unit06-dl-tips/6.2-learning-rates/6.2-part3-learning-rates-finder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e462c50-9f5d-4cc0-beb8-2f8918a6b1e2",
   "metadata": {},
   "source": [
    "# Unit 6. Essential Deep Learning Tips & Tricks\n",
    "\n",
    "# 6.2 Learning Rates and Learning Rate Schedulers\n",
    "\n",
    "## Part 3. Using an Automatic Learning Rate Finder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eec68953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch    : 1.13.1\n",
      "lightning: 2.0.0rc1\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 lightning.pytorch.loggers import CSVLogger\n",
    "\n",
    "from shared_utilities import CustomDataModule, LightningModel, PyTorchMLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa7bcf14-8046-4213-accf-544b67e74883",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(123)\n",
    "dm = CustomDataModule()\n",
    "\n",
    "pytorch_model = PyTorchMLP(num_features=100, num_classes=2)\n",
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81329c62-461b-4e57-a2e0-7ed4ed39cad3",
   "metadata": {},
   "source": [
    "## Lightning < 2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e57241-bd53-48c2-85eb-d90f591489ab",
   "metadata": {},
   "source": [
    "```python\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=100,\n",
    "    auto_lr_find=True,  # New!!!\n",
    "    accelerator=\"cpu\",\n",
    "    devices=\"auto\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"),    \n",
    "    deterministic=True,\n",
    ")\n",
    "\n",
    "results = trainer.tune(model=lightning_model, datamodule=dm)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7215709-24a9-4d67-8690-9ffe4acc00c6",
   "metadata": {},
   "source": [
    "## Lightning >= 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "17e18376-9168-439c-95bc-cfa94cdf9f8c",
   "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",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/setup.py:201: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/callbacks/model_checkpoint.py:612: UserWarning: Checkpoint directory logs/my-model/version_0/checkpoints exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: 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 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: 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 10 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": "1e4659ba81244d0cb7018fdfb1dc4e38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Finding best initial lr:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_steps=100` reached.\n",
      "Learning rate set to 0.10964781961431852\n",
      "Restoring states from the checkpoint path at /Users/sebastianraschka/Desktop/unit6/6.2-learning-rates/.lr_find_6cb119fd-ccbe-4c5e-b017-57b30b7becae.ckpt\n",
      "Lightning automatically upgraded your loaded checkpoint from v2.0.0rc1 to v2.0.0rc1. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file .lr_find_6cb119fd-ccbe-4c5e-b017-57b30b7becae.ckpt`\n",
      "Restored all states from the checkpoint at /Users/sebastianraschka/Desktop/unit6/6.2-learning-rates/.lr_find_6cb119fd-ccbe-4c5e-b017-57b30b7becae.ckpt\n"
     ]
    }
   ],
   "source": [
    "from lightning.pytorch.tuner import Tuner\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=100,\n",
    "    accelerator=\"cpu\",\n",
    "    devices=\"auto\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"),    \n",
    "    deterministic=True,\n",
    ")\n",
    "\n",
    "\n",
    "# Create a Tuner\n",
    "tuner = Tuner(trainer)\n",
    "\n",
    "# finds learning rate automatically\n",
    "# sets hparams.lr or hparams.learning_rate to that learning rate\n",
    "lr_finder = tuner.lr_find(lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a4ca1cf-5e65-4df9-875c-2424850fcf8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = lr_finder.plot(suggest=True)\n",
    "#fig.savefig(\"lr_suggest.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9abe1027-3ec8-4597-9188-1068ee91df3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10964781961431852\n"
     ]
    }
   ],
   "source": [
    "# get suggestion\n",
    "new_lr = lr_finder.suggestion()\n",
    "print(new_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "048040b6-b26a-43ae-853f-1561e5234324",
   "metadata": {},
   "outputs": [],
   "source": [
    "# update hparams of the model\n",
    "lightning_model.learning_rate = new_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "14d23b49-4c47-4bb1-bf6c-b87aabf0b22d",
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
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      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/callbacks/model_checkpoint.py:612: UserWarning: Checkpoint directory logs/my-model/version_0/checkpoints exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "\n",
      "  | Name      | Type               | Params\n",
      "-------------------------------------------------\n",
      "0 | model     | PyTorchMLP         | 15.3 K\n",
      "1 | train_acc | MulticlassAccuracy | 0     \n",
      "2 | val_acc   | MulticlassAccuracy | 0     \n",
      "3 | test_acc  | MulticlassAccuracy | 0     \n",
      "-------------------------------------------------\n",
      "15.3 K    Trainable params\n",
      "0         Non-trainable params\n",
      "15.3 K    Total params\n",
      "0.061     Total estimated model params size (MB)\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/fabric/loggers/csv_logs.py:188: UserWarning: Experiment logs directory logs/my-model/version_0 exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!\n",
      "  rank_zero_warn(\n"
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      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: 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 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: 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 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01a99579c1354c598f1acc206c976dbb",
       "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": "c8a00cbd182c461491ae983464ee1a56",
       "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": "83a9f8b2f5904525832a4abd17fcbc70",
       "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": "b8b7da9170d544e28e24e6f9ee16c04d",
       "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": "a9013ee5feac4aada48ed2f30841d127",
       "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": "e9b3ef8d39c34c5182b3cadd722bf712",
       "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=100` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer.fit(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d4b7eb3-9f3c-4095-ad85-b37acf4557dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zP3jEXiCU7VdrN0dD7szAXx9CRNx0J2mq181grYQTdTacTj3MCxIEQeijGGXgGSPV19HnqK/7V4LDFrz7OJ3w7ZPgaIKNT/s+p70eN+ARN42VYA+gGMVIS+X4EDcAJ10Fw89Uox3e+Vng6SkjapN7MlhjA3ttiBFx0524Ije52jEcTp3K+iD+AxIEQRD8xx25cYmbgTMgLh0aKiHv6+Dd5+gmTzpp9wrfYx6MieBtRW5iUsDkmmvtb8VUU61HwLVMSxloGpz/iKoWy1sLG/7t37UNDhopqcj224CIm+7FJW6GmqVLsSAIQlgx0jBG5MZkhpGL1HEwS8L3eqW56suViGiJW9y0EbkxmbxMxX6Km+LtgK4Ek+HZ8UXqYFhwjzr+5B6oyPPv+k4nHHbNqopwMzGIuOlejMgN0qVYEAQhbDTWQPVRdWx4bqB7fDfGteJcjfh2vdv6HKNaquVEcG8C9d24U1JtRG28mXEtDJoNTTWq/43uh2Xi2HZVWRaVoMrAIxwRN92Jq1oqkVqSqOFoRX2YFyQIgtAHKXNFbeLSVeWQwfAzwWSF8gPNRzN0loo8JQI0Eyz6vXps13utvS3uyI0f4sbfiim3mdgPcWMywQWPKmPwgU9hy/KOX2P4bQbPAbPVvzWFERE33UlUnNswNkgr4ZX1fob/BEEQhOBhiBvDb2MQkwTGfKRgRG/2fqS+5s6E8UtUlKP6qPLheOOPuAm0S3F7lVK+yBgJZ9yljj+8y1Oe3hZGf5se4LcBETfdj+G7MR3n64PlbCuoDO96BEEQgok/KY1wY4ibjBGtnzOqpoLhuzEE0uhzwBoDIxeq73e94znH3qi8ONC2oRg886X8SUvZm6Bklzr2Jy1lMPtnSgw1VMCK29s+z2FTDQ8h4vvbGIi46W5c4mZRf5WS+teag2FcjCAIQvDQdr3DBZuXoe1ZEe6ltE9pi0opbwzfTd7XanxBZ2ms9hhuR7kE09jF6uuudz0i0CgDN0f57k5sEMhk8OO7wWlTVVYpg/xfs9kCF/5DVWbtehd2vOX7vMLvlD8nNg2yJ/h//TAi4qa7SVW9buam1wDw/rYiCsV7IwhCL8C09wM0dEzbXg33UtqnrEWPG29SB0PWeNWteN/Kzt/jwGeqt03aMM99Ri5SIqb8gBIg4ElJJeT47k5sEEiXYm+/TXvX9EXORDjlVnX81k9g539bn+NOSc1Tfp0eQM9YZU/GFblJayxkzvB0HE6dZ748FN41CYIgBANXGbFWsD5y01O67hmY6StyAzDaFb3Z2wXfzR5XWmv0uR6BEZ0Iw85Qx0bVVEdl4AaBpKUCqZTyxam/gpFnqeGa/7kKvvpb859nDxm54I2Im+7GJW44cZjrTh0GwCvr86lqkIZ+giD0bLSKI+prbYn//VJCTXWRSqloZs//xy0x0kj7P1X+lUBxOtTMJvCkuQy8U1Pgn5kYAktLuWdK+WkmboklGi572TNQdOXv4L1blNfGVg/536rHh57eueuHARE33Y3xj6mygNOHpzAyK4GaRjuvfBuh/xEIgiD4g60ezejXAlCwPnxraQ/Db5M6GCxRvs8ZME2VXjdW+W661wFa4QaoK1OTuAfNav7k6HNVaXjxVjWV258eN+ApBa8rVeKpLZwOKN6mjjsrbkD5b859EM7+X0CDjc/CS99XqTpHIyT2VwNHewgibrqbhGw1iVV3oFUVct08Fb155qvD2BxdGDsvCIIQTirym38fqeKm5dgFX5hMMOosdbw7cHO0ZkRtRi5q3QMmPh0Gz1XHu94LPC2lO1XzvLYoPwi2WrDGNW9Q2Flm3aiiONY4OPg5vP4j9fjQUwP384QRETfdjcnkbubHiUNcOLU/GQnRFFU2sGJbUXjXJgiC0FlcKSk3Ruoi0mg5dqEtxpyvvu5+P2D/kGmfy2/TMiVlYKSmdr/X8VwpA7NVVScB1LRjKjbMxNnj1UiJYDDmXPjRB2qNTtdsrB4wcsEbETehIGuM+lqwkWiLmWVzlNh5cvVB9Eg14QmCILTHicMAVMa6So+Ltyp/RqThjtx0ENUYdrqKVlQVeASDH8Q1HkMr3avKqUcs8H3SmPPU17yv4dgOddxRWgr861IcaPM+f+k/Ba79FPpNUZ2dRywM7vW7GRE3ocBwyx/4FIAfzBxMjNXEjqNVrDtYFsaFCYIgdBKXuClNGIsen6V+ww9AFISM0nbKwL2xxsKI+ep49/t+Xz6n0tV9ePAciE3xfVLyQOXrQfd4bjoyFINXOXhp2+d0tVKqPZIHwPWr4LbdkJAZ/Ot3IyJuQsHwM9XX/G+hoZLU+CgunZ4LwL9WS1M/QRB6IK60VF1UJvqA6eqxSPPd2Bo8VVzteW4MvFNTfuIWN0bFVUfXNvBH3LjLwduI3Oi6V6VUN4gbUD6btozYEYyIm1CQOliFRHUHHFIdLH88dyiaBp/vOc6+Y9VhXqAgCEKAnFDipjbaS9xEmu+m/CCgQ3SSJwrSHiMXqZLxkh2u13ZAQyXpNXvV8eg2/DYGYy/wHJu8/DTt0VE5eGWBGuVgskDWuI6v14cQcRMqhrvCna7U1JCMeM4ap5T78+uOtPUqQRCEyMQ7cjPQK3ITST5C98DMEf5V+sSlwRBXZZMfVVPagU8x4UDPGKU6E7dHxgjIHKuOE7L96/Sb0IHnxkhJZY5VvWoENyJuQoWRmtr/qfsf/1kTVCngwdKacK1KEAQhcOpPQIMaAlwXlYmeM1lFPKqLoKowzIvzor2xC20RQGrK5CoBd47sIGpjMNZ17Y7KwA3chuI2PDfdnZLqwYi4CRVDTlGhyIoj7nBnapzKY1bUSbdiQRB6EK6UlB6ficMcDVHxkOMaqBhJqSmjDNwfv42BUdmU/3X7ow+aatEOfAKAPvIs/649/RoYfAqcfIN/5xtpqbY8N91VKdULEHETKqITPJ0rD3wGQIqIG0EQeiKulJSe7DWBeuDJ6mskmYrdkZsAmtslD1Tlz7oT9n7Y9nlrH0VrqKTW21DdEUn94Efvw+Sl/p3vjty0IbK6s1KqhyPiJpR4p6aAlFjVybKyXsSNIAg9CFfkhtTBnscGzlBfI0Xc6LqnDDyQyA10nJqqOqqGSwI7+y8NXvO8liR4iZuWXqbaUlcKUPNEzQQ3Im5CidFD4fAasDeREqfETU2jXUYxCILQc3BHbrzETa5L3BRtAXtjGBbVgroyaKgAtMBnIhmpqQOfQaMPT+Sn94GtDufAmRxNmdHVlbaNEbmxN0CjV1VtZSG86RpymT5cTR8XmiHiJpRkT1R/WZtqoOBbEmOsbgO/RG8EQegxuBr46SleaanUoRCXAY6myGjmZ0RtknNVg75AyBqr9uNodFe4uin8DrYsB8C58PfdO28pKh6s8erYiN5sehEem6XWZYmBM3/bfffvwYi4CSUmk6db8f5PMZs0kmJU9EZ8N4Ig9BiMtFSKV+RG0yIrNdUZv42BpnmiN96pKV2Hj+5Wx5OWovc/qWtr9AcjNVW0BV6+FP57s5pePmA63LAGxn+v+9fQAxFxE2pGNO93Y6SmKuubwrUiQRAE/3E63V1/m0VuwJOaioSKKbffppOTsg3fzd4PweH65XPXO5C3FiyxMP93XV+jPxipqdd/DPs+BnM0LLgXrvkYMkeFZg09EBE3ocaI3BRtgdpSt6lYIjeCIPQIao6pdI1mhqQBzZ+LpIqpsk6UgXuTe7JKszVUwpGvlI9opUvQzPmZqqoKBUY5ODr0PwluWA2n3NJ9JuZegoibUJOYrbw3AAc+J9lVDn5CxI0gCD0Bl5mY5AFgtjZ/rv9U0EyqiqcyzM38SruQlgIlHka75kXtfh++eUJ5jRJyYO4vgrJEvxh7vorezP8fuGYlZI0J3b17MCJuwsEIV0n4gU+9IjeSlhIEoQfgMhM389sYRCdA9nh1HKzojcMGX/wZtrwSwGvscOKQOu5s5AY8qamd/4XVD6rj+b9V+wwVU66AX+2HebeB2RK6+/ZwRNyEA6PfzYHPSI1Vf1mlWkoQhB6Brx433gQzNaXr8M7P4PM/wFs3+C9wKo6A0668MS1TZ4Ew7DRVrVRzTJl4cybB5Cs6fz0hZIi4CQeDZqt/dDXHGI4y5onnRhCEHoGRlkod4vv5YFZMfXqfu+waUEIn75uOX+dtJvZnQGVbWGM9RSAAZ/2xa9cTQob8lMKBJVrNmgLG1KiqggqJ3AiC0BNwl4EP8f18rityc3Qz2LuQbv/mSfjyYXV8waMwdrHqofPKFe5qLZ9U5MNnv1fHwfCnTHFFasYvgaHzun49ISSIuAkXrt8GBleo30LEcyMIfQR7E3z5VyjZFe6VdA7Dc9NWWiptGMSmqYqq4m2du8eOt+GDO9TxGb+Bk66Ci55QaaG6Unh5afOOvQYFG+FfZ8Kx7arKaN7tnbu/N6PPgZvXw5Inu34tIWSIuAkXw5W4ySjfSAyN4rkRhL7Cxmfgk3vgg1+HeyWBY29yzTPCt6EYWjTz60S/m8NfwpvXAbqaon3qL9XjUfFw+SuQkA0lO+GNa8Hp8Lxux9vw7LlQWwJZ4+G6z1Sn4WCQOap1ZZgQ0Yi4CRcZIyFpIGZnE7NMu8RzIwg9FVs9JmcAkddd76qvRVtaD0OMdCrzAV15BhOy2j6vs838ju2A5Veo9NOY8+HcB5uPN0geAJctV2MH9n4In/yPeg/XPASvXa1mMI08C675CFJyA96e0HsQcRMuNM1dEn6GaZOkpQShJ9JUi+WJuZy++7fqA7kj6srhyFp13FChpkv3JCq8KqXam6lkVEwdWg22Bv+uXVkIL14MjZWQOwsufsp3o7qB0+B7j6njtY/CM+co4zHAzJ/A5ctlkKQg4iasjFkMwGXmz8lsPILD2cN+ixOEvs6Ot9Aq80hsLEJrOWDRF3s/At0rlXJsR/etrTvwNVPKF4NmQ9JA5Y/Z9IJ/1/74bqgugswxSqC0N+xywsVw2p3qOG+d6pZ87l/gnD9J514BEHETXkYuxDliAdGanQesT1FV1xjuFQmCEAjfeT64Tdtf7/j83e+5DlxRj2Pbg7+m7qQjM7GBJUqNCAD48pGOq6aKtsKOtwANLv43xKV1vJbTfg3TfqSmfl/xHzj5uo5fI/QZRNyEE03DdP5fqdOjOdm0B/v6Z8K9IkEQ/OX4Hsj/2v2ttu8jaKhq+/ymOtjviu6Mv0h97WmRmwo/IzcAU3+ozL9VBc171fji8z+qrxOWQM4E/9ZiMsHiR+DW7TBygX+vEfoMIm7CTcog/hX1AwDS1v4BqorCvCBBEPzClW5xjjyL6uh+aPYGr8iMDw6uAnu9ijRMvlw91tPEzYkOGvh5Y42FOT9Xx18+rEYi+CJ/Pez9QM2kOv2uoCxTEETcRACfJl7IZucwzLZq+OBX4V6OIAgdYW+CzSoa4ZxyJQVps9XjW//T9mt2v6++jjnPE50o3aumTfcUKjoYvdCS6T+CuHSVzmorbWc03Jt8haoiFYQgIOImAkiKi+Uu23U4NYsqE93Vzm9/giCEn70fKLNsQg76iAUUpLrEzaEvoPpY6/MddtizQh2POQ8S+0FsqjIXH98dunV3hcZqqCtTx/6kpUD1ppl9szpe81DzvjSgqqkOfQEmK5x2R/DWKvR5wi5uHnvsMYYOHUpMTAzTpk1jzZo1fr3uq6++wmKxMGXKlO5dYAhIibOySx/MtsFXqQdW/BIaKsO7KEEQ2sYwEk+5AkwW6qKzcQ6YDroTtr/R+vz8b6C+HGJSYNAcVUad7Yre9JTUlJGSik2FmCT/XzfjOohJVlGqnf/1PK7r8Nn96njaMv+jQYLgB2EVN6+++iq33HILd999N5s2bWLevHmcc8455OW1MzcEqKys5KqrrmL+/PntntdTSIlTnS+/yPmRal1eXQSf3BvmVQmC4JPKAtj/iTqe+kP3w/r4S9TBNh+pKSMlNfocMFvUcfZ49bWniJtAzMTexCSp/jMAq/8CTqc63rdSiT5LTHDGJAiCF2EVNw8//DDXXHMN1157LWPHjuWRRx4hNzeXxx9/vN3X3XDDDVxxxRXMnj07RCvtXlJiowAobzLD4r+pBzf8G/K+budVgiCEhc0vAzoMmQfpw90PO8ddqPqtHN0Epfs95+u6x2g85jzP425x00PKwQMxE7dk5g0QlQglO1RKz+n0eG1Ovg6S+gVtmYIAYAnXjZuamti4cSN33nlns8cXLVrE2rVr23zdM888w4EDB3jxxRe5//77O7xPY2MjjY0ew15VlSrVtNls2GzBHXlgXC/Q6yZGK41ZXtOIbeBszJN/gGnLS+iv/ADn6PPQh56GPvgU/3o/hInO7r230Jf336f2rjuxfPc8GmCffAW61/8jtqgUzMPOwHTgExybl+M0mswd24G14gi6JQb7oHngOl9LH40F0I/twN4D3jtT2UHMgCM5F2eLn3mHP3trIqbpP8a89m84v/gzzqYGLMVb0aMSsM/8qfs96Wn0qb/7Pgj1/gO5T9jETWlpKQ6Hg+zs7GaPZ2dnU1xc7PM1+/bt484772TNmjVYLP4t/YEHHuDee1uneD7++GPi4uICX7gfrFy5MqDzj5RogJl9eYWsWJGP1XkKp0atJKGuBPOm52DTc+hoVMYO5njieI4lT6YsfnT77c/DRKB772305f33pL2n1+zGboqhMm5IQK/LrNrOnMp8bOY4PjxsxZm3wv3cypUrGWgfwTQ+of7b5/m0ZiJoGqOK3mYsUBw/lm8/+cJ9vtnZyHloaLXH+fS/y2m0Jgdpd93DzAPryQG2F1RzeMWKZs/587OPso1koRaFpWgz9v/+DIA9aQvYs+qb7lhuSOlJf/e7g1Dtv66uzu9zwyZuDLQWH9C6rrd6DMDhcHDFFVdw7733MmrUKL+vf9ddd3Hbbbe5v6+qqiI3N5dFixaRlBSAKc4PbDYbK1euZOHChVit/k+Qjd5VwssHNhOVkMq5585UDzadi/3Il2iHVmM6/AXa8d2k1B8mpf4wI0vex37xs+jeIe4w09m99xb68v573N6rirD848egmbFf8xlkjvb7pea33gTANOVyzj77e0CL/eunoj/yPAlNJZw3JQd9wDQsTz0IQOa8H3Hu5HObXzD/T1B+gAUT+6EPOz0Yu+s8deWY//sT9EGzcc69pdXTlidVo73xc89l3HA1Fy/Qn70Wtx2+/SdRjlr0mBSG/+BhhgdiTo4wetzf/SAT6v0bmRd/CJu4ycjIwGw2t4rSlJSUtIrmAFRXV7NhwwY2bdrET3/6UwCcTie6rmOxWPj4448588wzW70uOjqa6OjoVo9brdZu+2EEeu30JDVDparB7nmdNQXGna/+AFQXw8Ev4Lvn4MhXWPa+DxMvCvLKu053vq89gb68/x6z96L14LQDdqzv3gzXfgJmP9ZdW+Yu5zZPX4a5xV7V/uOUr2bba1h2vgkp/eHYNtBMWMaeBy3fn5wJUH4AS9luGL0wSBt0UbRVDecceqp/56/+Ixz8FA5+ijkxC6Zd7XlO16FCFXpYMke02offP/t5t8LGZ8DRiDb3F1gT0/3cTGTTY/7udxOh2n8g9wiboTgqKopp06a1CmetXLmSOXPmtDo/KSmJbdu2sXnzZvefG2+8kdGjR7N582ZmzpwZqqUHnZRY9QM70d5k8MQcmLwUzvyt+n7/J617RgiC0DH56z3HRZvhiz/797qtr6rJ3/0mQ79JbZ838VL1dcebsPMddTxoNsRntD63u8rBm+rgufPhucWeyq72OLoZNj7n+f792z3TywFqS8FWB2iQPLDz60rMgQv+D2ZcC7N+0vnrCEIHhLVa6rbbbuOpp57i6aefZteuXdx6663k5eVx4403AiqldNVVqveLyWRiwoQJzf5kZWURExPDhAkTiI+PD+dWukSyqxS8st6Gs6PJ4ANnqJ4R9SegcGMIVicIvYx8l8dj7AXq65qHoGBD+6/Rdc9065Ouav/c4Weorry1x2G1SknRVgrZqJgqDnLF1O73Pb2y3vk51Fe0fa6uwwd3ADpMuETNvXLa4NUfeiqkjIGZSf3B0joSHhCTL4PzHmp/6rcgdJGwipulS5fyyCOPcN999zFlyhRWr17NihUrGDxY9VEoKirqsOdNbyDZFbnRdahuaGP+ioHZAsNd/X32fdzNKxOEXoatHoq3quNF98PE76suwW9eD021bb9u17tQshMssUoAtIfZCuOXqOOGCvV19Lm+zzUiN8d3gyOIFSfegyqrCuGju9s+d9trSvBZ42HR7+HCx1R0qq4Mll+uOhN3tseNIISJsHcovummmzh8+DCNjY1s3LiRU0/15IefffZZVq1a1eZr77nnHjZv3tz9i+xmoi1m4qLMAFTUt5OaMhh1lvq696NuXJUghInuTLce3az8NgnZkDIIzn0QEvtD+QFY+bvW59ublDD4z5Xq+0mXQmxKx/eZdKnnOHsCpA31fV7KINX/xWmD0n2B7sY31cVw8HN1/L3HAQ02v+j7/4vGavjYleo+9XYVmYmKg8uWq/eoZIcSfuWH1DnSRVjoIYRd3AgKw3dTUdfxb2/68DMBTf0GKlPEhd7E14/D/VnN/R/BpOBb9XXgDNVKITYVvvcP9dj6p5r7U0r3wb8XwLq/q+9nXAvn/K9/9xk4w9Psrr2qRk0Lfqfiba+pMRC5M9V4CGO20zs/h7ry5ueueQhqiiF1KMy62fN48gC47GUwRysT9dpH1eOdaeAnCGFAxE2EkBKnuhRX1Lcvbv75xQHG/u9GajMnqwf8MQsKQk9gyyvw4Z0qsvLpfe2niTpLvkvc5HoVIAw/E06+Xh2/fbMSAN+9AE+cCkVblAC67OXAfCKaBuc8CGMXq9lK7RHsTsVbXlVfJy1VX8/8DaSPVCLmQ6+mqWUHYJ1L2J39AFhjml9n4HS4wCVqGl3+HUlLCT0EETcRgjFfqqK9iilg5c5jNNicrDOdpB4Q343QG9j3CfzXFTkwWdXE7fX/Du49dB0KXJVSuSc3f27BvR4B8PgceOenqjpo6Knwk7XtR1/aYtQiWPoiJGS2f14wIzfF21XpuTlKGYNBCbKL/gmaSVV87XKNgvjo/6nqrxELYNTZvq83eSmccqvne0lLCT0EETcRQopXxVR7FJ6oB+DlclfjsQOfB9eIKAihpnAj/OcqFbGZeCmc/1f1+Fd/C270puII1BxT4qnflObPRcXBkifUbKjqIjBZYP7/wJVvKx9KdxLMcvCtr6ivo85qPq5l4HSY+wt1/N4tKkq290O1z7P/1H638zN/B9N+pGZp9T+p62sUhBAg4iZCSHYNz2zPc2NzODlW3QDA59UDsMdmQFM15K0LyRoFIeiU7oeXvg+2Whh2Blz4D5h8ufKABDt6Y/S36TepdQoGYMA01YNlxAL48ccw7zYwmYN3/7bIGqu+Vh9t7YkJBKcDtr6mjidd1vr50++CzLGqRP2tG9Rjs34CGSPbv67JBIsfgWXv+X7fBCECEXETIXjSUm2Lm+LKBnRXGxwdE/uSZqlvJDUl9ESqi+HFi1TJcb8psPQFsESpdgen/kqdE8zojdtMfHLb50z9IfzwDRg4LTj39IeYJI+XpSvRm4OrVFotNhVGLmr9vCUavveYik4BxGfBqXd0/n6CEMGIuIkQ3NVS7ZSCF7hSUgYrGiaqg319e2ib0ANpqIQXL1Et/VOHwg9eh+hEz/OTlgY/euM2E88IzvWCiTs11QVT8VaXkXjCxUok+mLASTD/t0rgnPtnJawEoRci4iZCcHtu2oncFFYocTMsU3VjfqFkKLpmVg3AjE6igtAT+O9PlfE1PhOufLO16TbY0ZumOo9waC9yEy46qphy2KB4G+7QbUsaa1SjQfCdkvLmlFvhNyUew7Eg9EJE3EQIhuemvflSR13iZvrgVMbkJFKhJ1CWNkU9uV+iN0IPoakO9nygji9bDmnDfJ8XzOjN0U3KsJzYv2uzkbqL9iqmbPXw3AXwz1Pgtat9C71d76rqrrThyjzcEeawzUwWhJAg4iZCcHtu2qmWMiql+qfEcuaYLADWaq7qhb3iuxF6CEe/Ux15E/u1/0EczOiNMU8qd0b7lUHhwkhLlexq3qHZYYfXfwx5riGWO/8LT58NlQXNX29USU2+LDL3JwghRsRNhBBIWmpASixnuMTNc6WukvBDq9VveEL3UbQFHhoLm14K90p6NkZ136BZHX8QByt6Y/S3icSUFKjxDNY4sDdA+UH1mK7De79QHYItMXD2/0JchupM/uQZHg9RZSEc/EIde499EIQ+jIibCCEl1tOhWG8jr+4WN6mxTM1NITnWysaGfjTF9QN7PRz+KihrqWqwcdmT63jiiwNBuV6vYf2/Vbnut0+GeyVdR9fB3hiee+d9rb7mzur43GBEb3Tdy0wcoeLGZPaUhBu+m0/vhU0vKvPvJc/ArBvh+s9VlKe2BJ49DzYvV+MW0GHQHBmPIAguRNxECEbkxuHUqWlsPRlc13W3uBmYEofFbOK0UZmAxs4EVyv5IJWEr9lbytcHy3ngg928tamg4xf0BXTdM+qieCs0VIV3PV1l7aPwhxzY/X5o7+t0eITGID/EDXQ9enPikHqtOUpNu45UvH036/4BX7qaGS7+G4xxTRVPGQQ//gjGnK+6C799I6x+UD03eWno1ywIEYqImwghxmomxqp+HL563ZTWNNFkd6JpkJOsGmkZvpu3a13/Ke77qO1qigAoqvSkt+58YxvbCyu7fM0ez/HdUFWojnWn5wO6J9JYA2v+ovbxyb3gdIbu3iW7oLEKohI8PpOO8I7erP2/wCNO7uZ9k1Wvl0jFeD82vaRGI4DqknzSlc3Pi06AS1/wvCdNNWrA5bjvhWypghDpiLiJIIzUlK8RDEbUJisxmiiL+rGdNioTkwb/KRuObo6CE4fVMLwuUlypuiBbTBqNdifXP7+B0powpTAihZa9hI58GZ51BIMty1WfGYDSPbAnhNEbw28zcEZgFTuTLoWkAaq77o63ArunP837IgEjclN9VH2ddXPzuU7emExqIObF/4boZJhxDcSmhGSZgtATEHETQbTXpdiolBqQ4plKnBofxdRBqdQRQ3GKq6Pqvo+6vI6iKiVubj5jBMMy4jla2cDNL32HzRHC3/AjDaPU3khrHFkbvrV0BacDvn5MHaePUF9X/6VrEb+mGgaWf6XMsB1h+G0GzQ7sHmYrTP+xOv7mn4Gt110pFeHiJmuc53jSUlh0f8eG64mXwK8Pq6negiC4EXETQSS306X4qNtMHNfscSM19YU+RT2w9dUupxmMyM2o7ESevGoaCdEWvjlUzh/e39Wl6/ZYGmvgiCviMP936mvhd6pfS09jzweqGicmBa58S1XoFG2GA591+pKmz+9n2pEnMK15sOOT3eLGT7+NN9OWqfTL0U1QsMG/1zTWeHrHRLq4iUtTf79m3aRmbJn8/O/Z3/MEoQ8h/yoiiHYjNxWtIzcAZ4xW4ubR41PRoxJUubLRhr2TGOImJzmGEVmJ/HXpFACeXXuY1zbkd+naPZJDq1VfltQhMHy+agTntHnKi3sS6/6hvk7/sTKnTlumvl/zcOeu53Ri2vUOAKbtr7cvrCvyoapAVf/402iuJfEZKlIB8O0T/r3m6HfKW5Q0sPuneweDeberKIzZGu6VCEKPRsRNBNGe56bAnZZqPpV3bL9E+iXHUGhL4ODYn6gHP7lH/cbaCZxOnWOutFQ/l3F54bhsblmgJgff/fZ2tuRXdOraPRYjJTVioUoTDJ6jvu9pqanCjaoZnMkKJ1+vHpv9U/X9kS89UZVAKFiPVlsCgFZV2L7gM67fbzJExQd+L/Cse8dbavBmR0TyPClBELoNETcRhBG5OVHbOi3l3ePGG03TON0VvXmJc1XJbE0xfNm538RLaxuxO3VMGmQmeipLfn7mSBaOy6bJ7uSGFzaSV9YDUzKdwbsEfMQC9XXIXPX1SHD6CoWMdS6vzYSLIamfOk4eAFMuV8edid7sfq/599vfaPtc7+Z9naX/FNUfx2mHDc90fH6kN+8TBKFbEHETQSS3M4Kh8IQSEwNS4lo9Z/huVu6rQF90v3pw7d9V9VSAGCmpzMRorGbPXw+TSePhSyczMiuB4qoGLntyHUfKujjMsCdQuk9NrjZHwdB56rHBLnFTsB7sbc8CCzp15bB7hRqiGCiVBZ4qo9k3NX9u7i2gmZQZvWir/9fUdbe4yUtzvTc7324+PsCbrvhtvJnpit5seLr9978nNO8TBKFbEHETQbi7FLfw3FQ32KhqUI39WkZuAOYMTyfKbCK/vJ4DaafC0NPA0Qgf/zbgNRS5/Tat75MYY+Wla2cyPFNVUF325NccLu3lAseI2gye60mlZIxSbfDtDcrTESre+Rm8cjn85+rAe7188wToDhgyr3Uju/ThMH6JOg4k4leyC8oPopuj2T7gcvSYFKg55juiVV8BJTvVsT+didtj7AVqLlVtiRJTbVF2AOrLlQk5Z1LX7ikIQo9CxE0E4Z4v1aJa6miFEhzJsVYSolv3BomPtjBzWBoAn+05Dmf/Sf0mvusdOLQmoDUYkZt+STE+n89KimH59bMYmZVAUWUDS59cx8HjnfP39AjcfpsFnsea+W5ClJqqKlIzhkD1pXn1h2Dzo/QaoLEaNj6njuf8zPc5Rj+VHW9D6X7/ruvqbqwPPQ2bJQF99Hnq8e1vtj63YD2gqwngidn+Xb8tzFaYfo06/qYdY7GRIus/FSxRXbunIAg9ChE3EURKrO9qqcIKlZLqn9I6mmIw35WaemrNIfZrgzw9QT68q+00Qek+2PJKszRHkVellE9qy8hyHOfl62YxKjuBY1WNXPbk1xw43gsjOE11nnldIxc2f85ITYXKVLzlZVX1kzoULLFq1Mbyy/wrR9/0EjRWQvpIZYr2Rc4EGHUOoMNXf/VvTbvfBcDpEjVOI/qz87+tU2duv02A/W3aYtoylSos3AAFG5s/53SqqOWqP6rvx10QnHsKgtBjEHETQbTlufHVwK8lS6YNZFR2AiXVjVz25Dr2j/s5xCTDsW3w3fPNTz6+B964Fv5xMrx1g2ri5qLYNXqhny9xY2+Cfy+Af5xMpr2Il6+bxZicREqqG/nh0+sp7m0e48NfqvRecq5KRXljRG7yvgZH61lgQcXphO9eUMen/gp++DpY4+Hg5/Dype1Xxnk37Zt9U/s9Uebdrr5ueUWVbbdHRZ5qO6CZ0EeeBYA+eK5K19WXw6Evmp8fLL+NQUKmMkZD87JwWz28drUa0wBw+v9TfWMEQehTiLiJIFLjXKXgdc0ngxe60lIDffhtDJJirLxy/WzG9UuitKaJ77+wh6NTXamGz+53eR52w+s/hn/MVJOEdVdPkvVPuVMc7UZudr6tGsDZ6mD9v8lIiHYLnNKaJh7daSb/RC9SON4pqZadYrPHK/HYVKMGaXYnR75Swx+jEmH892DIKXDlm+r7w2vgpUtU6skXu9+HiiMQmwaTLmv/PrkzlCfHafeIg7bY7UqRDZqt+s8AmCxqfdA8NWVvVGXoxvnBwigL3/4mVB+DmuPw7PkqHWuywkVPwum/7rjLryAIvQ4RNxGE4blpcjipt3lSSW018GtJWnwUL183k8kDkzlRZ+P8daNoSBmhJiI/tQAem+XyIehqqvB1n6nmZnWlbn9CsbvHTYt76bqnARzAphfAVk9afBTLr5vF6OwEamwar20s7OK7EEEY86RapqQATGbPB3V3p6aMyNvEiz2m5kGz4Kq31VyhvHXwwkWw9yPlrVn1J3j3F/DSpfD+ber8GddAVOtKu1ac+kv1dcPTSgy3hVECPua85o8bqald73lMz0VblPk6Lt0z8iEYDDhJlXg7bfDpvfDUfJWmiklR741MyRaEPouImwgi1momytx6MrhRBt6e58YgJS6KF66dybTBqZQ3wC9OXKqeKNsH6DB2Mdz4JVz2EgyYBidfp57/+nF0p9MduWmVlspbp9r0W2JUh976E+7fzlPjo7hgsuqbYjQb7PGUHVDREpMVhp7q+5xQmIrrT6hIBMBJVzV/buB0uPodiE1Vht2XL4V3fw6rHoCNz6rS7trjKsIz4zr/7jfsdOW9cdphxS99z3CqLfPsuaW4GTRbVTI1VsL+T9Vjht8md1bwoygzb1BfN7+kIlSpQ+DaT1R0SxCEPouImwhC0zSP78Zb3LTRwK8tkmKsPP/jk5k5NI2PGifwf85LKRnxfbjxK1j6IuRM9Jx80lXKoHpsG9V7v6DJrlJVWUnRzS9qRG0mLfUIovX/cj9tRJWMyq4ej1ECPmgWRCf6PsfbVGyk+ILNttdV1CNrPPQ/qfXz/afA1e/CoDnq5zryLDjpajj9Llj8f3DFa/DTbwOrUDrnT0rEHl6j7t+SvR+q/WZPVGLCG5MJxl+kjo1qpTzX4Mpg+W28GXsBJOSo44Ez4NpPIWNk8O8jCEKPonVdsRBWUmKtHK9udA/PbLI7KalW4f2O0lLexEdbePZHJ3P9Cxt4eN/3+Nd+C19/fwytmt7HpcHky2DjM/D148DVpMdHEW0xe84pP+Qu+2XWTcpjseoB1wDDjTBwGv1dYyGOVvYScbPPRwl4S/pNVsbehgo43k4KpysYKamTrmo76pEzEX78QfDumToE5v0SPr8fPr4bRi1S/iIDIyU19nzfr59wsTIx7/kAmmqDXynljSUKlr6g7nHy9WD1/9+IIAi9l05FbvLz8ykoKHB//+2333LLLbfw5JNPBm1hfZWWwzOLKxvQdYi2mMhICKxXR2yUmX9dNZ2MhCiqG+zsPdaG6XTmjQAkHv6YgVpJazPxN08AuhoamTVGiRvDW+GK3hjC61hVAzZHN0UxQoWtXlVKgW+/jYHZ6u58a+oO383RzcqsbI6CSZcG//rtMffnkDZcNeX7/AHP4021ngniY9oQNwOmqaGctloV8asvV5Ggls0Dg0XuyTD3FyJsBEFw0ylxc8UVV/D5558DUFxczMKFC/n222/5f//v/3HfffcFdYF9jeQWXYoLKoyxC7FonfArxFjNjMpWaZU2e9FkjYHhZ6Khc7X54+Z+m4ZKZR6G5m37jdTU9jehtoyM+CjMmo5T9zQC7LEc+Qrs9cpblDWu/XNdqSktf13w12G872MXqwhbKLFEw7kPquNvn4Dibep4/6cqTZYyWFWM+ULTvDoeu3rmDJgujfQEQQgZnRI327dv5+ST1W+s//nPf5gwYQJr167l5Zdf5tlnnw3m+voc7siNKy1l9Ljxx0zcFsMzEwA40F4nYVcvkKXmzxmU4BV5+e4FVe6cOUZFbgwGTFOdXx2NsOl5TCaNVNdn19GKHm4qNoywI+Z3bIB1DdHU8tb5Nt92Fls9bH1NHU+9MnjXDYQR82Hchcpf8/4vVb8dd0pqcfvvjdGDxuZqDdAdfhtBEIQ26JS4sdlsREcrw+knn3zCBReoDqBjxoyhqKgoeKvrgxhdiitdkRvDoBuI36YlwzOV0+ZASTviZvh8SqJySdLqOb3eZaZ12D3t7Wf9pPmHmaZ5KnDWPw1OB6nR6sO9sKvipnQfvHk97PvE/9ecOKzEwK734OAq5QU6vgcqC9tvcteSmhLY/LI6Hrmo4/P7nwTmaLTaEuIbi/2/T0fsfEdVHKUMUrPCwsVZDyhfUf7XsOl5ZSaG1lVSLcmZ2Lzsuzv8NoIgCG3QKXEzfvx4/vnPf7JmzRpWrlzJ2WefDcDRo0dJT08P6gL7Gi09N8boBX8rpXwxPMuPyI3JxIq4CwGYWvSq+i19z/tQmaf6k0zy0TNkwhJVhlyZh7Z/JWmuAqvCrpSD6zq8fRNsfRVeuhjevaV9cWJvgi/+DH+fAW9eC6/+AJ6/EJ46U3Vg/us4+N/Bakq6P3zwa2UQ7jcZRp/b8fnWGFWSDWTUtDAV25sgf72axl26P7DIjmEknnpl+12Fu5vkAaoRHqjoTUOl6kKcO7P912maJ3qDphoECoIghIhOVUv97//+LxdddBEPPvggV199NZMnK6PgO++8405XCZ0j2dWl2J2W8rOBX3sYaakjZXXYHE6sZt8flq/ZT2GJ/jRJtUdUd951rrb903/s26xpjVUfvmv/D9PGp0mN/nGzNXeKfR9Dwbeq263Trqq4Dq6Ci56AQS0+UPO+Vs3qjEqlnInKuNpYo1JpjdXqq9MOH/9GzU8adnrb9977Eex4EzSzKqM2+/nPY/BcOPIVWVXb0PZ/AkfXq7UVblT+FIPYVFWuPHCGEkQDpjWvQjIoOwBHvlTDT6dc4d8aupOZP1HzqUr3qO/HnKuaGHbE5MuUoTh3pu99CoIgdBOdEjenn346paWlVFVVkZqa6n78+uuvJy7Ojy6oQpu0HJ4ZDM9NTlIMcVFm6poc5JXXucWON7quc6hK4xXnGVxveR8+vFONWjBZYca1bV98xjWw9lFMBz9jRPY5wIDOixunEz79vTqefbPy+Lx9k2qm98zZqiLm9LuUH+WTe5TwARVJOOd/VaSgpQ9E1+Gdnylz7hvXqgaGiTmt791YDe+5uvnOvkn1j/EXVzO//pUb4NUWIw5i0yB1MBzbqRry7ftY/TFIGgjpw11/RqgKpX0fqeeGz4fkgf6vo7uwRMF5D8FzruqotqqkWpI2DG7ZBlb5P0EQhNDSKXFTX1+PrutuYXPkyBHeeustxo4dy1lnnRXUBfY1jLRUZb0Np1N3941pb65UR5hMGsMy49leWMWBkhqf4qa60U5dk4PntUVcZ/0ArfygemLCxb7FgEHqEOVN2fcRpzd8yt+5Sokbp1OVMR9cpaIYI+Z7KqzaYudbatBndBLMvUVVCN20VqWKtixXlTd7P4K6MlWiDCpytPC+tquJNE1V/RR+ByU7lMC58u3WUZnP/gBVBcrjcvpd7a+zJbkz0RNy0GqK0dOGoQ2arQy0g2YrwaJpKkV1bBsUbFDdhAvWK59QVYH603LQJLTuSBxOhs5T73P5webG8o4IdZWXIAgCnRQ3F154IUuWLOHGG2+koqKCmTNnYrVaKS0t5eGHH+YnP/lJsNfZZzCGZ1bU2SitbaTJ7sSktTHIMgCGZyYocdNGObhRvl0d0x9tzHmw6131hHf5d1ucfB3s+4iJ1av5gbk/p1TuRH/wR2j15Z5z9n4ASf3bNqI67EpgAMz5medDMSYZLvqn8r+8dwuU7FSPp4+ExY/412bfGguXPgdPnq667n7xJzjzN57nCzbCN/9Ux+c/4pnf5C9Rcdh/8jWffPAeCy68DKvV2vocS5RKQw2Y5hkZUFeuzNPlB6Bsv0pHlR1Q32eOgVFnB7aO7mbuL8K9AkEQBL/olLj57rvv+OtfVf+K119/nezsbDZt2sQbb7zB7373OxE3XSA51lMKbqSkspNi2vTJ+EtH5eDNZkrNvUV1lx0+37/Ga8Pno6cOJfrEIf5gfVo9Vo+aaTTkFOUd2fM+vHUjXL9KpWBasuVl9aEel64qs1oy7gIVDVn1ACQNgNk/VWZef8kYCYv/Bm9cA6v/oqIqI+aDw6bSVujKND0igKiEN1EJNFmTAntNXJryEbX0Eum6TLIWBEHoAp0SN3V1dSQmqsZwH3/8MUuWLMFkMjFr1iyOHDkS1AX2NYy0VIPNyUFXlKUrZmKDjsRNcaUSUjnJruqfW7YrA6w/mEw4TrsL57u3ssM5iM+bxnPBkssZMeU01cXXYYNnz1flxP+5Cq5Z2XxCta0BVv2vOp53e9uznBKy4Py/+rcmX0y8RHUe3vgMvHmd8t9sWa7SVbFpcNYfO3/tYCLCRhAEoUt0KhwwYsQI3n77bfLz8/noo49YtEj1AykpKSEpKcDfXoVmJERbMJvUh9uOo1VA18zEBsOzPL1udB8lyUbkJifJFQ1J6hdQZEQfv4QPJj3OvRl/4f8cS9gXNV4JG1Bfv/8sxGfBse3w3q3Ny6I3PqN8J0kDYPo1ndqf35z9J1VVVVcGyy/3iKqz/qjGSgiCIAg9nk6Jm9/97nf88pe/ZMiQIZx88snMnq0adH388cdMnTo1qAvsa2ia5q6Y2nG0EuhajxuDIenxmDSoarBTWtPU6nnDc9NVb09/1+tbVUwl9YPvP6PKrLe+Ahv+rR5vrFFpIoDT7ggs1dQZrDHw/edUyqxos+qwPOwMVbYsCIIg9Ao6JW4uueQS8vLy2LBhAx999JH78fnz57u9OELnSXalpnYWqchNMNJSMVYzuWkqFeQrNdXMc9MFjCiTz3LwIafAgnvU8Qd3qsqhbx6HulJVNjzlB126t9+kD4cLH1XHllg4/2FJBQmCIPQiOuW5AcjJySEnJ4eCggI0TWPAgAHSwC9IGJGb6gY7EJzIDSjfzZGyOg4cr2HWsOadpI9VGZGbrt1rQIorctNWl+I5P1Nl0LveUf4bo/vwGXd70lihYPxFKnoTl6qElSAIgtBr6FTkxul0ct9995GcnMzgwYMZNGgQKSkp/P73v8fpdHZ8AaFdUuKaT08ORuQGvGdMtS4HD0nkBlSE5MJ/qFLuqkI1PylrvGeKdCgZuUCVZguCIAi9ik5Fbu6++27+/e9/86c//Ym5c+ei6zpfffUV99xzDw0NDfzhD38I9jr7FEbkxiB44sZ3xVRdk53KetUROViem3Yng8ckwdIX4F9nqqnRZ/4mvPOTBEEQhF5Fp8TNc889x1NPPeWeBg4wefJkBgwYwE033STiposYnhtQpeHx0Z3OHjbDGKC5v8V0cMNMHB9lJrGL9zLSUifqbNQ12YmLauN6WWPhRyvgxBE1q0gQBEEQgkSnfl0uLy9nzJgxrR4fM2YM5eXlPl4hBEJKrCctFayoDXgiN4UV9dQ3OdyPe1dKaV001ibGWEmMUYKmw+ng/afC+O916X6CIAiC0JJOiZvJkyfz97//vdXjf//735k0aVKXF9XXSfGK3ASjx41BWnwUqa5rHyz1RG88fpvg3MsQZAVdmQ4uCIIgCJ2kUzmIP//5z5x33nl88sknzJ49G03TWLt2Lfn5+axYsSLYa+xzeIubYEZuQEVvNhw5wYHjtYzvnwxAcVVwetwYDEiJZXdxdfu+G0EQBEHoJjoVuTnttNPYu3cvF110ERUVFZSXl7NkyRJ27NjBM888E+w19jm8q6W6Mg3cF25TcYl35EaJkK5WShkYpesdpqUEQRAEoRvotHu0f//+rYzDW7Zs4bnnnuPpp5/u8sL6Mt7VUkGP3BhjGLwqpoLVndhgQEfl4IIgCILQjUj9bQTSLC3VXZGb455eN63mSnURwyckaSlBEAQhHIi4iUC801LBNBSDR9wcPF6D06mGVwY9ciNpKUEQBCGMBKeBihBUkmOtLJszBICMhOigXntgaixRZhONdieFFfVkJkZTVqsGaQarWmqgS5AVVzVgczixmkVDC4IgCKEjIHGzZEn7LfIrKiq6shbBi3suGN8t17WYTQzJiGPvsRoOHK9BV8Eboiwmd5l4V8lIiCbKbKLJ4aS4ssE9sFMQBEEQQkFA4iY5ObnD56+66qouLUjofoZnJrjETa27g3C/IDTwMzCZNPqlxHCkrI6jFfVBFTdb8itIi48SwSQIgiC0SUDiRsq8ewfeM6YyEpS/J1hmYoMBKbEcKasLasVUXlkdFz++lsHpcXx6++lBu64gCILQuxAzRB/EXQ5eUuM2Ewerx42Buxw8iKbirYUV2J06B47XcszVeFAQBEEQWhJ2cfPYY48xdOhQYmJimDZtGmvWrGnz3C+//JK5c+eSnp5ObGwsY8aM4a9//WsIV9s7GJGZCKhycHcZeJDMxAbuiqkgRm72HfP05tmSXxG06wqCIAi9i7CKm1dffZVbbrmFu+++m02bNjFv3jzOOecc8vLyfJ4fHx/PT3/6U1avXs2uXbv4zW9+w29+8xuefPLJEK+8ZzMsU0VuSmsa2VNcDQQ/ctO/Gxr57fdqPLi1oDJo1xUEQRB6F2EtBX/44Ye55ppruPbaawF45JFH+Oijj3j88cd54IEHWp0/depUpk6d6v5+yJAhvPnmm6xZs4brr7/e5z0aGxtpbGx0f19VVQWAzWbDZrMFczvu6wX7usEmygQ5SdEUVzWy4Yia4p4Zb+3SulvuPSdRVV4VnqgL2vuxzyXEADbnn4io97mn/Oy7g768d+jb++/LewfZf6j3H8h9NF03ioFDS1NTE3Fxcbz22mtcdNFF7sd/8YtfsHnzZr744osOr7Fp0ybOOecc7r//frdAask999zDvffe2+rxl19+mbi4vltx84+dJvZWegJ3t0+0MygheNcvbYDfb7JgNek8eLKDrhZiOXT41TdmHLq6UJxZ548zun5dQRAEoWdQV1fHFVdcQWVlJUlJSe2eG7bITWlpKQ6Hg+zs7GaPZ2dnU1xc3O5rBw4cyPHjx7Hb7dxzzz1tChuAu+66i9tuu839fVVVFbm5uSxatKjDNydQbDYbK1euZOHChVitwekZ011scO5i7zf57u8vPmc+mYmdbxjYcu+Ndif3b/4Em1Nj1ukLSI+P6vgi7XCotBbH118RYzXh1KHO7mTCrNMZnB4ZArUn/eyDTV/eO/Tt/fflvYPsP9T7NzIv/hD2DsUte6vout5hv5U1a9ZQU1PD119/zZ133smIESO4/PLLfZ4bHR1NdHTrD22r1dptP4zuvHawGJnjEXYWk0ZOSjwmU9fDIMberVbITIimpLqRkhobOSnxXbruoXJlfB6RlYDZZGJLfgU7imsYkdN+76VQ0xN+9t1FX9479O399+W9g+w/VPsP5B5hMxRnZGRgNptbRWlKSkpaRXNaMnToUCZOnMh1113Hrbfeyj333NONK+2dGL1uALKTYoIibFoSzBlT+0uUmXhkViJTBipB0xVT8ZGyWv6zIZ+NR050eW2CIAhCZBG2yE1UVBTTpk1j5cqVzTw3K1eu5MILL/T7OrquNzMMC/7hLW6CNTCzJQNSYtmUVxGUiilD3IzISnA1HDzC1oIKv19fWWdj7YFS1uwvZc2+4+SXe9b0k9OHc/vCUVhkBpYgCEKvIKxpqdtuu40rr7yS6dOnM3v2bJ588kny8vK48cYbAeWXKSws5PnnnwfgH//4B4MGDWLMmDGA6nvzl7/8hZ/97Gdh20NPJTspmvgoM7VNjm4VNxCccnBvcTPcVcq+rbASu8PZrig5cLyGX7++le/yTuD0ss5bTBqjshPZWVTF46sOsCW/gv+7fGrQB5UKgiAIoSes4mbp0qWUlZVx3333UVRUxIQJE1ixYgWDBw8GoKioqFnPG6fTyV133cWhQ4ewWCwMHz6cP/3pT9xwww3h2kKPRdM0hmclsLWgkn5BHr1gEKy0lNOpe6WlEhiSHk9CtIWaRjv7SmoY269tY/hTaw6ywZV6Gp4Zz7yRmcwbmcHMYekkRFt4d8tRfv3GVtYeKOP8//uSf/zgJKYNTu3SegVBEITwEnZD8U033cRNN93k87lnn3222fc/+9nPJEoTRMb3T2ZrQSXDs4JYA+5FsCI3Ryvrqbc5iDKbGJQWh8mkMXFAMusOlrG1oKJNceNw6ny84xgA/7pqOgvHtfZyLZ7cnzE5idz44kYOHK/lsifX8ZvzxnHV7MFBGyQqCIIghBYxGfRhfnXWaB69fCpLThrQLdc3IjdHuyhu9rmiNkMz4t0pqEm5ylS8pR1T8cYjJyirbSIpxsLpozPbPG9kdiL//ekpnDexHzaHzv+8s4NbX92MwxmWFlCCIAhCFxFx04dJi49i8eT+RFvM3XJ9YwTDiTobdU32Tl9n/zGP38Zg8sAUgHZNxR9uV5V4C8ZlY+3ALJwQbeHvV0zlt+ePw2LSeHvzUT7fXdLpNQuCIAjhQ8SN0G0kxVhJjFGZz674brzNxAaTXOXgu4uqabA5Wr1G13U+2qHEzVnjc/y6j6ZpXHPKUC6coiJZu4r8bxglCIIgRA4iboRuxfDdFHQhNbWvRM2U8hY3A1JiyUiIwu7U2elDhGwvrKKwop5Yq5lTR7adkvLFqGx1n70lNR2cKQiCIEQiIm6EbmVgF303uu5VKZXtETeapjHJSE3lV7R6nRG1OX10JrFRgaXdjPvsO1bdwZmCIAhCJCLiRuhWDN9NZ9NSx6sbqWqwY9KUodibSe10Kv7QJW7OnuBfSsqbkVmJABw8Xovd4Qz49YIgCEJ4EXEjdCtdLQc3ojaD0+NbGZ8NU/HmFqbi/SXV7C+pwWrWOGNMVsD3HJASS1yUmSaHkyPldZ1atyAIghA+RNwI3YpRDr7xyAn+u7mQsprARmUYZeDe4yIMjMjNweO1VDXY3I9/5OptM2d4BkkxgQ9zM5k0t79HUlOCIAg9j7A38RN6N2Nc08cLTtTzi1c2o2kwoX8yp47KYN7ITE4alEqUpW2N7ctvY5CeEM3A1FgKTtSzvaCSOSMyAI/fpjMpKYORWYlsLahk77Eazp7Q6csIgiAIYUAiN0K3MiIrgbdumsMNpw5jbL8kdF3NhPrH5we47MmvmffnzyhtJ5rjrpTyEbkBT2rKaOZXWFHP1oJKNA0WjG1/unx7GBVT+6RiShAEocchkRuh25k6KJWpg1K5CyipamDNPjWZ+9PdJRyrauSNjQXccNpwn69tL3IDKjX1/rYidzO/j1yN+2YMTiMzsfNDMKViKjhUNdiIsZjbjc4JgiAEG/kfRwgpWUkxXDxtII9cNpW7zhkLwGsbC9D11qMOTtQ2UVrTBPj23ABMzk0BYIurHNzduK8LKSmQiqlgkFdWx9wHPuOGFzaEeymCIPQxRNwIYeP8yf2IsZrYX1LDZh+9avYfV1GbASmxxEf7DjJOGJCMpsHRygb2FFez/nA5AGeN73xKyrinUTF1uEwqpjrD698VUN1oZ9Xe41TW2Tp+gSAIQpAQcSOEjaQYK2e7RiO8trGg1fO+xi60JCHa4vbj/OXjPTh1mDAgiYGpcV1am8mkMdJ13/0lkpoKFF3XeXfLUdcxfOsSnYIgCKFAxI0QVi6dngvAu1uOtpoRtc/HwExfGJ2KV+5UJeBn+zlLqiNGuFJTe4+JqThQthdWcai01v39NwfLwrgaQRD6GiJuhLAya1g6A1NjqW6wu/0yBkZaamQH4mZKbnKz77tSAu6Ne8ZUB6Zih1Pnlv9s5dUD8s/J4J0thQAkuQanfn1IxI0gCKFD/jcWworJpHHxSQMB+M+G/GbP7T/WemCmL4zIDcCwzHh3xKWrjMpW19nfQTn4loIK3t9WzNoSU7tl7X0Fp1Pn3S1FAPzqrNEA7DxaRWW9+G4EQQgNIm6EsHPJNCVu1h4oo+CEMu/WNNo5WtkAdCxuxvRLxGrWgOClpLzv21HF1Jf7St3Hu4slhfXt4XKKqxpIjLFw6YxchqTH4dRhg/huBEEIESJuhLCTmxbH7GHp6Dq8sVGlMw64oiUZCdGkxEW1+/poi5nTRmUSZTHxvakDgrYufyum1uw77j7eI31xeMdlJD57fA7RFjOzhqUD8M0hETeCIIQGETdCRPD96Sp68/p3+TidurszcEd+G4NHLz+JL+84w51KCgbeFVNtNfOrbrCxKa/C/f0eP83Huq7z+e6SXpeqsTmcfLBNpaQumNIfgJnD0gD4WkzFgiCECBE3QkRwzoR+JERbyC+v55tD5X6VgXsTG2UmKykm6Osa6RJLbY1h+PpgOXanpwHhnmL/Ijcfbi/mR8+u59rn1vtsYNhT+XJfKSfqbGQkRDHbFbGZOVR93V5YSXVD7xJzgiBEJiJuhIggNsrM4sn9AHhtY767t0xbYxdChRG5aati6ktXSmreCPUBvq+kxq+OxkYUY/3hE3y6qyQYS40IjJTUeRP7YTGr/176p8Qy2O27ORHO5QmC0EcQcSNEDJdMUz1vVmwrYnthFeB/5Ka7MNJc+9pIN63Zr8zES6cPJNqkY3Pozfq7tIUx6BPgzx/txuHs+dGb+iYHH7vK+Y2UlMHMoa7UlJSEC4IQAkTcCBHDSYNSGJYZT4PNSXGVf5VS3Y0ROTpYWoOtRUSmsKKeg8drMWkwe1ga/VxNkXd1kJqyOZzsLFLiLdpiYu+xGt7aVBj8xYeYVXuPU9vkYEBKLCcNSm32nGEq/vqgmIoFQeh+RNwIEYOmaXzfFb0BSI61kpnQ+cnewaB/sqqYsjl0jrSomDJSUlNyU0iKtdI/TkVf9hRXtXvNPcXVNNmdJMVYuGXBKAD+unIvjXZHu6+LdN7dqqI2iyf3R9O0Zs/NHObx3dQ02kO+NkEQ+hYiboSIYslJAzC5PhdHZCW0+pAMNe1VTK129bc5ZWQmAP3jlbjZXdR+5GZboUpJTRqYwrI5Q8hOiqawop4Xv84L6tpDSZ0dvnC9HxdM7t/q+QEpseSmxeJw6tLvRhCEbkfEjRBRZCfFcNooJRb8LQPvboyKKe8ZUw6nzlcuv82pIzMA3JGb3R2kpbYWVAAwcWAysVFmd/TmH5/v77HVRFvLNZrsTkZkJTC2n+9yfKNqSlJTgiB0NyJuhIjjrnPHsmBsNj+aOzTcSwE8Imuf13TwHUcrqaizkRBtYXJuCoDbc1NYUd9u/5qtLjPxpAFqJtb3pw1kWEY85bVN/GvNoW7YQffzXamKsF3oIyVl4GnmJ6ZiQRC6FxE3QsQxKjuRp66ezuic4DXk6wq+KqbWuFIws4enY3WVPMdZoF+y6rXTVul4g83h7oUzySWKLGYTv3TNYHpqzUGOV/es+VSlNY3srVSCZrGPlJSBUTG1taCSWvHdCILQjYi4EYQO8FUxZYxcmOdKSRmMdp27u8i3qXhXURV2p056fBT9kz1NB8+ZkMPkgcnUNTn4+2f7gr6H7uTDHcfQ0Zg0IIkhGfFtnpebFseAFOW72XhE+t0IgtB9iLgRhA4YkBJLvLtiqpa6Jrv7w/mUEc3FzRhXtKmtcnCPmTi5WfpG0zR+ffYYAF7+No+8dmZZRRrrXB6aReOyOzxXRjEIghAKRNwIQgdomubut7PvWA3fHCzH5tAZkBLL0BaRilEdRG625CtxM3FgSqvn5ozIYN7IDGwOnYdX7gniDroXw0M0JTe5w3NliKYgCKFAxI0g+IF3xZTht5k3MqOVeXaM67w9xdU4fXQd3lZYAcDkgb6FgBG9+e+Wo1TUNQVl7d1JSVUDxVWNaOiM75/U4fmzXBVTW/IrqGsS3004KKlq4NmvDvXYyjxB8AcRN4LgB0ZEZm9JNV/uN/w2ma3OG5IRR5TZRG2Tg8KK+mbP1Tba3QNBJw7wLW4mDEhmYGosug67OuiXEwkYYySyYyEh2tLh+blpsfRPjsHu1PnuSEU3r07wxT8+38897+7k1fX54V6KIHQbIm4EwQ9GZqmIzDcHy9l7rAZNgznD01udZzWb3CmsXS1SUzuOVuHUIScppt0J5mP7qQjI7g46HUcCRs+ewQn+zcbSNM3drThcvpsmu5ML/v4ly575tldNZPeXwy4/19GKhjCvRBC6DxE3guAHRsVUaY0q0540IJnU+Cif5xqm4pbN/AwhMKmNlJSBIW5aiqNIZHN+BQCD/BQ3ALNcpuJw9bs5VFrL1oJKVu05zvo+OKX8mGtuW09IewpCZxFxIwh+YFRMGZzSogTcmzH9DHHTXJy4m/d1JG6MiqsIT0vpuu7ek7+RG/CYijfnV1DfFPp5WkWVnnThK+t77siLzmIMpS0XcSP0YkTcCIIfaJrGiGxPU8FTRrT22xiMyTHSSs3FifdMqfYwIjd7j1VjbzGJPJI4UlZHZb0Nq1lzd2f2h0FpceQkxWBz6O7ITygxIhcAK7YVUdWHjLUNNgcVdWq/J2pF3Ai9FxE3guAnxhiGuCgzJw1OafM8I3JzuLTWHZmorLdxqLQWaNtMbDAoLY64KDONdieHy2qDsPLuYYsrzTauXxKWAP4n0TSNqYNSAMIiboorPR2gG2xO3tl8NORrCBfewk4iN0JvRsSNIPiJUeo8e1g60RZzm+dlJkSTFh+FU/fMo9ruitoMSotr06tjYDJp7tETkZyaMnr2dJRm88UU1+iJzfmh97wYaZnMxGiAPlU1VFzpETcVtX0nYiX0PUTcCIKfXH7yIO44ezT3XDC+3fM0TfOYil3ixPCmTPRTCBiprUg2FRuRm0kDOu5v0xJD3BgCKZQY0YsfzR2C1ayxrbCSHUdDv45wUOwVualutNNkj9y0pyB0BRE3guAnMVYzN50+gty0jg0mLX037kqpDlJSBuP6+a64ihRsDqdbEPi7J28mDkzGbNIormpoFk0IBUWu+43tl8Si8TkA/KePRG+801IgFVNC70XEjSB0Ay0rpjyVUil+vj6yIzd7j1XTYHOSGG1hSHoAbmIXcVEW97T1UKemjA/4nKQYlk7PBeCtTYU02EJfuRVqvP1GIL4bofci4kYQugH3AM2iKspqGimsqEfTYIKfKRzj9UWVDRH527Xbb5ObjMmkdXC2b4xZVJtCaCputDsod1UJ5STFcMqIDAakxFLVYOfD7cUhW0eg6LpOWU1jxyd2wLHq5pGbE+K7EXopIm4EoRsYmZWISYMTdTY+3VUCwLCMeBJjrH69PjHGSm5aLBCZpmIjzTbZz0iUL9ym4ryKLq/HX0qqlECItphIibNiMmlc6oreRLKx+K+f7GPa/Z/wxd7jXbrOsRYpwBMRKJwFIRiIuBGEbiA2yswQ18Tw/2xQH5r+pqQMItlUbJRwB7onb6bkpgKq/4/Dx5DR7sAw1OYkx7iHnl4yfSCaBusOlnG4NDJL77/ar4a1bulilMvYf0aCqtgr72Kvm+2FVfx9h4kdRyPv76jQtxFxIwjdxFiXONlwRHlKAi2ZjtQZU3VNdva5BoAa0ZfOMCIrgfgoM3VNDnfJfHdjmImzvWZ7DUiJ5VTXEFRDiEYaB4+r97ukuvPma13X3ZErQzh3tZHfKxvy2Vdl4k8f7unSdQQh2Ii4EYRuwuhVYxCwuAlCr5v/bi7k6S8P4QxiZGTH0SocTp2sxGhyktseANoRZpPmjvyEKjVlpGVyWgwuvWyGSk29vrEg4rpCn6ht4oSrq/Dx6s77bsprm2hy7c34u2lct7Pkl6tRFl8fOsHeY5GXPhX6LiJuBKGbGOMlbswmjXH9Ohe56ewYhuXf5vGLVzZz33s7ue+9nUGbgG2kRiZ3IWpjMNndzK+iy9fyB++0lDfzx2aTHh9FSXUjq/Z0zdcSbA6W1riPS7ogbrxTUtlJqoFhVz03eSc8c7peWHekS9cShGAi4kYQuglDnIAa3RAb1XZXY190ZQzDF3uP85u3t7u/f3btYf7x+f6ArtEWW1xl7V1JSRlMCZe4aRG5ibKYWHLSAABeiTBj8YHjnp99VyI3Rgl8dlIMKXFd99zYHU53mg/gze8KqO5Dc7qEyEbEjSB0EwNSYkmItgCdG1HQ2TEMO49WcfNL3+Fw6iw5aQC/O38cAH/5eC8vfdP13663uM3Ege+pJcaMqb3HqqlttHf5eh1hNAz0lU5b6kpNfb6nhJKq0DYWbI+DLcRNZyNwRo+bnKQY0lzipiuRm6LKBhxOHYumMzwzntomB29+V9jp6wlCMBFxIwjdhMmkMdbVzK+zKZxAK6aKKuv58bPrqWm0M3tYOn9aMokfnzKUn54xAoDfvL2dFduKOrUWUP6PvPI6ACYNSOn0dQyyk2LolxyDU/dMTe9Oin0Yig1GZCUyOTcFh1Pvcsl1MDHMxACNdidVDZ0TgUbUKjs5xj3frCuRG+PvQXoM/HCmEobPrzsctPSnIHQFETeC0I38v3PHcv2pw7j4pIGder0xhsEfcVPdYONHz6ynuKqBkVkJ/PPKaUS5xnXfvmgUV8wchK7DLa9sdpcWB4oxT2poRjzJcf717OmIUKWmnE7dXW3UlhF6+mBVnh5Jpc0HW5SndzY15W2mTnOJm4ouGIrd4iZa58LJ/YmPMnPgeC1rD5R1+pqCECxE3AhCNzJ1UCr/79yxxFgD89sYeMrB209L2RxObn55E7uLq8lIiObpZTNIjvWID03T+P2FEzhnQg5NDifXP7/B3YgvEIwxEpODkJIymByiZn7ldU3YHDqaBlmuieAtMSa/R8ogTbvDyRGX3yre5dnqrLjx9huluoRpTaOdRnvnxk54xA0kxli4eJoS8M+vO9yp6wlCMBFxIwgRzGg/xjDous7v/rud1XuPE2s18/Sy6T6He5pNGo9cNoU5w9OpbXKw7Jn1vPxNXkBt/YNZKWXgnhDeCbEVCEZKKiMhGqvZ9399E1xDQHcerQpq+XxnKThRj82hE2M1Mb6/Wltne90c80pLJcVYMaZmdDZ6k+9OS6n36cpZgwFYufMYhRX1bb5OEEKBiBtBiGD8GcPw7tYiln+bj0mDRy+f2m7X4GiLmSevms7EAcmU1zbx/97axsl//JQr//0Nr3yb125TN13X3QKkK52JWzJxQDImTQm4llOrg0lxGz1uvBmWEU+0xURtkyPgCrXuwCgDH5IeT7YrldbptJRX5MZk0kjtYsVUvpfnBmBkdiKzh6Xj1OHlIBjXBaEriLgRhAinPVNxk93Jgx/tBuBnZ45kwbjsDq+XEG3h5etmcuc5Y5g4IBmHU2fNvlLufHMb0//wCVf++xve23q01UiEo5UNlNY0YTFp7vRNMIiP9kwI39SNqaniqrbNxAYWs8mdCowE341RKTU8M4HMBJVKO96JAZoNNoe7YZ/R48YwFXe2Ysrbc2Nw9RwVvXnl2/xOp7sEIRiIuBGECKe9MQwvfXOE/PJ6shKjueG0YX5fMzHGyo2nDefdn53CF786nTvOHs34/kluofPTlzcx/6FVvPxNnvtDykhJjc5J7LSHqC2MkvDuNBW7IxfJvv02BoZw2x4Bvhujx82wzHgyXT6h41WBixvvgaGGF8vw3XRmMnh1g80tltK9tOKCsdn0S46hrLapS1V5gtBVRNwIQoTT1hiG6gYbj36mGvPdsmAUcVGWTl1/cHo8N50+gvd/Po/Pbj+Nn585gpQ4K4fL6vh/b21j3v9+zhNfHGDtAVVhFUy/jYExXXxz/omgX9vASEv1S45t9zxv3024McrAh2XGu03QnYnc+BoY6k5LdSJyY4xdSI2zEuOlcy1mEz+YOQiA56VjsRBGwi5uHnvsMYYOHUpMTAzTpk1jzZo1bZ775ptvsnDhQjIzM0lKSmL27Nl89NFHIVytIIQeI3Kzp8UYhidXH6S8tolhmfFcOr1zpeYtGZaZwG2LRrP2zjP53fnj6JccQ0l1Iw98sJsXv84DYEoQ/TYGU1yRm20F3Tch3J+0FHhFbgorw96zxSgDH5aR4I7clHQicuNr70Y5eGeGZxopKcMP5s3SGYOwmjU25VWwrSD80S+hbxJWcfPqq69yyy23cPfdd7Np0ybmzZvHOeecQ15ens/zV69ezcKFC1mxYgUbN27kjDPOYPHixWzatCnEKxeE0GGMYWjyGsNQUtXAU2sOAXDHWWOwtFH901nioiz8+JShfPGrM3jwkkkMz4wHQNNg2pDUoN4LYGRWIvFRZmqbHOwvqen4BZ3AH0MxwKjsRCwmjRN1tmbjBUJNdYPNbR4elhlPVlLnIze+BoZ2xXNjmIlzU1pX5WUmRnPuxH6AlIUL4SOs4ubhhx/mmmuu4dprr2Xs2LE88sgj5Obm8vjjj/s8/5FHHuGOO+5gxowZjBw5kj/+8Y+MHDmSd999N8QrF4TQ4T2GYacrNfXIp/uotzk4aVAKZ43v2ETcWaIsJr4/PZeVt57GM8tm8PSyGQzPTAj6fcwmjYmu3jndlZoq9tNzE2M1MyJL7XF7CLomt4VhJs5MjCYxxuo2FJfXNmELcJCqr4GhHs9NJ8TNibYjNwCXzVCpqVUR1OlZ6Ft0LkkfBJqamti4cSN33nlns8cXLVrE2rVr/bqG0+mkurqatLS0Ns9pbGyksdHzm05Vlcqj22w2bLbgDnkzrhfs6/YE+vLeofv3Pzo7gU15FewsrGB0ZhyvuoY7/nLhSOz27p/JBHDKcBWxabnHYO19Yv8kvj5YzndHylkypV+XrtWSuiY71a6xBWmxlg7XOq5fIruLq9lWcIIzRqW3e253/ez3FSthNTQ9DpvNRoJVw2LSsDt1ik7U0q+NLsu+KKpQYiQj3upeZ1K0MsuU1TQGvPYjrnRZ/6QoKGu996FpSoiV1jRS19DYZl+hno78vxfa/Qdyn7CJm9LSUhwOB9nZzX/rzM7Opri42K9rPPTQQ9TW1nLppZe2ec4DDzzAvffe2+rxjz/+mLi41iHVYLBy5cpuuW5PoC/vHbpv//ZSDTCzeusB1m4/gMNpYkKqk+M717FiZ7fcMmC6undHmdrjlzsLWGH1mFF1HfZUanxdojE8SWdeTuA+mJJ6AAvRZp01n33c8QtOqLV8vnk/Ixr2+nWPYP/sP84zASYsdWWsWLECgHiLmcomjf9+9BmDAgig7TpiBjSOHtjJioodABxw7fFwUan7+n5fL19d79jBnSQnt967UwezZsaha7z6zoektR8s6/HI/3uh2X9dXZ3f54ZN3BgYzn0DXddbPeaL5cuXc8899/Df//6XrKysNs+76667uO2229zfV1VVkZuby6JFi0hKCl6vDlCqcuXKlSxcuBCrNThzd3oKfXnv0P37zz5ygtefWs+hWgv1NicmDf78g1MYmR38FFGgBGvvJ1U18PSDqymq1zh9wSIsJhPvbyvm318dZs8x5cPZXqHxP1eeGXAp+tcHy2HzBgakJnDuuXM7PD/ryAnefGo9pY5Yzj33tHbP7a6f/YevbIHCY5x20hjOnTsEgH8d+ZrKo1WMmjyDM0dn+n2tB3etBho457TZnOQyb/fLr+Bfu79Ft8Zy7rmn+n0tp1Pnl99+AuhcOH8uuzZ86XPvD+1eTUFFA+OmzXHfs7ch/++Fdv9G5sUfwiZuMjIyMJvNraI0JSUlraI5LXn11Ve55ppreO2111iwYEG750ZHRxMd3frXBqvV2m0/jO68dqTTl/cO3bf/8QNVSqjeprwWl0wbyLiBwTf2doWu7j033UpOUgzFVQ388YN9rNpbwjFXZVCca65SXZODHcW1zBrWfqqoJcddvVz6pcT4tcaJuWloGhRXNVLZ6CQjoePQQ7B/9ofK1G+pI3OS3NfNSoqBo1WU19n9vpeu65RUK1/NgLR49+syk1Tk+kSdLaB1F1c2YHPomE0auekJ7ML33nOSYymoaKC01v+19lTk/73Q7D+Qe4QtERoVFcW0adNahbNWrlzJnDlz2nzd8uXLWbZsGS+//DLnnXdedy9TECIC7zEM0RYTty4cFeYVdQ/GnKlXN+RzrKqRrMRo7jh7NOvunM8ZY1SEdv2h8oCv628ZuEFCtIWh6apCLBydip1O3V0ZNyzDE51z97oJYATDiTobTS4DclaiVym4q89NbZODBpv/3YSNMvABKbHtVukZ5uXibhypIQhtEVaX12233cZTTz3F008/za5du7j11lvJy8vjxhtvBFRK6aqrrnKfv3z5cq666ioeeughZs2aRXFxMcXFxVRWSi8FofdjNLr70dyhHTai66mcO0kZiUdlJ/DgJZNY8+szuOn0ESTHWTl5iCoc+PZw4OLGVyl0R4wL44Two5X1NNicWM0aA1M9P+vMTogbowQ+PT6KKIvnv/zEGAtm1/TMQIZnGuJmkI/hrN4Y73VxpQzRFEJPWD03S5cupaysjPvuu4+ioiImTJjAihUrGDxYzScpKipq1vPmiSeewG63c/PNN3PzzTe7H7/66qt59tlnQ718QQgpd583lnkjM7hoanAa9kUiF0zuz2kjM0mKtbTy3s1wiZvvjpzA7nAG1NvHiB4EUmE0YUAy720tYkdh6CM3Rhn44PT4Zvs0IjeBTAY/1kbUSg3PtFJa08SJuqZmZeLt0V4DP288kZvODfoUhK4QdkPxTTfdxE033eTzuZaCZdWqVd2/IEGIUPolx7LU1T+kN5Mc5zuvPjonkaQYC1UNdnYWVQU0mdyIXviblgJPp+JwRG7cYxcy4ps93qnIjY8eNwapcVFK3ATQ66bALW46iNwkS+RGCB+9s/mAIAi9DrNJY7qRmgrQd9PeB3xbjO+vmgoeLqujqiG0fUzcYxdaNEx0j2DoRFrKl7DrzHypgNNS4rkRwoCIG0EQegwzOiFu7A6nO9IRiOcmLT6K/i4xtCvEpuIDXgMzvTEMwcerG/2ee+Wehu5L3MQH3qXYb3Hjeu+OVfq/VkEIFiJuBEHoMZw8VJW/bzhywu8PzNKaJpw6WEwa6X6UdHsz3jUhfHuIxY3huRneQtwYJemNdifVjf51pm5v7IR7eKafhuIGm8MdNcpNbV/cGEKsyeGkvBMjHgShK4i4EQShxzBxQArRFhPltU3u6EZHGB/uWYnR7uogfwmH76auye4e2OldBg4QG2UmMVpZJf2dDu5XWspP8VHgmimVGG0hpQ1vlEGUxeQWY5KaEkKNiBtBEHoMURYTU13dbr895N+ATcPQmh2A38Zggst3E8qKKSNqkxpndU/u9iYzKTBT8bEODMXg/2TwPC8zsT+d5I1oUXEYp6t3lcp6G/e+u4NtBdJypCch4kYQhB6F0e9mvZ/9boo70ePGYPwAFbnZf7wmoEZ3XaEtM7GBMR38eE3H4qbB5nCnnHx7bgKL3OSV+VcGbpCTpM7ryZGbt74r4JmvDvPwyj3hXooQACJuBEHoUcwYGpip2OizEkgZuEFOUgzp8VE4nDq7i6sDfn1naKsM3MBdMeWHYDCiO1EWE8mxrdNIaYah2M/ITf4JFQXryExs0BsiN7uK1M99v59p0GBT3+Rgw+FyMWUHiIgbQRB6FCcNSsVs0iisqKewouMeKsc60cDPQNO0kHcqNtJSbUVu3BVTfkRuir0qpXylkdxpqVr/DMX+VkoZeLoU91xxs/uYEjcFJ+pptIcmeufN/364m0v+uY7XNhSE/N49GRE3giD0KOKjLUxwCQ5/5ky501KdEDegOhUDbA+R7+Zgqe8ycINAGvl1lJIL1HOT72cDP4Oc5J6dlnI6dfa5xI2ue9JyoeTzPSUAvLv1aMjv3ZMRcSMIQo9jRgBzpgIdmtkSo2Jqp4/IjdOp8/HOY+ypDKwKqy10XedQG2XgBoEMz3SPXmhD2Bmemzo/hmfqut7MUOwPwYjc1Dc5uPzJr7nllU0h8z0Z5J+oo67Jc88Drp9NqCipbuCIS1B9fbCMGj/L/wURN4Ig9EAM301HkRtd17tkKAZPxdSu4mpsrunaoDw/F/zjS25evoXHd5pYtfd4p67vzbGqRmqbHJhNGoPSghm58d3fJ8lreGZH0Zvy2ibqmhxompoI7g/BmAz++sZ81h0s4+3NR7nhhY0hFTgtfVaHSkMrbjYe9lQE2hw6X+7r+t+xvoKIG0EQehxG5GZfSU27lT5VDXbqXR+GnU1LDUqLIyHaQpPdyYHjNeSX13HTSxu59Il1bC+swqSBjsZtr21zm4E7i/H63NTYZhO8vQlkBENHUStN0/z23RhRm5ykGGKs5g7vDZ73vLrBTm0nog5Op87TXx12f//F3uMhFTh7Womb0JqKNxxR4sZoz/TprpKQ3r8nI+JGEIQeR1p8FCOzlOG2vZJwIy2TEmf1+wO5JSaTx1R8zzs7mP/QF6zYVoxJgytmDmLV7acyNFGnusHOdc9voLoLc6gOdFAGDp60VHltU7NIki/a63FjkBrnX8VUoCkpgIRoi7vpYGeiN5/vKeFQaS1JMRb+ffV0YqymkAocQ9xMdPmuDoY4LbXB9Xf7kmkDAfV+OJ1SNeUPIm4EQeiR+JOa6mpKysBITX19sJwmh5NTRmSw4hfz+ONFE+mXHMOPRznITormwPFabn11c6c/gDoqAwdlAjZSSWU17QuS4nbmSrmv52evG7eZuIOxCy3JTu687+apNYcAuPzkQcwfm80zy04m1mrmi73HuT4EAmd3sTKRnz0hBwhtWqquyc4O19iPm04fQWK0hdKaJrYUVIRsDT0ZETeCIPRI/Gnm197ogUCYNzIDUKLj31dP54VrTmZMTpL7+aQoePyKKURZTHyyq4S/frK3U/fpqAwcVCQpI0EJkpLqtgWDrusc86PHT5qfFVP55YH1uDHorKl4x9FK1h0sw2zSuHrOEABmD0/nmR/NINZqZnU3C5wGm4PDLjPvOS5xU1bbRKWfc7i6yub8CuxOnX7JMQxOj+PUUZkAfLZbUlP+IOJGEIQeycmuyM32o1Vt+jn8iVz4wxljsvjqzjP56NZTmT8222fPmIkDkvnTkokAPPrZfj7YVhTwfToqAzfwng7eFifqbDTZVdoqqw1DMXgiN/56bgal+2cmNuisqfjpLw8DcO7EfvT3MjDPGtZc4Fz3/IYO03OdYX9JDQ6nTnKslaEZ8e6/QwdD5LsxzMTTBqeiaRpnjskCxHfjLyJuBEHokfRPiWVASiwOp853eb7nTBV3UAodCANSYrGa2/8vc8lJA7nmlKEA3P7aFndawx/qmuwUuDoAD28ncgP+VUwZkZK0+CiiLW37jQL13IQiclNS1cA7WwoB3O+nN7OGpfPsj2YQF2Vmzb5SPtl5LKA1+YPhtxmdk4imaQx1pQpDlZpa7zITG+b500dnommws6iKosqOm1f2dUTcCILQYzm5A9/NscrOdyfuLHedM4ZTRmRQ1+Tguuc3UFnvXxpjS34luq7WaoiXtjDmS7VXMXXMz/4+aX54bmwOp/sDNVDPTWciNy98fQSbQ2fa4FSm5Kb4PGfmsHQunZ4LwLqDZQGtyR/2uJr3jclJBGCoK5oWClOxw6mz6YgncgOQnhDNSYPUsURvOkbEjSAIPZaOmvkVBclQHAgWs4lHL5/KwNRY8svreXtToV+v25SvPsyMqeftkeXHZHB3pVQ7KSnwr0vx0Yp6nDpEW0wdCq+WBBq5abA5eOmbPACu9RG18WbWMPXz/7obxM1ur8gNeEzeoYjc7CmuprrRTkK0xS2uAHdqSnw3HSPiRhCEHosRudmUV+H2l3jjb/Qi2KTGR7HUFVXwp4syqD0A7t/O28OvtJQfZeDgidy0J268U1K+/EbtEWjk5q1NhZTXNjEwNZZF43PaPffkoekA7D1WQ6kfs7YCYY8rpWiIC8MHdTAE4mbjEfV3ZuqgFCxeqdD5Y5W4+Wp/KfVNgRupiysbeHjlXir8HLfRkxFxIwhCj2V4Zjzp8VE02p386vUtlHl9wDXaHZS5Ui2dbeDXFaa7okr+THTWdd0tbvyK3Lgb+bUtGPwVdv4YijvrtwHPe19a09ih8VfXdf79pSr/XjZniLvkvS3S4qPc4uObg/6JSH+oqGtyV5qNynalpTKUD+pQaU2395pZ7zITTx+c1uzx0dmJDEiJpdHuZO2B0oCve+ebW/m/T/fxjFdjxN6KiBtBEHosmqbx8/kj0TT47+ajnPnQF7y6Pg+nU6fE9eEUZTG5TbOhZEpuClazxrGqRrdRuC0KTtRTWtOI1awx3tVTpz3ckZt2ohX+9vgx3pv2PDdGGXggDfwM0uKisJo1dL3jrspf7D3O/pIaEqItLJ2R69f1Zw1T0ZtgpqaMlNSAlFgSY9T7k5sai8Wk0WBzdvsg0I1uM3HzKJ6mae7ozacBpqYOHK9h1R41viEQo3tPRcSNIAg9mqvnDOGtm+Yyrl8SlfU2fv3GNpY+uY4v96vfbHOSYgJOpQSD2Cize6J4e714AHe117j+yX51Us5M8JSCtxUVKjZ63HQQtTIiN/W2todnBjoN3BuTSXNHj4o7qPIxojZLZ+S6RUVHdIe4MSqlvP0uFrOJQelq/91pKi6sqKewoh6zSWOKjyie23ezq6TDiKA3z6897D7eVxLaMRLhQMSNIAg9nim5Kbzz07n85ryxxEWZWX/4BHe9uQ0IrZm4JTP8aDQIHr/N1DYqg1piRG4abE6qffT40XXdXd3U0f4Toy1YOhie2ZW0lPcaiivbjtzsKa5mzb5STJpKSfnLrGFpaJr6wPZnmKg/tDQTG3hMxd0nDoyRC+P7JxEXZWn1/Kxh6cRazRRXNbg7GHdEVYON1zcWuL8/UlZHoz20E9ZDjYgbQRB6BRaziWvnDWPlbaexYGy2+/Fw+G0MprvKeNcf9t2Hx2BTnv+VUqCiQsbMJl8f6JvyK6iosxFrNTMkvf2GgJqmtTuCocHmYL/rN/0h6Z0UN36Yit/cpD58F43LCShClBIX5e4W/c2h4ERvDDNxK3Hj6j/Ukam4ss7Gl/tKcXTCm7OxRQl4S2KsZk5xdcz2t2rqtQ0F1DY5GJmVQGKMBYdT53BpXcBr60mIuBEEoVcxICWWp66ezhNXTuPUUZn8YOagsK3F+IDa38708gabw/0buD+VUgbu6eBVrcWNUX5+9oQcYqM6TnO5G/n5MBV/c6icepuanTUiq/3mgm2R40da6ltXr6JF47PbPKctglkSrus6e48pMec9YgNwN/LrKC1199vb+OG/v+HXb2wN2HxsCGEj6ueLBQH4bhxOnedcKallc4e4B87uK6lu51U9HxE3giD0Ss4an8PzPz6ZmS5PRjhIT4hmuKuE2PiNvCU7jlZid+pkJEQzMNX/0QZtmYptDifvbjkKwPemDvDrWkavm3IfaanPdqnuv2eO8T12wh88kRvfaaO6JjvbCioBT3l/IMx2/YzXHei6uCmsqKem0Y7VrLUag+FPr5tGu8MdUXl9YwH3vLvDb29MVYPNHTWa3kbkBuCM0UrcbMmvaLdiDuDz3SXkldeRHGvloqkD3AJ137GupdaOVTXwxOpDVEVoVbmIG0EQhG7E+LDe0Ibv5rsjFYBKSQUiHtrqdbN673FO1NnISIhm7nD/hJ3R66Zl/xNd193RgfkuI2tncIubNiI33x1RQyIHpMQyMMAOyKDeY02DA8drO/yw7wjDTDw8M6HVuA2jS3HBibY9KxsOn6CuyUGs1YymwfPrjvCnD3f7JXA25VXg1JW3Kasdr1RWUgyTBiqz+qrdx9u95jNrlUn7shm5xEVZGJmlUm37u2AqPl7dyNIn1vGXlfv4/GhkyojIXJUgCEIvwehV0lYzP6MzcSApKfAMz2z5Yf6WKyV14ZT+zRrAtUdbnpt9JTUUnKgn2mJi7oiMgNbnTb8OPDffurwyMzsRtQHluxlr+G662O+mLTMxqLEXCdEWnDrklfn2rHzuEoPnT+rHH76nBqk+8cVBHv1sf4f33uj6O9Je1MZg/hiVvnttY36bQmvvsWq+2l+GSYMrZw8GYER219JSlXU2rvz3N+6J6Ucj1Loj4kYQBKEbMbwT2wsrfXaVDaR5nze+IjfVDTZWuoZIXuRnSgq8PTfNxY0xw2jO8HS/vDttYZSCH6v0Xbr+jctv05mUlMFsV5Sqq3Om9rQjbjRN67BT8aq9KpJy+ugsrpg5iN+ePw6Ah1fu5ak1B9u9t7t5Xzt+G4PFk/sRZTGx/vAJbnhho88yfqNZ36JxOe6ImOG5OVRaG/A09dpGOz969lt2F1cTZVHyoaQh9G0W/KF1nZkAgMPhwGbzb+Cdgc1mw2Kx0NDQgMPRu8vsWhKJe4+KisJkEv0uhJfctFiyEqMpqW5kS0GFuy8LQFFlPUWVDZg03GkGf/Elbj7YXkyj3cmIrATG909q66Wt8Hhumv+fZ0QhzhwbuMnXGyPK1ORwUl7bRHqCZz5Vg83BpvwKoGviZtawdP795aEum4rd4ia7tbgBZSreWlDJweO1nDmqedovv7yO/SU1mE2au6LpmlOGUtdo56GVe7n//V3EWM38cNbgVte1OZxsdr0P04d0HLkZlpnA01fP4Nrn17Nqz3F+/Ox6nrp6urt8vKKuibdcFWg/mjvE/br+ybHERZmpa3JwpKzOb5N4o93BDS9s5Lu8CpJjrfzzh9O4/F9fU96oUd/kwGoNfaPM9hBx0wJd1ykuLqaioqJTr83JySE/Pz8sTcPCSSTu3WQyMXToUKKiosK9FKEPo2kaM4am8f7WIjYcLm8mboyozZgc3z1N2iPLh7gxqqQumjogoH+H7vlSXpGbiromNrhmHJ3ZBb8NqC7RGQnRlNY0UlzV0EzcbC2opMnuJCMh2l2N1BlOHqJ8NweP11JS1dCmZ2VXURXRFpO7rNubJruTA8eVF8VX5AY8FVO+et0YUZtpg1NJjvV82P/0zBHU2Rw8vuoAv3l7OyXVjVw9e3Cz92Hn0SrqbQ6SY62M8LE2X5wyMoPnfzyTHz3zLWsPlHHVv7/lmR/NIDHGyivr82mwORnbL6mZaDSZNEZkJbC1oJL9JdV+iRu7w8nPl2/iy/2lxEWZefZHM5g6KJXUOCsn6mwcLK1lSnz4Wi74QsRNCwxhk5WVRVxcYEPinE4nNTU1JCQk9LmIQaTt3el0cvToUYqKihg0aFDECC6hbzJjcCrvby1q1e/G6G9z0uCUgK/ZMnJTVFnvTslcMLl/QNdK9TE884u9x3HqqkvvgBT/q7jaIifZJW4qG5qNmPD223Tl32lynJXx/ZPYXljFuoNlXDildVruu7wTfP+f64i2mHj/5/NaiamDpTXYnTqJ0ZY292yIIl8VU6tcka7TR2c2e1zTNO44azT1TQ6eXXuY//t0H/9cdYDzJ/dj2ZwhTBqYwgav/jamDmZqeXPy0DReuHYmVz/9LRuOnOCHT33D08tm8MK6I4CK2rR8Xw1xs+9YDWdPaP/6TqfOr9/Yxkc7jhFlMfHUVdOZ6vKHDc+MZ8ORCiVuBoevKtEXIm68cDgcbmGTnh74D8rpdNLU1ERMTExEfMCHkkjce2ZmJkePHsVut0dcyFToWxgeiu+OnMDh1N0DIb9zdyYOzEwMHnFTXteEzeHknc1H0XUVwQh0TIKRlvKO3Bh+m65GbQxykmLZXljVylQcDL+Nwayh6WwvrOLrg+WtxE1to51bX92Mw6lT1+TgF69s4vUb57i9I+BJSY3KSWxTaA1ro9dNg83BV65hlkaptjeapvE/i8cxOTeZZ746zNaCSt78rpA3vytkSm6Ke6q9Pymplpw0KJXl183iyn9/w5aCSs56ZDWlNU2kxUf5FLpGxZQ/Yxge/HgPb3xXgNmk8ffLpzLHy1g+LMMlbrpxHEVniYxPoQjB8NjExXWuC6cQWRjpqEjxAAl9l7H9kkiItlDdaHcPLWyyO9lWqHq7BGomBjWQ0mxSAynLaprcVVL+9rZpeS3w9LmxO5ys2uMqAR8bJHGTrMSYMdDTuI/R/2fmsCCIG1fK7xsfvpv739/JkbI6+iXHkBxrZWtBJX/9ZG+zc9qrlDIY4hI3ZbVNVNZ7PErfHiqnweYkJymm2UwqbzRN46KpA/nvzXN566Y5XDR1AFazxub8CnYWGf1tOvc+TBiQzCvXz3al/9TP8YqTB/mcVeZp5Ne+uGm0O3jaNe/rzxdPYtH4nGbPd2SuDicibnwgKYzegfwchUjBbNI4yVXeu8GVmtpVVEWT3UlKnLVTXhOTSSMjQYmS1fuOqwoWs4nzJvYL+Fqp8Sqy2WBzUt/kYOORE1Q12EmNszKlE1ElX/RLVmkeb3Gz/WgVdU3KZzIqq21B4S8zhqZh0tSH7TGvCNHKncdY/m0+mgYPXTqZB5aoEu1/fnGAta5oC/gemNmShGgL2UlKqB32Kgf/fI8nJdXR/z2apjF1UCp/XTqFtXfO5/aFoxiQEsvEAclMzg3MWO7N6JxEXr1hFv2SY0iMtvg0LgOMdJWDHzhe0+6IiM15FTS6/FBLTmotmtuKYkUCIm4EQRBCwAz3nCmVhjEmgU/NDax5nzdGasooMT5jTCbJcYGnYBOiLVjNnuGZRofdM0ZnuVNoXcU9GdxLdBh+mxlD0gLymbRFcqzV7ecxqqZKqhv49RtbAbhu3jDmDM/g3In9uGxGLroOt726xZ2O66hSymCoj07FX+wxSsAzfb6mLTITo/nZ/JF8deeZvPuzU4i2dL7kHlTzwU9vP43Vd5zR5ly1galxRFtMNNmd7onvvjA8XGo4aeufj9F9+2BZXafmaHUnIm4EQRBCwHSvCeG6rnv1t+l8ZMQosTZmIV00dWCnrqNpGilxnkZ+n7pLwIOTkgLv+VLe4kYJvc427/OF95wpXdf59etbKa9tYkxOIrcvGuU+73eLxzEsI57iqgbuenMbVQ02CitUB+WWM6VaMjTDMBUrYXCkvI6DpbVYTFqXmh0Gi7goi9sk7guzSWN4ZsepKWOcxew2Ol0PSInFouk02Z0Unmh7blg4EHEjtGLIkCE88sgjQbnWqlWr0DStU6X1gtCbmJKbgtWscayqkYIT9Z3uTOxNplcpcVKMhTPGBBY18Mbw3WwpqGB/SQ0Wk8a8kZ2/XktaTgZ3OnW3uAmGmdhgltecqZe+yePzPceJspj422VTm0VF4qIs/O2yqVjNGh/uKOa+d3eqdSbFdBj9MiIWRuRm9V6V2po+JJXEmJ5RvDCyg07FDTaHW4DPbmM+m9mkkekqKjNK6CMFETe9hNNPP51bbrklKNdav349119/fVCuJQiCIjbKzIQBKmXywfYi8svr0TSY1AWPRVaSR9ycN6l/l1Iahu/mjY2q8duMIWnNerV0FUPcVDfYqW20s7u4mqoGO/FR5oAaDnaE4bs5XFbH799TguXXZ4/xaRKeODCZXy4aDaghl9C+mdjAnZZyeW6+2Nt2lVSkYpiK97cxQPO7IydocjjJTmq//1B2rEpHibgRwoKu69jtdr/OzczMlIoxQegGjFEMRlv8kVkJJHXhN33DcwOBjVvwhdHIzyhPD1YJuEFCtIXEaNV9pLiqwe23mTYkze8ZWP6QFGN1i8hGu5O5I9L50ZwhbZ5/3bxhzB3hiUy0ZyY2MHrdHCmrpckBX7siUKf3IHEzooNy8LWulNSc4RntesKyXbYeETc9DF3XqWuy+/2nvskR0Pnt/fFniizAsmXL+OKLL/jb3/6Gpmlomsazzz6Lpml89NFHTJ8+nejoaNasWcOBAwe48MILyc7OJiEhgRkzZvDJJ580u17LtJSmaTz11FNcdNFFxMXFMXLkSN55551Ov6dvvPEG48ePJzo6miFDhvDQQw81e/6xxx5j5MiRxMTEkJ2dzSWXXOJ+7vXXX2fixInExsaSnp7OggULqK2NPKe+IPjCGIhY5PKddCUlBaqVPijvgz/DFtvD6HVjEEy/jUF2ssd3YwwSDabfxsBITSXHWvnL9ye3a1Y2mTQevnSKe77WOD+iSANTY7GYNOptTjaUajTanfRPjmFUtn+dhSMBIy21v6QGpw8zsGEmbislZZAdp7uvE0lIE78OqLc5GPe7j8Jy7533neVXS/a//e1v7N27lwkTJnDfffcBsGPHDgDuuOMO/vKXvzBs2DBSUlIoKCjg3HPP5f777ycmJobnnnuOxYsXs2fPHgYNGtTmPe69917+/Oc/8+CDD/Loo4/ygx/8gCNHjpCWFth/TBs3buTSSy/lnnvuYenSpaxdu5abbrqJ9PR0li1bxoYNG/j5z3/OCy+8wJw5cygvL2fNmjUAFBUVcfnll/PnP/+Ziy66iOrqatasWeO3CBSEcDOthQDpTH8bb04fnckv5o/k1FGZXa428hY3Q9Lj3GW+waRfcgz7S2ooqmzoFr+NwVWzB3PweC0/njvEXYLeHtlJMbx47UxW7TnOuX6U0lvNJgalxXGwtJYvilSM4LTRWT2q/cTgtDisZo16m4OjlfXuwZqgGh5ucc25astMbOBJS0XWL5kibnoBycnJREVFERcXR06OarK0e/duAO677z4WLlzoPjc9PZ3Jkye7v7///vt56623eOedd/jpT3/a5j2WLVvG5ZdfDsAf//hHHn30Ub799lvOPvvsgNb68MMPM3/+fH77298CMGrUKHbu3MmDDz7IsmXLyMvLIz4+nvPPP5/ExEQGDx7M1KlTASVu7HY7S5YsYfBg1b9h4sSJAd1fEMJJekI0wzPj3R8EXamUArCYTdy6cFTHJ/qBd3XNmWOyu+WD2igHX3uglNKaJqItpoAHhvrDwNQ4nrp6ekCvGd8/udlYiI4YlhnPwdJaiuvV+3RGgCXg4cZiNjEsI4E9x6rZV1LTTNxsOHICu1NnQEpsh92uM11pqfLaJsprm9zpzXAj4qYDYq1mdt53ll/nOp1OqquqSUxKDMoIglgfnSUDZfr05v/Aa2truffee3nvvffcownq6+vJy8tr9zqTJk1yH8fHx5OYmEhJSUnA69m1axcXXnhhs8fmzp3LI488gsPhYOHChQwePJhhw4Zx9tlnc/bZZ7vTYZMnT2b+/PlMnDiRs846i0WLFnHJJZeQmhqcJmOCEApOHprGgeO1JEZb/B6QGArS4j3en2B1JW6JUQ7+8Y5jgIpcdbWvS7jwNtlazZFRAh4oI7KVuNl/rKaZGbqjEnBvos3QPzmGo5UNHDxeQ1p88CNxnUE8Nx2gaRpxURa//8RGmQM6v70/wfjNKT6+eWj5V7/6FW+88QZ/+MMfWLNmDZs3b2bixIk0NTW1cQVFy9lMmqbhdDoDXo+u66325Z1WSkxM5LvvvmP58uX069eP3/3ud0yePJmKigrMZjMrV67kgw8+YNy4cTz66KOMHj2aQ4cOBbwOQQgXp4xQv+HPHJYelMZ1wcJISyVEW9zG52BjVEzVNKrihpOHRtawxUAwet2AatAYH93zYgWeMQzNy8ENv80cP8QNeMYwRJKpWMRNLyEqKsqvGUpr1qxh2bJlXHTRRUycOJGcnBwOHz7c/Qt0MW7cOL788stmj61du5ZRo0ZhNqvf4CwWCwsWLODPf/4zW7du5fDhw3z22WeAElVz587l3nvvZdOmTURFRfHWW2+FbP2C0FXOnZjDk1dO449LOhjHHGJmDUtn0bhsfn3OmGbDJIOJEbkx6A4zcagwPtABThvV86I24HuAZlWDjW0FFYB/kRvw9P2JJFNxz5Oagk+GDBnCN998w+HDh0lISGgzqjJixAjefPNNFi9ejKZp/Pa3v+1UBKaz3H777cyYMYPf//73LF26lHXr1vH3v/+dxx57DID33nuPgwcPcuqpp5KamsqKFStwOp2MHj2ab775hk8//ZRFixaRlZXFN998w/Hjxxk7dmzI1i8IXUXTtFYDCCOBGKuZJ68KzKcSKN7jACwmrcuG6nDibbg+dWQPFTfZnl43RlR9/aFynLoylftjxgbPexFJpmKJ3PQSfvnLX2I2mxk3bhyZmZltemj++te/kpqaypw5c1i8eDFnnXUWJ510UsjWedJJJ/Gf//yHV155hQkTJvC73/2O++67j2XLlgGQkpLCm2++yZlnnsnYsWP55z//yfLlyxk/fjxJSUmsXr2ac889l1GjRvGb3/yGhx56iHPOOSdk6xcEofN4i5uJA5P9qgaNVLKSYvjJaUNZMMDpjlz0NIakx2M2aVQ32jlW1QgE5rcxGB6Baame+zdLaMaoUaNYt25ds8cMweDNkCFD3Ckeg5tvvrnZ9y3TVL5Krf0dp3D66ae3ev3FF1/MxRdf7PP8U045hVWrVvl8buzYsXz44Yd+3VcQhMgjLS6KKLOJJoeTmT3Yb2Nw24KRrGja16NKwL2JspgYkh7HgeO17CupJic5xmtYpv8/HyNyk19eR4PNQUwQimG6ikRuBEEQhJBgMmkMSFWpjpnDeq7fpjfh9t0cq6GiromdRVVAx837vMlIiCIxxoJThyNlbU8ZDyUiboQuceONN5KQkEBSUhIDBw4kKSmJhIQEEhISuPHGG8O9PEEQIozfXziBO84ezWlBHMopdB7PAM0avjlUjq7DiKwEslqYv9tD0zRGZHk6HkcCkpYSusR9993HL3/5S5xOJzU1NSQkJLh7/CQlBW8YniAIvYNTRmZwSg814PZGPKKkmmhXlVwgURuD4ZkJbMqriBjfjYgboUtkZWWRlZWF0+mkqqqKpKSkoDQwFARBELofQ9zsPVZDVb3qPxSImdhguKshpYgbQRAEQRDCyvDMBDQNKuttVNbbgMDMxJ7rRFbFlPyKLQiCIAh9lBirmUFe86PG5CR2aj7UcFcE6EBJrc8p46FGxI0gCIIg9GGMMQzQuZQUwKC0OCwmNWW8qKohWEvrNCJuBEEQBKEPM8JVDg6dMxMDWM0mhhidiiOgYkrEjSAIgiD0YYzIjabRpeaKkeS7Cbu4eeyxxxg6dCgxMTFMmzaNNWvWtHluUVERV1xxBaNHj8ZkMnHLLbeEbqG9nCFDhvDII4/4da6mabz99tvduh5BEAQhNJw8NI1oi4lTR2aSHGft9HUiqWIqrOLm1Vdf5ZZbbuHuu+9m06ZNzJs3j3POOafNuUiNjY1kZmZy9913M3ny5BCvVhAEQRB6H7lpcXz56zN5/IddmzPoFjcl4R+gGVZx8/DDD3PNNddw7bXXMnbsWB555BFyc3N5/PHHfZ4/ZMgQ/va3v3HVVVeRnJwc4tUKgiAIQu8kMzG6y4NMjYqp/REQuQlbn5umpiY2btzInXfe2ezxRYsWsXbt2qDdp7GxkcbGRvf3VVVqbobNZsNmszU712azoes6TqcTp9OpHtR1sPk3K0N3nas3mnAGY5CaNU4lQTvgiSee4P777+fIkSPNGuhdeOGFpKam8tvf/pbbb7+db775htraWsaOHcsf/vAHFixY0Gr97n13gPd7tG3bNm655Ra+/vpr4uLiWLJkCQ899BAJCeov+qpVq7jzzjvZsWMHVquV8ePH8+KLLzJ48GC2bNnCbbfdxoYNG9A0jZEjR/L4448zffp0f9+ldteo6zo2mw2zuXsHuRl/l1r+neoL9OW9Q9/ef1/eO8j+W+5/UEo0AMerGymrqiMptvMprvbu5w9hEzelpaU4HA6ys7ObPZ6dnU1xcXHQ7vPAAw9w7733tnr8448/Ji4urtljFouFnJwcampqaGpqUg/a6kj5x1i/75fSlcW2oOLmXUrgdMDZZ5/NLbfcwvvvv89pp52mXltRwccff8zy5cspLi7mjDPO4Ne//jUxMTEsX76cCy+8kG+//Zbc3FxACYGGhga3+OuI+vp6qqqqqKur45xzzmH69Ol8+umnlJaW8vOf/5wbb7yRxx57DLvdzkUXXcRVV13FE088QVNTE9999x01NTVUVVVxxRVXMGnSJD799FPMZjPbtm2jsbHR73W0R1NTE/X19axevRq73d7l6/nDypUrQ3KfSKQv7x369v778t5B9u+9/2SrmUqbxovvrGRIYjsv6gR1df4P5Qx7h+KWo+J1XQ/q+Pi77rqL2267zf19VVUVubm5LFq0qNXso4aGBvLz80lISCAmxjU0rCl8o9uTEhMhKr7j85KSOOuss/jvf//L4sWLAXjllVdIS0tj8eLFmM1m5s6d6z5/6tSpfPDBB6xatYqbb74ZAJPJRExMjN/zoGJjY0lKSuLVV1+loaGBF198EafTSWJiIiaTiQsvvJCHHnoIq9VKVVUVS5YscfukZsyY4b5OYWEhd9xxhztSM3XqVP/eHD9oaGggNjaWU0891fPz7CZsNhsrV65k4cKFWK3B/W0l0unLe4e+vf++vHeQ/fva/yvHNrDuYDk5oyZz7tQBQb1fIL/0hk3cZGRkYDabW0VpSkpKWkVzukJ0dDTR0dGtHrdara3+MjocDjRNw2QyedI70Qnw/476dS+n00lVdTVJrg/4rmLyMy0F8MMf/pDrr7+exx9/nOjoaJYvX85ll12G1WqltraWe++9l/fee4+jR49it9upr68nPz+/2TqNvfu1Ntd7tGfPHiZPnkxCQgJVVVVomsa8efNwOp3s27ePU089lWXLlnHOOeewcOFCFixYwKWXXkq/fv0AuO2227j++ut56aWXWLBgAd///vcZPnx44G9WG2vUNM3nz7q7COW9Io2+vHfo2/vvy3sH2b/3/kdkJbLuYDmHyxuC/p4Ecr2wGYqjoqKYNm1aq3DeypUrmTNnTphW5QNNU9ETf/9Y4wI7v70/AUSwFi9ejNPp5P333yc/P581a9bwwx/+EIBf/epXvPHGG/zhD39gzZo1bN68mYkTJ3pSb12gvUib8fgzzzzDunXrmDNnDq+++iqjRo3i66+/BuCee+5hx44dnHfeeXz22WeMGzeOt956q8vrEgRBEEKP0etmf5gb+YU1LXXbbbdx5ZVXMn36dGbPns2TTz5JXl4eN954I6BSSoWFhTz//PPu12zevBmAmpoajh8/zubNm4mKimLcuHHh2ELEEBsby5IlS3jppZfYv38/o0aNYtq0aQCsWbOGZcuWcdFFFwHqvTt8+HBQ7jtu3Diee+45ams9pX9fffUVJpOJUaNGuR+bOnUqU6dO5a677mL27Nm8/PLLzJo1C4BRo0YxatQobr31Vi6//HKeeeYZ91oFQRCEnoPR7TjcvW7CKm6WLl1KWVkZ9913H0VFRUyYMIEVK1YwePBgQDXta9nzxtuTsXHjRl5++WUGDx4ctA/rnswPfvADFi9ezI4dO9xRG4ARI0bw5ptvsnjxYjRN47e//a3fVVH+3PN//ud/WLZsGbfffjv19fX87Gc/48orryQ7O5tDhw7x5JNPcsEFF9C/f3/27NnD3r17ueqqq6ivr+dXv/oVl1xyCUOHDqWgoID169dz8cUXB2VtgiAIQmgZnqUiN06njtOpYzIFz0MbCGE3FN90003cdNNNPp979tlnWz2m6+GfNhqpnHnmmaSlpbFnzx6uuOIK9+N//etf+fGPf8ycOXPIyMjg17/+dVCqkQDi4uL46KOP+MUvfsH8+fOJi4vj4osv5uGHH3Y/v3v3bp577jnKysro168fP/3pT7nhhhuw2+2UlZVx1VVXcezYMTIyMliyZInP6jZBEAQh8slJimHXfWcTGxW+YhyIAHEjBA+z2czRo63Nz0OGDOGzzz5r9phRJWUQSOSrpcCcOHEin3zyCVVVVSQlJTUzJWdnZ7fpoYmKimL58uV+31cQBEGIbDRNC7uwgQiYLSUIgiAIghBMRNwIzXjppZdISEjw+Wf8+PHhXp4gCIIgdIikpYRmXHDBBcycOdPnc325j4MgCILQcxBxIzQjMTGRxMQg98wWBEEQhBAiaSkfSEVW70B+joIgCH0TETdeGGmXQIZzCZGL0YG5uyeCC4IgCJGFpKW8MJvNpKSkUFJSAqgeLYEM8XQ6nTQ1NdHQ0BCU2VI9iUjbu9Pp5Pjx48TFxWGxyF9zQRCEvoT8r9+CnJwcALfACQRd16mvryc2Njaok817ApG4d5PJxKBBgyJmPYIgCEJoEHHTAk3T6NevH1lZWdhstoBea7PZWL16NaeeemqfqyyKxL1HRUVFRBRJEARBCC0ibtrAbDYH7NUwm83Y7XZiYmIi5gM+VPTlvQuCIAiRhfxaKwiCIAhCr0LEjSAIgiAIvQoRN4IgCIIg9Cr6nOfGaOxWVVUV9GvbbDbq6uqoqqrqc76Tvrx36Nv778t7h769/768d5D9h3r/xue2Pw1a+5y4qa6uBiA3NzfMKxEEQRAEIVCqq6tJTk5u9xxN72M96p1OJ0ePHiUxMTHo/U+qqqrIzc0lPz+fpKSkoF470unLe4e+vf++vHfo2/vvy3sH2X+o96/rOtXV1fTv37/DNh99LnJjMpkYOHBgt94jKSmpT/5Fh769d+jb++/Le4e+vf++vHeQ/Ydy/x1FbAzEUCwIgiAIQq9CxI0gCIIgCL0KETdBJDo6mv/5n/8hOjo63EsJOX1579C399+X9w59e/99ee8g+4/k/fc5Q7EgCIIgCL0bidwIgiAIgtCrEHEjCIIgCEKvQsSNIAiCIAi9ChE3giAIgiD0KkTcBInHHnuMoUOHEhMTw7Rp01izZk24l9QtrF69msWLF9O/f380TePtt99u9ryu69xzzz3079+f2NhYTj/9dHbs2BGexQaZBx54gBkzZpCYmEhWVhbf+9732LNnT7NzevP+H3/8cSZNmuRu2DV79mw++OAD9/O9ee8teeCBB9A0jVtuucX9WG/e/z333IOmac3+5OTkuJ/vzXsHKCws5Ic//CHp6enExcUxZcoUNm7c6H6+N+9/yJAhrX72mqZx8803AxG8d13oMq+88oputVr1f/3rX/rOnTv1X/ziF3p8/P9v735jmjrbMIBfxUKFjhCUQUsWkSnK8A9xdipKZhyLwtgHHdPNsFliFlMnDONM3NQFNVH55KKJ6aJjRCMJCUFMjTr+TGSZi2KUSnWMscDcEm06Z9xAIyT0fj8YTzzW7X33ipz69PolJ2mf52m5r1NK7pyeQ61y7do1o0sbcSdPnpQtW7ZIfX29AJCGhgbdfGVlpcTHx0t9fb34fD555513xG63y19//WVMwSNoyZIlUl1dLVeuXBGv1yuFhYUyYcIEGRgY0NaonN/j8ciJEyeku7tburu7ZfPmzRIdHS1XrlwREbWzP6y9vV0mTpwoM2fOlPLycm1c5fwVFRUybdo0uXHjhrYFAgFtXuXst27dkrS0NCkpKZHz589LX1+ftLS0yM8//6ytUTl/IBDQve7Nzc0CQFpbW0UkfLOzuRkBc+bMEZfLpRvLzMyUTz75xKCKRsejzU0wGBSbzSaVlZXa2L179yQhIUG++OILAyp8ugKBgACQtrY2EYm8/CIiiYmJ8uWXX0ZM9v7+fsnIyJDm5mZZuHCh1tyonr+iokKys7MfO6d69k2bNklubu7fzque/1Hl5eUyadIkCQaDYZ2dH0s9oaGhIVy8eBGLFy/WjS9evBjff/+9QVUZo6+vD36/X7cvLBYLFi5cqOS++PPPPwEA48aNAxBZ+YeHh1FbW4s7d+4gJycnYrKvW7cOhYWFeP3113XjkZC/p6cHqampSE9Px7vvvove3l4A6mf3eDxwOBxYvnw5kpOTMWvWLBw8eFCbVz3/w4aGhnDkyBGsXr0aJpMprLOzuXlCN2/exPDwMFJSUnTjKSkp8Pv9BlVljAd5I2FfiAg2bNiA3NxcTJ8+HUBk5Pf5fHjuuedgsVjgcrnQ0NCArKysiMheW1uLS5cuYffu3SFzquefO3cuDh8+jMbGRhw8eBB+vx/z58/HH3/8oXz23t5euN1uZGRkoLGxES6XCx999BEOHz4MQP3X/mHHjh3D7du3UVJSAiC8s0fct4I/LSaTSXdfRELGIkUk7IvS0lJ0dnbiu+++C5lTOf/UqVPh9Xpx+/Zt1NfXw+l0oq2tTZtXNftvv/2G8vJyNDU1YezYsX+7TtX8BQUF2u0ZM2YgJycHkyZNwqFDhzBv3jwA6mYPBoNwOBzYtWsXAGDWrFm4evUq3G43Vq1apa1TNf/DqqqqUFBQgNTUVN14OGbnkZsnlJSUhDFjxoR0qYFAIKSbVd2DqydU3xdlZWXweDxobW3FCy+8oI1HQv6YmBhMnjwZDocDu3fvRnZ2Nvbu3at89osXLyIQCGD27Nkwm80wm81oa2vDvn37YDabtYyq5n+U1WrFjBkz0NPTo/xrb7fbkZWVpRt76aWX8OuvvwKIjPc9AFy7dg0tLS344IMPtLFwzs7m5gnFxMRg9uzZaG5u1o03Nzdj/vz5BlVljPT0dNhsNt2+GBoaQltbmxL7QkRQWlqKo0eP4vTp00hPT9fNq57/cUQEg4ODymfPy8uDz+eD1+vVNofDgeLiYni9Xrz44otK53/U4OAgurq6YLfblX/tFyxYEPIvH3766SekpaUBiJz3fXV1NZKTk1FYWKiNhXV2g05kVsqDS8Grqqrkhx9+kPXr14vVapVffvnF6NJGXH9/v3R0dEhHR4cAkD179khHR4d22XtlZaUkJCTI0aNHxefzycqVK8PissCRsHbtWklISJAzZ87oLo28e/eutkbl/J9++ql8++230tfXJ52dnbJ582aJioqSpqYmEVE7++M8fLWUiNr5P/74Yzlz5oz09vbKuXPn5M0335T4+Hjtb5zK2dvb28VsNsvOnTulp6dHampqJC4uTo4cOaKtUTm/iMjw8LBMmDBBNm3aFDIXrtnZ3IyQ/fv3S1pamsTExMjLL7+sXR6smtbWVgEQsjmdThG5f1lkRUWF2Gw2sVgs8uqrr4rP5zO26BHyuNwApLq6Wlujcv7Vq1drv+PPP/+85OXlaY2NiNrZH+fR5kbl/A/+d0l0dLSkpqbKW2+9JVevXtXmVc4uInL8+HGZPn26WCwWyczMlAMHDujmVc/f2NgoAKS7uztkLlyzm0REDDlkRERERPQU8JwbIiIiUgqbGyIiIlIKmxsiIiJSCpsbIiIiUgqbGyIiIlIKmxsiIiJSCpsbIiIiUgqbGyIiIlIKmxsiItz/ZuNjx44ZXQYRjQA2N0RkuJKSEphMppAtPz/f6NKI6BlkNroAIiIAyM/PR3V1tW7MYrEYVA0RPct45IaIwoLFYoHNZtNtiYmJAO5/ZOR2u1FQUIDY2Fikp6ejrq5O93ifz4fXXnsNsbGxGD9+PNasWYOBgQHdmq+++grTpk2DxWKB3W5HaWmpbv7mzZtYtmwZ4uLikJGRAY/H83RDE9FTweaGiJ4Jn332GYqKinD58mW89957WLlyJbq6ugAAd+/eRX5+PhITE3HhwgXU1dWhpaVF17y43W6sW7cOa9asgc/ng8fjweTJk3U/Y/v27VixYgU6OzvxxhtvoLi4GLdu3RrVnEQ0Aoz+WnIiIqfTKWPGjBGr1arbduzYISIiAMTlcukeM3fuXFm7dq2IiBw4cEASExNlYGBAmz9x4oRERUWJ3+8XEZHU1FTZsmXL39YAQLZu3ardHxgYEJPJJKdOnRqxnEQ0OnjODRGFhUWLFsHtduvGxo0bp93OycnRzeXk5MDr9QIAurq6kJ2dDavVqs0vWLAAwWAQ3d3dMJlMuH79OvLy8v6xhpkzZ2q3rVYr4uPjEQgE/t9IRGQQNjdEFBasVmvIx0T/jclkAgCIiHb7cWtiY2P/p+eLjo4OeWwwGPxXNRGR8XjODRE9E86dOxdyPzMzEwCQlZUFr9eLO3fuaPNnz55FVFQUpkyZgvj4eEycOBHffPPNqNZMRMbgkRsiCguDg4Pw+/26MbPZjKSkJABAXV0dHA4HcnNzUVNTg/b2dlRVVQEAiouLUVFRAafTiW3btuH3339HWVkZ3n//faSkpAAAtm3bBpfLheTkZBQUFKC/vx9nz55FWVnZ6AYloqeOzQ0RhYWvv/4adrtdNzZ16lT8+OOPAO5fyVRbW4sPP/wQNpsNNTU1yMrKAgDExcWhsbER5eXleOWVVxAXF4eioiLs2bNHey6n04l79+7h888/x8aNG5GUlIS333579AIS0agxiYgYXQQR0T8xmUxoaGjA0qVLjS6FiJ4BPOeGiIiIlMLmhoiIiJTCc26IKOzx03Mi+jd45IaIiIiUwuaGiIiIlMLmhoiIiJTC5oaIiIiUwuaGiIiIlMLmhoiIiJTC5oaIiIiUwuaGiIiIlPIfSsgmX8du9QMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\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",
    "\n",
    "plt.savefig(\"suggest_loss.pdf\")\n",
    "\n",
    "df_metrics[[\"train_acc\", \"val_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.savefig(\"suggest_acc.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ed97ce3f-c177-45f7-ac9c-5a90b0f765f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 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": "5c49b76447ba44a7aad2a463347858f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8550000190734863     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8550000190734863    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.8550000190734863}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, dataloaders=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c218eba8-60c6-4a5b-92d1-ede4ba089ce8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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