From 36b7247696c2abcd879c811b22395bb51a460767 Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Wed, 4 Sep 2024 17:49:50 +0100 Subject: [PATCH 01/27] Porting of 2D ControlNet tutorial using DDPM and ControlNet to guide the synthetic process. We used BraTS and conditioning on binary brain masks. Signed-off-by: Virginia Fernandez --- generation/controlnet/2d_controlnet.ipynb | 1210 +++++++++++++++++++++ 1 file changed, 1210 insertions(+) create mode 100644 generation/controlnet/2d_controlnet.ipynb diff --git a/generation/controlnet/2d_controlnet.ipynb b/generation/controlnet/2d_controlnet.ipynb new file mode 100644 index 000000000..1af09fbe8 --- /dev/null +++ b/generation/controlnet/2d_controlnet.ipynb @@ -0,0 +1,1210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e8b79aaa-6d2e-4408-b8a4-d684879b2fac", + "metadata": {}, + "source": [ + "Copyright (c) MONAI Consortium \n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); \n", + "you may not use this file except in compliance with the License. \n", + "You may obtain a copy of the License at \n", + "    http://www.apache.org/licenses/LICENSE-2.0 \n", + "Unless required by applicable law or agreed to in writing, software \n", + "distributed under the License is distributed on an \"AS IS\" BASIS, \n", + "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n", + "See the License for the specific language governing permissions and \n", + "limitations under the License." + ] + }, + { + "cell_type": "markdown", + "id": "63d95da6", + "metadata": {}, + "source": [ + "# Using ControlNet to control image generation\n", + "\n", + "This tutorial illustrates how to use MONAI Generative Models to train a ControlNet [1]. ControlNets are hypernetworks that allow for supplying extra conditioning to ready-trained diffusion models. In this example, we will walk through training a ControlNet that allows us to specify a whole-brain mask that the sampled image must respect.\n", + "\n", + "\n", + "\n", + "In summary, the tutorial will cover the following:\n", + "1. Loading and preprocessing a dataset (we extract the brain MRI dataset 2D slices from 3D volumes from the BraTS dataset)\n", + "2. Training a 2D diffusion model\n", + "3. Freeze the diffusion model and train a ControlNet\n", + "3. Conditional sampling with the ControlNet\n", + "\n", + "[1] - Zhang et al. [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)" + ] + }, + { + "cell_type": "markdown", + "id": "d6711d2e-554b-4f25-bd8d-836cfecd2a6d", + "metadata": {}, + "source": [ + "## Setup environment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "022890b1-ea44-4c60-8a80-ed1fc755f90b", + "metadata": {}, + "outputs": [], + "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "6b766027", + "metadata": {}, + "source": [ + "## Setup imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "972ed3f3", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "import os\n", + "import time\n", + "import tempfile\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from monai import transforms\n", + "from monai.apps import DecathlonDataset\n", + "from monai.config import print_config\n", + "from monai.data import DataLoader\n", + "from monai.utils import first, set_determinism\n", + "from torch.cuda.amp import GradScaler, autocast\n", + "from tqdm import tqdm\n", + "from monai.inferers import ControlNetDiffusionInferer, DiffusionInferer\n", + "from monai.networks.nets import DiffusionModelUNet, ControlNet\n", + "from monai.networks.schedulers import DDPMScheduler\n", + "\n", + "print_config()" + ] + }, + { + "cell_type": "markdown", + "id": "7d4ff515", + "metadata": {}, + "source": [ + "## Setup data directory" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8b4323e7", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/tmp8j8250od\n" + ] + } + ], + "source": [ + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "print(root_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "99175d50", + "metadata": {}, + "source": [ + "### Set deterministic training for reproducibility" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "34ea510f", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "set_determinism(42)" + ] + }, + { + "cell_type": "markdown", + "id": "c3f70dd1-236a-47ff-a244-575729ad92ba", + "metadata": { + "tags": [] + }, + "source": [ + "## Setup the brain tumour Decathlon dataset\n", + "\n", + "We now download the Decathlon tumour dataset and extract the 2D slices from the 3D volumes. The images have four MRI channels: FLAIR, T1, T1c and T2. \n" + ] + }, + { + "cell_type": "markdown", + "id": "87977bac-ff5e-4612-b9f2-b069d6ad9e9a", + "metadata": {}, + "source": [ + "### Specify transforms\n", + "\n", + "We use the following transforms:\n", + "- `LoadImaged`: load the nifti image\n", + "- `EnsureChannelFirstd`: to bring the channel into the first dimension\n", + "- `Lambdad`: custom transform to select the modality (0: T1, 1: T2 etc.)\n", + "- `EnsureChannelFirstd`: do it again to create a channel (as one channel was selected previously, the channel dim is gone)\n", + "- `EnsureTyped`: we ensure that the image is indeed a tensor\n", + "- `Orientationd`: we reorient the images to make sure that they match RAS\n", + "- `Spacingd`: we bring the image resolution to 3, 3, 2\n", + "- `CenterSpatialCropd`: We select the central 64x64x44 crop\n", + "- `ScaleIntensityRangePercentilesd`: we normalise with percentiles\n", + "- `RandSpatialCropd`: we select an axial slice per volume\n", + "- `Lambdad`: we squeeze along the axial dimension\n", + "- `CopyItemsd`: we copy the image into a \"mask\" element\n", + "- `Lambdad`: we threshold the mask (which is the image) to turn it into a binary mask.\n", + "- `FillHolesd`: with this, we remove any potential artifact that happened during the thresholding\n", + "- `CastToTyped`: we cast to float.32" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c68d2d91-9a0b-4ac1-ae49-f4a64edbd82a", + "metadata": {}, + "outputs": [], + "source": [ + "channel = 0\n", + "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n", + "\n", + "train_transforms = transforms.Compose(\n", + " [\n", + " transforms.LoadImaged(keys=[\"image\"]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", + " transforms.Lambdad(keys=[\"image\"], func=lambda x: x[channel, :, :, :]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n", + " transforms.EnsureTyped(keys=[\"image\"]),\n", + " #transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n", + " transforms.Spacingd(keys=[\"image\"], pixdim=(3.0, 3.0, 2.0), mode=\"bilinear\"),\n", + " transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(64, 64, 44)),\n", + " transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n", + " transforms.RandSpatialCropd(keys=[\"image\"], roi_size=(64, 64, 1), random_size=False),\n", + " transforms.Lambdad(keys=[\"image\"], func=lambda x: x.squeeze(-1)),\n", + " transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"mask\"]),\n", + " transforms.Lambdad(keys=[\"mask\"], func=lambda x: torch.where(x > 0.1, 1, 0)),\n", + " transforms.FillHolesd(keys=[\"mask\"]),\n", + " transforms.CastToTyped(keys=[\"mask\"], dtype=np.float32),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9d378ac6", + "metadata": {}, + "source": [ + "We create the training and validation datasets:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "da1927b0", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 387/387 [01:48<00:00, 3.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of training data: 387\n", + "Train image shape torch.Size([1, 64, 64])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:27<00:00, 3.51it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of val data: 96\n", + "Validation image shape torch.Size([1, 64, 64])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "train_ds = DecathlonDataset(\n", + " root_dir=root_dir,\n", + " task=\"Task01_BrainTumour\",\n", + " section=\"training\",\n", + " cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise\n", + " num_workers=4,\n", + " download=False,\n", + " seed=0,\n", + " transform=train_transforms,\n", + ")\n", + "print(f\"Length of training data: {len(train_ds)}\")\n", + "print(f'Train image shape {train_ds[0][\"image\"].shape}')\n", + "\n", + "val_ds = DecathlonDataset(\n", + " root_dir=root_dir,\n", + " task=\"Task01_BrainTumour\",\n", + " section=\"validation\",\n", + " cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise\n", + " num_workers=4,\n", + " download=False,\n", + " seed=0,\n", + " transform=train_transforms,\n", + ")\n", + "print(f\"Length of val data: {len(val_ds)}\")\n", + "print(f'Validation image shape {val_ds[0][\"image\"].shape}')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8e4d6164-00e5-4663-a678-1391438574e9", + "metadata": {}, + "outputs": [], + "source": [ + "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True)\n", + "val_loader = DataLoader(val_ds, batch_size=64, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True)" + ] + }, + { + "cell_type": "markdown", + "id": "5d86ba60-84d2-49f2-95c1-2ab611310d84", + "metadata": {}, + "source": [ + "### Visualise the images and masks\n", + "We load some images and masks to make sure things make sense:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "17a5e9a4-9756-400b-8dbd-0f1d457ad3dd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch shape: torch.Size([64, 1, 64, 64])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "check_data = first(train_loader)\n", + "print(f\"Batch shape: {check_data['image'].shape}\")\n", + "image_visualisation = torch.cat(\n", + " (\n", + " torch.cat(\n", + " [\n", + " check_data[\"image\"][0, 0],\n", + " check_data[\"image\"][1, 0],\n", + " check_data[\"image\"][2, 0],\n", + " check_data[\"image\"][3, 0],\n", + " ],\n", + " dim=1,\n", + " ),\n", + " torch.cat(\n", + " [check_data[\"mask\"][0, 0], check_data[\"mask\"][1, 0], check_data[\"mask\"][2, 0], check_data[\"mask\"][3, 0]],\n", + " dim=1,\n", + " ),\n", + " ),\n", + " dim=0,\n", + ")\n", + "plt.figure(figsize=(6, 3))\n", + "plt.imshow(image_visualisation, vmin=0, vmax=1, cmap=\"gray\")\n", + "plt.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "de29d929-bc99-4235-aea6-d6867c3d360c", + "metadata": {}, + "source": [ + "## Train the Diffusion model\n", + "In general, a ControlNet can be trained in combination with a pre-trained, frozen diffusion model. In this case we will quickly train the diffusion model first." + ] + }, + { + "cell_type": "markdown", + "id": "08428bc6", + "metadata": {}, + "source": [ + "### Define network, scheduler, optimizer, and inferer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bee5913e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\")\n", + "\n", + "model = DiffusionModelUNet(\n", + " spatial_dims=2,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " channels=(128, 256, 256),\n", + " attention_levels=(False, True, True),\n", + " num_res_blocks=1,\n", + " num_head_channels=256,\n", + ")\n", + "model.to(device)\n", + "\n", + "scheduler = DDPMScheduler(num_train_timesteps=1000)\n", + "\n", + "optimizer = torch.optim.Adam(params=model.parameters(), lr=2.5e-5)\n", + "\n", + "inferer = DiffusionInferer(scheduler)" + ] + }, + { + "cell_type": "markdown", + "id": "f815ff34", + "metadata": {}, + "source": [ + "### Run training\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9a4fc901", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_323202/1416541766.py:7: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", + " scaler = GradScaler()\n", + "/tmp/ipykernel_323202/1416541766.py:16: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with autocast(enabled=True):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:0/200: training loss 0.951951\n", + "epoch:10/200: training loss 0.437592\n", + "epoch:20/200: training loss 0.150791\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_323202/1416541766.py:48: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with autocast(enabled=True):\n", + "/tmp/ipykernel_323202/1416541766.py:65: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with autocast(enabled=True):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "validation loss: 0.052139\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:30/200: training loss 0.056416\n", + "epoch:40/200: training loss 0.032445\n", + "epoch:50/200: training loss 0.022699\n", + "validation loss: 0.032189\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:60/200: training loss 0.020905\n", + "epoch:70/200: training loss 0.025322\n", + "epoch:80/200: training loss 0.019249\n", + "validation loss: 0.014976\n" + ] + }, + { + "data": { + "image/png": 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MlraR1nz6d56TcViChQOlw2scdE13aFsRGG4WqVPrgWHyg05gpWjvm4CgNtP8oKaQTEqIx5qg1LYmwU47sNvtyuTT/COfrVwuS8a4mSShn3nWMlcQ8kXato18Po9cLodMJiPxVT1gOnTGz/UypzNJmBGjU6SANxpBaxSmZ9E+4/GaONYJqjw3JwQHnx40QcfMaO106KVROykEH8FGDa3zHfls+p3wfDwP079c10WtVvPVunASar6S2lQ7WlpmGTkxzaa3yVRAyKVtc3MTd+/exerqqhCzNNT5omhw83dtzJu5dPyb67q+nD1muGgvUmspahGCsNlsotfrySB3u10BiOu6cBwHx8fHvomzvr6OaDTq87RNjhF4ww+SPjK1pDYfCBw+g35/BCFXjZOTE1QqFR/XmUwmpQSVKWU6js0CLVMW0VmZindMQKVSKaytrWFtbQ25XM6nJRmZIBBop2mtwJ98mdVqFcDQCSHodIoUB5H3ofMCCW59TTPjhlQHBzUcDp+JX2tnxrTDtBMyis6h6KiIJsX5vNScvJ5OxNBZQIwwaYrruuUhXjizmvK22UTm/9atW3jw4AHu3r2L+/fvo1ar4eDgQGa5Pi81hZlM6nmeGPwnJydoNBri4HQ6HUlgoL3GPEPTMeA1qTVov3HAe70eGo0Gjo+PYVmWHJfNZhEOh31RG/1Pp3FpzagjN5oN4LU1N0gwM0WMS61ecjOZjMTK6cBYliVlAkdHRz6gm6DX73PR5FJJre+aaVwiNJWh/2ltpXkz097SNhdT9+mIAP4lx8wNHHWfmj4iaPhdYJjtTDDr2hQzwqLfCyklTT5roFJzaseDou3HUcX3puhJy3vWYU8N/EVcekfJxCmaYDCIW7duIZ1OIxQKSV0tB4r9XzgopCEikYgvL4/2k+u6qFQqaLVaqNVqaLVaaLVaPm4tHA4jk8n4Mm1oM46KXnAi0FakEc+YrAaLJrI1B6jjyfV6/Ywnz2PpSPAnn1un/PPYVqslE4W2rOd54iVT6/K9UUKhEPL5PGzblqxsPRlN7bxoMnGbkLUXsVjsjEYjlaI9Vb4sbaDrZYNeK/MDzbgttR/5P61lzCVJG/3A0PHR+YbkFzno2i7T5+H59bPw+fXyTO2mi6T4XR6v7VIzy5vf0RpfvyOdKkZnx1wJtI26iDIVx4TNgDKZDLLZrHjAXDa03cOwm3YSOCBMQmg0Gmg2m6hWq2g2m8hkMshkMlJfDECSXbmc6SRWesvdbhe2bSORSIjW5NLFkB4zoKmxRmW1aPKYANVOhQ7nEei095jixYRdwN/nkLYpz2tZlqwmiUQCKysrCAaDiMfj6HQ6ePLkCer1OkqlktRPm0kb+uciylTIaoKNYGSslYNtEq4mT8af1IC0/XR6OwuOmIZPXo+DadpndFy4FOvrUTua6V/8qTX6KE/YTJrlOQlCzVWaERz93nhO7ZAAkKJ6Ol2s0gPeBATK5bL03OG1tSwyAIEpaUKmyWezWezt7UlxOnDWwaGG1J1QSTYXCgV0u13EYjF0u1188MEHAIYOCVt7MH1eUyb0LvVyzaRT3bOaPCG1hybKuSxrrcT71pETAqvVavm6OOh2IwQh09J0yhgnBkFJflBHeDhxK5WK3L/neXjw4AEcx8E333yD169fCyC5TC86AIEJF7/zJRKEmUwG7733nm8AR4GQg8gXzeVqdXVVCO1+v4+1tTUkEgmpUKNW4Hk1CcylVkcUyKfpUgBmbOvohdaYBIv5Hsz4LycQJ5MOvennp23H89LB0d69zijiu2D2da1WQ7fblQn4wQcfIBAISLF8rVYb2UZk3DGch0xUE5Lczefz0jDyPDpHayLLssRu4zKrQ3MsXuJgRSIRpFIpGTTXdSW5leDi3xima7fbKJVKAIaDQeeDg6LzDE1QaW9bZ0YTyAAE8HRGAD/YTNNDR0AA+PhI0yalLc1kDdqf1PbNZhP1el1+5zW0mA7WosiFQfg2N9+23xSW3759WzKpR9EEms+jvcgICCMA2jukZuBnrEzTNRxcanX5p2VZwi+ySSar50iOe56Hra0t3Lp1y3dNTZTz2Rg9IVjMIiZqWM39MUFDe+V8H6StSI6TwCfwCVxW8/F7BB+bJw0GAxSLRWk7pyeifvfm/xdFJppZTYM7Ho8jlUqdcQL4U9tYJhVhnpeA1UJtpJdw2nIEgdaK7IzFpZ9EOm24Tqcj3CMBprtz8ZqjslRGvQtODgJQP7/ptWqbUoOVE5Mg15pQp5FpKmkU0b2IoDPlUpoQGP1wdBjW1taws7MjtbuaN9NhLG0rEVA6LZ+xXn1NLlHBYFDCdtQWPA+XSMuy0G63ZVB1Ebvrutjf30elUkGj0UC/30c0GpUaEGqUdDp9pqpOJ8RyWdWalZ229DvT/KEZgeE7YcyamUFs1k4tqetbyLey0SdJfzOaAwyBf5HMllnKhUH4LruCYNLemf7Hc5jn1J/p4L4GIMlqdtvnkqSP09ekrcn/MzQIDNP96Z1qbg3AGU2tSWT9d/5ff5eTxCTTeQ+WNdwXRYv5DKRmmOxBR44TQVcNag1qxrEXXS4Fwrc9XKvVwpMnT1CtVvHhhx/izp07Pv5ODywwrDHRBr4u3dSa5vnz59JilxV6enmjE0LS2XVdiYJo240ASKVSCIfD0uRcRzaSyaRPY5shQGpqOkzU4P1+HxsbG9jZ2fGlajFRl71myuUyms2mz0MmC1CpVNBut/H06VNUKhXs7e3B8zxks1msr6/Lfipc8gOBgPRrbDabCAaDUiAPLK4GpFwIhOPMrF6vh3K5jGg06mvZoT1WfS7OZFPrmV6r67qoVqvSSKjRaEjQnsJllM3R2Y+atRsm8Uytwh6GXN6Bs7ab1oTUbJpe0t+JRCJIJpO+cCK5P535re9bv49ms4lms4lyuYxKpYJ8Po9ms4lUKuXLoOHxtIHD4bBEhbRNvOhyIZ5Qa6fzxPM8HB8fo9froVgsol6vS6JpOBwWpl8vo1yeGL7jQNHzZaTk1atXODg4QLVaRbVaPeMsaM5NR0p0kiswzGrWnnmtVvNRInoC8PnpIJCKYT9BAp/38OLFC9RqNUmU1e+My7FO5+J98ngmIdTrdTE/nj59KhsDua6LYrGIfr8vjTTL5bIkd2gz5TrIRB0TCivq6vW6vBTHcRCLxcT7ZG9A8nt6UMylmuQ36ymoIbSG5ezPZDLS+Up70AB8IGRUBRhm7BBk2nbVWornJE3DaI2+BgCcnp7i+fPnaLfbqFarApZAIIDV1VUp72QIk0A+PT2F4zgolUo+brJcLsvk5jZnbJennQ7azbqa8DrIVJpkkpt78eIFvvrqK0k44FJIsPX7fanII9Vi2oykS1zXRTqdxvb2tiSc6qbobCXHPEBqLO2Z6hJNbSNqL1ffA/9GTd1qteS6/X4fz549g+M4ePnyJVzXlWtz3xRqck4Uz/NQq9XOUE4AhPPT1Xo6QYLaU9e+AJB6a44PV5TzQLiIS/RUklpJCD979gyJRAIffvghcrncmUB/v9+X9hVmRoq+Dl9yNpuVNhi2bcvGNLFYDBsbG6JlAUh+opldrZ+BIDXvicu6vhftzdq2jWKxiKdPn6LZbOLo6Aie52Fvbw+pVMpXZagdssFgIJqRS6ZObjATHHg9eu9sDKBNIxbBjyOLGC0BprzzO7d6IJ+li9jJ2Zk0Dl8ws2i0PaVDXOTOdDq/9gK1dtWfaU1sLrvA0F7UjoJlvdlUUU9EplfxX7/fx/HxsThNPL9Z4adLSnldrf1GaSnNJ+qS0ndlYZ83LosmUyl+B97MOhY69ft9HBwcIB6PI5vN+gaZdIz+Hp0TnRlDR4WznrFj1ttyMLVoh0QPGgddmwVMdjCL0nmvJI45EWq1muQ3sptrqVSCZVkyOXTtMLOv9TvU96Lfgbnq8N653S2/N+5YLLpMRRNyYNvtNur1umw2o180ATCKVDWD9/ozvWQznkt6Qh+vq+BGmRJa62gymqKBaDoePD8pEaZs8Vw8H21ZXbR+nmN3Hpj05+Y93RSZ2nLcbrfxv//9D51OB59++in29vZkeWbWtC7s4eDpJZfZM1zutHNBz5oOCTc21DHfQCAgzouuZjM1JgFMjai1Js0Gcn38G7sisD1bpVKR8J7mKVutls/JMM0Oynn2mjlZbqJMtT8hNQSJYR3KAnBmUPgZqQczvqw1FgHJzBZSPZqMBs6SziZprc9rAsVMkOCyznNGo9EzKWTa7hxVgG5qNlPOW5bPO/4myERByEEiiB48eICf/exn2Nra8jkbHFRz4DRpq7UePVkNLOYVsl6Ex5Lnozerg/68L0ZZeAy9VR6rbUFeV/OOeimMRCK4c+cOarUajo+PJaGC59VgHxeAN2mpHUcmCkLt2QaDQeTzeezu7koc1jTEdWWY9kZNm5BaisdqjaQr9HgPAHwep87NA+Db4IbHEjRmWE1rzlG/27aN1dVVhEIhVCoVXzH+qGX0Xdrvbe/2sppwnADDPGXimdWhUAiFQgHpdBobGxsS76R2YjWbBoumLEybSfN7mtIhCEwO0KRhAPgysvV5WNesNRftP04Q03HS1yI5ncvlEIvFsL+/f8Z00N8ztSjwhgNMJBKSdDENoCwq+ChTSe8vFArSkSuRSMhL6PV6kiBg9pyhNtLxYO0F81jtHRM82n7Ux1F7khCmY8IoA6MawLC5ummPatEaHIAv9BaJRATkoyIufCYzWTYYDEp/a8aZ5+GATEpbXuY8EwWhSbZqkAQCAUnBp9fJMBQzn3ncqDR4LnFaM+mYMetRCGYduqI9x+9wCY/H41LJRr6S32Gthhlloej9k2kz3rlzB9lsVkwBcogse+Bng8FANt+uVCqyQ71JPs9yGZ3UNS5znomC0Fy66GTQM47FYkin03BdF69fv5ZNZRqNBtLptDgZWgsNBsOGmqZtRZCQswMgGcZ0MrgMa+1EUprXoRbSURyaDYzIUKjRuO8yyfREIoFPP/3Ulx3+6tUrfPfdd1I5CEBi5T//+c9x+/Zt/POf/8Tnn38uwNVykQFddLvvbTJxEFILmW0+KHqppG2nC+VNW1BHNcxzELDUhp7nydKrgUPRoTJqVkZidI3GKNpIa2qT0iGYdQdYRknYRN6sPOx0Ojg5OfGVH1xFriP4KBMFIQt8qN2AYU9AgpNdFbj8cJDYw5CaRHvaOlymQalriOk90k5j6SWvRxtQc3/8XE8anT+oQcNd3k0gEXyBQEDqUZhFw3Z2oVAIuVwOtm1jY2MDg8EABwcH+O9//4tSqST1IzdBLuPFT5ysJhdo7ttmhr/MsJvuzULRdI/+TFM1pgbRTgxtPNPQ19rW/FxTOaOuqTlErbF1+n8wGBRnhbt56rplatput4tgcLiZpM6UuU75gFeVqbQLrlarkjnTbDZ9kQyChPYUw3hcrghGzfExLUtrK9NGJOD5fWopvT8JQa4jHkwMoGdKjctwG5d3nXTLe+L2DeQdmbxLyefzSCaTspk2Iyi9Xk+aW7LhZyaTwfb2NiqVCr744gucnJzg66+/lqq9cTTMVbjESYkZqRpHpgJCbgRICkTXY5gxYH6mvVcdSaFdaGqf88JhJiWiqRLabmZJAIExih7RVXi8V/bB0XwitRifhQ4Ra20Iat43l/CVlRWk02npass8xUAggIODA5nEmmc0WQg+37TlIiC/yP1MZTluNBro9Xo4OjrC/v4+1tbWpIkjMBxMLt0EgG4EzmP4QKbXrOPK2pnQ52UfGNbxchB1B3ztlGhKhvYfExXi8bjYe41GQ3YH0FEgncRqRoVo+7JCT+97x9T+b775Br1eD7u7u7h16xbu3buHTqeD/f19YRPYc6ZUKvnCjItG41zEnJha4/RWq4VqtYpyuSx2kdZMBITWQtQS2jY0NZz5z9SUBCHPq+tGtF3KY9jBQHvy1NTaPOA+clzidaNynZ/In4A/3Kh3G+C1CEbaga9fvxY6hxSQbdt49OgRIpGI7HdMM4c276ztx0nTQRPfYJE3GAqFUKvV8PLlSwEYu3SZmS4EgY5+aO5Ph9k0Gc30K01aU8sBozNoAoGAdLgC/G3Y6JnrqAk9Xu5Qz8wdHZbTWdTAMDFCd+ji8+k+ODqhgn10tCmi91z+yU9+IpqvXq9L1eFXX30lTMSsZNJa99LVdqOIY35O7VGpVLC/vy/1vLu7u7h9+7Zvd3VqKdIaBBQNf/J4OlrCJUhzkXq7MjNxQIfvgCFtxJAbtSq1t+d5AgzWtHBvZjZs4rLOa2l7jXUfLNcEIBOm2Wz6ykATiQTi8TiAIY3Fv9HJyWazyOfzMgEajQYODg5wdHSEZ8+ezRyEk5YrLcd6RpiREsuyUC6XYVlv2paVSiV4nod79+6JR6g9S+3d6vw8M4yl7TBe11w+6RyxEFwnN+hsGP6NmonfN/9uxrnNODXvxeQZ9XPovET9XJxk2snRzZk0+c4oD+3E09NTuedREZfrIpfuRXPeA3NQer0efvjhBxweHgJ487JPT09lh6ft7W3RKtrTpBbVdcF68HXPa22/sXspl+larYZGoyHX0APP6AcLr7ipNQDRSvrcZnsSTfdQuwHwtQLRNisLlEim68aenU5H9mZJJpPo9/syYTOZjLREpjcdDAZRq9Xw73//WzYXWllZkd6EiyJT9Y4vcnK9tABvdmN//fo1er0eVlZW4HmetMugDWYmgerr6WwYduECIJt7a8eHKfatVktS77mtBbWKtl9Nopz3xEJ63g//BsBne2pKSNNEBLB2jvR3+ZkZnyY9pf/RLtb9e2blGU9TLr2j0ziiSWXLsnB4eIjPP/9cQlf5fB6JREK0mF6a9ff4krVWYAu3fv9NpwKCulKpoNfroVQq4fDwEIeHh3j+/DkKhQJ++ctfIpvNolAo+BwSdlOg6Hsxn5/g0l49Jw9zG3XsmYBlSpsJIC00FxhDTyQSMuFisRiKxSKOjo5QLpel/oaUzXUO+13aJhyXuNTHsM+KbdvS4EfzhKamMQeKGodai2ClZtNRFAKAy6Xuk2jWolDDUuiZmxyk+by6SMs8RmtD/b64VOsEDv07MNxBXq8MXNK5rQbPqROCr6tcugPDRR6axzabTRwcHACAkMTFYlHsO8uykEwmsbKy4rO1zPJNcmMkkTUI2BHr7t27KBQKeP/993H//n3kcjl8+OGHCIVC0sNaA4HXoL1ITWiyACzeYoKuNh3Y1Fzbkfq72rwws7j5nKSFdATm1atXcBwHJycnODg4QLlcxsnJiexbcp2aH42SqVbbmcKCds2VsYcetRG9ZlIggH/3I00G62RRZkjTachkMkin04hGo9KBNZvNAoB0vdLA1eE3bd+ZHRk0Ma0Hn/fAxk+mR22aMzyHjrES/Pw+ExmYHFssFlEul1GtVsWZumjzo4sqkFnI1DowjPoOX3yn08Hx8TFs28bKyoq0i6O3yAQIvXQBkDCcJrnpEfIYxqkJCGrcer0uNSAEEr1yzTkyRYwgNMNipH7MLg40C9jknDUjuhEmHR2aE5qU5zEM7fFYnqter6PRaMhP9ru5aLRkVgC8yHVmqgk5aOxUGolEztRccNd10ybiMszlk/Yegamr32gfEoB0IhiB0FtU8Hs6gkE7UQOW965LB/i5dlBIydCG089Cz572p05h0/buqIgRkyCazaZowUVchi+jaScetnubUFu1Wi08ffpU+u2trKzgs88+w8cffyxBeoJO22m67yAHTS/ZWqPx/ug5ExQ6LGiG/DTQHcfxLaG8d7PGhWE3anHtrITDYaRSKTkHgUjtC8DnmXOCmQX3u7u7yGazePToEZ48eSL27E2RK4XtLgvEWq2GL7/8UmgN27axs7ODP/3pTzg8PMTR0ZGvyykHm5s1at5MR04sa5jlTC1FErjdbqNYLL55aOU48Ps6ZgtAwM0JYPJy+r50lEZ7qqRXeJ8EpvaYo9Go0C3UbNTu3JhyfX0d0WgUJycnKJVKvpqbUeMwb7vvonTexLv3j3sOBus5609OTvDdd99JzYVe7vgdLr9atE2kl2QdteDfdEjOfAZNd2jCmcDUP3lPDMHxXnUkRd+3WZuidxGg6cD/8ycnFIn4UCiEdDqNQqGARqOBYrF4htrS73decplrX9omvMqD6tlOrfX48WOEw2Hs7Ozg3r17CAQCUh7JQWHdBtsH663HOMi09xhxoNi2Ldnc5wntSB03psbRCRQ6+4X5k4PBQJZW7T3TC+cz0KQgsGhj0l7UcXBqQoL4vffew29+8xu8fPkS//rXv4SimadMQuvO1DHRojWF53kol8s4ODgQj1lrS4rumGV6siboTE2mnRcCUS+do2pLeKzmITUQzWxw7RTxnjjJzFi07m1j3rPmRckmMPdR25TvsgtnsSxP4vwzo2hGnUN7nE+ePMH+/j5SqRTy+Tw2Nzfx2WefSRsReqq01XSGiR5ctuagaGcB8Nt+jKSwewJTyPQ9au9dE+X6GvyOucOojsQQYLw/c3KQDtKlpeze//z5c7x69UrCdicnJ9K0821jMS0AUjFM6vxz04RaPM+TTv/VahWlUgmu6+Lhw4cIBAKIx+OiNWh7maSy+TtwdockXosA0AXy1Dw6O4bf5ff4O/+mCW7+To2qkxD0PRJkpj1nhgCZ1Oo4DorFIg4PD4UjJEk9D9vvqv7AKFkIEALDQWg2m7JtWLlcxvr6Ov7yl79ge3tbNA1Bo7k/OgB6V07btqUmhAAAhlvMWpYlGc3M6WOo0Bxg8ndM19Lg0sS2ZQ2L6YGhrcplWnd44LnM3ea5ZUS5XJZmo8+ePUOz2ZQWxfOiaCbhmJqyMCDUSx0A6QH98uVL/PGPfxQHRcd0dZo/B5k8IVO2GCc2a0J0Gpbe1FEnwAJnPWltEvDaBBqBTWDpmDRFU0AszNdlojo8WCqV0Gq1cHR0hMPDQ2m/rO9/FmIuv5PWwBPvTzjpG+z3+3j06BFOT0/x8ccfo1AoyPYLgL8kMxAISC2wSV0wAkEPmN6r7uygnQECgyAiOBmBIehMGgkY7nxPjpL3BgydK04SftZoNHByciLLL3fuZBY1TRS9fe2s+EBtT+vfJyUT14STfDGMHjx+/Bj7+/vY2NjArVu34DgO6vW6bEqobUAmLXC506lbTINnUyJqQIq2+0gIaxMAGGpCgpBOio5xE0BMngCGYUFqMe5upVuVHBwcoFKp4Ntvv5U6ZS7LjUbD916nYZuZMiuQT7w13CSFA8utJL788ktUq1UZtJ2dHfz0pz/1hfN0XFYvg3q515nc1IAEld6IGxi2FTHzHnXPHJMYbzQasgunzhbiJpGcHJZlyd7IjUYDr169kq3IaCfq2PS03/e8ZGFswvOk33+zSU0gEMDx8bFvj+Lf/e53+MMf/iCkLgDxHKn5GAqjU0DNFQ6HzyyNtVpNMrPNDrCkehjB6PV6aLfbPo3EY09OTnB4eIhwOIz9/X04joPj42O0220cHBxIIgK7+xNw53n3ozJlpg1A8x5GRWYmJQsNQv3Q7Bmj24oUi0X88MMPyGaz2NzclJ4xrB3WYTgd8wWGyQcmga1Tu/R9aD6PYtI32tYEIPUtzCh3HAe1Wk2cJaaFmefTANDL/Lxk6oD3xrzCLGyQ80R3+icgLMvC1tYWPvnkE9y+fRt//etfsbKyItnN2r7jPzoipGho99FOY1SCDYw8z5PusjqaoYGqtSz36aPWfPHiBZ48eeJrT6edGZ5j1h0UxpXzbMKLOCjjHLPQmtAUM42q1Wrh1atXiMfj0h+GHJ0GLL9rerPmuTWfp0ljTToDfvtSg9wEG4v+dUYQ750/z8uEWQSZ1T0tNAg1gPRPSrPZxP7+PgKBAL766ivk83mJruhG7Cb3p1+u/r/mG4GhCQAM+1vrBkqm80K79PT0FKenp2i320ilUkIp6UlgZuRcJ1kIimYWM1cb6Od5htwjmA4FQ2IaLLot3aj7N+1EHYYDhnmFBA29YnJ22kzRNBD75vB3897nbectkiycJjS9Msp5S2i//6Zh5e3bt7GxsXHGvmKtcqfTEX6Q59P1ugz50WGo1Wq+xAfWNxNA4XAY+XxenBXP80Tj0dt2XRelUumMibAUvywcCAGcsZPOGzhqp2AwiI2NDWxubopzQHuN1WvaMeH3SGiTY2y329L3r91u+0Cjw3q6YRO5Rt2DRpcPcCPJUZpv0ivKrGzLG5lFY8q4GoNL8uvXr/H3v/8d2WxWoht6oxumVXHPEgKDW+CSe2ROo+M4qFarvi4LBCqznWOxmOz4zsbu1KKlUgmVSkV2eT9v6Z00YGalZScelLgoRTOt+OFlhc5EKpVCJBLB5uYmkskkdnZ2kM/nJQcvn8/j4cOHGAwGePnyJXq9Hu7evYtcLifAevToEf7xj3+g0Wjg+PgY3W5Xioq4xCYSCemfQ16SBLfWwFex+Wb5jqetPadC0UwjlecqonPv+v2+kMMsD2g0GrJjEqviyuWyLKHshhoMBvH8+XNUKhXRaDpcprlGdowAhttm6MaXV3U6ZjnBF0GZXAuyWst5M1cXEjGDmfUZtNuYXsVd6JvNphDbBI/pyZo5g/S2CT7t6Y5Kor3p8i5NeuPIauDtTooZ4qLTwWWTsV4eywbvbxNz8hGoOvqxlKvJtdOE4wrvl/FhMy0fuFiHeTO5dUm3jCfjvKOZg3AeIarzKukuKosaXltkWUgQLuXHJePA62pbSy5F5Mc0SU3T5Kpy7RwTYPG4SmCx7mXaMulnvZQmnKR9OIvvL6KWmrQ2uc4y1e7975LrGmaahCziPc1LLty9fxZL4buucV5a1iyuPc6xoybqEnTny6XCdrOQca8zjfu56rUXLbS56HKp7v3Tkklp2atox0k9p06UXWrBt8vCUTQ3bcBu2vNMQxYKhJPUQkvv8/rIQoFwKT9OuZZk9TiyXAavjyw14RVklkv+TTYtliD8f1n0Qb7Jmn1qy/G7qIlJkMLm37WMc23zuHGuc5HjZynnVSiO+24mOR7nXffcY69DKtc0BnvUJDkPnFe5r+tahnlVMbtnvE1m4pjoAvHLJJfOMiqid4O67Dm0zAocFz3/IpHoS5twyrLotuYiyEKF7Zby45SlJlzK3GUJwqXMXZYgXMrcZQnCpcxdliBcytxlCcKlzF2WIFzK3GUJwqXMXZYgXMrc5f8AuI8jupoQI3EAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:90/200: training loss 0.021246\n", + "epoch:100/200: training loss 0.013711\n", + "epoch:110/200: training loss 0.017492\n", + "validation loss: 0.019058\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:120/200: training loss 0.020393\n", + "epoch:130/200: training loss 0.020519\n", + "epoch:140/200: training loss 0.015537\n", + "validation loss: 0.026484\n" + ] + }, + { + "data": { + "image/png": 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Ei4zH4xgMBnj16hUKhQKOj4/h8/nQbDbnVCkTFADg/PxcpA+Z0+/3o9/vYzgcCoPQLgsGg+Kg0EumGaBigbQfM5nMnGSjuqQJQEYlxtjv90Wyc5Elk0n87d/+rUA2kUgE+/v7yOVyqFQqsmBUUJtj65ZJ3NjsbunOvGOvSF3ZamSEYS+G29Scv36/j9FoNIfrEfvr9/uiVin1VIlFY1+NqpDZzAZP3R5A6aYyHKWpmjvYbDYxGo3QbrfR7XaRTCbFJgTe25UEuJPJJFKp1FzKWSaTwWg0Qq/Xk0iN22SAv0RamQmX2R8ko/1BuzCVSiESieDg4ADPnj3DxsYG9vb2BIqhZOz1enj8+DGy2ayk4vf7fTQaDYRCIYTDYQC3KpfSqNfrodPpCAMAt7AQPWBKNjLLZDIRm3M4HCIYDMp12WwWuVxOGLndbuOPf/yjOB29Xg+ZTEbsREpwXdcRCARwcHCAdDqN4XCIXq+Hra0t/PrXv0a1WsXp6Sm63S6urq6E6dflJVslNKzalps27zWBgeqTGc25XA7pdBrRaHRu49FwOBSVCczbHgSdyViqGqKHa0xK4CIw/qjJsvTE+VyG3fr9vsAuzNhuNBqo1+vo9/sSxqOTYVx88XhcnKfpdIpIJCKx5nA4LDFnVVO4ZUQrlewFY3u5QFZiwmUqw2y1cWBo7JdKJWxsbOAnP/kJvvzyS0nDH41GAjxvbm5iOp0iHo8DgNh7fr8fuVxOVCyZBLhluFQqJR54KpWaswmj0ajYccPhcK7fZCCG/rg4Go0GXr16hVwuh6OjIwDvgfZoNIrPP/8ckUgEmUwGg8EANzc3ODs7QywWw97enkhDev+dTgfD4VBS0prNpoDj6iS7ZSS797mxO72U0J6UAbFC1BfFJgmhUHU9evQI29vbYrxTCuq6jkQiIXYgAHEmotGoSE1OGu2pYDA4B5cEg0H5TdCb0A2loPpMAGILEmqJx+O4urrCxcUFdF3H/v4+gFvPfnd3F+l0GuPxGOPxGN1uFzc3N0in09ja2gJwuziYhkapSGZUM7OtGHEdKtpNm2be8FpxQrtkFiEh0fPMZDL42c9+hnw+j6OjIwnZUSoR8E2n03M5f6qa0nVd4sJsU02yCIfDCIVC4ljU63V8//33iEQi+PnPfy45gMD7vD/uIaFzQOiIz+33+wLdHB0dIRAI4N27dxIW1PX3GdgAhJkmk4lkelOqk+mazSYqlYqEAVVMU+23Cnibjel9kld2petDt9UHm60Cs/ijz+dDOBxGLpfDr371K2xubmJnZweJREJspcFggE6nI14kvWCzH0q7aDSKXC4nMIrP55PJZar99fU1vvnmGySTSXz++efIZDIiRcvlMrrdLvr9vuwvUb1oqvnZbCZ7SxqNBl6/fi22IpMbAEhqP6EnppapP+12G7VaDYVCAalUSiQz7WRjCNJoDy+yyczm5S5DgU5ppdJwRlqGvDOs5vP5sLW1he3tbUkuJbQSDAYFphkMBnPwC38TxKZzMJ1OcXp6KnuLmYrFyEkgEMDFxQUuLy8lvEfmnkwmkmvITGyqe+B2YzyTFsbjMer1Onq9nnxPKXZ+fo7xeIx0Oo1EIjEXtaG67Xa76HQ66Ha76Ha7mM1maLVaqFarkkyrMp4x88YY7bBLZir+YyHXsWMrMhq5xpAYQeGnT59if38fNzc3ouqIF6bTaQAQp4AgMxmC4DAlVaVSwddffz0Hk5yenqJcLkt+4mAwQL1ex+PHjzGbzeZgl1AoJM+k1Ds/P8dsNpOto7FYDKFQCI1GA9VqVd4zFAohn89jMpngq6++wuvXr/GLX/wCX3zxhXj+o9FIGLder6PZbKLRaKDRaODi4kK2ojILnExH58gsguKE3EZdVFqn4+KJTbhsdRmZUs1CZqSAP/RaqY54dIWa/KnGfNVow2g0kn3CDJ8Bt+qx1+uh1Wrh5OREHJ5IJCLSk/1TsUxjSI9qmIuAW0x5r9/vR6/XQ7VaRSQSkahMr9eTXMRGowGfz4dEIiHfqc/m307G2EirMpFXsWQ75El9wkV4lorBqcZ2v98X1cRJoFTa2NiQhAMynypFqRrJIMyCnk6nqFQqqNVqyOVy2NvbQ6/XQ7vdRq/Xk8jGv/3bv6FQKODv//7v8cknn8xFKfgc2oRUj5FIBKFQCH/+85/xu9/9DoFAAMlkEplMBl9++aV4yIVCAZVKBZVKBdFoVOy9UCiEXq+HFy9eoF6vI5PJ4OnTpzg7OxP1T0lIUu1fjqUdpliVAY33uvF+7xystisJ+Xs2m80xILG9yWSCeDyOYDAojE04Q4VfqAYJIjP60Ov15mrF0F4cDocCBvt8PrRaLQQCAckVVCUzSZW0DA+yLb4Ds3qazeZc7iE3wVP6hUIh2UpKBygej8+F54wM97GSG0xxGXlmE5pBM8ZoAQBJLvjmm29wfn4uG9o5ARsbG9je3kYoFEIikZA2p9Mpzs7OUK/XcXh4iMPDQ0mxf/fuHb766itUq1VxOhjfzWQyKJVKGI1GkjIViUSQSCSwsbEhWwlofzKTh1KQgPLJyQkajQai0Sh+85vfoNPp4N27dxiNRvjhhx9ESjNUSKiJYUVKRNqhL168EBOBnrdqEpiNrRMGtRNAsENeSlUrWmvYTpWQqjifTCaoVCpzhje9VjoJ3NBEm1DXdVGpDI9RAnY6HVSrVdTrddkCQKfFGBYjVMS9xwSwyYCMPbPYEZmj3W6jUqng8ePH2Nvbkw3urEFD8DwQCIhUrtfruLi4QCgUkpg3napGo4Fms/mB96uO1X3TXTlBtnfbMVqx7OEfPMCAa+m6jnQ6jS+//BLZbBZPnz6VpAAmM5D5mGFNqIJSk9hfp9ORyfzhhx8wHo8lWTSbzSKRSCAcDktSQr/fly2kkUgEx8fHKBaLYmMyYkJnYTgc4urqSvY9a5qGk5MTPH/+HN1uV9L6r66uMB6P5yQw4aJerydevxqb5p5ojp2ZQ2IVYls03sZxtztPVrSqJFS3MljRnSUwcKA5OeVyGYPBAM+ePUMmk8He3h62trbmnBBGLZiuzwzr77//Hq9fv0av10Oj0cBkMpFyHtvb24jH40in04jH43N2JaVeoVCYi5YwVV8Fq1W7VNM0bG1tIZPJ4Pnz5/jqq68wHA5lUTSbTQn/Abc7B0ncMMW/VVsT+HDTl5mN7cY79kJ12jEFVg0puo6YGF/QauDMvp/NZuj1egiHwzLwlHwMxQ2HQ9TrdZFKjFYQl6Pnm8lkxEHRNE3UMaETMhEnn7Yma80wT1FNBWMosNPp4PXr17i+vkar1cLGxobgmozuqNnVKqiubic1czrUyTWG5OxMpHH8F0lNr5jRql+Lnm2HPD102+7LkrFCoZBIEDVJIJVKCd5HlTYej6UwUqFQQDAYFMll5lWqZgCZEIBk04TDYVxdXYn3ysSGZDIpTDSbzXB6eornz5+j3W6jVCoJuEyzgBKH/ediUlPGGCkyMiH7xR12Vvt4zCSNlRBYxMir2pqL2jZjUrvP88Q7NlvhiygUCqFQKCCfzyOfz0tm8dXVlWS9qOGxcrmM8XgsSakEgAGIs6Fm0aiTylAcIRMC2lT1zNZWY8S03Rhaa7fbuLi4QK/Xk3R8VZ2qDMf/aUPrui6RHjpgqp1kVaFANQXszoNxLozkFQBt1QcjI9oVSo6ZcNkLLOoEGSMej+P4+Bibm5v49NNPsbu7i3fv3knxIMaXY7EYBoMB/vSnPwkD1Wo1SZVnxQZNu61w0G63MRqNZIsAJSvLw9HJoBeeTCbFqyVATvVYrVZxfX2NcrmMcrkMXb9NclX3CDPTm/fREaFtyxxG4P3GLJXxmOhgtsfEjAmtxnqZY/gx051mVnNQNU2TTemdTgetVkuAXmaRqF4mkwRUO0tVc1SfanSGdh0laygUEkZVy3QwRZ/pV9wcFYlE0Ov1pCpro9EQwJpkZBYWZOezCBVFIhEUCgVomiYhQjVpllhko9EwBc4/FsjGCa0lYmKVz7ZogFSVojLHbDbDyckJbm5u4Pf7pcpWJpNBLpfDo0eP0G63cXZ2Bl3XcXBwgNlshlKphEwmIyq22+2iUqkgEAhIyhSlIjNZVPiFSQrZbFYYHQC+++47vHjxQqRwKpXC06dPAQB7e3uIx+P4+uuvUS6XhbnpUBDH1DRNchQphfP5vNTFPjw8lJImfr8frVYLg8FAQnxv3rzBf/7nf0p0R91IZUWq+jPahBz/u2Rgt8/yJL3fjMxWAhmRao05eN1uF/l8XrxalulVt2oCmPN4+Xw6NmZGv9WE0IYjJkhVrdp0DP2xfAeBbVWq8hm0K5PJJOLxuNiYxD8zmQyKxaLspWG7tD91XUej0RC0gGpeVdurQjZ3SU5NAEdMaAeWsbqPPypWGA6Hsb29jSdPniCVSkktau79SKVSwgCqSmb1K+47YQ6iz+eT2PHV1RXK5bJsJa1UKnj+/Lns0gsGg3j9+jVqtRrq9ToGgwEeP36Mn/70p0in0zg4OJBQXLVaxS9+8QscHh5+oPpVZ+bp06dzlWLp2cdiMSSTSYlXA7e1uFmeJBQKYX9/H/1+Hzc3N3MOlJWnuw4mdONYGK9fGxPa9YiWdUwFrYH3qpHgMZNHKW0oGdLptKhA/tB2o92nSiaG+BhKm06nUrva5/Mhk8kgGAzi/Pwc5XJZVF8sFsPBwQFSqRS2trYkQXU4HGJnZ0e2EFBC04uv1Wrw+Xx48uQJisWimAKVSgVnZ2diEqg2IWEhMjK3KBCfBG4lthO4ZR0JBm5oLTahnQdaYVhGQDYajeKnP/0pNjc38cUXX+Do6EgkDCcslUqJLagyMiEP7tvg9kum2BcKBQGOyaR0Mh4/fgxdf186eDQa4fnz5+j1esjn8ygUCsjlcuIg0IvlvuNsNotUKiWnPxFY73Q6+PbbbzEej7G7u4tSqSSqezweiyfPvcrtdnsuUSEWiyGTyUgYkplFZNZV4rerzOUq5PT5nqZymb2AiuGRocLhMD777DPs7e3h6OgI+/v7Yi8yt4+RDZ4/wroyAMR2ZCQFgDgJLFrEJAgypK7rcp5Ju91Gs9lEMBhEr9fD3t4etre3kUqlJD2r1WoJE3BTfDAYxCeffILj42P5jDAON7Lv7OyIBGu321JwnVJU07S5La1ccJVKRexjpoKpTomd0NjHIAXV+bZLjiImVi9o10FR7ahGo4F4PC61WaLRqGSuqEF6NSLBttSyHHRi1IjDdDpFq9WSDGuG2Jj1wl19g8FA8vzOz8+h6zqKxaKoZ0ptlq3r9/uoVCp49eoVkskkisUidF1HqVQS86Db7YpJEY1GsbW1JWpX1/U5Kcn3mEwmuL6+loJK/FEBcS8jVk7ucyrV3NiFjiSh3Q5ZecZqqOr6+hoA8ObNG8n7Y8hMzdgxMiBwmxDAw3N0XZ+DNbing/FnZlu/fPlSwGMAUuOQaVqapuHw8HBOsjPEd35+Lqc6VSoVbG5uyv7lo6MjSbhg1jT3VMfjcWF6Xddl4z37SmD76upKmJAwjTruq6jYRaE+K7LSanZoGchupDsBq9XVYQxxMSNZ3eyk4n3qbxVvVAsnAbd2J5NGqfYpZTVNQ61WAwDs7+9LYqu6CX1zcxOxWAzA7T6WbDaLcDiM4XA4d7IAJTrtWFVqc1GwHWM0BYBESADMwVZGb9iIA9oho9ZyIyHNMMd1kSuw2u5LqdcxOqFu1QyFQhgMBrLxnJvHAUikQt3/ywll/Wi1Xz6fT6RUv9/H5uYmstksYrEY4vE4ms0mXr16hUQigX/8x3/Ezs6O4Hm8n4mt3BPCtC+WK+EGpuvra/HcNU0T6cYYNcOHxAUBIB6Pz21JZZo/s38oRTudjrSrmgVG588LWmZDrqKKPzpJSFLVirrC6ZDQEWDBdILUlHJqyE0tvaZ+TknKqArhHDo7dBRisZjgj0Z1R4aiQ0O8kJ42S9hRqquLSzUN+H5sU30XVbKb/fwlklvHyNVRs3bJ6BnzN3eWMTMmn8/j+voa9XpdSnKUSiXs7++LKpzNZhKZUFWcqtJCoRBKpRJ0XZfIytu3b/GHP/wB4/EYv/rVr6TaA+GRWCyGm5sb1Go18VSZ7zibzVAulwXXCwQCco4egA+YiVsGuPGeTMl3pmfOBRGNRmWTVDQalRS1er0uSRxGXNVKTRol5Sqeshf2pxNyBVavgl1xQNUtnCyjxt1orVZLIA2qOOBWQlECqe3yM4LClJys90dPVd2DQoYGgNFoJF4scHsURafTwXg8RiwWk8KWqVRKMEp1XGjvEVg32qvqHmuGIPk+hKTG4/EcPGQ23utUyau27Sbi4mlS67L71AwZbnQ6PT3FcDhEu92eq97PIxg0TROpFIlE5hiMjgEwb4fouo5ms4lOp4NarSbZMExa3dzclCgMGZdHl2WzWQAQiISSUc2g5s48blpSi2xq2m1ql6quyWz5fP79wP+//1kg6fHjx/i7v/s7XF5e4t///d/RaDTmMrdVuGaV+TFzcNxge3aeb7c9z9L7SYtce06QpmkS6WAWjFr8h5NJTI/GPgdL3aGmetvqs7kfuNvtSiIpbcJ0Oi2JtIRsWGaOnjQlEdtUnQgeAcEtAfRo1UiNsZqY6vioajYUCgnUdHx8jFgshj/+8Y9SHUxVxauSF07IKrFlK3LMhMserE4cf1OyFQoFfPLJJwiHw1JfZjgc4vr6Wl5OrZRPjJC2FZmoVquhVqtJxg2lHL1MJj1kMhns7Ozgiy++QD6fx2effSZFM1mLhgAy8TpKajVGrW5Sp/mgbmRa5FDwPhWaGY/HuLy8nNvyqWmanMP8/PlzXFxc4M9//jPq9bpn6nfViIrdoIRT8jyz2ngPpV84HMbm5iZ+/vOfy0RPp1NUq1U0m02RHgy3qUXOaWMR9+t2uzg/P5dtosQNNU0Tz5SFjPb29iT2+/nnn89taqJUZT1rVVL5/X6p7qVKadVzNnq+RlKlmOqpj8djvHr1Cm/evEE8Hkc8HkexWMRnn32GYrGIly9fIpvN4uLiArVaTRaE6sV77XSo82u8xkxVO9GEy2jtNiEA8YC3t7elKDoN/1gsNlenOZfLoVgsIp/PC/6mluBgpISbnli9gJJQ3VCv67ocBxGNRgHcVt0i0TlQQ4aqrWlUh6qXb7andhFQTMmu6+8rTQCQhcXFRBs1Ho8jn8/PmRVeqmazfpv1Wf3MzgJwI209KYi07Duu8uPjY/zyl7+UKgWEWFT4YWNjA7u7u1KtgPtKWJKDeYabm5totVo4Pz+XsnEqWMwfptZHIhEJ6bEAUyKRMK1LyCgN+2Tc2ETmY2IunQwyvpWdCtxuKX369Ck+/fRT0QBkaoLfuVwOBwcHSCQSODk5mcuqWWf0YhHZZS6n0nptYDVtLRYDYn0/FZ5R7TFSPB6fgy541ocaMVHBaDVPTw36q7YY+wHcOkcqsxgZTXV++L/qcKjAuMpoxkFXpaVxYrhxi+2yrg7T/Wu1mqR1qXHx+2JAN7Q279jOQ6jONjc3USgUsL29Lef+XlxciAMRCATwySefIJ1Oy2TQXlPV4s7Ojkgxlm1jGtTW1pZUyudntO2YbsXICxmGqo/EdlVg2LilUz2rjjYhFwDbNhKr8jOmDdxiiKenp7LHJhQK4fLyEv/93/+NVquF169fS8ElQldm+5bXRavCNU5pbY4JvVlGESjFiP2pWSKULCo0o06uypjq4KtAN7/n32a1c1htyxiqUys4qJ6xKr3UHxUDNDKD2kdd18X2VcuEABBgPhQKQdd1qdBFyUdQW3WK7oLuI2ToCURjNkCapkm5XOJvan0XSiymR6mRA2a+GFW1CvkEg0FJDg0Gg7InhDVu6F2zQDklnMpEjJ5w0qnegVu7j+1QGqVSKcm0UatHqPAQIRzVo1bbZeoaoy/pdBr7+/t49uwZOp0OTk9P0Wq18M033+Dq6govX77ExcWF5fh7IbmM7a4aRXHCzJ6m9wMfRkiouozHulL9EujlZFMKqpLMCFGooDDvZWSDFRJ4H7Nz1PtVRtT12xNEgVt1aTwzmfmC6jZP9lVNdKC5oEZy+L7ALXOTKdPp9NyOvG63i2AwKPYhTRjjWHspGRfBLXchgV0x4aLOqUY8U59KpZLYfTyJCYBImOl0KlVUSZPJBK1WS7KwJ5OJZL0wAYGMQkbihiFmQ/MZLA3H2jFkxNFoNBdaYztqeJHMRZOCUBDflU6MKhHZfzW0x30wjA6xKhjTukajkaR30Q5MJpPY2NiQlDa+K59tHPdF3y8iK1jlrkyAtXjHnByeSaLruiSQUmVR0tHg5iTxxVmSYzQa4fz8HKPRCIVCQSZEtQV5PdtRbUgyFP9XIxRkOG5oZztU9yr4TceEbQHz6Vl8FqUmpSRBdL4fAAHIg8GgRH9Y5o624XA4RDQalZxItVbjsrFfhXnMmNiqTaPN7Pa5rphw2cPUkBcLWVYqFQGOOclqFrUa5GcbZNB0Oi32V7/fn6sdw8KXZBrakZTGtPfoEKmMznuM5d2MjgAHmZKcCRGUcrxexTzVsTC+J3B7rgkZnLFtLlBuAWXkxwk8c1fOxSK70QlDenK2ndm1ak5epVKRMFs+n5e9wqr6VUFfriqqy1wuB03TUK1WZV8IS8N1Op25LGw6DcQaeYIS0/OpelTIh6A5pTNVs/quZKRut4tGoyF5iarkY7UwXst26WyRCZlfyPcjkB6NRue0As2OeDxumdq1Lob7qCEau0RbihGKVqs1JzkajQY0TcPm5ubcTjtmzdD2o1pXk2HpFQOQODMnl/uFyagqs6lRDQBzTM93pDQkqbYlJS4LefI9eS8dFGMKl9HDVzdzUcpyYdJeJEgPQBjdOA/rxgqNtC7GdM2ERhxN/ZxEpqlUKnj79q1Ip8FggOfPnwMAjo6OcHh4iEajIfuBK5UKMpkMstksdF3H5eWlHAdLh4XHx5ZKJVGpw+EQb9++RbPZxLNnz5BMJjEajeTwGkpCeuTM1Ga/uGhU/I9QEm1CwjpkOJXpVOZUtxyoqV28lhJb3bZA1UwNQoBfZWLVuzezw6zmxWw+1XsXOZpW3xs/c7soVjpgcdl3/J8hqVqthnK5jNFohOvra/j9fgGJSardRJuQdp0xm4UShRuI1G0DvIdOCCWzOkFG0Fn94WSbAdiqgwNgzg7kfhmz3XUqGZ+rFmln3Wwe0LOupIVVSWXkVaSy4912agcW4Uvq75ubGzQaDbx58wbfffcdZrP3hwqmUin8zd/8DbLZrExSMplEIpHAbDaTvbhkVE5+OBxGJpORiez3+zg5ORGIA3hfEL1arUp6WL/fR61Wg6ZpSCQSc04QmVZlLEpN9osMTGZXy8NxTzFrag8GA3S7XTmJQB0X9XgLVUL++OOP+Nd//VeMx2MUCgXEYjHJf2RtGnXcVajJag6cSjEzybhI2pk9X128dulOdtsxBYu2IaXbaDRCq9VCr9f7YLfccDiUpE++EB0AYL5YEDNser2eeKN8BreWkslUu49tLErL4g+lMCUs+zObzUS9k4EZBlRVNiEbqmu2Twlfq9Xw/PlzcegIYg8GAzlxFJhXj6rE9oLuCpw20spMaFc90ylgaTTgPSbHkho7OzsoFouCLQ4GA9msTmyOeBrPQqbXyRQvXdflkGueI6wyt3qYjpWqVYFnAFK9ldgdk2LpWJAJ6TiRUfkcloOrVCoSvmPlrcFggBcvXuDFixf4n//5Hyl13O12EQgEcHp6imAwiJcvX1qO7yLGccJQy661y+huHKeVHJNlZGYjUiLQ46vX67i8vEQ+nxd7ihPEQ6wppdrttjgvjUZDog7qKZ2scchcQTJXIPD+TGOVaVRpqjIhALHFeFwECxX5fD6BZlQppyZBsA0yM712vrOu67LYXr9+ja+//hrv3r0Tm5Zlg1mpVk32sBpjrz1ls/YWqXc791uR6ywat6Kb7dCZePHiBdrtthyA02q10Gg0EAgEUCqVMJ1OcXNzI6c1caccox3MydvZ2YGmvc/MVvMHidWpsWKGChmJUJlQ7R/jwCx8znbV/caUdgAE+6OKVQ9/rFQq6HQ6+I//+A9JsNA0DS9evMCPP/4oUp7q3ugIkeyO+apq2sres0NOn7vSvmMjI9pZKarkIRNeXFxgd3cXu7u7EmHJZrNSqJJnh5RKJRSLRWiaJjbXaDRCKBTC9va2hOxUSIPqkXmAlG6MV1NFq/2mraaqVDKaul+aTE1Gpjomg7K4EWtrX15e4ne/+x0uLi6wtbWFVCqFm5sbKV3CkwOsUAbj2HtBTtS58Zlm8+2mX44iJstWgpUXZtZ5Xst6gLVaDZeXl1KjmuE9HttF7I8pUIR3WGdQ13WJnKieqxrSU5NVqZYBzMWO6Uzwetp3xoO+1TGhRCTTMYWfcenBYIDvv/9eNi5RMnKjP2PkTnA7r8hrR2SZnWpGtplwVcTeKEkJTdDmOjs7QzKZxOPHj3F8fCzqr9/v4+LiAtVqFcFgEKVSSSTY+fk5Tk5O4PP50Gw2pVASzxCZzWZyMDZtKxWK6XQ60DQN6XRakh6M78zMG25IIrHcG3CblMswIrehMurRbrfx+9//Hufn52i323JQkLogzMhMwlhpGzswjHEenJDd+5ZBR2a0VojGuLLVgVRVn67raLfbuLm5QS6X+wCQVmtZUy3S9gLmMUweA6FuduJzaOex6gEjH0zz4nVqKpcxFMcs6X6/LzVjuFGd55FQXfd6PamLTSiKQDYXiXFsVHLCcPdBVv1am024qBOrGLG89uzsDNVqFX7/+6KTZLhYLIatrS2p7RyLxdBsNtFqtdBqtUQ1UwUaT3Nn4XQ6CoPBANVqFcPhEMViEcFgUA60IYNFIhFsbW3J81XskTZrtVrFjz/+KFsymXDAA7aj0ShOTk7wX//1Xzg5ORH1q46NlY21bKydkhcq3OlzrfZim5HnmdWLyMis6t/D4RC6/v48j3q9LvWkR6ORHL/AM4NVkJaRDzURgd+p4ToymNlvDrBxgxNwi2+qERMC7zy1vdvtziVINJtNRCIRlMtlvH37FtfX13M1qL226/7SyVVpOCMzLVMX6vVmUoBOwnQ6xQ8//IB2u42trS2cn58jn8/jr//6r+WoCQBzh2lTrZHpCAYTTplMJqhWq3OqmH2o1WpoNpt49OiRHPtFxlajLuPxGJ1OR+K6PPeEEA+lLCNCf/jDH/DmzRv5n8yqxrHVRWJnXJ1gdEZah0dt1pdFPLGI1nKEhBPiPZQ0tVpNsLVHjx5hOp0il8the3tbmIIxWJURmfRKO5LRFEY6yIS8jjAPmVLd0sl3oh1JWIdgOVU32+HeGDorJycn+Prrrz/Y2G6Etu5aIt7HM+2QJ2VArFaEFeRg5sJT5alq66uvvhLvN5/PyznDGxsb2NjYQC6Xw87ODgBI+pOu6+K0tFotgXw6nQ6urq4AvJekZA4yWqVSkb6oCRKker2Oi4uLObvy5cuXc8ea8aDG6XSKYrEomeVGp8zOmFrRKgve7D43TGmFW7pdZK6Y0Ekc0S52qO774E+/3xd7MJVKCcb26aef4tNPP8Xx8TGOj4/nEhmYc0h7L51OI51Oo9vtotlsSnWwWCwmgDRzDtk3lipWd/yxwj+Z9ubmBn/6058wnU6lZg5j2NPpFJlMBq1WSzJgVnHcFoHCd+0p2wG2nWKFrvcdW0Euy+5bRGqbBIp7vR7evn0riQpMKLi+vsbJyQl+/PFHZLNZPH36VPYqsybNYDAQ8DqRSGBzc1OeRYllBLC5x0ONRzPEyHPq0uk0AoGA2IY3NzcAIAxISczw4DKg32psFoH8iz5zQk7nzaqvq0RNPEvvN4pjdsxOqEcl1VPlZFIlc5Jfv349hyMeHh7iX/7lX7C9vY2dnR3ZStput5HL5WQ76ePHjzEajdBsNsVRUPvM7GxN0+QQRjXU1263kUgkkMvlEAqFUKlU0Gg0xDu2C+QvGp9l1y/63orM5sUuOe2jG3LtmCwCV1dp0yjWmYDAWKwRfJ5MJmg2m/j2229xcXGB8/NzRKNRYY63b9/i7OxMtk8Ct04QmZDPJNA9Ho9lEzpPZGIyK+EZVmQwnrzk5h2dRp68sgdXbc+q7bVLQiduudl3Kjxhpx3abOrnal9msxnevXuH3/72twgEAiL5mHrF4yKOjo7wT//0T8hkMtjc3JQaMGpkhUzYaDTw3XffIRgM4h/+4R9QLBYlfYweOU+C554UI4TlVNqYjcMiuMYLxlnkLC2yYZfZt077dqfnmADOVolZlMDMG9N1fa7wJZMGaMd1Oh0kEgm8efNGNpgzMZUAM3C704/JFH6/H6enp2i32xK/pgNUqVRM9xmvQnbsrXU+674gHE23+YZq0UjAfMUug3HcRlbMmNH4Ha9nnh6lEbE6FkznpvVAIICtrS1JfuX5I51OB61WCycnJ3NbR5vN5lyaPWPHZkC8GdmxEc3+t8uAdq5zKuW8YH47i/TOJeEisovsmw2a6lWr31Hl9no9dLtdAbpZ1SuVSgkj0vnodDqoVCpyoDadFoLlTJww9sFrsrPonJAXNrtVv1Zq164kVBNF1Q4Z/wZW7+Qi+3DRtYv6RcmoabfnjHDTvcrAjHywRrQxvEZQ3ezZi/q56Hq736ttO3n2Kk6D3Weo7as2sR1ecC0J12U/LMLGFnnkTiQEpSOBZILbqsNkbE9t1+iEmPXJLkhv/H7RO6zLNrRrRqwLGP+o1DHg3LYx88yMnxsH0lg1VYV97PbLCVBv1o9FZMWMi+xwJ/aoE8ZSx/PemdCu8e3EkDZT427VjFkfzRiSUtCqnWVGuhnQbAe+WhctGnM3WsKMls3pqu/p6TkmTj05M8/SjAnstGuUEnaY1ux6qwFddI0dW9UOuUEZvLK/l9mN61xgnpaGczMgVhCHW0DW6Egs6xuvNQPQ+b3aTzO70U0fF/XLakGZSXgnfbBjW9vps9fkWezYajDcSNBVXtjKRrR7/V3Qupw6q2dZfXYfwLQZrbT5nWRH6tiBLbyCD8z6Y3cxuF1sTsmujWumHZZJeruSepEUvsvF6al3fB9SRX32OqANJwvKjNyaFf8/kWOb0OkE8B4nUs6JejbaTMtWsl3p4JWqWsd4LfreSb/tOCCL7rOj3eyQ7X15RpXmZnDtPsfOZ07u/0sjp3iik3s+RnK92w5Yrmrcerdu7vGqH6tIEjv2svFes4Vtx6xwIumtvGG3C9autLTbvmMmVItWOn2YV3iaE/JKZdi532xc7EAfTj16p+TE4XJrBpiR3Wsdlwt2slrNwkVmkQcnHbZDi6IeXtp6iySh0Ya281w3WN0i+9nq2Wqkx8v+uSVXBZHMQmF2aV1MYEciOwmzucE37dzrRBs4hUvcMLGd6M8yWtUOX0sZELuT4FZKmXnqZotjWb+sVOaqkI5V9GXRMxeRHVW67L3tfOaGlmGbdmilIyTUAbeCF4zhpUVOjdXALANirVa4Ue3YkUJ2gHdjfxf1yYmjYPbMZVJbBa+9dhKt2ljUJ37nZNuD/dJJC8hrW2GRJDGjZROwTvpLhoQ+lr57mkXzQA/khjyRhA/0QKvQAxM+0L3TAxM+0L3TAxM+0L3TAxM+0L3TAxM+0L3TAxM+0L3TAxM+0L3TAxM+0L3T/wLcOyOyCATwGgAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:150/200: training loss 0.017830\n", + "epoch:160/200: training loss 0.017770\n", + "epoch:170/200: training loss 0.020470\n", + "validation loss: 0.017254\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:180/200: training loss 0.018951\n", + "epoch:190/200: training loss 0.021716\n", + "train completed, total time: 466.27585434913635.\n" + ] + } + ], + "source": [ + "max_epochs = 200\n", + "val_interval = 30\n", + "epoch_loss_list = []\n", + "val_epoch_loss_list = []\n", + "print_every = 10\n", + "\n", + "scaler = GradScaler()\n", + "total_start = time.time()\n", + "for epoch in range(max_epochs):\n", + " model.train()\n", + " epoch_loss = 0\n", + " for batch in train_loader:\n", + " images = batch[\"image\"].to(device)\n", + " optimizer.zero_grad(set_to_none=True)\n", + "\n", + " with autocast(enabled=False):\n", + " # Generate random noise\n", + " noise = torch.randn_like(images).to(device)\n", + "\n", + " # Create timesteps\n", + " timesteps = torch.randint(\n", + " 0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n", + " ).long()\n", + "\n", + " # Get model prediction\n", + " noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n", + "\n", + " loss = F.mse_loss(noise_pred.float(), noise.float())\n", + "\n", + " scaler.scale(loss).backward()\n", + " scaler.step(optimizer)\n", + " scaler.update()\n", + "\n", + " epoch_loss += loss.item()\n", + "\n", + " if epoch % print_every == 0:\n", + " msg = f\"epoch:{epoch:d}/{max_epochs:d}: training loss {np.mean(epoch_loss / len(train_loader)):4f}\"\n", + " print(msg)\n", + " \n", + " epoch_loss_list.append(epoch_loss / len(train_loader))\n", + "\n", + " if (epoch + 1) % val_interval == 0:\n", + " model.eval()\n", + " val_epoch_loss = 0\n", + " for batch in val_loader:\n", + " images = batch[\"image\"].to(device)\n", + " with torch.no_grad():\n", + " with autocast(enabled=False):\n", + " noise = torch.randn_like(images).to(device)\n", + " timesteps = torch.randint(\n", + " 0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n", + " ).long()\n", + " noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n", + " val_loss = F.mse_loss(noise_pred.float(), noise.float())\n", + "\n", + " val_epoch_loss += val_loss.item()\n", + " val_epoch_loss_list.append(val_epoch_loss / len(val_loader))\n", + " msg = f\"validation loss: {val_epoch_loss_list[-1]:4f}\"\n", + " print(msg)\n", + "\n", + " # Sampling image during training\n", + " noise = torch.randn((1, 1, 64, 64))\n", + " noise = noise.to(device)\n", + " scheduler.set_timesteps(num_inference_steps=1000)\n", + " with autocast(enabled=True):\n", + " image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler, verbose = False)\n", + "\n", + " plt.figure(figsize=(2, 2))\n", + " plt.imshow(image[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "\n", + "total_time = time.time() - total_start\n", + "print(f\"train completed, total time: {total_time}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "fd2b79a4", + "metadata": {}, + "source": [ + "## Train the ControlNet" + ] + }, + { + "cell_type": "markdown", + "id": "73524090-2924-4967-8774-45e795f45bb4", + "metadata": {}, + "source": [ + "### Set up models" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "06181aa6-1c4b-415d-9973-df6f44693935", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Create control net\n", + "controlnet = ControlNet(\n", + " spatial_dims=2,\n", + " in_channels=1,\n", + " channels=(128, 256, 256),\n", + " attention_levels=(False, True, True),\n", + " num_res_blocks=1,\n", + " num_head_channels=256,\n", + " conditioning_embedding_num_channels=(16,),\n", + ")\n", + "# Copy weights from the DM to the controlnet\n", + "controlnet.load_state_dict(model.state_dict(), strict=False)\n", + "controlnet = controlnet.to(device)\n", + "# Now, we freeze the parameters of the diffusion model.\n", + "for p in model.parameters():\n", + " p.requires_grad = False\n", + "optimizer = torch.optim.Adam(params=controlnet.parameters(), lr=2.5e-5)\n", + "controlnet_inferer = ControlNetDiffusionInferer(scheduler)" + ] + }, + { + "cell_type": "markdown", + "id": "94d2e5e7-8633-4d1d-a323-7e74c963641c", + "metadata": { + "tags": [] + }, + "source": [ + "### Run ControlNet training" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "78053aaf-2009-405b-904e-0e5d301018eb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_323202/2002720117.py:6: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", + " scaler = GradScaler()\n", + "/tmp/ipykernel_323202/2002720117.py:17: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with autocast(enabled=True):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:0/150: training loss 0.015892\n", + "epoch:10/150: training loss 0.019003\n", + "epoch:20/150: training loss 0.023880\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_323202/2002720117.py:53: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with autocast(enabled=True):\n", + "/tmp/ipykernel_323202/2002720117.py:74: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with autocast(enabled=True):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "validation loss: 0.012547\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:30/150: training loss 0.016548\n", + "epoch:40/150: training loss 0.014306\n", + "validation loss: 0.014935\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:50/150: training loss 0.016530\n", + "epoch:60/150: training loss 0.019910\n", + "epoch:70/150: training loss 0.015304\n", + "validation loss: 0.024789\n" + ] + }, + { + "data": { + "image/png": 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1gTGGSCSS8JlCocC6detw4YUXYvny5YN8uxOZd955J6W1S/NC5Pojy1Jc1+VyDRKgXCL+5hljWLNmDTQaDU4++eSU66tUKlxwwQV4/vnnU76N9/b25m2sQ6FSqaBQKBCLxfiyvXv34qWXXkpYb9myZQCAhx9+OGF5cvZqMZ5jRhb35MmT8ec//xkXX3wxZsyYkZA5+cEHH+DZZ5/l8ZhHHnkkli9fjnXr1sHpdGLJkiX45JNP8MQTT+Dcc8/FSSedlPFgb775Zjz55JM4/fTT8ZOf/AQmkwnr1q1DU1MTNmzYMOLYbTYbfv/738NiscBkMuHYY48d5N8CDvlfTzrpJPzyl7/E3r17ceSRR+Ktt97CX//6V9xwww0JE5GF4Oabb8ZTTz2FU089Fddddx1MJhP+8Ic/oLGxEQMDAxm9TWTDWWedhVWrVuGKK67Acccdh40bN+Lpp58e5A897bTTUF1djeOPPx5VVVXYsmUL1qxZgzPPPDPlpLZSqcRTTz2Fc889FxdddBFee+01LF26NK/nUgxcd9118Pv9OO+88zB9+nR+//zP//wPmpubud/0tNNOg1arxdlnn41rrrkGXq8XjzzyCCorK7lVnkv0ej3eeOMNLF++HMceeyxef/11vPrqq7j11lsHzY+I3HXXXXjnnXdw7LHH4uqrr0ZbWxsGBgbw+eef4+2338bAwEDOxzocZ555Ju677z6cfvrpuPTSS9HT04OHHnoIU6ZMSdCJY445BhdccAHuv/9+9Pf3Y8GCBfjnP//J3zLE+6rYzjGjOG5i+/bt7Oqrr2bNzc1Mq9Uyi8XCjj/+ePbggw+yYDDI14tEIuz2229nkyZNYhqNhjU0NLBbbrklYR3GDoUDnXnmmYOOkxzSxxhjGzZsYEuWLGF6vZ7V1dWxO+64gz366KMjhgMyxthf//pX1tbWxtRqdUJoYHI4IGOMeTwe9tOf/pTV1tYyjUbDpk6dyu69996E8CTGDoVGXXvttYPGPlSIU3I4YLrn/cUXX7BFixYxnU7H6uvr2X/8x3+w//qv/2IAWFdX16B9iCxfvpyZTKaUx0kVvpc8rmAwyG666SZWU1PDDAYDO/7449mHH344aJxr165lixcvZmVlZUyn07HJkyezn//858zlcvF1kuO4GTsUurVkyRJmNpvZRx99NOy5TARef/119v3vf59Nnz6dmc1mptVq2ZQpU9h1113Huru7E9Z9+eWX2RFHHMH0ej1rbm5md999N/vjH/+Y9m8p1e9zz549DAC79957+TL6jezatYuddtppzGg0sqqqKnbbbbcNCgVFUqgcY4x1d3eza6+9ljU0NDCNRsOqq6vZySefzNatWzfi9RjqXvn0008T1kv12xHHLvLoo4+yqVOnMp1Ox6ZPn84ee+wxvr2Iz+dj1157LSstLWVms5mde+65bNu2bQwAu+uuu3J2jrlGwdg4mP2TpOSGG27A2rVr4fV6ZTq9ZFSsWLECzz33HLxe71gPZcz58ssvcdRRR+Gpp57KeahxrpCVXcYJyVUX+/v78eSTT+KEE06Qoi2RZEmqaqb3338/lEolFi9ePAYjSo+MqwNKxoaFCxfixBNPxIwZM9Dd3Y1HH30Ubrcbv/rVr8Z6aBLJuOWee+7Bv/71L5x00klQq9V4/fXX8frrr+MHP/jBoHjxYkIK9zjhW9/6Fp577jmsW7cOCoUCRx99NB599NGitgokkmLnuOOOw//+7//ijjvugNfrRWNjI1auXIlf/vKXYz20YZE+bolEIhlnSB+3RCKRjDOkcEskEsk4Qwq3RCKRjDPSnpzMd3aeREIU67SLeA+I/1+o8dIxhzteOuuMdIxivf5DMdpzLkZGOhcZVSKRZEG+RGK0wjmabcejaAPjT7BzcZ2lq0QiSROlUgmlUjni2yf1NM0U2ma4bYtVpMbijTzVMce7ZyDd3460uCWSNCHRTEc882E151u0x8rSz+Uxi/XBli7pjl8Kt0SSJuneVNmKx3gXnWKkGP3fuRiLdJVIJBLJOEMKt0QiGZGRfK+F9i2n6wtmjBWVtZ0rpKtEIpkAFMolkOo4mQr6UGPMNNqiWNwgYxGNI4VbIpkAFFI4ko/FGBtSvDMZVy7OYSxEdCweHFK4JZLDgGRBSxbakcRnrK3aZNKN8BnuDSF5WTrnmMsHw2jeGKRwSySHCaJQkFiM57jnfIdlFjNSuCWSPFLs2Yi5GluxnGO2Yp7vUM9c70tGlUgkGZJuREMxWbPFIqyS3CAtbokkTTIV4nT8r4UQ1GJ6gEiGJ93vSgq3RJIhuYp+yGZyKpPwumwpljC7w41MatxI4ZZI0iTXQpZNcshoJxWlGE8MpHBLJDkiHddHrqzZbLYfiwm4bMi01vlEekMYLiZeRE5OSiQSSRGRzgNIWtwSSRaksq6L1eJLJ8V8LDr6DEU6HX7E9cZ6vLkik/OQFrdEkiGjjdIoRJTHcBNd4mdD/b+4bjGQ7diybWqRzj4zHU8uxyGFWyLJkLHwT2dLOuIyVhbrUAKYimKq8pdto4tcjl+6SiSSLJgozRJEd0O+reuxsN7zdb3z2bA5HaRwSySHCdkIZyHKxI7mIZiLqoTjESncEskEJJt6HIXuaZmLzNFU+xT/m2qdfJPN8TK9FtLHLZFMAHIxCZcvP/JYW8WZ+NLzcex018lkfNLilkgmAMXsGsjH2EaaICyW8MZ0qxVmanFL4ZZIckymTQoOZ5KFNlfulLG85tlMTmY6XukqkUgOA4olHnu0FFNY4FgiLW6JJMcUm7DkS7RzGfZG7oKJ8LZSiNBKaXFLJKNkLK3ZbLP1Mp2sG83kXqbbpbv+RHmLyAZpcUskoyRbqzAXFmu2IX7ZlJPNJEJC3H+2pWvF/WW7r7Ei2xrr6W4nLW6JJI+kEruxDE8bDamKaokuDvF8RnNuqcSrGOKzR8NI33em10ta3BJJnhhKtInxJDxDMZxYKxQKxOPxQZ9n0tA3eZvxes1yPW4p3BLJKCBLShSoVOtkWgI2F26UfNfMUCqVg/ZPywgxCkSlUkGhUCAWiw2Z8TheSuXmkmwiZaRwjwKFQgGDwQCNRpPT/cZiMQQCAcRisZzuV3L4UMiCUSTWWq0WOp2OP6gYYwiFQgiFQlCr1TAYDFCpVIjH44MsaFoWjUYRDofzOvaJgBTuUWC1WnHppZdi7ty5Od3v/v378fjjj2Pv3r053a8k9wxnLYkTemPVXiuf1fEUCgWUSiVMJhN0Oh3a2tqwcOFCqFQq+Hw+RCIR7NmzB3v37oXdbsfs2bNhNpuh1+uh1WoRjUYRDAYRDofhdDrh9/uxc+dObNq0KcFoiUajeTmH8YwU7lFgNBpx8skn44ILLsjpfr/88ku88sorUrjHGUNFVAwXkicymkiMsaqzTW+dRqMRra2tOOOMM6BWq+FwOBAIBGCxWBCLxVBXV4clS5agrKwMJSUlMBgMCIfD8Hg8CIVC2L9/P1wuF8LhMLZt25ZwjHg8PqwrKlfnARSmQFQukMJdhNjtdixbtgwtLS3YsGEDtm7dOtZDkqRBph3Y852okYtQuqFEiXz7Wq0Wzc3NqKmpQWlpKQYGBqDVaqFUKmE0GtHQ0IBYLAa73Q69Xg8A8Pv9CIVCiMViCAaDiEQiMBgMUCgUaG5uxpw5cxAMBrmoOxwOuFyu7C5Cmow3X7oU7iKkrq4ON910E3w+H+6++25s37497xaHZGzIVkyH25YxBqVSOSgELV2XA22XzgSqXq/H3LlzcfTRRyMSiWD//v3Q6/Vobm6G2WxGSUkJZs+ejXg8jmg0ing8DpfLhVAolHAsq9WKsrIyaLVa1NfXw+12Y/v27XC73di2bRu8Xm+Cb7yYSH4AF2KM41K4lUol7HY7zGZzWuszxuB0OuF2u/M8stygVqtht9thNBpRV1eHxsZG+P1+DAwMSH/fOCXXN7NCoYBOp4NarYZCoYBKpQIALtjiejR5GA6HEQ6HEYvFuHAOte+RHg5qtRpGoxFmsxlq9TcyIsZ2K5VKPr5oNMqjScj1kSr+W6/Xw2q1gjEGrVYLlUoFvV4Pi8WCSCSCYDCYsYCPhSsj34xL4TYYDLj88stx6qmnprV+OBzGE088gZdeemlcfYEajQbnnXce5syZg88++wwPPfQQurq6xnpYkhEYjX9anMwkkUu1f5VKhWnTpqGhoQFqtZqLnNVqhVarRTgcRigUglKphF6vh1KphMvlgtfrRVdXF7766isEAoEE61p09SQvS6a0tBRz5syB0WhEf38/PvjgA9TU1KClpQVGo5GLeSQSQSwWQywW4/+vUqmg0+mgUqmg1WoRj8fh8XgQDoehVCphNpsRDAbhdrsxMDCAmpoaTJo0CQ6HA7t27YLf74fP50tpxGQTepkLpI97BMjSOOKII3DGGWektU0wGMT69euhVCoTboTRxsjmO/tNqVSitbUVra2tUCqVePzxxyek9TBeyPe1F0V7qOQdOr5SqURZWRkaGxt5GJ5Go0F5eTkMBgMCgQC8Xi9UKhVMJhOUSiUcDgccDgcUCgW2bNmCYDCYlnWdCr1ej9raWuj1evT19aGvrw8WiwU6nQ46nY5b+STa4j96Q6AHTiwWQzgcht/vh8FggF6vh1qt5hZ2RUUFqqurodPp0NXVhXg8jmAwOEi4x1sm6mgYV8JdX1+PU089FbW1tZg5c2ba26nVaixatCghxCgQCODdd9/NauJv8uTJWLp0KWprazFlypSMt8+GpqYmrFixAh0dHXjnnXfkhOUYUIjWXiNNBup0OlitVhgMBlRUVKCsrAw6nY67LEwmEzQaDRc3pVIJpVLJBZzE7YQTToDH48HOnTvR3d2d4G4hS3u42tjRaBT9/f2wWq2YPHkySkpKUFJSwi1t2l6lUnH3TTQa5S4UMqICgQBCoRD6+/vhdDqh0+mg1+vhcrn424LP50NHRwfC4TCqq6ths9kQjUYHuXvGwqAZqzDPcSXcLS0t+OlPf4qpU6dmlPSiVqtxxhlnJLhW+vr64HA4shLAGTNm4Be/+AXq6uqg1Woz3j4bpk6dip///Ofo7e2F0+mUwj1BId8vQWJJwmo0GlFbWwur1YqamhpUVFTAaDTCbrdzkQQOCavX6+WirVarYTabYTabUV5ejkmTJsHr9eKll15CT08PF1TGGCKRyKBCT8mTnZFIBF1dXYjFYjjllFNw1FFHweVyob+/P8HNIwp3JBLhDx+NRgO/3w+32w2fz4euri709vZyKzwUCkGhUMBkMsHj8aCrqwtWqxUNDQ0ADt2//f39BfxmEr8P8W9itOKdyRtD0Qu3QqFAdXU1qqqq0NraipKSEh5WlAkajSZB7C0WC1paWjBnzhwMDAzg4MGDaWcqko8um3Fki1KphE6n49lnksKTL1fJSEIgWqkajQZWqxVWqxVGoxE6nQ5arRZqtTpBuBljCAaDUKvV3O8tCq9CoYBGo4HJZILBYEAsFkM0Gh32/MTxUJaj6MOm8ED6nCYjlUol/1z8TJxkpAlLij6JRCJ8MlWtVnM3TDQaHXGc+UZ8mOaSTPZZ9MKtUqlw7rnnYsWKFbDZbKiqqsrJfs1mM66++mpccMEFePnll7F69Wp4PJ6c7FsyMRmpLkkuhT15P2q1GjqdDna7HZMnT0ZpaSlqa2tRWloKjUbD/coajQYKhQKhUAgHDx6ExWJBQ0MDbDYbrxWi0WhgNBrh8/nQ3NyMnp4eOJ1OdHd3Ix6PJ4g/jYOMGnqAUGSK3+9Hf38/urq6oNfrUVZWhng8zmO16ZqR5a9UKhEKhbgAi5Z+LBZL+H+HwwG/34+mpibU1dXB4/HgwIED8Pl8CAaDUKlUQ07g5hvRRZLLh0i6+ypa4aYfmF6vR1NTE4455picWppqtRqTJk3CpEmT8PXXX8NkMiEcDiMSiciYacmwFHqCWLwXKFyOLG6KJkkVsx0OhxGNRqHVamEwGPi+tFotjEYjj5+mKA7RkhzJBSBazZFIBKFQCFqtlk820nJ6CKhUKn7/0mQkWdzJ0Sx07Hg8jlgsBq1Wy/3aYkhgcrDBWDBWln/RCndVVRW+853voKWlBQsXLhxUdSyXHHXUUfjlL3+JgwcP4rnnnsPOnTvzdizJ+GUk6yqbMMCh3CQAeBy0Wq3G7NmzMWvWLO7nNRgMMJvNXAzJ6Ojp6YHf7wcAzJo1CyaTCVarFRqNBoFAAMFgEFqtlvuyKysr0dbWhn379sHr9SIUCiEYDHI3hxjFIlqZWq0W5eXlKCsrQ2lpKfexU7w2iXIgEODuDr1eD4VCAY/Hg0AgwN0i0WgUFosFarWavz2EQiHo9XoEg0HY7XZoNBr+9kB1UMiVEggEMrruuWIs3TVFK9zl5eW45JJLsGDBgryH3c2aNQttbW3YuXMnPv74YynckpTk40YdTrzJ0tbpdGhtbcUpp5zCXRUKhYILHYmY3+/Hnj170Nvbi2nTpmHWrFl8XkSpVCISiXDRo/MpLS3FlClTEI/HsX//fvj9fi6+tA5NjCoU35RkVavVsNlsKC0thc1mg9Vq5X5y8kFTdUCfzweVSsX93B6PBz6fj8d2A4fq/lgsFmi1Wuj1esRiMW5l03jMZjNqa2vh9/u5e4ceDulcz1x9h8UQjlt0wt3Q0IDW1lbux8unpS1Cgf/z58+HRqPB7t27sWvXLv4lqVQqTJ06FU1NTTjqqKP4q6dEkgvECUgxCmTatGmw2WxoaWnhAkzuAcpKpO0p5C8cDkOn03GLVpx0J79wOBwGYwwGgwGlpaVobGxEKBSCy+XCjh074HK5EAwGuSjSccjCJ+u3vLwcarWaW9BiNAnFl0ciEX5OlCZPfngamzh/QD7wUCjEJz/j8Ti3sMV/hc4kLgbRBopQuJcsWYJbbrkFNpsNZWVlBT12ZWUlfvrTn8Lr9WLt2rW4//77uUWg0+nwb//2b7j88sthNBphs9kKOjZJcZGpn3uojD7Rr6vRaHjIn9VqRWNjI66++mpMnTo1IeKCan6QVatWq7kvuLq6GiUlJTCZTDxJRYw4ISGl8g92ux11dXWYNm0ali5div7+frz44ovYuXMnent70dPTw33NALjrpq6uDosXL0ZpaSmP6SY/NqWp0zHFNwQaAwB4PJ6EbEqVSoVwOMzH7fF4EoSZrPdAIAC32w2XyzVslmRyFmouRXes4reJohBusaZvTU0NGhsb065DkkvUajWqqqpQXl6O2tpalJeXw+/3w+v1AgBPdKBi8RJJJgwn3vTGZzAYoNPpuG+6rKwM5eXl8Hq93Poll0OqBBnKK9BoNPxzEi1R7CleXJz0NBqNUKlUsNvtsFqt8Pl8/KGQXFqVapXo9Xq43W6Ew2FoNBoeFSLWKhHDcMWoleQJUHpAiO6R5EznSCSCSCTCH16HK0Uh3FarFVdccQXmzp2LqVOnFjQ+OhVKpRKnnHIKqqursW3bNvzhD39AV1cXnnvuOXz11VeYO3currjiCtjt9jEdp2TsyNTSGml9s9mM888/H0ceeSR6e3vR0dEBk8mEvr4+MMZ4HkJyRAYAHm0Rj8eh1+t5bDcVoSL/dDAYhNPp5BYxhRhSHLhSqYTBYEBraytsNhs2bdrEJywdDgePFInFYnC5XDh48CB8Ph93WVgsloQuN2TtWywWBAIB9PT0JIxdnAilayTGhJeUlPBxkx/f5/NxSz2Z5AdBciZqsZSLyMU4ikK4jUYjFi9ejHPPPXeshwLg0IWdNWsWZs2ahY8++gjPPfccDhw4gM8++wyfffYZ/H4/LrroIinckpy9MlN51NNOOw3btm3DF198AQDwer2IRCI8BJB8vaIYiX5ssno1Gg2vcU1CQcKn1+t5arxGo+GiTRZ4XV0dTCYTent7+Zsv+Z/JMg4Gg3A4HAmiS7Hk4roqlQoajQahUAherxd+v5+Ls7g/eiDQsVQqFX8IiEIXDAa5D3yo70P8LpKTWopFvJPJdFwFF261Wo25c+di+vTpfJndbkdjY2Ohh5I17e3tePbZZ1FbW4sFCxagqakp78fs6OjAhx9+iM7OThn1UkSMdLOlinJQqVSor69HeXk5z0AsKSkBY4xbpTabDUqlEiUlJdwiplR0mqAk14LoMiBBI385AG6N63Q6lJaWQqvV8roilLAjprpTJiXFiZOwkxuEMQav14s9e/agpKQElZWVXOCpIqE4qa9SqfjkJXBIfIPBYILLhtYjH7oo2D6fDwMDA3A4HOju7uYJOOl+H/nOds3Ftpnur+DCrdfr8Z3vfAdXXHEFX0bdMsYLmzdvxu23346amhrcc889BRHubdu24c4778TevXt5nK6keEm28ui/5IeeM2cOjjnmGEQiEXg8Hu6T3rt3L5RKJaqqqqBWq7nAer1eXnuERJlcJGRVkuVKLhO1Wg3GGA/xMxqN/EFgNpsHxWbTtjSPQ8WsKA6bPo/H43A4HPjiiy9gtVqxYMEC2O12njEJgIcNkq87FApxcfL5fHA6nQDAI1DsdjtPDKJ2Z+SCcblcOHDgAAYGBrBnzx5utSeTr3DNfO4/Wwom3Hq9HpWVlbDb7aipqRnXboZIJAKXywWtVov29nbs3LkTJSUlKC8vz+mkJWOMWxrt7e28gpqkuEn+DZBgU4ifwWCA1WqFyWRCNBrlIYDkGybLk6zhofYnfibW/BCtcHFSUUw7T96v+DdNJoqWc6rMTK/XC4VCAafTif7+fhgMBp7GnlyDJBQKwel0IhQKwe12w+PxJExgUuy32CmHJiIpLJD+m04IYC5cIsUcgFAw4Z48eTJuvPFGTJkyBS0tLYU6bF5xuVxYu3YtXnjhBZxzzjm45pprchrfHY1G8cILL+Avf/kL+vr60Nvbm7N9S3LDcDc3iR2JZWVlJRYuXIjS0lJUVlbyyUCr1QqVSgWLxcI7oNPviCblKGRO7CoDgCfgkEVLURkUOgcgQaxpHXJpkPWuVCp5RqVGo+EPFLJ66bgA+MRiT08PHA4HvF4vrzpYU1MDvV6PkpISaDQaDAwMYGBggPvEI5EIz6ak9SiqhcIB3W43L/kaiUQwMDAAr9fLU93FaztcmF+ua8eMdQigSN6Fm56qdrsdCxYsQFtbW74PWTDC4TA2bdoEAJg2bRrC4XBOy7yGQiHs2LED//jHPw7r0KdiJZXFm+pzEj1qnltZWcn9xlShjxJWRD8vCSRZ0GKSDlnwokVNIkZ+c6oVQtuJY41Go/xYABL+n6BkHdHlQecUi8Xg9/t51IdKpeJhs+Qj1+v16O3tRXd3N4LBIPr7+7nFTJmQ9JZBKftiWCC5eILBIH+AZFNWYKjvJ53tipW8CrdCocBxxx2HE044AZMmTUJ5eXk+DzemfPHFF1i9ejVPLsgF0WgUH3/8cVE84SWDSS6QNNTnVNWSkmOoLjZFTdBvhqxrEjMSWBJZ6p4uijHwjehGo1Ho9XpuIZNw03Zk2QLg+/f7/WCMJYTgUvid0WhEc3MzXC4XYrEYAoEAT4pJfkgwdqiv6549e6DRaNDb28vT9UtKSmAwGKDRaBAOh9Hd3c2b/8bjcajVang8Hn4dkiNNvF4vfD5fQjTJSPfEaO+ZYr/n8ircSqUSixcvxq233spfhSYqn332Gb788suc73esaw9LUpMq7IyWi9XtFAoFysrK0NraitLSUpSWlsJisaC8vBxWq5ULMLkEQqFQgiVNFirFXIv7J+GmST0K+SORpRoh1N+R3DLRaBSBQID/NxgMwmQycYubYrNNJhNaWlq4q8LtdvNSrnR+APgbwcDAAHp7e/lDQqPRYNq0aTzz02KxIBwOo7+/H36/H8FgkFvp9GYhunRGessUM0+Tv4d8UEz3Yd5dJfTDSX4NGy+YzWbMnj0ber0e+/fvR3d3d8r16HVPcniQzk1M1rHBYIDdbkdJSQnv/kKfAYkdZsR6JZQoQxOWZOGK9bJpe4pKSS53SoKt0Wi4K4biq8V90aSgKJharZYLvdlsRjQa5Q8BOm5yWjmNi9wdwWAQfr8/oU0ZJeGIiJOp+fxOJgrjU00LSEtLC26//XYMDAzg/vvvxzPPPDPWQ5IUMaKYkq+6pqYGbW1tvMWYVqsd1HqP1qfqeORGoexBCgcEvrG0Ceo1SQIei8VgsVj4pCN1eSe/utiB3efzcZ81RXrQOZSUlKC5uRlWqxVutxslJSU8iYceLgC4zzo5miQajcLpdKK9vZ1HYoXDYe56GSr9fahrKl6rwx0p3CNgNBoxdepUeL1e1NTUwGw2J1gWYoUziQRIfIWnokpWq5W7D8TkEtFiFScmaT1qBRYIBBLqjABI8HfTPkmUqf616ONOHp9o5ZOvWlyH3haMRiNMJhMikQgMBgPPyBQTcsRoD3EfVG42HA7zVHUxXT05OSnVslTrpLrmhxNSuNNEr9fj3HPPTQhldLvdePbZZ/Pi25aMb8jiBA6F7NlsNm7xAt+kbpN/WqVSoaKigoslZSJS0gsJGNWijsfjaGpqQkVFBS8EJfq+qVaJWHhKTBenGiH0OSW8hEIhHDhwAB6Ph9fbViqVvLu6yWTClClTEAwG4Xa7EQgE0N7enpDKTuevUByqvU1+d1onuSGySLIApyvIxZooky+kcKeJWq3G4sWLsXjxYr6so6MDn3/+uRRuSUrEUqiUqUj+XYqYYIxx4bbZbLw6IDUTIOEj4Q4Gg+js7EQsFuM1RcSyq8A3nXPI0hZbhtGkpBgTrdFo+Jh8Ph/27t2Lvr4+NDc383GXlZUhFouhvLwc0WgUbrcbBw8e5B3YKWlGrDdCxyQftyioyX76VBwOApwteRVuxhi2b9+Ov/3tb6ioqMCcOXNgsVjyeciCIn1thy/pJnfE43G4XC7s378/oakvIe5D7BNJljb9TZN8VKs6Go2ir69vUHYluUyoibD4G6Ua1snx5+QWoSbClZWV0Gq1sFgsXHDpgUPx1lRvJBQKAThU8ljsFJ/cTzIV2XxGbp7D3e+dV+GOx+N4/fXXsX79esybNw/33HPPhBJuyeEJ+XeTrchUMMZw4MABfPzxx7BYLGhsbITBYODlV8X5EhJlEkDyXQOHmg709/fzMquRSATbt2/ntU1IsI1GI9RqNZqbm7kvmlwVXV1d6Ovrg8Fg4ElA5N5QqVQ8tnz69OncbULHorHRw8PtdsPpdMLv9/PSrZTBSdUDh5v7yXX0yOFmnefdVUKvT/RaOJGg19vq6mr4/X54PJ4Jd46S1Az3PSdbhH6/Hw6HAwASklfEe4MiO2idSCQChULB/dRitAr1bxSjOcTqfWq1Gj6fjxelEhsUpLJUxQlS8rGTtU7HEBNuyEI3mUzcn55cO2Wo6zLStcsHuUx9LxYULM0zyuZVRKlU4vzzz8eFF16IqqoqHH300bBarRnvp1gJBoP48ssvcfDgQbz11lv405/+NGS5SUn6FOtNlu49kCwUZWVlqKioQFNTE7797W+joqKCZyfq9Xre4Zyq8pHIiqF8DocDTqcTPp8P3d3dCT0XySKmYysUClgsFp6xSPVDqFyqGK1Cbw1UQ0ShUPDeltRzMhAIoLu7mzc+iEQisNlsaGxsRCAQwDPPPIPPP/8cwWAQPp8vZRp+ISimWiKjZaRzyHvK+/Tp03H++ecPiludCOj1eixYsAAA0Nvbiz//+c9jPCLJWJEs6qJ4e71eRKNRWK1WXrOEfMXANynrNJlIESlKpTKhQQLFalNmI8Vgi30oxVT23t5eWK1WnlVJSUBi6B+5WChMjxofUKINfU6Fp+g+Li8vx5w5c+D3+/HGG2/w4lVizfBCIl7/TCzs8WqN531y8l//+hceeeQR3lx0PJdzlUiSSccKJ1GjWGaqfidWAhRF0uFwoLOzEwqFAmazGWq1mhdaikQiPKyOsiEpsUecRKQMRaVSyRNfaPKRJiJFi1t0j4hlXamTjt1uh9Fo5M16Y7EYOjo6EIlE0NjYiOOPPx7t7e3YunVrQsQMnX8hvoNsjpNqm/Eg5nmfnPz73/+O9evX49hjj0Vra6sUbsmEIjk6g5aJNz5Z0FTalDrQmEymhKYGNBHZ09ODzz77jBeoomxImkQka9hut8NsNkOv18NqtXJLOxKJwOl0oq+vD/F4HL29vTz1nRopkICTD9toNMJgMCAej8Pn8yESiXDXCrU3o27ulPm4a9cuKBQKzJgxA21tbXj//ffR0dHB3SvJPvVMBXao9UcrrCNVdExnWyKdfeTjIZD3yUnyxVGJxomK3W7H9OnT4XA4+I9XMvFJ56akiUiqk00RG1S6VcykpGJNDoeDW+rkuqBEGkpgEZsmiIkvYq0Rqr5Hgk8ZmWKDA3KTkOsmuRmx2B8S+EY4aWKUeldarVZUVlbC6/Wiq6uLu01Gc21TuaCGWrdYSDXuXCMTcHLEkiVL0NTUhJ07d+Luu+/mdbolE5uh0rFFq5Cs5UAggIGBAahUKvj9/oTU8Xg8jr6+Pp6JuH37dm5hq1QqzJw5EzNmzODuECp3Sq4TKs9KzQe0Wi3sdjv0ej0qKiq4u8NqtSaknw8MDPCSqRTaV1VVxf3iABIShlIVUqOHRkNDA84++2x0d3fjlVde4bVVhrpW6T70kq/tcN9B8neRqaCLxxOrPA53LPGYhXIPFUy4xZAksTLaRKG6uhrV1dWwWq0oKSkZ6+FIihCKg6YGBGJ4XTQaRU9PDzweD3p7e+F0OhMKMU2ePJlPEIrlVEWrloSbJhH1ej2MRiNPVTeZTDxqhUTY7/fzetfUjs9isfBsSuAbV0+y1U3QmwJ18CH3ykhkOolIx0p33dEePxuXTKH84wUT7gMHDmDt2rWora3F6aefjrlz5xbq0BJJQRjpho1EIryLe01NDTQaDUKhELecPR4Pb+tFRg5ZuB6PB06nkyfqULKN2D2HYrgZY7BarbDb7VAoDvWEdDqd3GVC5Vp1Oh36+voQCoVgt9sxefJkXmlQrVbzBBzGGC8bS9uLbhqy8ikyht4SRnOtkskkYWckV8VIx05laWdy7EJQUOFet24dLBYLKisrpXBLxh1ifY1sLL9IJILu7m74fD6YTCZYLBZeNErMThSFm2qKeDweuFwubp3TWyslwVAfShKtkpISlJaWIhQK8TkXsrQrKytRVVXFy76Gw2HYbDbMmTOHJ+9EIhG43W6ewSlWHaTJUjH93ePx8Dj0YnijzlZARzMhOpoQxEwt9YL6uOnHKPsnSsYjo/Vfkl+aMcata4VCgcrKSsRiMbhcLp5IQ4JKBZqqq6t56z8SU7KMk5syiOOlhBqVSsUjSLRaLb8PqeQspd9TQg+JsjgRSohNHcTY8XA4DK/Xy8csyR9yclIiSZNsJrpEIaXKfpRMo9FoUFdXh3nz5kGtVmPv3r1wOBx8wjIajcLj8SAWi2HatGmYMmUK368omiJiIg71nKyurgaAhJBCr9cLhUIBm80Gi8UCo9HIa35T3DdFnADfRLKI3eBpf5QAFAgE0NPTg/7+foRCoZxYlqPdLlOGOkaujp2rqJgxEe5AIACXy8WD+w+3yl6Sw4Nk65exbxpvUG0b6nZOvuVQKMQTa6hTejQa5ROM4qs8NSgQjyF+TuF65NsmgaVsSwAJiT9iyVmKIhH3LSL6uOlcKbyQ/OL5uJ7FFPY3lhRcuEOhEJ5//nls3rwZRx99NL773e/KKAzJuCDdxIrh1iOR7OzshNvtRiwWQ1NTE+8wI5Z91Wg0qKys5FEaTqeTTw4C4KVVxU42JNQ02alUKnmrMXKVaDQamM1mPnEZDoe5Ba9QKHjqPSUHUS0TEmYKbSQfuE6ng06n482ExS71ySS/haQrxMUm2KMJN8yFoVpw4Y5EInj//ffx/vvvY2BgAOedd54Ubsm4Y7hJrOGEiT5zOBzo7++H0WhEd3c3LBZLQi9KihCx2WzQ6XQIhULw+Xx8kpAsZBJKiiahSUox5lpspBCLxRLaj5HlHw6HEQwGoVKpUFpaCqPRCJ1OB6PRyC11ALx0q9/vh8/nA2OMhwFSw4dkyziXLpNiYyQBz9d5Fly41Wo1jjzySEyePBnz58+HwWAo9BDywq5du7Bhwwbs2bMHfX19Yz0cSZ5JJ7Ik1efUsb2lpQW1tbWorq7mok1RJdS6TKvVcjcHCTNZ1eL+KXuRPiPhFmt6i2JO+yHLnuK7aRKTXDdkpdOEZTQahc/ng8/n440dAPCHDcWEe73eYScnC5WkUgiyOYdcnHfBhVun0+Hiiy/G5ZdfzktaTgTWr1+PVatWwel0wuPxjPVwJHkg3fjf5Aw6UWgpoWbRokU4/fTTeeRIKBTiiTc1NTWorq7mqe5kZQPg4izun+qQkO9aTGcnN4tSqeRZlowxDAwM8A70ZWVlCa4W8Rjk76YiV/39/RgYGEgoJQscSpN3OBzo6emB0+lMaAicfE1GupaprNhis9DT+S2ID/dcl5wtuHArFIdqBVdVVRX60HmFZtST03wlhzeimAPfCDllNZKoUuU+aiAsdnonMaW4a9ofxW/TctFNQ5+L/2giMllQ6DN6QKR6m6B1qC4JjYO63QSDQbhcroTqgSLFJLoTARkOKJGkyUhWE4lyspWV7PfWarVwOp3Yt28fKioqMHPmTCiVStTX1/MwPXGiMVlYaeLParXyfavVat4TUixMpVQqeRcdvV6PsrKyhKJUlL1JWZjxeJxncqrV6oQCVYwx3jSYEoXcbjdee+01fP3119x9Qm8R4nXJhKEmNccLhYiSK6hw0w9yopFuJp1kYpBKnJPdAMmiTf/ISibRo+44FJlBFiu528RtKVqD3BBU7Y9cMHTsWCzG90fHS7a4qeQqddEBvun2Tp+TJS/ug8SchFun08HpdGLHjh0J8eXD9ZtMvoaFIt+TpOkIdjoRNensp2DCXVNTg5NPPhm1tbWYNWtWoQ6bV/x+P959913s2LED77///iC/nmRiko6vNtm3TROCZAmXlZWhvr6eR5WQxSyKNE0mxuNxHnEi1gER/agkuBQuaDAYEixygoSf3BkUIijujyZJRaGnlmRUlpb24XA44HA4Eko2p7om4jjoc51OB7PZDADcys+nEZRvS364fWVynHTWLZhwNzY24oYbbkBbW9uEaWPmdrvx1FNP4a9//WtCQSDJxCTdycnkbUThJlGtrq5GS0sLQqEQ9u/fD4VCgbKyMt7ggKxrqvZHTXzFqBISXTHtnCxji8WC0tLShG701DNSbJZA+wiFQggEAgl1T+hY0WgUbrcb4XAYPT09cDgcCIVC8Hg88Hq96O7u5s2NaWz0cEmOYQe+qe9tNBpRU1MDxhi6urrg8XhSNjQeDbmeFCwWCibc5Ccbr+F/8XgcHR0d6O/v5z+Cvr4+9PT0JPjzUmEymdDQ0AC9Xp/xMbu6utDT05P1uCVjDwkaWdqUBUkTj2StkojSPzGkTuyQk2riUEygIbFOFtPkutKiu4dcLOKxaHy0LdVa6evr45ORVM0w1fmKxxInSak2itVqRUVFBQDwLFGKKyefvFKpTHmMbL6DVNduvCInJ9MkFArhv//7v/GXv/yFL4tEIti3b9+I27a2tuKWW25BU1NTxsd85JFH8OSTT06YH9x4ZriMyFSJJwSJYHl5OZYsWYKqqir+W6AJRcYY75pEyTAUOkhuEo1Gw8PwxM42JIx07FgsxuOt1Wo1j88msRYfCqLrhVx9BoMBer2er08hhpFIBHv37sXGjRvh9/t5WCA1LU6OahFdORSRotfrMXfuXEybNo2HA8diMfT29sLn82H37t3YunUrdDodmpqaYDAYsHXrVuzfvz+r74xcVuK4iq0AVjYPFSncaRKLxdDe3o7PPvss7W0oEqCsrAxHHHEEpk2bltExQ6EQXnvtNV6vQvrQxxeiaCiVSuj1elRWVqKmpoZ3mCE3BwkjNUsgcaWbWnSR0GeiRUyIbhSxb6SYcZn8wKGxErQ+jYuSdyKRCLe4A4EA+vv7E/zj4j7FWHDxAaTT6VBeXo76+nruNqJz8fl8cDgc0Ov1MBgM3HVkMpkS9p2t+BZbLPhokMKdJ5RKJZYuXYqlS5eiqamJl+TMBLVajWXLlsFut2Pbtm148cUXMTAwkIfRStIhleCl+kwkOcLCZDLBbDbztmFiBT6xJyWVfCWfNRVuEoWeJgnFTjg0LvJnUxSI2D0nFosl7JdqpBiNRqhUKj4hSsWu/H4/NmzYgL6+Puzbt48n44hRYqLFrdFoUFZWBq1WC4/HA4/Hg9LSUsyePRs2mw21tbUADj0gjEYjP3edTofa2lq4XC4YDAZMmzYNZrMZZrMZkydPhsfjQUdHB4LBIAYGBkZ0Uab6bqRwS4ZFpVLh+OOPx4033giNRpNVbKdKpcLixYuxaNEivPXWW/j73/8uhbuIGSpEUCy4ZDAYeEGpYDCYkJpOZVipwp7o16ZlYgq7KNqUxUgdaKieCFXgpMlJMZuSsW8qDNL8E2VYhsNhnvoeDAaxZcsWHDhwAAMDAwkJNsnhjuTLr6iogMlkQldXFwKBAEpLSzF37lyUl5fzcVNFRLKgdTodKisr4ff7YTQa0dLSAqvVipqaGvj9fnR2duKTTz7hvvV0hZu+i3REO1e+8Hxb91K4R8DlcuHrr79GX19fWn42k8mEGTNmoKKiApMnT075OpsJdDNUVFRg8eLFmDp1Kv+so6MD27ZtSwjFkhQPyRN00WgUXq8XHo+HW8KiK4VEWvx/ivwQMyNFy1qpVPIysXQcihOnCT3RohcTb8iiJ3cK8M0bQiwW4y3Purq6eFYkJfjEYrGUNUdojAaDARaLhVvRVVVVsNvtMJlM3NWj0Wj4Q4AsaCphazQaodfrodPpuFtEr9fzN4V8kSuxzTT8L1ONkMI9Art378aqVauwdevWtKzdmpoa3HzzzZg7dy7sdvuIvffSpa2tDXfeeWeCn/vFF1/EHXfcAZfLlZNjSNInnZhdsVIfpYZ3dXUBONRcurS0lPdpBMBD4SgCi5oLk3Uqpr+Tz1yj0fAwPcYYryYYDAbh8/m4X1mr1fK6I5RJSfugMZBVGgqFEI1G0dHRgYMHD6KnpwcHDhyAw+Hg7hkgMUpFdJvQvE5VVRWmTp2KKVOmpEzFd7vdPCpr9+7dcDgcqK+vx6RJk2A0GlFaWgq9Xg+tVguj0QiPx8PfKERSWck0WZuugI511Emmx5XCPQKhUAgHDhzA3r1701pfq9WipqYGkyZNyuk4DAYDGhoaEpZRrWZJ8SJaUmQZk3uCutEkW7uiuFEUiFjNL1kwgW8KWCWHEopx5MmWOiUEibHb4j4DgQB6e3vhcDgSmhgnC2LymwW5S4xGI080orcNOhc6R/LFBwIBBINB7i5KroJI7iaxccNEJN3zksItkeQYMfZZFBsyAsglEAgEePNs6lBDwkdiLro0KCVdTLgBwBNZyK1CvnBqj0ZWK+URUOs0sXIgRbUwxrh7YsOGDdi8eTPcbjdvkpAKSo0nf7tCoUBNTQ1aWlrQ0NCA6upqLtwUPhgMBhEKhdDf349AIAC1Wg2LxQKPx4OvvvoKFosF06dPh8Vi4e3Qent7uX+bmiRTo+JkizXTqJPxNmkphVsiyRPJlilFkkQiERiNRqjVasRiMVit1oQ6IaLPmeKvKUSPLGqCikjZbLaESBXRRSKmy4tp9yqVitfiJtcKxYQDhzrs7N+/n1vERHKstpjpSYJps9lQWVmJ0tJSlJSU8LcLsUwsdZIPh8M8wsTtdqOzsxMlJSW8V2ZPTw8OHjwIl8uVMJlKD7V0yyinO2GYbsTQSNtPiMnJnp4e/O1vf8PGjRtxzDHHZBzTXOxMmzYNxxxzDJqamiZcyVpJZiRHlxCRSAQOhwORSASNjY0wGo0wGAxcgJxOJ/x+P6xWK+x2OxdsAAlJOOSPjkajcLlcXNRpUpAEiqxfAAkTepTiTsto8tNisSAYDGLnzp1wOp3o6OhI8LsP5aagB46YeUkPDkosonUoXJCyIqmSIE06kgUdjUaxd+9emM1m7q4JhUL8gUPXeKS636m+l0y/x0wphPVeMOHeu3cv7r77bpSUlGDlypUTTriPP/54rFy5EiUlJQkJA5KJSTrWW3K0QDgcRmdnJ/R6PY9pNpvNfCKyq6sLnZ2daGhogNVqTajBLVb7I+u8o6MDDocDZrMZFRUVPKxOr9fD6/XC5XLxMYhp8STcCoUCwWAQGo0GdrsdlZWV6O/vx+eff44tW7Zg7969vNekWFI22Q1B4knXg+qqUL9Kl8vFz4X838ChSUyfzwe/388bRlCCj0ql4i4USiaimuW0H4VCMaT7Jpvvk85hrN0m6fi5CybcZB1Eo1EcPHgQu3fvhslkQnl5+YSYYNPpdCgtLeXVziSSZMhKpk40brcbwCG3Ak36JU9ApoqTJsQMRQoJTS5olTzpSJ/T/qklWjwe52Mj10iqMNNUNU4Ik8kEq9WKqqoqHidOfm8SW4paoSJX5Lah6BaTycR70Iop/UBikSpipPKx442inZwMBoN4+umn8c9//hMnnHACrrvuOpSWlhZ6GBLJqEjXKku2usna3bhxI/r7+9HY2AjgUIyy2WzmHdipcS+5BkS3BmVRWiwWmEwmBAIBdHV1cT8yFWiy2WzcQhctb7GmN9UlCQaD2LdvHwYGBngyT3JkixjdIZ4TPTzmz5+P8847DyaTCSaTiReLAg6JME167t69Gx0dHQgEAgnnr1ar0dLSwv3g1AKN/PoejwednZ284w5VLczme0n1PY01mUTKFFy4Y7EYtm7diq1bt8JiscDv9/MfWDEhFtgZ7ksVrR6JZCjEuh0A0N/fj1AoBI1Gw9vdiT5sCgsU46PpN5ncSCEcDvN62WKDYernSqIr/kZpHOTCIF+zx+PhxxYRRZv+pvOif9XV1Tj22GOhVqtx8OBBBAIBLsJUryQWi8HhcKCzsxNarRZWq5U/oChGm7I1KUGHUvWT31hIwA9HxjSqZPv27fj973+P2tpanHrqqQlZgWMJYwwff/wx3n//fezatWvIru0mkwmnnHIKWltbsWDBAp5IIZGIJD/4yV2gUChw8OBBfPzxxzCbzWhpaeE1tPV6PRQKBc+ATBZS0ZJWq9Uwm80Jrg21Ws33IYYl0vo0OWi1WmEwGHh3eRJ4snRFsU52jYix4RTCSGGH1dXVCZOjsVgMfX19CIVCvFSx1WrlPv6amhpewyUcDkOj0cBms0Gv1/Okn2g0ioqKCvj9/oS3B3ogjHcy8a+PqXBv2bIFO3bsQG1tLerr64tGuOPxON577z3ceeed/JUsFRaLBZdccgnOO++8hApsksObVBOXyX9TcadAIIDu7m5YrVaYTCbYbDbetT0ej8Pj8SSIqEqlgl6v5yIpCncsFuMWKtUoEcu90kNAq9XCZDJBrVbDaDRCq9VyHzcJYTQaTegYP9K5kV9dp9NxXzcAeDweuN1uBINBdHd3w+v1oqOjAx0dHQCA+vp6GAwG1NbWoqKiAi6XC06nE1qtFtFolFvVwWAQCoWC1zKhN2GKiBFFrxjcHsnkOkRwTJWGKpx5vV5s27YNn3zyCaqqqtDQ0DAmrpNQKIQ9e/bA4XBg7969PNZ0KKg+8VdffYXKyko0NjYWnctHUpyIER5UDbCnpwdGozEhZtrn83EhpqgSMcFH9AGLWZeEKMgAeNid6IqhCUAqeBWJRBAKhdK2YskXTnHZsVgMWq0WSqWSR4243W50d3fzaoHhcBgulwsdHR3w+Xyoq6vjxbdEtwj9i8VivHmD3+9PsOaJfBd2Gg25HldRmIhOpxMPP/wwnn76aVx00UW44YYbeLnHQtLT04P//M//xCeffIKenp4Rize5XC78/ve/xzPPPIMLL7wQN95445iMW1JcJPuTh4srJsGNRCL46KOPsHHjRtTX12PatGlcUBUKBRobG2Gz2QCAT0CSC4FinwHwDEsSZzGay2azwWq18tR7cpXo9XrehsztdnOrNxAIpIxJF6NTxGN6PB5s27aNF4jSaDS8oUNnZyfWr1/P652Ew2F0d3dj06ZNKCkpgVar5S4hCvNzu9084YbW37BhA7xeLx+3OIEqRuIUq4DniqIQbgq2B4D58+fD6/XycKZCWrChUAi7du3Chg0b0lo/Go2ivb0d7e3tmDdvXl7HTW8n4msrRRdIxheiGIo1RRwOB9xuN282QAJM1mxy/Q7aD1mlAPg2tA5Z5fQ5NUigUrLJ3eGpdypZ8sm/r1SiSGJLUR60H4rT9vl8cDqd6O3thdPp5OdBywOBAAYGBuByufjkrOgiofGTxe31enlce6rf/+FwTxSFcIt8+OGHWLlyJRobG3HJJZegubl5rIeUFh999BFWrlyJhoYGXHLJJTkvMrVjxw785S9/SZgo3bp1a0Y1iSX5I9nCFq3uVNZ3Kn8sCZTP54PL5eJuDaVSyd0DjDFerpUsWyrQpFAoYLVaYTQaeeME4NBcDNUgoWqFbreb+7OpuiQVvAoEAjwVPZW7RBwzCTZlSPp8Ph66xxjDwMAA+vv70dfXB6fTySsW0vioDdvHH3+M9vZ2GAwGGAwGvo9IJMLLu3q9Xu42cblcvKEDuVaKIXmmUBSdcG/atAmbN2/GzJkzceKJJ44b4aZxt7W1YdGiRTkX7vb2djzxxBPYvXs3X3a4/EiLneQEFyI5ZG64CUsxZZxcHyTcJKYkWj6fD8A3sc8AeAq72GOSKvEZjUYe200uFEqAIRE0m80oKyvjTRgovHCk86YHgdgpnkIAI5EIurq60NXVlVCoSrTQyareuHEjdu7cCZPJBIvFknC9qMEylbilpsV0HVJdz7GgkKVhi064gUMn7vF4sH79+gQLs7y8HLNmzcp5duKBAwewefNm7Nu3D/39/VnvJ59P/IqKCpx00kmYPHkyNm/ejIMHD+blOJLMIes62R+cbZRDKBSC0+nkoqxSqdDV1QWLxZLQLZ0s6EgkwmuURCIRXreakncoTVyso00p5hT37fV60d/fj66uLl5sKhOo1sr27dt5MpEYT04JQ1qtFvX19SgvL+ddegDwhwo1diA/PLlrSPQtFguP8x5t5/dcU8gHh4KlebRCJ5hQ/QSqawAACxcuxF133ZVza/bZZ5/F7bffjv7+fl7MJlva2tqwdu1anHDCCTkc4aGbeWBgAH19fbjtttvw4osv5nT/xcRYW05DIfqO01kvlcWajlVGoX4UIaJUKlFdXY3q6mqYTCbU1tbCYDAk1Dqh+jgUKkfCrdPpUFZWxiNSaCKP3Ci0bNu2bfjwww/R19eHzz77DL29vQnjJZLdJLQPKj1LSTVHHXUUysvLodfrodfr4Xa7sXPnTsRiMSxatAizZs2CwWDgYYNerxfhcJhPjvr9fuzfvx8+nw9dXV3o7e3lETCxWIy3URtqbOmQzgTySNvm67c60n6L0uIGDvn7enp6EpY1NDSgq6sLZrMZFouF1xfOBPpxiL67jo4OHDhwYFSdZKhVU2lpaV4Scaiqmjj5JCksmdyso7mhxSJQJKxerxcDAwO8FyTV/6AJSLoXRL8v+axpslBcX2w+TBOjVIUvW0uWwgipeFUwGOQWPQk6Ywx2u523MaNyFyqVijdaoIcKRZqQy4j2Q6n9ubjW2ZIcOVRoila4U7Fjxw7ccccdqK6uxuWXX44TTzwx431s2bIFjzzyCLcogEPtyUY7yXfCCSfge9/7HiorK9HS0jKqfaVi69at+MMf/oB9+/bhyy+/zPn+JSMzlEAk+68zLeI/1LHoH/V/DAQC0Gg06OrqglqtRnl5OaxWK2w2G2pqaqBQKLjlWlNTw5N4yD9OFnhPTw82btwIj8fD/c9OpxOdnZ08pnyocxYLXdFndL6U9KPT6XgNEUoQMhqNmD9/PvR6Perq6hJ6sYZCIezcuROdnZ0wGAwwGo0J0TC1tbVobm5GMBjk3XjIDSRmdmZzjdNhqAf2WL4Zjivh7u3txeuvvw6bzYZFixZlJdydnZ149dVX025Fli6TJ0/G+eefn7fqgF1dXXj11VcTJiclhSXVjTqS1ZVOTHEqYUj2jwcCAS6olKXo9XpRUlICt9vNrXOadKQ6JVTbhCxwiq2mUg47d+5Ed3d3ynKtQ41VzFQUx0x+d/JBKxQKGI1GRCIRWK1WNDc3cz+3OGEbjUbR29uL/fv3c/eKGONeUVGByspKHunCGOOFt7IRz6GEOFsLOtu48dG4W8aVcBPhcBjvvfdeVt3NN2/enHbHjELg9Xrx/vvvY9++fcOuRy2kJMXFSDddOlZaNv5VckdQdAalflOWZHl5OZ/U27hxI3w+H0+K6e/vx549e+D1evmbZrqiJSa6iGMBvqkAqNFo+Lrkl47H43A6nTzVn1w3arUafr8fW7ZsQUdHB3p7e9HR0cHbvAWDQV7XJBgM8g4/5FbJ5u1muISoVIwkzPm2+FMxLoXb7/fjmWeewQsvvJDxttTrr1hwOp344x//iDfeeGPY9Ypt3JLCkEpQaY6DQuIYY2hvb4dCoeA+5ZqaGsRiMfT39+O1117DgQMHEiw8muMZKlRxKEQrm8ZBbg+tVguDwZDQ3Z7mZqjIlEKhQF9fH48Sof6Te/fuhdPpTAgxpE44YvNgOofkZLR8kguXyISqVTIaiq2kI4VC0ey+QqFAWVkZysrK+DrxeBw9PT0Jk6AdHR3o6emR1rQkY0TrNx6PJ7gxPB4Penp60Nvby2uDpELsHp/N8Un0kx8K5HqhdH7K1qSJUJfLxesUUUlZiuYSMz6TLXwS8EKJdq7I9WRm0YYDjjeoq7XY5HX58uX43ve+x5d5vV48/PDDePXVV/l21OMvObTpcKZYwwFzdQ9k4/ce7tii+NL6FosFJSUlvF3acG6A5Nojw42HSPZtKxSH+l1S93iy/Gtra1FTU4NwOAyn04lwOAyHw8HrfpPlTIk7om9fHNPhUH9EZNyGA443Ojs70dnZyf/WaDRYvHhxwo8xFAph69ateO+998ZqmJIxJpUAZ/oanWo9sUNOPB6Hw+GAw+FIe1/pPkhoXVFIydqnUD6aPAXAsykDgQB6e3u5gHu9Xm6Ri2GJmUwYFrOY53tsUrjzRCwW4wJNP+JgMIivvvpqLIclGWMynZwcSbho+2QBHO2Ykknu8J5qG5qHoaJYFDdOvmqqNRIMBhMs6mwn/opVtIH8j026SvKIOKkCJN5gkqEp1utTyHsguUDVSNZotuFo+bjWlCwzVOo/uWfI4k61zuHEcOGgQyEt7jySbmysRDIU+czSzJdIkuWfbhjd4SbWuXhgSuGWSLIg05svE+s43XXzKXijERdKqhHPQ3yDEI2ZbGLYc3nehfaT58pnL4teSCQFplAul9FkAuaCVH7s5KiRkcYx3GTu4Yy0uCWSLMineyLbDL5MjlXI2tF0HPFYqc5luJDDfI6rkKSK585mDNLilkhGwVBWYTKphCvdbWl98b+jIZULI1dkkjqfzrJM1snkeo4luXhYSOGWSMaAoSbmhhKefE3kZRuKOBz5FM/h9p2O+2WiIF0lEskoGI1QZJJskumxROszuSjUcK6Y0VIod0c2k4rDuYdSfVbMCT7S4pZIioRiFYlMyXXUh2Qw0uKWSEZJLi2zbPczVLLLUPtPNeZsrM3k9XKZtyD69Yc7r3xNtI52f/mcAJYWt0SSJumGphXLJFmmhaOStx2JXE6YZkvyROtQYxmrEMx8/RZkyruk6ChWl8FQzYILHVqXirEYw2j82bl8SxmLMMLhyMV3MdK20uKWSEZJcg2OXFAsVjuRajypQhzT3Vcm64/E4RRNQkgft0SSQ8bK153v9PdcbCf6qfMRsZEquWWiIoVbIkmT8VpiNFvSbbKQvM1wf+e7sNRYfA/JD6FUD5Bcu7Kkq0QiKQBjbQkW0wTieKZYxi8tbokkx2RTXznfZHP8dLdJJ0Sv2P3Q6VrE6aTqpxL3XJ+7tLglkhyT7uTbWFhvmU56Ziu4yZUB0xG3QobsFeJY+TyGFG6JJMekG2kxFhboWFi+6abYj1WlwnzsX/xvPpDCLZGkSSpLLZ0wuWKn0Jb/WBxPoVAktBFMJtPvbCSrXQwRzQdSuCWSDCmWCaqRKLZYcGD8XLt0GavzkZmTkqKjWK3Vw+keSA5xy0U422hqdediHEM1byhUDPxILqNMJrOlxS2RFDEjNVzISx2MpMnVTML5srHyRVdGPhlqgjST8Q637kjnPtx3mOl1kxa3pOiQFnfxka9aKMkPhfE2PwCkZ7UPd/1SWeUj7U/GcUskGVBsBY0KRSEyHXNxjHy7PsTj5Oq42dR7ka4SiSRNDmeLe6wo1ms+1rVkpMUtkWTIRLWyC1kaNhP3QiYU8rvJZLJR/Huoc8/Ezy2FWyIpAIVIgx7qmCP5VUfaR6YCm876mTwkCuX+yCX5fghK4ZZI0iSXN2EhhCidY2TjX830OKmq5+Viv8VCqvPLtJIkRdWoVKq0jimFWyIZBUNZVtmKVSbWZToxwsONMZl8xzPnIgZcPJdCvLGk+/aQTVRJ8rYqlQoajSatscnJSYkkxxTrhFoxUuzXqljHl3Yct0QikUiKA2lxSyQSyThDCrdEIpGMM6RwSyQSyThDCrdEIpGMM6RwSyQSyThDCrdEIpGMM6RwSyQSyThDCrdEIpGMM6RwSyQSyTjj/wOcBSxGSxNSDwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:80/150: training loss 0.019710\n", + "epoch:90/150: training loss 0.015484\n", + "validation loss: 0.017555\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:100/150: training loss 0.014432\n", + "epoch:110/150: training loss 0.012640\n", + "epoch:120/150: training loss 0.012768\n", + "validation loss: 0.011241\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:130/150: training loss 0.011723\n", + "epoch:140/150: training loss 0.022256\n", + "validation loss: 0.012630\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train completed, total time: 464.29540824890137.\n" + ] + } + ], + "source": [ + "max_epochs = 150\n", + "val_interval = 25\n", + "epoch_loss_list = []\n", + "val_epoch_loss_list = []\n", + "\n", + "scaler = GradScaler()\n", + "total_start = time.time()\n", + "for epoch in range(max_epochs):\n", + " controlnet.train()\n", + " epoch_loss = 0\n", + " for batch in train_loader:\n", + " images = batch[\"image\"].to(device)\n", + " masks = batch[\"mask\"].to(device)\n", + "\n", + " optimizer.zero_grad(set_to_none=True)\n", + "\n", + " with autocast(enabled=True):\n", + " # Generate random noise\n", + " noise = torch.randn_like(images).to(device)\n", + "\n", + " # Create timesteps\n", + " timesteps = torch.randint(\n", + " 0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n", + " ).long()\n", + "\n", + " noise_pred = controlnet_inferer(inputs = images, diffusion_model = model, \n", + " controlnet = controlnet, noise = noise, \n", + " timesteps = timesteps,\n", + " cn_cond = masks, )\n", + "\n", + " loss = F.mse_loss(noise_pred.float(), noise.float())\n", + "\n", + " scaler.scale(loss).backward()\n", + " scaler.step(optimizer)\n", + " scaler.update()\n", + "\n", + " epoch_loss += loss.item()\n", + " \n", + " if epoch % print_every == 0:\n", + " msg = f\"epoch:{epoch:d}/{max_epochs:d}: training loss {np.mean(epoch_loss / len(train_loader)):4f}\"\n", + " print(msg)\n", + " \n", + " epoch_loss_list.append(epoch_loss / len(train_loader))\n", + "\n", + " if (epoch + 1) % val_interval == 0:\n", + " controlnet.eval()\n", + " val_epoch_loss = 0\n", + " for batch in val_loader:\n", + " images = batch[\"image\"].to(device)\n", + " masks = batch[\"mask\"].to(device)\n", + "\n", + " with torch.no_grad():\n", + " with autocast(enabled=True):\n", + " noise = torch.randn_like(images).to(device)\n", + " timesteps = torch.randint(\n", + " 0, controlnet_inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n", + " ).long()\n", + "\n", + " noise_pred = controlnet_inferer(inputs = images, diffusion_model = model, \n", + " controlnet = controlnet, noise = noise, \n", + " timesteps = timesteps,\n", + " cn_cond = masks)\n", + " val_loss = F.mse_loss(noise_pred.float(), noise.float())\n", + " val_epoch_loss += val_loss.item()\n", + "\n", + "\n", + " val_epoch_loss_list.append(val_epoch_loss / len(val_loader))\n", + " msg = f\"validation loss: {val_epoch_loss_list[-1]:4f}\"\n", + " print(msg)\n", + " \n", + " # Sampling image during training with controlnet conditioning\n", + "\n", + " with torch.no_grad():\n", + " with autocast(enabled=True):\n", + " noise = torch.randn((1, 1, 64, 64)).to(device)\n", + " sample = controlnet_inferer.sample(\n", + " input_noise = noise,\n", + " diffusion_model = model,\n", + " controlnet = controlnet,\n", + " cn_cond = masks[0, None, ...],\n", + " scheduler = scheduler,\n", + " verbose = False\n", + " )\n", + "\n", + " # Without using an inferer:\n", + "# progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110)\n", + "# progress_bar_sampling.set_description(\"sampling...\")\n", + "# sample = torch.randn((1, 1, 64, 64)).to(device)\n", + "# for t in progress_bar_sampling:\n", + "# with torch.no_grad():\n", + "# with autocast(enabled=True):\n", + "# down_block_res_samples, mid_block_res_sample = controlnet(\n", + "# x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=masks[0, None, ...]\n", + "# )\n", + "# noise_pred = model(\n", + "# sample,\n", + "# timesteps=torch.Tensor((t,)).to(device),\n", + "# down_block_additional_residuals=down_block_res_samples,\n", + "# mid_block_additional_residual=mid_block_res_sample,\n", + "# )\n", + "# sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n", + "\n", + " plt.subplots(1, 2, figsize=(4, 2))\n", + " plt.subplot(1, 2, 1)\n", + " plt.imshow(masks[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.axis(\"off\")\n", + " plt.title(\"Conditioning mask\")\n", + " plt.subplot(1, 2, 2)\n", + " plt.imshow(sample[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.axis(\"off\")\n", + " plt.title(\"Sample image\")\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "\n", + "total_time = time.time() - total_start\n", + "print(f\"train completed, total time: {total_time}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "b005e3bd-54b9-44bc-964d-ca0c9585a139", + "metadata": {}, + "source": [ + "## Sample with ControlNet conditioning\n", + "First we'll provide a few different masks from the validation data as conditioning. The samples should respect the shape of the conditioning mask, but don't need to have the same content as the corresponding validation image." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "262a5129-9445-4ecc-a37a-a97c59386747", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sampling...: 0%| | 0/1000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110, position=0, leave=True)\n", + "progress_bar_sampling.set_description(\"sampling...\")\n", + "num_samples = 8\n", + "sample = torch.randn((num_samples, 1, 64, 64)).to(device)\n", + "\n", + "val_batch = first(val_loader)\n", + "val_images = val_batch[\"image\"].to(device)\n", + "val_masks = val_batch[\"mask\"].to(device)\n", + "for t in progress_bar_sampling:\n", + " with torch.no_grad():\n", + " with autocast(enabled=True):\n", + " down_block_res_samples, mid_block_res_sample = controlnet(\n", + " x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=val_masks[:num_samples, ...]\n", + " )\n", + " noise_pred = model(\n", + " sample,\n", + " timesteps=torch.Tensor((t,)).to(device),\n", + " down_block_additional_residuals=down_block_res_samples,\n", + " mid_block_additional_residual=mid_block_res_sample,\n", + " )\n", + " sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n", + "\n", + "plt.subplots(num_samples, 3, figsize=(6, 8))\n", + "for k in range(num_samples):\n", + " plt.subplot(num_samples, 3, k * 3 + 1)\n", + " plt.imshow(val_masks[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.axis(\"off\")\n", + " if k == 0:\n", + " plt.title(\"Conditioning mask\")\n", + " plt.subplot(num_samples, 3, k * 3 + 2)\n", + " plt.imshow(val_images[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.axis(\"off\")\n", + " if k == 0:\n", + " plt.title(\"Actual val image\")\n", + " plt.subplot(num_samples, 3, k * 3 + 3)\n", + " plt.imshow(sample[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.axis(\"off\")\n", + " if k == 0:\n", + " plt.title(\"Sampled image\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a1ca8274-d85c-4dcc-9c16-08ac2b6ce0fd", + "metadata": {}, + "source": [ + "What happens if we invent some masks? Let's try a circle, and a square" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "393fca6c-2446-4822-8aad-44403761b40e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sampling...: 0%| | 0/1000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xx, yy = np.mgrid[:64, :64]\n", + "circle = ((xx - 32) ** 2 + (yy - 32) ** 2) < 30**2\n", + "\n", + "square = np.zeros((64, 64))\n", + "square[10:50, 10:50] = 1\n", + "\n", + "mask = np.concatenate((circle[None, None, ...], square[None, None, ...]), axis=0)\n", + "mask = torch.from_numpy(mask.astype(np.float32)).to(device)\n", + "\n", + "\n", + "progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110, position=0, leave=True)\n", + "progress_bar_sampling.set_description(\"sampling...\")\n", + "num_samples = 2\n", + "sample = torch.randn((num_samples, 1, 64, 64)).to(device)\n", + "\n", + "for t in progress_bar_sampling:\n", + " with torch.no_grad():\n", + " with autocast(enabled=True):\n", + " down_block_res_samples, mid_block_res_sample = controlnet(\n", + " x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=mask\n", + " )\n", + " noise_pred = model(\n", + " sample,\n", + " timesteps=torch.Tensor((t,)).to(device),\n", + " down_block_additional_residuals=down_block_res_samples,\n", + " mid_block_additional_residual=mid_block_res_sample,\n", + " )\n", + " sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n", + "\n", + "plt.subplots(num_samples, 2, figsize=(4, 4))\n", + "for k in range(num_samples):\n", + " plt.subplot(num_samples, 2, k * 2 + 1)\n", + " plt.imshow(mask[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.axis(\"off\")\n", + " if k == 0:\n", + " plt.title(\"Conditioning mask\")\n", + " plt.subplot(num_samples, 2, k * 2 + 2)\n", + " plt.imshow(sample[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.axis(\"off\")\n", + " if k == 0:\n", + " plt.title(\"Sampled image\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d9707e7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "formats": "py:percent,ipynb" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + }, + "vscode": { + "interpreter": { + "hash": "4f1513a79f82193cb81c96943579af15c6a44d6347609348bde584197ab7b1ab" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f1dbc6c70a3fc1e77669ba4fc6c325bffb0f94e2 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 4 Sep 2024 16:58:29 +0000 Subject: [PATCH 02/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/controlnet/2d_controlnet.ipynb | 81 ++++++++++++----------- 1 file changed, 44 insertions(+), 37 deletions(-) diff --git a/generation/controlnet/2d_controlnet.ipynb b/generation/controlnet/2d_controlnet.ipynb index 1af09fbe8..1366f1091 100644 --- a/generation/controlnet/2d_controlnet.ipynb +++ b/generation/controlnet/2d_controlnet.ipynb @@ -209,7 +209,7 @@ " transforms.Lambdad(keys=[\"image\"], func=lambda x: x[channel, :, :, :]),\n", " transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n", " transforms.EnsureTyped(keys=[\"image\"]),\n", - " #transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n", + " # transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n", " transforms.Spacingd(keys=[\"image\"], pixdim=(3.0, 3.0, 2.0), mode=\"bilinear\"),\n", " transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(64, 64, 44)),\n", " transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n", @@ -637,7 +637,7 @@ " if epoch % print_every == 0:\n", " msg = f\"epoch:{epoch:d}/{max_epochs:d}: training loss {np.mean(epoch_loss / len(train_loader)):4f}\"\n", " print(msg)\n", - " \n", + "\n", " epoch_loss_list.append(epoch_loss / len(train_loader))\n", "\n", " if (epoch + 1) % val_interval == 0:\n", @@ -664,7 +664,7 @@ " noise = noise.to(device)\n", " scheduler.set_timesteps(num_inference_steps=1000)\n", " with autocast(enabled=True):\n", - " image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler, verbose = False)\n", + " image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler, verbose=False)\n", "\n", " plt.figure(figsize=(2, 2))\n", " plt.imshow(image[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", @@ -916,10 +916,14 @@ " 0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n", " ).long()\n", "\n", - " noise_pred = controlnet_inferer(inputs = images, diffusion_model = model, \n", - " controlnet = controlnet, noise = noise, \n", - " timesteps = timesteps,\n", - " cn_cond = masks, )\n", + " noise_pred = controlnet_inferer(\n", + " inputs=images,\n", + " diffusion_model=model,\n", + " controlnet=controlnet,\n", + " noise=noise,\n", + " timesteps=timesteps,\n", + " cn_cond=masks,\n", + " )\n", "\n", " loss = F.mse_loss(noise_pred.float(), noise.float())\n", "\n", @@ -928,11 +932,11 @@ " scaler.update()\n", "\n", " epoch_loss += loss.item()\n", - " \n", + "\n", " if epoch % print_every == 0:\n", " msg = f\"epoch:{epoch:d}/{max_epochs:d}: training loss {np.mean(epoch_loss / len(train_loader)):4f}\"\n", " print(msg)\n", - " \n", + "\n", " epoch_loss_list.append(epoch_loss / len(train_loader))\n", "\n", " if (epoch + 1) % val_interval == 0:\n", @@ -949,49 +953,52 @@ " 0, controlnet_inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n", " ).long()\n", "\n", - " noise_pred = controlnet_inferer(inputs = images, diffusion_model = model, \n", - " controlnet = controlnet, noise = noise, \n", - " timesteps = timesteps,\n", - " cn_cond = masks)\n", + " noise_pred = controlnet_inferer(\n", + " inputs=images,\n", + " diffusion_model=model,\n", + " controlnet=controlnet,\n", + " noise=noise,\n", + " timesteps=timesteps,\n", + " cn_cond=masks,\n", + " )\n", " val_loss = F.mse_loss(noise_pred.float(), noise.float())\n", " val_epoch_loss += val_loss.item()\n", "\n", - "\n", " val_epoch_loss_list.append(val_epoch_loss / len(val_loader))\n", " msg = f\"validation loss: {val_epoch_loss_list[-1]:4f}\"\n", " print(msg)\n", - " \n", + "\n", " # Sampling image during training with controlnet conditioning\n", "\n", " with torch.no_grad():\n", " with autocast(enabled=True):\n", " noise = torch.randn((1, 1, 64, 64)).to(device)\n", " sample = controlnet_inferer.sample(\n", - " input_noise = noise,\n", - " diffusion_model = model,\n", - " controlnet = controlnet,\n", - " cn_cond = masks[0, None, ...],\n", - " scheduler = scheduler,\n", - " verbose = False\n", + " input_noise=noise,\n", + " diffusion_model=model,\n", + " controlnet=controlnet,\n", + " cn_cond=masks[0, None, ...],\n", + " scheduler=scheduler,\n", + " verbose=False,\n", " )\n", "\n", " # Without using an inferer:\n", - "# progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110)\n", - "# progress_bar_sampling.set_description(\"sampling...\")\n", - "# sample = torch.randn((1, 1, 64, 64)).to(device)\n", - "# for t in progress_bar_sampling:\n", - "# with torch.no_grad():\n", - "# with autocast(enabled=True):\n", - "# down_block_res_samples, mid_block_res_sample = controlnet(\n", - "# x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=masks[0, None, ...]\n", - "# )\n", - "# noise_pred = model(\n", - "# sample,\n", - "# timesteps=torch.Tensor((t,)).to(device),\n", - "# down_block_additional_residuals=down_block_res_samples,\n", - "# mid_block_additional_residual=mid_block_res_sample,\n", - "# )\n", - "# sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n", + " # progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110)\n", + " # progress_bar_sampling.set_description(\"sampling...\")\n", + " # sample = torch.randn((1, 1, 64, 64)).to(device)\n", + " # for t in progress_bar_sampling:\n", + " # with torch.no_grad():\n", + " # with autocast(enabled=True):\n", + " # down_block_res_samples, mid_block_res_sample = controlnet(\n", + " # x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=masks[0, None, ...]\n", + " # )\n", + " # noise_pred = model(\n", + " # sample,\n", + " # timesteps=torch.Tensor((t,)).to(device),\n", + " # down_block_additional_residuals=down_block_res_samples,\n", + " # mid_block_additional_residual=mid_block_res_sample,\n", + " # )\n", + " # sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n", "\n", " plt.subplots(1, 2, figsize=(4, 2))\n", " plt.subplot(1, 2, 1)\n", From 5e0059b542a00a6735dfae3de7df3d009df73b59 Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Wed, 4 Sep 2024 18:05:12 +0100 Subject: [PATCH 03/27] Removing blank line at end of file. Signed-off-by: Virginia Fernandez --- generation/controlnet/2d_controlnet.ipynb | 8 -------- 1 file changed, 8 deletions(-) diff --git a/generation/controlnet/2d_controlnet.ipynb b/generation/controlnet/2d_controlnet.ipynb index 1af09fbe8..942d1dd30 100644 --- a/generation/controlnet/2d_controlnet.ipynb +++ b/generation/controlnet/2d_controlnet.ipynb @@ -1168,14 +1168,6 @@ "plt.tight_layout()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8d9707e7", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From bfa92c9e05534e627aaf1fbd4cff3bd3d2183dfa Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Wed, 4 Sep 2024 18:15:09 +0100 Subject: [PATCH 04/27] Removing blank line at end of file. Signed-off-by: Virginia Fernandez --- generation/controlnet/2d_controlnet.ipynb | 22 ++-------------------- 1 file changed, 2 insertions(+), 20 deletions(-) diff --git a/generation/controlnet/2d_controlnet.ipynb b/generation/controlnet/2d_controlnet.ipynb index 1a6a40db9..73d1a042a 100644 --- a/generation/controlnet/2d_controlnet.ipynb +++ b/generation/controlnet/2d_controlnet.ipynb @@ -981,25 +981,6 @@ " scheduler=scheduler,\n", " verbose=False,\n", " )\n", - "\n", - " # Without using an inferer:\n", - " # progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110)\n", - " # progress_bar_sampling.set_description(\"sampling...\")\n", - " # sample = torch.randn((1, 1, 64, 64)).to(device)\n", - " # for t in progress_bar_sampling:\n", - " # with torch.no_grad():\n", - " # with autocast(enabled=True):\n", - " # down_block_res_samples, mid_block_res_sample = controlnet(\n", - " # x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=masks[0, None, ...]\n", - " # )\n", - " # noise_pred = model(\n", - " # sample,\n", - " # timesteps=torch.Tensor((t,)).to(device),\n", - " # down_block_additional_residuals=down_block_res_samples,\n", - " # mid_block_additional_residual=mid_block_res_sample,\n", - " # )\n", - " # sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n", - "\n", " plt.subplots(1, 2, figsize=(4, 2))\n", " plt.subplot(1, 2, 1)\n", " plt.imshow(masks[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", @@ -1065,7 +1046,8 @@ " with torch.no_grad():\n", " with autocast(enabled=True):\n", " down_block_res_samples, mid_block_res_sample = controlnet(\n", - " x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=val_masks[:num_samples, ...]\n", + " x=sample, timesteps=torch.Tensor((t,)).to(device).long(), \n", + " controlnet_cond=val_masks[:num_samples, ...]\n", " )\n", " noise_pred = model(\n", " sample,\n", From 536745a5336636be6a52f5e8fabf574021c4a0ae Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 4 Sep 2024 17:16:30 +0000 Subject: [PATCH 05/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/controlnet/2d_controlnet.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/generation/controlnet/2d_controlnet.ipynb b/generation/controlnet/2d_controlnet.ipynb index 73d1a042a..07e494d01 100644 --- a/generation/controlnet/2d_controlnet.ipynb +++ b/generation/controlnet/2d_controlnet.ipynb @@ -1046,8 +1046,7 @@ " with torch.no_grad():\n", " with autocast(enabled=True):\n", " down_block_res_samples, mid_block_res_sample = controlnet(\n", - " x=sample, timesteps=torch.Tensor((t,)).to(device).long(), \n", - " controlnet_cond=val_masks[:num_samples, ...]\n", + " x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=val_masks[:num_samples, ...]\n", " )\n", " noise_pred = model(\n", " sample,\n", From 906fb0588aeaa4312fbd2a8082fc10838276baca Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Thu, 5 Sep 2024 11:00:49 +0100 Subject: [PATCH 06/27] Fix flake8 issues to ControlNet. Signed-off-by: Virginia Fernandez --- generation/controlnet/2d_controlnet.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/generation/controlnet/2d_controlnet.ipynb b/generation/controlnet/2d_controlnet.ipynb index 73d1a042a..7b73f1bb7 100644 --- a/generation/controlnet/2d_controlnet.ipynb +++ b/generation/controlnet/2d_controlnet.ipynb @@ -1046,7 +1046,7 @@ " with torch.no_grad():\n", " with autocast(enabled=True):\n", " down_block_res_samples, mid_block_res_sample = controlnet(\n", - " x=sample, timesteps=torch.Tensor((t,)).to(device).long(), \n", + " x=sample, timesteps=torch.Tensor((t,)).to(device).long(),\n", " controlnet_cond=val_masks[:num_samples, ...]\n", " )\n", " noise_pred = model(\n", From 4f139d8aa4778c3846caa0ac8ef34d5e61710352 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 5 Sep 2024 10:02:19 +0000 Subject: [PATCH 07/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/controlnet/2d_controlnet.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/generation/controlnet/2d_controlnet.ipynb b/generation/controlnet/2d_controlnet.ipynb index 7b73f1bb7..07e494d01 100644 --- a/generation/controlnet/2d_controlnet.ipynb +++ b/generation/controlnet/2d_controlnet.ipynb @@ -1046,8 +1046,7 @@ " with torch.no_grad():\n", " with autocast(enabled=True):\n", " down_block_res_samples, mid_block_res_sample = controlnet(\n", - " x=sample, timesteps=torch.Tensor((t,)).to(device).long(),\n", - " controlnet_cond=val_masks[:num_samples, ...]\n", + " x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=val_masks[:num_samples, ...]\n", " )\n", " noise_pred = model(\n", " sample,\n", From bd0d7657c933528faf9adfe8beff3847b235361d Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Fri, 6 Sep 2024 11:05:15 +0100 Subject: [PATCH 08/27] Set download to True. Signed-off-by: Virginia Fernandez --- generation/controlnet/2d_controlnet.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/controlnet/2d_controlnet.ipynb b/generation/controlnet/2d_controlnet.ipynb index 07e494d01..f1ea3ec95 100644 --- a/generation/controlnet/2d_controlnet.ipynb +++ b/generation/controlnet/2d_controlnet.ipynb @@ -287,7 +287,7 @@ " section=\"training\",\n", " cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise\n", " num_workers=4,\n", - " download=False,\n", + " download=True,\n", " seed=0,\n", " transform=train_transforms,\n", ")\n", @@ -300,7 +300,7 @@ " section=\"validation\",\n", " cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise\n", " num_workers=4,\n", - " download=False,\n", + " download=True,\n", " seed=0,\n", " transform=train_transforms,\n", ")\n", From ef6c972c08b9604369779976e5e2f62511bb84f0 Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Fri, 6 Sep 2024 13:34:11 +0100 Subject: [PATCH 09/27] Change call to autocast. Signed-off-by: Virginia Fernandez --- generation/controlnet/2d_controlnet.ipynb | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/generation/controlnet/2d_controlnet.ipynb b/generation/controlnet/2d_controlnet.ipynb index f1ea3ec95..5d808c081 100644 --- a/generation/controlnet/2d_controlnet.ipynb +++ b/generation/controlnet/2d_controlnet.ipynb @@ -90,7 +90,7 @@ "from monai.config import print_config\n", "from monai.data import DataLoader\n", "from monai.utils import first, set_determinism\n", - "from torch.cuda.amp import GradScaler, autocast\n", + "from torch.amp import GradScaler, autocast\n", "from tqdm import tqdm\n", "from monai.inferers import ControlNetDiffusionInferer, DiffusionInferer\n", "from monai.networks.nets import DiffusionModelUNet, ControlNet\n", @@ -614,7 +614,7 @@ " images = batch[\"image\"].to(device)\n", " optimizer.zero_grad(set_to_none=True)\n", "\n", - " with autocast(enabled=False):\n", + " with autocast(\"cuda\", enabled=False):\n", " # Generate random noise\n", " noise = torch.randn_like(images).to(device)\n", "\n", @@ -646,7 +646,7 @@ " for batch in val_loader:\n", " images = batch[\"image\"].to(device)\n", " with torch.no_grad():\n", - " with autocast(enabled=False):\n", + " with autocast(\"cuda\", enabled=False):\n", " noise = torch.randn_like(images).to(device)\n", " timesteps = torch.randint(\n", " 0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n", @@ -663,7 +663,7 @@ " noise = torch.randn((1, 1, 64, 64))\n", " noise = noise.to(device)\n", " scheduler.set_timesteps(num_inference_steps=1000)\n", - " with autocast(enabled=True):\n", + " with autocast(\"cuda\", enabled=True):\n", " image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler, verbose=False)\n", "\n", " plt.figure(figsize=(2, 2))\n", @@ -907,7 +907,7 @@ "\n", " optimizer.zero_grad(set_to_none=True)\n", "\n", - " with autocast(enabled=True):\n", + " with autocast(\"cuda\", enabled=True):\n", " # Generate random noise\n", " noise = torch.randn_like(images).to(device)\n", "\n", @@ -947,7 +947,7 @@ " masks = batch[\"mask\"].to(device)\n", "\n", " with torch.no_grad():\n", - " with autocast(enabled=True):\n", + " with autocast(\"cuda\", enabled=True):\n", " noise = torch.randn_like(images).to(device)\n", " timesteps = torch.randint(\n", " 0, controlnet_inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n", @@ -971,7 +971,7 @@ " # Sampling image during training with controlnet conditioning\n", "\n", " with torch.no_grad():\n", - " with autocast(enabled=True):\n", + " with autocast(\"cuda\", enabled=True):\n", " noise = torch.randn((1, 1, 64, 64)).to(device)\n", " sample = controlnet_inferer.sample(\n", " input_noise=noise,\n", @@ -1044,7 +1044,7 @@ "val_masks = val_batch[\"mask\"].to(device)\n", "for t in progress_bar_sampling:\n", " with torch.no_grad():\n", - " with autocast(enabled=True):\n", + " with autocast(\"cuda\", enabled=True):\n", " down_block_res_samples, mid_block_res_sample = controlnet(\n", " x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=val_masks[:num_samples, ...]\n", " )\n", @@ -1129,7 +1129,7 @@ "\n", "for t in progress_bar_sampling:\n", " with torch.no_grad():\n", - " with autocast(enabled=True):\n", + " with autocast(\"cuda\", enabled=True):\n", " down_block_res_samples, mid_block_res_sample = controlnet(\n", " x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=mask\n", " )\n", From fa99612f4ac95d30ae5c568ce70f95c9f1a48621 Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Fri, 6 Sep 2024 14:03:35 +0100 Subject: [PATCH 10/27] Change call to autocast. Signed-off-by: Virginia Fernandez --- generation/controlnet/2d_controlnet.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/controlnet/2d_controlnet.ipynb b/generation/controlnet/2d_controlnet.ipynb index 5d808c081..6318c9ef6 100644 --- a/generation/controlnet/2d_controlnet.ipynb +++ b/generation/controlnet/2d_controlnet.ipynb @@ -315,8 +315,8 @@ "metadata": {}, "outputs": [], "source": [ - "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True)\n", - "val_loader = DataLoader(val_ds, batch_size=64, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True)" + "train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=2, drop_last=True, persistent_workers=True)\n", + "val_loader = DataLoader(val_ds, batch_size=8, shuffle=False, num_workers=2, drop_last=True, persistent_workers=True)" ] }, { From 079532cf0666aacde1876baf6098c6d8c5047f28 Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Fri, 6 Sep 2024 16:53:30 +0100 Subject: [PATCH 11/27] Addition of ControlNet to the README. Signed-off-by: Virginia Fernandez --- generation/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/generation/README.md b/generation/README.md index d6bc38279..422163001 100644 --- a/generation/README.md +++ b/generation/README.md @@ -60,3 +60,6 @@ Example shows how to train a DDPM on medical data using Pytorch Ignite. This sho ## [Using a 2D DDPM to inpaint images](./2d_ddpm/2d_ddpm_inpainting.ipynb): Example shows how to use a DDPM to inpaint of 2D images from the MedNIST dataset using the RePaint method. + +## [Guiding the 2D diffusion synthesis using ControlNet](./controlnet/2d_controlnet.ipynb) +Example shows how to use ControlNet to condition a diffusion model trained on 2D brain MRI images on binary brain masks. From 8491dfad4a3b074fa10c47e235d7dc2299c2198a Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Mon, 9 Sep 2024 17:09:36 +0100 Subject: [PATCH 12/27] Addition of tutorials for the 2D and 3D autoencoderkl. Signed-off-by: Virginia Fernandez --- .../2d_autoencoderkl_tutorial.ipynb | 782 +++++++++++++++++ .../3d_autoencoderkl_tutorial.ipynb | 787 ++++++++++++++++++ 2 files changed, 1569 insertions(+) create mode 100644 generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb create mode 100644 generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb diff --git a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb new file mode 100644 index 000000000..d6a4331e9 --- /dev/null +++ b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb @@ -0,0 +1,782 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "688d971d-fcab-41b5-b88b-c686c74935ac", + "metadata": {}, + "source": [ + "Copyright (c) MONAI Consortium
\n", + "Licensed under the Apache License, Version 2.0 (the \"License\");
\n", + "you may not use this file except in compliance with the License.
\n", + "You may obtain a copy of the License at
\n", + " http://www.apache.org/licenses/LICENSE-2.0
\n", + "Unless required by applicable law or agreed to in writing, software
\n", + "distributed under the License is distributed on an \"AS IS\" BASIS,
\n", + "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
\n", + "See the License for the specific language governing permissions and
\n", + "limitations under the License.
" + ] + }, + { + "cell_type": "markdown", + "id": "d6ae75bf", + "metadata": {}, + "source": [ + "# AutoencoderKL\n", + "\n", + "This demo is a toy example of how to use MONAI's `AutoencoderKL` class. In particular, it uses\n", + "the Autoencoder with a Kullback-Leibler regularisation as implemented by Rombach et. al [1]. The autoencoder was originally implemented by Kingma et al. [2]. \n", + "\n", + "[1] Rombach et. al \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/pdf/2112.10752.pdf
\n", + "[2] Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. CoRR, abs/1312.6.\n", + "\n", + "\n", + "\n", + "This tutorial was based on:\n", + "\n", + "[Registration Mednist](https://github.com/Project-MONAI/tutorials/blob/main/2d_registration/registration_mednist.ipynb)\n", + "\n", + "[Mednist Tutorial](https://github.com/Project-MONAI/tutorials/blob/main/2d_classification/mednist_tutorial.ipynb)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2caa73e1", + "metadata": {}, + "source": [ + "## Set up environment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c942c848", + "metadata": {}, + "outputs": [], + "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "76b639ff", + "metadata": {}, + "source": [ + "## Setup imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "350736c2", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "import tempfile\n", + "import time\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "from monai import transforms\n", + "from monai.apps import MedNISTDataset\n", + "from monai.config import print_config\n", + "from monai.data import DataLoader, Dataset\n", + "from monai.networks.layers import Act\n", + "from monai.utils import first, set_determinism\n", + "from torch.nn import L1Loss\n", + "from monai.losses import PatchAdversarialLoss, PerceptualLoss\n", + "from monai.networks.nets import AutoencoderKL, PatchDiscriminator\n", + "\n", + "print_config()" + ] + }, + { + "cell_type": "markdown", + "id": "6e3aacc9", + "metadata": {}, + "source": [ + "## Setup a data directory" + ] + }, + { + "cell_type": "markdown", + "id": "4c821bb6", + "metadata": {}, + "source": [ + "Specify a `MONAI_DATA_DIRECTORY` variable, where the data will be downloaded. If not\n", + "specified a temporary directory will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dbad31d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/tmp84mwb3ce\n" + ] + } + ], + "source": [ + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "print(root_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "d7964212-f198-4f5e-b564-452dbf23e9fd", + "metadata": {}, + "source": [ + "## Set deterministic training for reproducibility" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c9552991", + "metadata": {}, + "outputs": [], + "source": [ + "set_determinism(42)" + ] + }, + { + "cell_type": "markdown", + "id": "6cb6282d", + "metadata": {}, + "source": [ + "## Description of data and download the training set" + ] + }, + { + "cell_type": "markdown", + "id": "d3f33076-768d-44a9-b28b-b9a0332243e7", + "metadata": {}, + "source": [ + "We used the \"hand\" category in the MedNIST dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "83d59e68", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MedNIST.tar.gz: 59.0MB [00:01, 43.8MB/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-09-05 17:32:06,408 - INFO - Downloaded: /tmp/tmp84mwb3ce/MedNIST.tar.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-09-05 17:32:06,500 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2024-09-05 17:32:06,501 - INFO - Writing into directory: /tmp/tmp84mwb3ce.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:16<00:00, 2897.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-09-05 17:32:27,771 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2024-09-05 17:32:27,771 - INFO - File exists: /tmp/tmp84mwb3ce/MedNIST.tar.gz, skipped downloading.\n", + "2024-09-05 17:32:27,771 - INFO - Non-empty folder exists in /tmp/tmp84mwb3ce/MedNIST, skipped extracting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 2972.78it/s]\n" + ] + } + ], + "source": [ + "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\", download=True, seed=0)\n", + "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)" + ] + }, + { + "cell_type": "markdown", + "id": "537e5eb9-ff25-4014-9cf9-f6b8b31c8ffc", + "metadata": {}, + "source": [ + "## Prepare dataloaders" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "049ef46f-639f-47fa-903f-a3a799cb859a", + "metadata": {}, + "outputs": [], + "source": [ + "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"Hand\"]\n", + "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"Hand\"]" + ] + }, + { + "cell_type": "markdown", + "id": "8c477aa1-f089-4db2-845b-68dfd0ad3d02", + "metadata": {}, + "source": [ + "We apply the following transforms:\n", + "1. `LoadImaged`: to load the images\n", + "2. `EnsureChannelFirst`: to make sure a channel is added on the first dimension\n", + "3. `ScaleIntensityRanged`: scale intensity between 0 and 1\n", + "4. `RandAffined`: we apply random affine transformations, including rotations, translations and scaling to augment the dataset\n", + "\n", + "On validation, we do not augment." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "19a05912-5688-4736-a006-2972af6eafce", + "metadata": {}, + "outputs": [], + "source": [ + "image_size = 64\n", + "all_transforms = [\n", + " transforms.LoadImaged(keys=[\"image\"]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", + " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0,\n", + " a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n", + " transforms.RandAffined(\n", + " keys=[\"image\"],\n", + " rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n", + " translate_range=[(-1, 1), (-1, 1)],\n", + " scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n", + " spatial_size=[image_size, image_size],\n", + " padding_mode=\"zeros\",\n", + " prob=0.5,\n", + " ),\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "id": "89ca0e40-c7c0-49bb-b2ae-572c2c87e637", + "metadata": {}, + "source": [ + "Define datasets and dataloaders:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5058363b-2963-4eda-ae67-c4bf40ede8f9", + "metadata": {}, + "outputs": [], + "source": [ + "train_ds = Dataset(data=train_datalist, transform=transforms.Compose(all_transforms))\n", + "train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=2, persistent_workers=True)\n", + "val_ds = Dataset(data=val_datalist, transform=transforms.Compose(all_transforms[:-1]))\n", + "val_loader = DataLoader(val_ds, batch_size=8, shuffle=True, num_workers=2, persistent_workers=True)" + ] + }, + { + "cell_type": "markdown", + "id": "8e3d936d", + "metadata": {}, + "source": [ + "### Visualise examples from the training set" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ebeb6144", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot 3 examples from the training set\n", + "check_data = first(train_loader)\n", + "fig, ax = plt.subplots(nrows=1, ncols=3)\n", + "for image_n in range(3):\n", + " ax[image_n].imshow(check_data[\"image\"][image_n, 0, :, :], cmap=\"gray\")\n", + " ax[image_n].axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "id": "083aea1b", + "metadata": {}, + "source": [ + "## Define the network" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8cc39247-388c-4671-936b-8c9b96310567", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cuda\n" + ] + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"Using {device}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86b34ad5", + "metadata": {}, + "outputs": [], + "source": [ + "model = AutoencoderKL(\n", + " spatial_dims=2,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " channels=(128, 256, 384),\n", + " latent_channels=8,\n", + " num_res_blocks=1,\n", + " norm_num_groups=32,\n", + " attention_levels=(False, False, True),\n", + ")\n", + "model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "671ad579", + "metadata": {}, + "outputs": [], + "source": [ + "discriminator = PatchDiscriminator(\n", + " spatial_dims=2,\n", + " num_layers_d=3,\n", + " channels=64,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " kernel_size=4,\n", + " activation=(Act.LEAKYRELU, {\"negative_slope\": 0.2}),\n", + " norm=\"BATCH\",\n", + " bias=False,\n", + " padding=1,\n", + ")\n", + "discriminator.to(device)" + ] + }, + { + "cell_type": "markdown", + "id": "afb5f1bc-dcc6-4a1d-b9c0-a3d87d4044e3", + "metadata": {}, + "source": [ + "## Network losses" + ] + }, + { + "cell_type": "markdown", + "id": "ff6fca9d-a642-4caf-a661-b3eca53ff937", + "metadata": {}, + "source": [ + "Kingma et al. [2] trained the autoencoder on a reconstruction (L1 or L2 loss) and a Kullback-Leibler loss comparing the latent representation to a Normal distribution. In [1], Rombach et al. add perceptual and aversarial losses to improve the reconstruction quality." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f259580", + "metadata": {}, + "outputs": [], + "source": [ + "perceptual_loss = PerceptualLoss(spatial_dims=2, network_type=\"alex\")\n", + "perceptual_loss.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f39cfd6e", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer_g = torch.optim.Adam(params=model.parameters(), lr=1e-4)\n", + "optimizer_d = torch.optim.Adam(params=discriminator.parameters(), lr=5e-4)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b0656065", + "metadata": {}, + "outputs": [], + "source": [ + "l1_loss = L1Loss()\n", + "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n", + "adv_weight = 0.01\n", + "perceptual_weight = 0.001" + ] + }, + { + "cell_type": "markdown", + "id": "66041923", + "metadata": {}, + "source": [ + "## Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ea9e0a54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 0/100:recons loss: 0.023390,gen_loss: 0.292826,disc_loss: 0.244051\n", + "epoch 10/100:recons loss: 0.019977,gen_loss: 0.293877,disc_loss: 0.246095\n", + "epoch 20/100:recons loss: 0.017823,gen_loss: 0.290595,disc_loss: 0.242387\n", + "epoch 30/100:recons loss: 0.016301,gen_loss: 0.300653,disc_loss: 0.239462\n", + "epoch 40/100:recons loss: 0.016317,gen_loss: 0.324037,disc_loss: 0.241039\n", + "epoch 50/100:recons loss: 0.014776,gen_loss: 0.304615,disc_loss: 0.231920\n", + "epoch 60/100:recons loss: 0.015453,gen_loss: 0.371261,disc_loss: 0.216630\n", + "epoch 70/100:recons loss: 0.015820,gen_loss: 0.381722,disc_loss: 0.214916\n", + "epoch 80/100:recons loss: 0.015885,gen_loss: 0.387530,disc_loss: 0.217283\n", + "epoch 90/100:recons loss: 0.015494,gen_loss: 0.402209,disc_loss: 0.207687\n", + "train completed, total time: 5108.975979089737.\n" + ] + } + ], + "source": [ + "kl_weight = 1e-6\n", + "max_epochs = 100\n", + "print_every = 10\n", + "val_interval = 25\n", + "epoch_recon_loss_list = []\n", + "epoch_gen_loss_list = []\n", + "epoch_disc_loss_list = []\n", + "val_recon_epoch_loss_list = []\n", + "intermediary_images = []\n", + "n_example_images = 4\n", + "\n", + "total_start = time.time()\n", + "for epoch in range(max_epochs):\n", + " model.train()\n", + " discriminator.train()\n", + " epoch_loss = 0\n", + " gen_epoch_loss = 0\n", + " disc_epoch_loss = 0\n", + " for batch in train_loader:\n", + " images = batch[\"image\"].to(device)\n", + " optimizer_g.zero_grad(set_to_none=True)\n", + " reconstruction, z_mu, z_sigma = model(images)\n", + " recons_loss = l1_loss(reconstruction.float(), images.float())\n", + " kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3])\n", + " kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n", + " logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n", + " p_loss = perceptual_loss(reconstruction.float(), images.float())\n", + " generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n", + " loss_g = recons_loss + kl_weight * kl_loss + perceptual_weight * p_loss + adv_weight * generator_loss\n", + "\n", + " loss_g.backward()\n", + " optimizer_g.step()\n", + "\n", + " # Discriminator part\n", + " optimizer_d.zero_grad(set_to_none=True)\n", + "\n", + " logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n", + " loss_d_fake = adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n", + " logits_real = discriminator(images.contiguous().detach())[-1]\n", + " loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n", + " discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n", + "\n", + " loss_d = adv_weight * discriminator_loss\n", + "\n", + " loss_d.backward()\n", + " optimizer_d.step()\n", + "\n", + " epoch_loss += recons_loss.item()\n", + " gen_epoch_loss += generator_loss.item()\n", + " disc_epoch_loss += discriminator_loss.item()\n", + "\n", + " epoch_recon_loss_list.append(epoch_loss / len(train_loader))\n", + " epoch_gen_loss_list.append(gen_epoch_loss / len(train_loader))\n", + " epoch_disc_loss_list.append(disc_epoch_loss / len(train_loader))\n", + "\n", + " if epoch % print_every == 0:\n", + " msgs = [f\"epoch {epoch:d}/{max_epochs:d}:\",\n", + " f\"recons loss: {epoch_recon_loss_list[-1]:4f},\"\n", + " f\"gen_loss: {epoch_gen_loss_list[-1]:4f},\"\n", + " f\"disc_loss: {epoch_disc_loss_list[-1]:4f}\"]\n", + " print(\"\".join(msgs))\n", + "\n", + " if (epoch + 1) % val_interval == 0:\n", + " model.eval()\n", + " val_loss = 0\n", + " with torch.no_grad():\n", + " for val_step, batch in enumerate(val_loader):\n", + " images = batch[\"image\"].to(device)\n", + " reconstruction, _, _ = model(images)\n", + "\n", + " # get the first sammple from the first validation batch for visualisation\n", + " # purposes\n", + " if val_step == 1:\n", + " intermediary_images.append(reconstruction[:n_example_images, 0])\n", + "\n", + " recons_loss = l1_loss(reconstruction.float(), images.float())\n", + "\n", + " val_loss += recons_loss.item()\n", + "\n", + " val_loss /= len(val_loader)\n", + " val_recon_epoch_loss_list.append(val_loss)\n", + "\n", + "total_time = time.time() - total_start\n", + "print(f\"train completed, total time: {total_time}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "f17a5406", + "metadata": {}, + "source": [ + "## Evaluate the training\n", + "### Visualise the loss" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2a7e9061", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use(\"seaborn-v0_8\")\n", + "plt.title(\"Learning Curves\", fontsize=20)\n", + "plt.plot(np.linspace(1, max_epochs, max_epochs), epoch_recon_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n", + "plt.plot(\n", + " np.linspace(val_interval, max_epochs, int(max_epochs / val_interval)),\n", + " val_recon_epoch_loss_list,\n", + " color=\"C1\",\n", + " linewidth=2.0,\n", + " label=\"Validation\",\n", + ")\n", + "plt.yticks(fontsize=12)\n", + "plt.xticks(fontsize=12)\n", + "plt.xlabel(\"Epochs\", fontsize=16)\n", + "plt.ylabel(\"Loss\", fontsize=16)\n", + "plt.legend(prop={\"size\": 14})\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e2cc5b87", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# %%\n", + "plt.title(\"Adversarial Training Curves\", fontsize=20)\n", + "plt.plot(np.linspace(1, max_epochs, max_epochs), epoch_gen_loss_list, color=\"C0\", linewidth=2.0, label=\"Generator\")\n", + "plt.plot(np.linspace(1, max_epochs, max_epochs), epoch_disc_loss_list, color=\"C1\", linewidth=2.0, label=\"Discriminator\")\n", + "plt.yticks(fontsize=12)\n", + "plt.xticks(fontsize=12)\n", + "plt.xlabel(\"Epochs\", fontsize=16)\n", + "plt.ylabel(\"Loss\", fontsize=16)\n", + "plt.legend(prop={\"size\": 14})\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8c4701ac", + "metadata": {}, + "source": [ + "### Visualise some reconstruction images" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8adf85ac", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot every evaluation as a new line and example as columns\n", + "val_samples = np.linspace(val_interval, max_epochs, int(max_epochs / val_interval))\n", + "fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True)\n", + "for image_n in range(len(val_samples)):\n", + " reconstructions = torch.reshape(intermediary_images[image_n], (64 * n_example_images, 64)).T\n", + " ax[image_n].imshow(reconstructions.cpu(), cmap=\"gray\")\n", + " ax[image_n].set_xticks([])\n", + " ax[image_n].set_yticks([])\n", + " ax[image_n].set_ylabel(f\"Epoch {val_samples[image_n]:.0f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "fd170679", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# %%\n", + "fig, ax = plt.subplots(nrows=1, ncols=2)\n", + "ax[0].imshow(images[0, 0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + "ax[0].axis(\"off\")\n", + "ax[0].title.set_text(\"Input Image\")\n", + "ax[1].imshow(reconstruction[0, 0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + "ax[1].axis(\"off\")\n", + "ax[1].title.set_text(\"Reconstruction\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8ffdc149", + "metadata": {}, + "source": [ + "### Cleanup data directory\n", + "\n", + "Remove directory if a temporary was used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd57125a", + "metadata": {}, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(root_dir)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "auto:light,ipynb", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb new file mode 100644 index 000000000..ffb65c021 --- /dev/null +++ b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -0,0 +1,787 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ca6bdf5b-80bc-4285-9cde-fc5641e23742", + "metadata": {}, + "source": [ + "Copyright (c) MONAI Consortium
\n", + "Licensed under the Apache License, Version 2.0 (the \"License\");
\n", + "you may not use this file except in compliance with the License.
\n", + "You may obtain a copy of the License at
\n", + " http://www.apache.org/licenses/LICENSE-2.0
\n", + "Unless required by applicable law or agreed to in writing, software
\n", + "distributed under the License is distributed on an \"AS IS\" BASIS,
\n", + "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
\n", + "See the License for the specific language governing permissions and
\n", + "limitations under the License.
" + ] + }, + { + "cell_type": "markdown", + "id": "eca65c39", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "# 3D AutoencoderKL\n", + "\n", + "This demo is a toy example of how to use MONAI's AutoencoderKL. In particular, it uses the Autoencoder with a Kullback-Leibler regularisation as implemented by Rombach et. al [1]. The autoencoder was originally implemented by Kingma et al. [2]. The model uses the 3D brain tumour segmentation challenge (BraTS) [3] dataset available as part of the Decathlon Dataset.\n", + "\n", + "[1] Rombach et. al \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/pdf/2112.10752.pdf
\n", + "[2] Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. CoRR, abs/1312.6.
\n", + "[3] Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.-A., Arbel, T., Avants, B. B., Ayache, N., Buendia, P., Collins, D. L., Cordier, N., … van Leemput, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024. https://doi.org/10.1109/TMI.2014.2377694
\n", + "\n", + "This tutorial was based on:\n", + "\n", + "[Brain tumor 3D segmentation with MONAI](https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/brats_segmentation_3d.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "7e0fa3de-eadc-41cf-aebc-a2ceb0399ab7", + "metadata": {}, + "source": [ + "## Setup environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f82c364", + "metadata": {}, + "outputs": [], + "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm, nibabel]\"\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "7325d9ae", + "metadata": {}, + "source": [ + "## Setup imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a44e7a6e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "import tempfile\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from monai import transforms\n", + "from monai.apps import DecathlonDataset\n", + "from monai.config import print_config\n", + "from monai.data import DataLoader\n", + "from monai.networks.layers import Act\n", + "from monai.utils import first, set_determinism\n", + "from torch.amp import autocast\n", + "from monai.networks.nets import AutoencoderKL, PatchDiscriminator\n", + "from monai.losses import PatchAdversarialLoss, PerceptualLoss\n", + "\n", + "print_config()" + ] + }, + { + "cell_type": "markdown", + "id": "72bae2d5", + "metadata": {}, + "source": [ + "## Setup a data directory and download dataset\n", + "\n", + "Specify a `MONAI_DATA_DIRECTORY` variable, where the data will be downloaded. If not specified a temporary directory will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "48155dfa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/tmp0_29f8jr\n" + ] + } + ], + "source": [ + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "root_dir = \"/tmp/tmp0_29f8jr\"\n", + "#root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "print(root_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "f83013c7-8f1d-4bc2-9677-9912642e00f7", + "metadata": {}, + "source": [ + "## Set deterministic training for reproducibility" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1aaa77a6", + "metadata": {}, + "outputs": [], + "source": [ + "# for reproducibility purposes set a seed\n", + "set_determinism(42)" + ] + }, + { + "cell_type": "markdown", + "id": "319bff04", + "metadata": {}, + "source": [ + "## Description of data, download the training set and creation of dataloaders" + ] + }, + { + "cell_type": "markdown", + "id": "053fdee1", + "metadata": {}, + "source": [ + "The brain tumour segmentation dataset is available as part of the Decathlon dataset. Select the channel (MRI contrast) you wish to use for training (0: FLAIR, 1: T1, 2: T1-PC, 3: T2). \n", + "Note: The DecatholonDataset has 7GB. So make sure that you have enought space when running the next line" + ] + }, + { + "cell_type": "markdown", + "id": "9774010c-cc3e-49d8-9244-0dfe1b85fe1f", + "metadata": {}, + "source": [ + "We use the following transforms:\n", + "1. `LoadImaged`: retrieve the nii image\n", + "2. `EnsureChannelFirstd`: we ensure that the channel component is in dimension 0 of the tensor\n", + "3. `Lambdad`: this custom transform retrieves the specific channel that we desire\n", + "4. `EnsureChannelFirstd`: we add the new channel dimension (as the last transform has removed it)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1dbaf6af", + "metadata": {}, + "outputs": [], + "source": [ + "all_transforms = [\n", + " transforms.LoadImaged(keys=[\"image\"]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", + " transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n", + " transforms.EnsureTyped(keys=[\"image\"]),\n", + " transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n", + " transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n", + " transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n", + " transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "ac41ad56-1c09-4e17-aba4-7f00e1894ff9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [01:49<00:00, 3.53it/s]\n", + "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:26<00:00, 3.60it/s]\n" + ] + } + ], + "source": [ + "channel = 0 # 0 = Flair\n", + "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n", + "# Training\n", + "train_ds = DecathlonDataset(\n", + " root_dir=root_dir,\n", + " task=\"Task01_BrainTumour\",\n", + " section=\"training\",\n", + " cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise\n", + " num_workers=4,\n", + " download=False,\n", + " seed=0,\n", + " transform=transforms.Compose(all_transforms),\n", + ")\n", + "# Validation\n", + "val_ds = DecathlonDataset(\n", + " root_dir=root_dir,\n", + " task=\"Task01_BrainTumour\",\n", + " section=\"validation\",\n", + " cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise\n", + " num_workers=4,\n", + " download=False,\n", + " seed=0,\n", + " transform=transforms.Compose(all_transforms),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "eef45a74-aca0-4897-b1e9-185ef288812a", + "metadata": {}, + "source": [ + "Now we create the training and validation loaders:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "aa5875c0-a150-4561-876b-2a0233bc683b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image shape torch.Size([1, 96, 96, 64])\n", + "Image shape torch.Size([1, 96, 96, 64])\n" + ] + } + ], + "source": [ + "train_loader = DataLoader(train_ds, batch_size=1, shuffle=True, num_workers=1, persistent_workers=True)\n", + "print(f'Image shape {train_ds[0][\"image\"].shape}')\n", + "val_loader = DataLoader(val_ds, batch_size=1, shuffle=False, num_workers=1, persistent_workers=True)\n", + "print(f'Image shape {val_ds[0][\"image\"].shape}')" + ] + }, + { + "cell_type": "markdown", + "id": "617a46a9", + "metadata": {}, + "source": [ + "## Visualise examples from the training set" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8902c0a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "check_data = first(train_loader)\n", + "# Select the first image from the batch\n", + "img = check_data[\"image\"][0]\n", + "fig, axs = plt.subplots(nrows=1, ncols=3)\n", + "for ax in axs:\n", + " ax.axis(\"off\")\n", + "ax = axs[0]\n", + "ax.imshow(img[0, ..., img.shape[3] // 2].rot90(), cmap=\"gray\")\n", + "ax = axs[1]\n", + "ax.imshow(img[0, :, img.shape[2] // 2, ...].rot90(), cmap=\"gray\")\n", + "ax = axs[2]\n", + "ax.imshow(img[0, img.shape[1] // 2, ...].rot90(), cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "id": "19532ecb", + "metadata": {}, + "source": [ + "## Define the network" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0a839bfa-a494-4500-9cd0-7c6205be164c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cuda\n" + ] + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"Using {device}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e0514e5", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "model = AutoencoderKL(\n", + " spatial_dims=3,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " channels=(32, 64, 64),\n", + " latent_channels=3,\n", + " num_res_blocks=1,\n", + " norm_num_groups=32,\n", + " attention_levels=(False, False, True),\n", + ")\n", + "model.to(device)" + ] + }, + { + "cell_type": "markdown", + "id": "feac8b4f-a0b9-47e6-b8dd-6e83da1e8f04", + "metadata": {}, + "source": [ + "Rombach et al. [1] trained the AutoencoderKL using a patch-GAN adversarial loss and perceptual loss to boost the quality of the reconstructed images." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4ebda38-603c-440c-87bb-ff88198b424f", + "metadata": {}, + "outputs": [], + "source": [ + "discriminator = PatchDiscriminator(\n", + " spatial_dims=3,\n", + " num_layers_d=3,\n", + " channels=32,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " kernel_size=4,\n", + " activation=(Act.LEAKYRELU, {\"negative_slope\": 0.2}),\n", + " norm=\"BATCH\",\n", + " bias=False,\n", + " padding=1,\n", + ")\n", + "discriminator.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da14911d", + "metadata": {}, + "outputs": [], + "source": [ + "perceptual_loss = PerceptualLoss(spatial_dims=3, network_type=\"squeeze\", fake_3d_ratio=0.25)\n", + "perceptual_loss.to(device)" + ] + }, + { + "cell_type": "markdown", + "id": "ded7dc3f-56e0-402d-8ce0-d77ea6205f20", + "metadata": {}, + "source": [ + "## Definition of losses and optimisers" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "aa53b342-aeec-4817-8797-adf7b7080ea7", + "metadata": {}, + "outputs": [], + "source": [ + "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n", + "adv_weight = 0.01\n", + "perceptual_weight = 0.1\n", + "optimizer_g = torch.optim.Adam(model.parameters(), 1e-4)\n", + "optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=5e-4)" + ] + }, + { + "cell_type": "markdown", + "id": "b33436df-be7d-4e1a-ba34-0df8a8105b8b", + "metadata": {}, + "source": [ + "We also use automated mixed precision (AMP) to save memory:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c0b87e9", + "metadata": {}, + "outputs": [], + "source": [ + "scaler_g = torch.amp.GradScaler()\n", + "scaler_d = torch.amp.GradScaler()" + ] + }, + { + "cell_type": "markdown", + "id": "7d19616e", + "metadata": {}, + "source": [ + "## Model training" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "aa98bfa9", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 0/100:recons loss: 0.020644, gen_loss: 1.005610, disc_loss: 0.024810, perc_loss: 0.096774, kl_loss: 11961.354739, \n", + "epoch 5/100:recons loss: 0.019299, gen_loss: 0.963566, disc_loss: 0.032445, perc_loss: 0.080186, kl_loss: 19255.276533, \n", + "epoch 10/100:recons loss: 0.018521, gen_loss: 0.882888, disc_loss: 0.055547, perc_loss: 0.070705, kl_loss: 22346.606254, \n", + "epoch 15/100:recons loss: 0.018521, gen_loss: 1.024773, disc_loss: 0.014188, perc_loss: 0.068204, kl_loss: 26122.472853, \n", + "epoch 20/100:recons loss: 0.018183, gen_loss: 1.020731, disc_loss: 0.013762, perc_loss: 0.063751, kl_loss: 26065.798295, \n", + "epoch 25/100:recons loss: 0.017791, gen_loss: 1.017065, disc_loss: 0.008928, perc_loss: 0.061848, kl_loss: 27973.925384, \n", + "epoch 30/100:recons loss: 0.016497, gen_loss: 1.027929, disc_loss: 0.005776, perc_loss: 0.058142, kl_loss: 28598.175570, \n", + "epoch 35/100:recons loss: 0.018082, gen_loss: 0.922083, disc_loss: 0.044235, perc_loss: 0.061182, kl_loss: 29145.672766, \n", + "epoch 40/100:recons loss: 0.017112, gen_loss: 1.031649, disc_loss: 0.006293, perc_loss: 0.055899, kl_loss: 29358.829560, \n", + "epoch 45/100:recons loss: 0.017617, gen_loss: 1.031488, disc_loss: 0.006553, perc_loss: 0.056928, kl_loss: 30269.970220, \n", + "epoch 50/100:recons loss: 0.017587, gen_loss: 0.989403, disc_loss: 0.020194, perc_loss: 0.055261, kl_loss: 29611.149525, \n", + "epoch 55/100:recons loss: 0.014578, gen_loss: 1.035160, disc_loss: 0.001241, perc_loss: 0.048054, kl_loss: 29921.286118, \n", + "epoch 60/100:recons loss: 0.016188, gen_loss: 1.034492, disc_loss: 0.002137, perc_loss: 0.050492, kl_loss: 30739.220950, \n", + "epoch 65/100:recons loss: 0.018132, gen_loss: 1.026116, disc_loss: 0.009633, perc_loss: 0.053056, kl_loss: 30321.361550, \n", + "epoch 70/100:recons loss: 0.016364, gen_loss: 1.025895, disc_loss: 0.006020, perc_loss: 0.049412, kl_loss: 30252.894451, \n", + "epoch 75/100:recons loss: 0.013454, gen_loss: 1.019963, disc_loss: 0.001358, perc_loss: 0.043239, kl_loss: 29700.126062, \n", + "epoch 80/100:recons loss: 0.016021, gen_loss: 1.038090, disc_loss: 0.005187, perc_loss: 0.047805, kl_loss: 30522.813955, \n", + "epoch 85/100:recons loss: 0.015460, gen_loss: 1.024193, disc_loss: 0.007624, perc_loss: 0.045688, kl_loss: 30897.789541, \n", + "epoch 90/100:recons loss: 0.015317, gen_loss: 1.034723, disc_loss: 0.002935, perc_loss: 0.045348, kl_loss: 30689.129576, \n", + "epoch 95/100:recons loss: 0.015863, gen_loss: 1.037992, disc_loss: 0.003536, perc_loss: 0.044846, kl_loss: 30319.116891, \n", + "Training finished\n" + ] + } + ], + "source": [ + "kl_weight = 1e-8\n", + "max_epochs = 100\n", + "val_interval = 5\n", + "print_every = 5\n", + "epoch_recon_loss_list = []\n", + "epoch_gen_loss_list = []\n", + "epoch_disc_loss_list = []\n", + "epoch_perc_loss_list = []\n", + "epoch_kl_loss_list = []\n", + "val_recon_epoch_loss_list = []\n", + "intermediary_images = []\n", + "n_example_images = 4\n", + "\n", + "for epoch in range(max_epochs):\n", + " model.train()\n", + " discriminator.train()\n", + " epoch_loss = 0\n", + " gen_epoch_loss = 0\n", + " disc_epoch_loss = 0\n", + " kl_epoch_loss = 0\n", + " perc_epoch_loss = 0\n", + " for batch in train_loader:\n", + " images = batch[\"image\"].to(device)\n", + " optimizer_g.zero_grad(set_to_none=True)\n", + "\n", + " # Generator part\n", + " with autocast(\"cuda\", enabled=True):\n", + " reconstruction, z_mu, z_sigma = model(images)\n", + " logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n", + "\n", + " recons_loss = F.l1_loss(reconstruction.float(), images.float())\n", + " p_loss = perceptual_loss(reconstruction.float(), images.float())\n", + " generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n", + "\n", + " kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3, 4])\n", + " kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n", + "\n", + " loss_g = recons_loss + (kl_weight * kl_loss) + (perceptual_weight * p_loss) + (adv_weight * generator_loss)\n", + "\n", + " scaler_g.scale(loss_g).backward()\n", + " scaler_g.step(optimizer_g)\n", + " scaler_g.update()\n", + "\n", + " # Discriminator part\n", + " optimizer_d.zero_grad(set_to_none=True)\n", + "\n", + " with autocast(\"cuda\", enabled=True):\n", + " logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n", + " loss_d_fake = adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n", + " logits_real = discriminator(images.contiguous().detach())[-1]\n", + " loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n", + " discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n", + "\n", + " loss_d = adv_weight * discriminator_loss\n", + "\n", + " scaler_d.scale(loss_d).backward()\n", + " scaler_d.step(optimizer_d)\n", + " scaler_d.update()\n", + "\n", + " epoch_loss += recons_loss.item()\n", + " gen_epoch_loss += generator_loss.item()\n", + " disc_epoch_loss += discriminator_loss.item()\n", + " perc_epoch_loss += p_loss.item()\n", + " kl_epoch_loss += kl_loss.item()\n", + "\n", + " epoch_recon_loss_list.append(epoch_loss / len(train_loader))\n", + " epoch_gen_loss_list.append(gen_epoch_loss / len(train_loader))\n", + " epoch_disc_loss_list.append(disc_epoch_loss / len(train_loader))\n", + " epoch_perc_loss_list.append(perc_epoch_loss / len(train_loader))\n", + " epoch_kl_loss_list.append(kl_epoch_loss / len(train_loader))\n", + "\n", + " if epoch % print_every == 0:\n", + " msgs = [f\"epoch {epoch:d}/{max_epochs:d}:\",\n", + " f\"recons loss: {epoch_recon_loss_list[-1]:4f}, \",\n", + " f\"gen_loss: {epoch_gen_loss_list[-1]:4f}, \",\n", + " f\"disc_loss: {epoch_disc_loss_list[-1]:4f}, \",\n", + " f\"perc_loss: {epoch_perc_loss_list[-1]:4f}, \",\n", + " f\"kl_loss: {epoch_kl_loss_list[-1]:4f}, \",\n", + " ]\n", + " print(\"\".join(msgs))\n", + "\n", + " if (epoch + 1) % val_interval == 0:\n", + " model.eval()\n", + " val_loss = 0\n", + " with torch.no_grad():\n", + " for val_step, batch in enumerate(val_loader, start=1):\n", + " images = batch[\"image\"].to(device)\n", + " optimizer_g.zero_grad(set_to_none=True)\n", + "\n", + " reconstruction, z_mu, z_sigma = model(images)\n", + " # get the first sammple from the first validation batch for visualisation\n", + " # purposes\n", + " if val_step == 1:\n", + " intermediary_images.append(reconstruction[:n_example_images, 0])\n", + "\n", + " recons_loss = F.l1_loss(reconstruction.float(), images.float())\n", + "\n", + " val_loss += recons_loss.item()\n", + "\n", + " val_loss /= val_step\n", + " val_recon_epoch_loss_list.append(val_loss)\n", + "\n", + "print(\"Training finished\")" + ] + }, + { + "cell_type": "markdown", + "id": "a28c94e3", + "metadata": {}, + "source": [ + "## Evaluate the training" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "066417fe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "val_samples = np.linspace(val_interval, max_epochs, int(max_epochs / val_interval))\n", + "plt.plot(np.linspace(1, max_epochs, max_epochs), epoch_recon_loss_list, label=\"Train\")\n", + "plt.plot(val_samples, val_recon_epoch_loss_list, label=\"Validation\")\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "bb1b6dd8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Adversarial Training Curves\", fontsize=20)\n", + "plt.plot(np.linspace(1, max_epochs, max_epochs), epoch_gen_loss_list, color=\"C0\", linewidth=2.0, label=\"Generator\")\n", + "plt.plot(np.linspace(1, max_epochs, max_epochs), epoch_disc_loss_list, color=\"C1\", linewidth=2.0, label=\"Discriminator\")\n", + "plt.yticks(fontsize=12)\n", + "plt.xticks(fontsize=12)\n", + "plt.xlabel(\"Epochs\", fontsize=16)\n", + "plt.ylabel(\"Loss\", fontsize=16)\n", + "plt.legend(prop={\"size\": 14})\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "21bbecae", + "metadata": {}, + "source": [ + "### Visualise some reconstruction images" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "caf2b1e1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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99WEOAz/3eBwXRi3OVa6QTqdRKpVw9uxZp9PPZrMYDAZotVrodDpoNptuzLHmMBegNtITdu9R9t0eq+C1+Vv/1muRAJPY6mfWBEGNXFGeAcAtqnk+6zDn83m02200m00AQLPZPDBnjLMDp9FOzBVBtgjrMHzI00oejtqOSa477Jgw/eMkXuUwD3hYaHjYtew5J+VB97j3Ma5/HEf/mfaaYccnk0mkUilcunQJhUIBwH6mutYPtlpBez3dVnrYQl8NlhoxktdEIoFsNotiseiMNdtH2QZJ8t7envMO8Z48hkaOdZCpRQSAdrvt2nf+/HmsrKzg61//Ora3t0OfmZ1jZvHMx71/1PnW4/5GGBEbFkXiGFpYWMBDDz2EUqnkvL92QZtMJpHL5Q5cX6NK+tuOefvbEmbbds4Tqi3WMa5zjnqrOWeop5ikVz8Pt63u9/suEU+T/XTxwByGQqGAVquFF198Ea1Wy80n+nMvRaLnhiCHkT0ldsfpOZ2EOM5q5TMq5DMOwzqfJcT29bDzlTzfSx3aw0MxKlJDz3GhUHBbP2tiHTCc8IZJEuyGHPZ+NtlGw53qcWYZqWw2636rB5mkF0AgQ53e506nE7jusIU0Nc/ZbBaNRgOtVmvkImAW8MTXY15AQlkqlVAqlbCwsIB0Oh1IpONvrURD8Dja0DBSbq+jf4+y5cOq4/BeHOvAfp111Trzs6mXV5N+VW9MQqzJwyrd4LnpdNodQ/lXJBJx5eHuRcwNQQ4jd4f1WoxDGOEeFvo7DEYR2aN+ljAPsn191GvTwoc/PU4rRpFjGpjV1VVcuHAh4PUJ6/M6F2mNUz2HnmALm81Ow0OjpuFTEuR0Oo2FhQVks1kUCgVHivmjYVMaKBJiapaBfe+WeoXUoMbjcZw5cwbxeByvvPLKPWvoPO5PWIeQ/k/S9+ijj6JYLLrNM8KuQc9sOp0+YIM55kgY7SYbowjxNCDxZdk4/q+eW92MRCNLJPh2PuCGIry+7sDJDUMAuEV7tVpFJBLB2bNn3Xb2WuHCPvdh/58WzA1BJvQLP45r2/sMu9+9QArDJBqjjvHwuFdhF8TJZBJnz551Rf7VGwQEyS9Bry2hIVieq/o9GkfNime4lO2w8g0aOWqiqYtmBj0JshovtkM9QfQmsd4p76ekfDC4k5nPOq63b98eqkc+zEJ53DnDPNseHkfBOMcRx2M2m3WSJkZrOF616gMXlSTJVk6g3l4uPjkOh0XBOR/wOryGjmm+pzpj7pbH6+miVxME6S3Wa3B+0EiXRpl00W4XFnxmTNbjHKrVcijXmDSf4TRgrgjyMBJHHHXCHSdBsOfa43VQzMrjPMk5w0K29v9Jns009/XwOA04DHlLpVI4d+6cK6VmJRBhYVPdoIOGxGZ+a/a6GhbrwbEeaTWkqVQKmUwG2WwWuVwOxWIxQJCZLKM1UPmb12IbqtVqwDAqoafXOZvNAkBge1p9tod55nbhMAqeKN+7OGk7o+OKvzluGZ3J5/OByjVKVkk4u92uS54NG7O8PseO3f0ybBwo+eYiVwmp3RCI/9ukQL7O5Fu9L9vEYzgXEHasa7UdfYa6fTW915SldbtdtFotNyexss+9gLkiyMNI3DAJhMU0+jYr4VDtD42ZCvXZGbvdruto4wj9JFKLUSR71OcJI+/D2hIW+hh2/DAy7o2Xx7xi0r7J/s4KESxjNM28oRuDWK0ik154PA1TMpkMlH4L89jE43Hk83ksLS1hYWEBxWLRGe9isYh2u+0y6dvttiO+2iaGRjk/tVotFz5tt9uOWKuXir+5OUqlUsHW1tYBz/a0z3ySOdvPKR7HCe1/7OOLi4suMpPNZt0cwPGpNcvtgpIlF4F9by6vTwJJ8P12u31gIQ3AJedaj6tu5mGJuJZ/TKfTrkSbRo7o1dWIGBe9XKDrIl0/q9Zp5uflcZRYUOLBz8e2lEol7O7uotls4vr16wfKXJ5WzBVBHqZhsRqeSSbWsGOH/a01AxcXF3Hu3DmcP38eDz74IAaDgdu/vN1u49atW/jmN7+JF198MSBq1/sNu88kCFuV8vVJPN5hmIbgDnvGniR7HAbjFpGzxKg+qnMIC+lPWvfXSiDUMzzMU6qeIb6uoU09T41vJpNBKpVybUwmky6saxNueG6n03HeHc1ct95gLfUGBEl+LBZDoVBAv9/Hzs7O0A0MPDymwd0c/2FQx1cymUSpVEI+n0culwt4jdlWS0r1OpFIJCCL0HM4J9jzlGQrdO7gdfR6SqD1c0QiEVeHmeOX0i22T73gAAIbB/E4tpmk3uZJ8DV+Bt4vmUw64kxtM7BfxadarQaciqcdc0WQFZYsT+KRGOfN5YqJmwIAwMLCAiqVCpaXl7G6uoq1tTWUSiXUajVUq1UXUqARWVlZwb/8l/8Sm5ubKJVKKJfLKJfL2NjYQKVSQTwex82bN92uM9q2RCKBCxcuuA67sbHh7kEMM9qHnWRGhUj5XMZd+17o6B7Hi2Fj824aR3v/sL4di8Vw7tw55HI5AMFtXLWChZ6npdFoVMLkFSrTsNpAGjL1MNO7w3lJvclM1Eun065SBb1A3BKWxq7dbh9Y2PLzaEiV3iKteJFMJt1zuXjxInK5HHZ2dtBsNo8UKj3NXiOPewM6JrPZLFZXV/GGN7zBRVkymYwbSxzXHGeaZGerzxD0pDJJT0tDTtIurUXMv7lBie6ypzkGeqyNRFmiz7/5mfi52W4ArjKFzjucy9VbTlLOJEXOK+1220XkstksarUabty44arihD23sOfBZz1vmCuCHEba7Jc96tyw99nBYrEYfuiHfghra2soFArO48JtW1kHEAAqlQoAuOLh/FtDFiycnUgksLS0hOXlZXe/N77xjdjd3cU//dM/BTJEs9ksSqUSALjyTTdv3sTm5uaBTjLpQmCUxzcMljSMItDTtMnDYx6h/Z0aPWrn+H5YSSUL1QmOqkqhx9trK2nmuTyH3hmGavlDIm6jVZrEw8+h12fIWGsg23FvrwvAEYnt7W23KcA8Gi4Pj0kRjUZRKpVcfXHKlKze1nps7fjV5Fe+p4tPklrrGQ7jMHasEjxf8wbs/KTyT52TlHSrzVaPLr3TOifwOvpjHZTqiVdw3iK48CARtx7w08Yh5oogW2J8lOsA+1/++fPn8UM/9EMoFAquHIt2rFKphFarhXq9DgABw0cDw06guiOuvtjptONls1ksLCw4Ar63t4dms4l2u414PI5yuYxGo4FUKoULFy4gn8+j2WyiXq9jd3c3cB/7PIatuJTUTvr8pgmB3SthE497D7YPh42XfD6PbDaLfD7vvD3qxQm7lnpgrGHiPMHsbuCOAdG6oJwfdJMP/q/zUDwedzWZGf6l91gNYVgIVnWINEytVguNRsPNORo2VSPLe/Pai4uLWFlZwbe+9S1sbW25Z+BJssdpA/ttIpHAww8/7HbJVAmCklfWD7dEV3/rOGQEB4DzvgLBxDdduFrbzC2bNfGOMik64hjBZrsY+VGJBYl62OfSREMuDBS6LTZ/lJjbSBh/dJGv0otIJOLK5TFnggmE+tlPCw5NkAeDAf70T/8Uzz33HM6fP4+3v/3troPcTajR0LZFIhHk83k89NBDuHDhAs6dOwfgoC6P4Uklnex0LJHE8ASwv8qMRCLuOvxf6w/GYjEsLi5iMBg4w7yzs4Nyuew6cTweR7FYRC6Xw8rKCur1Ovr9Pmq1mivn1Gw2US6XXVLOMC+5Hcz6+qjQ8zSG77R1bo/7B2Ekzup8mfim2zNbz6/+b8Oleo6VWHCcUQ/I5BxeR5Nh9Fo0eEzAUUM1zBus3iWGOZvNpiPDjUYjsChnsp7VDPKHJJztSSaTKBQKKBQKaDQavjayx6mEjq+lpSXk83k3brVWOHXFas+HOafCok02F0GdfNYDrMeQaPJ/LrZ1AQ7AzQtcyFISoh5wG1nSdus8YhfqdBSESUP4PFR+ZhOTlfjy+qVSyTkKqtUqarXaAc/8acHEBPkd73gHfu/3fg8LCwvY3t7GO97xDnzta1/DysoKtra28OpXvxp//ud/jtXV1Zk0zHqSwzxE+kVpZ6AhPHfuHB5//HHnIU6n04HrqLhdwxM0EprZqtAVHrCvG7KdgP8nEgnk83lXfJvXYIZ7oVDA4uIiisWi0xt2u100m03UajVsbm5ic3PTEWYaPLtSPaqXZ9g1TlOH9rh/YcOC+jeT0BYXFw+EJRVWV6yVItQI2fCsao/r9bqbG3Qu0FAqr0Pjx4Q8q2/muZw7VP+nHmMSZDVK1WrVVa/gOeo1pq6Qv0kmUqkUCoUCFhYWhu6SNamhO20G0eP0Q/sc+3SpVEI2mw1smtFqtZyH1ZZfJNTG8tr2f7vQHtbnwxbiliSzzaqBth5jdUTq5iR6PUvwdaGtFXW0ss4o+8/31QOv8wmw73wslUrOg9zr9dBoNALcaBy3mydMTJC/9KUvOdH1f/kv/wWVSgXPP/88HnroIbzyyiv48R//cfzyL/8yPvOZzxy6MaNI8DgCyE6Zz+fx2GOP4dFHH8XKyorraDb8oJ2R8gnqkm0oRT06vE69XneSCj4Xaoc0+UXbzM7NsE8sFnP1Tm2IJhqNuhDr4uIi1tbW0O12sbm5ifX1dWf81IAN8y6Pgn2uwzxx3sh5zALH1ZfspKsGMpfLIZ/Pu2o06iHmMbZtVqbFcWG9NurF5fUYLtVqFNZ48YdzQS6Xc6SUpZNoyFqtFlqtlivj1u12HQmnAaLXuNPpuAQZzmVsg/UWkxgTfA69Xg/Ly8t4zWteg3q9jkajMVLCMsl34+cPj5NAJpNBJpNxC0zWM47FYo4c2vJsOvbDOIdyCiWm5AMqP+BxYU62sLrEej+ep4tm5RAkz6qF1uQ+/uZ97OJbq9rwt/IilpGzfInzHgm2bdfq6qqLQAHA3t6ee7YKO9/OI1E+lMTi//7f/4tf//Vfx0MPPQQAuHjxIn7t134NP/3TP32kxoQ9wFHH2RVIJpPBu9/9biQSCfR6PTSbzUDHVS0McGfFU6vV3EpSO0osFnM1Q2lkGeaMRqPOKLGD04sDwCX2hWl4aKDoLWItRg3PkpSzA3K3q8FggIWFBTz88MNoNBrY2trCyy+/jM3NzcCmAdM+T3vssL89PE4jkskk8vm8G/tKppWo6ryir/OcMEkGoRpgrVxBMm0905xjeBzLuZHMsq3qJeYP/1dvEH/USwTse5hSqZQrI6fPReccJfyZTAbLy8tuYwRi1Pzi4TFv4PbQKjEIG/9AUK45zDGnjrMwR5L9rT+WeCs3ICijsHIuXZTbvzX3yVbi0IW9jT6R69g26OexUbJRC2Uey+ogwB1ORgmLRtBOC6YiyHw4Ozs7eNWrXhV475FHHsGNGzdm17IQDJuQSSj/03/6T4Fi3wCcsanVaq7qBD0+lDskk0m3v7kNX3S7XbfPODtRNpvF0tKSI7DNZvNA+ZTBYIBcLhdYSfL6XJ0xdKLGMxaLuexPXp8FuXndTqeDZDKJM2fOYHFxEdVqFd/61rdQqVRCQ0LTepFHPWsPj2lwUp4BvW+xWMSFCxcCHh1g34McphMcFY1R8ktwkdxoNFykifdn8X8aKb02yyMVCgWk0+lAHVMtAddsNt34pjeMemMu5EmqOcflcjlXforzDq+vyYZa1J/PhNvvptPpA5nxh8EsvMhhoVk/T8037vbYt6SXSaccn0CwNrESVTsvWCI9bI5QGZRuMKJt4XxkSSeAwDzCManlF7m45fjVRS69trynepDVK0znYDabRSaTCcgfmNCri+Rer+ccd6qLVnkZjyeP4XPQzUqWlpawurqKzc1N50AMwzx6j4EpCfL73vc+pFIpdDodXL16Fa9//evde7du3XIlzI6CcZOenSSBO53owQcfBAAXmgTgOgC3Um2324EQI8+1ekJgP5uUnY6ENpFIuEQfhjlJqnk8DR07q8ogGOLJZrOBRBldXfGaFOa3Wq1AmTl2eK6GFxcX8brXvQ7PPPMMKpXKRCu1SSUrYc/bw2NS3O2JL8y7Q4OjBsNGSWxfD9PHWePLv9XDy/JRYQX77fk8TjcI0U0A1PCSENN48f+wfATel/VJmWGuniitoKEkgZ+Z7eeOXdQ/H/b7nMX8Ma9hWI/5xGAwQDKZRCaTcV5TIOjZ5XHAfsSXx+h1+BO2mAb2Iz38bWUPYf1f5VZsD+cEnT80N0HLqg0G+5pgcgFdhHNc8/Ow/SoB0QiUSj70GhoF0+cSltjH56i8iRLS08glJibI733ve93f//bf/ltXEo34/Oc/jze96U1HbtCwh6crMvs6AJTLZdeRmJRCsGPRiA0GA6ffVV2y7jjD80ioWTtVOy8NmpLuXq/nQjpcfdGzoyFeOxDZcUmueS/qphqNhkscVO0kt39cXV1FPp/HP/7jP2JjYyM0scaGjO0znOb78PCYF4SFOjW0qPp+mygXRpTD5gKeq6HKSCTiSHGj0UC73Ua9Xkc+n0cmkwmUaLPX55yUTqeRz+exvLyMhYUF57XVsc+FPqvc9Ho9Jw1jZIzGjdem3jqbzbrFuD4XdQiwTc1mE5FIJJCYXCwWUSqVsLGxcei5YJZziP2+POYbJxlBAuC2audCE9jX3pJc2lwEPd8uiNV22yoPtP/03vKa9vwwB5YmzzKHiaSY8wF/stmsu6ZqkDnfWQ8y5xFGxJk3pZF05jo0m82AczASiQRyKPi5VaphFxv8HADc3g/0IOtnPw3jd2KC/NnPfnbk+x/96EdDa4rOEsO8y4PBAPV63VWA4GvqLWKH1TqAKli3KyWCX7Q1KOw4Gtq0IRcAjsCyHUpybWgHwIHi/vQeMfuW57DtvV7PeZtzuRyWlpZQrVbdZif2uXkvjMdJ4m5Niuol0sleSaIlxGq8dAzrOFUtI6vMtFot7O3tubFLjwkNnJJzHffA/uJdF98c21qXvV6vu1KPJMiqSeZx9Cox+sRFPOdmXTgQWj6Sv3W3sUKhgK2trVCd4jDYxfhpMIYe9x4YRQmL5KjUwkITVgEEuIReg9fRCljq/OI28fyfC3bOOQTfYzRbS1Hyf/7WPCoep15w9XRrToTmYw0Gd3KeqtWqi3Azj4qftdVqBapWKFcaJ0dhe2x0zC4u5h0z2yiEGtmjYhQJHtaZlQCHlS3h/1ZfrJ1aiaqSYa1lau/H93RXGv2bcgqez3vYFaR2Gq7s6OnmZ2i3267+YdjzoTh/eXkZe3t7gdqD9rlOY6y8cfOYBCfVR+ycoMbIhgWth0WjN3q+fhYd06rno0HhjnPVatWdx0Wzlk4LayMQlFmovIIGioaM9+C9Ke9S/bAaMDVQNHLq/bKhWNs+fj56uMMSdA6LYaHWw8jrPDxGgUmvStysXGIct1AyrXIkvk59r845nANYMMAm7vI8Qs8lEebrunDWaBjfsxEzTdJTqZYSZJVAkBTrlvXRaNRptq0H2Tr1bOKizkfcnprzEKVap8VRN1c76R1F30ZPqwrBwwyFeoaA4H7magR0xcRVoBLMvb09J6VoNptuC1vtiJRwaOdLp9MBcq21GFnLtFAoBO7X6/Wwvb3tkn0YItJBxHDu2toayuUyNjY2AjtoDXvewxYjYX97eAzDPC6kEokEFhcXkUqlDmyuAYT3bQ0XKjS8SgOh9YOtwdXNQlg+Uucm7pa3srKCpaUlLC4uolAouAxwNb4aHlats9ZpVy+5jYrZ6BcQ1A/av6mppgeJiXpKxqeBPWeYcRx37XnrXx7zCx0DFhyf1iOq8wPHkpZQtdEl/WHf1OoylGBxDFMqwfN15z6ez7Kvi4uLrr1LS0uBHChWtFIPuC7iOYa1ug1wcEMUfnZGrDi3aBSL9yVHUWeDJc36fPk/KwidOXMGyWQS165dczzmNGCuCPKwCXDY6/Yha9kS3SJaE0xowBqNhiuon8vl0Gw2USqVArpBvS+zPpmpvre3F+gwTIZhR0qlUqjVagGSqyFedlJKPpgMQ50Ok2/4OZn8o/ue6+cfDAaubmEmk8HCwgLW19cPPC9Pfj3uF7DOuHpDgPB+b0OGYbDeJBo3O1fYclK8Jo0yjRLJp24rrbpIjYQxLKoSD42WWY8OsG/4raTCesnt/2pg1Us17SJo1LF+7vGYNcIiEsB+zWF7nHV6KfHVCAt/7HWUHKudJ0+wml2VOWkUWseVJu2yPZqXwE3HNBfJzg92czPlPdYBoPODrcnMz6B7Q+j19BmHXRvYz+PiRmlcaFvuMq+YK4I8KexETW8O/ybpZOejB7fRaGBjYwNbW1tOewPAJbQsLy8jnU6jUCjgwoULABD4MumN4UqM21UqmQXgVmHsuKp3ZEk5rtI6nY7TCzebTezu7gY6KT3fuVzOreZarZYzzhqq4TO4dOkSYrHYAYI8agEybEV3Wjqyx8lBvQrEqEXtcfYjtoGhvcXFRRed0ZJI2k7rcbWfQXWFwP5GIPSwAHAaZBq+RqPhFs28Fok18wVYSo0EmeObz4heYvVu2coVmgOhhFg9Q0AwoUg/n5ICvqYSEpWjnBavj8f84Lj6DPvxKLulY2HctTSCojaVYwAI1gEeDAZOAkX5pi5ceR314g4Gg0B0iLJJLpK13apdZqKtVtGil9q2WytSkF/U6/UDi2RyhXQ6jXa7HZhDOAeSRzHyRY86veG8L39TAkruQ74zGAywurqKdDqNl19+OfB8hzns5mWuOZUEmVCvcC6XC4Q7+bBJXLe2tvDSSy9hd3fXeWl5Psuo0djs7OygXq9jZWXFGS1FJBJxKzzrFaJGkVtCs74y9TjVatXtjFWr1VCpVNxOfErytQMyi71Wq7lyVQzF0pja1a2uGtXA2U44ihzbZ+3hMe9gP9cd7IBgKbdh0gr9TdhjNSEH2B+zmohHuRcNEqNZLDNJ8s5NgrhBiC01yTEdltyn7QszMtazY4/XH15fNyPRHbbUG+7hcVpAG2ir0SisF1kXi8BB+ZFimDdZ3+dY0h3neC/KJHUhzqRC1iAG4Ag0iTM5iUbMbXk2tfmWE/HZqL6ZHMdGvjiX0TmoUSvLKSzP0AQ+dS5ar7m2ad5wKIK8u7uLr33ta1hfXz/QaZ588smZNEyhD3OYpqjVaqFQKLhNPTgoWq0WqtUqnn/+eWxtbTmpBLAvIg+7HjsjCantXJRTsNPr7lXAnc5VLBYD9+FvSiFKpVJAwM8kPSXxqlPiKlJDQHo/LfOUy+Vw4cIFXLt2beyz9fA4SRzWqxxGAjlW8vm8W2RaY6RzlsqprKbOyik0GqQ1SQeDAQqFgqtq0e12Ua1W3T0ZpqxWq8hms0gkElhYWMDq6iqKxaLTI5PQ632BfdI9TgOsc6QaRNVOq25SPd5aOq5erwe8YSr1mifMo+7d4+4grC/qa2FRlLDFI8kl/7dEV3eoVW+0RoeHEXAm8eoCVBNmdVGu+mJuOc/7LC4uIpfLIRKJOImF5iDoOFWSzHvYEpfaXvVgD9uRk/NlLBZzclV+DjoWVbvN56gVdnhfSl5Py9idmiB/8YtfxLvf/W5Uq1UUi8UDnfI4CLIizOPJSZ7eW3pqIpEIGo0Gdnd3sbOzg0ajceB6YeGUweBOEl4ulwuEVzUUyy1a9TXbLnZM1SnSS80tGLXKBb3KOzs7jhyroD1stWsnAB6byWSwtrY2kiBP6j328JhXWO8FSwrxNxHmRVGEeZDVwKpnyRrLSCSCQqGAWCzmtp9nWJJjvN/vB0pRUoOsCXbqCdLC/eodsgbZeoO1berN4XyhSUecN214lkZQDd84T5x9lqfB+Hncu1BvLaHjVccdYeUVHB+E3VyHoFeUx3Ac03Gn4yisZJuWY9Rxrp9FtcJaOlIlDpaLWKKq84A+H3quNaFYCTeLHzCXg+1jO5QcU9ZBHsb3dLO004SpCfKHP/xhvP/978cnPvEJt0Pd3cawMCM7t3pTuY0iDY1q/HgOr8cvttfrYW9vD4uLi8hms6GDiUaTqyItzRKNRgMbqagB48YihULBDQoArkg/6xrreaqnslpCJciKRCLhsl8p4QC88fI4Phy2X83yvEwm4zS9Gh3SRaaFSo2GeaVpHMIWxNFoFPl83r2upLLf77vi+81m0xkNjSqpp5jzlnpv1KulRtLKJPRzqGfcEm8ArtyTJi+qxIJQ4xcm0zpJzEs7POYD6iFWm0lo/w17XxfQWhVC5wJLjq2Xmh5Vjn1LNOnRVVLcarWcU8/KNHS8AwiMfSXzlgPoglvnEpVz6OdnVHswGLg5gEUGKLGIRCKB8rNawo7kn9F0LrTJV8IiUfM0lwzD1AT5+vXreOqpp06EHI96mFbnopnk1PXU6/UDBoWGSotz829WtdCNQDTkwEHAzqMDZ3d390A95Vwuh5WVFSwsLGB5eTnQ8avVKqrVKpLJJG7evAkAgb3QuYW2ZsnqSlc7PhGLxbCysoLr16+HeovDPPEeHqcFYWSWJFbDgIwmDfMq2YUvr0nQIA3zoKou2VaeYZSJ12NGdz6fd3Moo0c6jpkkx12udDtpNWJM2GNUyy7YaRx1tyzOY5pQRIkFj9H7DAYD5PN5xGKxQMKOh8c8QeeDVCqFQqEQug+BjTqFwSbm8jV1rlkJE6/JHIN4PI5arebykbRUWyKRcEm62WwWy8vLyOVyyOVyyGazbl+JwWDgOIhKojg3cFxzZ+BGo+HmBi7M9flYch+Px5HP5wPOBHqM+ZveXy1GwLnVXpfzrFbj0WdfKBTQ7/exu7t7KvjG1AT57W9/O/72b/8WDz/88HG0ZyjsSk9BwxeJRALGMBqNolQq4fz5824lZLc7VG0ePcz80cQ/PRbY35iDmuZ6vY5KpYJGo4FWq4Xt7W28/PLLzlCtrKzgu77ru/DAAw+4+qwcyMCdFSuTA7vdrhsoAAIJP6qZAuB0TVZ7xP/PnTuHV155ZaxB08F+Gjquh4cF+z0XkDoOgGAWup6jfw/r/+PGD9+3i+dIJBIwUvQcaVhWI188XxfDNgFHzyF5Vs8VI1E0pvRe08hZzzRJtNZstZGqVCo1cYh0kvnDzzMeswTtHvsUS6VppIXHDTt/VH+0ZHhYFJvHMHdJHVvcMINzAMu3UQPM9tJpx2vavAi7wLU1j5U4a6ULtk1zLkiMM5lMwJlHp5yWjLPJi3YBYZ8TOVK/v1/lI6zowTxjopZ+4QtfcH//2I/9GH7hF34BTz/9NB5//PHA7nQA8M53vnO2Lfz/Y5z3mF8ANwOpVqtot9suU/zNb34zLl26hFarhVu3buHGjRuo1WoBDw1LwcXjcXQ6Hbzwwgt45JFHnCyDxodlnDhYYrEYlpaWcPbsWTSbTWxvb7uVIzvy1tYWnnnmGVy4cAGvetWr3JbT9O5wN5t6ve52nNGwBUl7t9t1g0/L2QFwOiEbehm1Uh72PMc9cw8PizDSM23fm/Q+vLbeB9j34GqINBKJuHCmSiB4nibEWUPKhXaYMVAogR0myUomk8jlciiVSgDgvD5MftHawwzJ2u2kdTHf7XYDUTHOEVxQa7iT3h96hPXz0DOlZJzt19qr457BpDhMf/CE2mMUrJY4l8thbW3NJdGGeX7VoaTRYdXz2j0UrMYYgOMcwP4inHKKpaUlZDIZNJtNZLNZRz4TiYRLulcPLMs4UnIJwJV15dhVvqJ6X3qVm82mk3jqZ9Q5jnOeSr30sycSCZw7dw7lchnNZjMwt9i8LV6z1WoFuJISYZaAW1xcPJCkZwk8cRy2Y1pMRJB//Md//MBrH//4xw+8Rs/FcWHYJMlOw86gLn1d/Vy4cAHdbhcXL150xqJWqwUIM70xuorT0if80jSLXT3X7LyLi4uus2liTLlcRiqVQj6fdwJ+YL/WYbFYRK1Wc2EY6n64S5+2gYaUnZudlIOUGxGM6mSTdEJvmDxOC1SLq2UTVe5kvaxAuC4uTH5hjSv/J7nU8/ijBoiEk3OWZrhzTtJFbliZNzXqNsmOpFgzzJk0qIaN7eQ8x7lFPeF8TpwPJ03S8/A4CeiYTCaTrjSa2i9dDE96LY3qaH7TOK80x75uO28Xz/xtvcG045r7wLZrroAlyCTJNrkfCMrHLCm1cyMX9JRrqZyCz1G5DT+D8iN1RmgU7DTNIxMR5Fl5DmYN7QA0LlqQm9AybPrlsXRaqVTC0tISbty44apddLtdXL9+HZ1OB5cuXXIeqGg06jok/+71eiiVSk5bmE6nXYIccGcTAXp39/b2sLGxEchCZadKpVIolUpu9aj7prNj0YtsB5l6kLjKbTabbpvYw5JcT449JsXd6ithizrVILO8mnozwhJpeC1Lkvk3zyV4ni6aeT4lDGpgOF613BzDqb1ezyXn8F40cByvrKOuc5vOFzzGhlJt1juNrCW9WnJSw7Sa58BnoQb+qN/zYTxDfh7ymAa0wWr7uIikh9SOd7vo1XEB7G8UppuHaOIrx5baZY4zLoo1J6HX6wWiv4PBHa1/s9lEuVx2TrJ6vR4g6ZFIxOmaGQGv1WpOg8yfVCrlSKxGwoYtuDlfklyn02m33byFlrVV0qyLbp2TlLizPeNkLfOAuRaDTPrwNDyoqzx+0alU6oDkQDtrOp3GysoKMpkMbt++jfX1dSeR2N7eDmwJywHS7XYDZZpoCDmI2u02tre30Wq1nL6INU8ZbtCkoXQ67Yi9Js4wqabb7QbKVpEwa+iV+iZLmK3n3U4M9jUPj+PCLCfEUVILGoBWq+USXnShb0OydpMOJXGap2A9uHb8WGOhYct4PI5kMol0Ou2INyUVnDOYMU6Jha1FatumxNZm3HN+4fWsc4CblZCYMwpGw6bJhwDcXGKf+bTwc43HcYMaZB2X7Ns2uYxOJ7sotvOBfY3QKDUJuV6LkWiOPS0GoIt2klmVeOjcQ2LOuYILaHIdJu3yh22jBzqMlIZ9LpVlcv7QCJL1anNhoATZetrZXs5pYRjm9DhJTE2Qn3rqKTzyyCN46qmnAq//zu/8Dp577jn81m/91qzadgDD5BXAvkFSLZESZxsa5TE0SNFoFMvLy67iRaVScSFReohIQtXYUHjPLWMpvie55cYlyWQSpVIJZ8+eRT6fD6w0eR12RgAuLFqtVjEYDAJJR+pNIjjwgGAJOm5jPe1zDgs5e3gcBrPoP1b6YN+z9+IYV09HGEhglfjZiJn1tug9rKc2jEAqQeb8QkOq21aTIPOzUpdsybl+Zk3WsSSZMisrNeFvzlXcSZRzjC7QdderYTtgnRTmqS0e8wP2+0wmE4iccKzYrdnVUTXJAjBs/tE5Qd9nrWArVdLFqpJptlEJskqo6Pgi4eTY16oWliBzK2kt5WblIRoZslEzRqVt5I2/+ZnVO64edgCBdnExMux5zhOmJsif//znA0l7xPd///fjk5/85EwJcpjhU9iVkHYqGiIaP61GQaghJGFOp9N4+OGHA+XdOp0OyuVywNNEbxC9LizbFI/Hsbi4iIWFBbzpTW9Cu91GrVZzGZz0TKtXh/eg0WISHvdQ7/V62N7exmAwcBIQtiGsDjIHWb1exzPPPDP02Y16vt7weEyKcSHzWRCZcV4PeyyNYrFYdOFNew1dLANBw2AXiNZry7mDcwSTVzQ6xMXz6uqqq3+eyWTcnEAvDIkzQ5BsR6PRCBgfzZ6nJzqXy7nKN2HPilpCkgCtzEPPGb1Q6vHSZD71BNGY+/nBY96gTifV2w6r5ADsE1DKE5T4qdeWBJU/ujjWBXjYApvXpF22i062T0u0aXS63W67XCJuRsREPn4GnqtRJ26Mpgtgne+4CAeCm6DoArnX6zkdMj3X9tlpjodGnjRJj3PQ8vLyqZBWEFMT5K2tLbcNoqJYLGJzc3MmjRqGUVIB6orUwKgmSMMGAAKeWoJfHDup7nSXz+cPeHLUk0wtcyqVwtmzZ5FIJFAoFAKdkjtpVSoVFxbR1asm7DABkEaWHZwrMQ5Sgp0VgKthyG2rLcI657jFiIfHYXCc3gEdz5Y004Bx7NgEVx3LFhqB0XvZewM44CmhDIHjl7v6UYLFMCq9tVq9QucdYL8iR5julwYonU47o6XeKEKlXPRmhc05YV52NWQ2ic/DYx5hx4ASVssd7FxgN8gZDPaT/8OiNNYxZcfLMG80Pa06ljgPkKhWKhU3TpPJpJNAcdyqlxhAIKpso11h5d7YRn5mklrOlVwQ6OvW4z1qLgjzMNMBSI/6aZhLpibIjzzyCL70pS/hQx/6UOD1P/qjP7rrtZEtVFNEr+3CwoKTRly/ft11lHa7jXK5jMFggJWVFSe1yOfzyGQyB0IQKmmgl4UDJJvNYmFhAYuLi0ilUjh37pyrVEFPkSbwcLVHT7Im7LFUinqHk8kkWq2W0wzW63VHvDV8k8lkXJtbrRZu3LiB73znO4EV42GfqyfNHqNwlP5xWF3rsPM4B2jCqho/nmMXmXbBzeNsJQklrpwT6CGhLIvbyXN8qhaRZZOi0WigXCTvwWtpKJIGWY0eNzOigWdijN05U5Np6vV6gCCr7pEaRi6wSQqsR/qoSdvjIg4eHtNCPce0s2HeXLso5vuM2AJBGaZ6aUkSOZ60VrESY+uMU+cV22iThHks71etVgOfJZVKIZfLIZlMAtgn1HoPvZbOFZz/Wq3WAUeYFi7QOUwJMqPklK4A+wnLWvVLn6nKXZV/cG7WpMV5xtQE+ed//ufxoQ99CBsbG/iRH/kRAMBXvvIV/OZv/uax6o/DJlTrQSLpbLVaTspAMlqv11GtVp1uh9dkhjklFTaTk9pjbQc7EhAsz8bSbqlUyoU/AQSKd1NT3Gw2UavVXKk3TYBJp9MusXAwuKNfXlpaQi6XcyEK7cSakMiwCtv8wAMP4LnnnhtLPoaFrT0x9pgHTLtIU48qJ2ydK3TM8LUwD4uONf1fjTANDBesnEdU/6tGS2uYa7hS8yV4vXGVI1SiASDUW6SkWq+n0g2SYS7SVdLhvccepwUci8B+2THtxxr91dcZcSXUgwwEcxrU82zHmWLc/5ZIAvs7aGqZN3IQTfbTDYJ4LSX/1DNrG7ngJglWQq2fxz4jPlf9rfMfF9xhiwVLkDmv3LMe5Pe///1otVr41V/9VfzKr/wKAODBBx/EZz7zGTz55JMzbyAR5nWwHiR2Xn6p6XTaFbgvFosHwg38crkq4xfNL5TaPHYoNbhMptnb20M0GkW1WnWJer1ez+1jTlLOAt62dBMNJbU/ANyGJZrhevbsWdcGXd3pLlmUZvBamUwG3/u934sXXnhhaAj1tIjlPe5dzHIRpl4KXXRq/+fYqdfrzstMuZMaAI5zW9ZJdXcqrSDx5DV1Jy+2idErAG7c2sUuE3aZzGfHqH1eiUQCuVzO6Q/VI80262ePRqMHKuKwXa1Wyy2y+dw0nDyL78rPMx6zgtow2mfac1u20P7QvtNel8tld131pBIadeF9eH+dF4ZpkMMIst5PCXitVgtopblo5QZj/FxaelYJsiXwXPhaWYhWoOBiW+cibbv10qvUhPJW3ovnkevwGnQK6vc1z064Q5V5++AHP4gPfvCD2NjYQCaTQT6fn3W7JoZ+gdo5mCyjlR007KI/OsB4DS1vZDsV70cPMwlts9lEoVBAJpNxMggtb8J9zRm6ofdYjbiuXNlhgTsebmajMiyiGmYaea37GI1GXS3IYXpLIowoz3PH9ZgvzMNEp94QJs2y6owuii0pVc8HjVLYmA8Lz1oNMsejepLpqVYPsnqhgH3PL69JjbH13PBv9RwzOVhLKNFLrfOcPiP1XPOzNxoNtyDgfZQgz+r7DXN2eHgcBezDqVQKCwsLSCQSB+RBwH6f7vV6brxw5znKG8Kq0dDpxLnBjodh/Vn5hkauOQ/oZmQEOYFKoTiPJZPJA55ibaeeYxfmw3IqdCGtiYiUcVjNNOdYPgctHaneYvUg6/MatmCYRxy6DvLGxgauXLkCAHjta1+LlZWVmTVqWthQATuFen5JKjV8wN8qPie0k+mqkN4hXp8eYUooNBSiq1c1xsB+Ao4N/dpQEMMo9OqotojnsHPaEDJXmgzJ8D0Pj3lGGNmetN9yPHDTHno2VL6gWdphnuFhY1L/JyzxJDEmSebYo0HRZB8NgypRp2col8sFxrUlydQN8jcNPj3f1BXbMCsXzPpM+v2+y5dg1rtGtXS3vVHf092En8s8FJQfFAoFV8FqmGOIBJkVZTSvBwg6xUgEtWa5tcO8v0oYlGOoN1b5AM9T6CKZcwSJMaWXfE9LRrKtbBc5Cu9ntc+8F3+rZ1136WPbdR7SaJkSZuUlKt0K22zkNGBqglyr1fBzP/dz+NznPucebiwWw5NPPolPfepTyGazM28kMD4sZwmy7qin4QAAAbLKjgMcLPPEQUTCaz03wL6nRr3E5XLZlWir1WrOcLF8C8s9qT4SCFbWsO3t9/vuXH5eLdEEwIUuBoOBS7ZhO07amHl4TINJoxhhHqIwfSBwMMGM91DJVJiXxb6uEg5CE21pUNUr1Ov1UK1WA55mEmqepwSZ3phutxuQPOhcRgOkUSQATqLBe9lwMqNP9praNs5xNOLcLIlEPCwrflJ477HHLBDmkYzFYlhcXEQ8Hg/YQ/XSWm8uoQtkBW20VnWIRO4kvHIxy/twTggbG2rnyS80eVedY+QGlmjyf01CpsxCJaBWhqlRKY2QkVzzfc4b+rpyDuVCw74TRrN1sc62VyoVx4mOMofcLRwqSe/P/uzP8MUvfhE/8AM/AAD46le/iqeeegof/vCH8ZnPfGbmjVSMInqqE7YrFvUKqfHlTnf09tK7w+vRoCiRZbJdLBbDwsICstkszp49i4cffhirq6vOgA7zGrOjaYY7STHDPJpARMmIzaxn+9rtNur1OsrlcmC1piHXYc9sXLhT35v3zuwxfzjOcHoYOR4F1dCFeahVhmEnb118M6qkxDnMy6ttHAyClTRUx8z/7XlqHAn+zfb1ej1nKHl/GjHWVubxnN9sm8OeEz1l6pmiMVdPm4fHvIDjkrbS5t3oGLPjTTGsUovVDLO8oq02E9auYbDOK477YZ5YYL/mus4pKndQb7D1AOs4Vk6k3EnnEqs31mdhZaucL2z0ncertFSjd/OMQ20U8od/+Id429ve5l57xzvegUwmg3e9613HSpBHhV3Va0RCCwS1xGqsB4OB2w6aWaIkoolEwhkeNTZEIpHAmTNnEI1GcenSJbzlLW/BysoKIpH9uqZcQTUaDZeoxxVVOp12hFyrapDskuCrTGN9fT3QPnY8DhqSdRb/r1araLfbeP755wOVOPicJvHIj/rfw2MewX6tSSyc9DXrm+8THO86h/B6YRo+Qg0AcNDrzPtqpMreg6/Rg0Piqztl2fGqJNVGmlTuocZKE2+slISfhf/TgNpQczqdDmiUDzMvHOeiyeP+Bfurlkm045OeW03g45jh+GOeD2HHvcqOgDtRdY61MKJrc504dnWPBKsr1nvqhmW8dyqVcrxE+QntP+WevI+VlHEMhtl51Uzbz0HoM7NyEss3otGok7HcunXL5V6dBk4xNUGu1+s4e/bsgdfPnDnj6ggeB8atwmwWqGqGw67F1QzbrANJd8phcf9MJuMMXjabRT6fxwMPPIA3v/nNAcNHo8bMWHbOTqfjMlOZQa6aH/UGW6heSRMFbJiXWbVsezQaxYsvvjh0VTsqTOLhcVIYNnlbjDrGTtCczCOR/Sx3DSPac/RcGjf1rFijR6gRscYxzNBw/IZVmbGbByih1UQ/NVIq1bAEeBT0OYaFmfm6Jd2HgSfHHscFjgf1jBLWA8rj9bfmDujrKn/g/1y4cgc+rQyj56pHepTNHWaT1YvMa2livrZJCbLOFTYSpTpq2wb1YNsFNJ+dPl+dd8hn+FnIcVhmV6PapwFTE+QnnngCH/3oR/G5z33O1RBuNBr42Mc+hieeeGLmDZwUqtnRv/X9ZrPpVl5qFFV7o4ZQE2WYFV8oFHDmzBlcunTJrTQZ8qDXh/fn9pXlctmVN9H6xqo5tqtI3ShEDS1XhNQ8Z7PZQChEM04jkUigdM1h4Qmzx93GUfqcemWA/SiSEjsboQmTHXAcqbdEvdE0kGpE9UdLrum1baSLx3IOUY2iFvBX42+NjNUsWkOnx9kfnQ95D+vF4hyTTqdRq9UO/d14eMwC1rbzN8eLShL0eMsNNNITj8fd7rUEF9Fa3UHzfpjkt7CwEJB3WjmVto/jzVaG4G87pygJ1VwBjm+WlGS0jI4/Lr6bzWZgTuC9+X7YHAYgMCfaaBXnK8613W7XOQdZ4paLh1arhVu3bmFvby/g2T4NmJog//Zv/zbe/va34+LFi/iu7/ouAMA3vvENpNNpfPnLX555A6eBdiZ2YiWP3LmOpJZl0YD9LG0mwnGwLCwsIJVKYWlpCYuLi27HvHPnzqHRaATCNfT0NhoN7O7u4vbt2y5TliXZuMKzHcQO2nQ6HRD8q9idqzRgv6yLLRhOhHmmhj073jvsdQ+PecQwL3KY/l4JsmrmVKLAa9rQLHBQ58fjLNG1C25eU9usf9NwkpjS0OnnUo8x/9fPb71jSnot0dW/1SMV9ln1uprkM2uogfbwGIewcc8+O8wLy/MsyeTr8Xjc7YqrY4ckkNs7q0OMYCJtOp0OOOq0DrqtlmUXovY4tmtYzgCdZfzNa5JzcOHPuULHM6PPmkuh2mUb7dL2KFkmz9IIuEpWy+Uytre3Ua/XA5V6TgumJshveMMb8Oyzz+J3f/d38cwzzwAAfuqnfgrvfve73VbHx4VJwq4kqTYphR2FXya/+GKx6MgwALf7FYlyLpdDJpNBqVTC0tISCoUCksmkWzFFIhHnKWYt5M3NTayvr2NjY8NlfmvWqRo31TmHeYX4mbRzq7dYO50ex2MKhQK2t7dn+j14eEyKkwinc+Kmt0LbohEZLorDPDla2UYNjBJkzi1qdK0eb9TnV2OpsikNn/J99YipQeTn4ZjXZ6DGP4yo83zOq2rM9fNrAo/mPxwVvK+XXHgcFRr5sAjjDJYgA3AEuVgsHkgg6/f7bvOuWq0W8KAOBgNX0aJQKABAYJyELbyBfeeWHsd5QEkxjwsjyJyHNC+AJJ55BOoo5O94PO4kIZp8Gxblsk42ne9I0Pk3dzPmfLK3t4fNzU00Go1TJa0gDlUHOZvN4qd/+qdn3ZaZgRO5GhvNxgTudGBucqKdnV88w6MszdZoNFCpVJyXmaEEnre+vo4bN26gUqmgXC47LXGxWHReaA1raLmVWCzmyrKpfpkdvN/vI5fLHchA7XQ6bsVKLxCPj0TuaC0vX76M7e1t19mHLTCsZ+k0rfI85hcn1Y94XxoH6xEB4GoIK5nl2LIhRb0ux7waNkuOCStZoDeKhkrbpwbH1krmwl6zz3n8MK+ZRpr0c+mxltDrPKHZ6zpvauLzUeaKYc/Yw2McwrzHKysrKJVKobIiXeTxfI4bOrBSqRQymYxzWPFcJYvs/9xxkr+5ay8dajzXSqK0zyvfINhOlVrxeM4VNgdAJV3AvvxBd7YDgtFkSj7Jjyj9tIl5+rx1rmJUnDlcvD897bVaDevr64EN0U4jDkWQr1y5gk996lP49re/DQB47LHH8KEPfQivfe1rZ9o4xTgvg3pX7RfL39Ty8f/BYOBWg8O0edT5MmzBVRfJLCtG3Lx5Ezdv3nTJeP1+H/l8Hul02lWeICnWMCwA570mGo2GO4fSCraH5yrhZzu1MDgJd5gOa9QztM/Zhz49TiusFzfMqHJcaV3UcaRvlEfKHme9uDpP6TE04jxWybTVFKvR1lJONiysVSuGyUa0DTpX6j2UIId5sqaF9xp7zBLsT7S3diE8DBpV0QoWygEIXZwqiY5EIo4XpNNpZ+ftuLcyCtv2MEJOpxfvb6UWOr8psbUVesKeBUkrP7cm+vLaOidoxEy91qq1pueaBHlnZyeUbJ8mHKrM20/+5E/ie77ne1xS3l//9V/j8ccfx+///u/jJ37iJ2beyEmgHU31uuzU8XjcVaJgJ2D1CEI1TDQ+rVYLlUrFbcPaaDRctYher+dkFZpko1pn6pbsCgzY10RyAKlRpGSDUo7t7W1H8LmrDj8nf/OzkxzXajXcunXL3WMSox/mMfPwmDeMinhY4qgyCCDo8bXn625ZfM9KF6y2NxK5U5mGRkPnIRqwwWA/MZBt4GIW2CeeKqvSv22OgpJirSva7/fd/KTVcWhA9TPzOvY5WAPO62pt6KMQXE+OPY4K69mNxWI4e/asK6FqF3lhEio9Nx6PO4Krnt8wcpdKpVx0mra/UChgaWkptCSkddqpfEnln/oa/9YxqccQHLvJZNKN6WaziXa7HfgMnMdUO00+otElrZShTgPykVQq5RYFWgUsEonghRdewO7uLnZ3d92cA5zuBfHUBPkjH/kIfumXfgkf//jHA69/9KMfxUc+8pFjJ8hhqy3+rasoJruwM+mqyoYMmfWqYUb13tRqNcRiMWxtbTlJRD6fRzwedzWOWalCB2CpVHKrWq2/GubRsgZf6zRylZbL5QIGle2lJpqhH+BOktLVq1exsbExUecc9lw9POYNk/Rnjg2OYS2VpgTZenJ1Mh/miQrTOVpyq57bTqfjcg1oOOixUWKuVWssSbVebkt4VSbGxbKSZr5nPeW6cCAsOddrMfnHw2NewH6cTCZd1JWv6zFAUFphzw/TMIfZQXVokVxrudVxTibrndbjdEFt220/E7C/YZBW2FGiq+Pczi06r2h5XI55m/hnk+/q9Tp2dnZcW8iBbO7HacbUBPnmzZt48sknD7z+nve8B7/xG78xk0YRkzxkftk0OOy0LIWmIQrWNFbDUK/XXQYqPUGqE+agoXyBxm9rawvxeBzNZhPpdBrnzp1DIpFwJJnXLhQKgS1btaOqgWb9xUwmExgkDGO02223zStwZ8WYSqVcJQ56rnd3d3Hz5k1cu3YNr7zyysjVm30v7DhrqD08LI7qTTxqvwo7n95cSo94Ly4+1TNEr49dHKvBGgZdaPNYLR3H8c7QK+cl3Q6aP6rv5ZyjHmAl1Gyf3ockmc9EPU4aNeNr6lXia/pM9TnQiFYqldBa7R4eYZi193CYN5iJ9tw90hJLm2ugFWz4P73HPG/YAlkJLvdF4ELY1he280qYPdWIk0aV9DNyPNuEPc4NupsmJRDUVuscRMcb28k5gtUodHOVZrOJSOSO7rparWJ7e9uVwLt27RrK5bLjGPZzhj2r04ipCfLb3vY2/MVf/AUeeeSRwOtf/epX8da3vnVmDQPCjeew1+ygYbFsyhKsEeBA4TF2Fcb3bTiE3ptms+m0w8ViEaurq0gmk9jb2wNwRwR/8+ZNJ7fgualUKrQjcfBwC2relx1/MLhTBJyrM3Zc/Vzb29t48cUXce3aNWxubqJSqYzNOLcrytPcmT1OBkfpN8ex6FJyV6/Xkc1mA4llVq+rurpWqxV4jREa/m/nA5Jtjllew5LwZDKJTCbjpE+MJOlueZlMBtls1oV6tQ3Dnpcu+Pm+eoN0vlTSrOfSIA/bVIlet36/fyCv4bDfj59n7g8c9/esNlP1tPyxCWI69nm+yiyA0f2Tx1oJViqVQi6XQyqVCkSHVJ4EBKvO6DE6ZxCpVCogp7RSTnIc6qMBOOegch/eVx18ujMwsO8k0M+u0epGo+FqKw8GA5TLZZeHpfOLfVanHVMT5He+8534xV/8Rfzd3/0dvu/7vg/AHQ3yH/zBH+BjH/sYvvCFLwSOPU4M68hWbE4Pq66OgP3OTk8sz1XtooZiddcr6ptzuRwWFxdRKpUOlHGjgWYHTqfTgaoTapy0zWHaIf2tA4ztbrVaeOmll/DCCy9gZ2fHlaCb5lnq73HP2MNjHjCMYJMMciFL7RxwsFa4SpqAg8SR3hclvLYUE8c+iTbzB1SewPZUq1V3r0ql4gwd66+qd0rHZViYVUOohM5l2k62gddTD7IaRyXdaiSHGfJRGBYh8NEoj1lBCTL7LqWTJJDAfm6ChcolhnmNFZovoAvgbDbrSCnzjoDgGLDEGYBzuDFazPdJkMlHKpWKy4li0QCVVum9dB6x3nQ7l+hxYfOJnqvP0v6297sXMDVB/pmf+RkAwKc//Wl8+tOfDn0PQGByPQxGGb6w/9U7QkLKsAdfY2hBV40cWOykNqlFr8H7kPRGo3fqDGezWZfFmkqlUKlUkE6n8cwzz2B3d9eVkFleXg7IKyzZteFcEmCWcgL2NzShHpCGfX19HVeuXMHW1pZLzhkXvg7TOtnnao2xN2weFkedEMMiGJNEjsKg5RPp0dUoEeVPKjlQDR49Q/Y1q+njApnHq+6XhJoljiqVCmKxGMrlMiqVCqrVauA+HP/U+bGNOu8QtsQToeSW7dXPyb+V3KpsjPOhjTjxHErR6P2eFMO+Mz+XeBwGdq5RMler1dw4B4IeUB5r5QkE7b8lh1YuoDkLfI0Otkql4hbiSiS1GACj1kpShxHyTqeDbreLnZ0dNJtNlMtlNy/pota20ZLXYXOq/YyTOMPutzE7NUEet7qaNcZ9ucA+kWQoMJlMusS1arXqahnTg6thSNYcVg1QIpFwoRJ2/FgsFtjdLpPJoFgsYnl52R1LnSGNMjXKNJrUSbVaLSeP4PPksfysNJxsD71gvd7+LoA8bzAY4MqVK9jY2HD6wEkM0Lj3h5FmD49pYBdidkIP+/uwUI2eLkD1R+cwjSqFEXT1OlmvkUK9OABcQh43ESKprtVqqNfroV4atnthYcFFojR6pERdJSKjDJ+SBUuaOZdpGFZ/a3jYVuu52/CE2mMYOI5YQUZ1vJb0WQ+pvq/a3DAirtD5TAsB8Hw7RiwJHUaONULFOaVer6NWq6FarR6QQYRhUmI86txJX7/Xx+Wh6iDfDYR1gGFfBldsv/u7v4v3ve99AOA8PIuLi47olsvlgJcoEongzJkzzngBdzIx19fXAwaoVCo50k2i2mg00Ol0sLe3566pOqBEIoF8Po9sNuvCJ5ubm1haWgponVk3mcdks1lkMpkDdQxJjEmsd3Z2sL6+jmvXruHv//7vnfY5zPB6eMwLZjmZhnl3+KNeWSbNMfyp2djq7bGGVOuNDxtXJJ0qwWByDAAXASLJ5KI37FokoZRuFQoFlyGvP+o9svrKMI+Sfh7OVWFGlp5kOgo4p1EawrDxNN9h2Jw9alE0bH6f9Noe84VJvJJHuR4XsLpLrkolrCRAx7JKpAC4cykn0vfsph28PheMHJO8ribD2uiwrZhlJRK8BucuEmR9fxSGRX3DSP9hxs+4xcO9hIkJ8jve8Q783u/9HhYWFgAAn/zkJ/GBD3wApVIJALC1tYW3vvWtePrppw/dmHFf2LjVTaVSwf/6X/8LP/iDP4ilpSVXTomTPCd/dvawsieRSCSwwxZF/joo2u02arVaoHxTNpt1x/b7fVSrVbeS5SDudDqoVqtOh0yN1N7eHhqNBhKJBHK5HBqNhhvskcj+9q43b97ElStXcPXqVWxvbzuSPi40NOxZj3vGdrXr4TFr2D42zWRr+6Ydyxyvmkhiz9djw9qlsgP7vzXAqinkglbDqeM+I4+hx5kZ49peTbYJG58kt2rgdcFAzzYRFjLWRQGPUdnZLDxS+v4kIeB72Qh7HA0qAwL2N94aNuatrbQ6fAu7oFVSy2i0jjP1FisJtlViwv7WdvLaNrFQj7HnD5tL7ydSO0tMTJC//OUvuxq7APCJT3wC73rXuxxB7na7uHLlypEaMympC/NI8L319XX81V/9FR577DE88MADTr/LcizcvENXe7rzDZP2IpGICylq0W0AgQ1A+DeAwEpRS8dRAtJoNJzkIxqNotFouJJsu7u7WFhYwOrqKh588EFHxOv1Ora2tvD3f//3qFarbtMSq0Oa5PnZZz3MS2+/D0+SPUZh0pDfYd+f5hrWONF40osLBPu49f5oaTXNIgcQ2G2L1So492iybbPZdFvTc1fNScB2VSoVNJtNl9egRJlG2G4aYBPygP0omlbE4LGUgPGzqIeLx9OLBezrIWcxD4QtRjw8DoNIJOI0vuvr6yiVSigUCoHICX8rTwiLCoVp/oFgXXA6u9TpVavVkE6n3Txgo1L0cI/ahdJKtNieRqMRyCcK8w6PI79h95x2zN2vhHpignzSk9qkkot2u43r16+7kObFixcD9Q058VOz3O12nbaXsEX41eiosSOB7nQ6gZIqrIdcr9fdfuUk01tbW8jn8xgMBnjppZfw0ksv4ebNm86znE6n8fLLLyMSibgtG8vlMq5evXogOeZ+7bQepweH0cEdFiqbsF5bTUQj6SUBVO+OEuthpNvuKMcfSqtqtRr29vbcphrTfnYaX25C1Gw2nVxL28qFP18b9qzVm26T8PR8YL8iABMQSfBtHWYPj5NCWNSh37+zoRcXfITdOlrzE4YRx7CxT1hZRavVQrVadbvsMt+JPIMRm7Bd8JRIcwHL9iYSiUC5V+sl9rg7mFsN8jjoytB6UTudDq5du+a8tcvLy1hdXXUeIDWC6oW1A0e3nWTGN++hZY+oCeax6XQa5XIZrVYLjUbDlUbqdrvY2tpyJVueeeYZvPLKKyiXy46w93o9XL16FZFIxJHvwSBct3iUVeA0Hjk/ID1mheOMRtiwpQ2ZcmxrDVCdB1SKYMOaKqcI80QDd8gmd7ssl8uBElOTwHqpKpWKKw9JYs8sfS1NxfaFSUbCnoO2VyNoOtepgdb50j7nu4Wwe3qy7kFQ1qibcgH7fTzMcxxmD3W8sN/zWM4LuoU7t1tuNBousTaVSiGbzbo8BKtTVq5BcOyRW+iGZR4nh4m/gbAV1zwQp1Erq52dHfzlX/4lEokE/vk//+dYXV1FKpUCECyMXa/XncGhESJarZbLOmfZKGA/DEniu7Oz4/YqT6fTrpST3dO9Wq2i1+vhhRdewNWrV7G3t3fA+LCaxbBVrmIaD90oIxP23Q7TNnl4HBazlFuEjQ1KKTTUyeNVK8jzrRHSQv+ZTMZVvbH3s55mJv/dvn0bt27dciWZDgsu9LlIBuAqWGQyGdceNbY0/iqroCeYXnOGfFkdh5ISGmjqjfnsNDmP8HOBxzxBvchKaoH9aLBGjtnPdfMgLpzVXvPavCZtvTq8qtVqgB/QqcZKNLy2jdxYxxPf58KcEk7d6GQaB5fHbDCVxOJ973ufI5jNZhMf+MAHkMvlACCgTz4sbMccFgIZJk4PuxZlFF/60peQTCbxute9Dg8//DAWFxddEg0rU+h59NxS78tkOP5P7RMNC8+ltIPEmnVQub3s5uYmAOCFF15wdZf1s4R1/FED4ygDZdS5fgB6nAZYsmYTalUSYaUGGnbltUgq+aNGFEDAAGvyGreQ3t3ddQm8h3Ug8DOpx7rVarmdNEmA7Tn6ebhbliYV6mfW9jG8q542PkctKenhMY+wZNa+p9VZhnEMXWDyfyXeANw4Z3UJ1i3XhTeJszq37G+NVtn761ylEWRPjk8GExPk9773vYH/3/Oe9xw45sknnzxUIyYRno97fxhxVKLcbrfx9a9/Hd/85jeRz+dx6dIl/LN/9s9cKTZmnPM8rXjBBBVqhNR4Ws8LvUnr6+toNBrY3t5GtVpFJBLB9vb20HJM4xYFYYsDff04Q9fzEC3wmF8M63vHPZnrOOAClf8DcMlurVbLJd+SXJIMk0QD+1IKGjDrBeY5BDXCL7zwAlqtltMdhxnaMEwyrgaDAWq1GhqNBtrtNjKZDC5fvuwMul1cM8rF52G3vVZPG4+11Tn4Wev1Osrl8onKKzw8iFF2P2wBrOfZH0KTcHVhqvfTZN96ve6cZbrPgY4xnTcom7ALeatHBvY9yZHInSIBe3t7R4pEeRwNExPkz372s8fZjokxytM6CmpIVAP8/PPPI5VK4TWveQ0effRRFIvFQC1VGj0m2QH7lS4YRuFmI5VKBZVKBTs7O3j22WcDYRnNID+Mnm7Y5w4jx+MWD/aZTGqkPTzmCeMiIKoZtAlzYRpAveawMaFyKODO7l1ax1z1j5OS5ElAss4aztxultEv3UHM3j8MavgJzbHg/bRUnYfHPGGShSft27Dyh3pM2DVshEmhdp3X0AW0yp8Utv4yj9VxF1Z5x4/Bu4+5UIHP2kM5CUGkoWNo9Omnn8ZLL73kdHmpVAqLi4uo1+s4e/YsBoOBe4/JNzdu3ECr1XKbAdBAMqPVaiCPS8c3CWnW+9pzJvHYe3jMK8L6NP/vdruu5GKhUACwX8lGjZfdwtl6oBg+VeLd6/Xw8ssvOy/POMmXtivsGPt+2Oej94qVc3K5HM6dO4dCoRDY0pafKcz7RKJgSTKlFHxuqrH0c4DHPGDc2AEQiPBYXbJGkBgNHtW3eS1uBMTSiRp5UiKczWaRzWYPeKrDIj3qdVY5WLPZdFFnH709WcwFQT5MBxjVscd1Kru6pEemUqkEVoEbGxtotVrY3t4GACeroPHY3t52RsmGIMdphiYxOGFEXz+3vce45ziNLMXDYx4wzQJt2LEkydxc58yZM25HSibF6AYcQNADrZUiuACuVqtO9nCYUm7TjLEweRU94v1+H/l8Ho1Gw81PlhTbChcqKVEZCT8nS8yxHrOHx2kAx4hNvA2zjWHVLMJKqhFaTSYSubOZGDcmoTNMfzimbHIesO+ZtjXMed9ms+lKxw1rj8fdwVwQ5GlgCaIldcOkCMNCKGF/07O8vb3tyLNeUwfUKC/QrL2vuhod9l7Ya9MQjGGv+0HqcbcxTqIwKclkuLLf7yOdTrvNjdRzw2gPoe+xqgXnhVqthp2dHWxubh7Q907btnGwY573arfbiEQiaLfbWFhYQKPRQC6XQyKROKB/VO8ZPd/8PFzsAwiUpeLnm6ZMnYfH3YKNgBIkyLTNurmNlVeohp+vhVWMYMULlnAlQWalGiW8SrjtvKBjme1UvT/bOBjsV7vyOFnMBUG23hH72rBzwjDMmI663jgP77BwaZhhHNe+YdccBrsYmJb02mvZRcFRvXMeHuNwXH1nWs0tM8I3NzeRz+exuroKAI4Ex2KxQMUGGqtarYZut4v19XU0m03UajWXyT5NG8KM+iRysLDPoqHZzc1NJwVjaTqNdjWbTRdW3tjYcAlGJMvWA86FgE/M85g3jJMq0bGVTqcD5VoVHDfqxCLBtdfkwlmJcCqVQjQaxerqKuLxOPb29lzUOZlMBqrH2Co42m5dXFPPzJwGfc9HdE8Oc0GQj+LpHOYxHoZRxHrcdaYNi457b5hXbBRhn1RXPAzTLhQmec/DAwjvu/M0udPQse651ky2m2LwdWDfA723t+c2/wkbR2Gfddy4mcQRMO4ZUpOcTqcd2aWRZikqXoslJ+v1esCbTGPMH1shw8PjNKDf7zvJAxe9w46zSanjrqvJdfQmU3ahm4lZT/KwnXjtmGPdcSbF+rF38pgLgjwpsZsE47TJ9tiw86e571EIQNi9rEEMa/Mw7fCo6w8LSQ275rhre3goxskh9LhhsqhZ3V9hI0H9fh/b29sol8vY3t52GwOdOXMGqVTKeW+oAWQSrpZLG/Y5jzpHjRuDNuyrhrXf7+PWrVuIxWLIZDJIJpMoFovOW0yZCDcmsvOB3mOW34uHxywxatxQcrS9vY1+v+92g6SUgZIiW2JNrxM21vi31kNm1CmRSCCXywXGjb2+lSnRxnM+6nQ6bh+G27dvB5Ji58nBcD9iLgjyJJ6naeQWk3pyZmEADuP1nvSYo7b5qF51tskbSo9Z4jj61DShSBolYN87vLu7i1Qq5Qr/05PDyjTDyjMetq2zgn5mGm5NEmKJOz7zsIRCP749TgvGzR30IHPhS82wQheWk8DmGmn1Cr4PBCvhKNm2v1XnzHHKTcW4IOf7HieLuSDIh8W0GkR77t0if5N09EkN/DgN1qjrT9M2bzQ9jguTyqIOM0YnjY4MBgNX4xfAgUTcSe8zC2+rHfvDrjksCmRzImhoWZVnWPvulgEe5gDxc4zHNFDPK/8Hgvk51CBTRpTL5ZBKpRwhptfW7nZnr2evGyaN0EoV6hHudrsuYqPHsw16Ps+9ceMGKpUKarVaYKMhj5PFXBDkaSQWw2QRhzGid2uCnsY4TeMJm8SrPuwYnWTG3c8bM49JMclCbNJF2VHJsb3fOM9TmPxj3H2mGa+zwGEJ+SR5D7Ma52HPcZr5xsMjDGFR5bDxysUvE97sRh8kskyi0+gKk+uU8EajUafvtxVitC26Pf2w8m6xWMxt9sNIFXfMazabvmrMnGEuCPI0E7ROsNbojrvGPBG9MA9Q2HuTXmOaY7yB8rgbmLafTUKijjKGx5HLWZPDw2LaRcawuWSYM2HUtW37pyHkkyyC/NzjMWuE9SnKo8rlsqswQeJK8sqSiCxvqFtUkzjzuHQ67ZLneL56gMOS9Ah6jHUra9ZT39vbw97eHur1eqhX2+NkceIEOYzsAtN7GsZ5hcPuM41+eBTRHKfhnfRe02iBj5voz9NiwmP+MUzGEBYOteeEvTYrEnvYPmzDt5Pe46j3G3fMNCT/OMn6UY/18JgFRi3EIpEI1tfXsbu7i1KphFQq5XbTBO4QYibwDQaDQIlH/Zvafur56ZHm7rnxeByJRMKVf9PztbayJgrW63Wsr687OZTWKveYH5w4QbaYxAMcRp4P07GmOWcaffNhjplEKziJh0ZhPep+8HmcNIYR6XnFvLXNE1YPj8nB5DduFZ3JZNy4IJklidV6xwCct1i3YmfCLgD3Nz3IuuOmepr5GnCHbLP0IivmULbh7fP84cQJ8ihPcZicYlJiqFD5xVHbajGJPGIUSQ1r6zT3D7vHsHOG3Svs2dxtnbbHvYl56j9hY2uaKNIk5x0mQnUYTDJ2D3O9cdfwkSWP0wLqfHu9HnZ3d9FqtQL6YMor1NaxJjpwhzgnk8lAvWStQKGEmV5mlUmw5CLrlJNAb21toV6vO8mGtteT5PnCiRNkxTB98bTnhr13mPMsRoWLhx2j9wjzfk/rxbbnj5N+eIPmcTdwFBnSacS8jKtZGNRxWuVhDgd77FGfx91aXHjcf6Dmt1arOfKq3mHg4CYekcj+ltSUWNAbDezrnG1lCl6f/yt55jW4qyfhifF84sQJ8mk0oJMYx2Fe60k8yOO80HYhYY8/SpKfX8V6TItJFrXT5hPMqg+GRVCmnXNGeZ4nSSwMu9aksqdZa4oPg7C5LOwZjot+TXPOLLziHiePk7AlYWOy2+26qhFMqOt2u0ilUoGNRNrttpNj2LaT3HILa5VIcFt3Ju4lk0lEIhFHotvtNsrlMprNJqrVqi/ldkpw4gQZOFqSi514Gd44ivGb5t7DrhNGZofdP0wrfDeS8LSd9vWwdnl4jMMoGU/Y//pa2Pmjzp2GXE7y3mH6+igCPuz4o+Q+2DbOwgs1aa7CMJI7yfUPe47H6cZJOlzC7qteYsoc6BEeDAaOPCuXIEiyO52Ok1io5AKAq35hq2ZEo1GnO57lpkMex4sTJ8iH8XZaT8ZJhHZH6ZqnvWeYB+6onpWwZ3TYicqTZI9ZYZxOfprXDhvmP+7F82HGyiTjeNRrh71v2LnjPO3TLjzG3XcScu7hcRRoX6a3mDWS6e0F4Goj6zkEt5qn9ILHqP3Wsm9KjqPRqNu8xNc6Pj04cYJ8GISF+6YxqLOahCclseNen8bQ22PHXXPSzzpLo+dxf2KSfhI2VsfpXfX1cYu1SXSsky74DivJsPcIC/kOk17ZzznNwnbWToFJFy6zaI9fhHscJ8IWgNwumnaUWmPKH6LRqCvXpjpjHkcyrYl/fI3JfsViEZubm85bHbbVu8f8Ym4J8jjDMM2ka3/b12bVXt5zFteZ1bHTyFcmIRceHkfFYcnQYRaksxhPsxoL0xLdcZjnMTrMgeGJ8P2FeY4I0JtM3TChMk3KKazX13qRCRJmJd+9Xg/NZtMl683zM/EIYu4I8lGJ6zivxyQyhsPgMNcK00xPcp1R+s0wkjutx0zP8cbM46QxTsN6FN3wcWHYPDRufE86Px3VuztLhM1ds9aeDos8+PnJ47Cgfet2uwCCUkf+kCADCOySp9IJnhONRpFIJBCLxVzFjFarhVdeeQU3b948UT22x+EwFwQ5bKIbJaOYppNZz7G9L4+ZpE36+jgt8Ljw4jQ6wqOGTkc9t3GD1hshj1lj1lGSexWnTZc7yQLmsIuc+7kfeBwvdAE+ykGnBJmEeDAYuKQ+3Q2v2Wy6pD5uI61eao/TgRMnyLpqm4VXZNbG15LTcd4s+zlmkYAyKcmeNNQ8Tps9jSzDw2MajBrj80wEh2mK7wZOYgzO4jNOeu5h5xs/N3nMArb/6VgPS6jj2Gi1WkilUkgkEm43PFax2N7eRq1Ww97eXuAcj9OFEyfIwxBGnCfxZk7jDZ3GyztMumBfC2vDpNrJsGQexbQhmmHtD0sICjvXGyCPo+JeITxHWTSexgXnaWmnh8esMarvq13kttF7e3uo1WqIx+PY2dkBAFQqFbdTnifH84Np57UTJ8ijQm7jZAzjOt8wT+m4e09y7XHnDzsmLHGQr+sxk0KvMeyZ2XuNeg7DjvGE2WMYvBHw8PC4H2AXu61WC81m88D708opTkMU7V7BNM/4xAmyxbR6teMkbYe59qRaunFSjWmuP+55THOvw2oEPe5v+P7h4eFxP2Jc1Pqo1/CYHaZ19J0IQR5G6LyXcv5xWGLvcfrhv3uPeYbvn/OD0ygrIqaNiM0id8rj7mGaeeJECHKlUgEQLoD3OB2oVCpYWFg46WZ43EX4cetxGuDnppMH5wqWUPPwmDdMMk9EBiew1On3+7hx4wYKhYIPLZwyDAYDVCoVrK2t+bI19xn8uPWYZ/i5aX7g5wqPecU088SJEGQPDw8PDw8PDw+PeYVfZnt4eHh4eHh4eHgIPEH28PDw8PDw8PDwEHiC7OHh4eHh4eHh4SHwBNnDw8PDw8PDw8NDcCJl3nyG6+mFzxS/f+HHrcc8w89N8wM/V3jMK6aZJ06EIN+4cQOXLl06iVt7zAjXrl3DxYsXT7oZHncRftx6nAb4uenk4ecKj3nHJPPEiRDkQqHg/vary9MFVgXU79Dj/sCwccu/Z1kxcpJ5YZL7he3OGXZtu6PnuNfHtWPaeW3U7l3T7uw1rE2HwTTf7XHvhDqqLbz3YDDwc9McgN9BNBr1Nv4U4DBzzGnFYDBAv9+faJ44EYLMLyISidw3X8q9hPtpMHnsY9i4naYvTEqiZtW/hl1n0tdte/m/krVp73HYY09izE16T+0bky4qRi1ejrLo8HPTyeN+tvGT9P95hHUMTDLHnWZM8plOhCB7eHjcG7BEeR73HQpr12EmfTX64+4x7FnYc6cxpveSoRr3OYZ58T085h33whi9Fz7DLOAJsoeHx13FMC+hksphHkY9f9aEnNc6rOfXepdH3WPYe7MgyaOe693AvC6UPDxOC6YZP57MHh88Qfbw8Dg0DuvVHGcARnlr7TUO620cFd4/rNGZhLyPurZdPOg1dNEwyTX0vMNg3D1s22YJ/YzjFkqejHvcaxgn9Rp2/HGN9fsVniB7eHjcVRwnmbob9xt330gkgmg0ikQigWg06koJ9ft9dLtddLvd0OvxWolEIlS7ORgM0O120e/37wopPG5JR9hnGLYg8PBQ3E250WEiS0eF9vtRzoKwhfTdxDzJvo6jLZ4ge3h4HAlH8dqOe2/Saw+bHA9rOMI8mJMaykQigVQqhde+9rVYXFxEqVRCt9vF9vY2XnrpJbzyyivodruBa/K60WgUDzzwAEqlEkqlEmKxGGKxGPr9Pnq9Hq5cuYLt7W2Uy2X0+/2xCZNHlVsMO/ZufS+jvNSeON+fOMz3Pi3JHTYXjerLx1VpxpLgsDExbKFufx/12U3zWe/mwmLc93VY8uwJsoeHx9zhJEubhWGYtIGkdmFhAclkEplMBul0GrlcDpcuXUI+n0exWES320UqlUIkEkE6nUan00Gv10O320U0GkU8Hkc0GkUsFsPa2hry+Tzy+bzzQLM0UaVSQT6fR7VaRbfbRaPRQL1eR6PRcM9g3hInraE86fZ43F84bG6BjWAcJT/Ajsl4PI50Oo1kMolUKoVYLBY6t8TjcbeQZuRI/+71euj3+2g0Gm4+6ff77mdSGdSkEo5pP/ck78/yvFl7kT1B9vDwmEucBJGapMyYGs9IJIJUKoU3vOENWFlZwUMPPYRCoYBisYhOpxMwahcvXsTrXvc69Ho9tNttR3BjsRiy2Szi8Tji8Tja7TZ6vV7gvvQgnz171hnGWq2Gl156Cc899xxeeOGFA+0c9RlP4tl6YuwxDHcrVD+soo2+b/8eprefRLfb7/cDtaBJeguFAi5fvoyzZ8/i7NmzyGQyiMfjgeun02mk02l0u11HhCnT6nQ6aLfbaLfbaLVauHr1Kur1Ovb29tBsNtFqtVCv19HpdAK7xWnEadJnPk7aYefMUfPLqLyMUe0Z996k95kWniB7eHh4CMImf0UkEsGZM2ewurqKpaUl5HI5LCwsIJPJAAB6vR6azaYzRrwOPcHRaBT9fh8AnFeZx/V6PUQiEcRiMUes1fDYc8+fP49sNouHH34YX//617G1tTXx5/PwOI2YRiYRpuWfxf2HJfjq/xzHqVQKqVQKZ86cQS6Xw9LSEtLpNBYWFlAoFJDP512+Asd2v99HIpFwkSX1CHOe6PV66HQ6Tq5FUkxP8ubmJhqNBra3t9HpdNBsNt1zmeWzGPeMppFj2Dl3EplE2Puz+s49Qfbw8LivoZP5KM8x/45Gozhz5gwee+wxPProo8jn81hfX3de306ng8FgEPAKKUGmVykWiyGZTLpr0+jRKPJ6NJq8DtsTj8dx9uxZPPjgg0gmk7h27Rp2dnbcscfpWZkHWEPqcTpxGqIZwwjwMO8zpVHxeBy5XA75fB6PPvoozpw5gwceeMBJJzgP8DoqneA9eB+dB/Rz9Pt9pNNp510mrl+/jt3dXRdtarfbB/Ie7POYNLo06SJhkvPGYdLjJ/HoTwtPkD08PO5LTDOhJpNJvPGNb8Ti4iLW1taQzWaRzWaRSqUwGAyQz+ed5waAk0QAdwgzrwHsGzUNv8ZiMXeveDyOSCSCVqvlSLMl6VrpgobxiSeewOtf/3rs7u5ia2sLzz77rPMw2c8VZthPK8k8re32uIPjklYcNaFuWM4BfzhOo9Gomw9KpRKy2SwuXLiARCKBRCKBZDKJRCKBpaUlZDIZFItFxGIxt3jmvQaDgZNT6JinVIvzCYm1jn2NOMViMUSjUZRKJaTTaSQSCZfQe+vWLdy+fdvJL+h9Hvfcwp7HYb+3YR7iUcedFDxB9vDwuO8wKTmORCJIJpPI5XI4f/48VldX8eCDDwY8wTRKAAIEmUaJiXgqmaCnSD3C1AoqWdZz1PvCY/W1c+fOYWlpCZubm0gmk1hfX0ej0UCj0XDJO6OewTwk9E2L09Zej9MLjlV6h3O5HOLxOBKJhPMSr66uIp/P46GHHnLEmF7idDqNWCzmpBOJRCIwvoeRd84HmnRnJVtWShKNRt39OHekUin0+300m02X61Cr1Q4k9dn7T+tdnvaZzjM8Qfbw8DgShoW6R4XhjovYhCWMhLVtmDFSDRs9u48//jjOnj2LS5cuIZ1Oo91uO6NEQsyku06n44wXcMdQ8fh4PO48Qc1m04VGFZpQo4bLGkCScxrfeDyObDbrPEZnzpzBa1/7Wrzwwgu4evUqXnrpJVQqlaM9XINJn/VR7zHKw3UaSb3HySOMBOqCNazP8SeVSiGXy+GJJ55AsVhEqVRy18hkMkgmk46c6mKX45RzgEqueD6T8TRvQdvIc+nB1mRelW0lk0lEIhF0Oh1HxKPRKC5fvozV1VUkk0n0ej0888wzLqlvd3cXu7u7BxbSkyTzDZOanHZ4guzh4XGiOAppPo7JmMQzn8+jUChgZWUFS0tLrhzTsHNYr3gYkdU2k2BTbwzAhUb5P0O4PMdel9fheQzZ0ggDQLPZdIZ1b28Pm5ubLowbhmk0iKP+t6+Pyl5XhBlaT4I9jgvjQvzqnSXxZOnG5eVlLCwsYHFx0cmr0um0G9Nh0in+z2tzHFtCHCa/UjLNa2nCLuUSlHbwGopMJuPe7/V6WFxcRLvdRqfTQTqdRiaTcYv9VquFbreLdrs99jneq2PUE2QPD4+ZYJLySaMm0bBs5GmPnwZh56gX5qGHHsJrXvMarK6uOs8sCayGWmnEer2eM0wqo2ClCj2PnuNMJhPwTlnyrF4lNZq60Qg9VcyG573b7TYWFhbwmte8Bq961auwubmJP/7jP0alUjlQI/UoC41JknFGaZ/DzrsX5B8eJ4NRc0fY3KKLTULHNXfFXFxcxOLiIs6cOYOFhQVcvHgR+XweCwsLaLVaaDabyGaziMViaLfbgTHc7/eRTCad5IELWnqDbXTLEmnqk4E7Mi4SZJJkEt5Wq+UkHLFYzOmT6XnmXMQdPdfW1gAA2WwWrVYLrVYL29vbqNVquHHjBqrVKra2tgLzjz67UePysPPyPMETZA8Pj0Nj0glwUnIzDQkKk0yEHTNJ0gnJ7vLyMgqFAtbW1rCysoJ8Pu88ubyHenqHeUDp2VHSSyOjr6tnWD3CbJPqmDUUS8PHUC41kYlEwh2rhrBQKAAA3vKWt2BjYwPPPPMMWq3WRN6huwk+v1H9YNIFlMf9i0kjFkBQ1kTSyQTcixcvOplUOp1GKpVCsVhELpcLjFsm1nHcablGva7OGSS+bC+Js53X9Ph+vx/wKOvnIpHXiJJN5uP80ul0EI/HUSwWXTupi45GoygUCkilUmi1WqhWq1hfX8f6+rpbWNt2Dnu2o76f00CePUH28PA4cRwn0Rl3bTUuy8vLOHfuHB5//HH3Pg0SdYNhBNkm3PV6PfeahkL1nvxtQ6yqLda6qDyeXuVYLIZMJuOOz2azSCaTrvoF0ev1kM1mkUgk8Ja3vAXXrl3DzZs3sbe35zxdkzynaXCYax12ceThMQxh+nhLzNQrGo/HkUqlsLCwgIWFBTz++OOIRqMol8uOBBcKBWSzWXcO76HVJzSBV8ezSjaUZOr4Zpts23WhzXN03tBz6b0mYVcvuZaR1J36er2e00/3+32USiUnHXn66aexs7PjKmpME+Eb9j2cBniC7OHhMRWOY5KzE+5xZEsPM5bRaBSvfvWr3U54JJpsh02e0fJOSogZxux0Ouh0OgeItN6Xr5FIU56hRNuGeNPptDuPW1uXSqWAxCIej7uqFdxlq9lsIplMot1uo9Fo4OLFi/j3//7f4+tf/zq+9a1voVqtulJ0x42wkKwnux6zxDjSFibzSSQSyGQyeOMb34jHH3/cLYZXVlYwGAywuLiIvb09lMtlDAYDtNttbG9vuw2BONZSqZS7rkaEKK1QmYJKp2wbSWp1PtTj2W4r5+LCmfKKMDKrx5Nwk9wz4sTzuGC4ePEiYrGYk11Uq9WJZBajvofTAE+QPTw8Do1Zh8ruttaUBmB5eRlnz57F6urqgTJranzCEmuAYMUJGhvrBdIEHGvg1Dutx6vXiAZNdcwkxepNpiYxFosFCDwNOXf+W19fx/Xr19FqtaYmyGEyh7B+cFo9Rx6nD2FzxyjPMRGNRpFMJl2N84cffhjNZhO9Xg+ZTMaNnVarhVqtBmB/t8xoNOqS2ayESqNBtjqNTZ4bJRFTL7OtasP5SYmyJdg8zv6wnfo+78nPQelFoVBAt9tFrVbDYDBAs9l0n/leXuB6guzh4XEkHHWCPIonYtp7EDRgFy9exAMPPIC1tTXk8/nQhB0FtXz2GJU+hHlIVbdHrzCvx+Qavp5KpZzHOB6PI5lMOkPEECdLxxWLRZeVztApjTr1g8D+ZgNMJmw2m3j00Udx4cIF/O///b9x9erVUOM5Tgsc9v8oouzhcdyYJMlT5Qj5fB4XLlzAD//wD6NQKLg6wboZD8emLp77/T46nQ7q9fqBCJFqeu17PDes3VayYcmvEmQr0bAeas5HJMt6DOcp1UhHInc2INLnxbbncjmXvFytVnHlyhXU63WUy+VAu4Z9H5N8R/MIT5DhM6Q9PKbBcU1ud9tznMvlUCqVsLi46GQV1sioB1dDo8C+TIJ/q+GxUNmEfX4k3RrWTCaTjiinUikX/qSuMBaLIZVKudJzJMma8c7jKAmx3xkrc5w9exbNZhO3b98O7Lp33Djq9+3nbY8wjFugqfwhkUjgwoULuHDhgivlGI/H3ZjhmOQ40rHE+YDjkmMv7F6TIszTO2zhHfZZNWoE4MBCP2zhwONU9kWNsn52bphE6Um1WkUkEnGbEQ0bj/Z19VTPe9TJE2RMLjTX4/3k7OFxemBDrdlsFg8++CAuXbqECxcuIJlMumx1gl4jJb4kn0BwIidxZdIbDZXVE2sYlNegtzebzSISiSCdTiObzSKfzyOZTLrapO1229U67vV6TmJB40ayHI1GnceYpaV00wL1iEUiEXz3d3831tbW8Ed/9EeufBzb5+c5j9OCSfqpLnxZkeIHf/AHcebMGZw7dw7AvgRqMBig1Wq5Y1knmPMASbLKo9RrO+m4CSPEnHPsLncADuiZ2QZ+Ll5TPclhyYF6jBJhlqpkDXU+N9aBzmQySKVSqNVq2NjYwPXr13Hz5s0DiYZHmTfmgRwDniAPRdjKL2wV5OHhMd+w5Jh63XPnzqFYLA4t2WbnAK09rAQS2Pcgq+HUzHUan2ESDG7ewbAnPcfctYseKnqJu93uga1sqUXma5lMxmkmVYvMz0HQM5TL5dDr9dBoNNw51sszybzn50aPu4lRES2rtdWkuZWVFSwvL6NUKiGfz7sEOwCBiA6ryTBfwUZZwoin9SSHQfMZbJuVKA/TVY/KA7BcxVa+0N983eZMpFKpAxpm9ZzTCcDPsLu762opD2sz/57WKXlSuC8JclgnCjOO7AjsOLqSC/sSh13XGwwPj5OBnZiVIJ85cyZAPhVhlSc4/llPVD3FTMyjV4bllLT+KT0/1oAqQaahUpkF9X+RSMR5urmFrN6Df6dSKecNt/IKzVLnnMZtqrPZLDqdDprN5lTPd1To97jnPj+/egDjZV+6aE0kEkgmk1hdXXWbfuRyObc9cyQScUlouislCTIJsybE2ZKP+jMJ4QsjyZwP+L8eO4q7DCPndr4BgtzGEmRW8rHzIOeadDrtNjzi3FapVFz0Sq81iTxkGtwtDfPUBPlrX/sa/uqv/gq3bt0CAJw7dw5PPPEE3vKWt8y8cbPGMP2NitEZYlDtXjqdRqfTccaFWzPy3HGamrtlLDw87meMMxwAXL1geky1DqgaUV5LQ528JkmqHkuS2mq1AOzrikmYdYFN46EeHCbAsG3ZbBaZTMaFdYE78wdLve3u7jpvs/7wMzNE2ul0nNe40+mg0Wg4D48+n3Q6jde//vW4efOmq/ka5t2aFHfLgPk51WMcOD65TfTDDz+Mhx9+GAsLC8hkMo4IaiUXLlg5vrmQJInmvGC90kCwNCSTaUkg+b7KH5QEdzqdwLzDxTgAl1Og41yT7ICDm4/w87PN/J/vk+yTMId5iHUR0Ol03N/UZCcSCSwtLSGfz2NnZwdPP/10wIs8a3I8i/MnxcQEeX19HT/xEz+Bv/zLv8Tly5dx9uxZAMDt27fxn//zf8YP/MAP4POf/zzOnDlzbI09CjRcaEOG9CDlcjk8+uijePWrX41cLuey2qkNHAwGaDQaeOmll/Bnf/ZnyGaz2N3dRbPZPFB6RTcKGIZRko15CjN4eNwL4PjkjliUIgAHjYn1BqlHRA2iSizCQp7DPDlhXh6VStA7TK8w32NWvJ6j11Rir15p/pAwa71UGtfV1VW0221XM3lYVrqHx2mBjs9kMomlpSWcO3cOly9fdnOATcgDEBjrSkx1+3dC5VR23lBtsJ0TNKLE18MkEGEeXMtl9LdyHV2g2+vo8WHPzeqW6X3n+5oEnMlkXKJeMpkMSMZOMyYmyD/zMz+DXq+Hb3/723jNa14TeO/KlSt4//vfj5/92Z/FH/zBH8y8kUeFdgIW1C8WiyiVSjhz5gx2d3dRKpVw7tw5V3i/0+mg3W47r7EaqDe84Q147Wtfi3g8jk6ng1qthnK57MrDbG1t4R/+4R+ws7Pj7s/OdenSJSQSCXQ6Hbz88suoVCqBpAAe7+HhEY5hEZmw/60BicfjWFtbw5kzZxzZVOOhujoaCO5Mp0kxJK/aJq10wa2g9bossWbDpnZBbQ0f76UGSw02dX/JZNIRanqvuFEIo156f25VTYKQzWaRTqdx8+ZN3L59G+vr6xPNScOkZbOAj755HAUcL9zs4l/9q3/ltP2sXJPJZJxtt55jzg82wZURGWDfycYqOIQuri1x5hwStpmHyh3oubaL4kkcb8PGI6+tHnAez88O7Ccpq0NAk5Y5xxCsG01Otb29PbYEnLZzHnnPxAT5y1/+Mv78z//8ADkGgNe85jX4//6//w9ve9vbZtm2mUCNyTvf+U6sra25ci4kwSxTokk47AwMv2gZF2r02u22+3JLpRKAO2GQxcVFXLp0CX//93+PRqPhOgdDPMyIXVlZwUsvvYQbN2640Kz1aNm/PTw8JoeS5Ewmg2w265JyCEtIw0iZhidJfG3YVI/lDz1IKrGwYVW9LsmwJcQ8Vj1bPK7dbrtwLLC/5S0X9/yh15jX4D1IqoE789ijjz6KbreLjY0NP/d4nHrEYjEsLi66H5VFae7BsKiPelDtrnhKeDVRl7DEUCM9OjZJxDWBdljO0zCPryW7ei8ew7lLSbB6uXXO0cU4X6cHne+RZEciEWQyGXS7XZRKJQwGA9TrdVd9Rx2ApwkTE+RUKoVyuTz0/UqlEsgCPWnwS0skEjh//jz+9b/+1zhz5owjvWoU0uk0ksmk2zZSy7Xwh7pD1R7xfK7yuCKMRCIoFAp4y1veglarhd3dXWxtbaFWq6Fer6Pb7TpSnsvl8MgjjwAAWq0WyuUyyuWyGyhqSK00xMPjtGEWE+UoTduoa6+urmJxcREPPPAAstmse12TcTWrW1/r9XqOUFLPqJpDklPKGKjVY8kkbkGtO25pW5kApNpjnaNUNsE5jAtt7vrF47jwbzabaDabaLVaLhrGsDKwn2yTyWRchY2LFy/i0UcfRb/fx7PPPnvAAxb2XRy358fPdR5HQSKRwKtf/WpcvnwZS0tLAe+pSpLCtnLWvkcvs8oWeC2OJfUI8z214fzhcZYgU6bAKDOTcW2ECggSXl30WokGy8BRP80qOP1+P1AFx2qleawu1nl9jYZxcUBP+qte9Sqsr6+j0Whgb28v4GU+bZiYIP+H//Af8N73vhf//b//d/zoj/4oisUiAKBcLuMrX/kKfv7nfx4/9VM/dWwNnRTsvJFIBIuLi3jsscfwwAMP4MEHH0QkEnGGgsc1m01n7BjKYKdTwbxeX73MfM2WeIpEIlhaWkKv13Ohy62tLezs7ARWb/l8HgsLC0in0y5kwQxa/t9oNFCtVnH79m20Wq0DBssbEI/TgrCw37T9N+y8UaSZ4c/z58/j3LlzLtHNelKAfbLMMW8rU6jB0NCnepAobeC1GLa0W7PquZyD9D5sR6PRQK1WQ6VSAQAXCmaYlxILSsMo+9rc3ES5XEaj0XBeZp3j+JPJZJwRjkbvbGF94cIFPPbYY7h69SoqlYqfYzxOJTi2Ll++jPPnzwd2tuOiUqVS6kUOW/gpGeR4t5KHMNs8bBGphNzqka0nV73EJLI67yiBBfbnrnEL2GFSNT4frdqjn5X3o3yUhLlYLKLX62FtbQ0AUK/XA5rtsHvOKyYmyP/tv/039Pt9/ORP/qSrwQnAlRL6j//xP+K//tf/emwNnQbpdBqrq6t49NFH8Za3vAXLy8toNpsBzaGu5GwmqNXaqGcJ2Ncb0tjpSoqdR4X9hUIByWTSGWYe1+v1AgSZyTk8v9vtBrzKmUwGOzs7qFarbv934PR0Ng+PMBxn+I3emHQ6jbW1NaytrQVChkAwW1uNlEokeC0lsJrgxtc4F7RaLWd47XWs0aKBobHRMCcJcrVaRblcdl4gkmP+6P06nQ4qlQo2NzedB5kLblboYSk4VvXQ+8diMZw/fx6PPfYYNjY2HDEfhVl8f6c1DOsxP7CLZxLkS5cuYWVlxXmBVZpEUmnrnOs4VeeXXUzrfS0mkUoqHwmTd1hyrHOEchVLkPkZ2E6d42yb7TnAPkFWR4ImCfN11mOnQzGfzztnZLVaxfr6+oGImW3HvI77qSQWn/nMZ/Brv/Zr+Lu/+7tAmbc3v/nNzqN8ElDDViwWcfHiRXz/938/3vjGNwIIdm7WNWRH564w9PjoCk47BYkwr0H9MRNfeF31CqkHiyFUGmx6gZhR3+l03DVUrxiNRlEoFFxC4cbGBq5evYr19XVUKhVXdm5eO5iHBxA+Cc6yz47yRq+srODChQvI5XIBL4gaQsImldjwo/WkcLzWajVXRk0NL7dz1qQbzhOqI85kMm7ba5ae0o1DuFjudDqIRqNukRyPx12eAxfMbAMAt/tVoVAIkOFcLueemxp6esAvXryIdDqNb3/729je3h45x4S9fpjvdlbX8fDgfMAyiSxnVq1WHaGzMkaN8CgJBIIcgmPZVrHh+2HEOqxtmitAUK6hZFx1zyrvIqegtAOAc7Cp7Iuv20W6rXBjP5+NZrHt+tl0MU8ZaqFQcPKvWq3mEvZY+i1sIRCGuyHfGoep6yAXi0X88A//8HG05dDgA49Go3j/+9+PtbU1RKNRl/jGzpJOpwN1/xqNhtMEs7Oo/IJF81W3x05WLpfdSrLb7TojyCLk7NTqeY7FYlhYWHCrrrBi/+p9UsPI81lpY3d3F+vr67h9+zZu376NRqMRIPceHvOGk/AURCJ3qjOsrKwECt8D4aHFsOQ5jSBZj46WT6Mn19YUVbmELnw5z9Cjy8U6Pbl2xy4l2NQt6v0IGnkaUd4jHo+7aBYJshosGuter4dCoYBYLOY2UNCMfQ+PecMwIsVxBQTlDPyfY3CUF5fH2gQ2K8ca1y7bRrtgHja2OH41Kk2Qa+iCm9dWbzL5Dz+z6p7D2qtEOOwzWQkIsB+BV/5TKBRQLBZRqVTcInuePcYWM9tJ7/bt2/gf/+N/4Jd/+ZdndcmJwAdNmceZM2cC2mC+l0qlXDalFs4H9gcOj+W5JMWsc8xj0uk0FhYW3H3oMWIyDFeB2Ww2EK6lnth6n7STJZNJ7O3tORE8SzSx/ByPyeVyWFlZwcWLF1Gr1XDt2jU899xzods8engcN0ZNerP2AgzzNA67z9LSkkuEVcNGY6jeYmBfEqGeYpJkHa96fLfbRbPZRLlcDhBgLYWkGeoMj5IUc9MCRpeY3MfjKBPhPTOZjCPR9N5kMhk3r/AclYPE43Hk83l3La20wXYyTEqcO3cOOzs7eOGFF1zi4ajv4W7ipO/vcbKw8gWdg/j74sWLWFtbc44mlmJTImxlVPqb17ILZmB/vtBEOXvsKNgFM1+zMitdaFOGyTHNggOUTNEJoM+A81AqlXJzWKPRCCTz1uv10GcbRuotOda2AfubrKRSKVy4cMF57hkBCxu3YfeZB8yMIN+6dQsf+9jHjp0gW0PMLyiVSuGRRx5xKy1dnamnV1eLwL7e2K6YSHKBO18WQ6U0LpFIxIU2Wb2D19GEFw196vvJZNJlkWrhbxorLWLO+sqUePA3r88tH3u9Hl5++WXUarVTt1LzuHdxN/piWBiTY5BjlgR1mOHSsk2cN6xHyXqauNBuNpsuIsUxrZo/GilCS8Vxwcs5htEj9XTrNYbJNXg/zbbX3a5IyGlQSY6ZhxFm/BYXF7GysoIXX3zRteWkw54eHqOgYzaXyzn5pxLSUTkBfE2vpYveUV5m2wYl3JZ0hhFqHcOcj7SajZaA7Pf7AcJsCbKdezTiRc85ALfI1vOsc8B6ixXDXmOd9UKh4CqFqZNgFOaFu0xMkL/5zW+OfP/KlStHbswkGOU9YukmVnpQYkwPL73CfM12Vv2y2eGi0agrvaTGDwhffVIawb9JaiORiEtqJBFWPSQ7LRNxgH3vFMu/qIeIq0ga1Te84Q3odru4fv06arXaTJ+7h8dRMMvJzkokwsKYmsRCPZyVI4QZRUqt+LeWclRvK8d0o9FAs9nE9vY26vW623RIE994vm5ly/alUink83msrq6iWCy6ijdcGGspKGqc6cnVOUC3zNZM/Uwm4zzTfBY8Xgmyti2RSLg59OGHH0Y8Hsc//MM/BELBYc98Hgyax/2BUX1NSSd3zotEIoGkdiuZAHCAACu5tItn/V/vaV9X/S6hESyOQS2lxnEK7G9tz4hSKpVCLpdz9+FcwfOVIPN6WlKObUsmk2i1Wi6PSUF+Q2LOyjdh86VyF/1MnIdyuRwWFxdRLBZRq9UC29yfhvliYoL8pje9aeiHCps07xZ4X5JGTuIaNlVtnep3dICErZLYkdn5CHYeenxtRQs13KoDGgyCpeG0HQoaRHqoqN2xO+pQ58MBwlJW5XIZ9Xr9VHRAj9OPScb9SZAoGhqSwzDohA7syyDUY6TkmHMLk3O3trZQqVSwvr7uyruVSiVHTJkk1+l0UK/X3fzCusj0AKmWkH83m01HXFnrmCRfk3H4WdXIsr2pVMq1QWUXlHBx/tRFPz8fQ9KUZej86uFxGpDNZpHP552jS+chHeOERn2A/VJpSiKtree1wrzEYZxJ+YDe18qhVPZJgsxxyHYribVzAPkCNzbLZDKBXApKQhuNhuNIzH9STmITEPVz0KnHOcTOR6zetbi4iGaziZ2dnQOOyXnGxAR5aWkJv/7rv44f/dEfDX3/n/7pn/Bv/s2/mVnDJgU7IDurXd1pqELLoVhSqh4ZDXNwBcZr625VfE8HDzU2NILsNLpSpNfZivypUyaZ1/cYBrGG3q48z507h/X1dWxsbBxIOPLwmAWOQpBm4TmY9HxKr8J2pSM4LnWcaTTJhmVpJDqdDvr9Pra2trC7u+vGG8c0PcHUCKrWkPMGxywNiUqwEomE8xYDcIRco058FhpJsoaKhpMJyir9UL2jDQ9TNpJMJgPecPU0e3jMMyKRCNLpNHK5XEAnDOxrfTWvQM9T6OIYCPcW67VVvhBGBK3Xmedbgsz36ACjpELzpDimbflGLrR5HKNVujBoNpuoVCqOkzBXws6T2jbbfuVTnBv5rLnwT6VSKJVKaDabgYX4OJI8DwvxiQnym9/8Zty4cQMPPPBA6Pu7u7snSsQ4qSth1ZAJO4waQ2C/XjGhX56GQWmc6FnZ3d11Or92u+3KyWi5Fd2+mgY1m826qhQA3FaMyWQSjUYDlUrFSUVUS723txfwaHHg8LMzvLK2toZbt27h2rVrTovs4TFLqEG526EynTTH3TebzeKRRx5BsVh0m++oB4XX0PnARoqAoLwCuFP4nnNGLpfDYDDA5uamWxxzwc7ID7005XLZkV96dEulEpaWlrC0tBRIztG5BtgvQaXeHU2OUVmWRsX0mgACi3OVV9B7ZH84f2UymYBH28PjJDFqHrARYDvGCZU/6nlKXO0CWeUEep49Vu9H7qDlHtk+jj0ubOlxLRQKjhuUSiVHbtPptNvKmXY/Go0GFsBcFNsScrwn5wzNTaCHmnMWF9lcuNvqGfqcNGpuEwz5mc6ePYt4PI5nnnkmdMOksLn8pFQJiokJ8gc+8IGRutbLly/js5/97EwadRjQqNhQqZU06Kqv1+s5otvv95HP551Gj+RXr6cJOXt7ewHPkGaCJhIJV1tZk/B08PB/6glZD7nRaABAYMc//s+EI3p/dICwI7ZaLaysrGBtbQ3f+c537up34OExDHeDRNuJNpFIYHFx0UkcdFIOO56v6W9Cx6xKEzjWiTCjCsCRXV2sA3D66HQ6fcDLZNvLdlkpWJhnfFhijc6DVletdVn5WxfjuuPYPOC06Bg97j6036sd5/8AAg4nG1VWkqxkWd9XuZEd91qtQbeY5z1JHm0EWiNKvG8qlXJzDB1xNkGXFW60bjL3dtB5QBfN6q1mpF3nLBY14H3Gedn5WyUZWj2n1Wo5XmS3n57XsTzxjPfv/t2/G/n+4uIi3vve9x65QdOCXxxDErYzAnDeHG7wsbOzg+3tbVSrVeexHQwGOH/+PBYXF13WJUk3sO+lVQ0fAOdV0dUawzEqk2AH0ILdyWQSu7u7LszBtpVKJWeEaZh4fx5H2ASCSCSCixcvotPp4Nlnnz0Q/vHwOElM0xdH6ff0GP7WH0oDVlZWEI/HUavVAuE9nmO3Yw2rPsHxrh5hep+UHLNsUr1ed/cD7hhiJtpykZ3NZl2NViba6P14HtulpZw4D9mcBD4fTZJRL7gaeco8dDc+tr/RaKDRaDgvcqfTCdSCt5iXucXPc/cP1Ps47HUlgvZ9C0qX9H860VRzrLJFen6VhJJ7cKyTDJJ/sI9S9sQxz7rj/GGVKybmZbPZwIJcHW3RaBT5fN61n3MDF7ycq/g6nX+xWAz5fB6NRsN5oPmZ+FkAOLKu4ytMsqaVNDg3co5ZXFxEIpHAgw8+iPX1dVy9ejXUATBvmC+XwCFB4kiiTM8rOzQ7ysbGBl5++WXs7Oy4+sKUPfAL39nZQSaTwdraGlZXVw+sLHk/dmB6gmz4geEKXUnRkxWJRNDpdLC1tYXt7W3UajWsr687CYiWYeK1KJfQHfhsUXCbMKiJOB4e84Bp++Io0hMWfqOByWazblwD+4tUHadh11VPi+oTw/SH9I4Ad+QcmkNAw8jz1WvC+YM1kDUBzuZPkDhz0a2VK5TEq5dsmPdLF+00gEws1I2TqH3W9igR8POJx7zC9k/V2A/rtxwbeozyBvWq6nyiHuSwhF7dWbPdbjvnGqVSXHCqHEE9yFryVSNOjHizbcp/dL5SL7GVW6k0i9e3jgGtvMX2qRfdEmT1NGsb9HMxcVmPsR7+ecI9QZCJdDqNZrMZ8ChXKhVsbm7i2rVr2NnZQb1eDxDPbDZ7oNg2M85pBMNCs1oKBdjfLERXm+l0GsB+FiqPZcgkkUhgYWEh4KWu1+uoVCpuILGt9CRrJrqWrWFn5opyYWEBjz76KJ555hlv1DxOLabtt5QqnT171kVitG44xwfHDICAZ1m9snbM67yhdYUHgwEuXLjg5pdOp4NKpQJgf5t6kk/ev1AoYGVlBYVCwe1Wpx7dwWDgwqZ8DYAr+aQEWWUf2m7VTOpr/Dz0dNNTzMX61tZWIBJntc+zgJ+TPI6CcdpUki/q/VnJQfudjajoWOECkWNRxz7vrZsJ6ftaEWJnZ8eNXyYL0iusXlgSVcol6DEG4OYIOsiy2ayTZtDjy3lLOQG5AtupY5iENZfLoV6vI5/Pu42CWq2WI/icP3k9la7aRbqNMCnx53PK5XLOcXGU8a9Rv+PEqSXIYR1dvyySUSbU3bx5E+VyOUA0qSHmRhzU/wJ3VoTLy8suYU47lnpzrFHSxEAl0RoS1QGhnazb7aJWq7kNSLSaha5cdcBar452xMuXL+PKlSveEHkcG+alb9EjQmNSKBTcFrM2DGrPC/N66FxCz6tmpvM4vn/mzBlEo1G88sor6Ha7KJfLLszICFClUgnMHfSkqOdJJRK2TVzI0yMEILSOMRDMpAeCiYYq1arVas4wsqazVtJR58AsCfJxYF76osfxYxxBUmKmr4WdExZx0Wisjkdex96H0HGmeUss0ajyKeYVAfvJ+twNU6Ve6mm1nyXMM8wfklx6mbkhED8zf2uVG/Im5lopP2IVG10wM2JPnqISVMqzdKFtiyTMO04tQdbOooaMv9kRbEhVVz4kuPxyuRkHj2s0GgESy4HBa2pdZb0er6nGl+3l8VwhsqNxMNGTTanFsMx7/c1Ore8nk0nnoSqXyzN99h4e8wBdFHJyLxQKjiRr4psSXwsdp+pBtoTZaoPpVY7H41hZWXGSq263i0qlEtitrl6vo1qtOq8WSzIBwVJzYQQZ2B/juqsWj2UyjYZ79X393el03BxIgsy5hwSZHiS2jZG0u+GxmRTz1BaPk8EwkmyJon2P5/J3mDRJSa4uEO39LEHW/COOK16LUgW2j95Zyhg6nY7bQt5WgwiTiYR9TquH1oUzx7T9rJHInQ2F1OPM65PYs/qObsIWidyp8EUPOec28heVnOliQ73wozAPY/zUEmSLSCTiJBZKgtPpNFZWVrC1tYVmsxkosE8iSYE8JRGDwQDZbNaFRJSAWk+xJgOqR4kyCYYluPJi2HRlZSUgiOee6Ox89DiTbCcSiUC4R7Nk7aAmYrEYHnroIXzjG9+4a9+Dh8fdghJZjgUmmVGn3+/3sbCw4KI4JLVKnPVHIzpc8IZ5jBWMWFlZ1O7urhvDJJ2UVZVKJRQKBTeW2WZr1JT0KnmnbpheKdUm04vDNkWjUbfRSLPZdJ9hb28P1WrVXatSqbhEPV3Yx2IxrK6uotvt4tq1a3NhuDw8hpFj4I4nkwmw3EXTyo3CiLHaUh3r1iEHIJCUr4jFYlhcXEQ6nXZeXJVY9Ho9ZDIZt8NfNBrFuXPnXHvz+TzS6XRgLHIhrHOJapAp39KxSz5hF/hKVMl7lpeX3WerVCqo1WpuYU+uoWUs+WxsJJtzRjQadaRb5aALCwunymF3KIK8u7uLr33ta1hfXz/gLn/yySdn0rBpQGMAwInbSZKXlpaQzWYRj8dRr9edUaHrn55aYJ8YZzIZ54XSWolhhpXnqX6w3+/jhRdecDtRsT2rq6tYWlpy20Wy7f1+H7u7u2i1Wk7mkcvl3GqOO25xcOnmAWwLcHCFGIlEcP78eXzrW986UOjcw+NeAfszjQjzAQAEvKFhIVYlx/YYJacq4dLj1FjweM6JmujGxTC3luYmIpoMpLXWLXGnAaK3Sree5nucIxhK1eQcEnDumjUYDFxCHudD9faot4weJnUgeJLsMa/gWMhkMm5MWImCIowYWzI97ByOdevZZd3ibDbrKljkcjkXFabOmGM0k8k4nqIyUCuTIilV7zb5hxJkbaOVl+nn4rNSzTPH/+7u7oHqHmHPgdex/2tVDuBO9Is5IpNgHuaZqQnyF7/4Rbz73e9GtVpFsVgMfIBIJHJXCbLeWzU28XjcbQzAwXH27Fn8i3/xL1wyyubmJvb29ty2zL1ez5VbS6VSWFpawvLyMorFYsCLDOzr/oB92QYHIzXPmUwGt2/fdm1jSad+v4/z588jlUq5DHJ6vVgiivdQ3bNmurOShXq4OPBIooH9cKyt6ezhcRowrL+GTca2LBMJKWUPxWLReZAbjcYBUgsgYJD02raSg5JHTbBVyRONAr00AJDP57G2toZXv/rVWFhYcNno6nXW+2t5JuoYubDe29tDo9FwGzRp4iDPU9JM8q1JytQ98toaHeOc1+l0EI1GUSqVUKlUDiweTmpO8XPZ/Qv1/IYRqH6/j8XFRTzwwAMHcoj0GtYrrLpjnUf4vo0k63jhHKFeWtZhpyOOOl9KEbhZECM9/Dwcw5Q1qKSTv8lB7Bxmc6D0+uQPNsrM5ED1SicSCaytrWF3dxe1Ws3Va282m4FnSXmXzlt6HZVUaI6Itnmex/LUBPnDH/4w3v/+9+MTn/iE63wnDRoIdmiGLblDHb/ApaUl9Ho9rKys4JFHHnHHsNza7u6u6wA0KmF6QC1dwo7FTsaM8H6/74yZivRrtZor8UTiTGLMuoH01pDYMuuVA01XhDaT1mbsc2Wq4REPj9MMaxjV20ONLV8P0yKGeYuH6Qs5rniM6uc0emaT2WiMGN1iSaVCoeAkH2yjGmQd37r45XX1PHqbOS+QzGoyMI2szo/6/NRDrUaaz5Kfi5IvHnMUzMorNMor6HF/gv0hk8mgVCo5iRPfs6RY37OJsiR6fF8jOnq86vVVbgnALcotZ7B5TXYO0EWzeqm1XToX6Y69PI730yiXXsOSfIKkmlEuXXCT71gHAhCskBOmObb/n7R3eBJMTZCvX7+Op5566sTJsTWQ3W7XeV+B/XqothQJsF8bMRa7UyibJZe2t7exvr6OcrmMXq+Ha9euodVqYWlpyen6NHucdU2pLc7lcuh0Om5rW953d3fXeW52d3edFIPX0vBLPp/HYDBwGe6qQ1KvFNuvHd96mtmubDaLSqVyqrJHPTwmhU78rMrALVuV2NnFrp4XlvGu3iB6kMJCsdHofik2/s/FbSQSQS6XcwZmeXkZZ86cOZCgR3kDE3XVoNILzHmB92bVG8ot+Jk0cZghTfV4kziTSKuHGdjPsKdUZTAYOC0nj/XE1OOkoItHC47JQqGAy5cvu2gr7aEmqeqilmOP55OAaq4P760Jr4PBwOUBcL7Q+YUEk+MTQMB268Ke5zIfSavJsH2UUOmYVw0yF8zdbheFQsERWj4bLd+mCwKVcTFPi841OhnJH2xUjeAGSnyd0i1+Nkq8VCc97ns+aUxNkN/+9rfjb//2b/Hwww8fR3sOBf3i6eHQFZLV01kDR9c/PbtbW1tuN6nt7W3n1dWwSKvVAgBnhLlzF3BHP7y4uIhvfetbrtwTk/7y+byTY3BQsQg4NUQMnTAkqlrpYSRXQ7L80V34LLz3xeNegU7UNHKUXKlxUZAYqkdVJ2Sr57dek1HEut/vu/mBZDOVSmFhYQHFYhELCwtukxBrZNRbo95dHqN6Ps51zKvQa7DdNIIk2Zy/1LNFOZpKU/h5tQ0k63weHh7zAOsAA+7Y5YWFhQNRH4242nNUssCxM+p+hMqu7HhVe8zXOAZ1ztH2qTxCJRdaWo1jUOcLjZx3Op3AYppzAt9TTzl/WwKvemh+BvUW6zPiPZRssy2cgxlB1y2ww57nPGEigvyFL3zB/f1jP/Zj+IVf+AU8/fTTePzxxw8Uh37nO9852xaOAR+sXSWpZ9V2RF0l6nW4y0s2m0W5XEa1WnWdQldrvBaLdlODnEqlnIYZuJMgdOvWLVdHsFgsYm1tzW0MoCtJAG6lybaxI3W7XVSrVWfQuQMgENzG2uonB4MBqtWq01yGPTcPj3nFuIkz7H0SPS2RqItnnQdURsXXgP0sb0JDksC+1IKLVhofNSJqbDknFItFFItFFAoFl5CjkSQ15pYk22Qj/s97aTlI+ywAuDmJcxSlF/Qkc2HOeY4bK5BIqC7S6hg9PO4mJrFd6XQaxWLR2U9dQHPsW+mBEk717o6C5QRKkvUYJcQqgbJQZ5h1dpEvcAzyf410caMPLsrpVeb5Gumy0TC2UyPsjIapvEJJtc6V6oykJ5xzk25hz+g6n808c5GJCPKP//iPH3jt4x//+IHX6Hk4CbDD8Itj/WJ2hjCyDCDwhdPLlMlkUCwWA2FQZsMzBMGOzgWCbj5SKBSQTqfxYz/2Y6jX66jX6y6soFnk2rHYPuqPmaFO41Uul9Futx3hZYdVPY9+tn6/j2q1imeffRbVanWuO6GHx7QIC7HqwjCRSCCXy+HcuXMuKUS1giS1quFVGYZeX1/jPKEJPDy/1Wq5pF4lvaVSCYuLi3j00UdRLBZdUjDboERXSbdmsxO8bqFQcEZQia0aPGvkKDvReUuNpD5HGltCtcg27DyL787D4zBQu67eS2r+2W/pxaTEQh1k/J/9nUSQ0SVdENJTa+2uRZhDytp7/SG34GZAGxsbLp+J8gnmR1G+0Gq1Ah5enad6vZ7bdIxzCj3HGl3XOZOLYM2ZIiexiwCd++xiXq/L74N87Pz58+j1evjOd74TkKSEPbN5wEQE+bRoV0ksSZbDdLq6YlENkg4WGisV03OwKXTQsBMwOY8GOplMolQqYTAYuFJLDDOoTlqrT3S7XWfAOp0OarVaQAPN1RmNNY0828J6p5VKBc8///yp+f48Ti9m7Qk4CoHSxSulUVZWxblAF8jWY6REznpbNKFGj+f9dN5hnkOpVHKElgZNy7bp1rGEJsVp+5LJpPuh9o/GO+x7CPNKqxfIepTCPhsQXunDw+NuYthcowROE13DxrgdU9bma6Uq/s0Iro04EXpN21a9p5ZcVbkHxxVJMb299ACT2NpdLm00mWD7OZZ1frDPoN/vO86h438w2M+HIjFXb7gm7w1bLAD7so1sNutyGfQ7mlfcMxuFKGKxmNPusoJDIpHAxsaGM1Asot1ut1EsFp1hymazAYNKaCjBhlFoiDOZDLrdLlZXV91GITyHGwRQA8QwCT1F7NyqkWLWOwC38i0UCqhWq67wvy4ESqWSGwCtVgsvvvgivvGNb4RKLDw8ZoVJyGyYl+C4Jsdo9M428QsLC4Hsbr0/vSAazVHirARSyyppnWJg3yjFYncK7l++fDlAIFWWoaFQnUPoBdJxbw0Pr6eSiUKhgLW1NWxvb7u5pt/vO00xPwtDnIPBHcmVepABoNlsutBsq9VyUSomIeu9c7lc4HX7TI+CafqD90B7EHZBSWcSsB/dAfaJLskunWmx2J1dc/f29pBKpZBMJl2Uh32Sm+lw8w8AziFG6PxhF9o2J4qvc4yTMFIeVq1WA+OZ80cikXAEU0u0kbiTQ1hC3uv13B4LAA6QfJVs2ChUPp93bWH+FJ8t5RIa5QL2vfKcL3XuovOQXupT70FWPPXUU3jkkUfw1FNPBV7/nd/5HTz33HP4rd/6rVm1bSrQCJKANhoNZ8joub1586Z7H4ArY8KkGZY2UY2PDi52aA3PcjMPhh/pPcpkMsjlcq4DM5t2b28P2WzWGaNarQZgX39IT/XS0hIAuGutrq4iHo9jaWnJCd9VMM9tKqkd5MD/vu/7PvzJn/yJ9yJ7HBuOEjKfFUnWNkQiEWfo+J56Q60UwYZRSUotebb3UiLN81gxQt+zSSn0AtEA0XOlyYS6cOY51hiTXKdSKecI0Pqoms/AOY/5C/p56KVSvSDJgz6zecE8tcVjvsAxoVII9bRqiTT93el0nO3k2KEMSuUZ6vRi0qu+ZqFE1Y47cgzek8d2Oh1XkYuRZ0axdSEc5hHnMyB3sRJSK4PQhD/rgdaoEf9XXTTnNFtOjj9aKYN/h21ENM+YmiB//vOfDyTtEd///d+PT37ykydGkIGgVpBklYl3AFAsFp0xUHkENTaaXaodUVdZRCQScaunSCTiiO/i4qJbGaneWDNRaWB1D3NuMqKZ91quaXl5OdDJ2PlZIooDTatWZDIZvO51r8M3v/lN3Lx50yfWeBwbxpHcu+EdUOJKLb/1iOgiVL0XuqmQXXiqd0alWvTCcr7gQphIp9OIRO5sO6/GzWqeNeObu27F43EsLCwESjtaUss2Z7NZRKNR5/Wlfpifl1pCfo5kMukSeNQ71mw20Ww2AxEqQkmHeowPszg6DYbRY34RtlgF9pPhaM81v4BjgmSNC0EuSGu1mvPaAnD9n9IG7m2gi2jOI0qQLVkFECDiSiBZ0pGyTl53MBhgc3PTzUfcBZgSLbZJZaG8t+ZJaZIyq0gomVWe02q1XG6VtpvXVyeDzndsc5gGWhfdXGDU6/WAlnre54KpCfLW1hYWFhYOvF4sFrG5uTmTRh0W+qVTFE5SGolEnFvfEkx2Ui3ZpAl/vDYQrBpBrW8sFkOlUkE2mz2QpENoR1UvDfXGusKlcdN2aQY726eeLqu1ZrtzuRxKpRLW19cPlLry8DhJHAdpppdjcXERCwsLLi9BDYouLNVIqLG1+jj1NNu/ATidnhoOLsxpeOg1pkTBSqq4YGamuuqnrYyBRrBYLDrvMbeNBuAMOqUVulue1oWlcWRuRKPRQLVadW0ltNoO7z/L72/eDaXH3cewxVeYhIGvJZNJnD9/HrlczvXzYWNXyRsXh7yfShg5ZoB9J5lWydE5xHpolRyrphnYL0lpHW9KTGOxGKrVquMpJNX8/IxQE7oIV8LM+YXPS3XLVkZGXsI5hNdRgs/PyQRB/Z6UM6kXm9Byk2Ee8HnC1AT5kUcewZe+9CV86EMfCrz+R3/0R3e9NnKY8eIXZ7W91qCx07AD6WqG57ND6/WBYI3URqOBZDLpBhiJLRNo9BqshKFGyn6WsMxO6pBUp0hQs8TOraEVrpBpoI9L8+nhMUtMSpaG9eVIJIJSqeSkU4z2EOpRUQ+OLrDDJvywcK3OQcB+iJcepkgkgnq97sal1iC1HliNHMXjcZRKpYBxIfnnWE4kEsjn8+7/Wq2GZrPpJB3UFqvni4aWf3Nu6XQ6KJfLzoOsRF/nDev1mcWcMsn37ecuj1FQwnj+/Hm3aFRSzON0cajkWAmyVq0ieLySUPUgW2mVzhMqdWAkhiRWuQUX0hyniUTCbQZE6SdJsUocdP6hvJJzDu/JvCvlQXaxoN51RqNI2u25vLd+bvvZ9fOrRIMEed4xNUH++Z//eXzoQx/CxsYGfuRHfgQA8JWvfAW/+Zu/eaLyCq5WSCKBoOTCdmytHUpJhk7+PIcSCO3g/IJbrZYLF8TjcRfW7Xa7rqQSSbPKLDj4+GO9VmGDkm3SEK4ac16XHVPrIWq5Jg+PexG6uNWJm5P8MM1b2AKar3GCB/bJKccfcw048ZMU81ieTy/LYDBArVYLGEMl02rE1bCoHhHYD6fqIp1t4cKcpJnzjBo7mxFPj7ZqorVWsnrM0uk0SqXSoZJ+RxHcSWQanhx7DIMStlQqhQsXLiCTyQQ2z2Ff17KJyhVU7qCRZJ5rcxi0vBrHNfuxenj1Ohzn9jWNVql3FkCAJ3DccR5i/oFdvJIzaE4CeYY62VQmEolEXMSL86fWROZ1eTxJeSaTOeDNZ46U5oKRb5XLZezs7ODWrVuo1WoHpGPzhqkJ8vvf/360Wi386q/+Kn7lV34FAPDggw/iM5/5DJ588smZN3AaMGmOKycSS/Uk0ygR3JaWhJhEOBqNOqPC0IYSaq7GCoUCFhYW8Mgjj+D1r389VldXA23SOoFKsFX6ocaR91CdtA4au/KjdrFWq+HmzZuu3inby8FtO6H3yHjcqwjzcqpHVI2ULqb1PRv2sxEmzfjW92k8rNcV2B/bOhdxftAxbr3UKo1Sjw6vbT1IugmI6qrVyCspDwuB8nxN3mGlDp0/ZwU/F3lMi7CFVSwWQy6Xc4tAe7xGX3Uxrc4pJYY8T72ijC5z4UnJlMoydRwr7Lwz7HOF3Zf2Xj3BJPa8pla0UHKt45vPSSvlkItYzqHPKSyipO1UR4NeS73S1WoV1WoVtVrNyb8Og0mjjEfFoWa6D37wg/jgBz+IjY0NZDIZ5PP5WbdrLOyDpWEICwWqZ0YT3waDO2WPmKyj2mUlqOpdAeA0v7FYDD/4gz+IV7/61VhcXHQrMJZfqdVqiEajKJfLqNVqzqvLcm/tdtvpfOxnsZ5srlLV2OsgHAwGOH/+vPNqcXOQ7e1tXLt2LXSy8PCYR0ziURwGHTfq6QH2w6Zc/FKDDCAQ7tNJ3uYl8Lf1LutCVLeEtaFHGgu2j5ErRofofaIEREFSTu+wGkSWr6REI5lMIpPJuAx4IJhkzLbSkKq324ZveY9I5E4ZTEpH9HmNgzWoRzFwfu66f6DECxhOjNhXGfmgbeSYVU29JY5cgKbTaVfZSuUEjMLogpWJe/V6HdVqFalUykVc7NjQqBGh12abeIwWCojFYo5IcrOPdruNdDqNTCbjaqDb+uSad6E72il07wV6fvms1BloJSLWcaBys36/7zzDuiinE+/KlSvY29tDo9EIvd684dCugI2NDVy5cgUA8NrXvhYrKysza9S0UKmBnYj5W7VC/NIbjQZqtVqg9rFq9nq9ntvZhiuiXq+HRCKB5eVlPProo3jTm97kpBXcZYoaIU3K6/V6rvh3Pp8/MEDtKky9y6r/4edRw04DzMxVSiqSySReeeWV0JCo9yB73MtQ+ZKVXXAe0FAoYRfYSpx5vuYn8BwloapZ5vtKRNWLTOKpBpTXomSK91aCTHKvr6sRAxDwEFmwTdaA63NSYk/SrFISfV7T4G55fzxOP9SLOayvWU+nVn4hlIjZhS3PZd4QZVSaLwQg8JtcodPpuOoXYeNMHXV8X/XKYbAchg45tkfntbAScHTEcc5Rgqyfl+NanQphZN4+Y9tO660mOWdbut0udnZ2sLe3h0qlEiDH846pCXKtVsPP/dzP4XOf+1xgxfPkk0/iU5/6FLLZ7MwbGYawAaOrNxoh9Rhz5WVlB/aL1s6gW7NyhVooFPCmN70JwH4pKCW07KCbm5uu6L4mz7DDU9phV2C28zLkqYTargY1OU8zUp999tkDE8G8r9o8PA4L9u1ms+nKPZFEaogUgPO6KqENq/lpF630oOoY5bXVCGkd4n6/74wWj6MnGwjOI5xrdnd3XTUL6iPpHSNB1rKRGood5nHTyJM+L0IJtSYfce47rMTCL8g9jopRCyuVCqitDHOS2fM4jujo4mYgGhmi/WZOD39Xq1W0220sLy+7CIven/OO3TluWP6RTQzkseQUfI3RZ2qgC4WC20SEJet473K5fMADnMvlnJOAz07nD7vQ5mdQAs2/tUQlo9eqg261WnjmmWdw+/Zt7O7uHvhe5hmHStL7sz/7M3zxi1/ED/zADwAAvvrVr+Kpp57Chz/8YXzmM5+ZeSMngQ1ZKNlV0qxkWDswcFCTFI1GsbCwgGQyiWKxiHw+j1wuh4WFBVy+fBl7e3vOcCkxLpfL2NzcxObmpruHenlpbLXzq5HWnbUI/Qw8RzswBwAHHs+xYVoPj3sRJIkkf3t7e0gmk1heXnZjWRecunDWcUYJhnqLlSDTCKsXGNgnw+o9sfOKao2toeY9rSGloeGYZruZuBeLxZDP5wNa5larFajfrIRX28L3rIzEtk010zaBaZrvZ9bwpNsDOGi3NTqifdUm6moUdjAYONlCOp12x/Ba3ISr2WwGNttRWROvwfPCxrzyDWvj7XFa4lXHpHIA7vgHwElDqtUqKpWKaxM1y/YzKUdS4mvnLz4rbTuvoxFyLhp4P8pLd3Z2UC6XUa/XTxU5Bg65Ucgf/uEf4m1ve5t77R3veAcymQze9a53nRhB1tADgABRDstg54DKZDLOOwLA6QD5k8vl3E52i4uLru4oOzMJMuUa1WoVN27cwPr6ulvZ5XI5p23i5gFhyTPsPOr9Vq+ytj3MWxTmPVpeXsatW7dOVaf08DgK6DXhBhrqJaLX18oICBoJEmH1lqoXN4wga3THav7VwITJnZS4awIgr6n3GwwG2Nvbc5+F92JbNWuc8xRL2dmQr86P+pvt1LnEPjc/l3jcTVjCa/9Wgszjw47VyIgufuk95gZD2scHgwHq9Trq9ToqlYrzmmoSbbfbxeLiopsnOOfw/joX0farvEHbqvOP1RerI5C7BPf7d8q/AUC1WkW5XHYEnCUuLclWWZX1tFsiO4wg62Ys9CJz8Z5MJrG3t4dbt25hb28vUI3HXnteMTVBrtfrOHv27IHXz5w5E3gAJwFdJakXhl+u7hTDaheFQiG0yoOezwFFobnVMbdaLdy4cQPXr19Hu93Gzs6Oq1ucTqdRKBRc6STKKkjEaXQYfuUKjJ4sEuhMJuNeoweLUg0bPmLb2u02HnroIfzTP/2TN2Ye9w2sh8RKJ2iYstmse5+yBo573TmOsNo8LXSv0Rtr7DjXWA8u2wMEC/yT9OpGJgQNLj9fp9Nx29VrCTg1WAyPhj0n+7+SYhuRAxDYmdTD425iFJGKRqNYWVlxyfJ2q3TgYA1kS5qZR6QLVb1GOp1GNBrF2tqaS9BrNBrY3NxENptFoVBwDjFLPu1iU51ahLZLPeDabp1PdG6gR7lWqwX2W9B7aJSN84LOd3q8ttl6l3u9nqtCUa/XAzlWzWYTe3t72Nvbw0svvYRarYZarRZKjsd9p/OAqQnyE088gY9+9KP43Oc+58IJjUYDH/vYx/DEE0/MvIHDEOaFCRPBK4kkIVUyyYHEFSjBY1qtlqupmEqlkEqlAt6hWq2G3d1dvPLKK9jc3HQZ3yS11ETp4ONuWex4lF1kMhnXOUnmmXCnHiHttFw96nbTNI7cAMCTY4/jxrx5FNUQknBaUqpRHEtewzw7/N+Gb9Xw8X89T8kx/9d2WvLOa9PIqdeX/6uWmZnsqmMkeba7fYUZvWEG3HqVABxI0hv2nDw8joppyFM0GkU+nw9snBN2vu3rvI/mKlhvL4/h4jSXy7mkOL7O6DCT/OzeA2HjI4wkh72m7w2LeHG8DwYDJ3VQPqTPSaNOlvfoM1IvszoAyDdarRYajYZ7vVKpOIK8vb2N69evuzkoLHJ2GjA1Qf7t3/5tvP3tb8fFixfxXd/1XQCAb3zjG0in0/jyl7888wZOCvX2DAYD55FlPUTV++ikTm+sTcjh39QUN5tNJ6NgcWx++cB+SDaRSLgVHACnSyYhZqhTt65UA2a9wDy/Xq+7AcrjSKx1IHBVV6/Xsbu7i+985zt36Rvw8AjHsDCife0o17bQ7dx1tzzgoMZOCeewrZ3Z1rD3IpGIC8vSMHKDIWtwlQQTnCtUo6hRKi5+6T3iwpnh3X6/7zYsYnsGg/2Njrhgt0bKeon0XN5bPdU0kurZOorhm8Wiat4WZh7Hg7Axp997LBbDo48+iqWlJVf9Rccux79dFOrYZMUqlTTomFIOobXGa7UaFhYWUCqVDiSwqkMujJTzGNseW3YS2OcKmqBfq9UQiURc5ax4PI5KpeI0yHzNRrC4MLdcQp+NesGZoMi2NhoN1Ot1bG9vuzb9zd/8DTY2NtBsNg+UijutmJogv+ENb8Czzz6L3/3d38UzzzwDAPipn/opvPvd70Ymk5l5A4fBDhAbtlBPC8ucWA8SO5b1CAFwXplGo4FGo4FoNIqdnZ2AGD+VSiESuVOqaXd313VKYF+vaHfYopFRUqthVnZiGkCtx6yhTZ6vG5FwNReN3smOf+655/DCCy+EPi8Pj1liXN8K88QeBziGdScpvs5xokQvrJ0aflVvqRLDMP0jxx4X6CrT0OgVj7MeZ93lS+/JMa4GTsmrVs6w56mRUqOnCJOO2KRf3of13GcBPx95HAU6FpmsyvygMA2tXUxrxEh3xtOxb6tKWHkUq1GpZtnKJmw0xkZo7CJc8yDs/KOfm046bctgcCcpr1aruT0dwoi4JvBafqLcgue1Wi1XGaPX6+H27duoVqtYX193i+nd3V1Uq9VA1FqJ/2nEoeogZ7NZ/PRP//Ss23JoWDkFv1DuMsOJPpfLHehwlUrFna/ieuuxsaL93d1dR4AzmQyWlpbc6pWo1+sBKQUH0rBBFI1G3S54fF23fLVJe1ZS0m63sbW1hVdeeQXPP/88nnvuOfdMPDxOCrMmQsMmW31dyzKxrinHsCa9hRmhsBCsDbfaag7ULbKYvxJkzi3FYtHpgrVcGj20LBGl92KeA69Pcq3El+RVSbOtA83Pw7lF78G5yCYOq6a62+2iXq/jxo0bKJfLR/j2ZpOY46Ud9xeGOXhoBxOJBFZXV5HNZl30WMeyjjU9T8mxOqC0yg0Q3CaaHKPXu7MnQjabdbXR1cuqJFHbawkyj1GpJCUczFkiGdbFuNY45tzT79+pYrG7u4tcLodUKhWQg+i11LvNCBGjaAAc+Y7FYtjZ2cH29rYrefcP//AP2N3ddQUARkWTTis5Bg5JkK9cuYJPfepT+Pa3vw0AeOyxx/ChD30Ir33ta2fauGmhJJI7UbEj03jY0CDlD5YYAwe3l1VSSq9NNpt1JeCSyaQj5kzcazQaTnvMzqfX0baTJKuh4r05CNSwa9i43+9jd3cXL774Iq5evYpXXnkFOzs7Bwarh8dpwCQkyhoe/uZ4YVJtPp930R4AARKpIUfrPea44bnD5AkAXIgTACqVigt9AndILr05TFjhscxC73Q6OHfuHM6dO4dCoeC2t1dPt85P/DvM0OomATYDXr3NljTTkNJwquctmUy6fAtmxR8WszCYp9kr5XF06HefTCbd+GKVGYIlx5SY6thXWYPV7FrPsR6nERnqj1kmjht2aaRKx3BYFIsVMbj4rFaryGazyGazbn5g8n+tVnPn6k68XCgwMY6SEc5NmlSsz4Okm22xCcqRSAS1Ws1t8NHtdrG+vu6KCdzL3OJQZd5+8id/Et/zPd/jkvL++q//Go8//jh+//d/Hz/xEz8x80aGIcxYqeGz4VDtHPqjA8NKH9QI6SDjwInFYk57RCPM3XUajQZu3boVSJhjO6y+SBOK2KZxKzE1vqyHeO3aNTz33HO4desWqtWqO+5e7sAe9y4O02+VBHNcMPuc1wyTWKg+kf/zN7PG1chxkap1kxntqdVqzjBz/DNJd29vzxXL73a72Nracgaw3+8jn8+79mqCC+9rPVqWqA8Gg0CdVp3jrGRCw638m59DFxz6mUmQ/aLb425iWF/j4o1VojRPJxKJOAJp7X/YNXVsqa3l/0oieXyv13MEmR5brQjBecaWfdQ5iF7ZTqeDvb09d2w2m3XbSTebTbz00kuoVCq4fv26+xzDcgt0LrMSjrCxqwtvLeVoz1MNt32GNqpzLyxgpybIH/nIR/BLv/RL+PjHPx54/aMf/Sg+8pGP3DWCbEGDSK8qK05Qv6fCdhXe629qZ7gTDg0GjQlDGDQ6qVQKxWLRrR4Zzmi328hms4G6pM1m03mStSMpMeZrdqWrRpLHcotaZpJev34dTz/9NF5++WVUKpWATMPD414HJ/RsNotUKuXGX7VaRSaTQTabdeNOCSET+fgavU86Lpkgq3pFhlQJelXVc0vDduPGDfT7fVy9ehV7e3vOS8RQJ8c6vcaPPPII8vk8arVawADZeYt/Ww+VRrk4T/AeJLckDRpKDqu3Sh03Nz+qVCpu97/D4jDeX7/Q97BQQheNRrG7u+tKI1LWxIUsxzfJK204cFDeYBeiPIb31P85H9hotM4xunkXEEy2s4Rd78P8p2vXrrkNN1g9gphmoRpGXC0JBvaThsMI8iS4F4gxMTVBvnnzJp588skDr7/nPe/Bb/zGb8ykUdNAv9R2u+3qdHJw0BglEolAGJSGjAZAvcTqMWKolq+zjEs6nXZJAZqQp51IByjF/FwRAgd1TtQF2QFt9Y6qLySuXLmC73znO65Osz4bD49ZYl4nQI5RLlS5gGT4Usup2R/OAczmVskFjZnWGVaJlHqblXxy/iiXyy7kWqlUAtEdYjAYYGdnB+l0GisrKwD2STc9YzaKNUz2oVEv+54m3ymZtkmD+tm4pS4TcHRBf1hMc/689jePk4MleyS4ureAQmUICiXDqsW3i7iwPhgmu6TdVkJsxyKAA1GcMG82o9Hb29vY29tDtVoN9RaPauMwWOdgmEfYtumw9zrNmJogv+1tb8Nf/MVf4JFHHgm8/tWvfhVvfetbZ9awSaEd/H/+z/+Jn/3Zn0U6nXbEtFgsOlF7vV53u2uxcy4sLADYD4WwdImGUfP5fGBg0avcbDadh0pXnuzE6XTaDZpyuexCqLyuZoazPFsikUCpVAp4c3icaqipSXr66afxp3/6p85LFWbkPDxmicN4AI8b9OSQHJPYlctlFAoFdDodV4pNE9o0MgMEk2ish0o1iPybcgb1PDNxp1qtOu+r1iTXuUTH68bGBra2thCLxbC6uuq0yDT21BiqUda2sy36uTQ5h8TdfhZCy81ZzSW3i7XPzc8zHncTdu6hbWRCvm4DzwUqjyO07Jkl0/o6ECTB1r7zeBJi2nSNLmkOlH4GXZQD+4SZ8w3lmizZWq/Xh5J0/R32nLStYc8z7PxJjpk3G3AcmJogv/Od78Qv/uIv4u/+7u/wfd/3fQDuaJD/4A/+AB/72MfwhS98IXDs3QC/wEqlgj/5kz/B93//92NlZSVg9Gx9QtUX82+GObV8mxXaq1e5Xq+jUCi4Tq3El1tRai3Sfr+Pvb09J/2Ix+PY3d3FxsYGGo2Gq4gB7A8Yeq17vR6uX7+OZ555Bi+99BI2Nzexu7uL7e1tNBqNuSQtHh7HgWGkTCdy9eLYbZc5RnmOGiebj6CEktfm9XldILhbleqfWRaN458IMzY0pNvb2wDgok7pdNq9F7bDl/UqcaMCJQDWg0yibvMddAGgnrVms+kiX54Ue8wTaGPphMrlcgGJhI4VTcbXv628YRg0usQxSRkTALfZl81nCEsUDBtHSszVMabOr0ns/Djvt31v1JgOkzfdDa4xD5xmaoL8Mz/zMwCAT3/60/j0pz8d+h6wP+HfLbDzXblyBbVaDd/93d+Nhx9+GIPBwA0EGg5KK/r9PpaWlgLt1CxYGjrbOfh6t9vF5uamK91EnWMkEglkequeb3d315VuqdVq2NjYwPr6usu2TyQSePDBB9Hv99FoNLCzs4ObN2/i29/+Nvb29txuNaqd1Pt4eNwvsFp+/k3yyvd1MaseWU2iU+8qr6fklOAxOvbsJM6NfW7duoWtrS13fUWYMaZx5K6c8XjcRa+YAMjFfph2kNdgrkO320UymXQLbBpZyis0IU8JM50JqqUsl8sBTfTdnGvmwVB6nCyGff+0tf1+H88++yzOnj2L17zmNYH3tZwZibEufK3EQl+zciMAjrTqJl48h7sLc25g9Fr3YVBJFhD07KrkqdvtukoRti2zhn7esLnlfh1/UxPkeU/6qlQquHr1KrrdLvb29vDwww8jkUgE6iBy5UfyqxpfS+o11GrBrFMaERqgeDzuSqKoJxkAtre3kcvl0Gw2ce3aNdy8eRPr6+vY2dlxbbxy5YpL7KtUKtje3saNGzdce8Pa4smxx/0MDXNy90xg39A0Gg10Oh2XQ6B6Yg2h2p3pNK+A4dIwbxNrohYKBWxtbeH69evY2dlBtVodOWeGGaB+v+/KRGazWZTLZZw/f95Fl/Qcu0MY39ekIx6jc5sSZLZfy2TxbxpoLsjD9JIeHicF2sNer4ebN29iMBhgbW0NwJ1xpBVs2OfDCLLqga33VyvADAYDl9NAeeT29rZrB6WVuhBXSRfbrGNYpRZaMUPzj0YtEsPkFEd9pvOAeSDlh6qDPK9gx2s2m3juuedc1ufZs2dx7ty5UO0fvSwaftVOqrvkaIiDGeesY8pr0cCwVmGr1XL6Q+qgm80mNjY28Mwzz+D27dvY3t7G7u6uI+3cbEAHlYY55qUDe3jMA9TAcQzTg0zSy3E6GAycRhnY3xJaDSUzxbWajd5Ly0gSLPdUKBTQbrdx8+ZN7OzsoNFojNQH6mv6Wfr9Pm7duoVkMomFhQXkcjmcOXPGGWzVR4dFuEhq6VG29ZS1LB29x0oeUqkUEokEdnd3A9t2+7nH4yRhZVQqd7p16xYikQjq9bpzgrHalI4Xu++BXtcSZKtjZkk2yquq1So2NzcDbcxms1hYWAgsPsNqrvNz2CiSHYvalmHzxqhxOcmYnQcyOo+YmCC/4x3vwO/93u+5pLZPfvKT+MAHPoBSqQQA2Nrawlvf+lY8/fTTx9LQcbCe3n6/j42NDfzxH/8xkskkfviHfxiXL192u94AdwqM03CqJkkL5qdSqQMF94H9zPJer+eS7Pb29tz+7JVKBcVi0YVJWF6JVSaee+45PP/8884DrZ4pGm2dALxh8vAIwhqZSCSC7e1tVKtV58WxXl9GdXgeE+r4fyaTcdVustkscrmce4/31IiRLmJ3dnbw/PPP49lnn8WtW7emKoemY51QY/z888+jUqng9a9/PUql0gE9skbC+Lm1BKUmCnERoLv/KfEeDO6UrGL1Ci72Z1G94rAYdt8wfaTHvY0wLS4jSFwMqwOM/VqT4BhVtsl71hGllWkYDWY0ija/Uqm48ZvJZBCJRLC8vBzYsETbyR/9HCqtUM2/zl3T9PVJZEmeFI/HxAT5y1/+cqD+3ic+8Qm8613vcgS52+3iypUrM2/gNLCeHr7W6XTwJ3/yJ1hYWMD3fu/34uGHH0Y+n3c73gEIeJEBOA0iOyjfb7Va7px6ve46rxYpp2Hc3d3FYDDA3t4earUaEokEtra2sLe3h6tXr7q6jTpo7ODRz+MNgYfHQei44E6WmmyniS4kggSNJcccF880oDZ6pCXS1JvcbDZRLpfx8ssvu8TZMC2f/h4XFlXNI+eSy5cvu01HbGjYepVZ7lKTkXm9sGQ8q73k/EiNp59/PE4ao+SFSmSV7FqZhE3Ks9fW13UhTK8xF48cG8qLmJugi82wdoZ9LhuRsnsgHPU5DdMZewzHxATZfknzPlna9vV6Pezu7uKP//iPkclksLa2hje+8Y149NFHA0SYYCIOAJcwx1UqvTUaqgVwoID31tYWqtUqXnzxRVy7ds1t6sEEQdvWMD3isM/j4eGxD5VT8CeVSmEwuFPbuNlsum3fWepRqzswihP2w+vb8cloU6PRwN/8zd+4ijS2Rvm4sTssbKqoVqtoNBr4f//v/2FxcRFPPPGESwpm++ktV49yr9dzcxevTRKhmkgAgUUFn+fW1hZu377twsyzmoemXfB7B4HHKKhzif3a7h8wbJzZChPaz5Qcc+Mf7pRZqVRQr9cDJd1Yu9jaeCXldkHK/7VaDjkHI8xHIbRhzjaPyXBPaZCHwYYnuG3j9evX8ZWvfAVnz57FW9/6Vly4cMFJMFhihWEOTZBjaDafzzvPMkMu3PL5b/7mb1CpVNxq04Z5w9ro4eGxj8OSIiWB9PJQqmBLJum9tOSZeoDUE6VeKOBOlKhcLmN3dxeVSmVine60RoqLc24ycvv2bWQyGeRyObc5CmF3whrmsdJnpefp/FSr1UI3KDgq/HzncVQM0+RyLFPiwD6u+n7tz3bhG3ZdJa9aJ9mSXur6LeyC2ZJj9VS3223UajXU63W3VbbHyWBigmxDAHzttEAHAT0urBTRarXwf/7P/0Emk3GavUKhgMuXL2Nvbw+XLl0CAKfZo1fm+eefx9bWltML0XNVr9fdnuphoUlvHDw8Zg8d4yTIlCZ0u11cuHDBeYoBBBJgdFdLGlcSZhpDLnS5s2YsFsM//uM/4tatW7h9+3aot9li2jnTHk+v1V//9V8jnU5jaWkJDz30EB588EE339CYJxKJgCREr2l3FqQu2WouNzY2sLGxEUgW1md9UjhNtsfj+BAmk6AOP5lMAtiPiCgp1s1ylCiHkWbafer2mfRXq9UCJeQAuEWrkmm7IA+7N73U3Hzs9u3b2Nrawu7urrv+KImGx/FgKonF+973PuepaDab+MAHPuCSWFReMM8I0ymzpJLWG81kMrh58yaazSZu3LgR0PexM9+8eRPVatWRY5udGnZPDw+P8TiMh3UYMaXE4ObNm2i323jggQeQTCZddIgeYTWiSrI17MmfcrmMvb093L5921WgmbX8YFh4uN/vo16vO73j4uKi24WTMgser58PQKBUG0mvJgORUG9tbWFjYwOVSiUwr53EXObJsMcwWB0+ALe5ViqVClS34fFKUPW3lkzU8aNl4dLpNPL5vMszikajuHXrVqCGuG4Lb72/6jnWEpJMlq3VagDuSKroPQ4j7h53BxMT5Pe+972B/9/znvccOObJJ588eotOAOodp0FptVrY2dkBcKdwP49T46Jhl0m8xF4c7+FxB2FkdhJJxSTH6DgbDAZOYsHKEufOnXPGi15XTWbT62iJJtUlb29v4+WXX8bm5iaq1WpoCcmjIIwcq9Sj0Wg4PeS5c+fcRii6zS0AJ/1iAjEdGfb58DNSWnbz5k08//zzB7SUs8AkXuhxz3DUgsjj/gT7BBNvk8mkc1xZgswkPrubrl5Hj0+lUq6mMqPPHC9aPYY/HKcapeH1gH1CTD6hO/kOBoMAQfY4OUxMkD/72c8eZztOBHZVpt4jXbXZCX3Y5GyN62mWpHh4HCeGLSD/f+y9eYxt2Vkdvu48TzXXm/v1e+3uphuT2Ay2GdxOhBkMceSAAiYNsUSCkWkkLIb8kYBBJoiICGQLi0QJkhUEChgkWwxO7AiCI7CDY4OHHunXbx5quvM8/P54v7VrnV3n3rq33q2qe+vtJZWq6t5zz9nn3L33t/b3re/bNiYhQcPGK//f3NxEpVIBACwtLeHSpUtGisDSbiTFAEzt32KxiFarhatXr6Jer6NcLqNer6PRaKDVankWyocBe76hYeZrV65cwfb2Ns6fP49cLodMJmNKVdZqNVQqFbPD1/Xr141UhAsHepBVXsFk4sMgoOOc0xFfB4VfZJavK+lkDXLuSQDsShrUrrOf+9l8P7uuMg3gvtZ4aWkJg8EA6+vrJpqTTqeRSqU8tYyH6fd1zDEqXS6XzfyiC119Bg5Hh4ciSW9STKIZnpbW0MHB4XDBDXs2NjYAAKurq2bXOBo0jSDxh2T4zp07qFarKJVKntDtUYz1YQlJwH1dcqPRQCKRMJV3WL+9UqmgXC4bsru5uWmMOe+L2mO/knEODvMAjQBXKhVTxpWRE7+xM8xBZo81EljdlCcQuL+tNDXHzGVieUh6nnXrej9Hm0apOp0O6vW6IcfT8h47uefB8dAS5FlJNnFwmDccJSGcZLE66lxEr9fDvXv3sLW1hStXrpjE3FOnTiGZTKLRaKBWq+Hu3buGSNJYkWTaJRpt7+60MeoaJLbdbhevvPIKQqEQUqmUSVKiFIPnUQ2yrUXmuR0xdpgFjJIk2jIq4H7fbbfb2NjYQCKRQC6XQzab9WwYxEQ76vX5ObuihB951rKPwK6eP5fLmbGmyb72+fwkZVoVJxAI4ObNm54F+DT4iZN2HhwPLUF2xNjB4eGEFuFnKbhwOIx4PG48QZVKxVS8sOsFK8bVyk4Tw+YuEnpgt+IONcj8nG10neF0mFUcpG8ygbVSqWB7exvJZNLIi/i+Liq1kg2wd4wMS7bT87GtJOHdbtdUkLHlmrwGP6u7/9XrddRqNbRaranzE0eSD4aHliA7ODjMLuzEtMMAt3RtNpsolUq+11fv1EEqa0wLwwycX+jYr6KQ7RHz+6yDwzxC5wruOtnv91EqlbC0tIRcLufZh0A9ydyEg5+1z2lLI2yizH0OmLTPscfNiNRLTHDRzc8x+rO9vY3t7e092uNpwTkFJ4cjyA4ODjOLw5jUR0k3ppFYO0oWctjJfMPgVzpuvzaN0j07OMwyms0mBoMBisUi4vG4qUJTr9f3VK6hZpg/WpGGOuJoNGoiNNQ1U37FpFebSJMcD0v644YglUoFm5ubZu8Eh9mBI8gODg4zh8MICfqVTlNiPCn5G7cSh/3+Qe9r2GfHbfekWulplatzpNrhQTFuX2RfY4Ltzs4OYrEYIpHInugKE3Q1OsSEXXp/mZynJeMAGDkFiS6TfAkSZCW8JN66S2+xWDQ5Edx4xGF24Aiyg4PDTGFcKcGk2O8zGqqdRYzKRj+ONvslOg97za+9+3moRyVSH4UEx2F2MSrSw34VDAbx8ssv48aNG1hZWUE8Hjcbm/X7fUOQtQZ4NBpFJBLxnK/T6Zhdd5vNJqLRKAKBgHmdBDoajZpKF0qm6VHmDyVdV65cQaVSMTWPZ3XeeZjhCLKFUeHGUce4SdrhYcG0vbujSM6w60wr9P8g93EY5GycZzsNT/ek17TPM8obvx9GLUSGveaSjBzGhcolSqUS6vU6wuEw0um02X6aumAtA+cnh9DX2+22SXgFvLtUqgeZRBjwkmMe32g0UK1WsbW1ZUg3r+UwW3AE2cI4k7wjww4O04OfRm+UsfAzYvuR1UnON845joqQ29nv0yrDtt8zn3TBMsxDPOzYSe9h1PHT+F4c5ht+37tuwnH79m0kk0l0u10juYjH4wiHw54tqVn2kRKLeDxudr0jmaXHmRVi6EW2K97Qq8xkPG5lffPmTezs7Jht3B1mF44gOzg4nAgchJRNQqim7cU8Kq/oJJ7uSb3Nw4j2tLXY49bEdXCwMRgMTDnHarWKTqdjNMnhcNjohLVGOLB3W+hWq2U8yIFAwHiU6RnWahXUKKu0ot1uo9lsolKpoF6v+3qsHWYLjiA7ODgcKg6aADdtCcO0iu5PA0cdqRqXtI6DSc4z6thRsrVxEiCd/thB4Zdwq+XaGo0Gbt++jXg8jng8jna7jWg0ilgsZhL4Go2GqZtMLzEJMjXHsVgMgUAAjUYD3W7XbDcfDAb3lIprNpum0sXdu3dRqVRw584dD9F2fXh24Qiyg4PDoeKgBmDaWttJcByGy69W8bBjbDzshlY9fw4ONlQnT1LLLZ1Jalmujf8z0Y8EWXfUDAaDZot29r1AIGA0zZFIBJFIBLFYDOFwGKFQCLVaDdvb26Yms8PswxFkBweHY8O0iN1hGJyjJJ3j6KcPQpznFcOevb5+0u7Z4cGg48Tvb/5PosuNQiiL0LrGer5wOIxwOGykEqyf7KcfZtk4bvUejUbNtWq1GnZ2dtDtdl3fnRM4guzg4HBsmNRQzAppfVDY97GffvqkY9wkwYfhWTgcHPbiyW9ckejW63Uji2DfoweZkol2u71n+3b+T88wayfzetzinSR5a2sLm5ubKBaLqNVqboE3R3AE2cHB4VAxLql92GUCD4KT8Oxcsp3DtDGq1jaJMQkuxxC3jQ6FQmYTEC01yPcB7Pk8vcsk1p1OB9VqFTs7O0az7DYDmR84guzg4PDAGBUSnybGSd46rOsc5XknJbzzTo4PgknrLzucfPiNm2HRCf5uNBrmsyS6JL3cwCMQCCCZTJoEvlAohEgkAgCIRqOGGKfTaUQiEZRKJdRqNXzhC1/AzZs3cfv2bUeO5xCOIDs4ODwQnNdvOnAbYuwP94wc9sN+Uh2bRHNbaE224+s8nj88liRar0fvc7vdxubmJmq1GorFIsrlstspb07hCLKDg8OBMc6ub4dtGPwM4rCEnQc9v42DaKjHfV/J4H4bdzyIF3WccxxEwvEg5f2GnU9/Ozjsh1F9aVg5QhJdlnkj6vW6p7JFs9lELpdDoVAwiX7lchmdTgef//znjaRiVDscZhuOIDs4OBwY+xFgP1Iz7QSscSpA6N+jEngOG5MuGMY59iju4SDXmPb36TzHDtPEfnKMYe9xQ5BqteqpgsHEvn6/j1arZUrA+SXkOswHHEF2cHCYCmYlGW8cA6TEfVyCPU3Ynu55wUlIBnRwIEZpkxV+r3U6HVPb2D5OtcbjLOzmdT446XAE2cHB4cCwJ/RxJ/jjNAQHbfM459pvI4+DygTG8dKP045R5xpWO3aSthwHxolOOBwPZq2vjIIu/saRZoySdunxo85nH+cwWzgWgjxM++Mw+3A1HB9ejFNj1OE+htU4noXnNU4bZqGdk2KWnvHDjnn+LvbbkGfSDXvcPDlbmKRvHgtBrlQqAOZz8DjcR6VSQS6XO+5mOBwhOG4Bt7XvJHDP6Wjh5qbjB+cK6nMfNrgxP/sYZ54IDI7hm+z3+7h16xYymYwLLcwZBoMBKpUKTp065Wo6PmRw49ZhluHmptmBmyscZhWTzBPHQpAdHBwcHBwcHBwcZhVume3g4ODg4ODg4OAgcATZwcHBwcHBwcHBQeAIsoODg4ODg4ODg4PAEWQHBwcHBwcHBwcHwbGUeXMZrvMLlyn+8MKNW4dZhpubZgdurnCYVUwyTxwLQb516xbOnj17HJd2mBKuX7+OM2fOHHczHI4Qbtw6zAPc3HT8cHOFw6xjnHniWAhyJpMBAIRCIbe6nDP0+330+33zHTo8POB3HgwGnYfOwrSqZe63bbKbL+/D3qmQ6Ha7bm6aAfA7CIfDJ6LP6jbsfu9Nu1ruflte7/e+w3AMBoOx54ljIci6P7n7gucLwWAQ/X7ffW8PIfidB4NB9/0LBoPBoTyPYecc91qH1a5ZgN99OdIwO5g3G/+gRPSo71Gf77TPexRbY8zC9tvjPLtjIcgODg7zi5NMvPwwzEiOmuCHeTgPsz0ODg4HA+e0g5C2cT93GITQPueDzglHRVqPmxyPC0eQHRwcHhhKmsch0PNIsschxH7Gctjn9Nhxntewz/p9nu/N43PeD26B4HAY2I+0PQipm/Szo+YTG/vJsoYd67A/HEF2cHCYGDYh1t/2+ycV9j1qONnv/v2I3TjP6EEN5HF9F8OewSRtOUxPvIPDuBhncTxNr+ikc4TfZ4fNBcOu47AXjiA7ODgcCCeRBB/Uo6uIRCImOckmr71eD91uF/1+fw/5Y/IjNd5+3iN+rtPpTHxfD/JdTeq19fNgT0Igxg1Zn7T+5zCfOEzJwGF6iN0YGg1HkA8JB/WU0Ci6ju4w63gQErnf+36k6ij797C2+cknNGEmFArh0UcfxaVLlxCJRBAMBhEOh031l5s3b+LGjRvY3t5Gs9n0nCufz6NQKGBpaQmJRMJDlMPhMILBIDY2NlAul/Hyyy+j2+1OpJscd44YR7ox7Lqjkufsdhw07OzgcFQ4aJ+bJInU7xpaJWjU8bb3etSYHDWn7SfZeljhCPIh4aBhkYN89qCfcXCYBsYlO7YnZJw+O0v9etg9BoNB5PN5RKNRxONxLC8vI51OG4IcCoUA3Pf+FgoFtNtthMNhtFotALve43Q6jWw2i1wuh2g0aj5HohwMBpHNZhEMBrG+vo5Wq4VarYZOp4Nut3tkBHLSDHrbQI+qzjHJommW+oaDw36wF9KRSMT8+FUGCoVCCIfDvrKIXq9nIlL9fh+tVgu9Xg+tVguDwWBPhGo/uMWnPxxBdnBwmBj2ZP4gC8JZwCQGwiZykUgETz/9NAqFApaXl41xotHjYiAajeLUqVNYWlpCs9k0xi0UCiEej+9ph15nMBhgMBigUCigUCjg0UcfRalUwvPPP4+dnR0Ui8WJ7mkUyRz3tVEY5xwHvc6s9R2H2cJBJD2TnHucRZydgKt9NhgMIh6PY2FhAUtLSygUCojH4wiFQqaMaqfTQTqdRiqVQqfTMXNIKBRCKBRCq9VCq9VCs9lEp9PBvXv3UC6Xce/ePXS7XfMzzCvM1+mp7vf7nvuYJNp0ksejI8gODg4OE4BENZ/PY3FxEclk0hg5aoj7/b5nkwTVGFMuQYIcDAYRiUTMcbZut9vtmmtHIhHzfiKRwCOPPIL19XU0m028+uqrKJfLvsaO53ZweBgwqnKM/f4k0oJRSbD2Z0k+o9EoIpEI1tbWkE6nTYQoHo8jHo8jkUh4Nk1jjkE8Hkc0GjXeYl4jGAyaz6dSKXS7XYTDYRQKBWSzWbNA39nZQbPZRLVaRa/XM+cZ557cXHEfjiA7ODg4WPDzjOj/+Xwejz76KC5evIhMJoOtrS3zPmUV9Ajxs6pHZpiUf/N9v8Q+fY+e5G63i1QqhVwuZ47f3t5GvV43hlDDrMMM3mF5gBwpdzgujCLHk2r2xwXHqHpm+VosFkMymcSjjz6K1dVVnD17FoFAwIxPHav8mwvncDjsWSDz3DyGxDeRSKDT6WBhYcHMOdevX0epVEKv10O73TbyCzsqxb8nlU49DHAE2cHB4aHCOOTNfi8cDuPcuXPI5/M4e/as0Q6Gw2H0ej2kUiljsGjc1BiTMLOCBWHrlGn8+DmbQPN/epz1fh5//HGcO3cOg8EAtVoN9+7dQ6lUQrlcHnnPNkmeRnm2aRrZ/byBDg7jQAmhYj+vsh8od+DYTCaTSCaTJqK0urqKcDhsokjBYBCrq6tIpVJIJpNmPiDRVcLb7/fR7XYN0WXUyJZFqAY5FAqh0+mY9gQCASwvLyOVSqFQKKDb7aLdbqNUKqFUKpnchWEykHFx0segI8gODg4PJcb1EEUiEaMZXFlZwblz54zBIWmlsez1egDg8QLxf15TCTDJcTi8OxXrVu5quGho7fMRCwsLyGazGAwGqFQqaDab6Ha7aLVa6HQ6pm0K9UqPE14+CoM4qYfvpBtph+lj0iRhW4YRDAaRTCYNCSbxXV1dRTabxalTpxCJRDwJutls1iTfhkIhRKNRDymmtIJjm2Ofc4n2d41M9Xo9s2CmfIvtC4VCSCaTJoFP56hOp4N2u23OrddwSXv38VAT5HHE9g4ODl6MW43C1tIOO8csQknpE088geXlZayvrxudMQ1jJBJBIBBAu902RkbPQQMHwOiOaZQALxmmIQS8ukb1QlE+oSFcDeUOBgM0m01EIhHEYjHU63XUajWTzOent7TDzuMkzx22NMPB4aAYR0bh15/5e5jEiceEw2Ekk0l827d9m8lDoIQhmUwiEokgGo3uIbiahKcSKy5Q7TZrFAmAIba8v1AoZM7B1zkvhcNh5HI5k6jHxL+FhQWcP3/eRLNeeukltFot9Pt9lMtlVCoVz0J6WJtmff6eFk48QR4VUnlYvmQHh6PGfp6IYQRr3Al4P4J2kIlcSWkoFEIikUAymUQul0Mmk0E0GjVklZ6hUcTR9sza5Jm/bQ8xDWgoFPIYTttQ2wRZvVX0MIXDYSQSCayuriIajWJ7exvdbneqRnCSJCcHh3mA9mOOUUqqEokEUqkUstksstks8vk8ms0mms2mpxoFwTFr5yToeNff/IxN1O2x79dmnQs0KsXPh8Nh48XudrvI5XJmcU9iz/mBlXba7bbvc3kYcCIJshOcOzgcHw7qBdxPA2h7o8chZqO82H7XpnGJRqM4e/Yszp8/j3Q6bQyfTWzVq2Sfi+SWRo/eHBJfYFebrHpEnos1UDUpJxqNAoCH4Gp1DNZX7fV6iMfjpr2ZTAaVSgV/+Zd/iVqtNlSPud+z2887N4lneVre4qOSfjjMB8YZ58DwcnC6UNbE2Uwmg2w2a2QU2WwWqVTKbOrDRTXHMuDNKYjFYp6FNce3epH5WSXYdk4Cz8sfHZskxqp/5j2RoAcCATSbTQQCAayvr5vrtdttdLtdNBoNNJtN3LlzB/V6Hdvb23sW++OM3ZMwLk8kQZ73L8XBYV6gE+Y4oU3FpATpsCfcUCiE9fV1JBIJ5PN55PN5JBIJxONxz254fgktmjjD5Bk7+Y4E2V7A+2mMaUD5N6/NJL9er+epnxwIBBCLxTxkWj3Q8Xgcg8EATz75JLa2tvDKK694qmgMwziSjEngJBQOh41R/dP2yuprXIzyJxQKYWFhwaMZZmm1RCJhPqPn5HiyJRt+Hmn+zTHr9z6wS7Lt+UFJPKtdKDnW7e5JxHlvAMwcxWt3Oh1TXq7T6SAcDqPZbGJhYQHFYhHFYnFPqbiTPp5PJEF2cHA4OhxlYsekko1xQUK6vr6OfD6P9fV1Y1Si0aghuHaNYV5TvTkknvTyavhU28/P2AaT96LaY4Y9mXDHY5icwxApiTz/7/V66HQ6hjw//vjjuHXrFq5du2ZCq9P+3o7KaE7izXJwUHCc2fMGPbCsNby6umoq1hDJZNIsTHU+4HyhY1gJsh9J3o9AE37eYv7m6/RIk9Dr/ME5gV5mvhaPx81cpRuMsIxkt9tFs9nE1atXUa/XzY59D7JIniecOII8jqE87C92P4+MDgwHh3nDLPbdcb2gwz772GOPYXFxEadPnzZaY02e0VJMdl1Swq4SoYZRfzTUaeuVARiPUCwW8+iX1dMTjUYRjUaRSqWMEWTWOr08g8HAZKpHo1Hz97lz5/Ad3/EdePnll/H3f//3aDQaxuiNwix877ZhfhiMtMNkGNUnlBjzf9Xtvu51r8Njjz1mxng+nzcRnFqthmazaRLwms0mAGBnZ8ezEYfWQPerbe73N38z6qNbTCvx1vuz5w77eC7UtUpFIBDweIH9yDg96ZwHw+EwTp8+jVQqhevXr6NaraLZbHoSiodFmuzv5Ljnj0lx4gjyOF+AX4fY79hpT8Tz1lEcHE4iqNldXFzEysoKstnsnlrEwF59tB+xHbUxh3qaNVzqZ6CoJaYnS0OkNMqJRMIk1dAgU/9I405jTQNPTxMrXpRKJWxsbOxJ2ptVTDJvOzy8sPuFn63VccqdLNPpNFZWVnD+/HmzqQalSe1224xDLenYbrfNmGT0SEn3sDaoB3lYW0d5le2o07D7t4mz/f4osq6bGqXTaQSDQSOz6HQ6ALBnnvTDPI/TE0eQR2EcXdIw2KuhUYNulJfYEWOHk4BZ8CYqJtFAa7vX19dx+vRprK6uGiNAb5KWUALgCU3qdQn1wtJAqheJBlM9yGpEaXjpHc5ms4YsMwTa6XTQarUMOeZWtQz7MtmGx9sEmUS41Wrhsccew8WLF/HJT34SN27c2GPs9nue82z4HE4+/GQUdv9m5GV9fR3f8i3fYhJymeDKsRMIBIz0QDEY3C+ryKiPLoQ1WmTPBfrDRSsXwQBM7oJqhzWpj4hEIh75hBJ4lWTo+345ENFoFIPB/URC1k9XRCIRJBIJ9Pt9lEolXL16Fc1mE/V63TwjW35mfw/ziIeKII9j0Mf9IvczyLNGICbFvLff4WTjQSddJt2k02nk83lTpUINkn2dYSFNfQ+A57PDXrfLONlto8SC+kcm0OjmBCzZRIOukgzdDMDP+6TXWFxcRKPRwPb29p4kHAeHkwiOwXA4jOXlZaytrWFxcdFTyhHY9QpT309CynPwmFHXGfUz7Hg/sunnMR51blt7bJ/XduJpaUgl03p8MpkEACwuLqJarSIQCKDVaplNSCblT+PiuPjIQ0WQ/WB/oWoM9/tSJgkt6GByxNNh3nGcfXjYtcfxdvKz0WgUKysrWF9fx6lTp4zXhZ6TUYZM9X16rHpnbI8RZRA8l3qIeE7+z537MpkMYrEY4vG4qbPKxDuSeB6XyWRM+5jRztCw3/NTjeOlS5dQKBTwV3/1VyZczLb4SUaOi0A74n6yMWzBedBz+PUXe8OPVCqFN7zhDVhZWcHy8vKe49vttvECawIbz6+k0I9Q+nmPhyXm6t86nzAyZUeveD/AbvRJNdCcY9QbrOdQwqykWhP3dK7h5iOpVArpdBrlchm3bt3C1taW55noOUfNH+NyIT8vuH3Ow8JDSZD9Bo4fUR6l7SHG/ZLmjRjPU1sdHPaD7XWJx+M4deoU0um0eU1LtQG7YVK/BBi75JudYa4G0DaItndak2ioiY7FYojFYmZTAnqSuT1tt9s1RoseZG0Xr8FwqSYYavJOr9dDNBpFIpEwiXyqR95vDpzn8KnDbGEaNmdYf1QvKglvIpEwkgqONyWl9mcYsSGxZKlFHc/D7knnCf7WRbJN7P1yIIY58wB4xr5ud29/zpZj8Dx6D1oFQ5P8eDylW8Fg0JSDi0ajKBaLaLVaZnE+6plMMmcc5xxz4gnyg3wRuprz0+eNM6DtjsjfjoA6OEwHB5lw4/G42Toa8Hpg9Di7KoQSZI5je8c7+1qKYYl5JMeBwH09IA12PB5HOp02BDkWixl9sd5LNBrds3VtOBxGqVTyZNgrWeZ5WA0jGo2aMnLEfuHjWcCstMNhNuAnfdBFKxehyWQS6XTajDfqcBl5sRPuVGKh45hkV2VTfgtk/W0fy+vaHl4/kuznJbfnJ2BvpIvHafttLmK3k+fR82q9ZQBmbmq32x6ivt+cfNBxe5Tj/YEI8pUrV/DKK69gfX0dTz311LTadCDsFwrczy2v+htmj7MD0EhqyKHb7XpCFMM6g71as1+f5P6cIXBw2ItJyDE9R8lk0tQGpjeW41THvk06AW+ynhoTO1FF6yCrsdHEPNX8Uk/MTUpyuRzi8Tiy2Sza7TZarZbZ7YrSiUAgYDYzaTabHi9PrVZDo9EwyX2dTsdsH6shUeC+kTt79ix2dnZw7do1z33zOg4O8wS1u6xUkUgksL6+jvX1dbMoTCaTCAQCpi64EjyO736/7ym9qByAkiXlDkqAOebtTXk0YsV2ag115Rp2pMkejyT+ftErXoO/bRKv0gw9j0rC1Kuum5IEAvcrXKytrSGRSGB7extXr141Cwzem71oOahUYhoynEkwNkH+8R//cfzqr/4q0uk0Go0G/sW/+Bf4oz/6I3Pz3/Zt34aPf/zjJmR51LBDoQq/sCBf15VSOBzG5cuX8cQTTyCfz2NxcRHdbtcMoE6ng62tLbzwwgv467/+aySTSVQqFd+6qPbqTMOp9opN2+lClg4Ohwd6aFkdghi2Qx6wV1fnZ2D09WH1Qe1z6jE0xnbSHatU2CFQfpZkXkk3AFOCSnfe449d1o3nKBQKJnyq9+zgcJSY9qKMNj4SiSCbzWJhYQGrq6umVKImpdneTx3rKjsgQbTfG1b7WOcCv/nBjk7ZPzpP+JFEXbDz/3HI5DAJhK2bVu+2zkX6fz6fR7/fx61bt/YssA/DwXcUTsOxCfJv/dZv4Rd+4ReQTqfxS7/0S/jsZz+LT33qU/jGb/xGfOELX8AP//AP44Mf/CD+/b//94fZXoNhD8eWNGgYRI0cB8zi4iKazSaeeuopnDlzBtlsFoHA/SLgrPlXq9XMaiqZTOKbv/mb8W3f9m0IhULodDqoVCq4deuWyQK/cuUKbt++vSfbNRgM4vTp00gmk2g2m7h58ybq9bp5bz9ivN/EcdSrKweHo8ZB+rV+htvH5vP5PZUk1GOkHl/V9dmVKJRkE7Z3SY9XL7N6hbjtKzXHqVRqD0EmkdUFuSbj2NevVCpoNBqe0Cc9O71ez3i8IpEIAoEALl26hHQ6jbt376JaraJWq40VKh313I9ise+iaycLk36X+9lFLorX1tbwrd/6rYhEIqZuOMkxSyt2Oh2PE4tji2NWnWmqteXnCXtuUU2zvm+TXl282gmBSqJ1XrGvqT8c5zqH6XX1PHZ0i3NLJBIxx8diMTOPcDHNay8uLiIUCiGXyyEcDqNSqXjkXX6OSn191PfuN5fMlAdZG/eJT3wCv/qrv4pnnnkGAPCWt7wF//E//kf89E//9JEQ5FETon7xdid/5pln8MQTT2B9fR2JRAKtVgv1et3sDMNVD0MpoVAI8XgcjUbDGKlut4t6ve4podLv93Hq1CmcOXMG3W4XTzzxBO7evYvPfvazAHa/yHg8jsXFRYTDYaytrWF9fR1Xr141NUh10PBeJoEzEg4PCyYZG5wP6JllJrbqfoG9yXW2p0eNiJJoXXxrMpx6qbW9tiYQ2N2whIaWni3NSNeaqmqYAPhqizudzp7XuRDnYoDX5XlyuRzOnTvnWbyP+x1MEjZ1kTKHowDHaS6XQ6FQMDvjAfD0e7/+qItknocSAz/ZFV9X6Hiwx71NkFXGMEw/bDvBVEKh/9sk2b6+fY/qHNDz284CfQ7Abkk4Vr9IpVLI5/MAgEajYe5pGrCdGUeBiTTIbOCdO3fwtV/7tZ73Xv/61+P69evTa9kY8NOz8H8NC2SzWbzrXe/C0tKSR1tIA5HP51GtVlGv101H105qe4JUpK4aPtYr7ff7uHDhAjKZDPr9PsrlMra2tlCpVFCtVpFIJMy2leFwGOvr68bjwyxyP22zHUbV53BQTY+DwyzC1qwpxp0c7ePoOT579qwnY932Ctvn0LGoHiB6XtUT3O/3jd6XJdnU80OPit6fVqNIp9PGg0yyHA6Hzec6nY5HI03pF7eR7na7aLVaaDabqNVqaLVahjD3+31Pkh/nMHrSut2u2TjlM5/5DO7evTtxss04xNf2fvkdP+o9Bwc/+aLfe8B9EvfYY49hfX0dhUJhj5wB2F3k2iRSIz+2tADYTWCj/VevMtviR4Y5brUttjeZY94mveop1k1BAoGAZ14gj1CSzPMzwkUOw+vY858e4/ce8zfoOY5EIrh48SI2NjZQrVbNtexxrE7Mg4zxo+I4ExHkf/tv/y2SySSCwSBu3bqFr/marzHvbW1tIZVKTb2BozBMYkEsLCzgySefRKFQwPnz5xGNRo3RYIdvNBpIJpMmC5wdgiVMNEShKyft0HbnYoc5deoUBoMB8vk8stksbt++jY2NDfP5UCiEbDZrMmmZJADsivXb7TYajQYqlYoxWH5w5NjBwQs7/Li+vo6VlRWzKYi98LU9MMDwWqp2rVFu8Uyyyr/VY6sk2ibl9Brz3LrNtL4WCoX2aA31epR8VatVtNtt8xrnE03AobyCBJntCYfDWFxcxOrqKjY3Nz21kf0wzFFxkO/LEWKHcTBuOD4QCCASieDUqVNYXl72eDwpN+KC0yawtuTAJogcn/YOmsDw/RX0fz9y7Bdd0vvi2LVfU82wtlWJsn0tYDfipa/ZzkGVdNlE354LI5EIMpkMOp0O1tbWsLW15dmWepzvbxyMUhFME2MT5G/91m/Fiy++CAB48skncfXqVc/7f/Inf+IhzIeJ/R5MLBbDwsICXve61+GbvumbkE6njbdWjSKNCw0H9TVawoT/26ECdiD7dR009DBls1nE43FzHq40A4H7muZMJoN0Ou0JqbJdrVYLpVIJxWIR0WgUlUoF9Xrds/oEXAk5h4cDdrQE2J9Y0XCsr6/jzJkzJvTnR5DtsUMjoFntSli11BG9uNT9RqNRQ2j1B9g1FpRNaOIgf2vbOK8oQeb/9NLw+tVq1bSB5FkJsl2eKR6PG0kH748EuVqtotVqOeLqMLMY1jc1OrO+vo58Pr+nxi8Ao0lWAmnPA7YXma/ZjinORaO8o0oybYKqJNImlEqEtQ0aLbcX/vYxKuHQdut90+tMYkyuosfrfeucFQqFTKGGZrOJdruNYrG4x8H4IBj1fU+b+4xNkP/8z/985Ps/+IM/iB/5kR95wOYcHPTgZDIZrKys4M1vfjO+9mu/1uyEA+xma/OLZ8eJRCJoNpt79DUkugxL6oCi0WCnY+ewQ7b8YXkZAMbTHgjsZtTT22SHNCORCJaXl3HmzBlcuHABV65cwWuvvYbt7W00m03jZQa8JNmGI80O84hh/VaNz6hJlwvUXC5nZBXq1bWJsX0u9cjQUKi3huehMaBXCoCHRNtJf/yfHlzqI7PZrNm0g8dzsw+WaWPIkoaOcgpdtPPeuEhXT7Vqj6l7ViPaarWwtraGr/u6r8PGxoZJtjkoxpFL7Dc/6fuOrDuMgpI51fUHAgHU63VPPWOVJdDppORRSaqfB1nH8qgorj1fKTnWUo92jgMddOqp5TwG7GqAeQ2da9QT3G63Dc/gXKLkXPXFupEQf3gNO6eCxJhzC4/j3NJoNLCzs4NarbZnE6KDjuNRNmHamNpGIRcvXpzWqfaF7UUKBO7XMAwGg/iRH/kRkyxXLpdNZ2EnpFen1+uZJBRmeZPsknRq5id1gJ1OB4FAwOj8gN2tHLkSZahUt2kE7nf+paUlszlBu9326IjYLhbvVz3jYHA/g/Spp57C5cuXcefOHdy8eRPb29u4ffu2Icv6jAhHjh0eZrC2MA2hvRC1x4ftubE9H7a3yK4OoQZWz23rDHlu6o9jsZiZQ3T+oAdYf/TaaujsdmtNdxpXlVeot5peo3a7jVQqheXlZcTjcd+F+7jQ+3cSCofDwKh+RYLMfkj7zTHtFzHa71r8bXuJNaJrw+91W3bldy1bZsXcB56PC2AeT+5Bcq8L8k6nY6LTwC5vGSbbtNtuPytyFjoL7Kh7MBhEOp1GIpEwC3v1ns8DpkaQ7969i9/6rd/Cv/t3/25ap9wXaiRCoRB+9md/FrFYzBBXGptut4t4PG7IrtYCpayCJFa/ZHqb6vW6WXGSRGezWQAwHa7T6aDRaACAuT7PBdzvZAx5qpyD4U31TtdqNWMgudJLJpOe/5eWllAoFNBoNFAqlfDVr34Vt2/fNt5kB4eTgIPoW23Dt7i4iMuXL5s5wK+gPmFLIOx2AN6NPziX0HtM+RM/Q4LLaJRCdXucB3RHPxJhLu5pJNkGeo/D4TCSyaQpCxWPx83neI90DPA3NzxQjxATbrrdLrLZLEKhEDKZjEkq9kscHue7AA6HHLuFvwMwXN8LACsrK1heXkar1fIQWo4nlURoX/WTEpCoArubAOk51RNM2DkNPCedb3YCG4mtFgDQCjYs/UhoJQ6SZ93IiG0i7yDvqdVqaLfbaDabRprFc+h9+DkGOP/RY81nkEqlzPzKLexXV1fRaDSM51oXBfZ5Z1EeOjWCfOfOHXzgAx84EoJseyW4csrn8x4vDjttOp32JOZpFii/SK1zqiEHAKbcG40XvS6UWdADpG1RQ8sf/TxXgYlEArFYDPV63eMh0gx2ZqKrbkpDtKlUCrlcDv/7f/9vbG5uGs+0dsJZ7HwOJwOz5hm0+znHiF0veNh40PFva+/0fRoekmMugAeDgYeEqxRLjQywK8Og91hDpmpE+bdmtvP66rGmJ6ff75udAtVwKlFWTxPvSUO54XAY2WwW+XwejUZjX0/TNOFHevy8fW5Oc/Czdfw/lUohk8l4xrS9SOZnbG8wx4YeY3s//bzHSqr5ee2r9nnsfswxyHHJRTTlWlpvWT3GOs5JUnnfmkdFksxnobkR9nynfMleoOuCQR0Oemw8HkcmkzHzm86Bft/jg+Aw5oOxCfLf/d3fjXyfCXxHAbuzEfF43JBWJZLsaI1Gw3hu6fUFdsupEPyCg8GgIcb08Oh7w8It1PoQatzY4WlIuRUsOyrDQTSUmmjDveI56DTkEg6H8cY3vhGf//zncffuXc/1Z4m8OJwMHDY5meT8fgRdxyYlFlxoDrsegD1kUcc15xTdRater6PRaKBarRqyTMKrdZC1RJt6bmOxmEnU5WJZPVLqAdI5K5PJmJJu6oXi/do1lFX+pYl6bJuSXy7gY7EYLly4gGAwiI2NDd9yTcNgf3/7hZ4P2p8cSX44YX/vfuRzMBhgcXERp06dAgDjRaVdVU+snlcjyvYGQerwsrXD9nn4OT+Hnt1Om1iSpzDKTGdZLBbz7FaslW80t4CSULaDkSVGtILBoFn02gtsknPdSEjnQoXKz2wpVjgcNjlhuVzO5Gn4PatZHcNjE+Sv+7qvG+opsldIRw0aE/X+alYmDYkaKACe8IBNdjUjVAcB39dyMWyDhlloBHVrSg5QXodQTw47GfXRFLZztafXUqIcCoVw+vRpXL16FeVy2bMDz6x5+Bwc9sM05hKOVRoP9cwSSnx1XNleIl1EU8/X6XSwubmJWq2GarVqokSZTMYQTP50u10jvdLxqHpg1RNS8sA5huFQEl+2k9pn1TfrHGMnCqohte9f75eSsXQ6jYWFBY9nW4+bZePm8HDBz7NLD7Lfdux+3l37fHbCnpJAHXN+ZN2PUOocY+cs8Nx2pEvlDPbiVnMMVAtszxM8B6/JnC3KOemE83sO6hS0JSN6DXqptVhBNBpFPB5HoVAwJSjtOvDTmj8OYx4amyAvLCzgV3/1V/GP/tE/8n3/K1/5Cr7ne75nag2bFCTFulWjen7U6wN49cE8Vju03SFosAAYTwo7jpJQu/apbYii0ainLfToAPBogTSjlis6PY9NeDkoTp06ZbJG52ml5jBfOMp+pAZn3IWe34LX9mjq3KCv8W8eb0sbgN2xWiwWUSqVUC6XEQqFzCYfJL32FrQqveL8wLCplnbTBGElyOl02hggGjo74ZAkmHOCJuJpaNSPHPPelSBzC1nqE6eFaSza3XzmAAxPgkskEshkMnvIqu3BBfZ6oQF4CDLfU4Ks48eu8MDP2SRaPdD2xkHKJ/RHF7oqD9EoMse1SjjVaaclJ5msyIi1XTZWoQ5DP4LMhYYfQWZ+RT6fR7fbxY0bNzxe9Fkfv2MT5De84Q24desWzp8/7/t+sVg8Ni8lv0Bd0XFFxc03aIAArwdJyapdMkWNiWp3+v0+tra2PKFShkHa7bbprI1Gw3RAkupMJmM8SSTdnU4HsVgMrVYL1WrVhH60nVpqiYPLDvuEw2FcvnwZpVIJd+/e9Ww8MinmofM6zAYOY9xr/zvo+aPRKLLZLKLRqIkg2WWGhiWMKDQipZVsKKPQuUe1yJRDdDodtFotVCoVY2Qo2VpcXMTCwgKy2ayHxHNu4O9ms4lms2nyG3gdzgUaFlaDr6FRXWDwXtRQs938rUlEnJ8mIcjT7hcuCuYwLmxCrLaUnlQduzpG1K5q1NYmySo5UikVADP21VlGqQLzBtSDzPmAhHJpacnMO/l83lS5iUajSKVSe8h9LBYz98dNh/h5Fg9Q0syxrPkGnU4H1WrVPAN6lJU8a7Rbn4VyL94TjwsEAshkMia3ijkV/PwwzAIHGZsg/9iP/RhqtdrQ98+dO4ff/u3fnkqjxoFt1NRrwsmeEztd/TQQJMTNZhP37t0z2e00pkp8+SWpMWm1Wmg0Gh5CrQaGA6HVagHYDZ0Q2nm0RFwgEDBhVM2GB3aLddueIB20XGGypuq9e/c8z2iSkOhxd0wHh3EJ0bDjQqEQksmkyd5WMqweIiWUukDm+3odzgEc+xxzqk/Wsmz0sLTbbbRaLePdYfsSiQSSySTi8fgeD7Z6eDXhxtZA2sZSPVCEH0H2C/XajgANn2ro90HI6jQN3ywYUYfDwX4LV7/jhr1Ph5KSO33fHiu6IPY7n5JCAJ7FKT20ftcgL7A90XyfCbSJRMJcI51OGwdcJBJBMpncMwcwSkUirgSZPER3C1RvNCPlbDeJtM4NunBQ4ms7D/y88oHA/byIZDK5p1LPfjju8T02Qf6n//Sfjny/UCjgh3/4hx+4QePC9i7ZEz+NF+sJ05t78+ZN3LlzB8ViEc1mE61Wy3iCstksFhYWsLS0ZEICtsSBHTifzwPYTb5h52J4kiJ7AEYDrTWSB4MBkskk2u02arUaEokENjc3sbW1hVOnTpnP6vayHAS6qQnbqKL+S5cuAbi/u6E+G+K4O53DycIk0ofDhvZrlhniFvN+sNttJ+bpMVzMkjyGQiEsLy8jnU6b6jGNRsN4e5lzQM9ytVo1Rf65QQjLNrFEEr3T9NbQKLEesc4fgDdpSMe1X0iZv3mc7r5HLzU93Y1GA61Wy3jeaXRHPbthfcCeq6c997j57GRiv+90v+/dfk+jLHxfCbM6kYYtQNVTSiecEmruR8Dfuk0zF9XcyZMLbfICAJ7fbFcikfAQZCbbqX45mUyae4xGoybvgXMKr8/nlkwmEQgEkMvlTAIfPcl8bspndN4gmeb8xPdUWkFJho55bnoWCoXMs9M5Y9j8cZzje2pl3o4S9mqPvzVUogly/X4fkUgEzz//PK5du2bIMQcAO0ez2cTOzg7u3LmDhYUFrK6uIpFIIJFIeLwydljVr200LtzJj95tlnNrNpu4ffs2SqUS6vU6bty4sYcMaxjCrrqhWiUSZIZ8tOyc7b12cDhOHCaZ1rmASXKJRMIzoSvssKsdmbFJJ6FzAc/PHTppFCmrIBhd0hJsJMg0fDQuGs5UzbIaRHqq/RwFauhtkq+6ZnqLObdw8yOVo2iY+SBGyhFXh+OA8gCVCyj8vJ22TECT7lVzbI8LO1qkO1+SLNITSzuu85Um6jKxje0iQdYyb+QAbAOJtZJutpdEVe9fK15oCTn1hLO9SvJ1TtIIl94P/1airPIRVuqZh7lhLgmyH0iOAW8Nw3K5jFqthmvXruH27dvGk6QGiZ2Sr9VqNaMD1vApsHfgUT/EQaHH0nhqwgzJL//P5/N7PM3UDOmqjXVLFRxomuHO8+fzeVy+fBkvvfSSGfi2MXVwOA7sR5L9PJ+Tnj+VSpktpsPhMNrttpmYeV4aGc010EQ5YFfawM/wb47fdDqNTqeDZDJpPEjM1qa0i14cyrJYG3RhYQGpVArJZNJ4lrXusWohO50O6vW6aWO1Wt0jpbBLUvE928jzWHq6WX6pVCqh2WyaKjj0QNE7ZM8/DwrtBw9iLOfB0DpMH+N87yRm6u1VDzHHhF0Bi/1ePZ32gpn7DWiNYd1yvtlsYmtry1SyoYySvIOkNxqNIpPJmF3nstksCoWCuYeFhQXjpGPJWY5Pklf1KmuZN3IPJbW8vkq8WCsa2E1K5iKZRJzPSx0JdsUN/W60zGQsFkOv10MqlTLzGDmb7Um27cNcSCxmFbbxUo0NADQaDWxtbeHGjRtm62ntMFzxAfB8aQwxptPpPYZIQ7C6wx7Px9/UM5PgciBpZicNNEOctVoN9+7dMwOSNQv1+ra+h0aYq8hut4tYLIZLly7hpZdeGvrcnGFxOA7sR3oP2jcHgwFSqRRisRgWFhaQTqf3RHnsiIouePW3npNjXD3CKrsYDAZYXl5GNBrFnTt3TJ4C8whYQokeJc4LNGTqNbYNg5J3JfPq4VaZhYLH2VE2/q2yCrZNvcfMq2BCjxq0aUcC3HzkME1wzAyLpPBv9YJyblC9vx1FAeAhhSTIWi8Y2JVeUp7AfQwoYWBehJZJ07Gp1yDJJEFWqYRKMfn/ft5ZnodebQCGgAPwJOnyWTAJULmHSsCA3ZrMOlfopiR+VThmHXNPkAl+YYSuCOndJYkk8fQzLPzSNdNU6yUDe5NgaOzUc1Or1Tx6HHYsrtoKhYLpMACM5zgYDGJnZ8dTA9Ve/dlyD30GAEyHXFpaQiaTQblcPqzH7uAwEVE6zOjFYHC/rFM6nUY+n0cikTDv+ZV5U28I39fxpGPNjlARNAYLCwsIBAK4d++eIZ6UXTQaDeMZjkQinqQZXtdPi2fPG0qWdazb85h6iWwSwPvQRTk9XioLo0dMNyqyo1APSpKn1RccuXawoXzAXniqvVfHlkorOGZIcDVRzSbbWtKRY9r2wrLijJJklUzpGLW5DMc+x7R6rHkcX7P3erBlEHpOdRTam6aoN5kORN3dT+dO/YxKP9ke8i6S8nHGqs4txzm+55Ig6wPj72AwiFQqZQpgE6FQCOl0GoVCAY1GwxNi0Q7H3+w48Xgc6XTaY8T0+uptZrgCuD/QwuEwSqWS0T4DMN4iFs1eXl42+iB6rAeDASqViqfoN89J+Yeubm0jSFKvk8Gjjz6KL3zhC3uem4PDSYNOyswzAGBqB/MY/ra9GPqanxHQcKAdMUqn04YIcwxWq1XPApdyrng8joWFBRQKBRPGrdVqe7y16iliSBaA8VBrOTm7/jtDtyrt4rX4frlcRqlUMg6Eer3ukVXoAoG1kEul0tSI7bQkFg4OgHfDMup4qfOnfQW89Yp1YaleU3vBqbZar8f/ddxEo1EsLi4iHo+jXq8bmUc2vLPPmAABAABJREFUm/VsKc/EOx7PfCdWtWFbCC0hpyUZOa77/b7ZHY+vKSfQBEPOGdQ6s7BALBYzSbrVatVIxrrdrqfmOp+h8iOVcrGCEGUg5FWLi4umyMAo2JG048KBCHKxWMTnPvc53Lt3b8/q5Nlnn51KwyYBvxCu5Eh0o9EoTp06hUwmg36/b4yBrSWijqfdbhs9zuLiIpLJpKd8iv7YtRKpEaRRvnHjhtE7h8Nh5HI5PPbYYzh79qwpd8J28/PNZhP1eh39ft8cQ2836xRyEuCApKdZM+tplEOhEM6dO2cIsj24HRxOEujpVdlUq9UyxJKLzmFJZ8PCfn6eEkJ1fpq1rddQDzUTVmgYuejVraRtDxXnG12E0/BQEkFDCMATOlUiTwPK9tJ7zO2qte6xff+JRMJDMhwcZhksh6ZlxRS2d1nnDv7o+KM99YM63TjeI5EIer2eScKNxWJG/qXVKMhZ9HWtJKGklufXNtLuc9xyDKsXW+Uiej9chLOsHBcUnEeY+E8oyVaZFwDP3GRH4/RYkv954R8TE+RPfOITePe7341qtYpsNruHdB0HQea11TvSbDZNUkwymcSjjz6KM2fOoFwu49atW7hz545ni1h6UViqqdVqIZfLYXV1dY/hpHcoFAqZUk70UtMDVCqVsLGxYQYWd5G5dOmS6SD0BDF7XIuLs6wTO74mFQIwInzbWGsYRDuqfayDw0kDF47cfhmAqSKTSqX2aOQIP+Nnh2FtUk2jxImfY43n0vORkDIZZ2FhwZBVJq9w/vGLjpGEcyMiVpxoNBool8vmNfWQ0UFArxDnG27oFAgETIKe1m2mEVTNcygUMhsc2bKKBzF0tqzkoOeaF2PrMB3YoXf7++/1elhcXMSjjz6KRCJhxo8S2GHRCy5WGZmlV5mv+dUC1zKwJIWUUq6srJhFcSqVMlEdlWTwM+qxVllWq9XykFGOUbsoACUhbCcXzhoV03YDu7phlYiygtbKygoqlQra7bYh7+rV1vnOnrP4DHSBHgwGkcvlsLOzM1KDPEs8ZWKC/P73vx/vec978Mu//Mum9t5xQj02NJBa6F5lE4VCAYuLi7hw4QL6/T7q9TqKxSL+/u//Hrdu3TLeZXbYWq1mCKaGXGg4WLu01+uZUnFMCqxUKshkMsazEwzeLxBeLBaxvr7uIbFahkVXlvxhKITaaPVi60pXO28wGPTUWK1UKjPV8RwcRuGghEm9oJwTbO+rre0fBo3wAPCMNzuphtfWz3HccxzTQ5NKpTxRHybwaek2zjF2joSOdRpPyi0IEmT9HP/ns1GtoOoq2X6VkZCcq2fLzSUOxw3baaW/KWWkNlf7rWpkeR71srIcI+CVXWhSnV7LbgPPyeoN/JsE1K4yY88fHKe8rv5w3lAZCImqRpS17ep1JvRv8g6CfCmZTBoHoF3tx+/eOd/pc+a1xplvZxETE+SbN2/iueeemwlyTPDLoIYO8Ca1qDZRDWYul8P6+jpOnTqFL33pS7h+/Trq9brpYJubm0in02Y3LmC3fmC1WjV652AwaEo10ft76tQpJJNJo+NhmyihoE6K50yn08bbw41LgN3Sb7wuB4JfByWZZydtt9tG3lGr1fbokx0cpgHbqzgtTHpORpGYDEdCp4my1M/pAnPYxM25Q2uN0gipNhCAx1tDbzHHZDweN6XdlpaWsLKyYs5FYhsIBAxB1kQcYJfUq6HjvFCpVEz1CYJzXywWQ7vdNlpHAOY4Et5QKGQkFpxftC18Tul02qOpHve7cRpjh2lj1HxDopbNZnH69GnPfgIA9ix2gd1SrlxstlotM36VU9D2qsfUToRVKRUAU77RlkXYkXclkUzs5f0wMs5xF4lEPLkHgUBgTyUaPV7bSLAdbAvvdzAYGFKfz+cxGHh33dOqG7YEhefVeUp1yXTwqZNvHDxIdOlBMTFBfvvb346/+Zu/wcWLFw+jPQcGjY2tuVFvjl2gmx2nUCjg8uXLWF5exvb2tmcnqc3NTaRSKaRSKQ/pbjabGAwGJnQbjUaRTCaNdjmVSnkMXjAYNDv1KenmYGcoiO8FAgFTO1XJr5ZQUR00Db8dTrE1TYrj7HgODocBHftaxkw9KxpmtRNbdaIndC5Rbw+NBwkljSewW/6N1wqHw0ilUkbrp2Nfr8m/1Zus41n1x/rDuU0dABqSpdGkd4wSDG5eRGJg37vOO+Fw2JDuSbxBhz3HuHnMwQbtMUknbT3JphI3gmNJ5QscxzqvDJNo2Fpljh1dmKuN1s9pJFklXYB3t0z1HNuEVwmp7cFVvgDsVr/hvGZHjDSCbeu4Ve5h3z//Z8Rcvd29Xg/1et18B+OO2eMc22MR5I9//OPm7+/+7u/GT//0T+OrX/0qnn766T3bj37v937vdFs4BnRFw87l15F0NcUvrFwum5rEi4uLWF5exubmJnZ2dlCr1RCJRIwB0lUn9xbPZDJGFkGdTjqdNomBmUzGaHgWFhawtraGfD5vvM3stMlk0kg1KN3Qov1cUVIvDcDT4fV/HVBMHnSeY4fDxGF5kSeFHXLUSg92rWPmBuj8YBsoTXbh/zyWybPqZdbID4+nxk8TYdgGv9AnDTq9unpNO+SqJNmWVKjny3YKUPLBSGAgEDDRLRpr3h+fj+7iRUI+LTiC6zAtUDLAKgpKhP121KP3VOUUwN4qF/ai0I8g255hXajaUgU9j0a5bPmWEkw9Rq+nNl9rD3OeIunVSBTPa8sfOP6VJPM1zXGyn4ctQaXDUuWvTAy2CfIw+zHJvHAYC+WxCPI73/nOPa/94i/+4p7X1FAcNVRXRE+OklDd1Y7H0LBwVcNzLCwsYGFhwXSCUqmEXq/nyT6n4J5F/xnCBO4X3V5cXMTly5fNuSnDaDQaZiccrjZVk0StdK/XMxnnnU4HW1tbGAwGxrtNY2WvAtXD1Ww2cefOHdy7d2/o83JwOCnQOWAwGHiq2Kih4vzASds2FPSAaBhSiTew61Xm2G42m+j3+0in02ZeoCFYXl42O/uRYOoW9DTM9HTrVrEc5yr3GAwGyOVy5n75o/IHGlO2n8aNC3p61zTqRq8yF9Yk2PqcbOM+CpMsmh5kceXmMQe1f/qjY5g2V4mmRmjo8aRDTJ1RvAbBMQrsRpM0MqXeWMCrK9ZKUzwPIzTxeNy0g8l9lFRwPHNnQL7Gccz7Y/k3zluUfnE3XpWWcStsXYzrfMQ5jc+J7ylJV/5ifyf6WjAYxMLCAhqNhqmWwTlyFpwrfhiLIM+auHrYhBiLxQzpZMdSEspVFLC7kqzVaibM2O/3TfiTBpb1/PyMEQ0RDRirWVBjuLCwYMgrO9Hm5qbHMNGYaq1SGklbk6wZtfV63dw3M9Hp1aGeqlQq4dVXX/XtgDahdnA4CdBxGg6HkUgkDOnTCX1Y8oqeg38P+62eFBJo6o3j8bjJPaDnmPOJRrBobLRcG89v1xrV66suUBf+lHvovZD883MMmXL+UomW7T1Tjzqf3SzNGbPUFofZgPZRW5ag84BKENRhZssCbPLLxHlbJjWqL9oeV13M2wt3YHecAvDkNuiil7kWOgfp3KJ8QSNIXHyTtAO7fEDf41ygzj9NePaTmShsLznnR5a006o4w0jycXOUud8oRMGHHAwGUavV0O12zRaKuVwOV65cMWSS5dyA+/IGFhRnZ9DOowOGHYQllOiNoU45mUwil8shHA4jm80aXZ+WmCoWi0bXpyEdruhisZjx+HDXPZabi8fj2NnZMatHFvqORqNYWloy4ZRut2vK2fkV99+v4x13x3Q42RjHa3BQz0IgEEAymTR1zLvdLmq1micLm95YGhEaOP2x5QwcVyS+wO444VwQj8eN7p9aXY5lVqfRihpqgFUWpXOCvXEQnwujTZR20YOkScEa1RsMBsbTbesRSYI7nY5JLOb5lEjTsE3yXRwmRhloh5MPe45Qj7C98QedSFxEql63Xq9je3t7z8JVCWSn0/FsE08HnL2oJHRhaS/CdcxxfqBul/ac90IyDuxKvvi3ElZ9Tck3k+Io9YzFYh4PuS6MAW/tZUad+LwYidediBlZtxcT9rPkc4xGo4YbVavVod8lX5ukL0wbExPk5557DpcuXcJzzz3nef3DH/4wXnnlFfz6r//6tNo2EdiZWSOU4Urgvod1c3MT9+7dMx2QK7J4PI7V1VXT0VRTTf0SOwO/AHboTqeDbDZrvMy5XA6hUAjZbNas7pS0M1xK3V+j0UC9XjcZ52wz2xAMBk3t1OXlZYTDYSwsLHjqvAK7O2ul02mTPMh7fOtb34rf/d3f9ayYx4EzOA6TYNohsgftf+pdBXajYHpe9RIB3hJQhO0h5msaFVINoWZ7833q+fgaDSO9QDxOF+Gcd1RWoV5dAJ45iwaVoVrb0822kyCoV0k1zCTJSo4nnTv02bl5xOEwYZNjwOtB1jGunlHNP+DCkQta9cbq3MBzc1zyNRLMYf3dHkN+xJcRLULboqSZ1+f8wcgzF9h6HZ27dA6jjEKjZ1peUj/L++Nrqk1mm+1Fvz1X6UKB19Wo2DDMwtwxMUH+2Mc+5knaI9785jfjV37lV46NIANeb0IikTCENJfLod+/X/had52iMeKqSAeVgp0B2C0PQ4lDsVg0Jd64PXW9XvdojdgmrhDVGKnOj5qf7e1to58mVldX94R/VG/kJ5mIxWK4cOGC0SxN6kV2cDgMsM8dhvaM59N8AdX/MWGOHhWdwJVc2sm8auRoKNRw6Hb1HLepVAoAjOdViTC9OvQwa4Uayh9qtZqnHBuNOg0dP0dZmF9JKvVUhcNhswGBGkIuuDk3NhoNj4dI5wkl6NPGpP3BzV0OhM4puukF+z51vbowVN1uvV5HrVbbQy5p73URqQtYjn0/77GeS8m2npNyJ5JWjmmel5IH7sSnC2reLyNFdqKuLsJZFpLHcMxzvlIyrPKLwWBg5GIktgDMBkesiqO75im/4jOg85LRKd6bOh0Oa155EExMkLe2tpDL5fa8ns1msbm5OZVGHRTqJaKGV78Iaods0b7tMQJ2V6HaGbXDq3c6FAqhVqshmUwim83uWQ1qqINaQ90nnW1nJ2FigIZVaLR1taorR+3k6hXj1o66WnZwOMkIBALI5XLI5XKm2D29q+pN0UoM6gHSH4VO+GoodQxq7WUaatYx12gUADNH0SgSlH6pzGJYpnkqlTLtYmJgtVr15CRoJIzzGRcJnG+YrMzylroYUNI+a/OHk1g8nBj2fTOay8WkLZOibSQhpdxSqysAe7doV12/7nxHW88fW/+snmg9pzrHAHj2VFAvK+cTzmFcbGueBbCbDEiyzcgSz6PXtrem1/tS2Rl5CDmUVrXhNtS8Z94326i5Fpr0yO/AzqvgPDpr88vEBPnSpUv4sz/7M7zvfe/zvP6nf/qnx1obWVdwatyUrNqics1aV3BVRtiaHkJXliqXYAUNLRLOAaH/81w0nvybr/O1Xu9+RQu/feW1Y2qSng6CeDyOWq02pSft4DC74ESbz+eRz+eRTCbRbDY9440GS7dh1XCjjn+dsO3xz9foUQZ2NYCMXnE88zevyc/wN8m06hB1AazX5fjnblecA0KhkNmyXjPVVYdMo0fvOb1J3W4X1WrVyNP8Fgh6vzaRmAYmPdesGVOH6WKSCCePZb1xjbZohEgJMolbo9EwBFkTXvXcGunVWsqAt6665jfweLaBY1g10QTnCJ6D3l0SUnpqSU615JpyBvIP9USrI3AYQeY11DPcarUMkVX5is5tujDQ52YvBpQrAbs5WfYY9ouEHycmJsg/9VM/hfe9733Y2NjA2972NgDApz/9afzar/3akckrhj08+wvXTqPZ2hpypRRCCTOvQcLJRB/9MtkxuOuOepeYPa/6IA390pNj6yN5LI9XDZHfJiC8T3qXmeDX6/UMKeDAmpUO53DycFCicpihehJWhlbV+Kh3lMfbUgr9m/MC/1YyqwtbAJ7MciW5g8HAJOwGAgFjjOlN0XNyPuB5GBq1o1kqf6A3OhAIIJVKeSQc+ref94oeI1uGQg8UEYvFkM1m0el0zIL7Qbw+fsbwIBILR5QfbigpS6VSeOqpp0yujr2w1PwA5giRQKqcSecIXVyrPIoVsFSzr0RU26YLTh3HJJw8VucZXpvtqVarnooWnB9sx5mWrtSomHqXWYyASf7tdtskF2tkTZ8D2wXs5mHFYjFzLAsiJBIJ85rmg7FdjUYDN2/eRLlc9h27s8RVJibI73nPe9BqtfDBD34Qv/RLvwQAuHDhAj7ykY/g2WefnXoD/eC3wuBkqys4HTi93m45Nh4P3CeliUTCeFHYsehhUcOn56zX6wiHw0in00gkElhdXcW5c+ewsrJito0GYHRNdkjE7hhK3jV0w/9VS6kdltU4dnZ2sLOz46nbSJIQj8d9r+ngcFDM0ipfYcsjOGZ1Qcrj9LcSWiXE9rkBeBarNmwZhnqiNbfBDqfSk0yvELWU9ADp3KbXUQJArxHnHiUDNqHWe1PjrlpELT05GOxuLqJykP2+CweHo0QkEjG72drRHsA7P5AsciwqebTHgy5cubhUyaQ9fwB77a0959jQhTnnAUoqg8Gg8Vw3Gg3TRo5R9eiqtMMm6NoGknOVPKhzTttln8d2Kiqp9/PY01HRbDZRr9dRqVSMw2CWecmByry9973vxXvf+15sbGwgkUggnU5Pu10j4UeO1TuknUQT5YDdjs5zVCoVz0qT5Zqor9ENBjRTE7i/Ynr88cdx9uxZrKysGM0jQyDcwrVaraJWq5lNQ1gMXHfBYttU08Rrdbtds9mHdkx9PxAIoFAoIJfLod1uY2dnBxsbGyiVSrh27Zo5r+0Fd3A4ahwFuW42m2YbeJ3gtWyTvQuoRpd0nrDzEfxkD8DuWLQ1xfY1aEDU0DDSwzYFAgETuaKGWckuvTz6HNke3Rrazkrn/ave0g49M3dDveRsQzqdxvb29qHrBWdRj+gw2+CYzufzAPwr1wDe6LL+zTKv8XjcjG86xoDdpDcApnRkKBRCqVQyXlyec1TfVTLtt227zjcaAaK3u9PpIJFIGE8tybEm++nGZ0pg+VyUDOu1dIMRPjc/vTLvgedQQlytVs3/9XodzWYTxWIR1WoVL7/8MqrVKorF4r7PaRT8bMhh2JUD10He2NjAiy++CAB4/PHHsbS0NLVGHRTqfaGsQLXEujqi5o4eXt0ylvokYFeQz5AEDVc+n8eTTz6JRx99FNlsFqlUygjXmQVeLpc92eGlUgkAPNu7Al7vknZEHSjswNp5tTMEg0EzsHu9+7vtpFIpfPWrX/WEU/VZODgcFw5TXsHxzbHMMa8F7gF4PKRsk20IbI/TMC+QnkO9teo1tsOVbKetxeO1tKY5w6y8ribyct5TuYedOEP4edXUO6TX10Qb9a6N60EeF8OMnYPDMAwLzav2VjXC9nH8rTY2EomYalR+BNlOXiOhZII+CavOBdpeva4uuO17sdvOxTQjSSqX4Nyhm6TpopoEWyUXdrTK79lq1FsdA/rM+GN7jXWDEhYj2NnZQbFYNDsB+113FjHxTFer1fATP/ET+OhHP+rxrD777LP40Ic+ZMjfUcI2cH6e0kBgVy+sUgYNrdjl3QAYnRI3HWCR6zNnzng2CwHue620/vKdO3eMto+JAPS2q3ZHia8mFdoE2a5XyhCJdmYNDXU6HXzlK18xrzs4TBOzKrMAdseikkdq7GhgtJqNLV1QzR3nEnpVWQNd71/DmhqKVaOo85QuWtkeEmJ6o1gu0ia89OjSY6bzALCbqONHkjVUzLbY4VO7hjQjW1x0U6r2IB4gXlv/PkhfckTageA4YuSFY8fODwJ2a5cD8Cyc0+k0UqmUZwFLiQB/SJw7nQ4qlQq63S5WVlY82lv1Xqu0SctD+lWG4bhSaQe9uowK0f6TfHI8RqNRJBIJNBoNo48eDAZml2CVb5JQ+y2ilSDbUiu2TxfJ5BrkPkwU5hzcarXw6quv4u7dux5v9IOM3aOyOwdK0vuLv/gLfOITn8Bb3vIWAMBnPvMZPPfcc3j/+9+Pj3zkI1Nv5DgYDHbLFekKTDfd6HQ6hvDqlw/AQ0SDwaDpdPl8HuFwGPl8HqlUCvF43BDkZrOJeDyOQOB+0k2r1UK5XEaxWESr1cKdO3c88o5IJGKE7JRh6IAgAWb2qrYN2C37pEZdP68rWwroVXDPc+kAcHA4KGax/7B/V6vVPdIvtlc9J7ZnWKVItveYxJHjkgk0ADxjzzZwSk4JOyGYv3UeAnaNiGbR9/u7ddh1Ya/ec83g1/vXcKq+plCCbbfRT8t9nCT1uK/vcHiYZH7Rfmwn5Ov7dlQE2B2jTK5XJx/PZW9A1mg0jB1tNBqoVCoIhe7vOkeOQej1dAwqQbbHFNujY1Ftvf3b1vyq3Vc5mF+kS6UXdkk8ddopX7G9xPSkMyeKXmzyoVqt5sm3mJcxe6CNQv7gD/4Ab33rW81r3/Vd34VEIoHv//7vPzaCDOyGQJQgs4MBux1FJ3uWSVIDqAMmk8kgHo9jcXERmUzGbCudSCQ8odB6vY5SqYTt7W3cvXsXxWLR6CDj8Tji8bjxOAPwaJZUR6zaQg1d2B4bfc9vImAplvX1ddy4cWMPIZ5FcuPgMA0MBvczzJvNphmjSiLVmOrED2DP/MB5QT2rmiCjBFkjUzQ8ahhtKYXCNp62dliNV7fbRaVSMZ/jb5WJaVjV7779DDLnCNurpJ5p3YRhHHLqFuIORwUdw7qpB9+jl1XLnulYYxJqKpUyfVv5BOsla51xkudKpWKul0gkfJPY7LbammCdL8hb+OPn4bVJss4Tdgk5HYfaFl3IayTbJt88hvMLF+L0FpMgt1otE/nqdrvY3t7G7du3UavVPG3SqJUt8ZolTEyQ6/U6VldX97y+srJivBrHAZ3kWbFCNckaGgVgwhX5fN6zy5xqdOh91nAsP2uHXDY2NnDv3j3UajWUSiXU63Xjxc1kMmZlqm3hgFVxO73Rath7vZ7JymVYVRcBnBBYrYLe5Ha7jcuXL+P//t//O9Od0MHBxkE8DEoC/YwHwb8pt+Lfqvsl2bTHiybWqEfFPoaRLF1wa+KfVpiwjaVtlJXo8tlo1IieKN3anh4czhGadKcGUM/HY22PlUacbKnHON/huCT5IN/5vHiiHA4HutDV3TMB+C4Q2c+5gAR2F8LU1+s5OV5UwlQoFJBIJIz9LxaLRt6gHmgds2qrdeyoR5XH8xhdvPNvO5Klcx2rRLBdLAlnk2VbRtbv983cYS8cGJm3vfKsQlGtVo3Ug5HzdruNYrGIV1991dOeYd/dLGNigvymN70JP//zP4+PfvSjhpA1Gg184AMfwJve9KapN9APft5QlUcAu8krqunVEkVaQcJvZQPcNyoUu1O/COzuk95sNrG9vY1isYhisWiS8thpKZNgTVFbNkECz9JM3NWH2km2Rwe1HZ5QrRTbyr+bzaYJBfndn4PDSYUSY1tSpB5RgsZHjSYwOgN+2ATP8WrrfW2S7Pc5YNeYkqxz4xC9psqySJJ5TX5WI2p6fZvc2nOChl51oTFrc8estcfhaOD3vQcCAVN9xfa28n3+z/6sUSUlnja30A19er0e4vH4Hs8zf0iyOcbtqlRsg180WK9p35vfPek9aPlXe7MffQa2bELrLA+TYZBfUEbGa7BkG6WtTMBjgYJ79+75es/nCRMT5N/4jd/A29/+dpw5cwavf/3rAQB/+7d/i3g8jk9+8pNTb+Ak4ITO8iLArt5QBwCNBo2m1hBkZ2AnYjiFHYTJelw5saQJwy+xWMxTzo2hBv7WOsW8HtsWjUYRiURMp2enZWe0Q7866LlS7PXuF+KuVqvY2dnB5z73OXMt/e3g8DBAJUgqaeLr6r3l/3ZpSMImsAoucJX88m/1NuvcY5N2XTRzHgLg2ehDt55VrSErXaiXmMfY84ntQVZ9s96rhlt5bzSGk2KejaTDbMAvEqEezzNnzmBpaclTZsyWFPnJCyibisfjRkLkZy+VqCoZr9VqSCQSSKVSexbW9sJVZR9+5N2+rr6nVSKazaZnAZ5IJMzYL5VKKBaLJlKtibw8RyaTMVF2aq95PY57LRXXaDTQaDQMxyIZ3t7eNuf80pe+hO3t7T0blQwj//b3OW6k6SgxMUF+6qmn8PLLL+N3fud38MILLwAAfuAHfgDvfve7PVmcRwUdACpSZyfXkKjfii2RSOx5D4Dx5DJ0oAQb2E244wCt1+toNBrGYMXjcaTTaeO11rbYJVzsTUh0hateY+56oytDZrGqhgi4L4X58pe/jJs3b/qGih0cTjJIBjVZheNAx5pilFcX2FsizZZMcLGrnh2d9El+STZt0GBp+/Xa2habvAK7VTNsPaHOK/yfz4Xttb1M2gYl7Ey2cXA4DgxbaAWDQSwsLCCTyXjGhpLdYZ9X6cKw6+k5VB/M6LB6nveTH6l0ya9dnKd0kW7PBzyOOmhGmwKBgIloM2rNXX01h4GOOEbHbdgEuV6vo1qtmvd2dnZQq9WwsbFhvNfVahWNRsPXgTDsOdjPdtZwoIKWyWQSP/qjPzrttowNvwdKwwd4NcOhUMh4dym+V90Oi96z09NIUZNDY6OGC4DxLFP7lM/nsbS05NFhNxoNU61CvVY6cHkNtjUej6PT6ZjBwY5MnRDPY+98A9zf9GRjYwO3bt3Cq6++iueff35PONdvwDs4PAhsg7Df/0cBJch+Xie7PbqItD2tfE/PY4diOV8wYUVLvGk+gyb3cO7gQjidTnuq7LAtuoDWOUlJsB6j3iq9PwAeg6oytMFgYHSWw8h1vV7HnTt3jKGcNo6jnzjMD2y5gC4+I5EILl68iGQy6amWoLZW5RVKQOlFpQNM5UX0QmuCrSbgRSIRJJNJI6sYBXsu8ZM2KSfQSBRlDYDXG24vWAOBALa2trC9vW02LVtaWjL3x2g4JSPAfYmIlrbjHER5ZiAQwObmJorFoiHO165dQ6lUws2bN8ci+37f5TzgQAT5xRdfxIc+9CE8//zzAIAnnngC73vf+/D4449PtXHDMMwVr+EWCsc56ft1RuqIGZqgh5adUw2IXQ6u2WyaeoKFQgFLS0uIxWIolUrG07Kzs4NWq+Uh27pxiZaJIjhY7exbEmRKNOyQCQBsbm7i2rVruHbtGq5evYpiseirZ3JweFDs55U5DtjzAidzrRgDeGUXjO7YZdSo5e90OojFYh59MrA3ZKs6xHK5jGaz6ZFqMXzLnaWazabHA9zpdLC8vIxAIGCiTkqK9f7U86ILbbZddwvk5zR/gpIwW3Jmb1er3yvvt1wuezTRDg5HDdtTyw0+uFmHemh10cixwR8dP5qYC+yVGyn4Ga1mw+oXHLskoRrV1fOpR1gX4/QEM8mfeVCdTgflchntdtvUNQZgEnG1jYxmc96j9EOT/pVjBINBz74MOifyvKwKxPsplUrm/5M8FxyozNs//+f/HG984xtNUt5f//Vf4+mnn8bv/d7v4V3vetfUGzkO2OE4uavn1Cah9sSvg4crRUob1FtjaxUDgfsbiCwvL2NpacmUcaNeeHNzE41Gw6w41etEA6htVx2iXlevz8HFe2DIpFar4caNG7hy5Qru3LmDcrnse78ODkeN4+p/9CBr7WJglyDb48hv/LXbbUOQNX/AjijRGAWDQZOoQslVq9VCIpFAOp1GvV5HuVxGpVIxWkIm1QJAJpMx52YkSbXTNF5+kS0aL3qU7M1DeE5el+FhLS3Je+CzUY8WvcgkyG5ecThuMArDHfC4AQawm0xGKQGjMbrfgN3HbdjRXiXnjP5EIhFDkLnI1PFh73TJtqmXm/kCzWbT87lwOGz0vnfv3kWtVsPm5qY5Pxfh6r22x6btzBgWPeMz0PvlM7DnS50fhs0D+zlR5sFZNzFB/pmf+Rn8m3/zb/CLv/iLntd//ud/Hj/zMz9z5ARZQ4D0igAwcgSGEhhqVd2g/s2Jn/uxqyeGIRvVEAL3DVA2mzWfSSaTiEQiZvV29epVs8rigOS5NUNcV5iqh1JPk65stUwN6w7eunULV65cwfXr100NZh4/zOPu4DAN2JPkcRInJrqm02njzaXx0DrBBKUJKjPo9/ue8kXNZtPsdBcIBDxRKY4rlmbkOUmoqd2rVqtmy9WtrS2Pd5ljmtvV0+PMhTY3I+LiH8Aeg0hodj3Pq7rnWq1mXiP5pkHUBCWGljnPFItFlEolT+m4cTBupGFYpGucvuTmtocXgUDA7Ga7sbHh2Z/AriIDeDcOsyO6rAZjywJ0nNrXtuVTADxRGR2D/LzWKFeplC5utRrVjRs3TOIdE/1tomq3y+9/na+GjRnbucjP6W/Fftfe7/Vp4jCuMTFBvn37Np599tk9r//QD/0Q/sN/+A9TadSkYCesVCpYWloyCTPqLQHue2d0hyl6X1WzRANIdDodVKtVo0+OxWJGV7ywsIDV1VWz6x6JLTNc6Z3hYGAohu3RwUnZBo0SJRw0YKztyHsjceZg2dzcxKuvvmqML5+Lg8PDBBosjhtuL0/5FOBdNJIoqteVxkqNGEmjHY1SaYOeW6NSlHEBwM7ODorF4h7DRlK+s7ODdDptxjbvQw2xHzm2PUbq8bHnO94Tz2d7xoDdBER6qer1ugntzspiyOHhhO3wIdml/eT4tz2eaqPpsFKSzDFiJ+upZ1n1/baXlbp+zht2mzVapV5Z9STbY6vb7aJaraJcLqNWq+2ZNyYZf7Zu2++5DjvvOET4JC5UJybIb33rW/GXf/mXuHTpkuf1z3zmM/iWb/mWqTVsFPSLUE/wRz/6UfzkT/6k8fDEYjEkEgmzmtvY2EA2mzWrs16vh6WlJaPJA2BCGMCuISoUCgC8BcV5XXpktP6hllIJBAJoNpuo1Wq4ffs2MpmM6Wz0EpdKJaNbDAQCWFlZ8YR8GILhD1GtVvHqq6/is5/9LL785S874+Vw4jEspMfXGGqlB7dYLJrkV60Swc8Mm9TpBaamkBEf2+uqxo1t40KYJLder6Pb7WJra8uzQYE9j927d89khRcKBaTTaRP9Ys15wFt5Qr1Uang17KoJRjTqfA76PHX+sMOtxWIR1WrV4/V6UEziJXZwIGzvJr2quqCzodFkOsF08xyVQSoh1oQ8vSbHFMePVodgpBnwlp7VyM4wiZK+xghxpVJBpVLZQ/jt44fhQcfXMLmG4iSSY+AABPl7v/d78bM/+7P4/Oc/j2/6pm8CcF+D/Pu///v4wAc+gI9//OOeY48C/AK73S4+9rGP4Tu+4zuwvLzs8dySZHIw0JDRUHALSq1kwTAjDaGWU2KCX6/XQ6FQ8Hh3GBohmSWxrdfr2NnZQTQaNSHZYrGIjY0NY3zS6bQJ7w4GuwkI7XYbpVIJX/ziF/Haa69ha2sLpVIJtVoNjUZjKgbLweGkQBPtVEIB7HpH9VhCtf/DEmZpFLVcmxpAeo350+l0jO7Rb5wqQe33+yiVSqb93B2MXi/bg83Pq/HUmsv2hkmaB6EkWaHH8V45n02bzDpy7HBQqKSp0+mgVqsBuB8pBvz7lla0UHJs649HSRHshakm/NF5Zi86NcrE17VN9rWVc/h5pI8So8jvSSXGxMQE+cd//McBAL/5m7+J3/zN3/R9D/CWXTsKsJPdunULf/Inf4J/+A//IR577DH0ej0kEgmzPTPr9JHsrq6uml1nNJQZjUbRaDRQr9c92jxdsepWjlztaaIMCblqmEqlkiG8m5ubuHfvHra3t41kIh6P4/Lly0YyQu/ySy+9hFarZbax5vWHeY1tHZWDw2FhVkiOLStgGFV1e5pco9UsNATL8UpJlZ6b44pjnmSSY52VKra2trC5uWkiQzSmwzS56qW6deuWkVQlk0nkcjmPMVfyykWAVq7gYp8VdBKJhGezI96vlpJie5jRznPrrqGc38b9vvebf+x5a1y9ssPDC7/FHHXx165dw9LSEvL5vCf6Cng1x7pQ1miQHmdfQ0kvqzu0222zWRgjRiTIdp4DI1Jsky5oVRKli3Am+g6rZqOf5euTPkdtg31uwj7mYcLEBHnWPZXtdhvXr18HcL8k0enTp3HhwgXT6UgsueqjJ1kzxElw6fHxKyBuJ8rxf3qcqtWq0R6yNmq/38e9e/cQj8exsbGBa9eu4d69e6ZkCgfJtWvXTMY4E3zu3Lkz1Pv0MHZch+PFcfW7cY2AenAoc8hkMp6t3ymt0qQUXexGo1GPZ9jPqGgIledlvWDWKPULjY4yQlyEb25uIhqNolQqYXFxEYuLi8ZLpWFhGnVeh3McNxEaDAYeeZaWd1KDrORbo2tcbGjZp1mCmwMfXug4Z/4NaxOzsoRdt1i1xvYikZEbntvvR/OKKIFgYj4TbLmzHfmEklxts30fHIdM6KdcbNi4GybVGPe5jXrPlrM9jOPsQHWQZxHa+QaDAa5evYrt7W089thj6Pf7WFtbMwk42jFZhk0NIREKhUyJJ8JOYmEIFYDRCkajURSLRaNrZqknlmNjhYsrV65ge3sbjUbDE4p95ZVXTLhVV5Z6r3Z7hj0P+28Hh5MOnQtIkElWacAYwlTpFQBP9Ee3eCZUe2gn99CwkSDbO3iOA9U0bm1tmaQi4P6un5yPaOiV4LIttqRDXxtGjpUg84fXtZMWJ/0eJnndwWFc2BpgjcQWCgXk83lT1YaLSC3tZm/2w3Nqwi09wSq90t1wO50OSqWSR9bEala6gy7lUbbHl9fnNTn2otGoiWDxM34cYFpjxu88o157WPjE2AT5u77ru/C7v/u7yOVyAIBf+ZVfwY/92I8hn88DALa2tvAt3/It+OpXv3ooDR0H9hdaLpfxN3/zN/jiF7+Id7zjHabiBL0jg8EAOzs7niQcAKbcErVN9DIreI56vY5er4dyuYx6vW5WfvV6HYlEwuj3OHDL5TJ6vR42NjawsbGBSqVirsMwsJ9kYr///UJPfn87OJxEkOQxcpNMJj07RalsQHe5I9nk/xzXXOySTGt4VBeulDmx2s1rr72G69ev71sreJShsT1anU4Hm5ubaLVaeOSRR5DP503yII9RiRh/GPKlJ2qYHpLXpEGnB5vzGucsJSS2d2kYHOl1OCzYhHEwuF/daWNjwyM14uLOHmsqmxzmgOJ5uXBmqURGaUqlkmdnyW63i3Q6jaWlJc8CXD3Q9oKbbVViTnnnNPOLxuUB9pjVsf6wcYmxCfInP/lJU8weAH75l38Z3//9328IcrfbxYsvvjj1Bo6DYR4JXZ194hOfQDKZxFve8hZcvnwZ6XTaaOpUOwTAGAath5xOpxEIBEyn1SQ8ACZ0y88PBgNsbW0BAIrFImq1GuLxOG7evIlWq4Vbt25he3t7jyH18xjz9WFwZNjhYYctkaC0gB4cuy4pCaVqeQF4PE0kyxqG1SgVsOtlajQaqNVquHPnDiqViq9BHoVRXlUaTBpn3RnQTtrT8LAmH1GLTfiVgrS9WQDQbDY9RtoRXodZAsc3sDsWqZf323Jax75NhO3NfwB4Pk+S3Gq10G63TRIuI0XA7gJ7VLUXW8Kgc5B6xP0kYHb7Rj2XSY73++w4keqTjrEJ8n6ezOPEqC9PQ631eh3/83/+T3zqU59CNpvFY489hre97W3I5XKo1WqekCSwa0T6/T52dnZMeAWA0Qbxfa5eARjivLm5iXA4jBs3buDq1avodrtmq1bV89m/h5H9SUOWDg6HhVnub4PBANVqdU9JRm7fGo/HPeSRhpOGicaSIVNbK0jDRyPWbDbxxS9+EbVazUSUeOyw9o0ax36fo0TixRdfRDKZxFNPPWW2kCXs2q9c4Gv9Yv7Qi64Em8ew/f1+HxsbG9jc3NyjP56l+d/h4YQfJ7FtqR3x8ZMmKAG1Ca2Oc0aCy+UyGo0GKpWKcZiR5OqmQpxXAHgi0H7t1ogWABOd3tzc9DjiFON6diedqyedt6aNWeIzJ0aDPAz2ao0ol8v44he/iC9/+cuIRqM4d+4cvvmbvxlra2smmY8a48FgYIixbvXK3fO0jFytVsO1a9fwla98xex8owl/o1aV+3WKYe/PSmdycJgVcIyxmkM4HPbUIAa8Zdv4wzlCE9TshTDJYr/fR7FYNEX8NR9hXIzreODrjOJtbW2ZSFYymUQymTRttI3tqGvYmkudo7Rc3Si5iIPDLEBtaCCwuzMkADPu/UoljrK9uoi05wpNvldiSzKsY8bPK2vXVgZ2CXmj0TA/tgf5sOF4xi7GJsiaCKKvzRNsw8AwSSgUwiuvvILt7W1TEi4ej+ORRx5Bs9nE+fPnjUZP66LevHkTt2/fRqVSMcaRWz9TW6zGx2/1Oqx98/ZsHRxmDYz4cKG7traGZDK5R15BbzLJr2a9K4Fkcg7LQgLACy+8gI2NDSPXIkZJpMYZ2xp6VfR6PdTrdTz//POIxWLI5XK4ePEistmsZwtpbf+wkC6fkRJiJeL0ltXrdUeOHeYGgUDAs2EQoyJc4NKOj4qIKOEF7m9RTfLK7eC1vCKvm0qlzGZgfo4wJdgq6dBxyCTfjY0NI9McV/M/q5hEwzxL3GciicWP/MiPmLqgzWYTP/ZjP4ZUKgUAHn3yrMM2VoPBAPV6HbVazSS6xONx7OzsoNvt4vbt256ddgKB+zUKd3Z2TBUKeomHkeBJO/cshRkcHOYFfhMxyd+dO3dMIi2T77RGqZJGlV1wjqCukWFVLoT3q1bxIIZt2DxA8l+tVk2ol/ppVuVgQqJ6uJgwpAaXZSppqLkDIUPJ05bXuXnN4TDQ7/dN3e90Oo1cLmcivroo5OJRyzdq2UR7gzDmJHBnzXQ6jWaziW63i2KxaCphsbwck/OH7QPBayvpZmItpWDFYtFEboDpV604Ssxjm4mxCfIP//APe/7/oR/6oT3HPPvssw/eoiOCbXi0A/Z6PdRqNVQqFQDAtWvX9nxWPTUaPhmlIxwHzng4OEyOUQtKembu3buHcrmMxcVFk5hLgmxXsrGlUIPB/UQ5lozjBj+VSmWkRvAwQMNNrTUX93RWqL6YEi96q+r1OgAYDxo11CQFLEV59+5dbGxseO5jng2dw8mG2t5EIoFkMolsNmveszXGGmWhhEGlF/bmIVqTmItQRp9v375tNhdLJBKmYgwwPPKuzjZeA9jdBZNRabZff88T5rHNirEJ8m//9m8fZjuOHDY5VmI76kvle376oWH/29dzcHA4GOzElGFhUj/C3O12jTxhYWEBTzzxhK+EAYCHLDNr/fr162g0GqhWq+a1URnmD2Ic/M5pG1u+fvv2bZTLZaytrSGVSiGbzZqqOsyLiMViGAzu14dvt9sm2sVQMec9etvb7bZnPrQ12g+CeTeaDscDvzFt983BYIBarWb0u0pAbWmVLX/U7egJO1mXhHowGGBlZQXBYBB37941HuB0Oo10Ou2pgMNrEJwzON4o96T2mFWvtD12u9QbPssYN5FwGjiMqPuJT9IbF/tN/tohD2IEnWTC4aRiXvRxNEY7OzsIBoOoVCrGsAH3jSilYrrlNPW4Ozs7Rlox6QYghwXKw5rNJqLRqJFXxONxADDeZZLgUqlkpCI0+naW/1EZNAeHaYLzEKMmnU7H46n1c4qNY8v1OB0vsVgMiUTCRG64TTvJMQm5fQ57sakl5BgRGiXXnPSZPOg5poF5nUscQRb4fYl+xNaPEMxrB3BwmBT79fXDJsx6/UkNAL02W1tb+Ku/+iuz09bS0hJisZgpwchaqvyfnlUlxuNec9i8Yh+z3/n0fU0gZBmqGzduGHJM3SQz7QmtB73fPah3fNhx87I4cpg/qO3dL6qrEopWq4VarYZisYhcLufZXlrrk1N3DHjnEVtuqfWT9Zr0+iaTSfMZu0IGj1WvMc/JHAGS40ajgeeff95sPGITeB1rtpRz2JzsxuaDwRHkfTCs4zlC7ODgxayNCdu46OusbEH9cSQSQSQSMQar3W6bH7/Q61EsAPyu4WcY+Tc9ws1m06NB1vY7Q+pwEmE7sur1Ora3t82uuAolvYSSV/6vpNtvTPpVrRm1oY7tidbE2FKpZJJtuaOuH9yC9GjhCLKDg8NEGBZVOaprP+jn1NgxSe/27dt73p9WGw4abRpmDG1dMI/jebXmq99n/a4/blTMGWiHecDW1pZJyOUuuCqroBSJdYtZ3pGEldBt6bkAVTJtLz5Vy8xFKv/WZN7BYGCSYweDAa5cuYKNjQ0j3yLGlX7OMuahjcPgCLKDg8Nc46A6O798AptsHuS8+2Ga3iE/z7b9PPwSm/T1Sa55lJ50h4cbflKqcUHieffuXbTbbSQSCaMJpvyI5dXsnTXta1FbzCo2WjKRen6SbSXNmtBnjxPdnr5arZoddsfd0n2eSec8wRFkBweHhwp+xHEaCTEHxSTGzk8raRPdcUnsOPc6K0k+Dg6TgET13r17qNVqyOfzZoMQyqkGg4HZbY9lHlnlQnfhCwQCHoJMnTHzFLRMJKUT/X7fU0rR1kkzz2F7exv37t1DpVLxbEA0yxgWhZrW8bOEh54gT/LlzfMXPSkepnt1mC6Omkz5XW/c5B77+OPAsLE2SeLhtNo/jQRMJ8NwOE7oHgXFYhHVahXVahWxWAzZbBbxeByJRAIAEIvFDEHu9/t7Nguh9IJ7IzQaDXMdJb/hcBjJZNKQZ1ahCIVCe7aoj0ajqNVqePHFF1Gr1VCv103FjXFw3HZ50usfVXsP4zoPPUGe1Hsz6viTZBSOexA6zBeOuu8P08yO0tHOy/gcJcEY9v6wbPdJMKk22q8941T8mTbcXOXgB+p+uXtkt9tFNBo1nmJ6iLmjHbBb2YL9W3fZZO1zglUo9HPA3qoV/J9kmpuMFItFc855mZseNjz0BHlS6A45fpnk8zJZ22330ynOy704HD/GJUKThOyHnXOSBEHtx9Mia6POM8k1RhFNP4w7Jg/D2I565tPwOk+rXQ4PL2ybpslulEJ0u13EYjHE43Ekk0mEw2Gk02lEIhFTw5gyDNUrDwYD4+1VRxmr3CgBJrgDHzfmCQTub05y69YtbG1toVqtespGPkgUaVpwdt8LR5AnxDDDMI+dah7b7DAbeJCJdBLpwDjXPspJfRTZPkgb7DlkHJI8KxinLbPUXoeTjVHzAgmzShlIYFnukZIIVrUIBoPGu0xvtGqQ6Q3WcaueaG0H6zO3Wi0j+1ACPwxOrnS8cAT5IcV+5N6RZ4dJMG5lhGlN9qOqN4zzmWGVHiYh236fmcR7POz1g2p8D3PMzqqRdh4vB4WOZ1sSORgM0Gq1TJ1zYLeMWygUQqfTMdUsuMNmKpVCMBhEvV43cohw+D5t4lbzhNZQppaZnuVut4vt7W2Uy2Wz3fuw6K3D7MARZAcHh6lhlid8P4Nkk82jaPtBrzHuAmBWn7+Dw1HCb1yr/IIEV6UUJLZalQKA2ViEpeFIfnksyTjPwx/+z8S8YrG4Z0fO/eDG8/HBEWQHX8wy0XGYbRyXt/GovaoHwSRe7mHHjyvDmMfciIPAzVUOgH/0in3DT25B/TA9xrqxDsmtvcU0AEOQ6WVWTzWTADUZUDXMpVLJyDkUD8M4nUc4guzgCzdQHSaBX5LncbTBrxbwpPKHg1x3mscpJqlOMS2plCOcDvMOmxDbcwM1xbb2mO9HIhEP+eUx9CqrR1o9yPQik3jHYjGUy2Vsbm6iVCqh0Wjs2QxklsbaLLVlFuAIsoODw4nAuEl943xmv+tMWrlhGoZnmIf8MHTd80SS56WdDkcDjolRESV9v9PpeCQPfI+EmKSYsgkApvQbSbSePxAIGILcbrdRq9Wws7Nj9M+z4ExwGA+OIDv4Yp4MpMNsYBY9IoRN/sY5DhheR3k/KcRBkggnwYNWEBnHSM+L4XZzlYONSRbELP+mO9mR6FarVY9nOBQKGe9yOp0252CVikQiYapgbGxs4MqVKyiVSiiVSoYc+43BeRlrDxscQXbwhTM4Dg8ThpGsScjXqNrI42KUR3hU9YoHMbDzbpzdXOXwIBgVeVIyS08yvcuNRsNINSizYAIet48uFotmpzyex2F+4Aiyg4PD1OGXGDPrOIhEY9hnH4S0+nl3D0OicRTfyTS+/2Hebkc2HKYNW6/M14DdGsckuyz9lk6nEY/HEYvFUCwW0W638fnPfx6tVsucY5yyqvMwRz5scATZwcHhUHBSCIx9H0dtzEaVpRsH43i2J/F+TyovOQyclL7lMHvYbyGpWuVGo4E7d+6YEnEATN1jv89MMxfC4fDhCLKDg8OhYxgZOyyyaXsuxy15Nmlt0lFeZ/tah6mVPUg5OPvzfpum7HfsuG0Z97MODseNcfX4/X4fjUYD9XrdHMMxrjvp2edxi7v5gSPIDg4ORwo/j6zCT5rhVxViUjL7oDWI9yN+fucaVmLqoBj3ng9CQh/kfic596Tfw4Ne2+H4MG/f1SjvsZ/8AvDfgMh+fz/M23OaZ0wyNx4LQd7P6+Iwu/CrM+vwcGC/736afWJUWHNYveNxz/Wgx43z2VEVL/YrO/egHu6TMDYPcg92fVmH48O82on92jtuBZx5u++HCZP0zWMhyJVKBQD26HQc5geVSgW5XO64m+FwhOC4JRFxcJhFuLnp+MG5ggltDg6zhnHmicDgGJY6/X4ft27dQiaTcaGFOcNgMEClUsGpU6eG6qwcTibcuHWYZbi5aXbg5gqHWcUk88SxEGQHBwcHBwcHBweHWYVbZjs4ODg4ODg4ODgIHEF2cHBwcHBwcHBwEDiC7ODg4ODg4ODg4CBwBNnBwcHBwcHBwcFBcCxl3lyG6/zCZYo/vHDj1mGW4eam2YGbKxxmFZPME8dCkG/duoWzZ88ex6UdpoTr16/jzJkzx90MhyOEG7cO8wA3Nx0/3FzhMOsYZ544FoKcyWTM34FAYOwVptvH/Hjgt0OQfocODwf4nU8yZucFuqX1JO9N67qHcf6Hbb4cDAYYDAZubpoB8Dugh26a/XDcfj3t4yY9dtjnbfhtXz0tTOO+Jp3/ZnXe0Wfc7/fHmieOhSDz4QWDwX0f5DgGZBa/DMDbUWa50wwjBXzdHsCzei8OhwvtDyft+2cf97uvSe7VNnSTfPYwnulJ+572g5ubZgOHOVdMe0wd5hj14wC2PfV77bDac5DzTHqNWR1/+j3o/6NwLAR5P+gNjNNx2PEOsgry++yw6+33/qjrTvq5cTFum0Z1hmGLEH02Dg4nHYftIR7n2HEdBtM2/A4OJw1H1f/3s5H2uLaPfRD7+iAL8ocRkz7rYyfIOuGPIqbj3th+RsaPCNu/98MskcZJnoti1EJi1GB2cHAYDr+5wW+OG3f82edwcHA4euxHPIdxAr4+TRI8Sbv2O6f9+VniNrOAYyfI43o2x/msfrnjEO9h5xnW0R82+D07N4AcHO5jXK8RdZh+EZlh0Zv9ImHOc+TgcHSY1OaNE/Ue9rpfFHe/80waSd5vfjmpmJS/HDtBPoyV1X6yhlEenlHXOojE4rAxDrmfFLN0fw4Os4BxwqR8PxKJIBKJIBQK7dFgdrtddLtddDod9Pt9z5wSDocRDocRiUSGaqF7vR76/T6azSb6/f5h3KqDg8MhYVw+MimRc9r7w8GxE+RxCd64+uDDaNNBrjUNuch+1x9HGjLsepPISSY53sHhJGIcbTDHyOOPP46nn34ayWQSkUgE4XAYwWAQoVAI169fx40bN/DCCy+gVCp5Pn/q1ClcuHAB6+vrSCaThgAHg0FEo1FEIhFUKhWUy2V86lOfQq1WQ7fb9Y2k2W0b9z4cHBy8sMfPOBHmYXZzHM8tF9V2BMrveJ13bEffOEUNHha7ftAFxLET5P2w3+Q/DqbRCaaZyTlJBx3nPNMg9A4ODgdDIBBALpdDLBZDNpvFqVOnsLCwgHg8jlAoZAhyOBxGrVZDu91Go9FAtVpFr9dDIBBAKBTC2toaVldXkc/nEY/HzflJrkOhkDnPI488gmq1inK5jHq9jnq9foxPwMFhtnHYHtZxzq/2OhQKIRqNmh8thzcYDNDv900kSolvMBhEMBg0kSge22w20el0TGRp0uiSn1ba8YY5IMjjYlQH3S8hxi+kYZdmOS7YbXuQtvjdyySZ8w4ODyNGjb1+v49oNIpHH30Up0+fxhve8Abfc9C4ra6uYnFxEY8//jgGgwEajQbC4TCy2Sz6/T56vZ6v14nSilgshng8jne+850olUp45ZVX8NJLL+Hll1+e/o07OJwg2DLJcby8k8oc/M6hHmHOA4lEAisrK1hdXcXKyopZTAcCAXS7XdTrdSwsLGBhYQGNRgO9Xs9EkRKJBKrVKiqVCvr9PjqdDq5du4ZyuYwbN26g0WigXq8fSm6Vn+Z50oTjUec+LK510PPOPEEelxge5AHYHthhur+DYFxx/bB27dfxRrXNJv7jtFWvbbffJeY5PGzw6/86BlKpFNLpNE6fPo1MJoOFhQVks1mPl4cGkeeiBzgUChnPz2Aw8BwfCoX2eH9UatHv9xEMBjEYDBCLxYwc45FHHsEXv/hFbG1tOW2yg4MFeyw/iF23/+f5/JxsHNOhUAgrKytIp9M4deoUYrEYkskk0uk0UqkUQqGQ+Vyv1zPvxWIxBAIB9Pt9E40Kh8MmwtTv99HtdrG6uopMJoNEIoFms4lms4lGo4F2u41arYZOp4NGo+Fpsz4X+znt9xzHwTSj7seFmSbIx/HAjtJjPIp4jqNVGkZs7ff2O6d9/lnsqA4OR41hi8RgMIhMJoOVlRW88Y1vxPLyMur1uiG+lEHQI8Tz8PVIJGI8wpqoRwLN1/k3r882ATAh2PX1dTzyyCOIRqO4ffs2SqUSut2uOdYtbB0cvBjH4TPO+7Yzyc9rzDEfjUZx6tQprK6u4uu+7usQjUY9Y5+RI479Tqdj5Bc6r/CcsVjMLKYp0+p0OlhYWECr1UKn08Hm5iaq1So2NjbQaDTQbDZNe0cR5GnkSJ2UeWemCfK4mAap26/yhQ1b9jCpBML2Wg+rrDEuhq1ix2mXI8QODsOzxweDAaLRKC5duoTl5WU89thj5vVCoYBwOIx0Oo3BYIBOp2MS56g97vV6xqjRa6weZMC7qyi9TrxGs9n0JOzoeO33+2i32+h2u/jGb/xGPPnkk2g2m6hWq7h+/To2Nzexvb3txriDA8az8+PkBXGM8u98Po9cLoeVlRWkUiksLS2Z9xk1yuVySCQSyGQyxhNMYtztdtHr9dDpdNDr9QDsRoyUI+jcoW3VKFQsFkM4HEY+n0cqlcLy8rKZ28rlMkqlEsrlMprNJkqlklmsD/Mm7xftPsmYOYJ8kNXHNL6kB5Fo+H3ejzSPIqv2IBj23n7nGldTPE4oZRhhcHA4qfDr65FIBPF4HGtrazh16hQee+wxE8YkCaYxa7fbns8yoSYUCiESiRgPkRq7YWORr9N4MdSqCT18H4DRNzcaDRSLRTQaDRNaVcPr4OBwcHAsxuNxhMNhxGIxLCwsIJ/P4/Tp08jlclhbWzOLXBJXeoMZXWIpSJLkQCBg/rYXwsDeyhU2/1D5ViAQQCKRMLplJgUmk0lEo1GEw2Ejweh2u2Ze8suBmBQniS/MHEEmDntlYneww5AWTKrv2e+YUYQc8JdX+BH1/c4z6hwODicNwyJANIRnz57F0tISnnzySWQyGZOYFw6HzWfpAWK4Uz+v56SXSGUUhO3dIqGNRqNDk/e0QgYNZK/XQz6fx9d8zdfg9OnTKBaL+Lu/+ztsb2/7kmQ3th1OMia1X/tFhKkBfvrpp01Eqd1uo9PpIJFImDrm5BSaE6AkWKNIWnmCZJe/1VvMY+15JRwOm9/UPDNiFY1GzbkikQgWFhbMnPLqq68audaVK1dw9epVD2Ef5uTzw6RR9HnAzBJkP42P3zHA6Lqf417L/twkg8pPbjGs7X5e2f0+My72I9fDNMujPNoODg8b6BFmAs3q6ioKhYLxAOlxBInpsEWoJu1piJak1vYK8/w0XoHA/ex2PQ/byc/TU8TPkMQnEgns7OwgmUxic3MTnU5nj6fbweGkwl54TuKo0vHK/AGdF5aWlrCwsIBms4l6vY54PG7Gsj2mSVw1JwGAZ6HMsazXJYke5l1WYt3r9cx1+FmSZwCIxWKmfFy/30c+nzev12o1Uy6u1+uh1Wqh2+0a7bLey8OyqJ5ZgjwpxtUNH+Rz45xvnL+HwU8XdRCSPEli3n7nd8l6Dicdfl4SemPj8TguXbqEy5cvI5VKIRKJAIDHk6tGNBqNAoAxgDw/AENwSYr1PXp81IipAWTCTrvd9lS4oCGldwi4b+SCwSCSyaTxNtGrvbq6ilKphD//8z9HsVjExsbGnufhIkUO84xxPZjjSgx1ERoMBo3O+OzZsygUCnjssceQTqdNDXTKGbiYBbyLaCbW8frUIGsugibYanuVINuEmNIIeovZ3lar5ZFs6vNhJGllZcXkRyQSCayvr6PZbKLVauHWrVsol8u4c+fOnmRhbbM+s5OGmSXI45DMSYnotK47jfOPc08HPTdh65kn8RSfxM7u4DAKwWAQuVwO2WwW586dQ6FQQCwWM4aNWmIm3SmhVIPBcKkm8agnCNgdX3ZyHkm0hlf5Pr2+Oq4p8yBJtwk6NxSIx+Po9/t4+umnTbULGlWFI8kO84xJvMP2//TO0gMbCoWQzWYRjUZNcl0+n0ehUEAmkzHkVyUSWobRjhiRuPKaGlnimA6Hw3sS8fi+TVL1HHayHhf6fuObx0UiEVNGLhQKIZVKIRAImCoYoVAI9Xod+XweW1tb2NzcNHOYHRU/qZhZgnwUOOkrIFu6MY1zOTicRNBI5PN5rK2t4Wu/9muNZ4ae2VqtZoiubSA0+a7X66Hb7RryqqXb/AyWXziXXmUl3apdpDGnxwrY9VAp6H2mIfyar/kapFIpfOUrX0Gr1UK73X7owqYOJxM6jvy8psOO0bHM5DlKlJaWlpBMJpHL5ZDJZJDNZhGPxz06Y45NLqD1uoCXtPpJreyFs+qM/TTHNiHWBbMuyhllUrKu9wnAE7kC7ke72u02er0e0um02bTkpZdews7Ojqd2O+/zJHODuSPI00iuG/ZlHpeswB4c/NuvbZMK4ad1Pyd1ADg4BAIBXLp0CUtLS7h48aLJ/u71esaA0AiGQqE91St4Dhoy3fRDDRaAPR4kv/FJL1YikfBUx6BnhySbO/DRoJMgKwng5+LxOFqtFur1Oi5evIgf+qEfwuc//3l84QtfMFvWOjgcF6aZ4OXXl23brt7YQCBgKlE88cQTeN3rXodms4ler2ekVXpOyhNKpRJarRYCgQCazaaRQZGY0ourMio7YqTkdzAYmAgQ5VS6IOZ1tS08hmOYC3NGvFQS5reBkJ6r2+2i0+kYaYYm/p4+fRqhUAi3bt1CtVpFs9l8KOaMmSTI4wySB9UOjyLJw9pz0CQ+v3Pt99qwQT7s+GnDaZAdHgawiP/S0hLW19exsrJiPLc0YIA3N4B1jvn/sPlBybKfB2vYXKBZ6CTUNLQ03MBu+TnqH7ldrZ6/1WoZrSONZiQSQS6Xw+3bt3HlyhWUSiWXtOdwYrCfA8xP5pRIJLC8vIxz587hscceQ7VaRbvdNuOdJdE4Tnq9nom+NJtNI2XiOOPY10TcYXOAHenl/yqxUo+ynz4ZgCdKpREtJdr6HHR+s/kMSTrbn0qlsLi4iGq1ap4JveYnGTNJkEeRM7uTTXLOURiXpPq9N6wtByGZsxLudOTYYdYx6SLVb4F59uxZPProo1hfX0c6nTayCJJKTaLhe51Ox+NhoTHU8kh8z26vHWbVtvFzyWQS8Xgci4uLxjtMA7yzs2NqMEciEWQyGSSTSSQSCSQSCc91B4MBGo2G2WKW5LrdbqPZbOINb3gDnnzySfz3//7f8fd///eeZ3SQ5+vgMAn8FpnTPC/Pbb+mpJS7Yj7yyCN45zvfaSQGzD2wyzKSNOtCmdIrYLdSBT3GlDbpWOIC1ya8+jxUQqFbzSvJV3kHK09okh/brbkGKs/i+XSHT01GpoyE80ksFkM8Hkej0cBrr72GSqWCjY0N34XHSZk3ZpIgj8J+UoNhg20SEmt/uaO8ysPOud8x++FBJ4wHacNReqodHI4DkUgEyWQSi4uLWFhYQCqVQiwW851f1PgBMEl6auiAXQ8OsLfEE/+2CbJf+JNe7Wg0arzD7XbbeLr4eeokabi4gxbDtDSYTLixy8IlEgnE43GcPn0azWYTd+/edXILhxMJ20PLsRiNRnH69GmcOnUK+Xx+T54Ax2g0GkW32/WtSc6EXFs+odeyX9PzK+xj7LHo50XW4/xstk2Oh41v3dBE5WXA7pxEJ8LS0hJisRg6nY5ZsJ/ExfXMEuT9SNo4BHW/4/yOHxfjfGZaEoyjxknp3A4nFw8aQUqn03j88cdx9uxZnDlzxpRLo5dVySw/T+kFS691Oh1EIhFDOlWnbOuTeQwJLEGDzBrH3PGKWfMkyvQC5/N5E/Jl5nkymUQymTQEORqNeragtskxy0ER3/AN34BHHnkEf/iHf4hqtXqg78PBYVIclo0ZdV6N6kSjUeRyOTzzzDNYWVlBOp02USP1EtuLWi2tprBJsn29YfIqPw+yfR+cQ2yP87DX6MVmG2y5hZ3Mq/Mdy0Tyc1wY8HPpdBqFQgGVSgW5XA43btzArVu3TDtOEn+YWYJ8UPhpafbzEA87zu8Y+1qzioMSiFm+JwcHYtIJ2DZasVgMS0tLSKfTnmQ6nlu9TH5jQ0OcfE/Lu+kxADwGSucoSicajYanOoYaOXp09LMk8cy6Z0iYNZzp5SaRTyQSGAwGqNVq5jwMwbIaxtraGnZ2dnxrJDs4zBNssql6X44rbvRBmRIXryTJJMB25MWPK/hVqFCyrJ/xk14R2l6VR1Aywa3jKcdSaYXOKQD21EjWOciWa+j8p3kP6kGnx1jnOWqzAaBYLHp02icBc0OQ9yOwo94fx5gOk2XYq7lp66VsHNb5xyXAJ2Xl5+BgQ6USsVgMi4uLSCaTxmj6Ha8aQHs+oPGhMdP37B8lzZo0wzJs9NwyrEljrB5hen4HgwGq1aoxkiq1IEGmB4jnZx1kNYT9fh+tVsvsvLe6uopgMIjNzU03DzjMLUZFnbkY5IJwfX0dmUwG8XjcRIjC4TBarZb5jJJd/fHz2tpe4mESCt1Fj//7STvozabOuN1uo16vmwhQp9MxMipdnHe7XTNn6DmA3TmIxzHfgm0i2eemI1w0cFc9npdJw5zf+v0+qtWqydHw+y7mbV6ZmCDfuXMHn/3sZ3Hnzh0AwNraGr7xG78Ra2trU2+cn1bHr/Pb5HWUi98moDyWr3EVSeOkn2PHoteHhlHb4LdyfZBOoau8wyTOhPMgO5xEqGFKpVJIp9NGz8tQqho1jn0tlcSxosX8daMOemponNRwDttUQLPfmaDHzUpYuYKEV4k1pR3UKNtl55iM12g0UKvVUCqVUKvVcPfuXbRaLZN5z+MA4OLFi4jFYnj11VfNe/bzmzcD5+AA3B/38XgcS0tLOHPmDE6fPo1cLodEImHInW6prBti6JzA7dztRS+jODrOSWoJm3f4eY51tzx6jPk+ybDfLna8R/4w2Y6LfyXfXDwPI/BaeYOyMM5lvKdA4H4OBOepdDqNZDKJUqmEF198Ea1Wa49Wex4xNkGu1Wr41//6X+P3fu/3EAgEsLCwAADY3t7GYDDAD/zAD+C3fuu3kEwmJ2rAML3wJN7icSZvJZl+4ZFA4H4txMcffxyXL19GNBrF2toa+v2+GUTtdhsbGxt48cUX8f/+3/9DLBZDtVrds/2sTWr9BoL+bd+PfR+2F/sgJHbUqtqvDX7Xd3CYdwQCAZOcpgltKm+gkfDzuNBQ8HU7XKpElp/X3e30GDuEqoSXmmg7VKu7dflpHvX83W7X1EFutVpoNBqo1+tmK1ld9AeDQWSzWVQqFSSTSTQaDROqdYtmh3mFjmnKqtbW1nDmzBkTndFFJY/18w6rjELtJT23ds1zvme3hdC5g2NRCTIXz4TOFTYnGNZWvu7He+xr2+fSiBsAk89Ah4JW6QiHwyaRmJIwRtfmmT+MTZB/8id/Ep/73Ofwx3/8x/jH//gfezK6P/3pT+MnfuIn8JM/+ZP4z//5P0/UgEkn32FEzo942mSVHZmemWQyiUcffRTNZhOXL1/G0tISUqkUABgjQg8Li/Cvr6/j4sWL+M7v/E5TV7RUKuHu3bvY2dnBYDDA9evXcevWLVQqFU8buVNXJpPxeHM46PwKeU8L+3mg93tvnju5gwMRDodx+vRpLC8ve4wAxx6NnBog9Qir8bENnF8ijJaBU2KtEg0aRXqL6REmwW02myiXy9jZ2TFeKo2U0QOsXnLKNiqVCorFovEg1+t1Q5jZLiUJKysreNOb3oSXXnoJL7/8svMcO8wd1NaxjycSCZw+fRrf/u3fbsYiNwRKpVIefTHB8c7XWHfcHufcbZPvEfZ4t8k1/9b2Kknv9XqmnCP/txft9uJdF9QqmSDhJrSWup+Djt5yfl7nJyXwnGtYEi8cDmNlZQWlUslsTa3nnTeMTZA/9rGP4Y//+I/x5je/2fN6KBTCt3/7t+O//tf/ine84x0TE2Rgct2t3/Ha2fQ4Ih6P45/9s3+GtbU1pNNpo+ujZ6VSqaDX66FWq3lqAQIwJJZkuVar7ckQXVlZwerqKjqdDs6dO4dqtYrnn3/e7LDTbrcRDoextraGRCKBZDKJcrmMV199FdevXzdeHBJlP2/xME/yft7fUavHcZ+3g8O8g9Ue8vk80um0b5hxmDeW49PPkwzAY7g0OcbWNisxVuOmeuNIJIJwOOzxFPO8arQZSmW7qCOkJpGaRf7N0KjqJUmQec5MJoNz587h3r17e0KzDg7zBo6XlZUVLC8vI5PJmHGncsr97KNNPmlXdfzaumL+7cdLlK+oXaekguNYNx7hcXZVGm0j5w56dfl5ziXaLoW2RT3ZPK8m7g2TmzE5uFAoYDAYoFQqGS/4vHKIsQmyXRrIBgnnQTDKI7wfCdTO5odIJILLly/jmWeeweLi4h6NTbfbRSaTMVvI0sOs56YoniSZ7dJVKr3SWi8wlUqh1WqhUqlgZ2cHxWIR1WoV3W4X5XLZkPGzZ8+i0+mgVquh0Wh4tpPd7zn5PTM9fhrhUedBdph3DAYDrKysYHFxEefOnUMikTDjwzYy+hnVCnPhDMBjnFROAdwPRWpkSL0u9uYjvCa9U8yoj8fj5prcIa/X63myyblRCBfgJND0PNXrdbNRSKPRMLvqaX5FIBBAKpUyhi4ajeLxxx/HvXv38KUvfWnoXOTgMKuwbV4sFsPXf/3XY2VlxYx7P9iSR9vmcdxoBMgm24TmGRD04NoEWvOYms2mmVsYxQbgWRQzGZdkmFEkykj4eY5dfk4rWeh9sh16Xzb5t6NguhhXCUc8HscjjzyCVCqFYrGIWq0217vtjU2Q3/GOd+Bf/at/hf/yX/4L/sE/+Aee977whS/gve99L77ne75n6g0kRmlo/chbIBBAJpPBpUuX8PTTT+PcuXNmn/F2u206NYvoRyIRs8pSMkyjYYcrNGNU66YCuyu8QqGAbreLbDaLXC6H7e1t3Lx501yXGxUkEgkAMN4ednjWMW00GqhWq9jc3PQY4mHecn3N6QcdHnZw8l5dXcXa2prxmNpaPR4L7Box26DocX4SCsoi1Is87PPA7s5b1PORhKuHqlwuezTBtv6Y2eV8j6Wgtre3US6Xje5YvVPMq2BdU16X0rMzZ87g6aefxiuvvIJSqeQWyA5zBR1fsVgMKysrWFpa8kgsOZ40GX8UOMZtz68tpfCD39gfdiy5iSbK6Xyjtdf1POQunI+4cGc0itAFOrkLz69zFncN1DwNyj1sfTLnolAohGw2i06ng5WVFWxsbMx1wt7YBPnDH/4wfvAHfxBveMMbUCgUsLKyAgC4d+8eisUi3v72t+PDH/7wgRoxKZGzRfI2SQ6Hw8jn87h06RK+/uu/Huvr656VmeqBNBFFtYUcNPTU2Eau0+kYEq06IcCblMPBmEgkkE6njVeImh56mm1tI3enof6wUqng1VdfRaVSMaVUNNN2v86nz8tPvuHgMC8YVxdr6xBXVlZw+vRpzzjTChM8VsfUqIxxLqx17tB5wfYg6Wf5OucZ9Qxpu3u9HiqVyh6CbHtzWK4tEAiYSNTOzo75m4tv3i+lHLFYzGxIwnNTp/3000/j7t27KJfL5nm7aJLDvICR3Wg0itXVVeRyOQQCAROFoaPMlkaMcsZx7HLxas8XNvyIsd8xfpJRlXSQ5IZCIeNQA7w7djISxdd5Xi3jptIuEmSdR3isEmSdC/m86LlWYqwR+W63i+XlZTMP+Uk65gFjE+RCoYA//dM/xfPPP4+//uu/9pR5e9Ob3oTHH3/8wI0YRdSGkedhJDmdTiOTyeANb3gD3vCGNyCXy5kkGxJT1eWqJxmAR3ze6/XQaDSMd1kTeUhQmUSTTCbN6m2YRzuRSOD8+fOmg7NNXM3ys9QNxeNxRCIRpFIpXLhwAY888ghu3LiBF198EZubm2ZlNsyI8/n5/a3/j0uUnXF0mAVMshgMBALI5XImOZZEUEm2n0ZQNwnQBbJflvhgMECr1TI6Xy5+o9Gop1oGj9XxypqsiUQCuVzOtJFEWWuPArseZxJeEme/xS+9xFycq3FjghKvz8/zeoVCAefPn0c+n8fW1pbxULvx7zBNqBZ3EofNuMfn83ksLCyYLZE5ttWjaSfb2tfRMUVvrlZo8Bt76nUddQ+aO8DPc4xz3tEcgUgkYiqF0bvLz5Agk1iTG8TjcZMDxcg0o1x+90jYORa9Xs94r1UuYnuzY7EY0um0J1lPF/jzNIdMXAf5iSeewBNPPHEYbRkbfqSPq5l/+S//Jc6ePWsS8KgrZocDdkkwt5WlGJ6dlVIM1iLU0AqNG3e+sgecCv9VisHVFZODAHiSZrTuKnfH4fVoACORCB577DGsr6/j2rVruHr1Kra2tlCv181qz34+ilFab7/X9XjnbXaYB/j1Z0qZWInGHiejfqu+Tv+3r6njFvAn0rYuUce+ElYtF8X5xW6LElrOfepNomSDO/ExR0Qr+NjkmF6jTqeDeDyOXC5nFuksC+fgMA/geEkmk8hmswB2awyzXJk6yezcAyXufA3wenZVO6y/edw442UYidZxrmPbzpGyZZ6UZihJJ/+g443vk/vYPGdYaTp7a20+J43MA/DkYSWTScRiMVM1Z96iUBMT5Bs3bpgscEWn08Ff/dVf4Vu/9Vun0rBJJRfhcBi/8Au/YLyzXHmxk8ViMfO6JspwRcQOSAPFlVEkEjECeN1AZG1tzXiL9IeDTUOs7Fj0TGsohGDHiUajRrKhHTWZTJo2LSwsIJfL4cKFC7h+/Trq9Tqef/55lEolX63PJMR4GOalQzs4EByTKysreP3rX49cLufxitjaQdUd21Um9DhCiSVlDI1Gw5M5rtvW6pygUodwOIxUKmW825yj6vW6+Rw9wfxcJpPxyLu0pis/n0gk9mybS1Jse7Z53zRkrO7DTUsajcbIaNQ0ME+G02G6mKYDRsfsuXPncO7cOY80QqNLthNo2PjW/1X6oJ+zCSTbotdRrkCCSn4BwMNL1GkHwOMt5rmUOJOjaCULLgA4H+hufKzIxUUx4M2x0nu1F/zq+OMCXJ2NPLZUKpnCBJVKxbeqxyw738YmyLdv38Y/+Sf/BJ///OcRCATwgz/4g/jN3/xNQ5S3t7fxzDPP+HaSg2C/EIp25mAwiGQyiXQ6beQK+nmVVvCz2mlVDK8rSxownoOdjl8+QzYaIlINIeCta8iOquemgaQB4oCh94fPwr73aDSKQqFgiPTy8jK+9KUv4datW6jX676r4EkXHQ4O8wY/IxePx82OT+rp0PGh5FiNErBLpP3kTLoFK0OXtkaYx+uCmuclsU2lUp4MdHp8/Oqe2sbVTjTUBB9+jvfHeYyVMfRzPL+WmmO+ycbGxpS+IQeHowPlRcDuOOR48IsE2eTNdnzpnGEfC4wuy2pfQ8emnQehHEQTC8kZ+Hkl6gBMdQuNDCmZbbfbhg8Fg0EjtdA50W6LLRexfw+7by7E0+k0YrGYmSPnaRE8NkH+uZ/7OQSDQXz2s59FsVjEz/3cz+GZZ57B//gf/wOFQgHAg3kVHjSkPxgMkEqlEAgEjPxBV1AMgdLDYu9So+1npwBgdDuRSMRogYBdEbwK9fm6eqBto0aCTE9TPB439QMZDtHBy5JR1P/wGiTiNGbUHPV6Pdy+fRuNRmPfZzzuew4O8wTbS5RMJlEoFPZ4ZHTRqiWb+D8NhXplAe+ilxUidnZ2zHzCMa0LblajYdv4OreWXl9fRywWMzvpMeGPsgjmO7Bt9XrdlIbk51SnqPORnbBDr48aV94f74n38LrXvQ6pVAovvfSSR2bhvL0Ox4n9bBVtbi6XM+Vd2b9Vs69OKM4HHPvkCSpx4sJVF6y2/ef5/NpjX0fJuv5o8i43DyLhzGaznjbR9hPkKrxHSjmZO0UeRMKqGyGpfEMjU1r1B4DHCamRMco16EVOpVIoFArI5XLo9XrY2dl5oO/9qDE2Qf7Upz6FP/qjP8Ib3/hGAMD/+T//B9/3fd+Ht73tbfj0pz8N4Pi8jpzcmYGpRo5GQzuGGj4tW8LO5le1guRTV3Q6SHisdnZ2NtsjpcaYUJE+Q7YAjB660+mYa/IcGvrIZDJYW1vDxsYGKpWKZ195+1npSphw5NhhHmCHPcc53jYidmjUllTY45jjlAYW2M1j2NzcRKlUwu3btwHcJ8c0aiSjjPKot1dzFnRrac1n0NwILug5rlutlnnNb+EMeDcU4PPSsnKj0Ovt7iCayWSwsLBgQqYODtPCQRdZ40SYA4GAJ0EV2JU8ALsLXS4UdT5Qp5bt5fWz37ym2tdh98lzsuQsCaWtHeY51SvMH84husjVRF5GkQgm/fN6lHGUy2UP6fdrM9vERb2+b5e4Va+6epAXFhYAAOVyeWoqg6PA2AS5VCoZTzFwv/j2H/7hH+L7vu/78Mwzz+C//bf/9kANeRCCpqEKdjb7C7eNG7Cr21FNETuWrvSY4EKdDq/J3zpYNZxJgq5GnSReNxahYaMRDQQCphPzXFrWjeERgiQ5nU5jfX0dGxsb2NnZ8WTaDnvmoyYo51V2mCVM2hd1bNuaQf5WnbAfQWZEB9g7h3Q6HWxubmJzcxN37twx5SWZB6F6QJVf6EKeBo9hU9Uqcy7j3/RCkzBTZqHeLTVkds1SfSb2hgeA17hRvsFkm4WFBfR6vUMlyM4j7TBNkCCnUikTfdGKERxvXEja45+LVD8phI4bW5Jg/207onTBy3Gm8k31TCtB1qozSuoJ1Qbb+QWamEuZA2WeqjvWtmo7NLmP98S5yW4HnwsX/8lk0iywr1+/7ruAmFWMTZAvXryIv/u7v8Ply5d3PxwO4/d///fxfd/3fXjHO94xtUaN8/D0i9SqEAwZqJicxFTDAcM8sboqonFNJpNmcFGq0Ww2EYvFzLnscm3hcNjswKeSjlgs5qmFzPf4P+UcWo+w17tfC1UJOrAbHuKg537zt2/fxu3bt1Gv1/escvd7nroKdnCYJUxKoOLxOBYXF5FMJj1VJtSwKTlWo6QeJTWQXMRy3mCJNpJY/lAaRQJeq9VQLBY9muNEIoG1tTUsLi6acpTaRr1fnUNUhkXDqZri/aRUADwGkWSbJSsZseJrnU4HmUwG9XrdzQsOM4H9+iHtOcch9f8qaaBziuNAIy0axdHP+HlK/aQO6swiOWeUSOcF9WIzWTeZTGJlZcXMI6dPn0YqlUIweL9Wej6f91Ta0jHc6XSQSCSMbJPXIcchuVYZBCvekGeQXJNvNBoNT5KeOvB6vZ7Z7Y/tZZu4mGdETZOMdVE+yxibIH/nd34n/tN/+k9417ve5T3B/0+S3/Wud+HGjRsHaoQ9oStZ84Of55Nkkdmf/LLsXWhswkivjh0u0CoTuuKh0bBlDLr7Hj1HLDGn1yXxJVm3yTyNHrec1pWuXXKO4Lnj8Tj6/T7Onz+PYrGI559/fs/A93veep9+vx0c5hHhcBi5XA7RaNQYENXuqRGz5xx7PrJlVFoVggtlAMbwaI7DYHC/TnK9XjfVdDjOuVFQLBYz85oaTj/vDK+lXmM7pKrQ8a4EmddS48ofNa7Mf6BHepy5xMHhuKALXDtC5CcxHPZbpZrqeSZUrmFzCy3VqnWHNbkf2CXibDfnE3q92+02MpkMMpkMABjJE7eNJyFV7zhzEriQV6mV8hvyCZWM6gKcPIUbDDEfgu1Wb7s9v+qcyZwHv8TgWcfYBPmDH/wg6vW6/0nCYXzsYx/DzZs3p9Ko/UL/eowOBgBmglfPMI0AK1Jsb29ja2sLtVoNd+/eNVKH9fV1rK6uolAomLIlei0aIXYqXo8dgz8k5+w0fppHEuNisYh6vY5MJoNwOIydnR3kcjmzemOHZD1BfpbGkW3QmocXLlxAr9fDCy+8sGdQsx1+f9vYb6Hi4DBrUIOVTCZx/vx5xGIxFIvFPRnfwF4DpXIrPcYvpDkY3C8hl81m8eKLL5pdL1OplNm+mUanXq+jXC4jkUhgMBggHo8jk8kgl8shnU575huS4FgsZuY4zlE0XFyQM0xqZ61rG+3fOlfSw9btdlGr1cw9MAlQk4fpbJh1r4/D/GHchZYtV9DXbLvmV6FF3/cjyhxf7Pt+nmc9p30dvt5sNk3OQKPRMDv50oMcCARMvgIdarTp0WgU2WzWaJSz2SwymYzxMnMxTScbnWC9Xs9sWpZMJj1ONi7GuSgGYJLo2u02EomEuTdG2/nZVqvl4T36nh2NV0LO+U8jXYzG8/OzPp+MTZDD4bApuD3s/fPnz0/cAL+HM+w1Py8zsOtJIalUz4pqh65cuYJr165he3sbtVrNhDRZpL/X6+HOnTvIZrO4ePEistmsWfXwvOwo9orRligA3nIyNHKqO6pUKtjZ2UG1WkWpVDJ7mA8GA0PmCXrG1fNlD1h6rHQHL2qo/J7vOBPSrHdgBwdCyXEikUAikTDGxE7IUQ2hftbepEONjxJk/k0JFUOa6oXV7Z8pXeC4ZlnKWCxmolVqtFXype3T9+0f23Njzw/6Gok7vVQkytxciZ4pXtNvceHgMIvQcaSLOnssqXyKsJ1dKq2ynXHD9Ld0YrXbbVSrVTSbTTSbTePs6nQ6njFP4suSr/Qi8z0SXl2ocrHM+6Dzjh5gngfYTdojH1CvMavVMLIFeCtx2EmK6vnWXCuVbKkzQXO7KB21tcyzjIk3Cpk2Rsko9vvfb0VoZ2g3m01UKhVcuXIFd+7cMUWr+dl8Pm9CEkymqVar2NzcNJ1WNYv8nO1hpjdGMzRpPLVD0qgyZJlKpUwHB2B0gNoBqRviiiwej++5f81wD4VCiMViOHPmDF577TXfZzWqY6pn3sFhnsA5YG1tDYVCwZQ/oxeY0BCinfDKCdyWM+gco0ZnMBggn8+biFC32zXzDCVV9M4mk0kEg0GsrKzg7NmzyGQyxpOkC2kuwGmo2R56mzQRj3+rMePxCiUJwP25sV6vGw9ytVpFo9FAuVz2bG9tEw0Hh+PCuH3QXmjqWCbsBTIXjZo/QJuqEWkld7wGxxY5ACVV29vbZuGpMip6kDmP5PN5JJNJo9VNp9PmXIuLi2azHvIHeyGvib9cdHN+UNIMwPM/S8Zls1nzjHSBwHuyn6vODYxoq0NBuQMdjFpffV7mkmMnyMOwX8jFTzZAQ8cviNKKra0tXL9+HaVSac9x1BRrwlu320WxWEQul0MymdzjQfEbJH4eZPU8s2PwdQry9dwkudw+mkR+GGHVChn2BJBIJPDoo4/i2rVre8qq+HVMOzxlvzcPndnh4QaJaiqVwtLSkglL+hk2/rbnDK1zTtA4+s1HHFunTp1CPB43u0UxjGrvXEUtos5DGvbka3a0jNpCrbGsx6leUo2XnkN/AzC75dHjzVAwQ7taKYOytWHeOAeHB8G0+xHHMpNn7c3DAP8Ii3qUbS2yXzRGtf86jkmKdSMOJrMpD9DFtr2rr5JRjm0l4wqSTjui5Of55rXV8UbpF9vJ2sn9ft9Tv13vk6/R2afPnj+c8zhv6Fw8D7xiJgjyQfRHfmDYQc/V6/VMmENrhgK7WiGGMjXZhh4Uhg3slSK/XE0O1IxYrtIAr1eK3t1kMmnazM/xM9Vq1bMpgBolPgM14H5kPRKJYG1tDfF4HLVabQ85cIbN4SSB/TqXyyGfz2NhYcF4Zm1ya+sRdZz61fq0jYuORc4X6+vrCIVCeO211wxB5jnpPSbpVCmY6h7V+2LrkTXRJR6Pm3bYyXZqFJmEw/PocwJgiDvnHuoltYqFJt/YHjgHh1kFx7L2YV0QA3v5go5p+0fzELR8GuC/9XO73fbUKtfEXh6j5di05KsfEbfnA00w5rnUtpNT2Itl/tZrkyD7PUPeB+dFXpdzi02Q+T+vw0WC1mqfJ7nWTBDkSeD3YNkhUqmUbyIhOwArVgDeWsgMw6qnN5PJIJFI+Or8OFhUpK9l5ILBoPEgAfAQXXqOubMXBwa9NyTzDMHoaouDQI2cvRrTChmDwf0kotdee23fxYXtAffzPjk4zCo4Bhge3d7eRjweR6fTQS6X80zg/K1Z3cDeyXsYOdaduOjVobFhogzJJwCPByuVSmF1ddUk5pE8qzFnOSiVZ/E16hQZYaIXWDWJvA/OSToftNttcx/FYtFIQiixoEFUYx4KhbCwsOCp2jMP3h+Hk4lhfU9fKxQKOHXqFNLptEdmoV5W28OqEgmt0mDbW/uattc5EAggl8sZu85zZrNZxGIxU5t5dXXVzCPZbNaQVO6cx4UqCSo3DwuHwyYyxcgO54NqtWrKvGl7eG9aVKDf7xvPcT6fNxsWUWpFmYgfpyJfogxVazPH43Gjo6ZklFW9lpeXAQDFYnGP028WcSCCXCwW8bnPfQ737t3b41V49tlnJzrXJA/HlgEoqePqiysielGXl5eRTCYRCoXw8ssvexJTAHiS4vr9PtLpNAqFgjFi7Gg8nlCyqqs6Fv2+evWq2ayDSUNra2s4f/48lpeXUSgU0Gg0TG3AmzdverxLqVTK6BtZOo5GTpP0+B6NtoZhotEoLly4gNdee833uY37/Thj6DAvoCEgmWOuABeY6l1Rckwj6ReZsf/XRTLHoerqbL2zbuUai8VMhYtgcHc7aXpx7bCteqz0Hng8jah9jN4jj2fEjPMWF+U0muo51nk9GNzd7tbBYdYxGAyQTCaxuLhoFpZ+Y1ttGu2ckmItWUYwmsPP2OB1uN+BbsSRyWQ8OmMm3gEwjjLAK51UYksOosnA3PSDUpJarWbarJuQ2fdGsLoESTWfVygUMvXd7YWx/s25RkvJkRxnMhmz+OdPJpNBpVLxdXSOqyQ4SkxMkD/xiU/g3e9+N6rVKrLZ7J4HNilBHoZJHhRXbMBu6EBXV8FgEGfPnsWpU6dQLpexvb2N7e1t1Ot17OzsmDAAP8ckuaWlJdPRdEBxxcYQBACPhoilWa5du2YSddiBv+EbvgHVatV4h+hBqtVqZrVJuQeT/Cjl0K1nNcyign1gd1BFIhHkcjmP9GMY0R22+OD5HBxmFerp1R2qOF6YDMtxoBIHO/SohJLGTmsnA7teY83u1iL6dpQK2I1UcfHNjHLOO0y2U9i7bJI889ztdhvFYtETDVOjxWQeJdXVatUYWHqHeG6GhKk/5HX6/f4e7bMdmnZwOCqM6m+0cSsrK3j66aeRyWRM5MWWStjn4vs6d+iGGMBu3pINzgdKGAOBAFZWVswYKhQKpmKE33jXyLQuVrmoZWSZHmR7d13ObaySweswemYn3ZLIs5qFRsojkQjOnDmDSqViPssIlK3n5v0yisZcCVYBY45Xq9XCqVOn0Ol08MILL/h+p7NGkicmyO9///vxnve8B7/8y79sdLQPgmGSiWHwGxz9ft9Ttk1JLjsCV22FQgEXLlww3ptWq4WXX34Zr776qtEd0zPD2oW2tEE1xuq1DQQChnTfunULwK54np8Lh8M4c+aMCWUAu9nkWtg7Go16NhUgeVdNkxpoEnM17Ky1qttL2s9xHCPnPMgO8wD1ttBIcGyoRg/whkr1dZso63H2Z9RQ2uOJ8wI/RyPEkk1K1qnp04oV9E5ru2x9NL07qlPmcVxQ0zPF9miFHM4ddjhZ7433wPNEo1Ezzzg4HAfGsUdcFNvJrCSSHH/AbnUqW15BYmz/VrtvjxNei4SRxJyOLP7Y84rKPXhe/jA6zIU9OQrnDF6T3EA3+9F5T9uofIaEmByGC3KVpdpOA5WlEJy7/LgFnwH/nyUSPAoTE+SbN2/iueeemwo5Hga/B6ivDSN1Gl7gF6idla+zg4bDYSwsLCCdTmNxcRHXr1/H5uam8ei+9tprhkwr2EFJVDudjglTMDTBmsa6Cm2327h37x4CgYCniDc/n0qlzODVjQY0rMPrq+ZJV666Mh0M7peES6VSnrqmfIbDQkSODDvMC+y+SgIYCOwWp1cDBOytUMHaoDY5JtQ7pARSpRQardLP0+ubTCaxtLSEpaUlE9XhHEKSyh96fknsgd2Md4LzGbegt3cN5SJba4/2+33jEVJNs1bL4fOkd5sGmHkc6XR6T6lMB4ejxjB+wJ94PI5cLufxmgYCAQ9pZN/m3EAHFOcEVrji/0oK1THGa2hUhWORG2OoDIvX1PNolQxelz/kCkz8DYVCni3tAZh8C3t3X15LSa4dgQZgEus4JwQCAWQyGSPf4lynCw6Vl+qcyG2ryY0os2i1WibHal4wMUF++9vfjr/5m7/BxYsXD6M9BwJJJjMrVdMHwDMgOEi0c6fTaZw/f94YsVKpZHaUunfvHprNptEEEyS3iUTCJN5xO0iGU7gpCcukUBDPgaSayHQ6jXw+b8T67KT0hiux15CKGmLVAXFQcDeu/TAvKzoHh1HgmGeZJd24w+7jtpcW2LsVM/+29YjqidLPabIcsKtJjMfjWF5eRjqd9iTPAbuyMF3Q0xjpHKEGitfVqJkexwgZCTLbxIQfJvvR08y285y6WCCBDwaDyGQyRuOti3QHh+OEvbjV+uB2pETHvf72q9xib+Ws8wJBAqltsduhUk3C9sjqAlhlC/aiW5OJOWb95iatZUzpg75vR4E0aqbXZ7u13rFGnGxvtm6UxEh8IBAwRQjssnGzjLEI8sc//nHz93d/93fjp3/6p/HVr34VTz/9tKdsGgB87/d+73Rb6AP1JvPL4naIfE3DJGpk9HO9Xg+NRsN4XldXV5HP583Odtvb2+aLjcVie7LdASCdTiMQCCCdTpudsZi9mclkcOfOHdRqNYRCIeTzeUPQtQYhV22pVAqtVsvoJdvttvGKNxoN0+ZWq2WeAX+zIoZ2fkpPWPR83GdKzEMHdnBQ0KDRICiBVANFA+BHfNXbzNf0PRpdW78IwBTs19An5RUkyKpXZlvstukP5y5dIHP+YYRKq+bweJJjZvCzvVx8MwTM+YHzh583ncQim82iXq/7hlgdHI4TusDkIpA5QiSjfmOd76veWMmy7XVVTmETPR03tqzLlkmpg0sdW/yfn1fiyvf0f57Ldpix/QCMJpmwZQ/aRltTrHOTVsHRZ0GvNo+hrJMEGQAajYanStc8YCyC/M53vnPPa7/4i7+45zUStUkxSlIxzgTM1V84HDb1ju1VpBJcYG8H4kCglySdTuPixYvo9/vY2dkx96d1AxnK0fADvcrxeBxLS0u4dOmSpwIGJRbcQYuifUozotEo8vm8EeeXSiX0ej3zu1armQRBTd6xM2u52cmXv/xlI/K3MUrG4uAwb1CZAL22CwsLZrMfe26i98NPe6uGSY+3pU0k2PV6Hb1eD4VCwZBTjtlMJoPl5WUsLS2ZKJLWSbY35KDEggSX5Z90QcxyUYlEwiya2S5NROL/JNfJZBKxWMwswkmUSZC1Ood6zXSnUEeKHWYVJHi2plh/bFvJcaBkWCu58Fw8ny4Y7YgRz8e2qEMOgGf82DrkSCRinH2sngXsRodV7kQvLa+jZWoBeLa67/f7JmKkHnKOa94Pz6G7CZLPqOREI0y8N9Udc4FBeYo6Uc+cOYNAIOCprMXPzeK8MhZBPo6EDPuB+a3S/D6jnhd9zc7m5oRPETq/TBonACY0kcvlPJ2AHhnV9bKDJxIJLCwsIBi8n0XOmqga3qHHmBpBdl5gV1vIjHHWNgwGgyZkTMJNiQjbztqF3F77zp07uHXr1tgdT1e/dgjKwWGWYS+o6fng4pPHALueG/Ue+3mO9dx+XmV9jb+TyaQZ05xjUqmUIbN2lIfQ5BoaeI5HJtuprIOhz3g8jlar5Vks+90P50N+TuVidgjXjkIpYXALaIdZhe1ptceD39+AvyTBllVpRJp6fzs3ycYo+Zb+BnZzGEhUGdmmZ1YX/nofeu+c81QTTL4TCAQ8HnK9H/vZ6ecHg4GJcqv0wk8ioc+dRFyJM6WkdC768Y1Zw0xsFOI36fp1Lp3Ah03U/AJZOJvi9WQyiWvXrpki+Lq96tLSkgk3ktja3iTtkFpXNR6PYzAYGA0yQzvszPyfRo21BbPZLAAY48mVGcMTql3qdrtGKsGORklJKpUymyGsrq4a0l6tVvHlL38Zf/AHf+AZYH7P037NL+wyqx3Y4eGG3TfZh/P5PNLptCe5RY9Xggxgj2fIDlXSaNjEVgloPB7H6dOnPYak3W6bBF6Oa841sVjMI4fg5zSxTsOu9PaqvOvs2bMm8sQybbVazbSJ98a20mNkL7LVS9ZoNDxlMvk5AGbhftD5wEWpHA4DSvC4+NNSaEqWtU/z70ajgXK5vMdzzHHBKCwJLOVGflIs1TnbP2yrnh/YzQ3g+VutlnHWsQwkAMMnNMGXc02/3zeyMi6q1TPO58LXeH2VS+m9A/fnh0QiYZ4lo1ladYclaXXRwaiWEmWWnmU1n3q9buaVWcXEBPm5557DpUuX8Nxzz3le//CHP4xXXnkFv/7rvz6VhvmRt1HgF6C6Fxq9Wq2GnZ0dXLt2DbVazYRZ+GXTwNADbGer2ysh4L4nRusns6Mlk0nPpiX8zVVYPB43HaVWqxkPMACjM1YjFAqFkEqlcPr0aXS7XSwvL3uyy9meZrOJbDZr7nlxcRGJRAL1eh1/9Ed/dKDvwHmRHeYRDDVSQwd4jRGPUahXiUbKz2Do334eFNUXs7aoVqDgOekxVp2xEnHOObaGmL9VHkGDQ+KrkipbZ8hr6sZCGnLVEKy2Vw2+g8Osgg4u9m2/KAjtv443JpTxWD85pnIB9dT6we91257qeOJ5KWOgZIvEVuckLmqB4ZF19RyTSDMXyk5Y5Pk038AvYs7npot0XsvPm67zlc4xjKD7OS5mDRMT5I997GOepD3izW9+M37lV35lagR5FIZ5nPVL5TbR/DL6/b7ZvY4DgaSW9RJ1u2h7xacGh1826xczlJvP540mWeuNknRrbUReQ2uKcgORSqViwhAcmMlkEv1+H6lUynxG26IaS3bMeDyOxx57DPF43GwXad+Thjn8nvOwMJGDwyxgWFSEybKczDVMqvpcfk6rQ2iyr3qe9DU9B68bDAY9iTDpdNosgDV5j8aK0gguoGmEAJiFPT3LlEXQ2LEEJHXFoVDIVN9pNBp7dJd2CFUlFrpBCKNqnJ/4m89Vt6F2hNlh1hAKhYyTSnXDJLdcGFJjy/fq9bpJiKfUkvOAln7jmNUEPmD4WODrduRJySOwuwGJknbOYc1mE9Fo1NwXI08a8dJzKint97210jmPcAxz7mAbuHjmjy4GlMPw+errNl+wFye68186nfYkFs/qPDIxQd7a2jK71imy2Sw2Nzen0qj9MEweoOSPnhsA5je9LfZ+5JrM5ycvsFdV+sWzE9GbrGWVtLwLAI9h0fAPX2fnYeKgkl6Sdy36T8MHwLOqZPio2+0a3SMHoD6vcZ6zg8Msw09iEQwGsbi4iEKhgGw2u8cYAfDoDZUgc6zaXhWem79VWmF7XGwdJMedLqxJmjOZjOfzg8HA6A+pdWTb6MHh9bn4V8JMz0yr1TJEV71NDGmy/CPnrk6ng0qlYogCYc97dj11B4dZAPtoPB7HuXPnkEqlPMRQF4ualMeIM7ddV9h6XI26kCCqTVVbrLDHEGHzAf7Nsc3P6djWfAEunO2KFzpHKE9hKUg/8Jr6LPm8FFzMM2+K0XA7B4L3Z1cD4TnS6bTZclqdlbOGiQnypUuX8Gd/9md43/ve53n9T//0Tw+1NvKoh6dfqP2ahhTspDq/c9vhU35xdodjR+TfLOhNqQM7Nq+lZNdeoQF7N//gseppprfJFrjb7aXXip4heqEmDQk5OMwjAoEAFhYWsLCwYIrdc8Fojy3+6HbyOhb8xhdhk2RCE4W5WKVB5vWazaaRdLFNHNeaKMSxy2NUk0zdc6/X82z8wSo3NIjaNi3FROLADQnK5TIajYb5vNZltb1Sw56Jg8NRQxeusVgMp0+fRiKR8ITwdWMvjjXayFqthmazaQgyxxjHL/u7Rnd0sWgvpDV/YVwPshJknpfcgwSZzi6SY1vGqc+C5+R1lav4PT9bRqX3p5/heeLxuHmWqoHm5/jsqEXW58d9Iyg7nWVMTJB/6qd+Cu973/uwsbGBt73tbQCAT3/60/i1X/u1qcorDrKisDshsNuZ2Ul4biXVfkZQBwe9OdrhuXprt9uIxWLo9/uo1+vY2tpCKpUySTg0cgxrcF9yJu+oMdT75mqVqzB6tlQfzXtmJ+XW2ExM4vbVqVQK5XJ5j+H385azDcMwq1ohBwcdxySmuqGFel44rjT8x3EHeLd/1THPc+i1eE4aNx7LOYTzTr1e98wp1Euq95oeYyYAM2FHDRfbr2SfXmQAyOfzZjcrAGbBrN5rNdp8j781jKoL63g8burE28/cweG4oJ7bZDKJs2fPehZ3fI9VoXTMal1iHmdLFvS31hWm9MH2svpBpRDKUWhP1Zvs91tLrjEqzfthEp/t6NPSjPbOl/Z9awRdPcG2rbc5lM6FvD96jTVJT50D1WoVV69eRalUmvlo1MQE+T3veQ9arRY++MEP4pd+6ZcAABcuXMBHPvIRPPvssxM3YBgRHjbxjnpdSaZWhGBHos4P2CXTJMgaPtFVE2UN/NLZcTqdDur1OjKZDBKJBAqFAs6dO4czZ86Yahb04rADcyXFc5L08npq+NTbpeERW0pBw9ZoNHDnzh3T0TVZZ9i24LYOedR34UixwyzD7rcaVtWxTCJrhxM51vy8PYSt39Xz8Devp+OUxNoO93JRS6PDKhd6PZ5TQ8N2GFM3QuIGKfQq21vP8zw0XpxruFjgdTUng6WmKFHTbHiNsDk4HDU04ku7mk6n90SMh8GeG9QrC+yVLvJYlQ7QY7qfHR3WfuUt5AKqmyY5pmaXxFzHtib7cj5Tj7lNdu1j9VnaBFnvZZh8lefk521PNH+YI1Eul4fuzzBLOFCZt/e+971473vfi42NDSQSCaTT6QM3YFIPhF/n01XfsNWgdnTtCKwgwd8qydDVETsudTf1eh2RSARnz57FE088gZWVFWSzWWSzWVPOLRAImJJJpVIJlUoFvd79jQMY3qGXV9tk6xd7vR42NjaM8dPBrB6pCxcuGOO3vb2N7e1t3Lp1Czdu3BhrlWvDGT6HWcewKEi9Xjel1dTw0ejo1qi2bMLWH2vJNZ7f3jRDvS98TXeg4iJZ8x/oZVG5F8muveMWiS2vpV5gRqj4ee6apeWZ6B3WjZG44FcvGbBL1DWRj9UvUqmU2XTJzQ0OxwU7GspoTCwWM3lGgFcapP2f51CNvu5LYNtg2xvb6/VQqVRMZCUejxuZ02Aw2ONp1XlCz2tHqXgMSTLzEACYaBgrW6mswY4q+0kl7AU7r2FH2DTxXzkAo+baTtUea8IjHXT1et1s3tZqtfDlL38Z29vbKJVKnjlnVnHgOsgbGxt48cUXAQCPP/44lpaWDnSecVdbozyd/J9fnmqF+L4aQ36R1N0xbKGdmx2ExoBllNTgPfroo3jqqaeQyWSQyWSMp5bZ841Gw1yTHmeGSdRQUytMcMDTW6SaSd2BSzs6wy40wAy5svbzMNjPXicee2Xp4DAPGAwGpo6v6oHtUmtqYOz5ROcYjjPbS+T3226Hft4P9D7xb/X6KHHlfMS2MsKkhFyNmXp6tPScGk790c+wzfoZzWBX8j7p3OAkGQ7Thr2QtSVQo8YmjwkG71egIUG2oy4s66q5QyR91WoVgUDA7H8AeL3Ndjvt17U9+rqOfxJdLo5ZC1mjT/y8FiDwI8gasR4WTfNzPOhneF27veRVlLdxYbGzs4NqtYrt7W1T3nYeeMXEBLlWq+EnfuIn8NGPftQ88FAohGeffRYf+tCHhobzh2HYhOnn2h/1GX5p7ER+19EVm5Z84Y9tbFiSjaFFGolUKoWVlRXE43Gz0YduD8vrs7JFtVpFqVRCo9FAMpk0HZt7xbPsHLDrwdJ2qe5Y2w/Ak8TDz3NAdDodXL16dagRHfd7cHCYVfj1536/j0qlYrZTtkOmdtKsJproGOOxSnRVzqCLWvWiqGSDUPmUkmqVXREc0xpmVQM5GAxMeHIw2JVQqXZS20tSq+8rAdeFgH7Oz4sdj8c97XOE1+E4oYSTu9OqU2kYOEa0n3PDC3qSlTjSwdVsNo1dZ3S42WwiEAggk8l4SDDgHZM6BrkwttvpR5xVP6wVrjhf6cZCPEYX0Mx90Iiz7tfA86kX2F5o+0kx7Dbq7sSNRgPFYtFc74UXXsDGxobRHc8LrzhQkt5f/MVf4BOf+ATe8pa3AAA+85nP4LnnnsP73/9+fOQjH5la4/y8y8PIMb9cFYMD3jCnemsYLlBNHfWF9ERHIhHjGc7lcmaFSYLc7/dNyFE7/+bmJmq1Gra2tjzhnUDgfkYqDTIHowrkB4PdBB8NvVL3ROOmxpmrOQ5cDkauMv1WhAeBk1w4zAMGgwGKxSJSqdSe0KWGTvk/JQU0IGo01ODxGE2k49zDsUmvtZZn022hdSc7lXFo+/y8MppwxzAm74tkl/OI1jTWaJp9bk0itF/XxQENGudMznXzZOgcTj7YjzWB1Y6y2ItIOsJIkNm/bS8ypUzRaNRsNqYe2u3tbQQCASwtLXk2+tJ26FjRiJaSULX51Bhz3tG5gvJQnqPdbpsxW6/XzfzQ7/dNJNuOmEUiEU9ddbZF5VfaPs294HlUwsUFQ7VaRaPR8JSNpINw3uaMA20U8gd/8Ad461vfal77ru/6LiQSCXz/93//gQjyKJmF33vDjlfPMA2DisjtDqreVw036IoylUohn89jaWkJ6XTao+/r9/vGw8wC+91uF1evXsXm5ibK5bLHw8yBQyPMzs/d9bTd9v3ZCTr6m8eqQaQOenV1Fbdu3dq3U9oeZjtc6+AwD2C/LZVKZitYmxxyDPJvAHsqNthQTywr0ihB5oJVZQtKlBmNYhu0+gThJ5tS8s3rqP6R1+SCnnOEJt5xPPtF4TQZT8f+sKiaarrdvOAwK9Axrhp/WzIwjCAzuY/noa1W8Ljt7W3PzntMoO90Omb3THW6qU6Y1yTP4P/2e8Du2NTSbnZEh/NOs9k081O9Xvd4akmedfe/YDBoEm+ZhKv5VyrrshfM/Dy12SwTyTK3tVoN9XodlUoF29vb2NnZMQTZ9kLP+hwyMUGu1+tYXV3d8/rKyorZrW1SjHpI4zxAdjY7RMAJXid0FYUHAgEkEgnjZdWOys6iBoukmkav1+uZVdHW1hZu376NZrPp6QyFQsGUfWP4h9fm+VhTsF6vmxAFsOvh4eqV7QL26qrZfg4CGvDXve51+MIXvjByEWI/Z/VozdNqz8HBhvZrlU8MBgMzHhk5Ug+yJuooibWNnY49/V+vpWSTY5TziB0l4vhXmQfroAK7FS00YYgkmgSZcwONF4kyr015iF+oVA2g7fXiPGsnIDk4HCWGSQRXVlaQz+c9Glwer+NWF5YcFyqd0jHJc3PsxGIxLC4uIpVKodFooNFoYGtry9hxO1nPnn9sCRZttR21sqVhwC5hVseeSrt4Lxpp1oiYvfj1i1bpc9E26/NjBKvZbKJcLpt5qFgsotlsolqtYnNzE1/96lfN/MNKHPZznXVMTJDf9KY34ed//ufx0Y9+FPF4HADQaDTwgQ98AG9605um2rhxH6B2JjtRj51mMBh4QgT6PrV99opRPShKujlYSJBLpRLu3buHzc1NY+DoXaaXmEW+WYKJbVLvkho+7dRK/Nlx1eNNGYcaUWat8xzDDJmft9he3Tkj6DBvsPuu9mHbm2QbRj8jqeCYtK9nh0CHjRuda7QNPLfODyTtel0eQy+xzgFqOFVqYeuNeU2b6Os9+j072xPt4HCcUFuVTqdNZFd1tH6LVn1dnWF+SX78zcVhIpHwyJIoyaBEgxxEF7n2WLIX2jZ0PtH5yY9w02uu0Slbhmkn5Ok5/BYNuniw+Qe95txkhfdZrVaN57hYLBqJ6TwvpCcmyL/xG7+Bt7/97Thz5gxe//rXAwD+9m//FvF4HJ/85Cen3kAbfg/anthVswfsGiEtScJj+Ro7n01I6RHudruoVCpIp9PIZrPGy9NoNFCr1Yy8gt6cbreLaDRqvDj0cNtt1RWsFv/W1aV6lQFviEY7Ma/N3bBKpRL+1//6X+YZ+A2QUcZuv2ft4DALsCMeJJYATC10hV/Ik68DXn2gfQ0AnrmF7/EaKnkC/DPDbZJqk+N2u+3Jl7AxGAyMnIqhTb/qEpwPNCFPN0exZSbaPttQcr7THQnn3fg5zCfsyCZJ7unTp5HL5TxbpdvjQZ1G7NtMPNWtnHVnTRJUlTr0ej0kk0mUy2UUi0Vks1njWY7FYp4yc37zBe084I1Qce7y+1ECTCccnXsq36pUKmYzH1sWRfD4fr+/Z96wdwgMhUImv4nHUEqxublpFgUvvfQS7t27h42NDc+ifJ7nh4kJ8lNPPYWXX34Zv/M7v4MXXngBAPADP/ADePe7341EIjGVRu3nofCTAwAw7nx+OexUDDkA2FM8XMMrOvDYQRqNhungrVYLxWLRGAhuPBIMBs1WlQxhJhIJJJNJo1PkilYHhXYgtoPhT7teKnVRhEpKqCFSz3G9Xsfzzz+Pu3fvep6dH0n2e76218jPw+zgMKvguLJ3zgK8NUj9PmcvZP0iKTYptxNr9Rr08mregIY/1aPt54W25yvelyb/2qFUbYN6knThra8Ne4a6mGAmvxq/acLNLQ4HARe0qVTKyBX1PWDvQtUeVzyHn4fV/rx6dCnH0hJzPJ8dZSL87K+9WNXSjLbzjufQqBCPIfFutVoeOZTdJs4bfosNzhFaNYd7NvCzm5ubKJVKuHHjhlmcb29vm+3qh937qGcwizhQHeRkMokf/dEfnXZbDGwSp/8Pe7C68gkGg8YLo94jPwLPbE+7Xqi9YqvX655qExS10wtNkT+wOyhbrZYp5Qbsdnrej3ZuGllm1Ptln9udnccFAve1i9VqFXfv3sXt27fx2muv4cUXX9zj0Rr2/PbzLDvj5TBP4ATfbDbNhE8PkCa9KanlHEIjQXAM2vkDSryV2HI7Z61bygo4vC7nHZZo41zFJB9NltEykJwDSI7Vq8MfbbttqEh4WWrSnuuUPGgVALZ5c3MTlUrFzLVuTnA4LnDMMXG+UCggHo+bsmbAXl2t/hDDZBU2maazSollKBRCOp1GNBr1fEadX/YY0c/bRJ2cQCtXcJyq3phebnX+kV+0Wi1T5lKlIZSFMj9BnQe6KRDvn9UvIpEIisUitre3TTTp+eefx8bGBl555RXPffjNCfNChv1wIIL84osv4kMf+hCef/55AMATTzyB973vfXj88cen1jAlkfq/wibRKp2gF1ZLm9j7lQO75VF4fu28dnhGOxvbxcHJuoI0KiwgrttBqj5JO6Tej+qU9YefB2DCIhoaDQQCKJfLuHXrFq5evYqrV6+a0jN+oV2/573fMQ4O8wRKpFSXC8ATQiRpVlmTbcBsCQYXszoPaA4Ez89axZ1Ox+zqRwO3s7PjkX90u10UCgXk83kT6lXZlHqm1JCr3lE91v9fe98aI1tWXreqquv97Ne9d+5rLpdhHoSLsRnCIwEB/mGFJONIBOTEZEwsRcEI88NWBvMjQhCZoFiRExGNEzuKJeLESMSONUiJiexEjhllAoZ4ZvC875377L79rPf7lR/Xa/c6u09VV1VXd1f33UtqdXfVqXP2ObX3/tb+vvV9m++zvfyb90kPk50c5CcP0QXC9vb2SB4iB4eDgk3AcrkcFhYWjCeTC1HlEPyManWB3ePJvsYgu0guQIcW90SgvefYtf/mOW1SqcUFtOSazgPU+ObzeY/zjkSd80GlUkG1WjU5UBp55uLYHve6WFZHAj9D2SY/v7GxYYoKaMTqpGGiMm8/8zM/g8cff9wk5T333HO4cuUKvvnNb+LjH//41BtJ7CUPsFeNWpoE2NkQwCbequPl59gpVQpBaLg2mUwilUqZwaErMxoTOxNVr8H2akfVYueahKMaah5PA9poNHDnzh1cv34dKysrnjJQkzxn3qf9moPDcQFJKhextsyK44gRJ45zkl+VN/F8PEajSQBMsf5wOGwMB3eSKpfLSCQSSKVSZkzTwPAcJOfRaHTXvKNyMf3bTuTlfELDpgReCbLqljmnADtkwc+RwEXE1taW8X77ecccHA4b8/PzWF5eNgs+P4KsmnsbKpmw+7OfN1THCSWOJMhc3GqirRJw2+nGH0oj6dTTa3Fzks3NTeTzeVy9etXwjEHVaGxOQWKrc5g6BTnnhUIhs68DAMMvNEnPlnoMmwOOu8NtbIL81FNP4Ytf/CK+8pWveF7/0pe+hKeeeupACTLhJ8Gg15hfbDQaNZ1Di/ITfuEUlkoCdoyMHWLhQOr1eojH44Ygp9NpszFHs9lEOBzGnTt3zC47wD0DlEgkdnll9DpcBapWyJZlqIax0WigXq9jbW0NP/rRj3Dt2jWUSiVPGFnvc5Rn6+BwHNHv903CTTweRzAYRKFQMGOKOkGVJQA7HhLOH2p06NGxE/doCJV0U/JAWQfH5ubmpjE6+XweW1tb2NraQrPZ9IzLdruNaDRq6q2znYFAwOgc+f8waN6D6o1ZdYfRNXq/+BkN63LuoBEul8vY2tpCpVIZunX9fuDmHodxQaIaDodRqVQQjUY9Dil7m2WVU9Cm2s4oO5Ki19JFKf/nopYaXU3AA+DxDvM8tueYJJQONeUra2trKJfLuHHjhvHk2lIObaMuXLmwBeAhxnyPsOcLm8gPWhD7jdnjTooVYxPk1dVVPPnkk7te/9SnPoVf+7Vfm6gRw+QTw1YoasRIaGkI2ekajQYAmCxVgh2BxowDh7UMAXhIKAdCPB43xywvL2NpaclsKBKNRk31CpaC4Xl4DQ4eHYQcDOrp1uxzDYGofKTf7xt94+rqKq5du4ZCoeCptbjXCm+U78bB4biARI9k0t7e3U+bSCNCQ9Lv9z0LW8Bbjk2NkM5B+hMIBMw4JinN5/PY2NjAxsYGCoWC2WCA12FRfcomSERJ7NVw+s2PfmNeCbLqmXWxT0LtRwx4LIv/09nA6zk4zAI4hqm15Wu2LladVXb/ZXRGj7OvQeh44fjROcT2sLIN/IzaaCW7lD/oIr5YLJqyaao55vt2GwfZ/mHjVecy29Pud46TRIKHYWyC/OEPfxh/+qd/ioceesjz+ne/+1188IMfnKgRflo5e1Xkd7x2gHa7jW9/+9v46Z/+aUSjUQA7iWwATKmSWCxmOnOv18PCwoLH09rpdMwKTUMv2vFsIwzsGBIOMnqzOECZ2GJvTMKqG7rzFVekXB3z/migGI6p1+u4desW/vf//t948cUXdxmuQUZP4bc48ft+nDF0mGWwfyYSCbPAbbfbKBaLJvzJOqUMjfqVWQK8RlQ9qyptYDiXC3PVMkajUePJ1qSZtbU1s7mAfd1+v48333wTKysr+NCHPoTTp097toiNxWLm/Epo1TjrTpqE5ip0u11Tb51edTs5WX80U79UKqFUKnm02m5OcDhs+Hkw7c11SDJtaeMgyYOOEe33/B/wr4ahJJjRI3qwOQ5VqsXz8Hgl7+QPSqZZIvbOnTvY2tryyDZGeT57OcgG2X2/4/2I8f1AkscmyE888QS+8IUv4Ac/+AHe9773AbinQf7Wt76FL3/5y3jmmWc8x44DW/bgBz9Cxy/vRz/6EZaWlvDud7/b7KgD7Pb+2KWVAO8+5ZrUo52Fepxms2mkFP1+H9ls1nhzaXipQ+QqlsarVCoZoxkKhUwYloSXgn81vJFIxGgbr127hpdffhk3b95EPp9HvV5HPp/3kOK9ntc4uB8GgcPJgR0G1TCmvm+Pa8KuZGPvqEnY84NqlbWGer1eN9vSNptNz652Nlj5YnNz0xhttoPJf/Qu6zxlOxM0611LV3HBrUTb3iTBXixoRR56rxwxdjgq+DnQKK3gfgN+5doIu2KF3xi3xzqPs8mnPfY57v32J7DznChpGDaeyBvoFLPve9DzGWTzlSyPa9cHOSlPOsYmyJ/97GcBAE8//TSefvpp3/eAnVXVJBhG6mwjpa91u1388Ic/xPb2Nq5cuYJLly4Zj6xml3N1xsxx1QLav0lyiU6ng3q97jEyzWbTJOmx4zOUqQOB2zFyVdnv97G+vo67d++aUnLhcBhvf/vbzYDrdDoolUp49tlnsbGxYYqAMwGIK2Z9Hn7PahD2M2gcHGYV6t1hEgznlUEhWBJIGkjucGmTRmBHc2yT02AwiGq1inK5bJJqmBPARa9Cxx7J/BtvvIG7d+/i8uXLJhNdvdk81vYQ2clD/X7feMxtvaN62fjDcysJYbSrUqkYj7a228HhKKD9lBrdt73tbSbPh8cQ9L5yHOlC2vYY21FXmxNwzmDUptFomNeY+6A5RCS5eg1bomVfixyl0WgY3mJH2u2xap9Dj/V7bg57Y2yCPCgT9CCx14pI38vn86ZjlUolPPTQQybsocSUeiEtzWZfjwZFd9XRwvzAPd0gM9Z1cLCgvur/KLWIRqPI5/NYX1/H7du3sb6+bjxM/X4fKysrHhJfLpfxyiuvoFKpDNQc7fX8eOyg9xwcTgI0yURDn9QB9/t9U4qJ45mLYJVMdLtdI9UgaQZ2iKxGmYCdXTkTiQQajQZWVlbMgtZP+jAs0lOr1dDtdnHjxg2TO7G8vAwApgwksEOIaWg1uZBzUiwWM3kPmqzEz/t5kW2HAKNjB7VBiIPDJOCY4QL4tddew8LCAh588EFP2UX1CqueH/BKo/y0tmo77YgU55ZyuWwiN5w3OO7IB3RBy/NyTlC5FNur8ie7bYOI8TjPzYYjzf6YqA7yLIPejtdeew3FYhHAvTIwDz74oCcsyk6i2iXAK8Pg//b51QhXq1WzQmRiHr1ImkxHwkuCfP36dVy7ds2sfrm5QKvVwhtvvGE8VJqoN+h+hz2LcZ+dGygOxxkqk+p2u6YaRL1eN2ORGn+VRalWkBEgelXtuUAXyADMeSiHqtfrWF1dxebmpsls5+cGSaH03NQpV6tVzM3NGaLO5F9bO6n5D3pfjUbDs0GAer3Vm2ZX6dBqHXym9IA7iYXDUcOPtLZaLbz++utmu2fmCLEijBJl9SLzZ5DkQccrx5jaZu6uy2N6vR4SiQSWlpbMOOPxfjpmQqVUwE6xgUHaaft5DCLMozjQ7GfqsIORCfLHPvYx/O7v/i6y2SwA4Gtf+xo+85nPIJfLAQC2trbwwQ9+EC+99NKBNHQYebO/XHpTVlZW8MwzzyCRSODv/t2/i2QyaTb04MAoFAomtMjBw92sVCain9HQJrV5urVjLBZDvV5HJpMxq0z+vnnzJnq9Hq5evYrNzU3zHrdyVLLOe5nEINlhFZ7HDQCHkwj2a5Yho2H0y8imIbQ9TL1ez3ym2WwikUiYDT5smYUueknIS6US3njjDVy9ehUrKytGN2h/xg9+r9Owtttt3Lp1C4VCAfF43EPwKeuitALw1n+nvIz3SqkZFwVqhHWO4/kbjYZxAtiRNgeHo4aSZFaM2djYMHsTcHHIvg/sbLhlLwTtjbd0rgBgFomUPVQqFZRKJbPDHHAv8pRKpbC4uAgAnqgNz6MVdNSzzfmBhJ+cYK/o0zCbPup4dbzAHyMT5O985zue+pdf/epX8clPftIQ5E6ng1dffXXqDQSGf3nDROns4LVaDb/zO7+D06dP4yd/8idx5swZAPAU5VZDQ4PBHe1oOOmVarfbnnAsPbwceJRG5PN5NJtN4x1m2JQhUibj8Rh7QOqKca/7tFeQoxhiPe9ez9nB4TiAUgIucm0DpSSVYx2AMVCs8kDvM71PSpA1CsUx02q1UCqVcOvWLbOZhq33G9VY2YaUxrjVaqFarSKdTns2DSI0+kXtsV/iEX8rOfbL3FdPtCPHDrMMEksmyDPZXT23ADyLQT/42VGOM0aKmf9TrVZN+UOiXq+bqI7t7FLYr6s3mwtUykDHkbX62fNh49bZ/OEYmSD7aeYOC4NCCH4hB7+QAsOLq6ur+I//8T8il8vh8uXLeNe73oUzZ86YDFj1JAE7hhDY0eKx47KmspZZIkh6Gdbd3NzE2tqa2QoagCHEtpGz70fvyS+0YsMW8g8657DrOTgcV3DMlkolRCIRQ3opX+K2z7b0gGPR3j6a+QtKdlWHyGjR97//fWxubpqEW8Im5ONAP8e2//mf/zlyuRx+/Md/HPF43LRZ5SAc+5yjNA+C5xsUaubx9DJvb29jY2PD40F2RNnhKKERUWD3fgiVSsUk6vkdx3MQfH1QbWT+5gK1UCigVqsZCRWT9oPBe7vezc3NeaJH/X5/166WuqOuOqpUUtFsNpHP53cVChjl+Qz732F0zKQGeT9f8KDJW2sTFgoFvPDCC3jllVfMJh8f+tCH8OijjyIejxsPMKUPdjvYeWkguRrlKnN1dRVXr141HmTqCelFpjEe1Fa/+x31tVHeG+e6Dg7HDbqQZZiSxfcZ7aGRotTCLu+myTy291jrABeLRZTLZWxvb6NcLu+SVRD7GVtq4KvVKgKBADY2Nsz21UwK4r3ZBl//JzQxT++Ni31GysrlMkqlkkvOc5g52CRX+7lWZeH7AExkSCNAtjNJ/6fDzC4dR/ml6ospc6LUSa/LzyhJthemBEtE1mo1VKvViauBOewfIxNkvy/yoAjVXlrZUTW1Oji0o3I1yBVhpVLBH/3RH+F73/seUqkUUqkU5ufnceHCBdRqNSwuLu4KMTIZ5+rVqygUCiiXy6jX6+j3+6Zj0yBzINlapHGfgZ8nfRKJxDC5hoPDSQDHODWD5XIZ58+fN94YJqfxWHqbgXvjIxaLmRJvHCcMeSoJfuWVV7C2toaVlRWzVa2NUReyo4RCWW7t+eefRyaTwblz57C8vGzkEpzrtGqPJvny3rV0nCb2cZFPory6umruzcHhqDHM1rHfc2OgdDrtGVNar1jHhGqBbV7B8ULCzaRZ2xbzfJlMBgsLC56NRxR6Xjtpj4v6er2OtbU1bGxsYHNzc2iSvsPBYiyJxac//Wmjn200GvjMZz6DZDIJAB5P634xSmcYdIwtsdiLjNIw3L17F+vr62Y76VQqhZWVFXQ6HSwsLCASiRivEjsyS7VVKhVT1g3wSjMGhYJGIb/jkNhxB5AbcA4nGeodooTi1q1bqFQqiMViZozr9rD2BiGAV2/MyBJLSBaLRaysrJhSj4NCuHu1c1ywjCQNfSQSweLioiccDMAk5LHm8SDvMhfv3KikVqthe3sb29vbKBQKu+7NweGoMIgYAztEN5PJIJVKmV1rNWpke21t2ZFdjg3Y2ZE3kUggGAwiHo8bB9v29jZKpZKRbNHBNogYk8RrBEvbx9yCzc1NVKtVJ2k6YoxMkH/u537O8/+nPvWpXcc8+eST+2/RhLBDF4pRxOp8v9lsmoSblZUVz4qUesROp2M2A2AHHqTR3ksnbL/Gv4d9btjfk8J5lB1OAuzISiAQMCTx1q1byOfzuHDhAgCYJB5WdCBsPa56WLnl+9raGm7fvo07d+6gUql4dIQHGaHhXFOr1dBoNFCpVLC0tGS8Y7YHjG1muJlebq3cw02NaPSLxSJu3ryJ69evm/fc3OBwlNgrSsp+HwqFDElNJBKo1WoeAspzDIr02HZViSujSpRXMJJ069YthEIhRCIRJJNJX4Ks5wTgid6ovecmP5ubmyZPyRHko8PIBPm3f/u3D7IdHgwzLnu9bnemvYikTXD9Ps8scvtzfH9QB97LozTsNWeQHBzGh19khq8z+/zZZ5/FmTNn8O53vxuRSASRSMQsfpk0y5JnvV4PjUYDjUYD165dQ7FYxN27d02tYtZL1usfFDHWc/f7OxVvrl69iu3tbVy6dAnZbBYLCwse+Ui73TYJe7dv3zalKavVqrkH3UiE5bJ090EHh1mF2kxu1R6Px5FOpwHAExHSBSxh23J6dQFvJRk7snvq1CmEQiFsb2+bzycSiV3VY/gZPyea5jR0u10UCgVsb297Njaa5FkMgyPco2Mmk/QOGnt5Zwcd70d499IN2p/10xzu1alHOW4vQzaqXtvB4SSCUoJut4u1tTUEAgHk83nE43HEYjHjJdKdNkkYqftdW1tDoVDAysqKR0Z1FGNHDX2pVEKtVkMqlUKv10M0GkU0GkW/3zfJPvzM1taWIfyVSgXlctl4if3K1zk4HAeoZIKLO01uI1HV17R6lPZ9W5/MhTLBcR+LxZBIJDxaZ+7SycWsX2UMPQ/HHWWbpVIJlUrFzD/jyC3HkWM6kjwaZpIgTzo576Xjtb0wgz6rr9mC/XExLin1u4dBbRynk+/X4I0qU3FwmGWQ/K6uruK//bf/ZjYQuHjxIlKplCn5WC6XTQUHelg16VbPRwzzXk+r7TwfDb5Wnnj55ZcRDoexsLBgcikqlYrZ1ZNVeWwy4Jc8PCwS5+AwS9Bx0Wq1sLm5aWRHWp2G/d1PFrmXc0k/SwLMnCMlyNyYZxRpJc/J/Ri63S5eeuklVCqVoWPRD258HgxmkiADgys2jIphWiX7mEEdcJhsY9AA2K/3Rc8xyOAOase4nvFR4Uixw0mALU+oVqumosPm5iYqlYrZHKhWqxlyyd3y/BbdB6E33mteGnQ91ioulUoIh8Oo1+vGW0xyr2TYGVWHkwSVRLH04vz8vIkQEUp2+TlbWmGTWJVC0DbbURcer441P4eWXosRq2KxaCJVlDY5u3v0mFmCPEg7vJ+J3c/wjLtSG/dco2ijFcNCKoM0ywdp7NwgdThpsIlyt9vF7du3Pe8p9vI2jZsvMSn8xrnqn3k/5XLZ/M922Pfg53RwhNnhOMFepAL3JBSVSgW3bt1CrVbD448/jlQqBcBb7ULLFpLI+pHZbrdrXted7Zisz1rrBL3BzGHQdip51mhOt9vF1atXTWKeXff4IOYRh9EwswT5IDGMaA46fhwv817HTopBRNgZNgeH8THIuzPovaPGqI4CP2kWP3+UumkHh0mhizw/m2v353q9ju3tbWxtbZlt1ylF0jrglCtxoem3OVAgcK8kW7vdRrvdNu/zf92Ft9VqeXbJo9xCNcyUazFJttFomI2GJt2t0kWEDgbHjiAfVKLZsA45yODs97z2ccMkFQdFrvc63gn6He4H2GPtKPv8XmPVjyzYn9dj97rWMLjcA4dZwTBpI7C7Yg23ag6Hw2a/BuqSmXugu2gC8GzwoT/cYIQEORQKGaLbbDbNxkPtdtvsrKvbRitBZjlF5jkUCgUUCgVTLnJSOJI8fcwkQR5EFieVEwzTCx8mRvEs+7VrlPsd5bm4xBsHB38ctFRpXIy6YFdS4Dd37HeOm6Vn4uAwTM6kC0d6ea9fv4719XWcPXvWVJwAdrTF9PIqmQW8FS6AHa8vN9PhMayFTC81tcn0BPN/eq/7/Xt7LZTLZdy5cwelUgnlchmNRmNqz2iYnXcL3fEwkwR5mIxgksn6sDSC+8EoWsD9eM/HvddhGnAHh5OIWZoPJsVBLPzdmHc4LrD7f6lUQqPRMCUQuTlQIBAwJdkA7ErO0y3m+TorwZAgBwIBoxemDIPHah1lEmQS53a7bXbjLJfLqFarAyvjjAu/z7rxOzlmkiBPU1pwkNiP1vgoPDPjhl6POtTs4OAwGtw4dbif4Rc50frCN27cQDKZRK1WQzQaRTgcRjQaNXpkSiQovYhEIggGg+Y3AJOgx6o2rJuuFTH02mpjSZi73S7y+Ty2trawsbFhkoTtHAiH2cBMEuRh5O0wieW4JHGcdh0F8R+3fY4kOzg4ODgcV9B2tVotBINBlMtltNttRKNRQ3K581232zUEud/vexL5AoGA8SDzB9ipckGdMqEEmbKPVquFRqNhSrpNslOew+FiJgnyMBwmsdyPrOMk4H69bwcHBweH4wklplqirVarod1uIx6PI5FIIJVKGU9yIBAw5DgUCiESiSAcDhudsnqj1ZPcbreNvII6ZnqLeW1KK8rlMiqVCm7evIl6ve4p++Ywm5hJgjxLpYiGZc36HTPsPT1mWAb6Ydz3Xtdx3mMHBwcHh5MCrT9MuxYOh9HpdExSH0kuZRhaB1llG5RVdLtdj16ZRJkeaNUmFwoF5PN5szOns62zj5kkyMSwahb7OYf9vh+GkcNROvYoWejDzj+sxukkBHqScnNuADs4ODg4HFfYNcCp96UXmASZpd+IRqOBubk5Q541mkyvL73L+j5JNgBP+TjgXsLg5uYmWq3Wrs1AHGYTM0uQp1V5Yj/VHfxCNdO67qSY9Lyjfs5Vr3BwcHBwOIlQLzC3dab2OBKJmPcikQgikQgAmHrJ4XAYkUjEeH9JgCnHiEQiRqbBnfrC4TC2t7exubmJra0tlEqliTcDcTh8zCRBHrUKxDjH7wezIHk4bDgvsoODg4PDSYBfZFS3jdYqE1qijXWSeQyT9WwJBcky/26320bOUS6Xkc/nDSGfJTvvMBwzSZDtsAj/Vhx2JYuDvsaswtZLz3JbHRwmwawtTh0cHKYPP9vV7XZRr9dNJQqiWq0a8sv3otGo8RZHo1EsLy8jGo0ikUgYj3MymTQe5O3tbbz88ssoFosoFosmoW9Yu9w8NFuYSYI8yqYZwN7JfNPS604rafCoyb4f7GS8YZu0ODg4ODg4nCSo3MHmHna5NpZ463Q6iEaj6HQ6nprJfP/u3bvY2tpCoVBAtVo1O+75wSXDzy5mkiCPimnvHrffzx3VefeD+72UnYODg4ODAzDYOQbASCaAe/Zye3sb6XQa/X4fCwsLiEajqFQqqNfrePbZZ1GtVneVfHM4XjjWBNnBwcFhv3DGy8Hh/sA43tq9KlH1+31Uq1XcuXMHW1tbuHPnjtEos5ScX7lUN98cHziC7ODgMDacbtfBwWGWMGhOGiZpHOYxHnYd/u50OqhWq+a89Biz1Jtfu/z+H/eeHA4HjiA7ODiMDTdpOzg47IWj2PnWxjj6Xj8P8145Q/q/1j3W9/ZKzhvWHoejw5EQZF2BzbI4XSs47NVOvzDKoPDKoA1Qxm3bJJ+3azvbv/c6116bnDicXLjv3mGW4frn7MDPxu+VDD4rGMfDa78/bOfcYZ9zODyMM08cCUEul8sAZr+DjPMgB4VqRn190rbt53N2W8Y5Z7lcRjabnagNDscTOm5nfew63L9wc9PRg3PF/bpjnJsfZx+jzBOB/hF8k71eDysrK0in0zO9knTYjX6/j3K5jLNnz+4KJzmcbLhx6zDLcHPT7MDNFQ6zinHmiSMhyA4ODg4ODg4ODg6zCrfMdnBwcHBwcHBwcBA4guzg4ODg4ODg4OAgcATZwcHBwcHBwcHBQeAIsoODg4ODg4ODg4PgSMq8uQzX4wuXKX7/wo1bh1mGm5tmB26ucJhVjDNPHAlBXllZwYULF47i0g5Twq1bt3D+/PmjbobDIcKNW4fjADc3HT3cXOEw6xhlnjgSgpxOp83fbnV5fKAVAfU7dLg/4PedD9opkn/b8NtFchyMu2Ok32fHec++t1HbNe497ve5TIpB92c/D7/nM87zHOX+hn23oz6bfr/v5qYZAL+DUCh07Gz8UY1Fh8NBv99Ht9sdaZ44EoKs+5O7jnh8QPLjJpD7E4PGrd0XhvWN/fab/Xx+knaNer1h27SPco5RyeO0x92o9+133DjPcxrPYC8ch22M7xccRxtP+3YQ7R1ngbffawxarI+zDfag85+kbTNG+U6OhCA7ODg4nEQcFBk4LiTDwWG/2C8RG/b5vcbRoM9Oc/yNc2+jPgvbYeFH9IcthoctDPS9UaNF9v/D3vNry6zAEWQHBwcHC6NM1H7HDHqN2MtA2MfbGNVY3q9eIYfZgh9x83tv2Of2e92D/uw4Y2hccjzpMZPcw6Tt2IuIjxttnOS7G2VenQSOIDs4ODhYGGWCHUSEB/2mJ8WPJKhRYGa1Taz1R18bZGT83rPb7Mixw0FjGEGaZYyaV+A3hqYhldjrug47OKiFviPIDr5wOmMHh9Fgk9twOIxwOIxgMOjRYDI5pNPpoN1uo9frmc+HQiGEQiHMzc0NTGzq9/vo9Xro9XpoNBro9XrodrvmHI7sOjjsYFTZgN+CdZKxNKrNHDV6NI79PYljfxY4iCPIDr446o7p4DAqpk0O9/K6+l2v3+8jFAohGo3ine98J97xjncgFAohGAx6iPLdu3dx9+5dvPrqqygWi+j3+4hEIshms7h48SIuXLiASCSCUCiEbreLQCBgSHcoFEK9Xke1WsVzzz2HcrmMUqk09F5s4++ItMNxwbgEyT5+Pwm2o3x23KorgzzDfvOL/nD+4HFcWPOz+vlx9L6D7mkUT/mga4x6rlEwCxzEEWQHBweHv8S4ejvgntd4fn4esVgMmUwGp0+fRjabNeSYxwSDQTSbTXS7XZTLZaRSKQDA3NwcstkslpaWkM1mMTc3h2AwiF6vZ7zLwWDQeJjn5uZw7tw5lMtlRKNR1Go11Ov1PQ2gI8YODtPDME21X4KaRpPC4TCSyaSJGBEc9/1+H3NzcwiHw573eI5Op2MiSN1uF+12G51Ox8wv/LHbSwybCwaR3kkW17PgBd4PHEF2cHA4EkwroWK/n99r4h/k+SGBjUQieNe73oXl5WVcunTJvEfDp56gxcVFZDIZPPDAA8bIBYNBxGIxADsGklDvUbfbRSQSQSQSwQc/+EFUKhXcunULb7zxBq5du2aehd7PIKO430TAacB5sx2G4SCJ1bSSw1TrrwRY3+drHMvBYBDZbBYPP/wwcrkccrmcZ5x3Oh3U63Ukk0kkk0lzfi6Qk8kkqtUqKpUKWq0W2u02tra2UKlUcPfuXVQqFVSrVbRaLROFAvyrUdjeZwcvHEF2cHA4EszChLxXONE2JJQ7LC8vY2lpCalUCrFYDMvLy0ilUsbIqbdHCTK9wwDQbreN0bI9RXaolAaSbSKpPn36NKLRKM6dO4e/+Iu/QD6f9xDsYfczC54dR5IdpoVp9edx++Qgss2fcDiMSCSC5eVlJJNJzM/PIx6Pm6iTvTjmQjgWiyEej3vOSY9zJBJBIpFAOBxGu902nwkGg6jX62g0GiiXy2i1WqhUKuh0Ouh0OuZctjRj2L2Mcq+zgINohyPIDg4O9wVGNXyDjiHxjUajOHv2LB577DGcOXMGkUgEhULBEFu9nk2U5+Z2ptxwOIxOp2PIL4mt7dXRRDw1AtFoFMvLy3jggQcQDAaxsbGBarVqEgCZ1DfNzQcOm9A6Au1wlBgmN9BjONYJSqrm5uYQj8cRj8dx6dIlLC0t4cEHH/SMd/Zx/nQ6HUSjUUQiEYTD4V0SDS7SOZ+QGCcSCcRiMZMEvLm5iVqthm63i2az6VsJx++epjXeDkKXPEkb9nM/jiAfMtyE7+BwNBhUjslPhmBP4vF4HH/1r/5VZDIZLCwsIJlMIpFIGOOUyWTQ6XRQq9XMZ2KxmEm2C4VCCIfDHiPodx2+plIJkmhgZ+teNcrUG7773e/GI488gmKxiFKphFu3bqFcLqNSqQxNqhlGfg+yLNx+FisODnth3KQ7/ZzfOLEjQhzT9AYvLi4ikUggGo0inU4bkswKNel0GrFYDIlEwiOp4E+v10On0zFEm7kH9j0oseb7kUjEaJP1mGaziWw2CwAmB6LRaGB9fR21Wg21Wm3XInqUJD2/Y/eSdQ07bhoYpazluHAE+ZDhJnwHh8PHsIWpXxa5/qbBO3fuHObn57G8vGyMCg0dya8SX/UId7tdzM3NGUMI7GiY99ID7+W9pWFYXl5GLpdDKpVCPB5HuVw21261Wr7SCwcHh3sYxbtJUhoKhZBKpUwFmuXlZSwvLyOTySAaje5K0rWTbflbIz0ageIxg7yw2hYAJjIVjUbNcZ1OxyQDMleiVquh0WiY9zg/UNrB9vhVw5g2DoIoT9tD7Qiyg4PDiYZNeAk/4qnH0sC95z3vwQMPPICFhQXjNVaiy+MphbDDmDpps44xjZGtPeYx2r65ubldhlRDuPybnupMJoNYLIZcLodSqYRKpYIXX3wRW1tbvuFVv/s/jIW8i6Y5HCT2IkqjJOZqgh0JayKRQCqVwl//638d2WwWCwsLpr65X/1zlUZwntAIkc4PnAN4nlAo5Gkn5wZN+Ov3+wiHw2YRzrZGo1HPtVhtp9vt4q1vfSu63S6uXbuGZrOJdruNtbU1bGxsGInWMHI+KvYzxv3yMMaFk1g4ODg4jIlBkyYNSSQSMVnk8/PzyGQyHnkDP0+DNTc3NzC0aJNOO0SqxpReaR7Da1JGYddE5fEM45Jws9oFk32old7a2tqVsKNtHPZspg1Hjh1mEToWlIgy2W5+fh7ZbBaZTAaZTAbZbNZIF+jJ9Rv/Smj1bz2e45nX5jG6gLU3B9L3daMhEmS7Hf3+vRJy3W4XuVzOVLvgtVutlpGLUb9sP5dBcoujHtPDJGuTwBFkBweHY41xdGeD5BT8m6HIhYUFXL58GW95y1t21StVnR/Px6QZenL1PTWGNjnWUKrtTWJ7qU2kRIOv8YdtjkajCIVCSCaTxrCRDGezWRSLRTz77LMol8sol8t7Piu/5zTM++zgcNAYFpafRnhdF63AzsKXpdlyuRze8pa3IJfLGb1xIpEw44DktN1ue86pEgsAnrEPwLMgpkfYvmeVdREaWWIZOM1X0DlCP8fF9qlTp0wbT58+bSpgNBoNvPHGG6hWq57Ikx+pt5/foOc66DP2fQ4iuUcBR5AdHByONWzDOE6SiX4mFArhzJkzyGQyOH/+PObn540RDIVCaLVaxoNjJ8zo1tH09jK5jh5h3QTATvjhjxpAGkt+ludUQ8LNBKLRKJLJpDGSJNTtdhutVsuUoPsrf+WvIJ/P4/r166hWq56kwkHPxf5/kCFzZNnhOML2GLMyRDgcxqlTp4xsgQl2TLbTzzD/wM4pUMKtMg0SZJVW+BFPlWL4ScXsyjkKnWd4X4SSbZJoHkPSf/HiRdRqNWSzWRQKBRSLxV0eaT/J1rDnO46E66hIscIRZAcHhxONYV5jxdzcHB544AEsLy/jbW97GwAYuQUAQ4zpfdHwJ3XF/J87W6mEwk+T7Pdbk3gA79aytoGirIJGjZ5uJua1Wi0Eg0G0222Ew2FcuXIF6+vrqFQq6Pf7exJkP8yC4XJw2C9sjbAuOBOJBBKJBC5dumS2fufCNZlMmjmBn2eyG8ckofkCSo5tedUgUmzLK3hOW8IxKJ9CvddaMo6IRCLo9XpmnmDpSeYztFotLC4u4saNG6hWq54Fvt1W+7ny/eMMR5AdHBxmAgcxsQ4jx6oFfPvb347l5WWcP38e8XjcGI5er2c29KD3lsaEnlqel0ZFt4T2M0rqZVJ9I6UZatT4OxKJoNvtolqtGuI9NzeHVCplfigFicfjhrA3Gg00m03EYjG0Wi1Uq1WEw2FkMhk8//zzeP7551Gv13dpkgc9x/16iQcZcweHUTBscWaTtlHPFwwGkU6ncfHiRZw7dw5nzpwxC9F0Og0ARrLUbDYNoaR2NxaLme3eY7GY8cZyLrD/1vJvbIM9JjRRV2VWdvKvSrlI0knk7cW7/XlbB00PM+c9Rs6CwSAuXLiATCaD27dvm536OFf5kWQbw+QT9vc3KWxP9TR00o4gOzg4HBmOkiQFAgHjfV1eXsaZM2cwPz9vQqpsl2qKKZ8AdrTIatDsChP2pO+XgKceJZsgkzzTyLXbbQ9p5k5c8XgcsVjMVLzg8WwLNYr0JKfTaayurmJxcRFra2t7EmRilHDquCHXvbSMjkQ7HAQ4zugxPnv2LC5cuIDz58+j2WyaqhC60QZJJsdXs9lErVYzlSA0F0GlFLYnWUvA+bUJ2C2v0OiRHm+PDy0Zp3OTzmFaHk49zX7zV7/fRyaTwdzcHEqlklkgML9hkjn8KOZ9P4nKXnAE+S/hvBkODoeH/XgLRiVPfu/Z8oa3vvWtePjhh5HL5RCLxczGH4QmwxHc2lVLOtlJc1puiQaL11Rvkm0MufEAjTb/p0GOxWKGJJPoptNpU/tYZRmtVgvhcNh4huilpmH/iZ/4CVy5cgXf/OY3cfXqVSMR0ec8KSEe5/vd6/t0c7PDOPDre/a4V0nF+fPnceHCBbznPe8xpJC1g5VQtttt40EGYGRM+XzenFfJZyAQMPOJLnj1+tpmTYLz84brbzvZVxfoOl/ZHmcu9nUhzkW4tpHzB0tRRiIRpFIp4y1/4403UC6Xsbm5adpo11nfa24+bKnWJNd0BPkvsVcCj8vednCYHqYxdvZKThn2uWg0ilwuZ0o2xWIx46nVrHEFz0njoh4jJckAPIZQDabqCJWIa0KNJgnRS0zDpiXaSJ7pRY5Go4YMk+yGw2G0Wi1j+FgJgzKQSCSC06dPo1ar4e7du54kwElhP/tRDJObSx0OCn62m2QwGo3i1KlTWFpaQiqVMoRSiSIXo+Fw2ER9GJEBYCRPfuNZE/GUGNsE2YYSZH3NJrx+40YJs7bHrnjhd02bJPP8TBLmLoCMtLXbbSPjGsaJ9lq0zCocQR4AP6+GX6d1cHCYDkYdV34Tq99rwybsTCaDn/iJn8D8/LzxvgaDQeMh0g05NFSqekAAnuNU1qChS8LWH6rXRQ2RSia4ix81hKqNppeZ215za2uSd+Cet5uJe/yxdYnvete7cP78eXz7299GtVod+RkOC1nanxlmDAd5+h0c9gM/rbuO51gshmw2iytXrmBpaQnxeNzIKFSPy3FDD3Kn0zHjB9ghyFqajdC6yH7JeYQuvlVSoW23ibH9un2v+r+SY+ZK6LE6L/A+KNfScwD3FuZvectbTIWLlZUVrK2tea477bl8XPg9G/7vNMhDMKleBthdfsXBweFo4OcV8vvb7xiGPjOZDJLJpJEhBINB1Ot1M0GTyNpGgt5ZEmV6Y9Rw2fq/QW1ST48aUC3fFovFzLlYTo7XpWeL3mbeRygUQqfTQbPZNO8PCoGyAsbi4iJCoRCKxeLA5+vmPYfjBo3OMEITDodx9uxZLC4uGumARlf4OXu3TM4HgHfsknDqHMNxrCXVbL2v7Xzza7s9n/BHSaC2RbeL1mNZnUKlFiT/uvDWuuyqZ+bfXHQzmZnRqEKhYLzJ9j3YGMV7Pgu47wjyOBO8FvXWEk7ELH2RDg4Ou2ETOxo5EuR4PI5oNGq8JjQEukEAsDvxhQZG5RP8rC3FAOAJaeocYnuQ2T7KHyixIOi9YkiTxp7Hqre43W6b16g/5rXUuNITPT8/j36/j1KptKtNkzz3UedaJ1dzOGioDpib6pw9exZnzpwxdY3V26taXY4XzgfKC1TLa0dUSJDtXTD9jrVhL7BtcmznRfgtfvWHbdBEPT1W8xM0OQ/ALvLc6XSM7CudTpv5is+IORqEXzT+uGBfBLndbuP69es4deoUstnstNo0VQxy1/uFIuh5oZGhvjAWi5ksVYYlGcIc5JXh+UfxvOzHEDk4OOyGPe7VaC0vLxsNHQCP7paeHg2x6uKYekTVIWpZJQ2/0qtEg6N6RBoQe/5g3WXKKzTJjlDj6Jfsx2oXjUYD9Xod9XodtVoNtVoNGxsbpkQVr8e57JFHHsHKygpWV1d9S0Lt9bxH+U4cHPxwEHpUm4SmUilcvHgRCwsLmJ+fN7vhcSxz0Qns9sIC8Mgo+v2+p2IMF6Ka5Mv5QM9je5jte1dirh5fjmuVS/g9M0ol6NQLBAIm2mQvxgEYLlOr1Qxx5+d1UU/yrFU6OJ8tLi6aPIhisYgXX3zRSNX2GvOTfu/DZCXTxMgE+V/8i3+BX/zFX0Q8Hke328UXvvAFfP3rXzf6m3/wD/4B/t2/+3e+OpyDwF6rkkFE1V696XHz8/N4+OGH8dBDDyEej5stWzOZjOkUzWYTt2/fxp/8yZ+g3+9jfX3dDAJdHeqKi+2xPTf2F+uIsoPDZBhVyxYMBk3dYA0l2iWU1COsEz2JLrCjPVTDYnt1/DxMdhvt9tHjq9Uu9P29jAoJOokwd9NrtVqo1WqmRJPtDcvlcqhWq4jH476hUr+2O++vwyxD5QyxWAynTp3C4uIiFhcXPdIqwBvxUXttk1qSTY5FSg4oN1Dya0dtdB7xGztKkLWkpB9/UDmEfb/q8ebGQYO81pRu+c01NpG3kw4BmPyNdrttFvhcpPNz+puftV+flPcc5PwzMkH+4he/iE9/+tOIx+P49V//dfyH//Af8G//7b/Fe9/7Xvy///f/8Eu/9Ev49V//dTz11FMH1ljFXh1NH7Z+wfwCk8kk5ufncfnyZbTbbaTTaSwuLiKZTCIYDJoi4N1uF7VazXiV5+bm8Na3vhUPPfSQKRzebDaNp6bRaGBlZQU3b97E9va2KQXV7/cRiURw8eJFJJNJNJtNvPLKKygWiwPvYVQ4I+XgsHclGuBe4smlS5cwPz+/y0OiemEaIE70DEOqjtGuTqEL436/b6pK2KFZwOuh4jloZJl4R+lEOBw2x3Je0qQbVrbQGq2sz1qtVlEul1EqlVCr1dBoNEwkjAScyX3cSfA973kPrl69ijfffHPP5+vmHYdxcNhOII69TCaD06dP48d+7Mc8xG5ubg6xWMyMT5sUaySJu8zxPngvKttQW87xynNpzWD10OpCk/WW6X1WYuu3mLZ5jr6mJd8CgZ2kP42KATAeZC4YSIK5SNe5jlIKciNW8olGowDuyTGWl5dRLBaxvb3t67W2vx/baUgMcgTsd84Zp++NTJC1Uf/5P/9nfO1rX8M//If/EADw9re/HQDwz//5Pz80gmzD9gYr+v0+HnjgATzxxBPIZrPGcHU6HROGZEdWnRJXVMxq5TWYKENdUTQaNasoAHjwwQfxyCOP4Hvf+57HeHHnq2g0ioWFBZw+fRpXr17FtWvXPN6aUQiv3wrMweF+wCiyJT2Ox2YyGaTTaSSTSTOh6/v6GSWwagyBHcOjhFehXiBbQqEGw/aoUNZFPbFuMGAnC/E3DZ56r0ma6T2mB1lDrXpu9XylUimcPXsWGxsbu0o9uYW4w3FEKBTCmTNnsLy8jFgsZsaPjoFhZc8Ab9lGjiF7caveVb9EPoXabiXItiTDb56xo+C8F5Vx6WJdpRa8lnrLlUjrdXl+Ph8l9AQX2gCMLnlhYQEAUKlU0G63R96EaFLY89Je89Q4c9hYGmQ+0Js3b+IDH/iA570PfOADuzwOh4G9HkQsFsNHP/pRXLhwARcuXDBhAs1Oj8ViaDQaaLVaZotYNUqNRgORSMT3i1aPEj099AR9+MMfRqPRQK1WQ6lUQj6fR6FQQCqVQrVaRb/fRyqVwpUrV9But1EqlVCtVlEsFo2WcdDqitd2BsvhfoIdTvTr/37yq0AggMuXL+PUqVOYn58fKAXTaJPf9qw8ly6YeaztzWXolRhEmnVRnkgkjASEyYK6+QfnJb6mBJohTl34M3eCP9QKqpHnFtWtVgupVApnzpzB+vo6Xn31VbNZgIPDLGEvmZEiGo3i8ccfN1Ej6oUpX1ApgRJDeyGuuQnKHwCYRa2tu+WmPvTg0ntse5rV+aaLcibNqbSKn7crTnCxq/dCvkPuosnFfgmAulsn7y8ajXoW4DYn4aIegImwJ5NJFItFVKtV39rq++EtKiGxX5s2xiLIv/Vbv4VUKoVIJILt7W3Pe+VyeZdX5igRCASQy+Xwjne8A4899hhyuRyi0agJMbIDNhoNjwHkIGCnopeYncUOwdCI0mjyPHNzc1heXjZ1ExuNBvL5PN544w1TYDsQuKd75n7vbFutVjNhUHbYZrOJ1dVV1Gq1XaFZwL/mo4PDSYefN1nHpy5eFxcXcerUKVP2jONVjaKew/Ye6xhTyYR6ZRiZ4txhV7sYdA9cVEejUWNsSXgJW1oBYJeRDAQCHnkFo2OcTzi30aAB92pCh8NhMy8CwIULF3DlyhW89tprKBQKk305Dg6HCD/CHIlEEI/HzW6THF/kAhw/KpcCvBWsON7U22zzAL/fdlTbPkaJqR254vv2/XHuUXmHLdugZ9yuvKVzCc/DazK6ZC/4SbBJ3u12MzLOTVeYywDc4zO3bt3ycCw/fuJHeGcFIxPkixcv4rd+67cA3FuR/fCHP8SHPvQh8/7/+l//C4888sj0Wzgi9CHH43Fks1k89NBD+MAHPmAK7Sv4hVHzY5NfrjBp9Aatnuh5Brx6JYLJAfF4HKlUCq1WyyT7zc3NIZlMGi0UyXkgEPCERZvNJlqtFmKxGDY2NlAqlYwWkYaZz0B/O6LscJIw7gTK41nGbHFxEUtLS2a86Bi3J2l6U/wMFo2LLog5DtXb61cqisf6LcpVc8xolNY0tksz8YeGi+fWyhWcnxiR4rm5lW4wGEQ2mzX6Snqiz507h16vh5WVlYkJslusOxw1WOM7nU4jkUgYzXEsFjNjSqURwO6cAn1NCbMtb9D+rsepfMP2TCs5BXaiTLasSj+vBFkXx1qBS3fD4+e01CPnJ76n84kmCHIOsucZkmj1cFOzvLCwYMrm1et1rK6u7unt9ZvbB0UCDxMjE+Tr168Pff+9732vhzAfBYLBINLpNM6dO4crV67g7W9/OzKZDHq9HiqVCoDdWh16bhqNhie8wAFUq9VMViYJKQ0WPdCNRsOcW40mwU4djUbxtre9zeiBNFGGA4nvcbXa7XaN0Zyfn8fKygreeOMNrK2toVarAYDRRDtj5HCSYXsZbO/MoDDeqVOn8OCDDyKRSHiMjibm8VjbQ2t7fv20bv1+37MFNI2Faol5PTWunEtIWJPJpPFy2XWTSVyBnXlG8yAYwtXrMNeC5+31esaDRrJAYq6L8mKxiFwuh16vZ2pF05FgP4NBcHIwh2E4SI+h9rPFxUWcPn3alD1kXx/WBltSBXgXxSTOSjAB7CLTPA8XufZcovOAzmU24dbP6qYe/F+jVySmKqFSEk7pV7PZNJyGcxfnFYJ5DJRqcct6tt2WgfIZMTq1tLSETCZjJKyaGDgu/OaTw8DUNgp53/veN61TjQ12pnA4jM9+9rPIZrMe7woNGI2VrRckEWUYsl6vo1Qqmdd6vZ4p1s8OUSwWPbWR2ZFZOBvYMWxq0AgOVBopfp4dVvVOgUDAtP3y5ctYWFjA1atXcfPmTfR6PTQaDRSLxV0hFWeUHO4nDCLJiUQCy8vLHlkB37M9xPzfNn7AzrazNmxjoRIMvzKPmthCg0jiyqo3mo2uXmjOCRzr6u1S3SENFa9Jj3QsFjNVfDj3EHQOcJcsbmEdjUbRarUmmktmMWzqcPLBvppMJpFOp80Ypa33G99+xJjQMe0nWfA7nue0I7vavmGft4knr6m78ylhJ89Q+Zgu4DVPga8B3oi3n3ebzjwl9LZDTgk92wTcqxQSi8UMTzpunGRsgnz79m3kcjmkUinP6+12G//n//yfQ/ci68run/7Tf2r0MsDODngkmPS4kvhSGsHzaC1DnqPdbqNer3u8vZFIBOfOnTMaJl0p2oaQ16pUKh45B6+hZeXYwVWzw3NoKabFxUUsLCzgne98JzY3N1GpVLC+vo7XXnsNtVrNd9c/B4eTBO3bfp4gfT+VSuGBBx7wJLv4HUujYCfS2SRZQ6A0HLo5hy210E2FNMSq5ZRSqRQSiYSnooSGPFX6pZrDQOBeEo0m0tAbZM9HAAxBTiQSu47hznucG2u1GpaXl1GpVFCr1TweslHnlnHmoONmPB1Gw2F+pzpmAGB5eRlnz571EElb90uO4Cez4LjThS/PoxEjW9ao7bBzENSz6/dsdNGri2BeSxe1Oobn5ubMHMIkZH6Wu9/Rg0w+Uq/XjSyLnm51ErCeui1R5blJxrW4gXqkl5eXcenSJVy9enXXhkzHASMT5NXVVfz0T/80fvCDHyAQCODv//2/j6efftoQ5e3tbXzkIx/xfZDThK0VBO51mvn5eVPChV8E9bvJZNJ0aIYi1esD7IQt1Tgx/KnhUnZQuzSMEl8AnjawM2gdRT4nDZ+wY7Fqhnqh7NUX2zA/P49sNotEIoFgMIhr166hUCh4ai46OJxk+Onb9LW5uTlPArHfmLDJqE1mgd276qm0giFIFty3d9hTj43OMSS9JK52dQw1supRstumicScm+hB1mfD3fk4v6hhp6dZ58FMJoP5+XnfUlNubnGYFPuNLIzy+X6/bzTI2l9J/lS/y3NyHGgUeNCPXmfYa/b7NkH2G0d21Qq2lR5iQucLlUxpzXZ+DoCprMNFu0rDbE+33xxjV9Dg6/yt0SzmX2UyGU9p3eM0b4xMkH/lV34FwWAQ//f//l8UCgX8yq/8Cj7ykY/gf/yP/4H5+XkAh7MqsFd0/LteryMejxvCyw5AwswyR+FwGL1ezxgzuwOruJ0rpG63a0IFmu1K+YWdAUvvMDumDgy2oVKpeLZ85fVs49pqtdBsNlGtVs3OfupJojCe7WFZpnw+f+DfhYPDUWEYQdOJnaSQYT71LimU/KrXR0kuP6+VbTiOq9Wq8SCzzrJ6d7jw1V2tSGJjsZipssPNBtTzzEU9F/S2/ELvWb1catD4t2ac6/zJZ8mcBxrb8+fPIxQK4YUXXvAQiYPAcTKcDqNjHGnBtMA+nUgkkMlkPOSYziOOT1sSycQzwJvf45dwaxNcHZe23bffU2+2PU7V+caxyEh4IpEw79nb3qsH2a6JzFK1uvER28FraiIfnw95hnIvdSwq/9F5d25uDtlsFmfOnMGtW7dQr9dRrVY9885RYJz5a2SC/Ed/9Ef4r//1v+Lxxx8HADz77LP4xCc+gY9+9KP44z/+47EvPCn8Hmy/f2/HmnA4jHq9bjqt7kJDgqwdW8Ot/NLYSdQbA8BsJMLOGo1Gd2V12qFZ3UWP7QF2OjwNrX09LQ9FEs0Ozs+o8J+DgUa4UCigUCh4OrSDw3HHXvOL7QnSkCOJqc4JhC2VUAOgBoGGjQvgTqeDQqGASqVikoABeBJl7KRd9UAzsYalqOi95TWp+6VRYwUczinaZtVUaghWja794/f8bCLBElnxeNzMoYO+GzfPOMwKOMZjsRgSiYQnwdVPCmknupJEax10nRf8+rrtgbWJo15Pybgd8WI7eQydZhrR5vEqG7Mj3lqdg/MhI1zkM7VazbOIp8OOz0Llpna1D22rXV6O804sFkM6ncYDDzyAUCiEN99809dBcVgYd54amSAXi0XjKQbulU/5/d//fXziE5/ARz7yEfzO7/zOeC2dMnS1ptnp2gk1bGGvfLQ+qN8KTrecZrIPO5teWweUvU2kGlcaMd0xS40eSfGgrHvVOfJ8yWQSS0tLWF1d9YRRnPFyOO4YlRzzb47xVCplIj92yFDHphJNJYo0FBybapBIkPP5PCqVCkKhkIliqSZP5x69FgkxCbJKtHQO4FjmAl9Dw7YXCtgpNzmIECvJHjQv8Jw0cPF43NRpH/e7cXAgDtMWBQIBU2KV19VxoDIHtbOqL7Z1s6pdHnRNAL7zjUZp7LGrY5+fV68y5xNKPdWhp5EjEmTOLYx0sW3kGiw6oHMU8yhU1qnJebrw1vtVhx2vwXuNxWJIJpM4ffo0QqEQbt265ZF0TLsvTHsuGpkgX758GS+88ALe9ra37Xx4bg7f+ta38IlPfAJ/62/9rak2bBAGrdwoDteEFmCnMwIwxku9udrRSI4Jan0ymYxHptFqtbC2tub5MqjtI1kNBAIolUqe1WOv10MymTQDlcRbd+orl8uewcKOV61WPeRYt43kc4nFYpifn8f8/DwSiQRKpdJQI+jgcFxge1nsPu032SaTSTz00ENIp9MmeVWNHz1EuvC0PTc6vjRpxZ6IuaVqKBRCo9FAs9k0iXMsq1QsFs14pmc7lUohnU6bTHutvcwx7uftpWeapJ1RKHuhYCcb6vyoNVF7vR7q9bqnbjLvtd1uI5fLodvtolqtTvR9OTgcJjFWrawm5Ou4ob2nB5nVITje+FkdR/b7tgxLZRScazjOeByj3LY3mtyBBJbzCUs/hsNhRKNRT4EEJaW8B5Jov+eijrler4d4PG6i7wBMtS/lLPSm63jWXDMl84FAwES+lJNFo1EjPbPLw80yRibIf+Nv/A385m/+Jj7+8Y97T/CXJPnjH/84bt++PfUGDoN2SM3aDAbvbcTRbDbNcbqq0S+GX2a5XDZljdSLw2PVkFITzLBtMBg021Szk4bDYU9IlAOGlSyUqJOcc0ODSCSCSqWyq66p1iH084bFYjH0+32cO3cOpVIJzz//vBnEx6EzOjgMw16SIX2Per2FhQVTmcGWQfl5UVXLa48xYCcipZpelUtpbVEeq8m3JLJaEScWi/lKtuy2aXvtv+2wsd1m3psSZPVm0xCS6KvXnJ5kGt1Rya+bcxyIw+gLfuNYbS3/t6NE6hWmrda5wq7fq+SQn7XHHj/HChCdTsdwFK0pTmhpNM4rBHMoSJDj8bhnniPxVYkoP29Hw8gf1CNNjzPbbD87LUtpL8D9ngvL6el7ugnS3Nycp4LYJBh1DlIHxyQYmSD/6q/+qimTtuskc3P4vd/7Pdy5c2eiRkwKe0VDD6+KzAF4vmSGRbe3t1EqlVAsFk34MJlM4ty5czh//rxJiOOXDdzruFx1srPrLlX6Y3ce/m9vIUsPMjvl2toacrmcMejs+PF4fNfKVoX3PFcoFML58+fRbrfx4osvOg+yw4mCbQTZv1X/x8ViMpn0bKNsT5YqqeB7GubU4zkP2Mbw9OnTSKVS2NzcNCRY8wZ4nVarZUrAMRkvnU5jYWEBuVwO6XTaLOppdDU3wSbsdjtVvmHvrOe3mCbx1bmEW1OXy2WzRTXnNiYp7/WdODj4wV7YKmmbpP8MI0i2BpjH01HG/23vLwDPopaLRo066fl5DvUq8/zdbtdEjbjo1Gvw87qZkJJd5R+s0MVj0+n0rugQk255TibbKfkn/+l0OmajILaNOm31ipMs8xg7gmc/Uzu5j4v/WCxmFgoLCwsIBoOeErnTgj3H2bZC2z4qRibIc3NzyGQyQ99/8MEHR77wpBg0oOjFVY8IOzbLHV27dg1bW1sol8uoVCpoNBrodDqmMzabTTQaDeTzeVy8eBGLi4sAdsKuSpTZ2fQ1NaDsWAzHqq6ZbaxWq0a/GAze22mv1+shkUh4Enb4GQ3z6Hn6/b4pIUfPFEMapVJprGcLOIPncHzBxDhqjwH/DQBszwp/+xldDdnqD8cs65SzfjmTXeglYQm4TqdjQrz0Bml5N9sI2Uk+el1bdmFHpey22/cD7ETF+JvzH0m+GkFGx/R6Dg6zBnss22Par9+SRKrHWH/4GqGOKv5PYqmRl1arZSLBumhV6ZZ6t5UnMPGeJSDJCSKRiJFuqWebDjtGsmw9s851fF+3t6fjT+cgWyNt8wN1TqhURaNrnDftesn6fR3EXDKtc05tJ73DhHpMCC2dxJBgq9VCuVzG9vY2VldXcffuXWO0uDLUXewYKi2Xy8jn88b7ZK92+YWzwzBkoGGDXq+3a7c8DiStTxoKhbC0tIRUKoVAIIBGo2FKR2lJN14HGDzIKekgQX7kkUfwZ3/2ZwM/Y8MZPYdZgz0p64Rqe6ToOY7H4zh79iwymYxnG3hO1oQ9ZwDYFZpUeYL+cCFKT8zi4iKq1SpKpRI6nQ6q1arRDXI81+t1hEIh493OZrNIp9NIJpPGy01DwzbzeprIQ+Nje734w+iTTez1mZIQ02Pd7XZRKpVQq9VQKBTM1rC8f3q9HRwmwWHaFnshqWTRz2tJ7y6dTM1m0+M55ljRZHo9Fx1oSlrb7Taq1Sq2t7fNuFRZlUah1XNMwqoyikwmY3a+ZN6CyqEAGE7B82ibdPMPnbv6/b6ZL5PJpPEua/4BF/x8nuq15jPgPWiScDgcNtV8AJj/+dpeScKTYNi5DlxiMUsY5A1S48UVGHCvAsedO3dQLBYNWeU5ODh0+8Z6vY58Po+FhQVkMpmhXiUlyqrxA+AxZtoedhAmDVJ7TI3yysqKp6PyvNQ8AztZsrxv9TAD9wzaAw88gEgkYrTYDg4nHel0GqlUCvF43FOLGPCGUfmjSS78bYcvlajyPErSg8EgFhcXMTc3h0qlgk6ng1qthmQyCQAmSa/RaBjvD40gt5jmeKYx0+urEeJCnsZK26TeYB5vy0/0PPR0kyDX6/Vd0gpbl+jgMC1Mqz9xLGqEg+ME2Kn+pBERfm4QcdLFpV5HP+Mnf+KYocOMji4dS+QNJO+ag6ReYl6TOQp8X2uYazsG3YcfOM9xLuBcOTc358nd4pxC0q1eayXgnJM0os55lPOsapCPy3xyLAnyINADrIYvELiXqc6JH/CGLrUaBA0BSXK9Xjclovy8ORxEHIi6avMzTBp60Lqn7DCUSND7DXiNuoL3qFDPOjNh5+fnsbq6ekBP3MHh4DHK6p/jMZVKIZfLIR6Pe8oh6bmUIOuEbodk+b49kfuN8YWFBQD3dhylFpkeYeqPmefADHoSZC6y1cvNH5VO2ARZZV80ZkoG/ELMavhJ3OklYhUL9aDZCUp+Rs1Fnhz2g4MIs1MDy3ExLC9ICbbaaiWedmIvsJPzo58jP1CZleYiqHNOHWi6A54fQY7H42bsa4UK3e5atc1siz329dr8nBJknR9VKsLr6u7DPJYOSZ2feJ88ht+FXX5OOcu0CLNff5q0jx1Lgqw3q0YglUp5qj/wi2R1CoZb1SOjG3/o/6wmYXtdFKo90o5P42OHQLkKC4fDRr7BkEO9Xkez2UStVkOz2TQDhR2Mn9cO6vels2OTbD/44INYW1vbRbAdHI4DxpnUuMhsNpvGI9rpdLCwsODZBVM9on7SCQ2V0gDQMNKjwutxfmCije52t76+buRXasi47So9Ms1mE6VSyXiZtYqESqz4t95noVDwvN7v9z2OAC3ebxNtlYR0Oh3TBtUfA/eMaTqdRrlcnuh7cXAYRlCm1Zf0PKdOnTLJ9ranl+1RgsZkVa1WRaI7jLzZ0R5yD5JcrfzCLZcpl9DKDjxWt5vXdur4pTSq2WyiXq97iHe73TZRKnUEaFRMF90kx9ls1iyemaTLRTP10yq5suUUbDO947yXVCplHAXtdhunT59Gt9vFjRs39iTE0yDN++1bExHkQqGA733ve1hfX99FvJ588sl9NWgSqO5GVzDxeByXL1/GxYsXkU6n8dxzz3nqfAIw27+2Wi3jcT19+jTOnDmDbDZriDMT7mg4UqmUp/PRS0QvzsrKitEcUqu4vLyMy5cvY2FhAefPnzcyi36/j+vXr5vycf1+31StYLhGtdBsOw02/yboqWbH58Jh2pofB4dZAxeHNGy6+6TtHSIGeVn8zg14JRp2kq4uUGu12i5ZlCaq8HitwMNMclvbpxErJev1et28Ry8OE4NJ9tkGGny+x23s+bxY81gTktgGNdzqCHDzicOsod/vI5lM4tSpU8ZucszYUV0dx2rPbSnGXtfjb11Ukvjy9WQyafIPNFGOP+QKGuG2r6H5BirD5DU0UV+JsJ/t53Nh1Jp/c75k0QDdJVSh85F6pFVuEQ6HDccJBALmGZxYicW3v/1t/OzP/iwqlcoufW4gEDgwgqyueH0NuPelnDp1ypPURs8MyeTy8jL+9t/+2yiXy1hbW8PGxgZKpRLu3r1rjEKr1UKhUDChR80cBXZWXiwVx9UcjVMikUCn00GxWAQAU0GCnl+GfdPpNObm5tBoNFCr1Yxh5KqN98RSLVo1o91uG720erIYUqK3iZ4s1Tc6Y+Zw3DGMvKqnRTX7gUDAFKhXz8owKYWSRHsRqnpCPb/WWmdGOPV7jEpls1ksLCyYCJWSXW27yidoNFXqxePUA0zDw5J2jIwxEkXpGM/JuUcNrpa21OfB83ButXXOCjfXOPjBXlz5vb7f8xILCwt49NFHzbgEdsgloVUX+L96SSm7pH2m3bWJncouOFZ5zVOnTpn3crmciRhrxEq9sBxnKt3wSw5Ur7DtCdco17DnSt7AZ8TPUs6xvLyMSqXiyWFSMs5754+9mFcPNZ97JpNBKpXyTR72a980sJ/+NTZB/uVf/mX8/M//PL761a8ikUhMdNFJMMxr0ev1DLlkZ6YGkF9QJBJBMplEMpnEmTNnANzb0aZYLOL111/HjRs3jGGg17bZbBophNZP7Ha7xltje6iYrU4DrV5fhj4p+VBvUKvVQjweR7PZRDweNxuWcMAyVKsDgcZKV7AcLFwVkuQ7ONwPsI0DvSk2dB6xf6vBUa8wDamdvMbz6YSvXmomqHDc0wBp+JTeW1uapedXY05vEUOgJLck5nb2Po0ajR1DsrY3Xa9tG0LVFtpeZoUjxw4HDZs82f+zBq89HrVv0l7yPR1HHJu6gPQbK359XcmvVqyil9hOZvMjgioLUU8wsDPHadvYHo599ULbzkXbo6zyUfIWknVWxuJ7mnxn3y/PPez70vYeB4xNkO/cuYPPf/7zh0qOFcN0KSTFADylmwAYzR6wU0UiFAohk8kgnU5jeXnZVLpgePTatWvo9Xo4deqUMQzsJFxZkviyXBO92alUCv1+32zNymtWq1UPiWbnI2Gmdkc3ItHVqxon24ByANKLTNlINptFsVh0OmSHEwtOzMwzYFJJLBbzLFSVFKr3lgTUlioRfgteDWfaEghGjQCYherS0hIWFxfN7n4ATBKPaguVPOs51RAxEbBQKJg5gpnxGt6kIeU9VKtVj8eb9ZttfaJ6kznnUrZGraODw7QwTW8h+z6T3TnWabeVNAI7i0XmLpAM9/t9I5vUhSs/r/ZXnVNKkNU5pdIDe0GtJFjrJmtESMk8k35ZppFzk1alsaNOSozV+2wTZfKHRCJhCLJ61Xku+x54/5rroGSa8w+32rajCcO43Tjf/zQxNkH+qZ/6KfzZn/0ZLl++PNWGjIpBD0D39+aXpHIFElnb6xoMBpHL5RAMBpFOp1EqlVAul1Gr1VCpVLC2toZut4tcLucpcF2r1dDtdo13Oh6PI5PJIB6PY3FxEe12G5FIBOvr62i1WohEIqaeIUOrmv3J0AOJNo1QrVZDtVo15Nmuc+pX+k2fk2476eBwUsH+zqoMlFRoyJGTMo0osLsuqi2nUDmDHRLUBS6P1QQbGqZYLIZUKoXFxUXkcjmkUqldyTh++kA1GJwvtFIO5zjmR9BQ8v5sSQkjYxrWjUQixoBx5z7OL+oh58KCO25RquG8xQ6zAiVsSgx13KqtVHtJe6syDHpkNXHPJnH2Oez3tOKE7S3Wccy/VVM8TD+s59Tz6T3SW23PXbYcTcms8grOE+pd1nbaXIvzCCUirOJBpwUdg4yk7xUFmAWMxJyeeeYZ8/ff/Jt/E//kn/wTvPTSS7hy5cqu8P0TTzwx3RaOAEos1P2vkgabIGrnY+eJxWI4e/YsTp06hWq1iq2tLeTzeYRCIdTrdePh5fVoPChuZ+iUYVSS3oWFBZOsl8lksLy8jEQi4amaoYO62+2azE+tm8ptvtXY+4Vn1BNEw8nEPweHkwxKnGgg+LdfWE/Hjv2enYBnGyiOUzvMqN4Xnocav2Qyifn5ebMQphFRqJfbDova+j6VeShB1mO0Iofqm5k8pHkO/X7fEGQ6EewQbyAQQCKRMHPRtLEfraCDA8G+bzuMdKHLsas2nQtD5QYkzZRr2jtK8tx+ZI/2nRiWfKfjVHW+tpfX71418mUTa50P7NJruhjQscd2qiSEcwsJvHrUWe6O5yInYkUcHkuyTKnoQcDPC72fOWUkgvx3/s7f2fXaV77ylV2vBQKBQ9eX8OYZmuTqBtjZfpG7QGkWthoXYIdYsgRbKpXCww8/jF6vh0Kh4Mkw7fXubQdNo8IBGQgEjCc5EongzJkzuHDhgglT6CYBvCZDIbwXLY1Cgtzr9bC9vY12u222yKa2GoAZvHY4o1QqYWVlBXfv3j1Wuh8Hh0mg/Z/eGybGAP5bp9phR/W+6Hl1ktXzcAwzFJtOp81xLL9EzzHLOzGcqx4arZtK75Z6hAm2i1KsaDTqqWdse7lUCsH5KRqNIpVKeRJ/OWfqcySpVsM9it5wUuz3fI5gzz4O6jtSIqxeTdsjq9IDmzgCO2PdTl6lF5SLY3qodXzoPKELWyWSSkq1vf1+3+OtrVQqZnyzlBuvHw6HjRxLobbfloAEAgFPFE1lXTwv20JvMOUbusseI0e8R7/8BUapWCRBvdLLy8umYIFytWlhFOI9DjkfiSAfB+2qTtqq/WFHIElmZ9RySqrzYcfXEGM6nfaEZrkQaDabhvim02nkcrldXmIm0XBgNJtNk9WpA01DIRTaRyIRtNttk2jH42mQWbqNA0h3v2o0GlhbW8OtW7eOxffn4DANqBfE3sHSjhrR86KvqaTBz+AN8tSQiHLiVw8O5x6VO9naYACGqNLw7mVYmUjTaDR2hXr1eWgSsYZOeW3bG+7nYVINooPDfnBQfUjJoL3IA3Ynp/FYHm8vCJVgMw+IttzW9QK7JVcKW5/M9tiSSCbqKnFVSQLPr8mEfh5se+7ycwZwjNuw39PdgrUykN/3qM/SfvbAPe8yc6xsj/osLm6PpTjVdqPbup9gMGg23GCyTigUws2bN004kkW2y+UyFhcXjR4vnU4bzwoNnHpP1LNEA8zyTYuLi0in08hms4bg2iWTlKRre5VAa4g0FoshnU7j7NmzZieucrnskY/0ej1DzJlo0Gg08Oabb+IP/uAPjHfLweGkg5M/t2fVzTiUIAPweI5svWIoFDKE1dYlsh46sGP46C0+c+aM+RzHPomxyipodGlsVRJlSyl0Ea2vRyIRnD17FrFYDOFw2Hi9uNinYdN75lzGY/wias1m05xLPeV8ZoNIgIPDUYNjlzaQY0xlQ4B3DwGG/svlsunrtP+cO7gBWafTMdFoTV6zF9Cca2zPMuCVdfAz6pDjnBCPx42dpzdZ5w86AzmGuWjWec6+T5Js21lgg3MNy1NygcA2cl7UbajVaw7sOAHUQch5MpPJmByrw4ByrnGI+NgE+fOf/zweeughfP7zn/e8/m/+zb/BG2+8gX/1r/7VuKfcN9jBGKbsdrseglwul7GxsYHbt297ag3zoYXDYSSTSUOS1XCooaBXSbWLwWDQGMRAIGA0yCql4GfY8amZZjYnyycxm1xXbOygi4uL6Pf7OHv27K5wKO89kUgY49ZoNDA3N4ePfexj+MY3vmEGvIPDSQfJoXqPAa9nRHWI+j4/r4bUPm6QNzUYDJrSUlpNwq6G0ev1DMnm2Ae8HmRem3/T66tGPhC4lwPBrWgZCSMx5znUaHNXK70nmwhzzlMjq+dy84jDrEHD+NTA6uKWpE4rVGj/ZlUIe0HIz5Nb6EYeGkHSH9sj6jeH2J9XvhEIBNBoNDwcgCSXMlG2Sb3KfmNT5zytusPxzWvYETRtv24eopubKC+y5xS9P1umRW7Eue2wpVFTl1gofu/3fs+TtEd84AMfwNe+9rUjIcj2DZOoJpNJADsZ4KdPn/Zs5coVTiqV8uiI7dWfhmM1HKp6xWq1imq1araLpYaHHmF6q9ip2ck56HgODmQSdbaFJeQAb7Fu1RBypcbOFolETGmp9fX1qet9HBxmDRyvqVTKJMP5EV57UqZHyDZYNC76GpNS9LycI7gRgGp/NSxKFItFY8wplaDBoeHg3MFaqmqotA57KpUCcG9jIkaLdH7Q9vN+uIhXQkwjql4dXVCwTU6y5TCL4IKTZRRphzXkr2OQ45naWm7+pf2dDjCWZSXZi0ajiMfjnkoxfiRP5xCdd3hdW56hxFejObrBWCwW85BrzVOw5y2Oad4Do96cnxjRJkfRxblKLHgtSlcZHVfizM/xfuwKICy1S36jVXz0eU0qwTkI6c7YBHlrawvZbHbX65lMBpubm1Np1KTQh0vSyfAKa3jSw0zDQB2xEmFg904/JJ+8DkMHDMUywUYNqq5egZ3VKMO0g9pC75IacXqjdQXJ95Qc6wDkvbMCx2Gv1BwcjgLBYBDz8/NDE/R0fAIwmjiOL1vTqxO/avBUuqSGQr3GNM7UCmt9Us4hutudeqiYT0EDo+ckQWbyXzAYNNegMfLTGPO66s1ut9vI5/OoVquoVCq7SsopCbDnJgeHo4R6amOxGC5evGgS3QnaS3sM9Xr38nWq1SrK5bLnvDpGSVo5JskZdDxzXOjC2ZZ1ATsVNWzZo016deySpLK2OecnW8urxJ6v6Z4KfmXhuIDQxEPOHzofATBzgu72x8/pPKMORfUg8964k6hWv2B7R/2+/V4fpIueFGMT5Iceegh/+Id/iM997nOe1//7f//vR1Yb2S/MoR5g/cJZckQ7B7CbEOsXZxtT22DyNWr42KnU8KlOiOSaHUo9xYB/bUOGRv1K1vE+2+22qZRB0k7Nz0GsrhwcZg0kt7lcDtls1nhbdILmWFPvhm4BO0gaoQtT/d++thLtfr9vpFSaAc7jGPbkuFZiTt0yr6/SCf6mvpm78/Fa9CRr5roaQtVXc7Gez+dN3XVWybA977owdyTZYVrYj+dQzxGNRnHhwgWT4K6LTY519ll6M8vlMur1uomc0LEEwGz4o7xBd7QFdhNTLbumY8aO4ujc4bcAJ5+gDe/1eqZuOT3BjGZpfgJhL/JtHqDzAjXGwE4pWd6nzgN0ADDipXOmFgng+dWTzGfPSJu9Q/E0oXzLfm0cjE2Qf+mXfgmf+9znsLGxgY9+9KMAgD/+4z/Gv/yX//JI5BWEhlH4N8EvD/BmhNNosSaoXZdUNTZqGPjgNTOdIc9arWbeJ0Fmx2VnCQQCu1ZzAIy3yPYiadKNHcrR6xSLRTOAmITY6XTMClIHqYPDSYWONa25yXFIA0ZDoPo4TWjRCV/HHL02SoiVgGo7dLxpWSYtNaWVJbQuOpNy7PKVqt3Tc+nufPbOmyQDGhbl/fJemeDLNmmNexpl7kw4i3Bk/f4Gx0AulzM2nvIDOwdHCamWOWTfB3Y22lACqgm/pVLJM/btKIsfZ1Dbq95W/s2kN92sB9ipJMF7ImfhHKY1jrUdtnNAn4ESebuqDZ146h1WBx5/WGpWI9dsE+ccrX9cLpdRLpdx+/ZtbG1teXbxnDamwXPGJsg///M/j2aziV/91V/FP/tn/wwAcOnSJfzGb/wGnnzyyX03aFLYX5BKDuhFsjc10U7MhD6F1lNWUq06JJZ4W15expkzZ8zW1Wq8/EKnupq0V3J25qfuCMiwh65mmWW7tbVlvFT1et0cG4/HD/TZOzjMAuzQpu3t5G/N4vYzKDzWDoEC2EUO1Uuj3hNd/NrhVg3N2hphJc4q9VDvr17XDq+qJIOLd03iIVHQ0pC2VpBGUb07bJ/qFPXeHDl12A/2Q2bssc3a4CqFIvyix9q/7QgSx45yA55Xo7S6GFVpki1L0nYQShA5RvV6/FvJPhfAen5+3vZa68LfJqOc73SO0XP6XQPYmX9U/6zvqyNSSXK9XkelUkGxWPTUd57VaNREZd5+4Rd+Ab/wC7+AjY0NxONxkyRyWLAHE78o9eYwNKKGp9FoeEgyQ5Ekqo1Gw9TpY8cnKdVORgOSTCZx6dIlnDt3zpR4I2lmPdRut4tSqYRCoYBarWYMGDsQCS61TUqktbNvbGyYwW+Xb6IxPHv2rCkizhVuoVDA3bt3D2yV5uAwa+j37+08yWx2OzyoEgYlxTqeVGNIskhjwDwBelhUb9doNHYloKihVo+O6hVp8Pi3nlOz1TWcqa9p7WT1SHPXPCb8aXKyXX6J8xvbrwSZ4dRUKoVCobDrmc+qgXOYLdgL1mlAxzfLoQE7G/UA8OyIx3ZwDNPTSdvMalbkFN1u1ySl6SKzWq0iGAxia2vLjBm9R1tqoG21JZu2NEKlUaoj1gU8y9HaZJ4OQo5J5UG8nr1I4NxA4m/nOKm+WBcSGp3mPdE5V6lUjHSlVCqhWq3i6tWrqFQq2NjY8Dyfcb9v+zMHNfdMXAd5Y2MDr776KgDg0UcfxdLS0tQaNQy2vED/1pJJfE21QxqmZLZ2qVQyg0Y9vFp/kO9pp4jH43jLW96Chx9+GKlUCul02pRaonHRjG96qalP1BUaOzSPo2FWbzgNonqReI920o8OvLt37yKfz3sGloPDSQbHGo0EoR5X9dKqB0pzAvgaF64acuW41RAsr63tGNQ+jSDZXiGdr9SQ0YCpweV8pJEqvVfbeKpHScmx3qv9rNhWEghbGubgcNTwG8u2BEmjR/bxtMuRSMRTQx3YWeBqpIWLTNZPTiQSJhl+0Li3vdnA7jlikAyDC3e2gd5rEnt13pEzcJyqfETvx5aAaXuG3YPdTl3I89p0/HG76WKxiHK5jEqlglqt5iHgh4VJrjc2Qa5Wq/jFX/xFfOMb3zBfXigUwpNPPomvf/3rSCQSYzdiHAz6Um0CqAW0taNoKIHHMbHNLtkSiURMp2dxcCXH/KxqkdXIlEolNJtNlMtl5PN51Ot1pFIp06mj0agJPwA7iTjafhpAlWNoCIVGVeuk8v1KpYLXX3/dSC0cHO4HMHqSSCR21UG2k+CU6AI7xgPALmJJr5J6c+yNMzgu/eQTHMOqaVZDplEwGmSSc9vbpDttxeNxY/jV+JO8s41q1OyEGl6f85d+pt/fyZ7nrp52WNXNLw6HCZsDAF4nlkqOtE9rBQqtDEHvM51duskQCTFrJdOeciOSZrOJ5eVlE7lRkszxq+PRllzY0gV7YaqLdc4tvEdyF32v2WyiWq2aeYqOuXg8bgisjm99drZDgb/tH5tXqea4Wq2i0WigVCoZD/LNmzexvb2NQqGwS5s9DewVwZr0OhMl6f3Jn/wJvv3tb+Ov/bW/BgD47ne/i89//vP45V/+ZfzGb/zGRA3ZLzjZ64pLDVS/3/d0DA4IrhYBmPc5kGKxGHK5HMLhMObn5438Ip1O44EHHjA720UiEY9namVlBdVqFevr6wB2yrD1+30kEgljkHktDadSLqGeGg5ieqSVKKtBVMPLwVqr1Zy8wuG+Qr/fN54K21OiBoGTPBe3KstS0CDxGIZb6fWl12aYXk8TcAGY66iXSo2Geoxo4NTAVqtVDxFXzaHmMQzy1NiSDWAnfGt7nXk850SVeDk4jIOD6jc6ttUOUhNMcPyoU4mJ9LFYDMlkEvF43MizVFtcr9cRiUTMTrb6eqlU8lR4UPKr8gR1wCkBVa2zLnCVr+iClXMDF9iUYfR69zYhI0EOBAImaZfzmzoK1avMMT9ITqF6Y4ILCHqN6VUnMa5Wq6jVaqhUKqhWq1Mjx5NEw/0WJHthoo1C/st/+S/48Ic/bF772Mc+hng8jk9+8pNHRpCBnfIk7JSq4wF2yzOYlW2L4Nkp4/E4kskkotEoFhYWkEgkzNbPzCKlcW21WiZB7tVXX8X6+jqq1aoZcLpLn2oNdccvu/IGByjbZmuU9V70b5L/RCKBpaUl3Llzxxkzh/sGJMi6G5WGV2lQNEOci2IlyPzN99TTRMNgE1z9URmEEmTVHw4Kp6qcipU4lASUSiWPh8n26FCWpcbOfg78n4aUhpP3rMcBMATZr1yUg8NRQcc4sJMgpiQZ2JFRqG5XbXA2m0U8HjdREvUEM4IUDoexvr5u5go64SqVCvr9vqm7rmUebacWz8c2255u3ofmPuiiVecQRp9JbEnYa7WaaQOPZek7Ss/0fFysq9dcny/nFnu+0kRfloskQebmaZVKxTgsiINaJE0bYxPkWq2G06dP73r91KlTh7avtg07fGl7j4GdEmp8n4PIzsjWDqOhhFarZQxkvV43IUd2+EKhgM3NTdTrddy5cwfVahUAkMvlkEgkEI1GTW1RYGcAxWIxjxHTRAJgJxTKzs326ICyQ7ScAGKxGB599FG88MILzog53FfguLYT8FRKpZ4e9dQAO/kIGsXhObghEMebkkidW0iauTCmQQZ29M6aM6FzD9tpv85FMqNJ2iYadRqser3uIcqqV2Zb2RYN5eo9aXv0eTg4zApoE3O5nJEwkrjpYlj5gJJMOq8YCbYj0QT/zmazJorUarVQKpUQj8eRzWaNE0091XY1DeUpSuo0oqMRK84znKP4t30N3psusHXRYPMze9E9iGAqCeazI9GmzKTVaqFWq6HVapmKWn/xF39hJCnkQ/z8QcCW3fjJcMbF2AT5/e9/P770pS/hG9/4hlkt1et1fPnLX8b73//+fTVmFAx6uOxc1OHSUCipJDnVQaJhCr7P8xG2gVVNYK1Ww927d7G2tmbqEJO4qmeHg1ANJK8TDodNKToaO15Tr8e/1ZDrCpVhH3ZI+zwHscJycJhF6GIX2L2ZEAmurVnUcanwG4v2HKGwtcaEeoI4T/Bc6g3mdejZ1TJNfE89PSTi6tnS6jt++mSeT++Rv/0WFn734+AwDqbZf9SJFQgEkEwmjcNJxwXgLbGon1fPqXp9+b6fPJELa3piNbnP1jkzYjXsWfhpf3WesLXVqh1WLy7Jve0c5PXVK2wvfAkl7/ZiXSPyTL6rVqvmvXK5bHYlLBQK2NraMrILv2sdJPyuNQkHGpsg/+t//a/xUz/1Uzh//jx+7Md+DADw/PPPIxaL4Tvf+c64p5sKbOOlWaZqENmxtS5fOp32yBh4vmDw3m5T1WrVCPFrtZqRXXBFxD3ONdGO9f14jlarhWg0ajqvemhUbsH6jbplNT/TarV2ecW0oDnDLazOUS6Xsbm5if/5P/8nAG9RcAeHkwZ74lMDAnjrFZPgagSJr6mnVxeWtqaQr9vhW44x3XxAx7x6bbWtei5gJxqktVa1Pmq9XjdzA7eXtj1g1Ab6Rdb8kvTUgPA+1euu3nM3lziMikn7yiiERp1GDz30EHK5nMdbqaRXF4s6tpmHRH5gL5YJjnEScZZtbTabyGQyyOVynnwk1QXb8kldFPvdo85XNjj/dLtdU76Rks9gMIharWbapt5mzkc8h60p5jOxS+KFQiEjn+Bx1WoVzWYTxWLRfO7atWvY3t7G7du3TQWLgyLGft5ihS507M+N056xCfI73vEOvP766/hP/+k/4ZVXXgEA/L2/9/fwsz/7s0e+IYUWvddwJI0ASz9p8gr3bFePkG1AWG2i0Whge3vbkNVUKmW0PyTJNJI66DhIeU4N39Iga3iUnUvbCsCUkVNdku6iQyLPbWN/+MMfYm1tzXmOHU48/CZMGiZNtlHip3pkYHcRfTvy4jexqq6Z/wM7YUktzcZdLkk4Oefwf13w6gKa85FKsXSnLS6YNbSqn1WPEOcglXH5edX0GfJ/yjYYqXIk2WEUHLRenTaUpVbVM2tHjmzPqep+7YWw3WaOBR2nujAddg6C88Uwm2x/jv/b85bWTifhpQS0VquZiBLbbj8zu2SsLT8lwQ0Gg0YqwXlme3sb1WoVm5ubZp7Y2NhAsVj0JOMd9zliojrIiUQC/+gf/aNpt2UsqOFSA9fv981WjfS8sNRaOp0G4A2fFotFTxaohl1pvBhK0SSWdrtthPlcwSUSCU8Yk55f3dVHjZUm+XGgxmIx1Ot1c14ay0Ag4FmAUI+k+6fXajXcuXMHGxsbuHnzJl599dWBoRQHh+OMQR4CQuuFqvHieKY3SaUNCiXYflpjDaOqQVRvCw1Fo9Ew3imORZLMRCJhQpZcUJPg0siQ1Ha7XTNX6CLfDgWrdlnR6/XM1q5cWKuRVSKgz4DzVaPRwMbGBmq1mptPHCbCXuPWPnav9ziuWWkqHo+jXq8PXCyrVpefZaKejmOOAZVq6IKbn2e5VjqpyB3sMaXQvCcbPN4v4sWoVL1eN21uNBro9/tIJpMmCl0sFlEqlZBOp01CMaWnhD0vkjBz7uj1eiiXy+aeWMOYnujr169ja2sL165d88i+7IWF33d41HPHOA7DiQjyq6++iq9//et4+eWXAQCPPfYYPve5z+HRRx+d5HT7hq6wgHsPgFUltJOxdItCk1vsgWPrfICdZD8tl8LtphOJhKkFSMOoWkFeu91uG1kECbCGgiORiCHQqqdiFjzJtoaHO50OCoUC7ty5g9XVVaysrKBUKnmej4PDScGg8JkSV0Z9uPBVjR7HDMcfPbKcCzSbm5/nNXSsEvYmIkxYoSSB+QdceFcqFbOAZgQqm816DJntwVWjaRti1S/7aQ2BHYJMOZZW79Dz2ruN8vVOp2PquTs4zAoSiQQymYwZX+y/OmZtr656cfU926Fky4m0ogRzlpjgR6Ksib2jkHzOVQq18Zr0W6/XsbW1ZdpYr9fR6XTMAjwSiaBer5tdgUng9V7se+dcwrZw7wdu6EEZKcd9t9tFoVAwScAacfKL5I2zKJo1TFTm7Wd+5mfw+OOPm6S85557DleuXME3v/lNfPzjH596IxXDtCWqt6NxY8fRUCS/NFv/o+VfVFuopFS9wADMVtvz8/NGm8xwBOsqc1cZNT7aITU7FNjZMpNtVYNse7oYCikWi7h79y5u3bqF9fV1FAqFY9spHRwmhb2wbTQa5nVgZ7ypppeLWUafgB3jykWwfl69Lzo2mWzLCJCWPGJtVZLtcrlspFRMcKFsy97cRAkyf9Oo2fOXLqg5j+n80mg0jIyLZas0YVATgG1QusX7c3A4SnBcJpNJpFIpMyZ00cfoi5JAtaF+ybj6vv6tYwSAseWdTseQZF7XvibPrb9t+65cgE405iFwUVsul3Hz5k2z+K7Vap6d8kiotUY7F+120q7yIB37CwsLiEQinpwmXeyrXM0PszY37Kc9YxPkp556Cl/84hfxla98xfP6l770JTz11FMHTpCH3Sx1dUy+SSaTHkOnFSTYGbRWKkXo9BCzdiC1xayXyA5Iz3Eul8PCwoKpd8yEvtu3b3sG2NzcHFKpFHq9niehR1etmoBn78RlPweGW4rFIm7fvo3XXnsNN27cMNmjez0vB4dZxiSeBxoq6n23traQyWSQTqfNXKASC3pwSaRDoZDR/lMeRaPL9jDyo2OYRqfdbqNWq5kSSNVqFcViEfV6HdFo1NQHvXv3rgmV6gJ8fn7eo1tWuZfWJvbzdNlJd5qszPulx0k9Vvb5NVTKebBWqxlybHu7poXj7GlyGB3Tzolh/1Wbx8WubqJjk1Zbf6tlW+0qM3bbGW0CdoiyretXZ5q+pzpfW/fLe1A51vb2Nmq1GtbW1kyFKr5nb2ykpFvvUyUQOm+ovILHrq2teeYZbesk4/MgxrTdh4bNHfuZV8YmyKurq3jyySd3vf6pT30Kv/ZrvzZRIyaFEkx2QIYTksmk8czwdW6DrZ2LHh47xEgyrV6oWCyGQCBgdtRbWFjAhQsXjEFmhis36aCxZbiDeieucAGvLMTWHJJI2+FhruS4uut2u7h16xZefvllFAqFXR5pB4fjhkmNKBeyuuNcNBpFMpk0x+i4UOOp8gTOKeqBVeOjC1rAa4RoULRaTr9/b/OScrmMYrGIra0t1Ot1j7e2VCqhUqmYuYNEWWVX6m3y83bp/3xfZV72rns2Gddz6+cZGVMSMU1MmzQ53D/Qvqzj2K4cQ9jyAsLvOB0XdiSJnllNzFWJFz/DNtiSTZU88EeTd7mQzefzqFQqWF9fN1FybR/bY8tIlFvY3mv7+ekzGMQf7P+P65gdhxeNTZA//OEP40//9E/x0EMPeV7/7ne/iw9+8IPjnm5i6JfOTvjv//2/xz/+x//Y7E8eCoWQyWRMB67VaoaosvOdOnXKJKr0+zs1hO3Qqa48SVoZtlCBOw0OcK+gOFeEzWYTGxsbxrPFTkj5hX4+lUqZQUJDT0NLr1e73Tae4xdffBHf//73dxlHB4f7BTQQrEdKA7O2tmYiN3ydiSuq/1diqxEme4tXYGfHTRsartRQJ+eUjY0NI6vgWNXfN27cQLFYxOOPP46FhQVPsi4TdOnJtok9ACPXsKFVMTQETC84PXCE6prpHSsWiygUCh7D7uAwC6jVasZG07arrFI1+Tp2NffIHs92CTaVZJKQawI9bTrHi60hZhQagMcBpiTZTpLjFs13795FuVz21CEm/P7ei9jyfvyw1+J3UlKsXO0oMe71xybITzzxBL7whS/gBz/4Ad73vvcBuKdB/ta3voUvf/nLeOaZZzzHHhT8OkahUMAf/MEf4Cd/8idx5swZT8jCrhjB19R48DU77AnsrKxYSg2Aqbe4vLxsBonqhdSYkAgz25SDQ8unUO4RCAQ8IRka9Xa7jR/96Ed46aWXsLq6ikKhYDw7zmA5OOwuKaXeU75v6wKV8Kn3lPMDSaTqke3P6a5dnCMajQYajYZZ0PLH1jUTjUbDZKBHIhFPHVEmB2t1HTWW+rfOX2yTerdouJUck9TbORo8L3fMsg30QcHJLRxGhS5CVdpA+6vQse9Xms3vNfu3QiMtSmA59tSLreTXliz4jSueR0vXEsOInl9EaJTPDbvH+xVjE+TPfvazAICnn34aTz/9tO97wI5k4LDATnHz5k185zvfwY//+I/jkUceMeWTSIxZXokaw2w26+lMqvmlR5eyDQCmbBzPoeQ3nU7v8gyzRByfRz6fNzrlRqOBQqGASqViNFSdTgePPfaYZ2CUSiU899xzuHPnjim3wnCnnXzo4HAS4OfVsWH3+UHkjQtXek01IYVlz/QcNK7RaNR4nW05BRff+hp/cyep7e1tT11QJcZKQHkvlHvduHED5XLZyEJoHPkZLrB5PfueWSqOxJpGnwt8ggaciwZ1HvBZcI4pFouoVCp7fgeTYhSvl4ODHxhpWVlZQTabxcLCgum7gHcuUUmTHTnR15TcajKsLYEkOefcwzGuci3d6EslF37eWrZVy87xdRt+i2w/T+2gY4ZhGp5eW55ymBh0j+Mu8McmyAehP5smarUarl+/jl6vh0qlgkuXLnnKFrHTsnNzExBdzXH1p9mgWnBbvcTUMbdaLeRyOWOQ6D3SJJ9+v49isYi5uTlsbGxgc3MTm5ubKBaLxuDWajWsrKwYLxTrLb/66qsolUoDiYMzKA4nDXv1ab/31bDxf5Z8TCaTnvwCkkYlimpUNXGNx/OcaoABmJJxrE26sbFhFrIkq4MMle3hYVJOLBYzr/d6PcTjcUNw1cOtoWSNXnGzAFbq0Llby1WpLETbQW84nQm2F2uU78jBgRhl0TvqecIhtrYAABLpSURBVGywNu/S0hLi8bix2VrJwa7QYuuFdRFK6ALSXoyTQ7A+MMdZNBo144k76dm8ydYla2UMjsdYLIZWq+WpeDXq8xg234yCaX1Xh4mDaO9EdZBnEdrJ2+02rl69ahLWcrkcLl26ZDqwrhDZGenhtb0qdshRk2RIatUrzdqDrBFIgkyyTY/V7du3cfv2bWxsbKBcLhuPc61Ww7Vr1zzhWBXl6706ODjsYJDOTZNZAXhKudGzowaPY5Vl29SzanuSaLx0W9ZGo2ESayjHGsW48dzb29u75qFEIoGlpSVPmUi+pzIS1i3lXKOLfjuzXUmDXo+eZ5WN8ZyHJbFwcPCD3wKNC9vbt2+j1Wrh3LlznhrfwE5/Z7IrX9PzqPYegG8lCLuyQ6vVQqlUMovpbvfefgupVMqUagW8CXM8BxfVtuwpEAiYPIlkMulL4gc9D31tv+N0v58fl7AeJCnf65kNwsgE+WMf+xh+93d/F9lsFgDwta99DZ/5zGeQy+UAAFtbW/jgBz+Il156aeSLTxv2A97c3MQf/uEfIhKJ4IknnsDS0pLZAjoQCJhdZ/i/bvyhnijWJLTDFrFYzFPGhfrBWCyGZrPp2SWLRndtbQ2VSgUbGxtYW1tDuVw2cglqmovF4qE/OweH4wwatlKphEajYbaBV9LrZ1zp5WGEh/KFRqOBRCJhPLC2JpfGTSULlUoFt2/fxptvvomNjY1dHtdBukAbfhU1VlZW0Gq18Nhjj5nkX00WBuBJTgZ2KnrwHlgVh/egjgJgd51lACiXy8jn87sW6o4kO0yCQYvYUWH3PXs81Wo13LhxA7lcDtls1iwodVwAO1ILW+bEY21iy8hMq9Uym/zU63WUy2Vsbm6azyeTScRiMVy4cMHMJ3byvt/40wg1OYMdtfEjvn5tP644SI/1pM9lZIL8ne98x3hDAOCrX/0qPvnJTxqC3Ol08Oqrr07UiGnCb/B0u138/u//PpaXl/H+978fly9fRiqVMqFI7irFTstVHitOMEnOLhujq8K5uTmzOuXqslgsot1ue0okvfnmm8ajxZJPLCOnMg+7s+xllCZdITk4nCRQa8xIjuoBdXxpMo5ut8w651rlwV4c67gHdqJWrFVaKpU8c6WO3VG8JLaXttfroVqtIhi8twOfSi/oNebcxXtjiFc9VBqq9Yuk6T0R1EXPurTO4f6BX+4BwZwdlnbsdrue8U17ruPRT1phn5tOME2+ZU1z3VmSY4ubhGnNcV5HbTx/dOxq3eFheVzD5E57zTP7XajcLxiZIB9H7Zl2buCeR/mZZ55BOBzG0tISHn30Ubz3ve9FJBLxiOvt+oTAjiHSxD3thHydf6s2+e7duybBbmNjw2S1jyKoH/TaoHt1cLifwXGaz+cxNzdniPLc3BwajYbZPQ6AqUmuUgkAHuJMOYYNenyi0SiazSZef/11bG9v4/bt27uM2iRzp73Qr9VqaDQaePnll5HL5fDwww8DuOe1Vg2xahqphdTz2PWV1ZPG63JxEAgEsLm5ibW1NV8PvJ53WPsHfcY+xnmmTzb8vt/9htXt87XbbeTzecTjcaTTaU/SvULzCmwJI/u/De5pUCqVjIOL2zrbC1BGeCi/5HhSIm6TY7ZLo9LMd/Ij9MOew17P9CQSYz8+td/55MRokEcBO1e73cba2hq2trbwve99D7FYDO9+97vxzne+01S1IMlVYqxGhJ02Ho+bTs3V5erqKq5fv46NjQ3UajXU63WT8a5F+oHRQiO2sfQ71hkXB4cdg0uSSm1/tVrdZWzUYNmLVPs1XWyrN7pcLqNarWJ7e9vkEuzX+AzSFXa7XVNJgiSAZSPZbr8yVn6hWT8Dq+Fkhnbr9fpQD/JJNLQOh4eD6D/2+NbXx7WRHD9cTOrCkudTwqqLa00WtokxsLvWMttIDzSTi/e7Oc+sRJcPUmN8UBiZIPuFII7bzaqhIKFtNpuo1+v4/ve/j5dffhmxWMyI7B988EE0Gg2cOnXKE67kOba3t/Hmm296tmCl55j7l2u4RNtgt2ncexj1dQeHWcQoC7pRdXV+IVf1wrAUU61WQyKR8HiMqUHW6/R6PZOHoEZQF8u8xptvvonNzU3cunVrV33jURa9ex1rz7H5fB6lUgndbhfZbBYPPvig2dlTs+eBnWTEYbVgeR+Ad6tcho7z+TzK5bLnWfi1axxMGiVzcCCGRS1Ue8/xwL5LiYUm4Nlzh02kSYiZtJtIJExUR8cMJVo8hnItnkM91FpWUV/n/MJSkYVCwZSRGzZGRh0/R6VVPqzrDeKoB65B7vf7+PSnP21qAjcaDXzmM58xtTpVc3ccYGv8tre3TfZ4OBxGPB7HxsYG2u025ufnDTlmkksgEDC73FQqFeMhHuT9cXBwGA+jjpthYy4QCJhF6+bmJrrdLhKJBILB4K464urZIehxVmLc7/dRKpVQLBaxtrZmdpizNYb7vcdBJKDX65mdtXq9Hs6dO4dz587tqq+qekbbUPB+VcNM6Ver1cLW1hY2NzdRq9X2bKeDwyyg37+XFJfL5ZDL5ZDJZHbtljdIa0/PsJ8kg3af2v9AIGAS8Ofm5lAsFg0/yOVySCaTA+Uk/DyvZ3ueubhtNBqoVqtmnI9y73ovB43j4g0+NInFz/3cz3n+/9SnPrXrmCeffHJfjZkF0FC0Wi3k83kEAgFcv37ddF6tgaqDzi+M6eDgcPRgZIdbPZ89e9bokkmS7VJumltAUkpvUSAQQLFYxJ07d0xVGtsbNUqCzKQgQa5WqygWi8hkMkZjrQl7KhWj4aWXW7XVJNqMqBUKBdy5cwcrKyvmXvZq8zie5YMyrk5mdn9CIz9zc3NYWFhANptFKpUyx/jV+B4UyeDr1OvTg8wITTQa9eQQMdoUCoXMdYd5Lu3qMXpdvkZ5RblcHnvDtb3Glxsjo2Nkgvzbv/3bB9mOI4XqiPxWYvytW9ban3dwcBgdhzVmlLixBNwLL7yApaUlPPbYY7vkBxpi5XvcgW5lZQXVahXr6+tGm9tsNndtJLJXW8Zpu5+nSwlBq9XCnTt30Ov1cObMGSQSCdNu1mXlbp4AsLq6aqJdtVrNhInV+8xkpEHtHpYgNCr53a/UzMGB0MgP+3W1WjUb5fglyAHwjHOOK3qQGTVimUiSZfbTUCiETCYDADh79qx5PZvNIpFI7Io4D9JF6/W4oC2Xy7hx44anjvm4EoqDxrjXOax2TXvxfV8l6Q3zMEy64nITu4PD8QDrim5tbZmSaQRroQM7iTL0HnOrWOp/V1dXdyX7HeYcYUesKpUK1tfXTWiXCUL0KtOzzNKTukNnpVIxBJnnUyNz2KHb/cDNxScbo+hwGQFmHWGOBb/+7Kc9HvS3nUsUCNzbzIP5Snxfq+QA/lvBa1s0X4L11+k5HiTZdDg83FcE2XU2B4f7E+p1LRQKKJfLWF1dRTKZRDKZxOLiogmVsqYxjyOxphRBNYGjeE+HeVxHbfcwYkC5RT6fRyQSQTqdNsezio7u3mV7ye2kpFHaeFw0iA4nA8P0vIpOp4NCoYBEIoFsNotYLOaRQTJB1+7/dvKekliOeXvLaEaTY7GYIciUatmSSx0vuhjVTYxarRZef/11T06Dfe9+XnB9Dva4HLTY9Xu+g+ap4zTONeo/Db53XxFkBwcHB7sQP/WEkUgEAIwnp1qtolqtDk3AnQTTmLz9wrSNRsPcC9+nrngQER61XcfJSDrcH/Drk9TSVyoV5PN5pFIpRKNRT//XDTj85JV+XmZ6kalJ5rV4Lp1TbEJrk1RtAxfe3ISEtZVHvd9h1TwG/T8OjtO4P4i2OoLs4OBwIrCXZ0VB702lUkG1WvVsF+t3rr2I47ge1XGOtTWMg85FY9tqtYaeQz83qCySg8Msw2+ss2pLqVRCu91GoVDAwsKCkUD0+31PWTYSWibY2ZEhklv+sMoLvcTchbdWq3lKJTIB2J5LSJ4paWIxAG5fzXnIrgh2GGNyVPJ9nDDK4mEvOILs4OBwYjCJt8TvM6OGJolhk/GkhmZSz88gzeO0DJ5f2NjB4ajgJynodDqo1WqmdCulFtwFV8syUlrBShdakUYlFyS1JNK6Wy5Bgszz6CY+CkaptKxiqVTySKAcDgbjzFmOIDs4OJwI7CUfGHacH6kcJfFunBDnNMjkMFI6LBlomCd91OvqOYd93hl3h4PCqP2f2zRvbW2h3+8jm82a0mqUNzCC4rflvH0dkmst78YNiOiVDoVCaLfbnk2ItHqGrYVWD/L29jaKxeLYJd0cDhaOIDs4OJwI+JHacb2/45YomyThbr+wNY5+fx/UtXmdcT3sDg77ga3hHSYJ6Pf7xut7584dbG5uYmlpCclkEgsLC0YawVJskUjEJOHNzc2ZnfcCgYAhv8Fg0JBZXSyql1l/7PYCO5KOer2OWq2GtbU1lMtllEolU03mqMeSiwp54Qiyg4PDfYtx5QdHbcBsTNOgDfJA+8Em5rP2XBxmE/vtK6N8XscEt0yPRCLo9XpIpVKerZ4phyApJklWqQavS40/rx8KhTw6Zm2jgvpkTQpuNBqoVCpmR06VfDgcLMbpf44gOzg4nAhMS3983DCuYR1XVrHXNU/CM3Q4HEyjr9gRDPu8mlwH3COm6+vrpu55OBzG3Nyc+c0a6PQmh8Nhs2ueSiOoN6anNxQKmQo3wWAQ4XDYs2MfAF9vcqPRQKlUwu3bt031C78KGn73tRfGWeTu9dlRPzMuqT+oSjkHscBwBNnBwcHhPsE0CMogY+7gcJQYpvWn57ZSqSAcDiMcDiMWi3m8xcA97XIkEkG320UoFDKbflBuoSXd6HEOBAIIhUJGsqF1lvX6LOdWLBZHTsjbj8d9XLKoCwu/RchxxyTP0RFkBwcHhwFQAzWNskEHhXE9TQrbE+x3z44UO0wDgwjfOGRs3D5ID3Cr1cL6+jqi0SgikQgymQyi0SharZa5NkkuyXM8Hjfl4yin0B02+/2+8UInk0njddZkO5Lqer2OarWKN99801MabpT2j3Ov04BfpZpBz30/RHraBFzJ/TTgCLKDg4MDdhvv4+o9Gdc4DAvrOkLsMG3st+bufggQiS0AswW7bjOvJd20RJt6jLVeMo+nRIMEm15lvgYAhUIB+XzelHebdexVI33S6jwHOa86iYWDg4PDAWOWvcUODg47GJeo0aNrbw7CRDt6gJUgkxwD3g1GgsEgotGo+bxWsSBBJvL5PNbX19FoNEb2Hh82jsJbPS0cRHscQXZwcLjvMcrkOqyu8CgJa9Mow7ZXPedRMUo941mEq5jhAIxXRWXQe0y6IxmORqO7yshxExBbT0xpBathsL4yP6865UKhgEKhgPX1deTzec9xs0YyTxr2O184guzg4DCzGGeCOyzyNCqZnlZbBl1v3Gez1/uzTjwdmXAYhHGrNbBsGz29rEpB8mrvrkdNcTAY9JR+o+aY5+UY4jGtVgulUglbW1uoVqsevbPDwWFaZSgdQXZwcJhpjDrBDdIPj/vZYeca9TPT8O5Oy8M0Skb6UZPjSUK7fiWtjvo+HI4HNAmt0WggEAigVqsB2EnqswkwdcbUFSeTSXM+yi54zm63i0KhgFdffRW1Wg21Ws2Q5km1uw7TwTjP3RFkBweHmcV+knGOug1+5xkkzRh23DjJg34k0S8j3e/9o8QoUpW93puF+3CYTQzrO+r5VTIM7GzyYZNmaphZ/o0bkVDfvLGxgWKxiHK5bLajPgkJwPcbHEF2cHBw2CdGJWfDvM6DXh/1M5NsJDBrmERL6uAwKvaKLNnlDActxrhxSLPZRCKRQCgUQjQaNbrmP//zP0e9XveUe/Nrg8PhQJ/5OAmSjiA7ODg4HFOo5/R+JpD38707TBd79SWNxDQaDWxsbKBUKiEWiwG4R8C4w54dtXHk+OjgNgpxcHBwuM/g5AUODuPDT3YxboSn2Wyi0WiY89E7qeXdHGYLToPs4OBwZDioCg7TJoHDyrb5XX8UjFrmba972UsrPKyI//1Elu/X+z4umBWP6bSqvewFraU8jfM5TA+2rnwUHAlBvh92aprVZJj9woVz71/s9d2PUgt42Hnt8/i9N4zMDoJfzV+bxPvd23537Jr0+GHXHacix0klj4Puy81Ns4Pj9F1Mc5wMmxuPw7O4HzBO3zwSglwulwEcj8EzKU7yvQH3vsNsNnvUzXA4RHDcAsMTXPaLUTSA+z3fJKXf9otJE/kO8pjjiL3uy81NRw/OFcdhS2WH+xOjzBOB/hHMor1eDysrK0in0y4EcczQ7/dRLpdx9uxZp7O6z+DGrcMsw81NswM3VzjMKsaZJ46EIDs4ODg4ODg4ODjMKtwy28HBwcHBwcHBwUHgCLKDg4ODg4ODg4ODwBFkBwcHBwcHBwcHB4EjyA4ODg4ODg4ODg4CR5AdHBwcHBwcHBwcBI4gOzg4ODg4ODg4OAgcQXZwcHBwcHBwcHAQ/H9yAcUs52QerAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get the first 5 examples to plot\n", + "n_evaluations = 5\n", + "\n", + "fig, axs = plt.subplots(nrows=n_evaluations, ncols=3, constrained_layout=True, figsize=(8, 6))\n", + "\n", + "# Remove ticks\n", + "for ax in axs.flatten():\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + "\n", + "for image_n in range(n_evaluations):\n", + " axs[image_n, 0].imshow(\n", + " intermediary_images[image_n][0, ..., intermediary_images[image_n].shape[3] // 2].cpu(), cmap=\"gray\"\n", + " )\n", + " axs[image_n, 1].imshow(\n", + " intermediary_images[image_n][0, :, intermediary_images[image_n].shape[2] // 2, ...].cpu().rot90(), cmap=\"gray\"\n", + " )\n", + " axs[image_n, 2].imshow(\n", + " intermediary_images[image_n][0, intermediary_images[image_n].shape[1] // 2, ...].cpu().rot90(), cmap=\"gray\"\n", + " )\n", + " axs[image_n, 0].set_ylabel(f\"Epoch {val_samples[image_n]:.0f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "dd03417f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(nrows=1, ncols=2)\n", + "ax[0].imshow(images[0, channel, ..., images.shape[2] // 2].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + "ax[0].axis(\"off\")\n", + "ax[0].title.set_text(\"Inputted Image\")\n", + "ax[1].imshow(reconstruction[0, channel, ..., reconstruction.shape[2] // 2].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + "ax[1].axis(\"off\")\n", + "ax[1].title.set_text(\"Reconstruction\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "292506bf", + "metadata": {}, + "source": [ + "## Clean up data directory\n", + "\n", + "Remove directory if a temporary storage was used" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25551b82", + "metadata": {}, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(root_dir)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d6aec13ab791e7be09ca24d0e51cd8058e3fe438 Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Mon, 9 Sep 2024 20:10:24 +0100 Subject: [PATCH 13/27] Addition of tutorials for the 2D and 3D autoencoderkl tutorials. Passed PEP8 compliance. Signed-off-by: Virginia Fernandez --- .../2d_autoencoderkl_tutorial.ipynb | 1 - .../3d_autoencoderkl_tutorial.ipynb | 38 +++++++++---------- 2 files changed, 19 insertions(+), 20 deletions(-) diff --git a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb index d6a4331e9..ea9eb217d 100644 --- a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb +++ b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb @@ -78,7 +78,6 @@ "source": [ "import os\n", "import shutil\n", - "import tempfile\n", "import time\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", diff --git a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb index ffb65c021..316d2a7d6 100644 --- a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb +++ b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -74,7 +74,6 @@ "source": [ "import os\n", "import shutil\n", - "import tempfile\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch\n", @@ -119,7 +118,7 @@ "source": [ "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", "root_dir = \"/tmp/tmp0_29f8jr\"\n", - "#root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "# root_dir = tempfile.mkdtemp() if directory is None else directory\n", "print(root_dir)" ] }, @@ -179,16 +178,16 @@ "outputs": [], "source": [ "all_transforms = [\n", - " transforms.LoadImaged(keys=[\"image\"]),\n", - " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", - " transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n", - " transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n", - " transforms.EnsureTyped(keys=[\"image\"]),\n", - " transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n", - " transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n", - " transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n", - " transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n", - "]" + " transforms.LoadImaged(keys=[\"image\"]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", + " transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n", + " transforms.EnsureTyped(keys=[\"image\"]),\n", + " transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n", + " transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n", + " transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n", + " transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n", + " ]" ] }, { @@ -561,13 +560,14 @@ " epoch_kl_loss_list.append(kl_epoch_loss / len(train_loader))\n", "\n", " if epoch % print_every == 0:\n", - " msgs = [f\"epoch {epoch:d}/{max_epochs:d}:\",\n", - " f\"recons loss: {epoch_recon_loss_list[-1]:4f}, \",\n", - " f\"gen_loss: {epoch_gen_loss_list[-1]:4f}, \",\n", - " f\"disc_loss: {epoch_disc_loss_list[-1]:4f}, \",\n", - " f\"perc_loss: {epoch_perc_loss_list[-1]:4f}, \",\n", - " f\"kl_loss: {epoch_kl_loss_list[-1]:4f}, \",\n", - " ]\n", + " msgs = [\n", + " f\"epoch {epoch:d}/{max_epochs:d}:\",\n", + " f\"recons loss: {epoch_recon_loss_list[-1]:4f}, \",\n", + " f\"gen_loss: {epoch_gen_loss_list[-1]:4f}, \",\n", + " f\"disc_loss: {epoch_disc_loss_list[-1]:4f}, \",\n", + " f\"perc_loss: {epoch_perc_loss_list[-1]:4f}, \",\n", + " f\"kl_loss: {epoch_kl_loss_list[-1]:4f}, \",\n", + " ]\n", " print(\"\".join(msgs))\n", "\n", " if (epoch + 1) % val_interval == 0:\n", From 8eec20d809d836ab09469858e97d9340ca21c15c Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 9 Sep 2024 19:12:27 +0000 Subject: [PATCH 14/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../2d_autoencoderkl_tutorial.ipynb | 37 ++++++++++--------- .../3d_autoencoderkl_tutorial.ipynb | 20 +++++----- 2 files changed, 29 insertions(+), 28 deletions(-) diff --git a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb index ea9eb217d..cf47a232a 100644 --- a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb +++ b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb @@ -272,20 +272,19 @@ "source": [ "image_size = 64\n", "all_transforms = [\n", - " transforms.LoadImaged(keys=[\"image\"]),\n", - " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", - " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0,\n", - " a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n", - " transforms.RandAffined(\n", - " keys=[\"image\"],\n", - " rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n", - " translate_range=[(-1, 1), (-1, 1)],\n", - " scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n", - " spatial_size=[image_size, image_size],\n", - " padding_mode=\"zeros\",\n", - " prob=0.5,\n", - " ),\n", - " ]" + " transforms.LoadImaged(keys=[\"image\"]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", + " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n", + " transforms.RandAffined(\n", + " keys=[\"image\"],\n", + " rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n", + " translate_range=[(-1, 1), (-1, 1)],\n", + " scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n", + " spatial_size=[image_size, image_size],\n", + " padding_mode=\"zeros\",\n", + " prob=0.5,\n", + " ),\n", + "]" ] }, { @@ -552,10 +551,12 @@ " epoch_disc_loss_list.append(disc_epoch_loss / len(train_loader))\n", "\n", " if epoch % print_every == 0:\n", - " msgs = [f\"epoch {epoch:d}/{max_epochs:d}:\",\n", - " f\"recons loss: {epoch_recon_loss_list[-1]:4f},\"\n", - " f\"gen_loss: {epoch_gen_loss_list[-1]:4f},\"\n", - " f\"disc_loss: {epoch_disc_loss_list[-1]:4f}\"]\n", + " msgs = [\n", + " f\"epoch {epoch:d}/{max_epochs:d}:\",\n", + " f\"recons loss: {epoch_recon_loss_list[-1]:4f},\"\n", + " f\"gen_loss: {epoch_gen_loss_list[-1]:4f},\"\n", + " f\"disc_loss: {epoch_disc_loss_list[-1]:4f}\",\n", + " ]\n", " print(\"\".join(msgs))\n", "\n", " if (epoch + 1) % val_interval == 0:\n", diff --git a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb index 316d2a7d6..0187bb9df 100644 --- a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb +++ b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -178,16 +178,16 @@ "outputs": [], "source": [ "all_transforms = [\n", - " transforms.LoadImaged(keys=[\"image\"]),\n", - " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", - " transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n", - " transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n", - " transforms.EnsureTyped(keys=[\"image\"]),\n", - " transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n", - " transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n", - " transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n", - " transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n", - " ]" + " transforms.LoadImaged(keys=[\"image\"]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", + " transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n", + " transforms.EnsureTyped(keys=[\"image\"]),\n", + " transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n", + " transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n", + " transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n", + " transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n", + "]" ] }, { From c7a243e46001367d9ff6a33a4ce40c4ba26f8cb8 Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Wed, 11 Sep 2024 11:56:58 +0100 Subject: [PATCH 15/27] Incorporate suggestions made by Eric (fix of tempfile import and use, typo). Signed-off-by: Virginia Fernandez --- generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb | 3 ++- generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb | 4 ++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb index cf47a232a..94c719f81 100644 --- a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb +++ b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb @@ -46,7 +46,7 @@ "id": "2caa73e1", "metadata": {}, "source": [ - "## Set up environment" + "## Setup environment" ] }, { @@ -79,6 +79,7 @@ "import os\n", "import shutil\n", "import time\n", + "import tempfile\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch\n", diff --git a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb index 0187bb9df..6d15d6e4e 100644 --- a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb +++ b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -74,6 +74,7 @@ "source": [ "import os\n", "import shutil\n", + "import tempfile\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch\n", @@ -117,8 +118,7 @@ ], "source": [ "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", - "root_dir = \"/tmp/tmp0_29f8jr\"\n", - "# root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", "print(root_dir)" ] }, From e5fba2b9d71e18a6b93d04540ea04fce9865dfea Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Wed, 11 Sep 2024 15:33:14 +0100 Subject: [PATCH 16/27] Re-run 3D models to ensure that losses go down. Signed-off-by: Virginia Fernandez --- .../3d_autoencoderkl_tutorial.ipynb | 94 ++++++++++--------- 1 file changed, 48 insertions(+), 46 deletions(-) diff --git a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb index 6d15d6e4e..4a0eda3b4 100644 --- a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb +++ b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "0f82c364", "metadata": {}, "outputs": [], @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "48155dfa", "metadata": {}, "outputs": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "1aaa77a6", "metadata": {}, "outputs": [], @@ -192,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 24, "id": "ac41ad56-1c09-4e17-aba4-7f00e1894ff9", "metadata": {}, "outputs": [ @@ -200,8 +200,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [01:49<00:00, 3.53it/s]\n", - "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:26<00:00, 3.60it/s]\n" + "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [01:51<00:00, 3.47it/s]\n", + "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:27<00:00, 3.47it/s]\n" ] } ], @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 25, "id": "aa5875c0-a150-4561-876b-2a0233bc683b", "metadata": {}, "outputs": [ @@ -272,23 +272,23 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "id": "8902c0a4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "0a839bfa-a494-4500-9cd0-7c6205be164c", "metadata": {}, "outputs": [ @@ -412,14 +412,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 18, "id": "aa53b342-aeec-4817-8797-adf7b7080ea7", "metadata": {}, "outputs": [], "source": [ "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n", - "adv_weight = 0.01\n", - "perceptual_weight = 0.1\n", + "adv_weight = 0.02\n", + "perceptual_weight = 1.0\n", "optimizer_g = torch.optim.Adam(model.parameters(), 1e-4)\n", "optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=5e-4)" ] @@ -434,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "5c0b87e9", "metadata": {}, "outputs": [], @@ -453,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 26, "id": "aa98bfa9", "metadata": { "lines_to_next_cell": 0 @@ -463,33 +463,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch 0/100:recons loss: 0.020644, gen_loss: 1.005610, disc_loss: 0.024810, perc_loss: 0.096774, kl_loss: 11961.354739, \n", - "epoch 5/100:recons loss: 0.019299, gen_loss: 0.963566, disc_loss: 0.032445, perc_loss: 0.080186, kl_loss: 19255.276533, \n", - "epoch 10/100:recons loss: 0.018521, gen_loss: 0.882888, disc_loss: 0.055547, perc_loss: 0.070705, kl_loss: 22346.606254, \n", - "epoch 15/100:recons loss: 0.018521, gen_loss: 1.024773, disc_loss: 0.014188, perc_loss: 0.068204, kl_loss: 26122.472853, \n", - "epoch 20/100:recons loss: 0.018183, gen_loss: 1.020731, disc_loss: 0.013762, perc_loss: 0.063751, kl_loss: 26065.798295, \n", - "epoch 25/100:recons loss: 0.017791, gen_loss: 1.017065, disc_loss: 0.008928, perc_loss: 0.061848, kl_loss: 27973.925384, \n", - "epoch 30/100:recons loss: 0.016497, gen_loss: 1.027929, disc_loss: 0.005776, perc_loss: 0.058142, kl_loss: 28598.175570, \n", - "epoch 35/100:recons loss: 0.018082, gen_loss: 0.922083, disc_loss: 0.044235, perc_loss: 0.061182, kl_loss: 29145.672766, \n", - "epoch 40/100:recons loss: 0.017112, gen_loss: 1.031649, disc_loss: 0.006293, perc_loss: 0.055899, kl_loss: 29358.829560, \n", - "epoch 45/100:recons loss: 0.017617, gen_loss: 1.031488, disc_loss: 0.006553, perc_loss: 0.056928, kl_loss: 30269.970220, \n", - "epoch 50/100:recons loss: 0.017587, gen_loss: 0.989403, disc_loss: 0.020194, perc_loss: 0.055261, kl_loss: 29611.149525, \n", - "epoch 55/100:recons loss: 0.014578, gen_loss: 1.035160, disc_loss: 0.001241, perc_loss: 0.048054, kl_loss: 29921.286118, \n", - "epoch 60/100:recons loss: 0.016188, gen_loss: 1.034492, disc_loss: 0.002137, perc_loss: 0.050492, kl_loss: 30739.220950, \n", - "epoch 65/100:recons loss: 0.018132, gen_loss: 1.026116, disc_loss: 0.009633, perc_loss: 0.053056, kl_loss: 30321.361550, \n", - "epoch 70/100:recons loss: 0.016364, gen_loss: 1.025895, disc_loss: 0.006020, perc_loss: 0.049412, kl_loss: 30252.894451, \n", - "epoch 75/100:recons loss: 0.013454, gen_loss: 1.019963, disc_loss: 0.001358, perc_loss: 0.043239, kl_loss: 29700.126062, \n", - "epoch 80/100:recons loss: 0.016021, gen_loss: 1.038090, disc_loss: 0.005187, perc_loss: 0.047805, kl_loss: 30522.813955, \n", - "epoch 85/100:recons loss: 0.015460, gen_loss: 1.024193, disc_loss: 0.007624, perc_loss: 0.045688, kl_loss: 30897.789541, \n", - "epoch 90/100:recons loss: 0.015317, gen_loss: 1.034723, disc_loss: 0.002935, perc_loss: 0.045348, kl_loss: 30689.129576, \n", - "epoch 95/100:recons loss: 0.015863, gen_loss: 1.037992, disc_loss: 0.003536, perc_loss: 0.044846, kl_loss: 30319.116891, \n", + "epoch 0/50:recons loss: 0.028408, gen_loss: 0.899995, disc_loss: 0.068511, perc_loss: 0.047966, kl_loss: 84008.107885, \n", + "epoch 4/50: validation recons loss: 0.029058\n", + "epoch 5/50:recons loss: 0.026289, gen_loss: 0.890100, disc_loss: 0.075222, perc_loss: 0.044404, kl_loss: 82538.794358, \n", + "epoch 9/50: validation recons loss: 0.031279\n", + "epoch 10/50:recons loss: 0.025868, gen_loss: 0.897919, disc_loss: 0.067960, perc_loss: 0.043630, kl_loss: 81820.101361, \n", + "epoch 14/50: validation recons loss: 0.023571\n", + "epoch 15/50:recons loss: 0.027377, gen_loss: 0.953108, disc_loss: 0.047639, perc_loss: 0.044497, kl_loss: 80916.426989, \n", + "epoch 19/50: validation recons loss: 0.026467\n", + "epoch 20/50:recons loss: 0.025950, gen_loss: 0.983597, disc_loss: 0.041265, perc_loss: 0.041636, kl_loss: 80244.706749, \n", + "epoch 24/50: validation recons loss: 0.027087\n", + "epoch 25/50:recons loss: 0.024513, gen_loss: 1.003783, disc_loss: 0.033110, perc_loss: 0.040052, kl_loss: 81447.181137, \n", + "epoch 29/50: validation recons loss: 0.027916\n", + "epoch 30/50:recons loss: 0.024828, gen_loss: 1.052075, disc_loss: 0.010955, perc_loss: 0.038624, kl_loss: 79766.662814, \n", + "epoch 34/50: validation recons loss: 0.033613\n", + "epoch 35/50:recons loss: 0.025590, gen_loss: 0.984481, disc_loss: 0.034665, perc_loss: 0.040138, kl_loss: 81379.091817, \n", + "epoch 39/50: validation recons loss: 0.025075\n", + "epoch 40/50:recons loss: 0.024524, gen_loss: 0.956906, disc_loss: 0.044816, perc_loss: 0.039182, kl_loss: 78912.705622, \n", + "epoch 44/50: validation recons loss: 0.022967\n", + "epoch 45/50:recons loss: 0.025341, gen_loss: 1.032001, disc_loss: 0.021897, perc_loss: 0.040327, kl_loss: 78940.488946, \n", + "epoch 49/50: validation recons loss: 0.021878\n", "Training finished\n" ] } ], "source": [ "kl_weight = 1e-8\n", - "max_epochs = 100\n", + "max_epochs = 50\n", "val_interval = 5\n", "print_every = 5\n", "epoch_recon_loss_list = []\n", @@ -590,6 +590,8 @@ "\n", " val_loss /= val_step\n", " val_recon_epoch_loss_list.append(val_loss)\n", + " msgs = f\"epoch {epoch:d}/{max_epochs:d}: validation recons loss: {val_recon_epoch_loss_list[-1]:4f}\"\n", + " print(msgs)\n", "\n", "print(\"Training finished\")" ] @@ -604,13 +606,13 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 27, "id": "066417fe", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -634,13 +636,13 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 28, "id": "bb1b6dd8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -671,13 +673,13 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 29, "id": "caf2b1e1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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99WEOAz/3eBwXRi3OVa6QTqdRKpVw9uxZp9PPZrMYDAZotVrodDpoNptuzLHmMBegNtITdu9R9t0eq+C1+Vv/1muRAJPY6mfWBEGNXFGeAcAtqnk+6zDn83m02200m00AQLPZPDBnjLMDp9FOzBVBtgjrMHzI00oejtqOSa477Jgw/eMkXuUwD3hYaHjYtew5J+VB97j3Ma5/HEf/mfaaYccnk0mkUilcunQJhUIBwH6mutYPtlpBez3dVnrYQl8NlhoxktdEIoFsNotiseiMNdtH2QZJ8t7envMO8Z48hkaOdZCpRQSAdrvt2nf+/HmsrKzg61//Ora3t0OfmZ1jZvHMx71/1PnW4/5GGBEbFkXiGFpYWMBDDz2EUqnkvL92QZtMJpHL5Q5cX6NK+tuOefvbEmbbds4Tqi3WMa5zjnqrOWeop5ikVz8Pt63u9/suEU+T/XTxwByGQqGAVquFF198Ea1Wy80n+nMvRaLnhiCHkT0ldsfpOZ2EOM5q5TMq5DMOwzqfJcT29bDzlTzfSx3aw0MxKlJDz3GhUHBbP2tiHTCc8IZJEuyGHPZ+NtlGw53qcWYZqWw2636rB5mkF0AgQ53e506nE7jusIU0Nc/ZbBaNRgOtVmvkImAW8MTXY15AQlkqlVAqlbCwsIB0Oh1IpONvrURD8Dja0DBSbq+jf4+y5cOq4/BeHOvAfp111Trzs6mXV5N+VW9MQqzJwyrd4LnpdNodQ/lXJBJx5eHuRcwNQQ4jd4f1WoxDGOEeFvo7DEYR2aN+ljAPsn191GvTwoc/PU4rRpFjGpjV1VVcuHAh4PUJ6/M6F2mNUz2HnmALm81Ow0OjpuFTEuR0Oo2FhQVks1kUCgVHivmjYVMaKBJiapaBfe+WeoXUoMbjcZw5cwbxeByvvPLKPWvoPO5PWIeQ/k/S9+ijj6JYLLrNM8KuQc9sOp0+YIM55kgY7SYbowjxNCDxZdk4/q+eW92MRCNLJPh2PuCGIry+7sDJDUMAuEV7tVpFJBLB2bNn3Xb2WuHCPvdh/58WzA1BJvQLP45r2/sMu9+9QArDJBqjjvHwuFdhF8TJZBJnz551Rf7VGwQEyS9Bry2hIVieq/o9GkfNime4lO2w8g0aOWqiqYtmBj0JshovtkM9QfQmsd4p76ekfDC4k5nPOq63b98eqkc+zEJ53DnDPNseHkfBOMcRx2M2m3WSJkZrOF616gMXlSTJVk6g3l4uPjkOh0XBOR/wOryGjmm+pzpj7pbH6+miVxME6S3Wa3B+0EiXRpl00W4XFnxmTNbjHKrVcijXmDSf4TRgrgjyMBJHHHXCHSdBsOfa43VQzMrjPMk5w0K29v9Jns009/XwOA04DHlLpVI4d+6cK6VmJRBhYVPdoIOGxGZ+a/a6GhbrwbEeaTWkqVQKmUwG2WwWuVwOxWIxQJCZLKM1UPmb12IbqtVqwDAqoafXOZvNAkBge1p9tod55nbhMAqeKN+7OGk7o+OKvzluGZ3J5/OByjVKVkk4u92uS54NG7O8PseO3f0ybBwo+eYiVwmp3RCI/9ukQL7O5Fu9L9vEYzgXEHasa7UdfYa6fTW915SldbtdtFotNyexss+9gLkiyMNI3DAJhMU0+jYr4VDtD42ZCvXZGbvdruto4wj9JFKLUSR71OcJI+/D2hIW+hh2/DAy7o2Xx7xi0r7J/s4KESxjNM28oRuDWK0ik154PA1TMpkMlH4L89jE43Hk83ksLS1hYWEBxWLRGe9isYh2u+0y6dvttiO+2iaGRjk/tVotFz5tt9uOWKuXir+5OUqlUsHW1tYBz/a0z3ySOdvPKR7HCe1/7OOLi4suMpPNZt0cwPGpNcvtgpIlF4F9by6vTwJJ8P12u31gIQ3AJedaj6tu5mGJuJZ/TKfTrkSbRo7o1dWIGBe9XKDrIl0/q9Zp5uflcZRYUOLBz8e2lEol7O7uotls4vr16wfKXJ5WzBVBHqZhsRqeSSbWsGOH/a01AxcXF3Hu3DmcP38eDz74IAaDgdu/vN1u49atW/jmN7+JF198MSBq1/sNu88kCFuV8vVJPN5hmIbgDnvGniR7HAbjFpGzxKg+qnMIC+lPWvfXSiDUMzzMU6qeIb6uoU09T41vJpNBKpVybUwmky6saxNueG6n03HeHc1ct95gLfUGBEl+LBZDoVBAv9/Hzs7O0A0MPDymwd0c/2FQx1cymUSpVEI+n0culwt4jdlWS0r1OpFIJCCL0HM4J9jzlGQrdO7gdfR6SqD1c0QiEVeHmeOX0i22T73gAAIbB/E4tpmk3uZJ8DV+Bt4vmUw64kxtM7BfxadarQaciqcdc0WQFZYsT+KRGOfN5YqJmwIAwMLCAiqVCpaXl7G6uoq1tTWUSiXUajVUq1UXUqARWVlZwb/8l/8Sm5ubKJVKKJfLKJfL2NjYQKVSQTwex82bN92uM9q2RCKBCxcuuA67sbHh7kEMM9qHnWRGhUj5XMZd+17o6B7Hi2Fj824aR3v/sL4di8Vw7tw55HI5AMFtXLWChZ6npdFoVMLkFSrTsNpAGjL1MNO7w3lJvclM1Eun065SBb1A3BKWxq7dbh9Y2PLzaEiV3iKteJFMJt1zuXjxInK5HHZ2dtBsNo8UKj3NXiOPewM6JrPZLFZXV/GGN7zBRVkymYwbSxzXHGeaZGerzxD0pDJJT0tDTtIurUXMv7lBie6ypzkGeqyNRFmiz7/5mfi52W4ArjKFzjucy9VbTlLOJEXOK+1220XkstksarUabty44arihD23sOfBZz1vmCuCHEba7Jc96tyw99nBYrEYfuiHfghra2soFArO48JtW1kHEAAqlQoAuOLh/FtDFiycnUgksLS0hOXlZXe/N77xjdjd3cU//dM/BTJEs9ksSqUSALjyTTdv3sTm5uaBTjLpQmCUxzcMljSMItDTtMnDYx6h/Z0aPWrn+H5YSSUL1QmOqkqhx9trK2nmuTyH3hmGavlDIm6jVZrEw8+h12fIWGsg23FvrwvAEYnt7W23KcA8Gi4Pj0kRjUZRKpVcfXHKlKze1nps7fjV5Fe+p4tPklrrGQ7jMHasEjxf8wbs/KTyT52TlHSrzVaPLr3TOifwOvpjHZTqiVdw3iK48CARtx7w08Yh5oogW2J8lOsA+1/++fPn8UM/9EMoFAquHIt2rFKphFarhXq9DgABw0cDw06guiOuvtjptONls1ksLCw4Ar63t4dms4l2u414PI5yuYxGo4FUKoULFy4gn8+j2WyiXq9jd3c3cB/7PIatuJTUTvr8pgmB3SthE497D7YPh42XfD6PbDaLfD7vvD3qxQm7lnpgrGHiPMHsbuCOAdG6oJwfdJMP/q/zUDwedzWZGf6l91gNYVgIVnWINEytVguNRsPNORo2VSPLe/Pai4uLWFlZwbe+9S1sbW25Z+BJssdpA/ttIpHAww8/7HbJVAmCklfWD7dEV3/rOGQEB4DzvgLBxDdduFrbzC2bNfGOMik64hjBZrsY+VGJBYl62OfSREMuDBS6LTZ/lJjbSBh/dJGv0otIJOLK5TFnggmE+tlPCw5NkAeDAf70T/8Uzz33HM6fP4+3v/3troPcTajR0LZFIhHk83k89NBDuHDhAs6dOwfgoC6P4Uklnex0LJHE8ASwv8qMRCLuOvxf6w/GYjEsLi5iMBg4w7yzs4Nyuew6cTweR7FYRC6Xw8rKCur1Ovr9Pmq1mivn1Gw2US6XXVLOMC+5Hcz6+qjQ8zSG77R1bo/7B2Ekzup8mfim2zNbz6/+b8Oleo6VWHCcUQ/I5BxeR5Nh9Fo0eEzAUUM1zBus3iWGOZvNpiPDjUYjsChnsp7VDPKHJJztSSaTKBQKKBQKaDQavjayx6mEjq+lpSXk83k3brVWOHXFas+HOafCok02F0GdfNYDrMeQaPJ/LrZ1AQ7AzQtcyFISoh5wG1nSdus8YhfqdBSESUP4PFR+ZhOTlfjy+qVSyTkKqtUqarXaAc/8acHEBPkd73gHfu/3fg8LCwvY3t7GO97xDnzta1/DysoKtra28OpXvxp//ud/jtXV1Zk0zHqSwzxE+kVpZ6AhPHfuHB5//HHnIU6n04HrqLhdwxM0EprZqtAVHrCvG7KdgP8nEgnk83lXfJvXYIZ7oVDA4uIiisWi0xt2u100m03UajVsbm5ic3PTEWYaPLtSPaqXZ9g1TlOH9rh/YcOC+jeT0BYXFw+EJRVWV6yVItQI2fCsao/r9bqbG3Qu0FAqr0Pjx4Q8q2/muZw7VP+nHmMSZDVK1WrVVa/gOeo1pq6Qv0kmUqkUCoUCFhYWhu6SNamhO20G0eP0Q/sc+3SpVEI2mw1smtFqtZyH1ZZfJNTG8tr2f7vQHtbnwxbiliSzzaqBth5jdUTq5iR6PUvwdaGtFXW0ss4o+8/31QOv8wmw73wslUrOg9zr9dBoNALcaBy3mydMTJC/9KUvOdH1f/kv/wWVSgXPP/88HnroIbzyyiv48R//cfzyL/8yPvOZzxy6MaNI8DgCyE6Zz+fx2GOP4dFHH8XKyorraDb8oJ2R8gnqkm0oRT06vE69XneSCj4Xaoc0+UXbzM7NsE8sFnP1Tm2IJhqNuhDr4uIi1tbW0O12sbm5ifX1dWf81IAN8y6Pgn2uwzxx3sh5zALH1ZfspKsGMpfLIZ/Pu2o06iHmMbZtVqbFcWG9NurF5fUYLtVqFNZ48YdzQS6Xc6SUpZNoyFqtFlqtlivj1u12HQmnAaLXuNPpuAQZzmVsg/UWkxgTfA69Xg/Ly8t4zWteg3q9jkajMVLCMsl34+cPj5NAJpNBJpNxC0zWM47FYo4c2vJsOvbDOIdyCiWm5AMqP+BxYU62sLrEej+ep4tm5RAkz6qF1uQ+/uZ97OJbq9rwt/IilpGzfInzHgm2bdfq6qqLQAHA3t6ee7YKO9/OI1E+lMTi//7f/4tf//Vfx0MPPQQAuHjxIn7t134NP/3TP32kxoQ9wFHH2RVIJpPBu9/9biQSCfR6PTSbzUDHVS0McGfFU6vV3EpSO0osFnM1Q2lkGeaMRqPOKLGD04sDwCX2hWl4aKDoLWItRg3PkpSzA3K3q8FggIWFBTz88MNoNBrY2trCyy+/jM3NzcCmAdM+T3vssL89PE4jkskk8vm8G/tKppWo6ryir/OcMEkGoRpgrVxBMm0905xjeBzLuZHMsq3qJeYP/1dvEH/USwTse5hSqZQrI6fPReccJfyZTAbLy8tuYwRi1Pzi4TFv4PbQKjEIG/9AUK45zDGnjrMwR5L9rT+WeCs3ICijsHIuXZTbvzX3yVbi0IW9jT6R69g26OexUbJRC2Uey+ogwB1ORgmLRtBOC6YiyHw4Ozs7eNWrXhV475FHHsGNGzdm17IQDJuQSSj/03/6T4Fi3wCcsanVaq7qBD0+lDskk0m3v7kNX3S7XbfPODtRNpvF0tKSI7DNZvNA+ZTBYIBcLhdYSfL6XJ0xdKLGMxaLuexPXp8FuXndTqeDZDKJM2fOYHFxEdVqFd/61rdQqVRCQ0LTepFHPWsPj2lwUp4BvW+xWMSFCxcCHh1g34McphMcFY1R8ktwkdxoNFykifdn8X8aKb02yyMVCgWk0+lAHVMtAddsNt34pjeMemMu5EmqOcflcjlXforzDq+vyYZa1J/PhNvvptPpA5nxh8EsvMhhoVk/T8037vbYt6SXSaccn0CwNrESVTsvWCI9bI5QGZRuMKJt4XxkSSeAwDzCManlF7m45fjVRS69trynepDVK0znYDabRSaTCcgfmNCri+Rer+ccd6qLVnkZjyeP4XPQzUqWlpawurqKzc1N50AMwzx6j4EpCfL73vc+pFIpdDodXL16Fa9//evde7du3XIlzI6CcZOenSSBO53owQcfBAAXmgTgOgC3Um2324EQI8+1ekJgP5uUnY6ENpFIuEQfhjlJqnk8DR07q8ogGOLJZrOBRBldXfGaFOa3Wq1AmTl2eK6GFxcX8brXvQ7PPPMMKpXKRCu1SSUrYc/bw2NS3O2JL8y7Q4OjBsNGSWxfD9PHWePLv9XDy/JRYQX77fk8TjcI0U0A1PCSENN48f+wfATel/VJmWGuniitoKEkgZ+Z7eeOXdQ/H/b7nMX8Ma9hWI/5xGAwQDKZRCaTcV5TIOjZ5XHAfsSXx+h1+BO2mAb2Iz38bWUPYf1f5VZsD+cEnT80N0HLqg0G+5pgcgFdhHNc8/Ow/SoB0QiUSj70GhoF0+cSltjH56i8iRLS08glJibI733ve93f//bf/ltXEo34/Oc/jze96U1HbtCwh6crMvs6AJTLZdeRmJRCsGPRiA0GA6ffVV2y7jjD80ioWTtVOy8NmpLuXq/nQjpcfdGzoyFeOxDZcUmueS/qphqNhkscVO0kt39cXV1FPp/HP/7jP2JjYyM0scaGjO0znOb78PCYF4SFOjW0qPp+mygXRpTD5gKeq6HKSCTiSHGj0UC73Ua9Xkc+n0cmkwmUaLPX55yUTqeRz+exvLyMhYUF57XVsc+FPqvc9Ho9Jw1jZIzGjdem3jqbzbrFuD4XdQiwTc1mE5FIJJCYXCwWUSqVsLGxcei5YJZziP2+POYbJxlBAuC2audCE9jX3pJc2lwEPd8uiNV22yoPtP/03vKa9vwwB5YmzzKHiaSY8wF/stmsu6ZqkDnfWQ8y5xFGxJk3pZF05jo0m82AczASiQRyKPi5VaphFxv8HADc3g/0IOtnPw3jd2KC/NnPfnbk+x/96EdDa4rOEsO8y4PBAPV63VWA4GvqLWKH1TqAKli3KyWCX7Q1KOw4Gtq0IRcAjsCyHUpybWgHwIHi/vQeMfuW57DtvV7PeZtzuRyWlpZQrVbdZif2uXkvjMdJ4m5Niuol0sleSaIlxGq8dAzrOFUtI6vMtFot7O3tubFLjwkNnJJzHffA/uJdF98c21qXvV6vu1KPJMiqSeZx9Cox+sRFPOdmXTgQWj6Sv3W3sUKhgK2trVCd4jDYxfhpMIYe9x4YRQmL5KjUwkITVgEEuIReg9fRCljq/OI28fyfC3bOOQTfYzRbS1Hyf/7WPCoep15w9XRrToTmYw0Gd3KeqtWqi3Azj4qftdVqBapWKFcaJ0dhe2x0zC4u5h0z2yiEGtmjYhQJHtaZlQCHlS3h/1ZfrJ1aiaqSYa1lau/H93RXGv2bcgqez3vYFaR2Gq7s6OnmZ2i3267+YdjzoTh/eXkZe3t7gdqD9rlOY6y8cfOYBCfVR+ycoMbIhgWth0WjN3q+fhYd06rno0HhjnPVatWdx0Wzlk4LayMQlFmovIIGioaM9+C9Ke9S/bAaMDVQNHLq/bKhWNs+fj56uMMSdA6LYaHWw8jrPDxGgUmvStysXGIct1AyrXIkvk59r845nANYMMAm7vI8Qs8lEebrunDWaBjfsxEzTdJTqZYSZJVAkBTrlvXRaNRptq0H2Tr1bOKizkfcnprzEKVap8VRN1c76R1F30ZPqwrBwwyFeoaA4H7magR0xcRVoBLMvb09J6VoNptuC1vtiJRwaOdLp9MBcq21GFnLtFAoBO7X6/Wwvb3tkn0YItJBxHDu2toayuUyNjY2AjtoDXvewxYjYX97eAzDPC6kEokEFhcXkUqlDmyuAYT3bQ0XKjS8SgOh9YOtwdXNQlg+Uucm7pa3srKCpaUlLC4uolAouAxwNb4aHlats9ZpVy+5jYrZ6BcQ1A/av6mppgeJiXpKxqeBPWeYcRx37XnrXx7zCx0DFhyf1iOq8wPHkpZQtdEl/WHf1OoylGBxDFMqwfN15z6ez7Kvi4uLrr1LS0uBHChWtFIPuC7iOYa1ug1wcEMUfnZGrDi3aBSL9yVHUWeDJc36fPk/KwidOXMGyWQS165dczzmNGCuCPKwCXDY6/Yha9kS3SJaE0xowBqNhiuon8vl0Gw2USqVArpBvS+zPpmpvre3F+gwTIZhR0qlUqjVagGSqyFedlJKPpgMQ50Ok2/4OZn8o/ue6+cfDAaubmEmk8HCwgLW19cPPC9Pfj3uF7DOuHpDgPB+b0OGYbDeJBo3O1fYclK8Jo0yjRLJp24rrbpIjYQxLKoSD42WWY8OsG/4raTCesnt/2pg1Us17SJo1LF+7vGYNcIiEsB+zWF7nHV6KfHVCAt/7HWUHKudJ0+wml2VOWkUWseVJu2yPZqXwE3HNBfJzg92czPlPdYBoPODrcnMz6B7Q+j19BmHXRvYz+PiRmlcaFvuMq+YK4I8KexETW8O/ybpZOejB7fRaGBjYwNbW1tOewPAJbQsLy8jnU6jUCjgwoULABD4MumN4UqM21UqmQXgVmHsuKp3ZEk5rtI6nY7TCzebTezu7gY6KT3fuVzOreZarZYzzhqq4TO4dOkSYrHYAYI8agEybEV3Wjqyx8lBvQrEqEXtcfYjtoGhvcXFRRed0ZJI2k7rcbWfQXWFwP5GIPSwAHAaZBq+RqPhFs28Fok18wVYSo0EmeObz4heYvVu2coVmgOhhFg9Q0AwoUg/n5ICvqYSEpWjnBavj8f84Lj6DPvxKLulY2HctTSCojaVYwAI1gEeDAZOAkX5pi5ceR314g4Gg0B0iLJJLpK13apdZqKtVtGil9q2WytSkF/U6/UDi2RyhXQ6jXa7HZhDOAeSRzHyRY86veG8L39TAkruQ74zGAywurqKdDqNl19+OfB8hzns5mWuOZUEmVCvcC6XC4Q7+bBJXLe2tvDSSy9hd3fXeWl5Psuo0djs7OygXq9jZWXFGS1FJBJxKzzrFaJGkVtCs74y9TjVatXtjFWr1VCpVNxOfErytQMyi71Wq7lyVQzF0pja1a2uGtXA2U44ihzbZ+3hMe9gP9cd7IBgKbdh0gr9TdhjNSEH2B+zmohHuRcNEqNZLDNJ8s5NgrhBiC01yTEdltyn7QszMtazY4/XH15fNyPRHbbUG+7hcVpAG2ir0SisF1kXi8BB+ZFimDdZ3+dY0h3neC/KJHUhzqRC1iAG4Ag0iTM5iUbMbXk2tfmWE/HZqL6ZHMdGvjiX0TmoUSvLKSzP0AQ+dS5ar7m2ad5wKIK8u7uLr33ta1hfXz/QaZ588smZNEyhD3OYpqjVaqFQKLhNPTgoWq0WqtUqnn/+eWxtbTmpBLAvIg+7HjsjCantXJRTsNPr7lXAnc5VLBYD9+FvSiFKpVJAwM8kPSXxqlPiKlJDQHo/LfOUy+Vw4cIFXLt2beyz9fA4SRzWqxxGAjlW8vm8W2RaY6RzlsqprKbOyik0GqQ1SQeDAQqFgqtq0e12Ua1W3T0ZpqxWq8hms0gkElhYWMDq6iqKxaLTI5PQ632BfdI9TgOsc6QaRNVOq25SPd5aOq5erwe8YSr1mifMo+7d4+4grC/qa2FRlLDFI8kl/7dEV3eoVW+0RoeHEXAm8eoCVBNmdVGu+mJuOc/7LC4uIpfLIRKJOImF5iDoOFWSzHvYEpfaXvVgD9uRk/NlLBZzclV+DjoWVbvN56gVdnhfSl5Py9idmiB/8YtfxLvf/W5Uq1UUi8UDnfI4CLIizOPJSZ7eW3pqIpEIGo0Gdnd3sbOzg0ajceB6YeGUweBOEl4ulwuEVzUUyy1a9TXbLnZM1SnSS80tGLXKBb3KOzs7jhyroD1stWsnAB6byWSwtrY2kiBP6j328JhXWO8FSwrxNxHmRVGEeZDVwKpnyRrLSCSCQqGAWCzmtp9nWJJjvN/vB0pRUoOsCXbqCdLC/eodsgbZeoO1berN4XyhSUecN214lkZQDd84T5x9lqfB+Hncu1BvLaHjVccdYeUVHB+E3VyHoFeUx3Ac03Gn4yisZJuWY9Rxrp9FtcJaOlIlDpaLWKKq84A+H3quNaFYCTeLHzCXg+1jO5QcU9ZBHsb3dLO004SpCfKHP/xhvP/978cnPvEJt0Pd3cawMCM7t3pTuY0iDY1q/HgOr8cvttfrYW9vD4uLi8hms6GDiUaTqyItzRKNRgMbqagB48YihULBDQoArkg/6xrreaqnslpCJciKRCLhsl8p4QC88fI4Phy2X83yvEwm4zS9Gh3SRaaFSo2GeaVpHMIWxNFoFPl83r2upLLf77vi+81m0xkNjSqpp5jzlnpv1KulRtLKJPRzqGfcEm8ArtyTJi+qxIJQ4xcm0zpJzEs7POYD6iFWm0lo/w17XxfQWhVC5wJLjq2Xmh5Vjn1LNOnRVVLcarWcU8/KNHS8AwiMfSXzlgPoglvnEpVz6OdnVHswGLg5gEUGKLGIRCKB8rNawo7kn9F0LrTJV8IiUfM0lwzD1AT5+vXreOqpp06EHI96mFbnopnk1PXU6/UDBoWGSotz829WtdCNQDTkwEHAzqMDZ3d390A95Vwuh5WVFSwsLGB5eTnQ8avVKqrVKpLJJG7evAkAgb3QuYW2ZsnqSlc7PhGLxbCysoLr16+HeovDPPEeHqcFYWSWJFbDgIwmDfMq2YUvr0nQIA3zoKou2VaeYZSJ12NGdz6fd3Moo0c6jpkkx12udDtpNWJM2GNUyy7YaRx1tyzOY5pQRIkFj9H7DAYD5PN5xGKxQMKOh8c8QeeDVCqFQqEQug+BjTqFwSbm8jV1rlkJE6/JHIN4PI5arebykbRUWyKRcEm62WwWy8vLyOVyyOVyyGazbl+JwWDgOIhKojg3cFxzZ+BGo+HmBi7M9flYch+Px5HP5wPOBHqM+ZveXy1GwLnVXpfzrFbj0WdfKBTQ7/exu7t7KvjG1AT57W9/O/72b/8WDz/88HG0ZyjsSk9BwxeJRALGMBqNolQq4fz5824lZLc7VG0ePcz80cQ/PRbY35iDmuZ6vY5KpYJGo4FWq4Xt7W28/PLLzlCtrKzgu77ru/DAAw+4+qwcyMCdFSuTA7vdrhsoAAIJP6qZAuB0TVZ7xP/PnTuHV155ZaxB08F+Gjquh4cF+z0XkDoOgGAWup6jfw/r/+PGD9+3i+dIJBIwUvQcaVhWI188XxfDNgFHzyF5Vs8VI1E0pvRe08hZzzRJtNZstZGqVCo1cYh0kvnDzzMeswTtHvsUS6VppIXHDTt/VH+0ZHhYFJvHMHdJHVvcMINzAMu3UQPM9tJpx2vavAi7wLU1j5U4a6ULtk1zLkiMM5lMwJlHp5yWjLPJi3YBYZ8TOVK/v1/lI6zowTxjopZ+4QtfcH//2I/9GH7hF34BTz/9NB5//PHA7nQA8M53vnO2Lfz/Y5z3mF8ANwOpVqtot9suU/zNb34zLl26hFarhVu3buHGjRuo1WoBDw1LwcXjcXQ6Hbzwwgt45JFHnCyDxodlnDhYYrEYlpaWcPbsWTSbTWxvb7uVIzvy1tYWnnnmGVy4cAGvetWr3JbT9O5wN5t6ve52nNGwBUl7t9t1g0/L2QFwOiEbehm1Uh72PMc9cw8PizDSM23fm/Q+vLbeB9j34GqINBKJuHCmSiB4nibEWUPKhXaYMVAogR0myUomk8jlciiVSgDgvD5MftHawwzJ2u2kdTHf7XYDUTHOEVxQa7iT3h96hPXz0DOlZJzt19qr457BpDhMf/CE2mMUrJY4l8thbW3NJdGGeX7VoaTRYdXz2j0UrMYYgOMcwP4inHKKpaUlZDIZNJtNZLNZRz4TiYRLulcPLMs4UnIJwJV15dhVvqJ6X3qVm82mk3jqZ9Q5jnOeSr30sycSCZw7dw7lchnNZjMwt9i8LV6z1WoFuJISYZaAW1xcPJCkZwk8cRy2Y1pMRJB//Md//MBrH//4xw+8Rs/FcWHYJMlOw86gLn1d/Vy4cAHdbhcXL150xqJWqwUIM70xuorT0if80jSLXT3X7LyLi4uus2liTLlcRiqVQj6fdwJ+YL/WYbFYRK1Wc2EY6n64S5+2gYaUnZudlIOUGxGM6mSTdEJvmDxOC1SLq2UTVe5kvaxAuC4uTH5hjSv/J7nU8/ijBoiEk3OWZrhzTtJFbliZNzXqNsmOpFgzzJk0qIaN7eQ8x7lFPeF8TpwPJ03S8/A4CeiYTCaTrjSa2i9dDE96LY3qaH7TOK80x75uO28Xz/xtvcG045r7wLZrroAlyCTJNrkfCMrHLCm1cyMX9JRrqZyCz1G5DT+D8iN1RmgU7DTNIxMR5Fl5DmYN7QA0LlqQm9AybPrlsXRaqVTC0tISbty44apddLtdXL9+HZ1OB5cuXXIeqGg06jok/+71eiiVSk5bmE6nXYIccGcTAXp39/b2sLGxEchCZadKpVIolUpu9aj7prNj0YtsB5l6kLjKbTabbpvYw5JcT449JsXd6ithizrVILO8mnozwhJpeC1Lkvk3zyV4ni6aeT4lDGpgOF613BzDqb1ezyXn8F40cByvrKOuc5vOFzzGhlJt1juNrCW9WnJSw7Sa58BnoQb+qN/zYTxDfh7ymAa0wWr7uIikh9SOd7vo1XEB7G8UppuHaOIrx5baZY4zLoo1J6HX6wWiv4PBHa1/s9lEuVx2TrJ6vR4g6ZFIxOmaGQGv1WpOg8yfVCrlSKxGwoYtuDlfklyn02m33byFlrVV0qyLbp2TlLizPeNkLfOAuRaDTPrwNDyoqzx+0alU6oDkQDtrOp3GysoKMpkMbt++jfX1dSeR2N7eDmwJywHS7XYDZZpoCDmI2u02tre30Wq1nL6INU8ZbtCkoXQ67Yi9Js4wqabb7QbKVpEwa+iV+iZLmK3n3U4M9jUPj+PCLCfEUVILGoBWq+USXnShb0OydpMOJXGap2A9uHb8WGOhYct4PI5kMol0Ou2INyUVnDOYMU6Jha1FatumxNZm3HN+4fWsc4CblZCYMwpGw6bJhwDcXGKf+bTwc43HcYMaZB2X7Ns2uYxOJ7sotvOBfY3QKDUJuV6LkWiOPS0GoIt2klmVeOjcQ2LOuYILaHIdJu3yh22jBzqMlIZ9LpVlcv7QCJL1anNhoATZetrZXs5pYRjm9DhJTE2Qn3rqKTzyyCN46qmnAq//zu/8Dp577jn81m/91qzadgDD5BXAvkFSLZESZxsa5TE0SNFoFMvLy67iRaVScSFReohIQtXYUHjPLWMpvie55cYlyWQSpVIJZ8+eRT6fD6w0eR12RgAuLFqtVjEYDAJJR+pNIjjwgGAJOm5jPe1zDgs5e3gcBrPoP1b6YN+z9+IYV09HGEhglfjZiJn1tug9rKc2jEAqQeb8QkOq21aTIPOzUpdsybl+Zk3WsSSZMisrNeFvzlXcSZRzjC7QdderYTtgnRTmqS0e8wP2+0wmE4iccKzYrdnVUTXJAjBs/tE5Qd9nrWArVdLFqpJptlEJskqo6Pgi4eTY16oWliBzK2kt5WblIRoZslEzRqVt5I2/+ZnVO64edgCBdnExMux5zhOmJsif//znA0l7xPd///fjk5/85EwJcpjhU9iVkHYqGiIaP61GQaghJGFOp9N4+OGHA+XdOp0OyuVywNNEbxC9LizbFI/Hsbi4iIWFBbzpTW9Cu91GrVZzGZz0TKtXh/eg0WISHvdQ7/V62N7exmAwcBIQtiGsDjIHWb1exzPPPDP02Y16vt7weEyKcSHzWRCZcV4PeyyNYrFYdOFNew1dLANBw2AXiNZry7mDcwSTVzQ6xMXz6uqqq3+eyWTcnEAvDIkzQ5BsR6PRCBgfzZ6nJzqXy7nKN2HPilpCkgCtzEPPGb1Q6vHSZD71BNGY+/nBY96gTifV2w6r5ADsE1DKE5T4qdeWBJU/ujjWBXjYApvXpF22i062T0u0aXS63W67XCJuRsREPn4GnqtRJ26Mpgtgne+4CAeCm6DoArnX6zkdMj3X9tlpjodGnjRJj3PQ8vLyqZBWEFMT5K2tLbcNoqJYLGJzc3MmjRqGUVIB6orUwKgmSMMGAAKeWoJfHDup7nSXz+cPeHLUk0wtcyqVwtmzZ5FIJFAoFAKdkjtpVSoVFxbR1asm7DABkEaWHZwrMQ5Sgp0VgKthyG2rLcI657jFiIfHYXCc3gEdz5Y004Bx7NgEVx3LFhqB0XvZewM44CmhDIHjl7v6UYLFMCq9tVq9QucdYL8iR5julwYonU47o6XeKEKlXPRmhc05YV52NWQ2ic/DYx5hx4ASVssd7FxgN8gZDPaT/8OiNNYxZcfLMG80Pa06ljgPkKhWKhU3TpPJpJNAcdyqlxhAIKpso11h5d7YRn5mklrOlVwQ6OvW4z1qLgjzMNMBSI/6aZhLpibIjzzyCL70pS/hQx/6UOD1P/qjP7rrtZEtVFNEr+3CwoKTRly/ft11lHa7jXK5jMFggJWVFSe1yOfzyGQyB0IQKmmgl4UDJJvNYmFhAYuLi0ilUjh37pyrVEFPkSbwcLVHT7Im7LFUinqHk8kkWq2W0wzW63VHvDV8k8lkXJtbrRZu3LiB73znO4EV42GfqyfNHqNwlP5xWF3rsPM4B2jCqho/nmMXmXbBzeNsJQklrpwT6CGhLIvbyXN8qhaRZZOi0WigXCTvwWtpKJIGWY0eNzOigWdijN05U5Np6vV6gCCr7pEaRi6wSQqsR/qoSdvjIg4eHtNCPce0s2HeXLso5vuM2AJBGaZ6aUkSOZ60VrESY+uMU+cV22iThHks71etVgOfJZVKIZfLIZlMAtgn1HoPvZbOFZz/Wq3WAUeYFi7QOUwJMqPklK4A+wnLWvVLn6nKXZV/cG7WpMV5xtQE+ed//ufxoQ99CBsbG/iRH/kRAMBXvvIV/OZv/uax6o/DJlTrQSLpbLVaTspAMlqv11GtVp1uh9dkhjklFTaTk9pjbQc7EhAsz8bSbqlUyoU/AQSKd1NT3Gw2UavVXKk3TYBJp9MusXAwuKNfXlpaQi6XcyEK7cSakMiwCtv8wAMP4LnnnhtLPoaFrT0x9pgHTLtIU48qJ2ydK3TM8LUwD4uONf1fjTANDBesnEdU/6tGS2uYa7hS8yV4vXGVI1SiASDUW6SkWq+n0g2SYS7SVdLhvccepwUci8B+2THtxxr91dcZcSXUgwwEcxrU82zHmWLc/5ZIAvs7aGqZN3IQTfbTDYJ4LSX/1DNrG7ngJglWQq2fxz4jPlf9rfMfF9xhiwVLkDmv3LMe5Pe///1otVr41V/9VfzKr/wKAODBBx/EZz7zGTz55JMzbyAR5nWwHiR2Xn6p6XTaFbgvFosHwg38crkq4xfNL5TaPHYoNbhMptnb20M0GkW1WnWJer1ez+1jTlLOAt62dBMNJbU/ANyGJZrhevbsWdcGXd3pLlmUZvBamUwG3/u934sXXnhhaAj1tIjlPe5dzHIRpl4KXXRq/+fYqdfrzstMuZMaAI5zW9ZJdXcqrSDx5DV1Jy+2idErAG7c2sUuE3aZzGfHqH1eiUQCuVzO6Q/VI80262ePRqMHKuKwXa1Wyy2y+dw0nDyL78rPMx6zgtow2mfac1u20P7QvtNel8tld131pBIadeF9eH+dF4ZpkMMIst5PCXitVgtopblo5QZj/FxaelYJsiXwXPhaWYhWoOBiW+cibbv10qvUhPJW3ovnkevwGnQK6vc1z064Q5V5++AHP4gPfvCD2NjYQCaTQT6fn3W7JoZ+gdo5mCyjlR007KI/OsB4DS1vZDsV70cPMwlts9lEoVBAJpNxMggtb8J9zRm6ofdYjbiuXNlhgTsebmajMiyiGmYaea37GI1GXS3IYXpLIowoz3PH9ZgvzMNEp94QJs2y6owuii0pVc8HjVLYmA8Lz1oNMsejepLpqVYPsnqhgH3PL69JjbH13PBv9RwzOVhLKNFLrfOcPiP1XPOzNxoNtyDgfZQgz+r7DXN2eHgcBezDqVQKCwsLSCQSB+RBwH6f7vV6brxw5znKG8Kq0dDpxLnBjodh/Vn5hkauOQ/oZmQEOYFKoTiPJZPJA55ibaeeYxfmw3IqdCGtiYiUcVjNNOdYPgctHaneYvUg6/MatmCYRxy6DvLGxgauXLkCAHjta1+LlZWVmTVqWthQATuFen5JKjV8wN8qPie0k+mqkN4hXp8eYUooNBSiq1c1xsB+Ao4N/dpQEMMo9OqotojnsHPaEDJXmgzJ8D0Pj3lGGNmetN9yPHDTHno2VL6gWdphnuFhY1L/JyzxJDEmSebYo0HRZB8NgypRp2col8sFxrUlydQN8jcNPj3f1BXbMCsXzPpM+v2+y5dg1rtGtXS3vVHf092En8s8FJQfFAoFV8FqmGOIBJkVZTSvBwg6xUgEtWa5tcO8v0oYlGOoN1b5AM9T6CKZcwSJMaWXfE9LRrKtbBc5Cu9ntc+8F3+rZ1136WPbdR7SaJkSZuUlKt0K22zkNGBqglyr1fBzP/dz+NznPucebiwWw5NPPolPfepTyGazM28kMD4sZwmy7qin4QAAAbLKjgMcLPPEQUTCaz03wL6nRr3E5XLZlWir1WrOcLF8C8s9qT4SCFbWsO3t9/vuXH5eLdEEwIUuBoOBS7ZhO07amHl4TINJoxhhHqIwfSBwMMGM91DJVJiXxb6uEg5CE21pUNUr1Ov1UK1WA55mEmqepwSZ3phutxuQPOhcRgOkUSQATqLBe9lwMqNP9praNs5xNOLcLIlEPCwrflJ477HHLBDmkYzFYlhcXEQ8Hg/YQ/XSWm8uoQtkBW20VnWIRO4kvHIxy/twTggbG2rnyS80eVedY+QGlmjyf01CpsxCJaBWhqlRKY2QkVzzfc4b+rpyDuVCw74TRrN1sc62VyoVx4mOMofcLRwqSe/P/uzP8MUvfhE/8AM/AAD46le/iqeeegof/vCH8ZnPfGbmjVSMInqqE7YrFvUKqfHlTnf09tK7w+vRoCiRZbJdLBbDwsICstkszp49i4cffhirq6vOgA7zGrOjaYY7STHDPJpARMmIzaxn+9rtNur1OsrlcmC1piHXYc9sXLhT35v3zuwxfzjOcHoYOR4F1dCFeahVhmEnb118M6qkxDnMy6ttHAyClTRUx8z/7XlqHAn+zfb1ej1nKHl/GjHWVubxnN9sm8OeEz1l6pmiMVdPm4fHvIDjkrbS5t3oGLPjTTGsUovVDLO8oq02E9auYbDOK477YZ5YYL/mus4pKndQb7D1AOs4Vk6k3EnnEqs31mdhZaucL2z0ncertFSjd/OMQ20U8od/+Id429ve5l57xzvegUwmg3e9613HSpBHhV3Va0RCCwS1xGqsB4OB2w6aWaIkoolEwhkeNTZEIpHAmTNnEI1GcenSJbzlLW/BysoKIpH9uqZcQTUaDZeoxxVVOp12hFyrapDskuCrTGN9fT3QPnY8DhqSdRb/r1araLfbeP755wOVOPicJvHIj/rfw2MewX6tSSyc9DXrm+8THO86h/B6YRo+Qg0AcNDrzPtqpMreg6/Rg0Piqztl2fGqJNVGmlTuocZKE2+slISfhf/TgNpQczqdDmiUDzMvHOeiyeP+Bfurlkm045OeW03g45jh+GOeD2HHvcqOgDtRdY61MKJrc504dnWPBKsr1nvqhmW8dyqVcrxE+QntP+WevI+VlHEMhtl51Uzbz0HoM7NyEss3otGok7HcunXL5V6dBk4xNUGu1+s4e/bsgdfPnDnj6ggeB8atwmwWqGqGw67F1QzbrANJd8phcf9MJuMMXjabRT6fxwMPPIA3v/nNAcNHo8bMWHbOTqfjMlOZQa6aH/UGW6heSRMFbJiXWbVsezQaxYsvvjh0VTsqTOLhcVIYNnlbjDrGTtCczCOR/Sx3DSPac/RcGjf1rFijR6gRscYxzNBw/IZVmbGbByih1UQ/NVIq1bAEeBT0OYaFmfm6Jd2HgSfHHscFjgf1jBLWA8rj9bfmDujrKn/g/1y4cgc+rQyj56pHepTNHWaT1YvMa2livrZJCbLOFTYSpTpq2wb1YNsFNJ+dPl+dd8hn+FnIcVhmV6PapwFTE+QnnngCH/3oR/G5z33O1RBuNBr42Mc+hieeeGLmDZwUqtnRv/X9ZrPpVl5qFFV7o4ZQE2WYFV8oFHDmzBlcunTJrTQZ8qDXh/fn9pXlctmVN9H6xqo5tqtI3ShEDS1XhNQ8Z7PZQChEM04jkUigdM1h4Qmzx93GUfqcemWA/SiSEjsboQmTHXAcqbdEvdE0kGpE9UdLrum1baSLx3IOUY2iFvBX42+NjNUsWkOnx9kfnQ95D+vF4hyTTqdRq9UO/d14eMwC1rbzN8eLShL0eMsNNNITj8fd7rUEF9Fa3UHzfpjkt7CwEJB3WjmVto/jzVaG4G87pygJ1VwBjm+WlGS0jI4/Lr6bzWZgTuC9+X7YHAYgMCfaaBXnK8613W7XOQdZ4paLh1arhVu3bmFvby/g2T4NmJog//Zv/zbe/va34+LFi/iu7/ouAMA3vvENpNNpfPnLX555A6eBdiZ2YiWP3LmOpJZl0YD9LG0mwnGwLCwsIJVKYWlpCYuLi27HvHPnzqHRaATCNfT0NhoN7O7u4vbt2y5TliXZuMKzHcQO2nQ6HRD8q9idqzRgv6yLLRhOhHmmhj073jvsdQ+PecQwL3KY/l4JsmrmVKLAa9rQLHBQ58fjLNG1C25eU9usf9NwkpjS0OnnUo8x/9fPb71jSnot0dW/1SMV9ln1uprkM2uogfbwGIewcc8+O8wLy/MsyeTr8Xjc7YqrY4ckkNs7q0OMYCJtOp0OOOq0DrqtlmUXovY4tmtYzgCdZfzNa5JzcOHPuULHM6PPmkuh2mUb7dL2KFkmz9IIuEpWy+Uytre3Ua/XA5V6TgumJshveMMb8Oyzz+J3f/d38cwzzwAAfuqnfgrvfve73VbHx4VJwq4kqTYphR2FXya/+GKx6MgwALf7FYlyLpdDJpNBqVTC0tISCoUCksmkWzFFIhHnKWYt5M3NTayvr2NjY8NlfmvWqRo31TmHeYX4mbRzq7dYO50ex2MKhQK2t7dn+j14eEyKkwinc+Kmt0LbohEZLorDPDla2UYNjBJkzi1qdK0eb9TnV2OpsikNn/J99YipQeTn4ZjXZ6DGP4yo83zOq2rM9fNrAo/mPxwVvK+XXHgcFRr5sAjjDJYgA3AEuVgsHkgg6/f7bvOuWq0W8KAOBgNX0aJQKABAYJyELbyBfeeWHsd5QEkxjwsjyJyHNC+AJJ55BOoo5O94PO4kIZp8Gxblsk42ne9I0Pk3dzPmfLK3t4fNzU00Go1TJa0gDlUHOZvN4qd/+qdn3ZaZgRO5GhvNxgTudGBucqKdnV88w6MszdZoNFCpVJyXmaEEnre+vo4bN26gUqmgXC47LXGxWHReaA1raLmVWCzmyrKpfpkdvN/vI5fLHchA7XQ6bsVKLxCPj0TuaC0vX76M7e1t19mHLTCsZ+k0rfI85hcn1Y94XxoH6xEB4GoIK5nl2LIhRb0ux7waNkuOCStZoDeKhkrbpwbH1krmwl6zz3n8MK+ZRpr0c+mxltDrPKHZ6zpvauLzUeaKYc/Yw2McwrzHKysrKJVKobIiXeTxfI4bOrBSqRQymYxzWPFcJYvs/9xxkr+5ay8dajzXSqK0zyvfINhOlVrxeM4VNgdAJV3AvvxBd7YDgtFkSj7Jjyj9tIl5+rx1rmJUnDlcvD897bVaDevr64EN0U4jDkWQr1y5gk996lP49re/DQB47LHH8KEPfQivfe1rZ9o4xTgvg3pX7RfL39Ty8f/BYOBWg8O0edT5MmzBVRfJLCtG3Lx5Ezdv3nTJeP1+H/l8Hul02lWeICnWMCwA570mGo2GO4fSCraH5yrhZzu1MDgJd5gOa9QztM/Zhz49TiusFzfMqHJcaV3UcaRvlEfKHme9uDpP6TE04jxWybTVFKvR1lJONiysVSuGyUa0DTpX6j2UIId5sqaF9xp7zBLsT7S3diE8DBpV0QoWygEIXZwqiY5EIo4XpNNpZ+ftuLcyCtv2MEJOpxfvb6UWOr8psbUVesKeBUkrP7cm+vLaOidoxEy91qq1pueaBHlnZyeUbJ8mHKrM20/+5E/ie77ne1xS3l//9V/j8ccfx+///u/jJ37iJ2beyEmgHU31uuzU8XjcVaJgJ2D1CEI1TDQ+rVYLlUrFbcPaaDRctYher+dkFZpko1pn6pbsCgzY10RyAKlRpGSDUo7t7W1H8LmrDj8nf/OzkxzXajXcunXL3WMSox/mMfPwmDeMinhY4qgyCCDo8bXn625ZfM9KF6y2NxK5U5mGRkPnIRqwwWA/MZBt4GIW2CeeKqvSv22OgpJirSva7/fd/KTVcWhA9TPzOvY5WAPO62pt6KMQXE+OPY4K69mNxWI4e/asK6FqF3lhEio9Nx6PO4Krnt8wcpdKpVx0mra/UChgaWkptCSkddqpfEnln/oa/9YxqccQHLvJZNKN6WaziXa7HfgMnMdUO00+otElrZShTgPykVQq5RYFWgUsEonghRdewO7uLnZ3d92cA5zuBfHUBPkjH/kIfumXfgkf//jHA69/9KMfxUc+8pFjJ8hhqy3+rasoJruwM+mqyoYMmfWqYUb13tRqNcRiMWxtbTlJRD6fRzwedzWOWalCB2CpVHKrWq2/GubRsgZf6zRylZbL5QIGle2lJpqhH+BOktLVq1exsbExUecc9lw9POYNk/Rnjg2OYS2VpgTZenJ1Mh/miQrTOVpyq57bTqfjcg1oOOixUWKuVWssSbVebkt4VSbGxbKSZr5nPeW6cCAsOddrMfnHw2NewH6cTCZd1JWv6zFAUFphzw/TMIfZQXVokVxrudVxTibrndbjdEFt220/E7C/YZBW2FGiq+Pczi06r2h5XI55m/hnk+/q9Tp2dnZcW8iBbO7HacbUBPnmzZt48sknD7z+nve8B7/xG78xk0YRkzxkftk0OOy0LIWmIQrWNFbDUK/XXQYqPUGqE+agoXyBxm9rawvxeBzNZhPpdBrnzp1DIpFwJJnXLhQKgS1btaOqgWb9xUwmExgkDGO02223zStwZ8WYSqVcJQ56rnd3d3Hz5k1cu3YNr7zyysjVm30v7DhrqD08LI7qTTxqvwo7n95cSo94Ly4+1TNEr49dHKvBGgZdaPNYLR3H8c7QK+cl3Q6aP6rv5ZyjHmAl1Gyf3ockmc9EPU4aNeNr6lXia/pM9TnQiFYqldBa7R4eYZi193CYN5iJ9tw90hJLm2ugFWz4P73HPG/YAlkJLvdF4ELY1he280qYPdWIk0aV9DNyPNuEPc4NupsmJRDUVuscRMcb28k5gtUodHOVZrOJSOSO7rparWJ7e9uVwLt27RrK5bLjGPZzhj2r04ipCfLb3vY2/MVf/AUeeeSRwOtf/epX8da3vnVmDQPCjeew1+ygYbFsyhKsEeBA4TF2Fcb3bTiE3ptms+m0w8ViEaurq0gmk9jb2wNwRwR/8+ZNJ7fgualUKrQjcfBwC2relx1/MLhTBJyrM3Zc/Vzb29t48cUXce3aNWxubqJSqYzNOLcrytPcmT1OBkfpN8ex6FJyV6/Xkc1mA4llVq+rurpWqxV4jREa/m/nA5Jtjllew5LwZDKJTCbjpE+MJOlueZlMBtls1oV6tQ3Dnpcu+Pm+eoN0vlTSrOfSIA/bVIlet36/fyCv4bDfj59n7g8c9/esNlP1tPyxCWI69nm+yiyA0f2Tx1oJViqVQi6XQyqVCkSHVJ4EBKvO6DE6ZxCpVCogp7RSTnIc6qMBOOegch/eVx18ujMwsO8k0M+u0epGo+FqKw8GA5TLZZeHpfOLfVanHVMT5He+8534xV/8Rfzd3/0dvu/7vg/AHQ3yH/zBH+BjH/sYvvCFLwSOPU4M68hWbE4Pq66OgP3OTk8sz1XtooZiddcr6ptzuRwWFxdRKpUOlHGjgWYHTqfTgaoTapy0zWHaIf2tA4ztbrVaeOmll/DCCy9gZ2fHlaCb5lnq73HP2MNjHjCMYJMMciFL7RxwsFa4SpqAg8SR3hclvLYUE8c+iTbzB1SewPZUq1V3r0ql4gwd66+qd0rHZViYVUOohM5l2k62gddTD7IaRyXdaiSHGfJRGBYh8NEoj1lBCTL7LqWTJJDAfm6ChcolhnmNFZovoAvgbDbrSCnzjoDgGLDEGYBzuDFazPdJkMlHKpWKy4li0QCVVum9dB6x3nQ7l+hxYfOJnqvP0v6297sXMDVB/pmf+RkAwKc//Wl8+tOfDn0PQGByPQxGGb6w/9U7QkLKsAdfY2hBV40cWOykNqlFr8H7kPRGo3fqDGezWZfFmkqlUKlUkE6n8cwzz2B3d9eVkFleXg7IKyzZteFcEmCWcgL2NzShHpCGfX19HVeuXMHW1pZLzhkXvg7TOtnnao2xN2weFkedEMMiGJNEjsKg5RPp0dUoEeVPKjlQDR49Q/Y1q+njApnHq+6XhJoljiqVCmKxGMrlMiqVCqrVauA+HP/U+bGNOu8QtsQToeSW7dXPyb+V3KpsjPOhjTjxHErR6P2eFMO+Mz+XeBwGdq5RMler1dw4B4IeUB5r5QkE7b8lh1YuoDkLfI0Otkql4hbiSiS1GACj1kpShxHyTqeDbreLnZ0dNJtNlMtlNy/pota20ZLXYXOq/YyTOMPutzE7NUEet7qaNcZ9ucA+kWQoMJlMusS1arXqahnTg6thSNYcVg1QIpFwoRJ2/FgsFtjdLpPJoFgsYnl52R1LnSGNMjXKNJrUSbVaLSeP4PPksfysNJxsD71gvd7+LoA8bzAY4MqVK9jY2HD6wEkM0Lj3h5FmD49pYBdidkIP+/uwUI2eLkD1R+cwjSqFEXT1OlmvkUK9OABcQh43ESKprtVqqNfroV4atnthYcFFojR6pERdJSKjDJ+SBUuaOZdpGFZ/a3jYVuu52/CE2mMYOI5YQUZ1vJb0WQ+pvq/a3DAirtD5TAsB8Hw7RiwJHUaONULFOaVer6NWq6FarR6QQYRhUmI86txJX7/Xx+Wh6iDfDYR1gGFfBldsv/u7v4v3ve99AOA8PIuLi47olsvlgJcoEongzJkzzngBdzIx19fXAwaoVCo50k2i2mg00Ol0sLe3566pOqBEIoF8Po9sNuvCJ5ubm1haWgponVk3mcdks1lkMpkDdQxJjEmsd3Z2sL6+jmvXruHv//7vnfY5zPB6eMwLZjmZhnl3+KNeWSbNMfyp2djq7bGGVOuNDxtXJJ0qwWByDAAXASLJ5KI37FokoZRuFQoFlyGvP+o9svrKMI+Sfh7OVWFGlp5kOgo4p1EawrDxNN9h2Jw9alE0bH6f9Noe84VJvJJHuR4XsLpLrkolrCRAx7JKpAC4cykn0vfsph28PheMHJO8ribD2uiwrZhlJRK8BucuEmR9fxSGRX3DSP9hxs+4xcO9hIkJ8jve8Q783u/9HhYWFgAAn/zkJ/GBD3wApVIJALC1tYW3vvWtePrppw/dmHFf2LjVTaVSwf/6X/8LP/iDP4ilpSVXTomTPCd/dvawsieRSCSwwxZF/joo2u02arVaoHxTNpt1x/b7fVSrVbeS5SDudDqoVqtOh0yN1N7eHhqNBhKJBHK5HBqNhhvskcj+9q43b97ElStXcPXqVWxvbzuSPi40NOxZj3vGdrXr4TFr2D42zWRr+6Ydyxyvmkhiz9djw9qlsgP7vzXAqinkglbDqeM+I4+hx5kZ49peTbYJG58kt2rgdcFAzzYRFjLWRQGPUdnZLDxS+v4kIeB72Qh7HA0qAwL2N94aNuatrbQ6fAu7oFVSy2i0jjP1FisJtlViwv7WdvLaNrFQj7HnD5tL7ydSO0tMTJC//OUvuxq7APCJT3wC73rXuxxB7na7uHLlypEaMympC/NI8L319XX81V/9FR577DE88MADTr/LcizcvENXe7rzDZP2IpGICylq0W0AgQ1A+DeAwEpRS8dRAtJoNJzkIxqNotFouJJsu7u7WFhYwOrqKh588EFHxOv1Ora2tvD3f//3qFarbtMSq0Oa5PnZZz3MS2+/D0+SPUZh0pDfYd+f5hrWONF40osLBPu49f5oaTXNIgcQ2G2L1So492iybbPZdFvTc1fNScB2VSoVNJtNl9egRJlG2G4aYBPygP0omlbE4LGUgPGzqIeLx9OLBezrIWcxD4QtRjw8DoNIJOI0vuvr6yiVSigUCoHICX8rTwiLCoVp/oFgXXA6u9TpVavVkE6n3Txgo1L0cI/ahdJKtNieRqMRyCcK8w6PI79h95x2zN2vhHpignzSk9qkkot2u43r16+7kObFixcD9Q058VOz3O12nbaXsEX41eiosSOB7nQ6gZIqrIdcr9fdfuUk01tbW8jn8xgMBnjppZfw0ksv4ebNm86znE6n8fLLLyMSibgtG8vlMq5evXogOeZ+7bQepweH0cEdFiqbsF5bTUQj6SUBVO+OEuthpNvuKMcfSqtqtRr29vbcphrTfnYaX25C1Gw2nVxL28qFP18b9qzVm26T8PR8YL8iABMQSfBtHWYPj5NCWNSh37+zoRcXfITdOlrzE4YRx7CxT1hZRavVQrVadbvsMt+JPIMRm7Bd8JRIcwHL9iYSiUC5V+sl9rg7mFsN8jjoytB6UTudDq5du+a8tcvLy1hdXXUeIDWC6oW1A0e3nWTGN++hZY+oCeax6XQa5XIZrVYLjUbDlUbqdrvY2tpyJVueeeYZvPLKKyiXy46w93o9XL16FZFIxJHvwSBct3iUVeA0Hjk/ID1mheOMRtiwpQ2ZcmxrDVCdB1SKYMOaKqcI80QDd8gmd7ssl8uBElOTwHqpKpWKKw9JYs8sfS1NxfaFSUbCnoO2VyNoOtepgdb50j7nu4Wwe3qy7kFQ1qibcgH7fTzMcxxmD3W8sN/zWM4LuoU7t1tuNBousTaVSiGbzbo8BKtTVq5BcOyRW+iGZR4nh4m/gbAV1zwQp1Erq52dHfzlX/4lEokE/vk//+dYXV1FKpUCECyMXa/XncGhESJarZbLOmfZKGA/DEniu7Oz4/YqT6fTrpST3dO9Wq2i1+vhhRdewNWrV7G3t3fA+LCaxbBVrmIaD90oIxP23Q7TNnl4HBazlFuEjQ1KKTTUyeNVK8jzrRHSQv+ZTMZVvbH3s55mJv/dvn0bt27dciWZDgsu9LlIBuAqWGQyGdceNbY0/iqroCeYXnOGfFkdh5ISGmjqjfnsNDmP8HOBxzxBvchKaoH9aLBGjtnPdfMgLpzVXvPavCZtvTq8qtVqgB/QqcZKNLy2jdxYxxPf58KcEk7d6GQaB5fHbDCVxOJ973ufI5jNZhMf+MAHkMvlACCgTz4sbMccFgIZJk4PuxZlFF/60peQTCbxute9Dg8//DAWFxddEg0rU+h59NxS78tkOP5P7RMNC8+ltIPEmnVQub3s5uYmAOCFF15wdZf1s4R1/FED4ygDZdS5fgB6nAZYsmYTalUSYaUGGnbltUgq+aNGFEDAAGvyGreQ3t3ddQm8h3Ug8DOpx7rVarmdNEmA7Tn6ebhbliYV6mfW9jG8q542PkctKenhMY+wZNa+p9VZhnEMXWDyfyXeANw4Z3UJ1i3XhTeJszq37G+NVtn761ylEWRPjk8GExPk9773vYH/3/Oe9xw45sknnzxUIyYRno97fxhxVKLcbrfx9a9/Hd/85jeRz+dx6dIl/LN/9s9cKTZmnPM8rXjBBBVqhNR4Ws8LvUnr6+toNBrY3t5GtVpFJBLB9vb20HJM4xYFYYsDff04Q9fzEC3wmF8M63vHPZnrOOAClf8DcMlurVbLJd+SXJIMk0QD+1IKGjDrBeY5BDXCL7zwAlqtltMdhxnaMEwyrgaDAWq1GhqNBtrtNjKZDC5fvuwMul1cM8rF52G3vVZPG4+11Tn4Wev1Osrl8onKKzw8iFF2P2wBrOfZH0KTcHVhqvfTZN96ve6cZbrPgY4xnTcom7ALeatHBvY9yZHInSIBe3t7R4pEeRwNExPkz372s8fZjokxytM6CmpIVAP8/PPPI5VK4TWveQ0effRRFIvFQC1VGj0m2QH7lS4YRuFmI5VKBZVKBTs7O3j22WcDYRnNID+Mnm7Y5w4jx+MWD/aZTGqkPTzmCeMiIKoZtAlzYRpAveawMaFyKODO7l1ax1z1j5OS5ElAss4aztxultEv3UHM3j8MavgJzbHg/bRUnYfHPGGShSft27Dyh3pM2DVshEmhdp3X0AW0yp8Utv4yj9VxF1Z5x4/Bu4+5UIHP2kM5CUGkoWNo9Omnn8ZLL73kdHmpVAqLi4uo1+s4e/YsBoOBe4/JNzdu3ECr1XKbAdBAMqPVaiCPS8c3CWnW+9pzJvHYe3jMK8L6NP/vdruu5GKhUACwX8lGjZfdwtl6oBg+VeLd6/Xw8ssvOy/POMmXtivsGPt+2Oej94qVc3K5HM6dO4dCoRDY0pafKcz7RKJgSTKlFHxuqrH0c4DHPGDc2AEQiPBYXbJGkBgNHtW3eS1uBMTSiRp5UiKczWaRzWYPeKrDIj3qdVY5WLPZdFFnH709WcwFQT5MBxjVscd1Kru6pEemUqkEVoEbGxtotVrY3t4GACeroPHY3t52RsmGIMdphiYxOGFEXz+3vce45ziNLMXDYx4wzQJt2LEkydxc58yZM25HSibF6AYcQNADrZUiuACuVqtO9nCYUm7TjLEweRU94v1+H/l8Ho1Gw81PlhTbChcqKVEZCT8nS8yxHrOHx2kAx4hNvA2zjWHVLMJKqhFaTSYSubOZGDcmoTNMfzimbHIesO+ZtjXMed9ms+lKxw1rj8fdwVwQ5GlgCaIldcOkCMNCKGF/07O8vb3tyLNeUwfUKC/QrL2vuhod9l7Ya9MQjGGv+0HqcbcxTqIwKclkuLLf7yOdTrvNjdRzw2gPoe+xqgXnhVqthp2dHWxubh7Q907btnGwY573arfbiEQiaLfbWFhYQKPRQC6XQyKROKB/VO8ZPd/8PFzsAwiUpeLnm6ZMnYfH3YKNgBIkyLTNurmNlVeohp+vhVWMYMULlnAlQWalGiW8SrjtvKBjme1UvT/bOBjsV7vyOFnMBUG23hH72rBzwjDMmI663jgP77BwaZhhHNe+YdccBrsYmJb02mvZRcFRvXMeHuNwXH1nWs0tM8I3NzeRz+exuroKAI4Ex2KxQMUGGqtarYZut4v19XU0m03UajWXyT5NG8KM+iRysLDPoqHZzc1NJwVjaTqNdjWbTRdW3tjYcAlGJMvWA86FgE/M85g3jJMq0bGVTqcD5VoVHDfqxCLBtdfkwlmJcCqVQjQaxerqKuLxOPb29lzUOZlMBqrH2Co42m5dXFPPzJwGfc9HdE8Oc0GQj+LpHOYxHoZRxHrcdaYNi457b5hXbBRhn1RXPAzTLhQmec/DAwjvu/M0udPQse651ky2m2LwdWDfA723t+c2/wkbR2Gfddy4mcQRMO4ZUpOcTqcd2aWRZikqXoslJ+v1esCbTGPMH1shw8PjNKDf7zvJAxe9w46zSanjrqvJdfQmU3ahm4lZT/KwnXjtmGPdcSbF+rF38pgLgjwpsZsE47TJ9tiw86e571EIQNi9rEEMa/Mw7fCo6w8LSQ275rhre3goxskh9LhhsqhZ3V9hI0H9fh/b29sol8vY3t52GwOdOXMGqVTKeW+oAWQSrpZLG/Y5jzpHjRuDNuyrhrXf7+PWrVuIxWLIZDJIJpMoFovOW0yZCDcmsvOB3mOW34uHxywxatxQcrS9vY1+v+92g6SUgZIiW2JNrxM21vi31kNm1CmRSCCXywXGjb2+lSnRxnM+6nQ6bh+G27dvB5Ji58nBcD9iLgjyJJ6naeQWk3pyZmEADuP1nvSYo7b5qF51tskbSo9Z4jj61DShSBolYN87vLu7i1Qq5Qr/05PDyjTDyjMetq2zgn5mGm5NEmKJOz7zsIRCP749TgvGzR30IHPhS82wQheWk8DmGmn1Cr4PBCvhKNm2v1XnzHHKTcW4IOf7HieLuSDIh8W0GkR77t0if5N09EkN/DgN1qjrT9M2bzQ9jguTyqIOM0YnjY4MBgNX4xfAgUTcSe8zC2+rHfvDrjksCmRzImhoWZVnWPvulgEe5gDxc4zHNFDPK/8Hgvk51CBTRpTL5ZBKpRwhptfW7nZnr2evGyaN0EoV6hHudrsuYqPHsw16Ps+9ceMGKpUKarVaYKMhj5PFXBDkaSQWw2QRhzGid2uCnsY4TeMJm8SrPuwYnWTG3c8bM49JMclCbNJF2VHJsb3fOM9TmPxj3H2mGa+zwGEJ+SR5D7Ma52HPcZr5xsMjDGFR5bDxysUvE97sRh8kskyi0+gKk+uU8EajUafvtxVitC26Pf2w8m6xWMxt9sNIFXfMazabvmrMnGEuCPI0E7ROsNbojrvGPBG9MA9Q2HuTXmOaY7yB8rgbmLafTUKijjKGx5HLWZPDw2LaRcawuWSYM2HUtW37pyHkkyyC/NzjMWuE9SnKo8rlsqswQeJK8sqSiCxvqFtUkzjzuHQ67ZLneL56gMOS9Ah6jHUra9ZT39vbw97eHur1eqhX2+NkceIEOYzsAtN7GsZ5hcPuM41+eBTRHKfhnfRe02iBj5voz9NiwmP+MUzGEBYOteeEvTYrEnvYPmzDt5Pe46j3G3fMNCT/OMn6UY/18JgFRi3EIpEI1tfXsbu7i1KphFQq5XbTBO4QYibwDQaDQIlH/Zvafur56ZHm7rnxeByJRMKVf9PztbayJgrW63Wsr687OZTWKveYH5w4QbaYxAMcRp4P07GmOWcaffNhjplEKziJh0ZhPep+8HmcNIYR6XnFvLXNE1YPj8nB5DduFZ3JZNy4IJklidV6xwCct1i3YmfCLgD3Nz3IuuOmepr5GnCHbLP0IivmULbh7fP84cQJ8ihPcZicYlJiqFD5xVHbajGJPGIUSQ1r6zT3D7vHsHOG3Svs2dxtnbbHvYl56j9hY2uaKNIk5x0mQnUYTDJ2D3O9cdfwkSWP0wLqfHu9HnZ3d9FqtQL6YMor1NaxJjpwhzgnk8lAvWStQKGEmV5mlUmw5CLrlJNAb21toV6vO8mGtteT5PnCiRNkxTB98bTnhr13mPMsRoWLhx2j9wjzfk/rxbbnj5N+eIPmcTdwFBnSacS8jKtZGNRxWuVhDgd77FGfx91aXHjcf6Dmt1arOfKq3mHg4CYekcj+ltSUWNAbDezrnG1lCl6f/yt55jW4qyfhifF84sQJ8mk0oJMYx2Fe60k8yOO80HYhYY8/SpKfX8V6TItJFrXT5hPMqg+GRVCmnXNGeZ4nSSwMu9aksqdZa4oPg7C5LOwZjot+TXPOLLziHiePk7AlYWOy2+26qhFMqOt2u0ilUoGNRNrttpNj2LaT3HILa5VIcFt3Ju4lk0lEIhFHotvtNsrlMprNJqrVqi/ldkpw4gQZOFqSi514Gd44ivGb5t7DrhNGZofdP0wrfDeS8LSd9vWwdnl4jMMoGU/Y//pa2Pmjzp2GXE7y3mH6+igCPuz4o+Q+2DbOwgs1aa7CMJI7yfUPe47H6cZJOlzC7qteYsoc6BEeDAaOPCuXIEiyO52Ok1io5AKAq35hq2ZEo1GnO57lpkMex4sTJ8iH8XZaT8ZJhHZH6ZqnvWeYB+6onpWwZ3TYicqTZI9ZYZxOfprXDhvmP+7F82HGyiTjeNRrh71v2LnjPO3TLjzG3XcScu7hcRRoX6a3mDWS6e0F4Goj6zkEt5qn9ILHqP3Wsm9KjqPRqNu8xNc6Pj04cYJ8GISF+6YxqLOahCclseNen8bQ22PHXXPSzzpLo+dxf2KSfhI2VsfpXfX1cYu1SXSsky74DivJsPcIC/kOk17ZzznNwnbWToFJFy6zaI9fhHscJ8IWgNwumnaUWmPKH6LRqCvXpjpjHkcyrYl/fI3JfsViEZubm85bHbbVu8f8Ym4J8jjDMM2ka3/b12bVXt5zFteZ1bHTyFcmIRceHkfFYcnQYRaksxhPsxoL0xLdcZjnMTrMgeGJ8P2FeY4I0JtM3TChMk3KKazX13qRCRJmJd+9Xg/NZtMl683zM/EIYu4I8lGJ6zivxyQyhsPgMNcK00xPcp1R+s0wkjutx0zP8cbM46QxTsN6FN3wcWHYPDRufE86Px3VuztLhM1ds9aeDos8+PnJ47Cgfet2uwCCUkf+kCADCOySp9IJnhONRpFIJBCLxVzFjFarhVdeeQU3b948UT22x+EwFwQ5bKIbJaOYppNZz7G9L4+ZpE36+jgt8Ljw4jQ6wqOGTkc9t3GD1hshj1lj1lGSexWnTZc7yQLmsIuc+7kfeBwvdAE+ykGnBJmEeDAYuKQ+3Q2v2Wy6pD5uI61eao/TgRMnyLpqm4VXZNbG15LTcd4s+zlmkYAyKcmeNNQ8Tps9jSzDw2MajBrj80wEh2mK7wZOYgzO4jNOeu5h5xs/N3nMArb/6VgPS6jj2Gi1WkilUkgkEm43PFax2N7eRq1Ww97eXuAcj9OFEyfIwxBGnCfxZk7jDZ3GyztMumBfC2vDpNrJsGQexbQhmmHtD0sICjvXGyCPo+JeITxHWTSexgXnaWmnh8esMarvq13kttF7e3uo1WqIx+PY2dkBAFQqFbdTnifH84Np57UTJ8ijQm7jZAzjOt8wT+m4e09y7XHnDzsmLHGQr+sxk0KvMeyZ2XuNeg7DjvGE2WMYvBHw8PC4H2AXu61WC81m88D708opTkMU7V7BNM/4xAmyxbR6teMkbYe59qRaunFSjWmuP+55THOvw2oEPe5v+P7h4eFxP2Jc1Pqo1/CYHaZ19J0IQR5G6LyXcv5xWGLvcfrhv3uPeYbvn/OD0ygrIqaNiM0id8rj7mGaeeJECHKlUgEQLoD3OB2oVCpYWFg46WZ43EX4cetxGuDnppMH5wqWUPPwmDdMMk9EBiew1On3+7hx4wYKhYIPLZwyDAYDVCoVrK2t+bI19xn8uPWYZ/i5aX7g5wqPecU088SJEGQPDw8PDw8PDw+PeYVfZnt4eHh4eHh4eHgIPEH28PDw8PDw8PDwEHiC7OHh4eHh4eHh4SHwBNnDw8PDw8PDw8NDcCJl3nyG6+mFzxS/f+HHrcc8w89N8wM/V3jMK6aZJ06EIN+4cQOXLl06iVt7zAjXrl3DxYsXT7oZHncRftx6nAb4uenk4ecKj3nHJPPEiRDkQqHg/vary9MFVgXU79Dj/sCwccu/Z1kxcpJ5YZL7he3OGXZtu6PnuNfHtWPaeW3U7l3T7uw1rE2HwTTf7XHvhDqqLbz3YDDwc9McgN9BNBr1Nv4U4DBzzGnFYDBAv9+faJ44EYLMLyISidw3X8q9hPtpMHnsY9i4naYvTEqiZtW/hl1n0tdte/m/krVp73HYY09izE16T+0bky4qRi1ejrLo8HPTyeN+tvGT9P95hHUMTDLHnWZM8plOhCB7eHjcG7BEeR73HQpr12EmfTX64+4x7FnYc6cxpveSoRr3OYZ58T085h33whi9Fz7DLOAJsoeHx13FMC+hksphHkY9f9aEnNc6rOfXepdH3WPYe7MgyaOe693AvC6UPDxOC6YZP57MHh88Qfbw8Dg0DuvVHGcARnlr7TUO620cFd4/rNGZhLyPurZdPOg1dNEwyTX0vMNg3D1s22YJ/YzjFkqejHvcaxgn9Rp2/HGN9fsVniB7eHjcVRwnmbob9xt330gkgmg0ikQigWg06koJ9ft9dLtddLvd0OvxWolEIlS7ORgM0O120e/37wopPG5JR9hnGLYg8PBQ3E250WEiS0eF9vtRzoKwhfTdxDzJvo6jLZ4ge3h4HAlH8dqOe2/Saw+bHA9rOMI8mJMaykQigVQqhde+9rVYXFxEqVRCt9vF9vY2XnrpJbzyyivodruBa/K60WgUDzzwAEqlEkqlEmKxGGKxGPr9Pnq9Hq5cuYLt7W2Uy2X0+/2xCZNHlVsMO/ZufS+jvNSeON+fOMz3Pi3JHTYXjerLx1VpxpLgsDExbKFufx/12U3zWe/mwmLc93VY8uwJsoeHx9zhJEubhWGYtIGkdmFhAclkEplMBul0GrlcDpcuXUI+n0exWES320UqlUIkEkE6nUan00Gv10O320U0GkU8Hkc0GkUsFsPa2hry+Tzy+bzzQLM0UaVSQT6fR7VaRbfbRaPRQL1eR6PRcM9g3hInraE86fZ43F84bG6BjWAcJT/Ajsl4PI50Oo1kMolUKoVYLBY6t8TjcbeQZuRI/+71euj3+2g0Gm4+6ff77mdSGdSkEo5pP/ck78/yvFl7kT1B9vDwmEucBJGapMyYGs9IJIJUKoU3vOENWFlZwUMPPYRCoYBisYhOpxMwahcvXsTrXvc69Ho9tNttR3BjsRiy2Szi8Tji8Tja7TZ6vV7gvvQgnz171hnGWq2Gl156Cc899xxeeOGFA+0c9RlP4tl6YuwxDHcrVD+soo2+b/8eprefRLfb7/cDtaBJeguFAi5fvoyzZ8/i7NmzyGQyiMfjgeun02mk02l0u11HhCnT6nQ6aLfbaLfbaLVauHr1Kur1Ovb29tBsNtFqtVCv19HpdAK7xWnEadJnPk7aYefMUfPLqLyMUe0Z996k95kWniB7eHh4CMImf0UkEsGZM2ewurqKpaUl5HI5LCwsIJPJAAB6vR6azaYzRrwOPcHRaBT9fh8AnFeZx/V6PUQiEcRiMUes1fDYc8+fP49sNouHH34YX//617G1tTXx5/PwOI2YRiYRpuWfxf2HJfjq/xzHqVQKqVQKZ86cQS6Xw9LSEtLpNBYWFlAoFJDP512+Asd2v99HIpFwkSX1CHOe6PV66HQ6Tq5FUkxP8ubmJhqNBra3t9HpdNBsNt1zmeWzGPeMppFj2Dl3EplE2Puz+s49Qfbw8LivoZP5KM8x/45Gozhz5gwee+wxPProo8jn81hfX3de306ng8FgEPAKKUGmVykWiyGZTLpr0+jRKPJ6NJq8DtsTj8dx9uxZPPjgg0gmk7h27Rp2dnbcscfpWZkHWEPqcTpxGqIZwwjwMO8zpVHxeBy5XA75fB6PPvoozpw5gwceeMBJJzgP8DoqneA9eB+dB/Rz9Pt9pNNp510mrl+/jt3dXRdtarfbB/Ie7POYNLo06SJhkvPGYdLjJ/HoTwtPkD08PO5LTDOhJpNJvPGNb8Ti4iLW1taQzWaRzWaRSqUwGAyQz+ed5waAk0QAdwgzrwHsGzUNv8ZiMXeveDyOSCSCVqvlSLMl6VrpgobxiSeewOtf/3rs7u5ia2sLzz77rPMw2c8VZthPK8k8re32uIPjklYcNaFuWM4BfzhOo9Gomw9KpRKy2SwuXLiARCKBRCKBZDKJRCKBpaUlZDIZFItFxGIxt3jmvQaDgZNT6JinVIvzCYm1jn2NOMViMUSjUZRKJaTTaSQSCZfQe+vWLdy+fdvJL+h9Hvfcwp7HYb+3YR7iUcedFDxB9vDwuO8wKTmORCJIJpPI5XI4f/48VldX8eCDDwY8wTRKAAIEmUaJiXgqmaCnSD3C1AoqWdZz1PvCY/W1c+fOYWlpCZubm0gmk1hfX0ej0UCj0XDJO6OewTwk9E2L09Zej9MLjlV6h3O5HOLxOBKJhPMSr66uIp/P46GHHnLEmF7idDqNWCzmpBOJRCIwvoeRd84HmnRnJVtWShKNRt39OHekUin0+300m02X61Cr1Q4k9dn7T+tdnvaZzjM8Qfbw8DgShoW6R4XhjovYhCWMhLVtmDFSDRs9u48//jjOnj2LS5cuIZ1Oo91uO6NEQsyku06n44wXcMdQ8fh4PO48Qc1m04VGFZpQo4bLGkCScxrfeDyObDbrPEZnzpzBa1/7Wrzwwgu4evUqXnrpJVQqlaM9XINJn/VR7zHKw3UaSb3HySOMBOqCNazP8SeVSiGXy+GJJ55AsVhEqVRy18hkMkgmk46c6mKX45RzgEqueD6T8TRvQdvIc+nB1mRelW0lk0lEIhF0Oh1HxKPRKC5fvozV1VUkk0n0ej0888wzLqlvd3cXu7u7BxbSkyTzDZOanHZ4guzh4XGiOAppPo7JmMQzn8+jUChgZWUFS0tLrhzTsHNYr3gYkdU2k2BTbwzAhUb5P0O4PMdel9fheQzZ0ggDQLPZdIZ1b28Pm5ubLowbhmk0iKP+t6+Pyl5XhBlaT4I9jgvjQvzqnSXxZOnG5eVlLCwsYHFx0cmr0um0G9Nh0in+z2tzHFtCHCa/UjLNa2nCLuUSlHbwGopMJuPe7/V6WFxcRLvdRqfTQTqdRiaTcYv9VquFbreLdrs99jneq2PUE2QPD4+ZYJLySaMm0bBs5GmPnwZh56gX5qGHHsJrXvMarK6uOs8sCayGWmnEer2eM0wqo2ClCj2PnuNMJhPwTlnyrF4lNZq60Qg9VcyG573b7TYWFhbwmte8Bq961auwubmJP/7jP0alUjlQI/UoC41JknFGaZ/DzrsX5B8eJ4NRc0fY3KKLTULHNXfFXFxcxOLiIs6cOYOFhQVcvHgR+XweCwsLaLVaaDabyGaziMViaLfbgTHc7/eRTCad5IELWnqDbXTLEmnqk4E7Mi4SZJJkEt5Wq+UkHLFYzOmT6XnmXMQdPdfW1gAA2WwWrVYLrVYL29vbqNVquHHjBqrVKra2tgLzjz67UePysPPyPMETZA8Pj0Nj0glwUnIzDQkKk0yEHTNJ0gnJ7vLyMgqFAtbW1rCysoJ8Pu88ubyHenqHeUDp2VHSSyOjr6tnWD3CbJPqmDUUS8PHUC41kYlEwh2rhrBQKAAA3vKWt2BjYwPPPPMMWq3WRN6huwk+v1H9YNIFlMf9i0kjFkBQ1kTSyQTcixcvOplUOp1GKpVCsVhELpcLjFsm1nHcablGva7OGSS+bC+Js53X9Ph+vx/wKOvnIpHXiJJN5uP80ul0EI/HUSwWXTupi45GoygUCkilUmi1WqhWq1hfX8f6+rpbWNt2Dnu2o76f00CePUH28PA4cRwn0Rl3bTUuy8vLOHfuHB5//HH3Pg0SdYNhBNkm3PV6PfeahkL1nvxtQ6yqLda6qDyeXuVYLIZMJuOOz2azSCaTrvoF0ev1kM1mkUgk8Ja3vAXXrl3DzZs3sbe35zxdkzynaXCYax12ceThMQxh+nhLzNQrGo/HkUqlsLCwgIWFBTz++OOIRqMol8uOBBcKBWSzWXcO76HVJzSBV8ezSjaUZOr4Zpts23WhzXN03tBz6b0mYVcvuZaR1J36er2e00/3+32USiUnHXn66aexs7PjKmpME+Eb9j2cBniC7OHhMRWOY5KzE+5xZEsPM5bRaBSvfvWr3U54JJpsh02e0fJOSogZxux0Ouh0OgeItN6Xr5FIU56hRNuGeNPptDuPW1uXSqWAxCIej7uqFdxlq9lsIplMot1uo9Fo4OLFi/j3//7f4+tf/zq+9a1voVqtulJ0x42wkKwnux6zxDjSFibzSSQSyGQyeOMb34jHH3/cLYZXVlYwGAywuLiIvb09lMtlDAYDtNttbG9vuw2BONZSqZS7rkaEKK1QmYJKp2wbSWp1PtTj2W4r5+LCmfKKMDKrx5Nwk9wz4sTzuGC4ePEiYrGYk11Uq9WJZBajvofTAE+QPTw8Do1Zh8ruttaUBmB5eRlnz57F6urqgTJranzCEmuAYMUJGhvrBdIEHGvg1Dutx6vXiAZNdcwkxepNpiYxFosFCDwNOXf+W19fx/Xr19FqtaYmyGEyh7B+cFo9Rx6nD2FzxyjPMRGNRpFMJl2N84cffhjNZhO9Xg+ZTMaNnVarhVqtBmB/t8xoNOqS2ayESqNBtjqNTZ4bJRFTL7OtasP5SYmyJdg8zv6wnfo+78nPQelFoVBAt9tFrVbDYDBAs9l0n/leXuB6guzh4XEkHHWCPIonYtp7EDRgFy9exAMPPIC1tTXk8/nQhB0FtXz2GJU+hHlIVbdHrzCvx+Qavp5KpZzHOB6PI5lMOkPEECdLxxWLRZeVztApjTr1g8D+ZgNMJmw2m3j00Udx4cIF/O///b9x9erVUOM5Tgsc9v8oouzhcdyYJMlT5Qj5fB4XLlzAD//wD6NQKLg6wboZD8emLp77/T46nQ7q9fqBCJFqeu17PDes3VayYcmvEmQr0bAeas5HJMt6DOcp1UhHInc2INLnxbbncjmXvFytVnHlyhXU63WUy+VAu4Z9H5N8R/MIT5DhM6Q9PKbBcU1ud9tznMvlUCqVsLi46GQV1sioB1dDo8C+TIJ/q+GxUNmEfX4k3RrWTCaTjiinUikX/qSuMBaLIZVKudJzJMma8c7jKAmx3xkrc5w9exbNZhO3b98O7Lp33Djq9+3nbY8wjFugqfwhkUjgwoULuHDhgivlGI/H3ZjhmOQ40rHE+YDjkmMv7F6TIszTO2zhHfZZNWoE4MBCP2zhwONU9kWNsn52bphE6Um1WkUkEnGbEQ0bj/Z19VTPe9TJE2RMLjTX4/3k7OFxemBDrdlsFg8++CAuXbqECxcuIJlMumx1gl4jJb4kn0BwIidxZdIbDZXVE2sYlNegtzebzSISiSCdTiObzSKfzyOZTLrapO1229U67vV6TmJB40ayHI1GnceYpaV00wL1iEUiEXz3d3831tbW8Ed/9EeufBzb5+c5j9OCSfqpLnxZkeIHf/AHcebMGZw7dw7AvgRqMBig1Wq5Y1knmPMASbLKo9RrO+m4CSPEnHPsLncADuiZ2QZ+Ll5TPclhyYF6jBJhlqpkDXU+N9aBzmQySKVSqNVq2NjYwPXr13Hz5s0DiYZHmTfmgRwDniAPRdjKL2wV5OHhMd+w5Jh63XPnzqFYLA4t2WbnAK09rAQS2Pcgq+HUzHUan2ESDG7ewbAnPcfctYseKnqJu93uga1sqUXma5lMxmkmVYvMz0HQM5TL5dDr9dBoNNw51sszybzn50aPu4lRES2rtdWkuZWVFSwvL6NUKiGfz7sEOwCBiA6ryTBfwUZZwoin9SSHQfMZbJuVKA/TVY/KA7BcxVa+0N983eZMpFKpAxpm9ZzTCcDPsLu762opD2sz/57WKXlSuC8JclgnCjOO7AjsOLqSC/sSh13XGwwPj5OBnZiVIJ85cyZAPhVhlSc4/llPVD3FTMyjV4bllLT+KT0/1oAqQaahUpkF9X+RSMR5urmFrN6Df6dSKecNt/IKzVLnnMZtqrPZLDqdDprN5lTPd1To97jnPj+/egDjZV+6aE0kEkgmk1hdXXWbfuRyObc9cyQScUlouislCTIJsybE2ZKP+jMJ4QsjyZwP+L8eO4q7DCPndr4BgtzGEmRW8rHzIOeadDrtNjzi3FapVFz0Sq81iTxkGtwtDfPUBPlrX/sa/uqv/gq3bt0CAJw7dw5PPPEE3vKWt8y8cbPGMP2NitEZYlDtXjqdRqfTccaFWzPy3HGamrtlLDw87meMMxwAXL1geky1DqgaUV5LQ528JkmqHkuS2mq1AOzrikmYdYFN46EeHCbAsG3ZbBaZTMaFdYE78wdLve3u7jpvs/7wMzNE2ul0nNe40+mg0Wg4D48+n3Q6jde//vW4efOmq/ka5t2aFHfLgPk51WMcOD65TfTDDz+Mhx9+GAsLC8hkMo4IaiUXLlg5vrmQJInmvGC90kCwNCSTaUkg+b7KH5QEdzqdwLzDxTgAl1Og41yT7ICDm4/w87PN/J/vk+yTMId5iHUR0Ol03N/UZCcSCSwtLSGfz2NnZwdPP/10wIs8a3I8i/MnxcQEeX19HT/xEz+Bv/zLv8Tly5dx9uxZAMDt27fxn//zf8YP/MAP4POf/zzOnDlzbI09CjRcaEOG9CDlcjk8+uijePWrX41cLuey2qkNHAwGaDQaeOmll/Bnf/ZnyGaz2N3dRbPZPFB6RTcKGIZRko15CjN4eNwL4PjkjliUIgAHjYn1BqlHRA2iSizCQp7DPDlhXh6VStA7TK8w32NWvJ6j11Rir15p/pAwa71UGtfV1VW0221XM3lYVrqHx2mBjs9kMomlpSWcO3cOly9fdnOATcgDEBjrSkx1+3dC5VR23lBtsJ0TNKLE18MkEGEeXMtl9LdyHV2g2+vo8WHPzeqW6X3n+5oEnMlkXKJeMpkMSMZOMyYmyD/zMz+DXq+Hb3/723jNa14TeO/KlSt4//vfj5/92Z/FH/zBH8y8kUeFdgIW1C8WiyiVSjhz5gx2d3dRKpVw7tw5V3i/0+mg3W47r7EaqDe84Q147Wtfi3g8jk6ng1qthnK57MrDbG1t4R/+4R+ws7Pj7s/OdenSJSQSCXQ6Hbz88suoVCqBpAAe7+HhEY5hEZmw/60BicfjWFtbw5kzZxzZVOOhujoaCO5Mp0kxJK/aJq10wa2g9bossWbDpnZBbQ0f76UGSw02dX/JZNIRanqvuFEIo156f25VTYKQzWaRTqdx8+ZN3L59G+vr6xPNScOkZbOAj755HAUcL9zs4l/9q3/ltP2sXJPJZJxtt55jzg82wZURGWDfycYqOIQuri1x5hwStpmHyh3oubaL4kkcb8PGI6+tHnAez88O7Ccpq0NAk5Y5xxCsG01Otb29PbYEnLZzHnnPxAT5y1/+Mv78z//8ADkGgNe85jX4//6//w9ve9vbZtm2mUCNyTvf+U6sra25ci4kwSxTokk47AwMv2gZF2r02u22+3JLpRKAO2GQxcVFXLp0CX//93+PRqPhOgdDPMyIXVlZwUsvvYQbN2640Kz1aNm/PTw8JoeS5Ewmg2w265JyCEtIw0iZhidJfG3YVI/lDz1IKrGwYVW9LsmwJcQ8Vj1bPK7dbrtwLLC/5S0X9/yh15jX4D1IqoE789ijjz6KbreLjY0NP/d4nHrEYjEsLi66H5VFae7BsKiPelDtrnhKeDVRl7DEUCM9OjZJxDWBdljO0zCPryW7ei8ew7lLSbB6uXXO0cU4X6cHne+RZEciEWQyGXS7XZRKJQwGA9TrdVd9Rx2ApwkTE+RUKoVyuTz0/UqlEsgCPWnwS0skEjh//jz+9b/+1zhz5owjvWoU0uk0ksmk2zZSy7Xwh7pD1R7xfK7yuCKMRCIoFAp4y1veglarhd3dXWxtbaFWq6Fer6Pb7TpSnsvl8MgjjwAAWq0WyuUyyuWyGyhqSK00xMPjtGEWE+UoTduoa6+urmJxcREPPPAAstmse12TcTWrW1/r9XqOUFLPqJpDklPKGKjVY8kkbkGtO25pW5kApNpjnaNUNsE5jAtt7vrF47jwbzabaDabaLVaLhrGsDKwn2yTyWRchY2LFy/i0UcfRb/fx7PPPnvAAxb2XRy358fPdR5HQSKRwKtf/WpcvnwZS0tLAe+pSpLCtnLWvkcvs8oWeC2OJfUI8z214fzhcZYgU6bAKDOTcW2ECggSXl30WokGy8BRP80qOP1+P1AFx2qleawu1nl9jYZxcUBP+qte9Sqsr6+j0Whgb28v4GU+bZiYIP+H//Af8N73vhf//b//d/zoj/4oisUiAKBcLuMrX/kKfv7nfx4/9VM/dWwNnRTsvJFIBIuLi3jsscfwwAMP4MEHH0QkEnGGgsc1m01n7BjKYKdTwbxeX73MfM2WeIpEIlhaWkKv13Ohy62tLezs7ARWb/l8HgsLC0in0y5kwQxa/t9oNFCtVnH79m20Wq0DBssbEI/TgrCw37T9N+y8UaSZ4c/z58/j3LlzLtHNelKAfbLMMW8rU6jB0NCnepAobeC1GLa0W7PquZyD9D5sR6PRQK1WQ6VSAQAXCmaYlxILSsMo+9rc3ES5XEaj0XBeZp3j+JPJZJwRjkbvbGF94cIFPPbYY7h69SoqlYqfYzxOJTi2Ll++jPPnzwd2tuOiUqVS6kUOW/gpGeR4t5KHMNs8bBGphNzqka0nV73EJLI67yiBBfbnrnEL2GFSNT4frdqjn5X3o3yUhLlYLKLX62FtbQ0AUK/XA5rtsHvOKyYmyP/tv/039Pt9/ORP/qSrwQnAlRL6j//xP+K//tf/emwNnQbpdBqrq6t49NFH8Za3vAXLy8toNpsBzaGu5GwmqNXaqGcJ2Ncb0tjpSoqdR4X9hUIByWTSGWYe1+v1AgSZyTk8v9vtBrzKmUwGOzs7qFarbv934PR0Ng+PMBxn+I3emHQ6jbW1NaytrQVChkAwW1uNlEokeC0lsJrgxtc4F7RaLWd47XWs0aKBobHRMCcJcrVaRblcdl4gkmP+6P06nQ4qlQo2NzedB5kLblboYSk4VvXQ+8diMZw/fx6PPfYYNjY2HDEfhVl8f6c1DOsxP7CLZxLkS5cuYWVlxXmBVZpEUmnrnOs4VeeXXUzrfS0mkUoqHwmTd1hyrHOEchVLkPkZ2E6d42yb7TnAPkFWR4ImCfN11mOnQzGfzztnZLVaxfr6+oGImW3HvI77qSQWn/nMZ/Brv/Zr+Lu/+7tAmbc3v/nNzqN8ElDDViwWcfHiRXz/938/3vjGNwIIdm7WNWRH564w9PjoCk47BYkwr0H9MRNfeF31CqkHiyFUGmx6gZhR3+l03DVUrxiNRlEoFFxC4cbGBq5evYr19XVUKhVXdm5eO5iHBxA+Cc6yz47yRq+srODChQvI5XIBL4gaQsImldjwo/WkcLzWajVXRk0NL7dz1qQbzhOqI85kMm7ba5ae0o1DuFjudDqIRqNukRyPx12eAxfMbAMAt/tVoVAIkOFcLueemxp6esAvXryIdDqNb3/729je3h45x4S9fpjvdlbX8fDgfMAyiSxnVq1WHaGzMkaN8CgJBIIcgmPZVrHh+2HEOqxtmitAUK6hZFx1zyrvIqegtAOAc7Cp7Iuv20W6rXBjP5+NZrHt+tl0MU8ZaqFQcPKvWq3mEvZY+i1sIRCGuyHfGoep6yAXi0X88A//8HG05dDgA49Go3j/+9+PtbU1RKNRl/jGzpJOpwN1/xqNhtMEs7Oo/IJF81W3x05WLpfdSrLb7TojyCLk7NTqeY7FYlhYWHCrrrBi/+p9UsPI81lpY3d3F+vr67h9+zZu376NRqMRIPceHvOGk/AURCJ3qjOsrKwECt8D4aHFsOQ5jSBZj46WT6Mn19YUVbmELnw5z9Cjy8U6Pbl2xy4l2NQt6v0IGnkaUd4jHo+7aBYJshosGuter4dCoYBYLOY2UNCMfQ+PecMwIsVxBQTlDPyfY3CUF5fH2gQ2K8ca1y7bRrtgHja2OH41Kk2Qa+iCm9dWbzL5Dz+z6p7D2qtEOOwzWQkIsB+BV/5TKBRQLBZRqVTcInuePcYWM9tJ7/bt2/gf/+N/4Jd/+ZdndcmJwAdNmceZM2cC2mC+l0qlXDalFs4H9gcOj+W5JMWsc8xj0uk0FhYW3H3oMWIyDFeB2Ww2EK6lnth6n7STJZNJ7O3tORE8SzSx/ByPyeVyWFlZwcWLF1Gr1XDt2jU899xzods8engcN0ZNerP2AgzzNA67z9LSkkuEVcNGY6jeYmBfEqGeYpJkHa96fLfbRbPZRLlcDhBgLYWkGeoMj5IUc9MCRpeY3MfjKBPhPTOZjCPR9N5kMhk3r/AclYPE43Hk83l3La20wXYyTEqcO3cOOzs7eOGFF1zi4ajv4W7ipO/vcbKw8gWdg/j74sWLWFtbc44mlmJTImxlVPqb17ILZmB/vtBEOXvsKNgFM1+zMitdaFOGyTHNggOUTNEJoM+A81AqlXJzWKPRCCTz1uv10GcbRuotOda2AfubrKRSKVy4cMF57hkBCxu3YfeZB8yMIN+6dQsf+9jHjp0gW0PMLyiVSuGRRx5xKy1dnamnV1eLwL7e2K6YSHKBO18WQ6U0LpFIxIU2Wb2D19GEFw196vvJZNJlkWrhbxorLWLO+sqUePA3r88tH3u9Hl5++WXUarVTt1LzuHdxN/piWBiTY5BjlgR1mOHSsk2cN6xHyXqauNBuNpsuIsUxrZo/GilCS8Vxwcs5htEj9XTrNYbJNXg/zbbX3a5IyGlQSY6ZhxFm/BYXF7GysoIXX3zRteWkw54eHqOgYzaXyzn5pxLSUTkBfE2vpYveUV5m2wYl3JZ0hhFqHcOcj7SajZaA7Pf7AcJsCbKdezTiRc85ALfI1vOsc8B6ixXDXmOd9UKh4CqFqZNgFOaFu0xMkL/5zW+OfP/KlStHbswkGOU9YukmVnpQYkwPL73CfM12Vv2y2eGi0agrvaTGDwhffVIawb9JaiORiEtqJBFWPSQ7LRNxgH3vFMu/qIeIq0ga1Te84Q3odru4fv06arXaTJ+7h8dRMMvJzkokwsKYmsRCPZyVI4QZRUqt+LeWclRvK8d0o9FAs9nE9vY26vW623RIE994vm5ly/alUink83msrq6iWCy6ijdcGGspKGqc6cnVOUC3zNZM/Uwm4zzTfBY8Xgmyti2RSLg59OGHH0Y8Hsc//MM/BELBYc98Hgyax/2BUX1NSSd3zotEIoGkdiuZAHCAACu5tItn/V/vaV9X/S6hESyOQS2lxnEK7G9tz4hSKpVCLpdz9+FcwfOVIPN6WlKObUsmk2i1Wi6PSUF+Q2LOyjdh86VyF/1MnIdyuRwWFxdRLBZRq9UC29yfhvliYoL8pje9aeiHCps07xZ4X5JGTuIaNlVtnep3dICErZLYkdn5CHYeenxtRQs13KoDGgyCpeG0HQoaRHqoqN2xO+pQ58MBwlJW5XIZ9Xr9VHRAj9OPScb9SZAoGhqSwzDohA7syyDUY6TkmHMLk3O3trZQqVSwvr7uyruVSiVHTJkk1+l0UK/X3fzCusj0AKmWkH83m01HXFnrmCRfk3H4WdXIsr2pVMq1QWUXlHBx/tRFPz8fQ9KUZej86uFxGpDNZpHP552jS+chHeOERn2A/VJpSiKtree1wrzEYZxJ+YDe18qhVPZJgsxxyHYribVzAPkCNzbLZDKBXApKQhuNhuNIzH9STmITEPVz0KnHOcTOR6zetbi4iGaziZ2dnQOOyXnGxAR5aWkJv/7rv44f/dEfDX3/n/7pn/Bv/s2/mVnDJgU7IDurXd1pqELLoVhSqh4ZDXNwBcZr625VfE8HDzU2NILsNLpSpNfZivypUyaZ1/cYBrGG3q48z507h/X1dWxsbBxIOPLwmAWOQpBm4TmY9HxKr8J2pSM4LnWcaTTJhmVpJDqdDvr9Pra2trC7u+vGG8c0PcHUCKrWkPMGxywNiUqwEomE8xYDcIRco058FhpJsoaKhpMJyir9UL2jDQ9TNpJMJgPecPU0e3jMMyKRCNLpNHK5XEAnDOxrfTWvQM9T6OIYCPcW67VVvhBGBK3Xmedbgsz36ACjpELzpDimbflGLrR5HKNVujBoNpuoVCqOkzBXws6T2jbbfuVTnBv5rLnwT6VSKJVKaDabgYX4OJI8DwvxiQnym9/8Zty4cQMPPPBA6Pu7u7snSsQ4qSth1ZAJO4waQ2C/XjGhX56GQWmc6FnZ3d11Or92u+3KyWi5Fd2+mgY1m826qhQA3FaMyWQSjUYDlUrFSUVUS723txfwaHHg8LMzvLK2toZbt27h2rVrTovs4TFLqEG526EynTTH3TebzeKRRx5BsVh0m++oB4XX0PnARoqAoLwCuFP4nnNGLpfDYDDA5uamWxxzwc7ID7005XLZkV96dEulEpaWlrC0tBRIztG5BtgvQaXeHU2OUVmWRsX0mgACi3OVV9B7ZH84f2UymYBH28PjJDFqHrARYDvGCZU/6nlKXO0CWeUEep49Vu9H7qDlHtk+jj0ubOlxLRQKjhuUSiVHbtPptNvKmXY/Go0GFsBcFNsScrwn5wzNTaCHmnMWF9lcuNvqGfqcNGpuEwz5mc6ePYt4PI5nnnkmdMOksLn8pFQJiokJ8gc+8IGRutbLly/js5/97EwadRjQqNhQqZU06Kqv1+s5otvv95HP551Gj+RXr6cJOXt7ewHPkGaCJhIJV1tZk/B08PB/6glZD7nRaABAYMc//s+EI3p/dICwI7ZaLaysrGBtbQ3f+c537up34OExDHeDRNuJNpFIYHFx0UkcdFIOO56v6W9Cx6xKEzjWiTCjCsCRXV2sA3D66HQ6fcDLZNvLdlkpWJhnfFhijc6DVletdVn5WxfjuuPYPOC06Bg97j6036sd5/8AAg4nG1VWkqxkWd9XuZEd91qtQbeY5z1JHm0EWiNKvG8qlXJzDB1xNkGXFW60bjL3dtB5QBfN6q1mpF3nLBY14H3Gedn5WyUZWj2n1Wo5XmS3n57XsTzxjPfv/t2/G/n+4uIi3vve9x65QdOCXxxDErYzAnDeHG7wsbOzg+3tbVSrVeexHQwGOH/+PBYXF13WJUk3sO+lVQ0fAOdV0dUawzEqk2AH0ILdyWQSu7u7LszBtpVKJWeEaZh4fx5H2ASCSCSCixcvotPp4Nlnnz0Q/vHwOElM0xdH6ff0GP7WH0oDVlZWEI/HUavVAuE9nmO3Yw2rPsHxrh5hep+UHLNsUr1ed/cD7hhiJtpykZ3NZl2NViba6P14HtulpZw4D9mcBD4fTZJRL7gaeco8dDc+tr/RaKDRaDgvcqfTCdSCt5iXucXPc/cP1Ps47HUlgvZ9C0qX9H860VRzrLJFen6VhJJ7cKyTDJJ/sI9S9sQxz7rj/GGVKybmZbPZwIJcHW3RaBT5fN61n3MDF7ycq/g6nX+xWAz5fB6NRsN5oPmZ+FkAOLKu4ytMsqaVNDg3co5ZXFxEIpHAgw8+iPX1dVy9ejXUATBvmC+XwCFB4kiiTM8rOzQ7ysbGBl5++WXs7Oy4+sKUPfAL39nZQSaTwdraGlZXVw+sLHk/dmB6gmz4geEKXUnRkxWJRNDpdLC1tYXt7W3UajWsr687CYiWYeK1KJfQHfhsUXCbMKiJOB4e84Bp++Io0hMWfqOByWazblwD+4tUHadh11VPi+oTw/SH9I4Ad+QcmkNAw8jz1WvC+YM1kDUBzuZPkDhz0a2VK5TEq5dsmPdLF+00gEws1I2TqH3W9igR8POJx7zC9k/V2A/rtxwbeozyBvWq6nyiHuSwhF7dWbPdbjvnGqVSXHCqHEE9yFryVSNOjHizbcp/dL5SL7GVW6k0i9e3jgGtvMX2qRfdEmT1NGsb9HMxcVmPsR7+ecI9QZCJdDqNZrMZ8ChXKhVsbm7i2rVr2NnZQb1eDxDPbDZ7oNg2M85pBMNCs1oKBdjfLERXm+l0GsB+FiqPZcgkkUhgYWEh4KWu1+uoVCpuILGt9CRrJrqWrWFn5opyYWEBjz76KJ555hlv1DxOLabtt5QqnT171kVitG44xwfHDICAZ1m9snbM67yhdYUHgwEuXLjg5pdOp4NKpQJgf5t6kk/ev1AoYGVlBYVCwe1Wpx7dwWDgwqZ8DYAr+aQEWWUf2m7VTOpr/Dz0dNNTzMX61tZWIBJntc+zgJ+TPI6CcdpUki/q/VnJQfudjajoWOECkWNRxz7vrZsJ6ftaEWJnZ8eNXyYL0iusXlgSVcol6DEG4OYIOsiy2ayTZtDjy3lLOQG5AtupY5iENZfLoV6vI5/Pu42CWq2WI/icP3k9la7aRbqNMCnx53PK5XLOcXGU8a9Rv+PEqSXIYR1dvyySUSbU3bx5E+VyOUA0qSHmRhzU/wJ3VoTLy8suYU47lnpzrFHSxEAl0RoS1QGhnazb7aJWq7kNSLSaha5cdcBar452xMuXL+PKlSveEHkcG+alb9EjQmNSKBTcFrM2DGrPC/N66FxCz6tmpvM4vn/mzBlEo1G88sor6Ha7KJfLLszICFClUgnMHfSkqOdJJRK2TVzI0yMEILSOMRDMpAeCiYYq1arVas4wsqazVtJR58AsCfJxYF76osfxYxxBUmKmr4WdExZx0Wisjkdex96H0HGmeUss0ajyKeYVAfvJ+twNU6Ve6mm1nyXMM8wfklx6mbkhED8zf2uVG/Im5lopP2IVG10wM2JPnqISVMqzdKFtiyTMO04tQdbOooaMv9kRbEhVVz4kuPxyuRkHj2s0GgESy4HBa2pdZb0er6nGl+3l8VwhsqNxMNGTTanFsMx7/c1Ore8nk0nnoSqXyzN99h4e8wBdFHJyLxQKjiRr4psSXwsdp+pBtoTZaoPpVY7H41hZWXGSq263i0qlEtitrl6vo1qtOq8WSzIBwVJzYQQZ2B/juqsWj2UyjYZ79X393el03BxIgsy5hwSZHiS2jZG0u+GxmRTz1BaPk8EwkmyJon2P5/J3mDRJSa4uEO39LEHW/COOK16LUgW2j95Zyhg6nY7bQt5WgwiTiYR9TquH1oUzx7T9rJHInQ2F1OPM65PYs/qObsIWidyp8EUPOec28heVnOliQ73wozAPY/zUEmSLSCTiJBZKgtPpNFZWVrC1tYVmsxkosE8iSYE8JRGDwQDZbNaFRJSAWk+xJgOqR4kyCYYluPJi2HRlZSUgiOee6Ox89DiTbCcSiUC4R7Nk7aAmYrEYHnroIXzjG9+4a9+Dh8fdghJZjgUmmVGn3+/3sbCw4KI4JLVKnPVHIzpc8IZ5jBWMWFlZ1O7urhvDJJ2UVZVKJRQKBTeW2WZr1JT0KnmnbpheKdUm04vDNkWjUbfRSLPZdJ9hb28P1WrVXatSqbhEPV3Yx2IxrK6uotvt4tq1a3NhuDw8hpFj4I4nkwmw3EXTyo3CiLHaUh3r1iEHIJCUr4jFYlhcXEQ6nXZeXJVY9Ho9ZDIZt8NfNBrFuXPnXHvz+TzS6XRgLHIhrHOJapAp39KxSz5hF/hKVMl7lpeX3WerVCqo1WpuYU+uoWUs+WxsJJtzRjQadaRb5aALCwunymF3KIK8u7uLr33ta1hfXz/gLn/yySdn0rBpQGMAwInbSZKXlpaQzWYRj8dRr9edUaHrn55aYJ8YZzIZ54XSWolhhpXnqX6w3+/jhRdecDtRsT2rq6tYWlpy20Wy7f1+H7u7u2i1Wk7mkcvl3GqOO25xcOnmAWwLcHCFGIlEcP78eXzrW986UOjcw+NeAfszjQjzAQAEvKFhIVYlx/YYJacq4dLj1FjweM6JmujGxTC3luYmIpoMpLXWLXGnAaK3Sree5nucIxhK1eQcEnDumjUYDFxCHudD9faot4weJnUgeJLsMa/gWMhkMm5MWImCIowYWzI97ByOdevZZd3ibDbrKljkcjkXFabOmGM0k8k4nqIyUCuTIilV7zb5hxJkbaOVl+nn4rNSzTPH/+7u7oHqHmHPgdex/2tVDuBO9Is5IpNgHuaZqQnyF7/4Rbz73e9GtVpFsVgMfIBIJHJXCbLeWzU28XjcbQzAwXH27Fn8i3/xL1wyyubmJvb29ty2zL1ez5VbS6VSWFpawvLyMorFYsCLDOzr/oB92QYHIzXPmUwGt2/fdm1jSad+v4/z588jlUq5DHJ6vVgiivdQ3bNmurOShXq4OPBIooH9cKyt6ezhcRowrL+GTca2LBMJKWUPxWLReZAbjcYBUgsgYJD02raSg5JHTbBVyRONAr00AJDP57G2toZXv/rVWFhYcNno6nXW+2t5JuoYubDe29tDo9FwGzRp4iDPU9JM8q1JytQ98toaHeOc1+l0EI1GUSqVUKlUDiweTmpO8XPZ/Qv1/IYRqH6/j8XFRTzwwAMHcoj0GtYrrLpjnUf4vo0k63jhHKFeWtZhpyOOOl9KEbhZECM9/Dwcw5Q1qKSTv8lB7Bxmc6D0+uQPNsrM5ED1SicSCaytrWF3dxe1Ws3Va282m4FnSXmXzlt6HZVUaI6Itnmex/LUBPnDH/4w3v/+9+MTn/iE63wnDRoIdmiGLblDHb/ApaUl9Ho9rKys4JFHHnHHsNza7u6u6wA0KmF6QC1dwo7FTsaM8H6/74yZivRrtZor8UTiTGLMuoH01pDYMuuVA01XhDaT1mbsc2Wq4REPj9MMaxjV20ONLV8P0yKGeYuH6Qs5rniM6uc0emaT2WiMGN1iSaVCoeAkH2yjGmQd37r45XX1PHqbOS+QzGoyMI2szo/6/NRDrUaaz5Kfi5IvHnMUzMorNMor6HF/gv0hk8mgVCo5iRPfs6RY37OJsiR6fF8jOnq86vVVbgnALcotZ7B5TXYO0EWzeqm1XToX6Y69PI730yiXXsOSfIKkmlEuXXCT71gHAhCskBOmObb/n7R3eBJMTZCvX7+Op5566sTJsTWQ3W7XeV+B/XqothQJsF8bMRa7UyibJZe2t7exvr6OcrmMXq+Ha9euodVqYWlpyen6NHucdU2pLc7lcuh0Om5rW953d3fXeW52d3edFIPX0vBLPp/HYDBwGe6qQ1KvFNuvHd96mtmubDaLSqVyqrJHPTwmhU78rMrALVuV2NnFrp4XlvGu3iB6kMJCsdHofik2/s/FbSQSQS6XcwZmeXkZZ86cOZCgR3kDE3XVoNILzHmB92bVG8ot+Jk0cZghTfV4kziTSKuHGdjPsKdUZTAYOC0nj/XE1OOkoItHC47JQqGAy5cvu2gr7aEmqeqilmOP55OAaq4P760Jr4PBwOUBcL7Q+YUEk+MTQMB268Ke5zIfSavJsH2UUOmYVw0yF8zdbheFQsERWj4bLd+mCwKVcTFPi841OhnJH2xUjeAGSnyd0i1+Nkq8VCc97ns+aUxNkN/+9rfjb//2b/Hwww8fR3sOBf3i6eHQFZLV01kDR9c/PbtbW1tuN6nt7W3n1dWwSKvVAgBnhLlzF3BHP7y4uIhvfetbrtwTk/7y+byTY3BQsQg4NUQMnTAkqlrpYSRXQ7L80V34LLz3xeNegU7UNHKUXKlxUZAYqkdVJ2Sr57dek1HEut/vu/mBZDOVSmFhYQHFYhELCwtukxBrZNRbo95dHqN6Ps51zKvQa7DdNIIk2Zy/1LNFOZpKU/h5tQ0k63weHh7zAOsAA+7Y5YWFhQNRH4242nNUssCxM+p+hMqu7HhVe8zXOAZ1ztH2qTxCJRdaWo1jUOcLjZx3Op3AYppzAt9TTzl/WwKvemh+BvUW6zPiPZRssy2cgxlB1y2ww57nPGEigvyFL3zB/f1jP/Zj+IVf+AU8/fTTePzxxw8Uh37nO9852xaOAR+sXSWpZ9V2RF0l6nW4y0s2m0W5XEa1WnWdQldrvBaLdlODnEqlnIYZuJMgdOvWLVdHsFgsYm1tzW0MoCtJAG6lybaxI3W7XVSrVWfQuQMgENzG2uonB4MBqtWq01yGPTcPj3nFuIkz7H0SPS2RqItnnQdURsXXgP0sb0JDksC+1IKLVhofNSJqbDknFItFFItFFAoFl5CjkSQ15pYk22Qj/s97aTlI+ywAuDmJcxSlF/Qkc2HOeY4bK5BIqC7S6hg9PO4mJrFd6XQaxWLR2U9dQHPsW+mBEk717o6C5QRKkvUYJcQqgbJQZ5h1dpEvcAzyf410caMPLsrpVeb5Gumy0TC2UyPsjIapvEJJtc6V6oykJ5xzk25hz+g6n808c5GJCPKP//iPH3jt4x//+IHX6Hk4CbDD8Itj/WJ2hjCyDCDwhdPLlMlkUCwWA2FQZsMzBMGOzgWCbj5SKBSQTqfxYz/2Y6jX66jX6y6soFnk2rHYPuqPmaFO41Uul9Futx3hZYdVPY9+tn6/j2q1imeffRbVanWuO6GHx7QIC7HqwjCRSCCXy+HcuXMuKUS1giS1quFVGYZeX1/jPKEJPDy/1Wq5pF4lvaVSCYuLi3j00UdRLBZdUjDboERXSbdmsxO8bqFQcEZQia0aPGvkKDvReUuNpD5HGltCtcg27DyL787D4zBQu67eS2r+2W/pxaTEQh1k/J/9nUSQ0SVdENJTa+2uRZhDytp7/SG34GZAGxsbLp+J8gnmR1G+0Gq1Ah5enad6vZ7bdIxzCj3HGl3XOZOLYM2ZIiexiwCd++xiXq/L74N87Pz58+j1evjOd74TkKSEPbN5wEQE+bRoV0ksSZbDdLq6YlENkg4WGisV03OwKXTQsBMwOY8GOplMolQqYTAYuFJLDDOoTlqrT3S7XWfAOp0OarVaQAPN1RmNNY0828J6p5VKBc8///yp+f48Ti9m7Qk4CoHSxSulUVZWxblAF8jWY6REznpbNKFGj+f9dN5hnkOpVHKElgZNy7bp1rGEJsVp+5LJpPuh9o/GO+x7CPNKqxfIepTCPhsQXunDw+NuYthcowROE13DxrgdU9bma6Uq/s0Iro04EXpN21a9p5ZcVbkHxxVJMb299ACT2NpdLm00mWD7OZZ1frDPoN/vO86h438w2M+HIjFXb7gm7w1bLAD7so1sNutyGfQ7mlfcMxuFKGKxmNPusoJDIpHAxsaGM1Asot1ut1EsFp1hymazAYNKaCjBhlFoiDOZDLrdLlZXV91GITyHGwRQA8QwCT1F7NyqkWLWOwC38i0UCqhWq67wvy4ESqWSGwCtVgsvvvgivvGNb4RKLDw8ZoVJyGyYl+C4Jsdo9M428QsLC4Hsbr0/vSAazVHirARSyyppnWJg3yjFYncK7l++fDlAIFWWoaFQnUPoBdJxbw0Pr6eSiUKhgLW1NWxvb7u5pt/vO00xPwtDnIPBHcmVepABoNlsutBsq9VyUSomIeu9c7lc4HX7TI+CafqD90B7EHZBSWcSsB/dAfaJLskunWmx2J1dc/f29pBKpZBMJl2Uh32Sm+lw8w8AziFG6PxhF9o2J4qvc4yTMFIeVq1WA+OZ80cikXAEU0u0kbiTQ1hC3uv13B4LAA6QfJVs2ChUPp93bWH+FJ8t5RIa5QL2vfKcL3XuovOQXupT70FWPPXUU3jkkUfw1FNPBV7/nd/5HTz33HP4rd/6rVm1bSrQCJKANhoNZ8joub1586Z7H4ArY8KkGZY2UY2PDi52aA3PcjMPhh/pPcpkMsjlcq4DM5t2b28P2WzWGaNarQZgX39IT/XS0hIAuGutrq4iHo9jaWnJCd9VMM9tKqkd5MD/vu/7PvzJn/yJ9yJ7HBuOEjKfFUnWNkQiEWfo+J56Q60UwYZRSUotebb3UiLN81gxQt+zSSn0AtEA0XOlyYS6cOY51hiTXKdSKecI0Pqoms/AOY/5C/p56KVSvSDJgz6zecE8tcVjvsAxoVII9bRqiTT93el0nO3k2KEMSuUZ6vRi0qu+ZqFE1Y47cgzek8d2Oh1XkYuRZ0axdSEc5hHnMyB3sRJSK4PQhD/rgdaoEf9XXTTnNFtOjj9aKYN/h21ENM+YmiB//vOfDyTtEd///d+PT37ykydGkIGgVpBklYl3AFAsFp0xUHkENTaaXaodUVdZRCQScaunSCTiiO/i4qJbGaneWDNRaWB1D3NuMqKZ91quaXl5OdDJ2PlZIooDTatWZDIZvO51r8M3v/lN3Lx50yfWeBwbxpHcu+EdUOJKLb/1iOgiVL0XuqmQXXiqd0alWvTCcr7gQphIp9OIRO5sO6/GzWqeNeObu27F43EsLCwESjtaUss2Z7NZRKNR5/Wlfpifl1pCfo5kMukSeNQ71mw20Ww2AxEqQkmHeowPszg6DYbRY34RtlgF9pPhaM81v4BjgmSNC0EuSGu1mvPaAnD9n9IG7m2gi2jOI0qQLVkFECDiSiBZ0pGyTl53MBhgc3PTzUfcBZgSLbZJZaG8t+ZJaZIyq0gomVWe02q1XG6VtpvXVyeDzndsc5gGWhfdXGDU6/WAlnre54KpCfLW1hYWFhYOvF4sFrG5uTmTRh0W+qVTFE5SGolEnFvfEkx2Ui3ZpAl/vDYQrBpBrW8sFkOlUkE2mz2QpENoR1UvDfXGusKlcdN2aQY726eeLqu1ZrtzuRxKpRLW19cPlLry8DhJHAdpppdjcXERCwsLLi9BDYouLNVIqLG1+jj1NNu/ATidnhoOLsxpeOg1pkTBSqq4YGamuuqnrYyBRrBYLDrvMbeNBuAMOqUVulue1oWlcWRuRKPRQLVadW0ltNoO7z/L72/eDaXH3cewxVeYhIGvJZNJnD9/HrlczvXzYWNXyRsXh7yfShg5ZoB9J5lWydE5xHpolRyrphnYL0lpHW9KTGOxGKrVquMpJNX8/IxQE7oIV8LM+YXPS3XLVkZGXsI5hNdRgs/PyQRB/Z6UM6kXm9Byk2Ee8HnC1AT5kUcewZe+9CV86EMfCrz+R3/0R3e9NnKY8eIXZ7W91qCx07AD6WqG57ND6/WBYI3URqOBZDLpBhiJLRNo9BqshKFGyn6WsMxO6pBUp0hQs8TOraEVrpBpoI9L8+nhMUtMSpaG9eVIJIJSqeSkU4z2EOpRUQ+OLrDDJvywcK3OQcB+iJcepkgkgnq97sal1iC1HliNHMXjcZRKpYBxIfnnWE4kEsjn8+7/Wq2GZrPpJB3UFqvni4aWf3Nu6XQ6KJfLzoOsRF/nDev1mcWcMsn37ecuj1FQwnj+/Hm3aFRSzON0cajkWAmyVq0ieLySUPUgW2mVzhMqdWAkhiRWuQUX0hyniUTCbQZE6SdJsUocdP6hvJJzDu/JvCvlQXaxoN51RqNI2u25vLd+bvvZ9fOrRIMEed4xNUH++Z//eXzoQx/CxsYGfuRHfgQA8JWvfAW/+Zu/eaLyCq5WSCKBoOTCdmytHUpJhk7+PIcSCO3g/IJbrZYLF8TjcRfW7Xa7rqQSSbPKLDj4+GO9VmGDkm3SEK4ac16XHVPrIWq5Jg+PexG6uNWJm5P8MM1b2AKar3GCB/bJKccfcw048ZMU81ieTy/LYDBArVYLGEMl02rE1bCoHhHYD6fqIp1t4cKcpJnzjBo7mxFPj7ZqorVWsnrM0uk0SqXSoZJ+RxHcSWQanhx7DIMStlQqhQsXLiCTyQQ2z2Ff17KJyhVU7qCRZJ5rcxi0vBrHNfuxenj1Ohzn9jWNVql3FkCAJ3DccR5i/oFdvJIzaE4CeYY62VQmEolEXMSL86fWROZ1eTxJeSaTOeDNZ46U5oKRb5XLZezs7ODWrVuo1WoHpGPzhqkJ8vvf/360Wi386q/+Kn7lV34FAPDggw/iM5/5DJ588smZN3AaMGmOKycSS/Uk0ygR3JaWhJhEOBqNOqPC0IYSaq7GCoUCFhYW8Mgjj+D1r389VldXA23SOoFKsFX6ocaR91CdtA4au/KjdrFWq+HmzZuu3inby8FtO6H3yHjcqwjzcqpHVI2ULqb1PRv2sxEmzfjW92k8rNcV2B/bOhdxftAxbr3UKo1Sjw6vbT1IugmI6qrVyCspDwuB8nxN3mGlDp0/ZwU/F3lMi7CFVSwWQy6Xc4tAe7xGX3Uxrc4pJYY8T72ijC5z4UnJlMoydRwr7Lwz7HOF3Zf2Xj3BJPa8pla0UHKt45vPSSvlkItYzqHPKSyipO1UR4NeS73S1WoV1WoVtVrNyb8Og0mjjEfFoWa6D37wg/jgBz+IjY0NZDIZ5PP5WbdrLOyDpWEICwWqZ0YT3waDO2WPmKyj2mUlqOpdAeA0v7FYDD/4gz+IV7/61VhcXHQrMJZfqdVqiEajKJfLqNVqzqvLcm/tdtvpfOxnsZ5srlLV2OsgHAwGOH/+vPNqcXOQ7e1tXLt2LXSy8PCYR0ziURwGHTfq6QH2w6Zc/FKDDCAQ7tNJ3uYl8Lf1LutCVLeEtaFHGgu2j5ErRofofaIEREFSTu+wGkSWr6REI5lMIpPJuAx4IJhkzLbSkKq324ZveY9I5E4ZTEpH9HmNgzWoRzFwfu66f6DECxhOjNhXGfmgbeSYVU29JY5cgKbTaVfZSuUEjMLogpWJe/V6HdVqFalUykVc7NjQqBGh12abeIwWCojFYo5IcrOPdruNdDqNTCbjaqDb+uSad6E72il07wV6fvms1BloJSLWcaBys36/7zzDuiinE+/KlSvY29tDo9EIvd684dCugI2NDVy5cgUA8NrXvhYrKysza9S0UKmBnYj5W7VC/NIbjQZqtVqg9rFq9nq9ntvZhiuiXq+HRCKB5eVlPProo3jTm97kpBXcZYoaIU3K6/V6rvh3Pp8/MEDtKky9y6r/4edRw04DzMxVSiqSySReeeWV0JCo9yB73MtQ+ZKVXXAe0FAoYRfYSpx5vuYn8BwloapZ5vtKRNWLTOKpBpTXomSK91aCTHKvr6sRAxDwEFmwTdaA63NSYk/SrFISfV7T4G55fzxOP9SLOayvWU+nVn4hlIjZhS3PZd4QZVSaLwQg8JtcodPpuOoXYeNMHXV8X/XKYbAchg45tkfntbAScHTEcc5Rgqyfl+NanQphZN4+Y9tO660mOWdbut0udnZ2sLe3h0qlEiDH846pCXKtVsPP/dzP4XOf+1xgxfPkk0/iU5/6FLLZ7MwbGYawAaOrNxoh9Rhz5WVlB/aL1s6gW7NyhVooFPCmN70JwH4pKCW07KCbm5uu6L4mz7DDU9phV2C28zLkqYTargY1OU8zUp999tkDE8G8r9o8PA4L9u1ms+nKPZFEaogUgPO6KqENq/lpF630oOoY5bXVCGkd4n6/74wWj6MnGwjOI5xrdnd3XTUL6iPpHSNB1rKRGood5nHTyJM+L0IJtSYfce47rMTCL8g9jopRCyuVCqitDHOS2fM4jujo4mYgGhmi/WZOD39Xq1W0220sLy+7CIven/OO3TluWP6RTQzkseQUfI3RZ2qgC4WC20SEJet473K5fMADnMvlnJOAz07nD7vQ5mdQAs2/tUQlo9eqg261WnjmmWdw+/Zt7O7uHvhe5hmHStL7sz/7M3zxi1/ED/zADwAAvvrVr+Kpp57Chz/8YXzmM5+ZeSMngQ1ZKNlV0qxkWDswcFCTFI1GsbCwgGQyiWKxiHw+j1wuh4WFBVy+fBl7e3vOcCkxLpfL2NzcxObmpruHenlpbLXzq5HWnbUI/Qw8RzswBwAHHs+xYVoPj3sRJIkkf3t7e0gmk1heXnZjWRecunDWcUYJhnqLlSDTCKsXGNgnw+o9sfOKao2toeY9rSGloeGYZruZuBeLxZDP5wNa5larFajfrIRX28L3rIzEtk010zaBaZrvZ9bwpNsDOGi3NTqifdUm6moUdjAYONlCOp12x/Ba3ISr2WwGNttRWROvwfPCxrzyDWvj7XFa4lXHpHIA7vgHwElDqtUqKpWKaxM1y/YzKUdS4mvnLz4rbTuvoxFyLhp4P8pLd3Z2UC6XUa/XTxU5Bg65Ucgf/uEf4m1ve5t77R3veAcymQze9a53nRhB1tADgABRDstg54DKZDLOOwLA6QD5k8vl3E52i4uLru4oOzMJMuUa1WoVN27cwPr6ulvZ5XI5p23i5gFhyTPsPOr9Vq+ytj3MWxTmPVpeXsatW7dOVaf08DgK6DXhBhrqJaLX18oICBoJEmH1lqoXN4wga3THav7VwITJnZS4awIgr6n3GwwG2Nvbc5+F92JbNWuc8xRL2dmQr86P+pvt1LnEPjc/l3jcTVjCa/9Wgszjw47VyIgufuk95gZD2scHgwHq9Trq9ToqlYrzmmoSbbfbxeLiopsnOOfw/joX0farvEHbqvOP1RerI5C7BPf7d8q/AUC1WkW5XHYEnCUuLclWWZX1tFsiO4wg62Ys9CJz8Z5MJrG3t4dbt25hb28vUI3HXnteMTVBrtfrOHv27IHXz5w5E3gAJwFdJakXhl+u7hTDaheFQiG0yoOezwFFobnVMbdaLdy4cQPXr19Hu93Gzs6Oq1ucTqdRKBRc6STKKkjEaXQYfuUKjJ4sEuhMJuNeoweLUg0bPmLb2u02HnroIfzTP/2TN2Ye9w2sh8RKJ2iYstmse5+yBo573TmOsNo8LXSv0Rtr7DjXWA8u2wMEC/yT9OpGJgQNLj9fp9Nx29VrCTg1WAyPhj0n+7+SYhuRAxDYmdTD425iFJGKRqNYWVlxyfJ2q3TgYA1kS5qZR6QLVb1GOp1GNBrF2tqaS9BrNBrY3NxENptFoVBwDjFLPu1iU51ahLZLPeDabp1PdG6gR7lWqwX2W9B7aJSN84LOd3q8ttl6l3u9nqtCUa/XAzlWzWYTe3t72Nvbw0svvYRarYZarRZKjsd9p/OAqQnyE088gY9+9KP43Oc+58IJjUYDH/vYx/DEE0/MvIHDEOaFCRPBK4kkIVUyyYHEFSjBY1qtlqupmEqlkEqlAt6hWq2G3d1dvPLKK9jc3HQZ3yS11ETp4ONuWex4lF1kMhnXOUnmmXCnHiHttFw96nbTNI7cAMCTY4/jxrx5FNUQknBaUqpRHEtewzw7/N+Gb9Xw8X89T8kx/9d2WvLOa9PIqdeX/6uWmZnsqmMkeba7fYUZvWEG3HqVABxI0hv2nDw8joppyFM0GkU+nw9snBN2vu3rvI/mKlhvL4/h4jSXy7mkOL7O6DCT/OzeA2HjI4wkh72m7w2LeHG8DwYDJ3VQPqTPSaNOlvfoM1IvszoAyDdarRYajYZ7vVKpOIK8vb2N69evuzkoLHJ2GjA1Qf7t3/5tvP3tb8fFixfxXd/1XQCAb3zjG0in0/jyl7888wZOCvX2DAYD55FlPUTV++ikTm+sTcjh39QUN5tNJ6NgcWx++cB+SDaRSLgVHACnSyYhZqhTt65UA2a9wDy/Xq+7AcrjSKx1IHBVV6/Xsbu7i+985zt36Rvw8AjHsDCife0o17bQ7dx1tzzgoMZOCeewrZ3Z1rD3IpGIC8vSMHKDIWtwlQQTnCtUo6hRKi5+6T3iwpnh3X6/7zYsYnsGg/2Njrhgt0bKeon0XN5bPdU0kurZOorhm8Wiat4WZh7Hg7Axp997LBbDo48+iqWlJVf9Rccux79dFOrYZMUqlTTomFIOobXGa7UaFhYWUCqVDiSwqkMujJTzGNseW3YS2OcKmqBfq9UQiURc5ax4PI5KpeI0yHzNRrC4MLdcQp+NesGZoMi2NhoN1Ot1bG9vuzb9zd/8DTY2NtBsNg+UijutmJogv+ENb8Czzz6L3/3d38UzzzwDAPipn/opvPvd70Ymk5l5A4fBDhAbtlBPC8ucWA8SO5b1CAFwXplGo4FGo4FoNIqdnZ2AGD+VSiESuVOqaXd313VKYF+vaHfYopFRUqthVnZiGkCtx6yhTZ6vG5FwNReN3smOf+655/DCCy+EPi8Pj1liXN8K88QeBziGdScpvs5xokQvrJ0aflVvqRLDMP0jxx4X6CrT0OgVj7MeZ93lS+/JMa4GTsmrVs6w56mRUqOnCJOO2KRf3of13GcBPx95HAU6FpmsyvygMA2tXUxrxEh3xtOxb6tKWHkUq1GpZtnKJmw0xkZo7CJc8yDs/KOfm046bctgcCcpr1aruT0dwoi4JvBafqLcgue1Wi1XGaPX6+H27duoVqtYX193i+nd3V1Uq9VA1FqJ/2nEoeogZ7NZ/PRP//Ss23JoWDkFv1DuMsOJPpfLHehwlUrFna/ieuuxsaL93d1dR4AzmQyWlpbc6pWo1+sBKQUH0rBBFI1G3S54fF23fLVJe1ZS0m63sbW1hVdeeQXPP/88nnvuOfdMPDxOCrMmQsMmW31dyzKxrinHsCa9hRmhsBCsDbfaag7ULbKYvxJkzi3FYtHpgrVcGj20LBGl92KeA69Pcq3El+RVSbOtA83Pw7lF78G5yCYOq6a62+2iXq/jxo0bKJfLR/j2ZpOY46Ud9xeGOXhoBxOJBFZXV5HNZl30WMeyjjU9T8mxOqC0yg0Q3CaaHKPXu7MnQjabdbXR1cuqJFHbawkyj1GpJCUczFkiGdbFuNY45tzT79+pYrG7u4tcLodUKhWQg+i11LvNCBGjaAAc+Y7FYtjZ2cH29rYrefcP//AP2N3ddQUARkWTTis5Bg5JkK9cuYJPfepT+Pa3vw0AeOyxx/ChD30Ir33ta2fauGmhJJI7UbEj03jY0CDlD5YYAwe3l1VSSq9NNpt1JeCSyaQj5kzcazQaTnvMzqfX0baTJKuh4r05CNSwa9i43+9jd3cXL774Iq5evYpXXnkFOzs7Bwarh8dpwCQkyhoe/uZ4YVJtPp930R4AARKpIUfrPea44bnD5AkAXIgTACqVigt9AndILr05TFjhscxC73Q6OHfuHM6dO4dCoeC2t1dPt85P/DvM0OomATYDXr3NljTTkNJwquctmUy6fAtmxR8WszCYp9kr5XF06HefTCbd+GKVGYIlx5SY6thXWYPV7FrPsR6nERnqj1kmjht2aaRKx3BYFIsVMbj4rFaryGazyGazbn5g8n+tVnPn6k68XCgwMY6SEc5NmlSsz4Okm22xCcqRSAS1Ws1t8NHtdrG+vu6KCdzL3OJQZd5+8id/Et/zPd/jkvL++q//Go8//jh+//d/Hz/xEz8x80aGIcxYqeGz4VDtHPqjA8NKH9QI6SDjwInFYk57RCPM3XUajQZu3boVSJhjO6y+SBOK2KZxKzE1vqyHeO3aNTz33HO4desWqtWqO+5e7sAe9y4O02+VBHNcMPuc1wyTWKg+kf/zN7PG1chxkap1kxntqdVqzjBz/DNJd29vzxXL73a72Nracgaw3+8jn8+79mqCC+9rPVqWqA8Gg0CdVp3jrGRCw638m59DFxz6mUmQ/aLb425iWF/j4o1VojRPJxKJOAJp7X/YNXVsqa3l/0oieXyv13MEmR5brQjBecaWfdQ5iF7ZTqeDvb09d2w2m3XbSTebTbz00kuoVCq4fv26+xzDcgt0LrMSjrCxqwtvLeVoz1MNt32GNqpzLyxgpybIH/nIR/BLv/RL+PjHPx54/aMf/Sg+8pGP3DWCbEGDSK8qK05Qv6fCdhXe629qZ7gTDg0GjQlDGDQ6qVQKxWLRrR4Zzmi328hms4G6pM1m03mStSMpMeZrdqWrRpLHcotaZpJev34dTz/9NF5++WVUKpWATMPD414HJ/RsNotUKuXGX7VaRSaTQTabdeNOCSET+fgavU86Lpkgq3pFhlQJelXVc0vDduPGDfT7fVy9ehV7e3vOS8RQJ8c6vcaPPPII8vk8arVawADZeYt/Ww+VRrk4T/AeJLckDRpKDqu3Sh03Nz+qVCpu97/D4jDeX7/Q97BQQheNRrG7u+tKI1LWxIUsxzfJK204cFDeYBeiPIb31P85H9hotM4xunkXEEy2s4Rd78P8p2vXrrkNN1g9gphmoRpGXC0JBvaThsMI8iS4F4gxMTVBvnnzJp588skDr7/nPe/Bb/zGb8ykUdNAv9R2u+3qdHJw0BglEolAGJSGjAZAvcTqMWKolq+zjEs6nXZJAZqQp51IByjF/FwRAgd1TtQF2QFt9Y6qLySuXLmC73znO65Osz4bD49ZYl4nQI5RLlS5gGT4Usup2R/OAczmVskFjZnWGVaJlHqblXxy/iiXyy7kWqlUAtEdYjAYYGdnB+l0GisrKwD2STc9YzaKNUz2oVEv+54m3ymZtkmD+tm4pS4TcHRBf1hMc/689jePk4MleyS4ureAQmUICiXDqsW3i7iwPhgmu6TdVkJsxyKAA1GcMG82o9Hb29vY29tDtVoN9RaPauMwWOdgmEfYtumw9zrNmJogv+1tb8Nf/MVf4JFHHgm8/tWvfhVvfetbZ9awSaEd/H/+z/+Jn/3Zn0U6nXbEtFgsOlF7vV53u2uxcy4sLADYD4WwdImGUfP5fGBg0avcbDadh0pXnuzE6XTaDZpyuexCqLyuZoazPFsikUCpVAp4c3icaqipSXr66afxp3/6p85LFWbkPDxmicN4AI8b9OSQHJPYlctlFAoFdDodV4pNE9o0MgMEk2ish0o1iPybcgb1PDNxp1qtOu+r1iTXuUTH68bGBra2thCLxbC6uuq0yDT21BiqUda2sy36uTQ5h8TdfhZCy81ZzSW3i7XPzc8zHncTdu6hbWRCvm4DzwUqjyO07Jkl0/o6ECTB1r7zeBJi2nSNLmkOlH4GXZQD+4SZ8w3lmizZWq/Xh5J0/R32nLStYc8z7PxJjpk3G3AcmJogv/Od78Qv/uIv4u/+7u/wfd/3fQDuaJD/4A/+AB/72MfwhS98IXDs3QC/wEqlgj/5kz/B93//92NlZSVg9Gx9QtUX82+GObV8mxXaq1e5Xq+jUCi4Tq3El1tRai3Sfr+Pvb09J/2Ix+PY3d3FxsYGGo2Gq4gB7A8Yeq17vR6uX7+OZ555Bi+99BI2Nzexu7uL7e1tNBqNuSQtHh7HgWGkTCdy9eLYbZc5RnmOGiebj6CEktfm9XldILhbleqfWRaN458IMzY0pNvb2wDgok7pdNq9F7bDl/UqcaMCJQDWg0yibvMddAGgnrVms+kiX54Ue8wTaGPphMrlcgGJhI4VTcbXv628YRg0usQxSRkTALfZl81nCEsUDBtHSszVMabOr0ns/Djvt31v1JgOkzfdDa4xD5xmaoL8Mz/zMwCAT3/60/j0pz8d+h6wP+HfLbDzXblyBbVaDd/93d+Nhx9+GIPBwA0EGg5KK/r9PpaWlgLt1CxYGjrbOfh6t9vF5uamK91EnWMkEglkequeb3d315VuqdVq2NjYwPr6usu2TyQSePDBB9Hv99FoNLCzs4ObN2/i29/+Nvb29txuNaqd1Pt4eNwvsFp+/k3yyvd1MaseWU2iU+8qr6fklOAxOvbsJM6NfW7duoWtrS13fUWYMaZx5K6c8XjcRa+YAMjFfph2kNdgrkO320UymXQLbBpZyis0IU8JM50JqqUsl8sBTfTdnGvmwVB6nCyGff+0tf1+H88++yzOnj2L17zmNYH3tZwZibEufK3EQl+zciMAjrTqJl48h7sLc25g9Fr3YVBJFhD07KrkqdvtukoRti2zhn7esLnlfh1/UxPkeU/6qlQquHr1KrrdLvb29vDwww8jkUgE6iBy5UfyqxpfS+o11GrBrFMaERqgeDzuSqKoJxkAtre3kcvl0Gw2ce3aNdy8eRPr6+vY2dlxbbxy5YpL7KtUKtje3saNGzdce8Pa4smxx/0MDXNy90xg39A0Gg10Oh2XQ6B6Yg2h2p3pNK+A4dIwbxNrohYKBWxtbeH69evY2dlBtVodOWeGGaB+v+/KRGazWZTLZZw/f95Fl/Qcu0MY39ekIx6jc5sSZLZfy2TxbxpoLsjD9JIeHicF2sNer4ebN29iMBhgbW0NwJ1xpBVs2OfDCLLqga33VyvADAYDl9NAeeT29rZrB6WVuhBXSRfbrGNYpRZaMUPzj0YtEsPkFEd9pvOAeSDlh6qDPK9gx2s2m3juuedc1ufZs2dx7ty5UO0fvSwaftVOqrvkaIiDGeesY8pr0cCwVmGr1XL6Q+qgm80mNjY28Mwzz+D27dvY3t7G7u6uI+3cbEAHlYY55qUDe3jMA9TAcQzTg0zSy3E6GAycRhnY3xJaDSUzxbWajd5Ly0gSLPdUKBTQbrdx8+ZN7OzsoNFojNQH6mv6Wfr9Pm7duoVkMomFhQXkcjmcOXPGGWzVR4dFuEhq6VG29ZS1LB29x0oeUqkUEokEdnd3A9t2+7nH4yRhZVQqd7p16xYikQjq9bpzgrHalI4Xu++BXtcSZKtjZkk2yquq1So2NzcDbcxms1hYWAgsPsNqrvNz2CiSHYvalmHzxqhxOcmYnQcyOo+YmCC/4x3vwO/93u+5pLZPfvKT+MAHPoBSqQQA2Nrawlvf+lY8/fTTx9LQcbCe3n6/j42NDfzxH/8xkskkfviHfxiXL192u94AdwqM03CqJkkL5qdSqQMF94H9zPJer+eS7Pb29tz+7JVKBcVi0YVJWF6JVSaee+45PP/8884DrZ4pGm2dALxh8vAIwhqZSCSC7e1tVKtV58WxXl9GdXgeE+r4fyaTcdVustkscrmce4/31IiRLmJ3dnbw/PPP49lnn8WtW7emKoemY51QY/z888+jUqng9a9/PUql0gE9skbC+Lm1BKUmCnERoLv/KfEeDO6UrGL1Ci72Z1G94rAYdt8wfaTHvY0wLS4jSFwMqwOM/VqT4BhVtsl71hGllWkYDWY0ija/Uqm48ZvJZBCJRLC8vBzYsETbyR/9HCqtUM2/zl3T9PVJZEmeFI/HxAT5y1/+cqD+3ic+8Qm8613vcgS52+3iypUrM2/gNLCeHr7W6XTwJ3/yJ1hYWMD3fu/34uGHH0Y+n3c73gEIeJEBOA0iOyjfb7Va7px6ve46rxYpp2Hc3d3FYDDA3t4earUaEokEtra2sLe3h6tXr7q6jTpo7ODRz+MNgYfHQei44E6WmmyniS4kggSNJcccF880oDZ6pCXS1JvcbDZRLpfx8ssvu8TZMC2f/h4XFlXNI+eSy5cvu01HbGjYepVZ7lKTkXm9sGQ8q73k/EiNp59/PE4ao+SFSmSV7FqZhE3Ks9fW13UhTK8xF48cG8qLmJugi82wdoZ9LhuRsnsgHPU5DdMZewzHxATZfknzPlna9vV6Pezu7uKP//iPkclksLa2hje+8Y149NFHA0SYYCIOAJcwx1UqvTUaqgVwoID31tYWqtUqXnzxRVy7ds1t6sEEQdvWMD3isM/j4eGxD5VT8CeVSmEwuFPbuNlsum3fWepRqzswihP2w+vb8cloU6PRwN/8zd+4ijS2Rvm4sTssbKqoVqtoNBr4f//v/2FxcRFPPPGESwpm++ktV49yr9dzcxevTRKhmkgAgUUFn+fW1hZu377twsyzmoemXfB7B4HHKKhzif3a7h8wbJzZChPaz5Qcc+Mf7pRZqVRQr9cDJd1Yu9jaeCXldkHK/7VaDjkHI8xHIbRhzjaPyXBPaZCHwYYnuG3j9evX8ZWvfAVnz57FW9/6Vly4cMFJMFhihWEOTZBjaDafzzvPMkMu3PL5b/7mb1CpVNxq04Z5w9ro4eGxj8OSIiWB9PJQqmBLJum9tOSZeoDUE6VeKOBOlKhcLmN3dxeVSmVine60RoqLc24ycvv2bWQyGeRyObc5CmF3whrmsdJnpefp/FSr1UI3KDgq/HzncVQM0+RyLFPiwD6u+n7tz3bhG3ZdJa9aJ9mSXur6LeyC2ZJj9VS3223UajXU63W3VbbHyWBigmxDAHzttEAHAT0urBTRarXwf/7P/0Emk3GavUKhgMuXL2Nvbw+XLl0CAKfZo1fm+eefx9bWltML0XNVr9fdnuphoUlvHDw8Zg8d4yTIlCZ0u11cuHDBeYoBBBJgdFdLGlcSZhpDLnS5s2YsFsM//uM/4tatW7h9+3aot9li2jnTHk+v1V//9V8jnU5jaWkJDz30EB588EE339CYJxKJgCREr2l3FqQu2WouNzY2sLGxEUgW1md9UjhNtsfj+BAmk6AOP5lMAtiPiCgp1s1ylCiHkWbafer2mfRXq9UCJeQAuEWrkmm7IA+7N73U3Hzs9u3b2Nrawu7urrv+KImGx/FgKonF+973PuepaDab+MAHPuCSWFReMM8I0ymzpJLWG81kMrh58yaazSZu3LgR0PexM9+8eRPVatWRY5udGnZPDw+P8TiMh3UYMaXE4ObNm2i323jggQeQTCZddIgeYTWiSrI17MmfcrmMvb093L5921WgmbX8YFh4uN/vo16vO73j4uKi24WTMgser58PQKBUG0mvJgORUG9tbWFjYwOVSiUwr53EXObJsMcwWB0+ALe5ViqVClS34fFKUPW3lkzU8aNl4dLpNPL5vMszikajuHXrVqCGuG4Lb72/6jnWEpJMlq3VagDuSKroPQ4j7h53BxMT5Pe+972B/9/znvccOObJJ588eotOAOodp0FptVrY2dkBcKdwP49T46Jhl0m8xF4c7+FxB2FkdhJJxSTH6DgbDAZOYsHKEufOnXPGi15XTWbT62iJJtUlb29v4+WXX8bm5iaq1WpoCcmjIIwcq9Sj0Wg4PeS5c+fcRii6zS0AJ/1iAjEdGfb58DNSWnbz5k08//zzB7SUs8AkXuhxz3DUgsjj/gT7BBNvk8mkc1xZgswkPrubrl5Hj0+lUq6mMqPPHC9aPYY/HKcapeH1gH1CTD6hO/kOBoMAQfY4OUxMkD/72c8eZztOBHZVpt4jXbXZCX3Y5GyN62mWpHh4HCeGLSD/f+y9eYxt2Vkdvu48TzXXm/v1e+3uphuT2Ay2GdxOhBkMceSAAiYNsUSCkWkkLIb8kYBBJoiICGQLi0QJkhUEChgkWwxO7AiCI7CDY4OHHunXbx5quvM8/P54v7VrnV3n3rq33q2qe+vtJZWq6t5zz9nn3L33t/b3re/bNiYhQcPGK//f3NxEpVIBACwtLeHSpUtGisDSbiTFAEzt32KxiFarhatXr6Jer6NcLqNer6PRaKDVankWyocBe76hYeZrV65cwfb2Ns6fP49cLodMJmNKVdZqNVQqFbPD1/Xr141UhAsHepBVXsFk4sMgoOOc0xFfB4VfZJavK+lkDXLuSQDsShrUrrOf+9l8P7uuMg3gvtZ4aWkJg8EA6+vrJpqTTqeRSqU8tYyH6fd1zDEqXS6XzfyiC119Bg5Hh4ciSW9STKIZnpbW0MHB4XDBDXs2NjYAAKurq2bXOBo0jSDxh2T4zp07qFarKJVKntDtUYz1YQlJwH1dcqPRQCKRMJV3WL+9UqmgXC4bsru5uWmMOe+L2mO/knEODvMAjQBXKhVTxpWRE7+xM8xBZo81EljdlCcQuL+tNDXHzGVieUh6nnXrej9Hm0apOp0O6vW6IcfT8h47uefB8dAS5FlJNnFwmDccJSGcZLE66lxEr9fDvXv3sLW1hStXrpjE3FOnTiGZTKLRaKBWq+Hu3buGSNJYkWTaJRpt7+60MeoaJLbdbhevvPIKQqEQUqmUSVKiFIPnUQ2yrUXmuR0xdpgFjJIk2jIq4H7fbbfb2NjYQCKRQC6XQzab9WwYxEQ76vX5ObuihB951rKPwK6eP5fLmbGmyb72+fwkZVoVJxAI4ObNm54F+DT4iZN2HhwPLUF2xNjB4eGEFuFnKbhwOIx4PG48QZVKxVS8sOsFK8bVyk4Tw+YuEnpgt+IONcj8nG10neF0mFUcpG8ygbVSqWB7exvJZNLIi/i+Liq1kg2wd4wMS7bT87GtJOHdbtdUkLHlmrwGP6u7/9XrddRqNbRaranzE0eSD4aHliA7ODjMLuzEtMMAt3RtNpsolUq+11fv1EEqa0wLwwycX+jYr6KQ7RHz+6yDwzxC5wruOtnv91EqlbC0tIRcLufZh0A9ydyEg5+1z2lLI2yizH0OmLTPscfNiNRLTHDRzc8x+rO9vY3t7e092uNpwTkFJ4cjyA4ODjOLw5jUR0k3ppFYO0oWctjJfMPgVzpuvzaN0j07OMwyms0mBoMBisUi4vG4qUJTr9f3VK6hZpg/WpGGOuJoNGoiNNQ1U37FpFebSJMcD0v644YglUoFm5ubZu8Eh9mBI8gODg4zh8MICfqVTlNiPCn5G7cSh/3+Qe9r2GfHbfekWulplatzpNrhQTFuX2RfY4Ltzs4OYrEYIpHInugKE3Q1OsSEXXp/mZynJeMAGDkFiS6TfAkSZCW8JN66S2+xWDQ5Edx4xGF24Aiyg4PDTGFcKcGk2O8zGqqdRYzKRj+ONvslOg97za+9+3moRyVSH4UEx2F2MSrSw34VDAbx8ssv48aNG1hZWUE8Hjcbm/X7fUOQtQZ4NBpFJBLxnK/T6Zhdd5vNJqLRKAKBgHmdBDoajZpKF0qm6VHmDyVdV65cQaVSMTWPZ3XeeZjhCLKFUeHGUce4SdrhYcG0vbujSM6w60wr9P8g93EY5GycZzsNT/ek17TPM8obvx9GLUSGveaSjBzGhcolSqUS6vU6wuEw0um02X6aumAtA+cnh9DX2+22SXgFvLtUqgeZRBjwkmMe32g0UK1WsbW1ZUg3r+UwW3AE2cI4k7wjww4O04OfRm+UsfAzYvuR1UnON845joqQ29nv0yrDtt8zn3TBMsxDPOzYSe9h1PHT+F4c5ht+37tuwnH79m0kk0l0u10juYjH4wiHw54tqVn2kRKLeDxudr0jmaXHmRVi6EW2K97Qq8xkPG5lffPmTezs7Jht3B1mF44gOzg4nAgchJRNQqim7cU8Kq/oJJ7uSb3Nw4j2tLXY49bEdXCwMRgMTDnHarWKTqdjNMnhcNjohLVGOLB3W+hWq2U8yIFAwHiU6RnWahXUKKu0ot1uo9lsolKpoF6v+3qsHWYLjiA7ODgcKg6aADdtCcO0iu5PA0cdqRqXtI6DSc4z6thRsrVxEiCd/thB4Zdwq+XaGo0Gbt++jXg8jng8jna7jWg0ilgsZhL4Go2GqZtMLzEJMjXHsVgMgUAAjUYD3W7XbDcfDAb3lIprNpum0sXdu3dRqVRw584dD9F2fXh24Qiyg4PDoeKgBmDaWttJcByGy69W8bBjbDzshlY9fw4ONlQnT1LLLZ1Jalmujf8z0Y8EWXfUDAaDZot29r1AIGA0zZFIBJFIBLFYDOFwGKFQCLVaDdvb26Yms8PswxFkBweHY8O0iN1hGJyjJJ3j6KcPQpznFcOevb5+0u7Z4cGg48Tvb/5PosuNQiiL0LrGer5wOIxwOGykEqyf7KcfZtk4bvUejUbNtWq1GnZ2dtDtdl3fnRM4guzg4HBsmNRQzAppfVDY97GffvqkY9wkwYfhWTgcHPbiyW9ckejW63Uji2DfoweZkol2u71n+3b+T88wayfzetzinSR5a2sLm5ubKBaLqNVqboE3R3AE2cHB4VAxLql92GUCD4KT8Oxcsp3DtDGq1jaJMQkuxxC3jQ6FQmYTEC01yPcB7Pk8vcsk1p1OB9VqFTs7O0az7DYDmR84guzg4PDAGBUSnybGSd46rOsc5XknJbzzTo4PgknrLzucfPiNm2HRCf5uNBrmsyS6JL3cwCMQCCCZTJoEvlAohEgkAgCIRqOGGKfTaUQiEZRKJdRqNXzhC1/AzZs3cfv2bUeO5xCOIDs4ODwQnNdvOnAbYuwP94wc9sN+Uh2bRHNbaE224+s8nj88liRar0fvc7vdxubmJmq1GorFIsrlstspb07hCLKDg8OBMc6ub4dtGPwM4rCEnQc9v42DaKjHfV/J4H4bdzyIF3WccxxEwvEg5f2GnU9/Ozjsh1F9aVg5QhJdlnkj6vW6p7JFs9lELpdDoVAwiX7lchmdTgef//znjaRiVDscZhuOIDs4OBwY+xFgP1Iz7QSscSpA6N+jEngOG5MuGMY59iju4SDXmPb36TzHDtPEfnKMYe9xQ5BqteqpgsHEvn6/j1arZUrA+SXkOswHHEF2cHCYCmYlGW8cA6TEfVyCPU3Ynu55wUlIBnRwIEZpkxV+r3U6HVPb2D5OtcbjLOzmdT446XAE2cHB4cCwJ/RxJ/jjNAQHbfM459pvI4+DygTG8dKP045R5xpWO3aSthwHxolOOBwPZq2vjIIu/saRZoySdunxo85nH+cwWzgWgjxM++Mw+3A1HB9ejFNj1OE+htU4noXnNU4bZqGdk2KWnvHDjnn+LvbbkGfSDXvcPDlbmKRvHgtBrlQqAOZz8DjcR6VSQS6XO+5mOBwhOG4Bt7XvJHDP6Wjh5qbjB+cK6nMfNrgxP/sYZ54IDI7hm+z3+7h16xYymYwLLcwZBoMBKpUKTp065Wo6PmRw49ZhluHmptmBmyscZhWTzBPHQpAdHBwcHBwcHBwcZhVume3g4ODg4ODg4OAgcATZwcHBwcHBwcHBQeAIsoODg4ODg4ODg4PAEWQHBwcHBwcHBwcHwbGUeXMZrvMLlyn+8MKNW4dZhpubZgdurnCYVUwyTxwLQb516xbOnj17HJd2mBKuX7+OM2fOHHczHI4Qbtw6zAPc3HT8cHOFw6xjnHniWAhyJpMBAIRCIbe6nDP0+330+33zHTo8POB3HgwGnYfOwrSqZe63bbKbL+/D3qmQ6Ha7bm6aAfA7CIfDJ6LP6jbsfu9Nu1ruflte7/e+w3AMBoOx54ljIci6P7n7gucLwWAQ/X7ffW8PIfidB4NB9/0LBoPBoTyPYecc91qH1a5ZgN99OdIwO5g3G/+gRPSo71Gf77TPexRbY8zC9tvjPLtjIcgODg7zi5NMvPwwzEiOmuCHeTgPsz0ODg4HA+e0g5C2cT93GITQPueDzglHRVqPmxyPC0eQHRwcHhhKmsch0PNIsschxH7Gctjn9Nhxntewz/p9nu/N43PeD26B4HAY2I+0PQipm/Szo+YTG/vJsoYd67A/HEF2cHCYGDYh1t/2+ycV9j1qONnv/v2I3TjP6EEN5HF9F8OewSRtOUxPvIPDuBhncTxNr+ikc4TfZ4fNBcOu47AXjiA7ODgcCCeRBB/Uo6uIRCImOckmr71eD91uF/1+fw/5Y/IjNd5+3iN+rtPpTHxfD/JdTeq19fNgT0Igxg1Zn7T+5zCfOEzJwGF6iN0YGg1HkA8JB/WU0Ci6ju4w63gQErnf+36k6ij797C2+cknNGEmFArh0UcfxaVLlxCJRBAMBhEOh031l5s3b+LGjRvY3t5Gs9n0nCufz6NQKGBpaQmJRMJDlMPhMILBIDY2NlAul/Hyyy+j2+1OpJscd44YR7ox7Lqjkufsdhw07OzgcFQ4aJ+bJInU7xpaJWjU8bb3etSYHDWn7SfZeljhCPIh4aBhkYN89qCfcXCYBsYlO7YnZJw+O0v9etg9BoNB5PN5RKNRxONxLC8vI51OG4IcCoUA3Pf+FgoFtNtthMNhtFotALve43Q6jWw2i1wuh2g0aj5HohwMBpHNZhEMBrG+vo5Wq4VarYZOp4Nut3tkBHLSDHrbQI+qzjHJommW+oaDw36wF9KRSMT8+FUGCoVCCIfDvrKIXq9nIlL9fh+tVgu9Xg+tVguDwWBPhGo/uMWnPxxBdnBwmBj2ZP4gC8JZwCQGwiZykUgETz/9NAqFApaXl41xotHjYiAajeLUqVNYWlpCs9k0xi0UCiEej+9ph15nMBhgMBigUCigUCjg0UcfRalUwvPPP4+dnR0Ui8WJ7mkUyRz3tVEY5xwHvc6s9R2H2cJBJD2TnHucRZydgKt9NhgMIh6PY2FhAUtLSygUCojH4wiFQqaMaqfTQTqdRiqVQqfTMXNIKBRCKBRCq9VCq9VCs9lEp9PBvXv3UC6Xce/ePXS7XfMzzCvM1+mp7vf7nvuYJNp0ksejI8gODg4OE4BENZ/PY3FxEclk0hg5aoj7/b5nkwTVGFMuQYIcDAYRiUTMcbZut9vtmmtHIhHzfiKRwCOPPIL19XU0m028+uqrKJfLvsaO53ZweBgwqnKM/f4k0oJRSbD2Z0k+o9EoIpEI1tbWkE6nTYQoHo8jHo8jkUh4Nk1jjkE8Hkc0GjXeYl4jGAyaz6dSKXS7XYTDYRQKBWSzWbNA39nZQbPZRLVaRa/XM+cZ557cXHEfjiA7ODg4WPDzjOj/+Xwejz76KC5evIhMJoOtrS3zPmUV9Ajxs6pHZpiUf/N9v8Q+fY+e5G63i1QqhVwuZ47f3t5GvV43hlDDrMMM3mF5gBwpdzgujCLHk2r2xwXHqHpm+VosFkMymcSjjz6K1dVVnD17FoFAwIxPHav8mwvncDjsWSDz3DyGxDeRSKDT6WBhYcHMOdevX0epVEKv10O73TbyCzsqxb8nlU49DHAE2cHB4aHCOOTNfi8cDuPcuXPI5/M4e/as0Q6Gw2H0ej2kUiljsGjc1BiTMLOCBWHrlGn8+DmbQPN/epz1fh5//HGcO3cOg8EAtVoN9+7dQ6lUQrlcHnnPNkmeRnm2aRrZ/byBDg7jQAmhYj+vsh8od+DYTCaTSCaTJqK0urqKcDhsokjBYBCrq6tIpVJIJpNmPiDRVcLb7/fR7XYN0WXUyJZFqAY5FAqh0+mY9gQCASwvLyOVSqFQKKDb7aLdbqNUKqFUKpnchWEykHFx0segI8gODg4PJcb1EEUiEaMZXFlZwblz54zBIWmlsez1egDg8QLxf15TCTDJcTi8OxXrVu5quGho7fMRCwsLyGazGAwGqFQqaDab6Ha7aLVa6HQ6pm0K9UqPE14+CoM4qYfvpBtph+lj0iRhW4YRDAaRTCYNCSbxXV1dRTabxalTpxCJRDwJutls1iTfhkIhRKNRDymmtIJjm2Ofc4n2d41M9Xo9s2CmfIvtC4VCSCaTJoFP56hOp4N2u23OrddwSXv38VAT5HHE9g4ODl6MW43C1tIOO8csQknpE088geXlZayvrxudMQ1jJBJBIBBAu902RkbPQQMHwOiOaZQALxmmIQS8ukb1QlE+oSFcDeUOBgM0m01EIhHEYjHU63XUajWTzOent7TDzuMkzx22NMPB4aAYR0bh15/5e5jEiceEw2Ekk0l827d9m8lDoIQhmUwiEokgGo3uIbiahKcSKy5Q7TZrFAmAIba8v1AoZM7B1zkvhcNh5HI5k6jHxL+FhQWcP3/eRLNeeukltFot9Pt9lMtlVCoVz0J6WJtmff6eFk48QR4VUnlYvmQHh6PGfp6IYQRr3Al4P4J2kIlcSWkoFEIikUAymUQul0Mmk0E0GjVklZ6hUcTR9sza5Jm/bQ8xDWgoFPIYTttQ2wRZvVX0MIXDYSQSCayuriIajWJ7exvdbneqRnCSJCcHh3mA9mOOUUqqEokEUqkUstksstks8vk8ms0mms2mpxoFwTFr5yToeNff/IxN1O2x79dmnQs0KsXPh8Nh48XudrvI5XJmcU9iz/mBlXba7bbvc3kYcCIJshOcOzgcHw7qBdxPA2h7o8chZqO82H7XpnGJRqM4e/Yszp8/j3Q6bQyfTWzVq2Sfi+SWRo/eHBJfYFebrHpEnos1UDUpJxqNAoCH4Gp1DNZX7fV6iMfjpr2ZTAaVSgV/+Zd/iVqtNlSPud+z2887N4lneVre4qOSfjjMB8YZ58DwcnC6UNbE2Uwmg2w2a2QU2WwWqVTKbOrDRTXHMuDNKYjFYp6FNce3epH5WSXYdk4Cz8sfHZskxqp/5j2RoAcCATSbTQQCAayvr5vrtdttdLtdNBoNNJtN3LlzB/V6Hdvb23sW++OM3ZMwLk8kQZ73L8XBYV6gE+Y4oU3FpATpsCfcUCiE9fV1JBIJ5PN55PN5JBIJxONxz254fgktmjjD5Bk7+Y4E2V7A+2mMaUD5N6/NJL9er+epnxwIBBCLxTxkWj3Q8Xgcg8EATz75JLa2tvDKK694qmgMwziSjEngJBQOh41R/dP2yuprXIzyJxQKYWFhwaMZZmm1RCJhPqPn5HiyJRt+Hmn+zTHr9z6wS7Lt+UFJPKtdKDnW7e5JxHlvAMwcxWt3Oh1TXq7T6SAcDqPZbGJhYQHFYhHFYnFPqbiTPp5PJEF2cHA4OhxlYsekko1xQUK6vr6OfD6P9fV1Y1Si0aghuHaNYV5TvTkknvTyavhU28/P2AaT96LaY4Y9mXDHY5icwxApiTz/7/V66HQ6hjw//vjjuHXrFq5du2ZCq9P+3o7KaE7izXJwUHCc2fMGPbCsNby6umoq1hDJZNIsTHU+4HyhY1gJsh9J3o9AE37eYv7m6/RIk9Dr/ME5gV5mvhaPx81cpRuMsIxkt9tFs9nE1atXUa/XzY59D7JIniecOII8jqE87C92P4+MDgwHh3nDLPbdcb2gwz772GOPYXFxEadPnzZaY02e0VJMdl1Swq4SoYZRfzTUaeuVARiPUCwW8+iX1dMTjUYRjUaRSqWMEWTWOr08g8HAZKpHo1Hz97lz5/Ad3/EdePnll/H3f//3aDQaxuiNwix877ZhfhiMtMNkGNUnlBjzf9Xtvu51r8Njjz1mxng+nzcRnFqthmazaRLwms0mAGBnZ8ezEYfWQPerbe73N38z6qNbTCvx1vuz5w77eC7UtUpFIBDweIH9yDg96ZwHw+EwTp8+jVQqhevXr6NaraLZbHoSiodFmuzv5Ljnj0lx4gjyOF+AX4fY79hpT8Tz1lEcHE4iqNldXFzEysoKstnsnlrEwF59tB+xHbUxh3qaNVzqZ6CoJaYnS0OkNMqJRMIk1dAgU/9I405jTQNPTxMrXpRKJWxsbOxJ2ptVTDJvOzy8sPuFn63VccqdLNPpNFZWVnD+/HmzqQalSe1224xDLenYbrfNmGT0SEn3sDaoB3lYW0d5le2o07D7t4mz/f4osq6bGqXTaQSDQSOz6HQ6ALBnnvTDPI/TE0eQR2EcXdIw2KuhUYNulJfYEWOHk4BZ8CYqJtFAa7vX19dx+vRprK6uGiNAb5KWUALgCU3qdQn1wtJAqheJBlM9yGpEaXjpHc5ms4YsMwTa6XTQarUMOeZWtQz7MtmGx9sEmUS41Wrhsccew8WLF/HJT34SN27c2GPs9nue82z4HE4+/GQUdv9m5GV9fR3f8i3fYhJymeDKsRMIBIz0QDEY3C+ryKiPLoQ1WmTPBfrDRSsXwQBM7oJqhzWpj4hEIh75hBJ4lWTo+345ENFoFIPB/URC1k9XRCIRJBIJ9Pt9lEolXL16Fc1mE/V63TwjW35mfw/ziIeKII9j0Mf9IvczyLNGICbFvLff4WTjQSddJt2k02nk83lTpUINkn2dYSFNfQ+A57PDXrfLONlto8SC+kcm0OjmBCzZRIOukgzdDMDP+6TXWFxcRKPRwPb29p4kHAeHkwiOwXA4jOXlZaytrWFxcdFTyhHY9QpT309CynPwmFHXGfUz7Hg/sunnMR51blt7bJ/XduJpaUgl03p8MpkEACwuLqJarSIQCKDVaplNSCblT+PiuPjIQ0WQ/WB/oWoM9/tSJgkt6GByxNNh3nGcfXjYtcfxdvKz0WgUKysrWF9fx6lTp4zXhZ6TUYZM9X16rHpnbI8RZRA8l3qIeE7+z537MpkMYrEY4vG4qbPKxDuSeB6XyWRM+5jRztCw3/NTjeOlS5dQKBTwV3/1VyZczLb4SUaOi0A74n6yMWzBedBz+PUXe8OPVCqFN7zhDVhZWcHy8vKe49vttvECawIbz6+k0I9Q+nmPhyXm6t86nzAyZUeveD/AbvRJNdCcY9QbrOdQwqykWhP3dK7h5iOpVArpdBrlchm3bt3C1taW55noOUfNH+NyIT8vuH3Ow8JDSZD9Bo4fUR6l7SHG/ZLmjRjPU1sdHPaD7XWJx+M4deoU0um0eU1LtQG7YVK/BBi75JudYa4G0DaItndak2ioiY7FYojFYmZTAnqSuT1tt9s1RoseZG0Xr8FwqSYYavJOr9dDNBpFIpEwiXyqR95vDpzn8KnDbGEaNmdYf1QvKglvIpEwkgqONyWl9mcYsSGxZKlFHc/D7knnCf7WRbJN7P1yIIY58wB4xr5ud29/zpZj8Dx6D1oFQ5P8eDylW8Fg0JSDi0ajKBaLaLVaZnE+6plMMmcc5xxz4gnyg3wRuprz0+eNM6DtjsjfjoA6OEwHB5lw4/G42Toa8Hpg9Di7KoQSZI5je8c7+1qKYYl5JMeBwH09IA12PB5HOp02BDkWixl9sd5LNBrds3VtOBxGqVTyZNgrWeZ5WA0jGo2aMnLEfuHjWcCstMNhNuAnfdBFKxehyWQS6XTajDfqcBl5sRPuVGKh45hkV2VTfgtk/W0fy+vaHl4/kuznJbfnJ2BvpIvHafttLmK3k+fR82q9ZQBmbmq32x6ivt+cfNBxe5Tj/YEI8pUrV/DKK69gfX0dTz311LTadCDsFwrczy2v+htmj7MD0EhqyKHb7XpCFMM6g71as1+f5P6cIXBw2ItJyDE9R8lk0tQGpjeW41THvk06AW+ynhoTO1FF6yCrsdHEPNX8Uk/MTUpyuRzi8Tiy2Sza7TZarZbZ7YrSiUAgYDYzaTabHi9PrVZDo9EwyX2dTsdsH6shUeC+kTt79ix2dnZw7do1z33zOg4O8wS1u6xUkUgksL6+jvX1dbMoTCaTCAQCpi64EjyO736/7ym9qByAkiXlDkqAOebtTXk0YsV2ag115Rp2pMkejyT+ftErXoO/bRKv0gw9j0rC1Kuum5IEAvcrXKytrSGRSGB7extXr141Cwzem71oOahUYhoynEkwNkH+8R//cfzqr/4q0uk0Go0G/sW/+Bf4oz/6I3Pz3/Zt34aPf/zjJmR51LBDoQq/sCBf15VSOBzG5cuX8cQTTyCfz2NxcRHdbtcMoE6ng62tLbzwwgv467/+aySTSVQqFd+6qPbqTMOp9opN2+lClg4Ohwd6aFkdghi2Qx6wV1fnZ2D09WH1Qe1z6jE0xnbSHatU2CFQfpZkXkk3AFOCSnfe449d1o3nKBQKJnyq9+zgcJSY9qKMNj4SiSCbzWJhYQGrq6umVKImpdneTx3rKjsgQbTfG1b7WOcCv/nBjk7ZPzpP+JFEXbDz/3HI5DAJhK2bVu+2zkX6fz6fR7/fx61bt/YssA/DwXcUTsOxCfJv/dZv4Rd+4ReQTqfxS7/0S/jsZz+LT33qU/jGb/xGfOELX8AP//AP44Mf/CD+/b//94fZXoNhD8eWNGgYRI0cB8zi4iKazSaeeuopnDlzBtlsFoHA/SLgrPlXq9XMaiqZTOKbv/mb8W3f9m0IhULodDqoVCq4deuWyQK/cuUKbt++vSfbNRgM4vTp00gmk2g2m7h58ybq9bp5bz9ivN/EcdSrKweHo8ZB+rV+htvH5vP5PZUk1GOkHl/V9dmVKJRkE7Z3SY9XL7N6hbjtKzXHqVRqD0EmkdUFuSbj2NevVCpoNBqe0Cc9O71ez3i8IpEIAoEALl26hHQ6jbt376JaraJWq40VKh313I9ise+iaycLk36X+9lFLorX1tbwrd/6rYhEIqZuOMkxSyt2Oh2PE4tji2NWnWmqteXnCXtuUU2zvm+TXl282gmBSqJ1XrGvqT8c5zqH6XX1PHZ0i3NLJBIxx8diMTOPcDHNay8uLiIUCiGXyyEcDqNSqXjkXX6OSn191PfuN5fMlAdZG/eJT3wCv/qrv4pnnnkGAPCWt7wF//E//kf89E//9JEQ5FETon7xdid/5pln8MQTT2B9fR2JRAKtVgv1et3sDMNVD0MpoVAI8XgcjUbDGKlut4t6ve4podLv93Hq1CmcOXMG3W4XTzzxBO7evYvPfvazAHa/yHg8jsXFRYTDYaytrWF9fR1Xr141NUh10PBeJoEzEg4PCyYZG5wP6JllJrbqfoG9yXW2p0eNiJJoXXxrMpx6qbW9tiYQ2N2whIaWni3NSNeaqmqYAPhqizudzp7XuRDnYoDX5XlyuRzOnTvnWbyP+x1MEjZ1kTKHowDHaS6XQ6FQMDvjAfD0e7/+qItknocSAz/ZFV9X6Hiwx71NkFXGMEw/bDvBVEKh/9sk2b6+fY/qHNDz284CfQ7Abkk4Vr9IpVLI5/MAgEajYe5pGrCdGUeBiTTIbOCdO3fwtV/7tZ73Xv/61+P69evTa9kY8NOz8H8NC2SzWbzrXe/C0tKSR1tIA5HP51GtVlGv101H105qe4JUpK4aPtYr7ff7uHDhAjKZDPr9PsrlMra2tlCpVFCtVpFIJMy2leFwGOvr68bjwyxyP22zHUbV53BQTY+DwyzC1qwpxp0c7ePoOT579qwnY932Ctvn0LGoHiB6XtUT3O/3jd6XJdnU80OPit6fVqNIp9PGg0yyHA6Hzec6nY5HI03pF7eR7na7aLVaaDabqNVqaLVahjD3+31Pkh/nMHrSut2u2TjlM5/5DO7evTtxss04xNf2fvkdP+o9Bwc/+aLfe8B9EvfYY49hfX0dhUJhj5wB2F3k2iRSIz+2tADYTWCj/VevMtviR4Y5brUttjeZY94mveop1k1BAoGAZ14gj1CSzPMzwkUOw+vY858e4/ce8zfoOY5EIrh48SI2NjZQrVbNtexxrE7Mg4zxo+I4ExHkf/tv/y2SySSCwSBu3bqFr/marzHvbW1tIZVKTb2BozBMYkEsLCzgySefRKFQwPnz5xGNRo3RYIdvNBpIJpMmC5wdgiVMNEShKyft0HbnYoc5deoUBoMB8vk8stksbt++jY2NDfP5UCiEbDZrMmmZJADsivXb7TYajQYqlYoxWH5w5NjBwQs7/Li+vo6VlRWzKYi98LU9MMDwWqp2rVFu8Uyyyr/VY6sk2ibl9Brz3LrNtL4WCoX2aA31epR8VatVtNtt8xrnE03AobyCBJntCYfDWFxcxOrqKjY3Nz21kf0wzFFxkO/LEWKHcTBuOD4QCCASieDUqVNYXl72eDwpN+KC0yawtuTAJogcn/YOmsDw/RX0fz9y7Bdd0vvi2LVfU82wtlWJsn0tYDfipa/ZzkGVdNlE354LI5EIMpkMOp0O1tbWsLW15dmWepzvbxyMUhFME2MT5G/91m/Fiy++CAB48skncfXqVc/7f/Inf+IhzIeJ/R5MLBbDwsICXve61+GbvumbkE6njbdWjSKNCw0H9TVawoT/26ECdiD7dR009DBls1nE43FzHq40A4H7muZMJoN0Ou0JqbJdrVYLpVIJxWIR0WgUlUoF9Xrds/oEXAk5h4cDdrQE2J9Y0XCsr6/jzJkzJvTnR5DtsUMjoFntSli11BG9uNT9RqNRQ2j1B9g1FpRNaOIgf2vbOK8oQeb/9NLw+tVq1bSB5FkJsl2eKR6PG0kH748EuVqtotVqOeLqMLMY1jc1OrO+vo58Pr+nxi8Ao0lWAmnPA7YXma/ZjinORaO8o0oybYKqJNImlEqEtQ0aLbcX/vYxKuHQdut90+tMYkyuosfrfeucFQqFTKGGZrOJdruNYrG4x8H4IBj1fU+b+4xNkP/8z/985Ps/+IM/iB/5kR95wOYcHPTgZDIZrKys4M1vfjO+9mu/1uyEA+xma/OLZ8eJRCJoNpt79DUkugxL6oCi0WCnY+ewQ7b8YXkZAMbTHgjsZtTT22SHNCORCJaXl3HmzBlcuHABV65cwWuvvYbt7W00m03jZQa8JNmGI80O84hh/VaNz6hJlwvUXC5nZBXq1bWJsX0u9cjQUKi3huehMaBXCoCHRNtJf/yfHlzqI7PZrNm0g8dzsw+WaWPIkoaOcgpdtPPeuEhXT7Vqj6l7ViPaarWwtraGr/u6r8PGxoZJtjkoxpFL7Dc/6fuOrDuMgpI51fUHAgHU63VPPWOVJdDppORRSaqfB1nH8qgorj1fKTnWUo92jgMddOqp5TwG7GqAeQ2da9QT3G63Dc/gXKLkXPXFupEQf3gNO6eCxJhzC4/j3NJoNLCzs4NarbZnE6KDjuNRNmHamNpGIRcvXpzWqfaF7UUKBO7XMAwGg/iRH/kRkyxXLpdNZ2EnpFen1+uZJBRmeZPsknRq5id1gJ1OB4FAwOj8gN2tHLkSZahUt2kE7nf+paUlszlBu9326IjYLhbvVz3jYHA/g/Spp57C5cuXcefOHdy8eRPb29u4ffu2Icv6jAhHjh0eZrC2MA2hvRC1x4ftubE9H7a3yK4OoQZWz23rDHlu6o9jsZiZQ3T+oAdYf/TaaujsdmtNdxpXlVeot5peo3a7jVQqheXlZcTjcd+F+7jQ+3cSCofDwKh+RYLMfkj7zTHtFzHa71r8bXuJNaJrw+91W3bldy1bZsXcB56PC2AeT+5Bcq8L8k6nY6LTwC5vGSbbtNtuPytyFjoL7Kh7MBhEOp1GIpEwC3v1ns8DpkaQ7969i9/6rd/Cv/t3/25ap9wXaiRCoRB+9md/FrFYzBBXGptut4t4PG7IrtYCpayCJFa/ZHqb6vW6WXGSRGezWQAwHa7T6aDRaACAuT7PBdzvZAx5qpyD4U31TtdqNWMgudJLJpOe/5eWllAoFNBoNFAqlfDVr34Vt2/fNt5kB4eTgIPoW23Dt7i4iMuXL5s5wK+gPmFLIOx2AN6NPziX0HtM+RM/Q4LLaJRCdXucB3RHPxJhLu5pJNkGeo/D4TCSyaQpCxWPx83neI90DPA3NzxQjxATbrrdLrLZLEKhEDKZjEkq9kscHue7AA6HHLuFvwMwXN8LACsrK1heXkar1fIQWo4nlURoX/WTEpCoArubAOk51RNM2DkNPCedb3YCG4mtFgDQCjYs/UhoJQ6SZ93IiG0i7yDvqdVqaLfbaDabRprFc+h9+DkGOP/RY81nkEqlzPzKLexXV1fRaDSM51oXBfZ5Z1EeOjWCfOfOHXzgAx84EoJseyW4csrn8x4vDjttOp32JOZpFii/SK1zqiEHAKbcG40XvS6UWdADpG1RQ8sf/TxXgYlEArFYDPV63eMh0gx2ZqKrbkpDtKlUCrlcDv/7f/9vbG5uGs+0dsJZ7HwOJwOz5hm0+znHiF0veNh40PFva+/0fRoekmMugAeDgYeEqxRLjQywK8Og91hDpmpE+bdmtvP66rGmJ6ff75udAtVwKlFWTxPvSUO54XAY2WwW+XwejUZjX0/TNOFHevy8fW5Oc/Czdfw/lUohk8l4xrS9SOZnbG8wx4YeY3s//bzHSqr5ee2r9nnsfswxyHHJRTTlWlpvWT3GOs5JUnnfmkdFksxnobkR9nynfMleoOuCQR0Oemw8HkcmkzHzm86Bft/jg+Aw5oOxCfLf/d3fjXyfCXxHAbuzEfF43JBWJZLsaI1Gw3hu6fUFdsupEPyCg8GgIcb08Oh7w8It1PoQatzY4WlIuRUsOyrDQTSUmmjDveI56DTkEg6H8cY3vhGf//zncffuXc/1Z4m8OJwMHDY5meT8fgRdxyYlFlxoDrsegD1kUcc15xTdRater6PRaKBarRqyTMKrdZC1RJt6bmOxmEnU5WJZPVLqAdI5K5PJmJJu6oXi/do1lFX+pYl6bJuSXy7gY7EYLly4gGAwiI2NDd9yTcNgf3/7hZ4P2p8cSX44YX/vfuRzMBhgcXERp06dAgDjRaVdVU+snlcjyvYGQerwsrXD9nn4OT+Hnt1Om1iSpzDKTGdZLBbz7FaslW80t4CSULaDkSVGtILBoFn02gtsknPdSEjnQoXKz2wpVjgcNjlhuVzO5Gn4PatZHcNjE+Sv+7qvG+opsldIRw0aE/X+alYmDYkaKACe8IBNdjUjVAcB39dyMWyDhlloBHVrSg5QXodQTw47GfXRFLZztafXUqIcCoVw+vRpXL16FeVy2bMDz6x5+Bwc9sM05hKOVRoP9cwSSnx1XNleIl1EU8/X6XSwubmJWq2GarVqokSZTMYQTP50u10jvdLxqHpg1RNS8sA5huFQEl+2k9pn1TfrHGMnCqohte9f75eSsXQ6jYWFBY9nW4+bZePm8HDBz7NLD7Lfdux+3l37fHbCnpJAHXN+ZN2PUOocY+cs8Nx2pEvlDPbiVnMMVAtszxM8B6/JnC3KOemE83sO6hS0JSN6DXqptVhBNBpFPB5HoVAwJSjtOvDTmj8OYx4amyAvLCzgV3/1V/GP/tE/8n3/K1/5Cr7ne75nag2bFCTFulWjen7U6wN49cE8Vju03SFosAAYTwo7jpJQu/apbYii0ainLfToAPBogTSjlis6PY9NeDkoTp06ZbJG52ml5jBfOMp+pAZn3IWe34LX9mjq3KCv8W8eb0sbgN2xWiwWUSqVUC6XEQqFzCYfJL32FrQqveL8wLCplnbTBGElyOl02hggGjo74ZAkmHOCJuJpaNSPHPPelSBzC1nqE6eFaSza3XzmAAxPgkskEshkMnvIqu3BBfZ6oQF4CDLfU4Ks48eu8MDP2SRaPdD2xkHKJ/RHF7oqD9EoMse1SjjVaaclJ5msyIi1XTZWoQ5DP4LMhYYfQWZ+RT6fR7fbxY0bNzxe9Fkfv2MT5De84Q24desWzp8/7/t+sVg8Ni8lv0Bd0XFFxc03aIAArwdJyapdMkWNiWp3+v0+tra2PKFShkHa7bbprI1Gw3RAkupMJmM8SSTdnU4HsVgMrVYL1WrVhH60nVpqiYPLDvuEw2FcvnwZpVIJd+/e9Ww8MinmofM6zAYOY9xr/zvo+aPRKLLZLKLRqIkg2WWGhiWMKDQipZVsKKPQuUe1yJRDdDodtFotVCoVY2Qo2VpcXMTCwgKy2ayHxHNu4O9ms4lms2nyG3gdzgUaFlaDr6FRXWDwXtRQs938rUlEnJ8mIcjT7hcuCuYwLmxCrLaUnlQduzpG1K5q1NYmySo5UikVADP21VlGqQLzBtSDzPmAhHJpacnMO/l83lS5iUajSKVSe8h9LBYz98dNh/h5Fg9Q0syxrPkGnU4H1WrVPAN6lJU8a7Rbn4VyL94TjwsEAshkMia3ijkV/PwwzAIHGZsg/9iP/RhqtdrQ98+dO4ff/u3fnkqjxoFt1NRrwsmeEztd/TQQJMTNZhP37t0z2e00pkp8+SWpMWm1Wmg0Gh5CrQaGA6HVagHYDZ0Q2nm0RFwgEDBhVM2GB3aLddueIB20XGGypuq9e/c8z2iSkOhxd0wHh3EJ0bDjQqEQksmkyd5WMqweIiWUukDm+3odzgEc+xxzqk/Wsmz0sLTbbbRaLePdYfsSiQSSySTi8fgeD7Z6eDXhxtZA2sZSPVCEH0H2C/XajgANn2ro90HI6jQN3ywYUYfDwX4LV7/jhr1Ph5KSO33fHiu6IPY7n5JCAJ7FKT20ftcgL7A90XyfCbSJRMJcI51OGwdcJBJBMpncMwcwSkUirgSZPER3C1RvNCPlbDeJtM4NunBQ4ms7D/y88oHA/byIZDK5p1LPfjju8T02Qf6n//Sfjny/UCjgh3/4hx+4QePC9i7ZEz+NF+sJ05t78+ZN3LlzB8ViEc1mE61Wy3iCstksFhYWsLS0ZEICtsSBHTifzwPYTb5h52J4kiJ7AEYDrTWSB4MBkskk2u02arUaEokENjc3sbW1hVOnTpnP6vayHAS6qQnbqKL+S5cuAbi/u6E+G+K4O53DycIk0ofDhvZrlhniFvN+sNttJ+bpMVzMkjyGQiEsLy8jnU6b6jGNRsN4e5lzQM9ytVo1Rf65QQjLNrFEEr3T9NbQKLEesc4fgDdpSMe1X0iZv3mc7r5HLzU93Y1GA61Wy3jeaXRHPbthfcCeq6c997j57GRiv+90v+/dfk+jLHxfCbM6kYYtQNVTSiecEmruR8Dfuk0zF9XcyZMLbfICAJ7fbFcikfAQZCbbqX45mUyae4xGoybvgXMKr8/nlkwmEQgEkMvlTAIfPcl8bspndN4gmeb8xPdUWkFJho55bnoWCoXMs9M5Y9j8cZzje2pl3o4S9mqPvzVUogly/X4fkUgEzz//PK5du2bIMQcAO0ez2cTOzg7u3LmDhYUFrK6uIpFIIJFIeLwydljVr200LtzJj95tlnNrNpu4ffs2SqUS6vU6bty4sYcMaxjCrrqhWiUSZIZ8tOyc7b12cDhOHCaZ1rmASXKJRMIzoSvssKsdmbFJJ6FzAc/PHTppFCmrIBhd0hJsJMg0fDQuGs5UzbIaRHqq/RwFauhtkq+6ZnqLObdw8yOVo2iY+SBGyhFXh+OA8gCVCyj8vJ22TECT7lVzbI8LO1qkO1+SLNITSzuu85Um6jKxje0iQdYyb+QAbAOJtZJutpdEVe9fK15oCTn1hLO9SvJ1TtIIl94P/1airPIRVuqZh7lhLgmyH0iOAW8Nw3K5jFqthmvXruH27dvGk6QGiZ2Sr9VqNaMD1vApsHfgUT/EQaHH0nhqwgzJL//P5/N7PM3UDOmqjXVLFRxomuHO8+fzeVy+fBkvvfSSGfi2MXVwOA7sR5L9PJ+Tnj+VSpktpsPhMNrttpmYeV4aGc010EQ5YFfawM/wb47fdDqNTqeDZDJpPEjM1qa0i14cyrJYG3RhYQGpVArJZNJ4lrXusWohO50O6vW6aWO1Wt0jpbBLUvE928jzWHq6WX6pVCqh2WyaKjj0QNE7ZM8/DwrtBw9iLOfB0DpMH+N87yRm6u1VDzHHhF0Bi/1ePZ32gpn7DWiNYd1yvtlsYmtry1SyoYySvIOkNxqNIpPJmF3nstksCoWCuYeFhQXjpGPJWY5Pklf1KmuZN3IPJbW8vkq8WCsa2E1K5iKZRJzPSx0JdsUN/W60zGQsFkOv10MqlTLzGDmb7Um27cNcSCxmFbbxUo0NADQaDWxtbeHGjRtm62ntMFzxAfB8aQwxptPpPYZIQ7C6wx7Px9/UM5PgciBpZicNNEOctVoN9+7dMwOSNQv1+ra+h0aYq8hut4tYLIZLly7hpZdeGvrcnGFxOA7sR3oP2jcHgwFSqRRisRgWFhaQTqf3RHnsiIouePW3npNjXD3CKrsYDAZYXl5GNBrFnTt3TJ4C8whYQokeJc4LNGTqNbYNg5J3JfPq4VaZhYLH2VE2/q2yCrZNvcfMq2BCjxq0aUcC3HzkME1wzAyLpPBv9YJyblC9vx1FAeAhhSTIWi8Y2JVeUp7AfQwoYWBehJZJ07Gp1yDJJEFWqYRKMfn/ft5ZnodebQCGgAPwJOnyWTAJULmHSsCA3ZrMOlfopiR+VThmHXNPkAl+YYSuCOndJYkk8fQzLPzSNdNU6yUDe5NgaOzUc1Or1Tx6HHYsrtoKhYLpMACM5zgYDGJnZ8dTA9Ve/dlyD30GAEyHXFpaQiaTQblcPqzH7uAwEVE6zOjFYHC/rFM6nUY+n0cikTDv+ZV5U28I39fxpGPNjlARNAYLCwsIBAK4d++eIZ6UXTQaDeMZjkQinqQZXtdPi2fPG0qWdazb85h6iWwSwPvQRTk9XioLo0dMNyqyo1APSpKn1RccuXawoXzAXniqvVfHlkorOGZIcDVRzSbbWtKRY9r2wrLijJJklUzpGLW5DMc+x7R6rHkcX7P3erBlEHpOdRTam6aoN5kORN3dT+dO/YxKP9ke8i6S8nHGqs4txzm+55Ig6wPj72AwiFQqZQpgE6FQCOl0GoVCAY1GwxNi0Q7H3+w48Xgc6XTaY8T0+uptZrgCuD/QwuEwSqWS0T4DMN4iFs1eXl42+iB6rAeDASqViqfoN89J+Yeubm0jSFKvk8Gjjz6KL3zhC3uem4PDSYNOyswzAGBqB/MY/ra9GPqanxHQcKAdMUqn04YIcwxWq1XPApdyrng8joWFBRQKBRPGrdVqe7y16iliSBaA8VBrOTm7/jtDtyrt4rX4frlcRqlUMg6Eer3ukVXoAoG1kEul0tSI7bQkFg4OgHfDMup4qfOnfQW89Yp1YaleU3vBqbZar8f/ddxEo1EsLi4iHo+jXq8bmUc2vLPPmAABAABJREFUm/VsKc/EOx7PfCdWtWFbCC0hpyUZOa77/b7ZHY+vKSfQBEPOGdQ6s7BALBYzSbrVatVIxrrdrqfmOp+h8iOVcrGCEGUg5FWLi4umyMAo2JG048KBCHKxWMTnPvc53Lt3b8/q5Nlnn51KwyYBvxCu5Eh0o9EoTp06hUwmg36/b4yBrSWijqfdbhs9zuLiIpLJpKd8iv7YtRKpEaRRvnHjhtE7h8Nh5HI5PPbYYzh79qwpd8J28/PNZhP1eh39ft8cQ2836xRyEuCApKdZM+tplEOhEM6dO2cIsj24HRxOEujpVdlUq9UyxJKLzmFJZ8PCfn6eEkJ1fpq1rddQDzUTVmgYuejVraRtDxXnG12E0/BQEkFDCMATOlUiTwPK9tJ7zO2qte6xff+JRMJDMhwcZhksh6ZlxRS2d1nnDv7o+KM99YM63TjeI5EIer2eScKNxWJG/qXVKMhZ9HWtJKGklufXNtLuc9xyDKsXW+Uiej9chLOsHBcUnEeY+E8oyVaZFwDP3GRH4/RYkv954R8TE+RPfOITePe7341qtYpsNruHdB0HQea11TvSbDZNUkwymcSjjz6KM2fOoFwu49atW7hz545ni1h6UViqqdVqIZfLYXV1dY/hpHcoFAqZUk70UtMDVCqVsLGxYQYWd5G5dOmS6SD0BDF7XIuLs6wTO74mFQIwInzbWGsYRDuqfayDw0kDF47cfhmAqSKTSqX2aOQIP+Nnh2FtUk2jxImfY43n0vORkDIZZ2FhwZBVJq9w/vGLjpGEcyMiVpxoNBool8vmNfWQ0UFArxDnG27oFAgETIKe1m2mEVTNcygUMhsc2bKKBzF0tqzkoOeaF2PrMB3YoXf7++/1elhcXMSjjz6KRCJhxo8S2GHRCy5WGZmlV5mv+dUC1zKwJIWUUq6srJhFcSqVMlEdlWTwM+qxVllWq9XykFGOUbsoACUhbCcXzhoV03YDu7phlYiygtbKygoqlQra7bYh7+rV1vnOnrP4DHSBHgwGkcvlsLOzM1KDPEs8ZWKC/P73vx/vec978Mu//Mum9t5xQj02NJBa6F5lE4VCAYuLi7hw4QL6/T7q9TqKxSL+/u//Hrdu3TLeZXbYWq1mCKaGXGg4WLu01+uZUnFMCqxUKshkMsazEwzeLxBeLBaxvr7uIbFahkVXlvxhKITaaPVi60pXO28wGPTUWK1UKjPV8RwcRuGghEm9oJwTbO+rre0fBo3wAPCMNzuphtfWz3HccxzTQ5NKpTxRHybwaek2zjF2joSOdRpPyi0IEmT9HP/ns1GtoOoq2X6VkZCcq2fLzSUOxw3baaW/KWWkNlf7rWpkeR71srIcI+CVXWhSnV7LbgPPyeoN/JsE1K4yY88fHKe8rv5w3lAZCImqRpS17ep1JvRv8g6CfCmZTBoHoF3tx+/eOd/pc+a1xplvZxETE+SbN2/iueeemwlyTPDLoIYO8Ca1qDZRDWYul8P6+jpOnTqFL33pS7h+/Trq9brpYJubm0in02Y3LmC3fmC1WjV652AwaEo10ft76tQpJJNJo+NhmyihoE6K50yn08bbw41LgN3Sb7wuB4JfByWZZydtt9tG3lGr1fbokx0cpgHbqzgtTHpORpGYDEdCp4my1M/pAnPYxM25Q2uN0gipNhCAx1tDbzHHZDweN6XdlpaWsLKyYs5FYhsIBAxB1kQcYJfUq6HjvFCpVEz1CYJzXywWQ7vdNlpHAOY4Et5QKGQkFpxftC18Tul02qOpHve7cRpjh2lj1HxDopbNZnH69GnPfgIA9ix2gd1SrlxstlotM36VU9D2qsfUToRVKRUAU77RlkXYkXclkUzs5f0wMs5xF4lEPLkHgUBgTyUaPV7bSLAdbAvvdzAYGFKfz+cxGHh33dOqG7YEhefVeUp1yXTwqZNvHDxIdOlBMTFBfvvb346/+Zu/wcWLFw+jPQcGjY2tuVFvjl2gmx2nUCjg8uXLWF5exvb2tmcnqc3NTaRSKaRSKQ/pbjabGAwGJnQbjUaRTCaNdjmVSnkMXjAYNDv1KenmYGcoiO8FAgFTO1XJr5ZQUR00Db8dTrE1TYrj7HgODocBHftaxkw9KxpmtRNbdaIndC5Rbw+NBwkljSewW/6N1wqHw0ilUkbrp2Nfr8m/1Zus41n1x/rDuU0dABqSpdGkd4wSDG5eRGJg37vOO+Fw2JDuSbxBhz3HuHnMwQbtMUknbT3JphI3gmNJ5QscxzqvDJNo2Fpljh1dmKuN1s9pJFklXYB3t0z1HNuEVwmp7cFVvgDsVr/hvGZHjDSCbeu4Ve5h3z//Z8Rcvd29Xg/1et18B+OO2eMc22MR5I9//OPm7+/+7u/GT//0T+OrX/0qnn766T3bj37v937vdFs4BnRFw87l15F0NcUvrFwum5rEi4uLWF5exubmJnZ2dlCr1RCJRIwB0lUn9xbPZDJGFkGdTjqdNomBmUzGaHgWFhawtraGfD5vvM3stMlk0kg1KN3Qov1cUVIvDcDT4fV/HVBMHnSeY4fDxGF5kSeFHXLUSg92rWPmBuj8YBsoTXbh/zyWybPqZdbID4+nxk8TYdgGv9AnDTq9unpNO+SqJNmWVKjny3YKUPLBSGAgEDDRLRpr3h+fj+7iRUI+LTiC6zAtUDLAKgpKhP121KP3VOUUwN4qF/ai0I8g255hXajaUgU9j0a5bPmWEkw9Rq+nNl9rD3OeIunVSBTPa8sfOP6VJPM1zXGyn4ctQaXDUuWvTAy2CfIw+zHJvHAYC+WxCPI73/nOPa/94i/+4p7X1FAcNVRXRE+OklDd1Y7H0LBwVcNzLCwsYGFhwXSCUqmEXq/nyT6n4J5F/xnCBO4X3V5cXMTly5fNuSnDaDQaZiccrjZVk0StdK/XMxnnnU4HW1tbGAwGxrtNY2WvAtXD1Ww2cefOHdy7d2/o83JwOCnQOWAwGHiq2Kih4vzASds2FPSAaBhSiTew61Xm2G42m+j3+0in02ZeoCFYXl42O/uRYOoW9DTM9HTrVrEc5yr3GAwGyOVy5n75o/IHGlO2n8aNC3p61zTqRq8yF9Yk2PqcbOM+CpMsmh5kceXmMQe1f/qjY5g2V4mmRmjo8aRDTJ1RvAbBMQrsRpM0MqXeWMCrK9ZKUzwPIzTxeNy0g8l9lFRwPHNnQL7Gccz7Y/k3zluUfnE3XpWWcStsXYzrfMQ5jc+J7ylJV/5ifyf6WjAYxMLCAhqNhqmWwTlyFpwrfhiLIM+auHrYhBiLxQzpZMdSEspVFLC7kqzVaibM2O/3TfiTBpb1/PyMEQ0RDRirWVBjuLCwYMgrO9Hm5qbHMNGYaq1SGklbk6wZtfV63dw3M9Hp1aGeqlQq4dVXX/XtgDahdnA4CdBxGg6HkUgkDOnTCX1Y8oqeg38P+62eFBJo6o3j8bjJPaDnmPOJRrBobLRcG89v1xrV66suUBf+lHvovZD883MMmXL+UomW7T1Tjzqf3SzNGbPUFofZgPZRW5ag84BKENRhZssCbPLLxHlbJjWqL9oeV13M2wt3YHecAvDkNuiil7kWOgfp3KJ8QSNIXHyTtAO7fEDf41ygzj9NePaTmShsLznnR5a006o4w0jycXOUud8oRMGHHAwGUavV0O12zRaKuVwOV65cMWSS5dyA+/IGFhRnZ9DOowOGHYQllOiNoU45mUwil8shHA4jm80aXZ+WmCoWi0bXpyEdruhisZjx+HDXPZabi8fj2NnZMatHFvqORqNYWloy4ZRut2vK2fkV99+v4x13x3Q42RjHa3BQz0IgEEAymTR1zLvdLmq1micLm95YGhEaOP2x5QwcVyS+wO444VwQj8eN7p9aXY5lVqfRihpqgFUWpXOCvXEQnwujTZR20YOkScEa1RsMBsbTbesRSYI7nY5JLOb5lEjTsE3yXRwmRhloh5MPe45Qj7C98QedSFxEql63Xq9je3t7z8JVCWSn0/FsE08HnL2oJHRhaS/CdcxxfqBul/ac90IyDuxKvvi3ElZ9Tck3k+Io9YzFYh4PuS6MAW/tZUad+LwYidediBlZtxcT9rPkc4xGo4YbVavVod8lX5ukL0wbExPk5557DpcuXcJzzz3nef3DH/4wXnnlFfz6r//6tNo2EdiZWSOU4Urgvod1c3MT9+7dMx2QK7J4PI7V1VXT0VRTTf0SOwO/AHboTqeDbDZrvMy5XA6hUAjZbNas7pS0M1xK3V+j0UC9XjcZ52wz2xAMBk3t1OXlZYTDYSwsLHjqvAK7O2ul02mTPMh7fOtb34rf/d3f9ayYx4EzOA6TYNohsgftf+pdBXajYHpe9RIB3hJQhO0h5msaFVINoWZ7833q+fgaDSO9QDxOF+Gcd1RWoV5dAJ45iwaVoVrb0822kyCoV0k1zCTJSo4nnTv02bl5xOEwYZNjwOtB1jGunlHNP+DCkQta9cbq3MBzc1zyNRLMYf3dHkN+xJcRLULboqSZ1+f8wcgzF9h6HZ27dA6jjEKjZ1peUj/L++Nrqk1mm+1Fvz1X6UKB19Wo2DDMwtwxMUH+2Mc+5knaI9785jfjV37lV46NIANeb0IikTCENJfLod+/X/had52iMeKqSAeVgp0B2C0PQ4lDsVg0Jd64PXW9XvdojdgmrhDVGKnOj5qf7e1to58mVldX94R/VG/kJ5mIxWK4cOGC0SxN6kV2cDgMsM8dhvaM59N8AdX/MWGOHhWdwJVc2sm8auRoKNRw6Hb1HLepVAoAjOdViTC9OvQwa4Uayh9qtZqnHBuNOg0dP0dZmF9JKvVUhcNhswGBGkIuuDk3NhoNj4dI5wkl6NPGpP3BzV0OhM4puukF+z51vbowVN1uvV5HrVbbQy5p73URqQtYjn0/77GeS8m2npNyJ5JWjmmel5IH7sSnC2reLyNFdqKuLsJZFpLHcMxzvlIyrPKLwWBg5GIktgDMBkesiqO75im/4jOg85LRKd6bOh0Oa155EExMkLe2tpDL5fa8ns1msbm5OZVGHRTqJaKGV78Iaods0b7tMQJ2V6HaGbXDq3c6FAqhVqshmUwim83uWQ1qqINaQ90nnW1nJ2FigIZVaLR1taorR+3k6hXj1o66WnZwOMkIBALI5XLI5XKm2D29q+pN0UoM6gHSH4VO+GoodQxq7WUaatYx12gUADNH0SgSlH6pzGJYpnkqlTLtYmJgtVr15CRoJIzzGRcJnG+YrMzylroYUNI+a/OHk1g8nBj2fTOay8WkLZOibSQhpdxSqysAe7doV12/7nxHW88fW/+snmg9pzrHAHj2VFAvK+cTzmFcbGueBbCbDEiyzcgSz6PXtrem1/tS2Rl5CDmUVrXhNtS8Z94326i5Fpr0yO/AzqvgPDpr88vEBPnSpUv4sz/7M7zvfe/zvP6nf/qnx1obWVdwatyUrNqics1aV3BVRtiaHkJXliqXYAUNLRLOAaH/81w0nvybr/O1Xu9+RQu/feW1Y2qSng6CeDyOWq02pSft4DC74ESbz+eRz+eRTCbRbDY9440GS7dh1XCjjn+dsO3xz9foUQZ2NYCMXnE88zevyc/wN8m06hB1AazX5fjnblecA0KhkNmyXjPVVYdMo0fvOb1J3W4X1WrVyNP8Fgh6vzaRmAYmPdesGVOH6WKSCCePZb1xjbZohEgJMolbo9EwBFkTXvXcGunVWsqAt6665jfweLaBY1g10QTnCJ6D3l0SUnpqSU615JpyBvIP9USrI3AYQeY11DPcarUMkVX5is5tujDQ52YvBpQrAbs5WfYY9ouEHycmJsg/9VM/hfe9733Y2NjA2972NgDApz/9afzar/3akckrhj08+wvXTqPZ2hpypRRCCTOvQcLJRB/9MtkxuOuOepeYPa/6IA390pNj6yN5LI9XDZHfJiC8T3qXmeDX6/UMKeDAmpUO53DycFCicpihehJWhlbV+Kh3lMfbUgr9m/MC/1YyqwtbAJ7MciW5g8HAJOwGAgFjjOlN0XNyPuB5GBq1o1kqf6A3OhAIIJVKeSQc+ref94oeI1uGQg8UEYvFkM1m0el0zIL7Qbw+fsbwIBILR5QfbigpS6VSeOqpp0yujr2w1PwA5giRQKqcSecIXVyrPIoVsFSzr0RU26YLTh3HJJw8VucZXpvtqVarnooWnB9sx5mWrtSomHqXWYyASf7tdtskF2tkTZ8D2wXs5mHFYjFzLAsiJBIJ85rmg7FdjUYDN2/eRLlc9h27s8RVJibI73nPe9BqtfDBD34Qv/RLvwQAuHDhAj7ykY/g2WefnXoD/eC3wuBkqys4HTi93m45Nh4P3CeliUTCeFHYsehhUcOn56zX6wiHw0in00gkElhdXcW5c+ewsrJito0GYHRNdkjE7hhK3jV0w/9VS6kdltU4dnZ2sLOz46nbSJIQj8d9r+ngcFDM0ipfYcsjOGZ1Qcrj9LcSWiXE9rkBeBarNmwZhnqiNbfBDqfSk0yvELWU9ADp3KbXUQJArxHnHiUDNqHWe1PjrlpELT05GOxuLqJykP2+CweHo0QkEjG72drRHsA7P5AsciwqebTHgy5cubhUyaQ9fwB77a0959jQhTnnAUoqg8Gg8Vw3Gg3TRo5R9eiqtMMm6NoGknOVPKhzTttln8d2Kiqp9/PY01HRbDZRr9dRqVSMw2CWecmByry9973vxXvf+15sbGwgkUggnU5Pu10j4UeO1TuknUQT5YDdjs5zVCoVz0qT5Zqor9ENBjRTE7i/Ynr88cdx9uxZrKysGM0jQyDcwrVaraJWq5lNQ1gMXHfBYttU08Rrdbtds9mHdkx9PxAIoFAoIJfLod1uY2dnBxsbGyiVSrh27Zo5r+0Fd3A4ahwFuW42m2YbeJ3gtWyTvQuoRpd0nrDzEfxkD8DuWLQ1xfY1aEDU0DDSwzYFAgETuaKGWckuvTz6HNke3Rrazkrn/ave0g49M3dDveRsQzqdxvb29qHrBWdRj+gw2+CYzufzAPwr1wDe6LL+zTKv8XjcjG86xoDdpDcApnRkKBRCqVQyXlyec1TfVTLtt227zjcaAaK3u9PpIJFIGE8tybEm++nGZ0pg+VyUDOu1dIMRPjc/vTLvgedQQlytVs3/9XodzWYTxWIR1WoVL7/8MqrVKorF4r7PaRT8bMhh2JUD10He2NjAiy++CAB4/PHHsbS0NLVGHRTqfaGsQLXEujqi5o4eXt0ylvokYFeQz5AEDVc+n8eTTz6JRx99FNlsFqlUygjXmQVeLpc92eGlUgkAPNu7Al7vknZEHSjswNp5tTMEg0EzsHu9+7vtpFIpfPWrX/WEU/VZODgcFw5TXsHxzbHMMa8F7gF4PKRsk20IbI/TMC+QnkO9teo1tsOVbKetxeO1tKY5w6y8ribyct5TuYedOEP4edXUO6TX10Qb9a6N60EeF8OMnYPDMAwLzav2VjXC9nH8rTY2EomYalR+BNlOXiOhZII+CavOBdpeva4uuO17sdvOxTQjSSqX4Nyhm6TpopoEWyUXdrTK79lq1FsdA/rM+GN7jXWDEhYj2NnZQbFYNDsB+113FjHxTFer1fATP/ET+OhHP+rxrD777LP40Ic+ZMjfUcI2cH6e0kBgVy+sUgYNrdjl3QAYnRI3HWCR6zNnzng2CwHue620/vKdO3eMto+JAPS2q3ZHia8mFdoE2a5XyhCJdmYNDXU6HXzlK18xrzs4TBOzKrMAdseikkdq7GhgtJqNLV1QzR3nEnpVWQNd71/DmhqKVaOo85QuWtkeEmJ6o1gu0ia89OjSY6bzALCbqONHkjVUzLbY4VO7hjQjW1x0U6r2IB4gXlv/PkhfckTageA4YuSFY8fODwJ2a5cD8Cyc0+k0UqmUZwFLiQB/SJw7nQ4qlQq63S5WVlY82lv1Xqu0SctD+lWG4bhSaQe9uowK0f6TfHI8RqNRJBIJNBoNo48eDAZml2CVb5JQ+y2ilSDbUiu2TxfJ5BrkPkwU5hzcarXw6quv4u7dux5v9IOM3aOyOwdK0vuLv/gLfOITn8Bb3vIWAMBnPvMZPPfcc3j/+9+Pj3zkI1Nv5DgYDHbLFekKTDfd6HQ6hvDqlw/AQ0SDwaDpdPl8HuFwGPl8HqlUCvF43BDkZrOJeDyOQOB+0k2r1UK5XEaxWESr1cKdO3c88o5IJGKE7JRh6IAgAWb2qrYN2C37pEZdP68rWwroVXDPc+kAcHA4KGax/7B/V6vVPdIvtlc9J7ZnWKVItveYxJHjkgk0ADxjzzZwSk4JOyGYv3UeAnaNiGbR9/u7ddh1Ya/ec83g1/vXcKq+plCCbbfRT8t9nCT1uK/vcHiYZH7Rfmwn5Ov7dlQE2B2jTK5XJx/PZW9A1mg0jB1tNBqoVCoIhe7vOkeOQej1dAwqQbbHFNujY1Ftvf3b1vyq3Vc5mF+kS6UXdkk8ddopX7G9xPSkMyeKXmzyoVqt5sm3mJcxe6CNQv7gD/4Ab33rW81r3/Vd34VEIoHv//7vPzaCDOyGQJQgs4MBux1FJ3uWSVIDqAMmk8kgHo9jcXERmUzGbCudSCQ8odB6vY5SqYTt7W3cvXsXxWLR6CDj8Tji8bjxOAPwaJZUR6zaQg1d2B4bfc9vImAplvX1ddy4cWMPIZ5FcuPgMA0MBvczzJvNphmjSiLVmOrED2DP/MB5QT2rmiCjBFkjUzQ8ahhtKYXCNp62dliNV7fbRaVSMZ/jb5WJaVjV7779DDLnCNurpJ5p3YRhHHLqFuIORwUdw7qpB9+jl1XLnulYYxJqKpUyfVv5BOsla51xkudKpWKul0gkfJPY7LbammCdL8hb+OPn4bVJss4Tdgk5HYfaFl3IayTbJt88hvMLF+L0FpMgt1otE/nqdrvY3t7G7du3UavVPG3SqJUt8ZolTEyQ6/U6VldX97y+srJivBrHAZ3kWbFCNckaGgVgwhX5fN6zy5xqdOh91nAsP2uHXDY2NnDv3j3UajWUSiXU63Xjxc1kMmZlqm3hgFVxO73Rath7vZ7JymVYVRcBnBBYrYLe5Ha7jcuXL+P//t//O9Od0MHBxkE8DEoC/YwHwb8pt+Lfqvsl2bTHiybWqEfFPoaRLF1wa+KfVpiwjaVtlJXo8tlo1IieKN3anh4czhGadKcGUM/HY22PlUacbKnHON/huCT5IN/5vHiiHA4HutDV3TMB+C4Q2c+5gAR2F8LU1+s5OV5UwlQoFJBIJIz9LxaLRt6gHmgds2qrdeyoR5XH8xhdvPNvO5Klcx2rRLBdLAlnk2VbRtbv983cYS8cGJm3vfKsQlGtVo3Ug5HzdruNYrGIV1991dOeYd/dLGNigvymN70JP//zP4+PfvSjhpA1Gg184AMfwJve9KapN9APft5QlUcAu8krqunVEkVaQcJvZQPcNyoUu1O/COzuk95sNrG9vY1isYhisWiS8thpKZNgTVFbNkECz9JM3NWH2km2Rwe1HZ5QrRTbyr+bzaYJBfndn4PDSYUSY1tSpB5RgsZHjSYwOgN+2ATP8WrrfW2S7Pc5YNeYkqxz4xC9psqySJJ5TX5WI2p6fZvc2nOChl51oTFrc8estcfhaOD3vQcCAVN9xfa28n3+z/6sUSUlnja30A19er0e4vH4Hs8zf0iyOcbtqlRsg180WK9p35vfPek9aPlXe7MffQa2bELrLA+TYZBfUEbGa7BkG6WtTMBjgYJ79+75es/nCRMT5N/4jd/A29/+dpw5cwavf/3rAQB/+7d/i3g8jk9+8pNTb+Ak4ITO8iLArt5QBwCNBo2m1hBkZ2AnYjiFHYTJelw5saQJwy+xWMxTzo2hBv7WOsW8HtsWjUYRiURMp2enZWe0Q7866LlS7PXuF+KuVqvY2dnB5z73OXMt/e3g8DBAJUgqaeLr6r3l/3ZpSMImsAoucJX88m/1NuvcY5N2XTRzHgLg2ehDt55VrSErXaiXmMfY84ntQVZ9s96rhlt5bzSGk2KejaTDbMAvEqEezzNnzmBpaclTZsyWFPnJCyibisfjRkLkZy+VqCoZr9VqSCQSSKVSexbW9sJVZR9+5N2+rr6nVSKazaZnAZ5IJMzYL5VKKBaLJlKtibw8RyaTMVF2aq95PY57LRXXaDTQaDQMxyIZ3t7eNuf80pe+hO3t7T0blQwj//b3OW6k6SgxMUF+6qmn8PLLL+N3fud38MILLwAAfuAHfgDvfve7PVmcRwUdACpSZyfXkKjfii2RSOx5D4Dx5DJ0oAQb2E244wCt1+toNBrGYMXjcaTTaeO11rbYJVzsTUh0hateY+56oytDZrGqhgi4L4X58pe/jJs3b/qGih0cTjJIBjVZheNAx5pilFcX2FsizZZMcLGrnh2d9El+STZt0GBp+/Xa2habvAK7VTNsPaHOK/yfz4Xttb1M2gYl7Ey2cXA4DgxbaAWDQSwsLCCTyXjGhpLdYZ9X6cKw6+k5VB/M6LB6nveTH6l0ya9dnKd0kW7PBzyOOmhGmwKBgIloM2rNXX01h4GOOEbHbdgEuV6vo1qtmvd2dnZQq9WwsbFhvNfVahWNRsPXgTDsOdjPdtZwoIKWyWQSP/qjPzrttowNvwdKwwd4NcOhUMh4dym+V90Oi96z09NIUZNDY6OGC4DxLFP7lM/nsbS05NFhNxoNU61CvVY6cHkNtjUej6PT6ZjBwY5MnRDPY+98A9zf9GRjYwO3bt3Cq6++iueff35PONdvwDs4PAhsg7Df/0cBJch+Xie7PbqItD2tfE/PY4diOV8wYUVLvGk+gyb3cO7gQjidTnuq7LAtuoDWOUlJsB6j3iq9PwAeg6oytMFgYHSWw8h1vV7HnTt3jKGcNo6jnzjMD2y5gC4+I5EILl68iGQy6amWoLZW5RVKQOlFpQNM5UX0QmuCrSbgRSIRJJNJI6sYBXsu8ZM2KSfQSBRlDYDXG24vWAOBALa2trC9vW02LVtaWjL3x2g4JSPAfYmIlrbjHER5ZiAQwObmJorFoiHO165dQ6lUws2bN8ci+37f5TzgQAT5xRdfxIc+9CE8//zzAIAnnngC73vf+/D4449PtXHDMMwVr+EWCsc56ft1RuqIGZqgh5adUw2IXQ6u2WyaeoKFQgFLS0uIxWIolUrG07Kzs4NWq+Uh27pxiZaJIjhY7exbEmRKNOyQCQBsbm7i2rVruHbtGq5evYpiseirZ3JweFDs55U5DtjzAidzrRgDeGUXjO7YZdSo5e90OojFYh59MrA3ZKs6xHK5jGaz6ZFqMXzLnaWazabHA9zpdLC8vIxAIGCiTkqK9f7U86ILbbZddwvk5zR/gpIwW3Jmb1er3yvvt1wuezTRDg5HDdtTyw0+uFmHemh10cixwR8dP5qYC+yVGyn4Ga1mw+oXHLskoRrV1fOpR1gX4/QEM8mfeVCdTgflchntdtvUNQZgEnG1jYxmc96j9EOT/pVjBINBz74MOifyvKwKxPsplUrm/5M8FxyozNs//+f/HG984xtNUt5f//Vf4+mnn8bv/d7v4V3vetfUGzkO2OE4uavn1Cah9sSvg4crRUob1FtjaxUDgfsbiCwvL2NpacmUcaNeeHNzE41Gw6w41etEA6htVx2iXlevz8HFe2DIpFar4caNG7hy5Qru3LmDcrnse78ODkeN4+p/9CBr7WJglyDb48hv/LXbbUOQNX/AjijRGAWDQZOoQslVq9VCIpFAOp1GvV5HuVxGpVIxWkIm1QJAJpMx52YkSbXTNF5+kS0aL3qU7M1DeE5el+FhLS3Je+CzUY8WvcgkyG5ecThuMArDHfC4AQawm0xGKQGjMbrfgN3HbdjRXiXnjP5EIhFDkLnI1PFh73TJtqmXm/kCzWbT87lwOGz0vnfv3kWtVsPm5qY5Pxfh6r22x6btzBgWPeMz0PvlM7DnS50fhs0D+zlR5sFZNzFB/pmf+Rn8m3/zb/CLv/iLntd//ud/Hj/zMz9z5ARZQ4D0igAwcgSGEhhqVd2g/s2Jn/uxqyeGIRvVEAL3DVA2mzWfSSaTiEQiZvV29epVs8rigOS5NUNcV5iqh1JPk65stUwN6w7eunULV65cwfXr100NZh4/zOPu4DAN2JPkcRInJrqm02njzaXx0DrBBKUJKjPo9/ue8kXNZtPsdBcIBDxRKY4rlmbkOUmoqd2rVqtmy9WtrS2Pd5ljmtvV0+PMhTY3I+LiH8Aeg0hodj3Pq7rnWq1mXiP5pkHUBCWGljnPFItFlEolT+m4cTBupGFYpGucvuTmtocXgUDA7Ga7sbHh2Z/AriIDeDcOsyO6rAZjywJ0nNrXtuVTADxRGR2D/LzWKFeplC5utRrVjRs3TOIdE/1tomq3y+9/na+GjRnbucjP6W/Fftfe7/Vp4jCuMTFBvn37Np599tk9r//QD/0Q/sN/+A9TadSkYCesVCpYWloyCTPqLQHue2d0hyl6X1WzRANIdDodVKtVo0+OxWJGV7ywsIDV1VWz6x6JLTNc6Z3hYGAohu3RwUnZBo0SJRw0YKztyHsjceZg2dzcxKuvvmqML5+Lg8PDBBosjhtuL0/5FOBdNJIoqteVxkqNGEmjHY1SaYOeW6NSlHEBwM7ODorF4h7DRlK+s7ODdDptxjbvQw2xHzm2PUbq8bHnO94Tz2d7xoDdBER6qer1ugntzspiyOHhhO3wIdml/eT4tz2eaqPpsFKSzDFiJ+upZ1n1/baXlbp+zht2mzVapV5Z9STbY6vb7aJaraJcLqNWq+2ZNyYZf7Zu2++5DjvvOET4JC5UJybIb33rW/GXf/mXuHTpkuf1z3zmM/iWb/mWqTVsFPSLUE/wRz/6UfzkT/6k8fDEYjEkEgmzmtvY2EA2mzWrs16vh6WlJaPJA2BCGMCuISoUCgC8BcV5XXpktP6hllIJBAJoNpuo1Wq4ffs2MpmM6Wz0EpdKJaNbDAQCWFlZ8YR8GILhD1GtVvHqq6/is5/9LL785S874+Vw4jEspMfXGGqlB7dYLJrkV60Swc8Mm9TpBaamkBEf2+uqxo1t40KYJLder6Pb7WJra8uzQYE9j927d89khRcKBaTTaRP9Ys15wFt5Qr1Uang17KoJRjTqfA76PHX+sMOtxWIR1WrV4/V6UEziJXZwIGzvJr2quqCzodFkOsF08xyVQSoh1oQ8vSbHFMePVodgpBnwlp7VyM4wiZK+xghxpVJBpVLZQ/jt44fhQcfXMLmG4iSSY+AABPl7v/d78bM/+7P4/Oc/j2/6pm8CcF+D/Pu///v4wAc+gI9//OOeY48C/AK73S4+9rGP4Tu+4zuwvLzs8dySZHIw0JDRUHALSq1kwTAjDaGWU2KCX6/XQ6FQ8Hh3GBohmSWxrdfr2NnZQTQaNSHZYrGIjY0NY3zS6bQJ7w4GuwkI7XYbpVIJX/ziF/Haa69ha2sLpVIJtVoNjUZjKgbLweGkQBPtVEIB7HpH9VhCtf/DEmZpFLVcmxpAeo350+l0jO7Rb5wqQe33+yiVSqb93B2MXi/bg83Pq/HUmsv2hkmaB6EkWaHH8V45n02bzDpy7HBQqKSp0+mgVqsBuB8pBvz7lla0UHJs649HSRHshakm/NF5Zi86NcrE17VN9rWVc/h5pI8So8jvSSXGxMQE+cd//McBAL/5m7+J3/zN3/R9D/CWXTsKsJPdunULf/Inf4J/+A//IR577DH0ej0kEgmzPTPr9JHsrq6uml1nNJQZjUbRaDRQr9c92jxdsepWjlztaaIMCblqmEqlkiG8m5ubuHfvHra3t41kIh6P4/Lly0YyQu/ySy+9hFarZbax5vWHeY1tHZWDw2FhVkiOLStgGFV1e5pco9UsNATL8UpJlZ6b44pjnmSSY52VKra2trC5uWkiQzSmwzS56qW6deuWkVQlk0nkcjmPMVfyykWAVq7gYp8VdBKJhGezI96vlpJie5jRznPrrqGc38b9vvebf+x5a1y9ssPDC7/FHHXx165dw9LSEvL5vCf6Cng1x7pQ1miQHmdfQ0kvqzu0222zWRgjRiTIdp4DI1Jsky5oVRKli3Am+g6rZqOf5euTPkdtg31uwj7mYcLEBHnWPZXtdhvXr18HcL8k0enTp3HhwgXT6UgsueqjJ1kzxElw6fHxKyBuJ8rxf3qcqtWq0R6yNmq/38e9e/cQj8exsbGBa9eu4d69e6ZkCgfJtWvXTMY4E3zu3Lkz1Pv0MHZch+PFcfW7cY2AenAoc8hkMp6t3ymt0qQUXexGo1GPZ9jPqGgIledlvWDWKPULjY4yQlyEb25uIhqNolQqYXFxEYuLi8ZLpWFhGnVeh3McNxEaDAYeeZaWd1KDrORbo2tcbGjZp1mCmwMfXug4Z/4NaxOzsoRdt1i1xvYikZEbntvvR/OKKIFgYj4TbLmzHfmEklxts30fHIdM6KdcbNi4GybVGPe5jXrPlrM9jOPsQHWQZxHa+QaDAa5evYrt7W089thj6Pf7WFtbMwk42jFZhk0NIREKhUyJJ8JOYmEIFYDRCkajURSLRaNrZqknlmNjhYsrV65ge3sbjUbDE4p95ZVXTLhVV5Z6r3Z7hj0P+28Hh5MOnQtIkElWacAYwlTpFQBP9Ee3eCZUe2gn99CwkSDbO3iOA9U0bm1tmaQi4P6un5yPaOiV4LIttqRDXxtGjpUg84fXtZMWJ/0eJnndwWFc2BpgjcQWCgXk83lT1YaLSC3tZm/2w3Nqwi09wSq90t1wO50OSqWSR9bEala6gy7lUbbHl9fnNTn2otGoiWDxM34cYFpjxu88o157WPjE2AT5u77ru/C7v/u7yOVyAIBf+ZVfwY/92I8hn88DALa2tvAt3/It+OpXv3ooDR0H9hdaLpfxN3/zN/jiF7+Id7zjHabiBL0jg8EAOzs7niQcAKbcErVN9DIreI56vY5er4dyuYx6vW5WfvV6HYlEwuj3OHDL5TJ6vR42NjawsbGBSqVirsMwsJ9kYr///UJPfn87OJxEkOQxcpNMJj07RalsQHe5I9nk/xzXXOySTGt4VBeulDmx2s1rr72G69ev71sreJShsT1anU4Hm5ubaLVaeOSRR5DP503yII9RiRh/GPKlJ2qYHpLXpEGnB5vzGucsJSS2d2kYHOl1OCzYhHEwuF/daWNjwyM14uLOHmsqmxzmgOJ5uXBmqURGaUqlkmdnyW63i3Q6jaWlJc8CXD3Q9oKbbVViTnnnNPOLxuUB9pjVsf6wcYmxCfInP/lJU8weAH75l38Z3//9328IcrfbxYsvvjj1Bo6DYR4JXZ194hOfQDKZxFve8hZcvnwZ6XTaaOpUOwTAGAath5xOpxEIBEyn1SQ8ACZ0y88PBgNsbW0BAIrFImq1GuLxOG7evIlWq4Vbt25he3t7jyH18xjz9WFwZNjhYYctkaC0gB4cuy4pCaVqeQF4PE0kyxqG1SgVsOtlajQaqNVquHPnDiqViq9BHoVRXlUaTBpn3RnQTtrT8LAmH1GLTfiVgrS9WQDQbDY9RtoRXodZAsc3sDsWqZf323Jax75NhO3NfwB4Pk+S3Gq10G63TRIuI0XA7gJ7VLUXW8Kgc5B6xP0kYHb7Rj2XSY73++w4keqTjrEJ8n6ezOPEqC9PQ631eh3/83/+T3zqU59CNpvFY489hre97W3I5XKo1WqekCSwa0T6/T52dnZMeAWA0Qbxfa5eARjivLm5iXA4jBs3buDq1avodrtmq1bV89m/h5H9SUOWDg6HhVnub4PBANVqdU9JRm7fGo/HPeSRhpOGicaSIVNbK0jDRyPWbDbxxS9+EbVazUSUeOyw9o0ax36fo0TixRdfRDKZxFNPPWW2kCXs2q9c4Gv9Yv7Qi64Em8ew/f1+HxsbG9jc3NyjP56l+d/h4YQfJ7FtqR3x8ZMmKAG1Ca2Oc0aCy+UyGo0GKpWKcZiR5OqmQpxXAHgi0H7t1ogWABOd3tzc9DjiFON6diedqyedt6aNWeIzJ0aDPAz2ao0ol8v44he/iC9/+cuIRqM4d+4cvvmbvxlra2smmY8a48FgYIixbvXK3fO0jFytVsO1a9fwla98xex8owl/o1aV+3WKYe/PSmdycJgVcIyxmkM4HPbUIAa8Zdv4wzlCE9TshTDJYr/fR7FYNEX8NR9hXIzreODrjOJtbW2ZSFYymUQymTRttI3tqGvYmkudo7Rc3Si5iIPDLEBtaCCwuzMkADPu/UoljrK9uoi05wpNvldiSzKsY8bPK2vXVgZ2CXmj0TA/tgf5sOF4xi7GJsiaCKKvzRNsw8AwSSgUwiuvvILt7W1TEi4ej+ORRx5Bs9nE+fPnjUZP66LevHkTt2/fRqVSMcaRWz9TW6zGx2/1Oqx98/ZsHRxmDYz4cKG7traGZDK5R15BbzLJr2a9K4Fkcg7LQgLACy+8gI2NDSPXIkZJpMYZ2xp6VfR6PdTrdTz//POIxWLI5XK4ePEistmsZwtpbf+wkC6fkRJiJeL0ltXrdUeOHeYGgUDAs2EQoyJc4NKOj4qIKOEF7m9RTfLK7eC1vCKvm0qlzGZgfo4wJdgq6dBxyCTfjY0NI9McV/M/q5hEwzxL3GciicWP/MiPmLqgzWYTP/ZjP4ZUKgUAHn3yrMM2VoPBAPV6HbVazSS6xONx7OzsoNvt4vbt256ddgKB+zUKd3Z2TBUKeomHkeBJO/cshRkcHOYFfhMxyd+dO3dMIi2T77RGqZJGlV1wjqCukWFVLoT3q1bxIIZt2DxA8l+tVk2ol/ppVuVgQqJ6uJgwpAaXZSppqLkDIUPJ05bXuXnN4TDQ7/dN3e90Oo1cLmcivroo5OJRyzdq2UR7gzDmJHBnzXQ6jWaziW63i2KxaCphsbwck/OH7QPBayvpZmItpWDFYtFEboDpV604Ssxjm4mxCfIP//APe/7/oR/6oT3HPPvssw/eoiOCbXi0A/Z6PdRqNVQqFQDAtWvX9nxWPTUaPhmlIxwHzng4OEyOUQtKembu3buHcrmMxcVFk5hLgmxXsrGlUIPB/UQ5lozjBj+VSmWkRvAwQMNNrTUX93RWqL6YEi96q+r1OgAYDxo11CQFLEV59+5dbGxseO5jng2dw8mG2t5EIoFkMolsNmveszXGGmWhhEGlF/bmIVqTmItQRp9v375tNhdLJBKmYgwwPPKuzjZeA9jdBZNRabZff88T5rHNirEJ8m//9m8fZjuOHDY5VmI76kvle376oWH/29dzcHA4GOzElGFhUj/C3O12jTxhYWEBTzzxhK+EAYCHLDNr/fr162g0GqhWq+a1URnmD2Ic/M5pG1u+fvv2bZTLZaytrSGVSiGbzZqqOsyLiMViGAzu14dvt9sm2sVQMec9etvb7bZnPrQ12g+CeTeaDscDvzFt983BYIBarWb0u0pAbWmVLX/U7egJO1mXhHowGGBlZQXBYBB37941HuB0Oo10Ou2pgMNrEJwzON4o96T2mFWvtD12u9QbPssYN5FwGjiMqPuJT9IbF/tN/tohD2IEnWTC4aRiXvRxNEY7OzsIBoOoVCrGsAH3jSilYrrlNPW4Ozs7Rlox6QYghwXKw5rNJqLRqJFXxONxADDeZZLgUqlkpCI0+naW/1EZNAeHaYLzEKMmnU7H46n1c4qNY8v1OB0vsVgMiUTCRG64TTvJMQm5fQ57sakl5BgRGiXXnPSZPOg5poF5nUscQRb4fYl+xNaPEMxrB3BwmBT79fXDJsx6/UkNAL02W1tb+Ku/+iuz09bS0hJisZgpwchaqvyfnlUlxuNec9i8Yh+z3/n0fU0gZBmqGzduGHJM3SQz7QmtB73fPah3fNhx87I4cpg/qO3dL6qrEopWq4VarYZisYhcLufZXlrrk1N3DHjnEVtuqfWT9Zr0+iaTSfMZu0IGj1WvMc/JHAGS40ajgeeff95sPGITeB1rtpRz2JzsxuaDwRHkfTCs4zlC7ODgxayNCdu46OusbEH9cSQSQSQSMQar3W6bH7/Q61EsAPyu4WcY+Tc9ws1m06NB1vY7Q+pwEmE7sur1Ora3t82uuAolvYSSV/6vpNtvTPpVrRm1oY7tidbE2FKpZJJtuaOuH9yC9GjhCLKDg8NEGBZVOaprP+jn1NgxSe/27dt73p9WGw4abRpmDG1dMI/jebXmq99n/a4/blTMGWiHecDW1pZJyOUuuCqroBSJdYtZ3pGEldBt6bkAVTJtLz5Vy8xFKv/WZN7BYGCSYweDAa5cuYKNjQ0j3yLGlX7OMuahjcPgCLKDg8Nc46A6O798AptsHuS8+2Ga3iE/z7b9PPwSm/T1Sa55lJ50h4cbflKqcUHieffuXbTbbSQSCaMJpvyI5dXsnTXta1FbzCo2WjKRen6SbSXNmtBnjxPdnr5arZoddsfd0n2eSec8wRFkBweHhwp+xHEaCTEHxSTGzk8raRPdcUnsOPc6K0k+Dg6TgET13r17qNVqyOfzZoMQyqkGg4HZbY9lHlnlQnfhCwQCHoJMnTHzFLRMJKUT/X7fU0rR1kkzz2F7exv37t1DpVLxbEA0yxgWhZrW8bOEh54gT/LlzfMXPSkepnt1mC6Omkz5XW/c5B77+OPAsLE2SeLhtNo/jQRMJ8NwOE7oHgXFYhHVahXVahWxWAzZbBbxeByJRAIAEIvFDEHu9/t7Nguh9IJ7IzQaDXMdJb/hcBjJZNKQZ1ahCIVCe7aoj0ajqNVqePHFF1Gr1VCv103FjXFw3HZ50usfVXsP4zoPPUGe1Hsz6viTZBSOexA6zBeOuu8P08yO0tHOy/gcJcEY9v6wbPdJMKk22q8941T8mTbcXOXgB+p+uXtkt9tFNBo1nmJ6iLmjHbBb2YL9W3fZZO1zglUo9HPA3qoV/J9kmpuMFItFc855mZseNjz0BHlS6A45fpnk8zJZ22330ynOy704HD/GJUKThOyHnXOSBEHtx9Mia6POM8k1RhFNP4w7Jg/D2I565tPwOk+rXQ4PL2ybpslulEJ0u13EYjHE43Ekk0mEw2Gk02lEIhFTw5gyDNUrDwYD4+1VRxmr3CgBJrgDHzfmCQTub05y69YtbG1toVqtespGPkgUaVpwdt8LR5AnxDDDMI+dah7b7DAbeJCJdBLpwDjXPspJfRTZPkgb7DlkHJI8KxinLbPUXoeTjVHzAgmzShlIYFnukZIIVrUIBoPGu0xvtGqQ6Q3WcaueaG0H6zO3Wi0j+1ACPwxOrnS8cAT5IcV+5N6RZ4dJMG5lhGlN9qOqN4zzmWGVHiYh236fmcR7POz1g2p8D3PMzqqRdh4vB4WOZ1sSORgM0Gq1TJ1zYLeMWygUQqfTMdUsuMNmKpVCMBhEvV43cohw+D5t4lbzhNZQppaZnuVut4vt7W2Uy2Wz3fuw6K3D7MARZAcHh6lhlid8P4Nkk82jaPtBrzHuAmBWn7+Dw1HCb1yr/IIEV6UUJLZalQKA2ViEpeFIfnksyTjPwx/+z8S8YrG4Z0fO/eDG8/HBEWQHX8wy0XGYbRyXt/GovaoHwSRe7mHHjyvDmMfciIPAzVUOgH/0in3DT25B/TA9xrqxDsmtvcU0AEOQ6WVWTzWTADUZUDXMpVLJyDkUD8M4nUc4guzgCzdQHSaBX5LncbTBrxbwpPKHg1x3mscpJqlOMS2plCOcDvMOmxDbcwM1xbb2mO9HIhEP+eUx9CqrR1o9yPQik3jHYjGUy2Vsbm6iVCqh0Wjs2QxklsbaLLVlFuAIsoODw4nAuEl943xmv+tMWrlhGoZnmIf8MHTd80SS56WdDkcDjolRESV9v9PpeCQPfI+EmKSYsgkApvQbSbSePxAIGILcbrdRq9Wws7Nj9M+z4ExwGA+OIDv4Yp4MpMNsYBY9IoRN/sY5DhheR3k/KcRBkggnwYNWEBnHSM+L4XZzlYONSRbELP+mO9mR6FarVY9nOBQKGe9yOp0252CVikQiYapgbGxs4MqVKyiVSiiVSoYc+43BeRlrDxscQXbwhTM4Dg8ThpGsScjXqNrI42KUR3hU9YoHMbDzbpzdXOXwIBgVeVIyS08yvcuNRsNINSizYAIet48uFotmpzyex2F+4Aiyg4PD1OGXGDPrOIhEY9hnH4S0+nl3D0OicRTfyTS+/2Hebkc2HKYNW6/M14DdGsckuyz9lk6nEY/HEYvFUCwW0W638fnPfx6tVsucY5yyqvMwRz5scATZwcHhUHBSCIx9H0dtzEaVpRsH43i2J/F+TyovOQyclL7lMHvYbyGpWuVGo4E7d+6YEnEATN1jv89MMxfC4fDhCLKDg8OhYxgZOyyyaXsuxy15Nmlt0lFeZ/tah6mVPUg5OPvzfpum7HfsuG0Z97MODseNcfX4/X4fjUYD9XrdHMMxrjvp2edxi7v5gSPIDg4ORwo/j6zCT5rhVxViUjL7oDWI9yN+fucaVmLqoBj3ng9CQh/kfic596Tfw4Ne2+H4MG/f1SjvsZ/8AvDfgMh+fz/M23OaZ0wyNx4LQd7P6+Iwu/CrM+vwcGC/736afWJUWHNYveNxz/Wgx43z2VEVL/YrO/egHu6TMDYPcg92fVmH48O82on92jtuBZx5u++HCZP0zWMhyJVKBQD26HQc5geVSgW5XO64m+FwhOC4JRFxcJhFuLnp+MG5ggltDg6zhnHmicDgGJY6/X4ft27dQiaTcaGFOcNgMEClUsGpU6eG6qwcTibcuHWYZbi5aXbg5gqHWcUk88SxEGQHBwcHBwcHBweHWYVbZjs4ODg4ODg4ODgIHEF2cHBwcHBwcHBwEDiC7ODg4ODg4ODg4CBwBNnBwcHBwcHBwcFBcCxl3lyG6/zCZYo/vHDj1mGW4eam2YGbKxxmFZPME8dCkG/duoWzZ88ex6UdpoTr16/jzJkzx90MhyOEG7cO8wA3Nx0/3FzhMOsYZ544FoKcyWTM34FAYOwVptvH/Hjgt0OQfocODwf4nU8yZucFuqX1JO9N67qHcf6Hbb4cDAYYDAZubpoB8Dugh26a/XDcfj3t4yY9dtjnbfhtXz0tTOO+Jp3/ZnXe0Wfc7/fHmieOhSDz4QWDwX0f5DgGZBa/DMDbUWa50wwjBXzdHsCzei8OhwvtDyft+2cf97uvSe7VNnSTfPYwnulJ+572g5ubZgOHOVdMe0wd5hj14wC2PfV77bDac5DzTHqNWR1/+j3o/6NwLAR5P+gNjNNx2PEOsgry++yw6+33/qjrTvq5cTFum0Z1hmGLEH02Dg4nHYftIR7n2HEdBtM2/A4OJw1H1f/3s5H2uLaPfRD7+iAL8ocRkz7rYyfIOuGPIqbj3th+RsaPCNu/98MskcZJnoti1EJi1GB2cHAYDr+5wW+OG3f82edwcHA4euxHPIdxAr4+TRI8Sbv2O6f9+VniNrOAYyfI43o2x/msfrnjEO9h5xnW0R82+D07N4AcHO5jXK8RdZh+EZlh0Zv9ImHOc+TgcHSY1OaNE/Ue9rpfFHe/80waSd5vfjmpmJS/HDtBPoyV1X6yhlEenlHXOojE4rAxDrmfFLN0fw4Os4BxwqR8PxKJIBKJIBQK7dFgdrtddLtddDod9Pt9z5wSDocRDocRiUSGaqF7vR76/T6azSb6/f5h3KqDg8MhYVw+MimRc9r7w8GxE+RxCd64+uDDaNNBrjUNuch+1x9HGjLsepPISSY53sHhJGIcbTDHyOOPP46nn34ayWQSkUgE4XAYwWAQoVAI169fx40bN/DCCy+gVCp5Pn/q1ClcuHAB6+vrSCaThgAHg0FEo1FEIhFUKhWUy2V86lOfQq1WQ7fb9Y2k2W0b9z4cHBy8sMfPOBHmYXZzHM8tF9V2BMrveJ13bEffOEUNHha7ftAFxLET5P2w3+Q/DqbRCaaZyTlJBx3nPNMg9A4ODgdDIBBALpdDLBZDNpvFqVOnsLCwgHg8jlAoZAhyOBxGrVZDu91Go9FAtVpFr9dDIBBAKBTC2toaVldXkc/nEY/HzflJrkOhkDnPI488gmq1inK5jHq9jnq9foxPwMFhtnHYHtZxzq/2OhQKIRqNmh8thzcYDNDv900kSolvMBhEMBg0kSge22w20el0TGRp0uiSn1ba8YY5IMjjYlQH3S8hxi+kYZdmOS7YbXuQtvjdyySZ8w4ODyNGjb1+v49oNIpHH30Up0+fxhve8Abfc9C4ra6uYnFxEY8//jgGgwEajQbC4TCy2Sz6/T56vZ6v14nSilgshng8jne+850olUp45ZVX8NJLL+Hll1+e/o07OJwg2DLJcby8k8oc/M6hHmHOA4lEAisrK1hdXcXKyopZTAcCAXS7XdTrdSwsLGBhYQGNRgO9Xs9EkRKJBKrVKiqVCvr9PjqdDq5du4ZyuYwbN26g0WigXq8fSm6Vn+Z50oTjUec+LK510PPOPEEelxge5AHYHthhur+DYFxx/bB27dfxRrXNJv7jtFWvbbffJeY5PGzw6/86BlKpFNLpNE6fPo1MJoOFhQVks1mPl4cGkeeiBzgUChnPz2Aw8BwfCoX2eH9UatHv9xEMBjEYDBCLxYwc45FHHsEXv/hFbG1tOW2yg4MFeyw/iF23/+f5/JxsHNOhUAgrKytIp9M4deoUYrEYkskk0uk0UqkUQqGQ+Vyv1zPvxWIxBAIB9Pt9E40Kh8MmwtTv99HtdrG6uopMJoNEIoFms4lms4lGo4F2u41arYZOp4NGo+Fpsz4X+znt9xzHwTSj7seFmSbIx/HAjtJjPIp4jqNVGkZs7ff2O6d9/lnsqA4OR41hi8RgMIhMJoOVlRW88Y1vxPLyMur1uiG+lEHQI8Tz8PVIJGI8wpqoRwLN1/k3r882ATAh2PX1dTzyyCOIRqO4ffs2SqUSut2uOdYtbB0cvBjH4TPO+7Yzyc9rzDEfjUZx6tQprK6u4uu+7usQjUY9Y5+RI479Tqdj5Bc6r/CcsVjMLKYp0+p0OlhYWECr1UKn08Hm5iaq1So2NjbQaDTQbDZNe0cR5GnkSJ2UeWemCfK4mAap26/yhQ1b9jCpBML2Wg+rrDEuhq1ix2mXI8QODsOzxweDAaLRKC5duoTl5WU89thj5vVCoYBwOIx0Oo3BYIBOp2MS56g97vV6xqjRa6weZMC7qyi9TrxGs9n0JOzoeO33+2i32+h2u/jGb/xGPPnkk2g2m6hWq7h+/To2Nzexvb3txriDA8az8+PkBXGM8u98Po9cLoeVlRWkUiksLS2Z9xk1yuVySCQSyGQyxhNMYtztdtHr9dDpdNDr9QDsRoyUI+jcoW3VKFQsFkM4HEY+n0cqlcLy8rKZ28rlMkqlEsrlMprNJkqlklmsD/Mm7xftPsmYOYJ8kNXHNL6kB5Fo+H3ejzSPIqv2IBj23n7nGldTPE4oZRhhcHA4qfDr65FIBPF4HGtrazh16hQee+wxE8YkCaYxa7fbns8yoSYUCiESiRgPkRq7YWORr9N4MdSqCT18H4DRNzcaDRSLRTQaDRNaVcPr4OBwcHAsxuNxhMNhxGIxLCwsIJ/P4/Tp08jlclhbWzOLXBJXeoMZXWIpSJLkQCBg/rYXwsDeyhU2/1D5ViAQQCKRMLplJgUmk0lEo1GEw2Ejweh2u2Ze8suBmBQniS/MHEEmDntlYneww5AWTKrv2e+YUYQc8JdX+BH1/c4z6hwODicNwyJANIRnz57F0tISnnzySWQyGZOYFw6HzWfpAWK4Uz+v56SXSGUUhO3dIqGNRqNDk/e0QgYNZK/XQz6fx9d8zdfg9OnTKBaL+Lu/+ztsb2/7kmQ3th1OMia1X/tFhKkBfvrpp01Eqd1uo9PpIJFImDrm5BSaE6AkWKNIWnmCZJe/1VvMY+15JRwOm9/UPDNiFY1GzbkikQgWFhbMnPLqq68audaVK1dw9epVD2Ef5uTzw6RR9HnAzBJkP42P3zHA6Lqf417L/twkg8pPbjGs7X5e2f0+My72I9fDNMujPNoODg8b6BFmAs3q6ioKhYLxAOlxBInpsEWoJu1piJak1vYK8/w0XoHA/ex2PQ/byc/TU8TPkMQnEgns7OwgmUxic3MTnU5nj6fbweGkwl54TuKo0vHK/AGdF5aWlrCwsIBms4l6vY54PG7Gsj2mSVw1JwGAZ6HMsazXJYke5l1WYt3r9cx1+FmSZwCIxWKmfFy/30c+nzev12o1Uy6u1+uh1Wqh2+0a7bLey8OyqJ5ZgjwpxtUNH+Rz45xvnL+HwU8XdRCSPEli3n7nd8l6Dicdfl4SemPj8TguXbqEy5cvI5VKIRKJAIDHk6tGNBqNAoAxgDw/AENwSYr1PXp81IipAWTCTrvd9lS4oCGldwi4b+SCwSCSyaTxNtGrvbq6ilKphD//8z9HsVjExsbGnufhIkUO84xxPZjjSgx1ERoMBo3O+OzZsygUCnjssceQTqdNDXTKGbiYBbyLaCbW8frUIGsugibYanuVINuEmNIIeovZ3lar5ZFs6vNhJGllZcXkRyQSCayvr6PZbKLVauHWrVsol8u4c+fOnmRhbbM+s5OGmSXI45DMSYnotK47jfOPc08HPTdh65kn8RSfxM7u4DAKwWAQuVwO2WwW586dQ6FQQCwWM4aNWmIm3SmhVIPBcKkm8agnCNgdX3ZyHkm0hlf5Pr2+Oq4p8yBJtwk6NxSIx+Po9/t4+umnTbULGlWFI8kO84xJvMP2//TO0gMbCoWQzWYRjUZNcl0+n0ehUEAmkzHkVyUSWobRjhiRuPKaGlnimA6Hw3sS8fi+TVL1HHayHhf6fuObx0UiEVNGLhQKIZVKIRAImCoYoVAI9Xod+XweW1tb2NzcNHOYHRU/qZhZgnwUOOkrIFu6MY1zOTicRNBI5PN5rK2t4Wu/9muNZ4ae2VqtZoiubSA0+a7X66Hb7RryqqXb/AyWXziXXmUl3apdpDGnxwrY9VAp6H2mIfyar/kapFIpfOUrX0Gr1UK73X7owqYOJxM6jvy8psOO0bHM5DlKlJaWlpBMJpHL5ZDJZJDNZhGPxz06Y45NLqD1uoCXtPpJreyFs+qM/TTHNiHWBbMuyhllUrKu9wnAE7kC7ke72u02er0e0um02bTkpZdews7Ojqd2O+/zJHODuSPI00iuG/ZlHpeswB4c/NuvbZMK4ad1Pyd1ADg4BAIBXLp0CUtLS7h48aLJ/u71esaA0AiGQqE91St4Dhoy3fRDDRaAPR4kv/FJL1YikfBUx6BnhySbO/DRoJMgKwng5+LxOFqtFur1Oi5evIgf+qEfwuc//3l84QtfMFvWOjgcF6aZ4OXXl23brt7YQCBgKlE88cQTeN3rXodms4ler2ekVXpOyhNKpRJarRYCgQCazaaRQZGY0ourMio7YqTkdzAYmAgQ5VS6IOZ1tS08hmOYC3NGvFQS5reBkJ6r2+2i0+kYaYYm/p4+fRqhUAi3bt1CtVpFs9l8KOaMmSTI4wySB9UOjyLJw9pz0CQ+v3Pt99qwQT7s+GnDaZAdHgawiP/S0hLW19exsrJiPLc0YIA3N4B1jvn/sPlBybKfB2vYXKBZ6CTUNLQ03MBu+TnqH7ldrZ6/1WoZrSONZiQSQS6Xw+3bt3HlyhWUSiWXtOdwYrCfA8xP5pRIJLC8vIxz587hscceQ7VaRbvdNuOdJdE4Tnq9nom+NJtNI2XiOOPY10TcYXOAHenl/yqxUo+ynz4ZgCdKpREtJdr6HHR+s/kMSTrbn0qlsLi4iGq1ap4JveYnGTNJkEeRM7uTTXLOURiXpPq9N6wtByGZsxLudOTYYdYx6SLVb4F59uxZPProo1hfX0c6nTayCJJKTaLhe51Ox+NhoTHU8kh8z26vHWbVtvFzyWQS8Xgci4uLxjtMA7yzs2NqMEciEWQyGSSTSSQSCSQSCc91B4MBGo2G2WKW5LrdbqPZbOINb3gDnnzySfz3//7f8fd///eeZ3SQ5+vgMAn8FpnTPC/Pbb+mpJS7Yj7yyCN45zvfaSQGzD2wyzKSNOtCmdIrYLdSBT3GlDbpWOIC1ya8+jxUQqFbzSvJV3kHK09okh/brbkGKs/i+XSHT01GpoyE80ksFkM8Hkej0cBrr72GSqWCjY0N34XHSZk3ZpIgj8J+UoNhg20SEmt/uaO8ysPOud8x++FBJ4wHacNReqodHI4DkUgEyWQSi4uLWFhYQCqVQiwW851f1PgBMEl6auiAXQ8OsLfEE/+2CbJf+JNe7Wg0arzD7XbbeLr4eeokabi4gxbDtDSYTLixy8IlEgnE43GcPn0azWYTd+/edXILhxMJ20PLsRiNRnH69GmcOnUK+Xx+T54Ax2g0GkW32/WtSc6EXFs+odeyX9PzK+xj7LHo50XW4/xstk2Oh41v3dBE5WXA7pxEJ8LS0hJisRg6nY5ZsJ/ExfXMEuT9SNo4BHW/4/yOHxfjfGZaEoyjxknp3A4nFw8aQUqn03j88cdx9uxZnDlzxpRLo5dVySw/T+kFS691Oh1EIhFDOlWnbOuTeQwJLEGDzBrH3PGKWfMkyvQC5/N5E/Jl5nkymUQymTQEORqNeragtskxy0ER3/AN34BHHnkEf/iHf4hqtXqg78PBYVIclo0ZdV6N6kSjUeRyOTzzzDNYWVlBOp02USP1EtuLWi2tprBJsn29YfIqPw+yfR+cQ2yP87DX6MVmG2y5hZ3Mq/Mdy0Tyc1wY8HPpdBqFQgGVSgW5XA43btzArVu3TDtOEn+YWYJ8UPhpafbzEA87zu8Y+1qzioMSiFm+JwcHYtIJ2DZasVgMS0tLSKfTnmQ6nlu9TH5jQ0OcfE/Lu+kxADwGSucoSicajYanOoYaOXp09LMk8cy6Z0iYNZzp5SaRTyQSGAwGqNVq5jwMwbIaxtraGnZ2dnxrJDs4zBNssql6X44rbvRBmRIXryTJJMB25MWPK/hVqFCyrJ/xk14R2l6VR1Aywa3jKcdSaYXOKQD21EjWOciWa+j8p3kP6kGnx1jnOWqzAaBYLHp02icBc0OQ9yOwo94fx5gOk2XYq7lp66VsHNb5xyXAJ2Xl5+BgQ6USsVgMi4uLSCaTxmj6Ha8aQHs+oPGhMdP37B8lzZo0wzJs9NwyrEljrB5hen4HgwGq1aoxkiq1IEGmB4jnZx1kNYT9fh+tVsvsvLe6uopgMIjNzU03DzjMLUZFnbkY5IJwfX0dmUwG8XjcRIjC4TBarZb5jJJd/fHz2tpe4mESCt1Fj//7STvozabOuN1uo16vmwhQp9MxMipdnHe7XTNn6DmA3TmIxzHfgm0i2eemI1w0cFc9npdJw5zf+v0+qtWqydHw+y7mbV6ZmCDfuXMHn/3sZ3Hnzh0AwNraGr7xG78Ra2trU2+cn1bHr/Pb5HWUi98moDyWr3EVSeOkn2PHoteHhlHb4LdyfZBOoau8wyTOhPMgO5xEqGFKpVJIp9NGz8tQqho1jn0tlcSxosX8daMOemponNRwDttUQLPfmaDHzUpYuYKEV4k1pR3UKNtl55iM12g0UKvVUCqVUKvVcPfuXbRaLZN5z+MA4OLFi4jFYnj11VfNe/bzmzcD5+AA3B/38XgcS0tLOHPmDE6fPo1cLodEImHInW6prBti6JzA7dztRS+jODrOSWoJm3f4eY51tzx6jPk+ybDfLna8R/4w2Y6LfyXfXDwPI/BaeYOyMM5lvKdA4H4OBOepdDqNZDKJUqmEF198Ea1Wa49Wex4xNkGu1Wr41//6X+P3fu/3EAgEsLCwAADY3t7GYDDAD/zAD+C3fuu3kEwmJ2rAML3wJN7icSZvJZl+4ZFA4H4txMcffxyXL19GNBrF2toa+v2+GUTtdhsbGxt48cUX8f/+3/9DLBZDtVrds/2sTWr9BoL+bd+PfR+2F/sgJHbUqtqvDX7Xd3CYdwQCAZOcpgltKm+gkfDzuNBQ8HU7XKpElp/X3e30GDuEqoSXmmg7VKu7dflpHvX83W7X1EFutVpoNBqo1+tmK1ld9AeDQWSzWVQqFSSTSTQaDROqdYtmh3mFjmnKqtbW1nDmzBkTndFFJY/18w6rjELtJT23ds1zvme3hdC5g2NRCTIXz4TOFTYnGNZWvu7He+xr2+fSiBsAk89Ah4JW6QiHwyaRmJIwRtfmmT+MTZB/8id/Ep/73Ofwx3/8x/jH//gfezK6P/3pT+MnfuIn8JM/+ZP4z//5P0/UgEkn32FEzo942mSVHZmemWQyiUcffRTNZhOXL1/G0tISUqkUABgjQg8Li/Cvr6/j4sWL+M7v/E5TV7RUKuHu3bvY2dnBYDDA9evXcevWLVQqFU8buVNXJpPxeHM46PwKeU8L+3mg93tvnju5gwMRDodx+vRpLC8ve4wAxx6NnBog9Qir8bENnF8ijJaBU2KtEg0aRXqL6REmwW02myiXy9jZ2TFeKo2U0QOsXnLKNiqVCorFovEg1+t1Q5jZLiUJKysreNOb3oSXXnoJL7/8svMcO8wd1NaxjycSCZw+fRrf/u3fbsYiNwRKpVIefTHB8c7XWHfcHufcbZPvEfZ4t8k1/9b2Kknv9XqmnCP/txft9uJdF9QqmSDhJrSWup+Djt5yfl7nJyXwnGtYEi8cDmNlZQWlUslsTa3nnTeMTZA/9rGP4Y//+I/x5je/2fN6KBTCt3/7t+O//tf/ine84x0TE2Rgct2t3/Ha2fQ4Ih6P45/9s3+GtbU1pNNpo+ujZ6VSqaDX66FWq3lqAQIwJJZkuVar7ckQXVlZwerqKjqdDs6dO4dqtYrnn3/e7LDTbrcRDoextraGRCKBZDKJcrmMV199FdevXzdeHBJlP2/xME/yft7fUavHcZ+3g8O8g9Ue8vk80um0b5hxmDeW49PPkwzAY7g0OcbWNisxVuOmeuNIJIJwOOzxFPO8arQZSmW7qCOkJpGaRf7N0KjqJUmQec5MJoNz587h3r17e0KzDg7zBo6XlZUVLC8vI5PJmHGncsr97KNNPmlXdfzaumL+7cdLlK+oXaekguNYNx7hcXZVGm0j5w56dfl5ziXaLoW2RT3ZPK8m7g2TmzE5uFAoYDAYoFQqGS/4vHKIsQmyXRrIBgnnQTDKI7wfCdTO5odIJILLly/jmWeeweLi4h6NTbfbRSaTMVvI0sOs56YoniSZ7dJVKr3SWi8wlUqh1WqhUqlgZ2cHxWIR1WoV3W4X5XLZkPGzZ8+i0+mgVquh0Wh4tpPd7zn5PTM9fhrhUedBdph3DAYDrKysYHFxEefOnUMikTDjwzYy+hnVCnPhDMBjnFROAdwPRWpkSL0u9uYjvCa9U8yoj8fj5prcIa/X63myyblRCBfgJND0PNXrdbNRSKPRMLvqaX5FIBBAKpUyhi4ajeLxxx/HvXv38KUvfWnoXOTgMKuwbV4sFsPXf/3XY2VlxYx7P9iSR9vmcdxoBMgm24TmGRD04NoEWvOYms2mmVsYxQbgWRQzGZdkmFEkykj4eY5dfk4rWeh9sh16Xzb5t6NguhhXCUc8HscjjzyCVCqFYrGIWq0217vtjU2Q3/GOd+Bf/at/hf/yX/4L/sE/+Aee977whS/gve99L77ne75n6g0kRmlo/chbIBBAJpPBpUuX8PTTT+PcuXNmn/F2u206NYvoRyIRs8pSMkyjYYcrNGNU66YCuyu8QqGAbreLbDaLXC6H7e1t3Lx501yXGxUkEgkAMN4ednjWMW00GqhWq9jc3PQY4mHecn3N6QcdHnZw8l5dXcXa2prxmNpaPR4L7Box26DocX4SCsoi1Is87PPA7s5b1PORhKuHqlwuezTBtv6Y2eV8j6Wgtre3US6Xje5YvVPMq2BdU16X0rMzZ87g6aefxiuvvIJSqeQWyA5zBR1fsVgMKysrWFpa8kgsOZ40GX8UOMZtz68tpfCD39gfdiy5iSbK6Xyjtdf1POQunI+4cGc0itAFOrkLz69zFncN1DwNyj1sfTLnolAohGw2i06ng5WVFWxsbMx1wt7YBPnDH/4wfvAHfxBveMMbUCgUsLKyAgC4d+8eisUi3v72t+PDH/7wgRoxKZGzRfI2SQ6Hw8jn87h06RK+/uu/Huvr656VmeqBNBFFtYUcNPTU2Eau0+kYEq06IcCblMPBmEgkkE6njVeImh56mm1tI3enof6wUqng1VdfRaVSMaVUNNN2v86nz8tPvuHgMC8YVxdr6xBXVlZw+vRpzzjTChM8VsfUqIxxLqx17tB5wfYg6Wf5OucZ9Qxpu3u9HiqVyh6CbHtzWK4tEAiYSNTOzo75m4tv3i+lHLFYzGxIwnNTp/3000/j7t27KJfL5nm7aJLDvICR3Wg0itXVVeRyOQQCAROFoaPMlkaMcsZx7HLxas8XNvyIsd8xfpJRlXSQ5IZCIeNQA7w7djISxdd5Xi3jptIuEmSdR3isEmSdC/m86LlWYqwR+W63i+XlZTMP+Uk65gFjE+RCoYA//dM/xfPPP4+//uu/9pR5e9Ob3oTHH3/8wI0YRdSGkedhJDmdTiOTyeANb3gD3vCGNyCXy5kkGxJT1eWqJxmAR3ze6/XQaDSMd1kTeUhQmUSTTCbN6m2YRzuRSOD8+fOmg7NNXM3ys9QNxeNxRCIRpFIpXLhwAY888ghu3LiBF198EZubm2ZlNsyI8/n5/a3/j0uUnXF0mAVMshgMBALI5XImOZZEUEm2n0ZQNwnQBbJflvhgMECr1TI6Xy5+o9Gop1oGj9XxypqsiUQCuVzOtJFEWWuPArseZxJeEme/xS+9xFycq3FjghKvz8/zeoVCAefPn0c+n8fW1pbxULvx7zBNqBZ3EofNuMfn83ksLCyYLZE5ttWjaSfb2tfRMUVvrlZo8Bt76nUddQ+aO8DPc4xz3tEcgUgkYiqF0bvLz5Agk1iTG8TjcZMDxcg0o1x+90jYORa9Xs94r1UuYnuzY7EY0um0J1lPF/jzNIdMXAf5iSeewBNPPHEYbRkbfqSPq5l/+S//Jc6ePWsS8KgrZocDdkkwt5WlGJ6dlVIM1iLU0AqNG3e+sgecCv9VisHVFZODAHiSZrTuKnfH4fVoACORCB577DGsr6/j2rVruHr1Kra2tlCv181qz34+ilFab7/X9XjnbXaYB/j1Z0qZWInGHiejfqu+Tv+3r6njFvAn0rYuUce+ElYtF8X5xW6LElrOfepNomSDO/ExR0Qr+NjkmF6jTqeDeDyOXC5nFuksC+fgMA/geEkmk8hmswB2awyzXJk6yezcAyXufA3wenZVO6y/edw442UYidZxrmPbzpGyZZ6UZihJJ/+g443vk/vYPGdYaTp7a20+J43MA/DkYSWTScRiMVM1Z96iUBMT5Bs3bpgscEWn08Ff/dVf4Vu/9Vun0rBJJRfhcBi/8Au/YLyzXHmxk8ViMfO6JspwRcQOSAPFlVEkEjECeN1AZG1tzXiL9IeDTUOs7Fj0TGsohGDHiUajRrKhHTWZTJo2LSwsIJfL4cKFC7h+/Trq9Tqef/55lEolX63PJMR4GOalQzs4EByTKysreP3rX49cLufxitjaQdUd21Um9DhCiSVlDI1Gw5M5rtvW6pygUodwOIxUKmW825yj6vW6+Rw9wfxcJpPxyLu0pis/n0gk9mybS1Jse7Z53zRkrO7DTUsajcbIaNQ0ME+G02G6mKYDRsfsuXPncO7cOY80QqNLthNo2PjW/1X6oJ+zCSTbotdRrkCCSn4BwMNL1GkHwOMt5rmUOJOjaCULLgA4H+hufKzIxUUx4M2x0nu1F/zq+OMCXJ2NPLZUKpnCBJVKxbeqxyw738YmyLdv38Y/+Sf/BJ///OcRCATwgz/4g/jN3/xNQ5S3t7fxzDPP+HaSg2C/EIp25mAwiGQyiXQ6beQK+nmVVvCz2mlVDK8rSxownoOdjl8+QzYaIlINIeCta8iOquemgaQB4oCh94fPwr73aDSKQqFgiPTy8jK+9KUv4datW6jX676r4EkXHQ4O8wY/IxePx82OT+rp0PGh5FiNErBLpP3kTLoFK0OXtkaYx+uCmuclsU2lUp4MdHp8/Oqe2sbVTjTUBB9+jvfHeYyVMfRzPL+WmmO+ycbGxpS+IQeHowPlRcDuOOR48IsE2eTNdnzpnGEfC4wuy2pfQ8emnQehHEQTC8kZ+Hkl6gBMdQuNDCmZbbfbhg8Fg0EjtdA50W6LLRexfw+7by7E0+k0YrGYmSPnaRE8NkH+uZ/7OQSDQXz2s59FsVjEz/3cz+GZZ57B//gf/wOFQgHAg3kVHjSkPxgMkEqlEAgEjPxBV1AMgdLDYu9So+1npwBgdDuRSMRogYBdEbwK9fm6eqBto0aCTE9TPB439QMZDtHBy5JR1P/wGiTiNGbUHPV6Pdy+fRuNRmPfZzzuew4O8wTbS5RMJlEoFPZ4ZHTRqiWb+D8NhXplAe+ilxUidnZ2zHzCMa0LblajYdv4OreWXl9fRywWMzvpMeGPsgjmO7Bt9XrdlIbk51SnqPORnbBDr48aV94f74n38LrXvQ6pVAovvfSSR2bhvL0Ox4n9bBVtbi6XM+Vd2b9Vs69OKM4HHPvkCSpx4sJVF6y2/ef5/NpjX0fJuv5o8i43DyLhzGaznjbR9hPkKrxHSjmZO0UeRMKqGyGpfEMjU1r1B4DHCamRMco16EVOpVIoFArI5XLo9XrY2dl5oO/9qDE2Qf7Upz6FP/qjP8Ib3/hGAMD/+T//B9/3fd+Ht73tbfj0pz8N4Pi8jpzcmYGpRo5GQzuGGj4tW8LO5le1guRTV3Q6SHisdnZ2NtsjpcaYUJE+Q7YAjB660+mYa/IcGvrIZDJYW1vDxsYGKpWKZ195+1npSphw5NhhHmCHPcc53jYidmjUllTY45jjlAYW2M1j2NzcRKlUwu3btwHcJ8c0aiSjjPKot1dzFnRrac1n0NwILug5rlutlnnNb+EMeDcU4PPSsnKj0Ovt7iCayWSwsLBgQqYODtPCQRdZ40SYA4GAJ0EV2JU8ALsLXS4UdT5Qp5bt5fWz37ym2tdh98lzsuQsCaWtHeY51SvMH84husjVRF5GkQgm/fN6lHGUy2UP6fdrM9vERb2+b5e4Va+6epAXFhYAAOVyeWoqg6PA2AS5VCoZTzFwv/j2H/7hH+L7vu/78Mwzz+C//bf/9kANeRCCpqEKdjb7C7eNG7Cr21FNETuWrvSY4EKdDq/J3zpYNZxJgq5GnSReNxahYaMRDQQCphPzXFrWjeERgiQ5nU5jfX0dGxsb2NnZ8WTaDnvmoyYo51V2mCVM2hd1bNuaQf5WnbAfQWZEB9g7h3Q6HWxubmJzcxN37twx5SWZB6F6QJVf6EKeBo9hU9Uqcy7j3/RCkzBTZqHeLTVkds1SfSb2hgeA17hRvsFkm4WFBfR6vUMlyM4j7TBNkCCnUikTfdGKERxvXEja45+LVD8phI4bW5Jg/207onTBy3Gm8k31TCtB1qozSuoJ1Qbb+QWamEuZA2WeqjvWtmo7NLmP98S5yW4HnwsX/8lk0iywr1+/7ruAmFWMTZAvXryIv/u7v8Ply5d3PxwO4/d///fxfd/3fXjHO94xtUaN8/D0i9SqEAwZqJicxFTDAcM8sboqonFNJpNmcFGq0Ww2EYvFzLnscm3hcNjswKeSjlgs5qmFzPf4P+UcWo+w17tfC1UJOrAbHuKg537zt2/fxu3bt1Gv1/escvd7nroKdnCYJUxKoOLxOBYXF5FMJj1VJtSwKTlWo6QeJTWQXMRy3mCJNpJY/lAaRQJeq9VQLBY9muNEIoG1tTUsLi6acpTaRr1fnUNUhkXDqZri/aRUADwGkWSbJSsZseJrnU4HmUwG9XrdzQsOM4H9+iHtOcch9f8qaaBziuNAIy0axdHP+HlK/aQO6swiOWeUSOcF9WIzWTeZTGJlZcXMI6dPn0YqlUIweL9Wej6f91Ta0jHc6XSQSCSMbJPXIcchuVYZBCvekGeQXJNvNBoNT5KeOvB6vZ7Z7Y/tZZu4mGdETZOMdVE+yxibIH/nd34n/tN/+k9417ve5T3B/0+S3/Wud+HGjRsHaoQ9oStZ84Of55Nkkdmf/LLsXWhswkivjh0u0CoTuuKh0bBlDLr7Hj1HLDGn1yXxJVm3yTyNHrec1pWuXXKO4Lnj8Tj6/T7Onz+PYrGI559/fs/A93veep9+vx0c5hHhcBi5XA7RaNQYENXuqRGz5xx7PrJlVFoVggtlAMbwaI7DYHC/TnK9XjfVdDjOuVFQLBYz85oaTj/vDK+lXmM7pKrQ8a4EmddS48ofNa7Mf6BHepy5xMHhuKALXDtC5CcxHPZbpZrqeSZUrmFzCy3VqnWHNbkf2CXibDfnE3q92+02MpkMMpkMABjJE7eNJyFV7zhzEriQV6mV8hvyCZWM6gKcPIUbDDEfgu1Wb7s9v+qcyZwHv8TgWcfYBPmDH/wg6vW6/0nCYXzsYx/DzZs3p9Ko/UL/eowOBgBmglfPMI0AK1Jsb29ja2sLtVoNd+/eNVKH9fV1rK6uolAomLIlei0aIXYqXo8dgz8k5+w0fppHEuNisYh6vY5MJoNwOIydnR3kcjmzemOHZD1BfpbGkW3QmocXLlxAr9fDCy+8sGdQsx1+f9vYb6Hi4DBrUIOVTCZx/vx5xGIxFIvFPRnfwF4DpXIrPcYvpDkY3C8hl81m8eKLL5pdL1OplNm+mUanXq+jXC4jkUhgMBggHo8jk8kgl8shnU575huS4FgsZuY4zlE0XFyQM0xqZ61rG+3fOlfSw9btdlGr1cw9MAlQk4fpbJh1r4/D/GHchZYtV9DXbLvmV6FF3/cjyhxf7Pt+nmc9p30dvt5sNk3OQKPRMDv50oMcCARMvgIdarTp0WgU2WzWaJSz2SwymYzxMnMxTScbnWC9Xs9sWpZMJj1ONi7GuSgGYJLo2u02EomEuTdG2/nZVqvl4T36nh2NV0LO+U8jXYzG8/OzPp+MTZDD4bApuD3s/fPnz0/cAL+HM+w1Py8zsOtJIalUz4pqh65cuYJr165he3sbtVrNhDRZpL/X6+HOnTvIZrO4ePEistmsWfXwvOwo9orRligA3nIyNHKqO6pUKtjZ2UG1WkWpVDJ7mA8GA0PmCXrG1fNlD1h6rHQHL2qo/J7vOBPSrHdgBwdCyXEikUAikTDGxE7IUQ2hftbepEONjxJk/k0JFUOa6oXV7Z8pXeC4ZlnKWCxmolVqtFXype3T9+0f23Njzw/6Gok7vVQkytxciZ4pXtNvceHgMIvQcaSLOnssqXyKsJ1dKq2ynXHD9Ld0YrXbbVSrVTSbTTSbTePs6nQ6njFP4suSr/Qi8z0SXl2ocrHM+6Dzjh5gngfYTdojH1CvMavVMLIFeCtx2EmK6vnWXCuVbKkzQXO7KB21tcyzjIk3Cpk2Rsko9vvfb0VoZ2g3m01UKhVcuXIFd+7cMUWr+dl8Pm9CEkymqVar2NzcNJ1WNYv8nO1hpjdGMzRpPLVD0qgyZJlKpUwHB2B0gNoBqRviiiwej++5f81wD4VCiMViOHPmDF577TXfZzWqY6pn3sFhnsA5YG1tDYVCwZQ/oxeY0BCinfDKCdyWM+gco0ZnMBggn8+biFC32zXzDCVV9M4mk0kEg0GsrKzg7NmzyGQyxpOkC2kuwGmo2R56mzQRj3+rMePxCiUJwP25sV6vGw9ytVpFo9FAuVz2bG9tEw0Hh+PCuH3QXmjqWCbsBTIXjZo/QJuqEWkld7wGxxY5ACVV29vbZuGpMip6kDmP5PN5JJNJo9VNp9PmXIuLi2azHvIHeyGvib9cdHN+UNIMwPM/S8Zls1nzjHSBwHuyn6vODYxoq0NBuQMdjFpffV7mkmMnyMOwX8jFTzZAQ8cviNKKra0tXL9+HaVSac9x1BRrwlu320WxWEQul0MymdzjQfEbJH4eZPU8s2PwdQry9dwkudw+mkR+GGHVChn2BJBIJPDoo4/i2rVre8qq+HVMOzxlvzcPndnh4QaJaiqVwtLSkglL+hk2/rbnDK1zTtA4+s1HHFunTp1CPB43u0UxjGrvXEUtos5DGvbka3a0jNpCrbGsx6leUo2XnkN/AzC75dHjzVAwQ7taKYOytWHeOAeHB8G0+xHHMpNn7c3DAP8Ii3qUbS2yXzRGtf86jkmKdSMOJrMpD9DFtr2rr5JRjm0l4wqSTjui5Of55rXV8UbpF9vJ2sn9ft9Tv13vk6/R2afPnj+c8zhv6Fw8D7xiJgjyQfRHfmDYQc/V6/VMmENrhgK7WiGGMjXZhh4Uhg3slSK/XE0O1IxYrtIAr1eK3t1kMmnazM/xM9Vq1bMpgBolPgM14H5kPRKJYG1tDfF4HLVabQ85cIbN4SSB/TqXyyGfz2NhYcF4Zm1ya+sRdZz61fq0jYuORc4X6+vrCIVCeO211wxB5jnpPSbpVCmY6h7V+2LrkTXRJR6Pm3bYyXZqFJmEw/PocwJgiDvnHuoltYqFJt/YHjgHh1kFx7L2YV0QA3v5go5p+0fzELR8GuC/9XO73fbUKtfEXh6j5di05KsfEbfnA00w5rnUtpNT2Itl/tZrkyD7PUPeB+dFXpdzi02Q+T+vw0WC1mqfJ7nWTBDkSeD3YNkhUqmUbyIhOwArVgDeWsgMw6qnN5PJIJFI+Or8OFhUpK9l5ILBoPEgAfAQXXqOubMXBwa9NyTzDMHoaouDQI2cvRrTChmDwf0kotdee23fxYXtAffzPjk4zCo4Bhge3d7eRjweR6fTQS6X80zg/K1Z3cDeyXsYOdaduOjVobFhogzJJwCPByuVSmF1ddUk5pE8qzFnOSiVZ/E16hQZYaIXWDWJvA/OSToftNttcx/FYtFIQiixoEFUYx4KhbCwsOCp2jMP3h+Hk4lhfU9fKxQKOHXqFNLptEdmoV5W28OqEgmt0mDbW/uattc5EAggl8sZu85zZrNZxGIxU5t5dXXVzCPZbNaQVO6cx4UqCSo3DwuHwyYyxcgO54NqtWrKvGl7eG9aVKDf7xvPcT6fNxsWUWpFmYgfpyJfogxVazPH43Gjo6ZklFW9lpeXAQDFYnGP028WcSCCXCwW8bnPfQ737t3b41V49tlnJzrXJA/HlgEoqePqiysielGXl5eRTCYRCoXw8ssvexJTAHiS4vr9PtLpNAqFgjFi7Gg8nlCyqqs6Fv2+evWq2ayDSUNra2s4f/48lpeXUSgU0Gg0TG3AmzdverxLqVTK6BtZOo5GTpP0+B6NtoZhotEoLly4gNdee833uY37/Thj6DAvoCEgmWOuABeY6l1Rckwj6ReZsf/XRTLHoerqbL2zbuUai8VMhYtgcHc7aXpx7bCteqz0Hng8jah9jN4jj2fEjPMWF+U0muo51nk9GNzd7tbBYdYxGAyQTCaxuLhoFpZ+Y1ttGu2ckmItWUYwmsPP2OB1uN+BbsSRyWQ8OmMm3gEwjjLAK51UYksOosnA3PSDUpJarWbarJuQ2fdGsLoESTWfVygUMvXd7YWx/s25RkvJkRxnMhmz+OdPJpNBpVLxdXSOqyQ4SkxMkD/xiU/g3e9+N6rVKrLZ7J4HNilBHoZJHhRXbMBu6EBXV8FgEGfPnsWpU6dQLpexvb2N7e1t1Ot17OzsmDAAP8ckuaWlJdPRdEBxxcYQBACPhoilWa5du2YSddiBv+EbvgHVatV4h+hBqtVqZrVJuQeT/Cjl0K1nNcyign1gd1BFIhHkcjmP9GMY0R22+OD5HBxmFerp1R2qOF6YDMtxoBIHO/SohJLGTmsnA7teY83u1iL6dpQK2I1UcfHNjHLOO0y2U9i7bJI889ztdhvFYtETDVOjxWQeJdXVatUYWHqHeG6GhKk/5HX6/f4e7bMdmnZwOCqM6m+0cSsrK3j66aeRyWRM5MWWStjn4vs6d+iGGMBu3pINzgdKGAOBAFZWVswYKhQKpmKE33jXyLQuVrmoZWSZHmR7d13ObaySweswemYn3ZLIs5qFRsojkQjOnDmDSqViPssIlK3n5v0yisZcCVYBY45Xq9XCqVOn0Ol08MILL/h+p7NGkicmyO9///vxnve8B7/8y79sdLQPgmGSiWHwGxz9ft9Ttk1JLjsCV22FQgEXLlww3ptWq4WXX34Zr776qtEd0zPD2oW2tEE1xuq1DQQChnTfunULwK54np8Lh8M4c+aMCWUAu9nkWtg7Go16NhUgeVdNkxpoEnM17Ky1qttL2s9xHCPnPMgO8wD1ttBIcGyoRg/whkr1dZso63H2Z9RQ2uOJ8wI/RyPEkk1K1qnp04oV9E5ru2x9NL07qlPmcVxQ0zPF9miFHM4ddjhZ7433wPNEo1Ezzzg4HAfGsUdcFNvJrCSSHH/AbnUqW15BYmz/VrtvjxNei4SRxJyOLP7Y84rKPXhe/jA6zIU9OQrnDF6T3EA3+9F5T9uofIaEmByGC3KVpdpOA5WlEJy7/LgFnwH/nyUSPAoTE+SbN2/iueeemwo5Hga/B6ivDSN1Gl7gF6idla+zg4bDYSwsLCCdTmNxcRHXr1/H5uam8ei+9tprhkwr2EFJVDudjglTMDTBmsa6Cm2327h37x4CgYCniDc/n0qlzODVjQY0rMPrq+ZJV666Mh0M7peES6VSnrqmfIbDQkSODDvMC+y+SgIYCOwWp1cDBOytUMHaoDY5JtQ7pARSpRQardLP0+ubTCaxtLSEpaUlE9XhHEKSyh96fknsgd2Md4LzGbegt3cN5SJba4/2+33jEVJNs1bL4fOkd5sGmHkc6XR6T6lMB4ejxjB+wJ94PI5cLufxmgYCAQ9pZN/m3EAHFOcEVrji/0oK1THGa2hUhWORG2OoDIvX1PNolQxelz/kCkz8DYVCni3tAZh8C3t3X15LSa4dgQZgEus4JwQCAWQyGSPf4lynCw6Vl+qcyG2ryY0os2i1WibHal4wMUF++9vfjr/5m7/BxYsXD6M9BwJJJjMrVdMHwDMgOEi0c6fTaZw/f94YsVKpZHaUunfvHprNptEEEyS3iUTCJN5xO0iGU7gpCcukUBDPgaSayHQ6jXw+b8T67KT0hiux15CKGmLVAXFQcDeu/TAvKzoHh1HgmGeZJd24w+7jtpcW2LsVM/+29YjqidLPabIcsKtJjMfjWF5eRjqd9iTPAbuyMF3Q0xjpHKEGitfVqJkexwgZCTLbxIQfJvvR08y285y6WCCBDwaDyGQyRuOti3QHh+OEvbjV+uB2pETHvf72q9xib+Ws8wJBAqltsduhUk3C9sjqAlhlC/aiW5OJOWb95iatZUzpg75vR4E0aqbXZ7u13rFGnGxvtm6UxEh8IBAwRQjssnGzjLEI8sc//nHz93d/93fjp3/6p/HVr34VTz/9tKdsGgB87/d+73Rb6AP1JvPL4naIfE3DJGpk9HO9Xg+NRsN4XldXV5HP583Odtvb2+aLjcVie7LdASCdTiMQCCCdTpudsZi9mclkcOfOHdRqNYRCIeTzeUPQtQYhV22pVAqtVsvoJdvttvGKNxoN0+ZWq2WeAX+zIoZ2fkpPWPR83GdKzEMHdnBQ0KDRICiBVANFA+BHfNXbzNf0PRpdW78IwBTs19An5RUkyKpXZlvstukP5y5dIHP+YYRKq+bweJJjZvCzvVx8MwTM+YHzh583ncQim82iXq/7hlgdHI4TusDkIpA5QiSjfmOd76veWMmy7XVVTmETPR03tqzLlkmpg0sdW/yfn1fiyvf0f57Ldpix/QCMJpmwZQ/aRltTrHOTVsHRZ0GvNo+hrJMEGQAajYanStc8YCyC/M53vnPPa7/4i7+45zUStUkxSlIxzgTM1V84HDb1ju1VpBJcYG8H4kCglySdTuPixYvo9/vY2dkx96d1AxnK0fADvcrxeBxLS0u4dOmSpwIGJRbcQYuifUozotEo8vm8EeeXSiX0ej3zu1armQRBTd6xM2u52cmXv/xlI/K3MUrG4uAwb1CZAL22CwsLZrMfe26i98NPe6uGSY+3pU0k2PV6Hb1eD4VCwZBTjtlMJoPl5WUsLS2ZKJLWSbY35KDEggSX5Z90QcxyUYlEwiya2S5NROL/JNfJZBKxWMwswkmUSZC1Ood6zXSnUEeKHWYVJHi2plh/bFvJcaBkWCu58Fw8ny4Y7YgRz8e2qEMOgGf82DrkSCRinH2sngXsRodV7kQvLa+jZWoBeLa67/f7JmKkHnKOa94Pz6G7CZLPqOREI0y8N9Udc4FBeYo6Uc+cOYNAIOCprMXPzeK8MhZBPo6EDPuB+a3S/D6jnhd9zc7m5oRPETq/TBonACY0kcvlPJ2AHhnV9bKDJxIJLCwsIBi8n0XOmqga3qHHmBpBdl5gV1vIjHHWNgwGgyZkTMJNiQjbztqF3F77zp07uHXr1tgdT1e/dgjKwWGWYS+o6fng4pPHALueG/Ue+3mO9dx+XmV9jb+TyaQZ05xjUqmUIbN2lIfQ5BoaeI5HJtuprIOhz3g8jlar5Vks+90P50N+TuVidgjXjkIpYXALaIdZhe1ptceD39+AvyTBllVpRJp6fzs3ycYo+Zb+BnZzGEhUGdmmZ1YX/nofeu+c81QTTL4TCAQ8HnK9H/vZ6ecHg4GJcqv0wk8ioc+dRFyJM6WkdC768Y1Zw0xsFOI36fp1Lp3Ah03U/AJZOJvi9WQyiWvXrpki+Lq96tLSkgk3ktja3iTtkFpXNR6PYzAYGA0yQzvszPyfRo21BbPZLAAY48mVGcMTql3qdrtGKsGORklJKpUymyGsrq4a0l6tVvHlL38Zf/AHf+AZYH7P037NL+wyqx3Y4eGG3TfZh/P5PNLptCe5RY9Xggxgj2fIDlXSaNjEVgloPB7H6dOnPYak3W6bBF6Oa841sVjMI4fg5zSxTsOu9PaqvOvs2bMm8sQybbVazbSJ98a20mNkL7LVS9ZoNDxlMvk5AGbhftD5wEWpHA4DSvC4+NNSaEqWtU/z70ajgXK5vMdzzHHBKCwJLOVGflIs1TnbP2yrnh/YzQ3g+VutlnHWsQwkAMMnNMGXc02/3zeyMi6q1TPO58LXeH2VS+m9A/fnh0QiYZ4lo1ladYclaXXRwaiWEmWWnmU1n3q9buaVWcXEBPm5557DpUuX8Nxzz3le//CHP4xXXnkFv/7rvz6VhvmRt1HgF6C6Fxq9Wq2GnZ0dXLt2DbVazYRZ+GXTwNADbGer2ysh4L4nRusns6Mlk0nPpiX8zVVYPB43HaVWqxkPMACjM1YjFAqFkEqlcPr0aXS7XSwvL3uyy9meZrOJbDZr7nlxcRGJRAL1eh1/9Ed/dKDvwHmRHeYRDDVSQwd4jRGPUahXiUbKz2Do334eFNUXs7aoVqDgOekxVp2xEnHOObaGmL9VHkGDQ+KrkipbZ8hr6sZCGnLVEKy2Vw2+g8Osgg4u9m2/KAjtv443JpTxWD85pnIB9dT6we91257qeOJ5KWOgZIvEVuckLmqB4ZF19RyTSDMXyk5Y5Pk038AvYs7npot0XsvPm67zlc4xjKD7OS5mDRMT5I997GOepD3izW9+M37lV35lagR5FIZ5nPVL5TbR/DL6/b7ZvY4DgaSW9RJ1u2h7xacGh1826xczlJvP540mWeuNknRrbUReQ2uKcgORSqViwhAcmMlkEv1+H6lUynxG26IaS3bMeDyOxx57DPF43GwXad+Thjn8nvOwMJGDwyxgWFSEybKczDVMqvpcfk6rQ2iyr3qe9DU9B68bDAY9iTDpdNosgDV5j8aK0gguoGmEAJiFPT3LlEXQ2LEEJHXFoVDIVN9pNBp7dJd2CFUlFrpBCKNqnJ/4m89Vt6F2hNlh1hAKhYyTSnXDJLdcGFJjy/fq9bpJiKfUkvOAln7jmNUEPmD4WODrduRJySOwuwGJknbOYc1mE9Fo1NwXI08a8dJzKint97210jmPcAxz7mAbuHjmjy4GlMPw+errNl+wFye68186nfYkFs/qPDIxQd7a2jK71imy2Sw2Nzen0qj9MEweoOSPnhsA5je9LfZ+5JrM5ycvsFdV+sWzE9GbrGWVtLwLAI9h0fAPX2fnYeKgkl6Sdy36T8MHwLOqZPio2+0a3SMHoD6vcZ6zg8Msw09iEQwGsbi4iEKhgGw2u8cYAfDoDZUgc6zaXhWem79VWmF7XGwdJMedLqxJmjOZjOfzg8HA6A+pdWTb6MHh9bn4V8JMz0yr1TJEV71NDGmy/CPnrk6ng0qlYogCYc97dj11B4dZAPtoPB7HuXPnkEqlPMRQF4ualMeIM7ddV9h6XI26kCCqTVVbrLDHEGHzAf7Nsc3P6djWfAEunO2KFzpHKE9hKUg/8Jr6LPm8FFzMM2+K0XA7B4L3Z1cD4TnS6bTZclqdlbOGiQnypUuX8Gd/9md43/ve53n9T//0Tw+1NvKoh6dfqP2ahhTspDq/c9vhU35xdodjR+TfLOhNqQM7Nq+lZNdeoQF7N//gseppprfJFrjb7aXXip4heqEmDQk5OMwjAoEAFhYWsLCwYIrdc8Fojy3+6HbyOhb8xhdhk2RCE4W5WKVB5vWazaaRdLFNHNeaKMSxy2NUk0zdc6/X82z8wSo3NIjaNi3FROLADQnK5TIajYb5vNZltb1Sw56Jg8NRQxeusVgMp0+fRiKR8ITwdWMvjjXayFqthmazaQgyxxjHL/u7Rnd0sWgvpDV/YVwPshJknpfcgwSZzi6SY1vGqc+C5+R1lav4PT9bRqX3p5/heeLxuHmWqoHm5/jsqEXW58d9Iyg7nWVMTJB/6qd+Cu973/uwsbGBt73tbQCAT3/60/i1X/u1qcorDrKisDshsNuZ2Ul4biXVfkZQBwe9OdrhuXprt9uIxWLo9/uo1+vY2tpCKpUySTg0cgxrcF9yJu+oMdT75mqVqzB6tlQfzXtmJ+XW2ExM4vbVqVQK5XJ5j+H385azDcMwq1ohBwcdxySmuqGFel44rjT8x3EHeLd/1THPc+i1eE4aNx7LOYTzTr1e98wp1Euq95oeYyYAM2FHDRfbr2SfXmQAyOfzZjcrAGbBrN5rNdp8j781jKoL63g8burE28/cweG4oJ7bZDKJs2fPehZ3fI9VoXTMal1iHmdLFvS31hWm9MH2svpBpRDKUWhP1Zvs91tLrjEqzfthEp/t6NPSjPbOl/Z9awRdPcG2rbc5lM6FvD96jTVJT50D1WoVV69eRalUmvlo1MQE+T3veQ9arRY++MEP4pd+6ZcAABcuXMBHPvIRPPvssxM3YBgRHjbxjnpdSaZWhGBHos4P2CXTJMgaPtFVE2UN/NLZcTqdDur1OjKZDBKJBAqFAs6dO4czZ86Yahb04rADcyXFc5L08npq+NTbpeERW0pBw9ZoNHDnzh3T0TVZZ9i24LYOedR34UixwyzD7rcaVtWxTCJrhxM51vy8PYSt39Xz8Devp+OUxNoO93JRS6PDKhd6PZ5TQ8N2GFM3QuIGKfQq21vP8zw0XpxruFjgdTUng6WmKFHTbHiNsDk4HDU04ku7mk6n90SMh8GeG9QrC+yVLvJYlQ7QY7qfHR3WfuUt5AKqmyY5pmaXxFzHtib7cj5Tj7lNdu1j9VnaBFnvZZh8lefk521PNH+YI1Eul4fuzzBLOFCZt/e+971473vfi42NDSQSCaTT6QM3YFIPhF/n01XfsNWgdnTtCKwgwd8qydDVETsudTf1eh2RSARnz57FE088gZWVFWSzWWSzWVPOLRAImJJJpVIJlUoFvd79jQMY3qGXV9tk6xd7vR42NjaM8dPBrB6pCxcuGOO3vb2N7e1t3Lp1Czdu3BhrlWvDGT6HWcewKEi9Xjel1dTw0ejo1qi2bMLWH2vJNZ7f3jRDvS98TXeg4iJZ8x/oZVG5F8muveMWiS2vpV5gRqj4ee6apeWZ6B3WjZG44FcvGbBL1DWRj9UvUqmU2XTJzQ0OxwU7GspoTCwWM3lGgFcapP2f51CNvu5LYNtg2xvb6/VQqVRMZCUejxuZ02Aw2ONp1XlCz2tHqXgMSTLzEACYaBgrW6mswY4q+0kl7AU7r2FH2DTxXzkAo+baTtUea8IjHXT1et1s3tZqtfDlL38Z29vbKJVKnjlnVnHgOsgbGxt48cUXAQCPP/44lpaWDnSecVdbozyd/J9fnmqF+L4aQ36R1N0xbKGdmx2ExoBllNTgPfroo3jqqaeQyWSQyWSMp5bZ841Gw1yTHmeGSdRQUytMcMDTW6SaSd2BSzs6wy40wAy5svbzMNjPXicee2Xp4DAPGAwGpo6v6oHtUmtqYOz5ROcYjjPbS+T3226Hft4P9D7xb/X6KHHlfMS2MsKkhFyNmXp6tPScGk790c+wzfoZzWBX8j7p3OAkGQ7Thr2QtSVQo8YmjwkG71egIUG2oy4s66q5QyR91WoVgUDA7H8AeL3Ndjvt17U9+rqOfxJdLo5ZC1mjT/y8FiDwI8gasR4WTfNzPOhneF27veRVlLdxYbGzs4NqtYrt7W1T3nYeeMXEBLlWq+EnfuIn8NGPftQ88FAohGeffRYf+tCHhobzh2HYhOnn2h/1GX5p7ER+19EVm5Z84Y9tbFiSjaFFGolUKoWVlRXE43Gz0YduD8vrs7JFtVpFqVRCo9FAMpk0HZt7xbPsHLDrwdJ2qe5Y2w/Ak8TDz3NAdDodXL16dagRHfd7cHCYVfj1536/j0qlYrZTtkOmdtKsJproGOOxSnRVzqCLWvWiqGSDUPmUkmqVXREc0xpmVQM5GAxMeHIw2JVQqXZS20tSq+8rAdeFgH7Oz4sdj8c97XOE1+E4oYSTu9OqU2kYOEa0n3PDC3qSlTjSwdVsNo1dZ3S42WwiEAggk8l4SDDgHZM6BrkwttvpR5xVP6wVrjhf6cZCPEYX0Mx90Iiz7tfA86kX2F5o+0kx7Dbq7sSNRgPFYtFc74UXXsDGxobRHc8LrzhQkt5f/MVf4BOf+ATe8pa3AAA+85nP4LnnnsP73/9+fOQjH5la4/y8y8PIMb9cFYMD3jCnemsYLlBNHfWF9ERHIhHjGc7lcmaFSYLc7/dNyFE7/+bmJmq1Gra2tjzhnUDgfkYqDTIHowrkB4PdBB8NvVL3ROOmxpmrOQ5cDkauMv1WhAeBk1w4zAMGgwGKxSJSqdSe0KWGTvk/JQU0IGo01ODxGE2k49zDsUmvtZZn022hdSc7lXFo+/y8MppwxzAm74tkl/OI1jTWaJp9bk0itF/XxQENGudMznXzZOgcTj7YjzWB1Y6y2ItIOsJIkNm/bS8ypUzRaNRsNqYe2u3tbQQCASwtLXk2+tJ26FjRiJaSULX51Bhz3tG5gvJQnqPdbpsxW6/XzfzQ7/dNJNuOmEUiEU9ddbZF5VfaPs294HlUwsUFQ7VaRaPR8JSNpINw3uaMA20U8gd/8Ad461vfal77ru/6LiQSCXz/93//gQjyKJmF33vDjlfPMA2DisjtDqreVw036IoylUohn89jaWkJ6XTao+/r9/vGw8wC+91uF1evXsXm5ibK5bLHw8yBQyPMzs/d9bTd9v3ZCTr6m8eqQaQOenV1Fbdu3dq3U9oeZjtc6+AwD2C/LZVKZitYmxxyDPJvAHsqNthQTywr0ihB5oJVZQtKlBmNYhu0+gThJ5tS8s3rqP6R1+SCnnOEJt5xPPtF4TQZT8f+sKiaarrdvOAwK9Axrhp/WzIwjCAzuY/noa1W8Ljt7W3PzntMoO90Omb3THW6qU6Y1yTP4P/2e8Du2NTSbnZEh/NOs9k081O9Xvd4akmedfe/YDBoEm+ZhKv5VyrrshfM/Dy12SwTyTK3tVoN9XodlUoF29vb2NnZMQTZ9kLP+hwyMUGu1+tYXV3d8/rKyorZrW1SjHpI4zxAdjY7RMAJXid0FYUHAgEkEgnjZdWOys6iBoukmkav1+uZVdHW1hZu376NZrPp6QyFQsGUfWP4h9fm+VhTsF6vmxAFsOvh4eqV7QL26qrZfg4CGvDXve51+MIXvjByEWI/Z/VozdNqz8HBhvZrlU8MBgMzHhk5Ug+yJuooibWNnY49/V+vpWSTY5TziB0l4vhXmQfroAK7FS00YYgkmgSZcwONF4kyr015iF+oVA2g7fXiPGsnIDk4HCWGSQRXVlaQz+c9Glwer+NWF5YcFyqd0jHJc3PsxGIxLC4uIpVKodFooNFoYGtry9hxO1nPnn9sCRZttR21sqVhwC5hVseeSrt4Lxpp1oiYvfj1i1bpc9E26/NjBKvZbKJcLpt5qFgsotlsolqtYnNzE1/96lfN/MNKHPZznXVMTJDf9KY34ed//ufx0Y9+FPF4HADQaDTwgQ98AG9605um2rhxH6B2JjtRj51mMBh4QgT6PrV99opRPShKujlYSJBLpRLu3buHzc1NY+DoXaaXmEW+WYKJbVLvkho+7dRK/Nlx1eNNGYcaUWat8xzDDJmft9he3Tkj6DBvsPuu9mHbm2QbRj8jqeCYtK9nh0CHjRuda7QNPLfODyTtel0eQy+xzgFqOFVqYeuNeU2b6Os9+j072xPt4HCcUFuVTqdNZFd1tH6LVn1dnWF+SX78zcVhIpHwyJIoyaBEgxxEF7n2WLIX2jZ0PtH5yY9w02uu0Slbhmkn5Ok5/BYNuniw+Qe95txkhfdZrVaN57hYLBqJ6TwvpCcmyL/xG7+Bt7/97Thz5gxe//rXAwD+9m//FvF4HJ/85Cen3kAbfg/anthVswfsGiEtScJj+Ro7n01I6RHudruoVCpIp9PIZrPGy9NoNFCr1Yy8gt6cbreLaDRqvDj0cNtt1RWsFv/W1aV6lQFviEY7Ma/N3bBKpRL+1//6X+YZ+A2QUcZuv2ft4DALsCMeJJYATC10hV/Ik68DXn2gfQ0AnrmF7/EaKnkC/DPDbZJqk+N2u+3Jl7AxGAyMnIqhTb/qEpwPNCFPN0exZSbaPttQcr7THQnn3fg5zCfsyCZJ7unTp5HL5TxbpdvjQZ1G7NtMPNWtnHVnTRJUlTr0ej0kk0mUy2UUi0Vks1njWY7FYp4yc37zBe084I1Qce7y+1ECTCccnXsq36pUKmYzH1sWRfD4fr+/Z96wdwgMhUImv4nHUEqxublpFgUvvfQS7t27h42NDc+ifJ7nh4kJ8lNPPYWXX34Zv/M7v4MXXngBAPADP/ADePe7341EIjGVRu3nofCTAwAw7nx+OexUDDkA2FM8XMMrOvDYQRqNhungrVYLxWLRGAhuPBIMBs1WlQxhJhIJJJNJo1PkilYHhXYgtoPhT7teKnVRhEpKqCFSz3G9Xsfzzz+Pu3fvep6dH0n2e76218jPw+zgMKvguLJ3zgK8NUj9PmcvZP0iKTYptxNr9Rr08mregIY/1aPt54W25yvelyb/2qFUbYN6knThra8Ne4a6mGAmvxq/acLNLQ4HARe0qVTKyBX1PWDvQtUeVzyHn4fV/rx6dCnH0hJzPJ8dZSL87K+9WNXSjLbzjufQqBCPIfFutVoeOZTdJs4bfosNzhFaNYd7NvCzm5ubKJVKuHHjhlmcb29vm+3qh937qGcwizhQHeRkMokf/dEfnXZbDGwSp/8Pe7C68gkGg8YLo94jPwLPbE+7Xqi9YqvX655qExS10wtNkT+wOyhbrZYp5Qbsdnrej3ZuGllm1Ptln9udnccFAve1i9VqFXfv3sXt27fx2muv4cUXX9zj0Rr2/PbzLDvj5TBP4ATfbDbNhE8PkCa9KanlHEIjQXAM2vkDSryV2HI7Z61bygo4vC7nHZZo41zFJB9NltEykJwDSI7Vq8MfbbttqEh4WWrSnuuUPGgVALZ5c3MTlUrFzLVuTnA4LnDMMXG+UCggHo+bsmbAXl2t/hDDZBU2maazSollKBRCOp1GNBr1fEadX/YY0c/bRJ2cQCtXcJyq3phebnX+kV+0Wi1T5lKlIZSFMj9BnQe6KRDvn9UvIpEIisUitre3TTTp+eefx8bGBl555RXPffjNCfNChv1wIIL84osv4kMf+hCef/55AMATTzyB973vfXj88cen1jAlkfq/wibRKp2gF1ZLm9j7lQO75VF4fu28dnhGOxvbxcHJuoI0KiwgrttBqj5JO6Tej+qU9YefB2DCIhoaDQQCKJfLuHXrFq5evYqrV6+a0jN+oV2/573fMQ4O8wRKpFSXC8ATQiRpVlmTbcBsCQYXszoPaA4Ez89axZ1Ox+zqRwO3s7PjkX90u10UCgXk83kT6lXZlHqm1JCr3lE91v9fe98aI1tWXreqquv97Ne9d+5rLpdhHoSLsRnCIwEB/mGFJONIBOTEZEwsRcEI88NWBvMjQhCZoFiRExGNEzuKJeLESMSONUiJiexEjhllAoZ4ZvC875377L79rPf7lR/Xa/c6u09VV1VXd1f33UtqdXfVqXP2ObX3/tb+vvV9m++zvfyb90kPk50c5CcP0QXC9vb2SB4iB4eDgk3AcrkcFhYWjCeTC1HlEPyManWB3ePJvsYgu0guQIcW90SgvefYtf/mOW1SqcUFtOSazgPU+ObzeY/zjkSd80GlUkG1WjU5UBp55uLYHve6WFZHAj9D2SY/v7GxYYoKaMTqpGGiMm8/8zM/g8cff9wk5T333HO4cuUKvvnNb+LjH//41BtJ7CUPsFeNWpoE2NkQwCbequPl59gpVQpBaLg2mUwilUqZwaErMxoTOxNVr8H2akfVYueahKMaah5PA9poNHDnzh1cv34dKysrnjJQkzxn3qf9moPDcQFJKhextsyK44gRJ45zkl+VN/F8PEajSQBMsf5wOGwMB3eSKpfLSCQSSKVSZkzTwPAcJOfRaHTXvKNyMf3bTuTlfELDpgReCbLqljmnADtkwc+RwEXE1taW8X77ecccHA4b8/PzWF5eNgs+P4KsmnsbKpmw+7OfN1THCSWOJMhc3GqirRJw2+nGH0oj6dTTa3Fzks3NTeTzeVy9etXwjEHVaGxOQWKrc5g6BTnnhUIhs68DAMMvNEnPlnoMmwOOu8NtbIL81FNP4Ytf/CK+8pWveF7/0pe+hKeeeupACTLhJ8Gg15hfbDQaNZ1Di/ITfuEUlkoCdoyMHWLhQOr1eojH44Ygp9NpszFHs9lEOBzGnTt3zC47wD0DlEgkdnll9DpcBapWyJZlqIax0WigXq9jbW0NP/rRj3Dt2jWUSiVPGFnvc5Rn6+BwHNHv903CTTweRzAYRKFQMGOKOkGVJQA7HhLOH2p06NGxE/doCJV0U/JAWQfH5ubmpjE6+XweW1tb2NraQrPZ9IzLdruNaDRq6q2znYFAwOgc+f8waN6D6o1ZdYfRNXq/+BkN63LuoBEul8vY2tpCpVIZunX9fuDmHodxQaIaDodRqVQQjUY9Dil7m2WVU9Cm2s4oO5Ki19JFKf/nopYaXU3AA+DxDvM8tueYJJQONeUra2trKJfLuHHjhvHk2lIObaMuXLmwBeAhxnyPsOcLm8gPWhD7jdnjTooVYxPk1dVVPPnkk7te/9SnPoVf+7Vfm6gRw+QTw1YoasRIaGkI2ekajQYAmCxVgh2BxowDh7UMAXhIKAdCPB43xywvL2NpaclsKBKNRk31CpaC4Xl4DQ4eHYQcDOrp1uxzDYGofKTf7xt94+rqKq5du4ZCoeCptbjXCm+U78bB4biARI9k0t7e3U+bSCNCQ9Lv9z0LW8Bbjk2NkM5B+hMIBMw4JinN5/PY2NjAxsYGCoWC2WCA12FRfcomSERJ7NVw+s2PfmNeCbLqmXWxT0LtRwx4LIv/09nA6zk4zAI4hqm15Wu2LladVXb/ZXRGj7OvQeh44fjROcT2sLIN/IzaaCW7lD/oIr5YLJqyaao55vt2GwfZ/mHjVecy29Pud46TRIKHYWyC/OEPfxh/+qd/ioceesjz+ne/+1188IMfnKgRflo5e1Xkd7x2gHa7jW9/+9v46Z/+aUSjUQA7iWwATKmSWCxmOnOv18PCwoLH09rpdMwKTUMv2vFsIwzsGBIOMnqzOECZ2GJvTMKqG7rzFVekXB3z/migGI6p1+u4desW/vf//t948cUXdxmuQUZP4bc48ft+nDF0mGWwfyYSCbPAbbfbKBaLJvzJOqUMjfqVWQK8RlQ9qyptYDiXC3PVMkajUePJ1qSZtbU1s7mAfd1+v48333wTKysr+NCHPoTTp097toiNxWLm/Epo1TjrTpqE5ip0u11Tb51edTs5WX80U79UKqFUKnm02m5OcDhs+Hkw7c11SDJtaeMgyYOOEe33/B/wr4ahJJjRI3qwOQ5VqsXz8Hgl7+QPSqZZIvbOnTvY2tryyDZGeT57OcgG2X2/4/2I8f1AkscmyE888QS+8IUv4Ac/+AHe9773AbinQf7Wt76FL3/5y3jmmWc8x44DW/bgBz9Cxy/vRz/6EZaWlvDud7/b7KgD7Pb+2KWVAO8+5ZrUo52Fepxms2mkFP1+H9ls1nhzaXipQ+QqlsarVCoZoxkKhUwYloSXgn81vJFIxGgbr127hpdffhk3b95EPp9HvV5HPp/3kOK9ntc4uB8GgcPJgR0G1TCmvm+Pa8KuZGPvqEnY84NqlbWGer1eN9vSNptNz652Nlj5YnNz0xhttoPJf/Qu6zxlOxM0611LV3HBrUTb3iTBXixoRR56rxwxdjgq+DnQKK3gfgN+5doIu2KF3xi3xzqPs8mnPfY57v32J7DznChpGDaeyBvoFLPve9DzGWTzlSyPa9cHOSlPOsYmyJ/97GcBAE8//TSefvpp3/eAnVXVJBhG6mwjpa91u1388Ic/xPb2Nq5cuYJLly4Zj6xml3N1xsxx1QLav0lyiU6ng3q97jEyzWbTJOmx4zOUqQOB2zFyVdnv97G+vo67d++aUnLhcBhvf/vbzYDrdDoolUp49tlnsbGxYYqAMwGIK2Z9Hn7PahD2M2gcHGYV6t1hEgznlUEhWBJIGkjucGmTRmBHc2yT02AwiGq1inK5bJJqmBPARa9Cxx7J/BtvvIG7d+/i8uXLJhNdvdk81vYQ2clD/X7feMxtvaN62fjDcysJYbSrUqkYj7a228HhKKD9lBrdt73tbSbPh8cQ9L5yHOlC2vYY21FXmxNwzmDUptFomNeY+6A5RCS5eg1bomVfixyl0WgY3mJH2u2xap9Dj/V7bg57Y2yCPCgT9CCx14pI38vn86ZjlUolPPTQQybsocSUeiEtzWZfjwZFd9XRwvzAPd0gM9Z1cLCgvur/KLWIRqPI5/NYX1/H7du3sb6+bjxM/X4fKysrHhJfLpfxyiuvoFKpDNQc7fX8eOyg9xwcTgI0yURDn9QB9/t9U4qJ45mLYJVMdLtdI9UgaQZ2iKxGmYCdXTkTiQQajQZWVlbMgtZP+jAs0lOr1dDtdnHjxg2TO7G8vAwApgwksEOIaWg1uZBzUiwWM3kPmqzEz/t5kW2HAKNjB7VBiIPDJOCY4QL4tddew8LCAh588EFP2UX1CqueH/BKo/y0tmo77YgU55ZyuWwiN5w3OO7IB3RBy/NyTlC5FNur8ie7bYOI8TjPzYYjzf6YqA7yLIPejtdeew3FYhHAvTIwDz74oCcsyk6i2iXAK8Pg//b51QhXq1WzQmRiHr1ImkxHwkuCfP36dVy7ds2sfrm5QKvVwhtvvGE8VJqoN+h+hz2LcZ+dGygOxxkqk+p2u6YaRL1eN2ORGn+VRalWkBEgelXtuUAXyADMeSiHqtfrWF1dxebmpsls5+cGSaH03NQpV6tVzM3NGaLO5F9bO6n5D3pfjUbDs0GAer3Vm2ZX6dBqHXym9IA7iYXDUcOPtLZaLbz++utmu2fmCLEijBJl9SLzZ5DkQccrx5jaZu6uy2N6vR4SiQSWlpbMOOPxfjpmQqVUwE6xgUHaaft5DCLMozjQ7GfqsIORCfLHPvYx/O7v/i6y2SwA4Gtf+xo+85nPIJfLAQC2trbwwQ9+EC+99NKBNHQYebO/XHpTVlZW8MwzzyCRSODv/t2/i2QyaTb04MAoFAomtMjBw92sVCain9HQJrV5urVjLBZDvV5HJpMxq0z+vnnzJnq9Hq5evYrNzU3zHrdyVLLOe5nEINlhFZ7HDQCHkwj2a5Yho2H0y8imIbQ9TL1ez3ym2WwikUiYDT5smYUueknIS6US3njjDVy9ehUrKytGN2h/xg9+r9Owtttt3Lp1C4VCAfF43EPwKeuitALw1n+nvIz3SqkZFwVqhHWO4/kbjYZxAtiRNgeHo4aSZFaM2djYMHsTcHHIvg/sbLhlLwTtjbd0rgBgFomUPVQqFZRKJbPDHHAv8pRKpbC4uAgAnqgNz6MVdNSzzfmBhJ+cYK/o0zCbPup4dbzAHyMT5O985zue+pdf/epX8clPftIQ5E6ng1dffXXqDQSGf3nDROns4LVaDb/zO7+D06dP4yd/8idx5swZAPAU5VZDQ4PBHe1oOOmVarfbnnAsPbwceJRG5PN5NJtN4x1m2JQhUibj8Rh7QOqKca/7tFeQoxhiPe9ez9nB4TiAUgIucm0DpSSVYx2AMVCs8kDvM71PSpA1CsUx02q1UCqVcOvWLbOZhq33G9VY2YaUxrjVaqFarSKdTns2DSI0+kXtsV/iEX8rOfbL3FdPtCPHDrMMEksmyDPZXT23ADyLQT/42VGOM0aKmf9TrVZN+UOiXq+bqI7t7FLYr6s3mwtUykDHkbX62fNh49bZ/OEYmSD7aeYOC4NCCH4hB7+QAsOLq6ur+I//8T8il8vh8uXLeNe73oUzZ86YDFj1JAE7hhDY0eKx47KmspZZIkh6Gdbd3NzE2tqa2QoagCHEtpGz70fvyS+0YsMW8g8657DrOTgcV3DMlkolRCIRQ3opX+K2z7b0gGPR3j6a+QtKdlWHyGjR97//fWxubpqEW8Im5ONAP8e2//mf/zlyuRx+/Md/HPF43LRZ5SAc+5yjNA+C5xsUaubx9DJvb29jY2PD40F2RNnhKKERUWD3fgiVSsUk6vkdx3MQfH1QbWT+5gK1UCigVqsZCRWT9oPBe7vezc3NeaJH/X5/166WuqOuOqpUUtFsNpHP53cVChjl+Qz732F0zKQGeT9f8KDJW2sTFgoFvPDCC3jllVfMJh8f+tCH8OijjyIejxsPMKUPdjvYeWkguRrlKnN1dRVXr141HmTqCelFpjEe1Fa/+x31tVHeG+e6Dg7HDbqQZZiSxfcZ7aGRotTCLu+myTy291jrABeLRZTLZWxvb6NcLu+SVRD7GVtq4KvVKgKBADY2Nsz21UwK4r3ZBl//JzQxT++Ni31GysrlMkqlkkvOc5g52CRX+7lWZeH7AExkSCNAtjNJ/6fDzC4dR/ml6ospc6LUSa/LzyhJthemBEtE1mo1VKvViauBOewfIxNkvy/yoAjVXlrZUTW1Oji0o3I1yBVhpVLBH/3RH+F73/seUqkUUqkU5ufnceHCBdRqNSwuLu4KMTIZ5+rVqygUCiiXy6jX6+j3+6Zj0yBzINlapHGfgZ8nfRKJxDC5hoPDSQDHODWD5XIZ58+fN94YJqfxWHqbgXvjIxaLmRJvHCcMeSoJfuWVV7C2toaVlRWzVa2NUReyo4RCWW7t+eefRyaTwblz57C8vGzkEpzrtGqPJvny3rV0nCb2cZFPory6umruzcHhqDHM1rHfc2OgdDrtGVNar1jHhGqBbV7B8ULCzaRZ2xbzfJlMBgsLC56NRxR6Xjtpj4v6er2OtbU1bGxsYHNzc2iSvsPBYiyJxac//Wmjn200GvjMZz6DZDIJAB5P634xSmcYdIwtsdiLjNIw3L17F+vr62Y76VQqhZWVFXQ6HSwsLCASiRivEjsyS7VVKhVT1g3wSjMGhYJGIb/jkNhxB5AbcA4nGeodooTi1q1bqFQqiMViZozr9rD2BiGAV2/MyBJLSBaLRaysrJhSj4NCuHu1c1ywjCQNfSQSweLioiccDMAk5LHm8SDvMhfv3KikVqthe3sb29vbKBQKu+7NweGoMIgYAztEN5PJIJVKmV1rNWpke21t2ZFdjg3Y2ZE3kUggGAwiHo8bB9v29jZKpZKRbNHBNogYk8RrBEvbx9yCzc1NVKtVJ2k6YoxMkH/u537O8/+nPvWpXcc8+eST+2/RhLBDF4pRxOp8v9lsmoSblZUVz4qUesROp2M2A2AHHqTR3ksnbL/Gv4d9btjfk8J5lB1OAuzISiAQMCTx1q1byOfzuHDhAgCYJB5WdCBsPa56WLnl+9raGm7fvo07d+6gUql4dIQHGaHhXFOr1dBoNFCpVLC0tGS8Y7YHjG1muJlebq3cw02NaPSLxSJu3ryJ69evm/fc3OBwlNgrSsp+HwqFDElNJBKo1WoeAspzDIr02HZViSujSpRXMJJ069YthEIhRCIRJJNJX4Ks5wTgid6ovecmP5ubmyZPyRHko8PIBPm3f/u3D7IdHgwzLnu9bnemvYikTXD9Ps8scvtzfH9QB97LozTsNWeQHBzGh19khq8z+/zZZ5/FmTNn8O53vxuRSASRSMQsfpk0y5JnvV4PjUYDjUYD165dQ7FYxN27d02tYtZL1usfFDHWc/f7OxVvrl69iu3tbVy6dAnZbBYLCwse+Ui73TYJe7dv3zalKavVqrkH3UiE5bJ090EHh1mF2kxu1R6Px5FOpwHAExHSBSxh23J6dQFvJRk7snvq1CmEQiFsb2+bzycSiV3VY/gZPyea5jR0u10UCgVsb297Njaa5FkMgyPco2Mmk/QOGnt5Zwcd70d499IN2p/10xzu1alHOW4vQzaqXtvB4SSCUoJut4u1tTUEAgHk83nE43HEYjHjJdKdNkkYqftdW1tDoVDAysqKR0Z1FGNHDX2pVEKtVkMqlUKv10M0GkU0GkW/3zfJPvzM1taWIfyVSgXlctl4if3K1zk4HAeoZIKLO01uI1HV17R6lPZ9W5/MhTLBcR+LxZBIJDxaZ+7SycWsX2UMPQ/HHWWbpVIJlUrFzD/jyC3HkWM6kjwaZpIgTzo576Xjtb0wgz6rr9mC/XExLin1u4dBbRynk+/X4I0qU3FwmGWQ/K6uruK//bf/ZjYQuHjxIlKplCn5WC6XTQUHelg16VbPRwzzXk+r7TwfDb5Wnnj55ZcRDoexsLBgcikqlYrZ1ZNVeWwy4Jc8PCwS5+AwS9Bx0Wq1sLm5aWRHWp2G/d1PFrmXc0k/SwLMnCMlyNyYZxRpJc/J/Ri63S5eeuklVCqVoWPRD258HgxmkiADgys2jIphWiX7mEEdcJhsY9AA2K/3Rc8xyOAOase4nvFR4Uixw0mALU+oVqumosPm5iYqlYrZHKhWqxlyyd3y/BbdB6E33mteGnQ91ioulUoIh8Oo1+vGW0xyr2TYGVWHkwSVRLH04vz8vIkQEUp2+TlbWmGTWJVC0DbbURcer441P4eWXosRq2KxaCJVlDY5u3v0mFmCPEg7vJ+J3c/wjLtSG/dco2ijFcNCKoM0ywdp7NwgdThpsIlyt9vF7du3Pe8p9vI2jZsvMSn8xrnqn3k/5XLZ/M922Pfg53RwhNnhOMFepAL3JBSVSgW3bt1CrVbD448/jlQqBcBb7ULLFpLI+pHZbrdrXted7Zisz1rrBL3BzGHQdip51mhOt9vF1atXTWKeXff4IOYRh9EwswT5IDGMaA46fhwv817HTopBRNgZNgeH8THIuzPovaPGqI4CP2kWP3+UumkHh0mhizw/m2v353q9ju3tbWxtbZlt1ylF0jrglCtxoem3OVAgcK8kW7vdRrvdNu/zf92Ft9VqeXbJo9xCNcyUazFJttFomI2GJt2t0kWEDgbHjiAfVKLZsA45yODs97z2ccMkFQdFrvc63gn6He4H2GPtKPv8XmPVjyzYn9dj97rWMLjcA4dZwTBpI7C7Yg23ag6Hw2a/BuqSmXugu2gC8GzwoT/cYIQEORQKGaLbbDbNxkPtdtvsrKvbRitBZjlF5jkUCgUUCgVTLnJSOJI8fcwkQR5EFieVEwzTCx8mRvEs+7VrlPsd5bm4xBsHB38ctFRpXIy6YFdS4Dd37HeOm6Vn4uAwTM6kC0d6ea9fv4719XWcPXvWVJwAdrTF9PIqmQW8FS6AHa8vN9PhMayFTC81tcn0BPN/eq/7/Xt7LZTLZdy5cwelUgnlchmNRmNqz2iYnXcL3fEwkwR5mIxgksn6sDSC+8EoWsD9eM/HvddhGnAHh5OIWZoPJsVBLPzdmHc4LrD7f6lUQqPRMCUQuTlQIBAwJdkA7ErO0y3m+TorwZAgBwIBoxemDIPHah1lEmQS53a7bXbjLJfLqFarAyvjjAu/z7rxOzlmkiBPU1pwkNiP1vgoPDPjhl6POtTs4OAwGtw4dbif4Rc50frCN27cQDKZRK1WQzQaRTgcRjQaNXpkSiQovYhEIggGg+Y3AJOgx6o2rJuuFTH02mpjSZi73S7y+Ty2trawsbFhkoTtHAiH2cBMEuRh5O0wieW4JHGcdh0F8R+3fY4kOzg4ODgcV9B2tVotBINBlMtltNttRKNRQ3K581232zUEud/vexL5AoGA8SDzB9ipckGdMqEEmbKPVquFRqNhSrpNslOew+FiJgnyMBwmsdyPrOMk4H69bwcHBweH4wklplqirVarod1uIx6PI5FIIJVKGU9yIBAw5DgUCiESiSAcDhudsnqj1ZPcbreNvII6ZnqLeW1KK8rlMiqVCm7evIl6ve4p++Ywm5hJgjxLpYiGZc36HTPsPT1mWAb6Ydz3Xtdx3mMHBwcHh5MCrT9MuxYOh9HpdExSH0kuZRhaB1llG5RVdLtdj16ZRJkeaNUmFwoF5PN5szOns62zj5kkyMSwahb7OYf9vh+GkcNROvYoWejDzj+sxukkBHqScnNuADs4ODg4HFfYNcCp96UXmASZpd+IRqOBubk5Q541mkyvL73L+j5JNgBP+TjgXsLg5uYmWq3Wrs1AHGYTM0uQp1V5Yj/VHfxCNdO67qSY9Lyjfs5Vr3BwcHBwOIlQLzC3dab2OBKJmPcikQgikQgAmHrJ4XAYkUjEeH9JgCnHiEQiRqbBnfrC4TC2t7exubmJra0tlEqliTcDcTh8zCRBHrUKxDjH7wezIHk4bDgvsoODg4PDSYBfZFS3jdYqE1qijXWSeQyT9WwJBcky/26320bOUS6Xkc/nDSGfJTvvMBwzSZDtsAj/Vhx2JYuDvsaswtZLz3JbHRwmwawtTh0cHKYPP9vV7XZRr9dNJQqiWq0a8sv3otGo8RZHo1EsLy8jGo0ikUgYj3MymTQe5O3tbbz88ssoFosoFosmoW9Yu9w8NFuYSYI8yqYZwN7JfNPS604rafCoyb4f7GS8YZu0ODg4ODg4nCSo3MHmHna5NpZ463Q6iEaj6HQ6nprJfP/u3bvY2tpCoVBAtVo1O+75wSXDzy5mkiCPimnvHrffzx3VefeD+72UnYODg4ODAzDYOQbASCaAe/Zye3sb6XQa/X4fCwsLiEajqFQqqNfrePbZZ1GtVneVfHM4XjjWBNnBwcFhv3DGy8Hh/sA43tq9KlH1+31Uq1XcuXMHW1tbuHPnjtEos5ScX7lUN98cHziC7ODgMDacbtfBwWGWMGhOGiZpHOYxHnYd/u50OqhWq+a89Biz1Jtfu/z+H/eeHA4HjiA7ODiMDTdpOzg47IWj2PnWxjj6Xj8P8145Q/q/1j3W9/ZKzhvWHoejw5EQZF2BzbI4XSs47NVOvzDKoPDKoA1Qxm3bJJ+3azvbv/c6116bnDicXLjv3mGW4frn7MDPxu+VDD4rGMfDa78/bOfcYZ9zODyMM08cCUEul8sAZr+DjPMgB4VqRn190rbt53N2W8Y5Z7lcRjabnagNDscTOm5nfew63L9wc9PRg3PF/bpjnJsfZx+jzBOB/hF8k71eDysrK0in0zO9knTYjX6/j3K5jLNnz+4KJzmcbLhx6zDLcHPT7MDNFQ6zinHmiSMhyA4ODg4ODg4ODg6zCrfMdnBwcHBwcHBwcBA4guzg4ODg4ODg4OAgcATZwcHBwcHBwcHBQeAIsoODg4ODg4ODg4PgSMq8uQzX4wuXKX7/wo1bh1mGm5tmB26ucJhVjDNPHAlBXllZwYULF47i0g5Twq1bt3D+/PmjbobDIcKNW4fjADc3HT3cXOEw6xhlnjgSgpxOp83fbnV5fKAVAfU7dLg/4PedD9opkn/b8NtFchyMu2Ok32fHec++t1HbNe497ve5TIpB92c/D7/nM87zHOX+hn23oz6bfr/v5qYZAL+DUCh07Gz8UY1Fh8NBv99Ht9sdaZ44EoKs+5O7jnh8QPLjJpD7E4PGrd0XhvWN/fab/Xx+knaNer1h27SPco5RyeO0x92o9+133DjPcxrPYC8ch22M7xccRxtP+3YQ7R1ngbffawxarI+zDfag85+kbTNG+U6OhCA7ODg4nEQcFBk4LiTDwWG/2C8RG/b5vcbRoM9Oc/yNc2+jPgvbYeFH9IcthoctDPS9UaNF9v/D3vNry6zAEWQHBwcHC6NM1H7HDHqN2MtA2MfbGNVY3q9eIYfZgh9x83tv2Of2e92D/uw4Y2hccjzpMZPcw6Tt2IuIjxttnOS7G2VenQSOIDs4ODhYGGWCHUSEB/2mJ8WPJKhRYGa1Taz1R18bZGT83rPb7Mixw0FjGEGaZYyaV+A3hqYhldjrug47OKiFviPIDr5wOmMHh9Fgk9twOIxwOIxgMOjRYDI5pNPpoN1uo9frmc+HQiGEQiHMzc0NTGzq9/vo9Xro9XpoNBro9XrodrvmHI7sOjjsYFTZgN+CdZKxNKrNHDV6NI79PYljfxY4iCPIDr446o7p4DAqpk0O9/K6+l2v3+8jFAohGo3ine98J97xjncgFAohGAx6iPLdu3dx9+5dvPrqqygWi+j3+4hEIshms7h48SIuXLiASCSCUCiEbreLQCBgSHcoFEK9Xke1WsVzzz2HcrmMUqk09F5s4++ItMNxwbgEyT5+Pwm2o3x23KorgzzDfvOL/nD+4HFcWPOz+vlx9L6D7mkUT/mga4x6rlEwCxzEEWQHBweHv8S4ejvgntd4fn4esVgMmUwGp0+fRjabNeSYxwSDQTSbTXS7XZTLZaRSKQDA3NwcstkslpaWkM1mMTc3h2AwiF6vZ7zLwWDQeJjn5uZw7tw5lMtlRKNR1Go11Ov1PQ2gI8YODtPDME21X4KaRpPC4TCSyaSJGBEc9/1+H3NzcwiHw573eI5Op2MiSN1uF+12G51Ox8wv/LHbSwybCwaR3kkW17PgBd4PHEF2cHA4EkwroWK/n99r4h/k+SGBjUQieNe73oXl5WVcunTJvEfDp56gxcVFZDIZPPDAA8bIBYNBxGIxADsGklDvUbfbRSQSQSQSwQc/+EFUKhXcunULb7zxBq5du2aehd7PIKO430TAacB5sx2G4SCJ1bSSw1TrrwRY3+drHMvBYBDZbBYPP/wwcrkccrmcZ5x3Oh3U63Ukk0kkk0lzfi6Qk8kkqtUqKpUKWq0W2u02tra2UKlUcPfuXVQqFVSrVbRaLROFAvyrUdjeZwcvHEF2cHA4EszChLxXONE2JJQ7LC8vY2lpCalUCrFYDMvLy0ilUsbIqbdHCTK9wwDQbreN0bI9RXaolAaSbSKpPn36NKLRKM6dO4e/+Iu/QD6f9xDsYfczC54dR5IdpoVp9edx++Qgss2fcDiMSCSC5eVlJJNJzM/PIx6Pm6iTvTjmQjgWiyEej3vOSY9zJBJBIpFAOBxGu902nwkGg6jX62g0GiiXy2i1WqhUKuh0Ouh0OuZctjRj2L2Mcq+zgINohyPIDg4O9wVGNXyDjiHxjUajOHv2LB577DGcOXMGkUgEhULBEFu9nk2U5+Z2ptxwOIxOp2PIL4mt7dXRRDw1AtFoFMvLy3jggQcQDAaxsbGBarVqEgCZ1DfNzQcOm9A6Au1wlBgmN9BjONYJSqrm5uYQj8cRj8dx6dIlLC0t4cEHH/SMd/Zx/nQ6HUSjUUQiEYTD4V0SDS7SOZ+QGCcSCcRiMZMEvLm5iVqthm63i2az6VsJx++epjXeDkKXPEkb9nM/jiAfMtyE7+BwNBhUjslPhmBP4vF4HH/1r/5VZDIZLCwsIJlMIpFIGOOUyWTQ6XRQq9XMZ2KxmEm2C4VCCIfDHiPodx2+plIJkmhgZ+teNcrUG7773e/GI488gmKxiFKphFu3bqFcLqNSqQxNqhlGfg+yLNx+FisODnth3KQ7/ZzfOLEjQhzT9AYvLi4ikUggGo0inU4bkswKNel0GrFYDIlEwiOp4E+v10On0zFEm7kH9j0oseb7kUjEaJP1mGaziWw2CwAmB6LRaGB9fR21Wg21Wm3XInqUJD2/Y/eSdQ07bhoYpazluHAE+ZDhJnwHh8PHsIWpXxa5/qbBO3fuHObn57G8vGyMCg0dya8SX/UId7tdzM3NGUMI7GiY99ID7+W9pWFYXl5GLpdDKpVCPB5HuVw21261Wr7SCwcHh3sYxbtJUhoKhZBKpUwFmuXlZSwvLyOTySAaje5K0rWTbflbIz0ageIxg7yw2hYAJjIVjUbNcZ1OxyQDMleiVquh0WiY9zg/UNrB9vhVw5g2DoIoT9tD7Qiyg4PDiYZNeAk/4qnH0sC95z3vwQMPPICFhQXjNVaiy+MphbDDmDpps44xjZGtPeYx2r65ubldhlRDuPybnupMJoNYLIZcLodSqYRKpYIXX3wRW1tbvuFVv/s/jIW8i6Y5HCT2IkqjJOZqgh0JayKRQCqVwl//638d2WwWCwsLpr65X/1zlUZwntAIkc4PnAN4nlAo5Gkn5wZN+Ov3+wiHw2YRzrZGo1HPtVhtp9vt4q1vfSu63S6uXbuGZrOJdruNtbU1bGxsGInWMHI+KvYzxv3yMMaFk1g4ODg4jIlBkyYNSSQSMVnk8/PzyGQyHnkDP0+DNTc3NzC0aJNOO0SqxpReaR7Da1JGYddE5fEM45Jws9oFk32old7a2tqVsKNtHPZspg1Hjh1mEToWlIgy2W5+fh7ZbBaZTAaZTAbZbNZIF+jJ9Rv/Smj1bz2e45nX5jG6gLU3B9L3daMhEmS7Hf3+vRJy3W4XuVzOVLvgtVutlpGLUb9sP5dBcoujHtPDJGuTwBFkBweHY41xdGeD5BT8m6HIhYUFXL58GW95y1t21StVnR/Px6QZenL1PTWGNjnWUKrtTWJ7qU2kRIOv8YdtjkajCIVCSCaTxrCRDGezWRSLRTz77LMol8sol8t7Piu/5zTM++zgcNAYFpafRnhdF63AzsKXpdlyuRze8pa3IJfLGb1xIpEw44DktN1ue86pEgsAnrEPwLMgpkfYvmeVdREaWWIZOM1X0DlCP8fF9qlTp0wbT58+bSpgNBoNvPHGG6hWq57Ikx+pt5/foOc66DP2fQ4iuUcBR5AdHByONWzDOE6SiX4mFArhzJkzyGQyOH/+PObn540RDIVCaLVaxoNjJ8zo1tH09jK5jh5h3QTATvjhjxpAGkt+ludUQ8LNBKLRKJLJpDGSJNTtdhutVsuUoPsrf+WvIJ/P4/r166hWq56kwkHPxf5/kCFzZNnhOML2GLMyRDgcxqlTp4xsgQl2TLbTzzD/wM4pUMKtMg0SZJVW+BFPlWL4ScXsyjkKnWd4X4SSbZJoHkPSf/HiRdRqNWSzWRQKBRSLxV0eaT/J1rDnO46E66hIscIRZAcHhxONYV5jxdzcHB544AEsLy/jbW97GwAYuQUAQ4zpfdHwJ3XF/J87W6mEwk+T7Pdbk3gA79aytoGirIJGjZ5uJua1Wi0Eg0G0222Ew2FcuXIF6+vrqFQq6Pf7exJkP8yC4XJw2C9sjbAuOBOJBBKJBC5dumS2fufCNZlMmjmBn2eyG8ckofkCSo5tedUgUmzLK3hOW8IxKJ9CvddaMo6IRCLo9XpmnmDpSeYztFotLC4u4saNG6hWq54Fvt1W+7ny/eMMR5AdHBxmAgcxsQ4jx6oFfPvb347l5WWcP38e8XjcGI5er2c29KD3lsaEnlqel0ZFt4T2M0rqZVJ9I6UZatT4OxKJoNvtolqtGuI9NzeHVCplfigFicfjhrA3Gg00m03EYjG0Wi1Uq1WEw2FkMhk8//zzeP7551Gv13dpkgc9x/16iQcZcweHUTBscWaTtlHPFwwGkU6ncfHiRZw7dw5nzpwxC9F0Og0ARrLUbDYNoaR2NxaLme3eY7GY8cZyLrD/1vJvbIM9JjRRV2VWdvKvSrlI0knk7cW7/XlbB00PM+c9Rs6CwSAuXLiATCaD27dvm536OFf5kWQbw+QT9vc3KWxP9TR00o4gOzg4HBmOkiQFAgHjfV1eXsaZM2cwPz9vQqpsl2qKKZ8AdrTIatDsChP2pO+XgKceJZsgkzzTyLXbbQ9p5k5c8XgcsVjMVLzg8WwLNYr0JKfTaayurmJxcRFra2t7EmRilHDquCHXvbSMjkQ7HAQ4zugxPnv2LC5cuIDz58+j2WyaqhC60QZJJsdXs9lErVYzlSA0F0GlFLYnWUvA+bUJ2C2v0OiRHm+PDy0Zp3OTzmFaHk49zX7zV7/fRyaTwdzcHEqlklkgML9hkjn8KOZ9P4nKXnAE+S/hvBkODoeH/XgLRiVPfu/Z8oa3vvWtePjhh5HL5RCLxczGH4QmwxHc2lVLOtlJc1puiQaL11Rvkm0MufEAjTb/p0GOxWKGJJPoptNpU/tYZRmtVgvhcNh4huilpmH/iZ/4CVy5cgXf/OY3cfXqVSMR0ec8KSEe5/vd6/t0c7PDOPDre/a4V0nF+fPnceHCBbznPe8xpJC1g5VQtttt40EGYGRM+XzenFfJZyAQMPOJLnj1+tpmTYLz84brbzvZVxfoOl/ZHmcu9nUhzkW4tpHzB0tRRiIRpFIp4y1/4403UC6Xsbm5adpo11nfa24+bKnWJNd0BPkvsVcCj8vednCYHqYxdvZKThn2uWg0ilwuZ0o2xWIx46nVrHEFz0njoh4jJckAPIZQDabqCJWIa0KNJgnRS0zDpiXaSJ7pRY5Go4YMk+yGw2G0Wi1j+FgJgzKQSCSC06dPo1ar4e7du54kwElhP/tRDJObSx0OCn62m2QwGo3i1KlTWFpaQiqVMoRSiSIXo+Fw2ER9GJEBYCRPfuNZE/GUGNsE2YYSZH3NJrx+40YJs7bHrnjhd02bJPP8TBLmLoCMtLXbbSPjGsaJ9lq0zCocQR4AP6+GX6d1cHCYDkYdV34Tq99rwybsTCaDn/iJn8D8/LzxvgaDQeMh0g05NFSqekAAnuNU1qChS8LWH6rXRQ2RSia4ix81hKqNppeZ215za2uSd+Cet5uJe/yxdYnvete7cP78eXz7299GtVod+RkOC1nanxlmDAd5+h0c9gM/rbuO51gshmw2iytXrmBpaQnxeNzIKFSPy3FDD3Kn0zHjB9ghyFqajdC6yH7JeYQuvlVSoW23ibH9un2v+r+SY+ZK6LE6L/A+KNfScwD3FuZvectbTIWLlZUVrK2tea477bl8XPg9G/7vNMhDMKleBthdfsXBweFo4OcV8vvb7xiGPjOZDJLJpJEhBINB1Ot1M0GTyNpGgt5ZEmV6Y9Rw2fq/QW1ST48aUC3fFovFzLlYTo7XpWeL3mbeRygUQqfTQbPZNO8PCoGyAsbi4iJCoRCKxeLA5+vmPYfjBo3OMEITDodx9uxZLC4uGumARlf4OXu3TM4HgHfsknDqHMNxrCXVbL2v7Xzza7s9n/BHSaC2RbeL1mNZnUKlFiT/uvDWuuyqZ+bfXHQzmZnRqEKhYLzJ9j3YGMV7Pgu47wjyOBO8FvXWEk7ELH2RDg4Ou2ETOxo5EuR4PI5oNGq8JjQEukEAsDvxhQZG5RP8rC3FAOAJaeocYnuQ2T7KHyixIOi9YkiTxp7Hqre43W6b16g/5rXUuNITPT8/j36/j1KptKtNkzz3UedaJ1dzOGioDpib6pw9exZnzpwxdY3V26taXY4XzgfKC1TLa0dUSJDtXTD9jrVhL7BtcmznRfgtfvWHbdBEPT1W8xM0OQ/ALvLc6XSM7CudTpv5is+IORqEXzT+uGBfBLndbuP69es4deoUstnstNo0VQxy1/uFIuh5oZGhvjAWi5ksVYYlGcIc5JXh+UfxvOzHEDk4OOyGPe7VaC0vLxsNHQCP7paeHg2x6uKYekTVIWpZJQ2/0qtEg6N6RBoQe/5g3WXKKzTJjlDj6Jfsx2oXjUYD9Xod9XodtVoNtVoNGxsbpkQVr8e57JFHHsHKygpWV1d9S0Lt9bxH+U4cHPxwEHpUm4SmUilcvHgRCwsLmJ+fN7vhcSxz0Qns9sIC8Mgo+v2+p2IMF6Ka5Mv5QM9je5jte1dirh5fjmuVS/g9M0ol6NQLBAIm2mQvxgEYLlOr1Qxx5+d1UU/yrFU6OJ8tLi6aPIhisYgXX3zRSNX2GvOTfu/DZCXTxMgE+V/8i3+BX/zFX0Q8Hke328UXvvAFfP3rXzf6m3/wD/4B/t2/+3e+OpyDwF6rkkFE1V696XHz8/N4+OGH8dBDDyEej5stWzOZjOkUzWYTt2/fxp/8yZ+g3+9jfX3dDAJdHeqKi+2xPTf2F+uIsoPDZBhVyxYMBk3dYA0l2iWU1COsEz2JLrCjPVTDYnt1/DxMdhvt9tHjq9Uu9P29jAoJOokwd9NrtVqo1WqmRJPtDcvlcqhWq4jH476hUr+2O++vwyxD5QyxWAynTp3C4uIiFhcXPdIqwBvxUXttk1qSTY5FSg4oN1Dya0dtdB7xGztKkLWkpB9/UDmEfb/q8ebGQYO81pRu+c01NpG3kw4BmPyNdrttFvhcpPNz+puftV+flPcc5PwzMkH+4he/iE9/+tOIx+P49V//dfyH//Af8G//7b/Fe9/7Xvy///f/8Eu/9Ev49V//dTz11FMH1ljFXh1NH7Z+wfwCk8kk5ufncfnyZbTbbaTTaSwuLiKZTCIYDJoi4N1uF7VazXiV5+bm8Na3vhUPPfSQKRzebDaNp6bRaGBlZQU3b97E9va2KQXV7/cRiURw8eJFJJNJNJtNvPLKKygWiwPvYVQ4I+XgsHclGuBe4smlS5cwPz+/y0OiemEaIE70DEOqjtGuTqEL436/b6pK2KFZwOuh4jloZJl4R+lEOBw2x3Je0qQbVrbQGq2sz1qtVlEul1EqlVCr1dBoNEwkjAScyX3cSfA973kPrl69ijfffHPP5+vmHYdxcNhOII69TCaD06dP48d+7Mc8xG5ubg6xWMyMT5sUaySJu8zxPngvKttQW87xynNpzWD10OpCk/WW6X1WYuu3mLZ5jr6mJd8CgZ2kP42KATAeZC4YSIK5SNe5jlIKciNW8olGowDuyTGWl5dRLBaxvb3t67W2vx/baUgMcgTsd84Zp++NTJC1Uf/5P/9nfO1rX8M//If/EADw9re/HQDwz//5Pz80gmzD9gYr+v0+HnjgATzxxBPIZrPGcHU6HROGZEdWnRJXVMxq5TWYKENdUTQaNasoAHjwwQfxyCOP4Hvf+57HeHHnq2g0ioWFBZw+fRpXr17FtWvXPN6aUQiv3wrMweF+wCiyJT2Ox2YyGaTTaSSTSTOh6/v6GSWwagyBHcOjhFehXiBbQqEGw/aoUNZFPbFuMGAnC/E3DZ56r0ma6T2mB1lDrXpu9XylUimcPXsWGxsbu0o9uYW4w3FEKBTCmTNnsLy8jFgsZsaPjoFhZc8Ab9lGjiF7caveVb9EPoXabiXItiTDb56xo+C8F5Vx6WJdpRa8lnrLlUjrdXl+Ph8l9AQX2gCMLnlhYQEAUKlU0G63R96EaFLY89Je89Q4c9hYGmQ+0Js3b+IDH/iA570PfOADuzwOh4G9HkQsFsNHP/pRXLhwARcuXDBhAs1Oj8ViaDQaaLVaZotYNUqNRgORSMT3i1aPEj099AR9+MMfRqPRQK1WQ6lUQj6fR6FQQCqVQrVaRb/fRyqVwpUrV9But1EqlVCtVlEsFo2WcdDqitd2BsvhfoIdTvTr/37yq0AggMuXL+PUqVOYn58fKAXTaJPf9qw8ly6YeaztzWXolRhEmnVRnkgkjASEyYK6+QfnJb6mBJohTl34M3eCP9QKqpHnFtWtVgupVApnzpzB+vo6Xn31VbNZgIPDLGEvmZEiGo3i8ccfN1Ej6oUpX1ApgRJDeyGuuQnKHwCYRa2tu+WmPvTg0ntse5rV+aaLcibNqbSKn7crTnCxq/dCvkPuosnFfgmAulsn7y8ajXoW4DYn4aIegImwJ5NJFItFVKtV39rq++EtKiGxX5s2xiLIv/Vbv4VUKoVIJILt7W3Pe+VyeZdX5igRCASQy+Xwjne8A4899hhyuRyi0agJMbIDNhoNjwHkIGCnopeYncUOwdCI0mjyPHNzc1heXjZ1ExuNBvL5PN544w1TYDsQuKd75n7vbFutVjNhUHbYZrOJ1dVV1Gq1XaFZwL/mo4PDSYefN1nHpy5eFxcXcerUKVP2jONVjaKew/Ye6xhTyYR6ZRiZ4txhV7sYdA9cVEejUWNsSXgJW1oBYJeRDAQCHnkFo2OcTzi30aAB92pCh8NhMy8CwIULF3DlyhW89tprKBQKk305Dg6HCD/CHIlEEI/HzW6THF/kAhw/KpcCvBWsON7U22zzAL/fdlTbPkaJqR254vv2/XHuUXmHLdugZ9yuvKVzCc/DazK6ZC/4SbBJ3u12MzLOTVeYywDc4zO3bt3ycCw/fuJHeGcFIxPkixcv4rd+67cA3FuR/fCHP8SHPvQh8/7/+l//C4888sj0Wzgi9CHH43Fks1k89NBD+MAHPmAK7Sv4hVHzY5NfrjBp9Aatnuh5Brx6JYLJAfF4HKlUCq1WyyT7zc3NIZlMGi0UyXkgEPCERZvNJlqtFmKxGDY2NlAqlYwWkYaZz0B/O6LscJIw7gTK41nGbHFxEUtLS2a86Bi3J2l6U/wMFo2LLog5DtXb61cqisf6LcpVc8xolNY0tksz8YeGi+fWyhWcnxiR4rm5lW4wGEQ2mzX6Snqiz507h16vh5WVlYkJslusOxw1WOM7nU4jkUgYzXEsFjNjSqURwO6cAn1NCbMtb9D+rsepfMP2TCs5BXaiTLasSj+vBFkXx1qBS3fD4+e01CPnJ76n84kmCHIOsucZkmj1cFOzvLCwYMrm1et1rK6u7unt9ZvbB0UCDxMjE+Tr168Pff+9732vhzAfBYLBINLpNM6dO4crV67g7W9/OzKZDHq9HiqVCoDdWh16bhqNhie8wAFUq9VMViYJKQ0WPdCNRsOcW40mwU4djUbxtre9zeiBNFGGA4nvcbXa7XaN0Zyfn8fKygreeOMNrK2toVarAYDRRDtj5HCSYXsZbO/MoDDeqVOn8OCDDyKRSHiMjibm8VjbQ2t7fv20bv1+37MFNI2Faol5PTWunEtIWJPJpPFy2XWTSVyBnXlG8yAYwtXrMNeC5+31esaDRrJAYq6L8mKxiFwuh16vZ2pF05FgP4NBcHIwh2E4SI+h9rPFxUWcPn3alD1kXx/WBltSBXgXxSTOSjAB7CLTPA8XufZcovOAzmU24dbP6qYe/F+jVySmKqFSEk7pV7PZNJyGcxfnFYJ5DJRqcct6tt2WgfIZMTq1tLSETCZjJKyaGDgu/OaTw8DUNgp53/veN61TjQ12pnA4jM9+9rPIZrMe7woNGI2VrRckEWUYsl6vo1Qqmdd6vZ4p1s8OUSwWPbWR2ZFZOBvYMWxq0AgOVBopfp4dVvVOgUDAtP3y5ctYWFjA1atXcfPmTfR6PTQaDRSLxV0hFWeUHO4nDCLJiUQCy8vLHlkB37M9xPzfNn7AzrazNmxjoRIMvzKPmthCg0jiyqo3mo2uXmjOCRzr6u1S3SENFa9Jj3QsFjNVfDj3EHQOcJcsbmEdjUbRarUmmktmMWzqcPLBvppMJpFOp80Ypa33G99+xJjQMe0nWfA7nue0I7vavmGft4knr6m78ylhJ89Q+Zgu4DVPga8B3oi3n3ebzjwl9LZDTgk92wTcqxQSi8UMTzpunGRsgnz79m3kcjmkUinP6+12G//n//yfQ/ci68run/7Tf2r0MsDODngkmPS4kvhSGsHzaC1DnqPdbqNer3u8vZFIBOfOnTMaJl0p2oaQ16pUKh45B6+hZeXYwVWzw3NoKabFxUUsLCzgne98JzY3N1GpVLC+vo7XXnsNtVrNd9c/B4eTBO3bfp4gfT+VSuGBBx7wJLv4HUujYCfS2SRZQ6A0HLo5hy210E2FNMSq5ZRSqRQSiYSnooSGPFX6pZrDQOBeEo0m0tAbZM9HAAxBTiQSu47hznucG2u1GpaXl1GpVFCr1TweslHnlnHmoONmPB1Gw2F+pzpmAGB5eRlnz571EElb90uO4Cez4LjThS/PoxEjW9ao7bBzENSz6/dsdNGri2BeSxe1Oobn5ubMHMIkZH6Wu9/Rg0w+Uq/XjSyLnm51ErCeui1R5blJxrW4gXqkl5eXcenSJVy9enXXhkzHASMT5NXVVfz0T/80fvCDHyAQCODv//2/j6efftoQ5e3tbXzkIx/xfZDThK0VBO51mvn5eVPChV8E9bvJZNJ0aIYi1esD7IQt1Tgx/KnhUnZQuzSMEl8AnjawM2gdRT4nDZ+wY7Fqhnqh7NUX2zA/P49sNotEIoFgMIhr166hUCh4ai46OJxk+Onb9LW5uTlPArHfmLDJqE1mgd276qm0giFIFty3d9hTj43OMSS9JK52dQw1supRstumicScm+hB1mfD3fk4v6hhp6dZ58FMJoP5+XnfUlNubnGYFPuNLIzy+X6/bzTI2l9J/lS/y3NyHGgUeNCPXmfYa/b7NkH2G0d21Qq2lR5iQucLlUxpzXZ+DoCprMNFu0rDbE+33xxjV9Dg6/yt0SzmX2UyGU9p3eM0b4xMkH/lV34FwWAQ//f//l8UCgX8yq/8Cj7ykY/gf/yP/4H5+XkAh7MqsFd0/LteryMejxvCyw5AwswyR+FwGL1ezxgzuwOruJ0rpG63a0IFmu1K+YWdAUvvMDumDgy2oVKpeLZ85fVs49pqtdBsNlGtVs3OfupJojCe7WFZpnw+f+DfhYPDUWEYQdOJnaSQYT71LimU/KrXR0kuP6+VbTiOq9Wq8SCzzrJ6d7jw1V2tSGJjsZipssPNBtTzzEU9F/S2/ELvWb1catD4t2ac6/zJZ8mcBxrb8+fPIxQK4YUXXvAQiYPAcTKcDqNjHGnBtMA+nUgkkMlkPOSYziOOT1sSycQzwJvf45dwaxNcHZe23bffU2+2PU7V+caxyEh4IpEw79nb3qsH2a6JzFK1uvER28FraiIfnw95hnIvdSwq/9F5d25uDtlsFmfOnMGtW7dQr9dRrVY9885RYJz5a2SC/Ed/9Ef4r//1v+Lxxx8HADz77LP4xCc+gY9+9KP44z/+47EvPCn8Hmy/f2/HmnA4jHq9bjqt7kJDgqwdW8Ot/NLYSdQbA8BsJMLOGo1Gd2V12qFZ3UWP7QF2OjwNrX09LQ9FEs0Ozs+o8J+DgUa4UCigUCh4OrSDw3HHXvOL7QnSkCOJqc4JhC2VUAOgBoGGjQvgTqeDQqGASqVikoABeBJl7KRd9UAzsYalqOi95TWp+6VRYwUczinaZtVUaghWja794/f8bCLBElnxeNzMoYO+GzfPOMwKOMZjsRgSiYQnwdVPCmknupJEax10nRf8+rrtgbWJo15Pybgd8WI7eQydZhrR5vEqG7Mj3lqdg/MhI1zkM7VazbOIp8OOz0Llpna1D22rXV6O804sFkM6ncYDDzyAUCiEN99809dBcVgYd54amSAXi0XjKQbulU/5/d//fXziE5/ARz7yEfzO7/zOeC2dMnS1ptnp2gk1bGGvfLQ+qN8KTrecZrIPO5teWweUvU2kGlcaMd0xS40eSfGgrHvVOfJ8yWQSS0tLWF1d9YRRnPFyOO4YlRzzb47xVCplIj92yFDHphJNJYo0FBybapBIkPP5PCqVCkKhkIliqSZP5x69FgkxCbJKtHQO4FjmAl9Dw7YXCtgpNzmIECvJHjQv8Jw0cPF43NRpH/e7cXAgDtMWBQIBU2KV19VxoDIHtbOqL7Z1s6pdHnRNAL7zjUZp7LGrY5+fV68y5xNKPdWhp5EjEmTOLYx0sW3kGiw6oHMU8yhU1qnJebrw1vtVhx2vwXuNxWJIJpM4ffo0QqEQbt265ZF0TLsvTHsuGpkgX758GS+88ALe9ra37Xx4bg7f+ta38IlPfAJ/62/9rak2bBAGrdwoDteEFmCnMwIwxku9udrRSI4Jan0ymYxHptFqtbC2tub5MqjtI1kNBAIolUqe1WOv10MymTQDlcRbd+orl8uewcKOV61WPeRYt43kc4nFYpifn8f8/DwSiQRKpdJQI+jgcFxge1nsPu032SaTSTz00ENIp9MmeVWNHz1EuvC0PTc6vjRpxZ6IuaVqKBRCo9FAs9k0iXMsq1QsFs14pmc7lUohnU6bTHutvcwx7uftpWeapJ1RKHuhYCcb6vyoNVF7vR7q9bqnbjLvtd1uI5fLodvtolqtTvR9OTgcJjFWrawm5Ou4ob2nB5nVITje+FkdR/b7tgxLZRScazjOeByj3LY3mtyBBJbzCUs/hsNhRKNRT4EEJaW8B5Jov+eijrler4d4PG6i7wBMtS/lLPSm63jWXDMl84FAwES+lJNFo1EjPbPLw80yRibIf+Nv/A385m/+Jj7+8Y97T/CXJPnjH/84bt++PfUGDoN2SM3aDAbvbcTRbDbNcbqq0S+GX2a5XDZljdSLw2PVkFITzLBtMBg021Szk4bDYU9IlAOGlSyUqJOcc0ODSCSCSqWyq66p1iH084bFYjH0+32cO3cOpVIJzz//vBnEx6EzOjgMw16SIX2Per2FhQVTmcGWQfl5UVXLa48xYCcipZpelUtpbVEeq8m3JLJaEScWi/lKtuy2aXvtv+2wsd1m3psSZPVm0xCS6KvXnJ5kGt1Rya+bcxyIw+gLfuNYbS3/t6NE6hWmrda5wq7fq+SQn7XHHj/HChCdTsdwFK0pTmhpNM4rBHMoSJDj8bhnniPxVYkoP29Hw8gf1CNNjzPbbD87LUtpL8D9ngvL6el7ugnS3Nycp4LYJBh1DlIHxyQYmSD/6q/+qimTtuskc3P4vd/7Pdy5c2eiRkwKe0VDD6+KzAF4vmSGRbe3t1EqlVAsFk34MJlM4ty5czh//rxJiOOXDdzruFx1srPrLlX6Y3ce/m9vIUsPMjvl2toacrmcMejs+PF4fNfKVoX3PFcoFML58+fRbrfx4osvOg+yw4mCbQTZv1X/x8ViMpn0bKNsT5YqqeB7GubU4zkP2Mbw9OnTSKVS2NzcNCRY8wZ4nVarZUrAMRkvnU5jYWEBuVwO6XTaLOppdDU3wSbsdjtVvmHvrOe3mCbx1bmEW1OXy2WzRTXnNiYp7/WdODj4wV7YKmmbpP8MI0i2BpjH01HG/23vLwDPopaLRo066fl5DvUq8/zdbtdEjbjo1Gvw87qZkJJd5R+s0MVj0+n0rugQk255TibbKfkn/+l0OmajILaNOm31ipMs8xg7gmc/Uzu5j4v/WCxmFgoLCwsIBoOeErnTgj3H2bZC2z4qRibIc3NzyGQyQ99/8MEHR77wpBg0oOjFVY8IOzbLHV27dg1bW1sol8uoVCpoNBrodDqmMzabTTQaDeTzeVy8eBGLi4sAdsKuSpTZ2fQ1NaDsWAzHqq6ZbaxWq0a/GAze22mv1+shkUh4Enb4GQ3z6Hn6/b4pIUfPFEMapVJprGcLOIPncHzBxDhqjwH/DQBszwp/+xldDdnqD8cs65SzfjmTXeglYQm4TqdjQrz0Bml5N9sI2Uk+el1bdmFHpey22/cD7ETF+JvzH0m+GkFGx/R6Dg6zBnss22Par9+SRKrHWH/4GqGOKv5PYqmRl1arZSLBumhV6ZZ6t5UnMPGeJSDJCSKRiJFuqWebDjtGsmw9s851fF+3t6fjT+cgWyNt8wN1TqhURaNrnDftesn6fR3EXDKtc05tJ73DhHpMCC2dxJBgq9VCuVzG9vY2VldXcffuXWO0uDLUXewYKi2Xy8jn88b7ZK92+YWzwzBkoGGDXq+3a7c8DiStTxoKhbC0tIRUKoVAIIBGo2FKR2lJN14HGDzIKekgQX7kkUfwZ3/2ZwM/Y8MZPYdZgz0p64Rqe6ToOY7H4zh79iwymYxnG3hO1oQ9ZwDYFZpUeYL+cCFKT8zi4iKq1SpKpRI6nQ6q1arRDXI81+t1hEIh493OZrNIp9NIJpPGy01DwzbzeprIQ+Nje734w+iTTez1mZIQ02Pd7XZRKpVQq9VQKBTM1rC8f3q9HRwmwWHaFnshqWTRz2tJ7y6dTM1m0+M55ljRZHo9Fx1oSlrb7Taq1Sq2t7fNuFRZlUah1XNMwqoyikwmY3a+ZN6CyqEAGE7B82ibdPMPnbv6/b6ZL5PJpPEua/4BF/x8nuq15jPgPWiScDgcNtV8AJj/+dpeScKTYNi5DlxiMUsY5A1S48UVGHCvAsedO3dQLBYNWeU5ODh0+8Z6vY58Po+FhQVkMpmhXiUlyqrxA+AxZtoedhAmDVJ7TI3yysqKp6PyvNQ8AztZsrxv9TAD9wzaAw88gEgkYrTYDg4nHel0GqlUCvF43FOLGPCGUfmjSS78bYcvlajyPErSg8EgFhcXMTc3h0qlgk6ng1qthmQyCQAmSa/RaBjvD40gt5jmeKYx0+urEeJCnsZK26TeYB5vy0/0PPR0kyDX6/Vd0gpbl+jgMC1Mqz9xLGqEg+ME2Kn+pBERfm4QcdLFpV5HP+Mnf+KYocOMji4dS+QNJO+ag6ReYl6TOQp8X2uYazsG3YcfOM9xLuBcOTc358nd4pxC0q1eayXgnJM0os55lPOsapCPy3xyLAnyINADrIYvELiXqc6JH/CGLrUaBA0BSXK9Xjclovy8ORxEHIi6avMzTBp60Lqn7DCUSND7DXiNuoL3qFDPOjNh5+fnsbq6ekBP3MHh4DHK6p/jMZVKIZfLIR6Pe8oh6bmUIOuEbodk+b49kfuN8YWFBQD3dhylFpkeYeqPmefADHoSZC6y1cvNH5VO2ARZZV80ZkoG/ELMavhJ3OklYhUL9aDZCUp+Rs1Fnhz2g4MIs1MDy3ExLC9ICbbaaiWedmIvsJPzo58jP1CZleYiqHNOHWi6A54fQY7H42bsa4UK3e5atc1siz329dr8nBJknR9VKsLr6u7DPJYOSZ2feJ88ht+FXX5OOcu0CLNff5q0jx1Lgqw3q0YglUp5qj/wi2R1CoZb1SOjG3/o/6wmYXtdFKo90o5P42OHQLkKC4fDRr7BkEO9Xkez2UStVkOz2TQDhR2Mn9cO6vels2OTbD/44INYW1vbRbAdHI4DxpnUuMhsNpvGI9rpdLCwsODZBVM9on7SCQ2V0gDQMNKjwutxfmCije52t76+buRXasi47So9Ms1mE6VSyXiZtYqESqz4t95noVDwvN7v9z2OAC3ebxNtlYR0Oh3TBtUfA/eMaTqdRrlcnuh7cXAYRlCm1Zf0PKdOnTLJ9ranl+1RgsZkVa1WRaI7jLzZ0R5yD5JcrfzCLZcpl9DKDjxWt5vXdur4pTSq2WyiXq97iHe73TZRKnUEaFRMF90kx9ls1iyemaTLRTP10yq5suUUbDO947yXVCplHAXtdhunT59Gt9vFjRs39iTE0yDN++1bExHkQqGA733ve1hfX99FvJ588sl9NWgSqO5GVzDxeByXL1/GxYsXkU6n8dxzz3nqfAIw27+2Wi3jcT19+jTOnDmDbDZriDMT7mg4UqmUp/PRS0QvzsrKitEcUqu4vLyMy5cvY2FhAefPnzcyi36/j+vXr5vycf1+31StYLhGtdBsOw02/yboqWbH58Jh2pofB4dZAxeHNGy6+6TtHSIGeVn8zg14JRp2kq4uUGu12i5ZlCaq8HitwMNMclvbpxErJev1et28Ry8OE4NJ9tkGGny+x23s+bxY81gTktgGNdzqCHDzicOsod/vI5lM4tSpU8ZucszYUV0dx2rPbSnGXtfjb11Ukvjy9WQyafIPNFGOP+QKGuG2r6H5BirD5DU0UV+JsJ/t53Nh1Jp/c75k0QDdJVSh85F6pFVuEQ6HDccJBALmGZxYicW3v/1t/OzP/iwqlcoufW4gEDgwgqyueH0NuPelnDp1ypPURs8MyeTy8jL+9t/+2yiXy1hbW8PGxgZKpRLu3r1rjEKr1UKhUDChR80cBXZWXiwVx9UcjVMikUCn00GxWAQAU0GCnl+GfdPpNObm5tBoNFCr1Yxh5KqN98RSLVo1o91uG720erIYUqK3iZ4s1Tc6Y+Zw3DGMvKqnRTX7gUDAFKhXz8owKYWSRHsRqnpCPb/WWmdGOPV7jEpls1ksLCyYCJWSXW27yidoNFXqxePUA0zDw5J2jIwxEkXpGM/JuUcNrpa21OfB83ButXXOCjfXOPjBXlz5vb7f8xILCwt49NFHzbgEdsgloVUX+L96SSm7pH2m3bWJncouOFZ5zVOnTpn3crmciRhrxEq9sBxnKt3wSw5Ur7DtCdco17DnSt7AZ8TPUs6xvLyMSqXiyWFSMs5754+9mFcPNZ97JpNBKpXyTR72a980sJ/+NTZB/uVf/mX8/M//PL761a8ikUhMdNFJMMxr0ev1DLlkZ6YGkF9QJBJBMplEMpnEmTNnANzb0aZYLOL111/HjRs3jGGg17bZbBophNZP7Ha7xltje6iYrU4DrV5fhj4p+VBvUKvVQjweR7PZRDweNxuWcMAyVKsDgcZKV7AcLFwVkuQ7ONwPsI0DvSk2dB6xf6vBUa8wDamdvMbz6YSvXmomqHDc0wBp+JTeW1uapedXY05vEUOgJLck5nb2Po0ajR1DsrY3Xa9tG0LVFtpeZoUjxw4HDZs82f+zBq89HrVv0l7yPR1HHJu6gPQbK359XcmvVqyil9hOZvMjgioLUU8wsDPHadvYHo599ULbzkXbo6zyUfIWknVWxuJ7mnxn3y/PPez70vYeB4xNkO/cuYPPf/7zh0qOFcN0KSTFADylmwAYzR6wU0UiFAohk8kgnU5jeXnZVLpgePTatWvo9Xo4deqUMQzsJFxZkviyXBO92alUCv1+32zNymtWq1UPiWbnI2Gmdkc3ItHVqxon24ByANKLTNlINptFsVh0OmSHEwtOzMwzYFJJLBbzLFSVFKr3lgTUlioRfgteDWfaEghGjQCYherS0hIWFxfN7n4ATBKPaguVPOs51RAxEbBQKJg5gpnxGt6kIeU9VKtVj8eb9ZttfaJ6kznnUrZGraODw7QwTW8h+z6T3TnWabeVNAI7i0XmLpAM9/t9I5vUhSs/r/ZXnVNKkNU5pdIDe0GtJFjrJmtESMk8k35ZppFzk1alsaNOSozV+2wTZfKHRCJhCLJ61Xku+x54/5rroGSa8w+32rajCcO43Tjf/zQxNkH+qZ/6KfzZn/0ZLl++PNWGjIpBD0D39+aXpHIFElnb6xoMBpHL5RAMBpFOp1EqlVAul1Gr1VCpVLC2toZut4tcLucpcF2r1dDtdo13Oh6PI5PJIB6PY3FxEe12G5FIBOvr62i1WohEIqaeIUOrmv3J0AOJNo1QrVZDtVo15Nmuc+pX+k2fk2476eBwUsH+zqoMlFRoyJGTMo0osLsuqi2nUDmDHRLUBS6P1QQbGqZYLIZUKoXFxUXkcjmkUqldyTh++kA1GJwvtFIO5zjmR9BQ8v5sSQkjYxrWjUQixoBx5z7OL+oh58KCO25RquG8xQ6zAiVsSgx13KqtVHtJe6syDHpkNXHPJnH2Oez3tOKE7S3Wccy/VVM8TD+s59Tz6T3SW23PXbYcTcms8grOE+pd1nbaXIvzCCUirOJBpwUdg4yk7xUFmAWMxJyeeeYZ8/ff/Jt/E//kn/wTvPTSS7hy5cqu8P0TTzwx3RaOAEos1P2vkgabIGrnY+eJxWI4e/YsTp06hWq1iq2tLeTzeYRCIdTrdePh5fVoPChuZ+iUYVSS3oWFBZOsl8lksLy8jEQi4amaoYO62+2azE+tm8ptvtXY+4Vn1BNEw8nEPweHkwxKnGgg+LdfWE/Hjv2enYBnGyiOUzvMqN4Xnocav2Qyifn5ebMQphFRqJfbDova+j6VeShB1mO0Iofqm5k8pHkO/X7fEGQ6EewQbyAQQCKRMHPRtLEfraCDA8G+bzuMdKHLsas2nQtD5QYkzZRr2jtK8tx+ZI/2nRiWfKfjVHW+tpfX71418mUTa50P7NJruhjQscd2qiSEcwsJvHrUWe6O5yInYkUcHkuyTKnoQcDPC72fOWUkgvx3/s7f2fXaV77ylV2vBQKBQ9eX8OYZmuTqBtjZfpG7QGkWthoXYIdYsgRbKpXCww8/jF6vh0Kh4Mkw7fXubQdNo8IBGQgEjCc5EongzJkzuHDhgglT6CYBvCZDIbwXLY1Cgtzr9bC9vY12u222yKa2GoAZvHY4o1QqYWVlBXfv3j1Wuh8Hh0mg/Z/eGybGAP5bp9phR/W+6Hl1ktXzcAwzFJtOp81xLL9EzzHLOzGcqx4arZtK75Z6hAm2i1KsaDTqqWdse7lUCsH5KRqNIpVKeRJ/OWfqcySpVsM9it5wUuz3fI5gzz4O6jtSIqxeTdsjq9IDmzgCO2PdTl6lF5SLY3qodXzoPKELWyWSSkq1vf1+3+OtrVQqZnyzlBuvHw6HjRxLobbfloAEAgFPFE1lXTwv20JvMOUbusseI0e8R7/8BUapWCRBvdLLy8umYIFytWlhFOI9DjkfiSAfB+2qTtqq/WFHIElmZ9RySqrzYcfXEGM6nfaEZrkQaDabhvim02nkcrldXmIm0XBgNJtNk9WpA01DIRTaRyIRtNttk2jH42mQWbqNA0h3v2o0GlhbW8OtW7eOxffn4DANqBfE3sHSjhrR86KvqaTBz+AN8tSQiHLiVw8O5x6VO9naYACGqNLw7mVYmUjTaDR2hXr1eWgSsYZOeW3bG+7nYVINooPDfnBQfUjJoL3IA3Ynp/FYHm8vCJVgMw+IttzW9QK7JVcKW5/M9tiSSCbqKnFVSQLPr8mEfh5se+7ycwZwjNuw39PdgrUykN/3qM/SfvbAPe8yc6xsj/osLm6PpTjVdqPbup9gMGg23GCyTigUws2bN004kkW2y+UyFhcXjR4vnU4bzwoNnHpP1LNEA8zyTYuLi0in08hms4bg2iWTlKRre5VAa4g0FoshnU7j7NmzZieucrnskY/0ej1DzJlo0Gg08Oabb+IP/uAPjHfLweGkg5M/t2fVzTiUIAPweI5svWIoFDKE1dYlsh46sGP46C0+c+aM+RzHPomxyipodGlsVRJlSyl0Ea2vRyIRnD17FrFYDOFw2Hi9uNinYdN75lzGY/wias1m05xLPeV8ZoNIgIPDUYNjlzaQY0xlQ4B3DwGG/svlsunrtP+cO7gBWafTMdFoTV6zF9Cca2zPMuCVdfAz6pDjnBCPx42dpzdZ5w86AzmGuWjWec6+T5Js21lgg3MNy1NygcA2cl7UbajVaw7sOAHUQch5MpPJmByrw4ByrnGI+NgE+fOf/zweeughfP7zn/e8/m/+zb/BG2+8gX/1r/7VuKfcN9jBGKbsdrseglwul7GxsYHbt297ag3zoYXDYSSTSUOS1XCooaBXSbWLwWDQGMRAIGA0yCql4GfY8amZZjYnyycxm1xXbOygi4uL6Pf7OHv27K5wKO89kUgY49ZoNDA3N4ePfexj+MY3vmEGvIPDSQfJoXqPAa9nRHWI+j4/r4bUPm6QNzUYDJrSUlpNwq6G0ev1DMnm2Ae8HmRem3/T66tGPhC4lwPBrWgZCSMx5znUaHNXK70nmwhzzlMjq+dy84jDrEHD+NTA6uKWpE4rVGj/ZlUIe0HIz5Nb6EYeGkHSH9sj6jeH2J9XvhEIBNBoNDwcgCSXMlG2Sb3KfmNT5zytusPxzWvYETRtv24eopubKC+y5xS9P1umRW7Eue2wpVFTl1gofu/3fs+TtEd84AMfwNe+9rUjIcj2DZOoJpNJADsZ4KdPn/Zs5coVTiqV8uiI7dWfhmM1HKp6xWq1imq1araLpYaHHmF6q9ip2ck56HgODmQSdbaFJeQAb7Fu1RBypcbOFolETGmp9fX1qet9HBxmDRyvqVTKJMP5EV57UqZHyDZYNC76GpNS9LycI7gRgGp/NSxKFItFY8wplaDBoeHg3MFaqmqotA57KpUCcG9jIkaLdH7Q9vN+uIhXQkwjql4dXVCwTU6y5TCL4IKTZRRphzXkr2OQ45naWm7+pf2dDjCWZSXZi0ajiMfjnkoxfiRP5xCdd3hdW56hxFejObrBWCwW85BrzVOw5y2Oad4Do96cnxjRJkfRxblKLHgtSlcZHVfizM/xfuwKICy1S36jVXz0eU0qwTkI6c7YBHlrawvZbHbX65lMBpubm1Np1KTQh0vSyfAKa3jSw0zDQB2xEmFg904/JJ+8DkMHDMUywUYNqq5egZ3VKMO0g9pC75IacXqjdQXJ95Qc6wDkvbMCx2Gv1BwcjgLBYBDz8/NDE/R0fAIwmjiOL1vTqxO/avBUuqSGQr3GNM7UCmt9Us4hutudeqiYT0EDo+ckQWbyXzAYNNegMfLTGPO66s1ut9vI5/OoVquoVCq7SsopCbDnJgeHo4R6amOxGC5evGgS3QnaS3sM9Xr38nWq1SrK5bLnvDpGSVo5JskZdDxzXOjC2ZZ1ATsVNWzZo016deySpLK2OecnW8urxJ6v6Z4KfmXhuIDQxEPOHzofATBzgu72x8/pPKMORfUg8964k6hWv2B7R/2+/V4fpIueFGMT5Iceegh/+Id/iM997nOe1//7f//vR1Yb2S/MoR5g/cJZckQ7B7CbEOsXZxtT22DyNWr42KnU8KlOiOSaHUo9xYB/bUOGRv1K1vE+2+22qZRB0k7Nz0GsrhwcZg0kt7lcDtls1nhbdILmWFPvhm4BO0gaoQtT/d++thLtfr9vpFSaAc7jGPbkuFZiTt0yr6/SCf6mvpm78/Fa9CRr5roaQtVXc7Gez+dN3XVWybA977owdyTZYVrYj+dQzxGNRnHhwgWT4K6LTY519ll6M8vlMur1uomc0LEEwGz4o7xBd7QFdhNTLbumY8aO4ujc4bcAJ5+gDe/1eqZuOT3BjGZpfgJhL/JtHqDzAjXGwE4pWd6nzgN0ADDipXOmFgng+dWTzGfPSJu9Q/E0oXzLfm0cjE2Qf+mXfgmf+9znsLGxgY9+9KMAgD/+4z/Gv/yX//JI5BWEhlH4N8EvD/BmhNNosSaoXZdUNTZqGPjgNTOdIc9arWbeJ0Fmx2VnCQQCu1ZzAIy3yPYiadKNHcrR6xSLRTOAmITY6XTMClIHqYPDSYWONa25yXFIA0ZDoPo4TWjRCV/HHL02SoiVgGo7dLxpWSYtNaWVJbQuOpNy7PKVqt3Tc+nufPbOmyQDGhbl/fJemeDLNmmNexpl7kw4i3Bk/f4Gx0AulzM2nvIDOwdHCamWOWTfB3Y22lACqgm/pVLJM/btKIsfZ1Dbq95W/s2kN92sB9ipJMF7ImfhHKY1jrUdtnNAn4ESebuqDZ146h1WBx5/WGpWI9dsE+ccrX9cLpdRLpdx+/ZtbG1teXbxnDamwXPGJsg///M/j2aziV/91V/FP/tn/wwAcOnSJfzGb/wGnnzyyX03aFLYX5BKDuhFsjc10U7MhD6F1lNWUq06JJZ4W15expkzZ8zW1Wq8/EKnupq0V3J25qfuCMiwh65mmWW7tbVlvFT1et0cG4/HD/TZOzjMAuzQpu3t5G/N4vYzKDzWDoEC2EUO1Uuj3hNd/NrhVg3N2hphJc4q9VDvr17XDq+qJIOLd03iIVHQ0pC2VpBGUb07bJ/qFPXeHDl12A/2Q2bssc3a4CqFIvyix9q/7QgSx45yA55Xo7S6GFVpki1L0nYQShA5RvV6/FvJPhfAen5+3vZa68LfJqOc73SO0XP6XQPYmX9U/6zvqyNSSXK9XkelUkGxWPTUd57VaNREZd5+4Rd+Ab/wC7+AjY0NxONxkyRyWLAHE78o9eYwNKKGp9FoeEgyQ5Ekqo1Gw9TpY8cnKdVORgOSTCZx6dIlnDt3zpR4I2lmPdRut4tSqYRCoYBarWYMGDsQCS61TUqktbNvbGyYwW+Xb6IxPHv2rCkizhVuoVDA3bt3D2yV5uAwa+j37+08yWx2OzyoEgYlxTqeVGNIskhjwDwBelhUb9doNHYloKihVo+O6hVp8Pi3nlOz1TWcqa9p7WT1SHPXPCb8aXKyXX6J8xvbrwSZ4dRUKoVCobDrmc+qgXOYLdgL1mlAxzfLoQE7G/UA8OyIx3ZwDNPTSdvMalbkFN1u1ySl6SKzWq0iGAxia2vLjBm9R1tqoG21JZu2NEKlUaoj1gU8y9HaZJ4OQo5J5UG8nr1I4NxA4m/nOKm+WBcSGp3mPdE5V6lUjHSlVCqhWq3i6tWrqFQq2NjY8Dyfcb9v+zMHNfdMXAd5Y2MDr776KgDg0UcfxdLS0tQaNQy2vED/1pJJfE21QxqmZLZ2qVQyg0Y9vFp/kO9pp4jH43jLW96Chx9+GKlUCul02pRaonHRjG96qalP1BUaOzSPo2FWbzgNonqReI920o8OvLt37yKfz3sGloPDSQbHGo0EoR5X9dKqB0pzAvgaF64acuW41RAsr63tGNQ+jSDZXiGdr9SQ0YCpweV8pJEqvVfbeKpHScmx3qv9rNhWEghbGubgcNTwG8u2BEmjR/bxtMuRSMRTQx3YWeBqpIWLTNZPTiQSJhl+0Li3vdnA7jlikAyDC3e2gd5rEnt13pEzcJyqfETvx5aAaXuG3YPdTl3I89p0/HG76WKxiHK5jEqlglqt5iHgh4VJrjc2Qa5Wq/jFX/xFfOMb3zBfXigUwpNPPomvf/3rSCQSYzdiHAz6Um0CqAW0taNoKIHHMbHNLtkSiURMp2dxcCXH/KxqkdXIlEolNJtNlMtl5PN51Ot1pFIp06mj0agJPwA7iTjafhpAlWNoCIVGVeuk8v1KpYLXX3/dSC0cHO4HMHqSSCR21UG2k+CU6AI7xgPALmJJr5J6c+yNMzgu/eQTHMOqaVZDplEwGmSSc9vbpDttxeNxY/jV+JO8s41q1OyEGl6f85d+pt/fyZ7nrp52WNXNLw6HCZsDAF4nlkqOtE9rBQqtDEHvM51duskQCTFrJdOeciOSZrOJ5eVlE7lRkszxq+PRllzY0gV7YaqLdc4tvEdyF32v2WyiWq2aeYqOuXg8bgisjm99drZDgb/tH5tXqea4Wq2i0WigVCoZD/LNmzexvb2NQqGwS5s9DewVwZr0OhMl6f3Jn/wJvv3tb+Ov/bW/BgD47ne/i89//vP45V/+ZfzGb/zGRA3ZLzjZ64pLDVS/3/d0DA4IrhYBmPc5kGKxGHK5HMLhMObn5438Ip1O44EHHjA720UiEY9namVlBdVqFevr6wB2yrD1+30kEgljkHktDadSLqGeGg5ieqSVKKtBVMPLwVqr1Zy8wuG+Qr/fN54K21OiBoGTPBe3KstS0CDxGIZb6fWl12aYXk8TcAGY66iXSo2Geoxo4NTAVqtVDxFXzaHmMQzy1NiSDWAnfGt7nXk850SVeDk4jIOD6jc6ttUOUhNMcPyoU4mJ9LFYDMlkEvF43MizVFtcr9cRiUTMTrb6eqlU8lR4UPKr8gR1wCkBVa2zLnCVr+iClXMDF9iUYfR69zYhI0EOBAImaZfzmzoK1avMMT9ITqF6Y4ILCHqN6VUnMa5Wq6jVaqhUKqhWq1Mjx5NEw/0WJHthoo1C/st/+S/48Ic/bF772Mc+hng8jk9+8pNHRpCBnfIk7JSq4wF2yzOYlW2L4Nkp4/E4kskkotEoFhYWkEgkzNbPzCKlcW21WiZB7tVXX8X6+jqq1aoZcLpLn2oNdccvu/IGByjbZmuU9V70b5L/RCKBpaUl3Llzxxkzh/sGJMi6G5WGV2lQNEOci2IlyPzN99TTRMNgE1z9URmEEmTVHw4Kp6qcipU4lASUSiWPh8n26FCWpcbOfg78n4aUhpP3rMcBMATZr1yUg8NRQcc4sJMgpiQZ2JFRqG5XbXA2m0U8HjdREvUEM4IUDoexvr5u5go64SqVCvr9vqm7rmUebacWz8c2255u3ofmPuiiVecQRp9JbEnYa7WaaQOPZek7Ss/0fFysq9dcny/nFnu+0kRfloskQebmaZVKxTgsiINaJE0bYxPkWq2G06dP73r91KlTh7avtg07fGl7j4GdEmp8n4PIzsjWDqOhhFarZQxkvV43IUd2+EKhgM3NTdTrddy5cwfVahUAkMvlkEgkEI1GTW1RYGcAxWIxjxHTRAJgJxTKzs326ICyQ7ScAGKxGB599FG88MILzog53FfguLYT8FRKpZ4e9dQAO/kIGsXhObghEMebkkidW0iauTCmQQZ29M6aM6FzD9tpv85FMqNJ2iYadRqser3uIcqqV2Zb2RYN5eo9aXv0eTg4zApoE3O5nJEwkrjpYlj5gJJMOq8YCbYj0QT/zmazJorUarVQKpUQj8eRzWaNE0091XY1DeUpSuo0oqMRK84znKP4t30N3psusHXRYPMze9E9iGAqCeazI9GmzKTVaqFWq6HVapmKWn/xF39hJCnkQ/z8QcCW3fjJcMbF2AT5/e9/P770pS/hG9/4hlkt1et1fPnLX8b73//+fTVmFAx6uOxc1OHSUCipJDnVQaJhCr7P8xG2gVVNYK1Ww927d7G2tmbqEJO4qmeHg1ANJK8TDodNKToaO15Tr8e/1ZDrCpVhH3ZI+zwHscJycJhF6GIX2L2ZEAmurVnUcanwG4v2HKGwtcaEeoI4T/Bc6g3mdejZ1TJNfE89PSTi6tnS6jt++mSeT++Rv/0WFn734+AwDqbZf9SJFQgEkEwmjcNJxwXgLbGon1fPqXp9+b6fPJELa3piNbnP1jkzYjXsWfhpf3WesLXVqh1WLy7Jve0c5PXVK2wvfAkl7/ZiXSPyTL6rVqvmvXK5bHYlLBQK2NraMrILv2sdJPyuNQkHGpsg/+t//a/xUz/1Uzh//jx+7Md+DADw/PPPIxaL4Tvf+c64p5sKbOOlWaZqENmxtS5fOp32yBh4vmDw3m5T1WrVCPFrtZqRXXBFxD3ONdGO9f14jlarhWg0ajqvemhUbsH6jbplNT/TarV2ecW0oDnDLazOUS6Xsbm5if/5P/8nAG9RcAeHkwZ74lMDAnjrFZPgagSJr6mnVxeWtqaQr9vhW44x3XxAx7x6bbWtei5gJxqktVa1Pmq9XjdzA7eXtj1g1Ab6Rdb8kvTUgPA+1euu3nM3lziMikn7yiiERp1GDz30EHK5nMdbqaRXF4s6tpmHRH5gL5YJjnEScZZtbTabyGQyyOVynnwk1QXb8kldFPvdo85XNjj/dLtdU76Rks9gMIharWbapt5mzkc8h60p5jOxS+KFQiEjn+Bx1WoVzWYTxWLRfO7atWvY3t7G7du3TQWLgyLGft5ihS507M+N056xCfI73vEOvP766/hP/+k/4ZVXXgEA/L2/9/fwsz/7s0e+IYUWvddwJI0ASz9p8gr3bFePkG1AWG2i0Whge3vbkNVUKmW0PyTJNJI66DhIeU4N39Iga3iUnUvbCsCUkVNdku6iQyLPbWN/+MMfYm1tzXmOHU48/CZMGiZNtlHip3pkYHcRfTvy4jexqq6Z/wM7YUktzcZdLkk4Oefwf13w6gKa85FKsXSnLS6YNbSqn1WPEOcglXH5edX0GfJ/yjYYqXIk2WEUHLRenTaUpVbVM2tHjmzPqep+7YWw3WaOBR2nujAddg6C88Uwm2x/jv/b85bWTifhpQS0VquZiBLbbj8zu2SsLT8lwQ0Gg0YqwXlme3sb1WoVm5ubZp7Y2NhAsVj0JOMd9zliojrIiUQC/+gf/aNpt2UsqOFSA9fv981WjfS8sNRaOp0G4A2fFotFTxaohl1pvBhK0SSWdrtthPlcwSUSCU8Yk55f3dVHjZUm+XGgxmIx1Ot1c14ay0Ag4FmAUI+k+6fXajXcuXMHGxsbuHnzJl599dWBoRQHh+OMQR4CQuuFqvHieKY3SaUNCiXYflpjDaOqQVRvCw1Fo9Ew3imORZLMRCJhQpZcUJPg0siQ1Ha7XTNX6CLfDgWrdlnR6/XM1q5cWKuRVSKgz4DzVaPRwMbGBmq1mptPHCbCXuPWPnav9ziuWWkqHo+jXq8PXCyrVpefZaKejmOOAZVq6IKbn2e5VjqpyB3sMaXQvCcbPN4v4sWoVL1eN21uNBro9/tIJpMmCl0sFlEqlZBOp01CMaWnhD0vkjBz7uj1eiiXy+aeWMOYnujr169ja2sL165d88i+7IWF33d41HPHOA7DiQjyq6++iq9//et4+eWXAQCPPfYYPve5z+HRRx+d5HT7hq6wgHsPgFUltJOxdItCk1vsgWPrfICdZD8tl8LtphOJhKkFSMOoWkFeu91uG1kECbCGgiORiCHQqqdiFjzJtoaHO50OCoUC7ty5g9XVVaysrKBUKnmej4PDScGg8JkSV0Z9uPBVjR7HDMcfPbKcCzSbm5/nNXSsEvYmIkxYoSSB+QdceFcqFbOAZgQqm816DJntwVWjaRti1S/7aQ2BHYJMOZZW79Dz2ruN8vVOp2PquTs4zAoSiQQymYwZX+y/OmZtr656cfU926Fky4m0ogRzlpjgR6Ksib2jkHzOVQq18Zr0W6/XsbW1ZdpYr9fR6XTMAjwSiaBer5tdgUng9V7se+dcwrZw7wdu6EEZKcd9t9tFoVAwScAacfKL5I2zKJo1TFTm7Wd+5mfw+OOPm6S85557DleuXME3v/lNfPzjH596IxXDtCWqt6NxY8fRUCS/NFv/o+VfVFuopFS9wADMVtvz8/NGm8xwBOsqc1cZNT7aITU7FNjZMpNtVYNse7oYCikWi7h79y5u3bqF9fV1FAqFY9spHRwmhb2wbTQa5nVgZ7ypppeLWUafgB3jykWwfl69Lzo2mWzLCJCWPGJtVZLtcrlspFRMcKFsy97cRAkyf9Oo2fOXLqg5j+n80mg0jIyLZas0YVATgG1QusX7c3A4SnBcJpNJpFIpMyZ00cfoi5JAtaF+ybj6vv6tYwSAseWdTseQZF7XvibPrb9t+65cgE405iFwUVsul3Hz5k2z+K7Vap6d8kiotUY7F+120q7yIB37CwsLiEQinpwmXeyrXM0PszY37Kc9YxPkp556Cl/84hfxla98xfP6l770JTz11FMHTpCH3Sx1dUy+SSaTHkOnFSTYGbRWKkXo9BCzdiC1xayXyA5Iz3Eul8PCwoKpd8yEvtu3b3sG2NzcHFKpFHq9niehR1etmoBn78RlPweGW4rFIm7fvo3XXnsNN27cMNmjez0vB4dZxiSeBxoq6n23traQyWSQTqfNXKASC3pwSaRDoZDR/lMeRaPL9jDyo2OYRqfdbqNWq5kSSNVqFcViEfV6HdFo1NQHvXv3rgmV6gJ8fn7eo1tWuZfWJvbzdNlJd5qszPulx0k9Vvb5NVTKebBWqxlybHu7poXj7GlyGB3Tzolh/1Wbx8WubqJjk1Zbf6tlW+0qM3bbGW0CdoiyretXZ5q+pzpfW/fLe1A51vb2Nmq1GtbW1kyFKr5nb2ykpFvvUyUQOm+ovILHrq2teeYZbesk4/MgxrTdh4bNHfuZV8YmyKurq3jyySd3vf6pT30Kv/ZrvzZRIyaFEkx2QIYTksmk8czwdW6DrZ2LHh47xEgyrV6oWCyGQCBgdtRbWFjAhQsXjEFmhis36aCxZbiDeieucAGvLMTWHJJI2+FhruS4uut2u7h16xZefvllFAqFXR5pB4fjhkmNKBeyuuNcNBpFMpk0x+i4UOOp8gTOKeqBVeOjC1rAa4RoULRaTr9/b/OScrmMYrGIra0t1Ot1j7e2VCqhUqmYuYNEWWVX6m3y83bp/3xfZV72rns2Gddz6+cZGVMSMU1MmzQ53D/Qvqzj2K4cQ9jyAsLvOB0XdiSJnllNzFWJFz/DNtiSTZU88EeTd7mQzefzqFQqWF9fN1FybR/bY8tIlFvY3mv7+ekzGMQf7P+P65gdhxeNTZA//OEP40//9E/x0EMPeV7/7ne/iw9+8IPjnm5i6JfOTvjv//2/xz/+x//Y7E8eCoWQyWRMB67VaoaosvOdOnXKJKr0+zs1hO3Qqa48SVoZtlCBOw0OcK+gOFeEzWYTGxsbxrPFTkj5hX4+lUqZQUJDT0NLr1e73Tae4xdffBHf//73dxlHB4f7BTQQrEdKA7O2tmYiN3ydiSuq/1diqxEme4tXYGfHTRsartRQJ+eUjY0NI6vgWNXfN27cQLFYxOOPP46FhQVPsi4TdOnJtok9ACPXsKFVMTQETC84PXCE6prpHSsWiygUCh7D7uAwC6jVasZG07arrFI1+Tp2NffIHs92CTaVZJKQawI9bTrHi60hZhQagMcBpiTZTpLjFs13795FuVz21CEm/P7ei9jyfvyw1+J3UlKsXO0oMe71xybITzzxBL7whS/gBz/4Ad73vvcBuKdB/ta3voUvf/nLeOaZZzzHHhT8OkahUMAf/MEf4Cd/8idx5swZT8jCrhjB19R48DU77AnsrKxYSg2Aqbe4vLxsBonqhdSYkAgz25SDQ8unUO4RCAQ8IRka9Xa7jR/96Ed46aWXsLq6ikKhYDw7zmA5OOwuKaXeU75v6wKV8Kn3lPMDSaTqke3P6a5dnCMajQYajYZZ0PLH1jUTjUbDZKBHIhFPHVEmB2t1HTWW+rfOX2yTerdouJUck9TbORo8L3fMsg30QcHJLRxGhS5CVdpA+6vQse9Xms3vNfu3QiMtSmA59tSLreTXliz4jSueR0vXEsOInl9EaJTPDbvH+xVjE+TPfvazAICnn34aTz/9tO97wI5k4LDATnHz5k185zvfwY//+I/jkUceMeWTSIxZXokaw2w26+lMqvmlR5eyDQCmbBzPoeQ3nU7v8gyzRByfRz6fNzrlRqOBQqGASqViNFSdTgePPfaYZ2CUSiU899xzuHPnjim3wnCnnXzo4HAS4OfVsWH3+UHkjQtXek01IYVlz/QcNK7RaNR4nW05BRff+hp/cyep7e1tT11QJcZKQHkvlHvduHED5XLZyEJoHPkZLrB5PfueWSqOxJpGnwt8ggaciwZ1HvBZcI4pFouoVCp7fgeTYhSvl4ODHxhpWVlZQTabxcLCgum7gHcuUUmTHTnR15TcajKsLYEkOefcwzGuci3d6EslF37eWrZVy87xdRt+i2w/T+2gY4ZhGp5eW55ymBh0j+Mu8McmyAehP5smarUarl+/jl6vh0qlgkuXLnnKFrHTsnNzExBdzXH1p9mgWnBbvcTUMbdaLeRyOWOQ6D3SJJ9+v49isYi5uTlsbGxgc3MTm5ubKBaLxuDWajWsrKwYLxTrLb/66qsolUoDiYMzKA4nDXv1ab/31bDxf5Z8TCaTnvwCkkYlimpUNXGNx/OcaoABmJJxrE26sbFhFrIkq4MMle3hYVJOLBYzr/d6PcTjcUNw1cOtoWSNXnGzAFbq0Llby1WpLETbQW84nQm2F2uU78jBgRhl0TvqecIhtrYAABLpSURBVGywNu/S0hLi8bix2VrJwa7QYuuFdRFK6ALSXoyTQ7A+MMdZNBo144k76dm8ydYla2UMjsdYLIZWq+WpeDXq8xg234yCaX1Xh4mDaO9EdZBnEdrJ2+02rl69ahLWcrkcLl26ZDqwrhDZGenhtb0qdshRk2RIatUrzdqDrBFIgkyyTY/V7du3cfv2bWxsbKBcLhuPc61Ww7Vr1zzhWBXl6706ODjsYJDOTZNZAXhKudGzowaPY5Vl29SzanuSaLx0W9ZGo2ESayjHGsW48dzb29u75qFEIoGlpSVPmUi+pzIS1i3lXKOLfjuzXUmDXo+eZ5WN8ZyHJbFwcPCD3wKNC9vbt2+j1Wrh3LlznhrfwE5/Z7IrX9PzqPYegG8lCLuyQ6vVQqlUMovpbvfefgupVMqUagW8CXM8BxfVtuwpEAiYPIlkMulL4gc9D31tv+N0v58fl7AeJCnf65kNwsgE+WMf+xh+93d/F9lsFgDwta99DZ/5zGeQy+UAAFtbW/jgBz+Il156aeSLTxv2A97c3MQf/uEfIhKJ4IknnsDS0pLZAjoQCJhdZ/i/bvyhnijWJLTDFrFYzFPGhfrBWCyGZrPp2SWLRndtbQ2VSgUbGxtYW1tDuVw2cglqmovF4qE/OweH4wwatlKphEajYbaBV9LrZ1zp5WGEh/KFRqOBRCJhPLC2JpfGTSULlUoFt2/fxptvvomNjY1dHtdBukAbfhU1VlZW0Gq18Nhjj5nkX00WBuBJTgZ2KnrwHlgVh/egjgJgd51lACiXy8jn87sW6o4kO0yCQYvYUWH3PXs81Wo13LhxA7lcDtls1iwodVwAO1ILW+bEY21iy8hMq9Uym/zU63WUy2Vsbm6azyeTScRiMVy4cMHMJ3byvt/40wg1OYMdtfEjvn5tP644SI/1pM9lZIL8ne98x3hDAOCrX/0qPvnJTxqC3Ol08Oqrr07UiGnCb/B0u138/u//PpaXl/H+978fly9fRiqVMqFI7irFTstVHitOMEnOLhujq8K5uTmzOuXqslgsot1ue0okvfnmm8ajxZJPLCOnMg+7s+xllCZdITk4nCRQa8xIjuoBdXxpMo5ut8w651rlwV4c67gHdqJWrFVaKpU8c6WO3VG8JLaXttfroVqtIhi8twOfSi/oNebcxXtjiFc9VBqq9Yuk6T0R1EXPurTO4f6BX+4BwZwdlnbsdrue8U17ruPRT1phn5tOME2+ZU1z3VmSY4ubhGnNcV5HbTx/dOxq3eFheVzD5E57zTP7XajcLxiZIB9H7Zl2buCeR/mZZ55BOBzG0tISHn30Ubz3ve9FJBLxiOvt+oTAjiHSxD3thHydf6s2+e7duybBbmNjw2S1jyKoH/TaoHt1cLifwXGaz+cxNzdniPLc3BwajYbZPQ6AqUmuUgkAHuJMOYYNenyi0SiazSZef/11bG9v4/bt27uM2iRzp73Qr9VqaDQaePnll5HL5fDwww8DuOe1Vg2xahqphdTz2PWV1ZPG63JxEAgEsLm5ibW1NV8PvJ53WPsHfcY+xnmmTzb8vt/9htXt87XbbeTzecTjcaTTaU/SvULzCmwJI/u/De5pUCqVjIOL2zrbC1BGeCi/5HhSIm6TY7ZLo9LMd/Ij9MOew17P9CQSYz8+td/55MRokEcBO1e73cba2hq2trbwve99D7FYDO9+97vxzne+01S1IMlVYqxGhJ02Ho+bTs3V5erqKq5fv46NjQ3UajXU63WT8a5F+oHRQiO2sfQ71hkXB4cdg0uSSm1/tVrdZWzUYNmLVPs1XWyrN7pcLqNarWJ7e9vkEuzX+AzSFXa7XVNJgiSAZSPZbr8yVn6hWT8Dq+Fkhnbr9fpQD/JJNLQOh4eD6D/2+NbXx7WRHD9cTOrCkudTwqqLa00WtokxsLvWMttIDzSTi/e7Oc+sRJcPUmN8UBiZIPuFII7bzaqhIKFtNpuo1+v4/ve/j5dffhmxWMyI7B988EE0Gg2cOnXKE67kOba3t/Hmm296tmCl55j7l2u4RNtgt2ncexj1dQeHWcQoC7pRdXV+IVf1wrAUU61WQyKR8HiMqUHW6/R6PZOHoEZQF8u8xptvvonNzU3cunVrV33jURa9ex1rz7H5fB6lUgndbhfZbBYPPvig2dlTs+eBnWTEYbVgeR+Ad6tcho7z+TzK5bLnWfi1axxMGiVzcCCGRS1Ue8/xwL5LiYUm4Nlzh02kSYiZtJtIJExUR8cMJVo8hnItnkM91FpWUV/n/MJSkYVCwZSRGzZGRh0/R6VVPqzrDeKoB65B7vf7+PSnP21qAjcaDXzmM58xtTpVc3ccYGv8tre3TfZ4OBxGPB7HxsYG2u025ufnDTlmkksgEDC73FQqFeMhHuT9cXBwGA+jjpthYy4QCJhF6+bmJrrdLhKJBILB4K464urZIehxVmLc7/dRKpVQLBaxtrZmdpizNYb7vcdBJKDX65mdtXq9Hs6dO4dz587tqq+qekbbUPB+VcNM6Ver1cLW1hY2NzdRq9X2bKeDwyyg37+XFJfL5ZDL5ZDJZHbtljdIa0/PsJ8kg3af2v9AIGAS8Ofm5lAsFg0/yOVySCaTA+Uk/DyvZ3ueubhtNBqoVqtmnI9y73ovB43j4g0+NInFz/3cz3n+/9SnPrXrmCeffHJfjZkF0FC0Wi3k83kEAgFcv37ddF6tgaqDzi+M6eDgcPRgZIdbPZ89e9bokkmS7VJumltAUkpvUSAQQLFYxJ07d0xVGtsbNUqCzKQgQa5WqygWi8hkMkZjrQl7KhWj4aWXW7XVJNqMqBUKBdy5cwcrKyvmXvZq8zie5YMyrk5mdn9CIz9zc3NYWFhANptFKpUyx/jV+B4UyeDr1OvTg8wITTQa9eQQMdoUCoXMdYd5Lu3qMXpdvkZ5RblcHnvDtb3Glxsjo2Nkgvzbv/3bB9mOI4XqiPxWYvytW9ban3dwcBgdhzVmlLixBNwLL7yApaUlPPbYY7vkBxpi5XvcgW5lZQXVahXr6+tGm9tsNndtJLJXW8Zpu5+nSwlBq9XCnTt30Ov1cObMGSQSCdNu1mXlbp4AsLq6aqJdtVrNhInV+8xkpEHtHpYgNCr53a/UzMGB0MgP+3W1WjUb5fglyAHwjHOOK3qQGTVimUiSZfbTUCiETCYDADh79qx5PZvNIpFI7Io4D9JF6/W4oC2Xy7hx44anjvm4EoqDxrjXOax2TXvxfV8l6Q3zMEy64nITu4PD8QDrim5tbZmSaQRroQM7iTL0HnOrWOp/V1dXdyX7HeYcYUesKpUK1tfXTWiXCUL0KtOzzNKTukNnpVIxBJnnUyNz2KHb/cDNxScbo+hwGQFmHWGOBb/+7Kc9HvS3nUsUCNzbzIP5Snxfq+QA/lvBa1s0X4L11+k5HiTZdDg83FcE2XU2B4f7E+p1LRQKKJfLWF1dRTKZRDKZxOLiogmVsqYxjyOxphRBNYGjeE+HeVxHbfcwYkC5RT6fRyQSQTqdNsezio7u3mV7ye2kpFHaeFw0iA4nA8P0vIpOp4NCoYBEIoFsNotYLOaRQTJB1+7/dvKekliOeXvLaEaTY7GYIciUatmSSx0vuhjVTYxarRZef/11T06Dfe9+XnB9Dva4HLTY9Xu+g+ap4zTONeo/Db53XxFkBwcHB7sQP/WEkUgEAIwnp1qtolqtDk3AnQTTmLz9wrSNRsPcC9+nrngQER61XcfJSDrcH/Drk9TSVyoV5PN5pFIpRKNRT//XDTj85JV+XmZ6kalJ5rV4Lp1TbEJrk1RtAxfe3ISEtZVHvd9h1TwG/T8OjtO4P4i2OoLs4OBwIrCXZ0VB702lUkG1WvVsF+t3rr2I47ge1XGOtTWMg85FY9tqtYaeQz83qCySg8Msw2+ss2pLqVRCu91GoVDAwsKCkUD0+31PWTYSWibY2ZEhklv+sMoLvcTchbdWq3lKJTIB2J5LSJ4paWIxAG5fzXnIrgh2GGNyVPJ9nDDK4mEvOILs4OBwYjCJt8TvM6OGJolhk/GkhmZSz88gzeO0DJ5f2NjB4ajgJynodDqo1WqmdCulFtwFV8syUlrBShdakUYlFyS1JNK6Wy5Bgszz6CY+CkaptKxiqVTySKAcDgbjzFmOIDs4OJwI7CUfGHacH6kcJfFunBDnNMjkMFI6LBlomCd91OvqOYd93hl3h4PCqP2f2zRvbW2h3+8jm82a0mqUNzCC4rflvH0dkmst78YNiOiVDoVCaLfbnk2ItHqGrYVWD/L29jaKxeLYJd0cDhaOIDs4OJwI+JHacb2/45YomyThbr+wNY5+fx/UtXmdcT3sDg77ga3hHSYJ6Pf7xut7584dbG5uYmlpCclkEgsLC0YawVJskUjEJOHNzc2ZnfcCgYAhv8Fg0JBZXSyql1l/7PYCO5KOer2OWq2GtbU1lMtllEolU03mqMeSiwp54Qiyg4PDfYtx5QdHbcBsTNOgDfJA+8Em5rP2XBxmE/vtK6N8XscEt0yPRCLo9XpIpVKerZ4phyApJklWqQavS40/rx8KhTw6Zm2jgvpkTQpuNBqoVCpmR06VfDgcLMbpf44gOzg4nAhMS3983DCuYR1XVrHXNU/CM3Q4HEyjr9gRDPu8mlwH3COm6+vrpu55OBzG3Nyc+c0a6PQmh8Nhs2ueSiOoN6anNxQKmQo3wWAQ4XDYs2MfAF9vcqPRQKlUwu3bt031C78KGn73tRfGWeTu9dlRPzMuqT+oSjkHscBwBNnBwcHhPsE0CMogY+7gcJQYpvWn57ZSqSAcDiMcDiMWi3m8xcA97XIkEkG320UoFDKbflBuoSXd6HEOBAIIhUJGsqF1lvX6LOdWLBZHTsjbj8d9XLKoCwu/RchxxyTP0RFkBwcHhwFQAzWNskEHhXE9TQrbE+x3z44UO0wDgwjfOGRs3D5ID3Cr1cL6+jqi0SgikQgymQyi0SharZa5NkkuyXM8Hjfl4yin0B02+/2+8UInk0njddZkO5Lqer2OarWKN99801MabpT2j3Ov04BfpZpBz30/RHraBFzJ/TTgCLKDg4MDdhvv4+o9Gdc4DAvrOkLsMG3st+bufggQiS0AswW7bjOvJd20RJt6jLVeMo+nRIMEm15lvgYAhUIB+XzelHebdexVI33S6jwHOa86iYWDg4PDAWOWvcUODg47GJeo0aNrbw7CRDt6gJUgkxwD3g1GgsEgotGo+bxWsSBBJvL5PNbX19FoNEb2Hh82jsJbPS0cRHscQXZwcLjvMcrkOqyu8CgJa9Mow7ZXPedRMUo941mEq5jhAIxXRWXQe0y6IxmORqO7yshxExBbT0xpBathsL4yP6865UKhgEKhgPX1deTzec9xs0YyTxr2O184guzg4DCzGGeCOyzyNCqZnlZbBl1v3Gez1/uzTjwdmXAYhHGrNbBsGz29rEpB8mrvrkdNcTAY9JR+o+aY5+UY4jGtVgulUglbW1uoVqsevbPDwWFaZSgdQXZwcJhpjDrBDdIPj/vZYeca9TPT8O5Oy8M0Skb6UZPjSUK7fiWtjvo+HI4HNAmt0WggEAigVqsB2EnqswkwdcbUFSeTSXM+yi54zm63i0KhgFdffRW1Wg21Ws2Q5km1uw7TwTjP3RFkBweHmcV+knGOug1+5xkkzRh23DjJg34k0S8j3e/9o8QoUpW93puF+3CYTQzrO+r5VTIM7GzyYZNmaphZ/o0bkVDfvLGxgWKxiHK5bLajPgkJwPcbHEF2cHBw2CdGJWfDvM6DXh/1M5NsJDBrmERL6uAwKvaKLNnlDActxrhxSLPZRCKRQCgUQjQaNbrmP//zP0e9XveUe/Nrg8PhQJ/5OAmSjiA7ODg4HFOo5/R+JpD38707TBd79SWNxDQaDWxsbKBUKiEWiwG4R8C4w54dtXHk+OjgNgpxcHBwuM/g5AUODuPDT3YxboSn2Wyi0WiY89E7qeXdHGYLToPs4OBwZDioCg7TJoHDyrb5XX8UjFrmba972UsrPKyI//1Elu/X+z4umBWP6bSqvewFraU8jfM5TA+2rnwUHAlBvh92aprVZJj9woVz71/s9d2PUgt42Hnt8/i9N4zMDoJfzV+bxPvd23537Jr0+GHXHacix0klj4Puy81Ns4Pj9F1Mc5wMmxuPw7O4HzBO3zwSglwulwEcj8EzKU7yvQH3vsNsNnvUzXA4RHDcAsMTXPaLUTSA+z3fJKXf9otJE/kO8pjjiL3uy81NRw/OFcdhS2WH+xOjzBOB/hHMor1eDysrK0in0y4EcczQ7/dRLpdx9uxZp7O6z+DGrcMsw81NswM3VzjMKsaZJ46EIDs4ODg4ODg4ODjMKtwy28HBwcHBwcHBwUHgCLKDg4ODg4ODg4ODwBFkBwcHBwcHBwcHB4EjyA4ODg4ODg4ODg4CR5AdHBwcHBwcHBwcBI4gOzg4ODg4ODg4OAgcQXZwcHBwcHBwcHAQ/H9yAcUs52QerAAAAABJRU5ErkJggg==", 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", 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" ] @@ -712,13 +714,13 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 30, "id": "dd03417f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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85CMoFAq46qqrtjXXSqWyoSrn5MmTKBaLoVLgfD4fKZs2w8mTJ7G8vIwf/vCH7rUzZ85suL5rr70W8Xgc7373uzd4BFU+dDuHJz3pSZiensYnPvGJ0DX89V//NX72s5/hec973rauY9BgnoEd4LLLLsPTn/50vOY1r0G9XsfNN9+MiYmJkJv61a9+Nf74j/8Y11xzDX7nd34H586dwyc+8Qk8+tGPxsrKijsum83iUY96FD7zmc/g8ssvx/j4OB7zmMfgMY95DK644goAwBvf+EZcc801SCQSeNnLXoarrroK119/PW666Sb8v//3//DsZz8byWQSt99+Oz73uc/hwx/+MF7ykpe4utmbbroJz3/+8/Hc5z4XP/jBD/DXf/3XmJyc3Pf7BgDPfvazkUql8IIXvADXX389SqUS/v2///eYnp4OCc0jR47g93//9/GhD30Iv/Vbv4V/+k//KW677TY3d7UW/uAP/gC33nornv/85+O6667DFVdcgXK5jB/96Ef4/Oc/j3vuuefArtcwWPiDP/gD/PZv/zZuueUWfOxjH8PTn/50PPaxj8Xv/u7v4qEPfSgefPBBfOc738H999+P2267zX3m85//PH77t38br371q3HFFVdgYWEBt956Kz7xiU/g8Y9/PP7Nv/k3+LM/+zNcd911+L//9//ixIkT+PznP49vf/vbuPnmm1EsFrc1z1/+8pd45jOfiZe+9KV41KMehaGhIfzVX/0VHnzwQbzsZS9zx11xxRX4+Mc/jve+97247LLLMD09vcHq9vGyl70M//bf/lu88IUvxBvf+EZUKhV8/OMfx+WXX45/+Id/cMdddtll+KM/+iO85z3vwZVXXokXvehFSKfT+N73voeLLroIN91007bmkEwm8b73vQ+vetWrcNVVV+HlL3+5Ky08ceIE3vzmN2/rHg0cDqyO4YDQqbQwn89vONYvhWHZzgc+8IHgQx/6UHDJJZcE6XQ6uPLKK4Pbbrttw+c//elPBw996EODVCoVPOEJTwi+/OUvR5bd/N3f/V1wxRVXBKlUKlRm2Gw2gze84Q3B1NRUEIvFNpQZ/vmf/3lwxRVXBNlsNigWi8FjH/vY4A//8A+DBx54wB3TarWCd73rXcHRo0eDbDYbXH311cGPf/zj4Pjx4xdUWvi5z31uy/uq93B2dta9duuttwaPe9zjgkwmE5w4cSJ43/veF3zyk58MAAR33323O67ZbAbveMc7gpmZmSCbzQbPeMYzgp/97GfBxMRE8Hu/93uh86yurgZvf/vbg8suuyxIpVLB5ORk8NSnPjX44Ac/GCqBNBguFJ2e9SA4v95OnjwZnDx5Mmg2m8Gdd94ZvOIVrwhmZmaCZDIZHDt2LHj+858ffP7znw99bn5+Pnj9618fHDt2LEilUsHFF18cvPKVrwzm5ubcMQ8++GDwqle9KpicnAxSqVTw2Mc+NvjUpz4VGkdllA+VLXNzc8HrXve64BGPeESQz+eDkZGR4ClPeUrw2c9+NvSZs2fPBs973vOCYrEYAHAlfpvdgyAIgr/5m78JHvOYxwSpVCp4+MMfHnz605/eIE+JT37yk8ETn/jEIJ1OB2NjY8FVV10VfOUrX9lyDn5pIfGZz3zGjTc+Ph78y3/5L0Ol4EHQvcwfJMSCICLLzRCJe+65B5deeik+8IEP4K1vfetBT2cgsbS0hLGxMbz3ve91TUgMBoPBcGGwnAFDz6JarW54jfkOUa2eDQaDwbAzWM6AoWfxmc98Brfccgue+9znolAo4Fvf+hb+y3/5L3j2s5+Npz3taQc9PYPBYOgbGBkw9Cwe97jHYWhoCO9///uxsrLikgrf+973HvTUDAaDoa9gOQMGg8FgMAw4LGfAYDAYDIYBh5EBg8FgMBgGHEYGDAaDwWAYcHSdQLiTHtUGg2F3cRhTfNhL32SIwbD/4Lrbqi27VRMYDIZ9xWEnBZz/bhKzvRxToePr+4eRZBqi4e+Y2+13a2TAYDDsOw47IQD25hr2+r50Gr8fvg/DOnZCLi1nwGAwGAyGAYd5BgwGw74gCIINLsxOrmqzVA2G/YV5BgwGQ8/BYtgGw/7CyIDBYNhTdEpmMuvfYNgbBEGwbUJtZMBgMOw5zNI3GHobljNgMBh6BuYtMBguHH5uTjcwz4DBYOgJ9BIR2Imb1WA4zDAyYDAYDAbDgMPIgMFg2Hf0khcgCrFYrOfnaNhdDLonyMiAwWAwGAwY7PCQkQGDwbDvGFSBa+hN9OPzuF3PlpEBg8Fg6CHspmLqRyW3m4jyBAxqeMhKCw0Gg6GHsJvKaFAVW7ew+7MOIwMGg6En0SvdCs1yNAwCLExgMBj2BTt1WR+08tXzH/RcDIa9gnkGDAaDYQsYCTD0O8wzYDAYehIHHRqw5DvDIMHIgMFg6FnsByHwFb//t5ECwyDAwgQGg2Fg4St+CwfsDizp8vDBPAMGg2Eg0Y3Fb22JdwfmXel9mGfAYDAcalDRUGnvpCTRKgZ2D50U/4WWivrfc9S5osaN+txOzr1bc97NeXVz7m5hZMBgMAwcfEG514J5kLCZ4uv0/l6ccy/P148wMmAwGPYF+yWUfctxP5WBkYpo9MP9iHqWtiI5B0lSYrHYtrwDljNgMBgM/wirHjAMKswzYDAYDj02s7KiLLqtrKadurX9vIV+sIh7Gft5f7dr+SsO4nnYLqk1z4DBYDjU2KmA7bZS4EI8BeZl2F3s9fe129gLT9Neea/MM2AwGPoW3Vjq280x2K6VZ/0Ldgc7qQw5KPQSIekW5hkwGAwDg62squ0oEo7VjaV2GJXDoKLT97lZyWSnDpbdjrGduexV7wvzDBgMBsMFYivh3AvWqqE7DOp3ZWTAYDDsCw7SXb6bLn1NPuxUajaoCqVfsVslgr38XFiYwGAwDCS66ZTXCdam2NDr2O7zaZ4Bg8FguECYR8CwFYnc7rNxoXkm1o7YYDAYusB2O7QZDMCF732x3XPsF4wMGAyGgcVuWfLmERgsHIYyR2tHbDAYDgXMKjf0Kw5jTomRAYPBYDAYusRhU/LdwsIEBoNhoLCdLoPETvey71fFMeiIShg97N+1kQGDwTAw2M3ucAZDP8HIgMFgGBhs13q7EGvvsFuKhs7ox0oUIwMGg8HgwRS5YSvsxzOyn8+hkQGDwWDAurVnRMBAdLL++/EZMTJgMBgM/4h+FPKG7Sn1fnP/dwsrLTQYDAbDQKJXFf9B9Ckwz4DBYOgJWH9/w37BnrGNMM+AwWAwdIHd2DimVy3R7WAvr2GvxjblvzWMDBgMBkMXuFCFchhb1EZhL6+hF8be7Lio7zCK5B1G4mdhAoPB0DPoB2VpOPzw+wh081zulfLfrwoXIwMGg8FgGBgchh0Hif30LhgZMBgMPYFeEL79it3Ya6EfsBfXHRU22A3sd5jBcgYMBoPBYNhlHDbCZZ4Bg8FwIDhswvIgsN3dFbc6ZrPxBrW0c7dj8rt9//br+zDPgMFgMPQgopRAt4ls+rNd8DOHMSP+sGIv2h5v97szz4DBYDD0GHaqBHbLuldF0o1n4TD38O+FOXbKO9hPb42RAYPBYOghbFfw72dy4HatTdv46eCw3W2WLUxgMBgMPYLt5gj4Vvt+Kt5uzmdEoDv0QjjGPAMGg8FwwOiGBGzmou+ma95mRKLTHHyyEeW23m5DHh4fpQD3gjz0emJkVEhgs3u0V9djZGAfEIvFkEgkNjBp/0tlwk673Ua73d4wRhR6gVEaDIa9BWUHf+Lx805dyokombFddKPod6qAtuuyNmyN3SYFRgb2AalUCkePHkUmk8HQ0BDi8Tja7TaazaZ7Px6Po1KpoFKpoFarYWlpKbS4k8kkUqkUgHXS0Gq10G63HYEAgFartf8XaDAY9hyZTAaZTAapVAq5XA7xeBxDQ+dF+OzsLObn5yOtfcoGJRFqjbbbbadQ/N/bSQzcStl3IhZ7TRJ63SOgiLpHm92f3bw2IwP7gEQigeHhYRSLxRAZWFtbQywWQyaTQTwex/LyMuLxOGKxGFZXV0PMb2hoaAMZWFtbcxYBF70xcIOh/xCLxZBMJpFOp5HNZjE8POxkQjweR6lUCh3P0EGnUID/vv6/3RCAHrtfssfk3O7DyMAegos3nU6j0WigXC4jlUohmUwimUxieHjYhRB4/NDQELLZLMbGxgAA+XweyWQSmUwGuVwuFGpoNBpotVpoNptoNptYW1vD8vIyms0marUa1tbW0Gq1zFtgMOwzdsPqpeU/OjqKdDqNQqGAbDaLdDqNfD6PoaEhZ0gA52UF5UG73UatVkOr1UIikUAikUA6ncbo6CgSiQRarZbzLjabTTQaDczOzqLRaDi5EQXfNb2dMMJulh/2AxHo5hmxvQn6BMlkEiMjI0gkElhbW0Oz2UQmk0E6nUYqlcLo6ChisZhT2KlUCkNDQ0gkEo4YzMzMOCGQzWbdewBQr9extraGtbU11Ot1VCoVnD592oUZKpWKIwwGg2Hv0a2bd6scoHg8jng8jkwmg6NHj6JYLCKXyyGbzTrjYGhoyIULCoUCpqensba2hlqthmazicXFRdRqNWeQDA8P4/jx40ilUmg2mwiCwBkSi4uLaDabWF1dRalUwtraWmg+na5VSUGUR2GzHgW9otAPuvyx2wTMvZ6jkYE9QDqddhY+lXo6nUYikUAul0Mmk0E+n0c6nUY8Hnex/5GREQwNDblFkkgknFWQTCZdEiJzDfQ4WgkjIyPIZDJoNpuIxWLIZrMoFoshdyAtiUajgUqlgna7bYTBYNgjbLYdbichn0gkkM1mXY7A0NCQMxS4fjlWPB531j9wPpkwHo8jl8u5z+j7PoIgwNDQEMbGxpwHstFohObK38xRIulguLOb5MWDVrqdsJ059VJlglUT9Dji8TjGxsYwOjrq2D0XGr0BhULBKW9l1DMzM25hp9NpAOet/3a77dx3a2trToFzfOYdZDIZFItFNJtNFItFLC8vO+IBrCcXUjCcO3cOd9xxBxqNBqrVqhECg2GP0K37nH9ns1lMT0+HDAcaFvF43CX+qSdByUIQBMhkMiHZkUgk0Gg03Gc1RJBMJvHIRz4SANBsNkPhBQ1l8r3Z2Vncc889qNVqmJubc8aHXmtUKCGqvHGrHIWtSiK3ur/bRVSVVy9hr4iIkYFdBFl6KpVCJpNxr6dSKaRSKaTTaWQyGefuV7LABCFaACQD8XjcxfDW1taca89P+OGijcfjSCaTyOVyLiwxPDwMAC5OSC9DuVx2bkaOvVVcj+e80DImg+FC0YuW5oXOieSeoQB6D7Ws0D+ffrbdbiORSKDdbiOZTLq1Src/SYBWIbXbbefJZD4Bx6GconxpNBouHyGbzQIAhoaGNngrt3MvesnaHmQYGbhAqEJnWKBYLCKTyTj3Pb0ALA9Mp9OhxcjPB0GARqPhkgEBOCLA18jY/c1EdEElEgkcOXIEU1NTIcWtYYVEIoFLLrkE4+PjKJfL+OUvf4mVlRU0Gg00Gg1HLCiYSDKSySSq1SoWFxfNk2A4UBx25cF1q70D8vm88/AxeZiVQs1m08kbKn4N8XEcv/dAo9FwLn0NGfjVAzQydF3zfcojHpvP53HJJZegWq0iFouhWq26pGV6G4B1chMlh6Luh6KTZyHKYt9NYthLz1VUhYc1HepRUPmSDFDZp1IpJBIJV/pDUsAfWvr8vCYZMi9AFb1mCdMNqCSAC51zKhaLSCaTqNfrqNVqoWPJ9LPZLKamprC6uopz586h1Wq5h0+FDvMd6NmIx+NYWlo6qFtuMPQNohIGmTDMXAEAIeNBcxBarZaTB77ngESh1Wo55VytVp1c8huhKRHo1PyMYQqGPDOZDEqlkgtRVCqVkPwiGejkSdzMExml4PXa/VyMnRCCTgTkMGC3PWNGBnYItZqTySSy2SzGx8fdYmY+AJN9KpWK6zfAsABw/gutVqsAwguebvtqtRqK8zEJkS47Ll4KDbrrSqWSsx6o5LmQVQjQnfiQhzwEExMTePDBBzE3N4dcLofR0VE3bjwed16LZDKJfD7vyItaHHpd9XrdvAcGQwdwLbLqKJlMYnR0FLlczpUlRylDGhCafExZQKOBXkauzbGxMefdo9zyEwppnKg1z9/qVVRDYWhoyPVP0eTDSqUCYL2hGo2fVqvlvBRq3KisUkPHr1jwcaH9BjqRjq0+s1+4kGvb7meNDOwQVH7MDygUCjh69Cjy+bxbMJqkUy6X0W63MTU1FaoMaLVaWFlZQb1ed59bW1tDtVrF2toa5ufnUa1WXXlQPp93SpqhAwqHdrvtEg5VSWuogoSA526320ilUnj4wx8OAPjpT3+KVquFyclJPOxhD8PQ0JAb89y5c5ifn3d5CExmXFtbc14DzTq2KgXDIKKTcvGFM4lAPp/HsWPHkMvlXJiA60etdSpGbUDGtcm1TC+AyoJCoeAqitR40KoE5gEEQeCqoXh+dfFTuVOGsA/C8PAw6vU66vU6Go0GSqUSYrGY64fA12u1GhYXF105dLPZxNDQEJLJJJrNJqrVaig5UnMlOilhNUB24kLvNrlzu+PuJqy0sMfgJ+pxMVARsg4YQKh3OJsA8WGfnZ11VoEqTHXRERyf5+BiVKhLj+9xsbFJCV2JZN7K+HXRFQoFTE5OYnR0FNlsFvF4fAODZ3lku912ZIDCiK7OIAiQSCRcmIKCxhIPDYOAburzU6kUstmsW08ML7IPADP3uf4oSxgaABDyDmqID1i34unC9/MBdJ40LFhm2KkMUedPmZJMJkPKW8OVuVzOkZ61tTV3LPMKmLxI46ZarYY8BCQQO7n/h8nlvxU6eUV2E0YGugQVf6FQcCU6zWYT2WwWExMTGBkZwczMDHK5nHPXkcE3Gg3E43FUq1WcOnUKP/7xjzE8PIypqSknELhYuSBJOqampgAgZHnTva8LmZ9hcuKZM2cwPz+PI0eOYHJyEgBcuMEnNBwjHo/j+PHjOHHihHuv2Ww6zwUX7sTEBC6//HJ3bc1mE/fccw9uv/125PN5XHTRRUilUo75nzp1CnfddZcjKL1WqmMw7BZ8KzVKYFPZjoyMYHp62skQkv5kMhkq911eXg4pRPYTYWJfu912ZELLktPptCsxTKVSoYoCknISjUajgeXlZUcmGMrUfAS9Phoz/M15KdnQsISGP2kYcCzeDw0h0MA4ffo07r77bmds6Bw2u/f6ezuW/1bohcqHvTq3kYEuwEWhzYP4OssBtVJgbW3NLQS689LptIubra6uIh6PO9edjknQCqfiptUARO9QpolFXHhcmFTs2mZUrQdaDLFYzJU+tlqtUMyfJELzI9LptLNUFhYWQr0OGD5ptVpYXFx0cychMRj6FVEluqpMtXyYnUXZplx7BShR10z/RCIRSjRm91KO7xN9jfNrwrEqT7/k0J97FLHRUkaSCM1Hisoj0qRI7WFAMsCGafxZWlpyRIaGkn8/TZ7sDowMbAKtCKCSnJmZcYk9VLB8SGu1mnuwWeNbqVQQBAFGRkZc8x9m+rMUkT9RWb2aW8C8A4IueX6mXq9jbm4OrVYLY2NjOHr0qIvFaYiAeQYUPvQ0sKSQC5FkgJUHujnKysqKE1wML5w4cSLkYiThmJiYwMMe9jCsrq7irrvucgmTBsNhgq8Mo0q+/CofNSCYs8Muofl8HoVCwYUGaFSw2ohyhMfwXPl8HhMTEwDWLXuOwXkACFnS+uNbzjxGXf0sdVZywP81bKHXrWNRLvhzYCUCgBBx4LyUIAwNDeHYsWMoFApYXl7G7bff7mSH763wz+9f306+3yhE3budjLPZ+Ft9fq+8E0YGOoALgg18uHCPHDniLOBkMon5+XmcOXPGubbq9bprA8p4VyKRQD6fd8ycrnxgY+cwsm0qUn7hGjsj66eQ0ZyB+fl5tNttzMzM4OjRo6jValhdXQ2xfv7wfMB6QyL+zTwHHpdOpzEyMoJisei8GwBcPwWWKepYuoHK0aNHkU6ncd999+3TN2gw7A62ckvr/1E9PaioaQRMTU25LqT+j/YB0G6kKh8KhUIoTNBqtVxSMr2PUTX93V6HWuqq/Hh9mxEBQnOmKAuo9Lm3SpSngURAPbETExOYnZ3Fvffe6xIMt7qm7aLbcbrxRGw3X8EfLyossx8wMtABbCucz+fd9sMst1OLemRkxIUEyPw19gWc/3IZk2e/ccbOCCprPmyaFATAlRSqZ4CLVpOKxsbG3AIsl8uhBCNf8ZPl+9aCTxr4XqPRcJsjcYxareYWcaFQQLPZdN4QQknLpZdeinK5jHPnzjlCYTD0MrYS7r5yisfjGB0dRSqVcknFWs3DigEt0VPlqT9M2NXEPq4nTRhURe13GATWw4FM4NPPNJtNZ4lrBYNeN+enSp2Gi4YnCZU1PMbvkaAWLsfS+8hxKDdZrbW8vIxyubzh3vvoRpHuRZhhuwp8M0/GfuYoGBnogKGhIRw5cgRHjhzB+Pg4JiYmnNtOLYBisYijR486hU9mznp/AM4Fz3rf4eFhlEolLCwsOOIQZbkr6I0A1heYJvnROpiZmXHHLCwsAAi3SY7H4879zzlzcQNw3gudiyp+vs7QAxMCR0ZGMDo66ioHmIOgpY3FYhHDw8Oo1Wr4/ve/b2RgwNGLGd+dlEPUa36yILDu6Tt69ChGR0eRz+eRy+VCn2MfAU0y1LHohuc56AWgN5LnUaXKpGVtW65Jd5p7pNeiTYsYUlQjQUmA/5vv674ESk703mkFBO8xz0UPrB8CVfkxPDyMkydPolKp4Be/+AXK5XJHD8F+5xHsxTO8V2GIzTDwZICtdjXphm5xLj4+rL4bkMkzas3rbyC82xcAt2B1IavyJXv2wwT8LKFMmjF+zewlfEHD8aN2QPPnqe5AXrvf2ETnxnlwflHWAssVC4UChoeHd1w6ZDAcNHyBzTygdDqNXC6HXC7nEgN5vK5thd9JUNc6laLCTyD2CYkaCxxfG/roOtc251HnUhnk5xD459ex/eM1NLoZOIbKQHpEgiBAPp934cp6vb7pWN1gt5Trflrxe4FY0CWFOqwXuBlYJkfXPct62CiDi3l4eBjj4+NotVpux8Bu7ocfkwfgynwYk/friJlEw94AtOYrlQoqlYprewzAlfWxlp/Mn3E5f1H7c9BeB2oRaE2zjkWCwKZI7Xbb5UewkkLB2KW6FenduO+++zA/P48HHngAd955pzUn6hL7afHsFnyrTQmqb0Hqa70oXPX+63ql1++yyy5DNpt1SX9AuFmQb7X618j1x/WiMkDJuyp+fq7TZkFKFHh+EgDuRcK1yfNpFYB+X0yY1jXNY7iGWS4NwJVH6vGUS75ngPNSMsH+La1WC6VSyTViK5fLOHv2LO66667Q+JqMuFfwx96t5zXK07HZa90mGmou2GYYWM8AH0ptFsTs/uHh4VCXLWA9lucrOJ+R83enOLzuPEjrOoptc3wKGgCuTlitB+YEkEkro/bnov3CtY6Y4FzUK+DfA5IX9jpX7wSw3lVNx9T7Qw9LsVhEu912VQm+RWEw9Bo2W6NMdhseHnZGBNejbxHTexgF3T64k9dOwwuch6/wCYYRtdWwnofvafKirnl/TMocHuuHeqLkWRT56XR/NTzJ1zhvyhWWLq+srLg5dLr+vcBekY39Dm/4GEgykMvlMDk5GdpsgwmAGjOn8kwmkyFXdlQyjR4PwFncfmmNgsf6/QPa7TaWl5cRi8Wc5c0FG4/HnbtvdXUVlUrFtSj2x2cYg/X+JBCtVsvtNsbPsBsYF52SChIYEijmAADhXAZeE3sJ0PrgYg6C80mNDBfQkxCLxVAqlXDfffdZ2eEAYSuhfdDCUeErqKGhIUxOTjpDgkmBfKb9xjpqadMj6BNnYD25Vz0JSsx90qylxz7B8HOKou63b3DQaPDH8UsFeSzH4P9+4mLU+5ynEhLmI1F20BvJY9SIofdzcnLS5RGcPXsW9Xr90OcL+ON1+s72AgNJBjKZDKanp51XgItSW2Xqb7rGgHCjDWC9oQeP4yIHNi7sTrEyvsdFX61WXYIMFwYXLRd3u32+FXC5XEYul0OxWASwrrg5Fyp3bfShip/uf47JjGd6PzSmyFAAhSFdiyQb/FEyoOdpt9uu+kAzqpPJJBYXF/Hggw8aGRhg7Kfg2ym4JuPxOCYmJjA6OorR0VG3/lRmMEvft6C51a8mA+r4mvQHhMt+1Qr284n4OT9pkAret/p5bvUe+pY8z+l7CqIsf/7tdwv0PRf8m4pePZHMAaCXVsOn6oWJxWIYHR1FPB7HysoK5ufnHQEj9vLZiSIdep299tx2g4EjA37sin3BuWjVTb68vOxian6cjA8CFSsfVDJaLjJ2I1QyoB4EPkB63lgs5vr783ht0AHAlRxRmZLN+7FYLk5dpAxRsEMiLX0KOX5erQ6+xvOwCYqSIBWEUVYCx1Krh2PlcjnMzMwgn89jaWnJlQ4ZBhu9Ilgp5FkiyKZBzC8iuI40JKeZ8TyG1T/0qvkJxFHXrN4BygYt2+M69bPyfStdLX0e73cp9MMQ/K3dV/0KA/U+cC5RHgv/PU1M5v+aV8DcJQ0h8Lpp6Gy1n8J+ohee151goMiAJpgA670ERkdHNzBe9uquVqtu0dLlDqwv3lqthnK5jHQ67fYa4Lk0s56urai6fz7Ymo9QKBQAwG1wBKzXCvNzo6OjbqHTRaYbGanHQi31Wq2Ger3uwgua1KPEh0JLEyYpkLiNsm44op4BJgapcKEbkPeHr7PU6pGPfCRqtRp++tOfGhkw9EyIgAiC8w3Djhw5glwuh/HxcQwPD7tnmQ2GAISUOhUtPQHclIe7kAbB+nbfagX7hF6NEL5OMPav8XpWSikZ111LVZaoxR3VY4DHUfnqa1HeBHpS1fBRzwGw7vGgEdFqtULVAa1Wy5Ev32gi8WCyItu1R5GPrb5T3pvtoNOzqd9Z1JhRn+v23Nud63ZDJgNFBhRcILpjls+o2YZYmT8Xgc+2lT37ri0g+uHwE2/0dbrq9T1dgCpo/AXARe6ze46p3c38eei1qHuS1+y7+zTPQR9WtT7UDRp1zUqK9JwGQ69hs6x+tZw1GU9/eKzvruc66STo/fP52MyD4o/tyxrOR70Fek2+pyLq/6jXt0oIpozSfAV6VpS0aHI0sJ5rEOV53C/sd27CfmCgyIDGquj+p5Ks1WoolUoA1mNtF110EY4dO+ZyBjQbngyXe39rcl+1WkW9Xg+VzGjCnvbu1/CAsnQ/jsiSQn34NfSgLkJdwOrVoGchlUqhVquFth4lyLZrtRpWVlYQBEFoV0WdL7Ceo6BWBy0Mnpdgg6V2ux2Kl/L+33vvvVhaWsLS0tJefP2GQ4aDdLf6cW4+36owtbyW4QI+10xKpkJTFzy9BxyD4/O3v/419KAhOyCs2DV/Sdcj1yy9j74ip/xiD5BureuoMAINDvV+dsp30ARJGmO6BbwmWKrRwU6o9IaUy+XI+XZjSV+IUt/J83khz/Rer4eBIgMEHz7NdKc7ne/H4+cb42QyGZTLZayuroYsXd+Fpux2bW3NucPpVeCDzFi7CgktMSSi2Lda0ESUNcGHWz0A6sJUBc7juZjUtc9roJtOx9ZkJWX3SnaYlMlro6DRsAM/0263USqVsLKy4s5rMPQKfAuZ65ykV4k+FR0QJvYaTtBkYP2cv+75OYLr1lewfvjT/6zv0vePISnXdRzl/td5+Oegl5LrXN/3j+c10yvrj0m5pl5ZyhGSAP19IVb6birZnYYdegEDRQb4ELKBBRU5dyRkEx8mxK2srLg++0wMZOyfD7AyVp6Di63ZbLrzaCavKkjGydbW1lCpVFCtVpHP5zE1NeVCFUo2AIT+5kLoRBxITJjvEIvFXGmThjx0e2PgvCK/6KKL0G63US6XUalUQlm/PnkAwkRBhQ29LrweLVNqNBpYWVnB6urqBpJgMBwUfOWiLutqteqSeukdJMGmB1Dj4apYKRsymYxbR5pgrJa030xHQw2cI3OQlHRoBQ8AJ7tUufvkIBaLubwjJTz+feD7akDouqeyZuzfn6/mP/hQuaX3g+TCT1SmzOX95P0/yAZmh1l+DRwZoPIrl8sukz6XyznXE9vjauJdNpt19f4ME+jCo4WgrJ1kgAuMuQdkxOoaq9frqNfrWFxcxMLCAiYnJzE5Oelc9n6mrJ5bPQq6wLiImOBIAaWdBHUMMmy+Nzo6iunpabTbbdx1112oVCpubBKYWCwWyqbm+JowCJwnA9yXfHx8PNSDge+RbBzmxWToHr36PUdZsfxN2cGkXrrESeb9cjog7BrXDqB+BZCG2HishjF5nIbrgPWOgNr7Q89JTwSVpe9J0Lwfyjta7Op9AOCSggklLXxfGxmpF4TH+zJLoUaT/zlfRqk3gAnaPIZEJCqPYrPcikFH35OBWCzmWvsqMw+C8xm83BaTxwZB4HYH1A13ADhLn8hkMm5PAH6ev9X1xlwDPty6aNrttmuDzOS+YrEYYu6+pa3XBiB0fv6QQHChq1WhcX8VfhpvXFtbw8rKyobPk+T4FREqjPzQSTqdDlUtaIgkk8m4/d0pAJeXl1EqlRxhajabWF1d3dAMxWDYK/h5ML6VCqxnzJPMa9Ib8wVUAStZ0PUdFf+Pgh8K0BI87SXC4zgHv49Jp7H9udETAoTDgur50Pn7HlKSF99I0bwm/7o6hTz98CPnxnswMjLicpuSySQajYbb7jgqtGKEYCP6fm8C7j+QTqdRq9VC8WhV2idOnMDx48eRz+dx5MgRxGIxzM7OolwuI5/Po1AooF6vY3Z2FkEQuF3J6FHQkjpNyiP5oHuc52XXrXg8juHhYaRSKRemANbjb3RJsnRmK/BBz+fzSKfTbi46FksLaQHoZ5k8FOUiTKfTSKfTaDQaTlnT48FQC8fhNVCg6tbO6taMirOeO3cOCwsLqNVqrrHSL3/5S6ysrOz0MegbXEhs9KDgh7BU4WyG/Y6/qlKmkikUCk75AGGvGJ/98fFx18m0WCy6bYpTqVSoQoZrO5vNIpVKoV6vu7CCJhvyPPQM6N4gujZpCQdB4AwKrQDwibo2BNOQhHoHeby+p1VEeo+iwgQa/tSxgLBHE1gvfaRhxDlx7vpbjSf/Puj8FhcXUSqVsLS0hNOnT3c0IA6TPrsQ8qJyfKvwyUB4BnSLTLquGLfX+BNdbmrtRyXeqKWuFrdm8/pJfr73gGPqa9oxTN+LSlLsBD3vVqU9UfeK42sGsoY31ELqlKfA8+p1KBGKerDp6ovFYm6jF3WZcvc3DXEYDHsJlR2KqLXvk5so9/hWoFzx10fUWlNvnrr2fcuev/1112k8/zVdy1G/oxBl6fMa/JwF/xr9a/e9oupx0DAIP0evbr1edx5hv2zcEI2BIgNMoBsdHcWll14aatAzNzfndsMql8suzsZe4/V6HfF4HEePHnUPLlk9F77u2qduNT6crVYLuVwu5KUIgvN7DPhMmD9s2avCRV306i7ja5wvGwiRHVNY6Jx1e1Uybm06FI/HXb6EhiDS6bSbh8Y+1cLwOx7yGFoDmrAZj59vUcxFXigUkEgkXALWzMwMCoUCFhYWzEPQB+jG2olSUnsJJeCshNG22cD5Z50VQfQajIyMYGRkxMmAoaEhFAoF5HI5VKtVt9OpWtK+coq6H74s4Nz8ecZischdQzlup3HUZe/fA9/ijlKmUd+f75WIIiZR1x01P0KTtKNCphoK4X4zxWIRyWQS1WoVZ86ccSGDw5ikvF/z7XsyAKzHm+hqn5iYwEMe8hCXyBOLnY+RnzlzxinQRCKBI0eOhIRAOp12SqpSqbgsfY7hWxBUsEwk1Mxh3c6Urj4+0Kr4GV5Qt57vhue59LxMsCEpoOXgu+LUmleyoQlNjEdq/oKWRvGcGk8lqdB8AyU7JAmM7XEeFMLc5pkenXw+DwDWmdCw5+BzynJceqzi8XhoXw/2z6fRoGuXz7DW9at7f6vz83cnT5pvcfueCP72FWwU4ehECABs8Az45+ff6jXVeUVhqxBRlHfAJyR6LSQBlFc8vlqtYmhoCHNzcxv2LTBsRN+SgaGhIaRSKZeNT2XNRVupVELtO5PJJE6cOBFij4wJcgG3Wi0sLy8DgLO4aSUACG3ryzHZvpiCQtt0MlTBh9RPVuIi55hcDL4C1nI/Citl9Jr0Q/BzjH9SuafTaUcctL+AnzDIBki6JznnpyEBvsbz8POVSgX1en3DAvXDIyRx09PTLms6k8mgUqlgcXHR3H99jv204tSFTQVPMuBvZEbPALuUZrNZF2psNpuYm5vD0tISarWay5Nh+HFkZAT5fN69DqwnxUURgCglGLVuOik7lSPMHfDd9ppUzP81X6DbdUYC4pOBqERKDcH6x2iugHoaCRpGBO8tjSCVv6Ojo0gmk6hUKo4UDAIx2O419jUZyOfzoY0sVDlXKhXn6m+1WigWizh+/LhLcqPy1we42Wy647lw2Fe/3W6HevS3220Ui0UUCoXQAvd7/wPhjHy11gGElLF6GgiN7zebTdeNK8rd5i8mv0SQQhA4L5yo8Eulkkv6I6niHHmtdO8D4dwJej60CVMQBK6ngh+q8HMt+N0Vi0V37lwuh/n5eVftYDDsFhiDJhnIZDJuIy0myAJwYS3Kl0QigXq9jnK5jHa77bp3+uuVnj6O5Yfw/Lmol08VLBW5KratyADJuSpSyhXKN83Q11CBPy/97ROUKDKwWYfAqGtQA0x7unB8HyRommjIroqsVqIHWJM6D1vIwMduXkPfkgEuOCpcPiBRCSx86FSZq1XtLwo/aZCv6c5/fHA1e1d7BSgZ4P9RSYL+IuJ5dB66CDWJSB8SDQf4loIvWOj+Z5yNMX9N2InKceB94jXz/H5DI94PClJ6V0gsNAOZFRT6fzabdb0fGAYxUjAY0Gd9L8BnWkv11CDwrXY/Ds0SWWC974a/qyEre2jxdnLRR7nw/bl2cx/Ug+d7DSn71PPny8ao8241jyjr2yf72nyoUw6FhinUa7HZtRIkNMz54KZQjUbDkTaVeYcRuznvviUDmUzGNe4pl8suWc13y7G/eLVa3bCjXhQrpvKjO11d+tpHm59fXV1154va8EihFjUFhT6s/NFSJ86XQozWvN9ngBZ6EAShzHzN2NfkHJYgUnHr/GjdaB4C3aP1eh3Ly8uuAsBvckQwZEM369raGhYXF9118x4Vi0UX/2MCJu8lvQtzc3OuV4Shv7CXgtp3t+v6Yj4AyTYJsZJxPyEwkUhgeHgYAFwYUok3lZ/uXUKy6ytoP78mKhYfRfaj4O9o6BMBjq3yiXNSQ0HvkZ4/6txcxxqOUBngEyydqz8H/8ffX0Gbwen9oGwaGRlBEJxPSq7ValhdXcXp06edx7JT2MKfh15zP6LvyIAm8OjDrbHzKOjD6if8+Mkq/oLwX9NEP/+cUUo8aqF1mlNUZjAJhHoK1HrnWP4cohg9z+e723gchZ52HaMnQfuF8zg/6VHvGckEx9NzMKfCnxutNnoIorKLDf2BvYzrRskBP2av+Th+NUCnH82eJxGghcq1HtVCvJO1u5lXwH+/E3FS5a+Wth9m6OSh6HY+/IxeC9enf149TuXgZt951Ps+ofOvDVj3EJAwNBoN5y1QA3HQ0VdkIB6P46KLLsLU1BQAbHCNkUEykU8tXsamyZaVBFD5EEFwvhywVqthbGwMR44ccQpM22VScQEIWbw8J6Fle0DYM6FNRVKplBMmQHhnND7Yft/uWCzmmp/oveBxvqtSE3Z4zRo2icfjofarlUrFxes4b227Su+EhgY0E1u9C6Ojo84jsbKyEqrU4MJmWWMQnG/8VCqVsLCwYFUGfYzd9g74hDxKwVCB6Xpi7XqhUAAQbsPrh9vYjEzJAK1wkmYmEvqKlPPjJj7qLdB7ovBzbfz31LCJUtqKKPKu0Hwgfxx+lvfGv3YlVVFhQT2nb+yoHOkUqtHKLYYr/bmPjIxgeHgYtVoNd9xxBxYXF0PXsRUB61f0FRmIxWIYHh7GkSNHUK1WsbS0tKHhhCbS+BY5M+L9RaLuPADugSuVSigWi05JR9URqwKPGte30nWhqXeBc2d8j8f6cU1CXZdcaLrgKOg4Fo9jfJ+KW8kRx1WvweLiotsOWcsRNZdCrSUqf2Zna5glm826BE72daBLkMeSIGQyGQwPD7vvzdD/2A1PwVZEIOqcGkJTL5zm3/hk2++YB6wrv1qthrW1NVcuGzUPzcnhPKK8fDpPzieKGGzn3kUd28njEPW/XjPlmhIr3aWVcksrkPRznbwKnebNe6yJh374IJPJIJ/Po1qt4tSpUyESx7/96+13IgD0GRkA4JSlMmou0mw2i7GxscikOWbWq1LXmB2tbuYYJJNJjI+PY2hoyGW1U5Hyx8/aBdbDGGSxfMjYYEfjV/w8lTF3KKPlzhwHvuYn6fkxQhVWSjA01OHvMa7jaFtTLlK2Pe4UKtFsaU0WYtMl1nL7lQM8T9TneZ3MNxgfHwcA1zDK0B/oRllvF5spVVWiWoWkiiCqkibKFR61ra4fulPDQ9clrV99rZMr208MVgLBdeznHOi1bzdMoPepEynRMXm/fVmsiluJjD8P3TTJL2/W81CW+nkAm10XPZTFYtEZIJsRgn5HX5EBVezqkuIDl8vlMDEx4RS3Kku2IibUYuXxtVoN8/PzaLfbmJmZQbFYxNraGpaWljbEyTTMoA8Vex9oWR8fbt2BLJfLhYgC3ZTaIbBer7vrVOETi8VCDJnXpYuYAkwbF9ECHxoaclsq6yLU90lQNAzgCx4KRX5O7w/JC7umsX8ABTDrsFdXVx0RofVAgsBmMEeOHEEmk8HZs2eNDBhC2I5QV2+hJriqYtbNiPy8Gf81VVZca1T0viGi4TuuK64tzfbv5L7nmKrINHTHOar1roZClKziuDzev6+dyIAmJfoeTl6PGjmbfUfavySKhPA1ygQtY44iF/yfcjefz7tQLHc71GsZJELQV2QAQCg8oFYlsM72dateP9tVrWRCFwvd/kA4IxZYXzhRO5ZprWwUe/ZJjC5eZdLA+sPNh9gfR4WBHs/7oOMTUQtWr0c7nPljaxgiKpZHoabCiNfSbDZRqVRClpjOT8MNei163xk2YPMXEqDdtioNBwM/l+ZCsR0hr5YplbQfmuN60JbkVPrqCeQ5WcmjvTn8dcx1w+v2PW/6uh9v95MYo65f4XsU1MvBe+6PGWV56z3yPRl+yENJUSfLncfofPT+RMk7/X786/flsR/KGHT0FRlot9s4e/Ys5ubmnPuHfcJTqRQKhQIymYwrI2y32y7JTR9QWrFMjgPWy4LGx8fdw0O3kpYW8nzZbNa1211bW8Py8nJonwAlH2x/rMqYO5b5LBtYb9yj+6r7OxqqZwOAa6TChUVrg94FhijoLlNLfXR0FKlUCtVq1YVJtGpBLSaWZQHAwsKC8y747J7H614Qw8PD7vMUqqwN9lsbk+w1m03X/IVj12o1zM7OWpZwH+BChXSnz29GKtTDpkm6VGiaFKxrkyGreDy+IbGWx3BsNjWKsrjVSInH4y4BWKEyx4dPDtTw8cdRQsGQiIYv/WO1QigqLKkkQj9Hog9ggyzQe6P5FXqd6p1RRR4EwYaQTlQlFb2R9Ejy+6Fc17Csfv/6d7+jr8gAcD6OTkXK/a2pLDWDHwiXuGkinTJydb1pdru69ngsXdm0BNSqAMILxnez+e4y3+sQ5U6jElcLg3PR3/zbjyf651OvhHpJWE3B++pbDv71k5ho8qLvDQHWhRaFKomJJiNqWdZmrkR6bdj0ZRAW7yBjL124XFdUin4/EW0RrlayyhOue58M8PkGEHq2+Xn9rbJFjQXfS9ZJyfv3Sb0NUcdGeRP9Y1RpElGhBl++qAdWDRTOS9esH+4g/PNGXYeSGr1un+TovfS9Lt16n/otjNB3ZICo1+tYXFxELpfDsWPHMDo6CgBYXl5GLBZzjSjIFvUB9n/U3ceFrBsaaQOeRCLhNsbI5/MYHR11jJcxMs2qVQubbkZ109Oar9frLqbFRcSYJqGkQxc9FW2pVAKw7vYH1gUA58cWrJwzM/q5aFjbTzJUrVYdIVErIxaLuUqLWq3mdnjkPeKe7n4bYwDOc6M7TbJLIXeB04RDul3L5TLS6bQjLYbexW58P7stjDlWpVLBvffei3w+j3w+HyL5lAU8nuuEa3ppaSlkxSoBJ/yGXaqItBRPY9+q8P1Wujq+khMlDWqUdEJUEp8/dyBMUvi3Kmp6LJRI+efR3woeq42aeK1slc456D1WWa7J2jpfPz+K3khuKFWpVEJ5DIOGviUD2jGM/eyB84omnU6jWCwCgOuWp7GpTlasCgK6nbjQ9HepVML8/DwmJyddRzJ+xk+u4/yo8KjYqGxJQGg9K2OmUNGse/8h5qJhbL7VaoUUMRcIj2OCI+v9NaSgLksuOn9R6g+3c223224vCBIRdZNqIiQXdb1ed2EKFZzlchn1et1tL6tEhuTAz10wGLoBn1v2COCPKryoGD2wvrbZCVPJsa9kNRlWSYA+s/V63a1pP/lWLXEl/b4nMMqLAGwdIuGco641ymPgj+f3UOnkfo9SuupV5f2j0cWEYZU5mrAJrIdEKBf8uWoOEglGPB53HWWBjV0QeV+i7mE/eQf6lgwQrVbLNZWYmJhAOp1Gq9VCqVRyCpiv8SFgT34qr6hFoQqK4EOYTCbdVsdUqABCykpZMRcL++yrhc24FuPyXABctBreiFpc/J85E34YQmOYzKHg/aA1zoXFa1bLg/s/6L0gSdJOhGwrzB96EwAgl8uFyAAXtN57tSZ0Ieo9pJfA+g70D/ZT2CrRpsIplUpIJpMYHh7e4BXwrUydr1r8UQpUz8H1pbKk21i1Eg09typ1Xee+fOg0vk+oo64lypXP1/ykYl/e+J/R3ABgPfSnnsao1s1+yIEy01fcGnLlfePx9F743pMoAqDfeT8RAWAAyMDa2hrOnTuHUqmEVCqFiYkJNJtNLC8vI5lMYmZmBrlczvW5bzQabj+BQqEQYuW+S1sVIJWYss1YLOZK3WhV+/uj02qm5U6LeHR01LFisvRkMumSEjVOyTIoYGO/dB6TTqdx8cUXI51OY2VlxZ2zVqu5uXHutP5JOthnnV4K7TNAQsJzAucT+DiXIAicZ6bZbKJUKrmxNVGRBCEIArePBC2oKI+NeiMIjqXbShv6C3vxvfrKlGi3266HCC1TPn++wmeDLh2zUyzaNyD4mn5eFd1W8BVY1PtRZMAfv5MnQa8FwAavhjb38UOsUdej16yGjP+ahmE0TMD13263HfHnbyZpq/zTpmlRiZjaCM23+DdT+FGv7wVB2C/S0fdkAFh37zUaDSwtLbkud+p+12x73W6XD4gf//O7B2riiu6051sbWqfPh1a9BXzAOba6wP09z4GwNaDWtc7Lr1nWhefPVRcgQy0MCehiVpe+hhK0MsF3xQFwAlXdhxobjRKefpYyQw08J/eFL5fLKJVKboMlg2E78F3e9NbF4/FQkywN0/F5jYr9+4o1qnKgkyW61e+oUIV/LVHXtplS8T0TPiHQ61JZocmNUdVP/nz8uURZ8Xxdc6u0wkrPB8AZSL6M0//9MC2/X20jHxVa2A4OsxHS92SALupCoYDZ2VmcOnXKKe58Po/h4WHX9Y5NhEZHR91DqIoRWCcBZKma0Z/L5UKdBDWmv7q6imaz6dzw5XIZi4uLiMXWm+bUajXUarWQcmfogF4KWuK68Kjw2+22U4zMCchkMigWi4jFYlhdXQ1lSrMUSh9gCr1qtYrFxcXQgtPFBIQXLhUxrf8gCFyJ4NjYGCYnJ5HJZDAxMeE8HK1WC0tLSzh37hyGhoYwPj4e2n3MX9QkESMjI6jX6273tzNnzqBUKrkfehYMhx/7IVx9pUVF1Wq1MD8/H0oSZumr7xXg2tRummpBK4EmidC1pYYDwdfUCPCT7/zPKaGOUuabhQn89Q2sW9yaPKnZ+hrWazQaoTwjBY0gJQC+y525SOr5Y/6QGluq9EkCNLSr9wVYr1bSbdh5nZTVy8vLzmsZldh4UPA9FXuJvicDwPqDV6vVsLS05LLUyTb5QOnDrsxTPQO6mLjYNdMYQOg3wwz68PPBrNVqbrHx3LofAM+vTZJ815suDC4mdffpvNnBkMdScKl7j2PowtZ74XdpJFgxUK/XHemg6z+TybhNW3idftMhEjK+5itzdUXSEqnX66jVao4EsGeBf58Mhu2Ca7XRaDjPAIm230SIa4cEQNcff6uSoaGgcW8/LMB1SlLie/42ywGIissTUe7yqDAc4/60yjVPyfdmcF4awvQrHnR+nZSaehX5ecpnINw0jbKFLdrL5XLIS6q5RlFGHa9Rz6PGxyAiFnR59YfV/ZFIJDAyMuIUH5URy/ImJyfdDnpk4L4LTheez3hHRkYwMTEBAE5pz8/PY3V11ZUWAutuLDLQRqPhlOD09HQoz4Asm2MymY6NdfgaLWC/nh9YX3RKbjSG75dK+pUBbL+sDTrU4qZAZMMf3TGRJILHMFeC/R4o4FqtFqrVKlZWVgDAfUdavUCoMOIiZ/iCzZcqlQoqlUrPsfvdxGEUVr6FGrXGOmG/4qVR59XfuVzOJcvyGeazyARarifKF/bcUMueSbdacgtsTLSr1+uoVCoANtbj82/FZs+7VthE3Xeue92hUcN8sVgs1FRMS3pVkereID6Z1yRsv2zQn8dW3ws/p4aSNoAi/PCMbyQo8SHpUKIVda/2+1nsFGrZzuc5xlZN2PreM9BqtbCwsAAAGBsbc2EBKpS5uTnHgJm0Rne/33mMC4aWNRcnwwqrq6toNBo4deoU5ubmMDo66hL0qJDvv/9+nDlzxn2xQ0NDqNVqrr4/k8m4XRH5fqcYOpUgFwbnrlZElJWdy+VCOy2SZWssn2g2m1haWnKLhT0HtDkTSwiZeMiKA+32xUQslhEqM+dC1gWtrk6dJwWGn2XMZEsLD/QPDpL4qIUdBIHzPgHrils73im5jsfjLpGVjbD4HvOFGBbUMYHwzoYkyYTvCeTcoo7hNQDrVUycs16DKlauv6gxlNhzPIZKKYeq1arL1/HDGWqk0Cuiu6b6hDGqr4Mf+1cvp8oHn8j5OQlRSlWJwmE1fC8UfU8GFHzoorJL1TVGq5YsP5/Pu/I3X3kGQYD5+XkA656BdDqNkZERAMD8/HyofCWTyeDiiy92sccgCFz9PxUqd1dkrkGj0UA+n3f7qHPu/uIlc4/FYqHmG777TFuDEjqG3iu6+Wnt8xiShmKxGCJN/pjqbfAFDhk556oERo+nB0EtF95TCg61PAz9AT8sdxDnV1A+8Hn3LW569ehZU8Wlx6+trblGN6y88cMJuoOpD20/7mf2q/eS64N5SkpgeE5V2pRf/rnVq0FPh86BxzO/Sa8j6kePb7VaG7Yz5z32S/58y1a9A5QPNAh8gsDj/c8rIelFr5ufbLmXGDgyQC8A3cl0cWkZGzvc0eLMZrOueZBatHRRc09sehRIHhYWFnDq1CkEQeAY9aWXXoqZmRkMDw9jenoa1WoVP/jBDzA/P+9K8AqFAqanp9FqtfCjH/0Iq6urOHLkCE6cOLEh8UYTkVTxcqGptcKthql4+Z6OycWkyY/sqKiLU5sBaQKQnkfdjIw1qtIPgvP7K3D/AiC8vXGlUsHq6ipqtRoWFhZc2IIhBL/tKAWroX+wV0IwSvDruvJf5/G0iNVY8PNtADgFo429fOLgQ5UzEG7ew/+59ugB1LwfvTZ17TM5mmPoHPw8BcpEXodvLedyuVCYUT14fk4Dx+J9YTIg92mpVCpoNBoYHh7G8PCwC6so6fDDnMB6OFHPMTs760IrlB/0UvjbSasM111OtyIDB+Ux2K/zDgwZUKWUyWRcR0JtCqQPMRk1+wJQafJh1+RAKk9lsyQHxWIxZBnoeIy7ZbNZZLNZtymKhjG4KOiKB6Ljg/7C1vpbdblpApAKBI1LUphoGABAaIwogebPQeOOeoxa8AxXKHtX6ysIglCnQu0Z71tTvvA0GHYC3xKjYqWyyufzIS8cn1/1eqm84brRFr3+2vGf5ahEOxIBDT34LY11LXENaSa//vif4zx4/TpW1GfUi+gnTEataV43O4gCcJ5QGhBKBigzfe8HX+d90oRwnovGl1r/CnoPlPRsFVPvd/R9AiGwrsQmJiYwOjqKY8eO4Vd+5VeQSqVcctzi4iIWFxdRqVQwOzuLeDzu9jRIp9OhVpjqmqbbCwhXEWg4otlsolwuo9VquX7nbIncbrddTN6vTdYwwdTUFKanp92eC1rPS3ARKEsn4QDCCTuajKfuw07CSD+3traGUqkUqnAgyQHCCTtU0rowgXWSwk2Q9Dyal8FFS+FC1s+8irW1NXePTp8+jdOnT+/uw9Nj6EVX5lbwFav+HAQ63UN/nvr35OQkxsbGkM1mMTExEVJuJKirq6t44IEHQnt55HI5l2TL8mAmLGsYgfPS1tx+/guNBW3Nq6A80jABsL6HiZ8cp9+DehV9C59zU6WvXkEffshOjahWa32nVCbsaf6Rfk5llHo/lUxp/oGGIvQ6/ORCVnKtra1hbm7OJTEzR6NT7sBBPq87Pbd+hwOfQKiuOv7kcjkcPXrUbWe8trYWsqTZgXB4eNhVAwAbN/5QSzRKMVMRMzGRsUIuDrbtpVeADzyt4CBYz/xXd6A2yeCDwuvUZj56D3RsvqZeEHX1+QKDFoYKKbrrGRulBcIFrwuU4QYlAzw358zX1MXJ1ziXeHy9+Uuj0UC5XI7cmMRg6ITNyFSU0FVvYqFQQC6Xw8jISIi4a0kdLVaNfdPDSDJAYyAqvMeNttS9zfE4jlYB6XrWdQ6E5ZV//f66V6Wh1673hXPR931Sxznwb78MkzKFBoQvN6O8tHxdjQ/fw0h54V8r5awqQnpyG42Gk7tbyY79IgL7lR8Qhb4lA2TP6XQax44dQ6FQcO4oNiAaGhpyiSfsSMicAmC91pckQhcolfXQ0JDrqqeMlHOgcPAt3LW1NSwsLITch9wgRcfiAmV2MfMbmIikDzlZty4+XUzaT4GWDYUKz6s5A3S1aSkU3WpcyKyrJklRMqB5C7RMNKaaSJzfu6FcLocEi7+5iRKSWGx9B8MgOJ+AuLKyEul6NRi6hf/scG2zWdbMzAympqZCpYW+y519Q0gKmF+gYUauD7+5Ds/J599XijpH32IHwta4Vghwfn6fEt/lr9ftzwnABnmkljw/38nj44cO/XlEeTl5Ll+uqjvfb+oErHsc+VkaMf7cKNPZCp1yMIos7pVc6eSBOCj0PRkoFou49NJLMT4+DmCdrS4tLTm3c7PZDNXPAutMlMlqupBbrZZzVWezWfdQqjLkQuR4fikQE+Ti8bhzIzKZDtjI6llqqJ4OCg91NVLZcrGTAVNxUlApIaBwUte7tmGmMtc9Efg+yYC6NdULoF4XfU8zePVexWLr2c8cl8RAE6hUAPmJhIb+wG4Iy27G8C1bfe7Hx8cxPDyMI0eOYGJiIvSc+zFskmfm+miOkFqeJMK6bvX8ak2rccH/9br0bz8BUT8XVYrI9zuRAv1bFbOGEtWSVULC17VfiMoqX+kq+VEoGVHDR89JpR8VgvLf06ZQ/H5I/PzvYdDQt2SAMbrh4WGMj49jfHw80i3H1zQxjiV8TNpT9xtZLJUtrV397T+MfkyLylmtd47r5x1oeEPfBzYm43FewHqjEZ0Dx+VrGkvTWJxa46yLJvlRFs95a2yTyp1z1cxjvX+Mf/rCjuOyBWlU0pVaVyQwJHUGw07BdZJKpZDL5ZDL5RwZIOlXhQysK1quRZIBbr9N76SfSKgWdScFGGXNRilt9capjPPH8/9XY0LhkxS1xjcbV8m7v6b1M7pO1TPgz8P/nBIDflatfr7nV0jpPHgdzGHSsf1r6xVCsF/z6FsykMlkMDU1hfHxcRw/fhxTU1OuKUa1WsXS0lJIwRNDQ0M4cuSIK8lJpVKhzXrUuiarZKiB7/kxwXa7vUFZaQasZrv65TpRHf6UKMTj67sGqgVAC1qbDvleD5bfJBIJDA8Ph1yZdMcnk0kXo2cZpgoc3WGQ5yPZUTLFB5oEpFgsIp/Pu/uhoRd6J5h8pclKjAHyPGzcsrq66rwnBkM3iFJUQRAgl8vhkksuQaFQwPHjx93eHqp8dAyGyDS3p1AoIJPJIJvNujJAHkuvgSpYdcOrsaFhiCg3vIYd9fOKqHi4H3tXxUgPZ5SnoJNSjkKUF0OVvm/IqFL2q5D0PT88ql5CbYqkm7DpOYAwGfA9FQfpvj9IAtK3ZIDxILJyIHrRRbnpNLvdP16PU5fiZlDLNuph13F14UaV7/E6fOukk2Dj+1Fz7BQf0/HUE0BSxM/6sUVfOGjOBK9Hu4rptdIzoV4aLYnk9bAGmgmETCiMakdqMPjwXds++ByyG6gfg+Zno8bQsCAb6filcd0Ie39dRinlqDW92eeizuErdX1tM4WoHo2t5tEpF4DvqUz0f6Lm7OdlaSmkQkMZJA7akIhGkho5Ude4l8q5VzwPRN+SAXoGCoWC2zGQnoG1tTVUq9XQA6UldiyRU8uZi1yVmZ88Q6ibTEMPqvy0KYY209CMfCB60x52+dLxfIFVr9fd5kDqEdBFpjkSGt8jGWJDDj/ZScMTvGdqUfDesO0xO60lEglMTk46cqT3h9ccj8dd2RT7KwRB4CoH5ufnsbi4iGq1ioWFhdBmJbwvhv7AbgjLzZSZD66NdDqNqakpZLNZAOvJtb6CVKXGZNqxsTG022337EeV2XFta1KfT+657jWs4BsHGtr0XwfCCXb++IQqQZ/c6xqPknNR5Lsb9zrno+GMKKOMY/FvzTfwjQWVx5SF9NSSACwsLGB5edmdm+WgzN/y71+vKeu9Rt+SgXh8fUMQWrPseKUWrm8Jq3Jjkxu+p5mpCrWE9X+dC2Pk+ho75qnFQUvaZ88qgPzEH50boV4PPa8/Lz95SN2GmguhLk0uPvV4qAeBC5OllRoCoacmSrjy3NrhkK5ZKv1SqeT6QczPz4fCJuYZMOwGuF798j+fCBNqhZJIR4XIFFHeRt8yJxnQ59tPxlPPma/E/LyEzUiQ7+mL+ozv1o8aR9djFHwvR6c1GxWmiCJNzI3ymyox/Kv5TqVSySWO83ulkWH5Rn1MBtrt83tcx2IxjI6Outg/FZNauQBcIpp29OLf6tKm5c6kNVqvwHomP7C+ELmAaGX7QoAChOejgvVLd7iFKuuUVQFrbgIFF4UIiQ2VOherdhzUmKRCBaD2CtBFqRUECrZq5usUrLwPmkCoFn0sFnNhADJ2Nhbi71KphFqt5koXOVe9/wYDsL1EMD7TfHYBhNz8AEJrR2PRarH659bz+6FBKnLN0tcyZFWuUR4A35jRcyiUIHBuvlXuk/TN0IkYRMkQ/3cnb8Bm4DWqbKTRoGRAk7TZGI27xC4vL7sKLnoPohIuo65jENC3ZIBhAe3YRSUTZUU2Gg13PEuBokIDVK4MOXBsjZ/xwaJCphKka6rVarnz0B2u2fL6cPN1kgG63xOJBMrlcmhTDhVKbE4CbGy+od0UKWx8xq1KXwUWf1M4dcpcjsVizn3P8irG+ZmA6X8nWqLZaDSwtLSEM2fOuI6HJDNKxjotZsPhxWbx6p2Ot52x2K8D2BhK0woXjqvrzm9EtpmVzDEVmtSsZMIfh3LBV8pRrv2oc/leTv3tG0qd5h01d59MbJaP0C18z6t6S/hd8DcNBO59UK/XsbS05BLHq9Wq6/8Qda/9+zRI6FsyQEuxVqthfn6+44YUqvCi+v8zvkTLmOADyGoAACGrPGohUsmrK1zjXVTY/kLn35wf56a1sd0IUI2pkRmroufneQ7/ejU84HdA8++9v/j90km1gKLCKiQL+XzeVReo4OU9912bhv7AQQpkrkONV3M+JOnqmvcz4QmN6QMbcw00Z8D/revNT7bVMTRJLipL3/cYcByt/9exfNnIY/xr8EOLUWueY+r/PrYKX/ieD5Wvmjyo51VyBsDJEg2dapmzyY/z6FsyQHbfarWwuLgIAJiensb09DSAdWasljq7UfGHJXKEZrtrHT8TVdQlrm74IAhCbUo5FtksBQy9DfQ+KKlg+R8tbgCudIkuzVgsFmr363soWArJ8ht+ptVquV0c2fKUXRl5DIBQzwDNQ/Dd8741wNwDll/xnLVabYPVw3urVkCj0UA8Hne7G6oXxPIE+g+7SQS2MxaVCtcDPWkk6/SEUQHTGIh6Hql4lUBriIDn4XrV1sNUcNqKnOSdn+dx/Dx3BdSGYnpONQQIVcT+OopS0lHj8Vj9HK9NX9vqu4jKedI5Mz/CPzfH5/FMvKQXMZFIuO3nKeNrtRqWl5cjW7oPMvqeDDB5hK/50Li43yM/yjXO9wk/mShqbGX66vbzF5R/XrVKeLw/l80e4qjQhcK3cPxFwQWq1rw/b/Vg+MmO+jeFnX6WglaFhS9MtUPY0NCQ+z6N0fc/djtcsBV0rTBxWFv6ck4aWovK2emkRH0LOsptrnJCvQqEb3FHyZ9O7m7fS+Cf1//tH98J/jX56zLK86fXoK9vdl2EEoUoGaB5UTT0KGtU9uyFDNluSKqX0LdkYHV1FXfeeSeGhoZQKBScm0gb8WimKa3sZDKJXC4Xcr0xmY2MNwjO5wxwowsyT2X39BbwYfPr5YH1B56xb86PxwIbs2/Vpc8ySa2f9aFkRllwEAQhS18VMb0f2Ww2dD6dA39oiahVxPEY1iiXy1hZWcHQ0BCGh4edUufubrpJlB+zzGQyodDGysoK5ufnO16vwXAhiMXOJ7AuLS2FepT4bbCBdfLLMmHmKfEZ5tbkftIe5QnlhDbVUo8bn2+ucd8VHovFnNdAPRW+0cHr0rWsngxdt0B0mMGXQ2pY+V1d9TqiEhZVIWu4Queg0MRlvRadI2WIypPh4eGQF4UNyph8rIZHlALfitTsB6II216hb8kAFxzL2zRGzsVKN526/XS/cn2I6EbXtrxra2suJKBhASUCvtdBwXNwHlHuvSioK05j71GuPl2kPF4VOM+VTCZDYZOo/gQ6nnYp00RFXpcq8Hb7fGWHJmaybFOvl/+rdeCXGTYaDberpMGwF2AIS9cMEN6iHFh/3umWrtfrKJfLiMfP9xVhohqhpFxj1sB6CE7lkx9C0HOqdcy15ivuTgYFP6//87hOHgP//FEhC16f34NFZSnHVI+nYjPjR+XpZh4MDeP6uRg0Qnjv9b5thf20+jfzWOyVx6xvyQAxNDSEkZERjI6OOouf3oIgCFAoFFw8ieUnP//5z0NKUwkDFR9jUs1mE+Vy2S18LVkB1hNeqAjZMY9jAOuWdLVaRb1ed/sqqCeB3cyA9YdAuwHydbVk4vG4sypIONQ6SSQSrtmGLtYgCEI5DUSUoOJrVNrtdjuUic3qh4suusgtXLpSqeQZCvDnSMFZrVZx9uxZLC8vGxEYIHRyA+/l+QC4+nMqbF8hqnUNrJMBbUm+sLCAVCqF1dVV5x3we+b7oQDfC+f3z/eVp583oK8D63JFiT2h4/geyyjy4Ctmfd8PD/iJxxoe8cMg6rGIIgI6lu/xiCIbPIdWIC0tLaFer2N5edm1jvfH8K/JRzeKdzefVf+Z2+5cdjKfvicDiUQCExMTmJiYcL3wNRTAn0qlgmq1igcffBB///d/j1qt5rqIcTGxxp99rxOJhBME6XQaF110kdvaV11QTN7jzn+a9KOLdXl5GYuLi+68DGlwHL+pEb0T6urXfgiJRMKV7vHYIAjceXVx6/4FZNa8Ri4aEhJdeLzHJA8s6eFCjsfP78pYKBTcwuRi5GfYb0AZOwBXDlQqlXD69GnXMMQwONhP16x6qFgCq2G1KOuYzy1DBeytwTWmffKLxaKTISzvVctUFS1j3noP1ANJUq29PEjU1ZPBcJ3vOVBFyuvQ5GOFKmX/2jlvtb6Bddnm319gvQkaZWMU4eJ4NDw0sbhTUqaeh7KmUqngvvvuC1WTKQnrRp7s5TO4VShCvSh7jb4nA8B6sggVGhcSsP4gr6ysuIenWCwim81idHTUWftc2IwjMj5HQaCK01+MfOh00arV47Nfzsvf3cvf5c/fUIQxRAoF5jkoUdCHT92PtBzYElnn7c+TFhPPybAC/1fCQMFIoaXKXgWAWlurq6uuMxgFrMZMDYa9Bp9d1qWTJOt6IPxEtCjBrYZHJ2vUf91XvFEJdr7rPGp8XV+dFKBvWXfjldnMau2kvKISIn0PgM6H8sOXoX64wA8D0IvIXI7N7u9m2GslrDJwP4lvFPqeDJC5s0FQsVhEvV7HysqKW+zNZhP33nsvTp06hUKhgJMnT7qdy4aHh0Ms1d9+WPtaU4lqSEF7EWjMHlhns6ogSTZYTqdNk3K5XGgu/CwXTDKZxNjYGGKxGObm5hwbZo90WiNafsNjOM9isYhcLuc6eCkpoaWvr6knAYA7jyZf0mXnexM4ljYTCoIA99xzD+6++253j3iMwbDXUC8A98Iol8tubfFZ9BUJlZQfx9cfetY2U/qEWrpqeatFTOWoicdRykXJeieDRNc0ZYFPcvw5c236XkkNZ/iERuenHgk/PKjrXj2o8fjG1u78rtrtNpaWlrCysoJyuYy5ublQkmInItYJ21XOF6LMNyMF+0USBoIMsFsdHyzG99iCmE1t6L7O5XJui918Ph9yV1HZ6/h+B0JgoyXgZ81qvEyVpHoGgiAI5Qmoaw+AS84jaJWrEKA7TIkFocREhZYmHunC5vWqkFAvhZ9HQAXO2L++p1BWz06Q6tYzGPYDUTFrdsf0vXQKkmFdr1xzDAHqOlSo3IhaFzoX3wuna1DXKf9XRR5FOPz3ou7HZh4CP39A5VNUFcFm51Ui4H8uikDo9WhPGYZ7We2lSdJR5+7kxRlE9D0ZaDQauP/++3Hu3DmUSiU88MADGBoaQj6fR6vVwvz8PGq1GkZGRvDUpz7VxfYSiQSWlpZcwyIg/JD4LSyBsLuQizOqPSn3MtDFQ4XJ5EJ6Bkg+qtWqW2QUOAxRqLCghRCV0aubpzSbzZCQYpijXC5jeXk5FM/3XX9sSERWT8JVKpWQSqUwOjoaat2sewjwHgDhjZ1KpRLuvPNOVzpoMBwkqATZ0pbJvephI5iDpHF3WstUcJrhrpnuQLhUMGoOSqS5drR0T/MSeC79nJY161g6ZlS+AmP5jOtzTvytY3QKUSih8MODUcepbAiCAJlMJkSgfK8DvY+zs7OoVCoolUobkgT1s1F/G86j78lAq9XC0tKSeyCr1SoKhQKmp6cRBIHLMJ2ZmcGJEyfccc1mEwsLC04J001FZUr3ExUmsDGJRR9GbVXsWwc8Xhemusvo0mcFgJYx+gREkwl1cer8oxYlcyNWVlawurrq9nNXT4GC89RSqWaziVwuF0rwUQFJ+Gyd93Nubg4LCwuu86HBcFDgs0vPAEthmdirRJuKWKFyQvuPANHeMSA6cc/3xHVStrq21XrWEkR/DoQqdV2bnXIkNO+Ax+m8ouRFJ7Ljw5cLujcE74nmE9BY4gZmJG3+WPuBzbwNhwF9TwYIEoHFxUW0Wi1n/ReLRRcSIBumJUuLmaBHQRPx/MVC5chyRZbNac6AuuH92Bl7I7DXAZUq8w6A9UxdXbDMLL7//vud8FLrQQWalh75SY7qZdA2rLFYzG2uxLanmkNB91y73UapVAqFN3wyoNdPstNoNFzHyKhOkQbDXmMzd/bi4iLuvvtuFItFXHTRRW5tdyLLOp4f3ouqFPBd8uotoLzxcwK47tnmWz8HIET6/a3RVYHzN49nVY+eg/8zNKkhSSUEfqhls/uqREbn6hML36LnfSJ5YSI3kwUPChdKAvR+HQShGBgyAAClUsnt9MdKgfHxcVc5MDQ0FEoqpEuQFnAqlXId9Lgwme0OrC92lg+m02m3Oxaz4/VhVyHCZBluo8ymJXyPi5IPCn9YzkeFfPr0abRaLUxOTrqtjgnOU8slNeTA8YFw8w7+rSWCvC7mCsRi6zkCKysrbm5+BYOSD1pO7AjG3A6DoRegiufcuXOYnZ3F9PQ0CoUCcrmcS7TrFO/384f4v5bIRVXWANGt01V2KBFg2I5Z9z450ZwGzsMnAjpv/9waDgTWQyA0lKIIjZ9D5Y+nJELnrIYC/48qI9QQSCKRcKGcqHvJ16LmcpBWfCeSFHXMfsxzoMgAsL6IGJOn611rhHUTImB9YQZB4KoS9D0qeU3gARDaoIcbn7B0T4/zqwl010A+7JoRG+Vy05wBteaBjZuA6GLnuZhA6WfrqruxWq06K4E5AzxHIpFwGz35yVZ6b/zwBe9NpVIxj4ChJ6EudjYmoyuaG3v5DbqAjU2EVC5o9jzPoZ/X8l2VN5pISPgJvhzPn4fmDPBzPlHn8ToOPQK+5RpFLvQzPnzPI2VZ1P32x1NopVK1WsXq6uqGnAZ/vF5Ap/sURV70mP1CLOiGnqB3buhuIJfLYWJiAul0GiMjI0in0xgfH8f4+DjS6TSGh4edAqaipPuahIFMPJPJoFAohB5gWsSlUglLS0uh0rrp6WmMjo66ubRaLaysrGBtbc1ZHFq5UCwWXbUCF7Q2IuIYfumdbsfMr9jP/M9kMi6RcnFxEfV6Hblczln9/CxDHNxVkaGUeDyO4eFhpNNpZLNZ5HI5V7bJMAnJjD8X3q+zZ8/i7rvvRqVSwalTpxy7N0Sjy+XaU4hy8+53PPdCoEqQHTNTqRSOHDmCfD6PmZkZzMzMhNz/GhZkaI4EmtVLfuIdP8+QII0M5u6o4ozq4U/Xud9tcLNQhl6fGjN+Xg+P4XWpRa8GAQmGX5bIKi4aObHYejMkrUTyvQBMZOYcgiBwXtZz587h3nvvdeXd3RgT/vrZr2fQ91Ls5zk38zYpBs4zAKy75AE4azeXy7ktdQl1ZZPF8qEmGVB3GT9DcOMSfdi5YHUu/gJhuMB/aDgHLoqoxJ2o2B3PQ/iJSZrnEI/H3S5funhJALjo6vW68wZo18MgCFy4RcsVKaDUy8L7yVKgKCvBYDhoqAXHVsWNRgP5fN55C/1OoD6UyDM/RpW0NjZj9j5zAdTD4HsGdHMvzUciNKcoKjTgW+p83/foEVEKxVc6vkeE168/nPNm+RY+EVG5xU6nq6uroRDGdnBYyOhO0clD0wkDSQbYoSoej6NUKoX2DEin065Ebnx8HLlczin/IDjfUyCdTrvWxmxuQSYPAMViEZlMxlUbpNNpzMzMIJlMIpvNOkufc2GpYTabRaFQQLlcdr3Rq9WqCzdo4l8stl45AHS2GOnVANabIjExUkuGuFdDLBZzCZSs0yUpSCaTGB4eDpVA0mJhaWEikcDk5CSAdSuCrZwpDNvtNhYXF1EqlTA7O4tz58655kQGQy/CJ91BcL4SiWv1/vvvx+joKB760Ic6z1oQrLcqZoMvtjr2n3XNJ6B1z7I6bQOuIUUg7O3zu3tqZRJf8939nSoc/LwC9Xr6xzLXSMONlJd6jCYlai6AJjX7YQ7NDyCBWFxcxPLyMlZWVna8e2m/E4GdYCDJAJUsANfchq2Gk8kk6vW6c/+zfpi5BXR983e9Xsfq6qqzcOkWZNVBMplEJpPB1NSUi6mr257WQLvddkRDvQisAOBio/Kn0KAy9kuKgPXFwzwHeg60QoFzoOAhKSK5UTcniY1aLByDAoD3jVsXs6qBuyLGYjHn+SiVSm5RW76AodfhW9bsPLqysgIAmJmZwUUXXRRy4WtZMD0IXGO6ZpXoJxIJtz8JX6O3jeenjNC8IxoNUdUKVO5RZYZ+3gDXu3oZ9Zp0TPUu8loYy9c1redT2ePPwScnfu4EPTPLy8tuJ9nNPAybfY/bgR/v3yvsZ8Kgj4EkA1GoVCqYn593CpzxrJWVlZCC1Z0JW62Wa7LDhKJWq4V8Pu9KF1UR+hvxAHAkgIt9ZWXFhQg6NQ/h67qdsp9AQwVNtz1f4+u0PvymSIyHMqbZqcIACAsYkg6tLvDLBDVsMjs7i+XlZSwvLx/KOLjB4KNSqeDuu+9GJpPB8PCw60lAjxxbhqvC5W+SA93JM5vNuuP9ZD8qdI0HMxTANavNjeiho3LXcJ3v1o8KDyg00ZBj+cpdj4vKP9Br1nbEnJd2S6VRo/sOlEqlUD+BbnHQHgHf+6KvdZKD+0VEjAz8I9i0Alivyy2XyygUChgdHcXExIRbJLrAUqkUpqenXWJds9nE8PAwxsfHNzQIYh09lSUZN7c4rVQqmJubc3OiMqZVrosZAOr1+ob6XM7Rrw0Gwu68druNTCbjQhQcJ5fLIZvNOkJCsqHxTBUW/M0dBtUdSg8EY6RMLmw0Gm4Tkaj4osHQa1CvQJQQD4Lzm5396Ec/QjKZxPHjxzEyMoLx8XFMTEyE4uRscc526M1mE6VSyYUVKRNYvsjX+HkgvH0511BU+R8VLD0JXO9AeM+DTsrG9yz4iYt6fvV2aJ6FVkOpwtdW8JQFvC4SIaLZbGJpaQmNRgPz8/MhWX3QCj4KvgLfbI5RJMB/bT88BkYG/hHKsolareZK7Wq1WqRSVvcYodYAY+VcKJqop+zeV4jqDeA5NT6vn/M7oil0AUcllFDR84eLUdt5aiaz79brlNyjAkjvkQoACw0YegHdCFpVcJ2g8flKpeIs/EwmEyL/auVrmE6tdxIArcH3FZ8SFL8yQa9LE5S5rimHuGajSn+7VWZRCtmXO37+AqFGBQ0PJQV+ojGNKd/bYLhwGBnogFarhYWFBaysrLgeBGw7Go/HMTY25rKJ/RBAKpVyCYbaHUv3EdAWpmy0E4ud3ySpUqm4BMeHPOQhKBaLbuyVlRWcPXsWa2trLhkxm80ik8kA2MgoSUz4HpP46Dkol8uhnIFz586hXC678ksmG1KA8Bp8jwTjlWqJcOFmMhlXMslOhUYEDPuJ3bCstgpnqRJ+8MEHMTs7i4WFBSwsLKBYLOL48eMbmphxPRWLxdBY6g3QXiNAeI8C31sIYEPojzF8KlxNIlRvoj+2/1uP90MWOgZJkD8X3hs/z0B/+51a2UxobW3N9ROIIgJ7bTlvd9yo43WO3YRGuz1ut2BkYBMwM5alb3SVDw0NbXBj+QtVF4ifsENoTE1jfHyNn9cmRbSuG40GUqlU6LxA2MPhu/b8JB0Arvc658fEPoYaNOlQs4F9ls9FqnkJJAP+fTrIlqGGwcOFCNTtKhkex2qbUqkU2hKYx/id9/xGZJrkG2Wl839V2FFJg/zt5xPoWDo3/3o75Q5spqiiwogq1+gZiaoEoHyg7FIyQI/oZvPqVWz3GTwIj8dANh3aLthWOJlMukz5QqHgsv+z2awLBcRiMYyPj6NQKLhSQVrgrEVWYUDl7lvKDEGUSiWnWNk1kaQhn8+7HdPYepjJfVTE9ECoAFF3nLJsWjQLCwtOuAwNDbkGTXQ3sokSKwi0dlpbpC4tLaFarbo+7o1GA0tLS0YGLgCHMdkySpHtt4t3M6UelZgXhe3EfUmWmZCbTqcxOjqKZDKJiYkJt/eIhuKA9a6lrELS/VDozeN6U2XP/ieqiDlfknMgrJT52SAIXDiUXghd/zQofONGcwVarfPbB5ME8fy6pbp/b1ittLy87PIBlpaWNlRNaLKl/13sxjO0Xwl62/UMRGG78/STTDeDeQa6AOPc6XTabTy0urqKSqWCQqHgFg/zBKgUgyBwi55uP8b+NZFPy/NarZbbK6HZbOL+++/H7OysU7bFYtHVMtM7wcoH1iYTTB7kAqbA0f4C/rlpRZTLZddvoVAoOMGk9c4kBSybqlQqIZJTKpVcoo/B0IvolKi1XfhCmkqXZYWlUslVK62trWFiYsKRBK3Q0QZllDVRcXauMSpUvxpAwbJpzRMAwgmE7fb5/VPi8bgrsSZJ6CYngT8+IenUhImfZxUSqwPYRIjv+3kRe4X9SNDbj/EvBEYGtgGyX92oiBa7smgqTW5WpH0JgI2LkNYDmT+bILFMUY/leAru4+3PlW5+3zKjMNG8BY7PzYJ0r4Ll5WU3z2Qy6bwO9AwwjMJQihIMgyEK+y0UO51PrbSt4rw7Pa+vzLlWmc/DtaiJfexvwjWnlnxU9Y0mAkYRGg0j6v+qyGkg0KOoHUTVGOCmR36cnx1KKQs1RMD7x7yjVquF1dVVF5asVCqh8IBPBPYSezG+723wE653a9zdhJGBbaDZbGJ5eTn0kOqCJsunNT45OYl4PI5cLocjR44gnU67Wln1CjD0QJJQrVaxtLSEdruN8fFxTE5OOiuCG6WoQFhZWcHc3Jwr6wuCwHkNtIaXdbmFQgH5fB7ZbNb1Q6C3gvspAOvJR+wJMDIyguHhYUeAWCq4urqKarXqmrAQVjJoOAzYKlP+QsdUizYIAszPz2N+fj6UV0SvIn+4zrLZbKgbYScXs08S9DhtkxxFJhh64P4pvmufIQ7KIJ0rDQh+fnV11XkilOTwPlD512o1l6wc5dnoZQt6K+yW4t9vGBnYJqLcdkB4a16WArIcBkDkRhoaw/fHpdtQQxBk3KlUKlQORJc9FTTde/5GKQQte4YztB9CtVoNVQWQEARB4IgGSQfPRy+CKX+DYSM6xcz5HpW0uuJpZQMI5QrRaIg6h5+USPg5BPoZX/H6rX8pP+gRbDQaG0qrmeDHjcy0r4BPLHicyg3Km04koNeJwX7lHOw1LIFwF6AMX8sO1RU/PDyMZDKJmZkZTE5Ohtxmy8vLrjsZE4dY1seOfUwQYqIPALcIH3zwQdx///0usSiqaoG7rA0NDeHuu+/G2bNnQ7FKXgO3ZGX8j5/XkIj2S6DLz2f3hr3BYbM2gN5IIOwV+JU+RFScnz0KLr74YuRyORSLRRQKhQ3jRZX5AetGBhN4/cZAzOin3FheXg5VATEPKBaLOe8nz0FZpzJAG5T5uQI8H40MzXc4TIgKG/mvbbVGu0kg3Gkia9RcLYFwH6HxM2X5rKtn/D+ZTGJ0dDQUt6ObrVqtuiQfVikw2YiVCs1m07nqYrGYa4SkC43Q+BvJAH8ajYbrp66IxWKYmJhwMUq/DEm7NBoMhu3Dj4cTun41Fk8ZApxvhc48niiCFVVFQJm02Xy0HBpY30+B3gnKM51nVBKjPx8ez7n4eQBbKbULybrfC2w1F//9KMKwFfzvdT+v38jALoNxd20wRLdZMpnEfffdh6WlJWQyGYyMjLi8A206wk04dKExkajZbLr3lpaWXMlisVh0i5hJOmT92vITABYXFzvOna2EKUj0YdSyIYPBsDN0UhI+UaB3bmFhAZVKBZlMxsmJKCtUyQQtcO0V4p9LP9dqtVCr1VCv10MhQCYB+1alGj5RnQU7KcbteIN6iQh0wmZz3Cx8EKXouw2RdBuW2C6ZMDKwB6hUKqFkOsbjk8kkqtUqHnzwQYyMjODo0aNIp9Oujp/NhKrVKubn50N5AePj4xgbGwtt9kMykEgkXD8Dv+aXr62treHMmTNbZvizKsBgMOw9fKWuApzGwMrKCiqVCiYnJ0NVA1HeBf6mgmfHQe06SI+kkg4aDfRS0psZ1aSo0zXoPHzXuXoCDmN4qNeIyV7kKRgZ2AfQzd9ut0OlOcwR0D7crE7IZrOhRa9bFVO5r66uYmlpKZRYxKRAJudwgfu7JRoMht6BkoIoDwErmc6ePYt0Oo1cLodEIuHCiVoayM9oi3RuBETlzvg+cwMajYbzMtIbGRUf38zS3er1w0gCiN2a+3bv317OxYeRgX0A436xWMy5+8fGxlxJIV34mUxmQwmRxvMYBmB97v3334/FxcVQXE7DE0C45KjX2K3BYAgjysqmN+/06dN48MEHMTw8jOnpaWQyGZcUTCtec33YHZTKnqSCrzEvwCcKCr8yoZsyTD83wMeFyqEoknJQ6NYVfxiqJIwM7BPUdRePx104YGhoKLQjYiKRcIu13W6H6oMBhBY9ywk1gVG3MTUYDIcT6h3geqY1z43TgPMhScoCrn1a9T4ZoCeAngLKD21tzHPz93Yy5P257xV6LbGwE3pN2W8FIwP7DC7Gs2fPuh0DSRAe9ahHYWJiAvPz8/jhD3/oOvrpw8/dz5jxz0ZCQOceCAaD4fDBDxdwbZfLZZw+fRqJRAIPPPDAhlbBTBbUPiOsStKW41Gxff/vbq3wTnJnr+RRLynaXprLhcDIwD6DC9FPMmRPAXoNZmdnN3T0A857Bi655BKXd2B5AIZ+w14kRx1W+FYwwwaaZwQg1CKdvUMoG/yNyaLc+Pt5v3vJzW9Yh5GBHkEQBK5x0NLSklvsPiqVCu6++27E43Gr+Tf0LYwQrCMqjyDKa8CGP1r2B4Tbgm+22c9+3W/7XvcH2/XKWAfCHsNhiYcZDgaH8dmIapDTqelMN7XXhp3Vt28nB8Bw+KFJ5MDWHQj3bk9Iw45gi9RgOA8jAp3RTVZ/t8cbDICFCQwGwwHBt2CNCG8PO1Hw3d7jTscZqehfmGfAYDAYDA5bhSAMhwPbJW7mGTAYDAcCc2f3Puz7uDAcphwYIwMGg6En0MuCsp9hIYHdRTeelV68t0YGDAaDYYDRi4qpH9Hr99nIgMFgMBgMF4jNPAK9TgQASyA0GAwGg+GC0A+JlUYGDAaDwWDoAlH7v/QDEQCMDBgMBoOhh9ErG7D1whz2EpYzYDAYDIaexW7F26OU+XbG7vdW8eYZMBgMhm2gVyxVQ3fYze/rMPXG2O41GxkwGAx9h71U1p02WTL0Jjp9Xzv9Dg8LIdjunIwMGAyGvoK/W5vBAGzcPXMvxo8iHrt5Lno59uLZNjJgMBgMhr7HYSeHez1/SyA0GAyHFtZK19ANDpII+Ltz7nSMvYZ5BgwGQ1/BiIChl3BYnkfzDBgMhkMLCtpe3gDGcPDY7+dit8+3H/M3MmAwGA499lPYG/Ew9CMsTGAwGPoe1hvAYNgc5hkwGAwDg9206pkY5pOMg/YY9Krn4kI7ABq2h+2SXyMDBoPBsENECVx97SCU3SAo2F4lPIcZRgYMBkPfYy/72+83es0T0S0uNNlzK+LV6Xz+8Yflfu03LGfAYDAYukA3RMBaFW+N/bo/vUDcDhOMDBgMhr7DXrRt7dSTPqoN7V6SAn/cw6j0dnJvtttO+LDsIdArMDJgMBj6Cr5y3CtCcJCKZVCVmpKszQjXoN6fC4HlDBgMBsM20CuKplfmcZDoluhZrsDWMDJgMBj6FqYA+hfd7ktxobtYHuQztJ9VExYmMBgMhxqHMWZu2DtYg6mdwciAwWA4tFCrr9+UQD9dy15gK2u5356H7WK73gQjAwaD4VCinwX9hbq2exn9eE39AMsZMBgMfYvDmjh2GOfcLXr52np5btuFtSM2GAx9Bz+RapCty6h2xwfdAvkw4zDcr/0gtUYGDAZDz8MXhJsJxn5XjNZM5zx2gxDqfetlL9J+zMvIgMFg6CtwJ8HdFqCHdU8AYG92a9zqtb1CtyRgO50aNUejl77X/ZyLJRAaDAbDDuBXMhwG7MVcD+N9MGyEeQYMBoOhC0TF5zv93UvW5W6h07Vudlw38F31/ms7HbubY3vNE3CQMDJgMBj6Dt0K+MOmwKMU3E7mrUrwoBVip62Ju3HzMyS0W+c/DM/AXsHIgMFgGFhc6O55xH67xy9EaW0nGTPquL241p0q9b0IeQwqIbCcAYPBsO/oNUv0QtFpK+New27Mba+usVd2hFT0Yg5EN7kZO5m3kQGDwbDv2Gshu5nA3G9F04sKZTew3wRoN86z3bLMXiElir263xYmMBgM+4L9EKx7pXi7jSt3Ov9m7uftkBbfVb+d5LtuztnNHDY7dqf3fztz3ytCsN0QgX/8Zt9JN3M4aJhnwGAw9AUOmwW+m6V4vXbtO7FeD1pBbpcI6O9u3/OP6SWYZ8BgMPQkttsoZy+VyV40MurGku7m/Z3cn6jr2Y2mSlHJiVtdw0GTgL3AQSv7nXhpjAwYDAZDF+hmy9xu3t9J2eOFjttNrHyvlLKvmA678t/tNsi9AiMDBoOhJ7EbNeT7id0qU4xCN/HpXsZhmWc36HQth/0aLWfAYDDsCw6TYu81RFnxh135GHoLRgYMBsO+YC8tZ4PBcGEwMmAwGPYUUVasKXmDYW+x3TVmZMBgMBgMhgGHkQGDwbDvsPwBg2Fvsd0kUyMDBoPhQGCEwGDYH3RDCKy00GAw7Cv87XMNBsPeYDvry8iAwWDYU6jiJxE4TCTgsM3XYCC20/LawgQGg8GwCYwIGAYBscCedIPBYDAYBhrmGTAYDAaDYcBhZMBgMBgMhgGHkQGDwWAwGAYcRgYMBoPBYBhwGBkwGAwGg2HAYWTAYDAYDIYBh5EBg8FgMBgGHEYGDAaDwWAYcBgZMBgMBoNhwPH/Ac83aChtsPZvAAAAAElFTkSuQmCC", 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", 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" ] From 433fd62721727c2d3bb236cc02ff2c64a361afcc Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Mon, 16 Sep 2024 16:45:54 +0100 Subject: [PATCH 17/27] Adding autoencoder info to README. Signed-off-by: Virginia Fernandez --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 7e7ca7aaf..f90ffb862 100644 --- a/README.md +++ b/README.md @@ -331,6 +331,12 @@ Example shows the use cases of applying SPADE normalization to a latent diffusio ##### [Diffusion Models for Implicit Image Segmentation Ensembles](./image_to_image_translation) Example shows the use cases of how to use MONAI for 2D segmentation of images using DDPMs. The same structure can also be used for conditional image generation, or image-to-image translation. +##### [Spatial variational autoencoder for 2D modelling and synthesis](./2d_autoencoderkl) +Example shows the use cases of applying a spatial VAE to a 2D synthesis example. To obtain realistic results, the model is trained on the original VAE losses, as well as perceptual and adversarial ones. + +##### [Spatial variational autoencoder for 3D modelling and synthesis](./3d_autoencoderkl) +Example shows the use cases of applying a spatial VAE to a 3D synthesis example. To obtain realistic results, the model is trained on the original VAE losses, as well as perceptual and adversarial ones. + ##### [Evaluate Realism and Diversity of the generated images](./realism_diversity_metrics) Example shows the use cases of using MONAI to evaluate the performance of a generative model by computing metrics such as Frechet Inception Distance (FID) and Maximum Mean Discrepancy (MMD) for assessing realism, as well as MS-SSIM and SSIM for evaluating image diversity. From f9f329c2ee54cb509aec597d96f41c028d7e8046 Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Wed, 18 Sep 2024 22:40:37 +0100 Subject: [PATCH 18/27] Fix issue with the plots for autoencoder tutorials. Add anomaly detection nbs to the README. Signed-off-by: Virginia Fernandez --- generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb | 2 ++ generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb | 2 ++ generation/README.md | 4 ++++ 3 files changed, 8 insertions(+) diff --git a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb index 94c719f81..d869cb81e 100644 --- a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb +++ b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb @@ -92,6 +92,7 @@ "from torch.nn import L1Loss\n", "from monai.losses import PatchAdversarialLoss, PerceptualLoss\n", "from monai.networks.nets import AutoencoderKL, PatchDiscriminator\n", + "from monai.utils.misc import ensure_tuple\n", "\n", "print_config()" ] @@ -694,6 +695,7 @@ "# Plot every evaluation as a new line and example as columns\n", "val_samples = np.linspace(val_interval, max_epochs, int(max_epochs / val_interval))\n", "fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True)\n", + "ax = ensure_tuple(ax) \n", "for image_n in range(len(val_samples)):\n", " reconstructions = torch.reshape(intermediary_images[image_n], (64 * n_example_images, 64)).T\n", " ax[image_n].imshow(reconstructions.cpu(), cmap=\"gray\")\n", diff --git a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb index 4a0eda3b4..7112954a2 100644 --- a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb +++ b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -88,6 +88,7 @@ "from torch.amp import autocast\n", "from monai.networks.nets import AutoencoderKL, PatchDiscriminator\n", "from monai.losses import PatchAdversarialLoss, PerceptualLoss\n", + "from monai.utils.misc import ensure_tuple\n", "\n", "print_config()" ] @@ -731,6 +732,7 @@ ], "source": [ "fig, ax = plt.subplots(nrows=1, ncols=2)\n", + "ax = ensure_tuple(ax)\n", "ax[0].imshow(images[0, channel, ..., images.shape[2] // 2].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", "ax[0].axis(\"off\")\n", "ax[0].title.set_text(\"Inputted Image\")\n", diff --git a/generation/README.md b/generation/README.md index 422163001..d998bffcf 100644 --- a/generation/README.md +++ b/generation/README.md @@ -63,3 +63,7 @@ Example shows how to use a DDPM to inpaint of 2D images from the MedNIST dataset ## [Guiding the 2D diffusion synthesis using ControlNet](./controlnet/2d_controlnet.ipynb) Example shows how to use ControlNet to condition a diffusion model trained on 2D brain MRI images on binary brain masks. + +## Performing anomaly detection with diffusion models: [implicit guidance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb) +Examples show how to perform anomaly detection in 2D, using implicit guidance [2D-classifier free guiance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), transformers [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomalydetection_tutorial_classifier_guidance). + From 5145843dce53ccf3eb241c8f0cce540da8d3aa27 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 18 Sep 2024 21:41:39 +0000 Subject: [PATCH 19/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb | 2 +- generation/README.md | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb index d869cb81e..71535e722 100644 --- a/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb +++ b/generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb @@ -695,7 +695,7 @@ "# Plot every evaluation as a new line and example as columns\n", "val_samples = np.linspace(val_interval, max_epochs, int(max_epochs / val_interval))\n", "fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True)\n", - "ax = ensure_tuple(ax) \n", + "ax = ensure_tuple(ax)\n", "for image_n in range(len(val_samples)):\n", " reconstructions = torch.reshape(intermediary_images[image_n], (64 * n_example_images, 64)).T\n", " ax[image_n].imshow(reconstructions.cpu(), cmap=\"gray\")\n", diff --git a/generation/README.md b/generation/README.md index d998bffcf..7dfa3801b 100644 --- a/generation/README.md +++ b/generation/README.md @@ -66,4 +66,3 @@ Example shows how to use ControlNet to condition a diffusion model trained on 2D ## Performing anomaly detection with diffusion models: [implicit guidance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb) Examples show how to perform anomaly detection in 2D, using implicit guidance [2D-classifier free guiance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), transformers [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomalydetection_tutorial_classifier_guidance). - From 57ab96375313b6d79c07495dda590648a50c96dd Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Fri, 20 Sep 2024 16:26:45 +0100 Subject: [PATCH 20/27] Add download = True to 3D NB. Signed-off-by: Virginia Fernandez --- generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb index 7112954a2..0985bfa9c 100644 --- a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb +++ b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -216,7 +216,7 @@ " section=\"training\",\n", " cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise\n", " num_workers=4,\n", - " download=False,\n", + " download=True,\n", " seed=0,\n", " transform=transforms.Compose(all_transforms),\n", ")\n", @@ -227,7 +227,7 @@ " section=\"validation\",\n", " cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise\n", " num_workers=4,\n", - " download=False,\n", + " download=True,\n", " seed=0,\n", " transform=transforms.Compose(all_transforms),\n", ")" From dfc1ebfb25438a6f03a4dd972e3e886e8d3cf08d Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Fri, 20 Sep 2024 20:41:56 +0100 Subject: [PATCH 21/27] Tweak for the number of samples to render Signed-off-by: Eric Kerfoot --- README.md | 6 ------ generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb | 4 ++-- generation/README.md | 6 ++++++ 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index f90ffb862..7e7ca7aaf 100644 --- a/README.md +++ b/README.md @@ -331,12 +331,6 @@ Example shows the use cases of applying SPADE normalization to a latent diffusio ##### [Diffusion Models for Implicit Image Segmentation Ensembles](./image_to_image_translation) Example shows the use cases of how to use MONAI for 2D segmentation of images using DDPMs. The same structure can also be used for conditional image generation, or image-to-image translation. -##### [Spatial variational autoencoder for 2D modelling and synthesis](./2d_autoencoderkl) -Example shows the use cases of applying a spatial VAE to a 2D synthesis example. To obtain realistic results, the model is trained on the original VAE losses, as well as perceptual and adversarial ones. - -##### [Spatial variational autoencoder for 3D modelling and synthesis](./3d_autoencoderkl) -Example shows the use cases of applying a spatial VAE to a 3D synthesis example. To obtain realistic results, the model is trained on the original VAE losses, as well as perceptual and adversarial ones. - ##### [Evaluate Realism and Diversity of the generated images](./realism_diversity_metrics) Example shows the use cases of using MONAI to evaluate the performance of a generative model by computing metrics such as Frechet Inception Distance (FID) and Maximum Mean Discrepancy (MMD) for assessing realism, as well as MS-SSIM and SSIM for evaluating image diversity. diff --git a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb index 0985bfa9c..4e1c9e1a6 100644 --- a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb +++ b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -691,7 +691,7 @@ ], "source": [ "# get the first 5 examples to plot\n", - "n_evaluations = 5\n", + "n_evaluations = min(5, len(intermediary_images))\n", "\n", "fig, axs = plt.subplots(nrows=n_evaluations, ncols=3, constrained_layout=True, figsize=(8, 6))\n", "\n", @@ -783,7 +783,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/generation/README.md b/generation/README.md index 7dfa3801b..d9125a861 100644 --- a/generation/README.md +++ b/generation/README.md @@ -64,5 +64,11 @@ Example shows how to use a DDPM to inpaint of 2D images from the MedNIST dataset ## [Guiding the 2D diffusion synthesis using ControlNet](./controlnet/2d_controlnet.ipynb) Example shows how to use ControlNet to condition a diffusion model trained on 2D brain MRI images on binary brain masks. +## [Spatial variational autoencoder for 2D modelling and synthesis](./2d_autoencoderkl) +Example shows the use cases of applying a spatial VAE to a 2D synthesis example. To obtain realistic results, the model is trained on the original VAE losses, as well as perceptual and adversarial ones. + +## [Spatial variational autoencoder for 3D modelling and synthesis](./3d_autoencoderkl) +Example shows the use cases of applying a spatial VAE to a 3D synthesis example. To obtain realistic results, the model is trained on the original VAE losses, as well as perceptual and adversarial ones. + ## Performing anomaly detection with diffusion models: [implicit guidance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb) Examples show how to perform anomaly detection in 2D, using implicit guidance [2D-classifier free guiance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), transformers [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomalydetection_tutorial_classifier_guidance). From 699446de7d1931058efef3e811b7a99bfae693ba Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Fri, 20 Sep 2024 21:14:37 +0100 Subject: [PATCH 22/27] Fix Signed-off-by: Eric Kerfoot --- generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb | 2 ++ 1 file changed, 2 insertions(+) diff --git a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb index 4e1c9e1a6..72859b0f6 100644 --- a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb +++ b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -694,6 +694,8 @@ "n_evaluations = min(5, len(intermediary_images))\n", "\n", "fig, axs = plt.subplots(nrows=n_evaluations, ncols=3, constrained_layout=True, figsize=(8, 6))\n", + "if not isinstance(axs[0], tuple):\n", + " axs = (axs,)\n", "\n", "# Remove ticks\n", "for ax in axs.flatten():\n", From 92bee4992690e291da462d7749b2b76f8fbb5557 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Fri, 20 Sep 2024 21:42:55 +0100 Subject: [PATCH 23/27] Fix Signed-off-by: Eric Kerfoot --- generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb index 72859b0f6..97b637d24 100644 --- a/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb +++ b/generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -694,8 +694,8 @@ "n_evaluations = min(5, len(intermediary_images))\n", "\n", "fig, axs = plt.subplots(nrows=n_evaluations, ncols=3, constrained_layout=True, figsize=(8, 6))\n", - "if not isinstance(axs[0], tuple):\n", - " axs = (axs,)\n", + "if axs.ndim == 1:\n", + " axs = axs[None]\n", "\n", "# Remove ticks\n", "for ax in axs.flatten():\n", From 019a40e589a30e8029d5c70ff5028c0ef5ed8d92 Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Tue, 24 Sep 2024 09:21:35 +0100 Subject: [PATCH 24/27] Addition of 2D super-resolution notebook using normal PyTorch and PyTorch Lightning. Addition of notebook descriptions to README.md file within generation. Signed-off-by: Virginia Fernandez --- .../2d_sd_super_resolution.ipynb | 1231 ++++++++++++++++ .../2d_sd_super_resolution_lightning.ipynb | 1304 +++++++++++++++++ generation/README.md | 3 + 3 files changed, 2538 insertions(+) create mode 100644 generation/2d_super_resolution/2d_sd_super_resolution.ipynb create mode 100644 generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb diff --git a/generation/2d_super_resolution/2d_sd_super_resolution.ipynb b/generation/2d_super_resolution/2d_sd_super_resolution.ipynb new file mode 100644 index 000000000..e8b30d84a --- /dev/null +++ b/generation/2d_super_resolution/2d_sd_super_resolution.ipynb @@ -0,0 +1,1231 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "33a9aedb-b4d8-48c6-9590-58b221405ca5", + "metadata": {}, + "source": [ + "Copyright (c) MONAI Consortium
\n", + "Licensed under the Apache License, Version 2.0 (the \"License\");
\n", + "you may not use this file except in compliance with the License.
\n", + "You may obtain a copy of the License at
\n", + "http://www.apache.org/licenses/LICENSE-2.0
\n", + "Unless required by applicable law or agreed to in writing, software
\n", + "distributed under the License is distributed on an \"AS IS\" BASIS,
\n", + "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
\n", + "See the License for the specific language governing permissions and
\n", + "limitations under the License.
" + ] + }, + { + "cell_type": "markdown", + "id": "95c08725", + "metadata": {}, + "source": [ + "# Super-resolution using Stable Diffusion v2 Upscalers\n", + "\n", + "This tutorial illustrates how to perform super-resolution on medical images using Latent Diffusion Models (LDMs) [1]. For that, we use an autoencoder to obtain a latent representation of the high-resolution images. Then, we train a diffusion model to infer this latent representation when conditioned on a low-resolution image. \n", + "\n", + "To improve the performance of our models, we will use a method called \"noise conditioning augmentation\" (introduced in [2] and used in Stable Diffusion v2.0 and Imagen Video [3]). During the training, we add noise to the low-resolution images using a random signal-to-noise ratio, and we condition the diffusion models on the amount of noise added. At sampling time, we use a fixed signal-to-noise ratio, representing a small amount of augmentation that aids in removing artefacts in the samples.\n", + "\n", + "\n", + "[1] - Rombach et al. \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/abs/2112.10752\n", + "\n", + "[2] - Ho et al. \"Cascaded diffusion models for high fidelity image generation\" https://arxiv.org/abs/2106.15282\n", + "\n", + "[3] - Ho et al. \"High Definition Video Generation with Diffusion Models\" https://arxiv.org/abs/2210.02303" + ] + }, + { + "cell_type": "markdown", + "id": "b839bf2d", + "metadata": {}, + "source": [ + "## Set up environment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "77f7e633", + "metadata": {}, + "outputs": [], + "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "214066de", + "metadata": {}, + "source": [ + "## Set up imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de71fe08", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "import tempfile\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from monai import transforms\n", + "from monai.apps import MedNISTDataset\n", + "from monai.config import print_config\n", + "from monai.data import CacheDataset, DataLoader\n", + "from monai.utils import first, set_determinism\n", + "from torch import nn\n", + "from torch.amp import GradScaler, autocast\n", + "from tqdm import tqdm\n", + "from monai.losses import PatchAdversarialLoss, PerceptualLoss\n", + "from monai.networks.nets import AutoencoderKL, DiffusionModelUNet, PatchDiscriminator\n", + "from monai.networks.schedulers import DDPMScheduler\n", + "\n", + "print_config()" + ] + }, + { + "cell_type": "markdown", + "id": "c0dde922", + "metadata": {}, + "source": [ + "## Setup a data directory and download dataset\n", + "Specify a MONAI_DATA_DIRECTORY variable, where the data will be downloaded. If not specified a temporary directory will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ded618a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/tmpj53lse09\n" + ] + } + ], + "source": [ + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "print(root_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "645f97bb-6879-4b2e-8fc9-29dd1a6e904f", + "metadata": {}, + "source": [ + "## Set deterministic training for reproducibility" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9f0a17bc", + "metadata": {}, + "outputs": [], + "source": [ + "# for reproducibility purposes set a seed\n", + "set_determinism(42)" + ] + }, + { + "cell_type": "markdown", + "id": "d80e045b", + "metadata": {}, + "source": [ + "## Description of data and download the training set\n", + "\n", + "For this tutorial, we use the head CT dataset from MedNIST." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c8cf204a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-09-23 09:27:05,757 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2024-09-23 09:27:05,758 - INFO - File exists: /tmp/tmpj53lse09/MedNIST.tar.gz, skipped downloading.\n", + "2024-09-23 09:27:05,759 - INFO - Non-empty folder exists in /tmp/tmpj53lse09/MedNIST, skipped extracting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:16<00:00, 2923.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-09-23 09:27:22,258 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2024-09-23 09:27:22,258 - INFO - File exists: /tmp/tmpj53lse09/MedNIST.tar.gz, skipped downloading.\n", + "2024-09-23 09:27:22,259 - INFO - Non-empty folder exists in /tmp/tmpj53lse09/MedNIST, skipped extracting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 2964.04it/s]\n" + ] + } + ], + "source": [ + "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\", download=True, seed=0)\n", + "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n", + "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n", + "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]" + ] + }, + { + "cell_type": "markdown", + "id": "cacdb233", + "metadata": {}, + "source": [ + "## Prepare dataloaders\n", + "\n", + "Here, we create the data loader that we will use to train our models. We will use data augmentation and create low-resolution images using MONAI's transformations:\n", + "\n", + "1. `LoadImaged`: to load the images\n", + "2. `EnsureChannelFirstd`: to make sure there is a channel dimension at the beginning of the output tensor\n", + "3. `ScaleIntensityRanged`: normalise the images\n", + "4. `RandAffined`: affine augmentation (just training)\n", + "5. `CopyItemd`: we copy the image item to obtain the low-resolution representation\n", + "6. `Resized`: we resize the low resolution image (copy we just made) to obtain a low resolution representation to 16x16" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "c7997edf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 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rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n", + " translate_range=[(-1, 1), (-1, 1)],\n", + " scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n", + " spatial_size=[image_size, image_size], padding_mode=\"zeros\", prob=0.5),\n", + " transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n", + " transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n", + "]\n", + "\n", + "train_transforms = transforms.Compose(all_transforms)\n", + "val_transforms = transforms.Compose(all_transforms[:3] + all_transforms[4:])\n", + "\n", + "# Datasets\n", + "train_ds = CacheDataset(data=train_datalist, transform=train_transforms)\n", + "train_loader = DataLoader(train_ds, batch_size=32, shuffle=True, num_workers=4, persistent_workers=True)\n", + "val_ds = CacheDataset(data=val_datalist, transform=val_transforms)\n", + "val_loader = DataLoader(val_ds, batch_size=32, shuffle=True, num_workers=4)" + ] + }, + { + "cell_type": "markdown", + "id": "166e4242", + "metadata": {}, + "source": [ + "### Visualise examples from the training set" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8c0fe41c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot 3 examples from the training set\n", + "check_data = first(train_loader)\n", + "fig, ax = plt.subplots(nrows=1, ncols=3)\n", + "for i in range(3):\n", + " ax[i].imshow(check_data[\"image\"][i, 0, :, :], cmap=\"gray\")\n", + " ax[i].axis(\"off\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "76412555", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot 3 examples from the training set in low resolution\n", + "fig, ax = plt.subplots(nrows=1, ncols=3)\n", + "for i in range(3):\n", + " ax[i].imshow(check_data[\"low_res_image\"][i, 0, :, :], cmap=\"gray\")\n", + " ax[i].axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "id": "9fc99896", + "metadata": {}, + "source": [ + "## Define the autoencoder network and training components" + ] + }, + { + "cell_type": "markdown", + "id": "9b52c4a7-26eb-47e7-8aac-99c62ca88ee3", + "metadata": {}, + "source": [ + "To yield a 16x16 latent representation from the high-resolution images, we use AutoencoderKL. We train it using a Patch-GAN adversarial loss, as well as a perceptual loss, to boost image fidelity." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "610bd118", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cuda\n" + ] + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"Using {device}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0e4ef480", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "autoencoderkl = AutoencoderKL(\n", + " spatial_dims=2,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " channels=(256, 512, 512),\n", + " latent_channels=3,\n", + " num_res_blocks=2,\n", + " norm_num_groups=32,\n", + " attention_levels=(False, False, True),\n", + ")\n", + "autoencoderkl = autoencoderkl.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "bd5197a4-ec30-4f13-9b7a-5e3e43a42637", + "metadata": {}, + "outputs": [], + "source": [ + "discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1, num_layers_d=3, channels=64)\n", + "discriminator = discriminator.to(device)\n", + "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfd826c6", + "metadata": {}, + "outputs": [], + "source": [ + "perceptual_loss = PerceptualLoss(spatial_dims=2, network_type=\"alex\")\n", + "perceptual_loss.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "410911c9", + "metadata": {}, + "outputs": [], + "source": [ + "scaler_g = GradScaler()\n", + "scaler_d = GradScaler()" + ] + }, + { + "cell_type": "markdown", + "id": "c16de505", + "metadata": {}, + "source": [ + "## Train Autoencoder" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "2789508d-9fa8-483e-8b4c-dd17bf2f39b8", + "metadata": {}, + "outputs": [], + "source": [ + "# Loss weights\n", + "perceptual_weight = 0.002\n", + "adv_weight = 0.005\n", + "kl_weight = 1e-6\n", + "\n", + "# Optimizers\n", + "optimizer_g = torch.optim.Adam(autoencoderkl.parameters(), lr=5e-5)\n", + "optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=1e-4)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "830a3979", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 0/75:,recons loss: 0.153782,perc_epoch_loss: 0.505703,kl_epoch_loss: 2163.503702,\n", + "Validation. recons loss: 0.002920,\n" + ] + }, + { + "data": { + "image/png": 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igVqtpsx8uVxGMplUx+TzeWXGOfn1el0FA6lUCqVSCUtLS5ienkY2m1VaLx6P4/z580YQ7XUM5GcJJpkbpsaNStVR83GxMSCRpLPUijTReoGFHqxIjlXm4GUfOR9y0eiLWy6WfuPwxAht13XRaDRU5zkw2WwWMzMzauXISQiCAI8ePcKdO3cwPz+PIAiQTCZRr9dRqVSU1tNTbSyC8H1fTRoDEWq1drsNx3EUb1itVuE4DlZWVpQf1u124TgOZmZm4Hlejynci48oRU6wpEtMZlmWfUmTxnuSBLNMw+kA1F8SmHokS19RB6nMXknQyWOkDOMjPpGomTfDpzD4vo9Op6MoF9lhaqOtrS3cunULt27dQqlUQrFYVI/v4AD4vq8Gn4MjB7Tb7Sq6hxq53W7DdV2sra1hcnIS4+PjaDQaqNfrePjwIYrFIvL5vBpwZmgIbOkbDjIv0hTpWlFOoj7B1I66RmT/AajfSU/pYNTfw3DnYVQcL7bNxcpFqmtluRBMcxsFqn4ZlScCREaH8Xi854FImUwGx44dUwCitNttxfdVKhU4joN0Oo1qtQrXdZXGK5fLitmPx+MAAN/3Ua/XkU6n4bouarUaHMdBMpnsWfnr6+uoVCooFosAoGidSqWi/gaAZDKJyclJLC8v78qG7EWGPZ6aRAYgDOwIUPmsReZ+STrzM18MWuh7E9A8XwYZUmtK31JqY4JeN/vA7u0MMjLXSfsnBkSSpKQYGOFms1l1E7LTQRAgHo9jYmICQRBgY2MDjuNgc3Ozh3bI5XLK9FcqFRw+fFitag4oB51awXVdHD16FLFYDI8ePYLv+5iYmEAikUCj0UC5XFZBCwAcOnRIkcJcNP8NjcMxoU8sqSgJWI4XgUQ/l+4N/WkWiujmWKbx9EIIti8LbOn/Sa0v29OLICQgdY3ZD4im8YiSfQUiB6Rer6PdbqvyJpZHyZUdj8fx0Ucf4erVqyiXyzhy5AhyuRy2trZUFEkTUi6XleNdrVaxvr4Ox3GQSCSwsrKCIAgwOTmpAMbsALMYtVpNZWZWV1fhui48z8PRo0eRy+XUYGWzWcVv7pVP1P0lvVRKih6k8HgCiMFEGIaKUeD46cGIXsbFIghTJbY0nfybfZdEOQHIJ+XqfqlO2URFx3uxJvtKaAdBgHQ6rYpNJUXAgAOAMrO3bt3CvXv30Gq1sLm5qUhwAFhaWsLY2Bg6nQ62trYAQBU11Ot1LC8v49SpU3AcB+vr69ja2lJg5nXHx8eRSqUUhbS5uQnf9/HMM88glUqhUqmoZD+rwS3LUr5i1GNLdNG1AMdDz4IQgLxPWg0GJKRaCH49xysBoZtKLjzZH11T6gCSPJ+kfWjmpWaU3KQOZlnxrvvIT4S+YTECzWW9Xlc+HTvo+35P1Yltb+eVqclarZby8+RN8Ulc8joff/yxqjhJp9OK6snlcvA8D/fv31dVOel0Gvl8Hu12G1tbW0ilUigWi2i320oDO46DZrOpnkDLgGKYdJ/uE1Ej6vSNLCbgixYjHo/3FMNKMNKsy/0rklqhX0vQsz/8ftBL187Sh6QrYOItdc5S3rOJ7I6SfSe0aVoAKJ+n0WhgY2NDkc/AtlbkRPu+j0QigWKxiGQyiXw+r/g9rkZWWm9ubqrf1tbWYNs2pqamUCgU1CRms1mk0+keLm5ra0vxcaVSCRMTE4pABtBDN0kedK+EtvwsS7YIEFmWL6NdWS0eZeZMGQypCflOl0ICUtIw+oJhWzJ4MvmH0r/leSYKp1+fo2RfgcjVQUeYN7CxsYG7d+9iampK0Tuu6+L8+fMAgJs3b6LRaKiKE+ZTwzBUVM7Y2Bji8bgCWCqVwqFDh9DtdpHL5ZBOp9VgMlhJJpM9Dn42m1VajxG2/K8C3W5XZW1k/d8g86xHidRCJsJY0jey3It+KzMatBbMalAjytQcX9SaiURCmX2pmfQyMxM3KKkj/WXKLZuCLoopwzRI9j1qlh2hH7S+vo7bt2/jW9/6Vo+WOHLkCGZnZwEAW1tbyGQyyucrlUpKS9RqNQUSmleW0MscKs0UV7TruqhWqwCAXC6HWCyGfD6PTqejcs4yx8oIXg7iMANp4hGjCGDpq0mTL0Grk8n8XRdJmDPKBnb2zhC00uQTiKZUnQx+9OwNaR1dM+raUQf3sLLvZWDSpyLZXC6XMT8/3xPA0Pzl83kcP34cxWJRBRaO46j/zeF5nnKcpRnhCiVYmdHhw92ZrG80GqrShCT3+Pg4pqamkMvlesDDIIuEtqQ3BokOLKm9eK+yTEt/ycp0mUaTGk/uKuTYmqJoy7J6CG4CUWpgtiMBReCZiHOpGeV15WL9LACk7CsQ9cHmqmw0GlhcXES1WlVbOjnB2WwWzz77rFrRtVoNruvigw8+wOrqquIfJV8o+UqWUgFQPqA0kY7jIJVKIZVKYXV1FZlMBtPT04q2YVvU3PSTpIbZS2ZF/6w77rpWlD6cLDZg8ETNL/PT1E4yHxyGOxkTmmi6NzqQ5Tl6NC3LvDjWEoi8F90HpfTLwPSTfdeIXN28EWA7aJmfn8eHH36IL33pS2qrJG+GPk0Yhuqf+Tx48ADXrl3Do0eP1BYB8oKsReROvkQioVa+XPEymmy323jw4IHa1Tc+Pt4zqUtLS7h3754CL2XYgKWfsy5z5Jy4WCym/N9qtaoAw99lTprn6ZSNTP8BO4uVm79oRWiGCRgJaJPfJwlzfWH1YxA+K5kNfA4+YrvdRiaTUZoF2I7i7t+/j3/84x/4xje+oTopsxf8zra3K2u+8IUvAABu3LiBcrncMyG6+a1Wq6hWq8qHJGkeBAFKpRIKhQLy+TxyuRwmJiZQKBQUeEmSLywsYGFhQVFLNEl7Nc/yZZoQ3S+TIJJakZqQ/+MlmUz2aCUJGI4z99vIXX06sS61mqwblXyi1JR6lC/z4LpJ1mmgvWjHfc+sMLsB7ESJ3Aj129/+Fj/4wQ8wMzMDoHePipygVCqF06dPw3Vd1Ot1fPzxx1hZWVEZE2DbpJ84cQIfffQR1tfXcfbsWQBQm7nJES4vL+MrX/kKAODrX/86XnnlFYyNjakJIhjeeOMNtFoteJ6n/Eqp1fuJnAgT/SK1CjUVCzDIi+p+GqNfBlDpdLrHh9T9MWpZWcpFrcfjeC80vbpPKNN6DPh0EErA6vev//3EgGjqlFz5vu/jzTffxC9+8Qskk0k0Gg1VqcMByGQyyjdLJpM4duwYWq2Wqsjudruq1Mv3feTzeXiep56SUCgUekjvhYUFXL9+HT/72c/wwgsv9EwUzeLf/vY3vP/++6o4lamtvSTtdRpEN2HS1HE86Lv5vr+raNaUOZHvMurl9zJXLFN/wA61JlN2Mq9MIr/RaKgXv6d1kf9STveHo8bjiQAxygTx3fd9/OY3v8Hp06dx5cqVnkd0cHCBHT9zYmJC+VKsJWSQwowIAJTLZQDYVePXbrcxOTmJV199FSdOnECz2VRAD8PtlOTDhw/xhz/8AXfu3FGaW1beDDuQulmWk8N2OPEyEJB8qe/7aitFvV5XizWZTPYUkchCVelT6nNBc69TLDIzQgVAsHER8kVASo1oyphI//CJByuyA/pEyM+//vWvUavV8Nprr8HzvJ6ghU/lArazHfl8HsViEQ8ePEC5XEa1WlVFFcePH1cTzA1IdM75H0t/9KMf4cSJE8jn8z3RPAAsLi7iz3/+M/7617+i1WopMj3qER/D3HuUJpCakKBgerJWqwHY2RJAYIVhqPrSbDZ7ghPTpPO+ZI0j0BvlSt9Pf9YNX77v94CQWtPEI5rm2HT/g+Rz306qdzCRSODGjRt4/fXX4fs+vvnNb2JmZgaZTEYNFE01sE3OHj58GNPT0+rf1T569AjtdhtTU1MqnXf69GlVJGFZlgLimTNnAEAFOHToFxcXcfXqVfzlL3/B/fv31V4abmU1mddB9xnlN+kLlAuCe3BknabUNjSrLOtngkCW8MsgR6YlJQWjpxNNwKPPKs2y9BdNJtnkG/ZbKP3kc9GIXPmmFRMEAVKpFN5//31sbGxgfn4ec3NzOHv2LI4cOYJ0Oq2OJxAKhQJOnz6t8tC5XA6NRgPHjh1DoVBQjxfhnhiSybZtI5VK9dQrttttzM/P45133sHbb7+NmzdvotVqoVAowPd9ZdJoMocZyKhIWX420SByUxMnWUamTGdyC0MsFlMLTHKLBKXUmJJ8pgvAPeG+76t3ugQSmHJXoL4lQZ9r0/zr4zKM7Otj6UwrX/+bT2sIwxAbGxuIxWJ4/vnncfHiRbzyyiuYnZ1FNptVAOLgsi1W9ZTLZaTTaeRyOUVI02/iJMgKZ/pBt2/fxh//+Ee89dZbuHPnjrpOLpdT2w3i8bh6TuMwZWDUGDJjETUeBCEBI1Nv3NHHhwTI92w227MpX6brqA1ljpkAlEAMgqAHiFIbSi3JrRnNZrOnBtEUpESBzbIspfWH+Q/2nysQTZJIJFCpVGDbtvLbNjc3AQAnT57E3Nwcrly5gnPnzikujQQ4nXNqPCn07ST4WKVdKpXgui7+9Kc/4Xe/+516oJMsaODAsbiAPpm+vcEkMpWmR7NyPPSImtqXWk2WhZE75JMfUqlUT+ZFFkPITfuS99RJaj5yjppQj45lQSwDmSjKRt6XyWfkeLZarScLRJOQJ+NjRViYwCwLV169XsfZs2fV7rrvfve7uHDhAk6ePKn4NBa08oYJUqb5XHf7gUp37tzBu+++i9dffx13795VZpcg465B9k/6qQwehgEiizIGAVGfOOlLyapygpIPYeJj9+TjjaWvaFlWT7GDng+mZqP2832/pwBWFjtIADJlq8+xTsybwEhlsLGxMRg7+wnE/RQ61d1uF8lkEsViEVNTU2pHXiqVQqFQQLFY7OEiK5UKtra28PDhQ3zwwQdYW1tTmRnSMzKKHDZrMqivABQQTdIPiBKQkieUvp/+kqZZf/C7CSRc6DpdI31SGdhEBSeSvDddQzINBCIr7PvJgQSiNFnUorIKGNiJbpmV4HkEMLBd+sXaRql5eSzw2SpFdKHp0qtaTCCnmZTBmB4E8CWrb2RULAEpq6zlXmVeS4JEcofUgPyO/eJx8nx+1oFooojkAiOBXiqVBo7hgfv3FmEYqkyDXlcnJ4xFEFzhAHqc9m63i0ql0sPLATuTzsBmmBTeMH3mu/wsfVmaS52u0duRC0QWo8oAhwBl27KoQb+e3haBJ+kd2XedrJYaW/ZRLn6T+7LXBX7ggAhAVUkDvfloGbCQY5MTK1epfJd7T3SNuN+iaxApEhzSBzUFeXyXwYbOz+lFs/Je9T7pGphANPl1sk1+HgQsnap6ojzifoj0k/TVJlcrC2ilFuAxPI7ZCUnyyuN4vf0QvX1+ZwJk1Pn6S/bPVKmtuxf65Ou+ovTdZH91kJnONx0X9ZtpsQySAwdErn4CTAIyisfSfSIOOOsipd+zH8HJoP5TpDmNAule2zWZQP03U9TO303g1MUEIFNwJc+NiqyfWiACUOCRq4rURr8gQ488dZMk29grEIYVk0/Fe+HfUdpOaiipsfvxs/20qHwfZCp1cJk0uald2Sf9evLeBsmBA6Jt28hmsz2Ugl63KP09E5gkEIHdDvXnAUApUTxi1LUlSPi7DDp4jMka6KCV7/Jc3YfTX3sBrX5vUQA0JR6i5MABkfWGwE6xJ29OOth6fZ70fYAdrcIIUzro/H0vpkMXffKkgy6/47Gmz7zHYa4j3RK9rSi+T/ZR9zFNGpQSZdr1Y0xaVH63F6tz4ICoa5EoR7rb7aoompqTv/Ml/5mjrj0A9OwJMWkJBjyWZalNXZlMRpWivfDCC/j2t7+N27dv49q1awB6gRU1kSbNZQoMdGDRxJu0qzzetEj7BTCyjX7+nf67/pt+j1EWyyQHDojDiG3bSCaTPaQsiV/L2slxst5P+ph6YYCsgDFNYLVahe/7mJycVNzkl7/8ZVy+fBm2bePDDz/EvXv3jP2MApc+OTRhg0x5VFBC+kpqJJPm0/vF3/v5lKbjTG2Y+iot0CB5KoHY6Ww/mElP7rN0S2Y6dBDIHCoja32ApTY4evQoisUicrkcpqenMTk5iVqthmvXruH+/fsolUqqsFUXEwCjgGEKDqL8St2P4+8y2IkKcPqZXT3IMgVGen/7LbK91HQ+lUC0LEs9U3FyclKV/8t9H9wqwIGhNgTQsxNNPsmKbctJotNdqVSwtLSEu3fvYnNzE0tLS6pUzOS/sa0o0yQnkhpRTpopejWlAnXqyvRZHzv5bjLN8jfTcfLapvuL8of7yVMJRGB7MPifmeRDQOXTtQhEvRJFlr73AwCP5cNBy+UyarXarocc8domLaOD0RQYDBOlRo2BScPpRHWU+R3UNsUE6qhF1i8w6ydPLRDb7bbSUqurqz0BizyGYJRaRe5I082LKfLWj+OeGJr6fkCToptgk082CLjye+nL6tJPE/bzIU1+oMkUm65n+m3YxfXUAtG2bVQqFVQqlV2pO/lcHIrMU8voWS/bivIXacLlc3F4PZpW2e6giDnqmH6TaQp++D6sOdY1XdR5UX6gPE9v57+RpxKIDDKo3UjlMCKWmRkp+qPVGDn3uw6BLTcphWGoCnD14/i33g7F5JPpn/Xjor6TPl1UMBQVcJjaluMSBUKKdE3kAvx/ZZrpu7mu2/NEBvkkCGD3U1uBHZNm2jxv0g66vwns8HQcfEkjRTnwuvTTUnpf9POifMqooEne9zDX0xeFPFaCVDfvJhAOS98MXRg7kpF8nvL5lqKMZCRDygiIIzkQMgLiSA6EjIA4kgMhIyCO5EDICIgjORAyAuJIDoSMgDiSAyEjII7kQMj/AZaygcwcRnNPAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 5/75:,recons loss: 0.055186,perc_epoch_loss: 0.259082,kl_epoch_loss: 2720.869604,\n", + "epoch 10/75:,recons loss: 0.047029,perc_epoch_loss: 0.211522,kl_epoch_loss: 2314.558471,\n", + "Validation. recons loss: 0.001608,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 15/75:,recons loss: 0.042698,perc_epoch_loss: 0.174223,kl_epoch_loss: 2112.795490,,gen_loss: 1.012134,disc_loss: 0.002280,\n", + "epoch 20/75:,recons loss: 0.035483,perc_epoch_loss: 0.102338,kl_epoch_loss: 2271.543911,,gen_loss: 1.012229,disc_loss: 0.002381,\n", + "Validation. recons loss: 0.001392,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 25/75:,recons loss: 0.030027,perc_epoch_loss: 0.057049,kl_epoch_loss: 2255.536555,,gen_loss: 1.012236,disc_loss: 0.012159,\n", + "epoch 30/75:,recons loss: 0.025783,perc_epoch_loss: 0.041187,kl_epoch_loss: 2198.212172,,gen_loss: 0.993313,disc_loss: 0.022697,\n", + "Validation. recons loss: 0.000970,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 35/75:,recons loss: 0.026168,perc_epoch_loss: 0.023821,kl_epoch_loss: 2140.541582,,gen_loss: 0.633519,disc_loss: 0.151763,\n", + "epoch 40/75:,recons loss: 0.025719,perc_epoch_loss: 0.017355,kl_epoch_loss: 2062.614804,,gen_loss: 0.479063,disc_loss: 0.183541,\n", + "Validation. recons loss: 0.000736,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 45/75:,recons loss: 0.024476,perc_epoch_loss: 0.015675,kl_epoch_loss: 1969.286549,,gen_loss: 0.510046,disc_loss: 0.174579,\n", + "epoch 50/75:,recons loss: 0.023567,perc_epoch_loss: 0.014090,kl_epoch_loss: 1853.637946,,gen_loss: 0.489276,disc_loss: 0.174202,\n", + "Validation. recons loss: 0.000862,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 55/75:,recons loss: 0.022395,perc_epoch_loss: 0.012438,kl_epoch_loss: 1734.904024,,gen_loss: 0.462214,disc_loss: 0.178832,\n", + "epoch 60/75:,recons loss: 0.021742,perc_epoch_loss: 0.012144,kl_epoch_loss: 1622.974387,,gen_loss: 0.528392,disc_loss: 0.165096,\n", + "Validation. recons loss: 0.000838,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 65/75:,recons loss: 0.021505,perc_epoch_loss: 0.011629,kl_epoch_loss: 1531.166383,,gen_loss: 0.506063,disc_loss: 0.167129,\n", + "epoch 70/75:,recons loss: 0.020647,perc_epoch_loss: 0.010862,kl_epoch_loss: 1449.738802,,gen_loss: 0.516405,disc_loss: 0.174172,\n", + "Validation. recons loss: 0.000698,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "max_epochs = 75\n", + "val_interval = 10\n", + "print_interval = 5\n", + "autoencoder_warm_up_n_epochs = 10\n", + "\n", + "for epoch in range(max_epochs):\n", + " autoencoderkl.train()\n", + " discriminator.train()\n", + " epoch_loss = 0\n", + " gen_epoch_loss = 0\n", + " disc_epoch_loss = 0\n", + " perc_epoch_loss = 0\n", + " kl_epoch_loss = 0\n", + "\n", + " for batch in train_loader:\n", + " images = batch[\"image\"].to(device)\n", + " optimizer_g.zero_grad(set_to_none=True)\n", + "\n", + " with autocast(\"cuda\", enabled=True):\n", + " reconstruction, z_mu, z_sigma = autoencoderkl(images)\n", + " recons_loss = F.l1_loss(reconstruction.float(), images.float())\n", + " p_loss = perceptual_loss(reconstruction.float(), images.float())\n", + " kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3])\n", + " kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n", + " loss_g = recons_loss + (kl_weight * kl_loss) + (perceptual_weight * p_loss)\n", + "\n", + " if epoch > autoencoder_warm_up_n_epochs:\n", + " logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n", + " generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n", + " loss_g += adv_weight * generator_loss\n", + "\n", + " scaler_g.scale(loss_g).backward()\n", + " scaler_g.step(optimizer_g)\n", + " scaler_g.update()\n", + "\n", + " if epoch > autoencoder_warm_up_n_epochs:\n", + " optimizer_d.zero_grad(set_to_none=True)\n", + "\n", + " with autocast(\"cuda\", enabled=True):\n", + " logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n", + " loss_d_fake = adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n", + " logits_real = discriminator(images.contiguous().detach())[-1]\n", + " loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n", + " discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n", + "\n", + " loss_d = adv_weight * discriminator_loss\n", + "\n", + " scaler_d.scale(loss_d).backward()\n", + " scaler_d.step(optimizer_d)\n", + " scaler_d.update()\n", + "\n", + " epoch_loss += recons_loss.item()\n", + " perc_epoch_loss += p_loss.item()\n", + " kl_epoch_loss += kl_loss.item()\n", + "\n", + " if epoch > autoencoder_warm_up_n_epochs:\n", + " gen_epoch_loss += generator_loss.item()\n", + " disc_epoch_loss += discriminator_loss.item()\n", + "\n", + " if epoch % print_interval == 0:\n", + " msgs = [\n", + " f\"epoch {epoch:d}/{max_epochs:d}:\",\n", + " f\"recons loss: {epoch_loss / len(train_loader) :4f},\"\n", + " f\"perc_epoch_loss: {perc_epoch_loss / len(train_loader):4f},\"\n", + " f\"kl_epoch_loss: {kl_epoch_loss / len(train_loader):4f},\"\n", + " ]\n", + "\n", + " if epoch > autoencoder_warm_up_n_epochs:\n", + " msgs += [\n", + " f\"gen_loss: {gen_epoch_loss / len(train_loader):4f},\"\n", + " f\"disc_loss: {disc_epoch_loss / len(train_loader):4f},\"\n", + " ]\n", + "\n", + " print(\",\".join(msgs))\n", + "\n", + " if epoch % val_interval == 0:\n", + " autoencoderkl.eval()\n", + " val_loss = 0\n", + " with torch.no_grad():\n", + " for batch in val_loader:\n", + " images = batch[\"image\"].to(device)\n", + " reconstruction, z_mu, z_sigma = autoencoderkl(images)\n", + " recons_loss = F.l1_loss(images.float(), reconstruction.float())\n", + " val_loss += recons_loss.item()\n", + "\n", + " msgs = f\"Validation. recons loss: {recons_loss / len(val_loader) :4f},\"\n", + " print(msgs)\n", + "\n", + " # Plot reconstruction\n", + " plt.figure(figsize=(2, 2))\n", + " plt.imshow(torch.cat([images[0, 0].cpu(), reconstruction[0, 0].cpu()], dim=1), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "\n", + "\n", + "del discriminator\n", + "del perceptual_loss\n", + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "markdown", + "id": "c7108b87", + "metadata": {}, + "source": [ + "## Rescaling factor\n", + "\n", + "As mentioned in Rombach et al. [1] Section 4.3.2 and D.1, the signal-to-noise ratio (induced by the scale of the latent space) became crucial in image-to-image translation models (such as the ones used for super-resolution). For this reason, we will compute the component-wise standard deviation to be used as scaling factor." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "ccb6ba9f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scaling factor set to 0.8571630120277405\n" + ] + } + ], + "source": [ + "with torch.no_grad():\n", + " with autocast(\"cuda\", enabled=True):\n", + " z = autoencoderkl.encode_stage_2_inputs(check_data[\"image\"].to(device))\n", + "\n", + "print(f\"Scaling factor set to {1/torch.std(z)}\")\n", + "scale_factor = 1 / torch.std(z)" + ] + }, + { + "cell_type": "markdown", + "id": "b386a0c2", + "metadata": {}, + "source": [ + "## Train Diffusion Model\n", + "\n", + "In order to train the diffusion model to perform super-resolution, we will need to concatenate the latent representation of the high-resolution with the low-resolution image. For this, we create a Diffusion model with `in_channels=4`. Since only the outputted latent representation is interesting, we set `out_channels=3`." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "92f3e348", + "metadata": {}, + "outputs": [], + "source": [ + "unet = DiffusionModelUNet(\n", + " spatial_dims=2,\n", + " in_channels=4,\n", + " out_channels=3,\n", + " num_res_blocks=2,\n", + " channels=(256, 256, 512, 1024),\n", + " attention_levels=(False, False, True, True),\n", + " num_head_channels=(0, 0, 64, 64),\n", + ")\n", + "unet = unet.to(device)\n", + "\n", + "scheduler = DDPMScheduler(num_train_timesteps=1000, schedule=\"linear_beta\", beta_start=0.0015, beta_end=0.0195)" + ] + }, + { + "cell_type": "markdown", + "id": "8fb22b1a", + "metadata": {}, + "source": [ + "As mentioned, we will use the conditioned augmentation (introduced in [2] section 3 and used on Stable Diffusion Upscalers and Imagen Video [3] Section 2.5) as it has been shown critical for cascaded diffusion models, as well for super-resolution tasks. For this, we apply Gaussian noise augmentation to the low-resolution images. We will use a scheduler `low_res_scheduler` to add this noise, with the `t` step defining the signal-to-noise ratio and use the `t` value to condition the diffusion model (inputted using `class_labels` argument)." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "25d9d3e3", + "metadata": {}, + "outputs": [], + "source": [ + "low_res_scheduler = DDPMScheduler(num_train_timesteps=1000, schedule=\"linear_beta\", beta_start=0.0015, beta_end=0.0195)\n", + "max_noise_level = 350" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "aa959db4", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation loss: 0.005906,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 20/200:,loss: 0.144559,\n", + "Validation loss: 0.004613,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation loss: 0.004093,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation loss: 0.004793,\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation loss: 0.004594,\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation loss: 0.004505,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation loss: 0.004176,\n" + ] + }, + { + "data": { + "image/png": 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flRkaRw614Jf8DKJeqNOy2SyWl5dZNGVZFrvRcrnMfCzDMJi6B4BoNMqOkWWZRagE0mq1yjgwx3EYfZJOp6Gq6oF4RK8/Q6vTKDIUBAGmaaLZbDJtRROIr6QhM+xdqM73E2ls27ZhGAaL9Cl1SNmhcU0ab75pgubzeRQKBXS7Xbayrl6vM9+bagQpsKHsT6lUQqvVgqIoUBSFcYN+vx+KomBqaoqxHjRemqax+z1snv/QpnlQI2gBEHVUp9NBPp9nuVlawws8IpA1TWPqPhgMsuiNfB9FUVCpVFCtViFJEk6dOoVgMMg0JpmPg3aG6z6qniHahQaB/CBFUZjGHraG17udBo60H+Vy+fSbN6M0qp3evqfrArvrjykDYhgGtre3US6XWYaoVquxPm+32wB2V+Hpus7OQSCl6huK/kOhEOMaKaMFYGgRyDP1Ecmf4EGwubmJ7e1tpFIpxtC//PLLWF9fRzgcZp3RarXYoNMslSSJUSJ80UOtVmMUy9bWFiqVCjKZDAMigXi/6SWvY93tdhk/SO5GJBJhBRp81oLMHAmZeB7QJLyzT/dMx3Y6Hdi2PVYF0V4pNMMwUCgUUKvVWFqO2vXw4UMoioJQKATTNJlrEAqFoOs6ay9F7aTlyfUiv50+i6KIer3ep3S8bgTfv6PkifuIvV4PhUIBf/zjH/HDH/6QNe4LX/gCjh07ho2NDWYmaGBoITfdOD3/hUxwOBxmjjyBIhaLIZPJwHEcrK+vo1qtMhplHPEOKP+cmWKxiIcPH2J6ehrRaBSqqiKVSvUVVvj9frTbbQYeAiCdh48gKctBGpZ8QiqooKII8j95kz6uUHtu3ryJcDjM/EHSdj6fj/mnlFWJRCKQZZn5md76UD4P7jgOQqEQ4xFd10W9Xke1WkW9Xh/JcY6SJxI1u+6jdQqktf785z/j61//Oubn5+H3+xlvRlRIPp8H8KjGT9M0CIIA27b7HhUiiiJs20YkEoGqqqjX65ibm8PCwgLm5+fhOA7+8Y9/9JVr7Vco6AAA0zSxtbWFW7duIRwO48SJE0gkEpiammIlX3QMn+fmeTnvIFCFiyiKiMViSCaTSCQSjDkoFovI5XJoNBr7mkzU99Qeyguvrq7izJkzSKVSmJqaYgkDYiECgQADF7ETuq73+ev8GnFiB+hYGsP79++jVCqxvj+Mr3goIHo5I+pE13Xxn//8B7/5zW/wi1/8ArquQxRFSJKEqakpzM/PI5/PQ5Ikpvm63S5LrZH/SCVjgiCgVCohEokgHo9jamoKy8vLiMViuH37Nj766CPmaO+HvuE7jgBmmiY2NjbYUyOIdKZIUhAEFsUbhtGn9YBHT0YgTQnspi41TUMgEEA6ncbMzAyi0SjT8oVCATs7O6w+cRgQx4nMLcvC5uYmcrkcM6OBQKAPsDxPSG4CAYn3VTVNg23bsCyLmWC6x1wux6zboJV6Xk5xlDy1XLNhGHjvvfdw8uRJfPe732UFDIIgYGFhAYZhYGVlheVbTdOEpmlQFIXVv4miyJ5ZQ9okGo1ieXkZL730EhzHwXvvvYeVlZUDV97w1AewC34CBVXUhEIhBkoKpsLhMCO1ebeCDx7Il1QUhZ0rmUxC0zQ4joN6vY5SqYRisYhqtQrHcVgkOkjG0Ta9Xg+VSgX//e9/kUgkkE6nEQgEmD/e6/VQLBaZ5aF3cq9IOdCEKxaLLCgkiyOKIvL5PFqtFrv/g7aX5KkBUdM0bG9v41e/+hVM08S3vvUthEIhVk3zyiuvoNlsIpfLsUGlmUvak0wzAXJpaQmzs7MsSltZWcG7777LojgqkjiI8HlV8lnX1tag6zpc10W73UYymWT8WiwWgyRJSCaTzB3hF1NRcALssggUFNCA53I53LlzBxsbG6hWq3155oPmzGkSmKaJf//734jH4zh+/DiWl5eZeTYMA/fv32ef6X7o6WmkRQmQRGEBYJOSahNN00Sj0RgKuL0Cq8f2dcfc8yBPeiA+sNfr4Stf+Qp+9rOf4ezZs0wzdjod5HK5voEjaoMc60ajgePHjyObzbJlAeQL/fKXv8Sf/vQnNiMPw2N5syyCICAajWJqaoplWubn55HJZDA7O8scdwI+HxWTWSY3gYBImuT+/ftYWVnB1atXsbq6iocPH6JYLLLMixeI/JO1Rg0uT3KrqoqLFy/i29/+Nqt/DIVCaDab2NzcxNbWFprNJhsnCsZo8tdqNeYuTE1NYWFhAbVaDR988AGuXbuGnZ0ddDqdgYQ+38bt7e2R/f9U6xEJbH6/H++//z5u376NN998E2+88QaWlpYQiURYMSYJzS7iBSn1RCViVDn861//Gu+//z4LEA5bFMv7umRmG40GWq0WdnZ2UCwWsb29jRMnTsC2bWSzWcTjcTY5aAAJgPxA0MB2u12USiWsra3h+vXrWFlZYc4+0TmjqBtvWwf1O+XM2+02Pv74Y6iqijNnzmBqaoqxALOzs8hmsyxy58u4aDKUy2WWT06lUqjX67hy5QpWVlYY/UN9f9glGk9FI3oZf9u2mapXFAWRSARf/vKX8eabb+Jzn/scwuEwSxPxlBD5WbzWWVtbwzvvvIMPPvgArVaLmW/Kox62Q3gqhifJk8kkZmZmMD8/j8XFRaTTaVasQAPHP+2VzzhQm5rNJlZXV/HJJ5/gxo0buHv3LiOVyYzTcbxQ/1F/UjtH3QexAel0GtlsFrFYDLFYDOFwGPF4HJlMBtlsFuFwuO8x0DQJKZvUbDaxvb2N69ev4+HDh6hWq489mNNrnnkGY2tra2S/P5OHMA1qJG1Lp9M4e/YsLly4gNnZWSQSCRYcUBRHGYKPPvoIf/3rX7G6uvoYm3/YFBN/HnonlwEAo55CoRDi8Th0XUcgEGDrVIjWoLwtzzkahoFWq4VKpYKNjQ3s7OygUCigUqkwUpyPaocBcZj5G3bfXoVA7+Rzh0IhtvRhenoaiqKwavFCocAiYlp1SEkHMsd7QYdPBY5jmp/p08BIO9Czoi3LYtGx3+9nEWMwGEQ0GmUpNVrTQRmXvdYVPykZ1i18HlkQBAbASCSCcDgMVVXZA9rp+d6NRgOVSoVFx5TmI5B7wee9Nplu2nfQPl7fkaem+EnKA4S+UwBJ7gNVQ/FuEn/NYVpw2HWeq484SKjDiXfiNYdhGIwcbrVa2N7eZqaceD7ej9pvKu8gbeWvQTQFvchBJ2qmXC4jFAr1PYKYNCKtZ67X62i322zwKcc8KE1InwEcyN0YBBr+nb8mpUi990+Wi9o4aCH+oHZ7c9TjyDNfTuq6LhskGlDSgqQp6AYoAOGpkGcBwkHCp+mIUyOqyXXdxypa+MiXlj0AYJPJW60DPP5ECH7buDIouzHseK87wGeFCPwEKr5IY9A5qI9IKBJfWloaq93PXCNS4QD5GARKyj/zA0M3TrOKDyIOsz5lP+0l4cHlNU00iKMew+b3+we6FXv5h3xf7ZVCGwU2/ny82eVBxqcoKVgkKst7nmHf6RyURRvn+dnAc9KI9Jk0HqWavDOTboqO403ZXqaGtvOa5rCBzF4BwbAXfyy/vHTQ8bzfSXSPt9qHP2aY8ADztp9+4ws3+H7kM0T8f9Tw5+JNNX+f/Nj5/X6kUimIojiWfwg8Jx/RK95HbngrWOhY7yDzRRZUikacnmmarIpYkqSxZ+Z+74f/7AWgt/2DJgu1n+ciKZ1IAQStm+avOU6wMqgNPGPhjdbJbfAqDB6QpEG95+BBmMlkoKoqNjY2XqwHde5XW9GMtiyrr5KEL20Ph8NYWlrCiRMnkM/n8eGHHz6V9noHc9RxXgCRdqHAjYpwqUJ9UBn+XhH9sP0IQFTQyv9pEt9/vEUapF15JQD0FwIHAgFks1moqor79+8zd2scORJA3K/wfg3RDUQJRaNRXLhwAWfPnsXt27fx4YcfolwuP9X27Hci8YNJWpDoHr4ymrIee0Wre53f+50yXfxfyJFGpgCMrsWfg6dveC3o9/vZKsRkMolIJIJarYb19XU0Go19FSq/sP8qQHWMlmXh2LFjeP311zE/Pw8AuHHjBv75z3+yipN2u33ohzM9KeF9QiKWSQvyWoZoItd12V+6jUNd7QVWPh3nOA574gTQzwrwFebUJvLnadLQ40toiUGxWMS9e/dQLpf7aip7vR5yudzIfnkhgcj7JK7rIplMIhaLoVqtolwusxpGPgA4bOrvSQlpc6rWpgHlKS3TNBmvSmtL+PKwQUM2itCmbRSFA2APlaL1LNSn9DvlkvlMCr2IkqLClEG1lD7fbnq3UCiM7JejoSb2KTTryaeh9cYUiSuK0kcGkzk6akKThCJl2gY8WlTmXcsyKIDj3wcFKTyAyaQCQKFQQKFQeIyaGsRjDjP3Xp+R73eKvsfqi3E14kQm8jTl6P+R8UT+X8gEiBM5EjIB4kSOhEyAOJEjIRMgTuRIyASIEzkSMgHiRI6ETIA4kSMhEyBO5EjI/wECFUMO1FbZbQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation loss: 0.004405,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation loss: 0.004649,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation loss: 0.004160,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Optimizers\n", + "optimizer = torch.optim.Adam(unet.parameters(), lr=5e-5)\n", + "\n", + "scaler_diffusion = GradScaler()\n", + "\n", + "max_epochs = 200\n", + "val_interval = 20\n", + "print_interval = 20\n", + "epoch_loss_list = []\n", + "val_epoch_loss_list = []\n", + "\n", + "for epoch in range(max_epochs):\n", + " unet.train()\n", + " autoencoderkl.eval()\n", + " epoch_loss = 0\n", + " for batch in train_loader:\n", + " images = batch[\"image\"].to(device)\n", + " low_res_image = batch[\"low_res_image\"].to(device)\n", + " optimizer.zero_grad(set_to_none=True)\n", + "\n", + " with autocast(\"cuda\", enabled=True):\n", + " with torch.no_grad():\n", + " latent = autoencoderkl.encode_stage_2_inputs(images) * scale_factor\n", + "\n", + " # Noise augmentation\n", + " noise = torch.randn_like(latent).to(device)\n", + " low_res_noise = torch.randn_like(low_res_image).to(device)\n", + " timesteps = torch.randint(0, scheduler.num_train_timesteps, (latent.shape[0],), device=latent.device).long()\n", + " low_res_timesteps = torch.randint(\n", + " 0, max_noise_level, (low_res_image.shape[0],), device=low_res_image.device\n", + " ).long()\n", + "\n", + " noisy_latent = scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n", + " noisy_low_res_image = scheduler.add_noise(\n", + " original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n", + " )\n", + "\n", + " latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n", + "\n", + " noise_pred = unet(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n", + " loss = F.mse_loss(noise_pred.float(), noise.float())\n", + "\n", + " scaler_diffusion.scale(loss).backward()\n", + " scaler_diffusion.step(optimizer)\n", + " scaler_diffusion.update()\n", + "\n", + " epoch_loss += loss.item()\n", + "\n", + " msgs = [\n", + " f\"epoch {epoch:d}/{max_epochs:d}:\",\n", + " f\"loss: {epoch_loss / len(train_loader) :4f},\"\n", + " ]\n", + "\n", + " if epoch % print_interval == 0:\n", + " print(\",\".join(msgs))\n", + "\n", + " epoch_loss_list.append(epoch_loss / len(train_loader))\n", + "\n", + " if epoch % val_interval == 0:\n", + " unet.eval()\n", + " val_loss = 0\n", + " for batch in val_loader:\n", + " images = batch[\"image\"].to(device)\n", + " low_res_image = batch[\"low_res_image\"].to(device)\n", + "\n", + " with torch.no_grad():\n", + " with autocast(\"cuda\", enabled=True):\n", + " latent = autoencoderkl.encode_stage_2_inputs(images) * scale_factor\n", + " # Noise augmentation\n", + " noise = torch.randn_like(latent).to(device)\n", + " low_res_noise = torch.randn_like(low_res_image).to(device)\n", + " timesteps = torch.randint(\n", + " 0, scheduler.num_train_timesteps, (latent.shape[0],), device=latent.device\n", + " ).long()\n", + " low_res_timesteps = torch.randint(\n", + " 0, max_noise_level, (low_res_image.shape[0],), device=low_res_image.device\n", + " ).long()\n", + "\n", + " noisy_latent = scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n", + " noisy_low_res_image = scheduler.add_noise(\n", + " original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n", + " )\n", + "\n", + " latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n", + " noise_pred = unet(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n", + " loss = F.mse_loss(noise_pred.float(), noise.float())\n", + "\n", + " val_loss += loss.item()\n", + "\n", + " val_loss /= len(val_loader)\n", + " val_epoch_loss_list.append(val_loss)\n", + " msgs = f\"Validation loss: {val_loss / len(val_loader) :4f},\"\n", + " print(msgs)\n", + "\n", + " # Sampling image during training\n", + " sampling_image = low_res_image[0].unsqueeze(0)\n", + " latents = torch.randn((1, 3, 16, 16)).to(device)\n", + " low_res_noise = torch.randn((1, 1, 16, 16)).to(device)\n", + " noise_level = 20\n", + " noise_level = torch.Tensor((noise_level,)).long().to(device)\n", + " noisy_low_res_image = scheduler.add_noise(\n", + " original_samples=sampling_image,\n", + " noise=low_res_noise,\n", + " timesteps=torch.Tensor((noise_level,)).long().to(device),\n", + " )\n", + "\n", + " scheduler.set_timesteps(num_inference_steps=1000)\n", + " for t in scheduler.timesteps:\n", + " with torch.no_grad():\n", + " with autocast(\"cuda\", enabled=True):\n", + " latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n", + " noise_pred = unet(\n", + " x=latent_model_input, timesteps=torch.Tensor((t,)).to(device), class_labels=noise_level\n", + " )\n", + " latents, _ = scheduler.step(noise_pred, t, latents)\n", + "\n", + " with torch.no_grad():\n", + " decoded = autoencoderkl.decode_stage_2_outputs(latents / scale_factor)\n", + "\n", + " low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n", + " plt.figure(figsize=(2, 2))\n", + " plt.style.use(\"default\")\n", + " plt.imshow(\n", + " torch.cat([images[0, 0].cpu(), low_res_bicubic[0, 0].cpu(), decoded[0, 0].cpu()], dim=1),\n", + " vmin=0,\n", + " vmax=1,\n", + " cmap=\"gray\",\n", + " )\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "30f24595", + "metadata": {}, + "source": [ + "### Plotting sampling example" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "155be091", + "metadata": {}, + "outputs": [], + "source": [ + "# Sampling image during training\n", + "unet.eval()\n", + "num_samples = 3\n", + "validation_batch = first(val_loader)\n", + "\n", + "images = validation_batch[\"image\"].to(device)\n", + "sampling_image = validation_batch[\"low_res_image\"].to(device)[:num_samples]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "aaf61020", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 31.22it/s]\n" + ] + } + ], + "source": [ + "latents = torch.randn((num_samples, 3, 16, 16)).to(device)\n", + "low_res_noise = torch.randn((num_samples, 1, 16, 16)).to(device)\n", + "noise_level = 10\n", + "noise_level = torch.Tensor((noise_level,)).long().to(device)\n", + "noisy_low_res_image = scheduler.add_noise(\n", + " original_samples=sampling_image, noise=low_res_noise, timesteps=torch.Tensor((noise_level,)).long().to(device)\n", + ")\n", + "scheduler.set_timesteps(num_inference_steps=1000)\n", + "for t in tqdm(scheduler.timesteps, ncols=110):\n", + " with torch.no_grad():\n", + " with autocast(\"cuda\", enabled=True):\n", + " latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n", + " noise_pred = unet(x=latent_model_input, timesteps=torch.Tensor((t,)).to(device), class_labels=noise_level)\n", + "\n", + " # 2. compute previous image: x_t -> x_t-1\n", + " latents, _ = scheduler.step(noise_pred, t, latents)\n", + "\n", + "with torch.no_grad():\n", + " decoded = autoencoderkl.decode_stage_2_outputs(latents / scale_factor)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "32e16e69", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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xoNpKHxNbdZjz5s1LbPVL8LHvc/rdQYMGJfawYcOqY41pre2rZfLpel/VKapfgm+znPY5p9v05NrX98ko7vTmIKcTLfFp0HbX8eb7mPo0aP1qe0Txq7WP6H4d/r6qbdV+He2J0Ig/Su7ckj0eSvYBye3ToOWPnhc5n4YoTyXn5q4V+RTl+mu0l02pP0QU9z63Z44vU8keDl1FaR58eXLaa62rwYMHV8c6PnVc+N+apX5SOodo3440/Tl/JcWXUZ9hub4e+e9Eezpouv4255MT7Zeg5Y38MHLvRpFvgbZ9bg+WaN7PzYu+XfSdTNHyRvtBRX5vZvGeM7k8N9PHkZUGAAAAAAAI4aMBAAAAAABCNlme5KURuvShEgzFLzupHGf06NGJrel33313u7+dP39+YudkJu3lyaxeiuDDmXoZjFn9EpUu0WmIMo8uX2k+VqxYUR2rLEPRJSofmlLLo0uset8o7J+294QJExJ76NCh1bFKiHJh0/yyYm5JLcqX1lWJfCcKbWpm1q9fv3bzoZIWRa/llyi1n+TkE1HozWjJfHPQyNb12q+1Lf0yrrad2to+UahB7UMa6tXbuTlAKennSiQxUjsXVtTTSBtF8iulVKLix1cuLHFJGZop12mmLMrPibnyNdJmrUBOGtLRNLP6Z+mAAQOqYx2P+jxUWWMUkjMnryt51mi/8e2tedL5R+cnL5vO9UetS/8c0vvmiJ5DJTK+nJQpmttKpNqKXlffK/RdyfcHlSrnxrZKzjxRyGez9Jmek5B1phSRlQYAAAAAAAjhowEAAAAAAEL4aAAAAAAAgJAO+zREmtVcGLhI45fTC2q615dp+E7VmvvQn2apf8TkyZOTNA3J5sOzmqVaNNWaqU5aw4x6/WQujJaWd9WqVdWxauty4SX9vaLt6s3q68NrwaMwaWb1Or1FixZVx0899VR4ruoyvQ/E2LFjw/uo7f0FtA20jbTufB/WPOXC13k9+/Lly5O0KVOmhPf17a+6+Jw+PdJxapt1Bo3ox9X284vONSXhWnPjKQpJqmk5/wjvg5ILsdqI5lbxZSoJXWsW+0PkiH6fC/3qyx+FDmwL35dzc1HJtUt031F/bSvdP8dKw8L69i314SgJUdoKlPTB0vL4vqF+ls8880xi69j2bZbTi+uYi8IpK5EGXttafd60DL7v63NG8xyF91R/jxL/LB1/Ol6j0Ki5cdJIyFHFXzv3Dqrt4Osq9z6nZYieGVpXkf9jKVF9lI4rVhoAAAAAACCEjwYAAAAAAAjhowEAAAAAAEI6LABV/VWkvcvFLfbaLE1bsGBBYnt9vJnZRRddVB1H/g5m9br2ZcuWVcezZ89O0saMGZPYqpdfunRpdaxaQi2/7uPg9zFQDVxOa+/3PND7qA+Hau99fZRsBW+W6vo1T6q1U63l008/XR1r+/rymJntueee1h6aRx+H2qy+HXx76301z9pXfJ+MdO9m9b4HXg+reVQiDXpOFx5p+0tig28OcnstlOgqtV/4tlPdZ87fKvKl0HaPdOw5n42InB9CiW69JI5/Lo9Rf8y1V0n5tU20fb3WV9NUQ6y2r9tSHw5f3mhvkJytabnno79vM+Otb4k+DiV5jtogt3eU+jT6558+3/Q+kda81BfG2+pbp/f1+1CYmQ0cOLA61vJqH9Pnny+jvpPk/CEiHw4tg451/1zO+SuV7LGj5Y3m2JzfW5QP9f/I7dvg31H0PULnLn3v8tfKPe9zZWoEVhoAAAAAACCEjwYAAAAAAAjhowEAAAAAAEI2eZ8Gr79SvVgurrHXpao2y+vhzer3Szj00EOr4xtuuCFJmzFjRmLrHgFHHnlkdfzkk08maX5/ADOz8ePHJ7bXrvXt2zdJU31gnz59EtvXj9aN1p1ey9ez+lnouVqXXmuv11U70shpDGs9V/Ph21fznLu2r59czGNtB38v1T+q1lD9XTyqYVR9qOoyI92x1pX6PPh6L9nfRNF+pfXaGZTo5XPaXl9WTYv6l1naXjnNe6TPLY2n79sr5zsR+aOUxN5XO7dPQ8n+ELk20r4cnRvtMZLbd0PnjMinQa9VspdELs/+2jq+cvNpFI+9xM75Q+TK5NkSfBoa2ftF68r7MKr/3/r168Nz/bW1ztXHQfNcsp+AEu3voc8wfc/wY0Off7k8+zGYe8+I+ly0l01b+fJ9XTX8eh/1eYh8f0p8gUr3BfK+CDp29b6a7vud+kPoXK33jfb7inz1zNinAQAAAAAAuhA+GgAAAAAAIKTD8qRomT+3jN/IUpEuu/z0pz+tjq+44ookTUOdTpo0KbH9cs8ee+yRpOkSzfLlyxPbL/3pUqcuQekSnV9W0t/qcrue66UxufCCWle6nBehoW39tXNL5P37909sL/XS5cjhw4cnti65+qW/3FJ8FGYtFy5T68q3SySdaeu+vp41tKsuMc6aNSux16xZUx1rX8gtuTYr5GdXUFIWJbdcHEkltS11fEUyhJyU0C/j56SCjbRHVP6cHCmaI3JtoOWPKJH65PKo48DbWt6cpMrbJSF0zdI21LlU5/Ho2rlwl9on/RwYhXdu69q+DFuCHKkRcpIM/wwfNWpUkqYhVlXq6p9buZDAShTGOWf79szNe/ps9X0lJ0+N6i43V0VyLB0nubry8iWV66xevTqxNVy7l6XmJJ0l0kNF69KHelcpcy5sdyQZ1zyqhC7qk7mxHoXLRp4EAAAAAABNhY8GAAAAAAAI4aMBAAAAAABCOuzToFo1r6+KwrG2le5t1elp2C3ViHsdsYZJVY3Y1Vdfndinn356dfy6170uSfv5z3+e2H5LdrNUbxeFzTSrD9HpdfuqQ9RzNdxZ5NOQ02BH245rKDS9b+QfkAs56rWVmqb+D6pb9HmOtM1t4dtf61nbRPud1xpq3eR8SbweVn00tK7mzJmT2L78Q4YMCc+NfCsinf/mIKeLLdH450LRqe3Jac+jMKJ6X7X9XJXTsmqf8uk5nzAl0qCWhG/N6X6jPlQ650e+FLmwsL7fl4Tu1fScb4Gm+zJp+bQ9lSgcr5ZBNdMbNmyojlXXrD4OW3qIVaVkXsj1BV93+uzQtl6xYkW79ykJn6z5yOnloz6ZCx8dzRu50MRROO/ce4ba0fjVNPUB8PnUPOozXN8VfMjZ3LuB9pXomVEyPnNh0aPw4PpO7X0lzOrHuq+fkpCqufRSfztWGgAAAAAAIISPBgAAAAAACOGjAQAAAAAAQjosfFYNZ6Rjy8W19+T0vDfddFNie18E1bxNmDAhsVWL9r3vfa86vuqqq5K01772tYm9ZMmSxJ45c2Z1rP4OWjfqh+H9IXTr91WrViW26mx93S1btixJGzt2rEVE2krNh+5L4fe8WLt2bZKm9R7pJVXTp74TWs9+v4gTTjghvI/m6+mnn66OR48enaRpDGivS9R8HHvsseF9BgwYkNi+vadMmZKkab1+97vfTeyRI0dWx9qPtC9EvkGbw4dhc+0FUaLP1DlA+66v82gfBrN6PW4UJzyKva92Th+vRPtzlGhdG9G85/w9dNz7vpzrN9HeA9FeCrn0Ej8Ts7RMuX0aFF9+va7mUfPhtfi6n4D2o9yeF61GSfx8tUti0ZuldadjV/dleOyxxxLb7+ugzwrt29EeLTnNe5SufUz7YHTf3H410RjUNL1v5GuXe/eLfC30uroflrZD5LOZ6yv+Wrl6Vj9b/56Zm7s1X/5a6mepPg0l/kq5fajYpwEAAAAAALoMPhoAAAAAACCEjwYAAAAAAAhpbQGkmf3sZz9L7LPPPrs6/vKXv5ykvf/9709s9S2YPn16dfylL30pSbvssssSW/XMjz/+eHV81113JWlqq87N6/hUH6e26uu8NlHjFKvWXvF+C6rpVM215sOjdZHDa/61DXKxlz/1qU+1m8dPfvKTia0+LBMnTqyOly5dmqSpXjDypbn33nsTW+tG/SV8ebWufvvb3ya26jK9bjGnS4y0iDmtd6tRoqPM7YHgx4yOPa2XKDa/6oBV26p7cHg7F2Nc8WXIxUGPyPk0KP7auf0RGkHrw98r2kuhrfTIpyFX/mh8ldR75E/U1rm+n2mfi+K+q60+DHpupK/fGvZpiOa43Jzn53zdW0mfHd6Xziz1H9RnmBLtxaA+Jrk8R3u/5HyB/BjLPUuifUdye0eV9Kucft7nU/t2VDd6rvr+6Nyt7z/RfXL17utO60b7gtq+L+m8sHDhwsTWZ1k0tnM+DiX78+RgpQEAAAAAAEL4aAAAAAAAgJCWlyctXrw4sb1c6cILL0zSbrnllsT+61//mtg+JOf8+fOTNF1m0nOjZVJd+tNr+aUz3a5el6g0FKpPz4WPVNsv0UUhEM3ql6z8srgu3+mSeRR+UNM0xKouo11zzTXVsS45qq3l9WHzNI9Dhw5NbE33baZ1pWFTVfp01FFHtXvuV7/61fC+vt5VHpMLa+nJySc2N7nlVG9r3qOQlGapTE/rSJeltX182DuVIWhoXQ3T6M/VOUH7ahTuU8dISRhK/W0klcidm5MdRFKfnFzHp2taLiSpP7e0X0chHVXCoLbvKzmJRjSuNdyzSiVVZurlSbmwsDpn+LprNLRiZ5CTQpTkMQoraZaGM3/iiSeStF122SWx995778T2ElXtr3pfbQMvX8nJI1Um5PuZtrU+/7W/eiJpS1u2J1c+7eu+DLlQr5H0R++rdafjyNdVLixsNI60DfTc/v37J7Yvv87zei19hvhrqSRObZUt+n6YmwcjCVKjodJZaQAAAAAAgBA+GgAAAAAAIISPBgAAAAAACGl5nwbV8XmN+P7775+kff3rX0/sD3zgA4nt/RQOPvjgJE11i6qf89po1T4PGjQosVUTF+mMI22hWao/U12thoBUbaz34VCNm+rCBw4cmNjRFu1ankg/l9tmXdP/9re/VcdjxoxJ0vbdd9/E1jL4vqI6RNUaDh8+PLG95lV17xrqbb/99kvscePGVccaIvjBBx9MbM1XpOHNaZIb1SY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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n", + "fig, axs = plt.subplots(num_samples, 3, figsize=(8, 8))\n", + "axs[0, 0].set_title(\"Original image\")\n", + "axs[0, 1].set_title(\"Low-resolution Image\")\n", + "axs[0, 2].set_title(\"Outputted image\")\n", + "for i in range(0, num_samples):\n", + " axs[i, 0].imshow(images[i, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " axs[i, 0].axis(\"off\")\n", + " axs[i, 1].imshow(low_res_bicubic[i, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " axs[i, 1].axis(\"off\")\n", + " axs[i, 2].imshow(decoded[i, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " axs[i, 2].axis(\"off\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "7fa52acc", + "metadata": {}, + "source": [ + "### Clean-up data directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a6f6d5a", + "metadata": {}, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(root_dir)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb b/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb new file mode 100644 index 000000000..d7eca6096 --- /dev/null +++ b/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb @@ -0,0 +1,1304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51f79ac5-1e09-4933-9009-9ade92901b3c", + "metadata": {}, + "source": [ + "Copyright (c) MONAI Consortium
\n", + "Licensed under the Apache License, Version 2.0 (the \"License\");
\n", + "you may not use this file except in compliance with the License.
\n", + "You may obtain a copy of the License at
\n", + "http://www.apache.org/licenses/LICENSE-2.0
\n", + "Unless required by applicable law or agreed to in writing, software
\n", + "distributed under the License is distributed on an \"AS IS\" BASIS,
\n", + "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
\n", + "See the License for the specific language governing permissions and
\n", + "limitations under the License.
" + ] + }, + { + "cell_type": "markdown", + "id": "95c08725", + "metadata": {}, + "source": [ + "# Super-resolution using Stable Diffusion v2 Upscalers using PyTorch Lightning\n", + "\n", + "This tutorial is identical to '2d_sd_super_resolution' but uses PyTorch Lightning (https://lightning.ai/docs/pytorch/stable/).\n", + "\n", + "Tutorial to illustrate the super-resolution task on medical images using Latent Diffusion Models (LDMs) [1]. For that, we will use an autoencoder to obtain a latent representation of the high-resolution images. Then, we train a diffusion model to infer this latent representation when conditioned on a low-resolution image.\n", + "\n", + "To improve the performance of our models, we will use a method called \"noise conditioning augmentation\" (introduced in [2] and used in Stable Diffusion v2.0 and Imagen Video [3]). During the training, we add noise to the low-resolution images using a random signal-to-noise ratio, and we condition the diffusion models on the amount of noise added. At sampling time, we use a fixed signal-to-noise ratio, representing a small amount of augmentation that aids in removing artefacts in the samples.\n", + "\n", + "\n", + "[1] - Rombach et al. \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/abs/2112.10752\n", + "\n", + "[2] - Ho et al. \"Cascaded diffusion models for high fidelity image generation\" https://arxiv.org/abs/2106.15282\n", + "\n", + "[3] - Ho et al. \"High Definition Video Generation with Diffusion Models\" https://arxiv.org/abs/2210.02303" + ] + }, + { + "cell_type": "markdown", + "id": "b839bf2d", + "metadata": {}, + "source": [ + "## Set up environment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "77f7e633", + "metadata": {}, + "outputs": [], + "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", + "!python -c \"import pytorch_lightning\" || pip install pytorch-lightning\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "214066de", + "metadata": {}, + "source": [ + "## Set up imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de71fe08", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "import tempfile\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from monai import transforms\n", + "from monai.apps import MedNISTDataset\n", + "from monai.config import print_config\n", + "from monai.data import CacheDataset, ThreadDataLoader\n", + "from monai.utils import first, set_determinism\n", + "from torch.amp import autocast\n", + "from torch import nn\n", + "from tqdm.notebook import tqdm\n", + "\n", + "from monai.losses import PatchAdversarialLoss, PerceptualLoss\n", + "from monai.networks.nets import AutoencoderKL, DiffusionModelUNet, PatchDiscriminator\n", + "from monai.networks.schedulers import DDPMScheduler\n", + "\n", + "import pytorch_lightning as pl\n", + "from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint\n", + "\n", + "print_config()" + ] + }, + { + "cell_type": "markdown", + "id": "c0dde922", + "metadata": {}, + "source": [ + "## Setup a data directory and download dataset\n", + "Specify a MONAI_DATA_DIRECTORY variable, where the data will be downloaded. If not specified a temporary directory will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ded618a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/tmpkazhiy23\n" + ] + } + ], + "source": [ + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "print(root_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "e855e2b7-7e46-44d9-a567-3e91b5db2b6f", + "metadata": {}, + "source": [ + "## Set deterministic training for reproducibility" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9f0a17bc", + "metadata": {}, + "outputs": [], + "source": [ + "# for reproducibility purposes set a seed\n", + "set_determinism(42)" + ] + }, + { + "cell_type": "markdown", + "id": "aa76151c-0a37-471e-8312-10c2afcf11bc", + "metadata": {}, + "source": [ + "## Description of data and download the training set\n", + "\n", + "For this tutorial, we use the head CT dataset from MedNIST." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "298d964a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MedNIST.tar.gz: 59.0MB [00:01, 38.7MB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-09-24 08:29:18,175 - INFO - Downloaded: /tmp/tmpkazhiy23/MedNIST.tar.gz\n", + "2024-09-24 08:29:18,286 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2024-09-24 08:29:18,286 - INFO - Writing into directory: /tmp/tmpkazhiy23.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:16<00:00, 2894.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-09-24 08:29:39,365 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2024-09-24 08:29:39,365 - INFO - File exists: /tmp/tmpkazhiy23/MedNIST.tar.gz, skipped downloading.\n", + "2024-09-24 08:29:39,366 - INFO - Non-empty folder exists in /tmp/tmpkazhiy23/MedNIST, skipped extracting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3004.12it/s]\n" + ] + } + ], + "source": [ + "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\", download=True, seed=0)\n", + "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n", + "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n", + "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]" + ] + }, + { + "cell_type": "markdown", + "id": "46bafb78", + "metadata": {}, + "source": [ + "### Setup utils functions" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "4f8eff03", + "metadata": {}, + "outputs": [], + "source": [ + "def get_train_transforms():\n", + " image_size = 64\n", + " train_transforms = transforms.Compose(\n", + " [\n", + " transforms.LoadImaged(keys=[\"image\"]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", + " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0,\n", + " b_max=1.0, clip=True),\n", + " transforms.RandAffined(\n", + " keys=[\"image\"],\n", + " rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n", + " translate_range=[(-1, 1), (-1, 1)],\n", + " scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n", + " spatial_size=[image_size, image_size],\n", + " padding_mode=\"zeros\",\n", + " prob=0.5,\n", + " ),\n", + " transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n", + " transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n", + " ]\n", + " )\n", + " return train_transforms\n", + "\n", + "\n", + "def get_val_transforms():\n", + " val_transforms = transforms.Compose(\n", + " [\n", + " transforms.LoadImaged(keys=[\"image\"]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", + " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0,\n", + " b_max=1.0, clip=True),\n", + " transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n", + " transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n", + " ]\n", + " )\n", + " return val_transforms\n", + "\n", + "\n", + "def get_datasets():\n", + " train_transforms = get_train_transforms()\n", + " val_transforms = get_val_transforms()\n", + " train_ds = CacheDataset(data=train_datalist[:320], transform=train_transforms)\n", + " val_ds = CacheDataset(data=val_datalist[:32], transform=val_transforms)\n", + " return train_ds, val_ds" + ] + }, + { + "cell_type": "markdown", + "id": "d80e045b", + "metadata": {}, + "source": [ + "## Define the LightningModule for AutoEncoder (transforms, network, loaders, etc)\n", + "The LightningModule contains a refactoring of your training code. The following module is a reformating of the code in 2d_stable_diffusion_v2_super_resolution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d5d1caff", + "metadata": {}, + "outputs": [], + "source": [ + "class AutoEncoder(pl.LightningModule):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.data_dir = root_dir\n", + " self.autoencoderkl = AutoencoderKL(spatial_dims=2,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " channels=(256, 512, 512),\n", + " latent_channels=3,\n", + " num_res_blocks=2,\n", + " norm_num_groups=32,\n", + " attention_levels=(False, False, True))\n", + " self.discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1,\n", + " num_layers_d=3, channels=64)\n", + " self.perceptual_loss = PerceptualLoss(spatial_dims=2, network_type=\"alex\")\n", + " self.perceptual_weight = 0.002\n", + " self.autoencoder_warm_up_n_epochs = 10\n", + " self.automatic_optimization = False\n", + " self.adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n", + " self.adv_weight = 0.005\n", + " self.kl_weight = 1e-6\n", + "\n", + " def forward(self, z):\n", + " return self.autoencoderkl(z)\n", + "\n", + " def prepare_data(self):\n", + " self.train_ds, self.val_ds = get_datasets()\n", + "\n", + " def train_dataloader(self):\n", + " return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True,\n", + " num_workers=4, persistent_workers=True)\n", + "\n", + " def val_dataloader(self):\n", + " return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False,\n", + " num_workers=4)\n", + "\n", + " def _compute_loss_generator(self, images, reconstruction, z_mu, z_sigma):\n", + " recons_loss = F.l1_loss(reconstruction.float(), images.float())\n", + " p_loss = self.perceptual_loss(reconstruction.float(), images.float())\n", + " kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3])\n", + " kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n", + " loss_g = recons_loss + (self.kl_weight * kl_loss) + (self.perceptual_weight * p_loss)\n", + " return loss_g, recons_loss\n", + "\n", + " def _compute_loss_discriminator(self, images, reconstruction):\n", + " logits_fake = self.discriminator(reconstruction.contiguous().detach())[-1]\n", + " loss_d_fake = self.adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n", + " logits_real = self.discriminator(images.contiguous().detach())[-1]\n", + " loss_d_real = self.adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n", + " discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n", + " loss_d = self.adv_weight * discriminator_loss\n", + " return loss_d, discriminator_loss\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " optimizer_g, optimizer_d = self.optimizers()\n", + " images = batch[\"image\"]\n", + " reconstruction, z_mu, z_sigma = self.forward(images)\n", + " loss_g, recons_loss = self._compute_loss_generator(images, reconstruction, z_mu, z_sigma)\n", + " self.log(\"recons_loss\", recons_loss, batch_size=16, prog_bar=True)\n", + "\n", + " if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n", + " logits_fake = self.discriminator(reconstruction.contiguous().float())[-1]\n", + " generator_loss = self.adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n", + " loss_g += self.adv_weight * generator_loss\n", + " self.log(\"gen_loss\", generator_loss, batch_size=16, prog_bar=True)\n", + "\n", + " self.log(\"loss_g\", loss_g, batch_size=16, prog_bar=True)\n", + " self.manual_backward(loss_g)\n", + " optimizer_g.step()\n", + " optimizer_g.zero_grad()\n", + " self.untoggle_optimizer(optimizer_g)\n", + "\n", + " if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n", + " loss_d, discriminator_loss = self._compute_loss_discriminator(images, reconstruction)\n", + " self.log(\"disc_loss\", loss_d, batch_size=16, prog_bar=True)\n", + " self.log(\"train_loss_d\", loss_d, batch_size=16, prog_bar=True)\n", + " self.manual_backward(loss_d)\n", + " optimizer_d.step()\n", + " optimizer_d.zero_grad()\n", + " self.untoggle_optimizer(optimizer_d)\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + " images = batch[\"image\"]\n", + " reconstruction, z_mu, z_sigma = self.autoencoderkl(images)\n", + " recons_loss = F.l1_loss(images.float(), reconstruction.float())\n", + " self.log(\"val_loss_d\", recons_loss, batch_size=1, prog_bar=True)\n", + " self.images = images\n", + " self.reconstruction = reconstruction\n", + "\n", + " def on_validation_epoch_end(self):\n", + " # ploting reconstruction\n", + " plt.figure(figsize=(2, 2))\n", + " plt.imshow(torch.cat([self.images[0, 0].cpu(),\n", + " self.reconstruction[0, 0].cpu()],\n", + " dim=1), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "\n", + " def configure_optimizers(self):\n", + " optimizer_g = torch.optim.Adam(self.autoencoderkl.parameters(), lr=5e-5)\n", + " optimizer_d = torch.optim.Adam(self.discriminator.parameters(), lr=1e-4)\n", + " return [optimizer_g, optimizer_d], []" + ] + }, + { + "cell_type": "markdown", + "id": "c16de505", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "## Train Autoencoder" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9d903aaa", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/vf19/PycharmProjects/MONAI_tutorials/venv/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/home/vf19/PycharmProjects/MONAI_tutorials/venv/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n", + "/home/vf19/PycharmProjects/MONAI_tutorials/venv/lib/python3.10/site-packages/lpips/lpips.py:107: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False)\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 320/320 [00:00<00:00, 1478.97it/s]\n", + "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:00<00:00, 773.46it/s]\n", + "/home/vf19/PycharmProjects/MONAI_tutorials/venv/lib/python3.10/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:654: Checkpoint directory /tmp/tmpkazhiy23 exists and is not empty.\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "\n", + " | Name | Type | Params | Mode \n", + "-----------------------------------------------------------------\n", + "0 | autoencoderkl | AutoencoderKL | 75.1 M | train\n", + "1 | discriminator | PatchDiscriminator | 2.8 M | train\n", + "2 | perceptual_loss | PerceptualLoss | 2.5 M | train\n", + "3 | adv_loss | PatchAdversarialLoss | 0 | train\n", + "-----------------------------------------------------------------\n", + "77.8 M Trainable params\n", + "2.5 M Non-trainable params\n", + "80.3 M Total params\n", + "321.225 Total estimated model params size (MB)\n", + "251 Modules in train mode\n", + "41 Modules in eval mode\n", + "/home/vf19/PycharmProjects/MONAI_tutorials/venv/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (20) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e3c00d0cf81e4f5484624e5e3278d6bd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: | …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=75` reached.\n" + ] + } + ], + "source": [ + "max_epochs = 75\n", + "val_interval = 10\n", + "\n", + "\n", + "# initialise the LightningModule\n", + "ae_net = AutoEncoder()\n", + "\n", + "# set up checkpoints\n", + "\n", + "checkpoint_callback = ModelCheckpoint(dirpath=root_dir, filename=\"best_metric_model\")\n", + "\n", + "\n", + "# initialise Lightning's trainer.\n", + "trainer = pl.Trainer(devices=1,\n", + " max_epochs=max_epochs,\n", + " check_val_every_n_epoch=val_interval,\n", + " num_sanity_val_steps=0,\n", + " callbacks=checkpoint_callback,\n", + " default_root_dir=root_dir)\n", + "\n", + "# train\n", + "trainer.fit(ae_net)" + ] + }, + { + "cell_type": "markdown", + "id": "c7108b87", + "metadata": {}, + "source": [ + "## Rescaling factor\n", + "\n", + "As mentioned in Rombach et al. [1] Section 4.3.2 and D.1, the signal-to-noise ratio (induced by the scale of the latent space) became crucial in image-to-image translation models (such as the ones used for super-resolution). For this reason, we will compute the component-wise standard deviation to be used as scaling factor." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ccb6ba9f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scaling factor set to 0.6885251998901367\n" + ] + } + ], + "source": [ + "def get_scale_factor():\n", + " ae_net.eval()\n", + " device = torch.device(\"cuda:0\")\n", + " ae_net.to(device)\n", + "\n", + " train_loader = ae_net.train_dataloader()\n", + " check_data = first(train_loader)\n", + " z = ae_net.autoencoderkl.encode_stage_2_inputs(check_data[\"image\"].to(ae_net.device))\n", + " print(f\"Scaling factor set to {1/torch.std(z)}\")\n", + " scale_factor = 1 / torch.std(z)\n", + " return scale_factor\n", + "\n", + "\n", + "scale_factor = get_scale_factor()" + ] + }, + { + "cell_type": "markdown", + "id": "3baa2b0f", + "metadata": {}, + "source": [ + "## Define the LightningModule for DiffusionModelUnet (transforms, network, loaders, etc)\n", + "The LightningModule contains a refactoring of your training code. The following module is a reformating of the code in 2d_stable_diffusion_v2_super_resolution." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "731034ec", + "metadata": {}, + "outputs": [], + "source": [ + "class DiffusionUNET(pl.LightningModule):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.data_dir = root_dir\n", + " self.unet = DiffusionModelUNet(\n", + " spatial_dims=2,\n", + " in_channels=4,\n", + " out_channels=3,\n", + " num_res_blocks=2,\n", + " channels=(256, 256, 512, 1024),\n", + " attention_levels=(False, False, True, True),\n", + " num_head_channels=(0, 0, 64, 64),\n", + " )\n", + " self.max_noise_level = 350\n", + " self.scheduler = DDPMScheduler(num_train_timesteps=1000,\n", + " schedule=\"linear_beta\",\n", + " beta_start=0.0015,\n", + " beta_end=0.0195)\n", + " self.z = ae_net.autoencoderkl.eval()\n", + "\n", + " def forward(self, x, timesteps, low_res_timesteps):\n", + " return self.unet(x=x,\n", + " timesteps=timesteps,\n", + " class_labels=low_res_timesteps)\n", + "\n", + " def prepare_data(self):\n", + " self.train_ds, self.val_ds = get_datasets()\n", + "\n", + " def train_dataloader(self):\n", + " return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True,\n", + " num_workers=4, persistent_workers=True)\n", + "\n", + " def val_dataloader(self):\n", + " return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False,\n", + " num_workers=4)\n", + "\n", + " def _calculate_loss(self, batch, batch_idx, plt_image=False):\n", + " images = batch[\"image\"]\n", + " low_res_image = batch[\"low_res_image\"]\n", + " with autocast(\"cuda\", enabled=True):\n", + " with torch.no_grad():\n", + " latent = self.z.encode_stage_2_inputs(images) * scale_factor\n", + "\n", + " # Noise augmentation\n", + " noise = torch.randn_like(latent)\n", + " low_res_noise = torch.randn_like(low_res_image)\n", + " timesteps = torch.randint(0, self.scheduler.num_train_timesteps, (latent.shape[0],),\n", + " device=latent.device).long()\n", + " low_res_timesteps = torch.randint(\n", + " 0, self.max_noise_level, (low_res_image.shape[0],), device=latent.device\n", + " ).long()\n", + "\n", + " noisy_latent = self.scheduler.add_noise(original_samples=latent,\n", + " noise=noise, timesteps=timesteps)\n", + " noisy_low_res_image = self.scheduler.add_noise(\n", + " original_samples=low_res_image, noise=low_res_noise,\n", + " timesteps=low_res_timesteps\n", + " )\n", + "\n", + " latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n", + "\n", + " noise_pred = self.forward(latent_model_input, timesteps, low_res_timesteps)\n", + " loss = F.mse_loss(noise_pred.float(), noise.float())\n", + "\n", + " if plt_image:\n", + " # Sampling image during training\n", + " sampling_image = low_res_image[0].unsqueeze(0)\n", + " latents = torch.randn((1, 3, 16, 16)).to(sampling_image.device)\n", + " low_res_noise = torch.randn((1, 1, 16, 16)).to(sampling_image.device)\n", + " noise_level = 20\n", + " noise_level = torch.Tensor((noise_level,)).long().to(sampling_image.device)\n", + "\n", + " noisy_low_res_image = self.scheduler.add_noise(\n", + " original_samples=sampling_image,\n", + " noise=low_res_noise,\n", + " timesteps=noise_level,\n", + " )\n", + " self.scheduler.set_timesteps(num_inference_steps=1000)\n", + " for t in tqdm(self.scheduler.timesteps, ncols=110):\n", + " with autocast(\"cuda\", enabled=True):\n", + " with torch.no_grad():\n", + " latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n", + " noise_pred = self.forward(latent_model_input,\n", + " torch.Tensor((t,)).to(sampling_image.device)\n", + " , noise_level)\n", + " latents, _ = self.scheduler.step(noise_pred, t, latents)\n", + " with torch.no_grad():\n", + " decoded = self.z.decode_stage_2_outputs(latents / scale_factor)\n", + " low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n", + " # plot images\n", + "\n", + " self.images = images\n", + " self.low_res_bicubic = low_res_bicubic\n", + " self.decoded = decoded\n", + "\n", + " return loss\n", + "\n", + " def _plot_image(self, images, low_res_bicubic, decoded):\n", + " plt.figure(figsize=(2, 2))\n", + " plt.style.use(\"default\")\n", + " plt.imshow(\n", + " torch.cat([images[0, 0].cpu(), low_res_bicubic[0, 0].cpu(), decoded[0, 0].cpu()], dim=1),\n", + " vmin=0,\n", + " vmax=1,\n", + " cmap=\"gray\",\n", + " )\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " loss = self._calculate_loss(batch, batch_idx)\n", + " self.log(\"train_loss\", loss, batch_size=16, prog_bar=True)\n", + " return loss\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + " loss = self._calculate_loss(batch, batch_idx, plt_image=True)\n", + " self.log(\"val_loss\", loss, batch_size=16, prog_bar=True)\n", + " return loss\n", + "\n", + " def on_validation_epoch_end(self):\n", + " self._plot_image(self.images, self.low_res_bicubic, self.decoded)\n", + "\n", + " def configure_optimizers(self):\n", + " optimizer = torch.optim.Adam(self.unet.parameters(), lr=5e-5)\n", + " return optimizer" + ] + }, + { + "cell_type": "markdown", + "id": "b386a0c2", + "metadata": {}, + "source": [ + "## Train Diffusion Model\n", + "\n", + "In order to train the diffusion model to perform super-resolution, we will need to concatenate the latent representation of the high-resolution with the low-resolution image. For this, we create a Diffusion model with `in_channels=4`. Since only the outputted latent representation is interesting, we set `out_channels=3`.\n", + "\n", + "As mentioned, we will use the conditioned augmentation (introduced in [2] section 3 and used on Stable Diffusion Upscalers and Imagen Video [3] Section 2.5) as it has been shown critical for cascaded diffusion models, as well for super-resolution tasks. For this, we apply Gaussian noise augmentation to the low-resolution images. We will use a scheduler low_res_scheduler to add this noise, with the t step defining the signal-to-noise ratio and use the t value to condition the diffusion model (inputted using class_labels argument)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "936bbb9c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "Loading dataset: 0%| | 0/320 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: | …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "17aa3327b5084a8f96c140125efd91e6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: | …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fa63aaeaed78402f8f478a467bb73cd5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: | …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "66c3255d7df7478e9277de2e512ec1d7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=200` reached.\n" + ] + } + ], + "source": [ + "max_epochs = 200\n", + "val_interval = 50\n", + "\n", + "\n", + "# initialise the LightningModule\n", + "d_net = DiffusionUNET()\n", + "\n", + "# set up checkpoints\n", + "\n", + "checkpoint_callback = ModelCheckpoint(dirpath=root_dir, filename=\"best_metric_model_dunet\")\n", + "\n", + "\n", + "# initialise Lightning's trainer.\n", + "trainer = pl.Trainer(devices=1,\n", + " max_epochs=max_epochs,\n", + " check_val_every_n_epoch=val_interval,\n", + " num_sanity_val_steps=0,\n", + " callbacks=checkpoint_callback,\n", + " default_root_dir=root_dir)\n", + "\n", + "# train\n", + "trainer.fit(d_net)" + ] + }, + { + "cell_type": "markdown", + "id": "30f24595", + "metadata": {}, + "source": [ + "### Plotting sampling example" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "155be091", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "837b6b2a5e1f42ab862c9cf589dbb615", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00 x_t-1\n", + " latents, _ = scheduler.step(noise_pred, t, latents)\n", + "\n", + " with torch.no_grad():\n", + " decoded = ae_net.autoencoderkl.decode_stage_2_outputs(latents / scale_factor)\n", + " return sampling_image, images, decoded\n", + "\n", + "\n", + "sampling_image, images, decoded = get_images_to_plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "32e16e69", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n", + "fig, axs = plt.subplots(num_samples, 3, figsize=(8, 8))\n", + "axs[0, 0].set_title(\"Original image\")\n", + "axs[0, 1].set_title(\"Low-resolution Image\")\n", + "axs[0, 2].set_title(\"Outputted image\")\n", + "for i in range(0, num_samples):\n", + " axs[i, 0].imshow(images[i, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " axs[i, 0].axis(\"off\")\n", + " axs[i, 1].imshow(low_res_bicubic[i, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " axs[i, 1].axis(\"off\")\n", + " axs[i, 2].imshow(decoded[i, 0].cpu().detach().numpy(), vmin=0, vmax=1, cmap=\"gray\")\n", + " axs[i, 2].axis(\"off\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "7fa52acc", + "metadata": {}, + "source": [ + "### Clean-up data directory" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3a6f6d5a", + "metadata": {}, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(root_dir)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/generation/README.md b/generation/README.md index d9125a861..cf7822310 100644 --- a/generation/README.md +++ b/generation/README.md @@ -72,3 +72,6 @@ Example shows the use cases of applying a spatial VAE to a 3D synthesis example. ## Performing anomaly detection with diffusion models: [implicit guidance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb) Examples show how to perform anomaly detection in 2D, using implicit guidance [2D-classifier free guiance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), transformers [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomalydetection_tutorial_classifier_guidance). + +## 2D super-resolution using diffusion models: [using torch](./2d_super_resolution/2d_sd_super_resolution.ipynb) and [using torch lightning](./2d_super_resolution/2d_sd_super_resolution_lightning.ipynb). +Examples show how to perform super-resolution in 2D, using PyTorch and PyTorch Lightning. \ No newline at end of file From fac4f650aee2b4307c537407c9afcf8f7d7a51d5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 24 Sep 2024 08:25:06 +0000 Subject: [PATCH 25/27] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../2d_sd_super_resolution.ipynb | 12 +- .../2d_sd_super_resolution_lightning.ipynb | 113 +++++++++--------- generation/README.md | 2 +- 3 files changed, 61 insertions(+), 66 deletions(-) diff --git a/generation/2d_super_resolution/2d_sd_super_resolution.ipynb b/generation/2d_super_resolution/2d_sd_super_resolution.ipynb index e8b30d84a..0cf89b5db 100644 --- a/generation/2d_super_resolution/2d_sd_super_resolution.ipynb +++ b/generation/2d_super_resolution/2d_sd_super_resolution.ipynb @@ -243,7 +243,10 @@ " rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n", " translate_range=[(-1, 1), (-1, 1)],\n", " scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n", - " spatial_size=[image_size, image_size], padding_mode=\"zeros\", prob=0.5),\n", + " spatial_size=[image_size, image_size],\n", + " padding_mode=\"zeros\",\n", + " prob=0.5,\n", + " ),\n", " transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n", " transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n", "]\n", @@ -656,7 +659,7 @@ " f\"epoch {epoch:d}/{max_epochs:d}:\",\n", " f\"recons loss: {epoch_loss / len(train_loader) :4f},\"\n", " f\"perc_epoch_loss: {perc_epoch_loss / len(train_loader):4f},\"\n", - " f\"kl_epoch_loss: {kl_epoch_loss / len(train_loader):4f},\"\n", + " f\"kl_epoch_loss: {kl_epoch_loss / len(train_loader):4f},\",\n", " ]\n", "\n", " if epoch > autoencoder_warm_up_n_epochs:\n", @@ -1005,10 +1008,7 @@ "\n", " epoch_loss += loss.item()\n", "\n", - " msgs = [\n", - " f\"epoch {epoch:d}/{max_epochs:d}:\",\n", - " f\"loss: {epoch_loss / len(train_loader) :4f},\"\n", - " ]\n", + " msgs = [f\"epoch {epoch:d}/{max_epochs:d}:\", f\"loss: {epoch_loss / len(train_loader) :4f},\"]\n", "\n", " if epoch % print_interval == 0:\n", " print(\",\".join(msgs))\n", diff --git a/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb b/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb index d7eca6096..26026fa73 100644 --- a/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb +++ b/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb @@ -233,8 +233,7 @@ " [\n", " transforms.LoadImaged(keys=[\"image\"]),\n", " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", - " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0,\n", - " b_max=1.0, clip=True),\n", + " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n", " transforms.RandAffined(\n", " keys=[\"image\"],\n", " rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n", @@ -256,8 +255,7 @@ " [\n", " transforms.LoadImaged(keys=[\"image\"]),\n", " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", - " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0,\n", - " b_max=1.0, clip=True),\n", + " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n", " transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n", " transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n", " ]\n", @@ -293,16 +291,17 @@ " def __init__(self):\n", " super().__init__()\n", " self.data_dir = root_dir\n", - " self.autoencoderkl = AutoencoderKL(spatial_dims=2,\n", - " in_channels=1,\n", - " out_channels=1,\n", - " channels=(256, 512, 512),\n", - " latent_channels=3,\n", - " num_res_blocks=2,\n", - " norm_num_groups=32,\n", - " attention_levels=(False, False, True))\n", - " self.discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1,\n", - " num_layers_d=3, channels=64)\n", + " self.autoencoderkl = AutoencoderKL(\n", + " spatial_dims=2,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " channels=(256, 512, 512),\n", + " latent_channels=3,\n", + " num_res_blocks=2,\n", + " norm_num_groups=32,\n", + " attention_levels=(False, False, True),\n", + " )\n", + " self.discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1, num_layers_d=3, channels=64)\n", " self.perceptual_loss = PerceptualLoss(spatial_dims=2, network_type=\"alex\")\n", " self.perceptual_weight = 0.002\n", " self.autoencoder_warm_up_n_epochs = 10\n", @@ -318,12 +317,10 @@ " self.train_ds, self.val_ds = get_datasets()\n", "\n", " def train_dataloader(self):\n", - " return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True,\n", - " num_workers=4, persistent_workers=True)\n", + " return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True, num_workers=4, persistent_workers=True)\n", "\n", " def val_dataloader(self):\n", - " return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False,\n", - " num_workers=4)\n", + " return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False, num_workers=4)\n", "\n", " def _compute_loss_generator(self, images, reconstruction, z_mu, z_sigma):\n", " recons_loss = F.l1_loss(reconstruction.float(), images.float())\n", @@ -381,9 +378,9 @@ " def on_validation_epoch_end(self):\n", " # ploting reconstruction\n", " plt.figure(figsize=(2, 2))\n", - " plt.imshow(torch.cat([self.images[0, 0].cpu(),\n", - " self.reconstruction[0, 0].cpu()],\n", - " dim=1), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.imshow(\n", + " torch.cat([self.images[0, 0].cpu(), self.reconstruction[0, 0].cpu()], dim=1), vmin=0, vmax=1, cmap=\"gray\"\n", + " )\n", " plt.tight_layout()\n", " plt.axis(\"off\")\n", " plt.show()\n", @@ -650,12 +647,14 @@ "\n", "\n", "# initialise Lightning's trainer.\n", - "trainer = pl.Trainer(devices=1,\n", - " max_epochs=max_epochs,\n", - " check_val_every_n_epoch=val_interval,\n", - " num_sanity_val_steps=0,\n", - " callbacks=checkpoint_callback,\n", - " default_root_dir=root_dir)\n", + "trainer = pl.Trainer(\n", + " devices=1,\n", + " max_epochs=max_epochs,\n", + " check_val_every_n_epoch=val_interval,\n", + " num_sanity_val_steps=0,\n", + " callbacks=checkpoint_callback,\n", + " default_root_dir=root_dir,\n", + ")\n", "\n", "# train\n", "trainer.fit(ae_net)" @@ -732,27 +731,22 @@ " num_head_channels=(0, 0, 64, 64),\n", " )\n", " self.max_noise_level = 350\n", - " self.scheduler = DDPMScheduler(num_train_timesteps=1000,\n", - " schedule=\"linear_beta\",\n", - " beta_start=0.0015,\n", - " beta_end=0.0195)\n", + " self.scheduler = DDPMScheduler(\n", + " num_train_timesteps=1000, schedule=\"linear_beta\", beta_start=0.0015, beta_end=0.0195\n", + " )\n", " self.z = ae_net.autoencoderkl.eval()\n", "\n", " def forward(self, x, timesteps, low_res_timesteps):\n", - " return self.unet(x=x,\n", - " timesteps=timesteps,\n", - " class_labels=low_res_timesteps)\n", + " return self.unet(x=x, timesteps=timesteps, class_labels=low_res_timesteps)\n", "\n", " def prepare_data(self):\n", " self.train_ds, self.val_ds = get_datasets()\n", "\n", " def train_dataloader(self):\n", - " return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True,\n", - " num_workers=4, persistent_workers=True)\n", + " return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True, num_workers=4, persistent_workers=True)\n", "\n", " def val_dataloader(self):\n", - " return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False,\n", - " num_workers=4)\n", + " return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False, num_workers=4)\n", "\n", " def _calculate_loss(self, batch, batch_idx, plt_image=False):\n", " images = batch[\"image\"]\n", @@ -764,17 +758,16 @@ " # Noise augmentation\n", " noise = torch.randn_like(latent)\n", " low_res_noise = torch.randn_like(low_res_image)\n", - " timesteps = torch.randint(0, self.scheduler.num_train_timesteps, (latent.shape[0],),\n", - " device=latent.device).long()\n", + " timesteps = torch.randint(\n", + " 0, self.scheduler.num_train_timesteps, (latent.shape[0],), device=latent.device\n", + " ).long()\n", " low_res_timesteps = torch.randint(\n", " 0, self.max_noise_level, (low_res_image.shape[0],), device=latent.device\n", " ).long()\n", "\n", - " noisy_latent = self.scheduler.add_noise(original_samples=latent,\n", - " noise=noise, timesteps=timesteps)\n", + " noisy_latent = self.scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n", " noisy_low_res_image = self.scheduler.add_noise(\n", - " original_samples=low_res_image, noise=low_res_noise,\n", - " timesteps=low_res_timesteps\n", + " original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n", " )\n", "\n", " latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n", @@ -800,9 +793,9 @@ " with autocast(\"cuda\", enabled=True):\n", " with torch.no_grad():\n", " latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n", - " noise_pred = self.forward(latent_model_input,\n", - " torch.Tensor((t,)).to(sampling_image.device)\n", - " , noise_level)\n", + " noise_pred = self.forward(\n", + " latent_model_input, torch.Tensor((t,)).to(sampling_image.device), noise_level\n", + " )\n", " latents, _ = self.scheduler.step(noise_pred, t, latents)\n", " with torch.no_grad():\n", " decoded = self.z.decode_stage_2_outputs(latents / scale_factor)\n", @@ -1141,12 +1134,14 @@ "\n", "\n", "# initialise Lightning's trainer.\n", - "trainer = pl.Trainer(devices=1,\n", - " max_epochs=max_epochs,\n", - " check_val_every_n_epoch=val_interval,\n", - " num_sanity_val_steps=0,\n", - " callbacks=checkpoint_callback,\n", - " default_root_dir=root_dir)\n", + "trainer = pl.Trainer(\n", + " devices=1,\n", + " max_epochs=max_epochs,\n", + " check_val_every_n_epoch=val_interval,\n", + " num_sanity_val_steps=0,\n", + " callbacks=checkpoint_callback,\n", + " default_root_dir=root_dir,\n", + ")\n", "\n", "# train\n", "trainer.fit(d_net)" @@ -1200,18 +1195,18 @@ " noise_level = 10\n", " noise_level = torch.Tensor((noise_level,)).long().to(d_net.device)\n", " scheduler = d_net.scheduler\n", - " noisy_low_res_image = scheduler.add_noise(original_samples=sampling_image,\n", - " noise=low_res_noise,\n", - " timesteps=torch.Tensor((noise_level,)).long())\n", + " noisy_low_res_image = scheduler.add_noise(\n", + " original_samples=sampling_image, noise=low_res_noise, timesteps=torch.Tensor((noise_level,)).long()\n", + " )\n", "\n", " scheduler.set_timesteps(num_inference_steps=1000)\n", " for t in tqdm(scheduler.timesteps, ncols=110):\n", " with autocast(\"cuda\", enabled=True):\n", " with torch.no_grad():\n", " latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n", - " noise_pred = d_net.forward(x=latent_model_input,\n", - " timesteps=torch.Tensor((t,)).to(d_net.device),\n", - " low_res_timesteps=noise_level)\n", + " noise_pred = d_net.forward(\n", + " x=latent_model_input, timesteps=torch.Tensor((t,)).to(d_net.device), low_res_timesteps=noise_level\n", + " )\n", " # 2. compute previous image: x_t -> x_t-1\n", " latents, _ = scheduler.step(noise_pred, t, latents)\n", "\n", diff --git a/generation/README.md b/generation/README.md index cf7822310..351416fd1 100644 --- a/generation/README.md +++ b/generation/README.md @@ -74,4 +74,4 @@ Example shows the use cases of applying a spatial VAE to a 3D synthesis example. Examples show how to perform anomaly detection in 2D, using implicit guidance [2D-classifier free guiance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), transformers [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomalydetection_tutorial_classifier_guidance). ## 2D super-resolution using diffusion models: [using torch](./2d_super_resolution/2d_sd_super_resolution.ipynb) and [using torch lightning](./2d_super_resolution/2d_sd_super_resolution_lightning.ipynb). -Examples show how to perform super-resolution in 2D, using PyTorch and PyTorch Lightning. \ No newline at end of file +Examples show how to perform super-resolution in 2D, using PyTorch and PyTorch Lightning. From d46e6ce6484089f61e83dd102b8456723c3c8fee Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Wed, 25 Sep 2024 16:20:50 +0100 Subject: [PATCH 26/27] Add more information on super-resolution files. Signed-off-by: Virginia Fernandez --- .../2d_sd_super_resolution.ipynb | 24 ++++++++++++++++--- .../2d_sd_super_resolution_lightning.ipynb | 21 +++++++++++++++- 2 files changed, 41 insertions(+), 4 deletions(-) diff --git a/generation/2d_super_resolution/2d_sd_super_resolution.ipynb b/generation/2d_super_resolution/2d_sd_super_resolution.ipynb index e8b30d84a..03c07ddb6 100644 --- a/generation/2d_super_resolution/2d_sd_super_resolution.ipynb +++ b/generation/2d_super_resolution/2d_sd_super_resolution.ipynb @@ -24,7 +24,7 @@ "source": [ "# Super-resolution using Stable Diffusion v2 Upscalers\n", "\n", - "This tutorial illustrates how to perform super-resolution on medical images using Latent Diffusion Models (LDMs) [1]. For that, we use an autoencoder to obtain a latent representation of the high-resolution images. Then, we train a diffusion model to infer this latent representation when conditioned on a low-resolution image. \n", + "This tutorial illustrates how to perform **super-resolution** on medical images using Latent Diffusion Models (LDMs) [1]. The idea is that, given a low-resolution image, we train a spatial autoencoder with a latent space of the same spatial size of the low resolution, so that high resolution images are encoded into a latent space of the same size of the low resolution image. The LDM then learns how to go from **noise to a latent representation of a high resolution image**. On training and inference, the **low resolution image is concatenated to the latent**, to condition the generative process. Finally, the high resolution latent representation is decoded into a high resolution image. \n", "\n", "To improve the performance of our models, we will use a method called \"noise conditioning augmentation\" (introduced in [2] and used in Stable Diffusion v2.0 and Imagen Video [3]). During the training, we add noise to the low-resolution images using a random signal-to-noise ratio, and we condition the diffusion models on the amount of noise added. At sampling time, we use a fixed signal-to-noise ratio, representing a small amount of augmentation that aids in removing artefacts in the samples.\n", "\n", @@ -416,6 +416,14 @@ "## Train Autoencoder" ] }, + { + "cell_type": "markdown", + "id": "a93437fe-d6ef-42d2-bedd-4da735c59dd1", + "metadata": {}, + "source": [ + "In this section, we train a spatial autoencoder to learn how to compress high-resolution images into a latent space representation. We need to ensure that the latent space spatial shape matches that of the low resolution images." + ] + }, { "cell_type": "code", "execution_count": 30, @@ -733,7 +741,9 @@ "source": [ "## Train Diffusion Model\n", "\n", - "In order to train the diffusion model to perform super-resolution, we will need to concatenate the latent representation of the high-resolution with the low-resolution image. For this, we create a Diffusion model with `in_channels=4`. Since only the outputted latent representation is interesting, we set `out_channels=3`." + "In order to train the diffusion model to perform super-resolution, we will need to **concatenate the latent representation of the high-resolution with the low-resolution image**. Therefore, the number of input channels to the diffusion model will be the sum of the number of channels in the low-resolution, 1, and the number of channels of the high-resolution image latent representation (3). In this case, we create a Diffusion model with `in_channels=4`. Since only the output latent representation is interesting, we set `out_channels=3`. \n", + "\n", + "**At inference time** we do not have a high-resolution image. Instead, we pass the concatenation of the low resolution image, and noise of the same shape as the latent space representation." ] }, { @@ -993,7 +1003,7 @@ " noisy_low_res_image = scheduler.add_noise(\n", " original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n", " )\n", - "\n", + " # Here we concatenate the HR latent and thje low resolution image.\n", " latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n", "\n", " noise_pred = unet(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n", @@ -1098,6 +1108,14 @@ "### Plotting sampling example" ] }, + { + "cell_type": "markdown", + "id": "1a2813d4-9087-459e-8913-bce174ac31cd", + "metadata": {}, + "source": [ + "As mentioned above, at inference time, we only need to pass noise of the same shape of the latent concatenated to the low-resolution image, to get the latent representation of the corresponding high-resolution image." + ] + }, { "cell_type": "code", "execution_count": 47, diff --git a/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb b/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb index d7eca6096..fde5a8c65 100644 --- a/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb +++ b/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb @@ -404,6 +404,14 @@ "## Train Autoencoder" ] }, + { + "cell_type": "markdown", + "id": "e740cb2d-5a57-42ed-806b-e8c720a6f922", + "metadata": {}, + "source": [ + "In this section, we train a spatial autoencoder to learn how to compress high-resolution images into a latent space representation. We need to ensure that the latent space spatial shape matches that of the low resolution images." + ] + }, { "cell_type": "code", "execution_count": 10, @@ -708,6 +716,7 @@ "metadata": {}, "source": [ "## Define the LightningModule for DiffusionModelUnet (transforms, network, loaders, etc)\n", + "\n", "The LightningModule contains a refactoring of your training code. The following module is a reformating of the code in 2d_stable_diffusion_v2_super_resolution." ] }, @@ -853,7 +862,9 @@ "source": [ "## Train Diffusion Model\n", "\n", - "In order to train the diffusion model to perform super-resolution, we will need to concatenate the latent representation of the high-resolution with the low-resolution image. For this, we create a Diffusion model with `in_channels=4`. Since only the outputted latent representation is interesting, we set `out_channels=3`.\n", + "In order to train the diffusion model to perform super-resolution, we will need to **concatenate the latent representation of the high-resolution with the low-resolution image**. Therefore, the number of input channels to the diffusion model will be the sum of the number of channels in the low-resolution, 1, and the number of channels of the high-resolution image latent representation (3). In this case, we create a Diffusion model with `in_channels=4`. Since only the output latent representation is interesting, we set `out_channels=3`. \n", + "\n", + "**At inference time** we do not have a high-resolution image. Instead, we pass the concatenation of the low resolution image, and noise of the same shape as the latent space representation.\n", "\n", "As mentioned, we will use the conditioned augmentation (introduced in [2] section 3 and used on Stable Diffusion Upscalers and Imagen Video [3] Section 2.5) as it has been shown critical for cascaded diffusion models, as well for super-resolution tasks. For this, we apply Gaussian noise augmentation to the low-resolution images. We will use a scheduler low_res_scheduler to add this noise, with the t step defining the signal-to-noise ratio and use the t value to condition the diffusion model (inputted using class_labels argument)." ] @@ -1160,6 +1171,14 @@ "### Plotting sampling example" ] }, + { + "cell_type": "markdown", + "id": "19ba049e-fca6-4c76-b7b1-7e992d370583", + "metadata": {}, + "source": [ + "As mentioned above, at inference time, we only need to pass noise of the same shape of the latent concatenated to the low-resolution image, to get the latent representation of the corresponding high-resolution image." + ] + }, { "cell_type": "code", "execution_count": 26, From ca1cdfa215ededd7aff45054b5154d8ba516d18b Mon Sep 17 00:00:00 2001 From: Virginia Fernandez Date: Wed, 25 Sep 2024 16:35:27 +0100 Subject: [PATCH 27/27] Modify SR text Signed-off-by: Virginia Fernandez --- generation/2d_super_resolution/2d_sd_super_resolution.ipynb | 6 +++--- .../2d_sd_super_resolution_lightning.ipynb | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/generation/2d_super_resolution/2d_sd_super_resolution.ipynb b/generation/2d_super_resolution/2d_sd_super_resolution.ipynb index 7933c94fd..15d111bc4 100644 --- a/generation/2d_super_resolution/2d_sd_super_resolution.ipynb +++ b/generation/2d_super_resolution/2d_sd_super_resolution.ipynb @@ -41,7 +41,7 @@ "id": "b839bf2d", "metadata": {}, "source": [ - "## Set up environment" + "## Setup environment" ] }, { @@ -61,7 +61,7 @@ "id": "214066de", "metadata": {}, "source": [ - "## Set up imports" + "## Setup imports" ] }, { @@ -744,7 +744,7 @@ "source": [ "## Train Diffusion Model\n", "\n", - "In order to train the diffusion model to perform super-resolution, we will need to **concatenate the latent representation of the high-resolution with the low-resolution image**. Therefore, the number of input channels to the diffusion model will be the sum of the number of channels in the low-resolution, 1, and the number of channels of the high-resolution image latent representation (3). In this case, we create a Diffusion model with `in_channels=4`. Since only the output latent representation is interesting, we set `out_channels=3`. \n", + "In order to train the diffusion model to perform super-resolution, we will need to **concatenate the latent representation of the high-resolution with the low-resolution image**. Therefore, the number of input channels to the diffusion model will be the sum of the number of channels in the low-resolution (1) and the number of channels of the high-resolution image latent representation (3). In this case, we create a Diffusion model with `in_channels=4`. Since only the output latent representation is interesting, we set `out_channels=3`. \n", "\n", "**At inference time** we do not have a high-resolution image. Instead, we pass the concatenation of the low resolution image, and noise of the same shape as the latent space representation." ] diff --git a/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb b/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb index 9f64109c3..817efe233 100644 --- a/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb +++ b/generation/2d_super_resolution/2d_sd_super_resolution_lightning.ipynb @@ -43,7 +43,7 @@ "id": "b839bf2d", "metadata": {}, "source": [ - "## Set up environment" + "## Setup environment" ] }, { @@ -64,7 +64,7 @@ "id": "214066de", "metadata": {}, "source": [ - "## Set up imports" + "## Setup imports" ] }, { @@ -855,7 +855,7 @@ "source": [ "## Train Diffusion Model\n", "\n", - "In order to train the diffusion model to perform super-resolution, we will need to **concatenate the latent representation of the high-resolution with the low-resolution image**. Therefore, the number of input channels to the diffusion model will be the sum of the number of channels in the low-resolution, 1, and the number of channels of the high-resolution image latent representation (3). In this case, we create a Diffusion model with `in_channels=4`. Since only the output latent representation is interesting, we set `out_channels=3`. \n", + "In order to train the diffusion model to perform super-resolution, we will need to **concatenate the latent representation of the high-resolution with the low-resolution image**. Therefore, the number of input channels to the diffusion model will be the sum of the number of channels in the low-resolution (1) and the number of channels of the high-resolution image latent representation (3). In this case, we create a Diffusion model with `in_channels=4`. Since only the output latent representation is interesting, we set `out_channels=3`. \n", "\n", "**At inference time** we do not have a high-resolution image. Instead, we pass the concatenation of the low resolution image, and noise of the same shape as the latent space representation.\n", "\n",