CP3_CNNs&Autoencoders-quickdraw_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checkpoint 3: Convolutional neural networks and autoencoders\n",
    "---\n",
    "*Responsible:* Guillermo Hamity (<ghamity@ed.ac.uk>)\n",
    "\n",
    "\n",
    "\n",
    "In this checkpoint exercise we will use the Quick Draw dataset. \n",
    "\n",
    "The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. The bitmap dataset contains these drawings converted from vector format into 28x28 grayscale images.\n",
    "\n",
    "We will focus on a subset of 10 classes of type \"body part\"\n",
    "\n",
    "The class labels are:\n",
    "\n",
    "![listbodypart](./list_body_parts.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| <p align='left'> Label                   | <p align='left'> Description| \n",
    "| ---------------------------------------- | --------------------------- |\n",
    "| <p align='left'> 0                       | <p align='left'> arm        |\n",
    "| <p align='left'> 1                       | <p align='left'> ear        | \n",
    "| <p align='left'> 2                       | <p align='left'> eye        |  \n",
    "| <p align='left'> 3                       | <p align='left'> face      | \n",
    "| <p align='left'> 4                       | <p align='left'> foot       | \n",
    "| <p align='left'> 5                       | <p align='left'> hand       | \n",
    "| <p align='left'> 6                       | <p align='left'> knee       |\n",
    "| <p align='left'> 7                       | <p align='left'> leg        |\n",
    "| <p align='left'> 8                       | <p align='left'> mouth      |\n",
    "| <p align='left'> 9                       | <p align='left'> nose     |\n",
    "\n",
    "\n",
    "Players draw items as quickly as possible, and as you can see, some images are quite bad! We will try and train a CNN classifier to anyway see if we can train a model to distinguish the classes. We will then train an autoencoder to learn a laten space representations of the dataset.\n",
    "\n",
    "    \n",
    "This week, like last, I am not providing example notebooks like `lecture2.ipynb` and `data-science-tools.ipynb` for Unit 2, though these may still be useful to you. Instead, I am **providing the imports for all of the modules and classes that you should need.** \n",
    "\n",
    "### Notes on assessment\n",
    "* Try and calculate the answers to the exercises provided. If you are unable to complete the question, describe which approach you _would_ have taken to solve the problem\n",
    "* Code must be understandable and reproducible. Before grading the notebook kernel **may** be restarted and re-run, so make sure that your code can run from start to finish without any (unintentional) errors\n",
    "* If you are unsure on how to proceed please **ask one of the TAs** during the workshop\n",
    "- Notebooks should be submitted by **10am on Friday 22 October 2021** \n",
    "- This CP exercise sheet is divided into **5 sections** giving a maximum of **10 marks** in total:\n",
    "\n",
    "| <p align='left'> Title          | <p align='left'> Exercise nos. | <p align='center'> Number of marks |\n",
    "| ---------------------------------------- | --------------------- | -------------------- |\n",
    "| <p align='left'> 1. Visualisation and Abstraction   | <p align='left'>   1  | <p align='left'> 1.5 |\n",
    "| <p align='left'> 2. Image classification | <p align='left'> 2–5  | <p align='left'> 3.5 | \n",
    "| <p align='left'> 3. Auto-encoders        | <p align='left'> 6–9  | <p align='left'> 3.0 |\n",
    "| <p align='left'> 4. Anomaly detection    | <p align='left'> 10–11| <p align='left'> 2.0 |\n",
    "| <p align='left'> **Total**               |                       | <p align='left'> **10** |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "You can find the dataset for download at [this link](https://cernbox.cern.ch/index.php/s/obvLeyMR0JEEa5p).\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preamble\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard import(s)\n",
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keras import(s)\n",
    "if float(tf.__version__[:3])>2.8:\n",
    "    from tensorflow.keras.utils import plot_model\n",
    "else:\n",
    "    from tensorflow.python.keras.utils.vis_utils import plot_model\n",
    "from tensorflow.python.keras.models import Model\n",
    "from tensorflow.python.keras.layers import Input, Dense, Flatten, Reshape, Conv2D, MaxPooling2D, AveragePooling2D, UpSampling2D\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from tensorflow.keras import regularizers\n",
    "\n",
    "from tensorflow.python.keras.backend import set_image_data_format\n",
    "set_image_data_format('channels_last')  # Images are formatted as (N, N, 1), with colour-channels last"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing (given, not marked)\n",
    "---\n",
    "\n",
    "First, we'll load in the preprocessed dataset from a numpy data file, which has already been split into training and testing parts for us."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "* Each image has been **Reshaped to** $(N,N,1)$ dimension, i.e. image has one color dimention\n",
    "* Images have been **Scaled** to have pixel intensities in the range [0,1]. It is an important step before feeding them to the neural network model, by dividing the values by 255. It's important that the training set and the testing set be preprocessed in the same way.\n",
    "* Dataset was **Shuffled** for both training and testing datasets separately.\n",
    "* Check the shapes of the resulting arrays; make sure they look as you expect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load quickdraw bodyparts dataset\n",
    "X_train, y_train, X_test, y_test = np.load('quickdraw_bodyparts.npy',allow_pickle=True) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Image labels\n",
    "body_parts = ['arm', 'ear', 'eye', 'face','foot', 'hand', 'knee', 'leg', 'mouth','nose']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 160000\n",
      "Number of testing  examples: 40000\n",
      "Number of target classes:    10.0\n",
      "Image shape:  (28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "# Each label of the dataset correspond to a class name\n",
    "class_names = body_parts\n",
    "\n",
    "nb_train   = X_train.shape[0]\n",
    "nb_test    = X_test.shape[0]\n",
    "nb_classes = y_train.max() + 1\n",
    "print(\"Number of training examples: {}\".format(nb_train))\n",
    "print(\"Number of testing  examples: {}\".format(nb_test))\n",
    "print(\"Number of target classes:    {}\".format(nb_classes))\n",
    "\n",
    "# Get image shape\n",
    "shape = X_train.shape[1:]\n",
    "print(\"Image shape:  {}\".format(shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image visualisation and abstraction (1.5 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Display images of the training dataset, and check that the associated label makes sense (0.5 mark)\n",
    "\n",
    "You will use `matplotlib.pyplot`, the `plt.imshow` method. Use the `cmap=plt.cm.binary` color map for all subsequent img plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. plot 10 training dataset images, one per category. Label the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "category, position = np.unique(y_train,return_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 800x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 4))\n",
    "columns = 5\n",
    "rows = 2\n",
    "for i in range(0, columns*rows):\n",
    "    img = X_train[position[i]]\n",
    "    fig.add_subplot(rows, columns, i+1)\n",
    "    fig.tight_layout()\n",
    "    plt.title(\"Category: {}\".format(i))\n",
    "    plt.imshow(img,cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. plot the fist 25 images in a 5x5 plot. Label the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8))\n",
    "columns = 5\n",
    "rows = 5\n",
    "for i in range(0, columns*rows):\n",
    "    img = X_train[i]\n",
    "    fig.add_subplot(rows, columns, i+1)\n",
    "    fig.tight_layout()\n",
    "    plt.title(\"Category: {}\".format(int(y_train[i])))\n",
    "    plt.imshow(img,cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2 Apply different kernels to an image of type face (1 mark)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. pick an image of the face type and save it to `myface`. Plot it with `imshow` (nothing fancy needed for the plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myface = X_train[position[3]]\n",
    "plt.title(\"Category: 3\")\n",
    "plt.imshow(myface,cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Define 4 kernels: identity, edge_detection, sharpening, and blurring, as `3x3` numpy arrays. We add them to a dictionary of kernels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_identity = np.array([[0,0,0],[0,1,0],[0,0,0]])\n",
    "k_edge= np.array([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]])\n",
    "k_sharp= np.array([[0,-1,0],[-1,5,-1],[0,-1,0]])\n",
    "k_blur = np.ones((3,3))/9\n",
    "kernels = {\"identity\":k_identity,\"edge\":k_edge,\"sharp\":k_sharp,\"blur\":k_blur}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Given is part of the `run_kernel` function which runs over the figure and applies the kernel to every non-edge pixel. Complete the function:\n",
    "    - Define the shape of the output image `np.empty(...)`\n",
    "    - Define the ranges of the row and colum loops , i.e. `range(...)`\n",
    "    - Define the fig index on which the kernel is multiplied in each step, ie. `fig[....]`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_kernel(fig,kernel):\n",
    "    #empty array holding the output image. Define the output dimensions as excercise.\n",
    "    out = np.empty((26,26))\n",
    "    for i in range(0,26):\n",
    "        for j in range(0,26):\n",
    "            out[i][j] = np.sum(np.multiply(kernel,fig[[i,i,i,i+1,i+1,i+1,i+2,i+2,i+2],\n",
    "                                                      [j,j+1,j+2,j,j+1,j+2,j,j+1,j+2]].reshape(3,3)))\n",
    "       \n",
    "    return out\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. run the plotting code block below and check that the kernels are doing what you expect.\n",
    "    \n",
    "Since some kernels multiply by negatives, we use the norm=norm(0,1) to scale the image back to between 0 and 1 before plotting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the normiliser for the bitmap\n",
    "from matplotlib.colors import Normalize as norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "#Loop over kernel\n",
    "for i,kernel in enumerate(kernels):\n",
    "    plt.subplot(1,4,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    #return transformed image and plot it\n",
    "    plt.imshow(run_kernel(myface,kernels[kernel]), cmap=plt.cm.binary,norm=norm(0,1))  \n",
    "    plt.xlabel(kernel)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image classification (3.5 mark)\n",
    "---\n",
    "In this section, we will train a convolutional neural network (CNN) to perform hand-written digit recognition; a standard form of image classification. We will:\n",
    "- create the model\n",
    "- train the model\n",
    "- evaluate its classification performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Define a Convolutional Neural Network suitable for image classification (1 mark).\n",
    "\n",
    "* Use the **`Keras` functional API** to construct the model. \n",
    "* The shape of the input layer should match the shape of the prepared images.\n",
    "* Add some **convolutional** and **pooling** layers in a sensible configuration. The architecture is a bit up to you, but try to reach a good level of accuracy. Around 85% on validation seems easily achievable.\n",
    "    * By default, you can use $3 \\times 3$ kernels, ReLU activation, and $2\\times 2$ pooling; but feel free to play around with different values.\n",
    "    * By default no padding is applied. You could try \"same\" padding in order to use the whole image, though results don't change much for me.\n",
    "    * If sufferening from large overtraining could try regularisation: `kernel_regularizer=regularizers.l1_l2(l1=1e-5, l2=1e-4)`\n",
    "   \n",
    "* **Flatten** the output of the last convolutional layer.\n",
    "* Connect it, to an **output** layer with the appropriate number of nodes and choice of activation function.\n",
    "* Display the model architecture using the `<model>.summary()` method. Optionally display it also with the `plot_model(<model>, show_shapes=True)` method.  \n",
    "* Questions:\n",
    "    * _What are the number of trainable parameters in the model?_\n",
    "    * _How many trainable parameters does your first convolutional layer have? Why?_\n",
    "    * _Which layer has the most trainable parameters?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "visible = Input(shape=(28,28,1))\n",
    "conv1 = Conv2D(32, kernel_size=3, activation='relu')(visible)\n",
    "pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "conv2 = Conv2D(16, kernel_size=3, activation='relu')(pool1)\n",
    "pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "flat = Flatten()(pool2)\n",
    "hidden1 = Dense(60, activation='relu')(flat)\n",
    "hidden2 = Dense(50, activation='relu')(hidden1)\n",
    "hidden3 = Dense(30, activation='relu')(hidden2)\n",
    "outputs = Dense(10, activation='softmax')(hidden3)\n",
    "model = Model(inputs=visible, outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 11, 11, 16)        4624      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 5, 5, 16)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 400)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 60)                24060     \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 50)                3050      \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 30)                1530      \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 10)                310       \n",
      "=================================================================\n",
      "Total params: 33,894\n",
      "Trainable params: 33,894\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total number of trainable parameters in this model are 33894\n",
    "\n",
    "The first convolution layer has 320 trainable paramters. This is because there 32 different kernels that need to be chosen, each containing 9 weights and a bias, totaling 10 parameters each (32*(9+1)=320)\n",
    "\n",
    "The first dense layer has the most trainable parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Compile and train the CNN image classifier (1 mark)\n",
    "\n",
    "* Choose the appropriate loss functiton. \n",
    "* Fit for a reasonable number of epochs, use 20% of data for validation. \n",
    "* Plot the loss curve(s) by using `matplotlib.pyplot` directly with the information in the `<model>.history.history` dict.\n",
    "* If you run on CPUs, stretch your legs while you wait for the 20 epoches to complete. Now is a good time for a coffee/loo break!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "125/125 [==============================] - 13s 102ms/step - loss: 1.2690 - accuracy: 0.5945 - val_loss: 0.8268 - val_accuracy: 0.7463\n",
      "Epoch 2/20\n",
      "125/125 [==============================] - 12s 94ms/step - loss: 0.7220 - accuracy: 0.7761 - val_loss: 0.6693 - val_accuracy: 0.7908\n",
      "Epoch 3/20\n",
      "125/125 [==============================] - 12s 98ms/step - loss: 0.6317 - accuracy: 0.8013 - val_loss: 0.6224 - val_accuracy: 0.8040\n",
      "Epoch 4/20\n",
      "125/125 [==============================] - 14s 112ms/step - loss: 0.5899 - accuracy: 0.8137 - val_loss: 0.5903 - val_accuracy: 0.8138\n",
      "Epoch 5/20\n",
      "125/125 [==============================] - 12s 97ms/step - loss: 0.5539 - accuracy: 0.8234 - val_loss: 0.5739 - val_accuracy: 0.8175\n",
      "Epoch 6/20\n",
      "125/125 [==============================] - 12s 95ms/step - loss: 0.5290 - accuracy: 0.8309 - val_loss: 0.5405 - val_accuracy: 0.8283\n",
      "Epoch 7/20\n",
      "125/125 [==============================] - 12s 95ms/step - loss: 0.5073 - accuracy: 0.8376 - val_loss: 0.5209 - val_accuracy: 0.8353\n",
      "Epoch 8/20\n",
      "125/125 [==============================] - 12s 100ms/step - loss: 0.4910 - accuracy: 0.8424 - val_loss: 0.5087 - val_accuracy: 0.8363\n",
      "Epoch 9/20\n",
      "125/125 [==============================] - 13s 101ms/step - loss: 0.4760 - accuracy: 0.8470 - val_loss: 0.4933 - val_accuracy: 0.8425\n",
      "Epoch 10/20\n",
      "125/125 [==============================] - 13s 102ms/step - loss: 0.4660 - accuracy: 0.8492 - val_loss: 0.4933 - val_accuracy: 0.8408\n",
      "Epoch 11/20\n",
      "125/125 [==============================] - 13s 102ms/step - loss: 0.4554 - accuracy: 0.8525 - val_loss: 0.4790 - val_accuracy: 0.8454\n",
      "Epoch 12/20\n",
      "125/125 [==============================] - 15s 118ms/step - loss: 0.4461 - accuracy: 0.8552 - val_loss: 0.4735 - val_accuracy: 0.8469\n",
      "Epoch 13/20\n",
      "125/125 [==============================] - 14s 109ms/step - loss: 0.4365 - accuracy: 0.8580 - val_loss: 0.4674 - val_accuracy: 0.8490\n",
      "Epoch 14/20\n",
      "125/125 [==============================] - 14s 110ms/step - loss: 0.4306 - accuracy: 0.8596 - val_loss: 0.4724 - val_accuracy: 0.8482\n",
      "Epoch 15/20\n",
      "125/125 [==============================] - 18s 144ms/step - loss: 0.4265 - accuracy: 0.8611 - val_loss: 0.4705 - val_accuracy: 0.8472\n",
      "Epoch 16/20\n",
      "125/125 [==============================] - 16s 127ms/step - loss: 0.4191 - accuracy: 0.8627 - val_loss: 0.4604 - val_accuracy: 0.8515\n",
      "Epoch 17/20\n",
      "125/125 [==============================] - 14s 111ms/step - loss: 0.4130 - accuracy: 0.8647 - val_loss: 0.4514 - val_accuracy: 0.8540\n",
      "Epoch 18/20\n",
      "125/125 [==============================] - 13s 105ms/step - loss: 0.4077 - accuracy: 0.8665 - val_loss: 0.4509 - val_accuracy: 0.8541\n",
      "Epoch 19/20\n",
      "125/125 [==============================] - 15s 123ms/step - loss: 0.4014 - accuracy: 0.8681 - val_loss: 0.4417 - val_accuracy: 0.8556\n",
      "Epoch 20/20\n",
      "125/125 [==============================] - 17s 134ms/step - loss: 0.3965 - accuracy: 0.8694 - val_loss: 0.4417 - val_accuracy: 0.8563\n"
     ]
    }
   ],
   "source": [
    "history_model = model.fit(X_train,y_train, epochs=20, batch_size=1024, shuffle=True, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_model.history[\"loss\"])\n",
    "plt.plot(history_model.history[\"val_loss\"])\n",
    "plt.title(\"Model Loss\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.legend([\"training loss\",\"validation loss\"], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Evaluate the classification performance on the testing dataset (1 Mark)\n",
    "\n",
    "* Compute the testing accuracy, _e.g._ using the `<model>.evaluate()` method. Compare to the training accuracy; can be read off from fit printout, if you added the `'accuracy'` metric, or can be calculated here.\n",
    "* Display a few mis-classified images from the test dataset.\n",
    "    * For this you will need to use the model to predict from the testing features.\n",
    "    * This yields arrays of 10 probabilities, 1 per class. Get the index from maximum probability to find the class prediction. Maybe consider using `argmax`?\n",
    "* Compute and display the confusion matrix (normalise by truth label). Discuss the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1250/1250 [==============================] - 4s 4ms/step - loss: 0.4484 - accuracy: 0.8543\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The testing accuracy is:  0.8542500138282776\n"
     ]
    }
   ],
   "source": [
    "print(\"The testing accuracy is: \",test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/5000 [==============================] - 19s 4ms/step - loss: 0.3981 - accuracy: 0.8687\n"
     ]
    }
   ],
   "source": [
    "train_loss, train_acc = model.evaluate(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training accuracy is:  0.8686749935150146\n"
     ]
    }
   ],
   "source": [
    "print(\"The training accuracy is: \",train_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy on the testing set is slightly lower than the training set but both are very similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_category = np.argmax(y_pred,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_misclass = np.where(y_test!=y_pred_category)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jI82xY8es61944QUjyfTp0ydF3qVLFyPJvPTSS879/vzzz5774WwXWlAXLlzYHDp0yLsbmAZJf+w69zFfs2ZNI8m8/PLLXu0nMxfUF+KqMW+O59KhQwcjyQwePDhNc0hNavfvhW7rv//+a8LCws77HHT33XcbSeaNN97wZE8//bSRZBo2bOgc8+CDDzoX1B988IGRZEqVKuX8o82MGTOMJJM/f35z4sQJ63YEBQWZzZs3O495/PhxU6RIEZMrV64Uf6g0xphly5YZSSYqKirFH9kAXFx85BvIhvr06aOpU6fq/fff17XXXuv5+KOvH/GtXbu2AgMD9f777+vyyy/3fLfUV/Pnz5ck3XjjjSpdurTP+znbb7/9pgULFmjLli06duyYEhISJCV+rC8hIUGbNm1SrVq1JEl169aVJI0aNUpFihRRmzZtzvtx9Xr16mn9+vXq1q2bHn30UTVo0MB5Du/M8sUXX0iSunTp4rw+X758uuqqq/Tll19q5cqVatGihXbt2qVNmzZJkrp3726NCQ0N1S233KLXX389w+d7ww03WFlSE7bcuXM7v8OfdH1q39dMy8/3p59+kjFGderUcTZQq1atmqpXr67ffvstRb569Wrt3r1bFStWVJ06dZzzSPoO5tKlSz1ZvXr19OWXX2rAgAF66qmndO211yo0NNQ53lubNm3S/PnztWnTJh05csTz/cq9e/dKkjZu3Oj8+GabNm2c399M+oj72b0VsvIxkuS6665zdrV3zTchIUFfffWVpNRr4aqrrlK+fPm0evVqnTx50uufQ/PmzVNtJOiN3bt364svvtCGDRsUGxurM2fOSJLWrVsnKfHnlVQXMTExWrNmjXLlyqW+ffv6fMyMlpYa80V8fLy+/fZbSVL//v3TNDYt9683fvjhB504cULXXXddqs9BTZo00bhx47R06VINGjRIkrRo0SJJUrdu3ZxjunXrppdeesnKk3qSdO3aVSEhIdb1nTp1UqFChXTw4EH98ssvatSoUYrra9WqpQoVKjiPGRYWpv79+2vkyJEaP368XnjhBc91Y8eOlZT4sfnAwEDneACZjwU1kA01bdpU5cuX14wZMzRmzBhNmTJFBQoU8Pm7dxUrVtSrr76qhx56SIMGDdKgQYNUtmxZNWzYUG3atNHNN9+c4ntfDz74oA4cOGDtJ6lZTlKznYzoFn3s2DH16NHD+m7ouQ4fPuz5/yZNmuiRRx7R6NGj1atXLwUEBOiyyy5To0aN1L59e7Vt2zbF98xHjhyp33//XV999ZW++uorhYWFqXbt2mrSpIm6det23u/jZoR//vlHktSjRw/16NHjvNvu379fUmLzG0kqWrSo57va50pqdJTRXJ3ik+ZQsmRJ5x8j8ufPLymxgc7ZfPn5Jt32852zt1y5ctaCOul+3rx5c6pNhZIk3c+S9NBDD2nx4sX67rvv1KpVKwUFBalGjRpq3Lixunbt6vkDjjfi4+M1aNAgTZgwIdUmQ1LK23u21Lr0FyhQQFLK+zcrHyNJ0jLff//913O7o6KiLrjvf//91+s/2KXn/M5PPfWUnnvuOauR4dnO/nklNecrWbJkuhbxGcWXGvPFv//+q2PHjkmSKleu7PW4tN6/3kiq9QULFqSp1i/0uyW1POkPQ6nVU0BAgMqXL6+DBw9aDUXPt98kd999t0aPHq333ntPI0aMUGhoqPbv36/p06crJCRE/fr1O+94AJmLBTWQDSV1BR0+fLh69eqlmJgY9e/fX2FhYT7vc/Dgwbrllls0d+5cLV68WIsXL9a0adM0bdo0DR8+XD/99JPnXesZM2Z4Fs1nS637bHoMGzZMs2fPVnR0tF544QXVrVtXRYsW9Szw//e//2nZsmXW4uSFF17QXXfdpc8//1yLFy/WkiVLNHHiRE2cOFF169bVDz/8oLx580qSSpQooVWrVmnRokX67rvvtGTJEk/DpOeff14jR47UI488kuG3LUnSO0WtWrVSRETEebctW7Zsps3DW+drepfWhni+/nwlnfeFsuu6pPu5RIkSatmy5XnndXbDqjx58ujbb7/VypUrNX/+fC1dulRLly7VqlWr9Morr+juu+/2vFN0Ia+99preeustlShRQq+88or+97//KSIiwvNO62233aaPP/441cV2Wu/frJaW+Sb9fCSpV69eF9ze9U5ganz93Thr1iyNGDFC+fLl05tvvqlmzZqpVKlSCgsLU0BAgB599FGNHDnyvH8cuVjOvv/Olp4ay2yZdf8m3ReVKlWy3g0+l+sPv6n9brnQ4txXF3p8RkZGqlOnTvr000/1ySefqFevXnr33XcVFxenHj16qFixYpkyLwDeYUENZFO9e/fWU089pc8//1yS7x/3PltERIT69evn+Wv3hg0b1KdPHy1btkxDhw7V5MmTJSWeWul8kt6V2rBhQ7rn9Omnn0qSPvnkE1WvXt26/u+//051bLly5TR48GBPN/GVK1eqe/fuWrlypUaNGqWnnnrKs23SKVeSPvJ78uRJTZo0SQMHDtSjjz6qm266SRUrVkz37XGJiorShg0b1LdvX68/ZZD0ztyBAwd09OhR5zuQF/o5+QNffr5Jt/18t891XdK7nkWKFPHpjz9169b1vBt95swZffbZZ+rZs6fGjRunm266SU2bNr3gPpJu74QJE9SuXTvr+vM9ntMquz1GihYtqrCwMJ04cUIvvfSS1124M1PSz+u5555zfozZ9fNK+v23Z88excbGZti71EkL4CNHjjivd/2RU0rf79C0KFKkiPLkyaPjx49r48aNKTp5p8aX+9cbSbVeuXLlNNV66dKltXHjxlTrIrU8qdaS3hl3STqloq9fg7rnnnv06aefauzYserevbveeustSfJ8XB1A1slef+oG4FGmTBm1b99eRYoUUYMGDVS/fv0MP0Z0dLTnndk1a9Z4PS7pNCRffvllus9xmnT+a9c7s19//bXzo+epqVu3ru6++25JF749oaGhuuuuu1S9enUlJCTo999/937SadS6dWtJyS8uvREZGen5zt1HH31kXR8XF6fp06dnzAQzkS8/32uuuUYBAQH65Zdf9Ndff1nXr1+/3vq4tyTPO3Pr16/3fDfTV7lz59ZNN93keafb2/o43+1dt25dmursQjLrMZK0sEv6nmtGCQwM9JwWKC21kJnO9/Pat2+f5zvDZytRooRq1KihhIQEr0/v5c19mrQQ+/PPP63rjDGe75+fKyN/h57P2T+/d955x6sxvty/0oXvr+uuu07BwcFauHCh9u3b59VcJKlx48aSEk9F5eKqIym598Inn3xifa1FkmbPnq2DBw8qf/78qfZvuJBGjRqpTp06WrlypR5//HFt375ddevWVb169XzaH4CMw4IayMZmzZqlAwcOaNmyZenaz/fff68vv/zS+g6bMUbz5s2TlLaPGtesWdNzztX27dt7vlOY5MyZM5o7d65X+0r6/vIbb7yRIt+4caPuuusu55jZs2frxx9/tD4Cefr0aU/DtLNvz0svvWTNUUp8hz3pHZLM/Kh1//79VbZsWU2fPl2PPPKI8x2omJgY60XqkCFDJEkjRoxI8WmA+Ph4Pfjgg+n+Y8bF4MvPt1y5cmrbtq0SEhI0YMCAFPdXbGysBgwY4PyIaFBQkIYPHy5jjDp27KjFixdb28THx+v777/X8uXLPdm4ceO0ceNGa9uYmBitWrVKkvePj6TbO3bs2BSPzz179qhnz54ZvkjNjMdIsWLFFBwcrJiYGM+CKKMMHz5cwcHBeuihhzR58mTnx5jXrl2rWbNmZehxU5P083r77bd16tQpTx4bG6tevXopNjbWOW748OGSpMcee0wzZ860rl+/fn2KhbE392nz5s0lSR988EGK8yGfPn1ajzzyiFauXHne25CWGvPVY489pty5c+vNN9/UuHHjrDrctm2bfvnlF2tuab1/IyMjJSnV80JHRERo8ODBOnbsmNq2bas//vjD2iYuLk5z585NURd9+/ZVnjx5tHjxYutrHEuWLNG4ceOcx7v55ptVpkwZ7d69W/fff3+KOt6yZYseeOABSYlfrUpPQ8N7771XkjyNyXh3GvATWdJbHECanHvarAtJ62mzXn31VSPJFChQwDRp0sTcdtttpmPHjp7jhoeHm9WrV6dpzv/9959p0KCBkWSCg4M9+23WrJkpVqyYddqR1E6bNXPmTM8pkK688krTtWtX06xZMxMUFGSaNWtm/ve//1m359577zWSTNGiRc31119vunXrZtq1a2eKFy9uJJnSpUubHTt2eLYPDw83kkx0dLTp2LGjue2220yTJk1M7ty5jSTTs2fPFHPK6NNmGWPM2rVrPecqLViwoGncuLG57bbbTIcOHUyVKlVMQECAiYiISDEmPj7etG3b1nMft2zZ0nTt2tWUL1/ehIaGes4znNGnzUrruPPddl9+vsYkni826f4qUqSI6dSpk+nYsaMpXLiwueyyy0y7du2MJDN16lRrLg899JDntlStWtW0b9/edO3a1TRp0sQULFjQOs1UjRo1jCRTvnx507ZtW9OtWzfTokULExYWZiSZZs2amdOnT5/3fk2yfPlyExwcbPT/58K+5ZZbTKtWrUxYWJipWrWq5/za556qyNdTJmXGY8QYY2666SbP6XpuvfVW07dvX9O3b1/P9Rc6Dd75auHTTz81efLk8Zx2q0WLFqZbt26mdevWJjIy0kgyXbp0STHmQqfNSuvp+JL8888/nsdE6dKlTefOnU27du1MeHi4KVmypOnTp0+q+3/uuec8j+3o6GjTpUsX065dO89p+s6d64XuU2OMad++vZFkwsLCzPXXX2/atWtnIiMjTYECBTy/9879WfpaY76cNssYYyZPnmyCgoI8vw9uuukm06lTJ1OzZk0TEBCQ4r7y9f6Ni4vznAKvVq1apmfPnqZv375m1KhRnm1Onz5tbrvtNiPJ5MqVy9SqVct07tzZdOnSxTRq1MhzTumvvvoqxb4/+OADkytXLiPJVK9e3dx6663m2muvNbly5fKcNisoKMi63T///LMpXLiw53Z36dLF3HDDDSY0NNRIMi1btrTOBZ7WU53FxcWZiIgII8kUK1bMp/OqA8h4vEMNQG3bttWIESNUt25d/fPPP5o1a5YWLlyo8PBwDR06VGvXrlXNmjXTtM9ChQpp0aJFGj9+vOrXr681a9ZoxowZ+uuvv1SzZk2vmzh16tRJixYt0nXXXac9e/Zo7ty52rdvn0aMGKGvvvpKQUFB1pjevXtr6NChio6O1vr16zV9+nQtW7ZMUVFRev755/Xbb7953uGQEt8tvP3225U7d24tWrRIM2fO1JYtW3T99ddr9uzZmdJs7VxVq1bV77//rlGjRumKK67Q77//runTp2vFihXKmzevHnzwQatLb65cuTRr1iy9/PLLqlSpkhYuXKhvv/1W1atX1/Lly7PFRwF9+flKUqlSpfTzzz9r4MCBCgsL07x587Rq1SrdeuutWr58uY4ePSpJzu/hjho1SkuWLFG3bt109OhRzZ8/X1988YV2796tJk2a6N13301x2qbnnntOAwYMUMGCBbV8+XJNnz5d69evV/369TV58mTNnz/f69Os1a9fX6tWrVK7du107NgxzZ07V5s3b9bgwYO1bNkyT/frjJJZj5EJEybozjvvVEBAgGbMmKH33ntP7733XobM+eabb9a6det03333qWDBglqyZIlmzpyp9evXq1KlSnrhhRf03HPPZcixLqR8+fJavXq1unXrpsDAQM2bN0+//fabbr31Vq1evfq83cgfffRRLV26VLfeequOHDmiWbNmafHixQoKCtLDDz+sZs2apdjem/v0k08+0eOPP66SJUtq4cKFWr58ua655hr9+uuvqf6O9rXGfNWzZ0+tWbNGffv2Va5cufT5559rwYIFOnXqlAYOHKhbbrnFs62v929wcLC+/vprtWvXTjt37tSHH36o9957z3MKQinxaxlTp07Vl19+qQ4dOmjfvn2aO3euvv76a/33339q27atPvroI8/HvJN0795d33//va6//npt3bpVc+bM0ZEjR/TOO+/onnvukeT+vVK3bl2tWbNGAwcOVGBgoGbPnq2ffvpJtWrV0vjx4zVv3rwUZ8vwRXBwsOfj5XfccUeaGvMByDwBxvhBa0oAADLIoUOHVKFCBcXGxmrv3r1+0dwKQPY3ZcoU9erVS23btvX6a0sZ6dChQ4qMjNTJkye1ZcsWr04vByDz8Q41ACBb+vnnn61s//796tWrlw4ePKg2bdqwmAaQJtu3b1dMTIyVL1myRA8++KAk6fbbb7/Y05IkjRw5UseOHdMtt9zCYhrwI7xDDQDIlgICAhQZGakrrrhCRYoU0a5du7R69WodPXpUZcqU0eLFi3nRCSBNJk2apL59+6pGjRoqU6aMAgMDtXnzZs+ZA26//Xavu7dnhKVLl+r999/Xli1b9P333ytPnjz6448/PB38AWQ9zkMNAMiWHn/8cS1YsEC//fabDh48qODgYFWsWFFt2rTR/fffryJFimT1FAFkMw0aNNDtt9+un376SQsXLtSxY8dUsGBBNW/eXH369NGtt956Uefz119/6b333lNYWJgaNGigF198kcU04Gd4hxoAAAAAAB/wHWoAAAAAAHzAghoAAAAAAB+woAYAAAAAwAcsqAEAAAAA8AELagAAAAAAfMCCGgAAAAAAH7CgBgAAAADAByyoAQAAAADwAQtqAAAAAAB8wIIaAAAAAAAfsKAGAAAAAMAHLKgBAAAAAPABC2oAAAAAAHzAghoAAAAAAB+woAYAAAAAwAcsqAEAAAAA8AELagAAAAAAfMCCGgAAAAAAH7CgBgAAAADAByyoAQAAAADwAQtqAAAAAAB8wIIaAAAAAAAfsKAGAAAAAMAHLKgBAAAAAPABC2oAAAAAAHzAghoAAAAAAB+woAYAAAAAwAcsqDNBQECARowYkdXTAPwC9QAkox6ARNQCkIx6yN78fkE9btw4BQQEqH79+j7vY/fu3RoxYoTWrFmTcRPzM88//7w+++yzrJ5Glli4cKECAgJSvTz33HNZPcUMQz1451KuhyRz585V7dq1FRoaqjJlymj48OE6c+ZMVk8rQ1EP3qEecn49UAveoRaSbd68WaGhoQoICNCqVauyejoZinrwzqVeD0ePHtWQIUMUGRmpkJAQXXHFFRo/frxP+/L7BfXUqVNVrlw5/fzzz9q0aZNP+9i9e7eeeuopiiKHuuKKK/TBBx9YlxYtWkiS5785AfXgnUu5HiTpq6++UocOHVSwYEG98cYb6tChg5599lkNHjw4q6eWoagH71APOb8eqAXvXOq1cLb77rtPuXPnzuppZArqwTuXcj3Ex8erZcuWGj9+vG655RaNGTNGlStX1t13363nn38+zfvz6wX1li1btHTpUr3yyisqVqyYpk6dmtVTQjodO3Ysw/cZERGh7t27W5d//vlHl112merWrZvhx8wK1EPOkxn1IEkPPvigqlevrm+++Ub9+vXT66+/rmHDhmnChAnasGFDphzzYqMech7qwTfUQs6TWbWQ5Ouvv9bXX3+t++67L1OPkxWoh5wnM+ph1qxZWrp0qcaPH69XXnlFAwYM0GeffabOnTvrmWee0b59+9K2Q+PHnnnmGVOoUCETFxdnBgwYYC677DLndgcPHjRDhgwxZcuWNcHBwaZ06dKmR48eZv/+/eaHH34wkqzLxIkTjTHGlC1b1vTq1cva57XXXmuuvfZaz7/j4uLME088YWrXrm0KFChg8uTJY66++mrz/fffW2MlmeHDh1/w9p04ccIMHz7cXHbZZSYkJMSUKFHCdOzY0WzatMmzzejRo03Dhg1N4cKFTWhoqKldu7aZPn26dbxzL2ffpp07d5rbb7/dFC9e3AQHB5sqVaqY9957z5rP1q1bTdu2bU2ePHlMsWLFzJAhQ8z8+fONJPPDDz+k2PbTTz81tWvXNqGhoaZIkSKmW7duZufOnSm26dWrl8mbN6/ZtGmTad26tcmXL59p3769efLJJ03u3LnNvn37rDn069fPhIeHmxMnTpjdu3ebP//805w6deqC9+W5VqxYYSSZESNGpHmsv6IeqAdv6mHdunVGkhk7dmyKfNeuXUaSeeaZZ847PrugHqgH6iERtUAtpOW10qlTp0zlypXNQw89ZCZOnGgkmZUrV3o1NjugHqgHb+ph8ODBRpI5duxYinz69OlGknn77bfPO/5cfr2gjo6ONn379jXGGPPjjz8aSebnn39Osc2RI0dMtWrVTGBgoOnXr58ZP368eeaZZ0zdunXN6tWrTUxMjHn66aeNJNO/f3/zwQcfmA8++MBs3rzZGON9Uezfv9+ULFnS3H///Wb8+PFm1KhRpnLlyiYoKMisXr06xVhviuLMmTPmuuuuM5JM165dzZtvvmlGjhxpmjVrZj777DPPdpGRkebuu+82b775pnnllVdMvXr1jCQzb948zzYffPCBCQkJMddcc43n9i1dutQYY0xMTIyJjIw0UVFR5umnnzbjx4837dq1M5LMq6++6tnH0aNHTYUKFUxYWJgZOnSoGTNmjKlXr56pUaOGVRRJv4Dr1q1rXn31VTN06FATFhZmypUrZw4ePOjZrlevXiYkJMRUrFjR9OrVy7z11ltmypQp5u+//zaSzBtvvJHiPomLizOFChUyffr08YyXZLZs2XLe+9LlnnvuMZLM33//neax/op6oB68qYcPP/zQSDIrVqywrouMjDSdOnU67/jsgnqgHqiHRNQCtZCW10qjRo0yxYsXN7GxsTlyQU09UA/e1EP//v1NYGCgOX36dIr8iy++MJLMnXfeed7x5/LbBfWqVauMJPPtt98aY4xJSEgwkZGR5t57702x3ZNPPmkkmVmzZln7SEhIMMYYs3LlyhR/WTqbt0Vx5swZExcXl2KbgwcPmoiICM8PMYk3RfH+++8bSeaVV15Jdd7GGHP8+PEU1506dcpUq1bNNGvWLEWeN29e5+3o27evKVmypDlw4ECKvGvXriY8PNyz/5dfftlISlGQJ06cMNHR0SmK4tSpU6Z48eKmWrVq5sSJE55t582bZySZJ5980pMlPaiHDh1qzathw4amfv36KbJZs2alOJavC+ozZ86YiIgIU69evTSN82fUQyLq4cL1MHr0aCPJbN++3bqubt26pkGDBucdnx1QD4moB+qBWkhELXj3WmnPnj0mf/78ZsKECcYYk+MW1NRDIurhwvWQNPeffvopRT506FAjybRp0+a848/lt9+hnjp1qiIiItS0aVNJie3ku3TpomnTpik+Pt6z3cyZM1WjRg117NjR2kdAQECGzScwMFDBwcGSpISEBP333386c+aMrrrqKv36669p3t/MmTNVtGhRZ1OUs+cdFhbm+f+DBw8qNjZW11xzjVfHNMZo5syZatu2rYwxOnDggOfSsmVLxcbGevYzf/58lS5dWu3atfOMDw0NVb9+/VLsc9WqVdq3b5/uvvtuhYaGevIbb7xR0dHR+uKLL6x5DBgwwMp69uypFStWaPPmzZ5s6tSpioqK0rXXXitJmjRpkowxKleu3AVv69kWLFigvXv3qlu3bmka58+oh0TUw4Xr4cSJE5KkkJAQ67rQ0FDP9dkZ9ZCIeqAeqIVE1IJ3r5UeeeQRVahQQXfccccFt82OqIdE1MOF6+G2225TeHi4+vTpo2+//VZbt27V22+/rXHjxklSmp8b/HJBHR8fr2nTpqlp06basmWLNm3apE2bNql+/frau3evFixY4Nl28+bNqlat2kWZ1+TJk1W9enWFhoaqSJEiKlasmL744gvFxsameV+bN29W5cqVL9hhcd68eWrQoIFCQ0NVuHBhFStWTOPHj/fqmPv379ehQ4f09ttvq1ixYikut99+uyR5vnS/bds2VaxY0fpFUqlSpRT/3rZtmySpcuXK1vGio6M91yfJnTu3IiMjrW27dOmikJAQT7OI2NhYzZs3T926dUv3L7OpU6cqMDBQXbp0Sdd+/AX1kIx6uLCkJ9K4uDjrupMnT6Z4os2OqIdk1MOF5eR6oBaSUQsXtnz5cn3wwQd69dVXlSuXX778TxfqIRn1cGElSpTQ3LlzFRcXpxYtWqh8+fJ66KGH9MYbb0iS8uXLl6b9+WW//O+//1579uzRtGnTNG3aNOv6qVOnZtipkFL7AcTHxyswMNDz7w8//FC9e/dWhw4d9NBDD6l48eIKDAzUyJEjU/ylJCP99NNPateunRo3bqxx48apZMmSCgoK0sSJE/XRRx9dcHxCQoIkqXv37urVq5dzm+rVq2fonM8VEhLi/MVdqFAhtWnTRlOnTtWTTz6pGTNmKC4uTt27d0/X8U6cOKHZs2erefPmioiISNe+/AX1kIh68E7JkiUlSXv27FFUVFSK6/bs2aN69er5Nnk/QT0koh68k5PrgVpIRC145+GHH9Y111yj8uXLa+vWrZKkAwcOSEqshe3bt6tMmTLpuh1ZiXpIRD14r3Hjxvrnn3/0xx9/6NixY6pRo4Z2794tSbr88svTtC+/XFBPnTpVxYsX19ixY63rZs2apdmzZ+utt95SWFiYKlasqLVr1553f+f7q0WhQoV06NAhK9+2bZsqVKjg+feMGTNUoUIFzZo1K8X+hg8f7sUtslWsWFErVqzQ6dOnFRQU5Nxm5syZCg0N1ddff53i42oTJ060tnXdxmLFiil//vyKj49X8+bNzzufsmXLav369TLGpNjXuefvK1u2rCRp48aNatasWYrrNm7c6LneGz179lT79u21cuVKTZ06VbVq1VLVqlW9Hu8yd+5cHTlyJMd93Jt6oB68VbNmTUmJH7E6e7Gwe/du7dy5U/3790/zPv0J9ZCIevBOTq4HaiERteCd7du3a9u2bSpfvrx1Xbt27RQeHu78GWcX1EMi6iFtAgMDPc8TkvTdd99J0gVvuyVN37i+CI4fP27y589vfVk/yZIlS4wkM23aNGOMd40F/vzzT6szXZKbbrrJREREpGga8PnnnxtJKRoLdOrUyVSoUMHEx8d7suXLl5uAgABTtmzZFPtUBjUWuP/++02ePHlStHTfsmWLyZMnjzn3RxcREWHat29v7at3794mODjY/PHHH9Z1Z7eef+mll9LUWKB69erm5MmTnm2//PJLZ2OBvHnzpnofnDp1yhQtWtR07tzZ5MqVy7z88ssprvfltFnt2rUzefLkMUeOHPF6jD+jHqiHJGmph+joaFOjRg1z5swZT/b444+bgIAAs379+guO91fUA/WQ5FKvB2qBWkjibS18/fXXZvbs2SkuSacOeumll1J0gM5uqAfqIUl6Trm7b98+U6ZMGVO9evUUPzNv+N2Cetq0adYP52zx8fGmWLFipm3btsaYxNb3VapU8bS+f+utt8zzzz9vGjRoYNasWWOMSbzzCxYsaCpXrmzeffdd8/HHH5t//vnHGGM850pr2rSpGT9+vHnwwQdNiRIlTMWKFVMURdKDuF27dmbChAlm6NChpmDBgqZq1ao+FcWZM2dMkyZNjJTY+n7s2LFm1KhRpkWLFp7bvmDBAiPJXHPNNWb8+PHmqaee8jwgzy2KG264weTNm9e8/PLL5uOPPzbLly83xiS2vi9btqzJkyePuffee82ECRPMyJEjzc0332wKFSrkGX/kyBFTrlw5T+v71157zdSrV8/UrFnTSDILFy70bJvUFbJ+/fpmzJgxZtiwYSZPnjzO1vfnKwpjjBk0aJCRZAIDA83u3btTXJfWLt///vuvCQoKMl27dvVq++yAeqAezh7vbT18/vnnJiAgwDRr1sy8/fbb5p577jG5cuUy/fr1u+BYf0Y9UA9nj7+U64FaoBbOHp+W10pnyyldvqkH6uHs8d7WQ+PGjc0jjzxi3nnnHfPMM8+YqKgoU6hQIfP7779fcOy5/G5B3bZtWxMaGmqdaPtsvXv3NkFBQZ527v/++68ZNGiQKV26tAkODjaRkZGmV69eKdq9z5kzx1SpUsXkzp3bSCnb4L/88sumdOnSJiQkxDRq1MisWrXKan2fkJBgnn/+eVO2bFkTEhJiatWqZebNm2d69erlU1EYk/gXtccee8yUL1/eBAUFmRIlSpibbrrJc547Y4x57733PCdvj46ONhMnTjTDhw+3imLDhg2mcePGJiwszEgpT86+d+9eM3DgQBMVFeU5znXXXWedtPyff/4xN954owkLCzPFihUzDzzwgJk5c6aR5CmyJJ988ompVauWCQkJMYULFz7vydnP5+effzaSTIsWLazr0vok8dZbbxlJZu7cuV5tnx1QD9TD2ePTUg+zZ882NWvWNCEhISYyMtI8/vjjPv3F1p9QD9TD2eMv5XqgFqiFs8df6gtq6oF6OHu8t/Vw3333mQoVKpiQkBBTrFgxc9ttt6W4H9PC7xbU8C+vvvqqkWQ94DPKmjVrjCQzZcqUTNk/kJGoByAZ9QAkohaAZJdiPQQYY4wAJXbIPvsUIidPnlStWrUUHx+vv/76K1OOOWjQIE2ePFkxMTHKmzdvphwD8AX1ACSjHoBE1AKQjHpI5JddvpE1OnXqpDJlyqhmzZqKjY3Vhx9+qA0bNnjO95aRPv/8c61fv15vv/22Bg0a5DcFASShHoBk1AOQiFoAklEP/y+r3yKH/3j11VdN1apVTd68eU1oaKipXbu2pyNiRitbtqwJDQ017du3N4cPH86UYwDpQT0AyagHIBG1ACSjHhLxkW8AAAAAAHyQK6snAAAAAABAdsSCGgAAAAAAH2RaU7KxY8dq9OjRiomJUY0aNfTGG2+oXr16FxyXkJCg3bt3K3/+/AoICMis6eESYozRkSNHVKpUKeXKdfH/huRrLUjUAzIe9QAky671QC0go2XXWpCoB2S8NNdDZnwxe9q0aSY4ONi8//77Zt26daZfv36mYMGCZu/evRccu2PHDiOJC5cMv+zYsSMzHu7nlZ5aMIZ64JJ5F+qBC5fkS3arB2qBS2ZdslstGEM9cMm8i7f1kClNyerXr6+6devqzTfflJT4l6OoqCgNHjxYQ4cOPe/Y2NhYFSxYUDt27FCBAgUyemq4BB0+fFhRUVE6dOiQwsPDL+qx01MLEvWAjEc9AMmyaz1QC8ho2bUWJOoBGS+t9ZDhH/k+deqUfvnlFw0bNsyT5cqVS82bN9eyZcus7ePi4hQXF+f595EjRyRJBQoUoCiQoS72x4DSWgsS9YCLh3oAkvl7PVALuFj8vRYk6gEXj7f1kOFfkjhw4IDi4+MVERGRIo+IiFBMTIy1/ciRIxUeHu65REVFZfSUgCyR1lqQqAfkXNQDkIzXSkAinhuQE2R5l+9hw4YpNjbWc9mxY0dWTwnIMtQDkIx6ABJRC0Ay6gH+JsM/8l20aFEFBgZq7969KfK9e/eqRIkS1vYhISEKCQnJ6GngIli8eLEzX7NmjZXdeuutVlakSJGMnpJfSWstSNQDci7q4eL68MMPnfn1119vZee+M4TMx2slIBHPDcgJMvwd6uDgYNWpU0cLFizwZAkJCVqwYIEaNmyY0YcD/Ba1ACSjHoBk1AOQiFpATpAp56G+//771atXL1111VWqV6+exowZo2PHjun222/PjMMBfotaAJJRD0Ay6gFIRC0gu8uUBXWXLl20f/9+Pfnkk4qJiVHNmjU1f/58PlaGSw61ACSjHoBk1AOQiFpAdpcp56FOj8OHDys8PFyxsbG0vvdz2eU71Nn5MZWd5w7/lJ0fU9l57hcb36H2TnZ9TGXXecN/ZefHVHaeO/xTWh9TWd7lGwAAAACA7ChTPvKN7GHPnj3OfMqUKVY2ceJEK9u4caPXxzp9+rSVtW/f3rlthQoVvN4vAFzqDh8+bGU9evRwbvvWW29Z2Z133un1se666y4r6969u5VdffXVXu8TAIDsjHeoAQAAAADwAQtqAAAAAAB8wIIaAAAAAAAfsKAGAAAAAMAHNCXLgebPn29lY8eO9Wo7SYqPj7cy16lWRowY4RzfqlUrK3v66aetLLWmNTt27LCywMBA57ZIKS4uzspCQkKyYCYALpZDhw55vW3evHm92m7btm3OfMKECVZWr149K6MpGQB4b9++fVbWrFkz57YzZsywsujo6AyfE7zHO9QAAAAAAPiABTUAAAAAAD5gQQ0AAAAAgA9YUAMAAAAA4AOakmUTCQkJVvboo486t33xxRetrFy5clb2xBNPOMf37t3bysqUKXP+CV5AkyZNrOzVV191bvvjjz9aWdOmTdN1/Jxm0qRJzvzhhx+2sq1bt1pZnjx5nONdj7P169dbWbVq1c4/QQAXTVqakhUuXNir7ZYvX+71Phs0aOD1tgAAm6v57rp165zbfvfdd1ZGU7KsxTvUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAPWFADAAAAAOADFtQAAAAAAPiALt9+6Pjx41bWvXt3K5s9e7Zz/H333Wdlo0ePtjJXR8HM0rJlSysrWLCgc9tPPvnEyujynVJq3Rz3799vZXPnzrWyrl27OsfPnz/fym688UYr2759u3N8VFSUMweQeWJjY73etkCBAl5tt2LFCq/H010WANKnSJEiVhYZGenc9vfff8/s6SCNeIcaAAAAAAAfsKAGAAAAAMAHLKgBAAAAAPABC2oAAAAAAHxAU7Istnv3bitr3769lf32229WNmHCBOc++/fvn/6JZbCQkBAr69Chg3PbmTNnWtmbb75pZblzX7oP3/r16zvzyy67zMo+/vhjK0utKZm3jeoOHDjgzGlKBlx8aWlKllozyHMtX77cmbt+9+TKxd/mkf24mmv++eefVrZ27Vrn+M2bN1tZWFiYV5kk5c+f38ry5s1rZVWqVHGOb9iwodfHQkoJCQlWtmHDBiurXLmyc/zFaupbo0YNZ75mzZqLcnx4j2dBAAAAAAB8wIIaAAAAAAAfsKAGAAAAAMAHLKgBAAAAAPDBpdvV6SJLrYFA27Ztrezo0aNW9uWXX1pZ8+bN0z2vi8XVVO3kyZPObV0Nrw4ePGhlxYoVS//EsqmAgABnfvnll1vZvn37vN5v2bJlvdpu9erVzrxWrVpeHwtAxjh06JDX2xYoUMDKXL9ff/nlF+f4xx57zOtjAWn1119/OfMePXpYWZEiRaxsx44dVuZqPiZJhw8fTuPs/IurweCIESOs7N577838yfipv//+25l37NjRytatW2dlRYsWdY4fNGiQlT3++ONWlt7mZak1Rfvuu++sLD4+PsOPf9ttt1lZeHi4c9tx48ZZWWqvVXMi3qEGAAAAAMAHLKgBAAAAAPABC2oAAAAAAHzAghoAAAAAAB+woAYAAAAAwAd0+c4EP//8s5Vdd911zm2LFy9uZUuXLrWyK664Iv0TO8epU6ec+bZt26zM1Snx66+/do6fO3eulW3dutXKcud2P/xuvfVWK7uUO3qnxZ49e6zM287dkhQdHe1VNnv2bOf4Pn36eH0sABkjLV2+XZ2BZ8yYYWWpPT906tTJ62MBSVyP0aeeesrKJkyY4Bx/4sQJr45TtWpVK3OdTUVyv9ZxnSnD9RwouZ9vp02bZmX79+93jp84caKVtWnTxsp+/fVX5/g333zTyoYMGWJlERERzvFdu3Z15jnJqlWrnLmro7fr/vjkk0+c413d1H/88UcrmzVrlnN8ap2yz7V3715nHhcXZ2Wu3+NdunTx6jipqVevnpXdd999zm1djzPX/ZRT8Q41AAAAAAA+YEENAAAAAIAPWFADAAAAAOADFtQAAAAAAPiApmSZ4Pnnn7ey1BoQrFixwsoKFy5sZR9//LGVffvtt859/vPPP1a2ZcsWK9u5c6dzfEJCgjM/V4ECBZx5q1atrOy5556zstatWzvHFypUyKvjw3bgwAErq1u3brr22bJlSyubPHlyuvYJIOMcOXLEygICApzb5suXz8pcjSRTa8RUrVq1NM4OOVVsbKyVvfrqq85tXfnhw4fTdfxcuez3hFzNplJrCrZv3z4rczU1GzRokHN8WFiYlT377LNWllpTtHvuucfKbrnlFitr1qyZc3zJkiWtzNUw1HU7LxXXXnutM3c1n3M1lHPdx5K7Id369eut7OTJk87x3jYle/nll535ggULrOzee++1skaNGllZZGSkV8eW3E3uNm3a5NzW1WSwZs2aVvb44487x99+++1W9sADD5x/gn6Ed6gBAAAAAPABC2oAAAAAAHzAghoAAAAAAB+keUH9448/qm3btipVqpQCAgL02WefpbjeGKMnn3xSJUuWVFhYmJo3b66///47o+YL+A1qAUhGPQDJqAcgEbWAS0Gam5IdO3ZMNWrUUJ8+fdSpUyfr+lGjRun111/X5MmTVb58eT3xxBNq2bKl1q9fr9DQ0AyZtD/ZtWuXlX3xxRdWltqX8IsWLWplTZo0sbJFixZZWZkyZZz7dDVbcDWWKl++vHN8hQoVvMpq1KjhHB8cHOzMc5qsrAVjjDPfu3evlZUoUSJdx3IJCQnJ8H0ie+O5IescOnTIylJrGulq5LRx40Yru+qqq9I9r0tZTquHbdu2WZmrAemGDRsuxnQked9ANS1NuSZMmGBlX375pXNb1+sy1+uq3r17O8d///33VuZqdpVaEylXE6pixYpZWc+ePZ3jL5asrIVSpUo5899//93KXnjhBSubNGmSc7yrUdz7779vZREREReY4fmlNn7p0qVW5vqd3a5dOytbsmSJc5+uJnsur732mjP/6aefrOzFF1+0surVqzvHP/3001bWq1cvK3Otm/xBmhfUrVu3TrU7szFGY8aM0eOPP6727dtLkqZMmaKIiAh99tln6tq1a/pmC/gRagFIRj0AyagHIBG1gEtBhn6HesuWLYqJiVHz5s09WXh4uOrXr69ly5Y5x8TFxenw4cMpLkB250stSNQDcibqAUjGayUgEc8NyCkydEEdExMjyf6IQkREhOe6c40cOVLh4eGeS1RUVEZOCcgSvtSCRD0gZ6IegGS8VgIS8dyAnCLLu3wPGzZMsbGxnsuOHTuyekpAlqEegGTUA5CIWgCSUQ/wNxm6oE5qfnRuY6S9e/em2hgpJCREBQoUSHEBsjtfakGiHpAzUQ9AMl4rAYl4bkBOkeamZOdTvnx5lShRQgsWLFDNmjUlSYcPH9aKFSs0YMCAjDyU33jvvfe82q5v377O3NUR09U5cuTIkVb2yCOPOPcZEBDg1ZyQeTK7FlxdfaXE7xWdK71dJl3fTeLJC2lxKT43XExHjhyxsrTUaGxsrJXlz58/XXNC6vy5HlzdgyWpY8eOVnby5Ekru+yyy5zj/fE0SK6O2Pv377ey1N79dHV1fuaZZ6wste8Cu7oqu86gkdSs61zffvutlU2ePNnKChYs6BzvD7KqFlz38/Dhw73K/IGrm/y0adOszNWJ/84773Tuc8qUKVa2fPlyK5s9e7ZzvKt794cffmhlrm7qkjRz5kwre/75563slVdecY7PamleUB89elSbNm3y/HvLli1as2aNChcurDJlymjIkCF69tlnddlll3na35cqVUodOnTIyHkDWY5aAJJRD0Ay6gFIRC3gUpDmBfWqVavUtGlTz7/vv/9+SYnnCps0aZIefvhhHTt2TP3799ehQ4d09dVXa/78+X55XkUgPagFIBn1ACSjHoBE1AIuBWleUDdp0kTGmFSvDwgI0NNPP+08QTeQk1ALQDLqAUhGPQCJqAVcCrK8yzcAAAAAANlRhjYly8ni4+Od+bvvvmtlN9xwg5VFRkY6x3/99ddeHf/666+3MpqPXbrO7Yh5PultSuZqeJQvX7507RNAxnE1hwoKCvJ6vOscrrt3707XnOD/5s2bZ2U33XSTc9vcue2Xi0kf3T2bqymXv3I1IHO9rkrt3dVPP/3Uypo3b25lEyZMcI53PTdfeeWVVuZqDCq5G5D17NnTuS38i6uB7OnTp60sLa+1XOuE5557zsqGDRvmHF+vXj0rK1q0qJWNGzfOOf7o0aNWVrp0aSurX7++c3z//v2tbOzYsVbmWmNJ7tq7mHiHGgAAAAAAH7CgBgAAAADAByyoAQAAAADwAQtqAAAAAAB8QFMyL3355ZfOfMeOHVaWWgMKl/DwcK+2czWGwqXrzJkzXm+bK1f6/m7meuwVKFAgXfsEkHESEhKszNVEKjVlypSxsk2bNqVrTvAvW7dutbIePXpY2alTp5zjhw8fbmXPP/+8lbmaGEnSgQMHLjBD/3C+0zudq3r16lbWqVMnK3PVpyTt2bPHylq3bm1lL7/8snP8FVdccaEpwk/dfffdVuaq0QULFqTrOPfdd5+VzZ4927mtq8ngokWLrCy1Wl64cKGVlSxZ0spSO7/4iy++aGWrVq2yMleNSe651qpVy7ltZuAdagAAAAAAfMCCGgAAAAAAH7CgBgAAAADAByyoAQAAAADwAU3JvPTOO+8487Jly1pZy5Ytvd6vt82dYmNjvd4ncr5SpUp5va2r8UlaHD9+3MrS0vDoUudq8vP+++9bWeXKlZ3jmzZtmuFzQs4SHx9vZWlpRhgVFWVl33//fbrmBP8ydOhQKzt06JCV3Xzzzc7xrsasR48e9SrL7lJ7vnM1Qfrvv/+s7Morr3SOf+mll6ysRYsWaZwdsqPDhw9b2Zo1a6xs8eLFzvGuBmDffvutlf36669WllqN5s2b18pcrx9DQkKc471d+6S2nomLi7OyOXPmWFnDhg2d42+44QYrmzhxopW1atXqQlP0Ce9QAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAPWFADAAAAAOADWvU6uDqmurrnSdKQIUOsLC3dVU+fPu31tkCSQoUKOfPQ0FAr2717d7qO5eoy/fzzzzu33blzp5VFRkam6/jZxZ9//unMXR0p09K1/4knnrCyp59+2vuJIcc7c+aMlQUFBXk9vkyZMlbm6lYsuTvE5suXz+tjIXOldlaHWbNmeTX+sccec+auM52k1oHYW1dccYWVde7c2bntZ599ZmW7du2ysvDwcCvbunVrmud2Nld9SVJgYKCVTZgwwcr69u3r9Xj4l9Seq12v3YsWLer1fl988UUra9SokZVdc801Xu/T1Y3+nnvusbLWrVs7x7uOlVpH7/S4//77nfnUqVOtrF+/flb2wAMPOMcPHjzYyly39YMPPnCO7969uzP3Fu9QAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAPaErmsGnTJis7efKkc9saNWqk61h//PGHV9tVrVo1XcdBzhIQEODMS5YsaWWbN29O17HuvPNOK3M11JCkPn36WNnnn39uZXFxcc7xrgZq+/btszJXE6TUaiQzmmq4mtS4mo9J7qYmBQoUsLLDhw87x7/00ktWNnz4cCujwc2ly9VIMy2Ph6ioKK+3dTUejI6O9no8MteiRYucuauJUvv27a0stdc06X2t4+Jq5Pjss8+ma58HDx5M13iXwoULO3PXa8W8efNm+PGRdVyNriTp008/tbJbb73VylyNiyV3Pa1Zs8bKvv76a+d41+uihx56yMoGDhxoZRUqVHDu82JJramt63Xd22+/bWWprcdcbrrpJiu77rrrvB6fFrxDDQAAAACAD1hQAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+oCmZw9q1a73etlq1al5t52oaI0nvvfeelbkaS2V1EwFkD23btrWyCRMmWNn27dud48uUKWNlpUqVsrLJkyc7x992221W5mo04WrqlV65c7t/nbkaJl155ZVWVrNmTed4V/MQY4yVnTp1yuvjuxqV3HXXXc7xJ06csDJXo5IbbrjBOR45n6uecuXy/u/lrrpPjet3B03J/IerWVFqwsLCrGz16tXObVu2bGll5cqVs7KtW7d6fXx/dPnll1uZqzGSRAOyS8ELL7zgzIsVK2ZlrtfzkyZNco5v2rSplbleF3Tv3t05/u+//7YyV1OypUuXWllWryciIiKc+WuvvWZljz32mJX9888/zvEVK1a0MtfPKbPwDjUAAAAAAD5gQQ0AAAAAgA9YUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPqDLt8Mff/xhZcHBwc5tK1eubGWubr+33nqrc/zChQut7OOPP7aytHRsxaVr2LBhVvbOO+9Y2TPPPOMc79rWpUuXLs48JCTEyn799VcrS63zoqujuKsj5H///Wdlv/32m3Ofv//+u5WtXLnSyqZNm+Yc7+ro7ZJap2NXjbs66aZW4wkJCVbm6txJl+9LV0xMjJW5ajE1aen66joLRosWLbwej8zVpk0bZ/7II49Ymet3Xmq/By8VW7ZssTK62F+6XK9JJOnll1+2shEjRlhZal2+X3/9dStr3769lVWqVMk5/uabb7Yy12uIcePGWVlqncP9UfHixb3K/AGrNAAAAAAAfMCCGgAAAAAAH7CgBgAAAADAByyoAQAAAADwAU3JHNatW2dlruZjkhQUFGRlrgZks2fPdo6fMGGClXXt2vVCUwScSpQoYWWDBw+2spdeesk5vmnTplZ22223eX38Dh06eJVlhnbt2qVr/NGjR525q0nh7t27rSy1pmBhYWFW5mpU5mo+lprnnnvOyjp16uTctnbt2l7vF/5l//79VvbKK69Y2bJly6zM1YwmNUWKFLGy1B43X331lZXdf//9Xh8LmSu1BnM//vijlbkaK/3www/O8fv27UvfxDJBaGiolZ08edLKUmua16xZMyvLmzevlRUtWtSH2eFSkz9/fitzvf6SpIEDB1rZvHnzrGzMmDHO8a7adb2G2LNnj3M8Mh7vUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPkjTgnrkyJGqW7eu8ufPr+LFi6tDhw7auHFjim1OnjypgQMHqkiRIsqXL586d+6svXv3ZuikAX9APQCJqAUgGfUAJKMecCkIMMYYbzdu1aqVunbtqrp16+rMmTN69NFHtXbtWq1fv97TyGHAgAH64osvNGnSJIWHh2vQoEHKlSuXlixZ4tUxDh8+rPDwcMXGxqpAgQK+3ap0io6OtrLUGrR89NFHVla2bFkrczV7kqRJkyalbXJIs8x6TGWXeoiLi7Oyjh07OredP3++lfXt29fKnnjiCef4MmXKpHF2l6Z+/fpZ2Xvvvefc1ttf0Q8//LAzf/HFF1P8OzPq4WLUQmbN/WJzNRp78sknndu6nh9OnTplZd27d7ey1B5PuXN714t0+PDhzvyFF16wsgMHDliZq0GPP8qu9ZBZtZDa75tzF0CSuznj8ePHneNPnz5tZXPmzLGyq666yjn+qaeesrLY2FivjtOjRw/nPqdMmeLML1WX+mul7MT1+/2OO+6wsr///tvKKlWqlClzymnS+phKU5fvc19sT5o0ScWLF9cvv/yixo0bKzY2Vu+9954++ugjT/fEiRMn6oorrtDy5cvVoEGDtBwO8GvUA5CIWgCSUQ9AMuoBl4J0fYc66a+DhQsXliT98ssvOn36tJo3b+7ZJjo6WmXKlHGe1kNKfPfs8OHDKS5AdkQ9AIkyohYk6gE5A88NQDLqATmRzwvqhIQEDRkyRI0aNVK1atUkSTExMQoODlbBggVTbBsREaGYmBjnfkaOHKnw8HDPJSoqytcpAVmGegASZVQtSNQDsj+eG4Bk1ANyKp8X1AMHDtTatWs1bdq0dE1g2LBhio2N9Vx27NiRrv0BWYF6ABJlVC1I1AOyP54bgGTUA3KqNH2HOsmgQYM0b948/fjjj4qMjPTkJUqU0KlTp3To0KEUf2nau3evSpQo4dxXSEiIQkJCfJkG4BeoByBRRtaCRD0ge+O5AUhGPSAnS9OC2hijwYMHa/bs2Vq4cKHKly+f4vo6deooKChICxYsUOfOnSUldobcvn27GjZsmHGzzkBbt261MldXvJ49e6brOGFhYekaD/+TXerB9aTj6q4qSU8//bSVjR492sreffdd5/hGjRpZWZcuXazs2muvdY4vVqyYlRUtWtTKgoKCrOzQoUPOfe7cudPKdu3aZWWpfbTsiiuusDJXJ9pcudwf+HF1zW3VqpWVff31187xrr+8uzo1uzqHXyzZpRYuNlfH4fbt21vZmjVrnONdXVvvu+8+K6tQoULaJ3cBN9xwgzN3/Y747rvvrCy1MwlcCrJzPQQEBDhz19lPXFlapOUxMmjQICv78ssvrczVGT+1jvW4OLJzPfirX375xcpcr5Xo6H3xpGlBPXDgQH300UeaM2eO8ufP73kBGh4errCwMIWHh6tv3766//77VbhwYRUoUECDBw9Ww4YN6dKHHId6ABJRC0Ay6gFIRj3gUpCmBfX48eMlSU2aNEmRT5w4Ub1795Ykvfrqq8qVK5c6d+6suLg4tWzZUuPGjcuQyQL+hHoAElELQDLqAUhGPeBSkOaPfF9IaGioxo4dq7Fjx/o8KSA7oB6ARNQCkIx6AJJRD7gUpOs81AAAAAAAXKp86vLt71x/DXv77bed2z700ENWVqBAASu78cYbnePj4+OtzNUcKU+ePM7xQFZwNfWSpGeeecbKBgwYYGWffPKJc7wrv+eee9I4uwsLDQ21spMnT2b4cVLjav5x/fXXO7c9ePCglc2fP9/KUmsGlHSuzrMtWrTIygoXLuwcj6wzcuRIK1u2bJmVDRs2zOt9Dh061MqmTJliZQsXLnSOP378uJVNnz7dyq677jrneFdDvN9++83KLuWmZLg4XI3zUmumB+QkmzdvtrKqVatmwUyQhHeoAQAAAADwAQtqAAAAAAB8wIIaAAAAAAAfsKAGAAAAAMAHObIp2cMPP2xlL730knPbNm3aWNmECROsrFSpUs7xn332mZUdPnzYys49/x6QXbge+/fdd59zW1e+bds2K/v999+d4w8cOOBVFhsba2URERHOfUZFRVmZ6zYVL17cOf7XX3+1sq+//trKXI3GJGn//v1W5mqS2KdPH+f4wMBAZw7/5/rZu7ial6XG9XieMWOGld1+++3O8TVr1rSyX375xcqmTZvm9ZxcjfcAAJnDtc5I7TUMLg7eoQYAAAAAwAcsqAEAAAAA8AELagAAAAAAfMCCGgAAAAAAH7CgBgAAAADAB9m+y7eru+hrr71mZW3btnWOf+utt6zM1dV3+PDhzvGTJ0+2snr16llZy5YtneOBnK5s2bJeZf6qXLlyVtapUyevx8fFxVlZSEhIeqaEbGLUqFFWVqZMGStzdcKXpLFjx1pZsWLFrMzV0fvMmTPOfW7dutXKjDHObb1Fl28AuHiuvPJKK6tYsWIWzARJeIcaAAAAAAAfsKAGAAAAAMAHLKgBAAAAAPABC2oAAAAAAHyQ7ZuSFShQwMqioqKs7PPPP3eOTy0/V/78+Z35kCFDrGzYsGFWFhwc7NVxAOQsNCC7dIWFhVnZQw89ZGV//vmnc/yHH37o1badO3e2ssOHDzv3+dVXX1lZ7dq1rWzt2rXO8UWKFLGyW265xbktACDjvf3221k9BZyDd6gBAAAAAPABC2oAAAAAAHzAghoAAAAAAB+woAYAAAAAwAfZvilZYGCgla1bt87KXI1YJGnfvn1W5mrQUr16ded4Gg4BANLjiiuucOY7d+60stDQUCvLndt+Kj9+/Lhzn998842VNWnSxMoWLVrkHO+a6+WXX+7cFgCASwHvUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPmBBDQAAAACAD1hQAwAAAADgg2zf5dvF1QW1Y8eOWTATAAB8ky9fPp/H5smTx5l36NDBq/Ht27f3+dgAAFxKeIcaAAAAAAAfsKAGAAAAAMAHLKgBAAAAAPCB332H2hgjSTp8+HAWzwQ5RdJjKemxlZ1QD8ho1AOQLLvWA7WAjJZda0GiHpDx0loPfregPnLkiCQpKioqi2eCnObIkSMKDw/P6mmkCfWAzEI9AMmyWz1QC8gs2a0WJOoBmcfbeggwfvanqISEBO3evVv58+fXkSNHFBUVpR07dqhAgQJZPbUMcfjwYW7TRWaM0ZEjR1SqVCnlypW9vuVAPWQ//n6bckI9GGNUpkwZv72PfeXvjx1f+Pttyq71wHND9uPvtym71oJEPWRH/n6b0loPfvcOda5cuRQZGSlJCggIkCQVKFDAL+/s9OA2XVzZ7a+tSaiH7Mufb1N2r4ekj2L5832cHjnxdvnzbcqO9cBzQ/blz7cpO9aCRD1kZ/58m9JSD9nrT1AAAAAAAPgJFtQAAAAAAPjArxfUISEhGj58uEJCQrJ6KhmG2wRf5cT7mdsEX+TU+zgn3q6ceJv8TU68j7lN8FVOvJ+5Tf7P75qSAQAAAACQHfj1O9QAAAAAAPgrFtQAAAAAAPiABTUAAAAAAD5gQQ0AAAAAgA/8ekE9duxYlStXTqGhoapfv75+/vnnrJ6S13788Ue1bdtWpUqVUkBAgD777LMU1xtj9OSTT6pkyZIKCwtT8+bN9ffff2fNZL00cuRI1a1bV/nz51fx4sXVoUMHbdy4McU2J0+e1MCBA1WkSBHly5dPnTt31t69e7NoxjlHdq4FKefVA7WQtbJzPeS0WpCoh6xGPfgX6iHrZOdakHJePVxKteC3C+pPPvlE999/v4YPH65ff/1VNWrUUMuWLbVv376snppXjh07pho1amjs2LHO60eNGqXXX39db731llasWKG8efOqZcuWOnny5EWeqfcWLVqkgQMHavny5fr22291+vRptWjRQseOHfNsc9999+nzzz/X9OnTtWjRIu3evVudOnXKwllnf9m9FqScVw/UQtbJ7vWQ02pBoh6yEvXgf6iHrJHda0HKefVwSdWC8VP16tUzAwcO9Pw7Pj7elCpVyowcOTILZ+UbSWb27NmefyckJJgSJUqY0aNHe7JDhw6ZkJAQ8/HHH2fBDH2zb98+I8ksWrTIGJN4G4KCgsz06dM92/z5559Gklm2bFlWTTPby0m1YEzOrAdq4eLJSfWQE2vBGOrhYqIe/B/1cHHkpFowJmfWQ06uBb98h/rUqVP65Zdf1Lx5c0+WK1cuNW/eXMuWLcvCmWWMLVu2KCYmJsXtCw8PV/369bPV7YuNjZUkFS5cWJL0yy+/6PTp0yluV3R0tMqUKZOtbpc/yem1IOWMeqAWLo6cXg85oRYk6uFioR6yB+oh8+X0WpByRj3k5FrwywX1gQMHFB8fr4iIiBR5RESEYmJismhWGSfpNmTn25eQkKAhQ4aoUaNGqlatmqTE2xUcHKyCBQum2DY73S5/k9NrQcr+9UAtXDw5vR6yey1I1MPFRD34P+rh4sjptSBl/3rI6bWQO6sngOxp4MCBWrt2rRYvXpzVUwGyFLUAJKMegGTUA5Aop9eCX75DXbRoUQUGBlpd3vbu3asSJUpk0awyTtJtyK63b9CgQZo3b55++OEHRUZGevISJUro1KlTOnToUIrts8vt8kc5vRak7F0P1MLFldPrITvXgkQ9XGzUg3+jHi6enF4LUvauh0uhFvxyQR0cHKw6depowYIFniwhIUELFixQw4YNs3BmGaN8+fIqUaJEitt3+PBhrVixwq9vnzFGgwYN0uzZs/X999+rfPnyKa6vU6eOgoKCUtyujRs3avv27X59u/xZTq8FKXvWA7WQNXJ6PWTHWpCoh6xCPfgn6uHiy+m1IGXPerikaiFLW6Kdx7Rp00xISIiZNGmSWb9+venfv78pWLCgiYmJyeqpeeXIkSNm9erVZvXq1UaSeeWVV8zq1avNtm3bjDHGvPDCC6ZgwYJmzpw55vfffzft27c35cuXNydOnMjimaduwIABJjw83CxcuNDs2bPHczl+/Lhnm7vuusuUKVPGfP/992bVqlWmYcOGpmHDhlk46+wvu9eCMTmvHqiFrJPd6yGn1YIx1ENWoh78D/WQNbJ7LRiT8+rhUqoFv11QG2PMG2+8YcqUKWOCg4NNvXr1zPLly7N6Sl774YcfjCTr0qtXL2NMYvv7J554wkRERJiQkBBz3XXXmY0bN2btpC/AdXskmYkTJ3q2OXHihLn77rtNoUKFTJ48eUzHjh3Nnj17sm7SOUR2rgVjcl49UAtZKzvXQ06rBWOoh6xGPfgX6iHrZOdaMCbn1cOlVAsBxhiTMe91AwAAAABw6fDL71ADAAAAAODvWFADAAAAAOADFtQAAAAAAPiABTUAAAAAAD5gQQ0AAAAAgA9YUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPmBBDQAAAACAD1hQAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAPWFADAAAAAOADFtQAAAAAAPiABTUAAAAAAD5gQQ0AAAAAgA9YUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPmBBDQAAAACAD1hQAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAPWFADAAAAAOADFtQAAAAAAPiABTUAAAAAAD5gQQ0AAAAAgA9YUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPmBBDQAAAACAD1hQAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAPWFADAAAAAOADFtQAAAAAAPiABTUAAAAAAD5gQQ0AAAAAgA9YUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPmBBDQAAAACAD1hQAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAPWFADAAAAAOADFtQAAAAAAPiABTUAAAAAAD5gQQ0AAAAAgA9YUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPmBBDQAAAACAD1hQAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAPWFADAAAAAOADFtQAAAAAAPiABTUAAAAAAD5gQQ0AAAAAgA9YUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPmBBDQAAAACAD1hQZ4KAgACNGDEiq6cB+AXqAUhGPQCJqAUgGfWQvfn9gnrcuHEKCAhQ/fr1fd7H7t27NWLECK1ZsybjJuZnnn/+eX322WdZPY0s88knn6h79+667LLLFBAQoCZNmmT1lDIF9eCdS70eJGnu3LmqXbu2QkNDVaZMGQ0fPlxnzpzJ6mllKOrBO9SDdOTIET388MMqX768QkJCVLp0ad100006fvx4Vk8tQ1AL3rnUa+Ho0aMaMmSIIiMjFRISoiuuuELjx4/P6mllOOrBO5d6Pdx3332qXbu2ChcurDx58uiKK67QiBEjdPTo0TTvy+8X1FOnTlW5cuX0888/a9OmTT7tY/fu3Xrqqacoihxs/PjxmjNnjqKiolSoUKGsnk6moR68c6nXw1dffaUOHTqoYMGCeuONN9ShQwc9++yzGjx4cFZPLUNRD9651OshNjZW11xzjd5//33deuutGj9+vO655x6dPHlScXFxWT29DEEteOdSroX4+Hi1bNlS48eP1y233KIxY8aocuXKuvvuu/X8889n9fQyFPXgnUu5HiRp5cqVuuaaa/TUU0/ptddeU9OmTfXCCy+oVatWSkhISNO+/HpBvWXLFi1dulSvvPKKihUrpqlTp2b1lJBOx44dy5T9fvDBB4qNjdX333+vUqVKZcoxshr1kPNkVj08+OCDql69ur755hv169dPr7/+uoYNG6YJEyZow4YNmXLMi416yHkyqx6GDRumbdu2aeXKlXr++efVp08fPfLII5o3b16O+AMstZDzZEYtzJo1S0uXLtX48eP1yiuvaMCAAfrss8/UuXNnPfPMM9q3b1+GHzMrUA85T2Y9NyxevFivvfaaBg8erH79+mncuHF67rnntGTJEv38889p25nxY88884wpVKiQiYuLMwMGDDCXXXaZc7uDBw+aIUOGmLJly5rg4GBTunRp06NHD7N//37zww8/GEnWZeLEicYYY8qWLWt69epl7fPaa6811157reffcXFx5oknnjC1a9c2BQoUMHny5DFXX321+f77762xkszw4cMvePtOnDhhhg8fbi677DITEhJiSpQoYTp27Gg2bdrk2Wb06NGmYcOGpnDhwiY0NNTUrl3bTJ8+3TreuZezb9POnTvN7bffbooXL26Cg4NNlSpVzHvvvWfNZ+vWraZt27YmT548plixYmbIkCFm/vz5RpL54YcfUmz76aefmtq1a5vQ0FBTpEgR061bN7Nz584U2/Tq1cvkzZvXbNq0ybRu3drky5fPtG/f3jz55JMmd+7cZt++fdYc+vXrZ8LDw82JEyfM7t27zZ9//mlOnTp1wfvybFWrVk3xs8spqAfqwZt6WLdunZFkxo4dmyLftWuXkWSeeeaZ847PLqgH6sGbejh48KAJDQ01Dz/8sDEm8Wd18uTJ847JbqgFasGbWhg8eLCRZI4dO5Yinz59upFk3n777fOOzy6oB+rBl7VDkhkzZhhJ5quvvkrTOL9eUEdHR5u+ffsaY4z58ccfjSTz888/p9jmyJEjplq1aiYwMND069fPjB8/3jzzzDOmbt26ZvXq1SYmJsY8/fTTRpLp37+/+eCDD8wHH3xgNm/ebIzxvij2799vSpYsae6//34zfvx4M2rUKFO5cmUTFBRkVq9enWKsN0Vx5swZc9111xlJpmvXrubNN980I0eONM2aNTOfffaZZ7vIyEhz9913mzfffNO88sorpl69ekaSmTdvnmebDz74wISEhJhrrrnGc/uWLl1qjDEmJibGREZGmqioKPP000+b8ePHm3bt2hlJ5tVXX/Xs4+jRo6ZChQomLCzMDB061IwZM8bUq1fP1KhRwyqKiRMnGkmmbt265tVXXzVDhw41YWFhply5cubgwYOe7Xr16mVCQkJMxYoVTa9evcxbb71lpkyZYv7++28jybzxxhsp7pO4uDhTqFAh06dPH894SWbLli3nvS/PlVMX1NQD9eBNPXz44YdGklmxYoV1XWRkpOnUqdN5x2cX1AP14E09fP75557FQufOnU1gYKAJCAgw//vf/6yfTXZFLVAL3tRC//79TWBgoDl9+nSK/IsvvjCSzJ133nne8dkF9UA9pGXtcPr0abN//36za9cu8/XXX5vo6GiTP39+8++//3o1PonfLqhXrVplJJlvv/3WGGNMQkKCiYyMNPfee2+K7Z588kkjycyaNcvaR0JCgjHGmJUrV6b4y9LZvC2KM2fOmLi4uBTbHDx40ERERHh+iEm8KYr333/fSDKvvPJKqvM2xpjjx4+nuO7UqVOmWrVqplmzZinyvHnzOm9H3759TcmSJc2BAwdS5F27djXh4eGe/b/88stGUoqCPHHihImOjk5RFKdOnTLFixc31apVMydOnPBsO2/ePCPJPPnkk54s6UE9dOhQa14NGzY09evXT5HNmjUrxbFYUCejHhJRDxeuh9GjRxtJZvv27dZ1devWNQ0aNDjv+OyAekhEPVy4Hl555RUjyRQpUsTUq1fPTJ061YwbN85ERESYQoUKmd27d593vL+jFhJRCxeuhaS5//TTTynyoUOHGkmmTZs25x2fHVAPiagH79cOy5YtS/EufeXKla131r3ht9+hnjp1qiIiItS0aVNJie3ku3TpomnTpik+Pt6z3cyZM1WjRg117NjR2kdAQECGzScwMFDBwcGSpISEBP333386c+aMrrrqKv36669p3t/MmTNVtGhRZ5Ogs+cdFhbm+f+DBw96mqt4c0xjjGbOnKm2bdvKGKMDBw54Li1btlRsbKxnP/Pnz1fp0qXVrl07z/jQ0FD169cvxT5XrVqlffv26e6771ZoaKgnv/HGGxUdHa0vvvjCmseAAQOsrGfPnlqxYoU2b97syaZOnaqoqChde+21kqRJkybJGKNy5cpd8LbmdNRDIurhwvVw4sQJSVJISIh1XWhoqOf67Ix6SEQ9XLgekrq1BgQEaMGCBbrttts83x09ePCgxo4de4F7yr9RC4mohQvXwm233abw8HD16dNH3377rbZu3aq3335b48aNkySeG/4f9XBp1EOSKlWq6Ntvv9Vnn32mhx9+WHnz5s05Xb7j4+M1bdo0NW3aVFu2bNGmTZu0adMm1a9fX3v37tWCBQs8227evFnVqlW7KPOaPHmyqlevrtDQUBUpUkTFihXTF198odjY2DTva/PmzapcubJy58593u3mzZunBg0aKDQ0VIULF1axYsU0fvx4r465f/9+HTp0SG+//baKFSuW4nL77bdLkqcJxbZt21SxYkXrF0mlSpVS/Hvbtm2SpMqVK1vHi46O9lyfJHfu3IqMjLS27dKli0JCQjzNImJjYzVv3jx169YtQ3+Z5QTUQzLq4cKSnkhd3YtPnjyZ4ok2O6IeklEPF5b0eG/btq3y5cvnyRs0aKDy5ctr6dKladqfP6EWklELF1aiRAnNnTtXcXFxatGihcqXL6+HHnpIb7zxhiSlqI/siHpIRj14r0CBAmrevLnat2+vF198UQ888IDat2+v3377LU37Of9PJIt8//332rNnj6ZNm6Zp06ZZ10+dOlUtWrTIkGOl9gOIj49XYGCg598ffvihevfurQ4dOuihhx5S8eLFFRgYqJEjR6b4S0lG+umnn9SuXTs1btxY48aNU8mSJRUUFKSJEyfqo48+uuD4pJbv3bt3V69evZzbVK9ePUPnfK6QkBDlymX/3aZQoUJq06aNpk6dqieffFIzZsxQXFycunfvnqnzyY6oh0TUg3dKliwpSdqzZ4+ioqJSXLdnzx7Vq1fPt8n7CeohEfXgnaSzPkRERFjXFS9eXAcPHkz7xP0EtZCIWvBe48aN9c8//+iPP/7QsWPHVKNGDe3evVuSdPnll6frNmQ16iER9ZA+nTp1Uo8ePTRt2jTVqFHD63F+uaCeOnWqihcv7vwo1qxZszR79my99dZbCgsLU8WKFbV27drz7u98f7UoVKiQDh06ZOXbtm1ThQoVPP+eMWOGKlSooFmzZqXY3/Dhw724RbaKFStqxYoVOn36tIKCgpzbzJw5U6Ghofr6669TfHxz4sSJ1rau21isWDHlz59f8fHxat68+XnnU7ZsWa1fv17GmBT7Ovf8fWXLlpUkbdy4Uc2aNUtx3caNGz3Xe6Nnz55q3769Vq5cqalTp6pWrVqqWrWq1+MvFdRDIurBOzVr1pSU+BGrsxfPu3fv1s6dO9W/f/8079OfUA+JqAfv1KlTR5K0a9cu67rdu3crOjo6zfv0F9RCImohbQIDAz3PE5L03XffSdIFb7u/ox4SUQ/pExcXp4SEhDR/gsDvPvJ94sQJzZo1S23atNFNN91kXQYNGqQjR45o7ty5kqTOnTvrt99+0+zZs619GWMkSXnz5pUk54O/YsWKWr58uU6dOuXJ5s2bpx07dqTYLukvTkn7lKQVK1Zo2bJlPt3Ozp0768CBA3rzzTdTnXdgYKACAgJSfO9j69atzpOw582b17p9gYGB6ty5s2bOnOn8xbF//37P/7ds2VK7du3y3K9S4sdD33nnnRRjrrrqKhUvXlxvvfVWio+UfvXVV/rzzz914403nv+Gn6V169YqWrSoXnzxRS1atMj6C9OePXu0YcMGnT592ut95jTUA/WQxNt6qFq1qqKjo/X222+nuK/Gjx+vgIAA3XTTTV7Pyd9QD9RDEm/roXLlyqpRo4bmzJmjAwcOePJvvvlGO3bs0PXXX+/1nPwJtUAtJEnPa6X9+/frxRdfVPXq1bP1gpp6oB6SeFsPhw4dcm7z7rvveuacJmluY5bJpk2bZnWMO1t8fLwpVqyYadu2rTEmsfV9lSpVPK3v33rrLfP888+bBg0amDVr1hhjErvLFSxY0FSuXNm8++675uOPPzb//POPMcZ4zpXWtGlTM378ePPggw+aEiVKmIoVK6bo1JfUWa9du3ZmwoQJZujQoaZgwYKmatWqpmzZsinmKC9b3zdp0sTT+n7s2LFm1KhRpkWLFp7bvmDBAiPJXHPNNWb8+PHmqaeeMsWLFzfVq1c35/7obrjhBpM3b17z8ssvm48//tgsX77cGJPY+r5s2bImT5485t577zUTJkwwI0eONDfffLMpVKiQZ/yRI0dMuXLlPK3vX3vtNVOvXj1Ts2ZNI8ksXLjQs21S6/v69eubMWPGmGHDhpk8efI4W9/nzZv3vPfDoEGDjCQTGBhodVtNS6e+RYsWmWeeecY888wzpnjx4qZcuXKefy9atOiC4/0V9UA9nD3e23r4/PPPTUBAgGnWrJl5++23zT333GNy5cpl+vXrd8Gx/ox6oB7OHu9tPXz//fcmMDDQVK5c2bzyyitm+PDhJn/+/Obyyy83R44cueB4f0QtUAtnj/e2Fho3bmweeeQR884775hnnnnGREVFmUKFCpnff//9gmP9GfVAPZw93pt6mD17tomKijL33XefGTdunBkzZozp3LmzCQgIMFdddZXVnf1C/G5B3bZtWxMaGmqdeP5svXv3NkFBQZ527v/++68ZNGiQKV26tAkODjaRkZGmV69eKdq9z5kzx1SpUsXkzp3baoP/8ssvm9KlS5uQkBDTqFEjs2rVKqv1fUJCgnn++edN2bJlTUhIiKlVq5aZN2+e6dWrl09FYUxiW/vHHnvMlC9f3gQFBZkSJUqYm266yXOeO2OMee+99zwnb4+OjjYTJ040w4cPt4piw4YNpnHjxiYsLMxIKU/OvnfvXjNw4EATFRXlOc51111n3n777RT7+Oeff8yNN95owsLCTLFixcwDDzxgZs6caSR5iizJJ598YmrVqmVCQkJM4cKFz3ty9vP5+eefjSTTokUL67q0PEkk3Seuizc/C39FPVAPZ4/3th6MSXyyqFmzpgkJCTGRkZHm8ccfN6dOnfJqrL+iHqiHs8enpR6+/fZb06BBAxMaGmoKFy5sevToYfbs2ePVWH9ELVALZ4/3thbuu+8+U6FCBRMSEmKKFStmbrvtthT3Y3ZFPVAPZ4/3ph42bdpkevbs6TmPdmhoqKlataoZPny4OXr06HnHuvjdghr+5dVXXzWSrAd8RlmzZo2RZKZMmZIp+wcyEvUAJKMegETUApDsUqyHAGPO+mA/LmknTpxIcUqdkydPqlatWoqPj9dff/2VKcccNGiQJk+erJiYGM/3VQB/QD0AyagHIBG1ACSjHhL5ZZdvZI1OnTqpTJkyqlmzpmJjY/Xhhx9qw4YNnvO9ZaTPP/9c69ev19tvv61Bgwb5TUEASagHIBn1ACSiFoBk1MP/y+q3yOE/Xn31VVO1alWTN29eExoaamrXrm2mTZuWKccqW7asCQ0NNe3btzeHDx/OlGMA6UE9AMmoByARtQAkox4S8ZFvAAAAAAB84HfnoQYAAAAAIDtgQQ0AAAAAgA8yrSnZ2LFjNXr0aMXExKhGjRp64403VK9evQuOS0hI0O7du5U/f34FBARk1vRwCTHG6MiRIypVqpRy5br4f0PytRYk6gEZj3oAkmXXeqAWkNGyay1I1AMyXprrITO+mD1t2jQTHBxs3n//fbNu3TrTr18/U7BgQbN3794Ljt2xY4eRxIVLhl927NiRGQ/380pPLRhDPXDJvAv1wIVL8iW71QO1wCWzLtmtFoyhHrhk3sXbesiUpmT169dX3bp19eabb0pK/MtRVFSUBg8erKFDh553bGxsrAoWLKgdO3aoQIECGT01XIIOHz6sqKgoHTp0SOHh4Rf12OmpBYl6QMajHoBk2bUeqAVktOxaCxL1gIyX1nrI8I98nzp1Sr/88ouGDRvmyXLlyqXmzZtr2bJl1vZxcXGKi4vz/PvIkSOSpAIFClAUyFAX+2NAaa0FiXrAxUM9AMn8vR6oBVws/l4LEvWAi8fbesjwL0kcOHBA8fHxioiISJFHREQoJibG2n7kyJEKDw/3XKKiojJ6SkCWSGstSNQDci7qAUjGayUgEc8NyAmyvMv3sGHDFBsb67ns2LEjq6cEZBnqAUhGPQCJqAUgGfUAf5PhH/kuWrSoAgMDtXfv3hT53r17VaJECWv7kJAQhYSEZPQ00mXt2rVW9uWXXzq3dX0F3dUNrlKlSs7x1atXt7IKFSpYGV0Ls5+01oLkn/UAZATqIfOcOXPGyiZOnOjctlu3blaWJ0+eDJ8Tzi8nvFYCMgLPDcgJMvwd6uDgYNWpU0cLFizwZAkJCVqwYIEaNmyY0YcD/Ba1ACSjHoBk1AOQiFpATpAp56G+//771atXL1111VWqV6+exowZo2PHjun222/PjMMBfotaAJJRD0Ay6gFIRC0gu8uUBXWXLl20f/9+Pfnkk4qJiVHNmjU1f/58q+EAkNNRC0Ay6gFIRj0AiagFZHeZch7q9Dh8+LDCw8MVGxubZa3v+Q51zuIPjylfZee5wz9l58dUdp57ZuI71L7Lro+p7Dpv+K/s/JjKznOHf0rrYyrLu3wDAAAAAJAdZcpHvrOTjRs3WlmdOnWs7NSpUxdjOpKkggULWtmMGTOs7LrrrrsIs8GlxPU437lzp5V9+umnzvGLFy+2ssqVK1vZU0895RyfL1++C00RyNZWrlxpZZs2bXJue+utt3q1zz179lhZ//79ndsGBgZaWZ8+fbw6DgAAsPEONQAAAAAAPmBBDQAAAACAD1hQAwAAAADgAxbUAAAAAAD44JJvShYTE2NlrsZMixYtco5v3LixV+P//PNP5/jff//dyl5++WUru+2226xszZo1zn2WLFnSmQNJvvrqK2fer18/K9u1a5fX+y1RooSVffPNN1b2119/Ocd//vnnXh8LyI4+/vhjK/vggw+c23rblCwqKsrKUjtVo6v2aUqGnOLXX3+1sr179zq3DQoKsjLX6XFcjWold4M/AJcm3qEGAAAAAMAHLKgBAAAAAPABC2oAAAAAAHzAghoAAAAAAB9c8k3JvJUrl/d/ewgODrayGjVqOLd15f/73/+s7Oqrr7YympLBGx9++KGV9ezZ07ltrVq1rGz06NFWli9fPuf4MmXKWFnv3r2tbNu2bc7xQE5Xvnx5Kztw4IBz24MHD1pZoUKFvDpO69atnfnkyZOt7PTp01bmatgE+LuOHTta2fbt29O1T1ezTUnq2rWrlXXv3t3KUmtqBiDn4B1qAAAAAAB8wIIaAAAAAAAfsKAGAAAAAMAHLKgBAAAAAPABC2oAAAAAAHxAl28/VLFiRSvbs2dPFswE2c2uXbus7I477rCyVq1aOcfPmTPHymJiYqysU6dOzvG///67lQUGBlrZyJEjneOBnO7yyy/3ett169ZZmeuMDy6pdfl+4403rGzZsmVW1rhxY6+OA/iTX3/91cq2bt3q9XjXGShmzZrl3Padd96xsjFjxlhZdHS0c3yPHj2szHUGjsjISOd4AP6Dd6gBAAAAAPABC2oAAAAAAHzAghoAAAAAAB+woAYAAAAAwAc0JQNyEFdTsri4OCu7//77neODgoKs7MYbb7SyP/74wzm+TZs2VuZq3FKiRAnneCCnczUVCw8Pd247ZcoUr8a7NGnSxJmHhoZa2bfffmtlNCVDdlSkSBGvstTUqVPHylJrwnn06FErmz17tpV9+OGHzvFPPPGElb3wwgtWNn36dOf4li1bOnMAFx/vUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPmBBDQAAAACAD2hKBuQgrgZkLiEhIV7v84EHHrCy3r17O7d95JFHrIwGZECyvHnzWlnPnj2d27777rtW9uKLL1pZoUKFrCwsLMy5z6ioKCtzNTMEcH758uWzsh49eniVSdLOnTut7Oabb7YyV7NPSRo/fryV3XHHHc5tkbESEhKsbNGiRVZWs2ZN53jX72xkb7xDDQAAAACAD1hQAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAP6PIN5CCZ0eXb1ck0NYULF/Z6WwCJ+vfv78zfeOMNK5szZ46VpdZ136VgwYJWdujQIa/HA8gYkZGRVvb9999bWbdu3Zzj+/XrZ2WxsbFW5jpTB7zjuj8l6cYbb7SyJUuWWJnr960kXX/99VbWqlUrK2vdurVzfMmSJZ15VpoyZYqVzZ8/38oOHDjg9T5r1aplZa77XpIaN27s9X4zA+9QAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAPLvmmZP/8849X27maR1xMq1atsrJixYo5ty1btmxmTwd+6ujRo15tlydPHq/36Wq0kT9/fue2l112mdf7BZCoWrVqztzVeOann36ysrQ0JXM1GTxy5IjX4wFknrCwMCubMWOGc9ubb77Zyp599lkrGzhwoHN8aGhoGmeXs505c8bKUmsKtnr1aitzNZHcsGGDc/xXX31lZdOnT7/QFD169OhhZePHj7eyvHnzer1PF9dzw5133unc9uOPP7Yy1zolOjraOT4hIcHKXLdp1KhRzvHNmjWzspdeesnKXI3OMgLvUAMAAAAA4AMW1AAAAAAA+IAFNQAAAAAAPkjzgvrHH39U27ZtVapUKQUEBOizzz5Lcb0xRk8++aRKliypsLAwNW/eXH///XdGzRfwG9QCkIx6AJJRD0AiagGXgjQ3JTt27Jhq1KihPn36qFOnTtb1o0aN0uuvv67JkyerfPnyeuKJJ9SyZUutX7/eLxsg7N+/36vtoqKiMnkm5zd48GAr+/PPP53bfv/991ZWu3btDJ/Tpc4fa2Hbtm1ebZeWxnXfffedlTVp0sS5bVBQkNf7Rc7ij/WQ3V199dVWtnjx4nTts2DBgla2c+fOdO0TNuoBGSVXLvd7X/fdd5+VzZo1y8q+/vpr5/j27dunb2Jeyi61MHr0aCtbtmyZc9vZs2dbWYcOHdJ1/L/++svKPvroI+e2zz33nJWtWbPGylzrgaJFi3o9p5EjR1pZas3TXA3AWrZsaWWuZpuSFBgYaGWupmrvv/++c7yrId91111nZVu3bnWOL1CggDP3VpoX1K1bt061650xRmPGjNHjjz/uKdQpU6YoIiJCn332mbp27ZquyQL+hFoAklEPQDLqAUhELeBSkKHfod6yZYtiYmLUvHlzTxYeHq769eun+leeuLg4HT58OMUFyO58qQWJekDORD0AyXitBCTiuQE5RYYuqGNiYiRJERERKfKIiAjPdecaOXKkwsPDPZes/mg1kBF8qQWJekDORD0AyXitBCTiuQE5RZZ3+R42bJhiY2M9lx07dmT1lIAsQz0AyagHIBG1ACSjHuBvMnRBXaJECUnS3r17U+R79+71XHeukJAQFShQIMUFyO58qQWJekDORD0AyXitBCTiuQE5RZqbkp1P+fLlVaJECS1YsEA1a9aUJB0+fFgrVqzQgAEDMvJQGebYsWNW5uoq6Oo+dzG5Ojc2aNDAue3AgQOt7HzfRUHGy6pacHX5Lly4sJXlz5/fOd7V9f6PP/6wsr59+/owO1yqsuNzgz9wdfl2dVh1fTQytRej4eHhVrZ+/XofZgdfUQ/ICLVq1fJqO2/P/pEVsqoWXGfJeeqpp6yse/fuzvGuj5gfOXLEylJ7reXa9vLLL7eyESNGOMe7ule7Gr/179/fylzridSc+4cOSSpTpoxz2wceeMDKrrjiCitL+jmf65NPPrEyV+fyO++80zm+SpUqVta4cWMrW7VqlXN8s2bNnLm30rygPnr0qDZt2uT595YtW7RmzRoVLlxYZcqU0ZAhQ/Tss8/qsssu87S/L1WqVLrbyQP+hloAklEPQDLqAUhELeBSkOYF9apVq9S0aVPPv++//35JUq9evTRp0iQ9/PDDOnbsmPr3769Dhw7p6quv1vz58zmvInIcagFIRj0AyagHIBG1gEtBmhfUTZo0kTEm1esDAgL09NNP6+mnn07XxAB/Ry0AyagHIBn1ACSiFnApyPIu3wAAAAAAZEcZ2pQsO3I1BsiXL18WzOT8SpYsaWWvvfaac9tHH33Uyk6fPm1lQUFB6Z8Y/MrWrVutrFy5cl6P9/bUE9HR0V7v09UA4s0333Ru++uvv1rZ0aNHraxQoUJW1qZNG+c+XfUQEhJiZWvXrnWOnzBhgpW5GnWcOXPGOb5o0aJW1rx5cytr0aKFc3zBggWdObKnkydPWtn777/v3NbbZpJLly61sk6dOjm3dTUl+++//6wsPj7eOT6rG3QCSPTDDz94tV2ePHkyeSb+K7XfY3369LEy1+uKUaNGOceXLl3aylwNwB5++GHn+EqVKllZ27ZtrWzOnDnO8ddcc42VuZqqPfjgg1a2efNm5z4rVqxoZa6mtrt27XKOd72Gcr3+3Llzp3O8a02SK5f37/tWqFDBq+0y6xRrvEMNAAAAAIAPWFADAAAAAOADFtQAAAAAAPiABTUAAAAAAD645JuSnThxwsrCwsKyYCZpl9pJ71PLkfNt2bLFyrxt1CC5mxO5uBpVSNJDDz1kZS+99JKVFS9e3Dn+2muvtTJXEyVXU4zUTrkxb948r46TWqM0V0OXsmXLWlnu3O5fp0uWLLGyd955x8pSa4Z41113WdkDDzxgZSVKlHCOR9ZxNdlzNQvbtm2bc7yrzi677DIrK1KkiNdzql69upW5Gly6GudJ0kcffWRlrqaZADLGH3/84cxvv/12K6tfv76V9erVK8PnlF2sWLHCmS9fvtzKPvjgAytL7Xfb4MGDrcz12ju1prCDBg2ysjJlyji39VZqv7PPtX79emfuakrmauo6depU53jX4/HOO++0sjp16jjHu5q93nLLLVa2ePFi5/jg4GBnfq7Y2Fivtksr3qEGAAAAAMAHLKgBAAAAAPABC2oAAAAAAHzAghoAAAAAAB9c8k3JKleubGVvvfWWle3cudM5PjIyMsPnBPhq69atVtasWTOvxx88eNCr7SZOnOjMx48fb2VDhgyxsueee8453tUAzFtffvmlM3c1Cvn999+tbODAgc7xTz31lJW5GqWlxhhjZatXr7aysWPHOse//vrrVuZqoDZr1izn+NatW19oisgkrsdUQECAlS1dutQ5vmHDhhk+pz59+lhZ/vz5raxfv37O8d27d7eyBQsWpH9iwCUmPj7eylxNPIcPH+4c72ru+fHHH1tZUFCQD7PLGTZs2OD1tk2aNLGyX375xbntokWLrOyJJ56wsly53O9bup7XXWbMmOHMXa93Uvudfa7UGqC6FCpUyMpGjhzp3LZ3795WtmrVKisrWLCg18evVKmSleXNm9e57YABA7zap7fN29KKd6gBAAAAAPABC2oAAAAAAHzAghoAAAAAAB+woAYAAAAAwAcsqAEAAAAA8MEl3+X7pptusrL77rvPymbOnOkcf++992b4nIALOXz4sDM/dOiQlZUrV87r/f73339ebefq5i1JPXr0sLJXXnnFylydjtPrhhtucOabN2+2sjNnzlhZ+fLlM3xOkvu21q5d28ree+8953hXh9d33nnHyqKionyYHTLTjh07rMzVdT61bt5jxoyxslOnTlnZww8/nOa5ne3mm2+2stR+F9x1111WtnDhQitzdcwFLkVbtmxx5q6uyD/++KOVuepTcp+RpnDhwmmbXA4XGBjo9baus6SUKlXKua3rTCGu5+Vhw4Z5fXyXESNGOHNXh/c1a9Z4tc/LL788HTNK29knXJ3HT5486dzWdfYT19lLPvzwQ+d419lnXnjhBSurUqWKc3x68Q41AAAAAAA+YEENAAAAAIAPWFADAAAAAOADFtQAAAAAAPggwBhjsnoSZzt8+LDCw8MVGxurAgUKZMkc6tevb2UhISHObV0NJOBf/OEx5avU5v7bb785t69Zs6aVzZ0718ratm3rHD9y5Egre/TRR60stYZDX3/9tZUFBwc7t0XWyIn14K9q1KhhZXv27LGyggULOsf//fffVpY7t91L1NX0KDIy0osZps7V/EySKleu7NWxfvrpp3Qd/2LJbo+pJNl13jmJ6+WzqzHVAw884BwfFhZmZa5GY506dfJhdmmXnR9Tqc09Li7OuX2lSpW8yhYsWOAc365dOyv7448/rGzdunXO8fny5fPqWM2bN3eOnzJlipW9+uqrVub6Pb527VrnPr2V2rKxW7duVvbxxx9bmasZnySNGjXKymJjY63Mdd9L7vv0559/dm7rjbTWA+9QAwAAAADgAxbUAAAAAAD4gAU1AAAAAAA+YEENAAAAAIAP7O4mUOfOna1s6NChzm13795tZaVKlcrwOQFn27p1q9fblitXzuttK1SoYGVXX321lc2aNcs5ngZkQLIJEyZY2ZgxY6wsPj7eOb5nz55W9sQTT1jZsmXLrOzmm2/2YoapS62Wn3zySSvr06ePlX3zzTfO8S1atEjXvIDM5KpFV2NPSXr55ZetbMmSJVbWsWNH53hXA7LixYtfaIpIg9QaCj///PNW5vp9++CDDzrHP/zww1bmaiBWvXp15/hGjRpZ2cyZM60stddvrm1Xr15tZV988YVzfHoEBAQ486lTp1qZq1HusGHDnOMnTZrk1fFTe25K7TnnYuEdagAAAAAAfMCCGgAAAAAAH7CgBgAAAADAByyoAQAAAADwAQtqAAAAAAB8EGCMMVk9ibMdPnxY4eHhio2NVYECBbJkDv/884+VVapUybnt008/bWWPP/54hs8JvvOHx5SvUpv7e++959z+jjvusLKYmBgri4iIyLhJIlvJifWQU7k6DufPn9/K7r33XisbOXJkpszpzJkzVnbZZZdZWa1atZzjUztDQFbJro+p7DrvrHDo0CFn/u6771rZ2LFjrSy1s2pcfvnlVubqwt+9e/fzT9BPZOfHVEbM3dV9+oUXXnBue8stt1hZ69atrezFF190jo+NjbWy8PBwK9u1a5dz/LFjx6zM1TW+X79+zvFZyXVWCkn6448/rMz1fFelShXn+Bo1aqRvYudI62OKd6gBAAAAAPABC2oAAAAAAHzAghoAAAAAAB+woAYAAAAAwAe5s3oC/qhChQpWdtNNNzm3ffnll62sZcuWVla3bt30Twz4f2npJRgQEJCJMwGQWQIDA63M1Xhl9erVF2M6kqTcue2XDeXKlbMyV9McICP9/fffVvbmm29aWWpNPF2P0UaNGlnZqFGjnOM7depkZa6aRfbgauRYokQJ57ZDhw61sk8//TRdxz9x4oSVdevWzbmtq9lY7dq103X8i6Vhw4ZpyrML3qEGAAAAAMAHLKgBAAAAAPABC2oAAAAAAHyQpgX1yJEjVbduXeXPn1/FixdXhw4dtHHjxhTbnDx5UgMHDlSRIkWUL18+de7cWXv37s3QSQP+gHoAElELQDLqAUhGPeBSkKamZIsWLdLAgQNVt25dnTlzRo8++qhatGih9evXK2/evJKk++67T1988YWmT5+u8PBwDRo0SJ06ddKSJUsy5QZcLKNHj3bmLVq0sLLrr7/eytatW+ccX7p06fRNLAs9+uijzvzyyy+3sp49e1pZrlzZ+wMSl3I9AGejFi6eWrVqWdn06dOzYCZIDfXgnePHj1vZnDlznNtOmjTJyr755hsry5cvn5W5Xn9I0j333GNl0dHRzm3hu+xcD/fee68z79Onj5X99NNPVpZac0bX47Rp06ZWFhoaeqEpwk+kaUE9f/78FP+eNGmSihcvrl9++UWNGzdWbGys3nvvPX300Udq1qyZJGnixIm64oortHz5cjVo0CDjZg5kMeoBSEQtAMmoByAZ9YBLQbreIoyNjZUkFS5cWJL0yy+/6PTp02revLlnm+joaJUpU0bLli1z7iMuLk6HDx9OcQGyI+oBSJQRtSBRD8gZeG4AklEPyIl8XlAnJCRoyJAhatSokapVqyZJiomJUXBwsAoWLJhi24iICMXExDj3M3LkSIWHh3suUVFRvk4JyDLUA5Aoo2pBoh6Q/fHcACSjHpBT+bygHjhwoNauXatp06alawLDhg1TbGys57Jjx4507Q/ICtQDkCijakGiHpD98dwAJKMekFOl6TvUSQYNGqR58+bpxx9/VGRkpCcvUaKETp06pUOHDqX4S9PevXtVokQJ575CQkIUEhLiyzQAv0A9AIkyshYk6gHZG88NQDLqATlZmhbUxhgNHjxYs2fP1sKFC1W+fPkU19epU0dBQUFasGCBOnfuLEnauHGjtm/froYNG2bcrLNA2bJlnfl3331nZUkfYznbXXfd5Rz/+eefp29iWWj9+vXOfOTIkVa2fft2K3vyySczfE4X06VcD8DZLvVacHUrlqQff/zRyvLkyWNlBQoUcI535ed+LFKSDhw4YGU7d+507vPsF7LIHJdKPbie15cuXercdu7cuV5lqXVFrlSpkpW99NJLVta3b18rc9UMLp6cWA/58+e3shtuuCELZgJ/kaYF9cCBA/XRRx9pzpw5yp8/v+e7DeHh4QoLC1N4eLj69u2r+++/X4ULF1aBAgU0ePBgNWzYkC59yHGoByARtQAkox6AZNQDLgVpWlCPHz9ektSkSZMU+cSJE9W7d29J0quvvqpcuXKpc+fOiouLU8uWLTVu3LgMmSzgT6gHIBG1ACSjHoBk1AMuBWn+yPeFhIaGauzYsRo7dqzPkwKyA+oBSEQtAMmoByAZ9YBLQbrOQw0AAAAAwKXKpy7fSOY6992LL75oZQMGDHCOv/nmm61szJgxVla6dOm0Ty6TpdZ9MXdu+2HVokWLzJ4OUrF161YrK168+MWfyCXK1ZjqjjvusLKpU6c6x9etWzfD54SM9cILLzjzZ5555iLPJNn+/fudeWY0JduzZ4+VValSJcOPgwtLSEhw5suXL7eyZcuWWVlqTcVc27p+7qlxvV5w/R689dZbnePr16/v9bEA4GLjHWoAAAAAAHzAghoAAAAAAB+woAYAAAAAwAcsqAEAAAAA8AFNyTLBnXfeaWUnT550bvvkk09a2RVXXGFlI0aMsLLBgwc79xkUFHSBGabd559/bmUTJkxwbjt8+HAra9CgQYbP6VLWqFEjZ16oUCErczVzCQ8Pd46vXr26ldWoUcOr7SSpZs2aVlatWjUrCwsLc47PSkePHnXmK1eutLIlS5ZY2Zdffukc72rm47r/ypcvf6Epwk+tXbvWmVeuXNnK3n33XSs7cuSIc7zrMemqcVdzTNexJenQoUNWtnHjRitbt26dc7yryZ5r/GOPPeYcj8z1/vvvO/N+/fpZWUBAgJVFR0c7x7dq1crKXM9DqTUPczWpy5WL93QA5Az8NgMAAAAAwAcsqAEAAAAA8AELagAAAAAAfMCCGgAAAAAAH7CgBgAAAADAB3T5zgSuzplDhgxxbnvzzTdb2X333WdlDzzwgJU9++yzzn3eeOONVta2bVsrq1evnnN8QkKCld1xxx1Wllrn7scff9yZI+O4OsFL7m7D33zzjZX9/vvvzvFr1qyxsqlTp1rZm2++eYEZnl+JEiWceZkyZaysQIEC6TqWy4EDB6wstU7NZ86csbLcue1fnVdddZVz/Ouvv25lt99+u5Xly5fPOR7+r1y5cs7cVXv58+e3stQ6I//3339WtnDhQiubMWOGlf3www/Off7111/O3Fuuehw2bJiV3Xbbbek6DnzTtWtXZ16hQgUrq127tpUVLFgwo6cEADke71ADAAAAAOADFtQAAAAAAPiABTUAAAAAAD5gQQ0AAAAAgA8CjDEmqydxtsOHDys8PFyxsbGZ0owou1q0aJGVffTRR85t586da2UxMTHpOr6rYdLq1aud21aqVCldx8po2fkx5a9z37lzpzN3NTtzNftKbfy2bdus7Pjx42mc3YXlzZvXylJrKnb11VdbWd26db3apz/y18eUN/x17tu3b3fm//vf/6xs165dGX78okWLWlmTJk2c27oez1WqVLGy6Oho5/ioqKi0Tc7P+etj6kKy67zhv7LzYyo7zx3+Ka2PKd6hBgAAAADAByyoAQAAAADwAQtqAAAAAAB8wIIaAAAAAAAf5M7qCcA71157rVeZJL311ltW5mogtmHDBuf4/fv3W1nt2rWtzN+aj+HiiYyM9Dq/4YYbMns6QJYqU6aMM1+8eLGVffvtt1YWGxvrHB8aGmpljRs3trIrr7zSygICApz7BAAAGYt3qAEAAAAA8AELagAAAAAAfMCCGgAAAAAAH7CgBgAAAADAByyoAQD/1969vET59nEc/2iPTicPHchJdNBdi8CFpEjLJFfR6Q9oVVTjotqXS6OgRSG0q12FC4vahZoRqJEJEYW0iAo80cIcTFGc728Rjkg+z2P3b5r7ui7fL3Dh7b34fm98I5eTEwAAACLgXb4DtN67u673Lt3rXQMA5EddXd1v186ePVv4QQAAwF/DK9QAAAAAAETAgRoAAAAAgAg4UAMAAAAAEIFzf0NtZpKk2dnZmCdBKFa+l1a+t3xCD8g3egBW+doDLSDffG1Bogfk35/24NyBOpPJSJJqa2tjngShyWQyqqioiHuMP0IP+FvoAVjlWw+0gL/FtxYkesDfs9EeisyxX0Vls1mNj4+rrKxMmUxGtbW1+vbtm8rLy+MeLS9mZ2fZqcDMTJlMRtXV1Sou9uuvHOjBP67vFEIPZqZUKuXsM47K9e+dKFzfydce+NngH9d38rUFiR585PpOf9qDc69QFxcXq6amRtLqf/9UXl7u5MP+N9ipsHz7besKevCXyzv53sPKP8Vy+Rn/GyHu5fJOPvbAzwZ/ubyTjy1I9OAzl3f6kx78+hUUAAAAAACO4EANAAAAAEAETh+oE4mEOjo6lEgk4h4lb9gJUYX4nNkJUYT6jEPcK8SdXBPiM2YnRBXic2Yn9zn3pmQAAAAAAPjA6VeoAQAAAABwFQdqAAAAAAAi4EANAAAAAEAEHKgBAAAAAIjA6QN1V1eX6urqtHXrVjU3N+v169dxj7RhL1++1LFjx1RdXa2ioiI9fvx4zdfNTNeuXdP+/fu1bds2tba26tOnT/EMu0GdnZ06dOiQysrKtG/fPp04cUJjY2Nr7llYWFA6ndaePXu0c+dOnT59WlNTUzFNHA6fW5DC64EW4uVzD6G1INFD3OjBLfQQH59bkMLrYTO14OyB+tGjR7py5Yo6Ojr09u1bNTQ0qK2tTdPT03GPtiFzc3NqaGhQV1fXul+/ceOGbt++rbt372p4eFg7duxQW1ubFhYWCjzpxg0MDCidTmtoaEjPnz/X0tKSjh49qrm5udw9ly9f1tOnT9Xd3a2BgQGNj4/r1KlTMU7tP99bkMLrgRbi43sPobUg0UOc6ME99BAP31uQwuthU7VgjmpqarJ0Op37fHl52aqrq62zszPGqaKRZD09PbnPs9msJZNJu3nzZu7azMyMJRIJe/DgQQwTRjM9PW2SbGBgwMx+7VBSUmLd3d25ez5+/GiSbHBwMK4xvRdSC2Zh9kALhRNSDyG2YEYPhUQP7qOHwgipBbMwewi5BSdfoV5cXNTIyIhaW1tz14qLi9Xa2qrBwcEYJ8uPz58/a3Jycs1+FRUVam5u9mq/Hz9+SJJ2794tSRoZGdHS0tKavQ4cOKBUKuXVXi4JvQUpjB5ooTBC7yGEFiR6KBR68AM9/H2htyCF0UPILTh5oP7+/buWl5dVVVW15npVVZUmJydjmip/Vnbweb9sNqtLly7p8OHDOnjwoKRfe5WWlqqysnLNvT7t5ZrQW5D874EWCif0HnxvQaKHQqIH99FDYYTeguR/D6G38J+4B4Cf0um03r9/r1evXsU9ChArWgBW0QOwih6AX0JvwclXqPfu3astW7b89i5vU1NTSiaTMU2VPys7+Lpfe3u7nj17pv7+ftXU1OSuJ5NJLS4uamZmZs39vuzlotBbkPzugRYKK/QefG5BoodCowe30UPhhN6C5HcPm6EFJw/UpaWlamxsVG9vb+5aNptVb2+vWlpaYpwsP+rr65VMJtfsNzs7q+HhYaf3MzO1t7erp6dHfX19qq+vX/P1xsZGlZSUrNlrbGxMX79+dXovl4XeguRnD7QQj9B78LEFiR7iQg9uoofCC70Fyc8eNlULsb4l2v/w8OFDSyQSdv/+ffvw4YOdO3fOKisrbXJyMu7RNiSTydjo6KiNjo6aJLt165aNjo7aly9fzMzs+vXrVllZaU+ePLF3797Z8ePHrb6+3ubn52Oe/L+7cOGCVVRU2IsXL2xiYiL38fPnz9w958+ft1QqZX19ffbmzRtraWmxlpaWGKf2n+8tmIXXAy3Ex/ceQmvBjB7iRA/uoYd4+N6CWXg9bKYWnD1Qm5nduXPHUqmUlZaWWlNTkw0NDcU90ob19/ebpN8+zpw5Y2a/3v7+6tWrVlVVZYlEwo4cOWJjY2PxDv1/rLePJLt3717unvn5ebt48aLt2rXLtm/fbidPnrSJiYn4hg6Ezy2YhdcDLcTL5x5Ca8GMHuJGD26hh/j43IJZeD1sphaKzMzy81o3AAAAAACbh5N/Qw0AAAAAgOs4UAMAAAAAEAEHagAAAAAAIuBADQAAAABABByoAQAAAACIgAM1AAAAAAARcKAGAAAAACACDtQAAAAAAETAgRoAAAAAgAg4UAMAAAAAEAEHagAAAAAAIuBADQAAAABABP8AsJArfRPOhysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "columns = 5\n",
    "rows = 2\n",
    "for i in range(0, columns*rows):\n",
    "    img = X_test[position_misclass[i]]\n",
    "    fig.add_subplot(rows, columns, i+1)\n",
    "    fig.tight_layout()\n",
    "    plt.title(\"Actual category: {}\".format(int(y_test[position_misclass[i]])))\n",
    "    plt.imshow(img,cmap=plt.cm.binary)\n",
    "fig.suptitle('Mis-classified images and their actual category', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test,y_pred_category,normalize=\"true\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=body_parts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp.plot()\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix shows the highest number of predictions along the diagonal, showing that the majority of images were correctly classified.\n",
    "\n",
    "However there are some regions that stand out from the diagonal as cases where a large number of images have been incorrectly classified as another class.\n",
    "\n",
    "For example leg was mis-classified as knee in 0.091 fraction out of the leg images and conversly, knee was mis-classified as leg in 0.092 fraction out of the knee images.\n",
    "\n",
    "A similar trend holds for foot and leg & foot and arm.\n",
    "\n",
    "Hand stands out as being mis-classified as arm in 0.064 fraction of the hand images but the converse of arm being mis-classified as hand is much less significant.\n",
    "\n",
    "\n",
    "More generally, ear, eye, face and mouth have the highest true positive rates while foot and leg have the lowest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For your own interest (not marked) run the first layer kernels over `myface`. You should see several copies of the image, all with varying transformations. In your training you may find different kernels, but I can clearly spot a bluring kernel in my first layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(8):\n",
    "    plt.subplot(8,2,2*(i)+1)\n",
    "\n",
    "    sns.heatmap(model.layers[1].get_weights()[0][:,:,:,i][:,:,0], cmap='Greys',annot=True,fmt=\".1f\")\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.subplot(8,2,2*i+2)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(run_kernel(myface,model.layers[1].get_weights()[0][:,:,:,i]),cmap=plt.cm.binary,norm=norm(0,1))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto-encoders (3 marks)\n",
    "---\n",
    "In this section, we will construct and train an auto-encoder as an example of **unsupervised learning.** This type of model is made up of an **encoder** that maps an image of shape $(N,M,C)$ into a latent vector of shape $(L,)$; and a **decoder** which maps the latent vector into an image with the same shape as the original. The latent, or encoded, vector will typically be small in size compared to the input image (_i.e._ $L \\ll N \\times M \\times C$), meaning that it is an **information bottleneck.** The aim of the auto-encoder is therefore to learn the most efficient encoding of a class of images (here: quickdraw dataset) that allows the decoder to reconstruct the original as well as possible under the bottleneck constraint.\n",
    "\n",
    "We will break down this task into a few sub-tasks, to make it easier to get through."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Construct the encoder (1 Marks)\n",
    "\n",
    "* Use the `Keras` functional API to construct the encoder model. \n",
    "* The shape of the input layer should match the shape of the prepared images .\n",
    "* Add some convolutional and pooling layers in a sensible configuration. \n",
    "    * Using a decreasing number of kernels for each convolutional layer (_e.g._ $16 \\to 8 \\to 4 \\to 2 \\to 1$) works quite well.\n",
    "    * For this task it is worthwhile to give some thought to padding: You want to have intermediate layers with shapes that are easy to reconstruct with the decoder. As a starting point, consider using `padding='same'` for your conv. layers.\n",
    "    * **See also the note below.**\n",
    "* **Flatten** the output of the last convolutional layer, and use this as the latent vector. \n",
    "    * For convenience, we choose to make the latent vector have values in the range [0,1], so add the appropriate activation function to the preceeding convolutional layer.\n",
    "* Display the model architecture using the `<model>.summary()` method, optionally also the `plot_model(<model>, show_shapes=True)` method.\n",
    "    * These methods are also very useful for debugging, when you are building your network. For instance, start with the simplest possible `Keras` model; plot the architecture using one of these methods; modify the model slightly; plot the architecture again to see the effect; _etc._\n",
    "    * Make sure that the output of the encoder is indeed a bottleneck, _i.e._ that the number of output nodes is significantly smaller than the number of pixels in the image. (_Hint: A latent vector could have size $49 \\ll 28 \\times 28 \\times 1 = 784$._)\n",
    "    \n",
    "**Note:** Choosing the architecture for an auto-encoder can be a bit tricky. The input images we use have shape $(28,28,1)$, and when we're pooling we typically downsample by a factor of $2$. If we use \"same\" padding, this means that we can go down to $(7,7,F)$ with no issues. However, if we downsample further (_e.g._ $7\\to 3$) it will be tricky for the decoder to reconstruct, since upsampling would result in $3\\to 6$. Therefore, to keep things simple, it may be best to not downsample further than $(7,7,F)$. However, you can still add _multiple convolutional layers in a row without pooling._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_img = Input(shape=(28,28,1), name=\"encoder_input\")\n",
    "enc_conv1 = Conv2D(16, kernel_size=3, activation='relu', padding='same')(input_img)\n",
    "enc_pool1 = MaxPooling2D((2, 2))(enc_conv1)\n",
    "enc_conv2 = Conv2D(8, kernel_size=3, activation='relu', padding='same')(enc_pool1)\n",
    "enc_pool2 = MaxPooling2D((2, 2))(enc_conv2)\n",
    "enc_conv3 = Conv2D(4, kernel_size=3, activation='relu', padding='same')(enc_pool2)\n",
    "enc_conv4 = Conv2D(2, kernel_size=3, activation='relu', padding='same')(enc_conv3)\n",
    "enc_conv5 = Conv2D(1, kernel_size=3, activation='sigmoid', padding='same')(enc_conv4)\n",
    "latent_vector = Flatten()(enc_conv5)\n",
    "latent_vector = Reshape((7,7,1))(latent_vector) # this is added code from the next task to reshape the output back to before flattening. Flattening step was still included to demonstrate its correct usage.\n",
    "encoder = Model(inputs=input_img, outputs=latent_vector, name=\"encoder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 28, 28, 16)        160       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 14, 14, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_15 (Conv2D)           (None, 14, 14, 8)         1160      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 7, 7, 8)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 7, 7, 4)           292       \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 7, 7, 2)           74        \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 7, 7, 1)           19        \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 49)                0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 7, 7, 1)           0         \n",
      "=================================================================\n",
      "Total params: 1,705\n",
      "Trainable params: 1,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Construct the decoder (1 Mark)\n",
    "\n",
    "* Use the `Keras` functional API to construct the decoder model. \n",
    "* **Reshape** the latent vector to have the shape of the last convolutional layer in the encoder.\n",
    "* The shape of the input layer should match the shape of the latent vector.\n",
    "* Add some convolutional and **upsampling** layers in a sensible configuration. \n",
    "    * You can use the same order of kernels (_e.g._ $16 \\to 8 \\to 4 \\to 2 \\to 1$) as in the encoder.\n",
    "    * Many of the notes and considerations mentioned for the encoder hold for the decoder as well.\n",
    "* The output of the decoder should have the same shape as the number images. Also, choose an output activation that is appropriate to the dataset.\n",
    "* Display the model architecture using the `<model>.summary()` method, optionally also the `plot_model(<model>, show_shapes=True)` method.\n",
    "    * As for the encoder, these methods can be very useful for debugging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_layer = Input(shape=(7,7,1), name=\"decoder_input\")\n",
    "dec_conv1 = Conv2D(16, kernel_size=3, activation='relu', padding='same')(input_layer)\n",
    "dec_conv2 = Conv2D(8, kernel_size=3, activation='relu', padding='same')(dec_conv1)\n",
    "dec_conv3 = Conv2D(4, kernel_size=3, activation='relu', padding='same')(dec_conv2)\n",
    "dec_upsample1 = UpSampling2D((2, 2))(dec_conv3)\n",
    "dec_conv4 = Conv2D(2, kernel_size=3, activation='relu', padding='same')(dec_upsample1)\n",
    "dec_upsample2 = UpSampling2D((2, 2))(dec_conv4)\n",
    "dec_output = Conv2D(1, kernel_size=3, activation='sigmoid', padding='same')(dec_upsample2)\n",
    "decoder = Model(inputs=input_layer, outputs=dec_output, name=\"decoder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"decoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "decoder_input (InputLayer)   [(None, 7, 7, 1)]         0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 7, 7, 16)          160       \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 7, 7, 8)           1160      \n",
      "_________________________________________________________________\n",
      "conv2d_21 (Conv2D)           (None, 7, 7, 4)           292       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 14, 14, 4)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_22 (Conv2D)           (None, 14, 14, 2)         74        \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 28, 28, 2)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_23 (Conv2D)           (None, 28, 28, 1)         19        \n",
      "=================================================================\n",
      "Total params: 1,705\n",
      "Trainable params: 1,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Construct the auto-encoder (given, not marked)\n",
    "\n",
    "Connecting the encoder and decoder to form the auto-encoder is a slightly tricky use of the `Keras` functional API, so I'll just give that to you. Assuming you have given you encoder and decoder intuitive names, the following should do the trick:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"ConvAE\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "encoder (Functional)         (None, 7, 7, 1)           1705      \n",
      "_________________________________________________________________\n",
      "decoder (Functional)         (None, 28, 28, 1)         1705      \n",
      "=================================================================\n",
      "Total params: 3,410\n",
      "Trainable params: 3,410\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "i   = encoder.input\n",
    "cae = Model(i, decoder(encoder(i)), name='ConvAE')\n",
    "cae.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try to make sure that you understand what is going on here: In the `Keras` functional API, layers and even entire models work like functions (thus the name). What we're doing above is passing an input image `i` through the encoder (resulting in a latent vector); passing the output of `encoder` as the input to `decoder` (resulting in a reconstructed image); and finally using the output of `decoder` as the output of the auto-encoder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9. Compile and train the auto-encoder (1 Mark)\n",
    "\n",
    "* Choose an appropriate loss function.\n",
    "* Train for ca. 10 epochs with some data held out for validation. Time for another coffee break!\n",
    "* Plot the loss curve(s).\n",
    "* Perform the auto-encoding for the testing images (use the `<model>.predict()` method) and display the first couple of _(a)_ original and _(b)_ auto-encoded images to see whether the outputs resembles the original images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "cae.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "125/125 [==============================] - 43s 337ms/step - loss: 0.4498 - val_loss: 0.3259\n",
      "Epoch 2/10\n",
      "125/125 [==============================] - 22s 175ms/step - loss: 0.2690 - val_loss: 0.2406\n",
      "Epoch 3/10\n",
      "125/125 [==============================] - 18s 140ms/step - loss: 0.2337 - val_loss: 0.2286\n",
      "Epoch 4/10\n",
      "125/125 [==============================] - 17s 137ms/step - loss: 0.2253 - val_loss: 0.2225\n",
      "Epoch 5/10\n",
      "125/125 [==============================] - 21s 166ms/step - loss: 0.2202 - val_loss: 0.2180\n",
      "Epoch 6/10\n",
      "125/125 [==============================] - 21s 168ms/step - loss: 0.2159 - val_loss: 0.2140\n",
      "Epoch 7/10\n",
      "125/125 [==============================] - 23s 182ms/step - loss: 0.2119 - val_loss: 0.2099\n",
      "Epoch 8/10\n",
      "125/125 [==============================] - 17s 136ms/step - loss: 0.2081 - val_loss: 0.2062\n",
      "Epoch 9/10\n",
      "125/125 [==============================] - 19s 151ms/step - loss: 0.2046 - val_loss: 0.2030\n",
      "Epoch 10/10\n",
      "125/125 [==============================] - 17s 137ms/step - loss: 0.2015 - val_loss: 0.2000\n"
     ]
    }
   ],
   "source": [
    "history_cae = cae.fit(X_train,X_train, epochs=10, batch_size=1024, shuffle=True, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams.update(matplotlib.rcParamsDefault)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_cae.history[\"loss\"])\n",
    "plt.plot(history_cae.history[\"val_loss\"])\n",
    "plt.title(\"Model Loss\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.legend([\"training loss\",\"validation loss\"], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "cae_pred = cae.predict(X_test[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "columns = 5\n",
    "rows = 2\n",
    "for i in range(0, columns*rows):\n",
    "    fig.add_subplot(rows, columns, (i+1))\n",
    "    fig.tight_layout()\n",
    "    if i<columns:\n",
    "        img = X_test[i]\n",
    "        plt.title(\"Original image\")\n",
    "    else:\n",
    "        img = cae_pred[i-columns]\n",
    "        plt.title(\"Auto-encoded image\")\n",
    "    plt.imshow(img,cmap=plt.cm.binary)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10. Plot images in the laten space\n",
    "- Predict 10 images into the laten space using the encoder only.\n",
    "- Reshape the output to images and plot the laten space images. e.g. if your encoder outputs 49 features, make 7x7 image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_imgs = encoder.predict(X_test[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 7, 7, 1)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_imgs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#latent_imgs_shaped = latent_imgs.reshape(7,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "columns = 5\n",
    "rows = 2\n",
    "for i in range(0, columns*rows):\n",
    "    fig.add_subplot(rows, columns, (i+1))\n",
    "    fig.tight_layout()\n",
    "    img = latent_imgs[i]\n",
    "    plt.imshow(img,cmap=plt.cm.binary)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anomaly detection (2 marks)\n",
    "---\n",
    "\n",
    "We have introduced auto-encoders as a form of unsupervised learning, since we are not using the image labels during training. This means that auto-encoders are not ideal for image classification (at least not since we actually _have_ the labels), but they can be used for something else: anomaly detection. This is the task of identifying examples that the model considers \"anomalous\" with respect to the dataset used during training. \n",
    "\n",
    "First, we'll load in some \"anomalous\" data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_olivetti_faces\n",
    "faces = fetch_olivetti_faces(shuffle=True)['images']\n",
    "faces = faces[:,4:-4:2,4:-4:2,np.newaxis]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9. Inspect the face images and the auto-encoder result (1 mark)\n",
    "\n",
    "* How many samples does the \"outlier\" dataset (_i.e._ `faces`) contain?\n",
    "* What is the shape of the images and what is the range of pixel intensities? Does this conform with the preprocessed quickdraw images?\n",
    "* Display the first few face images.\n",
    "* Get the output/prediction of the auto-encoder from the previous section when applied to all of the faces.\n",
    "* Show the auto-encoded versions of the same faces you showed above. Discuss the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The outlier dataset contains 400 samples\n"
     ]
    }
   ],
   "source": [
    "print(\"The outlier dataset contains\",faces.shape[0],\"samples\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the images is 28 by 28 pixels\n"
     ]
    }
   ],
   "source": [
    "print(\"The shape of the images is\",faces.shape[1],\"by\",faces.shape[2],\"pixels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9710744, 0.020661157)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces.max(),faces.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixel intensities range from 0.020661157 to 0.9710744\n"
     ]
    }
   ],
   "source": [
    "print(\"Pixel intensities range from\",faces.min(),\"to\",faces.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indicates that the faces dataset conforms with the preprocessed quickdraw images as both datasets have images with 28 by 28 pixels and pixel intensities ranging from 0 to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "columns = 5\n",
    "rows = 2\n",
    "for i in range(0, columns*rows):\n",
    "    fig.add_subplot(rows, columns, (i+1))\n",
    "    fig.tight_layout()\n",
    "    img = faces[i]\n",
    "    plt.imshow(img,cmap=plt.cm.binary)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prediction of the auto-encoder from the previous section when applied to all of the faces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto_encoded_faces = cae.predict(faces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auto-encoded versions of the same faces shown above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "columns = 5\n",
    "rows = 2\n",
    "for i in range(0, columns*rows):\n",
    "    fig.add_subplot(rows, columns, i+1)\n",
    "    fig.tight_layout()\n",
    "    img = auto_encoded_faces[i]\n",
    "    plt.imshow(img,cmap=plt.cm.binary)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of plotting the the first 10 face images and their auto-encoded versions are displayed above. These results show that the auto-encoder is not able produce good decodings of the face images as the decoded versions are very different from the original images. This shows that the auto-encoder does not work well with images that it was not trained on and the kernels that were learned for the quickdraw images do not apply well to the images of faces. The conclusion of the results is that the images of faces have very different characteristics to the images from quickdraw and hence the trained auto-encoder model does not generalise well to the extra face images, and this is the behaviour the we expect to see."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Indicative answers:_\n",
    "    \n",
    "* The face images have the same shape and pixel intensity range as the preprocessed quickdraw images, so they are valid inputs to the auto-encoder.\n",
    "* We can see that the auto-encoder transformed faces are _very_ unlike the input images. This is because the auto-encoder was trained to learn an efficient representation of hand-written digits which is not necessarily an efficient representation for other image domains, _e.g._ faces. This examples shows that this is clearly the case. From the point of view of the auto-encoder, images of faces are _anomalies_ in that they are fundamentally unlike the images on which it was trained, and therefore we shouldn't expect it to do a good job in encoding them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11. Perform anomaly detection (1 mark)\n",
    "Imagine now that we had a dataset comprised of mostly quickdraw images, but also a small subset of anomalies or \"outliers\"; here, in the form of black-and-white images of faceses, with the same shape as the quickdraw images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combined MNIST images and \"outliers\" in a mixed dataset\n",
    "mixed = np.vstack((X_test, faces))\n",
    "\n",
    "# Shuffle the mixed dataset so the \"outliers\" are randomly distributed\n",
    "indices = np.random.permutation(mixed.shape[0])\n",
    "mixed   = mixed[indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The auto-encoder was trained to minimise the difference between the original and the auto-encoded image, so let's use binary cross-entropy (BCE) as our metric for the difference between an image and its auto-encoded version. The `binary_crossentropy` method provided below computes **pixel-wise BCE** for two (arrays of) images: the input and the output image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_crossentropy (img_in, img_out):\n",
    "    assert img_in.shape == img_out.shape\n",
    "    eps = np.finfo(float).eps\n",
    "    img_out = np.clip(img_out, eps, 1. - eps)\n",
    "    return - (img_in * np.log(img_out) + (1 - img_in) * np.log(1 - img_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "* Define a new method called `difference` which takes as input two arrays of images (`img_in` and `img_out`; similar to the `binary_crossentropy`); computes the **average BCE value for each image,** or row; and returns a vector of these difference measures.\n",
    "* For each image, or row, in `mixed`, compute the difference score of the auto-encoded image wrt. the original image. This is a measure of how \"inlier\"- or \"outlier\"-like an image is.\n",
    "* Make a histogram of these scores, and see if you can identify any structure. It might be useful to use a logarithmic x-axis (see `plt.xscale`) along with logarithmic x-axis bins (see `np.logspace`).\n",
    "* Show the 9 least and the 9 most outlying images, according to this difference score. Discuss the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto_encoded_mixed = cae.predict(mixed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def difference(arr1,arr2):\n",
    "    \n",
    "    return binary_crossentropy(arr1,arr2).mean(axis=(1,2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "difference_arr = difference(mixed,auto_encoded_mixed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40400,)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(difference_arr,bins = np.linspace(0,1,21))\n",
    "plt.yscale(\"log\")\n",
    "plt.title(\"Histogram of count against the difference metric\")\n",
    "plt.xlabel(\"difference metric\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This histogram clearly shows the formation of two groups of images that are separated by the difference score. Images that are in the group with difference score of 0.8 and above are very likely to be images of faces rather than the quick draw images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "least_diff = np.argsort(difference_arr)[0:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_diff = np.argsort(difference_arr)[-9:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "columns = 5\n",
    "rows = 2\n",
    "fig.suptitle(\"9 least outlying images\", fontsize=16)\n",
    "for i in range(0, (columns*rows)-1):\n",
    "    fig.add_subplot(rows, columns, (i+1))\n",
    "    fig.tight_layout()\n",
    "    img = mixed[least_diff[i]]\n",
    "    plt.imshow(img,cmap=plt.cm.binary)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6c6C+EqveR/1dJnj33Xdt6dKlVlRUZNOmTbM2bZIfhVatWiW3O9ax19TUWHV1ddBeVlZmZmbnnHOO/exnP0trX9u0aWOf/OQnGz7WsXv3bvvJT35iP/zhD+3LX/6yXXvttdapU6e0XhMAcOLx6+UA0IL8x3/8h5l9/J96NqX6z6k++uij8u9/85vfmNmHn7ttvOhurKyszH74wx+amdmbb76Z9Hf1x6m+jznVfXziiSfkdzNPmzbNdu3aZXl5eTZq1KiG9vr9XbFihXzdF154QbYfz77W+1//639Zz5497emnn7Z77rnHamtr7dprr234DYc4nXfeeZaTk2OLFi2y9957L/j75cuX21tvvRX7fhyP+n/oKS0tDRbcZma/+93v5Hb1X8/15JNPyuv3hz/8QW532WWXmZnZs88++5Hf/52KLl262A9+8AMrKCiwffv2yWsAAMg8LLoBIMssX748+LzpkSNH7F//9V/tV7/6lQ0YMMC++93vNtPe/Y8vfvGL1qVLF1u8eLH967/+q0VR1PB3S5YssR/96EdmZvZP//RPSe1PPPGE7d+/P3i95557zszM+vTpk9Teu3dvMzN755130t7HG264wcrLy23z5s02derUpMXUmjVr7Jvf/KaZmd12223Wvn37hr+76KKLrFWrVvbXv/416afuURTZT3/6U/vTn/4k369Hjx7Wtm1b27p1a8PiL125ubl266232pEjRxr+keXjBKh9HH379rUrr7zS6urq7NZbb036SX9NTY3deuutSdc5E51yyinWunVre/vttxuC9Oo999xzdv/998vtbrjhBuvZs6etXbvWvvvd7yZ9hODdd9+1u+++W243cuRImzRpkm3YsMGuu+46+ZPy2tpa+/3vf2+VlZVmZrZv3z77yU9+kvQZ73pz58616upqa926dcPYBwBkNn69HACyzK9//Wv71a9+ZaNGjbJevXrZwYMHbf78+VZZWWkDBgyw6dOnZ8SvnBYXF9vvf/97u+GGG+y73/2u/fa3v7WRI0fatm3bbPbs2XbkyBG75ZZb7Itf/GLDNuvWrbNPf/rT1qFDBzvjjDOsrKzMjhw5Ym+//batXLnS2rZta/fdd1/S+0yaNMkefvhh+/a3v20vv/yyFRUVWU5Ojv3DP/yDnX322R+5j+3atbOnnnrKPvGJT9gvfvELe/HFF+2ss86yPXv22MyZM+3AgQM2ceJEu+uuu5K2Kysrs9tuu83+z//5P3bxxRfbeeedZ4WFhfbWW2/Z+vXr7Z//+Z/txz/+cfB+ubm5dtVVV9lTTz1lI0aMsHPPPdc6duxoZmb//d//nfK5/fKXv2z33HOPHTx40E4//fSGn8KeCL/4xS9s6dKlNnPmTKuoqLDzzz/foiiy2bNnW7du3eyqq66yZ599ttl/08LTvXt3mzJlStK1Ky0ttZUrV9rixYvte9/7XsM/CCXq2LGj/e53v7PLL7/c7rvvPvvzn/9so0ePtqqqKnvllVfs6quvtgULFtj69euDY3/44Yeturra/vKXv9igQYNs+PDhVlFRYVEU2dq1a+2tt96yQ4cO2YoVK6y4uNgOHTpk3/zmN+2f/umfbNiwYTZw4EDLzc21tWvX2vz5883M7Lvf/a716NHjhJwzAMDx4SfdAJBlPvnJT9rEiRNt/fr19swzz9isWbOsvLzc7rvvPlu6dGks6dEf1xVXXGGLFy+2m266yfbu3WtPPfWULVq0yM477zx7/PHHG37FvN5ZZ51lP/7xj+3CCy+0zZs327PPPmt/+9vfrHXr1jZ58mRbunSpfeITn0ja5vLLL7f/+3//r5122mk2c+ZM+81vfmMPPfRQyr96e+aZZ9qbb75pkydPttatW9u0adNs7ty5NnLkSPvFL35hzz//vFxA3n///faf//mfdsopp9jrr79ur7zyig0dOtTmz59vEydOdN/vV7/6lX35y1+2nJwce+qpp+yhhx6yhx56KKV9rVdUVGQjRowwM7PJkyente3xKi0ttYULF9rkyZOtQ4cO9vzzz9sbb7xh//t//2+bP39+Q8J89+7dT+h+peP++++3hx56yEaOHGmLFi2yF1980Tp27GiPP/64/cu//Iu73UUXXWQLFiywa6+91qqqquzpp5+2jRs32j333GO/+93vbOvWrdaqVavgmwPy8vLsb3/7m/3hD3+wCRMm2Pr1623atGk2c+ZM279/v33mM5+xadOmWf/+/c3sw1T2X/7yl/apT33KDh48aNOnT7enn37atm3bZtddd53NmDGj4eMWAIDMlxNl+u+BAQCAJO+9954NHjzY8vPzbdOmTQ0/LW9u1dXV1q9fP6upqbHKysqMXng3tTlz5tj5559vw4YNs6VLlzb37gAAMgg/6QYAIMvceeedFkWR3Xrrrc2y4F64cGHQtn37drvpppts165ddsUVV7TIBff27dsb0uwTLVu2rOFjErfccsuJ3i0AQIbjJ90AAGSBZ5991p555hl75513bMGCBVZSUmIrVqywgoKCE74vOTk51rt3bxsyZIh169bNNm3aZEuWLLG9e/daeXm5vfrqqw1fldWSvPLKK3bhhRfa0KFDrV+/ftahQwdbs2aNLV682Orq6uySSy6xF198UaaiAwBOXtwVAADIAosXL7bf/OY3lpeXZxMmTLCf/OQnzbLgNjP73ve+ZzNmzLC33nrLdu3aZW3btrX+/fvbFVdcYVOnTrVu3bo1y37F7ZRTTrHJkyfb7Nmz7bXXXrM9e/ZYXl6enX322XbjjTfaF7/4RRbcAIAAP+kGAAAAACAmfKYbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJrEtuh988EHr27evtW/f3saOHWsLFy6M662ArEFdAMmoCSBEXQAh6gLZLCeKoqipX/SJJ56wz3/+8/bLX/7Sxo4daw888IA9+eSTtnLlSisqKvrIbevq6mzz5s2Wl5dnOTk5Tb1rOMlFUWR79uyx0tJSa9XqxP6iB3WBTJStNWFGXSA+1AUQoi6AUMp1EcVgzJgx0eTJkxv+/+jRo1FpaWl07733HnPbDRs2RGbGH/7E+mfDhg1xDP2PRF3wJ5P/ZFtNRBF1wZ/4/1AX/OFP+Ie64A9/wj/Hqos21sQOHTpkixYtsjvuuKOhrVWrVjZhwgSbN29e0P/gwYN28ODBhv+P/v8/eP/Hf/xHa9u2bUP7mjVr5PutW7cuaNu7d6/s26ZNeLiJ752orq4upe2PHDkit+/YsWPQ5v3rR5cuXYK2/fv3y77t27cP2rZs2RK0de7cOeXt8/LyZF9l+/btsr2wsDCltk6dOsnt1bndtWuX7KvOjbpeZmbV1dVJ/3/06FFbsWJFWsfcFJqqLi655BLLzc1taK+pqZHvd+DAgaBt9+7dsq86n951Vjp06BC0tW7dWvZVNZB4PInUWPWoOkycP44lPz8/aOvatavsW1tbG7R540+NM1UXAwcOlNv37ds3aBswYIDsW1JSErR5x5B4zXbv3m1lZWUZXxNmfl3069cvaWx547dHjx5BmzcvV1RUBG379u2Tfbdu3Rq0HT16NGjz7hft2rUL2rzxq9q9eUBdfzWvevcL9V6R88txhw8fDtoqKytl37POOitoU+egtLRUbq9413zDhg1B2549e2TfxHNTV1dn69evz+q6eP3115Ou7e9+9zu5vXpm2rx5s/teqWxvpu8D6n6jxo6Zfl7wno0U7x6i7o/euE51+3R4+6X2Qd0f+/TpI7fv3r170Obtq6r5Xr16yb5nnnlmw3/X1tbalVdemdV1ce211yadV28+UOdu48aNsq+aV71xre4NincPUOPEG1Pq/uY9r6jncPVs5NVgQUFB0ObN4du2bQva1D3ATD9jqueoU045RW5fXl4etPXr10/2Va+rzoFZ8v7u2bPHTjnllGPWRZMvunfs2GFHjx614uLipPbi4mJ79913g/733nuv/fCHPwza27Ztm3RA3oO5mtS9hyjVns6vx6SzfTr7pQa6t2hJ9XXT2V69v8d7XfUa6pp51zGdvmoi8yYRb39P9K8WNVVd5ObmJp0X79qlM6bU+Enn/Ki+TVGD6eyXuv7e8Sqpjl+vbzo3MfW63g1T/eOdt0BSk736Bz0zfRPL9Jow8+uiVatWSdfbO5Z05mV1nbx6S3WspTNPpTOHH2/fdOZ17xhUezrnVrV5D2GK94B6IufCptCUddG5c+ekecE7n+qe6s1/qf5Awiz154105s90nlfSuT+ms+hO596S6vubpX5uvWujrq93blVf7z6k7jnZXBe5ublJ84V3PtU/kjbF3JHqufPmz3Tm2uNd4xzv/SKd5yivLtRrqPn+RD9HqRo61rVt9vTyO+64w2pqahr+qH+VBk421AUQoi6AEHUBhKgLZJom/0l39+7drXXr1sGvlVVWVspff2zXrp3814Kqqqqkf8lI51ed1K/YmJmVlZV95L4nUr9C4v0KiqKOyfuXFfWvOOonUV5f9a9T3r/MqH/F8f4lVP0KydChQ2XfkSNHBm3qX5e8f/VK51fWqqqqgrZly5bJvo3Po/frnXFrqrrYtWtX0r8GNv71+cTtG/P+JV+NCe/XaQ4dOhS0qf1P5ycX3n6pX8Hy/oVV9VW/muj9qp13vIr6VSVvflLXQY31TZs2ye3VT6C8vmp+837dKvHXCNOZ15pSujVh5tdFdXV10jw4ePBgub0aP2effbbsq8aqNy+ruVL9yrk3znbu3Bm0eWM1nV8v37FjR9Cmft3P+xiCug7eHKpqXv26oZnZkCFDUtpe3UPM9JyhXtPM7J133gnavHp94403Gv471V8BbWpNXReJ18ubP9WzSe/evWVfdV68jy6pnzypsep9lEyNdXUPMkvvPqaoGk5nv7yf0qmx6tW2Ol+qLtQzkLdf3phR85NqM0v+GKf3kcy4NWVdrF69Oul6ecek5jqvr/q1ZPXRT0///v2DtnSeV7z5Su2vVxfqeNU9z6tBdR/xngXV/OJ9REiNa/VRr/Xr18vt1fy0du1a2bdnz55Bm7o2ZpYU3uetVxpr8p90t23b1kaNGmUzZsxoaKurq7MZM2bYuHHjmvrtgKxAXQDJqAkgRF0AIeoCLUGT/6TbzGzq1Kl200032ejRo23MmDH2wAMPWG1trd1yyy1xvB2QFagLIBk1AYSoCyBEXSDbxbLo/tSnPmXbt2+3O++807Zu3WojRoywl156KQhAAE4m1AWQjJoAQtQFEKIukO1iWXSbmU2ZMsWmTJkS18sDWYm6AJJRE0CIugBC1AWyWbOnlwMAAAAA0FLF9pPu43XkyJGk7zvzkuFUMt7AgQNl32HDhgVt3pe8n3766UGb+v41Lw1Upft5yXoqXVGlzprpNE2VRuulxqrEQO8YVAJ6t27dZN/TTjstaFNJhF6SoUrNVMdlppNnveTQDz74IOn/VRp0NunUqVNSArx37VRiqtdXpVN6ScCqDlVyqJfyrOrCq0GVxqkSKz3ppMaqY0jne+JVcqmZ/mYBdbze+VL7641hlVLqJa0m1lA65zRTdejQIelce6nDF110UdDmzWlqXk5MfU+kkpPToeZFL2Fb1eCsWbNkX5VGq8a1l1R98cUXB23eXKvGsJeKrs6XSrX2UtnVPniJvGofvOTaxBT5I0eO2Pvvvy/7ZYuDBw8mnSvvevTo0SNo82pIzWneuVfXWc1JK1askNs3Tqo28+c/1Z7Od3qrfU1ne+8eoJ6j4voWFXWP9+4BFRUVQVsqc46XXJ1N2rRpk3RtvWeQdKhn7tGjR8u+tbW1QZv61iXvmU3VRTr3IK+GvGeexrzzpY7B++5sVQNeDak6VMfgvVc6z1Hq/up9w0vivJnqcxQ/6QYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmGRskFoURUlBSIMHD5b9VDCICkUy0yEB3gfvVYjXu+++G7R5gVNbtmwJ2rywGvW6S5culX1VKIYKjPK+t1AFpqhADTMdjOOFaKggNBVWU1VVJbdX4XedOnVKeb+Kiopk38a8UJFs0bZt26Rx7I11FQLlHbsKAPFCOdQYPuWUU4I2FahhpoNBvECL6urqoM0L90kMQKqnQmG8YBJ1vCoAxyy9MD5VLyogLp3QNi+MUIWNeNc8cR/U/mSbz33uc0lzuTdO1Jjwzr26t3gBlypERYXNnHrqqXL7tm3bpvT+ZmbLly8P2latWiX7qlAYFYTlUfc3b65V9do4yLKeChJ67LHHgraJEyfK7dV59OZCZfjw4bI9sbYOHDhgL7/8csqvmYlat26dNC94QYAqCC2dEDEvCFI9X/Xs2TNo855X1HOUF66nnje8ACTVrsZ137595fbq2SSdkDrv/qjmdnW8qn7M9DzkzYUqGHjAgAGy76ZNmxr+O51xkalyc3OTjkM9w5jp+693T1XXuaysTPZV11mNtY4dO8rt1TXw7mPqmdurod27dwdtKhBZPV+a6SC0zp07y75q3ZJOSJ/aXo1pM30dvfugOude38SAtlTXFvykGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYZGwMYffu3ZMSXb3E1FmzZgVtXuKfalep22Zm69atC9qGDh0atKnUZDOdljdq1CjZ969//WvQVllZKfuqRGWVTqn230ynsqeTfOslV6rk0MRkv3peeuqKFSuCtmHDhsm+Kr134cKFsu/ZZ599zH3KJh07dkxKqfRSJFWSr5cwq9q9JEw1/lTipJfy7KW2prpfXvq4Og/qWntJ3eq91Pj33su7Dup8qbR4b79UIrRKxTbT85t3vhJTSr3XyyaXXHJJUkrqz3/+c9lPzX8qTdlMz+3e/KfmNTV+zjrrLLm9ug+pOdFMJ3c/8sgjsq+qTTUmvPTedJJv1X3IuxcvWLAgaFu9enXQ5t2fVSKu920XypQpU2T7ueee2/Df6STpZqp27dolzU3ec5RK3lXJ4WZmGzduDNq8OUSloqtvwFDJz2Zm5eXlQZv3XDBv3rygTY0pM51GrJLWR44cKbdXY81LL05M/q6nUsbN9PyivnHBuzelkyyuzsGRI0dk38R0+Wx/hjL78NsiEs+Vlzyv5krvG4/UM5f3HKXuOeoZwnsv9bzijT+1D943s6i5XY0Jb/ypuvDGuro3eN+ao86Deo7ynsPUfSydcew9O38c/KQbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGKSsUFqo0aNSvrwuhe0sWfPnqDNCxRYs2ZN0DZixAjZt7a2NmhLDB+qN3z4cLn9+vXrg7b7779f9r3nnnuCtqlTp8q+27dvD9pUCNPgwYPl9iowYtu2bbJvt27dZLsycODAoE2dg8RAjkTquD744APZV51zFaJiZrZy5cqk/8/2cJxu3bolBQx610iFZ6jgCTN9TQoLC2VfFcSjwtFUcIW3X14IjwqS8kI5VChM4nmq540pr10ZP3580KbCrcx0sIcKG1H7b6bnMu98qXOjzkHj1/X6ZJP169cnBbN487oKgXr11VdlXxWM44V1qdA1Ff6iQpHMdF2pe5uZHivnnHOO7KsCJtVc3b9/f7m9CpDxzoEKqZsxY4bsq8b12LFjgzYVxGWm5xwvvFGdW+916+rq5H9nq8OHDyeFuqqAVzMdgqfC6sx06J4XGKUCBr25TlHXwAs3U/OiV0NqXo6iKGjzgnLVM5MXxjVo0KCgzbtvqyApVZvePKKCsLy6UNfcC4xKPLZ0wtoyVY8ePZLue96cpuYJL4RMvYY3JlSQmrr23r1ZhZBt2LBB9lXX2XsO9oIvG3vvvfdk+/vvvx+0eWGu5513XtDmPUepcZnqvprp8EYVUmum5xEvoC3xPubNgY3xk24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYpKxMYS7d+9OStpU6XNmOsVRpWab6WS8P//5z7Lv3/72t6BNpZ+rNFwzs3fffTdo81LVZ86cGbRNmDBB9q2srAzaxowZE7R5acxLliwJ2gYMGCD7vvDCC0Gbl+KnEmKvvvrqoM1LoX/rrbeCtnXr1sm+L730UtDmjY+1a9cm/f+RI0dkv2zRunXrpPRQL5G8vLw8aPMS7RXvdb32xp5++mnZ3qdPn6DNS7RP9b3M9LcNqBTK+fPny+1Vqq+X4H7hhRcGbSrp2kyPd1UrXsKnsnr1atmuzsGWLVtk38TkTi8NPZuUl5cnpS2/+OKLst9TTz0VtHlJwio51puX1b3l7bffDtqmT58ut1fXrvHcVU+NtdGjR8u+l19+eUrv5aUhq2NQKbtmZs8991zQppKuzXSis7pfqHnMTCf1Ll++XPZV34Jw5ZVXyr6LFi1q+G8v6TubHD58OCmpWD0/mOkxMWzYMNlXJYp7KcvqNVSasjf+1HOUlzyfzretqP1Vr7t37165vUrP91KtVVr7pk2bZF+VyHzRRRcFbePGjZPbq/PoJU2r66C+7cAs+bnJe47NJvn5+UljwBtT6hsZTjvtNNlXpbqXlZXJvt5zdGPe+uT0009P+TXVsamUezP9LKC+FeXvf/+73F59k5JKajdL7zlK1Zb3zK+oZHF1bzPT59F7RnrjjTeO2acxftINAAAAAEBMWHQDAAAAABATFt0AAAAAAMSERTcAAAAAADHJ2CC18ePHJwXj5OTkyH6/+93vgraKigrZVwVaqHA0M7N77rknaFMhFSo4wEx/qH7btm2yrwoa8PZLhZgkfpi/3m9/+1u5/ec///mgLfE8J1IhECoEysxs586dQZs6hsQAp0QqSM0Lexg6dGjQ5p3bxmFY2R6O06dPH+vQoUPD/3shFSrsRIUDmungCG9MqKALFU5XVVUlt0/c92PZvXt30ObNAyoISIUceoE/KizECyZRY0iF0pjp41VtnTp1kturABDvOn7iE58I2tq3by/7Jp4vNS9mm//6r/9KOlevvfaa7KfOszeHqxClv/zlL7KvCpZRAV7qenrv5YXgfe5znwvavGNQYTEqHMgLW+rXr1/Q5o111dcLCFSBXiq4c8iQIXL7v/71r0GbV69qjvTObeJ9JNtDN80+rO3E+vbuqV27dg3a1JxqZnbgwIGgzQuv3b59e9Cm9sELUlOBZV4Ypno28sLNVEimmtfVsZrpseGF16YTPLZ169agTc1ZKrTLTO+vdw9QvHCqxOc7b67JJsXFxUnnpVevXrKfCo305ko113lhmCo8Vo0pb65V9eaFFKtnG4965lJj0nuOSieUVdXFjh07ZF81rqMoCtq8OVs9c6kAazOzs88+W7Yrffv2bfhvb75sLPuftgAAAAAAyFAsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmGRskNpf/vKXpKCD66+/XvZTIRFeeIsKylABImY6BEyFBKhQLzMd7OWFSL377rtBmwoQMTObNWtW0FZQUBC0nX766XJ7FeygwhPMzE477bSgzQurUe0qHEUFrpl9GBDWmBfwpILjNmzYIPs2Dv3I9hCQxx9/PGl8e+dIBXV44SuqrxeIoYJFVOiaN35VYJQ3/tIJtlFhH8uXLw/a1LF6++CFcqjAHC+IRYWYpFMX6tx64UCLFy+W7UrinOGNi2yyfv36pOPwxokal97csWfPnqDNC3xS11/Nafn5+XL72bNnB21emOGMGTOCNm9cL1q0KGh7/fXXg7YvfvGLcnt1vN6co47NC0JTAVsqZHPJkiVyexXo6Fm2bFnQ5gVvJs5PKpgr22zatMk6duzY8P/etVPzjwrhM9N14T1zqcAnNd94AYOqNr17kzo27/muf//+QZu6B3jBc4cOHQravMAqFYTmPQuqcKhnnnkmaFNBhGb6edgLclNzoRc0mxig2xICBmfNmpU0Dr2AVhVO5s0L6pp6Y1UFEqv7unq2N9PX3xsTalx690dVr6tXrw7avPlXrWW886XWIt79sVu3bkGbepZU9xVvH7w5S4V0ehJDOlMNkeMn3QAAAAAAxIRFNwAAAAAAMWHRDQAAAABATFh0AwAAAAAQExbdAAAAAADEJGNja0855ZSk1M1zzjlH9lOpdl5Cp+IlxKokut69ewdtKu3PTCcne4mVKl3y17/+teyr0ilVEqd3vt57772gbcuWLbKvSjRV59vMbOTIkUFbOmmgKk3RS5Q85ZRTgjaVqm0Wpp9me/LmwYMHk1IXvTR5lWTpJTsOHDgwaDv11FNlX/V+Kp21trZWbq/GupeuqtIp1bcKmOmUW5WS66VuqhrwUr3VuPaOwUslb8xLvlXp0V7au9pfL6W0sLCw4b9TTd3MZHl5eUnHr8aOmT5Haj7x+np1cfbZZ6eym/b888/LdpWuqhL5zcxGjx4dtL3//vuyr5pv1THs2rVLbq/ueSqh1kwnOntzjvpmDJX27lHXxkuWV+egbdu2sm9VVVXDfx89etS9v2eLQ4cOJZ2r+fPny34qPdxLslb3Wm+eUfdwlX7u3S/U846X0qyer7xnLq+9scTxcKx98J4tVG16z6jeuGxMnRczfX/2zq2qTe91V61a1fDf3rjIJjt37kx6jvK+EUR9U4l3b6moqAja+vbtK/uq66zmNO/erJ4XvPGnnkG85yg1rtXzXWJqd6KNGzcGbd5YV98g4X27kLoXqnGYzrOM98ylasCbBxL3IdW64CfdAAAAAADEhEU3AAAAAAAxYdENAAAAAEBM0l50z5kzx6688korLS21nJwce/rpp5P+Pooiu/POO61nz57WoUMHmzBhQtLnQYCWhpoAQtQFEKIugBB1gZNB2kFqtbW1Nnz4cPuHf/gHu+6664K/v+++++ynP/2pPfroo1ZRUWHf//73beLEibZ8+XI3pEEZN25cUriBF6qlPuRfWloq+6oPuhcVFcm+ql2Fo6UalGTmh5t17949aPMCAVRQhgoqWLZsmdy+vLw8aPMmLhX2sGPHDtn3jTfeCNrUMXiBFSpEIjFIL5EKksrPz5d9G4+PQ4cO2axZs2Tfj+tE1YTZh/ufGADihS2pMVFcXCz7rlu3LmjzAoRUEFnXrl2DtoKCArm9CozatGmT7FtdXR20eXWh5gEVlqP6melARe+90glSUwEcKmzEe690Ao5UwEsURcfs64UgHa8TWRdlZWVJ89WcOXNkPzWnqPFrpucU71xNmzYtaPPGhKKCzM4//3zZV+3vq6++KvuqMaHmBjV/m+nxp8I4zcwGDBgQtHnnQAX5qBrw6lVdR6+vCv3p0aOH7JsYunbo0CFbtGiR7Hc8TmRdHD58OCngLp2wJS9cT83h3uumGljmhVaefvrpQZsXAqXGWq9evVJ6f7PUw63M9PF6z0aq3XtddX1VDXvzkKrXxOeFY+2D2t4s+Rk1riC1E10XieN45cqVsp8av4khpIlU6Jr3HKXmcBVQ6M1TqgbXrl0r+3rhxYqaV1WbN37VWsabG9TznXcvVvcn9ezrjXUVmuYFDCqpvG6q9/u0F92XXXaZXXbZZfLvoiiyBx54wL73ve/Z1VdfbWZmjz32mBUXF9vTTz9tn/70p9N9OyDjURNAiLoAQtQFEKIucDJo0s90r1mzxrZu3WoTJkxoaMvPz7exY8favHnz5DYHDx603bt3J/0BWoqPUxNm1AVaNuoCCFEXQIi6QEvRpIvu+h/3N/411uLiYvfXYO+9917Lz89v+FNWVtaUuwQ0q49TE2bUBVo26gIIURdAiLpAS9Hs6eV33HGH1dTUNPzZsGFDc+8S0OyoCyBEXQAh6gIIURfINE266K4PWaqsrExqr6yslAFMZh8Gg3Xp0iXpD9BSfJyaMKMu0LJRF0CIugBC1AVairSD1D5KRUWFlZSU2IwZM2zEiBFm9mHS74IFC+zWW29N67X27NmT9P9eCq9KPFXpwGY6vXn16tWyr0pUVgXbrVs3ub1KFxw4cKDs++Mf/zhoUymAZjrhUFFJnGY6vfzyyy+XfV977bWgzUvxU8ebTuqhcvToUdmuEty91208IceV1Oxpypow+/A4E6+Bl9irkiFVkrb3Gt51VkmY6aTcq/1S6axmeqyrlHEznSiqEk29lFP1jQdvvfWW7KvOgZfWrhJNE9OEj7VfKvnTS49V3/Dg1UXiPqSTst1UmrouSkpKkuYFNUeY6XPn/fRDjWGvLtT1V/cLlaJqZg3nINFZZ50l+z711FNBW6rzn5keU963gwwbNixou/7662Xfl19+OWjzxpaaM9T58tKUVQK7d8/zvgVDSZwHTvS9wqzp6+Lw4cNJY8Mbv40XM2b++UwnDTnVpOs+ffqk/JreN8ao5GMvPV09T6p99e6vKtndS2n2UuCVVJ+PvPdSdew9O6vz5d2LE+vNe+84NXVd1NXVJZ0r77k6nWunxoo3f6mxptYyK1askNs3Xh+Z6WdwMz2vevO9eo5Sc4Y3j6j7zbvvviv7qrHm1aua79W3GHjnO9VvUTgR0l507927195///2G/1+zZo29+eabVlhYaOXl5Xb77bfbj370Ixs4cGBDrH9paaldc801TbnfQMagJoAQdQGEqAsgRF3gZJD2ovuNN96wCy+8sOH/p06damZmN910kz3yyCP27W9/22pra+1LX/qSVVdX27nnnmsvvfRS2t+jB2QLagIIURdAiLoAQtQFTgZpL7ovuOAC99dVzD78tYW7777b7r777uPaMSBbUBNAiLoAQtQFEKIucDJo9vRyAAAAAABaqiYNUmtKjz76qBuGk0gFT6iwJq+v9x4qaECFLalAAzMdQOMFUKgABY8KdlCBL/369ZPb19bWBm0qXM1MBxV4oTQq9EyF6HjBKipcygtr6NGjR9DmBZAsXbr0mO+TTUpKSpKOdffu3Slv6/VV18QLnlAhOl6wjaLCebxrp/rm5+fLvirERI0/LxxJ1aYXeqXavWASFVajAtq8/SoqKgravLpQx6ve3yw5XOejfrqQLf7yl78knZe+ffvKftu3bw/aVNidmdkZZ5wRtKVzv1Dhar169ZLbDx48OGjzvn9WBb95ATKq5nv37h20eUGA6nttvXuLGpfevUXNOekENKUa0GWm79teQNby5csb/rs5AgabWqtWrZJCiLxjUiFM3pyk5mXv2qnXVb8O7M3rKuDNu/aqNr37vXoOUvc8r95V8JZ3H1RzgxdqqgKj1PnyQrMU7xjUNfPCaxPfryXcLyoqKpLmTG+uVc/mXkisejb3nm3UOVRzuHft1Fj13kvNy+oZ2pPqs72ZHlPeuVX3HK8u1H1o8+bNQZsKwDbz55dUeR9hSDyGVNcW/KQbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJhkbHr5ggULktL4zjrrLNmvc+fOQdvcuXNlX5VsN2DAANlXpf6q1Nc+ffrI7d9+++2g7aWXXpJ9VRLh3r17ZV+VcKgSL71E9FWrVgVtXpLrjTfeGLSp5GUzs9deey1oGzhwYNDmpZyq5EcvIXH9+vVBm0pUNTMbOXJk0v8fOHDA/vjHP8q+2chLclXjxEs4VmNFpbua6YRMlezoXY/i4uKgTY1f7728sarOg0p49b7ZQI01LyFWpdGuXLlS9lWpm6oGvfOt0nu9/VIppV7fnj17Nvy3l7yeTdq2bZt0/F7q66BBg4I2lQZuZrZu3bqgzUt9TTyf9YYPHx60nX322XL7Rx55JGhr/M0L9dRY976ZQCWNq7HmJeLPnDkzaFuwYIHs++UvfzloU/dBM7OFCxcGbSotXiXUmulkba+GVNrwq6++Kvsmpt575ySbqdRtMz1fe0nAKs3Ym2e6dOkStKlvZPDSgdU3C3jXRbV7zxtqflDbe0ndhYWFQZuaq830fde7P6r9Vc+C3j1TJSh751btgzc+EtOf0/mWgUy1bdu2pOvtPS+rceKdo3TS4NXrqnWA98zWv3//oC2dZ0Fv/KnzoOrCO1/pJNurcblixQrZV90b1BrNuweo5z5vHKtz481v3rd+fBR+0g0AAAAAQExYdAMAAAAAEBMW3QAAAAAAxIRFNwAAAAAAMcnYILW9e/cmfYB/7dq1sp8K4Orevbvs261bt6DNCwRQfVUw2MSJE+X2P//5z4M2L2zptNNOC9q8D+iPGTMmaFOBU6rNTIcqDBkyRPZVYTUTJkyQfSsqKoI2Fe6jXtPMbM2aNUGbFyKheK/b+DWyPQTk4MGDSeEc3jlSoRxeAI3q64VrJQaq1FNhISpoxkwHwHihV+kEi6igC/VeXl2oABAvgEadx61bt8q+KshKBYDs3LlTbq/GtRdWos6jF66S2J7tNWH24flLvN7qvmBmNn/+/KDNO0cqtM8L9Lz44otT6quC3MzMvvvd7wZt3v1C1VZJSYnsq+4X6v7oBbGpkLnRo0fLvipgSJ0XM30vVYFnniVLlgRtXm2recQLtEuc37xng2zStm3bpGAgb05T58g7n6pevHlZ3RvU/Om915YtW2S7oo7BCz5U9zF1vb3AKRW86QVsqbBfFXjmva6an705W13fjh07yr7qmnl9E1/Xmy+zSZs2bZLGoRo7Znqses8r6hnEG9fqOqm+6lnBTF8D79qp+5g3t6n9Suf5TvHmBvUc4z0HKaWlpUFbTU2N7KuOwashdR289Vjic3KqgbT8pBsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYpKxQWqHDx9OCibwgifUB/+9UBj1wflevXrJvsOGDQvahg8fHrR5gQTf+c53grbq6mrZ989//nNK7+W1q9A3L+zitttuC9q2bdsm+6qwkX379sm+5eXlQdvevXuDtqqqKrm9CqzwrqN63VSDXFSgQjbJy8tLOlbvOtfW1gZtXliICm/xQtdUsIzaBy+wR7V7YSEq0GLTpk2yrzpeFSDiBYCkE4zj1YCijqGsrCxo27Nnj9xezRleAIgKjvMCQBKvQ6oBIJmscTCONy+rwDEvfEWNlcGDB8u+KlxMBZZ5c+2//du/BW3Lly+XfX//+98HbSNGjJB9r7766qBN1bYa/2Zmp556atCmAoPM9NygxqSZDsFRc4M3ftX2GzdulH1VQJu3X4n15oVzZpNWrVolXW/v2ileMJO6h3r3X29ub8yb09R7eddOvZfXV40J9Vzh7b8al16YprrvqmcrM3191Fym7neeoqIi2Z7OvJ94L2wJQWodO3ZMujd74WjphAamE6Sm+qr3Us8wZvo+5h2DqgHv3qLuhWpe9p4P1TOTN/7SmYtUX7Xu6du3r9xeBYJ6IYlqreldh8R5wLuHNsZPugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACIScaml3fu3Dkp+S+dtEYvpVkl0A0YMED2HTp0aNCmUvwWLlwot3/hhReCNi+9XCV0qnRWM7MxY8YEbSo10zsuldDppQs+8sgjQdugQYNkX/V+KuGwd+/ecvsuXboEbe+//77sq47BS0LctWtX0v+nmjCYqbp27ZqUuuils6ZDJcemc57UPng1qMa1lyiv0im3bNki+0ZRFLSpNFDvuFQiac+ePWXf4z3nKhXbq1eVuukl/apz4F2HxHprCSnNubm5SfNN586d3X6N9evXT/ZV18Sbv1Rqq0od3rx5s9x+yZIlQdvSpUtlX3X9vWNQ+6vSWb005VTTp830sXmpryo5Vu2Dt706Bq+2VSKvl26f+JyR7fcKsw/TkxOfo7z5QCWVe+nlXiKzot5PzX/pPN95yclqTHhzm7r+6j7kPVeoseol7avX9ZKT1VhV18HbXvFSylXatfrGBbPk85BO6nSmKikpSboXeHXhJYIr6ltNvDlEjQn1Xt5+FRcXB21r1qyRfdVzlDevqn1Qz1HemFI16D1HqXnEm1tS/RYDb32ino28a6PmFy+xPvG+7/VpjJ90AwAAAAAQExbdAAAAAADEhEU3AAAAAAAxYdENAAAAAEBMMjZIbe/evUkfaC8rK5P9qqqqgjYVeGamAym8oAL1Yfpnn302aHv88cfl9iqAxgv3USFiffv2lX3Va6igAy/safHixUGbFwanAntUiJmZ2bvvvhu0jR49Omjzgk3UNVMBEGY6BGXnzp2yb+Nr3hTBY82puLg4KezCC/pQgRRe+IUK4vPCzdS5V+/Vo0cPub0KtFi5cqXsqwJ3hgwZIvuqelXvtW3bNrm9CofxwqXUfnnBJOqcq3315rc9e/YEbV6QmqLmFrOPFwCSyfbv359U2971UNfUqyH1GuXl5SnvkxrXjz76qOyrgqS8YKZTTz01aFNzrZkOydyxY0fQ5s21Khjngw8+kH3//Oc/B20DBw6Ufc8999ygTZ1vLzRL3bdVmJ2Zvhd781viXNYSAgYLCwuTzqs3H5SUlARt3rlXY9Xrq+ZgNdYaB57WU0GA3nVR19m7D6mxop6j0nkvb85Rr+H1VecrHepe7M3vqoa8e146wW3ZoKysLGneT+dZU40TM7PKysqgTT0rmOl7uLoe3n69/fbbQZsXPKyu3eWXXy77qnpT+zV9+nS5vQqT88afCtX1zpd6DTUPeffyYcOGBW2rV6+WfdW18QLeEtdj3hzYWHavQAAAAAAAyGAsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJhmbXr5169akNFqVFmnmJzIrKtnOSxSfNm1a0LZq1aqgTaULm+k0zu7du8u+Z555ZtCmUgTNzHbv3h20LVu2LGirqKiQ28+fPz9o2759u+yrzq2XqFtTUxO0vfHGGynvl3pdLyldjQWVTG8WppqnmjCYqUpKSpKS3lXqtplO2FTXyEyfk3RSg1XiqZdGq2pIpSl77b169ZJ9J02aFLSpMfXqq6/K7ZcuXRq0eWNVJQB7yckq9VKl2XqpsUOHDg3a1q5dK/uqa+Mlhya2p5OGni28+VPN115CsZpTvL5PP/100KbmWjXOzPR9yLt2o0aNCtq8lFt1vCrl1ru/bt68OWh77LHHZF+Vkut9W8Bf//rXoG38+PFBm3e/UXXl1aCqLW/ePHjw4Ee+R7bp1KlTUlK2Sik30+Pa+6YPdR/xnoNS1b9/f9mu5jTv3qKer9SzlZn+BgA1D6r7lZn+thSVdO0pLi6W7epbXDZu3Bi0eeNXbe/dy9V9zHseTnx2bgnfdlFWVpY0tpYvXy77qTnA+3aknj17Bm3efV2l1Ktn+wULFsjtq6urgzZvvlJJ+W+++abse/755wdt6lnyxhtvlNs/99xzQdvpp58u+6r7xfHOuapWzPRY974JZ82aNUGb94xEejkAAAAAABmERTcAAAAAADFh0Q0AAAAAQExYdAMAAAAAEJOMTQr5xCc+kfRB+9WrV8t+KiTg6NGjsq8Kn/A+eL9169agTQVleGEjKhDAC33bt29fyvulQhxU2MyWLVvk9uoceOFUVVVVQZsXbqaCCtTxemEj6YR5JYbd1FPn0CwcH971yhZHjhxJCnVYsWKF7KcCUbyQChVW4/VV1/+tt94K2iorK+X26VBBGxs2bJB9Vb2qcdK3b1+5vQomSae21Tk00wEcaly/9957cvsePXoEbd4xbNq0KWhT4ZFmyfXaEoJxOnXqlHRevUAgNSbUPcRMn7vXXntN9lXhZCoI0AvTVGE13phSc6gXZKWOTZ0bL5hJ3XfVmPT2wZvD1X69++67QdvIkSPl9mp/vetYWFgYtKm5xSw5JKwl1EXnzp2Trrd3jtSc4h2/Cl3zXlfVm5o/VQiVmQ7484LU1LOR97oq5FA9Q6i6NNP3N6+G1P569Tpw4MCgTR2Xdx9UvOAvFfLlXfPEUMlsD6M1+/D8Jc7vXlBWOmNVBRqr5woz/36fKnVvSiU0tZ53DadPnx60jR07Nmjz7k3nnHNO0LZu3TrZV51b796i5hF1DrxnNhXoqV7TzKy0tDRo89afiffiVIMUs3sFAgAAAABABmPRDQAAAABATFh0AwAAAAAQk7QW3ffee6+deeaZlpeXZ0VFRXbNNdfYypUrk/ocOHDAJk+ebN26dbPOnTvbpEmTmuTznUCmoi6AEHUBhKgLIERd4GSQ1qJ79uzZNnnyZJs/f75Nnz7dDh8+bJdeeqnV1tY29PnGN75hzz33nD355JM2e/Zs27x5s1133XVNvuNApqAugBB1AYSoCyBEXeBkkBNFUfRxN96+fbsVFRXZ7Nmzbfz48VZTU2M9evSwP/zhD3b99deb2YeJpEOGDLF58+bZWWeddczX3L17t+Xn59vq1astLy+vof1Tn/qU7K9Ss1etWiX7qmS8dBJi1XupNjOdDjh48GDZVyWHJqZFJlL7qxI+PSpJ0EtYVMmd3bp1k33Va6SaXGqmz+POnTtT3i8vGb5xUu/+/fvt61//utXU1LjX7njFWRff+c53klIbvZR7dZ69JOu1a9cGbSoR30ynbqox6dWVSg32kkNVX+8YKioqgrZx48YFbUVFRXJ7xUuY3b17d9DmpWWrulBzi1eDqoa8hM/Eh5N63jc5JCaa7t+/3771rW/FWhNm8dbFP//zPyel9D7//POyvxprXrpvnz59grZDhw7Jvmq+9ubKVKkkYzOzYcOGBW2J98pEKqFY1ZCX5q2+HUHNF94+eKnWqobU2PPuF+qe56VHJyaS1/NSqROPbf/+/TZ16tSsrotFixYlzU2vvPKK7K/m68Y/ZayXakqvmU75VnVx6qmnyu3Hjx8ftK1fv172VdffGxPqvqm+8UXNqWZ6XHr3YpVe7n0LjBqX5eXlQZt3D1Cp5t49QF0H71kycXwcPHjQ/uM//iOr6+Jb3/pW0jy4efNm2V89B33wwQeyb//+/YM2lWhuputNvZc3T3nJ24qa273nM3VvUONvyJAhcnuV7O7VoPrGGY96DVWb3rcYqXbvvq/uWd5apPFz1He+851j1sVxfaa7/mZW/5UcixYtssOHD9uECRMa+gwePNjKy8tt3rx58jUOHjxou3fvTvoDZDPqAghRF0CIugBC1AVaoo+96K6rq7Pbb7/dzjnnHDvttNPM7MN/uWjbtm3wLzbFxcXuv2rce++9lp+f3/CnrKzs4+4S0OyoCyBEXQAh6gIIURdoqT72onvy5Mm2bNkye/zxx49rB+644w6rqalp+KN+PQbIFtQFEKIugBB1AYSoC7RU+gNTxzBlyhR7/vnnbc6cOda7d++G9pKSEjt06JBVV1cn/WtUZWWllZSUyNdq166d+zlNIJtQF0CIugBC1AUQoi7QkqW16I6iyG677TabNm2avfLKK0F40ahRoyw3N9dmzJhhkyZNMrMPwzjWr18vQ40+ypEjR5JCb8477zzZb8aMGUGbFxyhAi28EKfu3bsHbWeccUbQdsopp8jtr7322qDNC+XYtGlT0OYFqal2FcDghQSosAcv6ECFD3i/xqOOTYUPrFmzRm6vPmvjBRGp65jqMRw4cED2Ox4nsi7q6uqSjtUb6+kEvaggPy8sacCAAUGbCuXwApBUqJF3DCqMUAVDmX0YutLYwoULgzavXtMJWVTBJF5tqxu+Gqte4JQK7vKCjNQ5b9XquGI7jsuJrItbb701Kbxk+vTpsp8aa949QM1JXlCLqguVqusF7qnQNi/cbNu2bUFbYqBLIlUvapx426txOWjQINlX1YA3D6iQQhUm6IUWqfuIt1+qXrz7QOJ+xXGvMGve+4UXDqnGlHdPVXOauieb+bWV6nup+evMM8+UfdU9ywtAUmNV1aZXg955VLy5XdmxY0fQpp65vPOl7uUqPNfj5Son1lA6IV7paM668ILt1LhWc7WZHuvDhw+XfVUNqfHn7Zd63vHmKxVw6Y1r9Xym1ife+FfrJu85Xj3bePulngXV8XqBtOr5znuOUuNbvX/jdq9PY2ktuidPnmx/+MMf7JlnnrG8vLyGySA/P986dOhg+fn59oUvfMGmTp1qhYWF1qVLF7vtttts3LhxKSULAtmIugBC1AUQoi6AEHWBk0Fai+5f/OIXZmZ2wQUXJLU//PDDdvPNN5uZ2f3332+tWrWySZMm2cGDB23ixIn285//vEl2FshE1AUQoi6AEHUBhKgLnAzS/vXyY2nfvr09+OCD9uCDD37snQKyCXUBhKgLIERdACHqAieD5vvAHwAAAAAALdzHSi8/EXJzc5PCXT772c/Kfi+++GLKr6lCObwAkGHDhgVtY8aMCdq8QIt/+7d/C9pycnJk34EDBwZtPXv2lH1VuNPmzZuDNhUcYGZWVVUVtKmgBTMd2lZUVCT7qnOrgoi8wB4VgOCFoKjX8EIMDh8+/JH/n21qa2uTAju88afCIMrLy2VfFV4xcuRI2VeFfajwDHXtzXQojBd+oca6d7yqXdWFF4Slzo0KKDTT5zYxxOtY1NeWeKFtXiCi4tWWkvhTBS/ILpt06tQpKbAonaAX7x6gglq8uVJ9prBfv35B29NPPy23/8tf/hK0eXPtqaeeGrR5YTWqLlQNe4F7KgjNSwpW48h7XRUEpK6DCvbx9mv16tWyr3rd4uJi2TdxfogrSO1Eys3NTZrfvXlG3Wu9EDwV7uTNy6ny6nXOnDlBW9euXWVfNVa8EDM1JtT86T0vqNAs73wpKkTKzOT3SKvz7b2XCmLz5hEVJueFSyWOm1R+Ip3p8vPzk+6t3nGr52gvHFDdq0eNGiX7qvlavVdpaanc/rXXXgva1Jxqpo/Ne15W9aKe5bxnBvV85YWHqvPlJc2rkMx07gFeAHSqvPtr4rNgqnMgP+kGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYZm15++PDhpORIL41bJQl6qcMqcc9Lwuzbt2/Q1qtXr6DtyiuvlNurdFSVemxm1rt376DNSy9XSZQq3dJLZ1UJ6l5ioEqe9ZII1fsVFhYGbe+9957cXqUmemnMKinaS8BunNyY7Ym0Xbt2TUp99FJfVUptOinVI0aMkO0q/VnV2+DBg+X2b7/9dtD2/vvvy75evSjqeFU6q/eaah7xkpfVOfDSQFUdDx06NGjz6kKl0XrXXKVnemnFiTWUTupupqqurk4a36NHj5b9Vq5cGbR5105R32Bhpudw9V4PPPCA3F4lzKokYzOd5Dp+/HjZd/jw4UGb+gYLb65V9eqNv/z8/KBNfTOCmZ5f1Fj1xqZ6Xa9e1bXx7heJzxxeSnE2afwtMN5zhUr39epCnXvvOqn7upqXvZR61b5+/XrZV+2v+gaWj3q/VF7TTD9HeEnp6vnM2y9VA2pce+/l1YCiXsOr18T2lpBe3vhbYLyUcPVs7KVUq2vqzSEbN24M2pYtWxa0efPy2WefHbR536Ci6s37dhmV1q+S/r1EfLWe8pLD1b3US5FXNaTur15dqDr2xrG6Zt41T7y3qPOs8JNuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIhJxibotGrVKikUwvuAvfqAvBcepF7DC11TwW2LFi0K2vLy8uT2KlRh3759sq8KCfACGFTQQUlJScrbqzAtL3xABfl4gSkqnGf79u1BmxdMol7XO4Z0Am4aB2cdOnQo5W0z0aFDh5LC8LyAQXWdvfOmAu+8kAl1TVUQ2muvvSa3nzBhQtDmhXIMHDgw5ddVIRaVlZVBmxckpMbFtm3bZN/zzjtPtqf6ups2bQra0gnz8sJuvLlISTwPqQaAZLJDhw4lnetbb71V9vv2t78dtHlhmiqUyOurPPfcc0GbCuEzM+vWrVvQtnnzZtlXhQGqwDMzPf7UvckLmFT3TG/OUeGj3thaunRpSvvghXyqY/Du++qe6d0HEoOL0gmezFQHDhxIurequd4svfAgNf94zxDq2URdD4+6/l6Qn6oLb/x5wVmNeWNKvZcXYqbq2AvIUlRAlvc8rO7b6QRltoRQzVS0b98+KZDWGw8qRMx7hkh8vXoqMM1MP1uoGvRCyNRzkArYNNPPXBUVFbKvChhUoWteIK2aM70a7NOnT9C2ZMkS2VcFX6r98uYsdVwq+NNM15sXPJdYL6nWDj/pBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmGRtV2Lp166REXy+VbsCAAUHbzp07ZV+V7ltcXCz7qoTWn//850Gbl1inXtdLF1QJmWPHjpV9lV69egVtKknR46Wqf/DBB0Gbl1qoXkO1qUR0M72/3n6plEcvEbXx/nr7ny3atm2blOjqJTBu2LAhaPOSl1Vao0rYNtMp9SrZ0UtefvHFF4M2VcNmZtdee23QNnLkSNk3MdG93sKFC4M2VStmOuncS6NV5/Hdd9+VfVVyp0ri9L5FQc17Xl+V9OulZSemeXp1lk22bNmSlGr83nvvyX4q9ddLg1dzVe/evWXfd955J2hTieLe9VBJrl7C7ODBg4O2IUOGyL7q3qLug95YV/c37168cuXKoE3NFx6VXu4l8qv0Zy/RWfHuA4mvke33CrMw1d9LXlbn2Ut4V3O7Sq4309/KMHTo0KDtzDPPlNurlHvvOq9evTpo81LVVZqxSppOJ6nfS5q+8cYbZbsyZ86coE3dW9Q33pj5dayo5ygvVT3xddN5j0x14MCBpKR3L6W+vLw8aPPmv3S+MUZRz7De/UK97uzZs2Xfl156KWjzvoGlf//+QZuq186dO8vtVdq7N6aefPLJoM27j6X6jOKNTVXH6htDzPS87+1X4lzkjYvGsr96AAAAAADIUCy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYZGyQWhRFSUEHni9+8YtB249+9CPZVwUgecFer7/+etC2YsWKoM0LEFGhRl7QximnnBK0qUACMx24o4IKvEABFfbgBc9t3749aFMhUGb6eFV4gQqWMtPXxgupU/vrhd40DsjI9hCQurq6pMAGFT5kpgNAvHOkzol37tVrqGAbL4BGBXCoECozXYPjx4+Xffv16xe0qdAr7xykGoJhpoNJvNArb7w3psLozHQQkBfOooJB0glyyWaTJk1KCtPz5s+rrroqaEsnRMwLZnruueeCtjVr1gRto0ePltsr3v1P7a+3XyogSwXHedurMC0VEGf2YZhdY4nhdolUAKS6Zl5QpJpfVDiWma5BdW8zS75fqHDGbNP4fuEFRqm5w7t2aq705jkV6JlOQKt6tvGu88yZM4M2dR8006F/6nVVOJuZ2R133BG0qRA0M12DS5YskX3V/VwFbHl1oY7BC7JSz4JesHDiPmT7M5TZh8G9ieG93v1XnWcvRExdO6+G1POy2t6bl5Vhw4alvF8qNNBMjwkVZrhjxw65vVqfeIFlau3lXQfVrs6h2n8zPWd5c446Bm+/EoMiVWikkv3VAwAAAABAhmLRDQAAAABATFh0AwAAAAAQExbdAAAAAADEhEU3AAAAAAAxydj08oMHDyalrKp0WDOzP//5z0GbSsI200msXoLdG2+8EbSpJMO9e/fK7bdt2xa0ecmhq1atCtoqKytlX/V+HTt2lH0VdbxeUrVK/POSK1W7StLs3r273H7fvn1BW5cuXWRflaDupU83Tm/O9jTnNm3aJF0vL61RJR97iacqfdxLf1ZjTY0pL+FTtadTr17qphrD7733XtDmpe+rbyHwxqpKKvdeV10fdb4LCgrk9iq9NDF1NVE6ic6Jc6tKx812Ks3ezGz9+vVB26mnnir7qjlFbW9mNm/evKBNjWs1z5mZbdq0KWjz5lp1D/DSuIcPHx60qePy7gFq/HiJvN6coajXKCkpCdq8BHHV7s3t6nyp5Fuz5HPeElKaW7VqlXQcau4x09/08P7778u+PXv2DNrUXG2mx6Wav9RznPdeI0eOlH0vuuiioM37Fg01h6t7QFVVldxefbOG57XXXgvavOc7dX9M51sUVL2qbyvwXlclrZsl16v3zJFNjh49mnQc6ZzPdO6X3vhTzwtqrHvzn0rK9uY/lV7vfeuNel31fFdaWiq3974tQFH3Ie95Ra0F1LdlqHPo8ZL61bnx7o+Jc2uq4yL77yoAAAAAAGQoFt0AAAAAAMSERTcAAAAAADHJuM90138WtfHnsLzPIqnPMXi/W6/avc90q9dVn2XxPvel3sv7XID6/K33GSn1eQPvcx+Kel31OQ7vvbzzpY5BHa93HdU+eOdL7Zf3GZXGx1u//2p/M1n9/jY+/+l8xt6jrol3PtXrqjGhPkdkpuvC+yySGqtebat9UG3p1JU3VtXncr2+qZ7bdOrCyy+ora1Nqa2x+uPJtpow+599brzv3jlSc7g3ptRY8a6Tet107k3q3Hs1rPbLq1c1VtW58e4B6Yx1bx8Udbzqdb0xqca193n5jzu/Zeu9wux/9rnxefI+36vOnXeO0plXVbt6XvGeK9Q+eHOaOgavrxoTcY0p1e6dL3Ue1Nzijcl0covUPJBKbdf/dzbXhXdOGlP90nm28p7N1euq89kUn+lW48c7fnVs3lhVvPFzvNun+rn0dN4/lWejet7zROL5qn+9Y9VFTpRhlbNx40YrKytr7t1AC7dhwwYZHpOpqAvELdtqwoy6QPyoCyBEXQChY9VFxi266+rqbPPmzZaXl2d79uyxsrIy27Bhg5tknY12797NcTWTKIpsz549VlpamlXptNRF9sr048rWmjCjLrJZph8XdZHZMn38fFyZflzURWbL9PHzcWX6caVaFxn36+WtWrVq+FeC+l+t6NKlS0ae5OPFcTUP76uzMhl1kf0y+biysSbMqIuWIJOPi7rIfBzXiUddZD6O68RLpS6y65+pAAAAAADIIiy6AQAAAACISUYvutu1a2d33XWXtWvXrrl3pUlxXDgeLfU8c1w4Hi31PHNcOB4t9TxzXDgeLfU8c1yZLeOC1AAAAAAAaCky+ifdAAAAAABkMxbdAAAAAADEhEU3AAAAAAAxYdENAAAAAEBMMnrR/eCDD1rfvn2tffv2NnbsWFu4cGFz71Ja5syZY1deeaWVlpZaTk6OPf3000l/H0WR3XnnndazZ0/r0KGDTZgwwVatWtU8O5uie++9184880zLy8uzoqIiu+aaa2zlypVJfQ4cOGCTJ0+2bt26WefOnW3SpElWWVnZTHvcsmR7TZhRF9RF06MuMhN10byyvS5aYk2YURfNjbrITCdDXWTsovuJJ56wqVOn2l133WWLFy+24cOH28SJE23btm3NvWspq62tteHDh9uDDz4o//6+++6zn/70p/bLX/7SFixYYJ06dbKJEyfagQMHTvCepm727Nk2efJkmz9/vk2fPt0OHz5sl156qdXW1jb0+cY3vmHPPfecPfnkkzZ79mzbvHmzXXfddc241y1DS6gJM+qCumha1AV1gVBLqIuWWBNm1EVzoi4y10lRF1GGGjNmTDR58uSG/z969GhUWloa3Xvvvc24Vx+fmUXTpk1r+P+6urqopKQk+vd///eGturq6qhdu3bRH//4x2bYw49n27ZtkZlFs2fPjqLow2PIzc2NnnzyyYY+K1asiMwsmjdvXnPtZovQ0moiiqgL6uL4URfUBUItrS5aak1EEXVxIlEX2aMl1kVG/qT70KFDtmjRIpswYUJDW6tWrWzChAk2b968ZtyzprNmzRrbunVr0jHm5+fb2LFjs+oYa2pqzMyssLDQzMwWLVpkhw8fTjquwYMHW3l5eVYdV6Y5GWrCjLpAeqgL6gKhk6EuWkpNmFEXJwp1kV3H2BLrIiMX3Tt27LCjR49acXFxUntxcbFt3bq1mfaqadUfRzYfY11dnd1+++12zjnn2GmnnWZmHx5X27ZtraCgIKlvNh1XJjoZasKMukB6qIvsOU7q4sQ5GeqiJdSEGXVxIlEX2XOMLbUu2jT3DiB7TZ482ZYtW2avvvpqc+8KkDGoCyBEXQAh6gIItdS6yMifdHfv3t1at24dJNJVVlZaSUlJM+1V06o/jmw9xilTptjzzz9vs2bNst69eze0l5SU2KFDh6y6ujqpf7YcV6Y6GWrCjLpAeqiL7DhO6uLEOhnqIttrwoy6ONGoi+w4xpZcFxm56G7btq2NGjXKZsyY0dBWV1dnM2bMsHHjxjXjnjWdiooKKykpSTrG3bt324IFCzL6GKMosilTpti0adNs5syZVlFRkfT3o0aNstzc3KTjWrlypa1fvz6jjyvTnQw1YUZdID3UBXWB0MlQF9laE2bURXOhLjL7GE+KumjWGLeP8Pjjj0ft2rWLHnnkkWj58uXRl770paigoCDaunVrc+9ayvbs2RMtWbIkWrJkSWRm0U9+8pNoyZIl0bp166IoiqIf//jHUUFBQfTMM89ES5cuja6++uqooqIi2r9/fzPvue/WW2+N8vPzo1deeSXasmVLw599+/Y19PnKV74SlZeXRzNnzozeeOONaNy4cdG4ceOaca9bhpZQE1FEXVAXTYu6oC4Qagl10RJrIoqoi+ZEXWSuk6EuMnbRHUVR9F//9V9ReXl51LZt22jMmDHR/Pnzm3uX0jJr1qzIzII/N910UxRFH0b7f//734+Ki4ujdu3aRRdffHG0cuXK5t3pY1DHY2bRww8/3NBn//790Ve/+tWoa9euUceOHaNrr7022rJlS/PtdAuS7TURRdQFddH0qIvMRF00r2yvi5ZYE1FEXTQ36iIznQx1kRNFUdQ0PzMHAAAAAACJMvIz3QAAAAAAtAQsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYhLbovvBBx+0vn37Wvv27W3s2LG2cOHCuN4KyBrUBZCMmgBC1AUAtCw5URRFTf2iTzzxhH3+85+3X/7ylzZ27Fh74IEH7Mknn7SVK1daUVHRR25bV1dnmzdvtry8PMvJyWnqXcNJLooi27Nnj5WWllqrVif2Fz2oC2SibK0JM+oC8aEugFBz1gWQ9aIYjBkzJpo8eXLD/x89ejQqLS2N7r333mNuu2HDhsjM+MOfWP9s2LAhjqH/kagL/mTyn2yriSiiLvgT/x/qgj/8Cf80R10A2a6NNbFDhw7ZokWL7I477mhoa9WqlU2YMMHmzZt3zO3z8vLMzOyCCy6wNm3+Z/fee+892b+uri5oa9u2rezboUOHoG3Pnj2yr/oXvEOHDqX8Xqr9wIEDsm+nTp2CtoMHD8q+ieekXmVlZdCWn58vt2/fvn3Qps6LZ+vWrbK9V69eQVthYWHKr6v6eu+1f//+oK1169ayb21tbdL/Hz161JYvX94wzk6UpqqLq6++2nJzcxvaq6urZf/Gx21mtmPHDtn36NGjx3z/eocPHw7aVF1Ezi/QqJ+6qDFtpmuwXbt2sq8aE4nn6VjvpV7Xq+10/nVf1XaXLl2CNlWXZma9e/cO2srLy1N+r8GDB8u+ia+xZ88eO/3007OuJsz+py7+1//6X0nXe9euXbK/moO9e4Dqq8aZR9WFul95vPF35MiRlF/XG++pvKaZvo9450uNH+/9O3bsmNJ+efexYcOGBW3p1OXAgQNl+xlnnNHw33v37rXx48dndV2MHj066Rp4zxXq3Kl7iJlZjx49gjbv2UbNwYo3ftW9ybu3qGPzxp9q9+4tqW7vjT91bN6YUvcGVZtqrjfT59u7NureMGjQINk38T60d+9eu+iii054XQAtQZMvunfs2GFHjx614uLipPbi4mJ79913g/4HDx5Mmizrb+ht2rRJmtjSuaF6CzDV7r2uak+1Ld33Un3TOQa1kGmK91LSeV11Y/J+1U09YKZzw0znfH3UfsSlqeoiNzc36cbaFOcoHeohSI2JdBbd6dSgdwyp1mY64ySd9/Ko66AejLyHU/Ug6P0jmWrv3Lmz7Kse7jK9Jsw+ui4S5xDvfKoHWK+G4porU+W9VzoL91T34UTfx1L9x4B06iKd8+0t+lW9ZHNdNH6O8v5xJZ25Ul27dO5Dijem1bn37i3q2JriGFLdPp1Ftzeu1XOQet10ftjjnVv1D73eYj4T6gJoCZr9Axn33nuv5efnN/wpKytr7l0Cmh11AYSoCyBEXQBA5mvyn3R3797dWrduHfzKc2VlpZWUlAT977jjDps6dWrD/+/evdvKysqssrIy6V8pvX9FbPyvwWZmXbt2lX3Vr6p5/wq4e/fuoK2qqipoU79ua6b/FdH7tVD1K4vp/Lqg+qmV9y/5BQUFQZv360eqXW1vZjZixIigTf2rq7f93r17g7a+ffvKvurX6Tds2CD7Nv6Vs+YK/miquqitrU36V3Lv1wUV79wrNTU1sl39i7f66bf3EwY11r1/MVc/zfJ+DVv1TfVXWM10vaXzE2Xvpy+KV2/KypUrgzbvV5zV/LJq1SrZN/HXa9P5lemmlG5NmPl1ceTIkaTa9n5VVI1fb67t3r170KbmKTM93lVter+yq3g/zdq3b1/Q5v0atvrYjrq3eT8BVfvrXRv10zvvddW5UT9l8z4+s27duqCtT58+sq+q19dff132Xb58+Ufu44nQlHXR+CfdTfGbUeq+nM68rJ5XPGqse9RzgVevag5W9zHvN4XSeZZM5zdTVF/1Xt5zjJrLvblwy5YtKb1X4/1KZw4DkKzJVyBt27a1UaNG2YwZMxra6urqbMaMGTZu3Ligf7t27axLly5Jf4CWhroAkqVbE2bUBVo+6gIAWqYm/0m3mdnUqVPtpptustGjR9uYMWPsgQcesNraWrvlllvieDsgK1AXQDJqAghRFwDQ8sSy6P7Upz5l27dvtzvvvNO2bt1qI0aMsJdeekn+KjhwsqAugGTUBBCiLgCg5Yll0W1mNmXKFJsyZUpcLw9kJeoCSEZNACHqAgBalmZPLwcAAAAAoKWK7Sfdx2vXrl1JCY3e9weWlpYGbeedd57sq1JMvWRJldyp0i29hGOVELt06VLZV6VK79ixQ/Zdv3590KZCU3r27Cm3HzRoUNDmnYO8vLygzUvzPO2004K2rVu3Bm3pfH+uZ/PmzUGbSho2M5s7d+7Hfp9M1KFDh6RzWP99rKnwkkm9c6eoRGWVPu6l+iu7du2S7epaecerjk19i4GX5KrSxw8dOiT7qvZ0vvdX1ZuXRqvqxUsVVkm9an40S66hdNLUM1Xr1q2Tzot3vxgwYEDQ5p0jNa9685dKf1ZjTc3fZmYbN24M2rZv3y77qvHnjWt1DKouvPdK53vi1Xn09kvVsbpmXtq2qhfv3qTqQt2vzPR1yGY5OTlJ83NFRYXsl+r3Q5vp9HJvDunfv3/Q1qtXr6DNuwep9HJvXl6zZk3Qtnr1atlXJXerNm/8qrHqPVuocel9O4h6DbW9980G6j7k1ZB3bEriPSudb+oAkIyfdAMAAAAAEBMW3QAAAAAAxIRFNwAAAAAAMWHRDQAAAABATDI2SG3EiBFJoTVlZWWyX2FhYdDmhRqpcDMvwKOysjJoU4FRPXr0kNur8AsVQGKmAzzefPNN2XfTpk1Bmwr16Natm9xehZh4QUIqMMcLElJhNdu2bQvavPAOFSa3f/9+2VftrxcutXbt2qT/TyfgKxO1adMmKRjFu84qQMkLxlGBee3bt5d9+/XrF7Spa+eFt6jzr8a0mQ6dUmE5Zrq2VdiMFwKjxqUXxqVqQJ1vM7OioiLZ3pgXDqRC17z3UvOTF+6TOA94dZZNysrKkq5hSUmJ7Keuszf/qcAx73uS1TVR106FS5qF85SZ2YoVK2TfV155JWhTIZ9mfnBbY71790653bu/qr7e/VG1qxpWbWb63Hp91bnxwkMTj6El1EVeXl7SfOXdL9Q9wLvOar737i3qHKqx7gXHenOwop651P3KTM+Vavx486dq98LR1H6pQEczfc7VM5t3f1X3EfVsZqbvj959P/G+ne3PUEBz4ifdAAAAAADEhEU3AAAAAAAxYdENAAAAAEBMWHQDAAAAABATFt0AAAAAAMQkY9PLBwwYkJQ0+95778l+y5YtC9o++clPyr4q8dFLTlZptCpF0ks4raqqCtq8xMq33noraFMJn2app7auW7dObq/Sc71EXXW+vNRXtQ9Lly4N2s4880y5vUpK91Iy1f6q9zIzO//885P+f//+/fbUU0/JvtmgdevWSdfFS7Lu2bNn0KbSSj3edV6+fHnQptKBvfRolY7qJdqr5Fkvffz1118P2vbt2xe0eWm06tsRhg4dKvv2798/aPPSe1W7Spj1xrrqm8519M6tl0ycrfr165c0h6jkcTOzAwcOBG3evUXNy+pbLcx0yrIaU+mkeZ9zzjmyr0ovX7Rokeyrxp9Kn/bG+uDBg4O2gwcPyr7p3FvUvLVx48agrba2Vm6vqHuImU6P9tLeE/uqhOtsk5+fnzRfeHWvvv3Dm5PUs0U6819+fn7Q5qWUV1dXB23q217M9LhUqexm+hsodu3aldJrevvgJcOreeD000+XfdUzl7pned92oY7LSzpX9wYvvTzxOqZz/wGQjJ90AwAAAAAQExbdAAAAAADEhEU3AAAAAAAxYdENAAAAAEBMMjZI7dxzz00K1/DCM/r27Ru0eaEe27ZtC9pUOJqZ2c6dO4M2Fb5ywQUXyO3V/v7pT3+Sfc8666ygbf78+bKvCvJRoW0jRoyQ2xcVFQVtXphc9+7dg7a9e/fKvuo19uzZE7StX79ebq+Cu7xro47tsccek30bB8d4ASTZonXr1kmhNyokyEyPVTWmzcxqamqCNu91VYiKCoEqLCyU26vrvGrVKtk31RAeMx2Co9q8oJjS0tKgTc0tZjpgyAvsUeFO6hx6AW/qGLzwRlVv6tqaJV8H7/WySdu2bZOCgVSInpnZ+++/H7R5x6/GtReEpoID1VzrjT8VIqbmXzNdW+ram+mALLW9F0K2ffv2oM07Xyr4sE+fPrKvCvRSQVTqnm2m76/e/KaCv7z7UO/evRv+W4XuZZtWrVolzaPeMal27/6rQjrLy8tlXzXe1fZe8Ku6B3g19Pbbbwdtavx6r6tC07x7kwp+8+6Zp5xySsp9VRCa4gUMquczby5UteUFsyYGU7aEugCaCz/pBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmGZtevmvXrqQ0ydNOO032mzlzZtD20ksvyb633HJL0HbXXXfJvqtXrw7aVEK3l4atkjSjKJJ91f56xzty5Migrbi4OGhTKedmOo3TSwP9/e9/H7R56dHnnntu0Pbtb387aPNScnfs2BG0vfDCC7Kvug4qIdTMbOXKlUn/76VEZ4ujR48mJZN7ib0qYTgxmTfRgAEDgjaV5m2W+rcFLF68WG6vkvbbtNHTkNeuqHGlxpRKDjfTabRe6qtKhk9Mzk6kUqXPPvvsoM2rK5Vq7e2XuuZe+m5i+nNLSKOtra1NSmT3UovVWPeunWo///zzZV9Vhyqh++WXX5bbq9RhL+FYJaire4CZriGVUKy+VcDjzeHq/rZ27VrZVx3vZz7zmaDN+wYBlT6u0qfNdLL28uXLZd/Nmzcf8/WyycGDB5OurZrnzMxOPfXUoM0792pcl5WVyb5qXlPJ23PmzJHbDx8+PGjz5j+V/K3majP9HODNA4r6VgBve3XPUfdBM12b6tttvLR39Q0Aa9askX3VdfDuue+8807Df7eEb7sAmgs/6QYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmGRskFpubm5SMMYf/vAH2U8FqqjwDTOzn//850HbVVddJfv+93//d9DWOJTLTAclmelgnerqatn3hhtuCNq8oI0PPvggaKupqQnavBCUFStWBG0VFRWyr3pdL4hqxowZQVtiKE29UaNGye3ff//9oE2FSJnpkLkhQ4bIvo0DlQ4dOmSzZs2SfbPB/v37UwqD69WrV9DmhXWpIDRVV2Y6OFAFsrz55pvH2MP/4QUMqsAW9V5mZt27dw/adu/eHbR5YTcqQObw4cOyrwop9IK71Pv95S9/Cdq8kLutW7cGbTk5ObKvuo7eWCkoKGj475YQjLNu3bqkc60ChcySj7ueCi8yM1uyZElK25vpuV1dOzV/m+nQNC/sS42pvLw82VeFnqn53gtkVOfR66vqzQtk3LBhQ9D2xz/+MWhTgVVm+t7kBZ+lE5qVGFLXEgIGu3TpknTP7tmzp+ynnje8sC4VxuaF/nXt2jVoU/ONd65VCJj3DLJt27agzQv9S7WvNzd49aao5yDvnucFkDbmXcdly5YFbV6o7ogRI4I27z6WeM6zPYwWaE78pBsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYpKxQWo333xz0v8XFhbKfiro4q233pJ9VcCaFxaiglr27dsXtCUGryRSoUYqgMRMB9CMGTNG9l27dm3QpkJ8+vTpI7cfOnRo0KZCzMzMTj/99KDNC/BQQShqv+bMmSO3V8Em7du3l31VEJoXUrdly5ak/z969Kjsly2iKEoKYfGCXtQ4USF6ZvraeSFiKmxGtXljXQXubNq0Sfb19kFRIXHqvVRgmpkOVlJzgJkObPLCyNTrqmCd1atXy+1VcJ0XAqXOlwoyMkuen7wAqmzSvn37pPnCu84zZ84M2ry5Q52Xt99+W/ZV516NP3VfMNOBUV44lbr+3phQdajuDd4YUPdHL8xQhTB5r6va586dG7Tt3btXbq/OY8eOHWXfurq6oM07X4nnXNVetlm/fn1SIK0316ZDjXXvOqn5Rz3Lec9RKrTLCwZT87V3DdX+qoC2AQMGyO1V8JwaZ2ZmO3bsCNq84DhV86oGvXu52l6FFprpY/Dmp7Kysob/9uofwLHxk24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYpKx6eXDhg2z1q1bN/y/l7qp0hq9ZEiVTuklTqrUyuLi4qBNJY+b6URwlVBrppM7veNVqdQqITQx4TqRSo1Vr2mmE4C911Wp5l6CsKLOree9994L2rzr0DhZO9vTy6uqqpLOq5c8r9K8d+3alXJfL/Va1VZOTk7Q5iWPq4RZ79qlmghtpvdXjT8vEV/VhUplN9MpzV6i6549e4I2NeckJgwfa7+891Jj20vm7t69e8N/t4SU5gULFiSdQy/VXyXHe8nJ6pp4Kc0qDVudey/NW4313r17y77r169PaXszXQPq3Hjbb9y4MWjbunWr7KuSptX5NtM1oOotnWT9xt9UUU/NT2rOM0ueX9L59oRMtW7duqTnKG9eV8fqzUlFRUVBWyrfklAvcX/qeXWlxpT3rRLHO/7UMXj3C5Wgrt7fey/v2UjNxer+qO4rZroGvBpS++ullyfyvqkDwLHxk24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICZpL7rnzJljV155pZWWllpOTo49/fTTSX8fRZHdeeed1rNnT+vQoYNNmDDBVq1a1VT7C2QcagIIURdAiLoAgJNT2kFqtbW1Nnz4cPuHf/gHu+6664K/v+++++ynP/2pPfroo1ZRUWHf//73beLEibZ8+XI3lEI5ePBgUuCGF/ajwiCGDh0q+6pAFRWYZmbWr1+/oG3OnDlB24YNG1J+r5EjR8q+l112WdD261//WvZVgSPqGFauXCm3V0FUO3fulH3z8vKCNhViZqbDeVRAkQpcM9NhH15ghwpy8TQOD/PCTo7HiaoJsw/DABNDWDp16iT7rVu3LmjzgnFUMI03rlXQihpTHTt2lNur6+wFFqnr7IWbqWNT76Xq0kwflwpe9Nq9kMR9+/YFbWpfvYBCL+hRUQFFXvBcYl3EFRh1IuuisQ8++EC2q/r3xnqq25v5wW2pqqioCNq8a6fuASrwzMystLQ0aFPjxAtbUnWstjfToVVe6JW6vqoG0wmn8qjQKi+IKvGcx3GvMDuxdVFQUJB0/N48pV7XCx1VYV0qSNBMz5UqNNWb19O5dipg0rvndevWTbY35p1vNTa8OVzVa2KQZSJ1fVRte+db8WqlqqoqaFPPfGbJx+sFFQM4trQX3ZdddplcJJp9OOk88MAD9r3vfc+uvvpqMzN77LHHrLi42J5++mn79Kc/fXx7C2QgagIIURdAiLoAgJNTk36me82aNbZ161abMGFCQ1t+fr6NHTvW5s2b15RvBWQFagIIURdAiLoAgJarSb+nu/77Oxv/+lBxcbH73Z4HDx5M+tU27zt7gWz0cWrCjLpAy0ZdACHqAgBarmZPL7/33nstPz+/4U9ZWVlz7xLQ7KgLIERdACHqAgAyX5MuuktKSszMrLKyMqm9srKy4e8au+OOO6ympqbhTzqhNkCm+zg1YUZdoGWjLoAQdQEALVeT/np5RUWFlZSU2IwZM2zEiBFm9uGvOS1YsMBuvfVWuU27du1kEmOHDh2SUlK9ZEiVbqkSg810iuM777wj+6p2tZ/q/c3Mtm/fHrR985vflH2feOIJ2a54icqNeQmfKjm0sLAw5fdPJ/1ZpY+vXbtWbu+dR0Wl53r/st84FfjgwYM2Y8aMlN/reH2cmjDz66Jt27ZJiazbtm2T26t68ZKE1bXzEk9VX5V+7b2X0rVrV9muEqG98a/2IZ1z4KWtKyp51hvXKulXJUV76b2q3ry5UCXteum9iefRSymOU9x14V1nNa69b8ZQacje+NuxY0fQpuY0b65VY8pLFG+8IPuo/VLnqvE3OnwUtb/et12oNGRvflL7oM6Xl5Sujsu756lvQfDGfGIyc0uoi127drnnMJFK2PYSstV59p4LUk319/ZRzevedVFp/15KuBpr6h6QTlp8dXW1bFfzyKBBg2TfN998M2hTaeHevK7OTTop/N6ck3gdvD4Aji3tRffevXvt/fffb/j/NWvW2JtvvmmFhYVWXl5ut99+u/3oRz+ygQMHNnzdRWlpqV1zzTVNud9AxqAmgBB1AYSoCwA4OaW96H7jjTfswgsvbPj/qVOnmpnZTTfdZI888oh9+9vfttraWvvSl75k1dXVdu6559pLL7103N+7CmQqagIIURdAiLoAgJNT2ovuCy64wP31RrMPf83o7rvvtrvvvvu4dgzIFtQEEKIugBB1AQAnp2ZPLwcAAAAAoKVq0iC1pnTOOeckBXl4gSwqAES1mZkVFBQEbV5YiAo2Un1VsJSZDk3btGmT7KtCmLywChX2oYIy8vLy5PY9evQI2iZOnCj7/upXvwravPOlgj1UmxeYpq5Zt27dZF8VjuIFfzUOc0snsC0THT58OOmnJN71UME0KpDFTI81LxhHBbWo9/ICdLzAsFT7evWmjk39NMkLoFHjzwtkVKFVXr2pXwlV51uF7Zjp863ChczMOnXqFLR546NXr14N/+0FiWWT1q1bJ41Db5yosCN13sz0efbOlRoTqs2bf9T48e5jKrTNu85qXlQBl+reaKZru3fv3rLvxo0bgzYv4E3VlhrrXg2q76H25iwV/OVdh8R7SzoBVJkqJycn6bx4gWVeCJiixpR3ndVYU20eNa690EtVQ951VvOyOi7v+87V/OLdX9Wc4R2DCoPznn2VdOYcdR/y7tuJx9YS7hdAc+En3QAAAAAAxIRFNwAAAAAAMWHRDQAAAABATFh0AwAAAAAQExbdAAAAAADEJGOjnKuqqpKSGPv27Zvytm+//bZsVwmtXbt2lX1V4mPPnj2Dtv79+8vt582bF7R5SawqzdNLrlXtKrVTpXOafXheG1u3bp3se8011wRtzz77rOyrro9KA/WOS6V2VlZWyr7qOnppxXPnzk36/2xPpG3Xrl1SAreXBq5STL1z5CXlKyr9O52k9JKSkqAtPz9f9k3nGFTysRp/XnpvTU2NbFfUOVffCmBmtnnz5qBNnRvvGqgEbe8YUk3kNUuec7w09GySm5ubNDa9MaWSrNevXy/7euc5VWqe8vZLpTR7Kc9qDvfSx5XCwsKU90vVlTdW1T6o9zLTCcjq/uhdg1S/RSFd2f7tFo3l5+cnHZM6b2Z6TKmUfI+XHK++QUKdY+/bH1RdeHNaOt+ioZ4D1Ot6zwvpjLWdO3em3FfVy+rVq4M2L1VdPUd584i6Dt7rJt7fvDEE4Nj4STcAAAAAADFh0Q0AAAAAQExYdAMAAAAAEBMW3QAAAAAAxCRjU0PeeeedpLCK6upq2U8FMw0fPlz2HTBgQNDmBWWUl5cHbaeeemrQNnbsWLn9V7/61aDtzTfflH1ViI0XRHX++ecHbSrEyQvv+Pvf/x60eYFlN998c9D29a9/XfZVYR0vvfRS0LZlyxa5vReQpaxcuTJoGzRokOzbOAxu//799vLLL6f8XpkmJycnKbTGC0lRYScqmMxMh9h4YTUqREwFsnhBfkrHjh1lezpBaCrgTZ0DLzRMvVe3bt1k3+3btwdtnTp1kn3Va6gQKe/aqCAhjzrn3rlNvI5NEUDV3AoLC5POobpGZnqsqvAhMx366PVNNfTPux5qv7zQScWbB9T4U7WiAv/MdGiaFx6q5gYvoE3dd9UxePWq9ssL8yoqKgravLpKPDfe62WTVEOv1Jjwzn06IXZqTkrnXq+CxbzwUC80TVH7oNq88FsVEOc9s6n53qttdbzqHHrBn7t27QravDGgXlftq1ny/KaOHUBq+Ek3AAAAAAAxYdENAAAAAEBMWHQDAAAAABATFt0AAAAAAMQkY4PUGvOCYtatWxe0eWFdy5YtC9o++clPyr5DhgwJ2kaOHBm0eWFLZWVlQVtBQYHs+9ZbbwVt48aNk30vvvjioE2FQHnBTrW1tUGbOlYzvb9eWMmmTZuCtvHjxwdt5557rtxehbnt3r1b9q2qqgraVLCOWRi6kmqwTKZq27ZtUuiNFwikrr8XdqNqywsRSjUExwucUvWigo7MdCiMdwzq/VRolRdAo0JlvDGlxmXXrl1lXxVSp47BC7JS9eYFHKlr7l2HxHOjQryyzYEDB5LmQW+cqhAmLzRQjQkvREi9rrr2XmCeqkFv/KnQKy84U4U7qfdStWamx4Y3/lRtq/uNmZ5f1LyujtVMB1ylE7Lo3R8TQ0m948wm+/fvTzov3jGpse71VWPCm0PU66paSSd403svdf292lbtah+8+VONX++91P3Cew5Rc4a6t3jPRuqaeQFv6nylcx0ApI+fdAMAAAAAEBMW3QAAAAAAxIRFNwAAAAAAMWHRDQAAAABATFh0AwAAAAAQk6yJrfWSNIuLi4M2L9G5d+/eQVu/fv1kX5XorVI3V6xYIbf/whe+ELT96U9/kn2rq6uDtmHDhsm+gwYNCtpUEquXbjl27NigzUv63bNnT9CmUmPNzEpKSoI2lYTpJWmqlNAPPvhA9lWpvir5VrV7758tCgoKkhKQvVR/lZLsHbt6jXRS3lUKqleDqt1LOFbjOtX0dDOdcuuls6rjPXTokOyr0mS9VH91DGofvPOtzo13btU+5Ofny76JKbleSnY2KSgoSDov27dvd/s15qVeK+kkvavX9dK41b1JfaODmVmvXr2CtsLCQtn3yJEjQVtiQvdHvb+Z2d69e4M2L9FZ1YV3HdQYVjXg1btq92pbvZdXr4ntXv1nk9zc3KRx6M0dKpVfPe+Y6flCfVOEma4B1eZdD/W63lhX43LHjh2yrxo/6dwvVLv3zKXur951UPdoNWd590y1D96cpc6tl+qfeM3SmS8BJOMn3QAAAAAAxIRFNwAAAAAAMWHRDQAAAABATFh0AwAAAAAQk4wNUjt48GBSYIMX3qJCHXr27Cn79u3bN6U273W3bNkStM2ZM0duv3Tp0qDNC4VRYUenn3667Dt69OigTQXueMelQlBU2I6Z2RNPPBG0DRgwQPZVAW8qqMMLEFG8YBIVPOcFuTQOAsr2cJycnBzLyclp+P+ioiLZTwUPese+a9euoM0L9kr1/CUGdSWqqakJ2tT1NDO7/vrrgzYVKmNm9uabbwZt6QRGqSAfFQ5oZta/f/+gbcOGDbLv22+/HbSpYJtTTz1Vbr9t27agzQsNVCE4Xl0kXh8v3Cub5OfnJ4UbqVAvMz3/eONPzZV1dXUp75N6Ly+0Ts2r3vynXlcFnpnpMaxCOs8++2y5/dq1a4O2devWyb4q+NI7X6qOVQ2uX79ebq/umV4Yl5rLvDCudO5P2SA/Pz9pvvGeo9Q58kI6VV8vXEvNdarNu3bqOnvPUSpU16sLdR9Tbd74TScMU72GNw+oc6PmdS8gUz3LpRMGpwJYzZLrNdvDaIHmxE+6AQAAAACICYtuAAAAAABiwqIbAAAAAICYsOgGAAAAACAmLLoBAAAAAIhJxqaXV1dXJ6VUeknCipc4qVJ6vaTzjRs3Bm0qqfyZZ56R26v0Zi/5e+DAgUFbv379ZF+VaKsSZs844wy5vUpgf+yxx2RflV7qpYGqRNuxY8cGbV6qdYcOHYI2L6laJZ2mmjKa7cmbbdq0SSltWo3r7du3p/w+Kv3cTKf7qsTTrVu3yu3VdfISg1Uqv0rv9/ZBUemwZnpcenPOpk2bgjbveFVC7L59+4I2L/1cJfKmM2d559ZLtM1Wubm5SXXhpdSr+VOlA5vp5HivLtS4VinL3jylUu4vv/xy2Xfu3LlBm5c0reYKNaZeeuklub0a1949IJ2UZnUfUuN66NChcnt1bbxz0K1bt6DNS39uaenlBQUFH/vbCWpra2W7uod641oleqt6875tQCWKe/tVVlYWtPXq1Uv29cZlY1EUyXZ1v1F1ZabHmldD6hlR3bfVPcQsvW+nUdfBmwsTr49XZwCOjZ90AwAAAAAQExbdAAAAAADEhEU3AAAAAAAxYdENAAAAAEBMMjZIrVu3bta6deuG//eCNnbt2hW0qQAbMx2WtHjxYtl30aJFQdvmzZuDNhUUYqYDLVQAk5lZ//79gzYv0EWFYqg2LzBMha6lE4xRU1Mj21WQyrJly4I2L+AtndAhFbjjBbRVV1cn/X+2h4DU1tYmhTnt2bNH9lPjxwuPUecusfYSqXGlwpa8oBcVZObV9o4dO4K2119/XfY999xzU3ovL0zOCydTVAiUF+6jQozUtVEBX2Z6f3v06CH7qvCwxuO/XmJAkQorynbe/FlaWhq0vf/++7KvChXy5iQVsKbGhBfE9sEHHwRtXl3ceOONQdt7770n+6qAyvXr1wdt3vhT7d45UHO4N1Z37twZtKnrUFRUJLdX98yqqirZVz0jeKFXnTt3bvhvL3QxmxQUFCSFfqkQUjM9Vr1wSjUmvMAxVYdqH7xnNjXXevcWFUZ58803y75qvlfPBn/729/k9up8efcQdX9VdWlm1qdPn6BN1YUXFKnuN15dqP0qLCyUfRPrwrtWAI6N6gEAAAAAICYsugEAAAAAiAmLbgAAAAAAYpLWovvee++1M8880/Ly8qyoqMiuueYaW7lyZVKfAwcO2OTJk61bt27WuXNnmzRpklVWVjbpTgOZhLoAQtQFEKIuAODklNaie/bs2TZ58mSbP3++TZ8+3Q4fPmyXXnppUqjEN77xDXvuuefsySeftNmzZ9vmzZvtuuuua/IdBzIFdQGEqAsgRF0AwMkprXjOl156Ken/H3nkESsqKrJFixbZ+PHjraamxh566CH7wx/+YBdddJGZmT388MM2ZMgQmz9/vp111lkpv9eRI0eSEjFVkqyZ2d69e4M2L7lWpVOuWLEi5X3q3r170FZRUSH7qsRUtb2ZPjYvcVL1VQmxXkquSrsuLy+XfVVKZWKKZaJUU8FVcq73ul7Cpzo2L4G5cdKul7x7PE5kXRw9ejQpMd877yod2KPSYL1UfpVer8ZUx44d5faq3UsJVqn83vhZuHBh0DZ69OigbeDAgXJ7Nabefvtt2Vfxxqo6XpX066U8q3OQTnqsl0KfeM7jSmk+kXVRW1ubdK68nwoWFBQEbV4NeedOUa+hrpNKFzYzy8nJCdpmzpwp+44ZMyZoKykpkX3V/VHVq7pfmenz5X0LgvpmA+8+lOo8rMa/mb6+Xg2l87qJxxZXqv+JrIuePXsmPQ9532qizoc3h6txnZ+fL/uquUWNP5VSbqbHj7df6ti8byZQY7hXr15B26mnniq3V/dH77lTpcAPGjRI9lX3VzUOvQR39Szn3ZvUnOWd28Tr681hAI7tuD7TXT9B1H/NwKJFi+zw4cM2YcKEhj6DBw+28vJymzdv3vG8FZA1qAsgRF0AIeoCAE4OH/tHHHV1dXb77bfbOeecY6eddpqZffh9vW3btg3+dby4uFh+l6/Zh//imPivjt73hQLZgLoAQtQFEKIuAODk8bF/0j158mRbtmyZPf7448e1A/fee6/l5+c3/CkrKzuu1wOaE3UBhKgLIERdAMDJ42MtuqdMmWLPP/+8zZo1y3r37t3QXlJSYocOHbLq6uqk/pWVle5nzu644w6rqalp+LNhw4aPs0tAs6MugBB1AYSoCwA4uaT16+VRFNltt91m06ZNs1deeSUIERs1apTl5ubajBkzbNKkSWZmtnLlSlu/fr2NGzdOvma7du1kAEW/fv2SAhu8X5dSgRZeWIgKX/GCXhJvgvWGDBkStHlhK/369QvavCA1FQDjhRupEJKePXsGbd5xjRgxImgrKipK+b28Y0hMXq23Y8eOoM0LTFHhLF4wiQo48sKlGoeFpBr4lo4TWRcFBQUpBeOoYDoVqmRmwcOdmVleXp7sq86zN36UdILsVGigd/2WL18etKl679atW8rv74VLqf3y6lW1pxNE44XgKF74XXM5kXVx+PDhpLHpBQ2pbdMJN/Pme1UX6tp5gVOq3t59913ZN52QRHVsXbp0Cdo2b94st+/atWvQls687I1fNRepe65331fHoEKovH3w7o+Jx6YCD5vCiayLAwcOJB3Hpk2b5PZq/Kprb6bHcDrPXOl89Zkav16orgos++CDD2RfFRynxtrIkSPl9moe8J5B1Hn0zu3ixYuDNnVc3hygnq/69Okj+6p/mPHqNfH5zptbARxbWovuyZMn2x/+8Ad75plnLC8vr+HzRfn5+dahQwfLz8+3L3zhCzZ16lQrLCy0Ll262G233Wbjxo1LK3ETyCbUBRCiLoAQdQEAJ6e0Ft2/+MUvzMzsggsuSGp/+OGH7eabbzYzs/vvv99atWplkyZNsoMHD9rEiRPt5z//eZPsLJCJqAsgRF0AIeoCAE5Oaf96+bG0b9/eHnzwQXvwwQc/9k4B2YS6AELUBRCiLgDg5HRc39MNAAAAAAB8H/t7uuP2la98JSkw48c//rHsp0ImvKAHFcLkhQ+Vl5cHbeeee27Q1rdv35T3ywuBUmEhXuhV+/btgzYVbqZCbcx04IkKfTPTYSNeuFSqAWVvvPGGbFfHpcLAvPdq/J2m9RqHhHmBL9micWCOF0ymjtMbU+mE2KmxpkKg1Pg30+F83jVRQVaFhYWyr3qNbdu2BW3e+FXjL53QK+94VXCbOrfLli2T26u5zAvzUvOIt1+Jr+G9Xjbp1KlT0jX0Up5VsF06wUBe6Jp6PzV+0wnnUvOvmdmqVauCNi8YrLi4OGjbuHFjyturuSHx+6CPJZ3zpfbBuwek814qHMo7hhMRpHYiVVVVJc0B3nygxpoXrqeeLbx7i5pv1TX1ArzU+PMCy1Rte9fZuw805n0vunoW9Kj9VSGzZqmHYXpBp+o+qO65ZjqM0LN9+/aG/44jjBY4WfCTbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABikrHp5aeeempSQqOX5KrSOL00ZJUM2bt3b9n3jDPOCNp69eoVtF166aVye5UMqZKfzcyGDh0atI0ePVr27d+/f9C2ZcuWoM1LA929e3fQ5iVgK15qpkrJVfvgba8SMb1rrtKfvZTSxq/hvWa2aNWqVVIaqpewrZKAO3bsKPuq9OaqqirZt0ePHnKfGlNp4GZm11xzTdDmJbm+9dZbQZuqQTNdAzU1NUGbl2qtEl4Tvz0hkUr798aV6jt9+vSgzUsQT+dbENQ19+acxL4q9TfbdOzYMWnMqXnOLL3kbZXS3LVrV9lXfXuCGmtearKqTe8bGZTG39JQTx2vmj+9+4UaP943CKjx550vVVsLFy4M2j744AO5vbq+3jVPZ3wnHoOX6J7NvHu9mn+8RHL1jQxe6ra6X6jroZL+zXQivZderlLRvXpX32yhnhvTeV7x7sWKNw+oOUe1effydL6JRB2Dd99OPI/e+QdwbFQPAAAAAAAxYdENAAAAAEBMWHQDAAAAABATFt0AAAAAAMQkYxN06urqkkI/xo4dK/stWLAgaPPCIFSIiAo6MtNBaM8++2zK76VCSLZv357yfnnBOCtWrAjaVFCGF8Kzfv36oE0FdZjpc+OFS6nXVbywGxW4472XClfZuXOn7Ns4sCSdIKVM1KFDh6Qxp8apmdmGDRuCNi9USwWpeSE66vqpYJXVq1fL7ZctWxa0XXTRRbKvCpvxQhLfeOONlLYfP3683F7NIyowyMxsyJAhQdvKlStlX3W8ar+866jOrRc6pHhBVokhc1EUpfx6map3795J4ULePKPGtZpPzPQcrIKhvNdQbTfccIPcvrS0NGhTwWJmul6XL18u+6qQzs2bNwdt8+bNk9sPHz48aLvssstk37lz5wZtEydOTLmvN4crKrjLu44qdMoL/kqsQ+/1sklOTk7SfOEFPqogyXSCwbxAWnXu1ZzmhUOqgDfveUUFtHl91euqe6YXPPfee+8Fbd7zinoW886tep5UzyxeOKA6394xqOdGFdxp5gctAkgPP+kGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYZm17e2O233y7bP/OZzwRtXkqzSpcsLi6WfVVyqUoO91Ioy8vLgzYvoVMlf44YMUL2VemSKvm4trZWbt+zZ8+gTSVemumUUS/VWiX9quP1EqHVcXnnSyXPeqmsjZNDvfTrbFFRUZF0vZYsWSL7qWtaWFgo+6p0VO86qZRbdZ3y8vLk9ip5+YUXXpB9Vb163zZw6qmnBm0qtXXQoEFyezX+vHlEJaUnpoEnUvODOi4vTVldG+86ppMwm/h+3ntnk+HDhyeNucrKStnv8OHDQZuXBLx169agzUvYVvOi2t5LCT/77LODtj59+si+6t7kfSuDmh86d+6c8nuVlZUFbW+99VbK+/WXv/xF9l2zZo1sb8z7Fg5VV+p8m+nxXVRUdMz3U+nO2ebw4cNJ6eXeOFHztUr49vr2799f9lXPJmpMecnf77//ftDmfbOLuud5ta2eF9Q3Hqj7nZlORfeeo9RY9b4FQZ0v1derH3UfU9+WYabvj949L/G50Xu2BHBs/KQbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGKSsUFqq1evTgrX8IJxVHiLF56hgoZKS0tl31dffTVomz59etA2cOBAub0K2ujVq5fsq9q9ICoV/KZCSFRYjpkOz9i4caPsq0JMvCAWFVajQki84Dp1DGpfzXQYkrdfjdu9cJhsccMNNySFrahrZKbPkRc2p8L1vAAZ9brt27cP2rzAOnX9d+zYIfuqsBhvrKoQGzU3eAE0avytXbtW9lVzhjeu1XhTAYUe1dcLUlPznhfwlhjeqMLtsk2bNm2Sjn/w4MGyn7r+Q4cOlX1VQKAaU2b+GG7Mm9efeeaZlLY3+zBMsTEvcEwFDCreGFDna9u2bbKvF76oqLGq5nAvTDOd91eBaC0hJC0V7du3TwrX8p6N1Hzthaamc2/Zvn170LZhw4ag7eKLL5bbn3/++UHbb3/7W9nXqwFFzavquLxA23TCJ4cNGxa0efuq7sUquFPtq8d7jlJzkRfGmRjmlk5gJ4Bk/KQbAAAAAICYsOgGAAAAACAmLLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJhkbHr5zTffnJQwuWvXLtlv/PjxQZtKU/bavRTK559/PpXdlKnHZjoN1EvOTkyjruelR6uUZPVe3vYqkXT16tWyb3V1ddDmpZSqc6tSLqMokturROYtW7bIvirR1kvfbZxS671/tqiqqkq6BirJ2Eynm3rnU6Xnewml6jyrsZaYmJsoJycnaEtM0k6k6sKbB1Qq7+7du4M2ry7U8XppyF27dg3avNpWybHqmw1UrZmZlZSUBG3ppMd6SeeJx9ASkpzr6uqSzvWoUaNkv7lz5wZt6nqY6dry0vPVa6i5Uo1pM11X3pjYvHlz0Oalpz/33HNBm/pmC2+cqPtbWVmZ7KuS3b3kZHW/UOnlXtK/Ot/evbioqCho85K5E895OsnpmarxvJSfny/7qevkzeFqrlUp5Wb6+Uo9rzzyyCNy+7PPPjul7c3M+vXrF7Q99dRTsq+qt02bNgVtXkq4qkFvv9Q3CKj3MjNbvHhxSn29b0FQ9zfveVjVhVdviceg7qsAUsNPugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmGTcZ7rrP3Pb+DNG3mdx1efmvM/hqM9dep8FVq+rPp/kvZf67Kr3eRn1WSBvv9Rn0dTn27z9qq2tDdq8z66p/fI+062o41WfB/P2yzsHar+8z1M1Vv+5wWz7bHf9/jb+zJZ37dTnI73PHau+qs17DW+spbp9On2991LXU41Vb/yq9/LOgRpr3mdXVbvah3TeK53PmnqfV078vGZ9nWVbTZj9zz43nhfTmWvTmf/SqYt0rnM6Y907NiXVGkjnnukdg5rbvbpIdc5RnxM30+fAG+vq+nrXPPEeX3/9s7kuGl/Xphh/qq93ndQzUzrvpa6TdwzqGcK736j3i6te1TOb97yinnnUOfCeo9Lpq97Luz8mfo67/hkkG+sCaG45UYZVzsaNG92gFqCpbNiwwQ3wykTUBeKWbTVhRl0gftQFEMrGugCaW8Ytuuvq6mzz5s2Wl5dne/bssbKyMtuwYYOb+pqNdu/ezXE1kyiKbM+ePVZaWprWT4yaG3WRvTL9uLK1Jsyoi2yW6cdFXWS2TB8/H1emH1c21wXQ3DLu18tbtWrV8K9n9b+i3aVLl4ycfI4Xx9U8vK9NyWTURfbL5OPKxpowoy5agkw+Luoi83FcJ1621gXQ3PhnKgAAAAAAYsKiGwAAAACAmGT0ortdu3Z21113Wbt27Zp7V5oUx4Xj0VLPM8eF49FSzzPHhePRUs8zxwUg22RckBoAAAAAAC1FRv+kGwAAAACAbMaiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACISUYvuh988EHr27evtW/f3saOHWsLFy5s7l1Ky5w5c+zKK6+00tJSy8nJsaeffjrp76MosjvvvNN69uxpHTp0sAkTJtiqVauaZ2dTdO+999qZZ55peXl5VlRUZNdcc42tXLkyqc+BAwds8uTJ1q1bN+vcubNNmjTJKisrm2mPW5Zsrwkz6oK6aHrURWaiLppXttdFS6wJM+oCOFll7KL7iSeesKlTp9pdd91lixcvtuHDh9vEiRNt27Ztzb1rKautrbXhw4fbgw8+KP/+vvvus5/+9Kf2y1/+0hYsWGCdOnWyiRMn2oEDB07wnqZu9uzZNnnyZJs/f75Nnz7dDh8+bJdeeqnV1tY29PnGN75hzz33nD355JM2e/Zs27x5s1133XXNuNctQ0uoCTPqgrpoWtQFdYFQS6iLllgTZtQFcNKKMtSYMWOiyZMnN/z/0aNHo9LS0ujee+9txr36+MwsmjZtWsP/19XVRSUlJdG///u/N7RVV1dH7dq1i/74xz82wx5+PNu2bYvMLJo9e3YURR8eQ25ubvTkk0829FmxYkVkZtG8efOaazdbhJZWE1FEXVAXx4+6oC4Qaml10VJrIoqoC+BkkZE/6T506JAtWrTIJkyY0NDWqlUrmzBhgs2bN68Z96zprFmzxrZu3Zp0jPn5+TZ27NisOsaamhozMyssLDQzs0WLFtnhw4eTjmvw4MFWXl6eVceVaU6GmjCjLpAe6oK6QOhkqIuWUhNm1AVwssjIRfeOHTvs6NGjVlxcnNReXFxsW7dubaa9alr1x5HNx1hXV2e33367nXPOOXbaaaeZ2YfH1bZtWysoKEjqm03HlYlOhpowoy6QHuoie46TujhxToa6aAk1YUZdACeTNs29A8hekydPtmXLltmrr77a3LsCZAzqAghRF0CIugBOHhn5k+7u3btb69atg6TGyspKKykpaaa9alr1x5GtxzhlyhR7/vnnbdasWda7d++G9pKSEjt06JBVV1cn9c+W48pUJ0NNmFEXSA91kR3HSV2cWCdDXWR7TZhRF8DJJiMX3W3btrVRo0bZjBkzGtrq6upsxowZNm7cuGbcs6ZTUVFhJSUlSce4e/duW7BgQUYfYxRFNmXKFJs2bZrNnDnTKioqkv5+1KhRlpubm3RcK1eutPXr12f0cWW6k6EmzKgLpIe6oC4QOhnqIltrwoy6AE5azZvj5nv88cejdu3aRY888ki0fPny6Etf+lJUUFAQbd26tbl3LWV79uyJlixZEi1ZsiQys+gnP/lJtGTJkmjdunVRFEXRj3/846igoCB65plnoqVLl0ZXX311VFFREe3fv7+Z99x36623Rvn5+dErr7wSbdmypeHPvn37Gvp85StficrLy6OZM2dGb7zxRjRu3Lho3LhxzbjXLUNLqIkooi6oi6ZFXVAXCLWEumiJNRFF1AVwssrYRXcURdF//dd/ReXl5VHbtm2jMWPGRPPnz2/uXUrLrFmzIjML/tx0001RFH34lRff//73o+Li4qhdu3bRxRdfHK1cubJ5d/oY1PGYWfTwww839Nm/f3/01a9+NeratWvUsWPH6Nprr422bNnSfDvdgmR7TUQRdUFdND3qIjNRF80r2+uiJdZEFFEXwMkqJ4qiqOl/fg4AAAAAADLyM90AAAAAALQELLoBAAAAAIgJi24AAAAAAGLCohsAAAAAgJiw6AYAAAAAICYsugEAAAAAiAmLbgAAAAAAYsKiGwAAAACAmLDoBgAAAAAgJiy6AQAAAACICYtuAAAAAABiwqIbAAAAAICY/P8AUTlapy7U7o0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "columns = 5\n",
    "rows = 2\n",
    "fig.suptitle(\"9 most outlying images\", fontsize=16)\n",
    "for i in range(0, (columns*rows)-1):\n",
    "    fig.add_subplot(rows, columns, (i+1))\n",
    "    fig.tight_layout()\n",
    "    img = mixed[most_diff[i]]\n",
    "    plt.imshow(img,cmap=plt.cm.binary)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots of the 9 least and most outlying images, according to the difference score, shows the simplest quickdraw images as the least outlying and the images of faces as the most outlying. This is expected because the auto-encoder was trained to encode simple images that mostly contained empty pixels. This therefore explains why the auto-encoder performs the best on the simplest quickdraw images and why is does the worst on the complex images of faces. \n",
    "\n",
    "This exercise shows that the auto-encoder can indeed be used as a means of anomaly detection as it has been able to succesfully separate the quickdraw images from the anomalous images of faces by using the difference score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}