deepfake_train.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/taabishhh/deepfake-detection/blob/main/Model_and_train_csv.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_M_hIl_-C6P8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d081c771-fe19-47bc-b320-a2ba2a964993"
      },
      "source": [
        "#before running this please change the RUNTIME to GPU (Runtime -> Change runtime type -> set harware accelarotor as GPU)\n",
        "#Mount our google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/drive/MyDrive/Celeb_fake_face_only-20211222T125406Z-001.zip -d /content\n",
        "!unzip /content/drive/MyDrive/Celeb_real_face_only-20211226T113841Z-001.zip -d /content"
      ],
      "metadata": {
        "id": "T5sGuGWMriYk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd0a63bb-47c9-413d-f6cd-1f98ab275f26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/drive/MyDrive/Celeb_fake_face_only-20211222T125406Z-001.zip\n",
            "replace /content/Celeb_fake_face_only/id31_id38_0009.mp4? [y]es, [n]o, [A]ll, [N]one, [r]ename: Archive:  /content/drive/MyDrive/Celeb_real_face_only-20211226T113841Z-001.zip\n",
            "replace /content/Celeb_real_face_only/id11_0003.mp4? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mneMgm9j7ack"
      },
      "source": [
        "Note : Use the drive link for the processed dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tnFgwZBYCyYR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4805f00-2a94-4935-b3a1-2803e212d108"
      },
      "source": [
        "#Note : only needed when you have to download the processed data to the environment\n",
        "#download and unzip the data from google drive Colab environment\n",
        "from google_drive_downloader import GoogleDriveDownloader as gdd\n",
        "#use only file id of the link\n",
        "#Note: Below link is just an example, Not an actual link. Actual Links are in ReadMe file\n",
        "#https://drive.google.com/file/d/1ubvKLzBDe5i1acxgGUK6ObeNBYCKUS07/view?usp=sharing\n",
        "url = '1ubvKLzBDe5i1acxgGUK6ObeNBYCKUS07'\n",
        "gdd.download_file_from_google_drive(file_id = url,dest_path='./data.zip',unzip=True)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading 1ubvKLzBDe5i1acxgGUK6ObeNBYCKUS07 into ./data.zip... Done.\n",
            "Unzipping..."
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/google_drive_downloader/google_drive_downloader.py:78: UserWarning: Ignoring `unzip` since \"1ubvKLzBDe5i1acxgGUK6ObeNBYCKUS07\" does not look like a valid zip file\n",
            "  warnings.warn('Ignoring `unzip` since \"{}\" does not look like a valid zip file'.format(file_id))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hlPaQS4e5VI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf7f42fb-e45c-40a5-db7c-7ebd11993e47"
      },
      "source": [
        "!pip3 install face_recognition"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: face_recognition in /usr/local/lib/python3.7/dist-packages (1.3.0)\n",
            "Requirement already satisfied: dlib>=19.7 in /usr/local/lib/python3.7/dist-packages (from face_recognition) (19.18.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from face_recognition) (7.1.2)\n",
            "Requirement already satisfied: face-recognition-models>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from face_recognition) (0.3.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from face_recognition) (1.21.5)\n",
            "Requirement already satisfied: Click>=6.0 in /usr/local/lib/python3.7/dist-packages (from face_recognition) (7.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZ22Sj8d0JoT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5876cf29-07f8-446a-cd7f-34c2c6d2095c"
      },
      "source": [
        "#THis code is to check if the video is corrupted or not..\n",
        "#If the video is corrupted delete the video.\n",
        "import glob\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data.dataset import Dataset\n",
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import face_recognition\n",
        "#Check if the file is corrupted or not\n",
        "def validate_video(vid_path,train_transforms):\n",
        "      transform = train_transforms\n",
        "      count = 20\n",
        "      video_path = vid_path\n",
        "      frames = []\n",
        "      a = int(100/count)\n",
        "      first_frame = np.random.randint(0,a)\n",
        "      temp_video = video_path.split('/')[-1]\n",
        "      for i,frame in enumerate(frame_extract(video_path)):\n",
        "        frames.append(transform(frame))\n",
        "        if(len(frames) == count):\n",
        "          break\n",
        "      frames = torch.stack(frames)\n",
        "      frames = frames[:count]\n",
        "      return frames\n",
        "#extract a from from video\n",
        "def frame_extract(path):\n",
        "  vidObj = cv2.VideoCapture(path)\n",
        "  success = 1\n",
        "  while success:\n",
        "      success, image = vidObj.read()\n",
        "      if success:\n",
        "          yield image\n",
        "\n",
        "im_size = 112\n",
        "mean = [0.485, 0.456, 0.406]\n",
        "std = [0.229, 0.224, 0.225]\n",
        "\n",
        "train_transforms = transforms.Compose([\n",
        "                                        transforms.ToPILImage(),\n",
        "                                        transforms.Resize((im_size,im_size)),\n",
        "                                        transforms.ToTensor(),\n",
        "                                        transforms.Normalize(mean,std)])\n",
        "video_fil =  glob.glob('/content/drive/MyDrive/FF_REAL_Face_only_data/*.mp4')\n",
        "video_fil += glob.glob('/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/*.mp4')\n",
        "# video_fil += glob.glob('/content/drive/My Drive/DFDC_FAKE_Face_only_data/*.mp4')\n",
        "# video_fil += glob.glob('/content/drive/My Drive/DFDC_REAL_Face_only_data/*.mp4')\n",
        "# video_fil += glob.glob('/content/drive/My Drive/FF_Face_only_data/*.mp4')\n",
        "print(\"Total no of videos :\" , len(video_fil))\n",
        "print(video_fil)\n",
        "count = 0;\n",
        "for i in video_fil:\n",
        "  try:\n",
        "    count+=1\n",
        "    validate_video(i,train_transforms)\n",
        "  except:\n",
        "    print(\"Number of video processed: \" , count ,\" Remaining : \" , (len(video_fil) - count))\n",
        "    print(\"Corrupted video is : \" , i)\n",
        "    continue\n",
        "print((len(video_fil) - count))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total no of videos : 4545\n",
            "['/content/drive/MyDrive/FF_REAL_Face_only_data/id0_0001.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id0_0002.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id0_0000.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id0_0003.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id0_0005.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id0_0004.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id0_0007.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id10_0000.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id0_0006.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id0_0009.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id10_0002.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id10_0003.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id0_0008.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id10_0004.mp4', '/content/drive/MyDrive/FF_REAL_Face_only_data/id10_0005.mp4', 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'/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id5_0009.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id61_0001.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id61_0002.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id61_0000.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id61_0003.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id61_0004.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id61_0005.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id61_0008.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id61_0007.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id61_0006.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id60_id61_0009.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id61_id59_0000.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id61_id59_0002.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id61_id59_0005.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id61_id59_0006.mp4', '/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/id61_id5_0000.mp4']\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#to load preprocessod video to memory\n",
        "import json\n",
        "import glob\n",
        "import numpy as np\n",
        "import cv2\n",
        "import copy\n",
        "import random\n",
        "video_files =  glob.glob('/content/drive/MyDrive/FF_REAL_Face_only_data/*.mp4')\n",
        "video_files += glob.glob('/content/drive/MyDrive/FF_SYNTHESIS_Face_only_data/*.mp4')\n",
        "# video_files += glob.glob('/content/drive/My Drive/DFDC_FAKE_Face_only_data/*.mp4')\n",
        "# video_files += glob.glob('/content/drive/My Drive/DFDC_REAL_Face_only_data/*.mp4')\n",
        "# video_files += glob.glob('/content/drive/My Drive/FF_Face_only_data/*.mp4')\n",
        "random.shuffle(video_files)\n",
        "random.shuffle(video_files)\n",
        "frame_count = []\n",
        "\n",
        "for video_file in video_files:\n",
        "  cap = cv2.VideoCapture(video_file)\n",
        "  if(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))<100):\n",
        "    video_files.remove(video_file)\n",
        "    continue\n",
        "  frame_count.append(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)))\n",
        "print(\"frames are \" , frame_count)\n",
        "print(\"Total no of video: \" , len(frame_count))\n",
        "print('Average frame per video:',np.mean(frame_count))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KBiDzqs8hw6v",
        "outputId": "026a5476-38d3-454d-e13d-c46b7848ed11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Total no of video:  4545\n",
            "Average frame per video: 147.97381738173817\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OqGXNkqhDKZU"
      },
      "source": [
        "# load the video name and labels from csv\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data.dataset import Dataset\n",
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import face_recognition\n",
        "class video_dataset(Dataset):\n",
        "    def __init__(self,video_names,labels,sequence_length = 60,transform = None):\n",
        "        self.video_names = video_names\n",
        "        self.labels = labels\n",
        "        self.transform = transform\n",
        "        self.count = sequence_length\n",
        "    def __len__(self):\n",
        "        return len(self.video_names)\n",
        "    def __getitem__(self,idx):\n",
        "        video_path = self.video_names[idx]\n",
        "        frames = []\n",
        "        a = int(100/self.count)\n",
        "        first_frame = np.random.randint(0,a)\n",
        "        temp_video = video_path.split('/')[-1]\n",
        "        #print(temp_video)\n",
        "        label = self.labels.iloc[(labels.loc[labels[\"file\"] == temp_video].index.values[0]),1]\n",
        "        if(label == 'FAKE'):\n",
        "          label = 0\n",
        "        if(label == 'REAL'):\n",
        "          label = 1\n",
        "        for i,frame in enumerate(self.frame_extract(video_path)):\n",
        "          frames.append(self.transform(frame))\n",
        "          if(len(frames) == self.count):\n",
        "            break\n",
        "        frames = torch.stack(frames)\n",
        "        frames = frames[:self.count]\n",
        "        #print(\"length:\" , len(frames), \"label\",label)\n",
        "        return frames,label\n",
        "    def frame_extract(self,path):\n",
        "      vidObj = cv2.VideoCapture(path)\n",
        "      success = 1\n",
        "      while success:\n",
        "          success, image = vidObj.read()\n",
        "          if success:\n",
        "              yield image\n",
        "#plot the image\n",
        "def im_plot(tensor):\n",
        "    image = tensor.cpu().numpy().transpose(1,2,0)\n",
        "    b,g,r = cv2.split(image)\n",
        "    image = cv2.merge((r,g,b))\n",
        "    image = image*[0.22803, 0.22145, 0.216989] +  [0.43216, 0.394666, 0.37645]\n",
        "    image = image*255.0\n",
        "    plt.imshow(image.astype(int))\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1leMozhXa5LF"
      },
      "source": [
        "#count the number of fake and real videos\n",
        "def number_of_real_and_fake_videos(data_list):\n",
        "  header_list = [\"file\",\"label\"]\n",
        "  lab = pd.read_csv('/content/drive/MyDrive/global.csv',names=header_list)\n",
        "  fake = 0\n",
        "  real = 0\n",
        "  for i in data_list:\n",
        "    temp_video = i.split('/')[-1]\n",
        "    label = lab.iloc[(labels.loc[labels[\"file\"] == temp_video].index.values[0]),1]\n",
        "    if(label == 'Fake'):\n",
        "      fake+=1\n",
        "    if(label == 'Real'):\n",
        "      real+=1\n",
        "  return real,fake"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWMZn0YHDO2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "e99941cb-768c-4be6-e93c-2f74d7247fc0"
      },
      "source": [
        "# load the labels and video in data loader\n",
        "import random\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "header_list = [\"file\",\"label\"]\n",
        "labels = pd.read_csv('/content/drive/MyDrive/global.csv',names=header_list)\n",
        "#print(labels)\n",
        "train_videos = video_files[:int(0.8*len(video_files))]\n",
        "valid_videos = video_files[int(0.8*len(video_files)):]\n",
        "print(\"train : \" , len(train_videos))\n",
        "print(\"test : \" , len(valid_videos))\n",
        "# train_videos,valid_videos = train_test_split(data,test_size = 0.2)\n",
        "# print(train_videos)\n",
        "\n",
        "print(\"TRAIN: \", \"Real:\",number_of_real_and_fake_videos(train_videos)[0],\" Fake:\",number_of_real_and_fake_videos(train_videos)[1])\n",
        "print(\"TEST: \", \"Real:\",number_of_real_and_fake_videos(valid_videos)[0],\" Fake:\",number_of_real_and_fake_videos(valid_videos)[1])\n",
        "\n",
        "\n",
        "im_size = 112\n",
        "mean = [0.485, 0.456, 0.406]\n",
        "std = [0.229, 0.224, 0.225]\n",
        "\n",
        "train_transforms = transforms.Compose([\n",
        "                                        transforms.ToPILImage(),\n",
        "                                        transforms.Resize((im_size,im_size)),\n",
        "                                        transforms.ToTensor(),\n",
        "                                        transforms.Normalize(mean,std)])\n",
        "\n",
        "test_transforms = transforms.Compose([\n",
        "                                        transforms.ToPILImage(),\n",
        "                                        transforms.Resize((im_size,im_size)),\n",
        "                                        transforms.ToTensor(),\n",
        "                                        transforms.Normalize(mean,std)])\n",
        "train_data = video_dataset(train_videos,labels,sequence_length = 10,transform = train_transforms)\n",
        "#print(train_data)\n",
        "val_data = video_dataset(valid_videos,labels,sequence_length = 10,transform = train_transforms)\n",
        "train_loader = DataLoader(train_data,batch_size = 4,shuffle = True,num_workers = 4)\n",
        "valid_loader = DataLoader(val_data,batch_size = 4,shuffle = True,num_workers = 4)\n",
        "image,label = train_data[0]\n",
        "im_plot(image[0,:,:,:])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train :  3636\n",
            "test :  909\n",
            "TRAIN:  Real: 718  Fake: 2918\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TEST:  Real: 169  Fake: 740\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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koBqZ8PPq5+QTy/A6QAAxbNeFv2Whay4cZg5Yg4i8sha21Lw1ehlgpBohahT2MqvQGB33ytRHfeBSSs/RHEgThlL6uXMSULcZUjfTcQJKMIGpu3GgvqTaYmoTBgDauOOTaWjuDSC4p/mzYy8f3yvkRi6RjR76ITDAKqxkbeA8bQf2oB41qSfpu+9M7gBMkBOtKtviic3Zk4UInWxOSgEVG8dhManHSbyAyVggUHJOj5aqz55YlvFQ2vewQIrsWQbKqwABBtJQ3H1YPsyEsNaxG2awFwfHECcGgAQE8q0of6V7RACxD/fNgmdmgPVaGBjk5zIItNaxX49Thi2YHGdam51WNskpmEB6DnPn40lZ8v1ut5cI7+dgBnOYnYalEBjdhZtQBrxbpdB9AASQSoOXDcV8f2OK/h7DwWhpx7k5UFDIHIBRf0N5GQM4gPyxDqKHYmzLnyO8ChBAErbWdJxA78N4+4zEs2NwbDTHa2B2VHS3gB3K2IzCiSRP5HUOs5MyvMhqiGTGME99HmjiKNznowWDEXjG5cpJGc6WwNriNjbwnAb2qQHgnvMrO8iGI2wpsjUjMGYRhCChBQGUAIAdCFLXM8L+V6oIK0/rthzB29YblJGcM4CsQCC3Mc8vRnNgWSZzGZ6W3vr5VXgVIMCAT7S5puXFbHlngMfptmYO8NhlCCxAwGxKzgCQQaWC1I0wsARzCNripvuuIxl5sS4BlAnEIii2WIkFOvwBXZRCugQpLVJRfEXjaFTPYQFzmZ6/QTfv5rx9biawEnwR+q52PqNQvR1JMr+ECWhpmFwnJjA6ZMWjFCPLbXEZ6HDzWAuiFAKzTilWs6CS+JPCYAugk7ECQCmcBqfdLofl9TmbHyG8ChAIJhCrCdkiI9bPHoWilcZwAOjMvgpYBgFmlvXkeP5cpn8ytiA/ww4UygQ0XcZI5vrJvRmZCVgjyIwhB3Ew6VgAnyeQl0or6b0TzZD8E5SuuYfcnUw4xDWUAx2BLQvj/Pzq/GOFoYtsoNcL1T8HSyuZxHtu4cCQAWACAWBkAqTfJQOWpMlLiXUliwq1kNSxnM+YHtEEsrP5OJX7bWCfy+G5D74SEGCGThkWJtD25nMGkHzJuQBtbr91x1m/gGlqq+TR73J0tpQKrzy7Z1pdFgnNrMTSkZYQ6zEb8WAKuGBGY8jHWJ7KlhM7LiE2mCo8vv/ycr5BMxcNcNbKt55/aTqenVZTAn5DBRw3ykEFcQa5Z5lAOPoEViBomjzMAZ2oFu4/bVdW18EEcjAzRKJeaKzPEF4HCCCNC2jd5xAYC7CnZgplVNEqxzsIO/v44XsgMCNmjAuQgUpt6haUZ+JofgIDpJVDEBgBIByCL/AJqBo4NwtmH0Ccz0LMSo/PhGRph39iJ9Vp/IHOej6BWTD9Zyu/uR2MxzEWGv4OTmFJy3Vp7TNj98Aw/NlYYyDaRcrMoQjOwf9erTynTF4FCIBV4zb5zaaAUTEv5CLrAnRuvvS3C49tL6ZdA+L9T5/SPWXcT0RTbwPHaD/T7sNGJ1QAYvEfGGNgCAC0mFWYA00NYnQIxvJi8/iAIeH3lIQJQ7Dgmw1gDQz3AeBz+gjMY866CJilxVZusvkUoDSsmsLpFsKdtgTjGIVq3brxdx4gZO9aYvSEx29EfZVFvUWbZRZWOvoEshliNfZ8sP0YAAC8EhBgDu+6jL0elxMb212cyPJe5jzUwmfrPhqXrkpfc3JpY+9XwhCMYNTu0UwMMJSF9NSQQmkdhGat8c+ve37v1Li1UUpAcC+cmQAvOX+fcOZjWKfRXgrgsvH+QvsIKAxzAYjzbxTgnuoUWeDdx3MEgSF9WrizZS7ynhlAXB9YAQIIVvUc/poXWf0fLbwKEEA2B9J6gibEo4NF35gq00L3imOZ1DNrvPxZOgrfEFda8zAccCnVChL+LK8nIc3hjPp/zgZwz/OfmcH3NoyCkZmaT/vW6eR2fnCo5TbCLCs9cQwFz/M83AuVmIAB7CI52WODuf5ymzK2UIoYreMaFjmO71ufQAiR07NUGIOwTFrbew/yNfljYATj1zRe+1G+F5p8BpoTl9pBm1iaD+p4Ifzxm5jDxF5GW3+yk5EzkbTXHWo/vA46lPnzWQGl/xfJP5yNlna+v2YH47W132W90xGSz8jrMPUqDZPV2Mb55SG78K5l/yLn+FMdpqXuxizS4Zrhfmasx5yGH+JueAarOguvAgR67/ju01u826/YW8M7nf7bCdhqxcObRzw+PuLxzRtcLhdQBZg6GjeIO7B57yzrjrPcZcnwzjqc04SMS1hsRCDI+gEE8/gLn7aZgNf9nTKCXfwNWnmlMmpllAId3HRF446GDhT49OI8LFgWobAt1Au2csFGm65FBxAKiEmOiQF5Y+4ssx5ZgEC2P9fA2r+tfJYPzSkeDFs2bapp/ohUNsdj+mYCLSKAfCEHFepEtS1Z8LgB+JJvjM6IvSaRaLtlvvjC4FoeIqq9qSmGFibeBOD2d77WF3M6MhB0o//MYCI3G6XiBdzdoCSZVvzmzSOoEC6PD9jRwW+BRh17GiLc0LDzO42XxYzZGKUD3KQM0AZjVSHfzIkxzTM8vO+i5q8CBBjwUYKd01LeJnBpv7dobABz14ynytbCsgkUgrTdQSL+j69LHH1oqcwxb0GWN+sypgAM6d+N9IlgxYKlYWboYiAIZjAzAF+MkrS/2lKX7Prs4AJiARRS/8dQlouGP9+Dl4Ht0ESgXnwQVDa7xiPLBBw9Z8SqTTHcVmK/lQ4UkucNz9LfM6OK9475MHFpJ2wsut9GEGhNRWuw12dfgcbjIGDUvoxuGgpTxBYaKSU9k81HT3VXrFSfFQFUHNsHac9djSuWdASCl4dXAQLAaIPlkM2A3DjTm/6+OVnMiZTp8Ol3uyw+GtuHybsy6i/tcc88OJxEEFvaFGWkyh9SBhbmRpz9D/loz8yNfWUTu7mCs63PTpY8n8fFp3zm69ZYV/4aT6uOrbD05N6YMc3rcjkINY+2vddZ71qftggto3f4rNJI8xGAAgSKtz0AQCEdcajXyiZKrLWoj1SmJZkj1iVsdWfOwABaGk0EU1rJd/ABrP80vBoQAOBq4WhhZu19T8CC7grtAqaFaaanoZpo1DR5YRAZAmwPp3c7wCQ03e4P3v7D1068Chxdlhm0Bv9GasycHJBhpwpQGY1mZl+WbSWQxolM6G0Vo1p5En5jXuUg9Nn/Mfew3AKB/Nzq+Vkh8FR/87OtN/Qk/CKQsuKU1Z+BgJxjyEd8a0qHPARKbI7VHPDu3CrntgQ9YASRDuWVy2r00xgAjIrr2f6ADwyvAwREtYoQ6Q+QwqxI67iD/D4loRS0BAJAQphIKZfNE8pQQiCxAnID0x6B/WnXWYHSYmS+QkFBRWECN0ZHB7c9AICBvNI8URkq0uKQ60VHIV4d4r3hIWlvTjaqeqfObOc+CUdXjXto2K5lyHdwsg01xvPQaNu2LZmC7LJzNiDpqMX9/oIxjN2sWRtjKJehR6Z3XJuMF7lehZXZZjX7nhmBxQe0bnMCZiEN9uHBmYA+J2OFYwOSTQR33oxm1eOUQZVZ/En9xo5Xnyu8DhAA4P0wg2CrGYBReId3RvN+cNhYA1Ko1eOJPtbvHrcO0/slp8Na5azpgr4dTAKOZFi6Wx/3QTizac2Rda455f2umn8Ya6EORba0KhAUNhBoICrYtphN534UItS6abeWTZrhAQyAkhrzcZ7BEgQWAHD7BzfxTKhN03dmXPcQ/tY7rk8GAvsBBDqHOSAgoCbmgrll7RymKLsppZXjcxGOeaXjKSGBw2iKzIzU2s2nJgTvDQJE9HsA/NcAfhiS1G8z888R0e8E8BcA/BiAvwXgp5j5792LL1dUplHz5hxIjYyIFoU0+wjYG9CKltnLTi2bLHN+ve5qZ0aDoWQ7h1btKb6CWfiPZkbY+dd3O3iXNDOPOyPPGnLWUrKycRvSPrwzxWPC71qJBbJq0vTjsepquRtKqbhcmi9+mp+T9BGqzsE42xIul3ss1YYh/Wf5Vw7k8T09Pem0c7HD312vaL359afrE3oTcBgYEQzkDcis7z7qN5tB0mtSvd5HXhf1YCNFu6KVswBtjhkQCxV0Cn8A5/uTvZl1GNLxY4cPYQI7gH+bmf83IvpBAL9ERP8zgD8G4C8z888S0c8A+BkAf+pmTAxfhSeHXJh+TL9Bs7P/l47pAwoY4nuZPAzpNduUlFnle3jnjJPku7GhRtbSwQTIv9N2Bu+j5hcgkg8bE5kdhIAMitr33f0FQPgEZEVc9l6WnA47Slck0H0VXkZVoS7pCJD7CUYqW2GTbmOBjqMHO587KA+CsQaxAdASsDtQ20Iv3HWoeRNG0Dqu1zYwgZFdBQgwF53QU4BSUKa0Z4dosb91FSFbTQiT7S46wDx9IdhnAmx1k4/RqoCzpcg+ZnhvEGDm3wDwG/r3d4jolwH8bgB/EMBP6GN/DsAv4g4IMFim6upMPXPkFfMHlBrr+x9Ecao4jqWcKN0fh5WG2BIIxKlx7iL8vMuzBYTCBVX/FVSZzZf3EMxfZAA9jTZMWsK6e4x6tqcu/cMHEGhxrkcTbgtd2Upu4HODP9jWiEZpTMBnwNWs+QsuqukfHh70uCOvo29+g/F7R4Bc2bs8a8Az4TdmwFEOzOx7Ohhre3e9orWGd09Puhy8jDOxmZ8jG5t7B4rONaPBN7LVKn9vG0qtzpjKZXMQAAgdUKfksacFsDkM9vHRTxA/NT1jK01P69JE+cjho/gEiOjHAPyTAP4KgB9WgACA34SYC6t3vgXgWwDwjWoaJErM0NXFiwJdPQzwOiJoZgpWE0uPfbI3ew8tapWXxgetrI4xTRy0XroXZc3E0MRjBH1nAQF9N9NhYAIBmG8gNJqBxbNAwNjIBAJggEuYTKbNbch1eNxt7fz4pvkP7BiLus55Hbt2nwMAhx9SHXn56riSNm7/vtSqNALT2CUa29pVHcxV08/OzUdC1TaJhZAhLbfSk3lhawxMTeUYZkDMpTadfyIP4geDABH9NgD/HYB/k5n//uQhZsqrKqbAzN8G8G0A+KGHyocx+ghzYPAJTGLMhysev1LvBZymuO1Zsy939TDnPt8AITpUVvb+5unHsjoxD9po/AE+4DGleaSutx2Dc7/66pmgo5NG1G5NLqzMBugU9J4ZqDoQyASgd6H9tqmGMYFsJlgI30B4QjPIPIfBODPqIxPIazz0rubAct7J2E0X9U0H/0atFZdt001lEwhsF1RlRaUQyiYgYI3q2mLbPAZh2zZ07npk0JVANkIxOQUjLWsAyOEMzD5W+CAQIKILBAD+PDP/93r57xDRjzDzbxDRjwD4u/djUrVrik4lOwg7Dr9gDHkQD0d8BgDDZxaAoI+wUndrSNFLkWh+eh42exAyGtHGC3AXR5HF5X3HDNgKRmZ6HOGR1HUhgkMqjFQUNLRw5H8Cijkn09HsSxu/kN5iL1xNir4XYop4lnU4L6uTkRnUGVQYpIIJc9SqhxxpimzX+5TjJ3icK6HPfx9MgwQMUZ05R/AvjYIfAO4TjCD2vO0iXWsW/gCBcRFRHVlZRiGmTgEQneT9rvE0HTFatM4nHebk9kRJfWJXgIcP6R0gAH8WwC8z83+Ubv0PAH4awM/q8S/djYxNMylthwhIrO8uq7bIWAEcCiePKyATfGYvXQJAHaAi4wWICL7AS5eG06+6yOlTG7ccR0VBAbjIs00rs5A696J3wNiM7VtoeSL1PJuHgjjPONRFpmn0rEsWRnCbzQPm7fCcUeYzYbLndjQ09BiGrQLNEEFtCiiNld3ot9HlueveovekFGxQBqFxWV6Go1ZJy0xrSPexV6Szfnu6P9Q/ogtPhjEreJLZ+aPXf6uXZP+nPv/0cxDYNgWMqibAOIKw6BwIW6364eECIuByuYAZwgiMpVCHTVPM+2HMIa59Gvo/hw9hAr8fwB8F8H8Q0V/Ta38GIvx/kYj+OIBfBfBTH5TCg2dk8YzJu8v/NE7AHkp+Bj+qNjEqb11rNHl9WQGKO+vCpAICvTePUcyKaNDuetKGJW1XG6ztjehYlTRVMlPyUUBAGQcgi2PktEEZTek3wQCQ7ddcUDB2v0oazl7VdD8AACAASURBVKskCykRo3AHc3GNTYu0z4I7mCYngGWMaZV+p9XMqRyNARRnANmcjIFPaaPXSdsfzc+Rgptvxa+nb5Mqq+6+hOY9LVQIpRcw9YXg23ei4GcgmMvvY4YP6R34X3AOVT/5HvEdUH4WxJVP4BhRxHfPJwAFChN863s2O5dpbKi96yhB9YK3Jh768ZnIi410rMmxBJALp89BSlold8XNZZO7CsU2HnsReNKY83z57FsQ8NgHTZ0FwMBqToOFPDpOutrm3aLXWs66E2dmMucz/z2PH8h2tW36UQpC4NIahMMoyCLU/+HhYQkOWfDnc1ZmyV1MLesdoPQdELBtspv1RRnApt9urUnbKeWYB7IyS21T/hhY76t1DH6UoDTOhIvN3jTUn3wDagArFeU0+OJgdkl8zGAm11w2TI+I0Ixy6jZjvoS5IbzHqZ5eHX0nHukQLPt6Me0AaUi1lAQCujV2CT9Bgjidpjt50iUDrrU97yY4PTRvTw3MgExoqOS9UKLaterHaWqQQatnQQBwAKJRcCPfuexnUJjjXD03M4WV959IzcZCgC7/bcyIAB/8U2v1QWem9WcQmENmXyboUlzk32BuMgJxElQbQ2BH4mgjc94tL/K9hdZPwHCLDXwIPLwKECBIRdla/UZv7We+AenTh9LyHrZqyUNZNU593iosZm01cZppgfp4cx1q2ndZM7DonoY2H6CqADbtopKBOuJkAozyE0oJT3K2NUUTVRnnYNrGKh1wwLOw1pKjcPVSYWPke+/S2KZ4bCbkLESlFjQ+Vv8ZhbZvSm9HgIEBzb4DpYhZYPm2+M6+sWrYJQGZ57vzMh9QwKy1omg7WX0j8kKIvR3mVZ1HQfN0qaKR6dYdtFUAotkZQMsz0BWQCo/OxXl2YQbSoUdM8zScf+AK088JrwIEhpA1+kJjADg4BrP24EmTgqObzN4177e/wwY+8uV4f7LN56SWgko1NabZmzxpHYwNzunkFPct+zmKKTvfdJ1lY1BzcU1albVMzuIlA93JPp6F5viddVyr81uN+kUNPpmMWaMfQQCH9J/a/TkjuvCBd4ZoT4gzBcXd3JZye5nL+75tP98PYHgfIHjOO68PBIBlozvLitHCKKSgVTicCQBQEm5HY3860jB+Z4zJRtpdtstkR64BAADmXKzyddZIVtfzPAZZxXZeI5EPmjXnO09JtvSUqdxXzrJVw7rVSOfrt4HkvgNskFEAhUb/TXzjXPCf/f1JiXDLayHAu2tnv0YoFh6A4PDMKwivEgS09sY+WVvWSQ1pqwCzh4Hk8PJ4FrZSYgKA0PvmXvSxwVrcc4VlARFbX542EHAaPQDYCFDud6C8EApSgvPzMV3X8gCC59XWRsJAs4FC7GYC9w4q1QpAPPrLoleG4v6J1HMQDhIfd8AIWs7qRLG0WP7M92B1OvsHsl/heUwB4LSnY7HvDE5knMY1M6OBEWJsTyi2mpSVKwE9vt1AsFWEzVcz+0uyUxOdD9+K8/en/Gdv23dulecrB4EijdFWcdF67lBTySasnIGAXJ1KZ7S7bBNUHZ4/NhzCooGGD6DWqt1N9k6AgGvVG9n0WQ4JAOSYAYBBVLWxBngEHeUwoSjtrAx7luJowxNZtm1b0Xd/334DA7AHMwBJTtiBAAksEhAA0K0VhREtCuaelh6fjT/mATz3gncBu2CfAID8oQmXb8nq9gGMfXJFd8wOTPm11rzAzA9wHPfwIUDw/qziVYJA1rSr7ptxrO0NJgD4yDj4mwzi0Dijo2n8PpA9waJBh3ZGs+CM+chaZgYf+fZ+eH7+e0Ub11pkTnucg6IrK5IetnQOt7Vm5D9/eyV8sxZ2hpEBweOlIa6jQB+pvn+XxBcDykvELXwgA9M7ltFB+O19MiDW9qJAZ22yYYxvBQLODjrrZLV+qDtm9m3mzsKcNiFYViHynz1xqx7n8GpAYE5jXtUme6hXz8o1s7swFAQz+8pBK9PBJp/Y84cGaM/1DiJZM25lZ8YXxuCeYaskVzA20OiYj9Xx7H4OKwAwMM1UW1J6BIEzW9+OZmaswG4VjkCAdBypuxC60X+R2/dBAHIdzEN5aXTShVCm3D+DNSiR9oQwVP04IwsmYMyxY7EeQu86kUzUSl66bihjrFrQ7fTNUPk+POL1gIChmNFFHL3T8iADqYvPBR5p4xHo4g1spgDHargcbzEDjRuabmnFYFSqOkuYwr6FVRQfSl1GyGlzYbOPtTHOVcKGBQZWlBpmpMm+tZo4NB9nFnAETPbuLBewQsg+wZXj7iUhg+AZLR+uEQ3deZaueRitl8UN4LPyzPeDgssK0L7MmK/RmF6mqZ5cYajgE+WL4iMgGxwkKxFzyjOXtEDOlE4DopW5wLxeW/NecJv/7Poz6vJVgIABgBWbUdf1qC0szR9DQVb7tLMOjklxz2sTMttKtd0bg1WoAcGI0jwULoNjSyyYds0Ffz4IRQ6xZPiK4uc+5BV9XJkKtwb4APAlzp9jQd5rQC+xw/WN+D+ZKLMpYCHT+BnshsDHtRZsqnXvfVhhKM9WHVnH+giKOiIi0BYrDRkIDBSnjt3OQzJFa6S0JDAYSug8vES48/O3wqsAAWDUaoMzagUE6GexwLz/3rhUUE1oSY26wVa7V1CKMLlXwYRUVgEy4hA9AUTxYtb2nMyCzg1mD4fmj+nHeeIMTIN4HiMetjkFPGrS/LePky8VpRbzdvgz8+CepW8h/X3r/G5B4rjIp8QxmiwBAmsb2mBfc+/C3XThURtEJUygJ3DoA4PIgJT9QUQAo8AdtgDKJUCAqOiGt+KXIAJoq6MfKccL+LTjYV7HM0ruuSX7PuF1gABHobkYEYZuNkBpo/OFRPUjmvib8zDUpGl9+bA1AEidpoaY4kP+ugkn2kD/RyYwUtuIR1lIH9cK8PnyaensoLWjHRnx2hyB8G3kZ2wHJJszv/mceWm0RRsxel8OtrG/x+Paf3ILB0JoxzIe6pbm50cmcAQBubdzrMgk6wvE8mI2J8TKqfX1ikMipAvzk8YNXqhX32BEQCDMAyIdP0oxn2NsCaR1OIGaN45bZRfp1ItjoTtjfRlTAF4LCCAhJ0E8+tY4iXQrKASt162qBQdI7TbAimAEgrEQbYFLW5N+LnljGrbWvEGMfJF9HLg42qaGZP+8YQNupPAIAMzAU+to2ggY4xqDAwj0WDo7A6ZPEGo2rbel74xaaKsbtm3Ddtlka7fLhkvdYmRgjclOMYkoTwUO7ZgB4Kg9zxvfTLjuOVZnJmAhNheR+9eeaH/X1Ye5o+26WKyBwOQTOCoActZoP1vhUIQV6PsVIF1opRRlCuG8vmAcUeiLlNZyOobA4r/doTyV5aG07OrLw6sAAS/kOahdbs/4NFUAlBYRsKWwVg6DLBDSEEwLq7eWZtTk4Sg2n5wVDqQN6p1mzx3Wo6Whwc2a7Kl17Itx5HmtwQwC+ZfnDORfMJRIozGBbdtwaRdsWwXzI7CxC/yGCk4TXmZ7eWYGt5x/872o2xXzWi9OOjKAo6/E88yM636NFaIzA+jjvgOx9NnYlXiWV1KbP9P7XQdZ2YQw6E9AgUBbQale6WrS2t4ZYbZ5vnI6XijE2aRxuXhRDBJeBQgAo4BYkIqA2lw0PX+0U2/Z9mEOzEtQhS18n0JlKhp+BU8DjxtnQrVObkSmURiMr9894ak1r8ieGjksbtdg01RgZlyv16GRzyAT5UHBHHrDvm+yuAp3H/7MnJYJq3Uh8EcAODKBO6XH4/H+86NwmBYPWq8M4LorCFz1/CpMYKD9nL7fMZsakvyZ3ejcAETf/q71sm2bblAra62buVXbLgvSJh9LoeIDmmZT9Fab/VzhlYDAiprN9pQ/CVPhVnmktnpo4WRjTXZm1ib2wK0GPKch27bMHS3NIxUXxdg9N2oxOP0DM56uV7y7Xhfpi3eBBAbTQqPmO+DJF+AUs4cQzRpxrxV7kZmNNhWbAVQdT9A5TB+vjztsYC7FANrcxfs8XZXzYiYQNPfdZpC22C+yJQawt9EMGFoWB9j6oiw9LYVmadVzW2nO6mDn7u+XUsBFTEcrv0uPRVdzORVTaDiCYG6mbtBqO6OJ3Qasj+/adnuZD69X9zyGVwEC3IF+7aDCqER4qISHQrgUYCMG8VUFTOm3bj8OgoyyKrGbjG0DBhcmgXMXgGbXAaCgkCyhXVjetGPVozgVLKGxRh4gk/Z67qiYhMLX6NPFKOduoa+edlwtPUi9ANrtyDD/hQlvS+fj5iMwCDTwJHKWA2iD6ISnpwaQ9Eq01twH8NAZ26ZaDQSqDCZ206BC4pPtyMyxaI09yp1QIKsuIVicvmvTwc3UycJixWf2c9mqalrC3juuewuNbza/7jn4bu9o3LGzGGdNx3x0WdFMgTrKuUGXCe/dQcSBxgpRK9xNNNPaer/6kHax+d88vsF22fDw5gE2iQxEeOwdOxHatYIL483DG1xxxf6060pjxc1eZqDZmhqIJfddmpNQa3VqudnImi5A4OfPMw9eBQgAQG+MjcRaq4VQVbALGDI402C0w0e6MHSfe3NiEUiFl1XSbd937x/uWZsTKO0lIE24DEcnFUm7snt5gdaSV5toUHTeZdVj1SLz5DMzrq3jmiYvrZYDk3hOHIYtHGajhjaBmrR1YiVPdUcloOpMw1IUADqjaKMnBT1pg6TCH0tzLX0ASla4s2zeyQgg8DrMFD+zjdHxaK24sUzyurYm+wpoF6AsOc7Y1Tdg6yJ21cq2ik9XwWXI7Ildn21aN3tyNHo2jIUlP4tpfwMvKWNdVHTbgELO1tzXUivAMnitloqtbuDKoay8DEs4v3l0cJOaazgW8yTliT4jgOBeeDUgYA1XHH1hDrykq0N5Ns6yHqZtNF4Znpz6eRN1zVTSBdH3JBgdPELbZe3hrLmty6q1jq601fYJfGJgZ/jS2XMX4Gr6bgBDjFGYQcPyxXy+QhAzZGEUfb/1JmvgISh8LI1WsF0uqEU2H1k6BRdm1UDp4VZcqo91PbkgmAaewLGzDfrpg0/GpxQnJGYANZlge+9g3bTFNjB5errKjkYtVkeSfRXHlmRgAFImQITLJWYRRnuKJc2sLGVJsx3XckWzIfGctXik93OH1wMCdkyOwLNGsrJFg0SZPRW2kb0TFRxvZifQaaPMDfIg/EkwE8U073T24ueVcTozdhaOY9tlmYafBT9308W2VH2ZRgvSVhmcBGLU3OEnsMbK5m9BALDt02cNO69hMNXKIT1ZCMx/wzwC7QxuehIHNio/aulwsvJQxwChpDUGrV7comsN1Dpo6JURoL5eg2FSUWVUYgUoKJMASy/VYMoAQ73lvSuZZTXi3qL7sZQiS7g/Q8m9r/PwuW+9ChAgSIFvOqKtqsca0IaYXH5G6/K7AJLdrvZxolTRSPRDjsDHRSYdmU+8ONru/LJp3Nh4RDfD2Bv2tutGGU33z8tdVsCVCjoVv24C43a6bgEmdrg1fKiH3zZJYbD3f7Pnc558NbOBDYxKnAR7KoP8Thq0dQYC3mO7aNThjI3yutkecjpSesxhlwHV20BKG7m/gtwUMab87rqD6C2eqICbLCdHUN9Snk+hjceOkRkoSJAuO1ewlYqtHn9UirbtgrbtQGdfgnzbNnQA26Jn532D6QeiZC48I8pXAQKAmQN5ZZuRKhkMiEU+hVnaQ0VP30g2qtmpc2NDNEITSn9/kWZPAodmNRaw7/bbfRNN3zKcGa1s6BT9+jmdpnlrAgH7TmtyIpRTVj++BwIzu5Ae8H64jomeDt7yMyo/0fyhapJJEOBBw/0z1kdqumXvOslLhwdNMxMRim8ZlsBA42EQ3j3t6LUrAFYdVZnr2syhMEv1Q25NZlCMIdkl9sw0sFQTZayPcZ3DXE5D+bK19xeYxBTiYOf3wusAAcJQoDK+3dbue78osx0KwBugebNnwbfhs3lMuzdaImwwWpmXw2b3UMuuuA1P+xX7vuN6veJ6HZlABgBmBl8IXKJP2bTymzePqLXizeObxATSXAjbkbfP2iP7OMbf7L94IOCSGufD5YK6ieOqap/3DCJATCUe5u4TxiV3ETQ7N/KuQ5NnIBiaQgbAWrFtojVBwgBraXjad6Dpmv5Quu7th1Av21ButdgCtLpNGMg3VrUNSK7XHU9PV5jLUoQ/NMs4o5Pw8CBbtr9584ht2/D4+AaXy4bL5SJjBpwxwdudbHV2ARh4eHgASsE78xdZWQ1g+XnC6wABBBMYf4A3lqTlgDRTK6GE2LQYPPoDjhs1BPmY+sFDm57L71jLJ57p7jx6LY/eG2eKAXxQYKOWkwZaSlFBHDfFtObIHJph27ZImzZaKqoBQb67EFE4K21vgq1AemNceyr4JqEfGASGoh7LSdMw2u1jY87nK+o7MBL/OzYH6cziaYeuTJ38Aaymo43pjy3Eqjo3oxwaCyXnztgvF/EHqLOTaOqtSGWeAdfqyoR628wE2IbyMzbBFPXsiq5WlN5ltOvEPEf/iLKB99WGzwivAgQIQEXBRhUbCQWuug25bzt2SjfX9+auJwuVNhBKaMuF1szvDBVDs9UxDlENWk8w2x4AamUwb66FPY3bBVyqN1bTThf1xFfVZCXnj4Q9bIVQvvkNuZSE1oSmkPS3u6B0dpDi3lHBqJ4n8olFDw8XXLYNl8sWIGR99yV6UIY1C7wiwiwy4DljAjaE16LJ5V5LAdeKy0XMoAag1IZt23QGICc/iuwMJ+WgdagscqvhI7C4H5jwjTfApYq2vmwyl8I2OXWQ0jyJQxiyJ4VqdCoFDxfZsvzx4UFZmzKChwcZol2rADFr+dZNGYBsU7a3BzAkHcQ7mtZtn8DxRtP/aOFVgICFQE8R/qzjgxEYYVsAgLKAsWtqYgI0+gLc8zsJfwaOQcOla+6lDtej26CmvY1JrMgd1ws4aa7LZUOhkcbOaQHUt0kFwMXv5zX1bdlz24wj0hsMpXJHSWsh1LQj78EMmL6/oqorjX+PCQTJiue8XgZbu4Jr1OHD5YJWqztYC0f5g0ZQNFPTAhXtu++Mtm1SHpfmdjxrnNZVKuyJ0ZKXvxDhorsyP1wepAtV52ZsybGd81sLgS1PxlQya1D1MrY7BNNbtJ+PFV4NCMwVT1nTqAYVx45dW9tM5kiJ+9pbkIS/EPn2UKXEvgHAcVPQ4Vsm9BPLKLDRYzLJCJc1E5kdcL1UYEG/5++GgBzHMpitnh1hYlZYHos6xnIZAbTvoL4rkOksQZLts8yezkDAXRZgsW/mfvDVb2YClg97R1gTOTPIz5mtb2DYQai9+uCo7XIJ04vH/R/FPICWicSX11XOjMs2Gq216HbytvjIyGryzxjT5XLxqdkG4LVUPD4++v1o0xWty/Diy0XKZL88oHftOtRyyqtefc7wakDAggtr0vwifdY1aOpehYPNYxxxuMwyIp6k8I++h6MZcEgTEbIH2TTZIOAYl82a48xeagDguql2GNNgoGejzyw/9r47MYv5MqIbrahPoBZZCTkPFsrebaoF6NVtXfu2OcoMALxMNWGyl8FUQAM7M/Z1zgwMePICoytQjzzZ9mIS30ZA5+K+F4KAgSkJjhS749D9RQRUKjK8Ns32a/adHnM1smnAem27bD5oykBA2IUBb7AwQBlbYWFleuyLnpuzwIB3d+e29DHDqwEB89CrKMlPJ+OwCgMljcbMPqZ1KBYmbzByLrETm7MmF36M7DpjAmYHNvVGg2LVH2nUmn5tsMhU1M0OTcNETcvlovaqXZPKNk00LxJiaQvKnJ2mcc2Gsm7bRY9pNRx7rneQjr/Ps+1mYOQkEJGm0PJmDsWoPi90T/vKJPA9HHU59WwOIJkEzLJlGlJ6GFV6SdTJed21y1XXUozKl5TYykPeFqiAK+NR++zb46P08rTYbn32DVi4KAg4U6p1KC9jmLVWgGUYfCfx/xQQrpvMGn3YNrS2D1u2gaDjEpJ5lErzU5kErwYELMyCAsDtIvf663+Zpo80KrMIOvyzD5m5e4sJzJS38rjWv2m+QuLlkoEq2UMc6QlRl1BUOCObKuyaVW8cg4BongihbZImz95ns/HnPRGJCNRlspTE1Sbvt5geQzncYqk8Cj1Ntn4GmFnjy73JhzA9Q4nam3M2M6pCMs7Hux+zCZL8SdazQlS97565o3ZGr1WXJZtAYErLRc2BvL35bMqkahpaXYwtGIeqRz6jzCTOz2MafDAIkOx48b8C+HVm/gNE9OMAfh7A7wLwSwD+KDM/vTheJJtYTpTujXby3GBuFZv5A8Rxs95nb6bm9olaC5iRdiuKLkB/vxRQEjgQeT5yg3KaX8UnMDe0bPtbHPB4JJfBNsb8mRYSu14Gw8hxBoEYOts7ORuwbGezRx5S02swW0a6HBofDgRG4e352UcQ7yUgyN+FynQGleFduLDN/gVP54ClJpKZpWGq09y9O7Yq87fMDNJYiZldxtJsBeXcvvJAsLiHzyXzh/AxmMCfBPDLAH67nv+HAP5jZv55IvrPAfxxAP/ZvUi8atTGN6NAlvQCQBjmx0dPgFZuaoApUsdht8EWG4ZGpR19BIA16KINUGrLzITBq63mQBY2SVMIgTQUBQMFCgxmBQ4NaRQ6KQGABiZgYFEc4AzwKJlZ8F/kLX9jbZdbWXo+6Zgnt768AqKcsoAjPRcAMDIF8u8oUDN0VR5xwnZ1BHmKtf6HFZWiMFObSGVAkZfMWKScumvuGaCt+7VOdazGO2azz/w3tRRxBJpPQPNWS4w0ZJL1KehIhg7lRxDlmLI2rSew7pFahQ8CASL6UQD/IoD/AMC/RZLKfxbAH9FH/hyAfw93QKCAcKGCyuoN6NI3vvlc6nGgy2xztmETjyTI3g2zuS13uTyozVy9kWVqeWYWeFqHkk12OQBbg/65ofGxcg9pIGu6mKXXnZAZBCLPMqa9kE+KdpAFoCssjbMWc8ML/4C+V6LFEdEgcOagvaX1M2CGyRAjLx1rSvINEdB7AdDQqaBA+uptYVFGF5efdn22tLS4C2hmfO6vqapkhHUVzSQzoxODidC1uzCXt5WvAMQIArUe2w9DnZK9yxR5Ktj3DgKhbQ0Pl443j4/gDry7vMN1b9ibLDJT9JuWrqVAJ/xh6IQnDun4LCAA4D8B8O8C+EE9/10A/l9mtv21fg3A7169SETfAvAtAPhmCeS3yvfCdHsOyFojIjoXpEGjeGOYx9KPlXkLBBgzCEzP0axnb4cCXcD0FgicpOXIVkLzWH5nU8fKctb5xgKyzT5+0g0S+X/yAZxdm+8HEwqT4ZAviPDIvWgLqzZBZIJMzq7ydw0cHdB6V289oRQWHw5zjFgC3MF59Esc29Sq/IdjYiS5Xsa6yb1Dc+tJFwzoDfhzwuz+cH4s27Pw3iBARH8AwN9l5l8iop946fvM/G0A3waA37kVPjRYisY5vnc3XX4kKuEVL0czwDzpzwUBIIbIr4AHZETyuYUwZuhuo1rkM/KqIHAyd2AVH9txcERFmd8iNfeAYM7ozDhG7LQ0wY8GBOx+iAx0aXouM6pWSillYCg+biB9u1DBVnMf/jgScpWFYzXfruOxDUb5Wm9C1bTnGZx+JFmYptt+Z4g6+lThQ5jA7wfwLxHRvwDgDcQn8HMAfoiINmUDPwrg1+/GRGbLFt8hJwhsCmF4YkBJi4bM0WMFSzGri6bCngbozBV3ntTzCmGM4wTuBe487FHk3/VDMgeyWQCcnmdtk7VPPHP8XmhSAYC87ffdPDyjbXICTmMCK22qiUmHNRMoILAu904snn0xUdJYhN597r+sBhUGc+MdRLI+IJcClJriz2VFng7NgaRFQWyYZpx9OubERExusrYRCikz1FQWBGAo/wzOLwuf3Bxg5j8N4E8DgDKBf4eZ/xUi+m8A/CFID8FPA/hL9+IiELayoRYZdVWoyig8X2+N1DudmYGeT2pF6L1OES0FtdqmGxdF33GG3Ozlvc0EjiMW+2Q3vogJoCchoqHh5ziNgg4pOZzL0ZiAACD8Z9/IL2Q7fczL85oPp6pY3x8df88OiRHIUcpEBg2xaEuEn4FIR91Bxy8w0Ek2Ae0c4wnkHoER6xuWUnx15bm3yMZ8gICiA8UKxHooTCGfnmySZeooTK9ORYazqMe71uosZvO5GXpsXdnMOIArwPn5RQh8BhC4Ef4UgJ8non8fwP8O4M/eeyFXiNldFnwMN8xBEo5BSv+v4qr+q4G8EwM4MgGJc9QAOT0KTBw9BPne85mA6ImxK2xMw/H6LZNAjrkc89G/OnjvR7NmNA0WyfU4npfDOZ5gAsf4JxLkpordGzR1yiszY9tIHYjwbj3bXKb1pvRaGQgT0EcGMvRM5DKdjnOeaNE+VsrEzBTT/MEAwiwly2SOZ1HOM5jycG+8QnheXX0UEGDmXwTwi/r33wTw+14ahzdYOi60EN1QoVmAoFz2t/sBSLpkaq0xR76OU3OzgMRIvIgrHyOMTEAa28wEnhvGrrb8/VUa7jOBsdGdDUud7cvcQLMwjCn9eMG6DF8SJE+Ab+C6kA9m+Ko9tco8g1JkAFCpV1+NiBuD1UN/y/RbAZD9ZsWxejd8NMVnTxbIdOfK42Cu4szUvnGm9Xn4a3jEFWQ8wPy8kn41IwYHryngP/fU+k+elzLWtd4cXUtCWNJVi9OqL6XEKsY0NoKZiq+FbyVM4/j6l/oEJoY+fCfSRNP9Ma1xHo00bM3xvbFnhTAzkefQdqLbGuboXzkygsxEjOquii77K4SZy9TeIEuSQSsrA/RaO4hkEVfSkYF7a0Dr6KWrn2Xy9peCZa9KYiG57eQ6GMaLuE8LcD8CVvEW94XNoGLdrmYCLzosDmG4TfOF8/AqQMA1t1UCtH7do4QAAaRrgBd4QRoNWCjWfUvrv/nc+KwhKSoyA8Aa6Wko20DtQKaX+gSAUUSe55tYM4HckFfaStjiqE3yfiejCgAAIABJREFU+x/DA30vnhkA5PnbcYVW1eT72oJad5pXXb4B2yYToy66aMiD7kzUegfvHe1pPwjIDFyr8pd2msb6r943YY87KACYCIUNQKZeqhrTv80nkEpsOr8d3qcGXwUIWHCtYJ5cAHk5rzAFZmfgiLB526egWMd19iIc7e/h7iBICeOt/1nvv7R3AJrPW9T+LE3z9RVdPTRWU7fMbrasuvcO30v93fOz8gI8H7OPYWYi2dTLgGHmlYPExMjG5KQRm5D1BMxRCJhpKT4A8xV4l2Ht6LSt8zGV5SoswZWPjs/ZpNWLfsggEIppbJ/W3DnFk1N1MAn8aiICn8sn8DFCLjRWY8ZMAXcODv4AfW8RjzsEdZPI8JiPq+ou+SfOhW7FBOb3OPkNbuYXSKrtvu1/33YNGzQDQQ5jQx3nzR/vLxJ8prEBsYocCCxt59GZwBsQZBAwan+WVxNou8ZpAZeVcMoGrPqNxuCLSJbFMW/gekzr6EM50PYbYQDEpDRy71RxP5X5BmxbOE1zTysiuMI5pHK4ymuEWIZXAwIh5NaIYnEQ27EnGsgYZiaQr8321qCVbqTnTBsbEzgbLPSStWFpwUDugcFzzldxHDVvXM+abNWoLZ1Ex3hMYH1+x2TbE63rJ+zcPGMvViPOdDp/exlYS91U3wIYc89EIQMS+26eKDT6Wux6vh/5VsuEyAcm2asrrkkkPqNKsnxeLQWb/dRkNUXlefZ0fLp1Bl8NCFgIbS9I2JM5IPcnDZMq3EZ+Dd2Bhx14zqneHI7CFExgSRmJdAGLZ8Sd/l99631B4J7nGgiGNe9mPC99nhOb9xWYwdgV98K5d5aWEK5MpddIQn5pBHi/b0qDkxDP4K3fKEyolXx48FwGt5jADJgAQCVWpB6dg4ciFN8VFTDBAcDGC0gP1q49Gw2lkG92++Gemtvh1YBALtgB8SdtYXU0NwoLobVoOJ+/5dqMCKnN3KXjs6af/QVYNICzsGICq2+unrmXVrPHDWjO+pdnBjD7BvwbyRzITGDIyaBBR3/JUbiM9WUBo6CxLpA8RowM+AbqY7ysQLIsUxPENIBpXgYtp3kuF1vgNK5ldnC75sl+FHlwc2DwDWQg51z0nyS8EhCYGmgGhMM9OY7lnagmTUI00dHc+J5rf6+YwPj1+Y/nh5f6Ag7nceNE6/Lp37cAYPWNIPLrB/w+41SoPAKl5HTju7OT0cI8hdvqn90sCM18KK8i05EzCAz3B+DC4JhztpQaYZgJ+XijIVByWNvaAtO4Du8lsPHJUWTnYHBmKZ2nxMMrAQGAqUFWiysoVA3PBw3RebEEGTYUku2yiSoqXWS7cVxAXIFeZKYeM7h0GUJLtsIMoVRK8MwO16Tn0ihY7DSXhkHl+Z9RXSdhwRFzszFtSuodn8FlaNAMMMtSX4p1PiW2WB4sNRzuyqLJ7yxLjjOzz2ZkikU7yN8bk07Is/wiMSM7ioQXXTNeDt0TZstoScXKOH9Lr01/lm25vWS8qHd1lvmXtf5INw+s+l6ZVu6x9AMMmaWuvUYl34cLvtGS4kwU2CZzoOviZVW7ilvqwpa35R+RjFvYGGhgMMneD5cN2DfCtgGlAlxYfx1cbat12f26W9vAlN4pd542mu+vw6sBAVAHa0H50Fy9lT3ucq9CmzMIFUAFQeYcEG0KBHLNNnHvzChM6Np1JBUT3Yluhy6AQBpYFtIIg8hzn68cBDfdQJ/8EyU9f98sYbUZzQ6O8RUhNnLsJujJZVxYaTCSRj6kHiPIJXqapeu8qy3w1d9Tuz2bTcxiz7Pu+EuAj/swJ5m9xlAA6dEi2NNTQEXySixb3N+k6CpUBTXy0mUZdvGPsNSplhOYUXnaOdrXNogh7d1ZRmq1uhK1zePohVELY6syvmGrhFKhDzFQAK4CzB1wkPTc8HQOms6jvO6FVwMCo5eYU2OPe3Mwur+OLxpO/kZ+N9PgcHgdj/4+Tj/38nDwcL5feJYTEEdBNTNrvj7b+2cOy7ns5u/NaViV5yovJ3eUNIQJECbB0eGbzb9cPrfMjjn/RMp4mAbTYo7BXvW2a//MfEiDyQRWpvkiOb0fr3W9KLwSEFg3nJNbAEb5ueeQWX7xjuCvGrfR9NO0LtJ2+v1npvP9hGb61uB800aZgHUFBKvz2c5fltH07r1yXedpfd/esy3aV/GcxW2CdgDDQx7MSmG3REw4zVQa4xiV1wwCdk2ejP9Twtw0vQfonyq8ChCYHS/jrDcMwyYJavthLLw83HhmFXn7q3kxysw84hgNIWsfEZ4p7VODeG74GFUdE5+iS9TO5/RxV8FPC6Xm+4f0nTAAO+ZJR88NZ0CwcmZGvCP/irwexzzYN8aNT89ZzOqauSm8Hdx2x43tjKVL26cvA2iwAUksPpeamEDKy7z+5ecMrwIEcjfgoWGJhyYqmuJgFErMgrP+aF4eV8/NAHA0GcYG9yHhntf/Jc/fFijTYJyAQLfaeiEDOGMCK+36nDydxZ3juSfAMwDYcVYmZ3lcfdOftf9O6tychQO7OmUG1pTXpsA46cva9EdqbHfC6wABDQNd1XNx0qTKI3U4TfQp6nbUgkbF8kCYWVPmIai5UoQ5wN8nEgfhfU22Dish+5BqXtnAp2FiR/fQ7B4TyP3lEv39nNwS+CmpQ1opLeGTGU8M94002FqDuR5npvSi8IIKMqA1IDBm4PkhEs8/m1Kz4cNpmvuwroYhkJXXpzEVXg0IzHarX3cH1ggEmIDAGUGKTwp+pPB23RrEfWYw8/9RC+ZqydSR0v9AEoBEZTifD3Gch/y+aKpwKNm55yuk4yBY8/iLnMZDmhfCe6inBcW+F8cqSDzSLaxJx51XpvcxmAOzUnkOW/Tr9v/MmHDEBlNYmQkIM8hOw2APHhfFeAFbeiyYwPjcB2mMG+HVgACwrgxryyH/5DvIxsyrdJ/G+GSvunx9ZAS9NRDJMtKsvgVW3k+K1Ezs68eDofvJS6RUSqocFuFU8A78ngU9pTG/SuP94b1JGGwdO3tq5Vtmhm8agpOGnsvi1rm9a7/Wxu7QMHNFRGYgeC4IRFwmvB3MaTMWjL6QAI3cfsQGN7ZyZI0Ln8lwTHNVTLi1Ifra/p51gs+e0onkpPeN/h9BYPQ12bZmstlp2pTE2c/dIvug8KpAwMKqoToTSAxgGF5JRyYAADTpPKNhg8OMujkCwEUBQTtmmadhm6yagJHW4jcJ8IQGi8tHjyBQfajgCezycb5vYW4fdn6LnZ/Z7GcCOmv8uSHn9+VaYkQnYHC07Y9MMGvu2T8QKwsTiASQcnLmdN5yYo4AwOnYVfDZhRmcmOUhuqmvnhePLMos2vK0wMj3GxM4b4Bx39DRVxA2NkB5Hv8YT+dxNJuOR/GVaTv1oXFZgzGAGTUJdJSZfGcwUWDyf+xtmPPJvEKAl4Xn+iHuvX+LHtvfs2Ayjz6B2Tfx0vTNQjo7+ETIw563xTqjp6coO2nKCGxoTaR/nh+wyi+z2vR5YdLOoLzZagYEYPIByCYonIAkPwu2AVsatN3KQrj221B0dZRQAAqqKzvkI4TXAQJT3YyIfRSiM1/AXMmslD5zgVkjDN9i9o0oTh1dbGk6akOisUHP3zqei9Y8+A+Wx+P95Tmf/J3Os1/B0uWP3ND6JghnTGD4FIcQrr6xKpc1NT/22NhvZAJhimQAWIHLMa0jCxny50KP6VzfGXwAc3yhxJjhi2VbKgiJCdBxw5gxfDqb4HWAwK3AUam5gMKRMgJA0GFrAB25ALNGYAY6Ndmmi0ZPPbN4c0tJ22ITFFTgz/cdbv/z4UjhvMvx63Whf/HNWZitTc++ArtvNicKHXqTsu1qNqrHof+tQHPIP8Pn3M9TbudnZ+DLgpDLfTYHVoA5bPJKHaUcQSPXobxzTKfFZe8A8P0ZOb/s+Q0m0Lnr36xDlJUJ9A7kTWkNFLqYD51lDUQdfQzDBjlqugYTSQY/DbtHu1LDcPxU4fWDwCIM9HNmApM21gvDu3bdhMyYAqlfANYwAX/XdPbQ4LMGSEpo6QrI6U/3b/YOLIGAPD35uSHSw3cyQAaI5da1ZjBHzXi25sAZAzolVIvnV0AS2n1kArA8INUlom3Y8/PRBDJf93s8avWs+f35xAQ4/W1sYeyJyiAwMhRNLNy5q/Ux+rc+zwjCVwMC5zRo/Yw7Bcv4/EwdjUnY+Wwb9t6HjT1XIwv9HDIJKcc1pg8AckONWYirhnqLTr80hMCMYeUQM3Yyq5gVfTbBzxt4zMK4Skv8fRTinK7nAAHzUWDzt2fQ6UlT2/Ld9owsZluHuHJemS2fpvkn+j8zgbREmYwL4DRiMPsHEhhM5ZZNAjnmivr04VWAwFleRRsHIuaC8r8TWkalp8bMIYxzIwe0YhDgMQ+Hnc9nuT02zpSvzEYS2xgaMefnkbR+9gHQcFwUlDcsT8/UuHMaTXOdOQVDCEIoVmZADmMdzEASmDPLQO8yIGs0AeI3f3tORwaJOb2+4UeJulvlYYwX0zcmEJiZwFSmc7VkAHSi4EwgAYCvgjX6u1JslpQoc9w+f254FSAArDWWBVuB5bJtuFwu8ts2XB4uVqr+rIwLSHYgjz6D4ZsgWa4aVhlip4udNvYOSIMC+rQi7Pi3/UbGcvZ9mzUa6RmPbLY7FkeKXHvD0oYGFm3UJu092/VITMbKvrWG3hn7vg8MINfToRwnVpAFpLU20feoT/HX7CETGo8tv325XLT8aGBm845Krcn3rtcrWuvY96sLMxGJx73IehOtN+DaD3W4ApXe++ADAODMIDMAL1uvswhsW6Fx92nBUd9VeweKtumLjBXYbKu8BgK8Pgpi/cGo+HTK70ceXg0IzFTQw8IEiGGVyJ6uowHuALHurstUTdKApDVGlI/lrc/TfpavlBlkVsLZQE33/ZhV5+GIhBbrNM/XzkJ+JlPpWevOwmx/j466tZadAXWVthj3Ie+aJp+HdUdRhPNSdhjqfsxpDoEVRyyfjB5e5jtp+MwM5ue9HijiclAGxHFIc/nY30cGZExg8IkgCTkfz983vAoQcFtJw6BJqYye02cspPmib5/2HozXJWH+36HxB5M4u5evqTC0hsZH7RqNnZwy2vWcFpo0Yj7eC6SJzWaPgEAbRtvN3nULVfuy5/H4mYqfmRBzvMEAYmMPEXRjIzIWoJTYSHSMS37Xa9pubAIWByIiEGxfgiMT6F16BYwFoScHMMInMC9XbuMDek8+AbaeBj3XOjfFFgzDAAujGTDV+6dyEbwKEHBPLFbOvzQg6GRc9bM/M2m06LPV6/o/g8FdHHqctrv2oaCusNnHFXg0pD+9zJ1jVKmvzR/AJyMWJzagz3Fh4Zc+DjnbDsYWUhlGR7V9IJK6AM1BJyVhms0HnLw/g9PK97ACAhcwfXcGdju2ZrMEG4iyjV8GwTVzwIR/HtI8CGwhn5AWQKCsLPeGmAOUu4weBYey0vIdnH489zxomXIyMabnM5hJOkz4s1IZ/V6fIrwOEIDsGVfrOONr2zYUKrhsFz+XrcRPMGCSk/uBfSoBq6AxyfjvXtgRmUAoXFTuR9R284AEPOK6jC/oxCDdBdeO0o1IaCwaCWraeFRlqngTsmFQAaFsAyGcshbAZhjEnk5b5WbU3MYAeu/Y9/3AzvLxZqlyaNKZUdjfrfWhrkshXWo7tou3YykVsRY/eRuI+OS4783BgNS/Yxt52PdLIVBdOyABgBu7pmdlBZTWs+DW1Sdgmt92QRafQFeLrTdZfdB7DdQc2FvzfDARSpO1Na2aV3sT+t+zw+EjhVcBAslvCgBT40g7tSwK6BAoXGhndtJsl4kpp0IsCRhMc0fwLKknca2vHzIsjr3eRbDZvyzf6FBAiQZq9NFbPxi9h2mxyOR5ASAAoNsgl0SB5wE398LoU+jDT+zzPoHAkRkYmJmitUVDpB1IgY3lYTgXIGC+gBB4AIhdgWVNRUJP7cv9PdmcMnueR1Mg34/nMZVVcnBPTKjDFshRFuI+g3jXDgPopvMPsf3PwgeBABH9EID/AsA/DknevwbgVwD8BQA/BuBvAfgpZv57NyNi6yoKO856A0qpeLg8DMzAZlmdpOr5GWADAITNODX87JwiojSE3WjkaIsf1h9Y/AB1WKGjZXRXn0MQgGnsxDS80M5WPgXO9HVx7L2jMXtvgNnTdjw648Yhu2dOSGMSEd8+UPU5Hvv1TiBqmJ1kUqbGCGzHYevnN08+XBvnMrA5BvK3jRuR1YwtvZYXr/cu7DDb9MS53FiZgGr+7PsAtCcgGMDAtMDYNYmlEFAKarfyCCYws64oc3yS8KFM4OcA/I/M/IeI6AHANwH8GQB/mZl/loh+BsDPAPhT96Mac2iV7luK1/ANrOzHRRQvCpn6J+tsPJqtDVPmaWQhkLTT+G4OzPEcg8GUOo3czjehT/kipBV29Vl9l1MZDAJqXoLkLxhAoLN71OOXBsosWMbKyZcb+r439N7w9PSE1jv26w7bEdg04+zgXfkE4rvCBOSadN8eexdGQc7v2/f8vUK+qjGrs64QeTllFsAcqzFHvqfegLiMKGVL2zT+AEDnrnVJzjLMdHHww0n7/kThvUGAiH4HgH8GwB8DAGZ+AvBERH8QwE/oY38OwC/iWSAwOgRrKajbhq1WPDxc/Bq5mH6cYAuXE0YgMJ9bAIL6CHyBfE5STslZCORly/PPKD7DhLUfzLzxvq5tYCbO/OyMODdWzpmZQWsNe+uDD2D2rB/imLQ+EJrQNP/T0xX7vuPtu3dorel1YQKWjssmYz2krsnn0Fv+5yD+AHk2F0IGgVx+BiqXy+Z+BeYKQOYNyGwRLS9TwRGpgoAxgfAzzYCXWYgJOYOdARgr8He4o7GWQymAlj2RpNG3zCuEzyD7Hj6ECfw4gP8bwH9FRP8EgF8C8CcB/DAz/4Y+85sAfnj1MhF9C8C3AOCbZbiuBRI9AkPvgAoosLC7FzZ7DvPzSeRvBtH2BCabT6DXARCHL8EwwN+7EyeXPin31JUFBnqBbYDCOIIO9ZlFnOwliGigYUfPDODoCzAKalp0xQqMSTw9PaG1hrfv3mHfd3z99q2AwNPV6bGlZL/saL0ry5PBPL1WZ3lD+TGU8VAAoZc/K8tJLI4wdCcDYT6Ili26VkSYeDz08GQm4JI91tuJTyDK+Oyn5gQB6B0l7Z1gDMDSmb/3qcOHgMAG4J8C8CeY+a8Q0c9BqL8HZmaitb+emb8N4NsA8LsuhTOCuzNw27DVzXdr9S6VPlO09wvWwO8BhwsnTEvHvdwYgNhtXBTM7VVtmlHDFPy+0f3JQRQ+Axq2mGHAfQYrbTozgX1v2FsbutXWZWqNd/Sy23vX647WGr76+mtc9x1fffUVrtcrvv76LfZ9x/XpSW3niPfx8RHf+MYbnz9vo+VkPEgIZNa22eE2H81usvazbdUFv/fwJ4hQV2y0OQs5lBUnm5+VBaRp0cEC7O+UHvuXBb9nZtDRu+xRxKWgLEZjLsfBfGIc+BAQ+DUAv8bMf0XP/1sICPwdIvoRZv4NIvoRAH/3bkzu/bQBQtMxgQMrDT9odXX63Pfgz/eOtvsg+IfjaPePzqhYFDUcYGuHGqALmgx5CPueKKYuy9+s99LMOgeMVJAIzegAsfIRJM0/29henrC8BoPQGMMBuF+x7w3vnp5wvV7x9u1bBwExB9oUL9TJW107jo2evXx9Eg5L3Q7OSCR7XBoQCttiM6JV87yPWShjB+M1CEi56ISxBGDHEZUjExD3X0p7j+8iPTfnYRb8Q14/YXhvEGDm3ySiv01E/xgz/wqAnwTw1/X30wB+Vo9/6V5chOgrpgQAuYswO004CZYJW47thTlZvrMCAABiI9L4HJAFX67nse5hdvLQ2IXkTnb9xBi8cZjzyolCsmVJiWSyjU2BHbWmhOwQnE2AOVg3Ym78BgJv373D9XrFV199haenJ3znO7+Fp6crvvrqa/QO7LuVhwEAvJwul8sAojJSr3paTJDskSMVDyZgvQE+kKgCl4m99N5lX7/d6ssGHaUyTUzA8k6ZCUxl1nsGgXEW4XHkYPeVhfw6otylfMI89fy9YiYAAH8CwJ/XnoG/CeBfhXCzv0hEfxzArwL4qedERJT8ARkV1Rd01KT23sfzoMyacAYAN0kXlRIsYZyyHEBwTL/2WkckOmZg/nZRG6M7AdD75kyDCn1PgIRYymoFBCthyixnNnnOJiGZZp17GILGh+/NGnkpI722cQSNwnkIzkxgLPMRDIRtHa+fSE5iFMP4AaubxASYBQDyYKHs6DswAWZhbIdnso8hGNyxHa2VkZkYZ898aPggEGDmvwbg9y5u/eRL4xqEP2vBqTcgbONPGw4AgAQAJ9/PQDDvdJTjlBOhjoMzSAcDzSyk6XhzYwoutAhAQDKHpDFi8AEcjzMDGNrnkGYTfus9gH8XPhqute4/eXZs5ERArTFDMAcbqBTn8/Rly+2YLitI5rCnI38ndQRJa/YHDHU1MQEZ9xug5SMJz5gAYwCuATzdHDtJHJ20b56OHzm8ihGDBEJFjWG1uxrqMnYkzeUJe7GUqiieNBpi/QGoIAxOdXD6X64XXW78YwWXx0nrrhyDtkFlWPLBBPIxuh5GMCwTwPS0xwIj9xVoo4RqOcCnGS/ZF8YJRUb/MxMgovD6M2tXX0z/FZAIIds26wmo2GrFZbt4l5gBA8nOLu7lZ+sFUZvf0rfve9QhaVsgwsODOBffvHnEViseHx80XdIquDc0JlC1wUmEwozCaWAXI60uDHdCp5J0lsXKzhhAUxDYzQFLwpB2YzlqHuwkgF6poMFMhG5voZD5dw2M5NuFZKTjiB809C6VA0rws9r26wABImzYAHOWm/CrAoR2q9hsMiR/wdi3HfbbyG1NOMIet+8SFZz1rs+mx30oDmp6Fke2vRs4ntfGNwMAGQCksvK0J83X1cdgNqZPoXHiwe6wAmzWGwa/S9bQNm5A5vsHE7Cx70BMBDJQqCp4vXc8PFwAxOhPmfch39jqhq3UgeYGkIufhMj6y+k4VFztayuHyyYK4RtvHrBtG37gm99A1fElFre1EyLCFdn3VFB68j91nWshqCng2SdzA4xm6wmSOHAbRhAAgE6Mqwr53nY0MK7EoEJgCAA1bihc0CEDEnwfDfteF1AsVNTHE21NQDaruHHiFN1trxJeBQh4yK2WcFh9NRxkdp59YyocK+ybKbzbquSUeng8CWo+D5U8huzsy3GsvL05Tk4Ohnx97IkwoR/jOfztusQy56k7pJWVXXCi7DMbyJN47BwAqgpCUzCxSV9vHt+gc8fl8gBzhBlQFSLZbFM996UUVF0m3G3svFGqan8qeaOZvECLgpmmd6sCMI+PwgAulwtqLah1kxLxMsXgX4o2lOoEWdDOw2zfQ9/LdZDB0/0UNMZ/VDS5Dc+2WX5Xu6x5uD3Fk9rDjfAqQMCdMFnLJQGdQcDoooRx1ZnsZzuKJjz+lwRvJCcFOvgN6Oz6CChB9V26lfHT0GjD6ZVzJKkZPOs0NkDzYg6NLDV4NU6H9OVx63Yt1g1onv4RBCpKYR+nP4cMLJQFEOS2tfkRRh9FGi+iDMLmjGTbH4DOMSl4fHjAtmUQsLECqavwmMIoQ0MJL+H0/+CjiDsBHIy5pfH8XvJfYBE37J6V0yGtqf3g6K8Cp94GQGfG/gMCAkCig7mijBrNAyjSEmDM00i2AkBH8Rl/Il4UGPCc8nlpLmblcP7kxAQMACwC5jwK0ZxFYyPtk9DlIcjmpPIw+RWMSNqwaVv33oCgUezyY70d2Zs+AHcC6ZLPNU+TGSuLaXQbPNNRfQ0Didfovy0LdrlsqEU25yAiPOkQZSuOWipKITw8PKCUisfHC2wdCvNnZBDo2QeA4yhFK3nrpWCEcJl7z6dip59MENTnevgCmvWaQAaBFmdt6ltI50ACTqkcZxgdIRvh/wpz8tCcZwZ8El4lCACpQRdy2xDA8nig80AqIJvnT2EGzA9P4WgG5PfoLsU6vEe0vIYEeKOXPwGDpf/Aa2TuwZw6np4aoC+Bjr1hguu7OinAQjW7mQXWqInGdQLmqd5mHuSxHX3QlkBvTebb9zGumAIcawzWWvGoR5tv8PT0NA3Sku+YU/Jy2Q7mVR4rkKuPyIrGGBK5ScDKlrLj2c0XBYLuwBKa3nwdxpjcMajmXQg+e5wWjyXI2zwBoKPvxNpFzos5fq1yh/Mb4VWBgGiNQN05kNXYcwMJEDh1Wpj0LzMMnhOOaZ/9A3Yt53P0BdDNdy2sCWMkw4B0ZZYUInAZB2SZh180pDS8y2VzD7V05TUFhwJm+KSeWfgzCETjltD2HW3ffRCSrTlgQldUs18uQu8fLw8KApsLezwfZoZNOd626vm24b8Ae7psCzorw5UK8XLjUQCja1D/puSTT5pXJmi1ccHWIhOZHM7NZ5CARdJ4NM1yd6R8ikGIzXBN3bkCcVPyHzAQMLvIoRh4FmX3RpYaxezMy9o430n/DRrYI3rJ9ZNAWLEKo8mK7IMvYBTala1tr54BBGv6oijHOLPNnR1v4swjFAVOYQKZ4sZCHBZXjjPHbUcAPsORiLDXipbMirx+gTwqaXp4EOF/fHjApj0MZibk51f5svQaC2DW3iTusP4gY4e5flgLLNrjGgRgFJ7zMZ6z1Z5tZKWwAzHABjBh9lGIVp9mythsQknGyAS8lhO7zUZF5OkfIBCAU58x2bkbKa4Jehdyy20M/197bxdry5KcBX6RWbX2Pud0m7ZhZDXdFu3RtEAWEj9CYAQPCIOwLYRfEMIgMMioXxjxIyTG1jx4kHgYJDSMkZChxT9CbsBY2DIIGBqPeMJgAwKD8eAZg90tmzYa7Pa9Z69VVZnBQ0RkRmZVrbXOOffevZq786jO2vWflT9ffhEZGdEXVPOeeqidckFDC7aPd6JAf/9m2lUnFnlc3ieiANmvZn0FXikzAAAgAElEQVSPIQCmHNrXQXgs7ZMs3x2c0i3WeHggZCvlIB56AwD2yr8WB2GzGXKuB4NQ9DohEJYlIg1RO4CwAS7rF9qFQCFERJ0CtGeMoY0X6ZWJ1GTMBCkVeUAAhbW3YbYP4tLeeoVfEQ0AFQPM/0il9AwubsWXlJBywpyWOjsQAygPCARkJn0GIzEXWwEro2i+MwAA5p2JN+qzioGtZgGgnZbXpxsCATSfUCrAoSDcOZAac209ykbPPSB04rXFDfQniujQH+8exo7+lf29Lr/BBKSj1TyXHt3I7dsMAe7y5rk2MvetpctroNCszjS9QLFgVMALJGosirF5HLnVfl6eLyWgILD2F9hq+FlHQhlx7RuAnqlQqHYDkdp3t+UqGTAWYAKk6T9Iv729TwGA29F+padicxxa26fZZhTlnhvhU2aknNQwS74rMNRTEQoI2PUACiOrDnSgecuuo0uefXvcZAK1Ns6m2wABQtdImn66BgGjcVsjoH696RcuDddn5epL2V5R9esAQK5sEawf8RnaWLDDBDLEGzGs43e9fJXZli7HGHSkbZdv2/Ny914/2gIVBLg0XtexpBevO7Kj6k3ZKe3uy7bXL5Tfohsyit2lzGUWo1l8RmSCQLHxt9ev6sdAwC33ZeaSVc+APFtlBW1TeM5LKgBJAKjoBrhcV20JoDM0VIBvladmmHQ6A9jIz5v759JNgABhXdHWkHwq2nMrgG7UBEG9c8vx1ZRbp2jpFe7lSWdlfo9QjP6K1R3l2f3ITK2u4LWYgG/Elq/tSt+S2XsAaOR7e7A75p/lj28qL12HpW4lqAGBB4Et+Oo7sN9K6XQ3clffW/dZEyLSZcKNDmmLAdTjFQScbqBpJyoymPv2lJHUTVkIoRXROuYh3+N0AlS/z+sh3NfWOilnudu/nG4DBEjsvk0eDGVJKJT2c6FzegPU45RyLPewUg56rZ7zobqb1A+gPUvsj1N7aKug94q+r8BUcEiqzHc8USHV6UF2lQs32hg9lqJyT3MNnQiFTteRec0CPAiAVVjZaHwMrv4OffltddiuIwO6XiNQC5ANs6uWfGRiiu2DSoeoV6N8PRPAan6bs4ysQxzEdwOLqbbI2AYYNtnn6xQluGhDVoBC/23OPpWuZ/qCoHI+sDBjVsVgoAAOLBaX+q+6JFe7C7Jv1zoxwuPEjXWL2hqoLvLCJt0ICEAtztoRy1oFd9fqX4LktP5Y6u9B11b3MrKjC1gdb9B5fbwlAl3XX3FPzZ2BVGEC1b6hER0cExJHmCTLXUmMUOqsA5pOvZax90dYItI1GK6B+QLcbHFtqdYOXEWDJk+OUXlW1uYZBQT6xVKVQHkI0Mf4Z+n9IBGfTI7347bl1U+/FQUhalcTOd4ft45fy8mAoigPlREgsDJHN16v9A9UZgSolJt981bR74/0fR84l24CBABFa5bFFTFGhKhD/YoCdnduHXajCnUQsLr8jL7gMRMrByhiTX+es648Nlk3ABGVBqOCigeA3v9eDxRltFYZ36ausgRDKM/v7/euzbxY14sOgKw3kPDgbScw2r3xsRUgN1IBgO58CARGaHwIShix1tDoGtrcN5N29sBZb5ZOW5ccF51AdgwEFViMZTghR/PWvfNiLttrv8CYgCwwQeaKghuf0IwmGyNzO1o2BzYv74vVnr83L1+qzo/0K8Ugbz5j9cwNZN/o6iiMQEdOdqfYvctGrl527/UAfQdtAHav1VAVp5rDXedeiQPdOZ+nGGQVXBl1NeTbCge61tzUo8s3Obj31zKty6HRDbmYhFwfjMI0uUKEZwU+bbUW1vppZxb8PIK7W5GgOLIlVKZ2xbveNN0ECIQQ8OLFC8zLUowsMlSeIlm5FgIhDhEgApPMj88u7DXcjy84H8oP6BgCC3mmrlM31PAKptAqldb++nwqDW6jw/UdxtbTy98ongfsmoA6DWfiu83XUyBQdF6aHQDYzECMQ0vL0YkvBFBUfUOnqW4As3RCVWp1gNN8m32HLhsPILXXqBp7Vpm9jIyaxahrCmyKkLMQ8WKQpB1tmZdihNOLRiEEDGEAs7pBZ4hHI1/2FCCuDQgaicTFk9QWZmWWSfMgB5IymsSp+GxI6oiEOAOnI4YkQXQGZoRB6nCaF4QYMRwIwxDw/Nkdnj+7x/Pnz7AsGceHGZlllf2q3aDW3+ukmwABIvERn5kBtbM2+Su4Ea9QS6r26ACvgMA9eZMX9aygyl3b02J7aW+03wKQFcvoRh8/GlG3L/ncNv1oZOKizVf50uTKi1r2KqOWZ1J5avOevVSVfjaCUTF2Wd2rw30rz6/G8CLWGcDYGbsv2/PbxyoV5836IwICZP1++c7yC5gcLtPQtTwaIynWe5idY1gDInORVnUGgCq3c5Z1E8VGwnsoyuJnQNteHCLioLYc1Fs3rYuzL8FXSTcBAmYnPi8Lcu7MPcFuHbr4ErA6ALYaZ7tPXelQf+kVpXcNILxO2qKVhAoUlzre2dRR9fq7/e5Xf3wnCmBNu620fdnJCJ+EhXXPuIp12UNQHaT4Kcc61da/tyr0rhaWUeu+tAHuz7e/NkgZK2q+jRlLyiASV+8hVnsBb5wEQrHiFP0YIUYgJJwNuPu6QHATIAAYbVQHmQ4VWWWl1R3UeYktT6r/AyijyWZyCprVqfqiTiskRc16jrtnbP1ujXNYnbcFrNQAAYj8G90z2o9id639bsnshSFgo9ORMQ7PP9xzsbp8lSO/2TXWcetIrW9piIt9N8o7248rD1opEPvv2Abtnv/x6nhlBuVAYVfe/qDoBYhWUeUroXBlVsob6nVYdQDuuwrAcJ0Ol5kRKpaDPmtbaYP0XpVuAgSICGGIErsuJZzmSSoSAAfgLmcJ3lgFg/1Ro+/xXQ9kODQlcp3PXbPTufsr2W39tf3vdsdvr8vd8RoGXan2xidliF+6MsJbZ9oCAEJdoy6Sd8kTNU92e7TTsLrijwogZrEQ9BrS78iMGnuPCZTFCoKIQMFEbafFdy8lNayysI3iQIVg7pdlLUD9XgtlHmxKkAEic+rKUHNLiEgipVjovHkKCEGsBbWNFKctJoqSHgj6jSGU4iOLHh1CiUaTFe14XirLzVl8K7AqRXMGckJgxkjAIQYcxgGHIWCMpGbIbRX4aghYVctV6SZAwFLmjJQTliXJVGFZd+6oHIzaoU6H9aPGar/ueiUhVn9bT9oY3Ru5or3udVJlGuevMVZgoOgGznVyI5iw0tox6n5FjNYCsY56KO/zjiu6F7r66MUW0syTTimwjnDVFNc6it6QqXQmAFWZ60qC3cFisGNTfYrsnh367/LWePY30Dt/7exTbKaF0JZZGTYcULkhul6+PbVdOjxzbT/2dzlW23X19WBM6/Xb3F66CRBgMBaNY3c8nfD2y7fFu6pqd+/nCQBjGAZAZa3agLuCNoR2u9TtN305W4W3fdpT5iIXNlR5nd4t3UF5/s47S8MFSmc3ebQ6D22ZgSmmfL79b/M9WmheKChz3+4e3+h9h+zlXs4MTq6hB+nkts+aXa9g9cU6+ZV5QFnHsGX7wKgDxtmydfkv+XCguFsfykLKuzM3gXNilNWSNS/bmzgeISw66AHiMWkcRgnVNgyYlwWLa3+mH7Mhi1f+hnuusJ1uAgTA4oQhqROGZVkQQijHUubi4JKUypYkAlRtnt0wSdzs6ghlf8uoUNx8+UGP63NtRPS/fnBca/7tPetu285Rny+WbZm+uaC8j3S/NuI1E/CJ7fs7BrDJCBwAUgeyK9akDkDbS7h1JJoATg7AMjUWjJWBSQcuMrS+b9Gp5FIJajnJQd7tTaBXXWCN9lpuHZDZ30zlGrm+BSQq//XMUb+v/y79r+gYuQNJXRqrzRqBxKYiBFsOXZMtS6pC7YUGtQMINwECmTOOpyNeHo84Ho94eDg2S0iXZUGM6u+usamGVpB/2poJNN/uryWhoVKa/YX9DT0l1v3gRAMiiO1Bt0KuyYz7Rb9vjzXq14FAfy2goctdoy3bRmDL8gKzjUf1X9D9BluKu8UEWBqndUwyug9ZPmtXy6VV3pW+nIGFwfP2Ah8iAeUMFIu7hc3yjpt90piDiNVegkIQ5yOhOkm1sgkhAOrJaFW7RhmDlbzORDEBHAozEb8gVQxhVchksxPQvCMEIIquKzKLvUXp7NToATgHcDIfhLW+IhEOQ8BhCLgbI+Y5lbBuAJA0j/YVzKFpbn0AGrC72aWbAAFmxrKk4oVF6NA62ktJGyOsFwNKw+f9y/XF7n6PqSZ/Ows2jya+oza0cU2tG1ay+tumO7WzK66U/S0Q2HjO9tae859cTV6t3NYMoCwdKJ1dORGbm476QAaaoJ0Et75IG3suUY8zeGHwlLfzqSNmhgAGM2PJbVy/xNVIiGgNApnb8GK2orH3iNwwrI79tHmCgLvhrYH8Jrvy0ia1bMAqwMqljP5txCWfzMtyLIu83DkQupC2bX768XFHMrgJEMiZ8aDRbCXUtTSDpN5ZvHLQaJKl3Q7+mql24tou4Ed2d43ft7zkLipx/+xmP1BpKPIWNGJE2AEBf6xforunkLLUzJe77+p1Apm5ASHf8QkoBizWydOyCHVXByHIzud+zgUEcs7Ic0Y+Vdu3RuxRLM8ur4uutOtBQEb8IKMsqZ/DEDAexOHoMAwIUULcSzCSw1oEc+8uug375sKo8uq8VlVhGXU2icvzik5A/RsgZ50lQPE3sCwSaUfiKKB4RvY6hXEccXd3h+OUSuh2AaEAExCkrFomYPkpbWHtkQ3AjYCAKAZbZY9JfMzqwqk0XEl9Iy9KonPKu43D0gm7Z5RKbarbukRTsO0ou+8UdCvPUDZQ6T8KIym/gOt+6+fsMQFgDQSscjTryJ6dxtRGNzL068Qjk8/tnUkjlxSPwcsiYGBOQxf5TcmDgIB5nhLylPyQaR8OAsnMH1VdwNyDgIk0Kh5SkDlGGy0zGDHIkt6QAkbIJ8WY4OJ8NWXYAIAr10apRKWWYEO+f5K1Vy5CvYGBGrqxAxDPBMqWwZlKZCmCi+DkIi637aAZreBzVOeTzqfbAIGccTpNEtAyZ7EhN9mQ3Jpr/Xf2Wd2H98Xgq3EFm2eSNBR7Yj3mlYLUNJLtZ7QHgszrexCw5wJ19C1Ucv0M7wvAb2EFACjthVnLtWvCRlcJEiLL56dp8CyyP2dV0qWEdJqRUsY8z9Lp1aNOSknobsrFp2CeEvJxqWIJOWUaqHRUO7/k1Ib5Vjn8cDgIABRPx9KBhmksjkqHGHGf7zEMMsLGISBqeLKtupFyZ9fvFQjEMaCKDABn5drepJdraHKTHWKIyEE8KDNDdC3MUj5EyMsibT0lAOL/kPX9gYAhBBzGEfd3dxiHU61XIhACsiouRV8RnIdpGzwut/HbAAFAXVlny70cVzrQoqWda0dta6hvknyHlv0OJ6z8Ua9bj/w7nna6dwCoAODFAfgGSd3++hm2vyXHttfVqTJpMK0Gn+DEYqcLsfJtFE7MyLMwNwGBjGUSEJ/mWTq8LQZzIGDiAE8ZeVrKqGn0nyzPbrRmFhBINlI6EKjKPo1PSQICmYCoU3Z5GGThGZRqB0LsvrsZXYsC1OqlbVTGHHuREHZlJybuiWY9A9Aaqs9U1G6WgFPLBFY6KBijA8xDdM8it9IbgQAR/WEAv0+yg38D4PcC+DCATwH4uQB+AMDvYubp3HOYWXUBWedUK7thrpFqxGdcbvzGX6Lgb/Btq0q+dL3kZ//eJp+ls7ZMwAOA7Ovl+t9aNFiDgN1/NnUUybC3YLAHW267Qk4ZDw8PRabNKWF+OCGlhNM0CzNYZKSXlXq8AgE+ucUzqLIrEcm8v6PsC1fFIKCGgiRydIgBWEIpKAqEuIwIMWJcFoyDNPGUclltOeLQFuBmXdkf/qD9nNG5oLbJIttTjeZk7aJGKEqIuc58GVAD1enoEAeMw6hAUNtZIFLwpDLyFyC3+tzMZZteGwSI6CMA/gCAr2DmByL6mwB+O4CvBfCnmPlTRPRnAXwjgG8796ycGQ/zgpMqALP5Z0cAI4JZ0V43Mu0RqHBJT9UbpR1qHVd5zO8zmqLyDd5VOoPXHYvQKFsYrEtuuUZH7q/3z2hkAHdYR/JyomQcdaKiZK56LSYwTIvNjNViE7Nv4MzIQahk0Ym4ZzI0hBmrVxxmsfCzTpsyHo7S6dOSlAnMSClhmmZlCMIAbDlttROojd+eZ/oG6TiQOnamwAvnohcCAzRIAI8pJVDOCLkaCZVpwyROV1LKmCmAMmOJA8BAGAeQLkqT8gxuNNbZCVZv1iay6DcgVFFNqwSAsIzMDHbMB1ncyscQJF4igDTLNJ2YVQs45pDkPoLYX5cl7mISHgMQA2EIhDFGDWnmGqr9ybVts9UrLku9byoODACeEdEM4DmAnwDw6wH8Dj3/VwD8b7gAAktm/P8vJyxzUtkzYOCIHEYwBiBHIA+6RTEusY6SW1PRopSxfW76kOh5yjmp6b0xs/QLG4H7C0vhWwZEk7y+cDspZFUa7N+3NRL5+9w9Cc58FppXTgjicUD8+UGuT1lk4wwWeZZMA+6ezxANfOYi4/Mkir+cE9KS8fbn30Jakir+GGkWsWCeZ+TMmO24NtisD2ZI2PM5L2XfAEbKm0q52/4CmbK0Kb/7USIUneYZADB6JVoICEsGhwCaddnutGAZR8QlI97f4cSMw2HEs2fPajsisSFgBfyUobb6Zs2nTBTimi0qizN9xbIsEmxEQZAX+b4IEVHC3R3mEJCmWV2sA5QS0gQEZiwhADwgRCVBHGwIxBiAHAl3Y8T9GDEvjLSY/gGl8ycWPU9TjlgNc6v02iDAzJ8loj8J4McAPAD4hxD6/9PMxSrhMwA+snU/EX0CwCcA4F4ruDgU9e/R/0Qs6OZRldI2CjvaZwIAVh5yznZX2iDVqwNcqeiV2tjubvc666zytEY5tZm99XE/a1HmsjdagM1PG031x8HAMkunn6ZJpmgnkfXFijPjdDqJ+FZAQEbAWR16LG5atzjt1HfY+2yEBdzSLJ9npTSmNzDxYZ5nAbSUy3mCTBGmIBOrUam1TakRIIA2RCAl5DzU8nGsSoLXurLoytmmSwsZRW2brftw9Tbc2blI+LcaRZuanqq9Wf82BWF5r4oVhb1t9O69seNcq3wTceCLAXwdgC8H8NMA/haAr772fmb+JIBPAsAXxcAWlFKf3lybfSG7CMN7Cpc9JlDotNvf0P3sp9JgHMg08v8rPMz0AK7T+kbRsIPN27fl0nP3eHAUJVtoI/Ya3c8Zy2kRGf80Scc/noT2q1HX9PJYgopyFgtAmw3ImbE4px6FCWzkQw70+bT+XwEgu/wdj6eibyAAC4ReS1SlAF6Szg5ktRcQJeY4DMgxyAjsvABbWW7qcbROSwdslIhogC2nGnYseR0IO1Gmqe+2TDzQtXmRC4vDliq/vSPpTcSB3wDgR5n5pwCAiL4TwK8B8CEiGpQNfBTAZy89iHXkMEoowS3L2SqLZouFV+nyquJ6JtCN/Ft99FJnaw/0o32vmLy+cvrlvp4B7HVyn6+6OOjCe7oykgYXCkUq/v20Y+WcMU8T0pIwqew/HyfklLCoAnc6TiWGHjOQZ73f6LGzIMxAmfsHpAqyvld6Bpdl01XnUdOiUGCTxEvRHQmtvoNQ6MhiH8CQxTfZ2AFkSfC4zOBlwGDxAbkaoJV6sHzqVvRHnlkR6YBi7VLFpCxBSD0IsDrGMQc55mpdLCzdFDM2AKC829pDjRtJJqKiXtNbQq0Y9U56ExD4MQBfSUTPIeLAVwH4fgDfC+C3QmYIvgHAd116EDMwz1mVINVExjIviqrc0C3qp0vKs1olQD/yv0paT8Vh1UKbyDsu36/wkk0GQB1AbOXt2lF/a18X+FdRS8t1WWonX5YFx4ejMIKHk8wCzGIXMB/npiMtqTbiDBZfeJq9TAZ4QqXNvr4ICEF0M94GpLJdMQ7yTOBhWURW1+ueQZkAEiIIecmIIeCgswdjjliYEaYBeRxwcCbqFDbakNssIwV0zX05RJlpPjGXRRa/TRqF2BSfheZrHRQvzo7qy7kMZgueqgFKnWLI7CBKbEcKXVOrCmFfhk1h7qQ30Ql8HxF9B4B/AWFk/xJC7/8ugE8R0R/XY3/h4rMAcZYQdKD1oyFahNymS/acV+zlO2lrHr7+vX5rPf96OoEtBnANCGyd7+0nts7pDkB12m1R455pnoUBTBOWJWGalRFMJ6RFFX8pYz5Nri6k/so+ZCFNEeuJhIbrd2ZUh5xCk6sbcD/la/ldTCQwtsICMgYCJg5YiSZOQGakZBGUgZSWMpuxqF4jZ0bwrPFMWdvgYgMBu/wbiFoU4pQFZADBN1/+nvWRm77xOq/sRCkbIGTmw1zGr/VFnrGs8l/+205vNDvAzN8C4Fu6w/8fgF/5qs9K5n220MY6j+4LutoLvHMy0bVpq42E0OsEXkXJAFeZLQBcAoHrnr0NBEVSz7Vci6w/zViWBScTB07KCFQnYIrC+TSXRUMyItaG2NN/BJ2C028REKgdCG660OcT0AECbsoOwGzvwBoEGCirDMMSECKDFhmBlyVhSbJU3Wi7hVrfBQAYmOkHNddV5V9KWQFmKUvgAXPk5PUAyogoNxTVRIeUUl38FEqYrcJEKhO4xgyo/Y69dDMWgwlA5KrIK53BZf8cG3i3DIa6I3p87zojkZfz4sW+PRHgVSn/uet6FsCaVVPuzdOMZRFFoHR6AYHjSRjB8WR2ABNyZkxLar7BR4jOYCz6YcbkLFAKIHWd4JmAiQMui+6Pxd6BtnST/p389d1xMBeiUeIDcu62Qv7dc0xfsi1Zt4pUbvxh2AwKgDKVSEQg1Vm0b5FpPTvq2VVdVSplFxsmQBB/a3UAWpsJn28/lm4CBABbNWado+0Enh72W08dG2WOPvviPir1epX0ZsDDpUGfA4C9d5SR8hWA0DcwZFFO5UWj504LpnnGSXUBp+Mk3p6OAg5HZwyUMmPK7aI03xEzZLQusjCE8ZfzJOICoFKAA5D+157tm/sBMsJaZ1+AYgpMeq0MKmK0lbO+XzeZbRL6TswIXGbX22loqnlhf6ApU5PhDQSqvgGA2CtoPQaiRvyoQ4bqCFjEEy9WaWsoTCDG9ZJie1qgDfdqVi5n2sbNgACAgnpVAUKrmHlA1bXIB7dUjt1QwUBjNaeXe71hM7IYqPS/78qn6sjA3TH/eyn1U6V9fvd0KbIROKOMXtM0CwicTpiXBcfTSZmB/E6TLBCaEpdO7jtsDwjefYUxBUsWp6/Uo7vO//rn2TsulUzPJnbZkrGYbkCp4LpxSyeuNGyAW+W1iQNAVSpKOcgHm4WiiAJr1+OFIblUQvQVscCHU1MWTeu8X2pPtwUCsBHQRcstYkFNrTjQUSDaqPiLTKC++zwQtA3kEl0/l1hrbCVwvIYuYJMRub+9TqXsZ0bOrVu3eRYgmOe5KgYLA1AjINRO7kvZj9Q2EvcgUfa57l8CAH/vRm2vru0KZluR48rHyqW3lTiX2jLuRYMKBEDL6IQR+TZkgHAOrGsphWaKsNc/rWcHfDrXnG4GBEwpSI4NmEY0kFOGNGxg/cWmxa37bWenfh91oU5F0m0guNCmbiqtRqqcndaZMc+MZWGcTjOWZcbL4wnH6YSX6tzlpYoD0ywM4ARhAAvWnRxomQCjDZdlSrwtkNjr/Oiu99fa8h8TB/r7CSIumM9+8c2nm7UhY4uqJyAyYs7Nu4BWwSphxFF0Cdu/sgEAsbjDK2sfykDCJZ+m/OPunxkxi02F6QViNQfXD7c2uVWGtiry5hWDQJ3G8Eqyhgls9L5N6kadwtCdXzEBqu69pIOrQuYMI/BA0NPxW0wGAP6XWQxupjljmhfM84LjPMs2TZjnGQ/zjCUlzItYAJ647bw9CABth85n9hNaEDg37m4xAQMYnVVe3U/dZp2/t7jrmQDA1bdBeVL9spb6W1dF85vdeUCWwVnMBZBMjdrzCDIoiUeg8gS3hWIgxdqZLf6k3WtAsNXLKwBsLH5z6aZAIJApytozlVK1YZ22aVtrdKKHyk9w+2DW6Ss0HbxnBI+Rrnn3nj3DFqXsgeB0mnA8Ljgej5jmCW+/fBvH0wkPD0fM84KHJWNhXUDDlQH4DnmeNLtvuXDtWqe9/tvAoNfV9/c1G1FjFtwOKvUpbTui5iV93pq/L4gNdk0rqlVACY3HXOjotF/n5HVlRpmvSF+AOgGg7fh+a6+9BAKCga5YWc8UMYpWHb5nBP2z9TF6+1oOr99w+UN7xaD/rv7Z1wDCtl1A3TwQiDJwkm2eMJ1OmKYJp2nGMifMGUX+9yN/P8JvpUs0v8n3Fdf4a/079q4xACibVy5bJ0IVK3tBuuwpO/ByO1Z6g+2cbCkR5Wou72N2r96pXsl3685u89JrC3Ej3QwIGLARiQZ0GAbcHQ64G0fc398haHBGotqYE1xl60NIOdL+1JqJCFqhAbKufkX5a/Msx1Hrqu9w/vq63+ZjJaYQOTyi1b2XUs8W+r/tvBifyLTV8XTC6TTh4bTglBLensTN+88+HHE8zTguGUsGTljL9T0INHnpfnd8WjZpq1N7wOnlfTtmjEBMhaURDxBdwQjCXRwxhIBxHDHEiGeHu+Ks83A44HA3Stg7zgiseqHSSa1OqLg4K5klOQ+ghCnLjBI+b07iASnpilgGgGVxddG6N2RAGz0QYlCwco5ENScxRsQILDNjJolKSqHqzQRkRHzgzjbAwP9cuhkQ8MmmCOXjB/Eaq9MiNs4YEDT+9QAUeYtqSAYP9AUA7EkrJuCniqRjl+PGLDZEBatknxpFZn896oi1ld4ECPysQNVfSGNY5kWnATOWzJgXnRqcF5zmhImr9r9X9jG2O3cPAJ4JvMpI79+xN0B6UcTs6QIMEAgDEYYQMUQJ6z1o+xlH8Tgch4gQowS8hZ61KIwAACAASURBVExVxibvKn2XF6+VhfW3njN/A6yd33QCFn2oMhFXt6ygYuIKkdP819QyYXaKADgQMFBBU+f2e6453QQImJKjyO4N5WpHV274fN8JKr1uRnX3LtPKNgi/umdte3DDur8m9dNUsu8s2pItEZ7xcFrw8HDC8XjCtGTM3Brm9KOxrw2/fnFvRMfGcWDNJvprDTjI7ftzttXOr2wgBkSKGMYBQ4jF0ejhcMBhHHE4HCSsVxRX5Bbc5vz0am7KNDmlH6NaPdosg58ebL6/AHIL2DbgBdoyAOrzck706Gvs+nQTIOBTQTTb786b4m7vXhv6WS88xwTqfS2q7gNAOz7d2uzAWhm4nrvmYuteQ75ZqLd+2q5PWwq7vWv6Z+SNa84lrzLbAgH7O5RNlIAxyjbEqCP/gGGMsg2yGav0LNKLfJt2AHAA6661gYfRz+2vU6t/pCK2kjvWz4Q1ebH9jedfZFtnLrgZEPBEmT216uTuPQCot8vQz1nWkNsCjvIc7kQCXbK5FgXqO5tHb4gCt5DWNgHttpgjDZ2/nhcVC+YF85yKRZ/vWP4LQ/c7oO2QbdhTzZP79eAyo6XgvR3A1vSj7/iDvm/U7R7AgQjPDiMOw4gPPH+BcRjw7NkzjOOIF8+f4XAY8eLFC2CMyIex0bTbyFJGVMdIU06yOlA7f9LrisNPCACYLiCllgmwAxaZLgyqG6gzFrQRXciXYqH7xQ+iFQbXjVVTwhmk3pfdp5xNtwMCHS0X9LUivpxa9OVasU4qraJAc+WKHWzNCpiy8JaAoMqDLY3dmxYsHY282CDfZoXcj757XxixpuZb1/qObvuMqnQs5Y+2wZ5jIy0DEF1AVF3AGCPGUbzzmhhwd6+Kwfs75EiYY7VIrR8puScVN5tydJsvR3IoaO3VGJgpuX37Mh1CJZRUfsrcP9mpdd36QmlnJ+rfVDPU3nOmrd4ECBDauJ7ZUVaLYFNXc12kAvoc+c3Mq9PtaHUeZLbsB24tbYFAzm05mBLSVqJVhdkC5oy0ZIRUG7jFae0/144ZEwjq2yL2odA5SNmqsxDvFKRS6Gp74EEC7lzDoHWLug0EjASMYcAYIw6HEeM4Yhyl898fDhgPNisw4nAYkQIh1zilzTu3yrUyyPYqrziUNtK6F7OpvcbpDBFydmHMCU4fUBV/ezNLa/uXdds2ILqKNWu6CRDoUy/b9rJWT9nrqKyj2qqwuj/LqEdlVLTnbuXF2w/YnbeQthRGe/Jo0UCHgNhozyNyTBgzq/UalxWdDQh0A4yBQFRNegweBAhgpcFQc1pnBCudnss7erNiYA0KlgVjAaYMjApsQ4j1N8o3Wvgu0xPEGGCRxpuHljc51pjdqO9ZlLvRRnArF68XsGu8AppL+dUnFfN4T6u6VJ650azPKwv9R+4jws2AgFe+mZOGZUlYggVtRLETkOs30FL/7xU7vlyz2yfIIhqTz3ynMgRvf4ve8WZEgr1G0FjJWVReHXVijMgJONCAlzFiOh1wnCZ1tFFnE/yzI+v8tTKMAS70F0mnMzmbIfEEZfQ3WbrOVCycJZZAzkicV0uFZ1RDJX/cRI7nUF/3MWIMAR8cRhyGAc/iAYc44tkgjOBefw8HUQ7GISoU5V3GUX6t09r4a+1OfQTGUKf1qiWrhs5TIZSYUaIjoYoQRECMVJYFh0jq5kzbH3lTYVfXxWWr5JjsfPHowkB3xzXpZkDAUkFeNu22KbcqreqBAHCjIlotQq8DaPY3pobsuf10Yc8ITEdwC2kr/2ZlZnmPKgcbHT+MI/ggMfFIR7G4hOIgM6HWAwEILAE/mDLA5EDAIgIPZZ8JWIreQbpc0OWymRghEwKLd6HArL4BfP3VQbG3GSgKQSIcQsAYIsY4qD4gYDCmo2vvLUhpmQmwJ5myzbWWtT1Aq5XyRj/W6UFUOnav14LTCbTX1VkAv2wegDpfRQUdFUMbscQ6fJtZvX/dNuwZe+lmQKBYUrG6b84JyzJjDkH8xWtDDoEwDLFp4CulIDqq7DqsZwIAkJFLCPBz6RbtBrbEJPtdeyIWQXgcR+ScMdCIaVxwGEacpgn384x5WdTXICPNc53zZiCwUnzVUI+wOjAwiOW9GcAQDARkRZ25/GJmLCmB0oIQM0IKCJwR3CRiUHYQsgAFUPUUEcDzccQhBjx/do8xRjwf7jCGiPv7e4zDiDtTCB4OiKPEIowqFiTOoAyYwrj0IW57k5+d8q2rdyxbO7WX6+szPSOLgTCOkpe7uwPGccDhcCgglVlmIjxIbNY7tju1H0D9N11irDcBAi2KcvMx5sAxOC/DvnCB7sNRq7bIUb7SYJ3Y4JEapD+f1rMEt5Z660H/t1dW5ZFBiZAO1f1HpIAliGJrQXU9BpbRGwBYQ34NCgJBK89AgIiQiTSIr7jOyqYjYFEUggFWk91AvJqSDERIEE873nTZlgffDwMOMeJuGDHGAXeDmglHYQMxuCXExrLt3VIYtU1wyxz9qNuD7LpcW4pp7KC3N2mAIIqtQox1K3UD6u5pWV2b1ihwHflfp5sAAZ8YKHYCy7IgUsC8zCAwlkWyawU3xCja2NUTWmUKoRUXit1AR/e3UmtNtl7EdIupb3xeN8BKgw85ginjjgLmYcYUD2pRqH7zh6VEHGJmxEUUh8YMovEnQ/BQu3EmYA5G/zMWMGb4kXVBTkBCQgIhkcweyJw5YSFxM56ChaSX9wwqQ3/o7rnoAJ7dYwgRL4ZBFIQxIsSAOyIMBIS0gIiRp4AlZVBmpBhAh3iRIvtByGaYxKZA8sIKLqbkJKKqE3HAYS7BxFox4vnzA4Y44O7uDnEIuLurYdJTBnjJjsWR+12Hy9sCqNdJNwcClgyRmWWKsJ9iAqxSvGLQyXlOnvXlZDSwMIEOtf97Sh4E/MbMEvk3EoY4yAg5MJKOntlkecpIJEFKAjKIoaG/gMitYpY1rqGVpcUakPIXoYtJpy5NRmcuWyaurrOIkYiRyJxzSOcbBpkBOIwjDnHAwRiAgkAJ461LhosDEUAbVCkY/dszAKcJYL9VOm0DB3zns18y/UDLBGw0ryHGq3gyRGEGpa26Zc/+3uvG+NcHg9sDAW5R2AI7BJKQWSW2XKFQ7b1rnUA/66IiwAUm0DIARwEVZG4BNC4xmFZ2beXMMIQyJRhjwBJjDSvGjBSFCSzKBMJsvvzFyzClqmthADnU1W+ZgBw8uAaZImRGCCqwZScvQxRawzAgxIgh6DM0L6TGCOM4IIaIF8MBYxjw7P5OVgk6PwEhBIyHA2KMGMcRFAPYLUBjvbZRwPlBovTtLSBA+eayfoCriBgs0jHL1ooAsojp7k7yfHd3QBwiDoeh2MRYu6pty4D2fDuoeouihnyldDMgEHRgNsci8CwgJ6Qc9JeQ8yBLYzk6eV6ewyr/9SKAJRlXlNozl8Zrg4POAIGIy76ItFyneHRkksWb1IICuc29WzoLtw1up3atY9X9Nc5zUWk4NmR/9+dI2ZEyJVgZK7UtIzOolBvFiLIsNWcJtZ7FEYYYAJkQRioPV8//GZBVegoIidWLLjR+AExvEBAC63Qclbl+DoQcUKbKSP3Ojbrw584xgCEEWQxETsse7Rk69UZaXqFO4THpxvIthT3CRBCdjqPcjSRSuUG/V5b0slynm9ynS4cjIUQgDkG2SAgD6bRg6/k4l6jIPtJQLO0pZdRArzA7jMputSK6RmOD3mZTA3AjIEAEHAZxPy3jBoOQwMuCBMa0EDIWhIGRMSIERuYBDAmmGaJ+JAOkYZwKI+i6T9KOyKXohsbvnP2dVRljq7sMDAJJmCTrMHXzipxq/GGdTTFd8wQBqtAxGU2rDr9zDdvks5sJKMKSvtj2M1iCfGiKHBARkIcACtoQUpYQYZlxGAMoslhrMkBD0hFRRDObsal16PQQkMg7ZZo3Z6RZ/fIhYULAkQI4UuOS2xRmfeBPcz5pi3+GuztZJDSOcu3dAASpKyZgiTpaRwZFIA5qzzASFgImLAIAwaagE7J2/sQJmRMysX5EApl/fxawC4Hk3UQYl4yMDAozmE5ImJApgQYB1vGeMI4B988jDmPE8FxmKcIoZXnKYgcznSZtF7YUegSFCNAAIAGccFoe8NbLE05LRsqExKT+BwmMiBAG39iU6daesJduAwTcVlJBMWdq6ihaH6rJNf/6S+2HV2bgi0XFAlR/AT4LbEhaztffyiX9b/tdPSOoJ6n9fdXk5NtXfoJR3s2GYeDGdX5by8i+0bT0rOflLvk7dCBgJRViRCYSA6BIOKBSbgOBWECAmgU60MU1MQ7V936MDrwd4rry9jUD2ugMZFTa6Zuc3qlaDVTRocj47m/5bf1JhCCD0zCI1eIwRP2+ardgdjDMGrsQBNKp3GKHABTjLQmcwq5jO1VHaU59aygNZaOuJd0ECFyTtH7A3M7hAnVfrutke+xjYK9Y9Clz7QSvkkeiqkj6Qkyu7Z1N5ke/AoVjQSBELfQSupwktFaMEYEZgwP0AgLFp35rwWk9TqwSA8IwgLQzgagIJqJ0xOYHFEXzhW9bda4u1ShAURkiFVEKINUJBF22POD+ThYvPXv2DMMgeoqg8QPMdavNhBEFBb/Wvdxs8SDmpdVV9Jnb+bZL1Xn7IMBoOnjPBlaLNDRJWdIa/dGhJZus/2azBD0AXDN1c+11++98/XtXz0IVI9l0BeTYgp2zzhGs2VPRk5gijFjWFAAKFlll3aBxJFlEPq95l5FeO7mCQtGhKAjYFCJiaMC5KQeGKuXOfGh33549wGb5OgLnlXgyPWiyvBhljYMYA43jiGEYMAwe4OT5ErFIzOQDARQFWmXWQBSMgA10uRnwTND3PLTqxrjk81K6aRBoO/x6jbwYD63uOvtMzwwaMt913h4M9o6/SXoz0HkzAKjijCZC8W/PrPoGoAkfVnQLBJAaBwFSJoMDgQAqIABAtIEhlXpkImRlEoaeBLSu4lxdmELPzi8OnBisTKPWKocMsPkKcNnIDI5t+a3KcUOZ7JN8YzvlKuxFRJnDYQAz4/nze4zjAS9evMA4im8DKa9c8jLP4uFpnmecplnd6InIMwwDssZQBGr7933B52mzFZ2jwS5dBAEi+osAfjOAzzHzL9ZjXwLgbwD4GID/COC3MfN/JfnKbwXwtQBeAvg9zPwvLmejzfdeYtWklulDjeBqq/tIhXAia1tcVAtblW6N8FWZwKt03ndytH5Hn717q3WcC2DaKQJ7W4TQOCATBV0BWiKNuNsm37msBQsgVRCQ7FUWYXp8m6ZDViZQvqarJ3YAwvU57XTg+XIrA7EUgICgTgMeDhIa5f7+XhcvHTAMg9J/1o5dQ5VZyHRWimVAV9mt+9bsY0ds19Aq79eIdpcvwV8G8NXdsW8C8Glm/jiAT+s+AHwNgI/r9gkA33bF80va6ldCQVsmsKSl+MrLOW12hqJ3c8d6JDVrMO5AYmsr+dkbQTbSzQIAUJRhW+kaeGuo8GpUNMMYdfVlxjGD+DAYRlv3327WWczOX9yD2dRaaBSGQN+ZaiBQdnncwIBmUAC6ekdbv76EqHs351wYzDiOuL+7wwdevMAHP/ABfNEHP4gv+uAH8YEPPMfz5890lI9lhew0zZimGaeTBHuxkd3KzZdvbbP1G82ceytvcs8VlajpIhNg5n9CRB/rDn8dgF+nf/8VAP83gP9Fj/9VlhL8p0T0ISL6MDP/xNU5MpnGZFDXWGthVEOiVq72VlYl/4Xu2/5KhnR65HeK7r+KbuB1n//6N6O07kbBVPQAkGknArLOAphCLRdwrWasmWXqNQDOdbcbnbs62Mu/16sUJmB5LLMT0Gk8cf2VWUyMYUwBzpdUmTXoZ33WTKDuqyNR107aNmEa/dpeQghiBhylk4cQpONH8XAEQpkJSKn6d5SgpfIMA8hxHGHrYywW5LLM6h4ul/ZvefepgsZmre+m19UJfKnr2D8J4Ev1748A+HF33Wf02AoEiOgTELaA+67fWXiprA2rHb0JKal9AC0ajnlbXieiYhUnAq1WoLum7aytT4Gt31cRF7bEhneTHbxqahRMRGpAI4dyMB2A6QhE9s4me3N23yaNMiAgZ3H0oR7wSx2Q49mcM3hDHNgEBgjw+I47Q/z7y6ivy54JGEgMwGSCQDq/D3Aps/28ywTstwCBR8v6pTAgAKozlefPn5X6DiHg+bNnEksAMityPJ6QUsY8L0hJAr+Y9WXQxU/iAekOMQ5Iqc4KnE6TBohdHBNYg8DrpjdWDDIzE9ErZ4eZPwngkwDwc+L+/b3c1njQ5YqMIdTO52nUlXlpgOAVvqHr7HX0O8cEbgkI9tM5DXt1u61Xiu1EiQOpqwCtLjIjOHdnmaiE7u5ZW5kRKA+XdQigGgx0UT9Fdj2rJVcVSdbrJbY/o9cFdMzgijIKUUDu7u5QZfpAGA8jCFT8MywlBPykTEDNsVUJKP4QRX8AoESJNnFhWRZhPi6orOXhTdPrgsB/NppPRB8G8Dk9/lkAX+au+6gee43UVo4ZTBABKQX9FdSVBnV+ZViV+VZvga0qbDTSrwkgex3npjp+IwLtpB3NMuuInrJb1ab91kb+TAQNnQlADIckroTSeVRHo42cbd50jXUplTcmIGa1Eh7dGAsDgFvGbJr6EqzGzHt3gQArINgtEqLyTZZsRes4ilMV27eFQTlPwl403PvpdCy0Xq4TEUD0BiPGYaxh4qcJJw0PN88z0pJaJoBWJ/O66XVB4LsBfAOA/11/v8sd/5+J6FMAfhWAn7lGHyDyW+3AIj/WswXxHXL3PvXbqaWWajaKq9IB7KUKBI1I0OWtz+8GzW/Pe9m2Pb7z9e99MlYPrpF4yToXAcRgWUAoGm09l9QDadFcQzp4ZAWBQOAsOgPARDJGyI7NAVj8KK0FYytF4e8FVdEkCwNIQeookK4NUNsBGiMoRtAQZO2Drh2w2QUC9FvbsKrlW8oAkDVLhl6+2FqwCFCpI7TKvGVZkDPjpB14mk6Y5hlLqp6fQ4gYxrFsRBEMYElJwsVNM47TLJGhWdZgZNj6i9pyzCGPzaoUW8crm9Y1U4TfDlEC/jwi+gyAb4F0/r9JRN8I4D8B+G16+d+DTA/+CGSK8Pdel43aCOuoUlNmBjIjhzpNQkRqYJGx0GJ5BemilKq5tk7p0NLAplcgEq06sCkW++t2v4P9O9vj21/9zigiXykp8OUi4aLaB5Aa6SgwJAI4SOdniKcfWYRTjVZIxX0CgCQLhMSdiNaBOjGFdnJjAuZvv6XlTswwWd7yq5r/rAxhIF30FIN0/LINulRaFhKx+T4FqZtTk+kr4FT1iK09QTnXlp1UbNURQS0AQ2k/zCjyu1j6qcXfsmBONgsg+R7GA4bxgDiIX4HMjDktOE4THk4SIHZeEpaUJUBMro5gJQNSZ+SUn9kkqh0216drZge+fufUV21cywB+/+XXXp/6zrNiBOx1A1nNNuXaMk3YIOXWdErfsdvO+U6O1atGfquJ6vz/nl7DNmJz7V66vbgjs0cxy9Jj6GhOJL4L0JZHbwRjo6t6Nqs+JSg00U7qlGRsRAEDkcJuNr7B7a3d028kUxr6MbcOGCg2/qLVrwFepmlG5oxhGHQ2QNy+y9RoLIOaiQFHFQNMh9DOCthYf37dyLVt7eYtBj0dMy1/FQnEAWnOGTmYRVVYD8NolXWrd6z29xvMdR3Xjy49uGw/951K7xWw9GCcnAjAELGrMC4WNgDtQKx6gz6/puyFMrecMyhQnaHIMo6DuT6bWtuEEmPQmJ+CWcGkflBxSsCejWwVJZf3t4xA2hbKFOBJtfmi2BMZH0QY1KdgnRI0QyKUNQSTAsE0T2W1ZlGEN2zpnWGStwMCvU4ASgG14UBFgEwyTVisqnIGcgAnWaZqK9VCyGKVVnmerv3HxXIznYNNepiBSnAN1yui1rMEVRRxT12zmh3R4hpceJW+vnoHQfwDdMeKUwdGMRdOpguATNkmG/1IaLYFOQFzseQLkPvLoxkIuc75izhAIK1T6EjNVZpV8UJezKGeB0SMoEDIg1D+LMsayywCotLtMYh9gVCOIosn1czXIK2phBuDijm1HmunyzljHIditGTl6kfph4cHzMuCt99+Web5mVkdpgQc7u4RohgXifv3oUwhnk4THh6OeDieME1LmVnwJsNNfWodCjsBQD5SxPU2LzcEArWSLWVrWDqvLFQSBQSyHrcFKpmhIMBAjCKLWseF4iZh1YP8aGwNwPQOgEY/sg1oAMCu2+7Qa6Gsr8g9JnA9QdgCHDTvWIlBWhCF6rvDtmWquoJMYgyUwVhMcWf5DwRk0RFYR8vWKPUbxIiofv8CiS1AqnhYsTPreFZhGU3ZR6/wc4o/AwEiOVYWGpE+NWfkhKZjJbVANctD0mu3GKON9jG2U48GKDlnPByPmOcZb7/9th6Dzh6MiFGmAs3rEUyo4IzTacLxKCBwUlEiFxDgAgh9HVrl9VOrNfuXgeBmQOBSo9/vLKqt1X+2qMg6KeA6AdaEvAcAS73dgGm1TY7dk5WvTaa/2BIXrntsOx1pElAPRpt6EEKZzjMmk0m9/xS6KRpyz7oAYVjsNf0aJo7SUiIeQ0f+QtmhTEC4NFIISJGab+iVtOUYqz9EtHb1QSMOV/NiMUsOum4/uKXGTFw9DXsASOpQxI201tEbyu2KTszWZWpQ9I1tG7Hb6rJoofvPnj0T0+nxAMD8OGadBpzw9suXmE4TXj6cil5gWXLRMRRQfBfSzYCA/0J2u0YhC+qik9243TeUt+sbLzUeRR3v2OvMvd3AJSOgV/5i/6EX8rKdKqxpf9lkJVtAYEBpjwnchQ/fEn10y4RirCMyfK6jVsoyk1Oi4sBZDKqIF6NEe9gAAP9pAaFRUHoGVgOLVEcd4m24DT3OpO/v2o/NMvlFOTaANOVHqN9BVERTAxhXc+WXGQU0D4c7DMOA58+fad4G9cwkswfzLDoAsQyU32WR4zlVBmCizNrn8JunGwKBnaR0tVfa+NkB5uqlBbD4eLURW6VWKU/ZFKNTtKA8u9K9oB2mgoscPZfly/JY+4567Fzau7ad2twXDRoQCMFpy3wT1l/SNQNmjWe/MYBI9S2iAizXcdLQ3cVAt1diobC1VMq1GuCQ6gbsWKRKv4PaA4QQEA7isTccxDFpHEeEISKMsh+ihPu2abOs8RBT9p0/ayjxVFbxVRZpTE10SuQqW6i/mAuLURSXeyhAnYboIiAQ7u7vMQwDXrx4DoCwJMK8LJjmBafTjJ996yVOpxN+9q23MU8Jx+Mk+onF3Ln5d59tHq+dbhoEmKHz1270XQEBXAUSQhAKm6muyrJU5anKILABAj0D2PzdzfP1y5H9b5+2FYZrcaX9bQFgazQv+9lk5foxTXcl9+t1IiS2GAIC6oBU5e7ixBNVPGum0PQNCQIWVP61+oFAFtNAdJUhOBk/BOnwwwCykX+QX1EYVgCQwjBxESVfVpaeEfQgYHqh0DEDqwJrO17m8fYCxljuDrI68u4wIjMhZVl8ZOsIRBdwUr8CMouQ1Dq26nuc7F/agGvLfh97+/vpZkCAaEtiRznWM4C61dkCmWsFZD25GKZW88x4VT7aKaK+E0FGQSLxe4dWR2DX9p116x3o7nvdtJ6ZaN+7CQDkAGAvFRoeECgD6gQkMoOJEcdBdAEkgBsSIwc1+23Wva/rz2vXgzoI9d6C23n/gGEcnFuvgPH+TpcmjwgxFFflsmagLRs/ldmGFrP9Np/+XkDD1m3Uk7HQwGYmLOefPbsX0FF7B/PCfDweMS8Zn397wjTN+Pznfxan0wlvvfVWmRJMS51l0Ldgv6JMR3Cp838BKQbPJd/QGbQBBF65J6CQQwBx7s6dR8Wt0fkaRrDV8d8JncG16RIA+OOefpuYZXJEAyYFCKGjvwyRNi1LQabfsq0hUJAVObwtP1F91F9zIW4LfWKQDmw+BKMDgRhCmV4roGDxCQaT/yOaRUPNu1uaU/tX5QVcRvQ23zYuVYVh16FcFVu5hqG6DvPAM6nl31G1/8fjEadpwjTPqh+okY62eL+IKNvvftN0MyAgFWgNZf86o3ZeU992THP2KLTLKmLlxrq+WHU/24yj5s1vAOAYgXYkryu4hhE8aiIqfgJtFyyU1uQwafgBCGIXAGvUmQFKpWNI7EKdVh3EvVayxUD6fKYKAiBZ2BMCgXQZbQzilTdEiSocyC3GGWIJ7EEUMBwGAYso4dDJAtS6ZcOFyUCUnoAEMxVtRXaiioX67pMD+TPFWNsJqfgiYGh6huNRzIVfvnyJaUr46bfEgvDh4QHLLBaFvQ0AhQDKtpgK5TscNlutXcjddekmQKCRY3euqYXttPoNkveKM3fNBmOwlxGvMec6JuDO20iK9lmXZhO2jlfZcy2S7KV6TS3Li6Cjci9M+YVQC4OoTBcSuXJTUGC9zqbdbFowK0Mw+w5LDOhiJA+qVYM/xChAEJX2B6PYFvbcBe0kQhyr63HYN9j3UFcHqGBgkCcDiWtPTb58Oda67kW+PllnRUCZfmWWJcTzPOv8/4KHhwnLknA6TUhJomvJ/RVuCNY2KhOr7+l1AquctOe/0HQCUokMZAfoF5Fu7yPX04bNe6CVFcTTu1cOAetOv16pqBpruI5rcm7HHLZAyn/3m6RrQGIrScw/FwcPGyAJ13F9RwcwaEDSErZsScoUBJjF+UjVPeQaoEifSzrqCwjEUN2KmY5gzcCoPIKhrIXU5biKNmWlY5d/v59y9fBj8/C+Lpui3KMCBjiO8mcitYeg4j3o4eGo2v+3cJoWvPXWrJ1/WVkCmoy/OSitBgzWkeiMzgA4c76mGwEBR7Ob77JG1MBzm7hyg82OwG2HbuRefeee/H6OEcjfxlDarJmocvGrL+gSzjGJXtm39bv/YgHAQKGITeC8GxatPJPV7gLAIUSQigBgRo4u9gOcGzJy2TwVWwAABxFJREFU1nyWtP0WuT8GDKEa+ERbHaj5CaWuZD+pRyGb+UmOERpXtKjBZAo0uaB02rrZQLFluLQ/inpmAdR2oMslnUFSKoCwzLIlb5yUy3zF2Ve2QPHOphsBARQZVVoQFxmo1oxtbUPty6R2jkqrreK3RhWy97prATSMYE83wKyKMWusTjmVN97V5BMVlPZEgHURtY3uHBBcSrUozVtzO3tCCr72OO8ZiACMFDd1KQau7CwCE3Qpsl3DsspQ2Eg1/DEWZVER7XqLamR5WbLMVAQdjdV5efE3QKzafwPorL67c0ZeUnHVJXb9YjUIyPr+3fI6U65eL0ANo2jr1liIFyltywwUr9moY18daBhlTXRTi2+ebggENv8EUGm2AXpfH8zcKP56Sue1uyvZThvStpx3ngn0KecmLODZJIPG68N6q/V/tXuZuQCfMCEqjc4/05eTdTQDAZvL39RpdAFCDJNNDo8IiOxiEIZ25Z9YGNb3h+Z72QWNNRCQdSdEsnoxOxZgloqwUbeMzjYau2nDs/S6ayOb4N7rFrYGgV7sIqefkIyXe/RQMxC9Qx3fp5sBgaawLOCnFWJz3XXPqfJjdR+2VgxSkSuZq9xvzwHWQOBHvC2WUENSXx6lpXO9fvJgdy0QFBBUTdYeDlV2o3b7sXZ6Agpl7+8JIQgIDDWmXiIgUO1oEQGDuoODPisEJ/I0XjNQ+qWxk57/VdZCdTqy0H5dLZiFks+LuPo2im5ORKTu4m559OVSvlxlwQJoQ4TZBoQgbsdSGtSkeWsWoua3PJ/cmg5dcyELkfiVAf+adBsgsPqwy1+6VRibVP/ia/2Iep7yedncMwrb93mz3V6ub97hFD5bVH/vd+srrknb92+JVDXv9kt6Ye0E65Gwlr+t3lMgIQ1gqkAQCuGvI2Z5Xy245gv3q6YyhM1vLvoCVBmc283K5hzL67/zzNn6PV1EpWuT//J3o9Ov3nfth7+rmSD6KQBvA/gvj52XnfTz8JS310m3mrdbzRfw7ubtFzDz/9AfvAkQAAAi+n5m/hWPnY+t9JS310u3mrdbzRfwOHl7E5H0KT2lp/TfQXoCgaf0lN7n6ZZA4JOPnYEz6Slvr5duNW+3mi/gEfJ2MzqBp/SUntLjpFtiAk/pKT2lR0hPIPCUntL7PN0ECBDRVxPRDxPRjxDRNz1iPr6MiL6XiP4dEf1bIvqDevxLiOj/IqL/oL9f/Ih5jET0L4noe3T/y4no+7Ts/gYRHR4pXx8iou8gon9PRD9ERL/6VsqNiP6w1ucPEtG3E9H9Y5UbEf1FIvocEf2gO7ZZTiTpT2se/zUR/fJ3I0+PDgJEFAH8GQBfA+ArAHw9EX3FI2VnAfBHmPkrAHwlgN+vefkmAJ9m5o8D+LTuP1b6gwB+yO3/CQB/ipn/JwD/FcA3PkqugG8F8PeZ+RcB+CWQPD56uRHRRwD8AQC/gpl/MWSl1G/H45XbXwbw1d2xvXL6GgAf1+0TAL7tXclRb0L5Xm8AfjWAf+D2vxnANz92vjQv3wXgNwL4YQAf1mMfBvDDj5Sfj2oj+fUAvgdiYfpfAAxbZfke5uvnAPhRqKLZHX/0cgPwEQA/DuBLIGby3wPgNz1muQH4GIAfvFROAP4cgK/fuu6d3B6dCaBWkqXP6LFHTUT0MQC/DMD3AfhSriHWfxLAlz5Stv5PAH8U1R/WzwXw08y86P5jld2XA/gpAH9JRZU/T0QvcAPlxsyfBfAnAfwYgJ8A8DMAfgC3UW6W9srpPekbtwACN5eI6AMA/jaAP8TMn/fnWCD5PZ9XJaLfDOBzzPwD7/W7r0gDgF8O4NuY+ZdB1oE01P8Ry+2LAXwdBKh+PoAXWNPxm0mPUU63AAKfBfBlbv+jeuxREhGNEAD468z8nXr4PxPRh/X8hwF87hGy9msA/BYi+o8APgURCb4VwIeIyFaDPlbZfQbAZ5j5+3T/OyCgcAvl9hsA/Cgz/xQzzwC+E1KWt1BulvbK6T3pG7cAAv8cwMdVW3uAKG2++zEyQrLe8y8A+CFm/j/cqe8G8A369zdAdAXvaWLmb2bmjzLzxyBl9I+Z+XcC+F4Av/WR8/aTAH6ciH6hHvoqAP8ON1BuEDHgK4noudav5e3Ry82lvXL6bgC/W2cJvhLAzzix4Z1L77WiZkdR8rUA/h8A/y+A//UR8/FrIVTsXwP4V7p9LUT2/jSA/wDgHwH4kkcur18H4Hv07/8RwD8D8CMA/haAu0fK0y8F8P1adn8HwBffSrkB+GMA/j2AHwTw1wDcPVa5Afh2iG5ihjCob9wrJ4ji989ov/g3kBmOdzxPT2bDT+kpvc/TLYgDT+kpPaVHTE8g8JSe0vs8PYHAU3pK7/P0BAJP6Sm9z9MTCDylp/Q+T08g8JSe0vs8PYHAU3pK7/P03wCJFpR1NjClCwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtOXSqyBDRnD"
      },
      "source": [
        "#Model with feature visualization\n",
        "from torch import nn\n",
        "from torchvision import models\n",
        "class Model(nn.Module):\n",
        "    def __init__(self, num_classes,latent_dim= 2048, lstm_layers=1 , hidden_dim = 2048, bidirectional = False):\n",
        "        super(Model, self).__init__()\n",
        "        model = models.resnext50_32x4d(pretrained = True) #Residual Network CNN\n",
        "        self.model = nn.Sequential(*list(model.children())[:-2])\n",
        "        self.lstm = nn.LSTM(latent_dim,hidden_dim, lstm_layers,  bidirectional)\n",
        "        self.relu = nn.LeakyReLU()\n",
        "        self.dp = nn.Dropout(0.4)\n",
        "        self.linear1 = nn.Linear(2048,num_classes)\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d(1)\n",
        "    def forward(self, x):\n",
        "        batch_size,seq_length, c, h, w = x.shape\n",
        "        x = x.view(batch_size * seq_length, c, h, w)\n",
        "        fmap = self.model(x)\n",
        "        x = self.avgpool(fmap)\n",
        "        x = x.view(batch_size,seq_length,2048)\n",
        "        x_lstm,_ = self.lstm(x,None)\n",
        "        return fmap,self.dp(self.linear1(torch.mean(x_lstm,dim = 1)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYNhn10tDV90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "3342ef202e104b3cbed00cbda2c905a9",
            "7b0d9dc415474d40b2685f674d6fa0d7",
            "0c7ba4251b7d4420a40e7f36937a1f5a",
            "3e0d01581101464883ab9962c6529583",
            "4b05e9b7e9c8443395b02ac3d983158e",
            "63c66769a1b148ea867b3d43275b47ae",
            "d5540d30767745508e62602412aa898d",
            "febff0446f044d72b33619d3fef8d556",
            "a6cb6117f4be46fdbb8013b14eb64ebe",
            "d8da7eebaece4e13ba24375ca48a6784",
            "5432a344af524bb0a8b28e9557fe5f60"
          ]
        },
        "outputId": "b711fd58-f4e4-4d9f-e135-c7390f091e6b"
      },
      "source": [
        "model = Model(2).cuda()\n",
        "a,b = model(torch.from_numpy(np.empty((1,20,3,112,112))).type(torch.cuda.FloatTensor))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth\" to /root/.cache/torch/hub/checkpoints/resnext50_32x4d-7cdf4587.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3342ef202e104b3cbed00cbda2c905a9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/95.8M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKheLUWBDaNN"
      },
      "source": [
        "import torch\n",
        "from torch.autograd import Variable\n",
        "import time\n",
        "import os\n",
        "import sys\n",
        "import os\n",
        "import numpy\n",
        "def train_epoch(epoch, num_epochs, data_loader, model, criterion, optimizer):\n",
        "    model.train()\n",
        "    losses = AverageMeter()\n",
        "    accuracies = AverageMeter()\n",
        "    t = []\n",
        "    for i, (inputs, targets) in enumerate(data_loader):\n",
        "    # for i, batch in enumerate(data_loader):\n",
        "        if torch.cuda.is_available():\n",
        "            # device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "            # inputs = torch.from_numpy(numpy.array(inputs))\n",
        "            # targets = torch.from_numpy(numpy.array(targets))\n",
        "            # targets  = torch.stack(list(targets), dim=0)\n",
        "            from sklearn import preprocessing\n",
        "            # import torch\n",
        "\n",
        "            # labels = ['cat', 'dog', 'mouse', 'elephant', 'pandas']\n",
        "            le = preprocessing.LabelEncoder()\n",
        "            targets = le.fit_transform(targets)\n",
        "            #    targets: array([0, 1, 2, 3])\n",
        "\n",
        "            targets = torch.as_tensor(targets)\n",
        "            targets = Variable(targets)\n",
        "            targets = targets.type(torch.cuda.LongTensor)\n",
        "            inputs = inputs.cuda()\n",
        "        _,outputs = model(inputs)\n",
        "        loss  = criterion(outputs,targets.type(torch.cuda.LongTensor))\n",
        "        acc = calculate_accuracy(outputs, targets.type(torch.cuda.LongTensor))\n",
        "        losses.update(loss.item(), inputs.size(0))\n",
        "        accuracies.update(acc, inputs.size(0))\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        sys.stdout.write(\n",
        "                \"\\r[Epoch %d/%d] [Batch %d / %d] [Loss: %f, Acc: %.2f%%]\"\n",
        "                % (\n",
        "                    epoch,\n",
        "                    num_epochs,\n",
        "                    i,\n",
        "                    len(data_loader),\n",
        "                    losses.avg,\n",
        "                    accuracies.avg))\n",
        "    torch.save(model.state_dict(),'/content/checkpoint.pt')\n",
        "    return losses.avg,accuracies.avg\n",
        "def test(epoch,model, data_loader ,criterion):\n",
        "    print('Testing')\n",
        "    model.eval()\n",
        "    losses = AverageMeter()\n",
        "    accuracies = AverageMeter()\n",
        "    pred = []\n",
        "    true = []\n",
        "    count = 0\n",
        "    with torch.no_grad():\n",
        "        for i, (inputs, targets) in enumerate(data_loader):\n",
        "            if torch.cuda.is_available():\n",
        "                from sklearn import preprocessing\n",
        "                # import torch\n",
        "\n",
        "                # labels = ['cat', 'dog', 'mouse', 'elephant', 'pandas']\n",
        "                le = preprocessing.LabelEncoder()\n",
        "                targets = le.fit_transform(targets)\n",
        "                #    targets: array([0, 1, 2, 3])\n",
        "\n",
        "                targets = torch.as_tensor(targets)\n",
        "                targets = Variable(targets)\n",
        "                targets = targets.cuda().type(torch.cuda.FloatTensor)\n",
        "                inputs = inputs.cuda()\n",
        "            _,outputs = model(inputs)\n",
        "            loss = torch.mean(criterion(outputs, targets.type(torch.cuda.LongTensor)))\n",
        "            acc = calculate_accuracy(outputs,targets.type(torch.cuda.LongTensor))\n",
        "            _,p = torch.max(outputs,1)\n",
        "            true += (targets.type(torch.cuda.LongTensor)).detach().cpu().numpy().reshape(len(targets)).tolist()\n",
        "            pred += p.detach().cpu().numpy().reshape(len(p)).tolist()\n",
        "            losses.update(loss.item(), inputs.size(0))\n",
        "            accuracies.update(acc, inputs.size(0))\n",
        "            sys.stdout.write(\n",
        "                    \"\\r[Batch %d / %d]  [Loss: %f, Acc: %.2f%%]\"\n",
        "                    % (\n",
        "                        i,\n",
        "                        len(data_loader),\n",
        "                        losses.avg,\n",
        "                        accuracies.avg\n",
        "                        )\n",
        "                    )\n",
        "        print('\\nAccuracy {}'.format(accuracies.avg))\n",
        "    return true,pred,losses.avg,accuracies.avg\n",
        "class AverageMeter(object):\n",
        "    \"\"\"Computes and stores the average and current value\"\"\"\n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "    def reset(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        self.val = val\n",
        "        self.sum += val * n\n",
        "        self.count += n\n",
        "        self.avg = self.sum / self.count\n",
        "def calculate_accuracy(outputs, targets):\n",
        "    batch_size = targets.size(0)\n",
        "\n",
        "    _, pred = outputs.topk(1, 1, True)\n",
        "    pred = pred.t()\n",
        "    correct = pred.eq(targets.view(1, -1))\n",
        "    n_correct_elems = correct.float().sum().item()\n",
        "    return 100* n_correct_elems / batch_size"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8WneBZNfysN"
      },
      "source": [
        "import seaborn as sn\n",
        "#Output confusion matrix\n",
        "def print_confusion_matrix(y_true, y_pred):\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    print('True positive = ', cm[0][0])\n",
        "    print('False positive = ', cm[0][1])\n",
        "    print('False negative = ', cm[1][0])\n",
        "    print('True negative = ', cm[1][1])\n",
        "    print('\\n')\n",
        "    df_cm = pd.DataFrame(cm, range(2), range(2))\n",
        "    sn.set(font_scale=1.4) # for label size\n",
        "    sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}) # font size\n",
        "    plt.ylabel('Actual label', size = 20)\n",
        "    plt.xlabel('Predicted label', size = 20)\n",
        "    plt.xticks(np.arange(2), ['Fake', 'Real'], size = 16)\n",
        "    plt.yticks(np.arange(2), ['Fake', 'Real'], size = 16)\n",
        "    plt.ylim([2, 0])\n",
        "    plt.show()\n",
        "    calculated_acc = (cm[0][0]+cm[1][1])/(cm[0][0]+cm[0][1]+cm[1][0]+ cm[1][1])\n",
        "    print(\"Calculated Accuracy\",calculated_acc*100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fExJLjt2AtV9"
      },
      "source": [
        "def plot_loss(train_loss_avg,test_loss_avg,num_epochs):\n",
        "  loss_train = train_loss_avg\n",
        "  loss_val = test_loss_avg\n",
        "  print(num_epochs)\n",
        "  epochs = range(1,num_epochs+1)\n",
        "  plt.plot(epochs, loss_train, 'g', label='Training loss')\n",
        "  plt.plot(epochs, loss_val, 'b', label='validation loss')\n",
        "  plt.title('Training and Validation loss')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "def plot_accuracy(train_accuracy,test_accuracy,num_epochs):\n",
        "  loss_train = train_accuracy\n",
        "  loss_val = test_accuracy\n",
        "  epochs = range(1,num_epochs+1)\n",
        "  plt.plot(epochs, loss_train, 'g', label='Training accuracy')\n",
        "  plt.plot(epochs, loss_val, 'b', label='validation accuracy')\n",
        "  plt.title('Training and Validation accuracy')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUe1XrYnDdit",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2e1615b7-3109-4d01-835e-ef51952848a6"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "#learning rate\n",
        "lr = 1e-5#0.001\n",
        "#number of epochs\n",
        "num_epochs = 20\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr= lr,weight_decay = 1e-5)\n",
        "\n",
        "#class_weights = torch.from_numpy(np.asarray([1,15])).type(torch.FloatTensor).cuda()\n",
        "#criterion = nn.CrossEntropyLoss(weight = class_weights).cuda()\n",
        "criterion = nn.CrossEntropyLoss().cuda()\n",
        "train_loss_avg =[]\n",
        "train_accuracy = []\n",
        "test_loss_avg = []\n",
        "test_accuracy = []\n",
        "for epoch in range(1,num_epochs+1):\n",
        "    l, acc = train_epoch(epoch,num_epochs,train_loader,model,criterion,optimizer)\n",
        "    train_loss_avg.append(l)\n",
        "    train_accuracy.append(acc)\n",
        "    true,pred,tl,t_acc = test(epoch,model,valid_loader,criterion)\n",
        "    test_loss_avg.append(tl)\n",
        "    test_accuracy.append(t_acc)\n",
        "plot_loss(train_loss_avg,test_loss_avg,len(train_loss_avg))\n",
        "plot_accuracy(train_accuracy,test_accuracy,len(train_accuracy))\n",
        "print(confusion_matrix(true,pred))\n",
        "print_confusion_matrix(true,pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 1/20] [Batch 908 / 909] [Loss: 0.467272, Acc: 83.53%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.277973, Acc: 90.21%]\n",
            "Accuracy 90.2090209020902\n",
            "[Epoch 2/20] [Batch 908 / 909] [Loss: 0.303608, Acc: 89.96%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.176354, Acc: 93.18%]\n",
            "Accuracy 93.17931793179318\n",
            "[Epoch 3/20] [Batch 908 / 909] [Loss: 0.249925, Acc: 92.02%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.164460, Acc: 94.28%]\n",
            "Accuracy 94.27942794279429\n",
            "[Epoch 4/20] [Batch 908 / 909] [Loss: 0.204532, Acc: 93.67%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.144732, Acc: 94.17%]\n",
            "Accuracy 94.16941694169417\n",
            "[Epoch 5/20] [Batch 908 / 909] [Loss: 0.207415, Acc: 94.72%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.150919, Acc: 95.93%]\n",
            "Accuracy 95.92959295929593\n",
            "[Epoch 6/20] [Batch 908 / 909] [Loss: 0.173520, Acc: 94.99%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.168937, Acc: 94.83%]\n",
            "Accuracy 94.82948294829482\n",
            "[Epoch 7/20] [Batch 908 / 909] [Loss: 0.164025, Acc: 95.46%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.137676, Acc: 95.27%]\n",
            "Accuracy 95.26952695269527\n",
            "[Epoch 8/20] [Batch 908 / 909] [Loss: 0.149191, Acc: 95.82%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.144336, Acc: 96.81%]\n",
            "Accuracy 96.80968096809681\n",
            "[Epoch 9/20] [Batch 908 / 909] [Loss: 0.160350, Acc: 95.41%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.101977, Acc: 96.48%]\n",
            "Accuracy 96.47964796479648\n",
            "[Epoch 10/20] [Batch 908 / 909] [Loss: 0.151705, Acc: 95.74%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.139846, Acc: 96.15%]\n",
            "Accuracy 96.14961496149616\n",
            "[Epoch 11/20] [Batch 908 / 909] [Loss: 0.142808, Acc: 96.34%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.100370, Acc: 96.81%]\n",
            "Accuracy 96.80968096809681\n",
            "[Epoch 12/20] [Batch 908 / 909] [Loss: 0.147432, Acc: 96.15%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.134735, Acc: 96.15%]\n",
            "Accuracy 96.14961496149616\n",
            "[Epoch 13/20] [Batch 908 / 909] [Loss: 0.137948, Acc: 95.90%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.115786, Acc: 96.37%]\n",
            "Accuracy 96.36963696369637\n",
            "[Epoch 14/20] [Batch 908 / 909] [Loss: 0.127588, Acc: 96.53%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.121333, Acc: 96.48%]\n",
            "Accuracy 96.47964796479648\n",
            "[Epoch 15/20] [Batch 908 / 909] [Loss: 0.148114, Acc: 95.98%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.201045, Acc: 94.61%]\n",
            "Accuracy 94.60946094609461\n",
            "[Epoch 16/20] [Batch 908 / 909] [Loss: 0.137930, Acc: 96.42%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.141935, Acc: 95.27%]\n",
            "Accuracy 95.26952695269527\n",
            "[Epoch 17/20] [Batch 908 / 909] [Loss: 0.120944, Acc: 96.42%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.120139, Acc: 96.37%]\n",
            "Accuracy 96.36963696369637\n",
            "[Epoch 18/20] [Batch 908 / 909] [Loss: 0.134898, Acc: 95.93%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.221269, Acc: 94.83%]\n",
            "Accuracy 94.82948294829482\n",
            "[Epoch 19/20] [Batch 908 / 909] [Loss: 0.138076, Acc: 96.29%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.124337, Acc: 96.26%]\n",
            "Accuracy 96.25962596259626\n",
            "[Epoch 20/20] [Batch 908 / 909] [Loss: 0.138912, Acc: 95.96%]Testing\n",
            "[Batch 227 / 228]  [Loss: 0.150355, Acc: 96.48%]\n",
            "Accuracy 96.47964796479648\n",
            "20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[704  18]\n",
            " [ 14 173]]\n",
            "True positive =  704\n",
            "False positive =  18\n",
            "False negative =  14\n",
            "True negative =  173\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Calculated Accuracy 96.47964796479648\n"
          ]
        }
      ]
    }
  ]
}