brain-tumor-classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f52a1fa9",
   "metadata": {},
   "source": [
    "# Brain Tumor Classification using Deep Learning\n",
    "\n",
    "The following notebook will perform brain tumor classification using Deep Learning. Our objective is to use a [Kaggle](https://www.kaggle.com/masoudnickparvar/brain-tumor-mri-dataset) dataset consisting of 7022 brain scans of which includes brains with giloma tumor, meningioma tumor, pituitary tumor and healthy brains. \n",
    "\n",
    "<img src=\"https://www.stlukeshealth.org/content/dam/stlukeshealth/images/brain-cancer-banner.png\" align=\"left\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "580700ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import cv2, os, glob, random, itertools, elasticdeform\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from torchvision import models\n",
    "from torchvision.datasets import ImageFolder\n",
    "from torchvision import datasets, transforms, models\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c3634b",
   "metadata": {},
   "source": [
    "## Data exploration\n",
    "\n",
    "Before jumping into writing models or makingg any statistical assesment, it is important that we study and understand the data we have. Let's plot some graphs and explore some numbers.\n",
    "\n",
    "#### Place file paths in dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a83dcf36",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_images_paths = {}\n",
    "testing_images_paths = {}\n",
    "\n",
    "def add_img(d, imgType, imgPath):\n",
    "    if imgType not in d:\n",
    "        d[imgType] = [imgPath]\n",
    "    else:\n",
    "        d[imgType].append(imgPath)\n",
    "\n",
    "for trainingImgPath in glob.glob(\"data/Training/*\"):\n",
    "    imgType = trainingImgPath.split(\"/\")[-1]\n",
    "    for img in glob.glob(f\"data/Training/{imgType}/*\"):\n",
    "        add_img(training_images_paths, imgType, img)\n",
    "        \n",
    "for testingImgPath in glob.glob(\"data/Testing/*\"):\n",
    "    imgType = testingImgPath.split(\"/\")[-1]\n",
    "    for img in glob.glob(f\"data/Testing/{imgType}/*\"):\n",
    "        add_img(testing_images_paths, imgType, img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d494d3",
   "metadata": {},
   "source": [
    "#### Plot training and test images quantity vs tumor type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3c6679f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tumors = list(training_images_paths.keys())\n",
    "tumorTrainingLength =  []\n",
    "tumorTestingLength =  []\n",
    "\n",
    "for tumorType in tumors:\n",
    "    tumorTrainingLength.append(len(training_images_paths[tumorType]))\n",
    "    tumorTestingLength.append(len(testing_images_paths[tumorType]))\n",
    "\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.subplot(1,2,1)\n",
    "plt.bar(tumors, tumorTrainingLength, color ='maroon',\n",
    "        width = 0.4)\n",
    " \n",
    "plt.xlabel(\"Tumor type\")\n",
    "plt.ylabel(\"Number of samples per tumor\")\n",
    "plt.title(\"Training set\")\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.bar(tumors, tumorTestingLength, color ='maroon',\n",
    "        width = 0.4)\n",
    " \n",
    "plt.xlabel(\"Tumor type\")\n",
    "plt.ylabel(\"Number of samples per tumor\")\n",
    "plt.title(\"Testing set\")\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig(\"data-distribution.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfc7d68",
   "metadata": {},
   "source": [
    "The dataset are well balanced in both training and testing sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad534eb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 5712\n",
      "Testing set size: 1311\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training set size: {sum(tumorTrainingLength)}\")\n",
    "print(f\"Testing set size: {sum(tumorTestingLength)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b301a9",
   "metadata": {},
   "source": [
    "#### Data observation\n",
    "Let's observe some brain scans of different types of brain tumors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "617b73ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 5))\n",
    "for index, tumor in enumerate(tumors):\n",
    "    imgPath = training_images_paths[tumor][random.randint(1,len(training_images_paths[tumor]))]\n",
    "    img = cv2.imread(imgPath, 0)\n",
    "    img = cv2.resize(img, (128,128))\n",
    "    img = np.array(img)/128\n",
    "    img = elasticdeform.deform_random_grid(img, sigma=2, mode='constant')\n",
    "    plt.subplot(1,4,(index+1))\n",
    "    plt.title(tumor)\n",
    "    plt.imshow(img, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40a25e0c",
   "metadata": {},
   "source": [
    "### Data transformation\n",
    "\n",
    "Now that we are more familiar with the data, let's start thinking of transformations we can apply. We will start off by performing basic operations such as data loaders, label encoder and decoder. From there, we will apply transformations on the MRI scans and finally normalize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d1267a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class powerGammaEnhance(object):\n",
    "    def __call__(self, img):\n",
    "        c = random.uniform(0.8, 1.2)\n",
    "        gamma = random.uniform(0.8, 1.2)\n",
    "        gammaImg = c * (img ** gamma)\n",
    "        return gammaImg\n",
    "    \n",
    "class scaleImage(object):\n",
    "    def __call__(self, img):\n",
    "        rows, cols, ch = img.shape\n",
    "        a, b = 0.8, 1.2\n",
    "        alpha = (b-a)*np.random.random_sample(3) + a\n",
    "        cx = alpha[0]; cy = alpha[0]\n",
    "        \n",
    "        M = np.float32([[cx,0,0],[0,cy,0]])\n",
    "        I_scale = cv2.warpAffine(img.numpy(),M,(cols,rows))\n",
    "        t = torch.from_numpy(I_scale)\n",
    "        return t\n",
    "    \n",
    "class elasticTransform(object):\n",
    "    def __call__(self, img):\n",
    "        sigmaValue = [2,5,8]\n",
    "        choice = np.random.randint(len(sigmaValue))\n",
    "        img = elasticdeform.deform_random_grid(img.numpy(), axis=(1,2), sigma=2)\n",
    "        t = torch.from_numpy(img)\n",
    "        return t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a2bcb86",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform(transformType):\n",
    "    if transformType==\"nothing\":\n",
    "        dataTransformer = transforms.Compose([\n",
    "            transforms.Resize((128, 128)),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "        ])\n",
    "        \n",
    "    if transformType==\"brightness\":\n",
    "        dataTransformer = transforms.Compose([\n",
    "            transforms.Resize((128, 128)),\n",
    "            transforms.ToTensor(),\n",
    "            powerGammaEnhance(),\n",
    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "        ])\n",
    "    \n",
    "    if transformType==\"flip\":\n",
    "        choice = np.random.randint(2)\n",
    "        if choice == 0:\n",
    "            dataTransformer = transforms.Compose([\n",
    "                transforms.Resize((128, 128)),\n",
    "                transforms.ToTensor(),\n",
    "                transforms.RandomHorizontalFlip(p=1),\n",
    "                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "            ])\n",
    "        if choice == 1:\n",
    "            dataTransformer = transforms.Compose([\n",
    "                transforms.Resize((128, 128)),\n",
    "                transforms.ToTensor(),\n",
    "                transforms.RandomVerticalFlip(p=1),\n",
    "                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "            ])\n",
    "            \n",
    "    if transformType==\"rotation\":\n",
    "        degrees = [0,15,30,60,90]\n",
    "        choice = np.random.randint(len(degrees))\n",
    "        dataTransformer = transforms.Compose([\n",
    "            transforms.Resize((128, 128)),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.RandomRotation(degrees[choice]),\n",
    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "        ])\n",
    "        \n",
    "    if transformType==\"scale\":\n",
    "        dataTransformer = transforms.Compose([\n",
    "            transforms.Resize((128, 128)),\n",
    "            transforms.ToTensor(),\n",
    "            scaleImage(),\n",
    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "        ])\n",
    "        \n",
    "    if transformType==\"elastic\":\n",
    "        dataTransformer = transforms.Compose([\n",
    "            transforms.Resize((128, 128)),\n",
    "            transforms.ToTensor(),\n",
    "            elasticTransform(),\n",
    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "        ])\n",
    "        \n",
    "    return dataTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f64ad11",
   "metadata": {},
   "source": [
    "#### Preview transformation on brain image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cbaf4f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_set = ImageFolder(\"./data/Training\", transform=transform(\"rotation\"))\n",
    "train_loader = DataLoader(training_set, 64, shuffle=True, drop_last=True)\n",
    "\n",
    "for img, label in train_loader:\n",
    "    plt.title(training_set.classes[label[0]])\n",
    "    plt.imshow(img[0].squeeze()[0,:,:], cmap='gray')\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a213d4",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n",
    "Our data generator is now ready to feed batches of data into a model. Let's design a model and pass it our training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18587c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvNet(nn.Module):\n",
    "    def __init__(self,num_classes=4):\n",
    "        super(ConvNet,self).__init__()\n",
    "        self.conv1=nn.Conv2d(in_channels=3,out_channels=12,kernel_size=3,stride=1,padding=1) \n",
    "        self.bn1=nn.BatchNorm2d(num_features=12)\n",
    "        self.pool1=nn.MaxPool2d(kernel_size=2) \n",
    "        \n",
    "        self.conv2=nn.Conv2d(in_channels=12,out_channels=20,kernel_size=3,stride=1,padding=1) \n",
    "        self.bn2=nn.BatchNorm2d(num_features=20)\n",
    "        self.pool2=nn.MaxPool2d(kernel_size=2) \n",
    "        \n",
    "        self.conv3=nn.Conv2d(in_channels=20,out_channels=32,kernel_size=3,stride=1,padding=1) \n",
    "        self.bn3=nn.BatchNorm2d(num_features=32)\n",
    "        self.pool3=nn.MaxPool2d(kernel_size=2) \n",
    "\n",
    "        self.drop = nn.Dropout(0.15)\n",
    "        self.relu = nn.ReLU()\n",
    "        \n",
    "        self.fc=nn.Linear(32*16*16, 4)\n",
    "        \n",
    "    def forward(self,img):\n",
    "        output = self.conv1(img)\n",
    "        output = self.relu(self.bn1(output))\n",
    "        output = self.pool1(output)\n",
    "        \n",
    "        output = self.conv2(output)\n",
    "        output = self.relu(self.bn2(output))\n",
    "        output = self.pool2(output)\n",
    "        \n",
    "        output = self.conv3(output)\n",
    "        output = self.relu(self.bn3(output))\n",
    "        output = self.pool3(output)\n",
    "        \n",
    "        output = self.drop(output)\n",
    "        output=output.view(-1, 32*16*16)\n",
    "        output=self.fc(output)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29dd2c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, train_loader, validation_loader, optimizier, loss_fun, transformType, patience=2, epochs=40):\n",
    "    training_accuracies = []\n",
    "    training_losses = []\n",
    "    validation_accuracies = []\n",
    "    validation_losses = []\n",
    "    \n",
    "    # Early stopping\n",
    "    the_last_loss = 100\n",
    "    trigger_times = 0\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        # Training\n",
    "        model.train()\n",
    "        train_accuracy=0.0\n",
    "        train_loss=0.0\n",
    "        \n",
    "        for images, labels in train_loader:\n",
    "            # Zero grads\n",
    "            optimizer.zero_grad()\n",
    "        \n",
    "            # Predict\n",
    "            outputs=model(images)\n",
    "            \n",
    "            # Compute loss and update gradients\n",
    "            loss=loss_function(outputs,labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            predicted = outputs.argmax(axis=1)\n",
    "            train_accuracy+=int(torch.sum(predicted==labels.data))\n",
    "            train_loss+= loss.cpu().data*images.size(0)\n",
    "            \n",
    "        train_accuracy=train_accuracy/len(training_set)\n",
    "        train_loss=train_loss/len(training_set)\n",
    "        training_accuracies.append(train_accuracy * 100)\n",
    "        training_losses.append(train_loss.item())\n",
    "        \n",
    "        # Validation\n",
    "        model.eval()\n",
    "        validation_accuracy=0.0\n",
    "        validation_loss = 0.0\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for images, labels in validation_loader:\n",
    "                # Predict\n",
    "                outputs=model(images)\n",
    "                \n",
    "                loss=loss_function(outputs,labels)\n",
    "                validation_loss+= loss.cpu().data*images.size(0)\n",
    "\n",
    "                predicted = outputs.argmax(axis=1)\n",
    "                validation_accuracy+=int(torch.sum(predicted==labels.data))\n",
    "            \n",
    "        validation_accuracy=validation_accuracy/len(validation_set)\n",
    "        validation_loss=validation_loss/len(validation_set)\n",
    "        validation_accuracies.append(validation_accuracy*100)\n",
    "        validation_losses.append(validation_loss.item())\n",
    "\n",
    "        if validation_loss.item() > the_last_loss:\n",
    "            trigger_times += 1\n",
    "            if trigger_times >= patience:\n",
    "                print(f'EARLY STOPPING - Trigger times: {trigger_times}')\n",
    "                torch.save(model.state_dict(), f'{transformType}-model.pt')\n",
    "                return training_accuracies, validation_accuracies, training_losses, validation_losses\n",
    "        else:\n",
    "            trigger_times = 0\n",
    "\n",
    "        the_last_loss = validation_loss.item()\n",
    "        \n",
    "        print(f\"[INFO]: Epoch {epoch+1} of {epochs}\")\n",
    "        print(f'Training loss: {str(train_loss.item())} Training acc: {str(train_accuracy*100)}')\n",
    "        print(f'Validation loss: {str(validation_loss.item())} Validation acc: {str(validation_accuracy*100)}')\n",
    "        print('-'*50)\n",
    "    \n",
    "    torch.save(model.state_dict(), f'{transformType}-model.pt')\n",
    "    return training_accuracies, validation_accuracies, training_losses, validation_losses\n",
    "    \n",
    "def test(model, test_loader, transformType, metrics):\n",
    "    y_pred=[]\n",
    "    y_actual=[]\n",
    "    model.load_state_dict(torch.load(f'{transformType}-model.pt'))\n",
    "    with torch.no_grad():\n",
    "        testing_accuracy = 0\n",
    "        for images, labels in test_loader:\n",
    "            # Predict\n",
    "            outputs=model(images)\n",
    "            \n",
    "            # Accuracy\n",
    "            predicted = outputs.argmax(axis=1)\n",
    "            testing_accuracy+=int(torch.sum(predicted==labels.data))\n",
    "            \n",
    "            y_actual+=list(np.array(labels.detach().to('cpu')).flatten())\n",
    "            y_pred+=list(np.array(predicted.detach().to('cpu')).flatten())\n",
    "            \n",
    "        print(f\"Testing accuracy: {(testing_accuracy/len(testing_set))*100}\\n\")\n",
    "        y_actual=np.array(y_actual).flatten()\n",
    "        y_pred=np.array(y_pred).flatten()\n",
    "        \n",
    "        metrics[transformType]['testingAccuracy'] = (testing_accuracy/len(testing_set))*100\n",
    "        metrics[transformType]['y_actual'] = y_actual\n",
    "        metrics[transformType]['y_pred'] = y_pred\n",
    "        \n",
    "        return y_actual, y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d99a098",
   "metadata": {},
   "source": [
    "#### Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "498d0a86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================== Training model for brightness ===================\n",
      "[INFO]: Epoch 1 of 30\n",
      "Training loss: 0.8877727389335632 Training acc: 69.47483588621444\n",
      "Validation loss: 0.47633594274520874 Validation acc: 76.18213660245185\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 2 of 30\n",
      "Training loss: 0.4468303918838501 Training acc: 82.27571115973743\n",
      "Validation loss: 0.38771378993988037 Validation acc: 80.73555166374781\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 3 of 30\n",
      "Training loss: 0.364700585603714 Training acc: 86.10503282275711\n",
      "Validation loss: 0.36979061365127563 Validation acc: 83.18739054290718\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 4 of 30\n",
      "Training loss: 0.33621013164520264 Training acc: 87.3741794310722\n",
      "Validation loss: 0.3585236966609955 Validation acc: 83.36252189141857\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 5 of 30\n",
      "Training loss: 0.23685738444328308 Training acc: 90.61269146608315\n",
      "Validation loss: 0.31782999634742737 Validation acc: 83.36252189141857\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 6 of 30\n",
      "Training loss: 0.20933380722999573 Training acc: 91.64113785557987\n",
      "Validation loss: 0.2894397974014282 Validation acc: 84.76357267950964\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 7 of 30\n",
      "Training loss: 0.18011778593063354 Training acc: 92.80087527352298\n",
      "Validation loss: 0.35688120126724243 Validation acc: 83.53765323992994\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 8 of 30\n",
      "Training loss: 0.15691408514976501 Training acc: 93.61050328227572\n",
      "Validation loss: 0.3095038831233978 Validation acc: 86.07705779334502\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 9 of 30\n",
      "Training loss: 0.12409722805023193 Training acc: 95.49234135667396\n",
      "Validation loss: 0.2726171016693115 Validation acc: 86.95271453590193\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 10 of 30\n",
      "Training loss: 0.10571876913309097 Training acc: 96.01750547045953\n",
      "Validation loss: 0.39692553877830505 Validation acc: 81.9614711033275\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 11 of 30\n",
      "Training loss: 0.10133235901594162 Training acc: 95.86433260393873\n",
      "Validation loss: 0.22370563447475433 Validation acc: 88.70402802101576\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 12 of 30\n",
      "Training loss: 0.08133488148450851 Training acc: 96.67396061269147\n",
      "Validation loss: 0.24886249005794525 Validation acc: 88.2661996497373\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 13 of 30\n",
      "Training loss: 0.07723435759544373 Training acc: 96.84901531728666\n",
      "Validation loss: 0.2595672607421875 Validation acc: 87.82837127845883\n",
      "--------------------------------------------------\n",
      "EARLY STOPPING - Trigger times: 3\n",
      "\n",
      "TESTING MODEL...\n",
      "Testing accuracy: 89.54996186117468\n",
      "\n",
      "=================== Training model for flip ===================\n",
      "[INFO]: Epoch 1 of 30\n",
      "Training loss: 0.7774111032485962 Training acc: 72.88840262582058\n",
      "Validation loss: 0.4215012192726135 Validation acc: 79.6847635726795\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 2 of 30\n",
      "Training loss: 0.3960099220275879 Training acc: 84.48577680525165\n",
      "Validation loss: 0.41413867473602295 Validation acc: 78.28371278458845\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 3 of 30\n",
      "Training loss: 0.3346099853515625 Training acc: 86.3019693654267\n",
      "Validation loss: 0.343401163816452 Validation acc: 85.37653239929948\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 4 of 30\n",
      "Training loss: 0.2765858471393585 Training acc: 88.88402625820568\n",
      "Validation loss: 0.3029957115650177 Validation acc: 83.97548161120841\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 5 of 30\n",
      "Training loss: 0.2113686203956604 Training acc: 91.99124726477024\n",
      "Validation loss: 0.24241475760936737 Validation acc: 86.95271453590193\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 6 of 30\n",
      "Training loss: 0.16245116293430328 Training acc: 93.43544857768052\n",
      "Validation loss: 0.2463579922914505 Validation acc: 86.86514886164623\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 7 of 30\n",
      "Training loss: 0.12812189757823944 Training acc: 94.8577680525164\n",
      "Validation loss: 0.212534561753273 Validation acc: 88.79159369527146\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 8 of 30\n",
      "Training loss: 0.11773648113012314 Training acc: 95.18599562363238\n",
      "Validation loss: 0.24694602191448212 Validation acc: 87.215411558669\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 9 of 30\n",
      "Training loss: 0.08898133039474487 Training acc: 96.41137855579869\n",
      "Validation loss: 0.2197452336549759 Validation acc: 89.14185639229422\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 10 of 30\n",
      "Training loss: 0.0977749451994896 Training acc: 95.79868708971554\n",
      "Validation loss: 0.2100638747215271 Validation acc: 87.82837127845883\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 11 of 30\n",
      "Training loss: 0.10266817361116409 Training acc: 95.77680525164114\n",
      "Validation loss: 0.2573224902153015 Validation acc: 87.30297723292469\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 12 of 30\n",
      "Training loss: 0.09088588505983353 Training acc: 95.99562363238512\n",
      "Validation loss: 0.21482641994953156 Validation acc: 88.70402802101576\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 13 of 30\n",
      "Training loss: 0.061259739100933075 Training acc: 97.17724288840263\n",
      "Validation loss: 0.2294119894504547 Validation acc: 88.2661996497373\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 14 of 30\n",
      "Training loss: 0.05693759396672249 Training acc: 97.3741794310722\n",
      "Validation loss: 0.22113461792469025 Validation acc: 89.49211908931699\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 15 of 30\n",
      "Training loss: 0.05540888011455536 Training acc: 97.6148796498906\n",
      "Validation loss: 0.20785784721374512 Validation acc: 89.66725043782837\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 16 of 30\n",
      "Training loss: 0.049399469047784805 Training acc: 97.72428884026259\n",
      "Validation loss: 0.22192969918251038 Validation acc: 88.70402802101576\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 17 of 30\n",
      "Training loss: 0.031319357454776764 Training acc: 98.62144420131291\n",
      "Validation loss: 0.21888171136379242 Validation acc: 89.92994746059544\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 18 of 30\n",
      "Training loss: 0.034998465329408646 Training acc: 98.51203501094092\n",
      "Validation loss: 0.2630118131637573 Validation acc: 87.82837127845883\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 19 of 30\n",
      "Training loss: 0.02884327806532383 Training acc: 98.53391684901531\n",
      "Validation loss: 0.25235021114349365 Validation acc: 88.96672504378283\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 20 of 30\n",
      "Training loss: 0.0383259654045105 Training acc: 98.11816192560175\n",
      "Validation loss: 0.2272115796804428 Validation acc: 90.28021015761821\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 21 of 30\n",
      "Training loss: 0.024632474407553673 Training acc: 98.6870897155361\n",
      "Validation loss: 0.19487157464027405 Validation acc: 90.01751313485113\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 22 of 30\n",
      "Training loss: 0.023878494277596474 Training acc: 98.7746170678337\n",
      "Validation loss: 0.23633192479610443 Validation acc: 89.75481611208407\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 23 of 30\n",
      "Training loss: 0.02531319484114647 Training acc: 98.7746170678337\n",
      "Validation loss: 0.2238488495349884 Validation acc: 89.49211908931699\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 24 of 30\n",
      "Training loss: 0.024026965722441673 Training acc: 98.7746170678337\n",
      "Validation loss: 0.25819435715675354 Validation acc: 89.92994746059544\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 25 of 30\n",
      "Training loss: 0.02248586155474186 Training acc: 98.81838074398249\n",
      "Validation loss: 0.2322932630777359 Validation acc: 89.75481611208407\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Epoch 26 of 30\n",
      "Training loss: 0.018916983157396317 Training acc: 98.81838074398249\n",
      "Validation loss: 0.2659159004688263 Validation acc: 88.87915936952714\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 27 of 30\n",
      "Training loss: 0.025152014568448067 Training acc: 98.59956236323852\n",
      "Validation loss: 0.25760263204574585 Validation acc: 89.49211908931699\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 28 of 30\n",
      "Training loss: 0.030274085700511932 Training acc: 98.49015317286653\n",
      "Validation loss: 0.23395606875419617 Validation acc: 89.05429071803853\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 29 of 30\n",
      "Training loss: 0.03367271646857262 Training acc: 98.49015317286653\n",
      "Validation loss: 0.3287460207939148 Validation acc: 88.09106830122592\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 30 of 30\n",
      "Training loss: 0.014076011255383492 Training acc: 99.10284463894968\n",
      "Validation loss: 0.23223280906677246 Validation acc: 89.57968476357267\n",
      "--------------------------------------------------\n",
      "\n",
      "TESTING MODEL...\n",
      "Testing accuracy: 90.54157131960335\n",
      "\n",
      "=================== Training model for rotation ===================\n",
      "[INFO]: Epoch 1 of 30\n",
      "Training loss: 0.8509880900382996 Training acc: 71.48796498905908\n",
      "Validation loss: 0.46574917435646057 Validation acc: 79.6847635726795\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 2 of 30\n",
      "Training loss: 0.4652479588985443 Training acc: 81.70678336980306\n",
      "Validation loss: 0.4873327910900116 Validation acc: 75.83187390542906\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 3 of 30\n",
      "Training loss: 0.3837100565433502 Training acc: 85.44857768052516\n",
      "Validation loss: 0.38426005840301514 Validation acc: 80.29772329246934\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 4 of 30\n",
      "Training loss: 0.3550488352775574 Training acc: 86.41137855579869\n",
      "Validation loss: 0.3682190477848053 Validation acc: 83.0122591943958\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 5 of 30\n",
      "Training loss: 0.3351839780807495 Training acc: 87.3741794310722\n",
      "Validation loss: 0.3119358718395233 Validation acc: 83.09982486865148\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 6 of 30\n",
      "Training loss: 0.3034609258174896 Training acc: 88.22757111597375\n",
      "Validation loss: 0.2978343963623047 Validation acc: 85.20140105078809\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 7 of 30\n",
      "Training loss: 0.2603807747364044 Training acc: 89.89059080962801\n",
      "Validation loss: 0.32162269949913025 Validation acc: 85.28896672504378\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 8 of 30\n",
      "Training loss: 0.25295886397361755 Training acc: 90.45951859956236\n",
      "Validation loss: 0.35111650824546814 Validation acc: 82.39929947460595\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 9 of 30\n",
      "Training loss: 0.2665761113166809 Training acc: 90.1312910284464\n",
      "Validation loss: 0.34966301918029785 Validation acc: 85.55166374781086\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 10 of 30\n",
      "Training loss: 0.24475957453250885 Training acc: 90.76586433260394\n",
      "Validation loss: 0.315433531999588 Validation acc: 85.81436077057793\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 11 of 30\n",
      "Training loss: 0.20671892166137695 Training acc: 92.23194748358861\n",
      "Validation loss: 0.2956736087799072 Validation acc: 84.67600700525394\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 12 of 30\n",
      "Training loss: 0.2090938538312912 Training acc: 91.68490153172867\n",
      "Validation loss: 0.25767046213150024 Validation acc: 87.56567425569177\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 13 of 30\n",
      "Training loss: 0.1779404580593109 Training acc: 93.12910284463895\n",
      "Validation loss: 0.2842932939529419 Validation acc: 84.76357267950964\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 14 of 30\n",
      "Training loss: 0.16549164056777954 Training acc: 92.95404814004377\n",
      "Validation loss: 0.3167242109775543 Validation acc: 85.81436077057793\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 15 of 30\n",
      "Training loss: 0.14844860136508942 Training acc: 94.00437636761488\n",
      "Validation loss: 0.24574562907218933 Validation acc: 87.215411558669\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 16 of 30\n",
      "Training loss: 0.1465800404548645 Training acc: 94.02625820568927\n",
      "Validation loss: 0.23932865262031555 Validation acc: 87.91593695271453\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 17 of 30\n",
      "Training loss: 0.1475767195224762 Training acc: 94.11378555798687\n",
      "Validation loss: 0.3210913836956024 Validation acc: 86.86514886164623\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 18 of 30\n",
      "Training loss: 0.15256232023239136 Training acc: 93.80743982494529\n",
      "Validation loss: 0.28481265902519226 Validation acc: 88.2661996497373\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 19 of 30\n",
      "Training loss: 0.13689883053302765 Training acc: 94.48577680525165\n",
      "Validation loss: 0.274338036775589 Validation acc: 88.00350262697023\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 20 of 30\n",
      "Training loss: 0.11715802550315857 Training acc: 95.0328227571116\n",
      "Validation loss: 0.3292294144630432 Validation acc: 85.55166374781086\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 21 of 30\n",
      "Training loss: 0.14783406257629395 Training acc: 94.09190371991247\n",
      "Validation loss: 0.29272085428237915 Validation acc: 86.42732049036778\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 22 of 30\n",
      "Training loss: 0.13160227239131927 Training acc: 94.8577680525164\n",
      "Validation loss: 0.2442699670791626 Validation acc: 88.17863397548162\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 23 of 30\n",
      "Training loss: 0.11543707549571991 Training acc: 95.12035010940919\n",
      "Validation loss: 0.22244249284267426 Validation acc: 88.70402802101576\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 24 of 30\n",
      "Training loss: 0.10556123405694962 Training acc: 95.42669584245077\n",
      "Validation loss: 0.23954631388187408 Validation acc: 88.96672504378283\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 25 of 30\n",
      "Training loss: 0.10182271897792816 Training acc: 95.90809628008753\n",
      "Validation loss: 0.2513639032840729 Validation acc: 88.2661996497373\n",
      "--------------------------------------------------\n",
      "EARLY STOPPING - Trigger times: 3\n",
      "\n",
      "TESTING MODEL...\n",
      "Testing accuracy: 87.4141876430206\n",
      "\n",
      "=================== Training model for scale ===================\n",
      "[INFO]: Epoch 1 of 30\n",
      "Training loss: 0.7969643473625183 Training acc: 70.54704595185996\n",
      "Validation loss: 0.5925734639167786 Validation acc: 71.71628721541155\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 2 of 30\n",
      "Training loss: 0.49362605810165405 Training acc: 80.30634573304157\n",
      "Validation loss: 0.4671971797943115 Validation acc: 79.50963222416813\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 3 of 30\n",
      "Training loss: 0.40256670117378235 Training acc: 84.09190371991248\n",
      "Validation loss: 0.3629896342754364 Validation acc: 80.29772329246934\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 4 of 30\n",
      "Training loss: 0.34255552291870117 Training acc: 86.63019693654267\n",
      "Validation loss: 0.44257280230522156 Validation acc: 78.6339754816112\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 5 of 30\n",
      "Training loss: 0.3688927888870239 Training acc: 85.66739606126916\n",
      "Validation loss: 0.35785409808158875 Validation acc: 83.27495621716288\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 6 of 30\n",
      "Training loss: 0.293600469827652 Training acc: 88.75273522975931\n",
      "Validation loss: 0.3234330117702484 Validation acc: 84.23817863397548\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 7 of 30\n",
      "Training loss: 0.2629617154598236 Training acc: 89.75929978118162\n",
      "Validation loss: 0.32864782214164734 Validation acc: 82.9246935201401\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 8 of 30\n",
      "Training loss: 0.2447669506072998 Training acc: 90.59080962800876\n",
      "Validation loss: 0.7645522952079773 Validation acc: 69.70227670753066\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Epoch 9 of 30\n",
      "Training loss: 0.25490763783454895 Training acc: 89.82494529540482\n",
      "Validation loss: 0.26737573742866516 Validation acc: 86.95271453590193\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 10 of 30\n",
      "Training loss: 0.2272718995809555 Training acc: 91.2035010940919\n",
      "Validation loss: 0.3852023184299469 Validation acc: 81.78633975481611\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 11 of 30\n",
      "Training loss: 0.2490304559469223 Training acc: 90.43763676148797\n",
      "Validation loss: 0.27594447135925293 Validation acc: 86.1646234676007\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 12 of 30\n",
      "Training loss: 0.1818932592868805 Training acc: 92.64770240700219\n",
      "Validation loss: 0.3066645562648773 Validation acc: 86.33975481611208\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 13 of 30\n",
      "Training loss: 0.18645715713500977 Training acc: 92.6258205689278\n",
      "Validation loss: 0.3152386546134949 Validation acc: 84.93870402802102\n",
      "--------------------------------------------------\n",
      "EARLY STOPPING - Trigger times: 3\n",
      "\n",
      "TESTING MODEL...\n",
      "Testing accuracy: 83.67658276125096\n",
      "\n",
      "=================== Training model for elastic ===================\n",
      "[INFO]: Epoch 1 of 30\n",
      "Training loss: 0.8436649441719055 Training acc: 70.61269146608315\n",
      "Validation loss: 0.446267306804657 Validation acc: 78.19614711033275\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 2 of 30\n",
      "Training loss: 0.4250631332397461 Training acc: 83.56673960612692\n",
      "Validation loss: 0.42422670125961304 Validation acc: 78.7215411558669\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 3 of 30\n",
      "Training loss: 0.37443917989730835 Training acc: 85.86433260393873\n",
      "Validation loss: 0.40264227986335754 Validation acc: 81.52364273204904\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 4 of 30\n",
      "Training loss: 0.32082366943359375 Training acc: 87.92122538293216\n",
      "Validation loss: 0.37035420536994934 Validation acc: 82.9246935201401\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 5 of 30\n",
      "Training loss: 0.27277350425720215 Training acc: 89.08096280087527\n",
      "Validation loss: 0.2964419722557068 Validation acc: 85.55166374781086\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 6 of 30\n",
      "Training loss: 0.2326255738735199 Training acc: 90.87527352297593\n",
      "Validation loss: 0.4062110483646393 Validation acc: 82.31173380035027\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 7 of 30\n",
      "Training loss: 0.23047788441181183 Training acc: 90.91903719912473\n",
      "Validation loss: 0.3309558629989624 Validation acc: 83.80035026269702\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 8 of 30\n",
      "Training loss: 0.2467334270477295 Training acc: 90.19693654266958\n",
      "Validation loss: 0.49625164270401 Validation acc: 81.26094570928196\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 9 of 30\n",
      "Training loss: 0.19881899654865265 Training acc: 92.4945295404814\n",
      "Validation loss: 0.32873615622520447 Validation acc: 84.1506129597198\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 10 of 30\n",
      "Training loss: 0.18400248885154724 Training acc: 92.5601750547046\n",
      "Validation loss: 0.3986377418041229 Validation acc: 83.0122591943958\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 11 of 30\n",
      "Training loss: 0.16951757669448853 Training acc: 93.17286652078775\n",
      "Validation loss: 0.3050279915332794 Validation acc: 85.55166374781086\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 12 of 30\n",
      "Training loss: 0.16353881359100342 Training acc: 93.34792122538293\n",
      "Validation loss: 0.32696449756622314 Validation acc: 85.37653239929948\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 13 of 30\n",
      "Training loss: 0.16348695755004883 Training acc: 93.45733041575492\n",
      "Validation loss: 0.4781692922115326 Validation acc: 82.13660245183888\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 14 of 30\n",
      "Training loss: 0.15989989042282104 Training acc: 93.45733041575492\n",
      "Validation loss: 0.31814172863960266 Validation acc: 84.93870402802102\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 15 of 30\n",
      "Training loss: 0.12158558517694473 Training acc: 94.8140043763676\n",
      "Validation loss: 0.2727920711040497 Validation acc: 87.30297723292469\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 16 of 30\n",
      "Training loss: 0.12063553184270859 Training acc: 95.20787746170679\n",
      "Validation loss: 0.28948843479156494 Validation acc: 86.07705779334502\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 17 of 30\n",
      "Training loss: 0.11624085158109665 Training acc: 95.27352297592998\n",
      "Validation loss: 0.2845904231071472 Validation acc: 86.51488616462348\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 18 of 30\n",
      "Training loss: 0.11755123734474182 Training acc: 95.27352297592998\n",
      "Validation loss: 0.24265705049037933 Validation acc: 88.35376532399299\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 19 of 30\n",
      "Training loss: 0.10995904356241226 Training acc: 95.49234135667396\n",
      "Validation loss: 0.35796284675598145 Validation acc: 84.67600700525394\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 20 of 30\n",
      "Training loss: 0.1008979007601738 Training acc: 95.68927789934355\n",
      "Validation loss: 0.28086116909980774 Validation acc: 86.86514886164623\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 21 of 30\n",
      "Training loss: 0.11788591742515564 Training acc: 94.87964989059081\n",
      "Validation loss: 0.3206425905227661 Validation acc: 85.90192644483362\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 22 of 30\n",
      "Training loss: 0.09961091727018356 Training acc: 95.75492341356674\n",
      "Validation loss: 0.3082424998283386 Validation acc: 85.46409807355516\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 23 of 30\n",
      "Training loss: 0.09269724786281586 Training acc: 95.95185995623632\n",
      "Validation loss: 0.2969357669353485 Validation acc: 85.81436077057793\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 24 of 30\n",
      "Training loss: 0.09389136731624603 Training acc: 96.0831509846827\n",
      "Validation loss: 0.27424120903015137 Validation acc: 88.96672504378283\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 25 of 30\n",
      "Training loss: 0.08438847213983536 Training acc: 96.34573304157549\n",
      "Validation loss: 0.273006796836853 Validation acc: 87.91593695271453\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 26 of 30\n",
      "Training loss: 0.1119099035859108 Training acc: 95.38293216630197\n",
      "Validation loss: 0.43909716606140137 Validation acc: 84.23817863397548\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 27 of 30\n",
      "Training loss: 0.0873161256313324 Training acc: 96.38949671772428\n",
      "Validation loss: 0.3066037893295288 Validation acc: 86.60245183887916\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 28 of 30\n",
      "Training loss: 0.0927991196513176 Training acc: 96.2363238512035\n",
      "Validation loss: 0.2935858368873596 Validation acc: 88.00350262697023\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 29 of 30\n",
      "Training loss: 0.0739823430776596 Training acc: 96.69584245076587\n",
      "Validation loss: 0.26660799980163574 Validation acc: 87.82837127845883\n",
      "--------------------------------------------------\n",
      "[INFO]: Epoch 30 of 30\n",
      "Training loss: 0.1057177484035492 Training acc: 95.29540481400439\n",
      "Validation loss: 0.23013070225715637 Validation acc: 89.22942206654992\n",
      "--------------------------------------------------\n",
      "\n",
      "TESTING MODEL...\n",
      "Testing accuracy: 92.29595728451564\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from torch.optim import Adam\n",
    "metrics = {}\n",
    "EPOCHS = 30\n",
    "\n",
    "for transformType in [\"nothing\", \"brightness\", \"flip\", \"rotation\", \"scale\", \"elastic\"]:\n",
    "    model = ConvNet()\n",
    "    optimizer=Adam(model.parameters(),lr=0.001)\n",
    "    loss_function=nn.CrossEntropyLoss()\n",
    "    training_set = ImageFolder(\"./data/Training\", transform=transform(transformType))\n",
    "    testing_set = ImageFolder(\"./data/Testing\", transform=transform(transformType))\n",
    "    training_set, validation_set = random_split(training_set, [4570, 1142])\n",
    "    \n",
    "    train_loader = DataLoader(training_set, 64, shuffle=True, drop_last=True)\n",
    "    validation_loader = DataLoader(validation_set, 64, shuffle=True, drop_last=True)\n",
    "    test_loader = DataLoader(testing_set, 64, shuffle=True, drop_last=True)\n",
    "\n",
    "    print(f\"=================== Training model for {transformType} ===================\")\n",
    "    training_accuracies, validation_accuracies, training_losses, validation_losses = train(model, train_loader, validation_loader, optimizer, loss_function, transformType, patience=3, epochs=EPOCHS)\n",
    "    metrics[transformType] = {\n",
    "        \"trainingAccuracies\": training_accuracies,\n",
    "        \"validationAccuracies\": validation_accuracies,\n",
    "        \"trainingLosses\": training_losses,\n",
    "        \"validationLosses\": validation_losses\n",
    "    }\n",
    "    \n",
    "    print(\"\\nTESTING MODEL...\")\n",
    "    y_actual, y_pred = test(model, test_loader, transformType, metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f75e0b99",
   "metadata": {},
   "source": [
    "## Evaluate results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "56754405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OpW/fvhx//PFUVlby9ttvU1hYyI033tikQXZkZCSHHnooc+fO5ZxzzmHffffFbrczZswYDjrooAC+iT9geuONN5g+fToHHHAAY8aMoba2lnfffZeBAweybNkybLbOv76n879hRxfrVg8mERERERER6VSCg4P5/PPPufvuuwF48skneemll+jZsyevvfYa999/f7Nn/ve//3HCCScwffp07rjjDm677TZ++umnti69GWMM77//Prfddhu1tbU8+eSTTJ06lfPPP5+nn34aoMUT6TobY1lWoGtodQMHDrTmz58f6DJax/uXQ+4cuHZxoCsRERERERGRbSxZsoTevXsHugxppz7//HOOPfZYbr75Zu69995Al7NdO/vv2BizwLKsgS3d0wqm9i7WDaVroLYq0JWIiIiIiIiISAvWrl3b7NrmzZu5+eabATjllFPauqQ2px5M7d2WRt/FeZCgVFxERERERESkvbn22mv59ddfGTx4MPHx8axevZpPP/2UoqIiLr30UgYNGhToEvc6BUztXazb/7soRwGTiIiIiIiISDt06qmnsmHDBj766CNKSkoIDQ2lT58+XHTRRVxyySWBLq9NKGBq77asYNJJciIiIiIiIiLt0hlnnMEZZ5wR6DICSj2Y2ruwGP+PAiYRERERERERaacUMHUEsW4FTCIiIiIiIiLSbilg6ggUMImIiIiIiIhIO6aAqSOIdYNnNdTVBLoSEREREREREZFmFDB1BLFusHxQUhDoSkREREREREREmlHA1BHEuv2/tU1ORERERERERNohR6ALkO2z6uvxVVZibwyYcgNbkIiIiIiIiIhIC7SCqR3LOfEk1k+YCOHdICRaK5hEREREREREpF1SwNSOBfVIpSYnB4yB2EwFTCIiIiIiIiLSLilgasdC3Fl4c3Kw6uv9fZgUMImIiIiIiEg7lJeXhzGGCy64oNXmNMYwYsSIPZ5nxIgRGGP2vCDZIQVM7VhwlhvL66V2zRqIyYSSfKivC3RZIiIiIiIiIp1aa4VbXYmafLdjM378CpMaT2p2NsGxbvDVgWeVf7uciIiIiIiISCe2ZMkSwsPDA12G7CStYGrHbKFhlIcG483J8W+RA22TExERERERkS6hV69epKWlBboM2UkKmNqxmB5pVIQGU5OtgElEREREREQ6hqVLl3LyyScTGxtLREQEQ4cO5bPPPmsyZsqUKRhjmDJlCtOnT2fEiBE4nc4mvZK2t01t3bp1XHjhhSQkJBAWFka/fv146aWXmD17NsYYJk6c2GJddXV13HPPPfTs2ZOQkBB69OjBTTfdhNfrbVYXwJw5czDGNP5smXfrflN5eXmceeaZxMXFERoaysCBA/n444+3+928/vrrjBw5kpiYGEJDQ+nduzd33XUXNTU1zcbOnTuXk046idTUVEJCQkhMTOSwww7jjjvuaDJuw4YNXH/99ey3335ERETgcrnYb7/9uOCCC8jJabsMQVvk2rGYpBRq7TbKsldCVCI4wqAoN9BliYiIiIiIiLQoNzeXww8/nAMOOIBLL72UdevW8eabbzJ69Ghee+01xo0b12T8O++8w/Tp0xk9ejSXXXYZeXl5O5y/sLCQwYMHk5eXx7Bhwxg8eDDr16/niiuu4Nhjj93hs2effTZz585l9OjRREdHM23aNB544AEKCwuZPHkyAP369WPChAnccccdpKenN2lavm3YlZ+fz6BBg3C73Zx77rkUFRXx5ptvMnbsWL744gtGjhzZZPzFF1/Miy++SGpqKqeeeioul4vvv/+e2267jZkzZ/L555/jcPhjmunTp3PCCScQHR3NmDFjSElJoaioiCVLljBp0iQmTJgAQGVlJUOGDCE7O5tjjjmGk046CcuyyM/PZ+rUqZx++um43e4dfi+txrKsTvczYMAAqzO47qFXrYfOOMH6bsjhls/ns6ynD7esV8cFuiwRERERERFpsHjx4kCX0C7k5uZagAVY119/fZN7P/74o+VwOCyXy2V5PB7Lsixr8uTJFmAZY6xPP/20xTkBa/jw4U2uXXTRRRZg3XjjjU2u//LLL1ZwcLAFWBMmTGhyb/jw4RZgHXzwwdbmzZsbr5eXl1tZWVmWzWaz1q1b96ef3dK7Tpw4scm96dOnW4A1evToJte3vO8pp5xiVVZWNrk3YcIEC7Aee+yxxmunnnqqBVi//PJLs8/fuHFj43/+8MMPLcAaP358s3E1NTVWaWlpi++wrZ39dwzMt7aTxWgFUzu20RZNElBWX0vdxo0ExWbCphWBLktERERERER2xqc3w/rfAl3FjiUeCKPva7XpnE4nt99+e5NrAwcO5JxzzuGll17i/fff5/zzz2+8N3bsWI477ridmtvr9fL666/jdDq59dZbm9zr27cv5513Hs8///x2n7///vuJjY1t/DsiIoJzzjmHf//738yfP58TTzxxp+rYIj09vVkdo0aNIi0tjR9++KHJ9ccffxyHw8GLL75IWFhYk3u33XYbTz31FK+++ipXX311k3vbjgWIi4trdq2lccHBwQQHB+/0++wpBUztWEpaKtb3hooQf6PvoFg3rPgcfD6wqX2WiIiIiIiItC8HH3wwUVFRza6PGDGCl156iZ9//rlJwDRo0KCdnnvZsmVUVVUxcODAFj9j6NChOwyYBg4c2Oxajx49ACguLt7pOrbo168fdru9xTm/++67xr8rKyv59ddfiYuL47HHHmtxrpCQEJYsWdL49znnnMN7773HoYceyrhx4xg5ciRDhgwhNTW1yXPDhw8nJSWF++67j59++onjjz+eIUOGbLe2vUkBUzuW1d1JgT2SipAyarKzidjPDfU1ULYWnKl/PoGIiIiIiIgETiuuDOoounfv3uL1xMREADweT4vXd8aWZ7f3Gdu7voXL5Wp2bUvPo/r6+p2uY0fzbZnT5/M1/l1cXIxlWWzcuLFZg+7tOfXUU/n44495+OGHefHFF3nuuecAGDBgAPfeey/HHHMMANHR0Xz//fdMmDCBDz/8kBkzZgD+VU5XXHEFt956K0FBQbv8brtDy2Dasaz4CIqCY6gIDcGrk+RERERERESknduwYUOL19evXw/4t9BtbetT4/5MdHT0Dj9je9cDbcs79+/f/097Sm/thBNO4Msvv6S4uJiZM2dyzTXXsGjRIk488UQWL17cOC41NZUXXniBwsJCfv/9d5544gm6devGv//9b/7973+32XsqYGrHsuIjKQ5yURESRHV2tgImERERERERadd++uknysrKml2fPXs24A9ZdlevXr0ICwtj4cKFLX7G119/vdtzb8tms+3WqqaWREZG0qdPHxYtWkRRUdEuPx8REcGRRx7JI488wi233ILX6+XTTz9tNs4YQ58+ffi///s/Pv/8cwA++OCDPS1/pylgaseSXWGUh8TgM1CalwvRyWAPVsAkIiIiIiIi7ZLH42m2amb+/Pm8+uqrOJ1OTjnllN2eOzg4mHHjxuHxeLjrrrua3Pv11195+eWXd3vubXXr1o1Vq1a12nzXXnstXq+Xiy66iJKSkmb3i4uL+emnnxr/njlzJlVVVc3GbVmlFR4eDsDvv/9OXl7en45rC+rB1I7ZbYbw+ETYCKXlpdRXVGKPyVDAJCIiIiIiIu3SsGHDeP7555k3bx5Dhgxh3bp1vPnmm/h8Pp577rnGbW6767777uPLL7/kgQceYN68eQwePJh169bx1ltvcfzxx/PBBx9ga4VDsY466ijeeOMNTjrpJAYMGIDD4WDYsGEMGzZst+a76KKLWLBgAZMmTSIrK6vxtLmioiJyc3P56quvuPDCC3n22WcBuO6668jLy2PEiBFkZGQQHBzMggUL+PLLL0lPT+fMM88E4IsvvuDaa69l8ODB9OrVi4SEBFavXs3UqVOx2WzccMMNe/xd7CwFTO1cXEoqLMZ/klx2NmGxbijKDXRZIiIiIiIiIs1kZmby7LPPcvPNN/Pss89SU1PDwQcfzO23386oUaP2eP7u3bvz7bffcssttzBt2jTmzZvHfvvtx6RJk4iIiOCDDz7Y4xAL4PHHH8cYw8yZM5k2bRo+n48JEybsdsAE8PTTTzN69GieffZZvvjiC0pKSoiNjSUtLY0bbriBv/71r41jb7nlFt5//33mz5/PF198gc1mIy0tjVtuuYXx48cTExMDwKhRoxg/fjxfffUVU6dOpbS0lKSkJI455pjG4KmtmG2bSHUGAwcOtObPnx/oMlrFwzOWUP/iTexTWMSRV12LK+JHWDAFblkLu9AMTURERERERFrfkiVL6N27d6DLEOBf//oX99xzD9OnT2+VMKsr2dl/x8aYBZZlDWzpnnowtXP7dI/2N/oOC6Emp6HRd20llLfP7vgiIiIiIiIie9PatWubXfvtt9944okniI2NZfjw4QGoSrRFrp3Lio9kepCL8vDNeLNz4JTT/DeKciAqMbDFiYiIiIiIiLSxgQMHss8++3DAAQcQERHBihUr+OSTT/D5fDz77LOEhoYGusQuSQFTO5cZF0FJkJNKu6EqJ8e/ggn8AVN62+2lFBEREREREWkPLr30Uj744ANef/11ysrKcLlcjBo1iuuvv54RI0YEurwuSwFTOxcR4sCKjgcPlBauxxfaHZvNoUbfIiIiIiIi0iVNmDCBCRMmBLoM2YZ6MHUAzsRkACqCHHhXrQZXmn8Fk4iIiIiIiIhIO6CAqQNI7JEKQEVIEN7shkbfCphEREREREREpJ1QwNQBZKZ2p8YEUxESRE12Qx+molywrECXJiIiIiIiIiKigKkj2CchiuJgF+VRkdRkZ0NMJtR4oLIo0KWJiIiIiIiIiAQ+YDLGHGeMWWaMWWmMubmF+05jzEfGmF+NMYuMMRcGos5AykqIoMThonzrLXKgbXIiIiIiIiIi0i4ENGAyxtiBp4HRwP7AWcaY/bcZdiWw2LKsvsAI4GFjTHCbFhpgidGhVITG4MVHVX4+VnS6/4YCJhERERERERFpBwK9gmkQsNKyrBzLsrzAG8DYbcZYQJQxxgCRQBFQ17ZlBpYxhtC47gCU26C20g4YBUwiIiIiIiIi0i4EOmBKAVZt9ffqhmtbewroDawFfgOutizL1zbltR+xyf6vpSIkiJr81eDsoYBJRERERERERNqFQAdMpoVr2x6NNgr4BUgG+gFPGWOim01kzN+NMfONMfM3btzY2nUGxOJv1rJyQSEAqWlpQEPAlJ0NsZkKmERERERERESkXQh0wLQa6LHV36n4Vypt7ULgPctvJZAL9Np2Isuy/mNZ1kDLsgbGx8fvtYLb0qKv1vD7V2sA2Ce1G+X2cCqiI/Fm5/gbfStgEhERERERkXbkiSeeYP/99ycsLAxjDI899hjGGEaMGNFk3MSJEzHGMHv27IDUKa0v0AHTj0BPY0xmQ+PuM4EPtxlTABwFYIzpDuwHdIlkJT49mo0FZVg+i6z4SEqCXJSFh1OT0xAwVRVBVUmgyxQRERERERHhjTfe4OqrryY0NJTx48czYcIEDjvssECXJW3EEcgPtyyrzhhzFTADsAMvWpa1yBhzWcP9Z4E7gSnGmN/wb6m7ybKsTQErug0lpEex6Ks1eDZWkRkXgSfISUXNBrwrsrFi/urfX1icC2H9A12qiIiIiIiIdHEff/xx4+/k5OQdjr3qqqs488wzSWtoByMdX0ADJgDLsqYB07a59uxW/3ktcGxb19UeJKRHAVBYUMq+3RPxRcdTX7aEmuoq6nwugsC/TS5ZAZOIiIiIiIgE1tq1/o43fxYuAcTFxREXF7e3S5I2FOgtcrIDsUkR2INsFOaVARCVkARARXAQ3qI6/yD1YRIREREREZEA2tJPadasWQAYYxp//uyZbXswbenXtHbtWs4991wSEhIICwtjwIABvPbaa3vzNWQPBXwFk2yfzW4jvkckhfmlACSkpsLPDSfJ5a8hIioJinIDXKWIiIiIiIh0ZVsaeE+ZMoX8/HwmTJiwR/MVFxczePBgXC4XF154ISUlJbz11lucc845rFmzhhtuuKEVqpbWpoCpnYtPj2bJt+vw+SwyMnqwHkNFVAQ1OdmQppPkREREREREJLBGjBjBiBEjmD17Nvn5+UycOHGP5lu4cCF/+ctfeOONN7DZ/Buvbr75ZgYMGMC//vUvTjvtNNxudytULq1JAVM7l5AexW+zVlOyvpJ9kmJY4YiizGnwZudAv0xY8XmgSxQREREREZEW3P/D/SwtWhroMnaoV2wvbhp0U6DLaMJut3P//fc3hksAmZmZ/OMf/+COO+7gf//73x6vkpLWpx5M7VxCWjTgb/SdlRBBSZCTsiAHNTk5EOuG8g1QUx7gKkVERERERERaR1paGpmZmc2ub9mK9/PPP7dxRbIztIKpnXMlhhMUYqcwr4z9Dk2kIjSGquo11G3aRH1wEnaA4lxIPDDQpYqIiIiIiMhW2tvKoI6ie/fuLV5PTEwEwOPxtGU5spO0gqkdm543na/XziU+LYrC/FKMMQTHdseyfNQ47NSUNuSD6sMkIiIiIiIincSGDRtavL5+/XoAnE5nW5YjO0kBUzv2wm8v8PrS14lPj2LT6nLq6324kpIB/0ly3k1e/0AFTCIiIiIiItJJFBQUkJeX1+z67NmzAejfv3/bFiQ7RQFTO+Z2uskpySEhPYr6Wh/F6ypITksDoCIijJqCtRARr4BJREREREREOo36+npuuukmfD5f47Xc3FyeeOIJHA4Hf/3rXwNYnWyPejC1Y26nm2m504hKDgKgML+MfTJTWWjsVHSLpSYnGwa5oSg3wJWKiIiIiIiItI6DDjqIefPmMWDAAI499lg8Hg9vvvkmJSUlPPDAA2RlZQW6RGmBVjC1Y1ku//9oNoesIzjMQWFeKft0j8bjiMYTFoY3u+EkOQVMIiIiIiIi0knExMTw7bff0qdPHyZPnsyUKVPIzMzk1Vdf5YYbbgh0ebIdWsHUjrmdbgByPbkkpKdSmF/G4d3C8QS7KKtZR+2aNfgiRmArXQ21VRAUFuCKRUREREREpKva0iNpW5ZlNbs2ceJEJk6cuN25kpOTeeWVV1qpMmkLWsHUjvWI7oHDOMjx+PswbV5TjsMy+KLiqK2rwbIsvFVR/sHF+YEtVkRERERERES6LAVM7ViQLYi06DRySnKIT4vGV2+xeW054XGJgEVVsIOaEuMfrEbfIiIiIiIiIhIgCpjauSxXVuMKJvA3+o5LTQWgIjSEmo0V/oEKmEREREREREQkQNSDqZ3LdGYys2AmIS4boZFBFOaVkp6ZzjqgIiEOb8E6SHYpYBIREREREZEOr6V+TdIxaAVTO5flzMJn+SgoKyAhPYrC/DL2SU+ixgRTGhVFTU52w0lyCphEREREREREJDAUMLVzbpf/JLlsTzYJ6dEUrasgwxVOSZCTEocDb34BljNTAZOIiIiIiIiIBIwCpnYuIzoDgyG3JJf4tCgsn4Wv2EtlWAwVvjqorcXriwPPKqjzBrpcEREREREREemCFDC1c6GOUFIiUxpXMAEU5pficCXgq6ui3hi85WFg+aCkIMDVioiIiIiIiEhXpICpA9hyklxkTAjhzmAK88qITkzGAJXBDmqKfP6BxbkBrVNEREREREREuiYFTB2A2+kmz5NHna+OhPRoCvNLSezRA4CqhHi8G8r8A9WHSUREREREREQCQAFTB+B2uan11bKmfA0J6VEUb6gksyFgKu0WS03BGgiOVMAkIiIiIiIiIgGhgKkDcDsbTpIrySY+LQosiLeHU2kLozgkFG9ODlZMhgImEREREREREQkIBUwdwJaAKceT09jo2xTX4gl24cHCV1lJnSNVAZOIiIiIiIiIBIQCpg4gMjiShPAEckpyCI8OJjI2hM0FZdRFxVFdVw1ATXUMFOdDfV2AqxURERERERHpWDIyMsjIyAh0GR2aAqYOIsvpP0kOaGj0XUZot+7Y6qqptRm8pUHgq4XS1QGuVERERERERGTPGGMYMWJEq803YsQIjDGtNp80p4Cpg3C73OR4cvBZPhLSo/BsrCI2IRmAqtgYajZ7/QO1TU5ERERERERkl8ycOZOZM2cGuowOTQFTB+F2uqmqq2JDxQYS0vx9mBKiugNQnpSId12Jf6ACJhEREREREZFdkpWVRVZWVqDL6NAUMHUQjSfJebKJT48CIMYRiwUUR0ZSk78aHGFQlBvAKkVERERERKQrysvLwxjDBRdcwPLlyxk3bhwJCQnYbDZmz56Nz+fj2Wef5ZBDDiEyMpKIiAgOOeQQnnnmGXw+X+M8U6ZMadzKNmfOHIwxjT8TJ05sMu60007D7XYTFhZGdHQ0Q4YM4ZVXXmmxrjlz5gA0mW/rLXjb68FUU1PDfffdx0EHHUR4eDjR0dEcccQRvPXWWzv8DvLy8jjzzDOJi4sjNDSUgQMH8vHHH+/BN9z+OQJdgOycLJc/Sc0pyWFoylCi48Owlfgoc0RRZLdTX1REXVg6Dq1gEhERERERkQDJzs7m0EMPZd999+Wcc86hqqqK6Ohozj33XF577TV69OjBJZdcgjGG999/nyuuuIKvv/6aV199FYB+/foxYcIE7rjjDtLT07ngggsa5946ELr88svZf//9GTZsGElJSWzevJlp06Zx7rnnsmzZMu68804AXC4XEyZMYMqUKeTn5zNhwoTGOf6sqbfX62XUqFHMmTOHXr16ceWVV1JZWck777zDuHHj+OWXX7jnnnuaPZefn8+gQYNwu92ce+65FBUV8eabbzJ27Fi++OILRo4cuftfcDtmLMsKdA2tbuDAgdb8+fMDXUarG/bGMI5MO5KJgycy4/nf2ZBTysINr5BcX8bo7+eTfuG+hEdugCu/D3SpIiIiIiIiXcKSJUvo3bt3oMsIuLy8PDIzMwH45z//2SR4ef311zn77LPp378/X331FZGRkQBUVFQwfPhwFixYwKuvvsrZZ5/d+IwxhuHDhzN79uwWPy87O7vZljav18vo0aP56quvyMvLIyUlpfHeiBEjmDNnDtvLQLaETXl5eY3X7r33Xm655RZGjx7Nhx9+iMPhX6NTWFjIoEGDyM/P55tvvmHw4MHNvoOJEyc2CbNmzJjBcccdx+jRo5k2bdp2v8dA2dl/x8aYBZZlDWzpnlYwdSBbGn0DJKRFs3J+IY6oeOoK12MBNVWRhNd+Dz4f2LT7UUREREREJJDW33MPNUuWBrqMHQrp3YvEW25ptfm6d+/eJFgBePHFFwG47777GsMlgIiICO6//36OPvponn/++SYB059pqV9ScHAwV155JV9++SUzZ87kvPPO2823+KNuYwyPPPJIY7gEkJCQwG233cYll1zC888/3xgwbZGens6tt97a5NqoUaNIS0vjhx9+2KOa2jOlEB2I2+kmuyQby7JIaOjDFB2VgL3eS21EON4SA3XVULYuwJWKiIiIiIhIV9S3b19CQkKaXPvpp5+w2WxNtrhtMXz4cOx2Oz///PMufU5BQQFXXnklvXr1Ijw8vLGv0mmnnQbAmjVrdvsdAMrKyli5ciXJycn06tWr2f0jjzwSoMW6+/Xrh91ub3a9R48eFBcX71Fd7ZlWMHUgWa4sSr2lbK7eTHyaC4Do4G7UAFU9UqkprAIX/pPknCk7mElERERERET2ttZcGdRRJCYmNrvm8XiIjY0lODi42T2Hw0FcXByFhYU7/Rk5OTkMGjSI4uJijjjiCI499licTid2u528vDxeeuklampq9ug9PB4PAElJSS3e33K9pKSk2T2Xy9XiMw6Ho0lD885GAVMHkun07+XMKclhUNIgXN3DqSGGGqA4JoZuubmwL/6AKfOIgNYqIiIiIiIiXc+WE+C25nQ6KSoqora2lqCgoCb36urq2LRpE9HR0Tv9GY888gibN29m8uTJTZqAg7/f00svvbRbtW9bM8D69etbvL9u3bom40Rb5DqULGfDSXJb+jBlROErDaUeG5uCQ6hbX4ivPsgfMImIiIiIiIi0A/3798fn8/HVV181u/fVV19RX1/PwQcf3OS6zWajvr6+xflWrlwJ0Lgdbmtz5sxp8ZktW9a2N+e2oqKiyMrKYs2aNaxYsaLZ/VmzZgE0q7srU8DUgSSEJxAZFEl2Sbb/77RoasrqKQ9yUtzQCb+GHgqYREREREREpN246KKLAP/pcpWVlY3XKysrufnmmwG4+OKLmzzTrVs3Vq1a1eJ8W0582/aEuRkzZvD888+3+Ey3bt0Af++mXanbsixuuOGGJsHUpk2buPPOOxvHiJ+2yHUgxhjcTje5nlyAxkbfhMZS6S0CwOuNI6woN1AlioiIiIiIiDRx9tlnM3XqVN566y369OnDySefjDGGDz74gNzcXM444wzOOeecJs8cddRRvPHGG5x00kkMGDAAh8PBsGHDGDZsGFdccQWTJ0/mL3/5C6eddhopKSn8/vvvTJ8+nTPOOIM333yzWQ1HHXUUb7/9NqeeeirHH388YWFhpKenc+6552637uuvv55PP/2UqVOn0rdvX44//ngqKyt5++23KSws5MYbb2To0KGt/n11VAqYOhi3y83Xa74GIK5HFMZAWGgctk15WHY7NRVhULQILAta2PsqIiIiIiIi0tZef/11hg8fzosvvshzzz0HQO/evbnuuuu4/PLLm41//PHHMcYwc+ZMpk2bhs/nY8KECQwbNoyDDjqIWbNmceuttzJt2jTq6uro27cv7733Hi6Xq8WA6ZJLLiE/P5833niDBx54gLq6OoYPH77DgCk4OJjPP/+cRx55hNdee40nn3wSh8NB3759eeyxxzjrrLNa7wvqBIzVsLWqMxk4cKA1f/78QJexV0z+fTKPLHiEr8/8GmeIk9f/PY8Nm3+kfv10jinzERtjJ7XnV3DdcojqHuhyRUREREREOrUlS5bQu3fvQJchskd29t+xMWaBZVkDW7qnHkwdTJbL3+i7cZtcRjQOr79rvSchgZoNZf6B6sMkIiIiIiIiIm1EAVMHk+nMBLY6SS4tCur9AdOm8Ai86zZh+YBi9WESERERERERkbahgKmDSY5IJtQe+sdJcunRYCKoMw422uxQX4+3PFgrmERERERERESkzShg6mDsNjsZzgyyPf6AqVtqBHa7jbqgWEq8NQDU1CcqYBIRERERERGRNqOAqQNyO93klvi3wDmC7MSmRBDkiKG2uhwAb7VLAZOIiIiIiIiItBkFTB2Q2+lmbcVaKmsrAX+j7yBiCK4pxZaUSE1ZMGzOgU54QqCIiIiIiIiItD8KmDqgxpPkShtOkkuLwo4LGxaVqT3wbq6FGg9UFQeyTBERERERERHpIhQwdUBupxuAnJKGk+TSozH2GAA2uVzUrPf4Fy9pm5yIiIiIiIiItAEFTB1Qj+geOIyDHI8/QIpNjsAe7A+YNjiCsWq81FXaFTCJiIiIiIiISJtQwNQBBdmCSItOI7vEf5Kc3WEjPi0enwljU70PgJrSIAVMIiIiIiIiItImFDB1UFmuLHI9uY1/d0+PxmZ3UVbtb/xd4+2mgElERERERERE2oQCpg4q05lJQVkB3novAPHp0dhsMdgqirG7XHirohQwiYiIiIiIiEibUMDUQWU5s/BZPvJL8wFISI/CZoshtK4CMjKp8digKPdPZhERERERERER2XMKmDoot8t/kly2x9+HKSYxHFtwLABF8fF4N1ZD5Sao9gSsRhEREREREZG2MGXKFIwxTJkyJdCldFkKmDqojOgMDIbcEv8qJZvdRmT3ZADWh0VQX15NXbVWMYmIiIiIiIjI3qeAqYMKdYSSEpnSuIIJIL1XBgDrfQYAb6lDfZhEREREREREZK9TwNSBZbmyyPH8ESCl9IwHE0lxVQ0ANQqYRERERERERKQNKGDqwNxON3mePOp8dUBDo297DPUVHkxYGDVVTm2RExERERERkb3uww8/5KijjiIpKYmQkBCSk5MZPnw4kyZNajKuqKiIf/3rXxxwwAGEh4fjdDrp27cvN998MxUVFY3jFixYwNVXX03fvn2JjY0lNDSUnj17ct1111FcXLxLta1evZqrrroKt9tNSEgI3bp1Y8yYMfz444+t8u7i5wh0AbL73C43tb5a1pSvIT06HVdCODhiCa5eSrDbjbciWyuYREREREREZK/6z3/+w6WXXkpiYiInnXQScXFxFBYWsnDhQiZPnswVV1wBQG5uLiNHjiQ/P58BAwZw+eWX4/P5WL58OY8++iiXXXYZERERAPz3v//l/fffZ/jw4Rx99NHU19fz008/8cgjj/Dpp58yb948oqKi/rS2n376iWOPPZaioiJGjRrFqaeeyqZNm/jggw8YOnQo77//Pscff/xe/X66CgVMHZjb2XCSXEk26dHpGJvBER2Pr/BXqlP2IWjeSgVMIiIiIiIislc999xzBAcH8+uvv5KQkNDk3qZNmxr/81//+lfy8/O55557+Oc//9lsXGRkZOPf//znP3n66aex2+1Nxr3wwgtccsklTJo0iZtuummHddXV1XHGGWdQXl7OrFmzGD58eOO9tWvXcsghh3DxxReTl5dHSEjILr+3NKWAqQPbEjDleHI4kiMBcKaksLEQ1ka4SPHUUF+8Abu3AoIjAlmqiIiIiIhIlzP3reVsWlUe6DJ2KK5HJEecse8ez+NwOAgKCmo+f1wc4N/y9u2339KvX78Wg6Et47ZIT09v8XMuuugirr32WmbMmPGnAdMnn3xCdnY2119/fZNwCSA5OZkbb7yR8ePHM3PmTK1iagUKmDqwyOBIEsITyCn5Y5VSZh83G3+G1b5gUvCfJBdWlAuJBwSuUBEREREREem0zjnnHK677jr69OnDuHHjGD58OEOGDCE+Pr5xzPfffw/AqFGjsNn+vB10bW0tzz33HG+88QaLFy/G4/Hg8/ka769Zs+ZP5/juu+8AyM/PZ+LEic3ur1ixAoAlS5YoYGoFCpg6uCxn05Pk+gzsyQ+vGDZXegH/SXJhRTkKmERERERERNpYa6wM6giuvfZa4uLimDRpEk888QSPPfYYxhiGDx/Ogw8+yMCBAykpKQEgJSVlp+YcN24c77//Pm63m7Fjx5KYmNi4je2xxx6jpqbmT+fYvHkzAG+//fYOx5WXt+9VZh2FAqYOzu1y896K9/BZPmzGRkxiFNic1FaUgsOOt9QBxTpJTkRERERERPae8847j/POO4+SkhK+/fZb3n//fV588UVGjRrFkiVLcLlcwM6tPJo/fz7vv/8+Rx99NNOmTWuy9c7n8/HAAw/sVE1OpxOAqVOnMmbMmF1/Kdklf74uTdo1t9NNVV0VGyo2AGCMwQqNxV5TRHB6BjUV4Wr0LSIiIiIiIm3C5XJx/PHH89///pcLLriAoqIi5s6dy2GHHQbAjBkzmmx1a8nKlSsBGDNmTLO+Tj/88ANVVVU7VcuWz5w7d+6uvobsBgVMHVzjSXKe7MZrDmcCtroSrDQ33rIQBUwiIiIiIiKy10yfPp26urpm1wsLCwEIDw9nwIABDB48mF9++YX777+/2djNmzdTXV0NQEZGBgCzZ89uNt+VV16503WNHTuWrKwsnn76aaZNm9bimO+++47KysqdnlO2T1vkOrgsVxYAOSU5DE0ZCoAzNYWidd+zOjqN7p46rI05mEAWKSIiIiIiIp3WmWeeSWhoKEOHDiUjIwPLspg7dy4//vgjAwYM4OijjwbglVdeYcSIEdxyyy28++67jBgxAsuyWLFiBZ999hlLly4lIyODQw45hCFDhvDee+8xePBghg4dyoYNG/j000/Zb7/9SE5O3qm6goKCeO+99xg1ahQnnHACgwcPpl+/foSHh7Nq1Sp+/PFHcnJyWLduHeHh4XvzK+oStIKpg4sJjSE2NLZJo+/MPv7QaZUvBHzgXbMBaqsDVaKIiIiIiIh0Yvfddx+HH344P/30E5MmTWLy5MnU1tZy//33M2vWrMZtbpmZmfz000/ceOONlJWV8dRTT/HCCy9QUFDAddddR0JCAgB2u50PP/yQyy+/nLVr1/LEE0/w9ddfc8kllzBjxoxm2+Z25KCDDuLXX3/lpptuwuPxMHnyZJ555hkWLFhA//79+d///kdcXNxe+V66GmNZVqBraHUDBw605s+fH+gy2swF0y/AZ/l4efTLAGxcv46Xr/4bQfEjOeaL50kZUkT0fV9B/H4BrlRERERERKRzWbJkCb179w50GSJ7ZGf/HRtjFliWNbCle1rB1AlkObPILslmS1gYl9AdyziorSwFoMbjUB8mEREREREREdlrFDB1Am6Xm1JvKZurNwNgbDbqQ2KweYsxyT3wlipgEhEREREREZG9RwFTJ7DlJLmckj9CJLszAau+mCr3AGrKdZKciIiIiIiIiOw9Cpg6gcaAaatG35EpKVg+D5ucbryldqzN2YEqT0REREREREQ6OQVMnUBCeAKRQZFkl/wRIqVkpQM+1luhWHVQm68VTCIiIiIiIiKydyhg6gSMMbid7iYrmPbpmQmAp7IaAO/qQqivDUh9IiIiIiIiItK5KWDqJNyupgFTemY6APXVJdTZQ6nx2KCkIFDliYiIiIiIiEgnpoCpk3A73Wyq2oSnxgNAWFQ09fZQLF8JFYn7UVPqgKLcAFcpIiIiIiIiIp2RAqZOIsuVBUCuxx8iGWPwRcdh1RdTmXoQ3tIgnSQnIiIiIiIiInuFAqZOItPp77m0daPvsPgk6n3FlDjd1JQG6SQ5EREREREREdkrFDB1EskRyYTaQ5v0YeqWnILxlVHki8TnNdSvWh7ACkVERERERESks1LA1EnYbXYynBlke/5YpZSWmQZAVXUFtY4IanLyAlSdiIiIiIiIiHRmCpg6EbfTTW7JH428s3r6t81ZvmJKo3pQs2Yj+OoDVZ6IiIiIiIiIdFIKmDoRt9PN2oq1VNZWAhCfkgqAVV9CeYwbr8eAZ3UgSxQREREREZEubsSIERhjAl1GowsuuABjDHl5eYEupUNTwNSJNJ4kV+pfxRQcGkZtSBReiimP70mNTpITERERERGRLmbixIkYY5g9e3agS+nUFDB1Im6nG4Cckj9CJHtMAt76IjzhqXhLHQqYRERERERERLZy7733smTJElJSUgJdSoemgKkT6RHdA4dxNDlJLjohCXt9MdVWOBX1MdSvWRbACkVERERERETal6SkJHr16kVQUFCgS+nQFDB1IkG2INKi08gu+eMkue49UgnyVWP5qimL7IF35fIAVigiIiIiIiKd1bx58zj99NNJTEwkODiYHj16cOmll7J27do/fdbr9fLUU09x/PHHk56eTkhICLGxsRx99NF8+umnLT6zcOFCzjrrLDIyMggJCSE+Pp6DDz6Y8ePHU1tbC0BGRgZ33HEHACNHjsQY0/izxY56MP3www+MGzeOlJQUQkJCSEpK4thjj+Wtt97ajW+oc3MEugBpXVmuLFYUr2j8252VSTb+k+TKotKpyZtFWODKExERERERkU5o8uTJ/O1vfyMkJIQxY8bQo0cPVqxYwfPPP89HH33E999/T1pa2nafLyoq4uqrr2bw4MEcc8wxxMfHs27dOj766COOP/54/vvf/3LJJZc0jl+4cCGHHnooxhjGjBlDZmYmpaWlrFy5kkmTJnHXXXcRFBTE+PHj+eCDD5gzZw7nn38+GRkZO/1O//3vf7n88sux2+2MGTOGnj17UlhYyPz585k0aRJnnHHGnnxlnY4Cpk4m05nJzIKZeOu9BNuDScnw/w+41uGhLDoN79pi8PnApsVrIiIiIiIisueWL1/OpZdeSkZGBnPmzGnSy+jLL7/kmGOO4eqrr+b999/f7hwxMTHk5+eTmpra5LrH42HIkCHceOONnHPOOYSF+ZdMvPTSS1RXV/PBBx8wduzYJs8UFxcTHh4OwPjx4ykpKWHOnDlccMEFjBgxYqfeafHixVxxxRVER0czd+5c+vTp0+T+6tU6oX1bCpg6mSxnFj7LR35pPj1jeuLqnoiFocoqotR5INU5QPl6iE4OdKkiIiIiIiKd2qwp/6Ewv30ftJSQ7mbkBX/fozmeeeYZamtrefzxx5s1yj7yyCMZM2YMH330EWVlZURFRbU4R0hISLNwCcDpdHLRRRdx3XXX8eOPPzJs2LAm97cETluLiYnZg7fxe+aZZ6irq+O2225rFi4BLdba1Slg6mTcLv9JctmebHrG9MTuCMIXEUOVtwhvSBSlVXH+k+QUMImIiIiIiEgr+O677wCYM2cOP/74Y7P7hYWF1NfXs3z5cgYMGLDdeRYtWsSDDz7IV199xbp166iurm5yf82aNY3/edy4cTz++OOcfPLJnH766Rx99NEMGTKErKysVnmn77//HoDRo0e3ynxdgQKmTiYjOgODIbckt/FaSLfumHUbIQSK7G5865djyxgawCpFREREREQ6vz1dGdRRbN68GYAHH3xwh+PKy8u3e+/777/nyCOPpK6ujqOOOooxY8YQHR2NzWbjl19+YerUqdTU1DSOHzRoEHPnzuXuu+/mnXfe4X//+x8A++23HxMmTOCss87ao3cqKSkBaLYiS7ZPAVMnE+oIJSUyhWzPHyfJxSSnULVqBZblozQqDe+yhYQeFsAiRUREREREpNNwOp2Av19SdHT0bs1x1113UVVVxaxZs5r1Sbr33nuZOnVqs2cOP/xwPv74Y2pqaliwYAHTp0/nySef5OyzzyY+Pp6jjz56t2oBcLlcgH/VVK9evXZ7nq5EnZ47oSxXFjmeP/b5pqanEWTV4QivpCwqHW/2ygBWJyIiIiIiIp3JYYf5VzDMnTt3t+dYuXIlsbGxLTbhnjNnzg6fDQkJYfDgwfz73//miSeeAGgSSNntdgDq6+t3up4t7/Tpp5/u9DNdnQKmTsjtdJPnyaPOVwdAhjsDgMqQCkqj0qguWBvA6kRERERERKQzueqqqwgKCuKaa65h+fLlze57vd4/DZ8yMjIoKipi4cKFTa6/8MILzJgxo9n4uXPn4vF4ml3fsGEDQOMpcgDdunUDoKCg4M9fpsHll1+Ow+HgzjvvZPHixc3u6xS55rRFrhNyu9zU+mpZU76G9Oh04lL83e099UWEBHWnZEMQCZYFxgS4UhEREREREenoevXqxYsvvshFF11Enz59OO6449h3332pra2loKCAuXPnEh8fz9KlS7c7x/jx45kxYwZDhw7ljDPOwOl0Mn/+fL7++mtOP/103nnnnSbjH374YT777DNGjBiB2+0mMjKSRYsW8emnnxITE8Pf//5H/6uRI0dis9n45z//ye+//954ytytt9663Xr2339/Jk2axGWXXUb//v0ZO3YsPXv2ZPPmzcyfP5+oqChmzZq1h99c56KAqRNyOxtOkivJJj06nahucfhsDkorN5JAbzbWZrBvxUaITAhwpSIiIiIiItIZ/PWvf6Vv3748/PDDzJo1i88++4yIiAiSk5M5/fTTGTdu3A6fP+644/joo4+46667ePPNN7Hb7QwaNIhZs2aRk5PTLGC64ooriImJYd68eXzzzTfU1dWRmprKFVdcwXXXXUd6enrj2N69e/PSSy/x0EMPMWnSpMbT6XYUMAH87W9/44ADDuChhx5i9uzZfPDBB8TFxXHQQQdxySWX7OY31XkZy7ICXUOrGzhwoDV//vxAlxEw5d5yDn/9cK4++GouOdD/j/7hSy8hzxvK/iFj6bFqJic8NRaTeXiAKxUREREREenYlixZQu/evQNdhsge2dl/x8aYBZZlDWzpnnowdUKRwZF0D+9OTskfjb4jEhKJri0hMqyG0sh0apf9FMAKRURERERERKQzUcDUSbmd7iYnycWn9MBZW4otJsjf6Hvp7wGsTkREREREREQ6EwVMnVSWK4scTw4+ywdAujsdOz5KIi3qHWFszi4JbIEiIiIiIiIi0mkoYOqkMp2ZVNVVsaHCf0RjYloPANbXFgNQuDl8u8+KiIiIiIiIiOwKBUydVJYrC4BsTzYAMYnJAGwu2YDNV8smb/eA1SYiIiIiIiIinUvAAyZjzHHGmGXGmJXGmJu3M2aEMeYXY8wiY8yctq6xI3I73QBkl/gDpnCnCysoBG/xBly2zZQEpWNVbA5kiSIiIiIiIiLSSQQ0YDLG2IGngdHA/sBZxpj9txnjAiYBYyzL6gP8pa3r7IhiQmOIDY0l15MLgDEGR0x3wqtLcEXVUBaZhnfZzwGuUkREREREREQ6g0CvYBoErLQsK8eyLC/wBjB2mzFnA+9ZllUAYFlWYRvX2GFlOjMbVzABuBKTcdWWYO8eTr09hI3zFwWwOhERERERkc7BsqxAlyCy21rr32+gA6YUYNVWf69uuLa1fYEYY8xsY8wCY8x5bVZdB5fl9J8kt+UfS1JaGtF1ZWxKSgRg/UpPIMsTERERERHp8Ox2O7W1tYEuQ2S31dbWYrfb93ieQAdMpoVr20ZnDmAAcAIwCrjNGLNvs4mM+bsxZr4xZv7GjRtbv9IOyO1yU+otZXO1v9dSj4w0DJBdV4e9vppNRSGBLVBERERERKSDi4qKorS0NNBliOy20tJSoqKi9nieQAdMq4EeW/2dCqxtYcx0y7IqLMvaBHwF9N12Isuy/mNZ1kDLsgbGx8fvtYI7ki2NvnNKcgCITfYvDitcuwZnzRo21+l7EhERERER2ROxsbEUFxezadMmvF6vtstJh2BZFl6vl02bNlFcXExsbOwez+lohbr2xI9AT2NMJrAGOBN/z6WtTQWeMsY4gGDgUODRNq2yg2o8Sc6TzaCkQcQkJQNQsXEtsQ4fOY6Dqa/3YbcHOmcUERERERHpmEJCQkhLS6OoqIi8vDzq6+sDXZLITrHb7URFRZGWlkZIyJ7vcApowGRZVp0x5ipgBmAHXrQsa5Ex5rKG+89alrXEGDMdWAj4gOcty/o9cFV3HAnhCUQGRTauYAoJj4CwKCjdRLeYSFZWBLPp91y6980KcKUiIiIiIiIdV0hICElJSSQlJQW6FJGACfQKJizLmgZM2+bas9v8/SDwYFvW1RkYY3A73eR4chqvhcUl4ir0EJYSD8th3Q+LFDCJiIiIiIiIyB7R3qhOzu1qGjB1S07BVVdCUY90HHWVbMgpCVxxIiIiIiIiItIpKGDq5NxON5uqNuGp8QCQmpFGRH0Vv4WkE1VWwOaS4ABXKCIiIiIiIiIdnQKmTi7L5d/+luvJBSAhNdX/95rNxNTmU+KLpb7WF7D6RERERERERKTjU8DUyWU6MwHILskGICYpBYDNa9fQLWgDlnGwaU15wOoTERERERERkY5PAVMnlxyRTKg9tLEPk6t7EhaGms0biO9WCcCGlUWBLFFEREREREREOjgFTJ2c3WYnw5lBtse/gskRHIw9OpaI6mLCergI8paxYdHqAFcpIiIiIiIiIh2ZAqYuwO10k1uS2/h3VPckXLUlFCfvQ1RZAYUFZQGsTkREREREREQ6OgVMXYDb6WZtxVoqa/1b4hJTU3HVelgZsy/R5fl4KoKo9dYHuEoRERERERER6agUMHUBjSfJlfpXMSWl9SDE8vJ7WTgxtXlY2Ni0So2+RURERERERGT3KGDqAtxONwA5Jf5G37HJqQDkrfUQ78gHoDC/NDDFiYiIiIiIiEiHp4CpC+gR3QOHcTSeJBeTlAJA6Ya1OOMhuMZDYZ4nkCWKiIiIiIiISAemgKkLCLIFkRadRnaJ/yS56Ph4sNkxpZtwpCYSXZZPYXZxgKsUERERERERkY5KAVMXkeXKItfj78Fks9kJjk3AVVtCVUo6UWUFlGz24q2uC3CVIiIiIiIiItIRKWDqIjKdmRSUFeCt9wIQk5iMq7aE9Qn7EFVWABg2FpQFtkgRERERERER6ZAUMHURWc4sfJaP/FJ/U+/k9B4460pZZkshpta/sqkwXwGTiIiIiIiIiOw6BUxdhNvlP0ku2+PvwxSXkorDqmfh5mCiwosJrStho06SExEREREREZHdoICpi8iIzsBgyC3xr1bacpJcwdoSQmIMUZ58rWASERERERERkd2igKmLCHWEkhqV2riCaUvAVLV5A8HJsUR58vBsrKK6ojaQZYqIiIiIiIhIB6SAqQtxO93keHIAiHDFYIJCiKwphpRkossKANToW0RERERERER2mQKmLsTtcpPnyaPOV4cxhvD4RGJqSyhNzCSqzN/8u1B9mERERERERERkFylg6kLcTje1vlrWlK8BID4lFVeth4LINEJMBRG2CjaqD5OIiIiIiIiI7CIFTF1IljMLgOwSfx+m7j1SiaorY0l1N0Ki63BWrVajbxERERERERHZZQqYupBMZyZAYx+m2ORUbFj8vtFGcHQtERtXUFZUTVWZN5BlioiIiIiIiEgHo4CpC4kMjqR7eHdySvwBU0xSMgAFhZWExNiI3LQSQKuYRERERERERGSXKGDqYtxON9ke/xa5mMQUAGxlm3CkxBNVtgpQo28RERERERER2TUKmLqYLFcWuZ5cfJaP0MhI7OGRuGpLqE1JxVFfTXR4nVYwiYiIiIiIiMguUcDUxWQ6M6mqq2J9xXoAnInJuGo9bIpNx9gtXKaEjVrBJCIiIiIiIiK7QAFTF5Pl8p8kt6XRd/fUHrhqPeRb3QmOqiPKk02Fx0tFSU0gyxQRERERERGRDkQBUxfjdroByC7x92HqlpxCZH0Fi8pdhETXErH6N0B9mERERERERERk5ylg6mJiQmOIDY0l15Pr/zvZ3+h70SYbwdF1hK36HWN0kpyIiIiIiIiI7DwFTF1QpjOzcQVTTGIyAGs3VxEcY7DX1+KKdShgEhEREREREZGdpoCpC8pyZpHjycGyLFyJSQBE1niwJycAEBtWzcaCUizLCmSZIiIiIiIiItJBKGDqgtwuN6XeUjZXbyYoJJQQVzdcdR4qE9PAQHTtBqrKaikrqg50qSIiIiIiIiLSAShg6oK2NPrOKfGfJNctOQVXbQnrg5MJjqwnavNKADZqm5yIiIiIiIiI7AQFTF1Q40lyHn8fpviUVGJrPaysTSA42kto9nxsdqM+TCIiIiIiIiKyUxQwdUEJ4QlEBkU2rmCKSUoh2FfDovIoQqLrqCvIp1tyBIX5pQGuVEREREREREQ6AgVMXZAxBrfTTY5nS8DkP0lu6SY7wdF1UF9Pt1jYWFCmRt8iIiIiIiIi8qcUMHVRblfzgKm4rJZglz9QctlLqamsw7OxKmA1ioiIiIiIiEjHoICpi3I73Wyq2oSnxkN0fHew2YmuLYPE7gBEl68G1OhbRERERERERP6cAqYuKsuVBUCuJxe7w0FkXAKu2hI80T1wRNoIXbcEu8OmPkwiIiIiIiIi8qcUMHVRmc5MALJLGk6SS00lts7DGpNISJSXupxs4npE6iQ5EREREREREflTCpi6qOSIZELtoY19mGKTknHVeVheG09wVDU1OTnEp0X6G3371OhbRERERERERLZPAVMXZbfZyXBmkO3xr2CKSUrB7qtjkSeSkOg6rKoqujl91NbUU7yhMsDVioiIiIiIiEh7poCpC3M73eSW5AL+gAlgZZGd4Og6AJz1mwDYqD5MIiIiIiIiIrIDCpi6MLfTzdqKtVTWVjYGTNVeCHbWAxC2ORdHsE19mERERERERERkhxQwdWGNJ8mV5hIZE4stOISo2jJqYxKxhzuozckhPi1KAZOIiIiIiIiI7JACpi7M7XIDkFOSg7HZiElMwlXrYXNIKsExhpqcbBLSotm0qgxfvS/A1YqIiIiIiIhIe6WAqQvrEdUDh3E0niTXLSmFuHoPq0gkJKIKb3YO8elR1NX6KFqnRt8iIiIiIiIi0jIFTF1YkC2I9Oh0sksaTpJLTiGy1sPS6m4ER1RQX1xMtxgLgEI1+hYRERERERGR7VDA1MW5XW5yPX+cJGcsi0UlYYQ0nCQX5llNcKidjerDJCIiIiIiIiLboYCpi3M73RSUFeCt9xKTlAzAusqQxoDJm5NLfHqUVjCJiIiIiIiIyHYpYOri3E43PstHXmkeMUkpAHi9Bkd4PSbYgXdLo+815dTXqdG3iIiIiIiIiDSngKmLy3JlAZDjySEsKprgiEgia8upCk0gJC6UmoZG3746i81rygNcrYiIiIiIiIi0RwqYurj06HQMhpwS/0lysUkpxNaVUhiUQrDTR01ONgnp0QAUqg+TiIiIiIiIiLRAAVMXF+oIJTUqlRzPloApmW71HvLpTkh4GXVr1xEZVk9IhIM1y4sDXK2IiIiIiIiItEcKmAS30012STbgP0ku1FvG4qpYgkM9AHjz8uk9OJmV8wtZtaQokKWKiIiIiIiISDukgElwu9zkl+ZT56sjJtnf6HuZJ+KPk+SyV3LoSZnEJIbz5ctLqKmqC2S5IiIiIiIiItLOKGAS3E43tb5aVpetbjxJrqQmlOCoOrDbqMnOwRFs56jz96eipIav314R4IpFREREREREpD1RwCRkOf84Sc6VmARATa0NY4Pg+Ghqcvzb57pnRnPwceks/XYduQs3BaxeEREREREREWlfFDAJmc5MwB8wBYeGERETS0RtBZVBsYR0C8KbndM49pATMumWEsmsV5ZSXV4bqJJFREREREREpB1RwCREBkfSPbw7OSVbTpJLIcEqZb0jmeDoWrwFBVheLwB2h42jL+xNTUUtc95YFsiyRURERERERKSd2OWAyRgTY4zZ3xgTss31C40xU40xrxljBrVeidIW3E432Z4/TpJzej3k+roTElIC9fV4Cwoax8alRnHIiZmsnF/IivkbAlSxiIiIiIiIiLQXu7OC6R5g3tbPGmP+D3geOAk4E5htjNm/VSqUNpHlyiLXk4vP8hGTlIyjtpJF5TEEB28EoGarbXIABx+bRkJGNHNeX0aFpyYQJYuIiIiIiIhIO7E7AdMQYKZlWVVbXbseWAMMA85ouHbtHtYmbSjTmUlVXRXrK9YTk+w/Sa6gKoqQ6DoAvA2Nvrew2W0cfUFv6rw+Zr+yFMuy2rxmEREREREREWkfdidgSgFyt/zRsFKpB/CkZVlfW5b1DvAR/rBJOogs19YnySUDUOINxeawCIqPabaCCSAmMYLDT84i77fNLP1ufZvWKyIiIiIiIiLtx+4ETGFA9VZ/DwEs4IutrmXjD6Kkg3A73QBkl2Tj6p6IMTa8tQaA4O5R1GyzgmmLg0amktzTxddvLaesqLrFMSIiIiIiIiLSue1OwLQG6LXV36OAUuDXra7FAFtvoZN2LiY0htjQWHI9udgdQUQndCe6vopKezQhsQZvTi6Wz9fsOWMzHHleb3wWfPnyEm2VExEREREREemCdidgmgUcb4y5yhhzCTAGmG5Z1tbpwz7AqtYoUNpOpjOT7BL/SqXYpGTifGWssycRHFGFVV1N7dp1LT7njA9jyGn7sHppMYu+WtOWJYuIiIiIiIhIO7A7AdO9QDnwOPAf/NvlJm65aYxJAIYD37ZCfdKGspxZ5HhysCyLmKQUIqqLWVGbQEjwZqB5o++t9TkimR77x/LNuyvxbKxsq5JFREREREREpB3Y5YDJsqxcoA9wNfAP4ADLspZtNSQdeBqY0hoFSttxu9yUekvZXL2ZmKQUbPVellbFEmxfC9Bio+8tjDEceW4vbHYbM19ags+nrXIiIiIiIiIiXcXurGDCsqz1lmU91fBTsM29Hy3LusayrB9bp0RpK1safeeU5OBK8p8kt74mGkdwPXaXk+rfFu7w+ciYUIaN68m6lR5+nakdkiIiIiIiIiJdxW4FTC0xxsQZY04xxowyxthba15pO40nyXmyiU3yHwLo8YYC4BzWj9Jpn1L2xRfbfR5g30MTyewbx7ypORStrdi7BYuIiIiIiIhIu7DLAZMx5nJjzDxjTOxW1wYAS4B3gGnAt8aYiNYrU9pCQngCkUGR5JTkENUtDntQELW1BoD4k/oQesABrP3nLXhXbX91kjGGEef0IijUzsyXFuOrb37ynIiIiIiIiIh0LruzgmkcYFmWVbTVtQeBGGAy/oDpEOCyPS9P2pIxBrfTTY4nB2OzEZOYjAsvVbZwbGUFpDz2KBjDmqvH46up2e484dHBDD9rPwrzy/hpRn4bvoGIiIiIiIiIBMLuBEw9gcZmPMaYOPynxr1gWdYllmWdBPwInN06JUpbcrv8ARNATFIKsXUe1pgkKMolODWV5PvupXrxYgrvv3+H8+wzIIGeh3Tnx4/z2LiqrC1KFxEREREREZEA2Z2AqRtQuNXfQxp+v7/Vtbn4T5OTDsbtdLOpahOeGg8xSckEVxWz3BuPVeQPnaKOPJLYiy6i+LXX8XzyyQ7nGnbmvoRGBjFzymLqa7VVTkRERERERKSz2p2AqQiI2+rv4YAP+HaraxYQugd1SYBkubIAyPXk4kpKxvjqya7pBiX5UF8HQMI14wnr35/1t91OTU7uducKjQhi5Lm92Lymgh8+2f44EREREREREenYdidgWgKcZIzpZoxx4e/J9KNlWaVbjckA1u95edLWGk+SK8kmpuEkufVeJ8ZXBx5/c28TFETKo49gQkJYc/XV+KqqtjtfxoFx9B6SxM8z8lmf49n7LyAiIiIiIiIibW53AqbHgSRgNbAKSAQmbblpjLEDQ4FfW6NAaVvJkcmE2kPJ8eQQ2xAwlXpD/DcbtskBBCUmkvzgg9SsXMn6O+/a4ZxDT+9JREwIM19aQq23fq/VLiIiIiIiIiKBscsBk2VZH+I/IW4RsAy43rKsV7YacjT+7XEzWqVCaVM2YyPTmUm2J5uwaCch4REYX8M/k60CJoDIoUOIu/wyPO+9R8m77213zuAwB0ed15uSDZV8/0H23ixfRERERERERAJgd1YwYVnWfyzLGtjw8+g292ZYlhVjWdZ/WqdEaWuZzkxyS3IxxhCTlEy05aXGhEBR8z5KcVdeSfihh7L+zjupXrZ8u3Om9orlwJGpLPxyNauXFe/N8kVERERERESkje1WwCSdW5Yri7UVa6msrcSVmEy0t4QlZMLv70B5YZOxxm4n5aEHsUVFsmb8eOrLK7Y77+GnZOFMCOPLl5fgra7b268hIiIiIiIiIm1ktwMmY8xhxpjnjTELjDHZxpifjDH/NcYMbs0Cpe1tafSd68klJikFe6WHf1aei1XtgXcvAV/TPkqO+HhSHnoYb34+62+/HcuyWpw3KNjOUefvT3lRNd+8u3Kvv4eIiIiIiIiItI3dCpiMMXcB3wAXAf2BTKAfcDEw1xhzT2sVKG3P7fIHTDmeHGKSUwCLDbXRrDrsDsidA1892OyZiEMHEf+Pf1A6bRolb7yx3bmTspz0OyaNxXPXkr9o8956BRERERERERFpQ7scMBlj/gLcAhQAlwBuIKzh9yUN128yxpzRinVKG+oR1QOHcZBdkt14kpyr1sP8mBPgoDNh9n2QM7vZc93+/jcihh3Bhnvuper3Rdudf9BJmcQmRzDr5SVUV9TurdcQERERERERkTayOyuY/g/YABxiWdaLlmXlWZZV0/D7ReAQYCNwZWsWKm0nyBZEenQ6OZ4cXInJAMTWecjeVAEnPgJx+/q3ypWtb/KcsdlIvv9+7HFx/n5MpaUtzu8IsnPU+b2pKqtl7lvbbwwuIiIiIiIiIh3D7gRMfYF3LMva1NLNhutv498yJx2U2+Umx5NDSHg4Ea4Yetgr+HrlZqygcDjjJfBWtNyPKSaGlEcepnb9etbecst2+zElpEcz4PgMls/bQPbPhS2OEREREREREZGOYXcCJgdQ+SdjKhvGSQfldrpZVbYKb70XV2IymcGV/LqqhNnLN0JCbzjhYcib698ut43w/v1JuP46yr+YSdGUl7b7GQNGpxOfFsWc15ZRWerdm68jIiIiIiIiInvR7gRMK4ETjTEtPttw/Xgge08Kk8ByO934LB95pXnEJKXgKNtEakwYj36+3L8qqd/Z0O8cf8PvlTObPR97/vlEHXM0hQ8/TOVPP7f4GXa7jaMu6E1NVR1zXl+23dVOIiIiIiIiItK+7U7A9DrQG5hqjOm59Q1jTBbwDrA/8NqelyeBkuXKAhpOkktKpqrUw1VDU1m42sPnizf4Bx3/EMT3gvf+DqXrmjxvjCHp7rsJSkpizbXXUldc3OLndEuO5NAxbnJ+3sjyHzbs1XcSERERERERkb1jdwKmR4CvgBOAJcaYAmPMPGNMPrAMOBn4pmGcdFDp0ekYDDklOcQk+0+SOyLBIqNbOI98vhyfz4Lghn5MtVXwzkVQX9dkDnt0NCmPPUr95s2svfEmLJ+vxc/qd3QaiW4nc99cTkVJzV5/NxERERERERFpXbscMFmW5QWOAf4F5AKp+E+O69Hw97+AoxrGSQcV6gglNSqVHE8OsUn+gKl0wzquPronS9eX8envDSfIxe8HJz4KBd/C7HuazRPWpw/d/3ULFXPnsvk//23xs2w2w1EX9Ka+zseX/1uqrXIiIiIiIiIiHczurGDCsqxay7LutSyrJxCNP1yKtiyrp2VZ9wJ2Y0x0axYqbc/tdJNdko2zexIYQ/G6NYzpm8I+CZE8+sVy6n0NQVDfcdD/XJj7MKz4otk8rnHjiD7hBDY+8QQV389r8bNcCeEcfso+FCzazJJv1rU4RkRERERERETap90KmLZmWVa5ZVlrLMsq3+ryM0DRns4tgeV2uckvzQe7wdU9kaXfzqV8UyHjj+7JysJyPvp17R+Dj38QEvrAe38Dz5om8xhjSLzjDoLT01lz/fXUbdzY4ucdODyFlP1i+PrtFZRuqtqbryYiIiIiIiIirWiPA6YdMHtxbmkDbqebWl8tq8tWc8zfrqLSU8yrt1zDQbZN9EqM4vGZK6irb+irFBTm78dU723ox1TbZC57ZAQpjz+Gr7ycNdddj1Vf3+zzjM1w5Hm9wMCXLy/B8mmrnIiIiIiIiEhHsDcDJungspx/nCSXdkBfzrn7EcKionn3nts4P2YtuZsqeO/nrVYrxfWEkx6HVd/Dl3c2my90331JnDCByh9+YONTT7X4mdHdwhj6l56sWV7Cwtmr98p7iYiIiIiIiEjrUsAk25XpzAT8ARNATFIKZ9/9MOkH9WfttFc4tep7nvxiKd66rU6HO/B0GHAhfPM4LJ/RbE7XKSfjPO1UNj/zLOVz57b4ub0HJ5F+YDe+ez+b4vUVrf9iIiIiIiIiItKqFDDJdkUGR9I9vDs5JTmN10LCIzj5xts4ZOzppKz/mYFL3ubNr5c0ffC4e6H7gfD+pVCyqtm8ibfeSsi++7L2hhupXde8obcxhpF/7YUjyMbMl5bgq/c1GyMiIiIiIiIi7YcCJtkht9NNtie7yTWbzc6wsy9g9FXXkeQtJPeFu1mdvdWYxn5MdS32Y7KFhZHy2GNYXi9rrrkWq7bpfYAIZwjDz9qPDbml/Px5wV55NxERERERERFpHQqYZIeyXFnkenLxWc1XEe1/xEgO+vstWPX1vDnhBlbM+/aPm92yYMwTsPoH+GJis2dD3Jkk3XUnVb/8QuEjj7b42fsMTCDr4AR++DiXzWvKWxwjIiIiIiIiIoG3UwGTMaZ+V36A8/Zy3dJGMp2ZVNVVsb5ifYv3R404hOWHXcymoFg+fOQevn37NSxfQxh1wKlwyCXw3VOwdFqzZ6OPP56Ys8+maPJkymbObHbfGMPws/clJMzBF1MWU1+nrXIiIiIiIiIi7dHOrmAyu/EjnUCW64+T5FpijOEfJw7grfiTCOo1iO/eeY2PHruP2upq/4Bj74akvvDBZVCc3+z5hJtvIrRPH9be/E+8q5r3awqLDGbEOb3YtKqc+Z/mtdp7iYiIiIiIiEjr2amAybIs22782Pd28bL3uZ1uALJLsrc75lB3Nw7fN5Ep9sM4/OyLWPnD97x++w2UbiyEoFD4yxSwLHjnQqjzNnnWFhxMyuOPAbBm/DX4amqa19Avnv0OS2TBp/kU5pe22ruJiIiIiIiISOtQDybZoZjQGGJDY8n15O5w3LXH7ktRZS0/Rx/EqTdPoHRjIa/8czyrF/8OsW4Y+xSsWQBfTGj2bHBqKsn33Uv1okUU3n9/i/MfcUZPwqOD+WLyYupq61vl3URERERERESkdQQ8YDLGHGeMWWaMWWmMuXkH4w5p6PF0elvWJ/4+TDtawQRwcFoMI/eL5z9f5RDb6yDOvvsRQqOiefuuf/Hr55/C/mNh0KXw/SRY8lGz56OOOorYCy+k+LXX8XzySbP7IeFBHHleL4rXVzLvwx2HXSIiIiIiIiLStgIaMBlj7MDTwGhgf+AsY8z+2xl3PzCjbSsUgCxnFjmeHCzL2uG4a4/ZD09VLS9+nUtscgrn3P0w6Qf244vnn+aL5ydRf+QESO4PH1wJRc1DooRrryGsf3/W33Y7NTnN76ft340+w1L45YsC1q4oaa3XExEREREREZE9FOgVTIOAlZZl5ViW5QXeAMa2MO7/gHeBwrYsTvzcLjel3lI2V2/e4bgDU50cu393XpibS0mll5DwCE6+6XYOGXMav34+jXfvv4vK0U/5B79zIdQ17bdkgoJIefQRTHAwa8aPx1dV1ewzBp+aRXRcGNOeWah+TCIiIiIiIiLtRKADphRg66PDVjdca2SMSQFOAZ5tw7pkK1safeeUtHyS3NauOWZfymrqeH6ufwWSzWZn2DkXMvqq61i7Yimv3v8oGw//N6z9GT67rdnzQYmJJD/4ADUrVrD+zrua3Q8OdTD26n4EhzmY+tgvrM/x7OHbiYiIiIiIiMieCnTAZFq4tu0+rMeAmyzL2mFnZ2PM340x840x8zdu3Nha9QmQ5coCINuz4z5MAL2TojnhoCQmf5NLUcUfJ8btf8RIxk28D19dLa+/+DErks6BH56DRR80myPyiCPodtmleN57j5J332t2PzoujFOuO5iwyCA+fPwXbZcTERERERERCbBAB0yrgR5b/Z0KrN1mzEDgDWNMHnA6MMkYc/K2E1mW9R/LsgZaljUwPj5+L5XbNcWHxRMZFMmiTYt2avw1R/ekqrae5+Y0DaSS9tmPc+55lG490vjwywK+qzkUa+r/QVHzlVHxV11F+KGHsv7OO6letrzZ/ajYUE657mAiY0L46MlfWLW0aPdeTkRERERERET2WKADph+BnsaYTGNMMHAm8OHWAyzLyrQsK8OyrAzgHeAKy7I+aPNKuzBjDEelHcXU7Km8uuTVPx2/T0IUY/ul8NJ3eRSWVTe5FxnbjXET7mP/YUfybU4wH+WnU/v6+VDbdJyx20l56EFsUZGsGT+e+vKKZp8T4Qrh5GsPxhkfxidPLST/9x33iBIRERERERGRvSOgAZNlWXXAVfhPh1sCvGVZ1iJjzGXGmMsCWZs0NeHwCYzsMZL7friPVxa/8qfjrz6qJ7X1Fs/Mbr6tzhEczHFXXMPwcy9mpSeG1+fZKX3vxubj4uNJeehhvPn5rL/99hZPsQuPDubkaw4mNjmCac8uJOcXbY8UERERERERaWuBXsGEZVnTLMva17KsLMuy7m649qxlWc2aeluWdYFlWe+0fZUSZA/i4eEPc1TaUdz/4/28vOjlHY7PiIvgtINTeHVeAes8zU+DM8Yw8MRTOOXmiZRa0bwydSmrpz3dbFzEoYOI/8c/KJ02jZI33mjxs0Ijgxg7vh9xqVHM+M/vrFygwwZFRERERERE2lLAAybpOILsQTw4/EGOST+GB+c/yEuLXtrh+P87sieWZfH0rJXbHZPZbwBn3/MEoSEO3n5pGgun/q/ZmG5//xsRRxzBhnvuper3lvtAhYQHMfbqfnTPjOaz539n2bz1u/ZyIiIiIiIiIrLbFDDJLgmyBXH/sPs5Jv0YHpr/EFN+n7LdsT1iwzljYA/e/HEVq4srtzsutkcGZ9/9KGnR5Xz+2pt88d8nqa+ra7xvbDaSH7gfe7du/n5MpaUtzhMc5uDE/+tL8r4uvpiymMXfbNsvXkRERERERET2BgVMssu2hEyjMkbx8IKHmfz75O2OverIfTAYnpy5/VVMAKHJ+3HKdTczMHYVv34xg3fvvo3KUk/jfUdMDCmPPkLt+vWsveWWFvsxAQSHOjjxyr6k9Y5l1v+W8vtXa3bvJUVERERERERkpylgkt0SZAviviPuY3TGaB5Z8Agv/PZCi+OSnGGcfWga7/y0mrxNzU+C25qt12iGnzqW0cnLWLtsEa/eci0bC/Ia74f370/C9ddR/sVM1t9xB7WFLfdacgTbGX35gWQcFMec15bx68xVu/2eIiIiIiIiIvLnFDDJbnPYHNxzxD2MzhzNYz89xvO/Pd/iuCtGZOGwGZ6YueLPJz3yNvY/wM049xLqvVW8fuv1rPjxu8bbseefT8zZZ1Hy1ttkH3U06267jZrc3Oa1Bdk57u8HkNU/nq/fXsFPM/J3+z1FREREREREZMcUMMkecdgc3DP0Ho7PPJ7Hf3qc/yz8T7MxCdGhnHd4Oh/8soaVheU7ntAeBKe/SFJ0PX/tnU+3lFQ+fOhuvnv3dSzLwhhD4u23k/XpNJynnYpn6ofkHH8Cq//vH1T9+mvTqRw2jr2kDz0P6c5372fz4ye5291aJyIiIiIiIiK7TwGT7LEtIdOJ7hN58ucnefbXZ5uNuWx4FqFBdh77YvmfT+hMgVP+Q6Tnd8Yd4mX/I0by7Vuv8vGj91FbXQ1AcHo6SRMnss+XM+l26d+pmDePvHFnkn/ueZTPmdMYJNnsNo6+cH96HZ7IDx/lMm9qjkImERERERERkVamgElahd1m564hd3GS+ySe/uVpnvnlmSb3u0WGcMHgDD5euI6l61s+Ba6JnkfD0GtxLHyV44YmM/yvF7Hih+94/fYbKN34R+8lR1wcCePHs8+XX5Jw8014V61i1aWXkTv2ZDwffohVW4vNZjjy3N7sf0QyC6bn8827KxUyiYiIiIiIiLQi0xn/H+2BAwda8+fPD3QZXVK9r57bv72dD7M/5LK+l3FF3yswxgBQUunliPtnMXifbjx37sCdmKwOXh4Da3+Gv88md3UZnzzxIDaHgzHX3UJqrz7NHrG8XjzTplH0wgvUrFiJIzmJbhdcgOv00zFhYXz91goWzlrNAcNTGDZuX4zNtPZXICIiIiIiItIpGWMWWJbV4v9DrxVM0qrsNjv/HvxvTt7nZJ799Vme/uXpxtVCrvBgLhqayYxFG/h9jWcnJnPAaS9AUDi8dT6ZfXpz9t0PExoRyTt3/osl38xp9ogJDsZ18slkTp1K6rPPEJSczIZ77mXlyCPZ9OSTHHZ0N/ofk8bvc9Yw+9Wl+HydL2AVERERERERaWsKmKTV2W127hh8B6f2PJXnFj7Hkz8/2RgyXXxEJs6wIB75fCd6MQFEJ8Gp/4GNS2HaDcQmp3LWXQ+R1LMX0554kHnvv9XidjdjsxE1YgQZr7xC+uuvETZwIJsmPUP2UUeT+fvr9B8ay+Jv1vHlS0vw1fta8/VFREREREREuhwFTLJX2IyNCYdP4LSep/Hf3/7bGDJFhwbx92FuvlxayE8FxTs32T5HwbDr4ZdX4edXCYuM4rR/3UmvIcP5+o2X+ey5J6mvq9vu4+H9+9Pj6adwT/uE6BOOp+Sdt4m55xx6By1j2bz1fP7iYuo7eMhkWRZrV5ZQUVIT6FJERERERESkC1IPJtmrfJaPO7+/k3eWv8PFB1zM1QdfTaW3niMemEWf5Gj+d/GhOzlRPbw8FlbPh7/PgoTeWJbFt2+9wvfvvUn6Qf056Zp/EhIe/qdT1W4opOjllyh5403yYg9jZdappPWwMfqGI3AE2/fwjduWt7qOZd+v57fZqyleX4kzPozTbx5IaERQoEsTERERERGRTkY9mCRgbMbGbYfdxhn7nsELv7/Aoz89SniwncuGu5m7YhM/5Bbt5ER2OO15CImCt86HmnKMMQwZdy7HXvYPVi1ayBsTbqR008Y/nSqoewLdb7iBfWZ9yYAz+rLfuk8oWOXjvUtfpvjjT7Hq6/fwrfe+kg2VzH1zOS/d/A1fvbGcoBA7h451U1ZUzYz//q5tfyIiIiIiItKmtIJJ2oRlWdw9727eXPYmF/S5gMsPvJrhD83GHRfBG38/rPGkuT+VMxtePhmSDoIjrodeJ4DNTt7Cn/nokXsJDg3l5Jsm0D0za6dr89XUsGDSdH5YGklM8VIGeKaRcOG5OE8+GVto6G69795g+SwKFhexcNZqChZtxmY3ZB2cwEEjU+meGY0xhsXfrGXW/5Zy0JGpHHHGvoEuWURERERERDqRHa1gUsAkbcayLO6Zdw9vLHuD8/Y/j241p/Hvj5fw6iWHMmSfuJ2f6Ld3YOa/oSQfYrNg8P9B37PYuG497993B9XlZZx4zU24+x+yS/Ut+WYNs/63lJjadRzw/UOEuCKJPfevxJx1FnancxfftvXUVNWx9Nt1/DZnNZ7CKsKjg+kzLIU+RyQT4QxpNn7uW8tZ+OVqRp7bi/2HJAegYhEREREREemMFDBJu2FZFvf9cB+vLX2Ns/Y7hw9nDSTZGca7lw/e+VVMAPV1sORD+OZxWPcLRCTAoZdSvs+pvP/E42zMy+Woiy+j7zHH71J9K37cwOeTFxMXZxiw/m28X32JCQ8n5i9/IfaC8wlKStq1F94DResq+G32apZ+v566mnoS3dEcODKVrP4J2B3b393qq/fx8VO/smZ5CSdf05+kfVxtVrOIiIiIiIh0XgqYpF2xLIsHfnyAV5a8wgDXScz+bjCTLxzEyP0SdmcyyP3KHzRlz4SgCLwHncsnvxpyfvudgSedyrCzL8DYdr7dWPbPhXz2/CLiUiM59vhIyl+dTOkn08AYnCeeSLeLLyKkZ89dr3Un+HwW+b9tYuGs1axeWozNYdh3YHcOHJlKQnr0Ts9TXVHLO/fNx1tdx1/+eQhRse1nq5+IiIiIiIh0TAqYpN3ZOmQKrhhGsu9MPrxy6K6tYtrW+t/g2yfht3fwYfjSezS/rqxg30OHcNxV1xIU3Hw72fbkLdzEp//5jZjECMZe3Q9H6UY2v/QSJW+/g1VVReSIEXS75GLCBgzYs5obVFfUsuQb/za4ss3VRMaE+LfBDU0mLCp4t+YsWlfBu/fPJzo+jFOvH0BQSMc6IU9ERERERETaFwVM0i5ZlsVD8x/i5cUv4y06nCeP/TfH9knc84lLCuD7Z7Dmv8SCDS7mFLpJSkvm5FsfINzp2ulpChZvZtozv+GMD2PM1f2IcIZQV1xM8euvU/y/V6gvLiasXz9izjqTyJEjsUfv/AqjLTavKWfhrNUsn7eeulofyT1dHDgiFXe/OGz2PT/kMe+3TXwyaSFZ/eMZ9bcDWiUMExERERERka5JAZO0W5Zl8dCPD/PykpeIqDmCry9+Eoe9lVbaVBbB/BdYPv01Ps1JJDLUcMpFZxA79Fyw7dxnrFlWzMeTFhLpCmHs+P5ExvhXQfmqqih5/32KJk+hdtUqCAoi4rDDiDrmaKKOOgpHt27bndNX7yP3V/82uLUrSnAE2dh3kH8bXFxqVKu8+tZ+/qyAb99byaCTMjnkhMxWn19ERERERES6BgVM0q5ZlsUVn/6brze+w2FxJ/Lc8XdjM3u+eqdRbRVrP32aD96aieXzMbZ3ManH/R36nQNBYX/6+LqVJXz01K+ERQYx9pr+RHf74xnL56N64UJKP/+css8+94dNNhvhBx9M1LHHEHX00QQl+09yqyr3svjrtfw+Zw3lxTVExYZywIgU9h+STGhEUOu97zYsy2LmlCUsm7ee4y49gKz+u9HrSkRERERERLo8BUzS7tXV+xjy3+upDPuc03qezu2H39a6IRNQsm417915E6VFJRyXtIxeSRYceikccgmEx+7w2Q25pXz05C8Ehdo5+Zr+OOPDm42xLIua5csp++xzyj7/nJrlywGo6TeSNe5jWeVxUl9vkdorhgNHpJJxUBw2W9tsWaurreeDR35m85pyTrtxwF5ZKSUiIiIiIiKdmwIm6RA++nUN131xPyFxszit52ncfvjtrR4yVZWXMfXBu1izdBFDezsYZM3CBIfDwefBYVdATPp2n91YUMaHj/+C3WEYe01/YhIjtju2vt7HsumLWPhlAZsrwrDX15C4fh6ZjlySRw4g6thjCdlvvzbtiVThqeHte37EZrfxl38O3O3m4SIiIiIiItI1KWCSDsHnsxj9xFcUB39EVcQMTtnnFCYOntjqIVNdbS0znnmMpd/M4cDDB3FUj7XYF70NlgUHnAqD/wFJB7X47OY15Ux97GcwhrFX96NbSmST+xWeGv82uK/WUOnxEh0fxkEjUslyG7xfz6bss8+oXLAAfD6CevQg6phjiDrmaML69sXYWvc9W7Ihr5T3H/6JhPQoxo7vj92x9z9TREREREREOgcFTNJhzFi0nkv/N58Thv3GVxtfY2zWWO4YfAf2nWzKvbMsn49v3nqVee+/SfpB/Tnp4vMI+XUKLJgC3nJwj4Sh4yFzOGyzyqh4fQUfPPozvnqLMVf3I75HFOtzPfw2azUrFxTiq7dI6xPLgSNSSe/TDbPNNri6zZsp+/JLyj7/nIrvvofaWhwJCUQdfTRRxx5D+MCBGIejVd93a8t/WM/nLy5m/6HJjDinbVdRiYiIiIiISMelgEk6DMuyGPPUN5RUeTnj6CX857dnGZM1hn8P/nerh0wAv335GZ//9ym6paZx6s0TiQp3wPwXYd6zUL4Bkvr6VzTtfzLY/wh9Sgormfroz9TW1OOMD6Mwv4ygUDu9D0/iwBGpuLo379HUkvrSUsrnzKHss88pnzsXq7oau8tF5JFHEnXM0UQMHowtJKTV3/u7D7L5aXo+w87clwNHpLb6/CIiIiIiItL5KGCSDmXW0kIunPIj95xyIJ6QT5j06yROcp/EnUPu3CshU96vP/HRo/cSHBrGKTdPJCHDDbXVsPBN+PYJ2LwSXGlw+P9B/3Mg2N97qXRTFR8+/gvGZjhoZCr7HZZIcOjurzzyVVVR/vXXlH3+OeWzZuMrK8MWEUHk8OFEHXsMkUccgS1i+32fdoXls5j27G/k/76Zk/7Rlx69dtzkXEREREREREQBk3QolmVxyqRvKSytZtYNI5i86L88/cvTnOg+kbuG3LVXQqaNBXm8f98dVFeUc9L4m8js3/C/F58Plk2Dbx6H1T9AWCwM+jsM+htExGH5rGZb4FqD5fVSMW+e/0S6mTOpLyrCBAcTMXQoUcceQ9TIkdidzj36DG9VHe8+uICKkhr+8s+BLZ6MJyIiIiIiIrKFAibpcOau2Mi5L/zAHWP6cP7gDP6z8D/8P3v3HRbVmf5//D2NgRl67yBYQBB718TYE03Z9N57sqmb/HaTLdmSzTe76dn0atpuetNEY0li7AVFQbCAgPTeps+c8/tjELuxgBTv13XNdc6cOXPOM0QNfLif+3lx04uc0+8cHp/0OHpt5/coamuo58sn/0Zt6W6m3XgHQ2ecfeAJpWu8QdP270DvB8OvhvF3QWi/Th/L/lSPB+vGjbQuXkLr4sW4q6pAr8c8Zow3bJo2DX1ExAldu7nWyqf/twFToJGLHx6Jj1/X9X4SQgghhBBCCNG7ScAkeh1VVbnstTUU11tY/vBZ+Bp0vLn1TZ7Pfp6z+53NPyf9s0tCJqfdxvznnmT3pg2MPu8iJl9x3aGru9Vu906dy/kYVA9Mfwwm3tvpYzkcVVWx5+Z6K5t++AFnSQloNPgNH07AzBkEzZlz3GFTWUED37yQQ1JGKGffkYW2CyqyhBBCCCGEEEL0fhIwiV5pTVE9l7++hj/OSefmySkAvLX1LZ7Lfo6ZSTP5v8n/h0Fn6PT7Kh4Py955jZzF3zFw3CRm33U/Bp/DNNpuqYQFD8DOH+D2FRCZ3uljORpVVXHs3Enr4sW0Ll6Co6AAja8vIVdeSdjNN6EPPfa+Slt/KmP5/3YwYlYS43+T2oWjFkIIIYQQQgjRW0nAJHqtq95cQ0FlK8sfPguz0VuxNC9vHk9teIqJcRN5dsqz+On9Ov2+qqqycf6X/PzB28QOTOf8h/6IKfAwPY8s9fCfURAxCK7/Dg6udjqFHEVF1L/2Gs3fzkfr60vItdcQdsMNx9SrSVVVfvpoO9t+qWDGjYMZOCb6FIxYCCGEEEIIIURvcrSAqft+GhbiGDwwYxD1FifzVhd3HLsu4zoeG/8Yq8pXcfvi22l1tnb6fTUaDaPOvZBz7/89NbsL+e+ffkdjZfmhJ5rDYObfoXQ1bHq/08dxPIwpKcQ++SQp336D+cwzqH/1NXZNn0Htyy/jaWs76ns1Gg1nXDaQ2AHBLHu/gOrillM0aiGEEEIIIYQQfYEETKJHG5kUwpRBEby+vIhWu6vj+EUDL+JfZ/6LLXVbuGnRTTTYG7rk/gPHTeKSPz+Ow2Lhoz/+jrKCvENPGnYVJE2ExX+GttouGcfxMKamEv/ss/T7+itMY8dQ98KLFE6bTt0bb6BYrUd8n06vZfatmZgCfPj+lS1YmhyncNRCCCGEEEIIIXozCZhEj/fAjIE0WV28vaL4gOOzk2fzwlkvUNRcxPULr6fKUtUl948dmM6V/3gav4BAPvv7oxSsWn7gCRoNzH0OnBb44dEuGcOJ8B00iIT//IfkTz/Fd2gWtU8/w64ZM2mYNw/FcfjwyC/Ah3PuzMJh9/Ddq1txuzyneNRCCCGEEEIIIXojCZhEj5cVH8yMwVG8uaKIZqvrgNcmx0/m1emvUmOt4brvr6OkpaRLxhAcHcMVf/830f0HsuD5f7H2q085oH9ZxECYdB9s+RiKfuqSMZwovyGZJL7+OkkffYRxwACqn/g/CmfMpPG//0V1Og85PzzenxnXD6amuIWfPthOX+zTJoQQQgghhBCic0nAJHqFB2YMpNXu5o1fig55bVT0KN6a9RY2t43rvr+OHY07umQMfgGBXPzoPxg04QxW/Hcei9/4Dx63e98Jkx+E0BSY/wC47F0yhpNhGjGcpHffIXHePAwJCVT99W8Uzj6bps8+Q3UdGNylDI9gzLn92L62ik2LS7tpxEIIIYQQQgghegsJmESvkB4TyJwhMbyzcjcNlkOrbjLCMnh39rvotDpuWHgDObU5XTIOvY8Pc377O8b+5lK2Ll3EV//6G469fY0MfjDnGWgohBXPdMn9O4N57BiSPnifhDfeQBcWRuUf/0Th3Lk0f/MNqmfflLhR5ySTOiKS1V8WUry1rhtHLIQQQgghhBCip5OASfQa900fgNXl4bWfCw/7ekpwCu+d/R5BxiBu+eEW1lSu6ZJxaLRaJl1+LTNu/S0lWzfz0R8fpL58j/fF1LNgyCWw4lmo29kl9+8MGo0G/8mTSP7kY+Jffhmtn4mKh/8fReedT8v336MqChqNhmnXpRMe78/it/JoqLR097CFEEIIIYQQQvRQEjCJXmNAVADnD41l3upialoPPwUtzj+OebPnEecfx51L7mRZ6bIuG0/WtFlc/OjfsbW28OEjD7BjzQrvC7P+6a1mmn8/9PD+RRqNhoCpZ9Hvi8+Je+450ED5/Q+w+zcX0rp0KXofLefckYXOoOW7l7dgt7h+9ZpCCCGEEEIIIU4/EjCJXuXe6QNxeVSeXXzk6qAIUwTvzn6X9NB0HvjpAb4t/LbLxpOYOZRr/u95whMS+fbZ/+On997E4xsK0/8Kxb9Azn+77N6dSaPVEjh7Filff03sv/+FYrdRdtfdFF9yKZrcdcy+LZPWBjuL3shF8SjdPVwhhBBCCCGEED2MBEyiV+kXbubGicn8d10p32+tPOJ5QcYg3pj5BqOiRvHIikf4b0HXBT0BYeFc9tj/MWzWXDYu+IpP//4obf3OhYSxsOhRsNR32b07m0anI+jcc0ldsICYxx/H09DAnltvw/mnuxg/zkBZQSMrP9/V3cMUQgghhBBCCNHDSMAkep2HZqUxNCGYhz/bQmm99YjnmQwmXpr+EmclnMU/1/6TN7a8gdpFU9Z0egPTbrydc+5+kOrdu/jgkfspS78XHC2w+M9dcs+upNHrCb7oQlIXfk/0Y3/BVVGB8fFb6OfaxpZlZWxbWdHdQxRCCCGEEEII0YNIwCR6HR+9lv9cMRyNBu7+bzYOt+eI5xp1Rp6e8jRzU+bywqYXeHbjs10WMgGkTz6LK//xND5+fnzy4mts8L8EddMHULyyy+7ZlTQ+PoRcfjmpPywi6pE/kLL9U0Ia8vnpvTx2L8ru7uEJIYQQQgghhOghJGASvVJCqIl/XzKULWXNPPFdwVHPNWgNPD7pcS4bdBnv5L3D39b8DY9y5FDqZEUkJnPVP58ldeRYfl5bxvzaETi/uh/cji67Z1fTGo2EXnstA3/4nrOmmfBzNLL44z1sv+N32AuO/vUXQgghhBBCCNH3ScAkeq1ZGdHcOLEf764qZmHukfsxAWg1Wh4d+yi3DLmFz3Z8xh9++QMupetWRDOazJz34CNMvvJ6djb48+FGf+q/faLL7neqaE0mYm67gXP/PBV8TayxDGPXhZdRdu99OHZJbyYhhBBCCCGEOF1JwCR6td+fncbQ+CAe+mwLexqO3I8JQKPRcM+Ie7h/5P18X/w99/14H3a3vcvGptFoGHP+xVz8x8exa8x8+Mlatv/wWZfd71QKTwln1p0jaDPHUjjnMdp++YWic8+j/KGHcRYXd/fwhBBCCCGEEEKcYhIwiV7NR6/lP1eOAODuj7JxupVffc+NmTfy5/F/5peyX7h9ye20Odu6dIyJmVlc/fcnCfe1M/+td/nx3dfxuN1des9TISkzjAm/6U9ZaxDNj7xP2E030rp4MYVz5lLxyKO4Ko9eVSaEEEIIIYQQou+QgEn0egmhJv598VByypr5v++PrR/QJQMv4ckzniSnJoebfriJRntjl44xIGkwl916OcNDysn+/hs++dsjtDXUd+k9T4VhMxIYNC6aDUuqaJ12Lf0X/0DIVVfSMn8+ReedT/O333ZpU3UhhBBCCCGEED2DBEyiT5idGc31E5J5e+VuFuVVHdN7zu53Ns9PfZ7CpkKuX3g91ZbqLh2jbuzNTB0RzDn9yqnZvYv3f38ve7Zt7dJ7djWNRsOUqwYR1S+QJe9so8nhS/Qjj5Ay/1uM/ftT8dDDlD/wAJ6mpu4eqhBCCCGEEEKILiQBk+gz/nBOGlnxQTz0ac6v9mPa64z4M3hl+itUW6u5buF17GnZ03UD1Org3OdJ9yvhqplRGE1mPv37o6z/9oteXeWjN+g4+/YhGE0Gvnt5K9YWJz6JiSR98D4R999P6+IlFJ13Pm0rV3b3UIUQQgghhBBCdBEJmESfYdTr+M8VI1BVuPu/m46pHxPA6OjRvDXzLSwuC9cuvJadjTu7bpAxWTDuDsILP+KqO6+i/6hxLP/gbb599gkc1mMLxXoic5CRc+4YgrXVycLXt+JxK2h0OsJvu5Xkj/+HNiCAPTfdTNU/Hkex2bp7uEIIIYQQQgghOpkETKJPSQwz8a+Ls8jZ08STC4+tHxNARngG785+Fw0ablh0A1tru3Dq2pQ/QGA8xiX/j3Pv/R1nXn0ju9av4cNH7qduT0nX3beLRSYFMvXaNCp3NbP8fzs6qrL8MjLo9/lnhFx7DY0ffMDuiy7GlpvXzaMVQgghhBBCCNGZJGASfc7ZQ2K4fkIyb63YzQ/H2I8JIDU4lXlnzyPAEMDNP9zMusp1XTNAoz+c82+o2YZmzUuMOvdCLvnT4zisFj589AHyV/7cNfc9BQaOjmbE7CS2rajgl092Ymt1AqD19fZmSnjrTZS2Noovv5y6V19F7QOr6QkhhBBCCCGEAE1v7v1yJKNGjVI3bNjQ3cMQ3cjh9nDxK6spqbew4J7JJISajvm9NdYablt8G6UtpTw95WmmJEzpmkH+7yrYtRTuWgMhybQ11PPtc09SsX0bw2efy5nX3IhOb+iae3chVVH5+b/byVtRgd5Hx5Az4hg2IxFToA8AnqYmqv72N1q++x6/YcOI/deT+CQmdvOohRBCCCGEEEL8Go1Gs1FV1VGHfU0CJtFXldRbmPvCClIj/fnktvH46I+9YK/J3sQdS+4gvyGfxyc9zpyUOZ0/wOYyeGksJE2AKz8BjQaP283yD98h+7uviRmYxrn3/56A0PDOv/cp0FhlYcN3xexcX41OryXjzDiGz0jEHGQEoHn+Aqr++ldUj4eoP/ye4IsvRqPRdPOohRBCCCGEEEIciQRM4rT13dZK7vwwm1sm9+PROYOP670Wl4XfLvstG6o28OjYR7ks7bLOH+Dql2HRH+CSdyHjNx2HC1Yt54dXX8Dg68ucex4mMTOr8+99ijRVW9nwfTE71lah1WvJnBzH8FneoMlVWUnFHx7BumYN/lOnEvP3v6EPC+vuIQshhBBCCCGEOAwJmMRp7c9f5/Le6hLeuHYUMwZHHdd77W47D/38ED+V/cS9I+7l5iE3d+7gPG544yxoq4G714FvUMdL9WWlfPP0P2msrGDSFdcy+ryLenWFT1ONlY3fF7N9bTVanYaMSbEMn5mEOchA4/vvU/P0M2j9/Yn5x98JmDq1u4crhBBCCCGEEOIgEjCJ05rd5eHiV1exp8HGgnsmER9y7P2YAFyKiz+u+CPf7f6OGzNv5L4R93Vu0FOeDW9Og1E3wZynDnjJabOy6JXn2bF2Jf1Hj2P2nfdjNJk7797doLnWysbvS9i+pgqNVsPgiTGMmJ2EvraUiof/H46CAoIvuZio3/8erbl3f1YhhBBCCCGE6EskYBKnveI6C3NfXMGAKG8/JoPu+BZQVFSFx9c8zic7PuHSgZfy6LhH0Wo6cRHG7x6Gda/DzUshfuQBL6mqysYFX7H8w3cIjorm3AceISIxufPu3U1a6mxsXFhCwapK0MLgCbEMmxqD48M3qX/zTQzx8cQ++SSmEcO7e6hCCCGEEEIIIZCASQgA5m+p4O6PNnHrGSk8ck76cb9fVVWey36Ot3Pf5px+5/CPSf/AoO2kVd7sLfDSGDCHwy0/gU5/yCll+bnMf+5JHDYrM2+5m/TJZ3XOvbtZS72N7IUl5K+qBCBtQgyDY1to/ccjuCorCbv1FiLuvBONj083j1QIIYQQQgghTm8SMAnR7k9f5fL+mhLeum4U09KPrx/TXm9ufZPns59nSvwUnpryFEadsXMGt+1r+ORamPk4TLj7sKe0NTYw/7knKS/IY9isOUy59mZ0+k4KubpZa4Od7IUlbFtVAQoMHB1OYsFXuL/+CN/Bg4n9978wpqZ29zCFEEIIIYQQ4rQlAZMQ7ewuDxe+vIqKZhsL7plMXLDfCV3n44KPeXzt44yOHs0LU1/AbOiEXkGqCh9dBsUr4K61EJxw2NM8bje/fPQuGxd8RUz/QZz7wB8ICAs/+fv3EG2NdrIXlbJtRQWKopKS4CF64XP4NpUR+bvfEXLVlWi0nTg9UQghhBBCCCHEMZGASYj97K6zcO6LKxgY5c/HJ9CPaa/5RfP544o/MiR8CK/NeA2T4fiahx9WYwm8PA5SzoIrPjrqqdtXr2DRq8+jNxiYc+/DJA0ZdvL370HaGh1s+qGEvBUVKB6FOM9u4tfNI2L4AGKe+CeGqBOrQBNCCCGEEEIIcWKOFjBJGYA47fQLN/PEhUPILm3iqR+2n/B15qbM5d9n/pstdVu498d7cXgcJz+4kCSY8nvYvgDy5x/11EHjJ3HVP5/BLzCIzx//M2u//ARVUU5+DD2Ef4iRyZcN5Jp/jCdrSgKVPqmsHfsX1jcPYuslN9Hy/ffdPUQhhBBCCCGEEO2kgkmcth79cisfri3l7etHMTXtxKthvt71NX9c+UemJEzhmSnPnHzjb48LXjsT7E3eqXLGgKOe7rTb+OHVF9i++heSsoYz89bfEhgReXJj6IEszQ42LS4l96cyPC4PUTUbyexnZ8Bj96MLDOzu4QkhhBBCCCFEnydT5IQ4DLvLw29eXkVls43v7plM7An2YwL4X8H/eHzt45zd72yemPQEOq3u5Aa3Zx28NRPG3QGzn/jV01VVJeeH71j+4TsATL7yOobNnNMnexVZW5xs+qGYrUtL8SgQ3ZrPuGtHETd7fHcPTQghhBBCCCH6NAmYhDiC3XUW5r7wC2kxgfzv1nEn3I8J4K2tb/Fc9nNcNOAi/jL+L2g0mpMb3Pz7YeO7cMuPEDvsmN7SXFPN4jf+Q8mWTcQOGszM235LWNzhm4X3drZWJ+v/u5FtG5rxaPTEm5uY8NupRPQL7e6hCSGEEEIIIUSfJAGTEEfx9eZy7v3fZm4/M5Xfn512Utd6IfsF3tj6BtcOvpbfjfrdyYVMtib4z2gIioObl8IxVkWpqsq25cv4ad4buBx2xl98JaPOvRCdXn/iY+nBLLUtrH7yS3Y1ReDR+5Lc35exl2cRHu/f3UPrkVSnE3tBAbbcXHwSEjCPH4+mj/7ZEEIIIYQQQnQuCZiE+BWPfLmVj9aW8s4Nozlr0In3L1JVlSfXP8mH+R9yx9A7uHPYnSc3sK2fwec3wdn/grG3HddbLU2NLHv7VXasXUlEcgqzbr+XqH6pJzeeHqx+8c+sfXUZpSFj8Oj96Dc0jNFzUohIPHoPq77OVVmJLScH2+YcbJs3Y9+2DdXp7HhdFxpK4NlnEzh3Dn7Dhp185Z0QQgghhBCiz5KASYhfYXd5uOCllVS32Pnu3snEBJ14PyZFVfjLqr/w1a6v+N2o33FdxnUnPjBVhQ8uhD3r4e51EBh73JfYuXYVS956GVtrC6PPvZBxF1+Bwcd44mPqwdyNjZT++XHyd0JZ0jTcGiPJWeEMmRJH3IAQdIa+15Nqf4rdjn3bNmybNntDpZwc3NXVAGh8fPDNzMRv6FD8hg3DNyMDR0E+zd/Op+3HH1GdTgzx8QTOmUPQuXMx9u/fzZ9GCCGEEEII0dNIwCTEMSisbeO8F1cwODaQ/94yDv1J9GPyKB4eXv4wP5T8wJ/G/YlLB1164gNrKIKXx8PAWXDpeyd0CXtbGz+9/yZ5Py0hJCaOmbffQ3xaxomPqQdTVZXmr7+m7J9PsSdqEmVJM3C6NeiNOhLSQkgeEk5SZhjm4N4dsqmqiqu8fF+YtHkz9oICcLsBMMTH4zdsWHugNBTfQYPQ+Pgc9lqetjZaFy+hZf58LKtXg6JgTEsjaO4cAufMwRATcyo/mhBCCCGEEKKHkoBJiGO0tx/TnVNSeXj2yfVjcnlc3PfTffxS9gv/nPxP5qbMPfGLLX8Klv0drvzEGzSdoOKcbBa/8RIttdUMmzWHyVdch4+f6cTH1YM5y8qp/P3vac3OoTlpFA1Rw6n1S8GmeqvTQoMhcVAA/UbHETM4Bo22Z08NU6xWbFtzO8IkW04Onvp6ADR+fvgNGeINk4YPwy8rC314+Andx11bS8v3C2leMB97zhYATKNGETh3LoGzZ6ELDu6sjySEEEIIIYToZSRgEuI4/OGLLfx33R7evWE0U06iHxOA3W3nrqV3sbF6I09PeZppidNO7EJuJ7w2GZxWuGsN+JhPeExOu40V/3uPTQvnExAazoxb7qLf8MP++9DrqR4PTZ9/jm3LFtwVFTjLK2hqhtrAgdSHDaE5sB9otBhcbUS6SonxayI2RoMpPgpDbCz6mBgMsXHoI8LRaE/d9DpVVXEWF+8XJm3BsX07KAoAPsnJ+8KkoUMxDhjQJY26nSUlNC9YQMu383Hu3g0GA/6TJhE4dw4BU6ei9TvxqaRCCCGEEEKI3kcCJiGOw95+TDWtDr67ZzLRQb4ndT2Ly8KtP9xKfkM+/5n2HybETjixC5WsgnfOhgn3wMy/n9SYAMq35/PDq8/TUFHG4MlnMeW6W/ALCDzp6/Z0qqriqa/HVVFBW3EFpQXNlFdqqLIF4sIHjeohqGkX4fV5hDXkYrJWozEYMERHY4iN3e8R07Gvj4lBe4TpZ8fC09qKfetWrO2VSfbNOXiamwHQms34Dc3qmO7mm5WFPiSks74cx0RVVRz53n5NLQsW4K6pQWMyETB9GkFz53pXojMYTumYhBBCCCGEEKeeBExCHKfC2jbOfXEFmbFBfHTL2JPqxwTQ7GjmpkU3UdJSwmszXmNE1IgTu9DXd8Pmj+C25RCdeVJjAnC7XKz94n+s+/ozfP0DmHrD7QwcN/G0XElM8ShU7W6hZGsdxTm1NFTZAPD3dRPt20CkbTeBNXkoFWW4a2q8Ddj3o4sI3xc+xRwaROkCveGdqig4Cws7mnDbNm/GsavQez2NBmP/VHyHDsVv6FBMw4bhk5KCRqc7qc/mdnloqrbSUGmhsdK7baq2Eh7vz7Dpice10p7q8WBdv4GWBfNpWfQDSkuLdyW62bMJnDsXv+GyEp0QQgghhBB9lQRMQpyArzaVc9/Hm7nrrFQemnVy/ZgA6m31XL/weupsdbw5600ywk6gyba1Af4zCkJT4MYfoJOmbdUUF/HDay9QXbSL/qPHMe3GO/APDeuUa/dWLfU2SrbWU5JbT9n2RjwupaNReFJ6MLFRCj4tNbgqKnBVlOOqrMRdUYGrvAJXZSWq03nA9bT+/hhionFVVqG0tXmPBQUdUJ3kl5WFLuDYw56DuRweGqssNFZaaKjcGyhZaKmzdeRhGq2GoAg/AsP9qNzVhMvhIW5gMEOnJ5KcGXZcvagUpxPLL7/sW4nO4cAQF0fg3LkEzZ2DccCAE/4sQgghhBBCiJ5HAiYhTtDvP9/Cxxv28O4NYzhzYMRJX6/KUsV131+H1W3lnVnv0D/kBJaCz/kffHkbzHkGRt900mPaS/F42LjgK1Z98iE6g4Ezr7mJzLNmSDUK4HJ6KC9opDi3npKtdbQ1OgCISAwgKTOM5CHhRCYFdIQzqqLgaWhoD58qcFVUereVlegjwvEb6g2UfPoln9DX12Fzt4dI+8KkxkoLrQ32jnO0Og3BUSZCos2ExpgIiTETGmMmONKEzqDtuM62FRVsWbaHtkYHwVEmhk5LYNC4aAw+x1c15Wlro3XJElrmL8CyapV3JbpBgwg6dy6B55yDITb2uD+nEEIIIYQQomeRgEmIE2Rzevsx1bZ1Tj8mgD0te7hu4XUAzJs9j4TAhOO7gKrCe+dBRQ7cvR4Cok56TPtrqChn8esvUpafS2LmUGbc+luCo6I79R69maqqNFRYKN5aR8nWeqqKmlFV8AswkJQRRtKQcBIGh2L0O/mm23aLa78QaV+YZGlydJyjM2gJjjIRGnNgkBQY4YfuGKd2ejwKRdm1bF5SSk1JK75mA5lnxpF5ZhzmIONxj9tdV0fL9wtpmT8fW04OAH6jRhI0dy4Bs2ad8h5Sx6qxykLR5lqSh4QTFuff3cMRQgghhBCix5GASYiTsKumjfP+s4LMuCA+uvnk+zEBFDYVcv3C6zHpTcw7ex7R5uMMcOp2wisTIP08uPitkx7PwVRFYcvShSz/8B0URWHSZdcy/Oy5aLUn1wuoL7K3uSjJ806lK82rx2F1o9VqiBkQRPKQcJIywwiOMqGqCg3lZQRFRGHw3RdUqqqKrdV1YIhU5Q2SbC37ptnpfbSExpg7AqSQ9kApIMwP7XFMazsaVVWp3NXM5iWl7N5Sh1anYeCYaIZNSzjhwMVZWkrLggU0fzsfZ1ER6PXtK9HNJWDqWWhNpk4Z+4lqa7Szc0MNO9ZVUbfHO3XR19/Ahb8bQUj0ia/WKIQQQgghRF8kAZMQJ+mL7DIe+CSH307tz4MzB3XKNfPq87h50c2E+4Xzzux3CPcLP74L/PR/8NMTcPUX0H9ap4zpYC11tSx58yV2b9pATP9BzLz9HsITkrrkXn2B4lGoKmqhJLeO3VvqqC8rR3GVoNOV43aU4nHZMIdEkjn1Ztyu0I6m23aLq+MaPr66g0IkMyExJgJCfI+rP9LJaqq2smXZHvJXV+J2KiSkhzBseiIJg0NPaFpfx0p08xd4V6KrrvauRDdtGkFz52CeOBGN/uSrvo6F3eKiMLuGneurKd/ZBCpEJgcycHQUkcmBfP/aVnQ6DRc+NJKA0JOvWhRCCCGEEKKvkIBJiE7w8Gc5fLqxjPduHMPkASffjwkguzqb2xbfRmJgIm/PepsgY9Cxv9nt8FYxKW64cw0Y/DplTAdTVZWCFT+xbN4bOK1Wxl14GWMuuBidXpalP1hbQz2luTmU5uZQkptDW30dAHpjMGjiQROJ274OVBt+wTOJHjDOGyJF7wuUzME+Parvld3iIu+Xcrb8WIa12UlorJmh0xIYOCYKveHEKtpURcG6YQMt386nZdEilJYWjIMGEf3YXzANH97Jn8DL5fRQvKWOHeuqKc2rR/GoBEeZGDgmigGjowiO3FdJVbunla+ezsYcbOQ3vxuBn79Pl4xJCCGEEEKI3kYCJiE6gc3p4fyXVlDf5uS7eycTFdg5lQ2rKlZx99K7SQ9N5/WZr2M2HMe0nN3LYd65MPlBmPbnThnPkVibm1j27utsX7Wc8MRkZt12D9H9B3bpPXs6h9XCnrytlGzdTGluDg3lewDwDQgkMSOLxMyhJA4ZSnBUDG6XQsXOJlyONjZ88xrl+VsYMm0WU6+/Db1Pzw8wPG6FnRuq2bxkD/VlbfgFGBgyJZ7MM+LwCzjx8StOJ62LF1Pz76dwV1URfMklRD74ALrg4JMfs0ehLL+RHeurKNpch9vhwRzkw4DRUQwcE014gv8Rw7yKnY1880IOYbFmzr9/OD6+p6a6SgghhBBCiJ5MAiYhOsmumlbOfXElWfFBfNhJ/ZgAlpUu44GfHmBE1AhenvYyvvrjCK++vB22fga3r4DItE4Zz9Hs2rCWpW++hKWpiZFzL2DCJVdiMJ4e04jcTicVO/K9FUpbN1NduAtVVdAbjcSnZZA4ZBiJmUOJTOqHRnvkPxuK4mHVJx+y9stPiOyXynkP/IGgyN7RSF1VVcq3N7J5yR5KcuvRGbQMGuft03QyPYsUi4Xa/7xEw3vvoQsMJPLhhwm64PzjruZSFZWqomZ2rK9m18Ya7G0ujCY9qSMiGTg6ipgBwcfcs2p3Ti3fv5ZL3MBg5t41tGP1PSGEEEIIIU5XEjAJ0Yk+31jGg5/mcM/U/jzQSf2YAOYXzeeRXx5hUtwknj/reQy6Y5yCZqmD/4yCiDS4/js4SrDRWRxWCz9/8DZbly4iOCqGmbf9loSMrC6/76mmKB5qigopaZ/2VlGwDbfLiUarJab/IBKHDCMpcygxAwed0JTBwo1r+f4/z6DRaDjnt7+j3/DD/jvdYzVUWshZuofta6rwuBWShoQxbFoCcYNCTnian337dqr+8hi2zZsxjRpF9GN/wdi//6++r768jR3rqtm5vprWBjt6g5bkoeEMHB1F4uCwEw6HCtZUsvTdfFJHRDDz5sxOa6guhBBCCCFEbyQBkxCd7KFPc/gsu4z3bxzLpAHH2Zz7KD7d8Sl/W/03ZibN5MkznkSvPcZpOdnvwzd3w4R7YPID4HdqloEvzc3hh9dfpLm6iqxpsznj6hswmnrvyluqqtJQUebto7Q1hz3btuCwWAAIT0wmMXMoSUOGEZ+egY9f56x+1lRVyTfPPkFtyW7GXXg54y++vNet1mdtcZK7vJzcn8uwtboIT/Bn2LQE+o+KQqc//mBHVRSaPvuMmqefQbFYCLvhBsLvvAOt34F9xlrqbOzcUM2OddU0VFjQaDUkpIcycEwU/YaGd9q0ts1LSln52S4yJsdy5pWDelSPLCGEEEIIIU4lCZiE6GRWp5vz/7OSRquT7+6ZTGQn9WMCmJc3j6c2PMX5qefzt4l/Q6s5hh/QVRU+uxHyvgCDGYZfDeNuh9CUThvXkbgcdlZ+8iHZC77GHBLC9JvvInXkmC6/b2dpbaijdGtOR3PutoZ6AAIjotoDpaEkZGRhDu660M7ldLD0rVfI+2kJyUNHcPbdD2IKPI6G7z2E2+Vhxzpvn6bGSgvmIB+GnBVPxuQ4fM3HX+Hlbmig5t9P0fzllxji4oj646PoR01k10bvCnCVhc0AxKQGMWB0FP1HRp5UP6ijWfNVIRsXljDy7CTGnZ/aJfcQQgghhBCip5OASYgusLO6lfP+s5IQk4FH5wzmnCHRnVbZ8PLml3kl5xWuTLuS34/5/bFft3ILrHnZ25NJcUPaHBh/NySOgy6uuqjctZ0fXn2Buj0lpE08kyFTZ6L38UHvY/RuDT7tz73HtLruqdKxt7WxZ9uWjiqlhooyAPwCAknIHEpS5lAShwwjOOrU9kRSVZWtyxax7O1XMQWHcN79f+i1TdRVVaV0WwM5S0rZk9+I3kdL+vgYsqYlHLBa27FqXrmOLc9/SrmaSENYOipaQmPN3hXgRkURGN41KyjuT1VVfvpoO9t+qWDixf0ZNj2xy+8phBBCCCFETyMBkxBdJLu0kT9+mcu2yhbGp4Tx2HkZDIoOOOnrqqrKUxue4r1t73HLkFu4Z8Q9x3eBlkpY/wZseBtsjRA7AsbfBYPPh2Pt7XQCPG4X6776jDVffIzicR/1XK1Od5gAqn3fx+DdN/gceM6R9g8Kr/bf1xkM1JbsprR9pbfqokJUVcFg9CU+PaN9pbdhRCQmH7Ux96lSVbiTb599AktjA2ddfxtZ02f36ilZdWVt5CwtZce6ahRFpV9WOMOmJxLTP+ion8vjVijNq2fH+mqKc+pwuxRMPi4idi8numEz/W66mNBrrkZj6Lo/zwdTFJUf3silcFMt065PJ21czCm7txBCCCGEED2BBExCdCGPovLRulKe/mE7rXY314xL4v4ZAwnyO7kffFVV5a+r/8rnOz/n3hH3cvOQm4//Ik4L5PwXVr8MDYUQGA9jb4MR14Jf8EmN72ha6mpprqnC7XTidjrat07cLuehxw7ed7mOes6vBVdHotXpiBkwyBsoZQ4lZsCJNeY+FWxtrXz/4lPs3ryRwWdMZfrNd/b6lfoszQ62/lRG7vJyHBY3kUkBDJueSOqICLTtqzGqikrFziZ2rK+mMLsGh9WNr7+B/iO9K8BFpwThqqig+h//oO2nnzAOHEj0Y49hGjH8lH0Oj0th/ks5lO9o4uzbh9Avq/N6sAkhhBBCCNHTScAkxCnQaHHy9OLtfLS2lBCTDw/PHsQlIxNOatUpj+LhkRWP8N3u7/jDmD9wZfqVJ3YhRYGdi2D1S1D8C/j4w/BrvGFTaL8THl93UBQPHqcL15FCKpd33+N04mo/FhwdTXx6Jj6+XT+VqrOoisKaLz5m1WcfEZGQxLkP/IGQmLjuHtZJczk9bF9dyeale2iuseEfamTImfHYWp3s3FCDpcmB3qgjZVg4A0dHE58egk53YGWZqqq0LV1K1eP/xF1ZSfAlFxPxwAPoQ05Nc3un3c3Xz26ivsLCefcMJXbAqbmvEEIIIYQQ3U0CJiFOodzyZh77Jo8NJY0MjQ/isfMyGJ544j+AuhQXD/70ID/u+ZF/TPwH5/c//+QGWJnjrWjK/QxUBdLmevs0JYw56T5NHkXlu62VzFtVzKQB4dw3vXf2EOpJijdvZMGLT6F4PJx91wP0Hz2uu4fUKVRFpTi3ns2LS6nY2YRWpyExI4yBY6JIHhKOwfjrPboUi4Xal16mYd48dIGBRD70EEG/ueCUTCm0tTn58qlsLE0OLnhwBBEJJz81VgghhBBCiJ5OAiYhTjFVVfl6cwX//C6fmlYHl4yM5+HZaUQEGE/oeg6Pg7uX3s26qnX8+4x/MzN55skPsqUC1rX3abI3Qdwob5+m9PNAd3zLu9tdHj7PLuP15UWU1FsJMRlotLr4w9lp3HamrLh1slpqa/j22SeoKtzJ6PMvZtJl13Rbk/Su0FRtxddswNf/xKYs2rfvoOqvf8WWnY3fqJHE/OUvGAcM6ORRHqq1wc4X/96Ix61w4UMjT6iBuRBCCCGEEL2JBExCdJM2h5sXl+3k7RW78dXruHf6AK6bkIxBd/zNpK0uK7ctvo3c+lyeP+t5zog/o3MG6bTA5o+8q881FEFQAoy9HUZcA75BR31ri93FB2tKeHtFMXVtDoYmBHPHmalMS4/k/o83M39LJf+6KItLRyd0zlhPY26Xi5/mvU7O4u9JyMhizj0PYQ6WqVl7qYpC8xdfUPPvp/BYLITdcAPhd96B1q9rp0U2Vln44t/ZGHx1XPTQSMzBJxYiCyGEEEII0RtIwCRENyusbeNv327j5x21DIj057HzMpjY//ibA7c4W7h50c0UNRfxyvRXGB09uvMGqSiwY6G3T1PJCvAJ8IZMY2+DkOQDTq1psfPWyt18uKaUNoebMwZGcMeZqYxLCe2YnuR0K9z83gZW7Kzl5atGMjszuvPGehrL+3kpS958GV+zmbn3/4G4QendPaQexd3YSM2/n6L5iy8wxMYS9cc/EjD1rC69Z3VxC18/u4mAMF9+8+AIfM09s3m8EEIIIYQQJ0sCJiF6AFVVWZJfw9/nb6O0wcrZmdE8Oied+JDjm1bTYG/ghoU3UGWp4o2Zb5AVkdX5g63Y5O3TlPeFt09T+rkw/m52+2Xw+vJCPt9YjltRmJMVy21npJAZd/hKJ6vTzdVvriW3vIV3bxjNhBMI1cShakt2880z/6SltoYzr7mJ4bPPPSV9h3oT64YNVP31rzh27sJ/+jSiH3kEQ2xsl91vT0ED8/+TQ2RiIOfdO+yYekgJIYQQQgjR20jAJEQPYnd5ePOXIv7z4y4A7jizP7edmYKv4dh/IK2x1nDd99fR4mzh7VlvMyh0UNcMtrkc1r2Oe/3b6J0tZCv9eVedS/CIC7jpjIEkhZl/9RJNVieXvbaGskYrH90yjqEJwV0z1tOM3dLGwpefo3DDGgaNn8zM2+/pVavknQqqy0XDvHnUvvQyABF3303otdegMXRNhVHhphoWvZ5LwuBQzrkjC53++KfCCiGEEEII0ZNJwCRED1TRZOPx7/JZsKWS+BA//jhnMLMyoo65EqWstYzrFl6HW3Ezb/Y8koOSO3V8qqqyclc9r/y8i027yrnadwV3GH8gxFEGQYkw7nYYfg34Bv7qtapb7Fz86ira7G4+vX0C/SP9O3WspytVVVn/zees+O97hMTGcd4DjxAWL/2uDuYqL6fq8X/StmwZxgEDiP7rY5hGjOiSe21bUcGPHxQwYHQUM24YjEYrlWVCCCGEEKLvkIBJiB5sVWEdf/1mG9urW5k8IJy/nJtxzAFMUXMRNyy8AR+dD/NmzyPW/+SnAHkUlYW5Vbzy8y5yy1uIDDBy8+R+XDEmkQAf7X59mlZ6+zSNvM7bpyk48ajXLa6zcPGrqzHoNHx2xwTigqXaprOU5m5hwQv/wuVwMOv2exg0fnJ3D6lHal26lKrHH8ddUUnQxRcR+eCD6EM6v1H6xoXFrPmqiCFT4pl82QCZviiEEEIIIfoMCZiE6OHcHoX315TwzOId2JwebpiYzD3TBhDg++tTeQoaCrhx0Y0EG4OZN3seEaaIExqD3eXhi+xyXl9eSHG9lZRwM7edmcIFw+Mw6g8zfa8827vyXO4XgAqDz4fxd0P8Yf+tAWBbRQuXvb6aiAAjn942njB/WXGrs7Q21DH/2Sep2JHPiHPO54yrbkCn13f3sHocxWql7uWXqX93Hjp/fyIfeoig31yARtt509lUVWXV57vYvGQPo+f2Y8zcfp12bSGEEEIIIbqTBExC9BJ1bQ6eWrSdjzfsIdzfyO9np/Gb4XFof2WaTU5tDrf8cAvBxmCePetZMsIyjvmeLXYXH64p5e2Vu6ltdTA0Pog7pqQyY3A0umOZ3tNcButehw3vgqMZEsbCuDshbS7oDg041hc3cPWbaxkYFcBHt4w9phBNHBuP283yD94m+/tviB00mHPv+3/4h4Z197B6JPuOHVT99W/YNm7Eb+RIov/yZ3wHDuy066uqyrL38ilYXcXkywaSdVZ8p11bCCGEEEKI7iIBkxC9TM6eJv7yTR6b9zQxIjGYv56XyZD4w6/UtldefR73/XgfDbYG/jT+T1zQ/4Kjnl/TauftFcV8uKaEVoebyQPCuePMVManhp3YlB5HG2z+0FvV1FgMIf1g4j0w9Eow+B5w6rKCam55byNjkkN554bRx9XgXPy6glXL+eHVFzD4+jL33odJyOiClQb7AFVRaP7yS2r+/RSetjbCrr+O8DvvRGv69ZUdVVVFdThQbDZUux3FZke121DsjvatHbfFxvJ1OspqDYxPqSXBVO99zWZHsdtQbXYUhwPV5j2/45jdjmqzoTqdmCaMJ+Luu/FNSzsFXxEhhBBCCCGOTgImIXohRVH5PLuMJxcWUG9xcvnoBB6alUao2eeI72mwN/Dwzw+ztmotlw26jP83+v9h0B1YIVRcZ+G15UV8nl2G26NwzpAYbj8zlcy4owdYxz5wDxQsgBXPQkU2+EfD+Dth1I1gDOg47atN5dz38WZmDo7i5atGoNfJiludqb6slG+e/ieNVRVMvuI6Rp17ofQCOgJ3YyM1Tz9N82efo4+NwTRsuDfksdsPG/zsfe1YeLQGcrLupDkwlazcVwlv24XGzw+try9aX9+OfU3Hc1+0vn5o/XxRPQot332H0tpKwIwZhN99F76DumjFSCGEEEIIIY5Bjw6YNBrNbOB5QAe8qarq/x30+lXA/2t/2gbcoapqztGuKQGT6Eta7C6eX7KTeauKMfnoeHDmIK4am3jEQMatuHkh+wXeyXuHoRFDeWbKM0SaIsktb+aVnwv5fmslep2Wi0fGc+vkFJLDzV0zcFWF3T/DL894t75BMOZWGHs7mMMBeGflbv767TYuGRnPvy7OkgCkkzltVha99iI7Vv9C/9HjmH3n/RhNXfTfuw+wZmd7q5kaGvYFP+2Bj8bX2BH8aHz3e814YCik2bs1+qL184ZGTo0P898qpKnGznn3DScm9djDXE9zMw3z5tEw7z0Ui4WAWbMIv+vOTp3OJ4QQQgghxLHqsQGTRqPRATuAGUAZsB64QlXVbfudMwHIV1W1UaPRnA08pqrq2KNdVwIm0RftrG7lsW/zWLmrnrToAB47L4NxKUfur7OweCF/XvlnDBo/Im23sGlnCAFGPVePT+KGiclEBvge8b2drnyjN2gqmA96P+/Kc+PvhuAEnlm8gxeW7uTWM1L4w9lpEjJ1MlVVyf7uG5Z/+DaBEZGc98AjRCRJ0+lTzdri5It/b8RucfGbB0cQFndsK0Xu5Wlqon7ePBrfex/FaiVg9iwi7roLY//+XTRiIYQQQgghDtWTA6bxeAOjWe3P/wCgquoTRzg/BMhVVTXuaNeVgEn0VaqqsjC3in8syKe8yca5Q2N55Jw0YoL8DjjPo6gsyqvihV+Ws8fwClpDI5PDbuKJ6bcT5HfkKXZdrnY7rHgOtn7ifZ51GerEe/nLKhfvrS7h/81O444pqd03vj6srCCP+c89icNiYcatdzN48lndPaTTTkudjS/+vREVuOihkQSG+/3qew7mbmyk4d15NL7/PorNRuDZZxN+150YU+XvjRBCCCGE6Ho9OWC6GJitqurN7c+vAcaqqnr3Ec7/HZC29/wjkYBJ9HU2p4dXfy7k1Z8L0Wo03D21PzdP9lalfJFdzuvLi9hdZ6FfuJlrJ0ayru0lVlQs59yUc/nz+D/jqz+F1UuH07QHVv8HNs4Dtx01bS5P2ebw0vZAnrhwCFeMSeze8fVRlqZGFjz/L/Zs28rQGedw5jU3YjB285+F00x9eRtfPp2N0WzgoodGYgo8scDX3dhIw9vv0PDhh6g2G4HnnOMNmlJSOnnEQgghhBBC7NOTA6ZLgFkHBUxjVFX97WHOPQt4GZikqmr9YV6/FbgVIDExcWRJSUmXjl2InmBPg5V/LNjGorxqEkNN2F0ealodDIkL4o4pqczKiEan1aCoCq9teY1XNr9CWmgaz571LHH+Ry0EPDUsdbD2VVj3OtibyfMdzhMtZ3Pl5ddwTlZsd4+uT1I8HlZ8/D7rv/4MrU5PdOoA4tIziE/PIHZgOr7m45u6JY5fVVEzXz+3iaBIE795cARGP/0JX8vd0EDD22/T8OFHqA4HgXPmEH7nHRj7yTRIIYQQQgjR+XpywHRMU+Q0Gk0W8CVwtqqqO37tulLBJE43v+ys5akfdhDoq+f2M1OZkBp22F5Gy8uW8/vlv0er1fKvM/7FhNgJ3TDaw7C3wMZ3UFe9hMZSTY6Sis+UB0mfcgVoZXW5rlBWkEdR9nrK8nOpLtyF4nGDRkNEYjLx6ZnEpXlDJ3NwSHcPtU8qzatnwctbiE4J4tzfDkXvozup67nr66l/620aP/oI1ekk6Ny5hN9xBz7JyZ0zYCGEEEIIIejZAZMeb5PvaUA53ibfV6qqmrffOYnAMuBaVVVXHct1JWAS4shKW0q598d7KWou4rfDf8tNmTf1nMbaLju29e/TuOQpYpUq7MH98Z3yIAy5BHSG7h5dn+Vy2KncuYPygjzKCvKo3FGAy2EHIDg6Zl/glJZBUFR0z/nz0svtXF/ND2/nkTwknLNvy0R7hJUhj4e7ro76N9+i8X//Q3W5CDr3XMLvvAOfRJl2KoQQQgghTl6PDZgANBrNOcBzgA54W1XVxzUaze0Aqqq+qtFo3gQuAvbOeXMf6cPsJQGTEEdndVn5y6q/sLB4ITOSZvD3iX/HbOg5y9fXNLXxystPc7nzcwZRAkEJMOG3MPwa8DF19/D6PI/bTU1xIeX5eZQVbKO8IA97WysA/iGhxKVltE+ryyQ8PhGNVJmdsK0/lbH8fztIGxfN1GvT0Wg7J7xz19buC5rcboLOP5/wO27HJyGhU64vhBBCCCFOTz06YOoKEjAJ8etUVeW9be/xzMZnSA5M5rmznqNfUM/p21Jab+WiV1YymWyeiFiMsXI9mMJh3O0w+hbwC+7uIZ42VEWhvnyPt8Ip31vl1FZfB4Cv2Z/YQekdVU5RKano9FJtdjzWL9jNum93M3RaAhMv7t+pFWKumhrq33yTpv99jKooBF1wPuG334FPfA/owSaEEEIIIXodCZiEEEe0tnItD/38EE7FyT8n/ZOpiVO7e0gd8itbuOy11YT5G/lyrobgjf+BnT+ATwCMvhHG3QkB0d09zNOOqqq01NZQlp/bETo1VpYDoPcxEjNgEPHpGcSlZRA7IA2Dr6xUdzSqqvLLJzvZ+mMZ4y5IYeTs5E6/h6u6hvo33qDpk09QFYXg3/yG8NtvwxAnQZMQQgghhDh2EjAJIY6qsq2S+3+6n7z6PG7NupU7h96JTntyTYc7y4biBq5+ay2pEf7879ZxBDQVwIpnIe9L0Bpg2JUw8V4I7TnVV6cjS1Mj5du3eUOn/G3UluxGVRW0Oh1R/foT1x44xaUNxs8/oLuH2+Ooisrid7axc301U64aRMbkrgl+XNXV1L/2Ok2ffooKBF94IeG33Yoh9vCrNiqKitPqxm5x4XYpKB4FxaN6H4p64HPPwc+V9nP2Pfcc6dxjuVbHeSpZZ8V32ddICCGEEEIcmQRMQohf5fA4eHzN43y560smxk3kyclPEmQM6u5hAfDj9hpumbeBkUkhzLtxDL4GHdQXwqoXYPNHoLgh40KYdD9EZ3b3cAXgsFqo2J5PWUEe5QV5VO3agcftBiA8Ialjlbq49AwCQsO7ebQ9g8ej8N3LWyndVs+smzPpPzKy066tqipup4Ld4sLe5qJtTzV13y6iaWMuLr0J3eBhaFIH41T0HefYrS4cVjd08rcJWq0GrW7vQ7vf/kHPtQe/vm9/4JgoUod33tdHCCGEEEIcGwmYhBDHRFVVPt3xKU+se4JoUzTPnfUcg0IHdfewAPh6czn3fbyZaWlRvHr1CPR7V9xqqYQ1L8GGd8DZBgNmweQHIHFc9w5YHMDtdFK1awdlBXmU5edSsaMAl90GQFBkVEd1U9ygDELj4k/blepcTg/fPLeZmpIW5t49lIT00EPOUTwKdosbh7U9CLK0P9rc+/YtLhwdx13YLW48buWI99W57RjcFnwDjJgTozCFmPE1GzD6G/A1ex96H+1RQ5/DhUK6g17XaDWn7X9bIYQQQoi+QAImIcRxyanN4YEfH6DF2cJjEx5jTsqc7h4SAO+tLubPX+dx0Yh4/n1xFtr9V9yyNcK6N2DNK2BrgMQJ3qCp/3SQH2h7HMXjobZkN2X5uZTl51G+fRu2lmYAfAMCiRuUTtygwcSlDSayX3/0htOncbjd4uLLp7NpqbeTMjR8v5DIGxQ5be4jvler1ewXCum9W38Dvqb2bXtY5Ouvx2je91yprqTutddp+uILNFotwZdeStgtt2CIkiohIYQQQgixjwRMQojjVmer48GfHiS7Jpur06/mgVEPYNB2/w/5zy/ZybNLdnDTpH78cU76odUQTgtkvw+rXoSWMoga4l15bvAFYPTvljGLX6eqKo2VFZRvz6O8YBsV27fRWFkBgN7gQ3T/ge0VToOJGZiGr7lv/7e0NDtY+NpWrC3OjhDIaD4oJNo/QGo/ZvDVnVSFkLOsnPrXXqXpy6/Q6HQEX3YpYTffjCFSgiYhhBBCCCEBkxDiBLkUF09veJoP8z9kVNQo/n3mvwn3695+Oaqq8tdvt/HuqmIemjWIu87qf/gT3U7Y+imsfB7qtoPBDIPP9zYFT5oIWu2pHbg4bpamRip25FNesI3y7duo2V2I4vGARkNEQhKxaRneSqe0wQSGSwDSmZx79lD36qs0f/U1Gr2ekMsvJ3DuHIyDBqH18enu4QkhhBBCiG4iAZMQ4qR8W/gtf1v9NwKNgTw75VmyIrK6dTyKovLAJ5v5anMFj/8mk6vGJh35ZFWFPetg84feleccLRCcCEOvhKGXy+pzvYjLbqdy1459VU779XEKCIvoqHCKSxtMWEIi2h6yEmJv5iwtpe6VV2n+5hvweECvxzhwAH4ZmfhmZOCbmYlx4AAJnYQQQgghThMSMAkhTlpBQwH3/XgfNdYaHhn7CBcPvLhbx+PyKNz2/kZ+3F7Di1cMZ27W4ZdZP4DTCgULvGFT0U+ACkmTvFVNg8+XKXS9jOLxUFta3FHhVF6Qh6WxAQCjyUzswDRv8/BBg4nqPwCDj7GbR9x7uaprsG3ejD0vD3tuLva8PDzN3p5ZGAz4DhzYHjhleLcDBqCR0EkIIYQQos+RgEkI0SmaHc08vPxhVlWs4qIBF/HI2Efw0XXfD5E2p4dr317L5j1NvHXdaM4YGHHsb24ug5z/weaPoKFQptD1Aaqq0lJb7Q2c2kOn+rJSALQ6PVGp/dsrnLxT6/wCArt5xL2Xqqq4ysux5+Zhz/MGTrbcPJSWFgA0BgPGQYM6Aie/zEyM/fujOY2atQshhBBC9EUSMAkhOo1H8fDS5pd4Y+sbDAkfwjNTniHaHN1t42m2ubj89TUU11n48JaxjEgMOb4L7D+FLvcLcLbKFLo+xNbWSsX2/PYKp21UF+7A4/auwhYal7DftLoMgiKjTqpB9ulOVVVcZWUdFU623DzseXkora0AaHx8MKal4ZsxGL9M7xQ7Y2qqhE5CCCGEEL2IBExCiE63tGQpj658FKPOyFNnPsXo6NHdNpaaVjuXvLqaJquLT28fz8CogBO7kEyh6/PcTidVRTs7Vqor374Nh8UCgDkklLiB6YTExmMOCcE/OBRTcAj+ISGYg0PRy5Sv46YqCq49e/YFTrm52LdtQ2lrA0BjNGJMG+Tt6dQROqWg0eu7eeRCCCGEEOJwJGASQnSJouYi7l12L3ta9/DAyAe4ZvA13VYBsqfBykWvrEKjgc9un0BCqOnkLihT6E4LqqJQX1baUeFUvj2f1rpaVFU55Fyj2Yw5KARzSCjm4BDvIyQU/+D9j4ViNJulEuooVEXBWVKCPW9bR7WTfds2lPagT+Pri29aWkfg5JeZgU9KChqdNG0XQgghhOhuEjAJIbpMm7ONR1c8yrI9yzi739k8Nv4xTIaTDHdO0PaqVi55dRWhZh8+vX0CEQGd0NS5YwrdB5D75YFT6IZdASHJJ38P0aMoigdbSwuWpkYsjQ20NTVgbWqirbEBS1MDlsZGLM2NWBobcTsdh7xfZzBgDg7FHBzs3Ybs2/ffL5wyBQWjldAEaA+diku8/Zxy87Dl5eLYlo9itQKg8fPDNz29I3AyjR6NIfYYGvsLIYQQQohOJQGTEKJLKarC27lv80L2CwwIGcBzU54jITChW8aysaSRq99cS79wM/+7bRyBvp3Y38VphYL57VPofkam0J3eVFXFabPuF0R5t5amxo5je/ftba2HvF+j0eIXGHhARZR/SCimoOOclndQtdSxVE8d9pyDr3PQywZfX3xMZox+plMSjKkeD87i4vbpdbneiqdt21BtNgB8+vXDPGkS5okTMI8Zg9bUPcG2EEIIIcTpRAImIcQpsbJ8JQ8vfxgVlT+P+zOzkmd1y1Shn3fUcvO89QxPCGHejWPw8+mCH4ab9sCWvVPoirxT6DIu8IZNiRNkCp04gNvlwtoeNrXtrYJqajgkiLI0NaIqh07P62kMRl98TCaMfiaMJrN332TGaDLh47ff/mGOec83oz+B5t6qx4OjsBDr6tW0rVyJdd16VLsdjcGA34gRmCdNxH/SJIyDBqGRv4NCCCGEEJ1OAiYhxCmzp3UPD/70IPkN+QyNGMrvRv2OYZHDTvk4vs2p4J7/bWLqoEhevWYkBl0X/bCpqrBnbfsqdHun0CV5g6ahl8sUOnFcVEXB1tpCW2MD1qZGPB73ga8f/L/sgw6oHOb/6Qe/6ZCnv/4eVVVx2e04bVYcVisOqwWH1YrTasFhs+Lce8zm3bodh04dPJjOYGgPnvYPp8ztAZSf95jf3pDKjNHPDx+TGV9/f0KiY9FotSgOB7aNG2lbuRLLipU4tm/3XjssDPPECfhPnIh54kT04eG/Oh4hhBBCCPHrJGASQpxSHsXD14Vf8+KmF6mz1TEjaQb3j7j/lE+be39NCX/6Kpd+4WbOzoxmVkY0WfFBXVdVdbgpdMmTvWFT+nkyhU6cNjxuN06b9fCB1N5928HHvc+dNlv71nrE60f3H8iMW+4mMjnlgOOumhosq1ZhWbkKy8qVeBoaADCmpeE/yRs2+Y0ciVZWBBRCCCGEOCESMAkhuoXVZWVe3jzeyXsHl+LiirQruC3rNoKMQadsDN/kVPDx+lLWFDXgUVRig3yZmeENm0Ynh6DvqsqmI02hG3sbxAztmnsK0YeoioLTbtsvhLLisFlorq5izRcfY2tpYcQ55zLh0qvx8fU77PsdBQW0rViJZeVKrNnZ4HKh8fXFNGY0/pMmYZ440btCnaz6J4QQQghxTCRgEkJ0qxprDS9vfpkvd32J2WDmtqzbuCLtCnx0p66KoNHiZGlBDQtzq/hlZy0Ot0Ko2Yfp6ZHMzoxmYv9wjPou6NV0uCl0GRfC1D9CWGrn30+I04C9rY1f/vsuW5YsxD8snKk33Eb/UeOOGhQpFguWdeu81U0rVuAsLgZAHxPjnU43aRLmcePQBQefmg8hhBBCCNELScAkhOgRdjTu4JmNz7CyfCVx/nHcN/I+ZiWd+kbgFoebn3fUsjC3ih8Lamh1uPE36pkyKILZmdFMGRSJv1Hf+Te2N8OqF2H1y+C2w4hr4Mz/B4Gy3LoQJ6JiRz6L33iJutJiUkaOYdoNtxMYEXlM73WWlWNZ6a1usqxejdLaClotvkMy8Z84CfOkifhlZaHRd8G/BUIIIYQQvZQETEKIHmVV+Sqe2vgUOxt3khWRxe9G/Y7hkcO7ZSwOt4dVhfUsyq1i8bZq6i1OfPRaJvcPZ1ZmNNPTowg1d3KlVVsNLH8KNrwNWp132tzE+8AU2rn3EeI04HG7yf7+G1Z9+iEAEy6+khHnnI/uOIIh1e3GtnUrlvbpdLYtW0BR0Pr7Yx4/DnN74OQTH99VH0McI9Xlwt3YiKexCU9jA56GBu/zhkY8jY24GxsO2FeaW/Dpn4p59BhMY8diGj0KXUBAd38MIYQQoteSgEkI0eN4FA/fFH7Di5tepNZWy4ykGdw34j4SAxO7cUwqG4obWJRXzaK8KsqbbOi0GsYkhzI7M5qZGVHEBB3a6+WENRbDj0/Alo/BGAgT74Fxd4CPufPuIcRpoqWuhmXvvEbhhrWEJyYz45a7iB2YfkLX8jQ3Y1m9BsvKlbStXIG7ohIAn6QkzBMnYp40CdOYMej85e/qyVBVFdVqbQ+MGr1hUXs45GlswN3Q4A2SGhq8wVFjE0pLyxGvpw0KQh8Sgi4kBF1oKPrQELQmM/Zt27Bt3ozqcnmr1AYPxjR2DOaxY/EbMVL+OwohhBDHQQImIUSPZXVZmbdtHu/kehuBXz7ocm7Luo1g3+BuHZeqquSWt7Aor4qFeVXsqmkDYGhCMLMzopmVEUVKRCetCledB8v+Adu/A3MknPkwjLgO9LLSlRDHa9f6NSx75zVa62vJmjabyVdej6//if9dVVUV5+5iLCtWeKfTrVuHarOBwYBp6FD0MTFofAxojUY0PkY0RuNhnvugNfq07x/0vP1177H2faMRjbaLFiA4CaqigNuN6vGgejz79t3u/fY9qG4XSnPzfpVF+1UZNTTgbtpXcaQ6HIe/mcGwX1gUgj4ktGNfFxKCPjQUXUgoupBg735w8FGnMyp2O7bNOVjXrcWydp23Ss3lAp0Ov8xMTGPaK5xGDEdrMnXRV1AcTPV4sG3ahCE+HkN0dHcPRwghxDGQgEkI0ePVWmt5afNL3kbgejO3Zt3KlelXntJG4Eezq6aNRXlVLMqrYktZMwADo/yZnRHNzIxoMmIDT76XVOlaWPIYlK6CkGQ464+QeRH0wB80hejJnHYbqz75kOzvv8HXP4Ap195M+qQpndLvTXE6sWVvwrJyBZY1a/E0N6M6HKhOJ6rDgeJ0gtt98h/CYEDbHjx5gygftD7GIz/38QFVRfW4we1pD4Ha993uwx/3eMDjRnW5Dz2+X5C0N0DiJL5n1Pr7owttD4T2C4u84dDeEMlbeaQLDUVrNndpfz7FZsO2aROWteuwrl2LLTfX+xkNBvyGDNlX4TRsGFpf3y4bx+lKdbtp+e476l55Fefu3QD4JCdjGj8O87jxmMeOkYb7QgjRQ0nAJIToNXY27uSZjc+wonyFtxH4iPuYlXzqG4EfTXmTjR/yqliYW8X64gYUFeJD/LyVTZnRjEgMQac9wfGqKuxaAkv+CtVbISoTpv0FBsyAHvQ1EKI3qCkuYskbL1G5azuJmVlMu+kuQmPjuvy+qsezL3ByOFGd+wVQDgeqw4nqOui503veAc8dDhTnYZ47nd5jDof3Pe3P0WrQ6PRodDrQ6/btG/SHP67Xo9EfdFyvA93eff0Rjre/93DH29+nCwxoD5S8oZLWp2f8suBIFIsFa3Y21nXrsKxdhz03FxQFjcGA39Ch3uqmsWO8gVMP/yw9mep20zx/PvWvvIqzpATjwIGE3XQj7sZGrKvXYF2/HsVqBY0G38GDMY8fh2nceEwjR6D168Qp6kIIIU6YBExCiF5nVcUqnt7wNDsad5AVnsWDox5kRNSI7h7WIerbHCzJr2ZRXjUrdtbh9CiE+xuZmRHFrIxoxqeE4aM/gQokRYG8L7xT5xp3Q+J4b9CUNL7zP4QQfZiieNi6dBG/fDQPt9PBmAsuYcz5l6CXkEAchaetDeuGDVjbK5zs+fmgqmiMRvyGD8c8dgymMWPwGzLEWz0mjkp1u2n+5lvqXnsVV0kpxvR0wu+8g4Bp0w6YDqq6XN6G+6tXewOnnBxwubxB37BhmCeMxzRunPfrLis8CiFEt5CASQjRK3kUD98WfcuL2S9SY6theuJ07ht5H0mBSd09tMNqtbv4cXsti/Kq+LGgBqvTQ4CvnunpUczKiOKMgRGYfI7zG2KPC7Lfg5+fhLZqGDgbpv4JojO75kMI0UdZmhr56b03KVj5MyExsUy78U6SsoZ197BEL+FpaWkPnLw9nBwFBQBo/PwwDR+OaexYzGPH4JuRgcZg6ObR9hyqy0XzN99Q9+pruPbswTg4nYi77sJ/6tRjqkxWrFasG7OxrF6NZc1qHPkFoKpozWZMo0d7K5zGj8c4YECPqnQWQoi+TAImIUSvZnVZeW/be7yd+zYuj4vL0i7jtqzbCPEN6e6hHZHd5WHFzjoW5VWxOL+aJqsLX4OWa8Yl8eDMQfgadMd3QacF1r4GK54DRwtkXQpT/gCh/bpk/EL0VcVbNrH0rZdpqqokfdIUzrzmJszBPfffEtEzuRsbD6hwcuzcCYDWZMJv5EhvhdPYsfimp5+WlTaq00nT119T/9rruMrK8M3IIPyuu/A/6+R6obkbG7GuXYdljbfCyVlSAoAuPBzz2LEdU+p84rt+KqwQQpyuJGASQvQJdbY6Xtr8El/s/AKz3swtWbdwZfqVGHXG7h7aUbk9CuuKG/h8YzmfZ5cxMMqfZy4dRmZc0PFfzNoAK5+Hta+C4oGR18MZD0FAVKePW4i+yu10svarT1n/9afojUYmX3E9WdNm9ciV20Tv4K6vx7p+fUcPJ2dhIeBtbm4aNaqjwsmYltan/5ypTidNX35F/Wuv4aqowHfIEMLvuhP/M8/skgojV0UFltVrsKxZg2XNajy1dQAYEhIwjx/vDZzGjkUfGtrp9xZCiNOVBExCiD5lV+Muntn4DL+U/0Kcfxz3jriX2cmze0V5/E/ba3j4sy00Wp3cN30gt5+ZemINwVsqYfm/YOM80Bth3J0w8R7wPYHQSojTVH35Hpa+9Qp78rYQM2AQM265m4gkqQoUJ89dW4tl3TpvhdO6dTiLiwHQR0YSMH06ATNnYBo1qs9UNylOJ81ffEHd66/jrqjEd2gWEXfdhXny5FP2/2ZVVXEWFmJZtRrLmjVY161DaWsDwJiWhnncOG/gNGoUWrP5lIxJCCH6IgmYhBB90uqK1Ty94Wm2N25nSPgQHhz1ICOjRnb3sH5Vo8XJH7/KZcHWSkYmhfDMpUNJCjvBb3brC+HHxyH3c/ALgUkPwJhbwCCr7QhxLFRVJf+XH/np/bewt7Uycs4FjL/4Cnx85e+Q6Dyu6mosq1fTtnQpbb+sQLXb0QUF4T91KgEzZmCeOAGtsWdX4x6O4nTS9Nln1L/+Bu6qKvyGDSP8rrswT5rY7b/0Ud1u7Hl5HRVOtuxsVKcT9Hr8hg7tCJz8srKkUbsQQhwHCZiEEH2WR/Ewv2g+L2x6gRprDdMSp3HfiPtIDkru7qEdlaqqfL25gj99nYtHUfnT3MFcPjrhxL8hr8yBpX+DXUsgIBam/B6GXQW6vvHbcSG6mq2tlV8+epetSxcREBbB1Btvp/+osd09LNEHKTYbbStW0Lp4MW0//oTS2orWZMJ85hkETJ+O/5lnovP37+5hHpXicND06WfUv/EG7upq/EaMIPyuOzFPmNDtwdKRKHY7tuzsjsDJnpcHioLGZMI0cmTHlDrjoEF9ehqjEEKcLAmYhBB9ns1t4/1t7/PW1rdwepxcOuhSbh96e49uBA5Q0WTjoc9yWLmrnmlpkTxx0RAiA3xP/ILFK2DJX6FsHYT1h6l/hPTzQb5ZFuKYlBdsY8mbL1G3p4TUUeOYesNtBIZHdPewRB+lOp1Y1q2ndfFiWpcuxVNXh8ZgwDxhAgEzpuM/dWqP6h+k2O00ffIJ9W+8ibu2Fr9RI4m46y5M48b12GDpSDzNzVjXr++YUucsKgJAFxLi7Zk1bhzmcWMxJCX1us8mhBBdSQImIcRpo85WxyubX+GznZ9h0pu4JesWLhpwEUHGntubSFFU3l1VzJMLCzAb9fzzN0OYnRl94hdUVdj+vbeiqTYfYobB9L9Aylkg3yQL8as8bjcbF3zF6s/+i0ajYcKlVzHi7PPQ6o5z9UchjoPq8WDLyaH1h8W0Ll6Mq7wctFpMo0YRMGMGAdOnYYiJ6ZaxKTYbjR9/TP1bb+GprcM0ejThd92FaeyYPhO+uKqrsa5Z01Hh5K6qAkAfG4N5bHv/pnHjMERGdvNIhRCie0nAJIQ47RQ2FfLsxmf5uexnACL8IkgNTqV/cP8DtgE+Ad080n121bRy38ebyS1v4aIR8fzlvMEE+hpO/IKKB7Z8Aj/+E5pLod8ZMO0xiO/5faqE6Amaa6pZ9s6rFGWvJyKpH9NvvovYgWndPSxxGlBVFUdBgbeyafFiHDt3AeA7ZEh72DQdY0rXN6RXrFYa//cx9W+/jaeuDtPYsd6pcGPGdPm9u5OqqjiLizsCJ+vatXiamwHwSU3d1zB8zBh0gYHdPFohhDi1JGASQpy2NtdsZlPNJnY17aKwqZCi5iJsblvH65GmyENCp9SgVPx9uqf/hdOt8OKynbz04y5igvx4+tKhjEsJO7mLuh2w8V34+V9grYO0uTDtzxAxqFPGLERfpqoqu9avZtk7r9HW2MDQ6bOZdMV1+Jp7do8c0bc4du+mdckSWhcvwb5lCwA+/VMJmDGDwBkzMKand2olkWK10vjf/1L/9jt46usxjR/nnQo36rA/T/R5qqJgz8/fFzht3Ihqs4FWi+/gwR3VTaaRI9H6nsQ0dyGE6AUkYBJCiHaKqlDRVkFhU2FH6LSraRe7m3dj99g7zos2R3tDp6D20Kn9YTacmqWNN5Y08uAnmylpsHLL5BQemDEQX8NJTs9xtMKaV2DlC+CyQOpUGDgbBs6C4MTOGbgQfZTTZmXVpx+S/d23+AUGMuW6W0ibcEafmR4keg9XVRWtS5bSungx1vXrQVEwxMURMH06ATNn4DdsGJoTnM6pWCw0fPQRDW+/g6exEfOECYTffRemESM6+VP0bqrTiW3Lln0r1OXkgNuNxmDAb/jwjsDJb8gQNHpZbEMI0bdIwCSEEL/Co3ioaKvwhk7N+8Kn3c27cXgcHefFmGMOmWqXEpSCyWDq9DFZnW4eX5DPh2tLGRQVwLOXDWNwbCeU4lvqYfV/IP8bqPdOuyBysDdoGjgb4keDVnrNCHE41bsLWfLGf6gq3EnikGEkZw1Hq9Oh0erQ6nRotVo0Oi3avc91OrRaHRqdDu3+xw93TKdDo933/GjX0Wi0Em4J3I2NtC1bRuviJVhWrkR1udCFhxMwdSoBM2ZgHjsGjY/Pr17H02ah8cMPaXjnHTxNTZgnTSL8rjsxDR9+Cj5F76dYLFg3buwInBz5+QBozWZMo0d3BE7GAQNkhTohRK8nAZMQQpwgj+KhrK3sgGqnoqYidjfvxqk4O86L84/rqHLaGz6lBKXgp/c76TH8WFDDw59vocnq5IEZg7j1jBR02k76wbJuF+xcBDsWQskqUNzgFwoDZngDp9Rp4BfcOfcSoo9QFA9bFi9kxcfv4bBYum0cB4dORpMZX3MAvv7++Prv2/r5B7Q/3+9YQCBGsz96w0n0eRM9iqetDcvy5bQsXozl5+UoVivagAD8z5pCwPTp+E+ahNZkOuQ9jR98QMM77+JpbsZ8xmQi7roLv6FDu+dD9BHuxkasa9diWbMG6+o1OEtKANCFhmIeNxbTuHGYx4/HJyGhm0cqhBDHTwImIYToZG7FTVlr2YFT7Zp3UdxcjEtxAaBBQ5x/3L7eTsGpTIidQJjf8fdUarA4efTLrXyfW8Xo5BCevmQYiWGdXDVlb4bCZbB9Iez8AWwNoNFB0oT2qXSzIbx/595TiF5M8XjwuFwoigfF40FVFBSPp/250n7M+9r+x9W9+8p++5729yge77HDXaf9+ur+r+19r9uN02bF1taKva0Ne1trx0PxeI74GQxG347gyS8goD2g2j+kCsA3IAA/84HH9MdQFdPVVFVt/7q5vf8t3G4Ut9u79Xi3qqJgCgrGFBR8WlV8KQ4HllWraF28hLZly/A0NaHx9cV/8iQCpk/HNGoUTV9/TcO891Cam/E/80zC77oTv6ysUzrOtoZ6KnduJywhidDYuFN671PJVVGBZc1aLGtWY129BndtLQCGuDhM48dhHjce87ix6MPDu3mkQgjx6yRgEkKIU8StuCltLT0geCpsKqS4uRi36sZH68MF/S/g+ozrSQg8vt9cqqrKl5vK+cvXeSiqyp/PHcyloxK65ocmxQNlG7yVTTsWQU2e93ho6r6+TYnjQd/9P2QKIY5MVVVcdhv2tjZv+NTait2yN3w6/DF7Wyu21lYUj/uI19Ubjd5KKLP/gdVRAYEdDdA9bldH+OUNfTwdxw4OgzqOtQdDinvfuR3H9r7fve/cY6U3+BAQEUlgeASBEZEERUS170cRGBGJOSQEbR+dGqy63Vg3bPSuSLdkCe7q6o7X/KdOJfzOO/HLzDglY3E5HZRvy6V4yyZKtmyibk9Jx2shMbGkjBhNv+GjiU/PQKfvm9V1qqriLCpqn063Guu69SgtLQAYBwxor24ah2n0aHQBPWelWyGE2EsCJiGE6GYuxcWuxl18vP1jvin8Bo/qYUbSDG7MvJHBYYOP61rlTTZ+90kOq4vqmZ4eyRMXZhERYOyikbdrKvUGTTsWwe7l4HGAMXBfo/ABM8Asv3kVoq9QVRWXw35A6LQ3eLK3tWK3HOFYawse94HBj06vR6vTe7ftD51Oh1Zv8G47XtN1nOs9Z/9z9Qe9tt/7O17TodMb2rfe8zQaDZbmJlpqa9of1bTU1WJtbjpgjFqdnoDwcALDIwmMiOzYBkV4t/6h4ej6QLNmVVGw5+ZiXbcO0/jx+GV0bbCkqip1e0oozsmmZMsmyvJz8bhc6AwG4tIySB46gtiB6dQWF1GUvY7SvC14XC58/PxIyhpOyogxpAwfhSkouEvH2Z1Ujwf7tm0d0+msGzeiOhyg1WKIj8eYkoJPagrGlFSMqSn4pKZK8CSE6FYSMAkhRA9Sa63lg/wP+GT7J7S52hgXM44bM29kXMy4Y65GUhSVt1fu5l+LthNg1PPEhUOYmRHdxSNv57RA0c/7qpvaqgCNtzn43kbhURlwGk1HEUJ4qaqK2+kAjQadTo9G2zObkbscdlrqavcFT3U1B4RQbU2NsN/3yBqNFv/QMAIj2que9gZR7SFUQFhEj5g22BNYm5so2bq5I1SyNDUCEBafSPLQ4SRnjSAuPQOD0feQ97rsdkrzcijauJ6i7HW0NTaARkN06gBSRowmZcQYIpNTeuSfqc6iOJ3YNm3Gun49jl27cBYW4iwuRnW5Os7RR0Tgk5LiDZz2Bk8pqegjI/r010YI0TNIwCSEED1Qq7OVT3d8yvvb3qfOVsfgsMHckHkDMxJnoDvGqRo7qlu5/+PN5FW0cOmoeP40dzABvqdwWoGiQFVOe3XTQqjY5D0eGL8vbOo3GQwn3+xcCCFOFbfLRWv9EQKouhpa6+tQFeWA95iDQ/ZVP0XuDaEi2qfjRWLwPTRQ6QvcLhcV2/Mp3pJNSc4maooLAfANCCSpfaXFpKHDCQg9vipXVVWpLdlN0cZ1FGWvp7JwB6gq/iGh9GsPm5Iyh/bZr+v+VLcbV1kZjqIiHIWFOAuLcBQV4SwsRNlvoQGtv39HtZNPSj+MqakYU1IwxMej6QMVeD1F3Z4Ssr/7ml3r1xA/OJOsabNJGjJMVggUpw0JmIQQogdzepx8W/gt7+a9S3FLMQkBCVyfcT3n9z8fo+7Xp7453QrPL93BKz8VEhvsxzOXDmNMv9BTMPLDaK3yNgjfsQgKfwSXBfR+kDKlPXCaBYGx3TM2IYToJIrHQ1tDPc211YepgKqhpa72kB5RfgGBBEfHEBIdS0hMHMEx3m1ITCw+vr0nhFdVlYaKMkq2bKI4J5s927bidjjQ6nTEDkonOWsESVnDieqX2qk/cFubm9i9eSNFG9dRvGUTTpsVncFAQkYWKcNHkTJiDEGRUZ12v95AVVXcNbU4iwpxFBZ1bB1FhXhq6zrO0xgM+CQn49MeOHlDqBR8+vVDexoEdJ1BVVVKtmxi44KvKM7JRu9jpN+wkZTl52JrbSEoMoohU2eRMWU6/iHd9D2YEKeIBExCCNELeBQPP+75kbdz32Zr3VZCfUO5Ov1qLku7jECfwF99/8aSBh74JIfSBiu3npHCAzMGYtR3Y9Nalx1KVnjDpu0LobnUezw6y1vZNGg2xAwH+Y2fEKKPURWFtqYGWmprveFTjTeIaqyqoLGqgrb6ugPON4eEtgdPsQRHxxISG0dIdCzBUTE9Yuqdra2V0q05lGzJpnjLJlrrvKughcTEkZQ1nOShw0kYPAQfv05e3fQIPG4X5QXbKMpeR1H2BhorywHvNDzvVLrRxA5MR6vrm43bj4WnpQVnUVFH4LS36slVVuatPgbQaDDExR3Y46l9qwsK6t4P0EO4nU7yV/7ExvlfUV9Wijk4hOGzzyVr+mz8AgJxu1zsWr+arUsXUpq7BY1WS+rIMd6qpqHD++ziAeL0JgGTEEL0IqqqsqF6A2/lvsXK8pWY9CYuGXgJ1wy+hijz0X87a3G4+ceCfP67rpS06ACevWwY6TG/Hk51OVWFmvx9fZvK1oGqgDkSBsyEyDTwC9nvEdq+DQZ9FzcwF0KIU8zlsNNUVUljZTmNlRXeR1UFjZXl2Fqa952o0RAYHuGteIqOJTQmtqPyKSgiqssCFI/bTeWu7ZRs2URJziaqCneiqgpGk5nEzKEkDx1BUtYwgiJPUe+/X9FYWU5R9gaKstdRlp+L4vHga/YnedhIUkaMJnnYSPz8pTE2gOJw4Cwu7gifOqqfdu9GdTo7ztOFhR1Q7aQLCUEbEIAuIGC/bSBas6lP9n2ytjST88N3bP5hAdbmJiKS+jFyzgUMmnAGesPhWxE0VpazddkP5P60BFtLMwHhEQyZOpPMs2Yc9xRR0be01NaQ/f3XTLz8Wgw+vf/7WgmYhBCil9resJ23c99mUfEiNBoNc1PmckPGDaQEpxz1fcsKqnn4s6202Fw8OHMgN09OQaftQd8AWuph1xJv4LRrKTiaj3yuwbwveDKFHBREHRxI7fcwSNm/EKL3sVvaaNovcOoIoCrLcdqsHedpdTqCIqMJiYltf3hDqJCYOAJCw457elpTdVV7Y+5sSnO34LRZ0Wi0RA8YSHLWcJKHjiA6dWCPrwpyWK2UbN3kbRS+aT22lmY0Gi2xg9I6VqULS0jqk6HIyVA9Hlzl5d4eT0V7ezx5t0pLy5HfqNV6Ayd/f7SBgYduA/zR+gegCwzYtz0oqNKcoio9VVFQbTYUq/XQh8W7baiuJHdHHoWVe1BUhVhTIINMgYS71MO+N/755zBPmHDAfTxuF4Ub1rJl6SJKtmxCo9HSb8QosqbNpt/wkVLVdBppa6hn7VefsGXJIjQauPAPfyMxM6u7h3XSJGASQoherqy1jPe2vceXO7/E7rFzVsJZ3Jh5I8Mihx3xPfVtDh75ciuL8qoZkxzK05cOJSH01ExfOC6qCo5WsDWArfEwjyawHu61BlDcR76u3g9M+4Inm28gTb5mGgy+NBmMNOp0NGk0NOChSXXRpDhoctuIMEeRHprOoNBBpIemE+wbfKq+EkIIcUSqqmJrae4Im/YGUN4wqtK7el87vcHHW+m0d9rd3n5P0bGYgoLRaDQ4rFZK83IoydlEyZZNNFVXAhAYEentozR0OImZQ/E1+3fXRz5pqqJQVbSTouz1FG1c39GAPDAi0juVbvhoEjKyesQ0xJ5KVVU8jY14mppR2lrxtLS2b1tQWtvwtB5+q7S24mltRWlrO2BFxsPRGI1oAwPQ+QccfhuwXzhlNqM6HIcNhw7/sHSco1qth72/CtT5+1EcEUxtoAmtohDf2EaKxUmgwYjWZNr3MJu9VVvtz0MuvRRj//5H/GxN1VVsXbaI3B8XY21uwj8snCFnzSDzrBkEhkeezH8a0YNZm5tY9/Vn5PzwHYriIXPKDMZeeBmB4RHdPbROIQGTEEL0EQ32Bj7K/4j/FvyXFmcLIyJHcNOQm5gcN/mwv41VVZXPs8t57Js8AP5y7mAuHhnf639z61bcNNmbaGqroLGllMbWSpqs1TRaa2m0N9DobKHJ2Uqj20aj4qBJdWPXHP7/d1pVJVhRCPYoBCkeKnz8qNbuOzfaHE1aaNoBoVOMOabXfw2FEH2Hqii0NTbsV/FU3h5CVdJcXYni8XSc6+NnIiAsnIaKMlRFwWD0JSEzy7vaW9YIQmJi++y/b60NdezetIGi7PWUbN2M2+FAbzSSmDmU1BFj6Dd8FAFhMpWpM6mKgmKx7AucWvcPqfbb7v96WytKy76t6nAc9R4agwGtyYTGvDcIMh8YCh0QEO3bV4w+FO4pZmvOBhpqqzEFBDL0rJkMnXkOpvCITv174HG7Kdq4ji3LFlGck40GDcnDRpA1bTYpI0b3+MpAcWxsba1s+OZzNi2cj9vpZPAZUxl30eUER/WM6cSdRQImIYToY6wuK5/v/Jz3tr1HlaWK/sH9uTHzRmb3m41Be2hvgLJGKw9+ksPa3Q3MHBzFExcOIcy/Z8wBV1WVVlcrTfYmGh2NNNq9jybHvud7X2tyNNFgb6DV2XrE6/kb/Ak2BhPiG0KIb4h33xhCsG8wob6hBPsEE6L3I1jVEKqoBLgdaO3N3qqothoo/JHGsrUU+OgpCIn1PrQeiq1VKKq3MWqgT2BH4LQ3fEoOSkavlWWghRA9i+LxeBuM71f11FJbQ0RSP5KyhhM7MA2d/vA9Zfoyt9PJnm1bvdVN2etpqa0GwMfPD//QcALCwgkIiyAgLKx9G97xOFXNzIWX4nSi7A2fLBa0RqO3kshkQuvnd9xT7KwtzeQs/o7Ni9r7KyUmM3Lub47aX6kzNddUk/vjD2z9cTGWxgb8Q0LJPGsGmWfNPO1WQuwrHFYLGxd8xcYFX+O020ibcAbjL76C0Nj47h5al5CASQgh+iiX4uL73d/zTu477GraRYw5hmsHX8uFAy7EZDjwG2BFUXlrxW7+vWg7gX56zh0aS2SAL5EBRiICjEQGGonwNxJi8kHbyf2aFFWh2lJNaWspJS0l7Gnd07Hd07oHh+fwv500aA3eoGhvQGQMJdh3X2DU8ZqxPTwyBmPQdcI3h63VUDAf8r+B3b+A6sEWlMiO1Elsj0giX7VT0LCdnU07O8Zu1BkZEDyAtDBv4JQWmsaAkAH46XvP8uNCCHE6UlWV+rJSSrZsprm2ita6Otoa6mitr8PS3HTIFK+9VWAB4REEhHoDKP+DgigfX/m3v6epL9tD9ndfs235MtwuJ/2Gj2LknAtIzBzaLVV7isdD0aYNbF26kKJN3p9dk7OGe6uaRo5Bp5dfWvV0TruNTd9/y4Zvv8BuaWPAmAlMuORKwhOTu3toXUoCJiGE6OMUVeGXsl94O/dtsmuyCTIGcUXaFVyZdiUhviEHnLu9qpU/frWVvIoWrE7PIdfSazWE+7eHTgEHbiMCfA947mvYV9LtUTxUW6s7gqPSllJKWkvY0+INkZzKvtVpfLQ+JAQkkBiYSEJAApGmyI6wqKPqyDcEk74HrE5jbYDt33vDpsJl4HGCfxSkzcWdPofdwXEUNO2koKGAgoYC8hvyOyqstBotyYHJHVVOaWFppIWkSV8nIYToJTxuF20NDbTW19LaUE9rXS2t9fsCqNb6OqzNTYe8z2g2ewOnIwRQAWHhGIyyGEVXU1WV0twcNi74it2bNqA3+DD4jKmMOOd8wuITunt4HVrqasj9cTFbf1xMW30dpqBgMqdMZ8jUWQRHx3T38MRBXE4HOYsWsO6bz7G1NJMyYjQTLrmKqJQj9+PqSyRgEkKI08jmms28lfsWP+35CV+dL78Z8Buuy7iOOP+4Q861ONzUtjqoaXW0b+3Uduzv29ZbHO2/wFXQGJrQ+tSjNdTja2rAx7cBjaEep7YWlX1Ntw1aH2JM8SQGJpIanERSUBKJAYkkBiQSZY5Cqzm+FY56BHsL7PwBtn3tXQXPZfU2ER80BwafBylTUHU+VFoqyW/I7widChoKqLJUdVxG+joJIUTf4Xa5aGuop62+bl8QVV/bEUC11tdhazl0tVRf/wBvABUegX/ogQGUf2gYoEHxuFE8HhS3G4/H433u9m73Pfee49nvNcXjwdN+fN/D7X3ffucccN0j3EdVITA8gtC4eEJi4giNiyc0Nh6jyXzqv9jHyO1yUbDyZ7IXfEVtaTGmoGCGzZrD0BnnYAoM6u7hHZHi8bB780a2LltE0cb1qKpC4pBhZE2bTf/RY3v0dFaP2421pQmX3U5wdEyfXC3P7XKxdelC1n71KZbGBhKHDGPipVcTOzCtu4d2SknAJIQQp6HCpkLeyX2HBbsXoKoqs5JncWPmjQwKHXTU97kVN5WWSva07KGktYTSllJKW0rZ3VxChaUcj7ovRNLhg5FItJ5w3PYwLJYQHLYQFGc4qjsA8IZIBt2hVVH7V0MNjQ8mOqiX/SbXaYXCpbDtG9ixEBwtYAyEgbMg/VzoPx189n3z3WhvpKChgO0N2zvCp+KW4o6+TkHGINJC0g7o65QSnNI7gzghhBAHcDudtO0NnvarhGptr4Rqq6/D1trSZffXaLXodHq0eh1anR6tTtf+0KM74Jj3HN3e53o9qCrNNdU0VVeiKkrHNU1Bwd6wKSaekNi4jv3AyMhuCxesLc1sWfw9mxbNx9rcRHhCEiPnXEDaxDN73WqBrfV15P60mK3LfqC1rha/wCAyzpxG1rRZhMQc+kvDruBy2LE2N2FpasLa0oy1uRFr+76luemA5/a2ff0xTUHBpIwYTerIsSQNGYbBt5d9j3cQj9tN3s9LWPP5x7TW1xKXlsGky64hfnBmdw+tW0jAJIQQp7EqSxXvb3ufz3Z8htVtZWLcRG7KvIloU/QBPZFKW71BUllbGW5lX4jkp/cjISCBpMAk77S2gEQSA72VSJGmyEOqbiwO969WRNUeUBXllREbyLS0SKalRzEkLqjT+0B1KbcDdi/3VjYVLABbA+j9YMB0SD/fGzr5Bh7yNpvbxo7GHftCp/qCA/o69Q/uz81DbmZW8ixpIC6EEH2cy+lor4Kqx9JYD4BWvy/40bUHPvuCoSOHQlqdbl+gpNWh0Z78Lys8bjfNNVU0lJfRUFFGY2W5d7+yHPt+4ZhOryc4OpbQ2PbgKTa+Y9/X7H/S4zic+vL2/ko/t/dXGjaSEXMuIGnIsF5fHawoHkq2bGbLkoUUblyLqigkZGSRNW0W/cdMOK7G5Kqq4rBYsLY0YW1q8oZELU1Ym5vag6L2Y+3PXQ77Ya9jNJkxBQW3P4IwBYVgbt/X6vWUbs1h96YNOKwW9AYfEocMJXXkWFJGjG6vzusdFMVD/i8/sfrz/9JcXUVM/0FMuOzqPvHn6mRIwCSEEIJmRzMfb/+YD/M/pMHecMBrfnq/A4KjjjApMJEIv85dqncvt0ehweKkstnO6qJ6luZXs7GkEUWFcH8jU9MimJYexaT+4ZiNvShc8bihZKW3Z1P+fGirAp0PpEyB9PMgbQ6YQo/4drfipri5mM21m/kw/0N2Ne0iISCBGzNv5LzU8/DR9a7fwAohhOj7rC3NNFaU01BZ5t1WlNFQUU5zdSWKZ1+/R1NQ8GGDp6CIKLS646t6UlWVPXlb2LjgK4qy16MzGBh8xlRGnnM+YfGJnf0Re4S2xgbyflrClqWLaKmtxjcgkIwzpjL4jKloNBqszd4qI29w1OwNjZobOyqObM1NeNzuQy+s0WAKDPI+OoIj78N80HNTUPAxhVoet5vygjwKN6xl14a1HSs1RqcOIHXkWFJHjSU8MblHBjWqorB99S+s+uy/NFaUEZmcysTLrqbf8FE9crynmgRMQgghOtjddn4o+QGP4iEx0BsmhfmG9Yj/YTZanPy0o4al+TX8vKOWVrsbH52WcalhTE+PZGpaJPEhvWh5aEWBsvXesGnbN9BcChodJE/yTqNLPxcCoo/8dlXhxz0/8saWN8irzyPSFMn1Gddz0YCLDlklUAghhOhpOqqeKsppKN/jrXpqD6AOV/W0f4+nkBhvCOXr73/QNV0UrFzOxgVfUVuy29tfaeYchs44G1NQ8Cn+hN1DVRRKtm5m69JF7Nqw5oAQby+dXo/f/gFRYDCm4Pbngd6qI1Owd98vMLBLpzWqqkr9nhIKN66jcMNaKndtByAwItIbNo0cS/zgjG7vMaWqKrvWr2bVpx9RV1pMWHwiEy+9mv5jxveI75N7CgmYhBBC9Douj8L64gaW5dewrKCGojoLAIOiApiWHsm09EiGJYSg6y1T6VQVKnP2hU31OwENJIz1NghPPxeCD/8bV1VVWV25mje2vMGG6g2EGEO4evDVXJ52OYE+h069E0IIIXo6W2tLe9i0p73qyRs8Ha7qaW/wZDSZyV/xE5bGBsLiExk59wLSJ07pdf2VOpOlqZHinGwMRuMBVUZGk7nHhiKWpkaKstdTuHEtJVs243Y68PEzkTxsJP1HjiF5+Cj8/ANO2XhUVWX35g2s/PgDanYXEhITx/hLrmTQ+El9sln5yZKASQghRK9XVNvGsgJvddO64gY8ikqo2YcpgyKYlhbF5IHhBPr23NVVDqCqUFvgDZryv4Xqrd7jMcPaw6bzIfzwS91uqtnEG1ve4JfyX/A3+HNF2hVcPfhqQn2PPO1OCCGE6C28VU/V3j5P7VPt9u7bWltIHjqCkXMuIClreI8NUMSxcznslObmULhhLYUb12FtbkKj1RKflkHqKG91U3B0TJfcW1VVSnNzWPnx+1Tu3E5QZBTjL76S9ElTjnvK5ulEAiYhhBB9SrPNxfIdtSwrqOHH7TU0WV3otRrGpoQyNS2KaWmRJIf33OWTD1Ff6A2a8r+B8o3eYxHpEJkGvsHgF3zINt/ZxJt7FrK4YhVGnZGLB17MdRnXEW0+8pQ7IYQQojdzO52ndbVSX6cqClWFOync6A2b6kqLAQiLTyR15BhSR40luv/ATqkqKsvPZeUnH1C2LRf/sHDGX3g5GVOmo9P3or6f3UQCJiGEEH2W26OwaU8TS/KrWZZfw86aNgBSI8xMS49ialoko5JC0OtOfgWdU6K5zNscfPt30FIOtiawN4FymKacQJFBz1vBQSwwm9Cg4XyPkRv1kST6RRw2mNq3DfLu+waBlH8LIYQQoodprqnq6NtUlp+L4vHgFxhEyojRpI4aS/KQ4Rh8fY/rmpW7trPy4w8o2bIJc3AIYy64lKxpsyS4PA4SMAkhhDhtlNZbWVpQzbKCGtYU1ePyqAT66pkyyNu3acrASIJMvWQq3V6qCk6LN2jaGzgdtC1vq+SdtgK+dNXiVlVmu7Xc3GJlQFsjKK6jXFwDxkDwCzpyGOUXAhFp3il8PtJcXAghhBCnlt3SRvHmjRRuXMfuTRtwWC3oDAaShgwjdeRYUkaMxj807IjvrykuYuUnH1C0cR1+AYGMPv9ihs08B4Px+AIqIQGTEEKI01Sbw80vO2pZWlDDjwU11Fuc6LQaRiaFMC0tkmnpUaRG9NwmmCei1lrL+9ve5+PtH2N1Wzkr4SxuSb+aIaa4owZUR9x6nPsurtVDVCbEj4aEMRA/CkL6QR/6+gkhhBCiZ/O43ZQX5LX3bVpLc001ANGpA0gZOYbUkWOJSOqHRqOhbk8Jqz79kJ1rV2E0mxl97kUMnz0XHz/5hdmJkoBJCCHEaU9RVDaXNbEsv4Yl+dUUVLUCkBRmYmpaJNPToxidHIqPvpdMpfsVzY5mPsr/iA/yP6DF2cK4mHHcmnUro6JGHXugpqrgsoG1Hqq2Qtl676M8G1zeVf0whXsDp/hR3m3cCDCeupVfhBBCCHH6UlWV+rLSjrCpctcOUFUCwiMIi0+kOCcbH19fRpxzASPnnI+v2b+7h9zrScAkhBBCHKS8yda+Kl01qwrrcboV/I16+oWbCTX7EGr2IcTkQ5i/dxtqNhBqNhJqNhBi8iHY5INO2/MrdywuC59s/4R5efOot9czNGIot2bdyuS4ySdeueVxQ22+N2za0x461e/0vqbRQuTg9sBpjDd0CusP2r4R3AkhhBCi57I0NVKUvZ7CjWup3l1I+qQpjD73QvwCArt7aH2GBExCCCHEUVidblbuquen7TWUN9lotDhpsDppaHNicXoO+x6NBoL9DISYfQg1+ewLpcw+hJn3hlIHHjf76LptOp7D4+DLnV/yTu47VFgqGBQyiJuzbmZG4gx0ndHk29rgXQFvb5VT2UZwNHtf8w3eV+EUPxriRnp7OwkhhBBCiF5FAiYhhBDiBNldHpqsLuotDhotrvbgyUGD1UXD3mMWp/dhddJoceJWDv//Vh+9llDTfiGU2YdQk+Gg5z6E+nu3EQHGTg+kXIqL74q+482tb1LcUkxyYDI3Zt7I3NS5GLSd2PxcUaBuR3vYtA7KNkBNPtD+tQkfBAntgVP8GIgYJKvZCSGEEEL0cBIwCSGEEKeIqqq02N0HVEHtDZ72BlGNVif1ln3HWuzuw14rLtiP6emRzBgczdiUUAy6zptm5lE8LC1dyhtb36CgoYAYcww3ZN7Ab/r/Bl99F62oYm9pr3LasK/Sydbgfc0nwNu/KaF9Wl3cKDAfeTUYIYQQQghx6knAJIQQQvRgLo9Co9VJo2VfpVRNq52Vu+r4ZWcdDrdCgK+eKYMimTE4iimDIgj07ZxqI1VV+aX8F97Y8gabazcT5hvGtRnXcunAS/H36eJGmKoKDUXtvZzWebfVeaC2T0sMTd3XQDxhDERmgE7ftWMSQgghhBBHJAGTEEII0UvZnB5+2VnL4m3VLCuood7iRK/VMC4ljBmDo5g+OIq4YL+Tvo+qqmyo3sCbW99kVcUqAnwCuCr9Kq5Ku4pg3+CT/yDHymmBik3tFU4bvMGTpcb7msEEscNhzK2QccGpG5MQQgghhAAkYBJCCCH6BI+isqm0kcXbqlmcX01RrQWAwTGBzBgcxYzBUWTEBp5036bculze2PIGy/Ysw0/vx6UDL2V09GgCjYEE+QR1bA26TuzZdCSqCk2l+zUPXw+jboLhV3X9vYUQQgghxAEkYBJCCCH6oMLaNpZsq2bxtmo2ljaiqhAb5Mv0wVFMT49iXEoYPvoT79u0q3EXb+a+yfe7v0dRlUNeN+lNBBmDvI+9wZMxiECfwI5je1/vOGYMwlfn222r6QkhhBBCiBMnAZMQQgjRx9W1OVhWUMPibdX8srMWu0shwKjnzEER7X2bIgnyO7GKozpbHVWWKpodzd6Hs7ljv8XZQouj5YBjzc5m3MrhG5cD+Gh9OqqggoxBB+zvH0ztDaX2Blf+Bn+0ms5rdC6EEEIIIY6PBExCCCHEacTm9LByVx2Lt1WztKCaujZv36axKaFMT/dOpYsPMXXZ/VVVxea2dYRNhwugWhwth33d5rYd8bpajZYAnwB8db4YtAZ8dD4YtIYD9vU6PT7a9uM6Q8d+x+ta/WHft/+5Bp2h4/WDj/tofQ543U/vJ9VYQgghhDhtSMAkhBBCnKYURWXTniYWb6tmSX41u2raAEiPCWRGeiQzBkeTGXfyfZs6i9PjpMXZsi+MOkwI5fA4cCkuXB4XTsWJS3Hh9ri9+x4XLsV1wP7ec/ceP9x0vxNl1BkJ9wsn3C+cCL8I79YU0bG/93mIMQSdVtdp9xVCCCGE6A4SMAkhhBACgN11FhZvq2LJtho2lDSgqBAd6Mv0wd6waVxKKEZ93w5CPIrnqCGUS3Hh9DgPu7//OQ6Pg0Z7I7W2WuqsddTZ6qi11dLibDnknjqNjlDf0AMCqDC/MCL82sMo076Aykfn0w1fFSGEEEKIXycBkxBCCCEOUd/et2lJfjXLd9Rhc3nwN+o5c6C3b9NZgyIJMp2CleL6GIfH4Q2brLUdodPe/b2PWlstDfaGw1ZTBfoEHhA67V8ZtX+1lNlg7jGVZ0IIIYQ4PUjAJIQQQoijsrs8rCr09m1avK2GujYHOq2GMcmhzBgcxcyMru3bdDpyK+59FVAHBVIdW6t361Jch7zfT+93wNS8YZHDOCP+DJICk7rh0wghhBDidCABkxBCCCGOmaKo5JQ1tYdN1eysaePeaQO4f8bA7h7aaUlVVVqcLd4Ayn5QENUeQFVaKilvKwcgKTCJyXGTmRw/mVFRo2TKnRBCCCE6jQRMQgghhDhhxXUWTD46IgN9u3so4ij2tO7hl7JfWF6+nPWV63EqTkx6E+NixnFG/BlMjp9MpCmyu4cphBBCiF5MAiYhhBBCiNOI1WVlfdV6lpctZ3n5cqosVQCkh/7/9u49Rq6zvOP475nZmd313te7TnxZx6axiEPiC4rjgp3FYSlKkNpABYWoLSlCoqgghUqtihBSTCskVJVSVWpBvUQlKhAiUSBt6YWuE2wTEnCC7ZA4cSyyWa9tvPf13uf29I85M57dnd21Pbs+M873I43OOe85c+aZ1avj9W/f857t2r9xvzo3derOtjt5sh0AALgqBEwAAABvUu6u10Zf0+G+wzrSd0THB44r4xm1VLdo38Z96tzUqXdueKeaqpvCLhUAAJQ5AiYAAABIksZmx/TM+Wd0uO+wjp47qtHZUUUsol3tu3TPpnvUualT25q38YQ6AACwAAETAAAAFkhn0npx8MXs6KZzR/TK8CuSpPV163XPxmzYdPf6u1VbVRtypVgJlxKX1DPWo3MT59TR0KG3tr5VsUgs7LIAABWEgAkAAADLujh5UUfPHdXhvsP6yYWfaDo1rXgkrj3r96hzY6c6N3VqU8OmsMvEElKZlM5PnFfPpR69Pva6Xh97XT2XetQz1qOhmaE5x1ZHq/W2tW/TjvYd2tm+Uzvbd6p9TXtIlQMAKgEBEwAAAK5KIp3QsYvHsk+m6zus3vFeSdJbmt6izk3ZsGnXul2hjoBJZVKaTE7qUuKSJhITmkhOaDwxrvHEuCaSE5pMTqox3qj22na1r2lXe2272mrbFItW/qidsdmxOeFRLlDqHe9VKpPKH9dS3aItTVu0pXGLtjZt1ZbGLdpQv0GvX3pdJwdO6sTACZ0aOqVkJikpO3otFzbtbN+p21pvuyF+XgCAlUHABAAAgJK8cemN7FPp+g7r2MVjSmVSqo/V6x0b3qHOTZ3av3G/2mrbrvh8Gc9oIjmhicREPhCaSExkw6JcezIIi4L1/LHB9nRq+pq+S0t1Sz5wWmzZVtumeDR+TedfKalMSn3jfQtCpJ5LPRqeGc4fVxWpUkdDh7Y2bl0QJjXXNC/7OYl0QqeGT+lE/wmdGDihk4Mn808ejEfiun3t7drZvjM/0ummuptW6ysDwALurtn0bP4PBxPJCU0mJuduJyfzf2jItaUyKe25eY+6NnfplsZbwv4aNwwCJgAAAKyYyeSknj3/rI6cO6IjfUfUP90vSbpj7R3at3GfaqtqF4wmmh8eTSYn5Vr699B4JK76eL0a442qj9WrPl6vhniDGuINl7djwXawXti2JrZGlxKXNDA1oIHpAQ1MDah/ul+DU4P57YHpAQ1ODyrt6QWf31zdrLbaNq1bs27BsjCMKjWIGp0ZvXxL26XX82HS2fGzc0Yjtda0zgmPtjRl1zfWb1RVpKqkGua7OHkxGzYFo5xeHnpZiUxCknRz3c3a0RbcVrdup7a3bg89jANQftxd06np7HV/iVAot12srTAsWk5VpEoNsQbVxepUH69XKpPSmdEzkqRbm2/Vuze/W12bu7S9dTsPsigBARMAAABWhbvrleFXsqObzh3WiwMvyuWqsqp8IFQfm7vMBUL1sSA8CtYLw6OGeMN1Cy0yntHIzMic0GnBcnpAg1ODSvnC/+Q0VTdlA6clRkS11rTq4tTFore1jc6O5s8Vi8S0uWHz3JFIwXpTddN1+XkUk0gn9OrwqzoxcCIfPJ2fPJ+vefva7XNurbu57ubQagWw8tKZtMYSYxqZGdHwzLCGZ4Y1MjOS3x6ZHcm3FYZDGc8se+6aaE0+FKqL1ak+Nm9ZpD3X1hBrUF0821bs34wLExd06Owhdfd26/mLzyvjGW2o25APm3av261oJLoaP7IbFgETAAAAroup5JTMTDXRmhvuL8QZz2h0dnRB+NQ/1a/B6cE5YdRyf21fW7M2PwJp/vxIKz0aabX0T/Xr5MDJ/Cinl4Ze0mx6VpK0bs26OYHT9rXbVR2tDrliADmpTEqjs6OXQ6LZYQ1PZ4OifGhUsBxLjC0aFjXGG9Va06rWmlY1Vzfn/4iwWCg0J0CK1123ufxGZkb09Nmndaj3kJ45/4wSmYRaa1p1oOOAujZ3ae/6vVynrgABEwAAAHCdZDyjsdmxy8HT9ICGpofUvqZdWxu36pamW9QYbwy7zBWXTCf16sj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QRkgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (20, 10))\n",
    "\n",
    "for transformType in [\"nothing\", \"brightness\", \"flip\", \"rotation\", \"scale\", \"elastic\"]:    \n",
    "    plt.title(\"Training accuracy\", fontdict={'fontsize': 20})\n",
    "    plt.xlabel(\"Epoch\", fontdict={'fontsize': 20})\n",
    "    plt.ylabel(\"Accuracy\", fontdict={'fontsize': 20})\n",
    "    x = np.arange(len(metrics[transformType]['trainingAccuracies']))\n",
    "    plt.plot(x, metrics[transformType]['trainingAccuracies'], label=f\"{transformType}\")\n",
    "    plt.legend(prop={'size': 20})\n",
    "    plt.savefig(\"performance.png\")\n",
    "    \n",
    "fig = plt.figure(figsize = (20, 10))\n",
    "for transformType in [\"nothing\", \"brightness\", \"flip\", \"rotation\", \"scale\", \"elastic\"]:    \n",
    "    plt.title(\"Training loss\", fontdict={'fontsize': 20})\n",
    "    plt.xlabel(\"Epoch\", fontdict={'fontsize': 20})\n",
    "    plt.ylabel(\"Loss\", fontdict={'fontsize': 20})\n",
    "    x = np.arange(len(metrics[transformType]['trainingLosses']))\n",
    "    plt.plot(x, metrics[transformType]['trainingLosses'], label=f\"{transformType}\")\n",
    "    plt.legend(prop={'size': 20})\n",
    "    plt.savefig(\"loss.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "c5a2dfdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plt_bar_chart(X, y, xlabel, ylabel, title):\n",
    "    '''\n",
    "    Plot bar chart for model and metric\n",
    "    '''\n",
    "    plt.bar(X, y)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    \n",
    "testing_acc = []\n",
    "for transformType in [\"nothing\", \"brightness\", \"flip\", \"rotation\", \"scale\", \"elastic\"]:\n",
    "    testing_acc.append(metrics[transformType][\"testingAccuracy\"])\n",
    "    \n",
    "plt_bar_chart([\"nothing\", \"brightness\", \"flip\", \"rotation\", \"scale\", \"elastic\"], testing_acc, \"Model\", \"Accuracy\", \"Testing Accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f366c636",
   "metadata": {},
   "source": [
    "**Note: confusion matrix plotting function has been taken from the following source.**\n",
    "\n",
    "Source of code: https://deeplizard.com/learn/video/0LhiS6yu2qQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "a21a20a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "#     plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "099cc10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (20,10))\n",
    "for index, transformType in enumerate([\"nothing\", \"brightness\", \"flip\", \"rotation\", \"scale\", \"elastic\"]):\n",
    "    plt.subplot(3,3,index+1)\n",
    "    plot_confusion_matrix(confusion_matrix(metrics[transformType]['y_actual'], metrics[transformType]['y_pred']), testing_set.classes, title=f\"Confusion Matrix - {transformType}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "db4db163",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========nothing========\n",
      "Accuracy: 91.484%\n",
      "Precision: 91.932%\n",
      "Recall: 91.484%\n",
      "F1: 91.462%\n",
      "\n",
      "========brightness========\n",
      "Accuracy: 91.719%\n",
      "Precision: 92.173%\n",
      "Recall: 91.719%\n",
      "F1: 91.793%\n",
      "\n",
      "========flip========\n",
      "Accuracy: 92.734%\n",
      "Precision: 92.631%\n",
      "Recall: 92.734%\n",
      "F1: 92.584%\n",
      "\n",
      "========rotation========\n",
      "Accuracy: 89.531%\n",
      "Precision: 89.764%\n",
      "Recall: 89.531%\n",
      "F1: 88.91%\n",
      "\n",
      "========scale========\n",
      "Accuracy: 85.703%\n",
      "Precision: 86.27%\n",
      "Recall: 85.703%\n",
      "F1: 85.09%\n",
      "\n",
      "========elastic========\n",
      "Accuracy: 94.531%\n",
      "Precision: 94.503%\n",
      "Recall: 94.531%\n",
      "F1: 94.503%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score\n",
    "\n",
    "for transformType in [\"nothing\", \"brightness\", \"flip\", \"rotation\", \"scale\",\"elastic\"]:\n",
    "    print(f\"========{transformType}========\")\n",
    "    print(f\"Accuracy: {round(accuracy_score(metrics[transformType]['y_actual'], metrics[transformType]['y_pred'])*100, 3)}%\")\n",
    "    print(f\"Precision: {round(precision_score(metrics[transformType]['y_actual'], metrics[transformType]['y_pred'], average='weighted')*100, 3)}%\")\n",
    "    print(f\"Recall: {round(recall_score(metrics[transformType]['y_actual'], metrics[transformType]['y_pred'], average='weighted')*100, 3)}%\")\n",
    "    print(f\"F1: {round(f1_score(metrics[transformType]['y_actual'], metrics[transformType]['y_pred'], average='weighted')*100, 3)}%\\n\")\n"
   ]
  }
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