb3zaeP6SS7zEPx30CUpU4dC7mxhZeoW+PCievVeDJVRHLoFgFJIEVBK4Juat7/1HWa/+TVe+Ttf49Vf+iVeefkOs71DVuua64dTqkojVULwp007uQWK4Gk3LR/84Ee8+cffpW0Fs8OX2EjJ7S/8EucPHxK7FtlukQQynaOzIuEuIgkTgCTTEuROUTCdfEiRjIlyTZAhcXKHE0hKQaJuxsGiuEKYMegKLwzWe5rtGcfPHlJoz43xmH2TstggDV6kEQNKpxZWSPPshLtQEEBdykSnsVAcgJspAMpLoOHP+trlTrtEQCt9mQwoLdFqcH8Mga7v2Ww2PHj4kPV6w/n5OdPplFu3bvEbv/Eb/Oqv/ionp6d89zvf4dvf+hbO7YyyXjzXn3kcwzH4gVJ7dXQVLoWyriTGQyDeBXet9aWR0a713/c9bdtejgF2Y4Msyy67BLtkAYYEeUdlHMYLekddpX/RPbh64HEn5vVX9IF8ujBGsDcfE2UgxiztpxQ4Z5kWd1m0SyxrvPQ4lQJK82zDtcN9rDgjWocOGh0zCpV0CLJBEyTTOWU5ZzxR6KKgd46mFrha4PrIWEw4PHqF5eqY3Oc0rqdtLe2mxQ1iQsKXGCqMXJEZw7jcQ+uCi9WGEDuUyehbS24yMiWpspK8Mqw6ybpZI4VnUgRUD1pUVGYPkxtca2naDikjVV5ibU8USXirbXvKMqLVFK8USpRcbJZcnF/gyoDeK/BtRM4FddgynU/YO9znmbug7TpkLFjXa7wTLE4XmKJnko/ITIX0gr7eEjYrdLDMplOQGaOqou5Tcu9cj9Sa+f4hZ8fHjKdTjq59FiWgaxpc8MzHU6ZKkuUF296RFRWu65M5n5Tcunmbo/1DLk6e06wWqeNiMhAC5yNlrtN16QPBduztjdg26RoOpBBbFQUqMxTVlONnJ/iQxiFaK4pqxGQypun+GsYEb739NqenZ3R9g4+BYZsHBjc0QDG0/GUCrUU8IUSQCqENIlhc3yadZQAkUgskFik0ZTVh//pdXnr1dXKd8+GP36Jpm9S2HzY/KSRSaERUiJhkhsWuUmHnYZA2JD8oEu5EZoRQDMaIl4ApoTQ7Ar4Uw6Q9OoJPwio60xCTJXDfbXHNOefnZ3xm8lVe+tzrqL5ntd2yvzdmUujhMcQldiEOI4h+3fOjf/Pv+MEf/iu6zjPZu0l/+oTFkw/Y++xXmF+/Sf3hm8huixI+VQJCkJkKrQTZEAzUQBeUApK5oCEKgcqSGJL3HpWiySVmIxKSv0MxQ5R7kJUEpelsx8XFCWfHj5iVgs/cvM4Ujwlu5z5BYABYhoTsEGEwdooCRAqOSEVUBqRG6RwGwKjQKagSNLHPP8np9te+rlavHw3MgxEWL7AngiG5GkxAogJiZNs0rJcrnj875tGjR8znc9577z3u3LnDr/36r/Ff/df/FVprvve9711iaX7yGP7086e1w92ImNw5GUCtceD3x8uqPCW03nvMkATkeX4Z5Lfb7eUo4CN8/yuV/A4c6L1PQd9oRJBJh2PQrXjBbrhE9wwdwZTkhCtJxE97jZ+uv9jKhaNfXeB9SBLsxrDxC5TRLDcXrNZroraYvMMYRTnNCG0kH+WoMEZjCDagtEAajYuRprXQQN05ou6xW5CrNd61CCGYlnPyrISsoJIZ2eQ6Xu9R24a6bQljR3TQrBa0nSW4mITPZI4KGUFoghZ0tkX2DX3n6Dcd6JxykuOso+s9y/WW+ThjXR/jSS32IpuSZRlnq+d462i2kahquqbDjJPEvSpHVCPJ+iz5EtTLDX3T0W8dRuccTo8YHVU08pRYSrpactYuWPUtLkImAs1mBdGgvMKYMVU+oZQVejRGhZ7Y9fSrFdvNijoqpgf7zPfnGLWX8FlGIzHM9ufMDq4hENi+w3SD3ocqUEahdMb6+RllVvJ8tcEDZVkgiTTrNZnRtJmmmoxoGmjbDsQGZQzWOsbzA1bnJ/TWDZgQhR+0eqbjCiKsF+cJOKoLXPRkRlKWGYUxGPHxMvNPlAzcv3d/qEIjiDgwAWAHI5eQmAaXs+QhxkoJMdHZotBEafC2x/ueGNNBRDWg+aWgbVuOnz3hyaMPqTfr5OcqxUBrHObvMRJiAjG94DWn4/A7k51hnhnCi3aqEC8qnqQ8l5D5npjmooJBBz4QY0JsG5Xsm4WQiDzn67/62/zKr/0Khzev0W02zPOMvf05WfbCfng30Ni1c5vG8aM//j7f/R/+Oc35GbM7r6G1xi6eIU4fsLJrDm69gj2+R4FFFxkmT3MhrU1KABAYqRAiEOOONuYAe6kKGcKu/o6IEFLwlhJUQTAZcbSPr2ZIk9O3Navlgub8OTdLxZ39MaOYFCGRGUoZUHmSI5Yp4UMkJsIlOHGHIJeSXRCF1HVRamBvREmIkjgkjf8xrEv766HyTTP44b0Jw6zep9m5GgLper1ms9nw9NlTnjx9wvnFOU3bcnBwwEsvvcS9e/ewfc/Ha6Dv2v/pHU0tevni2IbvUgqij+RFQVEUePui6t9JCVtrP/rIP6FHEGJi6rwwtUqjshgjcedhMWBSIGkgXIoxXcUp7LoIn66/0tW2Ls3hiwqlIq3d0tgGrabUdoPtPcFG+lCjrUBhkHgse8RGM5ntQR6wbUvdNzSuBy8wvcHbgJUrAhUaj7MtdVvz+PE5i5MlX/zS3yFTNevVCapIInHaK4qiIh9X1NmEk+fPEXnPbHQdEQWZzplUJTYPkM1o6jVtaxGyRyKot1t83SU74lzT1g3KCqQaJdVS54hC0G02FFrQLS/Y4IjRUuRTxtUcCk0IDW0H148OaLKO6eE+15qI8xLv4dnT54TqHGnh8cWWeT5CmI48gOgcduWxbY9VDXvlNWzvKUsYlxUZJWuxpe8a9Kjg9sF1bty8RZ4bgu1pNxtW6y1BRsDQhsjy9Jz/H3t/HnRbetZ14597WMOenvHMfU53p4ckndAheZMKJSQMBqGQvD8DUuGnWBCkpEqxQFAUFEHAARAlCirxVwViUVRZhAgUUoDvK2gZQ8IQSQKkk3R6ON1nPs+0p7XWPVy/P+619t7POd3pbkhyEnt/k+f086y99hruda37+l7jvb9/jUCNsQblLKPBkNPn7mI0HOKbhvF4go+BIkYmh4c415BlGXsHY0QyjEnUWhtL2esjMqeuG06cOkOmYTyrMAWMjCbT3cJ7hsnkiNHmFo5UuTUcDdje2eLapWtM59PnJWcvbAljleLU0vpSldYYbQltxWTS2W3mfkytf4UULhDV5glog7YWiYEY2hpp5RAJbSy2JlOOo5tXufj4x/Guxq6UCS5cozFlmfrgFwHVNk8QVJev0HZvU20vAVJ+gg9+YWkh7UTv0kqDESGu1E5DRKInzwqUUZy6cDev+8Iv4Nxdp0CD1ZoTwx6FbS259jvpYJrgFUfjmouX9mgkQ1REZ5ZyuEWuDdVsn4IKNblM/eSUIgRMnmPKEpX1MDqtQqh0qhzQOvUOiBFidClxUhq0aKyodjmAkCowTKrzDwqMLdB2QGwrAiIeFR2bueLUiU2GyjEgYqJHbI7SBdqW7SJFFhHdWoCtl6dNFkxVBbFdvVohESQ4gm9QeIg+PWsB1GfJEsYrXqgokhZTMbrNQTFtd0eNEYvu+mdo1TYjEqKLHOwf8MEPfpCrV69x/vx5HnjgAW7evMn+/n67qNdzY7XPgEAbG1xkn6TP2+ciItRVRfBhGVJoFX7XRXBBcGBRUSAiOO9SGaEPWGMWDYm68/uQvAPdkuAxhtY70V5jS3jVSghvTQo+ecj6fYqybTeuoCx6lL0M1S/xlJigQFtiVpFnilxDXe8R45iN3kkGWQ9lLM6URA2NOJraESeO3uYIW8BgtJmSAanRjWfazHF1RKsCkxfYXkkzu4YBMl3ixzOynkHmc0LlyEsYFNsUeYlSOaFqCK6imUSctmSkfv7GZCityIwi620z3HL42RFKK/K8D05w4wlZOSCLmsZHbhxdZrSxTRCF1gU26xGUUHuV4uomp7S7XB9fYTwfU89Tl8Z8IyUz6ghnT++gRxFXK8IsQIDN7W2Gw9PUfkaZWYqdDUpb4Kcz9g6PiFpx9yteCSJkeQ+jLOOjKXU9xzeOqDQmM1jTw1UBk5dom1NVY+Z1g3Y9+sWApy4+zXBjgxgDm6MRNsvplSXRNwTnaaIwGm3hfAp1Dze22NrZpuwNOTw6WuR7TeqaufNonVNkJb0yAwKND5w5dxdRICOV2yvgxo2buBA5eerc85KzF9Z0iJRUpzsPuCSrRClJjQkDK3H6bjW8NJUpxUJxGEjWZbcsMRFCJKDY37vGB37vfRiTMd6/ATGgjGmrCGJSOBKB0NaGS2vhLF2Ui0lPteQkdrFrmyb19n6SpaVWPk9JV4smRTpZuEppoih8U1O5OTcPDjixu8Hu1ohRvyTPQBOO5SiIaKq5Z//6EU9+/ApPP32d/qBk576XM750hbIckjUz3HzSdgqMmFBhbUmWlZAVaFtgdY5VWYrZti5qrQUtCgkmleupNB6Ib3tAdG2AU6MkrcEWBaYc4LWmmR1hQ00ZG/qmoZcL1gcMgtUWsW2L4jaPA61Ij9gscgBSF0OFkoiOKckxhtQvHG2IwQFpbQIlAfCoz7Bqgme1z9tSPWkVp+7yThZypdqcl9TPoW6aRUihQ4iBel5x5dIlDvb3eeCBB9jZ3aWqKyaTyTIW/+yXsLjGVqySqz4mir3YIS6JgLG2ff5qYeHfGhZY3bYoK2zzEqBtFhTlWMfD5IWLiF7JJaAjK8take4yb03dWeNPhygRkyVPm9UZqJw6zlAS6GHZ2t3F+YAdnKDs51gTqZsRTV2h0PjG47XHYshUhmm9uKIUvUFJ41LYdjae4l2NMTknTvYZnD7P6btOMZkeMpuMCbOKnrWoQqF0hhbD5mjI0X5FX/foiUEqh4tTonJ4nxJUQ7+Pl4p63KAkrRI42Oyl0KZAKFOYUWGwJkeCYjyeEbzmxO4ZLpoPoYuCkT3J7qmz9Hf67E9v0tSREA95eu8xtrZ2kLmCRhN8JOiKomcgaIIK9PqRKlRYKRgWQ8piSGgUURnqKmBVn0ExQGUFKoD1EZ0ZMDmZUcznNcSqNWgtyip8XeNqR1Hk7bugKcoejVhC3aC95vBwgs0zykE/hXk0EAPVfIqra46mU2xRMOj3cN6lrqiNYV45InOyPCdKqmQoTWoO510y9qzRyKKZmGF7d4fZrMJ5T1M3eNcQPUzqT8ESxkLnCrzFBNAKRKOUYKxN5UcEbJYx3Bpx+tQJDm7u8fjHP4YKKdtdoxfKJDMK52okQjOb8ORHP4wxOb7tMaB1inkTVSIQrf89xEDwnsza5MJHtQ1xuqttL0/rdL5FDFgdm+CTRd9mUEeOfVu1iW9BILiajWHJ1mjA1qjPqF/Qy0xbMdBmUMREd5oqcOXyHo8/8iQ3n7rOjacuEg2cOnua3skSbQ0yuY4OFSqtmtS64DNs0QObymuMzjGJPrXueJuSBwVQBi0ewUN0CzaW7iiCpMQ3ZQti2UP3S8Jsipru09OegQ6U4smkQUmTqjbIWS0NFABJDYUUuitMWIl1CxI93s0herTOUNGgFn3rBVTKgo7h+QnlnUKn1Lq6/WWZaSs9K5nznavdWsu8qtrvH1/YKEpctDB+8skn2d7ZWfQBeK4+BJ2njZVrWlYH3NLMp/VO0BIFs9JLAFKYIM/zRUfC7jhd/4GItMm4kkJqK14BOF6xsFy5MMVLZKWnR5dl0WbKLPZavac1XjjKsofowGS6jyWn3xthbUFpLcVWj0wNODjcY1AOKMseQRoMDdVRjcMR8whG4SU1pHH1nBg91vYY9UtczOgVGV4pvM6x5QCxORu9DYKvqaZjZodHaO+xuUEpj9BQVzXWaObTGd4LoUgdEXVuKfo5vWEPVc/Zr6fM/RjjNaFpaLwj4Mk2UlL0vHY4r9DSUOYKo1OTOpOVlP0ROrMp1BnSPdRS4ahxrsHVjqzWTPc1bpaUtbIGXRpEAiYaTJ4WryulwEhJJkOM6mEyTz8vyOw2/f4Wg9FWUr5FD9/rtwvh5Uh09HTG9HDSeqKTxzUqzeToiBiEwXBrEVIzRqeFiCRHq5x+v4fNUnt2qyKZMSBC08DG5iZ5kTM53CdIIC8seZ5yq4L3jIYjrIX5fN5OBAqy1A7eaGEynUJM4Z3p9DC9j+KRUIN4Bv0BwX8K2hHDcmKgXVQoGeWqjV2keHKeaQZFwebOFmcvnOf8ubNcefopLl+5zDw40goEsQ3mKzKboSTgfABfM9m/gdIW06761C1L3LkkkzUf8N7RNDVFXrRJhZooYeE2TRGDZb8AaT0Bx+ui28msjb+Gle0pAVK3pRzCyRNbfO5DD3DPXafY2RhQZCa1SuoaIbRldNVkzs0bhzz12CUuPXmZOJ4xv/wEN49u0ivfQFluIPU+sTpE02CsTcl62qC0TUsR2yyxb52hpHXxolLXLFJvhrYHIYglBpWEv12ZTCmVQgP5ECk2oBggSrDNEZk7YmgihQrY6NCkhkCiLSEWaQEcFVoSooHQJoW1+QfeIGLRRqEkILEh+gotoc0XSQseiag2nJQsxbQ+wWc+pA0PqHYVKRFJJFYlazrEsKjV70Tp2ZdCTh0Y9/f301LfJA/PJ/KRdKRi1eJO4YpwzOLuaIFuiXDyviXya9v1FiBVGCxc/rd0IgwxldcuCMWt1QFqebJnusfOC9Dlxyz40yKP59nv8vnlTqzRuJrcQ91McdJgbIZVJZnqU+QBo3Iyqwm+Zj4ONK6i8VPCVKFLDT4SXSKK1byims+xWpNlijCbYnOLbmpKhGAyjC1QRYFGONy/jq9mqbdJPkAZQ4iKiGd8dIhWFpMVVHVIeUJorFfYqOnlQ/p5j0Nf4Yhok6Ms+BgYT8YUVugPejQxR5GStEUaMIGiLOkNc6zNyIuCzOY0rmJaTfB1TtRgTeoXs72xyXQ8gyagfCQfWLJig8IKRo9St8VoKOgRQ4aiR5EN0ASkbugP+/QHA8qil5JubYa3WfIy65R/1itzpkdTUol6MpJChKZuUFHIswLaxGmlU+VZlhmKssdwmIhF6toaMVrhXVrkb3N7iyKzzKb76CCMygH9wQY2y5EYKfIcTWDmXCrBd6lSSKNwtaOaT1E2Gc7TccRmeXqzgoPosRqMfn7z7gsiA0oli5+2pS/QKl5NiEK/32dje5ONjQ22d3Y4efo0J86cZWtzA60Np86c41JT09SySIDTKIy2FL0+89mUEHya+NqWw1EpnHdYm6UqgNZxqhS4pqGaz+kVqZWjMZYQPNK1TW5DGSkXgFYxmaXl0hKYVEKYjtv1VO9Ky1JCYmB3c4sveP2reePnvYZzJ7cpM9NWDXQxU4WIxrnApccv8tTjl7h+7QBXNRQEwuHTNNcvceWj25y/50FsdR09vYmWgNZ5yr3o1kzQBq1T18aUxJaIgNamTXNImeTJG2LQOkMrjXezhU9Z6wyTDzCDLWSwRdAW5ocMwpi+rsiCQ0ePiCeqSCQgYnBNRgwaIxqVRUybsBiiI61GkcoElc0QY1KIIjapMYYEYogoldGVEyqdYu0KTVdd8dmAhXdJ0ninuHpqrKR1KjG01i4U6KIGf+F9Wlr0AM479vf3yLJ8kQi4CAGsnLPNiFkkoa5czO1lfNAVwdD10EgnVyu/qsW6BAtPQHuty1DFUnN3ngZtbJsAmjwLEpPXKeXS3GrzLy80LQC2OgLPNK7L3z57JOLOYXx0xOZol35vQExcOy18LppqPqHIBa3h8HCPqqqT+7qZsVXskuUZYT5jVtdIljOb1VTTmtGoj9WWa089hS0zcmMxIUBWIFlOWWTMZ2P2Dw7pmZQIh0m1/1FSPlIEfBBOnNjm6MhBz+DEUTWO+uCIJipUmbetzksoBuS9HBs9jTvC+TG7WydQcYc6jxidgXagGvrlJv3+iKaZURQl/cEIHxzeO6wXev0thkZjCsvO4DTX4mWMCExrEEvfjOjlBUEEV9eUZkQRc+YuYDJLPy8RF7i6d5Uhirwc4esaXMqHca7BuYZAYLg5QKMJImR5yt2o6yZ15lQwn80IIdLf2EgLeOFTONwYikGOMt36IcmAcHXNbD4nRNIiaVkGSpMXOYP+gNFwhFZpDYJmNsdVKZHRB0mNjEg5TEJaiTRThrp2FGhC9GiE4DzB10gYM5vOnpecvSAykFpgppaVqk3UQwvStvd98KGXcuHuC9x9/i7yomQ8nTGv5hQ7W0jwPPjA/dy8dgXnquRSUAHVRrD6ZYGRQDWf08SkmBBp+1R7glLL0kGVVn1qmprpZEqvKFKtu85Qqk4xbrrYaevubglBIgVw62QkaiU2iqTafmMx1rKx0eOL3vA6vvr/fhOntwdYrVBt0iSQ8hhILZRvHoz537/3+9x46jLG9hkMtlB+ShxfoXT7TC9+kCO/j2FGz0/SokK6SKWSJsfmJdCGPVRayx4JiOi2jrBbpS60tf4qtRw1WZtQ6DC2BJMTswEq76EzS5wekM+uMZAjbJwQfUvGWhNOJIJEInNCiIRYEXxOpnNSz0GH1xElFoVF26L1ZJAWjWpXsKStJNEqa93qycoVkdtKzj4bcMwyl+QaJ0Dj1KKaxrexJdMmeupuQZm2M2CXeV9V1Uo8/tZIezrbgiCkEGTaayVccOt1LcihdMQ8dUt0rdO+SxLsSEAX3lj9McYs8gZU6xEryoLBYICIcOPGDbomX6jlkkPPlBuw2mhsSW7W+NPgxIltTp27B19X+KpCG0tR9BBRTKaB5mif2XzCdDqhdg0x1EhT0e9vUk9vUMWao3kglH2OJin+nylDmcOlyzeZuTn9wjIwlv7GNkNyjCmoq4rxZIYeDsnQ1LOaunGQGcrRgJ3TdzE+PGQ6m9LUQogOF2vmdU1oPPbqVWbOk21ZbE8wvQGFHWF0JMacRgViDbkUuBCxeUm/16dvS5RXjA8PUXiMytnc3CEGj/Q8g/4GG1snGNgBbuqQYNjcOYVRYIs+QVukUdRHjom/xumtu8jikOnhPBkxCOO9A3w0NCrj5s0jepvb1Ps3aSYNznl8dDTOYcqc82VBNCkEbq3BaEMtUM1n7aqfFUfjObtWg62R2ACRsszJC8PhwU16/RO0wW7qxtO4gC1y6qrC+0DjU3MgY1MYUSuNaxomBwcoAnk/rVSoQuDw8AAQRsNBKhE1MJnMCNFgcoMEj6srXDNHq5r4ySwt7KybEGpEp8xylMGYjLLocfrue0BrXv95r+Xd7/5tZpMjzpw6ycHhER/44IeYjif0+j0uX7zIZHyQrIyYypkUCu8DipwiM6iYE2tHIx4JOjHhzh+gFMTODDLMJ1MObuxx4fyF1A/AZlCntbu1ou0p0GY9awUqpn7qbSJWV2IYgk8LrnS/h5AyX61hZ2eTL3rj6/myN30+vSwyn46XFQNKUogkpCWIn7x6g6ev7HNU16njFlWyGm88hZteo5CayfQal5/YJ2YNWxnk5RDEYjKDzgZ4yambiA4OYzxGt4YYKT9AGZMqNX0DEtDKYLO0EliQtBaAjhCVJjSOeHiInkzQzSEbcY8pU5TUEGKbOWHonN1KaTAzHDOkTuKhxZKjMbkiakFhgAKtC1A2tS81QHC3aC0Lql0jwYCI53A6PiZPnw6sZuQ/r/2f5e/bt8ttx3bBo0PKIzCZoSgKjDVMxpO09LEIdVMvkluXwyC3HLvdEo9vu9WL0P0iIrRtt6D1BHStiFdzHLp3oQsjHGswFDwxti2+JTVUSiGOmOKkkAiwLJNzY9t86tnGTAM2s7jgaasXP2uJwUIO7oDsZnmf6VGFVhZth0TlmYzH7B+M6eU59XifS1efxilLNuhTjEZkYUQTPdNmyt7VS3h6hKLPdDrBTY+IseJafZODvSm1pMXUGGaYoMg9iDIMtnYYeIOThr3DfXJjyIoSby2Hszl+Erj37tPUs4pqdoS4Ob4+QolmuHWSxjvUdExTTVAGRHnm8ynz8SHT+R793eQVTZHmnEZpGhuQUFOPDzjYG9PvWeZTTzVrEDGMyk1GvS1iLTzx+Mch5qAzlHgm4zlV42lEEVXE6oZ8S+grw8HeHvvXD/A+ebFVZtg6fZq60Rzt3eTk3Xdx9emLTG4e0uv1yXo9rl+9ARryzNLLc1SM7DcVKGi853A2Rofkvp/Oa7h5HbEz6rCPqz03vGN4Yhuio5wrjLLLd1Qi04MDrl/fpwmCC56qadAijHsTFML16zeYTcacOrnD5LBhuHUCT6QKgoRA7iJ5UaKMoao9SjdU45rMKopMkRc53gWapn5esqvkeUj3U089xYULF/60sr3GGgBcvHiR8+fPf1rOtZbdNT6ZWMvuGp+teC7ZfV5kIMbIpUuXGI1Gx7qWrbHGC4GIMB6POXfu3KLhzacaa9ld45OBteyu8dmK5yu7z4sMrLHGGmusscYa/+fiM3/lmDXWWGONNdZY41OKNRlYY4011lhjjRc51mRgjTXWWGONNV7kWJOBNdZYY4011niRY00G1lhjjTXWWONFjjUZWGONNdZYY40XOdZkYI011lhjjTVe5FiTgTXWWGONNdZ4kWNNBtZYY4011ljjRY41GVhjjTXWWGONFznWZGCNNdZYY401XuRYk4E11lhjjTXWeJFjTQbWWGONNdZY40WONRlYY4011lhjjRc51mRgjTXWWGONNV7kWJOBNdZYY4011niRY00G1lhjjTXWWONFjjUZWGONNdZYY40XOdZkYI011lhjjTVe5FiTgTXWWGONNdZ4kWNNBtZYY4011ljjRY41GVhjjTXWWGONFznWZGCNNdZYY401XuRYk4E11lhjjTXWeJFjTQbWWGONNdZY40WONRlYY4011lhjjRc51mRgjTXWWGONNV7kWJOBNdZYY4011niRY00G1lhjjTXWWONFjjUZWGONNdZYY40XOdZkYI011lhjjTVe5FiTgTXWWGONNdZ4keMzggw8/vjjKKX40R/90U/aMX/rt34LpRS/9Vu/9Uk75hpr3Iq17K7x2Yq17K6xij8xGfgP/+E/oJTid3/3dz+Z1/MZg7e97W0opXjVq16FiNz2uVKKv/k3/+YduLI1/rRYy+5adj9bsZbdtex+qvAZ4Rn4TMYHP/hB3vWud93py1hjjReMteyu8dmKtex++rEmA58AvV6Pl770pfzAD/zAM7LUzzZ472ma5k5fxhqfBqxld43PVqxl987gU0oGmqbhe7/3e3nta1/L5uYmg8GAN77xjfzmb/7ms37nx37sx7jnnnvo9Xp80Rd9ER/60Idu2+fDH/4wX/M1X8POzg5lWfK6172OX/7lX37O65nNZnz4wx/mxo0bz+v6tdZ8z/d8Dx/4wAf4z//5Pz/n/teuXeObvumbOH36NGVZ8rmf+7n8zM/8zPM617333sub3/xmfuM3foNXv/rVlGXJK17ximdkxwcHB/ytv/W3uHDhAkVR8MADD/DDP/zDxBgX+6zGA9/+9rdz//33UxQFf/RHfwTAj//4j/PKV76Sfr/P9vY2r3vd6/i5n/u5Y+d5//vfz1d8xVewsbHBcDjkTW96E7/92799bJ/Obfnud7+b7/iO7+DkyZMMBgO+6qu+iuvXrz+ve/9MxFp217L72Yq17K5l908E+RPip3/6pwWQ3/md33nWfa5fvy5nz56V7/iO75B/9+/+nfzIj/yIvOxlL5Msy+T973//Yr/HHntMAHn44Yfl3nvvlR/+4R+W7//+75ednR05efKkXLlyZbHvhz70Idnc3JRXvOIV8sM//MPyEz/xE/KFX/iFopSSd73rXYv9fvM3f1MA+c3f/M3btn3f933fc97fN3zDN8hgMBDvvTz44IPyuZ/7uRJjXHwOyLd8y7cs/p7NZvLQQw9JlmXy7d/+7fKv//W/lje+8Y0CyNvf/vbnPN8999wjL33pS2Vra0u+67u+S/7lv/yX8vDDD4vWWn7jN35jsd90OpVXvepVsru7K3//7/99+cmf/En5+q//elFKybd927fdNqaveMUr5L777pMf+qEfkh/7sR+TJ554Qv79v//3AsjXfM3XyDve8Q75V//qX8k3fdM3ybd+67ceG+fBYCBnz56VH/zBH5Qf+qEfkpe85CVSFIX89m//9mK/Tg5e85rXyJ/9s39WfvzHf1z+9t/+22KMkbe+9a3Ped93AmvZXcuuyFp217K7lt1VfErJgPde6ro+tm1/f19Onz4tf/Wv/tXFtm4Ae72ePPXUU4vt733vewWQb//2b19se9Ob3iQPP/ywVFW12BZjlM///M+XBx98cLHtkyWUIiI/8zM/I8Axob9VKN/+9rcLID/7sz+72NY0jfyZP/NnZDgcytHR0Sc83z333COA/MIv/MJi2+HhoZw9e1Ze85rXLLb94A/+oAwGA/nIRz5y7Pvf9V3fJcYYefLJJ0VkOaYbGxty7dq1Y/v+hb/wF+SVr3zlJ7yet7zlLZLnuTz66KOLbZcuXZLRaCRf+IVfuNjWycGXfumXHntpv/3bv12MMXJwcPAJz3MnsJbdteyKrGV3Lbtr2V3FpzRMYIwhz3MAYozs7e3hved1r3sdv//7v3/b/m95y1u46667Fn+//vWv5/M+7/P41V/9VQD29vb4b//tv/HWt76V8XjMjRs3uHHjBjdv3uTLv/zL+ehHP8rTTz/9rNfzxV/8xYgI/+gf/aMXdB9f93Vfx4MPPvgJY1i/+qu/ypkzZ/hLf+kvLbZlWca3fuu3MplM+O///b8/53nOnTvHV33VVy3+3tjY4Ou//ut5//vfz5UrVwD4+Z//ed74xjeyvb29uP8bN27wpV/6pYQQ+B//438cO+Zf/It/kZMnTx7btrW1xVNPPcXv/M7vPON1hBD4jd/4Dd7ylrdw3333LbafPXuWv/yX/zL/83/+T46Ojo5955u/+ZtRSi3+fuMb30gIgSeeeOI57/szEWvZXcvuWnbXsvtikt1PeQLhz/zMz/CqV72KsizZ3d3l5MmT/Jf/8l84PDy8bd8HH3zwtm0vfelLefzxxwH42Mc+hojwD//hP+TkyZPHfr7v+74PSPGjTzaMMXzP93wP//t//29+8Rd/8Rn3eeKJJ3jwwQfR+viQPvTQQ4vPnwsPPPDAsQcL6f6BxRh89KMf5dd+7dduu/8v/dIvBW6//5e85CW3nefv/b2/x3A45PWvfz0PPvgg3/It38K73/3uxefXr19nNpvxspe97LbvPvTQQ8QYuXjx4rHtd99997G/t7e3Adjf33/O+/5MxVp217L72Yq17K5l94XCvuBvvAD87M/+LG9729t4y1vewnd+53dy6tQpjDH8s3/2z3j00Udf8PG6RI2/83f+Dl/+5V/+jPs88MADf6prfjZ83dd9HT/4gz/ID/zAD/CWt7zlU3KO54MYI3/uz/05/u7f/bvP+HknxB16vd5t+zz00EM88sgj/Mqv/Aq/9mu/xi/8wi/wb//tv+V7v/d7+f7v//4/0XUZY55x+7Mx+s90rGX3k4+17H56sJbdTz5eDLL7KSUD73znO7nvvvt417vedYx5dWzyVnz0ox+9bdtHPvIR7r33XoCF6yTLsgUj+3ShY6lve9vb+KVf+qXbPr/nnnv4wAc+QIzxGEv98Ic/vPj8udAx8NWx+shHPgKwGIP777+fyWTyp77/wWDA137t1/K1X/u1NE3DV3/1V/NP/sk/4bu/+7s5efIk/X6fRx555LbvffjDH0ZrzYULF/5U5/9Mx1p217L72Yq17K5l90+CT3nOABxnKe9973t5z3ve84z7/+Iv/uKx2NP73vc+3vve9/IVX/EVAJw6dYov/uIv5h3veAeXL1++7fvPVVLxQktcbsVf+St/hQceeOAZWdyf//N/nitXrvCf/tN/Wmzz3vPjP/7jDIdDvuiLvug5j3/p0qVjpTRHR0f8x//4H3n1q1/NmTNnAHjrW9/Ke97zHn7913/9tu8fHBzgvX/O89y8efPY33me84pXvAIRwTmHMYYv+7Iv45d+6ZcWbjKAq1ev8nM/93O84Q1vYGNj4znP89mMteyuZfezFWvZXcvunwR/as/AT/3UT/Frv/Zrt23/tm/7Nt785jfzrne9i6/6qq/iK7/yK3nsscf4yZ/8SV7xilcwmUxu+84DDzzAG97wBv76X//r1HXN29/+dnZ3d4+5Zv7Nv/k3vOENb+Dhhx/mr/21v8Z9993H1atXec973sNTTz3FH/zBHzzrtb7vfe/jS77kS/i+7/u+F5zMAukl+wf/4B/wjd/4jbd99s3f/M284x3v4G1vexu/93u/x7333ss73/lO3v3ud/P2t7+d0Wj0nMd/6Utfyjd90zfxO7/zO5w+fZqf+qmf4urVq/z0T//0Yp/v/M7v5Jd/+Zd585vfzNve9jZe+9rXMp1O+eAHP8g73/lOHn/8cU6cOPEJz/NlX/ZlnDlzhi/4gi/g9OnT/PEf/zE/8RM/wVd+5VcurvMf/+N/zH/9r/+VN7zhDfyNv/E3sNbyjne8g7qu+ZEf+ZEXOHKfmVjLbsJadj/7sJbdhLXsfhLxgusPWnSlDc/2c/HiRYkxyj/9p/9U7rnnHimKQl7zmtfIr/zKr8g3fMM3yD333LM4VleO8c//+T+Xf/Ev/oVcuHBBiqKQN77xjfIHf/AHt5370Ucfla//+q+XM2fOSJZlctddd8mb3/xmeec737nY55NZ4rIK55zcf//9t5W4iIhcvXpVvvEbv1FOnDgheZ7Lww8/LD/90z/9nOcSSSUuX/mVXym//uu/Lq961aukKAp5+ctfLj//8z9/277j8Vi++7u/Wx544AHJ81xOnDghn//5ny8/+qM/Kk3TiMjxMb0V73jHO+QLv/ALZXd3V4qikPvvv1++8zu/Uw4PD4/t9/u///vy5V/+5TIcDqXf78uXfMmXyP/6X//r2D7PVur0TOP/mYK17K5lV2Qtu2vZXcvuKpTIZ2mWzP9huPfee/mcz/kcfuVXfuVOX8oaa7wgrGV3jc9WrGV3ifXaBGusscYaa6zxIseaDKyxxhprrLHGixxrMrDGGmusscYaL3KscwbWWGONNdZY40WOtWdgjTXWWGONNV7keF59BmKMXLp0idFodFsP5zXWeL4QEcbjMefOnbutl/inCmvZXeOTgbXsrvHZiucru8+LDFy6dOn/+Baea3z6cPHiRc6fP/9pOddadtf4ZGItu2t8tuK5ZPd5kYGuO9Kf/b//vyhlqOs58/kRvpqjlCGKpyhHTI4moIRef8jm1jYbm1toY3HNjKoO2AzKvMCaDK0NIMTgyPM+b33rV/Pw57wiMRcBERDp2PCSFT9bioOs/IsopG3DIYvvCwJEEQQhRAgSiSEiApGIIEQBHyAEIQTwUfAhopQQRah8ZO4itYvEKCgDRiuCCAqNIEgUYhRCTPc3n+zzod/9f5hefQJxnkldYzJNAJrGowX6ZUk1r+iXBUYirnH4GBAliPfs7e0znc4wueGe+x/A9EeYzFLmJUXRYz6tEV/jncNqxXh2SD2fg0SMMfgmsHfzkI3tLXqbG/SKPloixIhW4IKnLEuKjdN8zue9ic3d02ibITGNn9Jq8RhyrSitxmidRlYiIFhj6GeaIrNYY9BGodvRV1oxmx7xxf/XS55XV7BPFrpz/b9//wvINs8T8jOYq0+Rffw9RAK/vXOasVScUxlV5imahjPzmgzF/9gdsG81TTC8ns/h1fe+lo35DY6efA+HVz6INYonC/ijnQJV5FwYV7zSDRg98GfZu+tzeOyxdzO/+T4GDdw7rjjtPJcMHGSGkGnEKLTzvLyKBO/5yKhgbEf88f6QP7yaoWeObz05QTc1/78/mnJx5jloGuYusJVpPv90yde/fMi/+cAh/+9lzzxCgwOEe/oFb7prxBvvGfI9//0JersFX/LnzjEYlezEIXfrDbaU54OX/pgn9gO//6EJr7ow5Ksf2GU29XxsKhwNe7z8vpyBv8bJWcX5uedmYZkUhtoKEDg5F3bnkdw5Lg8KDg00VuGtQhnohci5iZBJ4Eq/zxEeZyJKaQoyNoPi1HjKpFA8NSypbI9+/z5KCvqTawz2PkaVRX7/TJ9x1kdMZCME7jms2Z56rvfh986XTDJNiCWnsy0evjSmdJ4ruxt85Nwp5kpRuTGKilc+cZ2TNxr+aLPkkZ0SV2aUg4xMafr7+7zpY4dcObPJY2e3ud43zPyMyfQQP1H80nc9eUdk93c/9DjD0SjNaSznv8VUqBb/pM5DstKFaGW67L4vIu38KO2spxZfEKSde9P2tJscO5+086tImi9j+1mQNEdGQKFQCFoJVin0LZ6N7pJ1+4NKc3OMgkTVHlPamfvW76k0p6hjmgHV/qWU4lY/Srqf5G2J7TwfouAieFGEKEkfxPSZtPeJAs3x48nKfYbu4qQbn+UVd7esaO8RSdes0ryIsLiP5bNbGet2bIxun0+r0JKeSj+hHfd0byTds9CfEKMwn034zre+7jll93mRgc5FZbOcLCsx1iAEZj4QfEAphTWWLMuIErBZRpblWGvRJifLDMZ4fKhR2qCswdqcLMuSkIhiOBiysbmBVl1fbVpFtEoK5JhgrD6cxaiLWiETy5fi1kGMIgsBkNi+BJ2QhCQoPgguCM7FRBYkYgSsEwqfXiNtNKLA+QiiiJKUq3THDxGrFSbrEZUlKzQmNNjMkqHQEQxCv19S5BkKwc8rxpMx4+kEpTXDfp9Z7agCjPICa/P2SQeUChgLRWmJUVNKH1fN6JcDrLa4piHEQFEYTp3exVqLUoLRCqMsSEBrjVXpedksIy97lL0h2uaLMVRaLyYRazXGaoxOQq0RrFb0Mks/N+SZxhizeFkVYIzC2uXL+unC4lwvvUAY3ENTnIc8Em72wDmubeUUynBSFXzcekI1I+qIDRE1yDBFRq4yNkOfna1tRnaO6mdIT5EpGA4sasNQKM2JUHByYtkIFcYfcKDm9PKCAZGzReCkCpg8Z5QZnNEoregrzYX5jKg1hWQcVnBURx4LAlrYzYS7BppvePmIaVTse8Vh7enpwCs34MG+5//z0m2GQ9hrGo6qGuccFwYZrz2Vc/9QePWJPk/4wNG4IRsVoIXcRvIiY7rZ58rhjCsi3Hu2T7XV50Y152Oh4YYFcyKnzAv80YDT1+Zc3TXs94WoPRtNg73pKZQmhoyPn8m4tKFxhUasgIoMKmF4RdGrI390LmdaGIyO9CNs1ZrRodCvYTIyuJ4nhik6ThiojC0MIyIzpXjJuKHGYYFNHzlRBfohMIqQPR5x2qB1YEMqzo0rlFaMYqAnwjyzzHRDL8x46LChqCNqHuhPPU1Mc5fRkd2pcH8d6buIDsKGGMaScSQG8eGOye5oY4NhO5F329K7KLcoPrWc7+jm0KS0jm2TVaUjy22qM6lWCMFCMabf0/k7MqCSMoqy+G9cXFf73qv2p3VPL8dP0FpaJZkuLC6upT1Hd2eyeled8lSL7x4zBDvFrRTEFeLUkaD2cFE6Qw/8gsi0JmGn1GVxsqUuYnmfoSVTiF6MYfcUktJPylyhQMnqHS3mRcWSvKSr67amv5VakhwkLp7vqh7rlH6M6b6WP2nbctw+sey+oLUJYvCQRYzRZDbDWIt3Ht84aj0H0oBKjIQYcK5BBaEoMjKbFF2MkeADWgWstZgsw2pLlucordLYi4LWgu/Y0/KRLm7tGS6we1FWWJrI4sXpHnRoFXVkKbxRVoVMEJWUHK2QdMIiotFKkRkNShCl8CGmc0TQWmGNQiSxYo0iZhllOUCUBg1GWzKSVY3W6SH7gPOePLfU3nM4mXA0mVKUBYPhkLzfRxXCcDRAouDrBuUUBkEJ1C6CBDJTYDKLCZbMBLABaTzeeaqmweQlmc7JUagYISYyYIxFK016SVmOI4lzd2OEQAgRp9LEoxVk7ctutV4Kfzv+0h5PP4N18OmEynvovMBmFqxGG4HGYeKE8ypnG4/BUwbPKAZGIXCh8QxVhrKGk01FduNJzMETDCc3saT7POMUD82gj3Df1LMzn2Avf4zB7JC7ZlcIjZBhGChNJopcZRhjmRsIWqFjIADOaPat4abSHCpFA+RKEZVweeb54PUabTK0zTBasZkbdotIqSMXNjI+X5WgA843SHDs5vDAhmE393z1Q9s8XcMwy+k1wq5uOGcaeqbH/dunqcYT5O6Cu0+NGO1s08SSDTvmZhE4iA3DrEA2ziCHN5gOhBvlFE+gRsh7aeIZ1nC9r3l6K6MuIZoIIgyywL37ip5EPratqPsFVkHhYXMquMZzwmoe3c346JYl1nC6dwLy+7AHe/jJE1wbRP54S1OJpxDDqUowUQh15JrVXEajosEqIXcOXCBYQxM8YT6BRsjxbM1qtg4rbFCcnSnyEAm6QuVTjMDOpGJ70uCuTZBK2C1ynIrUzhOP3B2T3U7p34puWzdvpX/VQpmv7hNXlGWyUFVLCNKcKyKgdDvnpmNEtVRw3bvfKatuXoit4ifGY+dUraWuuqmg265W/9vOse2hjeo8uq1C7JTkwmvByrS/VKwotfRuLEettQu7LyZFojrDUtL1aZWUoFYQSfNfN+91hEDa8et0h1JJL+hOV6nVa1PtHJjGWB97bGqFBMjiuXXEqbsjpbjtGroPFuPRfqZJZE/rlUFKF5zmZ528ss8HL4gMzGeTtCJW+yCNMWitcSFQV3O0MWmQlYIYCa5BVMBaMConsxlBfBpUH/DakWeWXq+kKPLEgjrBPIYli12wPjgmZEsX1sq3ZMlwY6f8AR9i61ppiULLIJXS3Ri37iuFIS7OKbG7BoXVGlGCl7jCaEGrNCmhIKDQREQrcgOZ1iiEzGQYpdN5Mg0xtGGFCMYQlUKUxuQ5ea+HyQsKH1CNRwlISPsaqyAI4iK+cUDA14HRxrDlU5LuQWlcFKbzmp4tsUqhtE7Hal+OEAJaJc9GCIEoicgc86y0xCwx60iIHfXVGG1QOr2UibnSygJooXUZvhBp++Sivzclm90kqoi5cZW8moOvedl0yt1R2MZzwTgGruJMVdOLwstmNQdOo4zi9PwatvYwfopivo9WmtoaNlCcDRqjFIMQsaIRAk45ZtoTM83cWEqlybzhRmm41Ms4tOC0kDeG0cziUTwysNyMlksTw0wnOaqUYhYMv31UEa2nV3j6meYuBdsNnO0pPt7UPO2gn0GZR3oGBoWi3wMtkeGG5ZTPyBrNYC6cLITdAnJlOZ9v4Dcyzt8/Yne35PTGLlt5wI0GbIYpPQV9Cs6oEX0zYcs3HHqYA1EMR7liqxG2qkAfyCOEqIhKgwg2QBYjUUWiDojJCNowj4IQ2FTgNNzoKa5tFPgKGI4Yjc6xmfUJT/R5elTz8VMFc+2xIeNo6ulHoVHwsVHGI72CTGdYo6nmkd0mUpeaJ7czLo80XnyaA7zmXg0q04wHOZMyQyRgdCQLYENyEzcovPOoGCmALArhznGBBVbnxePk4BYLeLFNLZSnWvl36crutiajZjGvytIS7vzYKspSKXdzLSuTrtCq0u76nvEO0rHauVUpWZazdYRmhXB0pEIWnogVL0B3ewLSGibHfATSejpWvMZqZQwVbSldO4UtQiILq5yF7mh9QsSWEamWCLRUatWBcCwMoFbOuToeC9rWGk4rQ3/8+rtwt+oISvfcWJ5ZLa6gtSvbMdW0pC55ZZ8PXhAZmEwO0VqTZZbgHSIxPTCtcc5RGI3WbYRFIt43BBGsEZQFYzO0zgghJuvWOYLLGA5HFGWRbkpWYj8rt76K5eNSi4fGyr7HCAEtEZBIZMV9IrcwWbVkbdIxVw2iNd50zNzgfKCTrO7YsvLglFJo3bHv9Lp4CYSmItOCiMaYLHmxdNKUMSqMtm3GARirKXslWENZliggeEc9nSOuYWu00fJuS6YtuclwRKLS1L4hhECIAR8DSEQbnUI22lDklsxqjAaLSSQcIQSXrtt7nHPEGBdsNXY0Uy1zAJA0ljoK0m5M8StQsXPyCar1CiDqjtaxlk9cpjATVLxMcXiJ3mQOCh6aC7ZqCBK4q4iMQsOmS2OxGR0SFMpDb36E6m0hWuG1Zmws+4OMPAZmPc0kMxgyCjtAn30JN86c4Q+vT3AuELTivolCe83jQ+GxDcU4t0SJZE3kZGVQHh4bWo4kY/8oo7EG8YLXmnKjJJ7uccMotFVYJUwV7Ch40Cje52v+sPaYOrDZU2zkwgOZJidnVyne5+Yc+sBgZtlVGU4X9NBsRMXhUcW0cmwNegxUH6v7bGxoVGnZaCyYSOYVpxz0teXMdE5sYGosSoTCS3LXN8I9UyGoQJWnfAglmkFtODP3uCjcOwlUPhKNonGCnQY254EyCGUd6c8DVW0QM0fnM4rgUQpcptFlQW5Lgrc0vqKxDbNMuDnscXNrgM0tGsNg6hkfzdgfKh7bMVweZYSoidoyySP3HzqaLOeR0xtc2ioRHbAq0AuQiVBUjsd3+lzZKqk1EBzRB6Qwd1B61bE5blWVdx7QxdxH2iiqNbBplYh0VufxGTUppONvZoT0TnfHbhVc561NKvG4U/u4y37Fa9Geo9t74TEgxeL1ChtQyTnBkggs3eciqp23u3vsvMid4haW0eR0zatEYKk1unBHS0hWhmPhUZfWE0CazwRAt8ZeOx66IyktIekUv9ayuPbFaHTXurjT5S9p7NSx55JCNiwIQbrnLsSxJFGyoErpOBEWJGXh8YHnXf3ygsiAq2dMjjRZnoOKSZnHgDEFqJCsTRIxmFdzcrH4EPCuZtD39HqjZBVbmxRWcIQQ2N7aJs/zbniXo7Rwi3SW+y0M59nYQjegSyqZtrGSpNJu00qjdUs/Ws+DSv4rREsaSKNpYhLlEFMeQWitiBQ+UC2Tbh9PKxyoJCxBIo33iErCv/BAREFCQEJElxm+mRFmLiX1IUTXUEWPjp7xwZhqXqOGPTRCphWZFoyOaB0oMkNAUFLgq4YYYnLzodBak+eWrY0+vcImgY0x5W90Y2rMYpSk9XbomNh1J3Aa3TLezlNDm0QjuBjb2KQi0L4ggNHpe2KOk7RPN9ze0xT9s6AjEhwRhbclh8NNLvuG6axh60TGOYnoWcRrwwdO5VzpKQpfMpjcy9nP+4vES3/IwZP/g4+7x3h60/DgXHF903KxnzEuhN50m/7uGa6f2OVJZ3BeI8FThsiFKUxKYdwX5oVCi8ZYS8xVSubMNQaDzjTGGvp5Uu5ntix/5uRpHs0jjRYIka3KMaoaJAaKlxRs6B5z39Bkiv0QuOQanowebzMeL3MOvcUdBAojPOEN03rO3RL4fy4+xgevN2zkG5we50RjuDDMud7c5InqJr2iR+l7bGLZGoyIkzG5S+9NFiNFCAwqTxaFMweB8sgTtAZt0ErT85EzE09jFa+5BlPjqLIGT6SshQtjz/Yscv/NgKlnzINlkD3NmRuGnVmFmh1xbmJo9gIxH9BEw9bcc1etKL3mrCqZ6TIpbAInnKdoPW11PcepgBgNRboeJTC1moNCc72X4XWGIdILkftzw0FheHKQ8fRGSZWBjwrvHKq4c7K78M6tKH1QbUj89jnuVpd55/rvvtd5drt3dFWrCgolAlElxScpkbqzzLvktE7Z6NYETiH6Zdi1PdPiM4GlIdFZskphjmnJzrXe/XTXn+ZdFVtPQKsipHVVrnpMpB2TbltKSF96bhUKpVdpTPc9QXWJkQi6NRoX46Q64pK+3xmBHcHR0IZDOz3VzZqLUV88oE7HdAQCxWKskzG69IKIgkjnbW3D2e11Lv9eksNuODv1LwL2+TkGXhgZsFoTg6OpAmiFSERConO9XkmUQIiextUoLSido5TGe8fNvevsbAk9emRZD2MUShuquuapi08T2wQ8bhFkWJKAW8MHC4asugd9/KvdAK6ie8C6ZcvdC7HyqmBaYVQarInpdxfx7UGjJAXoY5eEmN4u3cbhXGBxJVEgKEs2GGF7A2I1xjcVRitUBOMCSoTgG7wLZEZTVzVHB0ccjccpZ6Do0e/1KXoDTp3cZNAvUCYnEvHBEZsIYoghIAFMnuOdT65/IARP9I6IMK0bcjFYK0TfINFhrSHLe4tEUG304h4XxGzlOXSx/0XWqgdUQBNwRpOZ9kUHCqvBGpTWXcDljuDwdffhdl+N9O6Fi3+M/cBNos74wL0nuHi95tplzenTGfdT88ChxkR4bMtyUFq2qh6hvJv57ksIs0Nm+9scNZc52Mox4ti08ESpIVjUXIi+JoQZStfkqsI0DdteGHphWAdG85osNGQCu05xTwU0kZd7zyFC5TUTUewoz6no2a4qPv/EJrs9xZEI2gsnc8XLrGVr6njZpiYbKmYqJypNcI6TlbA195QxcOJUidYGNyuQANJAE+cE7cnvGXDq3hMY3WfipzzubhKrnKfkJr8XL0OTQ9Dcy4C/cOIh3ufgCQJNDoW19Lzw8NTyWjPgd4ee60VKGMtQlDFyuhZOzTV7heVxq2gIKO/JxFO6gHWCKE9Gzqmpx7pI2Vxi5K+ja0evmXABw8ZejdJ9lDGMmjmb1RylNBmWC0eOuUr5LxfGFdvTOVmjiI2wZx0mL9Cl5fSs4fzRFIvi7klDZjLmShNE0fOBc0eBQRRKHylmnqgVFoEYGczCHZPdztvYxe2T3ZFmu0Usf6GkIbYeyeVkuPRUJhe2Wnx/1WRN8+XSeyqRZQi0M9NWlViLKMlwWnw/qkX4MX0nXYhZyRVoo6S3GXgLMqCX7vROk3ehi9V5Pq5cWzrfChlRq/Z566pfbFiOUafUVz0fEenSAbCd/jHpGQQUQaWKNGk9HRqwOt1j59bvrvWZqhsW4QGWY7n0bLTX0BlzJELQxqnbhPd0lCjxmIck3Xd3mE65PT8r7AWRgVTmkKUH3saVlVZobcnLjKZJsWdCRIJHokUbi80U3tUcHu0zrycUeZ9eb0BRlmRZRuMcWttlAiDPavDfhk/gHDiG5GKRxe+s/BZDXLp5UChJpU+dgrcGtBckBlgIi0qxKkXrWWgfWlu6sri+lCCB8jXaN4iP5KIggItC1THYWUNuS4bDPspHyrKPthmbmxuc2N7CGNV6UzxNSNUNxmQUNsfklqppiKEhU5YiS5NcE9qKB6Xwopg5YdTrk/f6ZGUJwROblDNRVXPyzKK8Zz6vGISIydRivlgQMtJjUiTZTKGXJHRaQYgRFyC3GqsUZiVR807i0MDUWuhtYHpDssygCVw3M+RUwaC/RdyMSAVKNZjgKIj0EDI7JBdFb75HNr7C5vSQ0yhqVZJZw111RRgrXrKnuWvvOt7/IefcLv+XcxS10DsU7plGzkw9uVfcPRZqIj1RDBrhxKyh9J5eGHPJN1zfj1z2QzKrwYCvJ9x7ZcZJDVVmQAl9gZ2gGDWBLzry3FsfUWUaZzKIwm4D99QaFQKvaWoeN0LINIUtOI/hZbOKbTflczYtGwOFxApf15w1wsmyoGbAVlYwIxJjzbQJ1DYyKTQ3Vc6s1JhckweD1XC+GPGh8oBrPSAHo4RMhFON4YSBS2Xk/ecLnFHECCpGdurAdK/hgYOa37y/4LBXoEXRj0Nezt08tB+Zf/y9/N55zaUyg0kgr2a8fB64tzWDLm44bmYNRiL9GZiqRkchI2MravpVoJyM0VE4MXcMZo5dHHq+z3lzSDQarTSjOnLX0QGi4HVPT3nAOuZK41XEimdjPOcH75DsdhZ1SnJbKn1pTUeJXalbV1bWqbT0TYnJtkxTmlooqFUvH0q1SXJtQvVqCQIrnvZuPlhsbMPT0gUG26z29vspI75LqmNBBhYlhawqr9YTIYJ0XshO7akuLHC8omxVkS5CCKsE5xZCdOzXVQLVkqxFqKC1MPWxYy3U7eI+pNXEGoXRiQh3V50U/YLSLK/1mH9nCd1tUQpRKTehC21rlkmcCw8NnZe582CkpHBpDbhOFp5vePYFkQEQtG3dyUEhIQCB4CsIKcYZXURrg8ZAFKLyKDKKoiRGaJo6eRMAZRSjfsnpc6fICwvoVqhWHuKqF6nz2aRLWfhGVPsgb1U3C7eJHB90iXHBcruDJXeKbh9+qnNNwpiqC4wBpdWihCN5EEgup5bpGmWp6rhksApUhNh4qumU2XSGSGBSVRR5jja2JRCBXl7ig0N8w6BXcvf5c6nPAJGmmXNz7ybjoynaWu5/6Usp+wOMzcmyVImhy0gzM8Qq0DgPIpj22D5GrDX0lKKwCoNHScBojeRZUtQ+JKsj+DaBMLHjRUyxfbGj6spVVqwLEWiT6JRtJ6MQEK3IWpdWEPDh1if06cO22cZ4yGZjytkMW0UamXFqkuHyHJtrNseB+w5mnN+vUTHwqpuWG6Wi78f0Dx/BX7uC3f84G7OneKBwbDtP2URs4xgYYdNDOW+IVcWZw8v0c8HnhsbkuJEhNI5Sa8osY14abmaap1Rkf6K4+8Dz6OkRH1cDLlJy6BU3h5Ynz/QYxYqnTyqm0bFnUw7CRtDcPdY8dF0zGQhVrLEBcj/HRsUwKPKo6TfCa29GXuqT61xrxwjNToCeMryxsuzFlCwXXGSHA/rBslGWlPYkh/6IKA0bQXFmNuOlxuK14qY4pIGeRM6YnJ4xbGcFVSG4XBAlmAAmRpSKNJln0je4TFCi0WKZ14FJFTiqMq5tWMaDHgCZ5GyrIWfQsNXjsdOOp0cK6gzrDMVBwO45nFb8/l0517OAQZE1hlgazkwcH98s+MiJgjrXlEpQvuHh6xFzseIPzw15bMMy1YqcjH6Auw+FE/Mp417B09tDGlugXYRmDpWnrJo7JrtKrYQfJU36sXPrt0qhUxoLR0GbyLPIZSIxiTTlpb26jPf0jsstcy1Lj2Bn5UtLHmRl7myVzoqma0OhsjhOR2R0F1roLGW16mlYuuhvN9paSrCwcNWKGdxZ+MfNQuku+PhI0vKJYwbnKl9Q3b1Imu+TBa9YllZ2IVOFNktylkqsW4u/vRx1y3kXf99yWUvX/irxUMk2llu/oLr/rxA01XqMBNWGewXQ7f1b8/zyXV4QGRBtCK3VrE2KNwcfqOsaaw1aZyijMCZlYMsKjTHGYLUmxEhsLezBoMfp0yc5eWoXa1eaDR2/76VQruifRSAqsniIt1r8kARPdGKqqmNYLetbfUApfrWSbCHLYylSbX1hDbVNfRX8oqY2PTStk4fAKwitZGilMaLIy4K86ONFYbXCh0DWuvCV94ls2PRiSHC4as7B/gHj6QRRQr/X52g8ZzyvGY4yJEZcUxFCQJGT5RaDQimN7WXJQ5BbtCpovKKqayRECqMJ8ymhzjFogkoeHq0NxpRkhUVlhixblhmmiSWNUnrp20zaFZINqdxQtAYXUwhI6W66afstJLfancLWE4dk+hFs8zHK/UsUh0doHF9wqaDSNSpErHdsNQ2DJqCV5r4bge0sgHGUzsPOiFo2sdwghIaxqrky0mAHHGSensB9NxUFhmtbGU9uW6ZZRqOFzdqjo+NgmPPkluVGCY1WzJXiYF+jAzyyq3la5eztWap9RV0qDkqofeSjG6Ct4YY11FrR8wpnYHsOj+x4nuilsBMx5XpsOzicwj1jy6PbmiMBJOC1MFLwkrnl7nmOKzL2+okgEzUqCqWBbV1wd7bBvolYHxhFIZ+PeXkUTmCYSCAQySRyoqnYiZrXAKejUGdpurQhslt5Tk0aZkZzdiY0uWA0lCLszgL3zDyj2nGiyrHGA1DInEF1GbOX3o+BytjUFl1oMh3Z0A1WHDMVqXNoSoOKGrHgCzhsIpdyzxM2o8kzykJjlOZc7TkaeJ7e1DyxaZgZg1U5WbSMS8X5gzGXRj0+fHqDMNjCommaI0K1x8uyAMzuiOymBLMus57brNrWQbCwrBMRWLHWpZsml56+xeS5Yukv/6tX9kmIq1bxqqnZeQkXDgZ1TGmmuUAtQq+arqqoTZrr5vhu7pfWJc7SKFz2L1i1itv6+mWW5EoyZUsmbtX2olZ/bYexdbu3yQgLfaCXxAVAokJ0W7YJC2NpVV8lwtM5FZYegdtMoNZCXRCi9tksPSSsKLRVStSWZ6/Ov60nRxSYThbam16Uft5+Bc+IF0QGQghEGqxJtdJKGzAZInPq2tHvZxhrsJnFZBZRQvSBaA2ZsanSQFmcBLxrmM+mNPWc3FqWRSTdELJyS7dwLLXyl04NF5ZxrKUTZrUjlUYwEpPFotTKPqC0XgqrXiZyAItcBIMit5pebsijIkS1aCrkJaJ1inPpXCdXU/sCEyMqt5RlhrUGg9ArC2zbva9j4N55ZrMpeZFTO8/BeMJ4OiUvC8qBwRYlpTL0hxuICPV8jjENRkWc1Xgv1LM52mSpAZE1SIxYsWjjCU3DbDJOjYVKRSkpsSaGiEIRVSAGQUtO8C5VW7Rj0DqeFi/tspfHMqMYpVIPidZLkhqSaEIEFwQbAv4OsgF7Yx+7YVD5Nn4wRM9K+nWqcKkzqIyhUcKBE87MNJvOsNfPeHwgGD1kQ+6lfNkXcHizRD15Az874qNbwuFQg9VMrKEXIn2nKVA8vaO5eCKj1havhHoWOeobDgaGvZFhr98RpYivLDGPUGQQciQakEhmNGWWU4aciFBgsAJTFDMFUyO4TKiKwP6GxoshSEp6nTVQ6MCGg8e2Aoe9HC1Qq0jphXgQKYLnYn/OhzZby0UrthuPrmdkKvCYnXKlnGAUjFzANpsMqj6WgDXJhaqjEOuInWecn9X0m4AzbUlT9PS8I/eBs+PIqy/VOJOacmUSGdaBM0ee4Szy8NOOvWKGAoqoODOv2D6KmLrmwauarZnHRE2v9pzfb9gcO7JSc34iDESjI4yqyPmjAHVk1AtsNpFGK3pKUxrDydqThUCO0FcaTIbWFhU0SqXC42muOCoNvkzz1Ty3BK24UNy5Whi1Mt8t/rPqEVBJWbF8G1mxHZcKUDpF1f3dKqUVS3MR01fdvkvFuvAwAEsisVTaS8W4VHSd1bwkGqsldasld7K4H8WyYY7vSq67swq3EYPV6zs2TkKqDFsO2cqZbhnjdui6Meh0QTdenZLtEveUklZft3Ngl2fWnjeuMKTuvhfkA1gmCKw8svYPaXMOurBFquhafmXpHJeFR6fTm8tzpDvWwPPt7/LCmg4JEELLHE3KLtcGazOc80QJoDTamkQGYkpwE5FkgXYsUWtCDEwnE/Zu3mR8ND7mdlre2C3UbuFeUouXYXUg1cpgdd6TDslDoJPrRC/jL10/gfSjjyV7LNluYl25UUhuiKKJkpoNOafwMXkUtFaUJltccWyVbdQRFR1WCxIV1maLB6RJSSDeBeaNw+QpCUx0SwAGA7K8R5anksYuL6FLZ0rVCCElDDqHbzy9cgQqtUgOsiQ/Vd1Qtgl+SpvkKdERrTVRHM4JWVGm0sI21tGR0+V4rhKz5dNR7UyzfGHadp8h4oLCeoW7g2GCyUaOG21gTz+Inm+g/WPsHnme2M25XgrzTFEbQ6/RxD2HTOHStubRncjQ59zt7yKcup/r4TH0dYsSxfXNjMNhiSiDJxIaYZanCbQygUYHRGkUEU1EK00pil5Q9H3qPmmD4kSt2PGWu+aaqoqUM0XRCNtOOFUrylBwfhLJtJBZRWEBUew0iqHAro/0Gsfc2mTxWJ0SXDOIVuMKqEYlIlCJo64ch1YYm8ANE7hW2NTfwgrTynPOT8njnKezGRcHNdFqyuDYngVO5H0uaWEvC5BZtBj2Ks3GuE9VByZZwGlpy1ED86DYkhLjI2X0SX61IES8AmctmSh2ZwrlUvttG4W8qjGVp984Tu1Z7By0aHIf2Rw7RlXEBHjJdcfuOL0XGxWcPvTECPdpyGLA5TWZ1ZTac+FGw8bcce7IgcqZFwExYLxwas+xOWvYGji26obKzEEpMl+jJ46d+Z1LIEyKbtV84ZhiOe4N73q1rLynqiMCS7Kwqjg7ZadYUYo6xayPH7n9WWimJZHoLO1ux04ZxU67d5Nte/4uHLs6xS+t7pSY3XUITNZt+l64hQhEaHlJ1wfhlpwCtRKWWNzFcuZSSyvyWC6D1ioleSd7rm3AtMzej92cSFd6qZcZ/BzvuXArume3ukMXYug2xmM/coz0LO6tIwIdwVt8PW1Qqg13fyrIAK2yTK12k0vPaEWWFTSNxwdPjJpMssSUrCVGjwCucahcg4q0HmiaxnGwf8iNazeSW7qjru2QLRQ/SwZ5rH70tsFJ/2olxx95a+lrxaKUpdNrHRFY7TOwCEGQmnGk8EIq59OZTRadCN5rrEpNVrRWKZmy3T/EmARaKWoRfF0vXpKIQpPGEqMgeFKyokFEMNrSK/uYrKDslUlZe0+sayrniDsbiMnTvaLIlMZrhTEKVweC88SYyv18SBNwbg1FXpDnZUoUXGQWG4zRSHTpJYu0XoHjL9UxwVZLdrqad9E9pNj2+laAUztYcGIAAQAASURBVK1nwEhq2XyH8Nh2j2m5zeaZ+8ibHK4X4DSPbsFeD7wGMYZhZTk5D2z6wLQXmW9qhhPwqk+tYC41hfKU1tKzJbUuQRTKwbCODKJmEGBnHplNUpmoIrI9j2w3ikFbN7QxiWgfKaJw9ihwYha5cODYn9WU05zCK7Zrz4mjBp0rzk01ITQMNGzblK561hl2vOFclXEtBCqrqHKFz2AU4ZTL2CDjjFPoxtAglAJZoxj55MHo+cimN9RK08SIjpHoahCFwaEzcLkwdZF9U5H3elw2nkt5heRZKh8sFGeN5SjmPDp0VLabPDW9xlCWAzanjsdPTKnyJKuiEiFqDqEXHQdbGfu5IurUpXQ2dygtnBrDOItM254cJlOUwZC3k13uFWWVYoA6WlRLuM46SzGDZl6jjKDFszn29JvI6UOHjhWzzBEN5D5wbj+wNXecG9dM96bMxj75JH1NOa25sO/vmOyuttHtFDK0CjjKIs/qNqxY47eFz0mE4ZinYPlBUrBLPbl4zxeu+XZ+1J0pvXqExfyZDLrQHQ/BKBCdlJSStmHOSo5AMiK6VsGSGpuR5udEEljE77sciRjVwmjpyESHhXJfsfpBVhLs25LBdvpK4V7aZMBEQoxJOU9RVFty2BKC7lroMgVolXA7+HQkZangF8+zIzLtQ+04UzeUi/UTZKX76/KxLjw3q8+RdvwkLjtMru73XHhh1QRGo0gKyzuf3MfWopVBa9OWs9E2ucnI8zyFDIyldg4TBYgos3QVIYqmcWhlWW1D2ZmjXW1nh4Urqn0r2mqLZ8Hyg+58umv/28nt4ifRh2ctX4S0xgC0NfiCNhGjFFF0e9ktH5Y2B6Ftz2utRUQjYtAqtv0YklXfueRtXpKZVJOuYkzncQ3zsUPynPlkwnw6peylPgNKtUkrImiJqSNibtE6o3EN3icHk0Bam8AYNgZDjLVoUSAuvWgxopTBqAybWZQyZNasdAt8FmlSnauvbSCywvqFtm2npL7fTRBMEJo7GCb4wLWr5CfvQ5OzYQu0zQlK4aLHuRQSKY1lUCv63tBDsxUtp53ljNdsRI+dXmZ7dpOe8wx9zmyWcegVJlpsA6Np4MIU+gLDqChCQLwjC4HtGZw80jRZwM48W3iUD5gojJoaGojBUAeDE9MS75rgZhwVcDkXVO052USGc8gibDuFDRn04GwFeYxUJQQT2fSae6qCTV9w/1TTq6YEiaAiPa+5q9KciQYawR4axhbmRug7OD0TCtHMsgyVCU0TqYNhc+4YUtG3kSJC7R3RCLXXTMOMygYO+kLVM6n0OEZcDTcrEG24dEYzLzKiMtB6SGwJO43hY3dn7A1Sr5HGB0ZHNc5qgjV84L6SG4MMa8BEYXzT09z0BGP44/MDDvKIaGHoM4qPTtioIjc2cq6ONHPVEJRA1PS0IouKWQ4TVTONAR+EDMUwtwSj6MXIicMZjVSoKGiEIgq9+s55Bjp39bJhWspTiosSM2HpBu3K+tLMleyr1gBaKI9l4uBx3DqRrsx+K8pzaaQdP8AytHBs5kW1ik1CJCqFjmrRer6LeXdnitD2b0mK8FhOAMvGQ10f/k6RLkiStJVeLYHqDDnd5jotqihYzvtqoV2Xt9T9atrPOhLQKWgVU5v1hXcCusjJ0mvTDtvqeNzWXVfUsQqGLlVAVs4nC4V3S57FyiOTlefVEafuWsKzK8hjeEFkwGiDUhkxekIM7X8FpTRFnuOjWwiDdwFrIkWRYXRG3dR4EZxryPMs/RQFG5ubnDp5KhGNTkC1PKP+Wb2nBQl4BvcJrAp++3f70yWvpM/SAVZ8BSvblyRgdagXx1LS1sKuDrwsv9VST20gLyy9foHNNbFuwNUp2U6kLWOMKImphEQUdVVxcLDP0WRMXhTkO7uU/R7KWra3txn0BtisaJmsUDdN8jZEwftAVuaEZk4MPr2IylC7wGw+o2ka8uGInWKIkkBwcyCnyPuLF9O5rrtkK4CLN+SZhWqVCa+OewiCW9yjUPk7Z131a89mU3Fyss92mJA1ijMh4+EjwxUb0AF6EtieOu6aR3bFUu4btmZQTA/p1f8bvfcId88/Qm8+xWiDuRGYxAoTTWpn6z2btcMXmrpvmeWRWEQgkCFUh4HrRcYTO5YbfUuMFuUDF/YdpybCH17QfPwoZ/+GYj4LHJyAp04IN4qGPzyv0THjJXNLFjXihJ2jhnjd89vbGZd6GZtVZOACo3nDaC6YWWRuhevaEOc1u9PARiOMgmIrGnpa2ELDtEmJrET6AtsSoMgpsx5nfIBY4+qGM1VDzgFFL3DGGqZEfK7wmeHstGZWKq5lkXHjCCYtRNOPGT0v5E1gy5Rk2tKolC9RKoXNhKgccxU4NDVoTdSRMteEPKfO4LCfs9fTKBOwMbKTCwcWZpniYk9T9QqUVcxqxX5ZMcsij50qefJETqMdzgWim5FVCmk0Hz7X4+ktzUQHXIhkkrE/15yohSdO7/LYTp95ZpJVGISs9rw8N9ypBELVLugTV5SAtJb3im+Y47PVcu6Lra9+1fF62/y6OMTSsl2dF7sGbp3eXPUbSlvrvko6llHf9hpXcg+SobCcvBc5Wq1i7DwEK06D9hIVSwf8ysS+sk/XoGcxBkBg6dbXshy15ClOO616OLqh0UqhTXf/oGLXlE6ISqWEaJGW7HR9CVaewTFFfQvValXE4jEmWrJSGtq1YO5Guru2dP+xiwvcdo6OMC1b8D/f6OwLqyaIMbVDNRlaGYJrcK4iImiVo5VeJOghqcWtRAMmJy8KBIWfC8YI/UGfM2fPcddd5zl31zmMSTH357rujjWtPvHnIj7LV6VV/rKItnUfLPaLK9tXRWO5sMftD5XVhyYrZT4SicHjmzmumqHEtcKjFi2SvSSLo6lqBE1elIgPZEXBANjYHHHyxAmMMmhrCNER8YznjhAhs4a8iDSNJwMyW5JbixQlwXtC9CkMAOyPD1FKYfUW2pRYYup8p8C5Glc7iixnXs1TvwjhllmjZZ7c7lFZHcelyyq59bxWuCi4+Dyl8lOALzycMZx+gOLpi/RlxqC6QV9H7t+LbJu2tFQiPQejRkAcVRa5jLDVfxlbm2dxWzly9Qn8PFBpxx+MIk/lFcP+FkZZ+rPIyy83RKv42G7ksROp3XPUka2tiG08T2/A46cz9kdFCi+5SN3zhEsV+wPHtPLU7Xdi3zHpN9RloNoSgmhumMCwigQdmYwigxlc27Dc3FSMxWO9QdWKK+NIvafZrGp+53SGN0MG3lL6yIlxxX17jpO18P6zhssDzVD1GYbIXQcOfQg+13z8pObKwJH5yGg+ZOdwk3LmUzWAUeRKU3hNv4ZTc0UzC+QTxVwEjwctWAUXJhpjNPWlHhMtzLWj0ZFc4OxUc+4QXnpd6E3mNEaBKE5UhrvmOadCwcuODP3GEXXKcr9rpjjhNGPRnBxHJsFBZikqYejSpIr3SC1EHZEmLWqkJXkaJhoOjVAVBqULApaJ8sys8GQeeLJocz9aWe73DKd7dzCBsP2nazW7+LlFoa8aPre/aas7yzN/ouS2PW/HrUdeKvnOKr31uCt6trXwb1fyx0IhK1bxrWda3KNqF+kRdex+V+nQclvrJWHpjl9c88pFWrUkMl2b/dUjdT0Flpp+VVfIUqnL8jrCrTdxy8Ue+1RWj9iRruWGW/ww7VeWFQ2x7THQLa+cflfPu9nbC6sm8B6lAsakODNiidERgsdkOUYZjDUUZRvnDgHnIqgc2tyCsh8wWqjrisPDA7Z3drCZWfTzv/26n/tGVuNez/g3wjEHdVcrSzuY3f3RsehO4S27RLU04lgma0cCFokeEboM3djFggK4OlLNHfNZQCmY1Q1FnmO0RQUhxJCqBpxHq8igX3D3hXNpfQANEhw3D28wOZrgY+TCPRcgL9DWkhcFg14f0TXia7xEVFO3VDg1eInBUVrL5sYWWmtGZYHBAQqtM7QBELQpQKBfFth2CeIVJ0c7tktPQceuU7dGdSwGhlom4aTuaREX7pyr9ek+bI52OXnyQWx9E7l4hbmv+Z9Dz9VNhTaGGIShV9x/JJyeeD7cd7x/ZHjV8BSj/CHcvSOuZY9ShI9hYs3HRorrI0O/qNGhYVgqNueCJnLQs0xKDcbgYkMIsF9AlUWCCSgbyVSWqlAQCu8YTg3lNGBdhmjNpjbsVpAhnJ0KczyZhw0HpU9Ks4dih4yZFoIRyDWhyKi058gFLJFpzxAzS0UqjD4oFFoUcqR4amC4tjUgyzIymXG97/Hi0cbx8U3DtSEYCnIvqL7hrmnOYz3FU72G2qQQVa+KfNHkLPN6ykeym2iV05M+hYeBgzATjBNMldE3MKw8dt7Qrz3bDfS94SX7it0DhZp7rIOBxFTOKBmvfKziXqnRymIi7DSRHlDlip06MskdczNnVCvuPRJ8ptDaMZxXTPFIUOQh8MqxYSNmvGyu2dCBWa4IuUJM5PwcToWc806oa8ehBJxOhL5fOTbrO0dktVJYpVLJclxO/qlVbUKXF7D8aee8hcJLvyzz5eLio1tXtbvVq9p5FNLUppbT9EL5Lc1TWXn3u120Th7P1WMmZX88+31JBFIexDGLX9pcJumUb9LaijTnLhT8ojfvrV3/ljRhUXrYXo8ohY1tBr5Wi0TpbhG7Lu7feYDTdxWLGksJdB7UheLtwhSty35hiq4o/FWJ6nIIbg0jaNXd56ovRrc6Thbt4EM8XoIZV47rnmd09gVWE8RUMdBmbsQYMbZA0O2CNB0ZKNBK07h5u+hNhbIaawx5nqNUJITI4cEhV56+zPzBWatwWkl6Dv3fyWRH1JKQywoBuPXvdlRUp9LbTbAQuki73sAi6aItfWGZyZr2717GzqXVCYtaxI/UYqsCZciKHsPNHa5cfRpNCgmomBp+qxhSDoBE8swAgaapODo4YjyZEBF6RcH+0RHj8ZyiV6REFpKAaJF2GVAhorCFJXhPjCGtI240rnFpfYhejxA9Lnq8b9LE4j02s2RFgTWtcAcPtG0uO7dhbBtwdEy4HaPEbtWCGAipDFPrVLVgNWjVMfjnx1A/FfjDUcmpU3cTH3wts/lF4viDbBxOuTowHIwMSllChHFU9KPHhsBRz9D0exjToxqcZpJ59nLFyCoGzoDO2+WqI7FrTG4MNgq9qNkMCiUNEiMDr9h2UMwVcewoo2CjpWgU912P3HsjoGPg8CDw1FQRveLem55zh46tIiBzzaQd6Z1o2AyazAvDw8DnXIv0dMZBHokmtX7ejZaTwTHwnjO+R6UyolF4Yxjm0M8UmXLkRmNVMoMbFZiamkp7dAjMGqhDhrEKn2sOcsf2NDDPNbNRztwKIToOrOex6NFlyWOFolaJhORKMQoZzWjEzszwwd2GJldon1M4w2YVuOvQc3os/NFdlqNBTlELw0pzYixwFNFB8fipnIlSZC5nWCl6+zNCDOznmstDi89LVGjYnDr0vKK2mlorlAQ2nac/FzamDSdnGeQlJ6pIb+qQOqCpwcKJGjbm8JKjmuG8YWwidWtUbFeBe/bvXL7LolQZSUukdxvb6bKLkXdYlO+1c6OsmKDLua1VPApSeUBrGt1iVHXHk7Z1O0ot5sFFRcExHwCL43RdD1OzIbU45qIrnup67LffWRhxz6y4l9eycq/I4pgprbabe5cm3SqW3gVZWOZRupr8dLxutda4GsrohlySzkjkpbsWvdQLC0KzkrsgakHcuuyIVU20sOw78qGWfSUWQQ+1XKshxlsqLuIKuZLl8Tqy8Hzztl8YGYhCjKFlYykmYU1K7HGuabvxZYBgrMaKxTWhXR43rbxndIFq0zdjhPFkSl1XLBpCrGStLuzx1QSYji8sqeDyga0GxVYfTKTtH7AULrpQQfvQUoylq61fPWbaXS0Y1zLLM6La7HsWdbF0DLoTRBVRJjXfsAaIqac7MdK4mnk1p6obUIpeb0DZK/C1Z//giKOjMTbPyGyBtQX9zZzhqE+WZelFipHa1eiGlDAYheADRmck778n1wJW4b1nPq1ovKO3mdPTOVa1DaAkItFT+0Bpc6r5nNB2JOwUftcr3CwSbZYvWVeZ0D2nzGhyY7BGpYxcSWPmnufqWZ8KXHUZWTZk3N9B5ABvDIUWojZYZVEqA9J4hDYGWgShL5aBKiiyknl9jV5dMfKRjaA5Oc/QEilMkpZNJ+yMkzVc+MhGFVDRoWJkawZ33Qg0GYzGMMkFFR2Zi5zda9iYRjasIz90UEessowaKAJM+j1ChNw12BixzlE0wlYjZE6xuz8jzhWNEUKWoY1ms4mcO2wwVeSVT3vGpcaVGpcJg1o4dxg5Mfbcv+8opcZrTdCK0VyxM40QAyfGbfuXTNBGGI3nDI4iW3nGtJ8xk9TbIIpBe0dGBgpm2oMBaw0NGU9FT5FtcHUwoSqTErFRM6gz6kxj8oKPD2aMtzU9sfR9xuRQqGxNPnc8ekI4zDWZy+hXGjGRsvJc7is+vmswvZwsaKoyZ7P2XN+MPHHScHNosSGjqCPbY8vJxzwHA81jJ3vMdI6qAsYLeQiMrjsacVzayNmzgvce5T1lIwxngfIOrk2wtJbp9EIbUj3e9Ob4d9pkM5aW8uqe3VSZpqpO+R5PKlSrv60omGOx7FtOro79rlbO384lx+zb41Zyt/pfezG3Wc+3rWOw+HypYo91Q4y37L3iIUlbloRmWaaZdIGWlKOhV76vlqp5QQQWc750NvKi+n+FFHSWf/fMls2LuvtcJES2247dqbCoeEj7pvwyHyGElKS9OP9KjsHiGp5RQm7HCyIDqp3MUzmLJCXSLmPsGocyafSdcynxQoHSJu0faUmBtEtVapS2oDXKpoVxNC19ap/AqrvqWRNfFk0Flu0100uzZJ1K2r7eMT3SVUFOjG/Z23sx/sKiDWfHLBalLJKSMkRaNzzdw0wNfBZVEQgiHt/MmE0OCa7C1Q3VvMI1NXVdU1Xpv0FguCFgLCoqYlQok5EXPYqiAGPIhTbjH0yWk3o2KMgyNJE4q/DRYXppgag0gURslko866bB+0C/7Q+hlUGJXzJO71srIT1HtfK/LhyQ6SXZWbB1pC3PTPvkRlFYjdXdhEBqgKRvfXifPoRDh64D1jlM41Au0POKnblikCXZCSFias+pceBEFQkoKhvYqo8YqKeI0yfp3dxne+IYBKG5GTllQ+ofIcKGj5w5CBQBMh/RHgSLjoqdWWA4D8xEU2SWymi8CXgVIHrqTLG3adivNLNMkxlNNcqonWFvc8jlHQPNDBMcY++RcWBjzzO3wuVBoNKGvrf0KkPphZ2ZZ2vi8EqzexgZzBxiJWXd1w0nDmuGc8fdNxt60yMiiqAio8Zz5jAgCB7HiUNQRlBac2bi2JkFCAW9KjIvLI1OxPjU/hRLyYWoKaymyRVEjVUR6grjG7LgqQB0RlCaOodJoXF5Ts2cKoIYRZNrbBkpS6HnHXslTPo5RjTjSnFybihV5FopPN0XTE8wUeOV5lzfcGNouL5pub6RXnrjI3t9y91XZ1wfaR7bshwVWarm8Iqeg2Ezo6g9H9/J2CsN+EDmPIPak1nHzrX6jsluZ2y01hLp3+PNgpax9OV8uTSgjyuD46725faFDdPi2OvaKc3OlmJZriiy+GflAM/hB1StgbVyL4tvt790ax4sL671ANyiBLql37vhWbS0V8fHZXHdK46MpD7ShlTuKOjWy6sWwf/V8Iq0/49proyeGBoa53A+pr4i2qK1RduMpd92SbgSaVm2OO7WVTi2fsvK7wKoluV0ZYYhJp0TOkP0GBHonrHceqhPiBdYTZClfvYtGRCRZO0bQwixdWNpnGvQdsWVbARFajSkfUBlhjzLKfpDtrc3GW4MjynnZZaHegZCsFJpoFYEuF2goAsPdO5/1XoFYlt7uRr/XjBdlvGoLjqzYJxtsW3nmkn7soghLcpNQhp83UpylEj0jmo25vrVp7h2+SkO9/ao5hWz2ZymrnGNw/m2P4NolCnI+yW9vCAveyhjKHtFsrB8g7hA5SvCxgZFlmGznMwasqKAxlFVdSJDMVkxqdcAWG3ItKUocjIbKI3GkIiLMiaNodYYmyMSyaxuG26kJalV+2IawyKU0L3/aTJSixihVVDaRBpSbW/7EugUMrhTuOuo5uz+TXauPM5o9jRqUrFTC/cdQt1ElErNm4o6cHbuOemFIlqUjmzofQr/KMIT9PYO2JwFtMDJvKY00tbUKzadZ1AFolZUIhwCMcsxAsbV7NjAQc9yaSvj2oamzhTKNRQOtqZw8XTOtTpnemjoWcNk0zI/gqNezs3tHh6N0FAGj95znKorau/4yBlDU/bYmRdsNDmbM8/QQ9SOqpczHRaIgI2e3CVS0m9StwuFolfXmAAqKnbnns0qdbXcmQtl7VObboRTY08vCKICxgeaQuOzpPRP79VkRjOPlt3cUGeaYBUigZ2jhi035u4cDhzEtgzVhsDW3DOY1pwqLQUKZSCaQF4JhYciRnoBYkzJydpErApYCVhRGBHEp8ZWY++olCJoCypDUETl8Sqis8gs10xzODLCoVFpqeVM0cstBwPPqG+41ss5GBSgUj5E6T0Ucy48emcqCWDp3Uxz3XICU0qhW1fwcp68XQUf/6xTuUu39qIaaLHuQKd40vf0ynn1yuS7qmMkrphSsppWrI45EtXKhN4RARbzyOLQSzIjSwv3lu4xC4eHkjS/p2tdqSbQrVKUdF5RK4mK3cyklh6G1NZ3GQaJbfngavfAtGfAiEczx9UHTPZvsrd3yNGsxolGZyVFb8Rwc5eN3XOg7fJBsiQEyQsRlyWE3S6dHlzsu3qNHCutXHxv5ed5a/9b8AL7DKScgBhCO1gp9p9W9jMECXSraEls6+lVUiqgibHBhbRk7mA04tSZs5w+fYoTp04c6zK1vP1WQDsWvEoCFj9dnSirEYP04LqmGov+jbJ86G2YoCu96BT7sSexPNgtCS/ttpaZJXeNYJQQCYSmpqqmjA/3uXrlaT76yAd58rHHONq/gfMe5wPeObzzeO9xwaeHqhQ616jRRuoAZgQfGuZVYHx4xGxaoXMDZ88k1t62xsq0JtgcegUqy6mrphXspPJd4ym1YXM0IorHZgoJTRL8EIhKyE2JzXJE6VRJQFeX2xIBlTow5u0aEqvJNBrQOpGKzECuwa7290QBGqvuHBt4PXDi+lOMqhlDd5Pe0YwiKl4ytszmOtWiG0M/RLYaRxFSSWguBTobEHoFjRMKBUFbphYe2wzcGEUalRExnJoG8qhpNDyyFfnIlkfnCiOakwNho4pc73meHCiujAzeBqwXzo6EQdV25JO0WJeNQuE91nn6U8fGLKfOFU4ZsggmBnwEjxBLzdFQ4/o5R2rIoROk1PRiYDbq8fRLTuGVQokjk0C4PiG/MiaEwCMPjDjIA0WEslHYy1PKmzXTMuOpM0MOczAYiqAZXZphmsDeVs71zYImT2tYjGYKbTJKLDtO0wsGqS0awTYNG1NFX4RX5pY9XeNpAE3uhc2q4czE8bAecTBReC3UVtgMljNTQ1ZHxgeBo5mi1o5M4NyRo5wFrBhkYqhM6gLaP6rZngRMbpn30oqKXqX5qucCW14xaxo2pgXRaWqERhw6eMrKE3RqHmWCIWgIRJyCaHP0C13T7VOAVaWQXq2krHRa63aZRqCeWR9ovWop3uoSaNWdrJCF7rydBd2Fa1sCcYx26NY+X0yfsnrwJeFYoRDJG3Drtvbv1tJIyy131nNq1du509Oe6d9lGPi4H0QgJWGz8tNO4Ctapr2fNq9pZW7r9ly2QY7o2JCHI4pwhcMbH+HGH3+YRx+/ypNX9zmsAkEX9DdPcP7+z+Hz3vQWiv7WsWMucgRoLfvFeRYZWSxz0Jb1D13J4dILsAxvdM9pMYbtM+68tc8HLyxnIATEKIw2aAsSHc55xFiKvKBqarQ2iEDwEWxq8du5VKy1eBdTUxzn0BqKoiCzFpGudeuStS0VfnuTq+GDTlGppdh2DO6Yxu4GX7pMy2WJyTLW3/6ssLKOtXZZnCnuo+ioyqJTVoi44JEYaFzF4d51Lj72EZ584uM8dfEJrly+zNHhPs18RlNVVE2Dcy6t8xDjonoBpRgfHaGMIlQ1zjXUTU1mLdtbmyiVYUrF5mafQmtoGjAeosERW4mPBNegTIZzNSEmWxItzNpeBKIUKs8hLxDviG0nSSQwm1cMtk4wn6ceBUalDpNKJ4u/yAyZTskyIcjKpKSwJj0zq5fuRUHatbfTc/B/Qsb6ycDNok+tNaPoOYnmRDZAVOCjG32ubRZM1BQyzUmX8ZIDxfas4dGNjI+dOcErd15PcfYhnjqs6KuPckoEpyKPbAX2dyBqi4twmHnOHAkmwjRL5WnaCjoaNmONhIZibuhPYKgVJssoPZwcB07uB15yTfORI01/LuxIwz1XZ2zWc1TYx0znVAUEqyl8YGdcs31Q4Qw8tCc8ESKNbRA7QQXQoUL5tJAV2YhGKyKRYBTVVFMXDc5X7A0Lrg09UXkyH+k3GVkd2C/ho9uO632FUZoMGEwjZyeRi5vCxZOKpmdBQ1krSgpO+JJHT1kOegZQFB42porekUFmDQflHlObYaJi0Cg2Auw0GT0C25WirNP7pZVlFHM2GlB1RJ6sUk5DdORNxfa4wnphN884ezPSqOS66zWOs+M508mcnf2SSS/HGYMYGDrHPVfnjMaKfpVz1E9Nlmo8PSe84uoM5Rse3PMcNJ65FhoChUQujIVNd+dk95lc4x2USu9cUAoVkzu5i113CjhZ/8s58oWebxkOkFta+95yLS35fzYycnz7CgloP1imJ3efH9818YSu0r6dmRfkZdVYXHoDjt0Xq2GO5c11Juhi5UFFu1Jtup/OgxJFpQXfwpyeu0ox/wiT/Q9ixk+jplNkckCcNdRBUY/3yYwl1GOkt7k4XxDVNlRqc/AWbR6Xlm7XaCgZq8uLXbQkXqUAauk91601rGWpG1MY+VOwaqEPDhs12mq0sShyfNMQYqTXL4kKyrJHlltA8C5gjEpeEknL6NLe5I0b16jrOVZlhFd3cepOy6/8qtJDVXoZI7t1EJZQK1Y8KaQTIYa02EWK9cdFU4aud3+IsujmdSxs017EsSQdgRhSf4DaVUynE/Zv3uBjj/whTz3+KBefeJSj/T3mbYOfpnbUdU1TN0Qfkvekvb3UoTCtYogoCJHx/hE6KjKbUdgeZa9ga3ObXn+A0posV+RK4WPASyITLgqzqoamIR/0UVZjDDgViXisTZarB4zJKPoDirxPbBo8sU3way0DX2OVkFtNbjXWpHadVqnUjrn1pugFMevWdViOW9fkI3bstXXPuTtIBt578iw6s5SFcLcYHjZD7p5NeGTU4+bWgL1mRllaGnpsxoys2WesA1XZx/W2UDajjj61wZVIKZ4dDDHkWJUj3nNirhhVDT2veMBmlDF5i7II5w81Z8ZwTsOZCo72IkoC+azhxGHDcB458/Ga4ZHDzi228BTBo1FU2uNjhfE5xmtKrykaTeE0/Tryig8ecd5OmG8NaEpLz0d2Dyu2Dium88BLbMaRtcigR8/knLk6Z/vqFEfDPVdraCLeQhkVO0ee/qSmCYrtucYZi1ZChtCrPdnc0ass/RqUFoLtSC3MomLPRA4KQ2MSwR4UBT1bIlrxyK7lejFHKUUulp0q48HDPg/sWz5wZsz1vgFl6DHgwizn7oOAri2Pnj6BKwpwFcOjA/JLRxQeDkY5l7cMgqPfCMObM9RsjusXzEd9qtyA99i6YWNvQl5FKAoGM0U59ykZ00dGc8/pcUWVwUsf38PFfSSmjPcC2K08ZlrdMdm9Das6lHYuaf9eZOWtJKTdmrh3K5bzaFeydjz9fKGmnoUILAxRWd22tGaf9TaEZyENnaK/9QvLc3yiO1K3/SWL39TSUmFRTdF+ZpVqWxEr9ErjOwFiVG0nv4goj1GeUnl6SlFajc0UZZkxiJAFULmlnylcXdOjqyxIBmRoQwMLL8Di/larAW6x+JcOm+UYyWpzvrbaBNWGc9N8nRmF9c/PI/uCPQNKpK2gUigsRZnjA6ACSkWUCZTlAKV0SowLAWMVWZ6nBYq0T54FBa5uuHb5OtXULWPLLUNb9QqodnlgVFuS0kmeKLpkvUV9ZWwb+gQW1n6MXUemSJR2zYCV8ozFdztTd+GWWcaPRCKIp55PuXnzBpcuPcnFpx5P1v+lixzcvEEzn9M0c1zjaZrkNQkhEENchCaM0WTGYFptmpYhTlQuBsGLZzIZMxxusLm9zdbOFsPhgPl8ymw6YzYfc+78eYqNbbRNmeOZtcQgxKpGuUCuAxkBpxUejQ9ppbjc2BT/reeEmNolZ1mO9w7Rmn5/SGYNZZG1ZEBSC1itUDG15Oy4Wreew9IFtXQLhgUZa3NrVVcPfOfIwHAIOo8o36CbmixCzwkXGEKV0WsGbOmC805xbhzY/f8T91/PliR5fif2cRHqqKtSV1ZVV+ue6ZnuwWCAxWLJ3TVb48MujWb7xFfyryP5sgYan2g02BptF0YAi9kBBoOentalUl91RAiXfPBQ52ZWTxWNPRlpN++5cUJ4uHv47/tT398hWVjWW/goPkNevaG8/C3rV3vObx0qejI0NwVkylK6wHIHF/tEmbvqPI+vIEhJzAMrJym8wMRIFyM7JWkLjShzYuzoMsVvP7jH889KbmwgWwuefbzhw5ua//h4wa8e5igtwUsKE/noWvBTGTlrA/W9Fc9WJV2MSNtBZ3DWEWNEdy0Xnz/nNEqkkOgg2HSGpWkJOL73Ny94XJaEPCd3lovbPYvOcpJL1g3sFjlRBQonefK6obSRzLWcXwd2StBlgK64uFHkmxUf3JZUbUurDVYFdGzQW4tqCyoyKpKFSkRohWcnBUZq9gKuCklQkkJE8uApC4/E8YvKUa8lMfRzsy1YRMHnpzm/OpNYJSiU5jtnQGz57IMlnz3asK00wRmi7fjhi4j+JfwvT0o+u6gIeU4VMlZO8cFtx9nfeT59KPnLjzReSIrGUe07zm8MJ20g5Dnwfhg050rKJA/FHWmYBIPqTfqQlKHx2ziVznmXcWBee0DMohDnh4o7x4ppqTxSbpOMHYTVV9kRZkmD46MMZtlhXZ+eebhJuuV0o6E/ptVnduBbLR9OS0fL/rsh1XIoUCRkBORI45v886K35/fOBCWJShCFJ0aDokULixRpjiRCuUDTGRZxyigYlNHUZtkTGdGfc2fMxranPprqTIkjy0fsaygkzpJkKcpVCuTOBMSvGbj9zbIJSIDAGnA9ghzyLJNZ2gM5SguUSqZ/axPistaidAEyMTtppdBaI1Folc2GS8zGcprtsecfnni3B7OP6KmJEz3kkKsZQhL0Q56nj2EsHjRYA/zgHgiMphni4BhII+ODx3QNby5f8fnvfs3vfvtLXjz/kuvLSw7bLfXhQNfUNG2DMRbnUnpaCGFsMwyphRqtJFqljIAYEk2vDwKlFEEFgvM4ZzjU+/Rmy4jrWra3V+wPB6QWPAwhMTb2FM5KpLoJJsspVktc5yAIFKl0dAge33RkhcJ7y6EL5JUg06lAkfcJwBln0WVF21kIHiUFWg5Cf0qzOYrIPbKkRIwf0num+AoRw++t4vUPsf3j7Q1FrpCd5aI98FG7Z206/vT5c56ojCg9pTKsrOHssGPZGbKoyMNLNP+GWMDK/5ZVU1N6iEKSuwJRVOwyuNWW07hkaRUKwfVJybOzkhvpMRrOfOCHXwTqTPCb85JnJxU+z3DuQF16PnkdeX1vTX2libcOUQbcIhKaHFktsJWgUzFlmihoGklXCKwHpOL1/Q03uSJGy6ptMG/2yDcNz081f/uwQpFThIKly/jwzY7717dY4fn0/hmHrAAEK58CBDPZ0pU5+/WSXaFBK6gdXkFwkegj2ipWcsOD1UdsPvlzVtmG/OIeq2rBob2la25x9Y64vWJR3FJlGX/S3bLb39DZBhscMkTOG1BO8UR+iIiautmio+Rk51jdHpDtgYtFThYkQlUsrGTVWrRPc3hhKqzQKePBeKIPtN5y8IZ96KsnKolVCisETZ6zrxSdhhvvKQBK6LTgcl3w7DTHaJmyEFrNxVJy6jqiep+zVzLLOJ9tvbiIx3sGDhbZl3eHdwOAYTtiDRSDub833Y9xAjNzxDuvcfx3UgRSi+4otaNAEzESZfotoCdt64X1oCVP1vMxmHt40sldMKXPTc2cNWhuXh/NvHE8TjJYn9N9RjP97GdQGAWpYq2PgSB6OWMDMqbCRlIM4CbgrONw2HEeB2bAnvOB43E80pEm2Y8kWVnn1vA0FtNOAeM1hzV6yPxSMqV2q6+gkb+7feMAwkAKOhufIkq0LjCuTpq39zjbEbxPzIRKjXl91jgCnqAkQSqEVGR5RlFkU2S6SLl6A9tV6PMuhrGd009GBuHPGP05Rvr3Qj8O1oEYkq+mN2nOgYCPcWQPTAQdga7dcXP1khfPP+fli+d8+flnvHzxjJvrS/a7HU1d03UGZw3eOlzweN/7GkiauFQ96FFp2hZFnmIpYsrfjjKlECamPokSEusdLgas7TgcetTqA9ZFhM6pliVCaoIxeGORUqLLAhE8A0mSynKcsQjh0EoSBVhgu93ifKBYrsmWKgX9BJdMTj7StYZy4foAwkQeNPAKSCVQPUqLA9Jl9sIALoQ0XsxzMo41gPe1Pa63VJQgBEsFWd9XeTBILbBZyUFrnATtNJKc3XLD5dPvE/SfozeR1zevObm94fE2JwuBT+9XvDrdUGtDjJJzI6mEoPCS63sbXj3ecBnr5OLqPPW1w2qBXy3x6xVBKlzrQLUUwbO2GYWTlDFwZj0PbhrWty1Py4xa5DitQERKF3h861nVHulg1QY2JnBYFPg8R2SK0HjM3nC7lLw+y3FSkaHIvSQLmtyXtFLw2ycn7DKJ8IrKObLQ4bTgZpnzy0crtoUGArmJaJOxqQUv71/QPPyIs/t/xKOP/oL1/R+g8gJVVSxUxok1BNsRupZY74jNDuEs3DzHdDtseyDEgMwySlWwrk75kwcXfCIMl5/+HVrCKtuzPPwWLv+OP3ruaF4diMpS+JoHl3Ui6zKO5d5jtEQSeHi95/TG8kHeEqPiptS4GNAh8tFrw0nd8vF+CVXkoD3eObSBi31H3rWsDwX3Dhl1qXASJJKlCOSuow72vc3d5EQd3yKGD2OsVBQTVe7cl0yqjRLHBPZjwp63t16siKGigBjN0cO+4V2fXBTHOvm4LjOkQw5Mh8dCcLgPveWw1xpmLRmE5GzRGAVg7IFKABFT4SBmVo0j1TLdY7xvb3RIeVJxtAgk/DO3BEAMEwAbov4FEdcDA0mKicOH9FkmN4MUkRg9wXV0zR4RPUQ19laYWWGOtz5qYijOF5P/f7BGjM8x9MNMITvuuQH4QDzujt+7fUMwoBMgCI7oQ/qJESX7gLIQcNbTtR1SOiKRLE+doJTqSX08AY/Ic/KiZLGoyPOsD/BL6W5S0LMw9Qjx6MHD0GUMUZ4MYGD+M1gJYpilYQwWgQlE+Pl3kRTQFwLPnj3j7372v/KbX/2My9evuHz9mv1uhzEp+HEMAvTJAjCYw5VW5CpDKYFSMlVtlAJrDFpJxGBqEqJ3EXgGwp7EG6CIiFTUyRia/T756/OKZbmkWpUgFMY6fAhjQE/b1ATnaWpJXq6JShFcspooIVFCY12Lj1CIxEyopEaqkIomCYGUihAjSil0H88w8gz0FRtHs1zv9hjAlGeg6+zLOBPHolDpXxhdI+9ju36woNlUBKGpuxJ5LZDXt7y5WPJ5VWKyAqsFZcyw2wi7jJuzCy4/+RFN/MdU5y1fvPif2GUZBRlrA9vTkvqswkqNiDldGzA3LZmRyKKkXJyziQUqejauo/A3lC5w/xAJMuKjRdSODy4dm63h3rNbqusFVQdn2vHwpqFoDVVteXCdgvUUUDrHxc5QNo4oJarp+ODFlmgjppAsneH8Np23WSguWs0uD1hl8QSCCH3NBDA60GSgdEaQilorWi3ZZ5qbsqRdLgneEjLPzWpJfvKI4vs/ofrWj7l3/4esz79Lnm0IEqTOEEKRIZJiYC3etIjgid6x3l4SrEF4g1ASkeWorCArlqyWS6zvuF9+nCrM7W8Ji7/D6IrHvqExJpVND5pctIjQsOgiqyxic4F2kdNdYNFFLnaeKCybIs3NLAQuri2LLvDkxqCcoJVJSCobuGhbis5y/7Lh+1LQljkukyhnOdta7m8tN/b9kQ4Bxwu+mIu3GSgfPg9mdzEJ3XdqzO/aRuVbzITv72/X+GF27iCwJ994PLrWsZXgzkfRHzHz7wtibyXwECzBNBz2O+r6gPWQFSWL5RpdLpGqQPTB6MmcPgn6+S3EuL6lAycrwHGqXwID9BlvU2bBYJ0QMfaB0/1aSZ+n5jp8V6d+IAG6OKCReVsGHCcGODb0VS8L4zSOg916BFO9wB8YfMc4hzhlyH1d7sxvBgZmERVDJLx3jij6wMAI3qeiOUpFpOoDx6wjzzMgMSbFkAR6WRScnZ1S5Fni4RcijfUQHCjGYRqDYOZocUwRvGPKGVDcQMc4CK0xRmAAAGE4b8j3TLEEMXiev3rFz/725/z67/4G0xzo6gZjOqwbAqZ6OuHBHKMVUimyTFPm2VQuU6Rca0uq2CiRZFmGFCLtE1NCiZQSJVP52uiTW6PrWqQQrE8KyiwnBoHzKe3Qx/SCOO9pW4OKHmsUUjui1HgEwaUUT93zDAQSIZDEk3T9vra4FImvQMjebza8RFNswPByDWQePqTiQ25AzYOs7w8cTFUDIAjvEQz88qMl5ckS0Jwe0vws25pXDzZ8ucrxShJEZEHBRkXOvMQtlsTTc+rDKTq7IeaKWEiEEuTAOgiwgaGa2qp2LBvLwkLcdYhrixEC5QOrnWGz65A+EL0ib8DFQN52PHnTUXSO/NAimwwVMwqtKDJFV+ZcrRe0RYHq3U7BeQZGx32VcdCCe5cHjFc0WSqJfL7dszp0PFIFXRHYFZZaW2SQ3LtuWR1alISHb3KKQqFkQFnPZt+xaC1rqbm4DVivcAhaX5CtLlg+/Sfc++P/DcXTH7Aoz9GySOtASNwLKbYn2Vu9CDRRQgChcsImgwhFlZMXGVIqIM13BCgC1eICQcQ3Bw7LC3Yy4/bVZ9S3N2jhCPGcymti+4arwvPlaYnLFVknKLuKsvN0RYHp4yAIkeAsRrV4nRGQCGPIREQGyH2kNDYt6J3j9KrD5RCUIHMd5/uW09pj3qdZq9/mpvYjWt6ZAJhTD3/j6/dWwEHYTFr8PHZguvi4d2AgHcR+v3ZPwcP9Gj6Kstm1esE4uCcGJS+BmjgeNhAUydgRzRXd1TNef/45Xzx7xaELVKsTHj7+gPtPv8vq7AFSFyQyo/7tHBavtzozjn03uAcmHpnUn4OMmMdCjGwFvWtAycFEn6y8ESA4ojWkaiVj76Sbz+spzPtUDI2b+ugYJB3HcUy4on9WMfTlNFp/EDCQeAUSOQuDZhgDMVpyXWAcSKmhz5UGifeBtmn7qoQpFz3ElF4YQ2BzsiHLND6ECf3c7RAxx5aMkyv20G1ExYOA5zgPfgggHAZ44gwYEPOQzBiJImm5qAxkhjGepu7omobg7dguIUBrSZ5lKRYgU6m4j5RombR+a93oLnE+LeRaacqiJ/Lp3QNSCJQSZFrhg0ZGiHhiTNaHpqkTq5UW+BA5Wa8oigyt032rvKBucqKz5HlJ8BGi71FswNjEMrjMS1ASISTBOnwQRO+w3lAt8tQmldgknfOTn0zQ+wzFCLB8BBvBxpjoMPvJJPp5Ivtr6Z5rYixT+p62N3HHOggyUVARcSL29UwjOouU2qKiZOEkKw8La1FtTXvzhrj/NWd2R35r2dzCvb1nUzuevq7ZH1yfsWLY7B3nlw15COgO5N7hBIhgKJsD+aHBZgpCh/IRlEDlkaAFh4sVVx88YHeA7qBo7y+4+SQnXkV+/d1H3CqIItWxqGrDB68zgm744rzkuir5i9/eolVGVD4tNzHpLdFLFlc1SyJSKQpnOd8eWLQtRab49osDByURoiXzjkfbA8vOUbQRZW5pC4/PMmy55ul3/zn3//S/IXvwLYRaIoIEQi8IZF9Yq9dqevtrZwzb2y0xgHEWJTMePHqIKlPxMjFIn96cHGSKH7IatmrNL9qMv/v0JfblS56c3WN58YDFKmAqx2eLPX/3oMDnQOex+Qrt4fMP1nxxf8Uuy8BHtO0IpaII8Munp3yxMhhhUUGysIqnl5IHTeCLe0s+O1kQZCLfKluwruOeVtxqAZj3MncHLXZ6h44Jf+5ub71pY63c378NslEwE8RjG9I1ZQ/uRxbDI+mTxLmARPiDHIOI5/bqyVUxraVSCgijiAUxKHK9khgFInqKcCA2X3B49dfc/urnfPbLV7w5eGRWcf/RU/7snyvW6xN0lo3gRggYQj5G68koM9K6NsmFSakc5MjQpwnYTBH/IxaSyZ0qZJ9ZJZP7YFAE74K2t9dBwVE4RA8Y4tDvYr6fI/eCGAasD65P6YR9RsQ4NF9v5f2GTBqR4D3B+9EEnsz2nqooUuqhylA6pRamXPref+89zntUT1HrneX25oYvPv9iBBlDT72r8XPLysQiNVgFJurhocsmX3YcBf58wMdBZTJtpfrVEKLio299h3q3ZX9zyy/+9q9BtuSJUo+hmNGiLFitlsQYEt9C8FjT0brQWw96kgpAxCSEpUwMgUN2g5BiLN8sgLIoCEIjjOlhYgrMPNS3yAzWqw2KSBbT4KsYkASWyxLfqjQBBXRti7MNMaSo8qZp0Uha61BFwSpfpRc2pDGKIdI2B/JC4kyXSJGcQ4qkvUUpCCR/WUBgQ8CGgAt9vWwxGchib9JLiLy3jvCul+AfbvvgxZ7VzpOjOWsc55d71tdbPv78Neo2sTyqAKvacv96z+mhprvZoa//JWfmZ+RVx6PwnNIcWLUmLTCtQ5AnCxiJpMYrRV3kvD5f8uLeGleU2FhzUivEF5Gb05LPH+W8PgEvPaUvaM9Klh18+WjJ7e8MJgiaU83rRxVKbqgfndFiUkAqknbRUXhY28jrk4oXp0u+fX3ALOCLhxuycIJ9vefses9//GDDlwtDESUnYcH5oUOrSGwytquK1/fXYD2VAZoWebC4UtFWGaaISN9SRcn9H/wz7v/Zf4e+9wTrBbFpycoCmWvoHUjOWaRMwXwIQZZnnJ+fo5TmZ3/zN/zuV7/h4sFjyqqiyHK0lsmN2IN453zvboKby2v+41/9R/6f/8O/YPv6P3HmHPH+93hic0LZIrVDB0+OxwhJzDU+j4Rc0RWCpvAccoWPmiyW1PuOtlK8Wkten1Y0OiciKI0kSPjRK8+X9xf8+uEJVhfEKJB1xWUOD5rAz6uvq1/9IbZBhN3d3qWvv0u7nP78vTED8ejX8XmDZh+PjxjEVrx7bl8bQYgJiwyW3KNLz7RdoeT4feyxeowCT+irEQaUiGgVWRWCzbqgXOYsbEMUHt/tibZB4yhUTGmE8zoAMxAzApMRCMwCyZk06lEB7S0Joe/gxP4oUVr0ada9gT9O8iYSkks9hjEOYO4SYGzBjGxoJq/eNYRTOqEYYjzHe0uR+GC0SFUuVe9WlH8IMDAK7L5pUiqyQrOs8rQrBKQU5LkGkRYH7yJKDCWKk68aZB9YCG3XjrUBxroCR4YNOZp0pprXcQYIjpHSEWd2384RCAwtFwMRx3BhMU7adEvF6ckZf/rTf8LF+T3+x//Xmn//r//fqGiS/zT0WRSRXotOXAJjzYZ0kz44JYWqDJuSqWgQpPslg2qaEL7PK9FaUpVlAggi0rQNwXu2NzdkKmO/21EWOWVeEAU0JgU35Rp8sKg8S4ujEwSfMg9CDOybhtoY1mWOzjO0UkQCea7QOrk4rLXoLEMqRSTxMogoCChijJgYExDwU/xFZKIBHSd03/PJOvc11JI/8PZPfnbFMi8QMqMIkZXrKEJkfYAHQeLzDC8sWWvAWzqtuVnfZ//df84NP0RsXnJ9/e84vf2CR9stUXr+8mnFi2WJxBK1pKwVf/xKoYTg8zPF87PIyeKETJ/wepeC8OrTFW/OJa8ri4sGYVsulgU4x4231LZn9ewM+saQH2rK29cEHFGBjxnKWhauZuE6ipDRZo439woeX+9xKPZVyXKVE92CL05OeF5uiTTIWLPYRA4q52Gdc7Up+NXjHGJAR5BW8NPPBFlneXZa8sW9UxbihO+e/ITv//H/ASNX1Id9Mu8LgfUdJZGsXKNUnrhHpDharJTWnJ2f8Wf/6B+hTMPLqx2/+Ltfsd03PH78gFO9SLEkAm63e252e2wQvPz8c/76r/4Df/OXf01hdyzun2GyitN1wXJ7wyK0LGPGPRnYCUuMgg8Pmnt7T3h+YHmw7KsCl5coIfn4Zc3m9sB3bpZsshPqYggWDTz2kMdbzqPikShpYoaNkahyFnlJJjX51+Nt+YNsR2bir3iVfs9XRxf5yuPu7hxe4HcAhGQ5GHZOQKD3DhxdI60BbzsI0gliZtKeghbTiekLH4He6iRDSMXZjONm13K7a6nrjs45dFGkeBh8SrVTcjJ1HGn9k+I4yITILJOB3sI8PMKEbkjVFieOGujjq3r3sJIKKR3C91Zo5zBtDdGjhBjvNe+7oePeKa5HUPH29xOJVBylS7II0GcRDLLl62/fzDIQAnIo1UrsNV+43d6SZwtG7msRRn8zpCp6kISH7imNs6JgfXLC/YcPe3Q2t7306FAIUnRgf/s4sATGcdBShwyWgNlEOkKr75qI6QKTr0uMNZKUiCAVy9Wab333B/zvq4LL55/y8rNfjrke1gWauqXrDEPVPujjKvp0vAF7xhgYqKSU6oFTTNr4+AKIoYsDXdeQFQVlUaQXwgdM14KHm9stUmc4qbinLzg5XaOkYLtvOBz25MuTtEAridVZKiHtPFIoykWFqnKWRYaKBhmSdpRYAh3FokpFZFxMtMk+IGQiR0qZGBETAi6Evn72DHDFtK8v1YQWomdpnGIi3uf2q4+ekpUKJTQbI3i4q2m6W/4/H5zzrNjglQfdUPmMj3cVj/eBl5t7iB/+c57tv48X/5ZnzZoTd486FKys4Y1b8cbnlLnidH0PkVvasKNSirP751Tn9ylYooXjuS3x1RvK7IQTNDYYgm9why84u2w5vTGceUNWK6oucP+64eN4w72ba9pDy75QhCxZX0prubdtON91yC5gtQaVszzsuHhxzbYoyLqCSt/jO4/+MVm84bL+Na2r6ULNgcjWe66s5NLlkJXYeEBmho+KSKUVt0vJYVPyYP1HfPDov6aLBc51CCnJRZ7G2juyskT5FpUVY2GvOL6CcfzJMsGqKvjN9a95dXmDynOePHmUorF7q9O23vLm9Rv+5mc/55e/+DlXz37Lt3/8I87LnHVRUT29hzutMaZDR01T5OxxxABrA0u/IBML1juDrB0XMiBkiwqei5sdRdvx8edbHr7yeKVAQOYsm92O5c2B7yC5eOUwOscS0LbjbL/n/tWBH+3z9zZ3RW8GHlLK5rz684I0g1BN0f/HImQU0pM6eecm42Xu3HtQmKZ9k8Yc3zpnUgCm+4k+D37Osn9kYRATHJgC4GaKVRDIKNACovcc6oY317dc3uzZbw/cbluiMhQHx5urGz6oW/JVHDX5lGrep/YN7mIGV/FgCRD948RBx393X0RJpA+oQiYQoAa3wJBhkNY/hKdr9+AN6GpSmOYumN6UP+yZu7eHts37811hV0KkLHRFqio78CcMQOLrrr7fjGdgNlApaCgt/dYErG3QsoQIzhlAEEOKE6jKCue7ZBbPC87PL9icnbI6OePk/Jwgenan3rQ0JqONHTMtMsM2Yofxcz8xRRJM8w7oLz3N3UH2DgtWD3XFiDD6lBMpybOc9WrFalFxrTVRQtd12OGe0Jv/5RhgOTBoiX4UkyVhSscc/GyxJ/keg/SQIALOO5wBpXOKLMfnAdfZpKk7y36/R2Y5Ks/IioLT0w35QhGDI19tsG0HPh0vtQKb2rCoSnTw4APWmLQIO48LBp1rgrWoXNN1HYe6YeM9WSFwpLgL5xNL5AheZr7ISMJJEFGkNEnRo+H02ogEJN/T9rcPFlTLEhUlp22gy+HhvuU3C8E+L5FkhHjCHseSW5bs2JFR5Wd0ssC2YNsFnTulQ7GSgbPsY+4/+JDvfeuHLMo1om1Y2Y6yLBDrDaEoiE7isayWT/jgcUYmNOc0vG7fYG5fsREPuP/lv2JpNA/f1Fy0J3i14IGPLJocJS7YhHuE3Z4QDSpaljawahSZXXC2q/jj5yU4Q3Wj+V6r6FZLlk//iNMf/Rfk9z7i83/zr3l6cZ+LDzeYwzPutV+w0pbq4oLl00fUNuDiDdef/iXr3ZYsBMoq41w/4nuP/ikLcYqpG0SmMfUNvijIlxvKaoOIEtu1KR5IZjAzJSftJQXbNoeaLz7/DNtZVmf3yfMKaxwiJF8rDiQKD3zx5adcXz3nw6dP+M///L/j9GQDAn5+8+/51f7fkRnPIkqeryO/XHts7JCm5YeHyA/kgt8sJG9KgVcaHSKVtfy4PKfaZ/z8gzVmuULJgtxLlnVLFgN5Z3nz+IJtXqCcQBtDYR1VkHitaBYVcPte5m7SL2YWtzgErY56z6itTyvfpItPboPfQwT0LvVzvP5MSHNHuYqzU0WvkI1r6Ew5G8zb08L7FigJ9BH5cWp/FCk9UgIKiW8dzaGmvt3T7WvapqWra7rQ0hjP9eUNh0PDqU8C3o/cBdM97j720F8JcImj/anmwSSPxj4Yge8sNi32gfUu4AUIZ6nrHSFY9PjcHFsCekDnY+LDGQjoxlit4Y53LChTL6YvJn6B6WcY76+bB/ONq28MgsuHlFaX/EgSZx1ZlW7ujEuNllnKc+87UUqJdZ7D4UBelaxPBFnWZxmEgBzLPcZZx/9+Q8cUDxBnwnxs7ISM7iLY2WWPAUcY/S/960P0FmtatBJEqXBOIVVyMCk5RN8nYed9wPfBdwOgkFIlf1gIX/XOMQ31QMvgiA6UysnzHJUlwiBBJDhDe9izu85ZlAtW6xUohVCJtyFDYruO3AeU0rgQMAKaumVXHyjXa/TiBK10ivRGEiN4Z5Fa9wGNKcbBx5QmJnpEraUYy1IP/T4EZ45BQuNETBofEYQ89hf+Q2+tSERYKoKTA9mJIHOhZ+rKiEGhrWNpDqyMp+0MVbAszBXt/pa1L3h09i0eP654uFxx//F3EJtTnjz8KJW07jrKTJPlqeS0DY6uadG54rsfLClDiulYisgD3+IPt+TNgbi/T7Fc8uhyx39hT6k2Gz6pWvLmS7So2Dx6yP7NZ3R2R8ySuhM7ifAlq/uPKKs13eGW4voFZ7ZD3fuQ6ts/QTz6NvWnz/nrf/mXiJXg//h//j/x0ff+KeuPOzIfCYsVdrOkNQ7bXfLlb7Y8ji8RUqLkfa7dd3BXitf+mvOTFTqmSodSKrIszcsQ6YNkPRLdV3lL1rAYE++IsZab2xs607FYLMmqJXXd8Ob1ax7cWyFQfcBt4PnLN+SLEz78+LtcLBTb22s2pxs++u4POFw2PP/lv+dGO0zwXGeKy4XASYV3gkq13A8nPN9IXizAKYGKiiLCSVFxb1vx2VOFKZcIqVEBFm3AZBs+1oLffLThNlMoJ8hcxaZbYa8XhC/hF/eq9zZ3RyvjTCCPWLwX1kN62VDONx03AwYjQGOm0IvpOHGssA561dzA/y4t80jJChNMGH3j40PcPXlYBY/XhVEbFmldUiJlJEhUyqYhIKNHxZgiVUJIhHLO441N5eE728eEDQreFMk/gKZjoSCm1VcMx81W6V51nywcAx+NwHlBbQJ7A3UXOHSBxgaCACUd7aEGAloOlQ9mzyomMCH7cQhD343dc9zjwzwYDpkZYBiYFI/qStzt9t+zfbNsghiTL0IJRJhHlErGUlb0rFeRVF5TpKh6pEdKjbee/W4PQlAUS4J1QPJpp2I782CPv79N45jG48k/+5bI1Lvz/487Mrkg5EzTTeOdtGpnUiRxEoI9De+QfkcagJRWmTIOpEp+1YEHQUtJ7ANNjt0Xg08ojiMspYI+fdFHi1SaPNOY6Kd8f++xxtA1NW1d0/kA1uKahjwrUVmON5YYTA9IJKYvfpTHNUIqpNJpIvZ5gVJKhFCJ/rl/sBBDMtOR2AgzSSrZO/RxmCHm0Pus+nTJ5BnpgSBijMx9H9u9FioZyIPjvG652O452zV8smu48Q25TFS/VdPwdHvLg8OOMuTw63+P3S/x8g08/ogHTz7i4eaUdblAnNzHCUUuCjwRWeZkeYnSOoHB0KBkwHeedXU/8TiEQKY0CymIyy5VH1TnUOV8sN2zjiXlcsk6c+TNDYUqUZsN7vIZxjcIFcmBygnyUOAX6yTYrEHX1xQxkp88QJ09ZOcFN5c3fP6r37KzLea/F9z7o++xWS7TkqYUKJliP7odm++/Jm5ecXn5hsvba778q+d8kRtktuKf/rO/YFNkFEWJQCYiMNIyqrI88XOIlMaalD5HcBZrDdbB5etLhIiURUm5XCCVxDozWzM8TdNweXXD+cVDMs6wty94/uXnnJ2fgyp4cPqYNRV5zCi8p7KB0kiMljif2DSFt+RCp+qaOpVoDiLSLcHX4EpJU0SstICn0Z5VV3Lv1nK5VlwXAuEUCsXBpgJc1faWl6fle5u7aX3pPw1IoAcCg3Cbfg+I4cg2emfdu/seTt+K/vskE/v9s2V11EZHc8T8LrPPc61nNPmL8aA5WdFxUaR4LPHmmrBIhD5i1iApJVIppEtyxllLCP6oX6arD/Tyw6NNquYcG8wV0AGYpAsmQ3+MEtMFtvsDl292vLqsef7G8Oqy4fpg6FzibtFGIW+3RD9wrExj04uFEQiM6fRj24aWzFs4gyj9WIvxm9kXDNaLP6CbAJKmK+hJRYJgqHkfY5+bLgVE1VuKIjEEbLCoPEXVhxCxzrLf7bm9uqFruhF9vktY/P3yQ4zI9vhExgk44dp0/NFBfYcmLT6x7omhaBFz01ZK03N9eV9xNNGSgDfGEYVguVyyWK4QQtA2DaZtGMxkqUnDyza0LGlSYkjunz18jJ4gEpjwQqbaAYuKYrmiWlXoTGG7lv3uQJVLmrhDrBVSaoKU2BBSKVmlCVmgqirKPEsc5gwui2SNiEonQRb8SKkceveLFBKtINcS15u1xpe0f3EFfSRrH0SW0k7Tfi94r2Dg6U1N2VgKZzmrax5s95w0HR8vKk67a6SqEUGyaA7c312yaWu0XuGuPifX98jvnVF+949YPHyaSj1HkFmJFApvHT4GsrIiEnHe9RwcHqVUKlVtAjqXWBeQwZNlGlRB0AL10Q9wAs7uBU6jQEiZiHfEh2iVowU8WN7DO4PzFtu2RGsxUWBCAKVR5QlueU62WBB1QRcC++01u90tTXfg+mZPs2uJsUAWm97yleatVBJVZpz84H/Lb+PP+Zsv/w0///XPePnyGVl+yvrRx/zkp39C2G/J84oYoFzu2TzwLE/O0DLHO4MQiuD7dCpv8dbQtR3WC3a3WwqdCLU2mwXFesN6tUjgoX9Hje1QWnOyOSO6PS8vLVdv3nBzfcXh0LCq1nwozrkvrlm4Ldm2wztPqyIuCu63cLbteJqVlELTZoIo0pJ7uqtZ3NZc1KeoYKilxQtPFiRZzBBBIp1F9m2MUREEeCnwUiZirve4Tcv+uCNtMwF2R8V59/GzPyduwMlOMKfhmV9tPHKW+z//8K5X+6ssgUNE/NtVBfs1tzdzpLTTOJr25dhOMUpoIUVPy+4TV0wiskGqo4sOS3gvJJOiKcWkoMRIUtbiHCoNPv7hUkMKraA5ONyrW65e7bi67ri8ari+adk2LgWfylQgSG/3qU30BELzrpsDndkzjqRRw/czZPX3kUYlOTqNfSrz8PvPGbZvBAZUbwqPMSaCHKVSyeIASgEiIrWEKIl+SEVJPnBV9EzSUqX0PUJi83M2acJHfv7p4Y8CTd7a3hmjyoB9v2IqThNXMGnBvdSSvSUn9AcIQW82lYljIY7fMFgEEGCsxYXIg0cPuHfvHucX9xBCcH11zZvXL9jf3ACT+2G4//wlTRNP4H1idBSk9nlnCS6VFNZacXH/gs3FOXm1TC4EIWialiKrEN5hTEuWF0SZfFfOeaRW5DJnk2WUeYH0HqQlRo83HXmuGNi+rU0llMMdfnHZMxMSI6bviyGIVAqR6i5I2VsGJpKnYcpb//7AQHb7iiLLyAPknUMbk6w2JqDNDVEHoszw3uLosHmOufeU6kd/wvr0W1QP7+OLHEdkfzjgvEcXkeVqg5ACZxLjpremZ6bs6bi1oqpKDvsD1kq8dQgp8UWO1jnGdKgsReIPeeTRWUIP4KJIXBBZXhEdBGPZXt7S3Nyg+llerjcgFVHldF4gqInR0zU11rWIzJPlBTYobEgL4OBTRPSan9R02Zp/97Of86/+1b/k8tUvWK5KTlZnfPzJt9BR8Lu//RmmsbjOsTw958Mf/hHf++lP+uc3KJlhfcDZjhh8H/lt6BqLCIGy0FgXOD1dcPHwIVVZMvAMxCiQWvHh06dA5LCt0++64fmzL/jk9oaH2YZv5U9ZZgeqWnCyu2V9U2MJRFVwGgQXraSKkfNDoM7AK48Kgceva8pdw3dOVlxVkVoYrPDkMedJU3HaSJ686lhWGQiPILB0kodby7KOnN++H44BGOReZK4JJp0iTjXs5+ta2sHxGjqhh2T1numcswVxtAr0f49i5UhHidPvv+eVvpvKNz/hjkd3duygxKV/PqZn9UOtFwFKKYQclFJPiJ5UaDuxuvbJaqn9fTtn8axjVsFguI9iCkp/l8iZ4g4E3kPXOLpth+skQuTEaIlBkKzckegFwQucTSn1MQZ6Evq3ZZOYATIxpGeng1IM3dvCfF6Jcmjz+DudeKfdf//2DQMIxcg2NsQLZFLRuY4YwbnEc5/leV/AweE9hOiQokQIidKJn0Dnmmq1oFwsRgHMbCpOQS/ThPvKaXWsTDMI/BHvzlDoEH17bHSJo2YroTcvxX63GCv0aZ0CnGJ0jDm0yWKKsY6ze/f5/g9/SF4ULFZLsixjfbKhLHN+awxdvU8vdD+QyaYyXR9A6hylNJ0/EL0lE2GMpBBAphUn6yVlkfereKAoSs7PzshUoCgXOCEwXYPpOiAglMAYh46Rtu3wIaYYA53iGJxP9Qhs10IQVMWiX3ziVAGNPj4AMfJ2D2ROg2kh1wo982/6AJDiJwjD3+9n+7ffWlDpBdrDo0PHD24V5wb+7YcP2QHkDUEoVi7wncOG7+lHVH/237L58/8KEStcMNzcvqY1DUSBzgqi8LR1jXcWFwNFUaTxDQHZ82vUux2LqgJnsG0ioYox4rucslrgnMM5h8ozDvv9EWWz0ppMZ0TvKJebXrOISJ2ByjDtHiUi26tX+MbiVY4JUC5LNqcnKCQlglW14mACb3Ydt7VlYz0yk2P8D6SplOc5by5fUTdXnJx5nny0oFze54d/9Mf88q//kv/0P/7fWa8u0PqUsw8+4d5HHxPw1G1NWVb4GLGmxdtUrdQ4T9e0BOMos4yagPeOqlCcbBZoneKFQh9fUFUV9x9UbG+uaESkLAqWqxWmrTnsrtmtCl5qRSECFyonFGteOoGREakFUWrOi4oQa0yI2KhQLpBbRx4UC1lw9sqRa0sQAYUgi4JTBOsu48e/2dMpRVAKoTKKINi0hmrr+Un7HumIB4PhEFV2R+H3Yvo8goS51ikG6+P8vOOY+UEgwmzdJKbFUE7MgPMtWQoY1+63NP0jX8EkTI8Pm1sWhrroceSMmSy70/3jcO1emRoAgYsW7x0x+rHU+vgk41IvjvrqSBAzrLKMQGR06Y4IISBFSGXi8wKb5RR5jtYNUgl6vnkQktD7AbwL472PuAvG+76tBs/FXwKDU7vp250E/XEciI+Ttfp4HP7+7RuBAWMtOvalgF3yy4gsJ/ikIUo9CXIlJWhFEBmZFD1YsJydn7A5P2G5WXHv3mMePHrYaweTYJ53zRFYHL45mk1xjhf6h5/Q0OhPi8edM74Kfe+PHd6HLiRXQej9UxFmFQgHb8/wujnvWK5W/OSnP0XnGZ3pcLsdZVmSZxmrzYYHDx7w5ac1sg9KCENFxWH69c9ULVec3X/C9dU1129eYps9uZZjSqdWiug9hVToPFWBxDvWyxLvO9qmpVyuQStM0+GNI881USma/Z7Xr99w8ehh4hooF+BSHEIQGVKlIkeZzlgWBaXWyRLQZz8Y5wgu4CIT2dBsEwPAkQLfayCpGFIkIDHv0TJQ6hMyrRDCo/KAyJM5r80FTS5Aa2x0EDPsox9QPv2vWX/rn/Ly+SsgorKM5nBDtliSVxsEEmcMclHReU9VVViTilaZtsV2XeLkDwHhLQhoD4fRMpBlFhEDQQhM63H75IoJQiSGTyFQytH4A7lOQbr7q9fUN5fJleM7rq9fs1oskd6Bh3q353pbc/rgHmWeQES0DX/203/Cb15uCUSub25ZL0o2y4I8S7Eokd6dozQXj7/FB9/7Fgsd+f73PuKT7/23rDb3+Bf/+l9yVm259/RDytNPuPfRj7j/9DFdc0DoHIgY2xFtQ7vfpb7wkaa1WBew3rKvDxiTiFm1kigl6BnbKYqck5NTxL6m1hIZHSJaskyzXq7omj2H3Zb73/pTvrx+jmkarIr8tezYSovSlry+QhYPeRk6fpcbbvIU7Kgryw/LFT+Oj/gPvOBF4fBZTqUKzmLJR4eCb7vI57lkv1ngpUZGxbq1PLrZ8aBeslcl8Nl7mbu9QZzeiNKbjqc1L/FAMio0aal8+117yzc/XGsQH2K+zA4L41xLP96OlLH49wmdgSTn+Jg4M2kPgGLYNzc8pEj9tD47H7DOEoOnqkpWXgANrk01MAaG3OOGiqmDhh4dOpKJk2Bua55jmUk5JQn6CM44pNTkWc6iWlDkHZmDqPvr9wyxQ+D3fByhdwkMIq3/7zjubdLw5/14/Od8UgztjvOB/MOAAdHbuGNMdZkJIeWxhwQQpFR4H+g6kyr2aUWR5QgkwSdGuxCSHzXPS7IsHxHnYEAZkdncvPT3PMs0eJG3zVIDehKzY/qz7nR0MtGkqoIhHNcJiz1dshAD+Ji+DQiePPmAjz76mO1+y4k+oSor6rpmu91yfnFBt9/zHJG4BXo/5lRbIY6ovygXbE7PKKolMXheP2/wIUxtFRLnA51zBO3BR2zXcLOtWVQZ1WKDFIJMaWyWYduGYB1aCnSes1gtWZUlmUgmfZFJRJ+GiFZUiyoVoHKWTETKTINMtb0742ljwDPl6I59HJKvXOss8eZ7nwo/xWQDCTFRF7+v7T//jWGRK0S0nJg999oDEPizZ5HbcoGsFkSZs3n8Q558+79k+fTPOZhUeMtJRyYkMUoW5Yo8L6h3O/bXlxRFTntokFFgrSHPNFr2QWtdi+la9vWWxXJB6CzWpCI9EAjRpYWFxPVQrFYIKXExgIA81zQHS1tbQKcqf6tNYgB1jmW1osxyrA/UpsFYmySDD+yvr6n3N6isYHN6j8fqhP/w7/4dwXvqn/4pH3/4AeebJdnJAiHAOM+zV5esLh7yX/7v/nvO17ecbio++tZ/xf/6l3+Fk89ZfyBQ9xzVxZqzJw9Yn6zpmppqkxOspWsN0bbgPd3ulu31DV7meF1xdX2JNY6uTRHfxjkKrUebl1CSsig5NA3OtTSHHfVuizM1Oj+nOexRSvDxxff41uOf4GrH5dWnrIVGKpcKfHUaQYsnYoSgEQFkQEjLZ+6aj0XJy8zyhW7xOifXOZdScSBytlnzqyLjdVXhRCoxvsosH0ZJFCW/yBbvbe7CpMXPraJipi2KOKyf/SaO5eGk/37V9d/1/R0pM//ma6zP71qLj79P9ziS270EHtKzx+N6evvoI9GTZEqEuj5wqDus8QiRYtWGujmpZOPQe73eP3RWGBgDe8lzJwI/CIHs6WFGg8xgvRASpTLyssKWJcbapAD6QOjJ2FwEQmThHE29J2mZoucamO41h0J3e2qQje/+drb3zlgfH//119xvCAZSUZGUOgTWOWJsE7lMTERDMQSs88kdoFVi4BMgRCpGtD/saT7vONnX5KrCGc88V3N8BobnmPwnb+fIzlIQBzPaW53RH9mDgOH3/Doh9PmtMhH8+HDHPDQI7FEgp8k1tCvPCx4+ekRd12xONjR1ivBv9gfq3Z62aWi7jj4BYUaw9HY7sywjxEjem0i3ZUXX7FAiOQtCjKiioNpsyFdLgvPY15b99kCZn6CUpGtrgtKpPLEEZx3BRaqiQJ9fkCmN6zqMUIgIpq4pFxVd29LmOxbrU7SEXAkKJYlCpIJEQmBj4hM4GrHZuATi+EL4HqAOQNWF9+cn+OXFI9ZVQWUa1M0r7nUNJhfIMmMRHHIfWZ9+xOnZP2Kx+IS6BdPuMW2DrAqiCwQvuX1zjVZbTNdw8+ZVqlqJwh72RAFtT5ipemFeVidst7d0uz3BObxzifuBgLcSYxzlcon1gbZOtSqccxhr+ippglxI2mZPtlgRhGC3u8W2LeVyhTMNnUkBuUrn4Cz720u6fcSZGuM8MXTcPz/j8OKGz373KR88/ZAnjx5jXSq7rXVKe9ofGqpqwfe+80948niDdZbbTnB92xCFZv0AipOCh08fc+/effBgmpbFOtI0ByBRbXdNy+76lturS1SxQm9yyjIntJL6sOf25pbDoSWqAuuST1UKzaFpuLm94ebmDXVzi3MNpqtpmgP1/pYik7jWcbq8j9BLNo3hIgZqH4jSkHWCJ86wyBSlElyJgBOJJvrMGTbqhvtlh28tXmsyGciF5SSCMp5SQg5k0oFwZM6hfMpvN7F7b3NX3Pnde+WO1qgp2yD2pu8558CgcIk7x6fPsncDzDOpjoD+kfga2jBd66sbPtzkq7Tbu0Bg+G6wDoTRMjEdm+LQgncomdKfY0yKqrc9R4vzx8yoI09Cf00mASpmHZEE/xDMPq1bzPplOHmyKiSG10xr8iJHW4FzfX8pRfCertn3dQIiiEGeibFv3+6XuYJ8d5sA2lFxKKb23nXX/L2VKvvtm2UT9EJQSo2UyU3gY0DrVCRHqlSqJvRmGqfdyN0ce1u8dw5nHbfhmqvFJbbtiAMzjehFbJynFca3JtwACo7MH/HtjovMLQF3ju+/C35Ig0uERz5MQXH9Qek4wog454hVCMFiseD84h7WWtq64fWrV2Q6FTBarVbITNLst8QQenZlMVrg5rG7KS5BslhUaJVhT0/Z32xo690IJFIBIInKUuR/DCkVUWU5UmuWqxVNZ1I1yRhRWuFjINgkYPaHmizPWOcXFFIhQh/UKQQqRpztqY0zhVb9ZItJuGmlUbEP2IHRXygEPeGJSEGCkcRP0JufI1MVyfe1GXFgKy1eBYyKWAm3leLVhaYJmkXxCecP/wJ98TFe5IQuBVAG7widQAiHDD6h/5AizouiSvO8qwmmr7wnwZmO4BzVYklWLaj3B3KtRnaw0BnazqQAKAQ6z1I5bevwQiSmT2TK2UfQ3G6x7ZbyxIDK6Jo60ZyKiK33CZC4gJCBrtljfYZYFCipCMFSrRbk6zP+4pM/4te/+4KXr95w/+UrFuUHGOsSiFCCum44PTnlZHOPqjwlth3XL17wt//pb/jgySec3295+MGPuf/oI8r1GhkjOSXOe+rbG4RIKbXGOqJU5OUKnZe0bYtWkkJLumbH1eVrymcv4PLAvnboTCZ3RbS8unzF7c0bmvoWa1tC8MToaOtbwmqJNwFVnaCWp7i8QDmLlwVKanI8WkrOokN4x4VI/YvIWFGwMYE/DjkfGo0XGTIKVDAswoETJ/gjI3mEIUhPxJE7z73Oc2I8H2XfOPHq/29bils6tkYOmdwxJg7+OArcKavgbr459AJDMHt5j/cfAQUYAeksmmsEG0Nr4uyu0/1m6YmD8aBfL+f2goEafvLJHz957DkUhjYCKC3I8rQ+ZUqTKd2Dg0gIPgWx9plukzLZP0XK757dSYwyYgBBQ3uSVXOKIkCIsd+VluRFhqoKMp0RfMRaj/UO5ye+AO8cXXNggCYQxnLDsT9mhEYzGXZ3NIedo1thPKkfC5HA313LwmBl+TrbN7MM0KMM2RO4aJ0EWJbhgxuzC4Tog6Ssw7uAypJZJQ1GEn/BeayxpLTEcaqO5qqvkht3gcBbAv5dWGqu5Y/IapgAE/wTDAQ609VCnJv0w+yefVcLgdYa5ywRwXJZobVmuVyipGS739MZw6GpmadPpuCt+XMz3ifTmqIoWC2XVIvl2PljMGMM2LZNZnifzE/VokJqTUSglCL6KS0wz7KxnkJnDVFJhFQonaeQHe8RSqPyDJ0VAIToGVMr+zYPZYwl00udCtcNfZqqKoLogw17wMXxRH8f26ObW4pWswqe83pPHhwianRjkMWCs4/+hGz9EZRrotKEkCinh+wZZxtC1ybWxqxAqpxquaYoC7yxNPstOtMYb+mamugTKZWPCRxHmYo7ycFiGQLOJhZP3WQpw0ApiGLMWlGZpswLbg47nKmxEbK8JDoPEUzb0u72KKWJIWK6lrY+EFuB63KUAAeURYkQgoePn3Kzs9zc3PDlF1/w+OEFnakoy4iIke12z4PzE/KsBDTGtFy+uaFQGd/58T/jwZOOk4s/JsvWPWiMSKkxdcvh9hadZ6iiSum3KkOXCwRQ326pViW77S377TWXr16yDyWvt54vXlyz2izIteBP//T7XN28wdga57pkHSEFINuuxnQdzhlsrghZwaHK+Cw4XgiNkJGToCnaDGVrbqPlSgS8UOio8aHgkZPouCeoiBUC4T2alCabCcFp2+GVw6kE/kvnWXpHGQSP2vdrGZgbSycjwJDiLEZBfmQ+mJ0TZrvjgCY4ruMyAoLZiX1JmVlAX7rIAAQGopsjZSwOaYFpfZ3aPMKJvg0zptZIrwQOFodhjZ0jidmf45qcpHYMaQ47azEmAflUQGzKEhD9806PMsRtHa9NIygQs3v3Tz0Puo0xvb8xQvCR4PpKR72ciyExvXZtPajs09CMoOv3C+opnLi37cyA2vGIHFsA/n+pEvuNwMAclUiVhI4QKd0wFSiRKVYgzxLbXow4a+gJatPDKYXu/S2L5ZKyqkaBPla3Gn7ecgt8Rbv6SfPWgB4Bhj6fOcbjY/oP7wQXkT5ucBaLIKYTh5dUCOi6FiEkVVmwWa97CuaUq7+9vWV7c5smZpz4Bo7Q6dDBIZnArDFEIMuzntthdnsfaJsGjOvRqk/uMR+o6yblbktJJBEhKSnI+mIweZ6R51kqZiETBbLvNQuhc1SWEYmYzuC9O0Kjw/1Ttq0Ysy6inBaNIcp1XgQkzj6/ry0jo3KRlTNUvgMR6TKFbARnxQVP7n8fwhIfIsFanIs0TU2uQBDwztHW+1SeNLNIXZDnFVJIYvC0TcMqW2G6FtO2SJK5X1rTx3gavLMonfUm3lR0xRmHrRt0pvrFMbl1rHNIKSjPTvHeIJSmORwIvndzIfGdwdYNVimMMXhvsW0q3NJqRZYpVFFQVDmdNYgYePj4ES+efUnbdnTG4Fyy4vV8WJRVico0zgcOh5rLy2s+/uhjPn6QszzRyOwM13had40QUC5WdE2LtZYoBEHa0SoVI1ib/P9RRrbbLW1Tc3NziQolv32+49/+5X+iqDQfPHnAd7/7AU2zJ/oWa1q6rh2tS9ZajO2wrqNTBZlUNLniNYZnmSCIwJ6SJ0hyKXgl4XMNFkUeMzZN5KkquQoNn1WBVstEhWwDJ17yyBTsW8OrImK0BKkoPHgd2HQ5vGcwkH73Sszw90wyyPmB/dGTqjFb82Jvru5jfoZLDMCdYV8vh4ePctCghxu+dbu7cGXWzplAncv1ecWCkXtlpnyMqbYzI0aMMbEOet9ntfUZOr28scZgjSGEgOoZUeMdBXMOdr5yTZo9+whhjgSM6EvFMxbnGuVB/y+EiDUW03VMkQJHSODoknfj4seHHvdNfTtaZmYf5sGP82t9TcPANwQDIRJlCrePfUMH30kyySQCiIG4xjtLdC5V+iNHZpDlBYvFkvXJKfcfPmS92fRBdQNCE0Qxw0Nz4X2kVU+dOU2zCUMPaSETKBgQ2wwRz+bvQK8L0wDFcQYF5icMU4zxd0wphj3ZjJKSuq5BCPI8w7Qt9X7P8Ws5bH0nji+fIDjDtmkwnUEqldwB3o3TIPiQUjt7y0JwjrZtUzljodBF1mcaJIKgru0otCaTgs1yidIaGT3RGwIpDVHrgijBW4MoCqw1CczMBDsxjkUkh35QYlpUJvfblCkx9Or7BgO/fPKYUhnuN1eorSGYyKfnKy7VGT+8933K7Jyu84kkxx1wncH5QMwzlEovt3cW6z2yM0jV4nSDtS2H3ZYQQWc5nVKJEbBnPbFd3adv2uQ+0w4b0vjJTBO8x3Y1wamUqROTq8q0HdZ2LPJU8VPlJXXT4ZwlkMyPrmkT6U5d402AmBZF7xyBiMoUwntU8AgMtt3x6MFjqrJIGSpCjpYbKQUP7p+zXBZIJXDe0jQNbdvyyQdPiO6GwCneSLr6QFfvBq2Atjmgi5KIwNpENuRNh+1auvaA95ZXb64IpJijut5z777m/sUp5nDLzeWOn/zJD9E64uwBs79ld3vJbn9DLiQhJrImZw3OOWxRkmUluS5Yt47zHJwXnAiFsg3aOXSlklJCgRA5t+0N+6ioc81VHjFlQSQmQiYb+FEoeLHI+NVS0gqBzCSawFVrOd9pXr4/LwEpS+f3RPPf+WYUErMdyUB9jOqP6+XEI6ExX1PnRDhzJWs6dhJQQJ+i2K+VgnHNnavg4/UHodv7J4Z7ESaLwfR7pjQN14nJItC74/u4nJRpMPXLHKjM+2h6YNFfN61l40I2Wl7Gg3reFCkkWmpUXlBkOUpOFpIh4CCKVOnXGZMAtxjSHSfn8DFsmrVudsupvbMOnH/Xo6UkRgTzTo5E5FsVGd69faMp7kxHjLrnph8C/xIZz5A/LwQ9r33SfqRWSAk+eoKHTVFwcnbG2cUFZxdnKco6Dn74NBnmiO1d6TDD57sDOt+GMYEpe2A6fzD33/V0zYHAMF0n9wD9hJsPUIxJi76+vOb+gwe8fPUKYw1KafIimdxN0+K6NpHEzDTt5Jsa2hUhSpq6QUlJnqeUPK1Sqp/xiYgmhJQKqEVK4ZNSElBIJfAiUq2WPUuiBKEIEbrOQuiLFtGniDqXxtN7jOnIdI7tatqYuAryLENLlQRZiNg+PXDU+HurzeC6OHqu2fgkXxbjy/q+tu0G6jxZWrY2R0XPy7LkarHhR49/wNXVnqpaI4i4dk9XbymKBc2hhuCwPibuAGfJ8hwpBPvtHnvzhhAly9MzbIhYm8iHUBLnDYfLa0QUlIVOdNUxaTUI0GUFKLyJZEUGMi0e3lhsZ7DOcri9oTMGnEUrSdc2CWxbS729RQqPd4mgyDpHXmR4AsYH2tZQ5JFD2BGKBbY9IITn8eMHVEVBWVbjAquE5P7FCU17wJqWMi9ZVhUfPn7EYqkQuxrTdAQkTb3HNTVK6zS3feDswQXOeW5vblJqZVNj2xpnWzIl2V9fk0Eibar3nMXA+cU9vv3Jt+iC4x/9Z/859eE1r5/9mvrqBVcvXlDfbrk4PaNtO/JCUu9ucLYlsEKUa070hu9fBh5cbYlWkwvJw7YhEw6MZlkoHB0ylxgL6+0lj0+XHKymUTlWBA4hUvgO3QSqQqMj5C6iZI6UkeAOdF2HUJv3NncnFaff+vVxXBuP+AGGQ44F4SiCxHwNnY4fLAujsBeMGQpjG3qzfmAuiOZ3nu45KAjJfDip9qKvHMjM1Trcf7rXpFmMy3uf7i1FcnuulguKLEtVL0lkcbJ3sTlre3r7SRsXcVp7h/6822pm+++Co/FZRbIE0KfLW+uIwRFDInCbw6gUk+aTgsVQ8+ZteTUzhoxK5rHIn8ZgbMv8/DvkmON3/alft/r2N65aGHwEb/HB451HitBXlpKE4HDOjgcLKcnLEqUErnPEGLi6ucI4i1SK8EHKQBhoa6cHCbN7vt150yCKoxk996fMzfAhHE88xu/jOMnnX8zRb4xxChrsYxUGf3ggSTprUpR4lmdIrRE6jc5ue8PzL57x/NmXxOCTtqbkdP8RBKR7SZJm7pyjWKz6spvx6IUb6leL5KhCCCgyzfnpSf8cgqIoca6vdGgjWmqc9xy85+ZwYHmyYVMt0XmFt4aignyxQLlUGwySUBroiF0INC4yQLaxb2MfZyFIFRf7Fz5ExlQdweD3mupuv4/t6eVryjzyeN/y5KqltJbHByg2p2iTEzOLbXd0bUe9v8WZDhUFIXhwBiU13vkUrWwtLQd8CBSLiqxcsDrZcLjdpVRP6+lMTVkWqdpjfaAoNuRlge0soW2wpqFpDmR5RZ6XOBGpd7dorRhMm1pLbl9fUqxX+DZl7YgY0CKBPykghqQ3Ou+IAuq24VDXWOeRSiPigu2bW04ePWS3e8W5/4BFVbBar1gvShbLHNkvA8uF5ubylm5RsCwXnJysePDwghdf/I4LnXG4uSZbLkFEok7ZLa5ryaolwaUysSBpDgfMYYszHbvtDQcTef3yNTpLIOnQHPjt736LlZfcHG6QKuNXv/oZL379P3H9/BeErmF7dUO7a/BNR1GsePzwMYqC7nCgK5bkaKRa40KOjh0lmpJI7h1awInLyEVG8B5hDNplnPiaYmeoXIY9dPhowUdyA/falkWXsW41xngQHYKO0hged4FMu/c2d3uja6+9MgrV4XN4h+Y76urx7srXHzk3jfbar+z3xyGob1wWpzVoCGac32iIJ5iW4pm/+q5A7QXUtIZMQpb5+WPw34z2XMTRJB9jQGcKrRVZoYku4G0ghmQZEDGO7RxK+k51DxiV74mHhiSzjoTxrOfizJ0hhlL1yaKmtERpRaYkSgZkSJkZUYgUtxAigoCUvdVjQF3zfhn7SzJv5gisgGShHprzrjT6eNz3DCP2boX57vYNswliIpRxPpW+dR7wKK1QWhKCx3Qtzjqk0iidKulB6jgRFc5YttstOnvFm/uvcdYxPe2UUHGMPOdtENPsufOMoU9dm7rkHUAiTgLt+Hr9NY5umKwCzifGxeHEKSglEkOi833+xec4Z3n89AOs67i8vOT6zSX7my22acbAsUmjHh9o9j/Udc3N9pbMWLxLJmMpdY+Wp6AZneeoLE9IPQbyvEQHQXSBKBxFXmBaQww+vQxKpTiBTKHzHEjuGKEypHSJ5W6xwruUjnh9c8NFXZMtzgk9n9cQCDj08Tg+EWIPCicfIG9N+Pe5/fnPvuRMwcZ2rE1HRPBjv4SHH9HGAmMsb65f0rYNhIBWktZ7kIrQNeRVxeH2Nr3gUvWBs5oyz9k3B5bdGhU8mZL4TNJ2hro+oKSg6RrYwXpzxna/wzZ7VHSILCLzAts2eKcwzQGfaaKQicdBKqL1ZIvEWdE0HVpJrs0ltjNE56k7Q1aW6OgQeUG53BCF4nA40DRdGk+dtPlwfUlTH1KxKlfgYobM5agq5Zlmv71lvVoSOScQud1v+dlf/zX/6AefoLMM1za0TY2xBiElNkARwBqDi4k6u9vvMPUBaw0319e0TrLf3rJvD2wWmto42vASuehYlC1t/Zpnv/ifefXb/wUZLK6NZFGyPDvj5OQsRYoTE/V2nmEJHBZLDh885dfxQ153vyH6JadK8eNXLaWBZ+uS58sKiyMqy7kR/GfZBdso+M1GcigtiEDpJffbU5bbSGcP3KwcQRfomFHagqLWKB9QtnnfUxhIy9Wscnjy3L0tW0ZBC+8yks8v+PZ3cwUpiD7ou1+73iXEjsz54xdTlkEcr9l/1y+Gg6IzksDNPt/V36IYPP/9kk3oZYfHOdeHW3m8M0RvR018/nTzQMe72Y5z8JJacCeLbayV0FPK92udUj1hURgjpHoFMvZZXBLTNQzVcL9avU//jUGCcbKUvKOVb1k35lu8c8zXgwLfEAxIIXvEkwoWaZXSx6Y6BYne1tg2WQXyHJ2XyYYTAlJmEAPRB5q6Ybfd4q1LPLVyQLh38ivfstWIseOmXM0B3Q2otp+GQrwFJiatvz83vt3l47GxL07kbE+/PEnCGEP/AqT2toc9zz//jP3+FmctzSFFP0ffR5APQzuA8f4ZxviH/u3tTIovOCkXKJVcMNZZQCKiJ5Jy/puuQ8bE0Y2AuusQxlGVi2TeVw6hIjITeBvxwVPqgtPVhlwqfNtiPMTgMW2NCh4loTMtKk8c/C4EXIApHSccA6nZ5keN5Sv6chqu97L97MEpH+QVT2+uELstTma8OX3Iujhhf7sHnVPvbmjqA8RIphV5lkCTNw3et8Q+Y8QLiVBJK2kOB7Ky4ObVCyDxKxhjcF1HCJaiyJBSUG93KASmqQnOIrRExJAEa2cTwySRXIgEokkcBJ3pCNstMQr2+z15USBJlQJVIYlZTr6oOFxdUeiCfJEREXTG4A8NwkcWVclut2XrP2dz7ylRlxjvQQSKMsNqRZ4LRL5AFQusV9RdwPqItRZrW2pjOFstuXn2JbZr0v3zAm9afFlitnuikNT7Pe1+i2kaus7StZZieQIxkGc5Ikp8e2C7/x1eJdbLtW65+ew5yuyJQdLtDVW54P69B9y7/4jDYUehktWjrXeoKIhZRi1L2vNHHA5bfCtw0nO9LKlU5FWpebZUWBWJLnArA58sC3ax5LPKs80LkA4VHF/0li0b4a/We5ySSJ+jneJiEfhpkfPm8P7AgODYvDyS9r2lGQ7r4Uz4HV1lUveHJWfSieJsHU1Xubv2TvTDd1WZOK5n/Z/HGv2do4e1bkiVHqRfmOWYp8cbcv4TOJAAMsVPSGTSxEVAxEB0/bWCh+jQKqLkkFboZ2vvBDpmTRmrGI59PB06dvWw1g9AyUePFBEtJbnWfdyUSEXGZJKXSElb7wjB9oBCjpr6WPSJKVVzSOWe7h1nY3LUmaM+fHc9vgsS/jBgQMqRQyBTCq1TWVwpkjk1xY30wRx9jrbtOqLKRhO2lMmUrlQfe9AHTI0T8y3hP0zCXoMXvYmMKT7zyMo09kzsAyvE3cu9U5i9a4uQmPVsMjtNWCD27ervGwIRhzWBmytH9KliXQpAkeOkTmYoOZqvRt/Z3PQTAjc3V5TLJVleQvRMhkAxWhbiELEnVQraROBCRJR5SsVyiQKzyDOcgLZLoML6gPcB0OieKyJVa9IYk2pMeO+RShF7NrwwljL9SiNav/Vg7uhl69N73jMY+M2DNbdZRVCWqCNWVnz66AFPhKTb3uKJuK7Ddx7nLEZGGlmTZTmlgnq3pbcLgpCIoOkiNE3N6cU9urohEGnaDud8WhSUTH72AKYxNPKAkCmcp246kIIyL9Eq8Tx4wPkISqR3JAqM2RPCns3JGWVZkhUVMYJSgrzIsdtbrHUUVYoVEUxpnUhBIAn0rm3wwvP65WfIcoXINFJrjAl8+cUlm7MV3jtevdjz4trz5bVBycjly1d0psVaR73d0TU1hFT4SEpFZxraqzfELgU2WmMIzo1023lR0jUtAtBScdgfEMCqUNhgqJs9ealouxbfBQSSUucUeUlelKyWa7RIoDcvF5i2RQtNDDkhdujGsTh4dFTkeKrOUpjA0masrKKLkUiGDB3GtEQhkV4ivMZHjYuOIC172yKI3DiJJUOEnBgdjTQ8yCV7/x5jBob0Paa1K72382Vf9DUJpu/v2DiZU+FOaYmTwBvj/mYnH1kVRrA/E5pxBkXGoO63yeGOf08ARw3LmICJKyGBghDBRUEIwwqYmD1T+faOGAJaazKtCaTibiH4ZA2VSWufmjuBm2RdTfJkBAPxeGVLy7IY25rW/Di2VUiJ1BqZ6dEyrjTMyyvHmPqrqfcE74CZ0X42nuIdgzupq+8Yzzjb+xbgmw3McMjXXHe/YYxs3zm9rwQG85HH20goIkpKpOwnWkxV3GKIKc1JKaoip1wsOTk95/TsrKe6DQyA6F1sVYLY+6AHIZyeMI5tEkfm/bdZCpkiURkm3ddBBL1lwNopiBCOIPUwYYYgOWdcX3hIjmMlpcRYhwigs7dH59g2EWh2O67fvCbLypSFEANjPQQx3TWGBB6E6qsqaonUCiUVWiiILd5qlGZktDvUDXklWCiFynMEkYyIygqIpi8pK3HOJ3a4IUbiXV3Wj8k4795lZXmfUYOzLWhwUhBUTtQlQRX4YkWHpu4aTNdivcfaNBclqdS2UjnGO9q2TtqwTpp3jBYhDUKAbQ4406YgS5fUDCH7tKkQUDrHxT1125FlGT5ErA9ED1J6ohYpR1kIVCYQWcT7QNO2fYBS7HP3VeKS8EnQOp9SlyKKDIl3JrkwpOxBR5q73nU47/Ftx2F3zfXlS3S5Issrbq53/F/+r/83lqsTsrJiWUi6riNEwWa14NFZhrGWGCJdeyD4vuoikehTrIxxLtExG9tz5KfUvCg9QkrqepsCuwhY76gWJUoFpLGITHOyXtPqkld1Q/CRRbWkXK6QMvEn5FnRzzFFtJaYpzie0no+3Ecunh/QOpLHwKPrW3SX4gSqbkGTSbzUCNdyel3jdMMhlqzyDKMEjoiMhlVTI5E8UBkuX2C9xnpL5R2y8+R29d7m7h35fCRMBskhesCd4nhmWuZ43u9f80atfzihP3xIIR4UraPlWTB3Y0/XF9MdB6336LReoKr+R46VY6drxJhiEUQQuOHafQzTkKJtnE0WA6WIJs3RScGKxzECs3VoENhxUFzGdZVexEz8KmOviRkfAhMoUD3tvlR9qnYyHfS8D0leNYc6xa4Qe+tCzxA54JMehU10UfMxnD4cWwem8ZyFj8xwz9Tj7wpafNf2jcCA92FsXDLJD1H5s2h7BEpNjFme5Hf3UaAFlFXF5uSEs/NzNqenCK3wfadNXqYBAsx6ov88twAMwRgDmcTb2+xliEM17K9vGYAUDdq2DT64WcAGwyimha+fAKNbYvC3xZT72s8rnLFksq9VOIfkTNYGfMT5ju3lJVL2ZEYz64DoHyB4DyIRv2jJZI2xnrzKKYuK4DzEBiFkSnWLAd9bK6ROKYsqoQogkuU5LkSkkH1qm0tU0yNcGXKBxeAwGDWGWXfP+udYE3iPhgE+2bacKsGjJnJqJF0meeg0wStqYzFtl2pARNlrKcnkH0Kk6Vq6tiNDoBBjRU4pBDrXdIcD1jusD6BzkH0Z0+DxzqG1JiqNDQ7XugSuQpq/nbGEKDjsa5SSFGVJVBkBx363IxFhieS+0DoREvkEFlxnMMaRqaQxEXwC1tC77QSdM9SmRZcVwQRsU9PWO7rmgO1JwX73u1/y4vkV9x895Sd/8j0+/+x3XF3d8OEHH3D60++neiPWIm3b12YHnIOQrClRCLJy2T9zwPu0gLuYAJUxLSFEjGuRWqV3PqQ6JVWZs1yuWSxz9jc7XNuxPDljtd6wWCwI3iGFwoXENRB8eh+CV5ReckJF20YQDZWPnO5qhOlTfL2nyxUxCnLbcO+mwZaWIDynmcaIZOpV3nG/6VCq4ocix5lI5zusbShdy4POYXz23uauHP3lo2rOyBNw51hxVzAwfJi9nG8j9rf/nF18WL/GYLuZJjsnKRrXiAETDMpQf/xQUngQpFrG0TIwNWMiBhrSmdPfA5tpTCXl+xoo3idWWO9T+rcYg5pmAXZvqdRxFNTHsuAYNonjk2cWkGNrSAoKj+PaPJw2pD2atuvfm9iXsE/3nrNKzm8w9PcERObtewdiY8QTk3VneE7m1orfv31DMJD4oINPfv9JM0yseUOUZaaTsPMhoGB0B1jXp3zE2FPoZmMRnBQxEmD0qRxJlpHVZjRTzU1SAPGYq+l4S4TDb4mmv7ePIs45drttzzDYgxUpkDGZi6WU4wsaAJ1lKdfa+/FZB5bCpm4oiiwpTgwMBUP76IW8IyDoWpJ1IUZEmKPcdFYIHrxEiNDHJUiCcVhaFIJlsQAhsMMklBIZBcvFgiLXfUU918dzeDpr0dkKa1qyrMI5l6iZGV5KJlrQcKfjBMdjNhzX7zt619/T9sNbw6mMXBhYO4EVktAKPu0cdWsILiVN6f7Fts5hnEXJBte1GGvxUqFCTNzn1qCFIPM5dZbhQ6q9EKwnSo3sfYd1XZMXPs1zH1O6nQtEkdw71gYWUrOrD6lE9hD3gqDrGqqyxHvHYbej2pzQuhYlFD5A3daJfU2E5FoQka5L75iUkjzPOByS62JdVgigrfcsTUN0yZx/enaPn/75X/D/+B/+BWWV8cl3vs311Uv2uy0PH97n7Oyc+uoZ17fX6Fz2qVI2MU1KSVfXFKs1+WIJmcbtd3TO0XQdpmtp2gbvEwAy1rJaLmnaFtMdELFDlznB+1TF9MFTuvrAYrXm/OKcZZWCK2NM6bPGGYytyUyJlBKvMprlipt7K7wzbFrB+U4SgmCXC26KiNeWzASqbk/uPZ4KLaHAkzmHtJaytZwYh1iUPPER428J3iONoWw7LjpDl9n3NndFL0Tm8U1xvsCLySQ9vIkwKVcT38cE6Y+tC73gYgYwZoKJ8fxBSE3v+UyfmUTp0bmMAdNyrlELUCKOlfsS9a8Y6wOESJ/SDD4kF1p67pSlprIMneckYjXfi4gppR2mwMp3YqE4tS0e7eh/Ddp4v2sq5T4cJEbEknC4H3+iF1Ng9WBZDr6PdZjfZ+jzu6DlGNRN7Z4OmLuLjrzhcwvC0NKvufB+M9IhkWoRuD6tUESBVCl1T+RTapmQU9OFkmR5nmqcdyZFZDtPVS4ILiCEwPmAEClAb0Ce4/P0gEOEu0j4rjCfvn07Vn94KeZ7xdG3xOOOp3/5nHcc9lusdwlvBIHWKfBpKGYRYsSYZLJ68uQe2+0W79vkC5YyxQ+IgRWwBw5xQttjmsiQuYBAkfxQQgy90ttLokz9rRRkGTLLyfIMheTl4RVZlmH69ET6SouhF96Z0mwqmXL/bYdpFE4qnOmAyH6faGBV1qKzFNsxpzF9B4g97nox60/xjiO/7qz8A2y/Pck5kzm+EcguIvSKtljQmZYYU7EcaxqKokArhbGB7b5J5nVjcT6Q4ZA2xcF451gtKqJ17JsGIRTOB2qzR2c5VVESrOX65ppq5VE6S1z7rucwd2ZcwMrFInFHKEmMHtOlYDXRx9O0TWK/My6g84LFconKNJlTyf0jHA6Js4HDfk8KukoZPzFGqmpJWzfoUnN9+RITBAENMscEyeOn3+Hxxx9xcu+MqHMW6xM+zCs++e53e5O+5MWzL1k/eYIgEQtF6xAi1SHIV8kt0HaWXU+/Xbcdh92O4AyRwO122/NcCJrDPvW1FjhhsE2DPpFszh7QlTVCRMpywcnJBlfk3Fxv0SpHCcHu9gotM6rFGlsWPPeBZ/cybFawMpqFWdB5z683GZ+vBVZZcq+4VysuhOZNVfLz+wuuC4g4cp9zuotUlynm45fnkrroiNaTN4qLw4LFFrpRHP3Db4mjI46g+y0dZthxJNyndXDy5Q+xS8dL50TJe4clr98Gnv27KtqgIEwCklF7H/YkOukpumsIQI9y4CFJAMBHcIG+0mmKefGesUz6CGAgpWk7i5aCPFPkWYoZsK5nIvR9ZP8g2Ad0M2jriKO1apAsc6/CsP4fEQ4NX4kpu8J71weTRyaw1QeXh0gMEmdaonMIElHcFP4Wpxa8w1w9rqN9nw4u4qE/pvYM43j893DyUSro79m+ERhQSo6BQQPqCR6E1giRfJXWO3ApZtJ7CCKQqVQWWKuC4B377ZbLV2+4ubqGvvDN5GcKHAkNMX9oMRvYdwWpJIE5pzGeHAt3cdZ01ls7eliWfFMpdaW/K5EUYGds2u9jQCk99s+DBw9SQaLYEwSJxGYWegfR8OIlIR1wzqXAr5Bog1OQoUr3cT7505RK/A6CPndVJnphQIVAJgQx0xRVQbVcoLMcYw1t1xDxyEzSNikVtG4bvJCsTu9RFksKJSA6pErabVGWqQKY92lcZn03drecI1Nx1MNvdey7FpH3sP3NhSbPFK8Piu/WmnW25sXZknqXKJt3ux0RT6YVPqQy3CFK9nWDVprFckMg4kxLZ3sw3BlUBt44TFcjhEiLVxR0Mc0TleU47+mso21qqqJI1jWVll7nE/VwVZUps6OP7cjygrzM0ZlOnBEh0HpH7hXaGUotKcqc5rAnRIuUimqx4ma3AyKyrxOipBxdHSpI2q7ly5d/xc9/9TPuPfn3PHj6Iz794jWmuUK4E/72P/wbPv/tryAGqiywvbfhtKpYyIK22YFLxZqG98qFiDWO3e0ttzc3OBswneWwP3B7u8XZjjzX6X1wjuZQI3yg1BmLRc6i1Bz2t6yaA2V1zr379zH1niLXae4oRZSCpm3IygUIaNoDVbdnUS5ZZgsKv0ZkHaKoidIgVQZlhqgUWucQNTaD7tWBWgq2quAmz/A6IITjUHqeOotuBZ8VK26rgCscWam4t4hkmYXt+57Bw3s0CahjC+i7V/y7797cbjDuE/QAsj9DHKfVzflBJiuAGNP9pjoE8xvG2fGzuw7aehQ9b4kYFOwEAgYrQRyuPz1HCtyL5JlkUWaUmU4l1vvo/SGQDxFHKvWpV+JbnTGY8Y/9FF/Vp8OCFxEytTjEVBxJa0GmFUWuyXOPjSSugRDxQiAIeG+Yr5LzTLc5QJhvYjDFzmIcUrtHHXk2Hl+ta/1B3ARCKLSCqCNe9IRBwaOkSuUbc03XOtrW9YMnyXKd/HzBkxUa57skTARTMRcfpvqZInXCiN7mgj1OHTkUK3qblGjo2GPt/+4x0+8ZShybkLo9hBSk5ZxDRtmbspJwliGAT+0cwIBUihcvXtA0TQqYcg7nXIqU9qkaYlWoFOhCcqP44Ed+BCX1UftiDFjrUX0aWwxDLn9AiYju6wsQPATJuixwXYuUmmq1wliDr/fIkKoXiiiQIUOoDKU0WVFQ5proO7wHIRPRjVKKuj7QNi0hhD6ocPaiDNrAHWvK3Z6eLztfNRL/UFseNKKzqRaAD7jM0wG1iVxe3xJCoMgkTVNjOkNrAiiNsQ1FUZGVaV55QOgMIXWKVLee1td0dU1ZluRFQYihDz5U6CwnIHGuS/wcWRgXNqV1AnoxAeAoJC54YhBIoZBCs68bcpWDSnEipks0v3WeoTPN1e01izInzwrKBWR5DgLyXJFpSdMcuL59je2BYGMafHfAtDuuX0ZOT0745PEZym/4ztMTbGcx93KccbS3L/jZlz/jz3/y53z45D77V18iRIaQGUid4gJCKjjm/S31Ycfu0KTy1T6idI7OMtr2kDRLmRbGRVWiRCBXMpE5+UiuFN40ECoWy4Ii00Qf2O0bnLcsFiUiJvdaVx84XF+x8JaHXcPm01u82JOHN3z45TUuy8iaBQ/3JTZXSOmojOBkH9C2w98GrltPJy1Ix8IrzncGFRQP9nsK4/EiQ8TI0jSU1qDs+5y9d7ckJI+E9B1hcvfd+33ioLc5DjrW1wLuw739rD1pdPv7xpRi3nU+VRbMJFpptErWTjVo133bXHCzq6TWK0hWU8S4ng9UwFJKVIp1RihB7ALRepyxdF1LZ1pWY2vk2ANzs/z0aZADTAvbUS8O25AOGae2+w6Fp8wlRSbRWqJd6B3eac4TIQSXWj/Xc98xDtOd4Ng3Ee8cH5kTTkwAcRpz2UOtmX33927fsDZBQGea4JNfX0iBtcnHmQRlimKORGJIAVjCeaTMEEqmhS9IqnLBan2S6rGHBCpiCKOQnzTRyRIwdOJUsfB432CUmhDXMF3DcBDH03yyFLw15H0fB5Kw7rpuetNiup/SGbnU4xWc9xhjefbseQrM8nYseRz6ylZSCrzXBJkms5SCLMtSNGzvZPIhgQipYyoE1de3B8iyRD4UowAhCUIkHvuupdvtESGiBUSXiIaklBBlCgQMgUwpyiwBgUpYhGtwZDjnMSZpVLvWsDnNqMqKosiZcyCMQLX/iTMQ9ZVLZWQG2N7fgvrnLz2l8Dy0cK+vJLi9cfzm1RVCZ8gokQq6umZ/aKhbS5CJA9WjYLdDihQUmGVJa++MoWm7lFcsJDLLUwaG9325ZwhCjfsQ9NTPGqUU3ln2dYNSgsPhQFmWIBRKpCIrZZZR395w22w5OUmpba7rsKZN1qGiYL/fkcsVIUTqly+RSiVyHqmQWSpIJWV6l6SC4A0EWJZLHpxe8GC9olovsc2KhXB89vIz2tvLVHEtSLp6z/XVa8Tj81SaOTiE9gSlcX3dgMw5zKFme9jTdA4QdMZinKUqMk5PTri63dK2B8rTDVWhUSIiQsBZWC5O2SwWXN5sOWwDRanRLBBC42NEZ3qsDxH7qpjDnBU4MrdD1zuWpkUZ0FFyVke0N3iRhEoZFKURYHY8dnChM6KIyODQ1nNe36KWS358m2NQSeP1hrxtuagN7uutp3+YbYak3wW+J+qVQaOfqcSR0R2Z/ozvfF+TG0D0LsskhMP8LvHIaDoFjQsxavEIP7q5bq9e8+z55+y3HYtihfGW8/NzHjx+xOnpKbnK0fTrbARrEmPmEP8QYyK4G/5ZUnhTFIHoDc62EC0C3/MLRLzzxLal3t1Q377m/HTBzc0Nujhlc3pGCvaWjFEUgrFzxoiJXt5EmFgeZ90gYs+3oxRCa3xwWNPie7csMcWKhUBfy0cmpc50iJ6+bbrY5B4QwyAMwzYbpFHxmsXJDftHC0Fft2CYKpIUnKklhOwPYBmIPhBU0hRVf9fkOgDvYlooRPKpp2C4VOLRWAcqsFrlZGXBYrVmc3pGtVjiezBATIgLMfm1JmE/ITfRd8Ck/Q/f9z9iJJ8cO3lIuRm231cJ8Sg6NsbRlB9jSG6RAC4GjBuYCdNC733oTevpnKHa2pglAH3q3UCo0Q+aEKBUmkyxB1E+EPqEGiUTkvbeIQRo3UeqqwxZLhIQM4bGtnjr0FmO9J76UGM7k16lAM4YpM4Qegrm7Joap1LqWIhpcR5ScwYSqQFCjYBZ3DFJzbWROxacu339PnWri9ZTZYp1ECxiBhRsyAhmS911KaApExyahu3hQGsdUUiUlpSLDVEpnG1RPdh1PcCSUqaxUSppxC4F2eZZKhPdNC1K50lDIKUCOmvJMo3OcvJM45zDWEdRJOuSkGpauKTCWpvSBWMq/uWcQ6OI3pNrjXfJzVMfDiyXSxL7WsroKXKNVorOpih8IRVK5uR6QaEq2v2e+/cf8v1vfQ8V4O/2P6Pe7cgzjZaa6C3N4aZ3CaaaB7pfDKVMSoDSGt+S2q18oueOSWd01iJEYL/f41yHPFtjTAsDM2YULBYLpIioaPEmYFHsnMF72NeGIpdsVpq2qVlWJVJlWB/ZhoBblXx2XiFOA5WHn2SCIDJ+t5a8WABEChFZxsA/doJdnvPbSrPv08AqueTEFJQhoLTg80ziizUqSqSpqbxlrQXKle9t7s4Ls33V2jWlnR3DhTE4TnAUgPiuk+Psz0BPqTvoQKPVoI886NfkIfUQAtbU7G/fcP3mObdXL6kPO3772Wv2daCxHZvVCY8ePuTDpx/x8Ycfc+/B/bS+BPDWEgMJEDAwwSZlS8aeaEgEUKQ4gVyntD4l0USUCgjpiaHl5vILPvvlX7HIDnz6+ReYuOL7P/opebmkyAuyPB9p4ftuGp9tHph53Ct97JZI8QiDbBh+7o5PYCrh7nteBCFisuQiegASR4XqrcYcucfTf6K3lA9p9sPYxpFEVAytTEBAgFbgvqaU/2ZuApnSqkRfui7GMDYw9CkeE7lF0nzDMFmCoG4aykWJlMmXJ1WGjyBDQERBEAIhBsQ5ddQYJ9BHpQ7WgwkszBs5WA1mYCDeLdAxgIuBFKjHheLY1JbeCo+zpk8TDIQIbdvRGjeWKPZ+AAo9IRBhFPrzt08p2Ve3mrc/cYKHmQ9pKHCRRijxM0DyQadcWpG0vqpMhqsAqCxRF1clWhc4l9qmlYSQ7umCx9mAzkvyokJXCzQCZw1Ka4TQifNeCbquxZiOEANC/P5SF8f0yl+9YH2dctR/qO0Xa0Gew7mFbznNvawgSIELLll/rCE6qNuWumlpjAWlKMsUsSyExDgHWhG6iHNx1MRCDDgBTddCjGipEMaC9zRth9YerZNQpy/w5bwjlbFKAXnE5DbSPfiLfYpnUZTUao/rXQlBDGbVgPBuzF7RWSrf2ggJ0SOKDCViIvdCEL2nawzep4VGCQHR0exv2W+v+fDDTzBtR1O3WONSJbY+h1r0fAUIRSTVZxAWnHN0LlDolBWk8xyswXmLVJKiyGnrPT4KWtORZynYpOtaBIEyz5FK4oOjPhwILtFix5jmn7MR4wIEnUoYdy15ocl1DkLgpabJNW+WJZY9hZd8dCOxAp5t4PNNJMpIqRVVcHx7K7gpM744WfEmGmKMbPIT7sdz1qZDhIbflgVuuUAJBQYWeeB0WbC4NO9t7s7BwHwfb4n2t2yc09rJpBTFO2fEmfVvOE72QGJwjU46zcwVQH+96OmaG65ef8abV1+y217iTUOwjtevPuf56xrjPWVR8vzZ53z6u9/x2Uff4o9//GM+fPoULQW72zd9ajP4ENFFyXK17i0bHus7hK+RXaqCCSRXZ5aT6Rydge65LLypafevOFxXYG758tkXXF1dobOK5WrDYrGkLAuqasFms0FlOVW5IC+KBLrFQAsck/CPMNYmoOcKkGk9j70LdajSOvVrsshFkTINkhVryiaYhwMcj0icLD2zff1IzYIFJ1fFEAA5OFgGNskQI86D9Xfnybu3b8ZAONQZILFdhRBx1idU5WMfVRl6VCcTw5RUCCEJMdC2NVFEtE6VwPrDJ0QaBsW+V5tFvBNoQf/osUdKcw107K/x73GOjx0Kc/tZ6sLAXX8LIj2fMR3b22u2Nzf4kDT3EAXWuqkWPMea8Ny8MwCMwd2htUIp2ZMSTa0ZgODYD6SBjAMgIAUNDlGhQojezZDYDYMElWmkcMgiS6mBXQcukQilmAPdFx1KhYtkVpFXSzIizjSJakCqnhVMYLoWY0wSUAMWEFNPjcauozUpzo6b98nQt19vUv4htp9vNKoI3G8ha2EhBZ2MfUZqTDXHfaTtWuougb1yuURI3c9vnwiAYiT4iDVJwPvgECrxNThj0UIQRMA6h9SKECJt51jIlK4pSJpCsk6KFODWTwbnHHlIpXpBsCgrqqpkn+e0pjsqD+58IvvRUtE5j+o6tE4skkRHdBqpgJ6ASEqBMRbjfZ+t4/C+wzvF7eVrvv2t75Cv1wQfiX56S5TUVGWRXFVSISTYnmzIhkhrPUWRSIFiCP264KGfoy4EbOtQSrFeLlCA8Y6hjKWU0JqG2+1NAsUxQEhsmt4nMENUGGN6wO2gp0DXQpGFyBpB7WtK68iNJ+iIRpFliYgLmeJ/vHRYJK4QdEJigyOqSKkk3WIBh446y2hVRMqYAHa2oXUL1OWX72HWpu3rgOh3BaDNvwMGQ/jbLoLZ52TJnJuuJxAwbIO9MPRrXbu/4cWn/4nPfvdz3rx+RnPYo4kUMuPm+TNePt/ie4uxkoov8k/57LNPeXP5hp/85Mc8uDjli88/pSxLOuuwHsrFms3mBCED60pjbU20NaW5wtXbvkzxYH1OsiiMlliHjAZvdqwqSbAHfv6z/0AUmrJcUhQVeVGwXC45OTkhywtOz865f/8h5xcXVNWqr6kzrVm9OBpXMCkgKtXLRGZW4Titd71ACcFhuhbijNJ+GIm51frIgj3JJO58St/OgcfxFnu3RyQgIhj/9ZSwb8ZAKPtYgT4ALviA6TqUznptJhCj79udUum06rXNoPHeYdqWXbil3u0TKY6PPROT6DFAj4ziuwX+UScMAIA+kHD2zAMYGL4bfWkj0GAm/ZncAz0QiDFSHw48//ILnj//Audsf52JE2A0Iwk40o0FJAZCMQpurSRakXy581yPGMc5kfJkxeQH6k1MxMQxp7OB1jmCdwTTpehbb5F4NCmy3auAygqktYQuFeXJdEbsX+BcSQShNxunuvbGG6RIYzSkIs6jiYYWzzWN8fHFhFTTQfHo+6G/j3Kj/4G3VikyKWmUp5MBRyKfQQqCSyx9JgS6rqXtWhyKxWpFleVEFzCdwbtkKnc+EHxEaEnbdehsMNVJvJQpPiBGMpGjtaYzbsY7ETDO9a4gKEtBkeejOyoBMI8yhqooWa5WiS+g2YOQrJZLhABXJ3AiC4X3nsOh5vTinO12h/OO1qRsEJ3noDRZllx33idOAh8c3hmIBfX2FtMeePjBRyyqirbeEnzARosLnlzneB9x/TtpfaDtDJ11PbGQwdlUtz30mTSud3shJU3TcXKy4WS1QHiDksnt1XYp/bYxLVmm2FQLfDCEILAuEKMkzzR5luGsJ1Mi+WVF0n6Us6waw7cPDU1dszaeh1tDq6ErciqV4zJJjIbCOU53DT6z3F8s0YWkCw7pL1lLzyY4Ymt4aAzbkNw+Wmk2aBZtg9q/fm9zF94GBHdT0d5tKXj3dd6VxjYqMHfMBlPo3Z1z4pCyFrh5+Tl/+2//Z169+Izdfk97OEDbcJIv2X/+mpuXW4KSqYqmlKAVl28uub6+5s2bZ3z/u9/ixYsvKIo8udqCSNU8i4Ii83z85ByBAddxIg15cwve4qzBdB1t19K2BmcDUYm+QqxDiECZZ5yelHz65Susl3RNw1B4SfXMrUJI1usNH370Id/7/o94+PhDymqFUBqhQErVA6BBuZsUPtlndfm+mN3gLvYeXBDIDLxztG3Tx1kMQmeKUZiL/ckG8PbwHMGEQbYNzoE4JIbO4UYaPf8168V+IzAQnEMIOU2YXutVUqK0SK6C6NMx9LUJYiDPM4TIkSrVTQ/O4o0F3/uow0xQhMHMMZlBBul4DATEDAxM5i4h4vhixHg8+b8KYMfpFuPfkAa4Phy4fPMGQjINxeiJhL4K46TOH+X0CsiybDQ5FWVBkWd0zeHOjWe68qhUT/SdEVIMQQwELEpIfJZY6Gzbpsp2gA8Wb9qk2W73tI1jsU7V6wISYz1KBLJMUQGSAN2BjoCXis4Yuq5jITRN26CKAiFBa51elqGDR4DVpxOOQGZmvhpHY96Tb6PXf+jtw31gUQrOg+Zh0CwQaBuom479bkdnWmS0NG1DZx0yz9is1+RKUe9qnAPRZ3+4vpqhVJKsLHHGYhpHUSwYwKfWyRqjlcKKRGeciKvAOIsxls50eO8psjy9tv9f4v6sSbYsu87FvtXtxpvoTpt9FlAACiBA3ktemelFkulFP0C/QP9QpifJJLumB0kmXZF2SYAECqg++zxNdN7sZrV6mGt7xMkqkJkmFnObRWacCA/37dvXXnPOMcccI2dC8MQY0EoxHFpBAlLGjzOlFDZdLyqVIRJCxFnhfAh6ZCiIwJS1lpAUc4in31lr6bTG13bROM61hXTg9ZsvePHxhzx7fsU07chZfCy899zv7pmmmWEa0UrkisdpJiYxmE8xMM9eVByjaCjEem/HlBjmmZcvXlL8SJw9Och7D3VGO8RI0zS0jSbnWaY9QsHoBnKLqsgNTvaPthMBtDTtaecdF9e3cH+gn2bODp6tUvQ+8eFtIhmDKgETPM92gfPOcJ4Hjp0jGdAq0oXXvNwnzN0ducwMbUOyFoNm4xNPdkc47n7U9csjOPkP72OPw8i79f+7wf8/dyeqWsA8uoMfFT0PssRSuMj3gbtXX/L2l/9EoxQ/efoe9v2G6c1rDq/eoI8jXcrEXPBTIOSMdg6nHUplrm/f8p9+LiRasQOWdRrCjJ8VdIr5mOlahS0ZqxJNo+n7XrRQjIwTNo1FkUlafGJi8KAUOUf6trDtDX4uVQBIiNatNfRdS0me6f5Lfvt3X3D/9W94/8M/5cmLD2n7Lf12zdnlOcY44WMZi8kRlcXXZUFdFaryB1IV5asVerHEEJmm6QE1rkXvQ4X13c9J2uWPP9FH+ccjBLt+Pr8XRJZfvvPo/+LxA10LrTAla4YjQQrQiqISCnuCC0vJaMA5JVW11ihTanAxVZZUs0wsiyGPerSO1Ymkwjs3QT4F+CXwnsZFFDVzy6eE4A/1204IjVoyvSWwvYu19as177//Ac+vznn77VEgYiWViVYKWwlUuXCaolj8trXRaC2bc6kkSVGl+/1QuaAWSut3fl7TCxarZO9ntMqEKDr1thNHSBuF7bwfBlZtKx9/JaqlFElKNuVQJTvRCdd3mEYSGlHRddjG0JQObRtCjITo5dz1u/Ci5AUniSgWu1BVv8vvZganT1D/iCnB/+q2sG0VfdNxplr57KdI9JHjNBFyJkfPMSSS0qxWK842a8I4sF4JAfDpxRklZ8bZM86eKQSscejWsB8GCrMkAkZjtWKaJvqur22GTMkCsS/ckhAju/2OxjqBHSOEmAjeoylYY5l84ub2VtzRrOLm+k3tBQqBkSyJhDFGTMGKiK+gEKVMs6xLx3azJZTC/fHIcRiZp8Czqwtco/ndr/+Bl++9pO8aGmeIlYw6Hg9cX7/h5uoFTkOYZ4b9TtoWtqFbrUg+UHKmaRvuJ3HtVEZ4RX6YRHcDZGJFCyelbRytbkgpcHc3cv36FeN4R9NaIZIpy7rX2MbQrToosnpkFl2IlEMJfNPcM77MqBdr1scVf/nFiEmRtxvFzVYTG41NW1yY2ZaAMR3pcovvDJ4EOTP5zCYkrnTGOVivN3gNaR4IaY8vMyvd/mhr99SNrjfhqU36qHg5EQVPN2rhwfjnMXfgPxcYHu+1D3us/OmjXasUTlNaRdFow/MnT/npv/gb3vuzv6Ro+PLnf8u//x//bxRiZddL0WeMpVmvWV2cYVtp4cwh0epGrNprMuJsYbtqeP5khXV1Qk0pCoYQFbPPaN2grcMYR2MtOQdS8pRURLFompiOA3Y48vFGcflyy2F3ZJ4mqFMPfaN4cnHBL3/9hrth5hAn/tNXn3N/TISiWW23tH3Har3l5fsfsD2/4HzV8NTNvCwjXZFWrTa6FsEygQS1laAgpISfxkqE5EFUT/FOvPlDhlQoHqSGT5/2dx/z6HPm8ePkD/UfY7RwebHlPwsULiS6JNliFlKeJAuiOKULVUZH5pKttRJ8nDtlnt+VIF7W9uM4/hCvy2mx6iUIn1oBf3ix/54uQXmobAXi/e6N8iDjKR+SBjIamXMVeKlmAllhjBE9dCOExBizjJwZfXKT++eD4SNY/p3EpX6spUCCsiQ5qpBiwE8DShmcc5xtzxmmmbbrsK5lmgPTPBNmX2dxDbkUQs7YzrHarFmfXWCzKNc5A9Y1KGVoXIufJubxSEwe7TpQisepiqKajCiZiVe19SEocZHRMVWXYlHM6ceczYJ/u4K1KZwrzyem4YN2RdNvcc1bfPAchpnD8Y7jcWK9Pefs7IycM33XiUOf0Zxte0LMZBcIZsIOA20jPfzdccc4ihVx6xwYse02qw3TNOGMBueY/Cwjt9pirGY6HohJjLCMclitKmEq4P2AyomQPBro+xV+mqX6r4gcuXAcB7q+5+b2huAjxlpiEp2B1WaDa1qG4UjXdpgcGSeD1xatDShF07bc39/xxeefcXNzjZ9GKElmyGPh9as3PDl/zcurM1IIIvCiFSl5plFhXYu2DQWZNOr7jkJhmmfImXXbQY6UHHGNw3tDCgFnLOeXV7jGcH19LeTFaHDW0LUKbeUOFC/46ulBYjoOrM877NkFx3XmG3tFxOLuPJd7yFbx2XPD6yuDtg2NeoKabui4xyrFZx833G0cc0qEeSYeZv7CeH5WGv7up08Zzs8ZS8B7ixsbPr6551//448nR/zO8SjYPwTvav1bvh8c/P/foR6I3acphIJrHHazJmpFil7Y/ks7NHPy4mjWLauLLe16ReNarDK0xtIYsDrRt4aLbcvVxYZt76DM4oSpxU+laHHIjdGzH+45HO5FG2TMHOcZXyKbcUUYJvTs+epXv+XN3ZFf/vYrXj69wk+eYRhRgHMt//C7/8AnH33INM8Mw8gnH75P260xyLTY8e6a43XmGsXvfvEPFGDlFH/6fMX/5m9e8t5ZixDfYi1oZdRRV153yVJAhsOBMOyJzkr7oXLplk/yuyXiEtT/uV3zMXr8h48fjsr+MG+CnN6psk+FdKGOVAnB0BiDUkoY+MDsZ9rG1Eq66khbUwVXgPKdzPVRTPyusNAD9C//z/nReehyCvIP7YLHrYJ3+2Wnp038nlmiWkhHKUnfnoeMS1FH/kolTdYPf4nlSilpKyBZXQqR4/EgSoGLwcgDDveQbAOURyMkLOpeC8qhsc5JbzZGbJFZXJ0SGsXlak0cR+bZ47qe1bZH6cgwDRI8jCbHglGKEhNp9hhl0Sj8PNK0DeM4gjG0Z+fCOzDu9LkUxIRkGVsxSmErOY2KipAz1hisUej6fucg8Lr+zyzdP/bx5eacRkfc6PEl0RlDMI6YEquu47Mvv2UYj8RUuHzac3Z2IfB1K+I+xjludgeO48xhmJlDwBiNjx6rhRmfi2caJ/w40rWO7WqNohBjIIWMcTJHLfaqlpSFs1BKYfITjdWoGDHGCcs+RhrbsD3bMh+HajFdKuqk0aWcuAf74UjXtDjjZGN2ur52ZNWvGMeRYX+DtZa+7cnJVGGszDiN9OuOgmUeZSJiu96y3WxxWbM7Hvjqm6+5PFuTUhCBmJIoKTH6wNn5Bc4avE/1fhGCn1GKrnU4Y8h+IMwjuo6LOSv+DdM0yutt13R9x3E4SnWFtB2999V5MbBqW8iRSIIiXAZrOs6KI5aGthT6Eog4et2z0h0ZQywRlQrKQM6BKc1MaGJjKLbF2EQ6TmSjGEzkmh3ROVTTo5ueEiGq1z/a2pVjkWqv39WNN6fAN6++ZpoiznY0TVNbVAbnWhlhNZoH7P9hT3z83O/+/7uthXcfWZa9ANlyFZEw3vP61z8nTQMlBr7++d8Rx1FOMyVRGGwcumuxXYOpqHAMgWINK9fz5HLF+aZh01taVyCPxBSZp5n1ekWYPLNVbCj0fUvTtHRdg21mSlY0WMSmQ3REUip8/eqWX33xit99c89hEjR3OB6JIfL0yQX3R8/+OIgDaEh1nzRoFVloeErLNa+xG0UiJ0+OHoVDS49PoP963YzRtE3P+dPnfPv2hi8//zV//x//Pe99+CHr7Rldt6Lv1jRVW6Scru27SIz+vQL3UYuWdz7SR4+o/63h1HzPbOAHIgOwEN5g6VfkusFJRWyseYBMivxMZ0OOCZ8z2rQ025au67HWPar2333TjycI3imYl39/57FKSYL2kFSo0+8eOAPfTSyEY1AetQeWvy9lUSAU8pfWSqSXeQj4CqmMi1raH+VhUkAtKoqL9dKj3/EA4jw+vwWF0EpVy+C6PB7eDkoV7u/uTovIuYTOmWGcQEPb9mAdpDqja+S8kg8obVg5h07ANOFLIStL9IGQZvw8EuNMCK62gmQY5gGmkfdpAafB6QcEoMAJym6tkBJFL7wmd8si/5GOtTY4k4SRXwyxwBATzjWV5KrJaEzj2GzP2Wy3TId7fEy4lPHJM82JYfKnXr/CchxHLrYrrHOstJDO/Cz9waVlNI4DbdNUg59EzoqSo/Br4MSbERnkGWUUyigiCR0DQSmsVYQYaKyrctFRPD1ywVjLOBwwStN3PbmI/XTfSgvBakGPMgptRIio5Bk/z1grjP5nn/wJJUuCoJRmtdrw5OopcU7cH3Z8++YV7714wboTsSTRO0ikmEUzQCEVfBGTJ1Xn+FWWievgjwyHPfOs0Ro65+jaFm0VxipQWaoon8AqgkoEm9DKUEpmtWppDJDEUjrFiPORS+Dq9YESdvSHkZdvjyRtsDFzfohE1xBUoh/2fPB6IOrM2E2sjgVvAQpt8nxy7bnYJV7cTzQpk61Aun0wvL83tD7+eIt32R/ewZBl7/V+5Bf/9I989tnXyOSrE1leI2OpXeNoW0FiG9fStB19v6Lre5x1uMay6les1ytMVQd8eNl3IeyHf5dTy2Y67jjcXRPGkddffMnu/ojWmvE4gdEP8sMo0VNREKInzCPOZIopzJNn0BMr5+nMCl0cx5KYxknQVy2oawN1skmk1RVaDL+UBqoRUC4YFJ11dI2jAMMU8SEz+wJWUNsYpXVRUpYpoSTTW7qKdKGKIF3qYQ9WWqFKxlDQpTzsz8aQMcwRfNKEDLpp2VxccHZ5wTfXt/zql79kfxw4v7hgu92y2Z7J19kFn/7kp3z6p3+Krvbo9RKf4sEfqqFOIevRprrEpkfpAKg/kjeByInKkXOSec8i0PgyCy9JAaeKWSFwpq5VfCZVEZ/HRL9HDZLTG+Kdnz1OVJc5SljAstpXr22D5eotCcZjhGB57nfb2u9mXssnkUuus/0yfriQDJfn0UDR0kJYkgFrZPN6R9cA6R/pd17iIYt8ANuoRBqNXvgYS6tCiyudKktuqEWjoWYUd/f3KKe5cg3ZF4E2EATAFkUshRQ9kxdijdJGWludPM5oA7nOpStTxXFmyAGtWlCyWByVfKMVzlRZUVkcWANWi5bCMoK3jNssUws/1vFn00CrEi7ACzIrNTOHAzEJi99Ziw+J3q0kSc2ySUgTasQHGZcb55mUIlqLh0GIkaINRmmcM7RN4qhgngdCnBknyzzNstEirZoUk3gY1FmlxVAo5UTRBVU0KhaKyti253A4sGpkg6fyNwql6lzIMmpdgzWGVJLoeBiDM2JbXUrGGkfTrsT6OGYRy4qBkhRguLx4QkygjaPtVrRtj3VdTToz94c9v/38K/7k42fYEklxlmQgyQhu0/eSyBqFzpIE5xyJYaZfi30yWTZwa6QVoLX4czhnCaHqMLCwo+t9oxUxBJp1L++l/iyXRE4Rl0GPmTzNtAdPExK6JPxOtBqCbcgqsZlGNkdP1oUXrw+sd55goJDpoufZ/cx6NHz0auRql8k6QBloQubpwWPDj2dU9IDMLUfdO0og+YH97pbrt2+YpjqbXYtCYyxdY2jcsmYUxjYVMWixTqZMPnj/Pf7yL3/GdrOphdzvR4/HZkaUTE6e3e6GN1//hru7bxli4ezqGf2LD7BNg1ltOB52GJ0rYlionsNEH5goNFYzFYWu5L4SG1ROmNpUViXjjGLVtWil6Iym0wWbhWmmckaljMpU0p4k1TornFKsWwdkYogyYZAjpbZSSq2wCggRNsmkj0D8kmjlnCpRvLCQ4smJXCI5mYf1aBxFW0LWJGXJRolxV0x88dU3HIYRNXtCjNy+ec3Z2Rnb7Zam62hXa4xWfPzJRyIZrxYu1gMS/dAPeDf6nzh1p4Wx4ArvIj0n7Z7/wvEDCYTLwuA04wk8mnmX3+csWb5UktIbESjZVOnJKEYvpz7+w5uR5z9F3UcVu3qHjcnyt/AofpeTGtQf9i14OE5oQ+Hki/DO3xUh/i0KUw+ntwg71Etf4fdFLtUYtUw/nU41F97pt7/73bvHSUOgyM1TX5JKz0AjG3/TCIRflKGowhwiSlvQBoqSiq2+uNUiazzHxP5wAKVwbU+73tBZK0lMVmhtsaZgtBXRoxQwRJwW6N9RcEZjFTRGnQK/ICT1GlQRp1Qyi/qiro/v7X9evOiPeTydPSsDfVJcKfG6TzkQa1A0WmSDN8bJqKUXUZppnpl9ZPIe5xzTNKG0pnWCqMgoUW0JVeRJCJeF2c+AqghJlDn6Kje93DulogGpiHIhWBHDzfmk1x5mT1AWZ1yd6JENICOTCSULl8U5iw8eWyzOPohpUapxC1qsmOvooySsGWOak6FP0/QYrXFth3aOdr2ilMw0zXzxzbc8uew57w1i4OUrvDqfpk5KySdUIEkPkK61+KFgjaJ1FudMdWgs1b2xytAiFuBt26KNmL8sJi6xKjumqqJYyPgSODrFcN5TuguOduTCB3QM3Hbwpo9EK2hItI6nO48umdl65kYRjfiEqKLwTqMm0FqqQyzk7ElhJpb8e23E/6ZHefcbaU9GSCM5DuTkT1Mupeiq1ZBF/jxoZiMJb4iZnBUoA0rQMKM1u7s7Ssl8+OEHPHnyhNVq9YfPQ8k55BKZpx3Xb37Lq1e/wocDAUV79ZzLT34qRNJGs3/9OWeXZ3THPTlkstU4Y2iUwgGtMahSaF2D04p129AbTQP1MzBoMs83K+73BzpraDU4NFnXyTHqjqweWFklFZKPECNGiejPSf+iLInVSUtRxm1LQlXdC1XfZ3lHBEcKMR6h0uLlYnGNTDWgFqEfEafz93vu9ntCKtg62qtSpHMW70TRdJpH7m7eklN8GKd/JwGoF14tqcCSEDwuiBcYQf6lTj8tD/Dz9zh+cJtggTQpj5mLkhCkLBWHrMWqRugAJWQ7ZQymLC5F6lTF/8FzXQJyRRcoC6P2QfBmCd7f7W89EAzrZVPL0/2h6QJVSY/Lyz6IHOUsSoA5J0SOuK4GTvVLzSSVmGaUKs/J4wUq55x5Z13J/+Xdv4OESGDlJFu5zPxrLc+tiixAa61I4CLXVzsnPX7boNFVhCOfUAusxVDwUUhuViuUNSjnUGQIAg/nKJm/NYrGanqrWRvoWnFKdKcEoJzMRpQCw/J5ihVpqe/BWnlMp0u1uf5xjrtGEZwlxEynFCtj0NmcNCVKvdZd2wgBMGe0NgzDWIWmAvM8Vx5BT8aQijClvZf+YjZamO5ZrmUqhZgTxjlizlChdR/FMKnUqniaJ1LJ9NbWz1N8K3MRTQNjNJTMPM0ikVU/U6maxfEyes9q3eNnT4gBVRK6ZJqmxRjNMAxM00yqVZIxyMiv0RjdEMLMaruh61Z4L94btmnpNxty0fgQGed73tze09ozrCqkFAghE6PHGcOxyq6iFMqIt7zIxtbq3lna1onsODJdUVJinhKZIhurcazWq5MSpiLjnGYcJ9brNWmBPVUh6sRdU3j74gzHU9TunqfeE8LIb54qvnxiiM7g7IZ+btgMniYVfv2B4vbcEm0LWeNiIb4Z2X525Mv3DLfnHbFVpJKxR8341tJfTz/a2l2Oh00+QfbkMEpCsNjoIntczjJ9FJO0cbSKte0piYC0rwR5zDHx9ddfcX39hr/+63/B3/zN39BXlOcPAaaKQkmReT5wf/MV+90blANjRa2ybS1Nq3GNYn1+xvP3XvJqgGZORK1wfSeW60Zz0bXElNm2Da5kts7RFTCTR2uwUyCmmfYyM98fcatA2xmSEt8bGaORDUhrke8uyPsMPjIcBqyxghggBSzL/Z5KDfBS1GryaS+XhECKWTk0ywifcAosrrUYp3Gdpe0cbaNprELlSA4RnxWpJvIZSFo4QIvWgCqVjF6KtBWr3XwV2Xk0KfKdLLQs8P+SLRTeCcTLh1Yej+L/EZKBslSrS5BMscrjSkAzWotZRFVhEuJFIeVISYa260QXve1O+tCLHYZkbAtD4+Hkl7HB7yTH3+Ncv3vR3n0fp4TgBD6cIIfT40tNbiSjfPTylYxzSoROJhFLsiGLZ9Gfjjlj0HXSoLY5HiUxi5TlcrMr9SBmsRAJTUUhSg0SpWTRayjCC1iteoy1lBQFgtaWrCUrJj/0wjarFVppmsZh6o0NME0jzmiG6YhuNGdGs+07rlYdm66hbaQ3Z2qylMoyLllvECq8RnUlMwoLtJJbyAZlf7zy6h8vG5pWY+PMxyHyacowL1IiD6JUTduwWa9YdS3Xw0H638pSUIzDgLbiATaHSMwFZVv55BRoazEVqUFJ0uSsJaQRmbmv8/jzTKxtB22M2ADHiMuZGLJobViZ0gkx0DSWtrHcH/ZVW0K4G13TiCBPDMQSpYKtf1NygCJBtl+fi5hRFEZ/yUnc1doeYzVKO2L0GKtou46UI7lKqRptWK2v8PELpjDz1bdvOVs7zteOUCI+eEKQ547eM08DGdFZMNLDYhwHEonNdk3bOubF2AUha/kYiDnTdS3OWpqmQWlqQgDeT6giY2clQ4oZu6odrhhBN7j2Gd4cySiCNfjWEVYtodEcJ8OAYu8KnVW8XWverg3JGkrW6KS4inD4es8XF/D6POKdJ+eJziVS1ryvv+fG88c4Kvyplmo1eXIYIM7iV5HTiXtUKqIZYuTm+pp52qFJktwZR9dvWK23WKtPaOXxMPL61Y6L8y0ffPgBT589k8+gvvy7e69U4Fppsp95++otW6c5267obUBNb8hZQ9gxHgeMbths1ri2EIqMMKeSmKcRwoY0zcRGM/uJcWtpOsui/be/u6fpOr791RckHMdhj1kZ+s7KGq/eMCF6vPf4mGqry9K2PcmLYdhyby8qhVnKH4k9NRlQJaMdJ47AKRKUUzQ4FWbKyJRK1lbuEVNoG03faTYrS0ChQhElxaYhKZnYuby8oOs7jDUiLIaBUmQkOKUKhj++6o+Djn5oPdeTUko8EB4tknc/sQUV+GMgA+8Gr+W1ZCOL2Z2Gf5Ze93KSJWe67YrLJ09RaHJ2mKaprnqPn7+aFlGp+TUR+O7xeypa3yG9nP71KA9YVKfe1XR+SBgek2OWJ1haIY/JjMvzPbxi5ZwWQUMCURZblt9orbGuw1oN6V0S0gOy8TixK6fn10oj60U4C0VB0QuXIWCyQM8khc4JQiYMRyGctS3KGFThBIUrqznbblFIoNIpEKcjZEWKIk3rGpEN1SXTqsCmKZz1BqOMWIoW8Es/Tcl8ra3ti6Xu16Zej5rGJWDOEL7novxjHOn5Fr+CcCwcdoX9buA47JjmiePxSPDSKlg1YussssGitfDk6RO+ff2a4+RZra0kZCmSC9h2zXEeIUecu6BxDTpFSplRyko/PHqMtkzjTAhRoPoY0dYy+AlUjw8BNc4Y48DBAgiKDDZo3QBiEBSTZ5wG5nFis91KMWEth+Oe6L2IRRkjTqGNxXWNPFeUKQBrxAApazCNQ1thZbtGY52YMykFMQVCCHz40U/4d3/7n4gp8ubuhveOHW23pqhIhcROKpvSZxVb5hwjViWmOdE0DmeFy2D6TsaRsyTa2ojvQ9d1jMOxChB1QGEYBhSwamXefIyRlCMmjmzvZ5rXN/S8xvMVq3Tk2dtbsgJlt6y1ZWhFAbIvmqdjwqTIe/stznRM1RehSYmne00zJPr7zMZ4Ro6kPNIkaCaDzj+eN8FylJIhz5R0hDigSoAsxOZSNMuuLEm6RWvHMMpnrlTC2MSF6ejXWgzO5Fmln+49X332GX/bOIKf+Bf/4m9o2m55RN0WVQ2ekTSOrJXiZz/5gMPdjSCKTIR5j8IxDwM//9Xn/H//4XdcH0PVvlA0TStWxlrTJfjkk48gZVbrc5xdc78/opXm7OyMw3DDX//Vz/j253/P85/9JToecf4WqwNd6yThpAihT2mMkR0npcB4PFLiFqMgneJWIhcjiFRtGxQKJVF9cWowXsjfeQHba+tLoGm8T7y53fOL32Q6o5h8JnoASybUiTlF0zlKyPiUUBQaZ2QkNyd8jMLP0okURsiJd4N/RRCgwtoyVqtyrAWlBKOUM0o3SDn2uGh+FKvz99t3f6BrYRGVM71IPi4zp5oc5cZWmEqEEMhGF0UJGV3h7abv6boLzs4vRKinPI7lS2B854e/fx7/DB/gXVDkUT+JSvarGUCBk870kh0/tAcWwSLRdj7xH+ohCOXDRc9FEWLBWShonOvQOWFaCSjkjLYNtjLAv3tIu+Ph3ykVFooCnLol77xJrQwoR645tDaiCldQrDYb5iBsa51r71CJBWgcRmIBnwvtasXV2Tnr9Zo4+3oemnW3oW1WpBAJw4AKM502KGqfmMq14WSLUd/Hu3VEpo5mZkFGQpJN5Mc6Lo63dLrBhcRLX7iYPa9jIIXA/e6e8TDKBuWEvBl9QCnYbrcUbYkl03Yt1jpizKKylwtN26JLIofMfpjoU0IVGTVcr1pi8uQSKUmRkvAIgg+UUtAlMvuAc46QE70qYqMcPeMUySnxZL0l5cQwT7Rtjw+zCBJpgUL3xz2r9YamaUXhMme6tmWz3mK0JUVNzLDeiDriOE5MwaNUpm0srXO8ub4RRcQxSD84TNgJztcr1q3jPt9zdr5hjoEPP73g5Z87ri4V873lzRcT+2EnmhrW1CQiYlyDUpr9/p7oZ9ZdR9srUglQRAZFGejahg7L/f6e6EecVUAihAkUGOMwVoMqTOORnBSUROMPbMKBzfXnPM2eQkKne7b7CVM0Lmie3EJsHFDQceDZXcTEwmryDOsR32iKyrgQeHIobPcz/8t/Muy6yKATUWmaXHh6jJwdfsxkIAGBkj05ika/Lp5FUjzGIOjr0u4CilJstmf4MDGOA1rBZr1ms17LSDe14KCgxxFzvWc3RX4xy2jrT3/6Z7i25TEGfSoAlcFYK0JOKH7z1T3Xbw9c+xu2z45stht216+5N2fMboPbalaNJaQgFsBFeFa26zFNAymwspbeGWyQllhDoLeR4fYb/Hjg+otfo9PI5cZiNw1ZFdqmZb06p98oigk4X7VV/Mz93Q3BX6BKJgdBsBfCayaTShZbaq2IqtAYhbG2GuhZSZZKqci1pqRMpNA1FqcN8zHw219+S5+AErjfH6qnycwcEsU4uq5nDMdqaS9Osvk4oK1hs17RWI0xhRgnvN+xihuUpbbVHz59KTcDjR7x446UqkBdiRwOns3Fc9AGX11qgco9M5Sc+OzXv/peq+wH6gxkuYPri51Y9fXMS0nV0vWhaqT2YeZh4Jsvv+L86or33junsfZRQP8Dgb885Dn/HLv19LuykEHqs6mHxyhVTn0YedoHWOBU6J+eS85lQRdOQfgd5OYBvhF4WGOdpWkcwUdc25LiLC6AiDVnLjXAl4fzrif4AO0tP9OKkvIpS1AsugPLuS7uTlGgYKXQqqHrO1IR2NmkRAgeVZZxRpmxVs5QUhaugNE4Y+mblqIN+xqgYkwoP7De9qxXHX3XnBIoUdOCtKAnGhLLCGKBWp2UnBnHkXEcmeaZw27HV198xhdffv7PrKw//vFv/vYVm87hKGyyI2dFzAPH4cDucOAwBZJtKEoTUyQmuRbWGV6/ecP52TkpeFT9PapgjGKeRy7PzvB4Qkg4q7EVIgkhcnN7w263I0aBV1MqUGfwFWJeJQRGXTkC4ZT1a6vxfiYmua+a1uBHT8niKVFUYhgmMI7WOdabM8ZhXzcT0YI3TSvTCP1KJKy7hlxknrptnfghpIBPgclP8t4y5JhJ3tO3huPxhhQCXaP4y796yk//+zPOn3bEg2F7dsfdL48cDnccR2FMj9NEyJ62tcQcGf0kqoPJsuoM0UdyBuvMifCqjeHy6pwcqyapeugFWyukzIxMTzgyymXeniV+/hQm5TDa4sae/+ELQxMs36y3vD5fUTaWxlnc2NKnQBM1b67Oue17glGgIm3ytCQ2hyOTdRzditT2WFVoxwnDAWUD8OMkBHneE6dISZ6SPSp7qVbr71OO5JwIMeODtKJKkQmh84tLtufniHafoSjNOAeUSjTGCmKQoU2JEiMqJVReoOUMGFQWwbVSHfxKLhz3I//z//Tv+OrtHb95NfDq5p5dKBT7O7quZbtqOFtv8aZhfbZlve4ppNq3l7gx5Zlvv/yaviTaTc8h7MnJE2LiYDWlzBzud1AS8zxgVSRnRQqBMXiOw8h+2HMcBqYhEKIgEKuuwTYtuehqBy6vJ8WqlFBGgdGFBnBmGZUWoOuxV54cgg/M84jSHarp8VFzc3/Euxum6cirb6+53Qd2c2ScIxnPMEX244QPwinyfsY4S9d3WK3JmxWyic7E6Q4/OEKoSKASYzprDF3byWPURDjuef32nm9eXfPm+o4pac4un7DebLG2YRxGxmlmvVrz7Okz/Dzzi3/4+fdaZz9stJAHqH3xvbfanOC/mDW/R3jQQi5RQJhm9rd3rJtb5qoC9c7z1+r8XbTgOwGU7yQH34Ht/xCgoJbHPaYFPHru3xM1Wp62lBPhY/mbyvV8SDy0wjgn9pshUUBG6ko8LcCUM6omB6XO7/9eP+6d59TiCJYf2BIP45GS+zdW0/ctqjoNXpyfUSrOYZUi5YUxW8WiKimmsQanDI3WqBTIfoYC8zjSdp1IHjtLSkF0tkuhVI5DqoQ2hTCVS5WF9TGSY+T+7pZX375mOO54/fpbXr36lt3dPePxyP3tNXe3t7//4fw3OvaXPXQNnSo0Y4Z9QKWAD55cyX6pKGLJzCGgEPJlioUUZsgNFAliYQ4EH3BtQ1u9Iqy1wqCPEbSYGPkgzoIxZ4Y50NiOlDMpRRQyRip+FxFrG4KfyUafCKLGKHyYSLlgksIPgWEcxU64EsVSLuTDkdx3lL6K7GQJCiiDLpKUjONE23UM8wRK/EKMs9zc7PA+EGM+EYCVBtsYTGtIKvHm+i27w8jlyvAX55qfdJqzfku3uiKEC754/U/c+hFKFuJaDBSfQSfmGNBOM6eJ/aBAt4IWlkyYZsZJcX5+iWsrKdZKkh2r4UvOEZRlTh49z3ifudyuaF1k50Y+O88cbYEMXeP46E3Edmt+dbbiiyuNOk+s2w1uspzf73DJ8IvnG667nqihqBkVM6HraYeRX3205ZvOEq2jUZZVWPHk2vIX+wAc/0vL7I9zhAMq5romxWhK1AY1WRly1lW7oqDQNM6hlEFrg6rE55Klu5zrl0wrJZIumN5RNi26UWhTW3tL0XNCrh8mmwBUhuQL37yd+OJmRypgrSKmmTQXSt9wGCdiVeVj8XOpBY5GobVje3lBWxKqMWzff8E8HrAls9lsePvVZ1y8+JBv93vaq5ecd5peDagyoqKsE5liqwUSqibJulohgzaGxmk2WK4uNzTWkFqFwnG5bglXWz78+CWH+zvWfUvnwJ/UBB926gJY65iniew9a6t5edlzZjybdcO87phn8EnuSR/FxC/HhFEaazXn5+ciYFZFoXLK4t8zD6wbOO8zqpd9X+VY28oFHfcc9/fYRrNymvNec2MU3ovD4/7+Fj8JcXe/33M8DigUv/214+bmms8+++x7LbMfmAwsQali10VQAVVAo08GKSgha8io34OegEKRk5hI5HfUDP9QCf7uB/F9zw/USSZzeQalHv9r+e4PoQ2PH7v8rJwQgbIkBY+ygWU+PMZITBGdArlC88u4i5CpzMnO+LvnXFtR9WUe3ru8jPxbP6B1lJKr6IyYPaEUIXhyqgY5xoBzD8G8yHVPIVUhDtEfmIZR2jdaer2iGy8CNSVnpnFinkY260iIER9mhmHgcNgzHo8cj3t293fc391wuL/n7vaGt2/eMI4HhuOe42HPPI3EEPEhiib4j3T89oOezaZhlQvPbmbOZhGtSVF6/yEmIp7JC9Rna3E6TrPAsDFQSsLPHj8Fci7o1mE15OBxrpV+YIzkGChZY6wDZSiVt5HyTApBHP1UoXEWazUr60hpccWUHrzS4pY2exkXazpJ0EQ1MFHqZrU4k43TJPBvljFAhYzU4WdUKdVMRfAzazSuroO7+3vmmIRA2k8iOqRFSGucRub5yOvrt4QQ2eB4/2bHh7/xdG9GGnuH3QeemsS/14U7JQlnCDM+FXy2+GnCGDHTwjjKsbBqRaq21PacNtD1HdM8sF6vhOQVY518gdZYYgzsxz15jvRdYTvNGHtkvTYkEqoYmpCxcaQo6YkXk0nacywjvUlkq/CqcHCJY1vwquBVQLnEzVCYTOGwOeOmT0w647Rmmzboccb/iI6bqnh0cY+KmLo4K+iZsszSywhd1fqwRiZOagGnS5LpDGOwztJaR6cN2ihibFFPerRV7KbEl99+y//z//Nvefb0kudXL2AZ6VSLg19m3B05Js3bw8gUEq5xklzESMkK7xPFZGKu67TOpStdYwUKjWG9WmGzIB5ZJTBg6p5k0RQ/o2Ik7/eEoAhtxFpxtCy5CFKQJNEpWVwDx2liv98Ts+d83fLRy0uKhk8/fME0zqQU6BsDGa5WDR999Iz724ZV37E563m995UTBQsJHBDvhAJGFVaN5qOnZ7x36fAxo2NEZHcUqmgGFfExk62hoGnahrPNBtc0ZMpp+i5FTfSRxloaXUTRFZkmKFGMloL3ZD8IYTFbES8LQpoMKVeFU8/19S2/++x3HI/HGuEUwc/s9t/PZOuHcQaQYJWXMbsiHIJTkrAMw1OlG08yurJ+TZV1dW0jdrz690v1d2PlH2oT/PNcAoH+yx98zDsjhfV8fi8wqz/w+vV9PsD5S4vg4chFqqrH56G1lvdXhGVutakkE3nP3z3LE9+hPHpupd69hstrF7GRxUdQogEwTZ5x8pxvtwIR1xGYxXPcx0RJheCDmBBVV7nGNahKOLO152uN9Mjub2/56vPP2N/tOBwG7u9vubu54e72lvvdLYfdPfv6dTzsmYaBcTwSq8FRqex26b1pmWz4kY7bM820LWyKoZ8NaxtoEFg0hUhMCZ+KyPYejzRWY13DNE7kUvAhUNKMnz0xRBSaEgO5Wm1pLYlfKYmURB9AGyuEQCCWwjwOMlJYkowLqkIuCqX7OkdvQVUkJxQUlpgANDGK1PcyC12KOgkYuSLWyil6ETRCgr+06wKuaXGuoeRI4xzaCM8kxMjxOOBTYpxHWj+Je6NocTMOA3e7e+7uD0Ch7Q30EFVg8ntCGFn5zKVVdCVilMFZRSmBYZjJE1WgqRBSYPCKvlVcnnX0jUMrg1WKyU/0fcP97iBqhCjmIBoQVhl89Ex+ImdFFzPnEzzzB8I4knLDzkiS0xwiz3aRpCcGZ1EN+DSRyGxS4XLnyaVwtR5BaWYLo5rRTKwOiWYaWU+ZlYasA1Zr2tyyCgYXfzxvjVLbAMA7G1TJGXLkctsRXpyTk0zxmIouOWuxRsmIr1ZYo7BO4xpJBhotY4b7446mMWQF//GXX/G3//QFv/72mhdPLvjX/+q/J1eipapGQ85q8ANTd0X/bOTZNuKMJcWED56iFM417MYB264ppiEWmRwzZcljxE79sNth00SvM+N9QykBimKKGRU9x5trkvdMu3us17TnVXwLqapTnWjLOZ8shGUMeKTEmQ+enfOv/uIjjNO8//yKaZopOdE2Bj8HUI7LyxX++TlN2xIVHPy1cOIWcYmadIFMKhhVaK3mYr3ixVXPOI8M+46b+5FhCoQsyoiLgFAqopnRVjQuVrJgTJEQYDhO/OPPf03XG7paIOgik14pBg6HI8F7nNVMMbK7n3h9veObmx0+CLnctQ2vvn3Fq9evcM6KbkrwUhzqP4LoUEmJpIRYtziRqYXsUEtwEb6omvn6QZXQOofr1pi2ZXV2Ttv3soG+g+G/82r1Yi6/5/SYd4L440r+UW7xQKRYvtSJJ7AkA8tzqXe+Vw/Pd+rxl6qbsMBlj86n9jUXFUZrLfEkx1plhRW8Oy65vMNHxwNoUQmLj97rI8UTIUEqctGERaXOOhl3m2Y4OxNmeinVI8BATQqiFoLNcRgIMdG0HcYI4dNYI/B/yUzHA2Ge+Oy3v2J/d4sqcHd3x831W27eXjMMR+ZpJIQ6IpbzQ7J0aq08UCwkwVl0Gn6cY+UTbvZ0uWXtE32MNCSsEu+IZSpmHmemYQRnKUUxzTPFNXg/E6ZB1v2yoaVIzAltLd6PZKurYmEdmFVCgEMZYirsjwM5Sv+/bS0OcRgMUXgltpJ+chaUyRipflJKzGPGukYEhhQUpWTzjR7jFW3TyIRDSlhjUAhruaTCyq7wxgjpquvQUe7haZqYg2cOicnPxBTx80TjGkrWhBQY58DxGGQc9WnD7Z9vsS8t2ihsNJzvEnqITIeJQofVoMh4P+GzjDaKTXFivptZrx2FyPmmp28aCoXDsEOZNeM0SKJkLUWgEZIq3Nzf4f1E03RcuYarcOTK31P2MyacMYjQHN0083zviVpabevsGHeZlCe2MfP07URSmU+M5WLKTK0iGI+KRz56Gzk7jrz3ekdZO/bGo0mcR3hv51lNP97aDdGTqiXvAzwoLRmbA3/+8XM+ffkUXeFoa7S0bquFttUi8rTM0GtdEd1aOr25aXFdw5Qzv/j6mm+vb/ji61/x4skZFxdXJCM8mbIEHmdwzlDO3+ODP7/kuc8y1VAgkcg5MA4jd59/gdsaIqpW8hFHAVVwVafi7u6WNs/YridMHsH/CwHhQsRSiAsSbSV5Wfr9pUgbSdqxMp1y6ujmAjHwwfMLzta9EGa7BnKuU06QUr2nNLhLcZm9Hz3G3CMKhMv1LnUPqyP0FIrSFSWRKStnwWiZ2njs4qq1aL5Qz7HEQFGFtrFsVw1kxd39nv/L//X/Tk6evm+Fd2S1cDqK4tWb26rPkUUvZJiYQsLngvcZTcE4y2G/p+s7Xr58jrGWu7tbMZIrf4RkICaRZ10c6mCBySM5N6eFmnIiJSFjGCftg3615uLpc4oxnF1IMqC0fhQZH6MDj75//N138oUTifFxRlCj/WmKoHoGL+TAB85ihfFVtZrksRiROiUPshCW/tkDhH/KO5TYx4o50wOZMidRLctVQz4phV2e79H7OrUGikheSqB5EBuCmkecyAynbEd6vDVhsa6RfpTRpGwYDgeUUXR9J5likYy6GI1zdfY2J1IIWAVhGrkbDyQvyUKMid/+8hditBQFTaDk08gkLApdp6bi6dtck5l3SJ3kUx/uxzg+/XxHsyls0pH3biPbe8UrJXChVQqjZLMKQUZ3jDF1JDPSdD3TNJJDrBKphq519KuOyc+4xnE8HkhRqjDxZXcCVZeC1kaIXTGKlwBVldJYyIHDOOK0RWuLrsqNRiuc08QUpAWgW6bRSxuhVhwhJLwXUttq1cuorik469BayIchzGxWG5qmYZ5TbeUZximwPxwEvSmZ4TCQryJaZ3IRDoGhsGoaVCmsneL5e2uGJw1vnzm0a3CxwTeRzevA3bcTs6NuznWMSll8jNLeMIbxMJOKonVeSH3OYY1ijjOHNweRwC6Zvm8xrqnXUPPV169wrebJueXs6ZouDJToGZrM/jwT+5YyWYodyPdJbhhj0KqjzR0uF/rxiA2yIjeHEZ0S0YDKM2b2vNgX+ghP7w60o2E2wkPow54rP/2oyMBwf8Pa6pPLJMAiM90Yy59/8mHdR2Re/rQN1g3qgfNcv1meogY4kReWtZ9ilD/T0K86is7kUINtEufGrCxFK4xtabTFuSyOtSgyopyZjSVgCUYREcKstMwiRmWsShitefn+M2wIFJ/on77ATzsgs7m8ZJhn3vvpn3EcDqj1Be1Zg3Uz6IAxVtj/xuCMZrYGqhqnoGSZHAu9c/RPHDkHtHHMc+Q4zRznwBQjMBOzwuSCNpZvbw68enuQdn1+mOJHqVOIyqWQSiRU9GD2cDz6h68xMaZCyBABKq9hHAe0VWw2Kz5++ZQ/++R97nYD//N//Cf+4e9/jlrUpHXhbLvio/dfsF2d8er1DcpIQlT8RIpVUt5amAuohCoKVw3YjNbkDLPPpBi4ufkjtAkE+tbvVLki11sIIaCMyIlqq2twEHhpGEeatheGpHViDmNdlV99uNj1m9973SVILwSWJetSFY0Q3WiqVvPy+EfJwKPnWAL9Mk+KqrS70785QfNANeyBkybA6TfLDVdO1WJOiePhiMqJIssAVbNI+TAfEojl70/tAZAbnDq0t7xe1V44vaakwZCSBBMlm0DfOmLfnTZ3lPytDxPeQ9+06FQwOdEoMAbm/T1fH/ZQpO8UvIfywFKmCItb63eVHk/J4Kmn8ehzPOUsS6LwwHwo+cfbUD/6/MBmZWlKYTsFbGjo1gprCuvthtvpFoUoNKaSQWuG4wjAPHpsFRqxutB3jYiJ5FTlthXzPNE2DmUt2hisdThr2U07pskzTIE5ZJw4+ghBS8vnPIyjuEcaS4mZtjG41soIVCU5PlldcH1zT8y5SpsKK9zHhGsUfvYYY5inGdWBTZoQM9M0Mk0DXbfGtQ3GW3IM+DBxGA40jZx/mI7onNis1tWDJKGy52rT8OHacknmf/3pFRttiMdI1pqOlgvW6HKkNC0gLav1qicrCCXR6YbgPcM8Y53j7c1ITgmtC5AZZ0MqgWmcICtc15KnkTgcUbrB2o67/cDz9hxnNe5ly3iAm7Xj7XnHbz52+N5i0gWrsOIiT0xqze+ePeGri56gPb1Z094PtESCLvzdS8vNhSFb0bO3o+Kvvym03xb+/gV8ezYzmkzxis2k+PNo+Mn1D/d0+6913N1dc77Z4Ex72gi0WIoA6SHtPt1ej8XbZI972Nce9saHChZEFSQiqiByX+ciDHiypeQkAmVFHhkUhKjEV6Pa1iutSKUQUmIcPfM042eRz7ZWQ0loA3kxAaJAKJQYMGTwAyZ6YspM9wfwgVe/+RXpeMD4xOGgcRctZtsI8TtlSvAnguTigxJjZJ69OHqWhEZIhQnDfpr47OtbPv/2lsOUOHrx8ojBs+k73t7suDnM+FJRbZJ4uFTROIkRqSqUZoyVFoxzjqbtsG2hzQmdMrEUkU3Wgp73OtM6x7Ntz6cvLviLD5/x5vqerz93rHTLPIsoUt85nlxseL5Z0TSa969WD4Wn6qVgKKUWgrquAYV1DTd3e7749e+4GyM+yXX24fvZb/+gFb74DMikJixV8zJhsEDOYORkM5QkWgT7/Z7pd5/x5Nkznj/N2JPb1OMA8RBUHjKyRwj6IxTgJA6hHow1vsstOHEATtD/Q8W/EPLU497BkgxUVOqkfb38/WK4UZ+yUPkC80SqWXXMQtCqz1x9Q0qVKV5q5UfJzeM3XE9f+Ac1mVHSJ6aSMAsL8hLolFRPumT85IHMPA0yvpZEaS7MmRQjYxDDpRgDMQdUelTKP75uZZkHqbtN5sQ7OB350d9WY+JHAMEJ6Xj8Dt/913/74//1V5rNqqXPmo9uAh+91ZjWikWx5mQA5GOQjF8pmYtWogSIEqKS1QLdey8jUKMPZAbWmw3OGfzkmUZPjMLgv7254fp2x+Ew4UOiaIWz4hFwHAackemPkCKTHzBJo3SLtRC8ELC6XhLsVBJz8FV0CnaHuVbXlmmeWa1XoCAEL7yAygFJKYLSzLNne7bhOCQykVIC1ilyVnz75i2XT5+TYqTvGlTxFL/nRd/xf/iX7/FpuOfP7q8Z/oNmahXJaqyybGMDu45u/QSOe6yRil/7meTDSfr4/GLLbtrT7D1aaWYfud8POKfJRTgsCoPeH+la4TuEMFCUzGv37YoPX77gbK1QBwgKRp25TQNTGDgns8odyThGVzhuI+N5wlPIjSbalvE13DeFby4sN31DsYVsDXqVeRImPh0zv33a8M25QbUtJSnUOKF84fmXW+DuR1m7//EffsuX3x5Qyp4s21dty9XlBU8uzjhbOZwztcgpFapeKojHGOTyPyk4Fq+XklPtu8u+kmIQAucUGIc9u1upLPuuo+s6lHakkKQfnmQOv23EzEwjhNppd89w/YbjcRBEsI7irltD0xlWrVTLv/r5W2xJvDjfcOMydoks40wJI9PxKOiqLqxWDm0VWolKZ8rStl6UBZUSUR9dEdRS0qllaVSh6EhBpp9uDxNffLvjd6/uubrYcHN3y1/96cf4kIipkEqSOJciJUkcM42j6EhrMiF37KbMr76+43Y3MHpHf/Wcj87E+EiKuEUPADQPpm6lJD7/5W/43T/+Y1XrLHx06fjsmx2zT8xRc+2P7K/fkiiEnMlB9oJMgVRE1fXUktSoDJ98/JSzVUcwLcxFFBlj5HD8flMwPyzdrdZ7JWUhESJkM2FNC4P1cc9YnOpsLcAVYRq4uX7N8HJ/GrmoHfmHRbsQEJeV+7iKVstQCjV2q3cTAfUoop5+Vp9JIZuN4pQpP+QJ9TWXtsVCFCylQvG1hi8L+P3Q1TidUSX4GF3d2OuNqSnVOORxYJQxvXdlexZBYlXneR/O8Z13pRQxJo67A6po+rUwd+dpYp4ncgiVSFM9FZbAnR/C8VLln5Kn7yQFC8gvI0E82lTqb077Sn1MrTiWVCem/DDJkBceASdm8Y9xvHl+yW0H5zRsdWA7e15xYMqRVedonKyNYZy4u9/jtGIYB6w2HMLIunPM3pOTIwYxhpljZhhHzs/XNO05WsuGGkNAKYt1Cp8Db+/2jFMkFihapk9CyvgUcVqxWbW0rdxbqWR8jJSpEL0813rVMoWJrKrhTKg2yEWQLx89VmlSTrRdI3PURsypjEGkW7WY/rRdz+3umslPdJ3FNZa7eWS32/H5b37Fiw9eEnOhpIGmGTj/0PKX75/xF1/uOTSJMUfiDERNNoEcAm0RN8RXr16jbCYayEozp4zWms26Ff+GXNi2mt5K5aS9ZlsTg31MhABjCJytu2pkpHCN5vmL51ysWj52lp+8veG9tzuaeWJ1bGnnFYcuo/ItH6grnl97uj4QVo6VKhTXYNtCFxRXk8WlwJ+lM25zS0oiNNN3HR+a17T+G15mR6PXGHtGsZoY96z2d3xf57c/xvHzX3zF5nxAORHEMVqg8dZ+y6bvOd/0nJ+tOT9bsVn3bFYNXWNpjMaoRKlqoYudMMgcuwB7iikEcoIhRo7DzPFwYBoHvvn6K/7Ozlit2Kx6nnz6KW1TeHvziq9fvcE4x/bsgtZ1zI0THZIQuH77lt/++jfcvvmWaaoIqYKu0axXhk4ZNlambG6Kpriegzas3v8Q/IzVmqfPrvj61//E008+5fNfDoym58n2DNVGEgmfIlPMTD7hvYj65FL3u6owl+uorHEivaxyQdcpxCTFPRaFVQVnxU/jtAdmCClzHGdyyKRc6DpHDIFt5yjrFSEpvni945efvyYkiX06Z1pTcEYx58Ld4Bl9RltH1zpy9JQS6k4peiCtVXz0/IJpSgyzBwVzEDO0lAtDkNiVQVxyi0aV2qxVCZnPKMSSaXQdBV9UE+t02Pc5flgyYCRg5izCM0UhxIBKopP2UyFEgSWkx1WzxSpEpErC2QcL06Kkbn4Xe69H0aeg/xgrfzwVsPTplV60+x8VrctT1oirKQ9cgHcQB0m35ce6dgmqdnepq+YUENUJUVh0EWRMpv5aG3RZJEEfIXWP6uLgPSmLAY2zywdVoKjqg1BHrpQ6mWc4Jza5TdOIslRKHPc7huNexgwXIl966OOrBflguSjC4F0u5buOkQ/Xd0F93h2/fEgeBJ2QhJDy4KOQU8GHyKPcCXWCJn+8zRRgrVc0raaPYLKQlGynaDrLetXitAg0xZgIIYgTXPIonCg4psgcvECCVjzjVSpi9qOUuBzmXKtyTds1WGdIxjLMkZAl5c0JTAZTE85sFNYHun7FcZSRw5QLjdGkJLPKhcycDdf3g7hRIudqrJP+q4FGG3IKtE7ab9ZYlDWkMJFyYJxvSXkkRcPxeOQ4TEIwBDARayGXga4zrPqebBL2YsB/MnOvG+ZpxatPn/JNl4jaiOkYcDYq3v+sJ3514Ob2Naa39NsVikAOM5FCaKs9cUmst5aLK8e2Uzw1mk+tY/aB/1ACX3w9crz1jCOsV5a+M/UeyJxtey4bRXc80owzjY9si+U9MpNNqAQX6Z5+P6OHiJoiT76dwXRg9tg8cf5mx9po2nBgWEVSY8AZGjyXtyPn+5l/+dXE7SETG5n0YPZc3Hs2dz+SxgAwzYl4sxf0xGi6rqXtOxpnOcwzN4cD7ds7nDX0rWXdt5xtOs43HVdna7rO0lgxB1LLfrowzI1lnDxffPMNt4eRL774mmE4UnIgzhLYjSkcdw0Xm46+X3F3c8frV18yTB5tHH3f07U91llAVC69F3dP2zQoROK6s9CYgqW6hhbZXouK2FKY9jsckaIU9zeJ6D3XX38N04xRMFlPUi26N6icpO1QQkUeHgDepSUZQ6itSWlK6BpAUxHe28X5mnXf0NvCi4uGVkv7yvaOoiypZMZWY7XFGpHqHseRxsBFb1ibTDGFVWNISmNQrKziSW/ZdpaQ4c1hYC4NxjS0jUXmD6uOCQalDNbB5brlybon1frX1DJ5NyX+7rO3+ARo+RsBXusDT7bwiuEYmceB13cjX9/uGSaPdTI++n2OH8wZ0EajkjhEaSMSw4u3cQgBUVlL8sHUi6+MxjYW13V0fc9qs6LpmhrAH8HH9fFLg2TpTZ/GFpdfnoKxXAStH5IBqLD2o+MEn9W/PSmc8W6HQH6TUUXEgnKYSH7i1POuf/G4fpZenSwuq2sroZRTnazUA2FwwTx0FRVamOIF4V5oW5m66EpK1JX1WyGngkx0VL+EongXiYF3MiG1NJpO11f+Ux5xIqQd8gAlSicXRE0QqO6Hy/OlnB9e97Qu5LucBcoyxjx6PUGIFOqHW2T+Vzw+fiMwcjdPPH87cnU7ks42nNueL6IkfIr6/ooQ+IwuoBKNdcwhMk2B1rq6sUgSuuo6QvDs90e6xkq+b0WVEmU4TpE55ndcK3NBRFuSXPdhihg3U3KWSj4EGmvQujD5QNEakybu9h4D9I2maTVda7g836CUprEWSq57Q0bpQte2KJ2Z5oFkElrDm9uZu92OcZSN0ppaRRTF2WZLa8VArLvo6D5cU65GhhiZG83uouF+BUFbFA06a7yZuLQCpwp/KHC5MejGsj9kxiAJTcgZaxWbbcNHf/qc83PH1s9cDZHSFD4+W3N9+JLj10dKjlgFnc20K0W/zaxeNFy/aPDjFlWO9FPgfr3m1eUZwWlcKOz3B7qDY+oNd2crhtUGTEdWCes1l7eKYi3HVrFvIBrZM5zydMxc5YTyM2XMlQipsTHRhYhJP14y++U3r6AofAhorehXPZvthq7tSSXX4koUXa0xdI2jbwUduDhbs16J38b5+Ya2kdHA7XaNs5Zhmrm5P/DNqxve3u65v9sLolmFxsZxEnVDFxiHmca1xOA57veMcyAXGI8H4YAZYfsvHLKSMyUm2W+KIRbIBvm3EpGkOHsx9ekMh90drcpYbdBa051f4lNBtWtsShzHmbDRUiRW225TH6vVg3+AflSo5ceTTmqpiQqqJFZN4fJ8Ra8XLFSRVh1ay6h1IZHSBo28Xs6B2ct+29uEnfesVeTjqx5QohVgFE29fq7Ak1VDLlpUQ0tFL4qMF0NA6MSK4/2ISmDK0nYVd861a9kfR3Yz8h5rwNRaxkhjKqJboArDOGCswWdxQ3Ra4YwSvtr3OH6Y6JB+IN8tAcRY+wgVqONj+aHHDdJr2W63dJsNTdux2m5p2kbkih/ZGuVcqt2kQiupCpZxDqmYZMPWlZAhSH5GKVvRAWql/hC4gVOV/fCjR4nAYrqTM36e6tfMOBx5/c0XvPnmS4HZHj3n8s7Kowp8aXhotQTnxyqD5R2oXWstleGjL5SSloup8qzVovaUVNU+fElLYlIenROnlsNSkp8gwZqJvNPTf4RSnMY3S5FMPWehEEnK/ojEKE/4mD9wCm6n6yMyylot7RZYPMNzFvXCH+t48dWe82Omnwcu3+7Z7AOzmVmVnuPkT71YQbnA1pstxCjeASGRyoMIbEFcOld9x/1eyJd9a0/rIuWEnwN398cFEGSRL1mWYi6CnPiYGaYg8qOZqs2QsRbmnCg+UqYR7wOt0VgtG/521XB5uWGePNZYYliUIStqowq2sewPd2STmVPi9uaeu7sd0xxEt90WnIKnTcsHz96ntT0lJtZW82y7BWbcNGGnQDNmutZQiOQiMrUlpdq/tShtUBYuLlvAMU8Db26OHAcPBtabnu1Fw7OPnnLxtKW5u6f9ekf/suejT9/nqy8n9jcB5wybxvBk5fjwwzM2PzlDv4RXF5l9XLM+dLSj4dV5y2fPGpIz9EVzdld4ehg5nMHXTzfcrjdk5cgl0AXF091MaBy/e9Jw2xmSXj5rEeTarjSfnUXeriC3MlPfZEVWjtXrH89X44uvvz3B3tZquq5jtdvhbFO5WjIhIuPMDyPOSkHftTTWYnTh7GxD01isVmw3K6xRHI4D94cjr9685XCcuN0N6JRk9C9LJUrWlGKZ5sQqZBZ1w8YpcQlUsmflJCOGEnAVJUVy9MI7ShqfIBhNTJaYhWj7wQcv2J6dc3G+5vnLC/I8MR5HxmI5v3xKv2rpO4f3I+Nxj2sjqEgKEzmUk09Kjfyn+1iS8up/mCVginsqtXWacSTWKpDHUTrHSqNLlfLWUEhi9V60cCTijI2RVDJzyWJhToXtKWzaLUYVpmlmmmcSgjTCAjDn0729iBplZchFkVPAZFDK1GKs4JyhPbeMs+fuGDEsHjqyDpxzTLOcj9aFe2TP2p6dcX6+qV4Lmtl/PxntH1asPWKUL+N4ErREUEbXaPxQCUsGaJ2j22xYb89o2p5utcY2DqMVEkYzIcSHqQRlca5malkkGYP37HY7+n5F23U418iFyQWl7EnAaLF5fIx+y/8VywhNSoUUAzF4op/x88Q8Tex3N+zv7xiOe/a7O67fvubNN18SQ6juUOICeBo1XPgNqtIDcwb9aJ6+BtpcCou45YlPoGvmcmoJcFItLBVPLrmcqvNSrzkF1ALvq8dReUkOykPgV4tngDqNET20Uco7hMycMzFKnz+fyH9LRlHRl0dJ1UIazSf+wQPCIOpykkikVAhJFAx/TNGh0R9Z7zJumGiGQFFwMJmkCzEXgdxNQlmpMkoOlAI+ZIyWREBXdc2CqsRO6Lqe4zijVKJrLXP1EvDec78f2B+OgvAIwHfSH1j2r4UnEkLBWglOKYOKBRElKgxTFFdFVURExmgaa1i1llXXyPooIvMPst5CkomBYgqxRMI48vrtjldvrskpitJZhYvPGsN/9+SMFy/e595HhuMN60Pk450l7zMX90c21wNPvtqjx5ZDk4jao1XLahhYTY4YDRnFylmenq04XxlWKaC951dfDZi+5/LZFdtLxflVx/m5w02Gs5K4Wjvyy3M+/uQJ15/t2fQ9z3rLJ1cNP/2TJ+z+pOegD/gS0MYQLCRXuHeJ22YmmUJsO7RyjJuO/bnl+nLN675hTpmMZqM7brctY9vwxdZy3SuKjhibsCrSzoXn55ZfPze87TQ0FmMdTdFMneH5Vz/a0mW3uz/5WTSNGEDlKDP1tnJDtKrFGvXONbUrrQ1kIRFrbU4IqDXC7p8nT0bIZtL+A3LBIslA33TkRuGMw/ssc+2mYdVviangQxbCaFWUzEXm93Oc65ifSG+TFJlCSpaUFDlquk3PX/+bv+HFhx+zXvVstx13N9d8+fnX3N4daFTD+dUzXn74khRGhvtbuvEt7K9JUaYGYogS6FM+yXOXyo9wXYPSRky4cmI4eu72gWEK5AwmFdJx5ObNt3U7N3UioQZrirTDrCPFIqhsSQ8Uq7rXllLQZFrjMBr2+yP7w4jIaqc6NaRPyG0pDzGiaENOcs10AWskcaFkUI6mfh9T5LR7LkWJKvhYiFUdUgGEzPocNmcrzrYbjNLc7+6/1zr7YclArlVgLrUX/MC2L6VglEZZgUAXj/ecI9M8sbvbo7SjcWvpwdTKlyKqUcf9Hkphmj3aWtquwxjNMA6k5Hn96ls+++wz3nv/Iy7OL9lWxz3nGgoOsSiWIPZACuTEBQDw88Du7prxeGC/u2d3d8P+7pbjfsdud89hf0vyMzlHcop47zkeB4L3xJxpu+5U+Z0q87IUwXLzlLyMfCw1uLBBnda1d/OozVHPr5RCTomooqAenB4m9aR87uIh/rgl8PCxLNG5wvy1319qQlA4cQhzyZXUl2UT0aW+vhjzaKN/D/2RhMc8vGYpdWRzQUbkveRT0qKJuTB76bOHKHZGSzvpxzj+33/S8nK14tO3hk/fgEuF//Rhx33WrDcrJp+4n6X6C95zOB7xWVGKYvIz2joaZU5qk8YtlbhAiuu1o+9b7nY7YlIkCpOfBcZtNKYs9JoCWtf55GWOWePDA6kzZ0gFnFKQDPMoDOfztVhMLy0va+Uec41mmkJVFZyYZ48yCuOqwqHVfP3NNV98fUOIAec0uEJjFa6zvNga/rc/fcHN5ZZ0mImHN/S3tzz7TWLljzzZ7dncz5x99i3uzZlAn33EMLI+jJjdmuQ1OYEzDS+U46crx5+8WHO5W3Pz1ZHQrrh68gFX5xPPTeEyjKz8xHth4mx/oL8f+KJzfHZxxuXFc/500/AX28gnTvPLcCQMb0FbVqphsx8wIXLWFC6HQrCwdoVtUXRhZpsd57TsiyOkCd1odGvIZUblhpQzgUJxMzQe5QvFKoLTxNUa30O2LeiWORe2LcTFfepHONrKS1FA3zi26xWNq0pLuk5TFQkapWqP5FKVP2MUtC9Kj37ZVYI6bRmUorDayICa9CLx1Tq7bzvQRmR2hZqK1g5jO2JOKJPFWdIYUkpV+GehVS/zj7q+akaDaPVrQ+ccz55d8uTJBY21lCTTJ4pEmI7cvPmGFI+sbMSWRGsKvTOkxmIahWkMtlWokbqXWXRJWA2ucbi+I6G42x+4HwZ++5tvOIyZIWl8zrgCJFO1RcSUaUEOSt0ntYVcNDmVirgtd+y7R0rLfl/FlyqKmnIdFdenCg2KPknmq1Qg5YeWrlaoyvtaWsu6EtGXEq0+ERJl64gm6tTyXVohWj087vscPxgZUMDSAFXIaImxomgWU8EZgzbSB8lZWPR5mrkb3jIej+hsIUVUiUzjnrlC87/77DPubu84HA5o60RVrySOhz3H446UxFXv+u0rQHN+ccUHH37ERx99zHZ7xnZ7Lmp7y4V63BWQs+GL3/wT/9P/43/kcH9HmCeCnwhhBlUwSsh7thIycqme9TnWvpNkuXohcDxcElJJZAU+ZVSJnESDlFTUCY3TpkLEsoGLWvMj/H753Kpgj1YPyYI6/aomIOrRgOLjiQkkIKeCzP8iyyWlRTdx0TCQR5cY6lpZlt3D+T1U+rUFhMzw6ooOGG14mB8op6woV6vQ4zQLGkARFARqW+jHOXYXllHP+M2MCrBOjs+2GZ4Yzi833NwcWDnHOM9Ms+cwa+YobGPXr/DjTNM2pJgZp0JMlpQ0x9FTEAVAsaiWizf5wHGaMFqSwxgli2+N+MykVJjnihcpRCZYabxPIiOsFV2r2LSGVUcNtNC1lqY1aKdIJXM47GnbjkIWEiOCBFhlKCoQU2E333Ocd1yuFSo3FKewvWJz4Xjv43N++tEF24srRrMhHWb0VY9/csFXT2Y215En08S0hc8/3jJuLzFNh/WB9c2eyyHhUqBzYI1DZQO3t3R+5nI6ctYo4sdP+Lf6krPz57zHnj/5/A3P0i3dfs/F8Yj6co86Tjy/7XiyPuf86j2ebxreT6958avf0H8ZubOBaBTbYvjgzR4bE5c3E0+vJ46tpbuaWd+PPP/qjul4zjpteHbW8A0DcevY+p737wLBHBjaC54AU8oQAnqCF/eZq/vE813GaE0oBrTDZsXFccIdfjxfDWcbjFY0jWPdr1j1vYxwK5F4rzQRSep5MCai/lyd7NMX1FTBo5HspRLOgPeBu91BJKhtyzwnjBV+y8LAj1FEfXTlqkhMkETWGUtSmeQcpmlxWlO7h1iTKLYw58JhDgzX9/wf/0//Z1x3Rtc0bNcN696y6R1XFx22MXQucP3ZL9Ax4HTh2UVHa0A7h2vaWlgmZl2LLC26LDlnhmni1d3AYRq5391wGAdiaUX3RUFRdaqrFm9Ssaslr3povNaJrMVxEWqr9aEPXP8n+2gpSDs3y/UXeW95zndUWOs+LEVU5cg94sctPgimIrxL6F/Q75OJ3sLBqGeRcj6hsVJ//hEUCFMIFCV9VDE0kXxl6W0HHyk2YdsGY4yQHNRpYp04jXzzxef83X/4d3z2u19xOOx4/eY1fp4YxxE/S29D16B+mhE1Ur2tVyussxQ0u/trPvvdL2mans32nL/+63/Jz372V2y32xO7/Z2jZIb9LcP9G8JxT4kJnTOmSMaca6Mpnty9FKlWhtY2aEL9qX6nws2pVOEXQ9EW7Zz0o5w9ZexKKVqnKT6807t/OLfy8MWDiuHDsWAP9TVZFtVCwCyklJnniROeoBYNBh6xSeV8NFSZzPrTmgCVAsYJ3JtSxvuADxJQHp+KUZquacQB0Qi0vbxXnxOHYSbkUsV3xM0vxB+vRQDQ3e1pLJwdYT0VdE7o4YgzDTrOpJQwTmGKIRTF9d4zjJ4/++gF2+2G9WYFWrHf7WuCaIkFbvYHWgdgccbRth2HaWR3f+TV1zu0l9bAFCUpyAacXZQjZR1ooGtaEQ0aB1KWMayVsVw08JONga7lt28HUIXWQt+J4cwwDhhnuL59i3UNbS+ImtKFUKQdcvbM8eFPP+BPssbczHzbZvbPLKv3O85edlydnfPNXUPZr/Dlnrzp2b9wfPYy0l5FXrYN5X7HL392zrR2YCMuwtluTfjW8MkXil41dKueUcEvL2B4WXg6JT7+1vE3/XO+ml9wcfmUrFu+WUeIifdMJBTP2/d7vtqs2KU1W3PF5uKc9UbThIaJyLFJOF1YTZnz+4HV4PHWUGjYpg3rY8TeTzy/H9gcC3MeUNNnXLqOSx2BwDYVnry9J7gGcz/ycWdILRQndeuz6yOb24n/xRTYbxuGfiI0DgM8OxzY3n4/qPWPclTV0LZ1GCvz+SlmMBpVJXNLRRAlqKVH+4kYVjnbit5+TicEyrhW2rggbckCzmVC1vRD4OAjX7x6fdprDsPA5199wTgNDOPM0iI9waMgBZCSHT/GR2PHS+ArhZAVQ1TobHDFkSJEClP23BxmwR+0wjjLtm/46KrjZ58+ZbO2OALj4ch+mBimQAxV+VBZYp5JOdcpNU1Iln/7n37Dv/4f/oQ//dPnUH7FqzdHchR0Lk0PRm6pJgBFCFMsSX1JEsCyaC2zvNElGVj2vVJRF1Xfe84PSqzyB7U4rscCBJSiTnNWpdS/q4V2rqOHUnhVtRr1wD3K+RFCXCqZvUjrfNF8q1Hiey2zH+ZNILs+KRdiEsgnVrMIrRTWCQPeLPP6Sok/lVLoCuGHNPEPP//bU3tgceBLMZKi6JIvbQdYyIKKYXfHTXXZqg0ylLYY29B2a8bDji8+/4I/+/O/4KOPP+bs7Axr7amCXnrYwhtIlfmeBTrLFeCpaoQPBkr5dOOoCnpR+0QsrQiNJD/WUlLCtR3Rz2gjXIpUmepiUrDAwssCKSeE5TE0X+rrSfsuV12HUmdpKxmmNhqUWgh6cl4nJn9NBt4lThZJzk5cg1qxo8glI4qrhXkOTLOv4hkCwaH1A0FUITKzORGMorUix5woTMETSpbsuxJLY0qokv9wkvbf6PhXX3vOFfxkV3hxzMxaMQbDjR7IK8vrTcfxxkvlnxJFFfrO8uRyK0mqL6zO17BZE2IhZjgcRpF2zoX9HaT1hpwL4zhzf3vApsK/ef+SX90c+e0+osvS11SoIqDrCbOpgiKCpglxq3OGT1aZ//1PLrk2hXmeeROgSgZVWdSET562d8KHiRPGiq220RnmkX/9sw9JzY5/sfc8V4XfXli+eK8lPXHQJeb7bynhTzFqhdINURe8ykxoktGMrgFniWvH3Hq0TkSjSWvoV4EXjUO3LY1rmF3m0Dnunlm6rDkeAvbiHPdmIyz0VebrJw3enJOsJqD4p5crXq/OGcsK/VXLNO64UYmvNgNv/qzjq4sJaxps2PL0W0/86o67vvDbFxt22y0xHmmS4b/7ekV8s+fvX6755uoC1Z2zVQb15lf89GbkbN/y5YtLvjhvmHTBhkw/JbbTzAdjYlo1vD5v8G0PymHnzGYaObsbacqPt3a/+PIbtuuVfK3WrLoWpQtZFW7udkzTTEqCwK2q7oAzUqHe7Qf2w8DZZsNm1eGsIqXEMAdudwdShsZonlyc0zUNRcnUgtIWpR8mlQpwc3+L0TLVFNLS266j0FQVQlWbA7VlEWOUta4V2RSyMaRiKcoCCgfoIiZboRjxH0CRIpSYiF6cPvejZ91qPrhyrExkmib87JlTwkfZx8UeXFrYw5x5u/fs5oyxMl2xXbUcV4GwO3LYH8hekVqROUoLAbKoCs0joGglg5f0iCugpN0nvnz6ofBWnLRqysmyGVRVkV6ayzU0yr6bi2gHoFBGkABJZkDZAosHgtInVIKlCaPAWUMOUV7zhFQUmeKImaw1Sn2/MP+DkoGm7fBexgeFUaqrn/Sj3rFS5CXNUlJZyzi69FdJifF4JISAtZZV37O/2xFDIqPYnJ+BgugD0Xv5YGqAE3RE4HeMQRuLcw3EyLfTwDCO3N1d89WXn/Pxpz/hgw8+4OzsXMYfKcQUTqhGrgzTpeI+uYKVUhdVksfGgEgW1y5/AXWaAJAAnGI6QfZGizmRrl+ZTPYzoRiRol2OBfKv5yDTEAYfAyXH0+8WrSJ1ev8PyQMnOLBgUFB155enF1/7XFXChEthjapuZrLAVCnV+0Beb5wCo/c14VF1kSnR6q/KiFobMRBJos0tYy66og6yUGteIQGuJmEPbZz/9seLt4HnShKBdYTcOp6OkTOX2F9uyZefEv7jL5i/eY0/DigF27Xjctty7T1RJZzOtM5IEuQ9IQZMI1yKm9tbxpTIqoj08Ox5trb87/7inLNfF74d9sxx0cGQtWQ1Yu2qFTFFFEYQg6woqWCawou/uoJ/+RxtMi9WlsMvDiIDmzKzHxlCIOwizhk6rcgLeucLxgc+MDP/arjj27d72tuRFzrSB8OVd0w3ltI7ik88bTLXa40pmsuUODseWF0HupxYvb5jdRx5+bZjd7EGY8V05jhzdYy4IdDpjFGavuk5Uw2XaeYqZi6DZ19m5uMNg7/DnUF+MrNpIyF5kobRKnJrSXpmmgfy3cxuA8c2MfVwvy60naGNDaOHw95xt0lcX8F9m8ml5aJpiG7DXfwdb580XD/pya7nNhQoa4ozrELHPz0547NNZrAJkxV9dJwdZy594rCy/PsXltCsMcZhKWy8489a+HCYgR+nVTBOI33nZNrEgDESMGIpjOOBaQ6EJPdeYxSKFmOkzz1MA/f7Pc4YVp1FKbE23x/2vH17QyzQOcum72isSAqXksglyCx/FNMzjaJxGl0r3IWbANLPPu1sWdVto1ZZKmFqgqALpFgYp0L2Ea3gdphBaabgKVrRWIdVlhwzISVso9l0TpIKBc/PWs5aTQyRYY7cDZ4hZnI24gdTIlOKDF9NDMPAZtuxXv8bsA3biy13x5l44xl2BwwF05+xOVuRsrQWlqJPIePiC/dN1SRh6crqavyklCbVTbrtWqxWnF9safqmxr8sZE0lxmFLy7Tit7UezKIdox5iqNAHNAFH27WcndWqX0kZqOtjC4rVkrDVn61XLba27qUn/EfgDFx0GyY9cx8lG9TGVIbqAnUUYhWd0caijZZKp0h/Rqsi/sxhJoZE37RcXpxze/2m9qxqgKqSAjElSkqPiGxyAcUSWKYQxOM9MBx2zGHisLvmsLvl/vaat99+wwcffMj7779PSTPXb17VTVxm9VN1uio1+C9JjfTQ8mlGVa5n7a8t/fFanS8BuaSMMuIQtbRFFJUss3iLVw0C6sdzer2aXKSqIbD8noXEp5TckEuPb2kt1XM5wUAVDYgpMvtISKmO3iwXrxCzQmkLGnSpU671tHxMjLMnFbB2CUwFNBgjsrUUqUAWRzx9Ov8lSRGt7JILpfbxZKynEMKPN57125cXHIm4NyODh2/OO379pEPbFmLPT7pPeXV7zzDPcsOZxPMXZ1xerpjnmQu3YZxmpuHIcRRVsRgjwxF645jmmfm4JxfFcZyJqqAuW5q/6PlpHvnwbuSLu5m5bjqL3qQ2C2GwYCq0q5WI7rz/0RmXP3vK7YszfANn+0L71US4D5XBXFCpEIlAputaivfEkIgxU6yi+7hn2HrMIXKBosmKPikuhkzwM0pNmAzdU8X1KqNK4Pz+yIfjNZfNSJcDV6+PtJPnk7+HYevJVoNKtMHz7Daw2Rsuz4X3kPWaJ4Pl069GPhwOPLmbieaWMmd2h0AzR14+KbzoC893Mxf7gZe7Bm1vUcnw5eg53A54WtrQs5oc63HGaUs/Q393xB4mWmvY+gpumZ5t0LjDAT2NmGDoSiHkgB+PEAKBjNeeo5o45MKQC861qL7FKMXoFLsucb3WTAZUA5jCalY8Hy+4WO2Bmx9l7ZZ6H8o+kyghks1SCQtqqB+z1VOixKpjEgIhBFKM5BDJdU9OMRFjktl/rcgxkKNMZZUU5SsmXInonClZdO6skdewQKyEusboOhkm55qyeG8Yo+mNwZSlzSkImPe5TkdIYgxVajtHgvY02iC7SEJlzTFaDuOIUprp6OiNFDGJgs+ZIcgkm9WiIppTZgyRV9egSOyPE9EfOQwzPopdecqRxoBtHe8/uxDwNj3yeaiH0gYWWfpcyEn0DGxjKolTrMRz1UzQSrF5ssK20vYLMVSXUUGY7Wki6YFWGVOohF8nv89iTe6nwJff7NhebGgurygUETXTghaUkokJGmdIWaZFrLNCppRPQ97DH6NN0ChFqjwA0cCu6nNKnchtiypdVjKaITP6Gu2MEMhiORn7OOtY9X1dyDUbqi6Dy5dI+dYPBgQRqP0ka01l7yemcSDnSIkelRJxOLK/ecPbrz/nmxfPyNHzu1/+I/vdjhQCKVcVvRzr+ZRTgH7Q1q+vuyQ7NeiVR8mBokoy19MWiLdUTeulT1TqyB4nFum7lr+lZr6Vg2GWqfRFPKNqAJSaOVbzIE05jRkuMwYhJqYQxXAnV+Ut9fAZFQUhF5IXcokzGmU0GQhJWOuqZryaIovUiGJkjAti8HD+eem1lYdr9XA89MwkufvxdAa+enrJEA5sx8TGFz47s/zq0ohDGA3PiuXsrOPqfENMHtrEhz95wfnVhuNhxFrLMBxFh8IHUoKSCiF5+rbDD4V59kwhoxycbVfYC83wXPPJvOGv7gr7X99we4yEKJuHAklqS3loyZRC7+DZhePjD9asX264pQAG07e0q4bxsAj8SD8RazHOsTm75JDvGMaBKQfoLeWTNV9uAiY1RAw32XB7ZrnfWIpRqBxRMXPVOAmsupAbS2lblAqY40gzVn32Y0CHvWw6SrHJhe2xYGKiazTWGaK2NLFls9ec7SLrMeLWmRhG9vczlzlxea94b0w8uxvojiNP3mjUCP3Y8duoOaaEDpn+EHj69Qj7AFtH7ycuvhm4vPXY2UKcmVYG5TJ9Sjz55h53N/De2tKYI4PxjMcdOgy8HBPrw5GrTnNImqMzmNax7gpnCfo5MraK9aiJLuDJRBNxPqGDsNJ/rOOk8ZGTtFMrVJ9qazCXR2qnuY7Z6To9VKv4Qi1uUqoqpY9fQOBw0YwQ86EU5d9OZxRZ1A+VjDcu+03JiZwSzray/1V0ca6W1Y1xdNpUtU2IRUjWkYJWVhCDKjRnENfBrDPFVbSRytrPRSZ3LPgQIcqoLkqsleYgYl1GVYWZUsdro4yRT1NgniaOo8fHVPe5BE5D43j56QcsscroIiJqWfhgSmuMo0LvwpvLZIwzdH1XVWpF/OfmzVvIhc35ls3FGcZavPcoJTFKG42t0v0LL8BoTQwzsWRs19C2LSVl4hQ47iZe3c6sNh2b9RklF4ZBhIWsteSUCSHTtg2zH3Cuoe06YkzM00yKQmb/vu3ZH5QMTH7Ap0AugVwS5AcZ4VwFgR7DIKUuRBTy4Wt9IpdoLVnSYn609LetMZWB/t2gIv0ZtMJaycqcs6QY0Eg15QAdE+G45xBmpv0Nb776Nb/+uSGnxHS4IwRfs9EK2eYEi6hOfR+Pj9/T75cfntj2Em6rP0EppOCrg1Y+VfwlZ7IRGMj9AahcTJ1y9RmXDV6CbOU1pNoLQ/r9FotdyIF12kCV6hZW+2jlxD1AxlW0FkEnbQhJPhdbtQOMWTgVVGiLB3gMmWVe2kHl9GksiczjmYJySuJOcFrVaVY/Ys8VYDANXWy4b1q8itzpwqFEAp7GaabdHqVhs13hy4xeJz780ydcuA37mz2H40z0AVR13EsFg5Bnz1aOw06RfCKRuHq24snLHm0nfJO4eN7w139xxdsc+ezVwO2tZxwEQbNKoYwkAzFlGg1Pzhs+fdHy0dOGrjHcHAeYHSY7Vt2aoQmkKG0117TY9ZrV9pxnz9/Dx88Iw4wvntVGEZ8ZvrGJzjR8FQOvkuLNpWV/0aDaBu0MTJ4/D2uKceAajhcXvL40TKqj+zbybOeZFfzuqcWbjCuKrjh8NKxjoiuJuDYYK8WCb1r2/ZqD79nMGv/sgnQ7kmOmb1taVXA+oMNMVJ4pHdE7R281m/6MMbZ0vUWXiNsdeXJM+HtDnzNPbhOrSVrVMSdiO2LMRB8CV2935Bi5uouQD6yMIoSBPkQ+PQQu9p737YDxjtk5lJtoLVyEyMXeo5PhI63ZrAODiyQiZ1Pk6XWinb6fcMsf41gKgoX1n7JUzsvkwCINbioszUIge8xQqmim/E5+bkxl+td7filWUioViSinsbZQydO1lc4pyV+M24rsqEtCW0pCF4NBCX8AgdpP50HdI3SVJauImGy30kYz1TVVOUNZiIl62ZeUeA08qhxPrdRSi6SKvi0IZ6hIRioy7h2K5pgyerPBVQOkxkFj66h0kDfbdoaSIzlC8kG0b6zYw1sliogxFSY/kENkfd5zdrVGW4ufnbSTSbW7bQFd/W4UjTGkYJmiRzWWrhfdkGQ1KRSatsE1Ftc3VXdGkgpjTJUmTzRtQ1aRpnE0wmbG1xaC4rsF2j9//KBk4PawR1kIeTGG0KSYK8NVql9r9CkhWMbkcqnylEWfWJfGGJqmxRhXP8yCVoau64g54mcZodBKqlmNVFHGWlpnaa3BGUOpJhNmu8JU7cESJ+YwMPMQjFVZIHUoWrP4CFIe5Cr/uYt2EoioX8aYusiEvyBBO0LKeP8dnYPlwyi5jnq0p57Q49/b2seXS6bJOeFjJIQgVSNLEgU6BdrK5ldaY2v7Yfae0XswFmWs3JRZMnqtRUtfoU/9/If7Uk5Ga4NQZh8gv5wTM4VGNWgtkPZyM57GH2uWuzCYjRIlLFNJlBgNVq7j8EMW3H/FI0+JTq8YW4PPI8nPrI/gTaKsC8N4pGhDv9lw3kTcxcjFe4Yrdc7br++5uRuZvKdxBl8JWMaILDAlkm1Eq8LVWctHf7Lh/Y96dPVv36vM8w/P+Nfb55x9vuMX/3THV1+Iu6RVBWtBWwcUVg4+/XDFz36y4r0N+CHSHwNzyazSOdvujH3r8XqkXfdY09JdPuPq+Qe8eP6Sm/sD6u0NzsycXViKHhkLeJf55VaRZjjimeeIVomu3+C6Br++RIczMD2+WXG3itzrkXLl+HDXc1SZn7/UxE7T0NOUnstJgRl4PsF+rXBOCIfHzZavnxTod/Qucvvx+7Q3b3hKx9knZ9y8P/HFWCCPGAL/8Cc9Q97iuoZsNlycn7E+65jP93yej/hpwkfFRhW6xpExvF23vDprCTqh08wF8MQ1jFZzbTSHVNBZs8otZxHWhyMUhUqJ7aA4BwwJmw5czPUap8T7WfE09STXoAKcHSc+2gWG+cebhlmKCjH/EtK1zLPXZCBKtV+MEbQAua1zTe51ddokFYrQpzBa46zBoKtg0EJvrhC2UlUES6r3BotZJofqvW+ttIiNrnspCqXBoum0FdQROQeFOApus9CHAACUGElEQVQ2Evqx2qCM7O+qosidQM6itFfbBxjot5ouyDizQQK+zg8JRu8cSn9n9r88JAiU9FDIKAVGU4xhToU3t0e+er2naSzOQN9p+s7SGIMq4kegi3DfKLmiZ1ocQbWRfVnpqnIoXKpcDChJYJQRmrCubr+lTggIHAMojXUNOifhCuWaNC1kTK0qgVD0QlbrdY09mVxdVfu+wzWmio4tf1eJ/LKCvtc6+0HJgC+ZRktmo+v8Y/IRWkPbtkzTVDPDmuFphcLKrH3J0rtKgZwzbduxWq1Yr9cnGEMruDy/YPIjYRqJRmiYxhrRfbYN1tlK3MiUMFNyxM+CU4dHePVjhv47awR4mM9cxHLr78oD25NHP4NHM/6FUzYeY6zKhNUVy5gTy5MlULKw90sVCOGUCDxOGpbxEVTB+8DkvSQezlF0IilDiWKti4KQEyXIBqCdJdfZUgm+piIBFdqr3gCq9v8VSyJQM4JSzZbqFMOjegIQgmSxha7toCCjhvUzWwL/8hYV0DUWrcxD0lNHT3P+8TgDZoab3jL0mV4FTMisJ8NZSMzmwOHQEKMw/YsDtS5kG7jaPMOub9DNjv0cOd+uyNqxGyJt17DpDVPyvPezDa6HFx8+4fmzDWtbCJPGG8/rlSK2A6s+8VcXW0pJHPeewy7ibMEY+OjDJ6w2jnWf+cnHHefvOybnifdHztLESCNKnn3PatXTBM1qvcE1a1Znz3ny5D3Wmy39akvTtXTNzNMnHYwzRVvmIfAKQ+osiUI6JBhmcjJc2p725VP81BEAP8yk3QHaAaMd19s1U4xMMZGMI5Q1B6+Z0siFm+lDQzAa1xnmoqDtmbrIde75JhveGsPzj3/CeOZpns2M24nbztLrNSUqrtuGmRU0iXTW8/8j7s+aJMmSK03w47uIiKqamXtEZCIXAIVCVVdRdw/NSz3N35+XoaF56SFq6qrprq7EkglkxuaLmamqyN14HvheUfVIFBBB1AmXTAt3MzdVlfUy8+HD57z98q9ZHic+xN/xYQuUx0ecPPBdakz6wpsS+f2bA3/7xnGRjdw8X+rEwb3yMW/8ty/ecj4sBssKPKbGU9pol8z/99cnnpcFCKbEuG78h2fHck38b7+I/NejCdl8NT/ylBtL2mi68d3xALz/LPduyZUyxIS4jRarWustl7rLlFdt1I7VDQnwUm7B0Ox+DXmd58nqfe+Q4LuzpeAjTFNgip6H+YDTBuIZAje9dGPpiIXvkP5ehUMPzrKvNU2E2hpTM4+MGAQJig/VCsycWca7NGhb19Y4KqefQ/KBnBQtilSgeaR6okr3RlFCbw9s1WB4nKftYwECOujNndCHkppw2Uxzwonwslaiq/iO0k5z4HhwTEEI4vBiEr/OB2rXnBHE7BKcoyI0caj4fiK8eY4EIxCCQ9X1to+NHXsXEDxalVJs6kqdtW/9EJXqAIjFSjWzWG5xyXvPHcPcYpL3e+v7x2w/zZtAoeVKKxVRJThn/fIO6Ucfev/TmJ+hS+56N1kd7oBmmdYcI3OMrJdXpiAwR6ZpppUNVysP08T89Mg+t2FlKpq3frPf9fcZ5Dq78KNLfXPl0z03+iTQjwDWCR1G4LMKIPjQdd4/PZHms166EmDP2vxof9DJYAapGWmQfdRG+kn84aUxToKZ2Wy5cFlXGo55XohTpKYNJ2I1u5q3QanFbqS+79pHLQVF+xiKdrjsXrdAetNfe4k/xi21J0ZDStj3G7BhHI3RUnCoiUy5sL9mnOMxVqRiUwvjivSz/SPvsj/N9j4nDm/ge5eIMXL0j6RrJjhllgW9rKRcef/8nnq88ObhxOzfsp2Vw8Nbmv9Hamic88o0TfzVv/kzvnx84Ms3b/nNN7/h3/6nmelUeVwcMTTWJiS3cN4a324v4A0SfPhS+fP/ceHD+YH/3//yEZzjECFfL/zf/h//I1/+hWM6Ji668mGrXLczwRdCEE5Lox4ibn7Ey8w0HXl6+4bl+JZDWNguHyFfOBwi0+PJjIqu5rC4JZiOj2Z2UhuhNRClbY7LBfxf/YItYRwRhbpWStk41sLfqZBlQS9K9h5FaaVAgXfTxGN2LFH44uEtkheCTFxS5Tm/cJVEfYXZ/wfk4UA4fAeSeAkbf/+ktDaTnAN/Ban4qYIEfHzDPL0QNk86zrQS8CnzcRLyYeJ5jlxy40NVWgw4qfzdwZOOjzwfFl680lxh8hNKYFsm8jxxflx4WTy5NUq2CvWLEvl6Vr7/6sA3DwGNM+985JgLv2wzv7wK/7i7i/7rbzuhWRu1w9xOTTq4FlMPbdVanaPdJH02/vZaTHZbFRWDuN9+8RY/RavUAd9HhB/F8+XPhJI3olNoFecj8zwTOxqgvUWVc2IKgRh6QVIMWZym0CXrjdAwRNysVlFUC8/bCx9fP1LbxmGKltykhmhgjgemGMhceE1n8pQ4PMw4PGRH5MiXb36Gd874NwrSlMu68u33H0nffSS1xvM1k1vXY9DOjehfTdW4FL0FMiQGqs0a0lrBZ+XD2fCSgBJ9Nc+AeeJwmHiYA4fZkgulkzq7Eu0uQgQdWeiRSEZRau0KC/qekhPSWm8DyO4xcb927kjLiHf38WlUsnr3/Sh8f8T2k5KBIGO8rpMHvU3D5+uG+P5vvbczgnWrCbRZRS/RekCHiTkI2+WZdG08HibaHGlN+fDN79md+AbnAHao69ar5xOi3364dy6BPclkNL8FY5o27vr59ZY53XgDSm3mEOZE7lz4et/euV0/wW50SNlm8+tQHhSD34J3LDEy+Q6lyQ8u4DhPreG8Z0uJqoKEPr63mSBOiJEGnA5Hs9gtBeVGIBr7X2uBrrUvfQwo59yPwbQUBFv094zzjgkwBY8PvidCAH5HNmRHEbDU/w7h0J7s7KiHSWaMI+yX5vMlBIevZg5fKKVE6ua4FtCDQ6aZh+OXXOQZNJH0jEwb0/ENsSy8f/+OeVn45V/8Bd+tH/j+/XvirPzlr37G//Trv+QPv3+hqrC2REoFH85MVLbieFkbnkA4LtTqmJdH3CS8/cvKv9kmvv2u8s3fvnCaJvI587f/x+95+NkvmZ4gaeWqgY1G8nA8LBTAxWDzZRqIp5n5jUNqw1N4/90/cH39gPdKOC7kGEml4P3Esixs2UYQxQeaWMtHNPBw+DVT/ILLx+/IuXIVpSalZiVV5b1Xq8zI+EulSkHFsyn8l5r5ehK+bJVpeYtrDt8CrQVWH/nm5NjylUMFKFAT61p41UqjYqPlD2R5hqpIcMAM8YF2OFHUkXxjarD4zPs3hT9wJtWMpmAKa6mSQ+W/vXEsNdCi2GRGXy8iwrfLxNezkOcDB2m8yeA3j5SC0khvjjwlOBRPniKXvLGuG6E2fueU7TPeu9a6G253BSQwdO9LrcYf6QqAtWonAff+eOcujV557WtNLYnmwEufovId7iZY5VwrUovJZfatNaFWCxlWESutZraysmHr0CiUYncwVK0MLRScmGS9NJomslv5+PIO6XbgKRW2SyVvisNzeljwM7w8rxwfIhobqRTO10q5rmQqh2WB5pkxAybzsqm0Vti2zEtQUilEsQmesdINZEV7q9gmoAQVZ60EDDloY71TJbfKNTdkVdylsryufPk487M3B6Y5cPfmfT20YrHWshuHWQiwNkjDDM0IwVqqeaDWrZMN/d6S3pnb+9/vt7383YvA/XecA/lxI90/TYFQGr5nfd455inixdnomIwRi0YIvhthNKran04rghDEDlTzxsuHhGql1LzD1WpHxZil3CcVWtuD0Cd50sDdR1Dr2d9QeBsue85JZ6UWVLQDaa6TF3WcSuiOVSPh2QWQ9vaDVeNg/afalGtKpJT7fvguMmCTAaU1rtsGUzRb2h9cRBHdr1vrvAU3DJya2QcPWWdKpaTceRC2j813ljC2r04E8cN1y86fdxbgh8Xp4O86bA3QfW8U5/u0yF7x307vMFTql4oxXbzTB5XdLVHHybprOzR+eBP/K27eTE1EZkQcqSSkKE4mRGE+nmjfrbgghKXLYZ8b18uV6c0v8fORn//FlyxfKcEpb54cTw8Ll1Pl3//VfyC331Pama3PfRYHhQ3xNuImMVKcJ6dM1szhFxP/8T/9GfnSeH135YvTzLvfn/n67zfcsjC/PbL4hLhC9ZmwQLleySVQS0GlwVzQ5czl+3e8vHqu12cUJU4TLjaSFqgbTYVSmlUfvVMj6pHmcWSOT4+8nq98/+73rNuZGqGmSNYZdZ66OD6cX5mDxyUTPVJvUOTFeZ610LaEr5UyWOs4MoFrVi6p8j+cviBUxwe+46UUshbMxxkeEKr3NIEoEecmXFyo08zHa+bKyhwz7aRsKVCap2RTuCuitAav18bqHcsMT6c3LDjWvLKWxNdy4eOD8Cpq17cKU5yZY0Rq5jxtfPN2YfWVN1O2gsUvJIRrvvC/fOE4b59RjngWDg+R09PCPMVdRre12jVT6KJju4TV7o8yft6aia3V6ozZDrx9POGjqQQE5w3a7wZWu4mZ621fJzZKLgP8x8blpoAffXBxqAOVClr6kx9tLNHHvXVoy1vhqq9ctheeL1fy1XE+F0pudm86z8tzJkxCSoqScMGTs3K+FNIls26Vw3zgNB/49Zdf8Pj0yJIz25pZpldaq/jozEvE9SKQPtmFw+kgQN+1TbvksIJNVqAWUNsY3ezVfrN2aTExTWLr7Qi6gBAYX8vdkGpBUOleAk6QRk/S2n7Od98bAR+kF9z2+rFSj7V3jJ3DfStA91Y23NbtH7P9pGTAoRbonc290/IeEGzUzWBkd2dkRGvGwi5GIuk0V+t33FKpcRyfbNoh/N1md/zZD1Pux2e0Z36DLSu9l9OTgVJ7m0PvlbI6EkCvIlx3YWQY/YxA/emOWSXcJYBLsXEX8d01zO0qVDB65Y3SlIgtXDfk4pYA2c+6TKjb77k73Ww71i0lq9y9dLvNcTbsa4rB2KVtiP30B9ABHbHZKQxyU1YWkb0I8O72fvt/x9gO+/1n5+fu3Ixr1GozVGNk4j1bLeXzcQZqbdRrY/K6T3s48bjgaVpRcazXFdXGYTnxdPiCiQNNGmF54Fr/Fv9GeXwz40VZjjMPX7zl3y6/5K3A//66cW6ZWoTkbAbae6FpIjgjvG7lypYLRW1O/PHXgf/4n/6M//z//kdqU7a18t0/rjx9+Zbl9ITEd7hYjTdCIteIakDEo67S4kaWzFaVnN8wH07Mx0qrV0LciD5RmyNtq6FVKCl1MheKtEaIivczr5dnarsSJ0cTT2mO1oRrTWjwiDTK5vAlgBM0QA2gPnAtjddScfkVlQe8C6gcEX2kbK/kVJDHicPhifdpoiTIqljFq2zpggqkpkwVZBKcn5BwpDBxTu/QWKnRU9uRmj2rZopkYvA4nViTcqlX1qZwubCEo7Xd2kr2mY+Lp1RYJDNH4eocDY9mJU2KTpHUGhq9FQIKOUQ2t7AdIq/n58927/7qrx44HiJQSbohGhCVPYg4H0CGHksnGja3Fxk2ymZB5wZjV6Dt7U0vfWS5xxfnXJ/oarjeY3derc2kPej7jJOKa0t/PaivZC6kcsHcFZVWhGN4Q4zmVCtiNuktZRQTI1M1ImJz2Ps3pRbl4fCEnxLX7ZXrGZqaw+f5mnmtlSVW9FGZf/1LDscFt5p0s+8ohEdote4BfxSIpbdVaoVarYbTARLIWJS7NH1fh/c3wJKIXJXXteBfNlKBy1pJqZKqITD0aQzrPQD9Mzr904qvcS2cJVM2Gmqkee1a/m6Yy4XRBbhFjL3Vy6hX+290dKLhdl7Bv7T9pGTAY0I1FtAbWnR4Fu09e+vC3CBoc8zKSHM4qXvWxICOGAcAt2Bz67XcJhP6TbxD+vZzE7ho7LKZ/YTsXIpO5NuFenp2ipqCXq1tN6sQJ4SgxOD20b1Rrd8224fWuREpF5oKIQZQ7TP9A9XZbXws4bhDGUaudH/chhTQcf/G4D+4/nM/vCAEE5cQuuaDvXqoUrmBhAx9yr2y3z9lP/e3oG/f7bntSERG3d/P++AEDMTiNhI0VCKtb8lIzsQBrkNlnw8ZaE0gBXQ3S3KG3jizWN6uhcvlFYnweHrizeFL5nZgmwpVhE2vtGmDYNk+h8j85i2/+vO/5jFXfvNf/jda+471WpDNRERcDKR0xbmFhnLZrlxztckTVbyPvP3ziZ/9+sjr1yupVt5/98r7P1SOb46Et4UtPxOqo7oMyZCMefbU4GBKbO1KdRFUeHz7Z7ychXxRpGXaVmlOqVpMmVKVmrtRUpfl9qK8nM+c3DOpXvABnDpabaSysdaL9WldoxaH1moTPgqoh+apWThLJXC1Hr1UHDNBHynbhSgLMcwcTg+QHaUJtTmcm/AeqhakBUgeJaDR4d2Md48EHinpHRkl1YZuFiRSbTQKrhWCn3A+Uusrl5zQ7ZnHBcIEzWcahTUqIUaK2LlYfWGtUF2D4KizJ1Xw0hCptNaFimbT2th+oqfb/5Xbw5uAo5LWwjUJjkh0UycCAtK1QbqMuj2bNwQP7HmtfVS5dfGcWgtRjWTWtOGaqaeKE2iyE8GbNMQr6ksnCBqKoOFCygnfnvDS9fMlsdZXXq7P1GZj0K0IucHpkInBAYVUM6/bpUPYFdeVFUU8rTq2a6VmJfqIasNJsHutVHJWSrGgmaSxpszL9QJBWa+Zl+tK1lt0qbWh7qbpYhb2dRfFq9UK2Nb6utyl17ec2Grh+PBAoE8w9OKSfn7PW6W0jfOqfP8xs1033LwSTxvewTID2ghqPKymGOFxINm1oq2iwcYmrbXeqL6Zgv2ODAhN3V2iQv/pLfiPbRSwqm2Pkz9m+8muhS1n0PZJ/1dVqT3ANm7V/BDR0aboPmKglgExcpheaSP7NzeNZboE7iDRVMa1EBl2wdpd/JQxVnGLrQabOyeE/vmlKw/mUsk5k6sRXoaFZGgN1YgEkxbm7u36Ltn+qNlTtqaIM2nk1ga1sWdvfWdNiOkHUI1yQwbGubzL7IC9TeIwKWMfjUziBUIIPWC3PZjvRMHOjB1iQwP/GO8tYiQZg6QGzHTjV6iOFsCdsFBPYqTvkwUTS8K864xkteMeqmiAmakIXbLz8yUDpRiqZaXHGKPs56uZvfWWzkwPB54enjjNT3DxSDQL4+YrTQpFMgXHppV2OPGzv/j3yPNHoj+RV0ipmJRw9CwPkXVTSstU73hdTTq1NoNQvRZ8Tjx9Fbk+J/K1cLlc+fbrjyxPT3wVH3h9rqgkJAgP3uNk4XB0tCCobFxKwoUCzvP09pd8+90G64VWIF8yOltfnqZdZQ1rTbViLHQVvn73NYv7Fdf8bGxoMrVZL3gtiSAOF4dstUFbrjhEPRTBZzEo3ym4wMv2jhAiKUPaGqfTA8t0NLvxJtQilOYJ0cSLmiqaQfNMCxFcwLmZKI/M8jNk+wO1BtI10dqKaERds754zVSdmXyE4HlJlUu74vHMLoCzpEdDwYWIeId62MhkrVSEKBMOTxZhawUJUFhJrVB0w7lA9Z+PQEgzilsp1SDz1igRaGLCUa71eX+3F6/ahlBZbxx0/oCrrScLnU/QGuo8LSd7hp0gTaxw69ovjco0FYhr51l5xDuqvHJOG76ZFLUl/4VrufLh5YV1a5yWI60oLy5zXBemyYHYJNY1VUpTcml7VRKCo6iQsxVquW7kkhE8NZtVd1ob2vq4tEAR4R+//Q7/Xti2ynZN5G4KV7WZbLof7RLdj92cai34ti4Qp6KdCJl5fj3z/cdXfvazxhePB4KoWTmLabggphyaSuFyVd69Zl5fNmq8wDIzBeG4mKXyFBzTZCJn3ndCtkonKzZ8MNGk2op5xFTDDnb4tpPChQFOdMO5voTdkoE/1hv8k7QJtOtH74nAfaUn96Ayd5X/XbU/+hsd4hC57fj49yHLu1+4DpE773blQic3fWwAH6du6iO77wDYuIULljH7kSWrsuXMlrIhDc6YnHaChdwamk25aZJ78sbdedC7uN0DLo7ug6079MY42jFSsode+/l9dqfjvIjgQ9gNmnrivxP+7GHfV/WbRrXq3ndyY4RxXBaxY7O1fCRPhla4PfDb71Qd7Zu2Q2qWDUu/Ll1He89GR0toXN7RhrHXmJzn4EF8vq2Ugp8c6u36eDxaIYSZo57YwgW0shwijw8nFj+Tto3iTEWMSUA9pRhJ6UU2XhXmt18x58IcH7h8C5dU0NIIUtnWSqoNtgvLNHNeM5fNTEW8g5IS+Vxoq0eODlFH88q7j+9xv5uQ6Re8vE48X76jUfnrX/8bljYzT4HsEq9rYvWJuVmbYzl9hY/fg4t9osQZOzlKH8syJblMYagebrlxvv6eyX9Breb6VrWR20ZTm1hx4ih5ZRIQjdBHwLyVlPiibPVsCYRsvL6/IC8TzRVqyzy5I0s8mAhZCrQsJoPbKi07vFvYLmdCq8wiuBBxEnF64hB+gaz/J9l5Sl7xIZv6aNcKoSklnTmEhePDxPZ6RWi0uJKc0FrG0xBf0LARw4I4IbdKkYa6iZr7cx8blUZtG7V5shaKZoKbyO3zJQN5FeIcLYGVQmmNsm2IOiQ4wiTkVDsi2acL6EnWzp6/my64K2Qa0GqlpESYfB9xtrXUYc6ma30hvG20cEGl0JqnVk9KiZd1M/dX50i5Ulsj5crLubClyratlFRxbsPLCz5CjBbI02aqnTFGKxh6AMz5htp+ePloGv1VyedCTpVcMKjXm1FXA37/7Xu2vFIbCJ6AJ4pjaY6UCkuX/R4opzbjvrRm8cbXnhhoIeXM67ry7uOF3/3D17z/+MJ//Hd/wcNhwjvfuQ9GEDQzI6XQWBu8pkI8F+aPBeeUGCpBlCmafkGMnmkKzNF8JLRiWiPR4fBGo9FKdYI23wvl2vsXnS/Xg8+IQZ9OvOldAdjJovXHaWT8RGSgIe0u87gPiqNvIbeRsnHiB+w/qnYLIQ2tY/SlV8LO7SM0hiz4blLUg00PckNPH9iRgVIql+tKraOqtqAYg+cwTV0D3pzfti1bEhAnC9ASdqXEW39NO2fhj6vZGxGuwzb2ty7XM0Q+egNFpPfJ2L//4wL59h5e7DhHMiCAv3+BCRr0SkD3E7ETHXdCCfu+oWPioJNnisGFqPaq3l5TtbGlHgC8VQV2fY2E2Dq5Bmde6tKll3dDjz27GZMIlpHI/Q3xmbbYncuaCopn8gthiZQMc5jxBJRgqn4ucooH1lCR+ID4SLlUrtdqgUo961r47vJMPB3Qbx2zf2R7EV5SQlSZCLQEWaCsjY/lavd9deRcqDWjtVGvsK2JeJith+g9SRNfv/+ay/955eHhwMf3wpYK//bnJ9MiDx5Vz8ePVy668mauyINnmh9QHynSKJqRljsPZOJyvVJKxscZwWaec829vVb57ff/lUA1d8AYaX6zS9acVUsZNi1EzKa7iZA1oUXZckUkEb3ZfOcVmka8nzgeZ47zo03fqDD7J1oSrukVXCMwU5udj4hn8YY85ppwTTjEr0ibMh2Uw2lmS5XcGpPzRL/QZCbnCxsXDsuJhxbAVcKUUHGkbO2EGDNxnjiq46KZRELixOIj9Tlx0TP+QSm6kq7gXexE4YBUk+/+XNvH9xshVko1Nz8L5glQvPf4KKh6qOyTA2PEeaytI0mo0tsI++/ZajbNE2++eMNymnHeCpjz+cK6vXCYC+EEWyuseeNyLXz8UCnJs16vOF15fHpLSpVtLWwpk3MjhMjra+ld5UwppsI3TYFpitSsnM8rThJOHE0bwRuCaZU6bFvDee1aKY1licQM50sipWZW3ekZaWqJnFjy2kRRaVxL5pt37zn88g3DQ6Dp4Jr1ybFenpamvLxc+P033/Ptu48UHJe18vU3vyNdE//+r3/Fl28emWJvOXdEeayRVjwJpTlK8yYZXcUC+bUSzhUR82yI0bEER3TKskS+aJFJHKXIriUzpOYtESiWfKjrpRo01/UNuF3jviu3ljS3Fvq/tP3kNsHogwvjP3frvPRec2ehCNbvDt73+fzOhO+vGtNpI70YiIDvojlgMErOGW3aVQvDTnRRFK2NlAqvlysNR4ixxyB7z4JyyQnvIgqs2aB87wM+3MJsGeerB80hZ/nHp+D2s1G5t943j94WPB+sTz7IJuJnor/14HZOwR+dW4N9/Kj4ud10f8TEv2VbNsnR2k6ErNqs19d0f+j17sG3j2s4gcl7gr+9p/PWW3beEjFTL7SvVIrtprK3IBz2Pnsnvo8OubsRnc9IFdi3p4cnlujNl6EvNtdtY1kOaK3k1UxG5jjzMB94Wg64E8jpl/z23d9SXIIYgEJwnrgsZG28e36hKEzxgUkX2qYUTahUpM4UmTguBz48X8i1skjE5UbNsCwHHv/sLd8/f7DJFAQ/BVqDtCW+f/8dL68e5wJ5nQjuiFePD4G6OT58v/JaVtzTBf1K8GFGgqO6ymu6cr1+BC+8efszvn+32mjXbAJZZtZSmSZbMKq8Z3LgZSLgLSkNnuhMpKi0iIqwVnBsTJjiYPaB5CqHw0TaVsSZQildMP9aC5f0wiV9xGfHcnrCfziiNaBaKeIoWyHVSpHMx/aR54d3PD2+wU8B12w9id4WwCpCqjYRJM6Bb8wPkVKVhFC9Iq7QXDalOCe4mDlGxfmMy1eyVja1HrArV4quPDw+sPrK7L7E9wJFHEQn1K0RP2Mm+/pS8WFUhh2OpxKCJekAldzX317h3o1Pj4JrTBQMF1FgX6dH8WWS74UmKzW+cN2+Jzrl/CHz/JrZslKqkFZPXivHhwOX18If/vBMzmZo5L1jmgLLPJO3KyXDumZqriCOLUCMSggR1Ynz62Yz/c4TgvXyazPUYEsFRDgeTPAs541SGlWtbbosgbI1Ss42RYW1lJNCE0XOjb/7+3/gl18cqa1+IhM/yJSm32AqsltNPF8v/OH791zWxDRHfv2rn/H09i3T4YG1wHm74ICH46H7IQDiUI3U6g2Z6a1AnN9bNbXd2riSlVcq0grOX/n2w5VDCAgmULQcZ6Ywcd2g7qLLAz8eUsOj0vrBvdlHulq7yez/mO2nJQOjV9IDkIipStWuxAcWTEzLXvuN2di2jdYaPgRk9yJgJ7vBMD8aoxVmuLNtG1suJrIhjuDNVviwxF4dNVJKvF5XlECcp5vdcT8J9iAUevZgynzNICir4m0UDzUo32ZAb2r794Mdt/cd6Yz5SbvQF88+p+d29qLuyMgABga0bv+/y+h0T7HYeQo9ITANjLvURHtPsJnL1hRtqaqt3ZQLQ+jHf2s9lC5N7ENAa2HYTI/3HGSUvbIX2XWw++XvkqSfklJ09E0GZNX6eBOAUxBnboyfMSs4p2dezg3xHj+Z5kKrDWkr8/QX1vNslehnIhMUh59OhOWJl/Serb1SJVPpRlKa+Pr73/H/+V//X/zy7S9YJXN4fOINX3DZzqAwHw8szhHDjL5cbGRRPdImQnQsDxOpZg5PCwRY3sy45hAN5Fyt6ur3RNBofUXnLWHIwpZB3MK6rdRacAQkKA3TvPhwzrQQyNuZ7z/a5y8PZgerWpm77GquxXq968bppDQmqKa1YfKqwrwcOG+J5XDCixA8hMkTwsQ0g2ghJaz/MQlQUZ9oYSb5zPfbP+CD55VnrmUj1wBMIBHvG8d44LquPG/f8O3l/yA8f+TSIuqvnA4LabuSQ6WFho8R5wV15g0R4gnJBfERoutcpd5qdEpTR2oVXyoeRWs0Bz8aVWwcsvRn8rA8ktrKeX1hyxXnZg7TCZ0+l5A23aFu9LsL9rBVcBM1VdZ1gw7rDydWaJTdlt0Y9a1pNzCyfvO+7sgYC6+d31S4lAvP6weuNfHyUnAucr161q1Si1JSoxbl/XeJbXU9BijeeaboyE5o18yWGueXbMZHautK2irOFZANwdGyrQutVnIf0XbO2RhsbXgXeE0bTeudgqIVI7kJaSQ4zUh32gy1rKJkD4fjk3G6tj7XD4g4Sqmmu1CNV/Dtu3f83e++4w9ff+DlxUyNajUUYn1I/Of/8huu64VpCvzyz77iL/78QHQ33lRtZsudSiCX0s2j1AiJSpeDt83WdSuXqzY2Vc5SzeCOhv+YERzf/P4DH14LT8dAn8czIbkeS1xfe29md54xmshPSATgJyYDW84dYryRUG6Vfu/F9go3iHTTC3soQwiEEChq/dtSjawRvGOOE9GbuE0phS1nrikZrOQDwdmJMPZnJZe6j8DUaouVi9bTGwx3U8ezdoG2W6C1m6g/ABiTtCte7xmykwHR3yYfxnazNLYWgndWyYcR7QcD4K51MoL6H/V2+q+MWKod3uuuH+g4xz0bHIQf3yE1ulf2FPyetBi5T/fKHXrLxblRrNm5HPPCd8dmqA74IVLRK4x734ahkS7eAa37PlgiEnZegKmNNQXxwXgbPziP/9pboyDRg3dUw++IzlG1AI5SE947nk5veDi+wYcDYbE5+3P5jjCZv7rz3loKufL+42/5X3/z/+TbL/+K377/G9rUmB8mwnG0towsdFqOvNVKbplDiJCFXArNFVIqTD6ivgDC5GeCC5RaiReIfmbbrrwRmI+C2wIhRuJh4vD2AMHhi5LFnAwfnx6Q7z3XD5lUHCkr2/pKBbY1U/1mokSzxx0CTRQiFAoFIdNAWtefEFJqhGABXrLDh65Z7xUNiltMTrXWxBfHtzRNpE3JKVEpxOOR+AAf+C0pJ2osxC+EJRwNSo4zc5g5TsLr+YyqkKav+XZ9z8fkKK6gfiOzgTriFFAXANknIpyvUIXMRJxnWmqUmmkdjXMkNr2ynROP4UBSzEPEK/PikOip6tnKhtveoaFSgll5g8HNMuXPdu8iQ/is7mtErUopm/EI1KpG8QYZ525tm0vpyVxXLrRBM1SNG5GLcl6v1GbTNi+XjfkYmU6CxI1BSDyfKyITL8+NbWu0YryAvgQZ/6AaKlC0kLdGWG0NSamQU6NV3YsGEYO4ncGXn6zXY30VcRwOB2qtbGvqNvA2xUAfFQTHes1dhbEig6fdl9/orE//8PSGv/ntd3jZ+tglhgymhk+V3/3hOy7bxnfvX/j+3SsfPpy5XDdqU1IK/Lfz7/jtP3zNw+nAX/zqS37+sy958+bJiIYNYLiImlV0yoXLmsmlsG2FOE+GlEzGdRmmTiI3tEvBBOyaKSrS7JheVuNeDCv5QQM3cb2bpsCIH3tV1gmHP2XN/UnJwLX7Ne91cQ8yI8DcyGvuLmB20wRvBi/b1ol7HRO3m3vDMbFME61WtrTZeJoP+GlCnNk1jmxHx2fvOLoJYrRqYgIWl3vg3W86vSn19R7+0M534ixgiQU4G83r1W+/cDd4/aY9MNj+NhWy8+/3143TtCcI/71Nxvs6G/0pBqeNlkzTZlrYY3ohdMOgIUYw3kZueIaOayA3QqoTd1OHtKbUnlUO/EM7jCadKCqq1u/1XYZU7cZ0A2mRGwqzt3/uCCwO3SHnf4J+8a+3tcJhmbj2dpVTiD5SisPNgVor0UeeTm85Ht7iwhHvDmSpXOp7NGRTVeutISeKlJVL+QO/+/7MS3lFDhvz7BA82jwpJ0IQGoXlGHiYF1pOpLWgrlEU4jGAK0yTs8Q0GCXDFYOogzd1vS8fH3h4PFKYcVNgiZGnny1ccsLlgJ89tWZ++ctf8nfvTuj3juX0QNkyYVG0KGXryvZOCJNjPhosGbxHWalakRiQCGC+7bNfjPldKn5xTKc+007DR/BzsylNhMMi5M3ZBPUwtZkTr/UbQoAaGk0a/hGWEAhbY5knW5ylcPIzRRy5Fp7zhqhJCs9TorqKcxOtKilvBB+YfECBrRacmylls2orQZNuyFUr0hR1yqbCLIFKRV1FXSOHjMwbOkXWtaC1Ih6qgzBNTLKgpeCnz8cZkE7erXUUYJaAtT5/PtYP0x4opJJo3Ua4au1CQL0qrrfWwTcfX6gtdWl1qyi9d8wHx+EEj28D163y4V1CG2ybjfXZWKK1IrVPU41GoTareCudcNwG96jdEYwt2LtekDnfpyA6z0rVWsaSb8WFiCDemwWz2tJXi7W6UG4EZQVVK+PG5MDf/u4PvPv2O375swNfPB2oudBKoabGuhZ+8zff8Id370i5UkqjFENQYxwKroUtV6ZpJsSFZTkQvKfk3J0iK+KU0pTXy8rLdeXd8yuvlwvXNeO8Y548f/Hnv+TnX75hiRHp8cO0JW2/XddjqarmylmVXLmN1OsNMWa0DMSE72SMl97984hTw1nyX9p+Ypug94nvAzHmTSDOlO2G2IPcySKKMzbodUsGF4sYO74zzWtT82WOo69lyYIfMnpqWZIO2LwH5JFNibidKDNgr9FrdzIkMA0/2nv7cufA1cc8pJfGO6Kgfe70vsmvuh/Xfa9m359xIewy7Ox/J+weDve52v0o5EjiSjV3LNeJerU1XLAWjcH3g7jY9mToVnnfX5uB2dwh9DoEjBrSGs3LLYPUTqhR7ees5196Q0lGZio7EtXPX78OrhNkIBLQniwKGsKPvin/FFstJvc8OaFpoebKpWScX5Awm2+5DyzTiRBONJlMcjc/c84fyXpFfLDzWCteM5O3x2etgrqVplfcpAbmNWVyJiC1XTcbhYuCtgzR+olaFPVm1uKjgLNqVTy7WxyS8ShxmYhhQb03jw9RwqKIFEQiYY7QKl9+9SVvn95yPC6UVomnI4lXJAvLFFgWhwQhLJ4QA6qJ6CcLlnmFqBBGz8fG7toFJAjTZOOSpTOvXRTCAjIJUQLBZ1rLTEdPkNlGGlGKf4bJ44Ihe87ZSKGbPME10looVE7HE602tnPq3vKCc4WCItOCl2hM65INLlfBi8e5hkyVKcK2FSuK+nia1koMRsxCTN7ZRbOfrtLImMrjHFYQTxGbBxdvHS6kopJR9+NIWH+KLZcGWntQ6IWXM3Sv1moeKB1ZrVpZ03UX1gHBBSOBpppxvW9dauX6wazoDSEJVkgpbGdhq7A8vKEWT1qVbd2sPVBs/QjRs8yRWgspNSM21sGfMtXZUrqbjtwVVjLs3vsIubtpsYhzfa2xiaRay47qtqZorTYG2LlPo0DcUdyuizaShabCujX+9//696R15enxFzzWasFbO1egggbP+bohEmzkGisYfee6gbH+L9eN798983g6ME+RKVhhqliydVk3Pr5eWZPZk398eaY3ZBDpI5QIX759YIreRhvpWjZ6E7ozzod21Fs/SQTaJ4Xl0IDpQ+F7D2LHtG29/+cK0bvtp4kOOX9DAnp8NLnfm6Lfnij0gDkgjC1ng/e96S3jhlKfQK2d3dnhDuk3S88QnZqxzhDcUels/Z4JuSHGY6X6DanAAnDYFQH7jH5nyvdfvwU8Ae16u2N8siG9L3Mf+Ad8bjdf6VnvqIxvsE1PXFo1nYAp/HEiMNoXPfLeV/cjGmu/wM45gyw7X2PYGvPJe1rWvefUdy2IoRngXNd+0Iaq+/TFsCMw4xyN3pcT10cxb5KlRnhkh/jMxcx1gaRPAZL2GaEBVU/F4b0j+ELaMmkrHKYZP82EKTItkcPxSJxPNDejOF5ev+d1faXGio9Ag5oaNDgeJrw4pmjKbFUzzoV+XgvBKYIzQxmXSDmDVMJkSFbdCmvLGL2jMlxb6QIpxreoZvrkIlUh64ZqIbGSdKWQCW6GYFMBp8OJL95+ydPTA6/rB6Yw87x6fPQcw5FpcjSB5loPzIKox4cFP1lV7IIhSFZheaZ5IU4QY2CazF1NW4XJArqotTi0JUM5Tobo4aBlpUhDoid4JW3ZRJdCsFn1uqKut/1ipdZCqRsAPgZDSUIghEdCVWrbLGjXAlpY4oQPnhYy0+xN4Ew8JSs1GZy8TBPbthKmiczG7B2O1qvt3hYTWOJMC4KXCtLMw54rLhTq+vmSgZRSR+X6ot9dSJ1zhnyI64md7mZrQxjNuWCEPG20ouNB7a1aEOeZD0JYnLVMMEGeqpVaBZEJR6TmzRKBZoZGx8PCtATSKqyXbW8FgKEwhl5a68C5UfXfMNK9Zdpl4ltHG3ekVlyv0i1mlFJw6vbX7X4uu1Ki7kJABqFbWzQV5cOHV/7sqyceH46E4Nik0RxsxYjDc5d5dm4yf5na13bGNIbQWuX8euUff/8t8xQ4HCbePCz7Wp0rvPvwwsfXC9fNxh0v10yIwbRtSkbbd0zzjDjhzeNxj0OqfbqrL/aj2K13mhCtI7R27NyhrHctlj01kR0t2iu6H7H9ZGSg9Q++J+gZjNWIIeD7Ad0v+6pKzkZu8CF+6p+tt50eyPc4pNpPkEjrsHRHGu7e24swRZNoHf1xs+K1PRiiQwZ5mxjPCFY7/C+yV7oGy/f96g/O4ODfPrdfrH4TU/t4nfYbpycDIyi31jrrV/Z4y905vKcS3Cr8UbCPkUybptBOCNTbm8B+tDfkYSRr4/zbn2NB69LJajfjaL2I3MiCw3HRABUjJEXnTMN8yBoz7rWe9LVumOLuNBHQ2/H9yBGXP8UWpiNKpOREyZUhYU0r5tY2Bx6+PPHmZ19wenpL0wC18v3z11zWhJeIBI9TgylLVc7X1fTte7/euYAjmHBI2WhVcASmcGRrK7UkYgwscQYNVDIpJyQYSlCLMkW7K2srVK2WKDSHC5Fz+cilrjRxnNWIeKk2TBbmStYN7x1v3nzJw5sn3q9/wLlKmD1BHCEakVSaUksmp41lmRBxzGGmLgIUW6S8WKJYI4eHCYkZad4gVG8BAa+0ajKqrXXx6QBhUvxUcc5R3URpGecj4mw0zSP4UG0kuGXiPOMcJK7k0iVcPYg3RMxNkWU+Ua6vRopzZgNbAbwjuIlrW9maCRHVinmQtMYUI0EmavNMXihkKh5xDdcUrUKcT/gSOBxPrCi5fURIeKfQHC0U6o9dUf8EWy7lZljT16T9ee9coJzrTVeg0dHSkfA58h4wuqdBKaAep47gPceD5+HNhNJYL8b7uKbC7KOt65KtHRQdy2Hm9HDgul5JW6E1q5xt6yZ2zuOlUbs43EgG2n2gohOu6QEx39oBBhrf4sBQ/nPeI3caM6PaGZA4Ql9/7OfihKc3j/yHf/drfv7VAy1fqAgJeN4K15ePTK9rF1+qnWg59AcsuRrxTcTxetl49+GFLz4+cJijjTDiOF8S3717z+v1ypaaIeXOk0slpUKrjY/lwj/+3gS5WjOr98NhRqjsMnw6iip6O8Y0Ie4lh0fgH8WXtoY6ufOFuRXhP+Wu/UnJwOglj7k3QfHdcWlLxbTO76rwe3jCOaGMoK+jurYvh1JaoTbr84l3SNX9MwQ1D2lx0EZQumnlRy97D9u5ETwHTMInfZQdjhk35RgjGshAP+GuJwhI20/qSBRGNthUybWBOKL3qFgmTqkobU9E6H4F4wEWuVXR40zZhe5UQXuaetC1kZ9mrKG7G6D39+/EfEav3kh99lCqsCtV2ZdB117t/b0MroTgxBZP3E3NUPegiaEF0BecYY5k58aPpGtkCPQPHUOHKjT5ceIXf4othkAQG1lCsT75BIcYeX79Ho6NX/7s5/ziz3/F2y9/zrYpr5dnPm7vqXiCRmpSEE90CzE4qqxIUFLZoG0G5ZZiLOXSkOJoLeGDJUohRFqB6s3ue46e3K5EZ/ek6xm/R4guIKHx9HBAa6BJ4fcv/83IW1vjXDdyUSb/wFQd4jIfXr/n4cMT3s08PfyMw8ffIRO4CqEql5asNacF7xQvE4LDBc80zUaYk0JrhuKJEx4PC4LnOX0kckCqMh8nZj9ZRZKi9aSrg6ngJ0WlsCZz8HQuglvQasiGiuA14KqQOyEuzL1lWJRcE0Ubs5uZ4wyu8XpZCe6VUjczwWGMFQprWTm/u3J4E/n4/EJE2C4CLRC9FR5t6GBET8tGlpwXYXKBlj1ePeeXgg+Z4jyrXhFZTd+jqUHrfv5s965WK51wd0m4qjHvgaLZFBxH8TDCfm/L1VJAhnaJdt3/zmUXs6KP3iYCbIqlkTfQcuHiPZfzRs6NGCemORC8I22JshWu541abJ0K3e10H2frhQaqBO97Ao4px6K2TqrSnNvXNMHuf4kR6W1am5qS/bj3dfwOdhyDUSKud7N7q1jhcr3w/uWFt28ekBpJyZGyUFR43RK+NEu2xNQOa5+6CsF350CzZ+6wNa/njXfvX3nz8MAcPSLKul3Nzt4Fal3J60atBfGBaZ5JLZlF+vtXVOH9+2fePC78u7/+S4IzE74+38bIbkrnRHxCitf7eNHPZWtdUXX82w1xtp/9uO0nJQMiQhz+yto1+umZHSZkojebn33npGdrrvVA0rPU4G47PZAeGvvNOarUW36hRkbsEah1qDu40M11wh6HGuzZ0b0kp83iV2r3eW6tmSRkTx5qs5GQKUTisC7eT8APz4fBzviAi6OiDlTd0GpjLmZ85D9RJbxPBOi3ufYkxfa3DtVXFCg5M002LWEqjLbv0uH7m9iEoSKT2LkVEWIvJ7wTwjztYkZ2kuy9Wqnj1NtW2c05bgjAjRhoVJHbubmfZb1d93Gs/acd8flc28v1QqoXWqs2b79MBJSvHn7Gi37D+nDh3/3bv+aLn3/BshxpLYN4km9krTxMwSC9nMwgCk+pdl+t+ZmHQwRxXC+rMa2rqf95J+YySGRLSq4b11QJsdCy4Jrj8fSGzIYL2IIqwjQ5wnyklCthcgQvtFI5fzyz1Y1K43iI/Opnv6K8XiAm3uff475vVCdInchJOMwLP3/7wJYuSM1M04RWQaupwBVNPJwmXKi4ZKNp25poCtM8U7I5vqW8Id2O+2F5QpxjvTwjrZDV01rmklfCbATEJh7xnnmOlKS8vF756ou3nBalbRs0Q5na1AlTW2GeIqU2vD8Q4sEqtLTx9PhI5YIGwZWpO28ahKp45nnh8vwe5xttcbQaMeNiMVqcU4hirHMXDUdZN7xvzLMpQU6nt2Sf2bZGojCa4CqNoDOlfL5pgjiEQFT3AiEna804J3sPfV/r+nrgnCk0jkqyacUHE2LzzrNtds+pg1wdEwFKYjtvXC+FFAPHYyCGhewTqKOW2xqQNjuntRZUTbsDrEI10zcjEToRnp6eKLlwva7kbC61rWvn613vfxDUty0xtACAvt43bjKC+4/t1LRGAaS1TqWyFmYD1iRc2hs+XB7RdOXb98I376+8XDZKU5qrNgERjDd0OEScd2zbZihgyQx5fcRxPq/89rdf8/r8ytPjkb/6y18jXZTNS+Dh+EiuhQ8fPlCSabNMU2CtJnG8pczr65Wcn/jVrzKH2IvenYwJos2sqWultdrXfe3IQK//tU9xdL0cc2DsaIpaG0xC+NMkA61Vtq32m8lg/ZQSYCOBTkCneFdJ6x6UBYjB4bpRzC7A02+s2Zs3eRWY/BC6ucHeJrfbf1l7xTpujB0EMGGHQbow5Eg7xG690lQKVXPPwOzlrhPBvHOIeAuYw7qPG8YxZHfduBy98BV/S35aq72Vcaus6f0t9Z4f5mmqdzr+sr9q328nQnQO32F/caZr7aPBhsHfeAOCMk/x1n7YM8iG1h6076Up5a7BIKbsuB+0jKr/Psvs8Bv0tgK3i9jPddddtL5bP/1jBPXHymL+KbZ5ObJeP+C8jc5paTw+PiGHyvvrP/AhrrTDxNYqr9sz162w1sT711fifMDPMAWYWJBmUwiUI8tx5nV7Zi2NaY4cHw/INbNeV2IIzPFEWpWaC4fFIQVyzaSS8OIIztoppRWW4wTV45oyec80T5yl0aRALCyHgF9OpC1Qa7aF61QpEfS48c3zf+W7b/6e4/JIWBp//ue/4t35eza9UF2mbCvBVWL0+CkaJ6ABshpruTqmKRKjSaI6UaJrbPWK2Q4ouEpeXxGnqF4JBCqRUs64Q+t8n0DNDlxDDopzlaKFUhOTP3LB+tr0+e44HxE8B/fEw5Mn+MUIlS3jtoXTcuT3Hz8Q3UJp1kIIzhT3ylqQWJnCQvAL4ipEaKWh1eTGQytEF3AaOB2eEL1Qs6duiWuuIJlp2tjqleAf8WliWze0mZYCeHT7sUvq//VbrTfxmLZrB9h/mvHb7tYOPmknoGajPjhAXm1EO3Yy6JsvFkpeuVxWSjbk1daVTqKuyrpmFMi50Na0P9O2Tzby2LqV8tgDs4HuCYkIr6/nXTxuFzFLCQGmaWLIAqM38bmRHLReEfveir3nDagqWhqD4qxqSIqAtRucZ5oWVAIfzldeP77n3buPvFyz6WdMnmWZ2dZMUxOwS90ZNoRICLafJutboDiKE1rNOBpfffWWpvDu/QvvP5758PG1o8U9anQivWntWJG3bsYpiqHy8cNH/BePOCpS6z61hQqlqI19qjCslveoqvSWT0E13OIdt5bJTk7/kd3Zn5gMdFOcQTrrwcqCn00S7PFhVIQjSxXwoUNV4/6lw/Gi3NnGmDui3CyGb4d5L7yjXZazUXf9gx9kxz2YBe+ZuCWUrvdzpHMXJJglaPCBUtKNXGhnvL/obgfHX3VwBiqitsA5/K5y5RDUuxuErrqLNt1vdymABX8fOr8i9IfBqkUZY5LAjWVqM/37XnUUxFQBbyOUt/ldy3DHZEY/mXfV/fAwGA6RHTHZr/dIzm/Q1V6ZqE2BmBlU3cegdM/kP9+CukTPlqQ7U3ojt0nmefvIa36hLcJzeubr52+4TJ4tNb5//prn9R9YjsJ8UoJXmzlr4AJIhtf0SlicLRyTomRcK0zec1yOPB6+5PxyJeWN+QBL9VwucLmu3aYULu2F5ioBc82z2fHcoVnHy6ps+czxEDksk1XsxUZpc3nG+caqH0jhSpAFnSvLcuTLxwfKtxfWlIgzHP2R47yY54CaQEx0SpgUqvDwtHA6zFTdWNOKNjjOJ744PPH7b//BnpEAq75SywauINGsgpfDhCwg7tEU4cqF1DKv4crT8cTDcTGCpQbEBSMHOtNlrzlxigu1TqSqZH1m9p5JJoJfoC7gJpbjYn4RzdTmQrBnJcbIx9eV4I64lmjtSvDWWvQBFi+8psS0nJDWiBKRqasW5oaLDtT0F3I+k/NqVZ4PBo27sLPwP8eWc9lRUmDnEN33kd1o3fb1n2biWLdHT/fKsTZFpRJ6xdqaUHLhvBlJzbvAskTO5wuoEUdDUJwUslgFGnBcLtduVnabmtrXEQOPbe0Ws15vd4VRCIFpMqQyhMDlcvk0yNOnFpzfW5KVdr8Es6stVuuvOemKkyPuqPGsLpczf/83v7EYhOliPT4+UXKmqfL05oEP+pGtWGZVm5EhmzRSypSSCcHvcc15WOaJxzdPpFz5xz98S8qNl/OVrSScc8zzzPF44HrdjLORN8DjvHJYFt6+eeDnX73Fh4ltK7iu7eFqd0UVR2mWyLWdazXKUvvvIBr2s7Gv1602SrYxfrSQ1h83FvuT2wS33k3vP3uHd8oUQ2dH0nvt7IFUsco8uD6GKLd6U3vAHSxIu3k61NH/rbZhtmEJwHgf6AlBsV597ZyGoVC1Q9u+s1CVHghddxq0kz5GUu7vtJ2iMwLpD0/GyEr7+35yjuSWO9ho45288Pio+4q6B2OPhyCIN/UM6Sfa0wNt064fYA0FN6C10WqhH7vcoS63PbuD7Pt56cjLnmH33xwzzcPgRNXcucZDqe3GBxkPcAhh7xcOeM9OTUct/jgH+lfd1FWOj/MuxapSaHguW2arSozwMX3D3737zyzuG1Q9l/qe+c2Gl8K0OGrNpJTRWkE9WTZUCkGc8QJcQmnI5IgxEueZ09sDHy7fMR8c8zThME0LFzypQNUzMgFqMGdt2aBsFxBKb8slk8aNvtufGqdAFTyFJgV1Z57eTuRc8UvGzRuSM8fHwKEFZEpstUO+KZFyAhouNHycqFnRVojLA64JqTbWkiiacSWj0tt3h9lcGBu2WMoKzpu0t8so5g55WCKhZbw05jizbkquK0sUfBxoYTOUZFUWP/fWnLP30URpNmWkLXF4POF9Y/G2SHYvT/wy4yQw18jD8QjFkjXvzQnOOWtVfCULdZ6hZrz3hDAjslCqo7aE1g+ksqFNmL23pCU6wuQNqg6fz8IYbs/Z/Tb0XOpQ4INP1tw2gqr9tj3HtYFUm49P8PJxwzlhCrFbwZvkcckV7yOH05GUNnIuhgyMhGJYkksf8eOmTHrrb9v3tZlmhZce2LVD/n3bUvqEJLhPWfVjGlwDp25fY8ZXa3WPR0YavK3bQ/3WOUdOK8fDzOl4NCSiNC4pk5IJ4I0xTB88WozwXqqpPYYQOB4PLIvxRkrKlJL4/t1H3n94MUTZ+T6JUKjV4lcMwb5v1joftvNxiuA8z+cL69/8ljdPR3725RNzn57xzo6llk7KrqY0KyPpGggIhhjUYryQLW1ct8y2JdbrxromRKWPNP7L20+6w4MTY4m6W09mcn4nyu3z/HZHfLL676x+2Sllt1/rIyLQe/gKYvglQzyw1tqNjOoeAPesV5w9rKX2sZu+f86GMW7yuSNtHjdsTz86qaxpVyv0g5xyq6zHsezZ697isOA6NBFsLMYexqFzYNX8LbD+U3rRNpVhmamXW4vCDu+W9QE7OXBcg3so/55UsyMb3LLIPZvcscRbJo7a/LHpl7c9QbAvy8LH2+7nAwv0UYyVLGIPf6mjUnE7iuKcZ7v+U3fWn35TzRxOsUNuhmxsNZG7g6WLM5f6Aa5/i+MbS0hD5vSFY90AX0wZ3neYToSoyjQvOAnUtJqlLtoFqMTQg5hwcyYeZxMtkUCYJuJyZE2NlCrONyqekiuVPt43deZ0aPhoPVnvpQfbiDhHKpktb/iorLxyCo/UCldeSWUl1QJTJcoEsZN0uaICVmhWxCfET7Ri5inW77XgHppJN5uipjJNs5laKeaHAWwt4T24eERxbHk1Dk80UpQPDmmYOttoD5j4L+Ics5vxkzAtk7U9mHFxQ8h2PtxG8TDPRnwMXnEqOCJOFhyRVhoPp5k3j0fS6nBikwvmkSEUpxyfHriqh1IhqLHAW6EScDJRdEJLoeahceCJIRJFWHPqpKbPtCk7bD6+B8wWXgXxdxhff5YbtoaJs7VpWLSrWvB24igKz+8vHB8mgrNEoVbrQ6eUCX4QmIWU8q72WrVR+0QO+zrZS7v7pEU//X5v847KS4anyg1IHpXMEPdS44vuo8z3LUtrN7SbmFxPIOz72ySaqjL5wOPpyMPpSGvKy2XlejV+z1aqcb/64lZbu2tV2DpbSqYUb+s8dPGrTG3KNE34YPoiqmJcCPMh7sls7T48duyW/HQNhla5rk8s84IeHTEI6u1za+kiU6OwqjbBZl8mhvRyXvn48UzJmdfzhfN17QWY65Swf1bu7pPtpyUDnXgyePHscHNf/O4u1S2IDMi+B/Z+8QHTFsD6YE5Md7mqiTNovym0G/9UOrx9k71h3ITOB9OebtrtdW+/8enWYW0dRjqdce/U+kxOQJtZg8oNrbi9/HZah9gOaouaQ3YNhuE7bb/Y30d1H5v8dL/k7jw1O4t7djyeD1MU3N/wbj+0B2arBO6Slf3BHD9vtH+CTyHIjcchsK7bXTVxO1btX0Pvc4wPilqgcF764gHV9b6aSjecsj3y7j4N/NfdnGTm+UBVASxpSWcjkQUnhOjAJ7K8p+l7qjY8gXCA2Qv4Ruy8ljbmmYk8PJzwYeblw2qkOcVGNnGESSl64XAS4uzwEeYYmA8z0wGmNbOuG00ztVVWhIoiUWGu3V++IUHJWyFrRqrgwmwLtCaSrsx4rjmB86SsOMyUqNSCd44lPJjmgatsdUVdRyck01yiaqJo4BQjW1mtcp484iemecLXxnKYOU5HMmfEgRNvNr+loP4MbiLKzHXdqFS8M9hzjoGcNmrdbMHUitAJWW5mcguPTyfCZLPw0xyIXY8jOeP4+Kgc5yfWVQ05EEzZrmNihcw8O1M5nASR0J8pk+F92T7y5mjaJI2MYot73l5p1TPHB/YVo1VoMMXAFCacKK1mov98FsZjwZCewN/QVOnPnrs9r5bxW+K6u58CjT1oqg6fA6tyRcCpSWCXzp1MKROCI56vIErJFVUbY6ylGrntLhmwtfVul+Vuf8SKrDqqdvsHi4U6gvatULtHnseYt/OuE/hub6v9HNyHO9U7ZGKcr2ZIay2NdU1sWzIOQ6mUauQ877sBVLsVOf0D0JaptbKuyUar1fa7DLXW0ky8SUZxKzZSWe4QkB0N0W7YZ5/hFGKc+Pa7Zy7HxDzHm/5AU9Y1sW6Fl5fV1udUerFV2bbEy/OF8/lCSpslN6UwLwfmecGm0PTuYvzz209EBrrYTG846w9uUisg9I4z0C96h/dbHqNx/eQgZixRC7MPhhgMNTxxNkaAmCyxq13ZSjvv4O7muc+Y9Qd/7Q+Hyk3a14KU9fRNHKeRtVmSI/fB+o9PosiA4q1S873SF4yBj94sIwecPyrpgVoMOGwcLspOmNzP5b4vfb8HYUfYIcEhHEQ/p6WpwYDcwYR6O/8jbWNkvNiI3eRD1xuA1Qmo2xOA0Y5QcTSVnTciwRH6qGdtpS8Etl9utJMGnIe5Ltb71eJfeYuxMs/KWkp/cE0EZzl5Dg8nJh+Yg6Cs5A6BGvM8Mx9A8SzhQKl5XwiFiWn2hGViTZ5GMGeyZp4b06RUvTIfHS5AYyUsE8FPuGStCusRBNZtw/mJWjPNbUhshhg0s53dauKSNxwedQmnsLYEUUw8JQmqJuMdxSNeyWXD+Qhh5kEnpihsLZG12LXHpmq2/IrKgveBj9cL8/HIMs/4Zi6NjcbbN49MbqFur0zB4zUg2ZFypulG0cxhWqxdYg0+vHhmP/N6TThpHOOEpxgJME6IW5DqeXr6gi0l1nTBYcRCcYHoA6or3jceQiSrYxJPdZVU6u4nkdqKb47X63uaNFDXF95GrZmXyzNOK+IWUrlAWSg5kdJqomLeWOA+NGYcWhdCDDYOJw3xEa/TZ7t3nTfEcSzq41kTP9Q/3Y5EqvWObsioc5RiqIzerZm1dinjBufXK61Z/742peZGLc1Y/yg+OFrryZLl/nsisPOW+r7uSOpdgB/txbF27LynWjoiOiTSsbW6Hw37+9j0jh37rcjZlWD7Wtp6cTP2YfxurZWX1yuv58tIWxCslYc4cs6mlbAnNrdjgc6/K5Wc6l7dj2RGgW2zhGpoySC9tdFPtvd+R7RHoacMcTphS42/++0fmKbAHCN+iOI5QZvY9fntHyi1cLmupm+jSkqZdc1c1+tOzhYRXJgI0QjATdmJ7//S9pMbYfc69PtcPN2oRvpYid7nfva3dc24MLLrWxbhfEDUmyzlXmU6VDzag4hiD8O4TZo20z+ntxVSsj5k665Vn0Bq0iWL706IDC3nPoihDWoxHsJdH707KNwf/I4sWAugJyt6QzZaD5zjMKXfHEO1cZy8WwU/kue77N7iSX+ILACXXG77dRfkhT6l0Z0B13XbobbxgYPRu/fkOrrjhH2WdqdNjNf19g+iuyCGLTbjge7CM81G3pzckUn7Tb/zKXrGXMrnmybwseBng/Bz2miqHA/YOJs6HpYHUr6Q04a4YhMcMbDlzATGVI8eh436SBOcmwGlugtuUk7zW15eL2z1DGLXpdYCXqmYGpmboLlEylc2uSCT4xgnTqcTOReu5cpWK7VtaM2ILjgcDw9PLPPRqg6c2QS7YLbHwDE+kFhxkxpSZg4yNGYu1wtuThz9gYijaKPRKDTOawL1vDnMhGOmrFdattZFdJF1u9Jy5uhPMFsSNPsFrwEPpBKRoKBCqhvHxxOlmGrn7AQnntPpibk5QmikcmHBEcOREJ5AZsQfWI4PbDUZm7tiM/Xeo2Hi+fqBlj9AhaSOLJlWBNcsWdpyIZfM4iMSAr7N0CCXlTW/ItWzho05BmpyTNM0Mndc8CwhULUSQ6IQjLtBNZRAHd4ttLZ8xns32PrQk36HcbUGonfbPkUTB3RvicEgIBoqMBLagWRezrkjUb2w6Wucd7FLAMsu2CZBLFEYwb4XPKo3xND1KZlbYTgKEFtf9tZns/aVv1ujZZePtzUu7A6sbReAgz510I9v8J/sfVxfh7p+inOd6FjHrqDQdTWMwGgFS93X5V0Jticro465s6yxSY4RP5x0mfCxht4nAsNTohGjTXvpuAYKHz48mxbED65h6FN1op5vv/9ocaDWbhQ4kiC/x7t9ye9yy3EKzNPMNM98/c0f/sX77CclA7W1HeSurZn1qRph6HK9clgWlhh7AtmzNgVV6T4E/nbxxpeT/WYZX0ZSuTHVaUqqlSmY1Gvjllk1HW5yAZto8MhgtRpms/fLQvDMffTRFLLArDIac7AAuVf5PYsdDn6jXTXGPvZ+/Q8gGDucDqlzy5JrM53tuJNsbq8REXLKVMzRUfsHtg5HcRdU2W+VPnkQvC1uquCULWX77IHiMOSah6KjoQGHZfpkPPMeoah70iC3xKUjCdGz9/G2bTO+h8EdvXqhk3m6+sQdOvAZgQGu7UJ9hWsqlNbsXvDC4mcCnsXPiGZcCyziCUskObi+JnyYyduGmzKNhAtKcBOQetshczhM1PVM9A2WiSZCUU+jklqipDMPeJ7Pz6R6ZUsXWnb4dkJ94elwYvaN7bwRfCYGQVoguAPH44lNPcs84Rqs1ytrrqhEQjjg2FivG5e2cTrOhhbVxiKROEVe8jNXgUf/yKF5nKs0l1G/UfLEtTlcPHJOqxEUq7Buheob0c9c11emqlSZSSUxh4NNT5SED4758AYRpRThqzdvrGrPGW2ZS7syS0S8Z9OEi8IUFmiO3FbcrLyUK9vWbXijx8elB6IJHx6pqVKqR9vG8/qCIkzMnMLC8Xji+GbiD9/+hpe1cTpNzDiCC8Q5EOIRTcYHSQriJqIzdnxrjlQKUy6oCOdSmd2BMNtoZWlKwHOKCyE/frZ7t9aK97cgO1pv9828Ws3TBTUGffDeeB3azNtk77ebJ4tzgVazPf8itGIseufFeCF9/T2fr3hn7aA7uHfnW+1EviHhq7JXtTa1I9TcjL9ytwZog1Yr1awh+/RGRzN6MqBqbPrazEwrTnFXVTSkgf08jABbaicUMtARz54QNGCkE3ewvSUxnxa3A50dRNdx7kzboq/fzX5vxAJkoDiWkDjnRkDoCdZtP1utaKsWGwVyGYnKLZ6kXAnRozRqyjuqoeMIDUphjwfOITSm4Hnz+MDbt2+Nd/RDvZz/zvbTXAvXlXme7cSiPasy7SQ3MrId/75/5U2PelTbQ/vftWbiHlMw/kCfPUbsItZmwgmtFPCe4PxOVBwmRJPvlskegvvB5RRs7K+rXfmesaI2hzwu5GD7Q7+Jxgn8QQDbH8i7f9qZ9R3iqnob/RsQ2TBJCsuyV87j/VUV+thgK33Pd9eNnknLp/uwjxr1fRm/sydXIxlxDheCPbAp7fs7hD9E7FYft4sTsT7aftxKKwYr+2kyC07vSG0Qde4gu/1qj6RpJHi3KYPPtV1KopVIagXnI4fjI49zZNHIpRS+f/6OGlKvBMClxqaN17Xgj6BM1HIFNkNK2kbJr1RXefPwhmX5klRfbNxOhaoRkUjiFW0XihRy2bheXyl6Rn3BhxOPy4m0fWDTF5vHd1fwFR+NLHt5ecfj6QvYVoJAiIFWlJIxcZW68vg2cs6vnD8+c1i+wLuI04JqZomRGibOJXPNV2Y58PbxK5TEefuGeHjD5jylCpvCeoUlToRgEsEuHgh+orVknCBxvF4/soYr4j1xPjDNB1p+z2H+CiHS6pWsF5o3FGvb3pNrQSZhcZFSLKBVSaTLM1M4grdFvnFhDiYxPKknsvAwPfJyOZPIaAi4LuYkwZG00dorfi6oN5GkGE54PyMRvGt8SGfSBm0Cx0TV2dTiQsO1DAi5ZIoGpsnhcaRmanS5OQ6TcC3ff7Z7F27Pzqj29166E1ppt8pSTSSnkPFTsEA/oHcdiIFjaI+0Pc7157hpF2Eb6GOhaBkPdX+O+0eJrRsigDOYPARHiI5pDizLzGXdzF+go6VNlVZaR3x7LHBWedc6iNT3SYfeZJXbmBCzfXZhkJr7mnff7++/11rF+8DxdOTLL7/k5fmF5+dnas7mmYCJgnl1xtbHfib9AG8Iix2k9EL303aM7IiBc+y8hV1OHxOOExHquJ5Nb8iCtJ3oqe02PWE1nU0ifJqKjf0ZGYnF4oZ0PoOds1watMDp8OZH3WU/zagoBNM2FyNlCArO96AaLIDKD14kBi2bEpX18nYTmwHlNIUbUG0Ves+ApRppKLiJKXhcH7VzAN4zdThnv39a2dELMF7Afq9jgj33VbYlDGKeCu2usXGPJ/UXqJo0Zi6FWm89I7QfQhcQsjjc0Y1m38+TEUPue1xjB7R1jQbvKMVGe8S5bjRiPgoxBq7Xa5eeFGL0DPYsd/C8iBAm62+Om0JL2ScqfBe1KKWiahk39AS2J0XaHb3kbmZ3+DTkbAIk6jrcJc5Mm+4XLMaNPLJmS37mOZA/0zRBKUJr2bgi3tPwnGui5cy5XEEcuSS0eMv+XQNvWvypCo0N18zmuORC3jKBQ1dyW8nuIyKFIpVSodVM01fWdqblBGGiSjExHAEJivcbVT6CrLQaEA/z0ZmIAZBSpdaNKSZqW5nCQowTMS5MR6vWWvtIWDzH44KWxjJ5jtEjcSJ3uH32R3Ld8BqBSmlXUt14uV6hCfHwyOQdrSqe2HNQQ8zW/Mx8mkh5I7dso8Qdtm5NOcRIBJoIiw9c1xcu+QV1BRcnWvNo3ZgnzzldERe6utqFSsFp4Hl7JsaK1IbmK/N8JIaZoK4v5h4R8zKIRELcICW2ciWKsNWVZXrkw+WFJkLyFcqV2io+RtycYa1GrgreEkKUpl0DRJTFHSn1RDpfOR6OUHrvvCk1bwT3+ZAB4Mb9aa0n8Erwdh2s7Sm3tWVMPozqfV/DBlhqWvlGbeoKhXr7d7jxfhTFBHT6c+2kw88B7doqtQLVMS+RabLxuVIKr68XfAgWVEd7c9cv0dtC0QP3XizqnWjbXTuV/WfsLch9P3uLgv57Q7BOARc9y8MJ76MhINnQ6ikEaqn4Bn8eJl5a4YVKklscqvu7CJ9ILe9SO2PiwIrie+Ii2Bo8eFyjiB7TAYP7Jt7duFhOjMTZ3RJFIeAw9kY/Z3uRdV9cjda0qT5erldzA26Ol+v2o+6xn6wzwMie+viFOVMNZmm/Zfascq8bLVj1XR7wtYJV+R4b7VAlhojzN52ANtiwmD2ukQw7cY7hktgjGe2TCwG3tEDcmHnghlJ0RyhErfVQzfO70ceinMO70ZNRRt99vLZWWyzE+f7eA1qSvfcuvjsnhkHS+2G29GmrYWSa3od+I5X+0NlLxz6VXHZIUCxFpdMDrae99wg/fW/fUY/SRvUv+3UbN6MRV2wRcQ4k+O5ZYP1mVSCYq52RDIcDpe6ohEi/1hjMJtJVHT/TpjXixBO9EVNTXjmvrxR1tKCo871KctSqlFpwrbHMEVHPPAdDfVqhocRp4RjecL68kuuG6tmy8dpoxVjCPkBLBgUuMuGk4mcPboEO3znfkElQCuIc0UdbCFrjYXI8fnVimpQiFRcLTjLqIAS7t1KpVCk8nQ6cQuC4zCxhthZevjLNj5SUOESHU0U1k2smiyJxIV8ziypvH77geUvMj45UC6lttNIIeJ5OT1R9QWRjir4TSUEpNo1Q4XE58OHyEbd4NPR6pVbmMLOKGeXkLsgU1bF4hw8LtTrS5SNJNx4PR06HJ+YYmcVcIy96xvnC2zdHnpMn+pnJBworLStRYLte0RhYXMRPEzJDaZlUVkgGe795fKRSKNrIZRCulBg9VYWDP1qizUatQvAPHOWA1kIuiaaf8d4dPBxua8UuyNaDq3NDS2WU7X0uvdl6cP8eJmP76ffjvcdSNJbyGxKgmMW25apusgA4iQcNbFu2AsVZLzxt2ar2Tka8bw3T3TxV7secx9TZJwdu+8WIgbcq7jbKyKev4ZZY3HPFpFmCUjtS651jdsKGIiq8sSPkqpD6Mu3U0aSyz/XfoaX312L/5D0xu123EfeGrk1rDXyPFc30d6wI7K0chWUyOe3WFRn30W+AH5ABx3femy9Eq0rOlfPlypoyqp6UfxxX66dxBmqlebMxbs20k035SbtIzThjn8LBIqYCGL2n+eEYCKB4+shIf63vzZeRNPpwY5oas30E3dvp2FHxnbhom3aU3cyIBg/gNlUwLCKtL+R2BqgTiP6mp3CDt+3C+h78q45+k5EJvffmuT1uYjccEm/KgWP/PoX9bBsohvl039S8RmbZX0AIoUtk1p2Ic3vYRi+sn8exePREZL9BZWhGWG+/Ne29PscUTSN+n3DAxgabjqRNGKYp2sc9TVO8Lya9JTJQARu7vB+P/Nff5jj345gQ7yk1UVviUiohBLNz1YhT3+8PodXCcY5QYCLSnOdarygQomnkewWVgIqjiJI7OS+4nkAqLMvCw2Quhc57g9LV74MzzVSlqK52LktEWmMOQpCFtZxR1yhttYTPH3E+WtUcJ7IWphg4zpElHljciTUlrjXRvCPOR3xd2VLGe7XxOhWWEHETnObAEiKX0nAnQVNCW8F7YXazOfzFivPV5JPNdIOKcq0JxCPTzPX8gYlAnGxKQGsjTo7iZ2q12qapiR0Ff2CaZkoJHKZMFZMzn2c7rzQbQ5wl4L0nSWLB4b0SxeO6cEt0nioRFyei80gIljSVZslPayZKdDqS85UgwpoSW042gudgozFJI8YZxwxBCGJty9IK87SwrfrP3V5/0u3GoL8lBq7j0a3q7uB3E1aDmxeK3tp2o5i7O5SbBosFYtmLGW6vd0KI3vgEwdnfHTsc76MQejFWmpJLIdfaEYFby9E4YvxRQBvIxr467MW33UOWI/xw3b/98q398YPkph8WqmirlGQaEgFYEB4VggpZ4CDCVfp0mFpxaadTdoC4jQRrxIyxtg7Z/JFEePvLUGYVGVbFdi5jDHti4JyNzruivU1gBZ535tngSsNvSnZQ+nkYfRIZxbE4fDeJUmc+NINX4pyaWuiP2H5aMlAqLSi+Kz3tkIjcZu7vIfhxsbz3zJNVuyJ34x8dxreAYzej3eRjNO8GTd1uqFsBCkYHMddB3YOwwVGfZp0N8/C2ONl7NneqhjLUE0SZQ+iaAbepAEM37CI4LztyMJwc7QL6HQayfZX+2huCB7eb1W6yUZXf/ldqRbTgvL+hK72KVwZv5X48pT8M/Z0G8jJuGu2Iwv3DsQfqe7JE3y3vh3hUJyEOZS/VOwGTToYRC5z3SfLI5EV6MtSTkjHn/Dm2w+INxdGIqCmXOYTSGlqV3DJRnNlcu4CXSOriSx4PJaBBaM3RcLTmqCJMHf4PfgZtFLnSQsJhI4whLjyeDkRf2ar9nlPTxKg0q6RqYpoWaiv9Xoo2utch2uYEp7Gzp4u1upyniWMKj9T8SisJ/7CgzoPMRO+J/pWkmcP8xOWysjWbHVdtOITgIMyCmxq5vlryPSuTA98a4h1zgMqZMDUIDdeEECJeIrnCWpWKsFal0JidQyT0LLwYl8d5tFwNIWrVuiCuUYEmnnlaaC2bvHJN4ANNhCCR2R9MmKWd8S7jA9A8Pk644IjqeDwdcFPgokLVTNMrqCmeqjPTliKGYnh/INSZ4BqtJaQp6pVUV5ZpQoOj+oLDQfG06ljmmaSXz3bv3o/Jje8F6ZV1TwAGg22YvoyiaRd0Y18r9vcYzz/Wyh0S4/a7dyiEF+bDZNoUrq/HfX1taB/b7FMK/efiXJcJ7gnI+OQ75GGgxjKKlrtaQXoRMSTmR6txLFLa9fr7u/2T6Ml433kyZcsgcJgDURaOrfEGR/DKazOxOuc8ASXAPq02ClN73xH3uhqs/bBX751r1N9nzIbtkszOvglTIEz+Nq7Z1+Y2XCXhjhPnOKnylXecRbg4W5OR7mgr9gzjrBC9FaXSLehv1/3HbD9ttHAkJf2E+44SOMCFuAcW7f2TvTXQoW03GOb0sNZvWNc1jBXLHJ32U6m3bFf3AG+30LASrtqJGm2M3BkzdRBsREx/e7h7bbl+MmYzyCGK6/vTcAO9ENlv4h+ezpEF3sZoDNK/S8A7rNxMi3uaPhHd2R9svY387ZV8Pybdq32Tu9Se4eZS+wLQURVGsvLpdYFRrXcGsfj9xnZ6y2ZhzP4aF2SIMo2ETQBR3WGsURUMsY97VKLndPsjNJKCTzGbf/3NRyWVBsX69mvaei/6iBaznxYCnoC4CcSTaqEUSw5rE65rojqDXlNVfAsc4hNXfc8xPnZ+RaC0M02v5FyYFrMqTvVqYjzS4csuOapUmibCHMjrSm4FCeD9AjUgXljkieoKV11pzVOaohQIwiG+peXKml+pzZGrQcaTHDnFt7yU78DBNWc2zYDfK5/mCzVmzm0j1Y0wHXuvX6maqbIZCVAaHkfVgkpjCpGwA0SeUhyX6wYSmPyJkk2dzfdgVcis9QUvwsTEHEwQJZWMEo342ya2/MpVNrJMzJ2fpBpILZO44EgEFwxK6etNa2rKqKJobaT1QksJ0QXvZ2PC5yvn9RkfN2pVRB6IYeqJHkQ/o3kjlTPiMqVdcWIyx74XBTF8RsGsvm4M9FWHpel43u7X0bsnTTqpW/fX3ScUGIOe2zM6JPA/iR1iqN6YMhgQfL0reEruLqtqPW7nfe9z51vhJvt/+me0fTm4D+Lj1wYC3ETQwUr6ZMd6oO7HMIh3AzUxUp9pCbx5euI0L0RxHA8TrTWmXDhshXYVNDqSj7TgmYmoYm2tWqA1wh1/y8Tb2p4M7VNzsMcbS05G/aQMjxbnBmqg1rpp2pHrvt43s3quqmit+FKZcuPfMfNb58kdXbWWq++jniYaN9oE3vm9xW7qt6NC/5e3n5QMTNPUYc6uRuhHr5p9xOwG1bDD92GQ18QgEwUYLPteMUqvlJQxUqe3k4RJZNZ6s+1ElVyrLfB7cSs7jKJ9v4IIU4xmAdsauVqP3x4A3eGbqlhPu+qOCNyr+e23oPYk5g6WsgyoZ6ddyarRkD5m88lNL/te3l67//kp3H/rsVlPPjoLNrd9uzk5DgKl99J1wLllLNwZL41P3BMd65lZ4kPPTI1bIHfXc6/3+80ldw/3bYZkT/f356PpQCw+PY//2tu5FlzwbPXCVsxB7BQe8H5h9gt6TNS1MrmZqkLNlSAH0ErFnAOvyaR/Vfri56HVQAuB6BeCTqzrlXUznocX2NZXnqmoVJoTWtkIvoGHoolIYFkWUtlIeTU1QBaelgOn5WdMQKkz53SmrF8TVPBdlRDfmOcDtEdEFS8Hgh5YwiOzW6Ap5+171vzKphuHo+NwiGxboRWFyeEc5G1DVXnwv0ZlI8srqVzZykaUjLgI6il141oS11SYw4R3gRhOiEZKrjwsTxzCW17yM6Vmmq+sWXCaqVp4WL7kq/hrYjyQ20ZuJoVcXaPUMx4jF5ea8C5TXUNZWf0Hk2UunlYVJ637ikBqKym/EtxEaY7zdSNUmJzaCKEsFM3kmpAIVV/R0om9NEQmFveGTd5Ry8bx4LrW/pVlnng6nnDzAeKPM3v5U2ytE4Z06Js4vycCKqNg0rsEYDyNd1M+d9DqADybKlo74XqsSzJamjddF8SMjHwvmLgzIEpb7tck7uiCCLjgCWrXa2wDcTQ/mdLf54Z8cNdyDR1pNfe+aohyXw93hEPoSJqR2gfx0Im1Updl5nA4cFgOe0sviBqiMgfyoREeFx6qsqkSRfgCR1VYc+b58ooKPJxOdgq7NsMwomu9zdw6b2P8295GsNO9iwiJs8Wx1trjmc1xiXR9hBAoSanFULJJlaiwOGUJnil4mgMROzfiXUfexkimdMVJu07Oif37j7zPflIyEIPDO24Dnu4WbPjBh96grFupXMZN24l7tdtWajPjjFIrqd0QgDEOc4MklBgChzDZDZ0yuaR9vt1+00blXL/prWc9dvemte16Pz94Z6OM45795KTSL+p9m+KGc9nD53Eu0KWe9v0cnyMi4McD/MkZGm9z9539OU2mjDUUC22kpZ/fMXRB70Gh+zEOIpHzDi/uVimMZOmOeqCMSZw/7rXdWxqO5MdefifCpDvAtycCtwT5jkCqjdZ7avcLw7/2dr4WTicHkgATHaqtILmyysbT4Q3v0tesaj1wxRPjQmsXpmXiup1BGoKnaQVfmSYlNuGlqM3oszEdPH750rgj6Uxez6YP7g7kekbJZN2ouVJaI7qJt8cTCIRwoBVbLNd65sP2PaQVL79gWg5E72htRYOagJEPTDNsNRF1YpZoxEESuTVS/sgyO5J4vnj6AtUXZh8oUk1ZT5QJx3L6OUEmgkxc28ZaCjmD1hmHiTXhM6040mWze2d2HJeZ0/SWtV6ZD5HD6S2RwBwNT6qtUurK29Mjk1PenP4tRzEiXysblWYmUEHwD29ZL43qKsEX0wFwmVatXUbziM/gvVnMagZppLLRgqP1ADZPXxI1EPF4bbiycXCxCzQlgo/dxhpa9MCEE8dyeuL1w7e83yoyO6ZYif5q42PukcT5s927rT/nN6S8r0m9eBgV6EjE6VynIXRDM5176PWBt2qyDRGw0RLYCwiDulXHWHR/lltvr3aPEu/dLRYM8rb24qeqVeceBsFZK5Rc+pg3vRU7xNtMfEibkRTFu33eW0QIMVrh2AbibMnIJ+JLashmKoUtJXLJXNYVJx8B+nrv96RE7pBUH0xq/Hg48HA48STCab3gXeCXv/oVIU78/h/+nvXySi2mnFi7zs447aVWrterSXT31rWrjaenJ1DlfHkliAkcFalUpCOpgqgjrZmSy85t8wIlRl6mE8ux8HbyqAsE8d2/RgYj/3YNgVGeeWdcL73nnP0z208bLcRG81QMsv7hRyjsvRtVE4BI/YSZNrTJn45+ywgeyxSZ5khRm8e8h+9HxnqfDPh+8fdJgm4O1LBgSLuhBULbs025PU2oQqnFWPruVtvSFfZulf+YXPjh1gPhSEJ68mPjeHe/JZbJ3fdvfpipjaxymDXdrIf3evwHKNutdWEGUZ8G8x1d2I/pn76e2nsxigk9jV9u+/Hd4K+x1owfDLhs4GHiYFnmPotu13/LuU9ruH185nNt+VqQg+8BXYguEkKkZbOs1uq6ZHJBaDYvPTleXwqn01ec1yvONxqWCHgfEa/M04x7dVy2K+IqcZ6Y/ELLZo+7HAIXvdAHMi0QdkUyL9aeWHPDV/PEOMoT6m1C4Fo+4HH49sykjrenQOqweyVBC5y376iccSEyBUd1jdf6nsnNHJ4WNC+U6jmEmefXj5RWoDlUGqVmJh8RmVEHH/N7ajMHOVEhNCG2CDjEK9V3TQrvmKaFOczUesX7ZNMr9SMZY+Q/zSei/4pUE5NfOL55gzpHLZ6t2ThnzhcWL2i98nB4w5vDr3jlPaVlvB6hRV6vZ5QzS4SrZur2ijYhTI05Gtn1ulpVlcqFp+WJklbmSTlMDmrjmhztKjye3uI6wmiETJuO8TGhHggBpx7vJtALSiaIo25t5wF9ju2mAGqCYcF7ajWxtVGhj7WX1sxv/PY474EihMA8T3jvzFK4G24NFHbHub0VaQjEKbAcpg5vY4hSuRHoYow0VXIy5MS52zo7EEjX19fhA6B1QOym3NfuizGg5EZruY892z5511cjN5QBOzjS7JlFb+vo8BfYtoQv4xy5u0m0e7RE7vbX8T//T/93/v1//J/5s1/+ggak60ZV2HLiN7/5Dd+/+0DJN0JeCM7G5r1NG93G721stdbK48NDVw1sLMcD3nvWtPF6PlNzoeSCehvV1WZtSAR0jpSvvuS3xwcenOdtnJAQCb2F27t9+3rvOsnbEpXWkwrdpYr/pe0nyhFLd65quz7ymFn3zrFM8ZMqszVTxVq3DXCfmu30P5wIPgRjdDfFSUWdBXhGJdrNFgYQodwYsns2NIQ0cF1KcyBaHbXocp7jdYzg3aFtbcYlGDAPdxXxwOQ+GSURC8RRbzOt+8TAPzlCd3e33/W+7kLuDsmJG/2ekRDIJ6TJ8W5j3/fkQPXuX+9Zw2OC4vZ7t2rCxji5cz4rpXsN9GRoJ/7tspx3I5y2FuH33sCeHfXelvUQxQW0fD6o1dFHB0Mn8BCJc6D6ahWvJoPyJSEu9PkpZVkOnK8faRQkGFzrQiS42URSnCJSuuHWCiVT6opWUK/gIlE8rTjWrDjXcKJG/MHh/ETKytNpAi346qmYAp62RMkT02Flk3eEObNuNgngEFQTup4hVi7pQtGC6gEoFCmsKrysF8J0Qqkc4gkJEJZu9qMFbZUpLlzbip8XJBcOMqPRsxVvvIJqyN1pOsKTUFrFOUvanQc/weMUWcuFFsBniDITmDmnC+vlOx5OEyqBVs7mFJfPaM1kSRRW6tZ4Mz1yjG94//qOuia8OHJKQCarcGmN4DLz8kh1iXNbiSI4majAKR6Jue1ErMqGjxXfIsvhDcfJkfOFEkpPsizhOcwHrtuZODnC9EDWSi0bTRwlHKgKYXv6XLcuN8VT4XQ88fj4yLptfP/u3a4cKnoLvtDXkv4z5x3OBd68ecNf/dVf8cUXX/C3f/s3/Lff/KYjCHUv2LgjCE9TxAfp62LtfW1bTaxXb897bRV6DWbkVNsn7w3dDCFQq5kBMQWcVEqH3HUoFva2z0hqFCFMM8flQPCRw2HBOcf7D+95/viRUvNdScQni+N+HmRMfHU64D16fbcG34iCCs4zLQcOxydzJSyOvG2sW+W6Js7XlVrMFtl7T67FhPEks7eP+4EMXsS6bTu6XDt6O4rigQTck/ycM7vj4/GEiwvXHqmqNlyrFBnFrqFFToyPV7rT4hCD2+Wj/3vV4A+2n5QMbLngvaO02kdHIITItmXmKcLtVPdekDF67TTLHoRHcEVvrHbhruuwA8/9z/sYvGeaPSNWBcfuqHcjBHJ7MHT/dUaGPVCF8XnjxKnEXd3vn0IE9llX1b0rMPQPduRhxOgbEIEb0NYf4QK347K/3KshyP732j/bibVedulMxRjwvX5ordGyUu7wiXGf1XEiYLdtNpW5QLh5Ue5+EIbIuNt5952RLw7pwhwyFqJ71bC73x/XS5yDzzir7afJjr+J3WhOqVSyK8RlIsmZGME101PwQUEKKnBeL4QYEWdoUmtGOPVBuhIn5KaomGiOjN6qOLSdwJ+Yg6O2hMpGMMVuagKPN0a9GMmwCMadaRUn4F3EzxvP17MpsDFha6+1KaRVGy9znud8ZsJxDDMiyjVfaFQcFdWNOEeSrp1X4igNkMZWnzmXRJSIb9m8k7zxXpr31LXg/MQ1AW4m+ITTQqsrLjhMllnxkyF0JVdyWakUalvJfuOcEw6HtjPOR0IzEaNcleIyKVWO7ogPR3Cxz89nHIlGIqspkzoCSZ0lYlppckVqwevCIRyZp8Z1gy1X1loIoSClEOXAeslUtSkJe1Sb8ZgGgVkENFLL1aymJdKakPQjgePnu3ed37kAPgSmGFnX1Z4trJq0Ft5tPTP426p0EwaqnM9nfve73/Htt9/y8eNHWldgHYid4Pp7daIbRsZOKVmlKR2AlhE6W7fZbWarPZDMHZUQUiqGGjQjm0YfTbirVHIqn6yx94hxCIGff/Vn/OLnv9qR1ZQz58uG8twL0R9USMgfra73vK8dbWY/iLFY3TQJnFBq47ptrFtGusTxEFvD22iteeBYDBxaNf0DAQg+cDoeeXp64ttvvmXbLFnIxdQcLRm/kcQHuVN6MYjANW188/G9jcxGQ8R994wZU3gxBqY4mw5MP17nPDEEpmh8BJd/3H32E70Jhl1m662i0U//NDu7F1twzndJRdcDi0V8u+xth8PH3OwNF7/vRdv7KaNUlv3jPi3Gpc/B+71yH6JUY/Y+hj4B4Ya0ZU8RBkfAsU8SjOr6Pmvbmf97cqFdXMdSkD0J0NsO3roTf9wi+OG2B/n+bq1ntrXdjc/0rNMufJ9m6Cff4Cij/9h5tvcU729tBGeCH5YN3OCm2/72xXK4cPXX2HVs7F0V1ZuKmYxbQPdrbJeqJ2/jHHymTcmoeNDOrm4VvLCVK/5gsLkLQO223K7QyOYk2Lau+GaVE80hGMvchUBzJp+dNyMJOjGVwclP/Pz4F8T5wPeXv2fNH6gC4kPvySoxzJSmSLGFWN1YIIU5HJj8IyrvUW2syd67pgpaaepw0mit0FQ4X8/4ecZNilDJdbOqoXqaU1r3rtNmr6+aESnknEhpo7bI7DI1qEkFs6LiaVrYzuCbcDod7b5oFVruj2qlCmhz3eYcc1+sGRGDmwuVljZcg8kvhGB3Q22Z6IUQJubpkdb6kyQK0oheqM6IU5ObzNO9YPoW6knacD3hKXkFXykOUi7UXAhFiHi+eviSrx5+zcv2Hef0jtIulG4B7XQjl4QHJnEUB4cwMflIqYlNL/j0+RJZgP2x6iVFKaYiyKjQGybmI35fu1qH/k2OuLKuK6WrkZpT303LRHrVYgmFPc+mUGomTibL1h/8AclrV9dru+L/XWy29xriRk4cNmRsa2v0DonShetqX98GR8HW/hgMfXt5ORuPZr3w8nrt6q//zFq6H9NYdO4SgLuE6a7muk1e0IXnOpmRpv3ZKKZe2pVi76vte9LmXuU7YVpmnt6+5cOHj6wp2bXoqOy+S3eaALuaoXPgoGphLYUiDqqto76PIPqeDIQcKFNCOXBYFkKIzHFi7tLxqsomfwKdAWVU1MNValThtwB9+839VCF3ScDtCozb5QfXa/x3//52QV0PMGOcw2HOTtJHAZ2zBTu4sJ9wJ7ebRgRil1O+/4LOyxMFdTe4/x6C38v923Fx977/3f68/hNZ6T95pjrM3pTSTMxpyGuOG+z+rcc0QcB1Ms74rJ5lupF02Q6OG44enEcy4Hqqvz9c2tsd+5xqf4D6+3l1e07X9pGlsU/jPN6O1GZy7f0+5zRB02RVNaaeZ2iUojUhMoFr0GK/tysqhdoSwc3WE20JqqV9Do8Xb9yOABoyQQ+UbSLK3NUuK56Fo3vEhwVRk9UeyayIt7ltH6FtJvrkQtdpL4gIwS8sfuaCVYelmnxqLiaY42XGOWFLG/N0MF92J6CZ3CzYT/4RT2CtK5WMBBPzNrnkQvAWkJ0WUnk24h5KlUyVRCuC4FjXxhGYH2bURzNskoJ3lSaYPoBaalwRsmofU7UxKlGbOxcFcV2vYgr4Bt4FPFY0XHJC1QqHpuCsrwGucfAH8pYAoamj1UCtheaU4DJrLYg2mjoKxUy+agBXCWHhZ6e/JOKo+cJZL6S2ca0bTiMNiD7gacwelmBErVoAVfK2frZ7t5aGOEORTD2w7c8ezdYe5z8tbnYp8r0yNqG1lBKD2DsKHRGMdIit5la59sJBTJl1f+/WK+G+HGo/P627l43KfrRZlV5cBZsMMBJxJyCKZ/QUhgEdvRhCQVvjcll59+EjJRcu1zMpX3erYttBbsF//LSvxyLSE/sb6tlXs9sCdb8ky03AzcaOPbklSw5aQURxXrGhANmTsHtjJefdXjDmUthysuq/F1KW6Oj+eaMQdr3NvUtLCyDaOWGK80LwQnSOGALB29hrCJ45TixL5HhYmOaZOU7EMNwRG/JPct7+ePtpcsTYhUNvlSCt/fEi3xe8G5x9/waj0pdbBBvV7jhBrn9hqnytV9quEwEHac05mDqrWgD26QG7+Df/7HFutffPLJrtDP3OMbgFT/ab8odB+Da2c8sVajcC2Y99f80Nzo/DA4BP778fogWq3cFKOqFQjMEro/pX+t9hyADfWgyy38hj/EhhHz2p9UbwqSP56DnKp3nOeNT6A7Uft0Fzdnp0/xrOivvkwUByOkphyl1+5yx8js3G0cySFueJPhCDsjQ4RE9BSNnhJJo+uFa0ZQiNIIGUrpRqpL8gkSAB7wUlIa4xEQjTE9E/EsKE8v9v79yWHDmS9Px5HDITqENzDiutKOkB9Vx6H13oQlc62MpMs9qdIbu7UAVkxsl14REJFEntNlcctnYHbkZWVzeQQGZGerj//vvvhZY3Xi8fOaeNtL3xMB840xUq+7XJamTB4oyEhEwIxYIRTVS5WLeLE6YWSM0yWBc8IUw4jbzmzxwfIw96ZI6R1Da2eqG6hgRrNcprMQGi8IiIZ+jKBwmUWgg+sLWV5hvNOVyAoIGyVaILtDhxYOLgJjKF6pUgDucKRSsln4lOSamwVii1WRG5WTki+AeO4YAWI29KFxoLzuPUs5XCpX3mUgreTagGWss9uLVndg5HIhYA5OqoBFyzjWiVM40ZqtA0I147+gIXPfMx/ZE/nv6GtL1y2V45txNnPbG1wswBcY8myFQy0YGXhleYdWIrhdftC7HWP4PV2rr/G77PeE2uB+gOR/Chv7ZSuljW6CgAdv9hb795DpVd7Gd4MVF65wDkZLomMQYQC0xqaX2zdUYoVAWvO1qpHTFUQHzvKENtM0R3zZJRqg0+4LxN8htDevxNKdh7T8ldq38gjfhOopd3yYfs/3UP1tn2twWE9ynZDaKhRrZLaSNthkCkLbGe33h7e6G13DV/RrByPZgMXwsdQVXeLm/wJ0g1I0FAu87O+MJCP39H9MHQl5sWSucMyZ69Y5onlhiZe+Y/xUjspYMRHDgfDYkX36u7ShMM0fwC+ycFA6q1Q41Ckx9xz29uyPX3kaka6cEyLBHp2gNGjkCEGCJ06Ps6w8DvNXnf2fNNTUEueIuOHND6RmVtdlhLm95kqVhAMIKZa5mn39U24Cc39rOb8x6v1R7l9dZIbUhruyCGqjD4+Lpj7yDLQri5KLf74s5D6FGujb4MSNd0wIML1o0xltwVUhrkQDuWExtd6rzrXIv3keaVk9ADmkE46VGQOMsE6FEl/boP51IHxC5dpatnKnb5jMC5l3F6diL6oxDoVzdtjnmeLONsvt/jCx+eDxwOE2st1BKgDqTJeqR9BIiQwcvM7B8JzhM9THMl1YpDCU0QiQQCi39mjjMlvHDevuNz/ROxVj7ER3IOZM1AppZEaZnlOHNuZ3w1px68p2nilL7nUj724V4TvgUe5ggSTFDFH3HtyJoueIk8PD4AypovrLyCKJM/4ILiNaBb6q2+BkFe0oVcHOe08rA8E6KpA1ZVfPMc/QEWz9xg845ZZoSKloQLBT9BaY2SPCqeGGyzSCVRtPayClCEg0w8+Ac0OFTE1BdV8S2yrbDWN9zhQkWZ/AccgcyF1C7UXJmCJyCo89SW8N5xdAeKjxQSL+kN3ISrjtySDQaLEzU3trLyt2//hZfz/yIwsaVssw28OWnnK9vlI1P8DTF6mi6kdTNRIpmYyoL/MkL2n8duUFLvPS54Si19jouzzoKSO+R+m7zo/mPvYvop5PIH56YCUpWt5P0oLRv5Vzvj/YfH2dEAbhKl/vqCMem9szTNS28fxPXEz/RgiJEhLhd8NNTSoA/jNdWISoRW9w2zYSiGfaebTgG4QeHsM67tb7IPXhsXYcjgv54+8zf/47/y93/3B2uBrI3Pp0+cTi+cLy82enjsCzedXABer581fOslXcANP2LnPAIh15PX4AJzjITgbfOPgWkKLDFyCBYExOh3Aaxb/23jVHpkUjO1ZIqaVk/ts1K2nPgS+9kKhK1vHpZU2te5ZflbCVn3xScixDDhe2TpxIDWK5nQmNV2KxpT6Hr4vc607583Gfn+3t5S6Jzg1FCEsQzb+JNcs9gRtuhI6Ud4N5S3OlwVx0zqnzj/0Z8KBudvpSK9p19rM5RiwFbueg67ONiPn6Ob33snQXCd5CT7NcQJVcZwJt2/s/QH/UqytGBh1KYsaOjZfLMso2JZ6bgiXgGxaWP2/HVC0h642ECN8Rcjeh9Ttwbc9S4glBGdd/hs3MevZELAy7OR8ySBFNBGaoW6vYGvhPhIqkIgcAiRTRNKBiameOR5+h2zzEAGl1Cn0ISAkaJoAdWVnD/jeWQOD7hD4zW/8Lg8431FtldzWL5P4/SmKXHOK8de7jJ4sHDwM+f0xkN8xIsntYS6iPMBh4ficG3h6fBELifONRHjAzZFZiLXN97yia2sPIVvWR6esfp+skDTHWmu4OLCtmY2XZHFkZKpDB6XB6JEcvnEdv7M/PhbWrCe/0Il5Y1WBbINNzq4g6VireBFiN6yeBdgLRtSPH5WVsnkClEfefYPtLaxrhvfPP2W6IBqBMRSN2YXcfGA+kYuK6IbuIJXD8XKLcfjE81lFvc7tvWVwtnIhbUhzcqYa1uZ/DNKYU2bEcBqQFLiPH2EpqxNEH8kN0fKFyQVFrfgfeTo56+5eO1H7//3vb7dWrF6dh3JBz92LsN+wu/c/tuN69x/v91XqzbGBFXlOolvvM8NKFyvsvD7WOTuiJWe9NEMEQqDY2LewblrCTr2undXj6fkTKlWt/ehowKq+zRaFb2iIP1/Vz5EL3F04dkxXv72HKXzmv70+e/5ePqOseUaqbxZGUQgBsGrvwZXYysal27f9zqnyDm8dwQ/GcwfAtFHpmCbvQUBgVoaT8cDMYQeJIxATm6Cua4pIdfusNKglVG6KeRSqcX26D5XmPKF+i4/KxgYLXTeOVyMjE4A5z0heN6x5Ttc7FCm6LswxBUeGvyBXe624yxDRnJsoqP29EOT210H2xtbx6ZU5TqASG3xKJ100zKDIT9gLTqU7bxHW7Xai/c/vXnJdXHZaZgKoATX2xNvzq2XPILIT53C/t2dyE0vqIwYaQ++bMVep5IZk/UqSToYpnZOSq7ZYLzO4lfYhT32ZSHg1CZB3hJgRK7v28eAqu4dFiCm1jgOYp9k0NcPT8/RNRPGYJWfuqC/jsVlorSNBtRaqHWjamJWJVF4eLSHIYgNL/FVUC2ouq7Q6Jj8zNFFGpmkmVyEyT2yyJHzdmLmSPAmjlO4oPVCiI5n+QYQahNifEZboLLipYAWNCU+TI/Msw0o0mYlA9MsCDz6A1oDJ/lMqi8gEe8OePF4f+Yhzmxl5aLNFP/cRPTjWY2oGv/B+YD3gTlEoptxbSbXTxyWB9bzK+ki0MwZaQ1sF6wrpS2IO/OHl++I8Q3nxbgXeCYJTHohSiBtQpGIZoc4JU4TyxRZwoHTJbEsE2s5s2WTBXYEais41/C+cd7eWMKCIyF9zLCPvWNJN1pZWZyjOVNvbILxPZh4CN/gfcDLwkP4htQatQbCHPigf8V2ecWXmZxXFNjWQvDK8SFyzmfQwJw/kfKGSrBnqCpFM06Fc5m+2todtpf42pC4Nc6JOGvV++IH7AeP6l5l/MFf3Na2AR4fHwjBk1Pi/Ha+vkktOx3p2g0A2n0PuziOYEN14hwNvXEmoOMwPxaCx4WADxPRey7FoPNlDgQ3EcWzTMI0+e77gvnenu3brJqrKqAlS42qNo1WBwzSv6ANq6Nv3F3T/yYRNL9oRE1RI2aP/cqmy14F7GIIhOANrRZnyWlTHg+LnaM3AnOtSunquqOr4tPHV06fLoQQiMEToycuk+2rIjcqh+xZf6kj0Bqqg3avpEsS9xoJlC+DtX6e6FAnlblbQoYqg3M++Laj/jJCVQcdIpd3C8XuyVDPu8kk6exUxQgQ+4bfpxaqZb6lVkrqtTEdmzt7i4qI6ZaHcNjRilTKDUv+CqEPzQSLWzs59+Z7DtLi2IBlP69+k2G/Ef2P+5kamiI4Wm9fGge9gdVuwvBWe8tJRyD2qLAppeRObrkOGhpSlG1s0q2ZhjVXVGSQGIe0qb2sT2kk9Kj5Gsjtgk77Vx2sYJvouJNIEdT1hT/er1dlRIMBuwLZV4wGcruwldIJYT0jB+tR307kVdEu5NNEyVWppSMg7cI0CWl7Q1yjyRvVZYI+4uSA94Gin9CWmVmgl7Eu9UKUmUt6w3l4DL/hOR5IPLDVE1t+RSXhQmOKgroJfKVmy7q3UggusNUTjicepkckr+SWUAlIaKieqc6zVSjNEdyCFk/Kidrg8cOMEshlI7czU11wzUMTooB3M06ddSEEb4Fzu4AmWjtRt8LjstDi76lJye1ijrUJrTpKAImKF/j8tjEdA815QyBozNNCkEguJ5wLNjSriy21ljm371jzG14ca6kEp0yiSFSgktyFNa8UPfPglZo8WuilhkLRV7Q2Hud/Ra4bIQYep79mrZXXciaRyJviwkzKZ5IU3tpq4lESyRqgHnicv+G1fo/IauNky5GJhdYK57yyfUW+iw89qHZQW6XWxmE58rvfPbOuK6fXTz/oVe9E6z3t/fHzDCM10F0Qrb/z+hjfvE6b8VRUhKL6bhz5oIDtXKnBF9dB6DMlwmnyzHFmnsKeEY9BZhTT6m+l9YAskdaNIpGWEg5hdiC1Ui+VtFqf/jQvTA8Lv/+r3+NjtHI0I6mRvdMLNRLgGCjkvbeBWyHifSAEE2Nyg9A4UAaRTuyjj4SuvZOmQdcvGKVna7HTfYBUa5VWCoc57r4v10paM69vK6dLIudKUzF/syWqrjjnmOaZYzbRpz05RHDi8THgp0BErNW5KOp05KB9kqydh4ji40jk/mH7ecFAh+711rG3qwazfV3dF9ntJrRjBiLvhjyINvod6EN9FMogu1xvCmJw8xIDeG/1kFJJpSI+9Hr/FRraYaLxa89+rTx/DTrGpmohrEOa27NZQff9fz/dm+BkP0dVI9wgXdd8fLb2B6Sho2j0D9h4dmvtgzn2GdV1lM72F46NWfpDyM1goRsV4vE191LFVffJYH9Hb5e58QCC3BSkRoDRHYfIzmW4hjLXe3/FDHqQd4MEfU0C4XZZCbGgzRP9gckfqG2G6omSCZ0wpWqSuLXCesm4IEQPwU9c8mdWzeBMwU5bIsaVJToTK/LQnKfoRq0mlV1dpcbNIndvTkR1Q10hRE/JRuwsTolxIdcTargf0jkbZ5dp+sI0OZqrtKI20KgrGzp5RAmmKeBmarFnyBMIMlF0xUUhyZmkSmgzXgNTnIj+wFv6W6Y5sm4rIgHvZ0QTiFC9I9WEKMwh4rShoVFKI9VKzpWkCm1lS5E2N0oz8mXzgo8m/oMquWykUqm9K6J17sTGxtE/k0XJklDX2OpGqtlg2lxxcSM5SKgp7OENndBKZWOriVRPPBxmtHdLaDVicCkbtVS8tN6pAxI81Ts2FQ7+icV9w6qFrGekQnQTUSbjzzT/08jer2TTFEGvLc9DbyDGaNoLweOK9DkgcvV93GS479C/8b8r1D3Iv++Z9sKYIaMNQuwkYNeR0PGypntJc8xKQSzbjt4zxYl5Xpgng8enEAj+2t69vl34/PkzpRTqEJjznjhNxHlh6eqHQkMCiHi8Dzsn6dN3f2SZAn/97bfMy3zdjPo3lNase6Y528BlINB+T3CDt0x67Dl7MOCE4ON1kqsMoR/zd14sYc3bRk7JOAXNEAitSq1Cyr2ttym1QUXx08SCRyVRmxIPgdGV53qCGaaJZVk4Hh94eHgwQmPKlJw7PwsLlJyVt8U5E/CLoas3QkmZ8oVibz+TQDgEhEY948ev2TUBeiBQm1K04tqoQQsM1im2oS2zQXCl2TxsEbdHmkM50I0IjR9k0nZn+xQtI3RYANL3u95Hv3/ZvuC1/9zbCPsuudf4+xkzApt96+s/RzDYkQhT4FLow49GkU7VSIa7PsHNYW/PYQ8w9JrN75n3mHvtb/rbOx9htMiNSGJEvSLXgRXXh15QHeWPjntx7Q4Y5zXUzLpU1P5wSa/9v7s0/VDoj5fD8AvmIG7O/yuYZmM+O0cnnUZcAxcWtCWkGJoh3uNdoOZG3ZSUN0rs16+CSO2iQZ5SMqpvODeRW6WwojrUGGecTHgvFF/IFS7FNvrmK86rBdfZUZgIbqLmjdoyAYd3QkuFkht1CmRZ8TLh3ESQAC3SEC7lzCQzqtbf39qGuGAbCBOqlZQz4jxJNyiFQ7Tac9aEc5EkZ2Z5QqXiRIjuQHQLwQl1embdXqjtgkg2MZowerAdW6rkKtSUgYAWh5ZIbY20Vba4kcVxqZm3vLJVEJ2ITkFXVC5UaSRMECi1gshC1UptGY/VyHPbQD1ZbIuT5qnNNiTxgTWfye6NqTre9Mxl20DheXqk+rOJKvmFVVbiPOPUhi8ZKujZSkLcgriN4AOzTAQ1omNQh3xBMP9nW7sj9RbZN7JlXnh6fAZxvF1eSN4ho4zfg4H3pT/Z/e8IBkbSMBDVvf15DyYAdf3ZHsmNrd3YHauq0buH5v8YYGcEV9v8Q3BM00T0FhyEXkcf7c5RlJJmLhdM9a8L2jkanjIugtXSnQkiWauz+bnZweMhcojC5AEGfN4TRKeoc6aeOAan7QFLJ8TXDVzck6QhkzzEaFufBmr/bHuO92JZuvPgZypnLuc3Usp2XcKM+JlzraQekNq38jTviDNInM3LulGAYPe54qxjSFwgF1NrVPpnS2+nFxDniHHi+cM3LMsBgG1bOb+eWGv9yRLuT9nP1BkYUsRtJI0G49MHItwsXtuwre2u1LqLrDjXkYGOGOya0ww4/Ro0DIhZsBnPdUSve/QpuxhO64p4ehusyA2xbZBGdtxinNP7DPp6svpuvxt9pLcvHZqCI/N+tyEPln9tN9eloyE/2BVvZYQF7azbazQvvgcAAs5f+QEDCWCcXz+K9zes03cZgt6UKW5lM34AMTJqYz9BouSmrXT/x2vb4zt05ubd1wDr69jiFpwWws6FUKtReo/6BWrqxCxvve00aELNG955Ui7MYSaIjbN1YfA8Cqk2Uqs9Jlpxau1zMXiqbrRiKoFnPVFawkfHJNHa4jo/QMSmAlprrHFXSlG2c7JNa9Y+nc2bU+hQYC0Z9SYrrK6S6hvRPeCjR6uQ8oXLet6hobKtOF2QOViAXZWCQt46suCsNhvUxvb6I62upLLS/Ia42Mc9Dx0MU0H0IlYLJhJ8NKEUVgtwxJNb4y0XiiqRZtoCfqLpxUqaPtNK6uuoGWGNxiQzU1j4uJ1IZLQFWnHQPBCR4GjNsdULbc68ra8gGyU3ju6B43Lg5L2tZQ1W0gtC4IAjmGBTqyRdGYI4vrc+otXIkA60/Uzl9l/Y5BqTW+nKu13G3f58DQbsDZ0n1BP93ZfI7b/bL62PHt79ZedsDR/oOnExRI82MZEo76xzSZXgnI219iY9bPB/b9/1wcq1sfsvsYTNu6G5AUv0TNGxpkTaEqUUBBs/HGNg+BzvpD+j/WKIDRgKAg/HA9ErruX3Pn3YgEVH6eIGAVGt+3wE54JpoqhSq+K9WGBbMnIzAl4A5yHGyLTYhk48QFBKtsRQwsIyLwSE0DJp26ilGL+jd6BNYh0iSuet9VaEIZIXfEBQLpcL3gf2EiwWyIRgKMrzh2f++t/8a47LkVoKL58+0rZXSEr5c+gMjBnWNhmNvQYtYhrrxrTXQRXYM/ih5lR7dGOLzvX+95sNY0SwN3i2dAhbnEPagO9vbnUPDK6wmO55/E6uQHa4/x1yobpHZEY2kZ4l827j3z9nfMJeO5Ceqdt3d96Ikr63jFigYPKVMg6hV3Shn/S1PtA/JwZvIXDP+kXH1qz7Gt4h+v4Q73MHGB0bV+dB/7s9aBgZAf2z30WO3dEPx9D7Xq/KiCMwGkGO9Ov5HiwYp3RbZvmKVQIeHx6o/ryXVWopxCBo2zjEA7lWohtQoZEGvUz4ljj6hSyNOEUmFwhRUJ/7wKxGrhu5KpOLvVOj4bTRNHNePxsfoClrTaSy4ZKjRIjOkbvuf1OlCjZCuSRySuTSuOSVEKwt0jOZcEwutFoN2mwFq+8rRY0cKRLwBHLJaL2Y8EkLxNmTS2PNZ1wMTHHpQ2Nm1i2hcfQ5W+9+KZXYGlEDLsysLltC0FqXuBULrij4MOH8xOQiIpGqQtJMaxvOeUQ9uXpqW2ntAnHhEBwBj7ZC9Y5S3lAqqhkRx+RmFn/g4D/w0j5yyZ/wGkjJ+szjNBPCRE7WoqkFXtczeI9vE8Tu6GmIBHJuhNjRhR50OddQNhvM5GdqJ2OV1ihqXSfiIzl9vd7CGI0jFDvzXlUppXJ6fSX1QVsxWpAo0Pv6+6YOWLbSa+gygoJbX3p9MI39HjohHGjVJISDqWZaEI31uYeIKgQnnbxpm/VOABcbbCfQWwlHTDr8dfcts/B4nI2rtQ/Y6Yp7XnDeggcvPWjR4YkcjGSyb5JD/MhEgejP80jeoDs+6MN8Sm+LtkFMdQ+2c7ES2OF4QMSRtoR31tKr/TnLxVr2pikSp4n5+Eicnzj6A7krPbo4czgciVMkpdT/29jSRi0Jbdappf2Z8uP4xeZAROcJDppUnNj1t0mUrbt+OzkvFamJsjVaKUi9EDXTnAmofYn9rGCg1Iq4vvH0/aO2dtN2NsLQaxYtIj16DVYPeqeE5/dfRxS6t4r0z2j970fkNhYR2kVtWkNcRbUyat2D8GGiR7cZfQ8YbnZJ2f/asj0bGM11XO++k72/FqMUEXvty4XeEuj8QOb7MQzbGjoE73boH9m1PLDD68hA4Xumf930RyAwdtlBNDT2+7hFusOG4zrvPwdactVO3kOpa3T9nvjXVHEdrvopVcRbaNKiXUw0qd/Lr2WHgyM535nYDbRwmAONM5P/BvER7zF4UD3NCQ/xiUkbj/ORi5yNdOQXWruwpTeowjQfyDVZT3KIe5lBm/K6nkg+8ZvHv+L1/ELeMuu50KqSJ8fT0weaW0h542k6ImyktNFyouRKqaAB/ORpwOt2wblAKmLtQ63SxJnOAIGi0gleG7kU0pbBN8I8k85KqZnaGlUTWd+Q2vBlBmbEN7xrNE0QPBUlJUXripZGOELbmpWg1OrTkw8clgfKaiU+8Y7obcW0WmlaUQ08zs+kdEJkBt0oJffMSpinxuMy0Yhk9VSsfUzE1NicCzjxRH/k9fyKuBltE85FvHiiGIcouAOXpJTyhjjjPeAcr8lEm4JEqlSW+Yi0Qsu1lz4qYa5QI4tfkKoEPJe8seYVyCx+sVksX8kejgsOsdr7ZLDx5Wx1ducdk/cs8WBP6vC/vSPIi/GChqLoaHeLIdogoptWaNRKjDFOLPOEd46SLngMmb36JvOHRtDrSOKArLlxcbsP6yhE9097MNBbk22+x82YdtiPqdq6aBlXnyag2jUGdMgttz2B1L4HKNZV1hpktTp+61MBa6mslwsvpzemKfL0aLMnmgopF97OK+uWeP7wwUYQCxyWiePTMyGGPifhjbStrDmz5srrVnh6+sDj07NNJry88fLymU+fPjEvC4+PH3h8+sA0zYhAzhculwvaGiUnXl9P5GQBgnO9vVwrmhNBoJRKLTZ1JnpHcAGCQ1rl/P0f+Zvv/8S4AKo2or2UsutF/GP2RcHAraTlyKaHVO5oQ2vNRIOkj7ZtbfAGeuzZ2n4s7VwAaw1y+yjcVjtnQG3xKrxXy2qNUrvD6ypbTRU3Jg1aBLAvpvF5rdX+GddDuR41O7FFNVaaCx7jOrR9j7x5Vnova9t7Wp13NvBvbHQt0/ZgW/bPHGIgI7PerQdO+7CLgRy0MRjkCufvyMZ+bv3nTUDQVFGby/wOdRnzC0ZHIjrEM0YwsMcHJr/Jlaw0Tm/fzJupgQ1dhnGPYm8fFbEWmFL6QKs9LHm/nn4NG5+VtozGQMmZWja8L7Rowejl8opXz/a2ol5RDVAm5vLIMgVOpz+iMRHKkeI9qhHNHqpSs6OIwObJSYnHBZGJsiolN6bDA+UsUAN53ZAUaWthXcHrjEqgamNePUka6/qGsmJixwFxC1ut6KVQixDEkZKz6xsqpjqeulRtpDWlehty1HIgnRvz0VnG3rrzzSvn189c9EJsTwS/AJ6wzOQKlIx4KFkp9YW8FtjeSOWVEI82zKlOOA206ElrpLCh+RWmRxa/oAW2NRPdB5Df8vbxM/Mh0zoPQNSynbUWDuFILYrXhbMKOamRKCVxCW+8pBc2heB+h29LB80cJGVLK4pQilJIkAVtMzXMrAXWy8ZpS7S64f0DUWaMxWUEw6KVGipOC2v+zG+ffssSFtL6kbq9GWk8Z/LFvVtPv+ba/fb3v+/ysjZ5c0uFw7KAWAY/uZmnhz7vwWEJlzeS3WjvHbVw21QtGAhhDDJq7/yjZb6NWqy90kqVA9HtPh+rW9+CtMoVLRxJW7txXjJ0Ywaq2gZiKx1ZdfsXUFW0VFottFp2DzLIfQ2llGZTCbt+ivYEcXe9anwQMNKe+OETTRa5NIebF5rzJMY8FpMBf54PPHRBna0knDjq+ZXLtvLw8MDzh2/4t99+w/cfP/L9938yH5gLHz9+z8vpxMPxgW+++cC/+7f/nj/+6Y+8vLzw8nKCXhZY5gPPz8/89je/4eHxSKuF7777O15PL6S00mqzUq0WKCuiFQp4NdREmyPVQHOYJJ+C4HsABFUhNyhNSPn9evq/2RcFA6fTCYDvvv/4JS//s9rpFznK5Rc5yt3+aXY6nfjw4cOv9lkA//E//M9f4Gjff8Frfs4z8r//qV/kn5F9B/z3/4f3/90v9UV+Efsaa/c//ef/9qt83t3+Zds/tnZFvyDUba3xhz/8gaenp/dZ7d3u9jNMVTmdTnz77bfvepv/nHZfu3f7Jey+du/2z9W+dO1+UTBwt7vd7W53u9vd/uXa1+z2utvd7na3u93tbv8f2D0YuNvd7na3u93tL9zuwcDd7na3u93tbn/hdg8G7na3u93tbnf7C7d7MHC3u93tbne721+43YOBu93tbne7293+wu0eDNztbne7293u9hdu/wdWi3zEhkBdOAAAAABJRU5ErkJggg==\n"
},
"metadata": {}
}
],
"source": [
"data_dir = \"data/vww-s256/val\"\n",
"\n",
"val_data_loader = build_val_data_loader(data_dir, resolution=128, batch_size=1)\n",
"\n",
"vis_x, vis_y = 2, 3\n",
"fig, axs = plt.subplots(vis_x, vis_y)\n",
"\n",
"num_images = 0\n",
"for data, label in val_data_loader:\n",
" img = np.array((((data + 1) / 2) * 255).numpy(), dtype=np.uint8)\n",
" img = img[0].transpose(1, 2, 0)\n",
" if label.item() == 0:\n",
" label_text = \"No person\"\n",
" else:\n",
" label_text = \"Person\"\n",
" axs[num_images // vis_y][num_images % vis_y].imshow(img)\n",
" axs[num_images // vis_y][num_images % vis_y].set_title(f\"Label: {label_text}\")\n",
" axs[num_images // vis_y][num_images % vis_y].set_xticks([])\n",
" axs[num_images // vis_y][num_images % vis_y].set_yticks([])\n",
" num_images += 1\n",
" if num_images > vis_x * vis_y - 1:\n",
" break\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "3a8ffb30",
"metadata": {
"id": "3a8ffb30"
},
"source": [
"Cool, now you have a basic idea about the dataset. Let's then construct the OFA super network! The `OFAMCUNets` super network is composed of $>10^{19}$ subnets in the MCUNetV2 design space. The subnets are composed of [inverted MobileNet blocks](https://arxiv.org/abs/1801.04381) with different kernel sizes (3, 5, 7) and expand ratios (3, 4, 6). The OFA super network also allows elastic depths (base depth to base_depth + 2) for all network stages. Finally, the super network supports global channel scaling (specified by `width_mult_list`) by 0.5$\\times$, 0.75$\\times$ or 1.0$\\times$."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4f580577",
"metadata": {
"id": "4f580577"
},
"outputs": [],
"source": [
"device = \"cuda:0\"\n",
"ofa_network = OFAMCUNets(\n",
" n_classes=2,\n",
" bn_param=(0.1, 1e-3),\n",
" dropout_rate=0.0,\n",
" base_stage_width=\"mcunet384\",\n",
" width_mult_list=[0.5, 0.75, 1.0],\n",
" ks_list=[3, 5, 7],\n",
" expand_ratio_list=[3, 4, 6],\n",
" depth_list=[0, 1, 2],\n",
" base_depth=[1, 2, 2, 2, 2],\n",
" fuse_blk1=True,\n",
" se_stages=[False, [False, True, True, True], True, True, True, False],\n",
")\n",
"\n",
"ofa_network.load_state_dict(\n",
" torch.load(\"vww_supernet.pth\", map_location=\"cpu\")[\"state_dict\"], strict=True\n",
")\n",
"\n",
"ofa_network = ofa_network.to(device)"
]
},
{
"cell_type": "markdown",
"id": "e36f9fbb",
"metadata": {
"id": "e36f9fbb"
},
"source": [
"We then verify that the checkpoint is correctly loaded. We will sample some networks in the MCUNetV2 design space and evaluate its accuracy on the VWW dataset. The evaluation will take less than one minutes, and you are expected to see an accuracy around 83.6-88.7%. As you can see, we can directly extract these subnets from the design space and get their accuracy very quickly without **training**. This is a unique advantage brought by once-for-all (OFA) super networks.\n",
"\n",
"Let's first define a helper function `evaluate_sub_network` that testes the accuracy of a sub network directly extracted from the super network."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "cb6cc640",
"metadata": {
"id": "cb6cc640"
},
"outputs": [],
"source": [
"from mcunet.utils.pytorch_utils import count_peak_activation_size, count_net_flops, count_parameters\n",
"\n",
"def evaluate_sub_network(ofa_network, cfg, image_size=None):\n",
" if \"image_size\" in cfg:\n",
" image_size = cfg[\"image_size\"]\n",
" batch_size = 128\n",
" # step 1. sample the active subnet with the given config.\n",
" ofa_network.set_active_subnet(**cfg)\n",
" # step 2. extract the subnet with corresponding weights.\n",
" subnet = ofa_network.get_active_subnet().to(device)\n",
" # step 3. calculate the efficiency stats of the subnet.\n",
" peak_memory = count_peak_activation_size(subnet, (1, 3, image_size, image_size))\n",
" macs = count_net_flops(subnet, (1, 3, image_size, image_size))\n",
" params = count_parameters(subnet)\n",
" # step 4. perform BN parameter re-calibration.\n",
" calib_bn(subnet, data_dir, batch_size, image_size)\n",
" # step 5. define the validation dataloader.\n",
" val_loader = build_val_data_loader(data_dir, image_size, batch_size)\n",
" # step 6. validate the accuracy.\n",
" acc = validate(subnet, val_loader)\n",
" return acc, peak_memory, macs, params"
]
},
{
"cell_type": "markdown",
"id": "2dce5d06",
"metadata": {
"id": "2dce5d06"
},
"source": [
"We also provide a handly helper function to visualize the architecture of the subnets. The function takes in the configuration of the subnet and returns an image representing the architecture."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "28bee75e",
"metadata": {
"id": "28bee75e"
},
"outputs": [],
"source": [
"def visualize_subnet(cfg):\n",
" draw_arch(cfg[\"ks\"], cfg[\"e\"], cfg[\"d\"], cfg[\"image_size\"], out_name=\"viz/subnet\")\n",
" im = Image.open(\"viz/subnet.png\")\n",
" im = im.rotate(90, expand=1)\n",
" fig = plt.figure(figsize=(im.size[0] / 250, im.size[1] / 250))\n",
" plt.axis(\"off\")\n",
" plt.imshow(im)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"id": "9642838b",
"metadata": {
"id": "9642838b"
},
"source": [
"Now, let's visualize some subnets and evaluate them on the VWW dataset! We provide an example to randomly sample a subnet from the design space, and get its accuracy, macs, parameters on the VWW dataset. We also visualize the architecture using `visualize_subnet`.\n",
"\n",
"In the architecture visualization, the legend of each block `MBConv{e}-{k}x{k}` means that the current block is a mobile inverted block with expand ratio `e` and the kernel size of the depthwise convolution layer is `k`. Different colors of the blocks indicate different kernel sizes, and gray blocks are network stage dividers. Different widths for the blocks indicate different expand ratios. We also annotate the output resolution close to each block.\n",
"\n",
"Note that we assume that the image resolution is fixed to be 96. Feel free to add another cell below and play with the input resolution.\n",
"\n",
"Hint: you can change the `sample_function` argument of the `sample_active_subnet` method to control the sampling process."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d422ccad",
"metadata": {
"id": "d422ccad",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 453
},
"outputId": "08db0d70-4b25-4d4b-fceb-2603af3c0492"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"cfg={'wid': 0, 'ks': [5, 7, 3, 5, 3, 7, 3, 7, 3, 5, 7, 7, 7, 5, 7, 7, 7, 3, 5, 3], 'e': [4, 3, 6, 3, 4, 3, 3, 4, 6, 6, 4, 6, 6, 4, 4, 4, 3, 4, 4, 4], 'd': [0, 2, 0, 2, 2, 0], 'image_size': 96}\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Validate: 100%|██████████| 32/32 [00:14<00:00, 2.25it/s, loss=0.327, top1=86.6]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"The accuracy of the sampled subnet: #params= 0.7M, accuracy= 86.6%.\n",
"cfg={'wid': 1, 'ks': [5, 7, 3, 5, 3, 7, 3, 7, 3, 5, 7, 7, 7, 5, 7, 7, 7, 3, 5, 3], 'e': [4, 3, 6, 3, 4, 3, 3, 4, 6, 6, 4, 6, 6, 4, 4, 4, 3, 4, 4, 4], 'd': [0, 2, 0, 2, 2, 0], 'image_size': 96}\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Validate: 100%|██████████| 32/32 [00:17<00:00, 1.81it/s, loss=0.297, top1=87.9]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"The accuracy of the sampled subnet: #params= 1.5M, accuracy= 87.9%.\n"
]
}
],
"source": [
"image_size = 96\n",
"\n",
"# cfg = ofa_network.sample_active_subnet(sample_function=random.choice, image_size=image_size)\n",
"cfg={'wid': 0, 'ks': [5, 7, 3, 5, 3, 7, 3, 7, 3, 5, 7, 7, 7, 5, 7, 7, 7, 3, 5, 3], 'e': [4, 3, 6, 3, 4, 3, 3, 4, 6, 6, 4, 6, 6, 4, 4, 4, 3, 4, 4, 4], 'd': [0, 2, 0, 2, 2, 0], 'image_size': 96}\n",
"print(f\"{cfg=}\")\n",
"acc, _, _, params = evaluate_sub_network(ofa_network, cfg)\n",
"visualize_subnet(cfg)\n",
"print(f\"The accuracy of the sampled subnet: #params={params/1e6: .1f}M, accuracy={acc: .1f}%.\")\n",
"\n",
"# cfg = ofa_network.sample_active_subnet(sample_function=random.choice, image_size=image_size)\n",
"cfg={'wid': 1, 'ks': [5, 7, 3, 5, 3, 7, 3, 7, 3, 5, 7, 7, 7, 5, 7, 7, 7, 3, 5, 3], 'e': [4, 3, 6, 3, 4, 3, 3, 4, 6, 6, 4, 6, 6, 4, 4, 4, 3, 4, 4, 4], 'd': [0, 2, 0, 2, 2, 0], 'image_size': 96}\n",
"print(f\"{cfg=}\")\n",
"acc, _, _, params = evaluate_sub_network(ofa_network, cfg)\n",
"visualize_subnet(cfg)\n",
"print(f\"The accuracy of the sampled subnet: #params={params/1e6: .1f}M, accuracy={acc: .1f}%.\")\n",
"\n",
"\n",
"# largest_cfg = ofa_network.sample_active_subnet(sample_function=max, image_size=image_size)\n",
"# print(f\"{largest_cfg=}\")\n",
"# acc, _, _, params = evaluate_sub_network(ofa_network, largest_cfg)\n",
"# visualize_subnet(largest_cfg)\n",
"# print(f\"The largest subnet: #params={params/1e6: .1f}M, accuracy={acc: .1f}%.\")\n",
"\n",
"# smallest_cfg = ofa_network.sample_active_subnet(sample_function=min, image_size=image_size)\n",
"# print(f\"{smallest_cfg=}\")\n",
"# acc, peak_memory, macs, params = evaluate_sub_network(ofa_network, smallest_cfg)\n",
"# visualize_subnet(smallest_cfg)\n",
"# print(f\"The smallest subnet: #params={params/1e6: .1f}M, accuracy={acc: .1f}%.\")"
]
},
{
"cell_type": "markdown",
"id": "d4a62146",
"metadata": {
"id": "d4a62146"
},
"source": [
"### Question 1 (5 pts): Design space exploration.\n",
"\n",
"Try manually sample different subnets by running the cell above multiple times. You can also vary the input resolution. Talk about your findings.\n",
"\n",
"Hint: which dimension plays the most important role for the accuracy?\n",
"\n",
"**Answer:**\n",
"- wid is important\n",
"- image resolution is important\n",
"- ..."
]
},
{
"cell_type": "markdown",
"id": "c84a4301",
"metadata": {
"id": "c84a4301"
},
"source": [
"## **Part 1. Predictors (3 Questions, 30 pts)**\n",
"\n",
"Neural architecture search requires sampling a large amount of sub-networks from the OFA supernet and evaluate the performance of these sub-networks. Such performance evaluation is time-consuming.\n"
]
},
{
"cell_type": "markdown",
"id": "9e76446e",
"metadata": {
"id": "9e76446e"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "2b9e0174",
"metadata": {
"id": "2b9e0174"
},
"source": [
"In this lab, we explore very fast neural network search with **efficiency predictors** and **accuracy predictors**."
]
},
{
"cell_type": "markdown",
"id": "05588448",
"metadata": {
"id": "05588448"
},
"source": [
"### Question 2 (10 pts): Implement the efficiency predictor.\n",
"\n",
"For the efficiency predictor, we use an hook-based analytical model to count the #MACs and peak memory consumption of a given network. Let's build it from scratch using our provided APIs.\n",
"\n",
"Specifically, we define a class called `AnalyticalEfficiencyPredictor`. There are two major functions in this class, `get_efficiency` and `satisfy_constraint`.\n",
"\n",
"The function `get_efficiency` takes in the subnet configuration and returns the #MACs and peak memory of the given subnet. Here, we assume the unit for the #MACs is million and the unit of the peak memory consumption is KB.\n",
"\n",
"Hint: take a look at the `evaluate_sub_network` function above. Let's use `count_net_flops` to get the MACs of the network and `count_peak_activation_size` to get the activation size of the network.\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "5730239c",
"metadata": {
"id": "5730239c"
},
"outputs": [],
"source": [
"class AnalyticalEfficiencyPredictor:\n",
" def __init__(self, net):\n",
" self.net = net\n",
"\n",
" def get_efficiency(self, spec: dict):\n",
" self.net.set_active_subnet(**spec)\n",
" subnet = self.net.get_active_subnet()\n",
" if torch.cuda.is_available():\n",
" subnet = subnet.cuda()\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # Hint: take a look at the `evaluate_sub_network` function above.\n",
" # Hint: the data shape is (batch_size, input_channel, image_size, image_size)\n",
" data_shape = (1, 3, spec['image_size'], spec['image_size'])\n",
" macs = count_net_flops(subnet, data_shape)\n",
" peak_memory = count_peak_activation_size(subnet, (1, 3, spec['image_size'], spec['image_size']))\n",
" ################ YOUR CODE ENDS HERE ################\n",
"\n",
" return dict(millionMACs=macs / 1e6, KBPeakMemory=peak_memory / 1024)\n",
"\n",
" def satisfy_constraint(self, measured: dict, target: dict):\n",
" for key in measured:\n",
" # if the constraint is not specified, we just continue\n",
" if key not in target:\n",
" continue\n",
" # if we exceed the constraint, just return false.\n",
" if measured[key] > target[key]:\n",
" return False\n",
" # no constraint violated, return true.\n",
" return True"
]
},
{
"cell_type": "markdown",
"id": "92c1d32d",
"metadata": {
"id": "92c1d32d"
},
"source": [
"Let's test your implementation for the analytical efficiency predictor by examining the returned values for the smallest and largest subnets we just evaluated a while ago. The results from the efficiency predictor should match with the previous results."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "02e8bf45",
"metadata": {
"id": "02e8bf45",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "75263a24-aed0-4533-f66d-977928e4b0b5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Efficiency stats of the smallest subnet: {'millionMACs': 8.302128, 'KBPeakMemory': 72.0}\n",
"Efficiency stats of the largest subnet: {'millionMACs': 79.416432, 'KBPeakMemory': 270.0}\n"
]
}
],
"source": [
"efficiency_predictor = AnalyticalEfficiencyPredictor(ofa_network)\n",
"\n",
"image_size = 96\n",
"# Print out the efficiency of the smallest subnet.\n",
"smallest_cfg = ofa_network.sample_active_subnet(sample_function=min, image_size=image_size)\n",
"eff_smallest = efficiency_predictor.get_efficiency(smallest_cfg)\n",
"\n",
"# Print out the efficiency of the largest subnet.\n",
"largest_cfg = ofa_network.sample_active_subnet(sample_function=max, image_size=image_size)\n",
"eff_largest = efficiency_predictor.get_efficiency(largest_cfg)\n",
"\n",
"print(\"Efficiency stats of the smallest subnet:\", eff_smallest)\n",
"print(\"Efficiency stats of the largest subnet:\", eff_largest)"
]
},
{
"cell_type": "markdown",
"id": "d01a95ab",
"metadata": {
"id": "d01a95ab"
},
"source": [
"### Question 3 (10 pts): Implement the accuracy predictor.\n",
"\n",
"For the accuracy predictor, it predicts the classification accuracy of a given sub-network on the VWW dataset so that we do **NOT** need to run costly inference every time when we encounter a new subnet during architecture search. Such an accuracy predictor is an MLP (multi-layer perception) model trained on an accuracy dataset built with the OFA network. The inference of an MLP network takes only a few milliseconds, thus the accuracy predictor can speedup the search process by **orders of magnitude**."
]
},
{
"cell_type": "markdown",
"id": "d3cc986f",
"metadata": {
"id": "d3cc986f"
},
"source": [
"The accuracy predictor takes in the architecture of a sub-network and predicts its accuracy on the VWW dataset. Since it is an MLP network, the sub-network must be encoded into a **vector**. In this lab, we provide a class `MCUNetArchEncoder` to perform such conversion from **sub-network architecture** to a **binary vector**."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "129080c6",
"metadata": {
"id": "129080c6"
},
"outputs": [],
"source": [
"image_size_list = [96, 112, 128, 144, 160]\n",
"arch_encoder = MCUNetArchEncoder(\n",
" image_size_list=image_size_list,\n",
" base_depth=ofa_network.base_depth,\n",
" depth_list=ofa_network.depth_list,\n",
" expand_list=ofa_network.expand_ratio_list,\n",
" width_mult_list=ofa_network.width_mult_list,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "9407531e",
"metadata": {
"id": "9407531e"
},
"source": [
"We generated an accuracy dataset beforehand, which is a collection of `[architecture, accuracy]` pairs stored under the `acc_datasets` folder.\n",
"\n",
"With the architecture encoder, you are now required define the accuracy predictor, which is a multi-layer perception (MLP) network with 400 channels per intermediate layer. For simplicity, we fix the number of layers to be **3**. Please implement this MLP network in the following cell."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "2041d237",
"metadata": {
"id": "2041d237"
},
"outputs": [],
"source": [
"class AccuracyPredictor(nn.Module):\n",
" def __init__(\n",
" self,\n",
" arch_encoder,\n",
" hidden_size=400,\n",
" n_layers=3,\n",
" checkpoint_path=None,\n",
" device=\"cuda:0\",\n",
" ):\n",
" super(AccuracyPredictor, self).__init__()\n",
" self.arch_encoder = arch_encoder\n",
" self.hidden_size = hidden_size\n",
" self.n_layers = n_layers\n",
" self.device = device\n",
"\n",
" layers = []\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # Let's build an MLP with n_layers layers.\n",
" # Each layer (nn.Linear) has hidden_size channels and\n",
" # uses nn.ReLU as the activation function.\n",
" # Hint: You can assume that n_layers is fixed to be 3, for simplicity.\n",
" # Hint: the input dimension of the first layer is not hidden_size.\n",
" # use self.arch_encoder.n_dim to get the input dimension\n",
" for i in range(self.n_layers):\n",
" layers.append(\n",
" nn.Linear(self.arch_encoder.n_dim, self.hidden_size) if i == 0 \\\n",
" else nn.Linear(self.hidden_size, self.hidden_size)\n",
" )\n",
" ################ YOUR CODE ENDS HERE ################\n",
" layers.append(nn.Linear(self.hidden_size, 1, bias=False))\n",
" self.layers = nn.Sequential(*layers)\n",
" self.base_acc = nn.Parameter(\n",
" torch.zeros(1, device=self.device), requires_grad=False\n",
" )\n",
"\n",
" if checkpoint_path is not None and os.path.exists(checkpoint_path):\n",
" checkpoint = torch.load(checkpoint_path, map_location=\"cpu\")\n",
" if \"state_dict\" in checkpoint:\n",
" checkpoint = checkpoint[\"state_dict\"]\n",
" self.load_state_dict(checkpoint)\n",
" print(\"Loaded checkpoint from %s\" % checkpoint_path)\n",
"\n",
" self.layers = self.layers.to(self.device)\n",
"\n",
" def forward(self, x):\n",
" y = self.layers(x).squeeze()\n",
" return y + self.base_acc\n",
"\n",
" def predict_acc(self, arch_dict_list):\n",
" X = [self.arch_encoder.arch2feature(arch_dict) for arch_dict in arch_dict_list]\n",
" X = torch.tensor(np.array(X)).float().to(self.device)\n",
" return self.forward(X)"
]
},
{
"cell_type": "markdown",
"id": "0ed1215e",
"metadata": {
"id": "0ed1215e"
},
"source": [
"Let's print out the architecture of the `AccuracyPredictor` you just defined."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "1b85d9ea",
"metadata": {
"id": "1b85d9ea",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "42e9ab13-a012-4fb4-dd95-7ae5271ecd2e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"AccuracyPredictor(\n",
" (layers): Sequential(\n",
" (0): Linear(in_features=128, out_features=400, bias=True)\n",
" (1): Linear(in_features=400, out_features=400, bias=True)\n",
" (2): Linear(in_features=400, out_features=400, bias=True)\n",
" (3): Linear(in_features=400, out_features=1, bias=False)\n",
" )\n",
")\n"
]
}
],
"source": [
"os.makedirs(\"pretrained\", exist_ok=True)\n",
"acc_pred_checkpoint_path = (\n",
" f\"pretrained/{ofa_network.__class__.__name__}_acc_predictor.pth\"\n",
")\n",
"acc_predictor = AccuracyPredictor(\n",
" arch_encoder,\n",
" hidden_size=400,\n",
" n_layers=3,\n",
" checkpoint_path=None,\n",
" device=device,\n",
")\n",
"print(acc_predictor)"
]
},
{
"cell_type": "markdown",
"id": "ac409b74",
"metadata": {
"id": "ac409b74"
},
"source": [
"Let's first visualize some samples in the accuracy dataset in the following cell.\n",
"\n",
"The accuracy dataset is composed of 50,000 `[architecture, accuracy]` pairs, where 40,000 of them are used as the training set and the rest 10,000 are used as validation set.\n",
"\n",
"For **accuracy**, We calculate the average accuracy of all `[architecture, accuracy]` pairs on the accuracy dataset and define it as `base_acc`. For the accuracy predictor, instead of directly regressing the accuracy of each architecture, its training target is `accuracy - base_acc`. Since `accuracy - base_acc` is usually much smaller than `accuracy` itself, this can make training easier.\n",
"\n",
"For **architecture**, each subnet within the design space is uniquely represented by a binary vector. The binary vector is a concatenation of the **one-hot representation** for both global parameters (*e.g.* input resolution, width multiplier) and parameters of each inverted MobileNet block (*e.g.* kernel sizes and expand ratios). Note that we prefer **one-hot** representations over **numerical** representations because all design hyperparameters are **discrete** values.\n",
"\n",
"For example, our design space supports\n",
"\n",
"```python\n",
"kernel_size = [3, 5, 7]\n",
"expand_ratio = [3, 4, 6]\n",
"```\n",
"\n",
"Then, we represent `kernel_size=3` as `[1, 0, 0]`, `kernel_size=5` as `[0, 1, 0]`, and `kernel_size=7` as `[0, 0, 1]`. Similarly, for `expand_ratio=3`, it is written as `[1, 0, 0]`; `expand_ratio=4` is written as `[0, 1, 0]` and `expand_ratio=6` is written as `[0, 0, 1]`. The representation for each inverted MobileNet block is obtained by concatenating the kernel size embedding with the expand ratio embedding. Note that for skipped blocks, we use `[0, 0, 0]` to represent their kernel sizes and expand ratios. You will see a detailed explanation of the architecture-embedding correspondence after running the following cell."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "37c85b7e",
"metadata": {
"id": "37c85b7e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 783
},
"outputId": "3e069fd7-729d-4624-9177-1ad7570bbd13"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Loading data: 100%|██████████| 50000/50000 [00:00<00:00, 65878.05it/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train Size: 40000, Valid Size: 10000\n",
"The basic accuracy (mean accuracy of all subnets within the dataset is: 90.3%.\n",
"====================================================================================================\n",
"network embedding: [0 0 0 1 0 | 1 0 0 | 0 0 1 | 0 1 0 | 0 0 1 | 0 1 0 | 0 0 0 | 0 0 0 | 0 0 1 | 0 1 0 | 1 0 0 | 1 0 0 | 0 0 1 | 1 0 0 | 0 0 0 | 0 0 0 | 0 0 1 | 0 0 1 | 0 1 0 | 0 1 0 | 1 0 0 | 0 0 1 | 1 0 0 | 0 0 1 | 0 1 0 | 0 1 0 | 0 0 1 | 1 0 0 | 1 0 0 | 1 0 0 | 0 1 0 | 0 0 1 | 0 0 1 | 1 0 0 | 1 0 0 | 1 0 0 | 0 0 0 | 0 0 0 | 0 0 0 | 0 0 0 | 1 0 0 | 0 0 1]\n",
"image resolution embedding: [0 0 0 1 0] => image resolution: 144\n",
"width multiplier embedding: [1 0 0] => width multiplier: 0.5\n",
"**************************************************Stage1**************************************************\n",
"kernel size embedding: [0 0 1] => kernel size: 7; expand ratio embedding: [0 1 0] => expand ratio: 4\n",
"kernel size embedding: [0 0 1] => kernel size: 7; expand ratio embedding: [0 1 0] => expand ratio: 4\n",
"kernel size embedding: [0 0 0] expand ratio embedding: [0 0 0] => layer skipped.\n",
"**************************************************Stage2**************************************************\n",
"kernel size embedding: [0 0 1] => kernel size: 7; expand ratio embedding: [0 1 0] => expand ratio: 4\n",
"kernel size embedding: [1 0 0] => kernel size: 3; expand ratio embedding: [1 0 0] => expand ratio: 3\n",
"kernel size embedding: [0 0 1] => kernel size: 7; expand ratio embedding: [1 0 0] => expand ratio: 3\n",
"kernel size embedding: [0 0 0] expand ratio embedding: [0 0 0] => layer skipped.\n",
"**************************************************Stage3**************************************************\n",
"kernel size embedding: [0 0 1] => kernel size: 7; expand ratio embedding: [0 0 1] => expand ratio: 6\n",
"kernel size embedding: [0 1 0] => kernel size: 5; expand ratio embedding: [0 1 0] => expand ratio: 4\n",
"kernel size embedding: [1 0 0] => kernel size: 3; expand ratio embedding: [0 0 1] => expand ratio: 6\n",
"kernel size embedding: [1 0 0] => kernel size: 3; expand ratio embedding: [0 0 1] => expand ratio: 6\n",
"**************************************************Stage4**************************************************\n",
"kernel size embedding: [0 1 0] => kernel size: 5; expand ratio embedding: [0 1 0] => expand ratio: 4\n",
"kernel size embedding: [0 0 1] => kernel size: 7; expand ratio embedding: [1 0 0] => expand ratio: 3\n",
"kernel size embedding: [1 0 0] => kernel size: 3; expand ratio embedding: [1 0 0] => expand ratio: 3\n",
"kernel size embedding: [0 1 0] => kernel size: 5; expand ratio embedding: [0 0 1] => expand ratio: 6\n",
"**************************************************Stage5**************************************************\n",
"kernel size embedding: [0 0 1] => kernel size: 7; expand ratio embedding: [1 0 0] => expand ratio: 3\n",
"kernel size embedding: [1 0 0] => kernel size: 3; expand ratio embedding: [1 0 0] => expand ratio: 3\n",
"kernel size embedding: [0 0 0] expand ratio embedding: [0 0 0] => layer skipped.\n",
"kernel size embedding: [0 0 0] expand ratio embedding: [0 0 0] => layer skipped.\n",
"**************************************************Stage6**************************************************\n",
"kernel size embedding: [1 0 0] => kernel size: 3; expand ratio embedding: [0 0 1] => expand ratio: 6\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"The accuracy of this subnet on the holdout validation set is: 91.0%.\n"
]
}
],
"source": [
"acc_dataset = AccuracyDataset(\"acc_datasets\")\n",
"train_loader, valid_loader, base_acc = acc_dataset.build_acc_data_loader(\n",
" arch_encoder=arch_encoder\n",
")\n",
"\n",
"print(f\"The basic accuracy (mean accuracy of all subnets within the dataset is: {(base_acc * 100): .1f}%.\")\n",
"\n",
"# Let's print one sample in the training set\n",
"sampled = 0\n",
"for (data, label) in train_loader:\n",
" data = data.to(device)\n",
" label = label.to(device)\n",
" print(\"=\" * 100)\n",
" # dummy pass to print the divided encoding\n",
" arch_encoding = arch_encoder.feature2arch(data[0].int().cpu().numpy(), verbose=False)\n",
" # print out the architecture encoding process in detail\n",
" arch_encoding = arch_encoder.feature2arch(data[0].int().cpu().numpy(), verbose=True)\n",
" visualize_subnet(arch_encoding)\n",
" print(f\"The accuracy of this subnet on the holdout validation set is: {(label[0] * 100): .1f}%.\")\n",
" sampled += 1\n",
" if sampled == 1:\n",
" break\n"
]
},
{
"cell_type": "markdown",
"id": "334114c1",
"metadata": {
"id": "334114c1"
},
"source": [
"### Question 4 (10 pts): Complete the code for accuracy predictor training.\n",
"\n",
"\n",
"Now let's train the accuracy predictor using the dataset we provided! In this part, you are responsible for the implementation of the training and validation of your accuracy predictor. The training process will take roughly 1-2 minutes.\n",
"\n",
"Hint: you may refer to Tutorial 2 on how to train a neural network with PyTorch."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "b37224da",
"metadata": {
"id": "b37224da",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "23e9b67b-c6d9-4304-ae6c-feeee8157c3c"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Epoch1: 100%|██████████| 157/157 [00:01<00:00, 104.11it/s]\n",
"Val: 100%|██████████| 40/40 [00:00<00:00, 43.30it/s, loss=0.00443]\n",
"Epoch2: 100%|██████████| 157/157 [00:01<00:00, 107.59it/s]\n",
"Val: 100%|██████████| 40/40 [00:00<00:00, 43.15it/s, loss=0.00504]\n",
"Epoch3: 100%|██████████| 157/157 [00:01<00:00, 107.79it/s]\n",
"Val: 100%|██████████| 40/40 [00:00<00:00, 42.27it/s, loss=0.00309]\n",
"Epoch4: 100%|██████████| 157/157 [00:01<00:00, 93.52it/s] \n",
"Val: 100%|██████████| 40/40 [00:01<00:00, 26.44it/s, loss=0.00193]\n",
"Epoch5: 100%|██████████| 157/157 [00:02<00:00, 66.77it/s] \n",
"Val: 100%|██████████| 40/40 [00:00<00:00, 43.27it/s, loss=0.00216]\n",
"Epoch6: 100%|██████████| 157/157 [00:01<00:00, 106.36it/s]\n",
"Val: 100%|██████████| 40/40 [00:00<00:00, 43.11it/s, loss=0.00183]\n",
"Epoch7: 100%|██████████| 157/157 [00:01<00:00, 106.20it/s]\n",
"Val: 100%|██████████| 40/40 [00:00<00:00, 42.06it/s, loss=0.00163]\n",
"Epoch8: 100%|██████████| 157/157 [00:04<00:00, 36.92it/s]\n",
"Val: 100%|██████████| 40/40 [00:01<00:00, 28.94it/s, loss=0.00159]\n",
"Epoch9: 100%|██████████| 157/157 [00:06<00:00, 25.95it/s] \n",
"Val: 100%|██████████| 40/40 [00:03<00:00, 10.64it/s, loss=0.00164]\n",
"Epoch10: 100%|██████████| 157/157 [00:01<00:00, 105.31it/s]\n",
"Val: 100%|██████████| 40/40 [00:00<00:00, 42.65it/s, loss=0.00169]\n",
"100%|██████████| 10/10 [00:36<00:00, 3.66s/it]\n"
]
}
],
"source": [
"# the default value is zero\n",
"acc_predictor = AccuracyPredictor(\n",
" arch_encoder,\n",
" hidden_size=400,\n",
" n_layers=3,\n",
" checkpoint_path=None,\n",
" device=device,\n",
")\n",
"acc_predictor.base_acc = nn.Parameter(torch.zeros(1, device=acc_predictor.device), requires_grad=False)\n",
"acc_predictor.base_acc.data += base_acc # this is not good.\n",
"acc_predictor.to(device)\n",
"\n",
"criterion = torch.nn.L1Loss().to(device)\n",
"optimizer = torch.optim.Adam(acc_predictor.parameters())\n",
"\n",
"for epoch in tqdm(range(10)):\n",
" acc_predictor.train()\n",
" for (data, label) in tqdm(train_loader, desc=\"Epoch%d\" % (epoch + 1), position=0, leave=True):\n",
" # step 1. Move the data and labels to device (cuda:0).\n",
" data = data.to(device)\n",
" label = label.to(device)\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # step 2. Run forward pass.\n",
" pred = acc_predictor(data)\n",
" # step 3. Calculate the loss.\n",
" loss = criterion(pred, label)\n",
" # step 4. Perform the backward pass.\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
" ################ YOUR CODE ENDS HERE ################\n",
"\n",
" acc_predictor.eval()\n",
" with torch.no_grad():\n",
" with tqdm(total=len(valid_loader), desc=\"Val\", position=0, leave=True) as t:\n",
" for (data, label) in valid_loader:\n",
" # step 1. Move the data and labels to device (cuda:0).\n",
" data = data.to(device)\n",
" label = label.to(device)\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # step 2. Run forward pass.\n",
" pred = acc_predictor(data)\n",
" # step 3. Calculate the loss.\n",
" loss = criterion(pred, label)\n",
" ############### YOUR CODE ENDS HERE ###############\n",
" t.set_postfix({\"loss\": loss.item()})\n",
" t.update(1)\n",
"\n",
"if not os.path.exists(acc_pred_checkpoint_path):\n",
" torch.save(acc_predictor.cpu().state_dict(), acc_pred_checkpoint_path)"
]
},
{
"cell_type": "markdown",
"id": "d75e18dd",
"metadata": {
"id": "d75e18dd"
},
"source": [
"Now let's plot the correlation of predicted accuracy against ground truth accuracy and make sure our predictor is reliable. To receive full score, you are expected to see a linear correlation in this part."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "fb98dbfd",
"metadata": {
"id": "fb98dbfd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 507
},
"outputId": "633b7598-f3d8-4da1-c5b1-c4f72efc778d"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Val: 0%| | 0/40 [00:00, ?it/s]\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Correlation between predicted accuracy and real accuracy')"
]
},
"metadata": {},
"execution_count": 22
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
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\n"
},
"metadata": {}
}
],
"source": [
"predicted_accuracies = []\n",
"ground_truth_accuracies = []\n",
"acc_predictor = acc_predictor.to(\"cuda:0\")\n",
"acc_predictor.eval()\n",
"with torch.no_grad():\n",
" with tqdm(total=len(valid_loader), desc=\"Val\") as t:\n",
" for (data, label) in valid_loader:\n",
" data = data.to(device)\n",
" label = label.to(device)\n",
" pred = acc_predictor(data)\n",
" predicted_accuracies += pred.cpu().numpy().tolist()\n",
" ground_truth_accuracies += label.cpu().numpy().tolist()\n",
" if len(predicted_accuracies) > 200:\n",
" break\n",
"plt.scatter(predicted_accuracies, ground_truth_accuracies)\n",
"# draw y = x\n",
"min_acc, max_acc = min(predicted_accuracies), max(predicted_accuracies)\n",
"plt.plot([min_acc, max_acc], [min_acc, max_acc], c=\"red\", linewidth=2)\n",
"plt.xlabel(\"Predicted accuracy\")\n",
"plt.ylabel(\"Measured accuracy\")\n",
"plt.title(\"Correlation between predicted accuracy and real accuracy\")"
]
},
{
"cell_type": "markdown",
"id": "0fff1b13",
"metadata": {
"id": "0fff1b13"
},
"source": [
"## **Part 2. Neural Architecture Search (6 Questions, 65 pts + 10 bonus pts)**\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "fc92dfce",
"metadata": {
"id": "fc92dfce"
},
"source": [
"So far, we have defined both the efficiency and accuracy predictors. Let's start fast model specialization with these two powerful predictors!\n",
"\n",
"\n",
"\n",
"In this part, you are required to implement two typical search algorithms: **random search** and **evolutionary search**. The search algorithm aims to find the model architecture that provides the best accuracy while satisfying the efficiency constraints (e.g., MACs, peak memory)."
]
},
{
"cell_type": "markdown",
"source": [
"### Question 5 (5 pts): Complete the following random search agent."
],
"metadata": {
"id": "FP9c4Xcf6Aff"
},
"id": "FP9c4Xcf6Aff"
},
{
"cell_type": "code",
"execution_count": 23,
"id": "38fefc52",
"metadata": {
"id": "38fefc52"
},
"outputs": [],
"source": [
"class RandomSearcher:\n",
" def __init__(self, efficiency_predictor, accuracy_predictor):\n",
" self.efficiency_predictor = efficiency_predictor\n",
" self.accuracy_predictor = accuracy_predictor\n",
"\n",
" def random_valid_sample(self, constraint):\n",
" # randomly sample subnets until finding one that satisfies the constraint\n",
" while True:\n",
" sample = self.accuracy_predictor.arch_encoder.random_sample_arch()\n",
" efficiency = self.efficiency_predictor.get_efficiency(sample)\n",
" if self.efficiency_predictor.satisfy_constraint(efficiency, constraint):\n",
" return sample, efficiency\n",
"\n",
" def run_search(self, constraint, n_subnets=100):\n",
" subnet_pool = []\n",
" # sample subnets\n",
" for _ in tqdm(range(n_subnets)):\n",
" sample, efficiency = self.random_valid_sample(constraint)\n",
" subnet_pool.append(sample)\n",
" # predict the accuracy of subnets\n",
" accs = self.accuracy_predictor.predict_acc(subnet_pool)\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # hint: one line of code\n",
" # get the index of the best subnet\n",
" best_idx = torch.argmax(accs.flatten())\n",
" ############### YOUR CODE ENDS HERE #################\n",
" # return the best subnet\n",
" return accs[best_idx], subnet_pool[best_idx]"
]
},
{
"cell_type": "markdown",
"source": [
"### Question 6 (5 pts): Complete the following function."
],
"metadata": {
"id": "wBth-HDX6FDs"
},
"id": "wBth-HDX6FDs"
},
{
"cell_type": "code",
"execution_count": 34,
"id": "ae668f76",
"metadata": {
"id": "ae668f76"
},
"outputs": [],
"source": [
"def search_and_measure_acc(agent, constraint, **kwargs):\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # hint: call the search function\n",
" if 'n_subnets' in kwargs:\n",
" best_info = agent.run_search(constraint, kwargs['n_subnets'])\n",
" else:\n",
" best_info = agent.run_search(constraint) # use later\n",
" ############### YOUR CODE ENDS HERE #################\n",
" # get searched subnet\n",
" ofa_network.set_active_subnet(**best_info[1])\n",
" subnet = ofa_network.get_active_subnet().to(device)\n",
" # calibrate bn\n",
" calib_bn(subnet, data_dir, 128, best_info[1][\"image_size\"])\n",
" # build val loader\n",
" val_loader = build_val_data_loader(data_dir, best_info[1][\"image_size\"], 128)\n",
" # measure accuracy\n",
" acc = validate(subnet, val_loader)\n",
" # print best_info\n",
" print(f\"Accuracy of the selected subnet: {acc}\")\n",
" # visualize model architecture\n",
" visualize_subnet(best_info[1])\n",
" return acc, subnet\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "eb7a181a",
"metadata": {
"id": "eb7a181a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 900
},
"outputId": "5e7b7d2b-c20f-4d8b-e669-c652571f82b7"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Random search with constraint: MACs <= 50M\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 300/300 [01:24<00:00, 3.55it/s]\n",
"Validate: 100%|██████████| 32/32 [00:10<00:00, 3.19it/s, loss=0.187, top1=93.3]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the selected subnet: 93.27543427346657\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Random search with constraint: MACs <= 100M\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 300/300 [01:23<00:00, 3.61it/s]\n",
"Validate: 100%|██████████| 32/32 [00:12<00:00, 2.48it/s, loss=0.184, top1=93.4]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the selected subnet: 93.44913154393804\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Random search with constraint: Peak memory <= 256KB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 300/300 [03:04<00:00, 1.63it/s]\n",
"Validate: 100%|██████████| 32/32 [00:10<00:00, 3.04it/s, loss=0.204, top1=92.8]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the selected subnet: 92.8287840963889\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Random search with constraint: Peak memory <= 512KB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 300/300 [01:30<00:00, 3.32it/s]\n",
"Validate: 100%|██████████| 32/32 [00:12<00:00, 2.60it/s, loss=0.19, top1=93.2]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the selected subnet: 93.15136479455839\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"random.seed(1)\n",
"np.random.seed(1)\n",
"nas_agent = RandomSearcher(efficiency_predictor, acc_predictor)\n",
"# MACs-constrained search\n",
"subnets_rs_macs = {}\n",
"for millonMACs in [50, 100]:\n",
" search_constraint = dict(millonMACs=millonMACs)\n",
" print(f\"Random search with constraint: MACs <= {millonMACs}M\")\n",
" subnets_rs_macs[millonMACs] = search_and_measure_acc(nas_agent, search_constraint, n_subnets=300)\n",
"\n",
"# memory-constrained search\n",
"subnets_rs_memory = {}\n",
"for KBPeakMemory in [256, 512]:\n",
" search_constraint = dict(KBPeakMemory=KBPeakMemory)\n",
" print(f\"Random search with constraint: Peak memory <= {KBPeakMemory}KB\")\n",
" subnets_rs_memory[KBPeakMemory] = search_and_measure_acc(nas_agent, search_constraint, n_subnets=300)\n"
]
},
{
"cell_type": "markdown",
"source": [
"### Question7 (20 pts): Complete the following evolutionary search agent."
],
"metadata": {
"id": "3LGlOW1b6Yzu"
},
"id": "3LGlOW1b6Yzu"
},
{
"cell_type": "markdown",
"source": [
"\n",
"\n",
"\n",
"\n",
"Now you have succesfully implemented the random search algorithm. In this part, we will implement a more sample-efficient search algorithm, evolutionary search. Evolutionary search is inspired by the evolution algorithm (or genetic algorithm). A **population** of sub-networks are first sampled from the design space. Then, in each **generation**, we perform random mutation and crossover operations as is shown in the figure above. The sub-networks with highest accuracy will be kept, and this process will be repeated until the number of generations reaches `max_time_budget`. Similar to the random search, throughout the search process, all sub-networks that cannot satisfy the efficiency constraint will be discarded.\n"
],
"metadata": {
"id": "JUyevsrLy3KQ"
},
"id": "JUyevsrLy3KQ"
},
{
"cell_type": "code",
"execution_count": 45,
"id": "e02bd308",
"metadata": {
"id": "e02bd308"
},
"outputs": [],
"source": [
"class EvolutionSearcher:\n",
" def __init__(self, efficiency_predictor, accuracy_predictor, **kwargs):\n",
" self.efficiency_predictor = efficiency_predictor\n",
" self.accuracy_predictor = accuracy_predictor\n",
"\n",
" # evolution hyper-parameters\n",
" self.arch_mutate_prob = kwargs.get(\"arch_mutate_prob\", 0.1)\n",
" self.resolution_mutate_prob = kwargs.get(\"resolution_mutate_prob\", 0.5)\n",
" self.population_size = kwargs.get(\"population_size\", 100)\n",
" self.max_time_budget = kwargs.get(\"max_time_budget\", 500)\n",
" self.parent_ratio = kwargs.get(\"parent_ratio\", 0.25)\n",
" self.mutation_ratio = kwargs.get(\"mutation_ratio\", 0.5)\n",
"\n",
" def update_hyper_params(self, new_param_dict):\n",
" self.__dict__.update(new_param_dict)\n",
"\n",
" def random_valid_sample(self, constraint):\n",
" # randomly sample subnets until finding one that satisfies the constraint\n",
" while True:\n",
" sample = self.accuracy_predictor.arch_encoder.random_sample_arch()\n",
" efficiency = self.efficiency_predictor.get_efficiency(sample)\n",
" if self.efficiency_predictor.satisfy_constraint(efficiency, constraint):\n",
" return sample, efficiency\n",
"\n",
" def mutate_sample(self, sample, constraint):\n",
" while True:\n",
" new_sample = copy.deepcopy(sample)\n",
"\n",
" self.accuracy_predictor.arch_encoder.mutate_resolution(new_sample, self.resolution_mutate_prob)\n",
" self.accuracy_predictor.arch_encoder.mutate_width(new_sample, self.arch_mutate_prob)\n",
" self.accuracy_predictor.arch_encoder.mutate_arch(new_sample, self.arch_mutate_prob)\n",
"\n",
" efficiency = self.efficiency_predictor.get_efficiency(new_sample)\n",
" if self.efficiency_predictor.satisfy_constraint(efficiency, constraint):\n",
" return new_sample, efficiency\n",
"\n",
" def crossover_sample(self, sample1, sample2, constraint):\n",
" while True:\n",
" new_sample = copy.deepcopy(sample1)\n",
" for key in new_sample.keys():\n",
" if not isinstance(new_sample[key], list):\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # hint: randomly choose the value from sample1[key] and sample2[key], random.choice\n",
" new_sample[key] = random.choice([sample1[key], sample2[key]])\n",
" ############### YOUR CODE ENDS HERE #################\n",
" else:\n",
" for i in range(len(new_sample[key])):\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" new_sample[key][i] = random.choice([sample1[key][i], sample2[key][i]])\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" efficiency = self.efficiency_predictor.get_efficiency(new_sample)\n",
" if self.efficiency_predictor.satisfy_constraint(efficiency, constraint):\n",
" return new_sample, efficiency\n",
"\n",
" def run_search(self, constraint, **kwargs):\n",
" self.update_hyper_params(kwargs)\n",
"\n",
" mutation_numbers = int(round(self.mutation_ratio * self.population_size))\n",
" parents_size = int(round(self.parent_ratio * self.population_size))\n",
"\n",
" best_valids = [-100]\n",
" population = [] # (acc, sample) tuples\n",
" child_pool = []\n",
" best_info = None\n",
" # generate random population\n",
" for _ in range(self.population_size):\n",
" sample, efficiency = self.random_valid_sample(constraint)\n",
" child_pool.append(sample)\n",
"\n",
" accs = self.accuracy_predictor.predict_acc(child_pool)\n",
" for i in range(self.population_size):\n",
" population.append((accs[i].item(), child_pool[i]))\n",
"\n",
" # evolving the population\n",
" with tqdm(total=self.max_time_budget) as t:\n",
" for i in range(self.max_time_budget):\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # hint: sort the population according to the acc (descending order)\n",
" population = sorted(population, key=lambda x: x[0], reverse=True)\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
" # hint: keep topK samples in the population, K = parents_size\n",
" # the others are discarded.\n",
" population = sorted(population, key=lambda x: x[0], reverse=True)[:parents_size]\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" # update best info\n",
" acc = population[0][0]\n",
" if acc > best_valids[-1]:\n",
" best_valids.append(acc)\n",
" best_info = population[0]\n",
" else:\n",
" best_valids.append(best_valids[-1])\n",
"\n",
" child_pool = []\n",
" for j in range(mutation_numbers):\n",
" # randomly choose a sample\n",
" par_sample = population[np.random.randint(parents_size)][1]\n",
" # mutate this sample\n",
" new_sample, efficiency = self.mutate_sample(par_sample, constraint)\n",
" child_pool.append(new_sample)\n",
"\n",
" for j in range(self.population_size - mutation_numbers):\n",
" # randomly choose two samples\n",
" par_sample1 = population[np.random.randint(parents_size)][1]\n",
" par_sample2 = population[np.random.randint(parents_size)][1]\n",
" # crossover\n",
" new_sample, efficiency = self.crossover_sample(\n",
" par_sample1, par_sample2, constraint\n",
" )\n",
" child_pool.append(new_sample)\n",
" # predict accuracy with the accuracy predictor\n",
" accs = self.accuracy_predictor.predict_acc(child_pool)\n",
" for j in range(self.population_size):\n",
" population.append((accs[j].item(), child_pool[j]))\n",
"\n",
" t.update(1)\n",
"\n",
" return best_info"
]
},
{
"cell_type": "markdown",
"source": [
"### Question 8 (10pts): Run evolutionary search and tune evo_params to optimize the results. Describe your findings.\n"
],
"metadata": {
"id": "n0sLPepw6dFx"
},
"id": "n0sLPepw6dFx"
},
{
"cell_type": "code",
"execution_count": 46,
"id": "d0f39e69",
"metadata": {
"id": "d0f39e69",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 917
},
"outputId": "7c6fbb88-563e-4f0a-a680-42ed9e8d429a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evolutionary search with constraint: MACs <= 50M\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 10/10 [00:24<00:00, 2.44s/it]\n",
"Validate: 100%|██████████| 32/32 [00:11<00:00, 2.72it/s, loss=0.215, top1=92.3]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the selected subnet: 92.25806454641943\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evolutionary search with constraint: MACs <= 100M\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 10/10 [00:27<00:00, 2.75s/it]\n",
"Validate: 100%|██████████| 32/32 [00:11<00:00, 2.81it/s, loss=0.198, top1=92.8]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the selected subnet: 92.8287841493971\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evolutionary search with constraint: Peak memory <= 256KB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 10/10 [00:32<00:00, 3.23s/it]\n",
"Validate: 100%|██████████| 32/32 [00:11<00:00, 2.76it/s, loss=0.211, top1=92.3]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the selected subnet: 92.25806449341123\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evolutionary search with constraint: Peak memory <= 512KB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 10/10 [00:26<00:00, 2.64s/it]\n",
"Validate: 100%|██████████| 32/32 [00:12<00:00, 2.62it/s, loss=0.188, top1=93.3]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the selected subnet: 93.27543427346657\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"random.seed(1)\n",
"np.random.seed(1)\n",
"\n",
"# hint: tune hyper-parameters below\n",
"evo_params = {\n",
" 'arch_mutate_prob': 0.1, # The probability of architecture mutation in evolutionary search\n",
" 'resolution_mutate_prob': 0.1, # The probability of resolution mutation in evolutionary search\n",
" 'population_size': 10, # The size of the population\n",
" 'max_time_budget': 10,\n",
" 'parent_ratio': 0.1,\n",
" 'mutation_ratio': 0.1,\n",
"}\n",
"\n",
"nas_agent = EvolutionSearcher(efficiency_predictor, acc_predictor, **evo_params)\n",
"# MACs-constrained search\n",
"subnets_evo_macs = {}\n",
"for millonMACs in [50, 100]:\n",
" search_constraint = dict(millionMACs=millonMACs)\n",
" print(f\"Evolutionary search with constraint: MACs <= {millonMACs}M\")\n",
" subnets_evo_macs[millonMACs] = search_and_measure_acc(nas_agent, search_constraint)\n",
"\n",
"# memory-constrained search\n",
"subnets_evo_memory = {}\n",
"for KBPeakMemory in [256, 512]:\n",
" search_constraint = dict(KBPeakMemory=KBPeakMemory)\n",
" print(f\"Evolutionary search with constraint: Peak memory <= {KBPeakMemory}KB\")\n",
" subnets_evo_memory[KBPeakMemory] = search_and_measure_acc(nas_agent, search_constraint)\n"
]
},
{
"cell_type": "markdown",
"source": [
"### Question 9 (15 pts + 10 pts bonus): Run evolutionary search under real-world constraints.\n",
"\n",
"In real-world applications, we may have multiple efficiency constraints: https://blog.tensorflow.org/2019/10/visual-wake-words-with-tensorflow-lite_30.html.\n",
"Use evolutionary search to find models that satisfy the following constraints:\n",
"- [15 pts] 250 KB, 60M MACs (acc >= 92.5% to get the full credit)\n",
"- [10 pts, **bonus**] 200KB, 30M MACs (acc >= 90% to get the full credit)\n",
"\n",
"Hint: You do not have to use the same `evo_params` for these two tasks."
],
"metadata": {
"id": "1gL-cyMO6hTx"
},
"id": "1gL-cyMO6hTx"
},
{
"cell_type": "code",
"execution_count": 48,
"id": "fae35786",
"metadata": {
"id": "fae35786",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 251
},
"outputId": "35a638d8-581f-4bd2-a443-957ea54881d4"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evolution search with constraint: MACs <= 60M, peak memory <= 250KB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 10/10 [01:09<00:00, 6.91s/it]\n",
"Validate: 100%|██████████| 32/32 [00:11<00:00, 2.76it/s, loss=0.203, top1=92.9]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the selected subnet: 92.92803967951545\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evolution search finished!\n"
]
}
],
"source": [
"random.seed(1)\n",
"np.random.seed(1)\n",
"# hint: tune hyper-parameters below\n",
"evo_params = {\n",
" 'arch_mutate_prob': 0.3, # The probability of architecture mutation in evolutionary search\n",
" 'resolution_mutate_prob': 0.2, # The probability of resolution mutation in evolutionary search\n",
" 'population_size': 20, # The size of the population\n",
" 'max_time_budget': 10,\n",
" 'parent_ratio': 0.3,\n",
" 'mutation_ratio': 0.2,\n",
"}\n",
"\n",
"nas_agent = EvolutionSearcher(efficiency_predictor, acc_predictor, **evo_params)\n",
"\n",
"(millionMACs, KBPeakMemory) = [60, 250]\n",
"print(f\"Evolution search with constraint: MACs <= {millionMACs}M, peak memory <= {KBPeakMemory}KB\")\n",
"search_and_measure_acc(nas_agent, dict(millionMACs=millionMACs, KBPeakMemory=KBPeakMemory))\n",
"print(\"Evolution search finished!\")"
]
},
{
"cell_type": "code",
"source": [
"random.seed(1)\n",
"np.random.seed(1)\n",
"# hint: tune hyper-parameters below\n",
"evo_params = {\n",
" 'arch_mutate_prob': 0.3, # The probability of architecture mutation in evolutionary search\n",
" 'resolution_mutate_prob': 0.2, # The probability of resolution mutation in evolutionary search\n",
" 'population_size': 20, # The size of the population\n",
" 'max_time_budget': 10,\n",
" 'parent_ratio': 0.2,\n",
" 'mutation_ratio': 0.2,\n",
"}\n",
"\n",
"nas_agent = EvolutionSearcher(efficiency_predictor, acc_predictor, **evo_params)\n",
"\n",
"(millionMACs, KBPeakMemory) = [30, 200]\n",
"print(f\"Evolution search with constraint: MACs <= {millionMACs}M, peak memory <= {KBPeakMemory}KB\")\n",
"search_and_measure_acc(nas_agent, dict(millionMACs=millionMACs, KBPeakMemory=KBPeakMemory))\n",
"print(\"Evolution search finished!\")"
],
"metadata": {
"id": "ejJAqLBRRw3G",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 260
},
"outputId": "b0be7c71-e906-43fe-a1d5-1ac88b65016e"
},
"id": "ejJAqLBRRw3G",
"execution_count": 49,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evolution search with constraint: MACs <= 30M, peak memory <= 200KB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 10/10 [01:06<00:00, 6.68s/it]\n",
"Validate: 100%|██████████| 32/32 [00:10<00:00, 2.91it/s, loss=0.248, top1=90.1]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the selected subnet: 90.14888334061312\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evolution search finished!\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "wNCGN6jhdYMR"
},
"id": "wNCGN6jhdYMR",
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Question 10 (10 pts): Is it possible to find a subnet with the following efficiency constraints in the current design space?\n",
"- A: The activation size of the subnet is **at most 256KB** and the MACs of the subnet is **at most 15M**. \n",
"\n",
"- Yes\n",
"- B: The activation size of the subnet is **at most 64 KB**.\n",
"\n",
"- No\n"
],
"metadata": {
"id": "RwS6eKOG6mXl"
},
"id": "RwS6eKOG6mXl"
},
{
"cell_type": "code",
"source": [
"smallest_cfg = ofa_network.sample_active_subnet(sample_function=min, image_size=96)\n",
"print(f\"{smallest_cfg=}\")\n",
"acc, peak_memory, macs, params = evaluate_sub_network(ofa_network, smallest_cfg)\n",
"visualize_subnet(smallest_cfg)\n",
"print(f\"The smallest subnet: peak_memory={peak_memory / 1024: .1f} KB, macs={macs / 1e6: .1f}M.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 245
},
"id": "_0Xy_ll8cTBf",
"outputId": "8bef2151-81b6-4668-e1a0-a234697ad9e8"
},
"id": "_0Xy_ll8cTBf",
"execution_count": 52,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"smallest_cfg={'wid': 0, 'ks': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], 'e': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], 'd': [0, 0, 0, 0, 0, 0], 'image_size': 96}\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Validate: 100%|██████████| 32/32 [00:13<00:00, 2.33it/s, loss=0.379, top1=83.4]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"The smallest subnet: peak_memory= 72.0 KB, macs= 8.3M.\n"
]
}
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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}
================================================
FILE: Lab4.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "07b998ce"
},
"source": [
"# **MIT 6.5940 EfficientML.ai Fall 2023 Lab 4: LLM Quantization with AWQ**\n"
],
"id": "07b998ce"
},
{
"cell_type": "markdown",
"metadata": {
"id": "e9df27e7"
},
"source": [
"## Introduction\n",
"\n",
"This colab notebook provides code and a framework for Lab 4: LLM Quantization. You will learn how to quantize a large language model that can run efficiently. We will implement AWQ (activation aware weight only quantization) for 4 bit weight-only quantization.\n",
"\n",
"Running large language models (LLMs) on the edge is of great importance, which not only enhances user experience but also addresses privacy concerns, as sensitive data remains localized and reduces the risk of potential breaches.\n",
"\n",
"However, deploying LLMs on the edge presents significant challenges. Edge devices operate under tight power constraints, setting them apart from workstations or cloud servers. This translates to restricted memory bandwidth and limited peak computation throughput on the edge. For instance, the NVIDIA Jetson Orin Nano, with its 8GB DRAM, cannot accommodate even the most compact LLaMA-2 model in half precision. Thankfully, AWQ presents a push-the-button solution for weight quantization, empowering LLM inference on edge devices with constrained memory.\n",
"\n",
"Furthermore, by using the AWQ 4-bit weight-only quantization algorithm, combined with an efficient 4-bit kernel, we can achieve the following acceleration on the RTX 4090. In the next lab section, we will also use TinyChatEnigne to achieve actual performance acceleration."
],
"id": "e9df27e7"
},
{
"cell_type": "markdown",
"source": [
"### Demo on an RTX 4090:\n"
],
"metadata": {
"id": "RGQwcN7RRcBs"
},
"id": "RGQwcN7RRcBs"
},
{
"cell_type": "markdown",
"metadata": {
"id": "_STJPzJPNEsF"
},
"source": [
""
],
"id": "_STJPzJPNEsF"
},
{
"cell_type": "markdown",
"source": [
"### Demo on an Apple MacBook Air (M1, 2020):\n"
],
"metadata": {
"id": "gp1-o1-oOi4G"
},
"id": "gp1-o1-oOi4G"
},
{
"cell_type": "markdown",
"source": [
""
],
"metadata": {
"id": "BhtSaOhrOX_Z"
},
"id": "BhtSaOhrOX_Z"
},
{
"cell_type": "markdown",
"source": [
"# AWQ (activation aware weight only quantization)"
],
"metadata": {
"id": "MKX1i6epRi3G"
},
"id": "MKX1i6epRi3G"
},
{
"cell_type": "markdown",
"metadata": {
"id": "da9c5bc6"
},
"source": [
""
],
"id": "da9c5bc6"
},
{
"cell_type": "markdown",
"metadata": {
"id": "41b79e03"
},
"source": [
"Large language models (LLMs) have shown excellent performance on various tasks, but the astronomical model size raises the hardware barrier for serving (memory size) and slows down token generation (memory bandwidth). LLM sizes and computation are increasing exponentially, while memory bandwidth is increasing slowly. This gap is a major bottleneck for LLMs. In this lab, we will explore the use of an novel quantization algorithm (AWQ) to reduce memory footprint of LLMs and achieve accelerations for inference."
],
"id": "41b79e03"
},
{
"cell_type": "markdown",
"metadata": {
"id": "9f55aea4"
},
"source": [
"In previous courses, we have learned the basic methods of quantization.\n",
"There are two types of quantization:\n",
"\n",
"- Quantize both weight and activation\n",
" - Better for computation-bounded scenarios: context stage, large batch inference\n",
" - For example, SmoothQuant: W8A8 quantization\n",
"- Weight-only quantization\n",
" - Better for memory-bounded scenarios: decoding stage, single batch inference\n",
" - For example, AWQ that will be introduced in this lab: W4A16 quantization"
],
"id": "9f55aea4"
},
{
"cell_type": "markdown",
"metadata": {
"id": "a4a1ad9f"
},
"source": [
"For the LLaMA-65B model, in the decoding stage of single batch inference, we need to perform GEMV $[1, 8192] \\times [8192, 8192]$. Taking the NVIDIA A100 80G as an example, its half-precision (FP16) performance is 312TFLOPS, and the memory bandwidth is about 2000GB/s. Therefore, its computation intensity is:\n",
"\n",
"$$\n",
"\\frac{\\text{FLOP}}{\\text{Byte}} = \\frac{2\\times 8192^2}{8192^2} << \\frac{3.12\\times 10^{11}}{2\\times 10^9}\n",
"$$\n",
"\n",
"This is very memory-bounded (~$10^2$ gap), which is why we need low-bit weight quantization."
],
"id": "a4a1ad9f"
},
{
"cell_type": "markdown",
"metadata": {
"id": "174d2d1e"
},
"source": [
"## Setup"
],
"id": "174d2d1e"
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "2797112a",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "80a5d0db-2b1b-4db8-f045-bfd9b6ade9a1"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Installing packages...\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
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]
}
],
"source": [
"print('Installing packages...')\n",
"!pip install torch transformers==4.31.0 accelerate==0.21.0 sentencepiece==0.1.99 tokenizers==0.13.3 datasets==2.14.4 tqdm zstandard"
],
"id": "2797112a"
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "cfe5330c"
},
"outputs": [],
"source": [
"import tqdm\n",
"import torch\n",
"from torch import nn\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"from datasets import load_dataset\n",
"from functools import partial\n",
"import gc"
],
"id": "cfe5330c"
},
{
"cell_type": "markdown",
"metadata": {
"id": "2a86da42"
},
"source": [
"Here we use wikitext-2 dataset for evaluation. The dataset is automatically downloaded by the code."
],
"id": "2a86da42"
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "84ce637b"
},
"outputs": [],
"source": [
"def evaluate(model, tokenizer):\n",
" testenc = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')\n",
" testenc = tokenizer(\"\\n\\n\".join(testenc['text']), return_tensors='pt')\n",
"\n",
" testenc = testenc.input_ids.to(model.device)\n",
" nsamples = 40\n",
" model = model.eval()\n",
"\n",
" nlls = []\n",
" for i in tqdm.tqdm(range(nsamples), desc=\"evaluating...\"):\n",
" batch = testenc[:, (i * 2048):((i + 1) * 2048)].to(model.device)\n",
" with torch.no_grad():\n",
" lm_logits = model(batch).logits\n",
" shift_logits = lm_logits[:, :-1, :].contiguous().float()\n",
" shift_labels = testenc[:, (i * 2048):((i + 1) * 2048)][:, 1:]\n",
" loss_fct = nn.CrossEntropyLoss()\n",
" loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))\n",
" neg_log_likelihood = loss.float() * 2048\n",
" nlls.append(neg_log_likelihood)\n",
"\n",
" return torch.exp(torch.stack(nlls).sum() / (nsamples * 2048))\n"
],
"id": "84ce637b"
},
{
"cell_type": "markdown",
"metadata": {
"id": "Kq-32KkcyKcM"
},
"source": [
"The following code is used to calculate the model size."
],
"id": "Kq-32KkcyKcM"
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "32204623"
},
"outputs": [],
"source": [
"def get_model_size(model: nn.Module, data_width=16, group_size=-1):\n",
"\n",
" if group_size != -1:\n",
" data_width += (16 + 4) / group_size\n",
"\n",
" num_elements = 0\n",
" for param in model.parameters():\n",
" num_elements += param.numel()\n",
" return num_elements * data_width\n",
"\n",
"Byte = 8\n",
"KiB = 1024 * Byte\n",
"MiB = 1024 * KiB\n",
"GiB = 1024 * MiB"
],
"id": "32204623"
},
{
"cell_type": "markdown",
"metadata": {
"id": "2a9be846"
},
"source": [
"Let's first evaluate the perplexity and model size of the FP32 Model."
],
"id": "2a9be846"
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "b723b52b",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c5de4fc2-2dba-47b5-d84f-32c90b6ef109"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"evaluating...: 100%|██████████| 40/40 [01:19<00:00, 2.00s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"model perplexity: 14.47\n",
"model size: 5043.73 MiB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
],
"source": [
"model_path = \"facebook/opt-1.3b\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)\n",
"model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\")\n",
"\n",
"# Evaluate the model\n",
"model_perplexity = evaluate(model, tokenizer)\n",
"model_size = get_model_size(model, data_width=32, group_size=128)\n",
"print(f\"\\nmodel perplexity: {model_perplexity:.2f}\")\n",
"print(f\"model size: {model_size/MiB:.2f} MiB\")"
],
"id": "b723b52b"
},
{
"cell_type": "markdown",
"metadata": {
"id": "VB2bNIrKySLZ"
},
"source": [
"Uniform quantization is to map real values in the range $[\\beta, \\alpha]$ to lie within $[0, 2^{b} - 1]$.\n",
"\n",
"Notation:\n",
"\n",
"- Quantized Weight: $w_q$\n",
"\n",
"- Scale factor: $s_q$\n",
"\n",
"- Zero Point: $z$\n",
"\\begin{equation}\n",
"s_q = \\frac{\\alpha - \\beta}{2^{b} - 1} \\tag{1},\n",
"\\end{equation}\n",
"\\begin{equation}\n",
"z = -\\text{Round}(\\beta * scale) \\tag{2}\n",
"\\end{equation}\n",
"\\begin{equation}\n",
"w_q = \\text{Clamp}(\\text{Round}(\\frac{w}{s_q}) + z) \\tag{3},\n",
"\\end{equation}\n",
"\n"
],
"id": "VB2bNIrKySLZ"
},
{
"cell_type": "markdown",
"metadata": {
"id": "au0OJ3lV_irH"
},
"source": [
"### pseudo quantization\n",
"The following code is for pseudo quantization.\n",
"\n",
"Pseudo Quantization is used to simulate the effects of quantization on a model without actually quantizing the model's weights. (i.e. rounding to the nearest quantized value and then **dequantizing back to a float**.)"
],
"id": "au0OJ3lV_irH"
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "d63486a4"
},
"outputs": [],
"source": [
"# core quantization method (simulated quantization)\n",
"def pseudo_quantize_tensor(w, n_bit=4, q_group_size=-1):\n",
" org_w_shape = w.shape\n",
" if q_group_size > 0:\n",
" assert org_w_shape[-1] % q_group_size == 0\n",
" w = w.reshape(-1, q_group_size)\n",
"\n",
" assert w.dim() == 2\n",
"\n",
" # Calculate the maximum (\\alpha) and minimum values (\\beta) in the tensor.\n",
" max_val = w.amax(dim=1, keepdim=True)\n",
" assert max_val.dim() == 2 and max_val.size(0) == w.size(0) and max_val.size(1) == 1\n",
" min_val = w.amin(dim=1, keepdim=True)\n",
" assert min_val.dim() == 2 and min_val.size(0) == w.size(0) and min_val.size(1) == 1\n",
"\n",
" # Calculate the scale factor and zero point. (Formula 1 & 2)\n",
" max_int = 2 ** n_bit - 1\n",
" scales = (max_val - min_val).clamp(min=1e-5) / max_int\n",
" assert scales.shape == max_val.shape\n",
" zeros = (-torch.round(min_val / scales)).clamp_(0, max_int)\n",
" assert scales.shape == min_val.shape\n",
"\n",
" assert torch.isnan(scales).sum() == 0\n",
" assert torch.isnan(w).sum() == 0\n",
"\n",
" # Quantize W: Map values in the range [\\beta, \\alpha] to lie within [0, 2^b - 1] (Formula 3)\n",
" w = torch.clamp(torch.round(w / scales) + zeros, 0, max_int)\n",
" assert w.dim() == 2 and w.size(0) == scales.size(0) and w.size(1) == q_group_size\n",
"\n",
" # Dequantize W (pseudo quantization, the inverse transformation of Formula 3)\n",
" w = (w - zeros) * scales\n",
" assert w.dim() == 2 and w.size(0) == scales.size(0) and w.size(1) == q_group_size\n",
"\n",
" assert torch.isnan(w).sum() == 0\n",
"\n",
" w = w.reshape(org_w_shape)\n",
" return w\n",
"\n",
"@torch.no_grad()\n",
"def pseudo_quantize_model_weight(\n",
" model, w_bit, q_group_size,\n",
"):\n",
" for n, m in model.named_modules():\n",
" if isinstance(m, nn.Linear):\n",
" m.weight.data = pseudo_quantize_tensor(m.weight.data, n_bit=w_bit, q_group_size=q_group_size)"
],
"id": "d63486a4"
},
{
"cell_type": "markdown",
"metadata": {
"id": "hU8nK-JY0iKA"
},
"source": [
"Let's evaluate the perplexity and model size of the quantized 3-bit Model."
],
"id": "hU8nK-JY0iKA"
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "fc985610",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a07d3047-f3cf-40f6-9fe1-e2689fca78ee"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"evaluating...: 100%|██████████| 40/40 [01:16<00:00, 1.92s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"model perplexity: 121.90\n",
"model size: 495.06 MiB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
],
"source": [
"del model\n",
"gc.collect()\n",
"torch.cuda.empty_cache()\n",
"model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\")\n",
"pseudo_quantize_model_weight(model, w_bit=3, q_group_size=128)\n",
"\n",
"# Evaluate the model\n",
"model_perplexity = evaluate(model, tokenizer)\n",
"model_size = get_model_size(model, data_width=3, group_size=128)\n",
"print(f\"\\nmodel perplexity: {model_perplexity:.2f}\")\n",
"print(f\"model size: {model_size/MiB:.2f} MiB\")"
],
"id": "fc985610"
},
{
"cell_type": "markdown",
"source": [
"We can see that the model size has decreased, but the perplexity has significantly increased."
],
"metadata": {
"id": "KrDoPrp7Ncma"
},
"id": "KrDoPrp7Ncma"
},
{
"cell_type": "markdown",
"metadata": {
"id": "-BAC3SPY0swu"
},
"source": [
"There is a observation in LLM activations that **outliers appear in a small fraction of the channels**. If one channel has an outlier, it **persistently appears in all tokens**. The variance amongst the channels for a given token is large (the activations in some channels are very large, but most are small), but the variance between the magnitudes of a given channel across tokens is small (outlier channels are consistently large).\n",
"\n",
"According to the observation of AWQ, weight channels corresponding to activation outliers are more salient, and preserving those salient weights can lead to a significant performance improvement. Next, let's try to find the salient weights and retain them as original values to observe the change in perplexity.\n",
"\n",
"The following code is used to load the calibration dataset, so as to obtain activation outliers to identify salient weights."
],
"id": "-BAC3SPY0swu"
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "fb52620d"
},
"outputs": [],
"source": [
"def get_calib_dataset(tokenizer=None, n_samples=256, block_size=512):\n",
" dataset = load_dataset(\"mit-han-lab/pile-val-backup\", split=\"validation\")\n",
" dataset = dataset.shuffle(seed=42)\n",
" samples = []\n",
" n_run = 0\n",
" for data in dataset:\n",
" line = data[\"text\"]\n",
" line = line.strip()\n",
" line_encoded = tokenizer.encode(line)\n",
" if len(line_encoded) > block_size:\n",
" continue\n",
" sample = torch.tensor([line_encoded])\n",
" if sample.numel() == 0:\n",
" continue\n",
" samples.append(sample)\n",
" n_run += 1\n",
" if n_run == n_samples:\n",
" break\n",
"\n",
" # now concatenate all samples and split according to block size\n",
" cat_samples = torch.cat(samples, dim=1)\n",
" n_split = cat_samples.shape[1] // block_size\n",
" print(f\" * Split into {n_split} blocks\")\n",
" return [cat_samples[:, i*block_size:(i+1)*block_size] for i in range(n_split)]\n",
"\n",
"@torch.no_grad()\n",
"def get_calib_feat(model, tokenizer):\n",
" input_dict = dict()\n",
" def stat_input_max_hook(m, x, y, name):\n",
" if isinstance(x, tuple):\n",
" x = x[0]\n",
" x_max = x.view(-1, x.shape[-1]).abs().mean(dim=0).cpu().detach()\n",
" if name not in input_dict:\n",
" input_dict[name] = [x_max]\n",
" else:\n",
" input_dict[name] += [x_max]\n",
"\n",
" hooks = []\n",
" for name, m in model.named_modules():\n",
" if isinstance(m, nn.Linear):\n",
" hooks.append(\n",
" m.register_forward_hook(\n",
" partial(stat_input_max_hook, name=name)))\n",
"\n",
" print(\"Collecting activation scales...\")\n",
" device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
" samples = get_calib_dataset(tokenizer)\n",
" pbar = tqdm.tqdm(samples)\n",
" for input_ids in pbar:\n",
" input_ids = input_ids.to(device)\n",
" model(input_ids)\n",
"\n",
" for hook in hooks:\n",
" hook.remove()\n",
" return input_dict"
],
"id": "fb52620d"
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "bfd04606",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "de77c367-461e-4537-c3fa-2a1087b47845"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting activation scales...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/repocard.py:105: UserWarning: Repo card metadata block was not found. Setting CardData to empty.\n",
" warnings.warn(\"Repo card metadata block was not found. Setting CardData to empty.\")\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" * Split into 127 blocks\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 127/127 [00:50<00:00, 2.51it/s]\n"
]
}
],
"source": [
"del model\n",
"gc.collect()\n",
"torch.cuda.empty_cache()\n",
"model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\")\n",
"input_feat = get_calib_feat(model, tokenizer)"
],
"id": "bfd04606"
},
{
"cell_type": "code",
"source": [
"print(type(input_feat['model.decoder.layers.0.self_attn.q_proj']))\n",
"print(len(input_feat['model.decoder.layers.0.self_attn.q_proj']))\n",
"print(input_feat['model.decoder.layers.0.self_attn.q_proj'][0].shape)\n",
"print(sum(input_feat['model.decoder.layers.0.self_attn.q_proj']).shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "X_pPpL0FGful",
"outputId": "4dfda277-dd96-4099-88bb-b8fb8d4f4786"
},
"id": "X_pPpL0FGful",
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"127\n",
"torch.Size([2048])\n",
"torch.Size([2048])\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Cu1fUdHnBnDP"
},
"source": [
"### Question 1 (50 pts)\n",
"#### Question 1.1 (20 pts)\n",
"Next, please add codes before and after the quantization to protect 1% of the salient weight channels (1% channels with highest importance), ensuring that their values remain unchanged after quantization. (**The desired perplexity is 17.15**)"
],
"id": "Cu1fUdHnBnDP"
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "b2654479"
},
"outputs": [],
"source": [
"@torch.no_grad()\n",
"def pseudo_quantize_model_salient_weight_fp16(\n",
" model, w_bit, q_group_size, input_feat\n",
"):\n",
" for n, m in model.named_modules():\n",
" if isinstance(m, nn.Linear):\n",
" importance = sum(input_feat[n]).float()\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
"\n",
" # Step 1: Find 1% of the salient weight channels according to importance (hint: use torch.topk())\n",
" outlier_indices = torch.topk(importance, int(len(importance) * 0.01))[1]\n",
" assert outlier_indices.dim() == 1\n",
"\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" # Back up the values of the salient weight channels\n",
" outlier = m.weight.data[:, outlier_indices].clone()\n",
"\n",
" m.weight.data = pseudo_quantize_tensor(m.weight.data, n_bit=w_bit, q_group_size=q_group_size)\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
"\n",
" # Step 2: Restore the 1% salient weight channels to their original FP16 values\n",
" m.weight.data[:, outlier_indices] = outlier\n",
"\n",
" ############### YOUR CODE ENDS HERE #################"
],
"id": "b2654479"
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "2aad125d",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "70777158-e706-4bd1-c45d-e50855b96083"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"evaluating...: 100%|██████████| 40/40 [01:17<00:00, 1.95s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"model perplexity: 17.15\n",
"model size: 495.06 MiB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
],
"source": [
"del model\n",
"gc.collect()\n",
"torch.cuda.empty_cache()\n",
"model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\")\n",
"pseudo_quantize_model_salient_weight_fp16(model, w_bit=3, q_group_size=128, input_feat=input_feat)\n",
"\n",
"# Evaluate the model\n",
"model_perplexity = evaluate(model, tokenizer)\n",
"model_size = get_model_size(model, data_width=3, group_size=128)\n",
"print(f\"\\nmodel perplexity: {model_perplexity:.2f}\")\n",
"print(f\"model size: {model_size/MiB:.2f} MiB\")"
],
"id": "2aad125d"
},
{
"cell_type": "markdown",
"metadata": {
"id": "y2MnArYw4BtE"
},
"source": [
"#### Question 1.2 (15 pts)\n",
"Let's conduct an ablation experiment: randomly protect 1% of the weight channels, ensuring that their values remain unchanged after quantization, and then observe the perplexity. (**The desired perplexity is over 100**)\n",
"\n",
"\n"
],
"id": "y2MnArYw4BtE"
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "eZnTvBxw3nrJ"
},
"outputs": [],
"source": [
"@torch.no_grad()\n",
"def pseudo_quantize_model_random_weight_fp16(\n",
" model, w_bit, q_group_size, input_feat\n",
"):\n",
" for n, m in model.named_modules():\n",
" if isinstance(m, nn.Linear):\n",
" importance = sum(input_feat[n]).float()\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
"\n",
" # Step 1: Randomly choose 1% of the weight channels\n",
" outlier_mask = torch.randint(0, len(importance), (int(len(importance)*0.01), ))\n",
" assert outlier_mask.dim() == 1\n",
"\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" # Back up the values of the selected weight channels\n",
" outlier = m.weight.data[:, outlier_mask].clone()\n",
"\n",
" m.weight.data = pseudo_quantize_tensor(m.weight.data, n_bit=w_bit, q_group_size=q_group_size)\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
"\n",
" # Step 2: Restore the 1% selected weight channels to their original FP16 values\n",
" m.weight.data[:, outlier_mask] = outlier\n",
"\n",
" ############### YOUR CODE ENDS HERE #################"
],
"id": "eZnTvBxw3nrJ"
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "FZpUl8Xo3zII",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3a2b1b17-26d4-42b5-e52b-85a15b72979f"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"evaluating...: 100%|██████████| 40/40 [01:18<00:00, 1.95s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"model perplexity: 116.01\n",
"model size: 495.06 MiB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
],
"source": [
"del model\n",
"gc.collect()\n",
"torch.cuda.empty_cache()\n",
"model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\")\n",
"pseudo_quantize_model_random_weight_fp16(model, w_bit=3, q_group_size=128, input_feat=input_feat)\n",
"\n",
"# Evaluate the model\n",
"model_perplexity = evaluate(model, tokenizer)\n",
"model_size = get_model_size(model, data_width=3, group_size=128)\n",
"print(f\"\\nmodel perplexity: {model_perplexity:.2f}\")\n",
"print(f\"model size: {model_size/MiB:.2f} MiB\")"
],
"id": "FZpUl8Xo3zII"
},
{
"cell_type": "markdown",
"metadata": {
"id": "w5skvspRDH18"
},
"source": [
"#### Question 1.3 (15 pts)\n",
"Please provide a possible explanation for why the salient weight channels are so important.\n",
"\n",
"#### Answser 1.3\n",
"############### YOUR ANSWER STARTS HERE #################\n",
"\n",
"Salient weight channels correspond to those **outlier channels in activations** (with very large values). If we quantize them, there will be a significant drop in numerical accuracy.\n",
"\n",
"############### YOUR ANSWER ENDS HERE #################"
],
"id": "w5skvspRDH18"
},
{
"cell_type": "markdown",
"metadata": {
"id": "x0OVYFc2DbSr"
},
"source": [
"### Question 2 (50 pts)"
],
"id": "x0OVYFc2DbSr"
},
{
"cell_type": "markdown",
"source": [
"Despite keeping 0.1% of weights in FP16 can improve the quantized performance\n",
"without a noticeable increase in model size (measured in total bits), such a mixed-precision data type will make the system implementation difficult. We need to come up with a method to protect the important weights without actually keeping them as FP16."
],
"metadata": {
"id": "h5GGLwczKghz"
},
"id": "h5GGLwczKghz"
},
{
"cell_type": "markdown",
"metadata": {
"id": "srM0CaQw3xyc"
},
"source": [
"According to the methodology of AWQ, simply scaling up the salient weight channels can protect them. The principle is as follows:\n",
"\n",
"- Consider a linear layer channel $\\mathbf{y} = \\mathbf{w}x$ (from $\\mathbf{W}x$). We care about the quantization error from $Q(\\mathbf{w})x$.\n",
"\n",
"- $Err(Q(\\mathbf{w}) x) = Δ\\cdot RoundErr(\\frac{\\mathbf{w}}{Δ})\\cdot x$, $Δ = \\frac{\\max(|w|)}{2^{N - 1}}$.\n",
"- The scaled version is $Err(Q(\\mathbf{w} \\cdot s)(\\frac{x}{s})) = Δ\\cdot RoundErr(\\frac{\\mathbf{w}}{Δ})\\cdot x\\cdot \\mathbf{\\frac{1}{s}}$.\n",
"- The $RoundErr$ is always ~0.25 (average from 0-0.5).\n",
"- When the group size is relatively large (e.g., 128), scaling up one channel usually does not increase the maximum value in a group (i.e. $Δ$ remains unchanged).\n",
"- Thus, $Err(Q(\\mathbf{w} \\cdot s)(\\frac{x}{s})) = Δ\\cdot RoundErr(\\frac{\\mathbf{w}}{Δ})\\cdot x\\cdot \\mathbf{\\frac{1}{s}}$ < $Δ\\cdot RoundErr(\\frac{\\mathbf{w}}{Δ})\\cdot x = Err(Q(\\mathbf{w}) x)$."
],
"id": "srM0CaQw3xyc"
},
{
"cell_type": "markdown",
"source": [
"Taking the following figure as an example, if we assume 3-bit int quantization, then the quantization error caused by the value in the last column of the second row of $W(+1.4)$ should be $Err(Q(\\mathbf{w}) x) = Δ\\cdot RoundErr(\\frac{\\mathbf{w}}{Δ})\\cdot x$ = $\\frac{4}{2^{3 - 1}} * |1.4 - 1.0| * (2 + 2 + 2) = 2.4$.\n",
"\n",
"If the second channel is scaled up by a factor of $2$, the resulting quantization error would reduce to $\\frac{4}{2^{3 - 1}} * |2.8 - 3.0| * (2/2 + 2/2 + 2/2) = 0.6$."
],
"metadata": {
"id": "ui06UW1kK_Ep"
},
"id": "ui06UW1kK_Ep"
},
{
"cell_type": "markdown",
"metadata": {
"id": "oXb_q3e08jKG"
},
"source": [
""
],
"id": "oXb_q3e08jKG"
},
{
"cell_type": "markdown",
"metadata": {
"id": "qLOfsUrW8xrv"
},
"source": [
"#### Question 2.1 (20 pts)\n",
"Please write code to scale up the salient weight channels, then quantize it, and finally scale it back down, and observe the changes in perplexity. (**The desired perplexity is 18.93**)"
],
"id": "qLOfsUrW8xrv"
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "TdO4KtnsNKf5"
},
"outputs": [],
"source": [
"@torch.no_grad()\n",
"def pseudo_quantize_model_weight_scaleup(\n",
" model, w_bit, q_group_size, input_feat, scale_factor\n",
"):\n",
" for n, m in model.named_modules():\n",
" if isinstance(m, nn.Linear):\n",
" importance = sum(input_feat[n]).float()\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
"\n",
" # Step 1: Find 1% of the salient weight channels\n",
" outlier_mask = torch.topk(importance, int(len(importance) * 0.01))[1]\n",
" assert outlier_mask.dim() == 1\n",
"\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" # To simulate applying the scale factor, we can simply multiply it before quantization, and then divide by the scale factor after quantization.\n",
" # Scale up the values of the salient weight channels\n",
" m.weight.data[:, outlier_mask] *= scale_factor\n",
"\n",
" m.weight.data = pseudo_quantize_tensor(m.weight.data, n_bit=w_bit, q_group_size=q_group_size)\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
"\n",
" # Step 2: Scale back down the values of the salient weight channels\n",
" m.weight.data[:, outlier_mask] /= scale_factor\n",
"\n",
" ############### YOUR CODE ENDS HERE #################"
],
"id": "TdO4KtnsNKf5"
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "GoTh5CzuPhtV",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e7f96a57-e6ae-4728-f7b5-9c9a935ddb9d"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"evaluating...: 100%|██████████| 40/40 [01:16<00:00, 1.91s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"model perplexity: 18.93\n",
"model size: 495.06 MiB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
],
"source": [
"del model\n",
"gc.collect()\n",
"torch.cuda.empty_cache()\n",
"model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\")\n",
"pseudo_quantize_model_weight_scaleup(model, w_bit=3, q_group_size=128, input_feat=input_feat, scale_factor=2)\n",
"\n",
"# Evaluate the model\n",
"model_perplexity = evaluate(model, tokenizer)\n",
"model_size = get_model_size(model, data_width=3, group_size=128)\n",
"print(f\"\\nmodel perplexity: {model_perplexity:.2f}\")\n",
"print(f\"model size: {model_size/MiB:.2f} MiB\")"
],
"id": "GoTh5CzuPhtV"
},
{
"cell_type": "code",
"source": [
"for scale_factor in [1,2,3,4]:\n",
" del model\n",
" gc.collect()\n",
" torch.cuda.empty_cache()\n",
" model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\")\n",
" pseudo_quantize_model_weight_scaleup(model, w_bit=3, q_group_size=128, input_feat=input_feat, scale_factor=scale_factor)\n",
"\n",
" # Evaluate the model\n",
" model_perplexity = evaluate(model, tokenizer)\n",
" model_size = get_model_size(model, data_width=3, group_size=128)\n",
" print(f\"{scale_factor=}\")\n",
" print(f\"\\nmodel perplexity: {model_perplexity:.2f}\")\n",
" print(f\"model size: {model_size/MiB:.2f} MiB\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4SBUNQc_MDcP",
"outputId": "6b18bb00-b0ce-41fc-ae32-e6212e6fff6f"
},
"id": "4SBUNQc_MDcP",
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"evaluating...: 100%|██████████| 40/40 [01:18<00:00, 1.96s/it]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"scale_factor=1\n",
"\n",
"model perplexity: 121.90\n",
"model size: 495.06 MiB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"evaluating...: 100%|██████████| 40/40 [01:18<00:00, 1.97s/it]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"scale_factor=2\n",
"\n",
"model perplexity: 18.93\n",
"model size: 495.06 MiB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"evaluating...: 100%|██████████| 40/40 [01:18<00:00, 1.97s/it]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"scale_factor=3\n",
"\n",
"model perplexity: 19.25\n",
"model size: 495.06 MiB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"evaluating...: 100%|██████████| 40/40 [01:18<00:00, 1.97s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"scale_factor=4\n",
"\n",
"model perplexity: 21.26\n",
"model size: 495.06 MiB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kDIYt2xJEt84"
},
"source": [
"#### Question 2.2 (15 pts)\n",
"Please try different scale factors (e.g. 1, 2, 3, and 4) in the code and observe the changes in perplexity.\n",
"\n",
"Did you observe the perplexity first decreasing and then increasing? Please explain why this would happen based on the principle mentioned above.\n",
"\n",
"#### Answer 2.2\n",
"############### YOUR ANSWER STARTS HERE #################\n",
"\n",
"Scaling up at a large factor may increase the maximum value in the group (i.e. Δ will increase). It will affect other channel's quantization.\n",
"\n",
"############### YOUR ANSWER ENDS HERE #################"
],
"id": "kDIYt2xJEt84"
},
{
"cell_type": "markdown",
"metadata": {
"id": "ueFx8RCHF2zu"
},
"source": [
"### Question 2.3 (15 pts)\n",
"Due to the instability of fine-tuning, it would be a better choice to find the optimal $s$ within a predefined search space. We can find the optimal scale in the search space to protect the salient weights while also considering other values. In practice, it can be observed that considering just the activations is sufficient to yield good results. Please add the code for search and run it to observe the perplexity. (**The desired perplexity is 17.92**)"
],
"id": "ueFx8RCHF2zu"
},
{
"cell_type": "markdown",
"source": [
"$$\n",
"𝐋(\\mathbf{s})=\\lVert Q(\\mathbf{W}\\cdot \\mathbf{s}) (\\mathbf{s^{-1}} \\cdot \\mathbf{X}) - \\mathbf{W}\\mathbf{X} \\rVert, \\quad\\mathbf{s}= \\mathbf{s_X}^{\\alpha}\n",
"$$\n",
"$$\n",
"\\mathbf{s}^* = \\text{argmin}_{\\mathbf{s}} 𝐋(\\mathbf{s}),\\quad \\alpha^*=\\text{argmin}_{\\alpha} 𝐋(\\mathbf{s_X}^{\\alpha})\n",
"$$"
],
"metadata": {
"id": "bA4b4L2OL05N"
},
"id": "bA4b4L2OL05N"
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"id": "ff_sv6k0R2Eb"
},
"outputs": [],
"source": [
"@torch.no_grad()\n",
"def scale_ln_fcs(ln, fcs, scales):\n",
" if not isinstance(fcs, list):\n",
" fcs = [fcs]\n",
"\n",
" scales = scales.to(ln.weight.device)\n",
"\n",
" ln.weight.div_(scales)\n",
" if hasattr(ln, 'bias') and ln.bias is not None:\n",
" ln.bias.div_(scales)\n",
"\n",
" for fc in fcs:\n",
" fc.weight.mul_(scales.view(1, -1))\n",
"\n",
" for p in ln.parameters():\n",
" assert torch.isnan(p).sum() == 0\n",
" for fc in fcs:\n",
" for p in fc.parameters():\n",
" assert torch.isnan(p).sum() == 0\n",
"\n",
"\n",
"@torch.no_grad()\n",
"def scale_fc_fc(fc1, fc2, scales):\n",
" assert isinstance(fc1, nn.Linear)\n",
" assert isinstance(fc2, nn.Linear)\n",
"\n",
" scales = scales.to(fc1.weight.device)\n",
"\n",
" # fc1.weight.div_(scales.view(-1, 1))\n",
" fc1.weight[-scales.size(0):].div_(scales.view(-1, 1))\n",
" if fc1.bias is not None:\n",
" fc1.bias.div_(scales.view(-1))\n",
"\n",
" fc2.weight.mul_(scales.view(1, -1))\n",
"\n",
" for p in fc1.parameters():\n",
" assert torch.isnan(p).sum() == 0\n",
" for p in fc2.parameters():\n",
" assert torch.isnan(p).sum() == 0\n",
"\n",
"@torch.no_grad()\n",
"def auto_scale_block(module, name, w_bit,\n",
" q_group_size,\n",
" input_feat):\n",
"\n",
" # find the best scale ratio\n",
" def _search_module_scale(block, linears2scale: list, x, kwargs={}):\n",
"\n",
" x = x.to(next(block.parameters()).device)\n",
" with torch.no_grad():\n",
" org_out = block(x, **kwargs)\n",
" if isinstance(org_out, tuple):\n",
" org_out = org_out[0]\n",
"\n",
" s_x = x.view(-1, x.shape[-1]).abs().mean(0)\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
"\n",
" # Step 1: Initialize the best_error, best_ratio and best_scales\n",
" best_error = torch.inf\n",
" best_ratio = -1\n",
" best_scales = 0\n",
"\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" n_grid = 20\n",
" history = []\n",
"\n",
" org_sd = {k: v.cpu() for k, v in block.state_dict().items()}\n",
" for ratio in range(n_grid):\n",
" # ratio is the \\alpha in the formula\n",
" ratio = ratio * 1 / n_grid\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
"\n",
" # Step 2: Calculate the scales by the formula: scales = s_x^ratio\n",
" scales = torch.clamp(s_x, 1e-5) ** ratio # must clip the s_x, otherwise will get nan later\n",
"\n",
" assert scales.shape == s_x.shape\n",
"\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" scales = scales / (scales.max() * scales.min()).sqrt().view(1, -1)\n",
"\n",
" for fc in linears2scale:\n",
"\n",
" scales = scales.to(fc.weight.device)\n",
"\n",
" # Scale up the values of the weight channels\n",
" fc.weight.mul_(scales)\n",
"\n",
" fc.weight.data = pseudo_quantize_tensor(fc.weight.data, w_bit, q_group_size)\n",
"\n",
" ############### YOUR CODE STARTS HERE ###############\n",
"\n",
" # Step 3: Scale back down the values of the weight channels\n",
" fc.weight.data /= scales\n",
"\n",
" ############### YOUR CODE ENDS HERE #################\n",
"\n",
" out = block(x, **kwargs)\n",
" if isinstance(out, tuple):\n",
" out = out[0]\n",
"\n",
" loss = (org_out - out).float().pow(2).mean().item() # float prevents overflow\n",
" history.append(loss)\n",
" is_best = loss < best_error\n",
" if is_best:\n",
" best_error = loss\n",
" best_ratio = ratio\n",
" best_scales = scales\n",
" block.load_state_dict(org_sd)\n",
"\n",
" if best_ratio == -1:\n",
" print(history)\n",
" raise Exception\n",
"\n",
" best_scales = best_scales.view(-1)\n",
"\n",
" assert torch.isnan(best_scales).sum() == 0, best_scales\n",
" return best_scales.detach()\n",
"\n",
" # attention input\n",
" inp = input_feat[name + '.self_attn.out_proj']\n",
" inp = torch.cat([x.unsqueeze(0) for x in inp], dim=0).unsqueeze(0)\n",
" qkv = [module.self_attn.q_proj, module.self_attn.k_proj, module.self_attn.v_proj]\n",
" final_scales = _search_module_scale(module.self_attn, qkv, inp)\n",
" scale_ln_fcs(module.self_attn_layer_norm, qkv, final_scales)\n",
"\n",
" # attn out\n",
" inp = input_feat[name + '.self_attn.out_proj']\n",
" inp = torch.cat([x.unsqueeze(0) for x in inp], dim=0)\n",
" final_scales = _search_module_scale(module.self_attn.out_proj, [module.self_attn.out_proj], inp)\n",
" scale_fc_fc(module.self_attn.v_proj, module.self_attn.out_proj, final_scales)\n",
"\n",
" # fc1\n",
" inp = input_feat[name + '.fc1']\n",
" inp = torch.cat([x.unsqueeze(0) for x in inp], dim=0)\n",
" final_scales = _search_module_scale(module.fc1, [module.fc1], inp)\n",
" scale_ln_fcs(module.final_layer_norm, module.fc1, final_scales)\n",
"\n",
" # fc2\n",
" inp = input_feat[name + '.fc2']\n",
" inp = torch.cat([x.unsqueeze(0) for x in inp], dim=0)\n",
" final_scales = _search_module_scale(module.fc2, [module.fc2], inp)\n",
" scale_fc_fc(module.fc1, module.fc2, final_scales)\n",
"\n",
"@torch.no_grad()\n",
"def pseudo_quantize_model_weight_auto_scale(\n",
" model, w_bit, q_group_size, input_feat\n",
"):\n",
" from transformers.models.opt.modeling_opt import OPTDecoderLayer\n",
"\n",
" for name, module in model.named_modules():\n",
" if isinstance(module, OPTDecoderLayer):\n",
" auto_scale_block(module, name, w_bit, q_group_size, input_feat)\n",
"\n",
" for n, m in model.named_modules():\n",
" if isinstance(m, nn.Linear):\n",
" m.weight.data = pseudo_quantize_tensor(m.weight.data, n_bit=w_bit, q_group_size=q_group_size)"
],
"id": "ff_sv6k0R2Eb"
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"id": "cXuQUykZMdKa",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b7884eb2-c2c7-4e8c-badf-f9a352b5444a"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"evaluating...: 100%|██████████| 40/40 [01:18<00:00, 1.97s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"model perplexity: 17.93\n",
"model size: 495.06 MiB\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
],
"source": [
"del model\n",
"gc.collect()\n",
"torch.cuda.empty_cache()\n",
"model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\")\n",
"pseudo_quantize_model_weight_auto_scale(model, w_bit=3, q_group_size=128, input_feat=input_feat)\n",
"\n",
"# Evaluate the model\n",
"model_perplexity = evaluate(model, tokenizer)\n",
"model_size = get_model_size(model, data_width=3, group_size=128)\n",
"print(f\"\\nmodel perplexity: {model_perplexity:.2f}\")\n",
"print(f\"model size: {model_size/MiB:.2f} MiB\")"
],
"id": "cXuQUykZMdKa"
},
{
"cell_type": "markdown",
"source": [
"## Bonus point\n",
"Any optimization techniques without mixed precision on your mind? Try to implement them to improve the perplexity further! If you can further improve the perplexity to $x$, you can get $\\max(0, (17.92 - x) \\times 10)$ bonus points here!\n"
],
"metadata": {
"id": "MIZG376WRyzs"
},
"id": "MIZG376WRyzs"
},
{
"cell_type": "markdown",
"metadata": {
"id": "AjMKgVqq5BFG"
},
"source": [
"In conclusion, we can significantly reduce perplexity without using mixed-precision. Through an efficient kernel implementation, the 4-bit model can achieve decent acceleration for inference. Through learning about TinyChatEngine in the next section, we can run a LLaMA-7B model on our own laptops like the demo presented in the introduction."
],
"id": "AjMKgVqq5BFG"
}
],
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================================================
FILE: README.md
================================================
# MIT-6.5940
TinyML and Efficient Deep Learning Computing 6.5940 • Fall • 2023 • https://efficientml.ai
## Introduction
This is my implementation of MIT-6.5940 Labs.
I passed all the tests in the notebook and fixed minor errors in the original notebook. You can use it as a reference.
- [x] Lab1
- [x] Lab2
- [x] Lab3
- [x] Lab4
- [x] Lab5 (x86 arch)
### Note on Lab 5
The naive implemenation can be extremely slow. You may have to wait more than 10 minutes to get llama2's answer.
I provide my benchmarks of each accerleration implementation on two devices.
**MacBook Pro (15-inch, 2018)**
- Arch x86
- CPU: 6 Intel i7 Cores (2.2 GHz)
- Mem: 16 GB 2400 MHz DDR4
```
➭ ./evaluate.sh reference
-------- Sanity check of reference implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
reference, 3377.129150, 337.712006, 10, 0.776233
➭ ./evaluate.sh loop_unrolling
-------- Sanity check of loop_unrolling implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
loop_unrolling, 3335.317139, 333.531006, 10, 0.785964
➭ ./evaluate.sh multithreading
-------- Sanity check of multithreading implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
multithreading, 887.180054, 88.718002, 10, 2.954801
➭ ./evaluate.sh simd_programming
-------- Sanity check of simd_programming implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
simd_programming, 2073.361084, 207.336014, 10, 1.264343
➭ ./evaluate.sh multithreading_loop_unrolling
-------- Sanity check of multithreading_loop_unrolling implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
multithreading_loop_unrolling, 838.462036, 83.846001, 10, 3.126486
➭ ./evaluate.sh all_techniques
-------- Sanity check of all_techniques implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
all_techniques, 227.922012, 22.792002, 10, 11.501479
```
**Linux Desktop Computer**
- Arch: x86
- CPU: AMD Ryzen 9 5900X 12-Core (3.7GHz)
- Mem: 64 GB 2400 MHz DDR4
```
$ ./evaluate.sh reference
-------- Sanity check of reference implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
reference, 2726.144043, 272.614014, 10, 0.961593
$ ./evaluate.sh loop_unrolling
-------- Sanity check of loop_unrolling implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
loop_unrolling, 2177.221924, 217.722000, 10, 1.204030
$ ./evaluate.sh multithreading
-------- Sanity check of multithreading implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
multithreading, 720.249023, 72.024002, 10, 3.639630
$ ./evaluate.sh simd_programming
-------- Sanity check of simd_programming implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
simd_programming, 1852.494995, 185.248993, 10, 1.415086
$ ./evaluate.sh multithreading_loop_unrolling
-------- Sanity check of multithreading_loop_unrolling implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
multithreading_loop_unrolling, 570.304993, 57.029999, 10, 4.596558
$ ./evaluate.sh all_techniques
-------- Sanity check of all_techniques implementation: Passed! --------
Section, Total time(ms), Average time(ms), Count, GOPs
all_techniques, 177.613007, 17.761000, 10, 14.759280
```
### Results
**MacBook Pro (15-inch, 2018)** 8 threads, but it is still slow.

**Linux Desktop Computer**
64 threads, it is obviously faster. But I can't get the speed shown in the tutorial. Any discussion is welcomed.

## Acknowledge
Many thanks to Professor Song Han for sharing such an excellent course!