2-training-resnet18-from-scratch-with-128px-images.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick, Draw! Doodle Recognition Challenge\n",
    "\n",
    "PLEASE READ MY BLOG POST : https://medium.com/@bouteillon1981/10-lessons-learned-from-participating-to-google-ai-challenge-268b4aa87efa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import ast\n",
    "from pathlib import Path\n",
    "import pprint\n",
    "import sqlite3\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cv2\n",
    "import torch\n",
    "\n",
    "from fastai import *\n",
    "from fastai.vision import *\n",
    "from fastai.callbacks import *\n",
    "import pretrainedmodels\n",
    "import torchvision\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define some hyper-parameters for our notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "LIMIT_SAMPLES = None  # 340*1_000\n",
    "NUM_VAL = 34_000\n",
    "sz = 128\n",
    "lw = 1\n",
    "bs = 1024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drawing functions\n",
    "\n",
    "`list2drawing` convert a list of strokes to an image.\n",
    "\n",
    "Some ideas come from:\n",
    "\n",
    "Beluga's kernel: https://www.kaggle.com/gaborfodor/greyscale-mobilenet-lb-0-892\n",
    "\n",
    "Radek's post: https://www.kaggle.com/c/quickdraw-doodle-recognition/discussion/69409"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE_SIZE = 256\n",
    "border = 2  # keep some margin with image border\n",
    "\n",
    "shift_colors = (\n",
    "    (255, 0, 0),\n",
    "    (255, 128, 0),\n",
    "    (255, 255, 0),\n",
    "    (128, 255, 0),\n",
    "    (0, 255, 0),\n",
    "    (0, 255, 128),\n",
    "    (0, 255, 255),\n",
    "    (0, 128, 255),\n",
    "    (0, 0, 255),\n",
    "    (128, 0, 255),\n",
    "    (255, 0, 255),\n",
    "    (255, 0, 128)\n",
    ")\n",
    "\n",
    "\n",
    "def list2drawing(raw_strokes, size):\n",
    "    img = np.zeros((size, size, 3), np.uint8)\n",
    "    coef = (size - 2 * lw - 2 * border) / (BASE_SIZE - 1)\n",
    "    nb_stokes = len(raw_strokes)\n",
    "    for t, stroke in enumerate(raw_strokes[::-1]):\n",
    "        rgb = shift_colors[(nb_stokes-t-1)%12]\n",
    "\n",
    "        for i in range(len(stroke[0]) - 1):\n",
    "            p1 = (int(coef * stroke[0][i] + lw + border), int(coef * stroke[1][i] + lw+ border))\n",
    "            p2 = (int(coef * stroke[0][i + 1] + lw + border), int(coef * stroke[1][i + 1] + lw + border))\n",
    "            _ = cv2.line(img, p1, p2, rgb, lw, cv2.LINE_AA)\n",
    "    return img\n",
    "\n",
    "\n",
    "def drawing2tensor(drawing):\n",
    "    rgb = drawing.transpose(2,0,1).astype(np.float32)\n",
    "    return torch.from_numpy(rgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics\n",
    "\n",
    "`map3` is the metric used for this competition\n",
    "\n",
    "These functions are from https://github.com/benhamner/Metrics/blob/master/Python/ml_metrics/average_precision.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apk(actual, predicted, k=10):\n",
    "    if len(predicted)>k:\n",
    "        predicted = predicted[:k]\n",
    "\n",
    "    score = 0.0\n",
    "    num_hits = 0.0\n",
    "\n",
    "    for i,p in enumerate(predicted):\n",
    "        if p in actual and p not in predicted[:i]:\n",
    "            num_hits += 1.0\n",
    "            score += num_hits / (i+1.0)\n",
    "\n",
    "    if not actual:\n",
    "        return 0.0\n",
    "\n",
    "    return score / min(len(actual), k)\n",
    "\n",
    "def mapk(actual, predicted, k=10):\n",
    "    return np.mean([apk(a,p,k) for a,p in zip(actual, predicted)])\n",
    "\n",
    "def map3(preds, targs):\n",
    "    predicted_idxs = preds.sort(descending=True)[1]\n",
    "    top_3 = predicted_idxs[:, :3]\n",
    "    res = mapk([[t] for t in targs.cpu().numpy()], top_3.cpu().numpy(), 3)\n",
    "    return torch.tensor(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submission Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_3_preds(preds):\n",
    "    return np.argsort(preds.numpy())[:, ::-1][:, :3]\n",
    "\n",
    "\n",
    "def top_3_pred_labels(preds, classes):\n",
    "    top_3 = top_3_preds(preds)\n",
    "    labels = []\n",
    "    for i in range(top_3.shape[0]):\n",
    "        labels.append(' '.join([classes[idx] for idx in top_3[i]]))\n",
    "    return labels\n",
    "\n",
    "\n",
    "def create_submission(test_preds, test_dl, name, classes):\n",
    "    key_ids = test_dl.dataset.x.items\n",
    "    labels = top_3_pred_labels(test_preds, classes)\n",
    "    sub = pd.DataFrame({'key_id': key_ids, 'word': labels})\n",
    "    sub.to_csv(f'{name}.csv.gz', index=False, compression='gzip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Create ImageDataBunch using the data block API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open database in read only mode\n",
    "db = sqlite3.connect('file:/home/eric/jupyter-project/quickdraw-doodle-recognition/quickdraw_master/data/quickdraw_dataset.db?mode=ro', uri=True)\n",
    "c = db.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'airplane',\n",
      " 1: 'alarm_clock',\n",
      " 2: 'ambulance',\n",
      " 3: 'angel',\n",
      " 4: 'animal_migration',\n",
      " 5: 'ant',\n",
      " 6: 'anvil',\n",
      " 7: 'apple',\n",
      " 8: 'arm',\n",
      " 9: 'asparagus',\n",
      " 10: 'axe',\n",
      " 11: 'backpack',\n",
      " 12: 'banana',\n",
      " 13: 'bandage',\n",
      " 14: 'barn',\n",
      " 15: 'baseball',\n",
      " 16: 'baseball_bat',\n",
      " 17: 'basket',\n",
      " 18: 'basketball',\n",
      " 19: 'bat',\n",
      " 20: 'bathtub',\n",
      " 21: 'beach',\n",
      " 22: 'bear',\n",
      " 23: 'beard',\n",
      " 24: 'bed',\n",
      " 25: 'bee',\n",
      " 26: 'belt',\n",
      " 27: 'bench',\n",
      " 28: 'bicycle',\n",
      " 29: 'binoculars',\n",
      " 30: 'bird',\n",
      " 31: 'birthday_cake',\n",
      " 32: 'blackberry',\n",
      " 33: 'blueberry',\n",
      " 34: 'book',\n",
      " 35: 'boomerang',\n",
      " 36: 'bottlecap',\n",
      " 37: 'bowtie',\n",
      " 38: 'bracelet',\n",
      " 39: 'brain',\n",
      " 40: 'bread',\n",
      " 41: 'bridge',\n",
      " 42: 'broccoli',\n",
      " 43: 'broom',\n",
      " 44: 'bucket',\n",
      " 45: 'bulldozer',\n",
      " 46: 'bus',\n",
      " 47: 'bush',\n",
      " 48: 'butterfly',\n",
      " 49: 'cactus',\n",
      " 50: 'cake',\n",
      " 51: 'calculator',\n",
      " 52: 'calendar',\n",
      " 53: 'camel',\n",
      " 54: 'camera',\n",
      " 55: 'camouflage',\n",
      " 56: 'campfire',\n",
      " 57: 'candle',\n",
      " 58: 'cannon',\n",
      " 59: 'canoe',\n",
      " 60: 'car',\n",
      " 61: 'carrot',\n",
      " 62: 'castle',\n",
      " 63: 'cat',\n",
      " 64: 'ceiling_fan',\n",
      " 65: 'cell_phone',\n",
      " 66: 'cello',\n",
      " 67: 'chair',\n",
      " 68: 'chandelier',\n",
      " 69: 'church',\n",
      " 70: 'circle',\n",
      " 71: 'clarinet',\n",
      " 72: 'clock',\n",
      " 73: 'cloud',\n",
      " 74: 'coffee_cup',\n",
      " 75: 'compass',\n",
      " 76: 'computer',\n",
      " 77: 'cookie',\n",
      " 78: 'cooler',\n",
      " 79: 'couch',\n",
      " 80: 'cow',\n",
      " 81: 'crab',\n",
      " 82: 'crayon',\n",
      " 83: 'crocodile',\n",
      " 84: 'crown',\n",
      " 85: 'cruise_ship',\n",
      " 86: 'cup',\n",
      " 87: 'diamond',\n",
      " 88: 'dishwasher',\n",
      " 89: 'diving_board',\n",
      " 90: 'dog',\n",
      " 91: 'dolphin',\n",
      " 92: 'donut',\n",
      " 93: 'door',\n",
      " 94: 'dragon',\n",
      " 95: 'dresser',\n",
      " 96: 'drill',\n",
      " 97: 'drums',\n",
      " 98: 'duck',\n",
      " 99: 'dumbbell',\n",
      " 100: 'ear',\n",
      " 101: 'elbow',\n",
      " 102: 'elephant',\n",
      " 103: 'envelope',\n",
      " 104: 'eraser',\n",
      " 105: 'eye',\n",
      " 106: 'eyeglasses',\n",
      " 107: 'face',\n",
      " 108: 'fan',\n",
      " 109: 'feather',\n",
      " 110: 'fence',\n",
      " 111: 'finger',\n",
      " 112: 'fire_hydrant',\n",
      " 113: 'fireplace',\n",
      " 114: 'firetruck',\n",
      " 115: 'fish',\n",
      " 116: 'flamingo',\n",
      " 117: 'flashlight',\n",
      " 118: 'flip_flops',\n",
      " 119: 'floor_lamp',\n",
      " 120: 'flower',\n",
      " 121: 'flying_saucer',\n",
      " 122: 'foot',\n",
      " 123: 'fork',\n",
      " 124: 'frog',\n",
      " 125: 'frying_pan',\n",
      " 126: 'garden',\n",
      " 127: 'garden_hose',\n",
      " 128: 'giraffe',\n",
      " 129: 'goatee',\n",
      " 130: 'golf_club',\n",
      " 131: 'grapes',\n",
      " 132: 'grass',\n",
      " 133: 'guitar',\n",
      " 134: 'hamburger',\n",
      " 135: 'hammer',\n",
      " 136: 'hand',\n",
      " 137: 'harp',\n",
      " 138: 'hat',\n",
      " 139: 'headphones',\n",
      " 140: 'hedgehog',\n",
      " 141: 'helicopter',\n",
      " 142: 'helmet',\n",
      " 143: 'hexagon',\n",
      " 144: 'hockey_puck',\n",
      " 145: 'hockey_stick',\n",
      " 146: 'horse',\n",
      " 147: 'hospital',\n",
      " 148: 'hot_air_balloon',\n",
      " 149: 'hot_dog',\n",
      " 150: 'hot_tub',\n",
      " 151: 'hourglass',\n",
      " 152: 'house',\n",
      " 153: 'house_plant',\n",
      " 154: 'hurricane',\n",
      " 155: 'ice_cream',\n",
      " 156: 'jacket',\n",
      " 157: 'jail',\n",
      " 158: 'kangaroo',\n",
      " 159: 'key',\n",
      " 160: 'keyboard',\n",
      " 161: 'knee',\n",
      " 162: 'ladder',\n",
      " 163: 'lantern',\n",
      " 164: 'laptop',\n",
      " 165: 'leaf',\n",
      " 166: 'leg',\n",
      " 167: 'light_bulb',\n",
      " 168: 'lighthouse',\n",
      " 169: 'lightning',\n",
      " 170: 'line',\n",
      " 171: 'lion',\n",
      " 172: 'lipstick',\n",
      " 173: 'lobster',\n",
      " 174: 'lollipop',\n",
      " 175: 'mailbox',\n",
      " 176: 'map',\n",
      " 177: 'marker',\n",
      " 178: 'matches',\n",
      " 179: 'megaphone',\n",
      " 180: 'mermaid',\n",
      " 181: 'microphone',\n",
      " 182: 'microwave',\n",
      " 183: 'monkey',\n",
      " 184: 'moon',\n",
      " 185: 'mosquito',\n",
      " 186: 'motorbike',\n",
      " 187: 'mountain',\n",
      " 188: 'mouse',\n",
      " 189: 'moustache',\n",
      " 190: 'mouth',\n",
      " 191: 'mug',\n",
      " 192: 'mushroom',\n",
      " 193: 'nail',\n",
      " 194: 'necklace',\n",
      " 195: 'nose',\n",
      " 196: 'ocean',\n",
      " 197: 'octagon',\n",
      " 198: 'octopus',\n",
      " 199: 'onion',\n",
      " 200: 'oven',\n",
      " 201: 'owl',\n",
      " 202: 'paint_can',\n",
      " 203: 'paintbrush',\n",
      " 204: 'palm_tree',\n",
      " 205: 'panda',\n",
      " 206: 'pants',\n",
      " 207: 'paper_clip',\n",
      " 208: 'parachute',\n",
      " 209: 'parrot',\n",
      " 210: 'passport',\n",
      " 211: 'peanut',\n",
      " 212: 'pear',\n",
      " 213: 'peas',\n",
      " 214: 'pencil',\n",
      " 215: 'penguin',\n",
      " 216: 'piano',\n",
      " 217: 'pickup_truck',\n",
      " 218: 'picture_frame',\n",
      " 219: 'pig',\n",
      " 220: 'pillow',\n",
      " 221: 'pineapple',\n",
      " 222: 'pizza',\n",
      " 223: 'pliers',\n",
      " 224: 'police_car',\n",
      " 225: 'pond',\n",
      " 226: 'pool',\n",
      " 227: 'popsicle',\n",
      " 228: 'postcard',\n",
      " 229: 'potato',\n",
      " 230: 'power_outlet',\n",
      " 231: 'purse',\n",
      " 232: 'rabbit',\n",
      " 233: 'raccoon',\n",
      " 234: 'radio',\n",
      " 235: 'rain',\n",
      " 236: 'rainbow',\n",
      " 237: 'rake',\n",
      " 238: 'remote_control',\n",
      " 239: 'rhinoceros',\n",
      " 240: 'river',\n",
      " 241: 'roller_coaster',\n",
      " 242: 'rollerskates',\n",
      " 243: 'sailboat',\n",
      " 244: 'sandwich',\n",
      " 245: 'saw',\n",
      " 246: 'saxophone',\n",
      " 247: 'school_bus',\n",
      " 248: 'scissors',\n",
      " 249: 'scorpion',\n",
      " 250: 'screwdriver',\n",
      " 251: 'sea_turtle',\n",
      " 252: 'see_saw',\n",
      " 253: 'shark',\n",
      " 254: 'sheep',\n",
      " 255: 'shoe',\n",
      " 256: 'shorts',\n",
      " 257: 'shovel',\n",
      " 258: 'sink',\n",
      " 259: 'skateboard',\n",
      " 260: 'skull',\n",
      " 261: 'skyscraper',\n",
      " 262: 'sleeping_bag',\n",
      " 263: 'smiley_face',\n",
      " 264: 'snail',\n",
      " 265: 'snake',\n",
      " 266: 'snorkel',\n",
      " 267: 'snowflake',\n",
      " 268: 'snowman',\n",
      " 269: 'soccer_ball',\n",
      " 270: 'sock',\n",
      " 271: 'speedboat',\n",
      " 272: 'spider',\n",
      " 273: 'spoon',\n",
      " 274: 'spreadsheet',\n",
      " 275: 'square',\n",
      " 276: 'squiggle',\n",
      " 277: 'squirrel',\n",
      " 278: 'stairs',\n",
      " 279: 'star',\n",
      " 280: 'steak',\n",
      " 281: 'stereo',\n",
      " 282: 'stethoscope',\n",
      " 283: 'stitches',\n",
      " 284: 'stop_sign',\n",
      " 285: 'stove',\n",
      " 286: 'strawberry',\n",
      " 287: 'streetlight',\n",
      " 288: 'string_bean',\n",
      " 289: 'submarine',\n",
      " 290: 'suitcase',\n",
      " 291: 'sun',\n",
      " 292: 'swan',\n",
      " 293: 'sweater',\n",
      " 294: 'swing_set',\n",
      " 295: 'sword',\n",
      " 296: 't-shirt',\n",
      " 297: 'table',\n",
      " 298: 'teapot',\n",
      " 299: 'teddy-bear',\n",
      " 300: 'telephone',\n",
      " 301: 'television',\n",
      " 302: 'tennis_racquet',\n",
      " 303: 'tent',\n",
      " 304: 'The_Eiffel_Tower',\n",
      " 305: 'The_Great_Wall_of_China',\n",
      " 306: 'The_Mona_Lisa',\n",
      " 307: 'tiger',\n",
      " 308: 'toaster',\n",
      " 309: 'toe',\n",
      " 310: 'toilet',\n",
      " 311: 'tooth',\n",
      " 312: 'toothbrush',\n",
      " 313: 'toothpaste',\n",
      " 314: 'tornado',\n",
      " 315: 'tractor',\n",
      " 316: 'traffic_light',\n",
      " 317: 'train',\n",
      " 318: 'tree',\n",
      " 319: 'triangle',\n",
      " 320: 'trombone',\n",
      " 321: 'truck',\n",
      " 322: 'trumpet',\n",
      " 323: 'umbrella',\n",
      " 324: 'underwear',\n",
      " 325: 'van',\n",
      " 326: 'vase',\n",
      " 327: 'violin',\n",
      " 328: 'washing_machine',\n",
      " 329: 'watermelon',\n",
      " 330: 'waterslide',\n",
      " 331: 'whale',\n",
      " 332: 'wheel',\n",
      " 333: 'windmill',\n",
      " 334: 'wine_bottle',\n",
      " 335: 'wine_glass',\n",
      " 336: 'wristwatch',\n",
      " 337: 'yoga',\n",
      " 338: 'zebra',\n",
      " 339: 'zigzag'}\n"
     ]
    }
   ],
   "source": [
    "# Get list of classes\n",
    "c.execute('SELECT y,label from classes ORDER BY y')\n",
    "db_classes = c.fetchall()\n",
    "db_classes = {c[0]: c[1].replace(' ', '_') for c in db_classes}\n",
    "pprint.pprint(db_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True number of labels in train: 49707579\n",
      "CPU times: user 21.5 s, sys: 10.2 s, total: 31.7 s\n",
      "Wall time: 2min 10s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Get labels for each items in our train dataset\n",
    "c.execute('SELECT key_id,y FROM train')\n",
    "train_labels = c.fetchall()\n",
    "print('True number of labels in train:', len(train_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(340463, 340000)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a limited piece of train dataset\n",
    "if LIMIT_SAMPLES is not None:\n",
    "    train_labels = train_labels[::len(train_labels)//LIMIT_SAMPLES]\n",
    "len(train_labels), LIMIT_SAMPLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key_id</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4503599667216384</td>\n",
       "      <td>watermelon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4503605690236928</td>\n",
       "      <td>basketball</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4503612736667648</td>\n",
       "      <td>passport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4503618898100224</td>\n",
       "      <td>saxophone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4503626024222720</td>\n",
       "      <td>hospital</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             key_id           y\n",
       "0  4503599667216384  watermelon\n",
       "1  4503605690236928  basketball\n",
       "2  4503612736667648    passport\n",
       "3  4503618898100224   saxophone\n",
       "4  4503626024222720    hospital"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels = pd.DataFrame.from_records(train_labels, columns=['key_id', 'y'])\n",
    "train_labels['y'] = train_labels['y'].map(db_classes).astype('category')\n",
    "train_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples in tests: 112199\n",
      "CPU times: user 67.3 ms, sys: 26.6 ms, total: 94 ms\n",
      "Wall time: 218 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Get list of test items\n",
    "c.execute('SELECT key_id FROM test')\n",
    "tests = c.fetchall()\n",
    "print('Number of samples in tests:', len(tests))\n",
    "tests = pd.DataFrame.from_records(tests, columns=['key_id'])\n",
    "tests.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callback of parallel workers\n",
    "from multiprocessing import Lock\n",
    "lock = Lock()\n",
    "\n",
    "def create_func(path):\n",
    "    global c\n",
    "    key_id = int(path)\n",
    "    lock.acquire()  # meh!\n",
    "    if path < 9000000000000000:\n",
    "        c.execute(f'SELECT drawing from train WHERE key_id=?', (key_id,))\n",
    "    else:\n",
    "        c.execute(f'SELECT drawing from test WHERE key_id=?', (key_id,))\n",
    "    drawing = c.fetchone()\n",
    "    lock.release()\n",
    "    \n",
    "    if drawing is None:\n",
    "        print('got None for', path)\n",
    "    drawing = drawing[0]\n",
    "    drawing = ast.literal_eval(drawing)\n",
    "    drawing = list2drawing(drawing, size=sz)\n",
    "    tensor = drawing2tensor(drawing)\n",
    "    return Image(tensor.div_(255))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_list = ItemList.from_df(df=train_labels, create_func=create_func, cols='key_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs = np.arange(item_list.items.shape[0])\n",
    "np.random.shuffle(idxs)\n",
    "val_idxs = idxs[:NUM_VAL]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_lists = item_list.split_by_idx(val_idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_lists = item_lists.label_from_df(cols='y')\n",
    "\n",
    "test_items = ItemList.from_df(df=tests, create_func=create_func, cols='key_id')\n",
    "label_lists.add_test(test_items);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['watermelon',\n",
       " 'basketball',\n",
       " 'passport',\n",
       " 'saxophone',\n",
       " 'hospital',\n",
       " 'bottlecap',\n",
       " 'diving_board',\n",
       " 'teddy-bear',\n",
       " 'toothpaste',\n",
       " 'traffic_light',\n",
       " 'lipstick',\n",
       " 'hot_dog',\n",
       " 'stethoscope',\n",
       " 'paintbrush',\n",
       " 'key',\n",
       " 'blackberry',\n",
       " 'giraffe',\n",
       " 'rain',\n",
       " 'toothbrush',\n",
       " 'snorkel',\n",
       " 'piano',\n",
       " 'moon',\n",
       " 'pond',\n",
       " 'golf_club',\n",
       " 'soccer_ball',\n",
       " 'mouth',\n",
       " 'spider',\n",
       " 'moustache',\n",
       " 'strawberry',\n",
       " 'crocodile',\n",
       " 'The_Great_Wall_of_China',\n",
       " 'tree',\n",
       " 'penguin',\n",
       " 'scissors',\n",
       " 'face',\n",
       " 'square',\n",
       " 'keyboard',\n",
       " 'skyscraper',\n",
       " 'ant',\n",
       " 'basket',\n",
       " 'megaphone',\n",
       " 'roller_coaster',\n",
       " 'spoon',\n",
       " 'cello',\n",
       " 'tent',\n",
       " 'whale',\n",
       " 'mountain',\n",
       " 'shoe',\n",
       " 'steak',\n",
       " 'violin',\n",
       " 'truck',\n",
       " 'flashlight',\n",
       " 'map',\n",
       " 'hamburger',\n",
       " 'potato',\n",
       " 'firetruck',\n",
       " 'brain',\n",
       " 'sea_turtle',\n",
       " 'pizza',\n",
       " 'television',\n",
       " 'compass',\n",
       " 'cannon',\n",
       " 'screwdriver',\n",
       " 'eyeglasses',\n",
       " 'canoe',\n",
       " 'The_Eiffel_Tower',\n",
       " 'leg',\n",
       " 'smiley_face',\n",
       " 'goatee',\n",
       " 'postcard',\n",
       " 'baseball',\n",
       " 'remote_control',\n",
       " 'knee',\n",
       " 'snowman',\n",
       " 'flower',\n",
       " 'pencil',\n",
       " 'harp',\n",
       " 'arm',\n",
       " 'chair',\n",
       " 'pineapple',\n",
       " 'flip_flops',\n",
       " 'sailboat',\n",
       " 'skateboard',\n",
       " 'duck',\n",
       " 'cat',\n",
       " 'sandwich',\n",
       " 'flamingo',\n",
       " 'sleeping_bag',\n",
       " 'mouse',\n",
       " 'banana',\n",
       " 'car',\n",
       " 'helicopter',\n",
       " 'bicycle',\n",
       " 'palm_tree',\n",
       " 'drums',\n",
       " 'toe',\n",
       " 'kangaroo',\n",
       " 'blueberry',\n",
       " 'pants',\n",
       " 'crayon',\n",
       " 'guitar',\n",
       " 'rainbow',\n",
       " 'baseball_bat',\n",
       " 'camouflage',\n",
       " 'tornado',\n",
       " 'computer',\n",
       " 'bed',\n",
       " 'finger',\n",
       " 'stereo',\n",
       " 'shark',\n",
       " 'fire_hydrant',\n",
       " 'shorts',\n",
       " 'dragon',\n",
       " 'rake',\n",
       " 'clarinet',\n",
       " 'telephone',\n",
       " 'garden_hose',\n",
       " 'fish',\n",
       " 'garden',\n",
       " 'church',\n",
       " 'calendar',\n",
       " 'lion',\n",
       " 'power_outlet',\n",
       " 'ocean',\n",
       " 'laptop',\n",
       " 'camel',\n",
       " 'pliers',\n",
       " 'horse',\n",
       " 'cooler',\n",
       " 'candle',\n",
       " 'donut',\n",
       " 'stove',\n",
       " 'bread',\n",
       " 'hat',\n",
       " 'umbrella',\n",
       " 'fork',\n",
       " 'waterslide',\n",
       " 'birthday_cake',\n",
       " 'hot_air_balloon',\n",
       " 'van',\n",
       " 'panda',\n",
       " 'mermaid',\n",
       " 'sock',\n",
       " 'cloud',\n",
       " 'raccoon',\n",
       " 'anvil',\n",
       " 'hedgehog',\n",
       " 'stop_sign',\n",
       " 'carrot',\n",
       " 'ear',\n",
       " 'school_bus',\n",
       " 'speedboat',\n",
       " 'wine_glass',\n",
       " 'tractor',\n",
       " 'snail',\n",
       " 'motorbike',\n",
       " 'calculator',\n",
       " 'broom',\n",
       " 'dog',\n",
       " 'dumbbell',\n",
       " 'cake',\n",
       " 'oven',\n",
       " 'peanut',\n",
       " 'foot',\n",
       " 'grass',\n",
       " 'hockey_puck',\n",
       " 'cell_phone',\n",
       " 'hand',\n",
       " 'lantern',\n",
       " 'parrot',\n",
       " 'octopus',\n",
       " 'The_Mona_Lisa',\n",
       " 'elephant',\n",
       " 'belt',\n",
       " 'feather',\n",
       " 'underwear',\n",
       " 'house',\n",
       " 'yoga',\n",
       " 'hourglass',\n",
       " 'windmill',\n",
       " 'parachute',\n",
       " 'vase',\n",
       " 'shovel',\n",
       " 'castle',\n",
       " 'lightning',\n",
       " 'marker',\n",
       " 'bulldozer',\n",
       " 'toilet',\n",
       " 'purse',\n",
       " 'zigzag',\n",
       " 'bush',\n",
       " 'asparagus',\n",
       " 'hockey_stick',\n",
       " 'squiggle',\n",
       " 'fence',\n",
       " 'rollerskates',\n",
       " 'butterfly',\n",
       " 'drill',\n",
       " 'paper_clip',\n",
       " 'toaster',\n",
       " 'cookie',\n",
       " 'swing_set',\n",
       " 'bridge',\n",
       " 'crown',\n",
       " 'campfire',\n",
       " 'grapes',\n",
       " 'bee',\n",
       " 'sword',\n",
       " 'table',\n",
       " 'ice_cream',\n",
       " 'diamond',\n",
       " 'bowtie',\n",
       " 'nose',\n",
       " 'owl',\n",
       " 'wheel',\n",
       " 'mushroom',\n",
       " 'train',\n",
       " 'rhinoceros',\n",
       " 'barn',\n",
       " 'teapot',\n",
       " 'envelope',\n",
       " 'river',\n",
       " 'sun',\n",
       " 'fan',\n",
       " 'helmet',\n",
       " 'mailbox',\n",
       " 'cactus',\n",
       " 'cow',\n",
       " 'pear',\n",
       " 'microwave',\n",
       " 'wristwatch',\n",
       " 'trombone',\n",
       " 'sweater',\n",
       " 'beard',\n",
       " 'star',\n",
       " 'monkey',\n",
       " 'octagon',\n",
       " 'lighthouse',\n",
       " 'nail',\n",
       " 'beach',\n",
       " 'coffee_cup',\n",
       " 'flying_saucer',\n",
       " 'dishwasher',\n",
       " 'bracelet',\n",
       " 'string_bean',\n",
       " 'trumpet',\n",
       " 'paint_can',\n",
       " 'tennis_racquet',\n",
       " 'tiger',\n",
       " 'picture_frame',\n",
       " 'lollipop',\n",
       " 'popsicle',\n",
       " 'pig',\n",
       " 'bat',\n",
       " 'spreadsheet',\n",
       " 'submarine',\n",
       " 'light_bulb',\n",
       " 'sheep',\n",
       " 'animal_migration',\n",
       " 'skull',\n",
       " 'bucket',\n",
       " 'sink',\n",
       " 'crab',\n",
       " 'microphone',\n",
       " 'mug',\n",
       " 'saw',\n",
       " 'frog',\n",
       " 'jail',\n",
       " 'alarm_clock',\n",
       " 'matches',\n",
       " 'bus',\n",
       " 'dresser',\n",
       " 'snowflake',\n",
       " 'police_car',\n",
       " 'triangle',\n",
       " 'snake',\n",
       " 'cruise_ship',\n",
       " 'stitches',\n",
       " 'camera',\n",
       " 'apple',\n",
       " 'ladder',\n",
       " 'bathtub',\n",
       " 'cup',\n",
       " 'dolphin',\n",
       " 'streetlight',\n",
       " 'frying_pan',\n",
       " 'pickup_truck',\n",
       " 'lobster',\n",
       " 'pillow',\n",
       " 'mosquito',\n",
       " 'see_saw',\n",
       " 'bear',\n",
       " 'zebra',\n",
       " 'floor_lamp',\n",
       " 'hot_tub',\n",
       " 'book',\n",
       " 'hexagon',\n",
       " 'squirrel',\n",
       " 'scorpion',\n",
       " 't-shirt',\n",
       " 'necklace',\n",
       " 'pool',\n",
       " 'tooth',\n",
       " 'elbow',\n",
       " 'stairs',\n",
       " 'bird',\n",
       " 'door',\n",
       " 'eraser',\n",
       " 'swan',\n",
       " 'airplane',\n",
       " 'house_plant',\n",
       " 'headphones',\n",
       " 'radio',\n",
       " 'rabbit',\n",
       " 'washing_machine',\n",
       " 'fireplace',\n",
       " 'leaf',\n",
       " 'binoculars',\n",
       " 'chandelier',\n",
       " 'onion',\n",
       " 'axe',\n",
       " 'angel',\n",
       " 'bandage',\n",
       " 'ambulance',\n",
       " 'backpack',\n",
       " 'suitcase',\n",
       " 'eye',\n",
       " 'line',\n",
       " 'ceiling_fan',\n",
       " 'bench',\n",
       " 'hammer',\n",
       " 'boomerang',\n",
       " 'wine_bottle',\n",
       " 'jacket',\n",
       " 'circle',\n",
       " 'peas',\n",
       " 'broccoli',\n",
       " 'couch',\n",
       " 'clock',\n",
       " 'hurricane']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = label_lists.train.y.classes\n",
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dl = DataLoader(label_lists.train, bs, True, num_workers=8)\n",
    "valid_dl = DataLoader(label_lists.valid, 2*bs, False, num_workers=8)\n",
    "test_dl = DataLoader(label_lists.test, 2*bs, False, num_workers=8)\n",
    "\n",
    "data_bunch = ImageDataBunch(train_dl, valid_dl, test_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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crZ6zj2k7p7LPpzbgDpLP209UokFS1eb48X4sLgH4Xgnfry7HAM4i6c66EvhIKd4kRab78VngWi+fW6G21JO0Xayg8HPqVX68H/ggyRiVWtNKEjxO9BtYcLgjSbfefKzbOf6gnkISOI7HxhrGY7casMH98S88O6iLl+7oy6rnBotx0LktqwzJEiHxRIDspTy6/edxvYvtlxTpzrqvh+2X9lhJMmYyrdJ2TmWfTxlsqETctXtmRVokteAXJBOG3kDpvvRpGRgRERERAWo4AzgJG1T5kNcXFvsNUuw1wD1efie2+bSUTtqyFTD6c+qLWJdWPHSi3icTZe+8EE80acMygnH38fisW/yc7Oc1k8zsjMvNWfX4eS0k2cns+7LfJ5611+S3eNJLvL1X9ub28bf74Le4vhZb+D7u0k6jtJ1TuefTNSQZ4DdT3931MnpnYUvBgC2LVaoJeHXXBfwQ9gFar2PhzvPjxdiuC08M81gZm7RdrGD051QrtlflEq+fNMxjpbSy93KNj7nl7C28svd0zS7HwWI8u/QybKZ1mofEpO2cyj2fLiYZK3s01b2MmFTOPOBeL3+OZDvaYqubZWDi6dU7Y2vs1FvgF4sHlJ4A/AHYz+sdgz9cBLClJ94H3O71E7Ftr6T8SpVVugob53mB19eX6H1q2WqS8aOZ4R4oMozVJFvBvZXSBYBD0RhAERERkTpTUwHgN7CxfguxXQXah394XXiHH3/mN5GR3Av8wG8/wmaXTx/2GVJNfomNZzzab1K4NdiYzRaSsaEiherFxpNeAxyMdQmXU010AZ/jx08Ch3h55RCPrTcdwJuAxV4/j9KuNyS14Yt+fAtwkZffVaG2SHEtwdZFfa/Xf1HBtlSrNSSTcyYO90CREdzpx3HYnujlXKKp6gPAo0nGsiwkCXQk8QTwbi//FNuOaLAtiURi8VjRM4Efe/kIktnlUr16sczuxV7fncps/1fN1pOsA1iLGcBmjwwaG+wG0NJoP2/xyLd5HPRHsM0XvdzWD31e7u6zcr/XB7D6QFZ9IGvl89463vMuXmz+KWwc4NVlfO+a6gIWERERkZFVdQZwEvZN9jav31rBtqRdvBbg0cAtwJ5e1wxAGU4Ptu4dVPmHhWznVpKdQU4D/quCbalG84GpXk77Z2imASY3w1zfymb6BJjqC1FOGg+zJsJUX6hy8nh77PRWq0/IQKtn/FoaoakBxnnaaG0nzGiF4AuMRBEM+GI52wasHGcE+yPYug36fE/Enn7LGMbljm7o8u1yevqgux82+hY43X3QtS0pd/TYMX5uTx8s2Wj15ZvG/vsqt3i2/8+xpWDi3YfKMYytqj/Tv4yl3/+l0g2pIh/HgsBfe70a9n2Vyslea65vuAdKVVmDfREEOBub8ANazy5f+wCbvbyxkg3J0upX81kTYcF0ONQjiaPmwfypsNkjjR1aLFgDC9S6+iw4A+jshS298PwGq2/qSQKx1Zst+Nrk3xzau2F8owWGAOPHJV3Hk8bndBc3WpDZ7CuVZz8v0wgzJkDTuOR1Gsclgee4kHRDNwQLQhs86AzB7l/tf7in/AyWV+laZ7cBF2ILQkN5xuaqC1hERESkzlRlBvBAP34I2+os7Sn4NOkGjgOe9vqlpHtHAKmsfmzrMLCB21I74qzflSRb/91fobZUm/kk151KbDYwpRl2ngSv2tHqx8yHA2dZee4ky4pt9IY9tBpueBrufdHqHT3bd6l29SVdtfFEjXJPysg0QEOc5cPKjVn1uNu5scGyifF9TeNg/5lw8QlWnzO5ejOAL2KTsU7zejkygFUXALaSrGf3R7SEwWisBA7y8h0kY4HUlS65dicZ67Smkg2RotvLjxPR/20hmtl+5nQ5AsBZbfC2/eB1c61+wCwbx9fvXbkrOmCxb9x90T3w2Fp40QOh9irYDqt3gFF/w5zQBFR+R9sx2wrcQLKs3XRKn9yqugDwIySDJN9UyYZUuWf8eAzJ0jl7A2egcUCSSONYJymOeHvITej/thDN2MX5tpEeWESf/Xs4eS9Y5rsbXPMo3L8SnvYIYfVm2KpBulXvBuCfvXwYpd+GU2MARUREROpMVWUA9wK+BHzM6yuGeazk52ksywO2yO+jwJFe124qsg+wystbK9kQKboFfnyRyoxjq1ZTgckk46jL4Zt/ga/eCZv8P6qeF06uZUtIdgJ5K6XPAFZVAHgF8DhwVaUbUmPiP7h9sOVhnvX6G4E/V6RFUmm+DBjzgYe83DvEY6X6tJHsO3oX+r8txFTgZca+ZdesNvj0kTZmD2wc31CGu09qx1bgJi+fCUwB2kv4flURAH7AjwdhM4D1bbU0OrAZwv/u9TuwbOt3K9UgqRhfJ5Zp2LdSqS0TgR28/GQlG1KFVmO/v1d7/d5Rvs6OE+CM/eD6p6yuIE8AfuXH84DXUNoNLjQGUERERKTOpD4DOJdkm6ILSWavSun8qx8fwLanOdzr56Dsa72Is0MT0TlXi3YkyfLq/7cwm4B1wCFev3yUr7NtwHayaEn9VVjKKR6CtRI4ldJmAFP/p/d9kskIl1ayIXXoV8ABwN1efxDbRg60+Hat29mPAVhayYZIScwnWXZNk70KsxVbOivekKCV0U2Q2tZvW7KNH1e0pkkNiJdhuwU4ERsHCKUZC6guYBEREZE6k9oM4D/68TjgCC9rGYryewLY08u3kKx+fxTwcEVaJOUQ7xLRTbIVnNSOBSSZhg2VbEiVWgQs9PJkRndt6u6Dvsi2dRPJ9b/Y31j8WVyKbRpTGQBOB77n5R+Q7FQhlRFfIA4h2T/0QeDdwNUVaZGU2t5+fJlkJxCpHfsCL3lZX6wL9zjQ4uWdsZnBheobgP4ByKgLWAaxBBteEI/Br5sA8CKgy8tfrGRD5BU+5Mf7gJ8Ah3r945VpjpTIrn5cg9aIqzWtwG4ky/soACzc8yTXqIOwCXOF6huAgQiaU3kVlkrbhE3QOs7r3y7Be2gMoIiIiEidSd13j8OwlOc7va61MdPpSmzbuNu9/hpsxlIpVy2X8mgDdvGydomoPa3ATsDvKt2QKrYJy46DZQBHY9sA9PTD1JaRHyv1pxe4E3i/16dT/NU3UpcBjLALUJ/fJL0eAHb32wwsXb0nyaQRqU7xLhE7AE9VuC1SfNOwz9inKe9+trUkXgomXg6mlWT7xHwNDNhSMC2NWgtQBnc3MMlv80vw+qn7s4tIYVQqQ4q/Bf8dtkfzY15/NVpgtlppkeDaNgf7jNX6jmOzyI8LsZnAUNh4ygEsC9jcVNRmSQ15Guj08usY3VjT4SjWEhEREakzqcsASnXqxtZu/KrX/wT8vZeVaagu87FMPMCLlWyIlMReaH3HYnjcjy0kO+cUshxM3wB09sLk8cVtl9SOTSTDNI4BLiny6ysAlKL6vB/3B2728iEki85K+u1CssSFJvXUnn3Q+o7FEC+K30UyEaSQLrrsMYAig4kngoBNBpnu5WJNBlEXsIiIiEidUQAoJXEettTETsC5FW6LFGZfrHtwLckAZKl+Gb8tAJZjWXll5kdvE8lyMAcxuuVgevoh02g3kcHcTTIbeD7FnQ2sPzspiRUk4wE/D1zrP5N0ywB7kIzb1C4RtSOe2b0zcGMlG1Ij4nMjXgoGbCmYfM+Z3gHo6IYZE6yeabCfiWSLxwB2YjOBoXizgZUBlJK5zG9rgW9WuC2Sn1ZgNvCk36R2TPbbFPR/W0yLgJl+mzzCY3N19UHTOLs16Go8KgPAuq12G4hGfHjViTPNT2MTQY4p4mvrT05ERESkzigAlJKJxxidD5wMHOk3Sa+pWBZDu0TUnhl+24j+b4vpcWwpmOzlYPLV0wfjx9lNF+PRWd6e7Kby+nmVbk3x9ZLMBt7Pb9OHfUb+9DcnJXcr8Fts/+ArsW2oJJ2mYksMLPeb1I5mv00HtlW4LbXkeWwpmOzlYPLV3g0tTXZr1NV4VNZvhSfW2e3Y3WwsZaYGf5el2BauBn9NkkafxLYY2xHNCk6zgGWJGtCHQ60Z77cGtM96McUzgePZwIXQGMDiuOlZu+07A3ZotVutibeFy54MMlb6kxMRERGpM1oGRspCy8JUB2X+ale841iEuoCLaSu2FAzYcjCFLAXT3QfjgpV13o3efSvt2NxoWUCA1TW2yGUptoVLXQC4Cvg1sLLSDZGiu8yPH8SWhTmjgm0RqTcKAEtnkR8XYpOo8g0AN3ZBZpyVx6fualw9lm6047pOWLiHlf9YY5vQD7Yt3Fi3hNOXDhEREZE6k7oAcCW2bEi9zEKc1Wa3SxbC3EmVbk1pxcvCvAs4ADjcbyJSevEs4AE0CaTYHidZDqaQpWB6+2Fcg900C3j02rvt9rfV8Lpd7NZagxnV3G3hxiqVf3L1tBtOW8Zub9sPDppV6daUx0xsKZh4hXMRkWr2PMlyMIXMBO7uS8bdKgAcuz8ugV2n2C3eYq+WxOuzFmsmsP7kKiz+5tLTB/OmVLo15bETNgap3W8iUnrKAJZO/GW20KVgNvUkGcCWptK0rZ7csyL5fR48u9KtKb7cbeHGSgGgiIiISJ1RAFhhXX1229gN86dWujXlMRObJRevni8ipZfxWz/KABbbVpLlYOKlYPJZi7inzxZfD0CTrsZjtmozvLTFbsfsWunWFF/utnDTGdu2cDU4TLK69Pon8ZotsOvkyralXGYDHdgfsoiUR7Mf1QVcOotIloKBkZeD2dILazutnNHVeMy29MJdy6x81K42vn5LDV5o7gY+QTIRZLTLwaT2Ty6eDzEV2OzlbqCHJHAYoLRBxCzftPYzR8J/3WPlFR3FfY9en/HywiY4YKdk5tLWAj6hM/ggYq83kqR2x2Mf/PF9rcA4kkWYN1H+QGwmsAFdhETKqcWPfVgWUIrvb9hnajwTePUIj3+xAyb5Ao0HzIQHtADumN2+zI6nvwpmT4RnXq5oc0oidyLIA6N8HSWdRUREROpMajOAn/bj6STfVrdh68jFM0e3kGzCDbAxq7wG62aMH7uZJIMIlvUayCoPpi1jx7ftB7f7quL5ZAD9acn0/qx6E0lXTCPJt/LeFTC7GU5sSB7bjK33A5YJjR87Bcvsxd0Mk/1141nvcZYvbksTSaTfAKzFlmEBeIZkdfG7sW8W8dIsxc4MxmNiZgEPleD1RWRo8edOP/W11FY5bQV2wD6v87GpO+kCPmQOXL5o+MfLyP62yo79A3DE3NrMAObOBB7ttnCpDQAv8uPNJN3Bk7DxYzO9PhnYHTjY680kQVITNrA28vo2kgASLDCMy3EQuTGnPuADOMIyODFK3mMSyQne7OU4GGshCdrasEAtu03j/QgWjMWBWsvjMPE5+K73i8YBarxlU09WucvrcfvX+c/iwG0jFuzG5Y6seoc/9lCvH4dtKwM2pqCTZL/BO0mCQhh7d3EcGE/CtvwTEaklXdg1J98AcGsfLH7Jygf6EKBChv/IK63zgHpZOxy3G/z4ocq2pxTiiSDxtXu028KpC1hERESkzqQ2A7gi5ziUZpLMUiNJN+hEL+/g9ZkkGcS4Hmft4ixi3EXSgmXp1nt/8fSb4Szvh34zlomLM2H9WNrfv3SwjSQTtwbL1MXd0F0kGTmwrFz82HnT4Otvgo/8weoPrLXB2nHmbhtJt02fl0fqwh7OvX78Nsk08vnYoNI4rfx+kqwgWCZwLN3FcSa0laSrXkTKIz7/sj9LpLi6sd9vIbt6LvLukIV7wORm2Lpl+MfL8OIM6l9egBP3gil+YW/vHvo51SieCQx27R5NBjC1AWC+ukmCJLDZpYWKg8j4lzEBCyCnBKt/42jo8vKHbtx+LGG8pEJfVn00Adm+EWybCR3+ybF07SheZJTWZx0fIBlPMJ0kKAQLDIfrLr6N5MIyWGp5rh9bUAAoUm7xF1wFgKUTr1JRyJKuj6+zY0sj7DwJVisALIrfPwfvOTBZX3fxSFOyq0w8ExjsGj2amcBVHwAWw5BBpH9KPpeB3f2PaHWj7d9YbBu77HXnpWgtwPUkQSFYYDhctvDMrPsHCwDjn60FHi52Y0VkWHEAGPcgSPH1YhMOC9nW/Xm/4HT12X7wD2iAdFE8sc6uqYfNsXqtBYDxRBCw6/BoJoJoDKCIiIhInVFxWuIPAAAgAElEQVQGMA9LN8JhvrJnc4kygJ290NGT/u3gRuouzmc/89XA0uI3TUSG4esN04MygKXSh3XLFdKRs8m7n9ZsgYNmY6tJy5i9vNWygG/e0+qXPVjZ9hRbPBMYrAduNDOBFQDmYXk7TPSZJhMzpRlM2t1n60GlqQu4EHF3sYhIvRrAlucq5GM8nrSw+KVkKZjsn8vo9A7AHUvhA75O3PRWWD/S3nxV5m4/xtvCFXoNVhdwHl7YBE3j7DatZeTHj0bvgC0yPWeS3ZoVmotIEbX4TZNASqcXW+Vhst8ywz98O4tW2Wf/jFa7ydjdugS2brPbbinvXRuNp3nltnCFUAAoIiIiUmcUAOZhQ1dSLlUGEGys4fQJdlMGUESKKeO3npEeKGPSju0C1UZhY6x+/xx0dMPPT7fbMbtCpsFuMjpRBDu02K2xBn+Pm3jltnCFqMFfSfFt6IJt/XbbpYRj9OKxhvFNRKQYMtgyMM3Yklfah7t0VmMLQU+isC7g5ZvgpJ/ZpJBN3fCbs+C2d9vtmF0VCI5GQ0hutaiXZDLIfthEkOnDPmN7+pMSERERqTMKAPOwuTe5zZtSuvcpx2QTERnaQX57FDi8wm0ptvF+U/avtDaS/K7zWRYr2/PtsPAqv/0UevrtFmcDj9lV2UB5pbuxjPN8v+VLI83y0N0H633PlVKu01eusYYiMriv+3FPYB+SPbOrXQNJMNI13ANlzDpIftfNwz1wBPesgON/YuUj5sKFx1ggCPDQavjS7bbfLdgqElK/cmcC57stnALAPHT32aKSADMn2LevUpxw8VhDKO1YQxF5pb1Jsn6rgP0r2BapXu0kF9axBIDZ4mDwCN9QPQ4GH/LtzT5zK/T6taNWx7uNxr4zoKEOsqW5E0Hy3RauDn41IiIiIpJNGcA8Ldtkx6PmQaYRekswkCYeZwilHWsoIq/0YZKV9J8EFpDM4qz2cXMNbL8VnJROB0lmpdjrOd+zwo5xNvBCT/lccSpM8XSjMoCJhgZYu8XKqzsq25ZSimcCv9/r+W4LpwAwT0s32vHUBdDSCFtKcEUo11hDEdneTOB04GKvtwJnkFzAayEAjD/sS7CTpWTZkFWeVsL3yR4jePjO0OiBn4YDbi8O/JZuqmw7Su1ubEs4yH9bOAWAeXrB/3hamuyb1roS7CnY3Qcr/Y917qTSjTUUke2dAYwDfub11wAfAnb0enslGlVE2ZNAFACW1laSIGxSmd7z3hfL9EaSWvFEELDJIPlMBNEYQBEREZE6owxgnuLM3NpOmFLCJVqWe6Zxvx1tO7hSjDUUEdPmx/OAXwMrvT4d+3Y8x+vPlLldxaZlYMqnG+jzsoZyS7nEM4HBZgPnMxNYAWCeBiI7zmiFphLmTeOxhpPGw4QMdCgAFCmZ4/y4E/DdrJ+vwrryXu3128vZKKlq3cA2L5erC1gknggCNhkk3hJuuLGA6gIWERERqTPKAOapz0f1NgQYP6507xN3ATc3wtQWWL2ldO8lUs8yJLPm7gMeybqvE3gROLjcjSqRBmySC2gSSKltI1lqR4s5SDnd5sczSbaEGy4DqAAwT9s8AIyA8SX8rcU7jhCSdZ1EymWA/JYPqAUHAQd6+ZSc+7YCS4DdvN7qP6tW40g+7DUGsLR6sbUAAWZVsiFSd+LZ59PJbx9qBYB5irdoi6LSZgDX+1Wmf8CWghEpp/Ow7MVDlW5IGfwz8JyXB1sy4W/AUV6eQHUHgI0k432UASytPiDuuNEkECmnhpxjvo8XERERkTqhDGCe4jGAA5GNzyuVTf71vLNX28FJ+cz040JsN4xqznaNZE8/vgH4mJcH+/c+SrITyGxgXYnbVUqNJGMAtbBAafViS3KAzQLOoN+5pJMCwDz9XwBIaQPAbl9AakMX7KYRxFImx/txPLYeXi2L98vcDNw4zONWkoypmQ88XMpGlZi6gMsrDgDj9SRF0kgBYJ7iALB/ADIlHAO41QPA1Zthl8m2HRxoSzgpnQzwPi/fDzxfwbaU2nTgbC//kO33bc21lmQLuAOA60vYLqktcQA4AV1kpTwyJLPONQZQRERERAalLyd5KtcYwNiyTXDUPMj4e2lLOCmV3YD9vfyhSjakDP4BiFdX+p8RHrsVWObl/Yd5XDVQF3B5rfbjRCwzI1JMk7Ddi/b1+muB15CMb15H8jc4HAWAeer3ALBvAFryWWBnjJZuhFMXQIv/D21RACgl8k6Sdcv+VMmGlFgb8BHgJq8vHeHxvSR7ax5Fsm9wNa7N3gQEL/cM90ApCt/Rkww2rlaqS27Q3jBIeahjdjkeLZYdaDUOUo81sf36fRnAV6DjSOB1Xt6PZKs3sLVbHwdu8PpvGPnzLbfNIiIiIlIHlAHMUzwHo79MXcAvbLJMY7wbyLpaXpdDKmYK8DaSCQ7DTYqodkcAuwDvKeA5i/34DyQDrJUBlJHEGfUmkiEHaTGa7NZgPxsquzXYEQbPbmXfl50pHZ9Tb84pZ4a4r2WQevbjsu8bn/O4wV4zfkx2u+OfNebcl/2Y3N9NQ859g/1ewc7RwPbW+rGNZEjKjcBfgSe8/hLJ31whFADmacAjwG39SbdsKa3thKDt4KTEXgPMAK6qdEPK4F+wgO7RAp7zuB83YmNuAFYUs1Flkn1xUwBYevEs4Aw2PmvXrPvG5xwrFdzEj8snuMkujxTcZP9spOBmKJHfYgM55f4h7uvLuW9bTrkvq96Tc192PR4nGwdV2dsn9uQ8pnuQx+T+LHvcbW9OPbvck9OOHpJ/3/Mkf1fFGhGmADBP8X/CtgFoLsMYwHWdtu3cHN8O7oFVpX9PqT/vB54Fnqx0Q0roYD8ejmU7C/nw3MePk7Fv2dWqH1jjZa0oVXrx+bQauCjnvijnWKngBizAySe4gVcGM0MFN0M9f7DgJruNvTn17H9fX045tx7r55W/z+zycPdlq5ch9xoDKCIiIlJnlAHMU9wF3NcPzSVcCDrW3g1d22wxaJFim+PH1wNfp7a3fou3e3sBuKfA5+7nxw6S7pdqNBHr6oftMz5SGsv9eAo2WzM7S7Ut56jsllSKAsA8xSdRTz+ML8NvrasPNnbDfG0HJyXw1qzyryrWitKbD7zZy5+j8AkcC/z4ItW9fl72tuK1HOynzXKSYFAkbRQA5qnck0B6+2DNFthVGUApslZs7T+AO7E9b2vVu0kCt18W+Nw2YJ6X76K6MydTSLJJ1RzIikjxaAygiIiISJ1RBrBAPf3J9myl1DtgawEe4GtPtDbC1r7hnyOSj31Itgz6XCUbUmLTgPeSLHGzvsDnTwR28HK1z5KeRDLmTBlAEQEFgKm2ZCMcO9/KzQoApUjOxvaKBHigkg0psROxIO6KUT5/R5I1054pSosqp4WkC7h/uAeKSN1QF7CIiIhInVEGME+9Pgmku8+ycZmG7X9eCsvbYYIv4T65GTao70bGYJofTwOu9nJ7hdpSSq1+/DhwG7bQ9WjsTZI1WzbGNlXaVJKuX3UkiAgoACxYbx9MHj/y44phRQeM80BzeissrcWrtZTNsX6cBPy0kg0psdf4cQ/g/DG8zj4kW0FtHFOLKm8Kyfp/CgBFBBQAFqyrD5rGQUPceV7CDGB7N/+3X9AOrcM+VGRE7/Pjw8CSSjakxP7Nj4uBRWN4nb2BeAfGal87bwrJGogKAEUENAZQREREpO4oA1ignj4YP648kfPGLhtzCDBPC0LLGOwOHOrlj1PdixoP5++Aw7z8bkb/72zFdhFZ7PVq/31lgJ5KN0JEUkUBYIG6+6Exuwu4hDp7ocM/tbUlnIzFW0gCgFsr2ZASOx94yct/HMPrTMCWgXlizC2qvAwwmWTpn2oPZkWkOBQAFqh7GzQ1lCcD2N0HazutrAygjFYbtvXbLV5fU8G2lMocP74F+KqXC933N9sMbO2858bSqJRoxP4GRjsbWkRqk8YAioiIiNQZZQAL9IpZwCXUO2BLwQDsPtXWH4RkXKBIPhZgJ/rllW5ICb3Dj/3AtUV4vb2wCfi1kgFsoTbXfBSR0VMAWKDuPhgXypc6XeoLkB22swJAGZ0GbCHgWjUF+KCXr6M4XdwLsC7kDUV4rUprxLa0q/a1DEWkuNQFLCIiIlJnlAEsUNc2aGxIdugoteXebzMxYzfwBaJFClDL3/SOB3bw8veL9Jr7YrOJq30BaIBx2Ad910gPFJG6ogCwQN190BAsCCyHFzbZsWkcTGuxcjwuUCRfDdhM0FqTAT4L/MXrT43x9eINd3bDdkuphQCwGWgi2dZORAQUABast9+yf+UKADdkfW2PA0CRQqzCxsV91+u/AW7w8iNU5+SAff14KTANOL1IrxsHgDsBvyvSa1ZaM/ZBX43/zyJSOrXcMyQiIiIig1AGsEDdfRY1lzsDuK0fdtFi0DIKK4FTgPO8fhLwXi+vBW6kejKCM4EvAu/y+jPAm4Dni/T60/zYBjxdpNestFbsM0tdwCKSTQFggco9BnBzb3KcN6U87ym1ZynwKS9/DVvmBOBtwImkOyCcQhK8fgIbl3eO168Hijknag8/Biy4rAXjsP9XTQIRkWwKAAvU0w8h2KSMcojX/Fvfqf2ApTjagfu8fB+2ddpIASFYUFiugDADvNXL3yAZm3cx8L0StiEOALtI9s6tdkdiGc1iZUlFpDZoDKCIiIhInVEGsEA9fdY91FSm0DnOAK7sgLmTrJxpsG3iRIohn4wgWFawHBnBucAvgX28/hPgy14uxi4fw4lnF68FOkv8XuXyOmAZsKnC7RCRdFEAWKCefjtmytQFHFu+Cfbb0crNjdDbW973l/oxWEAIFhTmExDGr1GoPf14O7YH72Fef2IUrzUamaw2LKX61wCMhwwfAFwD6CNDRLKpC1hERESkzigDWKBt/bCmE+sHLqOlG2HSeCtPyECHvs5LmcTZvPvILyMIyUzi4TKC8cSOycCxwFVe/yBweTEaXqBWYLaX767A+xfbzn6cCtxfyYaISCopACxQ5zaY0QpTm8v7vss3WdcvwNQWWL2lvO8vEhspIIRkJnF2QHgdsL/XDwEO9PLOQAS82uuPlaTVI5sK+DBbnqxQG4rpVX7sp3bWNBSR4lEAWKDNPdAXwawyb6z68lb+L+s4pczBp8hwcgNCSCaSZAeEC4EJXl8HLPbyj4CbgOUlb+nwdiX5QBzrnsJpcIwfVwCrK9kQEUkljQEUERERqTPKABaosxe6tsHMMmcA12+Ffl/6JV4ORiSt4pnE2RnBvbBsFNiSJGmbZbsXya4ia4d7YBVoJeliX0z6ftciUnkKAAvUO2Dbss2aWN733dRtwSdoSzipPu2kfyLCPsDLXt5cyYYUwSxsPUWAiyrZEBFJLQWABertg/Yu2GnCyI8tpu4+2OCbee6mLeFEiiqDjVmMxyFW+xyrBdgewFC5STUikm4aAygiIiJSZ5QBLFDvAKzrhBmeASzXtmxb+2C190vtMlnbwYkUUzO2HM2NIz2wShwKbPTyi5VsiIiklgLAUXipE/acbuVMGbdlW+abeR41r7zvK1LrJmNbp1X7+n8ZPx4FPOTlYu/VLCK1QV3AIiIiInVGGcBRWL0ZJvpX7UwZQ+il3qdz6gJoaYQtygCKFMUMrMu02nfMmOzH+dhWfCIiQ1EAOAprtkBLk5UnZGBD9/CPL5YXvAu4pcl2A1mnxb1EimIF0IaNnQO4t4JtGYtd/TgBeLCC7RCR9FMAOAprOqHJM38Tx5fvfV/ssOO6Tm0HJ1JMm4BngOO8/u0KtmUsXufHTqo/mykipaUxgCIiIiJ1RhnAUejogeDlCU3le985vvvIDq3QXqZuZ5F60AvcCXzQ6/sBS6m+LdRe68elWFZTRGQoCgBHob0bIi/v0Fq+9423gOvpUwAoUmxXA2d5+ffAMuAJr99DsqzKKiy4StscrCnAAV6+hvS1T0TSRQHgKGzugW2+CPPMMm4JN9+3gNvYDV195XtfkXqwHDjJy28EjgQO9PqJJGvsdWIZtnhv48XA4yQLLm+lMsHXzkC8S2Ta910WkcrTGEARERGROqMM4Ch09kLXNivPbCvf++7qi3yt2QK9ygCKFN1yP17ut3iExw7AHl5+NTbb9kSvvwsYR7L12lLgPi/fDzwHvOT1jpK02rwK6PeyZgCLyEgUAI5C7wBs9j6eWRPL856tjcl7PfSS9gEWKYetWccVXr4dWyZmktd3APYhWUPwAODtXv4wMACs9/oTwCKSLtrnSALHLWNs6zFZbVw9xtcSkdqnLmARERGROqMM4Cj09kF7l5V3KtMkkOZGmNZi5SUbh3+siJReR9ZxKXBT1n0+YZ+dsa7jw71+KHAO8C9e7wXWefkRbLbxw14vZDeSVmzCymKvV9vyNSJSfgoAR6F3wHbjAJgxwfYDLlWXbKv/D+2xA0zyXUeWt5fmvUSkONqzjo8B13s9g3Udz/H6ASQzjQ8Gjgb8uyXPAZ8mWYpmOLOAucBFY2m0iNQVBYCj9JIHgHtOh0wj9I5x3YfWRpjcDDv7wKKDZsFBs+HAnaw+ZxK87FeG+18c/DVEJN16sfGA8ZjAh4H/8XIrMBmY5/VvY5NJfuL1LwNrhnjdBdhElMeK3F4RqV0aAygiIiJSZ5QBHKXVm+04MWNdwPkYKssHlumb2QYt/j/S1WfLvSz29SOuWAS3PGflpdrjSaTmbPVbPIP3SOCtwDe8fjpwsZe/R9LNDDa2cCPJYtQiIiNRADhKa3zNhpYmmJCBDb41WxzkgQV6+QR5YIHeolXwuI8If34DbOqGrVrvT6Qu9QI/x7alAzgP+ISXPwR8hmTiyVHYVnUaHiwi+VIAOEprfAzg+EZ474E2Rg+SIA8s0MsnyAMFeiIyuDio+zpwhZe/iC1UvcTr04AbytwuEaluGgMoIiIiUmdCFEWVbgMhhMo3okDzfFu2354FU1uSMYFxlg8s06csX+2LoihUug25qvGcksLsC1zq5V2A47H1CGtB2s4pnU9SzYY6nxQAjtG8SbYGoIK8+pW2ixVU9zklhZuPbQNXKx8/aTundD5JNRvqfFIXsIiIiEidUQZQZIzSlq0AnVNS3dJ2Tul8kmqW6i5gERERESkfdQGLiIiI1BkFgCIiIiJ1RgGgiIiISJ1RACgiIiJSZxQAioiIiNQZBYAiIiIidUYBoIiIiEidUQAoIiIiUmcUAIqIiIjUGQWAIiIiInVGAaCIiIhInVEAKCIiIlJnFACKiIiI1BkFgCIiIiJ1RgGgiIiISJ1RACgiIiJSZxQAioiIiNQZBYAiIiIidUYBoIiIiEidUQAoIiIiUmcUAIqIiIjUGQWAIiIiInVGAaCIiIhInVEAKCIiIlJnFACKiIiI1BkFgCIiIiJ1RgGgiIiISJ1RACgiIiJSZxQAioiIiNQZBYAiIiIidUYBoIiIiEidUQAoIiIiUmcUAIqIiIjUGQWAIiIiInVGAaCIiIhInVEAKCIiIlJnFACKiIiI1BkFgCIiIiJ1RgGgiIiISJ1RACgiIiJSZxQAioiIiNQZBYAiIiIidUYBoIiIiEidUQAoIiIiUmcUAIqIiIjUGQWANSCEsCCE8FAIYXMI4aOVbo9IOYUQloUQ3pCCdtwRQvhApdshUstCCLuGEKIQQmOl21Lt9AusDZ8Gbo+i6IBKN0RERETSTxnA2jAPeLzSjRCpVco2iEitUQBY5UIIfwKOAS4NIWwJIXwshLA4hNARQlgRQrgg67Fx6vzdIYQXQgjrQwifq1jjRYrngBDCIyGETSGEa0MIzQAhhHNCCM+FEDaEEG4IIcz2n7+iGym7CzeE8J4Qwl9CCBeHEF4GLgghjAsh/JefN0tDCB8ZqisqhLB7COFPIYSX/fE/CyFMybr/MyGElT5s4+kQwnH+80NDCA/6+bsmhHBRiX9vIkXjwzH+NYTwRAhhYwjhRyOdi35fFEI4N4TwbAihPYTw3RBC8PvGhRC+6efREuDECv3zao4CwCoXRdGxwN3AR6IoagMeBt4FTMFOlA+HEE7LedqRwALgOOCLIYR9ythkkVI4HVgIzAf+DnhPCOFY4N/9vlnAcuDnBbzmYcASYCbwNeAc4ATgAOAgIPe8yhb8vWcD+wBzgQvAxuwCHwFeE0XRROBNwDJ/3reAb0VRNAnYHbiugPaKpME7sL/p3YG9gM/neS6eBLwGO39P99cAO+9OAg4EDgH+scTtrxsKAGtMFEV3RFH0aBRFA1EUPQJcA7w+52EXRlHUFUXRw1jAuH/ZGypSXN+OomhVFEUbgN9iQdo7gCujKFoURVEP8K/Aa0MIu+b5mquiKPpOFEV9URR1YRelb0VR9GIURRuB/xjqiVEUPRdF0a1RFPVEUbQOuIjkPOwHxgP7hhCaoihaFkXR837fNmCPEML0KIq2RFF0b0G/BZHKuzSKohV+Ln4NOJP8zsX/iKKoPYqiF4DbsXMY7Ly7JOs1/71c/5BapwCwxoQQDgsh3B5CWBdC2AScC0zPedhLWeWtQFvZGihSGoP9Tc/GMg0ARFG0BXgZmJPna67Iqc/O+Vnu/f8nhDAzhPBz7+btAK7Cz8Moip4DPo5lBNf64+LusPdjWZOnQggPhBBOyrOtImmRfV4sx86bfM7Foa5LuefdcqQoFADWnquBG4C5URRNBi7DuqNE6s0qbIIUACGECcAOwEqg03/cmvX4nXKeH+XUVwM7Z9XnDvPeX/fnv9q7c88m6zyMoujqKIqO9PZFwDf8589GUXQmsKP/7BfebpFqkX1e7IKdh8OdiyNZPchrShEoAKw9E4ENURR1hxAOBc6qdINEKuQa4L0hhANCCOOxoOw+73Jdh118zvZB5u/DxiwN5zrgYyGEOT6h4zPDPHYisAXYFEKYA3wqvsPX7TzW29QNdAEDft/ZIYQZURQNAO3+lIFC/+EiFXR+CGHnEMI04HPAtQxzLubxetcBH/XXnAp8tlQNrzcKAGvPecCXQwibgS+iQeRSp6Ioug34AvBLLIuwO/D2rIecgwVmLwP7AfeM8JKXA7cAjwCLgZuAPmxMX64LsYkim4AbgV9l3TceGz+4Huv22hEbEwU2keXxEMIWbELI2338oUi1uBo7T5YAzwNfzeNcHM7lwB+w8eqL2P5ckjEIUZTbyyEiIiMJIZwAXBZF0bwRHyxSB0IIy4APeMAnKacMoIhIHkIILSGEN4cQGr1b90vAryvdLhGR0VAAKCKSn4B17W7EuoCfxIZZiIhUHXUBi4iIiNQZZQBFRERE6kwqNjgPISgNKVUriqLUrbOoc0qqWdrOKZ1PUs2GOp+UARQRERGpM6nIANa8Vv81ZxqhpRHaMlafmIHggXnDGL/wPrBqbM8XSZE2YIGXK/ktNV7gb6PfwFZu7q5Mc0RqUgZo9vJkYAbJtjkjnf/9BTx2AFuYEJJV1uuZAsBiOGQ2zJ1s5R1bYaZvYTh7IsycALMmWn1SswWD4/3XvrYTZvhOVGMJADf1wKJV8Olbrb6iY/SvJVIBrcA+Xn4LcFrWfdOoXBC41o9Tgc1eXo9dRJZ6/WngXrRBqchQWv02zetzsA2vwc77BSR7LE7BvmzFG9iPdO6vwQLGfB67Hmjy8t0kazjdD6wb4bm1KBWzgKtyfMXcSXb85hvhgFlJVm9bP2zts3JHN2zoghc2WX3VZlizBdb4NqQvbRl75g/sNX54CuzkgedX74LLHrTylt6xv74MK23jlSD951QG2M3LJwKnA3t6fSPwe+BnFWhXrngPtrkkAep8bBuD+AI1FQsUT/G6AsGxS9s5lfbzqZL8qsNEbEsbsHNkAbCv13fDNtqOH9tAkkV/GTtnnvb6k17O90vfQIGPXejlk0g2J96KBYG/9Ppfse1KauXqqTGAIiIiIgLUYQaw0XtcmyfDpNlWXvsk9G3N8wXaMnDuIfD5o/zJnfC+65Os3pZe6PIMYG8f9A1Abxn2co/bBda2td6e82+EW5eU/v3rWNqyFZDOjMU84Hgvnwns7+VurDvmSq//DdhQ3qYVrJlkzNJC4BLgTV5/uCItqi1pO6fSeD6V2xQs8x1n9fbDsnxxFm0HknNiANiCbXQNtinwEyRZvqUkwys2+2PLrZWkF+J44FRgb68PAI8B13v9NuBFL+cbKqTJUOdTzQeAbbPsuNdJMOcQ2OlA//lM6PG/uv5uWPxjeMq3mO5YOcgLHbmLHa88FXacYN2sYF2taetmnTvJuqYBTt4bfvsUfPIWq2t8YNGl7WIF6bhgzQT+3svvBQ7Ouu9vwI+8fDc2jqdaLcT+Lcd5/YkKtqVWpO2cSsP5VE5T/LgAeJuXT8QmXPjgJzZhQVF21+0zXl6JfYmLg6VqCJoy2FAPgNdjY5EPzLrvOS//DrgZeNbrlQheC6UuYBEREREBajwD2DYL3nGTlSdMh82r4aXFVl+1CNY+auUD3w97vTl53nO/h0VXwAvLsyZ6nOzJ4TibVi2ZtCN3SbKWkEwQSVvWsoqlLVsBlclYTCPJ8r0Py/7FXUIPA9cAPk+9piZKnAp8H8saQJIZkNFL2zlV6xnA3IzfiV7fkaSr9kbgV8AKr2/Chm/U8pXEOxB5LZYRBHgdlgWNOwpvwSaP3FvephWkLruAd38jvPUnVr7uDFj5wNBj/SbNgb3fauWDPxjIzM6wpM2CvltXHEr3h/9od1bjeLrBxgeef6PVq/HfkzJpu1hB+S5YRwJneflN2IxYsCDoOuyiATYGqFYvFO8GvoJdGCC5QMrope2cqtUA8DAGD/ji8/YG4BEva908Mx0bvxwvVXUctp7e37z+z9gM4jQZ6nyq6XUA22ZBv191Ni4ZfqJHx0q4/2Eb5/fQ+BPYaXwnb+u+DoAFbY/waz//nx/yFVJsSy988x4rX/uYZTR/45dtjQ+UUfoo8EmSC8M1JOtqPUl1jPsphgw2Nqqv0g0RycNc4JtePhj7YpYb8CnYG9p64I9+A8ue7tYrlOIAACAASURBVEsyie1x4NNevop0LxqvMYAiIiIidaa2M4AzYVuXlXs7R3jwOQfBBcfYY+9axgvH3cp3Oy19+Mb/hLN+aw97+Cq45VPQnfZ1KoayogPO+MX2s5ofPc/KGh8oebjCj+8G3ogtoArVMRuuFJqxZSOUAZS0agPO9fLnScb1vQ9l/MaqHbgH8EFWfAJbFgrgfOx3vLgC7cpHTY8BXHgxzPN1KH50NPQOdYX6xvHwySPgTT5g8Lalr3jIrj7C+x9/bsdfv9uOz99StOZWxmDjAz/wG6vf9ULl2lVF0jZeCUpzTk3CJnHs5/XDsbWy6t0XgbcDR3hdF9OxS9s5Vc1jAI/EuifjXTq+Clzm5Xr90lZq8TZ338Emw/3A61+hMmuc1uUkkH+4Bib4JoFXvRkGchNb17/djgv3hNf+EBaPPHSzeZplBPc/2+pVnxHMFq8f+FpfDenax+ALt0O3chvDSdvFCop7Ts3x49+wcX2HeT0Ne2fOarMtt7N3VFzlV7WVZRrS+u/Am0nWPNRI2rFL2zlVbQFg9ji/k4HfYuN1QZOUyu0fgUuz6h8BflHmNmgdQBEREREBajgD2JCBs2+CTk9T/PJMv2NSxo53vQ92nmzl/b9fcLogt0v4St8ZbkMtLAJ2lI8P/M1Z8MImePPPrF6ulEqVSVu2Aop3Th0G3OnlO7ClD9I0q+3ihXDGfkkGsCHAas8AnvIzWF6GP9mLsezf0V5Xt9rYpe2cqoYMYJsfz2X7cX7nk6y/KZUxzY9fAD6E7X4E8E8ku6eUUt11AWfa4L13wHL/Tf/+E1h/0eIP2w/au+DwH3p59Je05mlw8vdhrg8A+tGxsLEWgkCAmW1ww5mwn/ejH/QDeOblyrYphdJ2sYKxn1Pv9OOPSbqSPjOWFyyRuZNg7uSkfugc+Kz3xR77Y3iiDP3UV2J7pMZ7AacpQK5WaTun0h4AxuP8wMb6aZxfeh1I8n+1J/ANL19M6f6v1AUsIiIiIkANLwPTkIHMRNv+DYB9Z8CDH4S7fBOq035elMkN3Rvgf8+A0/7H6ufcA5fuY+Wt68f88pW1Zgscdjl8xZbH4T/fCG+5xsoDlWuWlNY3SAaMvwf4aeWaMqIVHduvX77nNOjtt/LmnvK0oQXYhk4JKa/BJnqAnbua6JFei7Gt5QDOBv6fl98JnEB5N5uo2QAwMwGaWmBLf6v94OEPww8ehI/cVJL3u+l8O85dBG/1K+ZVJ5TkrUY2qw2mtlh5c48Fuj0e7PYOwMCAHfP1hdvtOH8KNHjSeECXu1oS79l7PTaWLV7S5L4ivkemAZr9E2d8I0zxN93aV7zhpTPboGublTvLtJxlBihTrCl1bqhxfqeicX7VJB4m8kOSXVgux8ZbH+v1cowNrNkAcPxEaGgOdH7WR+Z87S644I6SvV+8xuA1p8C5vurjwR+Cv/1g6OeUzKePhNN9tbb+CLb1J4s7t3dbeY03eM0W2NidlNd0JmMi731x+9ddqhXOatEMkkCvFRvPtnLohw/rUF8zZs4kG583e6LV502GGRNgxwlWb8tAS5OVr3/StqYuxleKWRNhs/+pF/IdZzR8OhmTsSVxtHy6lNJckiBvJzTOr1bEnZQnAV8DHvX6q7A91UtJYwBFRERE6kzNZgCbdsiwdXIbPQ/4d6MSZv+yrX8Kbv64lU+4BJbfaT8rq4vugZv9u8OsNpg0PknFzGyDyc2wu09MP3h20i/X0gRNDdDhHVqLVsE//yFZV0Nqzr7YVm6Pe/14Rr+Q8VeOgbP+zsrNjTbqIE48r+2EdZ3w4Cqrr9psKwyBrb9erGTdThNsgj9Ab4nXL4+/PTcBXaV9K6lz84G7gIe8fjwa51eLPgfs6uX7sVnCpZxKULMB4IbXH0BofJ69H7NVzMp5ssTdvvucBmfeAD84yOpDbkVXbLkj4wcTB32ZBmj0S9mEDEwcbwEjwI9Pg8fPh097x8NVj2hXkBox348PAz8B3j/K14lXCgJbLehEXzJy1WYbSRAPPe3uK22XbMb/hGdMsEATSt8FHAeA49EYQCmNff34MNY9eEHlmiJlEn8WL8K6/A8s4XvVXgA4dxIA3Z88lmf//CL7vMHWrbt7CnSXeQjbr94J//Q0vOE/rH7TR8r7/sOKA7nsRcs25Kxgdsh/wycOh0sWWv2cg+H9vk/wY2uR6jQX8Gk9Y7qoHLMrXH8mLPPzasGllVsrPOOfZFNa4NEy/WnGH54taP9fKb4ZWBYI7EvaBZVripRRfBU+ChsD6N+peUcJ3ktjAEVERETqTO1lAL/5Rjuu7eT+89az7++sesQn4U+fL29Ttq6Ha06Ft/gagbscCS/8ubxtGJMtvfCVu+BaHyF2+Slw3zlWvuSv8I0/Q4fmPlaby4E1Xv76KJ4fLwv5mb+Hb98Ln/+T1Ss5OiDuAp6YgdWby/OeGgMopRAv9bII+JuXRztEQ6rXemwrzse8/jDJmoHFUlsB4JG7wMl7W/nUq2l/vJef+Cow5z8KM/aBX3oeta9M+zWtfQz6fIDQgrdUWQAYi7d/e/2P4Gwf5X/xQhvx/waPbp/fWJm2Sd6OyDqe6uVCwvfcnQFPuRp+/1zx2jcWE3xNlpamZIWjUosDwHFo+zcpnhuzyidWrBWSBs+QbDH5U5IhAXcU6fXVBSwiIiJSZ2ojA9jmX/+vPBV+62uu3LoEgA2+nPZ3XwUfuAfO93zqFa+DLWsoue52eNY3H1lwItz9lfJPRimqqx6x4x+eg8tOhjvea/U3/Bie3lCxZsnILvTjQ8BfCnjevMn+vA/b0i0LLrV6pSZ8DGaiT1xvarC1zMvB35IMoPy3FMMVwMFeno8WeJZkwt7dQLyvxMEU52+jNgLAcw+x444T4JO3DPqQDc/Ct/eEs2+2+seWwI99z5WVxdzvahBP3WDHA98LOywo/fuVxbqt8A/X2v7AAIvPg32+A8s3VbZdMqgjsPEkYN2/+Xb9zmqDW95l5WsfhY//IZ0rATWPs+ParI1sSi3+8ByHdgGRsfso8G7gAK+vq2BbJH0+TbJLyLkk+0CPRfUHgHMnweePsvJX7xp2/bvudvih78J8wncsIwjw2w/Boh+WrolrPWnWuRb2e1uNBICxT3nAve8MuOcDFgSCJoekzIUki8gWkv2bPTHZs/cnKV4GMgQ7zmiFhlCe94w/PBvQGEAZveP9eDFwCsmgf5FsK7Dt/8D2gb6Wsa9vrDGAIiIiInWm+jOA33yj9fsAXPZg3k+7+Z+STNxpP4adD4cbPlD85kEy5u+ZG2GvE+Gur27/85pw5v/Ck/8E17zN6vGWEFJxrwHmAB/0eiG52YYAUVT8NhVbnPUrV/YPbAFosC5gDXyQ0XgV4KOS+ATbzwAWyXWZHz+IdQGfMcbXq+4AMF725dSrrb6lsG7HR66y45pH4f1/htk++vYnx9safsX21A3JOECosa7gjl448gp4wrc7+cJRtoagpMIUirffrpgmPwa0FZwUbgZwL+ALafHtCrZFqkM88eN84DfAkV4f7epy1RkA5s769Rm/o7XmYbhkN3ifbRvMR5+1WcLrnhhjO3OsfSQZBwg1FgACLG23iSEAN5wF9/gIhT/+f/bOOzyqMvvjnxsghBB6MyJFERCwobKURcSyKmDDsupi731l1/azrGvvi7rrWhAruuhaAAVcEVEERVEUFUSUIiiRHloIoczvj/Ne7xDSM5m5M/P9PM887zmZO3feJPPOPfec856zMHFzEkD65HqsKYx/DuBKZACKilO80LOKPIvKMhF4C3jG6QdQtV3B6XJdEEIIIYQQjuT0AFag7EtlKVgBTxxg8vEj4NJZ8F8XYP/ujZi8BYX5QR4gWC5gSuUBAoz/wcbbJsPbru1K13+Zd1CIGiAzAx5za2r+api6OD7ve5kbNxPssBaiPNTpI3G0awRLXMJusqfEXEP1y8IklwGYC3TPgVs7mv7MeNhlHbR0z/+ExWOqiN8e7vUhsGQ6/PG/pk97AN67oernjcbPA4TY1wT0I+Odm0W5disZDtuOXUShmvXU7pgCvdqYPPV8Kw+j0jCiBjixK3Rx7enOGR2f92wNDHTy3UBBfN5WJDkq9Bx/euxq4z1HwK8b4KwYOXQSTSzKwigELIQQQgiRZiSHB9A5khgPdNwCtZ1r7rRNtg/aL5leD8us9Y/9GPjZ6ZW8Rf/sn/DrLJPP+h/s5toovHwsFFXjts3fCAKxKwrdoQmcsBecsZ/pdTKgWbbJlc2HX1kQJNGP/wHGzoWv3Xwr7RE83f2f/PIwKg0jYkzjLLjvD/CCW6tz4tQ+4cQoOUUcCqIGucqN6vQRP/ZrBXcfAf3amT7rV7h1cvKHfqOpblmY5DAAh/tCHVg+CO5xvQxmOavO/48eAgxw8t1YgS4/H2g6VnDpS6evotyCaItdFZNH94TzXdeQy2fDiD6w7peq/Sp+HiBUvSZg82zo3x7O7W7671rD5q3w7nzTn55Z6ksrxOC9bDyms72HX2Zx3Dx4ze2MnrkUispbSX7I1y8Pc4vr2KLyMEnDtkjQW3d7COsBXtYDsuvA7R/G7z1zgAuAd5xexa8CkSb0w9p4ARyNOn3UJF1bwB2uxesfOsDclXDKq6a/v6AC16wkw/dFnQ08B/Ry+vQKvj78BmAf9wB4cU8Y0AZeW2968Vuo6Vg/HbB8wQOxFQe2Ck8j6Nk0h+Ab/ANgIVCKEbbuF3ism8l/eguu+hFeOMr0xVWwZYr3BobyPYE9W8NZzsN3TGeoXwdmLTN96DtWCWdZjBJK/Ootd31k+YSnuN99UCd7AKzeBA9/ElTgKdM7uDAfjnwRXhxs+uSFMLW6TWxEPPCw9moQ3yLL5dHK1dIY2huGfRK7z35F2BtoS+DVEaI0MjHPjL9J6L0EziVV6dgUrncF8U7pBgvXmHzWGzB+XuoZfSXxMxYAdb6bChuAygEUQgghhEgzwu8BjO5i33ZfmLcK1pbhbvLDuj+5h5+gkwO0JyidPRC42sk3AmsA3ws3DpgB5Dm9IMj7e+5QOOJeOHey6ROutnzByrD8axvLKwrdpw3cdqjJbRtB0VaTn/oCXp8DC9xu3Zq6w8kvhE9/sQfAnVNgr+YmX/97eOo4WLXJzelzeH5WGZ6Y71bAVjfRg9vLA5gkZHjh8vz5/O0QGwu2wL9nxPe9L8YyS76I79uKJOREoAtwToLnkWrs3tjGv/ax3PcVLk3l4rcsbx2gYGti5pYI1rrHXuUdWIzwGoB+2LcncFaWyQ+2ghEzq2bxbMCSL/wEjCewXjwA+2Ih4j84/WE3+g1GJgJ+qPdrKwnzy2emnvIKtOkFY1w5961l2Ka5wK5Ahh9qfgKO+L2J8xvC/HXQpqHpw4+DPm3hK2eEXjgGZv7qfpUEVVPJL4TpblPN4FfMGLy0h+lDe9vj1dmmPz7D8i9+Y2UBzHS/zFEd4KFp6eGbTwEyXJxgz2b2GdiyzfSt26Fwa2DXb9sepONu3Q7bo/TtTojFv7xriyAd4sKx1SxXVAlau/FoLMVYJTxEaTj7hPuAF7CMI1F9WuXANb3hfFdLZ20h/Hk8vPmd6elaaawQCwN3ruTrvEgIOr17nrfzJCa6sS5wvSvkM2YInPhKzVV6dblO5AI9CKp09gSaOLkAmIXtMgZa5MH5z8OGpaY/c4gVlY4mehNzK4KLYkYdqOdymfI2wYBC+L/jTN+zKdz2AUxzv2rYbaVWOXD2fnCRq9HdrB589JPJf57g6kCf4/a/3X049H4aflqbkLnGmkgkEjofWfSaOhDrN3qR0z+uxHlaN4Txrp73Ljm2EcT/ytgegS3bg80hW7fbphEwz9zWbbDZGYtbnLzOGWubttrGJYDCbbBmkxmTAJu2mLzO9Vcr3BqcZ/NW+Ft/aFDX9L4jgtfVNFe68UbgILT5oyYJ25oq8RpVBje6cSiWM7os5jNKH1pkwxWuCselB9l3wV3OITPq2/jdAIadewlMlt8D66KeK209KQdQCCGEECLNCKcHsA/BDt3jgf3c5uY/94LfPQUr4lx3vzFWth2gPxYD6ur0LMheDme1MrVpfRjhioQtmwQUwYcu0L5PI7hiCyxypnmzveDkv5q871DLn7vUxQ5+WRd+r19JNHbR+j/sATe6qi8t68NRL8K3LV3LlsnnwNlvBm3jkpyweStgxzWVDUwlqIBU2ebz7VxaQvumUK821K1lelZtaFjXRoB6dQK5ST3IqgV1/edqQ2ZtaOQ8d3VqBeepXctKudRyf8XaGZZ3WMfdnmZ44LnnamVY2Gfgi6b/sKaSv0wVyQFc8SlmAufG523TlrCtqcp4AFsRZBoNw9IFROVomgUXu2jSVb2Cnz86HZ78HFbL67cTZxN0BunDjl1BSltP4TQAJ2KhX4AjgP+ebnLdWnDcy4m3jDKBZk7ujtUedB/S47rB/vVMHj0Xcu6Gd1z945e6wHknwt6Pmb6sHvyng8l7fALrtwRhphh2iEsYme4CfuZ+8PTxMOB1swbeGXkhTFkEV05I3ORiSNguVrDzmrqSICyVyPCl/5nIyAjCDxkZzuhzei2n+8Zk7QwzGMEMyeUbYUl0fCMO9MLKiAKcAMSx7GBaErY1VRkD8DHgGCfvR6nVxUQxfOfBaXvDTf2CG8THP4d/uQtivH0/yURf4HUnH4PtY/VRCFgIIYQQQgBh3AXcA9tu52esN8yGbm67blV3AMeaIoISMXnY7g63gWTsbvDzDSYPPgdOWAJfOS/fn7+DhnPgM/e7dXwULptq8t+2WwLnGHfaCcDtWH3qZMX/V4340srFTDjNsvUHTPqcdw4/CHIy7YBEbWtOI94g8ACeCFSyclHM+G35hmAZVwa/9Auo/Isona7AWcCFTpf3r3waZsLgLvB3V/KsURaM+AIe/MT0eBZ5T2Z+IqiC15UdPYClET4DECznzr9AtG1kWwoBJi9K0IQqgO+angczzzOxzYNw42J49CHTM66H88fAzEtMn3gWdHfN/M7H7F6/x+glwNsEoYRkNgTBwuGDR5k84bQZDNjannfau4THb5cnbmJpwi8EabWXAc86Wd+tZVO89AvobyZK50Hge2B0oicScrJrw3GuaN0dh0GL+jDS9fN+6GNXNUJUirUENxxdKviacBqA0YHpBnWt+S1YfYgk4oo58Hkd+PVg06/6AUb8Hvo9Y/oPV8FLJ5k85HW7SPuemULg7ySdo6RMRrsCnf0n1Gdk9gTO6JsLwIcyAOPC7W78AHBOam5OzFSShhOj5DdKPUqkO26/Ah2wm3ntUdiZzAwY6FqJ/r0/7O5Kq/13Ntw3FX5YnbCppQR+LUCoeD1A5QAKIYQQQqQZ4fQARtO4biAXJIcH0DXHsFTGLTD9ANNPegku/xb+va/pPYfDt5eb/FM+3DgpOEcHYDXWoS7VmPP9JrZsz+aA/c2zqx2V8cFPIzgKq2UOFioYgjwWJZEDXODkd1DhZ1E6fofROlgIWBj+rv/D9oDbDwtaiU6cD39yW1bnrCj5taJyFBF8rw8CXPUuyiqYkAQGYBZsdTvw41XyP0b4qYx+e7hXToLfXwdHDzN95ADY998mTzob9mgCl71teudCc+em4oV57brtzCuoQ/8W6wEY1jzbWsWJuDAX604A1hVkJuBa66Lv4oC9gbZOviqRExGhx+1fYB6Wi5XM9GwNfs2balWJ8+DBI008IBem/GQtTQFmqTVKjeA64jEEaOTk5DYAG9YNmo8mmQFYUnx92v0wx935ZNQO+uX2ewamnAffuR3CBU/AK0XBrp5Uomg7fDhvG+c3tF+++V6NWDlVBmA88UtwdwQmE7S97ktwF5nuaOevqAiNAbefgf+QnN/ZfkGGe4+Ao/e0wuxgRdiryppCWOKs4cOegxlLqzVFUQFcmj1rgV2cvKSUY0E5gEIIIYQQaUf4PYBN6kV1lE+NPbFr5u/8s4zmMPcw2Ox2CB9dF3p3g6bOp5tqrW/e39KAIXXsl9qDQlYmeD7pSj7WzOZpp38B/BHteG2NSr+IirE74Da08lEiJ1JFDsyF1081uUFdGPyfIOuqOmR4MHVx+ceJ2DHAjXUIojxlkRwGoL/5Y2tyGYCVca/+8AM8+hSMvsb0tRNg7z1h9uGmn/QK5FnKHGsKoaAoHDWxq8rJbbeQ4b5k5uVtS+xkxG+bHaYD/wUecPoNJR+e8vjlX9LdEBblsx/gb0+cl8iJVAK/zeL/HQw39IUJzlq4YKzSsZOVbMClXPIZFStCrhCwEEIIIUSaEU4PYLRZ2qgubHRptUniAfRzXfOBKwgSM8urc7l5DL/1gmv0BpzyDAxwt5TPDw7u2tYXwdJ18KM74YI1MG8VLHQ1Y5ZvtGMAiraGz1PYOAtOaLCWMbt0BSB/zXflvELEi6cxL8b/nN4TONbJ6RAGzXHjBaj0i6gYhxGUWFqVyIlUkL2aByHfNo3grDfgldmJnZOoPrsBezr5wQq+JnwG4FJsFd3h9G1FQa/Y7SGzZErBv2gMwrou+GvrZKysS0lu107ASGCw/4MX4M23YIAL6o940xYrQOdm0KU57O+2+Ry2u+VueG7HVuEW670L1kfx+1Uw19X3mLcKfl4H+S6ncGMRFMRpc3WfNjbe9wegdi0e39U1rCnSt0+YmELwRfIxwee3D6lvEPnlcdqi0i+ibPybhX2wNQNBR9AwcvGBNj48AKa7lhFHPA956XBnlwb0BfxLeUWrFoTPAPwFa4Dr+sbS/T1Y4CyHJtshieoH/dAQ9mkAD7hg/HMboT47GoD+P2Az9kXyrf/EaBg8GCZMMHXAAHjlHXYg2704qzY0y4bdXOXHTs1grxYmt28E/drBYFenIKuO1Xbyu+ot3xh4Dr9bCd+vhPlOj0SqVwYgmjsOgz6uqNpXeTCwXT/mF7lfoCi5yvukA76h1w14y8k/YkWkp5T4itTgYjcuRqVfRNns6sZcrJRSWMnNgZdPhl67mX71BHhSH+6UYzCB/ZBXwdcoB1AIIYQQIs0InwcQYD7wBze1GX0hd7rJ3wLDANc9o0LbXGoKv89KI6ziYlendyHoxLwnrG4M548ztc9lUGs7RO959f8BP7lHNKOdFxDME+iHg99xnkA/dFuw1crE+M20Jy/a8Tw5mdDAFfpsWd+acLd14eSuLWDPpibvl2spl/4usBbZsfEArtkEyzbC8S+bPu3XDIqGtIQVG+0HYUtSFL+xgaDLwb2Yp8P3kj1d4iuSF7/0C1j5F0XGRFl0jZLnJGwWpXNqNxuHH28Fmbs/Yfpc1dxKOVoA+wIPO72ixcjDaQACbHfOyYK94GlnIKz4EIYSXIGuB0ZTM/3SGhL0UinJwPOTpBoDmQR/8XyshxvAOKw3ywemflzMzsnNheuuM/kf/6DEkt2jR9t4+OEwcqTJQ4bA5ErEHDZEpVHmbdi5DY/frzE7E5pkQXNr0UudWhV/j7LI8OCzn6PsvJza0LgefLM8Nm8g4sINWHmBV53ei6B8TCpwYpSs8i+iPPwbozyCjX9hoHk2PH0cDOho+r1T4Z6Pkq6RVujIdtfFZs3gl1/CtSVhXyy97INKvi68BmBtZ5Vk1YY898m9HxgB/M0dMxy4BrjS6UVULai9DUt2cndMvxl4jZ1ekoHnvHp8h3ksfffdWgKDtBwzvEkT+OMfTZ4wAZaU0bPl/fdh5kyT77sP+vaFohj1HPINs6JC2xyysKY9q5kZ5pL0CxuKpOENwOWSM9XJ/oUwkQ756pJDsPMXUn+zi6ge2QTr4Btq3lucm2NFMNa6a0vxoEnjLNvdC/DWn2xzXx/nov+ioglhYieauujYoEFwldsVNmcOnH9+uAzAE7DvrIoUf45GOYBCCCGEEGlGeD2Add3UMmtZEpnPMuByJz+GFbx51ukNqZpJuwzr5eMaV//m4fPL05Xk4YuB9239etjmEgJzc8s//qabbJw2DY45Bt5I1jhV/UyoV8dq1IikY5Yb9wA+JLjr/D3J0wmhOJ2xL8PhiZ6ISAqaYS3gID65sDccDMd2sjJeAB8ugvcXwskuNemYznapBBj1DfzfpCDtR1Sejh3hvPPgtNNMb9gQ3nvP5L//HbaGJJzulyLqh1VnqOwVNbwGoF/1uE4GrNtc8jFzgIFYMhJYA7yqdBXbjvlPfQMwRgZeeWzcCJucbduqVfnHz3JX3rfegrvvhnffNX1DstlRDera/3XZxkTPRFSDFcABwEtO/xbLRYGg+HmykEHQz1WI8uiEXW4guCGqSR6cBvNXw+F7mH7+ATBkv8DfMW4evPW9yR8W300oyiU7G/bdF4YONf2II2DdOhjlytE984y1aw0bLs2T1th2iMqiELAQQgghRJoRXg9gPXd/VSsD1pbiAfSZXvPTqQm2boVCl9TbpBLuh1tvhRkz4OSTTX/uuZhPrWapXwc8SvfsiqShEDjJyXcCrm45/ShxU3uo0d2wqCgHA65e/m9t4GqSJevg0U/tAbbTd4+mMM+VdMmviUoYKU7Tpra5A2yDR8eOgZfv6qth3DhYXV7/1gTT340bga+r8PrwG4CrC1LWUCgqgny3dbIiIWCfefNg+HC46y7Tx44N/wd1B5plQwR9a6UYs4DznZyVyIkIUYNkYllHfppDIna/rywI6rWKiuPn9oHl9zV09Xzfew+uucYcKwAFSfC3zQRcaWC+xlJyKkt4DcBvfrUxfzNc28fkwa8kbj41QGEhrHR3cK1aQWZmxUu7PPAAnH22yZdcYjmBSUOr+rBlO6xPTcM+XelC4BWpypeREMlAI2zT0COJnkiKk+maF+yzD2RUwz3vl2u55ho48kjL7QPL73vmGZPDmN9XHrkEvctvruI5FPUQQgghhEgzwusB9CtdPvwJPHWcyXs1T7k+NstdM4zu3aF27Yp7AJctg2HDTB46FEaMsJ8lBa1yYNMWq1YqUobdAX91KrgvUpU2wHqSNvU89OTkWOeriCHY7gAAIABJREFUG24wvVGjIEfeq0Jr0lWudM6GDUFuHyRZ2lQJHEhgwE2t4jnCawD6TFwAq1ytlEt7wJ8nlH18kuHnANarZwZgZXjC9Xa8/HK49lpzcScFuQ1gfZF6ACcpfsnK64B/EIR7O2AlM0EGoEhdPKA5Cp/FkubN4SS3m2zoUNhlF5gyxfS//jU27/HVV8mR21dRBgM/Ovnnsg4sg/AbgPmF8NTnJg/tDXd/lFIFhJe6JpI5OVC3buVe69/B3HQTPPIIPPWU6fPCXo13l/qQvwmKQlJNU1SKlm48Fas95Tf0aw68l5AZCRE/MpDxFwt2d5W0zz4bLrgAstzOsddfh8ceg6+rsq01TWiMbURyZQqpql2rz7EQQgghRJoRfg8gwPOu1vrQ3nD2fnD/tMTOJ4b4OYB165oXcEUVtk++9prlS9x2m+mnnx67+cWUTHe/0aI+rNioEHCSssWNHlCPoINGI+D7hMxICJEM7OtaBV1+eRDyLSyEp5+G5583fWE8CismOXu5cWI1z5McBqAf8n11Nlx0EDz1hekpUEfONwBr1w5qElWWDRvgxhvhhRdM32+/oG1cqMh0H7fG9eCb5Ymdi6gyfmfubZjR19zptUi+4s8+24DlWFdIIUT1yc62DRwAu+4KN98Mhx5q+q+/Br3tX389KIcmyqapG18HxmP9f6uDQsBCCCGEEGlGcngAfR6fAWftB39wHbH/Oyex84kBG6L2s9SvX/XzjB8Pc11p+nvugYEDqzevGsEPATfIhLz1ZR8rQou/dWc71vHDrUa2AXkJmVH1yQMaAPs4XSU+hCifaC/fbrvBAQeYfMABVtrM73C1cSOsWRM0L5g0acdrn6gYD7gxA7g+BudLLgNw7kr46Cf4i+sMMmZu0ueR+WVgtm+HFi2qfp7CQutnCJZL0cf9iT7+uHrziyn1XWn3enVSaid3uuEbgNuwdkTtnL6WoBNIsrEWK2dzkNOHJ3AuQoSV3Fw45hiTDzpoRyOvXj3Y5PJDli2DL7+EmTNN/+Yb+Pzzite5FTtzCHCGk48FYlHGMLkMQIAHp8ELJ5rcazeYsjix86km/l3Q5s3QsmXZx5aHv22+sBCGDDE5VAZgA1fnpk4GLNuY2LmIKuM38CvCNoC4ag6sJHnr/xUAXwLdnZ5N1UsrCJGq3HADHOf6MqxYsaORN3s2zHeFQNeuTa2ae4nEz/sbBYx08rsxOrdyAIUQQggh0ozk8wBOWQwjnatr3BnQ6VHIS95wou8y37TJ3OvVwfcmjhoFF11k8q23hmiHVf06NuYXwgbFApIVP+liG5CDdQAB6wKSrB5AgJnA0U5uhDyAQhTnwQfh3ntNlpcvPjwRJV8b43MnnwEIcPP7Nh7SHiafA/u7P1Fh8nWW2OqmXFAQ9DusLq+/bu10APr3tzqBCSczAx4bZPL81TA1uUP36Yy/ygqAZgRlYJK9C8hsrK4hwG4k74YWIWqKJcla5ylJuQfo5+SDiU3eXzTJHQIePApa5cDDR9sjCSkqskd+vvU/jAULFlgdwFmz4NxzY3POanNMZ+jawh43TUr0bEQ12O4eWzFPmf9I9iLQ87Eah5uAAxI8FyFEepLlHq8DfwUOc48fauC9ktsAFEIIIYQQlSY5Q8A+KwvgyBfg4wtM/2ARjPo2oVOqLP62+BUrrAxMZuaOP6/qOZ991uRhw6BDh2B3VkJomAn3H2mdXABmLUvgZER18XMAN2NfIGOdnuy189YC/idTHkAhRLxpBfiNbhsBewPzavD9ktsABJixFP48weTnB8PX7it8ThWa6iaQX3+Fjh1jYwACTHRNAjdvhhNOgIceqt75qsUZ+0GzenDHhwmchIgVvgG4AagDXF7s58mKXwoGrByMSsEIIeJFT+B9wG1x5SAgv4bfUyFgIYQQQog0I/k9gAD/nmHjgI7wvzNN7vYvWJc8pUbWrLG2OrVj9B9Z5hyh774LZ5wBTz5pelzb77TItvFv/eGxz2BhTd/PiHjgr6pCLFm5drGfJzOupi1Ho1IwonTWIO+JiA1+d4/ngX8DV8bxvVPrMzwkqt7JSycnbh5VIC/PWun4j1jx5JMWVu7a1R5x5c+97AHwSLJniInirMXqAGaQOl8kswnKweyW4LmIcDID2zH+D/fITOx0RBLzT8zwex44m/gaf5AqHkCfdUVw1Ismf3kJXNYj8A6GnOXLoW5dyMmJ7Xk7dYIGDQKPYNzYvTFc/juTb5oEK+RLSTU2A3VJHeMP7MIOQSmY5Pj2EPHmJoJk/WOANxI4F5FcNAbcrgX2Bfo4+dMEzCWVvruFEEIIIUQFSC0PIAS7f89+E148EWb8YvqMpYmbUwVYsQIyMqBx49ie98ADrRXcqlWxPW+53HIIrHJ97kbOivObi3hQiH2BpNJd5Fo3LkOlYETpzALecvLdwLtOTt6mpCIedAQ+Jvie2YOg9FQiSD0D0GfUt9C/Pbx7lukdH7W6gSFl40YbYxUC9svJ7LMPzJsXx80f+7Wy8Y/d4Kw3TU6izTii4qzBSqWk0peI/w3xJUEpmOifC+FzqxtnAH7G+XOJmYoIOX47t4nA28AQpye6d3oqfXfvzNXvmBEI8OZpcPAzCZ1OWaxbZ32BW7aMzfkaNrSxbVvbCRw37jnCxrmrYHxNNK8RYaEQqEVqeQB9ZhLsBAYZgGJn/AK9I4HbnDyamq/dJpKHpsADwACn3wLcn7jp7EQqfncLIYQQQogySG0PYOFWOPwFk7+/Ev41EK4Yn9g5lcKGDda1I1YeQP88DRrAN9/E5pzl0q8t7LuLyWe/aX9/kbKswUpg1E30RGqA4qVg8hI4FxFu7gVOc/K5wLAEzkWEg0PcOMqNrjox7yVgLmWR2gYgwC/rbDzgCfjgHIhETL9yQqkvSQSbN5sRuOuusTlf+/aBvGhRbM5ZJt1zYfK5cJlLjZ60IA5vKhJJERYCTsUvkfkEpWBA5WBE6SwBHnfy9VhIOLkakYpY0NSNDxAUdx4JXAusTsiMykchYCGEEEKINCMVb95LZt4q6P8sfHGJ6fvnwvEvm7w60XtxbAPIpk2xKwOzzz42rl9vRaZrjN3dhD+7EO6eAk/OLPt4kTIUYneQqfglshaVghEV5xE3XgD8Gbg5gXMR8ecQgnAvwLFujOf+y6qQit/dpTNvNXR61OTJ58C8q0w+7Hn4OpHVeMwAXLUqdjmA3bvbuHix7TCuEVpkw7eXmzzya7hlcg29kQgja7EQcAw7F4aGAoJSMGDlYLQTWJSGH/K9DzP+XOt1liRmOiIOFA/5jnR6mEO+xUkvAxAgzxXE2/8JeMrZ6Z9fBBe9Bc99lbBpFRVZu7YOHUzPyoLCKjomc3KsBRzAlCl27pjTMBNmXw7TFpt+7ugaeBMRZjYDHlAn0ROpIfxSMGDlYGQAivJ4FrgauMHplydwLqLmKMnjF3ZvX0koB1AIIYQQIs1IPw+gT+HWoFPF+wvNG3jY7qZf9FZCSpgsW2at28A6eVTVA9ikCeTmmjyrprqwTT0f1hTCcf+poTcQYWezGzMTOouawy8FA1YORqVgRHnkYx1C/JzARwgKRovkJhVCvsVJXwMwmue+gpl58P7Zpn91idUP/KWmkudKZs0aC/0C1K7Gf+bii2HLFpPff7/689qBd11Fo11yYM9HVOsvjXHdC4lR98LQ4ZeCAdsMolIwoiK8RhACvg04PYFzEbEhVUK+xZEB6PP1smCDyJg/WeHoQc7G//CnuExh2TKo51wO9evD6kreVrz0ko2nnAL772/yT7Gc+rMnwO/bmtx+mHr8pjlbgAipWQgagp3AoN3AouJsAG508gvAfkBNBWJEzeG3cYPU8fgVRzmAQgghhBBphjyA0fj1AA9+Bu48DF48yfRvfoUbJ5k8q+bKxSxbFoSAGzSo+OuaN4eJE4PuH3vvDfNimXhyx6E2nrFv4CVdoT2R6c4mYBu2QzYV8UvBgJWDyY76uRBl4TccnQs8DByawLmIypOqId/iyAAsjZvfh/fmm3z/kTDtfJPf+h5u/cAKS5dE69Zwg8sAuf9+WFLxSlDffQf5+SbfdBNceWXZYeCOHW387DPIywv0lSsr/Jbl838Hw439TD7oKViYH8OTi2RmK7AdyEr0RGoQv6z50QSGrgxAUR7+/r1Tsf6vrzn9UtQmLqyUFPK91umpEvItjkLAQgghhBBphheJRBI9BzzPS/wkyiIzA47pbPLdh0NuAxj+hekPfAzLNgTHNm8O06ebPHw43Hdfpd7K9+J98AFkZMBJLgq9ebPpPrVqwYcfmvzaa3D++VUvG7MTuW5f59PHwYG7wmHPmT4nlq7F1CESiXiJnkNx4rGmcoGPgbudPrym3zAB9HXjG8AgJ2s3cM0TtjVVnfXUkSB82ABwzUh/8wqKxFM85Hs2qRXyLW09yQCsLDmZcHJXuOtw0zNrwbBP4Al3WVhdCK++anLLlnDkkVVqxdG0KTzwAPRz0df69Xc0AAsK4IknTL7//ir+LsXJqm15fvf/wfSVBTBgJMxfE6M3SE3CdrGC+KyppsAnwAinx+pjGCZcOU2mExi6T5ZyrIgdYVtT1V1PDd14I9YrGGAqZgzOr86JRZVJp5CvDMBY09RlPl3SA4b2hqJtpt80CbJc0Yi/3Qm9e1erFkufPjbWrm39gn0yM81LGBO6u8vcM8dDx6Zw31TTh02HDSr1Uh5hu1hBfNZUY8wD6N85317Tb5gA/I0fHxBsCLk4MVNJK8K2pmK5nvz+0s8BexCUjBlOkDsoao5D3JjKHr/ilLaelAMohBBCCJFmaBdwVfFLxtz9EYz4Eq51rrpHBsAq1yQrezH07lEtD+DHH1dznqXhezBv6Q8Xu/5zHy2GU/9b+g5nIaLYiu2IbZLoidQg/o7fLwk8N9loJ7CoOr4nuSdwIUFqwQXAOVHPi9jih3zPcHoqh3wrikLAsaZTM7jjCJNP3htWZ8DxLllvzgrID4GT/+Su8K+BgX6Fq1r12pzEzCfJCVu4CuKzprKB9wkuWJfW9BsmkIsJQnW9UF/gmiZsa6om11MHNz6BbTry+wjfDcS3GWlqUjzk6xq+pnTItzjKAUwE/xkGg7Jg+VzTt22Hj36C8T+Y/skSWOV8CUXba3YunZrZ+M+BcHBbeNLtYr7jg8CbKapE2C5WEJ81lQX8jyCJ/byafsME0gt43slnY5tCRM0RtjUVr2vUyZghCNZre4iTvwVUgbVkMglqkdbFcpP9n/+FnT1+6ejtUw6gEEIIIYQA5AGsWQYOhOefhz+7Yn571YKBnaCtKwpQtM3CwgCj58J7C+DntaYXbN35fFUhJxOG9oLrXUWzH1bDeWPgSwWxYkXYvBUQnzWVCbxJsHPxpJp+wwTzHzd2Bw4CNpRxrKgeYVtT8bxGtXDjQ4DLLGcb8BkwzukzsDSEVMxF9T16dZ3emMCr1xpoC+zq9HbY36ul03OA+lHyeuAsp6dTyLc4CgEngtxc69N2112mP/EEZNeG3VxTqSP2gAGu8nP3Xaym4GKX9TFhHoz9Hra7P01GZb8P3fEvDoaW9eG6iaaP/BoKY2RcCiB8FyuI35p6nSD8MhhI5aJBfk3A2cAw4I4EziXVCduaStQ1yjcAjwaOwMrG+CwAXId6JgJfOzlMoeJMN/oGnW/INcaMOQgMunZO9w06146Aevx2NSOC9SD3b76WY631/G2WSwluRH8BriA9Q77FUQhYCCGEEEIA8gDWLJmZ8NZb1scN4LjjSj82Nwd6t4GBziN4SHso3ALNXCnaynoA17r3nLkU/vI/yFPAqqYIm7cC4remniHYxXgU6VHI9gLgYcCVe2deAueSqoRtTYXhGpVN4IXugbUm7OX0JoDfr2k6Fir2WxbGMlRc0oaL4uFZCDx6fjjb9+jVc7qHefMg8Ogtd7rv0Vvq9MXFnssH3NWNQnaOOvjfR+qwEqAQcKIYOhSuvtrkgw6CFSsq9rrGWbZzt3YVnbS+wTh1cdVeLypM2C5WEL819ThBfbzDSM2cpOJkAR8SGLuHlHGsqBphW1NhvUb5xldn4BgnH07JoeIxTo9QduhvG9DGye0oO9+upPAsBAadf7XzDTr/ahT9nG/Q+espldNIEoUMwETRvTu869JPTzsNJk0q+3iRdITtYgXxW1PDgIOd3J/02RixN/Cpky/GSkyI2BG2NZVM1yjfU9jD6b6n0DewmlG2AbjcHQNmmJWVb1eSd85/XUneOZEYlAMohBBCCCEAtYKreRYuhDUuO6NfP3kARUqxBvM4QHp9mXyL5QGCeUH/RxDSEiKRFGD5b34O3CgsVNzJ6eWt0+2A3wy0vHw7kdwoBBwPXnzRxg4d4IgjoCAdMqXSh7CFqyB+a+o64Hwn9ya9Si64ap7MAqYQtJgS1Sdsayrlr1EipVEIOJGMG2ePPfaw2oBCpAhrsJ2BmQSFW9OFde5xEXAK1se1b0JnJIQQFUcGoBBCCCFEmpFOaTuJY8aMQO7RA+arQpFIDYqAWk5O1y+TicBbWE1EsPqA6bIbWgiRvMgDGA/y8uyxYAEMGpTo2QgRMwqxL5EM0tcABLgaaO4eQxM8FyGEqAgyAIUQQggh0ox0vmmPH/6u34kT4fTTobGr354fprbdQlSetQQh4HplHZji5GE7osHKw7ziZLWJE0KEFXkA48mUKdCkCey+uz2ESHI2Y62gPKBOgueSaEa6x2xguHsIIURYkQcwnnz9tXkD+/c3/csvEzodIapLAbDSyelWBqY4fqut8wnaxJ2B2sQJIcKJPIBCCCGEEGmGOoHEm7Fjoa7zlRx7LBSpuU6yE7auBRC/NTUC6O7kvphHUMBdbrwI6IraxFWWsK2ptLpGiZRDnUDCwvjx0LWrPZo1S/RshKgyrYGBwLPuIeMv4D732AA8mOC5CCFESSgHMN5MnQpZWSZ37271AYVIQk504xsJnUU4WefG04HngV5On56Y6QghxE7IAyiEEEIIkWbIAIw3ixbB0qX2GDAg0bMRotLkuMcFwDvAL+4hdqYjkAUscw8hhAgLCgHHmw0b4FNXJKJXL8jONrlAGVQiOdjbjW2BqxI5kSSgGxYOXpXoiQghRDHkARRCCCGESDPkAUwE77xj4/HHw267mTxPTaNEcnCxGxcDXyRyIklAZ+BngiLRQggRFmQAJoIv3GVz2zbo08dkGYAiCWgNHO3ku7EyJ2JnGrpxT2AcoGqfQoiwIQMwEfilX+bNg4EDTX7uuYRNR4iKcmKUrPIvpdPIjc2BuYmciBBClIJyAIUQQggh0gx5ABPJ1Klw5pkmN28OK1cmdj5ClEF06RdQ6ZfS6APc6+R1wIcJnIsQQpSGDMCaJjMzaPnWtSv87ndwyCGmd+sG69ebXL++DEARag7AQpovJXoiIaQPcJuTewJfOfkYYGFCZiSEEGXjRSKJ73GdEo22/Xp+zZrBPvuYfMgh0KMH7Lmn6VlZsG4dzJ5t+uTJMGaMyQt1mUhWwta4HmK7pvxNHxOwHMA3Y3XiJMdt3+I2djT6bgWmOVmbP6pG2NZUSlyjRNpS2npSDqAQQgghRJqhEHBVyMmBXXc1uWtXOPRQ6N3b9NatoU4dk5cvN2/fq6+a/umnsGSJQr0iaTga8/wBDAZGJ3AuYcEP9/Z0+lfA8cjrJ4RILhQCLouGrppX27Zw0EEm9+5teXy5ucFxeXnw2Wcmf/IJfP65yYsXW8hXpDRhC1dB9ddUdNh3sJPT2fgrKcfvVqdPQ0ZfrAnbmgrtNUqICqAQsBBCCCGEABQCDmjeHNq0MblnTwvrdutmesuWsGWLyb/8AlOm2AYOgDlzYOlS2KCeCCI1OAR4xslHAhMTOJdEUtrOXj/cK6+fECKZSd8Q8EEHQd++JvvGnh/yLSyEH3+EGTNM//BD+OYbk1etgoKCuE9XhJewhaugemsqGxgfpZ/gxvxqzSg50M7ecBC2NaUQsEhmSltP6WUAtmkDw4eb3K4d1K1r8rx5ZuT5eXxz5pihV6SvelE+YbtYQfXXVAc3jgPcliY+drq7LSIPSJVboZI2dsjoSxxhW1MyAEUyoxxAIYQQQggBpJMHMDcX3n03yNW79lr4/nuT166Vt09UmbB5KyB2a6odcK6TjwT2iHpuATCJIEfwa5IrTKydveElbGtKHkCRzCgEfOCBMH48/PGPpn+oDp0iNoTtYgU1s6aygVygh9MHAb2AJk5fA0x3sh8qznN6WELFpW3s8MO9MvrCQdjWlAxAkcyUtp7SZxdwRgZEIsFuXiFEpSgA5rsHwCigMdDZ6ccAhzv5YTcucOMkYCyw3emJyD25AzMAi+/mBRl+Qoj0QzmAQgghhBBpRvp4AJcuhTfftDp+QoiYkA986uRPgXuc7IeKBzn9NKycTDOnx/POc7UblyOvnxBC+KRPDiBYGHj79vKPE6IShC1fCcKXs9QY6ERi7jh9Y/MzZPQlC2FbU2FbT0JUBpWBEUIIIYQQQLp5AIWoAcLmrQCtKZHchG1NaT2JZCbUZWCEEEIIIUT8UAhYCCGEECLNkAEohBBCCJFmyAAUQgghhEgzZAAKIYQQQqQZMgCFEEIIIdIMGYBCCCGEEGmGDEAhhBBCiDRDBqAQQgghRJohA1AIIYQQIs2QASiEEEIIkWbIABRCCCGESDNkAAohhBBCpBkyAIUQQggh0gwZgEIIIYQQaYYMQCGEEEKINEMGoBBCCCFEmiEDUAghhBAizZABKIQQQgiRZsgAFEIIIYRIM2QACiGEEEKkGTIAhRBCCCHSDBmAQgghhBBphgxAIYQQQog0QwagEEIIIUSaIQNQCCGEECLNkAEohBBCCJFmyAAUQgghhEgzZAAKIYQQQqQZMgCFEEIIIdIMGYBCCCGEEGmGDEAhhBBCiDRDBqAQQgghRJohA1AIIYQQIs2QASiEEEIIkWbIABRCCCGESDNkAAohhBBCpBkyAIUQQggh0gwZgEIIIYQQaYYMQCGEEEKINEMGoBBCCCFEmiEDUAghhBAizZABKIQQQgiRZsgAFEIIIYRIM2QACiGEEEKkGTIAhRBCCCHSDBmAIcXzvEWe5x0Rw/O19zwv4nle7VidU4h0JnqNep73d8/zRiZ6TkIkCs/zPvA874JEz0NUHBmAoly0sIVIDLpxE+mM53nPeZ53Z6LnkarIABRCCCFE0qEbo+ohAzDc9PA8b47neWs8z3vW87wsz/PO8TxvavRBzkOwp5PreZ73kOd5P3met9bzvKme59UrfmLP805yIay9nd7L87yPPc/L9zxvlud5/d3P7wIOBv7led4Gz/P+VeO/tRAVwPO86z3P+8XzvPWe533ved7hLhT7mud5r7ifz/Q8b7+o1+zqed7rnuet8Dxvoed5V0U9l+F53g2e5833PG+V53mvep7XNOr5M926WuV53k0lTCmrjPft4jzp+Z7nzfY877io5wZ5nvel53nrPM9b4nne36POOcWN+W799Y7F306I0nDXhf8r4drTxPO8t93aWePk3Uo5xzme503zPG+Y+8wv8Dyvj/v5Es/zlnued3Y587gIGAJc5z77b0XN73rP874GNnqeV7s66zqdkQEYboYARwEdgE7AzRV4zYPAgUAfoClwHbA9+gDP884F7gOOiEQi33qe1xoYB9zpXnMN8LrneS0ikchNwEfAFZFIJCcSiVwRk99MiGrgeV5n4AqgRyQSaYCtk0Xu6eOB/2Kf5ZeB0Z7n1fE8LwN4C5gFtAYOB672PO8o97orgROAQ4BdgTXAY+79ugKPA2e655oBxS9+pb1vHfe+7wIt3fu85H4HgI3AWUBjYBBwqed5J7jn+rmxsVt/n1TpDyZE5Sjp2pMBPAu0A9oCm4CyHAI9ga+xtfIyMAroAewJnIE5FXJKe3EkEnkKeAm43332j416+nRsrTTGrm9VWtdpTyQS0SOED+xidkmUPhCYD5wDTC12bARbVBnYotyvhPO1d8ddA8wBdot67nrgxWLH/w8428kfABck+m+ihx7+w33elwNHAHWifv53YHqUngHkYV7snsDiYuf5P+BZJ38HHB71XC6wBagN/A0YFfVcfaAIu4kq730PBn4FMqKe/w/w91J+t4eBYU72123tRP/N9UiPR2nXnhKO2x9YE6X/dp1w16kfop7bx32OW0X9bBWwfzlzeQ64s4T5nRelV3ldJ/pvneiH4ufhZkmU/BN291IWzYEszFAsjWuB2yORyM9RP2sHnOJ5XvQdVh1gciXmKkTciEQiP3qedzVmeHXzPO9/wF/c00uijtvued7P2NqJALt6npcfdapamIcbbB286XletMd8G9DKvT76vBs9z1tVbFqlvS/AkkgkEn3enzBvBZ7n9QTuBfYGMoG6mCdRiESx07XH87xsYBhwNNDEPdfA87xakUhkWwnnWBYlbwKIRCLFf1aqB7AS82tH1df1L1V8/5RABmC4aRMltwWWYuGibP+HnuftEnXMSqAQc9vPKuWcRwLveJ73ayQSed39bAnmAbywlNdEqjB3IWqUSCTyMvCy53kNgSextIb5RK0bF/bdDVs7W4GFkUikYymnXIJ5FqYVf8LzvDygS5SejYW2ointfQHaeJ6XEWUEtgXmOfllLJQ2IBKJFHqe9zB2MwdaeyIxlHTt+SvQGegZiUR+9Txvf+BLwKvBeZT2+Y/++RKquK7THeUAhpvLPc/bzSWs3gS8ghl23TzP29/zvCzMAwKY1wF4BviHS4qt5Xleb8/z6kadczZ2B/dYVCL6SOBYz/OOcq/J8jyvf1SC7zJgjxr9TYWoBJ7ndfY87zD32S7EvAm+cXWg53knerZD8GpgMzAd+AxY7xLI67nP+t6e5/Vwr3sCuMvzvHbuPVp4nne8e+414BjP8/p6npcJ3M7O35+lve+nQAGWzF7Hsw1Wx2I5UQANgNXO+Psd8Keoc65wv5fWn4gnJV17GmDrLN/URzuNAAAgAElEQVT9/NY4zKMi157qrOu0RgZguHkZSxxfgHk27oxEIvOwi897wA/A1GKvuQb4BpgBrMa8Ijv8nyORyCzgGGC453kDIpHIEiyB/UbsgrMECxX7r3sEONnt/Ho01r+kEFWgLhY2XYnl17XE8n4AxgCnYsneZwInRiKRLS5MdQyWu7TQvfZpoJF73SPAWOBdz/PWY8ZbT4BIJDIbuBxbk3nu3NFpFGW9bxFm8A1w7/lv4KxIJDLXve4y4Hb3nn8DXvVPGIlECoC7gGluN2Wvqv7BhKgEO117sNzUethneDrwThzmMQLo6j77o0s6oDrrOt3xXFKkEEIkPZ6VUNkzEomckei5CJGMeJ63CNvM8V6i5yJqFnkAhRBCCCHSDBmAQgghhEgInhVG31DCY0ii55bqKAQshBBCCJFmyAMohBBCCJFmhKIOoOd5ckOKpCUSidRkHawqoTUlkpmwrSmtJ5HMlLae5AEUQgghhEgzZAAKIYQQQqQZoQgBJzOZGdAoC9o0NN3zIKOc4MU2169gTaE9AAqKoGh76a8RQogw07MnRCL2KM7SpfBLWnddFbGgXTv7HG3dmuiZpAYyACtIw0wbd2kAXVtAb9cpsceusHsTWF9kevN65RuAyzfa2KRe8Lql6+DH1bBgjenzVsHCNcGx64ugyH3oZSgKIcLCHXfYePrpkJ0NGVFxJV/Oy4PjjoOffor//ERys/vu8MQTJrdvD0ceqc9RrJABWAoH7Qp9nZHXuw10a2ly82wbVxbYOHs5jP0eprumUOUZfwDb3R1ym4bQpYXJXZrD/rvAYbub3qCueRMLt5i+ahMs22Dy96tg7gozEgF+Xgf5hbDRGZMFujsSQtQwrVrB2LHQrZvpgwbBli07HtPGfYc+8gjsv3/6Xbhbt4aNGyE/P9EzSS4aNoQrrzT5uutg/XqTMzJguxwgMUM5gEIIIYQQaYY8gFH0cXertx0KuzWEurVMX5QP434w+ZMlMGcF/OruSNYVVf39phfTs2tDlvuPNMu2OXRqZvpeLaC9a23drx0M3guy6pgeicCmLUG4eOEa+G6lyd+vhJ/WBh5KIYSoDu3a2fjVV7B4MXTubHpJOX4tXISjoCB4XTrQurWNEyZA/frw8cemjxsHM2aYnJdnfxexIyeeCA8+CI3c9e7GG6FLF5P79IE1axI3t1RDBiBm+N12KPTczfSv8uDCMRZqBVhbGJ+8u4KtQfh2dSH8sBomL9r5uJxMaJAJLeubvnsTaNvIchMB9mwK++Wa3KgurNsM81ebft1EM2CFEKKytGkD779v8n//C1ddBYWFpR+/aZONa9ZYLle6sNHdjGdlwa+/QocOpj/8cHDMggUwaRJMnGj611+nb6i4a1d47DGTf/c7ePVVM/wAVq0yQxpg3jzYsCExc0xFFAIWQgghhEgz0tYD6Hv9wDx/X+XB8S+bPm1xuHfabiiyR567E5q1bMfnMzMg2+1abpIFrXLg0QGmf3oh/Hc23DrZ9CXr4jNnIUTyM3w4rHARhCuugKJyUmD855ctsx2c6YLvyRs71jbHHH646fXqQY8eJg8aBKedBpdeavqaNTB9uoWJwULFeXkmp2Ko2E8PuOEGuPhi+PZb0/v2hS+/DI5r3hzatjX53XfjO8dUJ60MwJKMPjDDL+xGX2Uo2g5FLiyTXwgL86HvCNNP7Ap3Hg5fuS+dx2fAP1x+yuoyQjlCiPSlT59gPP54k8sz/nr0sBAoWJ5yx45WJgZS06ApibFj4eSTYc89TZ8yBebPN3nUKGjcOMihPOYYMxSLh4nBQsXjxpmBmMz4n4c//QnuvdfkbdvgoovgjTdML55S0LIlNGhg8jffxGee6YIXKalqZ7wnUYN9Fnu5vL7b+0OvtoHRd+tkM/ogdQy/itIwE84/wOTrDw4KU980CV6bY95FUXHC1rcU1LtUxBY/T61uXTjiCJOLisyA6djR9B9+gH794J57TM/NhZwck2s7V8PFF9s4fHjZ7xe2NVWd9XTddUGtxL33tr9TaWRn298NzIAeNMjkXr3MiP7iC9P/8pfAO5gs9OwJjz9ucseO8OijJj/0EKxeXfrrBg6EZ54x+bDDYM6cmp1nKqJewEIIIYQQAkhhD2CnZvDAkbC3K+C8ZC3c9kH6ev1Ko0U2XN/X5AsPhLz18Jf/mf7+QihUUelyCZu3AuQBFLHjoIPgxRdNvvBCWLjQ5KuvtrCl5z79ublW8sS/pKxcGYTzGje2sh7+sUcdVXY+V9jWVHXX00sv2ThwYOAxXbmy4q9v3Nh2yvqesJYt4ZZbYIRL7SlrJ3Yi8QuBP/CAdYKZMsX0q66yHb0V4frr4YILTO7du3J/N2GUtp5SygBsmgU3HWLyRQfCT/lwuUuo/WSJjL7y6NQMbusPx+5l+te/wp8nwIylCZ1W6AnbxQpkAIrYcfHFlqgPcNJJ8PzzJmdlweTJMHiw6Y0aWajXzw1cvRp+dvVHf/7ZLt6tWpnueTBgALzzTsnvGbY1Vd315Oe+zZwJmzeb3L175c/jh9SHDoVrrw3yCS+5BD79tDozjC05Ofa5uflm05cvtzmPH1/5c40aFWwYGTCg/NxTsTMpawBm14bT9jH5rsODn980CUZ9o7ZoVaHHrjbe9wf43W7w1lzTb/0gaD8nAsJ2sYKd11Qu4P6tocj72Ab45SjXAoWAvtfDyZNPBsbKtGnm9QMYMsQ8W35dtr33tjZda9eafvzxMGuWySedBHfdFfR0vf12O+7EE03/8MMd3zNsaypWN1TNmwc5gOPH29+wqnToAP/+t8kHHwwvvGDyLbcEO7XjzcCBNg4bZl7KO+80/cknK1+/zzd2p0wJPIdXXx2beaYbygEUQgghhBBAknsA2zSEt4fAHk1Mf+oLuMvdSaqkSWwY2BH+cZTJuQ1guNuF9sDHsEwV2YHweStg5zU1PBP+5GZZsCXY+Z0olgFu2bIW+Bn43unfAfMAv7PYasCvGpIm1UNCQ3Y2fPABzJ5teq9eQZ26nBw499wgpFlUZPXv/F2seXkwerTJ9etbfTff+3XssbB+fVBeZu7cHd83bGsqlikVnTrZ+O231vLM73hRHQYNMi8b2E7toUPhtddMr+n8QP/3efRR2wUOVv7m2mthyZKqnTMzM8iVfO+9YPfwuHGQUUm3lZ93+tlnVZtLKpBSIeA2DW18/2xYkA9XurwChSdrhhxXVPrkrkGYPbMW3D0FRsw0vTo9kZOdsF2sYOc11S0L3nYlJT5eAo8l+MtwO+C+3+kGdAb8VrHNgCx3DMAG4FcnLwDmEBiLC4HlwPqoY0XsyM212nMvuyL5F11kCfxgGxKWLIErrzR92DCrd+cX9J04EU44weRWrcz428vlF0cisGVLYDwUD1mGbU3VRE7tXntZDuXbb5t+5ZXVM9aaNrXxr3+1UKn/fzj//ECOJU2b2nv5n4cffgiKWpeXj5iTE9T2a9nS2gT6xZ67drXP0R57mN6uXbDxKDu78gagn5IwfTpcc03iwuOJRCFgIYQQQggBJKEHMDMDpp5v8voiOPz5mpqVKImmbjfbJT3gr31gjbtjvXkSjJ6bnmVjwuatgJLX1INH2jioE/R+2uT8EKVKuJxvGgAtgd2d3hno6uQ9gF2ijs3ANpD4zv+f2DGUPN/9DLTZpCoccAA891zQgeGAA8zTB/DYY+a1ev110xctst2u/saOMWOCEO+HH5oXp0MH09essdFvD1d8g0DY1lRN7arv0AE+/9zkvDzbOFNWoeiKsvfe9v8BK+Pz8MNWcBnKLrpcHtH/34ceglq1gh3iL78ceOeysqBZM9jNNWLo1Mk8nv7/u21be94/NhKBTZtMX77cPit165p+yCFw3nkmV8V75xchf/558zr6G0lGjqz8uZKVlAkBn9gFXnBlB34/Yuc+uCJ+tGkYtNY7pRt8vwqucTUEP1iUsGnFnbBdrKDkNdXPhVjePB0Gui+/T38pflS4yXYPF+2iNdAJ6OL0zoC75tAYyCQw+PIpOdcQLN9QuYY7s/vuMHVqoP/nP0H9vvHj4Z//tAs2wN13Wy5fly7B8d262fjVV5YrWKuW6WvXQp06dn6AdcV6kodtTdVkWSW/NM6rr8LvfheEUZ97LjbnP+MMM9r9XM2LLw7yOCtD9+6Wi7ePq7rx5psWwm7d2vQuXYL/Z9OmZmz5+XeFhbBqlfWEBvj++yDvc948KxPk90/euNFaBfot8fr1C3ILK7uTOJqGDS3f0g9ZT5tm5XMgKKeTqqSEAdgwE2ZeClMWmX7emBqclKgUXVuYh2nfXUy/YzI8OTOxc4oXYbtYQclrqrHz3n5yAYxzls81KdZcPRPLHwRohHkLfe9hF0rONQTLN6xIriFYvmG65Bo2bmy16/ySHCeeaL1bwTaHzJtn3iZf798fTj3V9FdfDcqCjB0LW7cGF/Bt2yzR3zcY/Iu/T9jWVLzqag4dGvTIfecd22QD1fPagdXR89vRnXUWfPSRyZddVrbxs9decP/9Jh91lBnqfp5ifj40aRIY70uXwo8/mrxggX02/Ny95ctt049fw6+8Wn6ZmTBhgskrVsBpp1X8dy0P//P60ktBnuEdd8C//pW6PaqVAyiEEEIIIYAk8wBe1sN2oR7giokuzC/7eBF/Dm1v43tnw7BPTE41L1NxwuatgLLX1INHWh4gWC5gmPIA40HxXEOwfMOK5BqC5RsWzzWcg4WYcaNLZ6KI5M45zMqyMhz+7t0xY4Jizh9+CPXqwdlnm75ggYUWG7oqDZdfbqVBwHK8fvwxyPuqVcu8PAccYHrxvLewral4dtbZd18b33rL/r5gu6k//jg25+/ZM/gfduhgbdqGDze9eXPL2/Rbr/n/HzCP3xdfmKcXLPy7bFmQz1lQELsuHc2bwyfu+vH003DffbE5bzRZWdbaEKxg9S+/BIW5v/wy9u+XSJI6BNwi28ZvLoenv4Cb34/HrER16J4L092XyOjv4NTXEjufmiRsFysoe031bw8jXSL3mW/A5EXxmVMyUVquIQSh5OhcwzVYSBlgs9Nx4xIshAxW//AnIM/p67DNKWAG5lbCZzA++6x1mgALBV92mclPPml5fH/4g+mLFlmtNb/23yWXBDlgjz9uGxx84zA728LBBx5oevF6cWFbU4lordiwITzyiMlDhpgRdNddplenXEx2tpVWAavV96c/Bf+nggKr2ehv5pgyJdj08/nnlscXj1ZsXbvC++46f955VWshVxnatLHP6OGuzNmTT1q3muqG3sNCaeupdrwnUhX+3CuQH5meuHmIivNlHuzpvrxmXw6fXQT9XCPzdNwpHCY+WBT0yH7/HBgwEt75MZEzCh8F7uH3nZ8HTI56vqRcQ7/VXjuCHcy7AB2Ank7PjnodWEu8jU5e7R6Lnb4AMxb93MNlBIblJsxgjIfzduFC+P3vTfY8u1iC5VJ99hkcd5zp339vXr2LLzZ9xQrr3eofe+aZllMIZmBcdlnVCwWnA+vWBTmAo0dbzpr/tx440DxWZZHtHCctWthmHP9/0atX8D8E24zhH9uwof1P/Z2y330Xm9+lsvi7hcFuLGqaJUusxeHJJ5v+73/DKadY3Uuo2qaZZEA5gEIIIYQQaUboQ8C7N4aZbqv2TZPg3zPiNSsRK5pnw+cXBfpBT8HKFNptFbZwFVQ8ZHXCXvDmaeYFBHkCa4IsAq9fPawFnqv8QUuCXcl7uJ+7Tmo0BeoDtaLO5Xv8fO+k7wT6FQsz+97D5YBfMs3PSfQjd5WN4J16ahCKnDYtKPMydqx5+/w8tfx8KzXSqJHpl18e5Id16GBlPfzQ4j33lN0CLWxrKhEh4OK0bg1vvGFyt24WFh7jKmFkZgb5lV27WtkUPzTfvr2F6pcuNf3TT4Pw6owZVn/Q3/3at691zNia4CjN9dcHeYi9e8PKlWUfH2uaN7eyRmeeafrbbwflY/LySn9dWEnaHMBnjod+7U0+4PH0bjmWzGTVhimumOdezaDbY7BkXdmvSRbCdrGCyl2wfCMQFA4OAy4aRxYWXnZpc+QSGIutsDCzH8lr4h7OFsMD/A/AJix07IeP/ZxEv4RqSTmJvqG5FchtZyVJwIr1+obGtm1m9PnFfs86yzYFPO+K8++6644hX8+zUiAARx9d9qaGsK2pMBiA0dx1F1x3HcyaZfratUFbvawsM7z9dmyTJln+nh9KrU4tvXgxapSFrsFC1/HIOyyJXi79bOTI4HN/7bWm13SP5ViiMjBCCCGEEAIIsQdwPxcjmXY+nPWmyW8kKCFVxJZXToYTukAv147syyR0qUcTNm8FVN5jcYIr8+GHg+UFTA4yo8Z62I5k3OgaNNAW8xa6Gu20Bpqzo6fRx9+U4m9+9Del5Dvvx9EXQWe3I3juGrj9n3DnP0xv2BLyVgclXX7+GTp2NHnwYNvUUL++6UOGwCuvlP57hW1NhcEDmJ0ddMS48Ubbyet34SgqClq9vfGGbdwpXlw7WcjJsd3HU6aY7m9ISSQ5OfCXv5h8/fUwe7Z1WAEreB12ki4EPN7V42mZA31HmKzdo6nDg0fC0N4md3sM5sY5xyOWhO1iBVW/YCknMPXxcxL9cHFJOYmuQcJvOYl+OZz6QL06Ts6BLbXgV1fHZnszyGgDOXuaXq8LbHUGyp8egO1trH0YWH/Wzp3hJ79RczHCtqYSZQD6HVgGDbKeu34v5U8/hdtug19d65qHHrKwOthxfumWZKRNG0sPuPlm0/2UgrDQqROMGBHUSLzvPvjHP8IdWk8qA7DHrvCiq1N2wViYurikV4lk54a+Nl7VC37vvIHJWNw7bBcrqN4FSzmBIpro0jXFcxK7N4NM5y5sH7GcxGYusahlLWjsdrC8vh0e2w/GvGX6ggXmwfI3lKRzL+Di+HmTxx9veX5gnr6JE4O2bDNK2Ax5zjk2Pv64lXL54x9N9/vvJgutW8Mtt9hGISj9JiHR+JtUHnjA6iP6HsHpISxVpxxAIYQQQggBhNQDOKAjjHAFL494AeasKOlVIlV4+3TY0+UYHfQUbEiynd5h81ZA9T0WxXMCQZ5AUT6ZBHmJA9vDPw8z+bQxsGEPa28GVl7juefgq69MHzRox/OEbU3FwwPYtKntpPbLjTRpYuVHwHb9zp1bsfN07GjlYfz8wFNPDXZxJwsZGbB9e6JnUTFyc63l4THHmP7ii0GJo3iXrymNpOoE0jATtrvltmlLYuciap4L37JuIQBDe8EdUxI7HwGj3cVm8CiY4EIbCgeL8ojufezVhy0unLlom2088dm40WrOzZlj+i23wB13xHGiIaFVqyB0e8UVVsJl1CjTH3105x7JFeGHHyw/7c47TR871s7l59QlQ/mSZDH+wOoCnnJKcBPz1FPWuxms281rIW6DGkoDsGV92LzN5GTzBonKk7cBrpto8sNHwyuzYd6qxM5JGKPnmhEIZgjKCBQVpWFd2OIu5CVt4Fu4EI44wuQXX4TJrtfe1KnxmV+i8D1zV10VGH9gHtFHHy2/xVtFKCyEa64xedw4ayU3cKDpxx9fNcNSlI3fLm6ffeBvfzP5xRftf3zppaaHrfWhcgCFEEIIIdKMUHoAm9QLPIBbksgVLKrOyK9tvPBAGH4cHPJsYucjAv6/vTuPrrM67z3+tSwdS7IsWR4x2HgCGweIQ8BMISTEQAGHMbe3MUlWgEIS7uoiITdNCyR3rbaU3KQhJm2alqFlCoHAhWDMPJQpMcQsQzEUbOMBTygeZcm2JEuydP949u5+LY4GWzp63/e8v89aZ71767ySt2Uf6Tl7eJ6uy8HaEyh9MWEENLuZv+ZuUnj55cjycqsUUqx8PsRrroEvuxP2LS2WPuSuu6xfqNO6L75oqUsefND6y5aFGSn/Z8vA2bEj5C68+2647z54913r/+AHcPvtyVmGT2QAOKEKGt03qFW5/zLBLxH9+UL4w1Xw1U9a3weGEj+/HKw9gdIXtRXQ5PZwt3fzRt7XBi4psdJyxeaoo+CGG8IBgfp6O9ABcM89FiwMhs2b4XOfs/a118Ktt1r74ovh8ssHbxxZ89ZbcMIJtr8TrL7wlVdaInQIgWFctAQsIiIikjGJnAEcWR4Of3T3zlGK07tb4JbXYIHLav/MKtjaFO+YJNChEOmrmmGwJ4M/x+fMCQmczzrLDnX4JcGFC+Mv0bZgAbzwgrUXLbL0Mv7U6uLF8Y2rWDU1hQTeDz8M//qvlqgb7NDPTTdZu2sy9MGQuAAwVwJjh8PWPdZvLfIfHD5nVknkka8/lPCPVRp5AJS5h0u6T717ALS4R5r8+HdwqVsC/unZ8PVH4x2P7E97AqUvaiJv5NOU1uNgTJpk19tvh1NPhdWrrf/Nb9rp0KSVCVvmttYceyz8/Ofw0kvW//GPbYk6KXvUis3q1famwFcNueUWmD/f2meeOfinsxMXAJaWQFUOPogxDUg5Fpi5uuVUASNcewRWQ3NY5F5/v+9XdGmXR/rDIs/nsNJKYAHcMLoP6krZPxiMBodD3GOL69cCu1x7G7AaWOv6K4ANQJ3r1xMCxKS85htbYb7LnXT3xXDyRHh9Y7xjko/TnkDJp9z9EBtTCavd3rJifiN/5JHwH/9h7bo6K8H2istl2pTw1YvGRtsD+Kh7k33ffXDBBSFlzECkpJGP+5V70/zMM1a6DyxZ95lnWnqkwaI9gCIiIiIZk7gZwFwpVJdbcuB+fy3CzJyf0Rvl+qOAw117PFbYfIrrjwZGEoqeDyPMro3FZtt8XaAO99gX6fuDy/uANvfAfXxvpL8X8FXuml0/OhvXHOlH263sv7S71z2aXX8S4GqsMxWYDpzk+jXYDKIfbwM2SwhhpnCF6/uZwjiWk7e7d85VOUsnIcmUb08gaCYwy0rdtMLwHDTsjXcshXSO26f81FO21AuWVqU1hcULFi6068yZ8MgjsML9EvjKV8JzMvC2brXyf2D7L3/0o5AmaDAkLgAsK4FhQ6G+ufd7q4CZhEBuHBbwgAV1/gFh6bYs8vk+EGvGlkx9GqbVru1nYjcDf4zc2zWo8w/fjwaD/pGvD6Fs0kB6vUvfL1ODLQ+PAaa5/kzC98wHin5Z2geKDa7vl5MhBIr+e/QuMJCrHTtdpLm3HSZV93yvxEtl4yTKB4CVZX37OZ5G55xjgR9YKpVHi2Sf8qZNcNJJoSzfQw/ZsjDAt78dz0GFYue3Cdx8s+0J9DkjB2M/YOICwKqcBYBb9uR//jDgEte+EvsLuHKTtAH+/+dOYDvgy8rWYYHcetff4R4Ae9i/hmUK38D1KDpztxML2t7Ic58PFGtd3weKM13fzyZCCBT97OBe4H7gIddf188x+8Sx9S0wtbbneyUZdEJYACpKw7VuV8/3ptFpp9lhj7lzre/3/xWTH/7Qrk89FYLbL3wBzj8/HCCRgfXEE/C3fwtXXGH9664r/J+pPYAiIiIiGZO4GcCKMhhasv/ekU+76/8BTiEsoT4N3E7YR7cbm82DsE9O+s7PFPo0VflmCqMnmmuxZXeAbwDXAi79Fb8Hfunab0S+Zl/5CjCbd8OUmp7vleRQihjxp4DLSqCxCPcAtrXB0KHZSJWyeLFVMwG480544w3467+2/oIF8Y2rGO3YAQ88EPYA/sM/FL5CS+ICwPJSW/7dGXlx+bKws4A/AJe6/obBHZqw/yEVv5wM9u8yHjjf9S8HXOlJtgL3EpaH19D7MrtPG7G+AT51CFS6/6lNKg2YCkoRk10VbqN11zfyxaKjI5SwywIfhFx4IVx2WUhbctFFlvKmUDWMs+jf/x2+8Q1rz5sH995b2D8vQ/+NRURERAQSOAM4BBhfZdVAwGZ+vulmfSqxU6iKWpNpM3CHa98DHOvaX8UO7LhqSLyNLQ+/5Po+DU0+a+rhC1PDspJmANNDh0Kyyc8Abm8qziXgLLvrLvj97629cCGsXGkzVQC7doUTrS0t9tjnUmK0t4eHl8Z0OYX2wQfw/PPWvuYaKx1XyGTiiQsA/7gHtuyGX7r/VHW74C2Xg+XNj2DXSviVO1n2IhZUZGArRuq0Aktdeynw98DnXf8K4FZszybAI4DbKsY77L88vG4nVA+DWrf5cIf+sVNFewKz5/JP2bWjE5YV4fJgR4flbyvW8nZjxsAwV+oqXyUQn57k05+GG2+Eu++2fkVFuKe93fZK+oCvpQWam0NJvL17Q0qZnTvtUe9SSjQ323O+ZnJjI+xxm/vffTf51VX6y++tvPNOOOaYUDe4EBIXAG5qhPPvg7OPsP4Jh8Fxh1j7nCNgyXCodbldvgKcDlzoPneQy+jJAdgGuOpu/D/gKOBPXf9rwGWu/R625/PNKutfPcf2ERVzKaks0J7AbBhVDue6PGb3vxNqAReT1ashlwuJoF/vmng1RcaMgalT4TOfsf4ZZ8DRR8MLL1j/6qu7D3RbWuB737PE0WBBY7XL2TpypLV9UFhbax8b6XK25XIwdqy1p061+8rdm/zSUigrsyvYfsvtrjTs7t3wi1+EHIzbelo+SqlV7udiWZml3ilkAKjVVBEREZGMGdLZ2dn7XYUexJAhPQ7CnwCtKbdExA+6aeTrsOXEE919lwMPFGiMUjgjgTmu/b+AzwDD3bvBxonwvzfDg27ZP4lbADs7O4fEPYauentNxeUil1Lit1/WcnAxOmsa/MZN7Z91DyytC8/NmQOLFln7kkssxcgc98JftMg+BvbxpL2mur6e7rgDPuWWuk8/PfnLkn6mD2y274wzrH300TZT55dYV6yAl1+GX//a+uv6m9G/BzlXp7W0NDzAUuyUl4cZwcrK0P7Od+Dss8P3+1e/siXo5csLN864PPggjBtnf1/o357J7l5PiVsCzsdv/G/abfnn/Ix0C/A5LP8c2MGDi1z7G4SqIJJsO4HnXPs54ATgl+7ffOoq+L+E5WNJN5WNK27zj4V1rnbkiu3xjmzF4QwAABf0SURBVKWQfvtbS4sCMG2a7U1Liqoqq+n72c9a3y/r+uXZPXtCrd9/+zd49dXQb2gYvMMZ/s85kD9v/nwrlTZ/vvUvv9zqML/6qvV/9jNY6jaf+/2GafXkk3DTTTBhgvULEYynIgCMaiHU4fUlYn0+yt8Bvm71cmxvYL6SZ5Jso4DZLgDcB/wPkjnzJwdPJ4SLz5hKmDsNbvO/gLv8Yt+3L+SMS/sBiiVLwizUWWfFFwD6WbSjjrIybWDB0ZAhYf/dypUh0AML9hpckJ7Gk7gffGAl08D2A86dC9e6WaCFC0Og9ItfwGOPpTdP4eLFNht6/PHWL0QAqD2AIiIiIhmTuhnAdsIScHmX597ATpcC3IaVI/sr11fVmmRzh345Blvu9e9M/ifwciwjkkJTipji8vkpMLwMHn4v//P79oWTnz7NSFpt3RpOZ154IfzzP1t7sGbUJk+GL34xlA37xCfCkufjj8N994V0LQe6rDsZ+IlrX4/l3k2qHTvgoYds5g8sNc3VV1v7ppvgb/7G9tKBVdlYtiyecR6MjRth/fpw2tyfth5IqQsA2wCfW7Q2z/N+39+X3cOXkbsICyZSOhtclA5z10uwRNEAI4DHgDNdf2vXT5KioxQxxWH+sfD+NljTTf3S6EEJnw4kzR5+2K633AKTJll7dYGipfHjbU/f5Zdb//jjobPTlgkBfvITeO01a/cnNcp44D8JCf2THPxF+QD39ddDWp7Jk+HP/gyuusr6X/+6Pefz7C1enOx9gk1N8NJLIdH2yJEhN+JA0RKwiIiISMakbgawg3AIpKKnG7GUML4axUJgJXCe668E3D5YUrgPNpWiy7zfBM6JPPe0u94FNBFmACUbdCgkvQ5zp/FOnQQ3vtx90vaWllAZws8A+lJhW7ak72DIG5EThj6dzUDMAPrvzZw54bTr2WdbKpS337b+D35gyZDzVeroj8eA9cAPB/bLxmLdOpsZve0263/2s/Dd74Yl4Y8+gn/5F2s/8sjAfy8HwtNPw2WXWXvqVHjrrYH9+qkLANuBZtfuyyqCrw7yaeBG4N7Icyvd9WXgeSwdCViJsj2EwFDVx/rnMPZf5j0c+yFzk+s/AkRfe3OQLNKewHQ6Y4pdS0t6/rdqabHyYBBSkpx2ml2rqgq3fFooGzbYdfly+NKXrP3AQSSirXLvjGfOhEsvtb19YPsl/cnPBQvgiSdgzRrrD/Rew79z16OBmRTX7zy/bLpoETzzDBzritRfdRXccIO1b7gBHn0Ubr/d+u+8k4wT0suWha0Tn/+8AkA6CHsAD2QfcQvwPSzYADiJUJv2z4FLsfQjYDOMjYSAcBPgyhFTB6wDtrj+ZmCXe4AFje2RdpZUAzWuPQ5L6gxh1tXP8l2DzcwmePuFxKjrnsCvPAxvuHcIO1tgbzu0uBeZSgTGb777hbpsM2xo6P6+tjarAQtWGgxCCbIPPwypSdLCBwgLF1qCYrCgbWsvG5d96pZp02x/11fd//PJk+1zH3/c+r/+dcjPV8i9aidjZVUB5rH/m/Fi09oa8gQuXQp/5yLfSy6xwyP+UM1bb1lOQZ86Z6D33vVVQ0NIcn3GGWH/4kDRHkARERGRjEnlDKB/M1TT043dWBy5+mB6DDAVONT1D3H98a4/CZjh2iOA4cDQyNdsI8wANgL+ENx64CNsxhBstrCOcFJ5N2E5u5XkzxhGZ/gOAT4BzHL9mcARhGX5RsJM7U18fJlXpCePLoejXWqNZ74GOfeC29NqCYa3uNJVW/dY5YmP3AtwfcP+z/kZQ7BZQ80YDqzptXCiO85/7dM9f3/b28NyVm2t7XXz5dTuvz8ZS24H47nn4PrrrX3iibZU29Vh7nt07rlhX9/s2bYs/uyz1v/+921f4WDPNp1H2Ff/9uD+0bHz+/7+6Z/gzjvh1FOtf+21cM894UT17bfDb35j7UKWx+uqtdX2AYLNMvs0Sr3NMvdV6gLAVsL+hHIgR/8Dp23u0Z2ce/h2DaEKyQQsUJzs+ocSAsdPAadjQSNAmbu6fc/sBepdux7YQEhTs9a1fdDUweBN1/rxHU3YEwL7B3j+++5/Vm0EngDed/0PsHQCYIc6RA7Ue+6H3Kl3wAnuF+ikajh0BEx270TGDoejx0GVe4FWlFkVBLA0Gc1toSLFlj0hYAQLGtc37P/cTvfDxS8zK2Ds2TlHQLv7Hr34Yc/3treHmrM1NTBxYlgK9jn10mjNmhAUXHyxBYBjxlj/lFNsE78PLIYMCUuQ3/qWLTHGWamiEjtw57/9Ma105jXB7Y38q9PgZjdzs6GA9V137w7B+LPPwic/CVdcYf3vfAf+8i+t/eSTdnjkzTetX+g3Li+9ZNfrr7cxAbzwwsB87dQFgBBO745hcIKirrNz0TRXPb1jqsQCJR8AjsCCw3GuPx6baQSbUZsOuKovVGN7HP1ew7EMXgDofx7VYt/rja4fDfBWYzOb/t+iheTPYEo6bWjs/gd/rgTKS2GY+0k2stweYKdTD6+xgBEsaPQBI1jQWOHelQ0ZEgJGCLOMW13Akm+W0T+X1X2JuRL48rGw2B2G2NTLL+eOjrCXraYGjjkmnAL2e93SqKnJTuSC7ee77Tb4kz+xflUVvPce3Hij9R9/fHBnkHozAZgG/GPcA8njzOl2vXhWCAAH07JlYW/nj34EF1xg7b/4C5uVe88lPF+wwAKyHd3kv+yvtWvtunVrSAo9UAGg9gCKiIiIZEwqZwCjp4CTHME2uUd0Wr2bKklAWNIG22dYRVhyLcv7GYXhJzA2YTN8fsldM3ySNK0dbgnG/efcGtlv8MZHH7/fzxiCzRr62cKR5WHGEMIy89jh1s+3zNzZaX2/zNx1XyKEZeZi3Jc4bRTMGgM/f71v97e2QqObJRw7FubODalU6uoKM8bBcocrnfGlL1m+OZ97btEiO8WZ1P2NPuXWGz3eFY/jJ9h1exPUx5yXZvPmkCLm/vutEst3v2v9W2+107p33hme92X4BoLfE/q731kqGIDKyv0r6xysVAaAPqCqIKV/gW60EIKtAm51EMms/w4YAVr7FjBC/mVmnwDZLzN33ZcIYZm5t32JEJaZ/b7ETY2wLfLLryWyzJwE84608by6vu+f43+ZTZ8Oxx0X9sMNxC+zOPll3XnzbKkuySXGouYBa7DDiUmSK4GZbh/lup3h9ZIEu3fDyy/bA+Coo6zMnC859+1vw/PPW/vmm+3NTX19+NyD9fTTVncabP/sypU9398XSZ5AExEREZECSOUEmk+dUoYiWBEpHL88m2+ZOd+MIeRfZu7tYAqEZWZ/MGXLHhhdAY3uz922B1a7mYS19bBiWzgcU7fLZgpbIkvLheKXwr86G55dDZsPYFbDzwBWV9sJ4J/+dODHFye/YT/p/Naik7GSqUmbgK3MwRQ3yN+8G+9YerN8OVx3nc32gaX6ueYaa991FwwfDtu3W3/lSksy/c471l+1ysoggs2C97RVYOnScGjq1FMHZgYwlQGgT50yjMHdGyci0pv+LjP/977ECphSA7Nc7q+ptZZ3D+CkiVAzDIa6z93XAQ17LUgECxTXuh+UPlCsc6eYfaB4sEHizNF2nVwD33/2wD7XL4WNGAFDh8K7Cf/lXqx8aq9a4Lk4B9KN2nKodqW+3h+gnHeF5nMG3nsvPPywtY85BmbNsu0OYGXorrzS/v+DbQnxb4o+/NBqPftyb8uXW71inzqpri4EfeedZ8Flf6UyAPT75EpI6V9ARMTpOsu4X8DYJXu6n1ksL7VfkmMqrT+t1vZMTXUB4vRaCxIhBIr73J/jA8Wus4kQAsWe9h1e7LK/17fA0m5mQbvjA8DKSkubsXFjz/dLYbhyw9QDy+IcSDemjLTa0gDLe0rSm1B+T+uSJfa4+27r53I2+z3O7RGeMSMkQ5892/aQfu1r1i8tta/jXyP/9V/h9XPSSZZrcls/vzdaQRURERHJmFROoPkTsmVY6hQRkSyI7vHb2QJr3fJR16Xl8tIwW+hnCqe52UE/U9h1ORnCTGGDy7XlZwr9cvK6nfCnR1v76VWw4wDTc/iZkYoKeP/9wS97JlagYK5rv06yqn94M0aH/+s+hVIxaG21WTs/c/fee/Doo+H56moY7bZYTJliS8bHu+oQxx1np3/B0s4MH97/GcBUBoBaAhYR6V506dYHivmCRH+tde+kfaDoU3D4QNEvJ4+qgHp3Cu9nvz/wcbW7MZWWho3wMrh89Q9IZgUQgOMmwB/d4aKGmHMADqbGxpArc+1aePHF8FxlpVXQAXv9+Bya/ZHK+MnPAJZg72ZEROTAdJ1NhO4DRR8sji4HvyVw3UFMHflTsitW7D/zIYNnTqSdxATQuRI4cnSYdW5KUO7LODU1DXy+TO0BFBEREcmYVM4A7o20c93eJSIi/dV1Obk/liyx6+zZYTlYBpev/gHJqwACUF1uuTKfXRX3SIpfKgPA6J7QqthGISIiB0PB3+DrmvwZkpcAGmyfaVUOVmyPeyTFT0vAIiIiIhmTyhlA/66lA6iOcyAiIiIpkPTqH94Ro2DIEFipGcCCS2UA6FcPOlAeQBERkd4kvfqHd8QoaG6DrUWU/y+pUhkA+n3IbWgGUEREpCdpSP7sHT8BNjbCntbe75X+0R5AERERkYxJ5QygloBFRET6Jg3VPypdNDJtFKzZoQTQgyGVM4Bt7rEX29AqIiIi+SW9+gfA8Jw9JlbD0iQmKCxCqZwB7HDXNqAizoGIiIgkXNKTPwOMHW7XijJYtSPesWRFKmcARUREROTgpXIG0G8NaCZkNxcREZH9jST51T8AZoy2a2enZgAHSyoDQL8EvBcYFudAREREEmwmyU/+DDDTBYC7W2FHc7xjyQotAYuIiIhkTKpnAHcDNXEOREREJMG+SPKrfwAcN8Gu6xugsaXne2VgpDIA9AnCW7A8gLkuHxcREcmySnedS/Krf1SWwlSX0+2tOmjt6Pl+GRipDAC9BmAMWscWERGJchNqTCO5yZ+9mnI4pMrabyU1T00RUuwkIiIikjGpngH0p4AVxYqIiARpqP7hjRsO5S4aWbk93rFkSaoDwJ1YJZBU/yVEREQG2Dx3XUNyq394R42Bdrfv78Mkb1YsMqmOnZqBMjQDKCIi4vnkz2AJoJOa/NmbNRYa91q7XieAB41iJxEREZGMSfUMYD22B7As7oGIiIgkhK/+AcmvAAIwe3xY+m1SPrdBk+oAsAWbwkz1X0JERGQA+eTPkPwE0FU5mDwSXllnfeUAHDxaAhYRERHJmFRPnjViy7/lcQ9EREQkASoJ1T8g2RVAAGrLYexwJYCOQ6oDQC0Bi4iIBBNIR/UPb1wVNLTACuX/G3Spjp0asQCwsrcbRUREMsAngE568mfv/BlQNhSWb4t7JNmjPYAiIiIiGZPqGUCXN5JcrKMQERFJhnmko/pHpYs+zp4OSzbBTiWAHnSpDgD3uGtVrKMQERGJ10h3PZl0VP+YMMKu00bBP/4h3rFkVaoDwCagA6iOeyAiIiIxmumutaQj+fPnpti1owNe2xDrUDJLewBFREREMibVM4Dt2Ayg8gCKiEiWfdFd60l+9Q+Ac4+068rtULcr3rFkVaoDwBagDS0Bi4hIdvnkz2AJoJOe/HlMJRw/wdp3vKnyb3HRErCIiIhIxqR6BlBLwCIiknW++gekowLI7PFQPczaT62KdyxZluoAsA3LBVgb90BERERiMifSTkMFkHOOgC0uT83a+njHkmWpDgA7sCCwIu6BiIiIxMQnf4ZkJ4D2yZ/PmAaL11tbCaDjoz2AIiIiIhmT6hnAdqCZkAFdREQkK7pW/4BkVwCZWGPXw6vhplfiHYukPADswPYADot7ICIiIoMsbdU/znQnVVr3qfpHEqQ+ANwN1MQ9EBERkUGW1uTP722F7UmeqswI7QEUERERyZhUzwC2YtVAfB7AnPuYiIhIMUtb9Y+xlfDJ8da+5TVV/0iCVAeAAA3AGNfWdKaIiGRB2pI/n3gYVJZZ+7k1Pd8rg0Mxk4iIiEjGpH4GMHoKWNGsiIhkQRqrf3y0y9ofJn29OiNSHwDuJFQCSf1fRkREpA/SUv0DoCoHp0+BVz60/m5t1k+E1MdMzYDbVqAZQBERKXojSU/yZ4ApI+HQEfD0qrhHIlGKmUREREQyJvUzgPWEPYBlPd0oIiJSBKZjKdCeiHsgfXTWNGhqgyWb4h6JRKU+AGwhTGOm/i8jIiLSi2lY+bfNcQ+kFzn3y/ncI2HZZtia9LXqjEn9EnAjNvNXRkgILSIiUqxmYb/76t0jqUZX2uMTY+GpD+IejXSV+gBQRERERA5M6ldNtQQsIiJZMgv4iOSf/j1lkl1zQ+F5Vf9InNTHTI2EALAyzoGIiIgUkN/mdBiwGkh6Or3zjrTr+kbY2BDvWOTjtAQsIiIikjGpnwHcG2nnYhuFiIhIYflVrkOAx+McSB+MLIdTD7f2EyugqT3e8cjHpT4A3BNpV8U2ChERkcIa7a4jgJVxDqQPptbCOBexqgJIMqU+AGwCOly7Os6BiIiIFNBEdx0CrI1zIH1w7hHQ6Jbo3k56wsKM0h5AERERkYxJ/QxgO2EGUImgRUSkWLktdbQBO+IcSC9yJXD2dFhaZ/1tSc9Xk1GpDwBbsBcDaAlYRESK1yx3rQeSnFVlwgiYMRrueTvukUhPUh8AagZQRESyYKa7bsQmP5LqlElQUgIvfxj3SKQn2gMoIiIikjGpnwFsI+QCrI1zICIiIgVSRdgD+ArJrgIybwas2QF1u+IeifQk9QFgB2EPYEWcAxERESmQCsIkR1JTwIx0+7BOnggPvKPkz0mnJWARERGRjEn9DGA70OzaI+MciIiISIGMJZSCS2oVkBGuHuuLa+GON+Mdi/Qu9QFgB2EP4LA4ByIiIlIgh0fa62MbRc82NNr1W4tCdg5JrqIIAHe7dk2cAxERESmQGVjpU4CtcQ6kDxT8pYP2AIqIiIhkTOpnAFsJCTHLgVzk4yIiIsVgKlYBBMK+d5H+SH0ACKEkzhg0pSkiIsUlh1UB8Xv/dvdwr0hfFUUAGD0EogBQRESKSTkwEXgy7oFIUVG8JCIiIpIxRTEDuNNdKyiSv5CIiIhTg1UBeT/ugUhRKYp4yW+ILUNTmiIiUlxGYb/fkpr/T9JJ8ZKIiIhIxhTFDKA/Gj8Me5ckIiJSLKYCncDGuAciRaUoAkCfB7CEIvkLiYiIODOAXcD2uAciRaUo4iVXfpAy7Li8pI8vcl4OHIr2JoiIeLOAPxJKwYkMBP2eFREREcmYopgB1BJwMpVHriOwk2wAE4DJwFGuP8V9DHdPNbDF9esKPkoRkWTypU0nAZsIv+tEBkJRxEt+CbgUOMG1/ZJiW+S+aLs9TzvfxwD2RdodvVx7antprlOci1xHYLmpAMYDh2OBHdim5emuPcbdGz2g00LYz7IZ+E/XXgOsA5a4/toBHLuISJr432OHAq/EORApSkURAK5x1zpgQZfnOrtp5wvS8gV6kD8YbOtyjd6zN9Juy/Px6Lu4aDDY3Mu9LV3uiz7XnOe+6NfP93W6jnVvl2srFshNdf1pWJAHFvCNZv89l23YRmWAbcBq134eC+p8DqvN2Mltf28r6Q6KRUQKwb/BrgFWxDkQKUraAygiIiKSMUUxA7jOXS8ADnPtYV2uXdvledq5bp6vyPPxii796P3RPyf6ubk899ZE2tElUv81SvM8769DI89F74tG9UPzfCzaHtJNG2wfXi1hdnEHYU/eK8Bywve+zj3vZ/Va0H4VEZH+8CsjjwGvxzkQKUpDOjs7e7+r0IMYMiT+QQyiXDcfzxek9fSxfIEe7B8MlvbwMdg/6PTt6Pi2AQ2u3YLSEOTT2dnZNXaOXdZeU1Jckvaaivv1VEL+/eQifdHd60lLwCIiIiIZUxRLwGmjAw8iItJXmv2TQkjEErCIiIiIDB4tAYuIiIhkjAJAERERkYxRACgiIiKSMQoARURERDJGAaCIiIhIxigAFBEREckYBYAiIiIiGaMAUERERCRjFACKiIiIZIwCQBEREZGMUQAoIiIikjEKAEVEREQyRgGgiIiISMYoABQRERHJGAWAIiIiIhmjAFBEREQkYxQAioiIiGSMAkARERGRjFEAKCIiIpIxCgBFREREMkYBoIiIiEjGKAAUERERyRgFgCIiIiIZ8/8BfeTkuqITCNgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_bunch.show_batch(rows=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_head=nn.Sequential(\n",
    "    nn.AdaptiveAvgPool2d(1),\n",
    "    Flatten(),\n",
    "    nn.Linear(512, len(classes))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'resnet18-simple'\n",
    "learn = create_cnn(data_bunch, models.resnet18, metrics=[accuracy, map3], pretrained=False,\n",
    "                   cut=-2, split_on=None, custom_head=custom_head,\n",
    "                  ).to_fp16()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU(inplace)\n",
       "    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (4): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (5): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (6): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (7): Sequential(\n",
       "      (0): BasicBlock(\n",
       "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BasicBlock(\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): AdaptiveAvgPool2d(output_size=1)\n",
       "    (1): Lambda()\n",
       "    (2): Linear(in_features=512, out_features=340, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = f'{model_name}-{sz}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()\n",
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 1e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 17:36:45\n",
      "epoch  train_loss  valid_loss  accuracy  map3    \n",
      "1      0.817358    0.847132    0.777118  0.840279  (4:23:41)\n",
      "2      0.782329    0.800338    0.787706  0.849358  (4:23:28)\n",
      "3      0.687505    0.694399    0.815824  0.870549  (4:24:33)\n",
      "4      0.610510    0.612972    0.836382  0.886265  (4:25:01)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(4, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save(f'{name}-scratch-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fine-tuning with larger images\n",
    "\n",
    "As it is not possible to change the batch size easily with fastai, the batch size set at the beginning takes into account the model to work with below image size too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "sz = 128+16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "data": {
      "image/png": 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iIsO4YXQ3ps7bwG9mrfnRewYHCe0jw4hrE07GoRIK3MOBg4OEr7fm8N7tYxzfKMYYf7Ajt4Rzj9BP6gRLGG5twkO4fFgCs1Iz+MvF/YiJDPvB+cXbD/DzN1fQr3M77jq7l0NROuPyoYlcPjSR4vIqNu8vZOPefBanH+A/6/czKzUDgPP7deSZCUMPz9sY2jWGuXeeytfbcsgrqaS6RqlWpbSimtyicrILyskpKmdQYhRjerZnVHJ79hwqYeL0ZdwwfSkzbxtNdOuwhsIyplkrLKskt6jcb/ovwBLGD1w/uhtvLdnN+yv2cNsZ389bWLn7EJPfWE7X2Na8OXkUbSNCHYzSOZHhIQzvFsPwbjHcMKY7VdU1rM3MJyu/jPP7d/rRSKqgIOGsPt7/ddQpKoKXJ6Zw8+vLufHVZbx1S8v9tzZmZ65r8IjdkvJTfTu1Y0T3GKZ/u4P80kqSYloTGR7Cn+auo0PbcN6+ZRSxkfZXb62Q4KAj3r47Xqf1juOFnw3j52+tYNJry5l+Y4q1NEyLVDtCyp8ShnV613HP+X2ICA3mX/9N594P1jHl3VW0iwjl7VtH0+EYOozN8TuvX0eenTCUdRn5XPHid+w5aMN0TctTu6x5t/b+019qLYw6RvVoz39/dzZV1TXsyy8jM6+Uvp3a2l+5TezCgZ1p3yacW2ekcvkL/2P6jSMYnBTtdFjGNJkducUkRLfyq/XcrIVxBCHBQSTFtmZ0j/aWLBwyMjmWOXecQkRoMOOnLWHBhv2N+v5780qZ8u4qtuwvbNT3NaYx7Mgtprsf7LLnyRKG8Wu9OrRh7p2nclLHNtz+5gqeWbTtR3NAPGUVlHHfR+tZufvQUd/7sc+28PGavVw7bTFrjjL/xpimtvNAsV/ssufJEobxe/Ftw3nv9jFcPjSBJxZu5RfvrKSkoupH5ZakH+DiZ75lxuJdXPOvxfzrv9upqak/uaRlF/LhqkzGDelC24gQrnt5CYu3H/B1VYzxyqHiCvJKKv2qwxusD8MEiIjQYJ64ZjAnd27Lw59uZmtWET8d1IVRPWIZkhTNG9/t5JEFW+jWvjUv3TCM6d/u4OFPN/Pd9gM8cc1g4tr8cCLgk59vo1VoMPdd0o/KauX66UuZ9Noy7jq7F4XlVew5WEJ2YTkTx3Rj3JAEh2ptWip/HCEFljBMABERbjujJyd1bMujC7bw1KKt6OcQJFCjcNHATjxy1WDahIcwrGsMby/dzf2fbOTiZ77htUkj6delHQAb9xbw77X7mHJOL9q7E8ms28cw6bVlPL5wK2EhQSTGtAKFu99bjYhw6eAuTlbdtDC1I6T8adIeeJkwRKQnkKGq5SJyFjAImKGqduPXNLmz+nTgrD4dyC+tJHXnQZbtPEiPuEiuSfl+4cja9cGGdY1h8hvLufalxbw0cTin9IzjiYVbaRcRwi2n9zj8nrGRYcy981QOFJcTFxlOUJBQWlHNja8t4+73VhMWHMTYAZ2cqrJpYXbkFhMcJCT52RJE3vZhzAGqRaQXMA1IAt7xWVTGeCGqVSjnntyRP154MteO6FrvKsP9urRjzh2n0Dk6gkmvLuexBVv4fFMWt53Rg6hWP5xFHhwkdGgbcXjZ91Zhwbw6aQSDEqOY8u5Kvtyc3ST1MiY9p5ikmFaEhfhXN7O30dSoahVwOfCsqv4O6Oy7sIxpPF2iW/H+7acwJCma575MIzYyjEmnJnt1bZvwEF6/aSR9OrXllhmp3DojlUWbsqiqrvFx1KYl25ZdSK8Ox7d9sy9524dRKSITgBuBn7qP2SI/JmBEtQ5lxuSRPPKfLYxMjjmm/QWiWoXy9uTRvPDfNOasyGDhxiw6tgtn4pjuTD4t2a8mVpnAV1Vdw47cYs7p29HpUH7E2xbGTcAY4B+qukNEkoE3fReWMY0vIjSY+37aj7EDjr1xHNU6lD9eeDKL/3guL90wnL6d2vHogi2c/+TXLNyY1eDcEGOOxe6DJVRWa+C2MFR1I/BLABGJAdqq6j99GZgx/ig0OIgL+nfigv6d+HZbLlM/3sCtM1I546R4LhvShSFJ0STHRfr91r3Gf23LLgII3IQhIl8Bl7rLrwCyReR/qvobH8ZmjF87rXccn/7qdGYs3sUzi7bx9dYcwHUL66w+8Tx4+UAi/WRrTRM40gI9YQBRqlogIrfgGk77VxFZe7SLRGQs8DQQDLyiqg/XOf8kcLb7ZWugg6pGu889AlyM67bZQuBXau1+42dCg4OYfFoyk07pzrbsQlbvzmPl7kPMWZlJ5qFSXrtphO3pYY7J9uwiOkdF+M0+3p687cMIEZHOwDXAJ95cICLBwPPAhUA/YIKI9PMso6p3q+oQVR0CPAt84L72FOBUXPM9BgAjgDO9jNWYJhccJPTt1I7xI7vyyFWDeWb8UFbvyWPiq8vIL610OjwTQLZlF/ll6wK8Txj3AwuA7aq6XER6ANuOcs1IIE1V01W1ApgJjGug/ATgXfdzBSKAMCAc14isLC9jNcZxFw/qzPM/G8b6zHxumL6UvJIKp0MyAaCmRtmeE+AJQ1XfV9VBqnqH+3W6ql55lMsSgD0erzPcx35ERLoBycAX7vdfDHwJ7HM/FqjqJm9iNcZfXNC/E/+6fjib9xVyyxupVFTZ3A3TsH0FZZRUVAd2whCRRBGZKyLZ7sccEUlsxDjGA7NVtdr9eb2Ak4FEXEnmHBE5vZ64bhORVBFJzcnJacRwjGkc557ckceuGUzqrkNM/XiD0+EYP7cty7U3S6/4AE4YwGvAPKCL+/Gx+1hDMnEtIVIr0X2sPuP5/nYUuGaUL1HVIlUtAj7FNQ/kB1R1mqqmqGpKfHy8VxUxpqldOrgLd5zVk3eW7uatJbucDsf4sdoRUr07tnU4kvp5mzDiVfU1Va1yP14HjvYbejnQW0SSRSQMV1KYV7eQiPQFYoDFHod3A2eKSIiIhOLq8LZbUiZg3XN+H87uE8/UeRtYmm77bpj6pWUXERsZRmykf+7y6W3COCAi14tIsPtxPdDg//XutafuwtVZvgmYpaobROR+EbnUo+h4YGadIbOzge3AOmANsEZVP/YyVmP8TnCQ8PSEoXRt35o73l7JLvd+B8Z4SvPjEVIA4s3UBnen9LO4bgsp8B0wRVX3NHhhE0pJSdHU1FSnwzCmQdtzirjqxe9oHRbCe7ePJtHPlq82zlFVhty/kIsHdebBywc22eeKyApVTfGmrLejpHap6qWqGq+qHVT1MuBoo6SMMXX0jG/Dm5NHUVhWyXUvL2V/fpnTIRk/kVtUQX5ppd92eMOJ7elty4IYcxwGJETxxs0jOVhcwXWvLCGnsNzpkIwf+L7Du3kmDFtdzZjjNLRrDK/dNIJ9eWWMfepr/m/uOr7ZlkOl7bPRYqXl+O8aUrVOZLESW9fJmBMwonss79w6ile+2cHcVZm8vXQ3Ua1CSYptRevQEFqHB5MQ3YrfXdCH6Nb+OWrGNJ60rELahIfQqV2E06EcUYMJQ0QKqT8xCNDKJxEZ04IM7RrD8z+Loayymq+35rBoUza5ReUUV1RxsLiC79IO8N32A7w2aQTd4yKdDtf4UFpOET07tPHrpfEbTBiq6p+zR4xpZiJCgzm/fyfO79/pB8eX7zzIbTNSueyF/zHthhRGJsc6FKHxtbTsIk7r5d8TkP1rh3FjzA+M6B7Lh784ldjIMK5/ZSnz1+1zOiTjAwVllWQVlPt1/wVYwjDG73VrH8ncO05lQEI7fj97LVkFNhS3ufHnTZM8WcIwJgBEtQ7liWuGUFFdw9//bavkNDdpWa6EcZIfD6kFSxjGBIzucZHceVZPPl6zl/+l5TodjmlEW7MKCQ8J8vuZ/5YwjAkgPz+zJ93at+YvH62nvKra6XBMI6ndZS84yH9HSIElDGMCSkRoMH+7tD/pOcW88s0Op8MxjSQtu4jeft5/AZYwjAk4Z/XpwIUDOvHsF9vYc7DE6XDMCSoqryIzr9Rv98DwZAnDmAD0l0v6ERIUxG9nraG6xhZdCGSH15CyFoYxxhe6RLdi6qX9WbbzIK98k+50OOYE1G7Lai0MY4zPXDksgbH9O/HYZ1vYuLfA6XDMcdqWXURYSBBdY/17hBRYwjAmYIkID14xkKhWYfxm1mrKKm3UVCDallVIz3j/HyEFljCMCWixkWE8etUgNu8v5IFPNlJQVul0SOYYbQuQEVJgCcOYgHd23w7cOKYbby/dTcoDn3PTa8uYlbqHovIqp0MzR1FcXkXGoVJLGMaYpjP10v7MueMUJo7pxtasIn4/ey1jn/qaFbsOOR2aacD2nNpd9vy/wxssYRjTLIgIw7vF8OdL+vHtH87mnVtHAXDNS4t5ZtE2G3rrp7Zl+f+2rJ4sYRjTzIgIp/SMY/6vTueSQZ15YuFWJkxbYv0bfmhrdiFhwUF0C4ARUmAJw5hmq11EKE+PH8oT1wxmxe5D/PWjDU6HZOpIyyqiR3wkIcGB8as4MKI0xhy3K4YlMuWcXsxdlcm8NXudDsd4qF10MFBYwjCmBbjr7F4MSYrmz3PXsTev1OlwDFBaUc2eQyX07hAYHd5gCcOYFiEkOIinrh1CVY3y21lrqLFOcMdtzylC1f83TfJkCcOYFqJ7XCT3XdKPxekHeGLhVpsZ7rBt2bVrSFnCAEBExorIFhFJE5F76zn/pIisdj+2ikiex7muIvKZiGwSkY0i0t2XsRrTElw7IomLB3bmuS/TGP3QIh74ZOPh1VJN09qaVURosNCtfaTToXjNZwlDRIKB54ELgX7ABBHp51lGVe9W1SGqOgR4FvjA4/QM4FFVPRkYCWT7KlZjWgoR4dkJQ3n7llGc2jOON77byXlP/Jfnv0xzOrQWZ1tWEclxkYQGyAgpgBAfvvdIIE1V0wFEZCYwDth4hPITgL+6y/YDQlR1IYCq2p9AxjSSoCDh1F5xnNorjpzCcu7/ZCOPLthCx3YRXDU80enwWoxN+woY0jXa6TCOiS9TWwKwx+N1hvvYj4hINyAZ+MJ96CQgT0Q+EJFVIvKou8VS97rbRCRVRFJzcnIaOXxjmr/4tuE8fvVgTusVx71z1vLNNvs5agp7DpaQmVfKyO6xTodyTPylLTQemK2qtb1wIcDpwD3ACKAHMKnuRao6TVVTVDUlPj6+qWI1plkJCwniheuH0atDG+54a6XtrdEEFm8/AMCYnu0djuTY+DJhZAJJHq8T3cfqMx541+N1BrBaVdNVtQr4EBjmkyiNMbSLCOW1m0bQJjyEm19fTqEtI+JTi9MPENcmLGBWqa3ly4SxHOgtIskiEoYrKcyrW0hE+gIxwOI610aLSG2z4RyO3PdhjGkEnaNa8cL1w9hfUMZbS3Y7HU6zpap8tz2X0T3aI+L/myZ58lnCcLcM7gIWAJuAWaq6QUTuF5FLPYqOB2aqqnpcW43rdtQiEVkHCPCyr2I1xrgM6xrDmSfF88o36ZRW2DwNX9iRW0xWQXnA3Y4C346SQlXnA/PrHLuvzuupR7h2ITDIZ8EZY+o15ZxeXPWvxbyzbDeTT0t2OpxmZ3G6u/+iR+AlDH/p9DbG+ImU7rGM7hHLtK+322xwH1i8/QAd24WTHBc4E/ZqWcIwxvzIlHN6k1VQzuwVGU6H0qyoKkvSDzImAPsvwBKGMaYep/Rsz7Cu0bz41XYqq2ucDqfZSMsuIrcoMPsvwBKGMaYeIsKUc3qTmVfKh6uONBreHKvv+y/iHI7k+FjCMMbU66w+8fTr3I7p3+7AYxCjOQGLtx8gIboVSbGtnA7luFjCMMbUS0SYMDKJzfsL2WCzv09YTY2yJP1AQM6/qGUJwxhzRJcOTiAsJIj3U/ccvbBp0JasQg6VVAZs/wVYwjDGNCCqdSjn9+vIR2v2Ul5lQ2xPxP/ScoHAWz/KkyUMY0yDrk5JIq+kks832pY0J2LBhv307dSWhOjA7L8ASxjGmKM4rVccnaMieH+F3ZY6XtkFZaTuOsSFAzo7HcoJsYRhjGlQcJBwxbAEvt6aw/78MqfDCUgLNuxHFS4c2MnpUE6IJQxjzFFdNTyJGoUPVtnM7+Px6fr99IiPDLjlzOuyhGGMOarkuEhGdI9hdmqGzck4RgeKylmSfoCLBnQO2OG0tSxhGGNByHuKAAARpUlEQVS8cvXwJNJzi3l60TaKy6ucDidgLNyYRY3C2AGBfTsKLGEYY7z008FdOO/kjjz1+TbOeORLXv7a9szwxqfr95MU24r+Xdo5HcoJs4RhjPFKq7BgXrkxhQ/uPIV+Xdrxj/mbOPfxr1iXke90aH4rv6SS/6XlNovbUWAJwxhzjIZ1jeHNyaOYdfsYRISrX/qOj9fsdTosv/T5piyqarRZ3I4CSxjGmOM0MjmWj+46lYEJUUx5dxWPf7aFmhrrEPf06fp9dImKYEhStNOhNApLGMaY4xbXJpy3bhnFNSmJPPtFGn/7eIPTIfmN4vIqvt6WywUDOjWL21Hg4z29jTHNX3hIMP+8chBtwkN59X87GNMzrtncgjkRqbsOUVFVw9l9OjgdSqOxFoYx5oSJCPde2JeBCVH8Yc5a9uaVOh2S45amHyAkSBjeLcbpUBqNJQxjTKMICwni2QlDqaqu4dczV1PdwvszlqQfYGBiFJHhzedGjiUMY0yj6R4Xyd8vH8CynQd57os0p8NxTElFFWsz8hndI3CXMq+PJQxjTKO6fGgiVwxN4OlFW1m+86DT4Thixa5DVNWoJQxjjDma+y8bQFJsa349czX5pZVOh9PklqQfILiZ9V+AJQxjjA+0CQ/hqWuHsL+gjL98uL7FLVi4JP0gAxOiaNOM+i/AxwlDRMaKyBYRSRORe+s5/6SIrHY/topIXp3z7UQkQ0Se82WcxpjGN7RrDL8+tzfz1uxl7qpMp8NpMq7+i7xmdzsKfJgwRCQYeB64EOgHTBCRfp5lVPVuVR2iqkOAZ4EP6rzNA8DXvorRGONbd57di5HdY7nvow3sPlDidDhNYuWuPCqrlVE9Yp0OpdH5soUxEkhT1XRVrQBmAuMaKD8BeLf2hYgMBzoCn/kwRmOMDwUHCU+OH4II3PZmKnsONv+ksXSHq/8ipZn1X4BvE0YC4LkJcIb72I+ISDcgGfjC/ToIeBy4x4fxGWOaQEJ0K1782XD25pVy8TPf8MXmLKdD8qkl6QcYkBBF24hQp0NpdP7S6T0emK2qtYvr3wnMV9UG94MUkdtEJFVEUnNycnwepDHm+JzWO45PppxOUmxrbn49lUcXbG6WE/tKK6pZvSeP0cnN73YU+DZhZAJJHq8T3cfqMx6P21HAGOAuEdkJPAZMFJGH616kqtNUNUVVU+Lj4xsnamOMT3Rt35o5d5zC+BFJPP/ldn41cxVV1TVOh9WoVu0+RGV185t/UcuXY76WA71FJBlXohgPXFe3kIj0BWKAxbXHVPVnHucnASmq+qNRVsaYwBIRGszDVw4iOS6Shz7djIjw5DWDCQn2l5sdJ2ZJ+gGCBFK6N7/+C/BhwlDVKhG5C1gABAOvquoGEbkfSFXVee6i44GZ2tIGahvTgt1+Zk9qFP75n80ECTxxzRCCgwJ/CfBFm7MZmBjdLPsvwMfLm6vqfGB+nWP31Xk99Sjv8TrweiOHZoxx2B1n9aRGlUcXbCFIhEevGhTQLY31mfls2FvA/eP6Ox2KzzSvaYjGmIDyi7N7oao89tlWCsuqeO66oUSEBjsd1nF5d9luwkOCGDek3sGgzULgpnNjTLNw1zm9+dul/Vm0OYuJ05cF5NpTJRVVzFu9l4sHdiaqVfO8HQWWMIwxfuDGU7rz9PihrNpziGtfWkx2QZnTIR2Tf6/dR2F5FeNHdnU6FJ+yhGGM8QuXDu7Cq5NGsPtgCbe+uSKg5mnMXL6HHvGRjGimo6NqWcIwxviN03vH89AVA1mzJ4+3l+5yOhyvbMsqZMWuQ4wfkYRI4I/0aoglDGOMX7l0cBdO7x3HI//ZQlYA3JqauXwPocHCFcMSnQ7F5yxhGGP8iojwwLgBVFTX8LePNzgdToPKq6r5YGUGP+nXkbg24U6H43OWMIwxfqd7XCS/PKcX89ft9+vFCt9ZuptDJZVcO6J5d3bXsoRhjPFLt53Rk14d2vCXDzdQUlHldDg/smV/IQ99upkzT4rnjN5xTofTJCxhGGP8UlhIEP+4bACZeaU8NH+z0+H8QFllNb98dxXtIkJ57OrBzb6zu5YlDGOM3xrVoz2TT0vmzSW7+HJzttPhHPbg/E1sySrksasHEd+2+fdd1LKEYYzxa7+7oA99O7Xld7PXkFtU7nQ4fL4xixmLdzH5tGTO6tPB6XCalCUMY4xfiwgN5qnxQygoreLeOetwcmHr9Jwi7pm9hn6d2/H7sX0ci8MpljCMMX6vbyfXL+jPN2Xx7rI9R7/AB3IKy7nxtWUEi/DCz4YRHhKYiySeCEsYxpiAcPOpyZzWK46/zlvPR6uPtHmnbxSXV3Hz68vJLaxg+qQRdI+LbNLP9xeWMIwxASEoSHj+umEM6xrDr2au5vkv03xye0pVWZ+Zz+o9eew+UEJeSQW/eGclG/bm89x1QxmSFN3onxkobD8MY0zAiGodyozJI/n97LU8umALew6W8MBlAwg9ho2Xqqpr+Pe6fWzeX8ivzu39o/03pn+7g7//e9OPrnvoioGce3LHE65DILOEYYwJKOEhwTx17RC6xrbm2S/S2LivgAcvH8iAhKjDZfJLKnntux1kHCplQJd2DEyMJjkuko/X7OXlb9LJOFQKuBYOfPH64YcTzn+35vDg/E2c368j40cmcbC4krySChJjWjN2QCdH6utPpLlspZ2SkqKpqalOh2GMaUKfrN3L1HkbOVhczsQx3bn1jB7MWr6HV7/dQWF5FbGRYRwsrvjBNcO7xfDzM3uyL7+U+z7awE8Hd+Gpa4ew+2AJ4577li7RrZhzxylEhreMv6dFZIWqpnhTtmX8ixhjmqVLBnXh9N7xPP7ZFt5YvJPXv9sJwAX9O/Lr806ib6e27C8oY11GPtuyixiVHEtK99jD15dWVPPQp5sJDRbWZuQTHCS8PDGlxSSLY2UtDGNMs7BmTx6frt/PJYM6/+D21NE8sXArzyzaRnCQ8ObkkZzSs2WsC1XLWhjGmBZncFI0g49jBNPd5/UmulUonaMiWlyyOFaWMIwxLZqIcPNpyU6HERBsHoYxxhivWMIwxhjjFUsYxhhjvGIJwxhjjFd8mjBEZKyIbBGRNBG5t57zT4rIavdjq4jkuY8PEZHFIrJBRNaKyLW+jNMYY8zR+WyUlIgEA88DPwEygOUiMk9VN9aWUdW7PcpPAYa6X5YAE1V1m4h0AVaIyAJVzfNVvMYYYxrmyxbGSCBNVdNVtQKYCYxroPwE4F0AVd2qqtvcz/cC2UC8D2M1xhhzFL5MGAmA504nGe5jPyIi3YBk4It6zo0EwoDt9Zy7TURSRSQ1JyenUYI2xhhTP3+ZuDcemK2q1Z4HRaQz8CZwo6rW1L1IVacB09xlc0Rk1xHePwrIP8ZzdY839PpIz+OA3CN8rrcaiv1YytV33ptjR6tnY9TxSLEcazn7Lhs+Zt+li32XP9TNy3KuzUJ88QDGAAs8Xv8R+OMRyq4CTqlzrB2wEriqEWKZdqzn6h5v6HUDz1N9GfuxlKvvvDfHjlbPxqhjY9XTvkv7Lu27bNzvsu7Dl7eklgO9RSRZRMJwtSLm1S0kIn2BGGCxx7EwYC4wQ1VnN0IsHx/HubrHG3p9pOeNwdv3O1q5+s57cyyQ6mnfZcPHAqme9l02fKyp6vkDPl2tVkQuAp4CgoFXVfUfInI/ruw3z11mKhChqvd6XHc98BqwwePtJqnqap8F6wMikqpergIZqFpCHaFl1LMl1BFaRj19VUef9mGo6nxgfp1j99V5PbWe694C3vJlbE1kmtMBNIGWUEdoGfVsCXWEllFPn9Sx2eyHYYwxxrdsaRBjjDFesYThJRF5VUSyRWT9cVw7XETWuZdIeUZExOPcFBHZ7F4G5ZHGjfqY42z0OorIVBHJ9FgC5qLGj/yYY/XJd+k+/1sRURFxdCceH32XD7iX6lktIp+5V2FwlI/q+aj7Z3KtiMwVkWPflakR+aiOV7t/59SIiPd9Hb4YetUcH8AZwDBg/XFcuwwYDQjwKXCh+/jZwOdAuPt1h2ZYx6nAPU5/f76up/tcErAA2AXENbc6Au08yvwS+Fdz/C6B84EQ9/N/Av9shnU8GegDfAWkePt+1sLwkqp+DRz0PCYiPUXkPyKyQkS+cQ8Rpk6Zzrh+0Jao65uaAVzmPn0H8LCqlrs/I9u3tWiYj+rod3xYzyeB3wOOdwz6oo6qWuBRNJLmW8/PVLXKXXQJkOjbWjTMR3XcpKpbjjUWSxgnZhowRVWHA/cAL9RTJgHXsii1PJdIOQk4XUSWish/RWSET6M9PidaR4C73M37V0UkxnehnpATqqeIjAMyVXWNrwM9ASf8XYrIP0RkD/Az4D78U2P8P1vrZlx/mfubxqyj1/xlaZCAIyJtgFOA9z1uY4cf49uEALG4mowjgFki0sP914DjGqmOLwIP4Ppr9AHgcVw/hH7jROspIq2BP+G6leGXGum7RFX/D/g/EfkjcBfw10YLshE0Vj3d7/V/QBXwduNE1zgas47HyhLG8QsC8lR1iOdBcS3rvsL9ch6uX5ieTdpEINP9PAP4wJ0glolIDa41YPxlJcUTrqOqZnlc9zLwiS8DPk4nWs+euBbPXOP+AU4EVorISFXd7+PYvdUY/796ehvXHCu/Shg0Uj1FZBJwCXCuv/wB56Gxv0vvOdmZE2gPoDseHU/Ad8DV7ucCDD7CdXU7ni5yH/85cL/7+Um4VveVZlbHzh5l7gZmOv09+qKedcrsxOFObx99l709ykzBtWBos/sugbHARiDe6br5qo4e57/iGDq9Hf+HCJQHrr069gGVuFoGk3H9VfkfYI37f7D7jnBtCrAe1xLtz9UmBVzLtr/lPrcSOKcZ1vFNYB2wFtdfPZ2bqj5NWc86ZRxPGD76Lue4j6/FtWZRQnP8LoE0XH+8rXY/HB0N5qM6Xu5+r3IgC4+FYht62ExvY4wxXrFRUsYYY7xiCcMYY4xXLGEYY4zxiiUMY4wxXrGEYYwxxiuWMEyzJiJFTfx5r4hIv0Z6r2r3yrDrReTjo62aKiLRInJnY3y2MfWxYbWmWRORIlVt04jvF6LfL0znU56xi8gbwFZV/UcD5bsDn6jqgKaIz7Q81sIwLY6IxIvIHBFZ7n6c6j4+UkQWi8gqEflORPq4j08SkXki8gWwSETOEpGvRGS2e9+Etz32Gfiqdn8BESlyL9a3RkSWiEhH9/Ge7tfrROTvXraCFvP9QodtRGSRiKx0v8c4d5mHgZ7uVsmj7rK/c9dxrYj8rRH/GU0LZAnDtERPA0+q6gjgSuAV9/HNwOmqOhTXSqwPelwzDLhKVc90vx4K/BroB/QATq3ncyKBJao6GPgauNXj859W1YH8cDXRernXCDoX10x5gDLgclUdhmtPlcfdCeteYLuqDlHV34nI+UBvYCQwBBguImcc7fOMORJbfNC0ROcB/TxW+mznXgE0CnhDRHrjWl031OOaharquSfBMlXNABCR1bjW+vm2zudU8P1iiyuAn7ifj+H7fTTeAR47Qpyt3O+dAGwCFrqPC/Cg+5d/jft8x3quP9/9WOV+3QZXAvn6CJ9nTIMsYZiWKAgYraplngdF5DngS1W93N0f8JXH6eI671Hu8bya+n+WKvX7TsIjlWlIqaoOcS+fvgD4BfAMrr0o4oHhqlopIjuBiHquF+AhVX3pGD/XmHrZLSnTEn2Ga7VVAESkdpnoKL5f/nmSDz9/Ca5bYQDjj1ZYVUtwbYn6WxEJwRVntjtZnA10cxctBNp6XLoAuNndekJEEkSkQyPVwbRAljBMc9daRDI8Hr/B9cs3xd0RvBHXMvMAjwAPicgqfNv6/jXwGxFZC/QC8o92gaquwrVK7ARce1GkiMg6YCKuvhdU9QDwP/cw3EdV9TNct7wWu8vO5ocJxZhjYsNqjWli7ltMpaqqIjIemKCq4452nTFOsz4MY5recOA598imPPxsy1pjjsRaGMYYY7xifRjGGGO8YgnDGGOMVyxhGGOM8YolDGOMMV6xhGGMMcYrljCMMcZ45f8BdjMAHsw9C00AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find(end_lr=2e-1)\n",
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 6:08:20\n",
      "epoch  train_loss  valid_loss  accuracy  map3    \n",
      "1      0.593504    0.579562    0.844088  0.892059  (6:08:20)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(1, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save(f'{name}-scratch-2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds, _ = learn.get_preds(ds_type=DatasetType.Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_submission(preds, data_bunch.test_dl, name, classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !kaggle competitions submit -c quickdraw-doodle-recognition -f subs/{name}.csv.gz -m \"{name}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key_id</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9000003627287624</td>\n",
       "      <td>radio stereo stove</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9000010688666847</td>\n",
       "      <td>hockey_puck bottlecap pool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9000023642890129</td>\n",
       "      <td>The_Great_Wall_of_China castle camel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9000038588854897</td>\n",
       "      <td>mountain tent The_Eiffel_Tower</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9000052667981386</td>\n",
       "      <td>fireplace campfire fire_hydrant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9000057427034623</td>\n",
       "      <td>fence jail spreadsheet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9000065506980882</td>\n",
       "      <td>wine_glass hourglass cup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9000087586309806</td>\n",
       "      <td>submarine dolphin lobster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9000092580281382</td>\n",
       "      <td>bracelet hand wristwatch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9000096661653918</td>\n",
       "      <td>hourglass vase wine_glass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9000102548572430</td>\n",
       "      <td>octopus octagon snowflake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9000109525154374</td>\n",
       "      <td>stove oven dishwasher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9000117423882596</td>\n",
       "      <td>squiggle roller_coaster snake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9000118400618039</td>\n",
       "      <td>string_bean sandwich peas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9000119463725679</td>\n",
       "      <td>harp cello violin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9000156567772007</td>\n",
       "      <td>shorts underwear pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9000159584429954</td>\n",
       "      <td>sailboat cruise_ship speedboat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9000161602785100</td>\n",
       "      <td>hospital church ambulance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9000182604013479</td>\n",
       "      <td>giraffe clarinet roller_coaster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9000183669464573</td>\n",
       "      <td>shark dragon crocodile</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              key_id                                  word\n",
       "0   9000003627287624                    radio stereo stove\n",
       "1   9000010688666847            hockey_puck bottlecap pool\n",
       "2   9000023642890129  The_Great_Wall_of_China castle camel\n",
       "3   9000038588854897        mountain tent The_Eiffel_Tower\n",
       "4   9000052667981386       fireplace campfire fire_hydrant\n",
       "5   9000057427034623                fence jail spreadsheet\n",
       "6   9000065506980882              wine_glass hourglass cup\n",
       "7   9000087586309806             submarine dolphin lobster\n",
       "8   9000092580281382              bracelet hand wristwatch\n",
       "9   9000096661653918             hourglass vase wine_glass\n",
       "10  9000102548572430             octopus octagon snowflake\n",
       "11  9000109525154374                 stove oven dishwasher\n",
       "12  9000117423882596         squiggle roller_coaster snake\n",
       "13  9000118400618039             string_bean sandwich peas\n",
       "14  9000119463725679                     harp cello violin\n",
       "15  9000156567772007                shorts underwear pants\n",
       "16  9000159584429954        sailboat cruise_ship speedboat\n",
       "17  9000161602785100             hospital church ambulance\n",
       "18  9000182604013479       giraffe clarinet roller_coaster\n",
       "19  9000183669464573                shark dragon crocodile"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.read_csv(f'{name}.csv.gz')\n",
    "result.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMsAAADhCAYAAABmzEFjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAGsZJREFUeJztnXmcHVWVx7+nt3Q6nU46C5iEkEQiIROCENlhCIJg2EHFFUUiIyi4gIAMH2AUw6iAOiPDGEBZBFRQ3BgEQVYRWRRCIIJsIftK0t3p9JbuvvPHucWrvPRyk37v1avX5/v53E/Vvbeq3nnv1a/udu4tcc5hGEb/lCVtgGGkBROLYQRiYjGMQEwshhGIicUwAjGxGEYgJhbDCKTkxSIi00XkYRFpFJHXReSUWN6RIvKKiLSIyCMiMimWN0REbhKRJhFZLSLnZ103X+deJSLL/LlLROSSrHPLRWSeiKwUkU0i8ryIjPR5p4vI3/25y/21KmLnNmeFLhG51ucdKCIPisgGEVknIr8UkXFZ32m+iKzxx9wjIhNCru3za0Tkf0Vkvf8vHo/lXSgiL/nvs1hELuzlv5wtIk5E5vX2f+cV51zJBqACeBU4HygHjgA2A7sDY4BG4FSgGrgaeCp27reBPwP1wHRgNTDH5+Xz3GnAML8/AVgEfCiWPw94GJgECLAnUO3zvgD8K1Dlz/07cHEvv00t0Awc5uPHeJvqgBrgJuD+2PEXAS8AO3u7fwr8OuTaPu124BfAWP9fvC/r2rP8/zUNWAJ8POualcAC4ClgXiL3U9I3dJ7Fsqf/0ySW9gDwLeDzwJOx9GFAK7CHj68Ejo7lfwv4hd/P27lZ9k8AXgQu8vF6/312C/z+5wP39JJ3OvBm/LfJyp8FbIrFfwRcFYsfB/wz5NrAHkATUBdo9w+Ba7PSLgauAm5JSiwlXw3rgehpPAN9UgLgnNsMvAHMEJF6YFw83+/P8Pt5OfcdA0UuFpFmYDkqpp/5rJlAJ/ARX717VUTO6eO7HoaWTD1xOvBT5+/EgHN/AhwiIuNFpAb4FHBf4LX3R0uLb/pq2Isi8uGeThQRQUvHRbG0ScBc4IpePq8glLpY/gmsBS4UkUoRORqYjVYzatHqUJxGYLjPIys/yiOP5wLgnPuOj88CbosdvwswAq1GTgE+AnxDRI7K/uIiMhfYF7imh7xJ6O9wa3aez98LuByItx1eA5YBK9BSYjo93Ly9XHsX9AHVCIwHzgVuFZHpPXz8N9D78uZY2g+By5xzzT3ZWyhKWizOuS3AyWiVYTXwNeAu9IndjNbP49QBm3weWflRHnk8N267c849j1bRvumTW/32Cudcq3NuIdoOODZ+roicjLabjnHOrWdbPg084ZxbnJ0hIlPREuMrzrk/x7KuA4YAo9HS7tf0XLL0dO1WYAtafepwzj0GPAIcnfXZ5wKfAY5zzrX7tBOA4c65O3v4rIJS0mIBcM4tdM7Nds6Nds59EHg38AxazL83Ok5EhgG7AYuccxuBVfF8vx9VDfJybi9focLnAyyMvlb8K8YPFpE5wI3ACc65F3u55mfooVTxpcKfgG85527Lyt4buMU5t8HfyNcC+4vImIBrL2Rbsu2ei7ZLjnTOLY9lHQns66udq4GPAV8Vkd/18t3yRxINpUIGYC+096YGuABYjD4hx6LVgg/7/O+yda/Ud4DH0Eb1HqgAoh6tvJyLPrzO8ucJWtdfBXw5du3Hgev9d5iOVjOP9HlHAG8T64Xq4fc4GO0RHJ6VPgFtO13Qy3k3A3ej1cBK4BJgReC1K4HXgctQ8R+ClqRRh8in0JJ/eg+fOxx4VyzcCfwAGFXweynpm7kAYrka2IhWf+4DpsbyPgC8glYTHgUmx/KGoN2nTcAa4Pys6+b8XC+W+4EN3t5X/U0Z782b4I9pRnuczorlPYJ2ADTHwn1Zn309cFsPv9N/oE/7+LnNsfzRwB1enA3AE8D+Idf2eTOAv3ox/QM4JZa3GK2mxT97fi/XuYWEesOirj3DMPqh5NsshpErTCyGEYiJxTACMbEYRiAmlh1ARCZ779cKH79PRE5P2i4jv5hYcoBz7hjnXI+uIzuKiLwlIh/I5TXzQVrszAWDXizx+R6lgiiD/r/NOUkPGiYRgLeAr6NuGO3oqPLF6Aj2JrYdNCtHHRLXowOB56ADeBU+/1HgzNjA4qWol+1adN7HiF7sGAP8HzrItwGdA1OGOk92o4OWzWRc9A8EnvTHvwAcHrvWo8CVwF/8eVPR0fafoF4AK9C5MOUFsPNE1H2nwds13ad/HfhV1rX/G/ih3+/V3mIIiRuQoFgWABOBoT7tVNQjtgz1P9oMjPN5Z6Mj7hOBUehIeW9imYu6drwb9TD+Nb2Pan8bmI+6g1SirukSs/EDsWMnoK4sx3obj/LxsTEblqIj5RX+er9BR9WHATuhPnFn5dnO3f1vd5Q/9iL/OVXohLUWvDsM+hBaBRzo473aWwwhcQMSFMvcfo5ZAJzk9x8Gzo7lHd2HWB4Cvhg7dhrqylHRw2dcAfyOmAtOlo3xm/Dr2Tcz8Efg9JgNV8TydkZLzaGxtE8Aj+TZzsuAu2LxMrSUONzHnwA+4/ePAt4IsbcYwmCu1y6LR0TkMyKyQEQaRKQBnX8RedSOzzp+SR/XHZ+VvwR90u/cw7FXo0/dB0TkTRG5uI/rTgJOjezzNh6KTjTr6TtNQp/sq2LHX48+sfNp51bXdc51e7ui+fo/Q0UA8EkyE9v6szdxSq5xux284xTnXdNvRN3B/+qc6xKRBajnL2hVYWLs3F37uO5K9I+PH9uJOlRubYBzm9A5Nl8TkT2Bh0XkWefcQ2S5sKM33G3OuX8L+U7++HZgjHOus4B2rkRndALvzHyciJYuAL8EviciuwCnAAcF2ps4g7lkiTMM/dPXAYjIGWjJEnEX8GUR2cVPG+7ryfpz4DwRmSIitcB/Anf2dAOIyPEiMtXfUI1AF9pgBr1p3x07/HbgBBH5oOgKL9Uicri/6bbBObcKXW/geyJSJyJlIrKbiMzOs513AceJrmBTiYqsHe2YwDm3Dq0y3gwsds69HGhv8iRdD0wikFXP9mlXoj0964Hvo/NRonZIBTqH4m3Unby/3rDL0SflOvQmr+/FjvO8LZvR2ZuXxfJOQhvsDfg5JsAB3q4N/tr3Artm2xC7xgh0oYnl6E3+PH7VlDzbeQrao9jo7Z2Rdb1P+9/vwlB7iyGYi75hBGLVMMMIxMRiGIGYWAwjEBOLYQRiYjGMQIpiUFJErEvOKBqcc9JTupUshhGIicUwAik+sUxC1xzcrb8DDaOwFJ9YlqDOE39D/V978oE1jAQoCneXbRr41cDTQJuPH1Boi4zBjDXwDWOAFKdY2tDJszN8+Fay5hgGFGs1LOIQv70NOA0/I8Iw8ks6q2Fv+lBGbO6dYSRDcYvFMIqI4hZLow/L0HdKGUaCFIVvWK+0+O0/gH3QF93F0w2jgBR3yRLxJLrAzmgfDCMBirtkiViArmc41ceX9XGsYeSJdJQshlEEpEMsK9FFeGZiXchGYqRDLI3oal2HkBmoNIwCkw6xdKDrqf+LD3XJmmMMTtIhFtC1CUdjPWJGYqRHLIaRMOkRyyL01TflwPSEbTEGJekRy3Jgow/7J2yLMShJx6AkqIvLYr+/d5KGGIOV9JQshpEw6RFLBzov/2lgD2BksuYYg4/0iAV0rOUZoB7o8X1XhpE/0iWW133oJuNUaRgFIl1iWe3DeuDAhG0xBh3pEothJEh6uo4Bmvz2H+hYSxXa8DeMApDOkuU59K3t5lBpFJB0iuUZVCgTkjbEGEykUyyGkQDpFMvraFvF3F6MApJOsWwE1qHLIxlGgUinWJqBhcD70LXEavo+3DByQTrFArqW2K7ACB8MI8+kVyyGUWDSNSgZ5zl01ZfIoXJVgrYYg4L0iqUBXbhifNKGGIOF9IqlGdgCvCtpQ4zBgrVZDCOQ9JYsrei8fHv1t1Eg0iuWDtQL2dosRoFIr1g6gQ1YyWIUjPS2WTqApcA4H2wU38gz6RWLYRSYdItlJTqvpQ6dNWkYeSTdYllDxpFyaMK2GCVPehv4oGIZ4vdrkzTEGAyku2QxjAKS7pJlFdp9DLpKpWHkEXHOJW0DIrJjRlQBj/j9tcApubLIGMw456Sn9HSLBeBUv70BOAh4JRcWGYOZ3sSS/jbLgz68DXwhYVuMkib9YjGMApF+sTT4cAPwUcxXzMgb6RdLxK1+e3qiVhglTOmIZQ1wF/B59K1g9mYwI8eke5wli31/AG4vaDpI4233QRs6+xjUq78TfReSLb5vbC+lU7IYRp5J/TjLRL+9Bl36eHg97zwCOjfAFgft/pgWYDO61kX0qpeof2Cjj2/0eY3oC8YAlvi4lUaDg97GWVJbDasFzgYu9fG1wFygvBpGfkzTRv4U6jZkPGHqySxgOdanTUEdlqt9vAqoBMrR5ZSjz3oLeNbHHwP+iTaTmnP7tYwiJpUly6HAj9FVkOb5tPnEbtzoEdDtQw9UxQ6NAqgTczUqoGE+7SDgEGCGj+8EdKGvt3zRpz0MvAAs8/EmrCQC/Z2j1XUnAkJu6/7R3/siufu9S3cE3zAKRKpKlsv89izgz8BFZJ7khSB6K9+uwJ5oifM+nzYFrb5FbaHX0eraM8CrPu1ttN1UqtSgyyEATENf+3kY+tsAbALGkNsn9Nt+2wBcBTzk4wOpHqfakbIO+DlwhI8fh1Z7ioFonYzRwO7oDQL65vEZqO1R1/Vi4O/AX4GXfNpSMgJLEyPRZab39PH3o6/LiTpcytHOkgXo9wV4ivxVZS70Nqz28R8Ad5PppNkeUimWSX77pN8e6reL825RbhiD3jzv9fEjgJloW6vcp60FFpG5oZ4AHMVXP+4i8/sfgvY81vt00BL+eTIzJl4ClqNP/EKxF3CO3/8wOsb2YzLOHaH3TerEMgW9iUD/gE+QzidwNrWo+9o0H58N7Ed+qyq5YC2ZmdtvAY+jVcx/+rRVFFcVcwrq+XQmmZ7Ou4Hr0Pdg9YU18A1jgBRtyfIrtFgHuLKw5hScePfqZLSjoNjoBt7w+2kaoB2DVskAzkOrwI/7+FfouWqWumrYe4DX8vBZ42ph/HAo6/Hn6JuVvotlRSnUBwchtcCRwOU+Xgccy7b3WerE0h/j0K7j7/t4SBfylJFw/6ehvhq6d+Br/+Zl3Z5zb69jnUYKiNpeD6Kl5JysfGuzGMYASa1v2HjgY8AvfbyvkmWcf5Qs/CLc9BzcuaiPg/sgqn5ZqRLGuHFQWwsNvv+4tRU6OjQkSTRgeQnwO+BgMsMTfZFasZQRVixWV8Ajn9X9BavhK/fn0SjjHd7zHvjTn6C6GjZv1rSNG2HNGli6VONvvglLlsBTT+m20PwFHTT9JnBUwPGpFUsoN5wAY/ww+8E/TtaWwcAc3wC47z4480xYvx4m+dHlKVNg8mTYe2+NH3EEDBsGv/89nONHE7sLWGx3oO3em1GPC1Avg94oabF8dm/4+J6w7w0a39BWeBsm1MFmX+1oyOPnl1VBWRmU+X+0rALKKqGiWgNAZQ2UV8Ba31HRmeNRxDlzVCQAp5wCv/1tz8fV+IdXdTWMGKHVskKKJE70EoYQXzJr4BtGICVZsuzll0O64QT4/D2wcE1ytlzyr/D+ybr/h9fgumdgcYDDVO04GLO77g8ZCdUjYYh3ex5ar2HICKj2o5kV1VBVC1V+Ek5lDZQPgfJKEP8vSxm0rIcuP3X0+VvglV9D04qBf8/Zs+Hmm+HoozX+4IO9H9vSktlu2ND7cQATJmibpyFPTmb1qC/e5oBjS04so6rhYb8c0i9eglsWJGvPVU9Ai6+GnTwdPjgVjr8dlvQzsHnoRTDjY5m464Qu777c1Q5bWqBjM3T4+kNnGzSvgfZGjbdu1NDeBG3+Rmtv0nP3+ZzGD7tEw+v3w6NXaFrDdnqp1vqexhtugEcf7VskO8Ill8D73w9/+IO2bQAWLsydeHZCp52HVMNSOyi5H3AP8CEfj7r+/jwXxvo68d7zoa0zFxbmhtmT4M6Pwod+AU/2M4paNxGGjtL9LS0qhk7f5uneAt2dPvi6fvcOdMfWTYA9PgT7nZ1Ju+P47RPMBRfo9tJLYeZMWJbjCUaTJsG558Lxx8NOO2na2rVw771w3XWweIAu6JcDH0e7j0G9pG1Q0jAGSElVw+YdAfuMg2nXaryYShWAli1oBTmA7k7Y5wzdf/J70JSHKaFNK+CZa2HVAvi4H90dPiG8ZJk4UUsUgHnzcl+qgI6/XHghXHklTPPzGk49FU46SXvfjjsuc9yOUI9OLQi5VUpGLAftAp+cCcfdXhqOjlW1MMO/TmPxI/kRS0RXW0zD29GFe801WiUCmD8/11ZtTUMDPP207j/9NLz2Glx++cC7nN+FVr1CarElI5Yup6P1W0rEF6WtATp9r9XISX0fmwT77aeDi3Pnary5wGtCTZmiPWmNjTt+jSp0jCV0ykHJiMW5HXO7L1Y6W6HNr/xXP6XvY5NABIYPT24wcdo0WL4c2gYw0FuGeiCHjixYA98wAjGxFCmdHTpu0rwGRkxO2pptcU7da5Kgrg6mToUXXhiYF3MVukqNlSwpp7sDGpdqGD4OKmr6P2ewMGIEjBwJL788sOtUoItZbOzvQI+JpYjZ+KaGoaMyzpCGDk42NWmP2EAYhi7TayWLYeSYkukNSwNd3bB2c/j8/wY/0FY9HCYeBG/7dWA723TQsrNdtwB0D9z9JS2ccQZ0damP2ECoRgUQ6mZmYikgM3aGEdXhg6bL/Uyk5rVw8k3Q7Zd/dJ2639Wxtb9YtmNlW6M6Vm7xx7R558ouL6S2BnWuHDoqI65iru6NGaPbE05Qx82Bju3UoFWr0DFsE0sBmTEWmtqhMXBsoNGXLHecCKMmq6s+qLt+eZW66Vf7l89UVm/rsj9qjLrsl1Vm0sqroMyvHSsVGu9sh9uO0bR1A3xa55PDD9ftsGFw990Dv573U6WfWQLvYGIpINNGw/Km7fdZa1qiYXvYauakb5mWVUDFkMxsyopqnSOzeXVu5rTkmzO8r9wLL+j8/YFSh3r3hE4YtQa+YQRSOiWLQGN7cbq81PnXjE0dDfe+Ch0FcBHp7iitJZt22w329+/zOO+83CynNBL1Ng71mCmZkuXZFfDcSvjxiVBbpaFYGFGtYWQ1vLyu/+PTQiFH8E8+GdrbNeRqNubOQCthU4qhlEoW4KIH4cUvwtn7avyakJXTCsC7/NTbli3wRuhwcZGzcqW6zZ97rsZfeaX/+fQ7Sm0tnHYaPPCAxtfkaE2FerRUCW1ClpRYljXBvMfh0sM0fudLmpYktVVwm5/7/NxKeGJpsvbkihUrdOLVo49qfNEiOP30zA2dS2bNgrFj4Y47cnvdnQmfywIlVA0zjHxTcmKZ/zcdJV+7Ga45Omlr4LwDYadhGs7/Y9LW5JbXXtNFKmbO1NVX7rkHfvITDaNG9X9+CHPmwGOPwZe+BA89pCFXjEBXdQnuCHHOJR7QWa3bFfYDtxrcwT7E8456t4aWS3GH7rr9185V2H00rvkS3JmzNCRlR6HC7Nm4Vasy4ZBDcOPG4WpqNGzPtebM0eAc7uSTc2tnlQ9/AvfzHvJ7u09Lqs0S8aAfsLrnFbjpJJh1vcabC+wvdeOJsGgd3F7Eo+K55LHHYMYM3b/6arj1VqioyMzTf/55eO45bd8AvPGGTgtuyRoVDF0GdkeJOkpHAi9ux3klVw0zjHxRkiVLxAUPJNeVfNpesO94OODG4luSKZ9E3cef+xwcfLC2Z2bN0rR99tFSY+hQjbe2ajdwVOJEabfeqiUK5L5UgcxNX0P4xK/4eSVJEl3J0WqYP5gD//VXeGltfj+vmHnySQ0RNTU6y3GXXTQ+a5aGffbRFScjZs6El17Kn13RC26HYGLZivl/g8+/T/f/51gtbRraoNU/7Ts6c+t+Mv8E3W7eAt99InfXLQVaWjSsWqXxZ5/VbU0NjB6t+93dOoaTT6J3Sg4BtudZllqxdKFftL/7vLkDjrld9/9wmq6F3LIFNnqHoDXNsGYzLPaPmCUNsLQRNrRqANjUoVWpvqpTo6rhR8fD/hM0fsTN0FTCE7BySSSiQuFrgZSja4aFYg18wwgktSXLMrQ49Y6ofb7eLPLHOvpWmDUBJtbBFD9pavII2K0eDvD16OFVUOknR23xMxM3dcD6zfC2f/q91agl0dJGHfwE+OVH1eP58Js0/lqJ+ICVItFk0HWETymGFL9yogr4PfpuDYCTBvD51RUaQMUyaqiGXf2sw0kjVVw7+xcF7VwL9dUwtFKFBPDYYvj6g8m8is/YPr7st18FDkBFE6e3V06kViwA/w6c5fc/h/ZsCPmrW1b4C1eVwZAKdZKs9xXgJN8u1hddwCa/34A+XCI9D9Ym1e/8dghwItv+DvZ+FsMYIKkuWSahb/8CdbdeDYzFngBx1gB+UZR3XgcXdZeuA5YAK4Fo5sBaMnX5qIrbRumUQmOAv/n964Fv93BMSVbDQAUDMAHtRjahbE03+iAB/a3Gk/nNxqLvVKwl050qqDdhK5n3LEYCitbMiMS1gkzXfSewxYcoHg+gVcLu2DndWSEiX8I8GPgO+k4WgA8Ci3s4rmTFYuw4VWjP0BDUqRC/nQDsigoLVFyRsCAjrrU+HfQhFbntQkYAkUBARdNFRlBbfFp7LC1qU0WCaY2ltcXS2mJb/PHxY9p9aAW+4dMOBhag7VuA3lZ/NbEYAyISFmTENZKtXUeigD+2Ouu8oX5/aNYxQ2JpcQHjrz8EHeOojKWV+7SoJlHu96O4+LCBTLXzm8Bf6L/ksga+YQwQK1mMoiO+ME9ZVoinRaULflsRi1f6MBTwDs3BbaHeSpbUjuAbpUux9rxZNcwwAjGxGEYgJhbDCMTEYhiBmFgMIxATi2EEYmIxjEBMLIYRiInFMAIxsRhGICYWwwjExGIYgZhYDCMQE4thBGJiMYxATCyGEYiJxTACMbEYRiAmFsMIxMRiGIGYWAwjEBOLYQRiYjGMQEwshhGIicUwAjGxGEYgJhbDCMTEYhiBmFgMIxATi2EEYmIxjEBMLIYRiInFMAIxsRhGICYWwwjExGIYgZhYDCMQE4thBGJiMYxATCyGEYg455K2wTBSgZUshhGIicUwAjGxGEYgJhbDCMTEYhiBmFgMIxATi2EEYmIxjEBMLIYRiInFMAIxsRhGICYWwwjExGIYgZhYDCMQE4thBGJiMYxATCyGEYiJxTACMbEYRiAmFsMIxMRiGIGYWAwjEBOLYQTy/61o2yv+ByllAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5QekMVEyEqkk6gVqI+rnQsKsu186GCVOhsja7nQDb+rNuSOdmfZKuelSfsqCuStu6rDXY1uetRK/Ogw7T+Ynj4c8+PPrGs/REtsXrLtFyTvpYPKn3r4O+HnjqLnjllWx6ulotK8CERr1GkxdA496aNml3mLEfVNZ5Nw+9Pt2t6sZB1pWL3DjIunL5unEmljyoRV2wvirIRHWGOqieAtWTdbnOC6BuLtTO0rQJU1QsZTE/v78XerZC+0Zd3vIWrHlaRdDqW4k6NkHHRuhq1eW+rjz/8Iy6kE2bdbFrcx77GEXmzIHOzrfHhfV1ZM+vo0lv+hUPZvNTFVBRrS5otR9QtmE+TFmobhxkXbnIjYOsKxc9YCJX7s0ns9d6KEwsg5CuVgEATJyxY53h3W9C9R/gY7dAnx/otWKCPuWiLp99XdC9xf85fuTk134DLav0Sdfpb9TOzSqWuF8/VpSnoLIMmoukGS/fUJdtPdDVA10t0OJv/DV/3nGdyDpFlgmy1mmSH/A2sk7L74P7vxLtfPDjmljwFWg/KGrjIph9KMw8IHuRu1uhanK2YjrtWtiWhrXLYPXDmtaySiubXf4p2dnsTX5X8TSFZtJQXgZbiiQOayxDXSLrFFkmyFqnqBk8sk7begjqX2EVfMMI5B1lWSpqtGm1cS+YdZCmTd8fpuyZbabd1g/t62Hd8/Cc/1To+ue03hBZjUmtsHcvPHg2lEA3jO1k0pAWaEk4aLHCP9mTiguLLH3kyoUy7sQSr2vU7gIz3gOzD9Pl6fvChGlat+hs1rSWlfDinfDWU7rctExNd0/b4MeoRb+WVARBuztFdTmkJHk3LOoROXUqPP10smXZGUpaLMPVNdJV+qIsajp8/few+o/QtBS2+A9R5xNekUGFUgrdiONM8E/0toTrUJU+EqCmBtasGXrdYsLqLIYRSElYlnhdA7S+EVLXaFoKm1+NvZ8YpZiiSqCb0rMstZXatN2ecN/7eh80WVUFb5TQ0OlFKZaaGbDggzBnsS7H6xqg9Y186hqjRRX6UrLUxFJXCf3bkh+oIhKLiDYdlwpFJ5aaGfDp+9WStLyuafG6Bmh9I8lQ7grUspQaUf/7pPvez/IRDc5pa1ipUHRiSaVg/VL40w9ggxdHUiHZuURdOurQ7+QVybvGYKL+9zvd9z6DnryP16IG7dQj5FXrjTyGzm3QsivaR7sQrGFEw4ZaBd8wAik6y7LlLbj3FIqyQhA9WcopzV6S0WAVfdHIjxnUQkwk+zX5qX5+V2C6T5uFPv2j4V4rgQ1o/+o8HrczfX+atkrovo3C/df3Al/J/3hFJxagKIUC2fsiag0rCipivxVkv+1biw7IPA39Gj1QX6Mi7/uVX6ceFUosCpp+9OSa/QTwKrAe/YYwfn4defslcy/V3w310HUOhXu7+xYjureKUyxFSnRvpCnw4HpT0Bvev4BlOvrknwZEg2k3oDd+VK/wLYf0oh8UB+ofg7Z26Hve561BP9C9Hljr07YAbaiqokrZKFbOKiqg0XcrbloBPQX5lvPoYHUWwwjELMtOEF2sarIeyqhTjVqQw4DjfNoBQAfge1fSi1qAFrKtOy+g1iHqxLQBtRhbocL3sGz8MDS1Q8/dY1X44YmGP4LSigsDE8tOEZnhMkbRDWsE9gWO9ctHAHPQusPLPu3HwMPAJr/chkZy9oQVJO3/5ZoyeKV9NAqdP5WVGhMGpRUXBiaWnSKqBqTJszWsBpgHLCZrNfZDrUn0cu5x4ArgGbL1iBHWGdJe5VXlyYfn19drmAuUVqgLmFh2iuhipQh4oFcAk4F3Ax/wae9Dv6vXBfjee1wDPEK2pWkMOshEYsmkobkIxCK+gl9KoS5gFXzDCMYsy04QvcsrZ4AvfdWjzblH+OVjgb39RpFv/ijwAPAs2fpHAWJmKv2/XFEEg1XMmpX98lgpxYWBiWWn2O6GCXQ1onWPqGK+GK2sR3FszwGXo3WQ131aASKiByLjC16eSr6XZDT8EZTGp/HimFhCqAYWQfn+uij3QPfV6FdYo0rq/cCDQPTCr4iemlX+DX1ZCloTFks0/BGUxte+4lidxTACMcsyGNOAD/n504EaKPeD5cnd0H0rcCn6YUnIul9FyARvWYTk3bBorDAojU/jxTGxREQBie8CvoAKJQow/B1wLZT55t71rdD5YO4Oipea2LdV2hPshBP/LB6Uxqfx4phYpqDvQb7sl/dGW6+uAe7yaT6EJKqGNAB7AE8WqowjpNaPprLNQUeCXYqjUJdSC3OJeGeKZV/gVD//UbQC/7hfPhF4ggFbrqJ4sI1ouNatY1nGUaTet3n3uWS7FEehLqUW5hJhFXzDCOSdYVmiPt6HA2cAh6DmAeBG4A7glQG2yyEyNi+jVZsKSqMffuSG9fYna1miuLBSiwmLGN9i2QP4BPAZvzwF+AtwMvCYT8vjxdizwGloZ8SNw6xbDDT4wMXufh3dJSmiuLBSiwmLGF9iqUH7gUTf2liMNune45dvApYzYnPwAtopcSqlJZaOhD/nHYW6lFqYS4TVWQwjkNK3LH4wBj4OfBYNgY/C37+OBi6O8uN/BToM6oLYoYqZOl9nae9J1rIM9lm8UqF0xTIL+B4anwXa1fY+tML+gk8bo9r3BrSqsz/w87E5xKhS55uO23pyPrZaYPL9LF6xULpi+RYwH7jQL9+H9jkvAB1oIPF+hTnciKhIZS1LUzv0JCiWsfwsXiEoXbFcgb5STyAmqweNwD+e7Lhzb+vfUiSkU9lwl1c2Db3uWJH0l75GC6vgG0YgpSuWVSQa6fssOjLRZD8VK+mU9mcp1GAVs2frtGQJ7LqrpmUy2biwVQHfmy9WStcNS5jl6MWb55dXDr5qoqRT2Z6ShRisYvVq/S0vhwcegAMPLN3P4uVSupYlYdaghu1dfipWKtPa977Q/e/PPx+mT4cvfUnf3Jd6qAuYZcmbdrTf1wFJF2QYMmntew+F7fi1ejVcdhlcdBEsX65pzc2wsRRCHgbBLIthBGKWJU86gKXoGHqgXWKKsWdxVbkOVAGFH6zihhvg9NPhxht1+aWX4KmnCluG0cQsywh4Fv36w3Syn0UpNlq74efLdFpW4ADGtjY47TR9Y9/dDRdfXLrvWADERSOeJVkIkeQLkQeHADf7+c9RvN2MoydiEi/vUymdIvoS/vhrCM45GSjdLIthBGKWZYTc73+jASpL2MswPINZFhPLCNnb//4JHUHpzgTLYowOJpYx5kfAB8lGIpdolw0DE8uYMw14EbjaL38nwbIYI8Mq+IYxQsyyjCIXAv/k5/ehYH3RjFHGLEsBuB59i98BXJxwWYzRxyzLKHOS/70ROJjSGNDC2BGzLIYxQsyyjDLRdycfR0eBOW6IdY3ixCxLgejy08eBVrLDmhmlj1mWMSTlpxKIHTRiDGZZrD/LGLKNZCJ9jbHB3DDDCMTEYhiBmFgMIxATi2EEYmIxjEBMLIYRiInFMAIxsRhGICYWwwjExGIYgZhYDCMQE4thBGJiMYxATCyGEYiJxTACMbEYRiAmFsMIxMRiGIGYWAwjEBOLYQRiYjGMQEwshhGIicUwAjGxGEYgJhbDCMTEYhiBmFgMIxATi2EEYmIxjEBMLIYRiInFMAIxsRhGICYWwwjExGIYgZhYDCMQE4thBGJiMYxATCyGEYiJxTACMbEYRiAmFsMIxMRiGIGYWAwjEBOLYQRiYjGMQEwshhGIicUwAjGxGEYgJhbDCMTEYhiBmFgMIxATi2EEYmIxjEBMLIYRiInFMAIxsRhGICYWwwjExGIYgZhYDCMQE4thBGJiMYxATCyGEYiJxTACMbEYRiAmFsMIxMRiGIGYWAwjEBOLYQRiYjGMQEwshhGIicUwAjGxGEYgJhbDCMTEYhiBmFgMIxATi2EEYmIxjEBMLIYRiInFMAIxsRhGICYWwwjExGIYgZhYDCMQE4thBCLOuaTLYBglgVkWwwjExGIYgZhYDCMQE4thBGJiMYxATCyGEYiJxTACMbEYRiAmFsMIxMRiGIGYWAwjEBOLYQRiYjGMQEwshhGIicUwAjGxGEYgJhbDCMTEYhiBmFgMIxATi2EEYmIxjEBMLIYRiInFMAL5f4A/LHAUFx8sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, (index, row) in enumerate(result.iterrows()):\n",
    "    create_func(row.key_id).show(title=f'{row.key_id}\\n{row.word}')\n",
    "    if i == 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save elements for Ensembling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(preds, f'{name}-raw_predictions.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame(preds.numpy(), index=test_dl.dataset.x.items, columns=classes)\n",
    "sub.to_csv(f'{name}-raw-predictions.csv.gz', compression='gzip')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}