ICSR-MultiHEAD.ipynb

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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gpu?True\n"
     ]
    }
   ],
   "source": [
    "#!pip install torch torchvision torchaudio\n",
    "import torch\n",
    "print('gpu?'+str(torch.cuda.is_available()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HEART-MET Assistive Robot Challenge with VideoMae Transformers\n",
    "\n",
    "Olmer Garcia-Bedoya, Jose Tomas Lorente , Sebastian Murcia\n",
    "\n",
    "**[Ekumens Inc](https://www.ekumenlabs.com/)**\n",
    "Copyright 2022- Ekumen Inc. All rights reserved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mPZJoSgn3_AN"
   },
   "source": [
    "## Colab Configuration"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "names=['Opening the door and walking in/out',\n",
    "        'Putting on a jacket',\n",
    "        'Touching a hot surface',\n",
    "        'Opening the fridge',\n",
    "        'Drinking water',\n",
    "        'Colliding against something',\n",
    "        'Eating food with a fork',\n",
    "        'Coughing or sneezing',\n",
    "        'Wiping a table',\n",
    "        'Reading a book',\n",
    "        'Neck roll exercise',\n",
    "        'Freehand exercise',\n",
    "        'Lying down',\n",
    "        'Limping',\n",
    "        'Talking on the phone',\n",
    "        'Using a computer',\n",
    "        'Falling down',\n",
    "        'Brushing teeth',\n",
    "        'Writing',\n",
    "        'Unknown activity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mY5TJSm4YRhs"
   },
   "source": [
    "The next code is adapted from (higgingFace->VideMAE-> VideoMAEForVideoClassification)[https://huggingface.co/docs/transformers/main/en/model_doc/videomae#transformers.VideoMAEForVideoClassification]"
   ]
  },
  {
   "cell_type": "code",
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    "id": "DB0vY8SrZGyS"
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   "outputs": [],
   "source": [
    "from decord import VideoReader, cpu\n",
    "from transformers import VideoMAEFeatureExtractor, VideoMAEForPreTraining,VideoMAEConfig,VideoMAEForVideoClassification\n",
    "import numpy as np\n",
    "import torch\n",
    "import os\n",
    "import json\n",
    "import csv #I make in a venv here more easy pandas..."
   ]
  },
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   "source": [
    "name_model=\"MCG-NJU/videomae-base-finetuned-kinetics\"\n",
    "#name_model=\"MCG-NJU/videomae-base\"\n",
    "feature_extractor = VideoMAEFeatureExtractor.from_pretrained(name_model)\n",
    "output=json.load(open('training_labels.json'))\n",
    "validation_labels = json.load(open('validation_labels.json'))\n",
    "path='training_set'\n",
    "validation_path='validation_set'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uUdrM7onc2Im"
   },
   "source": [
    "## Fine tuning VideoMAE\n",
    "\n",
    "Fine tuning is the gold of transformers, from few videos/images/sentences we can make a classifier.\n",
    "\n",
    "VideoMAE is based of a ImageMAE which is based in ViT (Vision Transformer), which is Based in Transformers, so let try.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4hXykNNbdm3F"
   },
   "source": [
    "### Create DataSet\n",
    "\n",
    "based in [image dataset](https://huggingface.co/docs/datasets/image_dataset). I think that it is not used in the best way the library [link text](https://huggingface.co/course/chapter5/4?fw=pt)"
   ]
  },
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   "metadata": {
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     "status": "ok",
     "timestamp": 1667574841424,
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     },
     "user_tz": 300
    },
    "id": "batqReKX57xm"
   },
   "outputs": [],
   "source": [
    "from datasets import Dataset\n",
    "import numpy as np\n",
    "\n",
    "my_set=[{'file_name':path+'/'+a,'labels':output[a][0],'description':names[output[a][0]]} for a in output.keys()]\n",
    "validation_set=[{'file_name':validation_path+'/'+a,'labels':validation_labels[a][0],'description':names[validation_labels[a][0]]} for a in validation_labels.keys()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'file_name': 'training_set/video0000.mp4', 'labels': 14, 'description': 'Talking on the phone'}\n",
      "{'file_name': 'validation_set/video0000.mp4', 'labels': 8, 'description': 'Wiping a table'}\n"
     ]
    }
   ],
   "source": [
    "print(my_set[0])\n",
    "print(validation_set[0])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "path_set = {}\n",
    "path_set['train'] = my_set\n",
    "path_set['validation'] = validation_set\n",
    "\n",
    "with open(\"path_train_set.json\", \"w\") as final:\n",
    "    json.dump(path_set, final)\n"
   ]
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-aa7a85bd564d0680\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset json/default to /home/tomas/.cache/huggingface/datasets/json/default-aa7a85bd564d0680/0.0.0...\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-fb539fc0c82eff47\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset json downloaded and prepared to /home/tomas/.cache/huggingface/datasets/json/default-aa7a85bd564d0680/0.0.0. Subsequent calls will reuse this data.\n",
      "Downloading and preparing dataset json/default to /home/tomas/.cache/huggingface/datasets/json/default-fb539fc0c82eff47/0.0.0...\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset json downloaded and prepared to /home/tomas/.cache/huggingface/datasets/json/default-fb539fc0c82eff47/0.0.0. Subsequent calls will reuse this data.\n"
     ]
    }
   ],
   "source": [
    "dataset = Dataset.from_json('path_train_set.json',field='train')\n",
    "dataset_validation = Dataset.from_json('path_train_set.json',field='validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 194,
     "status": "ok",
     "timestamp": 1667421550894,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "ohfjncJwfSNz",
    "outputId": "89189f43-ecd5-4c17-9684-aa7ee9bc3a15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'file_name': 'training_set/video0000.mp4',\n",
       " 'labels': 14,\n",
       " 'description': 'Talking on the phone'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gZaUiZpT6cos"
   },
   "source": [
    "### Training and validation\n",
    "\n",
    "Based in [Fine tuning ViT](https://huggingface.co/blog/fine-tune-vit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 917,
     "status": "ok",
     "timestamp": 1667575068999,
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     },
     "user_tz": 300
    },
    "id": "3EyE_Een6fK5"
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collate_with_path_multi_head(batch):\n",
    "    pixel_values = []\n",
    "    labels = []\n",
    "    for elem in batch:\n",
    "        file_path = elem['file_name']\n",
    "        videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0))\n",
    "        videoreader.seek(0)\n",
    "        file_size = len(videoreader)\n",
    "        batches = int(file_size/16)\n",
    "        for i in range(batches):\n",
    "            indices = np.linspace(i * 16, (i+1) * 15, num=16)\n",
    "            indices = np.clip(indices, 0, len(videoreader) - 1).astype(np.int64)\n",
    "            pixel_values.append(feature_extractor(list(videoreader.get_batch(indices).asnumpy()), return_tensors=\"pt\")['pixel_values'][0])\n",
    "            labels.append(elem['labels'])\n",
    "    return {\n",
    "        'pixel_values': torch.stack(pixel_values),\n",
    "        'labels': torch.tensor(labels)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1667575074037,
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     },
     "user_tz": 300
    },
    "id": "3G56RcBI6uOk"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_13695/2425197347.py:4: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
      "  metric = load_metric(\"accuracy\")\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from datasets import load_metric\n",
    "\n",
    "metric = load_metric(\"accuracy\")\n",
    "def compute_metrics(p):\n",
    "    return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4861,
     "status": "ok",
     "timestamp": 1667575087394,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "vsUZpL-66zPa",
    "outputId": "4b41f94c-11ee-4e14-c643-75bee01b3f8c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file config.json from cache at /home/tomas/.cache/huggingface/hub/models--MCG-NJU--videomae-base-finetuned-kinetics/snapshots/4800870825aa2ee4430280b6a6a3f6cabf2d68b7/config.json\n",
      "Model config VideoMAEConfig {\n",
      "  \"architectures\": [\n",
      "    \"VideoMAEForVideoClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.0,\n",
      "  \"decoder_hidden_size\": 384,\n",
      "  \"decoder_intermediate_size\": 1536,\n",
      "  \"decoder_num_attention_heads\": 6,\n",
      "  \"decoder_num_hidden_layers\": 4,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.0,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"Opening the door and walking in/out\",\n",
      "    \"1\": \"Putting on a jacket\",\n",
      "    \"10\": \"Neck roll exercise\",\n",
      "    \"11\": \"Freehand exercise\",\n",
      "    \"12\": \"Lying down\",\n",
      "    \"13\": \"Limping\",\n",
      "    \"14\": \"Talking on the phone\",\n",
      "    \"15\": \"Using a computer\",\n",
      "    \"16\": \"Falling down\",\n",
      "    \"17\": \"Brushing teeth\",\n",
      "    \"18\": \"Writing\",\n",
      "    \"19\": \"Unknown activity\",\n",
      "    \"2\": \"Touching a hot surface\",\n",
      "    \"3\": \"Opening the fridge\",\n",
      "    \"4\": \"Drinking water\",\n",
      "    \"5\": \"Colliding against something\",\n",
      "    \"6\": \"Eating food with a fork\",\n",
      "    \"7\": \"Coughing or sneezing\",\n",
      "    \"8\": \"Wiping a table\",\n",
      "    \"9\": \"Reading a book\"\n",
      "  },\n",
      "  \"image_size\": 224,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"Brushing teeth\": \"17\",\n",
      "    \"Colliding against something\": \"5\",\n",
      "    \"Coughing or sneezing\": \"7\",\n",
      "    \"Drinking water\": \"4\",\n",
      "    \"Eating food with a fork\": \"6\",\n",
      "    \"Falling down\": \"16\",\n",
      "    \"Freehand exercise\": \"11\",\n",
      "    \"Limping\": \"13\",\n",
      "    \"Lying down\": \"12\",\n",
      "    \"Neck roll exercise\": \"10\",\n",
      "    \"Opening the door and walking in/out\": \"0\",\n",
      "    \"Opening the fridge\": \"3\",\n",
      "    \"Putting on a jacket\": \"1\",\n",
      "    \"Reading a book\": \"9\",\n",
      "    \"Talking on the phone\": \"14\",\n",
      "    \"Touching a hot surface\": \"2\",\n",
      "    \"Unknown activity\": \"19\",\n",
      "    \"Using a computer\": \"15\",\n",
      "    \"Wiping a table\": \"8\",\n",
      "    \"Writing\": \"18\"\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"model_type\": \"videomae\",\n",
      "  \"norm_pix_loss\": false,\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_channels\": 3,\n",
      "  \"num_frames\": 16,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"patch_size\": 16,\n",
      "  \"qkv_bias\": true,\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.24.0\",\n",
      "  \"tubelet_size\": 2,\n",
      "  \"use_mean_pooling\": true\n",
      "}\n",
      "\n",
      "loading weights file pytorch_model.bin from cache at /home/tomas/.cache/huggingface/hub/models--MCG-NJU--videomae-base-finetuned-kinetics/snapshots/4800870825aa2ee4430280b6a6a3f6cabf2d68b7/pytorch_model.bin\n",
      "All model checkpoint weights were used when initializing VideoMAEForVideoClassification.\n",
      "\n",
      "Some weights of VideoMAEForVideoClassification were not initialized from the model checkpoint at MCG-NJU/videomae-base-finetuned-kinetics and are newly initialized because the shapes did not match:\n",
      "- classifier.weight: found shape torch.Size([400, 768]) in the checkpoint and torch.Size([20, 768]) in the model instantiated\n",
      "- classifier.bias: found shape torch.Size([400]) in the checkpoint and torch.Size([20]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "labels = names\n",
    "\n",
    "model = VideoMAEForVideoClassification.from_pretrained(\n",
    "    name_model,\n",
    "    num_labels=len(labels),\n",
    "    id2label={str(i): c for i, c in enumerate(labels)},\n",
    "    label2id={c: str(i) for i, c in enumerate(labels)},\n",
    "    ignore_mismatched_sizes=True,\n",
    "#     patch_size = 32,\n",
    "#     num_frames = 64\n",
    ")\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.hidden_layers = 16\n",
    "        self.lstm = nn.LSTM(20, self.hidden_layers)\n",
    "        self.label = nn.Linear(self.hidden_layers, 20)\n",
    "        self.vit = model\n",
    "\n",
    "\n",
    "    def forward(self, pixel_values: torch.Tensor, labels: torch.Tensor):\n",
    "        \n",
    "        h_t = torch.zeros(1, self.hidden_layers, dtype=torch.float32).to(device='cuda')\n",
    "        c_t = torch.zeros(1, self.hidden_layers, dtype=torch.float32).to(device='cuda')\n",
    "        \n",
    "        temp_x = None\n",
    "        output = None\n",
    "        \n",
    "        for i in range(pixel_values.size(0)):\n",
    "            temp_x = self.vit(pixel_values[i].reshape([1, 16, 3, 224, 224]))\n",
    "            output, (h_t, c_t) = self.lstm(temp_x['logits'], (h_t, c_t))\n",
    "        output = self.label(output)\n",
    "\n",
    "        return output\n",
    "    \n",
    "multi_model = Net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classifier.weight\n",
      "classifier.bias\n"
     ]
    }
   ],
   "source": [
    "for name, param in multi_model.vit.named_parameters():\n",
    "    if param.requires_grad:\n",
    "        if 'videomae' in name or 'fc_norm' in name:\n",
    "            param.requires_grad = False\n",
    "            \n",
    "for name, param in model.named_parameters():\n",
    "    if param.requires_grad:\n",
    "        print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "792"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 490,
     "status": "ok",
     "timestamp": 1667575094273,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "BmvmWI4a57xr",
    "outputId": "bafabebc-6568-4f83-acfd-14f46f7d57f0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
     ]
    }
   ],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "  output_dir=\"./videomae-icsr-heart\",\n",
    "  per_device_train_batch_size=1,\n",
    "  per_device_eval_batch_size=1,\n",
    "  evaluation_strategy=\"steps\",\n",
    "  num_train_epochs=8,\n",
    "  fp16=True,\n",
    "  save_steps=400,\n",
    "  eval_steps=400,\n",
    "  logging_steps=400,\n",
    "#   weight_decay =0.05,\n",
    "#   learning_rate=1e-4,\n",
    "  save_total_limit=5,\n",
    "  remove_unused_columns=False,\n",
    "  push_to_hub=False,\n",
    "  #report_to='tensorboard',\n",
    "  load_best_model_at_end=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 378,
     "status": "ok",
     "timestamp": 1667575104288,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "WYSSr6k157xr",
    "outputId": "43f59bb8-5f1c-4d81-8f75-1ce809c54371"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cuda_amp half precision backend\n"
     ]
    }
   ],
   "source": [
    "from transformers import Trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=multi_model,\n",
    "    args=training_args,\n",
    "    data_collator=collate_with_path_multi_head,\n",
    "    compute_metrics=compute_metrics,\n",
    "    train_dataset=dataset,\n",
    "    eval_dataset=dataset_validation,\n",
    "    tokenizer=feature_extractor,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "vfyxQlfx57xs"
   },
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 4359410,
     "status": "ok",
     "timestamp": 1667579468076,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "jxWG5dOA57xs",
    "outputId": "7cf52156-2971-4d4f-f8fc-dd4326645ea7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running training *****\n",
      "  Num examples = 792\n",
      "  Num Epochs = 8\n",
      "  Instantaneous batch size per device = 1\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 1\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 6336\n",
      "  Number of trainable parameters = 18152\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "grad can be implicitly created only for scalar outputs",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [47], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_results \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/trainer.py:1501\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_wrapped \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\n\u001b[1;32m   1498\u001b[0m inner_training_loop \u001b[38;5;241m=\u001b[39m find_executable_batch_size(\n\u001b[1;32m   1499\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inner_training_loop, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_train_batch_size, args\u001b[38;5;241m.\u001b[39mauto_find_batch_size\n\u001b[1;32m   1500\u001b[0m )\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1503\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1504\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1505\u001b[0m \u001b[43m    \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1506\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/trainer.py:1749\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1747\u001b[0m         tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining_step(model, inputs)\n\u001b[1;32m   1748\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1749\u001b[0m     tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   1752\u001b[0m     args\u001b[38;5;241m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m   1753\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_tpu_available()\n\u001b[1;32m   1754\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39misnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch\u001b[38;5;241m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m   1755\u001b[0m ):\n\u001b[1;32m   1756\u001b[0m     \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m   1757\u001b[0m     tr_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m tr_loss \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_globalstep_last_logged)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/trainer.py:2518\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2515\u001b[0m     loss \u001b[38;5;241m=\u001b[39m loss \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mgradient_accumulation_steps\n\u001b[1;32m   2517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdo_grad_scaling:\n\u001b[0;32m-> 2518\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscaler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscale\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2519\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_apex:\n\u001b[1;32m   2520\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m amp\u001b[38;5;241m.\u001b[39mscale_loss(loss, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimizer) \u001b[38;5;28;01mas\u001b[39;00m scaled_loss:\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/_tensor.py:487\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m    479\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m    480\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    485\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m    486\u001b[0m     )\n\u001b[0;32m--> 487\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/autograd/__init__.py:190\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    186\u001b[0m inputs \u001b[38;5;241m=\u001b[39m (inputs,) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(inputs, torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;28;01melse\u001b[39;00m \\\n\u001b[1;32m    187\u001b[0m     \u001b[38;5;28mtuple\u001b[39m(inputs) \u001b[38;5;28;01mif\u001b[39;00m inputs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m()\n\u001b[1;32m    189\u001b[0m grad_tensors_ \u001b[38;5;241m=\u001b[39m _tensor_or_tensors_to_tuple(grad_tensors, \u001b[38;5;28mlen\u001b[39m(tensors))\n\u001b[0;32m--> 190\u001b[0m grad_tensors_ \u001b[38;5;241m=\u001b[39m \u001b[43m_make_grads\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_grads_batched\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m retain_graph \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    192\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/autograd/__init__.py:85\u001b[0m, in \u001b[0;36m_make_grads\u001b[0;34m(outputs, grads, is_grads_batched)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out\u001b[38;5;241m.\u001b[39mrequires_grad:\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m out\u001b[38;5;241m.\u001b[39mnumel() \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 85\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrad can be implicitly created only for scalar outputs\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     86\u001b[0m     new_grads\u001b[38;5;241m.\u001b[39mappend(torch\u001b[38;5;241m.\u001b[39mones_like(out, memory_format\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mpreserve_format))\n\u001b[1;32m     87\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mRuntimeError\u001b[0m: grad can be implicitly created only for scalar outputs"
     ]
    }
   ],
   "source": [
    "train_results = trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1433,
     "status": "ok",
     "timestamp": 1667579469478,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "sz6jG1Oi57xs",
    "outputId": "c0c43c9f-1371-4bcf-a8fd-ef3e26c79c59"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to ./videomae-icsr-heart\n",
      "Configuration saved in ./videomae-icsr-heart/config.json\n",
      "Model weights saved in ./videomae-icsr-heart/pytorch_model.bin\n",
      "Feature extractor saved in ./videomae-icsr-heart/preprocessor_config.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** train metrics *****\n",
      "  epoch                    =           4.0\n",
      "  total_flos               = 33186001607GF\n",
      "  train_loss               =         1.424\n",
      "  train_runtime            =    1:07:49.50\n",
      "  train_samples_per_second =         7.026\n",
      "  train_steps_per_second   =          0.22\n"
     ]
    }
   ],
   "source": [
    "trainer.save_model()\n",
    "trainer.log_metrics(\"train\", train_results.metrics)\n",
    "trainer.save_metrics(\"train\", train_results.metrics)\n",
    "trainer.save_state()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 211
    },
    "executionInfo": {
     "elapsed": 38754,
     "status": "ok",
     "timestamp": 1667579508228,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "UWK9TSk457xs",
    "outputId": "40156d0d-7f99-4068-b73b-801f5c039f2c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 1220\n",
      "  Batch size = 32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** eval metrics *****\n",
      "  eval_accuracy = 0.6131\n",
      "  eval_loss     = 1.6013\n"
     ]
    }
   ],
   "source": [
    "metrics = trainer.evaluate(dataset_validation)\n",
    "trainer.log_metrics(\"eval\", metrics)\n",
    "trainer.save_metrics(\"eval\", metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 187,
     "status": "ok",
     "timestamp": 1667426738132,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "AZXFxB-LQQ-7",
    "outputId": "5d2fa64a-bde2-4d04-cf87-eede2f5ad788"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 1.6012746095657349, 'eval_accuracy': 0.6131147540983607}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "executionInfo": {
     "elapsed": 1134,
     "status": "ok",
     "timestamp": 1667579509321,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "j4yIILWEt-1p"
   },
   "outputs": [],
   "source": [
    "kwargs = {\n",
    "    \"finetuned_from\": model.config._name_or_path,\n",
    "    \"tasks\": \"video-classification\",\n",
    "    \"dataset\": 'HEART-MET',\n",
    "    \"tags\": ['video-classification'],\n",
    "}\n",
    "\n",
    "trainer.create_model_card(**kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "executionInfo": {
     "elapsed": 15981,
     "status": "ok",
     "timestamp": 1667579525293,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "Ev4EBAIBAdZa"
   },
   "outputs": [],
   "source": [
    "#!cp -R /content/videomae-icsr-heart/ /content/drive/MyDrive/ICSR 2022 HEART-MET Activity Recognition Challenge/models\n",
    "# !cp -R /content/videomae-icsr-heart/ /content/drive/MyDrive/models/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3QjKR9fwlq5h"
   },
   "source": [
    "## testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
     "referenced_widgets": [
      "e79a955f009e447ea03841ebd51f5475",
      "b56c2b116f4e43a89dcb1597f69deea2",
      "28828f3642a24b7984ed84748aa83c1d",
      "4a4bd6bca11747dab469e933548c31c8",
      "73578108cdb24b4586b123f9271e2fdf",
      "95889cc921824cfd90c53b7454175378",
      "35929e7e4214489b8559a583fd28d171",
      "cb03eff0750d46bca00a21f58e81f841",
      "dc0071c2fe704b7e8042069d1b6db79d",
      "03b78f48e2ba4253b34d0946d5b40e15",
      "ba01c404bddf435eba574cd35aa411e0"
     ]
    },
    "executionInfo": {
     "elapsed": 370,
     "status": "ok",
     "timestamp": 1667581438622,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "CiRrYrDBluLB",
    "outputId": "5e727387-d201-42b3-f0c9-0b50c478dea1"
   },
   "outputs": [],
   "source": [
    "# prepared_validation.to_json('/content/validation_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
     "referenced_widgets": [
      "b9ecdecdc0af4ed5a8b14fe484eff014",
      "fa915a0ccb534634b3b580fb3cb09c16",
      "9024ea625a284800811b19d7a0e26079",
      "7b96d51d2819479c9adf009d0d1b6710",
      "a07073612892430e94915351ad1c32e2",
      "7121d94ad31140a5a9d6b88edd690aa9",
      "2436895f138e4ae59b4ab936287554ed",
      "bfbbfff4a70e4d7183742f5e5118f6bb",
      "c74dd5761ca349ed9eba8b7073484ae7",
      "e6b09c270dac4dc19263fe6596985c0c",
      "cae454ab61954479a24dad6620b3d937"
     ]
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1667581461835,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "SgvuAkrKmZ26",
    "outputId": "5aa31c01-1542-4625-b04e-0f4002627370"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "846dc55c83574149a6febc7c3bcea0e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating json from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "56355"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepared_train.to_json('/content/training_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jRYvXjTEoBfj"
   },
   "outputs": [],
   "source": [
    "data['pixel_values']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 51665,
     "status": "ok",
     "timestamp": 1667582462627,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "yQGkGkfqmm4q",
    "outputId": "d5778354-97ff-44ac-8802-1ea55fa5dce4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "3 3\n",
      "6 6\n",
      "9 9\n",
      "15 15\n",
      "14 14\n",
      "17 17\n",
      "9 9\n",
      "6 6\n",
      "17 17\n"
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     "ename": "KeyboardInterrupt",
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     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [141], line 12\u001b[0m\n\u001b[1;32m     10\u001b[0m indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(videoreader), num\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m128\u001b[39m)\n\u001b[1;32m     11\u001b[0m indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mclip(indices, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(videoreader)\u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mint64)\n\u001b[0;32m---> 12\u001b[0m pixel_values \u001b[38;5;241m=\u001b[39m \u001b[43mfeature_extractor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvideoreader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindices\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_tensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m pixel_values \u001b[38;5;241m=\u001b[39m pixel_values\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/models/videomae/feature_extraction_videomae.py:166\u001b[0m, in \u001b[0;36mVideoMAEFeatureExtractor.__call__\u001b[0;34m(self, videos, return_tensors, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m     videos \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcrop_video(video, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize) \u001b[38;5;28;01mfor\u001b[39;00m video \u001b[38;5;129;01min\u001b[39;00m videos]\n\u001b[1;32m    165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdo_normalize:\n\u001b[0;32m--> 166\u001b[0m     videos \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnormalize_video(video, mean\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimage_mean, std\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimage_std) \u001b[38;5;28;01mfor\u001b[39;00m video \u001b[38;5;129;01min\u001b[39;00m videos]\n\u001b[1;32m    168\u001b[0m \u001b[38;5;66;03m# return as BatchFeature\u001b[39;00m\n\u001b[1;32m    169\u001b[0m data \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpixel_values\u001b[39m\u001b[38;5;124m\"\u001b[39m: videos}\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/models/videomae/feature_extraction_videomae.py:166\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    164\u001b[0m     videos \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcrop_video(video, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize) \u001b[38;5;28;01mfor\u001b[39;00m video \u001b[38;5;129;01min\u001b[39;00m videos]\n\u001b[1;32m    165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdo_normalize:\n\u001b[0;32m--> 166\u001b[0m     videos \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize_video\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvideo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage_mean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage_std\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m video \u001b[38;5;129;01min\u001b[39;00m videos]\n\u001b[1;32m    168\u001b[0m \u001b[38;5;66;03m# return as BatchFeature\u001b[39;00m\n\u001b[1;32m    169\u001b[0m data \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpixel_values\u001b[39m\u001b[38;5;124m\"\u001b[39m: videos}\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/models/videomae/feature_extraction_videomae.py:101\u001b[0m, in \u001b[0;36mVideoMAEFeatureExtractor.normalize_video\u001b[0;34m(self, video, mean, std)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(std, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m     99\u001b[0m     std \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(std)\u001b[38;5;241m.\u001b[39mastype(video\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[0;32m--> 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m(\u001b[49m\u001b[43mvideo\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "i=0\n",
    "predicted=[]\n",
    "for data in all_set['validation']:\n",
    "  #if i==2:\n",
    "  #  break\n",
    "  i+=1\n",
    "  videoreader = VideoReader(data['file_name'], num_threads=1, ctx=cpu(0))\n",
    "  videoreader.seek(0)\n",
    "  #indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader))\n",
    "  indices = np.linspace(0, len(videoreader), num=16)\n",
    "  indices = np.clip(indices, 0, len(videoreader)- 1).astype(np.int64)\n",
    "  pixel_values = feature_extractor(list(videoreader.get_batch(indices).asnumpy()), return_tensors=\"pt\")\n",
    "  pixel_values = pixel_values.to('cuda')\n",
    "  with torch.no_grad():\n",
    "        outputs = model(**pixel_values)#\n",
    "        pred=outputs.logits.argmax(-1).item()\n",
    "        predicted.append([data['file_name'],data['labels'],data['description'],pred])\n",
    "        print(data['labels'],pred)\n",
    "  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 370,
     "status": "ok",
     "timestamp": 1667582567088,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "xJwAm11IqFzd",
    "outputId": "e2cd5c3d-3d4f-479e-cbd7-e5caded5d79e"
   },
   "outputs": [
    {
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       "      <td>6</td>\n",
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       "      <th>1</th>\n",
       "      <td>training_set/video0528.mp4</td>\n",
       "      <td>4</td>\n",
       "      <td>Drinking water</td>\n",
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       "      <th>155</th>\n",
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       "      <td>17</td>\n",
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       "      <th>156</th>\n",
       "      <td>training_set/video0361.mp4</td>\n",
       "      <td>12</td>\n",
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       "      <td>12</td>\n",
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       "      <td>15</td>\n",
       "      <td>Using a computer</td>\n",
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      ],
      "text/plain": [
       "                           file  label              description  predicted\n",
       "0    training_set/video0694.mp4      6  Eating food with a fork          6\n",
       "1    training_set/video0528.mp4      4           Drinking water          4\n",
       "2    training_set/video0598.mp4     12               Lying down         12\n",
       "3    training_set/video0606.mp4      8           Wiping a table          7\n",
       "4    training_set/video0120.mp4      9           Reading a book          9\n",
       "..                          ...    ...                      ...        ...\n",
       "154  training_set/video0433.mp4     15         Using a computer         15\n",
       "155  training_set/video0467.mp4     17           Brushing teeth         17\n",
       "156  training_set/video0361.mp4     12               Lying down         12\n",
       "157  training_set/video0079.mp4      1      Putting on a jacket          1\n",
       "158  training_set/video0148.mp4     15         Using a computer         15\n",
       "\n",
       "[159 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(predicted,columns=['file','label','description','predicted'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 357,
     "status": "ok",
     "timestamp": 1667582611044,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "9ekeTxX5qsQ6",
    "outputId": "50260d97-65d5-46b0-e325-22952daf0302"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     precision    recall  f1-score   support\n",
      "\n",
      "Opening the door and walking in/out       1.00      1.00      1.00         7\n",
      "                Putting on a jacket       1.00      0.88      0.93         8\n",
      "             Touching a hot surface       0.75      1.00      0.86         3\n",
      "                 Opening the fridge       1.00      1.00      1.00         3\n",
      "                     Drinking water       1.00      1.00      1.00        15\n",
      "        Colliding against something       0.50      1.00      0.67         2\n",
      "            Eating food with a fork       0.94      0.84      0.89        19\n",
      "               Coughing or sneezing       0.50      1.00      0.67         4\n",
      "                     Wiping a table       1.00      0.83      0.91         6\n",
      "                     Reading a book       0.90      1.00      0.95         9\n",
      "                 Neck roll exercise       1.00      1.00      1.00         5\n",
      "                  Freehand exercise       1.00      1.00      1.00         4\n",
      "                         Lying down       0.75      1.00      0.86        12\n",
      "                            Limping       1.00      0.50      0.67         2\n",
      "               Talking on the phone       0.89      0.62      0.73        13\n",
      "                   Using a computer       0.87      0.87      0.87        15\n",
      "                       Falling down       1.00      0.75      0.86         4\n",
      "                     Brushing teeth       1.00      1.00      1.00        15\n",
      "                            Writing       0.91      0.91      0.91        11\n",
      "                   Unknown activity       0.00      0.00      0.00         2\n",
      "\n",
      "                           accuracy                           0.89       159\n",
      "                          macro avg       0.85      0.86      0.84       159\n",
      "                       weighted avg       0.90      0.89      0.89       159\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(df['label'], df['predicted'], target_names=names))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 473
    },
    "executionInfo": {
     "elapsed": 2996,
     "status": "ok",
     "timestamp": 1667582709066,
     "user": {
      "displayName": "Olmer Garcia",
      "userId": "02129671575696093186"
     },
     "user_tz": 300
    },
    "id": "9s-30mn9rE4c",
    "outputId": "6c7b766b-6ad7-4914-fc5a-43ec39eea6ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fc85069aa10>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ehhugNN3MDsunl/Rl7I/zcn+LWwiidfozZ9+7vRE3v3sVcLqZPZ9uX/ODv640swlp+1+Me9b8GfAeMNfWLBqSihkBfGNmVym7FnQj3G3kLXC98OOB/+E0nb0ltQWeBi7CPW+/Ebf0ZBPgPjP7q9x61bfgevRD0p8bpwgGrkCg9NTWwNWjbzMbNbZzrNhBm8+ubB1moGEjv1yk7z1eD8w2s6vLkMd1uIuIO+q77kwU0hgXQs3/2yZ2bNV/X6vz+gOBJFEXjfGNYzeLFbvH5u/VW2McnhkHiuEYP6DrTdxiHzfXdwKSLgK2x410TiQDBi3h1knvcMfktznkpAV1EtukySquu+hxbrr0UW654hF+NyR34xu33FLkGmKTUX+lxxaKmai2qliv+iQ0xvWI3EL2r/vXZ5I+jbxvmr+EnGVnXE9ZRVqqcmFmV/sBTj3N7DAzWxapb7Tc6kuxkLNh/aaIHM4zs+1szdKImcpeT9Iz/vxme7ZdEkplHFq5shFnXvxThp99IMPPPoABW3/KVt0y35oOlqjyx5a7/kqPLZYaFOuVD0m3S/pcGYx+kv4oyeRW+ctLaIzrETP7KjVSF7gJSDVq/Uq1cEU9WqqKpQtu3ec6xc+73gbAn9/767qOXJTOOCS+W+GmeTRuVEPjRjVYllGfwRJV/thy11/pscXgBnBVxXrFYDTws/SN3p2wN27GRixCY1xmJO3hDVAz/VXWen77nNQVlaQBcms1I6mV1pit3pB0cKSsv3nL1BQ/cCndUjVB0mWSXpH0nqRd/fYWkh6Q9Ja3Vb0saZ3nJIphxfLsJulFSR+meslyXOH3nRnpqV4K7Op7r6el1bexpIla495O5bs0EjNEbt3kVK/8Jkkv4wZt3Q0M9Pt3zZa/pG6+Bz1D0nR5y5akM338G5IujPs7hdIah6pUw02XPMqDN9/H9Jmb8M4Hmb0rwRJV/thy11/pscVgiJXWONYrb1lmE4GFGT66GjegNPagrNAYl5dmuCurQ/382Ma40b65OA9YbGZ9zKwv8Jzf3hKY4k1TE4Fjsuzf2My2w5m1LvDbTgAWmVlPX37/LPvGtWJtjJu/ux+usQX4Jc68tTWwJ04vuTFwNjDJ917TB4H9BnjK30nYGnid/GwK7GRmRwG/j5T9QY787wGu9+duJ9wUqb2B7sB2Pu/+knLPO6onaqyK4X8+kF+fOJQeXb+ky6aLyp1SINCgqDbFehWDpAOBT/20yNiExri8NAI+MrP3/Ps7gXxf+HviRjADYGapb+LvgZQj+lXc7d9MjMkQswtwny9vFvBGln0H+17zTJzZqleWuEfMrMbM3gJSa5/tAtxrZtVmtgD4LzAwy/4ppgJH+ilMffxSmPl40Ju2YuUvqTXQ0cweBjCz7/wz8L396zVgOrAlrnFeC2XRYdaHcejbZesx460fM2DrTzN+HixR5Y8td/2VHlsMKQNXzNvUHVL/f/tXJtvgauSc+H8Bzi80r9AYJ5eoRSvOjPeVtmaeWi5b1ooYMeugwqxYUcNW0bPr/S2g3XACj9GRgWjRWz/pOXybqawC8weX9yWRZ/rdzOy2DDmOMrMBZjagScSeWSrjUJvW39GyhTu9TZusYts+8/lkfrBEJTW23PVXemyx1FhVrBfwZer/b/8alaforsDmwAxJc3B36qZL+nG+nIKBq7xUA10kdTOz94Hf4nqMAHNwt4ufBA6O7PM0bhGGUwEktYv0jotlMnAIbjWnnrgFHNIpxooVZRJwnKQ7ca7p3YAzgY5ksVtJ2gyYZ2a3+Gfp2wJ3AQskbYUzeh2EM2TlI2P+ZvaNpHmSfmFmj/h6GuHc1hdJusfPqe6Iu+CJZdUolXGofbtlnHX8C1RVGZIxcUoXXn4t0zobwRKVhNhy11/pscWQGsBVCsxsJvCj1HvfIA8wsy+z7pSKDdKP8uBvvS7F3Qa9EndhNBU43sxW+MFKtwFLgAm4X+gg35Bcj2uoq4ELzWyMIiYuP2hqP2+gGgEsNbMr/SCwM8xsmh8cNs3MukhqibtF3hN4B2erGmpms9NyjmPFGo2zWz3k36eMXgIux60sZcDFZna/nL7yKWADYHT0ubGkI3AN9kp/rn5nZh/547sMZ9uaBrTyx5pe9yDWtohlzF9Sd9xc6Q6+rqFm9qGkP+CeO+PrPzybfQuC9CMQqA9qK/3YvE8r++uY3rFif/eTl3PWJeleYBDuu2MBcEH0DlpojAMFIedsbmJm3/mRxM8APUo13apSCY1xIFB6atsYd+ndys4f0y9W7NE9JtebgSvcpg6AW6bwed9LFXBCaIiTQyENbGi4A4F8xBN61DdhAFcAM/vGD07Y2sz6mtmT5c6pEii3KrAQdWa5c63k2HLXX+mxhWIQdJiBukXSppIelTRb0geSrlEttZpZ6qkzpaakU/3w/9T7jBrPAsq710s5TsvwWca85RSc6+jr6pIkqALjqjOTkGulxpa7/kqPLZY6NHDVGaExbqD4AVFjcHN6uwM/wa3V+7e6rquOlZqn4m6L1xo/XWCg781fnfZZ43KqQJOhCoynzkxCrpUaW+76Kz22GAxRY/Fe9UlojBsuuwPfpZYP9KKL04Cj5PSWw3yveYLvOadsW0g6XE6J+bqkm/0ALiQtVQmVmpJOATbBPZ9+PrI9U50bSvq3nI5yqtyawumMBzr649jV5/ZPSdOAP6Tl3d/XMQM3NSxVd9a8Je0t6SU5ReaDfiR7LJKiCoyjzkxCrpUaW+76Kz22WELPOFCX9MJZtFZjZktwYvJuftN2uDnKfYGhco7rrYBDgZ29ZrIaOMzHl1SpaWbXAvOBwWY2OE+d1+AW0hjoj+HWDHkcAHzgpRyT/Lam/vn3VWmxdwAn+3qiZMzbT/06F9jTzLbFTaE6Pcv5SCxBnRkIrI1zUzeK9apPwmjqyuZp80sMShqDU1KuwjU4U92dbpoDqQeJ6UrNvbKUm02peQ04paakbErNdLLVuSfQU2vWolhfUiszy/eMeZ3VmSS1Bdp6oxfA/+HmO+fKewfcvOvJPoemwEsZyj4WOBagWeTue9JUgVF15px57eql/hBb/vorPbYYDFJ2rUSRvIwCcXmLtN6npPWBzsD7flP6JHLDTV26M6J57BERd5RUqZmFbHVWATtE8uwYoyGGLDrMIhDuYiZVf08zOzo9qL51mKVQZyYh10qNLXf9lR5bLNUo1qs+CT3jhsuzwKWSfmdmd/nnvlfhLFbLfG9uL0ntgeXAL4CjgGXAo5KuNrPP/eetzWxuLfOJo9QEp65sDeQz0owHTgauAJDUz8zirNq0Dmb2taSvJe1iZi+w5rZ8rrynANfLq0q9payjrVnUIydJUAXGVWcmIddKjS13/ZUeWwxmSmTPOBi4GjByC1jfgFtRqAr4D07/uELSMFwD3AYnK7/bzC70+x0K/NnvsxI40cymqH6UmicDJwHzzWxwjjo74LSfW+EuGiea2fC0srrg9Je9/fvVufn30bz7A7fj7g6MB/Y1s9658pa0O067merynmtmY7P9Pkpl4CqEIP0IVDq1NXB17NXWjrs/3mqoF/R5rN4MXKExrlB8YzzAzE6qp/oapFKzLvMOjXEgUHpq2xhv0qudHX3foFixF/d9JOgwAw2OhqrUbKh5Z6SQBva6uZNjxZ28WaZZZYFAw8QN4EqeDjM0xhWKmY0GRtdjfd8A9XIFWZeUMu8Bg5Yw/KL5NKoynry3PQ+M3ChRsfec0Y1Zz7Wj9QYr+cvT7nH8f67uxIv3bkSrDdy8zv3P/DgRuTbk2HLXX+mxxVDfc4jjkLyMAmVDUrUXaLzpBRl/lJTxb0TSJpLyrmesDLrLuPuWEjkl5m9KVX4SVIH5Yrcf+jkn3PnWOvsNPno+Zz85g7OfnEGv3deel9wQjitJseWuv9JjiyEYuAINgeV+Gk8v3HzffVgj9ViNV03ON7MhxVRSm33rkC5AQY2xpNh3kpKgCswX2237JbRouyruITWY40pSbLnrr/TYYqmhKtarPgmNcSAjZvY5TmZxkhzDJI2V9BzwrCKLLfjPxkgaJ6fevDy9PEkdvFry53H3lXS0nHLzFUm3SBqZodyZktr6HL+SXxhC0l2S9vJ1TfJKy+mSdvK7Xgrs6u8EnCapkaQr5NSbb0g6zpczyO8/Fje3OxZJUAUWqxWceNfGXPLTftxzRjeWLV7bQpSEXBtSbLnrr/TYYjCDalOsV30SnhkHsmJmH/rRxj/ym7YF+prZQj+tKEo/YBucEORdSdeZ2ScAcr7psbipQU/H2RcnADnP1/kN8BwwI0Oak4GdgbnAh8CuwF3AjsDxuPEae/nR0t2Be3HPiM/GTYPaz+d4LLDYzAZKWg9n3hofOe7eZvZReuXZDFwNlV0O/4yfnfIJCJ64sjMPX7R5uVMKBOoUQ6yqqV/VZRxCzzhQCE+b2cIsnz1rZovN7DtcD3Izv70JTlBylpk9XcC+2wH/NbOFZrYSeDDLvpOA3fzrRqCPpI443/S3vv5bJM30ZfTMUs7ewO8kvQ68DGwAdPefvZKpIYbsBq4kqAKL0Qquv+FKqhpBVRXs9OsFzJ2x9toYSci1IcWWu/5Kjy2WJBq4QmMcyIqkLXA91JS7OpdqckXk56jWchXOOf3TIvaNw0Rcb3hXYALwBTAE10iDW8lqAbA1rkecbb1n4RaSSOkvNzezVM+4YMVmElSBxWgFFy9Y86U346kN2LjHssTl2pBiy11/pccWQ2pqU9IGcIXb1IGMSNoQuAkYaWYmFf2HaTgN54OS/mRml8XcbyrwT0ntcLepDwZmrlO42Sfe1tXU31Z/ATgDZ/kCZyCbZ2Y1ko4AUvenUlrOFE8Bx0t6zsxWSvoJ8Glhh7qGJKgC88XecfJPeP+lNixd1Jjzth/Avqd9zOwpbZj3VkskaL/pCn719/eZ/tiGsctMwnElKbbc9Vd6bHHUnQ5T0u3AfsDnERPgFcD+uEVwPgCONLOv85YVDFyBFJKqcQ1eE1yP9v+Af/iGbBgRo1dURZnhs8eBK81sQkp36Z/DjgUexWk74+x7LHAmsBCnqpxnZudkyPv/gEZm9hs/QOsFYEMz+8o/J/437qJgHE792cpLPp7C3Y4ejVu56WLc/0TC9bB/gXuWvfrZci6SYOAqhCD9CDREamvg2rBnBzvwrrz/OwNw28A7c9YlaTdgKXBXpDHeG3jOzFZJugzAzP6Ur67QGAcSi/ySiX5K0cPA7Wb2cLnzykZDa4zj0qhXj9ix1W++W8JMAoHaN8YdtupgP7/zwFixd21/e9660h35aZ8dBAwxs8PSP0sn3KYOJJkRkvYEmuEWd3ikzPkEAoEGTkr6EZMOkqZF3o8ys1EFVHcUGdZYz0QYwBVILGZ2hh9MtaWZnWIN7DbOgEFLuHXSO9wx+W0OOWlBomPjxnXYcBmXXPE8N906jhtvGceBB+VeUbLcx5WE2HLXX+mxxVCDYr2AL1OzJfwrdkMs6Rzc47574sSHxhiQ9GNJ90n6QNKrkv7jB/Bki49KKwb555xIOkDS2Vn2Wer/LbsKMhOSbpVbz7fQ/fpJ2rcUOdUmD0kjJJ2RJfbFUueTBFVgKZSN1dXi1pv7Mfz3P+P0U/ZgvwPep1PnzHakch9XEmLLXX+lxxZDfYym9mNh9gMOi9uJ+ME3xnLDhB8GJphZVzPrj1vrt2AzuZmNNbNL88QkQQW5Dmb2ezOLbZiK0A8oe2NMAXmY2U75o2pHElSBpVA2LlrYnA/ebwfA8uVN+Pjj9enQYXkijysJseWuv9Jji6XGqmK9ikHSz4CzgAPMbFm++BQ/+MYYGAysNLObUhvMbIaZTfKKxSskzfLaxUNzFeTVjiP9z5vL6R9nSro4ElOXKsjtfB2vSXpRUg+/vYWkByS9JelhSS9LGuA/u1HSNLnFIC6MlDUhErNU0t/kFouY4g1aSBrqz8UMSRMlNQX+Chwqp5U8NC2/Xj7/1+UUk9399tN9ObMknRo5L+9IGu2P+x5Je0qa7M/Ndj6upaTbfbmvSTowRx49/XF9KOmUSF6puxSD/OcP+brv8RdnSNrXb3tV0rWpux9xSYIqsBTKxig/2uhbunb7mnfe2aDecm1oseWuv9JjiyJmrzhOz7hToZgAACAASURBVFjSvcBLQA9J8yQdDYzETZt82n8f3ZSzEE8YwAW9cVKKTPwS1+PaGugATJU0MWa51wA3mtldkk7MEVcbFeQ7wK5+CP2ewN9x83FPwBmoekrqDbwe2eccr7NshHNM9zWzN9LKbQlMMbNz/AXCMbhpP+cDPzWzTyW1NbPvJZ1PZGpSGsOBa8zsHt9gNpLUHzgS2B43hehlSf8FFgHdgKG4QQ9TcQs57AIcAPwFN9XoHNy0gaMktQVeAZ7xuUWnSI0AtsRdbLX25/ZGb/OKsg3QC5iPV2vKDdi4GdjNzD7y/8NlRBWmw4xLs2YrOef8Fxl1Yz+WL6tbO1IgUEoMWFVH84zN7NcZNt9WTFmhZ5ybXYB7zazazBYA/wUGxtx3Z5wHGdx83WzURgXZBifTmAVcjWtUUnnfB2Bms4BoY3uIpOnAaz4+03Pi74FUT/BV3ApH4Bqr0ZKOYY08IxcvAX+R9CdgMzNb7nN72My+NbOlwBicPQvgIzObaWY1wJv+3Bhu7nMqh72Bs+W0lRNwI607Z6n/CTNbYWZf4iximR49vGJm83ydr/t6tgQ+jCgwszbGlaDDLFQ/2KhRDedc8CITnuvMiy9smjWu3MeVhNhy11/pscWQVANXaIzdl37/EpUd58F9bVSQFwHP+/lt++MapqxI2hxnp9rDzPoCT2TZZ2Vk0MHqnMxsOHAu0Al4VVLm+5MeM/sXrle7HPiPpN3zHE/0XNRE3tew5rwIODiirexsZm/HKC/bua3N+c9KElSBpVA2gnHqH6fyycfr8/C/c88/LvdxJSG23PVXemyxJLExDrep3S3gv0s6NjVsXVJfXK9zEnCcpDuB9rjFCM4kT6PnmQz8CrgbyDvhO41YKkifY0rZOCyt7kOA5+VGSPfx29fHeZYX++fA++B6l7GQ1NXMXsbdWt4H1yinayWj8VvgepjXSuoM9MW5pEdLuhTXsB4E/DZuDjhr1smSTvaazm3M7LVceRTBu8AWkrqY2Rwg51iBTCRBFVgKZWPPXl+yx15z+ejDNlx3k1N333l7H6a9snHijisJseWuv9Jji6HAecb1RjBw4aYbAf/E9ZC/A+YApwLvA5fjGi0DLjaz+7W2CnIQXpeoiNrR90L/BbTCKSBP9RrG6L6r430eBakgJe0I3IlrYJ8ADjezLpJa+u09/b5bAEPNbLak0cBOwCfAYmCsmY2WNMEfxzR5haWvYwiwn5kNkzQGt5KRcCsxnQq0wzWQTYBLzGz1BHe5aV6/BVYCnwG/8c+rT8c9Fwa41cz+GT0vft/R/v1Daeesuf9d7YS7s/ORP/fto3kAWwFLzexKX94sfxxztEbRufp352NGAtP8+dgfuMKf26lA63wWnWDgCgauQOmprYGrzZYb2U6jfhUrdtz/u7ZWdRVCaIwTimqhgvSDs5qYW8O3K26AUw8z+z7PrgFP5PwLuB6YbWZX59qnUhvjQnhq/uv5gzz7brN37NjqBZ/nD/oB0GijH+UPIhnnK26uUFi+tW6Me2xkO4zKNO5qXcYPuqbeGuNwmzq51EYF2QJ3i7oJrhd7QmiIC+YYuVWemuIGu91c5nwCgUAdYMCqmuQNlwqNcUIxs4z2qJj7foNbuzdQJL4XnLMnnI8Bg5Yw/KL5NKoynry3PQ+MzO6RKXdsXZV51WmdePmZ9WnbYRWjnl9zy/rR2zowdnQHqhoZ2++xhN+f9791yj31gjfZbrcv+HphU04YmtvLUu7zVUhsqepvSOerkFwLzaFQkvrMOHmXBxWOpGo/ETz1yqjPjMQP88+0U++L0lZmKfsUSW9LiuVOzVPWHLl1hQvdb/XxSPpLZPtqOUpdIOleOfHIaQXsM9o/My+YJKgCy6Fs3PvQhfztng/X2uf1ya148ak23PjMu9wy4V2GHP9FxrKfeWwTzjtx24yflfoclCq2lBrIhnS+4uZaaLnFYqZYr/okNMb1z/LItJx++fSZuFHSqxvjWmgrM3ECsFe+gUmlJO14/pIzuEgk/RgYaGZ98z33jexTq7tGSVAFlkPZ2GeHb2ndrnqtfR6/awMOPWkBTddz41PadliVsexZ09vxzeL880nLfb4KiS2lBrIhna+4uRZabrEUsFBEvREa44Qg6XxJU+UUkaPkGIK73XyP70U3VzxtZVf/fqaki+X1j2n13YQbZf2kpNMktZf0iO89TvHTu8ixfQNJ4+W0mrfCun+5cvrMf/if/yDpQ//zFpIm+58nSBrgpzo198eZ6qk3klOBvunrap6hjv3ldJ+vSXomdQ7SGA909GXvKreoxBR/TA/LTSFL5fJPOQPXH9Lqucj3lOPIThKhCiy3sjHFpx80Y9bLrTjl590545fdePf1dX6NBVHu81VIbBI0kOU+B6XMtxjMkjnPODTG9U+qwUm9UnNYR5rZQD+1pzluGs5DwDTcyh/9vMEqSkpbuTVu/u4xfvs1OA1lH2BepiS8wGM+MNj3Fi8EXvMykL8Ad/nQbNsvAF4ws1640d6ZLFiTWGPX2hX4SlJH//NaWlEzO5s1dw1SPfXuwPW+jq9x863TeQHYwcy2wVnHzsoQcwDwgS97kj+GP/ljmumPJUVTb9S6KrVB0hXAhsCRZrZWt0/SsXKu72kr1/KHBFJUV8M3Xzfimsdn8/vz5vO347oQJnEEyoeorqmK9apPwgCu+me5mfXLsH2wpLNwI6Hb48xgj+UpK11buZf/eUecxxncXOcrY+S1C76xM7PnfM93/Rzbd8O5uzGzJyQtSi/QzD6T1EpSa5wg5F9+v11xGsx8fGRmqbkyUS1nlE2B+yVtjBv5/FGGmNVIagO0NbP/+k13srZuNH0h8POAl83s2EzleVHMKHBTm1Lbk6AKLLeyMRqz876LkWDLbZZRVQWLF8a6wVDrHModmwQNZLnPQSnzLZb6fh4ch9AzTgCSmgE3AEN8b/YW4lm+MmorE8aLuIUh3mVNT3lHnCUsH3FUldfh7ir0AY4j3nnLxbdp76cC/eWkIrFJgiqw3MrGFDv9bDEzJrcCYN4H67Hye9GmfXXOfXJR7vNVSGwSNJDlPgelzLcYkuqmTuKX9w+RVAPypaRWwBDgIb+tGM3jFFxv9n6ckjMOk3DazovkzFRfmtkSSdm2T8StqnSxnBqzXY5y/+pfr+FWUVpuZplGZKyU1MTWXVkpF1El6BH5gs1ssaRFknb1t6x/i1sAJBvjcGavJyTt7aeN5SUJqsByKBsvOX4z3nipFYsXNuaw/j357R8/46e/Wsg/Tu/EsYN70KSJceY1H6MM33NnXfIGffsvYv22K7lr3ETuvqkr4x/pmLjzVUhsKTWQDel8xc21mPNQMEYiH5MEA1c9I6matT3T48zsbLk1j3+N00a+B8w1sxGSDsYtjbgc16N8kvzayu44J3ZzXGNymJmt85cvaQ5Ox/ml7/ndjhvUtQw41szeyLF9A9xqRh1xvd+9gf7mVkiK1tEVpxXtYWbvSRoPvGNmp/jPJ0SO5zLc893puKUSo3rMM4BWZjYirfwDcfOBF+E84wPNbFBaTJe0svoBN+EeCXyIexa8KJqLjxvNGiXnUbiGe98Mz+6BYOCCYOAqNcHAVXsDV8vuG9uW1x6VPxCYvu/fgw4zUDySWuB6nybpV8CvzezAcudV6YTGuDCWHbR97NgWD79cwkwCDYnaNsYtum9sW15zdKzY137+t6DDDNSK/sBIScKNQo53GRgIBAIVj6iuCQO4AvWAmU0ys6295GI3M3u/3Dn9EBkwaAm3TnqHOya/zSEnLUh0bLnrT1GlGm47+99cNnxcWXKo1HNbybHFEAxcgXWQ9GNJ90n6QNKrkv4j6Sd1XMcI/8w102cv1mVdDYH6OOYkKAjLrWwsRms4dPAs5n7WNmdMEvJtSOe2kmOLwazuGmNJt0v6XBF1rxclPS1ptv832+DWtQiNcRnxt5EfBiaYWVcz6w/8Gag7K3oezCy/tb2OUC0Vk3VFfRxzEhSE5VY2Fqo13LDtUnbs/TGPv7hl1pik5NuQzm0lxxZLHU5tGg38LG3b2cCzZtYdt+57zvUHUoTGuLwMxs0Vvim1wcxmmNkkOa6Q02POTJm6JA2SlBJ9IGmkpGH+530lveN72NdG44CeXvf4oaRTIvsvjZQ7QdJDvox7/MVCvnJT5TSTdIfP9TVJg/32YZLGSnoO94cZ3aelpCfkdJ6zIsc4R9KFkqb78raMxN8u6RVfx4F+eyN/rqbKKS6P89v/qjWms08l3VGXx5yLJCgIy61sLFRreMqQl7jh4e3zfgkmId+GdG4rObZYzOK98pdjE4GFaZsPxMmE8P/+ghgkoqfyA6Y3ziyViV8C/YCtgQ7AVLm5vRmRE4fcDOxmZh9JujctZEtc498aeFfSjRnm824D9MJpMicDO8t5mnOVm+JEwMysj288x0dut28L9DWz9D/anwHzzezn/hiiM/u/NLNtJZ0AnAH8Hjfd6TkzO0pSW+AVSc/g5kEvNrOBktYDJksab2bnA+f72EnAyAx51+aYkXQscCxAM1pkCwvkYafec1n0TXPe+2RD+nWfX+50AhVOAc+DO/jvgxSjvHUvFxuZWWqN0M+IeaczNMbJZRfgXu9CXiDpv8BAYEmW+C2BD80spYO8F99IeJ4wsxXACkmf4/5A0r3Vr5jZPABJr+P0k0vzlBvN9zoAM3tH0lwg1Rg/naEhBjff+iq5+cWPewlHipQu81W8dhM3l/mAyPPvZjgn9t5AX61Z7rANzmv9ke/p3g38w8wyXfjU5piDDrOOYvtssYCd+8xlh14f07RJNS2bfc95RzzHRXfunsh8G9K5reTYYihwPeMvazO1yU8vjTV/ONymLi9v4qYhFcIq1v69xVXTxFFLxokphnTFJABm9h6u1zwTZ/I6P0Mu0TwEHBxZfrKzmb3tt58c2b65mY33+4wA5pnZHVlyK8kxJ0FBWG5lYyGxN4/djoPPPYxDzv8NI27fg+nvdszYECcl34Z0bis5tijqcABXFhbIufLx/8YymoSecXl5Dvi7pGNTtz7klihsg7utepykO3ELR+wGnAk0wT3/XQ9n2NoDt3LRu8AWkrqY2Rzg0PTKiiRuuSlt5nP+9nRnv2/WFcUlbQIsNLO7JX2NuxWdi6eAkyWd7K84tzGz1/z24yU9Z2Yrff2fArsDe+JuzxdCrc9lEhSE5VY2lkprmIR8G9K5reTYoimt62osTs17qf/30Tg7BQNXmfEN0j9xPeTvgDnAqTiF5OXAPrg/nYvN7H6/z+XAQbgVipYCY81stKT9gStwPdGpQGszO0zSCGCpmV3p95+FU2fOkVdqynmnzzCz/XzMSGBarnLTjqMZcCNu/eVVwOlm9rwfXDbAzE7KcOw/9eXWACuB470Wcw5rNJ0DgCvNbJDcesb/BHbC3R34yMz2k1QFXAzsj+slf4EbNPEIsDlOfII/T+fX1TGnEwxchREMXIFiqK2Bq1nXjtbp0uNjxb5/yHk56/LjSQbhxvUswC3H+gjwAK5DMhc4JMtjurXLytYYS7qOHNcPKbdwIDlIamVmS/1z0uuB2ebWKk5kuUmmmGMOjXFhFOIu/mLfrrFj29/xUjHpBBoIddEYb3pJvMb4g0NzN8Z1Sa7b1NNyfBZIJsdIOgK3ru9ruBHBSS43yfwQjzkQqHjMwGqSN1wqa2NsZndG30tqYWbLSp9SoFh8z63Oe6ylKjfJ1MUxDxi0hOEXzadRlfHkve15YGT2GQ7lji13/QCnXvAm2+32BV8vbMoJQ3N7WR49426WrWhKjYlVNVUcccPB9Z5vQzq3lRxbDEl8Opv38kDSjpLeAt7x77eWdEPJMwuUBUlXSzo18v4pSbdG3l8l6XRJB0jKaZaRNEDStaXMN62+fpL2jRE3zD8fzvTZ0rrIJQmqwIakbAR45rFNOO/ErOP91mH4bftz2MihORvich9bueuv9NiisZiveiROX/2fwE+Br8AZonAjewOVyWTcACn8wKgOOClGip2AF81srJldmqsgM5tWz2ML+gF5G+P6IAmqwIakbASYNb0d3yyuu/mkpcy3IZ3bSo4tjnjTmhK5UISZfZK2qboEuQSSwYvAjv7nXsAs4BtJ7fx0qq2A6dHepaTRkm6SNE3Se5JSo5NXqzvlFqu4XZmVnOdJelfSC5LuVYZFLSTtL+llOQ3mM5I2Svu8KfBX4FA5/eWhkraT9JLf50VJPSK7dPK5zJZ0QaYTIelMrVFsXljISUyCKrAhKRsLxUyMPPIJ7jrhIQ4a+FbWuHIfW7nrr/TYoklgzzjOPONPJO0EmKQmwB+At0ubVqBcmNl8Saskdcb1gl8COuIa6MXATDP73g0yXosuwHZAV+B5Sd0yFL+OkhPXmz0Yp/1sAkwnsyL0BWAHP7/498BZwB8jeX/vpSGrp1FJWh/Y1cxWSdoT+LuvC59rb2AZTjX6hJmtHrQoaW+cxWs73HSpsZJ28y5aInFBh1kGjrnlQL5Y0op2LZcz8sjHmfNFW16bs0m50wo0BKwgHWa9EacxHg5cg/tCno8TLJxYyqQCZedFXEO8E/AP3O9+J1xjPDnLPg+YWQ0wW9KHuIY3nUxKzp2BR83sO+A7SY9lKX9T4H45o01T3BzrfLQB7pTUHXedG70H+rSZfQUgaQxO5xmdQbC3f73m37fCNc5rNcZBh1kereEXS1oBsOjb5kx4qwu9Nv08Y2Nc7mMrd/2VHls0CWyM896mNrMvzewwM9vIzDY0s8NTX2KBiiX13LgP7jb1FFzPeCdcQ52J9Js6mW7y1EY9eR0w0sz6AMcRTwN6EfC8mfXGCUGi++TLV8AlEcVmNzO7LW6ySVAFNiRlYyE0a7KSFk2/X/3zDt3m8cGC9vWab0M6t5UcWzQN8Ta1pC1wPeMdcOm9BJxmZh+WOLdA+XgRt1LSh36hioVyKx/1Ao7Jss9QOXXn5sAWOKXkDjHqmgzcLOkS3N/jfvieZhptcIpLcIq5THyDuwWeaZ9habF7SWoPLMfZuo5K+/wp4CJJ93j5R0fccpexPLNJUAU2JGUjwFmXvEHf/otYv+1K7ho3kbtv6sr4RzquE7dBq+VcfthTADSuqmHcG914aXbnes23IZ3bSo4tmgRObcqrw5Q0BWcgSi0j9yuclD++yy7QoJDUCFgEXGtm5/pto4EdzayHfz8M/3zWf/YdToW5Pk6F+XhUN5lHyTkC+A1OJ/c5MM7MbknL6UDcvN9FOKf3QDMblBbTHteINgEuAT7GrSf6LfAEcLiZdfG5/wLXWG8K3G1mF/oylppZK//zH1jjy17q9/8g23kLBq7CCAauQDHU1sC1XpdNbeML4k3ymHvUn+rNwBWnMX7DzPqmbZthZluXNLNAg8E3xo+b2UNF7p9ST7bAPZM91sym12WO9UFojJPBe7fH/+78yVFBNNjQqIvG+Mfnx2uMPz66/hrjrM+MJbX3PY0nJZ0tqYukzSSdBfynPpIL/GAYJbeW8HTg3w2xIc7EgEFLuHXSO9wx+W0OOWlBomPLXX8pY9s+vYDNzpvFZufOou34cG4rLbYoEvjMONcArldxo0sPwQ2YeR6YABxP3S3PFygzkqr9vNxZkh7zz4YLwsyGpfeKJc2R1MH/nG3QV2r/3/hBUlua2SWF1p9Wbxd/C7zWRI+hUJJgJwqWKGg6bzltJn7Bx+duxdwLe9Fyxtc0WRDObaXEFotqFOtVn2RtjP0C7Vv4f9NfW9RnkoGSstw3hL2BhZRg2pqZ5RYNVyBJsBMFSxQ0/d9yvtu8FbZeI2gklvdoTavpi+othyScg0qOLYq4veIE9YxXI6m3pEMk/S71KnVigbKQEnwgqaukcZJelTRJ0pZ+e0YTlqQNJI2X9Kacy3r1ZaW879kbuSZIekjSO5LukbeHSNrXb3tV0rXy5q4ovtc7SdJ0/8rWyDf2Zb/t62rh99/D5z1Tzga2Xq7tkXqbS3pSUraR5OuQBDtRsETB9x2b03z2N1QtXYVWVNNy5mIaLwzntlJii0NunnGcVz0SZ6GIC3BzPK/D2ZMuBw4ocV6BesaPoN4DGOs3jcKNmu+Pm+aUWhwkZcLaBrgPZ8ICt6j2C2bWC3gYt7B2JrYBTgV64qZA7SypGW6Jwn18fRtm2fdzYC8z2xb3qCTbIhQ9gBvMbCtgCXCCr2M0cKifq9wYOD7b9khZrYDHgHvTR3gHks/3mzRn4T4/ZtOr3qPj1bNZ0alF5DIx8IMlgT3jONKFIThV4WtmdqTvCd1d2rQC9UhzP3iqI05z+rSkVjjBx4MR7WWqt5jNhLUb8EsAM3tCUuZ7gfCKmc0D8PV2wU0b+tDMUmXdi1dMptEEGCmpH04a8pMsdXxiZilT2N3AKcDTwEdm9p7ffifulvzzWbb/079/FLjczO7JVFE2HWYS7ETBEuVYstuGLNnNXd9t8O95rGrXNGNcOLcNL7ZoEjjPOM5t6uVec7jKu34/BzqVNq1APbLczPoBm+H6DCfi/i6+jtin+vleJhRnwopSGwvXabi5yFvj5jRn/laNZwOLy2TgZ9K6Mm5wOkwzG2BmA5qw5u52EuxEwRLlaLTE3eJs/NUKWr/6Nd/sUH+2riScg0qOLQoDahTvVY/E+SKc5kfY3oIbYb0U92wxUEGY2TK5lZQewd2S/kjSUDN70DdEff3ymdlMWBNx4o6LJe0DtCug+neBLSR1MbM5ZB+t3waYZ2Y1ko4AGmWJ6yxpRzN7yef0gq+ji6RuZvY+8Fvgvzm2pzjfv64HToh7QEmwEwVLlGPj6z+g0dJV0EgsOLwzNS0yf+2Fc9vwYotFddgzlnQaTg5kwEzgSO/aLzCnPNKPtEq7AOub2RuFVhRIJlHjlH//GPAArgG7EdgYd3v4PjP7azYTlqQNcLeXO+J0mnsD/c3sy1QdUSOXr2skMM3MRkvaH7gCZ8uaCrQ2s8PScu0O/Bv3Rz8OODGau4/p4j+bBvQH3gJ+6y829gCuxF2ETgWON7MVObbPwfXAvwJuB74ws7PIQpB+JIMg/ahsai396NzJNjnz1Fixc045I2ddcprcF4CeZrZc0gPAf8xsdKF5Ze0ZS9o212eVImb4oZPemJnZ/pG3P8sQ/yjuOWr69q9wDXDWOsxsAm6uemr7SZGw581sS98Lv561V1BKxc8Goja4P2WImUPmFaMws2dxA8jibu8SeXtkpjIDgcAPnsa4sTcrgRa41Q2LKiQbV+X4zIDdi6kwEMjCMf7Wc1PcsoU3lzmfQAOlkN7uwiN3jB0bnNeVQwG3qTtIiv5BjTK3bCoAZvappCtxHvzlwHgzG19MTlkbYzMbXEyBgUAxmNnVuNvfFcOAQUsYftF8GlUZT97bngdGbpTY2HLXn5TYR8+4m2UrmlJjYlVNFUfccHCty03CcVVybFHEn0P8ZZ7b1O2AA3Gr1X2Nm4FyuJkVPOMolvQj0LCQZJKuirw/Q25lpGLKWlpHOa3WVHr5xzpSj/pE0iaSilrYIg5JUAUGZWNxasXht+3PYSOH5myIw7lNRmxRGFAT85WfPXFTI78ws5XAGNy00IIJjXFlsgL4ZbFe5WKRI1F/U5Iy3v0xs/lmNqRU9SZBFRiUjaVTK4Zzm4zYYpHFe8XgY2AHSS38eJc9cL6GgknUF2egzliFM2idlv6BpA0l/VvSVP/a2W9vJekOr4R8Q9LBaft1kPSSpJ+nbe8i6V1JdwGzgE6SrpBbeGKmpNiLikhq5Ped6nM4zm8/TdLt/uc+vuwWyq7sHC3pJkkvA5dL6ian7pwhp9HsmtZT7yXpFbkFM97wo7aRdHhk+81ylrJYJEEVGJSNhasVzcTII5/grhMe4qCBb2WNC+c2GbFFU0cGLjN7GXgIt+LcTFybOirnTlnIO8/Yt/aHAVv4qS2dgR+b2SvFVBioN64H3pB0edr2a4CrzewF/7t8CtgKOA9Y7GUeqWch+J83wmkyzzWzpzPU1R04wsym+Ea8H07M0QGYKmlizJyP9jkMlPNDT5Y03uc8QdJBwDnAcX6q0ihguJnNlrQ9bn50amDhpsBOZlbtG+VLzexhOf1lFRBd2X44cI2Z3SOpKdBI0la4+c47m9lKSTfg/j+4K+axBBogx9xyIF8saUW7lssZeeTjzPmiLa/N2aTcaQXqmjqcZ2xmF+B0wLUijvTjBtzd892BvwLf4OZ6Dqxt5YHSYWZLfG/1FNwovxR7Aj21Rii1vpz+ck/gV5H9UzrLJsCzuDm9URlGlLlmNsX/vAvO41wNLJD0X9zfSpy56XsDfSWlbh+3Abqb2UeShvkybjazycqt7AR40DfErYGOZvawP67vALS2UOsl4BxJmwJjfOO+B26e8lQf2xxnn1sLBR1mxcQCfLHEzfRb9G1zJrzVhV6bfp6xMQ7nNhmxxVDALeh6Jc5t6u3N7ETgO1j9JZ1NQxhIFv/E9TZbRrZV4RZ6SGkuO5pZrkFaq3DmtZ/miPm29qkCTsd5ciS3zSPTBLrj7G+pb8Zcys6CcjKzf+EWP1kO/EfS7j6XOyNl9zCzERn2DTrMColt1mQlLZp+v/rnHbrN44MFtVNnJuG4Kjm2aBqoDnOlf1Zm4J45EnecWaCsmNlCb4Q5GmeQAhgPnIyzXSGpn5m9jltI4UTcikpIaucvvAw4CtcD/ZOZXZan2knAcZLuBNrjFpA4k3gO66dwKyk9528N/wSn3myMW6FpN9xCEUPM7CFJ2ZSd0XPwjaR5kn5hZo/4299rPfuVtAVuoYpr/a37vv48PSrpajP7XFJ7nBVsbozjSIQqMCgbC4vdoNVyLj/sKQAaV9Uw7o1uvDQ78+Jj4dwmI7ZYktgzzqvDlHQY7tnZtrgVbYbgnh0+WPr0AsWgiOLSP+/9CLfy0Ag/wvp63HPixsBEMxvub/tej7s1Ww1caGZjtEZluR7uufGjZnZDpK4uwONm1tu/F26ZRGR6PwAAIABJREFUzX1wDfnFZnZ/NE5pWsxIWVXAxcD+uJ7pF8AvcPOPX/eNZSfcSks74Xr8mZSdo31dD/lyu+MkIh2AlcBQ3AVlKp+zcU7qlcBnwG/8hcyhwJ9xvfCVuFv1qdvx6xB0mA2PIP1oeNRWh9ls007W6cTTY8W+/5fTa1VXIcRyU/tRqnvgviCfNbOihm4HApVMaIwbHqExbnjUujHu2Mk6x2yMZ59Tf41xnNHUnYFluAXWV28zs49LmVggEAiUmkIa2GUHbR87tsXDLxeTTqC+SOBt6jgDuJ4AHvf/Pgt8CDxZyqQCgUpgwKAl3DrpHe6Y/DaHnLQg0bHlrr8hxlaphtvO/jeXDR+X+FwrObYo6miecV2StzE2sz5m1tf/2x3YjrCeceKQVO3lFKlXlzoos05UmBnKHSHpjFKUXUAOB/hnxSUhCarAoGwsrYZx6OBZzP2sbdbPk5JrJccWSx0auOqMgg1cfunE+PdrAvXF8rRpPnNSH8jxg7StZbNmmdlYM7u0VPUmQRUYlI2li92w7VJ27P0xj7+YcbXOROVaybFF0xB7xpJOj7zOkPQvilyvMVB/KLOm8kytUU1eGInNqn2U9DevkZziR2YjaX9JL0t6TU4zmdo+QtLtkiZI+lDSKZFyzpH0nqQXgB5Zcs6m6nxU0u/8z8dJusf/vLeconO6pAf9iHAkzZF0maTpwFBJP/MxMyQ962OGSRrpfx4qp9icIW8LUxY1Z1ySoAoMysbSxZ4y5CVueHh7avKs/pOEXCs5tihi9oqT2DNuHXmth3t2fGApkwoURfPILeqH/bbuwA1m1gvXAKYeM/QD+kvaTWtrH/vhpjUd5vdvCUwxs62BicAxfvsLOHHINsB9wFmRPLbECUK2Ay6Q1ERSf5zdqx+wL9ntbSlV50DgYOBWv/1Y4HxJuwJ/BE72U7TOBfY0s22BaUB0iORXfvuzwC3Awf44hmao93zgp/7zA/y21WpOn+8xkjbPknfgB8ROveey6JvmvPfJhuVOJVAsCewZ5xxN7XtIrc2srM/3ArFY7htTYPX836imcm//es2/b4VrnPuSXfv4PW7wHjgL117+502B+yVtjLOxfRTJ4wkzWwGskPQ5sBGwK/CwmS3zuY3NcgwZVZ1mtkDS+bj5xQf5OcD7AT1x/mp8HtGxDPf7f3fAzaX+CJwIJUO9k4HRcoKUMZHztY6aM+1Ygw7zBxjbZ4sF7NxnLjv0+pimTapp2ex7zjviOS66c/d1Ysuda6XHFk1DGk0tqbH3C+9cj/kE6paoElLAJZFnyt3M7DZyax9X2pqJ6NWsuXi7DhjpF5U4jrXtWisiP0f3iUMuVWcf4CvW6DAFPB2J7WlmR2c59pyY2XBcL7sT8KqkDcit5ozuG3SYP7DYm8dux8HnHsYh5/+GEbfvwfR3O2ZsiJOQa6XHFoNI5m3qXF+Ur+CsW6/7nsyDRL7gzGxMth0DieQp4CJJ95jZUkkdcVapZylc+9gGp6kEOCJG3RNxPc9LcH9z++OMWOlkVHVK2g5n9NoG+K/cSk5TgOsldTOz9yW1xC0I8V5amVOAGyRtbm7BifbpvWNJXc0thfaypH1wjXJGNaeZxWrkk6AKDMrG8msYk5BrJccWhYESKHTOauCSNN3MtpV0R2Sz4S4szMyOqo8EA/FQRIHp33choqn02/4A/N6/XQocbmYfKIv2UWtrNYcA+5nZMEkH4hSVi4DngIFmNkjSCGCpmV3p95nl95kj6Rxcw/05bkHu6am4SH7rqDqBP+AuDI80s+mSDsCt07w7MBi4jDWrNZ1rZmMlzQEGmNmXvtx9gL/74/vczPaSWwVqgJmdJGkM7ha0cBcnp/qf11FzmlnWYZ3BwFXZBOlHMqitgav5xp1s86PiGbje/nsCdJiS5gH/wDe+/t8UZmb/KH16gUDDITTGlU1ojJNBnTTGR8ZsjC9Jhg6zEW6QT6ax+wl8/B0IBAKlo5AGdpMprWPHzt/hm2LSCdSCJK7alKsx/p+Z/bXeMgkEKowBg5Yw/KL5NKoynry3PQ+M3CixseWuv9JiF128nBWTq6lqJ370L7ec+OLrvmPFC9XQGBpvWkXbc5tR1Vqxy0zCcSUxtigS2BjnmmdcvysrByoGxdRoShqeknnUcf2DJD2eP7J0JEEVGHSY5Ytt8fMmtL+6+Vr7rLddYza8pwU/uqcljTtVsfTO7wsqM8TWEX4AV5xXHCS1lfSQpHckvS0p/lJgEXI1xuHhV6CkmNlNZnZXufMoBUlQBQYdZvli19umMVXrr92fabZ9Y9TYbWvSu4rqz2sKKjPE1iF1K/24BhhnZlsCWwNFLTGctTHOIkcIBApCUpWk2ZI2jLx/36svVy8Y4RWal8lpOd/zti0ktZD0gKS3JD0sp+FcZ0CFV16+4xWYv4xsby/pEa+0nCKpr98+01/RStJXWqPbvEvSXl6XOUbSOJ//5YUcdxJUgUGHmYzYTCx7bCXr7bj2U8Ik5NrQYoulruYZ/3/2zjvcjqpq4783DUJCCF2QEjrSQgkdMaCoSFEpUoUAUgQpKij6iVSlqfTekSLSBFE6hARCSyCELkqRriAJCTVlfX+sPblz58ycO3PvObknN/t9nnnumT1r9t4z59xZs1d5l6T5gE2BSwDM7HMzm9iZOc2RxQMiZh3MbAZwFW0Um18DnjKz/+aI9zGz9fDUoqND24HAB2a2CnAUzhbWDpLmxikvtwnHv5A6fCzwpJmtAfwSSFbiD+GENqviZUG/HNo3BMaEz2viVKGrAztJWjJn7P0kjZU0dmo7vpOIiHxMvuwz1Ef0/2YVPpyIhqL8ynih5P87bPtleloGT3u8TM7Vf3HgPKiMqIwjZgUuBRLf8N7AZQVyCZHMOGBI+LwJzn+NmT0DTMg5b2XgFTN7KTCGXZU6tgnwx3D+fcCCkgYBo/E32k2B84DVAxHKBylij3vNbJKZfQo8ByydHbiIgasVqAIjHWZryKbx8W1T+fShaQw+dm5StK8tM9fZTbZTKKuIXRm/l/x/h+3CTG99cHKs8wJX/0dAp0qzRmUc0XSY2evAu5I2xwtI3F4gmiwtq9Jodgaj8NXwl4GR+NvtDriSzs6n8pxagSow0mG2hmyCTx+expSrPmeBU/vTa+7a+NhWmOvsJtsZqMJWAm8AbwQGP4AbcOVcGdFOEjGrcDG+Yv1j4Dwvi4eA7wH3S1oFNxln8QIwJNBa/gvYJXVsNG4iP17ScPxN90Pgw8D41c/MXpaXdjwc+FHVC8tDK1AFRjrM7pP94KhP+OyJ6cyYaLyzzRTm3bcfU678HPsc3j/kEwD6rdabwT+fu3SfrXBdrSbbWTSKDtPM3pH0uqSVzOxFPPD5uU7NqYiBKyKis5A0g/Y1r/+AF5d4H1jPzF4IcscQ6DMljQQON7OxQUmONbMhwf9yBV6h6QVgWWBHM3spM+Y3gdOBj3EFvJyZbR24ti8N530M7GdmE8I5fwR6m9mukjbCS0MubGbvp+kyg+xtwO/MbGTRdUcGrogEkfSjeegqA9c8iy5pK+xcjoFrwpkdM3BJWhNfbPTD40/2MrMPqs4rrowjGg4zq3F/hAjopxJFHOSOSX0envr8Hm0+409xDu1PJS0H3APUFLEwsztw33G2/X/Adwrm+f3U5zGk3DZmdjlweWp/67w+IiIiZkM0cA1qZuOBLlNmRmUc0XRIOhL4IW0R1VUwD26i7ou7cQ40s887OCeigei96CKlZae/+5+OheYAVFntxvs7i9EN5RHLIAZwRTQdZnaSmS1tZg924tzJIYpxqJmtYWZFwV8th2HDP+Ti0S9w2UPP870fvdvSslX6POzoZ7nm3pGce/2YunLNmuvsJhvvbXNlO4XGkn40BFEZR7Qc8ug0G0mdGXIBV2lEX0VoBarAZtBhAtzz18U56qCOA0a7+7paQTbe2+bKdhaNIv1oJKIyjpgt0EjqTDP7gZl1KuKxLFqBKrAZdJgAzzwxP5MndZz32d3X1Qqy8d42V7azaCQ3daMQlXHEbIEc6szTAiPO85LWDdSVL0k6IcgMCfSYVweZGyTNkzp/WPg8RdJvJD0V6DIXDe3Lhf2nJZ1QtvhFglagCmwGHWYVdPd1tYJsvLfNle0UqpF+zDJEZRwxu+LzkHJwPnALcBCwGjBC0oJBZiXgXDP7EvAhTq2ZxQDgETMbihOB7BvazwDOMLPV8cT+XEQ6zIiI2RBRGUdENAy3hr9PA8+a2dtm9hme55dwSL9uZg+Fz1fh1JhZfA4k5RbTNJwbAteHz9cUTWJOo8Osgu6+rlaQjfe2ubKdgYg+44iIRiJZhs6gPW3lDNpS9rL/Tnn/XlOtjfmmYTScrUAV2Aw6zCro7utqBdl4b5sr22m04Mo45hlH9GQsJWlDM3sY2BVn2CqLR4DtgeuAnasO3ApUgc2gwwT42YkTWGOdDxg0eCpX3jGKq85fjrv+8sWWu65WkI33trmynYKBZrReonGkw4xoORTQaQ4inzpzePi8dTh3JM4x/R5wBzAWL6v4HPB9M/s4c/4UMxsYzt0B2NrMRkhaATdt9w/97GZmtU/FFHoqHWYkpWgu4v2thq7SYQ5YaElbZdsfl5Ide9lPuzRWFcSVcUTLIY9OM3N8eOrzSLzqUrtjkoYA08xs9w7OH5j6fANedQXgTWADMzNJO+PBYBERET0BLbgGjco4IiIf6wBny4vOTsTrMM+RqLIai6s8x8ffXb+07Dw3P9qxUMA/T9uglNzyP36kdJ9zIiIdZkTELIKZvWpmq3Xh/NEpCs5NzeyfVftoBarAZlA2RnrH6rK9NINLjryRkw+4o2F9zjfybZY86SmWPPkpFr3yJTS1mKWiFe5BpMOsj6iMWwySFpQ0PmzvSHoztd8vI5slwqjxbUj6u6TBs2r+RZA0QtLiqf1XQ6nERo9TiZyjWWgFqsBmUDZGesfqsgA7bvYMr71T/9+wSp+9J37O4NHv8MZPVuf1nw+FGcbAJ99r2XvQUnSYJdOaYmrTHA4ze9/M1jSzNXFCi9OS/c5UKzKzb5nZxMbPtDJGAIt3JNRT0ApUgc2gbIz0jtVlFx48hQ1X+ze3jamp8NnpPgGYYb4anm70mjqDaYP65Yq1wj1oJTpMEekwIzoJSftKejxQNt6Y0DoWyPaSdHmKFvJVSQsFesjnJV0k6VlJd0nqH2TWlTQhrL5PlfRMTr9KjgWKyJ1C+/CwKr8hRT+pzLk74PU+rw5j9A+HDpb0ROhv5SA7QNKlkh6T9KSkb+fMZbikUZL+JulFSedL6pU6nkdvOUTSfeE675W0VGi/XNKZksZIejnMNenniHDfJ0g6tuTXBbQGVWAzKBsjvWN12UN2eJhzb16fGabc453pc/rgfkwcvhhDjnuCZY4ex4y5e/PJyvkr71a4B63wG2sHs3LbLERUxrMHbjKzdQNl4/PAPgVyfYCrgZfM7Fc5x1cAzjGzVfGgpO1D+2XA/mE1Pr2g7+2ANYGhwNeAUyUtFo6tBRwGrAIsC2ycPjFEKY/F04PWNLNPwqH3zGxt4Dw8HQng/4D7zGw9YLMwzoCc+awHHBzGXC7MD4rpLc8CrjCzNcI9OjPV12I4O9fWwEkAkr4e7td64brXkbRpdhKRDjOiHjZa7TU+mNyff7y+cEP77fXxNAY88wGvHrUWrxy7Nvp8BgPH/rehY/RkRDN1RGexmqTRkp4GdgNWLZC7AHjGzH5TcPwVMxsfPo8DhgR/8ryBGAOKqR83Aa41s+lm9i7wALBuOPaYmb1hZjOA8bRRSnaEm9JzCZ+/DhwpaTyesjQ3sFTOuY+Z2ctmNh24ljaqy3r0lsm1/ZH21Jh/MbMZoZLToql5fB14EngCWBlXzu0wp9FhRnrHarKrL/suG6/+Gn8+7hqO2fte1l7pTY7a874uj9//H5OYtuBczBjYF3r34qM1FqD/q/nhEt19D5op2ynEQhERXcDlwI9C0YJjcQWVhzHAZpKKjqeXbg2jfuxCv8l56XMEbJ/yky9lZs/nnFtEddkZesv0/JX6e2JqHsub2SUl+gJagyqwGZSNkd6xmuwFt67H9r/aje/9eleOufSrPPHiFzn+is27PP60+fsx16tT0OfTwYz+/5jE54v0z5Xt7nvQTNnOotE+Y0m9g1vtto6l8xHzjGcPzAu8LakvvjJ+s0DuEmBT4M+StjOzaR11bGYTJU2WtL6ZPUox9eNoYH9JVwALhHGOwFeMZTA5XEdHuBP3JR8cCDfWMrMnc+TWk7QM8BqwE3BhB/2Owa/tj/g9HF1iHsdLutrMpkj6Iq7oSyXHtgJVYDMoGyO9Y+fuQxlU6fOzpeflo6ELsOTvn8Z6ic++OIBJG+XneLfCPejue1uDxq96D8VdiIM620Gkw2xhSDoGmAJ8BPwM+C/wKG5WHpEcz6GIPBZYEVc6L+PBUwOB25Lc25ASNdDMjpG0PnARXmThAWCYmbXz+4agrFOALfGf8glmdp1q6SjPBsaa2eWZ87cHfgt8gpuMnw/jvBdSsn5nZsNDcNfpwEa45eaVpO9UX8OB43AFvzxwP3Cgmc1QMb3l0rhvfKFwH/cys39LujzclxvCOenzDwV+EIadAuxuZv8q+r56Kh1mFUTSD0ck/WgeukqHOXD+JW3oVw8tJTvmxiM6HEvSEsAVwG+An2SfV2URlXEEkgaa2ZTw+UhgMTMr92vtBmRfAFoFURlXQ7MUd3whgK9M+KRjoYAH1sg3b7cqGqGM19y83OPtoZtKKeMbgBNxy1+nn0vRZxwBsFVIOXoG+DJwQndPKCIiIqJpKB/AtVCSLRG2/dLdSNoa+I+ZjevqlKIyjsDMrgtBSquZ2VZm1tI5EmY2stVWxXloBarA7qbD7G7qzCp9Vp1vK9/bF3/dlzFfmZvHv9sW4f/K2X0Yu/1cjN1xLibs34/PCowCrfBbaCYdpqiU2vReki0RtmxsysbAtpJeBf4EbC7pqs7MKyrjHoZAbvFMpm0mbWbBOdsG8/QcBWUoOhuJVqAK7G46zFagzizbZ7Ourbvu7aLbTmf189rnvS85YhrDbvyMYdd/xgKbTue1C2rThVrht9B8OsyShB8lXLhm9gszW8LMhuABovflVYorg6iMIzCzW83spO6eRzdgBBUpOiWVykBoBarA7qbDbAXqzLJ9NuvauuveDh42g76ZbKA+A9s+z/gknw2sFX4LzabDhEiHGdECkHSIpOcCxeOfQtuIEAVdSA8pp9k8N1Be3i0vQLFDTv8dUndKGijpskCDOSFEWiNpl9D2jKSTU/JT5FScz0q6R9J6cgrOlyVtm7qGW0L7S5KODu3tLAWSDg+WghqKTknrSHpA0jhJdyYMY6HP0yWNxVMYOkQrUAV2Nx1mK1BnNqvf2fXevnJmHx7ZYi7e/VtvhhzUmvOdFXSYzWDg6qr7LCrjOQ9HAmsFWsgDCmRq6CFxuskhOP3k9/H0pDyUoe48CphkZquHedwXzMUnA5vj9JPrSvpOkB+Am39WxdOZTgC2AL6LpzglWA+n+FwD2FE5VawSZCk6gWk4ZeYOZrYOcCmeqpCgX/AZ/T7dT6TDjJidsMwh09jg7s9YdKvpvHXtHEozYcAMK7fNQkRl3PNQ9AtK2ifgq8HdcQWUhzx6yE2A60P7O3hubx7KUHd+DThn5sTMPsCpNUea2X8DWcnVOLEIOMVlUgj2aeABM5saPg9J9Xt3qHr1CU61maa87AgrAasBdwcqzl8BS6SOX5d3UqTDbG3qzGb1O7vf20W2ms5/7+ndkvNtOh0mRDrMiFmC94H5M20LAEmx061wRbg28HiBDzSPHrIsLqccdWcVpCkuZxDmF7iw0/PPo8icRvvfedF8BDybor9c3cy+njr+UZUJtwJVYHfTYbYCdWaz+p0d7+3Hr7X9K79/fy/mWaZW27TCfGcJHWYLFoqYQ+0UPReBuvFtSZub2X2SFgC+CZwhLzO4pJndL+lBPPpvYN0O2/AQsGegw1wYGE5+UYky1J13AwfhlZ6QND/wGHCmpIWAD4BdcLNxFWwRrvcT4DvA3sC7wCKSFsRZtLambZWdpuh8EVhY0oZm9nCY/4pm9mzFOQCtQRXY3XSYrUCdWbbPZl1bd93b537Wl0ljezN1Ijz8tbkZcuBU/je6Nx+/KtQL5lrMWPGo2vLorfBbmDV0mK1HdhUZuHogJK2Cr36TFfKpZnZ1UDD3A/PhK8GrzOwkSSNwasofFdFDBkV+Lq6EXw/nn2xmd2fG/iE51J0ZmYFhfuvgxRyONbObJO0C/DL0/Tcz+3l6DuHzMQQK0Mz8RuAKeD7cvHyVmR0bZA7BA6/exOlBXw00oFmKzpXw0orz4S+qp5vZRUpRjda775GBqxoiA1fzEBm4ijHvoCVs2AYHl5IdefeRXRqrCqIyjiiNhDYzrDIfAzYO/uNuR/qForvm0FOVcVRuPRszvrJWadleD+TVbJm16KoyHjRoCRu2frnHxP33/GKWKeNopo6ogtvk9Y/7Ace3iiKOiIiIqIRZnENcBjGAK6I0zGx4CG5aJVuVqbthZpd356o4D61AFdgMysZm0Fb2ZNnuHr+KbN++0zjr+Ns4/6RbuOjUv7DHDvVXwt09385CZqW2WYmojCM6DUnTA2FGsg2pI5tUhZpJwiFpmKQzmzS3Kc3otyxagSqwGZSN0Bzayp4q293jV5WdOrU3R5zwDQ448tsccOS2DBv6Jl9aPt/10Arz7RTKpjXF1KaI2QifpFKB1jSzV6ucbGZjzeyQJs2tW9EKVIHNoGyE5tBW9lTZ7h6/qiyITz/z77ZP7xn06T0Ds9alzuwcGsdN3UhEZRzRMASay3slPRFoLb/dgfxwSbeFz8dIujRFc3lISu4oSS9KelDStcopeiFpGUkPh3FPSLUrUGk+E47tFNrPSVFp3izp0vB5b0m/CSv45yVdFGg475JUOuy0FagCm0HZWAXdfV2tINvd41eVBeilGZx/4i1cf8GfeOLpxXnhXwu39Hw7A82wUtusRFTGEV1B/5SJ+mbgU+C7ZrY2sBnwe0lVSENWBr6B01oeLamvpHVxisuhwJY4n3QezgDOC2Qjb6fat8PpNYfizF+nBs7p0XjtZoAv4jSfhLZR4fMKwDmBhnNimEc7RDrMiJ6GGdaLA37xbXY5aEdWWu49hizxQXdPqbGwWCgiouchbab+Lp4f/FtJE4B7cCW3aN0e2uNvZvaZmb0H/CecuzFwi5l9amaTgb8WnLsxcG34/MdU+ybAtWY23czeBR7AqTdHA18OOdnPAe8GJb0hkEQmvWJm48PncbSn3gTmPDrMKuju62oF2e4ev6psGh99PBdPPfcFhg3N4+1pvflWQjRTR/Rw7Iazc60Tii+8SzU6zPTScjrVU+9K//eY2ZvAYJydbBSunL+HE4pM7up8WoEqsBmUjVXQ3dfVCrLdPX5V2fnm/ZQB8/jPvl/faay9+lu8/lbrzrfTaMEArphnHNFIzAf8x8ymStoMWLoBfT4EXCDpRPz3ujVwYYHczsBV+EtBgtHA/oHGcwG8+MQR4dgjOCXn5sCCwA1h6zJagSqwGZSN0Bzayp4q293jV5VdYP6P+dkPH6RXL0MyRj0yhEefXLJl59tZzOq0pTKIDFwRnUaapjLsL4SbkQfi5Qk3ALY0s1dTtJVDcLrN1SQNx2kmt86huXwG2DqcewywK77S/g9wh5ldlJnLMjhX9kDgFuCwMJ6AU3B/swEnmNl14Zx9cPKSxQNV6ETg+4Gac+Y8g+zhwEAzO6bofkQGrsjANTtijmPgGvBF22DV/UvJ3v340ZEOMyIiQYqGcx7cpLyfmT3R3fPKoqcq44iIBGe99lBp2YOX3rgpc+iqMp5vwOK2wSrllPFdY4+ZZco4+owjZgdcGGoMPwHc2IqKOA+twE7UE1miZjfZ7h6/kbJXH748v1h7XX67xZoz2/5+2pL8ar1hnLTlUE7acijP3pet4Nrc+XYKMYAropWRYtR6KuQKb9Sgfo/Jyw0OxzrkVDSzXUPE9spmdmJBP2tK+lYX5jhE0q6p/RGSzu5sf63ATtRTWaJmJ9nuHr/Rsuvv+B8OvOK5mvM22+ctjrz9KY68/SlW3bw2FaqlGLigYcpY0pKS7pf0XOAjOLSzU4rKOCKNJFVpKPALoEbxSWpo0J+ZNUTh47nEnVbGeNrSrh0JlUUrsBP1VJao2Um2u8dvtOzy63/IPIOn5Z5bDy3FwGV4oYgyW8eYBvzUzFbBY2QOCumSlRGVcUQRBgEfwEymrNGSbgWeS/NLh+OHhyArJB0S3hInSPpTqr9VCti1pqTGGCnpBkkvSLo6IQyR9K3QNk7SmQlrV6qPfsBxwE5hZb+TpAGB0esxSU8mbGCSegdGrsfDHBPn0Ul43vF4ST8ObYtLukPSS5JOqXLzWoGdqKeyRM1Ost09fjNl0xh15WKc+I01ufrw5fl4Uu9umUMVNKpQhJm9nbjNQkrk8zi/QmXE1KaINPoH3+zcwGJ4yk+CtYHVzOwV1SkIARwJLGNmn8nLLSZYGWflmhd4UdJ5Zpb9D1sLWBV4C09V2ljSWOACYNMw9rWZczCzzyX9mlQ9Y0m/Be4zs73DPB6TdA+e9jTJzNaVNBfwkKS7wrwPN7Otw/kj8NX2Wni+8YuSzjKz1+tce0TEHIdNdn+Hbx7yOgj+9ruluPn4Zdjtd//s7mnVgcGMxtNrhefiWsCjnTk/rowj0kjM1CvjZBhXpugsHzOzV0r0MQG4WtLuuAknQR67VhaPmdkbZjYDGI+bjlcGXk6NXaOMC/B14MjwcjESf8FYKrTvEdofxfOLVyjo414zm2Rmn+IsXTV500V0mK3ATtRTWaJmJ9nuHr+ZsgkGLTyVXr2hVy/YaJd3ee2pgTUyLcXAZVTxGS+U/H+Hbb+8LiUNBG7EUyo/7My0ojKOyIWZPQy6NgZjAAAgAElEQVQshDNqAXyUOjyN9r+ddEb+VsA5+Er68ZSPuQybVVcZuNIQsH2KrnMpM3s+tB+cal/GzO4q6KPD+RTRYbYCO1FPZYmanWS7e/xmyiaY9G6bonzqzgVZbKWPZ/kcKqO8z/i95P87bDWEQ4Gj4EbgajO7qbNTimbqiFxIWhnoDbyfc/hdYBFJCwJTcFasOyT1ApY0s/slPYgzYtW+JlfDi8CykoaEEo07FchNxk3gCe4EDpZ0sJmZpLXM7MnQ/kNJ9wWmsBWBN3PO7xJagZ2op7JEzU6y3T1+o2UvO3hF/vnwfEz5oA9HrT+Mb/3437z0yHy88dwAJFhgic/Y+be1JuqeysAVLIeXAM+b2R+6OKdI+hHhkDQdeDrZBX5pZn9LM2WlZA8BDsUV2cvAq8BvgPtxWkwBV5nZSR2wayXMXO3GCGlFY83scknbAKfiq/PHgXnNLE15iaQFcEXbF48CvxU4HdgIX8W/Epi+egEnANuEOf4X+A7wcTh/QeByPHgt7YO+DfidmY0sun+R9COip6NHkH70X8w2GjKilOwdL5xUdyxJm+CUu0/TFn/9SzP7e9V5RWUc0fJIMXAJN4G/ZGandfe8sojKOKKno0co47m/YBsttWcp2TteOmWWMXBFM3XE7IB9Je0J9AOexKOrIyIiCtAsPvEqCvaNG1ctLbvE9s+Wlm0IWnARGgO4IloeZnZaCLZaxcx2M7PaCJEWRKtTG7bS+D1ZtrvHBzjs6Ge55t6RnHt9h4R3DZvD/Oe8yWJ7vcCih7X5kDV5Ggsd+yqLHvQSCx37Kpoyvctz6BQiHWZERHVIOk3SYan9OyVdnNr/vaSfZM45QNIe4fMISYunjl3cWZacspgdqA1bZfyeLNvd4ye456+Lc9RBaxceb8YcPho+mPeOap8NOOjm9/hs9QG8e84KfLb6AAbd/N8uX1tlGDDDym2zEFEZR8wOeAgPxCIEYC2Ek4Mk2AiY+covqY+ZnW9mV4amEcBMZWxmPzCzWoLdBmJ2oDZslfF7smx3j5/gmSfmZ/KkjnN1GzmHz1cdwIyB7dm45n58Mh9t5lxAH202mLkfm9zla6sOA5tRbpuFiMo4YnbAGGDD8HlV4BlgsqT5A4vWl4A/SDo9MHYdqlCcQtIOwDCciGS8pP6BdnMYOB2npN/Ii2M8ImnR0L5c2H9a0gkJbWdZtAJdYaRs7H7Z7h6/Kpo9h94TpzFjfn8pmDG4D70n1vJczwo6zGimjojoBMzsLWCapKXwVfDDOHvWhriifRr4HOgXEvN/nzr3BmAssFvwO3+S6X4A8EgojjEK2De0nwGcYWarA2807+oiIuZQSJ5cOKthwPQZ5bZZiKiMI2YXjMEVcaKMH07tJ/kW13Wi38+BpPDEOJyCE1zRXx8+X1N0cqTDjLL1ZLt7/Kpo9hymD+5Drw98ldvrg6lMn682oafpdJgQV8YREV1A4jdeHTdTP4IrzLS/+KP8U+tiqrUl21em4Ix0mFG21e9tFTR7Dp8Om5cB908EYMD9E/l03VrSu+bTYZZUxLNYGcc844jZBWOAw/GiEdOB/4VqTKvipuWf1Dm3M1SXjwDb46vtnatOdnagNmyV8XuybHePn+BnJ05gjXU+YNDgqVx5xyiuOn857vpLbaW/Rs5hgT+8zlzPfkyvydP4wr4v8uFOizB5u4VY4PdvMM+9E5m+cF/e/+kSXb62yjCaUrWpq4gMXBGzBST1xikqzzSzX4W2y4ENzWwlSSNxOs2x4dgxBApOSdsDvwU+wVfTtyeyCR1nOGcHnKZzhKQVgKuA/sAduM+5bp3SyMAV0SpoFulHFTSL9KPLDFx9F7GNFtyhlOwd754XGbgiItIIq+FBmbYRqc/DM8eOSX2+Ea+qkmB46tjA1OcbgBvC7pvABqHIxM7ASl28hIiIiFZBCy5CozKOiMjHOsDZgQ97IrB3N8+nx6EVVm8RzcMsp7gsCzNsej7zV3ciBnBFROTAzEab2VAzW8PMNjWz2rpwHaAVaBAjZWP3y3b3+NBz722nERm4ImYVJA0JpQrTbcdIOryD80aE8oWzJST9MrNf9+kjaZikM8Pn4ZI2asQ8WoEGMVI2dr9sd4+foCfe2y6hBaOpozKO6Glop4zNrK5yNbOxZnZI2B1OoN3sKlqBBjFSNna/bHePn6An3ttOw8yjqctssxBRGc+hCJSQJ0t6TNI/JH05R2YrSQ9LWkjS5ZLOlDRG0ssh8hg5TpX0TKCO3Cm0nyNp2/D5ZkmXhs97B/rJIZKel3SRpGcl3SWpf84ctpH0qKQnJd2ToqscKOmyMOYESdtLOgnoH2gvrw5yU8LfP0naKtXv5ZJ2CKvh2yQNAQ4AfhzO/7KkVyT1DfKD0vsdoRVoECNlY/fLdvf4VdEK8410mBFzIvqY2XrAYcDR6QOSvgscCXzLzN4LzYsBmwBbAyeFtu2ANYGhwNeAUyUtBowGEgX/RSCpkvRlnHYSYAXgHDNbFQ+S2j5njg/iUc1rAX8CfhbajwImmdnqZrYGcJ+ZHQl8Emgvd8v0cx3wvXBt/YCvAn9LDprZq8D5QFKucTQwEkgU+M7ATWbW7qlQxMAVERHRqvAArjLbrERUxj0XRa916fabwt80DSTA5sDPga3M7INU+1/MbEaoeLRoaNsEuNbMppvZu8ADwLoEZRxKFT4HvBuU9Ia0MWa9YmbjC+aQYAngTklPA0fQVq3pa8A5My+q/TzzcDuwWSgssSUwKoenOouLgb3C572Ay7ICRQxcrUCDGCkbu1+2u8evilaYb9PpMGMJxYhZjPeB+TNtCwDvpfaTpVyWBvJfOGPVipnz00u/uhTvZvYmMBj4Jr4SHo2vTKeYWVI3Ld1fERXlWcDZoWDD/kCnqHjM7FN8pfsNYCdK8Fib2UPAEEnDgd5m9kwHp8xEK9AgRsrG7pft7vGrohXm23w6TFqyhGLMM+6hMLMpkt6WtLmZ3SdpAVwxnlHi9NfwVehNknY0s3oJg6OB/SVdgSv7TcO54JSSh+Er7QVxQo0b8jqpg/lwAg6APVPtdwMHhf6RNH9YHU+V1DdrTg64DvgBXulpRM7xyWSIRYAr8UIRx1eZdCvQIEbKxu6X7e7xE/TEe9tZGGANXPVKSp6rvYGLzeykDk7J7yfSYfZcBBPxObStkE81sySwaSRtlJALAWPNbIikEcAwM/uRpLWAq4FtcB/tbYGlioRGMpBinIKbfg04wcyuCzL7AMeb2eIh8Gki8H0zuykETN1mZqsF2cOBgWnmrND+beA0nArzPmBdMxsuaWC4tnXwVfWxod+TgW2BJ8xstwzdZV/gXeAWM9srtA0P92FrSSviLwszgIPNbLSkLwCvAIuZ2cR69zvSYVZDJP1oHnryve0qHeYgLWAb9Pl6Kdm7p11Xd6xA0/sPYAu81OrjwC7BlVcJURlHRNRBiBr/tpl9vyPZqIyroScrjO5GT763jVDG6/faotxYM/7ckTLeEDjGzL4R9n8BYGYnVp1XVMYREQWQdBa+4v+Wmf2jhPx/cRN/FgvR3ldfhLJyUbY1xu/Jst09fj3Zpc1s4ZJ91EDSHaHvMpgbSDOOXGhmF6b62gH4ppn9IOx/H1jfzH5UdV7RZxwRUQAzO7iifO4DQtLYMm/yZeWibGuM35Nlu3v8qrJVYGbfbHSfjUCMpo6IiIiIiOgc3gSWTO0vQVvAaSVEZRwREREREdE5PA6sIGmZQCa0M3BrZzqKZuqIiObjwo5FKslF2dYYvyfLdvf4VWW7BWY2TdKPgDvx1KZLO0gFLUQM4IqIiIiIiOhmRDN1RERERERENyMq44iIiIiIiG5GVMYREd0EScuUaZsdEZjZsm1z5clGNAeSekv6cXfPoywCm9Uci+gzjohoMCSdbGY/L9H2hJmtnWkbZ2brFPS7CbCCmV0maWGcPvSVHLl5gJ8CS5nZvpJWAFYys9sycr2BZ81s5ZLX9Vdqq4FNAsYCF4RiHInspWa2d2p/IE5DWkNRVrHfsvdgHzO7JNN2UiizmZUVsBuwrJkdJ2kp4Atm9ljBfVg6zOEeeQ3uPqniJ1nZLwJLkwqWNbNRebJVUOE+PBbKpHbU31x4CdMhmbkeVyDf8OuS9DJwI3BZZ+gkZ3fEaOqIiMZjC7wEZRpbJm2SVsZLQc4nabuUzCAKqlJJOhovcLESXsqxL3AVsHGO+GV4ScoNw/6bwPVAO2VsZtMlvShpKTP7d4nrehlYGLg27O+EF9dYEbgISFOGviHpXDM7UNL8eO3oi7rSb8V7sL2kT1Nc7OdQXPHrXJyPfHPguDD2jXgp0HaQtC+wH14UZTk8r/R8vD52VvbkcC3P4fzp4C8dXVVaVe7DQ5LOxoukfJQ0mtkTGblb8BegcVC/MHezrguvib4zcLGkXsClwJ/M7MMu9jt7wMziFre4NWADfgg8jT/0JqS2V4CrUnLfxh+i74e/yXYmsFFB3+PxspVPptomFMiODX/Tsk8VyI7Clc+9eH7krcCtBbKPF7XhK+zssVNwRfU4sH2d+1aq34r3oD9e2WsX4ArgjDrjP1Hhfo0H+mVkny6QfRGYq8LvZzLwYWZ7HbgZX7V35j7cn7PdlyP3TIV5NuW6Mud8BX+J/Ch8f8uXHW923eLKOCKicbgGuB04EUibQyeb2f+SHTO7BbhF0oZm9nDJvj83M5NkAJIG1JMN5tNEdjmKVztHlRwfYGB6FR3MuQOTMUNbeqX/aOj/McAkbWdmN3Wm3+RzR/cglApN8APgL8BDwLGSFkh/DylMDSb7pN+F8ZVyHj4zs88Tl7ikPtSa2BO8jK9a6640Uzgdr/xzDa5sd8ZX30/gq8ThQa70b8HMNis59hhJq5vZ0yVkm3Jd4TvYCtgLN5f/Hq8a92Xg79TWV+9RiMo4IqJBMLNJwCRJWRP1QEkDrdYU/L6ke4FFzWw1SWsA25rZCTnd/1nSBcDgYCrdm2Kz79HAHcCSkq7GzZcjCub8QMYHOg9OXpCHnwIPSvoX/lBdBjgwKIMrgsw2mXOexB/c2+BKK08Zl+m37D0YF8ZR6u9WYTNg2Zzxz8RXaYtI+g2wA/CrgnvwgKRfAv0lbQEcCPy1QPZjYHz4jmcqLjM7pEB+WzMbmtq/UNJ4M/t5GDNB6d+CpEWB3wKLm9mW8rKqG1rGnw5sAoyQ9EqYq3yqtkaqr7Pwe9is63oJX7mfamZjUu03SNq0oO8egxjAFRHRYEh6mjZFMDeuXF40s1Uzcg8AR+BBSmuFtmcs1HjO6XcL4Ouh3zvN7O46c1gQ2CDIPmJmuZVy0j5QM1suBHudbzmBVkF+LiAJ+HrRUsFVKZnewCFmdlrR/DrTb5ArfQ+qIPjxvxr6vdfMni+Q6wXsk54DXlC+5kEqac+8Pszsirx2SQ/jtbtvCE07AD8xsw2C8lozJVvqPki6HXeB/J+ZDQ0r+SfNbPWM3NIFc30tJZN7PW2idmVXrkvSJmb2YObcjc3soTrj9hhEZRwR0WRIWhs40EKZtVT742a2rqQnU8q43UM3JbsM8HaipIIZelEze7VgvCwmAa+Z2bSM7HhgPeDR1Byezj6sU/IbURtxW/MQLhvFW7XfCv31xX34yYpqJP7SMzVHdjngDTP7TNJwYA3gSjOb2MEYCwBLmNmEguNfBcaY2Scl57wscAYeeGfAI8CPcd/pOllFVbLPUr8xScfj8QNjzOyjvL5Ssoea2RkdtVW9LuVnF9S09VREM3VERJNhZk9IWj/n0HtBESS+vx2Atwu6uR7YKLU/PbTVRPzi0cFr48FjAlYDnsWjt39oZnelZEv7QCX9Eff1jad9FG2e0iwbxdthv5ImF8wpMaUOyjl2Hm4ePzfsfz+0/SBH9kZgmKTlgQvwILZrgG/lzHUksC3+7BwH/EfSGDPLy+fdAzhP0v+A0biye9DMPsiRxcxeptbMn2CmIg5++ZOBRfB7UO8+fBSsJMlvbAP8xSyLl/FgtzPD/R4NjArxDVnsiSvXNEbktJW6Lkkb4r/thSX9JHVsEMUukx6HqIwjIhqMzAOlF64Y38oRPQgnw19Z0pt41PVuBd32MbOZwUxBgfYrkH0L2McCYX3wEx4H/Az32aaVcRUf6DBglTyTbA6SlVc6T9Xw9KFK/ZrZvCXGy2LdjJ/yPklPFcjOMCf83w4428zOkvRkgex8ZvahpB/gq+ejJeWujM1sTwBJi+Om2XOAxSl47obAsX2ptRDsnRE9BdimyJSewU/wl4vlJD2Ep5DtmDPXy4DLJH0B+B5wOO6+mHnvJe0C7AosIyldmWheIC8wrux19cMD9vqkx8OjrncocY09AlEZR0Q0HukHyjQ8x/bGHLn5zexrIVCpl5lNlrQ18FqO7H8lbWtmtwJI+jaQ6wcGVrRU5Rgze07Symb2smqJsY7EfaBPA/sDfzezosCwZ4AvULx6n4kKUbyV+g0m+E1wxf6gmRUpzemSljOzf4XzlqVt1Z3F1KBo9qBtBde3QLaPpMVwhfV/Hcx1dzwSeHX8uzobX3EW4ZZw/J46cwV4t6QiBreIfAXPSRaellTDvCjpYmAV4N0whx3waOc0xuDf0UJ4pHOCybgVpgh1r8vMHsBfCi9P+6jnNERlHBHRYJjZsTCTdQozm1IgepGkPczsmSC/M+5Luy1H9gDg6mD6FZ6nuUdBv89KOg/4U9jfCXguBEllfaYHB1/fTAVcx/+3UOjnMdpH0W6bFZQ0Hx7VnfhsHwCOCxHnnepX0q/xVV0SkX25pOsLos+PAO6XszoJZ4vaK0eO0H4A8BszeyX45/9YIHscHrT1oJk9HpT8SwWypwP/wnOt78/z72cwj2VY2gowVtJ1eNpW+n7lRao/HHyuM1/OJD2BW2vSWBA3CU/EV7nvZeMLgqJ8DdhQtSxk/XGlXPm6JJ1uZocBZyuka2XGrfl99UTEAK6IiAZD0mr4wzzJeX0P2DNRuim5ZfEI013xFdQewNYFCis5pyMFnwR3HYivIMHzbM8FPsUfjFNSsnlBMzODfTLtX8kbL6xssrI34iveJHL4+8BQM9suR7ZUv5JeDH2kg9jGm9lKeeeHl4/k2ItmVjYvtmGQtCr+QrIJsEKYx/cLZE/AA6j+3kGfl+U0m7WnH/0C8EWcmWtX/IUE3A97vhVQoEr6EvAN/KWwt5ktkSNTNQK/7nVJWsfMxlX5ffVERGUcEdFgSBqDp5LcH/aHA781s41yZFfEVzj/Br5bL/JW0lY4jeZMWkcr4A4uMcfE/7cJ7U2n8+I+1NwHa4X+8yJ2cyPFK/R5P36PJob9wcBNZlbjhy4TTS3pz2b2PbWlos08RG2O7c/M7BS15dq2g+Xk2EoahOd4fwV/2VoITzPLTREKgVMD8NXu1NQ88gKz6kKehjQC98ePTR2aDFyeXUUH98iX8fs1GI94Hm1ml+b0XTUCv9R1BZ/937rjpakVEM3UERGNx4BEEQOY2UilWJJyHv4L4CbCRyWRVgKpc84H5gE2Ay7GfXqPZWSy/bZDpt/S/j9JD5rZJjlRzfWUxSdK5Y1K2hho96JRtt+UApyEm+DvDvtbZO9BCmWiqQ8Nf7cu6CONxEc7tq5UezyY2s42szfqCZcNVJO0BHAWbVzUo4FD0/2b5zJfIWl7M8uLV8jim6GfM8wsL9gwjSosZFUC8LYBTpM0Co/CvyNrKu/JiCvjiIgGQ9LNePBL4nfcHc+n/G44nkuwkCAviEXSBDNbI/V3IHC7mX05JZP0e1D4mx7fLKdiUbMgaSiemjRfaPoAN9XXC/Qp6qse2UQuiYakpzLR1Lltob10Dndn0JFrIQTXvaD8/PCadLDwMnIN7b/f3cxsi5y+jyZ/JV9jUZGzdSWpco+Z2X8K5nsK7lveAzgYd4k8Z2b/l5GrdF3hnL54UZWdcKvN3ZbJz++xsBYgyI5b3HrSBsyPUyw+geeino5HTmflNgDmTe0PAtYv6POx8PcRPD1mLuCfBbJP5rQ9USC7AV7IYQrOAz0d+LDOtfUO4y+VbJnjh4a/G6euaVAH9+sSYM1M2zFd/A6eAJZL7S9b5x6MBfql9vuRU7wiHLsbGJz5ru8skF0NpwN9DXdDjANWy5G7MPwtW9RhfJm20P7T1PZ/wMPApTlyO4Z5XoG/RL0C7FDQZy88Vel6POZhX8LCrivXlTovoU+9CQ8k6/b/6VmxdfsE4ha3nrIBvwDWqiD/ZPohFh5yRQrjKNyXtz3wDm5iPq5AdnyiDMP+RnUe1mOB5cNceuORxScWyB6MB6M9i6dCPU2mWlAyTtF1FPT7BvAUvnKm6Hw8AOoGvHTfy8lW0OdXgwIciUdyvwpsVnS/ctoKqzblfY8FsmPSY+IFEcY04Hd2L74a7h223XEKzzLnzgWMzLteYJHU/sJF9yAc74/XyC4z5twl27YELg/f1eU46Uqfrt6v2WWLPuOIiMbhZeDQYKJ9Cq/gdJcVMC7hinimCdHMZgT/W5uAtKOZXY+XYJwI3CjpNvxhVhR1vQ9waUgvEm4izhJHzISZ/VNSbzObjhM/PIm/WGRxKP4Afr+oL+B5SS8Bi6s9GUZNUFQK/8F94VdJWi+MU5MQjXMsH43zHG+GvzjU5MyGa7o3RPmWiaauksM9Xe0rTC1Nsb+0buxAFpLmpi0K3nAf7vlWy9O9N+4zPi3IjaE4bSuLefAazFn0svZm6fcpuLeStgVOxS0Iy0haE38xLEpBGkNtKlVe2x64r3j/Ot9Vj0VUxhERDYKZXYc/TJC0Fh4Uc5O8cMI9eEBKOuDoZUmH4IFF4A/ilzPd/gI3B95IeHiFB1Xhw8rMxgFDgzKmjtIG+FjO5DU++ALfpuAhjOc21+sLM9slpNXcidNGloHCHLeRdAy+mp0vR65/ULIy96sfI2kc8OuaDnMUm6Q8xQbtc7jBV+q56UfAL3EKxwfwF4Yv42k+eXhZ0lG09+1mv980rsQD6M4K+7uGc9sxZoVrL3VvM0F9vfEVb14E/h2S7gSuDfs74WUL83A0Hk09MsxnfPC7Z8dO0qv6h/+HdHrVPFl5M9ulxCX1WMQAroiIJiOkuGwBfMPM9ku1L4L7ljfHH5j3AoelVyipyOH1cG7jdkivRiTtbmZXqT0dZ1r2DzlzWxpnXeqH55bOB5xrZv9MyST9rYqvNP9Ge7KJmn6rQNKxZnZ0an8b4MeWSVkKKWOb4Kbq+/BCAydZTp6xpD/jiu2q0LQr7uvdMSPXGzjZzA4vEWjVC49ivw/3tUP9iljzA8fSlu89GveF51pKJD1nZquUaCtLm5kNFpyGs3flRihL2p5UhLaZ3Vwg94h5xaV08YkJWatH2fSqTkbr9zhEZRwR0QSoQVWIwqp1bXyFVBNVailCBEn7m9kFIYK2BhaYwTL9d1hZqKi/tm47l+ucGSPN6DQPTjgxOSOzLp5iNBg4Hl9hnWJmj+b0V0qxhfZHzGyDbHvBPMea2bDSF1YBkq7CU6AeCfvrAweZ2R4ZuTG4Yh9Hil7SClKYVJ5CtOw8L8FfHI/EYxgOAfqa2QEF8mXTq+ZoRGUcEdFgqKAKkWWIIYIpdR9qiTz2zsj1xgkrOkzxUMVawpKuwEvbdVhZKOW/rttWFSrJ6FRl/LKKLRw7DzenXk/7ClM19JKSTsL9ydlqVP9LyfyV+nm3uSZmSc/jlod/h6alcC7paaT87apAnqJaCtHvADMpRHNWo9m51qxKw8vS/+H1lMFdEsdn/bwpS81P88bIWlQk/dEy7GR5bT0VURlHRDQY4aHaYXUjSdcDL+Am1OPwik3Pm9mhObIPm9mGJcevVEs4nJNUFjocWNzMauJJ1KR6syrJ6FRm/JSPtC9tis1wbuoXClbGHdJLpmRfKZBdNiXzlfBxO7wARmIq3wU3E+eVWyydf66StJlBthSFqLye8du4BUb4b3ExM8vzx5d6Kapqqcn5Lvvg0fo131lPRAzgiohoPMpWIVrezHaU9G0zu0LSNRRX9RkvL1vX4eqNarWEO6wsJGlLPM3ki5LOTB0ahK/aahD8mj/HKwGlV/15JRTrMjpVHL8Mm1Y7mFnZSGTMrCZQKUfmAQBJv8+YtP8qqZDBy8xeC37mJWnv3ngi9JesYgX8UtLntBX+KPKtvoXf/yRwbS7c157FttaeEOU8ecnJGmVMW1Bh3bagiHvjeeuFlhpJv8AD4/pL+jBpxvPeLyw6r6chKuOIiMajbHWj5EE6UV5c4h28YHwe5sbTTdLKzGgzP6ZRpZZwmcpCb+EBONvifsoEk/Ggrzxcjb8MbIVHK+8J/LdA9gHVr6lcenzrRAk+OT/4eTjr1mqS1sCVU001qGCi/QlOdrJfMKmvZGZ5lbYGSFrWzF4O5y6DczQXzeN4PODpX7S9jMz83qxzdZ1zKUSTl5qU6+QjSbvhlb4MX8V/lO6oMy9lZjZdzoNeqIzN7ETgREknmlleSt0cgWimjohoMFS+CtEP8JSlNfAc2oHAr83s/E6Oe6iZnaEUJ3TJ80pVFpJTFfbBFdGLHfQ5zszWSUfZSnrczNbNke2F+86/jq+I7gQuzpr5JfW1VKGHRkGepnQE7pdPzOTPmNlqObLX4S8EewTFPQ9uMq7x4Ur6Jr6yS5dx3M/M7iqYx4vA6mb2eYk5b0cqbcvM/lIgV4pKVNIQ4Aw8mtrwSl+HpV/O5Pnza+IveekV82T8Ra4oSvw03G1Q11Ij6bs4M9eksD8YGF50bT0NURlHRMwGCH7NvCCYdNm88Wa2ZhU/ripUFpKnHP0Op45cRnXIHtSW/nInnr71FnCDmS1XMI/+lFDyzUDykqD2qTq5QVIK0dQZ2VzO63BsLiApV/hCNsgpI3sj8EMr4IROyZ2Ls6alc4L/ZWYHFZ/VOJR9KZJ0GE7u8Qfc5DzzEG5Wz6au5VX6yi3n2RMRzdQREQ2CylchqpcPbHhk862ZlUbaDDo38F1cwaXRGfarKp+6wn0AACAASURBVJWFjqEE2UPACXLSkZ/iJBaDKDBpqzqjU6PxnqTlCN+ZpB0o9vd/Hl4cEtnlqE/A8hnOxlYGJwJPSnqG+u6NzYEvJZYDeUT8s3kdyksjHo+vyvtA13N3K1gnlsDdIF/CqVMfCtsYS0Wfp5BHNjPH6Kg55kIjIpoNM9sk/O3It5f4DYvklsFr8c7MfbVMnqaka3EFmh6/MvtVgYIuwlQzm5QEWiVdFPSbvDxMwqkr66EUo1MVSNoAfwn4Eq7kewMfFSihg3Bz8sqS3sSLJOxe0PUxwB3AkpKuxq0KpQPAOsAVwMm44ppRR+6feNpT4h9fMrTl4XQ8qvvprNm/2TCzw4EkV34YzpG+F3ChpIk5UdJjJf0BOCfsH0T7GIEejaiMIyJmMczsgvC3hoQjgaSOiDRWICfYy8zeAXJNpg3As5J2BXqHwKVDcDNkV1FKyVeM0D4b2BmP8B2G8x6vWDD+W2b2NTlvdC8zmyxpoTxBM7tLTsG5Ab7KPNQKGLg6gY/N7MyOxZgXt4I8Rhs729gQbZ9dSb8OPDOrFXEG/XHLyHxhewt/4cjiYLwgynVh/27ayoH2eESfcUTEbIAc0/c7wC+yK+YmzyFN9pAEWh1v+XzPVfotxegk6S78QX04qQhtM/t5Tp+JbzcdQJbrf5TnJu9rbQQh2+OVq2qUt5zQ5UepIKOl8ZKEX82RzfPbTwJesxxKyrAq/Ay4lfZm6mygU26AYEo+zcq2Lm6mfoA6FKYFLpNJwDgzG5+RzSM1mYRHvF9gbTnNF+KENpOBR/Hyn48UBXrN6YjKOCIioltRVslXjNAeBXwNuJi2kpMj8gKtJK0OXIqbyRcHFgR+kOc/l7Q/7vv+Cc7adQTwUzP7a47sIziV6YRwXavhvt358ECtuzLy92f7ICfQqQrCC8wUMqbvrFVGnuM+jLaUsq3DvIfgjF2npGTPwAtOpAPIPsQV9KAkEl/SHXhA4DO4BeVh6qzSg+XjZ9Qy0nX6+mcnRGUcETEbQNLGOHPSR3KijrWBM6xOXq2keczs4w76zTOLTgLGmtktGdlhODnDENqTUtT4nUMU8fY5sp3msa4Soa0SBTAy8t/B2acmA5sWyQXZTYD7cZKUtYJrIE/uJuAoM3s27K+CpwX9DLgpL1q70ShK0cqRGwV8y0KRDHnRjL/hlcfGpf27eS9AqYj0Z81s1VS7cOW6UdhWwwMUH7ZUcZAgW9ry0RMRfcYREQ1GjkkZ2sx4P7VAAhFkt6J2JZCnsM7DyyIOxSOUL8ZL7tWYLOVFKi7G85aXCufsb2YH5vQ7N556k7AnbY8HMA2VtJmZHZaSvRpfCXYUYARwS7jmcdSJNg7zXRF/AA+hveLOrohKR2inXlI+xSsn1Rv/EpxLfA3cr3ybpLPM7Jwc2e/jfs09gvzfJe1lZnkR0ysmijjM6TlJK5vZy2n/eAfR9V2tivV3SV/PrsJzsAjtv6epOAnKJ5Ky399Ata/pvBT+W4P2KUyEVfAzkibiv4dJ+Kp7PTxwL40FzewSeb78AzgZzOMlr3O2R1TGERGNx+l4TdxrcPPkzvjD/gncHDocQNL5eF3XzXDluQPwWG13AEwzM5MXvj87PLT2KZA9DfgG7nvEzJ6StGmB7BrAxmY2PczpPJwOcxNqg2z+a2a31rnuNJYws2+WlL0eZwC7mFQVoiwqRmhXwdO4WdqAV+RFJYoU4PbAJua5wNdKuhmPgs5b5T4b7uefwv5OODPbXLSxr0H96Poik27ZvOwfAoerY+rMq4FHJSXWkG2Aa0JQ23MZ2Z/iNZ3/hf++lwEODLJXpOZ4CG0r4qm4qXoM/j+QF8CVzO/t8JL6FrBAB9fXc2BmcYtb3Bq4AU/ltI3PHsNJ8NN/B+JsSnl9PoDz//4D573uhaer5Mk+Gv4+WW9Oof1FYL7U/nw4A1e788P+V3GFuQueLrMdsF1BvxfibFJl7te4FvjOlga+Fj73B+btQH6e1Od+BTL9ccV1c9gOx1++egEDS87rsJy2bcL39krYXxPPS+/qPVgXODRswzqQnQuP2h8KzF0g8wf85WWxkuNvHX5/q+FugHE4LWm3/jZm1RZXxhERjcfHkr4H3BD2d6CNqD+90vkkJb84zj29WEGfO+HVnfYxs3eCafDUAtnXg6na5BSWh+J1gPNwCl6EYiS+ytkU+G1Y5dyTkd0LN2n3pc1M3Y4fW21Vk/oAe0l6GTd/1iMe+aukA3GFlY74zSOGaDiUKuGIWzCWwFfqeRHSGwKXkHEB4Hza7WBeI/r3YctiSsnp/QS3tKRxDBXysuWkKollZKTl82iDW27eJFhM06boHKxDm1thqCQsU6/bzHLN7kWw5lk+ZgvEAK6IiAZD0rI4z++GuGJ6BPdtvgmsY4E3WtJRuO/zqzjRgQEXWU7ZuorjLxTG/xquBO/C82HfL5BfDH+4AzxuZllmr0TuRcuU3suRKVUGMHNOh2UJq6Js+k2QLVXCMbQ/ir9c3Wod81hvjCvOhP0qubDS1yXpdTNbMtOWBLKlKTkn5L3oyOsvr4ubocGtGmMtU5BB0sG4D/dd3FVQ+PKkkvW6I6ohrowjIhoM8wCtbQoOP5iSOz58vFHSbbi5b1IDxn8Pr0dbFr3wikp9gOUlLW9mo3LkxkhaxcyyPsT02EnN3dxC8UBNAQorUZYwnF86FxYvzpBNv5mMB2hdlJlH3RKOOfN9Xe0JSor83JfgL2Hj6sh0hLx5VCFf+RawppnNgJnUmU/iLo80DsWrT+W+sGUwjBL1uiOqISrjiIgGI+RL7kttdHBNsfrUsc/oIOq4GeNLOhlXVM/S3vScp4w3wE3ar9Cx6XnV9I68ru06BfPdEbjDnPnqV3ja1vFm9mRGdBj5ubAHSGqXCwtsZO3Tb/6aTr/J9PuA6pdwTKOKC2CSmd1ecGwmCqLvwe9v/5z2g/G87M/wl407cWKPIgzG04nAfbJ5eB1/sSmDsvW6IyogmqkjIhoMSWPwiOR2KyLrAltWSPc4o6O2quPLy/atYXWqCaVkc03QadOzUoXigSTHeWah+Kx5NJwzwczWCPm7J+C+8F+b2foZuSq5sM8D37D26Td3mtmXlGHiUskSjkG2tAsgmIh74z71QkatZkJeS/gkPCAqiQk40syuy8hdAqyE389Cpq4gez8eNNZRve6qc214bvrshKiMIyIaDBWU3+tinzVlEbNKpTPjS7od2DFRcCXkh+KlFsEjv3MrEqlCofjkOiSdiEeIX5N3bZJewCO0p4b9ufAo8ZVzFOy38CCsduk3eNDTvmaWDYpqONQERq3Qb9m87ER+MdxvDPCY5ZCUSMrm/CZ91uRoq2S97qqQM3Yluenpl8i8ALgeh6iMIyIaDEkn4GXi/l5C9ovUBviMSh3fBY+i3gRf7SaYF5hh+ZzIVca/EU9PuZf2q5yaYBxJh+Lm7yR6+rv4aveslMzKZvaC8nmZc1eFwV/+JrAFbqL+BFcaQzNyR4Ux07mwt+LRyhea2W4Z+XQt4RetixzarQJJT+EvGlmlVVPhSNJ3gfusjUt7MDDczP4yi6ZbGkWBcHMKojKOiGgwgg9wAK7cpkJ+DdmUv/Y52kelbpuSWRpf1Z2IF1JIMBnPT56Wkk18jyozfjhnz7xrMLMrcmQnABua2UdhfwBOa7hGSuZCM9uvyqpQzk39TXxV/FJYya1uOaxR8sIHG4Xdh8xsbN78g+xG1K4eryySbxTUXEYtFDi6S8rWWEkyUdinm9lhBdHnZH6Lpep1dxbywhJnmVkeIUiPRwzgiohoMKzjesYJvoNHsNYrTv8aXrd2Q0mL0mZufN4ylX8qjJs+p0bp1oFoHxWcpMCk+9sv/C2dJ2rOn31Tav9tioODSuXCFqXf4BSizUZH9ao7BUkJG1WVvOxeOW3p5/4fw9/fdTS+la/X3VlsAowoGSDY4xBXxhERDUJVE20Vf22IOP4d7vMU7rc9wsxuyJG9N2u+zrZJ+rOZfU9tJB3Zuebll/4EJ++/Oczh28Dlef5XSQ/irGGj8RXs5I6usSNUzIV9ng7Sb4pWgwkyq8K6BBYFgU4L5gV2dRZBSSWWj5wp1OYvS7oUmIjnsYPXB17AzEZ0cS69gUVpb3UoIggp22eHAYI9GVEZR0Q0CFVNtBX9tU8BW5hzIifpS/ek/aqS5sZXZffh/NfJQ3sQnjq0ckp2MTN7u+oDMLxobBJ2R1tt+lEitwz+wvBlPCXqsyCfW9ihDCT9E1i/jIKTdD1wSFhlF8lUqQucBDithFsnEo7ubXD/9u45/b+Er8wvA26v92JQBZLmzvq/89pC+wC8sMXXcEV+N/CblKsh92UsQcGLTvqlaEabaNdWsJKOx1PqxiTzm5MQlXFERIMhqZcFkoVUW94DtIq/th0jVEjHeSrTdihwGF6T903alPGHOLPX2Tn9bmmZXFhJB5jZ+TmyywFvmNlnkjYDVgeuNLOJedcRfL9fwRXyZsC/LVM8Iqyw7ilj1g4vOVtkzfN1ZJuRfjMK2CpZ6UuaF/ibmdUU4pAkXAnujSvwP+OWhH90cQ55kfU1bSX76gxjWumXoopz2Qv/rWyIx0SMBkZZppRnT0VUxhERDYakSy1FsBFWJ7dmTcfhWD+cFQo84ndqVibInYpXWEozSk2wnFqvkg62VIRzB3MdA/zKzO4L+z8DNjOzLXNkx+OkG0PwfNRbgVXN7Fs5sv/C6/1egz9Ux2dfUFKy9+IFJ+qSTqhaLmyH6Td1VoX1zN/t8rJDxPYE65gmdDPgKpzTejye6/twvXNy+vgC8MXQz660t3ycn7Z8NBNVXoo62f8XgO/h6VvzN9FH3VKIAVwREY3Hm5LONbMDJc2PK4+LskKShuMl517FH6xLStrTcqgozewISdsDG4emC83s5rzByyrigG3x+r1H4BHNK+O+4DzMMLNpkrbDyzieJSnXTA2ciZuzdwHWwlmuRpnZv3JkpwBPS7obmGmezDHX/zts/cJWCCuX87p1CZksrgQek5dOBA/Cyw2Ck7QgsDtOvfkuzpx1K75ivx6Pkq+CbwAj8EIWv6e95eOXFftK5liP/atdhHTKb/4yMFJShy9FFedyMbAKfq9G4xzgs4wgpbsRV8YREU2ApFPwFcs6wEmWz341DtjVQk1aOZnDtVYybaWBc10Er9A0Dti7yLcpL5JwOk7FuI2ZvaIOckPlLFl74aucJcysd45MaXN9iWvpVPpNJlL9scQ3XyC7Dm1+81F1/Ob/wKOVLzOzN0LbYWZ2uqSfm9nJlS6urd/t835PzYYKiEESWA5BSMX+b8ZdLM/hwX+jzHne5whEZRwR0SCEFePMXTxw5jHgDgAzuykjX1NpJ68t1ffJwCKh7y7lduYoq37AtNCW26+kVYAD8Nzia0OQ1vfylIqk3+MKayBexOBBPIAr9+Faz1yvCrmwnYG83OWplIhUD/KlIoklKftiI+nfZrZUV+ZbBSGobx+cK3zupN0KeNLDi1larm6EdIhdGGhmHzZkwt7nl3ArwI+B3ma2RKP6bmVEZRwR0SBIuqzOYcs+AEPayQzcBwheaal33oMyBM1sY2ZFRQnSsnmBPJOA15rl58uZww648n23hOxwMuZ6YKa5XtI6ZjaupB94gTyZlGxNLm6ZSPWUbOn0qjwopyRiMxGiyl/AfczH4b+x583s0Izctrjpe3HgPzgr3PNmtioZSLoGfymbDjyOW4DOMLOi+tpl57o1/iK0KV7c4hH8N3RpV/qdXRCVcURENyEE/xxEKlUIONdySEAkPWRmG2fbC/p9BKeVnIAri9XwqkzzAT+0DLOVOqDkbDaqmOvDCnplfIX8opl9njmezsVdCvggfB6MR3PX+GnLRKqnjnUpkrgbVsYJ73dSjKMvruA2yMg9BWyOv4SsFQLOdjezfXL6HG9ma0raDf+dHYkX6uhqatPZ+P/AaCuoqd2TEQO4IiIaBEln1jueDUgKSvcPYSvqMzF9j5V0HfAX2gfN3JRz2lvAPmb2bOhjFXxV9DOc6WqmMlYBJSf5JRSbhb6JIgYws38EpdEOkrYiU/xB0v6WSs1KlK2ki4CbLfBzS9oSD7bKwx2S7qR9pHoRr3eHpQY7CIrKK4lYGSpP9ZmY+ydKWg14B3d11MiZ2fuSeslT8+6XVFRMo2/4fr6DB/JNldTlVZ2Z/Sjx3QfrTl3ffU9DVMYREY1DDVF/A7BN6vPHeJm/BEaKRjKFFRNFDGBmz8nZwV6WasibOqTkTCBpRzO7vqO2TmBsiKRNm+vzOKd/j6dd/TOMvRweqZ5XM3gDM9s32TGz20NQXTvIb8iZePBWYqEojFSnRCRxs1NxVI3q88IQ0f8rPJJ7IB7LkMXEEGw3Crha0n9IRbZncAHuUngKGCXPVe6yz1i1LHNnSSr03fc0RDN1REQPQ1hB/w/4U2jaCVgIT7F50MzWTclWoeTskGyikz7bUuZ6SY9n5i589bQuGYSV7mjaK/hNzewbObLtzNT1UBRR3NVI4ipQCarPCn0tZWb/lufCf4JzWe+GuzSuLmuOl9Snq/EIVXz3PRFRGUdENBjhIfJzPGcyHZnapTq2Fcbvj9fuTZTbQ8C5wKfAPGnFqxKUnMHE+y2ciCFdlH4QrhTWS8lW9tlWuK7zcN/2n8MYO+J5x/eEOd+Ukl0AD7RKmLFGAccWvAxcgZtbH+/s3GYlVILqs0JfM1+mJN1oZtuXOOfXee1mdlwX51Lad98TEc3UERGNx9W40toKjzrdE/hvVqggVWcSbqK9wDpZf9fMPsFNunlF2bMr4Ftp41kuwlthTtvS3hQ/GU8/+f/27jxMrrLM+/j3l4AkBAJGMOMCAoIsImFHI3AByjig4yuCOgqisooKuOA2bggM+oK+LqjI4oLiMiIggsoOEoIIJKwJOMyAviCIDmpAYVjCPX88T9GnT5/qquo61ae78/tcV67uOnXq1NOdpO7zbPddfO+xzNl2awZpFXNrVfWfSHOw/0xpyD4H3SPLF2hjB2BfSb8jDc2OloFrbdLce3mr0LjcaGVrAUsl1ZHqszhvMaLQRBvF4esZpOQpHVf5d6GXufspxz1js5op15tVYc9weYg1H/sSsDbDP3weIgWW2RHx1jG+/8uBoxm5QrrbD9t211058v7fPA+5TkTc0ubcEUO/vQwH96uXoKkeimVIuph0o3UUhRutqEhLOijdbPHq4VrFnvFY81uvAlwUEbv0+tqKaxWzzC0YZe5+ynHP2Kx+rRWs9+cVwPcBVXOp80sB+vxW0Ja0pHxyvlY5uFQNDX6D1GNdxPD6w8Vr9VxCEbgk70ddKV/7j5KuiepKTPdJ+jjD52zHtF1F0oci4gRJJ7Vp64gqVwyNTryGUUYn8ut7KdH3rIj4hqQjc/D7paRxHd6OiF+qy4xhGp6IpmUZcGt+zTxJD5FXeufvgZ6SyqxKStHZt0iZxcY9u9hE4GBsVr/jJK0BfAA4iTS3WhWwVmstoIG0mIa02hWgvH/266QPvV2B00l5e69r8/7LolSJqUJrCLeX/MxrRMRDkg4iVWv6lKTKnjEpJ/WnSLWPIc3ZvrnqxE7D9QwNgVatsG5nUEGz2xutgdHIjGGjrTo+kFQFqVXWcxfSjdT6ko6JivSkXbx/8QZuOml0p6/54nzdWrPMTTYOxmb1uzTP9y4jBc92PgBcrVThSMD6wLvyytZyXub5OWnDLRHxaaV0k+0C7hVKVZ7OYfic4uLC9/crpXX8dnRRvjBbSaks4htJ+anbas3ZKpUYjA6rte9i5HD9w6T0mKcBiyRtT1rd2+2K3UEFzW5vtAbpY8B25VXHQFUwXgnYNHImtNyj/g5pnvwqUu7sXhVv4J4EHuh3JXV2Al1mmZuKHIzN6nebpFblmQWk7UQjEkVExM8lbUTKKAUpo1Rr0VY54cKj+esjkp4LPAg8p83775C/blt8O1KGpeL7L5f0lKQ1qtpX4RjgovzzXC9pA+DOqhMlvYT0oT8nP/5vUorL2ypO7zRc/wDp97FJ7pUtJOW7vqZqdXTWddBUDzWdI+KC/G2nG61BmlYaln6QtCWpyjoxPCXpH/OxP0uqLNc5mnwDd1EMplzjAytqIAYHY7PaRcSGech5J9KK6q9K+mtEbFlx+jYMZVKaJ6ldJqULJK1JGp5cTAquI8oy5vfvJUh0W76QSMk9zio8vgtotxXmFOD9EXEF0Mo/fSowv+LcUYfrI+KofPwZpBuM+aRKUKfm3+tmFW3tJWh+QtJjUarpTMr2NUzuhR7MyOxXlYUXBqSXVcdXSrqAob+3vfOxWcBfe33jfAP3m+LfV416yTI35TgYm9VM0vNJK0J3Iu3hXUKqWlQ+r+tMShFxbP727PzhOqPcm5W0X0ScqaG6s+VrVKXdPIfqLF5VP1cvgWhWKxDnc1oBoEq3w/UzST3cNfKf+4Bb27T1RcDJwNyI2FzSFsBrI+K4itN7qel8Hmm041LaLI4btEi1rV9PdxnD3k0KwK0Vyt8Bzo60jWasPftnAkvy1qriDVxf1bNIf7fdZpmbcry1yaxmkp4iVbM5PiLOG+W82jIp5esdGhGnqMcsUTnIEhGVq40L511DCkTDVmlHda3mc0k9+Nac5H7ANhGxV5trr0L1cD2STiWtIn8Y+DWpms+1EfGXUdr6S+CDpP3aW+VjbWsvq/uazje1GeFYYdS5tap03TnlaQdJ60fE3f1cd7JwMDarmaR5pF7LzqQsVHcCv4yIb5TOqy2T0hjaKNJq5/eQ5htFWoxzUpvtUj0FIqV9yJ9meIrLo9sFUI1S+EDShaREF7eR5op/Bdw22k1MYc75xkIwHtZ+DRV0UP7aTU3n40hz1ZMiGcWgVyhLWgt4sI4bSkkLgT0i10ZWqmt8VrsbqKnGwdhsAJSS7u9IGqreDyAiXlA65wpgS9IWpX4zKRWv23E4OQ9l7wEc0up55AVZJwMXRsQXKq7bcyDqZjV1u+H6GJ6SU6Te8fz8Z3NS/u1fRcSIkQClnNvvIX2Yb61UX/nAiNij27aXrlcM3LNIW88eZ4Jvv1EPdbC7uNZLgc+Sfu/HkkY91iLdzO0fERf2ef1XkxK1vBrYmDSkvm9E3NTPdScLB2Ozmkm6AViF1Itr1WetyubU9XCfUkm5smXA78rbSroZTpZ0Iykp/3+XXrs2cHGrN1l67mFSIHqMtHWobSAqr6YG2q6m7mW4vjAfP5+0xeZZEbFmxXkbMLRg7C/A3aQP9qq/h72Ay1tz8Hmh3C4R8ZNO7Zno1EMd7C6udQPwr6T5+lNJvdhrJW1Cqj894t/MGN7jdaSAvDqwd0T8R7/XnCwcjM1qJmntTvOvY7jmtaRC7reQguDmpIVhawCHRUSxRnHH4eQO86dtn+uhvdcAHyutpj4+Ikaspu40XC/pCIZ6xE+QtzXlP7dGxFOjtGMWqef2CPAvEfG9inNG/L6Kw9ul4yJlE1s/Io6VtA7wnIhol4CldqrOmtZKknJcFCotKaVc/QdqWKFc/D1Juj0iNi08V/n76vK65cxqryDVrP5tbmtVhrUpx6upzWrWxUKoqyNiR40sQj/akOd9pGHWJfkam5H2/X6ItNr04sK5F0jas8Nw8uPdPqdUC/mONr3zYclECnpZTd2p8MF6pK057+s0vy5pNmkF8fNIK58vzY8/QLqRGRGMqd6j2+6z8WvAU6Q928eStoZ9laHUlOPhF6QRj+/nx/9Cys72B+DbDK+BXecK5eJNz6Ol5/rp1ZUzqw2iLviE556x2SRQ1VttHWuzMGnUeU1Jy6kuHi/StqmVC+eeGhGH5Dnusojq4gtdr6auc3WupPNIw9K/IvWwWguXjmw39yjpm6Q9t1/Nh94NzImIt1ecuzjPQRcXht0c41hzV6PUldYAi3EU/s2ItM3skdZTlP7NWO/cMzZriKTvRqkyU9WxbIlSPd8f5sdvIvUmV2Eo9SMAEbF6p/eOHnISR8Qh+Wsv+1IPIK2mPofUa1qQj1Vdv68tMSUbtIKRpNOB+4F1Y/RylIcDnyAVlgjgElJArvKEUhaqyO+xNsN7jONhuqTtW0PjkrYj5YiGtBocja24xqh6+TczFhpZbax1E9lXtbHJwsHYrCaqrpDztIp5uheXXr8SKSNXlbcD7wLemx8vJJXxe4JS8oZBzmtK2hzYjOGVo75TeL51M7F/pw/8MQ7Xd/L0jUmkbFH3dgjERMTfgY9ImpW/H82XScUv5kr6N1LBjo+PoZ39OAj4Zl6xL1LZzYPyNMBn8jljKa7RtI7VxqYyD1Ob1UTSt/K3zyYtNro8P96VtCXoNfm8j5JWpZaH+h4HTouIj/TZjpPJ85oRsWne83txlOopj+G6nyJV/dmMlH5xD1Ke6n0K5ywFXkma19wFhhWvbxWQGJjS8HtxOHW0ld/zSZWwVouIdZX2iR8aEe9q8x6bkIbABVxWx7ahsVDKvU10l1ccSdNIP+NDHU9ugKRfR8QOnc+cmhyMzWqmVID+ba3FRkqVjr4dEa8qnfeZiPhol9csD+EBUDWEN6h5zbyKdx5wY0TMU6oAdGZE7F445wjgMGAD4PcMD8Zthxzz0O/c0s9Wd+7jSpJ+Terh/jS6y9a1I7BRRHwrD1OvFuOYJSpPTezNyH3kI5K1SPo+qZ7zclJWuNnAlyLixHFpbA8kfZY03N622thU5mFqs/qtU1r1+wApE1fZ9uUDki6LiFdUnNvLEN6g5jUfjYinJD2ZVy3/EVineEJEfBn4sqSTI+Kwbi4q6XBSNrAHCu0MYIsa2tyViLgnje4/rfJ3nEcHtiUlpfgWsDJwJkO5n8fDeaStTIsoBK02NotUg3pf0mjFR/LrJlwwpstqY1OVg7FZ/S7TyKo6l7aelDSDtNp5rTyE3IoCs0lbcqosi1KZv1EMal7zhpwQ4zTSB/rfSKuWR+g2EGdHAhsX98eOs3vyUHVIWjm3p93QWwrhXQAAFM9JREFU817AVqSV4kTEfUpZxsbT8yPin7o8d+X8M70O+EpEPCFpQg6H9rhAcMpxMDarWUS8Jy/m2ikfKlfVOZS0EOu55A/17CHgK20ue4WkE+liCC8ividpEWleE+B1dcxrFuZQv66UL3p2RNzS73WBe0g9vaa8E/gS6Ubo96Q92+1WUz8eEdEKaKPsnR6kayS9JCIqK1aVnEJKnnEzcJWkF5D+nU0YGlllLEgZ264ez+H/pnnO2Kwhkg6PiJO6PLfrPb75/K1JubEDWFjHvFubpB+VKTm7vF7rQ/jFpGHfnzH8RqOq5GOjJB0FbATsTlq5fADw/W7/Hmtqw1JgQ1KKz8cYWpzWcVg/r7SfPpa/r0FRdZWxOcCrSMVFfljx/JTjYGxWM3VZKUfS/lWvL24VGuP7fxJ4A3B2fu/XkQomVNXy7eW6Xafk7PJ6laUeW6JNyce65N/TKG//dA3p1vkCnk8q9fiPpN/BRRFxyeBaOVLu3Y4QhbzbU6G3KWkOcGk5wclU5WBsVjN1WSknJ2RomUEaVl5c2iq0X0ScWfHhClT3HiX9BpjX2l8raSZwU0Rs3PtPM+y65wCfiDYpOaO6PGFRK3/yByLirn7aUgdJH6g4PAs4kFSAYrWK1wwsw1UnkmbnxVhzqp4vbhubKr1N9ZHzerLxnLFZ/R7oZo42Ig4vPs6Lo8ofkq05yV4WCd1HCu6tZBerkOZC+/WiViAGiIilSnmr7yqtRAb4InAvKX+ySPmTX0iaI/8maQ8yAJLOp33gPqVT0o6xiojPF9qwOmnh1jtIfwefb/OyxZK2i4jrB9GmDr5PqlS1iKFyji1B2k6WHrQZVWj1Nhn572zCkbQrKbXpCsE9Y7OaaYyVcvKq19tq6MH+hFS44BLSh/TupJrJ9+Z2jKkKjqR/J9WyLabkXAt4K2n4c7vCuSP2NSvn0C4/l39fazN89flDue2zozo9aC1ycHo/KWPZGaQ9uG0DgKQ7SHPGv2UoT3NX87WDJEnR5Yf5ROttqroK1RzSTeX+EXHH+Ldq/LlnbFa/rirllHqE00iZrX5UdcG8V/hgRiZ6qMr3fG7+03JlL40fxdvpMiUn8IikNwI/zo/3YainXv7gnV/KDna+pOsjYjtJSxiQvDr99aTavC+JiL+Ncu66OQnJq9qdM14kHRMRnyw8nkYqyLFvF6+diL3N15QeB/BgdE5NOqW4Z2zWEA2vVvQkaVXyvW3OvYZUbGFY0o+IOLvi3Bmk1bYA/zmoYd7RSNqAtF3oZaQP12tJSUt+T6redHXh3NuBV7Uybklal7QwatNB9uIkPUUauXiSDrmxVaiUJOnsiNh7EG3qhlLa1f+IiM/kbFw/ImVFO7pwjnubk4yDsVnNcjA8kLRlp1hQ4YDC8+8kBcxbgW902mqiUpnENuesBBxP2m7zO1JQWYeUKepjEfHEKC/vSNJGpO085UIRfVXVkbQn8HVSQXkB65N64FcCB0fEF/u5fh00PLVoo8O8eVX390j/dnYFfl7+HVWsuF4he5uTiYepzer3XeAO0pDmMaThw+KCrjNIQ7sLSMUWNiMtHhrNBZL2jIifj3LOiaSFXutHxMOQVuACn8t/Or1HJ98ipa38AikIvIM0vD5CL8PqEfHzHOg3yYd+U+jNNx6Is2jz/bgp7fP+Eimhx0JSMo+ti3vJi9ucbHJwz9isZq2ek6RbImKLvDBrQUS8ND//9PaY3Ju9rt1eysIWIZFWVj9GCuRVQ6l3klY8R+ka04E7ImKjPn+uRRGxTan9iyJiRNnHHofVB7Lfuk4aqgZVrAQFbfaQD6gNVYlfWtomgLHJwT1js/q1hoP/qlT/9w+kBCDl54mIJyu2BVF4vpctTVG1ojZSXd867rofy4uF7pT0HtL874i9uNmqEfHhLq9bXLz19H5rYMIE44iYPgHasELnbp7qHIzN6neqUgGIjwM/JQWsTxSenyeplR9YwMz8eLSau3sBl0euXZv3JO8SET8pnLZU0v7lHqWk/UjD5v06ElgVOAI4ljRU/bY253YzrA50vd96hdcu8UtLVQIYmzw8TG02CVQt4CovJJL0PNL2qUdJw8OQytHNBPaKiDoSf3Tb3ofpMKw+ymtr2W891TSdPtQGyz1js8mhaqHUsP+/OdjuIGk30kpuSCttLxt048p6GV4v7beeDmxKm/3WKzIH26nNPWOzSUDSN4G/Al/Nh94NzImItzfWqAo5PeYdqq7wVFnysZf91tZ565xNTg7GZjXKC5xeGhHX1HzdWaR551fmQ5cAx020faOSTo2IQ9qs/G274lfSXIYWcl0XEX8cWCMnOUlnkdYAvIXC1rmI6HfrmjXIwdisZk0nhRgUSV+uOLwMuCEiziudO6Oc+avqWD7+RtIe6StJc8s7AR+MiB+Xz12RSVopr74fdeucTU6eMzar32WS9iaVFazlbjf3NKu2LY3n3tIZpMQcZ+XHe5MK3M+TtGtEvLdw7jWk2sd0OAbwMWC7Vm84Jwy5lKG81pZcR/r9ddo6Z5OQg7FZ/Q4lVQJaLulR6kkMcVTh+xmkQDhqCs0B2AJ4eUQsB5B0Mimxx46k1IxI+gfgeaTtWlsxVOZvNmlbVJVppWHpB2mT2cuAzlvnbBJyMDarWY+JOrq95qLSoYWSrqv7fTp4JumDf1l+PIu0iGy5pFapyFeRqjs9Hyjue30Y+Nc2171Q0kUML6H4ixrbPVU8u7DX+B35a2tB36yK820ScTA2GwBJrwV2zg+vjIgL+rzenMLDacA2wBr9XHMMTgBuknQlqce7M3B8Xlx2KUBEnAGcIWnvqtSXVSLig5JeT+phA5waEeeO9poV1HTSzVBVyjYv/pnkvIDLrGaSPktaGfy9fOjNpEVOH+3jmnczlKP6SdJc7THFUoTjQdJzgO3zw+sj4r5Rzn01I7ffHFN4fkNgbkQsLL1uR+D+iPivOts+2RXLONrU456xWf32BLaMiKcAJJ0B3AiMORhHxPo1ta1f04A/kT47NpS0YURcVT5J0tdJc8S7AqcD+5AWIBV9kerfybL83D/X2O6poH0Sc5v0HIzNBmNN4M/5+76Hk/P2lcMoDH0Dp/Rbo7jHNvxf0nzuEuCpfDiAEcEYmJ+33dwSEZ+W9HlGzgPPjYhbyy+MiFslrVdfy6eMVzTdABscB2Oz+n0GuDFvR2rNrX6kz2ueDKwMfC0/fms+dlCf1+3F64CNI+Kxjmem/NgAj0h6LmmF9HNK56w5yutnjqF9U1pE/LnzWTZZORib1SwifpAXObUySn04Iv4wlmu1Ej2Q9uHOKzx1uaSb+2xqr+4i3RB0E4wvyNWXTiSVQwzScHXRDZIOjojTigclHcRQoQuzFYIXcJkNQGk19S8j4vwxXmdxRGwtaTHwhtaiJkkbAD8ezwU9ks4G5gGXUQjIEXFEh9etAsxolX8sHJ8LnAs8zvAqU88gVZka0w2M2WTknrFZzSpWUx8h6WUR0W6f7aiXy1+PAq6QdFd+vB5De03Hy0/zn7YkfSgiTsjfvyEizsrD2o9JOr74O4iIB4D5knYFNs+HfxYRlw+o/WYTlnvGZjWTdAvDV1NPB26MiC3GcK17GUqeMZO01xRgOfDoRCsoX9x+U96K4605Zu25Z2w2GHWtpm6X6GEloPZMX1Uk/Sgi3ijpVqrzYxdvMtTm+6rHZpY5GJvVr87V1PcXE2U0pFWa7zVdnBttvq96bGaZh6nNBiBnqirW5x3raupJVY5R0nLg76SbkJnAI62nSIu4Vm6qbWYTmYOxWU0kjTofGhGLx3DNOU3vL5X0MKP0avusRmVmeJjarE6fz19nkLbo3EzqEW4B3AC8rNcLNh2IcxtWB5B0LHA/8F3Sz7UvIxN5mNkYuGdsVjNJ5wCfaqV6zAXgj46IfZptWX8k3VxKPFJ5zMx65wLeZvXbuJhzOSJuAzZtsD11+bukfSVNlzRN0r6k+WEz65N7xmY1k/QDUpA6Mx/aF1gtIt7cXKv6l4s3fAl4OWkOeSHw3oj4bXOtMpsaHIzNaiZpBsMrLF0FnBwR/9Ncq8xsInMwNrNRtVJcSjqJ6qQfo+amNrPOvJrazDq5PX+9odFWmE1h7hmbmZk1zD1jswGRtGpEPNL5zIlN0vmMnvTjtePYHLMpycHYrGaS5gOnkwo8rCtpHnBoRLyr2ZaN2eeaboDZVOdharOaSfo1sA/w01ZeaUm3RcTmo7/SzFZU7hmbDUBE3CMNqxi4vKm29Ktd6URSSswYS51mMxvOwdisfvfkoeqQtDKpBOHtHV4zkXVTOtHM+uBharOaSVqLlKnqlaTe48XAkRHxYKMNq4GkuQwvDfnHJttjNlU4GJtZVyS9ETgRuJJ0k7ET8MGI+HGT7TKbChyMzWomaW3gYGA9ClNBEXFAU22qg6Sbgd1bveH8c17qqk1m/fOcsVn9zgMWAJcyiRduVZhWGpZ+EFd+M6uFg7FZ/VaNiA833YgBuFDSRcAP8uM3Ab9osD1mU4aHqc1qJuk44JqI+HnTbambpNcDO+aHCyLi3CbbYzZVOBib1UTSw6T9uAJmAY8BTzC0H3d2g80bM0kbAnMjYmHp+I7A/RHxX820zGzq8HyPWU0iYvWImJ2/TouImYXHkzIQZ18EHqo4viw/Z2Z9cjA2q5mky7o5NonMjYhbywfzsfXGvzlmU48XcJnVRNIM0vD0WpKeSRqeBpgNPK+xhvVvzVGemzlurTCbwhyMzepzKPBe4LnAIoaC8UPAV5pqVA1ukHRwRJxWPCjpINLPaWZ98gIus5pJOjwiTmq6HXXJKTDPBR5nKPhuCzwD2Csi/tBU28ymCgdjM+uKpF2BVhnIJRFxeZPtMZtKHIzNzMwa5tXUZmZmDfMCLrOaSdq64vAy4HcR8eR4t8fMJj4PU5vVTNK1wNbALaQV1ZsDS4A1gMMi4uIGm2dmE5CHqc3qdx+wVURsGxHbAFsBdwG7Ayc02jIzm5AcjM3q96KIWNJ6EBFLgU0i4q4G22RmE5jnjM3qt0TSycAP8+M3AUslrUIqHGFmNoznjM1qJmkm8C6GSg0uBL4G/A+p1vHfmmqbmU1MDsZmZmYN8zC1Wc0kvRw4GngBhf9jEbFBU20ys4nNPWOzmkm6A3gfKY/z8tbxiHiwsUaZ2YTmnrFZ/ZZFxC+aboSZTR7uGZvVTNJngenAOcBjreMRsbixRpnZhOZgbFYzSVdUHI6I2G3cG2Nmk4KDsZmZWcM8Z2xWE0n7RcSZkt5f9XxE/L/xbpOZTQ4Oxmb1mZW/rt5oK8xs0vEwtZmZWcPcMzarmaS1gYOB9Rie9OOAptpkZhObg7FZ/c4DFgCXUkj6YWbWjoepzWom6aaI2LLpdpjZ5OF6xmb1u0DSnk03wswmD/eMzWom6WHSyurH8x+Rkn7MbrRhZjZhORibmZk1zMPUZjVTsp+kT+TH60javul2mdnE5Z6xWc0knQw8BewWEZtKeiZwcURs13DTzGyC8tYms/rtEBFbS7oRICL+IukZTTfKzCYuD1Ob1e8JSdOBgKeTgDzVbJPMbCJzMDar35eBc4G5kv4NuBo4vtkmmdlE5jljswGQtAnwivzw8oi4vcn2mNnE5jljs8FYFWgNVc9suC1mNsF5mNqsZpI+CZwBzAHWAr4l6ePNtsrMJjIPU5vVTNJvgHkR8T/58UzgpojYuNmWmdlE5Z6xWf3uA2YUHq8C/L6htpjZJOCesVnNJP0E2A64hDRnvDtwHXAvQEQc0VzrzGwicjA2q5mkt432fEScMV5tMbPJwcHYrGaSZgAb5of/2Zo7NjNrx3PGZjWRtJKkE0jD0WcA3wHukXSCpJWbbZ2ZTWQOxmb1OZG0nWn9iNgmIrYGXgisCXyu0ZaZ2YTmYWqzmki6E3hRlP5T5TzVd0TERs20zMwmOveMzeoT5UCcDy4nF40wM6viYGxWn6WS9i8flLQfcEcD7TGzScLD1GY1kfQ84BzgUWBRPrwtKTf1XhHhxB9mVsnB2KxmknYDXpwfLo2Iy5psj5lNfA7GZmZmDfOcsZmZWcMcjM3MzBrmYGxm40bSckk3SbpN0lmSVu3jWt+WtE/+/nRJm41y7i6S5o/hPX4raa1uj5fO+VuP73W0pKN6baNNDQ7GZjaeHo2ILSNic+Bx4J3FJyWtNJaLRsRBEbF0lFN2AXoOxmbjxcHYzJqyANgw91oXSPopaa/2dEknSrpe0i2SDgVQ8hVJv5F0KfDs1oUkXSlp2/z9P0laLOlmSZdJWo8U9N+Xe+U7SVpb0tn5Pa6X9PL82mdJuljSEkmnA+r0Q0j6iaRF+TWHlJ77Qj5+maS187EXSrowv2aBpE3q+GXa5Damu1Azs37kHvAewIX50NbA5hFxdw5oyyJiO0mrAAslXQxsBWwMbAbMBZYC3yxdd23gNGDnfK05EfFnSV8H/hYRn8vnfR/4QkRcLWld4CJgU+BTwNURcYykVwMHdvHjHJDfYyZwvaSzI+JBYBZwQ0S8T9In87XfA5wKvDMi7pS0A/A1YLcx/BptCnEwNrPxNFPSTfn7BcA3SMPH10XE3fn4PwJbtOaDgTWAjYCdgR/k9KL3Sbq84vovBa5qXSsi/tymHa8ENpOe7vjOlrRafo/X59f+TNJfuviZjpC0V/5+ndzWB4GngH/Px88EzsnvMR84q/Deq3TxHjbFORib2Xh6NCK2LB7IQenvxUPA4RFxUem8PWtsxzTgpeVa04UA2RVJu5AC+8si4hFJVwIz2pwe+X3/Wv4dmHnO2MwmmouAw1o1oCW9SNIs4CrgTXlO+TnArhWvvRbYWdL6+bVz8vGHgdUL510MHN56IKkVHK8C3pKP7QE8s0Nb1wD+kgPxJqSeecs0oNW7fwtp+Psh4G5Jb8jvIUnzOryHrQAcjM1sojmdNB+8WNJtwCmkUbxzgTvzc98BflV+YUT8CTiENCR8M0PDxOcDe7UWcAFHANvmBWJLGVrV/WlSMF9CGq7+/x3aeiGwkqTbgc+SbgZa/g5sn3+G3YBj8vF9gQNz+5YA/6eL34lNcU6HaWZm1jD3jM3MzBrmYGxmZtYwB2MzM7OGORibmZk1zMHYzMysYQ7GZmZmDXMwNjMza9j/AplL2X+TUmhXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay,ConfusionMatrixDisplay\n",
    "cm = confusion_matrix(df['label'], df['predicted'])\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=names)\n",
    "disp.plot(xticks_rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "validation_path = \"validation_set\"\n",
    "onlyfiles = [f for f in listdir(validation_path) if isfile(join(validation_path, f))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# onlyfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.load_state_dict(torch.load('videomae-icsr-heart/checkpoint-5000/pytorch_model.bin'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eval_results = {}\n",
    "# for video in onlyfiles:\n",
    "#     videoreader = VideoReader(validation_path + \"/\" + video, num_threads=1, ctx=cpu(0))\n",
    "#     videoreader.seek(0)\n",
    "#     indices = np.linspace(0, len(videoreader), num=128)\n",
    "#     indices = np.clip(indices, 0, len(videoreader)- 1).astype(np.int64)\n",
    "#     inputs = []\n",
    "#     inputs.append(feature_extractor(list(videoreader.get_batch(indices).asnumpy()), return_tensors=\"pt\")['pixel_values'][0])\n",
    "#     inputs = torch.stack(inputs)\n",
    "# #     print(inputs)\n",
    "#     output = model(inputs.to(device='cuda'))\n",
    "# #     print(names[torch.argmax(output['logits']).item()])\n",
    "#     eval_results[video] = torch.argmax(output['logits']).item()\n",
    "# eval_results\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7306c79357c84d2fa7fde3142c5dc119",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/136 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'video0029.mp4': 9,\n",
       " 'video0023.mp4': 15,\n",
       " 'video0054.mp4': 17,\n",
       " 'video0050.mp4': 18,\n",
       " 'video0038.mp4': 18,\n",
       " 'video0090.mp4': 2,\n",
       " 'video0092.mp4': 16,\n",
       " 'video0063.mp4': 3,\n",
       " 'video0110.mp4': 7,\n",
       " 'video0022.mp4': 0,\n",
       " 'video0117.mp4': 17,\n",
       " 'video0077.mp4': 6,\n",
       " 'video0087.mp4': 3,\n",
       " 'video0032.mp4': 0,\n",
       " 'video0088.mp4': 6,\n",
       " 'video0098.mp4': 4,\n",
       " 'video0005.mp4': 16,\n",
       " 'video0046.mp4': 13,\n",
       " 'video0089.mp4': 2,\n",
       " 'video0119.mp4': 6,\n",
       " 'video0105.mp4': 16,\n",
       " 'video0086.mp4': 17,\n",
       " 'video0125.mp4': 5,\n",
       " 'video0001.mp4': 18,\n",
       " 'video0134.mp4': 9,\n",
       " 'video0011.mp4': 17,\n",
       " 'video0017.mp4': 8,\n",
       " 'video0109.mp4': 13,\n",
       " 'video0043.mp4': 1,\n",
       " 'video0031.mp4': 7,\n",
       " 'video0018.mp4': 15,\n",
       " 'video0115.mp4': 1,\n",
       " 'video0121.mp4': 3,\n",
       " 'video0006.mp4': 3,\n",
       " 'video0083.mp4': 0,\n",
       " 'video0009.mp4': 2,\n",
       " 'video0111.mp4': 10,\n",
       " 'video0003.mp4': 11,\n",
       " 'video0107.mp4': 13,\n",
       " 'video0118.mp4': 12,\n",
       " 'video0024.mp4': 3,\n",
       " 'video0030.mp4': 17,\n",
       " 'video0019.mp4': 12,\n",
       " 'video0008.mp4': 1,\n",
       " 'video0065.mp4': 17,\n",
       " 'video0037.mp4': 1,\n",
       " 'video0058.mp4': 17,\n",
       " 'video0126.mp4': 4,\n",
       " 'video0114.mp4': 4,\n",
       " 'video0062.mp4': 6,\n",
       " 'video0000.mp4': 18,\n",
       " 'video0066.mp4': 3,\n",
       " 'video0093.mp4': 3,\n",
       " 'video0044.mp4': 17,\n",
       " 'video0079.mp4': 17,\n",
       " 'video0039.mp4': 9,\n",
       " 'video0113.mp4': 17,\n",
       " 'video0016.mp4': 3,\n",
       " 'video0020.mp4': 15,\n",
       " 'video0128.mp4': 6,\n",
       " 'video0069.mp4': 3,\n",
       " 'video0080.mp4': 4,\n",
       " 'video0047.mp4': 10,\n",
       " 'video0045.mp4': 2,\n",
       " 'video0034.mp4': 12,\n",
       " 'video0059.mp4': 17,\n",
       " 'video0120.mp4': 7,\n",
       " 'video0130.mp4': 0,\n",
       " 'video0060.mp4': 4,\n",
       " 'video0085.mp4': 3,\n",
       " 'video0102.mp4': 6,\n",
       " 'video0028.mp4': 16,\n",
       " 'video0132.mp4': 14,\n",
       " 'video0099.mp4': 18,\n",
       " 'video0007.mp4': 15,\n",
       " 'video0127.mp4': 4,\n",
       " 'video0040.mp4': 6,\n",
       " 'video0035.mp4': 14,\n",
       " 'video0053.mp4': 13,\n",
       " 'video0075.mp4': 2,\n",
       " 'video0027.mp4': 15,\n",
       " 'video0076.mp4': 4,\n",
       " 'video0112.mp4': 6,\n",
       " 'video0012.mp4': 4,\n",
       " 'video0081.mp4': 16,\n",
       " 'video0068.mp4': 18,\n",
       " 'video0036.mp4': 12,\n",
       " 'video0025.mp4': 16,\n",
       " 'video0071.mp4': 12,\n",
       " 'video0135.mp4': 12,\n",
       " 'video0026.mp4': 7,\n",
       " 'video0082.mp4': 1,\n",
       " 'video0055.mp4': 4,\n",
       " 'video0052.mp4': 13,\n",
       " 'video0033.mp4': 6,\n",
       " 'video0129.mp4': 0,\n",
       " 'video0051.mp4': 13,\n",
       " 'video0048.mp4': 14,\n",
       " 'video0133.mp4': 2,\n",
       " 'video0049.mp4': 4,\n",
       " 'video0021.mp4': 1,\n",
       " 'video0091.mp4': 16,\n",
       " 'video0116.mp4': 8,\n",
       " 'video0056.mp4': 14,\n",
       " 'video0101.mp4': 1,\n",
       " 'video0103.mp4': 10,\n",
       " 'video0010.mp4': 14,\n",
       " 'video0073.mp4': 15,\n",
       " 'video0067.mp4': 15,\n",
       " 'video0014.mp4': 6,\n",
       " 'video0131.mp4': 4,\n",
       " 'video0074.mp4': 17,\n",
       " 'video0072.mp4': 17,\n",
       " 'video0070.mp4': 13,\n",
       " 'video0042.mp4': 11,\n",
       " 'video0064.mp4': 18,\n",
       " 'video0004.mp4': 18,\n",
       " 'video0100.mp4': 4,\n",
       " 'video0041.mp4': 18,\n",
       " 'video0094.mp4': 12,\n",
       " 'video0123.mp4': 17,\n",
       " 'video0095.mp4': 16,\n",
       " 'video0097.mp4': 13,\n",
       " 'video0015.mp4': 13,\n",
       " 'video0057.mp4': 12,\n",
       " 'video0124.mp4': 0,\n",
       " 'video0078.mp4': 9,\n",
       " 'video0084.mp4': 0,\n",
       " 'video0096.mp4': 13,\n",
       " 'video0104.mp4': 9,\n",
       " 'video0106.mp4': 15,\n",
       " 'video0122.mp4': 18,\n",
       " 'video0002.mp4': 10,\n",
       " 'video0108.mp4': 6,\n",
       " 'video0061.mp4': 9,\n",
       " 'video0013.mp4': 0}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_results = {}\n",
    "eval_labels = {}\n",
    "eval_estimations = {}\n",
    "for video in tqdm(onlyfiles):\n",
    "    eval_estimations[video] = []\n",
    "    videoreader = VideoReader(validation_path + \"/\" + video, num_threads=1, ctx=cpu(0))\n",
    "    videoreader.seek(0)\n",
    "    file_size = len(videoreader)\n",
    "    batches = int(file_size/16)\n",
    "    input_list = []\n",
    "    for i in range(batches):\n",
    "        indices = np.linspace(i, len(videoreader)-batches+i, num=16)\n",
    "        indices = np.clip(indices, 0, len(videoreader)- 1).astype(np.int64)\n",
    "        inputs = []\n",
    "        inputs.append(feature_extractor(list(videoreader.get_batch(indices).asnumpy()), return_tensors=\"pt\")['pixel_values'][0])\n",
    "        input_list.append(torch.stack(inputs))\n",
    "#     print(len(input_list))\n",
    "    results = []\n",
    "    for frame_seq in input_list:\n",
    "        output = model(frame_seq.to(device='cuda'))\n",
    "        eval_estimations[video].append(output['logits'])\n",
    "        output = torch.argmax(output['logits']).item()\n",
    "        results.append(output)\n",
    "#     print(np.bincount(results).argmax())\n",
    "    eval_results[video] = [np.bincount(results).argmax()]\n",
    "    eval_labels[video] = results\n",
    "#     print(names[torch.argmax(output['logits']).item()])\n",
    "    \n",
    "eval_results\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "video0000.mp4, predicted Writing vs real Wiping a table, 18 vs 8, Posible cases: [8, 18, 8, 18, 8, 18, 18, 18, 18]\n",
      "video0003.mp4, predicted Freehand exercise vs real Coughing or sneezing, 11 vs 7, Posible cases: [1, 1, 1, 1, 11, 11, 11, 11, 11]\n",
      "video0009.mp4, predicted Touching a hot surface vs real Unknown activity, 2 vs 19, Posible cases: [2, 2, 2, 2, 2, 2, 2, 2, 2]\n",
      "video0011.mp4, predicted Brushing teeth vs real Freehand exercise, 17 vs 11, Posible cases: [17, 17, 17, 17, 17, 17, 17, 17, 17]\n",
      "video0014.mp4, predicted Eating food with a fork vs real Talking on the phone, 6 vs 14, Posible cases: [6, 6, 6, 6, 6, 6, 6, 6, 6]\n",
      "video0015.mp4, predicted Limping vs real Colliding against something, 13 vs 5, Posible cases: [13, 13, 13, 13, 13, 13, 13, 13, 13]\n",
      "video0019.mp4, predicted Lying down vs real Falling down, 12 vs 16, Posible cases: [12, 12, 12, 12, 12, 12, 12, 12, 12]\n",
      "video0022.mp4, predicted Opening the door and walking in/out vs real Colliding against something, 0 vs 5, Posible cases: [0, 0, 0, 0, 0, 0, 16, 16, 0]\n",
      "video0025.mp4, predicted Falling down vs real Colliding against something, 16 vs 5, Posible cases: [16, 16, 16, 16, 16, 16, 16, 16, 16]\n",
      "video0026.mp4, predicted Coughing or sneezing vs real Eating food with a fork, 7 vs 6, Posible cases: [7, 7, 15, 15, 7, 7, 7, 7, 7, 15]\n",
      "video0027.mp4, predicted Using a computer vs real Writing, 15 vs 18, Posible cases: [15, 15, 15, 15, 15, 15, 15, 15]\n",
      "video0030.mp4, predicted Brushing teeth vs real Talking on the phone, 17 vs 14, Posible cases: [17, 17, 17, 17, 17, 17, 17, 17, 17]\n",
      "video0031.mp4, predicted Coughing or sneezing vs real Limping, 7 vs 13, Posible cases: [7, 7, 7, 7, 7, 12, 12, 12, 12]\n",
      "video0034.mp4, predicted Lying down vs real Colliding against something, 12 vs 5, Posible cases: [16, 12, 12, 12, 12, 12, 12, 12, 12]\n",
      "video0035.mp4, predicted Talking on the phone vs real Reading a book, 14 vs 9, Posible cases: [15, 14, 14, 14, 14, 14, 14, 14, 14, 15, 14, 14, 14]\n",
      "video0041.mp4, predicted Writing vs real Wiping a table, 18 vs 8, Posible cases: [18, 18, 17, 18, 18, 18, 17, 17, 17]\n",
      "video0043.mp4, predicted Putting on a jacket vs real Freehand exercise, 1 vs 11, Posible cases: [1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
      "video0044.mp4, predicted Brushing teeth vs real Neck roll exercise, 17 vs 10, Posible cases: [17, 17, 17, 17, 17, 17, 17, 17, 17]\n",
      "video0046.mp4, predicted Limping vs real Coughing or sneezing, 13 vs 7, Posible cases: [13, 13, 13, 13, 4, 4, 4, 4, 13]\n",
      "video0048.mp4, predicted Talking on the phone vs real Reading a book, 14 vs 9, Posible cases: [14, 14, 14, 14, 14, 14, 14, 14, 14]\n",
      "video0050.mp4, predicted Writing vs real Coughing or sneezing, 18 vs 7, Posible cases: [18, 18, 18, 18, 18, 18, 12, 17, 17]\n",
      "video0053.mp4, predicted Limping vs real Using a computer, 13 vs 15, Posible cases: [13, 9, 15, 15, 13, 13, 13, 9, 13, 13]\n",
      "video0057.mp4, predicted Lying down vs real Talking on the phone, 12 vs 14, Posible cases: [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12]\n",
      "video0059.mp4, predicted Brushing teeth vs real Freehand exercise, 17 vs 11, Posible cases: [17, 17, 17, 17, 17, 17, 17, 17, 17]\n",
      "video0060.mp4, predicted Drinking water vs real Freehand exercise, 4 vs 11, Posible cases: [4, 4, 4, 4, 4, 4, 4, 4, 4]\n",
      "video0066.mp4, predicted Opening the fridge vs real Touching a hot surface, 3 vs 2, Posible cases: [3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
      "video0075.mp4, predicted Touching a hot surface vs real Unknown activity, 2 vs 19, Posible cases: [2, 2, 2, 2, 2, 2, 2]\n",
      "video0079.mp4, predicted Brushing teeth vs real Neck roll exercise, 17 vs 10, Posible cases: [17, 17, 17, 17, 10, 17, 17, 17, 17]\n",
      "video0082.mp4, predicted Putting on a jacket vs real Coughing or sneezing, 1 vs 7, Posible cases: [1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
      "video0088.mp4, predicted Eating food with a fork vs real Wiping a table, 6 vs 8, Posible cases: [6, 6, 6, 6, 6, 6, 6, 6, 6]\n",
      "video0089.mp4, predicted Touching a hot surface vs real Wiping a table, 2 vs 8, Posible cases: [2, 2, 2, 2, 2, 2, 2, 2, 2]\n",
      "video0091.mp4, predicted Falling down vs real Coughing or sneezing, 16 vs 7, Posible cases: [16, 16, 16, 16, 16, 16, 16, 16, 16]\n",
      "video0093.mp4, predicted Opening the fridge vs real Touching a hot surface, 3 vs 2, Posible cases: [3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
      "video0094.mp4, predicted Lying down vs real Limping, 12 vs 13, Posible cases: [12, 12, 12, 12, 12, 12, 12, 17, 12]\n",
      "video0096.mp4, predicted Limping vs real Colliding against something, 13 vs 5, Posible cases: [13, 13, 13, 13, 13, 13, 16, 16, 13]\n",
      "video0097.mp4, predicted Limping vs real Colliding against something, 13 vs 5, Posible cases: [13, 13, 13, 13, 13, 13, 13, 13, 13]\n",
      "video0102.mp4, predicted Eating food with a fork vs real Drinking water, 6 vs 4, Posible cases: [6, 6, 6, 6, 6, 6, 6, 6]\n",
      "video0107.mp4, predicted Limping vs real Unknown activity, 13 vs 19, Posible cases: [13, 13, 13, 13, 13, 13, 13]\n",
      "video0109.mp4, predicted Limping vs real Colliding against something, 13 vs 5, Posible cases: [13, 13, 13, 13, 13, 13, 13, 13, 13]\n",
      "video0110.mp4, predicted Coughing or sneezing vs real Eating food with a fork, 7 vs 6, Posible cases: [7, 7, 7, 7, 7, 7, 7, 7, 7, 7]\n",
      "video0112.mp4, predicted Eating food with a fork vs real Lying down, 6 vs 12, Posible cases: [16, 16, 12, 12, 12, 6, 6, 6, 6]\n",
      "video0114.mp4, predicted Drinking water vs real Freehand exercise, 4 vs 11, Posible cases: [4, 4, 4, 4, 4, 4, 4, 4, 4]\n",
      "video0117.mp4, predicted Brushing teeth vs real Freehand exercise, 17 vs 11, Posible cases: [17, 17, 17, 17, 17, 17, 17, 17, 17]\n",
      "video0118.mp4, predicted Lying down vs real Talking on the phone, 12 vs 14, Posible cases: [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12]\n",
      "video0119.mp4, predicted Eating food with a fork vs real Talking on the phone, 6 vs 14, Posible cases: [6, 6, 6, 6, 6, 6, 14, 6, 6]\n",
      "video0121.mp4, predicted Opening the fridge vs real Touching a hot surface, 3 vs 2, Posible cases: [3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
      "video0125.mp4, predicted Colliding against something vs real Unknown activity, 5 vs 19, Posible cases: [5, 5, 5, 1, 1, 5, 5]\n",
      "video0129.mp4, predicted Opening the door and walking in/out vs real Limping, 0 vs 13, Posible cases: [0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "video0131.mp4, predicted Drinking water vs real Neck roll exercise, 4 vs 10, Posible cases: [4, 4, 4, 4, 4, 4, 4, 13, 13]\n",
      "video0135.mp4, predicted Lying down vs real Limping, 12 vs 13, Posible cases: [12, 12, 12, 12, 12, 12, 12, 12, 12]\n"
     ]
    }
   ],
   "source": [
    "for x in dict(sorted(eval_results.items(), key=lambda item: item)).keys():\n",
    "    if eval_results[x] != validation_labels[x][0]:\n",
    "        print(f'{x}, predicted {names[eval_results[x]]} vs real {names[validation_labels[x][0]]}, {eval_results[x]} vs {validation_labels[x][0]}, Posible cases: {eval_labels[x]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ -7.1302, -15.0484, -12.1665,  -9.0507,  -1.7132,  -7.9959,   4.8482,\n",
       "          -6.6244,  10.4697,  -7.1200,  -3.9928,  -7.8140,  -0.8858,  -8.7205,\n",
       "           4.7883,   7.2673,  -8.6436,  -4.5161,  10.7741, -11.0969]],\n",
       "       device='cuda:0', grad_fn=<SumBackward1>)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.stack(eval_estimations['video0000.mp4'], dim=0).sum(dim=0)\n",
    "# for result in range(len(eval_estimations['video0000.mp4'])):\n",
    "#     print(eval_estimations['video0000.mp4'][result] ,eval_labels['video0000.mp4'][result])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in eval_results.keys():\n",
    "    eval_results[x] = \"[\" + str(eval_results[x]) + \"]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'video0029.mp4': '[9]',\n",
       " 'video0023.mp4': '[15]',\n",
       " 'video0054.mp4': '[17]',\n",
       " 'video0050.mp4': '[11]',\n",
       " 'video0038.mp4': '[18]',\n",
       " 'video0090.mp4': '[2]',\n",
       " 'video0092.mp4': '[16]',\n",
       " 'video0063.mp4': '[3]',\n",
       " 'video0110.mp4': '[7]',\n",
       " 'video0022.mp4': '[5]',\n",
       " 'video0117.mp4': '[7]',\n",
       " 'video0077.mp4': '[6]',\n",
       " 'video0087.mp4': '[3]',\n",
       " 'video0032.mp4': '[0]',\n",
       " 'video0088.mp4': '[6]',\n",
       " 'video0098.mp4': '[4]',\n",
       " 'video0005.mp4': '[16]',\n",
       " 'video0046.mp4': '[5]',\n",
       " 'video0089.mp4': '[19]',\n",
       " 'video0119.mp4': '[14]',\n",
       " 'video0105.mp4': '[16]',\n",
       " 'video0086.mp4': '[17]',\n",
       " 'video0125.mp4': '[1]',\n",
       " 'video0001.mp4': '[18]',\n",
       " 'video0134.mp4': '[9]',\n",
       " 'video0011.mp4': '[19]',\n",
       " 'video0017.mp4': '[8]',\n",
       " 'video0109.mp4': '[5]',\n",
       " 'video0043.mp4': '[7]',\n",
       " 'video0031.mp4': '[7]',\n",
       " 'video0018.mp4': '[15]',\n",
       " 'video0115.mp4': '[1]',\n",
       " 'video0121.mp4': '[3]',\n",
       " 'video0006.mp4': '[4]',\n",
       " 'video0083.mp4': '[0]',\n",
       " 'video0009.mp4': '[2]',\n",
       " 'video0111.mp4': '[10]',\n",
       " 'video0003.mp4': '[11]',\n",
       " 'video0107.mp4': '[0]',\n",
       " 'video0118.mp4': '[12]',\n",
       " 'video0024.mp4': '[3]',\n",
       " 'video0030.mp4': '[14]',\n",
       " 'video0019.mp4': '[16]',\n",
       " 'video0008.mp4': '[1]',\n",
       " 'video0065.mp4': '[17]',\n",
       " 'video0037.mp4': '[1]',\n",
       " 'video0058.mp4': '[17]',\n",
       " 'video0126.mp4': '[4]',\n",
       " 'video0114.mp4': '[4]',\n",
       " 'video0062.mp4': '[6]',\n",
       " 'video0000.mp4': '[8]',\n",
       " 'video0066.mp4': '[3]',\n",
       " 'video0093.mp4': '[3]',\n",
       " 'video0044.mp4': '[10]',\n",
       " 'video0079.mp4': '[10]',\n",
       " 'video0039.mp4': '[9]',\n",
       " 'video0113.mp4': '[17]',\n",
       " 'video0016.mp4': '[3]',\n",
       " 'video0020.mp4': '[15]',\n",
       " 'video0128.mp4': '[6]',\n",
       " 'video0069.mp4': '[3]',\n",
       " 'video0080.mp4': '[4]',\n",
       " 'video0047.mp4': '[10]',\n",
       " 'video0045.mp4': '[2]',\n",
       " 'video0034.mp4': '[5]',\n",
       " 'video0059.mp4': '[19]',\n",
       " 'video0120.mp4': '[7]',\n",
       " 'video0130.mp4': '[0]',\n",
       " 'video0060.mp4': '[4]',\n",
       " 'video0085.mp4': '[3]',\n",
       " 'video0102.mp4': '[4]',\n",
       " 'video0028.mp4': '[16]',\n",
       " 'video0132.mp4': '[14]',\n",
       " 'video0099.mp4': '[18]',\n",
       " 'video0007.mp4': '[15]',\n",
       " 'video0127.mp4': '[4]',\n",
       " 'video0040.mp4': '[6]',\n",
       " 'video0035.mp4': '[6]',\n",
       " 'video0053.mp4': '[15]',\n",
       " 'video0075.mp4': '[2]',\n",
       " 'video0027.mp4': '[15]',\n",
       " 'video0076.mp4': '[4]',\n",
       " 'video0112.mp4': '[16]',\n",
       " 'video0012.mp4': '[4]',\n",
       " 'video0081.mp4': '[16]',\n",
       " 'video0068.mp4': '[18]',\n",
       " 'video0036.mp4': '[12]',\n",
       " 'video0025.mp4': '[16]',\n",
       " 'video0071.mp4': '[12]',\n",
       " 'video0135.mp4': '[7]',\n",
       " 'video0026.mp4': '[6]',\n",
       " 'video0082.mp4': '[7]',\n",
       " 'video0055.mp4': '[4]',\n",
       " 'video0052.mp4': '[13]',\n",
       " 'video0033.mp4': '[6]',\n",
       " 'video0129.mp4': '[0]',\n",
       " 'video0051.mp4': '[5]',\n",
       " 'video0048.mp4': '[10]',\n",
       " 'video0133.mp4': '[2]',\n",
       " 'video0049.mp4': '[4]',\n",
       " 'video0021.mp4': '[1]',\n",
       " 'video0091.mp4': '[16]',\n",
       " 'video0116.mp4': '[8]',\n",
       " 'video0056.mp4': '[14]',\n",
       " 'video0101.mp4': '[1]',\n",
       " 'video0103.mp4': '[10]',\n",
       " 'video0010.mp4': '[14]',\n",
       " 'video0073.mp4': '[15]',\n",
       " 'video0067.mp4': '[15]',\n",
       " 'video0014.mp4': '[6]',\n",
       " 'video0131.mp4': '[4]',\n",
       " 'video0074.mp4': '[17]',\n",
       " 'video0072.mp4': '[17]',\n",
       " 'video0070.mp4': '[13]',\n",
       " 'video0042.mp4': '[11]',\n",
       " 'video0064.mp4': '[18]',\n",
       " 'video0004.mp4': '[18]',\n",
       " 'video0100.mp4': '[4]',\n",
       " 'video0041.mp4': '[6]',\n",
       " 'video0094.mp4': '[13]',\n",
       " 'video0123.mp4': '[17]',\n",
       " 'video0095.mp4': '[16]',\n",
       " 'video0097.mp4': '[5]',\n",
       " 'video0015.mp4': '[13]',\n",
       " 'video0057.mp4': '[12]',\n",
       " 'video0124.mp4': '[0]',\n",
       " 'video0078.mp4': '[9]',\n",
       " 'video0084.mp4': '[0]',\n",
       " 'video0096.mp4': '[5]',\n",
       " 'video0104.mp4': '[9]',\n",
       " 'video0106.mp4': '[15]',\n",
       " 'video0122.mp4': '[18]',\n",
       " 'video0002.mp4': '[10]',\n",
       " 'video0108.mp4': '[6]',\n",
       " 'video0061.mp4': '[9]',\n",
       " 'video0013.mp4': '[0]'}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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