autoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93be1050-b3f6-47f1-ae97-52e5f40881ab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from torchvision import transforms\n",
    "import torch.nn as nn\n",
    "import random\n",
    "from torch.utils.data import DataLoader,Dataset\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9d7d2427-f6ce-4cf7-ad26-42130688d10b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113\n",
      "Entering Epoch:  0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "======> epoch: 0/60, Loss:0.03394058346748352\n",
      "Entering Epoch:  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "Entering Epoch:  2\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Entering Epoch:  3\n"
     ]
    },
    {
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    {
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     "text": [
      "Entering Epoch:  4\n"
     ]
    },
    {
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    {
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     "text": [
      "Entering Epoch:  5\n"
     ]
    },
    {
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    {
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     "text": [
      "Entering Epoch:  6\n"
     ]
    },
    {
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     "text": [
      "Entering Epoch:  7\n"
     ]
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    {
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     "text": [
      "Entering Epoch:  8\n"
     ]
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    {
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    {
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     "text": [
      "Entering Epoch:  9\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Entering Epoch:  10\n"
     ]
    },
    {
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    {
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     "text": [
      "======> epoch: 10/60, Loss:0.000522487738635391\n",
      "Entering Epoch:  11\n"
     ]
    },
    {
     "name": "stderr",
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    {
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     "text": [
      "Entering Epoch:  12\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Entering Epoch:  13\n"
     ]
    },
    {
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     "text": [
      "Entering Epoch:  14\n"
     ]
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    {
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     "text": [
      "Entering Epoch:  15\n"
     ]
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    {
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     "text": [
      "Entering Epoch:  16\n"
     ]
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    {
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     "text": [
      "Entering Epoch:  17\n"
     ]
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     "text": [
      "Entering Epoch:  18\n"
     ]
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    {
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     "text": [
      "Entering Epoch:  19\n"
     ]
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    {
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      "Entering Epoch:  20\n"
     ]
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     "text": [
      "======> epoch: 20/60, Loss:0.0031264247372746468\n",
      "Entering Epoch:  21\n"
     ]
    },
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     "text": [
      "Entering Epoch:  22\n"
     ]
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      "Entering Epoch:  23\n"
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      "Entering Epoch:  24\n"
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      "Entering Epoch:  25\n"
     ]
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      "Entering Epoch:  26\n"
     ]
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      "Entering Epoch:  27\n"
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      "Entering Epoch:  28\n"
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      "Entering Epoch:  29\n"
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      "Entering Epoch:  30\n"
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     "text": [
      "======> epoch: 30/60, Loss:0.000806483265478164\n",
      "Entering Epoch:  31\n"
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    },
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      "Entering Epoch:  32\n"
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      "Entering Epoch:  33\n"
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      "Entering Epoch:  34\n"
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      "Entering Epoch:  35\n"
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      "Entering Epoch:  36\n"
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      "Entering Epoch:  37\n"
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      "Entering Epoch:  38\n"
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      "Entering Epoch:  39\n"
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      "Entering Epoch:  40\n"
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      "======> epoch: 40/60, Loss:0.00041955907363444567\n",
      "Entering Epoch:  41\n"
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      "Entering Epoch:  42\n"
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      "Entering Epoch:  43\n"
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      "Entering Epoch:  44\n"
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      "Entering Epoch:  45\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  46\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 173.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  47\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 167.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 178.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  49\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 165.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 166.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======> epoch: 50/60, Loss:0.0004019361222162843\n",
      "Entering Epoch:  51\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 158.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  52\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 166.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  53\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 173.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 164.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  55\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 173.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  56\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 193.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  57\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 180.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  58\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 163.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Epoch:  59\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 113/113 [00:00<00:00, 160.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1024,)\n",
      "(1024,)\n",
      "(1024,)\n"
     ]
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L2S233JKdffbZWW9vb5ZlWbZs2bJs9erVI+t/8pOfZNOmTcseeOCBbN++fVl7e3s2ffr07KWXXirVS5gyij3rDRs2ZNXV1dlTTz2V/epXvxp5HD16tFQvYcoo9qz/0IoVK7LPfvazOU07tRV71gcPHszOOuus7B/+4R+yV155JfvBD36QzZw5M/va175WqpcwZRR71u3t7dlZZ52V/du//Vt24MCB7N///d+zCy64IPvc5z5XqpcwZRw9ejTbu3dvtnfv3iwisoceeijbu3dv9otf/CLLsixbvXp1tmzZspH1Bw4cyM4888zsn/7pn7J9+/ZlmzZtyqqqqrLOzs5SvYQpRRbJjyySH1kkP7JIfmSR/Mgi+ZJF8iOL5EcWyY8skh9ZJD+lyiKnRWGSZVn2jW98IzvvvPOy6urqbNGiRdl//ud/jvy3q6++OluxYsWo9d/97nezCy+8MKuurs4+8YlPZNu3b8954qmrmLP+8Ic/nEXEmEd7e3v+g09BxX5f//8JBsUp9qxffPHFrKmpKSsUCtn555+fff3rX8/efffdnKeemoo563feeSf7yle+kl1wwQVZTU1N1tjYmH3xi1/M/vd//zf/waeYH/3oR+P+/P3d+a5YsSK7+uqrx+yZP39+Vl1dnZ1//vnZv/7rv+Y+91Qmi+RHFsmPLJIfWSQ/skg+ZJH8ySL5kUXyI4vkRxbJjyySj1JlkYos894fAAAAAAAgbSX/DBMAAAAAAIBSU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJU5gAAAAAAADJK7ow+fGPfxyLFy+O2bNnR0VFRTz77LMfuGfnzp3xyU9+MgqFQnzkIx+JJ554YgKjAgDIIgBAackiAFC+ii5Mjh07FvPmzYtNmzad1PrXX389rrvuurjmmmuip6cnvvSlL8VNN90Uzz33XNHDAgDIIgBAKckiAFC+KrIsyya8uaIinnnmmViyZMkJ19x5552xffv2+NnPfjZy7W//9m/jrbfeis7Ozok+NQCALAIAlJQsAgDlZdpkP0F3d3e0tLSMutba2hpf+tKXTrjn+PHjcfz48ZF/Dw8Px69//ev4kz/5k6ioqJisUQFgSsmyLI4ePRqzZ8+OykofS3YisggATA5Z5OTIIgAwOSYji0x6YdLb2xv19fWjrtXX18fAwED85je/iTPOOGPMno6Ojrj33nsnezQAKAuHDh2KP/uzPyv1GKctWQQAJpcs8v5kEQCYXKcyi0x6YTIRa9asiba2tpF/9/f3x3nnnReHDh2K2traEk4GAKePgYGBaGxsjLPOOqvUo5QdWQQAPpgsMnlkEQD4YJORRSa9MGloaIi+vr5R1/r6+qK2tnbc36KIiCgUClEoFMZcr62tFQwA4A/4swzvTxYBgMkli7w/WQQAJtepzCKT/kdGm5ubo6ura9S1559/Ppqbmyf7qQEAZBEAoKRkEQCYOoouTP7v//4venp6oqenJyIiXn/99ejp6YmDBw9GxHtvG12+fPnI+ltvvTUOHDgQX/7yl2P//v3xyCOPxHe/+9244447Ts0rAACSIosAAKUkiwBA+Sq6MPnpT38al112WVx22WUREdHW1haXXXZZrFu3LiIifvWrX42EhIiIP//zP4/t27fH888/H/PmzYsHH3wwvvWtb0Vra+spegkAQEpkEQCglGQRAChfFVmWZaUe4oMMDAxEXV1d9Pf3+1udAPD/uD/mx1kDwFjuj/lx1gAw1mTcHyf9M0wAAAAAAABOdwoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeRMqTDZt2hRz586NmpqaaGpqil27dr3v+o0bN8ZHP/rROOOMM6KxsTHuuOOO+O1vfzuhgQEAZBEAoJRkEQAoT0UXJtu2bYu2trZob2+PPXv2xLx586K1tTXefPPNcdc/+eSTsXr16mhvb499+/bFY489Ftu2bYu77rrrjx4eAEiPLAIAlJIsAgDlq+jC5KGHHoqbb745Vq5cGR//+Mdj8+bNceaZZ8bjjz8+7voXX3wxrrzyyrjhhhti7ty53tGDcgAAYA9JREFU8elPfzquv/76D/ztCwCA8cgiAEApySIAUL6KKkwGBwdj9+7d0dLS8vsvUFkZLS0t0d3dPe6eK664Inbv3j0SBA4cOBA7duyIa6+99oTPc/z48RgYGBj1AACQRQCAUpJFAKC8TStm8ZEjR2JoaCjq6+tHXa+vr4/9+/ePu+eGG26II0eOxKc+9anIsizefffduPXWW9/3racdHR1x7733FjMaAJAAWQQAKCVZBADK24Q+9L0YO3fujPXr18cjjzwSe/bsiaeffjq2b98e99133wn3rFmzJvr7+0cehw4dmuwxAYAyJYsAAKUkiwDA1FHUO0xmzJgRVVVV0dfXN+p6X19fNDQ0jLvnnnvuiWXLlsVNN90UERGXXHJJHDt2LG655ZZYu3ZtVFaO7WwKhUIUCoViRgMAEiCLAAClJIsAQHkr6h0m1dXVsWDBgujq6hq5Njw8HF1dXdHc3DzunrfffnvMzb+qqioiIrIsK3ZeACBhsggAUEqyCACUt6LeYRIR0dbWFitWrIiFCxfGokWLYuPGjXHs2LFYuXJlREQsX7485syZEx0dHRERsXjx4njooYfisssui6ampnjttdfinnvuicWLF48EBACAkyWLAAClJIsAQPkqujBZunRpHD58ONatWxe9vb0xf/786OzsHPnAs4MHD476zYm77747Kioq4u67745f/vKX8ad/+qexePHi+PrXv37qXgUAkAxZBAAoJVkEAMpXRTYF3v85MDAQdXV10d/fH7W1taUeBwBOC+6P+XHWADCW+2N+nDUAjDUZ98eiPsMEAAAAAACgHClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5E2oMNm0aVPMnTs3ampqoqmpKXbt2vW+6996661YtWpVzJo1KwqFQlx44YWxY8eOCQ0MACCLAAClJIsAQHmaVuyGbdu2RVtbW2zevDmamppi48aN0draGq+88krMnDlzzPrBwcH467/+65g5c2Y89dRTMWfOnPjFL34RZ5999qmYHwBIjCwCAJSSLAIA5asiy7KsmA1NTU1x+eWXx8MPPxwREcPDw9HY2Bi33XZbrF69esz6zZs3x7/8y7/E/v37Y/r06RMacmBgIOrq6qK/vz9qa2sn9DUAoNyken+URQDg9JDq/VEWAYDTw2TcH4v6k1yDg4Oxe/fuaGlp+f0XqKyMlpaW6O7uHnfP97///Whubo5Vq1ZFfX19XHzxxbF+/foYGho64fMcP348BgYGRj0AAGQRAKCUZBEAKG9FFSZHjhyJoaGhqK+vH3W9vr4+ent7x91z4MCBeOqpp2JoaCh27NgR99xzTzz44IPxta997YTP09HREXV1dSOPxsbGYsYEAMqULAIAlJIsAgDlbUIf+l6M4eHhmDlzZjz66KOxYMGCWLp0aaxduzY2b958wj1r1qyJ/v7+kcehQ4cme0wAoEzJIgBAKckiADB1FPWh7zNmzIiqqqro6+sbdb2vry8aGhrG3TNr1qyYPn16VFVVjVz72Mc+Fr29vTE4OBjV1dVj9hQKhSgUCsWMBgAkQBYBAEpJFgGA8lbUO0yqq6tjwYIF0dXVNXJteHg4urq6orm5edw9V155Zbz22msxPDw8cu3VV1+NWbNmjRsKAABORBYBAEpJFgGA8lb0n+Rqa2uLLVu2xLe//e3Yt29ffOELX4hjx47FypUrIyJi+fLlsWbNmpH1X/jCF+LXv/513H777fHqq6/G9u3bY/369bFq1apT9yoAgGTIIgBAKckiAFC+ivqTXBERS5cujcOHD8e6deuit7c35s+fH52dnSMfeHbw4MGorPx9D9PY2BjPPfdc3HHHHXHppZfGnDlz4vbbb48777zz1L0KACAZsggAUEqyCACUr4osy7JSD/FBBgYGoq6uLvr7+6O2trbU4wDAacH9MT/OGgDGcn/Mj7MGgLEm4/5Y9J/kAgAAAAAAKDcKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHkTKkw2bdoUc+fOjZqammhqaopdu3ad1L6tW7dGRUVFLFmyZCJPCwAQEbIIAFBasggAlKeiC5Nt27ZFW1tbtLe3x549e2LevHnR2toab7755vvue+ONN+If//Ef46qrrprwsAAAsggAUEqyCACUr6ILk4ceeihuvvnmWLlyZXz84x+PzZs3x5lnnhmPP/74CfcMDQ3F5z//+bj33nvj/PPP/6MGBgDSJosAAKUkiwBA+SqqMBkcHIzdu3dHS0vL779AZWW0tLREd3f3Cfd99atfjZkzZ8aNN954Us9z/PjxGBgYGPUAAJBFAIBSkkUAoLwVVZgcOXIkhoaGor6+ftT1+vr66O3tHXfPCy+8EI899lhs2bLlpJ+no6Mj6urqRh6NjY3FjAkAlClZBAAoJVkEAMrbhD70/WQdPXo0li1bFlu2bIkZM2ac9L41a9ZEf3//yOPQoUOTOCUAUK5kEQCglGQRAJhaphWzeMaMGVFVVRV9fX2jrvf19UVDQ8OY9T//+c/jjTfeiMWLF49cGx4efu+Jp02LV155JS644IIx+wqFQhQKhWJGAwASIIsAAKUkiwBAeSvqHSbV1dWxYMGC6OrqGrk2PDwcXV1d0dzcPGb9RRddFC+99FL09PSMPD7zmc/ENddcEz09Pd5SCgAURRYBAEpJFgGA8lbUO0wiItra2mLFihWxcOHCWLRoUWzcuDGOHTsWK1eujIiI5cuXx5w5c6KjoyNqamri4osvHrX/7LPPjogYcx0A4GTIIgBAKckiAFC+ii5Mli5dGocPH45169ZFb29vzJ8/Pzo7O0c+8OzgwYNRWTmpH40CACRMFgEASkkWAYDyVZFlWVbqIT7IwMBA1NXVRX9/f9TW1pZ6HAA4Lbg/5sdZA8BY7o/5cdYAMNZk3B/9ygMAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJC8CRUmmzZtirlz50ZNTU00NTXFrl27Trh2y5YtcdVVV8U555wT55xzTrS0tLzvegCADyKLAAClJIsAQHkqujDZtm1btLW1RXt7e+zZsyfmzZsXra2t8eabb467fufOnXH99dfHj370o+ju7o7Gxsb49Kc/Hb/85S//6OEBgPTIIgBAKckiAFC+KrIsy4rZ0NTUFJdffnk8/PDDERExPDwcjY2Ncdttt8Xq1as/cP/Q0FCcc8458fDDD8fy5ctP6jkHBgairq4u+vv7o7a2tphxAaBspXp/lEUA4PSQ6v1RFgGA08Nk3B+LeofJ4OBg7N69O1paWn7/BSoro6WlJbq7u0/qa7z99tvxzjvvxLnnnnvCNcePH4+BgYFRDwAAWQQAKCVZBADKW1GFyZEjR2JoaCjq6+tHXa+vr4/e3t6T+hp33nlnzJ49e1S4+EMdHR1RV1c38mhsbCxmTACgTMkiAEApySIAUN4m9KHvE7Vhw4bYunVrPPPMM1FTU3PCdWvWrIn+/v6Rx6FDh3KcEgAoV7IIAFBKsggAnN6mFbN4xowZUVVVFX19faOu9/X1RUNDw/vufeCBB2LDhg3xwx/+MC699NL3XVsoFKJQKBQzGgCQAFkEACglWQQAyltR7zCprq6OBQsWRFdX18i14eHh6Orqiubm5hPuu//+++O+++6Lzs7OWLhw4cSnBQCSJosAAKUkiwBAeSvqHSYREW1tbbFixYpYuHBhLFq0KDZu3BjHjh2LlStXRkTE8uXLY86cOdHR0REREf/8z/8c69atiyeffDLmzp078jc9P/ShD8WHPvShU/hSAIAUyCIAQCnJIgBQvoouTJYuXRqHDx+OdevWRW9vb8yfPz86OztHPvDs4MGDUVn5+zeufPOb34zBwcH4m7/5m1Ffp729Pb7yla/8cdMDAMmRRQCAUpJFAKB8VWRZlpV6iA8yMDAQdXV10d/fH7W1taUeBwBOC+6P+XHWADCW+2N+nDUAjDUZ98eiPsMEAAAAAACgHClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5ClMAAAAAACA5E2oMNm0aVPMnTs3ampqoqmpKXbt2vW+67/3ve/FRRddFDU1NXHJJZfEjh07JjQsAECELAIAlJYsAgDlqejCZNu2bdHW1hbt7e2xZ8+emDdvXrS2tsabb7457voXX3wxrr/++rjxxhtj7969sWTJkliyZEn87Gc/+6OHBwDSI4sAAKUkiwBA+arIsiwrZkNTU1Ncfvnl8fDDD0dExPDwcDQ2NsZtt90Wq1evHrN+6dKlcezYsfjBD34wcu0v//IvY/78+bF58+aTes6BgYGoq6uL/v7+qK2tLWZcAChbqd4fZREAOD2ken+URQDg9DAZ98dpxSweHByM3bt3x5o1a0auVVZWRktLS3R3d4+7p7u7O9ra2kZda21tjWefffaEz3P8+PE4fvz4yL/7+/sj4r0DAADe87v7YpG/+zClySIAcPqQRd4jiwBAaUxGFimqMDly5EgMDQ1FfX39qOv19fWxf//+cff09vaOu763t/eEz9PR0RH33nvvmOuNjY3FjAsASfif//mfqKurK/UYuZBFAOD0I4vIIgBQSqcyixRVmORlzZo1o3774q233ooPf/jDcfDgwWRCWKkMDAxEY2NjHDp0yNt8J5mzzo+zzo+zzld/f3+cd955ce6555Z6lLIji5SOnyP5cdb5cdb5cdb5kkUmjyxSOn6O5MdZ58dZ58t552cyskhRhcmMGTOiqqoq+vr6Rl3v6+uLhoaGcfc0NDQUtT4iolAoRKFQGHO9rq7ON1lOamtrnXVOnHV+nHV+nHW+KisrSz1CbmSRdPg5kh9nnR9nnR9nnS9ZRBYpR36O5MdZ58dZ58t55+dUZpGivlJ1dXUsWLAgurq6Rq4NDw9HV1dXNDc3j7unubl51PqIiOeff/6E6wEATkQWAQBKSRYBgPJW9J/kamtrixUrVsTChQtj0aJFsXHjxjh27FisXLkyIiKWL18ec+bMiY6OjoiIuP322+Pqq6+OBx98MK677rrYunVr/PSnP41HH3301L4SACAJsggAUEqyCACUr6ILk6VLl8bhw4dj3bp10dvbG/Pnz4/Ozs6RDzA7ePDgqLfAXHHFFfHkk0/G3XffHXfddVf8xV/8RTz77LNx8cUXn/RzFgqFaG9vH/ftqJxazjo/zjo/zjo/zjpfqZ63LFLenHV+nHV+nHV+nHW+Uj1vWaS8Oev8OOv8OOt8Oe/8TMZZV2RZlp2yrwYAAAAAADAFpfPJbAAAAAAAACegMAEAAAAAAJKnMAEAAAAAAJKnMAEAAAAAAJJ32hQmmzZtirlz50ZNTU00NTXFrl273nf99773vbjooouipqYmLrnkktixY0dOk059xZz1li1b4qqrropzzjknzjnnnGhpafnA/zf8XrHf17+zdevWqKioiCVLlkzugGWk2LN+6623YtWqVTFr1qwoFApx4YUX+jlykoo9640bN8ZHP/rROOOMM6KxsTHuuOOO+O1vf5vTtFPXj3/841i8eHHMnj07Kioq4tlnn/3APTt37oxPfvKTUSgU4iMf+Ug88cQTkz5nOZFF8iOL5EcWyY8skh9ZJB+ySP5kkfzIIvmRRfIji+RHFslHybJIdhrYunVrVl1dnT3++OPZf/3Xf2U333xzdvbZZ2d9fX3jrv/JT36SVVVVZffff3/28ssvZ3fffXc2ffr07KWXXsp58qmn2LO+4YYbsk2bNmV79+7N9u3bl/3d3/1dVldXl/33f/93zpNPPcWe9e+8/vrr2Zw5c7Krrroq++xnP5vPsFNcsWd9/PjxbOHChdm1116bvfDCC9nrr7+e7dy5M+vp6cl58qmn2LP+zne+kxUKhew73/lO9vrrr2fPPfdcNmvWrOyOO+7IefKpZ8eOHdnatWuzp59+OouI7Jlnnnnf9QcOHMjOPPPMrK2tLXv55Zezb3zjG1lVVVXW2dmZz8BTnCySH1kkP7JIfmSR/Mgi+ZFF8iWL5EcWyY8skh9ZJD+ySH5KlUVOi8Jk0aJF2apVq0b+PTQ0lM2ePTvr6OgYd/3nPve57Lrrrht1rampKfv7v//7SZ2zHBR71n/o3Xffzc4666zs29/+9mSNWDYmctbvvvtudsUVV2Tf+ta3shUrVggGJ6nYs/7mN7+ZnX/++dng4GBeI5aNYs961apV2V/91V+NutbW1pZdeeWVkzpnuTmZYPDlL385+8QnPjHq2tKlS7PW1tZJnKx8yCL5kUXyI4vkRxbJjyxSGrLI5JNF8iOL5EcWyY8skh9ZpDTyzCIl/5Ncg4ODsXv37mhpaRm5VllZGS0tLdHd3T3unu7u7lHrIyJaW1tPuJ73TOSs/9Dbb78d77zzTpx77rmTNWZZmOhZf/WrX42ZM2fGjTfemMeYZWEiZ/39738/mpubY9WqVVFfXx8XX3xxrF+/PoaGhvIae0qayFlfccUVsXv37pG3px44cCB27NgR1157bS4zp8S9ceJkkfzIIvmRRfIji+RHFjm9uTdOnCySH1kkP7JIfmSR/Mgip7dTdW+cdiqHmogjR47E0NBQ1NfXj7peX18f+/fvH3dPb2/vuOt7e3snbc5yMJGz/kN33nlnzJ49e8w3H6NN5KxfeOGFeOyxx6KnpyeHCcvHRM76wIED8R//8R/x+c9/Pnbs2BGvvfZafPGLX4x33nkn2tvb8xh7SprIWd9www1x5MiR+NSnPhVZlsW7774bt956a9x11115jJyUE90bBwYG4je/+U2cccYZJZrs9CeL5EcWyY8skh9ZJD+yyOlNFpk4WSQ/skh+ZJH8yCL5kUVOb6cqi5T8HSZMHRs2bIitW7fGM888EzU1NaUep6wcPXo0li1bFlu2bIkZM2aUepyyNzw8HDNnzoxHH300FixYEEuXLo21a9fG5s2bSz1a2dm5c2esX78+HnnkkdizZ088/fTTsX379rjvvvtKPRowBckik0cWyZcskh9ZBDiVZJHJI4vkSxbJjywy9ZT8HSYzZsyIqqqq6OvrG3W9r68vGhoaxt3T0NBQ1HreM5Gz/p0HHnggNmzYED/84Q/j0ksvncwxy0KxZ/3zn/883njjjVi8ePHIteHh4YiImDZtWrzyyitxwQUXTO7QU9REvq9nzZoV06dPj6qqqpFrH/vYx6K3tzcGBwejurp6UmeeqiZy1vfcc08sW7YsbrrppoiIuOSSS+LYsWNxyy23xNq1a6OyUm9/qpzo3lhbW+s3Oj+ALJIfWSQ/skh+ZJH8yCKnN1lk4mSR/Mgi+ZFF8iOL5EcWOb2dqixS8v8j1dXVsWDBgujq6hq5Njw8HF1dXdHc3Dzunubm5lHrIyKef/75E67nPRM564iI+++/P+67777o7OyMhQsX5jHqlFfsWV900UXx0ksvRU9Pz8jjM5/5TFxzzTXR09MTjY2NeY4/pUzk+/rKK6+M1157bSR8RUS8+uqrMWvWLKHgfUzkrN9+++0xN//fBbL3PrOLU8W9ceJkkfzIIvmRRfIji+RHFjm9uTdOnCySH1kkP7JIfmSR/Mgip7dTdm8s6iPiJ8nWrVuzQqGQPfHEE9nLL7+c3XLLLdnZZ5+d9fb2ZlmWZcuWLctWr149sv4nP/lJNm3atOyBBx7I9u3bl7W3t2fTp0/PXnrppVK9hCmj2LPesGFDVl1dnT311FPZr371q5HH0aNHS/USpoxiz/oPrVixIvvsZz+b07RTW7FnffDgweyss87K/uEf/iF75ZVXsh/84AfZzJkzs6997WuleglTRrFn3d7enp111lnZv/3bv2UHDhzI/v3f/z274IILss997nOleglTxtGjR7O9e/dme/fuzSIie+ihh7K9e/dmv/jFL7Isy7LVq1dny5YtG1l/4MCB7Mwzz8z+6Z/+Kdu3b1+2adOmrKqqKuvs7CzVS5hSZJH8yCL5kUXyI4vkRxbJjyySL1kkP7JIfmSR/Mgi+ZFF8lOqLHJaFCZZlmXf+MY3svPOOy+rrq7OFi1alP3nf/7nyH+7+uqrsxUrVoxa/93vfje78MILs+rq6uwTn/hEtn379pwnnrqKOesPf/jDWUSMebS3t+c/+BRU7Pf1/59gUJxiz/rFF1/MmpqaskKhkJ1//vnZ17/+9ezdd9/NeeqpqZizfuedd7KvfOUr2QUXXJDV1NRkjY2N2Re/+MXsf//3f/MffIr50Y9+NO7P39+d74oVK7Krr756zJ758+dn1dXV2fnnn5/967/+a+5zT2WySH5kkfzIIvmRRfIji+RDFsmfLJIfWSQ/skh+ZJH8yCL5KFUWqcgy7/0BAAAAAADSVvLPMAEAAAAAACg1hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJA8hQkAAAAAAJC8oguTH//4x7F48eKYPXt2VFRUxLPPPvuBe3bu3Bmf/OQno1AoxEc+8pF44oknJjAqAIAsAgCUliwCAOWr6MLk2LFjMW/evNi0adNJrX/99dfjuuuui2uuuSZ6enriS1/6Utx0003x3HPPFT0sAIAsAgCUkiwCAOWrIsuybMKbKyrimWeeiSVLlpxwzZ133hnbt2+Pn/3sZyPX/vZv/zbeeuut6OzsnOhTAwDIIgBASckiAFBepk32E3R3d0dLS8uoa62trfGlL33phHuOHz8ex48fH/n38PBw/PrXv44/+ZM/iYqKiskaFQCmlCzL4ujRozF79uyorPSxZCciiwDA5JBFTo4sAgCTYzKyyKQXJr29vVFfXz/qWn19fQwMDMRvfvObOOOMM8bs6ejoiHvvvXeyRwOAsnDo0KH4sz/7s1KPcdqSRQBgcski708WAYDJdSqzyKQXJhOxZs2aaGtrG/l3f39/nHfeeXHo0KGora0t4WQAcPoYGBiIxsbGOOuss0o9StmRRQDgg8kik0cWAYAPNhlZZNILk4aGhujr6xt1ra+vL2pra8f9LYqIiEKhEIVCYcz12tpawQAA/oA/y/D+ZBEAmFyyyPuTRQBgcp3KLDLpf2S0ubk5urq6Rl17/vnno7m5ebKfGgBAFgEASkoWAYCpo+jC5P/+7/+ip6cnenp6IiLi9ddfj56enjh48GBEvPe20eXLl4+sv/XWW+PAgQPx5S9/Ofbv3x+PPPJIfPe734077rjj1LwCACApsggAUEqyCACUr6ILk5/+9Kdx2WWXxWWXXRYREW1tbXHZZZfFunXrIiLiV7/61UhIiIj48z//89i+fXs8//zzMW/evHjwwQfjW9/6VrS2tp6ilwAApEQWAQBKSRYBgPJVkWVZVuohPsjAwEDU1dVFf3+/v9UJAP+P+2N+nDUAjOX+mB9nDQBjTcb9cdI/wwQAAAAAAOB0pzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSpzABAAAAAACSN6HCZNOmTTF37tyoqamJpqam2LVr1/uu37hxY3z0ox+NM844IxobG+OOO+6I3/72txMaGABAFgEASkkWAYDyVHRhsm3btmhra4v29vbYs2dPzJs3L1pbW+PNN98cd/2TTz4Zq1evjvb29ti3b1889thjsW3btrjrrrv+6OEBgPTIIgBAKckiAFC+ii5MHnroobj55ptj5cqV8fGPfzw2b94cZ555Zjz++OPjrn/xxRfjyiuvjBtuuCHmzp0bn/70p+P666//wN++AAAYjywCAJSSLAIA5auowmRwcDB2794dLS0tv/8ClZXR0tIS3d3d4+654oorYvfu3SNB4MCBA7Fjx4649tprT/g8x48fj4GBgVEPAABZBAAoJVkEAMrbtGIWHzlyJIaGhqK+vn7U9fr6+ti/f/+4e2644YY4cuRIfOpTn4osy+Ldd9+NW2+99X3fetrR0RH33ntvMaMBAAmQRQCAUpJFAKC8TehD34uxc+fOWL9+fTzyyCOxZ8+eePrpp2P79u1x3333nXDPmjVror+/f+Rx6NChyR4TAChTsggAUEqyCABMHUW9w2TGjBlRVVUVfX19o6739fVFQ0PDuHvuueeeWLZsWdx0000REXHJJZfEsWPH4pZbbom1a9dGZeXYzqZQKEShUChmNAAgAbIIAFBKsggAlLei3mFSXV0dCxYsiK6urpFrw8PD0dXVFc3NzePuefvtt8fc/KuqqiIiIsuyYucFABImiwAApSSLAEB5K+odJhERbW1tsWLFili4cGEsWrQoNm7cGMeOHYuVK1dGRMTy5ctjzpw50dHRERERixcvjoceeiguu+yyaGpqitdeey3uueeeWLx48UhAAAA4WbIIAFBKsggAlK+iC5OlS5fG4cOHY926ddHb2xvz58+Pzs7OkQ88O3jw4KjfnLj77rujoqIi7r777vjlL38Zf/qnfxqLFy+Or3/966fuVQAAyZBFAIBSkkUAoHxVZFPg/Z8DAwNRV1cX/f39UVtbW+pxAOC04P6YH2cNAGO5P+bHWQPAWJNxfyzqM0wAAAAAAADKkcIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABI3oQKk02bNsXcuXOjpqYmmpqaYteuXe+7/q233opVq1bFrFmzolAoxIUXXhg7duyY0MAAALIIAFBKsggAlKdpxW7Ytm1btLW1xebNm6OpqSk2btwYra2t8corr8TMmTPHrB8cHIy//uu/jpkzZ8ZTTz0Vc+bMiV/84hdx9tlnn4r5AYDEyCIAQCnJIgBQviqyLMuK2dDU1BSXX355PPzwwxERMTw8HI2NjXHbbbfF6tWrx6zfvHlz/Mu//Evs378/pk+fPqEhBwYGoq6uLvr7+6O2tnZCXwMAyk2q90dZBABOD6neH2URADg9TMb9sag/yTU4OBi7d++OlpaW33+ByspoaWmJ7u7ucfd8//vfj+bm5li1alXU19fHxRdfHOvXr4+hoaETPs/x48djYGBg1AMAQBYBAEpJFgGA8lZUYXLkyJEYGhqK+vr6Udfr6+ujt7d33D0HDhyIp556KoaGhmLHjh1xzz33xIMPPhhf+9rXTvg8HR0dUVdXN/JobGwsZkwAoEzJIgBAKckiAFDeJvSh78UYHh6OmTNnxqOPPhoLFiyIpUuXxtq1a2Pz5s0n3LNmzZro7+8feRw6dGiyxwQAypQsAgCUkiwCAFNHUR/6PmPGjKiqqoq+vr5R1/v6+qKhoWHcPbNmzYrp06dHVVXVyLWPfexj0dvbG4ODg1FdXT1mT6FQiEKhUMxoAEACZBEAoJRkEQAob0W9w6S6ujoWLFgQXV1dI9eGh4ejq6srmpubx91z5ZVXxmuvvRbDw8Mj11599dWYNWvWuKEAAOBEZBEAoJRkEQAob0X/Sa62trbYsmVLfPvb3459+/bFF77whTh27FisXLkyIiKWL18ea9asGVn/hS98IX7961/H7bffHq+++mps37491q9fH6tWrTp1rwIASIYsAgCUkiwCAOWrqD/JFRGxdOnSOHz4cKxbty56e3tj/vz50dnZOfKBZwcPHozKyt/3MI2NjfHcc8/FHXfcEZdeemnMmTMnbr/99rjzzjtP3asAAJIhiwAApSSLAED5qsiyLCv1EB9kYGAg6urqor+/P2pra0s9DgCcFtwf8+OsAWAs98f8OGsAGGsy7o9F/0kuAAAAAACAcqMwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkqcwAQAAAAAAkjehwmTTpk0xd+7cqKmpiaampti1a9dJ7du6dWtUVFTEkiVLJvK0AAARIYsAAKUliwBAeSq6MNm2bVu0tbVFe3t77NmzJ+bNmxetra3x5ptvvu++N954I/7xH/8xrrrqqgkPCwAgiwAApSSLAED5Kroweeihh+Lmm2+OlStXxsc//vHYvHlznHnmmfH444+fcM/Q0FB8/vOfj3vvvTfOP//8P2pgACBtsggAUEqyCACUr6IKk8HBwdi9e3e0tLT8/gtUVkZLS0t0d3efcN9Xv/rVmDlzZtx4440n9TzHjx+PgYGBUQ8AAFkEACglWQQAyltRhcmRI0diaGgo6uvrR12vr6+P3t7ecfe88MIL8dhjj8WWLVtO+nk6Ojqirq5u5NHY2FjMmABAmZJFAIBSkkUAoLxN6EPfT9bRo0dj2bJlsWXLlpgxY8ZJ71uzZk309/ePPA4dOjSJUwIA5UoWAQBKSRYBgKllWjGLZ8yYEVVVVdHX1zfqel9fXzQ0NIxZ//Of/zzeeOONWLx48ci14eHh95542rR45ZVX4oILLhizr1AoRKFQKGY0ACABsggAUEqyCACUt6LeYVJdXR0LFiyIrq6ukWvDw8PR1dUVzc3NY9ZfdNFF8dJLL0VPT8/I4zOf+Uxcc8010dPT4y2lAEBRZBEAoJRkEQAob0W9wyQioq2tLVasWBELFy6MRYsWxcaNG+PYsWOxcuXKiIhYvnx5zJkzJzo6OqKmpiYuvvjiUfvPPvvsiIgx1wEAToYsAgCUkiwCAOWr6MJk6dKlcfjw4Vi3bl309vbG/Pnzo7Ozc+QDzw4ePBiVlZP60SgAQMJkEQCglGQRAChfFVmWZaUe4oMMDAxEXV1d9Pf3R21tbanHAYDTgvtjfpw1AIzl/pgfZw0AY03G/dGvPAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMmbUGGyadOmmDt3btTU1ERTU1Ps2rXrhGu3bNkSV111VZxzzjlxzjnnREtLy/uuBwD4ILIIAFBKsggAlKeiC5Nt27ZFW1tbtLe3x549e2LevHnR2toab7755rjrd+7cGddff3386Ec/iu7u7mhsbIxPf/rT8ctf/vKPHh4ASI8sAgCUkiwCAOWrIsuyrJgNTU1Ncfnll8fDDz8cERHDw8PR2NgYt912W6xevfoD9w8NDcU555wTDz/8cCxfvvyknnNgYCDq6uqiv78/amtrixkXAMpWqvdHWQQATg+p3h9lEQA4PUzG/bGod5gMDg7G7t27o6Wl5fdfoLIyWlpaoru7+6S+xttvvx3vvPNOnHvuuSdcc/z48RgYGBj1AACQRQCAUpJFAKC8FVWYHDlyJIaGhqK+vn7U9fr6+ujt7T2pr3HnnXfG7NmzR4WLP9TR0RF1dXUjj8bGxmLGBADKlCwCAJSSLAIA5W1CH/o+URs2bIitW7fGM888EzU1NSdct2bNmujv7x95HDp0KMcpAYByJYsAAKUkiwDA6W1aMYtnzJgRVVVV0dfXN+p6X19fNDQ0vO/eBx54IDZs2BA//OEP49JLL33ftYVCIQqFQjGjAQAJkEUAgFKSRQCgvBX1DpPq6upYsGBBdHV1jVwbHh6Orq6uaG5uPuG++++/P+67777o7OyMhQsXTnxaACBpsggAUEqyCACUt6LeYRIR0dbWFitWrIiFCxfGokWLYuPGjXHs2LFYuXJlREQsX7485syZEx0dHRER8c///M+xbt26ePLJJ2Pu3Lkjf9PzQx/6UHzoQx86hS8FAEiBLAIAlJIsAgDlq+jCZOnSpXH48OFYt25d9Pb2xvz586Ozs3PkA88OHjwYlZW/f+PKN7/5zRgcHIy/+Zu/GfV12tvb4ytf+cofNz0AkBxZBAAoJVkEAMpXRZZlWamH+CADAwNRV1cX/f39UVtbW+pxAOC04P6YH2cNAGO5P+bHWQPAWJNxfyzqM0wAAAAAAADKkcIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABInsIEAAAAAABI3oQKk02bNsXcuXOjpqYmmpqaYteuXe+7/nvf+15cdNFFUVNTE5dcckns2LFjQsMCAETIIgBAackiAFCeii5Mtm3bFm1tbdHe3h579uyJefPmRWtra7z55pvjrn/xxRfj+uuvjxtvvDH27t0bS5YsiSVLlsTPfvazP3p4ACA9sggAUEqyCACUr4osy7JiNjQ1NcXll18eDz/8cEREDA8PR2NjY9x2222xevXqMeuXLl0ax44dix/84Acj1/7yL/8y5s+fH5s3bz6p5xwYGIi6urro7++P2traYsYFgLKV6v1RFgGA00Oq90dZBABOD5Nxf5xWzOLBwcHYvXt3rFmzZuRaZWVltLS0RHd397h7uru7o62tbdS11tbWePbZZ0/4PMePH4/jx4+P/Lu/vz8i3jsAAOA9v7svFvm7D1OaLAIApw9Z5D2yCACUxmRkkaIKkyNHjsTQ0FDU19ePul5fXx/79+8fd09vb++463t7e0/4PB0dHXHvvfeOud7Y2FjMuACQhP/5n/+Jurq6Uo+RC1kEAE4/sogsAgCldCqzSFGFSV7WrFkz6rcv3nrrrfjwhz8cBw8eTCaElcrAwEA0NjbGoUOHvM13kjnr/Djr/DjrfPX398d5550X5557bqlHKTuySOn4OZIfZ50fZ50fZ50vWWTyyCKl4+dIfpx1fpx1vpx3fiYjixRVmMyYMSOqqqqir69v1PW+vr5oaGgYd09DQ0NR6yMiCoVCFAqFMdfr6up8k+WktrbWWefEWefHWefHWeersrKy1CPkRhZJh58j+XHW+XHW+XHW+ZJFZJFy5OdIfpx1fpx1vpx3fk5lFinqK1VXV8eCBQuiq6tr5Nrw8HB0dXVFc3PzuHuam5tHrY+IeP7550+4HgDgRGQRAKCUZBEAKG9F/0mutra2WLFiRSxcuDAWLVoUGzdujGPHjsXKlSsjImL58uUxZ86c6OjoiIiI22+/Pa6++up48MEH47rrroutW7fGT3/603j00UdP7SsBAJIgiwAApSSLAED5KrowWbp0aRw+fDjWrVsXvb29MX/+/Ojs7Bz5ALODBw+OegvMFVdcEU8++WTcfffdcdddd8Vf/MVfxLPPPhsXX3zxST9noVCI9vb2cd+OyqnlrPPjrPPjrPPjrPOV6nnLIuXNWefHWefHWefHWecr1fOWRcqbs86Ps86Ps86X887PZJx1RZZl2Sn7agAAAAAAAFNQOp/MBgAAAAAAcAIKEwAAAAAAIHkKEwAAAAAAIHkKEwAAAAAAIHmnTWGyadOmmDt3btTU1ERTU1Ps2rXrfdd/73vfi4suuihqamrikksuiR07duQ06dRXzFlv2bIlrrrqqjjnnHPinHPOiZaWlg/8f8PvFft9/Ttbt26NioqKWLJkyeQOWEaKPeu33norVq1aFbNmzYpCoRAXXnihnyMnqdiz3rhxY3z0ox+NM844IxobG+OOO+6I3/72tzlNO3X9+Mc/jsWLF8fs2bOjoqIinn322Q/cs3PnzvjkJz8ZhUIhPvKRj8QTTzwx6XOWE1kkP7JIfmSR/Mgi+ZFF8iGL5E8WyY8skh9ZJD+ySH5kkXyULItkp4GtW7dm1dXV2eOPP57913/9V3bzzTdnZ599dtbX1zfu+p/85CdZVVVVdv/992cvv/xydvfdd2fTp0/PXnrppZwnn3qKPesbbrgh27RpU7Z3795s37592d/93d9ldXV12X//93/nPPnUU+xZ/87rr7+ezZkzJ7vqqquyz372s/kMO8UVe9bHjx/PFi5cmF177bXZCy+8kL3++uvZzp07s56enpwnn3qKPevvfOc7WaFQyL7zne9kr7/+evbcc89ls2bNyu64446cJ596duzYka1duzZ7+umns4jInnnmmfddf+DAgezMM8/M2traspdffjn7xje+kVVVVWWdnZ35DDzFySL5kUXyI4vkRxbJjyySH1kkX7JIfmSR/Mgi+ZFF8iOL5KdUWeS0KEwWLVqUrVq1auTfQ0ND2ezZs7OOjo5x13/uc5/LrrvuulHXmpqasr//+7+f1DnLQbFn/Yfefffd7Kyzzsq+/e1vT9aIZWMiZ/3uu+9mV1xxRfatb30rW7FihWBwkoo9629+85vZ+eefnw0ODuY1Ytko9qxXrVqV/dVf/dWoa21tbdmVV145qXOWm5MJBl/+8pezT3ziE6OuLV26NGttbZ3EycqHLJIfWSQ/skh+ZJH8yCKlIYtMPlkkP7JIfmSR/Mgi+ZFFSiPPLFLyP8k1ODgYu3fvjpaWlpFrlZWV0dLSEt3d3ePu6e7uHrU+IqK1tfWE63nPRM76D7399tvxzjvvxLnnnjtZY5aFiZ71V7/61Zg5c2bceOONeYxZFiZy1t///vejubk5Vq1aFfX19XHxxRfH+vXrY2hoKK+xp6SJnPUVV1wRu3fvHnl76oEDB2LHjh1x7bXX5jJzStwbJ04WyY8skh9ZJD+ySH5kkdObe+PEySL5kUXyI4vkRxbJjyxyejtV98Zpp3KoiThy5EgMDQ1FfX39qOv19fWxf//+cff09vaOu763t3fS5iwHEznrP3TnnXfG7Nmzx3zzMdpEzvqFF16Ixx57LHp6enKYsHxM5KwPHDgQ//Ef/xGf//znY8eOHfHaa6/FF7/4xXjnnXeivb09j7GnpImc9Q033BBHjhyJT33qU5FlWbz77rtx6623xl133ZXHyEk50b1xYGAgfvOb38QZZ5xRoslOf7JIfmSR/Mgi+ZFF8iOLnN5kkYmTRfIji+RHFsmPLJIfWeT0dqqySMnfYcLUsWHDhti6dWs888wzUVNTU+pxysrRo0dj2bJlsWXLlpgxY0apxyl7w8PDMXPmzHj00UdjwYIFsXTp0li7dm1s3ry51KOVnZ07d8b69evjkUceiT179sTTTz8d27dvj/vuu6/UowFTkCwyeWSRfMki+ZFFgFNJFpk8ski+ZJH8yCJTT8nfYTJjxoyoqqqKvr6+Udf7+vqioaFh3D0NDQ1Frec9Eznr33nggQdiw4YN8cMf/jAuvfTSyRyzLBR71j//+c/jjTfeiMWLF49cGx4ejoiIadOmxSuvvBIXXHDB5A49RU3k+3rWrFkxffr0qKqqGrn2sY99LHp7e2NwcDCqq6sndeapaiJnfc8998SyZcvipptuioiISy65JI4dOxa33HJLrF27Nior9fanyonujbW1tX6j8wPIIvmRRfIji+RHFsmPLHJ6k0UmThbJjyySH1kkP7JIfmSR09upyiIl/z9SXV0dCxYsiK6urpFrw8PD0dXVFc3NzePuaW5uHrU+IuL5558/4XreM5Gzjoi4//7747777ovOzs5YuHBhHqNOecWe9UUXXRQvvfRS9PT0jDw+85nPxDXXXBM9PT3R2NiY5/hTykS+r6+88sp47bXXRsJXRMSrr74as2bNEgrex0TO+u233x5z8/9dIHvvM7s4VdwbJ04WyY8skh9ZJD+ySH5kkdObe+PEySL5kUXyI4vkRxbJjyxyejtl98aiPiJ+kmzdujUrFArZE088kb388svZLbfckp199tlZb29vlmVZtmzZsmz16tUj63/yk59k06ZNyx544IFs3759WXt7ezZ9+vTspZdeKtVLmDKKPesNGzZk1dXV2VNPPZX96le/GnkcPXq0VC9hyij2rP/QihUrss9+9rM5TTu1FXvWBw8ezM4666zsH/7hH7JXXnkl+8EPfpDNnDkz+9rXvlaqlzBlFHvW7e3t2VlnnZX927/9W3bgwIHs3//937MLLrgg+9znPleqlzBlHD16NNu7d2+2d+/eLCKyhx56KNu7d2/2i1/8IsuyLFu9enW2bNmykfUHDhzIzjzzzOyf/umfsn379mWbNm3Kqqqqss7OzlK9hClFFsmPLJIfWSQ/skh+ZJH8yCL5kkXyI4vkRxbJjyySH1kkP6XKIqdFYZJlWfaNb3wjO++887Lq6ups0aJF2X/+53+O/Lerr746W7Fixaj13/3ud7MLL7wwq66uzj7xiU9k27dvz3niqauYs/7whz+cRcSYR3t7e/6DT0HFfl///wkGxSn2rF988cWsqakpKxQK2fnnn599/etfz959992cp56aijnrd955J/vKV76SXXDBBVlNTU3W2NiYffGLX8z+93//N//Bp5gf/ehH4/78/d35rlixIrv66qvH7Jk/f35WXV2dnX/++dm//uu/5j73VCaL5EcWyY8skh9ZJD+ySD5kkfzJIvmRRfIji+RHFsmPLJKPUmWRiizz3h8AAAAAACBtJf8MEwAAAAAAgFJTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMlTmAAAAAAAAMkrujD58Y9/HIsXL47Zs2dHRUVFPPvssx+4Z+fOnfHJT34yCoVCfOQjH4knnnhiAqMCAMgiAEBpySIAUL6KLkyOHTsW8+bNi02bNp3U+tdffz2uu+66uOaaa6Knpye+9KUvxU033RTPPfdc0cMCAMgiAEApySIAUL4qsizLJry5oiKeeeaZWLJkyQnX3HnnnbF9+/b42c9+NnLtb//2b+Ott96Kzs7OiT41AIAsAgCUlCwCAOVl2mQ/QXd3d7S0tIy61traGl/60pdOuOf48eNx/PjxkX8PDw/Hr3/96/iTP/mTqKiomKxRAWBKybIsjh49GrNnz47KSh9LdiKyCABMDlnk5MgiADA5JiOLTHph0tvbG/X19aOu1dfXx8DAQPzmN7+JM844Y8yejo6OuPfeeyd7NAAoC4cOHYo/+7M/K/UYpy1ZBAAmlyzy/mQRAJhcpzKLTHphMhFr1qyJtra2kX/39/fHeeedF4cOHYra2toSTgYAp4+BgYFobGyMs846q9SjlB1ZBAA+mCwyeWQRAPhgk5FFJr0waWhoiL6+vlHX+vr6ora2dtzfooiIKBQKUSgUxlyvra0VDADgD/izDO9PFgGAySWLvD9ZBAAm16nMIpP+R0abm5ujq6tr1LXnn38+mpubJ/upAQBkEQCgpGQRAJg6ii5M/u///i96enqip6cnIiJef/316OnpiYMHD0bEe28bXb58+cj6W2+9NQ4cOBBf/vKXY//+/fHII4/Ed7/73bjjjjtOzSsAAJIiiwAApSSLAED5Krow+elPfxqXXXZZXHbZZRER0dbWFpdddlmsW7cuIiJ+9atfjYSEiIg///M/j+3bt8fzzz8f8+bNiwcffDC+9a1vRWtr6yl6CQBASmQRAKCUZBEAKF8VWZZlpR7igwwMDERdXV309/f7W50A8P+4P+bHWQPAWO6P+XHWADDWZNwfJ/0zTAAAAAAAAE53ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5ChMAAAAAACB5EypMNm3aFHPnzo2amppoamqKXbt2ve/6jRs3xkc/+tE444wzorGxMe6444747W9/O6GBAQBkEQCglGQRAChPRRcm27Zti7a2tmhvb489e/bEvHnzorW1Nd58881x1z/55JOxevXqaG9vj3379sVjjz0W27Zti7vuuuuPHh4ASI8sAgCUkiwCAOWr6MLkoYceiptvvjlWrlwZH//4x2Pz5s1x5plnxuOPPz7u+hdffDGuvPLKuOGGG2Lu3Lnx6U9/Oq6//voP/O0LAIDxyCIAQCnJIgBQvooqTAYHB2P37t3R0tLy+y9QWRktLS3R3d097p4rrrgidu/ePRIEDhw4EDt27Ihrr732hM9z/PjxGBgYGPUAAJBFAIBSkkUAoLxNK2bxkSNHYmhoKOrr60ddr6+vj/3794+754YbbogjR47Epz71qciyLN5999249dZb3/etpx0dHXHvvfcWMxoAkABZBAAoJVkEAMrbhD70vRg7d+6M9evXxyOPPBJ79uyJp59+OrZv3x733XffCfesWbMm+vv7Rx6HDh2a7DEBgDIliwAApSSLAMDUUdQ7TGbMmBFVVVXR19c36npfX180NDSMu+eee+6JZcuWxU033RQREZdcckkcO3Ysbrnllli7dm1UVo7tbAqFQhQKhWJGAwASIIsAAKUkiwBAeSvqHSbV1dWxYMGC6OrqGrk2PDwcXV1d0dzcPO6et99+e8zNv6qqKiIisiwrdl4AIGGyCABQSrIIAJS3ot5hEhHR1tYWK1asiIULF8aiRYti48aNcezYsVi5cmVERCxfvjzmzJkTHR0dERGxePHieOihh+Kyyy6LpqameO211+Kee+6JxYsXjwQEAICTJYsAAKUkiwBA+Sq6MFm6dGkcPnw41q1bF729vTF//vzo7Owc+cCzgwcPjvrNibvvvjsqKiri7rvvjl/+8pfxp3/6p7F48eL4+te/fupeBQCQDFkEACglWQQAyldFNgXe/zkwMBB1dXXR398ftbW1pR4HAE4L7o/5cdYAMJb7Y36cNQCMNRn3x6I+wwQAAAAAAKAcKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkTagw2bRpU8ydOzdqamqiqakpdu3a9b7r33rrrVi1alXMmjUrCoVCXHjhhbFjx44JDQwAIIsAAKUkiwBAeZpW7IZt27ZFW1tbbN68OZqammLjxo3R2toar7zySsycOXPM+sHBwfjrv/7rmDlzZjz11FMxZ86c+MUvfhFnn332qZgfAEiMLAIAlJIsAgDlqyLLsqyYDU1NTXH55ZfHww8/HBERw8PD0djYGLfddlusXr16zPrNmzfHv/zLv8T+/ftj+vTpExpyYGAg6urqor+/P2prayf0NQCg3KR6f5RFAOD0kOr9URYBgNPDZNwfi/qTXIODg7F79+5oaWn5/ReorIyWlpbo7u4ed8/3v//9aG5ujlWrVkV9fX1cfPHFsX79+hgaGjrh8xw/fjwGBgZGPQAAZBEAoJRkEQAob0UVJkeOHImhoaGor68fdb2+vj56e3vH3XPgwIF46qmnYmhoKHbs2BH33HNPPPjgg/G1r33thM/T0dERdXV1I4/GxsZixgQAypQsAgCUkiwCAOVtQh/6Xozh4eGYOXNmPProo7FgwYJYunRprF27NjZv3nzCPWvWrIn+/v6Rx6FDhyZ7TACgTMkiAEApySIAMHUU9aHvM2bMiKqqqujr6xt1va+vLxoaGsbdM2vWrJg+fXpUVVWNXPvYxz4Wvb29MTg4GNXV1WP2FAqFKBQKxYwGACRAFgEASkkWAYDyVtQ7TKqrq2PBggXR1dU1cm14eDi6urqiubl53D1XXnllvPbaazE8PDxy7dVXX41Zs2aNGwoAAE5EFgEASkkWAYDyVvSf5Gpra4stW7bEt7/97di3b1984QtfiGPHjsXKlSsjImL58uWxZs2akfVf+MIX4te//nXcfvvt8eqrr8b27dtj/fr1sWrVqlP3KgCAZMgiAEApySIAUL6K+pNcERFLly6Nw4cPx7p166K3tzfmz58fnZ2dIx94dvDgwais/H0P09jYGM8991zccccdcemll8acOXPi9ttvjzvvvPPUvQoAIBmyCABQSrIIAJSviizLslIP8UEGBgairq4u+vv7o7a2ttTjAMBpwf0xP84aAMZyf8yPswaAsSbj/lj0n+QCAAAAAAAoNwoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeQoTAAAAAAAgeRMqTDZt2hRz586NmpqaaGpqil27dp3Uvq1bt0ZFRUUsWbJkIk8LABARsggAUFqyCACUp6ILk23btkVbW1u0t7fHnj17Yt68edHa2hpvvvnm++5744034h//8R/jqquumvCwAACyCABQSrIIAJSvoguThx56KG6++eZYuXJlfPzjH4/NmzfHmWeeGY8//vgJ9wwNDcXnP//5uPfee+P888//owYGANImiwAApSSLAED5KqowGRwcjN27d0dLS8vvv0BlZbS0tER3d/cJ9331q1+NmTNnxo033nhSz3P8+PEYGBgY9QAAkEUAgFKSRQCgvBVVmBw5ciSGhoaivr5+1PX6+vro7e0dd88LL7wQjz32WGzZsuWkn6ejoyPq6upGHo2NjcWMCQCUKVkEACglWQQAytuEPvT9ZB09ejSWLVsWW7ZsiRkzZpz0vjVr1kR/f//I49ChQ5M4JQBQrmQRAKCUZBEAmFqmFbN4xowZUVVVFX19faOu9/X1RUNDw5j1P//5z+ONN96IxYsXj1wbHh5+74mnTYtXXnklLrjggjH7CoVCFAqFYkYDABIgiwAApSSLAEB5K+odJtXV1bFgwYLo6uoauTY8PBxdXV3R3Nw8Zv1FF10UL730UvT09Iw8PvOZz8Q111wTPT093lIKABRFFgEASkkWAYDyVtQ7TCIi2traYsWKFbFw4cJYtGhRbNy4MY4dOxYrV66MiIjly5fHnDlzoqOjI2pqauLiiy8etf/ss8+OiBhzHQDgZMgiAEApySIAUL6KLkyWLl0ahw8fjnXr1kVvb2/Mnz8/Ojs7Rz7w7ODBg1FZOakfjQIAJEwWAQBKSRYBgPJVkWVZVuohPsjAwEDU1dVFf39/1NbWlnocADgtuD/mx1kDwFjuj/lx1gAw1mTcH/3KAwAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkDyFCQAAAAAAkLwJFSabNm2KuXPnRk1NTTQ1NcWuXbtOuHbLli1x1VVXxTnnnBPnnHNOtLS0vO96AIAPIosAAKUkiwBAeSq6MNm2bVu0tbVFe3t77NmzJ+bNmxetra3x5ptvjrt+586dcf3118ePfvSj6O7ujsbGxvj0pz8dv/zlL//o4QGA9MgiAEApySIAUL4qsizLitnQ1NQUl19+eTz88MMRETE8PByNjY1x2223xerVqz9w/9DQUJxzzjnx8MMPx/Lly0/qOQcGBqKuri76+/ujtra2mHEBoGylen+URQDg9JDq/VEWAYDTw2TcH4t6h8ng4GDs3r07Wlpafv8FKiujpaUluru7T+prvP322/HOO+/Eueeee8I1x48fj4GBgVEPAABZBAAoJVkEAMpbUYXJkSNHYmhoKOrr60ddr6+vj97e3pP6GnfeeWfMnj17VLj4Qx0dHVFXVzfyaGxsLGZMAKBMySIAQCnJIgBQ3ib0oe8TtWHDhti6dWs888wzUVNTc8J1a9asif7+/pHHoUOHcpwSAChXsggAUEqyCACc3qYVs3jGjBlRVVUVfX19o6739fVFQ0PD++594IEHYsOGDfHDH/4wLr300vddWygUolAoFDMaAJAAWQQAKCVZBADKW1HvMKmuro4FCxZEV1fXyLXh4eHo6uqK5ubmE+67//7747777ovOzs5YuHDhxKcFAJImiwAApSSLAEB5K+odJhERbW1tsWLFili4cGEsWrQoNm7cGMeOHYuVK1dGRMTy5ctjzpw50dHRERER//zP/xzr1q2LJ598MubOnTvyNz0/9KEPxYc+9KFT+FIAgBTIIgBAKckiAFC+ii5Mli5dGocPH45169ZFb29vzJ8/Pzo7O0c+8OzgwYNRWfn7N65885vfjMHBwfibv/mbUV+nvb09vvKVr/xx0wMAyZFFAIBSkkUAoHxVZFmWlXqIDzIwMBB1dXXR398ftbW1pR4HAE4L7o/5cdYAMJb7Y36cNQCMNRn3x6I+wwQAAAAAAKAcKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkKUwAAAAAAIDkTagw2bRpU8ydOzdqamqiqakpdu3a9b7rv/e978VFF10UNTU1cckll8SOHTsmNCwAQIQsAgCUliwCAOWp6MJk27Zt0dbWFu3t7bFnz56YN29etLa2xptvvjnu+hdffDGuv/76uPHGG2Pv3r2xZMmSWLJkSfzsZz/7o4cHANIjiwAApSSLAED5qsiyLCtmQ1NTU1x++eXx8MMPR0TE8PBwNDY2xm233RarV68es37p0qVx7Nix+MEPfjBy7S//8i9j/vz5sXnz5pN6zoGBgairq4v+/v6ora0tZlwAKFup3h9lEQA4PaR6f5RFAOD0MBn3x2nFLB4cHIzdu3fHmjVrRq5VVlZGS0tLdHd3j7unu7s72traRl1rbW2NZ5999oTPc/z48Th+/PjIv/v7+yPivQMAAN7zu/tikb/7MKXJIgBw+pBF3iOLAEBpTEYWKaowOXLkSAwNDUV9ff2o6/X19bF///5x9/T29o67vre394TP09HREffee++Y642NjcWMCwBJ+J//+Z+oq6sr9Ri5kEUA4PQji8giAFBKpzKLFFWY5GXNmjWjfvvirbfeig9/+MNx8ODBZEJYqQwMDERjY2McOnTI23wnmbPOj7POj7POV39/f5x33nlx7rnnlnqUsiOLlI6fI/lx1vlx1vlx1vmSRSaPLFI6fo7kx1nnx1nny3nnZzKySFGFyYwZM6Kqqir6+vpGXe/r64uGhoZx9zQ0NBS1PiKiUChEoVAYc72urs43WU5qa2uddU6cdX6cdX6cdb4qKytLPUJuZJF0+DmSH2edH2edH2edL1lEFilHfo7kx1nnx1nny3nn51RmkaK+UnV1dSxYsCC6urpGrg0PD0dXV1c0NzePu6e5uXnU+oiI559//oTrAQBO5P9r7/5Do67/AI6/5m13U5i/kM1NprKZGWpIjo3NZCgDQfHHXw6MeYll4YJwUImzTrR0iIkgU/G3f6iHiorkWKU1RDMi22C0ZdgtRehGQtHQar9e/SHe9zud5efYve4+d88H7I+uz9nrXh5+nvDuPFoEAADEEy0CAEByc/xXctXU1Ijf75eioiIpLi6WXbt2yf3792XVqlUiIrJy5UqZMGGCbNu2TURE3n77bSkvL5ePP/5YFi1aJMFgUL799lvZv3//0L4SAACQEmgRAAAQT7QIAADJy/GBSWVlpfz666/ywQcfSDgcllmzZkljY2PkC8zu3Lkz4CMwZWVlcuLECdm4caNs2LBBnnvuOTl//rzMmDHjmf+bPp9PAoHAoB9HxdBi13bYtR12bYdd20rVfdMiyY1d22HXdti1HXZtK1X3TYskN3Zth13bYde22LedWOw6TVV1yH41AAAAAAAAAAAAF0qdb2YDAAAAAAAAAAB4Cg5MAAAAAAAAAABAyuPABAAAAAAAAAAApDwOTAAAAAAAAAAAQMpLmAOT+vp6mTx5smRmZkpJSYl88803/3r96dOnZdq0aZKZmSkzZ86UhoYGo0ndz8muDxw4IHPnzpUxY8bImDFjpKKi4j9/b/A/Tt/XjwSDQUlLS5Nly5bFdsAk4nTXv//+u1RXV0tubq74fD6ZOnUqf448I6e73rVrlzz//PMyfPhwyc/Pl3Xr1slff/1lNK17XblyRRYvXix5eXmSlpYm58+f/8/nNDU1yUsvvSQ+n0+mTJkiR48ejfmcyYQWsUOL2KFF7NAidmgRG7SIPVrEDi1ihxaxQ4vYoUVsxK1FNAEEg0H1er16+PBh/f777/X111/X0aNHa2dn56DXX7t2TT0ej27fvl3b2tp048aNmpGRoa2trcaTu4/TXa9YsULr6+u1ublZ29vb9dVXX9VRo0bp3bt3jSd3H6e7fqSjo0MnTJigc+fO1aVLl9oM63JOd/33339rUVGRLly4UK9evaodHR3a1NSkLS0txpO7j9NdHz9+XH0+nx4/flw7Ojr0008/1dzcXF23bp3x5O7T0NCgtbW1evbsWRURPXfu3L9eHwqFdMSIEVpTU6NtbW26e/du9Xg82tjYaDOwy9EidmgRO7SIHVrEDi1ihxaxRYvYoUXs0CJ2aBE7tIideLVIQhyYFBcXa3V1deSf+/r6NC8vT7dt2zbo9cuXL9dFixYNeKykpETfeOONmM6ZDJzu+nG9vb2alZWlx44di9WISSOaXff29mpZWZkePHhQ/X4/YfCMnO567969WlBQoN3d3VYjJg2nu66urtb58+cPeKympkbnzJkT0zmTzbOEwbvvvqvTp08f8FhlZaUuWLAghpMlD1rEDi1ihxaxQ4vYoUXigxaJPVrEDi1ihxaxQ4vYoUXiw7JF4v5XcnV3d8uNGzekoqIi8tiwYcOkoqJCrl+/Puhzrl+/PuB6EZEFCxY89Xo8FM2uH/fgwQPp6emRsWPHxmrMpBDtrjdv3izZ2dmyevVqizGTQjS7vnDhgpSWlkp1dbXk5OTIjBkzZOvWrdLX12c1titFs+uysjK5ceNG5OOpoVBIGhoaZOHChSYzpxLujdGjRezQInZoETu0iB1aJLFxb4weLWKHFrFDi9ihRezQIoltqO6N6UM5VDTu3bsnfX19kpOTM+DxnJwc+eGHHwZ9TjgcHvT6cDgcszmTQTS7ftx7770neXl5T7z5MFA0u7569aocOnRIWlpaDCZMHtHsOhQKyRdffCGvvPKKNDQ0yK1bt2Tt2rXS09MjgUDAYmxXimbXK1askHv37snLL78sqiq9vb3y5ptvyoYNGyxGTilPuzf+8ccf8ueff8rw4cPjNFnio0Xs0CJ2aBE7tIgdWiSx0SLRo0Xs0CJ2aBE7tIgdWiSxDVWLxP0TJnCPuro6CQaDcu7cOcnMzIz3OEmlq6tLqqqq5MCBAzJu3Lh4j5P0+vv7JTs7W/bv3y+zZ8+WyspKqa2tlX379sV7tKTT1NQkW7dulT179sh3330nZ8+elYsXL8qWLVviPRoAF6JFYocWsUWL2KFFAAwlWiR2aBFbtIgdWsR94v4Jk3HjxonH45HOzs4Bj3d2dsr48eMHfc748eMdXY+Hotn1Izt27JC6ujq5dOmSvPjii7EcMyk43fVPP/0kP//8syxevDjyWH9/v4iIpKeny82bN6WwsDC2Q7tUNO/r3NxcycjIEI/HE3nshRdekHA4LN3d3eL1emM6s1tFs+v3339fqqqq5LXXXhMRkZkzZ8r9+/dlzZo1UltbK8OGcW4/VJ52bxw5ciT/R+d/oEXs0CJ2aBE7tIgdWiSx0SLRo0Xs0CJ2aBE7tIgdWiSxDVWLxP13xOv1yuzZs+Xy5cuRx/r7++Xy5ctSWlo66HNKS0sHXC8i8vnnnz/1ejwUza5FRLZv3y5btmyRxsZGKSoqshjV9Zzuetq0adLa2iotLS2RnyVLlsi8efOkpaVF8vPzLcd3lWje13PmzJFbt25F4ktE5Mcff5Tc3Fyi4F9Es+sHDx48cfN/FGQPv7MLQ4V7Y/RoETu0iB1axA4tYocWSWzcG6NHi9ihRezQInZoETu0SGIbsnujo6+Ij5FgMKg+n0+PHj2qbW1tumbNGh09erSGw2FVVa2qqtL169dHrr927Zqmp6frjh07tL29XQOBgGZkZGhra2u8XoJrON11XV2der1ePXPmjP7yyy+Rn66urni9BNdwuuvH+f1+Xbp0qdG07uZ013fu3NGsrCx966239ObNm/rJJ59odna2fvjhh/F6Ca7hdNeBQECzsrL05MmTGgqF9LPPPtPCwkJdvnx5vF6Ca3R1dWlzc7M2NzeriOjOnTu1ublZb9++raqq69ev16qqqsj1oVBIR4wYoe+88462t7drfX29ejwebWxsjNdLcBVaxA4tYocWsUOL2KFF7NAitmgRO7SIHVrEDi1ihxaxE68WSYgDE1XV3bt368SJE9Xr9WpxcbF+/fXXkX9XXl6ufr9/wPWnTp3SqVOnqtfr1enTp+vFixeNJ3YvJ7ueNGmSisgTP4FAwH5wF3L6vv5/hIEzTnf91VdfaUlJifp8Pi0oKNCPPvpIe3t7jad2Jye77un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      "text/plain": [
       "<Figure size 2000x2000 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Here , we create the trainloaders and testloaders.\n",
    "Also, we transform the images using standard lib functions\n",
    "\"\"\"\n",
    "\n",
    "sample_num = 10\n",
    "\n",
    "# 定义数据集\n",
    "class MyDataset(Dataset):\n",
    "    def __init__(self, data_dir, noisy_dir, is_train):\n",
    "        self.x = None\n",
    "        self.y = None\n",
    "        f_list = os.listdir(data_dir)\n",
    "        if is_train: \n",
    "            for idx in range(min(sample_num, len(f_list))):\n",
    "                # if idx % 2 == 0:\n",
    "                #     continue\n",
    "                if self.x is None:\n",
    "                    self.x = np.load(data_dir + str(idx+1) + '.npy')[:180, :]\n",
    "                    temp = np.load(noisy_dir + str(idx+1) + '.npy')[:180, :]\n",
    "                    random.shuffle(temp)\n",
    "                    self.y = temp\n",
    "                else:\n",
    "                    self.x = np.concatenate((self.x, np.load(data_dir + str(idx+1) + '.npy')[:180, :]))\n",
    "                    temp = np.load(noisy_dir + str(idx+1) + '.npy')[:180, :]\n",
    "                    random.shuffle(temp)\n",
    "                    self.y = np.concatenate((self.y, temp))\n",
    "        else:\n",
    "            for idx in range(min(sample_num, len(f_list))):\n",
    "                if self.x is None:\n",
    "                    self.x = np.load(data_dir + str(idx+1) + '.npy')[:200, :]\n",
    "                    self.y = np.load(noisy_dir + str(idx+1) + '.npy')[:200, :]\n",
    "                else:\n",
    "                    self.x = np.concatenate((self.x, np.load(data_dir + str(idx+1) + '.npy')[:200, :]))\n",
    "                    self.y = np.concatenate((self.y, np.load(noisy_dir + str(idx+1) + '.npy')[:200, :]))\n",
    "        # print(self.y.shape)\n",
    "        # print(self.y)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        x = self.x[idx]\n",
    "        y = self.y[idx]\n",
    "        return x, y\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Here, we define the autoencoder model.\n",
    "\"\"\"\n",
    "\n",
    "class denoising_model(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(denoising_model,self).__init__()\n",
    "        self.encoder=nn.Sequential(\n",
    "                      nn.Linear(1024,256),\n",
    "                      nn.ReLU(True),\n",
    "                      nn.Linear(256,128),\n",
    "                      nn.ReLU(True),\n",
    "                      nn.Linear(128,64),\n",
    "                      nn.ReLU(True)\n",
    "\n",
    "                      )\n",
    "\n",
    "        self.decoder=nn.Sequential(\n",
    "                      nn.Linear(64,128),\n",
    "                      nn.ReLU(True),\n",
    "                      nn.Linear(128,256),\n",
    "                      nn.ReLU(True),\n",
    "                      nn.Linear(256,1024),\n",
    "                      nn.Sigmoid(),\n",
    "                      )\n",
    "\n",
    "\n",
    "    def forward(self,x):\n",
    "        x=self.encoder(x)\n",
    "        x=self.decoder(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "#We check whether cuda is available and choose device accordingly\n",
    "if torch.cuda.is_available()==True:\n",
    "    device=\"cuda:0\"\n",
    "else:\n",
    "    device =\"cpu\"\n",
    "\n",
    "for to_dataset,from_dataset in zip([\n",
    "                                        # 'noisy_npy','noisy_npy','noisy_npy',\n",
    "    'noisy_npy',\n",
    "                                        # 'bg_npy','bg_npy','bg_npy',\n",
    "    # 'bg_npy',\n",
    "                                        # 'clean_npy','clean_npy','clean_npy',\n",
    "    # 'clean_npy'\n",
    "                                    ]\n",
    "                                       ,[\n",
    "                                         # 'bg_npy',\n",
    "                                           # 'clean_npy'\n",
    "                                           # ,'new-n-to-c','new-n-to-bg',\n",
    "                                         'noisy_npy',\n",
    "                                           # 'clean_npy','new-bg-to-c','new-bg-to-n',\n",
    "                                         # 'noisy_npy','bg_npy','new-c-to-n',\n",
    "                                           # 'new-c-to-bg'\n",
    "                                       ]):\n",
    "\n",
    "\n",
    "\n",
    "    trainset=MyDataset(\"data/\"+to_dataset+'/', 'data/'+from_dataset+'/', True)\n",
    "    testset=MyDataset(\"data/\"+to_dataset+'/', 'data/'+from_dataset+'/', False)\n",
    "\n",
    "    trainloader=DataLoader(trainset,batch_size=16,shuffle=True)\n",
    "    testloader=DataLoader(testset,batch_size=1,shuffle=True)\n",
    "\n",
    "    tsfms=transforms.Compose([\n",
    "        transforms.ToTensor()\n",
    "    ])\n",
    "\n",
    "    model=denoising_model().to(device)\n",
    "    criterion=nn.MSELoss()\n",
    "    optimizer=optim.SGD(model.parameters(),lr=10,weight_decay=1e-5)\n",
    "\n",
    "\n",
    "    epochs=60\n",
    "    l=len(trainloader)\n",
    "    print(l)\n",
    "    losslist=list()\n",
    "    epochloss=0\n",
    "    running_loss=0\n",
    "    for epoch in range(epochs):\n",
    "\n",
    "        print(\"Entering Epoch: \",epoch)\n",
    "        for dirty,clean in tqdm((trainloader)):\n",
    "\n",
    "            dirty=dirty.view(dirty.size(0),-1).type(torch.FloatTensor)\n",
    "            clean=clean.view(clean.size(0),-1).type(torch.FloatTensor)\n",
    "            dirty,clean=dirty.to(device),clean.to(device)\n",
    "\n",
    "\n",
    "            # print(dirty[0].dtype)\n",
    "\n",
    "            #-----------------Forward Pass----------------------\n",
    "            output=model(dirty)\n",
    "            # print(clean.shape)\n",
    "            loss=criterion(output,clean)\n",
    "            #-----------------Backward Pass---------------------\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss+=loss.item()\n",
    "            epochloss+=loss.item()\n",
    "            #-----------------Log-------------------------------\n",
    "            losslist.append(running_loss/l)\n",
    "            running_loss=0\n",
    "        if epoch % 10 == 0:\n",
    "            print(\"======> epoch: {}/{}, Loss:{}\".format(epoch,epochs,loss.item()))\n",
    "\n",
    "    \"\"\"Here, we try to visualize some of the results.\n",
    "      We randomly generate 6 numbers in between 1 and 10k , run them through the model,\n",
    "      and show the results with comparisons\n",
    "\n",
    "     \"\"\"\n",
    "\n",
    "    from pathlib import Path\n",
    "\n",
    "\n",
    "    PATH=str.format(\"autoencoder-{}-{}-100.pth\", from_dataset, to_dataset)\n",
    "    torch.save(model.state_dict(),PATH)  # We save the model state dict at PATH   \n",
    "\n",
    "    f,axes= plt.subplots(6,3,figsize=(20,20))\n",
    "    axes[0,0].set_title(\"Original Image\")\n",
    "    axes[0,1].set_title(\"Dirty Image\")\n",
    "    axes[0,2].set_title(\"Cleaned Image\")\n",
    "\n",
    "    path = Path(str.format(\"data/auto-{}-{}-100/\", from_dataset, to_dataset))\n",
    "    path.mkdir(parents=True, exist_ok=True)\n",
    "    output_list = []\n",
    "    for idx in range(2000):\n",
    "        dirty=testset[idx][0]\n",
    "        clean=testset[idx][1]\n",
    "        # label=testset[test_imgs[idx]][2]\n",
    "        dirty=torch.tensor(dirty).unsqueeze(0).to(torch.float32)\n",
    "        dirty=dirty.to(device)\n",
    "        # print(dirty.shape)  \n",
    "        output=model(dirty)\n",
    "\n",
    "        # output=output.view(1,28,28)\n",
    "        output=output.permute(1,0).squeeze(1)\n",
    "        output=output.detach().cpu().numpy()\n",
    "        output_list.append(output)\n",
    "        if (idx + 1) % 200 == 0:\n",
    "            np.save(str.format(\"data/auto-{}-{}-100/{}\", from_dataset, to_dataset, (idx+1) // 200), np.array(output_list))    \n",
    "            output_list = []\n",
    "\n",
    "    # dirty=dirty.view(1,28,28)\n",
    "    dirty=dirty.permute(1,0).squeeze(1)\n",
    "    dirty=dirty.detach().cpu().numpy()\n",
    "\n",
    "    # clean=clean.permute(1,0).squeeze(1)\n",
    "    # clean=clean.detach().cpu().numpy()\n",
    "    print(clean.shape)\n",
    "    print(dirty.shape)\n",
    "    print(output.shape)\n",
    "    axes[0,0].plot(clean)\n",
    "    axes[0,1].plot(dirty)\n",
    "    axes[0,2].plot(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12405271-2a85-458e-84f4-9530460a31e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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