dlnd_face_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face Generation\n",
    "\n",
    "In this project, you'll define and train a DCGAN on a dataset of faces. Your goal is to get a generator network to generate *new* images of faces that look as realistic as possible!\n",
    "\n",
    "The project will be broken down into a series of tasks from **loading in data to defining and training adversarial networks**. At the end of the notebook, you'll be able to visualize the results of your trained Generator to see how it performs; your generated samples should look like fairly realistic faces with small amounts of noise.\n",
    "\n",
    "### Get the Data\n",
    "\n",
    "You'll be using the [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) to train your adversarial networks.\n",
    "\n",
    "This dataset is more complex than the number datasets (like MNIST or SVHN) you've been working with, and so, you should prepare to define deeper networks and train them for a longer time to get good results. It is suggested that you utilize a GPU for training.\n",
    "\n",
    "### Pre-processed Data\n",
    "\n",
    "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. Some sample data is show below.\n",
    "\n",
    "<img src='assets/processed_face_data.png' width=60% />\n",
    "\n",
    "> If you are working locally, you can download this data [by clicking here](https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be7eb6f_processed-celeba-small/processed-celeba-small.zip)\n",
    "\n",
    "This is a zip file that you'll need to extract in the home directory of this notebook for further loading and processing. After extracting the data, you should be left with a directory of data `processed_celeba_small/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can comment out after executing\n",
    "#!unzip processed_celeba_small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'processed_celeba_small/'\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import pickle as pkl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import problem_unittests as tests\n",
    "#import helper\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the CelebA Data\n",
    "\n",
    "The [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations, you'll only need the images. Note that these are color images with [3 color channels (RGB)](https://en.wikipedia.org/wiki/Channel_(digital_image)#RGB_Images) each.\n",
    "\n",
    "### Pre-process and Load the Data\n",
    "\n",
    "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. This *pre-processed* dataset is a smaller subset of the very large CelebA data.\n",
    "\n",
    "> There are a few other steps that you'll need to **transform** this data and create a **DataLoader**.\n",
    "\n",
    "#### Exercise: Complete the following `get_dataloader` function, such that it satisfies these requirements:\n",
    "\n",
    "* Your images should be square, Tensor images of size `image_size x image_size` in the x and y dimension.\n",
    "* Your function should return a DataLoader that shuffles and batches these Tensor images.\n",
    "\n",
    "#### ImageFolder\n",
    "\n",
    "To create a dataset given a directory of images, it's recommended that you use PyTorch's [ImageFolder](https://pytorch.org/docs/0.4.0/torchvision/datasets.html#imagefolder) wrapper, with a root directory `processed_celeba_small/` and data transformation passed in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataloader(batch_size, image_size, data_dir='processed_celeba_small/'):\n",
    "    \"\"\"\n",
    "    Batch the neural network data using DataLoader\n",
    "    :param batch_size: The size of each batch; the number of images in a batch\n",
    "    :param img_size: The square size of the image data (x, y)\n",
    "    :param data_dir: Directory where image data is located\n",
    "    :return: DataLoader with batched data\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement function and return a dataloader\n",
    "    loader = datasets.ImageFolder(data_dir, \n",
    "                                  transforms.Compose([\n",
    "                                      transforms.Resize(image_size),\n",
    "                                      transforms.CenterCrop(image_size),\n",
    "                                      transforms.ToTensor()\n",
    "                                  ]))\n",
    "    \n",
    "    return DataLoader(loader, batch_size = batch_size, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a DataLoader\n",
    "\n",
    "#### Exercise: Create a DataLoader `celeba_train_loader` with appropriate hyperparameters.\n",
    "\n",
    "Call the above function and create a dataloader to view images. \n",
    "* You can decide on any reasonable `batch_size` parameter\n",
    "* Your `image_size` **must be** `32`. Resizing the data to a smaller size will make for faster training, while still creating convincing images of faces!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 128\n",
    "img_size = 32\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# Call your function and get a dataloader\n",
    "celeba_train_loader = get_dataloader(batch_size, img_size)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you can view some images! You should seen square images of somewhat-centered faces.\n",
    "\n",
    "Note: You'll need to convert the Tensor images into a NumPy type and transpose the dimensions to correctly display an image, suggested `imshow` code is below, but it may not be perfect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f004f4984a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper display function\n",
    "def imshow(img):\n",
    "    plt.imshow(\n",
    "        np.transpose(img.numpy(), (1, 2, 0))\n",
    "    )\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# obtain one batch of training images\n",
    "dataiter = iter(celeba_train_loader)\n",
    "images, _ = dataiter.next() # _ for no labels\n",
    "\n",
    "# plot the images in the batch, along with the corresponding labels\n",
    "fig = plt.figure(figsize=(20, 4))\n",
    "plot_size=20\n",
    "for idx in np.arange(plot_size):\n",
    "    ax = fig.add_subplot(2, plot_size/2, idx+1, xticks=[], yticks=[])\n",
    "    imshow(images[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Pre-process your image data and scale it to a pixel range of -1 to 1\n",
    "\n",
    "You need to do a bit of pre-processing; you know that the output of a `tanh` activated generator will contain pixel values in a range from -1 to 1, and so, we need to rescale our training images to a range of -1 to 1. (Right now, they are in a range from 0-1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Complete the scale function\n",
    "def scale(x, feature_range=(-1, 1)):\n",
    "    ''' Scale takes in an image x and returns that image, scaled\n",
    "       with a feature_range of pixel values from -1 to 1. \n",
    "       This function assumes that the input x is already scaled from 0-1.'''\n",
    "    # assume x is scaled to (0, 1)\n",
    "    # scale to feature_range and return scaled x\n",
    "    return (feature_range[1] - feature_range[0]) * x + feature_range[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-0.8588)\n",
      "Max:  tensor(0.8980)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# check scaled range\n",
    "# should be close to -1 to 1\n",
    "img = images[0]\n",
    "scaled_img = scale(img)\n",
    "\n",
    "print('Min: ', scaled_img.min())\n",
    "print('Max: ', scaled_img.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Define the Model\n",
    "\n",
    "A GAN is comprised of two adversarial networks, a discriminator and a generator.\n",
    "\n",
    "## Discriminator\n",
    "\n",
    "Your first task will be to define the discriminator. This is a convolutional classifier like you've built before, only without any maxpooling layers. To deal with this complex data, it's suggested you use a deep network with **normalization**. You are also allowed to create any helper functions that may be useful.\n",
    "\n",
    "#### Exercise: Complete the Discriminator class\n",
    "* The inputs to the discriminator are 32x32x3 tensor images\n",
    "* The output should be a single value that will indicate whether a given image is real or fake\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# helper conv function\n",
    "def conv(in_channels, out_channels, kernel_size, stride=2, padding = 1, batch_norm= True):\n",
    "    layers = []\n",
    "    conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels,\n",
    "                       kernel_size = kernel_size, stride = stride, padding = padding, bias = False)\n",
    "    layers.append(conv_layer)\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "class Discriminator(nn.Module):\n",
    "\n",
    "    def __init__(self, conv_dim):\n",
    "        \"\"\"\n",
    "        Initialize the Discriminator Module\n",
    "        :param conv_dim: The depth of the first convolutional layer\n",
    "        \"\"\"\n",
    "        super(Discriminator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "        \n",
    "        self.conv1 = conv(3, conv_dim, 4, batch_norm=False)\n",
    "        self.conv2 = conv(conv_dim, conv_dim * 2, 4)\n",
    "        self.conv3 = conv(conv_dim * 2, conv_dim * 4, 4)\n",
    "        self.conv4 = conv(conv_dim * 4, conv_dim * 8, 4)\n",
    "        \n",
    "        self.fc = nn.Linear(conv_dim * 8 * 2 * 2, 1)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: Discriminator logits; the output of the neural network\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        \n",
    "        # hidden layers and activation\n",
    "        out = F.leaky_relu(self.conv1(x), 0.2)\n",
    "        out = F.leaky_relu(self.conv2(out), 0.2)\n",
    "        out = F.leaky_relu(self.conv3(out), 0.2)\n",
    "        out = F.leaky_relu(self.conv4(out), 0.2)\n",
    "        \n",
    "        out = out.view(-1, self.conv_dim * 8 * 2 * 2)\n",
    "        \n",
    "        out = self.fc(out)\n",
    "        \n",
    "        return out\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(Discriminator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator\n",
    "\n",
    "The generator should upsample an input and generate a *new* image of the same size as our training data `32x32x3`. This should be mostly transpose convolutional layers with normalization applied to the outputs.\n",
    "\n",
    "#### Exercise: Complete the Generator class\n",
    "* The inputs to the generator are vectors of some length `z_size`\n",
    "* The output should be a image of shape `32x32x3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper deconv function\n",
    "def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    layers = []\n",
    "    \n",
    "    layers.append(nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, bias=False))\n",
    "    \n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    \n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "class Generator(nn.Module):\n",
    "    \n",
    "    def __init__(self, z_size, conv_dim):\n",
    "        \"\"\"\n",
    "        Initialize the Generator Module\n",
    "        :param z_size: The length of the input latent vector, z\n",
    "        :param conv_dim: The depth of the inputs to the *last* transpose convolutional layer\n",
    "        \"\"\"\n",
    "        super(Generator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "\n",
    "        self.fc = nn.Linear(z_size, conv_dim * 8 * 2 * 2)\n",
    "        \n",
    "        self.t_conv1 = deconv(conv_dim * 8, conv_dim * 4, 4)\n",
    "        self.t_conv2 = deconv(conv_dim * 4, conv_dim * 2, 4)\n",
    "        self.t_conv3 = deconv(conv_dim * 2, conv_dim, 4)\n",
    "        self.t_conv4 = deconv(conv_dim, 3, 4, batch_norm=False)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: A 32x32x3 Tensor image as output\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        out = self.fc(x)\n",
    "        out = out.view(-1, self.conv_dim*8, 2, 2) # (batch_size, depth, 4, 4)\n",
    "        \n",
    "        out = F.relu(self.t_conv1(out))\n",
    "        out = F.relu(self.t_conv2(out))\n",
    "        out = F.relu(self.t_conv3(out))\n",
    "        \n",
    "        # last layer: tanh activation instead of relu\n",
    "        out = self.t_conv4(out)\n",
    "        out = F.tanh(out)    \n",
    "        \n",
    "        return out\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(Generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the weights of your networks\n",
    "\n",
    "To help your models converge, you should initialize the weights of the convolutional and linear layers in your model. From reading the [original DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf), they say:\n",
    "> All weights were initialized from a zero-centered Normal distribution with standard deviation 0.02.\n",
    "\n",
    "So, your next task will be to define a weight initialization function that does just this!\n",
    "\n",
    "You can refer back to the lesson on weight initialization or even consult existing model code, such as that from [the `networks.py` file in CycleGAN Github repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py) to help you complete this function.\n",
    "\n",
    "#### Exercise: Complete the weight initialization function\n",
    "\n",
    "* This should initialize only **convolutional** and **linear** layers\n",
    "* Initialize the weights to a normal distribution, centered around 0, with a standard deviation of 0.02.\n",
    "* The bias terms, if they exist, may be left alone or set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weights_init_normal(m):\n",
    "    \"\"\"\n",
    "    Applies initial weights to certain layers in a model .\n",
    "    The weights are taken from a normal distribution \n",
    "    with mean = 0, std dev = 0.02.\n",
    "    :param m: A module or layer in a network    \n",
    "    \"\"\"\n",
    "    # classname will be something like:\n",
    "    # `Conv`, `BatchNorm2d`, `Linear`, etc.\n",
    "    classname = m.__class__.__name__\n",
    "    \n",
    "    # TODO: Apply initial weights to convolutional and linear layers\n",
    "    \n",
    "    if classname.find(\"Conv\") != -1 or classname.find('Linear') != -1:\n",
    "        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
    "    elif classname.find(\"BatchNorm2d\") != -1:\n",
    "        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
    "        torch.nn.init.constant_(m.bias.data, 0.0)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Build complete network\n",
    "\n",
    "Define your models' hyperparameters and instantiate the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "def build_network(d_conv_dim, g_conv_dim, z_size):\n",
    "    # define discriminator and generator\n",
    "    D = Discriminator(d_conv_dim)\n",
    "    G = Generator(z_size=z_size, conv_dim=g_conv_dim)\n",
    "\n",
    "    # initialize model weights\n",
    "    D.apply(weights_init_normal)\n",
    "    G.apply(weights_init_normal)\n",
    "\n",
    "    print(D)\n",
    "    print()\n",
    "    print(G)\n",
    "    \n",
    "    return D, G\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Define model hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discriminator(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (conv4): Sequential(\n",
      "    (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (fc): Linear(in_features=2048, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (fc): Linear(in_features=128, out_features=2048, bias=True)\n",
      "  (t_conv1): Sequential(\n",
      "    (0): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv2): Sequential(\n",
      "    (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv3): Sequential(\n",
      "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv4): Sequential(\n",
      "    (0): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Define model hyperparams\n",
    "d_conv_dim = 64\n",
    "g_conv_dim = 64\n",
    "z_size = 128\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "D, G = build_network(d_conv_dim, g_conv_dim, z_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training on GPU\n",
    "\n",
    "Check if you can train on GPU. Here, we'll set this as a boolean variable `train_on_gpu`. Later, you'll be responsible for making sure that \n",
    ">* Models,\n",
    "* Model inputs, and\n",
    "* Loss function arguments\n",
    "\n",
    "Are moved to GPU, where appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on GPU!\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import torch\n",
    "\n",
    "# Check for a GPU\n",
    "train_on_gpu = torch.cuda.is_available()\n",
    "if not train_on_gpu:\n",
    "    print('No GPU found. Please use a GPU to train your neural network.')\n",
    "else:\n",
    "    print('Training on GPU!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Discriminator and Generator Losses\n",
    "\n",
    "Now we need to calculate the losses for both types of adversarial networks.\n",
    "\n",
    "### Discriminator Losses\n",
    "\n",
    "> * For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_real_loss + d_fake_loss`. \n",
    "* Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n",
    "\n",
    "\n",
    "### Generator Loss\n",
    "\n",
    "The generator loss will look similar only with flipped labels. The generator's goal is to get the discriminator to *think* its generated images are *real*.\n",
    "\n",
    "#### Exercise: Complete real and fake loss functions\n",
    "\n",
    "**You may choose to use either cross entropy or a least squares error loss to complete the following `real_loss` and `fake_loss` functions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def real_loss(D_out, smooth=True):\n",
    "    '''Calculates how close discriminator outputs are to being real.\n",
    "       param, D_out: discriminator logits\n",
    "       return: real loss'''\n",
    "    \n",
    "    batch_size = D_out.size(0)\n",
    "    \n",
    "    # label smoothing\n",
    "    if smooth:  # smooth, real labels = 0.9 or as given by smooth parameter\n",
    "        labels = torch.ones(batch_size) * 0.9\n",
    "    else:\n",
    "        labels = torch.ones(batch_size) # real labels = 1\n",
    "    \n",
    "    labels = torch.ones(batch_size)\n",
    "    \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    \n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    \n",
    "    return loss\n",
    "\n",
    "def fake_loss(D_out):\n",
    "    '''Calculates how close discriminator outputs are to being fake.\n",
    "       param, D_out: discriminator logits\n",
    "       return: fake loss'''\n",
    "    \n",
    "    batch_size = D_out.size(0)\n",
    "    labels = torch.zeros(batch_size)\n",
    "    \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    \n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers\n",
    "\n",
    "#### Exercise: Define optimizers for your Discriminator (D) and Generator (G)\n",
    "\n",
    "Define optimizers for your models with appropriate hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "lr = 0.0002\n",
    "beta1 = 0.5\n",
    "beta2 = 0.999 \n",
    "\n",
    "# Create optimizers for the discriminator D and generator G\n",
    "d_optimizer = optim.Adam(D.parameters(), lr, [beta1, beta2])\n",
    "\n",
    "g_optimizer = optim.Adam(G.parameters(), lr, [beta1, beta2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Training\n",
    "\n",
    "Training will involve alternating between training the discriminator and the generator. You'll use your functions `real_loss` and `fake_loss` to help you calculate the discriminator losses.\n",
    "\n",
    "* You should train the discriminator by alternating on real and fake images\n",
    "* Then the generator, which tries to trick the discriminator and should have an opposing loss function\n",
    "\n",
    "\n",
    "#### Saving Samples\n",
    "\n",
    "You've been given some code to print out some loss statistics and save some generated \"fake\" samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Complete the training function\n",
    "\n",
    "Keep in mind that, if you've moved your models to GPU, you'll also have to move any model inputs to GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(D, G, n_epochs, print_every=50):\n",
    "    '''Trains adversarial networks for some number of epochs\n",
    "       param, D: the discriminator network\n",
    "       param, G: the generator network\n",
    "       param, n_epochs: number of epochs to train for\n",
    "       param, print_every: when to print and record the models' losses\n",
    "       return: D and G losses'''\n",
    "    \n",
    "    # move models to GPU\n",
    "    if train_on_gpu:\n",
    "        D.cuda()\n",
    "        G.cuda()\n",
    "\n",
    "    # keep track of loss and generated, \"fake\" samples\n",
    "    samples = []\n",
    "    losses = []\n",
    "\n",
    "    # Get some fixed data for sampling. These are images that are held\n",
    "    # constant throughout training, and allow us to inspect the model's performance\n",
    "    sample_size=16\n",
    "    fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
    "    fixed_z = torch.from_numpy(fixed_z).float()\n",
    "    # move z to GPU if available\n",
    "    if train_on_gpu:\n",
    "        fixed_z = fixed_z.cuda()\n",
    "\n",
    "    # epoch training loop\n",
    "    for epoch in range(n_epochs):\n",
    "\n",
    "        # batch training loop\n",
    "        for batch_i, (real_images, _) in enumerate(celeba_train_loader):\n",
    "\n",
    "            batch_size = real_images.size(0)\n",
    "            real_images = scale(real_images)\n",
    "\n",
    "            # ===============================================\n",
    "            #         YOUR CODE HERE: TRAIN THE NETWORKS\n",
    "            # ===============================================\n",
    "                        \n",
    "            # Train discriminator\n",
    "            d_optimizer.zero_grad()\n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "                \n",
    "            D_real = D(real_images)\n",
    "            d_real_loss = real_loss(D_real)\n",
    "            \n",
    "            z = np.random.uniform(-1, 1, size = (batch_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\n",
    "            \n",
    "            fake_images = G(z)\n",
    "            D_fake = D(fake_images)\n",
    "            d_fake_loss = fake_loss(D_fake)\n",
    "            \n",
    "            d_loss = d_real_loss + d_fake_loss\n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "\n",
    "            # Train generator\n",
    "            g_optimizer.zero_grad()\n",
    "            \n",
    "            z = np.random.uniform(-1, 1, size = (batch_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\n",
    "            \n",
    "            fake_images = G(z)\n",
    "            D_fake = D(fake_images)\n",
    "            g_loss = real_loss(D_fake)\n",
    "            \n",
    "            g_loss.backward()\n",
    "            g_optimizer.step()\n",
    "            \n",
    "            \n",
    "            # ===============================================\n",
    "            #              END OF YOUR CODE\n",
    "            # ===============================================\n",
    "\n",
    "            # Print some loss stats\n",
    "            if batch_i % print_every == 0:\n",
    "                # append discriminator loss and generator loss\n",
    "                losses.append((d_loss.item(), g_loss.item()))\n",
    "                # print discriminator and generator loss\n",
    "                print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
    "                        epoch+1, n_epochs, d_loss.item(), g_loss.item()))\n",
    "\n",
    "\n",
    "        ## AFTER EACH EPOCH##    \n",
    "        # this code assumes your generator is named G, feel free to change the name\n",
    "        # generate and save sample, fake images\n",
    "        G.eval() # for generating samples\n",
    "        samples_z = G(fixed_z)\n",
    "        samples.append(samples_z)\n",
    "        G.train() # back to training mode\n",
    "\n",
    "    # Save training generator samples\n",
    "    with open('train_samples.pkl', 'wb') as f:\n",
    "        pkl.dump(samples, f)\n",
    "    \n",
    "    # finally return losses\n",
    "    return losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set your number of training epochs and train your GAN!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   20] | d_loss: 1.2611 | g_loss: 2.7420\n",
      "Epoch [    1/   20] | d_loss: 1.7417 | g_loss: 8.0746\n",
      "Epoch [    1/   20] | d_loss: 0.5051 | g_loss: 4.5080\n",
      "Epoch [    1/   20] | d_loss: 0.9765 | g_loss: 4.2233\n",
      "Epoch [    1/   20] | d_loss: 0.7984 | g_loss: 4.9204\n",
      "Epoch [    1/   20] | d_loss: 0.5868 | g_loss: 4.4055\n",
      "Epoch [    1/   20] | d_loss: 0.9179 | g_loss: 2.0728\n",
      "Epoch [    1/   20] | d_loss: 0.6090 | g_loss: 2.6108\n",
      "Epoch [    1/   20] | d_loss: 0.7944 | g_loss: 5.1056\n",
      "Epoch [    1/   20] | d_loss: 0.6701 | g_loss: 3.8183\n",
      "Epoch [    1/   20] | d_loss: 0.7070 | g_loss: 3.4192\n",
      "Epoch [    1/   20] | d_loss: 0.9884 | g_loss: 2.2416\n",
      "Epoch [    1/   20] | d_loss: 0.7896 | g_loss: 4.3609\n",
      "Epoch [    1/   20] | d_loss: 1.0225 | g_loss: 4.9975\n",
      "Epoch [    1/   20] | d_loss: 0.5402 | g_loss: 3.8382\n",
      "Epoch [    2/   20] | d_loss: 0.7132 | g_loss: 3.0605\n",
      "Epoch [    2/   20] | d_loss: 1.2362 | g_loss: 1.3541\n",
      "Epoch [    2/   20] | d_loss: 1.7217 | g_loss: 7.2970\n",
      "Epoch [    2/   20] | d_loss: 0.7213 | g_loss: 2.0659\n",
      "Epoch [    2/   20] | d_loss: 0.5062 | g_loss: 2.4728\n",
      "Epoch [    2/   20] | d_loss: 0.9221 | g_loss: 1.6446\n",
      "Epoch [    2/   20] | d_loss: 1.4990 | g_loss: 4.5705\n",
      "Epoch [    2/   20] | d_loss: 0.6262 | g_loss: 2.1770\n",
      "Epoch [    2/   20] | d_loss: 0.6223 | g_loss: 2.5114\n",
      "Epoch [    2/   20] | d_loss: 0.8122 | g_loss: 3.5867\n",
      "Epoch [    2/   20] | d_loss: 0.9047 | g_loss: 6.3845\n",
      "Epoch [    2/   20] | d_loss: 0.6653 | g_loss: 3.6774\n",
      "Epoch [    2/   20] | d_loss: 0.7581 | g_loss: 1.6952\n",
      "Epoch [    2/   20] | d_loss: 0.6218 | g_loss: 2.5880\n",
      "Epoch [    2/   20] | d_loss: 0.5576 | g_loss: 2.4069\n",
      "Epoch [    3/   20] | d_loss: 0.5271 | g_loss: 2.9662\n",
      "Epoch [    3/   20] | d_loss: 1.1981 | g_loss: 1.5620\n",
      "Epoch [    3/   20] | d_loss: 0.6558 | g_loss: 2.9498\n",
      "Epoch [    3/   20] | d_loss: 1.0529 | g_loss: 4.7385\n",
      "Epoch [    3/   20] | d_loss: 0.6342 | g_loss: 2.3718\n",
      "Epoch [    3/   20] | d_loss: 0.5653 | g_loss: 2.7043\n",
      "Epoch [    3/   20] | d_loss: 1.2417 | g_loss: 0.7574\n",
      "Epoch [    3/   20] | d_loss: 0.6962 | g_loss: 2.9546\n",
      "Epoch [    3/   20] | d_loss: 0.7942 | g_loss: 3.6880\n",
      "Epoch [    3/   20] | d_loss: 0.8192 | g_loss: 4.1759\n",
      "Epoch [    3/   20] | d_loss: 0.7833 | g_loss: 1.4403\n",
      "Epoch [    3/   20] | d_loss: 0.4833 | g_loss: 2.8933\n",
      "Epoch [    3/   20] | d_loss: 0.7227 | g_loss: 2.1817\n",
      "Epoch [    3/   20] | d_loss: 0.7825 | g_loss: 3.4045\n",
      "Epoch [    3/   20] | d_loss: 0.4531 | g_loss: 2.9547\n",
      "Epoch [    4/   20] | d_loss: 0.7133 | g_loss: 2.1819\n",
      "Epoch [    4/   20] | d_loss: 0.8902 | g_loss: 1.2007\n",
      "Epoch [    4/   20] | d_loss: 0.9049 | g_loss: 1.9302\n",
      "Epoch [    4/   20] | d_loss: 0.6890 | g_loss: 2.2089\n",
      "Epoch [    4/   20] | d_loss: 0.8182 | g_loss: 1.3701\n",
      "Epoch [    4/   20] | d_loss: 0.8005 | g_loss: 2.2336\n",
      "Epoch [    4/   20] | d_loss: 0.6199 | g_loss: 3.3115\n",
      "Epoch [    4/   20] | d_loss: 0.6669 | g_loss: 3.4239\n",
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      "Epoch [   20/   20] | d_loss: 0.2021 | g_loss: 3.0804\n",
      "Epoch [   20/   20] | d_loss: 0.5952 | g_loss: 1.7302\n",
      "Epoch [   20/   20] | d_loss: 0.2607 | g_loss: 2.8091\n",
      "Epoch [   20/   20] | d_loss: 0.2810 | g_loss: 4.2584\n",
      "Epoch [   20/   20] | d_loss: 0.2831 | g_loss: 3.5729\n",
      "Epoch [   20/   20] | d_loss: 0.2536 | g_loss: 4.7317\n",
      "Epoch [   20/   20] | d_loss: 0.7012 | g_loss: 2.8562\n",
      "Epoch [   20/   20] | d_loss: 0.8847 | g_loss: 1.7604\n",
      "Epoch [   20/   20] | d_loss: 0.3347 | g_loss: 2.4844\n",
      "Epoch [   20/   20] | d_loss: 0.3249 | g_loss: 3.3135\n",
      "Epoch [   20/   20] | d_loss: 0.3350 | g_loss: 2.1988\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 20\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "# call training function\n",
    "losses = train(D, G, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Training loss\n",
    "\n",
    "Plot the training losses for the generator and discriminator, recorded after each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efff6266cf8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efff66d0f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "losses = np.array(losses)\n",
    "plt.plot(losses.T[0], label='Discriminator', alpha=0.5)\n",
    "plt.plot(losses.T[1], label='Generator', alpha=0.5)\n",
    "plt.title(\"Training Losses\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Generator samples from training\n",
    "\n",
    "View samples of images from the generator, and answer a question about the strengths and weaknesses of your trained models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper function for viewing a list of passed in sample images\n",
    "def view_samples(epoch, samples):\n",
    "    fig, axes = plt.subplots(figsize=(16,4), nrows=2, ncols=8, sharey=True, sharex=True)\n",
    "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
    "        img = img.detach().cpu().numpy()\n",
    "        img = np.transpose(img, (1, 2, 0))\n",
    "        img = ((img + 1) * 255 / (2)).astype(np.uint8)\n",
    "        ax.xaxis.set_visible(False)\n",
    "        ax.yaxis.set_visible(False)\n",
    "        im = ax.imshow(img.reshape((32, 32, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load samples from generator, taken while training\n",
    "with open('train_samples.pkl', 'rb') as f:\n",
    "    samples = pkl.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nyqpys8Wqn3bhIviHP1Qi9g8/UKyrc80HMzhDFoXwuR7cVocdlRcTzR80hR4CZx3DDbyL6xxqmLjFbNV+E7incmwO+kLsFsCVM7wfU6jrA23tAEvmUQY6j9Zo6xTHBNwURxXwc3fY0XVO3sJl/BpVu9rlzbGSTqLn7ML5/clrfXbBdpzBCRrIWowYNEfdVeX7j0NUIX/jh8sqXvdE0K5QOWCwdOOmUyt59cDjSBDWaBXm1rUE7zVxWmB7QEFzMIQBOiddK88vteZ58+q524ZqV7u6OaYQRKrzXkfPHEbvP351ORVSvsTYHALBv7Gr9dndY+Hyh0da24BKdQmPYiGW7R1p/dFv+tXzE/WpyVzlGIglk8hXJfoF6jnhZB3wyIZernEvIHHdAuu+7PJ1Wy5rxYQoUTAJqu0c/QijyA13j51zziXIJhDheA4dSFkuS65J6DiMGIRxQso0wprPo9+zf9NNPgnhknx1bAPfUSr8XR/XLoCwViXPFqG8xNEbWO7eGOrYzgVQ8zlQ1RJ9ICTOCkTWez7r5kexbEfSZDKZTCaTyWQymUwbyb5Imkwmk8lkMplMJpNpI22Etq5AgdW2Z8w/JTWB7dIecLgBEt8OK/3tq2fCtx6dCCfYiYWC3D5QMtCDHbECRYYtfGAZPeCyNw5WW+Hnp0JO1hKdl9yaxtZ3oW36HhKTLoHVRUxwCtTq9B0xV2EAD46FMDwrhdI5IIF5KpxgFsJN8zpVVy4sV/jU/kh1Ffew3Q4Xxwu61i5UjzPUP7EcYpZ0VCTmcmOsunh4S06G46HqKwUClDYsxoIJkTtwF2QyYaCa+VqiZGBX2O7/1rGQ5gD97meffdWW3wCHrgq6ZiHBK6rDAyMtt/ZzTd1moEbe4zXMqILDagHMwq9ZdgJPxBiogWp41IvviqEMgBozvzxx0rijPha61biKEjmMBXDWdYHafxQKY8kivWcxFVaWAMPrAT0LIn1+hH5SwjHRlaqPfKn2DSMg08DXc2Zkv07VtfONLW0I6n6O5NM17i8I0eeQaHx0eNyW+8AaX7xRYul0qmcOgcntwKmTrm1zuOjWwKRH41UfGKP+9/fUznsjXS9F/AjRLzqB/naEcR8BvcuBb80ndEhWh1+gKxMzfHcit+8f/79/3pbv7Cn2XKe88y5uEKGHt4R8fXr7dlsuzzU3nE91r13YSuZA82NkmGfy67BSvXz68YO2DGrPlSURdxyZgCNnvbd6z7uJ4jodWT2R9kDj0TGWMhk4jpjQYfst8LyAGFUCTGyMYyNwsQXwuna0oZjzX65Ptatc1Xwqj2nMF0KOXz570pbPgItzfRBE6tMxYmxYg5/DXFIjNnVwNCAF4hZjPERETptiDewtx7ELzllrxw6ACnsgdlzTYUnnPN6f1OpHOZObo1yg/1Z87In647uT7SDK3jkXNWhrggdac2fF63muGJulamvut+wPtVa4izF+5+5BW+4BL8fU5xxdOIEh9jHnDnurte6op7gbPH6m+1oICQ5/h8su265ecydFGW6iPLARYm1OzDVD3UzfCUVO45ttOV9u5+iHc94FzQInIkePBy1RF3RtZd8lyhmiLlz5fuR1rcNyHVmqYiK402dTIsWr9ijovssjgGiXoAu380yvF2jTku6vwLFHOM/i4ZqbXuDoA+uMsYfuzaib4PfYX7QdSZPJZDKZTCaTyWQybST7ImkymUwmk8lkMplMpo20mWurc843+AUNhTy3RYElhdiOTYDInAKj+fy5EM8YzMHDO0IF7hxqy7aP5MTxrsrdPhBNuoY1qM3xIVzTMrlw1Uvd4/imXLiO4doaAIM8P5Gz7MvXuHd8J6/gUHd5jsTZ2uV2u2NhQhUwwwhb7mW+HXSn3+24H336kXPOuacvhG++OnnZli/PiCLRKQxYEu41AhpXlO/fJu/A0e9wT25T+4dCl5kolsxF3bly+xQ2UqC/xClxU/ZHoNAdYEd056qFHNw9EKpxfld4y+lvH7XlJdx3yzX6gQwQMJJqW0ikc0HzmZ7OsMX7+xCpzjUOF1g2nyEAykk3sRr4lscYd0yinQPBStDxo6vPIRZNpAvjG8gPyZ1kJCyW2EaAcBbCjXAN9YUzZJYKH5pfCDmMeb/Etxn0rlG1q13VNE6Gjjk5hctzoZhZI7F3d6iY5XuqpKcvhXW+eC4UiXhNDHS5AraXp3rOPsbvIdD8Dz9c4ZQ39+RWOOwrTudwSazporcU0hbFcM5DG729AFY/xTGEBE6tDu6vwCzHQM8WSyXU/uK3v2rLP/3JHbcNhaF348at9s6+YtpkrrZYvFM70pGW3ayE62IHboF7I7XdnQ/lbr2zp/EQIn5W7K5ADCPgmlWDOaYZnQtVns4wDyDG04FwzT2SWDSOaWTP5Pp4PtH8WyLBegIcfbwDB805nL/XxuOWfgf3tXNXzshghSencBp9ozYtiEdGnCsxJyGW8rjHHAnuExwfqHhOAPEcFPGa2/bVO3h8JAZmzLjCauNxnhAcZkEHXU5rcIOM4Q7ucb8Zp3AeB0C8oZvm6Qutqa5TPvAubI41hIgX+dqkjT/AuqHCfffQpjxmde+WxuB4DIQRbsUREs/XdFYlQVmrrpMmo8Gd22qLJVyc315qnno9AX6Lo1iDru6lAr6YI65wScKjSDyfEgFz5Tpuyjl0omMTLt8O2uqDwCXNuo/u4lxvsLx2Fzyuxn7MdSYXRsUaF4yi+o/H+2usO2OPNXCzvgkRM0uuedgWmM9LIOip09zXKfF9YaEjb0Ffc/JwrD7w7lLXucQRgz7GLM1ZQ6yjSlbU15TtSJpMJpPJZDKZTCaTaSPZF0mTyWQymUwmk8lkMm2kjdBW77yLGtSGO8wx0CLv3p8oeQbO5uSN0KUFnAmP9pUo+nBXuE4XyEWfyWSZOBmufx4cXORW28YVXusVem+OLfHhLtzn8EzE6jzQyrMTIFhwkNs5hlsbHF/TU7gwEi8JuM2uShvGtPy6PuVp6k5++7lzzrlXQHRmGRwqgS/WTHYPxyomLo8COoIBLQYKm+DZ7t2Vk2EAFKMGDuQyfFbTfwKgzUzmvOgDvZwBvQTSEgKtreF6WADJ6ADTunlb/fHg5Zu2fPJC5WoNZwUeQHdZvx3kI/C+xWhquDLmuCf0LOfRFmX+fudZj6TjNdEWOHxWYCLozFjA5SycCV9b4C6SYNWn19BHouh4b7zUtRmoaEK2BOrm4ehYY9AGcIxOc+FAxaXGb7pQ/c2B3hF1GcGt9jrla+fiBhkD2esmC+GAARyMXU+YS9JTnDw/1zOcvtHfhgg2MRzfqoXqa4B+f/djjc07tzQGHjz8tC3v3FxhXf2+nAtrYNSzqbD/Yik8sqK7XqnXa7QRk4TPgZHXQCWJ2zv02S7ApsOxMDQ6df/m179221AUBG6/cdEr4Jb85InixQDYWxWpP8VArUKnvjsY6nnu3xOSe3hTSFMC99uYU0mi6ydwLg4DzStXrsRLjMEc88BwKhSXsbRETKsd2hTtGC81js520Tcv9Xq+RtUBE8OcswOkl8nGR8mWEqD7wI2j1dxGd++TU42pGnEhhAO1K4CdIZYNhqr/HcSR568RPzk2EMMrBDw+/85An7ts4ldNtBZzZQeOy544K3DiEjE+qjp4nfMJ5hDHIyz6rAT3viAqiL7pcfRgNkdsu0YFzrn+VTyAwywobhdzLqt5DAeO0lhzfnBfR552gZR3OjhikcClnHEKmCP7ehlggdnc3BAGyR8+vNeWz3As7O2F+toF8Egelepi/VnN4A5aq4w/XUNYo+r97T6GU+nbmeK8j3Qc7TrlnXO+qcc8fb+TKrFkOtUGOBrA4xMe30eI/9ZYCxVwda+wziiApXqUl4h3vjkesij0WjpTey0KrT2KmWJjGXGQ6N4vCzn3vnuGZ93V0bTRJ3+kP42FXWfnQGSJXdOhX5+6tl76urIdSZPJZDKZTCaTyWQybST7ImkymUwmk8lkMplMpo20sWvrFeZIR7CYiUGxlU9HsBwY1XSp7Xm6Wt2De+cOcNbhUJhBN1I5ZGJlbFXHfP3KKSwjUoUte6AXSyTr9oW27Psj3csOkNt7D5UA/PIXX7blkxM5ax3K1NDN4Ya3RELe3kjf57vA9i6RWPU6VRale/tudY/nE7YF0E/YitHVK0K7JAnRVnwAEB0iaDsjIax3bgiJ6wEjrmrdDzmtoEFUYHToCjrbpXSZQ/JWPEdVsI/oM+m6R85jCDfXb334rbb89FQ48OlLJVP2cJElXr0ODlyfale3qFNJDAWuW0zCm9BJFvVSBhy/TFwNFLskIqy/LZZAwHFvBZIvx7i3osGhAji/xQGuEdGJjUmTcY9wDgyAVBMHq4FEsj6iBHWDZ2KfLYBQJhH7/sbh8uvJO1c1Dozzc7hkAr+uxhprQ4xBD4R6ATe3BdxRQSK5MfDu3UPxUw/vCGf98OG32/LRkWLcaF/v6fRW7qgVcOIF6tw5xck0UJ1nMyE9xMsrIIQdHFO4dSiEMwOOfQmX2RBIXg7Uem+seyDudfpW4/da5Z2r49XnnJ7qOfd29JwhkkbHmIdCODoOE9XFrX0hSge7aq8xY2+HcyIdI/WeAOgyscK6sQGlQ3MOh88YTqI5mEAPrDHDuMvo9Aw062Bff/viVG66dap4n+KYCXOBV1hHDPrAy4vtxNUgDFx3uKq7xURjakpcn26cSyJwqoudfTkq37slLHnUUR+I4AR9OQO2mKm8nOHICeJmr48jIc1cuYQLPWM/HZIDzqs8sgDHyJpHVUKigk6vc84hHgikNsBcnGE9Q4RvVihWXbua2LPmqI46TxGDEtYX5sfdIVyh4c7aiYij8/gAxxpmRVKLOMNA1LhoHFwDrFXGI43jH3zvo7b8FvH28y+FXReo5xzxoMb6JMSxFTow14jVFeblAH0jiTH/prqH9FL45XWqrurWSdyjX3oeyUH7huyjAdcKer3CeqJmfeGoXY7jEFWG7At4vZjxeIbGadZgrvlS81SKsgeiXgGPLZdw3Pc4YuNxbAdtkb9Tu7/94idtef/7/5muk2A9XqsvhVw7oc4yt/l4tB1Jk8lkMplMJpPJZDJtJPsiaTKZTCaTyWQymUymjbSZa6t37srkK2HiWyBzMfaVE+wlFzMkkEbizj6QizEQnQjb5zHc7RIk3aY7U030LSNes9qSzpfAMLCVHzOhOXCa9BK4Crahw0iv3zyUS5X/UFvPP//iSVs+eS6MxwMdJXLpFnqOaaXt73i4HZQuCgN32CTungIruUQiciKDTPoeRdoa90A4mNy8Jh6JfnL/hjjf4yM4fHnUNZwBgxIOdM016e6YOyTyRZ+iEyBzcoMkcx6ciQf2FqAfdb0QpA/uC8XNgFb+6Z/+eVuepEy2DnRlO3nsnavhXAs0ocIHhsB/iXPQTTeigy3GbAksaS1/L12RMWazjMiU3l/CMfCKpArgTEn3tRgZtyskYi6AsdCVtqjZjrpkBtwsx3MkI72p31MbzSdAd5C0twO0z4fbQc2dq13QJCsu4PwW7ar/JTmSTKPvFqkw+ncvnrflG3D7vL0nPPTWzd22fO/e/bZ8cBsIa0/lzkjHDehOeYU8VnAMpVvzGiYHXKju4HUgllmu9/TZ2dBeR4jr2ULlyYnqLAPKFKN9B6XqclFsZ0DWVdU68IUd4Ij4bDqfFjhuUePZhncVaw7Gwll7kRjlCJ29A4Q1TDhnwNWU8dxzDl3dT4TjBbQUDzEefSWkqywZ43GsAOIc3oXD6O5Afeot+kknVh9fBPqsGPP1FG0XRFtqx9q7qljV0btX57qnDCj2Qm13CffMQay1ysM7ctv84Q/leLzXV5t++9vCxV9g/H75VJjg509P2vLbtzpKsZho7A96q7rrjNW2UyB4Hm3KuW8twzuT16NvVjxCQlQSbZHzmADiMI8ZlSSR1+L5luJqXbuwXbvh2ar3I9HVGnsKtHSo8TuAOyvHVBDTFRnrPKxFuF4tAy5G8P5mPUxDUqKvR0eKDX/4kY4gTM+EQp+c4fgAUGgQ8y4C3rzmmI17p2NxzcUxuPMy1+dWXij3dap2zlXNHE4H8qLg0Re6tsKdFa9X7IAl3ZLRF3FkokpVrpGVIYfLal7r9XT3+Td2AAAgAElEQVSies/zVUxYXgLdd/r3GM7ZeaT7KnBUJSMuDuy9yDXuAwcEHkNw56He08NxCjrOch+Ra8Py/cPj75XtSJpMJpPJZDKZTCaTaSPZF0mTyWQymUwmk8lkMm2kzdhJr616JgwO+XWUjoIgBbJamIWD21UXSM2oJ4xmAOe+bpd4GXBHYm1AASokZ68adCcGylcDtYvhZBUAMSAeSBesEmU6XB0dC9X8QxBFT14Kbb0AZpDn2H7nVjxwznS5HXSnct5lDXJS0QW3pisf6hZ/GwFhjZB4l2RUVhB11j/c+5ac6w6AizCZb10SdWKi+ubeU7gbxkKkyj6waLiKns/lqnW50HZ/ggSzFfCHnmf/Ur8bR+qP//hbH7Tl4lzuvj/57FFbngKHXrMzvUbVdd0mFK+BeYVEIoAlhWvOrkBb0M3obrf+KxNd6VQXvkfnPrwd+EtAl+Sry8DtM+wjlqxlxlVxzaFtLYEyMXY8B3CVtBL+UTPkAanqrTnd4lnZ36stMcq+btsvBc67uBAKM5/rXgMg1MTb7u5pTD24ddiW97tqr9vHGoPDvnDDpGC9AANb0A1ZMXzRxEciyhmTPBfEot/fqHSDRE5751H/SawYQxqsSxKTiCP7MmJsDOTzKnn7NnSFUsUVnLhpdYl7upgLidztCE893BP62AU6HGHMhnSGJD/nOK4RBzBXVhjwdfN+thdjA0NXhDm0zFnReD/+oMY9djLVfx8u7K/fqH8FWDt0ekJEz86ELpe4zyzdTjsWReZO3r5wzjl3ciaH3xTYOYhcN5trzI6Bo3/8HTl93zmCa2tP4+7+A80ln3xH+OvrE2Guv/jZb9vy//3jf9eWL1EvF9NV+ebxLX0O5vYF4m3OuQlOvKRTqxrYNQZeyQenQy/7FPq7r9g3cIwIc1Q2x9rwOuUdbGbxPFiHhTjGUFXoi3iGoyO1aRdjrQecNUFAIiZO1/oSuDDHbFm/Jz5i7ESYFEOcGbl7W0cWvnX3ZltezjUuZnCxrgLOXzjaAgfiiOt6OJyHWDNfTnV9Yu0+1zi4TtV17Yom3tQ4huSxrijg+Bx6xjq4w9OtmlMG5q0yQv9OMWfAvbkqdc0s1xxd1HBozbPmfnW5rte459EfdMe1Y2Fch1RAxOeVyhWcYKdwcL21UGzgdfJKcbWo3t+v1tzsv6ZsR9JkMplMJpPJZDKZTBvJvkiaTCaTyWQymUwmk2kjbYS2Bs67brM97PkdFNvhRAKWSM47QaJ3+gaNgLCOdrVVv3+gcg2XOZ9jHxhuRwG2tunkGDQoEZOu9+Dml3S01RvjejkRoRp37JHkeaHt4LwnPONwJKysgnvn4jfCVVJgDjO6HXZ1zXS+pUT2deUWjZNiD9nKo4FQGDrf1msWYuQHgRCgvlJgT0NgWjduCePp7cnhq14IPUyAN+VIql42ycjDLhKdAyc5faH6/OWXv27Lj1++aMsRMMjDfV3nAIhsfqDP3xnSpU8IwV6i5/uPgJSESBr7qycv2/IMbX29qt0VyxcwqS2ek25myHnuYibkJc6LcV0iOjDhr0uIrKm+6gTY95KJgDEeG9SmwlhzwGZ8qPHFaxD/qYhQkqVDvyswZgsgcCVQK79EnVXvR61mSELORNPXKe+8ixuIMJ3ps88uhdVNT4VQZ0uNlzsHwq5uHsFVdanrDPblwhoP1V5ZQGgdxwHgVheiHjPg/p0GkanpsgtkLgeCREe4PETSZoyLHLcSEpnDHcaIpfzbALEnQPsWcKQO6TQcbyeuBs65foPr7o5ULzmPXaBdFgvV7b1DzXdJF3MM3BXpkOuALZZ4NlruEati0usK0/4Velj798/hFWIDQ39FEhrHMVyiewkw58dACPsDzbl0Cn17JofUHlxeI5ydmWCML7PtoK15lrlXj79yzjmXAYOme+laQnHgnjdva+6/f0+urYO151H7RonasQt35WSoMufNvZuaT//1v/mztvyTX62OVdRe884R8PYoUx1eAH1kcvMqZgOryKM3Idw7GddJZwZc4OEY09r7MWYrDv5rlK9rFzRzRb52fgNz0xrPq/LOrup5v6+2GOL4VUinVrrZRu93tvUdIK/43JrIbzOWclisrsU31Oewo8//4I6Q5hdvdJxq+lJzSAGMGad2/j1HZ7xcch0BN1egzp5cZrKddY53OtZQ0/Wax1qwPqkzoMvAeSsNOxejr4eBKqMb6PvIfKF6zJ2OSM2milM10NI01LzcbfqG78CxG5gt8XKP+yXaWiL9wGTNaFltMYO7dQ40+/nTL9pyNFTf6Ow+bMsB6izFeaHw95gebUfSZDKZTCaTyWQymUwbyb5Imkwmk8lkMplMJpNpI22Y8d473yRCDrH1HsdwTwLSVGMreQm0gA5fdL7ysZCLCZyh4h62sIG2ciedyZ2rig5lq/th0vV8jq18ujcBKeoP4D7bFYqTwF2vxDZ0kWnrO4O720GuKr6c6JlSIAcL4E5uqWtO0+0gWGVdu4sGMQyAR6yZKwKLYjJjMi85XBwzPbLLmYy8gEOuUz3OwHt1gGyVha4ZINH2skEPT18K3fnJr3/Tlr96oUTNOdC4pKsE7P2x7qVEnZ8CJ3w3xbMeKVl0MKItrRCGErjIA+C6HYyDz76Sm9b1qnZlgyR6uOyRwCyB0ERwkqyIf/OKQNzo4stQQQe3EpiFh6MuE6w73NsVWhpi4FVwdAwqoSUBoxP6S8CE0kCtciDYRao2WqB9e8C3U6CFOd0ggcwVaOscSYSvW1WDRNIhms/w4pXixXCgeh53hVbnM8Wg3kgo3RjHBwLmjgcqWsK1tfaqr2qhuo6ApabRqo464KXpOB2ir+XoLzU5T2CKIXgauujRoZeO0QGCVQpHygx9gJ/VwxwSzwjMXp+C0LtB40gaAHXjfHQ+0wP1gLrtj4XSxTHxVIy7mAMCLpEeaHhENo2oIsYm6q5qYnuwljkdMYDOgfj4ADE+JMrI4w5E5unEi/vllHM2QXJt3INHQCtD4uvbQSKLsnRvpyuUnC6lFfrZZK577eA4yrc/ut+W6QhfoK9npcbpvERsQj+p4XrdgWv9j/7JH7Xl4Vhj/Nm7/80559zT54oTS8S03T0lsucRh7QAYoexkwDPLPB6yfgB0d06h/Ml111cRtC1kvP1dar2tcua4ybrmDv6KOoowdg5hKP1/ljxkyhygrWTQ5+mu3GAfl+734HXEkG/+h/EQGYnCDFi+h3dy+0j4bd3gTSfTtTXnp5qjZRgLos9cWXdS4F2Ie7uEYc66ONzOJhep7z3Luk1dY16LuC8X8bvx6Y9jhsFv2PbjJkgyoXq4uKt1m1ffP6zttzHWN49Vj8Z0XU6Xq11QxyFy2otkudwQ59gnfVuqve85NGWVO3I+a7C944AyHzn/Kneg/h8AGS+At8c4/hg+rsG+d8j25E0mUwmk8lkMplMJtNGsi+SJpPJZDKZTCaTyWTaSBuirdqqZ3JPumESGgrAR8YVHau0dXpwINzx9TthHr88k+vQEC6rx/tyQdrtI1Es0AoPt6krt7ppDqw0Bf6E9/qutrgHgwHKuscREJHRgbDJeEdoUicGSlbou/rOjpKH7070rJcozzIidtty+/QuapyqmGO9BHpRk0MhLgVMjRiTB5oWwMkx7KpeFnCb/Nu/EirggH/cv3/Ulnf7avc0W9XR+Vxb+UkolODTb33clodICn04hhNeR/cewskzR50/Oztpyy9ev2nLs5mwjf09YQA1PIhnuLceEqkfHwp5vU7VrnZlg6Uwt3tJl+GKKBjwshAOkMRseH2ypXD48kBLPRBeussxDvD9V/9CrMLjc+hmVmJMl8D0cmBfoGbX0KEciCgTl3vEKjrUpXAInl+qnxIDLHMlJb5O1XXt8ivEDFWVFnD+pCv1AjcOZGs0VL8/fni3LXd35UrsyayTqcIYLODwWCImEnHrN9fJ11BGXS6nix4TYaNjBHDjrPEcNI+k2yJvN8TYJ5acIgH3Emhrt6M6qIiYXaNCH7hRssKLMtx3uiQeCefbvp5hMNIcE1ZwwkW/PwP21MV1RqiY3lDXCZyuEw/gpor3XzUp54EIwaRGo9KNMix4xAEJyhljmOQaYzwkiovrzKZyQMzpPgv72RzYOd2br1N1Vbm0Qc+YUH4OLPlqPnLOuWEfzvM3tSaowJCeLDCXAOFNga/3gaDv7iB5OcbMeF/42iff/UFb/pd//M+cc879yf/+f+kz38p18s1rYXox5iail8UcxyNwXCmGE28GTo4oewbcz8e65iJA38iAOtM1c0uIsqt1bX4EXc1rBCQ6oh7dUjvu4JhAhDVEirkqiYmyYz6FOznryzPIIQ626CqcbMu1+Q6vIx7HcN/n/Y5wLMuXGLNwW+UcSpdoT5ddHEUh5hquBeVtIcrOZc08vIs4WVaYA3DkrcK6ucw1TotU5fOJxuPblxobj774VVv+1V/9ZVuOvPrxrV0dJ0n29Pw7+/pu0GmOJCzQjgvgo2c4CvZnz3RE65c/U5aBU7jDxoyljv1B7dLFWmUy0ftvfw/jOpGDaxdZMhaYE8t886MftiNpMplMJpPJZDKZTKaNZF8kTSaTyWQymUwmk8m0kTZCW4PAu6RJAt8FqhLApYuYWoWt9BgOV5kIFreYAadcaOt5Akzw7ET4x8Ub4ToPbsidan9f29wVEnwGDabz7gKOsOfaV353LmQxwx7/GO5cg5FwkgNgAztHB225syN8cXdP2+8RttP7qIPRWO/pnKrOFnOhI2m6HXTHe+/CaHUvNRPNE18E1lDBvYsoMBONr7OMTBAOJ03gxRlwqOVbbeF3YAFYHal9Ow0uu9sFknmgOs+BS+0Eaq/DBAmsL4Umnk+BEwMX2YfL68TJIfb0lZDXCZxd+319VgGHyxJOaN3tNKPzXm6IoECdA96XA/PodoDPoX3p7EqHXo/+R1fixQKOq8B7AriJRUiOGxB/vMKegP4VwDZruKblcBRMZ0Da4HRI57YCn7+4xHsKJuDWezqoD7oUToAmzeDO+PZ8O2ir83LDXksMD+SYzwny1MWJcLidMfDNUrHm3anuO4SzXNjBeO/qonGkPl0Bc+nDjfCKFo6ANpdoxxyJ3ImeeqCSHgnrF+dwpQPKtSiBqs6AYwFDq+H8PbnQdSKMweUCRzHKzdGdryXvnEsaV+IUDrPI0L42Jyaqzy7cWS+mmpPenOk4RAwcvDtGUvvXQhgTuBIfH95pywc3dWQgBhbsGlQvCnXtItcEff5Gsfn0RAjYBbDJGs6I6A4uSoDQAv2L4MKaYAxmmFsckNcEwW0x17j22yHpXO1EFjKsLhB3UvT1Q8z9/VjrgxLriWKKvruA6/dSY/PZk8dt+RFQQq45bt0SPnfv4bfb8vf+4BPnnHP/PeLY3/z137Tlx19p/roE6h959YUa+B7PSvCEQ4h+wrXAdI74jLklwPWLEu8Brpy47YxH732Lq/LYRYnPq3EMZACn1uNDYX8RXJSnF2q7qsJCNoF7OXoN18b9COMXWQHCCOPxyrQ1J4aK4zNYt1xcKE5cnqtu32I9NYeTd43+GBCzxfj1OLJWoZ5K7DkRTR8g3lTT7RzFSpep+/Kzx8455+5/+0H7er8HR1SsM7988bgtv3j+pC1/8Tc/1etPhJB+/pOft+WnZ8/b8gxxmA65McbYTk9t9+Ge+szRwaocIdY+naldXryUg+6LS30fmS4VJ0K4QfN0CIzyXRf3kqG9fvXbr3T9ua7/B7FcpUc40jUaKIZ14VL8dWU7kiaTyWQymUwmk8lk2kj2RdJkMplMJpPJZDKZTBtpM7TVezdMVlu1TCqcEXGEk5QD0hTBoS6a0TVMW+yXwMgyYE9T4Hnzmd7jsD1fFHBNxZZwna62ik/OhSG8ARJAlz8igXUPyayBdJ2V+lu6omVAAg4P5J54746Qog7crvYG2pbvw+V0iozhSbAttDVwcfOZJdyuAvyuUK454tG9C6+i/plAmX1gthBa8fJEmNYB0OF3aJu0EE66ty9UYORW99kDdhiWcPuE81nk9EzLc93j43fACYBLZ0yIjBExAm52CVQvBn5Q7cH1jcm7C2IhW1LtXNDgRcQtcj4EnHJjOBFHCZFEuONeCL+YnKq9Lt8pOe450HC/0JjZA3Y1iJm4GYhGg8XOvOownepzMmbLRtLeNRPhBOgUsLcO8KL+GM90STdXoPSVniPpK34Mhirj7S7G+69TgfOu17RZF213cSZnuSAioozk9XB6nCOPcHmmvp6hfS/RHzLYQXZjoOFwoD6+qTFY1EK/Bk28rRIgxBgjMyL6U33myzPgkXBFXsI118PhG/nM3TmOCZQYsyyTkqvBWWa4tzWbwmuUd97FDZtWc3zN6G6t8k5P43EC5NUvVO5g7CQ9YaBzRJUZ4lr+FHU9Vv1+WgiDvPeBykmDywaYz5eZ+vnz10LDnn7xZVs+nylmh3BIHqI/HgLJ7O4KnUpzOJWir8WoG7pQM+l5hLmo19nYdP7rq+lSc2DxFxfTf/+fnXPOjUc6gjHGcZvRCA7vOAKRz1QX86Xa9GBHxyoWC60tUhwDKAvV3SzVWmSvcWz+4fe/07727ds32vKjx2q7z36p8jvMzzERS9TzyWvFfsaMnFM+4vMMCHoBrNvXwCDXaFYCxNcn75yLm+eokY2en01X1b2x6r/XUz/m/Bh12PJCVReF6nFGnBT128UaeC9FxoGxxkmvceessJYgnvoWGOTLZ5iTUed0qx5gDI6nwqhnwJtrrG9pwgpzUFetOctKcaQ27fe3045np2fuT/7XP3HOOffq/FH7erR/3JZ3e2qL1GueePq3ckSdzLW2OX+N+RHr/qyEYztGOXF/B/fVk4na5kusl/wXK7Q0pbs1PifCsYY+1jCHIx1P2T9UHxz2cQTgUuP+ArGXY9YDua3xfehXnysbxne/q/5++5aOQSS/B2luO5Imk8lkMplMJpPJZNpI9kXSZDKZTCaTyWQymUwbaTM2xHvnW4QO7nNw6cqQgDN1xLGw7TrU1vASCGsAR790qS3bCRwbez1tpZ9PtIW904db3U3hJVG9QgjoNNWFI9oEuBSoPncBTszDcW4Xe/9z4EhdYKvv3mrr2cFxa39XOAOxnKND4T0LIAdlsR03s9opkXhJbBXtVfn3O7WGcNyjYydR2BCYZYHX30xUwT1gN6nH9v8SPAX4pitnURB+LsO/vwImNwP21ekKD/jFK2Gz7ybCEKYTtXU/EULwyUdyuLp9W0lo317IAa8gWeDoRAoH0Wo7KF1dFm55sUKgAqBTIa0TkWC429W4c7gn5I92ORCgJRwSXzwXarVEH+0jQfAALq8BkLAwgHNfEzdYP2dw2pzi9RBo8RjujvNc/bEL5zaPcgdJt0caXi5dAqOGE2oF7CQBojOA++lyR+P3OuW9d1G8uvd35+qXnRBHAzBOx4ifx3tCYYZwyUzhyJYVPCYAF+UCLpToAync5TyQrZvutl7fW7VNBSfG9ELXuwTy8+yV+s7JiXCsBfDI4Y6eIwGifAw8cA8OiC+AWidJB2XVzY2Rxv6tPfWNKdCg69TK7XPVTnkOHBModgesbozxeAC36P6R+vr0Ev0V467CGAhp2TxSH81SxbWT56/a8vGRYlmnQU5LOJ1PEEuzmeqqi4F0ABfDFK6ACV6fZsLEdiK14/ENxf4lfstOfo7k6TjCcADUvA/cbAmn0OtUVTu3bMbMjG6rWPPkdJrG2BwB36znOBqAcY0Q6zIcLekN1Y8HO0LNmLs+iHUP4yFcpxtMug8kcgZcfPRt4cy3D+U2f3Gm9n1Nt0/EjG6XbY0jCYHe8/L1mf6WCeEz9FNOP6iDckv2u7V3rm6QzDDQM/gaaywcpegijox76nMB5tOLywVe15xRYo2IUzuugqN0gXQFM8w3Mee8weo6AebnFNkMcpSjjmLaMFTZwfl8B3PokkzvqeLndKlYXcAlu4Cb+5DMK56Va40oWFsMXZvm0wv3sz//P51zzv3tq6ft6yW+L4wxN//ht4V3H/WE9vYj3d+hV33tJSrPcHwlxxGIGGOgqvVZz9/qqMYcx3ziZl3UQb0FPKpWcM2p57g11LUP93TvIfra+AMdl9sZ6rvOSar+9TdfPWvL3X1d8+BYf3t4U9evMCbqevPxaDuSJpPJZDKZTCaTyWTaSBvtSHrvXNjk1PH4JYe7Wjl+9Zgilxt+DHADHFYNsavHX/Y6+LVlhB0uh12wt/hF+1apX3SH+HX38EbzrTtV3ph0rl/hEvyS08WvFF9hl+r4gXLX3Lkr84k3L/XryMVb/ZLBX4Jdrl+5YvzyluDX6NFI79kd6FfGMt3SL3V17crml6caB7OLNacK7Abi1zFfYkcSG20ev7wU2A25qGm2c4b3w8Boqnb85KZ+iU2n+pVk3vxinuEX/Ro5qnL0kSXMdp5d6jPxI74bjvXLzGyh3EE3bunX+uMb+uV2jN2uKNLznU/U7il+AU7xC3+2JdOkuq5d1eyqLWDCwB2QPnJjlfjFqi5oOqPy69caJ3/x08/b8gRmJX38alZxF3KuDjHC7tF4R/dwlT9yfql+cQnzqxp5ESsYWD091/O9AToQ8tfRTPd4NNRn7mHXtDfEzi36wxKdg7t//NWTO1/XqcAHbtiQFukcJib4NXsMc64hdlt7yPkUVgqyXz2RScrrSz1bBhojQvnWrnYEB3vaPVoi9+t8ojY4Olhdk/kqaZhTgPQIkM8xHCk2X2DH+/Vz0QIh+uNjr3xY927f0z2iHQf4RZk5B+/c1GcFC43TBXbKrlPeORc2cdPDIIVxMoapUQSToNEN1T8n5SeXGo+oXtfD+xcYy8cwtemgXgCSuMmlxk+/mXumiGOvTvWZFczwIhg4nEzUT5/CuOLmgeo8vdQuXIG57/bth235aFd9bb8LMidT37iFX+bTUHH1y/l28rrWdeWKxlCtxs5cGCmOuACxHrn9Ssw9T77SLvBf/+yv2/ICZkoxclT3sIP+4EhGIrfvak7aiRV7a6f7iaPV6xmMUxalSIAcBngBYuYU1NMz7CqeYcfsxWsROBl2xe9id2MMI6hLjx0uUE411noB5v8AOWGvU97JGMZjJ7uGXQzbt0af6yHP4wXGyxdPtdMzxW5rDfOUDDuMR9gp2x/DEBI7pAH61VVe4HRJ0xepjjF/IQ8w1xtnMJXh2qpAnOhhPmGMz0EIVojDGXbEaMKzVq9b2pYaJ5H7rx+sKIaP7qg+//YFzINAzjx6pDnjOx9p7d4FyTM5xxqOBnyRYk0wxLPl+tscxMzwhsbpUaB2vHl7tVN4Z1dj5NW51pn/9i9+rPsK9ExHI605JzBHeoE10o2u+sbdjxQzushjmZTacd4Z6h7/m//0v2zLx8eKK0t8r2LO7q8r25E0mUwmk8lkMplMJtNGsi+SJpPJZDKZTCaTyWTaSJujrQ06lPCANA/xRnxd2+E5cM8usNUjbMdGQBwGMMKYA0U4R87BHj0GRtp63t0XLjNs0JljUVHuxbm2fS9xwDwLgJIBx7sL857DOzIKGAFbePVIONCLZ8If9vaEGu0e65oFXFpoZtNHPpyL7aTlcXVdu6zBHGq0HQ/ZElMgklLWMCGCyRL5i7JCnkXgjpOZ0JnPvxAOtQvuaoy+EWXAdLIValLhIP8M6Gvc0RZ/L9I1doGV3rsBEwugoIdAth7clqHIbbRdXQHx62rY+IkePAeqtiQWkm0HbQ184JIm9xfxlxRGJw5tF5Y8+K2/CCr1ud5YY+cWDIb2kBewqDjGga+jz3jkG4uQC69szF5KIDQhXBgGyKk1AeYZ4zP3hsD3YO7x9lQI1nKBfIlAfQfAyIOB/pY5EIsSedCYZzXcTjv6wLmwt6o8jzxlN4H9jXCUYNjDEQCYiPWByXV31ddvIU4VMEcoEWM/+fhbug6MwOpceI2HEYN3q89aM3oBHjyI9fk7yOmZIE7D68dlI7V1WSIP2JeP9bdnwl9v9tQ3D29oDqlC9ashMLDnJ4rPzAl7naqdc0UTT0vUBdG9xKvP9WDgMMC9doHtHh5q7ilwzfGxXs8QP+/QSAfxOURMZnnZ4MoZTGU8nJf6wLUimJs9uE1cXWPqxpHQ1vM3mgdna0lOgfT2YS5xoHa8WKjf7Y7GeB2GMIvtmHvUtXPZFe62lsBWxSBC3jrEiwrxdudYzxN/oefMYbpyAAOjLgyn+kDzu+gbARY9AVxdfIMxFzBj8SFyd9MADxhkDzhxiGc6xJGIBYxBnj4WNriEERNR0C7XMFj38QiFAzK9Ja+d1Xi8MvID2lvARMXD6O9wDMwYMbCLIy6vzoULh0vM/ZiBk46ec7yDXNjIIzlAfdFULmjyMgaYP4NQ7dxBm8/R7/rop+FEfSTE80UDfc7Ia70UgVU9BUJZIE4UvD7zFaLxii2tc+I4djdu3XLOOffiQkZg92DM+NULzff8XrDAkbPvHcuEZ7av9WcK46wSa7sICPgCpnL7aK8CY+bRK6Gr3z5cjZ9PkAvy5AvdSx9odx9Y9OJca7cK7XsE7L3XQ57lTPEwv0B/6Kkt9hDDeyP0HxgipgXyrP4eA9J2JE0mk8lkMplMJpPJtJHsi6TJZDKZTCaTyWQymTbSZmirC1zcuE15OP5V2D4P4IIVMOcTHKMWcHQcwcnqcF84RQ95/p691pbwDhCN/oGQgw/u32rLUQfOZk1epb37Ylv/ELkoP/viy7b86JG2pvtAVKpU2/3T13R3lO7c0effvyfsaAxMqZsIxXn3SnhthXw9zKUZ/B75XL6u6gYVBGW07mZWvt/Blb89VEyiiO3wGDUzPhCeF8Ah7gBuk//5D37Ulr91oLpbzoDpNP0nr4CVAqOOgTje2hFucDdTOegIJ/F41tkYLr5wCqWDZoU6CAq4xQJnLYDR5qjYotxOO3ofuLjJd5XDCbCG+/D0jVAcN4YDY6x7ZepEEG0AACAASURBVF0EyN96F5hvCIvT+QUc5bzKTF/ZTYC5AH9M0xW6kaFLjQ4UA3b2hGHs9tXXcmBEFfIlVk7t+/BI4z5inwXWVMFNM3Zqx7QGhr9Qf1gi52BQbQfdqd0qd51zzu0fymFt6YSCd0rV87ijOLlzgDxZcOK7u1BsTOF0XNYqD4fCrsY7QGeQi84Bo10AZaubmFUiRuWw5o6dxuMI3b+HvpY8lKPdORw4p6nKH9/5J235APl25+in/R3VTRdIWAIU8+WJ3pO77bTjiqVbFVO6QSJpXjwAigyH4h6wsy4cMB8+uNuWS7RjDzkEa6D8RN9YroFTFnADL+YN2grMOQKaGAOPpMN6H7jd6MOHbXktd3SkvtwDVk8MMkcS2yO47IYv4AANzPD5VPeZAo+/VtXOXYXyFLE7K9WfCoyRPEXsxZGB0YH69x/90/+kLfO4R38PmBpyUPKYQB85dAcDuFpzAdIg1QVdNIHp9eD0fIjjLMOPND8f3Vd7IRWhyzLkmnwMR99KY+oMLr7pTIjdO+RBzRA/a6D6a0klr1G+di5s2m+5lvNaz7+H4w1HI6HFPbgre4yjjx9oHXkJt14MTefh7t0Fdj4EKjnsA2cFNnq1LqvRATzWNnGo14/7ioeXiIdxV++ZTzSmahwZcIjbX2EMTuGkncEmuuKRNfTxsmJ5O3nPk17X3fv009VHv1Eb3RwInb+D+zhDuwQ4inXYV//79EB5VS8u1KfP0Ndnufr3wKl+d4GDP36Lox8T/e0f7K/e8xA5qb8Yq188gZP8KIDzPI5Q3cVYv8DiilksjoHDLwdY/yDf6aintUAY6N4LxLMSfXCnu3m+bNuRNJlMJpPJZDKZTCbTRrIvkiaTyWQymUwmk8lk2kgboa21c65sHJuYZJ0Iaw/leQynLGx7Z0Bkor4c2T68d78td17KoS8CDuWRGPTgQFuwo762/0On6y8bB8s+UIIYaO2PfiC3wntwe32KpKZjYCzIS7+WjLuYqj4SvIkufUwym8MpCgTWmmNkWmwHFVgRWE1CcU+E9f0Ormu2anTmBE4QApnr4fmP+nCii/WgP/xEDlofAmmavRHy6+A6GMerfpIEdHvT/XaBTyJntQuINcLRDsZbrg8MsgKyzSpYwrVrClwiA5a8hKNsTsfiekvugj5wdYOe+USoxBDIy8zrsy/OX+tv4Vy3A/w46artxhWRdTV8V83lipIOn8BcgcTVyMieNmM/AeYTApPrYjB0doV21EvVM6nripgi+qCHmynd5FLcSwb3QiaQ570NOnru2cW23D5rlzXtsX9PiPyiUAyKzhRrbsExcxACnQde9eC2rkOUMwBy6piwHohwgNg+m6Dv0tW5KdPttsxUHiD583CsGO+A7xHNm+aK5elcHSyhWyDa3QMHmsBN9/iWPmvyRo52eFQ3u9zOeKzq2s3LqyMDCJRI5p0jZnaHwPQRg0LgoeMh3BXhEBh4lauY0zjiI/p6jiTpAfp62Dj6eVjoVhgvscK36yNx+toRDCCUOeJnOYezKea+KOIkquuMB+rXs66w7gx/y+tsaXp0zjtXBavBlCF+5lhXMLn7PAcOCPfbwW3Esn25vXeP0V6Y+zlPlDxuAMyxDPQ6zIBd2txDgLk6xN8lfbVFF47HdOCuUPaIAWWpe79/LNT67EzHJl4+17Gg1691LCl6LXfM5ZyurcCr/XYQ5drXLm/WNymx2rX5WHU0hqM0x0gHdUGX3b2+xmaKub/kWgRoa6+D6yMm1+hXaTMn0RA/xpxIB9cITvWDvt6zB+w9P8RaZQFHUBxLmsw1pr4KX+n9iEk1BlsIzNVjHVFv6ciAjzqud/TQOefcPuplAAfnvVs6pjEBZv2L1+qjXz5+1JYPvqc55sZHmivDNxizZ7p+FGk9M4TT9M9+qzg1Hqt9b+6vAmcPdXL/UMH0sxM41Xtde3eM7xpAWD3muL2x7p1HYeY4PhD1NQYz4LJFjXKFdRdw7HiINcLXlO1Imkwmk8lkMplMJpNpI9kXSZPJZDKZTCaTyWQybaSN0Naqrt2iQWRCuFWu4RTAI4hdzZDMOlxqm/z2vpwhByNt0z5wSFS8B1QCLqgXb/T6GFhqHwmMZ40j0+RcjkrVUk5Ll0jyfHQol7XdjrbKLyfavk7w+XgktyhZH3o9nyGRu9e2cojPzeFEN5/p9XRL5oLO1W1y4HrNtrVce8/7lAApy4DhRkiqegcYz8cfyOVsxwtdunuo7fmXT5VkNgIaNUAy36jheECquiSBu1QAtMRh+z4ljgc0CW0XJxgGXSEHOVwBI6AxQU7GTkWHMVECBanW3nSdql3VPFMHWBjoJ9ev5YKbAy3KJkKIC1ioJgFwbThgFgESmmNcV4EqsiqRIBs/URUVkZ7VzV0lXnbOuYgOdUSkkYh7re2QILsE6lNhQBYFcWW0BdqlRv8lph1hGFy5VDvn3M7OdtqxrusWi+4hUXSBZxvArS+Am54rhCX5Avfa1XV8RJwUjrd4tsIBgZrDrQ+YJUNCVS2ba7wfjc+A/u304GLYAReNIwbjUjE7HQNlnApTmkyEZs1zuJBmiuf1UnG+O0SbMij77TCRVe3cMrtC6fQZHrE+iVSfa/nZgY7XmB+TIdBeJqaHc7HHeHTABNcThMNRGnGqE/3dSabIET9z/ftwqM/kuKiAt4XoawsghDD4dDXGeMa/BeIX4GjAbCbX9reIYbNiS0ik8670q/5SINZnmJAr1Es208NN4Tbvc82DAySj98CSfZdu4EISC4y7Av2BsczXwJuvXCXhqElEGiHTdeBeHuLoTdwBFo2zH8Qga6dx151rPA6Hmn86CRFsYLlo6xL1Gmy0Cv36qmu5iuYF537gynAExtJjrS+y2ME6pwSb3+1zTsR8hvhYIw7Q4ZRIsQtXdVRzjwf/XuMmExwVCnm0ZaD6r/E5y7naejLRmOpiLNeYN+nI7Xg0DfHD45yJ39K2lA8DF++s1nqHtb4vXGbCVtMLtentmx+25e6Ojsh9ea6++7ePftOWPx59ty2HXtfv7Gl9OZ0qJj55rrXT56eae/7ZH/ygLfc7qzpKESce3BMWfveFjhm9eCv8uxjrO8jeQCjscE9IdRdOwxUc+k9PVB8XZ/i+BVz27FQY/h6OBkZwiI06mw9I25E0mUwmk8lkMplMJtNGsi+SJpPJZDKZTCaTyWTaSBu6ttYub/CxxBFHw/Y9EKwEiVS7IzgBAWvI4fRXg3/xQCiyuXClKXDWAFhOmAqDfHuhbWMXr1CBQZdYkP7u9FdP9HcvtWX98DvaHj9EAlIPhGECx8KqC1cr1EEF9MyFQA7gxEWHthldQIv346X/oarr2uUNFhQCu6zIjRIzArNQIbGvA7bR7wqtONwXprYbAdXzasdHj4QL39nXFv6gp79lzuJFg1aQSgs9kvrSzbWCGyXwjzWzNtE9a05ovKaP1AfncNktgNEmcGgLaL8LDrDKt4NgOeeFgvr119t7gtNiDIdkj4qMwx5eBy6Ddg/4kxMQpaDQ89dEdIAAOfTpqzpKaAoM7C7C50dgnkDMuRIoLF0HczhA1uinAZ0JczruAjNEE6UYsr2e8I8AWOh1qq5qlzdufT0kKi4HwkCHh8LkEic8ZfJWznK9RO8J1pwDEXfQpnMgrAUwwQrxuebrcMwsG8ySbqMOCY4d+kWAwdbrqD5rHolwRMB0ndy932ExAHbukYybJO6jJ0KGSsTSo7FiknMX7rpUOefmTZwKEbwY65cLuEHC9bEA58oxyGTtIR1sAzpM6u01MqPXPHuBdvQcG27V39izkxpOzMAq2Y4BXH45b/hl/d5yhP5Yw353DmfdLmLG7Qfqy199ofn8DFh3iPn0OlW72uVNRyJKWAP/rjEHeIyvyblQN1foXgPUaYB4yynXA4OrCpYRq3+Ha/nVeRrGUk92GkdmKjhARsClwwTXxjxYoA8mQKc7cN+N6/djm0RK6QRPDNL/jmM0/6FauWGvxgDdvYnxLxeYSzBGctR/HMNNFzEuxKTIqb+s3t+ONV+vODYxfhrseY3EL4mnag1FjHyMrAUdjDV+To16zjDiE2DMJeqJ82PMIxGcr0Ouu7Zz9MMHgfNNrOzG+Gz3QVu+fII4ngnb/XBPR3uGI43Hv/jFF235F3/612253xeiXQE7z3Ds65cvn7Xl432N5R99T98ZguZIVdhTvY12tc59cF+Y65MLrYV/8ZWc2l/hyNXtY8XDkt8R3qHPjhSfZwvN1YcYs/s39V1mBFy239W83E02b0fbkTSZTCaTyWQymUwm00ayL5Imk8lkMplMJpPJZNpIm9nz1M75BjsqsY0dEQ2kkyeSgo/H2jq9ONX2/KuXSmR7+xjOk0s5gr19JzeixbnKvY62Zp8/1XVOz3QPu+PVfd69oy3uXbgxJUAyz19ry/rsTIlZbx4pYSmdSocZnLe4xQ/sJUQiYHcOV0niYzFdmICh1dtB6VztXJtjFxafwe8gTLyjUxnQAuBVYyRp3RuqXNTaYn/1UnW6A/fb0B+05eWlsIQ8oYPoqquGwKvGTMQNB7UQKA7dBUMkl3dIDhwBz/N4PXN0W1S790P1zSUwS7q/xnDQrLfm2upapobIMV3VaA1Zoz5jp7EW4ZndEhgqqpfJhpdACYmmM0m8J7rKNmiQdb/mpKprp0AW8xhOyAnYVnA/xGnXUD7iPSk+C3hVTfc+9Jl5qmfa3ZEb9HAEd8xrlPfO9ZpHymLd9/5dOcglcDidT4UDzkuNl9lC6DLdGB3zCwOzrpFsnbgj3W9n50gkTwSqGQ6kmZdAMpeZ8NubaK+SKB/aly6VNV2Xge8tgOuczoQDnb6RM9/sUp/78gQJpYHuHIz4++lLd12q69rljTNjjbhaoP/xiEfc0xiscQwkx7zZRYWVODIRAXPlZ1U5uGy0R4HrJLFiaKdBG5eJ6rwE1ljnxNsQJ5gYHWuBDNhgiLYLERsWcOKdF2qvgxtCv4q5Pusvz77U3wIZDpMtjcfaueDKvZJOnms4vlRhGbXEH0wWGkcR3HpdD+sDumoilpap6qWA22cFp9YuMLwrV/MUbVEAFy/A7sewcPVdXSMkn0lsE30nBOIY4/3Et6sMx0DorEteE/032NZ+Ru1c3ayzKjxDnak+F4g7s5SoPfrrEu2FMVV1cbwL189R1zVQ7ArxM0eMrYmsX8UKDLWsLN9bTuYaRznGNI//FIjDBVx2C+C0KfDXtCR+i7UDj68h3pQl62xLCrzzjdMw16jxHvr0ULHj7JXi/u5A7bXT01GC//bT77XlaqRjcVlPY+PFE30feHSi8nePdOTkH31H1zke63tCUa6+48SJxtcEjuIHOP716W1lq3j09qQt13Amv7zQPN/DenV0qHuvDxQP95a6/u2HcqWNO7qf3QFcW7F2TX4P0tx2JE0mk8lkMplMJpPJtJHsi6TJZDKZTCaTyWQymTbSZq6ttRCw0BNbYfJ1KQaGMgIWUyDp++dPn7blOzvaeu5iezW/hGvrROhOfqqt+hKf3K+BIRarz0oKbeOGoZDY3hAMwQnQg7e6xqyS+18XrooZEIYAWEQBfK6A49d0JmepK5dG55wbAi0kTriotuNm5px3VYNoRHQH8+93XqOLIJ3XQvyxx+sBnDF7lRCoD24JLz480nb+LSRezWeqr2UEZKhJwD0FHnyW4nPw3oiOwk4KgQ32e8IAvKcjGRAwYMadAM6TBcpzfW4P2A+R13Rr7SjXvzyni6D+nUnJ6YjqgX6yfSu63AK1SoHR1BXQKNRdRAQK1487RBib6xfEY4lL69pzr/HNhMgBLWQjWlYCrqmAidERk6waHUeZdBuXuZwIoawyYSHXqcDVLmkQq5yOeOhDF0D9z891Tzd3hYVntTCmZa5O0FkSS9LnlnQpRKL3DFhdRUYZaGXYOE92gXcFFzp2EHeBwZbEj3XtADBUBIycSZzdUu/PU/XBCgj0yzfCfhYT1VOvp9jT7/OztvP7aVnV7rKJSV0E8sMdxZoR5r4LoGZzuLn2dxVfiCcSSVxz5aWjMRxvK6DbdE4OOzjO0OCGnYiOrOwvmBPQRh2MHYY3unfmaF+POWE5UzumwNr7HJrAIBeeWChiyYyrjetT7Zy7okl5H3TlZv/mM7w7k2trBqxxjqCcAD/NHbF7xEQ4KteITUEMF27E+ahB2Qu4xHseU2BoxP3yKJLP0V6IsSWPL+QqE5P3iCV00saJARejDiI49NLN9Dq1cm1t3OnxPBXWLRn65dlcbZfmRLRV/zjN5DpY/y05lwCXLdeOEuD9QNArrFLCeLUG9oXud4E1L9doeYqjUsD+eQylgnPwAkc2CuC6KfopnbRx+mo9SwNdpcF7l8V22tE5347DFHU1HmnuG374sC2/efGiLSd0Pu8pxkWHeJ6OXp+grpc7qpdeR2vUG/s32/Iejsnlpb7LlNnqmkWOeFBjDXUJ/BbH3z4NhaTeOpYr7cVCfzujEzLGXXautjj+zsO2/AHw21t7OqozwPxYof8GbvN2tB1Jk8lkMplMJpPJZDJtJPsiaTKZTCaTyWQymUymjbQR2uq9b5Ny12QW6NyEXVEPRoxYXafSVu6bM23xPn512pZvj+CeOdUWcxeZbOkwFQMVPcR2+7DBLHpdbV8f7skNcYwt7jevtQ09QeLXCPjcfCGMKoZrXIqtYSYMBYHkxj1tW7u+tqRP32HLGxRPVm8LiaxdcMVErrlr0lUNxd9BEJU5UViV310K2fr0ew/a8s6+MIP+QBhzBw6TvR0gQEAlgmYLf5ao/ksgkTBGXEu8G8IZMmESb6DWMVAvItKDYoz3A/nIhD/MB2q7GEhgmAIDjLaTONs778LGCY7OgVVNpAn9ErgMHVaJsxBOzzOhMwtgeFEM5Bdukx5usWGAQJAT31qN08ml6hCkhutiXBR4jgWcYmMglkyCXAE1Ik5JHMiD8Ypwj3TK7MAZkYnsZ3BIvVZVlXMNuhqjjaao/+WFyk9eK07u7wjvqRZAW4GnlHA6pmtqgPhVI/DUwMDSORAo1FfiV7GywL/T4fXWfY0d4vMpUK8In1nAxbAAzrpIFYdTxJsM80+Ftrt1905b3sNRifN3OlZABPja1cSsqKfxNejrPmKgoo++lIv18ZEwJt73Gu5IV2LUaVyoHatQ82CBfp8iNmXA4IrRqg3OpxrfsxnmUjipc27Pic06lSvGYRx5WeDzlySnEPvjgbDNL379WFcn8prDXXkJh9prlg9X9x6jb3Fc9GIhbSnWBF8+k3t8sfy+ypGev54jHnpg95gnArgo1nRLBso2eyuUPGk41xdP9PnncDO+efuoLQ8GcKkEHpnwXnB8oc7p/ImjRehHKeKzR0Dvgr+tcDwk8lwjbSmRvZMDpcexkyJ9P8J7caExMJ1pLdinAzjG73LJ4xbAU/FZwRrCq9d5hIdHO6rOqt7fXWhdcfLqTVs+OFK8j0LVZwpsNQxwTAODp1yqTSc8toKYXOREHDlXqh0DnI/wNfvy9o7wBFfjMFK8SDE2R/sPdR/9X7TlKdYzdB8uS/XdoIYLf615or5Uvdy/q3ofHN5ty6f4/rI4g8N5teo/S3xfWaT6nOeXwKiXauvTuY6tDPd0BK/O4Xwf0ilfMeMVcOwk1jw42lW5Bl5e4eghW672m+8v2o6kyWQymUwmk8lkMpk2kn2RNJlMJpPJZDKZTCbTRtoIbXXeu6BxvuxgyzgAouSx7V1N4N4FuyuihAtsx7651LbyEbZjF05bvx1sbfdh7doFfjCAE2uws9r+D/eEVfobSDIeCd05uK9t6pePft2WCyTrdjESzeO5o0BYjquIB9DhionU6XqL1/HVvqi3g3w4p1sMcX8046IrYI33FMAg6Ni1gPNXCfzj4FjurEFX1ySmli+05d/xwrqKruo6a26O+I+PgVWURPngOEdHuAjYF7DrKqOLIZJ+Y3Qk2O7vduCImQDZBnLZAf653JIrnfPeBQ2q4HGzbFPioR73XQF5qUrgr8CpsxLJlPGxHbQLKB6XI4HuAu6FBdw2356sHJCXqT5/Z1eOaJ0EyDFwGo87KOFeWQGjYtL1mlaSeL6QCbuZCDp6Pw7M5NJ+w3D5dVXXzhVNfJxPVOfLmcZFPVKc6gyJbKpu+zEQRwzTCq65Aeq3KoHvd/S3fWB19+5o/BYFf3dcXbO41OffviNX5t09lQv+Xol4n4JxJGJ3kQoRukBS96JEZ4sxxhG3bn4gR70crs7VheaW9AKVc40KvG/HRpdO0EC+gpHa7sWFMKbPfvt5W35wX3MV89gHqAu6ImcBkCpgkAnmysN9oVke7XF1VIVTXPcertEXwukR1wu4QdKtsYJj+gxtmgIRB3XuBgca+4+fC/X94ithmRlwzkvMM8uMeO01ytfON0ciAsxTEY/zYM6O4JD8GsdzzmcaX/0R8HIgoRFicoRg6hNghWjHsobzJtYHs4vV2qXAumJ8Q/0o6Wt8ByFxS71/OQMeyXkAGGQ+ByINJ+8g5PkEFQOs9aKKz+Tw+paQSO9c0LiQh4j1ccK5X313DnT8DHEtHgEzroF4Yv0T44gHMdc4VrvT4TyMNR5zDIgWO6/VR3YPhCLv72vt2ulpzYkkCmtOscVCbTctcGQAiHKIeBAi9gdwDY87KNPZHfNpCNff65R3gQv9qh5LYpdw1fcDYaD9Wzfa8gIxtt/BvD5XnZ+/0HtyIK+7+6r3wa7mwXSpezjn8Z9IjZA1Ltzhk2fta5eZ6n+KOS4aasDwiM2Tl/o+wvVkivUkHbanU93XzY+E3w6xXiixtpljvdRnTPg9EGXbkTSZTCaTyWQymUwm00ayL5Imk8lkMplMJpPJZNpIG7FagXOu2/xJCLem0JNvIz4Ih7UCSYiRDJUugicX+tuH2MqNxnIALJD0OoC74hQuY2/OtYU8aJCa0562r8+6wmYefKhrHz+Qm+tkovcskVS0D5QxQfVlIhhcOIBDGJ1NgQNlwPMWwDLP4b41L7eERDrXWrH6CEguHa5CujsBH8R7UmzrT9HuOfCPENgiXauYhHgBp7A+kJqC6HCTMDsA5hPQDpImwkCqKqCqBdCsHEmDYzqP0bWMFrpwT0zw/oiZnkNiF8BFgu24tjrnnQvj5jN0r0xwTOSogOMh7RWrWG1RT+jCprcnfWA0wCO9U3+NgLwsPbA21N1gvLrOINH1+sD94qFQOibbLdfwp/e/zvevuZDBbbQkZhijfdnUcJskzcnrX6+8C5pYEsFxLwWaudfXOJosVc+DSu8fpWqXIZw0PccMniEBzurh8hp11TYJXDVDJG6+SpI+3BNyW8CZM/e02lQxJ4pcwC0PY3YxFfaTZXAepRMvErOXcOz+7edfteU+7nfGOOy2c2Qg8L7FiznkL6aYE9G3nr8SuhRVT9ryfxX+U72eAVPjKQkkqfcJ8DmMNc84DMQuGqgcNuNnB6zhIpUL6WIOd8kJHLMR92rcS4bPL3DvOfojHUlzzHF//md/2Za/fCMUOcG9pzgiU27p6Id3vk3AnmH+oAV7VRB5ly6Ae37+9HVbvnUkJDHEvFUibjs4g3eATXocCYk9UGMcs4kbV/rxDSGTJVA6uuymcGGtccSjKDUeeRrDo86zEtfE/Div9PrLE63RiNj7Do+N4DjJtghl51zStE61Rl3i+BXWM+dwK6Zj/gwIPl2XiZdzIc01B3HwCP0+cjjCgbVCr0GQOztYN8GdOIq6+Dt95hy4dLnWLqrcEuvuAkhvzbbAWp7u6P2EztNEvN+PSV+rvG+d4ukYy6wNbqSxMMDRtQLHc5boAynG7//H3ps0yXKl6Xnn+BxzznlH4AIoTFWN7lI1u9lkkzRywYVEybSk9tpKW/0Fmf4AZVroJ8i0kJk20oKSFhTLqsnuqkKhJoz34k45Z4wePmkRnvE+aY3qRpRFWplB37vBQVxPj+Nn9jjPeb8J7jPcxxG4XT3zGHPPGOcAwkTzX17rPWHRtpkM0SLQ1VwEbDj3QMcxT+XA2KdwR09RzDnKPxvoOGAXR2HiiO0d13PNjHVU4jcfV21H0mQymUwmk8lkMplMG8leJE0mk8lkMplMJpPJtJE2tiH07RZoRHwO6FgOPOOW+w/dXOFuVMFJ6WoJ9BFZi7CXezEROrK4UPoVnD/pstoZrBCo4bX+/enJ2Tr92ccKXtoBy3d9Jfe1ERzPsn24PjKAMNARmGO5CnZac6AmIfCDaSEsYYxg3FVzV6iAk+NqyMDAqKOazmN6zmUNtz4gAXy2WUjHN7iG5vr8Yqpn7jhhHA1wuxhI3qLFa1IgISXQxAIIXAWs7Nb2PfhFD5zEV+gGcDldzogAMYAwWJzqmzGABkhLc2eurU7R3sHugLhxDcorRH8s8PwBnBZrII6Jiv9WefF6D+w5ASKzdyTXzgH6RtG6xbKtRQFt/nAtAiIHcNQr4AAZ0EmSDCXapkcbDD3aDPEtomqMvA68tCrv5nc3772LWgSmvJbTY6cjJ7oIzmvJUJ9XQLELYMy3Yr6j3QfAViqn9h0sEOgZzrEhyn2Rqs+OhivELgQ2U+ZwzV0Ig8zhFFeH34zJlUCheVKCJn2BI+quPluiXb++1Ng+7AnvCYm1l3eDRNbOuXk73pdLfcdgpLzO5sgH0MzPXijo+Nm50MADBCBv0BbRpVzoNLf5Evgl6s5jvl4AQY5aBpeOkvUcWD6wvgncO5fIAB1BG/YvIK8V/rYCDv8Mc/F//O3XeCjg2yO0MSDT/q4C2XvnwvbeIRHThkc8gPdjDJzg+MrnJzoe82fFm+v0CC6NS8wltRfqHCxxlADYN500Axw3WA9ZYEkbrIkc1hV0uy1xxCZIsZ7BER7iqc2Sjtk42oJg7Fd0f6XbJ6bZEHMLccVtysO1tQaaSNfRGXDPi4XKazJTOzvooq5vnZlAVIKGc4/KhcHdl7f6gNpMgTpw7fGTAIUVFjhOa0CJ7AAAIABJREFUNBbyPQt4fADjCpFPtN9bBDGcZbmO43OEmM+zlMdZsC5l2wjvqB4D78L2+yugnzDfdUs47/fhsDqHO2oIVPQcY6wfqj8mOxpvr2c6DjdGxff6Wq+Gqcqu3xDXXuWXjsvFAvWIcSKfcc2F43KosayLeR5HW66mwml3j/Ucx0Dc2WgjzIMV1kt4DXP+90CUbUfSZDKZTCaTyWQymUwbyV4kTSaTyWQymUwmk8m0kTZCW5umWQekjxCAFYapLpgD/wAeWd/aOtUWcAL3tylwmajR/d/cPVqnTxu5210Ah/rLf/IP1+nBnpxYr8sVOnMlCtY9PXm+Tr98qq3hOVzpTi/1+fsHCkz6QayA17t9OFaBJwwcXVsZPJRBmVVOp6f6LrozNf6uAryCeADSFwRwmEL+6P4XeAa11zXTWmX38kRY8PPXQjEO+0JxYCbmaiARQSBXugqoQNViNLfcf1G2FXAdj5vnGbbvS7UpEDrO0zkPWHQDpqcsGAgaLoX4roLutsAp787s07ugxaubkkg5EV6gWai7EPgmaF4XhUTWEfS8ULkThYlR7lVFjhh9uQMH5rZvlCJOXOnVRgoippG+ZzyvvvGarKc6zYA73oKJiQABRQ6B6MxK3h+oD9rp0t1NRXrvXdI6MHZTuNl21eb2BuoXB32hrUuMWSEwmoiOtCgXYsFoxu7FU42r86fAUoEGeeBhVbq6fx+BoDmu13AnjTv6/s49jaX9ewocnQFpD4AgLebAqCM90/IKCHCg+5+fCUeKctV12tP9x8Dqt6m6btx8ssr7DDh7JxMKNYS7YOeeXMK/+vrlOv3jn/xsnX7rP/un63QUY3yBGyGdz6/Gmktm58TxMZ6ndHNt3YJDjf0V7l0Ap61xrGEJl797PbkF3ib9MX5Wus8UY+wnH3+Oa+AcHKicZhMcMWAXvKP50TnnmhY7joFdLrluASpcAQEsEAz+Ker0/Fro8sGOystjzJxcCq28fPkb5WVC3FBjaTJQm74xX69v2a3SXRx4MI6hvLrWuP7WBx/p+i7GDK++1qACLqdCcT/+WONHDfww+B2UnL/lcH43iLJr9P38Os53NdYQ52eah56eqr7eevDeOp3EBERxPAbPwKgEF1MhlONTjTvTicZYYuJley6KazGuFZeYmxLgqaP7Qhl39nWsJIzh8psAp82VxzPMIXOMjd0OjpxwHYFO3qB/NHd0FMt756KWYy2xzr7lwI41ah3rGg+32STTNXFH4wuHlAIvKjO43Iexxr4GeHGDNUSDeTYZru5zhvKsMabVyFeNSA0XqN8+8OY40zGNKsL8jLEhHumavaHuH9DeF/f3GCsqOg3/HsOq7UiaTCaTyWQymUwmk2kj2YukyWQymUwmk8lkMpk20saurTcbwSkwqhlQszIQwtLAYQ278C7FFrOnMyTc9GbgdTq7wqH2DhQw9OVMKMinz56t02fPlJ+ff7pygrsEIrUA10i3ohRbycd72tbfPQC+hWdiENg4BAYABIt4CQgsN7lUHl6dwa0W+YnuKsCrc863W9nc3i4bYq66lsG1wxLObnCQK1CmF3C8/fg3n6zT//IvFWh7BPTxcix05uS50s4LUc7nK+SjgUNhsdQWf4N210mUxy7yG6VwNkU9VnRABLJVwzVrCRazzFFmcNSrFkIUHDCPurjll7Zdte0lRNsiilyhfj0CYXs6tRbfjGUnRESAwxHBOnsu18UMfba4EGJX10RQVnmI0NA9+Cc/UJuaYlwhDtfvC9dJ+3Joo4OrJ84Kp1CiX0TpSmDMFeorRMFWM/DQW5QPvIvbMXEA9LGcqSx6HT3zu+8+Waf/+sd/rethHMjA0h7P3wXq5FBeSaogzi9L9cF4rGcOgTrfIEB0Z+WwVyVAlAOhnYMO0F1gvAUwJTpZLnKi5rr/OFf7uoLr9XUuVI+TW5x9M3q2XTWubrm5Bs/QhePhGw8frdPDkeayKZDUT78Q7vnsszfW6QcP1dajWxMFcpCrzcwncDKfoB6BEYfxCqFbRghoDrfqCcq5wSTeO1CdBsS74ExJ19YZ8K3Xr9RmTk41VzBIPeeiW/0O6L2v7waJbJrGLVv8M8X3hRjfODMTm4wx9p6PhUf+9K9/vk7fG2le68dC0HZ39HlvjoDpk1frdHmlsvM4+tEUqzoNuJ6Ci/J0KcSSx3ZiHAPqdoRT0p2UOPzVpXDWX3z86Tr94kR5pFO9o6tzgTYIp9+quHUQYau6iRzAeZ1rshrrgAJz4pdfyUF4/t79dXqYai14yw2TTtMJ+heGmnmpuqsQcSDEfk6artpDjj6YT7F2hat590Bjdr+nekwxxi5yjQE1/naO82hfv1LdNZiLOziSQgfoqsSxhTDG53dYj+1/o5T9EUdv0Ae7OxqbinONLxnWM+lQHWV8pv6wAGoeoy/lMz3z2ZXeNW7Wpc45Nwd2O27XhVd4R9iJ1C4KHJXKcX7Gw2W3u6O2Nuzp3ucvVacBHMHffPK9dfpoqHemGON2iLZfYc0T4EWs/D0QZduRNJlMJpPJZDKZTCbTRrIXSZPJZDKZTCaTyWQybaTN0Na6dn7RbskP4VCUIOjpnA5TwJIYABWkXwbE4fq1tmx/9UqOZx+9J0eqx491/eWJ0l+/EIrw46+E2122yGUBl7UMaO0QQb/ffUuo0fc/eLxOjzJt3zcTbVUvFkrn2AZ3wChC8KwemNbJCZyy4KjH4LxENLeqxjnX4gk02kwJ7ACNC1FfCfK0xLOVTqjE5FqowE8///U6/cc/+nCd3keQ3yQXilEChamxzT+oVsjHFBjMGO6CCbDknZHuNxwJ1wlrYQgegXeDkogj2inQrICfw1mXjsUNLQsRkHwxB/K6ZTWtW1wDZIGuowweXHpmFkmipegnDF6f9RAgG210ciJkqoQ7WPdQfYmOsr51sw3ATofgfwrUfx/5HcDxt7uvPtuFa2gJB8J5CafHkBiz8jIDGr+A2yJIXzeZAAcq7gZtDRrn0hbrD4DSlWjTgdfzHN+Xi/VoV8+fTzWm0P2YFOQi0/OkscZP/0jl2y9Vd6ef6Z5T4IlBf5W3uIG7YAxEq6vPdx8JyfS7ytcC88M1EKz8Ws9aVUAFr4QKnqPdxbfaNdoyMOaCrs4MHr5NNc4FbVsfjvScP3jvyTr96Imcautcz/bgQHPcGK7XH38s987Rju457AmJ9HB47OypHo97coVtMPfUC7jrtuXlOX704YS8hOsjpqN0qM/5a3QF/HOMIxvzmcr8+SvV3QLu1hEY0cUELon4PI3I8d4N2uob5zr1TSB75S9siIjp+pqfJyq7FH/7q19rffLu20p/8NaTdZrHf7JDFXa5UP3WMXBKuLaGrZMzg4yXXuXva7WL/QfK4/BNtREXK7851jbXV6qvz3792Tr95QshkQvMOV2MWws4u5c84oBWUzV3s5/hnXNR2x/nCADvMR+FmO/yucaIM7iqvjrV8+91cAyEbRHYZIR1S7qja4683FQ7WIPOF3RXXvVTjyKJMtXXXqZ+l2EeTDoaD5YLjZ/TGdaoQMRPX2mNdnahZ+31hchmmP/ncE4O4f7u0m92hd+uAudu5hmsVSqM4w2OssSJ+kuM6BJLHNnogFvtxFgv8tgM2daRyq7a0xieI4rAlDh6i/h34dgdYX5MFjwiJ6fxsIf5NFKbvcJ4OJvoex6/9c46/f6PfrhOHwzgCgsEu5zpe1OswZawMva/B6JsO5Imk8lkMplMJpPJZNpI9iJpMplMJpPJZDKZTKaNtPledIs8NjUQTKAMDZy/8gmCpwIDWAI5ijzQmUjbxy8uhRNcw53zYF+Y1PceaLs3A2bZDbStGw1W29YR3OQGR3I0SgZCEvYGQg+6Qwavh6MdAn2fXurzEJjlrcCk4EJrIKxTOHExqGuaMKj43aCtjWtc0dZBBFfKBr8rMIBxA8yV9Rhj63+BwOXEm84vhGn97Ne/WKc/uv+2vsupDYwGQCLpHNtuz9dj3TtDYPawh+DjmdpgU6qc6WoZAbNZAsdOab/mUP50YUX7bZYMvgwXMbjYVs3duETWde0mbTBh74jrwDHzd7h0Ee+J0Jcd0jGGhwAIUAQsZ6cv57h6KZQqAVISoh3f4L9BA/ddoL/EzsMUzpCo3xBIZgx0dwEntAqYEl3IFnDDoyOoc8BfUTZ0co57O+5O1NTOtWNMyrEgRADjCZ4/Un853HtznV68EAbp4VJKd8EQCNayorOtvqv/rnC34L6+a/laONSyDWAfAXUPOhj3cBwgQPDlKVwS58DnGvTBumF7gdvnFzqywCDeUaj+PkC76xEzRN+MBnBe3KK89+sx+8035PR4/6Hmm44DuoQ6enAsXPklHKLHUwSAfy00K3sIBAsoXeyBdfVQppnwrTpRHdTlTbnAwa9S/0oyILEYG+jqmXNeu1L/Gs/oeg10FscESBlHaINJBmwNY88t9D74PSJnf0vdTGERXClB1bkl5o8SY32EuSdNgAbC0fvsuVC26T25+EZA8tJIbXTwAPPyfeUnDFWnLhi0eQFOXOFa4JE1xlg6yV9eK9B6DeT47ESfX+CaBthqB+NKE+k5XCX0z8M1PanosM1zQdtT45wr23kxxhgUor9MgZWWGGvmOV1zF7hGdZoAueZQGuB/AvDKyUj9qhtp3gwutb6dTVd/G+IoSeOBMHO8RTmzH01xTCMHQnl6jsD3eKYucNlepu+qS6LIOOaCiA3sEy69m3qsm9rNWjS5KVTmAZDyAGu1CP3CJ5i/rtXvFjgNEURwp+XpLqzzHI5YdPCY/UxHEg5HPK7TvndgXZaPNa7XCZDYWOuKYqnPF7Xy9fxabskZjq2894M/Wqd3h1g7Y82JbupqD/ddtOWG659q8yM8tiNpMplMJpPJZDKZTKaNZC+SJpPJZDKZTCaTyWTaSBuhrU/eed/9z//Lv72jrJj+Lnm/PYe6H/7wh+4nP/nJ1u5n+vbaZj32+iP3F//sP9/a/UzfXv/d/7jFehyN3D/6V//p7/W3/+RfbS0b/7/UNvvjDz76yMbVP5C2WY8ffPh99++sHv8g2mY9vv/h993/ZfX4B9E267Hb7bh/8KM/+vsv/Eb9063lw/S7ZTuSJpPJZDKZTCaTyWTaSL7ZIKaW9/7EOffl3WXH9HfozaZpDv/+y/5+WT3+QWX1+N2Q1eN3Q1aP3w1ZPX43ZPX43ZDV43dD36oeN3qRNJlMJpPJZDKZTCaTydBWk8lkMplMJpPJZDJtJHuRNJlMJpPJZDKZTCbTRrIXSZPJZDKZTCaTyWQybSR7kTSZTCaTyWQymUwm00ayF0mTyWQymUwmk8lkMm0ke5E0mUwmk8lkMplMJtNGshdJk8lkMplMJpPJZDJtJHuRNJlMJpPJZDKZTCbTRrIXSZPJZDKZTCaTyWQybSR7kTSZTCaTyWQymUwm00ayF0mTyWQymUwmk8lkMm0ke5E0mUwmk8lkMplMJtNGshdJk8lkMplMJpPJZDJtJHuRNJlMJpPJZDKZTCbTRrIXSZPJZDKZTCaTyWQybSR7kTSZTCaTyWQymUwm00ayF0mTyWQymUwmk8lkMm2kaJOLDw4OmidPntxRVkx/l/7qr/7qtGmaw23cy+rxDyerx++GrB6/G7J6/G7I6vG7IavH74asHr8b+rb1uNGL5JMnT9xPfvKT3z9Xpt9b3vsvt3Uvq8c/nKwevxuyevxuyOrxuyGrx++GrB6/G7J6/G7o29bjRi+SX3z2a/df/+t/6Zxzri6W689Hx/fX6UF/Z53u9Hrr9PG9g3V6d2e0TodFvk5Xi4W+rNH9T0+m6/TFxfk6fXk+0Xel4Todd5J1Omjvs8wL/Xukxz5+8+11ujfcXafPLnTv169e6zvH1+v0dHa1Ti+rep32pV+nQ3zXq1en6/SLs1fr9E5f5fSf/PD9dfrDJ2+5u9DJyUv3b/7Nf++cc65aVuvPl1W5TucLpafTi3W6Gp+t02Wuerk607NdoL6KSveP0s46HScqlzhWfaWRys4HqtOiWtVjWep+i7nay/VU7eh6ps/nS9V7lsbr9OGR2ukg0+fLhepxeq3nyLrK+2A3XacfHqrtF6Hu8/lzlUedqy1vU7/4+GP3o48+dM451zTKdxWoDKuyUT7Q3ZNIVDsud96p3uNYzxPg/o3TPfP5XH/rdaNRR3/79mP1/QeHwzZjqvNPPv1qnZ4sVF/zQt9ZOrUFHypdI19lwfFDeWlqXVN7Pfci1/UlninEdyWp8tksdZ9t6pcf/9z94w9X/T7Bd/eHw3U6CJWPGnmtK5V/06hvoCqcQ//KUHZZjIsqtFFd7g77auvHexq3u+Hqb7ux+oVv9IdXZXedfjVVm/r89df6/t7ROv2Df/5f6jvvv6n7LHVPH+uenZ4+76Yqj6TWc9S5vrfEIY4aY8I2dfLVZ+5/+m//tXPOuadfvVx//u/QvseT2To97Khs38B4tHuocvZe/ejVucaj6yvNT9cLPXNe6tnYBg67mPP2VY67aXtRrQKaFyq3uZIuX+p/UowNHnNfB+1lWWlMHo+VxwpzQq+b6T4p+vJC16SB8nbvgcaS/V21n23qlx9/7P6yHVcf7O2tP4+6ysfDXfXNpVe5cFxNUEYV1ktFjufP1H+ynu55a4zDsNNJ9Plw1F+nB205Bo36QoPyrxvla1nq88UM8+ZU65kZxuGzK6y5rtV+51hTzSs1tpOx2ukCbbMbKe9/+sHDdfoHDx+4u9BnX710/9V/8z8455x7uDNYf354oHp5/tlfrdNff/HxOt1xqqPdPaxp0amKEm0a9ZsXumaGOWa6UFmHXvW0s6N677f9dNjTGDBIlN98pvF+OtFaFLdzPYyBicM86LEWwxo5Ql9b4jlO56rfcar0BPUejdR+RwP1lW3qk49/7v7so/ecc7f7V6+rcklRL2lH1+xhPDrcHeBz/W0/0zV7HfSpvq7vJninCFXYAeZN9tmmXtVTU6isXr+8XKd/8Up/99evxuv0p0/1rlHFei84+JN/rPw+/HCdzvYfr9NJpPz6gdpJulB+O3jj69T6H59qDIuKzedHOyNpMplMJpPJZDKZTKaNZC+SJpPJZDKZTCaTyWTaSBuhraH3rtdiUjkQirrUtqj32t49PBR6cnCk85qdVF9bgkar8T8XZ9rufX0iTGh8rS3bnYG2ofeAH8ShtqoLv9pC9sBJrohqvBZiGgMZ2+9omzi7t79Op8DBlrvCcmZX2qp+fXKyTnughUlf7+39ibbWpxM96+e/EQZ12AdjtkV551zqV3VQNEChVERujvoFKeAKoAXlRKjGbKJ6SVBG94A0HwAT6o9UvkmgsqgDYJNAoHK3Kt/z50Jrz07V7ibAWTNgzhW4oHypOhpfCL/xB0IIeh1hX10wnyXwrTFwzrNL4UA5+JJnXz5dp1MgGNtV48oWZQs82T30TTy/d8SegIrGRDmBv3qiGvpbD9SqN1QfbFBGfGQ+/Wi0wnh6iXCeqyshOl+9VN9kWwiA3pU1EE4gPUEIXBefF8g7MdeGP6NVTAKfXgAdLe8GbW2axuUtTsKx0QFRAqnrAgcEEChjjc99qoeLfgfyGgB76vVUSztDjZ/7qMgs0/3jdhwIGyAxwNKHib6/2VWfqlHv40Lj3mIp5HVSYizvv7FOF6XKBl3Q1UAu8RiuxJhfB+wfqOwtqq4bN56t6uyvPn2+/vwztOkhUM7RQOUcZnq2HGjpl+f62xz4/hTP3BRq3zHG58c7ws7ee1PHNobEutJVHrJGfT1H33l5oTr6+rXQrBzM6xLtq5pjnOgBox4oLwXa7AS4H6h6NwL2G0Rqd89PVB6hinKr8qFzcW9VHzXaNMhMF2LMHKLNBcDbYnS2JtBcngD7z/oql25X/SQCMh6HahspjmcwDzfpAEc/PMb4gJit1/e4ua7f2dHnM4x7vZHy3se67ATjdoRjCEsMvhxjZ1PN0S9PNG8e79zN/BinsTt6Z3X05BD96+qrX63TX33+23W6yYXtlqi7l8+1/pxjvGtQ1jnwwQn6xgJjOI8RcbpOXqscs7ZvdgOtc9juOMb6EPMtlvIjzPM7OLYzT/X9nRnmVvTButA117WeYwb89QrrqMU5yuCOtqWCwLusRXFjrMkSpIddjRc7GN/2dzRI7I2U3hkoPcSRqwGQXy6/ictGWBu7Ut/VhJhvbvboFqqX4Z4q8mGkvysGOGaV6pm+nqj8a6/+OKnVXppUz1HhuFgn1Jq26WPdQjQb/TT1QHeTzY9i2Y6kyWQymUwmk8lkMpk2kr1Imkwmk8lkMplMJpNpI22EtvogdJ1OiyYlQgObSrfp9YRH7O0JCd3F9m1JBzM4IU6u4fJ2IQxiNtc1u7tys3r48NE6nWDLP8c9O3615R0OsPW/L7xqPtH3jC/kTuqcto8DODd2YXtUjLXdvH+oex4c61kvLoUDudfa+h7jntOpkIrzEyENr54JG9yqGud8SxcRz3BwHU2BE01h4lTAgfTkRGW0mOnzd95Uvbz/gVxxd4C2Ekto4EwYAEvNgRLmrUPsdFdt6vxEzqhfv1S5vTwF/nqpOhqPVc6X13I9zOGsVu2rPLpwHS7hLpwDY55cCHvpA6MYdOFEe0c/13jnXdwiEsScCmBGFdCaCHxkBMSBuEgADDQAOhOBryE22gFKl2VKHx+q7O7D7TNrsb0gUTk/eXS8Ti+XKucvgdItUf4RnJArOpgy70A4Q4wNJcrGVURUgLzimaryd1y/RXnnXdIicUGs7y6AUYVwgk5jOB4DPyaQ4pHvOlTZJUDmBinc6vq6z9GOxp39nq6J8L1Ji64GKJ4IDpRNX+PhIZDj+zI5dpNLoppCFrvAxGY9DT7LAs8dqmwqlNM1nFo924ODezMdbbeoWZ67v/nNyi39+UTjS5KoDPtwT73VFsGLf3GmeWiKwXeJ9lfArTiDo+Bj4Fv/6D0V9jtvauxN4WTYaes0ZDtCM38AHO/NU9XRy+dCEy8vNQ+cAXurkPcEmG1W3WJB18kx7rNAmxkBz+OxgosLXb9NeadjLk2k5++nGtP6Gbg3NCjQZc6jIDvo172O7jPc0VohhXs7x1gf0W0bXwDk9cattcEfcnF3a+jCUaQqVrqD4wPJQvWSJnq+PpDjGC62r07gTgqX5lvriEb9/emp2s/9/bthlEPv3U57bOby02frzz/56Y/X6XKmOaapsdABpjjGER4eMagC9hk959UE6Ccccjke0fmcTulRi693Qt0j7bLOVebTmdYzDmveHjDeLvrOCDjn0UBtbTfVmn1eqH8tcTRgwT2nRvdfAnk9LfVOsE15713WjlN0TI1wXCFB/xoAF9/b1XPu4RjOIY6l9TLNVYMhEHQ8c+CB4AMZrxn1gNe3/6Vz8mBX/36AubQCtrqbKI+jC13/K5RtAGyVeGpc6plyOGB34ahcxFjn1GjM6KdZs/mROtuRNJlMJpPJZDKZTCbTRrIXSZPJZDKZTCaTyWQybaSN0Namdm7RokMFgq7uPdZ27NEj4TQHB8I2+EWLuZCUKQI0e0cXMm1P3zu+p3seygU0BlcF4zg3gN1S07IIIdADD1fX0VCo5BKY5xjo4wXwhxhoQb8HJ7qF8IQBttOP772v7zoS4gfjOnc5lhNaDrziJRz7tqmVu+AKRaELGN1FF0B+b4KrOufci6/kcjsBvvXWWwqM+qOP/mSdHgIPDbtwUwVrE6FMHQOmA3MtWgyvA6u+FIHWe32V7ehA6devVHfnV8Jpzk8ZfFn4wempru8gcHkPjnohAtjSSfF8IvygGyPA/R0Fsm8C55o2QHUAF7CgERZDt9MUKGMH6QABtcPwmxHKFBhPF46cHVyzP1R/eHgolG4XuN1N4HO6L4fAfPYQNPgCmGJ+obZWw6qV7nckFisacxITw29nFf6CaTrBNryrvxu0tXHOLdt7z9D/M4yHNXDbPpyFU+DUO3jQGO0vQ5/a76nPHBzqPsO+7rPfhZNkRrdJjM+t63OCMmkitCkEVy9YbIHqd7anMebkQuPEHON6Ccy1ALwbAM1ewF2Q0ds9+kEMjCeKNw+4/G20KEr3y1crx25+w96eyjNh0GqU3RgOfRXmkoKB5IF90xH0LdTjX35fgd4/elPpPqxFI7rFtqgkiDG3BHLbw/fs7qvujvc0h796pXnq9Frj6m+eKbh2jA4Zgv+MMrW7ENjVDOuCKxTmAhPn69d3g7auMrP6TwMXyxBu5GRYI2BkIeprZ0d1PcrkmrszBOrfxfyYob/T7pT9x7MdR3/rc3/LkJhrHvSjCs6jS/XBBTDFxAPBBk47BEbtdvQczqkex2g/13QqxRw1B/L32SseKdqeynLpzs9WqPnVc7mzBkAJc0cUHg6kwH9LrB0DzP2BU1kUcI2vUWEJjjDRtZVnArJY9+y2rs5D4M993CMHKpugLVzV6i8XMxwRW6i/nCM9w1GJfFdjdYbjTeMAbQOWrNc1HEfhKHvrmNQW1XjnyhbhDDCOJAMcq8Hask9XahzNOLin95Sdntouj+NlxMg7dO3XfdwCdQ2kNoqVt6adtxqvMi/h5rvXpYO+7sc6nWM+PT3DmhP96AIW27NU9+8HmnMWOAbSxf3DCG2fa41s83WO7UiaTCaTyWQymUwmk2kj2YukyWQymUwmk8lkMpk20kZoa900btE6Oe0eCdV49Piddfre8YN1eu3w6py7Pn2xTs+uhBYwCG/gtX2bwCmL1yTY1q0RAD3OGKhXmEXSInmk0hjINUKg3gwuaNlQCG1/KvT0eiJH0ORaKOMcrnsLoEk7fW1tv31PwbWzP1MeC2AeL5+pnPIlom5vUXVdu8V8tZ0+HAqhWADlXM6FeP7y5x+v09dn+vydt+W2+eF7b63TXSCyMVCuEChIiqDMFYPdo0USX4uT1p0UgbPjhAie2sgIOMPRQG3wAgGUL4+Fgrx+DfdXoK2LGYLRA9faI6igAAAgAElEQVTpEP2DI+OyEKIwB+qyBCazTQXOuyxalSkdzGg02gOaOBwA4QATWgP7Gw3VLm/K3DnneoEqhs6TO3AxpqPywQj4FtCRqF7VX7cD/GaqeqmBYxVoFyHa0dVkjmuIla2TCgjsnMunQhw9sK4Q96+BL7lS3+URfPhuwB3nnJcZX8hsALXqAj0pG/XTexhjM9TLHpw56SC8h/6+s8u2oXQWYrwFzhcyGHr4t1G6MAGmmAiDRNNxFftICZdKpF+hzGEm50Jgxs2tYw2EmoHzAd+qULDV3ZCtrqpqd9Ui2yPOWQGODMAhuoMg2vOF8jeGY+YSKBcdzj94eLhO/xd/8YN1+u23dZykBzfsCCimz4R4KWtwQEQfISMeAI0L0XZ8SJxT7bEC9v98rLkyJd4LYmy5VH6v0d4dgqezby6KuxlXvVs5fjp3e9yZYV5flKrHtAQujnrfQ1nwiEc3IdqKPoPg4p7jOeY8ush6tKubKypw5A2QNhL6DECf8zjRLbwfjrM8FoSq6CJfezsYY+Bm//VLzadsVk2l5zuHW/82VS1zd/3sU+ecc8up1lULzDdLHEepUeYNcXm6ytfqR4uGztG6fIgIBQEGMI8ZpIN1Z68LN9Hhqs3s76If4whPgLF/CiTy2anWqK8QfeByrLItgfGe6k9dPVW+jkY8iqRnnRc4fkWHcxxBW5R3s151TeOatg1yzg7hWNrt4LgNHMg5Dnc6mpPSodKe5QuM2MFcOiXqXKLfeRQk6rpp+9US/THFUa0AZ/GGgdaicY15fqpx4utIz/FirmcNMbdXkcaPJcomxWteGAG7BqLsE6yjks33F21H0mQymUwmk8lkMplMG8leJE0mk8lkMplMJpPJtJE2QlvDKHTDw9V26+HB0frzzlB8Cl3I+Jo6nct1MUQQ3Ngz2LC25DtABnspAl3DGSoGghUAr4nBR8Y3rnTgqxpsQdOlkohOB65pvUQOlKORMISX0dfrtLuGkxPwgzkCKO9kKpv33/lwnY6A9P7s3//7dfpkLNe7rco71ySrLW4GMZ9dCvP4xU9+sU6//FpOrQwu/zaiiw/hMJUAfYiA4mRwiK2RDmvgBMBxPJGaNsJ8AKw0xr+DwnTdRHjAoCu8aHcoXOTqWqjGPpDMJBEC88XT5+v0BC62c7h29dHe0xiutLAzq5q7Yem89y5uv7OGw2kDbCJgX2i+GbMZAmE56AmP6CTfHMy4D5y1Cxe/HSA9A7gh06UxbNH0KFOZdOHw2QMSOzwU8nFwIjdIoshXU7XZM2BK3oGZQ8DlC+Do9RwoCtzPiiWwpkDtNCD+ukV571zY9oeGaCbQbg/cZAgksoc2t492/3BP6M4AmOtwoPErG+r6BNhq4tXHQoyVMKhzUZsf74HmRUCE4ERYwxW4KfW5L+GqinbULOkmp3ZSBQjcjaDQFTC0EO26JtqHINq3SNgtKggCl7Wo0S7ch+el2nENBi6FS+f1XM85BnoYY1zdHcKd9Yc6TvLhB4/W6Q7m1jDQ9Q6Iu0e/Dm5wqwrB7ek5i+MODuXf1Kq7YUd9NoYj5g5c28/xfAVw2QwO6td0EMU1xPZDIPne39Hv4IF3vq0bjqUVgsRPLlWnfWCKQ+Csg576Wgo0je7ZIfpsiPVHg/VKAP63gjNkiDYd3MyPcHeugd0FaHdFgn5Uq6/XQB/jQs9aAoEjas66TuAm2sFAUePYToOjSDzuMZvfkRt2U7oiXzkKT6dCbKdz1V0Bh9UAY2/p6dipMp8vVC4F0OoYx6J6PR79UPkOsXYZYUxO6IqbrtpMH+vfFHVaIV9ZhXrZV//udzQmv0I7fXkld+UlHGRP4Gy/mOpvM2CQMdxcp5hnGyVdEN4Nau6cE9oa8iiLniFF39nF+8IA7reDROkYc1XCsxdwTfdcw1VclyJjRJfphtwedQtR526Jtoa2U2GtUqWq3wEw6qDEERv0zQJto0mIvcNR2P9t5NY555Yd1G+NI1rx5od4bEfSZDKZTCaTyWQymUwbyV4kTSaTyWQymUwmk8m0kTZCW+M4cffutRhNCpe3UAhHvyecZQkEsJrTTQxOaGNttzNy9bAPLId2jA3RFlwCRIGBeuMWf/FAwALgrOS1ArhX1tgq51Z2v6/t8TewPf4sEf45uZCzq8N3TYFT7hzfW6c/+P4fO/zBOvX5r/8DPv8btz15F7S4ysvXwme/+vTTdfr05OU63YVT7ltvyC2wD0dU0BkuAg4IYs75DMFbgSVVQCjothlmxODa/xL5Aa4TApVtgJlEKa4B8hHERPJU5vlDbevPgMDkuXDK2UJIz4IBXkNgt2iDnujuFhV473ot+jYBguaBFg8GwK4Q/HuIAOX3duTAPEAQ3i7cHeME/Qs4RReOZ2kf7QFISQy8OGwxtagLdKgQ/uTh3tmBa/HerlyUHxwJNb9CAPRncDx+fqG+RpwjjvRM+deq3yXcXxs4FhLHauq7QbB849xNLPZ5SQwUuD6wqwanB7JAz3YAtPh4V89Jl+Gsh3ENLqu3JgL0Xzo58ndHf+O6mNHlTukS4y2x6wJfRFfevJCTYpMrX3WkekxCPTgx15h1BzfXpAEaDLwnqzea9r61oih0By363zlQXi+/0LPtDYCsdYDzwuU5w3ibYvx650h94P3vva3rgRFHsBr0A/UTjnENvjdoy8sDXWsK4Jy3jqGovzTAHV2g64kE9jMg0sA/r4E79rvCYneHyterUzFzF3CLjTBH3+8AX9+ivJN7co32NJ8oH4MdpWM4V6ZwD/ep+qDHnFRHnBSVrLFWaIBTVh6u00BLGzpMtmMcisd5uKqWIRBlYOFNQhxRfxz1cG986RLX1AXuCZPdDHmvgamX6Kcl1nqT6o78sKvKNa1T+3KGtotnyEK2Yz0zjyeVmFuLQvehC+3evur6jV3Np4c4EkLcnWhrhDXtDSnJ/kgn+wpO1ClcYz3misDrOWJg4XEDN90T9a8JHmQ21RG0CIu3FEdeen2l85nqbh+u8M595bYl75yL6tVz1DjG0GAyiSs9c7enct491JyY4N0ki3nMRwpw2KuEs7DDMYyqUDkG7JtwOPdtW68xlzqsUR2RduDBWPK6KOfRLbXBZqk/CODsHoWqlwptKsULTIGjZhHmwWusIw6X6MzfUrYjaTKZTCaTyWQymUymjWQvkiaTyWQymUwmk8lk2kibMT7eO9finCn22w+O5eAaAGGZXwkHzOHqFSLQewnXxQ4CagbAIOrfZXqJrf2gxjsx3Jxc0rqZwZEsAFYZ8u/oCIfPKyJtubZ9I6CCBwO5VFa58Ic5grTOcpXBxZlwpwMEl37/B+8rbx2idP+b25aWxdJ99fJL55xzr58LLa4LYmSq38MDBFweCtvIEPTUoYwCoE4erE2AgNownHMB0JnwllsdcKsWQPAI2NqEcLDDtn5NzgYOVw0wAxiFurIBagXEbGeki+YzNcKnJ0rnwA+qUPdJ6DRY3ZErnXOubJHHAi5st/gnr7LoA2nby4R/7MENMkvhPgcnzeSW8yr6AK5P6fwJZ8Kkq++K2vGhhvtyAfe5YKn7FcB70hDo3w7wOeDKCYJ7d74+WaefAd+OwGB3esrj1QzjE7D2iqhpfTeudI1r3LJ1pQuARFfA9a/gaJldaOx4A4jU8Z7SIChdDKc7ELIujOhqij6D76o9xztOF6vPQ+KjwGNCtMEKc4WHe+cthBK4Y1Xp+RKv8WaJMTlBMPayIeaKLN5yn1MeyrshW10QBq67s0JafaFyefO+nuH4WCinv+UACfw61JxRo4yeHADZQn9k3wwyBMtGH/Toyx795CaXHmN5jfaSAfG75XDZCIHzNZAqIOuDfc2Jg1Md97g8U10PgUZ3H+q4h1/qOT75XMcs6BycF3eDRNaNc8sWb+cxDdeovha5cNYeMNAsUJkzr+gmt+ZH4usN3HJrAHcVHHIDNF4fIhh5i6+FQIsb1JfH/Axy33mslQi01Z75QtsE6h8St8/xTED8GjhdB8hP6Tj23M38WNeVm81XRx/mucY0HoPxwMID4IDNQvkuSq1RF3jOUU/z5psPhJ0/wdGL3T7mXCCXaUI3cPS99vZc/5ZAEyuM6zlcQHtc8qBeCq/8LvD9ea7+NcERnpLOuli33IfzuQuwIIcrbXd0N6h50zhXtOUR4VhcBCfzBmNg6ukyjM+xXkQXdCHGODrsN8BJq6XGrHoJvBljUIijW03rshpg3qnR1zwmKiK6DtEB/FhuurHTvOG47o2++YiBx1psCdf0HsahPMZ7WK36PYnMtdVkMplMJpPJZDKZTHcse5E0mUwmk8lkMplMJtNG2gjy8T5wSbTayt67r+370WgPV2m79PJcaOtiJmwygutQXBGp0dZ4CaTMw4XKAcEhEkm3uICBQW+SuPct90UG/cZrdQD2ssZW8nyhbe2ISCaQud2+UFU/F9JzcS3cbo5t69klcLuR8IM3Hj90d6EiX7rnn69ctUAiuwTBvHd6DEAvnCCFGyPdLUHCupoYWUAnOuA1AZDThriO7hPAfTRocUqirR74aAAckXRbAxyOO/Yx8JYs1bOmcMLrw+3y/pEwLboFvz6Va2i+ULtbAE3q0CV4i6qryk0uVt9flUAyUADsIzVKpg8kkihnQndj4HABUIkoYHuAcxrQ9AhBz2P+besyWwZE19nxiJcDhQIi3cDJMkPDOwIKO4Ab5BBOyy9ONSZNr9WXr64QNB6YMLH2NLkbJjLw3g1aRDiCE/Ql3CqJXWWxMJRhqmdL4X4Y4jxABNyOCGNQfTOCVja3IDfdB1jRTSB7H9MJWX/FgOoRHFYLjqtAkEKP/GK8veVoiO/imMwg9cTtaGFJtLBp7ub3004Su48ePVjlA32Kwdcf7SFY+NOLdXqBttXMMffBsfxohAD3wMjoQB0D9faJxq+A7roIdn7zUzLoJ+cR2JrzYIh+sfTEKoV9xRgz9oZwZMUxgaevNfcVcMS8twt34cdaUxwCHZ1N6W6r/Pyvf/PMbUtR4N2oLccE88TVFVyk9/V53CFaDMQTDpBRRFQVExGHFLTLqoJrKJs0gr6D9ndxi2g2jqgbkVHM23Sa5PGREHMyg6FzruZUhrk1xp7EoO584+d0vw9vsb53hLZWtVtcr+osJj6LtaBH/pYYPxdz4P0IBt/HXPbeI6HY72KtdoSjIv2uru8ARabbJtedTXvko0Kle+CmMdereKQK82mGepwD1RziWctd5XGRq6+9vtJ6Zgm8tww0rpRw/f3qVGPYQSCEf5sKA79G4HvgxVOMNV2sN0KMe1yreZR5gHG1qVimeHfIgZrnemeZ40hCgyM6CefHdi0d0AE15Byrj12B78TRqsUU/WsJfL6LNTDa1AzpW8c30Jfpdh7k+jwHarvkAvpbynYkTSaTyWQymUwmk8m0kexF0mQymUwmk8lkMplMG2kjVisMAzdsA692EKycLqlETy4u5ZZYz+TElzIwJ7Z4A2BJdGGN4ERHKpVuVw7b01FCPnL1H897A+egg2tTfnMgTn4NsVi6aREziIAXdUPhPa+fvVqnc7i5Ejmks9lgRwFUt6m6rtx8vkKNmyXzqm3vIfCMhO64+O2hA/fOGFxbpwsEC0hVGAnDW2KLnQhFwDKticWs0kRYQc/ddlyL8PtIReSTbnkI9gqcsIcg2qMeAkoD/Yrvy6U4QiZOLoSFzBbCvfL8jtw+m8YVrRuyByoUp8CogNUVS7U5OnlmcLeMgEfE6BsgZ1xIhOMWngh8ihgznr9s8+lx7S3MBIZwjfsdSDCNlhnvF/nqJrrng3056qFo3OWFcJUoUt25BfF5BIAO7+Z3N++9i2/ujbLK4BLb7anv7PRU5qOu8poQM4ZjJ51qA2BdERwLGcCYrr8cEemM7G8wMBSoJ2KK8cDTaQ/Xx3BU7tLZt1DfGQz0txUG4tkM/d1zfELwZUe8l893N26faZq4d956wznnXDnXMxRzta0R3JKvOpoTq3PlNQS2m6Cx78FlOEG7jIB7cXy8zXV981y1RvswlgdEDeEoWAN7ZzrBmEGuvoJ18PGRjnsMvpIL69WZ0Lj9gdrMwYFwO44xBx24JwZgO7co7/3aCXcO/D3CODUaqD/GNd3jVY8ZXLLZ/EjIe8y5RK5Jfnoglx6VFxTfgJzy3nRtpZsrxl6P4yw8EuExb9T4/obtCOUR4o9roLA8QhKi//LzW31zi/LOOd/mKwH6uKSTLMpiNoUbJ55/CDx1F2u7J4da290fat0wGADtRUVGmM9CIMiMKFC15RIhXyWLB3VUAkeMMN9G+IMEx2qWWBdlOArTh4vzFSIOnL2Wo/9srrXD0b0H6/QLoLCf/+YLdxcKAuf62eo5BnC7fbCr/nX/UOMqjHJvHfEIKj2Dj3j0AscKGE6gIF4MPJTHuBK4veMffN5Gi+BxDGD84a2oFHAgd3DXRpSHhwdqay/Y7450/Vcc2PWnrkZ5VDhG1DDv+N6r3yPKgO1Imkwmk8lkMplMJpNpI9mLpMlkMplMJpPJZDKZNtJGaGsQBK7bbtVit9TNZtoynsGldApshVhOQJwCW+we2YnIf8CZMSHtBgQnYkBlcHhxvdo2buhGGQG/Ic8BbLXANjExvQrokM+B+iTCE6Jb7lxEGKQc6FMJl1kYdLkeULJtyjeNi9tAqjUCgQcdONuCxkzgDOlRjku4dM6BztTXumeK8qWzbQZ3VAbRpquku4USrj6vAro1oo0A22D8dTrkhRWuQdDkFM5aKVzZsq7KY1EAkZ3qnvf2gOSh3b14BQc4/O121azd37rAaQqgSEtggg2CzoYhESW4gwH780BubgVxRjqAK29JV7QGaG8hx+a4DV7exKr/Bmh8jb7pEPy5RL0X6CQw5nR1wz6LPAK5jDOVwU4HCDZxr+AWX6v7hBsNl99aTV27vD0SEALzGtKVDuWSFUL6BsABe0j7Un+7TPWcMcrdw/2VxV6Di4mAT1WMsN5+vAR+k0QIWk3HXwxjIXChBG66Fydy8oyQrwKYXMJA23T3BanqgQxxrqgwzqUBZ6/tKQxCt9O6BefAqKacS0o4KoLMnOPzBcpoH5xWH0cGSGCVwKdcQ3dOoFlAvDwdOdtZifcL0anqCB0A82PNwoVRaJTBgfxa42dnpLrefywH7Kef6/jL3/zs63X68feEz3UxV5xfTZSHucpsm6qqxk0vV5VTogz7wK97sDsPgaQmmZ65vnVN+I2fBxjXah4lyNEhUQcB6qDC5zcoahnQbRUPxfEN42QZEd/T5VVAzBZ4JM4eLLFuKW8Fe8f4SeyX6B2dIYlgb1V+7TBe0a0aK7EC49dsIqwzw5GcUUdz65sHch8+HsEZfAdza0B0mH1fOavhOFwCeb1xZV1iHOOaq8Ba9NbRH4zNfol2BEfhBMepElRR1HxzvZRwof/ymfrmARzsP3zzjXX6p4hosE3FQeDutZj84VBj3b2Hcok92NVz9lKs84BvVsB8PV3uMX7SKb6m2z7K0c24BuU7AN4x2vV1CUfWAphzjvXJHGXOEuw8Vrs7yfUu9daDP16nLwb64y7+eIlB+ZabOxH7UtdMsAbLms3fO2xH0mQymUwmk8lkMplMG8leJE0mk8lkMplMJpPJtJE2YrWapnZFuUI+uMVezrR9fPbqua6H81USCQvxufCUJd0w4R5JjCZLiGXAAZBxzIFc0KXQtZhBCATvFkILFKSiW1EJ9JTBYYGt1jG5Ojg2Ad3teLg2Ib+zS21Vj6+ES2TAvbpe6W2qqmt3MVvVQQZ8djDUs10v9fz7e3ToVfLqXK6Dp6+AGSdCGQMG04U72A4caXf25dA33BUuEmfYYr8pO6KGQEhC+EvWDNpLu1VH3BEfo17oNpkCqdodqgwugYIQA+s9Bg7TVfrzL4VvbVXeuaZFzEI4djbAjOoF0WK45iGocASMhhi3B+sUwFksTG9xSUgD11moP8ZwzyzbvhzdKnQ4CgI/bgK6KwPBZkcCfst6D5Avj3sGwAaDSPcnSu9n34zxNjWfe3uq6tpdL1bjah+YzYiOpfjqDpDrbqYxYgZ2ajERKnqNIwYLoFyDHY3JHRxWSLoqiyHwrVGCcWBth62yrdEXGo6fc9X/fCb+5sUL9Ytff/1U+br33jpdjHX9IqYTLAJkow1EaIPFLcc+OOPFd4Oa+yBY47oF+mAvV9lenmjMXDZyDc5rIOgIZD/soY4wHRTAzrJ9PVtxqztyPkXfIIZ4M58yEDfaWkPHTgylBYJ1V7nyy8DoNQZrukd24ZIdwsX35FT3PPtYKN3hkeYKj+8aX92Na2td127Scscebp87wB0zuB+PBkDEGyDdOHpRh3TcBQtcYw5DuZQYH3l8oK70/CTv6vaa8JbjIsY0LJYq4HjoLs5xnLxlrA8HfTDQMfE8tMeSbu74KjrOJljT3Q1o7pzzjSvbOa/AGi5AwS3gpInlhOth7j8Yaaw52tFabW9fHTKNhCE6Rg0gAc4jHJwfgd2q+oCqYrgKGdSe6DT6Wop5vkf0Em0jAeof4z7MMIMYTODa+vzpl+v0ozfkiH7QUxn8ym1PQehdv79qd/uIJvAGMOMe1pbpLTdbtl1gxpwnllqvYhh2i0bjy3IMRBv3J5ca1rpP07qjep6zQn+sPBFxrLn6OJowJRat76e79RmiAzQcDrvAoWP9bVxr/KjxsFmtuX0acM387WQ7kiaTyWQymUwmk8lk2kj2ImkymUwmk8lkMplMpo20sQ3hTbBij+DmU2yT55OTdTpNgKlhW38KFJZBOpcI6FlPETx9oS3jBDhaChfFDhxHB3ABjVqkw3P7PlS6AhK5rIRRTYFUTQul/QLfD5whBpJGtJY4HDHay9ev9EwIXNzrabu+nxIl256axrmyRfzohjmFu2gITLAEqlEAZauWcJLEM3TgLkj3txJt4GIprCuYqe5COKT1O8IYOq0b3q2A9diCL5a6dw73sNlY33NxrQC7RcFAzEDvgOHlMyCZJVytgIt0RspvtRQ2sIMA3BHK6f/42WduW/I+cEmUtvlb4nNdA8rINXAQW8KZE7SY6wDJDW85J+N7iY3C1ZCBq2twQuz7WWsLx7ZAYrQGchIAC6HzakAHWQRMDzDe0Cl32WgsWZTERYCso04b8khAv3J/N0hk45wrW5SfOOB4oYrpAakfjjRGzPAMkxdq6wtg91PgOg7t+MWVxqCXF6frdIoGtNsVvvXee4/X6QdvrNz6Oj2g+B2guDMiiApa/dmnwln/zx//h3U6xpjxwa6cAMOYrntoG0DWecTBoV3FwAbpTFwy2vsW5b13UTsndYABXyx1lKNAgPsKzpx+oXrP53qG3RHdmHX9CXDlEm6iKTBLOgpyPA/gGBm02BNdjhswrMup2lSJ4xgl5sQGuGUEbJOYvANW1sG4EuZKlxgnz3Fs4nqsNjAcaJ4N67v7HfwGh89RR9OZyiXdBdYI5NXB8bkuldekB6yNRyzohIw5qY6JQSpdkULE4OTbtt7QvZ5j7JLu+HCsxPhBytUTYYWT+RJzXI68VKj3CMePepjDA7pBA/tN74htbRo9X4D2V1V02Nf1MQaSHtYz9/fkUnr/0bGuB3Y+xlGgHH2ZFLHD93bg9NvtYx3ZznkN5infUdku4CxbEHkGgl0s9XnluEYm3gxMe6ljEN0UeUngQIz1xasTRWYYws100N34deJbKU1i9/bjlYvzIfp/gn6X8tgD1x631jB4frT20xcaa16eaB7kkYwJnIgTrDlqrJ/7GdaFbR8f9OCY3sd6plbZxhmQY6/+8otfa63467E+P9hTXUxSzI+6pUvhnh1jGE4w9ncw9o47ON5Ed+VvKduRNJlMJpPJZDKZTCbTRrIXSZPJZDKZTCaTyWQybaSN9qKDwLmk3fmuFkJerl6/XKcbBEAfDIRgLS50/fiVto8DYBB9BBXtwqKuHAtNmxf624KWYHDWyuBaFUYr10HfYOsbyMdirG3ti3NhuWOgXtwGj4HWNqnQhmog96oYTodBQKdBOBxiq3x5qTxUBVyVbkXt3aK8c0HLdOTAHedL2T4NY+2TL5fAKSpt1Xd6es7jIyEfCXDWJXCsEgzjotJ3NcAWmZ8UjqNpm504Am6Be18Buzo5E7J3dS6sbkq3YPCcOwPV4xQuWBcX+tvZWH/bwCWyC7ewDp3W8Ey95G7YncD7NVZV50RbGflYfaEAfjOdCBcPG/wtEKABcKxBHygGg14TA/VE2fV5VAPNapHTAOhlU30zHktMiy6vrPfrSz3HqzMhN+MrOAcjj0ugnVM4i9bAZfnrWsR8FpsjH99G3gmxYt0RHd/dFXJ0BHwxDYDUo50d7cgJeeeBXC8Rf9xdXam8nsE19Rp9KYLD4Qzj1HlnhajuRer3wVzlM7/Uc7x6rvnh3/7fP12nX070PT98V9jsqNIzjeFWFwA9C8BaVwh6zrGnAdPDoPEM8L5Nee9d3HJ6MdxIC019bgr8+lbgbDpdpkp3E82DGeabaa52fP7zT9bpzkDt5P59lelgCO4JWFvZzkM5xobTp2oLv/3Zb3Qt+suDPY39Dx8J4w+BmBWwEQyAmvd6wOowrlSXRI5VR5c4ZpLjCEM/JTe4PYVB4Aa9VRuspnTT1XfvYH3icSRnjHkigvvu/FpzieOaAKhZ4hAYHe0nwlhKR3hfYi6MVuVbwga/xri75LoCzo3FHJ8Daa7hbj2r4V4JZH2J9UJR8tiE0oM9lNNzIZQpkN5BeHf7GeujAviKBrbEnm6nOMKTYaxJgG8ucNzg5dca1wrayWNZPcGaIwxUX/cfqM+kwHzj1vU4Baq6xJrk9anmta9e6vt7wGPpZp+j3qczDUTNLVdg9ccIY5KPeDwFRyXmqt+TV2rX1d0Mqy4OA3c0Wq07EdTAdTDWe2DuYazn4YiSX2ocOTtVvp99rfXii0vNideXQEiBteeYl0c9vL+kqt9hGwHhyS6JNc4AACAASURBVD0cW0P/Yj+lS+p//Mnn6/T/88mLdTrYf3udTiZqLzmeMAQiG6CNR3DS5rGVaKD7HAJN7wWE3L+dbEfSZDKZTCaTyWQymUwbyV4kTSaTyWQymUwmk8m0kTZCW71zro1/7i6nwhRevVbw4Af37q3TdFI9xZZ8BZw168N5CQF5AwYzhhtghiD1NDzLc91/NtM2f9IimjXcqOpCuMF8ISfP/FqOgg3Q0y62rNM9oZ3drvCeCJhnnMBpD+5MQaHnA3XiIrhPlTkDjMJOc4tqmsYt2wDCDYPzwqUqGgCFckgD7WVQ5vPXag8zoBhE4+IeHAXhOFaB/CzhALgohM5k01UbSId03NWW/eWV6vHiDM5bQB6Gvd11unsIPBAYUeda+MerF8/X6Su6pcGxeD5XHntwORuwvTV3gyhHYegOhitsMcMz3HatVb8g1nk+VR+YwtEvOtHnnb6erd9XueweC5scwbWTQccTdVPXAK+NWmSK7qx00SvgVlcCk5/Nlb4Cdn55pXwtgcAnXZQ/UI1yQtwLKBfKJsTAAsNXl8Z3hyj3W0w/Qlt5MFDZvnNf487DA6Gqo2PhjouZ8l0UqvcvPhNCejEWurPAM6cIfpz1df/eAG7MfVRqix3n4a0IzvqeicaDH/9UOOvridrX8Y6w2B30lxxzy+Vc33lwLDfXqobrIxxHA/40CtQKTdBlwS3ry+3JB8637roJHGN39tRf5ic6PjHpqJw7PfW1BRwSOyPVbzQQDtfD2FfgeMbkVHPYqRf6FkfKQxf4aX29agOTicbPl08/XafpvHoAt+B0pLzPKj0rg5jHFeZhYFR9IOvHx3ARPlU7nc2BozP+N53Fm7tDzdO2wdA5fAT0tJeqLHIc2chzDRjLudqxK1W/MwQOL0LUdaLviuGmmmEcGIyUjhK1jWC5KnjihQXQ0ynmqcVE7WUMB/0i17yxBMqYYL9hjucYX+s+C8whYfXNRxJiuNlnOBJyjOdz7gu3LXnnXNgi8MuayC9wQKxDQqw/YyDXDuhnjaMOuzs6zhQBfwXl63pYizqsRbgiKG+5rLcu7Di+8ewCR64wkC3RMejK20cw+pDr5YWuOcc654K4Mtod3ZsDuj4Dzz+fs/3ezb5U4APXb+eHToqSQ3viErUB2kvH9oup2uvT11+u0z/H/FjChb430pwbYl16/1B9P+sCk0a7r/zq+jO8l9Q4ljUZ6+9+9ZnWmT/+rcbhrKP+/d6f/GidfvRQ8+CnOAZShKh3nvpDG+93lYcRkOoZ+sSg2tx913YkTSaTyWQymUwmk8m0kexF0mQymUwmk8lkMplMG2mjPcy6qtzseoVrjIEyZkACBn3hg/UUSNlUW8wBXISu4Mg2n2jrPYBbXQhnpOGOPt/tCgVp5gisCwe6GzexGMhJDTymRmDlyhN/0Db19UKfZwiOvKyFaQ1L5SXZ0/ZxDzZTyVBb1USt6hzfS5fTkpHEt6e6adzsxs2rUNke7COvkbbM94fCdneA8F6cCo94BmSLTotEZ3eAOj84vr9OR30Eep+qSSYhUJsWh+pMdb+yQJtiOOUYrlaXyuPJa2Ffg108ExEGuAKmuM/OUPV7OUawYDiYNnTlpUtcdFconXd1i0TWRNB21c7Knsq8hGtbCPTucF/Ym4+E4iwXKuuXJ+rv1+hLGbDvATCaowONA8NdYXBBsspnANRoCTzmeg535wk+B0Y1AZa7BFaWAC2+dyxssoAT7GUkjKU3FaYVvhTymaJOwxTBh8vN3cy+jXzgXNSeGXhyT3XxD78vp7Z7u/r8+P7ROj2dqo+8fClE5udPddzg4lrlOAsZ3Fz1+2Bf5TVAEPZ7mCJ8oHL3O6uyrq4wRsHJ0gXqUy5WX+vjKEPUU1t7eqG+eQh8aQH36Bh1nXWB8cLBNGmIWaK+KrgU3lV3dN4FfpXfuAun71jlEgMPjq6FPdHlOYF75OlruQv+9pNfrtMzsEtRH8ctemon18DxhxMg+LuYfFoH8wpuo71E6Nbgkca9HPjTiwv1xwLHVhIgcEO4C/c7eu5uF8j2O/r8cq4+WH6i8eE5bG+v4E49ru+mPzrnXdOOAXRpP3pbKGOKQOe+QTD6MbhGIPtZpjIdjYDd07EcRyboEn51ojGrAKI63MPY1yK4XDNcnateroA/wzDSBXQaB9LGOZHurzuN6m6Y6TmurpXH8RXGVUCcMdosaDs3LzH/b1GN865s2yOH7iCsedFaEaIA9EOsf3Y0n+7uYF4bKV3iSEII3D8CUp7AYXMyV103OM5Ut8cqeBzm1YnqLsZRkiHmigALyoM95csDY18ONQ8Pz3BU55nmjRk42z7WrtWtRgM3bNQv167blPfexS02G2JsjODa3HDBBTx1hrJdYq0/LfScRzuanwaP9nWbjvrA4hL4Ot5l0hRH8GrdP25R513gqVFDtFnXXhdEqvWdg4Hqd4AjXZ2hnqm7BMYbwv23g/kEecQpOlfBMTor8beN8vZtZTuSJpPJZDKZTCaTyWTaSJvtSNa1W0xWb+Pe6y2339cbfYhfK66wixCmeqM+G2t349lLGWecXsOABcYWj96Qgc97+JVgBkMJHtcO+IvJrP1ljwemPeI74df3MXZ1XuCXxa8v9avwwaE+j0r9ih6Xir314fsfrdMPv/dwne539atkiV+Qxjj4vEB8m2pXvyxtU2EQuEH7q3COXwMZa3MHv6QM+8p3VfHANn6FLPUrRhnpb2c48N8Z6Fe+Ej9hXMNU6BD1lMOYo2h3i68W+jUoxy/BS8QGneKX3V98rnrhofa3YRbx9LMv1unPfimjiaOh2treLnbCa5VBjl+d0xRmUfj1ycO4YJuqq9otr1fl3vCQOOqOMSwnMFNIsFN5CsOaC8RRCmEmxV+u54it9iADFYAd5xlilXXwK3nS/oq4hI/UPCdBoM+xieRmiH33Am3gBDFpu9g9+OTTz9bpx/sP1ulHb8iwZB9mJyP0zabWuHWFX9fL5m52QNIocO8crNran//gvfXnT95RHED++s8fhycL5ekCO3O/+kqxsT57ifhtPbXpP/qLP1X6n/3zdfr6TL9Q1yANZohJ5qerPtChwRLGq26mtjOvFOewRB7ffesv8Uzqd57xOkOaq+CXWOycL/BrNA2f0CVcji5YM/7wFuUD77Js1Qb7A8xT2EWKsWM6xS6hx6/5HtPyJZ07QIDsPRbR8asXaq83xJBzzn3wRG39ANRQ0dCwY3XP82uNDTV2UJtU9XgJGucKMeZ+9cWv9bcz/CoPs6zvPdIc/sG7j9bp+4fqg3/yUM+0hOlXDmJnMWc8N5TNFhVHsbt/uMrLRamx5nuPRAJgKnNFiFhxDrGzsSN8ipit51/D9AxjX5ypke5j17YPI6opiJkYpJRvTXvmM/WXK8SunIxh9oM56xKmH5w3H2GeZ51WaMs72MnpZ9h1b1Qec+zS9DHnJGg/g+7m5h7fRo1r1mSZ5yb8rZ0hUHMwrMlAIEUYMz9/LtLj6Y81rlXY5Qywa/boSH3wGEaUOzAeLEHC3ewon4H0aDApct4sYbbzGQiUAWIo97GDydjK5UL1yHk2gWFOwniUoDsCjB+Jxy6zuyPzK9+4qI21GGCN2uC7Q+z25phjKgz8JXaZG7iyxTCwugCp9ZtfKo7ji9+qL3UP1R4eDdXW9wNVzuNwVb8VTP96mMODHLFkE411GXYJSYe9fqr1zP13/3yd3tuDgSUW1XWMWOeYEzOUmYOxUol3ooXffFy1HUmTyWQymUwmk8lkMm0ke5E0mUwmk8lkMplMJtNG2pApaFzdbjFHwPhouLGYcVsUMQQ7iKcDBG2GbfsAGN7OobCJe0BbR/s68L4409ZzDkONJFZ60aIgN/HrnHOuwV4+t7gjxHzc6QJTHOrQK5GlAjH6Tp8qL8+/Fsay/1A4TATToAZGF5cwXbhaCE06yu8mjmQQBK7Xxkny2lV3YQSsAXGe+jAciTpCd4o3VUbL17pmibhumWgs9/iJDjI/2FGdpg4HqLG1XyF2z7JFFEAR3TIaGV/oQc5OYQqCWEMB4uY0cNyoUO/TqfCWSy+85BCGMbuHeo458CU6KJUwkymrOzKF8M7dUDoF8K8GiEkFrDbAAf4B0PEQeKqHydJoV0joAsZGHk4Jx491TRYSMdR3Mf5i2hpkBaHyeH4mRHoKbHYKhGa4qzJn7LNFX2U77Os5Tl8J7ayARw5h+rEcqsx6Q30+nwPzA8YcB3eDKHeyxH30/qoc33hD5TkCWu094oFFKpcuMOZ7T1TOH6H8y5/8Yp1uAt1n94BmKGrfdUcYzyeXigHZR1v/47ffdc45N9gVupUdKu9dmIt8723hx/nlT9bpIRDiow//eJ2eVsKuLs41BgSV2mAn1DzgOYvBGKSC8UsPOKu/G7LVBUHguq3BTAwzoKwA5t4DIg7TqgXMNxinbXegcnzyCMckjlXu4Q7iO8bARvdVv0PEBW4wrs7b2KshkLH+ntpCscRxh54KGqHq3ORUc7VD/L0aSHucYC2AmLcZrj++r2d68FzHRs6BZb6Gedo4vxuTljiJ3NGjVV7qFxpHdnuI24ixLgPyHmB94HBs5OoKRjaYS1K00Rqo2eE+DI8y1UGEIwMB0MNue7wo6WhMu8K42okwP+D4SHaoddnZRGU7HAhTz7HWe3751TodE4Xd0zon9KqvBEZq7IMR1j9Zdldoq3PLds7zWIvWMIgplxpLU6x/GK/8/Ext8eNffaH71JjXYKLV4ViDdUYH82DgVB8h4yh/w2c8JjODydUEawwa45ydoY9cai1E862LUx1ZiJGvDtpvg/jLHvVVzIEDo41H1d2gra5x67VVhbVigiMQNeoXPljOE/dsVEd7R2rfv/yVjnJ88SliV6PcmwT1gXH7fKyyRvh4l1er+uD7Qgwzuh7iIB8c8OgPzOXQT+MHMsM7xpGFDkxJS6yLGqd2x3V9gPVg3ajuOjCWnPwe8XltR9JkMplMJpPJZDKZTBvJXiRNJpPJZDKZTCaTybSRNmIKvHfuhrrxibZOYyCpdJhKgMz1sH3OmFmdGE5GHjHpjoDbeQT+uhJ2NeroGtfob5d0/ctX31v0gDLmxG+Vry7isHSA8RwMEffJIQ4Y8v54X9hRhC3sGtvKXTjnpUAezqfnSp/r+XLgTtuUd86lbR0sU+WvhAteROQX5ZICzTocAtsF/jpfYKsemMfurnDW/o62/BP8nlHmiHcFV7Ib19AiYJw4PVOnr7o42kNcyAxxFIGezmeIqwaM+c//wY+Ux44wgAMg1Q0wz9dfC2mezoQoL6dAV/zd/F4TNM5lrWMlwiK5fIGYqsCuUqAS9x/qeTLEK9qFkyTJsSGc6wLwxXTJ7MKVDGSQqxD/L22x9hr4bdAFfluqX2RwsqwL5WsX7n9xqDbogAu9cfSDdfpoT0haiLYWJsJSilz3X5TCsWiamcR3U49pHLk329iQIzjIRRnyDZfDCs6fe7sYvyKVy9H7wkwfHgilOzsVqjcHCvOz//d/X6dPESP4cqKx6c8+VFzLNx5/3znnXGdPmE0EjDzbF4r84Y8+XKcXM90vPVbhHiCPb3TVHp/t6ZjAgnga8Jsu4tPVGLcSuHfPKzoN3k09eudd2LoLe6BpIXDqGihYMVeemgYNDRjzwSHih8KptTvC0QOnvpklxE9xJAEYcQF3yqody1LEZMtwBGMHmOvuQpjceKY+cvDD763TjH8XAqdM0e/SFHGT4ZpZo74Yq64/QBsHgjV2dxNnOY4i96gd7yuMO/1YuGmEualE3NkOytFzzkAMThjFuxGDmgJzJWrWxdopSeGoC4fJXuuaOstVR2GH/y78uAe2u3EaMw7R1hh/MJ/p+s6776zTwxFiwgL7z+HUHgM17yGI3XKhNngBZ+ytqnHrKYHzY7XEOIJxoQesMMNY38XnH76jPph1NMbt38M8hOMDKdYQAdxvE3xvCmy0f7O+0Ve6tKNymyIW4jEcOztdzRU58MXXiPHazYSz9rEuS7AGdDGOfmCcOMO6m1qWOEZzh3FdfbtODzBMFiHwTbiO0pU3wNgRoVDvDxCDtdG65f49PecMZya+/ELrvC8+hws6jgQcvqG28f67K/f14X3Nw+VY984OEbcRzuiDCnHd94CCH6teDjEGVIhXPMR4wLzHWAMmfB/BeHPtdc3urRgY3062I2kymUwmk8lkMplMpo1kL5Imk8lkMplMJpPJZNpIG6GtVVW5cbs920npKAgEjTcHcpQCJxqBe+sCgQrw1x5bsx4ubw2yDIrGlbh/AlQgaN2LEE/VebjiBZGeo5MKFUjgWFXwqSIwEqW2gJeR0gGcTbvYfi8auailQCfo1jaZaPu7AOa5TXnvXHDzHMA8KgQiHdNFkMgR8NAYOMHeAIFRkY6A0cQdlUUAxzGPaME1kTCUO11ub5QEQhVG+0IYhiNhHvtnKs8FsbJS9RuirUVAPjqoOw9scoa/HcCNeAKHtDkCNwf+b+d9K/LO3VCOIfDvskFwWTisJUBIR6naaAdY8jAWjuWAxtXAZUK4+IXAtQO4iToEtG6AVtzcxzOyMpz9hh3VaQR0vQGuXo2AvQN1btA2QyC3CTDIHKjaBVC9aS5kKACmBarMxdHd2H0GQegG/dU4FAExSegUBzfdBOhjA/fILgKEu1D1OwIie3r5cp3OgTFPxqqvMZC8AYIvf++dt9bp/XSFtNLNruD4Cfz5/htvrtPRvyCmp/69uyfsvar1THtAVc/hsBgBx/Il2iaQ6SVQ2BTjkPd3g2A1rnFV64RXN8Iul3ONQdMp5rgU/QXzSoxy6QAXHw2UjoE+Jph/0RxcFgL3Wigdwxn7JqB4jfEtAYqOodxFcE4+wDhdg//OF8TF1aY8ysPVGqtqBEafzuhyKiSvgHt5iD5elVxtbE+BD1w3W7X73V25O5ZAaQvMWTHQ1uAWbgrk9Ujo42SssSYv0BbZBoCpd7o4KoNyJ/5bt+gsg5J3gM1GQFWJx1YI3k7ne0/X7b7GlWhHqHOGvlkUwv0mWPdxp2KI4O0XYBQv4Li+TTXOubIdy9lU6FZZ4vhEF2PK/Z7Gz8MDlNeR8N8UR7fiUH1ziagEvH+FeShJ4Y4+0P19i3oPMuDlV8rvAo6hCe59jPEgxBpmL9JzXPWx7r2PeRPrk1eIRDDG8Z8LfJ4v1U4aoJJ0wN2mmqZxebt2i9EuE/TBBmNWgPZH+r3D9SeOT4we6RmOctXF6xfCWSO4un/wp5oHjw/Vr/felLPqwd7KPdsHGruugcF2lsrYHo7xnS80VwwG6ne7cEXuDvHaBhS2iOGSjTbgUC9BrPzkeIGii2zoNl+v2o6kyWQymUwmk8lkMpk2kr1Imkwmk8lkMplMJpNpI22Mtl5ft2grUMJyATwDqEYAfNM3cBECZhT2tK1LVCXAVn0DrGuBYLfzK+A6cCgL6UrWBiP3QNRSIJEOgdE7CHbakDiB+xydohq48QUBHBYDbY8vSuU3ioEUNcQsEUh0rr8tFneEfDTOVS0WsQQqM78EWjRWnnJsjWeF6ijCFniX5c+6ToETJOQEdU8Qp66A8+MCTppNi93UUf23PnPOOY863yXC0AeSikDzZcbfULD1778ZCSiBpQRAAjx+i8mBYF1PlN7fV9vYttZ0Ex4HpqNuCRflAI7DFerRw+W1B3fjEAhUSGdm1FENZrws0KaJKMNFzbdIM4NCR0RG0fEyIHtxV4hlCvy1QX90cICsgMwtFsKBqkLftYBbYAX3uVt5Q3sgArxNBd65JFl9TwM3wwrIYByj3ZOITOj6iM/hKO3grhj0v9lBlN8VNMLXYrCNKZwnbzDiKmQAa/QFBE4fjIQHMmZ1jEbb1MpLBjQsTXT/HurXY6wuib0jjyHRv1s0692g5nVVudnV6jiCx5xxei2n2jMEuO/hOETcCB1LMX52+mrrHONiYJAJA1fHQE4LoooqCz79zXAaoDwdnCGJrcZwtw6AMdfAt6JCnxdTHc3Ir9R+ixpHAICzLmfCIx1Q9oszXX8Jt1g6zW5TZV27Vzd5x9j06kSB6bMdLihUj0lI5Fp1l8BNt4f+kALV9URCgX42S/YTXI/2ELRjZYN6znCEyMPpuQMnc9/VONEHNtigTTVAwWkuXFcaV4sCrvg4thLwJBDw0tcXxOoxYW1ZdbXKQEUkmPMR1mH9ROUy7AHBx/XdLo5eAD+uA+DltcplgTLqdNU3urtCTkk3J/Hqmgvgo6dTrMWA8XdDzLGVyrCD+Zl9di8VzpnjeM4Yx3DGnO9Q754DKOcfHt0K7gY1b1zj6vb5yhwNClMfloWucjgCgCgDHhmPMccs8DpApPzeY0ViePBA6STS0Y8C68gU7q++RW1zjB9BF+vSUN+zP1JbCA9V18GlHiq7D+fqAN+DtsmTRYVTu/v/2juzHkmOLDub+RYeW26VWVUsskh29zQJ9YwASWADeta/1Ise9COkR7UAPQygntFI0zPTbJLNKhaLteQSmbH7anoIzzhfQGyxoicTAxTueXIGIyPcza5dsyj77Fw6KmfAmEc4QoIloKvb/fvRdiRNJpPJZDKZTCaTybSX7IekyWQymUwmk8lkMpn20v6sVof1cWu4QeFSog+DjO6p+s3a0DETRWpJH7XYjm2AzF3PhLl4QDo5CplHuIlbV0e+lgAdCrgugbQRu4rgXpkDs63hGOngBJvAZTbZcZbT3vPhCKheRMctoCvhfhCs4JxrunshHDSDI9iL18J4VtXn2+sRncf8j7dpD05ejBMPJ81AvBlRuIYTmSem1rlAtkAyG6CtxQrFr1msewC8B/1bIgaJVMdw/62IdpT6/BqOpBHQhQr33h8prg8P4KZ5l/LO+S5eMvQkHUiXKDrv4cC5muv1EQouM+ZSdAyd0P4U+uDZjhibCVnMro0IpA6BYK1Wui9iM6PR4EdfbzF2GrR/C+faCHFUAxla0t0WLmoV8FI6vR0dwMLyTuVd0mEmbUrkmEWW8XoEN0ygOJ74K5Djnge745Cr4brsME6jBM8JXDUA3W47pJaOxCwuH8GVN4X17cFYmPdiLpQxKZD38HwACF0MHChKie2hbRgDQKOJbpbhfhAs51vXRJux1wAve/38h+311UJj8+FjoWZXUz3pcAy3TaDpMRyH40bPn2JeoWst8yMRVY8cF7qc7IHGtQWrfgOxRG6IEBeezs1sW5wnqRu1xxJ882om/PVmqrm9xRzSYC5OiPT+GcuXd1HdNu5iubmvFqhwdak5MVsrpz99DBdlBOyO0zQwsizm0Q89D0woXdv++FiOyYoC17wt2M42IabHNRRzfJ5xTOFoD/rUoS+KpVx5S+QDoun07iyB+p5fa31xDVwzju5nPyM4t/Ui9nQXxXGjGHPTg0MgkZgzesiZvQZuuljztei8FdyKU+SdGGl1/uZie/39d8oPiy5/fftKufHyWk6eI/R/dKoP/BmqHyTIyTGQ1Apjkw6yMXPmWv2ywrWnK6rjGhVO2u6eFFrXdH3W4PljhaKriCsDeY+WcOTHsZbKc30AB2xcO1Z/wLG0FmuIjEf5PI7pdfeZca8u4tzOtaJiKoYLbEj1gMsbOOsudQxihaMfUa5nGmIsx5iXB8i9nCubKY65NLC6fUfZjqTJZDKZTCaTyWQymfaS/ZA0mUwmk8lkMplMJtNe2ms3OvLe5dkG48iG2o6l40+8UxwZ7o5w5vTAQ2O6+MGptcEfXK+FvywvhQSMH5zpHuB86iqiS5tt3WFP2EINzAGUjyuBkrVAFnW3zuUo7j0A7rfuEYXFFjbspKJLff7JodCYIZA5usXW5ITvUN55l3YoqkcIsDjx+UTb59OFXj851vtr4Kwx3dwyALPALALai4gOe6tFQKTos362ua6xTT9FYWcPdMeVQsPSFM55LAAewW201efEKKReeqE4BRAOYjJLuINWwEhOj4WtjXK5+t2pQtjiCR4Y+QoubCxaHTNe4VZXAHfo47oBchQDS/ZAuj0sYsOO6zGKBQOT8tv3SnW0kwT0nR6xya7Dve/8W1j4cQyvAdKzBLZRr1Ccd0VnPBRkhzPz8bHcTO9S3nmXdk7SdNcMdDVtFKNE5ti/PkfbrTC+4AAZeyAs/JiVYr1OhFUtJ3DtzNQW/Q6bLBMgrK3Ga1HBNY5OyEATw1rPtINQ4vmKAlhjLnwrAMXNkUFqjOsc45G1sv3qfo4MOBe5qHPUe/X65fbVf/rj93oL4vvwTDniEZDXHbS01OtNBUc/T7wb8xmdWlmUGmPTR5jRuvxVOvVj3cAZEvmWzs1tTBwe7tYcX8AXK4ypeq7PXAG5Xc8VM0QFF0A4j+EifJRj+fKV3HD/uVosV+5v//YfNv+Bth1luo9TII4PT9W2PbYFcLsI+cVnGoNJ+PF50KFdHOawCH1D59yo+64m+X+x5c3H0e0czr48vgHkNtBJG4h0QO5vcMSjJiaHYyPLQq+/QYwXWKP91adPttf/88sX7s4UnGu68bDznA3GC9arGWMaH1PXwKzZFnwP1h/zEm62HL9Ltd1Xz5QTzi+EdCddTEdw2V2UYDgRFjdwhuYxq34P7uzod2Lya0TbquZ4xFOxkD3nIs6zJaI2MN/cpfzWdTiC0/gKMRq3xHMRlz1gxnQ1v8HRJuDXLudvFiCyWOc0ifqrWaqNRodaH+Rd3K+RP1PktAYO8H22W1+vF4XicYU+nbxWfC0eKQfcOv4651yFowrJHG0GvFZQrHPHcCnutzxQ8m6yHUmTyWQymUwmk8lkMu0l+yFpMplMJpPJZDKZTKa9tBfaGpxzTcdDxdj+HMB5rIRbX7NTxBSIXR/FbgERtHCYXF4Lr1pNz/U5+MxD7AjXQGHnc7lcrRebLeEVMFsX9D39ByO8l+iF3gPDORedaWu4N9Tf9lPhi3QF5K55Bcw1AUby0QNhruuIiNH9FOr1kXdpV1g3pwsr8MgK7M48gQAAHy5JREFUW/kTFIr+JHqs97R0L4U7VksURt+bYzufLor1kogb3CZZmLxzfsyBxM57+LsdlFEvx/ieXqY+ShLgN3SK1Z+6QOc8MMcVEODlUpjBgxNhz2dnKlobhf1RgXdV0znnLoCLxYg/Fp9eAeWsgaS0QFUK9GkCXLzhPzkBT9xBfWq1BfGmBK6DvhuHKdo/BkJTof9LoDg5HUEbtieceIk3897xOQUQzjmuS7L3iJnxgWJwMLwvX7qwdTxl4fAMGEqLvOqI2ZRwzCQ6A7ycbpAB7R5axAPoqTUKyS8KIe6jR3KUa26Bf6CyFfJ6mwnzCXC4DHC/m/zwbHudI9+y0HozUr4JQ6GtvUwP1cJhkQ6mRAVjuGbW8Q5EeGdq28bNVpu55+vnf9i+7ge6p0Gm4tPHY+Wj/i+E913faL6rW2FUrlYediX6F+gjTVMDcEoWsk8fwGF7uonvfqY4Xy3AzzU8boJ2xpht6fiKYyj1WkEVCj1Hjb4OGJsRjrYsce/HR3q+AZxIH/V4ZOArd1cqytJ9+eyZc865BM6cx0PMR2jPzz7/dHvtSxwBSOFkijmR6F3LdqQbM3ytWWjcA2lmgfXbAucJEGXfwtUVY70scPQhVX/1GrVnPMQYJC5d4igHPie06q8W8+mzV6+31zcT5dsUa4Gjh/fjat6G1i27+82QD/vAB90K83qFvArn1YA83LQYazxChfTcj+DAfITi8UBn60px9cEj/W0Wb/Jq5dWeD79HwXqg+wH9wiNEDpgr23nF42VARJkoA45K8DgAj4rkOKqybnm0hbDv3SmE1jVdjilws1lKpB6/I+BuXS3Rvz3EPRDehMeZBhi/OyUNcBQKL/tc/dgf6/dA47q+nuk760TX1+iMNzfIn0u9p53pHm8e6T0z9EUWwWmYbr3ovD5w3TGPjiGHjZHn0mj/I3W2I2kymUwmk8lkMplMpr1kPyRNJpPJZDKZTCaTybSX9mO1gnO3Rl3EpaIE2/coal+iiHtKVAOOZ3GmW5jB8W0RtMWb5vr8EbEEOOD1gS7Ua31vOdmgG9fY4u/1gdMU+AwUgl5eCsu5nAnLWWHb/AjOnD2arI2EgLV0B13BKQuo2uER0IWp8KUajl93qcg5l3UYwiGKoc5vgKYBT7k+n2yvq1+qj3pwASUewQLSoKFcg630FuhGDRc5IkANYmZebfqgQjHYNfCnfKAYYfHnkjeAm0wQOwlisABysENm4zNBFjiPZ3364cfb635ffVoQubxTBReaWySSBcd1gxzg1yj4fQVE6RgscA5EqUCs0y031HD3Q5u2QGpahEYErLzX28RYSaQLDVqwyDYQuDWw9wKxE9PxDM58FfCx9RKuZXAwKxdwMAWuPIQzZA7sZTjQWLlr3bZjBBvpdgcbpqsnsDOvPo2ncB9OcXygokshkGIeDUBbv/i9HAXTU+W4GLZ3N5cb5JUIfH4sNG78SEhqHQn5XiFOz7/7bnu9fiPXzf6Jxk76sRDZEY4VNCysTGfoHQyQaDowQH8/rq1lUbgXX2+Q1gRun0+eKC9EU80r476QvrMTXbdArhug8yXm1roSkpiWaq8IfcTxS6fFcgJX3nITP5fnOg6SDNRfdOolvleVcEn3wp9rHA9pgLoTO29wgCB4jalb52LnnBvCpfAprg/g+H6GowR3qTRO3IedI/wKR02Wc7X5FdYENyv16cEI6GkBLJhHVgpcA8Gnk/lgrHjIenrOEvk50H20Q/LKKXF4zGVw7yzgUt7MgUjj3qOlninCfVVwKScWWq7Rp3Ba7gHn/OCxXPYfjIQBPnhA/8i7U+S9G3XIdtbTswXkBfKLO679ABg5V2U9tf8K7vAeyTRJFaMJ7ezxvadjjdmKk2Vz6/ap7/wA68PxCA7/OwsRrrlwVCfWvYQMaydgvHSkd3DijxBfWQ/OwTjGVMLVu/b3c2TAeefC7Toaj7wmug1UFdOmc1gvpjWwVTxbjTksXWjezAaYZwv1+yVyeIaKC2mmPHiz2qyZi1bjpcQaqoAb/OJceXJ5ozxc15pDv8VvrGusXT85U198MtL1EA7bPTwfnXsjrMdvgHLnf8b2ou1Imkwmk8lkMplMJpNpL9kPSZPJZDKZTCaTyWQy7aW90FbvvUu6IscLICwjuM+12KZfTy+21304rLHQfMD7WfQ0A87iiMnh5R1UEYjM0UO5+9XHx5t7AeaTANdJgcr0ciEk2SPhFt++lCNcuUbBcGwTj4DenfT1XTHckK4XQoqIvB4eEtHR33Lr+S4VnPDPCFjDYKht+hpb3RO44C7mur/+AyEXrJ+cEDNgfduCzoxw6lwpllLYDuYZHSxvr+FGlaJQMFCNNbCyHjEqPCvR7AqISAm0son4HMBCFkIRTk4V14+fCGm+Xgh9evbsubsXee+ibjwmjmiH2iJGgeoG71nN9AzrE1334JzonV6P4UBIVzg6MCfAe2q0Vw/OsVG8wTLoQtoiSKo1cOUMeQJx0aBgO2GaEqgLHeQajLUW42sJVLBFXw+BwR8/VB7I/hzm4x0UXHB1536X0IkSOCKof1cRfQSK7TE26b6XoR1btEsKp87L75Wr13DYzNrj7fWbl3ITLSeb9yTAZuqJ8kQLpMjlysdJrO9sMe7XUzjaFRqPJw/UBoMa6F2mmNopvszxDhy9x5jNyD7dnSLvXL+3+Z6Pfv759vXrxdX22o91H0dP1C+Dvtr5avFye/32H3DU4VroVHSg3Js4Il66n7wHR98YMbDD7G9iyT9U7krhRp7je+j8Wq3gzjpXP1ZrXS/piozC9BXjGkco6BU4httlg1ga9dCP0X2hdMFF3Wc3S7qo6/umC7grYk2wwjogBkJaN1w3qF0CLFmzA+YaXXse98C8ksAp/rYwegnH5brU9xQzvZ4hx3i4PpZzWDcDmW/oogyXzuUOOq3vimKNwf5Yn38KvPTkVK+7SK/fpbz3Lrs9tgIXccafx5pgjlxW4fU+8kWJfozwOTHyToNZif2egQ3fOSTRqG/K7ohFH3F+eqA1aowxHTyu4Tq+xjGNNNbrC7y+gIN7BXSXxQ0cHL7TXPczHCh+boB7u+J+8qoPzkWd/X+NnywxEPEK+aXEfMA1+qrUfBDBXTlLcSQHz1yW+q7FQp+5wNG5uNSYuZnpqMb2qBv6xWOsXwMhPp8r/qdA5qu+2va8gevwU+Xqn50JgY8xNvt0icbfBsRj63RvKY4rFYm5tppMJpPJZDKZTCaT6Z5lPyRNJpPJZDKZTCaTybSX9nRt9S4Jm23YxbW2dHNsdbdwc9vBm4AH0OkP9TFdQ/QQKFIDPLQBLlPjZ3CJ7eYMjkhxV6g0gvsiXT2X2BKvS6FDDnjbcCw06+3NW332UuhKlKk9RkGoKl0fp3AmrIFFHJ6p/ZalUKYouh9UILRCjVDf2GWZ0NYWblOzhe7jBjjBIfouDcQ/gBgSw8N2e9SgaGypPohr9HsPOFRXqHXH8Q/9z0L2y4lcZmNglT1gIWt8T2iJSjJO4SwLl9MoCAnoAROq0B5f//GH7fXv/vGZuy/dunzCkMzFQEz9TlF7NdgMCNYSaEsyg6Mf/p0ph/tbD45gKd3igBQHp/dMbhTT6/kmHlbIEwu4C/bGGjvJjqsocBWv9xcoCh0FOsjClQ1tcI0xW+AeBgM93wAugoeHQkf8Pbl9Oudc27HWdBptYjwD7rVEXm2Ar7VwZ03gtNigcHMEPHcGjPj7Fy+218cfPdleHz5UfOdwKWyPNm3kUby+ZlFyHBNwwKtXTnkSVLR7RSazUu6tUAC838c8w76gGzSSQkY0HSz7sL0f993+8MD9m1//B+ecc4taiNLyhWJ3MNZDj88+2177Rv3oeoq5+eSb7fV0rDZ6ACyJeTVOUJQ6BVIOF98E4zfpcuIo6L0Nxst6heMYcOws0e8VjmwUK7qTYpwix67R1yXORHi8JwFOuQaWeBUUY5NrtfFdyjvvki6vxYihCA7gDZC5KdrlDLjhogVut+PGqL89OAWa1lPe4TwUAP3WDdq0FI7ubjavn78Vfr4Cup8hv9HhPmABVjZqzzXXbpjPa+QVBydYOlIGIHY5BnkGl/sEQ7AO95NXY+/duEPgCyCGq5mua6wbFhXWFrnaC8vFHVdoLB1djPmpxfwbY04KXp9Z4HjMHOsMt9y0dVWogZZANZsFjn6AQ42B+vsYcwiOGZVrfecKuX+FmCLGXBYcm8BC4ZSeeDgTY76+U/nIJWm3NkXeX0fEfBW7LX4YlNWPzwGsslDjiFQMN9coBto6vdQ1bGHp7Hr7W2Nzy5vrGAFzDbfX14Vef7XikSDEyxAOuh8+3V7/+sPT7fUQ6+EMxwTLhmtqHElADsvQlgVigMdA3lW2I2kymUwmk8lkMplMpr1kPyRNJpPJZDKZTCaTybSX9kJbQ9u6dYeGxcCMcqCnZQ/YKIraDlAwvjdAQVO4CBGF2cUW5V60ONcWdort6SGcrZqZ0Ma8Yyj8QNhIBZes1ZW2rGkbF4AnpHBVjYFzrFEsuj+iCxZQI7hjXi31vbOVnPyePJJj38Gh8DFQeHeqsm3cy85tKwI+Nx4Jj1gRVQHWeXGlPv3gTMXCIyAyEbbVWxQtboBvFUArHBy0Urj11sA4qs62smVxZPRFA7ytBsKwXAhXrgLwRaA+tP5czPX++Q1wCbzJ50KAr+BY+OXr32+v/+73f9hefw/c6C7lvXdR57Kawo2zAmZTVz+Oi6+BZxREzQ80run4SkdWh6L2NXiWBBh3DuR3hcLk16tNLOXARvpDuAii71h0vllxMKAvUIC9RTrLeuqj5WuN8fML5YYa+OvJscb4hw+Fjjz59APd++vX7l4UwtZVkC6sdFUFJbfjRMfxxYRY0C1woOsCPNb5tdC4Fnn7CPkuA3GZHxHB2eRzuhtGcH7zMZBgotDAqOfAbIbHen+TPdK9fKDrAdxEV3AIrHfIOBxVwL2liP1kvwMd7yyfRC472dzj87/7evv6m5lcWP/VL/7t9jo/U5yt18qr31zI/e83/yREPgWS+PTnH22vY2BJrkXhdSChLTC1qI9C9m2XB4mFI+8GuhwDo26Qvxv0RYsC7BU/EzEbcC8B84wHFlqXGu9lqvdMpsrPkxc4inKHal1wqw6FbIFdEjtf43lmN8op4QPNiTFxbRyVGQD37MMpHY+/03YOWDvdihv077xzPY7hB3ow1vUAhexjnmcBGlfgO5Mczro4epIABa0wh8SaQtwaxw2I2w1H+t51UFzVQJrvUj7yLut33wncMUXsLhCLKzoII6n0OF6ALyZ4Pw8hFURIS33+9WutAy4mOiI1gZt96IrN9xAMRYwjQUBrewO1Z4b5+TFyZoRjIOUKYxk4NtcLs6XeP6PLPj5/SYyX9vf31I8uOBdu14hwBB5gflyhakOMY1YxHaoxifJohAcinPTo1Kp+fI2jfL2+/jaqtHZJDhQnVff2NcbU1QrXc+DKXp8xpXv8xz/bXj/8/C+212cj9UUPazEQ5S5D+sBKe+e4R4n8kWNdXf4ZqLntSJpMJpPJZDKZTCaTaS/ZD0mTyWQymUwmk8lkMu2l/dDW0LqqKyQe59p6b5faJj/sC6F4ib3WGq5ZOzVPUaR20YOT5ExbyZMLYaCrS+EsAXjcYKDCyUcPdA/9w819xkBbAxC8Fi5z66lQxi+/VhH59FqYTehrO3h4Jlw3wVZyXeE5KmBHwK6efftqe30y0r2dPRZW1sT3xLY6v0WgloX6bg0MwwFbqeEeez6V+9xijkLjR+qLHjDXXl/IQQIspAROibrNrkXldY+t+qpzSl0Bm2FB+WWNdgZq7RLFF1HBgNdXC6GPV5fC/eJM+Ov4QBhaC2Tpf/x3Iaxf/SBc5Waqh5qh2Ppdynvvko7TI85Bt9VxHzgrKRQgKVcTxf3jM2HWIxTIbujKu1ZbV179la2Az/U0NoYYe65z4qXzW4ki5kSLmwUK1sPZNRuikPtQSCQR2clEGPlXb1Dg/UJ5JUIh7BQOcB73fnWuviuW9+SiHIILHS5UAT+i41wCJ74oRbIhDgenZZ8BAUc8EGNu4Bx3MFDOvAGOf/5WsVEgBmazjVPncganXjj4HR3JofrssfLEcKB8EBq1f34oVDP56Ffb68NHH26v6YbIcwgRnIMDUVtiV1AU3c+/ny5mM/fXv/lvzjnnfvd/vty+/uAXP99ex7HGQtno+hVw1v/yX5VT3k40Bl7COXEK1+8RwrIE+lnMFCcp8KnRI42ZqEOdYMzpqp15m3OC4gKEo0uAwDdAGYnV0W0xOLhKt3D1RMzOOS8D23eYK4o159Y7VBtce+sCDzQOTb5z1OGbb5VfPv1Ejscnx8RW4aYLZ8waeOTqSn2X4YhH5hQndJ8n2ph3Dr0t0PU2poM+jxApT0xulCd7wF/zkXIg3XRr9IubAgXFqq6Ao2/siFozVyCHkdu/Q3nnXdzlJI852+PcVAqscT5XfN/g2NLjA7nNpj1ipkBe2ad4fV2ofZdzOPuPhUCPP9K1645qMJdeTrX+neIz5pf67Gyk73wUFIMxHElDpPhq4X68wLGR9Rzxg+MRi4W+d4VYalqu6e5nftx8z+a+6p0hD8QTjvwxktlOpge+GedEWxX3JaaY53DeXwCHbkf6rZFiDTGvNGbTzh39Gi74b2bA4bnmHetelnN9xpOTL7bXjx8oZ/MooUeb02yVbr0B+ZPrZ4SyW6GlfLz/2Q/bkTSZTCaTyWQymUwm016yH5Imk8lkMplMJpPJZNpLe+1h+si52xrHPaBFKbABInDHB8IjajhZURG2m0dANdpCW9VLOGxOroRaVUBIz2dyxKqP5Fp16DaoaJ4CYwNSVQE5OV8IFZgAq/vZw8f6WziuDXNtcdOlsgbqSyul80sUQ8f2++RGrz9+KgwsCnRXuzt5712Sbbqebn41CvI2RJoSFFkGOrWq1KfDlfoowHGMbokez1M7uLM2QCum+swko4vv5nNiuHT26WyGYujVWveesGAscKQKqMbNhXDdFi62R3CSjBE/b94qTr55KYTv5WthsXSRbdt7QiJdcKErHh7wHeND4YP5AEV7F3hPH0Xfgbm8/l7OpAdArod9uB7Do25xKcfCCEXSq1LjMcbnZNkmJ7RgVJZXas+iAdpZsB+BdMF1MEUB9hVi85uv/ri9fvFCyPElXXzpbAZnxNd//bvt9YePhfqePtQYv2uFzj2xRbFyBww17OQCtX9C3g7xHVrig4i/BXlxuD7ie2dvv9cnAq+dwq0vDpv4GSAuhsDYhpEQ/WEiNCzu6fP6cLwb/kI4a/xYOGvvWO8p6BIJjAfmgi6FC6UDwpoCPYvvJ626tqzc4sXmyEIO5HsYwRWxUvz9/f/6zfb6P/3H/7y9/t9/L8yVyNYKTqEL9IUvOAaALb5SXksKjcf8jebl7HYeQJvUSx7N0P16uhw/Pdtex6gun8AVECSjA33sdsgpDMI13M6XOGYxnelZF3M93/PL+zn6EVxwTTeuIsyDcQJcDOjYm2vlwMu3mgOOHsp1MYObaozgzWK9PkcOuvhOOStba45pnNoih9N03uH4K8xrBY4BuVj5lghnPFZ8nWCsZUBoS86byCVJCmQOa5gF+i46hCM3HDd5tGHHNv0OFZwKvLMiQA5nzgp5krjyBea1z59qDkiBaBPpJorqG10f4vjPrFW7T4GlFt0xAeecq7vxMEfuX8LJ+yXcXnOsrX71l4o1zu1FifmuhlNrqPAefdcE6GyJOXqNtXaJtU0GJ+nQ/vhRgn++/DZeuIZrUU3AJ0DqMz3/EHOGH+n14LX+nN7omd/8oDz57ddaC70BDl6/QFUIVKBosR6tumMFb1dqtxptfjLWb4f1pT57/Mtfb68PPv7l9nqAPHENfL0PLLxEH6Vwiy3QLzmOnfHkR4LxGOL9x6PtSJpMJpPJZDKZTCaTaS/ZD0mTyWQymUwmk8lkMu2l/dDW4FzabRWzKGeKrdA10DGQDDuFw8tCGESGLdgYDq5RKvxmLMLQLa+EAaynwLTgslW9wnb+LRLZ6DdzD46zDZwYWcTz330hx6SzJ8J46F7Fz2Sx9yWcJz3cJqulnnsK/HKN7ekCaFYcYMl4h/IhuLjrR5CBrgVm3IBLYmHWC7i8reEw2cAFq260DV8B0aBrVgI30cgBtRqib1qFZ3RbRRxBRRzxCOgQq/amcPJMgKXM0Y/9nv42RSH7CPGwngmp/sMf5Kp4/kbxWMFBK6YjKCmeu1QbXOgwaiLlcQrEAkgPyNOdfj8Gll2gTycXwrQGH6uP+rneX6XAuIGRDHK1Yw0kzneFhVl8vDcUolWv4aYGl8heXygKKCI3vdFYm06FnEyAb63gWpaioPcoVx99ciqnuxYN9eTp0+11FO6nALpzwbWhc8NmSka7FaXiL6BYdoNi7QVseWOgqoEcKPCecSTkdHKtzx+kwlVPToV1Pf5ERZH7HeLfFCjmnKFPD4V0ORSpb2gVN9YRhPJU7Zwi4RfA0Fq4zFbAdUDr7BaXxrxEnDK9p38+ravKTX7YoK0JxsLiXK7Qi5fChv/xy2/1OjDEDHmqj5xJZ8im1rN5cKnjU42lXobch7bz+PfjuisMHydqoCXssssFjnLsFLiHqyecyUu4kMJAl76rrmnp5orXS41ZFtcmtpYA8ToaKifcpaI2uF6H1ddYn6QIogJ4+RQux8/fyI39w8+UUw7gGM7YzXFs5/BA7p1coyx/UF4Lmn6dg0t2fbWZzwLiLsNYKODAeXiiGDl6KjfyGJjibIV8A4SwRhzdwG37OZDABvljNNY8EBC/6wooXXlPaGsbXNM5+5ZroOAxcwTcwxeKy0tUDSByfTAGCot1BrdkPJjB3pGe7fRUsXsMx2F0o5s18+7/a3xVGGuPsEZeNbrHh5jD24X6roSz/WoCh/41MN4LOD2vUOCe61LgtRnWbh5u55xb71pRN+lHSPYhwvcFxaJH/vQ4LrfG2GgrtdGalQsirSkj5MQMc6uv9fwLYKkL3EOvG+8eyGiBo0Ut4miBIya/+uzfb6//4kj5YMK8iqMcl8ilvMc5Xh8Bw3cNcWy0pQcC7PaX7UiaTCaTyWQymUwmk2kv2Q9Jk8lkMplMJpPJZDLtpb0rT4but2cNvIpFoPuZsIkroGCeKAPcGBtALyzay+qaiQfO0hcyFfCZ41w4Rd7TPaTFZsv/gG5yN8BgSznbHQAtOXqk6wjNNO6hSHtBjAcoGbaqW2ybX93AWRQ8wxr4YYuCv+3+3fNOqpvGXU42SCYMxlzMgq0pnM3gMHs5FRIwuRFy8/hQ+AXjoYIrHx02Y7hQ9oZw00LBcgd3zrZzhAwRXONwX562gECHSrgnlkBUekCnawcEDK6hAfjY5I3wtNevxBd5OvDBCSwBltjeU91sF4JruuLARJoaxGKCNkyAMiTo+CEw3PwAbYF/Z6qATUQ5UCQ4IDdw9EsxBge52iUKm+sAhGRd614G4IkDXPFqFEE+hxNd/SecLB3cQcdA4Eo41PXhNvnoTM99cqqxP8gVJ999p8LQdyvvQru5Rw+ULkJubOHMSYw+OKFFOfJntUZfgCaNGhRuzvRsIyB2y5nGwMUrYHXXcrONu+LLCbDD4wdC+YZ/heLMI7i2PgCyNdSRgQboXw0H0Ra4+BJuoou5MCKkLTeGq3aLnNCLeJSATrd3p7pq3EXn6JwOMf7h6nnxRqhwDrvZf/2pnGpHmD941CFHrC9nio3TEebfWO0+Al3s0O8J4qdtN/fZODh5AvkeHyt5pchvjE3irBFcfonPEaf1QNIC8Mjpldojx9gc5MoPDbD9EjnPuVfuPpQnaos+MWsUBb9ZCzH89jv19V++0tGAgw/l/J7AGbwFdgYS1bUVnLcPlEsbHBtJaTvdHfkYAB0vCt37ADk7HmOewly9RjzWiMH1HO0MjHmGI0QtHN/PHulZH5wo3udzxW+GIbhkgrpDtaF1i3WXy4i847qP/LWE8/tkqriky+3JAMXogVC2QAZ5pCtFMnv6RBhxevCJroHarro5rMbaY45cPl1q7XHxSmuSBFhyQRQZqGqBeCmwBq84ZuH8ydcTOKHSSTpFjk3uCW0NIXLrevOdacS1DdYKnDQixWjdEptGnsK6bczqEq3m/v5nWuvXmGfpoPqK68JLOeqePdzMbYNTJeEcZ3Leok9//1xr6k+fag7F0s095HNjjVRgzVM0OJIAdJqz3UHKfEMnc91PWey/YLUdSZPJZDKZTCaTyWQy7SX7IWkymUwmk8lkMplMpr3kiSH+lL744ovw29/+9h5vx/Sn5L3/mxDCFz/9zp+W9eO/nKwf3w9ZP74fsn58P2T9+H7I+vH9kPXj+6F37UfbkTSZTCaTyWQymUwm017aa0fSe3/unHt+f7dj+v/okxDC2U+/7adl/fgvKuvH90PWj++HrB/fD1k/vh+yfnw/ZP34fuid+nGvH5Imk8lkMplMJpPJZDIZ2moymUwmk8lkMplMpr1kPyRNJpPJZDKZTCaTybSX7IekyWQymUwmk8lkMpn2kv2QNJlMJpPJZDKZTCbTXrIfkiaTyWQymUwmk8lk2kv2Q9JkMplMJpPJZDKZTHvJfkiaTCaTyWQymUwmk2kv2Q9Jk8lkMplMJpPJZDLtJfshaTKZTCaTyWQymUymvfR/Aa5OQ9BEd1Z8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efff6662cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(-1, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: What do you notice about your generated samples and how might you improve this model?\n",
    "When you answer this question, consider the following factors:\n",
    "* The dataset is biased; it is made of \"celebrity\" faces that are mostly white\n",
    "* Model size; larger models have the opportunity to learn more features in a data feature space\n",
    "* Optimization strategy; optimizers and number of epochs affect your final result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** (Write your answer in this cell)\n",
    "- If I can use more faces from my google photos archive, preprocess the faces exactly to this input dataset and use them for training, it will definately improve the model as it is only using white hollywood celebrity faces.\n",
    "- I tried with SGD optimizer first. But it takes a lot of training time so I think Adam is better for this problem.\n",
    "- We can also apply image augmentation for improving the result.\n",
    "- Image resolution is very low. So probably using higher resolution photos will help to improve the model.\n",
    "- Some photos looked unrealistic in the first trial. So I added extra convolutional layer in the discriminator model. Increased batch size and convolultional dimensions for both discriminator and generation. Additionally, I've also added center crop image augmentation and label smoothing in the loss function of discriminator as recomended by the reviewer to reduce overfitting. But I think it is still over fitting. Not sure yet where to improve from here.\n",
    "\n",
    "**Update after review:**\n",
    "- Changed transorm with augmentation as suggested (https://knowledge.udacity.com/questions/838393).\n",
    "- Updated scale function as suggested by the reviewer\n",
    "- Updated weight initialization as suggested by the reviewer.\n",
    "- Added label smoothing as suggested by the reviwer in real loss calculation to reduce overfitting.\n",
    "- The model was not producing good enough result. So I made following changes:\n",
    "  - Replaced relu activation with leaky_relu in Discriminator model as suggested\n",
    "  - Added one extra convolutional layer in Discriminator as result was bad, training was fast and overfitting in less than 10 epochs\n",
    "  - Changed hyperparameters to \"batch_size = 128, d_conv_dim = 64, g_conv_dim = 64, z_size = 128\" from \"batch_size = 64,  d_conv_dim = 32, g_conv_dim = 32, z_size = 64\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"problem_unittests.py\" files in your submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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