CNN_100x.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b48e344",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms, models \n",
    "from torchvision.utils import make_grid#\n",
    "import torchvision\n",
    "import os\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b733e6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ab1d890",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "460616f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "369f03f4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f44a880f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_datasets():\n",
    "\n",
    "    transform = transforms.Compose([\n",
    "        transforms.Resize((256,256)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "    ])\n",
    "    \n",
    "    trainset = torchvision.datasets.ImageFolder(root='Breast_cancer_100/train', transform=transform)\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=32,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "    validset = torchvision.datasets.ImageFolder(root='Breast_cancer_100/valid', transform=transform)\n",
    "    validloader = torch.utils.data.DataLoader(validset, batch_size=32,\n",
    "                                         shuffle=True, num_workers=2)\n",
    "    testset = torchvision.datasets.ImageFolder(root='Breast_cancer_100/test', transform=transform)\n",
    "    testloader = torch.utils.data.DataLoader(testset, batch_size=32,\n",
    "                                         shuffle=False, num_workers=2)\n",
    "\n",
    "    return trainloader,validloader, testloader\n",
    "\n",
    "classes = ('Adenosis', 'Ductal_Carcinoma', 'Fibroadenoma', 'Lobular_Carcinoma',\n",
    "               'Mucinous_Carcinoma', 'Papillary_Carcinoma', 'Phyllodes_tumor', 'Tubular_adenoma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba00af87",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "Found no valid file for the classes .ipynb_checkpoints. Supported extensions are: .jpg, .jpeg, .png, .ppm, .bmp, .pgm, .tif, .tiff, .webp",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-1539f958aadf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainloader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidloader\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0mtestloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_datasets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-8-394662aa9df6>\u001b[0m in \u001b[0;36mbuild_datasets\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     ])\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mtrainset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorchvision\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImageFolder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Breast_cancer_100/train'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     trainloader = torch.utils.data.DataLoader(trainset, batch_size=32,\n\u001b[1;32m     11\u001b[0m                                           shuffle=True, num_workers=2)\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, root, transform, target_transform, loader, is_valid_file)\u001b[0m\n\u001b[1;32m    308\u001b[0m             \u001b[0mis_valid_file\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCallable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     ):\n\u001b[0;32m--> 310\u001b[0;31m         super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None,\n\u001b[0m\u001b[1;32m    311\u001b[0m                                           \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m                                           \u001b[0mtarget_transform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget_transform\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, root, loader, extensions, transform, target_transform, is_valid_file)\u001b[0m\n\u001b[1;32m    144\u001b[0m                                             target_transform=target_transform)\n\u001b[1;32m    145\u001b[0m         \u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_to_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0msamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_to_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextensions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_valid_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36mmake_dataset\u001b[0;34m(directory, class_to_idx, extensions, is_valid_file)\u001b[0m\n\u001b[1;32m    190\u001b[0m                 \u001b[0;34m\"The class_to_idx parameter cannot be None.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m             )\n\u001b[0;32m--> 192\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmake_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_to_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextensions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextensions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_valid_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_valid_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfind_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirectory\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36mmake_dataset\u001b[0;34m(directory, class_to_idx, extensions, is_valid_file)\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mextensions\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34mf\"Supported extensions are: {', '.join(extensions)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minstances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: Found no valid file for the classes .ipynb_checkpoints. Supported extensions are: .jpg, .jpeg, .png, .ppm, .bmp, .pgm, .tif, .tiff, .webp"
     ]
    }
   ],
   "source": [
    "trainloader,validloader ,testloader = build_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58097cf9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4bdb1d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNN(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 96, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (6): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (7): ReLU(inplace=True)\n",
       "    (8): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (9): ReLU(inplace=True)\n",
       "    (10): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace=True)\n",
       "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (maxpool): MaxPool2d(kernel_size=(6, 6), stride=(6, 6), padding=0, dilation=1, ceil_mode=False)\n",
       "  (classifier): Sequential(\n",
       "    (0): Dropout(p=0.5, inplace=False)\n",
       "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
       "    (2): ReLU(inplace=True)\n",
       "    (3): Dropout(p=0.5, inplace=False)\n",
       "    (4): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "    (5): ReLU(inplace=True)\n",
       "    (6): Linear(in_features=1024, out_features=8, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "# define the CNN architecture\n",
    "\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self, num_classes: int = 8, dropout: float = 0.5) -> None:\n",
    "        super().__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            nn.Conv2d(96, 256, kernel_size=5, padding=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            nn.Conv2d(256, 384, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(384, 384, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(384, 256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "        )\n",
    "        self.maxpool = nn.MaxPool2d((6, 6))\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(256 * 6 * 6, 4096),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(4096, 1024),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(1024, num_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        x = self.features(x)\n",
    "        x = self.maxpool(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.classifier(x)\n",
    "        return x\n",
    "\n",
    "net = CNN()\n",
    "net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "28902bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "656d6723",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_accuracy(y_pred, y):\n",
    "    top_pred = y_pred.argmax(1, keepdim = True)\n",
    "    correct = top_pred.eq(y.view_as(top_pred)).sum()\n",
    "    acc = correct.float() / y.shape[0]\n",
    "    return acc\n",
    "\n",
    "def train(model, iterator, optimizer, criterion, device):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    model.train()\n",
    "    for (x, y) in iterator:\n",
    "        x = x.to(device)\n",
    "        y = y.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        y_pred = model(x)\n",
    "\n",
    "        loss = criterion(y_pred, y)\n",
    "\n",
    "        acc = calculate_accuracy(y_pred, y)\n",
    "\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "        epoch_acc += acc.item()\n",
    "\n",
    "    return epoch_loss / len(iterator), epoch_acc / len(iterator)\n",
    "\n",
    "def valid(model,iterator):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "\n",
    "    model.eval()\n",
    "\n",
    "    for (x, y) in iterator:\n",
    "        x = x.to(device)\n",
    "        y = y.to(device)\n",
    "        y_pred = model(x)\n",
    "        loss = criterion(y_pred, y)\n",
    "        acc = calculate_accuracy(y_pred, y)\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "        epoch_acc += acc.item()\n",
    "\n",
    "    return epoch_loss / len(iterator), epoch_acc / len(iterator)\n",
    "\n",
    "\n",
    "def epoch_time(start_time, end_time):\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_mins = int(elapsed_time / 60)\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
    "    return elapsed_mins, elapsed_secs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ec167f1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 01 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.724 | Train Acc: 42.05%\n",
      "\tValid Loss: 1.866 | Valid Acc: 43.54%\n",
      "Epoch: 02 | Epoch Time: 1m 38s\n",
      "\tTrain Loss: 1.624 | Train Acc: 43.46%\n",
      "\tValid Loss: 1.926 | Valid Acc: 43.61%\n",
      "Epoch: 03 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.577 | Train Acc: 45.58%\n",
      "\tValid Loss: 1.750 | Valid Acc: 44.64%\n",
      "Epoch: 04 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.523 | Train Acc: 48.53%\n",
      "\tValid Loss: 1.684 | Valid Acc: 47.22%\n",
      "Epoch: 05 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.472 | Train Acc: 48.85%\n",
      "\tValid Loss: 1.588 | Valid Acc: 45.74%\n",
      "Epoch: 06 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.413 | Train Acc: 50.00%\n",
      "\tValid Loss: 1.747 | Valid Acc: 47.18%\n",
      "Epoch: 07 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.403 | Train Acc: 51.03%\n",
      "\tValid Loss: 1.448 | Valid Acc: 50.89%\n",
      "Epoch: 08 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.299 | Train Acc: 53.33%\n",
      "\tValid Loss: 1.392 | Valid Acc: 50.82%\n",
      "Epoch: 09 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.212 | Train Acc: 55.64%\n",
      "\tValid Loss: 1.366 | Valid Acc: 54.16%\n",
      "Epoch: 10 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.199 | Train Acc: 56.03%\n",
      "\tValid Loss: 1.487 | Valid Acc: 54.50%\n",
      "Epoch: 11 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.159 | Train Acc: 56.92%\n",
      "\tValid Loss: 1.419 | Valid Acc: 55.43%\n",
      "Epoch: 12 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 1.025 | Train Acc: 61.15%\n",
      "\tValid Loss: 1.311 | Valid Acc: 58.86%\n",
      "Epoch: 13 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.925 | Train Acc: 64.94%\n",
      "\tValid Loss: 1.342 | Valid Acc: 65.28%\n",
      "Epoch: 14 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.890 | Train Acc: 65.96%\n",
      "\tValid Loss: 1.314 | Valid Acc: 69.47%\n",
      "Epoch: 15 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.675 | Train Acc: 74.94%\n",
      "\tValid Loss: 1.175 | Valid Acc: 77.37%\n",
      "Epoch: 16 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.520 | Train Acc: 80.38%\n",
      "\tValid Loss: 1.329 | Valid Acc: 78.78%\n",
      "Epoch: 17 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.532 | Train Acc: 81.28%\n",
      "\tValid Loss: 1.286 | Valid Acc: 78.57%\n",
      "Epoch: 18 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.352 | Train Acc: 88.46%\n",
      "\tValid Loss: 1.092 | Valid Acc: 76.55%\n",
      "Epoch: 19 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.268 | Train Acc: 91.15%\n",
      "\tValid Loss: 1.256 | Valid Acc: 78.81%\n",
      "Epoch: 20 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.206 | Train Acc: 93.14%\n",
      "\tValid Loss: 1.395 | Valid Acc: 83.10%\n",
      "Epoch: 21 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.203 | Train Acc: 92.82%\n",
      "\tValid Loss: 1.262 | Valid Acc: 81.70%\n",
      "Epoch: 22 | Epoch Time: 1m 38s\n",
      "\tTrain Loss: 0.146 | Train Acc: 95.45%\n",
      "\tValid Loss: 1.626 | Valid Acc: 83.62%\n",
      "Epoch: 23 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.203 | Train Acc: 93.59%\n",
      "\tValid Loss: 1.402 | Valid Acc: 82.66%\n",
      "Epoch: 24 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.130 | Train Acc: 95.38%\n",
      "\tValid Loss: 1.520 | Valid Acc: 82.69%\n",
      "Epoch: 25 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.110 | Train Acc: 95.96%\n",
      "\tValid Loss: 1.287 | Valid Acc: 81.22%\n",
      "Epoch: 26 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.101 | Train Acc: 96.15%\n",
      "\tValid Loss: 1.655 | Valid Acc: 82.18%\n",
      "Epoch: 27 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.085 | Train Acc: 96.73%\n",
      "\tValid Loss: 2.001 | Valid Acc: 81.01%\n",
      "Epoch: 28 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.099 | Train Acc: 96.28%\n",
      "\tValid Loss: 1.396 | Valid Acc: 82.66%\n",
      "Epoch: 29 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.080 | Train Acc: 96.41%\n",
      "\tValid Loss: 1.795 | Valid Acc: 81.94%\n",
      "Epoch: 30 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.064 | Train Acc: 97.12%\n",
      "\tValid Loss: 1.707 | Valid Acc: 82.66%\n",
      "Epoch: 31 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.055 | Train Acc: 97.44%\n",
      "\tValid Loss: 1.873 | Valid Acc: 82.38%\n",
      "Epoch: 32 | Epoch Time: 1m 37s\n",
      "\tTrain Loss: 0.059 | Train Acc: 96.99%\n",
      "\tValid Loss: 2.179 | Valid Acc: 82.93%\n",
      "Epoch: 33 | Epoch Time: 1m 20s\n",
      "\tTrain Loss: 0.047 | Train Acc: 97.63%\n",
      "\tValid Loss: 1.930 | Valid Acc: 82.18%\n",
      "Epoch: 34 | Epoch Time: 1m 14s\n",
      "\tTrain Loss: 0.112 | Train Acc: 95.71%\n",
      "\tValid Loss: 1.159 | Valid Acc: 81.63%\n",
      "Epoch: 35 | Epoch Time: 1m 14s\n",
      "\tTrain Loss: 0.061 | Train Acc: 97.37%\n",
      "\tValid Loss: 1.903 | Valid Acc: 82.93%\n",
      "Epoch: 36 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.048 | Train Acc: 97.76%\n",
      "\tValid Loss: 1.877 | Valid Acc: 82.93%\n",
      "Epoch: 37 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.046 | Train Acc: 97.37%\n",
      "\tValid Loss: 1.888 | Valid Acc: 82.62%\n",
      "Epoch: 38 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.054 | Train Acc: 97.56%\n",
      "\tValid Loss: 1.893 | Valid Acc: 82.14%\n",
      "Epoch: 39 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.047 | Train Acc: 97.50%\n",
      "\tValid Loss: 1.944 | Valid Acc: 82.62%\n",
      "Epoch: 40 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.047 | Train Acc: 97.31%\n",
      "\tValid Loss: 2.201 | Valid Acc: 82.62%\n",
      "Epoch: 41 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.071 | Train Acc: 97.24%\n",
      "\tValid Loss: 2.035 | Valid Acc: 82.66%\n",
      "Epoch: 42 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.088 | Train Acc: 96.47%\n",
      "\tValid Loss: 1.600 | Valid Acc: 78.61%\n",
      "Epoch: 43 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.085 | Train Acc: 96.79%\n",
      "\tValid Loss: 1.569 | Valid Acc: 81.46%\n",
      "Epoch: 44 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.048 | Train Acc: 97.24%\n",
      "\tValid Loss: 1.960 | Valid Acc: 82.90%\n",
      "Epoch: 45 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.043 | Train Acc: 97.44%\n",
      "\tValid Loss: 1.937 | Valid Acc: 82.90%\n",
      "Epoch: 46 | Epoch Time: 1m 13s\n",
      "\tTrain Loss: 0.046 | Train Acc: 97.63%\n",
      "\tValid Loss: 2.264 | Valid Acc: 83.17%\n",
      "Epoch: 47 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.040 | Train Acc: 97.82%\n",
      "\tValid Loss: 2.028 | Valid Acc: 83.38%\n",
      "Epoch: 48 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.045 | Train Acc: 97.12%\n",
      "\tValid Loss: 1.770 | Valid Acc: 82.86%\n",
      "Epoch: 49 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.043 | Train Acc: 97.63%\n",
      "\tValid Loss: 1.995 | Valid Acc: 82.69%\n",
      "Epoch: 50 | Epoch Time: 1m 13s\n",
      "\tTrain Loss: 0.035 | Train Acc: 98.08%\n",
      "\tValid Loss: 2.109 | Valid Acc: 83.62%\n",
      "Epoch: 51 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.038 | Train Acc: 97.76%\n",
      "\tValid Loss: 2.033 | Valid Acc: 83.17%\n",
      "Epoch: 52 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.037 | Train Acc: 97.56%\n",
      "\tValid Loss: 2.358 | Valid Acc: 83.10%\n",
      "Epoch: 53 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.039 | Train Acc: 97.56%\n",
      "\tValid Loss: 2.164 | Valid Acc: 82.45%\n",
      "Epoch: 54 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.036 | Train Acc: 97.50%\n",
      "\tValid Loss: 2.372 | Valid Acc: 82.69%\n",
      "Epoch: 55 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.036 | Train Acc: 97.69%\n",
      "\tValid Loss: 2.299 | Valid Acc: 83.38%\n",
      "Epoch: 56 | Epoch Time: 1m 46s\n",
      "\tTrain Loss: 0.038 | Train Acc: 97.82%\n",
      "\tValid Loss: 2.598 | Valid Acc: 81.66%\n",
      "Epoch: 57 | Epoch Time: 1m 57s\n",
      "\tTrain Loss: 0.039 | Train Acc: 97.18%\n",
      "\tValid Loss: 2.803 | Valid Acc: 82.93%\n",
      "Epoch: 58 | Epoch Time: 1m 51s\n",
      "\tTrain Loss: 0.051 | Train Acc: 97.05%\n",
      "\tValid Loss: 1.976 | Valid Acc: 81.49%\n",
      "Epoch: 59 | Epoch Time: 1m 53s\n",
      "\tTrain Loss: 0.044 | Train Acc: 97.24%\n",
      "\tValid Loss: 1.976 | Valid Acc: 82.18%\n",
      "Epoch: 60 | Epoch Time: 1m 52s\n",
      "\tTrain Loss: 0.042 | Train Acc: 97.05%\n",
      "\tValid Loss: 2.232 | Valid Acc: 81.90%\n",
      "Epoch: 61 | Epoch Time: 1m 53s\n",
      "\tTrain Loss: 0.040 | Train Acc: 97.63%\n",
      "\tValid Loss: 2.285 | Valid Acc: 82.62%\n",
      "Epoch: 62 | Epoch Time: 1m 54s\n",
      "\tTrain Loss: 0.037 | Train Acc: 97.50%\n",
      "\tValid Loss: 2.570 | Valid Acc: 82.59%\n",
      "Epoch: 63 | Epoch Time: 2m 5s\n",
      "\tTrain Loss: 0.035 | Train Acc: 97.82%\n",
      "\tValid Loss: 2.435 | Valid Acc: 82.42%\n",
      "Epoch: 64 | Epoch Time: 2m 3s\n",
      "\tTrain Loss: 0.036 | Train Acc: 97.88%\n",
      "\tValid Loss: 2.565 | Valid Acc: 82.62%\n",
      "Epoch: 65 | Epoch Time: 1m 58s\n",
      "\tTrain Loss: 0.039 | Train Acc: 97.63%\n",
      "\tValid Loss: 2.595 | Valid Acc: 82.62%\n",
      "Epoch: 66 | Epoch Time: 1m 56s\n",
      "\tTrain Loss: 0.035 | Train Acc: 97.82%\n",
      "\tValid Loss: 2.595 | Valid Acc: 82.90%\n",
      "Epoch: 67 | Epoch Time: 1m 56s\n",
      "\tTrain Loss: 0.040 | Train Acc: 97.18%\n",
      "\tValid Loss: 2.537 | Valid Acc: 82.38%\n",
      "Epoch: 68 | Epoch Time: 1m 55s\n",
      "\tTrain Loss: 0.037 | Train Acc: 97.63%\n",
      "\tValid Loss: 2.418 | Valid Acc: 81.73%\n",
      "Epoch: 69 | Epoch Time: 1m 55s\n",
      "\tTrain Loss: 0.037 | Train Acc: 97.63%\n",
      "\tValid Loss: 2.595 | Valid Acc: 81.46%\n",
      "Epoch: 70 | Epoch Time: 1m 54s\n",
      "\tTrain Loss: 0.035 | Train Acc: 98.01%\n",
      "\tValid Loss: 2.743 | Valid Acc: 83.14%\n",
      "Epoch: 71 | Epoch Time: 1m 56s\n",
      "\tTrain Loss: 0.034 | Train Acc: 97.56%\n",
      "\tValid Loss: 2.605 | Valid Acc: 82.21%\n",
      "Epoch: 72 | Epoch Time: 1m 55s\n",
      "\tTrain Loss: 0.039 | Train Acc: 97.56%\n",
      "\tValid Loss: 2.597 | Valid Acc: 81.73%\n",
      "Epoch: 73 | Epoch Time: 1m 54s\n",
      "\tTrain Loss: 0.033 | Train Acc: 98.01%\n",
      "\tValid Loss: 2.779 | Valid Acc: 83.38%\n",
      "Epoch: 74 | Epoch Time: 1m 52s\n",
      "\tTrain Loss: 0.037 | Train Acc: 97.63%\n",
      "\tValid Loss: 2.654 | Valid Acc: 82.21%\n",
      "Epoch: 75 | Epoch Time: 1m 56s\n",
      "\tTrain Loss: 0.032 | Train Acc: 98.21%\n",
      "\tValid Loss: 2.783 | Valid Acc: 82.90%\n",
      "Epoch: 76 | Epoch Time: 1m 55s\n",
      "\tTrain Loss: 0.038 | Train Acc: 97.44%\n",
      "\tValid Loss: 2.789 | Valid Acc: 82.86%\n",
      "Epoch: 77 | Epoch Time: 1m 53s\n",
      "\tTrain Loss: 0.032 | Train Acc: 98.08%\n",
      "\tValid Loss: 2.809 | Valid Acc: 82.21%\n",
      "Epoch: 78 | Epoch Time: 1m 57s\n",
      "\tTrain Loss: 0.031 | Train Acc: 98.40%\n",
      "\tValid Loss: 2.830 | Valid Acc: 82.90%\n",
      "Epoch: 79 | Epoch Time: 1m 54s\n",
      "\tTrain Loss: 0.036 | Train Acc: 97.37%\n",
      "\tValid Loss: 2.913 | Valid Acc: 82.45%\n",
      "Epoch: 80 | Epoch Time: 1m 53s\n",
      "\tTrain Loss: 0.039 | Train Acc: 97.44%\n",
      "\tValid Loss: 2.451 | Valid Acc: 83.62%\n",
      "Epoch: 81 | Epoch Time: 1m 55s\n",
      "\tTrain Loss: 0.052 | Train Acc: 97.44%\n",
      "\tValid Loss: 3.007 | Valid Acc: 82.42%\n",
      "Epoch: 82 | Epoch Time: 2m 15s\n",
      "\tTrain Loss: 0.075 | Train Acc: 96.28%\n",
      "\tValid Loss: 1.936 | Valid Acc: 80.98%\n",
      "Epoch: 83 | Epoch Time: 2m 20s\n",
      "\tTrain Loss: 0.043 | Train Acc: 97.56%\n",
      "\tValid Loss: 2.151 | Valid Acc: 81.94%\n",
      "Epoch: 84 | Epoch Time: 2m 23s\n",
      "\tTrain Loss: 0.035 | Train Acc: 98.08%\n",
      "\tValid Loss: 2.674 | Valid Acc: 82.21%\n",
      "Epoch: 85 | Epoch Time: 2m 31s\n",
      "\tTrain Loss: 0.034 | Train Acc: 98.01%\n",
      "\tValid Loss: 2.546 | Valid Acc: 82.42%\n",
      "Epoch: 86 | Epoch Time: 2m 36s\n",
      "\tTrain Loss: 0.037 | Train Acc: 97.24%\n",
      "\tValid Loss: 2.427 | Valid Acc: 82.38%\n",
      "Epoch: 87 | Epoch Time: 2m 32s\n",
      "\tTrain Loss: 0.043 | Train Acc: 97.44%\n",
      "\tValid Loss: 2.472 | Valid Acc: 82.45%\n",
      "Epoch: 88 | Epoch Time: 2m 30s\n",
      "\tTrain Loss: 0.040 | Train Acc: 97.44%\n",
      "\tValid Loss: 2.613 | Valid Acc: 82.42%\n",
      "Epoch: 89 | Epoch Time: 2m 27s\n",
      "\tTrain Loss: 0.034 | Train Acc: 97.56%\n",
      "\tValid Loss: 2.701 | Valid Acc: 82.18%\n",
      "Epoch: 90 | Epoch Time: 2m 29s\n",
      "\tTrain Loss: 0.036 | Train Acc: 97.95%\n",
      "\tValid Loss: 2.661 | Valid Acc: 81.46%\n",
      "Epoch: 91 | Epoch Time: 2m 22s\n",
      "\tTrain Loss: 0.033 | Train Acc: 97.82%\n",
      "\tValid Loss: 2.922 | Valid Acc: 81.22%\n",
      "Epoch: 92 | Epoch Time: 2m 23s\n",
      "\tTrain Loss: 0.032 | Train Acc: 97.95%\n",
      "\tValid Loss: 2.705 | Valid Acc: 82.62%\n",
      "Epoch: 93 | Epoch Time: 2m 21s\n",
      "\tTrain Loss: 0.037 | Train Acc: 97.50%\n",
      "\tValid Loss: 2.798 | Valid Acc: 82.21%\n",
      "Epoch: 94 | Epoch Time: 2m 18s\n",
      "\tTrain Loss: 0.118 | Train Acc: 95.96%\n",
      "\tValid Loss: 1.718 | Valid Acc: 81.25%\n",
      "Epoch: 95 | Epoch Time: 2m 21s\n",
      "\tTrain Loss: 0.042 | Train Acc: 97.88%\n",
      "\tValid Loss: 2.044 | Valid Acc: 82.18%\n",
      "Epoch: 96 | Epoch Time: 2m 19s\n",
      "\tTrain Loss: 0.039 | Train Acc: 97.56%\n",
      "\tValid Loss: 2.267 | Valid Acc: 81.01%\n",
      "Epoch: 97 | Epoch Time: 2m 20s\n",
      "\tTrain Loss: 0.044 | Train Acc: 97.50%\n",
      "\tValid Loss: 2.431 | Valid Acc: 80.46%\n",
      "Epoch: 98 | Epoch Time: 2m 19s\n",
      "\tTrain Loss: 0.050 | Train Acc: 97.24%\n",
      "\tValid Loss: 2.457 | Valid Acc: 81.73%\n",
      "Epoch: 99 | Epoch Time: 2m 22s\n",
      "\tTrain Loss: 0.035 | Train Acc: 97.82%\n",
      "\tValid Loss: 2.504 | Valid Acc: 81.70%\n",
      "Epoch: 100 | Epoch Time: 2m 22s\n",
      "\tTrain Loss: 0.037 | Train Acc: 97.69%\n",
      "\tValid Loss: 2.449 | Valid Acc: 81.70%\n",
      "Finished Training\n",
      "Accuracy of the network test images: 81 %\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import time\n",
    "\n",
    "train_l = []\n",
    "train_a = []\n",
    "valid_l = []\n",
    "valid_a = []\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    for epoch in range(100):\n",
    "        start_time = time.monotonic()\n",
    "        train_loss, train_acc = train(net, trainloader, optimizer, criterion, device)\n",
    "        valid_loss, valid_acc = valid(net, validloader)\n",
    "        \n",
    "        train_l.append(train_loss)\n",
    "        train_a.append(train_acc)\n",
    "        valid_l.append(valid_loss)\n",
    "        valid_a.append(valid_acc)\n",
    "        \n",
    "        end_time = time.monotonic()\n",
    "        epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "\n",
    "        print(f'Epoch: {epoch + 1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
    "        print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc * 100:.2f}%')\n",
    "        print(f'\\tValid Loss: {valid_loss:.3f} | Valid Acc: {valid_acc * 100:.2f}%')\n",
    "    print('Finished Training')\n",
    "\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in testloader:\n",
    "            images, labels = data\n",
    "            outputs = net(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "    print('Accuracy of the network test images: %d %%' % (\n",
    "            100 * correct / total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6c04c08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for class Adenosis is: 68.2 %\n",
      "Accuracy for class Ductal_Carcinoma is: 92.8 %\n",
      "Accuracy for class Fibroadenoma is: 75.0 %\n",
      "Accuracy for class Lobular_Carcinoma is: 67.6 %\n",
      "Accuracy for class Mucinous_Carcinoma is: 75.0 %\n",
      "Accuracy for class Papillary_Carcinoma is: 82.1 %\n",
      "Accuracy for class Phyllodes_tumor is: 70.8 %\n",
      "Accuracy for class Tubular_adenoma is: 70.0 %\n"
     ]
    }
   ],
   "source": [
    "# prepare to count predictions for each class\n",
    "correct_pred = {classname: 0 for classname in classes}\n",
    "total_pred = {classname: 0 for classname in classes}\n",
    "\n",
    "# again no gradients needed\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = net(images)\n",
    "        _, predictions = torch.max(outputs, 1)\n",
    "        # collect the correct predictions for each class\n",
    "        for label, prediction in zip(labels, predictions):\n",
    "            if label == prediction:\n",
    "                correct_pred[classes[label]] += 1\n",
    "            total_pred[classes[label]] += 1\n",
    "\n",
    "\n",
    "# print accuracy for each class\n",
    "for classname, correct_count in correct_pred.items():\n",
    "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
    "    print(\"Accuracy for class {:5s} is: {:.1f} %\".format(classname,\n",
    "                                                   accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27a72ec5",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'valid_l' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-ee5601c3263d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_l\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Train Loss'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_l\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Valid Loss'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Epoch'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Loss'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'valid_l' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(valid_l,label='Train Loss',linewidth=2)\n",
    "plt.plot(train_l,label='Valid Loss',linewidth=2)\n",
    "plt.xlabel('Epoch',size=20)\n",
    "plt.ylabel('Loss',size=20)\n",
    "plt.legend(fontsize = 20)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2efb26da",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(valid_a,label='Train Accuracy',linewidth=2)\n",
    "plt.plot(train_a,label='Valid Accuracy',linewidth=2)\n",
    "plt.xlabel('Epoch',size=20)\n",
    "plt.ylabel('Loss',size=20)\n",
    "plt.legend(fontsize = 20)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a6fe620b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for class Adenosis is: 77.3 %\n",
      "Accuracy for class Ductal_Carcinoma is: 96.7 %\n",
      "Accuracy for class Fibroadenoma is: 76.9 %\n",
      "Accuracy for class Lobular_Carcinoma is: 44.1 %\n",
      "Accuracy for class Mucinous_Carcinoma is: 50.0 %\n",
      "Accuracy for class Papillary_Carcinoma is: 46.4 %\n",
      "Accuracy for class Phyllodes_tumor is: 54.2 %\n",
      "Accuracy for class Tubular_adenoma is: 80.0 %\n"
     ]
    }
   ],
   "source": [
    "classes = ('Adenosis', 'Ductal_Carcinoma', 'Fibroadenoma', 'Lobular_Carcinoma',\n",
    "               'Mucinous_Carcinoma', 'Papillary_Carcinoma', 'Phyllodes_tumor', 'Tubular_adenoma')\n",
    "\n",
    "# prepare to count predictions for each class\n",
    "correct_pred = {classname: 0 for classname in classes}\n",
    "total_pred = {classname: 0 for classname in classes}\n",
    "\n",
    "# again no gradients needed\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = net(images)\n",
    "        _, predictions = torch.max(outputs, 1)\n",
    "        # collect the correct predictions for each class\n",
    "        for label, prediction in zip(labels, predictions):\n",
    "            if label == prediction:\n",
    "                correct_pred[classes[label]] += 1\n",
    "            total_pred[classes[label]] += 1\n",
    "\n",
    "\n",
    "# print accuracy for each class\n",
    "for classname, correct_count in correct_pred.items():\n",
    "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
    "    print(\"Accuracy for class {:5s} is: {:.1f} %\".format(classname,\n",
    "                                                   accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1aafc3d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[ 15   0   4   0   1   2   0   0]\n",
      " [  2 168   2   1   6   1   1   0]\n",
      " [  1   8  39   2   1   0   0   1]\n",
      " [  0  10   1  23   0   0   0   0]\n",
      " [  0   5   3   1  33   1   0   1]\n",
      " [  0   3   1   0   1  23   0   0]\n",
      " [  2   0   3   0   2   0  17   0]\n",
      " [  0   4   3   0   2   0   0  21]]\n",
      "F1 score: 0.816867\n",
      "Accuracy score: 0.816867\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "\n",
    "def test_label_predictions(model, device, test_loader):\n",
    "    model.eval()\n",
    "    actuals = []\n",
    "    predictions = []\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            prediction = output.argmax(dim=1, keepdim=True)\n",
    "            actuals.extend(target.view_as(prediction))\n",
    "            predictions.extend(prediction)\n",
    "    return [i.item() for i in actuals], [i.item() for i in predictions]\n",
    "\n",
    "actuals, predictions = test_label_predictions(net, device, testloader)\n",
    "print('Confusion matrix:')\n",
    "print(sklearn.metrics.confusion_matrix(actuals, predictions))\n",
    "print('F1 score: %f' % sklearn.metrics.f1_score(actuals, predictions, average='micro'))\n",
    "print('Accuracy score: %f' % sklearn.metrics.accuracy_score(actuals, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ca3a416",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [[ 15,   0  , 4   ,0  , 1  , 2  , 0 ,  0],\n",
    "     [  2 ,168  , 2  , 1  , 6 ,  1  , 1  , 0],\n",
    "     [  1 ,  8  ,39  , 2  , 1  , 0   ,0   ,1],\n",
    "     [  0  ,10 ,  1  ,23  , 0 ,  0 ,  0 ,  0],\n",
    "     [  0  , 5,   3  , 1  ,33 ,  1 ,  0 ,  1],\n",
    "     [  0 ,  3 ,  1 ,  0  , 1,  23 ,  0 ,  0],\n",
    "     [  2  , 0 ,  3  , 0 ,  2  , 0 , 17   ,0],\n",
    "     [  0  , 4  , 3  , 0 ,  2,   0 ,  0 , 21]]\n",
    "\n",
    "\n",
    "b= [ [ 15/22,   0,   4/22,   0,   1/22,   2/22,   0 ,  0],\n",
    "     [  2/181, 168/181,   2/181,   1/181,   6/181  , 1/181  , 1/181 ,  0],\n",
    "     [  1/52  , 8/52 , 39/52,   2/52,   1/52 ,  0 ,  0  , 1/52],\n",
    "     [  0  ,10/34 ,  1/34,  23/34,   0 ,  0,   0 ,  0],\n",
    "     [  0  , 5/44 ,  3/44,   1/44,  33/44  , 1/44 ,  0 ,  1/44],\n",
    "     [  0 ,  3/28 ,  1/28 ,  0   ,1/28 , 23/28,   0,   0],\n",
    "     [  2/24,   0 ,  3/24  , 0  , 2/24,   0,  17/24 ,  0],\n",
    "     [  0  , 4/30 ,  3/30,   0 ,  2/30  , 0  , 0,  21/30]]\n",
    "\n",
    "\n",
    "c = [[ 17  , 0  , 0 ,  1  , 1 ,  2 ,  1 ,  0],\n",
    "     [  7 ,150  , 3 , 4  , 10 ,  4  , 3  , 0],\n",
    "     [  0 ,  4  ,45  , 1  , 2  , 0   ,0   ,0],\n",
    "     [  0  ,3 ,  1  ,29  , 0 ,  1 ,   0 ,  0],     \n",
    "     [  0  , 4,   3  , 1  ,35 ,  1 ,  0 ,  0],    \n",
    "     [  0 ,  2 ,  1 ,  0  , 0,  24 ,  0 ,  1],     \n",
    "     [  1  , 1 ,  1  , 0 ,  1  , 0 , 20   ,0],     \n",
    "     [  0  , 1  , 0  , 0 ,  2,   0 ,  1 , 26]]\n",
    "\n",
    "d = [[ 17/22 ,  0/22  , 0 ,  1/22  , 1/22 ,  2/22 ,  1/22 ,  0],\n",
    "     [  7/181, 150/181,   3/181,   4/181,   10/181  , 4/181  , 3/181 ,  0],\n",
    "     [  0  , 4/52 , 45/52,   1/52,   2/52 ,  0 ,  0  , 0],   \n",
    "     [  0  ,3/34 ,  1/34,  29/34,   0 ,  1/34,   0 ,  0],     \n",
    "     [  0  , 4/44 ,  3/44,   1/44,  35/44  , 1/44 ,  0 ,  0],     \n",
    "     [  0 ,  2/28 ,  1/28 ,  0   ,0 , 24/28,   0,   1/28],   \n",
    "     [  1/24,   1/24 ,  1/24  , 0  , 1/24,   0,  20/24 ,  0], \n",
    "     [  0  , 1/30 ,  0,   0 ,  2/30  , 0  , 1/30,  26/30]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa36be0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'confusion Matrix x100 ohne Augmentation'}>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "group_counts = [\"{0:0.0f}\".format(value) for value in\n",
    "                np.array(a).flatten()]\n",
    "\n",
    "group_percentages = [\"{0:.2%}\".format(value) for value in\n",
    "                     np.array(b).flatten()]\n",
    "\n",
    "labels = [f\"{v1}\\n{v2}\" for v1, v2 in\n",
    "          zip(group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(8,8)\n",
    "\n",
    "categories = ['Adenosis', 'Ductal_Carcinoma', 'Fibroadenoma', 'Lobular_Carcinoma',\n",
    "               'Mucinous_Carcinoma', 'Papillary_Carcinoma', 'Phyllodes_tumor', 'Tubular_adenoma']\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "plt.title('confusion Matrix x100 ohne Augmentation')\n",
    "sns.heatmap(b, annot = labels, fmt='', xticklabels = categories, yticklabels = categories)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e23b6070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'confusion Matrix x100 mit Data Augmentation'}>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZQnaHYosrl73IkT1bwPvs7lnx8rpuY0T20bpQKnIuLs6MnDqQZQtWs3bFBrvDUXGAnjsTPm2Uh0NEmoqIEZEC4azfKCKlYyGObCIyP6b3E9N2HTtH7qzpyZwutd2h2GLP3oPky5ebXLly4OrqSsuWjVm2fI3dYdlC60KpyPX79UfOnDzPtPGz7A5FxRF67gyDv1/MvGyijfLwtQG2ArbORWWMuWqMaW5nDC/im4kL6dh/Gheu36b2VyNYuOUAAKt3J96hKwB+fn588ukPrFwxiyOHNzJ//jI8PU/aHZYttC4CTZgylFVrZ5Mvf24OeW6iXYd481GPdloXgUqWK0aTlm9SvnJpFq2fyaL1M6lSs6LdYdlCj4tAeu4Mg/GPmZdNJNGPRwqDiKQETgDVgaXGmAIikgz4DSgEHANyAT2MMXtFpA7QG0gKnAHeNsZ4i8h5YDrQEHAFWhhjjotIOmAqkAd4BHQzxhwWkarACEcYBqgCpAeWG2MKi8hrjhiSYF1QNTPGnIqoLE+2zNA/MJCy5rd2h6DioJeSpbQ7BBUHpU+aOL/RC8vtp/ftDiHOuPvY2+4Q4hTfZ1dCD3KPZU+PbYiRNk7SgtVtKZv2lIetCbDaGHMSuCMiJYH3gUfGmKLAz0ApABHJAPwA1DLGlAT2Ap8H2dYtx/JxwJeOZb2BA45tfQf87lj+JVZDvzhQGXgcIq7uwAjH+tLA5egqsFJKKaVUvKJTIiYKbYDZjt9nO95XAf4AMMYcBg471pfH6j3fJiIHgU7Ay0G2tdDxcx9W7zpAJWCGY1vrgfQikgbYBgwTkY+BtMYY3xBx7QC+E5FvgJeNMSEb7QCISDcR2Ssie6cs1RuElFJKKaXiOm2UhyAi6YEawGTH8JOvgFaAYA0pCZUFWGuMKe54FTLGdAmy/qnjpx+BU1CG9bWIMcYMALoCyYCdIW8yNcbMAhph9aD/JSI1wiqDMWaiMaa0MaZ0l0bVIy90OM5fu03L3pMCXhU/HMQfa3fx1fiFAcvqfzOKlr0nhcp77c49ugyeQZMfxtG053hm/r07YF14+Q+cukTzXhNp228KF6/fAeD+oyd0Hz4rzk37FJVHHQ8f1ofjnlvZv28tJYoXjjRv/1++Y/++tfw2dUTAsnbtmvHRh12Iy7QugnNycmL9lkXMnDM+1LqKlcpy5uJeNmxZzIYti/ni68Ay7zu8jk3bl7Jhy2LWblwQsPzH3l+ycdtSRo8fGLCsRavGdOveMWYLEg20Liwd3m3N0k2zWbZ5Dh27tQkzzfc/f8FfuxayZOMsChV5NdK8X/z4IUs2zmLA6J8CljVqUZ8O79p6G1SU6HFh0XNnNEhgY8p1nvLQmgO/G2Pee75ARDYB+4F2wAYRKQwUdazeCYwRkXzGmNMikhzI7hj6Ep7Njm31FZFqWENc7otIXmOMB+AhIhWAAsDBIHHkAc4aY0Y6fi8KrI+WUochV5b0zO31LgB+/v7U/nIENUq+Svva5QLSDJmzlpTJk4bK6+zkxJcta1Hw5aw8fPKU1n2nUL5QbvJmy8jg7m+Fmf/3NTsZ+kFzrt76h7kb9/Flq9pMXLaFrm+8Hub8rHZ5/qjjem+04fJlL3buWMmy5Ws4dixweH/9ejXIny83BQpVolzZkowZ3Z+KlRqGm/fKlWtUKF+akqVq8/v0URQuXIDTp8/TqUNL3mjQzsbSRkzrIrRu73fk5IkzpEoV9nj1nTv20q5V9zDXNW3QiTt37ga8T5U6JWXLlqDa640YN2kIBQu9wrmzF2jdrimt3or7jx7XuoD8BfLSon0TWtbrhM8zXybNGcmmtVu5cO5SQJoqNSvycp6c1C33FsVKFabXoG9pVf/tcPPevnWHEmWK0rhaWwaP68srBfNy4dxlmrZqyLutP7KxtFGjx4WeO1XYtKc8tDbAohDLFmANPUkpIoeBr4HdAMaYm0Bn4E/Hup1YjemI/ASUdqQfgDXkBeBTETkiIoewesNXhcjXCjjiGCZTgMCx6DFu17Fz5Mj4EtnSpw1YZoxhzV5P6pctHCp9xrSpKPhyVgBSuCUlT9YM3Lj7IFiakPldnJ15+syHJ898cHF25tKNO9z45wGlX3051PbtFJVHHTdsWJcZM62ZLHft3k+atGnIkiVTuHn9/f1JksQVgGTJ3PDx8eHLL7ozaswUfH1DjmKKO7QugsuaLTO161bjj9+jZxZTf3+Dq6Mu3JIlxcfHlx4fd2XS+BlaF/GkLvLkz8WhfR48efwUPz8/9mzfT603qwVLU7N+VZbMXQHAoX1HSJ0mFRkzpQ83r/E3uLpadZHUzaqLLj06MGPybHx97ZvOLSr0uLDouTOa2DSmXETqicgJETktIqFmkhCRNCKyTEQOichREXk7KsXRRnkIxphqxpjVIZaNNMa8bYxpbYwpaozpaIypaIzZ61i/3hhTxrGuqDFmqWN5LmPMLcfve40x1Ry/3zHGNHakLe8Yo44x5iNjTGFjTDFjTBtjzFNjzHljTGHH+v7GmNccw2TqGWPuxFa9rN7tSb1ywac03H/qIulTp+TlzOkizHvl1j8cv3iNInncI8zf5Y2K9Jmxkj/+3k2bGqUZtWgjPZpUi9ZyRIeoPOrYPVsWLl8KTHPlshfu2bKEm9fb+yELF61k7541nD93iXv3HlC6dHGWLYvbc9BqXQT384Dv6N1zMP4RnNRLly3Ohq1LmD1/Eq8WyBew3ADzFk/h700L6NC5JQAPvR+yfOkaNmxZzMXzl3lw/wElShZm9cp1MV2U/0zrwnLq+BnKVChB2pfS4JYsKVVrVSRrtszB0mTOkhGvq4EPgbl29QaZs2YKN+/Dh49Ys2I9i9bP5MrFq3jf96ZI8UKsX705tov3wvS4sOi5M3oY4xcjr4iIiDMwBqiPdU9hGxEJ+WTEHoCnMaYYUA0YKiJJIiuPDl9RkfLx9WPToZN88lbw8emrdkU+9/ijJ8/4Yux8vmpVh5TJgg9zCZm/QM4s/PGddTG57+QFMqZNhTGGr8YvxMXZGg6TPo39U9hF5VHH4aWJKO+QoeMYMnQcABPGD+an3oN55+021K5dFQ+PY/zSf0SovHbTughUu241bt68w+GDR6lYqWyYaQ4fOkrJwjV4+PARtWpX4fdZYyhX0uode7NOG65fu0GGDOmYt/g3Tp88y47texk9YjKjR0wGYPiofgz8ZSTtOzanWo1KeB45wbAh42KtjFGldRHo7KnzTBr1O1PmjebRw0ccP3oqdG92OJ+FiPJOGT2DKaNnANB32PeMHDie5u0a83q1cpzwPM344VNjvGwvSo+LQHrujNfKAqeNMWcBRGQ20BjwDJLGAKnE+mOlBO4AkX5doT3lKlJbPU5TIGeWYA1iXz9/1u0/Qb0yIS8OA/n4+vH5uPm8Ub4wtUoFH9ETUX5jDBOXb+W9BpWYsGwzHzSuQoMKRZi1bk/0Feo/iMqjji9f8SJ7jsA07tmzctXrepTyFi9uXaicPHmWDu2b06Ztd1577VXy5csdE8X5T7QuApUrX5J69Wuw7/A6Jk0dRqUq5Rk7cXCwNN4PHvLw4SMA/l67GRcXF9KlewmA69duAHDr1h1WLl9LiVJFg+UtUrQgAGdOn6dl6yZ07fwpBQrlJ0+euDW8C7QuQlowaynNanWgQ+P3uHf3frDx5ADXvW4E6z3Pki0TN67djFLegoVfAeD82Ys0bvkGn737HfkL5OXl3DliuFQvTo+LQHrujCb23OjpDgT9IF52LAtqNFAQuAp4AJ8YE/mGtVGuIrVq91Hqh+gR33XsHLmzpidzurAfsmGM4afpy8mTNQMd65QPtT6i/Eu3H6ZK0fykTpGMx898ERFE4Mkzn+gp0H8UlUcdL1++hg7trCfPlStbkvv37nPt2o0o5e3d62t+6j0EV1dXnJ2dAfD39yd58mSxU8AXoHURqF/vYRQrVJVSRWvy7jufs3XzTj7o9lWwNJkyZQj4vUTJIjg5OXHnzl2SJ09GipQpAEiePBnVarzOcc/gzwX79vtPGPDzSFxcXYLVRbLkbjFcshendRFcugxWozKre2Zqv1mdFQv/CrZ+/erNNG75JgDFShXmwX1vbt64HaW8n3zbnVEDJ+DiElgXxt8ftzhYF3pcBNJzZ9wWdGppx6tb0NVhZAk5RVxdrIk6sgHFgdEiEulTyXT4iorQ46c+7PQ8x48d3gi2fPXu0ENXbvzzgN7TljPm0zYcOH2J5Ts8yO+eKWDKw4+aVqdy0Xzh5n++v6XbDzP+s7YAdKxdji/GzcfV2ZkB3ZrGRBFfWNBHHTs7OTFt+hw8PU/S7d0OAEycNIOVq9ZRr14NThzbxqPHj+na9fMI8z7XqFFd9u47GNDrsXPnPg7s/xsPj2McPuwZOhibaV1ErtM71hR106fOpmHjunTu0gZfXz+ePHlCt3esusiYKT3T/hgDgIuLMwvnL2f9ui0B26j/Zk0O7PcI6Cncs+cAm7YvxfPoSY4eORHLJfr3EmtdjJw6kLQvpcHX15c+3w7i/r0HtOpkzUI1Z/pCNv29jSq1XmfN7kU8efSE7z7pE2He52rWr4rHQU9uXL8FwMG9Hizd+Ccnjp3mxNEIH/YcpyTG40LPndEkhh70Y4yZCEwMZ/VlIOhXUdmxesSDehsYYKxxRadF5BzWBB27iYDEtfmfVfR6smWG/oGBlDVD3RytFC8ls/8eBRX3pE8aaYdWonH76X27Q4gz7j72tjuEOMX32RXb5yp+sm9xjLRx3Eo1CbdsIuICnARqAleAPUBbY8zRIGnGAdeNMT+JSGasabWLPZ/8IzzaU66UUkoppVQUGGN8ReRD4C/AGZhqjDkqIt0d68cDfYFpIuKBNdzlm8ga5KCNcqWUUkopFR/52zMvvzFmJbAyxLLxQX6/CtR50e3qjZ5KKaWUUkrZTHvKlVJKKaVU/BP5LIPxijbKlVJKKaVU/BNDs6/YRYevKKWUUkopZTPtKVdKKaWUUvFPAhu+oj3lSimllFJK2Ux7ypVSSimlVPyjY8qVUkoppZRS0Ul7ypVSSimlVPyTwHrKtVGulFJKKaXiHWPseaJnTNHhK0oppZRSStlMe8qVUkoppVT8k8CGr2hPuVJKKaWUUjbTnnKllFJKKRX/JLCHB2mjXCmllFJKxT8JbPiKNsoTuGxv9LE7hDjh4dF5docQZ6Qp0truEOKMRz5P7Q4hzkidJLndIcQZl7xv2h1CnOGXwBo9SsVl2ihXSimllFLxTwIbvqI3eiqllFJKKWUz7SlXSimllFLxTwIbXqWNcqWUUkopFf/o8BWllFJKKaVUdNKecqWUUkopFf8ksOEr2lOulFJKKaWUzbSnXCmllFJKxT/aU66UUkoppZSKTtpTrpRSSiml4p8ENvuKNsqVUkoppVT8o8NXlFJKKaWUUtFJe8qVUkoppVT8k8CGr2hPuVJKKaWUUjbTnnKllFJKKRX/JLAx5dooV0oppZRS8Y8OX1EqavLlz82mbUsDXheuHKD7B53tDitG9fx1ClXbfUzTD34IWDZ25mJqdfyMFh/1pMVHPdmy51DAuslzl/Pmu9/Q8L3/sW2fhx0hx7qkSZOyZctSdu9ezf79f/Pjj5/bHZLtnJyc2L5jBfMXTLE7lFg1bHQ/PE5tYcP2JQHL0qZNw+xFk9m2bxWzF00mTZrUNkZon8R6TISk54vg6tapxtEjmznuuZWvv+phdzgqmmmjXMWY06fOUfX1RlR9vRHVKzfh0ePHLF+2xu6wYlSjWpUY1zv0P432Teowb1Qf5o3qQ+UyxQA4c/EKqzfvZtHYfozr/Tk/j5uBn1/CuuoPy9OnT6lXrzVly9ajbNl61K5dlbJlS9gdlq169HibE8dP2x1GrJs7axFtm3cLtuzDz7qyddNOXi9Vn62bdvLhZ11tis5eifWYCEnPF4GcnJwYOeJnGjRsT5Fi1WnVqgkFC+a3Oyx7+fvHzMsm2ihXsaJqtYqcP3eRy5eu2h1KjCpd+FXSpEoZpbQbdh6gXpWyJHF1JXuWjOTMmokjJ8/GcIRxw8OHjwBwdXXB1dUFY4zNEdknm3sW6tWrwbRps+0OJdbt3L6Pu3fvBVtW940azP1zMQBz/1xMvTdr2hCZvRLzMREWPV9YypYpwZkz5zl37iI+Pj7MnbuERg3r2h2WikbaKFex4q3mb7Jg3nK7w7DN7OXraPbhj/T8dQr3vR8CcOP2XbJkTBeQJnOGdFy/fdeuEGOVk5MTu3at4tKlA6xbt5U9ew7aHZJtBg3qyfc/9MffP3E2NELKmCk9N67fAuDG9VtkCPIZSSz0mAhOzxeWbO5ZuHQ5sGPr8hUvsmXLYmNEcYD2lCv1YlxdXan3Rg2WLFpldyi2aPVGdVZMGsS8kb3JkC4tQyZbvV9h/bsVkdgNzib+/v6UK1efvHnLUaZMMQoVesXukGxRr34Nbt68zcEDR+wORcURekyEpucLS1j/HxLrtwYBjImZl020Ua5iXK06VTh80JObN2/bHYot0r+UBmdnJ5ycnGhWtyoeJ88BkDn9S1y7eScg3fVbd8iULq1NUdrj3r37bN68kzp1qtkdii0qlC/Nm2/WwvPYVqb/PoqqVSsyZcpwu8Oy1c0bt8mUOQMAmTJn4FaQz0hioMdE+BL7+eLKZS9yZM8W8D67e1a8vK7bGJGKbtooVzGuWfMGLJifeIeu3LzzT8Dv63fsI//L7gBUK1eC1Zt388zHh8vXbnLh6g0Kv5LHpihjT4YM6QJm1HBzS0qNGpU4ceKMzVHZo1evQbySvwKFClaiU8eP2LRpO126fGZ3WLZas2oDLds0AaBlmyb8tXK9vQHFMj0mgtPzRaA9ew+SL19ucuXKgaurKy1bNmbZ8oQ9eUKkEtjwFZ2nXMWoZMncqFbjdT775Ee7Q4kVXw8az16P4/xz35tanT7ng3ZN2OtxnONnLyIiZMuUgZ4fdgIg38vu1Klchibvf4+zszPfvd8eZ+eEf52cJUsmJk8ehrOzM05OTixYsJxVq9bZHZaywdjJg6lYqSzp0qdl39H1DBkwmtHDJzFh2nDadGjGlctedOuUeBukSs8XQfn5+fHJpz+wcsUsnJ2cmDZ9Dp6eJ+0OS0UjicnxSCLiB3gAroAvMB341ZgXn+1dRKoBz4wx2yNJ1xkobYz5MII0HYGvAXG8phpjhrxoTCG2mQ0YaYxp/l+2E93SpcqfyAecWbz2T7c7hDgjTZHWdocQZzg7JfyLoKhKnSS53SHEGfefPbI7hDjDL4E9MfG/8PX3szuEOMX32RXbb4J6PPPHGGnjJGvX15ayxfR/pMfGmOLGmNeA2sAbQK9/ua1qQMX/GpCI1Ac+Beo44ioJ3IswU/D8YX67YIy5Gtca5EoppZRSKnqJSD0ROSEip0Xk2zDWfyUiBx2vIyLiJyKRTiUVa91ExpgbQDfgQ7F0FpHRz9eLyHJHb/jzwu4XkUMisk5EcgHdgc8cBawsIg1FZJeIHBCRv0UkcxRD+R/wpTHmqiOuJ8aYSY79visiexz7XSAiyR3Lp4nIMBHZAAwUkXyOfR5yxJlXRHKJyBFH+s4islBEVovIKREZFKScbUTEw/FHGhhkubeIDBSRfY5tlxWRjSJyVkQaOdLkEpEtjn3uF5H/fJGilFJKKRUvGf+YeUVARJyBMUB9oBDQRkQKBQvLmMGOTuniWO3OTcaYSO9aj9Ux5caYsyLiBGQKL42IZAQmAVWMMedEJJ0x5o6IjAe8nw8zEZGXgPLGGCMiXbGGo3wRhTAKA/vCWbcwSAO9H9AFGOVY9wpQyxjjJyK7gAHGmEUi4oZ1cROyTMWBEsBT4ISIjAL8gIFAKeAusEZEmhhjFgMpgI3GmG9EZBHQD+vbhUJYw36WAjeA2saYJyKSH/gTKB1GHXbDugAiedKMJHVNE4VqUUoppZSKR+wZXlUWOG2MOQsgIrOBxoBnOOnbYLXXImXHgMrIxumUBzYbY84BRHBlkR34S0Q8gK+A16IhtsKOnmgPoF2Ibc5zNMhTAe7GmEWO+J4YY8IagLjOGHPPGPME6w/1MlAGq+F90xjjC8wEqjjSPwNWO373wLqq8nH8nsux3BWY5IhvHlaDPRRjzERjTGljTOnoapAnTZqEtRvms3n7UrbvXsm3330cKk2q1CmZNXdCQJq27ZsBkC9/bjZtWxrwunDlAN0/6AxArz5fsWXHMsZOCPgygZatG/Pe+52iJe7oNGPxXzT94HuafvADXw8az9NnPgydOodG3f9Hsw9/5NN+o7jvHfZY1LDyAnw1cCwtPupJi496Uu+dL2nxUU8ADnieotmHP9Lms95cvGpNeXXf+xHdfxwS5+alnTBhMBcv7mffvrXhpqlSpTy7dq1i//6/Wbt2bsDyNGlSM2vWeA4dWs/Bg+soV64kAP36/Y89e/4KNhVc27Zv0aPHOzFXkP/I3T0rK1f9yb79f7Nn7xo++ODtUGlatWrMrl2r2LVrFevWL6BIkYIB68aNH8T583vZs+evYHn69v2WXbtWMWnS0IBlbdo0DXP7ccnuw2tZv20xa7csZPWGuaHWV6hUhhMXdrF2y0LWblnIZ1+/D1gPSJm/7Dc271rGxh1L6dq9fUCe73/6nHXbFjFyfP+AZc1bNQyWJq7R4yKQniuCq1unGkePbOa451a+/qpHmGmGD+vDcc+t7N+3lhLFC0eat/8v37F/31p+mzoiYFm7ds346MMuMVeQxMcduBTk/WXHslAcIy7qAQuisuFYbZSLSB6s3uIbWDd+Bt2/2/NkhP1clZBGAaONMUWA94Lkj8xRrJ7qsEwDPnRss3eIbT4MEl9UPA3yux/WtxIR5fUxga0t/+f5HTfFPv9G4zPgOlAMq4c8SRRj+c+ePn1GkwYdqVKxEVUqNqJmrSqULlM8WJqu3dpz4vhpqlRsRMM32tP3529xdXXl9KlzVH29EVVfb0T1yk149Pgxy5etIVXqlJQtV4LKFRri7OxMwUKv4OaWlDbt3mLKpJmxVbQouX7rLjOX/c2fw3uxaGw//P39Wb15FxWKv8bCMf1YMLovL7tnZkoYTy0NLy/A4G8+YN6oPswb1YdaFUtTs6J1aP6+aDXD/teDjzs2Z+7KDQBMnL2Uri0bxLkHDM2YMY9GjTqGuz5NmtSMGPEzzZt3oWTJWrRt+37AuqFDf2Lt2o0UK1aDMmXqcfz4aVKnTkWFCqUoU6Yuzs5OvPbaq7i5JaVDhxZMmPB7bBTpX/Hz8+W7//WjVMlaVK/WlG7vdaBAgXzB0pw/f4m6dVtRrlx9Bg4YxajRgY3LP2bMp0mT4BejqVOnolz5kpQrVx9nZ+eAumjfvjkTJ86IlXL9F80bdqZ25beoV71lmOt37dhH7cpvUbvyWwwfNA4AX19fev8wiCrlGvJm7dZ07tqWV17NS6rUKSlTtgQ1X2+Ks5MzBQrlx80tKS3bNmXa5Lj7OHo9LgLpuSKQk5MTI0f8TIOG7SlSrDqtWjWhYMH8wdLUr1eD/PlyU6BQJd5//xvGOI6L8PKmTp2KCuVLU7JUbZydnShcuABubm506tCSceMT6GQHMfTwIBHpJiJ7g7y6BdlrWP+Ew2u3NgS2RWXoCsRio9wxLGU8VkPaAOeB4iLiJCI5sL4OANgBVBWR3I58zwfGPwBSBdlkGuCK4/cX6VbtDwwSkSyO7ScVkefdvqkALxFxxeopD8UYcx+4LCJNguSP6rQFu7DKlsExJqkNsOkFYk8DeDka6h0A5xfI+589fGj1Aru6uuDi6hKqx9YYQ8qUKQBIkSI5d+/ew9fXN1iaqtUqcv7cRS5fuorxNyRxta4r3JK54evry0efvMvE8b+HyhcX+Pn58fTZM3z9/Hjy9BkZ06WlYsnCuDhbf4air+bl+q27Uc4blDGGv7bupn6VcgC4uDjz9JkPT54+xcXFmUteN7hx+y6lixSI0TL+G1u37ubu3X/CXd+qVWOWLFnFpUvW46GfP0QqVaqUVKpUlt9+sxpUPj4+3Lt3H39/f1xdXQFwc3PDx8eXzz/vzpgxU+PkcfHctWs3OXjwKADe3g85ceJMqEdg79q1n3/+uQ/A7t37cXcPXL9t227u3Al+z7m/vz9JkgR+Rnx8fPn0s/cYO25anK6L/+LG9Vt4HDoGwEPvR5w6eZYsWTNZx0USx3GRLCm+Pr68//E7TJnwR5yuCz0uAum5IlDZMiU4c+Y8585dxMfHh7lzl9CoYd1gaRo2rMuMmfMB2LV7P2nSpiFLlkzh5rWOC6s+kiVzw8fHhy+/6M6oMVPifH3ENUFHHDheE4OsvgzkCPI+O3A1nE21JopDVyDmG+XJHDdmHgX+BtZg9UADbAPOYQ3PGALsBzDG3MQaD71QRA4BcxzplwFNn9/oCfwEzBORLcCtqAZkjFmJNUD/b0dc+wjsif4Rq+G8FjgewWY6AB+LyGFgO5AlgrRB9+2FNeB/A3AI2G+MWRLV2IGxQCcR2Yk1xv1hJOmjlZOTE5u2LeXE2Z1s3LCNfXsPBVs/ecIfvPJqXjxPbWPrzuX875t+oRrubzV/kwWO3mRv74csXfoXm7Yt5eKFS9y/94ASpYqwakXcm4M2c4aX6NS0HnXe/pKaHT4lZfJkVCxZOFiaRWu3UKl0kX+Vd9/Rk6RPm4aXHf+Mu7RoQO/R0/hjyVpaN6jJqN8X0KN905grYAzKnz8PadOmYc2aOWzfvoJ27axhTblz5+TmzTtMmjSUnTtXMm7cQJInT4a390MWL7a+yj9//hL37z+gVKliLF8e/lfecU3OnNkpVqwQe/YcDDdNp06tWLNmY4Tb8fZ+yJLFq9ixcyUXzl/i/v37lCpVlBXxoC6MMcxeNJm/Ns6jfacWYaYpVbY4f29dyMx5E3glRO8xQPac2ShSpCD79x3mofcjVixdw9otC7l44Qr37z+geMnC8erhQnpcRCwxnSuyuWfh0uXAdtzlK16hLtbcs2Xh8qXANFcue+GeLUu4eb29H7Jw0Ur27lnD+XOXuHfvAaVLF2fZsgT8gCF7Hh60B8gvIrlFJAlWw3tpyEQikgaoCkS5nRejN3oaY8LtyXX0lofXG70KWBVi2UmgaIikoQpqjJmGNQwlorh+A34LY/k4YFwYyzuHeH8KqBHGpguHFYMxpkGQ32cBs8LYR8ogv/8U1jrHfoPWwf/CiCHG+Pv7U/X1RqROk4oZs8ZSsGB+jh07FbC+Rs3KHDl8jMZvdiB3npwsXDKNKtv38uCBNwCurq7Ue6MGfXoFTgk/6tdJjPp1EgAjRv9M/34j6NCpBdVrVOLokRMMHTw2NosYrvveD9mw6wCrpgwiVYrkfDlgLMs3bKdBdWsCnIlzluHi7Myb1Sq8cF6AVZt2BfSSAxTIk5OZQ60HLu09coKM6dJisMaguzg782WX1qR/KX7cwOvi4kyJEkWoX78NyZK5sWnTYnbt2o+LiwslShTm8897smfPQYYM+YmvvvqA3r2HMmzYeIYNGw/AuHED6dNnKG+/3ZqaNatw5MgxBgwYFcle7ZMiRXJm/TmOr7/uE3Dsh1SlSgU6dmpF7VqRz6I6fPgEhg+fAMCYsQPo13c4nTq3ombNyhw5cpxBA0dHsgV7NKrbjuvXbpI+QzrmLJ7M6VNn2bk98B57j0OelClSi0cPH1GjdhV+mzmK10vVD1ifPEVypvw+gp7f9cf7gdX/MHbkVMaOnArAkJF9GPzLaNp2aEbVGq9z7OgJfh0yIXYL+QL0uIhcYjpXhDUMMWQnVnhpIso7ZOg4hgy1mjETxg/mp96DeeftNtSuXRUPj2P80n9EqLzxmg03ehpjfEXkQ+AvrBELU40xR0Wku2P9eEfSpsAaY0yUO1D1yRnqhd2/94BtW3ZRs3aVYMvbdmgWcEV+7uxFLly4TP4gj42vVacKhw96BnwlGVSRotY9q2dOn6NVm6a80+kTChbKT568L8dgSaJu50FPsmfOSLo0qXF1caFmhVIcPHYagCXrtrJ59yH6f9ktzJNlRHkBfP38WLdjH3WrlA2V1xjDpDnLeK9NI8bPWsIHbZvyZvWKzFz2d8wVNppduXKNtWs38ejRY27fvsvWrbsoWrQQV654ceWKV0Cv4aJFKylePPg3CMWKWfdanzp1lnbtmtG+/QcUKvQqefPmiuVSRI2LiwuzZo1nzuzFLF3yV5hpChcuwJixA2jV8l3u3PknytsOWhdt275Fxw4fxum6uH7tJgC3b91h1fJ1FC8ZvE/F+8FDHjmGxK1fuxlXVxfSOYZ1ubi4MOX3X1k4bzkrwzjWCxe1boQ8c/o8Ldo05r23P+fVgvnJnSdunC9C0uMiahLTueLKZS9yZM8W8D67e1a8vK4HS3P5ihfZcwSmcc+elate16OUt3hxqz5OnjxLh/bNadO2O6+99ir58uWOieIkOsaYlcaYV4wxeY0xPzuWjQ/SIMcYM80Y80JP60uwjXIR+V4CJ25//vre7rjiq/QZ0pE6jTWk380tKVWrV+TkybPB0ly+dJWqVa2e4owZ05Mvf27Onw+8QblZ8wYsmB/6RkiA7378lP79RuDi6hLwqHl/f0OyZMliojgvLEvGdBw+cYbHT55ijGHXIU/y5MjG1n0e/DZ/FSN7fkwyt6QvlPe5nQc9yZ09K1kyhH6uwNJ126hcuiipU6bgydNniJPgJMKTp89irKzRbdmyNbz+elmcnZ1JlsyNMmVKcPz4Ka5fv8nly17kz29duFWv/nqwb14AevX6kj59huHq6oqzY+y+Mf4kTx43jouQxo0byIkTpxk1akqY67Nnz8asP8fTtctnnD597oW2/WPPz+nbN0Rd+MfNukiWPBkpUiYP+L1q9YqcCPG3zZgpQ8DvxUsWwUmcAhqjw0b35dTJs0wYE/bNaV9/9xGDfxmFq6sLTk7Pzxf+JEse1fv9Y5ceF1GTmM4Ve/YeJF++3OTKlQNXV1datmzMsuXBh5ksX76GDu2sb03KlS3J/Xv3uXbtRpTy9u71NT/1HhKsPvzj6XERIRvmKY9JsTpPeWxyXLn8bHccCUXmzBkZO2EQzs5OODk5sXjhKtas3kDnd9oAMG3qnwwZOIYx4weydedyRITePQdz57Z142OyZG5Uq/E6n33yY6htv9GgFgf2HebatRsA7Nl9gK07l3P0yAmOHoloaH/sKfpqXmq9XppWn/6Es5MzBfPmpHm9qjT94Aee+fjw3g9DAtL9+GEnbty+y08jf2Ns78/Dzfvc6s3Bh6489/jJU5au28b4vtb0+x2a1OXzX0bj6uLCwK+7x07Bo+D330dRuXIFMmR4idOnd9Gv3zBcXKybjSZP/oMTJ06zZs1G9u5dg7+/P7/9NhtPz5MAfPZZT6ZNG0mSJK6cO3eRbt2+DNhuw4Z12LfvUEAP0M6d+9i7dw1HjhzDw+NY7Bc0EhUqlKZtu2Yc8TjGjp0rAfip1yCy57BmypoyeSb/++5j0qV7iV9H9AOsmUYqV2oEwLRpI6lcpTzp07/EyVM76NdvOL9Pt6aEa9CwDvv2Heaal/UZ2b1rP7t3r+bIkeNxsi4yZkzP1JkjAXBxdmHR/BVsWLeVjm+3AuD33+bQoHEdOr3TGl8/X548fkr3LtZxXrZ8SVq0bozn0ROs3bIQgP59fmX92s0A1HuzJgcPHAnoid+35xDrty3m2NGTeB45EdtFjZQeF4H0XBHIz8+PTz79gZUrZuHs5MS06XPw9DxJt3c7ADBx0gxWrlpHvXo1OHFsG48eP6Zr188jzPtco0Z12bvvYLD6OLD/bzw8jnH4cHhTaau4QOLanMcqeqVLlV//wIDX/gQ6HdS/kKbIC32blqA5OyXYLwtfWOokUZ1EKuG7/yzs5w0kRn72PJwlTvL197M7hDjF99kV2+fnfTTxsxhp4yTvNtyWsiXYnnKllFJKKZWAJbCLRu0mUkoppZRSymbaU66UUkoppeIfG2/KjAnaU66UUkoppZTNtKdcKaWUUkrFP/4Jay4LbZQrpZRSSqn4R2/0VEoppZRSSkUn7SlXSimllFLxj/aUK6WUUkoppaKT9pQrpZRSSqn4J4E9lV57ypVSSimllLKZ9pQrpZRSSqn4J4GNKddGuVJKKaWUin8S2DzlOnxFKaWUUkopm2lPuVJKKaWUin9Mwhq+oj3lSimllFJK2Ux7yhO4+08f2R1CnFCwQg+7Q4gzHlzeaHcIcUbqHNXtDiHOeOrnY3cIccZTX60LpeKFBDamXBvlSimllFIq3jEJbPYVHb6ilFJKKaWUzbSnXCmllFJKxT8JbPiK9pQrpZRSSillM+0pV0oppZRS8U8CmxJRG+VKKaWUUir+0eErSimllFJKqeikPeVKKaWUUir+0SkRlVJKKaWUUtFJe8qVUkoppVT8o2PKlVJKKaWUUtFJe8qVUkoppVT8o1MiKqWUUkopZTMdvqKUUkoppZSKTtpTrpRSSiml4h2jUyIqpZRSSimlopM2ylWMqlunGkePbOa451a+/qqH3eHYzsnJiWXr/2TyrBF2hxLr/Pz8aN65Bx981QuAMVP+oEbj9jTr1INmnXqweftumyOMfePHD+bChX3s3bvG7lBs5+6ehSUrZrBz72q2717Je+93sjskW+m5M5DWRSCtixD8Tcy8bKKNchVjnJycGDniZxo0bE+RYtVp1aoJBQvmtzssW739XlvOnDpndxi2+GPeEvLkyhlsWYdWTVgwfQwLpo+hSsWyNkVmnxkz5tG4ceJufD7n6+vHj9/1p3zpetSp0YIu3drx6qv57A7LFnruDKR1EUjrIgzaKFcqasqWKcGZM+c5d+4iPj4+zJ27hEYN69odlm2yZM1E9dqVmPPHIrtDiXXXbtxk8/bdNEvEf/+wbNu2mzt3/rE7jDjh+vWbHD7kCYC390NOnjhD1myZbY7KHnruDKR1EUjrIuHTRrmKMdncs3Dp8tWA95eveJEtWxYbI7LXjz9/xYDeI/BPYDemRMXAERP4/IMuiAQ/5fy5YBlNO77PD78M4979BzZFp+KaHDndKVq0EPv2HrI7FFvouTOQ1kUgrYswGP+YedlEG+UqxohIqGXGJKw5RaOqRp3K3L51hyOHjtkdSqzbuG0X6V5Ky2sFgn/N2qrpm6yaO5UF08aQMX06Bo+eZFOEKi5JkSI50/8YzXff/syDB952h2MLPXcG0roIpHWR8OmUiCrGXLnsRY7s2QLeZ3fPipfXdRsjsk+pssWpWa8q1WpVImnSJKRMlYJh4/rx+fs/2B1ajDtw2JONW3eyZccenj7z4eHDR3zTexADe30dkKZ5o/r0cNwAqhIvFxcXpv8xmvlzl7J8aeK9+VXPnYG0LgJpXYTBpvHfIlIPGAE4A5ONMQPCSFMN+BVwBW4ZY6pGtl3tKVcxZs/eg+TLl5tcuXLg6upKy5aNWbY8cf6jHdxvFK8XrUeVkm/ycbdv2bF1T6JokAN89v7brFv8B2sWTGdw728pW6oYA3t9zc1bdwLSrNu0nXx5XrYxShUXjBzzCydPnGHs6N/sDsVWeu4MpHURSOsiNONvYuQVERFxBsYA9YFCQBsRKRQiTVpgLNDIGPMa0CIq5YlXPeUi4gd4BFnUBJhljKnouCL50hjTIIZj6AyUNsZ8GJP7SQj8/Pz45NMfWLliFs5OTkybPgdPz5N2h6XiiKFjp3Di1FkQcM+SmV5ff2x3SLFu+vSRVK5cgQwZXuL06Z307Tuc6dPn2B2WLcpVKEXrtk05euQ4m7YtBaBv76H8vWaTzZHFPj13BtK6CKR1EWeUBU4bY84CiMhsoDHgGSRNW2ChMeYigDHmRlQ2LPFpPJKIeBtjUoazrhpRaJSLiLMxxu8/xNCZeNQod0niHn/+wDEoZ+pMdocQZ5w4vsDuEOKM1Dmq2x1CnJHMJYndIcQZ958+sjsEpeI832dXQg9yj2UPPm4QI22cVCOXh1s2EWkO1DPGdHW87wCUC9ouFJFfsYatvAakAkYYY36PbL/xfviKiAS9Eyi1iCwSEU8RGS+OqR5ExFtE+ojILqCCiHwuIkccr0+DbGuxiOwTkaMi0i3I8rdF5KSIbAJeD7I8o4gsEJE9jtfrjuU/ichUEdkoImdF5OMgeULtW0RyichxEZnsWD5TRGqJyDYROSUiZR3pyorIdhE54Pj5aoxUqlJKKaVUIiUi3URkb5BXt6Crw8gS8uLABSgFvAnUBX4UkVci22+8Gr4CJBORg47fzxljmoZYXxZrfM8FYDXwFjAfSAEcMcb0FJFSwNtAOayK3SUim4wxB4B3jDF3RCQZsEdEFgBJgN5YlXsP2AAccOxvBDDcGLNVRHICfwEFHesKANWxrpBOiMg4oGhY+wbuAvmwxhx1A/ZgffVRCWgEfIc1VOc4UMUY4ysitYBfgGb/tjKVUkoppeKtGJpi2BgzEZgYzurLQI4g77MDV8NIc8sY8xB4KCKbgWJAhOON4luj/LExpngE63cHGePzJ1ajdj7gBzz/zr4SsMhRUYjIQqAyVkP7YxF53tDPAeQHsgAbjTE3HennAM+vdmoBhYJMU5RaRFI5fl9hjHkKPBWRG0DmCPa9FOsiw8Ox/CiwzhhjRMQDyOXYZhpguojkx7oqcw2rEhxXdN0AxDkNTk4pIqgypZRSSikVRXuA/CKSG7gCtMbqSA1qCTBaRFywOnfLAcMj23C8H74SQsivD56/fxJkHHmY44QcY9JrARWMMcWwGulu4Wz3OSdH+uKOl7sx5vkTUJ4GSeeHdQEU0firoOn9g7z3J/DiqS+wwRhTGGgYJL5gjDETjTGljTGlo7NBXrdONY4e2cxxz618/VWPMNMMH9aH455b2b9vLSWKF440b/9fvmP/vrX8NnVEwLJ27Zrx0Yddoi3u6JY738ss3zA74HXo3Bbefi/k5xHKvV6K5Rtms3rrfP5cOjlgeedubVi1ZR6rt84Plu+bnh+zctMchozpG7CsSYs36dytTcwW6F/4ffYiGrd7jybtu/NVrwE8ffoMgJnzltCgdVcat3uPoWOmhJm3TrNONO3wPs069aDlO6Fv7vxt1nwKv16fu//cA2D/4aM07fg+rbp8zEXHgzPuP/Cm22ffx7k5esePH8yFC/vYuzfsGRHSpk3NnDkT2L17NVu2LKFQIev6Pnv2rKxePZsDB9axb99aevR4OyBPv37fsnv3aiZPHhawrE2bpsHSxDVJkyZh7Yb5bN6+lO27V/Ltd6H/zqlSp2TW3AkBadq2D/zS7+CRDWzduZxN25aybtPCgOW9+nzFlh3LGDthUMCylq0b8977nWK2QP+RnjsDaV0E0rqIBv4mZl4RMMb4Ah9ijY44Bsw1xhwVke4i0t2R5hjWiI3DwG6saROPRFachNYoLysiuR1jyVsBW8NIsxloIiLJRSQF0BTYgtULfdcY80hECgDlHel3AdVEJL2IuBJ8Wps1WH8YAESkeCTxhbfvqEqDdVUG0PkF8v1nTk5OjBzxMw0atqdIseq0atWEggWDPwymfr0a5M+XmwKFKvH++98wZnT/CPOmTp2KCuVLU7JUbZydnShcuABubm506tCSceOnx2bxXsi50xdoUL01Daq3plHNtjx59IS/VmwIliZV6pT0GfQd3dp/Sr1Kzfnwna8AeKVAXlp1eIumdTrwZtVW1KhThVx5cpIqVUpKlinGG1Vb4ezsxKsF85HULSnN2zTkj6nz7ChmuK7fvMXM+UuYM3Uki/8Yj7+/P6v+3sTufYfYsHUnC38fy5KZE+jcNvyRVVNHDWDB9DHMnToy2HKv6zfZsecAWTMH3pg7/c+F/PrzD3zyXmfmLFoBwIRpf/Jux1ZhPkzDTjNmzKNx4/AbiF9//SGHDnlStmw9unT5nCFDfgLA19ePb7/tR4kSNalatQnvvdeRAgWsz0j58qUoW7Yezs7OvPbaq7i5JaVDhxZMmDAjlkr14p4+fUaTBh2pUrERVSo2omatKpQuUzxYmq7d2nPi+GmqVGxEwzfa0/fnb3F1Dfzyr9GbHaj6eiNqVn0LsD5TZcuVoHKFhjg7O1Ow0Cu4uSWlTbu3mDJpZmwW74XouTOQ1kUgrYtoYkOjHMAYs9IY84oxJq8x5mfHsvHGmPFB0gw2xhQyxhQ2xvwaleIktEb5DmAAcAQ4BywKmcAYsx+YhnXlsgvr6uUA1hWNi4gcxuqR3ulI7wX85Nj238D+IJv7GCgtIodFxBPoHlFwEew7qgYB/UVkG9aE9bGmbJkSnDlznnPnLuLj48PcuUto1LBusDQNG9Zlxsz5AOzavZ80adOQJUumcPP6+/uTJIn1TzhZMjd8fHz48ovujBozBV9f39gs3r9WsUpZLpy/zNXLXsGWN25Wn7+Wr+PqlWsA3L51F4C8r+Tm4D4Pnjx+gp+fH7u276POm9XxN/64OurCzS0pPj6+dPuwE9MmzY6TdeHr58fTp8/w9fXj8ZOnZMyQjjmLV9ClfUuSJLFm8Uj/UtoX3u6gkRP4/IMuBG1ru7i48OTpM548fYqLizMXL1/l+s1blClRNJpKE322bdvNnTv/hLu+QIH8bNy4DYCTJ8/w8svZyZQpA9eu3eDgQasTxdv7IcePnyZbtsxhfEZ8+eyz9xg79rc4eVwE9fChNYOJq6sLLq4uob7VMMaQMqX1TV6KFMm5e/dehGUy/oYkrtax5ZbMDV9fXz765F0mjv89TteFnjsDaV0E0rpQYYlXjfKwpkN8vswYs9EYU8MY08pxZdLdGOMfVj5jzDDHlUvA1Ysx5qkxpr4xpqgxpoUxppoxZqNj3W+OK6KqxphPnk97Y4y55dhf0ef7dCz/yRgzJMj+Chtjzkew7/OOISnP03c2xswPuc4Ys8MRx+vGmB+NMbmipWKjIJt7Fi5dDryP4fIVL7JlyxIsjXu2LFy+FJjmymUv3LNlCTevt/dDFi5ayd49azh/7hL37j2gdOniLFsWfx6G0LBpXZYtXB1qee68L5MmbWpmLZnEknUzadrSmqnz5LEzlK1QkrQvpcEtmRvValUia7YsPPR+xOrl61i+YTaXLl7lwQNvipYoxN+rNsZyiSKXOWMGOrdpRq23OlK9cVtSpUjO6+VKcf7iFfYdOkKbdz+lc4+v8Dh2Isz8IkK3z76n5TsfMW/JyoDlG7bsJFPGDBTInydY+nc7tKT3wBHMmLOYNs0aMnLidD56t2OMljGmeHh40rhxfQBKly5GzpzuuLsH/xzlzJmd4sVfY8+eg3h7P2Tx4lXs3LmS8+cvcf/+A0qVKsby5WvtCP+FODk5sWnbUk6c3cnGDdvYt/dQsPWTJ/zBK6/mxfPUNrbuXM7/vukX0HA3xrBg8W+s37yITm+3AqyLlaVL/2LTtqVcvHCJ+/ceUKJUEVatWBfrZXsReu4MpHURSOsiehhjYuRll/h2o6eySVjDBEIeuOGliSjvkKHjGDJ0HAATxg/mp96DeeftNtSuXRUPj2P80n9EqLxxhaurCzXrVWVwv1Gh1jm7OFO4WEHav/Uebm5uLFg9nYP7DnPm1DkmjJzG7wvG8ejhY44fPYmfn9WDMXHUdCaOsr5i7P9rT4YPGEfL9k2pXK08xz1PMWbY5FD7scO9+w/YsGUnf837jVSpUvLFD7+w7K/1+Pn5cf+BN7MmDufIsZN8+WN/Vs/7LdTff8a4oWTKmJ7bd//h3U+/I/fLOXitQH4m/j6bicN/DrW/Aq/kZdakXwHYe9CDTBnSY4zhix/74+LizFcfvUuGdC/FRtH/syFDxjFkSC927lzJ0aMnOHToKL6+gY9NSJEiOX/+OZ6vvurDgwfWbK/Dhk1g2LAJAIwdO5C+fYfRuXNratWqjIfHcQYODH38xQX+/v5Ufb0RqdOkYsassRQsmJ9jx04FrK9RszJHDh+j8ZsdyJ0nJwuXTKPK9r08eOBN/dqtuXbtBhkypGPh0mmcPHmWHdv2MOrXSYz6dRIAI0b/TP9+I+jQqQXVa1Ti6JETDB081q7ihkvPnYG0LgJpXaiwxKuecmWfK5e9yJE9W8D77O5Z8fK6HizN5SteZM8RmMY9e1auel2PUt7ixV8D4OTJs3Ro35w2bbvz2muvki9f7pgoTrSoWqsSRw8f59bNO6HWXbt6g83rt/P40RPu3vmH3dv3U+A166a+uTMX06hGW1o37MI/d+9x/szFYHkLFbGmnz935gJvtWrAR12/4ZWC+ciVJ2fMFyoKdu49iHu2zKR7KS2uLi7UrFqRgx6eZM6UgVpVX0dEKFLoVUQk4GbNoDJlTA9Yw1tqVqmIh+cJLl3x4srVazTr9AF1mnXi+s1btHjnI27dDqxbYwwTpv3Je53bMG7qTHp0bU/DujWYOW9JrJX9v3rwwJv33vuK8uXfoEuXz8iQIR3nz18CrGE6f/45njlzFrNkSehvX4oVsz4jp06dpV27t2jfvgevvfYKefPmis0ivLD79x6wbcsuatauEmx52w7NAnrwzp29yIULl8n/ivUtybVr1sPvbt26w4playlVKvhQpSJFrSdanzl9jlZtmvJOp08oWCg/efK+HNPFeWF67gykdRFI6yKa2DSmPKZoo1xFyZ69B8mXLze5cuXA1dWVli0bs2x58K/Eli9fQ4d2zQEoV7Yk9+/d59q1G1HK27vX1/zUewiurq44O1vD5f39/UmePFnsFPBfaPhWvTCHrgCsXbWRMuVL4OzsjFsyN4qVKsyZk+cASJ/B6tXN5p6Fug1qsDTENj7/3wcMHzAOFxcXnJysj6jx98ctWZiT7cS6rJkzcvjIcR4/eYIxhl17D5Ln5RzUqFyB3fsOAnD+4mV8fH15KW2aYHkfPX4SMNb40eMnbN+9n/x5cvFK3txsXjGbNQums2bBdDJnzMC8qaPIkD5dQN4lK/+mSsWypEmdisdPn+Ikgojw5MlT4os0aVIH3Mz49tut2bp1d0CP+Pjxgzhx4jQjR4b9jUjPnl/Qt+/QEJ8REyc/I+kzpCN1Gmt2WDe3pFStXpGTJ88GS3P50lWqVq0AQMaM6cmXPzfnz18iefJkAWPNkydPRvWalTgW4lHi3/34Kf37jcDF1QVnZ+sz4u9vSJYs7tWFnjsDaV0E0rqIJgmsUa7DV1SU+Pn58cmnP7ByxSycnZyYNn0Onp4n6fZuBwAmTprBylXrqFevBieObePR48d07fp5hHmfa9SoLnv3HQy40t+5cx8H9v+Nh8cxDh/2jP3CRoFbMjcqVS3HD5/3C1jWtrN18pw1bT5nTp1j0/rtrNw8F39/f+b+sYiTx88AMPa3IaRNlxZfH196fT2A+/ceBGyjdv1qHD5wlBvXbgJwYO9hVm2ey3HPUxw/GuEzB2JN0dcKULt6JVq+/RHOzs4UeCUvLRrXR0T44ZfhNGnfHVdXF3754QtEhBs3b9NrwK+MG9qX23fu8sl31pSPfr5+vFGnGpXKl450n4+fPGHJqr+Z+Ks1vKVTq7f47PufcXV1YdBP38RoeV/E9OkjqVy5AhkyvMTp0zvp23c4rq7WaXby5JkUKJCPyZOH4efnx/Hjp+ne3ZqVp2LF0rRr1wwPj2Ps3GmNs+/VazB//WXN6tOwYR327TuEl5fVg7xr13727PmLI0eO4+FxzIaSRixz5oyMnTAIZ2cnnJycWLxwFWtWb6DzO9b0ntOm/smQgWMYM34gW3cuR0To3XMwd27f5eVcOZgxawxgfXswf+4y1v0dOEnVGw1qcWDf4YDe9D27D7B153KOHjnB0SPHY7+wkdBzZyCti0BaFyosEtfm+VXRyyWJu/6BgZypM0WeKJE4cXxB5IkSidQ5qtsdQpyRzCWJ3SHEGfefPrI7BKXiPN9nV2yfk/be27VipI2T5re/bSmbDl9RSimllFLKZjp8RSmllFJKxT82jv+OCdooV0oppZRS8Y+/3QFELx2+opRSSimllM20p1wppZRSSsU7JoENX9GecqWUUkoppWymPeVKKaWUUir+0Z5ypZRSSimlVHTSnnKllFJKKRX/JLDZV7RRrpRSSiml4h290VMppZRSSikVrbSnXCmllFJKxT8JbPiK9pQrpZRSSillM+0pV0oppZRS8U5CG1OujXKllFJKKRX/6PAVpZRSSimlVHTSnnKllFJKKRXvmATWU66NcpUoJHHSQ/259C/XsjuEOOPemZV2hxBnJM9Vx+4Q4ozkrkntDiHOeOTz1O4QlEo0tKWilFJKKaXiH+0pV0oppZRSyl4JbfiK3uiplFJKKaWUzbSnXCmllFJKxT/aU66UUkoppZSKTtpTrpRSSiml4h0dU66UUkoppZSKVtpTrpRSSiml4p2E1lOujXKllFJKKRXvJLRGuQ5fUUoppZRSymbaU66UUkoppeIfI3ZHEK20p1wppZRSSimbaU+5UkoppZSKdxLamHJtlCullFJKqXjH+OvwFaWUUkoppVQ00p5ypZRSSikV7yS04SvaU65iVN061Th6ZDPHPbfy9Vc97A7HNkmSJmHu6mks3jCTZZvn8NHX3ewOyTZjxg3kzPnd7Nyzyu5QbHHtxi3e+awnjTp9RJPOn/DH/OUAnDh9jnY9vqXpO5/y4Xe/4P3wkc2Rxj49X1gS+2ckJD0uAmldxA0iUk9ETojIaRH5Noz11UTknogcdLx6RmW72ihXMcbJyYmRI36mQcP2FClWnVatmlCwYH67w7LFs6fP6NzsfZpUb0fTGm2pVL0CxUoVtjssW8z8Yz5vNXnb7jBs4+zsxJfvd2Lp9FHMHDuA2UtWceb8JXoNGcun73Zg0dRfqVmpHL/NWWx3qLFKzxeBEvtnJCg9LgJpXYRmjMTIKyIi4gyMAeoDhYA2IlIojKRbjDHFHa8+USmPNspVjClbpgRnzpzn3LmL+Pj4MHfuEho1rGt3WLZ59PAxAC6uLri4umCMsTkie2zftoe7d/6xOwzbZEyfjkKv5AUgRfJk5M6Zneu3bnP+0lVKF7PO6xVKF+PvzTvtDDPW6fkiUGL/jASlx0UgrYvQjH/MvCJRFjhtjDlrjHkGzAYaR0d5tFGuYkw29yxcunw14P3lK15ky5bFxojs5eTkxKL1M9nmuYbtm3ZxeP9Ru0NSNrty7QbHT5+jaMFXyJc7Jxu27QHgr43buXbjls3RxS49X6iw6HERSOsiznAHLgV5f9mxLKQKInJIRFaJyGtR2bA2ylWMEQn9FVBi7R0G8Pf3p2mNdlQr9iZFS7xG/gJ57Q5J2ejR48d81nMQ3/R4h5QpktPn6x7MXrKKlt2+5NHjx7i6Jq778PV8ocKix0UgrYvQjL/EyEtEuonI3iCvoDeChTW+JeQfYj/wsjGmGDAKWByV8iSus76KVVcue5Eje7aA99nds+Lldd3GiOKGB/e92b19H5VrVODU8TN2h6Ns4OPry2c9B/NmrSrUqlIegDw5szNxcC8Azl+6yuad++wMMdbp+UKFRY+LQFoXsccYMxGYGM7qy0COIO+zA1eDJjDG3A/y+0oRGSsiGYwxEX4Fqj3lKsbs2XuQfPlykytXDlxdXWnZsjHLlq+xOyxbvJQ+LalSpwQgqVtSKlQpy9lT5+0NStnCGEOvQWPI87I7nVo2Clh+++4/gPWNysQZ82iZyMaK6vlChUWPi0BaF6EZEzOvSOwB8otIbhFJArQGlgZNICJZxPHVhoiUxWpv345sw9HWUy4i3saYlFFMuxH40hizNya2H8l2UgJDgVrAE6xK+soYs+s/brc78MgY8/t/jTGh8PPz45NPf2Dlilk4OzkxbfocPD1P2h2WLTJmzsCAUT/h7OyEiBOrl/7NxrVb7Q7LFlOnjaBS5XKkT/8Sx05u45d+I5jx+1y7w4o1B44cZ9naTeTP8zLNu34OwMdd23Hxshezl1hT4NWsXJ4m9WvYGWas0/NFoMT+GQlKj4tAWhdxgzHGV0Q+BP4CnIGpxpijjnYgxpjxQHPgfRHxBR4DrU0UxhpJdI1HikuNcsfViRgT+h5aEZkNnAO+N8b4i0geoKAxZkUUt+1sjPGLatx2c0ninrgHnDnkS5st8kSJxNWHkV6sJxq3Ti2zO4Q4I3muOnaHEGckd01qdwhxxiOfp3aHoOIo32dXbH/G/YWStWKkjfPy/r9tKVuMDl8RkeIislNEDovIIhF5Kcjq9iKyXUSOOLr2EZGfROTLIPmPiEiuENtMKSLrRGS/iHiISGPH8lwickxExmINsA863ud53rxAOeCH5w12x5Q2KxzrF4vIPhE5GnRQv4h4i0gfEdmFdTdtR0eZDonIjJCxi8hGERkoIrtF5KSIVHYsdxOR3xxxHxCR6o7lnR37XiYi50TkQxH53JFmp4ikc6R7V0T2OPa7QESS/6c/kFJKKaVUPBVTN3raJabHlP8OfGOMKQp4AL2CrEthjKkIfABMfYFtPgGaGmNKAtWBoc/H7QCvAr8bY0oYYy6Ekfc14GAEPd3vGGNKAaWBj0Uk/fNYgSPGmHLAXeB7oIbjrtpPwtmWizGmLPApgeXuAWCMKQK0AaaLiJtjXWGgLdb8lz9jDYUpAewAOjrSLDTGlHHs9xjQJawdB71r2N//YTjhKaWUUkqpuCLGGuUikgZIa4zZ5Fg0HagSJMmfAMaYzUBqEUkb1U0Dv4jIYeBvrLkhMzvWXTDG/JcnbnwsIoeAnVg97c8fleUHLHD8XgOY//wOWmPMnXC2tdDxcx+Qy/F7JWCGI99x4ALwimPdBmPMA2PMTeAe8Px7dY8g+QuLyBYR8QDaYV1khGKMmWiMKW2MKe3klCIq5Y6SqDzed/iwPhz33Mr+fWspUbxwpHn7//Id+/et5bepIwKWtWvXjI8+DPN6I87o9F4blm2ew9JNsxk6vh9JkiYJtj5lqhSMmzGMxRtmsmzzHN5q3RCALNkyM33hOFZsncuyzXPo8G7rgDxf/PghSzbOYsDonwKWNWpRP1iauMbdPSvLV85kz7417Nqzmvc/6BwqTdq0qZn55zi271rJhk2LKFjolWDrnZyc2LJ9GXPnTw5Y1rvvN2zftZIJk4YELGvdpkmY27fb7/OW0aTzJzR9+xO+7juMp8+eMXbabGq26Erzrp/TvOvnYc6kcu3GLd75rCeNOn1Ek86f8Mf85QHrTpw+R7se39L0nU/58Ltf8H74CIADHsd4q8tntO7+FReveAFw3/sh733VJ85NjabnC4t+RoLT4yKQ1sV/Z9ONnjHGztlXQhbbAL4Ej8mN0NoBGYFSxpjiwPUg6SLrFj4KFBORUOUWkWpYN39WcPREHwiy3SdBetcljNjD8nwgnh+BN9RG9J1I0IF7/kHe+wfJPw340NHT3puw6ydGROXxvvXr1SB/vtwUKFSJ99//hjGj+0eYN3XqVFQoX5qSpWrj7OxE4cIFcHNzo1OHlowbPz22ivbCMmXJSIeurWhepyONqrbGydmJN5sEH4/b7p0WnD55libV29Gx6Xt83fsTXF1d8PP1ZWCvX3mzUkta13+bdu80J+8ruUmZKgUlyhSlcbW2ODs780rBvCR1S0rTVg3587d5NpU0cr5+vnz/3S+UKVWHmtWb8W63DrxaIF+wNF989QEeh49RsdwbdHv3CwYO7hls/fs93ubkicCpIVOnTkW5ciWpWO4NnJ2dKfTaq7i5JaVt++ZMmvhHrJQrqq7fvM2shSuYPWEQi34bgZ+fP6vWWzfwdmjegPmThzF/8jCqlC8VKq+zsxNfvt+JpdNHMXPsAGYvWcWZ89bzKHoNGcun73Zg0dRfqVmpHL/NWQzA9HlLGd77Kz7u2o45S1YDMOH3uXRt1yzMOYztoueLQIn9MxKUHheBtC5UWGKsUW6MuQfcfT6eGugAbAqSpBWAiFQC7jnSnwdKOpaXBHKHsek0wA1jjI9jTPbLLxDTGWAv0DvIVDX5HePS0wB3jTGPRKQAUD6czawDWj4f2vJ8vHcUbca6qEBEXgFyAideIH8qwEtEXJ9vJ7ZE5fG+DRvWZcbM+QDs2r2fNGnTkCVLpnDz+vv7kySJKwDJkrnh4+PDl190Z9SYKfj6+sZm8V6Ys4sLbm5JcXZ2JlkyN25cvxlsvTGQIqX1LUXyFMm59899fH39uHnjNp4e1p/84cNHnDl5nsxZM2L8Da6uVl0kdUuKj48vXXp0YMbk2fj6xt37iq9fu8mhg9aTSb29H3LixOlQT5grUCA/GzduB+DUybO8nNOdjJkyAJAtWxbq1qvO9GlzAtIHPS7c3Kzj4pNPuzF+7LQ4eVz4+vnx9OkzfP38ePL0KZnSR+2UkDF9Ogq9Yj1AKkXyZOTOmZ3rt6ybcM9fukrpYoUAqFC6GH9vtr4AdHF25snTZzx58gwXFxcuXbnGjVt3KFM8Sg+LizV6vgikn5FAelwE0rqIHjqmPHzJReRykNfnQCdgsGOoSXGgT5D0d0VkOzCewLHRC4B0InIQeB8Ia66fmUBpEdmL1TA9/oJxdgWyAKcdw0AmYU36vhpwccTaF2sISyjGmKNYY743OYa6DHuBfY8FnB37nQN0Nsa8yK3tPwK7gLW8eLn/k6g83tc9WxYuXwpMc+WyF+7ZsoSb19v7IQsXrWTvnjWcP3eJe/ceULp0cZYti9vzrt64dpOpY/9g/YFlbPFYxYMHD9m2MfiMmjOnzCVv/lxs9ljF0k1/8sv3Q0MNL3DPkZWCRV7l0L6jPHz4iDUr1rNo/UyuXLyK931vihQvxPrVm2OzaP9JzpzuFC32Gnv3HAy23MPjGI0aW/9sSpUqSo6c7rg7jp0Bg36k5/cD8PcPnCjJ2/shS5asZuuO5Vy4cIn79x9QslRRVq74O9bKElWZM6anc8vG1G71HjWadSFliuRULFMcgD8XreKtLp/x48DR3HvgHeF2rly7wfHT5yha0Bq2kC93TjZs2wPAXxu3c+2G9byJru2a0WfoOP5YsJw2TeszcspMPnynTcwV8F/S80XYEuNnJCg9LgJpXUQPYyRGXnaJtnnKjTHhNfBD9TgbY6qFs43HQJjzcj2fDtExlrtCOPsqHM7yoNu5D7wbzur6Ee07yPvpWGPkgy77Kcjv1YL8fgvHmHBjzBOgcxjbn4Y1NOX5+1xhrTPGjAPGhRN7jArrq/GQjczw0kSUd8jQcQwZahVpwvjB/NR7MO+83Ybatavi4XGMX/qPCJXXbqnTpKJmvSrUKt2YB/ce8OuUATRsXp9l81cFpKlUvTzHjpyk01vvkzN3dqbOHc3enQd56G2NsEqeIhkjpw6k/4/DApZNGT2DKaNnANB32PeMHDie5u0a83q1cpzwPM344S9yP3TsSpEiOTNmjeXbr/vyIEQDdPjQ8Qwc3JOtO5bjefQEhw954uvnS716Nbh18zYHDx6hUuVywfKMGD6REcOth6mNGtOfn/sNp2OnltSoWZmjR44zeNCYWCtbRO498GbD9t2s/nMcqVKm4IufhrBs7SZaNqrHex1aICKMnvonQ8ZOo+83H4a5jUePH/NZz0F80+MdUqawJlTq83UPBoyawvjf51L99TK4ulqn6gL5cjNz7EAA9h46Ssb06TAGvuw9BBcXF758vzMZ0qWNlbJHRM8XoSXWz0hQelwE0rpQYdEneqooicrjfS9f8SJ7jsA07tmzctXrepTyFnd8/X7y5Fk6tG9Om7bdee21V8mXL6wRTPaqUKUsly9e5e7tf/D19WPtig2UKFM0WJqmbRqydsUGAC6eu8zli1fJk98aaeXi4szIqQNZtmB1QJqgCha2ekvPn71I45Zv8Nm735G/QF5ezh1qls84wcXFhT9mjWXunKUsW/pXqPUPHnjzQfevqVShAd26fkH6DOm4cP4y5SqUov6bNfHw3Mxv00dSpWoFJk0J/sVTUccQjtOnztGm7Vt07vgRhQq9Qt68uWKjaJHaue8w7lkyky5tGlxdXKhVuRyHjhwnQ7q0ODs74+TkRLMGtTly/FSY+X18ffms52DerFWFWlUC+y/y5MzOxMG9mDtxCPVrVCZHiB40YwwT/5jPex1bMG76HD54uzUNaldh1sIoPW4hxun5IrjE/BkJSo+LQFoX0cP4x8zLLgm2US4iu0TkYIhXEbvjiq+i8njf5cvX0KFdcwDKlS3J/Xv3uXbtRpTy9u71NT/1HoKrqyvOzs6ANW4yefJksVPAF+B15RrFShXBLZn1gJEKlctw9uS5UGkqVCkDQPqM6cid72UuXbgCQL9ff+TMyfNMGz8rzO1/8m13Rg2cgIuLS0BdGH9/3JLH2n29L2TMuAGcOHGGMaOmhLk+TZpUAePlO3VuxfZtu3nwwJvevQZT8JXXKVKoCm93+pjNm3bwbpfPg+X94cfP+bnvcFxdXXB2tk5X/saQLI7URdZMGTjseZLHT55ijGHXfg9yv5ydm7cDJ2Vat2UX+XLnDJXXGEOvQWPI87I7nVo2Crbu9t1/AOszMHHGPFqGGGu65K8NVClXijSpUvLk6VOcxAknceLx07jxoBc9XwSXmD8jQelxEUjrQoUl2oavxDWOOcVVNAnv8b7d3u0AwMRJM1i5ah316tXgxLFtPHr8mK6OR4hH9mjgRo3qsnffwYAr/Z0793Fg/994eBzj8GHP2C9sJA7vP8qa5etY+Pcf+Pr6cezICebMWESrTm8BMGf6QsYNnUL/Ub1YuvFPEGFI39H8c+ceJcsVo0nLNznheYpF62cCMPznMWxeZ93kVbN+VTwOenLjujWG+OBeD5Zu/JMTx05z4mjYva12Kl+hNG3avsWRI8fZusOa0q/PT0PI7ujFmTplFq++mo8Jk4bi5+fH8eOn+fCDb6K07Tcb1Gb/vsNcu3YDgN27D7Bj9yqOHjnOEY9YvaUiXEULvULtqhVo2e1LXJydKJA/Dy0a1KHXkLEcP30OEcE9S0Z6ft4dgBu37tBryFjGDfiBA0eOs2ztJvLneZnmjs/Kx13bUaV8KVat28rsJdZwqJqVy9Okfo2AfT5+8pSlf21kgmOGjo4tGvFZr0G4urgw6MfPiQv0fBEosX9GgtLjIpDWRfTwt3H8d0yQuDa3rYpeLknc9Q8M5EubLfJEicTVh7ftDiHOuHVqWeSJEonkucK8nSdRSu6a1O4Q4oxHPnHj2xcV9/g+u2J7i/hEgfox0sZ59fgqW8qWYIevKKWUUkopFV8k2OErSimllFIq4bJzTvGYoD3lSimllFJK2Ux7ypVSSimlVLyT0G6L1J5ypZRSSimlbKY95UoppZRSKt5JaGPKtVGulFJKKaXinYQ2T7kOX1FKKaWUUspm2lOulFJKKaXiHaM95UoppZRSSqnopD3lSimllFIq3kloUyJqo1wppZRSSsU7eqOnUkoppZRSKlppT7lSSimllIp39EZPpZRSSimlVLTSnnKllFJKKRXv6I2eSimllFJK2Syh3eipjXKlEpmX3FLaHUKc8VK+N+0OIc7w3jHG7hDijPSVP7U7BKVUIqSNcqWUUkopFe/ojZ5KKaWUUkqpaKU95UoppZRSKt5JaGPKtadcKaWUUkopm2lPuVJKKaWUincS2IyI2ihXSimllFLxjw5fUUoppZRSSkUr7SlXSimllFLxjk6JqJRSSimllIpW2lOulFJKKaXiHX+7A4hm2ihXSimllFLxjkGHryillFJKKaWikTbKlVJKKaVUvONvYuYVGRGpJyInROS0iHwbQboyIuInIs2jUh5tlCullFJKKRUFIuIMjAHqA4WANiJSKJx0A4G/orptbZQrpZRSSql4xx+JkVckygKnjTFnjTHPgNlA4zDSfQQsAG5EtTzaKFdKKaWUUvGOQWLkFQl34FKQ95cdywKIiDvQFBj/IuXRRrmKUXXrVOPokc0c99zK11/1sDsc2zk5ObFw3R+M/2OY3aHYJmnSJCxZO5NVm+axdttCPvvmA7tDss248YM4f34ve/ZE+dvNBOXpMx/a/jCKFt/+StOvhjJ2/hoAxs1fS60eP9Pyf7/S8n+/suXAcZsjjV2J/bgISf+PBNK6iB0i0k1E9gZ5dQu6OowsIUei/wp8Y4zxe5H96pSIKsY4OTkxcsTP1HujDZcve7Fzx0qWLV/DsWOn7A7NNh27tebsyXOkTJXC7lBs8/TpM9o06cqjh49xcXFh/srpbFy3lQN7D9sdWqz7Y8Z8JoyfzqRJifMiLYmrC5N/6EZyt6T4+PrRufc4KhV7FYAO9SvRqUFVmyO0R2I/LoLS/yOBtC5Ci6l5yo0xE4GJ4ay+DOQI8j47cDVEmtLAbBEByAC8ISK+xpjFEe1Xe8pVjClbpgRnzpzn3LmL+Pj4MHfuEho1rGt3WLbJnDUTVWtVYt7MJXaHYrtHDx8D4OLqgquLC8ZE4Xb3BGjbtt3cuXPP7jBsIyIkd0sKgK+fH75+fiAJa97hfyOxHxdB6f+RQFoXccYeIL+I5BaRJEBrYGnQBMaY3MaYXMaYXMB84IPIGuSgjXIVg7K5Z+HS5cCLx8tXvMiWLYuNEdnru36fM6TPSIx/QnsG2YtzcnJi5ca57D++kS2bdnBwn4fdISmb+Pn70/J/v1K9e1/KF8lP0Xw5AZi9ZgfNvxlOzwnzuO/9yOYolV30/0ggrYvQ7BhTbozxBT7EmlXlGDDXGHNURLqLSPf/Uh5tlKsYI2H0eCXWHtFqtStx+9Zdjh5OXGNjw+Pv788b1VpSvkhtipcozCsF8tkdkrKJs5MTc/t/yprR33HkzCVOXbpGy9rlWf7r18zt/wkZ06ZiyMwVdoepbKL/RwJpXYTmH0OvyBhjVhpjXjHG5DXG/OxYNt4YE+rGTmNMZ2PM/KiURxvlKsZcuexFjuzZAt5nd8+Kl9d1GyOyT8myxahRtzLr9i5h6MRfKFepDIPG9rE7LNvdv/+AHdv2Uq3m63aHomyWOkUyyhTMw/ZDJ0ifJhXOTk44OTnxVo2yHDlzKfINqARJ/48E0rpI+LRRrmLMnr0HyZcvN7ly5cDV1ZWWLRuzbPkau8OyxbCfx1CteANqlm7MF92+Y9fWPXz9QU+7w7JFuvQvkTp1KgCSuiWlUtXynD51zuaolB3u3PfmvuP+gifPfNh55DS5smXi5t37AWnW7zlKvuyZ7QpR2Uz/jwTSugjNrp7ymKKzr6gY4+fnxyef/sDKFbNwdnJi2vQ5eHqetDssZbNMmTMwbEw/nJydcXJyYvniv1i/ZrPdYdli2rSRVK5SnvTpX+LkqR306zec36fPtTusWHPrnwf8MG4u/v7++BtDnfJFqVqyIN+Nnc2JC14IkC3jS/zY5S27Q41Vif24CEr/jwTSukj4JLLxSCJigD+MMR0c710AL2CXMabBC+/QGgT/yBjz+7+IN1qIyJdAV8AX8AOG/td4RKQ00NEY83E0hBhtXJK4J+4BZw750maLPFEi8djvqd0hxBm3Ht+PPFEicXvLr3aHEGekr/yp3SHEGU99fewOQcVRvs+u2D5V0orMbWKkjfPm9T9tKVtUesofAoVFJJkx5jFQG7jyb3cY1iD42OS4KKgNlDXG3BeRNECTF8jv4rjzNhhjzF5gb7QFqpRSSimlEo2ojilfBbzp+L0N8OfzFSLyk6Pn+fn7IyKSy/F7RxE5LCKHRGRGyPQislFEBorIbhE5KSKVHcvdROQ3EfEQkQMiUt2xvLOIjA6yr+UiUk1EnEVkmmPfHiLyWQRl+Q5rvsj7AMaYe8aY6Y7t9RSRPY7tTBTHrc6OOH8RkU3AJyJSRkS2O8q1W0RSOeJYHqSMUx35zopIQO+5iHzu2P4REfnUsSyXiBwXkcmO5TNFpJaIbBORUyJS1pGurGO/Bxw/X43i308ppZRSKkHxl5h52SWqY8pnAz0djc6iwFSgckQZROQ14HvgdWPMLRFJF14MxpiyIvIG0AuoBfQAMMYUEZECwBoReSWC3RUH3I0xhR37ThtOTKmAVMaYM+FsZ7Qxpo8j7QygAbDMsS6tMaaqY6L440ArY8weEUkNPA5jWwWA6kAq4ISIjMOqu7eBcliPad3laOjfBfIBLYBuWBPTtwUqAY2wLiSaOPZbxRjjKyK1gF+AZmGUs5tjO4hzGpycEu/TI5VSSimVMPlHMqd4fBOlnnJjzGEgF1Yv+coobrsGMN8Yc8uxjTvhpFvo+LnPsQ+wGqMzHPmOAxeAiBrlZ4E8IjJKROoB4Q0UFSCi8UfVRWSXiHg44n8tyLo5jp+vAl7GmD2O+O6HNZwFWGGMeeoo/w0gs6Nci4wxD40x3lhlf35xc84Y42GM8QeOAuuMNeDfg8B6SQPME5EjwPAQ8QUwxkw0xpQ2xpSOzgZ53TrVOHpkM8c9t/L1Vz3CTDN8WB+Oe25l/761lCheONK8/X/5jv371vLb1BEBy9q1a8ZHH3aJtrhjQod3W7N002yWbZ5Dx25twkzz/c9f8NeuhSzZOItCRV6NNO8XP37Iko2zGDD6p4BljVrUp8O7rWOsHNEhdepUjPttKOt2LmHdjsWULF002PpUqVIyZeYoVm2ax9ptC2nRtjEAWbNlZvbiyazbsZi12xbydrd2AXm+7fUpqzfPZ9jYnwOWNW3ZIFiauMbdPSsrV/3Jvv1/s2fvGj744O1QaVq1asyuXavYtWsV69YvoEiRggDkz5+HHTtXBry8rnnQo8c7APTt+y27dq1i0qShAdtp06ZpmNu30/mrN2n5v18DXhW79OSPVVs4ceEqHXqOodk3w/lo8DS8Hz0JM3/9jwfQ7JvhtPzfr7T5fmTA8nHz11Krx88B291ywJrr/8CJ8zT/ZjhtfxjFxWu3ALj/8DHd+0+OU3M3J/bjIiT9PxJI60KF9CJTIi4FhhBk6IqDb4jtuDl+RtYAfu75XWd+BPbch3fpE+a+jDF3gWLARqxe9slhZXYMWXkoInlCrhMRN2As0NwYUwSYFKQsYI2tfx7bi5QLAssW0SVd0PT+Qd77E1gvfYENjm8EGoaIL0Y5OTkxcsTPNGjYniLFqtOqVRMKFswfLE39ejXIny83BQpV4v33v2HM6P4R5k2dOhUVypemZKnaODs7UbhwAdzc3OjUoSXjxk+PraK9sPwF8tKifRNa1utEk+ptqVanEi/nzhEsTZWaFXk5T07qlnuLnl/8Qq9B30aYN2WqFJQoU5TG1dri7OzMKwXzktQtKU1bNeTP3+bZUcwo69X/Gzat20bN8o2pV6U5p08Gn96wY9fWnDp5hvpVW9CqURd+6PMlrq4u+Pn50a/nUGpWaEKTuu3p2KUV+V/NQ6pUKSlVpjj1qjTH2cmJVwvmJ6lbUlq0acyMqXPCicJ+fn6+fPe/fpQqWYvq1ZrS7b0OFAjxUKTz5y9Rt24rypWrz8ABoxjl+IycOnWWCuXfoEL5N3i9YgMeP37C0qV/kTp1KsqVL0m5cvVxdnbmtddexc0tKe3bN2fixBl2FDNcubJlZG7/T5nb/1P+/Plj3JK4UqN0YXpPWsAnbeqzYOBn1CjzGtOWbwp3G5O/7xaQP6gO9SsFbLtyiQIA/L5yM0M/7cBHreoy9++dAExctI6uTWqE+ZAVuyT24yIo/T8SSOsiepgYetnlRRrlU4E+xpiQz8M+D5QEEJGSQG7H8nVASxFJ71gX3vCVsGwG2jnyvQLkBE449lVcRJxEJAfwfKx1BsDJGLMA+PF5POHoD4xxDDtBRFI7hns8b+DeEpGUQPNw8h8HsolIGUf+VI4ZaaJariYiklxEUgBNgS1RzAtWT/nzm2w7v0C+/6xsmRKcOXOec+cu4uPjw9y5S2jUsG6wNA0b1mXGTOuhVbt27ydN2jRkyZIp3Lz+/v4kSeIKQLJkbvj4+PDlF90ZNWYKvr5hffkQN+TJn4tD+zx48vgpfn5+7Nm+n1pvVguWpmb9qiyZaz2F8NC+I6ROk4qMmdKHm9f4G1xdrbpI6pYUHx9fuvTowIzJs/H19YvtIkZZylQpKFehFLP/sL7w8vHx5f79B8HSGGNImdL6xiZFiuT8c/cevr5+3Lh+iyOHjwHw0PsRp0+dI3PWTPgbf1wdx4VbsqT4+vrw3oed+W3izDh9XFy7dpODB48C4O39kBMnzoR6BPauXfv55x/ri7zdu/fj7h76EdnVq7/O2bMXuHTpiuMzkgQAt2Ru+Pj48uln7zF23LQ4XRe7jpwmR+b0ZMv4Eue9blKqgPVvoUKR/KzbcyRa9uHi7MxTHx+ePPXBxdmZS9dvc+POPUoXDNXnYis9LgLp/5FAWhcqLFFulBtjLhtjRoSxagGQTkQOAu8DJx3pjwI/A5tE5BAw7AXiGgs4O4aRzAE6G2OeAtuAc1hDOoYA+x3p3YGNjhimAf+LYNvjgA3AHscwkE1YUzT+g9U77gEsxhrXHYox5hnQChjlKNdaothjbYzZ74hvN7ALmGyMORCVvA6DgP4isg1wfoF8/1k29yxcunw14P3lK16h/rG4Z8vC5UuBaa5c9sI9W5Zw83p7P2ThopXs3bOG8+cuce/eA0qXLs6yZXH7YQinjp+hTIUSpH0pDW7JklK1VkWyZgv+cJPMWTLidTXwSWvXrt4gc9ZM4eZ9+PARa1asZ9H6mVy5eBXv+94UKV6I9avj9vzdOV/Ozu3bdxgyui8rN8xh4K8/kSx5smBppk/+k3z5c7Pn6Dr+2rKA3t8NDDW8IHuObLxWpAAH93nw0PsRq5b9zcqNc7l04QoP7ntTrERh1q7aGIsl+29y5sxOsWKF2LPnYLhpOnVqxZo1G0Mtb96iIfPmLQWsRtySxavYsXMlF85f4v79+5QqVZQVy9fGUOTRY/WOQ9SrUByAfNkzs3GfJwBrdh7m2u1/ws4k0H3AZFp/N5L563YFWzV7zQ6afzOcnhPmcd/7EQBdGlWnz+QF/LF6K23qVGDUnNX0aFE3rC3HGYn9uND/I4G0LqJHont4kDEmZRjLNmINFcExTWKdcPJOB6aHWPZTkN+rBfn9Fo6x08aYJ4TRE+wYYx3eoNKIesdDbmOQ4xVy3Q/AD2Esrxbi/R6gfIhkGwmsk59CpC8c5PdhhLhAMcacB4Km6RzWOmPMDoKPrf8xZKwxJayvg0M2rMJLE1HeIUPHMWToOAAmjB/MT70H887bbahduyoeHsf4pX9Y14H2OnvqPJNG/c6UeaN59PARx4+eCt2bHU6ZI8o7ZfQMpoy2vnruO+x7Rg4cT/N2jXm9WjlOeJ5m/PCpMV62F+Xs4kzhogXp9e0ADu7zoNcv3/DBJ+8wtP+YgDRVq7/O0SMnaN2kKy/nzsHMBRPZvbM53g+sEWHJUyRj/LRh9Pl+UMCyCaN+Y8Ko3wAY+OtPDBswhtbt36Jy9Qoc9zzJqKGTYr+wUZQiRXJm/TmOr7/uw4MH3mGmqVKlAh07taJ2reBfyLm6uvLGG7Xo1TPw9DR8+ASGD58AwJixA+jXdzidOreiZs3KHDlynEEDRxOX+Pj6smmfJ5+0rgdA724tGPD7UiYsXEe1UgVxdQn73870nz4g00upuX3Pm+79J5M7W0ZKFcxDy9rl6fZWTQQYM28NQ2auoM97LSiQKxt/9PkQgH3HzpLxpdQYDF+NnImLszNftn+T9GlSxVaxI5XYjwvQ/yNBaV1ED/84NFQtOrzI8BWViF257EWO7IEP4MnunhUvr+vB0ly+4kX2HIFp3LNn5arX9SjlLV7cumf15MmzdGjfnDZtu/Paa6+SL19u4qIFs5bSrFYHOjR+j3t373Ph3KVg66973QjWe54lWyZuXLsZpbwFC1vXXefPXqRxyzf47N3vyF8gb6hx63HBtavX8bp6nYP7rFFtK5eupXDRgsHStGjbmNXL1wFw4dwlLl28Qt781t/VxcWF8dOGsXj+ioA0Qb1WxBo/fPbMBd5q1ZAeXb7ilQL5yJUnZ0wW619zcXFh1qzxzJm9mKVL/gozTeHCBRgzdgCtWr7LnTv/BFtXp241Dh08wo0bt0LlK1bM+oycOnWWtm3fomOHDylU6FXy5s0V3cX4T7YePEGB3O4BDeLc7pmY8L+uzP7lY+pVLE72TGGPZMz0UmoA0qdJSY3Sr3HkzCXH+1Q4Oznh5OTEWzXKBix/zhjDxMXrea9pTSYs+JsPmtWmQaUSzFq9LQZL+WL0uLDo/5FAWhcqLAm2US4iY0TkYIhX3L4tPQ7bs/cg+fLlJleuHLi6utKyZWOWLQ/+ldjy5Wvo0M7q4SlXtiT3793n2rUbUcrbu9fX/NR7CK6urjg7WyNz/P39SR5iKERckS7DSwBkdc9M7Ters2Jh8H+061dvpnFLa2r/YqUK8+C+Nzdv3I5S3k++7c6ogRNwcXEJqAvj749b8li7rzfKbt64jdeV6+TJlwuA16uU49SJs8HSXLlyjderlAMgQ8Z05Mn3MhfPXwZg0MjenD55jsnjwr457Yv/9WDogDG4urjg7Gydroy/IVmyuFcXAOPGDeTEidOMGjUlzPXZs2dj1p/j6drlM06fPhdqfYsWjZg3b1kYOeHHnp/Tt++wYJ8REwc/I6u2H6R+hWIB72/fs3qF/f39mbRoPS1qhfySER49ecbDx08Dft/hcZJ8Oayv8m/eDZxMa/2eo+TLHnyo2NLN+6hSvACpUybn8TMfxEkQEZ48iztPotTjwqL/RwJpXUSPhHajZ1RvUIx3jDFhzy+k/hU/Pz8++fQHVq6YhbOTE9Omz8HT8yTd3u0AwMRJM1i5ah316tXgxLFtPHr8mK5dP48w73ONGtVl776DAVf6O3fu48D+v/HwOMbhw56xX9goGDl1IGlfSoOvry99vh3E/XsPaNXpLQDmTF/Ipr+3UaXW66zZvYgnj57w3Sd9Isz7XM36VfE46MmN61aP2MG9Hizd+Ccnjp3mxNFTsVvIKOr1bX9GTOiPq6srFy9c5ssPf6Rd5xYAzJw2j5FDJjB0dF/+2rIAEWFA71+5e+cfSpcrQbNWDTl29CQrN84FYHC/kWz4eysAdd6ozqEDRwO+Ydi/5zB/bVnA8aMnOXb0ZNjB2KhChdK0bdeMIx7H2LHTmjn2p16DyJ7DHYApk2fyv+8+Jl26l/h1RD8AfH19qVypEWDdmFWjRiU+/ui7UNtu0LAO+/Yd5prXDQB279rP7t2rOXLkOB4ex2KjeFHy+Okzdh45zY9d3wpYtnr7QWav3QFAzTKFaVK1NAA37t6n98T5jPnmHe7ce8Bnw60LM18/P954vQSvF7OmER3+50pOXPBCgGwZX+LHLm8F29/SLfsY/21XADq+UZkvfv0DV2dnBnwU9lSlsU2Pi0D6fySQ1oUKi8Sl+VxV9HNJ4q5/YCBf2myRJ0okHvs9jTxRInHrcXiPNEh8bm/51e4Q4oz0lT+1O4Q446lv3PnGQcUtvs+u2D6ge07WdjHSxmnlNdOWsiXYnnKllFJKKZVw+dt+WRC9EuyYcqWUUkoppeIL7SlXSimllFLxjn+ED0qPf7SnXCmllFJKKZtpT7lSSimllIp3EtpMFtpTrpRSSimllM20p1wppZRSSsU7CW32FW2UK6WUUkqpeMff7gCimQ5fUUoppZRSymbaU66UUkoppeIdvdFTKaWUUkopFa20p1wppZRSSsU7eqOnUkoppZRSNtMbPZVSSimllFLRSnvKlVJKKaVUvKM95UoppZRSSqlopT3lKlE4/c9VcqTKYHcYccL9p4/sDiHOeOrrY3cIcUbKCj3sDiHOeHxpvd0hxBnJctSwO4Q4I1WSZHaHoEIweqOnUvGPNsiVUkqphEWHryillFJKKaWilfaUK6WUUkqpeEd7ypVSSimllFLRSnvKlVJKKaVUvGPsDiCaaU+5UkoppZRSNtOecqWUUkopFe/465SISimllFJK2Utv9FRKKaWUUkpFK22UK6WUUkqpeMc/hl6REZF6InJCRE6LyLdhrG8sIodF5KCI7BWRSlEpjw5fUUoppZRSKgpExBkYA9QGLgN7RGSpMcYzSLJ1wFJjjBGRosBcoEBk29aecqWUUkopFe+YGHpFoixw2hhz1hjzDJgNNA4WlzHexpjnm0oRtc1qT7lSSimllIqHbJp9xR24FOT9ZaBcyEQi0hToD2QC3ozKhrWnXCmllFJKKQcR6eYYC/781S3o6jCyhOoJN8YsMsYUAJoAfaOyX+0pV0oppZRS8U5MTYlojJkITAxn9WUgR5D32YGrEWxrs4jkFZEMxphbEe1Xe8qVUkoppZSKmj1AfhHJLSJJgNbA0qAJRCSfiIjj95JAEuB2ZBvWRrmKUXXrVOPokc0c99zK11/1sDsc22TNlplZiyezdsci/tq2kM7d2todkm1Gje3PyXO72L57pd2hxAn6GQmUmOvC6/pN3v7kexq2/4DGHXswY16w//H89uciCldpxN1/7tsUoX0S83ERlJ47Q7PjRk9jjC/wIfAXcAyYa4w5KiLdRaS7I1kz4IiIHMSaqaVVkBs/w6WNchVjnJycGDniZxo0bE+RYtVp1aoJBQvmtzssW/j6+fFzzyHUrtCUt+q2p2OX1uR7NY/dYdniz5kLad7kHbvDiBP0MxIosdeFi7MzX33wDsv+GMus8YOZvWglZ85fBKwG+469B8maOaPNUca+xH5cBKXnztD8MTHyiowxZqUx5hVjTF5jzM+OZeONMeMdvw80xrxmjClujKlgjNkalfJoo1zFmLJlSnDmzHnOnbuIj48Pc+cuoVHDunaHZYub129x9PBxAB56P+L0qbNkyZrJ5qjssX3bHu7e/cfuMOIE/YwESux1kTFDOgq9mheAFMmTk+fl7Fy/aX3bPWj0FD5/vzOOb8MTlcR+XASl586ETxvlKsZkc8/CpcuB9z5cvuJFtmxZbIwobnDPkY1CRQpwcJ+H3aEom+lnJJDWRaArXtc5duosRQu9yoatu8iUIT0F8uW2Oyxb6HGhImLXEz1jijbKVYwJq1cnCkOqErTkKZIxbtpQ+n4/GO8HD+0OR9lMPyOBtC4sjx495rMfB/DNR11xdnZm4ox5fNgl8d6DoseFSky0Ua5izJXLXuTIni3gfXb3rHh5XbcxInu5uLgwbtowlsxfyV/L19kdjooD9DMSSOsCfHx9+fTHAbxZuyq1q1bk0hUvrnhdp9k7n1CnZVeu37xFi66fcuv2XbtDjTV6XKiI2PREzxijjXIVY/bsPUi+fLnJlSsHrq6utGzZmGXL19gdlm0GjvyJ0yfPMmXcDLtDUXGEfkYCJfa6MMbQc+Ao8rycnU6tmgDwSt5cbF46gzVzJ7Nm7mQyZ8zAvMm/kiH9S/YGG4sS+3GhEhdtlKsY4+fnxyef/sDKFbM4cngj8+cvw9PzpN1h2aJ0uRK81aohFSuXZcXGOazYOIdqtSrZHZYtJv82nDXr55Evf26OnNhK+44t7A7JNvoZCZTY6+KAxzGW/bWBXfsP0+ydT2j2zids3rHX7rBsl9iPi6D03BlaQhtTLv9mbJaI+AEeWE8EPQZ0MsY8esFtZANGGmOai0g14EtjTAMR6QyUNsZ8+MKBRX3f9bEeeZoC63Gpy40xX0bDdrcbYyr+1+1EJ5ck7jr4DsiRKoPdIcQZ/zzVsezPPXj22O4QVBz0+NJ6u0OIM5LlqGF3CHFGqiTJ7A4hTrnrfdr26YB65moXI22cPudn2lK2f9tT/tgx92Jh4BnQPbIMIRljrhpjmv/L/QcQEZcXTF8YGA20N8YUBAoDZ6Njf3GtQa6UUkoppeKH6Bi+sgXIJyINRWSX/L+9O4+zcvoDOP75zpJJGxXSQiWkSKWFJGXfKoqSpGyJ7PuubPlZy1YiCSFR2km0ifZ9kyjaUGlfZ5rv74/nuXPvLE0Tc++5M8/33eu+7n3W+31O59459zxnEZkjIuNE5CgAEekmIh+JyPci8ouI3OyvrywiC3M78QHO2VdExgIfishkEakdcdwUEam1n9M+CDynqkvBm5lJVd8+yPc7SkSGisg8/9HI32+7/9xURCaIyBcislREBkZMt3quf/4FIvK+iBzir18pIs+LyE8iMlNE6orINyLya2iGKBEpLiLfichs//iWB//fZYwxxhhT8LmaPCha/lOh3K81vhivKcsPwOmqWgf4DK/wG1ILuBQ4A3jSb7qSF7md8zSgpapeA7wHdPJjOgE4RFXn7+ecJwOz/uP7vQ5MVNVTgbrAohzOVQe4G6gBVAXOFJEU4AO86VZPwWv+c2vEMatU9Qy8HzofAFcCpwNP+9t3A1eoal2gGfBKqLAfSUQ6+wX7menp1lTBGGOMMSbe/dtCeVERmQvMBP4A+gEVgW9EZAHwAFAzYv9hqrpLVTcA44EGeXyf3M45XFVDDUIHA5eJSDJwA16B9t/I6/udA/QGUNV9qrolh3NNV9XVqpoOzAUqAycCK1Q11EtlANAk8j385wXANFXdpqrrgd0ichhe+/fnRWQ+MA6oAByV9Y1Vta+q1lPVegkJxQ4qAXJz4QVNWbRwEksX/8CDD3TNcZ/XXn2apYt/YPasb6lT++QDHtvj+UeZPetb+r/fK2Nd+/atueP2G/Mt7vxWtdqxGZ01R00YxPyVU7j+lvaZ9zm+Ml9+/SFL187g5q7XZTtHQkICI8cP4r1P3shY99BTdzNm0mBeefvZjHVXtLmMTp3jd4ziQw4pwrgJXzL5pxH8OGMMDz92V7Z9zjyrIb+vmcOkH4cz6cfhPPCw112k2vFVMtZN+nE4v6+dS5fbOgHQ7ekH+GHqSHr3fSnjPG2vvpxbbusYk+v6t+wzEhb0tPjw82G0vK4rl3e8nQe6v8SePXt56/1POKdVp1w7cq74Y3XG9tY33EXDi9ry0efDAPhm/A+0vK4rp5zdkoVLf8k4ZvaCxVzR6Q7adr6XP/yJdrZu207n+56KuzG9g54vQuy7M3/YkIieUJvy2qp6h6ruBd4A3vRrgG8BUiL2z3qNeb3m3M6ZUQXsdzL9FmgJtAE+yeWci/Bqvf/1++XRnojX+/BqxQ/UcSB0THqW49P949sDRwCnqWpt4K8sMUZNQkICr/d6jsuaX8sppzajbdvLOemk4zPtc/FF53B8tSpUr9GYW299iLfe7JHrsSVLluCM0+tR97TzSUxM4OSTq5OSkkLHDm3o3WdALC7rX/lt+e9c2rQtlzZtS/Nz2rF7527GjsrcMWzLpq10f+R/vPdWztdx/S3tWb4s3JWhRIninFb/VC5uchUJCQmceFI1Dkk5hNbtWvDx+59H9Xr+iz179tLy0g6cdUZzmpzRnHPPO4t69Wtn2++nH2fQpFELmjRqwUsvvAnA8l9WZKxr2vhydu3axagRYylZsjgNTq9L49MvIzExgRo1TyAl5RDaXduKfn0HxvgK884+I2FBT4u/1m9k4BcjGPTuq3w14E3S09MZ8/1kADpc1ZIv3+/Fl+/3oskZ9bIdW+WYihnbP3/3VVJSDuHcJmcAUK3KsfR89hFOO7VmpmMGfPYVPZ95hLtu7sCgr8YA8M6AQdzc4aocJ99xJej5IpJ9d+aPwjb6Sn4OiVgKWOO/zvqTrKWIpIhIGaApMCMfzpnVe3jNSmao6j+57PcS8KjfzAURSRCRew/y/b7Db3YiIokiUvIAsYUsBSqLSDV/uQMwMY/HhuL7W1VTRaQZcOxBHPufNKhfh19/XcmKFX+QmprK558Po0XzCzPt07z5hXw08AsApk2fTanDSlGu3JH7PTY9PZ0iRZIBKFo0hdTUVO6/rwtvvNWPtLS0WF3af3Jmk4b8vnIVa1avy7R+44Z/mD9nEamp2a+jXPkjaXbBWQz6eGjGunRNJ9lPi5SiKaSmpdH59k4M6PtJ3KfFjh3ewEvJyUkkJyf/q5q5s5s2YuVvf7Bq1VrS05Xk5Ii0SE3jjrtv5p3eH8Z1WthnJMzSAtL2pbNnz17S0vaxa/cejihT+qDPMXXWfCqVL0f5ckcCcFzlSlQ5pmK2/ZKSkti9dw+7d+8hKSmJP9as468N/1A/opY5Hli+yMy+O01W+Vko7wYMFpHJwIYs26YDo4CpwDOqujYfzpmJqs4CtgL9D7DffLy23p+KyBJgIXD0Qb7fXUAzv5nLLDI3c8ntvXcD1/vvsQDvB1mfvBzrGwjUE5GZeLXmSw/i2P+kfIVyrFod/m9bvWYd5cuXy7RPhfLlWL0qvM+a1euoUL7cfo/dvn0HQ4aOZuaMsaxcsYotW7ZRr15tRowoOBNDXNbqIkYM+fqgjnnyuQd5odtrpKeHf4/v2L6Tr0eMY9SEQaz+fQ3btm7n1Do1+XbMhHyOOP8lJCQw6cfhLFsxjQnf/8CsmfOy7VO/QR0m/zSCwUP6UT1LzRhAqysv5csvRgKwffsORgz7xrstu3I1W7dso27dWowZNS7q1/Jf2GckLOhpcdQRZeh09eWcd9WNNLuiIyWKFePMBnUA+HToKK7odAePv9CLLdu253qeMd9P4pJzm+S6D8DN115J95fe4qMvRtCu1aW8/u7H3HFj+wMeF2tBzxdZ2Xfnf1fYOnoe1HCCIapaPId1w4Bh+zlkmap2zrL/SrxOl6jqBGCC//oD/Dbh+zunqnbLus7vPJoAHPCTqKojgZF5vYas76eqf+E1lcm6X3H/eQL+9fjLt0e8/g6vE2jWYytHvP6AiHbxkdvwOsvGXE63QLP+qt/fPrkd+/IrvXn5ld4AvNPnJbp1f4kbrm/H+eefzYIFS3i+R69sx8aL5OQkzrvobF56Ju8xnnNBEzZs+IeF85bQ8MzMt67feeMD3nnjAwBe6PkUr77wNm2vvYKzmp3B0sW/8OYr7+Zn+PkmPT2dJo1aULJUCT7+tDcn1TieJYvD7V3nz11ErRpns2PHTs6/4Gw+/rQ39Wqfl7E9OTmZiy89l6e7vZyx7vWe7/J6T+96e735PM8/25MOHdvQ7NzGLFq4lFdefDt2F5hH9hkJC3pabNm2nfE/TOObQe9Songx7nvyf4wYO562l19Ml45tERHe6DeQl97qx7MPZ29LDJCamsqEKdO5u3P2PilZVT++Kp/08T4/M+cu5MiypVGU+556kaSkRB7oegNlS7ufBTTo+SIr++40WRWKGT1F5DpgGvCY37HS5LM1q9dRqWJ40JyKFY5m3bq/Mu2zes06KlYK71Oh4tGsXfdXno6tXdu72bBs2W90uPZK2l3ThZo1T6RatSrRuJx80fS8xiyav5QN63NrLZXZaQ1rc95FTZk8ZzRvvPs/Gp1Vn9f6PJ9pnxqnVAdgxa+/06ptc26/8UFOqF6NylWPydf489vWLdv4YfI0zj0vc83etm3bM27Tfjt2IsnJSZSOmCb8vAvOZt7cxaz/e2O2c55SqwYAvy5fwdXXXM4N191JjRonUPW4mLXcyjP7jIQFPS2mzpxLhaOPovRhpUhOSuLcJmcwd+FSypY+nMTERBISErjysgtYuOSX/Z5j8tRZnHT8cQdVmFZV3vnwc27p2Jbe/T+j6w3X0PyCpgz8MlsdlBNBzxf7E/Tvzv/COnoeJFXtpqovH3jP//QeH6pqJVUdHFonIteLyNwsj7eiGUdhNmPmXKpVq0LlypVITk6mTZuWjBiZ+abEyJFj6dDemw+qYYO6bN2ylT///DtPx3Z/6kG6dX+Z5ORkEhMTAa8W4dBD43cGteatLmb4kDEHdcxLz7xOo1Mu4Kw6l3DHzQ/x4+QZ3NPl0Uz73PtIV1594W2SkpJISPQ+ounp6RQtGpM+vQelTNnSlCxVAoCUlENo2qwRv0R0YAU48sjwbKp1T6tFQkIC/2zclLHuyqsu48vBI3I8/2NP3E2PZ3uSlJxEYkJEvigaf/nCPiNhQU+Lo486gvmLf2bX7j2oKtNmzaPqsZVYvyH8A/67yVOpVmX/BaTR303mkvMO3HQl0rCvv6fJGfUoVaI4u/bsISFBEElg9+49Bz44BoKeLyLZd2f+KGwdPf9V85WCQFX7c4D25Sbv9u3bx113P87oUZ+QmJDABwMGsXjxMjrf3AGAvu9+xOgx33HRRefw85Ip7Ny1i5tuujfXY0NatLiQmbPmZtR6TJ06izmzx7FgwRLmz18c+4vNg5SiKTRuejqP3ftMxrprOl0FwCcfDKbskWUY/t2nFC9RDE1P5/ou13JBoyvYvi33QXzOv6QZ8+cs5O8/1wMwZ8Z8xkz+gqWLlrFk0bJcj3Wh3FFH8Hbfl0hMTCAhIYGhQ0bzzdfjuf7GdgD07/cpLa+4mOtvuoZ9aWns2rWHGzuFb9cXLZpC02Zncs+dj2c79yWXncfs2Qv488+/AZg+fQ5Tpo1i0cKlLFwYs+4UeWafkbCgp0WtGidyftMzaXPT3SQmJlL9+Kpc1fxCnnzxDX7+ZQUIVCh3FE/dfxsAf2/YyFP/e5PeLz0FwK7de/hp5tyM7SHjJv1Ej159+WfzFm576GmqV6tK31e6Zxwz7OvvM5Y7tmnJPY+/QHJyEi8+dX8Mr37/gp4vItl3p8mJxNsYpiZ/JRWpYP/BQKUSZQ+8U0Bs3mMTSoVs27vrwDuZwNm16vsD7xQQRSud4zqEuFGiSOGqZf6vNm1f7ny8zXsrXx2VMs6rKz9zcm2Fok25McYYY4wxBVmhbb5ijDHGGGMKr8LWFMBqyo0xxhhjjHHMasqNMcYYY0yBU9jGwLZCuTHGGGOMKXC0kDVgseYrxhhjjDHGOGY15cYYY4wxpsApbM1XrKbcGGOMMcYYx6ym3BhjjDHGFDjphaxNuRXKjTHGGGNMgVO4iuTWfMUYY4wxxhjnrKbcGGOMMcYUOIWt+YrVlBtjjDHGGOOY1ZQbY4wxxpgCp7ANiWiFcmOMMcYYU+DYjJ7GGGOMMcaYfGU15cYYY4wxpsCx5iumQDkspZjrEOLCttRdrkOIG4kJdoPMZGffFWFFK53jOoS4sXPpUNchxI1Dq1/hOgRTyFmh3BhjjDHGFDjWptwYY4wxxhiTr6ym3BhjjDHGFDjWptwYY4wxxhjH0tWarxhjjDHGGBNIInKRiPwsIstF5OEctrcXkfn+40cROTUv57WacmOMMcYYU+C4qCcXkUTgLeB8YDUwQ0SGq+riiN1WAGer6iYRuRjoCzQ80LmtptwYY4wxxpi8aQAsV9XfVHUv8BnQMnIHVf1RVTf5i1OBink5sdWUG2OMMcaYAifdzZCIFYBVEcuryb0W/EZgTF5ObIVyY4wxxhhT4ERrnHIR6Qx0jljVV1X7hjbnGErO52mGVyhvnJf3tUK5McYYY4wxPr8A3nc/m1cDlSKWKwJrs+4kIrWA94CLVXVjXt7XCuXGGGOMMabAcTRO+QzgeBGpAqwBrgauidxBRI4BhgAdVHVZXk9shXJjjDHGGGPyQFXTROR24BsgEXhfVReJSBd/ex/gSaAM8LaIAKSpar0DndsK5cYYY4wxpsBx1NETVR0NjM6yrk/E65uAmw72vFYoN8YYY4wxBU60Onq6YuOUG2OMMcYY45jVlBtjjDHGmALHUUfPqLGachM1vd56niW//sTkqSNdh+KcpUWYpUVmF17QlEULJ7F08Q88+EBX1+E4Y/kisyDniz/Xb+SGh56nReeHuPyWh/n4q28A2LJtOzc/+gKX3ng/Nz/6Alu27XAcaewFOV8EgRXKTdR8NnAIbVvd6DqMuGBpEWZpEZaQkMDrvZ7jsubXcsqpzWjb9nJOOul412E5YfkiLOj5IjExkftvvobhff/HwNee4rOR4/j19zX0+3wEDWvXZFS/l2lYuyb9Ph/hOtSYCnq+yImqRuXhihXKTdT89ONMNm3a4jqMuGBpEWZpEdagfh1+/XUlK1b8QWpqKp9/PowWzS90HZYTli/Cgp4vjih9GDWqVQag2KFFqVKpPH9t/IfxP82m5XlnAdDyvLMY/9Msh1HGXtDzRRBYodwYYxwpX6Ecq1aHJ4JbvWYd5cuXcxiRiQeWL8LW/LWepb/+Tq0Tq7Fx81aOKH0Y4BXcN27Z6ja4GLN8kV06GpWHK9bR0xhjHPEnlcjE5a1TEx8sX3h27trNPc++zkO3tKd4saKuw3HO8kV21tHTGGNMvlizeh2VKpbPWK5Y4WjWrfvLYUQmHli+gNS0NO559nUubdaI886sD0CZw0qy/p/NAKz/ZzNlSpV0GGHsWb4o/KxQbowxjsyYOZdq1apQuXIlkpOTadOmJSNGjnUdlnEs6PlCVXmq53tUrVSejq0uzljf9PS6DBs3GYBh4ybT7Iy6rkJ0Iuj5IicapX+uOC2Ui8g+EZkrIgtFZLCIHCoilUVk4UGeZ4KI1PNfrxSRsgdxbDcRuf9gY9/PuWqLyCX5ca7CoO/7r/L1uEFUO74K85dMon2HK12H5IylRZilRdi+ffu46+7HGT3qExbOn8AXX4xg8eJlrsNywvJFWNDzxZxFyxjx3RSmzVvMlV0f48qujzFp+lxubHMZU2cv5NIb72fq7IXc2Ka561BjKuj5IgjEZXskEdmuqsX91wOBWcAQYKSqnnwQ55kA3K+qM0VkJVBPVTfk8dhuwHZVffkgw8/pXJ389779v57rIN4zSVXT9re9bMkTgt3gzJhcbN4dvHGO9+ewlGKuQ4gbli/Cdi4d6jqEuHFo9StchxBX0vauyd7IPcYuOeaSqJRxRv8x2sm1xVPzlclANf91ooi8KyKLRGSsiBQVkeNEZHZoZxE5XkRyHQ9JRO71a+EXisjdEesfE5GfRWQccGLE+uNE5GsRmSUik0Wkur/+Kv8c80Rk0n7eqwjwNNDWr/1vm7UW3j9HZf+xVETe89cNFJHzRGSKiPwiIg38/UuLyFciMl9EpopILX99NxHpKyJjgQ8PKpWNMcYYYwoBG6c8CkQkCbgYWOCvOh54S1VrApuB1qr6K7BFRGr7+1wPfJDLOU/z92kInA7cLCJ1/PVXA3WAVkD9iMP6Aneo6mnA/cDb/vongQtV9VSgRU7vp6p7/f0GqWptVR10gMuuBvQCagHVgWuAxv77Purv0x2Yo6q1/HWRBfDTgJaqes0B3scYY4wxxsQ514XyoiIyF5gJ/AH089evUNW5/utZQGX/9XvA9SKSCLQFPsnl3I2Boaq6Q1W34zWLOct/DFXVnaq6FRgOICLFgUbAYD+md4Cj/XNNAT4QkZuBxP9ywRFWqOoCVU0HFgHfqffzbEHE9TYGPgJQ1e+BMiJSyt82XFV35XRiEeksIjNFZObuvfk7GUdCQgLfT/6KTz5/J9u2asdXZcy4QaxZv5Cud9wQXl+tCuN/GJbxWLF6Nrfc1hGAJ7vfz8Qfh/PWOy9m7H/V1S3pfOt1+Rp3NFhahFlaePIyBfZrrz7N0sU/MHvWt9SpffIBj+3x/KPMnvUt/d/vlbGuffvW3HF7/M9+afnCE/R88eHQMVx+y8Nc0eVhHnzhLfbs3csbH35Bq1sf5cquj9H50f/x98ZN2Y7bs3cv7e56ita3PcrltzzMWx99mbFtf8fPWbSMVrc+ytV3Pskfa72RSbZu38Etj70Yd8MHBj1f5If0KD1ccV0o3+XXKtdW1Tv82maAPRH77CM8nvqXeDXqlwGzVHVjLufOrT1QTp/MBGBzRDy1VfUkAFXtAjwOVALmikiZA18aAGlkTuOUiNeR15gesZxO+HpzuoZQ7Ptt9KiqfVW1nqrWSylSan+7/Su33NqRX5b9muO2zZs28+iDz/LW6/0yrV++fAXNGrekWeOWnNvkCnbu2sWoEd9SomRx6jesy9mNWpCYmMBJNU4gJeUQ2l3Tivffze33VnywtAiztMjbFNgXX3QOx1erQvUajbn11od4680euR5bsmQJzji9HnVPO5/ExAROPrk6KSkpdOzQht59Bri4zINi+cLyxV8b/uGTYWP57PWnGdrnBfalpzNm4lSub30pQ3o/zxdvPcfZDWvT55Ovsh1bJDmZfi88wpdvP8/gt55lyqz5zFuyHGC/xw8YMobXHr+TOztdxaBR3wHwzqfDuKlt8xzH+XYl6PnC5Mx1ofygqOpu4BugN9D/ALtPAi73R3QpBlyB1259EnCF3069BNDcP/dWYIWIXAUgnlP918ep6jRVfRLYgFc4z8k2oETE8kqgrn+OukCVg7zkSUB7//imwAY/TieOLn8U51/YlI8HDM5x+4YN/zBn9gLS0vbb75QmTc9g5Yo/WL1qLZquFCmSDEBKSgqpqWncftdNvNvnw1zPEQ8sLcIsLTx5mQK7efML+WjgFwBMmz6bUoeVoly5I/d7bHp6ekZaFC2aQmpqKvff14U33uoX12kBli9CLF9A2r509uzdS9q+fezes5cjSx+eaTKgXbv35FgDJSIcWtSry0pL20da2j5C5er9HZ+UlMjuvXvZvWcvSYmJrFr7F39v+If6tU6K0tX9O5Yv8ocNiejeQLza4lwH51TV2XhtzqcD04D3VHWOv34QMBev5n1yxGHtgRtFZB5ek5KW/vqXRGSBP1TjJGDeft52PFAj1NHTP39pvznMrcDBjl3UDagnIvOBF4COB3l8vnruhcfo/uSLpKf/+5s7V7S+lCFfjAJg+/YdjBz2DeN/GMYfv69m29Zt1Kl7CmNGf5dfIUeNpUWYpYUnL1NgVyhfjtWrwvusWb2OCuXL7ffY7dt3MGToaGbOGMvKFavYsmUb9erVZsSI+B+b2PKFJ+j54qiypenU+hLOv+5uzrnmDoofWpRGp50CwOsfDOa8DncxavyPdO3QOsfj9+1L58quj3F2u66cXudkalWvlrEtp+NvatOcp3u9z8dffUO75ufz+oDB3H5d/A2vGfR8kV/S0ag8XEk68C7RExoOMcu6lcDJEctZhypsDLyvqvsi9mka8bpyxOtXgVdzeI/ngOdyWL8CuCiH9a1yvZDwfv+QueMowAX72T3yGjtFvF4Z2uafr2XWA1W1W17iyU8XXNSUDRs2Mm/uIs5s3OBfnSM5OZmLLjmXZ7u9krHujV7v8Uav9wDo+cZzvPBcL6697iqannMmixf9zKsv9c6X+POTpUWYpUVYXqbA3t8+uR378iu9efkV73rf6fMS3bq/xA3Xt+P8889mwYIlPN+jV7ZjXbN8ERb0fLFl2w7GT53F1/1fpUTxQ7nv+TcY8f0Ump9zJnd2uoo7O13Fe4OG8+mIb3MsmCcmJvDFW8+xdfsO7n6mF7+sXMXxlb2b1TkdX/24YxnYsxsAMxcs5Ygyh6Oq3N/jTZISE7n/5msoe3j+Nuv8N4KeL0zOClRNuYgMBa7DG7XExFCDhqdx0cXnMnvB9/Tt/xqNm5xO73dfOqhznHd+E+bPW8T69dm7Apzi31r8dflK2ra7nJs63c1JNU6g6nHH5kv8+cnSIszSIiwvU2CvXrOOipXC+1SoeDRr1/2Vp2Nr164JwLJlv9Hh2itpd00XatY8kWrVDrZVXPRZvggLer6YOnchFY46gtKHlSQ5KYnzGtVn3uJfMu1zSdNGjJsyI9fzlCxejPq1qjNl5vxs23I6XlXp++kwbml3Ob0HDuW2a1tx2Tln8smw+Kg1Dnq+yC82JKJDqnqFqtbK68RA0SIiF/pNVCIfhXqGhWe7v0Ktk5pQ95Rz6Hz9PfwwaSq33vzAQZ2j1VWXMWTwyBy3PfL43bzwXC+SkpNISPSyZXp6OkWLFs1xf5csLcIsLcLyMgX2yJFj6dDeu5XesEFdtm7Zyp9//p2nY7s/9SDdur9McnIyiYneIFDp6ekcemj8pYXli7Cg54ujjyjD/KW/smv3HlSVaXMXUaVSeX5f82fGPuOnzqZKRCEz5J/NW9m63RvTYPeevUyd4x0LHPD4YeMm06RBbUqVKMbuPXtJECFBhF179hAPgp4vTM6cNl8pqFT1G7wOp4HX6YarAfjg/c848siyjJs4hBIlipOens4tt3WiUYOL2b5tB0WLpnB2s0bce9cT2c5x8aXnMWf2Av78828AZk6fy6SfRrB40c8sWrg0ptfzX1hahAUxLSKnwE5MSOCDAYNYvHgZnW/uAEDfdz9i9JjvuOiic/h5yRR27trFTTfdm+uxIS1aXMjMWXMzasOmTp3FnNnjWLBgCfPnL479xf5Lli+Cly9qVa/G+Y3r0+aOJ0hKTKD6cZW56uJmPPTi26xcvQ6RBMofWYYn7rgegL83buKpnu/R+5kHWL9pM4+/3Jd96emopnPBWQ05u2EdAHr2H5Tj8eB1/Bw+7gfeee5BAK5rdTH3PPc6yUlJvPjQbbFPhBwEPV/kF5ftv6NB4m3cTpO/ypY8wf6DjdkPm0497LCUYq5DiBuWL8J2Li3UN4EPyqHVr3AdQlxJ27vG+RiTzSqeH5UyzvjV3zq5NqspN8YYY4wxBY7L4QujwQrlxhhjjDGmwEkvZK09ClRHT2OMMcYYYwojqyk3xhhjjDEFTuGqJ7eacmOMMcYYY5yzmnJjjDHGGFPgFLYhEa1QbowxxhhjCpzCVii35ivGGGOMMcY4ZjXlxhhjjDGmwClsE2BaTbkxxhhjjDGOWU25McYYY4wpcApbm3IrlBtjjDHGmAJHC1mh3JqvGGOMMcYY45jVlBtjjDHGmALHOnoaY4wxxhhj8pXVlBtjjDHGmALHOnoaY4wxxhjjWGFrvmKF8kJu8+4drkOIC8WKpLgOIW7s2LvbdQgmDh2adIjrEOLGZux7M+SIWu1chxA3dq2d7DoEU8hZm3JjjDHGGFPgpKNReRyIiFwkIj+LyHIReTiH7dVF5CcR2SMi9+f1eqym3BhjjDHGmDwQkUTgLeB8YDUwQ0SGq+riiN3+Ae4ELj+Yc1tNuTHGGGOMKXA0Sv8OoAGwXFV/U9W9wGdAy0xxqf6tqjOA1IO5HiuUG2OMMcYYkzcVgFURy6v9df+ZNV8xxhhjjDEFTnqURl8Rkc5A54hVfVW1b2hzDofkSyBWKDfGGGOMMQVOHpqa/LvzegXwvvvZvBqoFLFcEVibH+9rzVeMMcYYY4zJmxnA8SJSRUSKAFcDw/PjxFZTbowxxhhjCpxoNV/JjaqmicjtwDdAIvC+qi4SkS7+9j4iUg6YCZQE0kXkbqCGqm7N7dxWKDfGGGOMMSaPVHU0MDrLuj4Rr//Ea9ZyUKxQbowxxhhjCpxotSl3xQrlxhhjjDGmwHHRfCWarKOnMcYYY4wxjllNuTHGGGOMKXAKW/MVqyk3xhhjjDHGMaspN8YYY4wxBU5ha1NuhXJjjDHGGFPgWPMVYw7ChRc0ZdHCSSxd/AMPPtDVdTjOvPn2CyxfMZ2fpo9xHUpcsHwRZmnhObrCUXw2rB/fTR3GuB+HcsMt7V2H5JTlC0/QvzvX/bWe629/iObXdKZl+1v46POvMrYNHDyMy66+iZbtb+GVt/q5C9LkG9FCVvVvMksqUsHZf3BCQgJLFk3mokvasXr1Oqb+NJprO9zGkiW/xDyWYkVSYv6ekRqdWZ8d23fS592XOaPBxU5j2bF3t9P3j6d84Vo8pUX54qVj/p6RjjyqLEcedQQL5y+hWPFDGfX9IG7ucBe//PxbzGNZu/2fmL9npHjKF/bdGbZh5bcxf8/1G/5h/cZ/qHFiNXbs2EmbG+/k9R5PsPGfzfT98DPefqk7RYoUYeOmzZQ5/LCYxpZctqrE9A1zUKXMqVEp46zYOM/JtVlNuYmaBvXr8OuvK1mx4g9SU1P5/PNhtGh+oeuwnPhxygw2bdrsOoy4YPkizNIi7O+/NrBw/hIAdmzfyfJlKyh39FGOo3LD8kVY0L87jyhbmhonVgOgWLFDqXpsJf5av5FBX43ixmvbUKRIEYCYF8hNdFih3ERN+QrlWLV6bcby6jXrKF++nMOITDywfBFmaZGzipXKU7NWdebMmu86FCcsX5icrFn3F0t++ZVaNU9k5R9rmDVvIe1uvptOXR9gwZKfXYfnRDoalYcrVig3USOS/e6PNZcyli/CLC2yO7RYUd4Z8BrdH/0f27ftcB2OE5YvTFY7d+7insee5aE7b6F4sWLs27ePrdu280nf17iv603c/0QPyyOFgI2+YqJmzep1VKpYPmO5YoWjWbfuL4cRmXhg+SLM0iKzpKQk3hnwGkO/GMXXI79zHY4zli9MpNS0NO5+7FkuvaAZ5zc9E4CjjizLeWefiYhwSo0TERE2bd5C6YA1YylsP0SsptxEzYyZc6lWrQqVK1ciOTmZNm1aMmLkWNdhGccsX4RZWmT20uvdWb7sN957+0PXoThl+cKEqCpP9uhJ1WMr0fHqVhnrzznrDKbPmgvAyj9Wk5qWxuGHlXIUpTuFrfmK1ZSbqNm3bx933f04o0d9QmJCAh8MGMTixctch+VEv/49aXxWQ8qUOZzFP/9Aj+d68dGHg12H5YTlizBLi7D6DevQ+uoWLFm0jDETvc/Gi8+8zvhxkx1HFnuWL8KC/t05Z/4iRnz9HccfV5nWHb2hMe+6pSOtLruAx59/jcuv7UJychLPP35fjs2eTMES9SERRaQMELoPWQ7YB6z3lxuo6t6IfZsC96vqZQdx/m7AdlV9OT/izcP7TcCLcWYs3u+/cjkkYjxxPaxXPHE9JKKJT66HRIwnrodEjCf23RnmYkjEeBYPQyJWOLxmVMo4azYtcnJtUa8pV9WNQG2IfQE6JyKSpKpprt7fGGOMMcaYrJy0KReRD0Tkyojl7RGbS4rIUBFZLCJ9RCQh6z4icqWIfJDDeW8WkRkiMk9EvhSRQyPe71URGQ/8bz8xNRCRH0Vkjv98or++qIh8JiLzRWQQUDTimAtE5CcRmS0ig0WkuL9+pYh099cvEJHq/vrSIvKVf66pIlLLX99NRAaIyFj/2FYi8qJ/7Ncikuzv96R/fQtFpK/YvSpjjDHGBFS6alQersRjR88GwH3AKcBxQKvcd89kiKrWV9VTgSXAjRHbTgDOU9X79nPsUqCJqtYBngSe99ffCuxU1VrAc8BpACJSFnjcP2ddYCZwb8T5NvjrewP3++u6A3P8cz0KRPZmOg64FGgJfAyMV9VTgF3+eoA3/es7Ge/HQY7NfESks4jMFJGZ6enBHFLMGGOMMYWbRumfK/FYKJ+uqr+p6j7gU6DxQRx7sohMFpEFQHugZsS2wf4596cUMFhEFgKvRRzbBK+QjKrOB0KzWZwO1ACmiMhcoCNwbMT5hvjPs4DK/uvGwEf+ub4HyohIqLv0GFVNBRYAicDX/voFEcc3E5Fp/vWdk+X6MqhqX1Wtp6r1EhKK5XLJB+fCC5qyaOEkli7+gQcf6JrjPq+9+jRLF//A7FnfUqf2yQc8tsfzjzJ71rf0f79Xxrr27Vtzx+03Eq8OOaQI308Ywg8/jWTqjDE88thd2fa55NLzmDJ1FJN/HMGESV9x+hmnHfDY7k8/yJSpo+jTN9y6q+3Vl9Pltk5Rv6b/wvJFmKVFWMmSJejzwSt8P3U4300dRt36p2bb5/Qz6zFm4mDG/TiUz0f0B6BqtcqMmTg447Ho95+4scu1ADzy1D18M/lLXnv7uYxztGpzGTfc0j42F/UvWb7w2HcnfPjZUFq2v4XLr+3CA0+9wJ49Xre6gYOHcdnVN9Gy/S288la/HI/dum079zz2LM3b3Uzzazozd6E3A+59T/SgdceutO7YlQtad8zoEDp7/iKuuO5W2t54J3/4k1Ft3badzvc8VuiGEiwsXI2+kob/g8BvglEkYlvWnKI5rN9fz5MPgMtVdZ6IdAKaRmw7UJXxM3i101eISGVgQi4xAQjwraq228/59vjP+winc07NTULn3gOgqukikqrhT0w6kCQiKcDbQD1VXeW3z49ZD5yEhARe7/UcF13SjtWr1zH1p9GMGDmWJUt+ydjn4ovO4fhqVaheozENG9TlrTd70Khx8/0eu2bNn5xxej3qnnY+Hw54g5NPrs7y5Svp2KENl1wWv39k9+zZS/NLr2XHjp0kJSXxzbeD+HbsRGbOmJuxz8QJPzJ61DgAatY8kQ8+eoP6dS/Y77HLfl5Og9Prcubpl/Juv1epUfMEfvv1d9pf25pWl1/v6EoPzPJFmKVFZt16PMSE76bQpdN9JCcnUbRo0UzbS5YswXMvP06HK7uwds2flCnrdTT9bflKLj77KsBL0+mLvuPrkd9RokRxTmtwKhee1Zpe77zAiScdz8oVf3BVu5Z0uOrWmF9fXlm+CAv6d+df6zcw8IthDBv4DimHHMJ9TzzPmHETKV/uSMb/MJUhH75NkSJF2Lhpc47Hv9CzD2c2rMdrzz1Oamoqu3Z7xYxXnnkkY5+X3niX4sUOBWDAp0Po+dzjrFn3F4OGjuKBO27mnQ8+5ebr2haakVoK248LVzXlK/GbgeA110iO2NZARKr4bcnbAj/46/8SkZP89Vfs57wlgHV+G+yD/WYqBazxX3eKWD8pdC4RORmo5a+fCpwpItX8bYeKyAkHeI/IczXFa+KyNY/xhQrgG/y261fmtnN+a1C/Dr/+upIVK/4gNTWVzz8fRovmF2bap3nzC/lo4BcATJs+m1KHlaJcuSP3e2x6ejpFinj/9UWLppCamsr993Xhjbf6kZYW331xd+zYCUBychLJyUnZvhhC2wEOLXZopu05HZuerhRJjkyLNO68+2b69B4Q12lh+SLM0iKseIliNGh0Gp995N0wTE1NY+vWbZn2aXnlJYwZ8R1r1/wJwMYN2Uc8OfPshvyxchVrVq8jXdNJ9tMiJeUQ0tLS6HLH9bzf95O4TgvLF5kF/bszbd8+9uzZS1raPnbt3sMRZUsz6KtR3HhtG4oU8eony+QwAdD2HTuYNW8hrf28k5ycTMkSxTPto6p8/f0kLjm/KeBNxrV7z15279lDUlIif6xey1/rN1C/Tq2spzdxwlWh/F3gbBGZDjQkcy32T8ALwEJgBTDUX/8wMBL4Hli3n/M+AUwDvsVrI34wXgR6iMgUvOYjIb2B4iIyH3gQmA6gquvxCu+f+tumAtUP8B7dgHr+/i/gNXnJE1XdjJduC4CvgBl5PTY/lK9QjlX+7S+A1WvWUb58uUz7VChfjtWrwvusWb2OCuXL7ffY7dt3MGToaGbOGMvKFavYsmUb9erVZsSI+J8kIyEhgck/jmD5iumM/34Ks2bOy7bPZc0vYMbssQz+4j263vpwrsdu376D4cO+YfKPI1i5chVbt2yjbt1aGTVG8cryRZilRdgxx1bknw2beOXNZxk94XP+16sbRQ/NXFNetdqxlDqsJIOGv8+o7wfRum3zbOdp0epihn05BoAd23cyZvg4xkwczKo/1rBt6zZOrVOTb8eMj8k1/VuWLzIL8nfnUUeUpVO71pzX6jqatbyGEsUO5cyGp7HyjzXMmreQdjffTaeuD7Bgyc/Zjl295k8OP6wUjz/3Kld26sqTPXqyc1fm4W1nzVtImcMP59hKFQC4uUMbuv+vFx8N+op2rZvzet8B3HHzdTG51lixyYP+A1XtFrF4esTrR/ztE8jcbCTy2C+AL3I7p6r2xitEZ92nUx5i+wmvM2jIE/76XcDV+znme6B+DusrR7yeid+MRlX/wbszsN9r8JeL57RNVR/H61wacznd6spaw7G/fXI79uVXevPyK95/2Tt9XqJb95e44fp2nH/+2SxYsITne/TKdmw8SE9P56xGzSlVqgQff9qHk2qcwJIsk3uMHDGWkSPG0ujM+jz+xD20bH5drsf26tmXXj37AvDGm8/z/LM9ua5jG845tzELF/7Myy++FfPrPBDLF2GWFmFJSYmcfOpJPPlwD+bOWkC3Hg9x29038srzb2bsk5iYxCm1T6Ld5TeTknIIX33zMbNnzmfFr78DXm3o+Rc15X9Ph6+vzxv96fOG1/b8f7268UqPt7i6QyuaNGvEkkXLeOOVvrG90DywfJFZkL87t2zdxvjJU/lmcH9KlCjOfY8/z4hvvmffvn1s3badT/q+xsIly7j/iR58Pbh/pv//tH37WLJsOY/ecyu1alanR88+9Pvoc+7oHC5kj/52Apecf3bGcvUTjuOTd3sCMHPuAo4sWwZV5b4nepCUlMgDd9xM2dKHx+z6o8Gar5hAWrN6HZUqls9YrljhaNat+yvTPqvXrKNipfA+FSoezdp1f+Xp2Nq1vT6ry5b9Rodrr6TdNV2oWfNEqlWrEo3LyTdbtmzjh8lTOe+8Jvvd58cpM6hS5RhKl8n85be/Y2vVqgHA8uUruPqaK+h03Z3UqHECVY+rnO/x/1eWL8IsLcLWrf2LdWv/Yu6sBQCMHvYtJ9c6KdM+f679i4nfTWHXzl1s+mcz036aRY2TT8zY3vS8s1g4fwkb1m/Mdv6ap3g3JX/79Xdat23BbTfcz4knVaNy1WOieFX/juWLnAXxu3PqzLlUKH8UpQ8/jOSkJM49uxFzFyzmqCPLct7ZZyIinFLjRESETZu3ZDq23JFlOeqIstSq6eX9C5o2ZvGy5Rnb09L2MW7ij1x0bvb0VFXe+eBTbunUjt7vD6TrTdfS/MJzGDh4WHQv2By0wBXKReR6EZmb5REfP6Pj2IyZc6lWrQqVK1ciOTmZNm1aMmJk5lulI0eOpUN7r6l7wwZ12bplK3/++Xeeju3+1IN06/4yycnJJCZ6rYfS09M5NMst73hQpmxpSpUqAXhtW5s2O5Nly37NtE/VquGBeE49tSbJRZL5Z+OmPB372BP38NyzPUlOTiIxISItisbfzHqWL8IsLcLW/72RdWv+pGq1yoDXNvyXnzPn87FjvqfB6XVJTEwkpWgKdU47hV+W/ZaxvWXrcNOVrO5/9HZe7fEWyUlJJCZ6f8bS05Wi9hkB4jdfBP278+ijjmD+wqXs2r0bVWXazLlUPbYS55x1BtNnzQVg5R+rSU1L4/DDSmU6tmyZ0pQ78ghW/L4agKmz5nJc5fCP0Kkz51D12IqUO/KIbO87bPQ4mjRqQKmSJdi1Zw8JIogIu3fvybZvQVPYxil3NfqKM6raH+jvOo6CZt++fdx19+OMHvUJiQkJfDBgEIsXL6PzzR0A6PvuR4we8x0XXXQOPy+Zws5du7jppntzPTakRYsLmTlrbkYN0NSps5gzexwLFixh/vzFsb/YAyh31BH06fsSCYmJJCQkMHTIKL75ejw33OgNxPN+v09p0fJCrr7mClJT09i9azfXd7wz12NDLr3sfGbPns+ff/4NwIzpc/hx2mgWLVzKwoUH200i+ixfhFlaZPbkQz14/Z0XSC6SzB8rV3P/7U9wbSdvVJWPPxjM8mUrmPD9FMb+8CXp6el89tEQli3xav5SiqZwVtMzeOSep7Od94JLzmHenIX89ed6AGbNmMfYH4awZNEylixalm1/1yxfhAX9u7NWzeqc36wxba6/g8TERKqfcBxXtbwYEeHx51/j8mu7kJycxPOP34eI8Pf6jTz1Qk96v/IMAI/ecysPdX+R1LRUKpU/mmcevSfj3GPGTeTi85pme89du3czbMw4+vb0hhHt2LYV9zz2HMnJSbzY7aGYXLfJOyls7XFMZklFKth/MFCsSHzUlMSDHXt3H3gnEzjli5d2HULcWLs9+0gwQWXfnWEbVn7rOoS4kly2qvNxFQ8vXi0qZZxN25c7ubbANV8xxhhjjDEm3gSu+YoxxhhjjCn4XA5fGA1WKDfGGGOMMQVOYWuCbc1XjDHGGGOMccxqyo0xxhhjTIHjcvjCaLCacmOMMcYYYxyzmnJjjDHGGFPgqHX0NMYYY4wxxi1rvmKMMcYYY4zJV1ZTbowxxhhjChwbEtEYY4wxxhiTr6ym3BhjjDHGFDjW0dMYY4wxxhjHrPmKMcYYY4wxASUiF4nIzyKyXEQezmG7iMjr/vb5IlI3L+e1mnJjjDHGGFPguKgpF5FE4C3gfGA1MENEhqvq4ojdLgaO9x8Ngd7+c66sptwYY4wxxpi8aQAsV9XfVHUv8BnQMss+LYEP1TMVOExEjj7Qia1QbowxxhhjChyN0uMAKgCrIpZX++sOdp9srPlKIZe2d424jkFEOqtqX9dxxANLizBLizBLizBLizBLizBLizBLi7BolXFEpDPQOWJV34g0z+k9s5bl87JPNlZTbmKh84F3CQxLizBLizBLizBLizBLizBLizBLiyhT1b6qWi/iEfkjaDVQKWK5IrA2yynysk82Vig3xhhjjDEmb2YAx4tIFREpAlwNDM+yz3DgOn8UltOBLaq67kAntuYrxhhjjDHG5IGqponI7cA3QCLwvqouEpEu/vY+wGjgEmA5sBO4Pi/ntkK5iQVr+xZmaRFmaRFmaRFmaRFmaRFmaRFmaeGYqo7GK3hHrusT8VqBrgd7XilssyEZY4wxxhhT0FibcmOMMcYYYxyzQrkxxhhjjDGOWZtyY4wxxpg4JyKXAjWBlNA6VX3aXUQmv1lNuYkKETlORA7xXzcVkTtF5DDHYRljTIEhIkeKyDGhh+t4Yk1EEkXkY9dxxAMR6QO0Be7Am5jmKuBYp0GZfGcdPU1UiMhcoB5QGW/YoOHAiap6icOwnPHHKX0DOAkogjeM0g5VLek0sBgTkeOBHkANMtf2VHUWlEMikgLcSPbarxucBeWI5Y0wEWkBvAKUB/7GK3wtUdWaTgNzQES+AZqr6l7XsbgkIvNVtVbEc3FgiKpe4Do2k3+sptxES7qqpgFXAD1V9R7gaMcxufQm0A74BSgK3IRXSA+a/kBvIA1oBnwIfOQ0Irc+AsoBFwIT8WZ92+Y0Incsb4Q9A5wOLFPVKsC5wBS3ITmzEpgiIk+IyL2hh+ugHNjlP+8UkfJAKlDFYTwmCqxQbqIlVUTaAR2Bkf66ZIfxOKeqy4FEVd2nqv3xCh5BU1RVv8O7S/e7qnYDznEck0vVVPUJvLsmA4BLgVMcx+SK5Y2wVFXdCCSISIKqjgdqO47JlbV4f0MSgBIRj6AZ6TcBfQmYjfdj5TOXAZn8Zx09TbRcD3QBnlPVFSJSBQhy28Cd/nS8c0XkRWAdUMxxTC7sFpEE4Bd/RrQ1wJGOY3Ip1X/eLCInA3/iNfkKIssbYZv95gmTgIEi8jfeHYTAUdXuACJSwlvU7Y5DckJVn/FffikiI4EUVd3iMiaT/6xNuTExICLH4rUNTQbuAUoBb/u154EhIvWBJcBheLfoSwEvqupUl3G5IiI3AV8CtfCabxQHnoycGS4oLG+EiUgxYDdeh772eGkx0K89DxT/x+pHQGl/1QbgOlVd5C6q2BORRLw7aZWJqFBV1VddxWTynxXKTb4Skc9VtY2ILAAiM5fg1XLUchSaMcYUKCJSkswFsH8chuOEiPwIPOY34UFEmgLPq2ojl3HFmoiMxvuhtgBID60P3UkwhYM1XzH57S7/+TKnUcQZEbkMr/bvWLzPXehHStBGX6kHPEY4HQAI6o81v43odWSv/brTUUjOWN4IE5FbgKfxOvel439fAIEbiQYoFiqQA6jqBP9OQtBUDOJnIWisUG7ylaqu819uAHaparqInABUB8a4i8y5nkArYIEG+/bUQOABstT2BNhoYCqWHmB5I9L9QE1V3eA6kDjwm4g8QXgknmuBFQ7jcWWMiFygqmNdB2KixwrlJlomAWeJyOHAd8BMvIkP2juNyp1VwMKAF8gB1qvqcNdBxJEUVQ3i8G45sbwR9iuw03UQceIGoDswBO+OwSS8gQSCZiow1O8MnUpA77YWdtam3ESFiMxW1boicgfeUGcvisgcVa3jOjYX/E5sz+CNRb0ntD5onXRE5Fy88dq/I3M6DHEWlEMicg+wHW/It8j0CGLbYcsbPhGpg9fxdxqZ0yJwzZqMR0R+Ay7H7rYWalZTbqJFROQMvJrxG/11Qc5vz+EVvlLwZvQMquvxmjIlE26ioHi1YEG0F2/c4ccId4wOatthyxth7wDfY015Qn0NHiV7v4ugta/+BbvbWugFuZBkoutu4BFgqKouEpGqwPjcDynUStt0yACcqqpBnRwnJ/fiTSBkbYctb0RKs2ZNGayvgWcdMEFExhDgu62FnRXKTVSo6kRgooiUEJHiqvobEORbr+Oskw4AU0Wkhqoudh1InFiEtR0OsbwRNl5EOgMjCHizJqyvQcgK/1GEYN9tLdSsTbmJChE5BfgQb8IHAdYTwAkfQkRkG94MnnsJz+IYuE46IrIEOA7vj8seAj5+vYgMBWri3UUKdNthyxthIpLT6CKqqoFr1mR9DTIL+symhZ3VlJtoeQe4N8uED+8CgZrwIURVS7iOIU5c5DqAOPOV/zCWNzKoahXXMcQR62tA9plNRSSQM5sWdlZTbqJCROap6qkHWhckItICaOIvTlDVkS7jcUVETgXO8hcnq+o8l/G4JiJFgBP8xZ9VNTW3/QszyxseEUkGbiXi+wJ4J4h5Q0QWWF8Dm9k0KBJcB2AKrd9E5AkRqew/HieYEz4AICIv4M12uth/3OWvCxQRuQuv49aR/uNjf9jMQPL/sP4CvAW8DSwTkSa5HVNYWd7IpDdwGl6eeNt/3dtpRO5MFZEaroOIA9lmNsVrEmkKEaspN1HhTxrUHWhMeMKHbqq6yWlgjojIfKC2qqb7y4nAnKC1l/XT4QxV3eEvFwN+Clo6hIjILOAaVf3ZXz4B+FRVT3MbWexZ3gizO41h1tfA4/c/mU3mmU3rqerlzoIy+c7alJuo8AvfgeusdgCHAaHRE0o5jMMlAfZFLO/z1wVVcqhADqCqy/ymC0FkeSNsn4gcp6q/AvhDyu47wDGFlfU18NjMpgFghXITFX6N3/1kn/DhHFcxOdYDmCMi4/G+UJvgjeMeNP2BaX6tD3gz1PVzF45zM0WkH+Har/bALIfxuGR5I+wBvGERf8P7vjiW4BbA7HY+VtEVFNZ8xUSFiMwD+uAVMDJqeFQ1qAUORORooD7eH9lpqvqn45CcEJHTgDPxa3tUdY7jkJwRkUOArmRu5vW2qu7J9cBCyvJGmJ83TsRLi6UBzhML8ArmgjcjchW8DtE1nQYWY1bRFQxWKDdRISKzgtguNjciUgGvxivyC3WSu4jc8NvTH0XmdPjDXUQmXljeCBORRmQvgH3oLKA4ISJ1gVtU9RbXscSSVXQFgzVfMdEyQkRuA4ZiM9IhIv8D2uLN4Bg51m6gCuX+aBpPAX8RbjOsQKA6bYWIyJlAN7L/WAviJDGWN3wi8hFe58a5hAtgijchW6Cp6mwRqe86DgfSVDWoI/AEhtWUm6iwGekyE5GfgVpBvQUdIiLLgYaqutF1LPFARJYC95C99itw6WN5I8wfcaSG2h9oROTeiMUEoC5QRlUvdBSSEyLSDfgbq+gq1Kym3ESFzUiXzW94M9IFulAOrAK2uA4ijmxR1TGug4gTljfCFgLlgHWuA4kDkbMhpwGjgC8dxeJSR//5gYh1CgSyoquwskK5iQoRORS4FzhGVTuLyPHAiUGdxRLYCcwVke/IXMsRtN70vwETRGQUmdPhVXchOTVeRF7CG+YsMj1muwvJGcsbYWWBxSIyncxp0cJdSM4sVtXBkStE5Cpg8H72L5SsoisYrFBuoqU/3i350BTAq/G+RINaKB/uP4LuD/9RxH8EXUP/uV7EOgWCOKKC5Y2wbq4DiCOPkL0AntO6Qs0quoLB2pSbqBCRmapaT0TmqGodf10gZ6Qz2YlIsdDMjcYYk5WIXAxcArQBBkVsKonX3r6Bk8AcEZFBeBVd16nqySJSFG/G29puIzP5yWrKTbTs9b80FEBEjiOA7alF5HNVbRMx1m4mAZwq+gy8CWGKA8eIyKl4w5vd5jay2BKRa1X14yyd2DIEqcmGiPRU1btFZAQ5f0YC02RDRH5Q1cYiso3MaRGaWr6ko9BcWAvMBFqQeUKtbXido4PmOFVtKyLtAFR1l4gEdcbbQssK5SZangK+BiqJyEC8CUE6OY3Ijbv858ucRhE/egIX4jflUdV5ItLEaURuFPOfS+S6VzCEZjN92WkUcUBVG/vPgc8XqjoPmCcin6hq6v72E5EvVbV1DENzxSq6AsCar5ioEZEywOl4tTxTVXWD45CcEZEqwDpV3e0vFwWOUtWVTgOLMRGZpqoNrVmTyUpEigG7VDXdX04EDlHVnW4jiz0ROR1YpKrb/OXiQE1VneY2svgT+V1SmInI+cDjQA1gLH5Fl6pOcBmXyV8JrgMwhYuI1A098CZEWYd3G/IYf11QDSY8aRB4Y1IHqqOSb5U/U6GKSBERuR9Y4jooV0RkgIgcFrF8uIi87zAkl74DDo1YLgqMcxSLa72B7RHLO/11JrtA1Cyq6rdAK7w7zp8C9axAXvhY8xWT317xn1PwRpSYh1dTXguYBjR2FJdrSaq6N7SgqntFJIgjTHQBegEV8EbkGQt0dRqRW7VUdXNoQVU3iUihr/XbjxRVzSiIqup2f8SJIJLIiYNUNV1E7O91AOVQmRUau/4YETkmoMOnFlr2ITf5SlWbAYjIZ0BnVV3gL58M3O8yNsfWi0gLVR0OICItgcA15/GbMLV3HUccSRCRw1V1E4CIlCa438s7RKRuqJAhIqcBuxzH5MpvInIn4drx2/DGcTfZFfbOjlbRFSBB/fI30Vc9VCAHUNWFIlLbYTyudQEGisib/vJqoIPDeGJKRN4gl9vMAZxEKeQV4EcR+QIvfdoAz7kNyZm7gMEistZfPhpo6zAel7oAr+O1IVa8pj2dnUYUB0TkcKCSqs6PWP2Qq3hiwSq6gsUK5SZalojIe8DHeH9UrgUWuw3JDb/DWhdVPd3vsCWhDlwBMtN/PhOvo1Jo3OGryDzcWWCISAKwHGiNN1mQAK1UNXCfE/8zchZQHTgRLy2W5jbqRmHlp8Wrqnq161jigYhMwBsWMQmYi3fXcaKq3gugqmPdRRdTVtEVADb6iokKEUkBbsX7QyvAbKCyqt7oNDBHROR7VQ3iLI2ZiMh44IJQYUtEkoGxodqgoBGRn1T1DNdxxAMRmaCqTV3HEQ9E5BugeWQ/lKAKja4iIjfh1ZI/JSLzAzjHw6fADjJXdBVX1XZOAzP5ymrKTVSo6m6/ABa6BX048IXbqJyaIyLD8UZcyZjJUlWHuAvJifJ4Y3P/4y8X99cF1VgRaQ0MUashmeI37xpE5s9IEDuyrcRLj+FkTovATCoVIUlEjsZr2vWY62Acuh6vois098UkbESeQscK5SZficgJwNVAO2AjfjMFqwGjNF56RNaWKxC0QvkLeD9QxvvLZwPd3IXj3L14EwmlichugjlzY0gj//npiHVK5s9MUKz1HwnYBFNPA98AU1R1hohUBX5xHFPM+XNcvOY/TCFlzVdMvhKRdGAycKOqLvfX/aaqVd1GZuKFiJQDGvqL01T1T5fxGGNMvBOR44EeeH1yUkLr7W9r4WI15Sa/tcarKR8vIl8Dn1H4h6w6IL+N/Y1ATTJ/od7gLCh39uCNtZsCnCAiJ6jqJMcxOeOPKHE8mfNFINNDRC4l+2fk6f0fUTiJyBHAg2RPi8DdNfDvvvbGmwH5ZBGpBbRQ1WcdhxZr/YGn8GrKm+E1Zwn839bCxmb0NPlKVYeqalu8URQmAPcAR4lIbxG5wGlwbn0ElAMuBCYCFYGgjcCC31lrEt7t6O7+czeXMblk6REmIn3w+p/cgVfYuApvVuAgGggsBarg5YuVwAyXATn0LvAIkArgD4cYxJFpiqrqd3gtHH5X1W4Es2lXoWaFchMVqrpDVQeq6mV4BdC5wMNuo3Kqmqo+AexQ1QHApcApjmNy4S6gPvC7P+JKHWC925CcsvQIa6Sq1wGbVLU7cAZQyXFMrpRR1X5AqqpO9O+one46KEcOVdXpWdalOYnErd3+MKq/iMjtInIFcKTroEz+skK5iTpV/UdV3wnirdcIofGWN/uTPpQCKrsLx5ndfoclROQQVV2KNy51UFl6hIVm79wpIuXxPjNVHMbjUuj7Yp2IXCoidfAqN4Jog4gchz/5mIhcSXiq+SC5GzgUuBM4DW9IxI4uAzL5z9qUGxMbff22w48Dw/GGAnzSbUhOrBaRw4CvgG9FZBPeKBNBZekRNtJPi5fw5jVQ4D2nEbnzrIiUAu4D3gBK4jUFDKKuQF+guoisAVbgFUgDRVVDzZe247Unz0RE3lDVO2IblclvNvqKMcYJETkb747B1zZJiqVHJBE5BEhR1S2uYzHxQUSKAQkBnA05T0RktqrWdR2H+W+sptyYKBKRe4EtfvvQyPV3AImq2tNJYA6JyKl4M70CTA5iAVRE6gNlVXVMaJ2qThSRFnh9DWY5Cy7GRORavAqij0LrVHWPiFwnIjtU9ROH4cWUiLwI/KaqfbKsvwcop6oPuYks9vzvzpzWA4GdSMkUctam3JjougFv5JWs+vrbAkVE7sIbWeJI//Gx/wMlaF4CluSwfrG/LUjuw2u+k9Vn/rYguQzvuyGrXnidw4OkhP+ohzeTZQX/0QVvrG5jCh2rKTcmujSnmmC/JjCIY8zeCDRU1R0AIvI/4Ce8drNBUkZVV2ZdqarLRaSMg3hcSsypSYKqbhORZBcBOaSqmp7DyvSgfV/4I/AgImOBuqE8IiLdgMEOQ4tXgcofhZXVlBsTZSJyVF7WBYQA+yKW9xHMPyZFc9lWLGZRxIdkv71wJiJSAijiIB6XdvozN2bir9uVw/5BcAwQWbGxl4CNXCUiiSJyoDtovWISjIkqK5QbE10vAaNE5GwRKeE/mgIjgJedRubG+8A0Eenm13hNBfrlfkihNE5Ensta+yki3YHvHcXkSj/gCxGpHFrhv/6M4OWNJ4ExItJJRE7xH9cDowjmaE3gNf+bHvGdMQ0Y4Dak2FLVfcBpud0tUdUPYheRiRYbfcWYKBORi/EmTjoZb5i3RcALkZ38gsCf+OJ0YDfQGK+GfJKqznEamAN+zfB7QAO8ibUATgVmAjep6nZHoTkhIl3wZm0sjvcZ2YH3GentNDAH/HkMHsD7vgBYCLysqgvcReWWiNTF6xyueJ3Dg/id8QpwPF7TnR2h9ao6xFlQJt9ZodyYOCAij6hqD9dxRJuI/KSqZ7iOI16ISFWgpr+4SFV/y7K9pqouin1kbohIcby/S9namItIR3823MAL2pjU/ohNTQgXyuc5DinmRKR/DqvVn+3VFBJWKDcmDgRljFm/ecZ8YIjal88BBSVf5IWlRViQ0sIfselm4Eu8u2tXAH1VNWidw00A2OgrxsSHoHR2vBevI+M+Edntr1NVLekwpngWlHyRF5YWwWQjNgEikoKXFjWBlNB6qykvXKyjpzHxIRC1xqpaQlUTVDXZf13CCuS5CkS+yCNLi2CyEZs8HwHlgAuBiUBFwGY3LWSsptyY+BCYPzIi0gqvo2eofehXbiMyBURgPiN5EKS06I83YtNQf/lygjcqD0A1Vb1KRFqq6gAR+QT4xnVQJn9ZTbkx8SEQk2GIyNt4M/ItwBtVoouIvOU2KjfEU+kAu2WbeCrAprgOIFb8EVhyE5gxqVX1VeB64B9gE3C9qvZ0GpQbqf7zZj9/lCJg47UHgXX0NCaKROQNcrntrqp3xjAc50RkEXByqJOnP0ziAlWtmfuRhZOIzFLV01zHEQ/8Dn398W7JvwfUAR5W1bFOA3NARH7AmzjpA+ATVd3sNCAHRKR0bttV9Z9YxRIPROQmvM6utfA+J8WBJ1W1j9PATL6y5ivGRNdM1wHEmZ/xZuj73V+uhDcaS1BNFZH6qjrDdSBx4AZV7SUiFwJH4NWO9gcCVyhX1cb+LJ43ADNFZDrQX1W/dRxaLM3Cq9AINdUJVW6I/7qqi6BcUdX3/JcTCdi1B4nVlBtjok5ERuD9IS0F1Aem+8sNgR9V9TyH4TkjIouBE/B+pOzAL3Coai2ngTkgIvNVtZaI9AImqOpQEZmjqnVcx+aKiCTitaF+HdiKlz8etQljgkNE7s1tu9+8xxQSVlNuTAyIyBHAQ0ANMg9ndY6zoGLrZdcBxKmLXQcQR2aJyFigCvCIiJQA0h3H5ISI1MK7U3Ap8C3QXFVni0h5vOEAC32h3J/Fc79UdXasYnGshOsATOxYTbkxMeAXNgYB9+N1dOwIrFfVh5wGZuKCiBxJ5h9rfzgMxwm/f0Ft4DdV3SwiZYAKqhq45k0iMgl4F/hCVXdl2dZBVT9yE1nsiMj4XDZrgCo0TIBYodyYGAh16AvdovfXTVTVs13HFgsi8oPfTnYbmTu+hpprBHKschFpAbwClAf+Bo4FlgSx46uINMlpvapOinUsLvlNVj5U1fauYzHxQ0T6k8OgATZ5UOFizVeMiY3QcFbrRORSYC3e5A9B0R68yYNcBxJnngFOB8apah0RaQa0cxyTKw9EvE4BGuB19gtUjaiq7hORMiJSRFUDPySmiMwE3gc+VdVNruNxaGTE6xTgCry/I6YQsUK5MbHxrIiUAu7Dmx66JHC304hiayhQF0BEvlTV1o7jiRepqrpRRBJEJEFVx/vTiAeOqjaPXPbHcH/RUTiu/Q5MEZHheB2AgcB26rsar339DL+A3h8YqwG7za+qX0Yui8inwDhH4ZgosUK5MbGxSVW3AFuAZgAicqbbkGIqcgZCG84rbLOIFAcmAQNF5G8gzXFM8WI1cKBJdAqrtf4jgYB39FPV5cBjIvIEcBlerXm6iLwP9AraeOURjscbXtYUItam3JgYEJHZqlr3QOsKq8hrDdJ1H4iIFAN24/1oaY83ZORAVd3oNDAHsky0Fer0uVJVr3UWlIkLEaPRXII3tfxAoDHQQVVrOwwtZiL644TGaf8TeCRrDbop2Kym3JgoEpEzgEbAEVnGmy0JJLqJyolTRSQ0znJR/zUEvKOnqu6IWBzgLJD4EDnRVhpeG+IproJxyR9C9UGgJsEcQjWDiMwCNgP98GZ43eNvmhaku43WHycYrFBuTHQVwZsOOYnMt6G3Alc6icgBVQ3SD5ADymEUmoxNBPRHiqoOEJEieJMpgTf7a1ANxBtC9TIihlB1GpE7V6nqbzltUNVWsQ7GJRFphXeHQIHJqvqV24hMfrPmK8bEgIgcq6q/H3hPY4JJRJri3S1YiffjpBLQMWhDIoINoRpJRA4BWgOViahIVNWnXcXkgoi8DVQDPvVXtQV+VdWu7qIy+c1qyo2JjfdE5CpV3QwgIocDn6nqhW7DMi6JSI4dtYI4eRDeeO0XqOrPACJyAl4B5DSnUbkR9CFUIw3D6yA/C9hzgH0Ls7OBk0OjzojIAGCB25BMfrNCuTGxUTZUIAdQ1U3+LI4m2EZFvE7Bm2L+Z7y2xEGTHCqQA6jqMhFJdhmQQzkNoXqP25CcqaiqF7kOIg78jDfaSuiOayUgcLPdFnZWKDcmNtJF5JhQDaiIHEvObYpNgKjqKZHLIlIXuMVROK7NFJF+QGgK+fZ4taOBo6qhiWIyhlANsB9F5BRVDWStsIiMwPtbUQpYIiLT/eWGwI8uYzP5z9qUGxMDInIR0BeY6K9qAnRW1W/cRWXiUVCHjPTbDnfF68gmeGO3vx0x2kahl2VYyGxU9c4YhuOUiCzAS4skvDG5f8NrvhLqDF3LYXgxIyK59iNQ1Ym5bTcFixXKjYkRESmLN6W6AD+p6gbHIRnHsgyTmYA362kZ62sQTCLSMbftqhqYYTP9u4n7ZR3nTWFkhXJjYkBEmuS0PogjS5gwEXkqYjENb+SRL1V1t5uI3PHHnO4GHEvmUTZsBtgAEpEUvOEgq+F1aOynqoGd7TbLMKpFgGRgRxCHTy3MrFBuTAz47QJDUoAGwKwgTgZishORkni35Le5jsUVEVmK15lxFrAvtD5Is5tGtB/Okaq2iGE4TonIILxRaCYDFwO/q+pdbqOKHyJyOdBAVR91HYvJP1YoN8YBEakEvKiq7VzHYtwRkXpAf8ITS20BblDVwHVwFJFpqtrQdRwuWfvhMBFZEOoILSJJwPSA9rVI2t8dAhGZqqqnxzomEz02+ooxbqwGTnYdhHHufeA2VZ0MICKN8QrpgejElsV4EXkJGELEeNSqOttdSLEVpEJ3HoTGakdV00TEZSwuTQfq+rN5hiQA9bARvAodK5QbEwNZRlVIAGoD85wFZOLFtlCBHEBVf/DbjgZRqJa8XsQ6BQLTxEtEPlfVNhEjj2RsIkAjjvhOFZGt/msBivrLobQIWlvq5oTzRKj/SWCaMwWFNV8xJgayjKqQBqxU1Smu4jFu+eORA3QADsWbuVLxps7epKqPuYrNuCMiR6vquv2NPGIjjgSPiKwGXsX7MRJJAVT11ZgHZaLGCuXGxIiIHAGgqutdx2LcEpHxuWzWIHUAFpFrVfXjLMNDZghqoUNEyuF1CFdghqr+6Tgk44CIrAN6k71QDoCqdo9tRCaarPmKMVEkXkPIp4Db8b5UE0QkDXhDVZ92GpxxRlWDPktjpGL+c4lc9woQEbkJeBL4Hu974w0ReVpV33cbmXFgnf2tCA6rKTcmikTkHuASvNk7V/jrquLVfHytqq+5jM+4JSKl8H60hcaxnwg8rapb3EVlXBORn4FGoeEgRaQM8KOqnug2MhNrIjJHVeu4jsPERoLrAIwp5K4D2oUK5ACq+htwrb/NBNv7wDagjf/Yijf6SuCIyAAROSxi+XARCWrN8Gq8fBGyDVjlKBbj1rmuAzCxY81XjImuZFXdkHWlqq4XkWQXAZm4cpyqto5Y7i4ic10F41gtVd0cWlDVTSIS1BrCNcA0ERmG16a8JTA91O4+qO3sg0hV/3Edg4kdK5QbE117/+U2Ewy7RKSxqv4AGVPN73IckysJInK4qm4CEJHSBPdv1K/+I2SY/2zt7o0pxKxNuTFRJCL7gB05bQJSVNVqywNMRE4FPgRK+as2AR1Vdb67qNwQkeuAR4Av/FVXAc+p6kfuojLGmNixQrkxxjgmIiUBVHWriNytqj0dh+SEiNTAmyxIgO9UdbHjkJzwh099EKgJpITWB2moTGOCyArlxhgTR0TkD1U9xnUcsSYiOV6zqv4R61hcE5GxwCDgfqAL0BFYr6oPOQ3MGBNVVig3xpg4IiKrVLWS6zhiLcvU8kWBKsDPqlrTXVRuiMgsVT1NROarai1/3URVPdt1bMaY6AlqJxpjjIlXgawpUdVTIpdFpC5wi6NwXEv1n9eJyKXAWqCiw3iMMTFghXJjjIkxEdlGzoVvwaslDjxVnS0i9V3H4ciz/sRS9wFvACWBe9yGZIyJNiuUG2NMjKmqDW2XRWgMbl8CUBdY7ygcJ0QkBa8NeTWgAtBPVZu5jcoYEytWKDfGGBMPIn+opAGjgC8dxeLKALymK5OBi4EawF1OIzLGxIx19DTGGGPigIgsCLWtF5EkYLqq1nUcljEmRqym3BhjjDMiMjy37araIlaxxIFQB09UNU1EXMZijIkxqyk3xhjjjIisB1YBnwLT8Dq7ZlDViS7iciHLDMChTr87/deqqiVdxWaMiT4rlBtjjHFGRBKB84F2QC28tuSfquoip4EZY0yMJbgOwBhjTHCp6j5V/VpVOwKnA8uBCSJyh+PQjDEmpqxNuTHGGKdE5BDgUrza8srA68AQlzEZY0ysWfMVY4wxzojIAOBkYAzwmaoudBySMcY4YYVyY4wxzohIOuHOjZF/kKxzozEmUKxQbowxxhhjjGPW0dMYY4wxxhjHrFBujDHGGGOMY1YoN8YYY4wxxjErlBtjjDHGGOOYFcqNMcYYY4xx7P9SitLULWvEcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_counts = [\"{0:0.0f}\".format(value) for value in\n",
    "                np.array(c).flatten()]\n",
    "\n",
    "group_percentages = [\"{0:.2%}\".format(value) for value in\n",
    "                     np.array(d).flatten()]\n",
    "\n",
    "labels = [f\"{v1}\\n{v2}\" for v1, v2 in\n",
    "          zip(group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(8,8)\n",
    "\n",
    "categories = ['Adenosis', 'Ductal_Carcinoma', 'Fibroadenoma', 'Lobular_Carcinoma',\n",
    "               'Mucinous_Carcinoma', 'Papillary_Carcinoma', 'Phyllodes_tumor', 'Tubular_adenoma']\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "plt.title('confusion Matrix x100 mit Data Augmentation')\n",
    "sns.heatmap(d, annot = labels, fmt='', xticklabels = categories, yticklabels = categories)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4999ab26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z41dUldmzN9Ow4RSuv/4Djhw5DUBYWIglCT9QMhOFMcZvJCYeoXv397j99jn88ccJ6tWLIjk5xddhmRxKVtOTMcZvpKZm8OqrK3nppRWkpmZSsWI4r77alX/844rswXTGP7idKESkjKqe9GQwxpiSo1+/j5g/fzsAd9/djNde68pFF5XxcVQmLwU2PYlIWxHZAmx1bjcTkakej6yo2R1PxviVhx++ipiYaL766m5mzbrBkoQfc6ePYjyO6cAPA6jqBuBqTwblEXbHkzE+k5WlzJjxI8OHL8l+rmPHWmzePJBrr63tw8iMO9xqelLVX3PNqZLpmXC8wO54MsarNm3aT1zcIr77zrGO2d13N6NZs4sBCA62+2mKA3cSxa8i0hZQEQkFHsTZDGWMMfk5eTKN555bxrhx35OZqVx8cVkmTOhG06ZVfB2aOU/uJIo4YCKOpU2TgM+BQZ4MqkjZ0qfGeN0nn2xnyJBP2bs3GREYPLglL73UifLlw30dmikEdxJFA1W9M+cTItIOWOmZkIpYziRh/RPGeMW8edvYuzeZK664mDff7E3LltV8HZK5AO4kijeAK914zr/Z+hPGeExGRha//fYnl15aAYAxY7pyxRWXEBcXaxP4BYB8E4WItAHaApVF5JEcL5XDsWKdMcbwww9JxMUtJDU1kw0b4ggNDSY6ujRDhrTydWimiLhK9aFAWRzJJDLHz5/AzZ4PzRjjz44ePc3AgQtp2/YtNmzYT0pKBnv2HPN1WMYD8q1RqOoyYJmIzFTVX7wYkzHGj6kqH3ywmWHDlnDgwElCQoJ47LG2PPXU1ZQuXcrX4RkPcKeP4pSIvAY0BrJvWVDVTh6Lyhjjt+68cy4ffLAZgA4dajJtWi8aN77Ix1EZT3Knl+k9YBtQG3gO2AOs8WBMxhg/1r17XaKiInj77ev55pt7LEmUAO7UKKJU9S0ReShHc9QyTwdmjPEPS5fu4uefjzBgQCwA/fs3pXfv+lSqFOHjyIy3uJMo0p2/94lIL+B3wFY1NybA7d9/gkce+Zz3399EWFgwXbrU4bLLKiEiliRKGHcSxYsiUh4YjmP8RDngYU8GZYzxnawsZfr0dTzxxFKSk1MJDw/hmWeutvWqS7ACE4WqLnQ+TAauheyR2caYALNhwx8MGLCQVat+A6BHj7pMntyTOnUq+jgy40uuBtwFA7fimOPpM1XdLCK9gZFABHCFd0I0xnjL448vZdWq36haNZKJE7tz000NyTVztCmBXNUo3gJqAKuBSSLyC9AGeEJV53khNmOMh6kqp06lU6ZMKACTJnUnPn4tzz13LeXKhfk4OuMvXCWKWKCpqmaJSDhwCKirqn94JzRjjCf98ssxhg79lJMn01m6tD8iQoMG0Ywf393XoRk/4ypRpKlqFoCqpojIDksSxhR/6emZjB//A889t4xTp9KJjAxl584j1K8f5evQjJ9ylShiRGSj87EAlzm3BVBVberx6IwxRWrlyr3ExS1i8+YDAPTr15hx47pRtWqkjyMz/sxVomjotSiMMR43dOhiJk92TKpQp05FpkzpSffudX0clSkOXE0KaBMBGhNAKlcuQ6lSQYwY0Y6RIzsQEWET+Bn3eHRFERHpLiLbRSRRRJ7Ip0xHEUkQkZ9sahBjis62bYf4/POfs7dHjGjHxo0DeeGFTpYkzHlxZ2R2oTjHYUwBuuJYa3uNiCxQ1S05ylQApgLdVXWviNjsYsZcoNOn03n55RWMGbOSChXC2bZtCJUqRRAWFkJMTLSvwzPFkFuJQkQigJqquv08jt0KSFTVXc5jzAb6AltylLkDmKuqewFU9cB5HN8Yk8vnn//MoEGL+PnnowBcf30DbLycuVAFNj2JSB8gAfjMud1cRBa4cexqwK85tpOcz+VUH6goIt+IyDoRudutqI0xf7Fv33Fuu+0junV7l59/PkrjxpVZseJeZsy4nooVbQI/c2HcqVGMwlE7+AZAVRNEpJYb++X1PUbzOH8LoDOOaUG+F5EfVHXHXw4k8gDwAEDNmjXdOLUxJcvf/vY/fvghiYiIEEaN6siwYVdRqpQtbW+Khjud2RmqmlyIYyfhmALkjOo4pijPXeYzVT2pqoeA5UCz3AdS1emqGquqsZUrVy5EKMYEHtWz37teeaUzvXvXZ8uWwTz+eDtLEqZIuZMoNovIHUCwiNQTkTeA79zYbw1QT0Rqi0gocBuQu8lqPtBBREJEpDTQGth6HvEbU+IcP57KsGGfMWDAwuznrrmmFp98cju1alXwXWAmYLmTKIbiWC87FXgfx3TjDxe0k6pmAEOAJTg+/P+nqj+JSJyIxDnLbMXR97ERx+SDM1R1cyHehzEBT1WZM2cLDRtOYcKEVfz73wns2XPM12GZEsCdPooGqvok8OT5HlxVFwOLcz0Xn2v7NeC18z22MSXJ7t1HGTLkUxYv3glAq1bViI/vZTUI4xXuJIpxInIJ8CEwW1V/8nBMxhgnVeXVV1fy3HPLOH06g/Llwxg9ujMPPNCC4GCPjpc1Jps7K9xdKyIX41jEaLqIlAP+q6ovejw6Y0o4EWHHjsOcPp3B7bc3Ydy4blx8cVlfh2VKGLe+kqjqH6o6CYjDMabiGU8GZUxJdujQqezZXQHGjOnK55/fxfvv32RJwviEOwPuGorIKBHZDEzGccdTdY9HZkwJo6rMnJlATMxkbrnlQ9LSMgGIji5N166X+Tg6U5K500fxb+AD4DpVzT0OwhhTBLZuPUhc3CKWL3dM2tys2cUcPXqaKlWsBmF8z50+iqu8EYgxJdGpU+m89NJyXnvtO9LTs6hcuTTjxnXjzjsvR2ySJuMn8k0UIvI/Vb1VRDbx16k3bIU7Y4qAqtKp0yxWrfoNgAEDWjB6dGebm8n4HVc1ioecv3t7IxBjShoRYdCglpw6lc6bb/amTZsaBe9kjA/k25mtqvucDwep6i85f4BB3gnPmMCRmZnFG2+sYty477Of69+/KevWPWBJwvg1d26P7ZrHcz2KOhBjAtnatb/TuvUMHnzwM0aO/JLffz8OOGoVNoGf8Xeu+igG4qg51BGRjTleigRWejowYwJBcnIKTz31FVOmrEEVatQoxxtv9KBq1Uhfh2aM21z1UbwPfAqMBnKud31cVY94NCpjijlV5cMPt/Dww5+xb98JgoOFYcOu4tlnO1K2bKivwzPmvLhKFKqqe0RkcO4XRKSSJQtjXHvzzXXs23eCq66qTnx8L5o1u9jXIRlTKAXVKHoD63DcHpvzpm4F6ngwLmOKndTUDI4dS6FKlbKICFOn9uSbb/bwf//XgqAgGxNhiq98E4Wq9nb+ru29cIwpnpYt20Nc3CKqVo1k6dL+iAgNGkTToEG0r0Mz5oK5M9dTOxEp43x8l4iMExFbuNoY4ODBk9xzzzw6dpzFtm2H+PXXZPbvP+nrsIwpUu7cHjsNOCUizYDHgV+AdzwalTF+LitLeeutH4mJmcKsWRsICwvmuec6snHjQJvh1QQcdyYFzFBVFZG+wERVfUtE/u7pwIzxV6pKt27vsnTpLgC6dKnD1Kk9qVcvyseRGeMZ7iSK4yLyT6A/0EFEgoFSng3LGP8lInToUJNNm/Yzfnw3brutiU3gZwKaO01P/YBU4B+q+gdQDVvj2pQwixbtYN68bdnbI0a0Y9u2Idx+u83yagKfO9OM/yEi7wEtRaQ3sFpV/+P50IzxvaSkP3nooc+YO3cr0dGlufrqS6lUKYKwsBDCwtypkBtT/Llz19OtwGrgFhzrZq8SkZs9HZgxvpSRkcX48d/TsOEU5s7dSpkypRg5sj3lyoX5OjRjvM6dr0RPAi1V9QCAiFQGlgIfeTIwY3xl9erfGDBgIQkJfwBw440xTJzYnRo1yvs4MmN8w51EEXQmSTgdxr2+DWOKnaws5d5757Nly0Fq1izP5Mk96NOnga/DMsan3EkUn4nIEhzrZoOjc3ux50IyxrtUldTUTMLDQwgKEqZM6cmnn+7kmWeuoUwZm8DPGHc6sx8Tkb8B7XHM9zRdVT/2eGTGeEFi4hEGDVpEjRrleOutvgB07FiLjh1r+TYwY/yIq/Uo6gFjgcuATcCjqvqbtwIzxpNSUzMYM2YlL7+8gtTUTCpViuDVV08RFVXa16EZ43dc9TW8DSwEbsIxg+wbXonIGA/76qvdNG0az7PPfkNqaiZ//3sztm0bbEnCmHy4anqKVNV/OR9vF5EfvRGQMZ6SmZnFvffO5513HAs2NmgQRXx8b2tmMqYArhJFuIhcwdl1KCJybquqJQ5TrAQHBxESEkR4eAhPPdWBRx9ta4PmjHGDqGreL4h87WI/VdVOngnJtdjYWF27dq37O7zuzHPD836fJrBt2rSflJQMWrasBsDhw6c4diyFyy6r5OPIjPEuEVmnqrGF2dfVwkXXFj4kY3zr5Mk0Ro36hvHjf6BevSg2bIgjNDSYqKjS1hdhzHmyercJOAsWbGfo0E/ZuzcZEejSpTbp6ZmEhgb7OjRjiiWPjrAWke4isl1EEkXkCRflWopIps0hZS7E3r3J3HDDbPr2nc3evclceeUlrF79f7zxRk8bOGfMBfBYjcK5bsUUoCuQBKwRkQWquiWPcmOAJZ6KxQS+zMwsOnacye7dx4iMDOXFFzsxaFBLQkJsthljLlSBiUIck+3fCdRR1eed62VfrKqrC9i1FZCoqrucx5kN9AW25Co3FJgDtDzf4I1RVUSE4OAgRo3qyCef7GDChG5Uq1bO16EZEzDc+bo1FWgD3O7cPo6jplCQasCvObaTnM9lE5FqwI1AvKsDicgDIrJWRNYePHjQjVObQHf06Gni4hby8ssrsp/r378pH354iyUJY4qYO01PrVX1ShFZD6CqR0XEnQbfvJb9yn2P6gRghKpmulolTFWnA9PBcXusG+c2AUpVef/9TTzyyOccOHCSyMhQhgxpRfny4bbSnDEe4k6iSHf2Iyhkr0eR5cZ+SUCNHNvVgd9zlYkFZjv/g0cDPUUkQ1XnuXF8U8Ls2HGYQYMW8eWXuwHo0KEm06b1onz5cB9HZkxgcydRTAI+Bi4SkZeAm4Gn3NhvDVBPRGoDvwG3AXfkLKCqtc88FpGZwEJLEia3jIwsXnxxOaNHf0taWiZRURG89lpX7rmnudUijPECd6YZf09E1gGdcTQn3aCqW93YL0NEhuC4mykYeFtVfxKROOfrLvsljDkjOFhYsWIvaWmZ/OMfzRkzpivR0TZozhhvyXcKj+wCjruczqGqez0SUQFsCo+SYf/+E6SkZHDppRUA2LnzMPv2neDqqy/1bWDGFFMemcIjh0U4+icECAdqA9uBxoU5oTGuZGUp06ev44knlhIbW5UvvuiPiFCvXhT16kX5OjxjSiR3mp4uz7ktIlcCAzwWkSmxEhL+IC5uIatWOdbHCg0N5sSJNCIjw3wcmTEl23mPzFbVH0XEBseZInP8eCrPPvsNEyeuIitLqVo1kokTu3PTTQ2ts9oYP+DOyOxHcmwGAVcCNurNFIm0tEyuvHI6iYlHCAoSHnqoNc8/fy3lylktwhh/4U6NIjLH4wwcfRZzPBOOKWlCQ4Pp378pn3yyg/j4XrRoUdXXIRljcnGZKJwD7cqq6mNeiscEuPT0TMaP/4GaNctz221NAHjiifY8+WQHgoNtAj9j/FG+iUJEQpxjIa70ZkAmcK1cuZe4uEVs3nyAypVL07t3fcqWDbV1Iozxc65qFKtx9EckiMgC4EPg5JkXVXWuh2MzAeLIkdOMGPEFM2asB6BOnYpMndqTsmVtjQhjigN3+igqAYeBTpwdT6GAJQrjkqryzjsbGT78cw4dOkWpUkGMGNGOkSM7EBFRytfhGWPc5CpRXOS842kzZxPEGTbM2RQoPT2L0aO/5dChU1xzzaVMm9aLhg0r+zosY8x5cpUogoGyuDdduDEAnD6dTlpaJuXLhxMaGsz06b3Ztesod9/dzMZEGFNMuUoU+1T1ea9FYoq9JUsSGTRoMR07Xspbb/UFoEOHS+nQweZnMqY4c5Uo7Oufccu+fccZNmwJ//3vTwCUKVOKU6fSKV3a+iGMCQSublzv7LUoTLGUmZnF5MmriYmZwn//+xMRESGMGdOFdesesCRhTADJt0ahqke8GYgpXlJSMrj66n+zZo1j0cLevevzxhs9qFWrgm8DM8YUufOeFLBYmdvL1xEErPDwEJo0uYh9+04waVJ3brghxjqrjQlQgZ0odi92/K7d07dxBABVZe7crVSpUpb27R1rWY0b143gYLFpwI0JcIGdKM742yJfR1Cs7d59lCFDPmXx4p3ExESTkDCAsLAQKlQI93VoxhgvKBmJwhRKWlomr7/+HS+8sJzTpzMoXz6Mhx5qTUiITd5nTEliicLkacWKX4iLW8SWLY6lR+6443Jef/06Lr64rI8jM8Z4myUKc47Tp9O5+eYPOXDgJHXrVmLq1J507XqZr8MyxviIJQoDODqrMzOVkJAgIiJKMW7cdezYcZh//rMD4eH2Z2JMSRaYnwBze52948kUaMuWg8TFLaRr1zo8/fQ1ANx5Z1MfR2WM8ReB2SuZM0nYrbH5OnUqnZEjv6RZs3hWrNjLjBnrSU3N8HVYxhg/E5g1ijOG2yS3+fn0050MHryY3buPATBgQAtGj+5MWFhg/0kYY86ffSqUMCdPpnHPPfP56KMtADRtWoX4+F60aVPDx5EZY/yVJYoSpnTpUhw5cpoyZUrx3HMdeeihq2xchDHGJUsUJcDatb9ToUI4detWQkSYMaMPwcFB1KxZ3tehGWOKAfsqGcCSk1MYOnQxrVr9i7i4hag6+mxq165oScIY4zarUQQgVeV///uJhx9ewh9/nCA4WLjyykvIyMiiVKlgX4dnjClmLFEEmJ9/PsLgwYtZsuRnANq0qU58fG+aNq3i48iMMcWVJYoAcvx4KrGx/+LYsRQqVAhnzJgu3H//lQQF2ToRxpjC82iiEJHuwEQgGJihqq/kev1OYIRz8wQwUFU3eDKmQBYZGcawYVeRmHiEsWOv46KLyvg6JGNMAPBYohCRYGAK0BVIAtaIyAJV3ZKj2G7gGlU9KiI9gOlAa0/FFGgOHjzJY499QefOtenfvxkATz99ta00Z4wpUp6866kVkKiqu1Q1DZgN9M1ZQFW/U9Wjzs0fgOoejCdgZGUpM2b8SIMGk5k1awNPPvkV6emZAJYkjDFFzpNNT9WAX3NsJ+G6tnAf8GleL4jIA8ADADVr1iyq+IqlzZsPEBe3kJUrHZe2S5c6TJ3a0+5mMsZ4jCcTRV5fbfOcfElErsWRKNrn9bqqTsfRLEVsbGyJnMDp9Ol0Ro36hnHjfiAjI4sqVcowfnw3brutidUijDEe5clEkQTknECoOvB77kIi0hSYAfRQ1cMejKdYCwoSFizYQWZmFoMGxfLSS51tzWpjjFd4MlGsAeqJSG3gN+A24I6cBUSkJjAX6K+qOzwYS7GUlPQnpUuXolKlCMLCQpg509HF07q1deUYY7zHY53ZqpoBDAGWAFuB/6nqTyISJyJxzmLPAFHAVBFJEJG1noqnOMnIyGL8+O9p2HAKjz32efbzrVtXtyRhjPE6j46jUNXFwOJcz8XneHw/cL8nYyhuVq1KYsCAhWzYsB+A5ORUMjKybIZXY4zP2MhsP3HsWAojR35JfPxaVOHSS8szeXJPeveu7+vQjDElnCUKP3D06GkaNZrKH3+cICQkiOHD2/D001dTpkyor0MzxhhLFP6gYsUIevSoy44dh5k2rReXX24T+Blj/IclCh9ITc1gzJiVXHPNpVxzTS0AJk/uSXh4iE3gZ4zxO5YovOyrr3YzcOAiduw4TMOG0WzaNJDg4CBKly7l69CMMSZPlii85MCBkwwf/jnvvrsRgJiYaKZO7UVwsN3NZIzxb5YoPOzMBH4jRizl2LEUwsNDeOqpDjz2WDtCQ21+JmOM/7NE4WHJySk8+eRXHDuWQrdulzFlSk8uu6ySr8Myxhi3WaLwgJMn0wgJCSIsLISKFSOIj+9FZqZyyy2NbAI/Y0yxYw3kRWzBgu00ajSVV19dmf3cTTc14tZbG1uSMMYUS5YoisjevcnccMNs+vadzd69ySxZ8jNZWSVyRnRjTIAJvEQxt5dXT5eensnYsd/RsOEU5s/fTmRkKBMndmfZsntsTIQxJiAEXh/FbucchLV7evxUhw6donPn/7Bxo2MCv1tuacT48d2oVq2cx89tjDHeEniJ4oy/LfL4KaKiIoiOLk3t2hWYPLknPXvW8/g5TfGRnp5OUlISKSkpvg7FlCDh4eFUr16dUqWKbhBv4CYKD1BV3ntvE61aVaN+/ShEhHffvZHy5cNtZLU5R1JSEpGRkdSqVctuZDBeoaocPnyYpKQkateuXWTHDbw+Cg/Zvv0QXbq8Q//+HzNo0CJUHR3Vl1wSaUnC5CklJYWoqChLEsZrRISoqKgir8VajaIAKSkZjB69gldeWUlaWiZRURHcdVdTX4dliglLEsbbPPE3Z4nChaVLdzFw4CISE48A8I9/NOfVV7sSFVXax5EZY4z3BE7T09xe8HrRZdL9+0/Qu/f7JCYeoVGjyixffg9vvdXXkoQpVoKDg2nevDlNmjShT58+HDt2LPu1n376iU6dOlG/fn3q1avHCy+8kN2kCvDpp58SGxtLw4YNiYmJ4dFHH/XBO3Bt/fr13H+//66mnJqaSr9+/ahbty6tW7dmz549eZb773//S9OmTWncuDGPP/549vPx8fFcfvnlNG/enPbt27NlyxYADh48SPfu3b3xFhxUtVj9tGjRQvM0lrM/c3rmXaYAmZlZmpWVlb09Zsy3Onr0Ck1NzSjU8UzJtmXLFl+HoGXKlMl+fPfdd+uLL76oqqqnTp3SOnXq6JIlS1RV9eTJk9q9e3edPHmyqqpu2rRJ69Spo1u3blVV1fT0dJ0yZUqRxpaenn7Bx7j55ps1ISHBq+c8H1OmTNEBAwaoquoHH3ygt9566zllDh06pDVq1NADBw6oquPfaenSpaqqmpycnF1u/vz52q1bt+zte+65R7/99ts8z5vX3x6wVgv5uRt4TU/DCzcaOiHhD+LiFjJ4cEv6928GwOOPtyvKyExJVoS13b84j7/3Nm3asHGjY5r7999/n3bt2nHdddcBULp0aSZPnkzHjh0ZPHgwr776Kk8++SQxMTEAhISEMGjQoHOOeeLECYYOHcratWsREZ599lluuukmypYty4kTJwD46KOPWLhwITNnzuSee+6hUqVKrF+/nubNm/Pxxx+TkJBAhQoVAKhbty4rV64kKCiIuLg49u7dC8CECRNo1+6v/x+PHz/Oxo0badbM8f919erVPPzww5w+fZqIiAj+/e9/06BBA2bOnMmiRYtISUnh5MmTfPLJJwwdOpRNmzaRkZHBqFGj6Nu3L3v27KF///6cPHkSgMmTJ9O2bVu3r29e5s+fz6hRowC4+eabGTJkCKr6l36EXbt2Ub9+fSpXrgxAly5dmDNnDp07d6ZcubNjsk6ePPmX/W644Qbee++9c66LJwReojhPx4+n8uyz3zBx4iqyspTU1EzuuqupdUKagJKZmcmXX37JfffdBzianVq0aPGXMpdddhknTpzgzz//ZPPmzQwfPrzA477wwguUL1+eTZs2AXD06NEC99mxYwdLly4lODiYrKwsPv74Y+69915WrVpFrVq1qFKlCnfccQfDhg2jffv27N27l27durF169a/HGft2rU0adIkezsmJobly5cTEhLC0qVLGTlyJHPmzAHg+++/Z+PGjVSqVImRI0fSqVMn3n77bY4dO0arVq3o0qULF110EV988QXh4eHs3LmT22+/nbVr154Tf4cOHTh+/Pg5z48dO5YuXbr85bnffvuNGjVqAI5kW758eQ4fPkx0dHR2mbp167Jt2zb27NlD9erVmTdvHmlpadmvT5kyhXHjxpGWlsZXX32V/XxsbCxPPfVUgde7KJTYRKGqzJu3jQcf/IykpD8JChIeeqg1zz9/rSUJU/QKWdO9UKdPn6Z58+bs2bOHFi1a0LVrV4BzvtXmdD5//0uXLmX27NnZ2xUrVixwn1tuuYXgYMdaLP369eP555/n3nvvZfbs2fTr1y/7uGfa4wH+/PNPjh8/TmRkZPZz+/bty/4WDpCcnMzf//53du7ciYiQnp6e/VrXrl2pVMkxvf/nn3/OggULGDt2LOC4jXnv3r1UrVqVIUOGkJCQQHBwMDt27Mgz/hUrVhT4Hs9QPfffPff1rVixItOmTaNfv34EBQXRtm1bdu3alf364MGDGTx4MO+//z4vvvgis2bNAuCiiy7i999/dzuWC1EiE8WhQ6e49975LFzo+EOIja3Km2/25sorL/FxZMYUrYiICBISEkhOTqZ3795MmTKFBx98kMaNG7N8+fK/lN21axdly5YlMjKSxo0bs27duuxmnfzkl3ByPpf7nv4yZcpkP27Tpg2JiYkcPHiQefPmZX9DzsrK4vvvvyciIsLle8t57Keffpprr72Wjz/+mD179tCxY8c8z6mqzJkzhwYNGvzleKNGjaJKlSps2LCBrKwswsPD8zzv+dQoqlevzq+//kr16tXJyMggOTk5O2Hl1KdPH/r06QPA9OnTsxNpTrfddhsDBw7M3k5JSXF5fYpS4Nz1dB4iI0NJTDxCuXJhTJ7cgx9+uM+ShAlo5cuXZ9KkSYwdO5b09HTuvPNOvv32W5YuXQo4ah4PPvhg9h03jz32GC+//HL2t+qsrCzGjRt3znGvu+46Jk+enL19pumpSpUqbN26NbtpKT8iwo033sgjjzxCw4YNiYqKyvO4CQkJ5+zbsGFDEhMTs7eTk5OpVq0aADNnzsz3nN26deONN97I/ra/fv367P0vueQSgoKCeOedd8jMzMxz/xUrVpCQkHDOT+4kAXD99ddn1wA++ugjOnXqlGdiPXDgAOC4flOnTs2+k2vnzp3ZZRYtWkS9emenCdqxY8dfmt48qcQkipUr93L48CkAwsJCmD37JrZtG8zgwa1s3WpTIlxxxRU0a9aM2bNnExERwfz583nxxRdp0KABl19+OS1btmTIkCEANG3alAkTJnD77bfTsGFDmjRpwr59+8455lNPPcXRo0dp0qQJzZo14+uvvwbglVdeoXfv3nTq1IlLLnH9Jaxfv368++672c1OAJMmTWLt2rU0bdqURo0aER8ff85+MTExJCcnZ3+7f/zxx/nnP/9Ju3bt8v2QB0fNIz09naZNm9KkSROefvppAAYNGsSsWbO46qqr2LFjx19qIYV13333cfjwYerWrcu4ceN45ZVXsl9r3rx59uOHHnqIRo0a0a5dO5544gnq168PODrUGzduTPPmzRk3blx20gH4+uuv6dXLO7NlS15taP4sNjZW8+pgyr6rJFdb8OHDp3jiiaXMmLGe++67ghkzrvdClMbA1q1badiwoa/DCGjjx48nMjLSr8dSeMrVV1/N/Pnz8+wXyutvT0TWqWpsYc5VvL9Knxlkl8eth6rKrFkJxMRMYcaM9ZQqFUTVqpF5di4ZY4qngQMHEhYW5uswvO7gwYM88sgjbt08UBSKd2f2mbUnznCuQbFt2yHi4haybNkvAHTsWItp03oRExOd+wjGmGIsPDyc/v37+zoMr6tcuTI33HCD185XvBPFGTmam5KS/qRZs3jS0jKJji7N669fR//+Ni7C+Iar21CN8QRPtJoU30SRz5Kn1auXo3//pgQFCa+80oVKlbxz+5gxuYWHh3P48GGbatx4jTrXo8jv1t7CKp6d2SOrZDc77avQl2Ff3EVcXCwdO9YCICtLbb1q43O2wp3xhfxWuLuQzuziWaPYvZjMLGHa9r/z5IcN+PPPn0hMPMKaNf+HiFiSMH6hVKlSRbrKmDG+4tG7nkSku4hsF5FEEXkij9dFRCY5X98oIlcWeND96/gx6RKumnQ/Q9+qxZ9/ptKnT33mzLnVqvfGGOMBHqtRiEgwMAXoCiQBa0RkgapuyVGsB1DP+dMamOb8na9fj5Wj5cT/I0uDqF69HG+80YO+fRtYkjDGGA/xZI2iFZCoqrtUNQ2YDfTNVaYv8B/ndOk/ABVExOUwziOnIpCgYB555Cq2bh3MDTfEWJIwxhgP8mQfRTXg1xzbSZxbW8irTDXgL3MFiMgDwAPOzVR4dvO4cZDH1DMlTTRwyNdB+Am7FmfZtTjLrsVZDQoukjdPJoq8vubnvsXKnTKo6nRgOoCIrC1sz32gsWtxll2Ls+xanGXX4iwRyWPuI/d4sukpCaiRY7s6kHvydHfKGGOM8SFPJoo1QD0RqS0iocBtwIJcZRYAdzvvfroKSFbVc6eoNMYY4zMea3pS1QwRGQIsAYKBt1X1JxGJc74eDywGegKJwCngXjcOPd1DIRdHdi3Osmtxll2Ls+xanFXoa1HsRmYbY4zxruI9zbgxxhiPs0RhjDHGJb9NFB6Z/qOYcuNa3Om8BhtF5DsRaeaLOL2hoGuRo1xLEckUkZu9GZ83uXMtRKSjiCSIyE8isszbMXqLG/9HyovIJyKywXkt3OkPLXZE5G0ROSAim/N5vXCfm6rqdz84Or9/BuoAocAGoFGuMj2BT3GMxbgKWOXruH14LdoCFZ2Pe5Tka5Gj3Fc4bpa42ddx+/DvogKwBajp3L7I13H78FqMBMY4H1cGjgChvo7dA9fiauBKYHM+rxfqc9NfaxQemf6jmCrwWqjqd6p61Ln5A47xKIHInb8LgKHAHOCAN4PzMneuxR3AXFXdC6CqgXo93LkWCkSKY76fsjgSRYZ3w/Q8VV2O473lp1Cfm/6aKPKb2uN8ywSC832f9+H4xhCICrwWIlINuBGI92JcvuDO30V9oKKIfCMi60Tkbq9F513uXIvJQEMcA3o3AQ+papZ3wvMrhfrc9Nf1KIps+o8A4Pb7FJFrcSSK9h6NyHfcuRYTgBGqmhngk0W6cy1CgBZAZyAC+F5EflDVHZ4OzsvcuRbdgASgE3AZ8IWIrFDVPz0cm78p1OemvyYKm/7jLLfep4g0BWYAPVT1sJdi8zZ3rkUsMNuZJKKBniKSoarzvBKh97j7f+SQqp4ETorIcqAZEGiJwp1rcS/wijoa6hNFZDcQA6z2Toh+o1Cfm/7a9GTTf5xV4LUQkZrAXKB/AH5bzKnAa6GqtVW1lqrWAj4CBgVgkgD3/o/MBzqISIiIlMYxe/NWL8fpDe5ci704alaISBUcM6nu8mqU/qFQn5t+WaNQz03/Uey4eS2eAaKAqc5v0hkagDNmunktSgR3roWqbhWRz4CNQBYwQ1XzvG2yOHPz7+IFYKaIbMLR/DJCVQNu+nER+QDoCESLSBLwLFAKLuxz06bwMMYY45K/Nj0ZY4zxE5YojDHGuGSJwhhjjEuWKIwxxrhkicIYY4xLliiMX3LO/JqQ46eWi7IniuB8M0Vkt/NcP4pIm0IcY4aINHI+Hpnrte8uNEbncc5cl83O2VArFFC+uYj0LIpzm5LLbo81fklETqhq2aIu6+IYM4GFqvqRiFwHjFXVphdwvAuOqaDjisgsYIeqvuSi/D1ArKoOKepYTMlhNQpTLIhIWRH50vltf5OInDNrrIhcIiLLc3zj7uB8/joR+d6574ciUtAH+HKgrnPfR5zH2iwiDzufKyMii5xrG2wWkX7O578RkVgReQWIcMbxnvO1E87f/835Dd9Zk7lJRIJF5DURWSOOdQIGuHFZvsc5oZuItBLHWiTrnb8bOEcpPw/0c8bSzxn7287zrM/rOhpzDl/Pn24/9pPXD5CJYxK3BOBjHLMIlHO+Fo1jZOmZGvEJ5+/hwJPOx8FApLPscqCM8/kRwDN5nG8mzrUrgFuAVTgm1NsElMExNfVPwBXATcC/cuxb3vn7Gxzf3rNjylHmTIw3ArOcj0NxzOQZATwAPOV8PgxYC9TOI84TOd7fh0B353Y5IMT5uAswx/n4HmByjv1fBu5yPq6AY96nMr7+97Yf//7xyyk8jAFOq2rzMxsiUgp4WUSuxjEdRTWgCvBHjn3WAG87y85T1QQRuQZoBKx0Tm8SiuObeF5eE5GngIM4ZuHtDHysjkn1EJG5QAfgM2CsiIzB0Vy14jze16fAJBEJA7oDy1X1tLO5q6mcXZGvPFAP2J1r/wgRSQBqAeuAL3KUnyUi9XDMBloqn/NfB1wvIo86t8OBmgTmHFCmiFiiMMXFnThWJmuhqukisgfHh1w2VV3uTCS9gHdE5DXgKPCFqt7uxjkeU9WPzmyISJe8CqnqDhFpgWPOnNEi8rmqPu/Om1DVFBH5Bse01/2AD86cDhiqqksKOMRpVW0uIuWBhcBgYBKOuYy+VtUbnR3/3+SzvwA3qep2d+I1BqyPwhQf5YEDziRxLXBp7gIicqmzzL+At3AsCfkD0E5EzvQ5lBaR+m6eczlwg3OfMjiajVaISFXglKq+C4x1nie3dGfNJi+zcUzG1gHHRHY4fw88s4+I1HeeM0+qmgw8CDzq3Kc88Jvz5XtyFD2OownujCXAUHFWr0TkivzOYcwZlihMcfEeECsia3HULrblUaYjkCAi63H0I0xU1YM4Pjg/EJGNOBJHjDsnVNUfcfRdrMbRZzFDVdcDlwOrnU1ATwIv5rH7dGDjmc7sXD7HsbbxUnUs3QmOtUS2AD+KyGbgTQqo8Ttj2YBjWu1XcdRuVuLovzjja6DRmc5sHDWPUs7YNju3jXHJbo81xhjjktUojDHGuGSJwhhjjEuWKIwxxrhkicIYY4xLliiMMca4ZInCGGOMS5YojDHGuPT/jMhMCGF6Bx4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def test_class_probabilities(model, device, test_loader, which_class):\n",
    "    model.eval()\n",
    "    actuals = []\n",
    "    probabilities = []\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            prediction = output.argmax(dim=1, keepdim=True)\n",
    "            actuals.extend(target.view_as(prediction) == which_class)\n",
    "            probabilities.extend(np.exp(output[:, which_class]))\n",
    "    return [i.item() for i in actuals], [i.item() for i in probabilities]\n",
    "\n",
    "which_class = 1\n",
    "actuals, class_probabilities = test_class_probabilities(net, device, testloader, which_class)\n",
    "\n",
    "fpr, tpr, _ = sklearn.metrics.roc_curve(actuals, class_probabilities)\n",
    "roc_auc = sklearn.metrics.auc(fpr, tpr)\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC for label= %d ' % which_class)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e36ac8af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4354395605623722, 0.43612637370824814, 0.4464285717560695, 0.47218406601594043, 0.457417582663206, 0.47184065958628285, 0.5089285717560694, 0.5082417583236327, 0.5415521980478213, 0.5449862640637618, 0.5542582422494888, 0.5885989012626501, 0.6528159345571811, 0.6947115384615384, 0.773695055108804, 0.7877747256022233, 0.7857142858780347, 0.7654532967851713, 0.7881181320318809, 0.8310439563714541, 0.8169642858780347, 0.836195055108804, 0.8265796704934194, 0.8269230769230769, 0.8121565935703424, 0.821771978185727, 0.8100961538461539, 0.8265796704934194, 0.8193681320318809, 0.8265796704934194, 0.8238324179099157, 0.8293269230769231, 0.821771978185727, 0.8162774730187196, 0.8293269230769231, 0.8293269230769231, 0.8262362640637618, 0.8214285717560694, 0.8262362640637618, 0.8262362640637618, 0.8265796704934194, 0.7860576923076923, 0.8145604397241886, 0.8289835166472656, 0.8289835166472656, 0.8317307692307693, 0.8337912089549578, 0.828640110217608, 0.8269230769230769, 0.836195055108804, 0.8317307692307693, 0.8310439563714541, 0.8245192307692307, 0.8269230769230769, 0.8337912089549578, 0.8166208794483771, 0.8293269230769231, 0.8149038461538461, 0.821771978185727, 0.8190247256022233, 0.8262362640637618, 0.8258928576341042, 0.8241758243395731, 0.8262362640637618, 0.8262362640637618, 0.8289835166472656, 0.8238324179099157, 0.8173076923076923, 0.8145604397241886, 0.8313873628011117, 0.8221153846153846, 0.8173076923076923, 0.8337912089549578, 0.8221153846153846, 0.8289835166472656, 0.828640110217608, 0.8221153846153846, 0.8289835166472656, 0.8245192307692307, 0.836195055108804, 0.8241758243395731, 0.8097527474164963, 0.8193681320318809, 0.8221153846153846, 0.8241758243395731, 0.8238324179099157, 0.8245192307692307, 0.8241758243395731, 0.821771978185727, 0.8145604397241886, 0.8121565935703424, 0.8262362640637618, 0.8221153846153846, 0.8125, 0.821771978185727, 0.8100961538461539, 0.8046016486791464, 0.8173076923076923, 0.8169642858780347, 0.8169642858780347]\n"
     ]
    }
   ],
   "source": [
    "print(valid_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a4d34c0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "name": "python3"
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