unit05-lightning/5.6-logging/5.6-part3-logging-csv.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a753d5ad-a6e6-439e-b975-a86330d47208",
   "metadata": {},
   "source": [
    "# Unit 5.6. The Benefits of Logging Your Model Training\n",
    "## Part 3. Using a CSV Logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eec68953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch    : 1.12.1\n",
      "lightning: 2022.9.15\n",
      "\n",
      "conda environment: dl-fundamentals\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning --conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e80fef60",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightning as L\n",
    "import torch\n",
    "from shared_utilities import PyTorchMLP, LightningModel, MNISTDataModule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67046f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightning.pytorch.loggers import CSVLogger ### New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eaf13b54",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastian/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/lightning/pytorch/trainer/setup.py:200: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./mnist/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb069b6589b54af396c81a3dec748593",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9912422 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist/MNIST/raw/train-images-idx3-ubyte.gz to ./mnist/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./mnist/MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a0cc5cf24fb41cbb73d804089f6b509",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28881 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist/MNIST/raw/train-labels-idx1-ubyte.gz to ./mnist/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8668bb1a541a49fb8bb5beef1c9d037d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1648877 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to ./mnist/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "809b993a4bd94fd4b9fc777edb743d2f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4542 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Missing logger folder: logs/my-model\n",
      "\n",
      "  | Name      | Type               | Params\n",
      "-------------------------------------------------\n",
      "0 | model     | PyTorchMLP         | 40.8 K\n",
      "1 | train_acc | MulticlassAccuracy | 0     \n",
      "2 | val_acc   | MulticlassAccuracy | 0     \n",
      "3 | test_acc  | MulticlassAccuracy | 0     \n",
      "-------------------------------------------------\n",
      "40.8 K    Trainable params\n",
      "0         Non-trainable params\n",
      "40.8 K    Total params\n",
      "0.163     Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./mnist/MNIST/raw\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastian/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastian/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f8081f76e494a8695d28a869a8c8639",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=10` reached.\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(123)\n",
    "\n",
    "dm = MNISTDataModule()\n",
    "\n",
    "pytorch_model = PyTorchMLP(num_features=784, num_classes=10)\n",
    "\n",
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.05)\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=10,\n",
    "    accelerator=\"cpu\",\n",
    "    devices=\"auto\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"), ### NEW\n",
    "    deterministic=True,\n",
    ")\n",
    "\n",
    "trainer.fit(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "897d5146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAGwCAYAAABSN5pGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABtBUlEQVR4nO3de1zUVeL/8dfMwHCRm9wFUZS85q00yXLtZrpatJXblnYxK8uSviXbr7Q0tdZoazPbstx21d3NXN22665mKq2W5qW11LxRaobKRTTlKjAw8/tjYGC4CCgwA7yfj8c8mPnM53JmDsa7c/sYbDabDRERERFxMLq6ACIiIiLuRgFJREREpBoFJBEREZFqFJBEREREqlFAEhEREalGAUlERESkGgUkERERkWo8XF2A1spqtZKeno6/vz8Gg8HVxREREZEGsNls5OXlERUVhdFYdzuRAtJ5Sk9PJyYmxtXFEBERkfNw9OhROnfuXOf7Ckjnyd/fH7B/wQEBAU12XovFwtq1axk1ahSenp5Ndl45f6oT96L6cC+qD/ei+qhfbm4uMTExjr/jdVFAOk8V3WoBAQFNHpB8fX0JCAjQL7ebUJ24F9WHe1F9uBfVR8PVNzxGg7RFREREqlFAEhEREalGAUlERESkGo1BamZlZWVYLJYG72+xWPDw8KCoqIiysrJmLJnUxdPTE5PJ5OpiiIiICykgNRObzUZmZiZnzpxp9HGRkZEcPXpU6yu5UFBQEJGRkaoDEZF2SgGpmVSEo/DwcHx9fRv8h9ZqtZKfn4+fn985F7CS5mGz2SgsLOTEiRMAdOrUycUlEhERV1BAagZlZWWOcBQSEtKoY61WKyUlJXh7eysguYiPjw8AJ06cIDw83MWlERERV9Bf4GZQMebI19fXxSWR81VRd40ZPyYiIm2HywPSwoULiY2Nxdvbm/j4eLZv317nvhaLheeee464uDi8vb0ZOHAga9ascdonNjYWg8FQ4zF16lTHPldffXWN96dMmdLkn03jV1ov1Z2ISPvm0oC0cuVKkpKSmD17Nt988w0DBw5k9OjRjvEf1c2cOZM//elPvP766+zbt48pU6Zwyy238O233zr2+frrr8nIyHA81q1bB8Btt93mdK7Jkyc77ffSSy813wcVERGRVsWlAWn+/PlMnjyZSZMm0bdvXxYtWoSvry9Lliypdf933nmHp59+mrFjx9K9e3cefvhhxo4dyyuvvOLYJywsjMjISMfjP//5D3FxcVx11VVO5/L19XXarylvFyIiIiKtm8sGaZeUlLBjxw5mzJjh2GY0Ghk5ciRbtmyp9Zji4mK8vb2dtvn4+LBp06Y6r7Fs2TKSkpJqdJm8++67LFu2jMjISBISEpg1a9Y5xwwVFxdTXFzseJ2bmwvYu/2qj1OxWCzYbDasVitWq7XOc9bGZrM5fjb2WHfTvXt3HnvsMR577DFXF6XRrFYrNpsNi8XiqAeNR3IPFfWg+nAPqg/3ovqoX0O/G5cFpJMnT1JWVkZERITT9oiICA4cOFDrMaNHj2b+/PmMGDGCuLg4UlJS+OCDD+pcUPGjjz7izJkz3HvvvU7bJ0yYQNeuXYmKimL37t089dRTpKam8sEHH9RZ3uTkZObOnVtj+9q1a2sEKw8PDyIjI8nPz6ekpKTOc55LXl7eeR13oW688Ub69+9PcnLyBZ9r/fr1+Pr6OsJka1JSUsLZs2f54osvKC0tBXB014p7UH24F9WHe2nN9WG1wdlSKCiFIDOYm3jd3sLCwgbt16qm+b/22mtMnjyZ3r17YzAYiIuLY9KkSXV2yS1evJgxY8YQFRXltP3BBx90PO/fvz+dOnXiuuuu49ChQ8TFxdV6rhkzZpCUlOR4nZubS0xMDKNGjarRPVdUVMTRo0fx8/Or0eJVH5vNRl5eHv7+/i4ZKOzh4YHZbK6zy9Fms1FWVoaHR/2/Oq2527KoqAgfHx9GjBiByWRi3bp1XH/99bo7thuwWCyqDzei+nAv7lIfNpuNgpIycs9aOHPWQu7Z0vKfNV/nnLWQU2Qhp9BCTlEpeUWljvMsu28I8d2Cm7RsDf2fdpcFpNDQUEwmE1lZWU7bs7KyiIyMrPWYsLAwPvroI4qKijh16hRRUVFMnz6d7t2719j3p59+Yv369edsFaoQHx8PwMGDB+sMSF5eXnh5edXY7unpWeOXsKysDIPBgNFoxGg0YrPZOGtp2G1DrFYrZ0vK8LCUNck6SD6epgYHrXvvvZeNGzeyceNG/vjHPwKwdOlSJk2axOrVq5k5cybfffcda9euJSYmhqSkJLZu3UpBQQF9+vQhOTmZkSNHOs4XGxvL448/zuOPPw7YZ4b9+c9/ZtWqVXz22WdER0fzyiuvcNNNN9VbtrKyMh588EE+//xzMjMz6dKlC4888kiN7rslS5bwyiuvcPDgQYKDgxk3bhxvvPEGAGfOnOGpp57io48+Iicnh4suuogXX3yRG2+8scb1jEYjBoPB6bYjtdW1uI7qw72oPtxLU9VHcWkZORXBptAeZhw/HdtLHK9zzpYHnbMWSq22C7p2B7MJi83Q5L9XDT2fywKS2Wxm8ODBpKSkcPPNNwP2cJCSkkJiYuI5j/X29iY6OhqLxcL777/Pb37zmxr7LF26lPDwcG644YZ6y7Jz506g+VZNPmspo++znzXLueuz77nR+JobVs2vvfYa33//Pf369eO5554DYO/evQBMnz6dP/zhD3Tv3p2OHTty9OhRxo4dy7x58/Dy8uLvf/87CQkJpKam0qVLlzqvMXfuXF566SVefvllXn/9de68805++ukngoPP/X8IVquVzp0789577xESEsJXX33Fgw8+SKdOnRz1/9Zbb5GUlMSLL77ImDFjyMnJYfPmzY7jx4wZQ15eHsuWLSMuLo59+/bpnmsi0uZZrTbyiko5c7bk3AGnfHvF40yhpcH/c18Xs8lIoK8ngT6eBPnYf1a+NhPo40GQr7nadk8CfDzxNLl2JSKXdrElJSUxceJEhgwZwtChQ1mwYAEFBQVMmjQJgHvuuYfo6GjHeJht27Zx/PhxBg0axPHjx5kzZw5Wq5Unn3zS6bxWq5WlS5cyceLEGl1Bhw4dYvny5YwdO5aQkBB2797NtGnTGDFiBAMGDGiZD+6mAgMDMZvNjhl+gGM82HPPPcf111/v2Dc4OJiBAwc6Xj///PN8+OGHfPLJJ+cMuPfeey/jx48H4IUXXuCPf/wj27dv55e//OU5y+bp6ek0Bqxbt25s2bKFf/7zn46A9Lvf/Y7f/va3Tq1Kl112GWAfD7V9+3b2799Pz549AWpteRQRaS2KLGWknznL8TNnOX7a/vPYzwXs+9HIX9K2knO21N76U2TBdgGNOQYDBHh7ElQeYCoeQU5Bp0rAqbLd29PYateVc2lAuv3228nOzubZZ58lMzOTQYMGsWbNGsfA7bS0NKdupqKiImbOnMnhw4fx8/Nj7NixvPPOOwQFBTmdd/369aSlpXHffffVuKbZbGb9+vWOMBYTE8O4ceOYOXNms31OH08T+54b3aB9rVYrebl5+Af4N1kXW1MYMmSI0+v8/HzmzJnDqlWryMjIoLS0lLNnz5KWlnbO81QNoR06dCAgIKDOda+qW7hwIUuWLCEtLY2zZ89SUlLCoEGDAPttQdLT07nuuutqPXbnzp107tzZEY5ERNyZzWYj92wpx84Ucvz02cogVCUMncyvaxKQEXJqjrPxNZtqDzgVLTh1BB9/bw+MxtYZci6EywdpJyYm1tnisGHDBqfXV111Ffv27av3nKNGjXJMl68uJiaGjRs3NrqcF8JgMDS4m8tqtVJqNuFr9nCre7F16NDB6fUTTzzBunXr+MMf/sBFF12Ej48Pv/71r+udtVe979dgMDRoOYMVK1bwxBNP8MorrzBs2DD8/f15+eWX2bZtG1B5/7S61Pe+iEhLslptnMgrrhZ6KsJQEcfPnCW/uLTe8/iaTUQH+RDd0YfoIB86BXiR8eMBRlw+hFB/7/LQYw86Zg/3+ZvSGrg8IIl7MZvNdS6bUNXmzZu59957ueWWWwB7i9KRI0earVybN2/miiuu4JFHHnFsO3TokOO5v78/sbGxpKSkcM0119Q4fsCAARw7dozvv/9erUgi0uyKS8vIKA86FS0+VZ9n5JzFUlZ/v1dIBzPRHX2ICqwMQRU/O3f0IdDH06kLy2KxsDp/P9f2CtOg+QukgCROYmNj2bZtG0eOHMHPz6/O1p0ePXrwwQcfkJCQgMFgYNasWc26sGWPHj34+9//zmeffUa3bt145513+Prrr+nWrZtjnzlz5jBlyhTCw8MdA7I3b97Mo48+ylVXXcWIESMYN24c8+fP56KLLuLAgQMYDIZ6xz+JiFSXW2Rx7vo6fZZj5T/Tz5zlRF5xvecwGQ1EBng7hZ6oKs+jg3zwaepFgKTBFJDEyRNPPMHEiRPp27cvZ8+eZenSpbXuN3/+fO677z6uuOIKQkNDeeqpp5p1QciHHnqIb7/9lttvvx2DwcD48eN55JFH+PTTTx37TJw4kaKiIl599VWeeOIJQkND+fWvf+14//333+eJJ55g/PjxFBQUOKb5i4hUZbXaOFlQXNnyU/4z/cxZjpU/r7pWT128PY2O0NPZqfXHl+iOPkT4e+Hh4plaUjeDra7BOnJOubm5BAYGkpOTU+tCkT/++CPdunVr9EKRVquV3NxcAgIC3GoMUntTtQ5NJhOrV69m7NixarJ2AxaLRfXhRlpbfeQXl5KdV8yJ3CKy84s5kVvs+JmZW94ClFNESWn9LeJBvp6Olp6qrT4Vz4M7mFt8Bldrqw9XONff76rUgiQiIq1amdXGqYLKsJNd8TOvmBN5ReU/7a8LSxq2ro/BAJEB3vYur6Ca43+ig3zo4KU/oW2ZalfcwpQpU1i2bFmt7911110sWrSohUskIq5WWFJaGXrqaPXJzi/mVH4xjVm02ddsItzfizB/L8L9vQkrfx5RPh6oc0cfIgO9Xb5QobiWApK4heeee44nnnii1vda8z3dRMSZ1Wrj58KSKiHHOfRkV9le0MDWHrC3+IR0qAg9zj+rBqFwfy+1/LQkmw3KSuyP0hIoK4bS4nq2FVf+7DkaAqLqv04z0G+JuIXw8HDCw8NdXQwROU9FljIyci1k5xc5t/BUdHOVtwKdzC+hrBHNPd6eRsL9vesNPcEdzBrwDGCz4llaAPlZYLBWCyCW8ue1basSShq8rQHBp+zca+PV6+4PFZBERMS95ReXcjg7n0PZ+Rw6UcCh7HwOnsjj6CkTRVtSGnWukA5mR9CpHnaq/vTz8mi1t6poEcV5cHwHHN0OR7fjcWw7Y4ty4DtXF6wORg8wme0PDy8weYGH2f7T5Fm+rcp7Ph1dVlQFJBERcbDZbGTmFjkC0KEqgSgzt6iOo+wBxsvDSHiAF2F+dYUe++sQP7PG95wPmw3O/FQehrbZH1l7wVY5484pSpq8qgWOimBiruU9zwZuMzs/P9e22t4ztp51nRSQRETaoSJLGT+dKiwPPxVBqIDD2fnnHPsT6udF97AOxIX5ERfWgdhgbw5/9zW/vuF6Ovr5qLWnKVmKIGNXZRg69rW966y6wC4QMxRi4rF0uoQ1O37ilzfchKfZ3PJlbkMUkERE2rCfC0pqhKBD2fkc/bmwzplfJqOBriG+5SHIHoTiwv2IC/Uj0Nd5bR2LxULBQfD39lQ4ulB5mVVah7ZDxs6aY3iMntBpIMTEl4eioc5jdCwWrMZ0+6h1uSAKSCIirVxpmZWjp89WCUGVQehMoaXO4/y9PSpDUHgHx/Muwb5139i0pABO/lD+SMV0IpXBmScwbtgJoRdBcHf7wy9cf6TPpawUTux17i47k1Zzvw5hVcJQvD0ceerm2y1BAUmaVGxsLI8//jiPP/64q4si0ubkFVk4nF3gNC7oUHY+R04V1HnjU4MBooN8agSh7mEdCPPzqrvVp+AUnPweTqZCdpWfOc5/xI1AZ4DNW52P9+wAwd3KH92hY7fK8BQQDe3tTgGFP8Ox/8Gx8kB0bAdYCqrtZICIiyvDUMxQ+/emoOkSCkgiIm7EZrORkVNUpVusMhBl5dZ9A1RvTyPdQ/3sXWFhla1B3UI71H3DU6sVco7VDEEnU6HwVN2F9AmGsF4Q2pOy4IvYv28ffSK9MZ05Aj8fhpyj9j/+WXvsj+pMXtCxa2VgcgSobhDUxT44uDWzWuHUwcqWoaPb7d9pdV4B0PmyyjAUPRi8te6bu1BAEhFxgSJLGUdOFdSYLXY4u+Cct8MI9/dytADZW4TsgSgq0AejsY6WhjKLPbhkpzqHoJMHa2nFqCIwBkJ7OsKQ/Wcv6BDi2MVqsXDo1Gp6jRmLqeLeX6Ul9pD08+Eqjx/tP08fsa+Pc/J7+6M6g8kekoK71QxQHWPBs3H3t2wRJQVOU+05th3Onq65X3Ccc3dZWO/215LWiiggtQSbDSyFDdvXarXvW2Jqmn84nr4Nbp59++23mTNnDseOHXO6Ue6vfvUrQkJCeOaZZ0hKSmLr1q0UFBTQp08fkpOTGTly5HkVbf78+SxdupTDhw8THBxMQkICL730En5+fo59Nm/ezDPPPMP27dvx8vJi6NChrFixgo4dO2K1WvnDH/7A22+/zdGjR4mIiOChhx7imWeeOa/yiDSX0wUl7MvIZV96LnvTc9iXkcuh7II6F0z0MBqIDe3g1BIUF24PRQHe52hdKc6vDB7ZqZU/T/8I1jruPm/0sP/hDutpDz8VYSjkIvDyq/2Y+niYISTO/qjOWmZvtTr9Y83w9POPUHrW/t7pH+HQ59UONti756p23VVtffLyP7/yNobNZg9/VccOZe4BW7VQ6+FtbxGqCEOdL4MOoc1fPmkyCkgtwVIILzRsJVAjENSU1346HcwdGrTrbbfdxqOPPsp///tfrrvuOgB+/vln1qxZw+rVq8nPz2fs2LHMmzcPLy8v/v73v5OQkEBqaipdunRpdNGMRiN//OMf6datG4cPH+aRRx7hySef5M033wRg586dXHfdddx333289tpreHh48N///peyMvt/iGbMmMGf//xnXn31VYYPH05GRgYHDhxodDlEmorNZuPY6bPsy8hlb7o9EO1LzyE9p/b1gwJ9PCtDUHjljLGYYN+61wmy2aDgZHlLUGqVQPQ95B6ru3BmPwjtUR6CysNQaE97sGjJLi2jyd691rErdL/a+T2bzT6TqyI4VQ9Rxbn2z5h7DI58WfPcHcLLQ1PV1qdu9gDlG3x+5S0thozdzlPt8zJq7hfQGWKqdJdF9LcHRWm1FJDEoWPHjowZM4bly5c7AtK//vUvQkNDueaaazAajQwcONCx//PPP8+HH37IJ598QmJiYqOvV3Ugd2xsLL/73e+YMmWKIyC99NJLDBkyxPEa4OKLLwYgLy+P1157jTfeeIOJEycCEBcXx/DhwxtdDpHzYSmzcig7n73Hc8sDUQ770nPJLaq9paZriC99OwVwcVQAfaMCuDgqkHD/cwyStlrtA6Id3WHfVz6vrfumQoewaiGoh71VKCDa/Qf7GgwQ0Mn+iL3S+T2bzT7QuWq3XdUAVXgKCk7YH0e31jy3d5BzaKrafdchrPK7yT/hPNU+/Vt7l2BVRg+IHOA81T6wc7N8JeI6CkgtwdPX3pLTAFarldy8PAL8/Z26uS7o2o1w5513MnnyZN588028vLx49913ueOOOzAajeTn5zNnzhxWrVpFRkYGpaWlnD17lrS0WqamNsD69etJTk7mwIED5ObmUlpaSlFREYWFhfj6+rJz505uu+22Wo/dv38/xcXFjiAn0pzyi0s5ULVVKCOX1Kw8SkqtNfb1NBnoGeFfJQwF0qeTP/51dY2VFsOpQ+Uh6IfKcUInD9q7m2plsI/TqegOqzpO6HxbStydwWAf+9QhxN5SU11RTpWuuvIWp4oAlZcBRWcg/Rv7ozrPDvagVJJvP6Y63xDoPLSyuyzqEjA37r+t0vooILUEg6HB3VxYreBZZt/fBYP3EhISsNlsrFq1issuu4wvv/ySV199FYAnnniCdevW8Yc//IGLLroIHx8ffv3rX1NS0vibER45coQbb7yRhx9+mHnz5hEcHMymTZu4//77KSkpwdfXFx+futf6ONd7IhfiRF5Rle4xexg6cqoAWy3Dhfy9POgTVR6EOtlbhS4K96u5hpCjW+wHOFW+htCpg+Xjg47UHL9SwWS2jwWqHoJCLtIf6Oq8AyFqkP1RXUmB/Xt2Gu9U/twx467i5mUGCO9TZap9vD08uXvrmzQ5BSRx4u3tza233sq7777LwYMH6dWrF5deeilgHzB97733cssttwCQn5/PkSNHzus6O3bswGq18sorrzhayv75z3867TNgwABSUlKYO3dujeN79OiBj48PKSkpPPDAA+dVBmnfrFYbR04V2MOQYwB1Lifza59K3ynQu0YXWeeO1W6tUVoCP3/vHIJO/mDvHis6U3dhvAKqhKAqg6WDuoJJ/5m+YOYO9vWFIi6u+V5psX2Bxp8P27vOogeDT1CLF1Hcj/7lSQ133nknN954I3v37uWuu+5ybO/RowcffPABCQkJGAwGZs2ahdVas4uhIS666CIsFguvv/46CQkJbN68mUWLFjntM2PGDPr3788jjzzClClTMJvN/Pe//+W2224jNDSUp556iieffBKz2cyVV15JdnY2e/fu5f7777+gzy9tT5GljO+z8hwhaF9GLvszcmudTm80QPcwP6dWoT6d/Anx87LvYLPZx6mc+gYOVwlBp34obw06x7+JwBj7mKCQHuUDpssHTftHqoXCVTy8KutCpAoFJKnh2muvJTg4mNTUVCZMmODYPn/+fO677z6uuOIKR0DJzc09r2sMHDiQ+fPn8/vf/54ZM2YwYsQIkpOTueeeexz79OzZk7Vr1/L0008zdOhQfHx8iI+PZ/z48QDMmjULDw8Pnn32WdLT0+nUqRNTpky5sA8vrd6Zwsop9RWB6GB2fq1T6r09jfSOrGgRsgei3pEB9oUVLUXw8yE4+R1884N9TNCp8p/FOXUXoGK2WEUICrnI/jM4Tt1iIq2IwWarrWdd6pObm0tgYCA5OTkEBDivfFpUVMSPP/5It27d8PZu3KJmVquV3NxcAgICmmaQtpyXqnVoMplYvXo1Y8eOxdOzla/w2wZYLBZWr17NmDFjyC4sY+/xHKdp9cfP1D6wObiD2RGCKgJRt5AOmAqyyoPP91VC0A/l98Wq6z+P5YOkQ3s6h6CQHu2uNaiiPvTvwz2oPup3rr/fVakFSURahZJSK18f+Zn/Hshi414jz+78Lzlna59S3yW42pT6ME8iLMcwnDoIJzfAoR9g2w/22WMleXVf1CvQfgPWql1iIT3sg3bdcUVnEWkyCkjSLN59910eeuihWt/r2rUre/fubeESSWuUlVvEfw+c4L+pJ9j0w0kKHGOGjEApniYDPcL96RsVQN9Ify4JKqCnZxYdcg/aW4SO/wC7D9pnKtXFUL5wYfUQFNrDeX0cEWlXFJCkWdx0003Ex8fX+p6afaUuZVYbO4+e5r8Hsvn8wAn2ZTiPcQvz92JknC9Rp/7HTX39ibam4/FzebfY94fOfUsfn47VxgWVd4917KYVj0WkBgUkaRb+/v74+7fAfZGk1TtdUMIXP9gD0cbvszlTaAHAgJVYwwl+GXaKa4JO0Nt4lICcVAwHyhfy21jLyYwe9sBTdVxQaE97MKpyg1URkfooIDWj850CL66nums+NpuNfRm55V1n2Xybdho/Wz69DUe5yZjGAO9jDPZOJ8ZyBI+ys5CL/VFFsYc/np0uxujUJdbT3lXWkvcVE5E2SwGpGZjNZoxGI+np6YSFhWE2m+u+31I1VquVkpISioqKNIvNBWw2GyUlJWRnZ2M0GjGbzY6b48r5yy8uZdMPJ/niQDqHD+wkvPAQvY1pTDWk0ducRpThZ+cDKu7t6uENYb0hop9joT9LcE/WbNzO2LFjMaq7VkSaicsD0sKFC3n55ZfJzMxk4MCBvP766wwdOrTWfS0WC8nJyfztb3/j+PHj9OrVi9///vf88pe/dOwzZ86cGisv9+rVy+ku70VFRfz2t79lxYoVFBcXM3r0aN58800iIiKa5DMZjUa6detGRkYG6ekNuwdbBZvNxtmzZ/Hx8WlwqJKm5+vrS5cuXTAajQpI58Fms/FT2hH2f7uFn3/8Bp/TB+hFGrMNx/EylEJtQ36CulQGofC+9ufB3WuuJG2xtMhnEJH2zaUBaeXKlSQlJbFo0SLi4+NZsGABo0ePJjU1lfDw8Br7z5w5k2XLlvHnP/+Z3r1789lnn3HLLbfw1Vdfcckllzj2u/jii1m/fr3jtYeH88ecNm0aq1at4r333iMwMJDExERuvfVWNm/e3GSfzWw206VLF0pLSxv1B9ZisfDFF18wYsQIDWZ2EZPJhIeHhwJqQ1mK4GQqJenfkfX9DkrSv6Nj3vfEkkNsxT5VGkNLPTpgjLwYY2RFq1A/+72vvANdUHgRkdq5NCDNnz+fyZMnM2nSJAAWLVrEqlWrWLJkCdOnT6+x/zvvvMMzzzzD2LFjAXj44YdZv349r7zyCsuWLXPs5+HhQWRkZK3XzMnJYfHixSxfvpxrr70WgKVLl9KnTx+2bt3K5ZdfXutxxcXFFBdX3qOpYgVpi8WCpZ7/ozWZTOd8vyqr1UppaSkmk6lRx0nTKi2tXF+non7rq+c2z2aD3OMYTuzFcGIfhhP7KM3Yi8eZQxhtZZiBmCq7W20GMj2jKerYi8DYgQR2HYQt4mIIjKHMYKTG/zY08PtVfbgX1Yd7UX3Ur6HfjcsCUklJCTt27GDGjBmObUajkZEjR7Jly5ZajykuLq6xMrWPjw+bNm1y2vbDDz8QFRWFt7c3w4YNIzk5mS5dugD2m6RaLBZGjhzp2L9379506dKFLVu21BmQkpOTa71p6tq1a/H1bfrbB6xbt67JzykXpj3ViamsiICiYwScPep4BBYdxbPMeRp9RU/ZaZsfB6xd+NEYQ75PDKagGIJCo/DwLL9/WSlwCDi0F2iaNbDaU320BqoP96L6qFth4TmWA6nCZQHp5MmTlJWV1Rj3ExER4TReqKrRo0czf/58RowYQVxcHCkpKXzwwQdOXVjx8fH89a9/pVevXmRkZDB37lx+8YtfsGfPHvz9/cnMzMRsNhMUFFTjupmZmXWWd8aMGSQlJTle5+bmEhMTw6hRo865VHljWSwW1q1bx/XXX68uNjfRpuvEZoXTR8pbhCpbhgynf6x1dwsmDlqjSLXFcMDahVS64NmpHwN69+aqXmH8OsKv2bsm23R9tEKqD/ei+qhfQ+8h6vJB2o3x2muvMXnyZHr37o3BYCAuLo5JkyaxZMkSxz5jxoxxPB8wYADx8fF07dqVf/7znxd0l3cvLy+8vLxqbPf09GyWX8LmOq+cv1ZfJ2fPQNbe8sce+88T++pcXDHfM5Tv6cL2s1EcsHbhgK0Lh2xR+Hfw5eqeYVzdO5wpPUIJ8nXNIoutvj7aGNWHe1F91K2h34vLAlJoaCgmk4msrCyn7VlZWXWOHwoLC+Ojjz6iqKiIU6dOERUVxfTp0+nevXud1wkKCqJnz54cPHgQgMjISEpKSjhz5oxTK9K5rivSapUUwP7/wK5/wI8b7S1G1Xl4Uxrai3SvOL4pimJNdgjbCzvxc1Fly2i/6ABG9Qrnmt7hDOgchMmoAewi0ra5LCCZzWYGDx5MSkoKN998M2AfoJySkkJiYuI5j/X29iY6OhqLxcL777/Pb37zmzr3zc/P59ChQ9x9990ADB48GE9PT1JSUhg3bhwAqamppKWlMWzYsKb5cCKuZC2DI1/CrhWw7xOwFFS+F9gFIi7GFnExGV5xbMwJ5+Oj3nydlkuZtfLO9X5eHozpEco1vcO5umcY4QG6MauItC8u7WJLSkpi4sSJDBkyhKFDh7JgwQIKCgocs9ruueceoqOjSU5OBmDbtm0cP36cQYMGcfz4cebMmYPVauXJJ590nPOJJ54gISGBrl27kp6ezuzZszGZTIwfPx6AwMBA7r//fpKSkggODiYgIIBHH32UYcOG1TlAW6RVOLHfHop2/xPyqqy/1bEbDLyDs33GsflUAP9NPcGGr7M5fuYsUFz+gIvC/bi2dzhX9wpjSNdgzB5aqFRE2i+XBqTbb7+d7Oxsnn32WTIzMxk0aBBr1qxxDNxOS0tzWk26qKiImTNncvjwYfz8/Bg7dizvvPOOU1fZsWPHGD9+PKdOnSIsLIzhw4ezdetWwsLCHPu8+uqrGI1Gxo0b57RQpEirk58Ne/5l70LL2FW53TsQ+o2DAXdw0KsvS746wgdvHKTIUtnF5uVh5Iq4EK7pHc41vcKJCW762ZgiIq2VywdpJyYm1tmltmHDBqfXV111Ffv27Tvn+VasWFHvNb29vVm4cCELFy5scDlF3IblLKR+am8tOrgebOWzOI0e0GM0DLwdW4/RbPkpn798/iOfH/jCcWh0kA/X9g7n2t7hXN49BB+z1toSEamNywOSiDSA1QpHt9pbivZ+BMVVpqlGD4YBd0C/cZR4deTfu9L5y5tfsz/Dvo/BANf3ieD+4d0Y2i1YK4SLiDSAApKIOzt1qHxc0Qo4k1a5PTAGBtxuf4T15HRBCcu3p/G3r77lRJ59TJGPp4nfDOnMpCu7ERvawUUfQESkdVJAEnE3hT/D3g/swejY15Xbzf5w8a/srUVdrwSjkcPZ+Sz56Dv+teOYY3xRRIAX917RjQlDuxDoq3VQRETOhwKSiDsoLYEf1tq70L7/DKzl9woyGCHuWhg4HnqNBbMvNpuNbT/+zF++/JGUA1nYymfnXxwVwORfdGds/06agSYicoEUkERcxWaD4zvsoWjP+3D2dOV7kf3tLUX9fw3+9gVMLWVWVn17nL9sOsye45VjkEb2Cef+4d25vLvGF4mINBUFJJGWdvon+1pFu1fAqYOV2/0iYcBt9mAU2c+xOafQUj6+6AiZuUUAeHsa+fXgztx3ZTe6h/m19CcQEWnzFJBEWkJRDuz72D6u6KfNlds9faH3jTDwDuh+NRgrp90fOVnA0s0/8t6OYxSW2Kfyh/l7ce8VsUwY2oWOHVxzDzQRkfZAAUmkuZSVwqHP7V1oqauhtKj8DQN0G2EPRX0SwMvfcYjNZuN/P53mz18cZt3+yvFFvSP9eeAX3UkY2AkvD61dJCLS3BSQRJqSzQaZu+0tRd+9BwXZle+F9rKHogG/gcDOTodZyqx8uieTxV8eZtexHMf2a3qF8cAvunNFXIjGF4mItCAFJJGmkJtuH1e0awVk76/c7hsC/W+zB6NOg+yrNlaRc9bCyq/T+OvmI6Tn2FuYvDyM3HppZ+4fHstF4f6IiEjLU0ASOV/F+XDgP/YutMMbgfL+MJMX9Bpjn5p/0XVgqrkW0dGfC1my+Uf++fVRCsrHF4X6mblnWCx3xnchxM+rBT+IiIhUp4Ak0hjWMvjxC3tL0f5/g6Wg8r0uw+wtRX1vBp+gWg/f8dNpFm86zJo9mVjL81SvCH/u/0U3bhoYhbenxheJiLgDBSSRBvA/ewzj53Nhz78gL6PyjeDu9mn5A34Dwd1qPba0zMpne7P4y6bDfJt2xrF9RM8wHhjejV/0CNX4IhERN6OAJFKXwp/hu/fw+HYZ12burtzuHQT9brV3oXW+rMa4ogp5RRZWfn2UpZuPcPzMWQDMJiO3XBLNfcO70StS44tERNyVApJIVVYr/LgBvl0G+/8DZcUYACsm6DkK46AJ0HM0eNQ9RujY6UL+uvkIK74+Sn5xKQDBHczcfXlX7rq8K2H+Gl8kIuLuFJBEAM4chZ3vwrfvQk5a5faI/pQNHM/ajEBG3nQ7Rs+6b/668+gZ/vLlYT7dk0lZ+QCji8L9eGB4N26+JFrji0REWhEFJGm/Sovts9C+eQcOb8AxC80r0H7Lj0vugk6DsJaWUrJ6da2nKLPaWLcvk798+SP/+6nyXmrDLwrl/l9046oeYRiNGl8kItLaKCBJ+5P5nb0LbfdK5xvExv4CLr3Hvrq1p885T5FfXMp7/zvKks0/cvRn+/giT5OBXw2K5v7h3ejTKaA5P4GIiDQzBSRpH86esc9A++YdyNhZud0/Ci65EwbdWecstKrSz5zlb18dYfn2NPKK7OOLOvp6ctflXbn78q6EB3g3T/lFRKRFKSBJ22W1wk+b7KFo/yeV90IzekLvsXDJ3RB3rdMNYuuSlg/T/rmbT/dmOcYXdQ/twH3DuzHu0s74mDW+SESkLVFAkrYn5zjsWm7vRjt9pHJ7WB+49G4YcDt0CG3Qqf535Gd+v+YAXx/xADIBGNY9hAd+0Y1reoVrfJGISBulgCRtQ2kJfP+pvbXoUArYrPbtZn/oPw4uuQeiL61zzaLqTuYX8+KnB/jXjmMAGA02bhoQxQMj4ugXHdhcn0JERNyEApK0bif220PR7hVQeKpye9cr7bPQ+v4KzB0afLoyq43l29N4ec0BcsvHGN02OJp+tp+YcEt/PM8xzV9ERNoOBSRpfYpyYe8H9mB0/H+V2/0iYdB4+9iikLhGn3bX0TPM+ngPu4/lANC3UwDP39yPAVF+rF79U1OVXkREWgEFJGkdbDZI22IPRfs+AkuhfbvRA3r+0h6KLhoJpsb/Sp8pLOHlz1JZvj0Nmw38vTz47aie3HV5VzxMRiwWS9N+FhERcXsKSOLe8jJhZ/mA658PVW4P7WkPRQPvAL/w8zq11WrjX98c48VPD/BzQQkAt1wSzYyxvQn313R9EZH2TAFJ3E+ZBX5Ya28t+mEt2Mrs2z07QL9b7AOuY4Y2eMB1bfal5/Lsx3scq1/3CPfjuV/1Y1hcSFN8AhERaeUUkMR9ZH8P374Du1ZAwYnK7THx9taii28BL78LukRekYVX1/3A37Ycocxqw9ds4rHrenDf8G54mowX+AFERKStUEAS1yrOh70f2oPR0W2V2zuEwcDx9ploYb0u+DI2m41PdqUzb9V+TuQVAzC2fySzbuxLp8Bz31ZERETaHwUkaXk2Gxzdbg9Fez+Eknz7doMJeoyyh6Keo8HUNFPqD57IY9ZHe9ly2L4MQGyIL3N/1Y+reoY1yflFRKTtcXmfwsKFC4mNjcXb25v4+Hi2b99e574Wi4XnnnuOuLg4vL29GThwIGvWrHHaJzk5mcsuuwx/f3/Cw8O5+eabSU1Nddrn6quvxmAwOD2mTJnSLJ9Pqsg/AZv/CAvjYckoe0AqyYfgOLhuNkzbCxNWQJ8bmyQcFZaU8uKnBxjz2pdsOXwKLw8jSdf3ZM3jIxSORETknFzagrRy5UqSkpJYtGgR8fHxLFiwgNGjR5Oamkp4eM2ZSTNnzmTZsmX8+c9/pnfv3nz22WfccsstfPXVV1xyySUAbNy4kalTp3LZZZdRWlrK008/zahRo9i3bx8dOlQuGDh58mSee+45x2tfX9/m/8DtUVkpHFxvD0PfrwGrffFFPH2h78321qKuV1zQgOvqbDYbn+3N4vn/7OP4mbMAXNc7nDk3XUxMsOpZRETq59KANH/+fCZPnsykSZMAWLRoEatWrWLJkiVMnz69xv7vvPMOzzzzDGPHjgXg4YcfZv369bzyyissW7YMoEaL0l//+lfCw8PZsWMHI0aMcGz39fUlMjKyuT6aFP4MX71un6Kfn1m5PXqIPRT1GwfeAU1+2Z9OFTD7k71sSM22Xy7Ihzk3Xcz1fSOa/FoiItJ2uSwglZSUsGPHDmbMmOHYZjQaGTlyJFu2bKn1mOLiYry9nden8fHxYdOmTXVeJyfHvipycHCw0/Z3332XZcuWERkZSUJCArNmzTpnK1JxcTHFxcWO17m5uYC9268pFxKsOFerXpzQUojp7zdjzNwFgM03BGu/27AOvBPC+1TZr+k+Y5GljLe//JE/fXmEklIrniYDD1wZy8NXdcfHbLqg77NN1EkbovpwL6oP96L6qF9DvxuDzWazNXNZapWenk50dDRfffUVw4YNc2x/8skn2bhxI9u2batxzIQJE9i1axcfffQRcXFxpKSk8Ktf/YqysjKn8FLBarVy0003cebMGacQ9fbbb9O1a1eioqLYvXs3Tz31FEOHDuWDDz6os7xz5sxh7ty5NbYvX75c3XNV2axcdmQhUWe+ptjkx66Ye8kMvBSbsfmy+N7TBt7/0cipYns3Xc9AK7/uZiVCk9NERKSawsJCJkyYQE5ODgEBdfdktKpZbK+99hqTJ0+md+/eGAwG4uLimDRpEkuWLKl1/6lTp7Jnz54aLUwPPvig43n//v3p1KkT1113HYcOHSIurvZ7eM2YMYOkpCTH69zcXGJiYhg1atQ5v+DGslgsrFu3juuvv75V3hjVuOEFTGe+xmb0xDThH1zSZVj9B52n42fOMm91KusO2NdMivD34ukxvRjTLwJDE45pau110taoPtyL6sO9qD7qV9EDVB+XBaTQ0FBMJhNZWVlO27OysuocGxQWFsZHH31EUVERp06dIioqiunTp9O9e/ca+yYmJvKf//yHL774gs6dO5+zLPHx8QAcPHiwzoDk5eWFl5dXje2enp7N8kvYXOdtVrtWwub5ABhu+iMecSPqOeD8lJRa+cumw/wx5QeKLFZMRgP3XRnLYyN74ufVfL/SrbJO2jDVh3tRfbgX1UfdGvq9uGyav9lsZvDgwaSkpDi2Wa1WUlJSnLrcauPt7U10dDSlpaW8//77/OpXv3K8Z7PZSExM5MMPP+Tzzz+nW7du9ZZl586dAHTq1On8PoxA2jb4JNH+/MrHYdCEZrnMVwdPMua1L3hpTSpFFitDY4NZ9X/DeeaGvs0ajkREpH1x6V+UpKQkJk6cyJAhQxg6dCgLFiygoKDAMavtnnvuITo6muTkZAC2bdvG8ePHGTRoEMePH2fOnDlYrVaefPJJxzmnTp3K8uXL+fjjj/H39ycz0z6DKjAwEB8fHw4dOsTy5csZO3YsISEh7N69m2nTpjFixAgGDBjQ8l9CW3D6J1gxAcpKoPeN9jWNmlhWbhG/W7Wff+9KByDUz8yMMX249dLoJu1OExERARcHpNtvv53s7GyeffZZMjMzGTRoEGvWrCEiwj4lOy0tDaOxspGrqKiImTNncvjwYfz8/Bg7dizvvPMOQUFBjn3eeustwL4YZFVLly7l3nvvxWw2s379ekcYi4mJYdy4ccycObPZP2+bVJQL/7gDCk9CZH+45U9gbLqGydIyK3/b8hOvrvue/OJSjAa46/Ku/HZULwJ91HwsIiLNw+V9EomJiSQmJtb63oYNG5xeX3XVVezbt++c56tvUl5MTAwbN25sVBmlDtYyeP8BOLEP/CJg/MoLvplsVf878jMzP9rDgcw8AAbGBDHv5n70iw5ssmuIiIjUxuUBSVqxtbPgh8/AwxvG/wMCo5vktCfzi3nx0wP8a8cxAIJ8PXlydG/uuCwGo1HdaSIi0vwUkOT87PgrbF1of37zWxA9+IJPWWa1sXx7Gi+vOUBukf2WJLcPieGpMb0J7mC+4POLiIg0lAKSNN6PX8Cq39qfX/MM9Lv1gk+56+gZZn28h93H7Cuf9+0UwPM392Nw144XfG4REZHGUkCSxjl5EFbebb/pbP/bYMT/u6DTnSks4eXPUlm+PQ2bDfy9PEga1ZO7L++Kh8llq1CIiEg7p4AkDXf2NPzjdig6A50vg5vegPOcYm+12vjXN8d48dMD/FxQAsDNg6J4+oY+hPt713O0iIhI81JAkoYps8A/74FTByEwBu5YDp7nF2T2pecy6+M97PjpNAA9wv147lf9GBYX0pQlFhEROW8KSFI/mw1W/z/72COzH4xfAX7hjT5NXpGF+eu+5+9bfqLMasPXbOKx63ow6cpumD3UnSYiIu5DAUnqt20R7FgKGGDcXyCyX6MOt9lsfLIrnd+t2k92XjEAY/pFMuvGvkQF+TRDgUVERC6MApKc2/dr4bOn7c9HPQ+9xjTq8IMn8pj10V62HD4FQGyIL3N/1Y+reoY1dUlFRESajAKS1C1rH/zrPrBZ4ZK7YVjtK57XpshSxoL1P7B402EsZTa8PIxMveYiHhzRHW9PUzMWWkRE5MIpIEnt8rNh+e1Qkgddh8MN8xs1Y+2PKT+waOMhAK7tHc6chIvpEuLbXKUVERFpUgpIUpOlCFbeCTlpENwdbn8HPBq3kvUXP2QDMPOGPtw/vBuG81wOQERExBU0dUic2Wzw7/+Do9vAK9B+A1rf4EadoshSxoEM+w1mx/TvpHAkIiKtjgKSOPvyFdi9Egwm+M3fIKxno0+xNz2XUquNUD8vogK16KOIiLQ+CkhSad/H8Pnz9udjX4K4a87rNDuPngFgUEygWo9ERKRVUkASu/Rv4YOH7M+HPgSXPXDep9pVHpAGdg668HKJiIi4gAKSQG46/GM8lJ6FuOtg9AsXdLpdx84AMDAm6MLLJiIi4gIKSO1dSaE9HOVlQFhvuG0pmM5/cuPpghJ+OlUIwIDOgU1VShERkRalgNSeWa3w4UOQsRN8Q+z3WPO+sFBT0XrULbQDQb6NWxpARETEXSggtWf/nQf7PwGTGW5/F4K7XfApdx3NAWCQutdERKQVU0Bqr3athC//YH+e8EfoOqxpTlsx/kjdayIi0oopILVHadvgk/L7qg2fBoPGN8lpbTZb5Qw2tSCJiEgrpoDU3pz+CVZMgLIS6H0jXPtsk5362OmznCoowdNkoE+ngCY7r4iISEtTQGpPinLhH3dA4UmIHAC3vg3GpvsVqOhe69MpAG9PU5OdV0REpKUpILUX1jJ4/wE4sQ/8Iu0z1swdmvQSWiBSRETaCgWk9mLtLPjhM/DwhvHLITC6yS9RMYNN449ERKS1U0BqD3b8FbYutD+/ZRFED27yS5SWWfnueMUUf81gExGR1k0Bqa378QtY9Vv782uegYtvaZbLfJ+Vz1lLGX5eHnQP9WuWa4iIiLQUBaS27ORBWHk3WEuh/20w4v8126UqBmgP6ByI0WhotuuIiIi0BAWktqrwZ1j+Gyg6A50vg5veAEPzBRetfyQiIm2JAlJbVGaB9ybCz4cgMAbuWA6e3s16yZ2awSYiIm2IywPSwoULiY2Nxdvbm/j4eLZv317nvhaLheeee464uDi8vb0ZOHAga9asafQ5i4qKmDp1KiEhIfj5+TFu3DiysrKa/LO5hM0Gq5+wjz0y+9mn8/uFN+slC0tK+T4rD9A92EREpG1waUBauXIlSUlJzJ49m2+++YaBAwcyevRoTpw4Uev+M2fO5E9/+hOvv/46+/btY8qUKdxyyy18++23jTrntGnT+Pe//817773Hxo0bSU9P59Zbb232z9sitr5ln7WGAcb9BSL7Nfsl9xzPxWqDyABvIgObt6VKRESkJbg0IM2fP5/JkyczadIk+vbty6JFi/D19WXJkiW17v/OO+/w9NNPM3bsWLp3787DDz/M2LFjeeWVVxp8zpycHBYvXsz8+fO59tprGTx4MEuXLuWrr75i69atLfK5m833a2HtM/bno56HXmNa5LKV4480vV9ERNoGD1dduKSkhB07djBjxgzHNqPRyMiRI9myZUutxxQXF+Pt7dxC4ePjw6ZNmxp8zh07dmCxWBg5cqRjn969e9OlSxe2bNnC5ZdfXue1i4uLHa9zc3MBe7efxWJpzEc/p4pzNfqcJ/bj8a9JGGxWrAPvpGzIQ9CE5TqXb9N+BqB/VECTfhfu4rzrRJqF6sO9qD7ci+qjfg39blwWkE6ePElZWRkRERFO2yMiIjhw4ECtx4wePZr58+czYsQI4uLiSElJ4YMPPqCsrKzB58zMzMRsNhMUFFRjn8zMzDrLm5yczNy5c2tsX7t2Lb6+vvV+3sZat25dg/c1W3K56vs5eJbkk+3Xhy1ch+3TT5u8THXZ+oMJMHD2+AFWr97fYtdtaY2pE2l+qg/3ovpwL6qPuhUWFjZoP5cFpPPx2muvMXnyZHr37o3BYCAuLo5JkybV2SXXlGbMmEFSUpLjdW5uLjExMYwaNYqAgKa7c73FYmHdunVcf/31eHp61n9AaRGmd2/FWHISW8duBE36hDE+HZusPPU5lV/Mz1s2YjDA/beMxN+7AWVuZRpdJ9KsVB/uRfXhXlQf9avoAaqPywJSaGgoJpOpxuyxrKwsIiMjaz0mLCyMjz76iKKiIk6dOkVUVBTTp0+ne/fuDT5nZGQkJSUlnDlzxqkV6VzXBfDy8sLLy6vGdk9Pz2b5JWzQeW02+Pdv4dh28A7EcOd7eAY074y16vZl2bvX4sL8CPZv+pY0d9JcdS3nR/XhXlQf7kX1UbeGfi8uG6RtNpsZPHgwKSkpjm1Wq5WUlBSGDRt2zmO9vb2Jjo6mtLSU999/n1/96lcNPufgwYPx9PR02ic1NZW0tLR6r+t2vnwFdq8Egwlu+xuE9mjxIuysuEGt1j8SEZE2xKVdbElJSUycOJEhQ4YwdOhQFixYQEFBAZMmTQLgnnvuITo6muTkZAC2bdvG8ePHGTRoEMePH2fOnDlYrVaefPLJBp8zMDCQ+++/n6SkJIKDgwkICODRRx9l2LBhdQ7Qdkv7PobPn7c/H/syxF3jkmJUzGDTDWpFRKQtcWlAuv3228nOzubZZ58lMzOTQYMGsWbNGscg67S0NIzGykauoqIiZs6cyeHDh/Hz82Ps2LG88847Tl1l9Z0T4NVXX8VoNDJu3DiKi4sZPXo0b775Zot97guW/i188JD9efwUuOx+lxTDZrM57sGmW4yIiEhb4vJB2omJiSQmJtb63oYNG5xeX3XVVezbt++Czgn2LrqFCxeycOHCRpXVLeSmwz/GQ+lZuGgkjJrnsqL8dKqQM4UWzCYjvSObbqC6iIiIq7n8ViPSCCWF9nCUlwFhveHXS8Dkuoxb0XrUNyoAs4d+lUREpO3QX7XWwmqFDx+CjJ3gGwITVoK3a8f97HSMPwpyaTlERESamgJSa/HfebD/EzCZ4fZ3oWOsq0ukW4yIiEibpYDUGuxaCV/+wf484Y/Q1fXLEVjKrOxJty+2NSim5RamFBERaQkKSO4ubRt8Uj7gfPg0GDTeteUpl5qZR0mplQBvD2JD2vYCkSIi0v4oILmz0z/BiglQVgK9b4Rrn3V1iRx2OrrXgjAYDK4tjIiISBNTQHJXxXnwjzug8CREDoBb3waj+1TXLg3QFhGRNsx9/uJKJZsV04eT4cQ+8IuE8SvA3MHVpXLiWCBStxgREZE2yOULRUpN/Y7/A2P2evDwhvHLITDa1UVykl9cyg8n8gEYoBlsIiLSBqkFyc0Yvvkbcdmf2V/csgiiB7u2QLX47lgONhtEB/kQ7u/t6uKIiIg0OQUkd1Jagmn7WwCUXTUDLr7FxQWqXeX919R6JCIibZMCkjvxMFN6zyr2dboN65VJri5NnXamnQE0/khERNouBSR34xvCD5EJ4MZT5ytbkIJcWg4REZHmooAkjZKVW0RGThFGA/SPVhebiIi0TQpI0igV6x/1CPeng5cmQYqISNukgCSNogHaIiLSHiggSaPsOpoD6Aa1IiLStikgSYNZrTa1IImISLuggCQN9uOpAvKKSvH2NNIzwt/VxREREWk2CkjSYBUDtPtFBeJp0q+OiIi0XforJw1WEZC0/pGIiLR1CkjSYDuP2QdoKyCJiEhbp4AkDVJcWsb+9FwABukWIyIi0sYpIEmDHMjIo6TMSkdfT2KCfVxdHBERkWalgCQNsrPK+CODG98nTkREpCkoIEmDOAZoq3tNRETaAQUkaZCd5QtEDtIAbRERaQcaHJA+//xz+vbtS25ubo33cnJyuPjii/nyyy+btHDiHnLOWjicXQDAgM5aQVtERNq+BgekBQsWMHnyZAICAmq8FxgYyEMPPcT8+fObtHDiHr4rn94fE+xDiJ+Xi0sjIiLS/BockHbt2sUvf/nLOt8fNWoUO3bsaJJCiXvZ5ehe0w1qRUSkfWhwQMrKysLT07PO9z08PMjOzm6SQol7ccxgU/eaiIi0Ew0OSNHR0ezZs6fO93fv3k2nTp0aXYCFCxcSGxuLt7c38fHxbN++/Zz7L1iwgF69euHj40NMTAzTpk2jqKjI8X5sbCwGg6HGY+rUqY59rr766hrvT5kypdFlbw9sNpsjIGmAtoiItBcNDkhjx45l1qxZTmGkwtmzZ5k9ezY33nhjoy6+cuVKkpKSmD17Nt988w0DBw5k9OjRnDhxotb9ly9fzvTp05k9ezb79+9n8eLFrFy5kqefftqxz9dff01GRobjsW7dOgBuu+02p3NNnjzZab+XXnqpUWVvLzJzi8jOK8ZkNHBxlFqQRESkffBo6I4zZ87kgw8+oGfPniQmJtKrVy8ADhw4wMKFCykrK+OZZ55p1MXnz5/P5MmTmTRpEgCLFi1i1apVLFmyhOnTp9fY/6uvvuLKK69kwoQJgL21aPz48Wzbts2xT1hYmNMxL774InFxcVx11VVO2319fYmMjGxwWYuLiykuLna8rpjNZ7FYsFgsDT5PfSrO1ZTnvBA7fjwFQM9wPzwMViwWq4tL1PLcrU7aO9WHe1F9uBfVR/0a+t0YbDabraEn/emnn3j44Yf57LPPqDjMYDAwevRoFi5cSLdu3RpcwJKSEnx9ffnXv/7FzTff7Ng+ceJEzpw5w8cff1zjmOXLl/PII4+wdu1ahg4dyuHDh7nhhhu4++67nVqRql4jKiqKpKQkp/evvvpq9u7di81mIzIykoSEBGbNmoWvr2+d5Z0zZw5z586ttUznOq61++QnIynpRq4It3J7XPsLRyIi0rYUFhYyYcIEcnJyap2ZX6HBLUgAXbt2ZfXq1Zw+fZqDBw9is9no0aMHHTs2fnbTyZMnKSsrIyIiwml7REQEBw4cqPWYCRMmcPLkSYYPH47NZqO0tJQpU6bUGo4APvroI86cOcO9995b4zxdu3YlKiqK3bt389RTT5GamsoHH3xQZ3lnzJhBUlKS43Vubi4xMTGMGjXqnF9wY1ksFtatW8f1119/zkHxLeUfS74GTnPjFf0YO7izq4vjEu5WJ+2d6sO9qD7ci+qjfrWt51ibBgeksrIy9u7d6whEl112meO9wsJCDh48SL9+/TAam29x7g0bNvDCCy/w5ptvEh8fz8GDB3nsscd4/vnnmTVrVo39Fy9ezJgxY4iKinLa/uCDDzqe9+/fn06dOnHddddx6NAh4uLiar22l5cXXl411wDy9PRsll/C5jpvY5RZbexJzwPg0tgQl5fH1dyhTqSS6sO9qD7ci+qjbg39XhqcZt555x3uu+8+zGZzjffMZjP33Xcfy5cvb3ABQ0NDMZlMZGVlOW3Pysqqc2zQrFmzuPvuu3nggQfo378/t9xyCy+88ALJyclYrc7dPz/99BPr16/ngQceqLcs8fHxABw8eLDB5W8PDmfnk19ciq/ZRI9wf1cXR0REpMU0OCAtXryYJ554ApPJVOM9Dw8PnnzySd5+++0GX9hsNjN48GBSUlIc26xWKykpKQwbNqzWYwoLC2u0UFWUp/pQqqVLlxIeHs4NN9xQb1l27twJcF7LFLRl35ZP7+8XHYjJaHBtYURERFpQg7vYUlNTufzyy+t8/7LLLmP//v2NunhSUhITJ05kyJAhDB06lAULFlBQUOCY1XbPPfcQHR1NcnIyAAkJCcyfP59LLrnE0cU2a9YsEhISnIKb1Wpl6dKlTJw4EQ8P54946NAhli9fztixYwkJCWH37t1MmzaNESNGMGDAgEaVv63bpfWPRESknWpwQCooKDjnwKa8vDwKCwsbdfHbb7+d7Oxsnn32WTIzMxk0aBBr1qxxDNxOS0tzajGaOXMmBoOBmTNncvz4ccLCwkhISGDevHlO512/fj1paWncd999Na5pNptZv369I4zFxMQwbtw4Zs6c2aiytwcVtxgZ2DnIpeUQERFpaQ0OSD169OCrr76qs5Vl06ZN9OjRo9EFSExMJDExsdb3NmzY4PTaw8OD2bNnM3v27HOec9SoUTW63CrExMSwcePGRpezvSmylHEgwz5Ae2CMFogUEZH2pcFjkCZMmMDMmTPZvXt3jfd27drFs88+61jAUVq/vem5lFpthPp5ER3k4+riiIiItKgGtyBNmzaNTz/9lMGDBzNy5Eh69+4N2FfSXr9+PVdccQXTpk1rtoJKy6ocfxSIwaAB2iIi0r40uAXJ09OTtWvXMm/ePDIyMnj77bf505/+REZGBvPmzWP9+vWkpqY2Z1mlBWn8kYiItGeNWtXR09OTJ598kp07d1JQUEBhYSFffPEFQUFBDB8+nIEDBzZXOaWFVbQgDdQMNhERaYfOe9nrL774gokTJxIVFcUf/vAHrrnmGrZu3dqUZRMXOVNYwpFT9hmJAzprgLaIiLQ/jboXW2ZmJn/9619ZvHgxubm5/OY3v6G4uJiPPvqIvn37NlcZpYXtOpYDQLfQDgT51lw5XUREpK1rcAtSQkICvXr1Yvfu3SxYsID09HRef/315iybuIije02tRyIi0k41uAXp008/5f/+7/94+OGHz2u9I2k9NP5IRETauwa3IG3atIm8vDwGDx5MfHw8b7zxBidPnmzOsokL2Gy2yhlsCkgiItJONTggXX755fz5z38mIyODhx56iBUrVhAVFYXVamXdunXk5eU1ZzmlhRw7fZaT+SV4GA307RTg6uKIiIi4RKNnsXXo0IH77ruPTZs28d133/Hb3/6WF198kfDwcG666abmKKO0oIrWoz6dAvD2NJ17ZxERkTbqvKf5A/Tq1YuXXnqJY8eO8Y9//KOpyiQuVDn+SAO0RUSk/bqggFTBZDJx880388knnzTF6cSFdh21T/HXCtoiItKeNUlAkrahtMzKd8ftAWmQBmiLiEg7poAkDj+cyOespQw/Lw/iwvxcXRwRERGXUUASh4rxRwM6B2I0GlxbGBERERdSQBIHrX8kIiJip4AkDjs1QFtERARQQJJyhSWlfJ9lX+xTA7RFRKS9U0ASAPam51JmtRER4EVkoLeriyMiIuJSCkgCVFkgUt1rIiIiCkhit9OxgnaQS8shIiLiDhSQBKgMSBp/JCIiooAkwMn8Yo6dPgtA/866B5uIiIgCkrC7fP2juLAOBHh7urYwIiIibkABSSrXP1L3moiICKCAJFTOYNP4IxERETsFpHbOZrM5bjGigCQiImKngNTOpf1cyJlCC2aTkd6RAa4ujoiIiFtQQGrnKqb3940KwOyhXwcRERFwg4C0cOFCYmNj8fb2Jj4+nu3bt59z/wULFtCrVy98fHyIiYlh2rRpFBUVOd6fM2cOBoPB6dG7d2+ncxQVFTF16lRCQkLw8/Nj3LhxZGVlNcvnc3e7ygdoq3tNRESkkksD0sqVK0lKSmL27Nl88803DBw4kNGjR3PixIla91++fDnTp09n9uzZ7N+/n8WLF7Ny5Uqefvppp/0uvvhiMjIyHI9NmzY5vT9t2jT+/e9/895777Fx40bS09O59dZbm+1zurOK8UcDY7T+kYiISAUPV158/vz5TJ48mUmTJgGwaNEiVq1axZIlS5g+fXqN/b/66iuuvPJKJkyYAEBsbCzjx49n27ZtTvt5eHgQGRlZ6zVzcnJYvHgxy5cv59prrwVg6dKl9OnTh61bt3L55Zc35Ud0a5YyK3uOl0/x1z3YREREHFwWkEpKStixYwczZsxwbDMajYwcOZItW7bUeswVV1zBsmXL2L59O0OHDuXw4cOsXr2au+++22m/H374gaioKLy9vRk2bBjJycl06dIFgB07dmCxWBg5cqRj/969e9OlSxe2bNlSZ0AqLi6muLjY8To3NxcAi8WCxWI5vy+hFhXnaspz1mVvei7FpVYCvD2IDjC3yDVbo5asE6mf6sO9qD7ci+qjfg39blwWkE6ePElZWRkRERFO2yMiIjhw4ECtx0yYMIGTJ08yfPhwbDYbpaWlTJkyxamLLT4+nr/+9a/06tWLjIwM5s6dyy9+8Qv27NmDv78/mZmZmM1mgoKCalw3MzOzzvImJyczd+7cGtvXrl2Lr69vIz55w6xbt67Jz1nd5iwDYKKTVwlr1nza7Ndr7VqiTqThVB/uRfXhXlQfdSssLGzQfi7tYmusDRs28MILL/Dmm28SHx/PwYMHeeyxx3j++eeZNWsWAGPGjHHsP2DAAOLj4+natSv//Oc/uf/++8/72jNmzCApKcnxOjc3l5iYGEaNGkVAQNNNj7dYLKxbt47rr78eT8/mve3HFx/uAdK5dlAcY0f2aNZrtWYtWSdSP9WHe1F9uBfVR/0qeoDq47KAFBoaislkqjF7LCsrq87xQ7NmzeLuu+/mgQceAKB///4UFBTw4IMP8swzz2A01hxzHhQURM+ePTl48CAAkZGRlJSUcObMGadWpHNdF8DLywsvL68a2z09PZvll7C5zlvVd8ftvySXdg3RP6QGaIk6kYZTfbgX1Yd7UX3UraHfi8tmsZnNZgYPHkxKSopjm9VqJSUlhWHDhtV6TGFhYY0QZDKZAPuK0LXJz8/n0KFDdOrUCYDBgwfj6enpdN3U1FTS0tLqvG5blF9cyg8n8gEY2Fkz2ERERKpyaRdbUlISEydOZMiQIQwdOpQFCxZQUFDgmNV2zz33EB0dTXJyMgAJCQnMnz+fSy65xNHFNmvWLBISEhxB6YknniAhIYGuXbuSnp7O7NmzMZlMjB8/HoDAwEDuv/9+kpKSCA4OJiAggEcffZRhw4a1qxls3x3LwWaDqEBvwgO8XV0cERERt+LSgHT77beTnZ3Ns88+S2ZmJoMGDWLNmjWOgdtpaWlOLUYzZ87EYDAwc+ZMjh8/TlhYGAkJCcybN8+xz7Fjxxg/fjynTp0iLCyM4cOHs3XrVsLCwhz7vPrqqxiNRsaNG0dxcTGjR4/mzTffbLkP7gYq1z8Kcmk5RERE3JHLB2knJiaSmJhY63sbNmxweu3h4cHs2bOZPXt2nedbsWJFvdf09vZm4cKFLFy4sFFlbUt2ld9iRCtoi4iI1OTyW42Ia1QEJLUgiYiI1KSA1A6dyC0iPacIowH6R2uAtoiISHUKSO3QrmP224v0CPeng5fLe1lFRETcjgJSO1TZvabWIxERkdooILVDmsEmIiJybgpI7YzVaqtsQeoc5NKyiIiIuCsFpHbmyKkCcotK8fIw0ivS39XFERERcUsKSO3MzvLWo37RgXiaVP0iIiK10V/IdkbdayIiIvVTQGpndpZP8dcMNhERkbopILUjxaVl7E/PBXSLERERkXNRQGpHDmTkUVJmpaOvJ12CfV1dHBEREbelgNSOVF3/yGAwuLYwIiIibkwBqR3ZqQHaIiIiDaKA1I5UzGDT+CMREZFzU0BqJ3KLLBzKLgBgQGfNYBMRETkXBaR24rvy6f0xwT6E+Hm5uDQiIiLuTQGpndD4IxERkYZTQGonNP5IRESk4RSQ2omqU/xFRETk3BSQ2oHMnCKycosxGQ1cHBXg6uKIiIi4PQWkdmDn0dMA9Izwx9fs4eLSiIiIuD8FpHZg51H7DLZBukGtiIhIgyggtQO7NINNRESkURSQ2rgyq43vjttbkDRAW0REpGEUkNq4w9n55BeX4ms20TPC39XFERERaRUUkNq4igUi+0UHYjIaXFsYERGRVkIBqY2rWP9IC0SKiIg0nAJSG7erfAabBmiLiIg0nAJSG1ZkKWN/Ri4AAzXFX0REpMEUkNqwfRm5lFpthPqZiQ7ycXVxREREWg2XB6SFCxcSGxuLt7c38fHxbN++/Zz7L1iwgF69euHj40NMTAzTpk2jqKjI8X5ycjKXXXYZ/v7+hIeHc/PNN5Oamup0jquvvhqDweD0mDJlSrN8Plequv6RwaAB2iIiIg3l0oC0cuVKkpKSmD17Nt988w0DBw5k9OjRnDhxotb9ly9fzvTp05k9ezb79+9n8eLFrFy5kqefftqxz8aNG5k6dSpbt25l3bp1WCwWRo0aRUFBgdO5Jk+eTEZGhuPx0ksvNetndQVHQNIAbRERkUZx6Y255s+fz+TJk5k0aRIAixYtYtWqVSxZsoTp06fX2P+rr77iyiuvZMKECQDExsYyfvx4tm3b5thnzZo1Tsf89a9/JTw8nB07djBixAjHdl9fXyIjI5vjY7mNXce0QKSIiMj5cFlAKikpYceOHcyYMcOxzWg0MnLkSLZs2VLrMVdccQXLli1j+/btDB06lMOHD7N69WruvvvuOq+Tk2MPCcHBwU7b3333XZYtW0ZkZCQJCQnMmjULX1/fOs9TXFxMcXGx43Vurn3ws8ViwWKx1P+BG6jiXBd6zjOFFn48aW816xvRoUnL2N40VZ1I01B9uBfVh3tRfdSvod+NywLSyZMnKSsrIyIiwml7REQEBw4cqPWYCRMmcPLkSYYPH47NZqO0tJQpU6Y4dbFVZbVaefzxx7nyyivp16+f03m6du1KVFQUu3fv5qmnniI1NZUPPvigzvImJyczd+7cGtvXrl17zmB1vtatW3dBx+8/YwBMhHrb+GrDhZ1L7C60TqRpqT7ci+rDvag+6lZYWNig/VzaxdZYGzZs4IUXXuDNN98kPj6egwcP8thjj/H8888za9asGvtPnTqVPXv2sGnTJqftDz74oON5//796dSpE9dddx2HDh0iLi6u1mvPmDGDpKQkx+vc3FxiYmIYNWoUAQEBTfQJ7cl23bp1XH/99Xh6ep73eQ7/9xDsP8Swnp0YO3ZAk5WvPWqqOpGmofpwL6oP96L6qF9FD1B9XBaQQkNDMZlMZGVlOW3Pysqqc2zQrFmzuPvuu3nggQcAe7gpKCjgwQcf5JlnnsForBxznpiYyH/+8x+++OILOnfufM6yxMfHA3Dw4ME6A5KXlxdeXl41tnt6ejbLL+GFnndPeh4Al3QJ1j+SJtJcdS3nR/XhXlQf7kX1UbeGfi8um8VmNpsZPHgwKSkpjm1Wq5WUlBSGDRtW6zGFhYVOIQjAZDIBYLPZHD8TExP58MMP+fzzz+nWrVu9Zdm5cycAnTp1Op+P4nZsNlvlLUa6BLm0LCIiIq2RS7vYkpKSmDhxIkOGDGHo0KEsWLCAgoICx6y2e+65h+joaJKTkwFISEhg/vz5XHLJJY4utlmzZpGQkOAISlOnTmX58uV8/PHH+Pv7k5mZCUBgYCA+Pj4cOnSI5cuXM3bsWEJCQti9ezfTpk1jxIgRDBjQNrqijp85y8n8EjyMBvp2arruPxERkfbCpQHp9ttvJzs7m2effZbMzEwGDRrEmjVrHAO309LSnFqMZs6cicFgYObMmRw/fpywsDASEhKYN2+eY5+33noLsC8GWdXSpUu59957MZvNrF+/3hHGYmJiGDduHDNnzmz+D9xCKu6/1qdTAN6eJheXRkREpPVx+SDtxMREEhMTa31vw4YNTq89PDyYPXs2s2fPrvN8FV1tdYmJiWHjxo2NLmdrUtG9pvuviYiInB+X32pEmt7OKrcYERERkcZTQGpjSsusfFe+gvYgraAtIiJyXhSQ2piD2fmctZTh5+VB9zA/VxdHRESkVVJAamMqblDbPzoQk9Hg2sKIiIi0UgpIbczOo7pBrYiIyIVSQGpjKgZoD9IMNhERkfOmgNSGFJaU8n2W/RYjakESERE5fwpIbcje9FzKrDbC/b2IDPB2dXFERERaLQWkNqRigPbAmCAMBg3QFhEROV8KSG1I5fijIJeWQ0REpLVTQGpDKm4xooAkIiJyYRSQ2ohT+cUc/fksAP07awabiIjIhVBAaiN2l99eJC6sAwHeni4ujYiISOumgNRG7KwyQFtEREQujAJSG6HxRyIiIk1HAakNsNlslVP8Owe5tCwiIiJtgQJSG3D057OcLrRgNhnp3cnf1cURERFp9RSQ2oCd5d1rfaIC8PIwubYwIiIibYACUhuwM+0MAIM0vV9ERKRJKCC1ARUDtDWDTUREpGkoILVyljIre47b10BSQBIREWkaCkitXGpmHsWlVvy9PegW0sHVxREREWkTFJBaOUf3WucgjEaDawsjIiLSRiggtXIV6x9pgUgREZGmo4DUyu06qvFHIiIiTU0BqRXLLy7l+xN5AAzUFH8REZEmo4DUiu05noPNBlGB3oQHeLu6OCIiIm2GAlIr5rj/mrrXREREmpQCUiumBSJFRESahwJSK+YYoN05yLUFERERaWMUkFqpE3lFHD9zFoMB+muAtoiISJNyeUBauHAhsbGxeHt7Ex8fz/bt28+5/4IFC+jVqxc+Pj7ExMQwbdo0ioqKGnXOoqIipk6dSkhICH5+fowbN46srKwm/2zNqaL1qEe4H35eHi4ujYiISNvi0oC0cuVKkpKSmD17Nt988w0DBw5k9OjRnDhxotb9ly9fzvTp05k9ezb79+9n8eLFrFy5kqeffrpR55w2bRr//ve/ee+999i4cSPp6enceuutzf55m5JjgLa610RERJqcS5se5s+fz+TJk5k0aRIAixYtYtWqVSxZsoTp06fX2P+rr77iyiuvZMKECQDExsYyfvx4tm3b1uBz5uTksHjxYpYvX861114LwNKlS+nTpw9bt27l8ssvr7WsxcXFFBcXO17n5uYCYLFYsFgsTfBt4Dhf1Z91+TbtNAD9ovyb9PpSU0PrRFqG6sO9qD7ci+qjfg39blwWkEpKStixYwczZsxwbDMajYwcOZItW7bUeswVV1zBsmXL2L59O0OHDuXw4cOsXr2au+++u8Hn3LFjBxaLhZEjRzr26d27N126dGHLli11BqTk5GTmzp1bY/vatWvx9fVt/BdQj3Xr1tX5ntUG3xwxAQbyjnzH6pPfNfn1paZz1Ym0PNWHe1F9uBfVR90KCwsbtJ/LAtLJkycpKysjIiLCaXtERAQHDhyo9ZgJEyZw8uRJhg8fjs1mo7S0lClTpji62BpyzszMTMxmM0FBQTX2yczMrLO8M2bMICkpyfE6NzeXmJgYRo0aRUBAQIM/d30sFgvr1q3j+uuvx9PTs9Z9fjxZwNmtm/HyMHLfuF/iaXL5ULI2rSF1Ii1H9eFeVB/uRfVRv4oeoPq0qtG9GzZs4IUXXuDNN98kPj6egwcP8thjj/H8888za9asZr22l5cXXl5eNbZ7eno2yy/huc67NzMfgH7Rgfh61yyTNI/mqms5P6oP96L6cC+qj7o19HtxWUAKDQ3FZDLVmD2WlZVFZGRkrcfMmjWLu+++mwceeACA/v37U1BQwIMPPsgzzzzToHNGRkZSUlLCmTNnnFqRznVdd6P1j0RERJqXy/pmzGYzgwcPJiUlxbHNarWSkpLCsGHDaj2msLAQo9G5yCaTCQCbzdagcw4ePBhPT0+nfVJTU0lLS6vzuu5mp+MWI1r/SEREpDm4tIstKSmJiRMnMmTIEIYOHcqCBQsoKChwzEC75557iI6OJjk5GYCEhATmz5/PJZdc4uhimzVrFgkJCY6gVN85AwMDuf/++0lKSiI4OJiAgAAeffRRhg0bVucAbXdSUmplX7q9/3SQbjEiIiLSLFwakG6//Xays7N59tlnyczMZNCgQaxZs8YxyDotLc2pxWjmzJkYDAZmzpzJ8ePHCQsLIyEhgXnz5jX4nACvvvoqRqORcePGUVxczOjRo3nzzTdb7oNfgAOZuZSUWQny9aRLcNPPnhMRERE3GKSdmJhIYmJire9t2LDB6bWHhwezZ89m9uzZ531OAG9vbxYuXMjChQsbXV5Xq7pApMFgcG1hRERE2ijND29ldlYM0Fb3moiISLNRQGpldh07A8AgDdAWERFpNgpIrUhukYVD2fY1kAZoir+IiEizUUBqRb47loPNBp07+hDqpwUiRUREmosCUitSuf5RkEvLISIi0tYpILUiFTPYBql7TUREpFkpILUiFQO01YIkIiLSvBSQWonMnCKycosxGQ30iw5wdXFERETaNAWkVqJi/FHPCH98zS5f31NERKRNU0BqJbT+kYiISMtRQGolqt5iRERERJqXAlIrYLXa2H1MtxgRERFpKQpIrcDhk/nkF5fi42miR7ifq4sjIiLS5ikgtQIVN6jtHx2Ih0lVJiIi0tz017YVcIw/0gBtERGRFqGA1ApogUgREZGWpYDk5oosZezPyAU0g01ERKSlKCC5uX0ZuVjKbIR0MNO5o4+riyMiItIuKCC5ucrxR0EYDAbXFkZERKSdUEByc1ogUkREpOUpILm5XeULRA7qEuTagoiIiLQjCkhu7ExhCT+eLABgYGdN8RcREWkpCkhurOL2IrEhvgT5ml1cGhERkfZDAcmNVR2gLSIiIi1HAcmNORaI1ABtERGRFqWA5KZsNpvjHmxqQRIREWlZCkhuKiOniJP5xXgYDVwcFeDq4oiIiLQrCkhuqmJ6f+9O/nh7mlxcGhERkfZFAclN7T6u+6+JiIi4igKSm6poQdL4IxERkZbnFgFp4cKFxMbG4u3tTXx8PNu3b69z36uvvhqDwVDjccMNNzj2qe19g8HAyy+/7NgnNja2xvsvvvhis37OhiqzwZ7j5StoKyCJiIi0OA9XF2DlypUkJSWxaNEi4uPjWbBgAaNHjyY1NZXw8PAa+3/wwQeUlJQ4Xp86dYqBAwdy2223ObZlZGQ4HfPpp59y//33M27cOKftzz33HJMnT3a89vf3b6qPdUGyCuGsxUoHs4m4MD9XF0dERKTdcXlAmj9/PpMnT2bSpEkALFq0iFWrVrFkyRKmT59eY//g4GCn1ytWrMDX19cpIEVGRjrt8/HHH3PNNdfQvXt3p+3+/v419nUHP+UbAOjfORCT0eDi0oiIiLQ/Lg1IJSUl7NixgxkzZji2GY1GRo4cyZYtWxp0jsWLF3PHHXfQoUOHWt/Pyspi1apV/O1vf6vx3osvvsjzzz9Ply5dmDBhAtOmTcPDo/avpLi4mOLiYsfr3Fz7IGqLxYLFYmlQWRvCYrGQVh6QBkQHNOm55fxU1IHqwj2oPtyL6sO9qD7q19DvxqUB6eTJk5SVlREREeG0PSIiggMHDtR7/Pbt29mzZw+LFy+uc5+//e1v+Pv7c+uttzpt/7//+z8uvfRSgoOD+eqrr5gxYwYZGRnMnz+/1vMkJyczd+7cGtvXrl2Lr69vvWVtjJ/y7dP6S7MOsXr1wSY9t5y/devWuboIUoXqw72oPtyL6qNuhYWFDdrP5V1sF2Lx4sX079+foUOH1rnPkiVLuPPOO/H29nbanpSU5Hg+YMAAzGYzDz30EMnJyXh5edU4z4wZM5yOyc3NJSYmhlGjRhEQ0HQLOeYWFjFty0YA7r3pGjoFetdzhDQ3i8XCunXruP766/H09HR1cdo91Yd7UX24F9VH/Sp6gOrj0oAUGhqKyWQiKyvLaXtWVla9Y4MKCgpYsWIFzz33XJ37fPnll6SmprJy5cp6yxIfH09paSlHjhyhV69eNd738vKqNTh5eno26S/hD9mnsWIg3N+LmBA/DAaNQXIXTV3XcmFUH+5F9eFeVB91a+j34tJp/mazmcGDB5OSkuLYZrVaSUlJYdiwYec89r333qO4uJi77rqrzn0WL17M4MGDGThwYL1l2blzJ0ajsdaZcy2pYoHIAdEBCkciIiIu4vIutqSkJCZOnMiQIUMYOnQoCxYsoKCgwDGr7Z577iE6Oprk5GSn4xYvXszNN99MSEhIrefNzc3lvffe45VXXqnx3pYtW9i2bRvXXHMN/v7+bNmyhWnTpnHXXXfRsWPHpv+QjVCxQOSAzoEuLYeIiEh75vKAdPvtt5Odnc2zzz5LZmYmgwYNYs2aNY6B22lpaRiNzg1dqampbNq0ibVr19Z53hUrVmCz2Rg/fnyN97y8vFixYgVz5syhuLiYbt26MW3aNKcxRq5ytqQMIzYFJBERERdyeUACSExMJDExsdb3NmzYUGNbr169sNls5zzngw8+yIMPPljre5deeilbt25tdDlbwp/uuoQP/72ay7q6tiVLRESkPXOLW42IMy8TmD1UNSIiIq6iv8IiIiIi1SggiYiIiFSjgCQiIiJSjQKSiIiISDUKSCIiIiLVKCCJiIiIVKOAJCIiIlKNApKIiIhINQpIIiIiItUoIImIiIhUo4AkIiIiUo0CkoiIiEg1CkgiIiIi1Xi4ugCtlc1mAyA3N7dJz2uxWCgsLCQ3NxdPT88mPbecH9WJe1F9uBfVh3tRfdSv4u92xd/xuiggnae8vDwAYmJiXFwSERERaay8vDwCAwPrfN9gqy9CSa2sVivp6en4+/tjMBia7Ly5ubnExMRw9OhRAgICmuy8cv5UJ+5F9eFeVB/uRfVRP5vNRl5eHlFRURiNdY80UgvSeTIajXTu3LnZzh8QEKBfbjejOnEvqg/3ovpwL6qPcztXy1EFDdIWERERqUYBSURERKQaBSQ34+XlxezZs/Hy8nJ1UaSc6sS9qD7ci+rDvag+mo4GaYuIiIhUoxYkERERkWoUkERERESqUUASERERqUYBSURERKQaBSQ3s3DhQmJjY/H29iY+Pp7t27e7ukjtUnJyMpdddhn+/v6Eh4dz8803k5qa6upiSbkXX3wRg8HA448/7uqitFvHjx/nrrvuIiQkBB8fH/r378///vc/Vxer3SorK2PWrFl069YNHx8f4uLieP755+u935jUTQHJjaxcuZKkpCRmz57NN998w8CBAxk9ejQnTpxwddHanY0bNzJ16lS2bt3KunXrsFgsjBo1ioKCAlcXrd37+uuv+dOf/sSAAQNcXZR26/Tp01x55ZV4enry6aefsm/fPl555RU6duzo6qK1W7///e956623eOONN9i/fz+///3veemll3j99dddXbRWS9P83Uh8fDyXXXYZb7zxBmC/31tMTAyPPvoo06dPd3Hp2rfs7GzCw8PZuHEjI0aMcHVx2q38/HwuvfRS3nzzTX73u98xaNAgFixY4OpitTvTp09n8+bNfPnll64uipS78cYbiYiIYPHixY5t48aNw8fHh2XLlrmwZK2XWpDcRElJCTt27GDkyJGObUajkZEjR7JlyxYXlkwAcnJyAAgODnZxSdq3qVOncsMNNzj9O5GW98knnzBkyBBuu+02wsPDueSSS/jzn//s6mK1a1dccQUpKSl8//33AOzatYtNmzYxZswYF5es9dLNat3EyZMnKSsrIyIiwml7REQEBw4ccFGpBOwteY8//jhXXnkl/fr1c3Vx2q0VK1bwzTff8PXXX7u6KO3e4cOHeeutt0hKSuLpp5/m66+/5v/+7/8wm81MnDjR1cVrl6ZPn05ubi69e/fGZDJRVlbGvHnzuPPOO11dtFZLAUmkHlOnTmXPnj1s2rTJ1UVpt44ePcpjjz3GunXr8Pb2dnVx2j2r1cqQIUN44YUXALjkkkvYs2cPixYtUkBykX/+85+8++67LF++nIsvvpidO3fy+OOPExUVpTo5TwpIbiI0NBSTyURWVpbT9qysLCIjI11UKklMTOQ///kPX3zxBZ07d3Z1cdqtHTt2cOLECS699FLHtrKyMr744gveeOMNiouLMZlMLixh+9KpUyf69u3rtK1Pnz68//77LiqR/L//9/+YPn06d9xxBwD9+/fnp59+Ijk5WQHpPGkMkpswm80MHjyYlJQUxzar1UpKSgrDhg1zYcnaJ5vNRmJiIh9++CGff/453bp1c3WR2rXrrruO7777jp07dzoeQ4YM4c4772Tnzp0KRy3syiuvrLHsxffff0/Xrl1dVCIpLCzEaHT+k24ymbBarS4qUeunFiQ3kpSUxMSJExkyZAhDhw5lwYIFFBQUMGnSJFcXrd2ZOnUqy5cv5+OPP8bf35/MzEwAAgMD8fHxcXHp2h9/f/8a4786dOhASEiIxoW5wLRp07jiiit44YUX+M1vfsP27dt5++23efvtt11dtHYrISGBefPm0aVLFy6++GK+/fZb5s+fz3333efqorVamubvZt544w1efvllMjMzGTRoEH/84x+Jj493dbHaHYPBUOv2pUuXcu+997ZsYaRWV199tab5u9B//vMfZsyYwQ8//EC3bt1ISkpi8uTJri5Wu5WXl8esWbP48MMPOXHiBFFRUYwfP55nn30Ws9ns6uK1SgpIIiIiItVoDJKIiIhINQpIIiIiItUoIImIiIhUo4AkIiIiUo0CkoiIiEg1CkgiIiIi1SggiYiIiFSjgCQiIiJSjQKSiEgTMRgMfPTRR64uhog0AQUkEWkT7r33XgwGQ43HL3/5S1cXTURaId2sVkTajF/+8pcsXbrUaZuXl5eLSiMirZlakESkzfDy8iIyMtLp0bFjR8De/fXWW28xZswYfHx86N69O//617+cjv/uu++49tpr8fHxISQkhAcffJD8/HynfZYsWcLFF1+Ml5cXnTp1IjEx0en9kydPcsstt+Dr60uPHj345JNPmvdDi0izUEASkXZj1qxZjBs3jl27dnHnnXdyxx13sH//fgAKCgoYPXo0HTt25Ouvv+a9995j/fr1TgHorbfeYurUqTz44IN89913fPLJJ1x00UVO15g7dy6/+c1v2L17N2PHjuXOO+/k559/btHPKSJNwCYi0gZMnDjRZjKZbB06dHB6zJs3z2az2WyAbcqUKU7HxMfH2x5++GGbzWazvf3227aOHTva8vPzHe+vWrXKZjQabZmZmTabzWaLioqyPfPMM3WWAbDNnDnT8To/P98G2D799NMm+5wi0jI0BklE2oxrrrmGt956y2lbcHCw4/mwYcOc3hs2bBg7d+4EYP/+/QwcOJAOHTo43r/yyiuxWq2kpqZiMBhIT0/nuuuuO2cZBgwY4HjeoUMHAgICOHHixPl+JBFxEQUkEWkzOnToUKPLq6n4+Pg0aD9PT0+n1waDAavV2hxFEpFmpDFIItJubN26tcbrPn36ANCnTx927dpFQUGB4/3NmzdjNBrp1asX/v7+xMbGkpKS0qJlFhHXUAuSiLQZxcXFZGZmOm3z8PAgNDQUgPfee48hQ4YwfPhw3n33XbZv387ixYsBuPPOO5k9ezYTJ05kzpw5ZGdn8+ijj3L33XcTEREBwJw5c5gyZQrh4eGMGTOGvLw8Nm/ezKOPPtqyH1REmp0Ckoi0GWvWrKFTp05O23r16sWBAwcA+wyzFStW8Mgjj9CpUyf+8Y9/0LdvXwB8fX357LPPeOyxx7jsssvw9fVl3LhxzJ8/33GuiRMnUlRUxKuvvsoTTzxBaGgov/71r1vuA4pIizHYbDabqwshItLcDAYDH374ITfffLOriyIirYDGIImIiIhUo4AkIiIiUo3GIIlIu6DRBCLSGGpBEhEREalGAUlERESkGgUkERERkWoUkERERESqUUASERERqUYBSURERKQaBSQRERGRahSQRERERKr5/wiQwCTz9HcaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "df_metrics[[\"train_loss\", \"val_loss\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n",
    ")\n",
    "df_metrics[[\"train_acc\", \"val_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}