09_pytorch_model_deployment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d53d613-7fb2-4d9e-828f-9ceea249bdc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch version: 2.1.0+cu118\n",
      "torchvision version: 0.16.0+cu118\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "print(f\"torch version: {torch.__version__}\")\n",
    "print(f\"torchvision version: {torchvision.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bbcb597-d1fc-404f-9a83-f56d156cc52a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Continue with regular imports\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from torch import nn\n",
    "from torchvision import transforms\n",
    "\n",
    "# Try to get torchinfo, install it if it doesn't work\n",
    "try:\n",
    "    from torchinfo import summary\n",
    "except:\n",
    "    print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
    "    !pip install -q torchinfo\n",
    "    from torchinfo import summary\n",
    "\n",
    "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
    "try:\n",
    "    from going_modular import data_setup, engine\n",
    "    from helper_functions import download_data, set_seeds, plot_loss_curves\n",
    "except:\n",
    "    # Get the going_modular scripts\n",
    "    print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
    "    !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
    "    !mv pytorch-deep-learning/going_modular .\n",
    "    !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
    "    !rm -rf pytorch-deep-learning\n",
    "    from going_modular.going_modular import data_setup, engine\n",
    "    from helper_functions import download_data, set_seeds, plot_loss_curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a325f62-7209-4091-8b41-d702b278f09a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d508d4d-bfa8-4549-89a4-992fdf23bf8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] data/pizza_steak_sushi_20_percent directory exists, skipping download.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('data/pizza_steak_sushi_20_percent')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download pizza, steak, sushi images from GitHub\n",
    "data_20_percent_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip\",\n",
    "                                     destination=\"pizza_steak_sushi_20_percent\")\n",
    "\n",
    "data_20_percent_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c14bd57c-d88b-482e-90b2-86953c8358e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup directory paths to train and test images\n",
    "train_dir = data_20_percent_path / \"train\"\n",
    "test_dir = data_20_percent_path / \"test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38a6307f-03c4-4bfc-bdb6-f0ac22f9d035",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Setup pretrained EffNetB2 weights\n",
    "effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
    "\n",
    "# 2. Get EffNetB2 transforms\n",
    "effnetb2_transforms = effnetb2_weights.transforms()\n",
    "\n",
    "# 3. Setup pretrained model\n",
    "effnetb2 = torchvision.models.efficientnet_b2(weights=effnetb2_weights) # could also use weights=\"DEFAULT\"\n",
    "\n",
    "# 4. Freeze the base layers in the model (this will freeze all layers to begin with)\n",
    "for param in effnetb2.parameters():\n",
    "    param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88d5207b-6468-4f96-b18e-34d5b01da190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Dropout(p=0.3, inplace=True)\n",
       "  (1): Linear(in_features=1408, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out EffNetB2 classifier head\n",
    "effnetb2.classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4cfed418-17ab-4fe2-aedf-7e2499149511",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. Update the classifier head\n",
    "effnetb2.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.3, inplace=True), # keep dropout layer same\n",
    "    nn.Linear(in_features=1408, # keep in_features same \n",
    "              out_features=3)) # change out_features to suit our number of classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7f5b9cc2-079f-4b66-b309-c85092c1a250",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_effnetb2_model(num_classes:int=3, \n",
    "                          seed:int=42):\n",
    "    \"\"\"Creates an EfficientNetB2 feature extractor model and transforms.\n",
    "\n",
    "    Args:\n",
    "        num_classes (int, optional): number of classes in the classifier head. \n",
    "            Defaults to 3.\n",
    "        seed (int, optional): random seed value. Defaults to 42.\n",
    "\n",
    "    Returns:\n",
    "        model (torch.nn.Module): EffNetB2 feature extractor model. \n",
    "        transforms (torchvision.transforms): EffNetB2 image transforms.\n",
    "    \"\"\"\n",
    "    # 1, 2, 3. Create EffNetB2 pretrained weights, transforms and model\n",
    "    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
    "    transforms = weights.transforms()\n",
    "    model = torchvision.models.efficientnet_b2(weights=weights)\n",
    "\n",
    "    # 4. Freeze all layers in base model\n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "\n",
    "    # 5. Change classifier head with random seed for reproducibility\n",
    "    torch.manual_seed(seed)\n",
    "    model.classifier = nn.Sequential(\n",
    "        nn.Dropout(p=0.3, inplace=True),\n",
    "        nn.Linear(in_features=1408, out_features=num_classes),\n",
    "    )\n",
    "    \n",
    "    return model, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "211ae3cb-796c-4220-9ab0-7337025a26f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3,\n",
    "                                                      seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e40419cb-d70a-444b-bb08-a1f00d55dc83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "============================================================================================================================================\n",
       "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
       "============================================================================================================================================\n",
       "EfficientNet (EfficientNet)                                  [1, 3, 224, 224]     [1, 3]               --                   Partial\n",
       "├─Sequential (features)                                      [1, 3, 224, 224]     [1, 1408, 7, 7]      --                   False\n",
       "│    └─Conv2dNormActivation (0)                              [1, 3, 224, 224]     [1, 32, 112, 112]    --                   False\n",
       "│    │    └─Conv2d (0)                                       [1, 3, 224, 224]     [1, 32, 112, 112]    (864)                False\n",
       "│    │    └─BatchNorm2d (1)                                  [1, 32, 112, 112]    [1, 32, 112, 112]    (64)                 False\n",
       "│    │    └─SiLU (2)                                         [1, 32, 112, 112]    [1, 32, 112, 112]    --                   --\n",
       "│    └─Sequential (1)                                        [1, 32, 112, 112]    [1, 16, 112, 112]    --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 32, 112, 112]    [1, 16, 112, 112]    (1,448)              False\n",
       "│    │    └─MBConv (1)                                       [1, 16, 112, 112]    [1, 16, 112, 112]    (612)                False\n",
       "│    └─Sequential (2)                                        [1, 16, 112, 112]    [1, 24, 56, 56]      --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 16, 112, 112]    [1, 24, 56, 56]      (6,004)              False\n",
       "│    │    └─MBConv (1)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
       "│    │    └─MBConv (2)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
       "│    └─Sequential (3)                                        [1, 24, 56, 56]      [1, 48, 28, 28]      --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 24, 56, 56]      [1, 48, 28, 28]      (16,518)             False\n",
       "│    │    └─MBConv (1)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
       "│    │    └─MBConv (2)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
       "│    └─Sequential (4)                                        [1, 48, 28, 28]      [1, 88, 14, 14]      --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 48, 28, 28]      [1, 88, 14, 14]      (50,300)             False\n",
       "│    │    └─MBConv (1)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
       "│    │    └─MBConv (2)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
       "│    │    └─MBConv (3)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
       "│    └─Sequential (5)                                        [1, 88, 14, 14]      [1, 120, 14, 14]     --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 88, 14, 14]      [1, 120, 14, 14]     (149,158)            False\n",
       "│    │    └─MBConv (1)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
       "│    │    └─MBConv (2)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
       "│    │    └─MBConv (3)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
       "│    └─Sequential (6)                                        [1, 120, 14, 14]     [1, 208, 7, 7]       --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 120, 14, 14]     [1, 208, 7, 7]       (301,406)            False\n",
       "│    │    └─MBConv (1)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
       "│    │    └─MBConv (2)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
       "│    │    └─MBConv (3)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
       "│    │    └─MBConv (4)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
       "│    └─Sequential (7)                                        [1, 208, 7, 7]       [1, 352, 7, 7]       --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 208, 7, 7]       [1, 352, 7, 7]       (846,900)            False\n",
       "│    │    └─MBConv (1)                                       [1, 352, 7, 7]       [1, 352, 7, 7]       (1,888,920)          False\n",
       "│    └─Conv2dNormActivation (8)                              [1, 352, 7, 7]       [1, 1408, 7, 7]      --                   False\n",
       "│    │    └─Conv2d (0)                                       [1, 352, 7, 7]       [1, 1408, 7, 7]      (495,616)            False\n",
       "│    │    └─BatchNorm2d (1)                                  [1, 1408, 7, 7]      [1, 1408, 7, 7]      (2,816)              False\n",
       "│    │    └─SiLU (2)                                         [1, 1408, 7, 7]      [1, 1408, 7, 7]      --                   --\n",
       "├─AdaptiveAvgPool2d (avgpool)                                [1, 1408, 7, 7]      [1, 1408, 1, 1]      --                   --\n",
       "├─Sequential (classifier)                                    [1, 1408]            [1, 3]               --                   True\n",
       "│    └─Dropout (0)                                           [1, 1408]            [1, 1408]            --                   --\n",
       "│    └─Linear (1)                                            [1, 1408]            [1, 3]               4,227                True\n",
       "============================================================================================================================================\n",
       "Total params: 7,705,221\n",
       "Trainable params: 4,227\n",
       "Non-trainable params: 7,700,994\n",
       "Total mult-adds (Units.MEGABYTES): 657.64\n",
       "============================================================================================================================================\n",
       "Input size (MB): 0.60\n",
       "Forward/backward pass size (MB): 156.80\n",
       "Params size (MB): 30.82\n",
       "Estimated Total Size (MB): 188.22\n",
       "============================================================================================================================================"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchinfo import summary\n",
    "\n",
    "# # Print EffNetB2 model summary (uncomment for full output) \n",
    "summary(effnetb2, \n",
    "        input_size=(1, 3, 224, 224),\n",
    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
    "        col_width=20,\n",
    "        row_settings=[\"var_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9da57d3d-dce4-4159-8854-76af16e5dccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from going_modular import data_setup\n",
    "train_dataloader_effnetb2, test_dataloader_effnetb2, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
    "                                                                                                 test_dir=test_dir,\n",
    "                                                                                                 transform=effnetb2_transforms,\n",
    "                                                                                                 batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82f6ac86-1a2f-43f4-bae4-cda5aedede6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ed178af0c50443fbf4fa0678e1a8663",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 0.9843 | train_acc: 0.5667 | test_loss: 0.7410 | test_acc: 0.9347\n",
      "Epoch: 2 | train_loss: 0.7171 | train_acc: 0.8396 | test_loss: 0.5877 | test_acc: 0.9409\n",
      "Epoch: 3 | train_loss: 0.5878 | train_acc: 0.8938 | test_loss: 0.4901 | test_acc: 0.9563\n",
      "Epoch: 4 | train_loss: 0.4481 | train_acc: 0.9125 | test_loss: 0.4349 | test_acc: 0.9409\n",
      "Epoch: 5 | train_loss: 0.4302 | train_acc: 0.9062 | test_loss: 0.3921 | test_acc: 0.9443\n",
      "Epoch: 6 | train_loss: 0.4400 | train_acc: 0.8875 | test_loss: 0.3522 | test_acc: 0.9597\n",
      "Epoch: 7 | train_loss: 0.4253 | train_acc: 0.8792 | test_loss: 0.3266 | test_acc: 0.9563\n",
      "Epoch: 8 | train_loss: 0.3900 | train_acc: 0.8979 | test_loss: 0.3461 | test_acc: 0.9352\n",
      "Epoch: 9 | train_loss: 0.3770 | train_acc: 0.8771 | test_loss: 0.3130 | test_acc: 0.9131\n",
      "Epoch: 10 | train_loss: 0.3769 | train_acc: 0.8625 | test_loss: 0.2814 | test_acc: 0.9688\n"
     ]
    }
   ],
   "source": [
    "from going_modular import engine\n",
    "\n",
    "# Setup optimizer\n",
    "optimizer = torch.optim.Adam(params=effnetb2.parameters(),\n",
    "                             lr=1e-3)\n",
    "# Setup loss function\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "# Set seeds for reproducibility and train the model\n",
    "set_seeds()\n",
    "effnetb2_results = engine.train(model=effnetb2,\n",
    "                                train_dataloader=train_dataloader_effnetb2,\n",
    "                                test_dataloader=test_dataloader_effnetb2,\n",
    "                                epochs=10,\n",
    "                                optimizer=optimizer,\n",
    "                                loss_fn=loss_fn,\n",
    "                                device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eac24e28-13a1-4876-a8eb-caf4e642ba60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_functions import plot_loss_curves\n",
    "\n",
    "plot_loss_curves(effnetb2_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "972e7aa2-1269-4671-ab74-b8b6544e38fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving model to: models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\n"
     ]
    }
   ],
   "source": [
    "from going_modular import utils\n",
    "\n",
    "# Save the model\n",
    "utils.save_model(model=effnetb2,\n",
    "                 target_dir=\"models\",\n",
    "                 model_name=\"09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "231cca31-b693-458d-8d72-e162a9576d0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrained EffNetB2 feature extractor model size: 29 MB\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Get the model size in bytes then convert to megabytes\n",
    "pretrained_effnetb2_model_size = Path(\"models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\").stat().st_size // (1024*1024) # division converts bytes to megabytes (roughly) \n",
    "print(f\"Pretrained EffNetB2 feature extractor model size: {pretrained_effnetb2_model_size} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e795e0ff-b0eb-4021-adbe-64967f64df79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7705221"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count number of parameters in EffNetB2\n",
    "effnetb2_total_params = sum(torch.numel(param) for param in effnetb2.parameters())\n",
    "effnetb2_total_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f50764e6-d084-4235-872f-f98b0eec1ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.2814320981502533,\n",
       " 'test_acc': 0.96875,\n",
       " 'number_of_parameters': 7705221,\n",
       " 'model_size (MB)': 29}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary with EffNetB2 statistics\n",
    "effnetb2_stats = {\"test_loss\": effnetb2_results[\"test_loss\"][-1],\n",
    "                  \"test_acc\": effnetb2_results[\"test_acc\"][-1],\n",
    "                  \"number_of_parameters\": effnetb2_total_params,\n",
    "                  \"model_size (MB)\": pretrained_effnetb2_model_size}\n",
    "effnetb2_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5bf309ac-fd7c-49ed-ad33-a39a30206467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (head): Linear(in_features=768, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out ViT heads layer\n",
    "vit = torchvision.models.vit_b_16()\n",
    "vit.heads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e15fc080-3670-49be-a742-21815447f61e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_vit_model(num_classes:int=3, \n",
    "                     seed:int=42):\n",
    "    \"\"\"Creates a ViT-B/16 feature extractor model and transforms.\n",
    "\n",
    "    Args:\n",
    "        num_classes (int, optional): number of target classes. Defaults to 3.\n",
    "        seed (int, optional): random seed value for output layer. Defaults to 42.\n",
    "\n",
    "    Returns:\n",
    "        model (torch.nn.Module): ViT-B/16 feature extractor model. \n",
    "        transforms (torchvision.transforms): ViT-B/16 image transforms.\n",
    "    \"\"\"\n",
    "    # Create ViT_B_16 pretrained weights, transforms and model\n",
    "    weights = torchvision.models.ViT_B_16_Weights.DEFAULT\n",
    "    transforms = weights.transforms()\n",
    "    model = torchvision.models.vit_b_16(weights=weights)\n",
    "\n",
    "    # Freeze all layers in model\n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "\n",
    "    # Change classifier head to suit our needs (this will be trainable)\n",
    "    torch.manual_seed(seed)\n",
    "    model.heads = nn.Sequential(nn.Linear(in_features=768, # keep this the same as original model\n",
    "                                          out_features=num_classes)) # update to reflect target number of classes\n",
    "    \n",
    "    return model, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "84416422-aacf-455f-8869-8de396c22de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create ViT model and transforms\n",
    "vit, vit_transforms = create_vit_model(num_classes=3,\n",
    "                                       seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5ba1aec9-f108-4398-8f27-d956810b7171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "============================================================================================================================================\n",
       "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
       "============================================================================================================================================\n",
       "VisionTransformer (VisionTransformer)                        [1, 3, 224, 224]     [1, 3]               768                  Partial\n",
       "├─Conv2d (conv_proj)                                         [1, 3, 224, 224]     [1, 768, 14, 14]     (590,592)            False\n",
       "├─Encoder (encoder)                                          [1, 197, 768]        [1, 197, 768]        151,296              False\n",
       "│    └─Dropout (dropout)                                     [1, 197, 768]        [1, 197, 768]        --                   --\n",
       "│    └─Sequential (layers)                                   [1, 197, 768]        [1, 197, 768]        --                   False\n",
       "│    │    └─EncoderBlock (encoder_layer_0)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_1)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_2)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_3)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_4)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_5)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_6)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_7)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_8)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_9)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_10)                  [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_11)                  [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    └─LayerNorm (ln)                                        [1, 197, 768]        [1, 197, 768]        (1,536)              False\n",
       "├─Sequential (heads)                                         [1, 768]             [1, 3]               --                   True\n",
       "│    └─Linear (0)                                            [1, 768]             [1, 3]               2,307                True\n",
       "============================================================================================================================================\n",
       "Total params: 85,800,963\n",
       "Trainable params: 2,307\n",
       "Non-trainable params: 85,798,656\n",
       "Total mult-adds (Units.MEGABYTES): 172.47\n",
       "============================================================================================================================================\n",
       "Input size (MB): 0.60\n",
       "Forward/backward pass size (MB): 104.09\n",
       "Params size (MB): 229.20\n",
       "Estimated Total Size (MB): 333.89\n",
       "============================================================================================================================================"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchinfo import summary\n",
    "\n",
    "# # Print ViT feature extractor model summary (uncomment for full output)\n",
    "summary(vit, \n",
    "        input_size=(1, 3, 224, 224),\n",
    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
    "        col_width=20,\n",
    "        row_settings=[\"var_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2ec09677-dac0-4712-a35b-581a4704a44c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup ViT DataLoaders\n",
    "from going_modular import data_setup\n",
    "train_dataloader_vit, test_dataloader_vit, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
    "                                                                                       test_dir=test_dir,\n",
    "                                                                                       transform=vit_transforms,\n",
    "                                                                                       batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "886d0a28-49e4-4e53-add4-00fdb055f296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eefb6a992f1444a0aa2c0d7376b37221",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 0.7020 | train_acc: 0.7521 | test_loss: 0.2714 | test_acc: 0.9381\n",
      "Epoch: 2 | train_loss: 0.2532 | train_acc: 0.9062 | test_loss: 0.1672 | test_acc: 0.9602\n",
      "Epoch: 3 | train_loss: 0.1764 | train_acc: 0.9542 | test_loss: 0.1273 | test_acc: 0.9693\n",
      "Epoch: 4 | train_loss: 0.1276 | train_acc: 0.9625 | test_loss: 0.1074 | test_acc: 0.9722\n",
      "Epoch: 5 | train_loss: 0.1159 | train_acc: 0.9646 | test_loss: 0.0953 | test_acc: 0.9784\n",
      "Epoch: 6 | train_loss: 0.1274 | train_acc: 0.9375 | test_loss: 0.0832 | test_acc: 0.9722\n",
      "Epoch: 7 | train_loss: 0.0897 | train_acc: 0.9771 | test_loss: 0.0845 | test_acc: 0.9784\n",
      "Epoch: 8 | train_loss: 0.0919 | train_acc: 0.9812 | test_loss: 0.0764 | test_acc: 0.9722\n",
      "Epoch: 9 | train_loss: 0.0922 | train_acc: 0.9792 | test_loss: 0.0734 | test_acc: 0.9784\n",
      "Epoch: 10 | train_loss: 0.0658 | train_acc: 0.9833 | test_loss: 0.0644 | test_acc: 0.9847\n"
     ]
    }
   ],
   "source": [
    "from going_modular import engine\n",
    "\n",
    "# Setup optimizer\n",
    "optimizer = torch.optim.Adam(params=vit.parameters(),\n",
    "                             lr=1e-3)\n",
    "# Setup loss function\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "# Train ViT model with seeds set for reproducibility\n",
    "set_seeds()\n",
    "vit_results = engine.train(model=vit,\n",
    "                           train_dataloader=train_dataloader_vit,\n",
    "                           test_dataloader=test_dataloader_vit,\n",
    "                           epochs=10,\n",
    "                           optimizer=optimizer,\n",
    "                           loss_fn=loss_fn,\n",
    "                           device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "59a972b7-308a-4cf8-90b1-8dde6f3159d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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nhIQEPfPMM7Wuf/HFF9W1a1f5+/srNjZWl112mfu6jz76SH369FFAQIAiIyM1atQoFRcXN7gWtBxmvVdb0vvV4XDo+uuvV+fOnRUQEKDu3bvrueeeO+y82bNnu9/Dbdu21ZQpU9zX5eXl6a9//atiY2Pl7++v3r176/PPP5ckTZ8+Xf379691XzNnzlRCQkKt78/FF1+sxx57TO3atVP37t0lSW+//bYGDhyokJAQxcXF6eqrr1ZWVlat+/rjjz90/vnnKzQ0VCEhIRo+fLhSU1P1ww8/yNfXVxkZGbXO/8c//qHhw4fX6XsDAGiZ6PgCALR6pZUO9Zr2VbM/7saHRyvQXrd/ip977jlt3bpVvXv31sMPPyzJ9QOgJN199916+umnlZiYqDZt2mjnzp0aO3asHnvsMfn5+emtt97SBRdcoC1btqhjx45HfYyHHnpITz75pJ566im98MILuuaaa7Rjxw5FRETU63n98ssvuuKKKzR9+nSNHz9eK1as0M0336zIyEhNnDhRP//8s/7+97/r7bff1mmnnaZ9+/bpxx9/lCTt3btXV111lZ588kmNGzdOhYWF+vHHH+sVOqDlMuu9KrWc96vT6VSHDh304YcfKjIyUitWrNBf/vIXtW3bVldccYUk6aWXXtLUqVP1xBNPaMyYMcrPz9fy5cvdtx8zZowKCwv1zjvvKCkpSRs3bpTNZqvT96bGkiVLFBoaqsWLF7svq6ys1COPPKLu3bsrKytLU6dO1cSJE7Vw4UJJ0u7duzVixAiNHDlS3377rUJDQ7V8+XJVVVVpxIgRSkxM1Ntvv6077rjDfX/vvvuunnzyyXrVBgBoWQi+AADwAmFhYbLb7QoMDFRcXJwkafPmzZKkhx9+WGeffbb73IiICPXr18/99SOPPKIFCxbos88+q9W1caiJEyfqqquukiQ9/vjjev7557V69Wqde+659ar12Wef1VlnnaUHHnhAktStWzdt3LhRTz31lCZOnKj09HQFBQXp/PPPV0hIiDp16qSTTz5Zkiv4qqqq0iWXXKJOnTpJkvr06VOvxwfM5snvV19fXz300EPurzt37qyVK1fqgw8+cAdfjz76qP75z3/q1ltvdZ83aNAgSdI333yj1atXa9OmTerWrZskKTEx8fjflEMEBQXptddeq7XE8brrrnP/OTExUc8//7wGDRqkoqIiBQcHa9asWQoLC9PcuXPl6+srSe4aJOn666/XG2+84Q6+/ve//6msrMz9vAAArRPBFwCg1QvwtWnjw6NNedzGMHDgwFpfFxUVafr06friiy/cQVJpaanS09OPeT99+/Z1/zkoKEihoaGHLTOqi02bNumiiy6qddmwYcM0c+ZMORwOnX322erUqZMSExN17rnn6txzz3Uv2erXr5/OOuss9enTR6NHj9Y555yjyy67TG3atKl3HWh5zHqv1jx2Y/CE9+usWbM0e/Zspaenq7S0VBUVFe7liVlZWdqzZ4/OOuusI9523bp16tChQ63AqSH69Olz2FyvX375RdOnT9dvv/2m/fv3y+l0SpLS09PVq1cvrVu3TsOHD3eHXoeaOHGi7r//fv3000869dRTNWfOHF1xxRVsDAAArRzBFwCg1bNYLHVewuSJDv2h7vbbb9fixYv19NNPq0uXLgoICNBll12mioqKY97PoT9MWiwW9w+ejSkkJERr167V0qVL9fXXX2vatGmaPn261qxZo/DwcC1evFgrVqzQ119/rRdeeEH33XefVq1apc6dOzd6LfAu3v5elcx/v86dO1e33367nnnmGQ0dOlQhISF66qmntGrVKklSQEDAMW9/vOutVuthS5MrKysPO+/Q70NxcbFGjx6t0aNH691331V0dLTS09M1evRo9/fieI8dExOjCy64QG+88YY6d+6sL7/8UkuXLj3mbQAALR/D7QEA8BJ2u10Oh+O45y1fvlwTJ07UuHHj1KdPH8XFxSktLa3pC6zWs2dP9zygg2vq1q2bew6Qj4+PRo0apSeffFK///670tLS9O2330py/QA/bNgwPfTQQ/r1119lt9u1YMGCZqsfaAye+n5dvny5TjvtNN188806+eST1aVLF6WmprqvDwkJUUJCgpYsWXLE2/ft21e7du3S1q1bj3h9dHS0MjIyaoVf69atO25dmzdvVm5urp544gkNHz5cPXr0OKyDrW/fvvrxxx+PGKTVuOGGGzRv3jz997//VVJSkoYNG3bcxwYAtGwNCr5mzZqlhIQE+fv7a8iQIVq9evVRzx05cqR7N5uDj/POO6/BRQMA0BolJCRo1apVSktLU05OzlG7O7p27ar58+dr3bp1+u2333T11Vc3SefW0fzzn//UkiVL9Mgjj2jr1q1688039Z///Ee33367JOnzzz/X888/r3Xr1mnHjh1666235HQ61b17d61atUqPP/64fv75Z6Wnp2v+/PnKzs5Wz549m61+oDF46vu1a9eu+vnnn/XVV19p69ateuCBB7RmzZpa50yfPl3PPPOMnn/+eSUnJ2vt2rV64YUXJElnnHGGRowYoUsvvVSLFy/W9u3b9eWXX2rRokWSXJ/9s7Oz9eSTTyo1NVWzZs3Sl19+edy6OnbsKLvdrhdeeEHbtm3TZ599pkceeaTWOVOmTFFBQYGuvPJK/fzzz0pOTtbbb7+tLVu2uM8ZPXq0QkND9eijj2rSpEkn+u0CAJwID9mcqN7B17x58zR16lQ9+OCDWrt2rfr166fRo0cfdabA/PnztXfvXvexYcMG2Ww2XX755SdcfGP4fmu2LnhhmabOW2d2KQAAHNPtt98um82mXr16uZcBHcmzzz6rNm3a6LTTTtMFF1yg0aNH65RTTmm2Ok855RR98MEHmjt3rnr37q1p06bp4Ycf1sSJEyVJ4eHhmj9/vv7v//5PPXv21Msvv6z3339fJ510kkJDQ/XDDz9o7Nix6tatm+6//34988wzGjNmTLPVDzQGT32//vWvf9Ull1yi8ePHa8iQIcrNzdXNN99c65xrr71WM2fO1IsvvqiTTjpJ559/vpKTk93Xf/zxxxo0aJCuuuoq9erVS3feeae7u61nz5568cUXNWvWLPXr10+rV692h97HEh0drTlz5ujDDz9Ur1699MQTT+jpp5+udU5kZKS+/fZbFRUV6YwzztCAAQP06quv1lr2abVaNXHiRDkcDk2YMOFEvlUAgPpyOqW9v0s/vSTNvUZ6MlEqzDC7KlmMeu4PPmTIEA0aNEj/+c9/JLm2NI6Pj9ff/vY33X333ce9/cyZMzVt2jTt3bu3zoMmCwoKFBYWpvz8fIWGhtan3OP6YWu2JsxerS4xwfpm6hmNet8AAM9TVlam7du3q3PnzvL39ze7HJygY72eTfn5AY3nWK8T71c0xPXXX6/s7Gx99tlnxzyP/78A4AQ5qqSM36Udy6W05VL6Cqksv/Y5l74u9bms0R+6Pp/z6jUdtKKiQr/88ovuuece92VWq1WjRo3SypUr63Qfr7/+uq688spjhl7l5eUqLy93f11QUFCfMuslMdpVx47cYlU5nPKxMfYMAAAA8Db5+flav3693nvvveOGXgCABnBUSnt/k9KWucKu9J+k8kPyGnuw1PFUGZ2GKS9msEI6DzJ9V8V6PX5OTo4cDodiY2NrXR4bG6vNmzcf9/arV6/Whg0b9Prrrx/zvBkzZuihhx6qT2kN1i4sQP6+VpVVOrVzf6k6R7HdMQAAB7vxxhv1zjvvHPG6P/3pT3r55ZebuSIAR9Oa368XXXSRVq9erRtvvFFnn3222eUAgPerqpD2/Cql/VgddK2SKotrnWL4haqi3WDtCR+g9T59tKqsg7Zmlyr5uyLlleTrq39UqHucn0lPwKVZg7fXX39dffr00eDBg4953j333KOpU6e6vy4oKFB8fHyT1GS1WpQYFayNewuUmlVE8AUAwCEefvjho87oYQkh4Fla8/t16dKlZpcAAN6tqlza9XP10sVl0s7VUlVprVMq7WHaHXqy1vv01vfl3bR4f4zyNx28Kcse958sFmnnvhJ1jwtppidwZPUKvqKiomSz2ZSZmVnr8szMTMXFxR3ztsXFxZo7d64efvjh4z6On5+f/PyaLxFMjA7Sxr0F2pZTJCn2uOcDANCaxMTEKCYmxuwyANQB71cAQJ1Vlkq71rjmc+1Y7vpzVVmtU4psYfrddpKWlnXTj5XdtbksXkbBwSOinLJZLeoUGaiuMcHqGhOirrHB6hITrKToYPn72pr3OR1BvYIvu92uAQMGaMmSJbr44osluYbbL1myRFOmTDnmbT/88EOVl5frT3/6U4OLbSpJ0cGSpNSs4uOcCQAAAAAA4IUqiqWdq6S05TLSlku7f5HFWVHrlBwjTD85e+gnZy+tcvZUitFOhlxBl6/Noq5RQeoaE6IuMcHqGusKuhKiAuXnY37AdTT1Xuo4depUXXvttRo4cKAGDx6smTNnqri4WJMmTZIkTZgwQe3bt9eMGTNq3e7111/XxRdfrMjIyMapvBHVDLh3dXwBAAAAAABPUFHlVFpusZIzi5ScVajd+0sV4u+ryGC7IoJcR1SwXRFBfooIsivU30cWi8Xssj1DeaGq0laqcPP30o5lCt2/XjbDIUmq+Q5lGG20ytmz+uihVKOd7DabEqOD1CM2RBfEBLs6uWKD1SkySL5euCFgvYOv8ePHKzs7W9OmTVNGRob69++vRYsWuQfep6eny2qt/Y3YsmWLli1bpq+//rpxqm5k7o6vbDq+AAAAAABobmWVDm3LLlZyVqFSsorcQVdabokcTqPO9+Nrs6hNoCsQi6wOxCKDDoRkNX+uuS48wFdWq/cHZRVVTqXv2at9m76XNX2FonLWKL58q3zkVJuDztttRLqDrrWWk2SPTlK3uFB1iQnWXTHB6hobovg2AfLxwoDraBo03H7KlClHXdp4pKGS3bt3l2HU/X/U5lbT8bWvuEL7iyvUJshuckUAAAAAALQ8pRUOpWa7Qi1XuFWklKwi7cgt1tHyrWA/H3WJCVa32GDFtwlUUUWV9hVVaF9xhXKLXf/dV1yhovIqVToMZRWWK6uwvE71WC1yB2Wu7jG/AyFZ8MGBmevyNoG+poZCBweEO/fskTV9pWJy16hH+Xr1tKSpi6X2N3GnM1o/W3opPeRkFcWdqsgOXdU1NkS3xISofZsA2VpA6Hc8zbqro6cKtPuoXZi/9uSXaVtOkQYERZhdEgAAAAAAXqu4vMrVuZV1cMhVqF37S3W0vphQfx91i60Zjh7iXmIXF+pfp+WLZZUOdwjmCsTKlVtUcchl1X8uKldBWZWchpRbfV1dWCxSWIBvre6xg7vKai/B9FObQLvsPvUPyo4UEGZl7Fa7/LUabNmkIdbNusCSLmtN0FX9EHts7bQr9GSVtD1Vfl1GqGNid10U6t8iutoaiuCrWlJMsPbklyk1q1gDOhF8AQBwqLS0NHXu3Fm//vqr+vfvb3Y5AADAAxSUVVYvTazdwbU7r/Sot2kT6KuusdXBVvXyuq6xwYoO9juh+Vz+vja1Cw9Qu/CAOp1f6XBq/0GBWG5xhfYVlbv/nOvuKnNdlldaKcOQ8koqlVdSqW11HJcU4u9zeEgWfCA4axNkV05hea2gcNf+UkUa+Rps3aQh1k26yLpJ3a27JN/a950XmKDSdqcqoOsIhfUYqXZh7dWuvt+4Fo7gq1pSdLB+TM5RajYD7gEAnmnkyJHq37+/Zs6c2Sj3N3HiROXl5emTTz5plPsDcADvVwAtTV5JhSuUqe7cqpnDlVFQdtTbRAX7qWv1EsUuBwVdkcF+zVj50fnarIoJ9VdMqH+dznc4De0vqekYq+keKz8kODvw5/0lFXI4DRWWVamwrEppuSXHvP8Y7dep1k260bpJQ3w3qYt1z2HnVEV2l63zcFkShkmdhik8JFbhDXnyrQjBV7WaOV8MuAcAAAAaX0VFhex2Zul6rapyafdaaccyKW+n1O5kKWG4FJnkWvsFz1GaJ6X/JO1YLjkqpPghUsLpUnBMnW6eW1Re3XVUpJTMQiVnFWlrZpFyio4+Mysu1L96eWKwusZUL1WMDm5x87NtVouigv0UFewnxR7/fKfTUEFZpXIODcmKXMGYM2+X2u7/WZ2L16mf8w+1rdp9+J3E9pY6DZOqgy6foKjGf2ItHMFXtZqdHbfR8QUArY9hSJXH/g1ck/ANrPMPCxMnTtT333+v77//Xs8995wkafv27SoqKtIdd9yhH3/8UUFBQTrnnHP073//W1FRrg9FH330kR566CGlpKQoMDBQJ598sj799FM99dRTevPNNyXJvaTgu+++08iRI+v1FL7//nvdcccd+u233xQREaFrr71Wjz76qHx8fI75+EFBQVq6dKnuvPNO/fHHH/L19dVJJ52k9957T506dapXDWhFzHqvSl75fr3rrru0YMEC7dq1S3Fxcbrmmms0bdo0+foeWCfzv//9Tw8//LDWr1+v4OBgDR8+XAsWLJAklZeXa9q0aXrvvfeUlZWl+Ph43XPPPbr++us1Z84c/eMf/1BeXp77vj755BONGzfOvanV9OnT9cknn2jKlCl67LHHtGPHDjmdTi1atEiPPvqoNmzYIJvNpqFDh+q5555TUlKS+7527dqlO+64Q1999ZXKy8vVs2dPzZo1S7GxsUpMTNTq1as1cOBA9/kzZ87Uv//9b23fvv2wHebRQJVl0q41rvAkbZnrz1UHdfasdf0/qeDYg34oP12K7k4Q1txK9knpK6W05VLaj1LGekkHDdFa9bLrv1Hdql+r02V0Ok3ZlgilVC9N3FodcKVkFWnfMWZdtQ8PqA63XLO3usaGqEtMsEL9fY96m9bMarUoPNCu8MDqAHD/Dtd7au8y1/sqb8cht7BIcX1cQWWnYVKn06RARjGdKIKvajUdX+n7SlTpcMq3BW3dCQA4jsoS6XETpiHcu0eyB9Xp1Oeee05bt25V79699fDDD0uSfH19NXjwYN1www3697//rdLSUt1111264oor9O2332rv3r266qqr9OSTT2rcuHEqLCzUjz/+KMMwdPvtt2vTpk0qKCjQG2+8IUmKiKjfB6vdu3dr7Nixmjhxot566y1t3rxZkydPlr+/v6ZPn37Mx6+qqtLFF1+syZMn6/3331dFRYVWr159QnM90AqY9V6VvPL9GhISojlz5qhdu3Zav369Jk+erJCQEN15552SpC+++ELjxo3Tfffdp7feeksVFRVauHCh+/YTJkzQypUr9fzzz6tfv37avn27cnJy6vVtS0lJ0ccff6z58+fLZrNJkoqLizV16lT17dtXRUVFmjZtmsaNG6d169bJarWqqKhIZ5xxhtq3b6/PPvtMcXFxWrt2rZxOpxISEjRq1Ci98cYbtYKvN954QxMnTiT0OhEVJdKu1a7wZMdyadfPkuOQDp+gaNcP420SXNfvWiMVZUp/zHcdkhQY5fphPWG4KwyL7inxujSu4pzqQLL6tcr8Q7WCLkmKSHIFXD7+qtq+TD7ZG2XJ2SrlbJV+eUMWScXOWKU7e2qds6dWOXtqjw50EsVHBLg7t7pWD5lPiglWsB8RQp0ZhrR/uyvgqnmt8nfWPsdildr2qw66Tpc6nioFhJtSbkvG/7XV4kL9FWi3qaTCoR25JeoSE2x2SQAAuIWFhclutyswMFBxcXGSpEcffVQnn3yyHn/8cfd5s2fPVnx8vLZu3aqioiJVVVXpkksucXdR9enTx31uQECAysvL3fdXXy+++KLi4+P1n//8RxaLRT169NCePXt01113adq0adq7d+9RH3/fvn3Kz8/X+eef7+7y6NmzZ4PqADyNp7xf77//fvefExISdPvtt2vu3Lnu4Ouxxx7TlVdeqYceesh9Xr9+/SRJW7du1QcffKDFixdr1KhRkqTExMT6fitUUVGht956S9HR0e7LLr300lrnzJ49W9HR0dq4caN69+6t9957T9nZ2VqzZo074OvSpYv7/BtuuEE33nijnn32Wfn5+Wnt2rVav369Pv3003rX16qVF0k7Vx3o6Nq9VnJW1j4nOM69vEoJp7s6hg7+BUVlmbT7l+r7+FHauUYqyZE2feY6JCmgTXXnSnVXWGxvyWprvufZEhRluV6jmtcqe/Ph50R1r/VaGcGxevSLTfpg1U4Vlp+hUBVpsHWLhlg36VTrRvWy7FBna6Y6WzN1pZZKkooDO6iiw1AFdhshv6QRUngnuvfqwzCk3JSDXqvlUuEhM7qsPq5lwjXvqfghkn+oOfW2IgRf1SwWi5Kig7V+d75Ss4sIvgCgNfENdHVzmPG4J+C3337Td999p+Dgw//NSk1N1TnnnKOzzjpLffr00ejRo3XOOefosssuU5s2bU7ocWts2rRJQ4cOrdWlNWzYMBUVFWnXrl3q16/fUR8/IiJCEydO1OjRo3X22Wdr1KhRuuKKK9S2bdtGqQ0tlFnv1ZrHPgFmvF/nzZun559/Xqmpqe5gLTT0wA9Y69at0+TJk49423Xr1slms+mMM85o8ONLUqdOnWqFXpKUnJysadOmadWqVcrJyZHT6ZQkpaenq3fv3lq3bp1OPvnko3a1XXzxxbrlllu0YMECXXnllZozZ47OPPNMJSQknFCtLV5ZgSvoSvvR9QP53nWSs6r2OaHtDwRUCcOliMRjBx++/tXnDpPOuFOqqpD2rD3wg3/6Kql0v7T5c9chSf5hUsfTDoQ0cX0lGz+W1lKwp7pDqLpTKDf58HNietWa+3To/K4Fa3fp9WXbJbnmUkVHxsonposKYsdrW0ywfMMNJZasl33XCtdrtWedgkp2KWjrh9LWD113EtrBFc7UPMbx/n9obQxDyt7iek/tWC7tWOHqgDyY1VdqP6D6fXK61GGw5EfW0Nz4G+YgidFBWr87v85bkgIAWgiLpc5LmDxJUVGRLrjgAv3rX/867Lq2bdvKZrNp8eLFWrFihb7++mu98MILuu+++7Rq1Sp17ty5yes73uO/8cYb+vvf/65FixZp3rx5uv/++7V48WKdeuqpTV4bvJSXvlel5n+/rly5Utdcc40eeughjR49WmFhYZo7d66eeeYZ9zkBAQFHvf2xrpMkq9XqnuVVo7Ky8rDzgoIOf70uuOACderUSa+++qratWsnp9Op3r17q6Kiok6PbbfbNWHCBL3xxhu65JJL9N5777lnqeEgpXnVc5+qQ6i9v0mGs/Y5YR1rBxttEk4s2PCxu5ZqdTxV0u2So9L1uDVhW/pPUlm+tPVL1yFJ9hDX+TVhW9t+kq2VzYvK23mgm2vHcmnftkNOsLg65Wpeq46nSUGRR7273KJyPfL5RknS38/qqilndpHd50jLTTtIvca4/lheWB2MVodte9ZKBbuk3+e6DkkKaVt7nltU19YVhDmdUtbGg16rFa4Ox4PZ/KQOgw68pzoMkuwn9osTnDiCr4PUDLhPZcA9AMAD2e12ORwO99ennHKKPv74YyUkJLiHyR/KYrFo2LBhGjZsmKZNm6ZOnTppwYIFmjp16mH3V189e/bUxx9/LMMw3F1fy5cvV0hIiDp06HDcx5ekk08+WSeffLLuueceDR06VO+99x7BF1oEs9+vK1asUKdOnXTfffe5L9uxo/YQ5b59+2rJkiWaNGnSYbfv06ePnE6nvv/+e/dSx4NFR0ersLBQxcXF7nBr3bp1x60rNzdXW7Zs0auvvqrhw4dLkpYtW3ZYXa+99pr27dt31K6vG264Qb1799aLL77oXiLa6pXsc/0gXrPsMGODDpv71KbzgdAiYZgU3rFpa7L5Sh0Guo7Tb5McVVLG7wcFByul8nwpZbHrkCTfIKnjkANLwdqd4grUWgrDcA00T6v5HiyT8tJrn2Oxujrh3APOh7qWjNbRI59v1P6SSvWIC9Hf/q9L3eZX+4VIXUa5DkmqKJZ2rj5oKewvUuFeacNHrkOSgmJqL4WN7tGygjCnQ8rccGA+147lrg7Gg/kESPGDDryn2g90dULCoxB8HYSdHQEAniwhIUGrVq1SWlqagoODdcstt+jVV1/VVVddpTvvvFMRERFKSUnR3Llz9dprr+nnn3/WkiVLdM455ygmJkarVq1Sdna2e5ZWQkKCvvrqK23ZskWRkZEKCwurtdvb8dx8882aOXOm/va3v2nKlCnasmWLHnzwQU2dOlVWq1WrVq066uNv375d//3vf3XhhReqXbt22rJli5KTkzVhwoSm+vYBzcrs92vXrl2Vnp6uuXPnatCgQfriiy/cuzXWePDBB3XWWWcpKSlJV155paqqqrRw4ULdddddSkhI0LXXXqvrrrvOPdx+x44dysrK0hVXXKEhQ4YoMDBQ9957r/7+979r1apVmjNnznG/L23atFFkZKT++9//qm3btkpPT9fdd99d65yrrrpKjz/+uC6++GLNmDFDbdu21a+//qp27dpp6NChklzB+6mnnqq77rpL11133XG7xFqkouwDP4ynLZey/jj8nMguB0KJTsOksPbNX+fBbD5S+1Ncx2l/O3qwkPqt65Cqg4XBB55D+wHeFSwYhquD6+C5TwW7ap9jsUnt+h94rTqe6loS2gDfbcnSJ+v2yGqR/nVp34Zv2mYPkpLOdB2SVFnq2syg5rXauVoqzpL+WOA6JCkw8sDGBp2GuZZjetPGBjXBbM1rVRPMHsw30DWXq6ZDsaUFsy0UwddBanZ2TM0urvXbawAAPMHtt9+ua6+9Vr169VJpaam2b9+u5cuX66677tI555yj8vJyderUSeeee66sVqtCQ0P1ww8/aObMmSooKFCnTp30zDPPaMwY17KGyZMna+nSpRo4cKCKior03XffaeTIkXWup3379lq4cKHuuOMO9evXTxEREbr++uvdA7WP9fiZmZnavHmz3nzzTeXm5qpt27a65ZZb9Ne//rUpvnVAszP7/XrhhRfqtttu05QpU1ReXq7zzjtPDzzwgKZPn+4+Z+TIkfrwww/1yCOP6IknnlBoaKhGjBjhvv6ll17Svffeq5tvvlm5ubnq2LGj7r33XkmuXSXfeecd3XHHHXr11Vd11llnafr06frLX/5yzO+L1WrV3Llz9fe//129e/dW9+7d9fzzz9d6Lna7XV9//bX++c9/auzYsaqqqlKvXr00a9asWvd1/fXXa8WKFbruuuvq+Kp4ucLMAzOfdiw/xoDzg5YuhjRs85JmY7W5lja27ScNvfkIS8mWSyW50vbvXYd0+FKy+MGSrwcFn4Yh5STXfq0K99Y+x+rjCkxqXqv4Ia6OqxNUXF6l+xdskCRNGtZZ/eLDT/g+3XwDpM4jXId0yMYGy1xBWEmutOl/rkNydanVmufWx7M2NnBUSnvWHXit0n+SKgprn3PwUtxOp7sCyta2FLcFsBiHLs73QAUFBQoLC1N+fn6tgZyNrazSoZ7TFskwpJ/vH6WoYL8meywAgDnKysq0fft2de7cWf7+XvQbYxzRsV7P5vr8gBNzrNeJ9yuO5ZFHHtGHH36o33//vUG39/j/v2oNOF/m2i3uUDEnHQgVOg2TgqMPP8eb1QwPr/kepC13dRkdzGZ3dYHVzJ6KH9K8swANwxVCpi07MPfpiDUOrB3WNUGND/3vD72xPE0d2gTo69tGKNDejH0uVRXSnl8PDHpPXyVVHjI72y/MtWyz5rWK69e8Gxu4N1+onjm3c/VxajydzRc8WH0+5/EKHsTf16b24QHatb9U27KLCb4AAAAAD1NUVKS0tDT95z//0aOPPmp2OY0nL732Tn77tx9ygkWK631gltBxBpy3CBaLFNPDdQy6wRUy5abUXjZYuMc1xD99pfTj09XdVCdXL4083TUvrBG6qdycTtey0prXascKV6fTwWx+rnCrJuDpMKjJu9J+Td+vOSvSJEmPjevTvKGXVL2xwRDXUWtjg0OWDW5d5Dqk6m6qmnluwxu/m6qyTNr984HXaucaqaq09jn+4QdCroRhrk0EPKkrDY2C4OsQSdHB2rW/VKnZRRrc+cjDNAEAaIkef/xxPf7440e8bvjw4fryyy+buSIAR9Oa369TpkzR+++/r4svvth7lzkahrQ/7UB4c7wB5zVzn+ox4LxFslhcOwlGdZUGTjowP8v9fVwu5e90zaLatUZa9m/X/Ky2/Q4sVet4qhQQXvfHdDqkjPW1d/Iry6t9jnsO2fDqAecDJJ/ma6KoqHLq7o/XyzCkS05urzO6eUDnX62NDf5xyMYGy6X0Fa4dPlO+cR2Sa2OD+MEHXqv2p9Tv+1hR4nrdax5j1xrJUV77nMDI2nPvvG0OGRqE4OsQSdHB+n5rtlKzGHAPAGhdbrzxRl1xxRVHvK5VDo4GPFhrfr/OmTOnToP0PcrBA85rOmAKdtc+x2Kr7lQadqBTqYEDzlsNi0WKTHIdp1RvjrJ/x4GQKm2ZawfFPWtdx4oXqgPFPgd1zg2VAg9qeHBUuTqVDp77dNiA86BD5j6dbOqA8//+kKotmYWKCLLr/vN7mVbHMR1xY4M/as9zK90vbfvOdUiSj3/1PLeaQPGQHRMriqWdqw4sg939i+SsrP24tXaeHC5Fd29ZO0+iTgi+DlEz4H5bTvFxzgQAoGWJiIhQRATdzoA34P3qoZwOqShTyt/tCrbyd1XPFFouFWXUPtfq6woBDp5N1ZhL8lqrNp1cR/+rXV/n76o9K23fNlewtfc36adZkixS7EmuTqO89OoB54c0QfiFuoKumk6htv08ZsB5SlaRnl/imv/24AW9FBHkJTsMWm1S276u49SbXEtIszfVXu5bklM9j+tH121sfq4Ospiertdvz6+Ss6r2/Ya0PdDNlXC6a2dTgq5Wj+DrEEnRwZKk1Gw6vgCgJfOCvV1QB7yOrQOvM5pCvf+/cjpdQ8sLdh8Itg7+c/5u1+59huPIt2+mAec4RFgHqd941yFJBXtrdxnlbJUyN7iOGv5hB3YjTDhdiu3jkQPOnU5D985frwqHUyO7R+vCfu3MLqnhrFZXABl7kjTkL4dsbFC9jLUos3pe2PIDtwuLPxAedxomRSQSdOEwnvfuNVlSdcfXzn0lKq9yyM+HwXYA0JL4+rp+Q1tSUtLilwO1BiUlJZIOvK5oWXi/oinV+vvDMKTiHKlgV3WQteegP9ccew9fRnUkFpur6ySsvRTa3rW0qtMwV6dKEw84Rx2EtpX6XOY6JKmwOkzZ/YsrJOs0zBW+eMGA8/fXpGt12j4F2m169OLesrSkwOeIGxukurq/cpKrN3oY5uruA46D4OsQ0SF+CvHzUWF5lXbklqhbLO3GANCS2Gw2hYeHKyvLtdV4YGBgy/qg2EoYhqGSkhJlZWUpPDxcNpvn/4CC+uP9ikZhGK4liI5KyaiSUVWhktJSZWVlK3zvj7J9/a4r6HJUHP++LFYpOK461GonhXY4EHCFtnf9OTjWK0ITVAuJlXpf4jq8SEZ+mZ5YuFmSdPs53dWhTaDJFTUxi0WK6uI6gHoi+DqExWJRYkywftuZp9SsIoIvAGiB4uLiJMn9wzS8V3h4uPv1RMvE+xXH5XRKRpUr3HI6av+55utDlzU6KhS+40vFJb8nqeY6iyu0Cm1XHWZ1qP3nsPau0MsDl7yh9Zn26QYVllepX3y4rj0twexyAI/G39pHkBQVpN925jHgHgBaKIvForZt2yomJkaVlXVYtgKP5OvrS6dXK8D7tRUzDNeQ8cJM12yfokypKMv138IM17ytoiypqrRu9xcQ4Qq2gmPkGxgqW6duUt/Xqju32ruWJ5q4Mx9QV4s27NXXGzPlY7XoX5f2kc1KJyxwLARfR5AUUz3gPosB9wDQktlsNoITwEvwfm3hMjZIW7+U9qe5lh3WzNY6dHe9owmMrL3c8LA/t5N8/Jr0KQDNIb+0Ug98+ock6aaRSeoRF2pyRYDnI/g6gpoB96l0fAEAAABNI3+3tP5D6fcPpKw/jn6ef7hr6Lg7yDp0tlY7hsaj1Xjiy03KLixXYnSQbjmTeVdAXRB8HUFitKvja1tWkQzDYIgqAAAA0BjK8qWNn0m/z5PSlsk9X8tml7qeI7XtdyDMCquesWUPMrVkwFOsTM3V+6t3SpKeuKSv/H3pggXqguDrCDpFBspqkQrLq5RdWK6YUH+zSwIAAAC8U1WFlLLYFXZtWSQ5yg9c12mY1PcKqddFUkAb82oEPFxZpUP3LlgvSbp6SEcN7hxhckWA9yD4OgI/H5viIwK1I7dEqdnFBF8AAABAfRiGtHOVK+z6Y4FUuv/AddE9XGFXn8ul8I7m1Qh4keeXJGt7TrFiQvx095geZpcDeBWCr6NIig6uDr6KNDQp0uxyAAAAAM+XvVVa/4FrblfejgOXB8dJfS6T+o6X4vpIjBIB6mzjngK98sM2SdIjF/dWqL+vyRUB3oXg6yiSooP07WYpNZudHQEAAICjKsyUNnzs6u7au+7A5fZgqeeFru6uziMkK/OIgPpyOA3dPf93OZyGxvSO0+iT4swuCfA6BF9H4R5wn83OjgAAAEAt5UXS5i9cYde27yTD6brc6iN1GeVaxth9rGQPNLdOwMu9sXy7ft+VrxB/Hz104UlmlwN4JYKvo0iqDr7o+AIAAAAkOapcIdfv81yhV2XJges6DHItYzxpnBQUZV6NQAuyc1+Jnvl6qyTpvrE9mT0NNBDB11EkRru2Td6dV6qySgdbxQIAAKD1MQxpz1rXzK4NH0vF2Qeui0h0hV19Lpcik8yrEWiBDMPQvQvWq7TSoSGdIzR+ULzZJQFei+DrKCKD7AoL8FV+aaW25xSrZ9tQs0sCAAAAmse+7dL6D13dXbkpBy4PjJJ6X+oKvNqfwpD6FswwDBVXOLSvqEK5xeXaV1yh3OIK7SuuUJCfjy47pYMC7DQHNJUFv+7Wj8k5svtYNeOSPrLwXgMajODrKCwWi5Kig7Q2PU+p2UUEXwAAAGjZinOlP+a7urt2rT5wuU+A1OM8V9iVdKZkY0c5b2QYhgrKqrSvuEL7isuVW3QgyMotqr6s+uuakKuiynnU+/tmY6ZenTBQdh9rMz6L1iG3qFyPfL5RknTrWV3d86cBNAzB1zEkRgdrbXoeA+4BAADQMlWWSlu+dIVdKYslZ5XrcotVShwp9blC6nm+5Bdiapk4nNNpKK+00h1iHdyRdeDPB67bX1KhSodR78fx97UqMshPEUF297FoQ4a+35qt2+at0/NXnSyblW6kxvTI5xu1v6RSPeJC9JcRiWaXA3g9gq9jYMA9AAAAWhynQ0r70RV2bfxMqig8cF3bfq7Ort6XSiFx5tXYClU5nNpfUlkdWpUfCLCKDg6zDly+r7hCzvrnWAqy2xQRbFdEkJ+iasKsYLsig1yXRR4UcEUG2xVoP/xHxktOydZ1c9boi/V7FeLvw1K8RvTdlix9sm6PrBbpX5f2la+NjjrgRBF8HUNS9YB7Or4AAADg1QxDytzgmtm1/iOpcO+B68I6Sn0vd3V3xfQwr8YWqLCsUjv3lR4WWuVULy08uEsrv7RSRgOCrFB/H0UGH+jIOjS4OjTMaoxNu4Z3jdbzV56sW95bq7lrdio0wFf3jOlB+HWCisurdP+CDZKk64Z1Vr/4cHMLAloIgq9jSDyo48swDP4iBwAAgHfJ31U9pP4DKWvjgcv9w6WTxrm6u+KHSFa6ShrL/uIKLd6YqS/W79XylBxV1aMty2KRwgN8qwMsv0O6sey1Lo8KtqtNkN20jqAxfdrqiUv76s6Pftd/f9imsABf3XJmF1NqaSme/nqLdueVqkObAE09p5vZ5QAtBsHXMXSKDJSP1aKSCocyCsrUNizA7JIAAACAYyvNkzZ+6gq7diw7cLnNT+o22hV2dT1b8vEzrcSWJqeoXF//kakvN+zVitRcOQ4KuyLdnVf2WrOyIoMPfF1zfXiAr3y8aGnbFQPjVVhWpUc+36invtqiEH8fTRiaYHZZXunX9P2asyJNkvT4uD5HXGIKoGF4Nx2Dr82qjhGB2pZTrG3ZxQRfAAAA8ExV5VLyYtdSxq2LJEfFgesShkt9r5B6XigFhJtWYkuTVVimr/7I1Jfr9+qnbbm15m31bBuqsb3jNKZPW3WJadk78l1/emfll1bq+SXJmvbpHwrx99G4kzuYXZZXqahy6u6P18swpEtObq8R3aLNLgloUQi+jiMxOljbcoqVml2kYV2izC4HAAAAcHE6pZ0/ucKuPz6RyvIOXBfdU+o3Xup9mRQeb1aFLU5mQZm+XL9XCzdkaE3avlozufq0D9OYPnEa07utOkcFmVekCW4b1VUFpZWasyJNt3/4u4L9fHV2r1izy/Iar3yfqi2ZhYoIsuv+83uZXQ7Q4hB8HUdSTJC+2SSlZrGzIwAAADxA/i7p59nS7x9K+ekHLg9pK/W53NXdFdvbNTAKJ2xPXqm+3JChL9fv1c879te6rl98uKuzq3dbdYwMNKlC81ksFk07v5cKy6r08dpduuW9tZozaZBOS6Jx4HhSsor0wrcpkqQHL+iliCC7yRUBLQ/B13EkRblak7flsLMjAAAATLZjpTT3Kqm0OoCxh0i9LnKFXQmnS9YT37EP0s59JVq0IUMLN+zVr+l5ta47pWO4xvZpq3N7x6lDm9Ybdh3KarXoX5f2UWFZpb7emKnJb/6sdyefqv7sTHhUTqehe+evV4XDqZHdo3Vhv3ZmlwS0SARfx5EU42pTpuMLAAAApvpjgTT/r5KjXGrbTxr2D6n7GMmXObSNYUdusRauz9CXG/bq91357sstFmlQpwiN6ROnc3vHMff3GHxsVj1/1cm6/s01Wp6Sq4lvrNYHfx2qbrEhZpfmkd5fk67VafsUaLfp0Yt7y0KXJtAkCL6OI7G642tPfplKKqrYXQMAAADNyzCkFS9Iix9wfd39POnS1yQ73UYnalt2kb7ckKGF6/fqjz0F7sutFmlw5whXZ9dJcYoJ9TexSu/i72vTf/88UNe8tkrrdubpT6+t0kc3ntaql4IeSUZ+mZ5YuFmSdPs53ekeBJoQKc5xtKnebnhfcYW2ZRerd/sws0sCAABAa+F0SF/eJa151fX14L9K585gSeMJSMkq1ML1rrBrc0ah+3Kb1aJTE11h1zm94hQd4mdild4tyM9HcyYN0vhXftKWzEL96fVV+vDGoYolQJQkGYahBz7doMLyKvWLD9e1pyWYXRLQohF81UFSdJD2FVcoNbuI4AsAAADNo6JY+vgGactCSRZp9GPSqTcztL6eDMPQ1swifbF+r75cv1fJB40w8bFadFqXKI3tHaeze8UqMpiwq7GEB9r19vWDdfkrK7Ujt0R/fn2V5v1lqNowvF2LNmRo8cZM+VTPRbNZeU8DTYngqw4So4K1Jm2/tmUz4B4AAADNoChLem+8tGetZPOTLvmvdNLFZlflNQzD0Ma9BfpyvWtA/cGf431tFp3eJUpj+7TV2b1iFR5IENNUYkL99c71Q3TZyyu0NbNIE+es0bs3DFGwX+v9MTS/tFLTPvtDknTTyCT1iAs1uSKg5Wu9f+PUg3vAfTYD7gEAANDEcpKldy6V8nZIARHSVe9LHU81uyqPZxiGNuwu0MINrs6utNwS93V2m1UjukVrbJ84ndUzVmEBviZW2rrERwTqneuH6IpXVuq3nXma/ObPemPSIPn7ts7luk98uUnZheVKjA7SLWd2MbscoFUg+KqDpGjXgHs6vgAAANCkdqyU5l4lle6X2iRI13wsRfHD8dEYhqF1O/PcA+p37S91X+fnY9XI7tEa26et/q9HjEL8CbvM0jU2RG9eN1hXv7pKK7fl6m/v/6oXrzlFvjar2aU1q5WpuXp/9U5J0hOX9G214R/Q3Ai+6iCxJvjKKZLTacjKGmwAAAA0tg3zpQU3So5yqf1A6aq5UnC02VV5HKfT0K8792vh+gx9uX6v9uSXua8L8LXp/3rEaEyfOJ3ZPUZBrXhJnafp2yFcr04YqGvfWK3FGzN150e/65nL+7Wan63KKh26d8F6SdI1QzpqcOcIkysCWg/+JaiD+DYB8rVZVFbp1J78UraaBQAAQOMxDGnFC9LiB1xf9zhfuuRVyc5nzhoOp6FfduzXwvV7tWhDhjIKDoRdgXabzuoZq7G943RG92gF2vkRx1MNTYrUS9ecor++/YsW/Lpbof4+mn7hSbK0gg0bnl+SrO05xYoN9dNdY3qYXQ7QqvCvQh342KzqFBmklKwibcsuJvgCAABA43A6pC/vkta86vp6yI3S6MclK0ugHE5Dq7fvc4Vdf2Qou7DcfV2wn49G9YzRmD5tdUa3aJaMeZGzesbqmSv66R/z1unNlTsUGuCrf57T3eyymtTGPQV65YdtkqSHL+qtUJbdAs2K4KuOkqJdwVdqdpFGdKPlHAAAACeoolj66Hpp65eSLNLox6Sht5hdlamqHE79tG2fFm7Yq6//yFBOUYX7ulB/H53dK05j+8Tp9K5R8vMh7PJWF/Vvr4KyKj3wyQa98G2KQv19NXlEotllNQmH09Dd83+Xw2loTO84jT4pzuySgFaH4KuOXAPuM9nZEQAAACeuKEt6b7y0Z61k85Mu+a900sVmV2WagrJK/evLzVq4fq/2l1S6Lw8P9NU5vWI1pk9bDUuKkt2ndQ1Db8n+fGonFZRW6qmvtuixhZsUGuCj8YM6ml1Wo3tj+Xb9vitfIf4+eujCk8wuB2iVCL7qKJGdHQEAANAYcpKldy6V8nZIARGuIfYdh5hdlake/t9GffTLLklSRJBdo0+K1dg+bXVqYmSr2/mvNbl5ZJIKyir1yvfbdM/89Qr289V5fduaXVaj2bmvRM98vVWSdN/YnooJ9Te5IqB1Iviqo6ToIEmi4wsAAAANt2Ol9P6VUlme1Kaz9KePpcgks6syVXJmoeavdYVes64+RaNPipUPYVerYLFYdPe5PVRQWqX3V6frH/N+VZCfTSO7x5hd2gkzDEP3Lliv0kqHTk2M0PhB8WaXBLRa/ItSRzUdX5kF5SoqrzK5GgAAAHidDfOlty5yhV7tB0o3fNPqQy9JeubrrXIa0jm9YnVe37aEXq2MxWLRoxf31vl926rSYejGd37RmrR9Zpd1whb8uls/JufI7mPVjEv6toqdKwFPxb8qdRQW4KuoYD9J0ja6vgAAAFBXhiEtf076aJLkKJd6nC9d+z8pKMrsyky3bmeeFv2RIatFun10y97ZD0dns1r07BX9NbJ7tMoqnbpuzhr9sSff7LIaLKeoXA9/vlGSdOtZXdU5KsjkioDWjeCrHljuCAAAgHpxOqSFt0uLp7m+HnKjdMVbkj3Q3Lo8xFNfbZYkjTu5g7rFhphcDcxk97HqpWsGaHBChArLqjTh9dVe23DwyOcblVdSqR5xIfpLC92tEvAmBF/1wIB7AADgzWbNmqWEhAT5+/tryJAhWr169VHPrays1MMPP6ykpCT5+/urX79+WrRoUa1zpk+fLovFUuvo0aNHUz8N71FRLM29RlrzmiSLNHqGNOZfktVmdmUeYVlyjpan5MrXZtE/RnU1uxx4gAC7Ta9NHKje7UOVW1yhP722SrvzSs0uq16+25KlT9ftkdUi/evSvmzOAHgA3oX1QMcXAADwVvPmzdPUqVP14IMPau3aterXr59Gjx6trKysI55///3365VXXtELL7ygjRs36sYbb9S4ceP066+/1jrvpJNO0t69e93HsmXLmuPpeL6iLGnOedLWLyUff+mKN6WhN5tdlccwDMPd7XXNkE6Kj6ADDi6h/r56c9JgJUUHaU9+mf782irlFJWbXVadFJdX6f4FGyRJ1w3rrH7x4eYWBEASwVe9JMW4Or5Ss+j4AgAA3uXZZ5/V5MmTNWnSJPXq1Usvv/yyAgMDNXv27COe//bbb+vee+/V2LFjlZiYqJtuukljx47VM888U+s8Hx8fxcXFuY+oKOZWKXur9NpZ0p5fpYAIacJnUq+LzK7Ko3z1R4Z+25WvQLtNU/6vi9nlwMNEBvvp7euHqH14gLblFGvC66uVX1ppdlnH9fTXW7Q7r1Qd2gRo6jndzC4HQDWCr3pIinIFX9tzi+VwGiZXAwAAUDcVFRX65ZdfNGrUKPdlVqtVo0aN0sqVK494m/Lycvn7+9e6LCAg4LCOruTkZLVr106JiYm65pprlJ6efsxaysvLVVBQUOtoUXaskF4/W8pLl9p0du3c2HGI2VV5lCqHU09/vVWSdP3pnd0bSAEHaxceoHduGKKoYD9t3FugG95co9IKh9llHdWv6fs1Z0WaJOnxcX0UaPcxtyAAbgRf9dC+TYDsPlZVVDm1e793rTUHAACtV05OjhwOh2JjY2tdHhsbq4yMjCPeZvTo0Xr22WeVnJwsp9OpxYsXa/78+dq7d6/7nCFDhmjOnDlatGiRXnrpJW3fvl3Dhw9XYWHhUWuZMWOGwsLC3Ed8fHzjPElPsOFj6a2LpLI8qcMgV+gVmWR2VR5n/q+7lZJVpPBAX01m8DeOoXNUkN66brBC/X20Jm2/bnznF1VUOc0u6zAVVU7d/fF6GYZ0ycntNaJbtNklATgIwVc92KwWdY6snvOVw5wvAADQcj333HPq2rWrevToIbvdrilTpmjSpEmyWg98fBwzZowuv/xy9e3bV6NHj9bChQuVl5enDz744Kj3e8899yg/P9997Ny5szmeTtMyDGn5c9JH10mOCqnH+a7ljUEs+zxUWaVDz32TLEm6eWSSQv19Ta4Inq5Xu1C9MWmQAnxt+n5rtm6bt87jVt+88n2qtmQWKiLIrvvP72V2OQAO0aDgqz47AklSXl6ebrnlFrVt21Z+fn7q1q2bFi5c2KCCzZYUUx18ZRF8AQAA7xAVFSWbzabMzMxal2dmZiouLu6It4mOjtYnn3yi4uJi7dixQ5s3b1ZwcLASE4/eoRMeHq5u3bopJSXlqOf4+fkpNDS01uHVHFXSwtulxdNcXw+5SbriLcnOsPYjeXdVunbnlSou1F8ThiaYXQ68xIBOEXrlzwPka7Poi/V7dd+C9TIMzwi/UrKK9MK3rr/zHryglyKC7CZXBOBQ9Q6+6rsjUEVFhc4++2ylpaXpo48+0pYtW/Tqq6+qffv2J1y8GZKiqwfcZzPgHgAAeAe73a4BAwZoyZIl7sucTqeWLFmioUOHHvO2/v7+at++vaqqqvTxxx/roouOPqS9qKhIqampatu2baPV7tEqiqV510hrXpNkkUbPkMY8IVltZlfmkYrKqzTrO1dAcOuorvL35fuEuhvRLVrPX3myrBZp7pqdmvHlZtPDL6fT0L3z16vC4dTI7tG6sF87U+sBcGT1Dr7quyPQ7NmztW/fPn3yyScaNmyYEhISdMYZZ6hfv34nXLwZEqNdHV/bsun4AgAA3mPq1Kl69dVX9eabb2rTpk266aabVFxcrEmTJkmSJkyYoHvuucd9/qpVqzR//nxt27ZNP/74o84991w5nU7deeed7nNuv/12ff/990pLS9OKFSs0btw42Ww2XXXVVc3+/JpdUZY05zxp6yLJx1+64k1p6M1mV+XRXv9xu/YVV6hzVJAuH9DB7HLghcb0aasnLukrSfrvD9v04tJUU+t5f026VqftU6Ddpkcv7i2LxWJqPQCOrF5bTdTsCHTwh6Lj7Qj02WefaejQobrlllv06aefKjo6WldffbXuuusu2WxH/i1PeXm5ysvL3V970m4/dHwBAABvNH78eGVnZ2vatGnKyMhQ//79tWjRIvfA+/T09Frzu8rKynT//fdr27ZtCg4O1tixY/X2228rPDzcfc6uXbt01VVXKTc3V9HR0Tr99NP1008/KTq6hQ92zt4qvXupa+fGgAjp6nlS/GCzq/Jo+4or9OqP2yRJU8/uJh8bo4bRMFcMildBWaUe/WKTnvpqi0L8fUxZNpuRX6YnFm6WJN0xurs6tGF5M+Cp6hV8HWtHoM2bNx/xNtu2bdO3336ra665RgsXLlRKSopuvvlmVVZW6sEHHzzibWbMmKGHHnqoPqU1m85Rro6vnKJy5ZdWKiyAgZwAAMA7TJkyRVOmTDnidUuXLq319RlnnKGNGzce8/7mzp3bWKV5jx0rpPevcu3cGJEoXfMROzfWwYvfpaiovEontQvVeX1ayVJYNJkbhieqoKxKzy9J1rRP/1CIv4/Gndx8XYSGYeiBTzeosLxK/ePDmVcHeLgm/1WL0+lUTEyM/vvf/2rAgAEaP3687rvvPr388stHvY0n7/YT4u+r2FA/SSx3BAAAaFU2fCy9dZEr9OowSLp+MaFXHezJK9VbP+2Q5OqMsVpZDoYTd9uorpp4WoIk6fYPf9fijZnHvkEjWrQhQ4s3ZsrHatETl/aRjf+nAY9Wr+CrITsCtW3bVt26dau1rLFnz57KyMhQRUXFEW/j6bv9sNwRAACgFTEMadlM6aPrJEeF1ON86dr/SUFRZlfmFZ77JlkVVU4N6RyhM7q18GWwaDYWi0XTzu+lS05pL4fT0C3vrdWK1Jwmf9z8kkpN++wPSdJNI5PUI86zflYFcLh6BV8N2RFo2LBhSklJkdPpdF+2detWtW3bVna7d271yoB7AACAVsJRJX3xT+mb6hEdp94sXfGW5Btgbl1eIjW7SB/+4lq9cee5PRj+jUZltVr05KV9dU6vWFVUOTX5zZ+1bmdekz7mjC83KbuwXInRQbrlzC5N+lgAGke9lzrWd0egm266Sfv27dOtt96qrVu36osvvtDjjz+uW265pfGeRTM70PFF8AUAANBiVRRL866Rfn5dkkUaPUM6d4ZkPfIGTTjcs19vldOQRvWM0YBObcwuBy2Qj82q5686WcO6RKq4wqGJb6zW1szCJnmslam5mrvGFeQ+cUlf+fvydwHgDeo13F6q/45A8fHx+uqrr3Tbbbepb9++at++vW699VbdddddjfcsmhlLHQEAAFq4wkzp/fHSnl8lH3/pklelXheaXZVXWb8rX1+s3yuLRbp9dHezy0EL5u9r03//PFDXvLZK63bm6U+vrdJHN56mjpGNt9NiWaVD9y5YL0m6ZkhHDe4c0Wj3DaBp1Tv4kuq3I5AkDR06VD/99FNDHsoj1Sx13JFbrCqHk+2YAQAAWpLsrdK7l0p56VJgpHTVXCl+sNlVeZ0nv3Lt+n5x//bMQUKTC/Lz0ZxJgzT+lZ+0JbNQf3p9lT68cahiQ/0b5f6fX5Ks7TnFig31011jejTKfQJoHiQ2DdAuLED+vlZVOgzt3F9qdjkAAABoLGnLpdfPdoVeEYmunRsJveptRWqOfkzOkY/VottGdTO7HLQS4YF2vX39YHWKDFT6vhL9+fVV2l985A3V6mPjngK98sM2SdLDF/VWqL/vCd8ngOZD8NUAVqtFnaNcyx0ZcA8AANBCrP9IevtiqSxP6jDIFXpFJpldldcxDENPLtoiSbp6SMdGXW4GHE9MqL/euX6IYkP9tDWzSBPnrFFReVWD76/K4dTd83+Xw2loTO84jT4prhGrBdAcCL4aKKl6uSMD7gEAALycYUjLZkofXy85KqSeF0jX/k8KijK7Mq+0eGOm1u3MU4CvTVP+j13v0PziIwL1zvVD1CbQV7/tzNPkN39WWaWjQfc1Z0Waft+VrxB/Hz104UmNXCmA5kDw1UDuAfdZDLgHAADwWo4q6Yt/St886Pr61July9+UfAPMrctLOZyGnv7a1e01aViCYkIaZ74SUF9dY0P05nWDFezno5XbcvW3939VpcNZr/vYua9Ez3y9VZJ039ieimmkeWEAmhfBVwPVDLjflkPHFwAAgFeqKJbmXSP9/Loki3TuE9K5MySrzezKvNYnv+7W1swihQX46q9nsEwU5urbIVyvThgou49Vizdm6s6PfpfTadTptoZh6N4F61Va6dCpiREaPyi+iasF0FQIvhrI3fGVTccXAACA1ynMlN4YK21dJPn4S+Pflk69yeyqvFpFlVP//sbVHXPjGUkKC2AAOMw3NClSL11zinysFi34dbce+t8fMozjh18Lft2tH5NzZPexasYlfWWxWJqhWgBNgeCrgWo6vvYVVzTKTiEAAABoJtlbpNdHSXvXSYGR0rWfu+Z64YS8vzpdu/aXKibETxNPSzC7HMDtrJ6xeuaKfrJYpDdX7tCzi7ce8/yconI9/PlGSdI/RnVV56ig5igTQBMh+GqgQLuP2oW51niz3BEAAMBLpC2XXj9HykuXIhJdOzfGDzK7Kq9XXF6lF75NliT9/ayuCrCzXBSe5aL+7fXwRb0lSS98m6JXf9h21HMf+Xyj8koq1bNtqCYPT2yuEgE0EYKvE5AUw4B7AAAAr7H+I+nti6WyPKnDYOn6b6RI5lA1hjeWb1dOUYU6RQYyCwke68+ndtIdo7tLkh5buEnz1qQfds53m7P06bo9slqkJy7pI18bPzID3o538QlIrG55TaXjCwAAwHMZhrTs39LH10uOCteyxms/k4Iiza6sRdhfXKFXvnd1z0w9uxtBATzazSOT9NcRri6ue+av1xe/73VfV1xepfs/2SBJum5YZ/WLDzejRACNjH+VTgAdXwAAAB7OUSV98U/pm+mur0+9Rbr8Tck3wNSyWpKXv09VYXmVesSF6IK+7cwuBzgmi8Wiu8f00FWD4+U0pH/M+1VLt2RJkp7+eot255WqQ5sATT2nm8mVAmgsPmYX4M1qdnbclk3HFwAAgMepKJY+us61c6Ms0rlPSKfeaHZVLUpGfpnmrEiTJN15bndZrex8B89nsVj06MV9VFhWpc9/36sb3/lFd4zu4f5/+fFxfRRo50dloKXg3XwCanZ2TN9XokqHk7ZuAAAAT7LxM1fo5eMvXfoaOzc2geeWJKu8yqmBndrozO4xZpcD1JnNatGzV/RXUXmVlm7J1iPVuzheckp7jegWbXJ1ABoTSc0JiAv1V6DdpiqnoR25JWaXAwAAgIP1u1Iafrt07eeEXk1ge06xPvh5pyTprjE9ZLHQ7QXvYvex6qVrBmhwQoQkKSLIrvvP62VyVQAaGx1fJ8BisSgxOkgbdhdoW3aRulTP/AIAAIAHsFiksx4wu4oW69nFW+VwGjqze7QGVQcHgLcJsNv02sSBenN5ms7sEaOIILvZJQFoZARfJygpOlgbdhcoNZsB9wAAAGgdNuzO1/9+2yNJumN0D5OrAU5MqL+v/nZWV7PLANBEWOp4gmoG3Kcy4B4AAACtxNNfb5EkXdivnXq1CzW5GgAAjo7g6wTVDLhnZ0cAAAC0Bqu25Wrplmz5WC2aenY3s8sBAOCYCL5O0IGOr2IZhmFyNQAAAEDTMQxDT37l6vYaPyheCVFBJlcEAMCxEXydoM5RQbJYpPzSSu0rrjC7HAAAAKDJfLs5S7/s2C9/X6v+zkwkAIAXIPg6Qf6+NrUPD5AkBtwDAACgxXI6DT1V3e117WkJig31N7kiAACOj+CrETDgHgAAAC3dZ7/t0eaMQoX4++imM5LMLgcAgDoh+GoEDLgHAABAS1ZR5dSzi7dKkm48I0nhgXaTKwIAoG4IvhrBwQPuAQAAgJZm3s87lb6vRFHBfpo0LMHscgAAqDOCr0bAUkcAAAC0VCUVVXp+SbIk6e9ndVGg3cfkigAAqDuCr0aQVL3Ucee+EpVXOUyuBgAAAGg8c1akKbuwXPERAbpyUEezywEAoF4IvhpBdIifQvx85DSkHbklZpcDAAAANIr8kkq9vDRVknTbqG6y+/DjAwDAu/AvVyOwWCwMuAcAAECL8/IPqSooq1L32BBd1L+92eUAAFBvBF+NhAH3AAAAaEmyCsr0xvLtkqTbR3eXzWoxuSIAAOqP4KuRJMVUB19ZdHwBAADA+z3/bbLKKp06pWO4RvWMMbscAAAahOCrkSRGuZY6pubQ8QUAAADvtiO3WHNX75Qk3XluD1ksdHsBALwTwVcjqen42pZVJMMwTK4GAAAAaLh/L96qKqehEd2idWpipNnlAADQYARfjaRTZKCsFqmwvErZReVmlwMAAAA0yKa9Bfr0tz2SpDtHdze5GgAATgzBVyPx87EpPiJQkpSaxXJHAAAAeKenv9oiw5DO69tWvduHmV0OAAAnhOCrER3Y2ZEB9wAAAPA+P6ft05LNWbJZLfrn2d3MLgcAgBNG8NWIagbcb8um4wsAAADexTAMPbloiyTp8gEdlFj9S10AALwZwVcjqhlwT8cXAAAAvM3SrdlanbZPdh+rbh3V1exyAABoFARfjYiljgAAAPBGTqehp6q7va4d2kltwwJMrggAgMZB8NWIEqNdSx1355WqrNJhcjUAAABA3Xy+fq827i1QiJ+Pbh7ZxexyAABoNARfjSgyyK6wAF8ZhrQ9hzlfAAAA8HyVDqee/drV7TV5RKLaBNlNrggAgMZD8NWILBaLu+uLAfcAAADwBh/+vEtpuSWKDLLrutM7m10OAACNiuCrkTHnCwAAAN6irNKh55ZslSRN+b8uCvbzMbkiAAAaF8FXIyP4AgAAgLd4c0WaMgvK1T48QFcP6Wh2OQAANDqCr0bGUkcAAAB4g/zSSr24NFWS9I9RXeXnYzO5IgAAGh/BVyM7uOPLMAyTqwEAAACO7NUftim/tFJdY4J1ySkdzC4HAIAmQfDVyDpGBMpmtaikwqGMgjKzywEAAAAOk11YrtnLt0uS/nlOd9msFpMrAgCgaRB8NTK7j1WdIgIlsdwRAAAAnmnWdykqqXCoX3y4Rp8Ua3Y5AAA0GYKvJpDIgHsAAAB4qJ37SvTuqh2SpLtGd5fFQrcXAKDlIvhqAkkMuAcAAICH+vc3W1XpMHR6lyid1iXK7HIAAGhSBF9NIImOLwAAAHigLRmFWvDrbknSHaO7m1wNAABNj+CrCSTFuDq+UrMIvgAAAOA5nv56iwxDGtM7Tv3iw80uBwCAJkfw1QQSo1wdX3vyy1RSUWVyNQAAAIC0Nn2/Fm/MlNUi/fOcbmaXAwBAsyD4agJtguyKCLJLYs4XAAAAzGcYhp5ctFmSdOkpHdQlJsTkigAAaB4EX00kMap6wH0OwRcAAADM9WNyjn7atk92m1X/OJtuLwBA60Hw1UTcA+6Z8wUAAAATOZ2GnvpqiyTpT6d2UvvwAJMrAgCg+RB8NRH3gHt2dgQAAICJvtyQofW78xVkt+mWM5PMLgcAgGZF8NVEagbcM+MLAAAAZqlyOPXMYle31w3DExUZ7GdyRQAANC+CryaSFFMdfOUUyek0TK4GAAAArdHHa3dpW3ax2gT66obhnc0uBwCAZkfw1UTi2wTI12ZRWaVTe/JLzS4HAAAArUxZpUMzv0mWJN1yZheF+PuaXBEAAM2P4KuJ+Nis6hRZvbMjyx0BAADQzN75aYf25pepbZi//nRqJ7PLAQDAFARfTSgpmgH3AAAAaH6FZZWa9V2KJOkfo7rK39dmckUAAJiD4KsJJUYz4B4AAADN79Uft2t/SaUSo4N06SkdzC4HAADTEHw1oaTq4IuOLwAAADSX3KJyvf7jNknS7ed0l4+Nj/wAgNaLfwWbEEsdAQAA0NxmfZeq4gqH+rQP05jecWaXAwCAqQi+mlDNUsfMgnIVlVeZXA0AAABaul37S/TOTzskSXee210Wi8XkigAAMBfBVxMKC/BVVLCfJGkbXV8AAABoYs99k6wKh1NDEyN1epcos8sBAMB0DQq+Zs2apYSEBPn7+2vIkCFavXr1Uc+dM2eOLBZLrcPf37/BBXubxOrljgy4BwAAQFNKySrUx2t3SZLuoNsLAABJDQi+5s2bp6lTp+rBBx/U2rVr1a9fP40ePVpZWVlHvU1oaKj27t3rPnbs2HFCRXsTBtwDAACgOTz91VY5DemcXrE6pWMbs8sBAMAj1Dv4evbZZzV58mRNmjRJvXr10ssvv6zAwEDNnj37qLexWCyKi4tzH7Gxscd8jPLychUUFNQ6vBUD7gEAANDUftuZp0V/ZMhikW4f3d3scgAA8Bj1Cr4qKir0yy+/aNSoUQfuwGrVqFGjtHLlyqPerqioSJ06dVJ8fLwuuugi/fHHH8d8nBkzZigsLMx9xMfH16dMj1LT8cVSRwAAADSVJ7/aLEkad3J7dYsNMbkaAAA8R72Cr5ycHDkcjsM6tmJjY5WRkXHE23Tv3l2zZ8/Wp59+qnfeeUdOp1OnnXaadu3addTHueeee5Sfn+8+du7cWZ8yPYo7+MoplsNpmFwNAAAAWprlKTlanpIrX5tFt43qZnY5AAB4FJ+mfoChQ4dq6NCh7q9PO+009ezZU6+88ooeeeSRI97Gz89Pfn5+TV1as2jfJkB2H6sqqpzavb9UHSMDzS4JAAAALYRhGHpykavb65ohnRQfwWdNAAAOVq+Or6ioKNlsNmVmZta6PDMzU3FxcXW6D19fX5188slKSUmpz0N7LZvVos6R1XO+cpjzBQAAgMbz1R8Z+m1XvgLtNt1yZhezywEAwOPUK/iy2+0aMGCAlixZ4r7M6XRqyZIltbq6jsXhcGj9+vVq27Zt/Sr1Ykkx1cFXFsEXAAAAGofDaejpr7dKkq4/vbOiQ1rGigkAABpTvZc6Tp06Vddee60GDhyowYMHa+bMmSouLtakSZMkSRMmTFD79u01Y8YMSdLDDz+sU089VV26dFFeXp6eeuop7dixQzfccEPjPhMPlhh1YM4XAAAA0Bjmr92llKwihQf6avKIRLPLAQDAI9U7+Bo/fryys7M1bdo0ZWRkqH///lq0aJF74H16erqs1gONZPv379fkyZOVkZGhNm3aaMCAAVqxYoV69erVeM/Cw9HxBQAAgMZUXuXQzG+SJUk3nZGkUH9fkysCAMAzWQzD8PitBgsKChQWFqb8/HyFhoaaXU69/b4rTxf+Z7migv308/2jzC4HAIBWwds/P7QWvE4NM3vZdj38+UbFhvrp+zvOlL+vzeySAABoNvX5/FCvGV9omM5Rro6vnKJy5ZdWmlwNAAAAvFlReZVmfefaKOrWs7oRegEAcAwEX80gxN9XsaGuYaPbslnuCAAAgIZ7/cftyi2uUEJkoC4f2MHscgAA8GgEX83EPeA+mwH3AAAAaLj5v+6SJP1jVDf52vg4DwDAsfAvZTNxD7in4wsAAAANVFrhUPq+EknS8K5RJlcDAIDnI/hqJknRro4vgi8AAAA0VGp2kQxDigiyKzLYz+xyAADweARfzSQxmqWOAAAAODEpWa5fonaJCTa5EgAAvAPBVzNJinYtdUzLLVaVw2lyNQAAAPBGyVmFkqSuBF8AANQJwVczaRcWIH9fqyodhnbuLzW7HAAAAHih5ExXxxfBFwAAdUPw1UysVos6u3d2ZM4XAAAA6q9mqWPX2BCTKwEAwDsQfDWjmuWODLgHAABAfZVVOpSW65oXS8cXAAB1Q/DVjBhwDwAAgIbanlMspyGF+vsoOoQdHQEAqAuCr2ZExxcAAAAaKvmgZY4Wi8XkagAA8A4EX80oqbrjK5WOLwAAYIJZs2YpISFB/v7+GjJkiFavXn3UcysrK/Xwww8rKSlJ/v7+6tevnxYtWnRC94kTk5LJjo4AANQXwVczSqzu+NpXXKH9xRUmVwMAAFqTefPmaerUqXrwwQe1du1a9evXT6NHj1ZWVtYRz7///vv1yiuv6IUXXtDGjRt14403aty4cfr1118bfJ84MckMtgcAoN4IvppRoN1H7cL8JUnbcljuCAAAms+zzz6ryZMna9KkSerVq5defvllBQYGavbs2Uc8/+2339a9996rsWPHKjExUTfddJPGjh2rZ555psH3iRPjDr7o+AIAoM4IvppZzYD71CyWOwIAgOZRUVGhX375RaNGjXJfZrVaNWrUKK1cufKItykvL5e/v3+tywICArRs2bIG32fN/RYUFNQ6cHwVVU6l5VTv6BhL8AUAQF0RfDUz94B7Or4AAEAzycnJkcPhUGxsbK3LY2NjlZGRccTbjB49Ws8++6ySk5PldDq1ePFizZ8/X3v37m3wfUrSjBkzFBYW5j7i4+NP8Nm1Djtyi1XlNBTs56O4UP/j3wAAAEgi+Gp2STF0fAEAAM/33HPPqWvXrurRo4fsdrumTJmiSZMmyWo9sY+P99xzj/Lz893Hzp07G6nilq1mmWOXmGB2dAQAoB4IvppZYpQr+GLGFwAAaC5RUVGy2WzKzMysdXlmZqbi4uKOeJvo6Gh98sknKi4u1o4dO7R582YFBwcrMTGxwfcpSX5+fgoNDa114PiSM5nvBQBAQxB8NbOkGNdSx/TcElU6nCZXAwAAWgO73a4BAwZoyZIl7sucTqeWLFmioUOHHvO2/v7+at++vaqqqvTxxx/roosuOuH7RP0lZxVKYr4XAAD15WN2Aa1NXKi/Au02lVQ4tCO3RF34rR0AAGgGU6dO1bXXXquBAwdq8ODBmjlzpoqLizVp0iRJ0oQJE9S+fXvNmDFDkrRq1Srt3r1b/fv31+7duzV9+nQ5nU7deeeddb5PNJ4U946OISZXAgCAdyH4amYWi0WJ0UHasLtA27KLCL4AAECzGD9+vLKzszVt2jRlZGSof//+WrRokXs4fXp6eq35XWVlZbr//vu1bds2BQcHa+zYsXr77bcVHh5e5/tE46hyOLUt2zUfls+OAADUj8UwDMPsIo6noKBAYWFhys/PbxFzIG6d+6s+XbdHd53bQzeNTDK7HAAAWqSW9vmhpeJ1Or5t2UX6v2e+V4CvTX88NFpWK8PtAQCtW30+PzDjywTuAffZDLgHAADAsR28oyOhFwAA9UPwZYKaAfepBF8AAAA4juTM6sH2LHMEAKDeCL5MkBTt+tCSml0sL1hpCgAAABO5O77Y0REAgHoj+DJB56ggWSxSfmml9hVXmF0OAAAAPFhyJjs6AgDQUARfJvD3tal9eIAkV9cXAAAAcCQOp+Eej8FSRwAA6o/gyySJ7uWOzPkCAADAke3aX6LyKqf8fKyKjwg0uxwAALwOwZdJkqJdA+7Z2REAAABHU7PMMSk6WDZ2dAQAoN4Ivkxy8IB7AAAA4EhqBtt3ZbA9AAANQvBlkkQ6vgAAAHAcyVmFkpjvBQBAQxF8maRLdcdX+r4SlVc5TK4GAAAAniiluuOrCzs6AgDQIARfJokO8VOwn4+chrQjt8TscgAAAOBhnE7DHXyx1BEAgIYh+DKJxWJhwD0AAACOak9+qUoqHPK1WdSJHR0BAGgQgi8TMeAeAAAAR1Mz2D4xKlg+Nj62AwDQEPwLaqKaAfepdHwBAADgECmZ1fO9WOYIAECDEXyZiI4vAAAAHA07OgIAcOIIvkyUVP0hZltWkQzDMLkaAAAAeJKapY5d2dERAIAGI/gyUafIQFktUmF5lbKLys0uBwAAAB7CMAz3Ukd2dAQAoOEIvkzk52NTfPUOPalZLHcEAACAS0ZBmQrLq2SzWpQQGWR2OQAAeC2CL5MlRjHgHgAAALUlV3d7JUQGyu7DR3YAABqKf0VNVjPgfhsD7gEAAFCtZr5Xt1jmewEAcCIIvkxWM+Ceji8AAADUSGFHRwAAGgXBl8lqljpuyyH4AgAAgEvNUscudHwBAHBCCL5MVtPxtWt/qcoqHSZXAwAAALMZhuFe6kjHFwAAJ4bgy2SRQXaF+vvIMKTtOcz5AgAAaO2yi8qVX1opq0XqHMWOjgAAnAiCL5NZLBZ31xcD7gEAAJBSvcyxU2SQ/H1tJlcDAIB3I/jyADU7OzLgHgAAADXLHLuwzBEAgBNG8OUBEqOrB9wTfAEAALR6yezoCABAoyH48gAHOr5Y6ggAANDa1ezo2DWW4AsAgBNF8OUBaoKvbdlFMgzD5GoAAABgphT3jo4hJlcCAID3I/jyAB0jAmWzWlRc4VBmQbnZ5QAAAMAkuUXlyi2ukMVy4JejAACg4Qi+PIDdx6pOEYGSGHAPAADQmtUMtu/QJkABdnZ0BADgRBF8eYiaAfcEXwAAAK1XMsscAQBoVARfHuLAnC8G3AMAALRWKZns6AgAQGMi+PIQB3Z2pOMLAACgtarp+OpC8AUAQKMg+PIQNUsd6fgCAABovWqCr26xLHUEAKAxEHx5iJqOr915pSqpqDK5GgAAADS3vJIKZRe6dvhOouMLAIBGQfDlIdoE2dUm0FcSXV8AAACtUUp1t1f78AAF+/mYXA0AAC0DwZcHcQ+4zyH4AgAAaG2Y7wUAQOMj+PIg7gH3WQy4BwAAaG2SM12fAdnREQCAxkPw5UHcA+7p+AIAAGh1krMKJUldYwm+AABoLARfHoSOLwAAgNYrxb3UkR0dAQBoLARfHqRm955tOUVyOg2TqwEAAEBzKSyr1N78MknM+AIAoDE1KPiaNWuWEhIS5O/vryFDhmj16tV1ut3cuXNlsVh08cUXN+RhW7z4NgHytVlUVunU3oIys8sBAABAM6np9ooN9VNYgK/J1QAA0HLUO/iaN2+epk6dqgcffFBr165Vv379NHr0aGVlZR3zdmlpabr99ts1fPjwBhfb0vnYrOoU6ZrzxXJHAACA1qNmR8euLHMEAKBR1Tv4evbZZzV58mRNmjRJvXr10ssvv6zAwEDNnj37qLdxOBy65ppr9NBDDykxMfGECm7pEqOqg69sgi8AAIDW4sB8L5Y5AgDQmOoVfFVUVOiXX37RqFGjDtyB1apRo0Zp5cqVR73dww8/rJiYGF1//fV1epzy8nIVFBTUOloL95yvbHZ2BAAAaC22ZrKjIwAATaFewVdOTo4cDodiY2NrXR4bG6uMjIwj3mbZsmV6/fXX9eqrr9b5cWbMmKGwsDD3ER8fX58yvZp7Z0c6vgAAAFqN5EyWOgIA0BSadFfHwsJC/fnPf9arr76qqKioOt/unnvuUX5+vvvYuXNnE1bpWRKjXUsd6fgCAABoHYrLq7Q7r1SS1JWljgAANCqf+pwcFRUlm82mzMzMWpdnZmYqLi7usPNTU1OVlpamCy64wH2Z0+l0PbCPj7Zs2aKkpKTDbufn5yc/P7/6lNZiJEW5PuxkFJSpqLxKwX71eokAAADgZWo6/aOC7WoTZDe5GgAAWpZ6dXzZ7XYNGDBAS5YscV/mdDq1ZMkSDR069LDze/ToofXr12vdunXu48ILL9SZZ56pdevWtaoljHUVFuirqGDXB55tLHcEAABo8VjmCABA06l3O9HUqVN17bXXauDAgRo8eLBmzpyp4uJiTZo0SZI0YcIEtW/fXjNmzJC/v7969+5d6/bh4eGSdNjlOCAxOlg5Rfu0LbtYfTuEm10OAAAAmlBy9Y6ODLYHAKDx1Tv4Gj9+vLKzszVt2jRlZGSof//+WrRokXvgfXp6uqzWJh0d1uIlRQdr9fZ9DLgHAABoBVKyqnd0ZL4XAACNrkEDpKZMmaIpU6Yc8bqlS5ce87Zz5sxpyEO2KknVA+4JvgAAAFq+mo6vLix1BACg0dGa5YGSol2/7WNnRwAAgJatrNKh9H0lkljqCABAUyD48kDu4CunWA6nYXI1AAAAaCqp2UUyDKlNoK8i2dERAIBGR/Dlgdq3CZDdx6qKKqf25JWaXQ4AAACaSErWgR0dLRaLydUAANDyEHx5IJvVos6RrjlfKcz5AgAAaLGSM6vne7HMEQCAJkHw5aESawbcZxF8AQAAtFTJ7OgIAECTIvjyUAfP+QIAAEDLlHzQUkcAAND4CL48VFIMHV8AAAAtWXmVQzty2dERAICmRPDloRKj6PgCAABoybZX7+Ad4u+jmBA/s8sBAKBFIvjyUDUzvrILy5VfWmlyNQAAAGhsNYPtu8YEs6MjAABNhODLQ4X4+7p/87eNnR0BAABaHOZ7AQDQ9Ai+PJh7wH02yx0BAABampSaHR2Z7wUAQJMh+PJg7gH3dHwBAAC0OO6ljrF0fAEA0FQIvjxYzYB7gi8AAICWpdLh1PbqTYy6xtDxBQBAUyH48mBJMSx1BAAAaIl25BarymkoyG5T2zB/s8sBAKDFIvjyYEnVOzum5RaryuE0uRoAAAA0lppljl1iQ9jREQCAJkTw5cHahQXI39eqSoehXftLzS4HAAAAjeTAjo4scwQAoCkRfHkwq9Wizsz5AgAAaHEIvgAAaB4EXx4uMZqdHQEAAFqa5MxCSVLXWIIvAACaEsGXh0uKZsA9AABAS1LlcGqbe0fHEJOrAQCgZSP48nBJdHwBAAC0KDv3l6qiyil/X6vahweYXQ4AAC0awZeHo+MLAACgZalZ5tglJlhWKzs6AgDQlAi+PFznKFfHV25xhfYXV5hcDQAAAE7UgcH2LHMEAKCpEXx5uCA/H7UN85ckbcthuSMAAIC3O7jjCwAANC2CLy9Qs9wxleWOAAAAXu9AxxfBFwAATY3gywsw4B4AAKBlcDgNpdQEX7EsdQQAoKkRfHmBxJqOryw6vgAAALzZ7v2lKq9yyu5jVceIQLPLAQCgxSP48gLunR2Z8QUAAODVkrNc872SooNlY0dHAACaHMGXF0iKcS11TM8tUaXDaXI1AAAAaCjmewEA0LwIvrxAXKi/Au02VTkNpe8rMbscAAAANFByJsEXAADNieDLC1gsFiXWDLjPYrkjAACAt0qpXurYNZbgCwCA5kDw5SUSo6oH3Gcz4B4AAMAbGYbhXurYJYYdHQEAaA4EX17CPeA+m44vAAAAb7Qnv0wlFQ752izqFMmOjgAANAeCLy9RM+A+leALAADAKyVnupY5do4Kkq+Nj+EAADQH/sX1EgcvdTQMw+RqAAAAUF8p7h0dWeYIAEBzIfjyEp2jgmSxSPmlldpXXGF2OQAAAKinmh0du7CjIwAAzYbgy0sE2G1qFxYgiQH3AAAA3iiZHR0BAGh2BF9eJCmGAfcAAADe6OAdHVnqCABA8yH48iJJ0Qy4BwAA8EaZBeUqLKuSzWpRQhQ7OgIA0FwIvrxIYvSBAfcAAADwHjXLHDtFBsrPx2ZyNQAAtB4EX16kpuOLpY4AAADepWawfVcG2wMA0KwIvrxIl+qOr/R9JSqvcphcDQAAAOqqZr5Xt1jmewEA0JwIvrxIdIifgv185DSk9NwSs8sBAABeZtasWUpISJC/v7+GDBmi1atXH/P8mTNnqnv37goICFB8fLxuu+02lZWVua+fPn26LBZLraNHjx5N/TS8Ukr1UscudHwBANCsCL68iMViYcA9AABokHnz5mnq1Kl68MEHtXbtWvXr10+jR49WVlbWEc9/7733dPfdd+vBBx/Upk2b9Prrr2vevHm69957a5130kknae/eve5j2bJlzfF0vIphGNqayY6OAACYgeDLyzDgHgAANMSzzz6ryZMna9KkSerVq5defvllBQYGavbs2Uc8f8WKFRo2bJiuvvpqJSQk6JxzztFVV111WJeYj4+P4uLi3EdUVFRzPB2vklNUofzSSlktUmL1LzEBAEDzIPjyMnR8AQCA+qqoqNAvv/yiUaNGuS+zWq0aNWqUVq5cecTbnHbaafrll1/cQde2bdu0cOFCjR07ttZ5ycnJateunRITE3XNNdcoPT39mLWUl5eroKCg1tHS1ezo2DEiUP6+7OgIAEBz8jG7ANRPEh1fAACgnnJycuRwOBQbG1vr8tjYWG3evPmIt7n66quVk5Oj008/XYZhqKqqSjfeeGOtpY5DhgzRnDlz1L17d+3du1cPPfSQhg8frg0bNigk5MhL+mbMmKGHHnqo8Z6cF0ipHmzfhWWOAAA0Ozq+vEzNUsdt2UUyDMPkagAAQEu1dOlSPf7443rxxRe1du1azZ8/X1988YUeeeQR9zljxozR5Zdfrr59+2r06NFauHCh8vLy9MEHHxz1fu+55x7l5+e7j507dzbH0zFVcs18r1gG2wMA0Nzo+PIynSIDZbVIhWVVyi4qV0yIv9klAQAADxcVFSWbzabMzMxal2dmZiouLu6It3nggQf05z//WTfccIMkqU+fPiouLtZf/vIX3XfffbJaD//9aXh4uLp166aUlJSj1uLn5yc/P78TeDbep2apY1d2dAQAoNnR8eVl/H1t6tAmUJKUmsVyRwAAcHx2u10DBgzQkiVL3Jc5nU4tWbJEQ4cOPeJtSkpKDgu3bDbXfKqjdZ0XFRUpNTVVbdu2baTKW4aapY7s6AgAQPMj+PJCNQPut+Uw4B4AANTN1KlT9eqrr+rNN9/Upk2bdNNNN6m4uFiTJk2SJE2YMEH33HOP+/wLLrhAL730kubOnavt27dr8eLFeuCBB3TBBRe4A7Dbb79d33//vdLS0rRixQqNGzdONptNV111lSnP0RPtK65QTlGFJCkphh0dAQBobix19EJJ0cH6bks2HV8AAKDOxo8fr+zsbE2bNk0ZGRnq37+/Fi1a5B54n56eXqvD6/7775fFYtH999+v3bt3Kzo6WhdccIEee+wx9zm7du3SVVddpdzcXEVHR+v000/XTz/9pOjo6GZ/fp6qpturQ5sABdr56A0AQHPjX18vlOje2ZGOLwAAUHdTpkzRlClTjnjd0qVLa33t4+OjBx98UA8++OBR72/u3LmNWV6LtDWT+V4AAJiJpY5eiKWOAAAA3sE93yuW+V4AAJiB4MsLJVX/xnDX/lKVVTpMrgYAAABHU7OjYxc6vgAAMAXBlxeKDLIr1N9HhiGl5TLnCwAAwFMlZ9bs6EjwBQCAGQi+vJDFYnF3fTHgHgAAwDPll1Qqq7BcEksdAQAwC8GXl0qMYsA9AACAJ0vJdi1zbBfmr2A/9pQCAMAMBF9eKimmesA9wRcAAIBHqlnm2IVuLwAATEPw5aWSoms6vljqCAAA4ImSs5jvBQCA2Qi+vFRS9IGOL8MwTK4GAAAAhyL4AgDAfARfXqpjRJBsVouKKxzKLCg3uxwAAAAcIiXTNeOrayzBFwAAZiH48lJ2H6s6RgRKYsA9AACApyksq9Se/DJJUpdoZnwBAGAWgi8vdvByRwAAAHiOmjmsMSF+Cgv0NbkaAABaL4IvL8aAewAAAM+UzDJHAAA8AsGXF0us7vhiqSMAAIBnSXEPtmeZIwAAZiL48mI1HV/b6PgCAADwKDU7OnZhR0cAAEzVoOBr1qxZSkhIkL+/v4YMGaLVq1cf9dz58+dr4MCBCg8PV1BQkPr376+33367wQXjgJrga3deqUoqqkyuBgAAADW21ix1JPgCAMBU9Q6+5s2bp6lTp+rBBx/U2rVr1a9fP40ePVpZWVlHPD8iIkL33XefVq5cqd9//12TJk3SpEmT9NVXX51w8a1dmyC72lQPS92eQ9cXAACAJyipqNKu/aWSpK6xLHUEAMBM9Q6+nn32WU2ePFmTJk1Sr1699PLLLyswMFCzZ88+4vkjR47UuHHj1LNnTyUlJenWW29V3759tWzZshMuHgy4BwAA8DSpWa7PZZFBdkUE2U2uBgCA1q1ewVdFRYV++eUXjRo16sAdWK0aNWqUVq5cedzbG4ahJUuWaMuWLRoxYsRRzysvL1dBQUGtA0fmHnCfxYB7AAAAT5Cc5VrmyHwvAADMV6/gKycnRw6HQ7GxsbUuj42NVUZGxlFvl5+fr+DgYNntdp133nl64YUXdPbZZx/1/BkzZigsLMx9xMfH16fMVsU94J6ljgAAAB6hZrB9N5Y5AgBgumbZ1TEkJETr1q3TmjVr9Nhjj2nq1KlaunTpUc+/5557lJ+f7z527tzZHGV6JfdSRzq+AAAAPEJyputzWddYOr4AADCbT31OjoqKks1mU2ZmZq3LMzMzFRcXd9TbWa1WdenSRZLUv39/bdq0STNmzNDIkSOPeL6fn5/8/PzqU1qrVbPUcVtOkZxOQ1arxeSKAAAAWrcUljoCAOAx6tXxZbfbNWDAAC1ZssR9mdPp1JIlSzR06NA634/T6VR5eXl9HhpHER8RKF+bRWWVTu0tKDO7HAAAgFatrNKh9H0lkqSuMSx1BADAbPXq+JKkqVOn6tprr9XAgQM1ePBgzZw5U8XFxZo0aZIkacKECWrfvr1mzJghyTWva+DAgUpKSlJ5ebkWLlyot99+Wy+99FLjPpNWytdmVceIQKVmFys1q0jtwwPMLgkAAKDV2pZdLKchhQf6KiqYHR0BADBbvYOv8ePHKzs7W9OmTVNGRob69++vRYsWuQfep6eny2o90EhWXFysm2++Wbt27VJAQIB69Oihd955R+PHj2+8Z9HKJUUHKzW7WNuyizSiW7TZ5QAAALRaNTs6do0JlsXCCAoAAMxW7+BLkqZMmaIpU6Yc8bpDh9Y/+uijevTRRxvyMKijpJhgaWOmUrPZ2REAAMBMKdUbDnVhmSMAAB6hWXZ1RNNKjHINuE/NZmdHAAAAM7l3dGSwPQAAHoHgqwVIqv5gtY2OLwAAAFO5lzrGEnwBAOAJCL5agKQo1werjIIyFZVXmVwNAABA61RR5VRaLjs6AgDgSQi+WoCwg3YN2k7XFwAAgCnScovlcBoK8fNRbKif2eUAAAARfLUYidGuri/mfAEAAJhja6ZrmWOXWHZ0BADAUxB8tRBJ0Qy4BwAAMBOD7QEA8DwEXy1EUjQD7gEAAMyUklUTfDHfCwAAT0Hw1UIksdQRAADAVOzoCACA5yH4aiESq5c6bstxDVUFAABA86l0OLU9x9V53zWWji8AADwFwVcL0aFNoOw2qyqqnNqTV2p2OQAAAK3KjtwSVToMBdltahfmb3Y5AACgGsFXC2GzWtQ5ytX1lcJyRwAAgGaVUr3MsUsMOzoCAOBJCL5aEPdyRwbcAwAANKuaHR27MNgeAACPQvDVgjDgHgAAwBzJNTs6MtgeAACPQvAlSWUFUqX3z8Wq6fhKzSL4AgAAaE7u4CuG4AsAAE9C8LX9R+ml06RvHzW7khNW0/G1LYeljgAAAM3F4TTcHfddWeoIAIBHIfiqLJHyd0orZ0npP5ldzQmp6fjKLixXQVmlydUAAAC0Djv3laiiyil/X6vatwkwuxwAAHAQgq9uo6V+V0sypE9u9uoljyH+vooJ8ZPEgHsAAIDmUrPMMSk6WDYrOzoCAOBJCL4k6dwZUkhbaV+q1y95dA+4Z84XAABAs0jOKpTEfC8AADwRwZckBYRLFzzn+rOXL3l0D7hnZ0cAAIBmkZJZs6Mj870AAPA0BF81WsiSR/eAe5Y6AgAANIut1R1fXej4AgDA4xB8HawFLHlMqv7ARccXAABA03M6DaVk1ezoSPAFAICnIfg6WAtY8pgY5VrqmJZbrCqH0+RqAAAAWrbdeaUqq3TKbrOqY0Sg2eUAAIBDEHwdysuXPLYPD5Cfj1WVDkO79ntX7QAAAN6mZrB9YnSQfGx8tAYAwNPwr/ORePGSR6vVosRoljsCAAA0h2QG2wMA4NEIvo7Ey5c81uzsyIB7AACAppXMfC8AADwawdfRePGSxyQ6vgAAAJoFwRcAAJ6N4OtYvHTJY1J1xxfBFwAAQNMxDEMpma4ZX11jCb4AAPBEBF/H4qVLHms6vljqCAAA0HT25pepuMIhH6tFnSKDzC4HAAAcAcHX8XjhksfOUf/f3p2HR1Webxy/ZybJZA8hG1sgQMIqmwQQEFcUcKlYxaUqiFV/IlqV1hZaAVuraLVKBcVKRXFFUbFWBUUUEESBIIusCfuWFchKtpn5/XGySlgCSU7O5Pu5rnMlOXNm5hmOyMvN+7yvMfDKyi/WoWONv14AAAArKm9zbB8ZJF92dAQAoFHiT+gzYbGWxyCnj85rHSpJ+r+3kpRXVGpyRQAAAN4nmTZHAAAaPYKvM3FCy+OPppZzJl68pY+aB/lp08Fs3fdWkopL3WaXBAAA4FVSymZ8xUeHmFwJAAA4GYKvM1W15fG/jb/lsUNUsF6/s58C/RxakZKpP8zfILfbY3ZZAAAAXoMdHQEAaPwIvmqjvOUxK8USLY+9Yptp1u195WO36dMNh/T3z7fK4yH8AgAAOFcej4dWRwAALIDgqzYs2PJ4cacoPTeqlyRpzsrd+vfyXSZXBAAAYH3puUXKKSyV3Va5sRAAAGh8CL5qy2Itj5I0sk9rPXZ1V0nS0wu36cOkAyZXBAAAYG3JaUabY1xEkJw+DpOrAQAAJ0PwdTaGP2WplkdJuntIB917UQdJ0p8+2qhvtqWZXBEAAIB1JacbbY7xrO8FAECjRvB1NgLCLdfyKEkTh3fRr/u0lsvt0f3vrNO6fUfNLgkAAMCSKha2Z30vAAAaNYKvs2XBlke73aZnbuypiztFqbDErbveWFOxDTcAAADOXEpZq2OnmBCTKwEAAKdC8HUuLNjy6Ouw6+Xbzlev2GY6VlCiMXNWKzW70OyyAAAALMPj8WgHrY4AAFgCwde5sGjLY5DTR6/f2U8dIoN08NhxjZmzWtkFJWaXBQAAYAlZ+cU6VlAim03qGEXwBQBAY0bwda4s2PIoSc2D/DT3rv6KDnFqe1qu7nlzrQpLXGaXBQAA0OiV7+jYtnmg/H3Z0REAgMaM4KsuWLDlUZJimwdq7l39FeLvo9V7juh37/2kUpfb7LIAAAAatZSyNscE2hwBAGj0CL7qgkVbHiWpa8tQzR6dKD8fu77akqbJ/90sj8djdlkAAACNVvmOjvHRLGwPAEBjR/BVVyza8ihJF3SI0Iu39JbNJr23ep9e+DrZ7JIAAAAarfJWR2Z8AQDQ+BF81SWLtjxK0vDzWuqJ686TJL24JFlv/bDX5IoAAAAap/IZXwkxBF8AADR2BF91ycItj5J0+wXt9NDlCZKkKf/9WQs3HTa5IgAAgMblaH6xMvOKJLGjIwAAVkDwVdcs3PIoSQ8PTdCt/dvK45EemrdeP+zKMrskAACARiMlw5jt1bpZgIKcPiZXAwAATofgqz5YuOXRZrPp7yPP07DuMSp2uXXP3LXacijH7LIAAAAahYr1vWhzBADAEgi+6oPFWx4ddpv+dUsf9Y9rrtyiUo15fbX2HykwuywAAADT7UjLlcTC9gAAWAXBV32xeMujv69Ds8ckqkuLEGXkFmnMnNXKKlvPAgAAoKlKKV/YPjrE5EoAAMCZIPiqTxZueZSksABfzb2rv1o3C9CuzHzd9cYa5ReVml0WAACAaZLTjRlf8bQ6AgBgCQRf9cniLY+SFBPqr7l39Vd4oK82HMjWuHfWqcTlNrssAACABpd9vERpOcYM+HhaHQEAsASCr/pm8ZZHyRjYzbmznwJ8HVq+I0N//HCj3G6P2WUBAAA0qPI2x5Zh/gr19zW5GgAAcCYIvhqCxVseJalP23C9fPv5cthtWvDTQT29aJvZJQEAADSolPI2R2Z7AQBgGQRfDcELWh4l6dLO0frHDT0lSa8u36XZy3eZXBEAAEDDSU5jYXsAAKyG4KuheEHLoyTd0LeNJo7oIkl68outWvDTAZMrAgAAaBjJ5Ts6srA9AACWQfDVkLyg5VGS/u+iDvrthe0lSY/O36hlOzJMrggAAKD+la/xlUCrIwAAlkHw1ZC8pOXRZrPpL1d11XW9W6nU7dG4t5O0Yf8xs8sCAACoN3lFpTp4zJixzxpfAABYB8FXQ/OSlke73aZnb+ylIQmRKih2aewba7QrI8/ssgAAAOrFzrLZXlEhTjUL9DO5GgAAcKYIvszgJS2Pfj52zbq9r3q0DtOR/GKNnrNa6TmFZpcFAABQ55JpcwQAwJIIvszgJS2PkhTs9NHrY/spLiJQB44e1+g5q5VTWGJ2WQAAAHUqOT1XEsEXAABWQ/BlFi9peZSkyGCn3rxrgCKDndqWmqt75q5VYYnL7LIAAADqTEqaMeMrPibE5EoAAEBtEHyZyUtaHiWpbUSg5t7VT8FOH/24+4geeX+9XG6P2WUBAADUCVodAQCwJoIvM3lRy6MkdW8VpldH95Wfw66FP6fq8U83y+Mh/AIAANZ2vNil/UcLJBF8AQBgNQRfZvOilkdJGtQxUi/c3Fs2m/TWD3s145sUs0sCAAA4Jzsz8uTxSM2D/BQR7DS7HAAAUAsEX42BF7U8StLVPVvqr7/qLkl6fvEOvbd6n8kVAQAAnL3yhe3jme0FAIDlnFXw9dJLLykuLk7+/v4aMGCAVq9efdJrZ8+erSFDhig8PFzh4eEaOnToKa9vkrys5VGSRg+M0wOXxkuS/rJgk77cnGpyRQAAAGcnuWxh+04xBF8AAFhNrYOv999/XxMmTNDUqVO1bt069erVS8OGDVN6enqN1y9dulS33nqrvv32W61atUqxsbG68sordfDgwXMu3qt4WcujJP3+yk66OTFWbo/0u/d+0urdR8wuCQAAoNYqF7ZnR0cAAKym1sHX888/r3vuuUdjx45Vt27d9MorrygwMFBz5syp8fp33nlH999/v3r37q0uXbroP//5j9xut5YsWXLOxXsdL2t5tNlsevL68zS0a4yKSt26e+4abU/NNbssAACAWklhR0cAACyrVsFXcXGxkpKSNHTo0MoXsNs1dOhQrVq16oxeo6CgQCUlJWrevPlJrykqKlJOTk61o0nwwpZHH4ddM27to8R24copLNXoOT/qQNmuSAAAoGHVZrkKSZo+fbo6d+6sgIAAxcbG6pFHHlFhYeE5vabVFJa4tDcrX5IUT6sjAACWU6vgKzMzUy6XSzExMdXOx8TEKDX1zNZw+tOf/qRWrVpVC89+adq0aQoLC6s4YmNja1OmtXlhy2OAn0P/GZOohOhgpeUUafSc1TqaX2x2WQAANCm1Xa7i3Xff1cSJEzV16lRt3bpVr732mt5//339+c9/PuvXtKLdmflye6SwAF9FsaMjAACW06C7Oj799NOaN2+eFixYIH9//5NeN2nSJGVnZ1cc+/fvb8AqGwEva3mUpGaBfnrzt/3VKsxfuzLyddfcNSooLjW7LAAAmozaLlfx/fffa/DgwfrNb36juLg4XXnllbr11lurzeiq7WtaUXKVNkebzWZyNQAAoLZqFXxFRkbK4XAoLS2t2vm0tDS1aNHilM997rnn9PTTT+urr75Sz549T3mt0+lUaGhotaNJ8cKWR0lqGRagN3/bX2EBvvpp3zE98O5PKnG5zS4LAACvdzbLVQwaNEhJSUkVQdeuXbv0xRdf6Kqrrjrr15Sst6RFSpqxPmkCbY4AAFhSrYIvPz8/9e3bt9rC9OUL1Q8cOPCkz/vHP/6hJ554QosWLVJiYuLZV9uUeGHLoyTFR4dozp395O9r1zfb0jXxo03yeDxmlwUAgFc7m+UqfvOb3+hvf/ubLrzwQvn6+qpjx4665JJLKlodz3YJDKstaVE+4yueHR0BALCkWrc6TpgwQbNnz9bcuXO1detWjRs3Tvn5+Ro7dqwkafTo0Zo0aVLF9c8884wmT56sOXPmKC4uTqmpqUpNTVVeXl7dfQpv5YUtj5LUt124XvrN+XLYbfpo3QH948vtZpcEAAB+YenSpXrqqaf08ssva926dfr444/1+eef64knnjin17XakhbJ7OgIAICl+dT2CTfffLMyMjI0ZcoUpaamqnfv3lq0aFHFv/bt27dPdntlnjZr1iwVFxfrxhtvrPY6U6dO1eOPP35u1Xu78pbHd28yWh67/kpqO8DsqurE5V1jNO3XPfTHDzdq1tKdigp26q4L25tdFgAAXulslquYPHmy7rjjDt19992SpB49eig/P1/33nuv/vKXv5z1EhhOp1NOpzUWiS8udWtPprGjI62OAABY01ktbv/AAw9o7969Kioq0o8//qgBAyrDmKVLl+qNN96o+HnPnj3yeDwnHIReZ6jTMKnXrfK2lkdJuikxVo8O6yxJ+ttnW/TphkMmVwQAgHc6m+UqCgoKqv1jpiQ5HA5JksfjOeslMKxkb1a+St0eBTt91CL05BszAQCAxqtBd3XEWRo+zStbHiXp/ks66s5BcZKk33+wXt8lZ5hbEAAAXqq2y1Vce+21mjVrlubNm6fdu3dr8eLFmjx5sq699tqKAOx0r2l1let7saMjAABWVetWR5jAi1sebTabplzTTRl5Rfp842Hd91aS5t07UD3ahJldGgAAXqW2y1U89thjstlseuyxx3Tw4EFFRUXp2muv1ZNPPnnGr2l1O8p3dGR9LwAALMvmscCWejk5OQoLC1N2drZCQ0PNLsc8C+6TNrwnRcRL962QfAPMrqjOFJW6dNcba7QyJUsRQX76aNwgxUUGmV0WAMDCGD9YQ2O+T+PfXafPNx7Wn6/qonsv6mh2OQAAoExtxg+0OlqJF7c8On0ceuX2vureKlRZ+cUaPWe10nMLzS4LAAA0YSlp5Ts6hphcCQAAOFsEX1ZS3vIoGS2P+340t546FuLvqzfG9lfb5oHad6RAY19fo9zCErPLAgAATVCpy61dmWXBFzs6AgBgWQRfVuPFuzxKUlSIU2/e1V+RwX7afChH//dWkopKXWaXBQAAmpi9RwpU4vIo0M+hVmHes7wEAABNDcGXFXlxy6MkxUUG6Y2x/RXk59D3O7M04YMNcrsb/VJ0AADAiySnVe7oaLezoyMAAFZF8GVFXt7yKEnntQ7Tv+9IlK/Dps83HtbfPtsiC+zDAAAAvERKurGjYzw7OgIAYGkEX1bl5S2PknRhQqT+eVNvSdIb3+/Ry0t3mlsQAABoMpLTWdgeAABvQPBlZV7e8ihJv+rVSlOv7SZJevbL7fpgzX6TKwIAAE1BcsWOjsz4AgDAygi+rKwJtDxK0tjB7TXuko6SpEkLNundH/fR9ggAAOqNy+3Rzgx2dAQAwBsQfFldE2h5lKQ/DuusUX3byOX26M8LNunet5KUlVdkdlkAAMALHThaoKJSt5w+drUJDzS7HAAAcA4IvrxBE2h5tNlseuaGnvrzVV3k67Bp8ZY0DZv+nb7dnm52aQAAwMuUtzl2jAqWgx0dAQCwNIIvb9BEWh7tdpvuvaijPhk/WJ1igpWZV6Sxr6/RlP/+rOPFLrPLAwAAXqJiYXvaHAEAsDyCL2/RRFoeJal7qzB9+sCFGjs4TpL05qq9umbGd/r5YLa5hQEAAK+QnJ4riYXtAQDwBgRf3qQJtDyW8/d1aOq13fXmXf0VHeLUzox8jXxppV76NkUuNwvfAwCAs5dSNuMrPjrE5EoAAMC5IvjyJk2k5bGqizpF6cuHL9KI81qo1O3Rs19u1y2vrtL+IwVmlwYAACzI7fZUrPFFqyMAANZH8OVtqrY8fnKfdHij2RXVu/AgP7182/l6blQvBfk5tGbPUY3413f6eN0BeTzM/gIAAGfu4LHjOl7ikq/DpnbN2dERAACrI/jyRsOnSSGtpCO7pH9fJH36Oykvw+yq6pXNZtONfdto4UMXKbFduPKKSjXhgw164N2fdKyg2OzyAACARZS3OXaIDJaPg6EyAABWx5/m3iggXLp7sdT915I80rq50ozzpZUvSqXeHQK1jQjUvHsv0B+u7CQfu02fbzqsYdOXa0VyptmlAQAAC6hY2J42RwAAvALBl7cKayONel0au0hq2VsqypEWT5ZeHiBt+0Ly4hZAH4ddD1yWoI/vH6QOkUFKyynS7a/9qCc+26LCEpfZ5QEAgEasYn0vFrYHAMArEHx5u3YDpXu+la57SQqOMdof590qvTVSSttidnX1qmebZvrsdxfq9gvaSpJeW7Fb181cqa2Hc0yuDAAANFbJ6SxsDwCANyH4agrsdqnP7dKDSdKFj0gOP2nXUumVwdLnv5fys8yusN4E+vno7yN7aM6diYoM9tP2tFxdN3Ol/vPdLrnd3jvrDQAA1J7H46lY4yshmuALAABvQPDVlDhDpKGPS+NXS12vlTxuac1/pBl9pB9mSa4SsyusN5d1idGihy/S0K7RKna59ffPt+r2137UoWPHzS4NAAA0Eqk5hcorKpWP3aZ2EUFmlwMAAOoAwVdT1Ly9dPPb0pjPpJgeUmG2tGiiNGuQlLzY7OrqTWSwU7NHJ2rar3sowNeh73dmafj05frfhkNmlwYAABqB8vW94iKD5OfDMBkAAG/An+hNWfsh0v8tk66ZLgVGSpk7pHdulN6+UcrYYXZ19cJms+nW/m31xUND1Cu2mXIKS/Xgez/pkffXK6fQe2e8AQCA00umzREAAK9D8NXU2R1S4ljpd+ukQQ9Kdl8pZbE0a6C0cKJ0/KjZFdaL9pFB+vC+gfrd5Qmy26QFPx3UiOnf6Ydd3rveGQAAOLWU9FxJBF8AAHgTgi8Y/MOkK/8ujf9R6nyV5C6VfpwlvXi+tHq25Co1u8I65+uwa8IVnTT/vkFq2zxQB48d162zf9DTC7epuNRtdnkAAKCBlbc6xseEmFwJAACoKwRfqC6io3Tre9IdC6SortLxI9IXf5D+PUTa+a3Z1dWLvu3C9cVDQ3RzYqw8HumVZTs18qWVSk7LNbs0AADQQDweD62OAAB4IYIv1KzjZdJ9K6SrnpMCmkvpW6S3Rkrv3Spl7TS7ujoX7PTRMzf21Cu391V4oK+2HM7RNTNW6I2Vu+XxeMwuDwAA1LOMvCJlHy+R3WYsiQAAALwDwRdOzuEj9b/HWP9rwDjJ7iNt/0J6aYD01WPGbpBeZvh5LfTlwxfp4k5RKip16/H/bdGY19coPafQ7NIAAEA9Km9zbBcRJH9fh8nVAACAukLwhdMLCJdGPC2NWyXFXyG5S6TvZxjrfyW9IbldZldYp6JD/fXG2H7623Xd5fSxa/mODA2bvlyLfj5sdmkAAKCelC9xEE+bIwAAXoXgC2cuqpN0+4fSbR9KkZ2kgkzpfw9J/75Y2v2d2dXVKZvNptED4/T57y5U91ahOlpQovveXqc/frhBeUXet9A/AABNHet7AQDgnQi+UHsJV0jjvpeGP23sBpm2SZp7jfT+7dKR3WZXV6fio0O04P7BGndJR9ls0gdrD+iqf32npL1HzC4NAADUofLgqxM7OgIA4FUIvnB2HL7SBeOkB3+S+t0t2ezS1v9JL/WXvn5cKvKeHRH9fOz60/AumnfPBWrdLED7jhRo1Cur9PxX21XicptdHgAAqAMpZcEXrY4AAHgXgi+cm6AI6ep/SvetlDpcIrmKpRUvSDP6Sj+9Lbm9Jxga0CFCCx8eol/3aS23R3rxmxTdOOt77crIM7s0AABwDrLyinQkv1g2m9QxiuALAABvQvCFuhHTTbrjE+nWeVLzDlJemvTf8dLsS6W9q8yurs6E+vvq+Zt7a8atfRTq76MNB7J19Ysr9O6P++TxeMwuDwAAnIXyNsfY8EAF+LGjIwAA3oTgC3XHZpM6j5Du/1G68u+SM1Q6vF56fbg0f6x0bL/ZFdaZa3u10pePXKTB8RE6XuLSnxds0t1z1yozr8js0gAAQC2xsD0AAN6L4At1z8dPGvSg9OA66fwxkmzS5o+lmYnSN09KxflmV1gnWoYF6K27Buixq7vKz2HXkm3pGj59uZZsTTO7NAAAUAspacbapPExBF8AAHgbgi/Un+Ao6VcvSv+3XGp3oVRaKC3/hzQjUdrwvles/2W323T3kA769MHB6tIiRJl5xfrt3LX684JNKiguNbs8AABwBipnfLGjIwAA3obgC/WvZU/pzs+km96UmrWVcg9JC+6VXrtCOrDW7OrqRJcWofpk/GDdfWF7SdK7P+7T1S+u0Ib9x8wtDAAAnBatjgAAeC+CLzQMm03qdp00fo10+RTJL1g6uFb6z+XSx/dKOYfMrvCc+fs69Ng13fTO3QPUItRfuzPzdcOs7zVjSbJKXdaf3QYAgDc6VlCsjFxjjc6OBF8AAHgdgi80LF9/acjvpQeTpN63S7JJG9+XZvSVlv1DKjludoXnbHB8pBY9PERX92ypUrdH/1y8Qze/+oP2ZRWYXRoAAPiFlLLZXq2bBSjY6WNyNQAAoK4RfMEcIS2kkS9J934rxV4glRRI3z4pzewn/fyR5PGYXeE5aRbop5m39tELN/dSiNNHSXuPasS/luuDtfvlsfhnAwDAm5S3OcYz2wsAAK9E8AVzteoj3bVIunGOFBYrZe+XPrxLen2EdOgns6s7JzabTdf3aaOFDw9R//bNlV/s0h8/3Khxb6/T0fxis8sDAACSktNY3wsAAG9G8AXz2WzSeTdID6yRLv2L5Bso7VslvXqp9Ml4KTfN7ArPSZvwQL13zwX60/Au8nXYtGhzqoZNX65lOzLMLg0AgCYvOT1XkpQQQ/AFAIA3IvhC4+EbIF38R+mBtVLPmyV5pPVvSzPOl757XiopNLvCs+aw2zTuko5acP9gxUcHKz23SGPmrNbjn25WYYnL7PIAAGiyymd8xUeHmFwJAACoDwRfaHzCWku/flX67ddS60SpOE9a8lfppf7Slk8lt3V3SDyvdZg+e/BCjRnYTpL0xvd7dO2MFfpqc6oOHC1g/a9GrLDEpX1ZBXK5uUcA4C1yCkuUmmP8wxprfAEA4J3YugaNV2w/6beLpU3zpa8fl47tlT64QwqMlBKukOKHSh0vkwKbm11prfj7OvTX687TpV2i9eiHG5Wcnqd730qSJAX5OZQQE6JOMcHqFBNS8X2LUH/ZbDaTK28ajhe7tDMjT8npuUpOy1Nyep6S03K170iB3B7jL0Z/Gt5FQ7tGc08AwOLKd3RsEeqvsABfk6sBAAD1weaxwBSTnJwchYWFKTs7W6GhoWaXAzMU50srpks/viIV5VSet9mlNv2MICzhSqlFT2PNMIs4kl+s577arrV7jmhXRr5KTzKbKMTfRwnRwercIkQJ0SHqVBaIRYU4CV/OUn5RqRFwpeVpR3quUspCrv1HC066qajdJpXfon5x4Zo4oov6trNW8Ao0JYwfrMHM+/TBmv3640cbNSQhUm/9dkCDvjcAADh7tRk/EHzBWlwl0v4fpeSvpOTFUvqW6o8Hx0jxVxhBWMdLJf8wc+o8CyUut/Zk5mtHWp62p+UqOS1XO9JytecU7XVhAb4Vs8OMGWLG95HBzgauvvHKLSxRSroRaqWk52lHmjGT6+Cx4yd9TnigrxJiQpQQXTbzLjpY8THB8vd16JWlOzVn5W4Vlhgtt8O6x+jRYV1okQEaIcYP1mDmfXry8y2a/d1ujR0cp6nXdm/Q9wYAAGeP4AtNR/YBIwBLXiztWiqV5Fc+ZnNIbS8oa4u8QorpbqnZYOWKSl3aXRaIJaflantqrpLT87Q3K18nW26qeZBf5QyxmBB1KgtwwoP8Grb4BpR9vCzgSjN+fZLT85SSlqtD2SffFCEy2KmE6GAlxASXfTVCrojTBIep2YV6YfEOzU/aL7fH2LzgpsRYPTI0QdGh/nX90QCcJcYP1mDmfbrz9dVauj1DT13fQ78Z0LZB3xsAAJw9gi80TaVF0r5VlUFY5vbqj4e0khKGGi2RHS6RnNbevamwxFXZqpeWqx1lX0/VqhcZ7Kw2Q6xTjBH2WGldk2MFxWXrblVdhytXaTlFJ31OdIhTnWJCFF8RchkB17kGgclpuXpm0XZ9vTVNkhTg69DdQ9rr3os6KMTfOr+mgLdi/GANZt6nwU9/o4PHjmv+fQPVL47WdQAArILgC5Cko3uMACzla2nXMqm0Smub3bdsNtiVxhHV2ZKzwWpyvNhV0dK3oywY2pGWqwNHT97aFxPqLGvpK1tYv4URDJkZ3hzJLzbaEstmbiWn52lHWp4y804ecLUM8zfCrejyUC9Y8VEhCgus38+xZs8RTftiq9btOybJmHH3u8vi9ZsB7eTnw+a5gFkYP1iDWfcpv6hU3ad+KUn6afIVXj0rGgAAb0PwBfxSSaG0d2XZbLCvpCM7qz8eFlu5QH77iyS/IHPqrEf5RaVl4VH5+mFGW+CpWgFbhflX7CyZEBOizmWzpoKcdbMhrMfjUWZesZLTc6utv5WSnqes/OKTPq91s4DK9sToEMXHBCs+OlihJgZ1Ho9HX25O0z++3KZdGUbLbdvmgfrDsM66pkdL2e3eEawCVsL4wRrMuk8bDxzTr2auVGSwU2sfG9pg7wsAAM4dwRdwOlk7jZlgyV9Je1ZIpVXCH4ef1G5w2WywK6SIeK+ZDVaTnMISo12wPAxLNxbVP1XrYJvwgMrF9KND1LlFiDpGBSvAz1Hj9R6PR+m5RZXtiel5SinbTfFYQclJ3ye2eYDRllilPbFjdLCC6yh4qw+lLrfeX7tf079OVkau8WvYo3WYJo3ookHxkSZXBzQtjB+swaz79FHSAf1+/gYN7BCh9+69oMHeFwAAnDuCL6A2iguM8Cv5K+M4trf64+FxRggWf4UUd6HkF2hKmQ0tu6BEyem5ZTtMVq4jdrJWQ5vNmOFU3mYYHuhnrEFWtuB8TmHpSZ/Xrnmg4qPLd6U0Qq4OUUEK9Gu8AdfpFBSX6j/f7da/l+1UfrFLknRRpyhNHN5F3Vrx/zGgITB+sAaz7tPTC7fplWU7NXpgO/3tuvMa7H0BAMC5I/gCzpbHI2WllIVgi432SFeVljsffyluSFlb5BVS8w7m1WqSo2Vrb1VdUD85PU9HTtGaKEl2mxQXEVQ5e6usPbFjVLD8fWueKeYNMvOKNPObFL39w16Vuj2y2aTre7fWhCs7qU140whRAbMwfrAGs+7T3XPX6Out6Xriuu66Y2Bcg70vAAA4dwRfQF0pypN2L5dSynaKzN5f/fHmHStbItsNlnz9zamzEcjMK6pYo2t7Wq6yj5eoY2SQEspaIttHBsnp470B1+nszcrXs19u12cbD0uS/Bx2jRnUTuMvjVezQBZUBuoD4wdrMOs+Xfzst9qbVaD37rlAAztGNNj7AgCAc0fwBdQHj0fK2Fa5QP6+VZK7Svueb6CxMH7CFUZbZHg782pFo7XxwDE9vXCbvt+ZJUkK8ffR/ZfEa+zgOK+e+QaYgfGDNZhxnwpLXOo6ZZE8HmntY0MVGexskPcFAAB1g+ALaAiFOdLuZWVtkV9LuYeqPx7ZubIlsu0gyYdZPTB4PB4t25Ghpxdu07bUXElSyzB/PXJFJ91wfhs52AESqBOMH6zBjPv088FsXTNjhcIDfbVu8hWyefEmNgAAeKPajB+su3I0YDb/UKnrtcbh8Uhpm40QLOVrad8PUuZ241g1U/ILljpcIsUPNYKwsDZmVw8T2Ww2XdI5WkMSovTJTwf1/OIdOnjsuP744Ub957td+tPwLrqsSzR/EQOAepKSnidJSogO4f+1AAB4OYIvoC7YbFKL84xjyATp+DFp17fGTLCUxVJemrTtM+OQpOjuUsJQY32w2AGSw9fU8mEOh92mG/q20dU9W+qtVXs189sU7UjL02/nrlX/9s01aUQX9WkbbnaZAOB1ktON2bbxMcEmVwIAAOobwRdQHwKaSd2vNw63W0rbVLlT5IE1Uvpm41j5L8kZKrXpJ8V0k6K7SdFdpagukm+A2Z8CDcTf16F7LuqgmxJj9fKyFL2+co9W7z6i61/+Xlf1aKFHh3VR+8ggs8sEAK+RnGbM+OoUTfAFAIC3I/gC6pvdLrXsZRwXPSoVHJF2fmOEYClfSwWZ0s4lxlHOZpfC21cPw6K7S807SA5+23qrsEBfTRrRVWMGxumFxTv04boD+mJTqr7anKZb+sfqocs7KSqEBZgB4FxVtDrGhJhcCQAAqG8sbg+Yye2WDq+XDm+Q0rdK6VuMtcKOH6n5eodTiupUPQyL7mqsGcYaJV5ne2qunlm0Td9sS5ckBfo5dM+QDrrnog4KdhKAAqfD+MEaGvo+FZW61HXyIrk90uo/X67oUP96f08AAFC36n1x+5deeknPPvusUlNT1atXL82YMUP9+/ev8drNmzdrypQpSkpK0t69e/XCCy/o4YcfPpu3BbyP3S61Pt84ynk8Un6GEYClby1ri9wqpW+TSvKl1E3GUZUztCwIqxKGRXeTgiIa9vOgTnVuEaI5d/bTD7uyNG3hNm3Yf0z/WpKsd37cq4cuT9At/dvK12E3u0wAsJTdmflye6RQfx9m0QIA0ATUOvh6//33NWHCBL3yyisaMGCApk+frmHDhmn79u2Kjo4+4fqCggJ16NBBo0aN0iOPPFInRQNezWaTgqONo+OllefdbunY3sqZYelbjO8zd0hFOdL+H42jquCYE8Ow6C6SH+tFWckFHSL0yf2D9MWmVD375TbtySrQ5P9u1msrduvRYV10VY8W7EoGAGeofH2vhBh2dAQAoCmodavjgAED1K9fP82cOVOS5Ha7FRsbqwcffFATJ0485XPj4uL08MMP13rGF60KwCmUFktZKdXDsLTNRkhWI5sU3q5KGNZViukuRcSzu6QFlLjcmrd6n/61JFmZecWSpF6xzTRpRBdd0IEZfkBVjB+soaHv0/OLd+jFJcm6pV+snr6hZ72/HwAAqHv11upYXFyspKQkTZo0qeKc3W7X0KFDtWrVqrOrtgZFRUUqKiqq+DknJ6fOXhvwOj5+xiL4Md2qny/KkzK2V2mV3CKlbZHy06Wje4xj++eV19t9pchO1cOw6K5SWFujJRONgq/DrjsGxun689to9vJdmv3dLm3Yf0y3vPqDLusSrT8O76wuLfgLPgCcTEp6riQpnh0dAQBoEmoVfGVmZsrlcikmJqba+ZiYGG3btq3Oipo2bZr++te/1tnrAU2SM1hq09c4qsrPrJwZVh6GpW+VinPLQrLN1a/3C5aiulQPw6K7Ga2YME2w00ePXNFJt13QVjOWpOi91fv0zbZ0fbs9XTec30YTruikVs0CzC4TABqdqq2OAADA+zXKbcEmTZqkCRMmVPyck5Oj2NhYEysCvEhQpNT+IuMo5/FI2ftPDMMyt0vFedLBtcZRVWDkiWFYdFfJyV8kGlJ0iL+eGHme7rqwvZ79cpu+2JSqD5MO6H8bDunOwXG6/+J4hQXSwgoAktEuvjszX5KUwIwvAACahFoFX5GRkXI4HEpLS6t2Pi0tTS1atKizopxOp5xOdtkBGozNJjVraxydhlWed5VKR3ZWCcPKArEju6SCTGnPd8ZRVVhbo+2yIgzrZrRQ+vg17GdqYtpHBunl2/rqp31HNW3hNq3efUT/XrZL81bv1/hLO2r0wDj5+zrMLhMATLU3K1+lbo+C/BxqGeZvdjkAAKAB1Cr48vPzU9++fbVkyRKNHDlSkrG4/ZIlS/TAAw/UR30AzOTwkaI6G0f36yvPFxcYs8GqhmHpW6Tcw1L2PuPYsajyeruPsXh+eRBWHow1i2P9sDrWp2243r/3An27PV1PL9ymHWl5euqLbZr7/V5NuKKTRvZpLYedXcwANE3lbY7x7OgIAECTUetWxwkTJmjMmDFKTExU//79NX36dOXn52vs2LGSpNGjR6t169aaNm2aJGNB/C1btlR8f/DgQa1fv17BwcGKj4+vw48CoMH4BUqt+hhHVQVHpIxtxq6SFTtMbpGKso3zGdukzR9XXu8bJEWXrR9WvstkTHcpKMqYhYazYrPZdFmXGF3cKVofrTugFxbv0MFjx/X7+Rs0+7td+tOILrqkUxR/6ZPk8XhUvrexp/xnGd2/HlU+pl+cK7+28nnGN1UfVw2vJ0+V63Xi653q/WySWocHyNdBWAycreT0svW9aHMEAKDJqHXwdfPNNysjI0NTpkxRamqqevfurUWLFlUseL9v3z7Zq8zgOHTokPr0qfzL8XPPPafnnntOF198sZYuXXrunwBA4xHYXGo3yDjKeTxSzqGyIKxKy2TGdqkkXzqYZBzVXifiF7PDuhsBGeuH1YrDbtNNibH6Va9Wen3lHr28NEXbUnM19vU1GtghQpOu6qKebZrVaw1ut0dFpW4VlbqMryVuFZa6VFRSea6wxFV5TUnVn41zhSWVj51wfan7hMeLSlwqdrlPCI6k6kGUFUUE+en6Pq01KjFWnVvw+wGorR1pxo6OBF8AADQdNo+n8Q//c3JyFBYWpuzsbIWGhppdDoC64CqVju6uMjusLBQ7skuVMcUvNGtbGYiVh2IRCawfdoaOFRTrpW9TNPf7vSp2uSVJ1/RsqRv7tpGrLKCqCJVKfhEqVQuhqj5ePaQq+kUIVf4+MCYx2mTMyLNV/Fw2667iMeNc+bWqcn2J27gf5Xq1CdONZcFmWAAbGNSE8YM1NOR9Gj59ubal5mrOnYm6rEvM6Z8AAAAapdqMHwi+ADQuJ6wfVtYymXu45uvtPkb4VbGgflnLZLN2rB92EgeOFuj5r3ZowfqDDTrzycduk9PHLqevQ04fu/zLvhqHQ07fql+rPv6L68uu8/etfKzi8bJzvg6b7DZbtSBJKguTfhEulQdLUpUwylY9qCp/7gmvVyW8qjHYqsN20hKXW8u2Z2h+0n4t2ZquUrdx8/x87BrevYVGJbbR4I6RsrOGWwXGD9bQUPep1OVWtylfqtjl1nd/vFSxzQPr7b0AAED9IvgC4H0KjlQuop+2ufL7opyar69YP6ybsW5YeSgWHNWwdTdiWw7l6MUlydqTlV8RRpUHTf7nEkKdJJjyYW2qOpOZV6RPfjqoD5MOaFtqbsX5VmH+urFvG93YN1ZtI/hLPeMHa2io+7QrI0+X/XOZAnwd2vzXYYTEAABYGMEXgKbB45FyDlafHZa2xZgx5iqu+TmBkWWzw6oerB8Ga/J4PNp0MFvz1x7Qf9cfVE5hacVjF3RorlF9YzWiRwsF+tV6SU+vwPjBGhrqPn25OVX/91aSerQO0/8evLDe3gcAANS/2owfmuZIGIB3sNmksDbG0enKyvOuUunIzuqL6advkY7slgoypd3LjaOqZu2qLKZfdkTEW2/9MLdbcpdI7lLJVfb1pN+XGL9WfoHGr6F/M3bTtBibzaaebZqpZ5tm+svVXfXVljTNX7tfK1Iy9cOuI/ph1xFN/XSzrunZUqMS2+j8tuHs5okmK4UdHQEAaJIIvgB4H4ePFNXZOLpfX3m+ON/YTbJ83bDyhfXz0qRje41jx8LK6+2+UmRCWRDWVYrqIvk4Tx8k1fS9u7Ts5zMNpc7ydT3nsJi8X3BlkBjaWgqLrfy5/JzVgsAmxN/XoV/1aqVf9Wqlg8eO6+OkA5qfdED7jhRo3pr9mrdmvzpEBWlU31j9+vzWign1N7tkoEEll+3oGB9D8AUAQFNCqyMA5GdVhmHpZeuHpW2RinNP/1wrsDmMTQAcvsbXX35flGvMhDv9C0nB0VXCsF8GY22koEhmjTUibrdHq/cc0fy1B/TFpsM6XuKSJNlt0iWdozWqbxtd3jVGfj7euf4a4wdraKj7dPWL32nzoRzNHp2oK7qxoyMAAFZGqyMA1EZQhNR+iHGU83ik7P1VZoZtlTJ3GDOqHL7GbDC7jzG7zO5j/Fzte1/J7qjyfQ2BU8X3p3huxXv4nuT5ZdeeUFOV685kd8viAinnkPGZsw9UHjlVvi8tNGbH5aVJB5Nqfh0f/7LZYlWDsSo/h7Y2WivRIOx2my7oEKELOkTor9d11+cbD2n+2gNau/eovtmWrm+2pSs80Fcj+7TWqL6x6taKcAjeyeX20OoIAEATxYwvAMDpeTxSQVaVYOzgiSFZXuqZvVZgRPUg7JczyIJjziysw1nbmZGnD5MO6KOkA0rPLao4f17rUI3qG6vrerdSs0Drt7UyfrCGhrhP+7IKdNGz38rPx66tfxsuBzs6AgBgacz4AgDULZvNaGMMipRa9an5mtIiY9ZYzsGyMOwXwVj2Aak4zwjQCrKkwxtqfh27rxTaqnobZdVgLLS15E+IcS46RgXrT8O76PdXdNJ3yZman7Rfi7ek6eeDOfr54GY9+flWXdEtRqMS22hIQhQhASwvOd1oXe8YFcx/zwAANDEEXwCAuuHjlJq3N46aeDxSYXaVIKwsGKsIyg4YwZm7pHKzgZNxhp0kGCubQRbS0mj/xCn5OOy6tEu0Lu0SrSP5xfrv+oP6YO0BbT2co883Hdbnmw6rRai/fn1+a41KjFX7yCCzSwbOSjJtjgAANFkEXwCAhmGzSQHNjKPFeTVf4yo1WiZ/OVOsalhWeEwqypbSs43NCE7Gx1/yDTQOv/KvQVV+DjrJ+TO41uHndYv4Nw/y09jB7TV2cHv9fDBbHyYd0CfrDyo1p1AvL92pl5fuVL+4cI1KjNXVPVoqyMkQAtaRnEbwBQBAU8WoFQDQeDh8KmdwnUxRXtkssRpaKctnkLmKjcX4Swul40fqvk6b4yRh2akCtV+eD6j5Wt9A09c4O691mM5rHaZJV3XR11vSNT9pv5bvyNCaPUe1Zs9RPf7pZl3Vo6VuSoxVv7hw2bwsBIT3SSlrdUyIIfgCAKCpIfgCAFiLM1iK6mwcNXG7peNHpZJ8Y7fKiq8FUnF+2dey8yXHTzxX47Vlh6vYeA+PSyrKMY764BNQc1gWHCOFx0nh7aRm7YyvoW2MwLAeOH0curpnS13ds6VSswv10boDmr92v/ZkFejDpAP6MOmA4iICdWPfNrqhbxu1DAuolzqAc+F2eypaHeOjQ0yuBgAANDSCLwCAd7HbpaAISRF1/9qukuphWEU4VkNIVi1IqyFQOyF0K5BUttFy6XHjUNbpa7I5jLXNwuMqw7BmcZXhWHB0nbRltgjz1/hL43X/JR21du9RzV+7X59tPKw9WQV67qsden7xDl2YEKWbEttoaNcY+fs6zvk9gbpwKPu4Copd8nXY1C4i0OxyAABAAyP4AgDgTDl8JUeY5B9W96/t8Rhh2MlmphXlGov/H9srHd0jHd0rHdsnuYqMr8f21fy6voFSs7ZVQrGyr+VBWS13yLTZbOoX11z94ppr6rXd9cWmw5qfdECrdx/R8h0ZWr4jQ2EBvrqudyvdlBir7q1CaYWEqcpne7WPDJKvw9w2YgAA0PAIvgAAaAxsNqOt0S9QCoo8s+e43VJeWlkYVhaIlX9/bK+x3llJgZSxzThqEhD+i1AsrnLWWLNYY7fOkwhy+mhUYqxGJcZqT2Z+Rftjak6h3ly1V2+u2qsuLUI0KjFWI3u3UkTwyV8LqC8p5Qvbx9DmCABAU0TwBQCAVdntUmhL42h7wYmPlxYbmwBUDcOqBmQFWcZ6aMePSofX1/AGNimkZfUZYlVnjYW0lOxGS2NcZJD+MKyzHrmik1akZGr+2v36anOatqXm6onPtujphVt1eZcY3dSvjS5KiJIPM2/QQJLLF7ZnR0cAAJokgi8AALyVj58U0dE4alKUa7RI1jRb7Oheo80y95Bx7Ft14vPtvkYbZZUwzNGsnS4Ob6eLR7bXseu669ONhzV/7QFtOpitRZtTtWhzqqJDnLr+/NYa1TdW8YQRqGflrY4JLGwPAECTRPAFAEBT5QyRYrobxy95PMaMsKN7agjF9kjZByR3iXRkp3HUoJlfiEaHt9PoyHbKatVCa46FauFBp7bkNdeby7L172W7dH7bZvrbdefpvNb1sG4amjyPx1Ol1ZGQFQCApojgCwAAnMhmM9YaC4qU2iSe+Lir1JgJVlML5dG9Ul6qVJwrpf0spf2sCEnDyw6VLfWV6QnV/tRoRR7/j6Q+DfXJ0ISk5RQpt6hUDrtNcRFBZpcDAABMQPAFAABqz+FTtltkW0lDTny85Lh0bH+VMGxPlYBsr1SUrUhbjiJtOVJEeAMXj6aifH2vuIhA+fmwrhwAAE0RwRcAAKh7vgFSVCfjqMnxo5WzxULbNGxtTdhLL72kZ599VqmpqerVq5dmzJih/v3713jtJZdcomXLlp1w/qqrrtLnn38uSbrzzjs1d+7cao8PGzZMixYtqvviz0LfduH6aNwgHS92mV0KAAAwCcEXAABoeAHhxtGqt9mVNBnvv/++JkyYoFdeeUUDBgzQ9OnTNWzYMG3fvl3R0dEnXP/xxx+ruLi44uesrCz16tVLo0aNqnbd8OHD9frrr1f87HQ66+9D1FKgn4/6tmNGIQAATRlzvgEAAJqA559/Xvfcc4/Gjh2rbt266ZVXXlFgYKDmzJlT4/XNmzdXixYtKo7FixcrMDDwhODL6XRWuy48nKAJAAA0HgRfAAAAXq64uFhJSUkaOnRoxTm73a6hQ4dq1apVZ/Qar732mm655RYFBVVfJH7p0qWKjo5W586dNW7cOGVlZZ3ydYqKipSTk1PtAAAAqC8EXwAAAF4uMzNTLpdLMTEx1c7HxMQoNTX1tM9fvXq1fv75Z919993Vzg8fPlxvvvmmlixZomeeeUbLli3TiBEj5HKdfE2tadOmKSwsrOKIjY09uw8FAABwBljjCwAAAKf02muvqUePHicshH/LLbdUfN+jRw/17NlTHTt21NKlS3X55ZfX+FqTJk3ShAkTKn7Oyckh/AIAAPWGGV8AAABeLjIyUg6HQ2lpadXOp6WlqUWLFqd8bn5+vubNm6ff/va3p32fDh06KDIyUikpKSe9xul0KjQ0tNoBAABQXwi+AAAAvJyfn5/69u2rJUuWVJxzu91asmSJBg4ceMrnzp8/X0VFRbr99ttP+z4HDhxQVlaWWrZsec41AwAA1AWCLwAAgCZgwoQJmj17tubOnautW7dq3Lhxys/P19ixYyVJo0eP1qRJk0543muvvaaRI0cqIiKi2vm8vDw9+uij+uGHH7Rnzx4tWbJE1113neLj4zVs2LAG+UwAAACnwxpfAAAATcDNN9+sjIwMTZkyRampqerdu7cWLVpUseD9vn37ZLdX/zfR7du3a8WKFfrqq69OeD2Hw6GNGzdq7ty5OnbsmFq1aqUrr7xSTzzxhJxOZ4N8JgAAgNOxeTwej9lFnE5OTo7CwsKUnZ3NOhAAAOCMMH6wBu4TAACordqMH2h1BAAAAAAAgFci+AIAAAAAAIBXIvgCAAAAAACAVyL4AgAAAAAAgFci+AIAAAAAAIBXIvgCAAAAAACAVyL4AgAAAAAAgFci+AIAAAAAAIBXIvgCAAAAAACAVyL4AgAAAAAAgFci+AIAAAAAAIBXIvgCAAAAAACAV/Ixu4Az4fF4JEk5OTkmVwIAAKyifNxQPo5A48Q4DwAA1FZtxnmWCL5yc3MlSbGxsSZXAgAArCY3N1dhYWFml4GTYJwHAADO1pmM82weC/wzqNvt1qFDhxQSEiKbzVbnr5+Tk6PY2Fjt379foaGhdf76qBvcJ+vgXlkH98o6uFe15/F4lJubq1atWsluZ3WHxopxHspxr6yB+2Qd3Cvr4F7VXm3GeZaY8WW329WmTZt6f5/Q0FD+I7MA7pN1cK+sg3tlHdyr2mGmV+PHOA+/xL2yBu6TdXCvrIN7VTtnOs7jnz8BAAAAAADglQi+AAAAAAAA4JUIviQ5nU5NnTpVTqfT7FJwCtwn6+BeWQf3yjq4V8DZ4feOdXCvrIH7ZB3cK+vgXtUvSyxuDwAAAAAAANQWM74AAAAAAADglQi+AAAAAAAA4JUIvgAAAAAAAOCVCL4AAAAAAADglZp88PXSSy8pLi5O/v7+GjBggFavXm12SfiFadOmqV+/fgoJCVF0dLRGjhyp7du3m10WzsDTTz8tm82mhx9+2OxSUIODBw/q9ttvV0REhAICAtSjRw+tXbvW7LJQhcvl0uTJk9W+fXsFBASoY8eOeuKJJ8S+NMCZYZzX+DHOsy7GeY0b47zGj3Few2nSwdf777+vCRMmaOrUqVq3bp169eqlYcOGKT093ezSUMWyZcs0fvx4/fDDD1q8eLFKSkp05ZVXKj8/3+zScApr1qzRv//9b/Xs2dPsUlCDo0ePavDgwfL19dXChQu1ZcsW/fOf/1R4eLjZpaGKZ555RrNmzdLMmTO1detWPfPMM/rHP/6hGTNmmF0a0OgxzrMGxnnWxDivcWOcZw2M8xqOzdOE48QBAwaoX79+mjlzpiTJ7XYrNjZWDz74oCZOnGhydTiZjIwMRUdHa9myZbrooovMLgc1yMvL0/nnn6+XX35Zf//739W7d29Nnz7d7LJQxcSJE7Vy5Up99913ZpeCU7jmmmsUExOj1157reLcDTfcoICAAL399tsmVgY0fozzrIlxXuPHOK/xY5xnDYzzGk6TnfFVXFyspKQkDR06tOKc3W7X0KFDtWrVKhMrw+lkZ2dLkpo3b25yJTiZ8ePH6+qrr672+wuNy6effqrExESNGjVK0dHR6tOnj2bPnm12WfiFQYMGacmSJdqxY4ckacOGDVqxYoVGjBhhcmVA48Y4z7oY5zV+jPMaP8Z51sA4r+H4mF2AWTIzM+VyuRQTE1PtfExMjLZt22ZSVTgdt9uthx9+WIMHD9Z5551ndjmowbx587Ru3TqtWbPG7FJwCrt27dKsWbM0YcIE/fnPf9aaNWv0u9/9Tn5+fhozZozZ5aHMxIkTlZOToy5dusjhcMjlcunJJ5/UbbfdZnZpQKPGOM+aGOc1fozzrIFxnjUwzms4TTb4gjWNHz9eP//8s1asWGF2KajB/v379dBDD2nx4sXy9/c3uxycgtvtVmJiop566ilJUp8+ffTzzz/rlVdeYUDUiHzwwQd655139O6776p79+5av369Hn74YbVq1Yr7BMDrMM5r3BjnWQfjPGtgnNdwmmzwFRkZKYfDobS0tGrn09LS1KJFC5Oqwqk88MAD+uyzz7R8+XK1adPG7HJQg6SkJKWnp+v888+vOOdyubR8+XLNnDlTRUVFcjgcJlaIci1btlS3bt2qnevatas++ugjkypCTR599FFNnDhRt9xyiySpR48e2rt3r6ZNm8aACDgFxnnWwziv8WOcZx2M86yBcV7DabJrfPn5+alv375asmRJxTm3260lS5Zo4MCBJlaGX/J4PHrggQe0YMECffPNN2rfvr3ZJeEkLr/8cm3atEnr16+vOBITE3Xbbbdp/fr1DIYakcGDB5+wXfyOHTvUrl07kypCTQoKCmS3V/+j2uFwyO12m1QRYA2M86yDcZ51MM6zDsZ51sA4r+E02RlfkjRhwgSNGTNGiYmJ6t+/v6ZPn678/HyNHTvW7NJQxfjx4/Xuu+/qv//9r0JCQpSamipJCgsLU0BAgMnVoaqQkJAT1uQICgpSREQEa3U0Mo888ogGDRqkp556SjfddJNWr16tV199Va+++qrZpaGKa6+9Vk8++aTatm2r7t2766efftLzzz+vu+66y+zSgEaPcZ41MM6zDsZ51sE4zxoY5zUcm8fj8ZhdhJlmzpypZ599Vqmpqerdu7defPFFDRgwwOyyUIXNZqvx/Ouvv64777yzYYtBrV1yySVsc91IffbZZ5o0aZKSk5PVvn17TZgwQffcc4/ZZaGK3NxcTZ48WQsWLFB6erpatWqlW2+9VVOmTJGfn5/Z5QGNHuO8xo9xnrUxzmu8GOc1fozzGk6TD74AAAAAAADgnZrsGl8AAAAAAADwbgRfAAAAAAAA8EoEXwAAAAAAAPBKBF8AAAAAAADwSgRfAAAAAAAA8EoEXwAAAAAAAPBKBF8AAAAAAADwSgRfAAAAAAAA8EoEXwCaBJvNpk8++cTsMgAAAFAPGOsBOBmCLwD17s4775TNZjvhGD58uNmlAQAA4Bwx1gPQmPmYXQCApmH48OF6/fXXq51zOp0mVQMAAIC6xFgPQGPFjC8ADcLpdKpFixbVjvDwcEnG1PRZs2ZpxIgRCggIUIcOHfThhx9We/6mTZt02WWXKSAgQBEREbr33nuVl5dX7Zo5c+aoe/fucjqdatmypR544IFqj2dmZur6669XYGCgEhIS9Omnn1Y8dvToUd12222KiopSQECAEhISThi8AQAAoGaM9QA0VgRfABqFyZMn64YbbtCGDRt022236ZZbbtHWrVslSfn5+Ro2bJjCw8O1Zs0azZ8/X19//XW1wc6sWbM0fvx43Xvvvdq0aZM+/fRTxcfHV3uPv/71r7rpppu0ceNGXXXVVbrtttt05MiRivffsmWLFi5cqK1bt2rWrFmKjIxsuF8AAAAAL8ZYD4BpPABQz8aMGeNxOByeoKCgaseTTz7p8Xg8Hkme++67r9pzBgwY4Bk3bpzH4/F4Xn31VU94eLgnLy+v4vHPP//cY7fbPampqR6Px+Np1aqV5y9/+ctJa5Dkeeyxxyp+zsvL80jyLFy40OPxeDzXXnutZ+zYsXXzgQEAAJoQxnoAGjPW+ALQIC699FLNmjWr2rnmzZtXfD9w4MBqjw0cOFDr16+XJG3dulW9evVSUFBQxeODBw+W2+3W9u3bZbPZdOjQIV1++eWnrKFnz54V3wcFBSk0NFTp6emSpHHjxumGG27QunXrdOWVV2rkyJEaNGjQWX1WAACApoaxHoDGiuALQIMICgo6YTp6XQkICDij63x9fav9bLPZ5Ha7JUkjRozQ3r179cUXX2jx4sW6/PLLNX78eD333HN1Xi8AAIC3YawHoLFijS8AjcIPP/xwws9du3aVJHXt2lUbNmxQfn5+xeMrV66U3W5X586dFRISori4OC1ZsuScaoiKitKYMWP09ttva/r06Xr11VfP6fUAAABgYKwHwCzM+ALQIIqKipSamlrtnI+PT8WiovPnz1diYqIuvPBCvfPOO1q9erVee+01SdJtt92mqVOnasyYMXr88ceVkZGhBx98UHfccYdiYmIkSY8//rjuu+8+RUdHa8SIEcrNzdXKlSv14IMPnlF9U6ZMUd++fdW9e3cVFRXps88+qxiMAQAA4NQY6wForAi+ADSIRYsWqWXLltXOde7cWdu2bZNk7MIzb9483X///WrZsqXee+89devWTZIUGBioL7/8Ug899JD69eunwMBA3XDDDXr++ecrXmvMmDEqLCzUCy+8oD/84Q+KjIzUjTfeeMb1+fn5adKkSdqzZ48CAgI0ZMgQzZs3rw4+OQAAgPdjrAegsbJ5PB6P2UUAaNpsNpsWLFigkSNHml0KAAAA6hhjPQBmYo0vAAAAAAAAeCWCLwAAAAAAAHglWh0BAAAAAADglZjxBQAAAAAAAK9E8AUAAAAAAACvRPAFAAAAAAAAr0TwBQAAAAAAAK9E8AUAAAAAAACvRPAFAAAAAAAAr0TwBQAAAAAAAK9E8AUAAAAAAACv9P8G1ObEhYq1RQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_functions import plot_loss_curves\n",
    "\n",
    "plot_loss_curves(vit_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "883da75f-ab2a-406d-9153-0b69e629c551",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving model to: models/09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth\n"
     ]
    }
   ],
   "source": [
    "# Save the model\n",
    "from going_modular import utils\n",
    "\n",
    "utils.save_model(model=vit,\n",
    "                 target_dir=\"models\",\n",
    "                 model_name=\"09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c2d83c48-8fbf-4e9b-a535-e4a213d984ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrained ViT feature extractor model size: 327 MB\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Get the model size in bytes then convert to megabytes\n",
    "pretrained_vit_model_size = Path(\"models/09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth\").stat().st_size // (1024*1024) # division converts bytes to megabytes (roughly) \n",
    "print(f\"Pretrained ViT feature extractor model size: {pretrained_vit_model_size} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9d779dc7-f858-44fd-ac30-b8cca5c1e4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85800963"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count number of parameters in ViT\n",
    "vit_total_params = sum(torch.numel(param) for param in vit.parameters())\n",
    "vit_total_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "02d98e2a-207f-4a77-a917-7d57427f99f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.06443448746576905,\n",
       " 'test_acc': 0.984659090909091,\n",
       " 'number_of_parameters': 85800963,\n",
       " 'model_size (MB)': 327}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create ViT statistics dictionary\n",
    "vit_stats = {\"test_loss\": vit_results[\"test_loss\"][-1],\n",
    "             \"test_acc\": vit_results[\"test_acc\"][-1],\n",
    "             \"number_of_parameters\": vit_total_params,\n",
    "             \"model_size (MB)\": pretrained_vit_model_size}\n",
    "\n",
    "vit_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "968d964d-a1d0-4d99-a9a7-bdf6c72ec667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Finding all filepaths ending with '.jpg' in directory: data/pizza_steak_sushi_20_percent/test\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('data/pizza_steak_sushi_20_percent/test/sushi/2903125.jpg'),\n",
       " PosixPath('data/pizza_steak_sushi_20_percent/test/sushi/3177743.jpg'),\n",
       " PosixPath('data/pizza_steak_sushi_20_percent/test/sushi/715227.jpg'),\n",
       " PosixPath('data/pizza_steak_sushi_20_percent/test/sushi/3494950.jpg'),\n",
       " PosixPath('data/pizza_steak_sushi_20_percent/test/sushi/128616.jpg')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Get all test data paths\n",
    "print(f\"[INFO] Finding all filepaths ending with '.jpg' in directory: {test_dir}\")\n",
    "test_data_paths = list(Path(test_dir).glob(\"*/*.jpg\"))\n",
    "test_data_paths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7f84d36f-a30f-40cb-83c2-bde92b9050d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import torch\n",
    "\n",
    "from PIL import Image\n",
    "from timeit import default_timer as timer \n",
    "from tqdm.auto import tqdm\n",
    "from typing import List, Dict\n",
    "\n",
    "# 1. Create a function to return a list of dictionaries with sample, truth label, prediction, prediction probability and prediction time\n",
    "def pred_and_store(paths: List[pathlib.Path], \n",
    "                   model: torch.nn.Module,\n",
    "                   transform: torchvision.transforms, \n",
    "                   class_names: List[str], \n",
    "                   device: str = \"cuda\" if torch.cuda.is_available() else \"cpu\") -> List[Dict]:\n",
    "    \n",
    "    # 2. Create an empty list to store prediction dictionaires\n",
    "    pred_list = []\n",
    "    \n",
    "    # 3. Loop through target paths\n",
    "    for path in tqdm(paths):\n",
    "        \n",
    "        # 4. Create empty dictionary to store prediction information for each sample\n",
    "        pred_dict = {}\n",
    "\n",
    "        # 5. Get the sample path and ground truth class name\n",
    "        pred_dict[\"image_path\"] = path\n",
    "        class_name = path.parent.stem\n",
    "        pred_dict[\"class_name\"] = class_name\n",
    "        \n",
    "        # 6. Start the prediction timer\n",
    "        start_time = timer()\n",
    "        \n",
    "        # 7. Open image path\n",
    "        img = Image.open(path)\n",
    "        \n",
    "        # 8. Transform the image, add batch dimension and put image on target device\n",
    "        transformed_image = transform(img).unsqueeze(0).to(device) \n",
    "        \n",
    "        # 9. Prepare model for inference by sending it to target device and turning on eval() mode\n",
    "        model.to(device)\n",
    "        model.eval()\n",
    "        \n",
    "        # 10. Get prediction probability, predicition label and prediction class\n",
    "        with torch.inference_mode():\n",
    "            pred_logit = model(transformed_image) # perform inference on target sample \n",
    "            pred_prob = torch.softmax(pred_logit, dim=1) # turn logits into prediction probabilities\n",
    "            pred_label = torch.argmax(pred_prob, dim=1) # turn prediction probabilities into prediction label\n",
    "            pred_class = class_names[pred_label.cpu()] # hardcode prediction class to be on CPU\n",
    "\n",
    "            # 11. Make sure things in the dictionary are on CPU (required for inspecting predictions later on) \n",
    "            pred_dict[\"pred_prob\"] = round(pred_prob.unsqueeze(0).max().cpu().item(), 4)\n",
    "            pred_dict[\"pred_class\"] = pred_class\n",
    "            \n",
    "            # 12. End the timer and calculate time per pred\n",
    "            end_time = timer()\n",
    "            pred_dict[\"time_for_pred\"] = round(end_time-start_time, 4)\n",
    "\n",
    "        # 13. Does the pred match the true label?\n",
    "        pred_dict[\"correct\"] = class_name == pred_class\n",
    "\n",
    "        # 14. Add the dictionary to the list of preds\n",
    "        pred_list.append(pred_dict)\n",
    "    \n",
    "    # 15. Return list of prediction dictionaries\n",
    "    return pred_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "890b822a-3993-4dbf-8e30-1981ba072478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a863f0ad32c84efba516e38e7b2a460e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make predictions across test dataset with EffNetB2\n",
    "effnetb2_test_pred_dicts = pred_and_store(paths=test_data_paths,\n",
    "                                          model=effnetb2,\n",
    "                                          transform=effnetb2_transforms,\n",
    "                                          class_names=class_names,\n",
    "                                          device=\"cpu\") # make predictions on CPU "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3246188a-964a-47e4-8594-e5607950b638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/sushi/2903125.jpg'),\n",
       "  'class_name': 'sushi',\n",
       "  'pred_prob': 0.8019,\n",
       "  'pred_class': 'sushi',\n",
       "  'time_for_pred': 0.0334,\n",
       "  'correct': True},\n",
       " {'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/sushi/3177743.jpg'),\n",
       "  'class_name': 'sushi',\n",
       "  'pred_prob': 0.8293,\n",
       "  'pred_class': 'sushi',\n",
       "  'time_for_pred': 0.0175,\n",
       "  'correct': True}]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the first 2 prediction dictionaries\n",
    "effnetb2_test_pred_dicts[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "40e53ead-3e94-4986-9e4b-37d000ff9b55",
   "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>image_path</th>\n",
       "      <th>class_name</th>\n",
       "      <th>pred_prob</th>\n",
       "      <th>pred_class</th>\n",
       "      <th>time_for_pred</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/2...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.8019</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0334</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/3...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.8293</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0175</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/7...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.7241</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0154</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/3...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.6199</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/1...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9472</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          image_path class_name  pred_prob  \\\n",
       "0  data/pizza_steak_sushi_20_percent/test/sushi/2...      sushi     0.8019   \n",
       "1  data/pizza_steak_sushi_20_percent/test/sushi/3...      sushi     0.8293   \n",
       "2  data/pizza_steak_sushi_20_percent/test/sushi/7...      sushi     0.7241   \n",
       "3  data/pizza_steak_sushi_20_percent/test/sushi/3...      sushi     0.6199   \n",
       "4  data/pizza_steak_sushi_20_percent/test/sushi/1...      sushi     0.9472   \n",
       "\n",
       "  pred_class  time_for_pred  correct  \n",
       "0      sushi         0.0334     True  \n",
       "1      sushi         0.0175     True  \n",
       "2      sushi         0.0154     True  \n",
       "3      sushi         0.0165     True  \n",
       "4      sushi         0.0159     True  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn the test_pred_dicts into a DataFrame\n",
    "import pandas as pd\n",
    "effnetb2_test_pred_df = pd.DataFrame(effnetb2_test_pred_dicts)\n",
    "effnetb2_test_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5d2a2f17-8d79-4137-9765-543efba66ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "correct\n",
       "True     145\n",
       "False      5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check number of correct predictions\n",
    "effnetb2_test_pred_df.correct.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "631314a9-793f-4628-afb1-2514c1bbbe85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EffNetB2 average time per prediction: 0.0157 seconds\n"
     ]
    }
   ],
   "source": [
    "# Find the average time per prediction \n",
    "effnetb2_average_time_per_pred = round(effnetb2_test_pred_df.time_for_pred.mean(), 4)\n",
    "print(f\"EffNetB2 average time per prediction: {effnetb2_average_time_per_pred} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cd4582ad-6639-4414-bce6-90a7c449e13b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.2814320981502533,\n",
       " 'test_acc': 0.96875,\n",
       " 'number_of_parameters': 7705221,\n",
       " 'model_size (MB)': 29,\n",
       " 'time_per_pred_cpu': 0.0157}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add EffNetB2 average prediction time to stats dictionary \n",
    "effnetb2_stats[\"time_per_pred_cpu\"] = effnetb2_average_time_per_pred\n",
    "effnetb2_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ceea39c1-0b8d-448f-8392-5d781b151ff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98282ddda4974c8b8720d12bddfb3f66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make list of prediction dictionaries with ViT feature extractor model on test images\n",
    "vit_test_pred_dicts = pred_and_store(paths=test_data_paths,\n",
    "                                     model=vit,\n",
    "                                     transform=vit_transforms,\n",
    "                                     class_names=class_names,\n",
    "                                     device=\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5c1e6ee5-131c-43fb-a86b-7a6b69f08a53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/sushi/2903125.jpg'),\n",
       "  'class_name': 'sushi',\n",
       "  'pred_prob': 0.9975,\n",
       "  'pred_class': 'sushi',\n",
       "  'time_for_pred': 0.0901,\n",
       "  'correct': True},\n",
       " {'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/sushi/3177743.jpg'),\n",
       "  'class_name': 'sushi',\n",
       "  'pred_prob': 0.9629,\n",
       "  'pred_class': 'sushi',\n",
       "  'time_for_pred': 0.043,\n",
       "  'correct': True}]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first couple of ViT predictions on the test dataset\n",
    "vit_test_pred_dicts[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "29af17cc-80d1-4401-8acb-eaf75e7308af",
   "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>image_path</th>\n",
       "      <th>class_name</th>\n",
       "      <th>pred_prob</th>\n",
       "      <th>pred_class</th>\n",
       "      <th>time_for_pred</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/2...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9975</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0901</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/3...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9629</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0430</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/7...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9852</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0469</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/3...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9657</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0443</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data/pizza_steak_sushi_20_percent/test/sushi/1...</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.9876</td>\n",
       "      <td>sushi</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          image_path class_name  pred_prob  \\\n",
       "0  data/pizza_steak_sushi_20_percent/test/sushi/2...      sushi     0.9975   \n",
       "1  data/pizza_steak_sushi_20_percent/test/sushi/3...      sushi     0.9629   \n",
       "2  data/pizza_steak_sushi_20_percent/test/sushi/7...      sushi     0.9852   \n",
       "3  data/pizza_steak_sushi_20_percent/test/sushi/3...      sushi     0.9657   \n",
       "4  data/pizza_steak_sushi_20_percent/test/sushi/1...      sushi     0.9876   \n",
       "\n",
       "  pred_class  time_for_pred  correct  \n",
       "0      sushi         0.0901     True  \n",
       "1      sushi         0.0430     True  \n",
       "2      sushi         0.0469     True  \n",
       "3      sushi         0.0443     True  \n",
       "4      sushi         0.0422     True  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn vit_test_pred_dicts into a DataFrame\n",
    "import pandas as pd\n",
    "vit_test_pred_df = pd.DataFrame(vit_test_pred_dicts)\n",
    "vit_test_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f95a2220-a95c-4b5a-99d7-27dc7530be0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "correct\n",
       "True     148\n",
       "False      2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of correct predictions\n",
    "vit_test_pred_df.correct.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "64f9b195-8f84-46d9-a06a-c1277c3cb2e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ViT average time per prediction: 0.0467 seconds\n"
     ]
    }
   ],
   "source": [
    "# Calculate average time per prediction for ViT model\n",
    "vit_average_time_per_pred = round(vit_test_pred_df.time_for_pred.mean(), 4)\n",
    "print(f\"ViT average time per prediction: {vit_average_time_per_pred} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4c78f5da-492b-4f1e-a9f6-bde3fa81112b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.06443448746576905,\n",
       " 'test_acc': 0.984659090909091,\n",
       " 'number_of_parameters': 85800963,\n",
       " 'model_size (MB)': 327,\n",
       " 'time_per_pred_cpu': 0.0467}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add average prediction time for ViT model on CPU\n",
    "vit_stats[\"time_per_pred_cpu\"] = vit_average_time_per_pred\n",
    "vit_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7797cf73-d823-4a7d-bcbd-a6c3ee36895a",
   "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>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>number_of_parameters</th>\n",
       "      <th>model_size (MB)</th>\n",
       "      <th>time_per_pred_cpu</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.281432</td>\n",
       "      <td>96.88</td>\n",
       "      <td>7705221</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>EffNetB2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.064434</td>\n",
       "      <td>98.47</td>\n",
       "      <td>85800963</td>\n",
       "      <td>327</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>ViT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_loss  test_acc  number_of_parameters  model_size (MB)  \\\n",
       "0   0.281432     96.88               7705221               29   \n",
       "1   0.064434     98.47              85800963              327   \n",
       "\n",
       "   time_per_pred_cpu     model  \n",
       "0             0.0157  EffNetB2  \n",
       "1             0.0467       ViT  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn stat dictionaries into DataFrame\n",
    "df = pd.DataFrame([effnetb2_stats, vit_stats])\n",
    "\n",
    "# Add column for model names\n",
    "df[\"model\"] = [\"EffNetB2\", \"ViT\"]\n",
    "\n",
    "# Convert accuracy to percentages\n",
    "df[\"test_acc\"] = round(df[\"test_acc\"] * 100, 2)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "54898a47-09c1-4cf3-b1ba-b385d33a93c7",
   "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>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>number_of_parameters</th>\n",
       "      <th>model_size (MB)</th>\n",
       "      <th>time_per_pred_cpu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ViT to EffNetB2 ratios</th>\n",
       "      <td>0.228952</td>\n",
       "      <td>1.016412</td>\n",
       "      <td>11.135432</td>\n",
       "      <td>11.275862</td>\n",
       "      <td>2.974522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        test_loss  test_acc  number_of_parameters  \\\n",
       "ViT to EffNetB2 ratios   0.228952  1.016412             11.135432   \n",
       "\n",
       "                        model_size (MB)  time_per_pred_cpu  \n",
       "ViT to EffNetB2 ratios        11.275862           2.974522  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare ViT to EffNetB2 across different characteristics\n",
    "pd.DataFrame(data=(df.set_index(\"model\").loc[\"ViT\"] / df.set_index(\"model\").loc[\"EffNetB2\"]), # divide ViT statistics by EffNetB2 statistics\n",
    "             columns=[\"ViT to EffNetB2 ratios\"]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5328c1d1-5219-47df-855d-9b624fe53e11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: 无法创建目录 \"images/\": 文件已存在\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Create a plot from model comparison DataFrame\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "scatter = ax.scatter(data=df, \n",
    "                     x=\"time_per_pred_cpu\", \n",
    "                     y=\"test_acc\", \n",
    "                     c=[\"blue\", \"orange\"], # what colours to use?\n",
    "                     s=\"model_size (MB)\") # size the dots by the model sizes\n",
    "\n",
    "# 2. Add titles, labels and customize fontsize for aesthetics\n",
    "ax.set_title(\"FoodVision Mini Inference Speed vs Performance\", fontsize=18)\n",
    "ax.set_xlabel(\"Prediction time per image (seconds)\", fontsize=14)\n",
    "ax.set_ylabel(\"Test accuracy (%)\", fontsize=14)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "ax.grid(True)\n",
    "\n",
    "# 3. Annotate with model names\n",
    "for index, row in df.iterrows():\n",
    "    ax.annotate(text=row[\"model\"], # note: depending on your version of Matplotlib, you may need to use \"s=...\" or \"text=...\", see: https://github.com/faustomorales/keras-ocr/issues/183#issuecomment-977733270 \n",
    "                xy=(row[\"time_per_pred_cpu\"]+0.0006, row[\"test_acc\"]+0.03),\n",
    "                size=12)\n",
    "\n",
    "# 4. Create a legend based on model sizes\n",
    "handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.5)\n",
    "model_size_legend = ax.legend(handles, \n",
    "                              labels, \n",
    "                              loc=\"lower right\", \n",
    "                              title=\"Model size (MB)\",\n",
    "                              fontsize=12)\n",
    "\n",
    "# Save the figure\n",
    "!mkdir images/\n",
    "plt.savefig(\"images/09-foodvision-mini-inference-speed-vs-performance.jpg\")\n",
    "\n",
    "# Show the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1053f74b-c1e2-4c91-829b-fb3f36c70ce2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradio version: 4.22.0\n"
     ]
    }
   ],
   "source": [
    "# Import/install Gradio \n",
    "try:\n",
    "    import gradio as gr\n",
    "except: \n",
    "    !pip -q install gradio\n",
    "    import gradio as gr\n",
    "    \n",
    "print(f\"Gradio version: {gr.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "275f1ab1-5ebb-438b-9ca5-769df79996c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Put EffNetB2 on CPU\n",
    "effnetb2.to(\"cpu\") \n",
    "\n",
    "# Check the device\n",
    "next(iter(effnetb2.parameters())).device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c4ab532c-01b6-48ba-9f08-b98a0d3ea568",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Tuple, Dict\n",
    "\n",
    "def predict(img) -> Tuple[Dict, float]:\n",
    "    \"\"\"Transforms and performs a prediction on img and returns prediction and time taken.\n",
    "    \"\"\"\n",
    "    # Start the timer\n",
    "    start_time = timer()\n",
    "    \n",
    "    # Transform the target image and add a batch dimension\n",
    "    img = effnetb2_transforms(img).unsqueeze(0)\n",
    "    \n",
    "    # Put model into evaluation mode and turn on inference mode\n",
    "    effnetb2.eval()\n",
    "    with torch.inference_mode():\n",
    "        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities\n",
    "        pred_probs = torch.softmax(effnetb2(img), dim=1)\n",
    "    \n",
    "    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)\n",
    "    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}\n",
    "    \n",
    "    # Calculate the prediction time\n",
    "    pred_time = round(timer() - start_time, 5)\n",
    "    \n",
    "    # Return the prediction dictionary and prediction time \n",
    "    return pred_labels_and_probs, pred_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6f1f9dd0-eb5f-4838-8f2a-c2584c79d021",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Predicting on image at path: data/pizza_steak_sushi_20_percent/test/sushi/3834718.jpg\n",
      "\n",
      "Prediction label and probability dictionary: \n",
      "{'pizza': 0.2275046408176422, 'steak': 0.04974856227636337, 'sushi': 0.7227467894554138}\n",
      "Prediction time: 0.02668 seconds\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "from PIL import Image\n",
    "\n",
    "# Get a list of all test image filepaths\n",
    "test_data_paths = list(Path(test_dir).glob(\"*/*.jpg\"))\n",
    "\n",
    "# Randomly select a test image path\n",
    "random_image_path = random.sample(test_data_paths, k=1)[0]\n",
    "\n",
    "# Open the target image\n",
    "image = Image.open(random_image_path)\n",
    "print(f\"[INFO] Predicting on image at path: {random_image_path}\\n\")\n",
    "\n",
    "# Predict on the target image and print out the outputs\n",
    "pred_dict, pred_time = predict(img=image)\n",
    "print(f\"Prediction label and probability dictionary: \\n{pred_dict}\")\n",
    "print(f\"Prediction time: {pred_time} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a4445c91-275c-4d69-b015-887e41cb35fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['data/pizza_steak_sushi_20_percent/test/sushi/2819945.jpg'],\n",
       " ['data/pizza_steak_sushi_20_percent/test/sushi/3365273.jpg'],\n",
       " ['data/pizza_steak_sushi_20_percent/test/pizza/129666.jpg']]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of example inputs to our Gradio demo\n",
    "example_list = [[str(filepath)] for filepath in random.sample(test_data_paths, k=3)]\n",
    "example_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5291f1af-50eb-452a-80bd-b744f1adaea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "Running on public URL: https://ccc468a35dd9e071d3.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://ccc468a35dd9e071d3.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "\n",
    "# Create title, description and article strings\n",
    "title = \"FoodVision Mini 🍕🥩🍣\"\n",
    "description = \"An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi.\"\n",
    "article = \"Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/).\"\n",
    "\n",
    "# Create the Gradio demo\n",
    "demo = gr.Interface(fn=predict, # mapping function from input to output\n",
    "                    inputs=gr.Image(type=\"pil\"), # what are the inputs?\n",
    "                    outputs=[gr.Label(num_top_classes=3, label=\"Predictions\"), # what are the outputs?\n",
    "                             gr.Number(label=\"Prediction time (s)\")], # our fn has two outputs, therefore we have two outputs\n",
    "                    examples=example_list, \n",
    "                    title=title,\n",
    "                    description=description,\n",
    "                    article=article)\n",
    "\n",
    "# Launch the demo!\n",
    "demo.launch(debug=False, # print errors locally?\n",
    "            share=True) # generate a publically shareable URL?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9ac5aeb3-1f33-431f-b287-c53dee4bd6f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "from pathlib import Path\n",
    "\n",
    "# Create FoodVision mini demo path\n",
    "foodvision_mini_demo_path = Path(\"demos/foodvision_mini/\")\n",
    "\n",
    "# Remove files that might already exist there and create new directory\n",
    "if foodvision_mini_demo_path.exists():\n",
    "    shutil.rmtree(foodvision_mini_demo_path)\n",
    "    foodvision_mini_demo_path.mkdir(parents=True, # make the parent folders?\n",
    "                                    exist_ok=True) # create it even if it already exists?\n",
    "else:\n",
    "    # If the file doesn't exist, create it anyway\n",
    "    foodvision_mini_demo_path.mkdir(parents=True, \n",
    "                                    exist_ok=True)\n",
    "    \n",
    "# Check what's in the folder\n",
    "!ls demos/foodvision_mini/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a72444a9-eaf9-4485-bcc8-2b1a7a7bbf88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Copying data/pizza_steak_sushi_20_percent/test/sushi/592799.jpg to demos/foodvision_mini/examples/592799.jpg\n",
      "[INFO] Copying data/pizza_steak_sushi_20_percent/test/steak/3622237.jpg to demos/foodvision_mini/examples/3622237.jpg\n",
      "[INFO] Copying data/pizza_steak_sushi_20_percent/test/pizza/2582289.jpg to demos/foodvision_mini/examples/2582289.jpg\n"
     ]
    }
   ],
   "source": [
    "import shutil\n",
    "from pathlib import Path\n",
    "\n",
    "# 1. Create an examples directory\n",
    "foodvision_mini_examples_path = foodvision_mini_demo_path / \"examples\"\n",
    "foodvision_mini_examples_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# 2. Collect three random test dataset image paths\n",
    "foodvision_mini_examples = [Path('data/pizza_steak_sushi_20_percent/test/sushi/592799.jpg'),\n",
    "                            Path('data/pizza_steak_sushi_20_percent/test/steak/3622237.jpg'),\n",
    "                            Path('data/pizza_steak_sushi_20_percent/test/pizza/2582289.jpg')]\n",
    "\n",
    "# 3. Copy the three random images to the examples directory\n",
    "for example in foodvision_mini_examples:\n",
    "    destination = foodvision_mini_examples_path / example.name\n",
    "    print(f\"[INFO] Copying {example} to {destination}\")\n",
    "    shutil.copy2(src=example, dst=destination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "553dcd51-e5de-4371-a013-de5c507a3ba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['examples/2582289.jpg'], ['examples/3622237.jpg'], ['examples/592799.jpg']]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Get example filepaths in a list of lists\n",
    "example_list = [[\"examples/\" + example] for example in os.listdir(foodvision_mini_examples_path)]\n",
    "example_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6512240e-db31-40bb-9c0b-68bb6f125223",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Attempting to move models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth to demos/foodvision_mini/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\n",
      "[INFO] Model move complete.\n"
     ]
    }
   ],
   "source": [
    "import shutil\n",
    "\n",
    "# Create a source path for our target model\n",
    "effnetb2_foodvision_mini_model_path = \"models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\"\n",
    "\n",
    "# Create a destination path for our target model \n",
    "effnetb2_foodvision_mini_model_destination = foodvision_mini_demo_path / effnetb2_foodvision_mini_model_path.split(\"/\")[1]\n",
    "\n",
    "# Try to move the file\n",
    "try:\n",
    "    print(f\"[INFO] Attempting to move {effnetb2_foodvision_mini_model_path} to {effnetb2_foodvision_mini_model_destination}\")\n",
    "    \n",
    "    # Move the model\n",
    "    shutil.move(src=effnetb2_foodvision_mini_model_path, \n",
    "                dst=effnetb2_foodvision_mini_model_destination)\n",
    "    \n",
    "    print(f\"[INFO] Model move complete.\")\n",
    "\n",
    "# If the model has already been moved, check if it exists\n",
    "except:\n",
    "    print(f\"[INFO] No model found at {effnetb2_foodvision_mini_model_path}, perhaps its already been moved?\")\n",
    "    print(f\"[INFO] Model exists at {effnetb2_foodvision_mini_model_destination}: {effnetb2_foodvision_mini_model_destination.exists()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bd3e84e2-4de9-49a0-89ec-59a15c1be8c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_mini/model.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_mini/model.py\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from torch import nn\n",
    "\n",
    "\n",
    "def create_effnetb2_model(num_classes:int=3, \n",
    "                          seed:int=42):\n",
    "    \"\"\"Creates an EfficientNetB2 feature extractor model and transforms.\n",
    "\n",
    "    Args:\n",
    "        num_classes (int, optional): number of classes in the classifier head. \n",
    "            Defaults to 3.\n",
    "        seed (int, optional): random seed value. Defaults to 42.\n",
    "\n",
    "    Returns:\n",
    "        model (torch.nn.Module): EffNetB2 feature extractor model. \n",
    "        transforms (torchvision.transforms): EffNetB2 image transforms.\n",
    "    \"\"\"\n",
    "    # Create EffNetB2 pretrained weights, transforms and model\n",
    "    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
    "    transforms = weights.transforms()\n",
    "    model = torchvision.models.efficientnet_b2(weights=weights)\n",
    "\n",
    "    # Freeze all layers in base model\n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "\n",
    "    # Change classifier head with random seed for reproducibility\n",
    "    torch.manual_seed(seed)\n",
    "    model.classifier = nn.Sequential(\n",
    "        nn.Dropout(p=0.3, inplace=True),\n",
    "        nn.Linear(in_features=1408, out_features=num_classes),\n",
    "    )\n",
    "    \n",
    "    return model, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9a6b4e10-25ec-492a-9485-6a72764f7699",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_mini/app.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_mini/app.py\n",
    "### 1. Imports and class names setup ### \n",
    "import gradio as gr\n",
    "import os\n",
    "import torch\n",
    "\n",
    "from model import create_effnetb2_model\n",
    "from timeit import default_timer as timer\n",
    "from typing import Tuple, Dict\n",
    "\n",
    "# Setup class names\n",
    "class_names = [\"pizza\", \"steak\", \"sushi\"]\n",
    "\n",
    "### 2. Model and transforms preparation ###\n",
    "\n",
    "# Create EffNetB2 model\n",
    "effnetb2, effnetb2_transforms = create_effnetb2_model(\n",
    "    num_classes=3, # len(class_names) would also work\n",
    ")\n",
    "\n",
    "# Load saved weights\n",
    "effnetb2.load_state_dict(\n",
    "    torch.load(\n",
    "        f=\"09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\",\n",
    "        map_location=torch.device(\"cpu\"),  # load to CPU\n",
    "    )\n",
    ")\n",
    "\n",
    "### 3. Predict function ###\n",
    "\n",
    "# Create predict function\n",
    "def predict(img) -> Tuple[Dict, float]:\n",
    "    \"\"\"Transforms and performs a prediction on img and returns prediction and time taken.\n",
    "    \"\"\"\n",
    "    # Start the timer\n",
    "    start_time = timer()\n",
    "    \n",
    "    # Transform the target image and add a batch dimension\n",
    "    img = effnetb2_transforms(img).unsqueeze(0)\n",
    "    \n",
    "    # Put model into evaluation mode and turn on inference mode\n",
    "    effnetb2.eval()\n",
    "    with torch.inference_mode():\n",
    "        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities\n",
    "        pred_probs = torch.softmax(effnetb2(img), dim=1)\n",
    "    \n",
    "    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)\n",
    "    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}\n",
    "    \n",
    "    # Calculate the prediction time\n",
    "    pred_time = round(timer() - start_time, 5)\n",
    "    \n",
    "    # Return the prediction dictionary and prediction time \n",
    "    return pred_labels_and_probs, pred_time\n",
    "\n",
    "### 4. Gradio app ###\n",
    "\n",
    "# Create title, description and article strings\n",
    "title = \"FoodVision Mini 🍕🥩🍣\"\n",
    "description = \"An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi.\"\n",
    "article = \"Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/).\"\n",
    "\n",
    "# Create examples list from \"examples/\" directory\n",
    "example_list = [[\"examples/\" + example] for example in os.listdir(\"examples\")]\n",
    "\n",
    "# Create the Gradio demo\n",
    "demo = gr.Interface(fn=predict, # mapping function from input to output\n",
    "                    inputs=gr.Image(type=\"pil\"), # what are the inputs?\n",
    "                    outputs=[gr.Label(num_top_classes=3, label=\"Predictions\"), # what are the outputs?\n",
    "                             gr.Number(label=\"Prediction time (s)\")], # our fn has two outputs, therefore we have two outputs\n",
    "                    # Create examples list from \"examples/\" directory\n",
    "                    examples=example_list, \n",
    "                    title=title,\n",
    "                    description=description,\n",
    "                    article=article)\n",
    "\n",
    "# Launch the demo!\n",
    "demo.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8760f341-1548-46bb-812a-b2632ade7cf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_mini/requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_mini/requirements.txt\n",
    "torch==2.2.0\n",
    "torchvision==0.17.0\n",
    "gradio==4.22.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4d84c058-993f-4d0a-ae9b-d9535e4267d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\n",
      "app.py\n",
      "examples\n",
      "model.py\n",
      "requirements.txt\n"
     ]
    }
   ],
   "source": [
    "!ls demos/foodvision_mini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ea26657f-bbda-42d0-a2c3-a649878eee50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: 09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth (deflated 8%)\n",
      "  adding: app.py (deflated 57%)\n",
      "  adding: examples/ (stored 0%)\n",
      "  adding: examples/2582289.jpg (deflated 17%)\n",
      "  adding: examples/3622237.jpg (deflated 0%)\n",
      "  adding: examples/592799.jpg (deflated 1%)\n",
      "  adding: model.py (deflated 56%)\n",
      "  adding: requirements.txt (deflated 8%)\n",
      "Not running in Google Colab, can't use google.colab.files.download(), please manually download.\n"
     ]
    }
   ],
   "source": [
    "# Change into and then zip the foodvision_mini folder but exclude certain files\n",
    "!cd demos/foodvision_mini && zip -r ../foodvision_mini.zip * -x \"*.pyc\" \"*.ipynb\" \"*__pycache__*\" \"*ipynb_checkpoints*\"\n",
    "\n",
    "# Download the zipped FoodVision Mini app (if running in Google Colab)\n",
    "try:\n",
    "    from google.colab import files\n",
    "    files.download(\"demos/foodvision_mini.zip\")\n",
    "except:\n",
    "    print(\"Not running in Google Colab, can't use google.colab.files.download(), please manually download.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6e0b677f-f32f-4505-8a35-d0cedd8afa2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"750\"\n",
       "            src=\"https://hf.space/embed/frank-chieng/foodvision_mini/+\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7568521d5310>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IPython is a library to help make Python interactive\n",
    "from IPython.display import IFrame\n",
    "\n",
    "# Embed FoodVision Mini Gradio demo\n",
    "IFrame(src=\"https://hf.space/embed/frank-chieng/foodvision_mini/+\", width=900, height=750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "871e6c63-c7fd-4823-996e-1a01768abebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create EffNetB2 model capable of fitting to 101 classes for Food101\n",
    "effnetb2_food101, effnetb2_transforms = create_effnetb2_model(num_classes=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "12f739f3-5d07-43e9-afc6-6a9767fb0721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "============================================================================================================================================\n",
       "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
       "============================================================================================================================================\n",
       "EfficientNet (EfficientNet)                                  [1, 3, 224, 224]     [1, 101]             --                   Partial\n",
       "├─Sequential (features)                                      [1, 3, 224, 224]     [1, 1408, 7, 7]      --                   False\n",
       "│    └─Conv2dNormActivation (0)                              [1, 3, 224, 224]     [1, 32, 112, 112]    --                   False\n",
       "│    │    └─Conv2d (0)                                       [1, 3, 224, 224]     [1, 32, 112, 112]    (864)                False\n",
       "│    │    └─BatchNorm2d (1)                                  [1, 32, 112, 112]    [1, 32, 112, 112]    (64)                 False\n",
       "│    │    └─SiLU (2)                                         [1, 32, 112, 112]    [1, 32, 112, 112]    --                   --\n",
       "│    └─Sequential (1)                                        [1, 32, 112, 112]    [1, 16, 112, 112]    --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 32, 112, 112]    [1, 16, 112, 112]    (1,448)              False\n",
       "│    │    └─MBConv (1)                                       [1, 16, 112, 112]    [1, 16, 112, 112]    (612)                False\n",
       "│    └─Sequential (2)                                        [1, 16, 112, 112]    [1, 24, 56, 56]      --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 16, 112, 112]    [1, 24, 56, 56]      (6,004)              False\n",
       "│    │    └─MBConv (1)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
       "│    │    └─MBConv (2)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
       "│    └─Sequential (3)                                        [1, 24, 56, 56]      [1, 48, 28, 28]      --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 24, 56, 56]      [1, 48, 28, 28]      (16,518)             False\n",
       "│    │    └─MBConv (1)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
       "│    │    └─MBConv (2)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
       "│    └─Sequential (4)                                        [1, 48, 28, 28]      [1, 88, 14, 14]      --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 48, 28, 28]      [1, 88, 14, 14]      (50,300)             False\n",
       "│    │    └─MBConv (1)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
       "│    │    └─MBConv (2)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
       "│    │    └─MBConv (3)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
       "│    └─Sequential (5)                                        [1, 88, 14, 14]      [1, 120, 14, 14]     --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 88, 14, 14]      [1, 120, 14, 14]     (149,158)            False\n",
       "│    │    └─MBConv (1)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
       "│    │    └─MBConv (2)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
       "│    │    └─MBConv (3)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
       "│    └─Sequential (6)                                        [1, 120, 14, 14]     [1, 208, 7, 7]       --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 120, 14, 14]     [1, 208, 7, 7]       (301,406)            False\n",
       "│    │    └─MBConv (1)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
       "│    │    └─MBConv (2)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
       "│    │    └─MBConv (3)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
       "│    │    └─MBConv (4)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
       "│    └─Sequential (7)                                        [1, 208, 7, 7]       [1, 352, 7, 7]       --                   False\n",
       "│    │    └─MBConv (0)                                       [1, 208, 7, 7]       [1, 352, 7, 7]       (846,900)            False\n",
       "│    │    └─MBConv (1)                                       [1, 352, 7, 7]       [1, 352, 7, 7]       (1,888,920)          False\n",
       "│    └─Conv2dNormActivation (8)                              [1, 352, 7, 7]       [1, 1408, 7, 7]      --                   False\n",
       "│    │    └─Conv2d (0)                                       [1, 352, 7, 7]       [1, 1408, 7, 7]      (495,616)            False\n",
       "│    │    └─BatchNorm2d (1)                                  [1, 1408, 7, 7]      [1, 1408, 7, 7]      (2,816)              False\n",
       "│    │    └─SiLU (2)                                         [1, 1408, 7, 7]      [1, 1408, 7, 7]      --                   --\n",
       "├─AdaptiveAvgPool2d (avgpool)                                [1, 1408, 7, 7]      [1, 1408, 1, 1]      --                   --\n",
       "├─Sequential (classifier)                                    [1, 1408]            [1, 101]             --                   True\n",
       "│    └─Dropout (0)                                           [1, 1408]            [1, 1408]            --                   --\n",
       "│    └─Linear (1)                                            [1, 1408]            [1, 101]             142,309              True\n",
       "============================================================================================================================================\n",
       "Total params: 7,843,303\n",
       "Trainable params: 142,309\n",
       "Non-trainable params: 7,700,994\n",
       "Total mult-adds (Units.MEGABYTES): 657.78\n",
       "============================================================================================================================================\n",
       "Input size (MB): 0.60\n",
       "Forward/backward pass size (MB): 156.80\n",
       "Params size (MB): 31.37\n",
       "Estimated Total Size (MB): 188.77\n",
       "============================================================================================================================================"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchinfo import summary\n",
    "\n",
    "# # Get a summary of EffNetB2 feature extractor for Food101 with 101 output classes (uncomment for full output)\n",
    "summary(effnetb2_food101, \n",
    "        input_size=(1, 3, 224, 224),\n",
    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
    "        col_width=20,\n",
    "        row_settings=[\"var_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2e1e9e6d-9fbb-4949-b7fd-06af27200fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Food101 training data transforms (only perform data augmentation on the training images)\n",
    "food101_train_transforms = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.TrivialAugmentWide(),\n",
    "    effnetb2_transforms,\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "274e0943-3cf3-47c1-8902-abcac9c211a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training transforms:\n",
      "Compose(\n",
      "    TrivialAugmentWide(num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)\n",
      "    ImageClassification(\n",
      "    crop_size=[288]\n",
      "    resize_size=[288]\n",
      "    mean=[0.485, 0.456, 0.406]\n",
      "    std=[0.229, 0.224, 0.225]\n",
      "    interpolation=InterpolationMode.BICUBIC\n",
      ")\n",
      ")\n",
      "\n",
      "Testing transforms:\n",
      "ImageClassification(\n",
      "    crop_size=[288]\n",
      "    resize_size=[288]\n",
      "    mean=[0.485, 0.456, 0.406]\n",
      "    std=[0.229, 0.224, 0.225]\n",
      "    interpolation=InterpolationMode.BICUBIC\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training transforms:\\n{food101_train_transforms}\\n\") \n",
    "print(f\"Testing transforms:\\n{effnetb2_transforms}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f23b68e2-5063-4cd8-9412-7fd7992264be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PosixPath('data/food-101_100_percent/train'),\n",
       " PosixPath('data/food-101_100_percent/test'))"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# food 101 datasets\n",
    "image_path = Path('data')\n",
    "data_100_percent_path = image_path / \"food-101_100_percent\"\n",
    "# Setup directory paths to train and test images\n",
    "train_dir = data_100_percent_path / \"train\"\n",
    "test_dir = data_100_percent_path / \"test\"\n",
    "train_dir,test_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "44873494-5f0c-4fba-8b03-c7646808c65b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from going_modular import data_setup\n",
    "food101_train_dataloader_effnetb2, food101_test_dataloader_effnetb2, food101_class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
    "                                                                                                 test_dir=test_dir,\n",
    "                                                                                                 transform=food101_train_transforms,\n",
    "                                                                                                 batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d8557a5a-8a02-4cc9-8f83-262fed48c7c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple_pie',\n",
       " 'baby_back_ribs',\n",
       " 'baklava',\n",
       " 'beef_carpaccio',\n",
       " 'beef_tartare',\n",
       " 'beet_salad',\n",
       " 'beignets',\n",
       " 'bibimbap',\n",
       " 'bread_pudding',\n",
       " 'breakfast_burrito']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food101_class_names[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b61fd3fb-842f-4de5-bf48-c69d87cc8a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_dataset(dataset:torchvision.datasets, split_size:float=0.2, seed:int=42):\n",
    "    \"\"\"Randomly splits a given dataset into two proportions based on split_size and seed.\n",
    "\n",
    "    Args:\n",
    "        dataset (torchvision.datasets): A PyTorch Dataset, typically one from torchvision.datasets.\n",
    "        split_size (float, optional): How much of the dataset should be split? \n",
    "            E.g. split_size=0.2 means there will be a 20% split and an 80% split. Defaults to 0.2.\n",
    "        seed (int, optional): Seed for random generator. Defaults to 42.\n",
    "\n",
    "    Returns:\n",
    "        tuple: (random_split_1, random_split_2) where random_split_1 is of size split_size*len(dataset) and \n",
    "            random_split_2 is of size (1-split_size)*len(dataset).\n",
    "    \"\"\"\n",
    "    # Create split lengths based on original dataset length\n",
    "    length_1 = int(len(dataset) * split_size) # desired length\n",
    "    length_2 = len(dataset) - length_1 # remaining length\n",
    "        \n",
    "    # Print out info\n",
    "    print(f\"[INFO] Splitting dataset of length {len(dataset)} into splits of size: {length_1} ({int(split_size*100)}%), {length_2} ({int((1-split_size)*100)}%)\")\n",
    "    \n",
    "    # Create splits with given random seed\n",
    "    random_split_1, random_split_2 = torch.utils.data.random_split(dataset, \n",
    "                                                                   lengths=[length_1, length_2],\n",
    "                                                                   generator=torch.manual_seed(seed)) # set the random seed for reproducible splits\n",
    "    return random_split_1, random_split_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7411c325-c8e1-49bc-a320-6b362639dcf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Splitting dataset of length 75750 into splits of size: 15150 (20%), 60600 (80%)\n",
      "[INFO] Splitting dataset of length 25250 into splits of size: 5050 (20%), 20200 (80%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(15150, 5050)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchvision import datasets, transforms\n",
    "# Create training 20% split of Food101\n",
    "train_data = datasets.ImageFolder(train_dir, transform=food101_train_transforms)\n",
    "test_data = datasets.ImageFolder(test_dir, transform=food101_train_transforms)\n",
    "train_data_food101_20_percent, _ = split_dataset(dataset=train_data,\n",
    "                                                 split_size=0.2)\n",
    "\n",
    "# Create testing 20% split of Food101\n",
    "test_data_food101_20_percent, _ = split_dataset(dataset=test_data,\n",
    "                                                split_size=0.2)\n",
    "\n",
    "len(train_data_food101_20_percent), len(test_data_food101_20_percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0b0b45c7-91a6-4d9f-8373-62708e40683f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "NUM_WORKERS = 2 if os.cpu_count() <= 4 else 4 # this value is very experimental and will depend on the hardware you have available, Google Colab generally provides 2x CPUs\n",
    "\n",
    "# Create Food101 20 percent training DataLoader\n",
    "train_dataloader_food101_20_percent = torch.utils.data.DataLoader(train_data_food101_20_percent,\n",
    "                                                                  batch_size=BATCH_SIZE,\n",
    "                                                                  shuffle=True,\n",
    "                                                                  num_workers=NUM_WORKERS)\n",
    "# Create Food101 20 percent testing DataLoader\n",
    "test_dataloader_food101_20_percent = torch.utils.data.DataLoader(test_data_food101_20_percent,\n",
    "                                                                 batch_size=BATCH_SIZE,\n",
    "                                                                 shuffle=False,\n",
    "                                                                 num_workers=NUM_WORKERS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "84b1e4d8-d981-4466-b413-9f08c3263d15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7abfdf71515f4026ab63b621a0a6bd4c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 3.6508 | train_acc: 0.2822 | test_loss: 2.8961 | test_acc: 0.4635\n",
      "Epoch: 2 | train_loss: 2.8550 | train_acc: 0.4471 | test_loss: 2.5819 | test_acc: 0.5102\n",
      "Epoch: 3 | train_loss: 2.6468 | train_acc: 0.4898 | test_loss: 2.4941 | test_acc: 0.5184\n",
      "Epoch: 4 | train_loss: 2.5469 | train_acc: 0.5093 | test_loss: 2.4494 | test_acc: 0.5345\n",
      "Epoch: 5 | train_loss: 2.4795 | train_acc: 0.5297 | test_loss: 2.4519 | test_acc: 0.5318\n"
     ]
    }
   ],
   "source": [
    "from going_modular import engine\n",
    "\n",
    "# Setup optimizer\n",
    "optimizer = torch.optim.Adam(params=effnetb2_food101.parameters(),\n",
    "                             lr=1e-3)\n",
    "\n",
    "# Setup loss function\n",
    "loss_fn = torch.nn.CrossEntropyLoss(label_smoothing=0.1) # throw in a little label smoothing because so many classes\n",
    "\n",
    "# Want to beat original Food101 paper with 20% of data, need 56.4%+ acc on test dataset\n",
    "set_seeds()    \n",
    "effnetb2_food101_results = engine.train(model=effnetb2_food101,\n",
    "                                        train_dataloader=train_dataloader_food101_20_percent,\n",
    "                                        test_dataloader=test_dataloader_food101_20_percent,\n",
    "                                        optimizer=optimizer,\n",
    "                                        loss_fn=loss_fn,\n",
    "                                        epochs=5,\n",
    "                                        device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "87e1ce19-97b5-48f1-a5a1-6b5253548c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_functions import plot_loss_curves\n",
    "\n",
    "# Check out the loss curves for FoodVision Big\n",
    "plot_loss_curves(effnetb2_food101_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7fbcbf36-2a00-4400-b894-b37f5ea364c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving model to: models/09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth\n"
     ]
    }
   ],
   "source": [
    "from going_modular import utils\n",
    "\n",
    "# Create a model path\n",
    "effnetb2_food101_model_path = \"09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth\" \n",
    "\n",
    "# Save FoodVision Big model\n",
    "utils.save_model(model=effnetb2_food101,\n",
    "                 target_dir=\"models\",\n",
    "                 model_name=effnetb2_food101_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a0e609fa-0dd0-470e-8a42-d04b35b2ebe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Food101 compatible EffNetB2 instance\n",
    "loaded_effnetb2_food101, effnetb2_transforms = create_effnetb2_model(num_classes=101)\n",
    "\n",
    "# Load the saved model's state_dict()\n",
    "loaded_effnetb2_food101.load_state_dict(torch.load(\"models/09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f314dfd5-50b3-4e4b-ab28-7725fabfeac4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrained EffNetB2 feature extractor Food101 model size: 30 MB\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Get the model size in bytes then convert to megabytes\n",
    "pretrained_effnetb2_food101_model_size = Path(\"models\", effnetb2_food101_model_path).stat().st_size // (1024*1024) # division converts bytes to megabytes (roughly) \n",
    "print(f\"Pretrained EffNetB2 feature extractor Food101 model size: {pretrained_effnetb2_food101_model_size} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3c261cea-914e-4517-9980-c1fb62176553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7b1cf7e3b7d4823a5b1e108447e1748",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 2.6940 | train_acc: 0.4697 | test_loss: 2.3151 | test_acc: 0.5715\n",
      "Epoch: 2 | train_loss: 2.6235 | train_acc: 0.4905 | test_loss: 2.2652 | test_acc: 0.5836\n",
      "Epoch: 3 | train_loss: 2.6128 | train_acc: 0.4921 | test_loss: 2.2551 | test_acc: 0.5828\n",
      "Epoch: 4 | train_loss: 2.5935 | train_acc: 0.4964 | test_loss: 2.2396 | test_acc: 0.5934\n",
      "Epoch: 5 | train_loss: 2.5989 | train_acc: 0.4962 | test_loss: 2.2400 | test_acc: 0.5898\n"
     ]
    }
   ],
   "source": [
    "from going_modular import engine\n",
    "\n",
    "# Setup optimizer\n",
    "optimizer = torch.optim.Adam(params=effnetb2_food101.parameters(),\n",
    "                             lr=1e-3)\n",
    "\n",
    "# Setup loss function\n",
    "loss_fn = torch.nn.CrossEntropyLoss(label_smoothing=0.1) # throw in a little label smoothing because so many classes\n",
    "\n",
    "# Want to beat original Food101 paper with 20% of data, need 56.4%+ acc on test dataset\n",
    "set_seeds()    \n",
    "effnetb2_food101_all_data_results = engine.train(model=effnetb2_food101,\n",
    "                                        train_dataloader=food101_train_dataloader_effnetb2,\n",
    "                                        test_dataloader=food101_test_dataloader_effnetb2,\n",
    "                                        optimizer=optimizer,\n",
    "                                        loss_fn=loss_fn,\n",
    "                                        epochs=5,\n",
    "                                        device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d78dd330-ad73-4940-b3c9-f883fd497729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_functions import plot_loss_curves\n",
    "\n",
    "# Check out the loss curves for FoodVision Big\n",
    "plot_loss_curves(effnetb2_food101_all_data_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e6c5bd41-54be-423e-8389-d68fdc63fa4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving model to: models/09_pretrained_effnetb2_feature_extractor_food101.pth\n"
     ]
    }
   ],
   "source": [
    "from going_modular import utils\n",
    "\n",
    "# Create a model path\n",
    "effnetb2_food101_full_data_model_path = \"09_pretrained_effnetb2_feature_extractor_food101.pth\" \n",
    "\n",
    "# Save FoodVision Big model\n",
    "utils.save_model(model=effnetb2_food101,\n",
    "                 target_dir=\"models\",\n",
    "                 model_name=effnetb2_food101_full_data_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "70720e81-7310-4ea1-9b87-2a038a9e717e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Food101 compatible EffNetB2 instance\n",
    "loaded_effnetb2_food101_full_data, effnetb2_transforms = create_effnetb2_model(num_classes=101)\n",
    "\n",
    "# Load the saved model's state_dict()\n",
    "loaded_effnetb2_food101_full_data.load_state_dict(torch.load(\"models/09_pretrained_effnetb2_feature_extractor_food101.pth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c522852c-8cee-47cb-81cb-ff0c8cd7280a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrained EffNetB2 feature extractor Food101 model size: 30 MB\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Get the model size in bytes then convert to megabytes\n",
    "pretrained_effnetb2_food101_full_data__model_size = Path(\"models\", effnetb2_food101_full_data_model_path).stat().st_size // (1024*1024) # division converts bytes to megabytes (roughly) \n",
    "print(f\"Pretrained EffNetB2 feature extractor Food101 model size: {pretrained_effnetb2_food101_full_data__model_size} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9a7aa462-24d6-4e78-b6de-1f26c47ce756",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Create FoodVision Big demo path\n",
    "foodvision_big_demo_path = Path(\"demos/foodvision_big/\")\n",
    "\n",
    "# Make FoodVision Big demo directory\n",
    "foodvision_big_demo_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Make FoodVision Big demo examples directory\n",
    "(foodvision_big_demo_path / \"examples\").mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9ab2d12f-8bbc-4abe-8c96-2fe004878553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-03-25 14:40:14--  https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/04-pizza-dad.jpeg\n",
      "正在连接 127.0.0.1:7890... 已连接。\n",
      "已发出 Proxy 请求，正在等待回应... 200 OK\n",
      "长度： 2874848 (2.7M) [image/jpeg]\n",
      "正在保存至: ‘04-pizza-dad.jpeg’\n",
      "\n",
      "04-pizza-dad.jpeg   100%[===================>]   2.74M   864KB/s    用时 3.2s    \n",
      "\n",
      "2024-03-25 14:40:18 (864 KB/s) - 已保存 ‘04-pizza-dad.jpeg’ [2874848/2874848])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Download and move an example image\n",
    "!wget https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/04-pizza-dad.jpeg \n",
    "!mv 04-pizza-dad.jpeg demos/foodvision_big/examples/04-pizza-dad.jpg\n",
    "\n",
    "# Move trained model to FoodVision Big demo folder (will error if model is already moved)\n",
    "!mv models/09_pretrained_effnetb2_feature_extractor_food101.pth demos/foodvision_big"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8138dda8-6687-4fcf-abdf-26e58580a53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple_pie',\n",
       " 'baby_back_ribs',\n",
       " 'baklava',\n",
       " 'beef_carpaccio',\n",
       " 'beef_tartare',\n",
       " 'beet_salad',\n",
       " 'beignets',\n",
       " 'bibimbap',\n",
       " 'bread_pudding',\n",
       " 'breakfast_burrito']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out the first 10 Food101 class names\n",
    "food101_class_names[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "242aa286-35f1-453d-842a-00328acc1038",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving Food101 class names to demos/foodvision_big/class_names.txt\n"
     ]
    }
   ],
   "source": [
    "# Create path to Food101 class names\n",
    "foodvision_big_class_names_path = foodvision_big_demo_path / \"class_names.txt\"\n",
    "\n",
    "# Write Food101 class names list to file\n",
    "with open(foodvision_big_class_names_path, \"w\") as f:\n",
    "    print(f\"[INFO] Saving Food101 class names to {foodvision_big_class_names_path}\")\n",
    "    f.write(\"\\n\".join(food101_class_names)) # leave a new line between each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c44d9364-dbe5-4c76-af41-fd0dccc5fdc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare']"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open Food101 class names file and read each line into a list\n",
    "with open(foodvision_big_class_names_path, \"r\") as f:\n",
    "    food101_class_names_loaded = [food.strip() for food in  f.readlines()]\n",
    "    \n",
    "# View the first 5 class names loaded back in\n",
    "food101_class_names_loaded[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9b3e9351-03c7-462a-8d3a-876411c88c85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_big/model.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_big/model.py\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from torch import nn\n",
    "\n",
    "\n",
    "def create_effnetb2_model(num_classes:int=3, \n",
    "                          seed:int=42):\n",
    "    \"\"\"Creates an EfficientNetB2 feature extractor model and transforms.\n",
    "\n",
    "    Args:\n",
    "        num_classes (int, optional): number of classes in the classifier head. \n",
    "            Defaults to 3.\n",
    "        seed (int, optional): random seed value. Defaults to 42.\n",
    "\n",
    "    Returns:\n",
    "        model (torch.nn.Module): EffNetB2 feature extractor model. \n",
    "        transforms (torchvision.transforms): EffNetB2 image transforms.\n",
    "    \"\"\"\n",
    "    # Create EffNetB2 pretrained weights, transforms and model\n",
    "    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
    "    transforms = weights.transforms()\n",
    "    model = torchvision.models.efficientnet_b2(weights=weights)\n",
    "\n",
    "    # Freeze all layers in base model\n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "\n",
    "    # Change classifier head with random seed for reproducibility\n",
    "    torch.manual_seed(seed)\n",
    "    model.classifier = nn.Sequential(\n",
    "        nn.Dropout(p=0.3, inplace=True),\n",
    "        nn.Linear(in_features=1408, out_features=num_classes),\n",
    "    )\n",
    "    \n",
    "    return model, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "321a382f-86ca-45e2-ada9-67256eb7982e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_big/app.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_big/app.py\n",
    "### 1. Imports and class names setup ### \n",
    "import gradio as gr\n",
    "import os\n",
    "import torch\n",
    "\n",
    "from model import create_effnetb2_model\n",
    "from timeit import default_timer as timer\n",
    "from typing import Tuple, Dict\n",
    "\n",
    "# Setup class names\n",
    "with open(\"class_names.txt\", \"r\") as f: # reading them in from class_names.txt\n",
    "    class_names = [food_name.strip() for food_name in  f.readlines()]\n",
    "    \n",
    "### 2. Model and transforms preparation ###    \n",
    "\n",
    "# Create model\n",
    "effnetb2, effnetb2_transforms = create_effnetb2_model(\n",
    "    num_classes=101, # could also use len(class_names)\n",
    ")\n",
    "\n",
    "# Load saved weights\n",
    "effnetb2.load_state_dict(\n",
    "    torch.load(\n",
    "        f=\"09_pretrained_effnetb2_feature_extractor_food101.pth\",\n",
    "        map_location=torch.device(\"cpu\"),  # load to CPU\n",
    "    )\n",
    ")\n",
    "\n",
    "### 3. Predict function ###\n",
    "\n",
    "# Create predict function\n",
    "def predict(img) -> Tuple[Dict, float]:\n",
    "    \"\"\"Transforms and performs a prediction on img and returns prediction and time taken.\n",
    "    \"\"\"\n",
    "    # Start the timer\n",
    "    start_time = timer()\n",
    "    \n",
    "    # Transform the target image and add a batch dimension\n",
    "    img = effnetb2_transforms(img).unsqueeze(0)\n",
    "    \n",
    "    # Put model into evaluation mode and turn on inference mode\n",
    "    effnetb2.eval()\n",
    "    with torch.inference_mode():\n",
    "        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities\n",
    "        pred_probs = torch.softmax(effnetb2(img), dim=1)\n",
    "    \n",
    "    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)\n",
    "    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}\n",
    "    \n",
    "    # Calculate the prediction time\n",
    "    pred_time = round(timer() - start_time, 5)\n",
    "    \n",
    "    # Return the prediction dictionary and prediction time \n",
    "    return pred_labels_and_probs, pred_time\n",
    "\n",
    "### 4. Gradio app ###\n",
    "\n",
    "# Create title, description and article strings\n",
    "title = \"FoodVision Big 🍔👁\"\n",
    "description = \"An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt).\"\n",
    "article = \"Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/).\"\n",
    "\n",
    "# Create examples list from \"examples/\" directory\n",
    "example_list = [[\"examples/\" + example] for example in os.listdir(\"examples\")]\n",
    "\n",
    "# Create Gradio interface \n",
    "demo = gr.Interface(\n",
    "    fn=predict,\n",
    "    inputs=gr.Image(type=\"pil\"),\n",
    "    outputs=[\n",
    "        gr.Label(num_top_classes=5, label=\"Predictions\"),\n",
    "        gr.Number(label=\"Prediction time (s)\"),\n",
    "    ],\n",
    "    examples=example_list,\n",
    "    title=title,\n",
    "    description=description,\n",
    "    article=article,\n",
    ")\n",
    "\n",
    "# Launch the app!\n",
    "demo.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c1cee946-0f68-440d-b350-b2d117f60a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing demos/foodvision_big/requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile demos/foodvision_big/requirements.txt\n",
    "torch==2.2.0\n",
    "torchvision==0.17.0\n",
    "gradio==4.22.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "20afcbf2-8676-4a4d-9851-58ae0cfb37df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: 09_pretrained_effnetb2_feature_extractor_food101.pth (deflated 8%)\n",
      "  adding: app.py (deflated 55%)\n",
      "  adding: class_names.txt (deflated 48%)\n",
      "  adding: examples/ (stored 0%)\n",
      "  adding: examples/04-pizza-dad.jpg (deflated 0%)\n",
      "  adding: model.py (deflated 56%)\n",
      "  adding: requirements.txt (deflated 8%)\n",
      "Not running in Google Colab, can't use google.colab.files.download()\n"
     ]
    }
   ],
   "source": [
    "# Zip foodvision_big folder but exclude certain files\n",
    "!cd demos/foodvision_big && zip -r ../foodvision_big.zip * -x \"*.pyc\" \"*.ipynb\" \"*__pycache__*\" \"*ipynb_checkpoints*\"\n",
    "\n",
    "# Download the zipped FoodVision Big app (if running in Google Colab)\n",
    "try:\n",
    "    from google.colab import files\n",
    "    files.download(\"demos/foodvision_big.zip\")\n",
    "except:\n",
    "    print(\"Not running in Google Colab, can't use google.colab.files.download()\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7e285ee3-f415-47b4-8b78-2d4e0d2b4537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"750\"\n",
       "            src=\"https://hf.space/embed/frank-chieng/foodvision_big/+\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x756a697dc610>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IPython is a library to help work with Python iteractively \n",
    "from IPython.display import IFrame\n",
    "\n",
    "# Embed FoodVision Big Gradio demo as an iFrame\n",
    "IFrame(src=\"https://hf.space/embed/frank-chieng/foodvision_big/+\", width=900, height=750)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bea6ce3-74aa-4659-a954-0c4945b8a663",
   "metadata": {},
   "source": [
    "exercise:\n",
    "4.Evaluate the ViT feature extractor across the whole Food101 test dataset rather than just the 20% version, how does it perform?\n",
    "Does it beat the original Food101 paper's best result of 56.4% accuracy?\n",
    "train ViT_b-16 on full datasets of food101"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b490ef09-01a4-4680-a206-9c181126786c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_vit_model(num_classes:int=3, \n",
    "                     seed:int=42):\n",
    "    \"\"\"Creates a ViT-B/16 feature extractor model and transforms.\n",
    "\n",
    "    Args:\n",
    "        num_classes (int, optional): number of target classes. Defaults to 3.\n",
    "        seed (int, optional): random seed value for output layer. Defaults to 42.\n",
    "\n",
    "    Returns:\n",
    "        model (torch.nn.Module): ViT-B/16 feature extractor model. \n",
    "        transforms (torchvision.transforms): ViT-B/16 image transforms.\n",
    "    \"\"\"\n",
    "    # Create ViT_B_16 pretrained weights, transforms and model\n",
    "    weights = torchvision.models.ViT_B_16_Weights.DEFAULT\n",
    "    transforms = weights.transforms()\n",
    "    model = torchvision.models.vit_b_16(weights=weights)\n",
    "\n",
    "    # Freeze all layers in model\n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "\n",
    "    # Change classifier head to suit our needs (this will be trainable)\n",
    "    torch.manual_seed(seed)\n",
    "    model.heads = nn.Sequential(nn.Linear(in_features=768, # keep this the same as original model\n",
    "                                          out_features=num_classes)) # update to reflect target number of classes\n",
    "    \n",
    "    return model, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "aebb1938-86ed-4e00-87cd-1d1d519e544c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(food101_class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "66a2a629-e942-4f9e-bcbf-d63d02cd8927",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create ViT model and transforms\n",
    "vit_food101, vit_food101_transforms = create_vit_model(num_classes=len(food101_class_names),\n",
    "                                       seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7adf3625-e29b-4ec5-9cb9-fc3ace45b378",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup ViT DataLoaders\n",
    "from going_modular import data_setup\n",
    "train_dataloader_food101_vit, test_dataloader_food101_vit, food101_class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
    "                                                                                       test_dir=test_dir,\n",
    "                                                                                       transform=vit_transforms,\n",
    "                                                                                       batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9df65577-d54b-41a6-9ab7-8d3a8c3a3cde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "============================================================================================================================================\n",
       "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
       "============================================================================================================================================\n",
       "VisionTransformer (VisionTransformer)                        [1, 3, 224, 224]     [1, 101]             768                  Partial\n",
       "├─Conv2d (conv_proj)                                         [1, 3, 224, 224]     [1, 768, 14, 14]     (590,592)            False\n",
       "├─Encoder (encoder)                                          [1, 197, 768]        [1, 197, 768]        151,296              False\n",
       "│    └─Dropout (dropout)                                     [1, 197, 768]        [1, 197, 768]        --                   --\n",
       "│    └─Sequential (layers)                                   [1, 197, 768]        [1, 197, 768]        --                   False\n",
       "│    │    └─EncoderBlock (encoder_layer_0)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_1)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_2)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_3)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_4)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_5)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_6)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_7)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_8)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_9)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_10)                  [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_11)                  [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
       "│    └─LayerNorm (ln)                                        [1, 197, 768]        [1, 197, 768]        (1,536)              False\n",
       "├─Sequential (heads)                                         [1, 768]             [1, 101]             --                   True\n",
       "│    └─Linear (0)                                            [1, 768]             [1, 101]             77,669               True\n",
       "============================================================================================================================================\n",
       "Total params: 85,876,325\n",
       "Trainable params: 77,669\n",
       "Non-trainable params: 85,798,656\n",
       "Total mult-adds (Units.MEGABYTES): 172.54\n",
       "============================================================================================================================================\n",
       "Input size (MB): 0.60\n",
       "Forward/backward pass size (MB): 104.09\n",
       "Params size (MB): 229.50\n",
       "Estimated Total Size (MB): 334.19\n",
       "============================================================================================================================================"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchinfo import summary\n",
    "\n",
    "# # Print ViT feature extractor model summary (uncomment for full output)\n",
    "summary(vit_food101, \n",
    "        input_size=(1, 3, 224, 224),\n",
    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
    "        col_width=20,\n",
    "        row_settings=[\"var_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "41368af7-b647-4246-a878-8ef9fa84c917",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup ViT DataLoaders\n",
    "from going_modular import data_setup\n",
    "train_dataloader_food101_vit, test_dataloader_food101_vit, food101_class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
    "                                                                                       test_dir=test_dir,\n",
    "                                                                                       transform=vit_food101_transforms,\n",
    "                                                                                       batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7ea8d11f-61da-411f-a151-fe239222db1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dbce3cba042541a9b3aa02c0b52d0971",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 1.5287 | train_acc: 0.6147 | test_loss: 1.0113 | test_acc: 0.7243\n",
      "Epoch: 2 | train_loss: 1.0741 | train_acc: 0.7171 | test_loss: 0.9145 | test_acc: 0.7495\n",
      "Epoch: 3 | train_loss: 0.9528 | train_acc: 0.7468 | test_loss: 0.8897 | test_acc: 0.7550\n",
      "Epoch: 4 | train_loss: 0.8774 | train_acc: 0.7651 | test_loss: 0.8817 | test_acc: 0.7580\n",
      "Epoch: 5 | train_loss: 0.8265 | train_acc: 0.7770 | test_loss: 0.8774 | test_acc: 0.7575\n",
      "Epoch: 6 | train_loss: 0.7864 | train_acc: 0.7868 | test_loss: 0.8876 | test_acc: 0.7578\n",
      "Epoch: 7 | train_loss: 0.7523 | train_acc: 0.7958 | test_loss: 0.8900 | test_acc: 0.7576\n",
      "Epoch: 8 | train_loss: 0.7269 | train_acc: 0.8003 | test_loss: 0.8879 | test_acc: 0.7601\n",
      "Epoch: 9 | train_loss: 0.7034 | train_acc: 0.8079 | test_loss: 0.9005 | test_acc: 0.7593\n",
      "Epoch: 10 | train_loss: 0.6833 | train_acc: 0.8115 | test_loss: 0.9107 | test_acc: 0.7584\n"
     ]
    }
   ],
   "source": [
    "from going_modular import engine\n",
    "\n",
    "# Setup optimizer\n",
    "optimizer = torch.optim.Adam(params=vit_food101.parameters(),\n",
    "                             lr=1e-3)\n",
    "# Setup loss function\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "# Train ViT model with seeds set for reproducibility\n",
    "set_seeds()\n",
    "vit_results = engine.train(model=vit_food101,\n",
    "                           train_dataloader=train_dataloader_food101_vit,\n",
    "                           test_dataloader=test_dataloader_food101_vit,\n",
    "                           epochs=10,\n",
    "                           optimizer=optimizer,\n",
    "                           loss_fn=loss_fn,\n",
    "                           device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a041b16b-a609-48e7-a432-7aabe329773e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_functions import plot_loss_curves\n",
    "\n",
    "plot_loss_curves(vit_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "845293d8-1589-450e-8b38-c258b9299d75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving model to: models/09_pretrained_vit_feature_extractor_food101.pth\n"
     ]
    }
   ],
   "source": [
    "from going_modular import utils\n",
    "\n",
    "# Create a model path\n",
    "vit_food101_full_data_model_path = \"09_pretrained_vit_feature_extractor_food101.pth\" \n",
    "\n",
    "# Save FoodVision Big model\n",
    "utils.save_model(model=vit_food101,\n",
    "                 target_dir=\"models\",\n",
    "                 model_name=vit_food101_full_data_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2fbcac7a-8150-4d47-a963-86d4550ac8f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Food101 compatible vit_b_16 instance\n",
    "loaded_vit_food101_full_data, vit_transforms = create_vit_model(num_classes=101)\n",
    "\n",
    "# Load the saved model's state_dict()\n",
    "loaded_vit_food101_full_data.load_state_dict(torch.load(\"models/09_pretrained_vit_feature_extractor_food101.pth\"))"
   ]
  }
 ],
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