deep-image-classification-with-pytorch-and-comet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45bbcc6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:37:45.859064Z",
     "iopub.status.busy": "2023-01-23T08:37:45.858582Z",
     "iopub.status.idle": "2023-01-23T08:38:03.574813Z",
     "shell.execute_reply": "2023-01-23T08:38:03.573545Z"
    },
    "papermill": {
     "duration": 17.725519,
     "end_time": "2023-01-23T08:38:03.577254",
     "exception": false,
     "start_time": "2023-01-23T08:37:45.851735",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting comet_ml\r\n",
      "  Downloading comet_ml-3.31.23-py3-none-any.whl (450 kB)\r\n",
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      "\u001b[?25hRequirement already satisfied: wrapt>=1.11.2 in /opt/conda/lib/python3.7/site-packages (from comet_ml) (1.12.1)\r\n",
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      "Collecting wurlitzer>=1.0.2\r\n",
      "  Downloading wurlitzer-3.0.3-py3-none-any.whl (7.3 kB)\r\n",
      "Collecting everett[ini]>=1.0.1\r\n",
      "  Downloading everett-3.1.0-py2.py3-none-any.whl (35 kB)\r\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from comet_ml) (1.15.0)\r\n",
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      "Collecting dulwich!=0.20.33,>=0.20.6\r\n",
      "  Downloading dulwich-0.21.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (504 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m504.4/504.4 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hCollecting semantic-version>=2.8.0\r\n",
      "  Downloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\r\n",
      "Collecting requests-toolbelt>=0.8.0\r\n",
      "  Downloading requests_toolbelt-0.10.1-py2.py3-none-any.whl (54 kB)\r\n",
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      "\u001b[?25hRequirement already satisfied: simplejson in /opt/conda/lib/python3.7/site-packages (from comet_ml) (3.18.1)\r\n",
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      "Collecting configobj\r\n",
      "  Downloading configobj-5.0.8-py2.py3-none-any.whl (36 kB)\r\n",
      "Requirement already satisfied: importlib-resources>=1.4.0 in /opt/conda/lib/python3.7/site-packages (from jsonschema!=3.1.0,>=2.6.0->comet_ml) (5.10.2)\r\n",
      "Requirement already satisfied: attrs>=17.4.0 in /opt/conda/lib/python3.7/site-packages (from jsonschema!=3.1.0,>=2.6.0->comet_ml) (21.4.0)\r\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/lib/python3.7/site-packages (from jsonschema!=3.1.0,>=2.6.0->comet_ml) (0.18.1)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.18.4->comet_ml) (3.3)\r\n",
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      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->comet_ml) (3.8.0)\r\n",
      "Installing collected packages: everett, wurlitzer, semantic-version, dulwich, configobj, requests-toolbelt, comet_ml\r\n",
      "Successfully installed comet_ml-3.31.23 configobj-5.0.8 dulwich-0.21.2 everett-3.1.0 requests-toolbelt-0.10.1 semantic-version-2.10.0 wurlitzer-3.0.3\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Couldn't find a Git repository in '/kaggle/working' nor in any parent directory. You can override where Comet is looking for a Git Patch by setting the configuration `COMET_GIT_DIRECTORY`\n",
      "COMET INFO: Experiment is live on comet.com https://www.comet.com/tirendaz-test/experiment-tracking-pytorch-3/83e6ea947e674e278d865218ec356b86\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Installing Comet\n",
    "!pip install comet_ml\n",
    "\n",
    "import comet_ml\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Setup device-agnostic code\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "my_api_key = user_secrets.get_secret(\"COMET_API_KEY\")\n",
    "\n",
    "## import comet_ml at the top of your file\n",
    "from comet_ml import Experiment\n",
    "\n",
    "## Create an experiment with your api key\n",
    "experiment = Experiment(\n",
    "    api_key= my_api_key,\n",
    "    project_name=\"experiment-tracking-pytorch-3\",\n",
    "    workspace=\"tirendaz-test\",\n",
    ")\n",
    "\n",
    "hyper_params = {\"batch_size\": 32, \"num_epochs\": 5, \"learning_rate\": 0.001,\"hidden_unit\": 128, \"image_size\": 128}\n",
    "experiment.log_parameters(hyper_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4bca5134",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:38:03.598803Z",
     "iopub.status.busy": "2023-01-23T08:38:03.598277Z",
     "iopub.status.idle": "2023-01-23T08:38:04.289595Z",
     "shell.execute_reply": "2023-01-23T08:38:04.288471Z"
    },
    "id": "Dh5tUEeY-sWk",
    "outputId": "dd5445b2-7fb3-4d66-cef5-8476ad7af591",
    "papermill": {
     "duration": 0.705361,
     "end_time": "2023-01-23T08:38:04.292614",
     "exception": false,
     "start_time": "2023-01-23T08:38:03.587253",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random image path: /kaggle/input/cat-and-dog/test_set/test_set/cats/cat.4202.jpg\n",
      "Image class: cats\n",
      "Image height: 217\n",
      "Image width: 179\n"
     ]
    },
    {
     "data": {
      "image/png": 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mZG73bV785T7yemVN6axLiVmiSDAI1hiIiYcuClhbevKM0LVd04e0vocACcRWVZWXYHnHMKPIeb91RzPMnBXDa08jCycxxljXG1nCIbMtzLGM3i0725wW7npiYVRrYkhziUJorXGEFoRSFANYFEVZFATIISYywzCMXW+RoCiKonDeO+e6rlNZHMdBbZxuJACs12vnnCLcaj1xkVPMr4+IMBvZgz9t6tXpdBIRFCiKAgz5qqzWq/YqIGLXdcfjsSgKIijL75aJLP2zkhRBZJQxxWHoESICCUjgMYUe0mgsOcI4xBxV6aGyRDAjLst4UNfzNvaAhqwHRJQInAipMBQZeIghJYe2KtZgfexaGMbCWGus3naKMnDOUup+w6tqf3oIa5mDrk7OYOmDqlpWAFFvqDHbMOEZ5jW2xBILX/oZ2YqlOamrcqMYn35Sg8MQwuFwaMpV13Xn5+cP7t3fX9989tlnTdMU1lVV1Y0DEenHhmHQE68ZDQ03VD7UnyjLUkQ0WXo8Hk+nk9K00EBIkawBgHEcyZjSOWZ2ZVFYF0Jo2zbzxZm5qioNX5n5+vp6HPv79+9ba5um0WSbmiddTEQUkDAOSRiIIKWx7/u+Z8DSlyzDMAwpdpAG4jEOaQCQsdRlscY463Jg3/eD3tNYi5m3nFJhi5RC059iDzyIRVN6V1jLY2et3dSrzeZMIt1cN8Mw1nX98ma/2+2qdWn1ZCxlLe/TMlpR0VFcSB26pe5SHQOvhrvZEVnmNbL/8cqhWWRbQHL0Q/ot2dbAImmS0YVmaE7N4cXl88Ph9ubqen9zgwKxqnDAoijavnl5dXV9e9P2HTODIZA7spLeKsx+fj69vEgIAEzvKAiCQICqPIhIaNJtIcW276YdSmE2o6K+reYfzs522RDrEukRsq7QOwpAEh5DGmOKLO0wooyQInIiYUkJBEhAdZs+UpqvGKNqJl7QFfR1vCvjmIIIMBJaYem7YeAGJXVtc9ofjscWxDanGEawpjTFioZofLKv7XHW88vFWvoH6n/Igssjc4SZpSc/FjNb6xQVgEXsmm1T3gnImWUh56w6d5oFNcbqcmSrlBKnJABkjHPOJZDr6+uh62+vbyQlVSePHj165713Ly8vf/bpp4fDIQmPKTrn1CplyUgpqTvJzMsIPENbjJJADMxJx1ma1UfRjwFA13UTJ4OTQvK6LMxRX6RpTtvtdrvd+mKCDScdCagwMAPGlPoQ+sARDAFYdNaTFTRgUSykCJI4oCCRNTjnnojZOKY5t5zSxE411hBRFyAlgEjOlt55SobHIQz9/vbQt0EYIrdluTaFB0NElseBxyH21i5zoVlhLGOHvN/Zz1g6hkvvIS93Fi9EXJbr6FrrTdKCGZRxCxGxxqmOWSozWIBR+cRM2VfEMYTb46FvWhbebDZVVUlKF/fvffSDH7iiKH/yl8XYB07tYWARgzZ7u1k48otkycgmdRKIhY7RtP/N7a2zFgBijI6MCnFKqRuHZQys0pVSGoZeV4NMpazB/MooqoNMjGkMkgTIeIQERMagM95RJB4ljcjpdgAAREAETKxKC0WoH+J8Q0BESyRgCKhtAgoRWwDDbFOSFCEmvvfg4dCfRFJVrYjK5hRurk9t18q2SuMQDL2SA8z7KovQUTdSjeJSfywVxvIQZAmb/cqpsDNnQdXnUA8364w7R8/eJbqyS6h/6geyCtWv3p8OYHBd187Yi7OzRw8eGmOa0+n2uG+6JnJ0has3q8ipC4PmGrLay8KRb5tRnCzxxswpMXMH1aPMgbrKljGM4KMVEY1+NVgDAJHJMFXVVglBWVnq21lMLELWMqIIhsgM1vr6dLr1lhAFyUAUEsJkWJKQV9XFIjHdHVFrPQCQucP1GZEFnC0sOQtFGmnswtiOceyFU1l64wvnzGq7K4tVfQZ+3R6boZFY13VRFK8whGERKGbJ0MxWTvtqPiyzJV779aWQzf4BLsUiIzPLKCCrgemV5tDmNdBTFkyflAnMJG3fAcBmtRLCMYbQtYfb/ZdffdUO/TiO+9NREATRFV7GMbQ9zDHUa0+uboE6rTxDugyLKB0nQyMi3jpVDDRjgMMcjWe9iKiBFaSUuq7Tn4RY1nVdVdXkcKQ52SYUI49jDFHIOgGbRKKIZUFAFEJBYGt8rch1RlzysXxN3+uqhrpwxnmqOdDYxTCMIMlgGsaTwGgt+XpVrXdbW58/xDDKi+N1WZbOObu0FDJTIrLIf9t/1GXMZiU7qvmc0YLZy8wAUzSlulRmSkFaMOF4EdOK3GlgESSy1npjXAg9IgKQ/k9EVR0670QkcorMbd+N4xiHUZOQpx//OAqHFIFIEPq+78dBQcnvVI2qUfTc81wHkBa+OQAw3lne6bzOGEx2LQEgJ/3HcYrCbm5GzZjXq3K32wFAVVXGGB5ZEIRRSMKYmq7vxlCiIV8iCVBMkAiEgAEYUMgWxjlfFOq9KR5jrW3bFhfZiXxEO4MEjtAZX6xX1gAaAkPJeUjcjXEMKUbGQVJMMLBUq1LRnTvJeC12yAdl+a/MjHjHD1heGrnptbQRqo114dR70ANEi6zgUrfLDJQxs/6KXtndkVdzLsMwAU3q6AUBgAlCeHl9FUJwhR9jHGMYYxgHrooqf1H+Xj1wqjNgwVOH2S/GhSO1tAUiAosXIaJxHPUszbT1QV95vV4BgMbJSgecAHgwIgIiyioaxziOkUxS6U8MBALAxAlZUAQNJYExpjFOJlXXXLWdfrXCM1Omoq7CGGOIxNHbypFBYJbBDMlYJgtknScDVAibIuGnX33iB++cs3XlmTmlGMakj2gNGCIA8d547wBkHFpmNsZ4Z5kjCHBW5rNkGLpTedM6EohAn3qy5LwjgZS0HmQEZmTgyEgJwXgkRIOAmDCScqBjSgHAaHqLiL3PLrAwQ0o8879LABiHdHl129ZjXdcAMI5N27aJIQH17aBb601ZrDDGOIyNiBCh8xYxpTSyMAo5j4nbtusBkAwkHoSxRhdisuArqTjI2CQLq6qquq4jjsaid45lHMdehK2z4H12tIkI0ahHlBIgCpEYQ0PPzWl0tq7rIuIIAMaMDofans79YUP9atUBMGAETIgy/QkAiDwWABBC7PueE1jrjTESpO+nEwYQABLiRIxare9oRJq7M0jOucI6TqmqKg7ROUfQxRjff//9BuF0c/3w4cO7ROvS+VdPQo1Z1rq0QMTzKcl/Sd9VoJe9DRJgufMeRIQMzbL1yvHVQEEXVw1Wxt+yKso3z7+lJ7vv1eJMR/M1DyY/D05Z3JSSqsCck5m4RdMltEhbcv51mIwsiqA+KiDPOdh6TJxS0oOr2gJmzoomHRXmz1CyNX7GZqZWC5qyG4YGEECDENJnExCjOgkRrbWMEzU1jLGq6ilqShqtTCtze3src3orhjAMA7AoRkeIq9UqjQERad6FbIwm7rjugS6ZauzsSMPCdXjNzfzOaxnH6jcZQAHRTMowjDFGzPcEAAFGoFmrA+SkK2aSQGaBwCKJkxW4/mIIQRmR02LNyNtShgDAGEcERBzjyIkBNRnrU0oAqCEfACiRBgCSiBAKIes+WUREcmTFgoHEMUoEYeeNouMFA88J3qZpdNdVSibVNRfn6UMWhggRRISFEAkwRBm6IYSomSAkDbBF1UYcgynIO++MFUFhHMcYIRgCFABAQBDBGX/BNO8gAqQYOaYYo8RERKyL1nWqSBTOyVC1XS4uzLEJzpwMXKS4ZEavX5ODvPpLucmeKQkAzqWFwxhjJEAylJSzgoj6B6GGgiqX+tVaHPYajJHd3vxJfZk0E73ysX6D6Kozj0RWnQBhEkQQUrHIqcpJEBV6JBRkADDOEJFxWLmKmUNU9GIUIA0jC+8BQB2jtm2XAr2MGoZhQCV8p1ZEnHPGYBhHGTn0Y+h6kQQoKhBzLCoAgMzEYgHJGBACQ0YAk0/9KBqDMSIA0bR9xpWqt5xzhfOWTN+0mtyIYUp0GGOAUJ+q3pS6gHfWJO/xMvjR/VgKTV5xma98B3jV28+3nXCCcYIFDSmeEVWcDN75uYgokl67w3d+HS0S0DmKU1Olt8oVUMvfEhFOYMg466wRQjuGnlm9Ju2rxIsX0dOi+kmYIxGREWuJDM9MPtc02LYsIuMYm6Zz1mY3fLmqMAd6qgKbplHbx/1hhnkQERNHTgzAw9ADANKEseWDJ9By5DCov2+NkmcTC0+EQUIDQAgszIAoxGEYUkpSFM45gxDi2LbtqqpjjJX35W67Xq9F5HC777p2e77Wx76rN8lyYGZu37dX9he5XrMmKCCJ4zjlJpyxWUXJBC8C0CI7P475YGmuLiuJ1wRRrxCCGj6tNdXz6r1XyyKLckta4PFwV4oN4zimFIlUZ1i4Q8CT6gxDBCQMSYSJAAgF0Tjw3gEUgpyEY4yCMIYpXaI43h3oMsNCqksyS94Y886DR4fj7e3tbXvsYe65ACBpyMuowjq9eExNsMMMYFjnCkRMUdT1RjQAE3lbw/7i3I+qvNVVUUwoxNGMiKg1MhcXF8MwdF03xgBEgphE7opReU7G4MIl+7axSCkuD/RSsJYClF0BY2gCpkQMEdLkVC5OglJjBUBwAarKDDiqAohzrbos6EI4MzaqqspeUVmWRVFcX18vn1NvYoxBJMAclhtrc1XLZNlmoEQASCQxMRExsoiQ8jFJEnKEoXCekFzpXHCWvXMFkfXeKwyqUbQ+bUYdVJNpsKC8oVVVhKE4IUHSRwJIEGOy6EQEBACM8N3CchoZkVNKURBDtEHDHwQlQRsiOx+qlFKy641BtN4751CAAEtf0AbVQ9f6JQVyrq6u1uv1B/QBaBbpNYWR9d5rBzQfuHGMuIA68p+55vo1SbIKIedTCxMXyxgLAIKTc4ezf7OET3CuTlM3Ymlf8v2NMbrEcS56U9L24XDQd56sprWqWlJKzCkxAzrVU8YYZpg90OmAzu9OemQRUSCyiAEDaAQk8SgS0CAROucMFUVRGbLek0J5OSeXYQaV3TS3eyvLcrfbffHJf+26Tj2SqloVvkgppcBWydtCIpkxKABQOpjEC1hEDKKwQIrH2/38jg4RU4xpTDHG2+vRGFMURQTN/fYAXJf+bLvu+x5Jjqd9251CCMPY7ewmy4NlZmUsjuOoIZPSpZa506Vi0LqrpVpehpE4FxPwzCVOYzCApfOd1rIyA4BzLsZJyVtLzjnrnJ74dbU7HA5N09Bc46VaQXFJXuTSsgBpVKLwjuKM+pyKtKrS1rNbluWpvQppFEakisArkdRamp9cdRLObFMuiiLGUQSK0jhPgFEgCpB1hZbrWUdVVQkba5z3pUiva6K5+EwPa9tWn4Tn+mzViM3xCAClK0UEEvdNq9mPOEQAAMlZLdJ4A1jiGABIQyVJrO2gDAkIphBTiLpvhsB42wwdFUUcx65ptMa4ruuiqjS09t5vt1uF+M7PzzebzaltiqocY7BKcaM5Q5b1R95sePW6cyC+65+yDN3poZkxjizIIiyIyDONI6slng+GprCzqsiOQlmWep/XUNpct6LULACYw+MJExSRoii2262apEPTVrUHoL5ruu6EaDlRDOB9oYh7fgkRAeC27Verar2pEFPb71Pis/P6wYN7AORsMQ7SHlrC4vz8fuHrECT2bVoUduPsg+sKi4gqORXiq6srkNcY2jT/mRYtwpZx1rdjLp5+iAxz8iKH385Yzb9569za4hx1al0MzGkNNCaJNF1ny2KSXZUMlWU9N7jINi0l4LW449vCkXXMEjGTxPq/OcxRB/suqtS0Ic764NuSka0yzEmv2R5NKJxC2t57JfsrzDUMg7LARaSu691ux8y3t7erLQiPfQeRoahos94K2+Y0dN2gSJfaF0AWSYhokVIYu1O0TlZl8fjt+3/7f/q9H/7wh8YY7+oXz2/+05/95TdPriBx6EPTDNakXCyva8JzxlhfJHPhuq7b7/eru4Wl7EhN/ibKLBz8XQLxmnDoTaY1QZx8E2dIhCWJ874sK+PmFscxIE1QRbmqzsoS5tK9CYLLlg8W7sVr0NZSAr79k+/8ucxBfO7bQoDKUskfE5EkTJolgWlRMiZBc1ZWZgWT4xeeE544t7/J7irfZaWtnk5NUqSZY6e9tEsC4yAMfDxdGiqtK6GLgBbBICJOSAsCQF0Vh0PTtfDoreL3f+93//b/9MOPf/DB/fsXTdNZUwxdLJzr2va269ar86KoxhC1dn6pg9PMJcAZN+K5eLpGnAC/Vxn287Lm/0y/WF/p/KXTfxuwqo81xDcsugshBDNnzbTDzDiObds2wzh1mKmqKhfnZG2RVcjSOuiVN+zbD4ULOnH+ibc2AqQ5ZaxajmcogoUFEoNQvAPIMx6Q1a/iXbDI/WZBWf5FY0Wtv62qSjmeZVkS0TAM6/X6vffe4/JQV5vt9iKO5rNPv/nL//Jlc+pXtSWjp05msZhgDACwFuqV/eEPf+fv/t0/fO+Dhyzjp5/99NGjt558/eKnf/nlN0+fnE7HsTferXa7i1Mz5p4+S7RtStnPmT81c8YYYADgpViICECCbw9k+17BmA/b6zkKjggAgqDFVKjVwtZoqh3mg9v3/X6/f/HihS2nzh82g7W0KC9bXt+5/fnQf+vJXifvOHIA4FKK6nwlEbWiIqwaASWXWSeQgizMPqbeLeuPDIzqmupjKH/fLKpRVDJE5OrqSo+vPsn5+fmPfvSjd35UpiiFX6/qiz/6o/if/uyn//7f/vmXnz93zjELJxFR1xgBmIgk8XpVvfXW/bcfP95t196axKYqvbf22Tdf/Ze/+PHli8aZqlivCEx76jIJNK9GXrTXFJ662KkfAXBG1VAgqQQIQFYAr67/m8wKw0Ka7hy4GI0xCJhi7FNERF9OlzHGFd5aS25i0BVFUa5WkzXhmZVZluUc1L2SmPgeIci6Mb885+oMIt0PQ2aK3BA1rs2LhXd/ICIKIc1BjT5cJotk7wfmMBXm2uj8bBk2yPKtdzgej0S0Wq1CCKfTiUxApJi6MTT3Hzz4o7/3tw/75puvnyOYGEBYVO9OUDSiI3KWUHh/ff35pz+7uS6r2ntv/+3/919/8rMvP//0Vhge3Ntu1+ecXN/1mfWTJQMX+BAs/PRZud4FySKyUAy8+HNhVsS+Vk3/yscmsbjrv0MCKABIiGIQGFhSGAPIiZ1zK1wXxYYMisBms9pu1xEm2o3NLeg06svFxNnV+JbMfodYLJ7prnYNp8zvne6Z/hVERNDYLBlENCX+RdJc55I92ewivKbDUkrjOC572mXAIKtunJvRGGNevnw5DMPuvdNHH/7uZn1xfdU8f/HM4Prdd9/+W3/r937yl19wQqTIkfUQ45yxY+amaZ49e3Y4vUjc9sMxxtD3QAgXFxXhal2vi6I4HcN+v08myYJAn60zzVR4mPmOivBWdwneJU6YvlssAHjCM18VjnlVlBwmCyFTDaN8YiJSXyWlNDaNpku89xCDruRqs8Y4Ff5b/VFOGWdLQa9mpPKuUJoI1gkYCGXOBA59652FOBbC27oyAnHofIpcOUJGiowxCQ8pMgCgM0IgFhEQWBJgZEA2AtqNgxANEVqbYhSRMI5+tcrnAAEUd0OAOCZmBk5jH2KRfF0YM5dIJeAoHAUAhm68vd63p+6f/z+GP/iD0w9/+C7R+sXLl99881+fPHny/OVl4UqUMYTEEgw56xyDxJDGaKpqRVT1XVEWZ9vNpvZxv98f28uiKFQte+9ZYoQjuFMXYwKOPCM33lhryaAzZJBFwBuxPKaQUKRAnDriTcFmXm0FEeYtWBxATN83r5VwPs84nWFOkdPEp1le3hiJ3F8dTyOcnZ2tyxUnHl8cmu0KAME6mx3+LAFZOL7/QsSlVZvIHMZgEhUidRUjUWb1TndW72L2yScAZ5H0Wl75Jxk6y2dRzcqUUhJRzy5DjTAzu7z3qhe7ruv7Poz9j3/8F5988lkIISZFnNg5bUGJziVNxiISTPXpk2eg7bzUWq1WKy1kJSJ16buu0y5CRVGoxooz0JJXVRbo8Hdq3Ff2+A254hzy/ILXm7Yy161o61LtW1SWZZcn32Q0Oiv8Nz1Tvl57IVV9k/4nISGVDJGkVby6ZyGElNSsQkqpKAqFfjNzZ3HDV9JmsKDc4SL5PtmOOMmNuqLMrBXuSxQk342ICO3x0Oxvj9ba3fn5+fm5MLrmdDw2iIHIWosazGNKIigs2imQiDScwzk9popWX01ZOVVVNacbxTOSohdojJavaTQ+o//MjPJ90cabdvQXObTLZXzT5/N6avCsRTfGmK6ZshwTBXlpwn+uRCMioCAgIIOAgABwChGIDCAZMsZATCmlYRzJWUWvh2FgMQzEIillYsaUUgGckZrZn6C5vZq+gxoIVQmaAdF75nwNz320iqLIW5jmro8qJUVRxPGamYncarU6211s1mfDMFA7xnAchziOwRhjyBERoEFMYzuh8ioKMPtYp9OJmbUVWHaDckmB1mx5Y723VVl665rmdKcu5uvn7uuvf73pnA/DkDm26vFo0WX11iP9gKVX0yI/95tYBOkVBqiwiDBwQgFjrSN0zqUZFcdxnE8VCCRBYBH1EyaRmABdQCQSWNDpTEbhZFHemGMTxUOXmBgs5GO9Xi8hf+WqWGvPNm9NoSyaECSEZK0/P7+4ubllxhgYhMQiM/AU8VAOl2KMt7e3mo5p21ZF0zl3dna2Wq3UoJydncGM6BtAInDWGiRLlESUuSciKD9ntd/0r7+Iznjld9/w+Zx5wEXH1RDCZrvRD9ilTCzV+Bu/FieLgCQgE9EGRQjQEmgTLGOQERkEBca7gq1JO+hxnAO26euQEBC03RHMmn/yVObeLLrcmSI6dZSbqg0md0TznCKiFG3VHNoPQxfC+1VZrlSlW+escc4VRUV1vW5OA0CnSL0IxpkAoLEbEXVddzgclL/fti0AFEVxdnb27rvvbrdb7cbU8aDPEPohxigcx3FEAU2CT94G34Utv9DuLpf/F7Ym33+VZRnnRiAaK6mhuHr2Qj9w18Etu0jf81iL51OpBxAQScJsEIElcZAAI0IMIaWIiOM4VS1by4mJgYDITOSDux5IhIiEJHffvlS5cue0QoxRW9kZY3a73dDfUXtUXLRcIr9tVoqqfq4ub+q63m63ha8ZpOtC245oTFWunDsRWURS/oveAVLMa6c8Bu3N5b0/Pz9//PjxW2+9dXFxkW32eLhSouXkfKB46xwZQJHEHKKIqEJFAVpQcr61wr+Kn7HcuOm0v+H+sIAelltvZiTtlTby3++z6MUANKcVRBICqCclKaZAY+iRE0jSeRLWWk3sGGOs1QCN0Bgyk2QAwALwAkJMcySyjFByMKJ5KYXzd7vdarU67E8ZWyuKoq7r3KMzRw3K79IX9L42phAxIaQQo6bpXeGNucNF1KIhkXOuOx1htm76DFVVnZ2dlWX53nvvffjhh7vdTpsPN02jDabTOFHkDVJZ+c1qXRflzfVVJBJEYGERYJHvPX4/Nw74Ba83fYs2hMlFqXdrPk6dgO9YfVkgcm7w246SaPMPNCAQwgAiBsQgxMRhGAtDZ5u1NSalaK3Z1CvrqI0QIh+bth+PzAJzo2elblprDYGI5CI+mVEyfQazaJFg5lY7uu6Kb3rvNT9S17WSSzabzcOHD3/4wx9qSw8dSKOa5nA43L7cayal67p+HHQDEkxUDEScEm9EZMzZ2dnQNmqGlPYBAHVdn52dqZWJMdZ1nVJ6+fLlRLsdJnTcGbvZbHbbNSRumgYRLZIAirBBIm9jjByi3EWHd8zW5XZ8506/pk3zv37758pgyzoV51iPFgnLpV6Q2TP7VaZ/IyLDBNIhEgpaQlsUlgwCKC3KGAMEgrha1cMYhxCNMTyMzBGIYkIiYmXdLe8MSPh6R8vXVkQ9QZ5LLBNMSInqeTPXh+k+aQXp2dmZknKPx+OuPmNm7U6mvQZkLld0zikEstlsnHNJIpI8fPhQu0XHGIuimLqjxNi27aNHj+7du+e93+/3qp+urq4mX6frtaCjKn1dlJv1+vY2GGMSEYjoDjky0UgWgNc2+Pu34LX//E5l/6YA8/t1laMJyP/lO6WTIAqzCDMyo0VCMsYICHAMPRMp2g0ilFKK4ziOeSCNAN1Nr1RypWZfVRgIKZMGsljggqGj6iTPJCAicq/oQ5opHQqKa4t4dbWapmma5ql5fjwetVEOCxtjQhjavlORKksfYyxLv9vtGARRbl7e5IXWHO84jofD4cGDB+++++5HH31kjAkhbLfb+/fvM3NIY9d1zthxHKuytEgxxhSjAWRAYwxoHT0gq0bEV14WfgHUAF6lyHynSOG3Ulrf0v1vEr5fVWcAQJpCyKjZGkMigM77lIIIAxjnjfWeiAA5gReguZo+T9S6K02AuWjGGDJIQnfsoaVk5PdRwdKcsjGGcPIQ1ZTosS6KQsfPqHHR7SyKYrVa8QDG0H5/K1PMNoFmiFLXZdsX+33fNEdrCQ0Q0eFw0ISckjxU4Jxz77zzzna7BYC2ba+vr58/f35zczMMAxowxmy3W+9cWZbI6bDf397eeo3GEVmmGhwATRa9koLPLvnP3YVlULn8/Gs/VyDxNbH4vs2dcfRfWjLSVMSWmHkOzRGBV9UmxpElOeeqVVlUlXUEiK7Y9kNwRTmEGNNhjKon2MyzkmCKTYgICacWR9kDhYV7oVohc/NzKJsRSY1j7dzfJ/NOMkZSluW62Ox2W73V7eFG/1Igp5TqVXnGZyEOIQ5X1y/HOALApjpTyvE4jldXVyGEd95556OPPvqjP/ojlZVhGPb7/RdffPH111+HEJJEAFjV9b1790rnYxxTSnEYTeHzS/GcJfhlF1+vpfP3uhx86+evaaAshT9X+H55yZidI3WZLCrlBYUjAhi1JMZYR957pc5ZKwotqPEGIoG7ltNz1htICBBo0bNF5hI0mTvLxhizEChnX6VBw8hM6lEsq23b0+mkx10/hoi0MqpyQhzwKewPN92pO3UnIvIFVXXx6NFDROy6bn+8FZH79+9rQNS2bd/3m83mo48++rt/9+9++OGHGvWo05P54kPodQe895A4xlGjAI5zH1IW1gKFb+n8vMi/yM79gjrjO2/+PWojEwZ+eWuCjEhkDAhYAGvJCDLwMAygI6UShBBoJEC01o5DG5PkwFKBMEB3x2OYGioKkpCAtVM36vxKZu6ErwZC5iG5mTyXE104F7jGGF+8eAEAalloZp8YY8YmVFX11uOHZGCz3T599vVXX31xbI/MsWkaIKjrerNZDUNdVJ4IYCo4gPv373/wwQfvvPPO7/3e733wwQdaunN5efnVV199+umnGv6M47jZrI0xWsF2c3PTnA7MXDovM34j8/bMIdhdQwpYRI9LSsd3ysTyP7/HA13qjF9E4DL541eMTQiJLFoAY4hY64PvSkxhjr5EpCgKyzCEqGcaQkopCSAzA7LKBADcScbCEOYXg1mWlxKmq3k8HmVmBC49EvVSNSmqDeSnXyGp6qJePb53797jd9568OSec4YR2vbUts3p2KQUisIlDiLJGN+eWiI6Ozv7wQ9+8Hu/93vvvfeezvvMtUZPnjz58ssvNS7VJ58cGubj7V6B0XVVQ86RqrZ4dfPg1bP+/a4AvKoMfq4H+ktdIfsZOdJbitgy4wALA4aI59xImtjbZK0xJVkSnHqMBI7jIGhgtaoKY1OIlR36EErpzwrqHAxND2JsaUdBAGGJIoYQGSUmSZxkGHIazFgrSiI3ph+GoixjjKemqaqqXq2QaL/fW2sShxCHqZd7CApGOW8Ox9vPv/jUOqpX5Xa3JgNj6K1bGS099cX9slptzu8/fOtv/53/1cuXL1+8fPbll59fXl5eX94qo2gIwbni448//oM/+IMPPvjg3r17q9VK82q3t7d932vbUOecdkoxxmy3a03Z3F5d90PjvSusG/qm9AUwSwqYGBVdQCCg6B0AJJHIU55hpp8gAJgJURRmVr9/tar41bmhGuUzvzaJdhK+QduZuxnJEGFEIYoTPX35WYQFSciWzk/nLPEUKhAhoJ8HXKgaVAIZizRDh5jBEwMwkWxSSiGkufOOLYpKkfkYgyrzuk5l2RrTxKDtefMREQHlQggBir+bxaRwFszhyWs/n8LIebbe8gMxxpcvX242m7qub25uvvjiCyLSrR17RO2fwUJEdV0a83C9rh8/frw/vPf++++/ePFsv9+HMCUU3nr03qNHj959992LiwsF0wBAk5OaaFVahnOOFiRFHaelD+mc824KVnHBbgSU5ZET7Xqhh54n7DHvtSHimWC2tB2yuDDfeQlhvZor/U635tvyBAA2a4uMlCkeFxczm5eGMLSiRD4kJOuNK9Q37Ps+hjSEZC0mwSQYkiQGHidI0c1O6JCC5PwIAk+zCLRCCVKy2YfPkkFzbbs+qgLkiFiWZde2ORGf698VPlHmwX6/17reR48eEREizBCnFrsSM2+3axF5/PajDz74YL+/ORwOIYyagtmsL9SHtfMoD/Uw1O9pmiZ3/8wSrM6vIuWFmwZ8hmGc9mMWBUSQmXk19XCZxQIIJSb9AH3Lni63dikWr/38tS1/7SdvRN/nGlrLcQLCDZL209fCEJp1BgCAwNTTD8AYm0FWnSwxHXcyxlhjLCKNY7i93SucRTwyYBIMUUKKaI0xzPpKqnsYUTvaCMCrM4ll5o7j3Psl136llDRADeOoeHkmduQQJsZ4OByUgnU4HJ4/f55Sunf+dlkVRVkgFogIoD3BIiJaV2w2m/PzXZ5ZVxS+79LSc4oxHo/Hw+Gw3++vrq6ur6+1fYpqCyJKKarE5NlmMvfFIwEG0ZRyTjTjlPeCvAKASAIy4x93p1kAAGJK2Z3K77v0WPnVYtLv1Azf44dStiZx7jgGAGrJEkAAqKpKZsAgzs3Imbl2IDB3IepC2wVVcROsyYAoN7enm9uTPpij0A0hxATGshhmQDTZP1eFN5UNzpKR3+jOlJLmMUzmzsjc2nwcBoC7iYpZmnMNY2b3KJB6cf4WzS2RmKPqYCKqqkqbVShGntm8ipdnkrqqitPp9M033zx79uzm5kYNmZ3n5dzuL5cUKX0SkCQiLBOxUwi1/GxJ0sA5K6pyY63F/KYCdy+22N2lBbF2Lj5bFOYQkbYUXv62ckU5fTdrkJc6A1gTWpDZmgBwOhyzZZk3xqLFwk11SilKCCHFEe7gL8mGNhcFQYWTW0BOpu7awiKs7HVGkVdqr5Q29xpLI6u+Zf8J/TqtT9TVSQsebNM0Gjru93udNvLw4cP3338/JR1JNs0YJyLvrfdWV1ZAa5SnqoUYR2sKmSvn1Lc4nU63t7c6xVnmyhelvSDiJBaJrbXGkva/4llDCCKZWVkoDp2yaKCKhZ4KO3fexYk1N9udV0m7+SffVhDZDchnKf8Q3swnzTJkgYU5MUw4DCmfxVpm1tRUWZYKAmrYiSj6r0q4zQ6BBnIAoL6YuinOufUKAUDIxIRX+/3zF9fdcIgh4dQN8s6UKDsjy6wsOv4o9AmzwxHnS3V+ZgirCC7XLucts0Z58eJZjBfGGKXzWPvK5EAAMnbKTXvvUyqHfur3pTm5/X5/fX19fX293++bpklzWaJy/hAxpsBhavBinQEWMqbwNo4BEVHTjXgXC+T5atNGzjy3OxU4a81pU+c9Xno2sKiBM3c9fZmZybqlwlg6jt8tGVlnyEySU/HXWLwsyw8//DDzppRZqWiS9U5pxyoZeee2260aV3Uzs/HzPnrvjfWnpv/Z518c23i1PyZhIyKMrz4xTJZ4fjFYtLeC2abQYnQXM9sF70sNH81kMPVDrbXb7Xaz2Wiu/HhoAHizWa1WlZun6fCrE4RFKRQAhBZx6suuhdRK+Oi67quvvlJ+V3Z9AMAYE0e+0+dIYMB7X5a+SYkWYUlWEhDnUR5Tfcjk6iWeSvgAgWDRrerVgTJZOJT4OLGNFmMMSlcICM8bnrPZb6InS1bPZu666py7d+/ee++99+DBA43OlW6pdlSVdtu24TBNhhLGFEUEiIw1NkXxrlSO/9DPU3OYjYVhjGM7PH3+4rPPvvjq6yenpve+RGM4cIwRQTvhIclE+IOFTlMR7LpuvV5rR2ace/4Nw/D8+fPCe5qLRbVeVCVYvUINaNu2PR6Pn3/++ZMnT95794PVavX48WOZ23IAwGaz6ftetR3PlVQ5natnQLO1L168ePr06X6/f/bsWfYu1YpNNXAQz87OdNDOW48ealOKd955TADW2nHsD7f7ud8viEgKfDweV1X1+PFjZv7Zz3725edfNMOw2+0kJmtM4T0zt2079p1mrHAeA5gj5HEc8xQLnknheqrbrnOzEQjzODBFkPM659Veahd7Op00g7zb7c7Ozi4uLnQDVDhgtg76EN57cuAKb70rQNBM5aaqbJOw/i8HwIgYJBDRqe2ePnvxzbMXbdsRGev9OEYgLIpiSkPrbNqU0H03LJt789JiTiczD32fmyboiHZmVhr0er3WiRbDMDx79ux4PKpP8ODhvYeP7ltHLGfWeBFpmmbRmmxKdTHr/yb0OqV0e3v71VdfffLJJ9fX1zjz8TUy0oex1hpEY0xd16vV6sMPP7y4uHDO7XY7tcUcx81mM0dSklJa1xvFT9f1KqVkvf/ggw+stX/+539+c3W9398CQOULa229WjnnDE29DNq2JSLvvbJJrq6ucHFl3+jBgwcaWqvDbozRvh2r1erbi4yorVoAdMZztarfevvx/fv31Y6oRS/rSk2Xn/0GYfBlASyMMKaYQMAQIk49ThHAECIQTazwKdgTdkVhxhRYkjAZFxnatk8xkjFoLJC65YKI1hK/IaDK3avVodEsPDPX2r2bWY+RzOm0Bw8eKEtDi4j08ykl7+2jR4/Ozs5kHgSMcBcVi0yjQGfHZQo4VR9cXl4+ffr06dOnt7e3KhmqeBTD0KT/alsDgLV2s17rQBM9yjr/BpBTSmSInBVJIiwIRVWmlJqhi2Mwzu7uXZS++KM//uOrq6uXz59fXV0dDodD1xCgc672To+rnaexLDsaLrMTKhnHwwERq7IUEU6JU3LWXpyfq7LMAc6d2sg64/E7bz9+++2Hbz3abDbqcmpm0hiT2paZyRoiAk7WO++99KPKY/b71Bk0i2r0/JSIyGhAqBtv94dTP4zOORIcx+iLAhGBJcYoiQ2Kc8Ya178hmoK5jGD52ojYtm0mZ6gusdZWVaXxi7VWc2k0V68cDodPP/0ZAA9Dp3qlrusH9x8RkQgCaFNRVEBVv2Ucx9PpdHNzs9/vcyOavu/VAdfmNQp3eu/LogAAZ626xsrnGAZgZkEgAmutov5JOHLaNyfnnCGKiSMKeissp74lb99+95233n58c3Pz5Muvnj9/rlqz2+/zdEi1oYq+qM+E3+LCdV2nLe109UREKy5zJ93XhOMuDPzgo48ePHhwfu+emiI0RinYRATajDclAyCI1tqiqkZOMUZOiSyKgCQgQIOkTHFEBEQdLMUACJii3B73X3755ZdffnlzffBV5VxBVpwrQRIkZi1Akem2aN13ioWZO2nygtVIRG7uKkZzM0ntU9A0jVL+V6tVSkm5oqvVahy7zz///Pb2tmmam5uboqgePXpk/8Cv12vnCiJS327yM8ZIJg3DoEzB0+mkmlkxULVo+u3KBSyKQuXm/Pz80aNHOpEbCBNHAeHEIqBmK6UUU2RmQTQIgADWlM6p+hnbFhBcXdZFud5uqlW92m1ePn9xPB6rqtJ1yEiallWqgs8KTxeNiO7fP9dxf0SUyy2fPXum1mQpFlO0nCVje7Yr64qsAUIRiJzavuuGvizLMYYxjCFNBVhJWLqpQYfMZEacufyvSWsOum6Pp67rrCsevfW4XG9j4FPbhXYINiGAATEE1hgCTCnEGM0bJIPmYQnZ8Ju5olBbgr777rvvvvuujlvWw/Gzn/3sm2++UTOv3pIa+K5rh6G31rZtW5b1MAyPHj4ehqEoKu89oiEiwsl7RYoKdavauLq6urm50Sav6/V6t9tpIevpdGrbtmkaZKfM07fffts5q3mycexnDhGiNYxTvTkAWGciJ9EiTOc8GeNdgfVuvYkxdmE0RA8fv7XabnxVfvXVV23XL+EsnBtyaBu+nFjAOaGhQmzn8U26ZYr05Lj3u3WGNj5bJk30BORdj3PnbHXywUyhs8Mpt0RE3vqyKrM05C1UM7zZnm22Zx9+9PGp669v9ldXN6dj27Zt17bd6ZjiSFMvZIlvBm5fw0MzgPPixYvVavXxxx//w3/4D//wD//w/Pxcbcf19fW/+Bf/4l/+y3+53++zX6Yx3Xa7VeDcWnvv3oOu666uroZhqKpVXdfWemutSsYwDP1w1AKkq6urly9fNk2zWq0ePHhwcXHx4Ycfvv/++9rc5+bm5quvvnr69Gnf7NV/r6pK3VljDHPc7XYighbVRx6GgblwzjFO3RN0jxnEelet6uPhqBtROr/b7R4+elTV9f0HDz77j/9JRTDMQ6XU/7WLQR/LdTvuD3Vd12WlipOZ67pebVfH4zFXFE+MwFfjWHt27wLn3Kl1Tpn16u5p55q+72Hq0xi7cSi9z8p8iZnqg74WBQHA+cWFCBpjkMyY+O13wFpblasvvvji2dOnX3z6ycsXz4a+lRnh+U6xyPK+dLI0Ynz77bdVLP7kT/7k3r17qhU2m43yxe/fv//JJ5/85Cc/efLkSVmW5+fnp9P+/Pw8hHA8npqmuXfvnojc3t6qvwlA3rP3ntAogHFzezOOowJc6ni+++67H3744T/8h//w/fffv3//vh6YYRjUP/3X/5//txqyyfaB3BEQEdwrw2AFEcc090pHDMPQtq21dr1alXXlrdN49fLmWkf8GWP+wT/4B0+ePPnpT3/64sWLlFJZluogfvPNNzQne3GmQjLzgwcPxnHMsQkixhivrq4Uklj6nnrl/8T/8//p/6iLrhYhzg2a8z6J5C67AgAxjnpkeS4n1KdRdZ0xqAxUj3HSexo76B5rvBBCuLq6evLkyYsXL9SKn04nYRvnmRXZjdUH03ZsAKAmtq7r9Xr9f/gn/+AP//APf//3f1+1uptNtb6O3vbTTz/9Z//sn/34xz++uLhYn3+oVF4NLFer1cOHD9966y1tsnx+fq6Sp+AeAHz5zedXVy9vr28++eSTzz7/5MP3P/if/+f//R/8/u8XzgokEhBJ2jubDHrr/svn3/z0pz/NHAA9V9vtVhW7etBZgVlr++EoM79LgRPn3Hq9HoZBg8xsO9Rq2CSK5Tz9+pvPPvvs8sVLFCl9YWACsCCxIgAKoyY/6UsVlLTobCnf4pIxiJO5bvS4P+i2AUs+i0pmxMVvEkzp46TJDxYCRDIqUtbedRMHkJg4hWlQ7xDvcHczlxUxc9M0dV0/evTo/PxcTfiLFy8uLy+fPHlKBkLQIDajF7Gqi8Ph0PW0Xq9jGodhePz2oz/5kz/5R//oH73zzjsKLqmq09dWMXLOKS6n2a+rq6vVWdpsVut1jYgiqe/b43HvnBmGbhz7rmvUkdRCNz0JN1fXn3766el0+oPf/1t//Md//IOPPiIi55wAofZ9d2AsWWudsT/4wcdVVX711Vc3Nzen01GPtQiHEJmTMROdIITQdQMACEzVEnZuRpgp0BmihdmlcM6dbvfGu4uLi81mc3Fx8dUXXz79+uvD7d4by5pkYFEIABAJUUiybKEhN43KhhijzBAzZAdRJvAXEW1KXBSlGj8ik5HvWbEI4iu5/Ozp6NEXEe3JmlU9zN9nJnbdYgCMIAkSGjBGSCwaA1S6wm3tpl4/vPeg67of/c7h8vLy66+/fvnypYaF+uirXaVFoRYJrLt3dv6/+ZO//0//6T99762L3W5nje24JzDO+MIXbNmSLVxJYMTJ9oNd9/f7J19+/a/+1b9aldV2u62qahgGHd/tyIxd//LUNIfj7eq6LEtVJ1N6KLbPnz9/8vWXu8327/ydv/P3/t4fbVarMPREhCKMrMM3nHXOWGttZcw7jx8bxBRCezoZxNJ7b62kpINoVaUryh5jRDTqnFqy3vjSlcYYSzYOkYEBIIYZQzOGgHbbc0RkoLKsHz9+pyzr9Xp7c3n15RdfmLlgA/V0Midmm6eCI1igpRzkjHe2IyIyjhNupJN81sYYLdtSVEDD9DuTM2fnUkrOWeUl6VCgGGOMiXkiKEwuBRrtt0pEHqcuOa9ZMtWTyomCuc/rbrf7wQ9+cHl5ee/e+eeff/7ixQvNWgHA8+dPV6sVEe73N8aY3/mdjz/88P3NZtV1nVKttCbRzV2qYe65OY7jdrt9//33f/SjH/3FX/zF+cXu8ePHu91Ou31PJm8c27ZNHJr2qFUFXd/owRpid3N9KYkfP37rdz7+qCqKoWvruhZICKjVIqrerLE481K997vdLle6xrkneja7Mk9uI4K4GGMLM8KtkKOa0Yynqe0ehiEMo4jUVfl29fbZ2VnXtKvVqjmdbq+uD7f7cRxJdXnhI49AaNzElFNvYdrWXN/wXVwvuyqr0vmUUhpDSskAikgagy0rZR4iovJHhAVZwjx/3BaeqmkIVJobbOt9J/WlQzdIFHXILsskKCIi4p3TvEBKKYxjAAjjUHj3u7/zg7cfv3V1dXV1dXU8HsdxfPr0qYZRmrT74P33NutVGIey8tba1WpVFEXTNDnIUp2sn1dE68MPP/z444/rulrX1bquSu/Otht1dw6Hw/lum19EtySlZBDC0I99t12vfu+HP3zvvXeA0zAMbz2833UdEXDEUYAMlr7QQ1V61zSNpLhZ1bvNum+bse+cIYUcUhhTGDPAj4jGlECYJIkIijpqEseEiAattcagFRBgZJaUUsAEgEVVV0VZlqUBdLYofPUP/tH/9tnX3/z0L3/SDaEbxiRigci4vjlomklm3lMWQVAumQbAs0AY7ydgY7VaKTBsF2O9FWZehrJZlDTc10WnRSPp7DMuRY+I+hByQDU5QTP/WxaAVf6uvu+zPldUQDE+5dhlcCJnSVKSENIwBGaO8a5/l8YaRVEp4A3Au935Bx981I4BiWMaBMR5V9XeBgzR57K2WflNqIB1cvXyRVEU77/7zsX5rjs1kpwxaBCcdQlJgA1SbvFTFIV2vgIA1ccistls1PdMc9/BNE8sYO5xzvfCTECBRZSeN0UPIRMqGDXGEE8RALSPdFlVD956ZIy59+D+9eXVy5cvr66uXt5cOw9JBKbR9vPA8AWfdHnhYgSWLX0hImhht9lmnKQuK9VjPI/GBIAICCyrdamvoVN3hBkkITBzEhRAg6iJtkmeYiSjXViYITGyOJoGxI3jyDFpepgESue998ZWMcY4tojorK3O1kRT6kGBcG0u3vd9GJqrl0/TkE6H43q9xrl8Xn1PXXqdB6PMnTCM57uzHUGWaUKRFAxKXXrnLIqL5q68RW+1XZddc9xu17tNdbi60ilXt9c3zJw4Ass4p7/VrW76keOIksLQoaTz3Ubdr8KZEFw+gcr0EZEUcSaY3Y20dWTUBc54UhaX267hmRQtIjpI0Rt7s781SPcfPbz/6OHxeHz65OtPP/302bNnVUkZyDfG6Pj4bN0mPENym0tMMkmM1enUiFiVcwjOrEQN1cOQ+5FzSgiF8zFGJSKpWwTVJOA5lxPmAYtKDNR/vtMrxlhjUggoUhWFQsgTzzYlsiApcoze+7osVIC6riuK4tGD+4iovMtVVXlrri9ftqfu5uZKM8NZzzHz+fl527br9Vpr1fXZrKV7D+7DTP6OMQInQ7SqSmMMgUQzlcQpdlSVhbP43ttv3bt3zxvz1ZdfxBi3223XNsofBgAlRqgyOB6P/TAWRVE4u48hjoO1ljkdjofdbmf8nQeqKjClBOIVtFZB0QjAGRtjTBjjGLRwxsz9+VHEW2vzWMzEKSVJE9tyGEdV+Q/eeuSr8t0P3n/x7Murq6tu6MPcTCylNPb9NENpoovdMXrUvxQR/L/9X/8vWX9mhTY1OooxLIYe6gecnxSOfkbFX5n12S5kVUlEI2Nmeam/qf+02+1ydKPyO+VCC7yLaxbAl1aW6qMTkU4jAAAOkJ9Z5VK9UeX1V1WleRPdEmZ2pTkejzqLO/sTIpLBA/UZc77Kgmy32/Pzc+990zQ6UEep5Cml+OqMUhFpul5vlbuPa8ZcTYZzLpcm6JEbelb1nHkwapi++eabzC/R30opnU4nv66Z2Xu/rlfKTgKA0vvj8RhC0BmGS4zg9vL5119//cknnygypkPLh667f/9+3/c6DAUAFGVARKUOiYjdbCs7jZGaKgFzb7yM7uU8zQRXTIThJMLW2qLw1t41LM+6QW97eXMjRoz15BDBKfuCiMrCsEPmyezBHPt4/0qvwYyy77abtm3HcZQUUwLg5AwZYy4eXMS5kdc4jn0YAwfg4JzzFgtHVWGZyRjjnWbLxCBYQkPolF8CBACW0BqyhFAWzFP4MFpjIFojKYxBmGOwhMYY7xWO4zAbBWPMxCYlY1CL+g0aEBG2LrmJLENEBKhIlK5DCizzTCBri6rwReEB4OH9ewrPp8KrWANAXRYdB6U7JQ4UWYd7pRTK0pell7rSgypzjWfhH9vCk7N1XT9//vx4PEpiVxTH41FnHjozFYgDgEXqwjhJxnZTZ0xTJs6t16StISkL6+y02ZnfMI44jgIgCJEQrSOYWpqLCGs5OxFZS9aawpF35CwSGW8x+UkURNgYQGuWKEhK5JzJOiMrKmZe1yVKAp6y/96Std57X1Y+hOnckwFjkWZqOJIUpXPexCiIIpBYkidyRtiCMeCMZpIQEYnYEiOiJSczp6twWJpaNFpLo6RkUJxBg5QEnCEspkQdIhKAJTKFTl9Eg4YNMrNBb1BUB8s8Dl1REGvNuqpjjMrLMsiSxjhySmm3qU+nUwpiEI0BSaMxZlV5C2YYBklsgQ2RIRQRQi7Ku/F9uRGl996adVXXZ2dnDx48+Nlf/uQnP/nJ4XavRlASI07VThITIrKb+vwBgF1V2j5XQkgxRZYURzAo+/1eo9PSu6zWQgh1XQRvhmGa1kEkqDWyyPNB14We0l6bzSrbBUTSwkHlNyw+lvN+1HVN5ltYaxUXEaEQBgB2zmj0Y+bOCDGOKU3d/srSF4XLphBRjEGRFOMoIsw2pVSUNRkkg8YSmemBp4MBTGRsHmuNaIA2da1UvyQMAoQgwhxHScEVVe1rBhmGKSfOzGU51fwRkQgqr0GncRmDMnPNRay1tigKIisiw+DVf0ocILFKuUBiicYYgdQPvTGmoqr03uoEvLmOYQqAtfEmkSUsvUuT/E3uwfnFxdnZ2WazQcT/+hf/5ebmxhkjMPXvlJhijIaIifrxrqxLEQgmUsY/iBAzFYWORQEiIdLZA845AmRjsUDHPDXAUCtj7DRGZekiiEglTnIpDoIuh3POmtcHw4qQiIwDIAgI659Ek/IYh56IqrKYZQgBgFP03k9TonUerwhIFEbvTLBIyMJBOCCiNVYptgRiEAiEYKpPNwjGEICgsNHpkJxEUw8pcBwTBwTjDLFBABJhEcGp4yUo3qVvUTg/mV0d3AwoSGrdl6OKAcB7X5XV4XhbFEVVWoRiGMAYo1QjESk8IXiFYntMAEKYJA3OkFEv1CpnJQ0pppDiKIxzC0eCEOIYYmF9N/QEeHZ29sMf/tAAxhh/+pc/SSGkECWxAWQDMCMI3sxIV98dJ73NiTAhojVijWzv7dSJG/o+q3drqO0afT3raOkkjuMIIACMIISTIy0gZJiZgSOSctNNWVrnLItZ+iUI0/QkTX7m8Hp5adIyy5kaOGtJJM2V+DT/rs4BUjItAUz4BwAkDrPRAUAFUaaGDsw6bVsUkgNkY6jvmnEcQdA645xJggCAxlprESmmMYwppCklaa113oQQeJoUKQIJCa01Kb8wglZ1JcbEqhS16Q8Yo8kRq0Cn987ayanXVyOi0A+uLLVh/DR/T1j/p1QgnQRJhjhC5KRRgqZw79+79/5HHx6PRxT47JNPBulDGlU9WG2zbCzORWq2H1qVMgBAEiIkA2QgcWCJAkkgiQafQsKYkmatJoVB8xBeLQOMMTJHVZVqJlxCBrRummfjrDEgyMmAgAgISIySEoiQMdbYzWabUsqcWwAtowVnsHBGp78KsLUkBi1BkjitviPn7IyYQQgDSzTWFaWziYjIecPMGMCRVosCgrAwCRggEk4xCADpDNsUtW8MEdmp3+Ok4ECICCy5xCJTysOgsVPOmUySwCyIIMyizeoMmKlLMiEiskRhEiCB3WZCGsGSJS8iIYxdM6i9sITMkQDU6DOzpFCUriimyBEACHiGr6ZFNgYBRI+ERlIxxqZprDFFUTx69Khr2hfPnnFMYRiZ2eguCjDzqW0myTDzKFGcuT0aUyjhhYi0+QTMJQwahmg6aQbWAADX65UCKiklRLB2YvezDK/pAH0lmlP2+kO1VXrsYK5G5EVMSAtmg8wNM0Qk8DAjAYZoapQwA3lsDDpn1OKlFEIIhXXZ512qpTjPk9afp3l8WFl6dbOISAtqBQEYQxhYe7EZa61lEHXFdqsSFmx9XiQzcZF0VB3jnCOTNDspIt6r3xDGUTkxBhH0KBZFpTGqpFgV3nvXpRg4CQIRFs6IkBK28/daS9b6tu81J7W/ud0fDnVVEdF6vV6tVkPXt9Cogw8AOidVcQ4AwH/5//y/a3JByc273UZFLMZRdyLEQTdSmaXdca9wdY68ZcZuAUAp9rgYlRhmdDzNZUJ6nzgX8GSTpAZiuzlX1Cun4PVuYR4DPgfP0+U8hcWoHoXmjDGn0yl7PFppp/9a+FrjutmDnlgBiq8ov1KzJ1qWd5g9YiLSJJwS+U+n09RfSmPLlDQWUOaHc05Jh4ioHzudTpOz/erwqHv37qmJHO7acKPmGtfrtbVWWR1mrj2Oc4oqzXM5FKvVV9bCk1x7gohky5SkH4e2D+MQGTR3br/44ov/8O/+/c9+8pNVVV5fXt47OwNgDlGKiZtnETFT7xWP0pRVSmFyl2DKeuiB1pQEz3nhLBOZIwQzCJEWbS3obvj2lM5RSsus/XJx/VQXmiVAFdWyDBoXOBgiVrXXNaW5v6xueaY6ZqTOzH3cltsjczOnruv0QCsgpt+uDFOcaIJGQU8NCna7Xb4JLeqjMo1BRPI8DU0Gzfb3jvWv4FUmgqteya8Q5w5gWQPlFdYry4QGKfqoOSurC96PkXlSUQgmiTonpIVFy5UZxzGNYRinE2h1kzTFgIh932UEUBdx9hgyI7cQgRijNkQQrU1F1NbhxogxTKQ4ASASs8SYACYKk/48hDj3VtBpjBMmTSRhHtaaxUUlY7my+UJE7TMwl38Cs6iN1+niMUae0jtCBIgz1DEzKGHuHZgbeQGAQtcqcNV2LRM5b9oJmgsU9BRlpCtjMHn7cz4FFgxnmCd1wBx84rcSXSKi9WA5fZNz98M4ZuHQ+6tEMnNuSpaNl3MupGgMGbBkIVkJicMYmUHFUYWvKApr7TCIMebi7HySDJorE1VZ6cLlilARyc6qiGjUO7uc81CqmWqAczZV9aqSo1Q35GOHc5SbdYxZUBZEhObRdtkV0A02r86FzLuV+G7Ontoymhms3/Zg8kLTTDWSqVv+XY/npXODc7kRzJAuzoULpPT/BdqoN2maRgvEc2pG9VCmH2T7K3M9rT5PVpO6hqps9NWy2M3+06SV9dxmH1FR+WzTdYlWq1VKEjlBFAROEhGROd3e3ubqrKz8Kl+cDodJMph5tVqpNCDialXpcuQ3IQN5y4nIGQopKRKZTUneRZWVKQ6z1okY5zLbDIiQSNtCWD/F/USExoi2kBLxCw46AJCznhBDYADtV0OEyw5DMQS1caBoTEquKFbr9X6/VxKpA3DOFTM59tg2uoXGO09TTpWMuby5XiIxiFjUVUWkvnreTjMnpWRBIdAznXVAFuKsa2HuyJB/ovKUHY40z66evD9EnufdL81HvmTuOaNioVGhen7ZCqvgrn0tU5ci0SSH3mEcx/V6fXZ2ZgmHGFWOV2UFMHdwO51OyulSN6eqiqwJ5ye46xzIzCh302jyC2f9+dqj6zvnoxAXIxG1+x3PrT/zh70vca6hyJqGmXMDk4Ud0R5+/dL66pNouuvbCiaEoJF9xliX6iGf6azJnHNDGLOqUBFUyVg+j+6Nutj37t3TyiUA0DZOWuI2zmNjZS59kzta1/Q6WXvJIpAxi0RBli1YhGwppdwwOa82zknKtm1j5DGGJIRwp3fv3buHvwuH29v9zfU+JTuL7Gaz8EAVqNaVVZde7ZyulDaIzCeJGUQQ5lmSOIVtfn5uS5SIiBliDLna08wDKLLuMcYhMkDKUqzfpisFrzpcIuIWLdLyB0SEyGrlMxHpMU5Jum5wrsAJRmORqC3wYuTsr2QZnRRpVdEimoVXZ6EvPQCa422YmUoqdktVr3s/px5Z5kk8NE8imhdhimDtgsyHM3yw/Ex2iQDRzYMy85Oovsk+jV45hNGzbY0ltEkghim3fO/evfV6ffnieQihqCp9l8vLS11bqzwGjUIBoO9bRKyqqqoKfUOWuDyUSQN6QlTJBSBrrHdd1zlDZAzOUXUIsR8HYwxZo8p8okSoTtbyaMytlQCFUChysopEGUJEska/UWme2XlknsZx+bIYxzGkaNEaZ7VhyxDGzW7Lcys3IpqqcwlXq1XGiICUBi+RU9O13vvKapnanbXKuiGjHboBeVOXugoRtTOYAgGHw0HHaan3llWObqd+rGkaWQThKjQxRi2EzJYl0xXs1MH9FedDFzaPcVE/b7I1xhqDgoDGI5gxpqEfmfn29pZjur29ff78eXs6ufv39Q67zXbSGcfjUb9el8DP7SiUPaseaFZciKiWLyv57P0pK4KZlYypBNc4zwqR2VVWe4yIWuSvWl3LdpVFoVQG5WXFGDWxpzo5x5C5QUAIQVvQq/vJzAqviYg+J87IisypCtXzOAfhahqMMffu3dO/Z/BA/650PdXSyt5QFXJxcQEzN0V3QnFGpZPp6+t0C4UKHzx4kOY62GyzVA6yV5ddTpwZEWamYM2mc3Tz0IyMW1hrtewqS5taNz3hQJ7IJuExiiHripLQAJCIvHz+YhiG1WpVl2Vd133frlarm+Oc/aC5Q80cSvGsi6YggiXKPGhZRJwvpxcjIhRAY52zrmAZtQk/oEEiJEuI1vF6vdYXTnOXPhHhxIkhxDgGNT0ckyAaJMvSCzOlZEWsczQDCf0wIJHT1AmAKt6YEhmXWFLixGCtI+NYZBwnfyI7g8YYJGvQcG7sEeM4d3ZTGwswMdBymyNjLad099gzTqPWIev/LPFu7liiG6ZIYNb2+UmykwgAdV0vzVPWQGHBn82+hUpPPmN5XzTGyf9k535D4zgaZ1ISBtGhImEMp1OjBZvX19en02kYBo4Rmff7A4coOH2pVdlU5SMizHF2f2ZMdwHYMXPpXMZelu7b0qvPP1FFotiALoeZiXFKu9JiQ917O7GOYemiw1y3ufQAYC5fs4tmpvmH6qjToiqO5yJpdTnzTqhNyTAGzL5wtvdEhLNjqOuj8YL+aeYr/6tuT8Zks7OsboHumZkb/uEC9cp2JBumvMg4u6I4u9J6Ew3CVdeqaszGLoOqIQSyZUpREIxznEAJZsMwqmBZa1erlaS0W6+NwU292h/baa140feJmbUzpjEmw444V45M5mfR1ydHKEuhxledfH0+npt4ynzljy3hGuccwDT2l+eMQ1oMpdJd7/s+R5j6MX2qbDWMMXl4lprPDKTCwrfNpi0f9DRXe2fXzyyufGqXbnJWADJDI1n/q50yuRZIdfCCDZldy7wm2VuHhYMsi4CZZ9AWZpoxESk4u8QO4qIKlZnJGmMM5LZuRO++++5xf9CDetzvOYS2PUlMY5g2bnKJc70youQlUHUCr7YtNsYhJph6ASKzxMhEqShsSlPtgginJMxJRJpGJ7ndzVo2hqz1wxDGMTKLMSCCzBAji0ztKHimmuqVO+zkJ8nepXOVwgkhBA1DjDGafyIyAMQMKck4xnGMwxCshdyuSrW9NjdyixImWWDeNA9ZytKsn1Ham25zrtLDBV5Oi2pN9V4z2h3m+U75pOUDliVeNzsH8zDXBsOsmDMioGYru3T6Rur9VFV1bAYRnO0Oz+YMj8fj1dXV6XTKYqpqO4wjiBChzXnUhfLD/NzMLDzPc9dzpg09YQpYAUAQIzPGqcMmzz+5E4UF2gOKkxvTjyPolxnDAEl78olY78AQGGJmxonukUDaviOiytmiKBghhBBSaoe+BIvGCGKYs3eFdvtb6GhdVGZOIjFMmCkYsuARYEyxGwcwZEnDIxQR/WoV8zQXyEwu0awzMoCYFulf1U9uHqcicxdR7WQii74j2QYtdQ8v0PH8E140rrTzSOYlPKpHf2lb0zxsdhyPzhXM3DdNDMyiuD5fXV1dXl7GGNfrNQHsdrsYR2SJQe78jGzejJkS2Skl56YmCIlDttNEBHNCIYM8MLvN8Gq+SuZ4ZIp+Z1dA5Zdm2AcWpQyICDDVBGtWQtdF4zGZE2wygwoaCIxzg2m549JJthE4Rzr6u2OIS62TP6Z4A87xQpZjQUqLwnx9l3Hu3SZzViHNpOj8+mrFcM60Zd2wtIMamOS9xwWHXhZZ6LzfMUYkUscr7wjOFY78KmyYhcw5Fzm1bRMD+7Ky1vb9WM0ARkrpcDjEYWjbU+jHVb3Rb7RZ8Gd9CIqpAfjJrqcxqzJEVAK3fqt6MbDwmXUJslOCiGVZKgE9Lsq/VMD1/fU99ZyJSNeP2ZXJb27v6sc7VZXWWn2307HP/ZDszG5VD3Rp+/NOLAOWnPX23h+PR5XOJRwHAGhdPhgwF2AqlqpuhL5vjiz0rMcYNWWvyaPVaqW6BOZUrcyd5vLvZjdTr7ZtJwIHEc3VjiklnLvF5YAoOysqqTjT5JT678u19x5iiDGGkMp6pYWK2qKpaRqsKwCo67quS4mp7yb/0n718lJEdrvdrl7th5GIqmq9Wm2HYRjSANa5aptS6rqOey4KexywKHZosY+xtOV2uxWRpmnKdXk6nTx6U6z7EGKK6iLdSEIsO+T96RZS2m22u82mQNc0zTgGNFQUxcjDsW0dO1mdm/GIxgNCSO3QDm0Ti6KwriIo6xL7vr89HolgvfZtCH3fj5E3m812U4tI27Z9F4VTXdfjMPb9SESbTU1o+76PkY0pmlOje5Mmf9m4wpeV3++HwA6MA1NGCUOYtJpFc+pOKaWiqBKMGpJszh4eTkfva7S2R7LksaI+hNtjb8JUDVZVu4dvvYWI2tcLADebc2vt5eXl8Xh7cXGxXu+G5tZvVxNxXExpS2NM4DDI4OrVwHzsx5TGoijW6zONPozDkG7ZsADeHltEXK/XzlhEPD97EEJ4+vRp17R1XZd13TTN5e2YMDx58uRst7M2hXbY7/dd0/7pv//3V0+/eXzv3vPnzx+c31O9CwCCYAwJgB36YIxpTh2C2W63hsxhfxqGQe2Z977wFo0R7rt26NoBirUazuvra0WlFJxXh1nnl2pzo91ut16vA6ZnT59ODR6itKeTJbPbnq9XKzJmvd7U69Wpa2+u92jo7OwMxoPCROp7SUxj6Pu+P9+d1XWJiF3TMEetWhORY9s07VGj7kk3HkVEzs7OmJmEmvYIAMppjTG+8/j+MISmaYYcsg4TMWVyLGji7eE0DTQCkLWmqipflTGkpmlOp9OqWgMhM/fdCDAVMQ9hvNieqdYJAhAiAHRj6MZgrf36+Ysp6inKCBgBi9X65eWVYqNFURCZlLQ6F0WwKKqiqDS4Ox4bY3oAcB5DiAAQmI2xq9WKiK4ur733p2OTxjAM42q92azXXdcNwzgMoBwibTW2v775yU9+8uybp8fj8eXz55vVummawnmeM+Qq+gBgv3ny5J133gkAhxjrsnRV5YxZnZ1Za5umGYbhcHt7PB6/+eably9fDsPw5PJG/azLy8v9fq9mRW1qURT37t0zxujA43v37m02mwjy7Nmz9nha1bUj0546jqmqVuoMn59dVKv61Da3x8NmvXv8ztu7CouiKAo/WaWYTs3heDzeO784O9sWRTH2rfoEWih2aI6qpYuiUExQG6598MEHmVGsT6jzWr39vePxeDicQghkrbpjYwyr1Uowg35Odz2lVJgpPLm8vlFumAiO4xgjpyhtr33+7jr2aZNaNbXaP1P9g+vnT6+vr3e73UcffVQURdd1++dPQwibzUbj8JSSynruArter/OkBPUbEDFGVrZKVWrRUSQiNLaqVsMw9CH23dD34+nUKn4oApcvX/7Zn/5pCOF4PKYxPHv27Pb65sG9e8oCVKKWOu9lWSpGLCL23/3b/7D6323eeecdEQlj6trbtm3Pzs4+++yzy8vLy8vL4/F4c3Pz7Nmz29vblFI7RpdH3UQ288IR0XV3++Lr59ZSCGwMtPtjCIEtGaKU0v5q7y2tqrXE9PL2mxDFWvOyfg5kDqejIPzu7/5uVX74B3/wuxo+qTL3duoc/fL5M+2eE4Yutw4zxmROl5b4EVHhjLd09fL5ZrPR2HIK88IwdM3l5aVqb5yZFl3Xtf1wfX2dIkdR0oMXkX4chmHwiGdnZ8aYq9v96XTS3xr6wMwpiY5FEgFfFBog9DIVLXrvdUi9HsHnz5/v9/uLi4uPP/7Ye397e6sDhT/64MPD4TCO44MHD370ox+9//77Zb0m6y8vL0M6mlOrAUhRFEAmMTvv4xgSYALc74/7/X69Xu8222GMQx84gSsqRIzjqA0qyRaXl5c//vGPb6+uh2E4OztDAeVd5EAhctKSaPX09bTbP/uPf/bu2+9ast77vu2fPn3605/+VES+/PJLbZqpvq4upfd+VZTqMJdrp1pXg6iLi4v9fp8pNirsh8MBPJW+QDRD1wOzNwRA4Iv75ysii8b042CQiqp85/Hbv//DH1XVNIqXmUVSMXlhcL7beu+NRUgZkE3MvN6U2UbamToaY3z58qVKUo4IxnE8P9/dv38fFqzEMWoUKvV6FUIYhyBT7ZP0/TgMw1dffVVvts65BMgJLi8vf/azn/30p5/XdQGgFKeECN55NazBQwhJUVznQMsC1DFsmmAt/Ot/++/UAbfWDsPwH//Df2zbjhnu3z//yU9++vHHH2tD6s8//1wjg7Ozs48//vjDDz+s65W+dVnIOITrq9umabbr7dnZmcbm1hdlvbJkTqfTkyff/Omf/umnn376v/7jvz8MQxqD0r2cscMwpBBK73NA55yTsszg+mRN2l7+9E//0xdffFUUxWazub29/eyzz47HY13XipasVrWqRFWPt1fXskBhAWBIIhJD23s0dVWo6GHkoiiwXrtN2R5PRPzW/XsceH97CwCPHz4KIRhryToCOBISC7CgTIy6uq5FUoyRYIofmbkfWhzR6cxsRE3ZI4r3VjH1GKd5M2Xp33nnsVJz9RCoTt5sVuvVitACwBgVKGOyG+dcjDzY0RgDglpTWRRFCOU7b7+njtR6tX378ftXV1dN0331+RNvHCI6ct5ERDTkmBkSUOGsTRkZQ8RcMV5VTg1x38tqhcY45qHrBmsdIt7eHv71v/63/+7f/Yeqquq61oCOmc/Pz3/4wx/+8Ic/1L6Rbd8URXE6HJ8+fXr//v1//I//MQj+l7/4r8MwvPv2O+fn55cvX/7Zn/3Zf/7P//nJkyen08kKbTaboevruvbWGa3aS8l77627i6j9hFzk1ITdlP7Z18+ePnmqltsYI1EKWxS28Ma7eUBQH/rIMZnkLC3ROhExVNRVwcyFt3pGrUHNBDpLq83WMYjItl4ZpMo4RNxuty9fXhnvnHPAUjiPhlCYUxyGzloCmKBxzevFGKvSMyup4o4IiIhIQohkLJLImJgZiZDEEIY4JA6qRTQGVg73FGemqXaBKDIb720GQAkNWjNlIsS3bd80HU+Zd6x8udvtUpqwE28LnHtgGqJIRlDQvZIGU0hGMXtco6L7ItKLcXPaJcbYpz6NqY1t6IP33oAJY3j65PLqxdWf/f/+bIqoSZxzMYTjsXv06N7Nzb50/s///M+P+8Pv/M7vXFxcPP36m08++WQYhvPz80cPH3/1xRcPHz5sTycC0NS0htZd06pv2/e92jVVHpYmjqp1xqaUNtttXdfDMMQQC+cL5wFAtL+CNuqLyRlbOB9bRESDBhhSCKoYvff1pp6oacaUa6d84+HUpmGoimIchpsXl865uqpSlGffPLXWIivGF3GuADYwmaGpIULpCl8gYoyjlgMhgj46SwRk64hn1jhpbyGRFAIwq/kAACW7x3F0zhV1fTo08+x4V0GRWBEthRqjJA4pAgSTjCGLiFc3l0S02+0A4HRqDje3t7e3ISRrrUELCILiyDhXiMhoRnSEi/zihJlCeuvhxel0ggSIIIwEYK1zq61Yaprm1LTGmO1atVfs+/5we6zrcrveqM6PYzAFFc4fu/a475yjuipvrvf//P/1zwGgbePZtvrxj/8iDOPh0JalffTokQF88sUTw5EAC+e9dWM/jP2gEpAAFT5Xd15twnq9Ph2OkzV59OjR7e0tESl6U9f1brdTiKYsy8PhUBTFer2+ublR7syqrlNKq9WqbVuryPzxyCkZoqHv9Tju9/vmdKrreuj7kcOqrNrjqS6rqiz7pq2q6sG9e/v9flWVQKbDDlGUIcfMXddpmyXnJ6q7c6YoihiCtroHlpjGnANDSErhzy3PMqAkiyR1hlxL76whnUyGiAgMLEnYWmOM8R4s26mJmDCzWO/iGFTZaHrFWnt+fr6qau/L9njSzHvfDlVZEaA1GGMcu2G3211dXW232xE4Cg+HEw+DYkrMcO/8/Pr6+sHurJEILPfOL2a2ZdU0zeq8Br4qfCEiHNNusz0cDtt7m9PpFMdxt6lDCFVR3LT9uq5CCA/ubeuyGsfx/Py8dP78/HwYhs1uu1qtUt+IiHa3Simpncop33HuFa5cocPhoK1amNlyStaYzXpdlqWbOi74cRh0CpI1hhBBJMUYxtEQcdcxczAm9D0ARKI0jmDM/vr6uN8rSRVSMgAW0RGtN9u6rDim0vnCex6CQUKZSh3RTE6Asei9LQo3jK2x6Atrp3ZmcRxTjNG7maxgRCfVTe4OibOWmQ2RNQYBjALnziktkZlF048hjHMyKKUgkgAoCccYU5ToouhA4ilnpoPulMzsVAOFMCpw0rfN2A+VL5qm8bYQkaHvOYW+7x9szowQChXz/1DICD148EBbgHvvj0JrW3RoSzBUF8jinJOYRIRDDP0gMRXWFdqCPXHli+CLyhfJB9yd6SSGuq4l8aqqx3GsqmpVVoMdiqLgMRAip6STX/rD3jnX9z2wNMcTCnRta621fkrx9H1vrVW6SYxxs1pPb+0IC2tKZ70hSDFyGkCGtumbU1VVh8MhrVbOudB3BoTNBHtnMz/n4SZ9q8ddMRMlYqUwBkMpDAGQEIex11yD9m1g5iQxJ1zAkJL4YMpBi7C29Q/KCzQGZWYL3Lkac8YBFi3kZE6158yOXna67xSvpcgxxZTSGBUVNjL9iqLdlFKw1gqySCqKorDu/PysqqrS+6IorDHa6GwYqnVV932/si5G4whXZdGXxaosHGGM5nh7o5FhKsuxa2NZpHFI49CGZux7t1p5Qu+L9Xq9LouyLC8vLyf+pbObzcYIn2/WFuTp9TXHFMcQbZAxso1pDIlME1MagxFIY+AQZYxiI4ewKivvvUVaVzWyrFfrnC4wMxWtrmsRqVZ1Tm8xs0UU5jiOfYxj256MMd5bY5DIeG/L0ldVUZYlc1Sf+frFi+Wi08zazZlfEdHywxjj4XDw1d3ILS8OAHSEroagQQAAEiQWCZzGGIpCB7ahFg6BASSYxzMyoiVEAM4l15wiCApDDAmBiEgJTH03kfRJkSCD1lhrnMCUi5KJVIAiBpUmTdYYJDA8Sz+i+MKO49gPYiwS2aHrUhzJYEqRo01hjIQkwGFM1qYwdkOvKTdvqWuOzigsFquqsgQAWDjD3talX9flZlUZI72zWpgjnOLQD20DKaZxSIQAEIc+ODt2bd+c+ua0W9VFUQyGVqsaYtjUq8G7sizDOCaEqvTIabuqDUhVVSHY9rBPRClG1frjOMZhVNAsjKPqiRjjGIMNIYTgacIGbQxjikE4kaHCu6Io1qtaO7mUhR96YwhBOIZxJDSEMY3MHNOYOCBi4pA4AHI/xGHsrCNtCULGGovGTtO81OnT7hdFURhr29thckBFBIEFksTI4ayqnJuo90RkEAzpTNt5aiuBFn1PsERIQjTVqc2kCp28JcqT0PowAINoEMdcY43EE9ELCEyMkdBqW0JJd8UUq1W1398MA1lykePLl8+fPfumb07WWhTohw44CUPfdSCp7/tivUEgZAKDYBAtIRMCFXUpBCJCzsCICThwHFMgJGI2IpiSsmrTMDBi7X2hCbMYPZFDtADEbMg4MkzGG1s5XxYFApTOI0ticEADi4wxdkMQHMeRBJBFQaCUUopxHEfLbIu5z8esYqc8op0pWs4Za8k5UxSuKJwxOI798bgXkRCG0+kgkpxzXdeIJOX14FyBs7zu3bvnnMuN3BVIDiEkSNqmgpkjpzEOgGhSCmGk6AQhgRCR1irInNSO8wgVmL4Hes3BSmTMLUeFiDw5cjZASCmBEIIBEOFkyCFom1udLpuEGSGOYUpvqpipzUCZS9DAhBR1jpUhZ4hGjm3bOOfc2lmglAIzV3VhyZTecypK5yUBCFfekwiVliIQsamcqRyVlogpwr47qjWpIDZD40LRhM5Gj0FiCOKMQTGWqtIbFPUTi8IhInCsS98XrvR28HbfNSmOQ9cbkPZ0NAhj1yfXg0gIwRDEvovOchwlGZRUl5X1DgBWZaV4V0rJWKsMmKl6z081fLLg3dm6LMe+l5QkJWBGIo4xDMN6va6KIlXVuq7LskQRtSZXV1c8U/SWfgbPtUOaoVAV2rYtOQohjGm0wRLRMAycAAxp30lGQBSeo7tuHLJbwMxqR0QkpYnHIGBEeyjhxLAiN82L57kxIc+s/Nf8jAnDYWFYdi4gnh0UxGnqMk/Fx2KtbWOYqwFAUJxzZeXH3vTtgAJh6AyDiHAcE9vEYd+e1JqIpdvmKHZKmm+3WxIHAKb0TpKrS7+q/KpakdOxBJJ4GIa+7dq2TSHqJGIi6vseBfq266uub7vt7sxa64DqokxjqHwBcUJUHZmqKGWMdVkBy3q9TildXV356PuuM8b0fU/ODsNgp2EBAQ2N42jnsv0QQmknXWIBOIQhpaDuelWt1uu1tcTMZemHwRCBSAphIAJEGWNk5jHGUStHQhhCoJTkcNgfjwo7hpSs98Y5VxTGTsSZ3BzBOYfGTaOWaJpQGjl146Cd8HU7jZmwTtUQzjnryHvvFpJBRCkKAKSkpEMgUs5H0tAkwxz5z0kFveq3LpzWlHk0RGQMFsbtzrfO2q4d2qYZxr5tm8vLy8J5gxRjTCYgzMibQFF5mwwaKOui7Iqi8mjARKpWJZDEGIFkjEPbN8fmACSnLsQYtckwM5uydJbKwsVgS20lg7KqyzBWm3UtHNE7Y0ywZCxNcaMwpIREKcVx6Jv2JMCnw7Hv2hACGSsLpjHNpbBlWboYyroCgNVmrWH/OI40h+x2CGJcxcxlvT2dTlc3RyB/s28AIDKNEbrb02q1Cgn3l7cXFxeOXOQoUeIQnXOFLUYaY4wXDy8cuRDCdrs1YEII3nhIkIBSJAR3PDTMPAzdfn9zcXERx6GTVG+2MjImWJfbAquL9X0LZVn6EIIkWVUrRDye9mkcN9uVtRZiGnhQ/BtFxq7X4+4rV60LhWSY2RbGeEyJx9DHYTJMSipCcSmlFBNz4ElVGM2shsiqDl1REVFMqTucAlVVedEcT6ERD/XKbB/uHvfXg7ZZLk2lrJzB+dVmM+K+NnUf+nEcpRfpBQqAESRIc90cj8eyLE/HkzeeO17ZVYmlVCWmdP7w8fPnzwtr+2Hw5dpWa+6CKdd936Orbk79yHRz6vuRx36PiG3bMuCp78WYkdmWZZdSJHSFd5t1udkkZ8uqMiFMCeemsVUFYRBDCQGEj22jSZOuaQ3S0HSYJISAc/h5N2Y+zn1FhnkybGYZqRfJzGVZ9k2r2Vs9JXPa2uj4daU5aU55ImvFqFQPnEnPIlJVVRIka3xZWtvgTENSvM/YO/qaijnOlRdIJPAK6qzQVo5d3dx2c5ynTtr8u/otQWvbEaeBaZNkyJzmWNogRDydTlrylENifVTtlBLnOR5DGBGxaRrDUyl6Jtvpwr733nuKxWmBhT4hEQ3D2Latrp7W+mqXGzVJmfQlM9vv4VuPAOB4PK7Xa56Hn+PiyvpPr+fPnyuesdlsmqbRZ8a5QEv9DD9fWX+IiM1cU5yrK3WSD87UPfVTlDpQluX1yynOBgAtuxjn1r4Tqjo3q9cvs0WxWq2sNRJTURQ6r2cyn4YYJzNsjKWJ0ToaO3Fu1fRknybLosmNGefin9kzeKXXOs59KfJ2MnMcB/U9jTGwsCY48/loUR6h+4dziSnNw6SHYVhVNd5RJDHral0HY4wSL3SU1TAMt7e3h8MhC1kGl+p6pa0WiEiZgrodetazZKv+DyHs93sAUBaFJjsySBXmidTWWtWd4zhut1v1LlXstCOGLlqOR5aX9mgXEZsX+rW/KBdBYTsN9/u+10yuTFX3d71gNBZd1jfncEizR13XcYhI0rYnBer7vjfOonWKrpiZasozoVLXSIMgZwxLTCkZ1E4LpH1HjTFa0ZTmcW4y1xNrz52lWGR2cY6teC6ZEdT64LsDl3WGkkWAhUBwbvOSZqI2zx3BaU4x6iIohVhXTA+P0o/zx/SsawpztnRWowNd4cPhICJaDmmnvoPYdV293cC8VgoHwNyjTaVHve9hGFQH5HcPc0+UZeH8tyXDuckvsd77zPjVV9LRfjpjTHW1ChoAlGWpHeB00TVdrl+gjcCV3KXfrSQznpnNuhwAtbV2u90OIak18b5THluaioXuCMb6rIBARDFMW+jNHZiRKcEy89HzWYe863PUPjmfU+bZAUCYK3ZmC3LX3pRnPr32iuSYSKbmoXau74B5JvkwDGOc1OeuXqd5LHQuqVIDqvKRH5gnGroWSNyxjnEuOFgKDc+tYbNesfN44qXcp7lYK19Kvdav5nm4id4/x5g5wNT/vLMm+fQbo/FEoekPhU5Vpetp8963x5MaKp02MmUCY1QeM81FhTT3LyfnqqpCBGSdszQVz4QQDIhdtLtPcwNrmHsXTftt7gr6EjPzPAxFYNqVcdSFk0VzHDXnvCiYmGqNOFlrnfZznWJXRHNH/oa5sV82LrrTBJO9mMhjcNdOyRhjZNo5zd1rVWZWAzjP+lNend4khOCcJuFtTgfy3C9FN0KJFPoMqiG0Ql0Heqgd0fvoRti5GkX/DCHoOBi1JnrIeZ6xmi1svlJKMjdJu6sqyKdQH+v6+loJibphWSUqK1iV2J1iYM41HXqHNBfudX1vjGnblkMUSGpN9LRZEDc/Gcx14rPbcVcnp3Zp4uDoVEFE5miQNATnRXlIPgfZEZHF2BgiSmFkZtURPEMgxlmld8+nebImtChRRLxze0XEeZ/tnbWWQfTFlbqnWdlhvkIIZ2dn2QMzxmh5XF3XOukon2MzV6OovVftK3PtRdu25XqljrbqLS20nxgUMapG0fp9jUKza6/uke5F1kOvuRrqFUwSr1KsikgtgjFG08dT092UVppUC2G73Q5t570/Pz9XRVJVVZob0enTqzwpSeRwONiimOyi91VVEYH3frfbMZBxtqhXp3YwTTum1Pd90zS7tVWdJHfXJBkhBJ61S0pRUCOL6XDj3JRIdZK+eb7C3P0ijoP3PiUmojQ1HcTZfr0ii9mNhRytzBXPfd8r+1ZdnK7rxhicc23b7uq1mpu6rjUjqk+o1kTlO6WkZG4RITJd1yl7WUTU4dXeCpvNxs/Db/QnROSqUuZqU72P+oIqGRo0aZSU5sZAqvv1DKj7pSwQWNSH5tgkzv2ArK6jfoFMswrKhw8fwlxUkw2V7npVVTo3ULm16mEs9Zh6TERUluX9+/dPE/2f03gX+Kmia/suAaqiapq2bdu+71ertzUhl70zDWK7vinL0lszjJ2SNginwCFNA8kMLZhmbq7ZzxFEtvezDkAQGccxhGRGS0RhjLOqn0bRdF13GDrnXNd1+6Y7PzvTCNB7XxWlqmJNL4cU1+v1EIOyJPUrVGGoi31zc6ODSHku0VYBGsew3W7rut5utxpZXFxcFEVxdXWV5s4UvIha95eXuiPaYUAjDpU/FTsdLz2O47179/b7vaquzEFn5sPhcHZ2pqZN68r0Tauq6rrOuHlWgdJ2iqLQycc6DFglWqlgqnJVnYQQmtNJu1tmMVTvXeMxTfbrK6lS7brOe9/3XRqDdTSOvbrN+W31wyFy0zQ3Nzdt21pHmoFbVSUAjKFvh0FAh87BzMQBQMmrZheN93guPdU3pLmqVvVi6bXmjIhI0/kAAIQhhBAn7jFQym7dOE7BiIjc3t7m8Up6KDW8jzFGTkTUNE3qJwWem6npYzx48EBRYFUz6rLAXLeuAqe/osdGNZOup+o8dZ4ePHgQ5l68cZ5cDAC5wHNcXHrgMygSF1XjOcOl7qOfezHW69UkGffv31dloKwqnQ53Op3c3EEgu6x6qYlRpaSSoaumSiXOnYGzBs7xGLJ2s3Pqi1SVYZBqtWIwY2Im47xXVUEGRKTrOuDknAMEa21MLNo6dPKiyeAUieTQPC+3asGlH57twmwvplgsxghAQLi0SvBqEfo4D7l89vTp8+fPVRS8dTj3HgIASRPot1lvVNFuNhsFDNWcqc7Q06W6WXvT0lw5nXW7qnQVI/0KdZ5U293c3KjDVBRF0zQqUpqrWwRZd80gzs7OhmEAAKXo6jHW2DVHSfkvIqJICQDYw+GQdUOMUX2I1WqlkbS+FS1mEqiGVFWmd1eFqbMzcmwic/PUHHrBNOtr+snN/hYIhUzb9upFNk3z/Pnz0+lU1YUxJoTAMXjvfWHt3K0gNxrMb6KnUPd4Aj+c01orWAzZyEuQppKeOx9F95eISHL9hWQpd64YhsFaZ73Z7/fa4Kzv+/qswrn3DSICTc1usrerq6QGQnl4ODfcwXmSXlVVXdcvcY7lOivq85rOoJnbpnq6LEttP7pMOeUra0oRISLdMi3v47mTVnr1smaqlZ38jNVqtdvt1Jqo66CRSA611Rop5Awz0qJ+lqoN/XU1NKoS9RdVkvq+T2Pwg51/MIgIyF0PECIa2u7y8vLf/Jt/88GH73300Ue73U5HbcQYWURbDBIog4ZiTMATZqWbna1JmLuyyCKnrItujJEkOXSXKTyZZrLr4Eiei1Am5ZzGtm07gXVRnU6n4/G4Wa8z3pWD9sjT39lPscBs9aZ+RuphKFTIi64H49wTWGb31s11zN956ch4daQy2pYPxnKn1e3TrVRZpLkLkiJjS+c6XzST4uw777wzDMPpdNJTezweU0raCFd1XY6R9IB6Y40xmkaiuVuvymOMd31RclyRw3pIbK3VadAaaAGhLQo3RLQOne+HcRiGn/70pwJJw+O6LDabzXpTF9Z2fZNSAgSLitKICGcn3M4NilS4NfrnxUTI7IeOo8KgEyE0hJCSYKDT6UTGKXIwxklTGmN0Zv3hdn9ve6Yk4e1mQ3NngOnVAICnv69Wq+wHZOtARFp4znO7Ig21sp4zi87rNHdWyZ109GHUMGkXen14DQjUn8hb+xp4lUdNaBDq5kao2VZmScp4xiQZ+/1ew0WVI61N3W63asPS3NUlG+zT/qAF/GreNHAKIWw2mzQTtbNSUazxO/2M5maPhmziruvITfXKwzCsyqJpmqdPnx4Oh+169ejRIzLAzrGwSgaRVqui0FQ6oCKobl0WSs7e5QJGY+YUAiJqmoamXhcs2v+bTA59M4SlbuYXX3zRnZ90LGMWiO+UDA2ssi3QhQohXFxcmJmAqScKEeu6BsB8lowxKk/6pfq7dtEzou/7ertJM+qoyFV2bLNepMWVAxyY598cDgdcdLheOhkqQJNkKHJV1/XFxYVC48ysS5BxJ/W6dZJl1tVqXzebjZYqKUw+DENGQfQL3hSbqEZXk1Su1rasEou1FrmVuYOKHpFnz4ax695593GM0RnyhQKLE49Ax6+r36MPphpbcWt4NU6OMVaFn760LL2IMSaEJAgpJSSr4hXSBBioo0dEt7e3VjAHKYoXqeeu9i5yQm1HWUzGqyzL9Xo91eKGoH+audhTFTMzW+sUF1fzr3fTKmQNNV/TGcrmDXPDDG2DoT9RUZis27x9Oq9EZbEsy9VqNY7jbrc7Ho/L2ERVVEY+AMA+evSWUrCOx5N+U9O0ZVkCYN8P3hfjGGJMZ2fnxljnvLUWrVnvtm3btre3jEDOHppTFAaA0+mkvQa6cTB9h4bQGyosiUsEWJZkDKKhamUFReTs7Mza0+l0dBDXFLvmuBeOT18UVfXg/m63tbt1Iu73zYtw6OuqKMDBaW9xszu/J0KnY3NzeVMUxdnGZ8UIDMIQ+la14OnYKrKCWO33+/cfrW9ubiB2W39hvLuNfTM2ZMzt4bDZbZOkftivrGzON8MwPH/+xMHODfv7lRlP113XV6UfhiGBOGvruo4A5w/vX129XLkSkL2n1W6nEBMTjczNMChmcOr7vu83M3PAG2PLstpsuq6rNmvn3MipXK/6GMRQtVn3fc+EQTgmYeYhjNLTsTm149D3/Xq91m7PMUaVP60V0j81tFFfZLVZy+mkLvn/v6/3bJIjSa5F3UOkLtUKDUzPcLkkL814jU/9kGvv/XV+45rRlrML2apkylDvw8mITgC7Nw020w1UVWZFeLg4ftxdKHk8n7qhpxP3fV8ztW2rlHrZv1prj+dTPw4pka4+ffoENx7QPaqN67qGXw2DpGMVbAgBpud8PsPLhVGH6Um+xdKdmVle0SODIA/DgPAYjkJye4dhkNXb/FgsYtM0eUa5VnmmJKHXGxodSe99XddzOjRqRRyjoiiQgEbPb+dc0zSr1coNh+SoBjF3lRy7dhgGoebgFsX7czomUzhqWeaYxdBbWI10alO9NXMwxuz3exjs9BoYEfRqBozt4lwHAN6IMrCeQ5xND42SWsvB0BRFgTqxBIcnRxs57SlOmEAk7Jx7fX0FwpYCYxXbrgNUVZF4i1+TsldoR++917H3LD79+fkZTwOwDPfGTg9xoObSw1qv19hIKL1owsPhcIADAffHxXT51dVV3/fQdUVR7HY7tC96mcaiKFarVV3XIB3iMzMlyyLTQqKg1FrrvR3H8e7uGgYYYfZ2u63qsqyKtm3Lqthut9vd5vX1dZomIXmzXR+/HUEYGIZB+nn2kfUumfYsy+YOccZIKU+n0+Vy6ft+muz5fO7ayRh3OBy2m90yOMT/YV5xAKqqSu1fmPl0OuEAJDwjEXNk7DUL+BiOAjosYC8pzvGARwxY3cTmZib27Ma19EOVUuBYAarHpyGSQPgNuaQ4S6pt2xQ2q9VqJSI/JeU+UkzxVhAQ66KyTAOZx79CQpVSkHHkgRDm4AaJQ6Biui/P881mc7lc7KJH3RwuxqzparXabDaCJmOmrgtSWFVXQoiyLImE9976YIw1xrAPZZFrIc0wkvMiUF2UmVRuMuQ8+7Cqavbh9fV16ofL8QQFE0I4nU42+EwXCRcRSmZZJoVuhx4QcFEU1muovdQyEbF6cmAXCpIT1JhATHiRU+wxPYMfsfkHNiyBQLhE7OIN0UlAIpZ3s9n8/DlN00BnyNhLNB1O+BxTnNBjrYU1SKGTigMbIJopZlbPz8+AueBq4a74GtAZODoU+ROYfognNrGhrk093mNUgoHsQgjXnpNpwFeFJ5sWDm+EfTkcDmOmx8WkjxR54hBPcmKWxhjriVkWRfHy8lKW5c3NTdM0EMe2bRELDMNwPB5DbH+D+LPOQkpTndqLkgMRDdO42exYCimls6Hve0h/Xddq0f2NYjbAWutsxPWNid4f+dhEPOHQMmb/Zzp/bNZuI5kPi4+wH2siYtvhEElGHEcdwlu0cSwB7MUSZwoxXZAeDBIGVZHOM5CupSSln9+siVr0ulsGMKfTCauARDxsHjOvVg1kDfowHSN0d0CYE0JIQVSMTQY8n4lXyhePi3l9eZ6fxwFdfi6Xq6oQmZJlWdaVas+ncRzJeeeCMUaobLXarJrNx6+foRjl3B3b43nW67nxHAJyQDLjOD4/P6EPTlEUQTCTDCFIrYQQgWiapqGfEHlCUb++Xvb7/fF4dA5sMUYEV+TlTxgRCyEwCwHBhY6DmCCvyEmlbDBcgWQ+kmeWkNzlrCqKdQ8wbRSTojpOtFgizklhQLgpkr4SAAFkFsHU0pqAKfjmZ+BEitha0C/a50I4YE2stch/qjhnL4WF2HsEmThhx+MRygCIW5IeOI/b7fZwOCRFBxzFRuoXvL8wJ9NDcvSU5GyBQ0AT7Habrrt8/Nhh0qRSSkqhtT6fj1Lyet1IKUNwmBwuZRGmebABlLBgFUIwznbdMFkzDEPfjchYRizYRMEtiXgavRDqfD7/bE1wQe6hDtu2hXZE9gCf4xYpEnBF1U8MLvg3Qgg07AoxT3a5XMB/gB2nSBeFNUl+hoipLhUT4HgM7CzGjENzQKQoNnxCUDNLBhg6AUWFkU0Eh2CZgMAWCiGIAk6hcw5kcVoMfUmWj4jgwTwf9xzbsgJ1gMgnPCo5N3hvJmaDN89YsX3btmfXB2eLXOd5nuclrAnu8vDLL4+Pj9M0rVer6+trY8zhcDjs99M03d3dXV9d4ZxN4yiYldb3//RPr6+vLy8v0zSRFBSMc8565z3FJzRZWaCxxPF4REgvhGiaRindtROmwEuhkn2E7lRKpBAMerssy6ZpsFYUu+SaOIsEiAgIMdbatm2hoZe2X82z44X3Hh8VYpNrmC2sJ35QSsEd4TijCVfK1HCck13XdYqhOKZXYDHBGSPkTXA+oG8xoRM2AjKBe0NWpmkSYu7ci81DhcHlctlut0KI0+kEkzHGmqfBTiATzHF8lkHmoCfquobpgQgmccFNN6u8aZpghvbSPzw8dO356empqprNZlMVhXPu8fFxc7V+//69tfbLly8vLy8PDw/v3r1r2/bl5QVf/uHh4e7u7unp6fX11Tn37dN/39zcbDabw+Fw6btxMNZanWebzc5TMMZkukDNZ9L2q9VqHEzf94+PfznsL6vVBs4juBSwiUqhRQMlUAGBiTHmfD6DOQFuzhJFBSigYm4s0bu11p8+fcIyutjAGggktj8B5/CZYDqXOiDEZsUyjlSCzgBK6+OwnLDIimPxV6vIQKbFdCeAAdh15CPgnqS8idZ6vV4hRpBSwh/Gc3z79g0xFRzPkFI+TqXJNCnEDzNlZnZgE0BprTXWuEzjOHZdp0ReZXq1Wg3DcHV1xT6god3uOhNCDUOrL6wEV1V1e31ljLmcjn17CSHc3VxfLpdp6L99+bxer7UUkulwPHCcQiqE2O1242Cen59Pp5NSmVLq6urKGo/YBNG71gRJDWFWAwh6+fvBHclFOxwOiMYBwmIpiOj6+hqOFA7AEinA/sErAlAE3Y6dVnE8D3zP7XbbdV1ZlgnPgD+LdwFND7FefBxHlWmsfEJE4ITB+UhRsYhTiCI2wyppp/TPYIRDK9Z1Df8UQQozv7y8oL8boA58t77vr6+vsTRlWSZfmpm7/csPhsPEttlLu8gRIBF+FkdIRqZCnTdVVU1Dr5QqdDZNM6GwLOuqqqZp/PLlS1mWd3d3CE9g3aEzwTgBUIhDeTm9YvWLonh/d6tVnuf58Xw6n1uVaaWUNR6B0vl8nqYphNUwDKfTiVna2PkJO4E4Qioxjr2UUggahuH25oOK018pIktSSiBOKcmZ8tjJ058hy7ouiiLLstfXV/rpCiGgp5aNU1RUnCiF7An+BkoCPg0mDkNt444IyG3k8C6jYmg7nvt0aZ2Uj4oTTQFnQahTZg/jwdfrFdSgjixzfFsf++fhiRE0w1LMYV50b2kxDyX9nBzeggs8El4/v1EEpdThcFhVNZDN47lllh/eP7Bwnz9/Ph6PeZ6/e/cOrvg0TS8vL3Vdr9drMJJeX1+rqrq/v5fvb8/n85cvX4ZheHl5kUKfTqdxGokIA4KY5vZtCL6kTtl8aGDCUuLASSmFZFTlI2q1cQgjIkmoYXh8HIfQyjguGu4edAbWGa53ikKX0SIuFbOb+CdkoNADBw6Ai0Vc0BlA6uDrIFZA8gWcRRfbvSMvYxczjd6qhnwcEIroxceqL2gzFEgB64RJS56mi6NBEebN2bKIgXIkVvnvB5YmFMTF2skUm4RYDZDeOI6ToGDNSM5jVEry/u5u1/z+3el04uCG7lLXdVMVHfn2cgrO7Ha7VV3aaXh+fh66y83NDVoPYNWenp6cDd77oirX67XsuxACqMeIpPI8b/usruumaYxxxljv5/WdczREgWbsIQSCJzjGBs2QABgCCAH43OGt2GRKoHByGEWkpaULW4VwA/VBYtEeH6DIUnq+j6XnOIUW85RFJJL5BQEPW+NiLaRKvcMByEAO4Ff6SD1ND8rMp9MJahYUCqhBWBPgd6hfklIism+nIdljFykt6XZ4ZcK1hBDT0AsKCRjlmdrpur4riyyEAPAKhvnTp0+n4+Nut1uv15fLBZV9wJWht5xzMPBCiP1+fz6fiyLD8W2axlEYB8PM2+22KKr1dtP3/f71+Lx/TYP+RqNi2nMKkZFE9Fa8KjzjgErJzjmsGPwwEccAKqWQTU36QCmFaAJOZUqAASDQWh8OhxC5BCEmn2EL4AImi4z1hF8CNU9zU9S3HDhgBRvJ5W7RnstF6j9OC5yBNz8joRSzJonvQfgbQhiGAdbk5uZaxASPj0xlG8feQJxDCMD2UyyUdFSSgATvQFSTo2OMybVKQXzfUyaC4FlDeu2zTIYQzGStnZjcNJ4gDfDsALDCIAJSPJ/Pq9Xq5uaGmVEojONrrR3M9Pjt+Xw+G2evr29JvFFKTZyerdQ2nbOkCHFm5uOoxDQBkGAVR9sxcyqrQWyyXq9VrDeBzAFQgdTCmvg4Fy2hdsv/4spjZSLOKrCGqqo+f/6MD7Gxi4kQYhiGyZqu61CBgl0jIoBYEAJIEpR9FoeGMrOCm5qQLrgn9MaXnCvpkHiTUoJ7ruMkd+jPhOlCMpZqpp3m4ukQAgwNkJnz+ZyOHY7RTAGMbySiYRg4TJkIeUZVVZ2Oe8Xi/v5DCOH3v35yLty/+7BqNMAAiqwToONp0eEDXV1dVVV1OBy67kIxTYBeMdjC33//3XrnvVcyQ+Um7AKJIpLjQ1EUzjICbPC7IBnGYJCn6Pu+LNRy3ZL/n8eu7zj6iSV6Pp9hdPq+x4YheQHIWMcho8tAN+EikEI8yQ86A3rIOVevmiz2P4KQLUPCKRauwsYBTZkl49he6rruzQScjpT0zso8y33FzEVTT975EBzT6Kw3k6VwGYaCyIRw6jptTFEU7ThmVdXUdeF9VVUzrbks8zy/XNqpHfI87/u2NT7LsvP+6EcrpRSOcqFJUFFnq9Wqzdu+7y/eCu8zwWWuyQ2SWWm20yDrXAjenw7iy18eHn79l3/95/3x9PT8jeTdb7/9luf5x48fXw+HVRB1XTsORbMZx9Hx6Ina0ZrQEpEqarL2234vhLi5uZEkQdQUQkzWANm8uSnrurxcLqfD6+l0IumtoaKohv58PvWXS6dkbiZfbUoh1DAMTb06HNxqtR7HPsuKzWYj4njzp6enRGBu2xZn18cK1aIooHgQAyYfEKjGfr9nZtBCQyw96vs+LwvrnWKNHg95ngslV6vV8XhUmVbOCugbZ42zkzXcj203OD/PxVFKWRecJyE1C0UsWSgmkop0VmS5K5t6dnWTYwL1AgW12WweHx9FTHwkhGoZ3OJboQUpyI+I0WFQX19fIeDBxI5bRFiOuq4xRQBBGoQ3xCRQnueruoaKCkEkvASVnHBdv337Vha1zov7+/vnxxciur6+3u12qJJCNJQYyyhwQkYgnafz+QzSm5QSgeXDwwNO8zRN+/3+cDhAaT/vT103vL6+msnnec4sq6pC3pUWFP70A6JTIB/Jp1ZKgXuWzq6Mg+WSF4n81OFwQJiN2ZEpdqAIqMtFvTLFLlPWWsx0wtdM/lDbts1qk8gSFLtVwUe0i8JVF0ulL5fLLBkIoBNeC9GGUkrwBjNDGYYQ7u7udGQtg2lyOp32+z1C02EYoDM2mw1+uByOQEsRYuFSSmGMrF2U586Ewk21Xq+BjaZI2Bqz3jSyKIjIONt1nZlcWXvgTvAWb25udrvdbrcD7xK8EHyvhHBrrcu6QAHE6+srgkkAlNvtNoTQtu3xeERBFMWKSGT4KMgEPMN4mZnvqfq+z4tsGDpjzN3texErV4GawNNE75RlZmvpioFph2YbyLC8vLyEWKlFi7p+2DiOBb0mthnCTkGS4IEiUk1ggV8Q1lOkk8KWEKtAyjrqjOfn57quYWuBsXCkn8DmJfOPN7y8vAA8AMIN+dVaX11dAUuAXMN7wDeBmYQHY4xJwy5w+TiAE17CKAS4pbCsCSXUWgMAkVoppaXQLoTj8Vjm1W63y7IMpcbv3r27urpq2/bx8RHnGA4aJBsi2DTNZrNBU18k3OFlA3cCpt40jTHmcrkQZZB4wdp78n7urlEUhYzzOgAsEs1j2JKqgAcGbZc8zRCzGAj+U7CDu4C7b2PR4jI2SR4oUpsJCEDokeoQeVFm4hal7uH7axnZ0qK2AOc/hKDwxbDxqQNTURRYWQQpkHE4zwgXKYL2cOLwWUnAbWzvJ6WkRUMcyBDuCAHPFyV+MRJ7o6XgJAkRVJ53XVfmeZ7nLIXWWZFXgcU0TafD+f7+/vr6+nA4HA6Hz58/73a7oiiAIsPl3m63WZYBe358/gYN0TQNMx+PR4jU+XyGBwctgqjSWusPc9wvFTOTlOBJsPs7V+JDwN1O1gpaMNWn06KFRsL0lmkw9X12HqcIsY8xRseS5XRi+fsr4Uaw6Qm1SkGWjRc+5O3X1KdrtVohowY1ldL/S/SDF4PIoXVhayDvMMxwpwGvlmW5Wq1mV/R4gm8MRadjdyXgIghnoHtwwqz0oPA0tc63NRzoQrFUDD5APw6Xy8Ua36w36/VaywzDmm5ubrbb7fF4PBwOUspff/2ViE6nk13UuCKYhJ5A6gFkAKRCkBaAlACt2m63354PeGBmds5O09jl3ThOtOCjmwVde7u5Th5VwgOFENBMfjFyPKGl2FdgSFLK1WoF3exjLnSpM1JMYRf8DB+7VKRcDPNbzVyISOYPeFq6KLWdEQLISghBff36NWX34QHAScTjYslC7DuAJz6dTqiY+8GphIP9gzUBLuK9x2CRYRgOhwPYb5AYfDfQFYUQT1N7uVz2eynFKmyqcRzN1Mq6WK1riFHXddY6KWbq12a1fXx8RG+yu7u7FITjLgjwzucz0DlEd8wMX897j4dPhymdWthyqDcssVZZCDyOGAjSvn//HroBdqdpGq0lUhgJoobfhl5vq9VKRn6XiIVr0FvAHqBoUf5jI2tr6WdA9W42G9gmLC8+sGmajx8/JjyDIq+n67qirHnRciLpp7/nZyQ4VW02GxBeXKzrSrAVrE5KBAM7w3TFlAlE6AXMwMVSREBsfjHAV8ZEH9AVBO4hBBxcvHJm+dbzOF2EZMEHQwQtejgcjDGewmaz3Ww2nvh4PA7diFzux48fP3/+/O7du7u7u6qq/vM//xOoNnJOiFmcc6vVCi1U9/s9FpqI0E3g8fERKKr4fuI3RRJrVXGWFVplzgWYy3EctZZt2+pMjWM/TVNZNEk7ogeGi91msB8ucgHh5M5tKpXCXUDpgG4AfipijV0Wuw0kYkeIHS4S8rtErorYs2uKXaB8ZBQrpZDN+dmaqDxjZo/OKnnsMgZN1XXd6+sr0E8IuI9jUfC4q9UqscSAVUPQ4HNxJBsicHA6g9FFLgY8UHwmkqJY3xCLzLTWf/7zn2+uN7tt1ff9dlPWlR7bU9dftuv11dVVVuTMwhiTl9Uvv/zy8vTqnKuqqmkaPDkRffjw4Z//+Z9fXl4A29/c3HRd9/Ly0nXdOA5N00A4vn79+vT0BDWZMuCr1Qp5469fv379+lVnH3AwDofDMEwhsBTK+wCFBAgYpFGI8t3d3devX/PIsEdSPswlT3OXEYqTIhMzBucV3EekiGHOQJoMMUM2DIPK3gg4CW7ADlr7BhCAXmmtPZ1O6CkIuczjYLnEBk0ZNajGPLYgU/v93jkHCUKoI4RYr9e4KwxNShGFEI7H4xT7+iQTnhC69DUSE8z0A7TFtKi7TY8In5fnOj5SSr2M/TA4fCtjzDBwkYssy3RWwu5Ya70P3o2BRZZl79+/f35+btsW2gJPezgc7u/vUYN5PB4BmePQjOMIxZsgeSz0NE273Q76D87HNE1FUdjAOPpdO1rriYRWmTGOYkIrxOYtWJDX19fD4YDkEcw/viDmX+JrYtHwdpTqQD3j7VDPIGmnJU3QcBFH4aUTD0MJNTPEdgmQS+cczGtSP3ieoiig81Ikgn+dpsnzHAooWiR5fcx5WmtBdIODiUeBGD48PODc46uCGQDZMot2iFAbWZbJQIm3yIt59smDS0gtZLlpGnJuu92ivLYsxKrJV2XW9RcL7cLknDeTk3pe9BQKuliiLqVECxRrLYABkPGxQ/B4rq6unHOfP39GHP7bb7+FEEAGA+g0P54doOSHHp88E7sTnqG1HIYhL7JpGo0xV7tbUOBwQrIsw7Pt9/v0fbHaYAYB0YILBQ2EWM+lEuToH+DX0+kET9ZHLjQgEJTbQBnAQEC2ADwmJwG4ETb0b144G845hSANRxzLMcSZUDCWWHroEgRLyT1OlQGwO7To7adj6yd2PrFXUkolOcnm+1x8iH1S8XYiGscxz5irPM9zG31hKSl4k2VZXdd9O8DWPj4+6ng5556envLInceNYBCP54OKzHpgEmVZAt8DqAVjh/cOwzC5MRlva60Qc0P/zWYDtx2fU1WVlJzwDHgScKGG4S1zpCO7H8qyqqqnpyfAEksFnNI9SwQCv0KdZ7FXp4l9jhK9gyNLFMZos63wZRFeqTi6O6W6OPaqwzXFqgj1g0XA58KXYWZ4iElsjTGIA8fYUYljnhYLhPslg+K97y8XvNLEktyEq5o4ChBBEFak82NKIXrvnTfGyHEcdTY70nmeEzGFmTCAT0B92263u76+RgDFiz5o1lpsfFVVu90OOvyvf/0rHAXovP/6r/9awm5JdZvuDSFQkeTNsQIlXSkahGYF5pvwjBSMJGIHlHEKQEIEuW2kTahYY7gEx3AyYfeTg5weIIFGRITIBd6JiGxQEeuJwObER2HH3zzQMHeXmBVDSreknUb3IAgRvgO+M9p5IfiGwALIQwiXHiWZ8J+1RVjA3suVnW0Kz0Av/lKruYPPEPsfDsMQAo2D8cRSyiIrm6Z5//49DDaUHJ4tRKrSNE2n0wnAV7OuoQASYdN7D2IzXGbYFPC7yrK89Lyswk2LOMbWbC7WAsFs77YFwoQQE6Swbrg7nPSlkogZojcfUMaKoPDTheAfWg2yAlohOj9ByxaxWRu8TsQyeE7cAomwJVi1/FUl2nrathBTO4j7k11AzInIFt6Aj3wkoEbMjFRyyrqJZe/jOH1DxIZlkAAo52Q+8YHjOO6Htsr16XR6fHwcm/z6qgmhmKbJhxkw7rouBPKOAovj8diKzjm33W6h3p+fn/M8r6oq5SpT5MzMfd/vj69IlCA0gCgkDjPFrgpooYSDjo7x0+iSZPhYOlUUxWazMnMnwrkqGJdzLvVzhapLyhXKGMDa09MTJCMFI2Ns5e4iOTIs2kbc3FxDICBhACchqUIIkJXw/EBlNtsrHNpkp7AdS99iiY6PsbWVgmJAbJLcQIge4vLkluILh5gUheAnCcB+4CE4FnoLIY7HYxLeZO1mrCKEWDskEb8RkXYDwCVIJGJIzf542isM3QyeiDOtdV5orSWrT58+ffv27d27dygSadsW8BqMMTwhZNG6rru7v0XRcDINwOB///33p6cncN91ZFF77yE0du6Zr4iEs14t+pNoLS+XSyA/jr33vqklolwoISyO1jodXOxosjtpS1Sk1sISWWv/Zmzy/Pzc9z1w0qRmUtYTEUBCIHnRjc5E1v4YK1PShoZYmjVN0/V2M/sZy2z4AgZ2CdJHrjLE4utNszIsRKBcaUnMPpRZ7nN7uVxEoGBdP7VN0ygWl+Pp6urqarURSjZNI4mdc2WW29J676VS/TCotg2CTfDnvuvHQSopsushhMfDtHun/+Hdg9fy8Xi62tab66tvnz5Nw/jLu/ub3fXYT5dT65xR91f3D7+M4/j4+rJer+/v76dp+vjxY57nuXd1Xd9sN/BpnHO6LDZ6d3d/3/WXr1+/dO1FlX4Y++fL5bh/CXawpj8e+qvdzYf7d+Nonl9fni+2WG0Of/4dqEl/6XGC180GVhDxxWa9nabKWquqohbklYAoWEun82lWvVr2zogik8yBuS43Qoj2eF5hnG7TTES6rjtrz8NgjOmdG8cRRMa6rvdI7q8aVVUAUerV6nK51LudtXYiGoeBpOzA8pXyBAd5HL1zMsuKPIfTQyEUeW6N0UpZpfIsc85ZY5SUVVmeTodZUpNYQaagoGCqocGAamy3W9hReHwJE4tdWc7b7TaPHePguEDOuraTTiZferTGxLYZ2C1rPQIHhNrT6bJeN0WZAQojS1JMznTspiLTgLMOLHebq6ZeT4N57s/rprra3rZNdTweP/7lv8uyvLu5ArbWt+fgTF3XSlB7vpxOp1acyyrP80xKDuwPh7O1k+ew2+02m82270+n0zAMxjiWQqscZdAICKuqEkJJ4kSID7F3BYKaaZpWYTP0PbqVZkqVRUHeZ1l2uVxgtuFnETPsLjkfrPPGOjQyNzYoR86v6ybLskGqpqyCdVVeoNEZ4MiUWks/86JKA2qAI3oLhyH5c8vo0pi36ej4m6CZiZho5qm6WF4nI+kZHnUCvBN8aYYRf4nIuygKdMIA3cF9T0dOjzs/sZgRIWIGe6BsasQ+WZEHRh/M7P793f3d9W5b5nlelkIrnefy5duLXK90VljnTpeLktluc7XaNF03B1ZN09hIPEG+A74kDDBUcVmWthsPh05rWVR5lqt+8M5ZzPEjwUopKTQJ7zxJZinVdruapqlpGqTsh2EqdGaMOUxH8sF7X9cNsHMoc1ICKt3FfIeNNX9iQSNN+BjHxn4J/sLXmSO7vpdSphp/pZTMtLfu5z9SSqbo4xPR91Nqw0/9uOq6Bo0Zzr5Ivd6yGX1QKWqimFWiWFdOsa2FtRbrMo7j7fsPCfMwsaCljwNa3fede1VqhSM4CBZBCCFYCGYehqEbB9GrYRisd4W1SIH2nhUzslOXyymTBSbCX9/emL4fzXS12pCn/eHFWvvh/pfdZt113dfPJ/Rj2a5XwzCcj4eh00qpqsj7vj8d9nmeb7fbd7c3bMXLy9PhcGi7qSiyZrMSnez7fn88tG3bdb1U2Xq1y/NyGIbz+QK3//r25vX19fnxqW17udmEELbbnTNzVSrwA/huu+srZ6wScgrknbNspmH01mmtBTETB6JAHGj+XwoBBPaSGcOqhRDQ9kpKrZRWGJCoiTmwUN//CSzIv3GwOQoBE03GGGtVaowp0GVXXi4XFqIfBh+Cc64fBiHlNE19G2dJAelCjAeDgkMGq6EX7f2gIYJgR2Fytp/mtqOeCUltXeSOQghBaMXOspMkhfEuAa4+BKSrg+BcSWut9U5qpVgXRTFMo9TqutlcXW/rurS2G8d+mDhT2mq62qzO1gbng+AinydFvB5equst0BtALNvtFmL38vKy2WzghS216KZubuWO2H59+tYOl7LKfbD92BnjJmMDiaKoqtWaSZh26EeTl66u63/4h39Yr9fTNDkXmqYZuPPeuxA7WDhr3JykBTMtYQnQvjiXSXfCqUSU6Jyhv3WleD4dUVhntyguTx/IEZb94e9/UBs/x8DpvyESherVavYz0rOmI+5jK0x4ywmzwiOCkQUwB5j3zc0NkkAiNn73sahpppJj1qEQDMKZYGZebzejmcqyDEzTNA3T6Jwry/J//j//x6+//pIX6nLeU5i0ngPu/etRMLHgw+GwaVar9doZ+7x/3Ur+8OHDh/v3YFCej6fVavXh/r2djGThrct1tqobxPF/efy9WZVXV1fVqtZHeXzeX7q5Kmm12dbNimUupZ5Gdzyd2m5gkQXBOBKr1erx8bFteyI6d23lgnPO2JnUiXgqhAANEZwnH4gCBxLEgtjjV8FEJIiJWbAQLN5ezIGIyIfgvLfOe8+B7GTwxxnrrQtCokfl8o93zjtHi7qN2YiDzqMkK0lS+OBD8OTdaI2wSuWZyjMVvMoy773yTuWZZ7pcZmhPJcMjYhlSwtFCLOnHCxC7vnv3DoxRIEhLnAe6x/u3Brzee1RICSGC4EDsmQJToND3fdt36Iz/ejhgfW9vb29ursqq4OCVksFLa03vLJNTMpRaSeZAfrQ2Q/NoKeD/qkiix/NgIyGgcEFQFmWtfX19dcGXZV7V9Q71HcELpYXSQigpcudpmIZx8llWbreVyWrvPcUaEGYmwcMwPDz8Zsep6zqVze07gcVt1xsXh3wt/Yx0gun7phJLlZD+KcT6ek4OWVkyc5Zll6794ej/KBDfe3iB0esweApMhInJnkI/9CR4GAcXvPe+Hwcs5pvOULEfKgJUxLsIjhH0AxhomgbpldFMo5mGaZTjXGubDXk39MZZ652nwFKoTGfBk+CqqY/HowxeW2OMsd5pM43jSIIzMznnJFpljKOncHt7+2//9m95kTEHIp9lmZI6+IG8ybL8ercd+7OScl1XztrD4ZJl2c3NTd/3f/7zn//6178+PDxcXV0BCAdZBhsG4QDEtNvt+qlt23YYuvV2s73ajeN46TszuW/fHsfJeyekVJ5UUdVlWa9Wm/3ggH+c2stoTVmW4JnC4zbDKIS03qHveEK7f3C2fibnpWv5Glp0N8CpA+SI/irAP4T6cbr2Eq1aigX+m/y/ZL9wu8S9ymKzZaRyhtHMAMu/bGsUuBljUhNx4CT7/R5IC4gtRVEcj8e8LJAkvLq6ApP9w4cPIPX4mD3K47Q9JDb7vv/tt98eHx+VUh8efvn48WNZlmVdt20bmEwsCd/tdv/r//t/WRMHynJV5Mr7yYytklSXuZIkmTIlcp3VZSlZnE6nw+Hwr//yPyAK3vvVarXdboHYJDwYKNAUezM222q/3+/3+0C+Xq3quibiyZrXl1PXDZduDF6WZd2stnW1yrLipR/btj0fT6iyf305PH79+vr6ejm1MzHYhSFOeMnz3A5TcttxxzzOi3BxRpqIjd5CCOM4d3Bbhi0Uh8ssNTqM/qVrsXHLXImIA+SWF4Rj5LdewUsdk+LeEJuywxXro7ui0MllmRo2sWUnWH1AcxPW2axXc4Vk8Na7aZpOl5lLhzSP1jrjnASzFCS4aRoXPEshtQpEwzD04yi1Hk9HyGLZ1EopkkJoNQzDumgIURJLljrIQgrPJMdhFBwss8+D1vmqKre766Ks237YbHfbq+unp6evj0/74+kPf/jDb3/4xy9fvkA+WCqlVGDhAhljusGW9SYrmvPleDwevz2+aK2LovIkdF5uslrJIs8LrUoi0Q8T9ts1NUuhtdYqR0eGv/7l07u7OyHE509fTpeLFCKEcDwem2qGUH3sXsLMgd+KyEVkkOPXul697VkIaf4jSxG89w6uBHIDCGgoZTeS8whD87NrSSEM7q2sfml6JjsnOpiZKLi5aYLzHPt0QQFA4ST3IoQAjxqwN2AriCHoFNNizCSeDHW63ntoKsgjEB4AIfCtPVFZV5vd9uHhwVFASZJU6suXL4fDQURCTZjnwgshFJG3hou8cd5YM/WDYdELoaSUQcjJuHGydV1f39zVzZqZA4mv357yonKeyDkhtc5yYgkf7vOnp9VqlZd5CNk0cddNKiMSvigrFZhZKl1olYcgp9FO09RHTiUzCyXhjXrvx8H84x//GLw/Hk5d111fX1dVdTmdnp+fJ2tGM6XIgpnZM0uhlEzKPB16+j5ZmqzAMI5vXsgi8TWYiaQgKfCxztpZCsxEi4gm3d3KH10Q/CClFPTWDB9+ggvkaTaFKqVtODYYhDOIvCUAoqTrnHMU4TZgIwjo89hKxi0oF5C2yVoXgifabrd1XTfr1c3d7d37+3kKXByJ1XXd/njYHw/NqiYK3rElL4TgIEPgyfuqyoSQHIS10zi4E3dCCG9NlhVPL4fjudtut7fvPiilvn379t9/+e9ff/11mkwIIbCzfrLWDpOzjibDx9OgeyOEqFfbsl6HEDyFoqgm44xxtp8m4QNL54Lz/tx1zBxiEtEaL1kope5/+YBO7Tc3N2VZPjw8oNntf/zHf5xOJzSwy7KsripUmjw+PgpmTwQRSMhjngvEGIFneAp6QLGATPD384K9Ga133oYQ+0VBetzwFrWm7ScmHOCf/Y+UK4U6wJlXyo5+/lWBRzRNE0WAK8SUJhwfWKCUCUOwim47SCTCuMDQQjiQRQSszlJO07S92oGKXJalyrPb29vPnz8771HBkJcFy7k9KrSOEpIpUGBmYpLEvr0YpUlJnWkplSChKXjnxWQCEQ1jf74MT88HpKOKcvX12wsRaa3xAlgWKeXN7a/OG+ccc8glB54ThIHQxGgyzgom5jkexNqFEKxzc7DG7L1HrR4R/fLrgzMWHjoz/5//9//19PT0+PjonFuv15vNBnoefbfgG42R1G6cLaRgJrEYyoRoQOdZwhiMmRK47OPWpiONJ1SxQ4b4Xpic+htiwRFBdxTm8VDBOwqeiTlKBkr/3NwFVia5g9+UtEjim3TDIJSarKVh0Fr34+hCyLJME1VNgxtvNhsUGazX634cQJJAopXk3PalWa2ccy54OChFUeRVKbSiwFpleZZ577z3WkghSVBwZsx0nmeKyFNwTMFRcMSZzojITVM/DG0/lMM4+9iTkVJKzZ7YOWecZ2YlVQhCq0qrYMzY9Z1zTmmh83yajHGBpc6VZJIukBncZEweu3EnD5GZgwjW2lN70UJut1uKLLppmm4/3OsiX++2QgjU8kCe7n/50HXd58+ff//99+PxiE+T0xSYXYTGsdqAzMbLhRdQVbIOIlPIvQTJQikZCX8/K4z5h+B/+Pv069IpiQ4sufh6BWQ+3TvFQnBL1ffkTe/92HXFYkwcaCPX19egVGERQc4uiqJpGpYC5BQiMn7OcVzaVikllBTO4aNsmEs0nXNyphWy94aIldRKSs7LIlNCBDMNxhomT+SDJ6kz773KuNZZYiFdum6e3gCgQmkS0ntPQnZnq5RnScZY0GOFYiYdvCMKQijBSigpPHk3r5pzDo65i2N20dRAKcVKhzCvslBSB90Pg1Rqs93COluwk0KQQpRVdXN767zfbLeIrtH+ywU/jXOHk7QXJnadllIqqdKh9/TdvDARC3wSBhoWKRIYlPTr0gPFhb0OcT7Q8u0K5h8aFS4kluNyuaAXPdgVcDiyLCvLfLPZwPogbNlut/f3903T7HY7H5NAEH+ttT3bOU5jUp6llGVVgT8htYK2yMqitAZ6xaRZV56dc5IYuhCi65ybJmuMVZoypbMsG8Yxz/McNDjnAlgd6zXM8GQMIxYgss4Za6vydrLTNJpAVOSlkOyCHcdRaM0oQyKrmJhnP7F3zsV2YQgBYH85tKCY9JfOxpY3lOe9necOOef6cYAh1lq3fbder3/59eH23R1Cv6enp69fv57PbWAapnGyhomZhJBCCK6LfIlYvO2ZFGLBtPLeG4+WUp7+DqSRQtalfKQ8n8JHeW+snQwlEVHe++fnZ/iS0zTlZTGaSQiR12W1bpxzE/l8VfsQWjNmTfV6PuZN9esf/4CES1WVQEFWm1WQQQpZ5YW1VsoZVJbahjAxs2L27J1xUzfWZWmMD2Esy1IHGcLkhbuudJULR86L4Ml7tkKRyIQNpuvOZVl644QgkedVWQoBVSp57ENg51iITAgkLUMIoa5r0HXH0Qmk8kTmvT/2X3wke8qsDMxuCpOl4Ky1VrDURR6Yh3H0nqSUwgdrrBsdWZLEkoJmykRoSllVmoiUH4bRZpnQGqtvyU80EXuvKFCwdrB967fb7difnOurqtIqZ3J3767f3d+M7fSnP/1p7DsOKEFQaIzvXLBxxhvGa3gXnHPkpuADXDDFitWsEzz/mE8hIgrEKoRA3lNAdRkRkySiItOzAQkcfCCvOGgOxvk4+YZ5rhfFeqVe1xLToSN7A5GbcfYPf/jD3d3d3d0d2OTMb/38ooS/xTIAqhHopmhexPaXKVrz3o+xcc/6dp6cJYQQQmdZJoiJ55Gc6UDwgq0kYm8n8RMZWkaKZTpDOjbqnkOtWJcAkFuwFFq5uavEzKRKUYAPVoQ37h3gNRt5WRSRhmS2A8dSlECANYEiZHpu6ecXvDiozxB4smYcRynRt0kys49wB5b3b0gAvZ11+t6l8PTdgItlYPy/v5SIzc8vl4txM3fZeqcwTDCmAPI8z4o8z/M//vGPm80GPB0istak6WJYBaXfmvuP41hlmmItZLJqgN6SWCQ4OYQATx6ZF/D0BbH00nsfKEAw0uf8YDvTDlFEgZarEGJ/kuXf8Lyds0VnEp4pPhjg7bmPlLXWByUCUZw2Mgy9tVYLqXUmIntBKJUcdkw8kVJSmIdeIXHoC0r4kJSyqqqbmxv84Fw4Xc4gghARc5gnFs9OQ1BSLe3Cz97Djx7o9xfPvJy3F2DNlks3S4b5vhc9z2MpDRG5mHyvmnq1WoH1VK0aGefOCSGIAqCtLNPJLmIVUmYfthbHAjYb1FkXRwPBwYb9xkkCP5FIWms5zEZxKRnJL4YTk+K95Cfp2KrmhytSFOYgPL0CaWHBEr6w957orf9kkntJjOUqi8Jao5Sqi1JIQsGO1toTJYX05vnTPIsEnj9yKLAIPrj1epXlORIZxphz2w3D8Kc//Qnj3Ah+tBAzGsbyh8OAHxKta6lTmZneVEl6cQpcfw5YFpKBPVCZDjwX1VjvhJGbzUZgjutuCwQdEUc3DjAf2BJsOTOH4BaPRMnNlmKeUrxU9bQYouzjqNWl/veLFqWCODnPi/iKQ6AQgorvTaoyBVnL24XIfoMPr+YBM9LN+imM06iUkiIYPz+SEIqZp3HuZ5feyMzWuhRsN2UFgISIsizrxzFtQ1IMgQVhHlmeGWOchSFjZkYot7vO8jxnEuM4ZkXuXPj8+fOlG1AVLZSUMoO0WXqb5byUA7EYUP2z+5l+hs7g2Kp1qTN+OEIqK/LkAXjv+3EAeHd3f18UxWa3xST7mW/hLG6AUEVrjQ9VSsFXYuZALu2f1rrKM2aGqzHfMg6R99+PhkeOl+Iks6gIUPugmBnsuvS0HF0NEX0FirDP0qeBWkryNI5zO/BZbhZ9UtMrxZyYmMmOSacSJ7oNt21bFDlHVEDGK2W5tNaSOEmGMSbLtGIKIThrQghM8/YURZGXRVqlEIKUfH19LbMTlBkzE829l5c7vfxvKlr84fL+Oz8jCRMR/aAz4oJEyQCwjTB9mEZ0dLy7v7+7u1OZRtcfa+1gJuec8goeFt7snENPXewiPiROzrbp3KjY6tDGURUgmfo5nUNL+YA2Uho0ojhWKMOKO8SuYbGd1owyNuVfLty0cAx/uJKNoJigEkKwFERkjfMR93RuLnhPIkjsl4oaEtO2LYu56bOJ7dyT3obcOB9rVZyLey9ZCOdcUWRZpkQc+uTDDEvc3d0VdZXn+fFw7oZ5gESea7J/CwWPaPffcz6+2/7v/Yy/dymky0kwOLpFUVzf3v77v/+7UPPJ66cZ1EI7Csg1v5XvOTgQ3tvZMyeM1J51F5AP+JUmNqBESjZB9ypegNvzPM+UTrsIneRBivrpa/vYmCxheS6WRKRNcotstYiEGngSNIuUEhS899bMg3+898bYYRiKvIrGKyw9NYyUCyGM46S0SFW4MmYlcQszzwYP8zzbaRzHkUmC0em9L6pKSunJwxxrrQNT8LReN7qY+5n2Y2fTrBqa25nTT6uRfk1LQUTEePGbdqFFkPImLj9bE+tdVuRQFR8+fPjw8AAga9vsQgiDmca+DyHkec5CtF3nnUPBTAxNPc1NXudR0oEcM6PCwMR2jtg/jqWzSTiwT/C8jDFoIEmRwKJ1xsySBTOXZUmZn6YBmVvvvRDsnEPmc7kowCjHaO8hfIuv7Oc8iLVELGe2RLDOwmcKYmYqhBBQDBHmSiRJPNdeOOd0nqO8sdluJzN3MMMTLEKneXCkUDqEYO2Mo1CYZ4OUZalICCGGaURKMs/LwPNUOZKiaSpjrmQc2/n7779nQssFDT1JvI1zSH7YcjmTid4sJoU5Wg5hFpflAqb3/v8Wqh85/oNe3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=179x217>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "from PIL import Image\n",
    "import glob\n",
    "from pathlib import Path\n",
    "\n",
    "image_path = \"/kaggle/input/cat-and-dog\"\n",
    "train_dir = f\"{image_path}/training_set/training_set\"\n",
    "test_dir = f\"{image_path}/test_set/test_set\"\n",
    "\n",
    "# Set seed\n",
    "random.seed(42) # <- try changing this and see what happens\n",
    "\n",
    "# 1. Get all image paths (* means \"any combination\")\n",
    "image_path_list= glob.glob(f\"{image_path}/*/*/*/*.jpg\")\n",
    "\n",
    "# 2. Get random image path\n",
    "random_image_path = random.choice(image_path_list)\n",
    "\n",
    "# 3. Get image class from path name (the image class is the name of the directory where the image is stored)\n",
    "image_class = Path(random_image_path).parent.stem\n",
    "\n",
    "# 4. Open image\n",
    "img = Image.open(random_image_path)\n",
    "\n",
    "# 5. Print metadata\n",
    "print(f\"Random image path: {random_image_path}\")\n",
    "print(f\"Image class: {image_class}\")\n",
    "print(f\"Image height: {img.height}\") \n",
    "print(f\"Image width: {img.width}\")\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e51644a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:38:04.314933Z",
     "iopub.status.busy": "2023-01-23T08:38:04.314487Z",
     "iopub.status.idle": "2023-01-23T08:38:04.325640Z",
     "shell.execute_reply": "2023-01-23T08:38:04.324817Z"
    },
    "id": "RY-UzszvKrKW",
    "papermill": {
     "duration": 0.024181,
     "end_time": "2023-01-23T08:38:04.328129",
     "exception": false,
     "start_time": "2023-01-23T08:38:04.303948",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "IMAGE_WIDTH=hyper_params['image_size']\n",
    "IMAGE_HEIGHT=hyper_params['image_size']\n",
    "IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)\n",
    "\n",
    "# Create training transform with TrivialAugment\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.Resize(IMAGE_SIZE),\n",
    "    transforms.TrivialAugmentWide(num_magnitude_bins=31),\n",
    "    transforms.ToTensor(),\n",
    "     transforms.Normalize(\n",
    "         [0.485, 0.456,0.406],  \n",
    "         [0.229, 0.224, 0.225])  \n",
    "])\n",
    "\n",
    "# Create testing transform (no data augmentation)\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.Resize(IMAGE_SIZE),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(\n",
    "         [0.485, 0.456,0.406],  \n",
    "         [0.229, 0.224, 0.225])  \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c15171c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:38:04.356491Z",
     "iopub.status.busy": "2023-01-23T08:38:04.356072Z",
     "iopub.status.idle": "2023-01-23T08:38:06.527110Z",
     "shell.execute_reply": "2023-01-23T08:38:06.526072Z"
    },
    "id": "O_rQ4PllMe04",
    "outputId": "2a3ffefc-d582-4f28-b33d-2a0c2958db8d",
    "papermill": {
     "duration": 2.189,
     "end_time": "2023-01-23T08:38:06.530274",
     "exception": false,
     "start_time": "2023-01-23T08:38:04.341274",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Turn image folders into Datasets\n",
    "train_data= datasets.ImageFolder(train_dir, transform=train_transform)\n",
    "test_data = datasets.ImageFolder(test_dir, transform=test_transform)\n",
    "\n",
    "NUM_WORKERS = os.cpu_count()\n",
    "torch.manual_seed(42)\n",
    "\n",
    "train_dataloader = DataLoader(train_data,\n",
    "                              batch_size=hyper_params['batch_size'], \n",
    "                              shuffle=True,\n",
    "                              num_workers=NUM_WORKERS)\n",
    "\n",
    "test_dataloader = DataLoader(test_data, \n",
    "                             batch_size=hyper_params['batch_size'], \n",
    "                             shuffle=False, \n",
    "                             num_workers=NUM_WORKERS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f2da4d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:38:06.551339Z",
     "iopub.status.busy": "2023-01-23T08:38:06.550859Z",
     "iopub.status.idle": "2023-01-23T08:38:10.534816Z",
     "shell.execute_reply": "2023-01-23T08:38:10.533736Z"
    },
    "id": "j99yPcDCtnPG",
    "outputId": "45bba9bf-3063-4ab7-c820-8b95a351b6e8",
    "papermill": {
     "duration": 3.998051,
     "end_time": "2023-01-23T08:38:10.538135",
     "exception": false,
     "start_time": "2023-01-23T08:38:06.540084",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class CNNImageClassifier(nn.Module):\n",
    "\n",
    "    def __init__(self, input_shape: int, hidden_units: int, output_shape: int) -> None:\n",
    "        super().__init__()\n",
    "        self.conv_layer_1 = nn.Sequential(\n",
    "          nn.Conv2d(input_shape, hidden_units, kernel_size=3, padding=1),\n",
    "          nn.ReLU(),\n",
    "          nn.BatchNorm2d(hidden_units),\n",
    "          nn.MaxPool2d(2)\n",
    "          ) \n",
    "        self.conv_layer_2 = nn.Sequential(\n",
    "          nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=1),\n",
    "          nn.ReLU(),\n",
    "          nn.BatchNorm2d(hidden_units),\n",
    "          nn.MaxPool2d(2)\n",
    "          ) \n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(in_features=hidden_units*4*4, out_features=output_shape)\n",
    "        )\n",
    "\n",
    "    def forward(self, x: torch.Tensor):\n",
    "        x = self.conv_layer_1(x)\n",
    "        #print(x.shape)\n",
    "        x = self.conv_layer_2(x)\n",
    "        #print(x.shape)\n",
    "        x = self.conv_layer_2(x)\n",
    "        #print(x.shape)\n",
    "        x = self.conv_layer_2(x)\n",
    "        #print(x.shape)\n",
    "        x = self.conv_layer_2(x)\n",
    "        #print(x.shape)\n",
    "        x = self.classifier(x)\n",
    "        #print(x.shape)\n",
    "        return x\n",
    "\n",
    "torch.manual_seed(42)\n",
    "model = CNNImageClassifier(input_shape=3, # number of color channels (3 for RGB) \n",
    "                  hidden_units=hyper_params['hidden_unit'], \n",
    "                  output_shape=len(train_data.classes)).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d52f695",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:38:10.558959Z",
     "iopub.status.busy": "2023-01-23T08:38:10.558583Z",
     "iopub.status.idle": "2023-01-23T08:38:34.274409Z",
     "shell.execute_reply": "2023-01-23T08:38:34.273284Z"
    },
    "id": "iDdzcRfVRP2a",
    "outputId": "078bff53-f872-4aff-bdbf-de6487d7fd4a",
    "papermill": {
     "duration": 23.729058,
     "end_time": "2023-01-23T08:38:34.277300",
     "exception": false,
     "start_time": "2023-01-23T08:38:10.548242",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torchinfo\r\n",
      "  Downloading torchinfo-1.7.1-py3-none-any.whl (22 kB)\r\n",
      "Installing collected packages: torchinfo\r\n",
      "Successfully installed torchinfo-1.7.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    },
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "CNNImageClassifier                       [1, 2]                    --\n",
       "├─Sequential: 1-1                        [1, 128, 64, 64]          --\n",
       "│    └─Conv2d: 2-1                       [1, 128, 128, 128]        3,584\n",
       "│    └─ReLU: 2-2                         [1, 128, 128, 128]        --\n",
       "│    └─BatchNorm2d: 2-3                  [1, 128, 128, 128]        256\n",
       "│    └─MaxPool2d: 2-4                    [1, 128, 64, 64]          --\n",
       "├─Sequential: 1-2                        [1, 128, 32, 32]          --\n",
       "│    └─Conv2d: 2-5                       [1, 128, 64, 64]          147,584\n",
       "│    └─ReLU: 2-6                         [1, 128, 64, 64]          --\n",
       "│    └─BatchNorm2d: 2-7                  [1, 128, 64, 64]          256\n",
       "│    └─MaxPool2d: 2-8                    [1, 128, 32, 32]          --\n",
       "├─Sequential: 1-3                        [1, 128, 16, 16]          (recursive)\n",
       "│    └─Conv2d: 2-9                       [1, 128, 32, 32]          (recursive)\n",
       "│    └─ReLU: 2-10                        [1, 128, 32, 32]          --\n",
       "│    └─BatchNorm2d: 2-11                 [1, 128, 32, 32]          (recursive)\n",
       "│    └─MaxPool2d: 2-12                   [1, 128, 16, 16]          --\n",
       "├─Sequential: 1-4                        [1, 128, 8, 8]            (recursive)\n",
       "│    └─Conv2d: 2-13                      [1, 128, 16, 16]          (recursive)\n",
       "│    └─ReLU: 2-14                        [1, 128, 16, 16]          --\n",
       "│    └─BatchNorm2d: 2-15                 [1, 128, 16, 16]          (recursive)\n",
       "│    └─MaxPool2d: 2-16                   [1, 128, 8, 8]            --\n",
       "├─Sequential: 1-5                        [1, 128, 4, 4]            (recursive)\n",
       "│    └─Conv2d: 2-17                      [1, 128, 8, 8]            (recursive)\n",
       "│    └─ReLU: 2-18                        [1, 128, 8, 8]            --\n",
       "│    └─BatchNorm2d: 2-19                 [1, 128, 8, 8]            (recursive)\n",
       "│    └─MaxPool2d: 2-20                   [1, 128, 4, 4]            --\n",
       "├─Sequential: 1-6                        [1, 2]                    --\n",
       "│    └─Flatten: 2-21                     [1, 2048]                 --\n",
       "│    └─Linear: 2-22                      [1, 2]                    4,098\n",
       "==========================================================================================\n",
       "Total params: 155,778\n",
       "Trainable params: 155,778\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 861.58\n",
       "==========================================================================================\n",
       "Input size (MB): 0.20\n",
       "Forward/backward pass size (MB): 41.94\n",
       "Params size (MB): 0.62\n",
       "Estimated Total Size (MB): 42.76\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Install torchinfo if it's not available, import it if it is\n",
    "try: \n",
    "    import torchinfo\n",
    "except:\n",
    "    !pip install torchinfo\n",
    "    import torchinfo\n",
    "    \n",
    "from torchinfo import summary\n",
    "\n",
    "# do a test pass through of an example input size \n",
    "summary(model, input_size=[1, 3, IMAGE_WIDTH ,IMAGE_HEIGHT]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07d797f3",
   "metadata": {
    "execution": {
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   "source": [
    "def train_step(model: torch.nn.Module, \n",
    "               dataloader: torch.utils.data.DataLoader, \n",
    "               loss_fn: torch.nn.Module, \n",
    "               optimizer: torch.optim.Optimizer):\n",
    "    # Put model in train mode\n",
    "    model.train()\n",
    "    \n",
    "    # Setup train loss and train accuracy values\n",
    "    train_loss, train_acc = 0, 0\n",
    "    \n",
    "    # Loop through data loader data batches\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        # Send data to target device\n",
    "        X, y = X.to(device), y.to(device)\n",
    "\n",
    "        # 1. Forward pass\n",
    "        y_pred = model(X)\n",
    "\n",
    "        # 2. Calculate  and accumulate loss\n",
    "        loss = loss_fn(y_pred, y)\n",
    "        train_loss += loss.item() \n",
    "\n",
    "        # 3. Optimizer zero grad\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # 4. Loss backward\n",
    "        loss.backward()\n",
    "\n",
    "        # 5. Optimizer step\n",
    "        optimizer.step()\n",
    "\n",
    "        # Calculate and accumulate accuracy metric across all batches\n",
    "        y_pred_class = torch.argmax(torch.softmax(y_pred, dim=1), dim=1)\n",
    "        train_acc += (y_pred_class == y).sum().item()/len(y_pred)\n",
    "\n",
    "    # Adjust metrics to get average loss and accuracy per batch \n",
    "    train_loss = train_loss / len(dataloader)\n",
    "    train_acc = train_acc / len(dataloader)\n",
    "    \n",
    "    experiment.log_metrics({\"train_accuracy\": train_acc, \"train_loss\": train_loss}, epoch=hyper_params['num_epochs'])\n",
    "    return train_loss, train_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad623e83",
   "metadata": {
    "execution": {
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   "source": [
    "def test_step(model: torch.nn.Module, \n",
    "              dataloader: torch.utils.data.DataLoader, \n",
    "              loss_fn: torch.nn.Module):\n",
    "    # Put model in eval mode\n",
    "    model.eval() \n",
    "    \n",
    "    # Setup test loss and test accuracy values\n",
    "    test_loss, test_acc = 0, 0\n",
    "    \n",
    "    # Turn on inference context manager\n",
    "    with torch.inference_mode():\n",
    "        # Loop through DataLoader batches\n",
    "        for batch, (X, y) in enumerate(dataloader):\n",
    "            # Send data to target device\n",
    "            X, y = X.to(device), y.to(device)\n",
    "    \n",
    "            # 1. Forward pass\n",
    "            test_pred_logits = model(X)\n",
    "\n",
    "            # 2. Calculate and accumulate loss\n",
    "            loss = loss_fn(test_pred_logits, y)\n",
    "            test_loss += loss.item()\n",
    "            \n",
    "            # Calculate and accumulate accuracy\n",
    "            test_pred_labels = test_pred_logits.argmax(dim=1)\n",
    "            test_acc += ((test_pred_labels == y).sum().item()/len(test_pred_labels))\n",
    "            \n",
    "    # Adjust metrics to get average loss and accuracy per batch \n",
    "    test_loss = test_loss / len(dataloader)\n",
    "    test_acc = test_acc / len(dataloader)\n",
    "    \n",
    "    experiment.log_metrics({\"test_accuracy\": test_acc, \"test_loss\": test_loss}, epoch=hyper_params['num_epochs'])\n",
    "    return test_loss, test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec4d6e7a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:38:34.411612Z",
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     "status": "completed"
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   },
   "outputs": [],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "# 1. Take in various parameters required for training and test steps\n",
    "def train(model: torch.nn.Module, \n",
    "          train_dataloader: torch.utils.data.DataLoader, \n",
    "          test_dataloader: torch.utils.data.DataLoader, \n",
    "          optimizer: torch.optim.Optimizer,\n",
    "          loss_fn: torch.nn.Module = nn.CrossEntropyLoss(),\n",
    "          epochs: int = 5):\n",
    "    \n",
    "    # 2. Create empty results dictionary\n",
    "    results = {\"train_loss\": [],\n",
    "               \"train_acc\": [],\n",
    "               \"test_loss\": [],\n",
    "               \"test_acc\": []\n",
    "    }\n",
    "    \n",
    "    # 3. Loop through training and testing steps for a number of epochs\n",
    "    for epoch in tqdm(range(hyper_params['num_epochs'])):\n",
    "        train_loss, train_acc = train_step(model=model,\n",
    "                                           dataloader=train_dataloader,\n",
    "                                           loss_fn=loss_fn,\n",
    "                                           optimizer=optimizer)\n",
    "        test_loss, test_acc = test_step(model=model,\n",
    "                                        dataloader=test_dataloader,\n",
    "                                        loss_fn=loss_fn)\n",
    "\n",
    "        # 4. Print out what's happening\n",
    "        print(\n",
    "            f\"Epoch: {epoch+1} | \"\n",
    "            f\"train_loss: {train_loss:.4f} | \"\n",
    "            f\"train_acc: {train_acc:.4f} | \"\n",
    "            f\"test_loss: {test_loss:.4f} | \"\n",
    "            f\"test_acc: {test_acc:.4f}\"\n",
    "        )\n",
    "\n",
    "        # 5. Update results dictionary\n",
    "        results[\"train_loss\"].append(train_loss)\n",
    "        results[\"train_acc\"].append(train_acc)\n",
    "        results[\"test_loss\"].append(test_loss)\n",
    "        results[\"test_acc\"].append(test_acc)\n",
    "\n",
    "    # 6. Return the filled results at the end of the epochs\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ea15f128",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:38:34.449120Z",
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7af67d611b446a489ea32ab819b89b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 0.7667 | train_acc: 0.5941 | test_loss: 0.6083 | test_acc: 0.6676\n",
      "Epoch: 2 | train_loss: 0.6821 | train_acc: 0.6415 | test_loss: 0.6603 | test_acc: 0.6651\n",
      "Epoch: 3 | train_loss: 0.6284 | train_acc: 0.6740 | test_loss: 0.5493 | test_acc: 0.7196\n",
      "Epoch: 4 | train_loss: 0.5973 | train_acc: 0.6933 | test_loss: 0.6107 | test_acc: 0.7093\n",
      "Epoch: 5 | train_loss: 0.5553 | train_acc: 0.7284 | test_loss: 0.4845 | test_acc: 0.7706\n",
      "Total training time: 303.850 seconds\n"
     ]
    }
   ],
   "source": [
    "# Set random seeds\n",
    "torch.manual_seed(42) \n",
    "torch.cuda.manual_seed(42)\n",
    "\n",
    "# Setup loss function and optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(params=model.parameters(), \n",
    "                             lr=hyper_params['learning_rate'])\n",
    "\n",
    "# Start the timer\n",
    "from timeit import default_timer as timer \n",
    "start_time = timer()\n",
    "\n",
    "# Train model\n",
    "model_results = train(model=model,\n",
    "                      train_dataloader=train_dataloader,\n",
    "                      test_dataloader=test_dataloader,\n",
    "                      optimizer=optimizer,\n",
    "                      loss_fn=loss_fn, \n",
    "                      epochs=hyper_params['num_epochs'])\n",
    "\n",
    "# End the timer and print out how long it took\n",
    "end_time = timer()\n",
    "print(f\"Total training time: {end_time-start_time:.3f} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75ba0d6a",
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "def plot_loss_curves(results):\n",
    "  \n",
    "    results = dict(list(model_results.items()))\n",
    "    # Get the loss values of the results dictionary (training and test)\n",
    "    loss = results['train_loss']\n",
    "    test_loss = results['test_loss']\n",
    "\n",
    "    # Get the accuracy values of the results dictionary (training and test)\n",
    "    accuracy = results['train_acc']\n",
    "    test_accuracy = results['test_acc']\n",
    "\n",
    "    # Figure out how many epochs there were\n",
    "    epochs = range(len(results['train_loss']))\n",
    "\n",
    "    # Setup a plot \n",
    "    plt.figure(figsize=(15, 7))\n",
    "\n",
    "    # Plot loss\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(epochs, loss, label='train_loss')\n",
    "    plt.plot(epochs, test_loss, label='test_loss')\n",
    "    plt.title('Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend()\n",
    "\n",
    "    # Plot accuracy\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(epochs, accuracy, label='train_accuracy')\n",
    "    plt.plot(epochs, test_accuracy, label='test_accuracy')\n",
    "    plt.title('Accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend();\n",
    "plot_loss_curves(model_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fe7be963",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:43:39.788789Z",
     "iopub.status.busy": "2023-01-23T08:43:39.787076Z",
     "iopub.status.idle": "2023-01-23T08:43:39.819429Z",
     "shell.execute_reply": "2023-01-23T08:43:39.817658Z"
    },
    "papermill": {
     "duration": 0.043355,
     "end_time": "2023-01-23T08:43:39.821368",
     "exception": false,
     "start_time": "2023-01-23T08:43:39.778013",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Save the model for inference\n",
    "from comet_ml.integration.pytorch import log_model\n",
    "log_model(experiment, model, model_name=\"My_Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c9196e26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:43:39.840554Z",
     "iopub.status.busy": "2023-01-23T08:43:39.838952Z",
     "iopub.status.idle": "2023-01-23T08:43:39.869277Z",
     "shell.execute_reply": "2023-01-23T08:43:39.868310Z"
    },
    "id": "c2Cke99ToEZr",
    "outputId": "bc388d6e-6d02-4811-c807-f7fe4036331f",
    "papermill": {
     "duration": 0.042127,
     "end_time": "2023-01-23T08:43:39.871591",
     "exception": false,
     "start_time": "2023-01-23T08:43:39.829464",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dogs'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose a image.\n",
    "custom_image_path = \"/kaggle/input/cat-and-dog/test_set/test_set/dogs/dog.4001.jpg\"\n",
    "\n",
    "import torchvision\n",
    "# Load in custom image and convert the tensor values to float32\n",
    "custom_image = torchvision.io.read_image(str(custom_image_path)).type(torch.float32)\n",
    "\n",
    "# Divide the image pixel values by 255 to get them between [0, 1]\n",
    "custom_image = custom_image / 255. \n",
    "\n",
    "custom_image_transform = transforms.Compose([\n",
    "    transforms.Resize(IMAGE_SIZE),\n",
    "])\n",
    "\n",
    "# Transform target image\n",
    "custom_image_transformed = custom_image_transform(custom_image)\n",
    "\n",
    "model.eval()\n",
    "with torch.inference_mode():\n",
    "    # Add an extra dimension to image\n",
    "    custom_image_transformed_with_batch_size = custom_image_transformed.unsqueeze(dim=0)\n",
    "    \n",
    "    # Make a prediction on image with an extra dimension\n",
    "    custom_image_pred = model(custom_image_transformed.unsqueeze(dim=0).to(device))\n",
    "    \n",
    "\n",
    "# Convert logits -> prediction probabilities (using torch.softmax() for multi-class classification)\n",
    "custom_image_pred_probs = torch.softmax(custom_image_pred, dim=1)\n",
    "\n",
    "# Convert prediction probabilities -> prediction labels\n",
    "custom_image_pred_label = torch.argmax(custom_image_pred_probs, dim=1)\n",
    "\n",
    "class_names = train_data.classes\n",
    "custom_image_pred_class = class_names[custom_image_pred_label.cpu()] # put pred label to CPU, otherwise will error\n",
    "custom_image_pred_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e34c2555",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:43:39.897568Z",
     "iopub.status.busy": "2023-01-23T08:43:39.896778Z",
     "iopub.status.idle": "2023-01-23T08:43:40.147480Z",
     "shell.execute_reply": "2023-01-23T08:43:40.146426Z"
    },
    "id": "62tSSQHro-_V",
    "outputId": "f44c1e06-cb55-436b-e235-fec914c0d9b9",
    "papermill": {
     "duration": 0.270203,
     "end_time": "2023-01-23T08:43:40.150372",
     "exception": false,
     "start_time": "2023-01-23T08:43:39.880169",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot custom image\n",
    "plt.imshow(custom_image.permute(1, 2, 0)) # need to permute image dimensions from CHW -> HWC otherwise matplotlib will error\n",
    "plt.title(f\"Image shape: {custom_image.shape}\")\n",
    "plt.axis(False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ec42a516",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-23T08:43:40.172353Z",
     "iopub.status.busy": "2023-01-23T08:43:40.172058Z",
     "iopub.status.idle": "2023-01-23T08:43:42.572201Z",
     "shell.execute_reply": "2023-01-23T08:43:42.571204Z"
    },
    "papermill": {
     "duration": 2.413613,
     "end_time": "2023-01-23T08:43:42.574770",
     "exception": false,
     "start_time": "2023-01-23T08:43:40.161157",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.com/tirendaz-test/experiment-tracking-pytorch-3/83e6ea947e674e278d865218ec356b86\n",
      "COMET INFO:   Metrics [count] (min, max):\n",
      "COMET INFO:     loss [126]         : (0.3402763903141022, 1.4224852323532104)\n",
      "COMET INFO:     test_accuracy [5]  : (0.6651088169642857, 0.7706473214285714)\n",
      "COMET INFO:     test_loss [5]      : (0.48446191172115505, 0.6603429233655334)\n",
      "COMET INFO:     train_accuracy [5] : (0.594148406374502, 0.7284113545816734)\n",
      "COMET INFO:     train_loss [5]     : (0.5552971184372427, 0.7666507542608268)\n",
      "COMET INFO:   Parameters:\n",
      "COMET INFO:     batch_size    : 32\n",
      "COMET INFO:     hidden_unit   : 128\n",
      "COMET INFO:     image_size    : 128\n",
      "COMET INFO:     learning_rate : 0.001\n",
      "COMET INFO:     num_epochs    : 5\n",
      "COMET INFO:   Uploads:\n",
      "COMET INFO:     conda-environment-definition : 1\n",
      "COMET INFO:     conda-info                   : 1\n",
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      "COMET INFO: ---------------------------\n",
      "COMET INFO: Uploading metrics, params, and assets to Comet before program termination (may take several seconds)\n",
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    "- Thank you for reading 😀 If you like this notebook, don't forget to upvote it 😊\n",
    "\n",
    "- Let's connect [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [Instagram](https://www.instagram.com/tirendazacademy) |[GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
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