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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can comment out after executing\n",
    "#!unzip processed_celeba_small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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/stable/torchvision/datasets.html#imagefolder) wrapper, with a root directory `processed_celeba_small/` and data transformation passed in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
    "    \n",
    "    data_preproc = transforms.Compose([transforms.Resize(image_size), transforms.ToTensor()])\n",
    "    \n",
    "    dataset = datasets.ImageFolder(root=data_dir, transform=data_preproc)\n",
    "    \n",
    "    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
    "    \n",
    "    return dataloader\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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 16\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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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59ltvvF61n2HtzDIko562s1Rz1gLzLx23aHhROCUgagXnqcJ1tJkswIy5/yvcW2O6GAwGg8FgMBgMBoPBYDBcAeyli8FgMBgMBoPBYDAYDAbDFeBS8iLP8ySIzzchWSzISU8ETRtuPe3Yde4ZwvFlbwSZCJxdNjr6ncDl4FbNHHKf0ilBTX4UqEGBS2EMQIP2Y0ij+D0eD9eaQA7C729ASuKXKpeKfPfYUaDuOKTwwsTEqeycN5TqX5alZNk59fB4qvd7vNSYeHqokqL79z+s2nGh8gsRkW+9faNq/+K9/aq9g7L5w03cc1S5bvVVajSdozI1qMAlKK4PdtRR6VO0RUQ++OlPq/aTJ4+rNl0pCtB22126BUC6FijtbQLpw3ii7cenLm3zFE4RksOx67FKng5Rtf42aMFNw0VV/SP8xXHlrWfbi4hImxRstK8jN/3cDY2vX/iG0hxfuXuzavc2VY7W7mpcBEinG3BYGwzc+7+/p5Iwr2SM6DYFqI3PTo6r9uePNNYePH5atQ9Bp50tkK9CN8UXkM7QWYdyhxyuWy8+bVpx/zwv5OT0nGJLGRYdi9od7ZccEpH5Uh2ERFxXuwxU5yXoqxnkOIzBEPLSCDGXcVscmw4C9XtO6a4Pp5kONAQ9X2PZob5CTlBMlQq8k8OVC8dOa0c/hsQtw5ybUrKAVUE4a65jWlEUMpudx8CTJzrGSHPnvWXfJzU3A1K7HQlNsV4r4EqZiz+3fZHrj19ba1B2yRhjTAVBsPb7/NxpM++Scl2Tpv3ohz+q2t/8xjeq9te+9rW15142lOTveZ6E1ZpXxz3vDddzlAq1a86WlB7FkO112/q9YYz5pqX7GrU5z9HBA06amBfakHmNWjoHiYgMN/TvrV04+8G5M4CjUrLUfY2neuxHh7pmiR5r+/5Y88pZXnscgQuKH2hMsboAHbSm8ybLqEvxXsQ95b2UO2Csc4SkNRn1n/1Q5Xz/+B/9w6r98bsqKeriHt7Geuat27qmvrGj6+VtyDkCPA8djVVGfbJ0zyNHDklwwuO59vmzY30Genai7Qm+s8Bct8BcXCJ3HR/r+kdE5Lf/6f9ctV+5rXLwX/uv/lbV9lo6/79YhzVtnUMwPihz/SryIoLzBZ9zR4Ph2s9vQH5/9FxLNMye6fjsoaTIt37+m87xbl7X8hCLU5WvsURJBzkrC/lshfICuB/McY4rUc0xLcXaN7nQ0ROff4WpypguBoPBYDAYDAaDwWAwGAxXAHvpYjAYDAaDwWAwGAwGg8FwBbikvEgkWFGZY1RkJ6W542k7Bzcu9l3iFiU426AadUlfxff9krRrcnBBuUOb3yHVts4GotwohNSItHCP9N8ElPx8PeWXDjb9nl4naVqrraoWXUliIe1fz2m+cJ0RmoNSyuKcdp2VkIzFem8ODz+u2ou5VsV+5cbI2dO33nylar99Fy5FLaW6+R1QZHsq//Da+nkHtNQM994DrbGzq9KkrdY15zyudZXy+HhP+2gG6n4JGmbU0nERgFZMd5NnY604/jzQ/S9Td5genur/8ky36T5XGcrxgUqN7txyq4M3Df5q/IXQbXRbkG1EkFRAtuHVRnsMV4fNjvbZN++qA9F/8s2vV+3XX1VJUR+yR8G2tIcIkQdbocZtq6cxLyJy47bGUoycE4V089D4nOOavoZ+/fRzleR99KGOnwdwDzmauPK8Gai6eUkJZbmuqTKDhvFuPc+TIDzvjx4cOXj907nemwUkRXXHpiSh64x+7uNGUTZAqi0r3ReQETE3eMgHjFi/5l4UQN7Sxe8lG5AcMFOk7NO5Xt8GtKy3QdHexf6PQpd2+zTVPFPgvDIcg5K/wbKZEhGRcwnZ0dF5Lj0+1pxKmjalxZSHFbXYcWRBtEfE3M8Bd5F0iDIgz6E6624o/anLi9z//flSI8K/wJ0yhnuRK01ytz+Ds8UPITW6ASknnYz8oJm/BXriqdskc6sjhQXlHdfZdbtLWpjrRl0d3zt9bW9jXtlq6w4GEedJSJPgRNQBVZ/OHoOuK4XtDPXv9lC/F7VwwoxZaOOHbY33QYzv0BRENDYeTlx5yhzS6XzBcaTfC+FqGnUv9Tjz0uHFfWEGuWjapYvqwwefOP/7J//jP9L/fagOPzf7Ol7fuqlrm6/t71btV/dUmr+He9vGestH3+eZbpu7qc+ZJ+aYWw7grPP4WOUjDw801z6F1OjpoUqHHmKNfJZAllvLd5Qbffef//Oq/Yvf+U7VvveWSh2/YPLYQDCXU+aaOeuOcu33fzYwx8Pdc3dHZfLdvq6Pb13X9e3jJ/er9hLufdu7uu291+85R+vi3UAfMv0QoyFu4Vm9xLMAPqeUvt/T8+NcVdYMTpmbCkjZXqwlRWrrwwvkvT8LzZzdDAaDwWAwGAwGg8FgMBhecthLF4PBYDAYDAaDwWAwGAyGK8Dl+HieSBCc02m6kN8MOkoHmsPlYHIGyUzo0r82QRvqkipEmyJSd0iLAgfId9weQKd2aLN6rnlNclCCxkm6bBQpddOtcFyu/ZwV+ws4HHlOBeWag1Nf78EMVPcMbhtduEP4pbt9U1CUpSQricTJidJJP/j8mX4H3MQY9K/boKGJiLy6r9S1zc3Nqu1DYlKSDk2noJIyM9KhQRsvIOHCtlHbHSp7+ztVewAK7tlE6ZI5qspTUuRDikBZw9aGVoK/tqnxlIcub/PxWKmXU9BuA1byP9LzSNMah65B8ETEX43RGLmhx7FEun3KCv/ufQsLpVxeg8PVz39TXTRu3d6r2i3QawPEl+9DXgQ5ZQg6owfHCS+qp9n1UkS6gQTYb1RoLmq39dgjxN21kcroPhgplfLdn6oTmIjI/cc65qaJ5mcf0ibBdbwIKa9h+qKiLGS+OB9bM0hBJnC4SEET9R1HjBpPmk4ucAwJ6EzjSE1Be8ZuOLWxyn6Gb+WYj/zCvedtxMpWqH3fw1y8INUfnOlorjnghqcxdAfHboue96RwpaydGHK3BPcN1GXfmauksUjTVB6tXMJmU8zLdC+6QFJUj50Mc0CBeOGvXWR5Xyzx4fwRrP0Ol0v1/bjbr5cXEUWxXvLE7zuSasq0Q3edwvnuww/V9e/f/3tdN96E3OEbcDhqFDyRYJU7C/awY1TFdQ5k6LWl3TbkHNc2dLzuD3S/2z2950OsT9oB17RwMupBKgTntghzStxz44Z/cw0UoY9hjOaspSI4DgWQPL3u0SmPDjJu0jic6v05A9V/jvZiirGXNzjpiEo/OAcELJOA554SLiu/93/9O2c/P/nBH1btm1gLfPuOyvHf2Nf2zS2VQm/3dR3Qp9QCS5iyo3HXQomAMK1JK9EdfILaG2mf39rSdcvr13Wtf3SqMqIHz55X7R8+1PZPH6qz3MnCdUxb4nnsnXffqdr/9+//ftW+e+/Nqu1H63NqU8E1/xfWM18CjgwJn7NKSBsSxRtwiNre0WejHGuTJebPvWsaf9evu6UbPHyvh/IcKUs/0HkSOTXBvEwJ9ua2rvdDOLcVNU2c4x75Zdrh5ePGmC4Gg8FgMBgMBoPBYDAYDFcAe+liMBgMBoPBYDAYDAaDwXAFsJcuBoPBYDAYDAaDwWAwGAxXgEtbRocrvahj5YT6CtM59IiOLswVrXY7qh2MabsashYLBGTe+s8DR5IMHTLatIWOAleD5TsWWtDuBzxf6pvRpoaU1o20+qQ9VenqxzqoZbMxVO3l4ydad4Hn5BXNrM1RlqUsV3r2ByutvIjI/c+0hgm7pe2rLu/WjlpBi4hss84G7k0puDfQuxZnrK8wQ1v714NGOEedizTTWhB+5uqFe6ip0B3E+Fzby0TrtYjHekOwdIQusg/Lw05bz+Mscfv9k6dqG/zhU/1fithMFro9baWbhjAKZW/3XI/pl6x1BF0mxySGnufX+0z/+QbsoK/vb1TtAKVNCny/gB2078HeFGJnx+oR5xF6bpr10P8CDTzPNse1urUa9CgDWIaGN9S6MQxp/6wxLCKS5/r3Q1iMTxHrGXP1qh5W09TyRVnKLD2/pgnsknMolANoe3PYGXo1q2bad/N3isLxG9T+oma4B0twDzkkRz0w1jtL+HltrnplV/u4j0CfT/T6slT7kdNNH+0bmNv2UtaE0mPn7Vpth7bm4XeWWpfrDIHBcTjzmhYxijRN5emT83oBtAsvv4Q9ZP07rJ/CuicBa6mgoy4+hre27dZk4biv1a67YL/umoT7xaeORTViGPMsa5YVhRs7XEsdH+uc/93v/i9V+623tL7C3bt3z/f/FWoR/EXCE0/CVX+nqOvnXAfWrjHu2ahWM+7aQO/Zta5us9fT9ibqrfRg49uKUIcFdZaijuaiFmyoQ8ylWL6cb4PaZCFqxXDd7TvLXcQ40leIOGvBSpq1rcpc12ciIj/NUE+J9sOox5ej7tVyomueJmJtdSXaxuPj0yO1RP6j3/++s8kIz2Pffu3Vqn1vS+tjXNvU9tYI1tDoM9bIDFgTCmcS+6zz4+aYFPU10M0SY37sIL5GsDTfxHjYG+j5bWzrHNht6zX8yU8/c449Rt2xyUTnq+9973tV+2/+579atbd2z2uMNKtynQvm5osso3+WTfRFcwTr+bFOYgf1VjZRx2XrmtZF3L99p2o/+PSjqn3vntZRHMDOWURkcTap2sO+1vw5mur4ZjwGHmt1ouYY2hsbWv9ztKE1jB6cau1LERGPhYgumjO/tNX2ehjTxWAwGAwGg8FgMBgMBoPhCmAvXQwGg8FgMBgMBoPBYDAYrgCXs4wWqfhXtD/uwWZsNICVU6oyGd8Hb19E2rBgDUGz5Gsgx0rRoeNyT6QDgabrEMX089B3ZU6kupHyWzrWmzgEJEW05CwvsOp0rLdqdCXaxQ66StXKt5UKdXSs9Kewody3UkpJV/Kiw6OD6nNSjUvY+d69rlT2Ucel2AeQSxS0KxXEFynvkGnkkBfRKiyFpWw2h6QIFL2o3newAab8wC/1cw/UyxTWoYzxiLKVQGUuaaqUyN2BKw+6ta2Uu4+f6xg7W+g2R2O11pst1C6taYijSO7cuiEiIvOp0g4TWKwXGa1Yi7VtEZEhLDNfuamWde2I7561n1PILQJKCR1qNXPOepu9qP5uG5aZlJ152BfPnLINWhPzGC1YjL6QY4mI5MXXxAF44eHH96v250+UrnxwptZ8Lw79ZaQVLxOKspTFyjYxY7+A8+5YAKcwtaxdqudTnrpe2hG1YLXZVTp0XII+DzlSCNlRjO8vS1Cy6bMpIt0Y3P+l7itDtOS0QETcDCDxGCIvtXPt6z6Ot5G4S4Nwqf/8EJT+BBI+qvnKBv+ek2eZHB6ejwfGSE6LcEeWjDFVs5C86D5wPUMraa+8SFLz599Px5m4th9SxDknOsunC6TWpJQ79HTEdorvf8Gu2pFY6zGmsOP+/vdVIvH222+LiMjZmc5nzQM1ocgZlJ3i835NXrQJqfFmRzfqQoIRI+eHjqRIP29hv3EL34GFcxzB5tlz4zekTB8SSs9jLmN5AMjMsCtS/VtYc9/CXFjk7rGXucoJnj3Qtdgs0VzLOTfMGiwvKqUKGe8i+SDWIz/6wQ+q9k/f+Ymzq9c2VT6x29fnigElaNQR0dAZa1kvRqxRRoQ1bpjx2c19QMES2ZHUU60bUKeGZoa4zVFWYb+va7jXrqnU6NPHun4REZnDcrrEoHv3vfer9seffFy1N3fOJTFNW+cQjJUFygssl8t1X/9z9oU/nHui7Q5KPWztqbxoBHvmv/rX/3rVPn7ysGq/du9e1aYcW0Qkhh39aKjPgpND7eMcz34R1kXcF9dtQ0iKrl3Xtf+79x84x84hs6fVdhngeRHxSIntl0VzV0YGg8FgMBgMBoPBYDAYDC8x7KWLwWAwGAwGg8FgMBgMBsMV4JLuRb5ErXO62qivtJ/dXZVFnE6VEkpXol7bLYveBR0yIK/MqR4M2hsozZRz5E61Yjr9gBYJinBRl+hAbeSBTlSkSsnKM1IgQeUu2Ib7RUmZAKr913nroOP5oCWPRno/BXTPkxO30nJTUBYi6eK8D05PVVK0TJUC2EFF+g7oyWHgysHykM4qej+zgNRoSMYiuB2FlPuANn6qMVvCecEv9Zx8z3WBYWneLerUAAAgAElEQVT+jDKAlh6vBdpbjPgo4DBCt5GU0hHQ1votV5o36qlzWAT3m+kYEpGnKjtazNSpp3koId3DuCe1neOT7hri6jMGI71vgw4diOgqAtothivHd1CAxozzoAyIY7vwXPepkE4A4HF63JfjmIbrc/IjtgVNt9XWGNwDBVdEJIWMbwmKJlR1cpaoBPAsfeGM0zDabSmSvXAQQZ+mqV5zvkAbcRO03LmKLhyUCMXITT1oP9uIlRbyVZfV/tsai1twkhjAYaLfUiq1iAiYr/LJfZUPwqBNPMxbMdrO/Iu8dEp5EGKrW6P8diBn6iIeI0orwWLOy8srl18W5Hkhp6ezVZtxj3kJ96co1juNiVzs/OPkGfz0VfLz/AKpEY/h7J9SWndb5hAvp0QIOcTZIl/3lZprBPUDdI50f8vLuBbD+e7CjevJE3U1/O3f/mciInJ8pO5qTcGLSy0wrij5CyDT6EFiP4pdaQ3djIYdur1A8hFARgQtR0zpPvJPgO/7HPdol5EbBSXmFcaBB1mQB0lRANlSiG1zrKsKzHMdSJ72t911zmyi+/rpY12jvTtbL88NvJoes2EoV2OO604iwbzywUcqk5nXZHitTZ1bglKd7dq+zichXe4wXAvEUZ6i7yFlE5SD4LqDTlTn0NhLU0gyLlgnsSwDSy/E2G8r1j5uQyLVh8ORiEg8gRNWoHPq8Vjvx5/++E+r9s9/+xfOz6HB8iKe+xyOjcslZXeXd4RzMgLWOXFP1xE3bqtkJ0O+e/tb39Dvy9+u2ndGKvdZLuHyKiLjsT7nno31eZFuspOJfmcH80iM+XOJtR4dIrc29Pm6FbvrlHSGNSE+T/A8VuLZMUsuL90ypovBYDAYDAaDwWAwGAwGwxXAXroYDAaDwWAwGAwGg8FgMFwBLsUBjqKwoqvfvLVffd7rKS3whPIiyCK6XZcq3W6z4jAq5YPLRMo3LYQ8UBU9fz19l5vm3LZ0qw3nhdKD0gxyElDHKVkouD1dSEBGyj39TuHQdF36Y+EQmEDLhCxriOrNceTSL5uCsiwkWVWWzxKVFPmF3qce6F8DOGO1Oi7V34cDTQEZQAYNQBCAahgp1dIPlWYYgRo9iCElG+v5LeW0ak8Ddc4REceGoAMHogiuJB7o3vOZ7mt2pjKgJWj7rJxN2mzUcSVWna4eIwLtcz7XfR0d6vHyeU0a1SCURSnJqvp6xnuVaDspOPa4sUuVHvTV9akNt7AAcgsP494DHTsImRuQfxyTtPWSIK827kuhtGA9ldj5vlP53/E10s9B++cphZGb4nuQuPQ6oBvjQrqxjh9/dQ8Cv2nv5z0JV/c2Y3xk6x1oAoznKHCvldR9DzTpCDT+IebAASrdt7GvUadftfcGmtdvbynV9saeVv4fDjReRUQmCz33CdwZFoiPdIqq/pCZ3b6lEsN7O+o0IFPdT7fFuHTHTvdQ5/Xep+pCEID+u8R8dtJpWrwosiyTw8ODL3we0bEOMVX8LOmdIwXSpk9pMeVF+JzrCMc1kdJDOjYinv2ajtpxf3Q+p0sRzgP79S9wJvKYl5CLssyVUzr2j3TvwZx9+/adqr1cuQkGwZ+fG18mFKXIiyGagY4eol/6bc0Zm1jnjNru2q6PdU7chVyoA8dG5Bm2A58yDUF7/byVU6JWu6aADn6IKTozsk9TStHoGuc4Y6FfcQ+C0s0Zg5FeUxxqe0nnQkrJm2rvuYL34kY66xY602nfHyE/+TVJDCVoIcYu5cvLqa55vQQxBceiAFIeH/3tY43rPMPk7jpnudBjzM50nsm4NsLay3EZxC0IIbcKIH+LIRFv1yTB7RZLCaAMQabn9Kc//KOq/V/+6q+eX0JxeTealxF0LFouuf53Fshfal8XOU+xJMbmljrunpzqs8cIMqJvfEOlRkcfq3PUo08/dY538ORR1V5MdN3RwtyYYf3fQ7mFdkfb7jsDvYYhvs8SECIiwtgMOO/pVxZzuHsmtbnuS6C5KyODwWAwGAwGg8FgMBgMhpcY9tLFYDAYDAaDwWAwGAwGg+EKcCl5kR/40h+cU3MCUG1ZJZiV6ktwxJapK3FYwFmhEMhBSGu+gC1YggIUgAKUX+AUUArpky6lKqP1jKfnFFzghkNqpI927q2/lTy21Ci+pOdSJkU6b4zqyjxek1CKSLaiHuag59NFAUxBuQ0Hj+2Be19Z1dyHdKhFWYhPGrhumy9QJRs8Od5VqjEyVOIPcvfeR7GecK+LavE4vwTHS1nlHecaR3qtOaluoe4/r0mbKAOgy8gpqZEYX2GtOniTUBSFLGbn55/OtQp7AvpkwZxBGVBNXjTqqFyj09U2Ka4lpH0hPqcEyce7at/h5BdrPw9qUo2Qji/O9qBvM1d462m3DquYuYiSxtKtVh/RCQBtHzRyH9Xn2/55HHqNez9fSr5yZ0rh0sTK+j5yM6n3YS1Pd+joAOkEpUObkG1dh9SIrkYtUOn3dpR2exNyn31I4MKa5GBegqINN7XchxMF5LKbI5Uw7V/brtov5nARkWBHj3HturoA1H1AogM99i5j6l2VFy2wDihyOiY0C0VZynLlShAinxT5eup5DLp93b6IkUS3Q8qTKP2hlJkjzoPUxpEuFlxD4BpqJhUXCi+QmwKun5BPHGmlx3XKeslTnruU6wKyWZ6Hk+JwvGJ1IU3zESlFJFmdNIcI80QH/dhH/h123fVFvwO5ZwdSY8z9XA4WWK+WMZ2FKHOF5AmSaM6TYU3iQ4lQAZks192erJ/3uE5nfHCfAeSyUejGTSdGjkNeWyTO5Krn/hWcWV4qvJBvOeNKcQrZxsP796t2py4hhtSGzrBJiv5Dn7exHo0QF23KjJFcKB8sMVOkS7f/SjxbBXDR4nMMJUUF5mnKgAvIg9qIzx7W+e3IleDTSIl5N4ac++H9T6v2s8NzN8Asa668iM+8OR12LrimulOTd4EE9UI5K2JriLUG5yrKaI8PVBL3e9/7XtVOjl0p7+19dUIa7WFNAqcgyotOTtTlbkiJGvo6RMzReXnQU8m3SO3dwgVyYrbD4PLr4qatpA0Gg8FgMBgMBoPBYDAYGgF76WIwGAwGg8FgMBgMBoPBcAW4lLyoyAs5m76oHM6q+XCHAMNvnujnp6dKQxZxXSA6oPT3uqhIfbG+SJs5aWikF7Jqd3nBd0RI4ON1FJCW+I5zB1yNctLhQGHP9PsFnTMKl36X41y4L1aL5rHD0KXQNQVe6UlQnp87qYw5Kolv9JUS+fbda1V7s+tWl06XkKwVGr4JK7CDet9GhHu5fp5ScoN4IvV1OdPvFDUDIA8U6vlSaZ8JrmmOquHzBH2NYVeGSnWLeqBtltou5lrBW0RkiPGyCZnT80wp/cNQqaGdZdOI2oqyLCVZVcFPQWvNMO7DSK81Yy6qj/VM78PTB0+q9jNQu0ebWoX9+jXKT0CrhyDNcQqipBE0xcJ302xerKcPOzIDh4ePJui1pIN6zGOgkfs1ynWEVDagDKYDOQG3Kb9wCo1AWZZSrOR2vMcFroSSzgDyvzhyqf4t/J0usA3iseVTaqTynW06sSEZbW5Q6qbxW4LGPUtdkc8UlNoJqLbHp2Ocq+bLTVBn+3AyinCtw03NPyM4eklNZpDCfLADqV07pEsENk+bS9P2RMRb9QMpyhwDPq6bsVOXF+UOtRvU5yBa+7mTN6iioKyH6xGsLygB9P26QAznhByZcU2CA5KSX8zxOaXkuDZKnrJa3i0oN3Lo6XBmgdzhxeaLRcNksWVZrQvoKBMHOl5ixE2MZNypuX31wXrv9rW/4gCue9gXHUAZHz4o8o4DjdPmBOP2HUsE0MLDQ8w7LkrMtpzPsObJsMvCkR2562POmtNUjzFHrESIOx+S6kZidV2FM2dr+8ljdXV5/FAd5OLAnZ1p+kUp0AKLikFb56jRpjrmDeCa1eronMFzYqQ6Et2a02iro7HqQaLI/LCAZNxD+2ymctb5ErJyrOFC5JLNlivF7eCezDExtQJMZHOsFc/O1+p0iWsauB6kvChJ1rsXeV6dc0EZO0tyrB/3lMEFmCdHI13bJHBH/AyORR+885OqfWtrwzmLDTr2IpiTOdYaAaRGmIcWc50z6FAa49l5iOTar7kqUy3E6gQpn885x38J99E6jOliMBgMBoPBYDAYDAaDwXAFsJcuBoPBYDAYDAaDwWAwGAxXgEvJi5Isk0dPzysN9zq66R7cF+j0M18otc33XGlMGih17ZMnh1WbNKXbcHDpd+hKQno+K6eDlkdaJCiIubi0Z5JwSeFl9ew0VYrUtJJXiZxNICtJlAJHhhqZwFHo0gDpJEIngChgt9CRSRoJL4yks3NLRESuXdeq6x89/7xqX99Wmtf+njptBLHSIEVEjk6VKvfOhz+s2g8eaQXsNNE+GvX0Xm719Rht3O8YcdONcL9BvQ7FpZFFcJWaQKpECcCi1G0WkELNUu33JF9P7R0NlRpXLt2YjRBU2339Xis8qto3dkATlQbTbsuy4psnrCAOKmQXVOnpkpR3d1eTscq0HnyicThO9XM6dVzb1rx25/atte0e6Likdbuywtq7bdB8eY7YRHInx0EeyUr8oEy2W3Q50++ENR+aNijtu9tKA93bUcrlgyc6lqYram/dBellh+d5EsXn92cJ+mmZwc3DkWPotu3YnRYjcE6jHmR7kB2NIQGc5UqP3YWMpIV8ECUaA4spJBs47rL2m8jTsY7j5yeac84W2sejrm4zOdM8+OSZ9mnSJ4UcEs0OJHthLWYxqflwqdjw4ZSBsTNJanrMJsHzxF+tQ0JnKsYaBrfHUfuU7njLMBZJzcb0I7kztujmQWcQxHBOiQ8kl3CILDM35zvOFo7cmq4MWCdBAlBSLgTOdYm8u4S8l3IAEXHd12S9vIhU7hdSI1Lkm4BSSslW18S1XoB26LgY6v2LIpfmHrZ1Xg9bkNPEcJqJ6Aij22IJ40hPfOSxEkqQnGO99kRAp5kA+YuyEvZdwesTXb+HhW5bisZHmXPwuPIUSfV7syUd+ShFg6zNb+gCWUREPJVywSUqh/PXhz/9oGqPj3Wtd3vgSmv4VJPRURFS/d62SjpakHMEfV1vRyNdE9ChLTvV56EWJaWBu0bu7ahUew5nzRISEK5luX0CN66J6D04g+Q/wT67NReZPsbGMdbkUCc5Yy5aPWd4/l8OHsLF8iLFF+VFBKVGdK/jwy2akCPTCTLFfjJIoj06m9Xy/OmJPldHzpxJKa3gc21nKFvC+ZOauwjrlF5Nltby+cxGByi8y6BjWnz5kh9/OSLMYDAYDAaDwWAwGAwGg+Elg710MRgMBoPBYDAYDAaDwWC4AlxKXpTnhZycTkREpN9V6v1woPS0JwcqFWLh/rSmjenmcAUolKLzk8+fVe0T0KZfu6mONtt9pQSRHOTjHVJwUSXn3JVqZJAFJYXS5sYTuEOM9fPJRNsCulMEqjkLGrNyf40B51QK38A99D1StXC4huqLWr2B3PvOfyYiIvceHVef/+jTp1V7b0tpjUO4f4jnUhbHRxofjz/+s6r94L7SLcfTSdVeOJQ2vX9DSNd2IdHZ39Y2pUlhbaSUoJo/H2sMPR0r/fFgqrS+k7nGwcyJR1Rj9/WcdlAB/NWbbnXvPp1WwMgdDjVYvnZXK9J3wmZJQ+ooVzQ/Oh1EcGVoQWZzOtfxSecGEZHd/RtV++5dlQh9+lhlbodPNSY/fvahfv7gcdWenSr98bXXX63abcQROY/lwqV3Tk9VivL4kR7v2XON4TkkHAGosi3QIUcDjZHBSPPHxqaOn17PpWyT3r6B833z9dtV+z7OaXZ/vLqcZsWQ73vSWjkHTUA5pVSCcjDSnAddl3JKuVGvpfd5b2+3aj98pM4SHzzVHEWZxj7u99mR5sEkR+6aa6wcz9y4eXqqee2zY3UsWoCquyw1/yyXmvvoBLO/odeQg6KdLSFda7lxc3CkMb9Y6BjrwbFi6ZP+2yxpCOF5XkVBziEFLC+SxlBSVLjrC8p8EvRBMdM5Yw7XjiWo4KRdd5AD+m3INrz1upKgRnsOMVH4obZTULB5HinmzTJbL+ssIz2PM0jJKe8VceVMns/5DtIC5vbVZaQNs0zzPK9au3Hmafvr21CRyPHYde6ZJTpHeOjX/oauBa7vYf2IJdMo1L7zA0ikeWwfzjQ/Q1LBOZcyOA+yoDLD1ZYaE9OUUkeN6yXWZwmkaFHo5pzTmV43U6GX6TjysdjOpCZPahBKKat7TRnF5FTnifd/8k7VpvvroDdy9kWpWYkHiATS2qdHOuecnmk+72Dt3Z3q+uLajs51PT6fYI01z9wYPhrrnHE803g+Hus1hZQr4tlsAankdAE5GtUtkIl04LQkIjKAK000peulHiPFvP6gPD9G2rB1zkVIER/z+VdxgfsyyZdOZRftRf8xwjPu9Z29qp2cjZ1t3n/33ard7Wg+4bMw1//XdnVfg74eo421connSEqe9uBWKiKyM9SYfz7VmIX5rPiYi6P48jnHmC4Gg8FgMBgMBoPBYDAYDFcAe+liMBgMBoPBYDAYDAaDwXAFuJS8SEQqV6B+Xytbp6nSGXPQmtptpXydTdyK9h89VAp7C1RY0mifnel+J6Cvvn1XZQLbPUiN4MwRgXa7RPXqQzgOiYgcjJXadAR3k1kCGmys19rrKf1oc0NpfT3Q2ci0KiEv8kqXfldkoPOC+hcHdBGAG0veTOqbl6biPzvv75+7db36/N+iUnoftK5+Vz/Pa9S42XOVefRBpb/RRUVqTylfR4ihxRLUaFIZ50qHPhkrrTEMVQ4QZ67MaQ7HokdPNIboMHIMSRG9JHzQGjstjfcBuMdtXFs6c4/tD/T6Skimrm3AbWfjZtXeKOvV7ZsFbzWWY1Qg74Da7oM3nYD2H4bufZthLP7hB0phPDjR/hu1NGf1Oto3KaQaDz9V56M2ctf+Hc1LMT5Px0qnFhF5DqnS5x+rtOk5zoN0xk4fLkPXVGbZHSjVnOMnSSExOdScdn5NcN9paxze3tPY+fZbr1ft08MfiYhbkb4J8DxPWitNIN3u6AzXgbPQgOOwRlXuYo7pwVUkQgzGHb3/nx9ofBxP9P6/fUP77jpy3yncII5myB+JK1U5XWpuykDLLuCstqA7A75zAulQgLnYu6+xuDzTz0dDnfNERJagjnuentfGQOe9PIRUpSbjbRI8T+XCBWR+KaQQS+T/xVzHdzp3pTWc/9ugRHcxnnpdUPR90N8hS9yEpGh/RynULToiwm0hCF0psodYnyG5PDs80ePlerzhoLu23cI48cD3fnqMe5C4OWeCedCDHCTG/QihiXiR72dnNReklxyeeFV+CX2MN8iDcqztThId9+XEXdtFmfb3AjFUYK0Rf6TrgA24qt3Z1fh444auUa9t4N57rk/aC/iBS5f3IBlMC+2PxVL3NYck8uEzlVZ++kxlJM8nGh+tWM/1+ibm29oyZXKkY+x0AWkT5G4BnX5qDpONw0pWlOO54MlDXR989oG6F0UY3m3f1XZ0kCsijHsMb5ljnXR6qjFVQuLcbUM6uqffubO/r9+Hq9HpFKUXRGQy07+fHWnZiTEcYCln6nTg4IV1bYG+9zGntRCrg57rcrq5qXEfIzdx3d9H/lmuzrWoW142FJQXTWv9cllQIuQ828pFf3Bb/QflRbdvqrz/8Sdunj+F/MxHnPaxZvLwfE8XN8YQv0/HMy/RPPHGK684x352oC6PP3j/Pd0eYZHjOSRNL7/OadZK2mAwGAwGg8FgMBgMBoOhIbCXLgaDwWAwGAwGg8FgMBgMV4BLyYuCIJDhyiUjzZSrxgrkva7SBVugh9JFRkTk8FQpZnTn2L+uFP0paNdJqjRoygbKG1pVexMUy1as75POQNl+/EzptCIi4xlowqjIfroAzXem5xqeKZ3xGVxrYjio9OFw0Y71XDcGLnXTqbDtw2EDzg8F3BAcl4QGIT87ldPv/2sRcft6o63tuIV7ge9MTt3+OnqiNMXJCSQ4VG6BelaATliCIp/CVWKa6Pe7LaVSB5FSFIOai9J8oZTFCeh7S0gAyPrskgLX1XaXThSFnl8LdLYwdZ0ocpxvAXrbZgeyrAO9voMDl/LeJJRlWUnsqHCJUUGfrgpLUOyLzKX+ffKZUnXHidJl5+jbN26oi8+rt5V62EY1/XSKqvyPlY64CRnQ4JrmpWWN9pzy73YP39N+OkWszlOlaC4hgTz+5NOqvb+tUqO7t1TG0q85Gwj2m0O+Qse1V28qffjzG89FRKT1gR6rCQh8Xwa987F8NEH8wxGmh8rz9+4o3XUwcuVFiwRuGRjTy7mOsQKSDa/QmzmGm9YJXKt+/tadqr3hae6LM41Lv3SllUFKmZTG/xy/nSwxX+Slfr6AS9rJVPfrQYoWYBwFLTdmOVddH0LessT9hGShhKTkR9IspEkijx6dS8RO4VR2hrkoh+thD/PYtS3XaW53T50VWpgDpktImbG+GEDe2N5WidcAcsUB5EgdSCtDH/Np7ec0Si+yEz1ehO03ce4xpAUxZIgDSI1Gbbo46H5I0RYRIWPfh7yvhfEXMgGtKOl0cWwGSvFX4y9AoohwbR20t9DXr25tO3vaghtdUuq4Oso17uiIOIYU7ScPVLp/PNb10hu3dS64taf9iK6Wbttda0SRju/lVOP/4FDP4+Ez7af3HkCqjTVZf6Tz3Ktov3VdjzfquvL7T1LIcnO6UCIJwyWQ0pOmoSwKWa6k9I8fflJ9/v3f/T+r9pMHD6p2D/kgDtzHuAxrlRlkOh5uryfoZ3+9E+QIa3LBM9TJoY7vEuuO6dKVUedYC49iyFAzzEV4/pqhfEKJ5y9BaQlBfAbYZy92tWlDzFetrgZ4gee3Lubj5Q9+fH7c2Vdx+nn5kGHtS3kRHXAp0flqWL89j8EcPsT6eGdX5exh6r4bOEPZjiFyxTVs08Zz0wjrkS5KU0SI5QxrXSqhbsPRVETkzbsqbfr84UP9B2R6Y8qMs8s/kzdtVjMYDAaDwWAwGAwGg8FgaATspYvBYDAYDAaDwWAwGAwGwxXgUvKisiwlW9FpDo6UathpwUmE30+VatiN3KrQxVDpjQO4rvzCL3y7av/xD/6kaj8DReojSEz6kDMNIC/qgg7U7uixtnZcGqdMldp0eqDU7o+fPKnaGai5Z2dztCGRAkWv31Va8PaGUp+ub7qOEK/d0HO5tQtKVUeP53mkLzVTXlSKSLqi3BcTpRxeA611K4S8iBTXuUth8+DyVKZ6P8YzJY19cqyU8AJUxsjXA07hKtICdTWEA8fb7XtVu9sH1VJEPj/Q+H8Oh6RDtJegAhdwLOqD8rlb6udtUPF8H05LNWmTBBrnZai0ua22tk8/VgeVg7mOl6ahlFKyVdn9gBR0UBgFTmNRrvHSqw2X3VD7fw73hUdwHHnv80+xL4211zZ1rA4jvf9RqG3Pg2wMsp5l7TyivsaeP9B8Mn0OJzU4rkWBXt8cefTRRPv1nY8+q9qvP1SHsO+8/TXn2HubSr+kdNGDLCuGY8W13fOcFYbNej/viSfxSoITIWxCuK/sb29W7Y0Npb7OCleOlyLWSjh1gOEtX7uh93wAF70j9NF1SM7u3X1V9znTPBG1ddz6Nac9OVDq63QOxwnMugXo9nSjIQ2Wc+bmlsbpzZt6DXf2VaIm4jpZtBELI0hVAqH7X3OdRBbLhbz3/vsiIpJARlQWej+7GJN7A0icuy7NPYIm8vBY54wn6MslpNchZGNboE3HG3CsgzQgxrziyI7aNec2yN8iumLNdV9HcLVaol+ZauNA8+vNTV1XlVj/xKE7V/bauo2P+Ayx+gwhw34RqZ7fLLmIJ6WEwers4aDYQrsP54sArkEHp+oaIyJy/1DH9xOsM49I3ce6dKOn93wT93sGTcnBEx2fHThp7m7DVavtPhL4cKdpYZ0fwFXk6aHmqQ+e6TGOMt22O4Nj1nPNiU/v6+ffeUPHkYhIkmjOonsIZfYJXLq65QUWKg1AWRayXLlZHTzTUgoPf6Iui4uZ3tv+EPKZyO0zOg0eH2meebxUWZAPqaMgl+2MdB4UzFfxpuaiUzhRtfHMtayVkCjRaQvI4OkSe5BoTn38/NOq3accHzKWjQ2ds0fbcK51KzcIFPzSDeEE5uvx8hOV4Y3/5HfPP5u5Eqmmgi5MCe7xV0HptMu17QJSJa4UIzy7dCAnHcJts5W7a42d6yrJ7WKtMoSr5PamytpaHbx/wHmUOcsO6P7pcEqXURGRCHMOpaADnMd4AUn2BRKrn4VmraQNBoPBYDAYDAaDwWAwGBoCe+liMBgMBoPBYDAYDAaDwXAFsJcuBoPBYDAYDAaDwWAwGAxXgMvVdBGRbGU9OYcOfYmaChl0fcuZasnuXlcbUhERH3URjo5h3/pc9Yx96H/neD0U4PPZTGu9RC21/ez0tHaBF6l+dcN3L/lkofrSk7Hq+W6/clfP/d7rVfv3fv8/VO3TmVrQ+gF1/KrPnU61HZWqVRMRubuvevo+NLk91ATIlrQ7bZbG+QVyKWWyssQuT/QevwZrsN6G1ruJO3ov9kNX75e/pvGV+6rNmzxUfeZwjloVuGc+rFzDNupy4LZuorbD9Ztq6zqsWTp+8kDjJvb12B3YlEXY8RI1WiiSp5W6D9vN7YGex/ZQY7l+7ixzEnG/sLPNMreeUtNQaUdxDzPUV0hT1ibRsb7Vc+srfOvea1X7WqJ65WsT1Wj2oFu/s6Pf2YC9Z7nU728hr/U2NEaijsZzULj3f4hU2IUN8Owjrecxh/0lpKly65punMFq7/FH71ftJSxoF8iPIiL5UHW1LBVUwkq5hN1gf6WjpU68CSjKQs5WNqohrC+7XdgLDjX/PEeNp7m42nS4LUsI98oRfrPYRf0NlOSSvW0du9+8qxbkt29gLkAOaEfQ15/oOYmIbMESPkR9og2Bxr4AACAASURBVI+eq1Y/QUxEsFbd7Wn9jbdv6jz59bc0x72Kmi6jnmubXcJqPMB4K1GfauZpoB5nbtw1CUVeyKKyhddrYj2SNvTl1KpL6RZwSqD/np9pf3rQgg9gpVmithLF9KyDIejjwR3ts3Zfc06/VoMsQy262ULjhVaaw55unyNnsWZfjmMvF9rHGZJJq+WusUpfz4XXR1tl2pfGqzWa///a0vT/XwS+Lxvt83GGISIRltndUMdhD4kiqc3Rz0+0v5aoGxai1kULds6/8C1do8aZrs0fvP9x1T7I9Xgx8nl3oPG72XLrorR7eu4FarQ8eqrx+NkTPddFiXpDuL63v35bzxv21s8+U0P554eaQ0VEihL1QnBNSDkSYJ70BDe9aSg9CYvzexejxoq30JzhjCqsM9uxW7+p79RHQo0LfN5HfckINWGGrNGC8T051fhshbrPLNHcNT1z56sE/eyj3l2M7e9u6Ty4hZwVo36lh7mnRPwHqMU4DN21Xhc22hHqKHlIqixTd3v//Dzi6FKPxC8taNvMmi6s9fKFciTe+n+5NV10+4L/wTj0cewc9soF1pUDzFWD2j3326xZpn23iWf6DizCeU455t8CNYUyzDsZ1rqLhZ6fiMgYtWrbqGd1lmqO88r1c+OXRbNW0gaDwWAwGAwGg8FgMBgMDYG9dDEYDAaDwWAwGAwGg8FguAJcikvliSf+iqqVwor3GayWO6B/LWDHfHffpeHc2Ffq/myklEs/VDrR7j2lPkewhWq3QJMEpbANWYojLwr1PLyWK9XojCHfAS1qB5ZUHqxVSa3rd5R+1AM1N4Md7air3/nG22oTKiLy5j2lXJJaV8yVykQ2WFE004Yzz3M5Gp/Ttkbg6n/nG29X7eU1vd8hqGPBpks53YB16T64/stUaWL9QG8aZXAL2BaSsjuAfOfVOyrfuPHq3aod9dTWXERkY/Md/d6OnmP7TKl8pLS1cN4BqZZotzb1OzvY586Gew8WYKB3MF582NlmtHrcUpu9RmJF6y9AC8ww7nNSG0FL7bdciv2NLaXC3hpof96mzfdSY6qNPusjn2xtqTyD1nedPuyYQZ0nJVxEZAu2h18L9BhL+NodjZXa60ESyeMJ7sGdvn5nf0PP4+bulnPsfk+PFwqon7let4f7/OLbzSL6i4jnVRTs0bbeMz/QvjhNQbvFvaQNqUhNXoR8HIJmSvXVNmyDd3C8NyDr2RhCypHBKj7Xc23XfhNpI86HXZ0z92D7fAI5Swsn9equ0rjfuqPz6vVbGouklrcCt8eXkKnNMS5oJd1ZQvZSs2JsEjwpxa/iQe8DZ99OpH3chbworg2UyNOxdGtP+yloaf8FoL/TJTnwKDPWfvVhBR5Bcsm+GI40vkREMsTODeSEfle/x7HRgQdrC3LnLizXl5mex+dPnunBDl2r8wBrpjTVdotSAdqer/QjDXOMFt/3pLeixi8Wmk+iQsfFFuy0X4XkLwrc8fJKT2NlCunWHGPa8zV/72ONG0B27e+o5LXEvef6PSthueq7ssIQMg3qelqQ09+AfXGX9uDIo/uJPiO0B5C0/aLKx/db7jrn4TO9piSHdTvzbq73LRdX2tckeCLiv4h7ylb72h+jGBORr/cwrsVOkOp9uL7JMgs6pttdPCtBKomhKhj2ki70OSlHJkwoj685ds8xZwwGKCUA+YiXaF67g3W4l+l+Q1/PewqtdUlJXujOlX6scUvr+Rhr8h08Lw7bzZRRXwTKi+bouwzy+9CvPU+y/xwLaEjULpAXUeZXQLK6PNMYKFM9dh/xF3TdnBOhLEC7w7kVuY/PAjnWbljTpQme/SCLXSy0fXKsluoi7jw7RMmPE8g9ee5peflJ6i9HhBkMBoPBYDAYDAaDwWAwvGSwly4Gg8FgMBgMBoPBYDAYDFeAS8mLirKQZOWmA3ahHB4pRWdjQ2UipGptdFwK0Svb+r0SDg8B6PZBW2k8LdD756ANzc+UytoGXbWELscDBSiuVfq+ua+0618svl61J5ClTJ4/rNr7Iz2n7Y7StFtwlNkYwH3nmtK371xzHXA2HJodqJSg3CWgQpUNZU/GcUtu3zh3jpmAhjwErSyBZCxDlevAp5xCZHNX3RriEm5EMMvwlu9W7ZMSFbPR992O7rc/0M9v7uv+h3sqNZqKG78x4+C6UrTbicbaYq4xlIO2FtIZAnzODqikA0hhWjUnisWZ7ncXVcD95zoWUlB7BzdVytdEvDBFIbWxxH3jsChBeey03fTWQf/vYSzu3dTPJ6BD0hqK0sXeADRoOnBAW5DhrOpvtknX39+BrO5tdVd69lxdz+ZzpYe2UOm919MYGXQ1VgcYS51avitAL80gpSIdlTThFwZcDWP6i3ieBKv8mqMD8oIOF3qhhedwa+u70jZozJlTNV/3ex0V7TsRYm6IuRG0fbBjJUBsdNpu3212dJt2oPPhLvJojjmXFPLdLZWU7G3peURILTGujW5FIiJ5grgBfT1BO4cTl581LmJqWF0LLsNHPqAjxgZkhfubrny5jxzUgUS6hRgJMaZD9F+OeTBNVIrCvulgrA9berKjjhs7QaHHi2+ptDJFn9FRKUbc9hB3MeSsM2wbIt8loLOLiPj4M8V9iyHRakH68CLWKK9qAjzxJFhJL3wf7isCySok0dt9vX/9gXutKWItoTwC9ymKNP/jtkqB9W7/BtbNcBP1Az2/iHkwd+OGUhIfeebGq7quDQOsO+Ac2YPbWn+o8dvf0PVLFHMN5zo4PXqq30syDSKu52GYJlnzZqkKnudJtMoJI7h6/sff1GeS8Ym6ZH54qve8TFzXpjbcw3zILSJtyg7c9oZt7RvmoiWcZw4TPZ7f4nMWrqH2fFJCl5sil2309dgRVkedEMee02FGXZG6CPQCc+ipuLFDyUob13Qbst5f/tbP6bFXzqENM0wTEXV+owMcJXgLSI5zyMjrSiqfek4aE13gZcSvc82YYb06O1E3oBLy41i4TnHlcTFkPVRAFUvIwfG8vEBpCV5rhu8vIG2a4vyWc9dlsdfm2k3jZg/rpwxx+uTQlSd9GTRrVjMYDAaDwWAwGAwGg8FgaAjspYvBYDAYDAaDwWAwGAwGwxXgku5FhQT+OWUnBMXsaDqp2sfadCRFo75Lux3C1aeDKu58D5SDXpoUSiGajZV6zwr4jrwIdCLScb3A5cBtDPQ8vn7vZtU+PdULSRZKx6aVRYCq4XQoiSLlXbXQ7kY12nqh9KflUmlOi7nS6RZLpeV5Uqs23RDE7Y688va3RERk/DpcMI6fV+0g0XtB+VhUo07GuYbsxgakRj+PCvxwDzk4eFC1W3AJ8UCNLkD/3b6ujlLxpko20oUbN6ORxsQxqIwdOEj4I6WJZqDAJalS/1ohHLDgUtSGnK6scR5JEdzAsbOuHiPcU+pxuulKo5oET0SCF+4N5LIyT5AeDZqjF7jprYQjSxtU/z6kXNv7Sr0PMN5KUJqXCaj0oM2S+u3h/MLauCUNNAJV8faOUhi3cH5TuACE4HF20PcxnJpiuFcUcDoREVks9dx5TQVuXIF8GaxkM57UrAledngisqL4UwobgVNLlwgPDiFFnWOMv0PMSR1w2zPkcjrCjCBLa490fHs4qRJzZragLMG953QUIjV4hDjwEE+kgXdjbFBibkzg6LYEHTxx42Y6UUruFK6EpxPNOWenSu0N8mbOVSIipSdSVv18wRiBRHQTjmJfkBD3IPWD1IhuNR6lbZD55Tm/o2ukNNWc42OsjyBD7cVu7uNfXfwvCjXPcAxQy+whzulk0VloHE2Qr466rhw2TzR/JZQXQT4eYjwUqzj3G2ZflGWFHByfX2sRXiD1gjwr6mB81uboYKD3M4FTjZ9Teo3YZEJAf0UD7esu5DtlonEWU7eeuusc9kCE9fzOdb2Ofkdjcz7WZEbJagvSt34M5xLEn0TusReY7hPMSYxBj7LYoLmOaaXnSbEaG0PIPwu4kL1xTZ2eJlwK1eZ4uqH1IaOnpIMuRR3KYTFfPVvAMQrHo/tm5MN9M3IdahNIUgO0oxHc79D/yVTnmAJr9eFQ5fF+Wz+fYO5xNMQi0sZ+d+As+8Yruq9vvqlusqezc/nUXx55kY6Lo6PDqn14qO1+l8/dIiEkpRHyVOBh9sAahHJUD8802VifX7NTbQvlQXCR8mK37wKce0H5MuRuKco1LNFOITtKl3Ql1by5WGqcBbVlCiXW25D5hTjfU6yNwvLy62JjuhgMBoPBYDAYDAaDwWAwXAHspYvBYDAYDAaDwWAwGAwGwxXgUvIi3/elu5Ju5Al5/Eq9mYG6c+eaUvW7kF2IiMTg9bRa+B8okwvQkbI5qhKjOv6oo5T8EPSqFJQ7D44dkruuDK0Q9O8WKir39Zwy3KUSFGPS8x2HC1B+eYODzK2wnYIulSxIl1Ja1DylHKCZ78i8KJRwRbseiVK2PFT4n07HVbuANIxVqkVEStC/xIdEDY5RN195pWoPe/qd2TOl1j19pNImAT2/e1sphzLS2GoJNAAiMhho9f52pPTFZKJxN6BDxUC/UyLeY1TLFlxbgWrsxcKlj+ZwRepC1ubfVieufEepgyeFew+bBE88CV/kipL0SVCrkYoKjJGylt5y4f3Fvny4h4S4nwUkJ9l6ymOAGIyRS0g6rLPkWek9xDlFiJdeC3TzTY1DEaVJFnAMyXE/JCMt2KU/ljkcdyAtyUC/ZNX3Ijtvl1+BRvkXCd/zpLuS13iQBPkXtB2qbN01BZfuI9EHmPeOQXHNkU+27qhcMdjV3OdjLsifH1Tts1O42E3cnEMnO8pQAjqgIYZi5JkQ/PBspudaYohkoNByDhIRmc/07xwSW8pqe6CmD3tw+GoYPPEqp6Ki5HxPlxcdLy3IdbY23Ove7EJeDHmRI6sA6DrBmGS+S0BvDkADH45UStCurbcCHw46rfUS6QK5gY4VgcPBhpwj1Tm7h7lrb+g6Di4gmTs40zjyMceFkMUF8fm5N01elJeFjKs1HdyHSki4kO9j0PY7NWlMD5KIoIdBCjuPEu3c03hMMqwlMW6X6NPcY76jXKdG9WccoI8iTE9tXF+nrX1fYN3hzHmQknmhxkZR6nmLiIwh4Uswj3kYI3Q3aUXu+rpR8ERemHG2uiovSrE2GcGp7FXIGJNT1y0sQ37u9XUduIl1BKXQPchh6dBWjLWflrjnJebHCJIib1CTNMZ6jCXlqjjG5rbKffK+5qhkoZJEzm8zlGEoURYgS9y+D+GC+/qb96r2XUh8BY6DkuUvdtpAnN+fi+Sv7737XtX+nX/1r6v2sO/KizrorzaeS9qQgbYhMeRa4/pAY6s90zEdQlaWQq6W4d7nrZr8voA8FW6TLBNSQtZfwqGrpBVkrueRorxDjrxEN08RkSHcrfp4ZgvxfP4Ibs3hV0g5zXyKNxgMBoPBYDAYDAaDwWB4yWEvXQwGg8FgMBgMBoPBYDAYrgCXkhfFcSy375zLL44g1Tg4URp0DopqvwdaUujSJwPQGH20U2H1flCiWb0cNKoIDiWk5hagJRVwawgy9z1TGKNiOrcX8oZAhWKld9y+KKKTkdKlPOyHEiIRkSVo/K4jChwl4FSQN5Q9mRa5PFueO05tzODAAdox+zHghU5din0egsaPvsxAh1+cKd0yPdLvHz58VrXPIFd78+tvVu1uT+lzxVhjXCaowi0i21sqL0rf0u0/e++jqn30VI+3sQ1qJ90jEHMhqKQp7sHJzI2bdKZUuY22UgSDbaXDPWzrdyZLl37aKHjq1OI5zmaUF1FftN5tRMSl6GcYbwHkW0VECZJiNqX7lG47Qhy0QMlMIJHLPdeVIaMVQKm5wpW+IIeAZV84biqsHg+adr5eCiUikmPM5QtIimZ6D5KZxktF6WygvKjzQsKB+8fs78qIKDW6+LeIgvNTAIcAOEwVkIbt31GpY4vOZsjrCeJmAFlOGboSkadPn1Ttydmpfg/s3AiORVEM9xu6fFBKhfDIEStJXdKIeKabTbtNeZw6rgVFc3/P8T2volovE8phFCWyQw4ac6vtutB0IW8dwFWPMkauW3w4GeVYB8yQ81ux7me0qVKEAWQ9dEUTEZlNz9a2wwBON4jPCMfwIYXKELdz0Lq7cCzqtnQsiIiMIJUZzzSOzsYaw5s7mkf9F5KpZqmLREQVsAXGSEqXM6wfS+SSqO3KzVpYh4QDjZXA1/vsw7EozyFJn3MNDdcOjGHKAkOM4cJ3qf45+p5OWb63Xr4fBTg/rE0o/S/hxlR4et6UuIqIHMHNJs/0vvl0A/R4Dxq8zpFSxFvdRw/OlV2dD0K4O/Wwljk7cfeEbhYapsaQ67G8Q4C+OTnT9fYEZRw8zCUBZKsjrGuTwu2/0bGO6Sn6co71aI48GiM/BiHXMBprseMECblc4M43Oa671+/jcz3eGHP2ciVPapqMWkTXuZRjUl5EWXKJcXTw5Kmzny76dT7Ve8O5IEFfbMBhtf3Wt6r2XgjZ0lSfXQrMYbOlzkFF6cZNd6T7dZ7jISnKcR7McVyn0IHPzV3ax/2hK7HqQz6V4BlhAandZh8y3vDyjmnNXRkZDAaDwWAwGAwGg8FgMLzEsJcuBoPBYDAYDAaDwWAwGAxXgEvJizzPlzA4p9/EoBKHoEGzuvQcMo8scasEO1Qh2AM59PmSVFul+nigZbZQhdsDvSoHZSlJlV7VKl06UL50z2vd+TkV+0HFY3XuTo/3QylwC1D00po+KKWDBRUHlFHg8yR1aVhNwTJfykfHn4mIyGuxVisP6EQ0J61P22dTlzuZk8aPbWbox9MzpZ6dPlQK3eOHD6v21g11+tmA68bRB1rpu1i+r6c3duVFM9Brw75uH8ba98+P9dglePybcJ0pp3oNcVtjSEAPn6Hqt4gIGJISQBKQgk53BlreybK57kXnRP7zC45AJaZEh/R+n2Mnc6U1lPNRguahOn4eaxxNQcuc0DFqAHphD5KBGM5rdJrJ3ftP14453GpS9Hmvp8cIggsojK5tk7aRV4rEzRnZXO9BCmeLdI6K7ogpr0pADaPdllJxjDlfUBbiGnWsd6I730a3z+gYBdkpTdbKOaisyEUl3OpIqR2AorpxbR/fce95gjg/PlXnGErZEpxIDGeJMKLbINp0s0K7/msMjWQijJc+XP6omvOyhsUL4HmetFcS0Jxuh3B3iqKQG2jbr8uXdW4I0M/cnFTwDPPYGC4Jc4zbjS11Lhli3cE2JYYiIktKshHP4wN18YshORnB6SSEYipkGGH+DiE/CGuU6x4cnMJQ5QsLSHYnkDXsbp7nPq9xIeSJ763cPTk/YTTN5zrul0uNp6wmhRXIXKkRyWCXEWA+LOaQ6M8gTX6ieSKFfKfXp+se8kTpzpkFZYJwW1qMdR12cl9lj3FPJSW9XTjFtLBGolwW0v9JbS3+EOdeYE4LfThuQWJbNtilUUqp9MweJF4hJINtyBq2ML6fHrhrUzrPlUvMd3A5ykO914eF3ufDM92XBxl8B043rYG2I5SQSJeuDN7Dc1AXa+wF5MvPDnWNvLupsUNZVIHnN8pKErrZBBeXr2AFCbroZpjUks75NRV+83gI9fWKiDhOn8zrX39TSyEEdSkVnnmnyMc5bslkrLFya0efoba6GhOzx1qWYXGs3/ex5kkRoyfH7hp1C67HFPlT6st1DufPEnN0Uax/Z8DyDnFUewWCdZIf6Hn18SwxwLV24/UuhD8LzYswg8FgMBgMBoPBYDAYDIYGwF66GAwGg8FgMBgMBoPBYDBcAS4lL1oulvLJez8VEZEcW7JSdJErpQxsfhknLvVvnMOFZqmUniDRnS1TpWOnoCGyMn8bEh+/JNUQkoEUdP7MPY8Ukp3sAklRBLpsHJKWic9B9fRAd8qxz6ysu5jgb0pGQOMsPVDYS1ee1BQkRSGfL8/pakcLpaW2Z3r9bVEO85u499605tzzXOVGS1B155B/zE+PqvZTuGzNwMOLQe393X/7e7pPUBZvgeY2G7tuHu8+UUrtHLR/VrPOfaWhJWOlVC5AYVyClt2agjIHaUBWk3ZkKOA+bek9GPvqBjFO4IxVruMfNgWeeCsnHx8V6lmFPQU1ewnK6Gnm0pWP50qZ3DqFMwwrpGMcT5ErArhLTCdK+3/+VDuj3dL+fvpYnauWM9fNY3tjVLXJZk1xHrO50nz7qL7fgsMVjEckL6hD1PP2Ejfn+JQbIUF7kJaUyD/Zyo2ibNj7+bIs4cCDfMKxRF4u6MhejWJcULYKbV8O/cMc9++TTz+r2h9A/trrQkaLvN4NIFP14RSQu303hfud5zgW6R9UAyzhKiLIBz71iZBLFXRWqVGPfdBwI45DnGPJz6PGaUMq+J4nnRUle4G1Q4I1Cx0KuSbwapY7MSSHEeIqyeGGBne66YnmijFcISPIM87gpHYC2SvdjuYzV3Lw9Onjqt0GxZ7SzFO4CXFd1OtD9oqxkC64hkPOCVzKtd+GVBZSy0NIrxNoToad8+8XDVvveCLywiQjhDtMhpgYL3RcTM5Al5+765wELk9hC7kJ4z6n8w9i8/RI10jzsX7eHUAn5iMnYk6RmkzDw/o1hLYsQO6bHmo/Tk+4kIUcbxNSKLglBZBG3j921+bvPNOxkNHhBKklLjQ2XWeb5iFfnT9dDNtDXYP6fZVrbWFs9LsYnyKSzzRvJHO4hQXaT7GvOSBFUAVwnDo41rXzJNOcM8a6imvLJHNlIieQR/bgahViWuPa6AzzUg/lKyjVc56ZfDbd+caRyUEimmOCTDDX3j85z5dJUy1iRRy3t9KRA2tfX99V+fLGUNeVIiIzyL4CSHBmkEV3sN8AEv3kQJ+5kuODqt2mtJv9CDn79Mwtn7CEc1KAdUfCNT9l0Skdjng8lCphsCBU/PqjUQB3XchnB3Dn293U9futfUihfixfCs1aSRsMBoPBYDAYDAaDwWAwNAT20sVgMBgMBoPBYDAYDAaD4QpwKXlRURQyW8k4ZqDeg4HrOD08OVHK0QdP3ENFLd3oZqJVuUNQ65Z0igB93gd1swQ/iLRwj5XdSSGqUVYzUJNyOCxloD0GgZ57t6eUrBznkSd6PB+VxemYktbch+gW4ZDjeB2oZJ57652WXnbkUsrpinp4nEOmg+rSQ9C1d0ENjBeurKeE40qyQLXtiVLdjuEI83ip+z2NlbL42Ylu+/77H1XtCajeb9y7XbXDzOWh/fgTlRBM4Aqwu6XUszvXNK590O0LUIm7oMMtEZuLCBXb4Y4kIjLtgfIe6/fOIFmYgmobxi79tHGobhGdzfB/UJ0L6PTOQJcUEXl2oHTZLUghkjlpzLqvEFXK+3AOmIJu+fDjz/XY6MuDp0qxnNSo/q/culW1X7v7StXuocL/ZKJU/8kMUoSWUmL5xhzmWOKB/lvUKL8p8hGlBSXzDCjmZ6t91c01XnbkeSFnK3cUz5kj9DueQzlFO3B/i3DmGFBOS0gJp4jBHz3R3BAH2he3d9R15vCpusZMjjUf+JiSo1q+3x5qfNzY030NBkofLjzOkxgXJaRyoOqz3x3abVCj6oOqm1FBQLkJgiRvWsAAnudVjg+tGPcQ64PxVHPLKaStdfci5qZlpv2cJZBknIFiD7p9DGeQNtYddLN7DNnQA0gHpzN33pzP9Xg7WxtV+zriqMQcdwLXiQXyHeneKWSMc8iGKU8UEZlgDn9ypHltBolvCanMcev8XPOGUf1LTyRfSXBCjIsCY2GKe/NorNe8feBS7DtD7a92F+58WBfQiY2uV0mi+/LwnRgyopJudTFkpgP3nnuOMwio93Qjgkzh2VOdY7OnGu8trOVbIzidLHT/f/RjleSKiDw+otsbzr1AbvJxvnlzZdSl50nhfVEe5WHcF7GuQQaIqa3hhrPNNNX1xhGkRnmga1MY2kje0fnjFFKcQ7gYLlI4vkDSdTqFU2vq5nwv1vOaIXduIq/FkG34kOJSShv6XN+hhATLBWBbEZEED6Wcv0vI9meYa//so/M5e75082Yz8MW5lp/Q5Smle2NYizc8g3VGGmvlXO/TCOuCBx/qOufZO+r62oLT3qijY53zyMMnOm/Nczf33XlFn7t6Izxv4zpYCoTP6qVjoci9Inchbr6QMSAvwmO/xFgr7m7rnPnma8yX/0d9b2thTBeDwWAwGAwGg8FgMBgMhiuAvXQxGAwGg8FgMBgMBoPBYLgCXEpeFASBDFfOG+MnT/UfoB63QFE6nimV9AcfKc1NREQKpSBFN29U7c7/w96b/cqapeld7zfEHLHn4cxDDpU1dld1V7e7abdojwIJZIEAIS4AIYTgwgiBkBD/AjdwA0jGlkHC8gUe2gNu28Lu7urBru6q6qw5M6tyOHnGPe8dc8Q3cLHjfM9vRZ3T5ClriwzrfW5yZZyIb1jrXe9a37ef5306qrAegeY9Ae2tjgrbpDQb6P0J2hnorRML6WMJKH1NOMlQqjSGg84sE+22DRlRUtP1RTg3qV3TJeraHPKYAseao81q3dmKVfN/jqg0ixfU1Cno7BOyREF7G0/gVBWqIyzjuEIewdr/R6Zx/EFfVMujHBRohM1ZpmlwPtAJn739nr6fhkS0CaiXGabRAA5ER09B1WyL/vjatihzt+HQleIcGWh80bKLSarfnNY1rzLKbHLRNjv1kH66SoiiyGoVdRSVyUmTpDsNqKT9JXnR42eiL+83NB5TSANZIX2nprzUSJCXIPGZ9UWnHl8o1vY66vP7t18LrmN9Z7Nqt7fUrtXg3IbrGJ5KppkjF9FFhvmjpGvTEvN0PielH7TbOmi3oP1/fPq8qn8Yg592lGVpswXtNP6JEvXPv4M25HiBm4eZJTX1c4o+a/C4jKdYOeQIUp43dkVLjY/lMDI6kRPaWkcU8O0dUXzNzG7fkvPAJnJIHXkjnyk+CsgzWO1/jjWFsrSSZNulPivoDIh/g6tWIwAAIABJREFUQthZEcj8VldeFMeRtduX8yFwSci1royQt7/1/e9X7S++rpxhZra3ea1qj2daf3JIQIaQC5UNULn3d6t2oyVKfxtSkgmkSWcj5YlaPXShuXn7M1V7E/KiNqQFSVO/OftQuWx4qlitQV7EjDCn3Hxpn/Kd935UtQ9Odb2UqGSQrpwtXAlXT6IWVe53Nexzylw5YI78McG+4dmh9spmZnU4nUVt5eYm5i5CyGYjUOZBkU+buo5hH/E3h/MR1otmM5Qc1FPtWUtISajq2d7eqtoTuC6dHEhiG891TRszraUfPtP3f/uPHgXnHmGvFzHaAjdGSK8sjPmVQmmVgynzaGt3v2pPO1o/nnzv3ap9BCmgmVm3pX6YIvZG6LcDrD9HmLtnyEVNrIlrTa1FxQR7E8gCp0tukUldQdIfap86QImBYzhKXt/UXmgbUutGir0erDsbTcVRloTyogHkmyxN8fGRHJWeHmg/+M+++87l/YxDqcsq4Ln6KtjPIIZO+4qPQ5T8qDfDPjs7U99kmFenfY3RHDnrne9o3Tv4/jtVe3iofo1QQoKSZe7Tr9/UvsbMzLDmFi+RDr1MahQFNBJIioJPkT+icJ/Da0yxh8lxHxtwE420RH9iONPF4XA4HA6Hw+FwOBwOh+MK4C9dHA6Hw+FwOBwOh8PhcDiuAP7SxeFwOBwOh8PhcDgcDofjCvBKNV3KsrRsof+j1TLtVOuoM3ACq9SzEatumP3okSwz37guC9Xb+zerdgIt3sOHD6v2cV/as0ZDWmfWXhmeSbOY5dIdpj3pAM3MWrAsS2E7Via0q5bOawRd7MkQ+jjURDDowgrcw3QS9kE2g1UfLVtRT2bO32eraRkdWWSNhdgugvaSer8a9LsxavBES2VscsTXHLUMqJV+/1gaxI+OoEeELex2V3WBbmwqfnfrsHpEfYR8qc6DQWvYh8Z+hOu46GuO/LAPS0e86+xtKv42UpwDevbaPKynkc+lV5021KamOYZGPo1XWOtsl3nHzCwr6IsMi3baAKP/Z0s1AY5Qc2UAC8OkgbmHIhXjgfS9J8+ki01SWB6W0HfiQhqI0+1WKPzsren/S+j154mCPVnXvc5hHz2+UF5rI+fQopRWruVSWQTWLjFYBtIOlLUGnl5czgHWmVoFxFFk7cZl3FO1WxYvqZ3FWFmqSRFBC88swLIZ1Ed3kOMy2PjOoK/fvS59fgOf722pPkKnE65V69trVTtuwLoaa0TNdO458miKsR5j7TbqoTGn8izsgxyBxPo+c7QzXEexHHgrhDRNbGvzsrZOkb3YgvJirPvj3ubwPKzNYYn2NnNDfQXUUZigTsfGhuqtrKFdSxULZU3fX6tr/7PR0joWLS2czS5tWhXFSV1xUV/Tmtjc0LHOP9Y9RVjrUtSDaaCOxPgkrItwiL1YlCpnRchFM8yTSePy/laugl1ZWvzcNreG+Id1O/eAaaq1I22E+wtakqNsmw3G2JeOWDcGv8f+s45aU2WpfNBAvlrb0XVMZ2H8Zn2NXWsDOQvXm3Z0f5uIofmFYvb0ofbsp+eKga+9q/t871lY83DKtQp5KsFeOWnqOmovqd21CohKs/j5HpiWtk0836zvVe1f/+b/VbV3w2XCfvULqpHRQ22PAfL7h2d6jjkeqN+7DR1sF/viDdT62VnDCbF3Ksfh800Eq+cI689RH/tXPAM9PlPeeOu67vUzu+qDGHvkFu3ri6XaHAXqZZbKWd9+5/2q/fXvfK9qN7cv+6yMVi+Gntc0DJZc7Fkm2A/StjlJQs4F0pQVyPNz2MuXdf1miPE+n2hMz8aKrTHeAbTgU/76vXtV+4233gquo4Fn8vwldVWC/QViPOwDPFNiXGuJrqNccs1mXbsY+0CcwmLsFRvJq/NWnOnicDgcDofD4XA4HA6Hw3EF8JcuDofD4XA4HA6Hw+FwOBxXgFeSF2VZZkdHlzZwaQr6/KZsLjfbooLVDiQhmk5DsujhuShI7x3KWu76HcmLdhuiKnZStR8+kiXVd0/FvdzbFv1xtydq3Na6Pm+1dRwzsxgW16RHz0BPM9Dkykh0uIsLUSaTCFaubfL9QGGfh/KggBaeivJEMQklRcPR0FYSkVm5uL0U1NAGKFs12oRBDjYuwz6jzfQc43I20W/eeSw784NzjUsPtLLJXH3Z2xeN++ZrojU2cE3ZPKTLTyBVeXqmGDyaMm50sRdjff7gWOe+1xPNtwUJE2Uy8zL0zZ7Cbp32rwkoxnEOG9lydd+tlqUkesWLmf4W24uphnkSprfTMazjQb+8vyVbxi5yzofvP67aP/zeH1TtG3dlv9rpKXbqiK8p6NDZU1G0zcxOEAtRV1TrWhuW9bC2H+P7I9BxY8hPuqBuUlpQLkmsypL0SfQPYn1Ka8n+JR01y1eL7J8mse0s1gBSQyn3HINCO4TcLLeQYgwHVoP60BqQN3ZAt28j7lJaNx7JXvzeuuwxd27f1XE6sAZeC9eqFBdSwnZzMlEummMuQBUb2EdTPsXP84JSqFDSSBlRYBUMaRRpzHG8xNtdIdTS1K7tXcq8MtCxSW3PuK5PlM8Pz0L71gI5vdbSfK2B+jyZStIxHep8p4+1f4ozBN4MYznnHgLrRxKO38VT7bFo5x01sCYWmgOnkHCXoFA3GpAHgY8eYz94PlScm4XStDo0eZMpZMQQE8UGqfZKobR4kbfzucaigGz9eKTxPZuKkv96U3tUs1CmHxWwrEf/XRwo7s6PFEPcY/Vq6stWrHajAbr9BHKzZZtu5Egb0ypY134xUpzmQ30/sLJvKpf94ImO8633UYJg6dwF4ovSoTjV5wllA+lqrVFEaaUVC7lrgjwfFxrvN778i1X7q3/hadUevvt2cKw6pW3YC7z7SDngIfasu1uSI3UhTWMOb2Jf1E0QU5A0Jh21zcxGZ4rJBBL5eqTjHuJSPzhQ7jwbai3ZWP9s1d6CzCnBvjhdXrMDcaLOfTLQ88AF9vS3bl7KQNOPtedbFTy/Uz4zUCU1hQ32MfYgh3hWNzPLUJ5jfqH44F520lEuuvPmG1W7Bbliiuey2VA5qsR13NmHBG5beyEzswn2FJRAUfpVvGQPQ0kR+yPGnixGGYDljMESBiXk5zXMixzPXNFPsS9e3acxh8PhcDgcDofD4XA4HI5PMfyli8PhcDgcDofD4XA4HA7HFeCV5EVFWdp4dkmtSVG1egqXl9amqEK766Lenw1CaczpXFSjH8GZ6Nq+3Bvia6r8X+D9UKMU5W5wKnpi1ka1YVD1p3COKZYkOix6PZyjyjPo2xGosxnkPs2uqr7XSe8HvSrPQmlIANLBUN17PtV1jEaicf6EFcmKoLTSsoVzQ4IK6lGM8UpRmTpSH2dLBLACfZaCpjjFd+hMU8NYrMMdZnoqKuMh3ICyC1EkW6Br50tuEIOJ4vcALjdDuA70QCePIT0ZglY9hGtTxuHFWI+iJao45UX4UQkKehPU0Hjp96uF0iqJHjmTlBpxXqCZReE75VNQpY/gOBK1NY934aSWlaqi/vTs3aq9fft+1b7zGVFfY9Cph3B9yCehRC7D+M8hfxs/kRva2YkkcucT0YJvvSlaZrMH5yQcPwY9OVqibEegTJaQk+RwujmHVO+50wCll6uANEkqeVFizN+654tY87YoNW/HSy5xzCEN9G0XMqwmRqBGCSQC9eGhaPizQ6xD5+r7ASRwnQ25FZmZrfdE8W7VIDlIdE81uNG0O8o5TeSymNMF95BlyhOzpXVrAllbjntif85W1F1vGUkS29b6ZeycHKo/J8gfPcglxqCvPznSHDYzOx+p37a7cLhqgB7dUJ4ppjrWaKgc0oTMoA7LBcqRjs9FCZ9ZOO+7LV1vnXuNCE4Tse6vva61qwWHtQyU/gJU7Bni6Ogs3GNFiL06cjLCLZQdL/Zbq/YXwSgqLUqfjzekgJBjnOL73z/W+N6Ai6GZ2ZsN7aOzgfJxZ0sx1N2C+9NYfXz0kWQdlKR3I/22CYlOvYmc1gyvo4G97yxSfOVt5ct0T7GZIMYvkFOfDRU333ykz398BklD+vJ9Sk4nEcQdVZBxsrqSxtLM5gs5FbaplkAavgn3or/w7/0HVfvZ268Fxzr95j+p2hfnykcPnyjeNvDs8vn716r2ZKgxjhELjVuSwK7fUbveVJ7IxqH7VDl/VLWnhzru4FT7GUoUOcbvH+paPzjSM976Hci5KWFdctsrcuYmrK94NuDe+9vfu3QyGi05zK4EFtO3pLQG5QUKSLumeD4Z9dWvZmYJ+nCOfsogkT6cQjraUl7bff1e1W5AvtPlcwiklU2sA5OlPu+fauw7kFjX61wRkF8DNR12wnheKOmChPvMl5wq80C2hGcz/Kb4F9wLr9q65nA4HA6Hw+FwOBwOh8OxEvCXLg6Hw+FwOBwOh8PhcDgcV4BXkhflpdnFdEGBy0QJSlBZfH9rp2r3NiTV2JuIzmZmFieifNXACHr8sShpe039vjEDxdtEZ7y5JueR3Q3R5OKGaI456LTLEp0UlMROqt80UMnf8PN6U+dO65RYiQKaZXCWGIMWGeu3ZmYxrmsCmvYAlFDSvHY2JL1aKZSllQuKGx0yaCvSgFwtRvXqYolxmud6T9hEHGy2RYW8Cxr+bqw4fW1bdP2kEH23zHDughWyQS+zkG6/3lGsrNV03Hxd50tBlz0d6VjPIC+hFCECbTyHdOqsFC3PzGwAGVw0eXGF7jpowWlttWn/xaJaeBRpvAvEUQLqIFKRzaOQbjxAXD08E4Xx6VjzbQcx2YEb2r0336zao5mOcwCK+DqqsNe6cnRrtMPYmUOOViIFjzHXNzY111+/ITpvb09xPitAzZ5DDol+WlIZWEQXGpxvDMewJ6eSwVwsYi1fMWljEsXWXcg2SNGex5BUgYI7gtYhD40QaEIWxFdJjRsTFdxGclBczxF/7Ya+U29pTP/oe5KxDQeQlppZE+vF3gbd+fT7Zkvf2UW+u7arWO7hfFRNTuGul+VhzGaImwkcfXJW+6cEYKkPVwlJkth679LZodVCTh3opkihzpB3D09Dp7KP4KS3/ab2JzV00Dpkgsm61pVkrrEsB5D+QF69jz1Bqa2QzZf+nsbx4Brcz+HuB2eKxpbW0MFM0oDhUNKV+VzH+fiJhDMPD/QdM7ME+555rvvgvGojJp/HebSCQRTFz6VRcO7BHKHDy8djxcofPg4dn9JUso3bdN44Uv8ldX3e3VL/xZk+Hz1RDolnuqY2XI02GjpX1AjjprMhh5OyhjVmA1/a0nFPLnS+x5DW/d5Hutevf6z19gjxkC/JgZmIC8jXCjo7oZ9n09Vao4jSzLLy+R4Z8mDKMyBr6NY1P5P72puYmQ0+/E7VHp9KXrTTU4zcuaHntDttyKJHOscpJPgTk2PsRazjXLt/WyfOljbrkLXQAbDX1Pjt9RR7I0itD8917oNT5ZbpLa1p9RqlnywwYDYa6/9zfO8XfulXqvaH59jzPL10LcpXzKWRiF/SLjEuE0jHh+NQ1lODFD3BfON+KIObbM5SG1gPe3WNaa+G3AKJbAEno+kwvI7DI8WalbtVs7OvPUwdjnCUlnFPVhSU0uvrdDvivvny90KO37MdOj6+eukGZ7o4HA6Hw+FwOBwOh8PhcFwB/KWLw+FwOBwOh8PhcDgcDscV4BXdi8xGC4eUFPTjEZxcDs9EBbu+KwrbznboxNBsiLNUB5VyBrrPx0dPq/bNrqhFBurTZkPU3P2uqLa7929W7Rao2MNpSNkeXogKO7kQpa0ciDbUApWpAXkRJScZXl9NAxcH0ZJq9bC7SUyiO8Q5rqmJCuKtRsNWFgtGVgn5RlSH+0cDsq2JOnNahvStCSvXgybdAw38ZzD2p8eiqm33dNz1nmIlmSH+4BQwQCzms5B2OC31vZ22jku5yKgAtRdjn2Sihq436ZAEpwCc66IM5UFTcOXahX4fgXZbxOinNJQnrRZKyBYQO/jGi2uam9mSmwEpgs9AWX0GqdEduIX1So6TaNb1lvJJDPlg/6FkOYYxi5bGrx5DslBTu3dNVMr2lmi7zXXIYzLlqAIOTNQP0MVhudJ6iT6YwqFtMAEt/FBzZlDJi1aLdluWpRXz50kHpFHcRgz5WQ3uYunSvUZw/ypwgCE80yhRNFBt56yajzVzmEmy8camHLN6b31G31lyg2gjP2y04YwG95FRXbE2yDSmR2eSL2RjSBEixV9JCcqSnGzCXIj+iXGvKVxqVtVpz8wsjiJr1C/joQZqOunsM7gM0T3tYhjuL773g3eq9hfuaM3pQZqcYB1MG8o5NdN6P8C8f/xI8zPvKwfEDNs4lObQ9S/u6LjNfUkiO9egGalBsgkZbzLWeJ+Dnv7eB3KgPOuHcVsk2j8VWM+jl/Dhy+o7qxZDkdliPabjZQmp+gx9NsN6/d2TUJaWYl5GtoXPFR/NLeWTsqbBb2xBflNorHO4pJEVH2E9itJwrRrFcjjpbCtmm5s63wjS1CH2Tz98qLnwO++r/YjufchXlB+YmcWQaTI+GDelQW6VhM5Lq4WyGpSyoMycaxfkmxi/Rlv7ETOzzq5kjMkHP67at7f1rHR9T89jBV3L4D7Vqqs/e5C2biOGG8/0jNbMwnk/mcIdB5L/2s4NtfEcOKzr+x/B6c/wnDWjPBrxMRiFEhWaNq5tKMd9/pf+bNV+D45Kz/7h3zOzpf3jioFSu4TuPoELD+TVy3u68sVymhL7ScqMiRT7pxqegT58R/F3/kRS2wzjNRmHLkoHx/rebP561d7bQXkNDnDQfHF5CN4b5UFL5riBIx+lZujC4POX9ccfB2e6OBwOh8PhcDgcDofD4XBcAfyli8PhcDgcDofD4XA4HA7HFSAqX4EGHEXRoZl9dHWX43gF3C1LlHb+FMPj5lOFlYkbM4+dTxE8bhw/LTx2HD8NPG4cPy08dhw/DTxuHD8tPlHsvNJLF4fD4XA4HA6Hw+FwOBwOxyeDy4scDofD4XA4HA6Hw+FwOK4A/tLF4XA4HA6Hw+FwOBwOh+MK4C9dHA6Hw+FwOBwOh8PhcDiuAP7SxeFwOBwOh8PhcDgcDofjCuAvXRwOh8PhcDgcDofD4XA4rgD+0sXhcDgcDofD4XA4HA6H4wrgL10cDofD4XA4HA6Hw+FwOK4A/tLF4XA4HA6Hw+FwOBwOh+MK4C9dHA6Hw+FwOBwOh8PhcDiuAP7SxeFwOBwOh8PhcDgcDofjCuAvXRwOh8PhcDgcDofD4XA4rgD+0sXhcDgcDofD4XA4HA6H4wrgL10cDofD4XA4HA6Hw+FwOK4A/tLF4XA4HA6Hw+FwOBwOh+MK4C9dHA6Hw+FwOBwOh8PhcDiuAP7SxeFwOBwOh8PhcDgcDofjCuAvXRwOh8PhcDgcDofD4XA4rgDpq3w5SeIyTS9/UuLzKNK7mwjvcfI8q9plmQfHKoP/i1/6LzoH2/gffB2fWhwHP3jhMRcXpq/hNxGOVhYlvo7PX3bylyEK33HxHPV6vWr31npVu93uVO1Wu121v/P2t47Kstz9BGf9/x31NCmb9ZqZmc3nigkOS6eh+7dC34mXxi5J0Idloc8XxzczazZaVTsv5lV7ns2q9mSi3w7Gis38JUOaJOF1pAljXv+WFzquRTpYjOM265p2jQauu92t2hnmzmg8Ds49Hur/e13FRKerWBlNplW7fzGo2heT2crEjdll7LQWfRTH6vNaqj4s0OeYnpZlGAszm2ca5yAfYV4mOEcjZUypnSYJjon4mqudI2cwx5iZFSXzido52rwnfv8l6THMV4g73o+ZWRN5plZTH5b4fa3e1PcXMXVwcGgX5xefJMt9KrC9tVHevXndzMJ7eyk434b94J/Go2HV5hgzQzQaDXyKcczzF7aZ/BjXcazYyvJwzcwRa622clyE32eIccZNvaZx5/qZ5y+eE0kSbg14jiJTX1mh36SIrQbWre+8+9FK5Zytra3y5s1bZmZ2cX6mf8iVU9d6ytWHx/rOydlFcCzO72DqcnuCpJVgbPZ3t3A+9eez45OqfXau3J5g/7K5uRZcx3ikax+OtH6UEfc8wZXjAnF9yH3X97ardqehnPH08Cg4Un88qdrclnVaWrsYX/XFXuD07NyGw9HK5BzGDdehYOCR18/OTqv28VHYZ5yXhXG9eMnJy0+wF33Zb4nl375svWH8Bp/HL/kO5wFyH76TLP0JOMX6u7m9U7W765v4Pfdhwtvffnulck630y63Ni7n7Hwyqj6PsMdlv2U5Pi/CfQ7X/Bn225MZ9ioF9x0vvqZ/kTD6444VjDmCpI79SLur/JqkWLt4TOQirr9mZgX6Lcea2MAaFeP3z/H02TM7Pz9fmZwTRdFPMxzPf/3Sf4kxys1U8bSGfXAL41VP1ZdRyhyAvQ33PC95hr/8X+yf+PlLkl+BuZC/ZN/MNvfmc+5lzGyOY80wMUaYR/nLFnIrP1HOeaWXLmma2vXre2ZmluEmanVMEFPwD/vajEyn58Gx5nzoSbT4xsHGVB0Q1zWoNUyWCD3ACdxu6zriGjYWZRhofDHUwIN7Yniwmuo6sgwbXAYHrzXlAoMAjMPEUE+UAO7cvlO1f+1P/6mq/eWf+2rV/tmf//mqfXu3+5GtCJr1mv3Sm3fNzOzRgTYXDbzI+Lk3b1bteHRctVu1MDGu9RQrhpcoWzeuVe233vpi1b4YPavaj58+qNo//JEWtt/5rjax5zNdE4bRtnvh2G1uaOxS04ZziAWzTHV9bcTpZ29qXt57Tdf9uZ//lap9dK4++Na3vxec+zt/+O2q/ad+5eeq9s//yV+o2m+/837V/s1//LtV+ze+++HKxI2ZWatRs1/+wt1FW2NwfWe/ak9n6vN5pPxxeKrNvpnZk2fa5M5zPUiXicZvs6OHm9f296r2W3dvVe2dDT3QPDt4WrUfPlV7gBdl+dLLnykenid4iB9MdL2DsR6SptgsMT1iX2E1PKyXdX1pDQ82ZmZv3NJ93Lil+8szLQX7996q2p//lV80M7P/+r/672yVcPfmdfutv/1Xzcwsj/ByKVI/JXhhkPeVl97+/d8KjvXtb329aj95/LhqR1jrXn/tXtVOsRaMLhRz475e5sR4qdHEBrOFF6+n5+GaeXKkXPbFn/mCft/Rbw5O9ZvxVGvb7Zs3qnajpnXuFC8J5hM9wG9s6cHGzKzW0DmGp+qrYqTcuXf3XtW+/5VfUvvX/pOVyjk3b96yv/k3/46Zmf3j3/h1/cOpbuPP/9ovV+2/9Nf+TtX+a3/7N4JjzfgiFluPHHGYFoqFDexB/pv/9N+v2n/mT/9i1f4f//e/XrX/1j/4WtXewou4f+ff/nPBdXz32+9V7d//5ver9hxzI0XujBL8MQob6s2u/iD03/+X/3HV/uqbb1bt/+F/+SvBuX/7Oz+s2m3E/c996Weqdr2n496/d7kX+p/+179sq4SbN2/Z3/5bf9fMzOYpHjCwj8iGWqt+/W/9jar9f/6VsM8ukDdGxhf7fDH/4nbwQg+PLVHw8h574uBFSfhgk+F7fBCrI1Zi7ONqdcUg925pqb1Qhr11A3vztWb41mVvT+vTv/sf/mdV+0/+a/+Wfo+8VC90rN2bOyuVc7Y21uy//c//IzMze/bDb1Sfp9jbzPD8cHimXF3gO2Zm3Zb2M4+eKj//8IH2J08G2l+c4+8IUYk/QPGFD+IiC/7i/sLmT3wQ4zmog7drm03Fwq0berH287/8J6t2b0vPBin+2NbCi+i7r78WnHo60/1dnOg59N6du7oO5LLnd/pf/MW/uHwXn3o8f+zl2IWPucjlGIciXvrDCgasbdo7fG5H8fSn72ksvnRDfxS4s6O+bGxiTiIWWwlf2OB5Kvzbkk1z5YpZjJcg/KMpnqcGA+21+321J3hRMsKe+wz77EdHyrNmZgdn+v2H52q//UwxdMb5YrqnPBt/opzj8iKHw+FwOBwOh8PhcDgcjivAKzFdLl9d1hYtvXVqNfVXf/71tcRf/IolaU2Jt1YJ3pqn+AtLt6m3SOto803pOv66Q3oi5SAN/LYslq4D7QR/AczxqnA0xV+j8dfDk5HeME8CmjbpTqRRhe+CM/RJf6g31ycnevv27PCwav/wh/qL0SohMv01pI435tfW9Ea0AKU/wl/2I4yJmdkYVOU6qCgt/GVpcKw+O77Qm/6ButgGQ41RPYZ0xGb4XMds1sKpMuyLKTHqi5WSI+abXc2Lbl1vdi9ONb4nR/rO4ZGu+/s/eLtqk7ptZnYTDIUapEqcUyWYEq2VIUv+JIqytOn08l72dkRnH4w0mKQdDua674dP1Z9mZsOhYqddQxz21Iev3xaD5rN3xT67vS920t62WAB39tb1nT19PhgrN9SikK01hXzuHPO+D9nY6anmw7MTsRGenevzPsa4zPjXDMXzZBr+GeHjx/prV62p+97bUUydHD7S+R58YGZmGf5ytAoozays8iv/qgsWIv6i8uShWHAf/PhHwbGGZ2KPlDP95u5t/dVnf0N/6QG71rK2xiLb2qja46liMW1qDWvjr2+bm4otMzPL9ZvRQHGwta15sbujvxaOJ8qjp1hTepAhliVYV/gr/KAI/+K9tac+7HaVy/pz/PX1meZb+m7IzlsllFZasYiTP/jmH1WfX2tpDnAXcWNPfV5bkmWR3h8HEgv8FRl/zRuDEfDkRMymBHSJn/38Z6r2If4yd/+W4vEXfvYrwXWcHUKGVP9B1c6xV4koWcBfEil7HGLP8/SpGE+1z36ual/bVX+YmZXYA01A93nvg4dVO21qjTte/BV/NAqZiquA59K9IOPnmmM/+K7W9X/4G3+/al9cnPEXoVQVf3UOWZNkobxMz/THX+cyYgtZmSnWLu7tY+yZeCRKIss6JWPYp2BOFLmOP52H13SBvPub/0h9df3mvar9uS+LcTZf2iuuEubTqT2E3WJeAAAgAElEQVT68F0zM4sRLzPMyWfH2mdeYC+z1Q33h0f46/3JqeY9lgObQVVB9n8No5ni+SQnHQHDVOB/lsU6fNwJWFa4pwSPoGSCfv/bmidvfE7X0V7TmjgB031/GuYKnu4Ya9/etvZxvZ6OFT1/ePzjSlF8avGc6vLiHEBpX1imIzxKrVRQXO9oLv3sHe2J714Tu2VjS89vzY5ikBKuJhgtbZR9SPA8VSwxwVM8o8dY9xJKp/Ebri8FV2aUWDCEB2Vp2RLLZjTT+fYi9cGtmb44PdB+fFpQbv7J4EwXh8PhcDgcDofD4XA4HI4rgL90cTgcDofD4XA4HA6Hw+G4ArySvKi0yIpF4bUWJA91FMwaoZDovBD9K62FFLiERSBRKGx7TRSkm6Bsv7ktavYOKNvdltpxoC96sTtEli0VCiM1CUXlWPRuBBrb0UD3d9DXcQ/OJTcZQI40RuGz2VzHMQsLik1BW9/YFF38xnUVvnzySHTjVUJZFJb1L2mO6+AgbjbhuIJiYK0OXQ3CEJ1AI9RZE3VtDHru8Uh9+fBQ8qLjmU7+7EDjFaGCdRuxWAcNrZOEFLgZ4igFdTYDFXiO60hRRGq9oxjf2lBcs3B0PhaF7fVboveZmX329ftV+/0fq2DuR+9/XLWPECsbzdWl3dbS1PZ3LimhLHpNemGEefQEheOOT0MnkTr4lJQL/Ykvqgjkl7+gIsw3dvWdFgpcNlBYslgXxfLGjuRFGQp3pUsl5lhEfAYq8QQOI/2+5sPjA9GKv/eRZDDvPFD78RPd93gAijAKVJqZTeY694NHT6p2u638vIHidu9//7Jo83TJQevTD61VQUE/UGgnQ+WM977/nap99kwSLDOzyYXiqAeZYBPypGKo72xCbtLelJRnjvyfgas/g5Qnaer4ZRkSttP49apNKeIc1OpOG8UBMUcGkCscH0sW0ulp3AtwzgdDxZyZWVpq/Nf3NC/aPeWv/oXkAKePlJdWDaPRyP7wG5fFLN/+norOFrd1r3PI7W7uiXLdboYF14fYC8Sg6AduY9i3pJBqH8M5qUA+ef2aiq//TqGimz/+ga51fak64X0Umn/zpqSERwOtg/2B4miCWKB74wyfP3x6oBPEOt+1/bAIcwN9Msu1Vp70tZZHE8XXo4X70Wi8evKi5w4zEYp0P3siGdXf/XUVz33w/o+rNtcLsyVHPjpsli+WF7EYbmgtGu5bniM092Tx1CVf0YiFNyELwf66h4KmQ4wjnf0M8rFeWzEwG3NNX9qbY+988NGHVfu3/++/WbW3IBfZee2ztqooi8zm/cs1vI65+wRr//GF5gPd5Z4+CaVpMfZDBdpjSo3ppkkZPfZIDbTpdjQN3LTw23hJr4L/p3tMipBMIGdrwSylnCo3PPpAZRW2rt+u2vWuDA0mS7mijX9rwUyAcqjQuS1e/HcVcXnt4eyhvOhlCHNDG/P7rR3ta99cV/9to9h1s6XxanaU4+vYK7fhPNXC3imH9DCtLznD4toz7JVjyIWy8sW5JSv4W+RQ/JYupuuQdpuZ7cIkY1ZI7vb6ptb+IYpQP0JJgNAH6eVwpovD4XA4HA6Hw+FwOBwOxxXAX7o4HA6Hw+FwOBwOh8PhcFwBXkleFMdRRRUlVYh0+9OJaO4xqolHS0V+m3CbudYTBekteH+/eU007RsdOsGg2jbkRWWKdqxrimJW4Q5JWHmh781Bb2QVdlJiN0GN3IIH+WZdNKXHoOxeTHScwzN9bmbWh1Qmh57pAs44lE6026I+rRTK0qIFvX2vq37agOplvSv6VndDMdC/CPuMrjyWqc9OjkTvn49EyTzL1H+PcKjRWAHZAc2uW4NLB6j3u91QojOFy1YTdiWjqShpUKJZG3S4vR3RcT/3RTlRpOuSEZ1uK/aLgWj7l+fW+Z48k0RknunzM1Swb9Ve0aTsU4bnleWHcARqgF57caax78PdJ16qLP76retV+1/9ypeq9le/KOeN/T2NQbOu8Y9BRS1B/Y4jxo5yVAGHomWGN6v6F6Chl01d77yjON9bU7zs9BSHmzXJVb4OiclHT5VXpqNQFtTaVsX+IeRMjKNuT7k9WriYlMVSmfdPPSIrn1OGQVG1TPf8+CPR+x998F7VnvbD+dYElX4NOb+Bz+9ck2TjFlyNSjpVQS4yhdynk5LCr/ZkFgbO5rqkQ1TS8ri7TVGBY4xZBmnTEeRFUalcW0OeGA9Dad7FGZx7GoqPjS3NqaKlc4zPIT1ZMRwdHtlf/d/+kpmZHR9qLXlc01jSve76jtaudTjymZkd9DX/6ODRwP6k1dKauL8raXE7hdRsRkq0YnC/o9yA5cayCaz6zOz6TeW1P/Ozn6/aH2Od+BGkDB890z5uGKy5uoeHTyRxm0Ciu4d7MDOrwc0ijhWfOa6RefC5DK8sXyyN+fRCOSebajT+8J//ftX+ztvfqtpzOO0V5bLj0ItdZEq4rxWg0geKIsop8dvAuQTfZy6Ji3Cfw7UqhvyggMvjWldysnZDBzs/01psc8V7DSfEttlm02WJFX4O+f2Pvi9J3e/95v9dtf/sVijDXimUpUULOdbjA+XnI7gPzUvIsvrI+b2wdEMNpRwOTjWPa1gHu1CuQkVv3ZRyIZRlgJvZFPKMGAGyBxmKmQU2fswhc0gU6xHaKEex21MOGc8ggTzRutJBbM6mYemGegPuup0u2nTu0/ejfyn4B+UL2yUlVRivaCnnrENWfpuSIozjBqRDXchG21jD6tg311Gyo05ZPvYaUaz1wSzMcZz3MUo8TAObZMQQJG0N5JkMya+Gn+ZLcdPBPW00FbNRrmPdQxmBU+yrJuNPJjD6lyHSHA6Hw+FwOBwOh8PhcDg+dfCXLg6Hw+FwOBwOh8PhcDgcV4BX0h1EUWT1xiVFqKQDB2nQhd7j9NqicmV9UA3NbL+lU3/xtmQzb+2L/n59HfQlUOCacGOJ26JBlaDVJYnalOhktZACBFasTcdwl5iKWtQEta4OWnAj0jnaqe61DieQZ+eimVJCZGY2m4s2V0DONJ+BZlmqP1mVfJUQRZHVFq4MYOdbDzKxNqiMA1CeR6Q2m1kdsjaO5OkpqpfDoaVoKD4GA8VgDRS2na5isYPjt+Ey1IyXrgMyjzXE42ii6zgBpS3BvfZ6utfHB3IcOn5PbjTJEJTKMxLHzeZ9Uf8pe6FcZIwYarVW991qXhR2fnE5bggRy+ca/T77A44Jn70pZwMzs1/7yltV+xfeule197dEF+T8Zq8lcNEqqBcKtENwVMJUL5e6P8GROe8zfJ6C6kkq8B2THCSZy9mMVNF6/Lhqf3AYOhuMx4orVvU/PReN+QxuPXtb7cX9rFZd/9LKyvUjxrhMB7q3Jx/8SJ9TUpQtUU4hj1iDs9r91+5W7bv31e7gO3QloSzt9FQ5Lsf60oF0ZDwKx26KCvw7m4qDyUg5Zz7S+HYhA+o0lKMOMHcujnSOvR0dc1wLx3s0hVzuRH3YaWrt7rWVL+eT0MVnlTCbTu3B+wv3JYzNKaj+B0ei7d++JWegXUiNzMzefSwJDmXO6y3F1Bu3JE373Ouv4XNJTGO4nO1taY/053/1T1TtMdyjijiUA6aQt765r3HeQayugXqfYK/xEWREIxzn9FhxMMLac21P/WFmttHScY8ht+rA9XJ4ob5tLSQxy2Yon3aUVlqx2BcfPJFc8+v/TPIi7kHoULQMugO1a3D9iBWPw6HmfY61I5Aa8Zj4LdeLNMr5peA6SrpgRS92RJnDsfTatmQhBZw96hH3sdhvNSDvX1on6VxSYP2dQpb19h+ob/f2lINXDbPZ3B4+uIyZMzwzzOBgNxprjmx0IPNoh/Kiiz73uZDsICUXdfVninnYhQSknULOhO31dFTgO7qO12+GrmXc9xxhfvfp+AIJeBO2Ri1ISejw+RjHmY8gVVySiVB6l+dwCoRchSUkGpXLzoolHZOjGa+8oBw++BfIFpeOs9uFpAgx1YFsfg16wLUGno/w3FSDI1ANQVDD3rrE51m53Od4boI8ySJIHyM6AWp86zgW3UrniNMCe5vmkmq+P1Vs9hq672ysc2x3IfeH5PtkrD3dH4fVfRpzOBwOh8PhcDgcDofD4fgUw1+6OBwOh8PhcDgcDofD4XBcAV5JXlQUhY0X9OU2qEU10B/TRJScGug9G53wVJ+9porSn7kmus6NTR2X1MMGqD4NVKOOG2ondbRjXVORgV4egSdnZkny4krcpHEWcMkpQbMu8c4qgpuNQUrCY06X3Cj6Q9EkR3P9/gJuFKcnchSI49V0oanVUtu/dklp7paiTnY7L5ZyTEE/pIzNzMwK9Cf+qUBl9xTONpSPRai63mvoONst9f1mR3ETo7J6e4ktv9ER/W4DVPEBrr0FfnRrXTT8/V3RJY+moks+eSAa9xacAhILqZPb26KH37r7RtV++4cPq3YNzmGdblgdfJVQFoVNZ5fzpAPa4hTuEFmucVqDbPHLr90IjvV50PW32uqTFLTpBHKhGFXRWeo+eYnUprQXu0kssydj0MrLoIQ+KOIJqZS670Zb0oLdHTnHvAXJyAQyyZNBKOs8AX2ygSrzGaqzn54pJtfbl98v/xgq/KcVlftJrv4YnsLZ7NnTqh1BBrQs4qyj0v4unFlu3Za8qwHdZAmKfhpB4tNVDhhDKnQOWUgdlO5uO3SDOIMzl3UUNzf2Jec4wD31dvT7bhPuf6AO908lESkgW6HLg5nZBeRnE0hJhnDH6LXlHtLp6lirhsiwHiG3DEdaPx5DtvfGG/er9jVId8zMIuSWGsb2xo5i4Ze/qN9//v7tqr3W1TilkHk0sL+4fUPxmK9j3Vty/plhISxAid4dam5sbGjMNiAN/zby7jsPtcYY1i7m490d5Vkzs1/+yhd0rHferdpt7O/W37qj9kI693/8jb9vK4WysGx+mS+/+/Y3q48fvC+XtNlMc6eE7CFe0lI1QYG/saV97d17yjnf+6GO++RIOWROXQdlPTgF17yYsqEk3GvwqrjfjU15bY71ZqeHPfiePh/BfauBvJZgjW01Q+ekyUzxlUB6EuEZYwQpzTd+5x/aqmI2z+zhk8XaBDeX0UTrda2m/u8gh0+n4TNNBMnOWlO/2eIzGza0tQYcZnAcSjUmQ/V/hmed3TXtZe/thvKiDBKQbqrfnNXh0Ad3P0PM13GO7XXlojHmzBB7mWIWxm0DcpIL7LGmmH9zyIhVtmDZRWwFsJi/0bKOfQHuPxNIcdbS8HnyOpz3epAmtxqKtS7Wgjb2qI3AYU3HLOicBHltivGxJHy4wiOeRRFciOGcRwuiHGtbDVZcdDid4/1Bjuf5Vh6uk3XOJcy3WVPHqsFlbb0VSvs+CZzp4nA4HA6Hw+FwOBwOh8NxBfCXLg6Hw+FwOBwOh8PhcDgcVwB/6eJwOBwOh8PhcDgcDofDcQV4tSIhZWn59FIDlcDDOU6h54KWuAnbr+u9sK7E/V3pivdRH6MNfWETlr112A42WtJDpw0dJ6np+xH0ZkUhbWEtD68jm0vDlUHbOoMh8Qy60zr0+jn0f038dgu1V6JI19efwArazA7OdKw5aiY8/viDqv3gI9mabm/LWnKVUKsldv36pU67VUq/14SdHS3ekonarVao94tzxd35hXTkKWqg1OMaviMNZwNxsIE6DesIic0WbMYQG80orPSwDsvoXlvHilnfCHURWh3p5bvQ1q7vqB7D6YHOFw2l0d7shfURUtQZOH8qe+CLo4OqPYTWOVmX7n/lEJk9l2km0I3OZ6itBA3oNuoVvH49rC2w04P+Mn6xxSZtMQM76PBLLzwO7buLgscPdaMFciQtOWl9xxoyEc4XIcc1eqofsYf6Crf2ZF238XGokz8+hOUi9Kt1WN5fnMl2eNi9rNlR5DRoXwGUpZULS8hsqhxwgpon50dHVZuWk2Ue1pFivYWNDa09nVbLXoQYuSKBHSdjqNfTcfoD9fdsrjVicyOsD/Lkma530Ffu299VnF9Qp41aNrS63kbtjtG5arrMRtLIb2yG+vwB6o9lmdr9M13T7jb6BnXXVg2FFTZd3CP3DihdYQ+eqjZQBCvMO9dCm/oWrH8bsN68fU31b77whuq43N5Wv6Wpvs+YMthoNlDboTCNXyCMN7Mp8xdqmHVauqkW6pl1UfNqHfUf9reVXzvriiPWWnj/keySzcz29tQn92e6xgEscGlZ/+DgMqZY92gVMJ/P7PDZAzMze/sPZWXcR92jAvObts31Wri/6NT1b2/c0Fz8pS+p/s+9XfX/P/raN6r2w2PkeCSdmFasWLfqiFHWODQzY/myAvbFMfbXdaxh05HyyY1r2nc8hc18ib1XI8V6tmRz3mmifh9yTsTaadibn56EcbdKKMvIJos9wxxrUQZ77HXUBIuwh8iW1uYG+netqbnbRl2vVhf1XfBM08RmKkPCGzOPbamOy966YnNrM1yv5rmuPcE5OtgXD+BFPUMNmAbsi6djrXXba6jvcoS6ftMwVzDWR0Otr6xbRvvoWu1yXhbl6tV0eT5kEcYuCvao6tcUz1k7jTDn7DNWaFlfx1xPWNcJ9VCNe1o8y6G2ZIn1z7B3iuPwmbzAWpJjOArsbdi2hPbnqCmLa6qh5mGJx4BZEY53k/X4UvXBeMqaf7rvVvzqvBVnujgcDofD4XA4HA6Hw+FwXAH8pYvD4XA4HA6Hw+FwOBwOxxXgFT2II0sWVCDKQXJQcFugu9ZgdbvWDOmuW11RmdZANaLVVw1UoVZLtNsm7ChrsImOQMctIXnKYEsWZUtUpgyShUQUorgA9QrfyUDVmoPWl5p+m0Eq1KnrPne3RM01M3t0AmvogeQkF33JA8ZDyURy2A6vEuI0sfbWJS2wCduvxhwW3aBv0Sp8ay285wvIhWZn6v8OrPES2tuCVtYEFWwdFmLrXcXc1rridwJ6c7w0Vbo9xWyrAwlHQ/GxXte1z+e6pz4seZsNUSQbOEzaoSVgSJ08eCYZUdpSTKW5+nAy1DnOpQBYOcRRbI36IufM1bc57L9zUG27kCTuboayrBZs8Ax5JgOHmnbQlA1EkJgEdtBGGZDiq6DUaOnddkQadYLcBKp0gvsjxbvE/6SQqa2t6V53NhV3m2uhBCY+Ulxkc0hqCtB5R6L8np9cUuPzJcnNSmCRB2YTzbGjZ8+q9gzyqoyWk0sU45hWh6BulwXptUJosxocqGrWseZ1keOGY1GhN7YlQTEz63Y014+PJFmYwqZ1E/KnCewx17qiZbdhcd/uqD280FqzvR5SxZvIl5NcEpEs09y7ONMattsL17pVQlmWNlnMDdLiKRl8+Oywas/Bgb6xtyQvCuJFn3fbGrN10O07sKCMIb1IYD1ewq43gXVm3IRt+ZLDOxx3LYJ9K32EyxQ5J+beRrGwhZz67oHi5X/+y3+9an/8NFxwBkOsS5AcrG1JmlCa8nG+oJfPsqWb+JRjPBrad771z83M7MEH71WfF5D5Ub2aou+b9ZDqv9FWrN29JjnHta6+d+3zr1ftEkb3f+83v161n0GCzVxEK9Y0kBKE+5wk4Tr2YlltC3vtKWSuzabmwu6epPFPIWls9LTutOIw77ZN8Twaaq88KRWnScLAXj1pyHMUZWnjxf5mMta61EG5hQj3zbWrsyQTaeOZg2PT2VBfr62x32GHO4dsqabzFZA2dbCWbCDP12uhpDRH3CfITXFfMWJjXfsQuaHEHv78VM9D69uKqRj9weckM7MEcytFDmf5C8b2cHAZk8Uq7nOin2iEbTRrmGNbzXCubyHWWggpOHxbPWXO0vfZ31S21uvaN0Qp1rBU8RDVwmfyEs9s8Uv23THGMcJzDyX6SYkHqlj7qhquu7ksLxpCXgf76Tp+U0PcNH8K2oozXRwOh8PhcDgcDofD4XA4rgD+0sXhcDgcDofD4XA4HA6H4wrwSvKiKI4sXVCSSnuxNMQgAUhB50+K0LmHtNs6KuXXUMW9Dmpck65GaNfgbhOBjlumOs4UFePzJJRqpAnoTznKGtdEjctTVC6OcR+UA6CicVAZHv201Q2p/tc2RNN7eCg3hIuBKKHjoWiEDx9+bKuIJElss7egEs/gDnSisWizGv6mxqGBITEzu+hDzoFq3S1Q5ZI5qlnHcGfAwe69JheAvTWMXSEaWrspSmU2DOM3hgRhBkpiVOI6poqPeYaK9KDf9U9Fxd4H/fOir9/2B+G5ByPFVw0Uzk1w3S66utc+ZCSriHhBnZ6iD1nV3EAvbIGmXauH8y2pab6RWhpRjka2MuiFjDVSVOmkYDQYCTQmIU0++Df8KAY9uuSxcFExuJsp5E9ZQ9TeLpzedpecrxo1SUDGkEfmueK+RDX5s+HlubN8xajbZVnFxQSSnf65qMoR5EF0lKovOZW1QYslNTWLXiw1jUs6rmm8kPqsQFX/ZhsOd2dwZJiHVOetDckxTg6VNy7ORb3f3FIcTHOtHQ1cdxvn3ulKunB4KNnirAjXSTpfzIaU0SkuLuBcsjHTfaweIouiy/vNQWFPESNPjyQvGsOB4+bN0GFwbUtz8eBQc28yV18F0lXkqFoT8iKMWQ6nvhr2SEmpsc+WHNMixAKlggVo3g3kkxR5JlrTGE9yXXc21lw6O1L76AwuSmY25V4RzhQpXLt2t9Rvw9Fl7IWC9E8/RsOB/cHv/56ZmQ3H6if+aTOl6wbc8ZZlPbuQku/tygUoxvc6kEX/ys9/rmpznfwHX5PU6Hykzxt0JKHMKQn/Dttq6nsR5T9oN6CLTuEkQpXmzW3lriGks8en6qfrO6HbYA4nyHWWLUCeCdal1VKjBSjKwkbTy3lDV6sUe5AZ9nFZjc9GoayHbjMN7KvbDeWWNfwGCkVr4rgxpGxpjceBNKkOSWMZxvBsxmuHXDHBuoQxnmNjNMU1jTHeRSwZUYZ8fHqmtcvMLEKOvH//XtVOkONq2Ock6WXfRtGqZR0zW8hec+STYLcWSEshC2yEzpZdPJN3IXNtUbaKZFZm6su5UYKN4zKfYF9VYM0rolBeFMqhXuysNuE48Tt47q+XL47ZUfTi9xVmZl3sc86m/BrcuxBDjeTV48WZLg6Hw+FwOBwOh8PhcDgcVwB/6eJwOBwOh8PhcDgcDofDcQV4JXlRkiS2vnFJhZ7D7aEGPlAxFydnPFKV6lES0njqoDa1u6L+pDhWDVW4S1TYLikJAj0tYaVk0sjweT4NJQfFVBTXKaRHEShSBaqDz+G4M8v0nbJgNXfITUB/7CQhbX0XrhNNXGMfdPhjUMdvrd+yVUQaJ7a+uNfBieiBcVPjvtkCJa0Exes0rEoeodo0qbCdpqiTA1R2j0y0xjW4DK01xbnjubOZ4imGZGlkooabmcVw5irGOlYjZbV5XccYziAxnCuaha776EJjTXew6TIFbk1UXVIvO7iPFtxGBqNVpvpb5UKTzSnze7HjUB0UyWiJoB6B+hyjbaBcYhpbQepghH8A/ZdSQjo6kOkc58sURP2+wGVQElmw8jrkT/GMR4Z7Du6nDSr2Rq9jRAf0yUlfMoAS8UZq6nDhnFWUqyUvKs0sX1zzbKJ5OB9BRkWZBf7+wLXDzKzX1Xytw/XK5ogD0LITBFGEfMAwoBMIJa4cx7NT5QMzs3YL6ySucTigYwjlapAh4rhcazbgejUYKpeURShJ7NTgpgaZ5myuvp1lyrVTuKetHEozW0jPKPmr0ZFsBskyXKJu74fyoj/1r/xi1X777R9U7Qj8+dORfr8LSVENcRdBJlCSsY04mHNflIXztcU8Bce0KWSTU8RIgQxWwzzZ0E/t5r5kL/uQjzy7UEyYmeVYvyLkVLryTSDRqi/o7KtG9c/mWSX7o2SVexY6FnHsKN8zM9vdkmPURlfyw0ai/UkbuaXXVtz8uV/+StWejrX2/+43vqvP4QxVq9HVL9xrNLAH4p6iVqNMSs06SgrM4Ro6nOo6OqD6P34omd5xHO71bAvOlbHiv7uuOZZNdI7ROJRErhIiiyxZSI1jSLdS7k3QnylKKdDZ1cysgLwsSV4cezVIPRh5XezJu3BVKyALjCPI96mpXvobfk4pNL6X4vfBsXB9Od1xZ2qfnsi1zzq61uOjUF7Ux1762k09NyWIowLPZrVF3K5azjGTxL1c+rRqIcdDgWTJkoya0pwG+rZAOQ9UbrACLng1yPpj5DW6bRY8OZ6f4nmoCyy5tkLem+OdQ4G9BvfHNDBjNKbct6HsQ16E56Zjco58Ms8oj9P3Kcf7pHCmi8PhcDgcDofD4XA4HA7HFcBfujgcDofD4XA4HA6Hw+FwXAFeUV4UW29tIRO5EBW5SRp+JmpQlolSGC2VFm+AqphQIsQKx6guPQPdntX+E8iOElCDIkhPWDG+WQvpYyPQlwyV/JMWqPugO0WxaEakjickd81x36SWWojdnpwjdjfVPjkUzXKeieK0ta7vrBQis3gxHvOZ+pUuK71tVJe+EI1s0A+dEGZj9X8PzlXs/zHkBDEcpjZR1X14+FjfL0TP3NgSlbeEq9ZwGr6fHIMa3QPtv46YmNI1CPS7Oai9NgDNDqE4m+rzPu7ZzGzQl9OVzUXjr6e69m4gL1pd2q2VpZULaiudgmqY3wXaTVZOX1LE0OigDrp+jN9QohinpMFCMgLabPyS19akOS5/qaQbEfJdGb9YwlQwXkAxLnlDEe4B8o9Wm046Zj1UpT/F3IKRj5Wgh84WMpNyuTNXAYtLnkFSlEG6GbhK4Z6jJdptHTFBSdJsBpoq5Ip09OGREowRXQBySG8bkJecny3Ji0D9phPb2ZlywHiotafZgZQW99pEHpxAUtuBzDebhRKRTVDNZ5AyHA/pAKXvF7NVdkwrLVnIUpuQOL92517V/rkvf6FqP8V6PRk/C47U7Ui+tbUtWcQhXFu+/aNHVXtt/1rVrrUkr2i11Od1tOnGQSq25WHOqcM9soAkbI610uzJoO8AACAASURBVOBwVCZwSsEeK4NEugMZ47UdyWE+fIL1yczGM0j6MOVy/M95X324tbOIw1Wk+i9ydQGHHuYArlspJDrtxlKextxtYi1/mTNIkqov93e0D/g3/9yvVm26kPzuH32vap9CfhwvO+3VdKwW8k8D8yLYazf1Od1lDpDLioxuR+qdx0/DuTPnGphrrbp7fb9qr7WVl+L5Cq5RQLywLKSRFV0WM+ydC7qLLbkGlbnGM6GrDJ9FMMx0Du1BttTAM9cEZSPilPpGujou93/8/9kucR81SOfGOF8O+UlWKo/Ryev8DLIjM3v08YOqvYec2sY6NpgpptJydfkHz3vw5dFPzTwdjsJ7DmRBwfGx3w2cNDUWNbhTRRHXITy7NDDu9pJ9rIXuemUgk8I1QbKaUEoLGdwc5Qj4HM4HgXh5b459/hzXMYGEOEXctYol56VPgNWNNIfD4XA4HA6Hw+FwOByOTzH8pYvD4XA4HA6Hw+FwOBwOxxXgleRFZSkKWcGKwaDhr6+JEns6FKWwUQtPlYMGdHwqZ5gp3DnKFNXS26K+bmaiHO3XRYfrdCQTacAhJsO1FhY6uUQtuKDkok+eH+t7jw51fYeHqpI9GIpyRAFIAzSqWkP0TFuirdNJZB3Xnj0UzfL4QDKYbPg5W0mUipcINL71DTke1Hvqm2dP1PdFEb4XLEAl64JmPYNj0RTSnA7ouLug7LZqcFRA1fQ6qPNzsXGt2w1pZDEqqncgc0pBBy2hF6rPdH+joa7vGWROjZ7iLwel+/AspPpPpzr3Bi6rjSrjJejl8QrStIlyQVEk9TkGpTmGExqr/ZfL7kWoXG8pHWMgS0RKLEtITgL5CcYYcsrQswi0yCh0hIhAw+UVktkdkYJLCQEqxuegFWcl4hYU4TIJ824b9O8U1Eoyg2PMh/lCIlcu0c4/7YistHhRLX86Uv4u55ArQlOFAv9WlOF4lfh/tkc5ZHszySNyhEQ94lijMj7WP/Y9q+dnQWyZDfqSpNAIbwbq6xDqqRZyWYl7rdW4DiFHQUY1G4dOIg1K+CCFKAY64WCkvp1PVlfSGEVm6cJBhC4tCSQV33nng6r9T3/7a1WblGQzs1FOCY3WtS76cASZ2gC0+i+++XrV/vwbn6na1+7eq9qd3d2qTeeraRlKUtOZzj06kmPM08ei5T94+LBqn51JIjSYalxHWFtLrLl1SNa2N0IZ9AEcCHPENGUppIIPB5ffL5b68tOOsiwsez5+lHJQBoSJW6MbUD3M0y1ImynFpxwnRjtF/1OO9MYtyNv+jX+9at+8dbtq/4Pf+q2qfXSi2DAzm2O/VWD9pOJ5BjeQONc1ZZAWTMaQS1/oHFMM8QWln2YWn51W7Rwy+wxz5Mb+TtXuwnFlFfF8banVtfHMIYWYobPo3pLPQylnD3ubFvY2XZRxaMI1qF2HPD7G/sAQU4jPIqYjDfYKS/qWGHuNOp7BWpCGzCEvmmENPsNawv15jG0I18fBRegu+uH7P67aX/jSl6p20mNugnRr0Z8/KZH69KN8kXsRHYs4Rujj6VLJD8oVA0chzG9jeQ5spfIY632h+I1b2P9AglSiPEO+dBlcuQrsfVM8P8cln9v1/RLzIuHzAmT5dJZLl57JuR8aY02a4rh1lJNo/hS8FWe6OBwOh8PhcDgcDofD4XBcAfyli8PhcDgcDofD4XA4HA7HFeAV5UVl5T6TotL0BNXPG6ho3wI1d6OrqtFmZhkote3N7ap97Y7ogixqPByLy5SNJVsaHIoO1KvB3QR0uAzuH/k8pE3XQbuewQnm4uipfj/V/a3B4WEHjkOkypLaew53mmKJRlVAatNs0pEAxxrBRSBbTZlIaWVFNW2Awr4OGnLcEJVrBsphrycJkpnZ2ZHkXVO4Sg2HoC1DFrLVVTxu9nDuHVAte4rNCBTVWlMx1EghTTGzApXkE9C6Wem7nsENa6JrmkxFhTw7E6W2BilVd1dzYrYksaLTSrMJ6jKkD3Qvqi0z6FYIZVlWNFKqpOopJDdWW/6ZmYWyQjMzKBeD8cvh5hEnlDBBkoH53YCEKeU5SGHEeZed2wrQHgtWSwdBdAD65RCU/hmoxJTUUeo4HCi+JnmYMzj/EtB2yU2tI9ZnC87wqmWesiwsm17m8/6J5Jo5HAty5H5S4fOl8UrRT3TLK6EjGg8H+IU6s1EDWZYuE5SG4dx0/CiW4vcYuS/LFQdTxG80wVoXrG2U++Be4UBA94LltYqOAoFaEfE7noDuPQhlvKuG5/OSLOsPHsplKArum3LYsOMot+50IL1AHLa3JQE5H+tYB88Utx2saTVIAJrrcg2qb0qiHOeMR7P5hf7/8OHHVfvH77xTtR8cwD0GSw4lMflEcTCEtOxioPiiO9bl/yvnjCGFo8sXN6KTRR+snqQxqnIqVQpNyCwM+YNy88mS2xcdYkbI83S+m2INnNHVCPL2HvYme3CI/LO/+ktVe2dH+7Df+H8kNTIzOziCzGys6yjgyXQK2dwUrk2UyAby7wy6BPRHseQgc3Gh4z6X+5mZPR3pmoaQJO3taS6sJCrpsO41g6SoCdlQG3OslYb9tg75wxqdDJE3ogK5HrNvjnENxgNrYMl9Q4rPl3QiKXJkow5JfJfOsrqPU4wlt6xNyPenY+zbkGvH01CC/+TJk6o9hDPa7u71ql1QXpytnqzoOZ5fOWVEwRJN6TQ6drIkQaWMi05XdPGMEIMF1oUC0rUIz8gFSkCU2LuW2CskSVi6gc/COWMNz/F0+ixfct8F91WQyOZ4bpznYR8wfw1xjQXyVIyYrf0UrlfOdHE4HA6Hw+FwOBwOh8PhuAL4SxeHw+FwOBwOh8PhcDgcjivAK8mLIossXlDG6qCtnZ+rAn4T1X9boD/ub4fUv5v7+1X7BiqpN0DHpbxoPAE9aIIq56ANTY9F/x1RMgCHo2Qe0onyiahn00NV70/GojBudkUnqrfkRlRPXkw1H0FmUAMF62wSdvc8ozOCztFB366vifqZdkRDXiVEkVltEQtbmxrfNEFlfNJxG6CXTcLxiuEONAWlcAY5RkSKfuB4Ixpbb0Myts6urolOOAWrebdgZWShjCjGu8s56KBHT+AMcSB699GR2hN8P0OFf9ZYb6Rh3IzwPSoheN8bHV3vRk/9uWqgvCglzRGviyPQ1CnVODkPK9oPUIW9D3nRCDlhRikiYpJjcHNP0q/Xbt+o2h26KIHnWCtCfdcc43SGGP7+Rw+q9o8/Fj32HNKAGLz1NUg5NzZ0TXGinDgLp48l6Dg6bRnaCSiTUSVtWy2BUZHnNulful+cQpbDPDEDlZSuKukSXbuOsT+/UN/SMWGKSvctOOdtbSvPtNuQLsKliKTsyURzm/IgM7Pz8zP8H6rsQ3pLyeUGHIQyUqlxRq6rk6k+X1I72BD9ltEZAYk7x9w5WWV5URRZtBifknIySBxiSDkT7HOKSUhz32lBCgr5z5ND9WcHzng/84Yci+5sS1q7d137paSp6xiPFBPxOmQs09A9agbZUtpSnrp9/2bVvnX3mq4JKauGe43QHyNsH//oh3IL+doffDs493pX68/5SHu3OeQBdPOq11cr1zxHaaXkZVg7EsjsG6DbzyEXn0/lTGZm1h9qX/rwsdaCGDKiCLKtOdaFHlzL7l6XnIJ77jqu4ytfeqtqb22Ecu7f/f1/XrUfPJb8bADqfQmJ4miIfTrsbEqM9RzxVCAXJWm4x8r4e4TEfE5ZqOJphRUidhkwl/dFWUQH68QNyMC28UzSqofyDMrrM9jVnJ0pB8SQvT49x+eY301IRtotnaOJfU4HUpJoyQmmwCSg3G7OdeIMcY51+gBr6zCDlBbHGWHNns7Dcx8f6/nt5ET78Nt3EDtwpHmuYllF96IX7c34SYr9Zo2uaksyakroeygH0kDJhaKB2KKzH56F6fgXNyij1prEfs6LsM/5zMznt5zrEK41xT44mkPSiOeCHHukCWROzGNmZkPsgUaUF+EZL3BRWpInfRI408XhcDgcDofD4XA4HA6H4wrgL10cDofD4XA4HA6Hw+FwOK4AryQvskgOB1PQaMdwL1pLRVuky8dmL3QvasNd5fxUFNnROeiJkIO0QHdqNyWzYYXs8UgUzSITlanXg8tQEVbWH52I0tY/Bo0TTg7JmmiZWSKq1WAA2j8rM7MqNKiQtfqSRCWo3K7fbKFa9+1d0T3v3b9lq4g4iq2zoCe2II2ZLuj/Zma1NfVrG/KqSRK6MJB6PIdsowHaWw/HIq/x6amojLW2+ri3v4nPX+yEE2UhFY8yjz6okO+891HVfgRJUbutc9x+7b5+i9g/PFV/NCPFw2YznKbRGE5NoLfVMPfAeLcW7nX1UFZV6uuoWJ6AXliQpp1TKhRS/3Y24fQRQTZwrH4/evS4aj87FEW1AMX18IlyUVwoD75+W1R9Un7zJanGGLnpnQ/er9r/7Fui5R9f6DsNVHO/d0typtvX96o2aeFRqvtea4bSstNjxWoCeR/dCWJ4ByT2/D5Wi/Jf5Jn1Ty/H7+JE4zuGXHECJ5bZFNX007DPjg5ETz74QBT7A9CWz/tat+jIcH1fsq/XId+4hs8bTeWrLCc1PMw5g5HOMRopl52eK8/UQAm/6Cs2E+jd6nW4pmAdPzmBXHNJl1ZHDkphhzaHPC5C3Dw7DeUSq4Q4jq21GJNA4VWwT8oXtIJpZGZmLfzrdkdxdXyivvrw/Q+r9leuK4cMm8ohmzcULy2sj/lMcRAN9Hk5CZPOHK5BnQ3Jabf2dqv2DPOk/1hSx+lQaxTN0Lqbyj9f+exrVZuON2ZmB6fKOSldHhHfc0g8689d41aN6V+Wli1khgko6AncNegeR6erYkma0W5rjbl1627VblFijvVw0Nd8myI3XBxqf7uGeb+dSPbYhRzyzTva65qZtZKvVu0//ObbVfsHP5KcbJho7Eoq3CJKwSHNg6xzhj7IltxvIuz/C+zHM7g5FpBnxxehpG6VEEWR1Rf3WMOfwvcgSdzBvrbDLy05FDJLTSDJaHdUGqGxrmeiUzy/nZ4rjtJSn/eGyv/r2E9GmKRrXR3fzGyGvdg5ZKwHOMdHx4dV+7gvqVhUx732tG8b4pmBLo3TJZXH1HS+Y6zTc+hmufYVi/W/WJL0rgKeu4TRDS5Gmw5ma5CrbdfC54KNnsZvExL6Xg/rCsKO8tAY+4USa3/zJXKmArlhbmGfz+Dsx20nXZjmMWSJkLvFLUridN0Z5PNnM93E8ELxZ2Y2gsVp8MgHmVMTa1gtD+VJnwTOdHE4HA6Hw+FwOBwOh8PhuAL4SxeHw+FwOBwOh8PhcDgcjivAK8mLiqKw0aL6PKU1dEOIQBVah4NKtxVW2D6HJONiJGrSgwvRkYaowt3aFH1+C24sNzp6b7RbV7sEna2Lau5ZHPLQToeiwo4gR4jWRK86y/T7h4ei3F0cH1XtZiFq3CauowFJUdYK33EV4CInoLXtozL5Rk390am9eqXkTwOSJLb1RQXs+bno+ZOp+rK1JjotJWOtZhg3jTpCFuXqZ6BRznLQ1UDDJN2M8pTZUGOXdkELjjUO5RKL7OJMlOuTE7WzUr9p7Mo5YBophg7gMDJhBXbQ6qYDHbO3pHjKQO9P7cUxMRiAJprUX/idVUBpYpnHGDM6hozHGT5XfHR6otGbmU0g9TtC/jo/U1/RgaUNF611uK/tg/JbTkVXLSC1aIKCS7ciM7MZHTzGooJf31KOa9RElT4D7fbsRHTIcqZ7ePpIQdLugWK5RCFtYv40aso/dKRhH8wXDhKrVtQ/m8/t6OlTMzMbwklnSnkROMk5pA7FkrTmo4/kivcRKOwPMS6jMZ0QFJuHkP4MBqRGS2p0++adqt3siPadFWHcjEaKr+Nj5YchZEe9Nc31wQhOTXD563QUExPEb7+vfhovyVNqLcVHuw1pHtdM3PcZXExWDZFFVltwmaeQFJWkQcNhwSATjmqhhLg/Uj+uMR0V+s0AMfWtd35UtWfRvaq9gxh8657kJnOsJTNIMKIlTWMJ6TWlK9OhYur9B5JXj8+Vl87PFLcT7Ku2IL+8dlvXev+mZEdmZt/7kSS3dD4ZQmbCUJ8unOWKFUs6ZVlWLmhNSMAiUuHpeFlq3GtJuMg3ITkswOk/udB40QGE87DA/icvNG+PT7V2JE2dbxtSf1uS5DZjxdGbtzWu69jPf7AtCdPhhXLIDPJvjm82UN58ij300yW3wVEBt5NI5+PzBiVaebaa+2Mzu9zkLC6/04GsEM86TUhEU8z1NA33dzVI2BoYWzqzvPMjyZqf9jXXZ8gn1ze0FjW31WbphTqktPU0lMiNIL140td69R5cij4+VQ7pQzbfqCn+t1O1m5DX1SFfys7D9aZGR1FIF+fYb1Ne1F/kxyxbRXnRZVzQ0TOBLufN2ypL8VWsHelAsiuz8GXAHLKxAs9QQ+QW7psbcANdbyr3deCa18YzHvfpdI40Mxufas80hgvfFHO9RNzVGnSF1LlZAmKUo3PgkhYtPRvRDY25OsG5u5hTjY1XdxR2povD4XA4HA6Hw+FwOBwOxxXAX7o4HA6Hw+FwOBwOh8PhcFwBXlle9NypaD4XJShFVfQGKHC7oMblWVhZ/MmFaIsfPhGd6ONz0XiOUJk8S0WN/PLn3tBxxV4ya+u3m03Qy3LRjJI4pI9dgGJ2EYl2NJqJQvRH74t2++CZKJAZqvpvd0D9m4vq9lk4mjQa4blL0LHXQbfqbaqK9BYofvVkNd+RRVFkjfolpX0GCUY+0rhTlpZBKhQt3XIDtN1xX+M9RgX3C1S3n1Luc0OV+WH0YENQ6ZMzUS3pmDUbhhS442eg94PJnTckS2vv3q7a7z6SrOr4ROfoP1E81cdwmOjqoLUy7IQ2KnEb6MMlKHBQjtjeekh5XyVEZWTR8/snfR4SK0rFckh5HjyVE5GZ2eHRu1X7xx9rPM4nyg8ZKIk1SHFu7oBqixy3Drr8eKzYTvdFxU4a4fhlkKPNUUm9MMk+Hh3r+p6i2n8mZq4l6I8OHc+QM964IXcSM7MU19sBDXQ2BzW4JB33MibLJYnUpx1FlttwIcGZQV40hovLFPRn0kfLIpQ1jCG3OjiH0wMoq2dIDxnWulmhf+g1dNx9uPml9zQm3S3RVWdRSJcvHug6pmNKhyCZwrWeQcY2grtJlkseN6HcCs4OsyXK76CvXJ2UyCfIuzklavZiF7hVQbmQF0UpnUGQfyA9TeEUMS9DJ5HjieKlBqcOSiFyHPfHB5JbkI59/6EkPjdAsZ8WGJcI9PwlmvwU45fi2j9+Jor5P/n6t6r2Bx891D2NIV9Dnrm+pvz4VbiNdBuhpHGrhViP6UjD+QdJzHMt76rJiyyyfOEo1Gpp7zvHvZWUYESQFy3J3s/gtPhPP/5G1T7CnmcGCVlc0+e39uRMdGtbktXdCRzqUjpKIa7nYdwcHinuzoe63uFMxyoSje8Z5LJDyMfrkNJf39L37956q2o/XshBn+PdJ1rsnl0gH5VsQiZerNYaRUSRnL3acAHaWNNavt7QfGvWIH1YeozjUn14qjXgnQ8kUfwQ68e8heeNpsYmOtaa0W0pZ2ytK//Eqb5fxuE+p8Ta8OxI8fbgRMc9hPxyNMEYDxWHT7Ffvon9TIq4szx0Oe22UdoC5SXoLBvhmeu5E+qKpRwzMyvjn5RElSjNMUSpgR3kibgZ5uk+ZK5HGKMTrFUP4fhIx8b1mfa+a7HWsHWMw2fuS9q0Dge9oxN938zs5DHcJvH5INK1py39/uMz7fPHiOtbkNF2IcEr8cwfL1nktZCeuW0fIX+tr6vf3ti7py/9/d+zT4LVfIp3OBwOh8PhcDgcDofD4fiUw1+6OBwOh8PhcDgcDofD4XBcAfyli8PhcDgcDofD4XA4HA7HFeCVarpYWVq20MTH0O+Vc+miqIPqwZau15Xm18xsDruvBDq7L74u3dfZTMd9eCyd12fuqFZGJ5dWsJyrVsIYOrQRdOvtMrzlPGNtCGm11rrSUl7blC7t44+ly75z/Yba11TL4xtf+92q/awtXeudjmxnzcw6sJDepF5zTZrc1q7aRUNa/JVCWVq2sN6cYdwj1AIqIuiNYXk3g02qmVkNv5nMYPUMG697dxBDsDjbuqY6G8VIOsIp7GLnsNUrS517dK5zmYV2cznsDGvQSXZRW2N7rOvoptKYPh5ItZjBjjOCZrpcsoVudxUrcIs1yvg7sFSLG0t+1yuG57MkYb0V1N3o4l670DpHedhvo4m0wTnqZaRNWCDCYrnTUc5KYAN/eKI6CPdQJyiGdV2BXFJb0jrHqKlQqyl2+ufSXLehod6vS0M9gf3hBLUWcmjpo47yFXOwmVkd9os92LGfoT9nqEEwXwTVqmmdizy30aIWTh/24JO5NOQl7rOGWjebN8M6OGtzzUXWItvY1vfOUFclw9qzkSif7MP7fWtTa8HOjvLS3i3ZO9YaoZ3hR+9/ULUbqJsxRp2sGLrnKfLJEH3QRA2kAvEUIc80G+E6mcFGewIrapSQsQnajOtVQ2ml5Yv6EBH3OawZgfnAKi7RkkY8Rv2tzU3p0K+j7sYQdTT6E60zzBtNFOm6OMXalWl/0USNpsk8XDf7qOmTQN/eglXx3ob2MB99IJ38zVtaT7lXm/VVcyNHHDWa4V6vi71fjNhjLa4I67Tlq2fb+hzP82RaV79GOWqmID5i1K7b7GpPYGbWRNxMUWfgxr5iaIq6S62Wxv71u/erNtdDLHM2o10urJqnWRi/F6jzVNaVc9KODnZzQ3nwyVDxOz1/cY25szOd7+6O8uD/296ZNEuSXNfZY8h5emMNXXNPGEgAJEAZTcYNZaaVfqw22kgbLSmjRNFIUCC60d2F7q7xzflyjDm0eFl5Pg82RFSbPbHT7J4NvF5nRka6X79+I3HOPU9++XPvs6PffKb3fPa1rou+SWyFExQ7dkgBYRC4/ianT4aq80cj9VsZo8dcB+NGeegq1LOLGL000Z+KPU8i9Dx5tKf16JWoL0pds0BfolmtGGy0AfNrfTwj3u0rhoct9d24hpX4fK24Wy5hQ474itArKgj8Xlpj1N4HeIYqsf/6feWlbHOQhZFve70LqDd9HQPsixx9IL+ZqTb8HHbdDxoW34O2Yu3bSz1v92HRnrQVN72J4vS4q95Rw1prl0x1nSV6Ho7QryzN2JjVuWCoz3iwj3PyWtcKB7rXJc6b0yv9BsDWl8Uaz2+p4rrX9uvjuI8ch/59nNsB9mfxPfpIGdPFYDAYDAaDwWAwGAwGg+EWYD+6GAwGg8FgMBgMBoPBYDDcAt5PXnRjiuecc64C3SYGwRZOze5gIBrrMawwnXOuDfryw+N723EAq8kyEm2ojGUtB6dU14YF4SAUxSmdi2ZE3lve4OLRMorSKDCc3I/u6DMO/uzxdrxew/J4LYvXf/8TvWa4JypSu+NT4KJI91X1MZ/48D7mkLSmXUJd167a0PpT0J67PVLQv5u+FaxpGubccKC4GXZBJ0UkD9uiVN6biGY4xnx3IVMahaDBgi8fgo7ZcG91AezMh/gax6DglqCxldgY07nur3cImmdHNN0OLNkp93DOubCnL5vAtvbySp9H28469edwpxDULgjfrbPWO4aFWw+SrkPEzqMHkmo459wnH326HS9Ad52CXj2di4rZge3nHuzDj0H5vXcoOu6IcqSQqbVh+d3TPd6/K3kSJXJzSENobZyB7k2q+qCjez1GzE96foq/utS8PUeMXMBWeZ4oPpONbKbeMX1Rnufu7ckb55xzC9gZ5rT1LjQX44Hm7ykkrs45Nxzp7OrCqnAKC9Uq1j5uIW4OB5rjB3uKj8OxxgdH2vcDfFYKK3vnnHsC2WSAMy1GnqpBj64hpaLzcYEYwjZyw65iJW77NvOrJeIRUqocEglaa+du92jaW9TOVRvf1SCAvAh7gOM2JBx17XPsR6Duf/hEdcHHD7XvL0Gf/+xLScgOx4qF40PlnBKy2gIxki0V263An38wvl0BedidifLXX//lL7fjn//kE70e1sR1wf2j7zocKs7nqR+3A/w3yjTjBLEK2ZJ7R9nesZxT17UrNnsgQD0TQ0YVgqo/AZX9wbGkXc4594sfa/77kGdFyC0hCuEWZLG9js6qEmt0da4atYDkPqpQ55T+nFOyc3yg3Pf0keKmQs3+AWT2lLQFzEWeNBgS89ivj5/CNvvXX0p6uySjn5rq3XWMdmEQukHnJudOBqoPOpD/tbjeHHf8vd7qq47oQkITtXStBdYghBS6gzOxC0vm3gSSZdxfDX/q9drf9wGuNUR89keqTzqoW6bXsJKGLCXZw1kHGd4Msswo9uucg2Odqf0RJHmQybZhdx1t9qsfgbuCm/wScuvi+bzAHsZx7SZ9/9se9BVHlGd9sK/n6u4RxpSoZWiNAOni5Upn1QD1ag07+X5DTRriGWoICfwwUCxT7nNnX+1G8scfbcez2ZvteAk7+hA6yzr042bBmgt7xEFeN5vrub9Tvz9vxZguBoPBYDAYDAaDwWAwGAy3APvRxWAwGAwGg8FgMBgMBoPhFvBe8qK6ql2+cS+KQE3vd9DROBIlp0d5RuVTz0Y9vH+Mzu3oQh2E6CQMymQIaQia5rvZpSRFdS1aUwj6oyt9Kh7pnlkq2tDQiS51MBbF6R7oxouV7rVih/oj3XcKXcqy4SgQgBJ351gUuLql+ejBfWTY6HC/M6hr5/KbuGHX9RhrXYLWRepkUPm6niPESn2smBhMRMU+fCDqdgt0fboFZFONi4VojWGktY7hMtNu+Y4MpPYWWNdWpO8x6uveJ5AWFHuQwR0p/uZiw7nrN3LImSY+5TcDZZgylgLd4q9nit9xz5cK7BaCrbNVDTePCmNSq/ug89/Z8yWN+3tyiclBfV3uae+t4ATD/TkAxZtypjjSa9qI7ZC6jdrPOW240tqzdQAAIABJREFUjEwgVRogJxagY2cpZCyQnbV4f4h/0j6LlSjazjlXLpQje4idBJ+xgA3NOzputWNU/zzP3MmbGwcWOo1la33PPtali7x+0JBxPngsl7o7B4qh2ZUospTWdEC/3keX/iGkFaT9t/B5FZy4+g0XmMeUy0EyVRWKlQW69KepPqPE/78SgJIfI4b2Rvq8ulEaJAvNW5bpuindKzCHWbG7XP/aOVe9k1ngzKjgStWmFCumTMZ3Q+hCMphjjx1NQPuP4dKIuur6ShT7vNR7h5Bmz+eKg9lUaz8eK6c551wKl4UCNVoAF5p95JDhHcReoc8OeR6j5qn5dzhI3FxXOa6PHNluoY5DPi82koV6x7j+tau3ji0FHJgoLyoh7Zsg39+Z+DnnLv49Gmtc4rwJIYYIkJ/LQjk/SZSjilLjCOdTO9B9hJFfp0+xrjHyzAQxSxn2wRBn7rHuO8S5VULisYaU9SL3pXl3DlTHHfYld7jO9f0ytAsowoYGfIcQRZHbG97skz7qA2+NMY6QazstP1d3cYZ04M4y7OocowNRCNlRlTOOIAdBCTOCLK7ieZP561dXelPqUG9D67gPx7TxSN/7eF/n5hqSYPrchHD+bDdcaA7vKXY6fcjtIBMZQVI8Gt7cexTuoCw2eOcwSUs9fc8I67A/0Fw8PvAdBh+PNf8DyBgH2N/dtq7bRq6osaYXlxfb8eJaz9QZ3D0zyi9XfqIPQtQUkPL0enAtg2zbteFCCccth3OOEs024qHp1rZe67krRz6iAWkL51bc9+Puj4ExXQwGg8FgMBgMBoPBYDAYbgH2o4vBYDAYDAaDwWAwGAwGwy3g/dyLAufCDc8mhDSkhd9uWqA1dUHDYdd755wLK7jyQDoUgVJdVqLuBPg8V4n2k6zkPLKanuv6oJGF7L7vfDoQu4Mv5rrW7Ox0O74Dh5IjSHz2enBaQhfvZS7K3dlKdKe6IRNJ8J4SdNQuHJnCnqi5YVfztEuo68qlG5prF9TEmhRc0M0SaMaWK9HLnHOuB+nEZKJr0RkqAtUtwFrk6MxfoJO266Mbew/UMzhwFJFPl69ByaS8aJWI+hquFSutQPfdDuEe0sF1h+jkP9TWXGY+5fF6oXvP0GG7C8p7jj3iIlFzdw21c1tias0cAHptDEpoG/uZjj7OObc3EOWecsWs0v7mPnZ0ncDP0zmkPyWcpULIdQLcU93ozh6DDtwDxbXdUhx2QSN3kAY4xHBEpxp8Xo57Wmc+XbwLp4IQDicJKN8rutNsZCI7pi5ydVW5fENNpVvPEpTRAq5la6xpkPg06THWdf++pIHlA9CsQzrYgAYLyV8FSVCGPd2Kke/hRhY3HGgGPbgf7StmZ5eixEZYqHUt6n6A9a0Q4yHudYj9sk4bVHE6E8E1IkFsZtgjDab5zqHenA8FzqIaNUWO+aH0rnb+Zu93NadHB4qdoyPR6nmW3Lkj+drrFy+244SuZSVkuai9Iqzf4MCXFy1znaPXcF3ci0DDB6W85RRrIXMicgu/dwkJX78RO8dwwtiH/PPVJc52Jth3eS3YLX1RVdVuvXFumsORagJqewZXugDr1ev4dWkFN5BsiZyP90SUGvEswIETQH7TaUEOj33LaY5avuSA65JS0gjZUeR41iH31ZQ44J7w2XQyiir/kOnDxeQQ0rfnkN0FAR3F3M4iCkM32sQJ5UJ1xWcEuNRhS4aN7x1jIjqUDkFuUaO8KJGz2H2BTngR6qo21imh20vqy7tqx1pFcbREbCeQPx9B9tEbqF7OIYW7Qm6Z4bv1e74UdzDUc1MLTnwd7JkeZPerzXPGjqWcDW7muaKiHW0zEnypU+zhoOW3HehiL01wroxq5YQ6Q6xALpte6Dlrdar1Xc4UE7MYsbLQPa3X/plZdvHvFmoYh/uIkNcqSJByXXekt7p5CMk94p3tBJzznz0TuIa2kXc/+EBn5t27R+59YUwXg8FgMBgMBoPBYDAYDIZbgP3oYjAYDAaDwWAwGAwGg8FwC3g/eZFz7h2zrAbdtdUDrR4ykRJU+KDyKUQ1JD8BaGgtUOxjRykKO7WL9hNk4NaB3hZDfhDVcG6IfC5er6v/xgbYK3TlXqWQn8DRJorRQZmAbKYFKhNpmDf3pe80gztEuAfa3FDU3KjboH7uCGpXu6q+WZs61Hzka1Fwe6CrzudyE1qDjuucc1Uf7gddzWcPjlYh3BYqUnOxdq226GIB6P10B1isIBta+TQ0LLFr47o9yNdiyMnCgPcBdybIjsrWd7viuNCPs6uZ6HQ59s4Buowf7oMq/v4Ntn9AqF21yR0haLMh5A5tSDviClLH2N8vATc4qKWd8LtleyViku4clI8EcKEhvzPAn8MGZTX06NzITTFyVlv3zngJPckJ6Ma55qMC1T+oGjmH0sVA85GmkCSllBBsnKPcDnK3N3suh7QGQ1dBXrRa6zxKpvRIcC6DQ0wHcpEQ+7KF84Zxk+AcSZbat6TxR6Dwk3pfN3RpmScV09r34HaxmEK2hH3B8QqOJnUVfOe4zH2qeJErp+Zw8aGLWIlYq6odjBfgnXy6LBgwkNMgdioEVdioL1pIvqS8l9jHD598uB1//QrSCczt6zdyhLh/JJnSg6dPt+P9I0mTRvt6jXPOdTqqYU5fS7bkUjhTjJQHK8pVKOEAbd3V+t4BaptWy98/46Guuz/SfbRDfacabiq5exfnuxVDdV27bLNvrmeSTQTQghSp8kE1VP7otv2zKoEsPUANOcQZ1oYbSBvry6xRUUK61DUrrGMA2VEn8s/C0VgyOMqLSpyzPTg71siDNTSGNdxLyzLBayDPTXwpbIj37B3qvlqvkbNK5bUq20ltiHPuxu1wsJHUhJDiVNwDGDK/lnlDv4wWD3EbNUnrux/3GJ8l4oK5j+5YEaXW7PrQ0JS28XmTNh0Vdd0h62g4C8YR8hKukyJeWpiPduTXOTWePek0Sre+sqSL1s2+pOxud7Bp+eHJXIUC3IrfvjjZjn/V8+fs4V24TXU0NyM4jIXnyiGUpYVoBYIlciWeySmLXWSa54sVdEDOuRgFci+C5J5tSCBJii+VaytIgNe4TomaPcNvEXnunzHXiMElrjWBqy3rgPXaP+v+GBjTxWAwGAwGg8FgMBgMBoPhFmA/uhgMBoPBYDAYDAaDwWAw3ALeS15U1c6lxQ01pwc3F0p5SD1O4AJBpxrn/M7mNX776QxEwW331LE8W+taq5loqfOlqEUF6NitDjod9zRuRz4FsQMXiW5H4yXkJAXuvT8A/RI0pSwVzagL2m0P33MeNeRBoDbV6BDdObq/HY8nkhd14938jSyoaxduaHslXEKypai2V+kbjdmdvvDjJgDlKw4pDYPEDfzHArTDANKAGK5QKahuZBeSzlb3/PtYOMVgQVMduBBEGJMySgcacoHZhZ5uBE0jkDVkAAvQ90ZjUTi7HY1b4S5SJjeoa9n/MGeAGuogHwwhu2i6vwSgGNaIK88UibRrSIdqSBpLSN48ciLuo6Y8o0F9jZAvKcekyw7UQi6mCxPTF74f9wVdIDqNOQgpu4QjAbcZXb7eDXfNGaJ2tUuDmy+V8uwBLZ5U+CXOqnPQVZ1z7moqueMYZ0kV6LptSANzSHHOz0TnrfH6ydGx/g7VWxkqbtLCp90u4DozX+m8SUEvrxGDlFVx+ei4lWPhkzWkjoUvL4pbiq9hX3KCINfnrZaQYO7mUeWcu9li726fjkV0EnEx9gidEiP/i1NizXpohTPn08cfbcf9X3+ma+G8/+r5y+3442eSI9V0BUFe6pW+Yx1dGUZ7kh7RoSRAzeQgRysDSoqQZwq4rODwomzx5t9wuoSjZQufHUMenG+o/rsmFqlr1b+LErRz1qUOsnp8/zDyS/GCMYW1aEOG1B/JoWowkptZCpnHNRw5C+SJXl/1z+Gx3Dhake8CkyPxvzlRjcb7nUwUTyEk9xnkTNkcOdVzCEEN3HC/oZNgq63PGyFOZ3CjSxu14i4hDAPX2dSLIZ5R6FDoQp7lOMcqf6dUkF5XmOsWZKgdOEOFFeVvOnMKB5kyy1fcX4IzbV35lWq3oxjrlYqrDs61Mc6SPuswjEvcX5uOSNwjjRI3g0yk4hlXQOYWQx7zLrx2LekAAdtosI7FHru41DPXyaXfuiGFux4dQUvIS7vMRZHiKY01XuevdVHUzUPUTjQsWqf+fVAu1xsoPo4P9VwcdfRdk1J1UYvSPDwj1CvKo3X9ZcNp75ItJXCPh2hHEaGFRJW9/7PVDpdGBoPBYDAYDAaDwWAwGAw/XNiPLgaDwWAwGAwGg8FgMBgMt4D3di96x1QqQL2PYzgOobv6Et3Iy6ZMhJ248d/ovFFCjnN2Kpr2+YmotnUuatLhvuRIJP0MJqK59dtDRyyW+h79LlwIAt375dn5dnwEKuWQjjm4j6oUhcuj5nZ8F5qVXuaKDlyKJqJ7dg+OtuN2/N7L9YNA4JxrbSiClF2UC01ABkrZ4lLyortHWjvnnItAnaRSLAC9LcQad0BxdDEci0K8PoZkCY43LTiMtJ1e75xzy/lU/8j0PUJEXgfONAXdLmrISALEewk5AejaReBT2DJ88RRbeJXq8+YzXWsw9CUmO4VAzk+VQ+wE+t4Z3GKYl8pGN/1ijnVKFG8lpQJ4/yqB80ym/R2C2jgYKb6qkuute6J7WhMhpEApqI0XC8nXJj12cKd7juYgB5WywPcu1g3XLeTa2pOFgmKMVF1sXlO73aJuV3XtsvRmnxWU8yGHFuhu/3YuOcCLi0vvWvfO9W9KF8OBcva6Uqxcnp9ux1eXOrfuP7in+4M8qLiWBCCATImx6JxzvaHiYA66/inuL4XEJ8H5W2CPlKBop5C0VVPltHbXP2uGY8XdCi4EyxlyOGwL/pBTxs5gM0V0lipAcw/Af68hwcgaOWe11DqTJl+Cxz4cqm750Y8+3Y6/+Ow3uB3l8G++fbUd57//Yjt++uyZrtn2c86Xv/3H7fjgQHH74PFTfAYlLXCBY5lYU2qEPAOJQ9O5KkGMLZGPunB2evz44Xb8/OVNrF6CCr8bqLdrXEPCt8K89mC5QolO3fj/PzmDUQyZMuQ7QcyzX3O1hKMG/0456RXmNo5V33Y6/r598Ua1Nh2P5mvln33ULX1IWPpjybNDuv+lei9l1GVDJpNXlJzp/fcnklINBprn55A/7RyCwIUbBy86FnEr1X/AnYbyPeeci5BbKLcedbUe45FyzhquemvWDqjVK5z/yz9QY7Uazyd9nFcLxE52jvdDItSFo6QnDcFeivF9aBRXNGQi85kkwbQazSGBSiA56b1zjgp3j4fwzlmqossc5Z6Y+zX24WXjrKpQD7bxPBYVkKDC4Y6ur8sr5Zw1ZILhHtzq7utZtsLzSW+BtXJ+a5AMz+fnp8pTo2Ndq3tHz8tVqrOmvlI9wzgtmJtz3zFtuladT2fYNp4Fh2hNwbYDfyx2L8IMBoPBYDAYDAaDwWAwGHYA9qOLwWAwGAwGg8FgMBgMBsMt4P3ci6rKpRt6aBiK5vjmUvToGm2JH+8dbsdJ5lNO80T/DiJQelq61sW5KPYXM1GFej3Q6gtKfOgiADcbMOPL1KfJFyt0P78GHRtd5mF44WaJaFEhfrMKQLHMIJFKM9GS8tB3L0rB1CV1vDOgXEvdm+sddS9yrnaBu5mfKqXcAW5RoPd7+oaGtKZE93E2/OfMBHDucT3R4SraDIHyTJp0Wuj+6lrvna0Vl845t4LcZNwVbbeLbttxT3/3nC9q0PUwBxU7b0M6kmY+fTSDFKQzQAxiX5xASjNe+W4SO4UaUsTa+/MWdAhZLLQus6nvQhOzuztozSncJeZzUR3XoGa3sZbdodaYndYXiOFOC05ZLV+almTopA6nmy4cGlJ0dD+FDK8FWdUQblw1pFdrxHmx9umTKzhK5AirHN3qK+w5L253CXXg6ne2QJD29WJ9zwWC6ARp6ZtL5CLn3AOcQyVyRdBVrGRzfcZserYdDyaKuX/4u/+t2wMrdbIvqmwICnjhfFlgG3kmzZLvfN0l4j8HzbdEjBc40ArESlohFiP/vK4LuJhcKBder+A4gpgPq92So/mAYxplRAEdHTifkLyGjZIK8VbgPSVo7tlK83k8hlwRbmtrfMa3rySj+Ou/+tV2vL+nvDRo+2fGJ8+e6HuAQh9AYpflivvQkxqhbqkhiYH2ISvhSpj6++cM9eHppaQMe0PF+l/87BN9j8nNZ7w8lcx4F1C7wFWbSgTT6iuysK06oNFHDVdNb+tHjEH9OUFNUmG/VYgVunmUcKxp7UEaAKn09dTP9wFueDjgeaPPSzOtN++jgwKtqHHm8atSztFw2mPO6cR600fPJCd4jdg6Od1hGbVTDUtJUFBRUkTZEcYNeREdEUMETA73tCTWObFaq76YL3WmpZCpUWaZ8hkI4bK40t52zrmgLTnIyVR7eQnJ5RVkQGN8jQ5lZwiRAnNQwiV23ZCJnEJqVsL1b7Qnx6+rXPXhalMrVuXunVvhJhYKbiXEREC3zK7m9brxbLXAc0UK+TMdtIIU7o2QL6/wfN6CZOxirjrlH7/+WveMGK0ajydj1NoB3fJQ/yxniqcIr4kxCSme7XNIn3M4dF0lvkPkJb5TJ9R77h2iNQg+7/WF/4zxx2BXn+INBoPBYDAYDAaDwWAwGH7QsB9dDAaDwWAwGAwGg8FgMBhuAe9nMVCLyp+AHhukonz1QGMGo8et1z6NZwn5wygQ/T5yomaDHe1GfdFuI3RpruBwtIbbSF2Aqv9G9LKs1vWdc256+lr/bal7jMeiEw0mon9HcMAhxTsHlWmB6xSk48Y+ZTtL0IkbtLcVukJXoHZ1692kT9Z15fL8Zq5yUM/WCJDLqdaoO1Q85I3fBZfoYh/1QXsGjZbOGXWLVGpQzECjXMFGitKumtS2NoLROdftay1Itc3pTIRO4Q7yjQjc4xg0wByc4hWofrOmCw/21ainOQgCfB5o7WfnvoPNruEdk7b25EWgtoPSfHYlJ5fXDTcD0t55rTkkNwm65o+P5PIxGcu1jPTd07fKJycnGkegVoehv29XCRxNQJE9PIQzWk95poKLwPpa+6QDKUMb0qQkEA2zSHza7TqBcwRir005JnK7n7F2B3EcuYODjUNDABo+adlwsFogF31z7ksJH74VTXoxF601xbqm0Co9efDBdnx4LIntt6/ebsefffH1djxdfL4dr7C368bk00HoyVO5vXz00Yd6PyS2334N5xHkE08xBupxSB1E4n94ghy0WkO+Bm4w5QDhjrldEXXtXLWZC7qHtOG2Q6eOvFD+KBr0dJ53lPV04GS4nCm+wgoSRbjqJaBEr+DyUsDdoYx0jl3XDbkKlpZSlgCuH+UKDoxtyIvouANKegkp7hpy22Xiy4vevlUeLlCjPXn0eDvuQzr9k49vpEb/7W/+3u0W6q3kivk/wJxRCdKBPJRyV+ecVy/wTQFKgRrrWOLlOSSvAzgWdfoo97u8P+pMGxIfzxlQ692moxLq3SrUDc4ph6mYf/SayqtZGlJySGb3DpVHe3BFenkGmcEO/1/IQe1ca+PWFDKFIHZo7kS3sKzwc3UK5xouLd1cKAfJKuZ2ykogU0I9yfqaU96f+M6wp5c4N+EK02mrZl0k+vuiC7fQgdaYmroEMTWHNHuZ+s+XZxf4bLgzHd+/sx3vYc+dv7mp3ermobsDqDeLzLVmjdqCLGfvrlyr6oar6Qnm6QPUBSX2ej7T35kbeoi5SUfXTVCPpJCYDSGj7Q6VB51zLsBvCF3cxwjPR1ym9bnWmj9prOGUx5YfM/wWcb3y6+MErQpWcE/79Qs5Ur6e6f7+6fnv3ftih9OUwWAwGAwGg8FgMBgMBsMPF/aji8FgMBgMBoPBYDAYDAbDLeC95EVBGLruhhKZF+A5oit6HopylNeUYPi0rRWorJeVaE3LBSQAHXXj73Q1nrFbekEqHdwTupIGlH3R9quyIbU4EFUo6klyEgSkJomSv471XRPIFWZncrhIKtHkkg66Kdc+fdLhda1I9x610NWeNM6dlRfVrtzESwY26dszzTcUFK4Lam7dcHyaXYsW2QXd26G7fdTR3yvP8UlUMrp5RGiPHiI88pXist+QhtUjxcF6rteRnhn2JJNqY48EkMOUOTrKo5N2nuJeGw4yM7wnhDvWpK/r7o8lV3vzSnO2i3gnaeQs0DEkQ26ZolP9C0gHnXMuSfTfPOkYcstwX7lidHiwHfcgb2yD+h1DPhiHiruLqWRO6crPOQzJYV+U3CHcPChfYN7IQOOczvUZntlMW3FXFL4sriwVO5zDDvKMq5WbxVndLdptGAauP7jZG+kaMiC6QWSYV7hCfdtwYXh8onnOBng/8vRwItru3j2dPaM9xdAnn/50O55CjjT98tvtuAU7iKMHD7z7ePaR5BjPnuq/jUZ0PBLeXMD975w5ALKy8LvlRUXix80q1+tWmeYqo3MAYpPU491D7YpNjo5Aqx/0lCfoNMY9ts79732Ns+HbV8pHP4Y8bHop6nK/o7V5eE+x8/lXek0GHcVsrf384JGo862u75hGo4oK5wflh5QQkNJflnDPieAIkStPZJAXLSDXdc656bVo5R8cap88OtY+mV9Kmplv3N7KXXQS2ZxSLc5lSXkRaP+QWYStRimO17FWqWrUxw5uZnAbyVBkBSjxYziVDSgrhES5aLheFXQE9c4AOJcgBkOcW1H83XukRP1TQC9VhL6MOkMuPJvCKQ4ymdNz5eZuDw4jO4YwCFx7IwUrIHGG4aKrkV9L6CuKRn2YIr9Tk5TBMi+jm4sno4eEEucb92IH7qAtxG2vA0mQ82X0ez3VT9dwVKScewmpdQeSpzX2zxznygVq7aThXtSGOyndImkrFiKHR52bcdBwgtoF1Nt7RisF5G8ahLUgMQzHvqznDM6y867Wq8N4hEwsRtEZoOfBEDHRPlRrjuGB8n2X8vmGpKuEy1YEWXQCp7IO9noLqXaF3wYyOKZlcBeuvLnx824L8Z8g5v/2S0m1T85/sx1f1+9f5xjTxWAwGAwGg8FgMBgMBoPhFmA/uhgMBoPBYDAYDAaDwWAw3ALsRxeDwWAwGAwGg8FgMBgMhlvA+/V0CZyLNw0JSugGU/R3WUAfOIPncwjrOuecC6Ct9+yCoUXuQiO4mErPVcxgcwj7pgRac7ePfhpH6m9Rrn3d58Vb9WvJ59Cn0542hrXonu51MNDfr2BJRbvqGjrVOvR7BQQrfchwoXt8CNvhUS59dFD5/U12BYFTP5qrC32f1VLxMWxrrccj6QmzxNf5Li6k7T0eSBdYwqKUluIBdIe0MQ0xxx1oCgPYjKVr9Jxp6Mtj6Ezb6HfgaLMH7aDXWwMaU/ZGyqBDjYvvtpl0zrkrXGu5kMbyzljxsTdW/4HpzNe77hLqWjZ+Fftx0NIO652j88uq8Hup5IHmZNCFJrSPHkDQfhZL6UML9HKK0TOlDevXgyP0gMH81w1LxxzrTNtm9gEooFFmH6QYr+miZ1CF+GTrp7jxu3qJlD9H36Ac90FrW7+Tzu4gCIOtPreF75OzLwH2Z4T+UNOFHzdXc8XEIRryHPeVswewuHz5QtbQJy/UVyVd6axhL6CnR/d1HfRXOHqsHi7OOXcfVtSDPvq4IBe1WoqJ0VD3Nz1XLitoQQ7bTWqj89qPmyukkDn6BITYeyH09kHt5+1dQu2cq99p/z17bQ25b4dD9WUqkY+dc26x1Hn3zctX2/Hbc8VFWOo1f/aTj7fjJ48ebcfTpT7v5FJ1xK9xzQ9++om+A/rhOedcgDWnS2+EfiE1eop1Qh466B+BviAletrlqH/Wqb/2tN3+2U/V1+jZY/W1+ezz323HX778xjnnXNqwgf2hI3DOxZtc00VvgBQ9T3gSBOhdx/zjnHMBetTxTEtQz6zQ9+IEtqnlG/XQaZ0qVgLYi3fQU2GA/m9Ro0UBrcArlM71AeLrUO8P0ZdsD/3RQuQT5qsCzYZqfwq8Xm2//uwL3RNqwhb6cvT6fk+RXUJZ1265OfNbTL3eUay9SutwF/vPNBV6vxQ1ayPUFIipGL2qWujXwt4XzIMxEmFQcezXCgP0hOnie2ToEZXg/TPEc8zec5iEBZLJFXq65KUfuDHO/Ah9O2rcewzL6G0/q91r6bItY7zWKOz5g/0yvVQNctbIObOW4qiEb3m/B7twxNYafaSKjHWVXs++QG08F7slYrnxgBPCqrkI2BMVz/qIlXZbr2mjZ02Ah/gq0/nUDfU9+42eLHv4rmep3r/AbxkXGCet96+PjeliMBgMBoPBYDAYDAaDwXALsB9dDAaDwWAwGAwGg8FgMBhuAe8lL3J1vaW4haAFFqCClaBMni9F6VmmPsX+aCwaWxDQ00r0nhVszV5diCZZQCK0luqIqg23V4vWNAb9brX0qa+rue5jca17n61h6wsK9ehalKoPH4meFYSg5YGaG4OOXjQcnxeJrvUWNOG7T59tx/lcdLBiTzTOXUJdOVcmN3N4faXv3G2L1jjua8ygnM5WjkhWoIMVpFtCmgOqYQg64upKc3n5zcl2XIl572JwXCNQ7JKVTxunpXMCiVAOi/AA0pPxsawyU9Dwk0TXDTyZgN5buAZlvYR1IPbI6VL3VCMGHz2QVdtOYsOfLLHeBcYtejBDfhN1fTleD9a6R7CDnoPG/vVz2fd+BSvfB4+1J2klHSLf8dN6sMsss4b97kLrlCJuC1Adr64kPzg/l13sZCQpw5OHoufTLnONWM1WvrSM0pIZ6MBrMDxJSa4b/7srCILQtbs3FHpKwPKCFseai15XcbOc+/Ii2vIWE61yDQnA2VR56rdffK1rXWst7h7rPnoD5ZkwwPk0k9Tk+qvn3n18/qX+TTZvgvNpjrVPcb70I913v6PPXsM2O4HU6DL35ZSv5zjjA83VPiyz2BunAAAONklEQVSOO7inOORu2D2pUbnJxcwzNeyfq472dx9yWFphOudcr625uriUve3XbyRBa8X3tuOvvtFe3xvKbjOuXmzHa8jU3l6q7lhCfnx/fNe7j/RahdKrkzfbcRtrM57oWjHuO/BqPY1L2IfmqPWSdaPGSvWeg0ixfnRPtufL336+Hf/2+Y28aJ3tliw2cIGLNlT5kNa0IQs/SmTxmtgvxSNQ5mknHaIuyGFt//xv/3k7TiEheDA63I4PhpL7FG1dZ1rp+vnKl8Dnl7JnzmaKoXkBO9YDnUnDZzqT/vRXqnn6tIiNUau1IbXNGzJcp/zFkEqQ/LqQY8bh+z3O/JCQ5bl7fnLzDPD4WPu+h7YMIfZeDFUD69SbF0Iuj9qo39IaMDtf48x481p1cVThvYjhHmqeFuT0rIOdcy5HbZNC4nKBGmSK/LM3UX12gJzaCnS361IxuMoh22vYDrdxj308W9SoncMI328Tn2G4izyEm+9ES/cA4wr7agq54Vdzf71+/EzyZbbUGBwqVwTYY6dLnENXeoiq8ew97KGFBj4uQtzUof9gTAl4ipo4hwV0F3XHw3uqx+/sK+e0kF9TPK+tV/p75fzafIx80p1Dlp5RIsoWEu9fGe9ihBkMBoPBYDAYDAaDwWAw/OBhP7oYDAaDwWAwGAwGg8FgMNwC3s+9yAXbrv0hfq+hW8wKlNCXF6KCvbkWBcg55z4YSSrT7qIjfkyqHD6jJWrk5OFHus5TdexnF/awI5rQFJTJ5Vr0beec2xv9ZDsOSlFkT7+WtODFb15ux+nV6+2400KHaHR5p/FDiU7JZVu0Qeec++AXv9D43pPt+ONf/nI7Hn8gB4O81dAn7QiKvHBnb27kEhUYwwUoW/EQFLgrUVqXSaOzdSQKImnudUHnDFDG4Ka1pmSsQAfrgaiM7JztSt1sv+dLVRa5qHUpaNZrUCf59zCUxCcGXTiHxCPH91mB2nk69ylwM3R/Z2f26zU6u8/02ePerolDhCAItvRqLKWrcv2D/cNJSC8bX5uymS6cDjp7yk2UCX77QlT/zvHxdnzwsRxGQnSAr0CxX1/IQSL0zQVcb08UyDEkUAvkqZdL5c4UMpZ7j0TJn0BumEJbmYHWG8Z+zliCp30CqeQK1OWKjjSbOdu5pv6Bc8GGKtyDs5CDHK/TBl0VDlNRg+p/DonWvETeAJ36+IEkIn86UDx99dnvt+PRUGv98LHiaf/One14Bqp3gzTuFlivizPIRb7V+VSAWn78gT5j0Be1fAWZTPlWc5BCejVtONC8hdMcVXvHQ12XThaLdfPudwd1Xbty4wZWFpSDwP0O4zhXzogaEgfKKnoDxc7f/M//tR2X5a+24+m5zpWff/jhdnxnLAnH9VTXOT2RTOm//uf/sh3/p//wH7z7iOFudvZCNUxdqB768BNJQ/YOdSbSYS+BFLNGrBagby9WvjzvDSQqf/e7/74d//1ncix6fSZZw2wjbau+B3X73xSBaPM05OB57yAFSVErlw3nF9L4KYOIIIMc7SkmHkwkIzp9OcXr9Xm9Pa3p8UPVm8MD5assU/w559zqteLrDDXx+lQStTnqrT0khx5kHe2YNQ/qGbQWqBqPIwlkESnOe8qyOpCO0klt11C72hWbjM/9VndwftNlk+HS2CcBpBttuDv1IUGmoWKBf8xKxc75a+WJ07n2+hBrSRlPVjXkRahJVmvF+jlq5FVf3+/oZ3oWG/cVq9dwhFvRJS2nu5k/B5TatNuKyQr7rII8KdrUSU0nnV1A8B0i8JoOrpiaAE6rRaM2TPG8Pc/hxovn2f0DPcccQZr88kJ5Y4H1imPIbStdkx1Fel3faa9CoR+gum9haQ76yn17GMeIxwB5M8ffF3CunaW+hDUIVc/nyDnrPyAvijE56b+o2L4bxnQxGAwGg8FgMBgMBoPBYLgF2I8uBoPBYDAYDAaDwWAwGAy3gPdr9x0ELoxv6DftWtSkNahIBShAb0Ez+ubUd6F5NNJ7Dg/gGBNDAtAVHe5+LFrT6deiU89TfeDgJ6LjHjwWffLhnfu6v9CnoUWJ/v3i7z/bjrNXot3moPyODtEJeyx623JN6p9+yyprdIkvfInKT//kz7bjT3/84+14eCy6edAVdTncQRcI524cIC5Pb+QW66Viog0XDYcu0qtElK83U3+9+pDgXIJ+ukeaYwR5RFdrMXyo7tyDPVHvqwSODJBmJJB7RGt/7QZw34qPJF+Ih7qPeE/vKWutXeVRIXV/C7gIPL9WbD2/9GlraaV5GyCe12BbvjnTPB98ImneziFwLnjX3Zw0SU4hHKoKyEfWKemnzq0S/XuxUj66A3nZ3qPHGg80b9NU8VmBkjjc12tG90XPd8/gNNCQBeagyKZL5ZniW0lR7h5IivgnjyUxPBgp/2SIzwSLT+eqIvBT/Mlcn3cK94uq+u6O7IHzae+7gjAIXLd7891DzJmDe10XVP0OqMb9oU93fXutzvxnK83H6ApSoJHkZB89kHPMk8mn2/H8jea7tdB1hl2tSVArfquI+dG5LnLFBJTapz8VLTueiM6bQ7Z7CaeB6+mZXoNNhSPMzbJGzsF/G8O5ZzzE+QSJ2lXin/e7hcAFGwpxgL1UwhXPpyErHzTdEMpCr/vkx5JCP3+uvf53//BP2/EHx5KJhHCF/I9/9Ze4juqc//M7OVp99XvVRV99ITcb55z75U9/qvd//HQ7TlaSE1CCRjcQusZ5eQb0/gJzM1/5Eu5z5JzXyDlny690rQjSrfhm/9U7JmoMAufiDWW/gNw5prQG0pFr5JWkQXMvULeUqAtqFNhBT3nq8GeKLToipv+sOLt+KalQcFdxs4S8qGycmVdwzrtGDinbuo8Pf67a9SNIRNoj1b4JpCYVcxyOF0qInHNuOsP5hljrtSHppaSl3s2zyrkbt6vBph4OIAUsKfuFExH3RtX0FoT2iO5CEV1i8H+3DyERfYIa+aCrv89P5aYYwo1x1FXtu176zlfLQPs+rhXD6wCyXrgUtXHeJZDeXS903TWeDSg1aroXEZyf+VLn0hLOsP2Ns+XOSRqdc8HG3bPGoqKccwGksKyb04akcYr9dwaX2PkUzxJdrD1+A3hGN9CW1mXUUQyNBqpZItQQUeTXqFyDAv0oIjzXjUaQ9Hr7ReM52jsskRNn+HtS+Xk3x7P7fKk4TTz5vV4ffY8zypguBoPBYDAYDAaDwWAwGAy3APvRxWAwGAwGg8FgMBgMBoPhFvB+7kVB4FrtG1pPAArQGp2p2TF4BSrl89fn3rV+dIiOw+h43gWVMuqK3jMe6++9V/q8F//jH7bj/FvRNZcvND7+hei4wciXiaRfyAVi/k/q1j39nbqzrwtRcO8eSk6QrES5cyvdX9EVZe5kpfk4aThC/Lv7ciIZ3oXchY4o6KZNac4uoSort9pQjEPQsUag8bd7+s41HFZmiU93nYL+OjzT6x4/kZNLAWpiDMppGIsK2RprjdxQr0lAi8/QLrvu+vcxOFD8dlNdt4ogI2qB9gbaOenodMe4mouW9+s3l9vxS98MwoUO8iJQSUu0BD+51ps+Wu0u7da5wIUbF5ooBL0WdD+2Qi9BXZ7PfZp7uqd5v5hKDtKfaP0OBuimPxCFsVPAnewlnDZOkT/gVBKDmttu+/ZFCWjT5YISNt3vMzjgdAfIB3B3SyLMAfJuDJcCxpRzzr06VR5ud/S9Hx1Kvvkl7q/aUcp2GIauv5Gnxsg5RU75quY1hPNUp9FN/0xb0b261Bo97Crnz68UBxOcVaOOnKo6Q+WMAY6CAZzUyljjqCENK/ropt+CowyOtEVH8TGDG1+5Ar1/Dakc8se6gEvT2j9r6KAyHugD79/VeZiiDnh57efL3ULtqo31WStWjKSQ0FQVKNuYmzL3ZVkZzitSkX/2qWQYn7/6Zjt+da4aZNjWtT5+JsnaX/zFn2/Hg0Ode11IvX73lS8vWq0UxH/y8bPt+MFdyRi7PcUbXT74nQrKLCF/KiDzOLtCXeScu0KOywPNZ1lDGo49V/oKrZ1BGISuu6llS1D1A5xbBeb1/ELztFj4Z9X+AJIiUOYdaqMc9UkbueXxn/+pXg9XtfJUMVAlmuR1CAfFrv//w9YPdA7tDSXTv3esv48eSJISoiUA5bw8R7h3KF1bLf2z6uRU0qYc8qI+ZKFponnr9xT/u4bQBa77rs6hww7HkAfVtH+JG2sGSXDNnEUZJGSTrqa0T3/eO1JNMB6qVsjXWqeM50pDqhGgPun0tTb7aBGQI2dliIsppD/XkCuucC5lOR1l/DmI4MyT47pVypYQulZnsKnXdkxeFDjn3nUYYP1fc3khL6rhHJY07D1P4Wp4ivzzFjXPuK/nowp7sg156P5E9cEIzlEBWodULUjgev7atdB2Issoj4PsHVIjyhVTyDTnOHdKuntm+g6ULDnnXIV8RHe5gpJIvD76HvFiTBeDwWAwGAwGg8FgMBgMhluA/ehiMBgMBoPBYDAYDAaDwXALCP5fXZ//xYuD4Mw5982/+kLD/w88qev6+F9/2b89LG5+UNiZuHHOYucHBIsbw/eFxY7h+8DixvB9YbFj+D6wuDF8X/xRsfNeP7oYDAaDwWAwGAwGg8FgMBj+OJi8yGAwGAwGg8FgMBgMBoPhFmA/uhgMBoPBYDAYDAaDwWAw3ALsRxeDwWAwGAwGg8FgMBgMhluA/ehiMBgMBoPBYDAYDAaDwXALsB9dDAaDwWAwGAwGg8FgMBhuAfaji8FgMBgMBoPBYDAYDAbDLcB+dDEYDAaDwWAwGAwGg8FguAXYjy4Gg8FgMBgMBoPBYDAYDLcA+9HFYDAYDAaDwWAwGAwGg+EW8H8ByTQ7ZVmhYrcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4e9ca7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper display function\n",
    "def imshow(img):\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\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=16\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": 6,
   "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",
    "    \n",
    "    min_val, max_val = feature_range\n",
    "    \n",
    "    x = x * (max_val-min_val) + min_val\n",
    "    \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-1.)\n",
      "Max:  tensor(0.6784)\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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv(input_depth, output_depth, kernel_size=4, stride=2, padding=1, batch_norm=True):\n",
    "    \n",
    "    layers = []\n",
    "    \n",
    "    conv_layer = nn.Conv2d(in_channels=input_depth, out_channels=output_depth, kernel_size=kernel_size, \n",
    "                           stride=stride, padding=padding, bias=False)\n",
    "    layers.append(conv_layer)\n",
    "    \n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(output_depth))\n",
    "        \n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "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",
    "#         self.conv_dim = conv_dim\n",
    "        \n",
    "#         # 32*32*3 -> 16*16*(conv_dim)\n",
    "#         self.conv1 = conv(3, conv_dim, kernel_size=4, batch_norm=False)\n",
    "        \n",
    "#         # 16*16*(conv_dim) -> 8*8*(conv_dim*2)\n",
    "#         self.conv2 = conv(conv_dim, conv_dim*2, kernel_size=4, batch_norm=True)\n",
    "        \n",
    "#         # 8*8*(conv_dim*2) -> 4*4*(conv_dim*4)\n",
    "#         self.conv3 = conv(conv_dim*2, conv_dim*4, kernel_size=4, batch_norm=True)\n",
    "        \n",
    "#         # Final fc layer\n",
    "#         self.fc = nn.Linear(conv_dim*4*4*4, 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",
    "#         x = F.leaky_relu(self.conv1(x), 0.2)\n",
    "#         x = F.leaky_relu(self.conv2(x), 0.2)\n",
    "#         x = F.leaky_relu(self.conv3(x), 0.2)\n",
    "        \n",
    "#         # Resize the tensor for FC layers\n",
    "#         x = x.view(-1, self.conv_dim*4*4*4)\n",
    "#         x = self.fc(x)\n",
    "        \n",
    "#         #x = F.sigmoid(x)\n",
    "        \n",
    "#         return x\n",
    "\n",
    "\n",
    "# \"\"\"\n",
    "# DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "# \"\"\"\n",
    "# tests.test_discriminator(Discriminator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
    "        self.conv_dim = conv_dim\n",
    "        \n",
    "        # 32*32*3 -> 16*16*(conv_dim)\n",
    "        self.conv1 = conv(3, conv_dim, kernel_size=4, batch_norm=False)\n",
    "        \n",
    "        # 16*16*(conv_dim) -> 8*8*(conv_dim*2)\n",
    "        self.conv2 = conv(conv_dim, conv_dim*2, kernel_size=4, batch_norm=True)\n",
    "        \n",
    "        # 8*8*(conv_dim*2) -> 4*4*(conv_dim*4)\n",
    "        self.conv3 = conv(conv_dim*2, conv_dim*4, kernel_size=4, batch_norm=True)\n",
    "        \n",
    "        # 4*4*(conv_dim*4) -> 2*2*(conv_dim*8)\n",
    "        self.conv4 = conv(conv_dim*4, conv_dim*8, kernel_size=4, batch_norm=True)\n",
    "        \n",
    "        # Final fc layer\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",
    "        x = F.leaky_relu(self.conv1(x), 0.2)\n",
    "        x = F.leaky_relu(self.conv2(x), 0.2)\n",
    "        x = F.leaky_relu(self.conv3(x), 0.2)\n",
    "        x = F.leaky_relu(self.conv4(x), 0.2)\n",
    "        \n",
    "        # Resize the tensor for FC layers\n",
    "        x = x.view(-1, self.conv_dim*8*2*2)\n",
    "        x = self.fc(x)\n",
    "        \n",
    "        #x = F.sigmoid(x)\n",
    "        \n",
    "        return x\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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deconv(input_depth, output_depth, kernel_size=4, stride=2, padding=1, batch_norm=True):\n",
    "    \n",
    "    layers = []\n",
    "    \n",
    "    transpose_conv_layer = nn.ConvTranspose2d(in_channels=input_depth, out_channels=output_depth, \n",
    "                                              kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\n",
    "    \n",
    "    layers.append(transpose_conv_layer)\n",
    "    \n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(output_depth))\n",
    "        \n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "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",
    "#         self.conv_dim = conv_dim\n",
    "        \n",
    "#         self.fc = nn.Linear(z_size, conv_dim*4*4*4)\n",
    "        \n",
    "#         self.t_conv1 = deconv(conv_dim*4, conv_dim*2, kernel_size=4, batch_norm=True)\n",
    "        \n",
    "#         self.t_conv2 = deconv(conv_dim*2, conv_dim, kernel_size=4, batch_norm=True)\n",
    "        \n",
    "#         self.t_conv3 = deconv(conv_dim, 3, kernel_size=4, batch_norm=False) #No Batchnorm for last layer\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",
    "        \n",
    "#         x = self.fc(x)\n",
    "#         #reshape x for convolutional layers\n",
    "#         x = x.view(-1, self.conv_dim*4, 4, 4)\n",
    "        \n",
    "#         x = F.relu(self.t_conv1(x))\n",
    "#         x = F.relu(self.t_conv2(x))\n",
    "        \n",
    "#         #Last layer uses tanh\n",
    "#         x = F.tanh(self.t_conv3(x))\n",
    "        \n",
    "#         return x\n",
    "\n",
    "# \"\"\"\n",
    "# DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "# \"\"\"\n",
    "# tests.test_generator(Generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
    "        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, kernel_size=4, batch_norm=True)\n",
    "        \n",
    "        self.t_conv2 = deconv(conv_dim*4, conv_dim*2, kernel_size=4, batch_norm=True)\n",
    "        \n",
    "        self.t_conv3 = deconv(conv_dim*2, conv_dim, kernel_size=4, batch_norm=True)\n",
    "        \n",
    "        self.t_conv4 = deconv(conv_dim, 3, kernel_size=4, batch_norm=False) #No Batchnorm for last layer\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",
    "        \n",
    "        x = self.fc(x)\n",
    "        #reshape x for convolutional layers\n",
    "        x = x.view(-1, self.conv_dim*8, 2, 2)\n",
    "        \n",
    "        x = F.relu(self.t_conv1(x))\n",
    "        x = F.relu(self.t_conv2(x))\n",
    "        x = F.relu(self.t_conv3(x))\n",
    "        \n",
    "        #Last layer uses tanh\n",
    "        x = F.tanh(self.t_conv4(x))\n",
    "        \n",
    "        return x\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": 15,
   "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",
    "    if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n",
    "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
    "        # We have disabled bias in our modules but the following code snippet is used to set bias\n",
    "        if hasattr(m, 'bias') and m.bias is not None:\n",
    "            nn.init.constant_(m.bias.data, 0.0)"
   ]
  },
  {
   "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": 16,
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Define model hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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=100, 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 = 100\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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Least squares loss has been shown to produce better results so I chose it\n",
    "# LSGAN reference -> https://arxiv.org/pdf/1611.04076.pdf\n",
    "\n",
    "def real_loss(D_out):\n",
    "    '''Calculates how close discriminator outputs are to being real.\n",
    "       param, D_out: discriminator logits\n",
    "       return: real loss'''\n",
    "    loss = torch.mean((D_out-1)**2)\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",
    "    loss = torch.mean(D_out**2)\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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "learn_rate = 0.001\n",
    "beta1 = 0.1\n",
    "beta2 = 0.999 #default value\n",
    "# Create optimizers for the discriminator D and generator G\n",
    "d_optimizer = optim.Adam(D.parameters(), learn_rate, [beta1, beta2])\n",
    "g_optimizer = optim.Adam(G.parameters(), learn_rate, [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": 21,
   "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",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "\n",
    "            # ===============================================\n",
    "            #         YOUR CODE HERE: TRAIN THE NETWORKS\n",
    "            # ===============================================\n",
    "            \n",
    "            # 1. Train the discriminator on real and fake images\n",
    "            \n",
    "            d_optimizer.zero_grad()\n",
    "            \n",
    "            # Pass real images through disc & calculate real loss\n",
    "            d_out_real = D(real_images)\n",
    "            d_real_loss = real_loss(d_out_real)\n",
    "            \n",
    "            # Generate fake img through latent vector z & calculate fake loss\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_img = G(z)\n",
    "            d_out_fake = D(fake_img)\n",
    "            d_fake_loss = fake_loss(d_out_fake)\n",
    "            \n",
    "            d_loss = d_real_loss + d_fake_loss\n",
    "            d_loss.backward()\n",
    "            \n",
    "            d_optimizer.step()\n",
    "\n",
    "            # 2. Train the generator with an adversarial loss\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_img = G(z)\n",
    "            d_out_fake = D(fake_img)\n",
    "            \n",
    "            g_loss = real_loss(d_out_fake)\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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   50] | d_loss: 1.1819 | g_loss: 4.0737\n",
      "Epoch [    1/   50] | d_loss: 0.7348 | g_loss: 1.3972\n",
      "Epoch [    1/   50] | d_loss: 0.5987 | g_loss: 0.7552\n",
      "Epoch [    1/   50] | d_loss: 0.4917 | g_loss: 0.5571\n",
      "Epoch [    1/   50] | d_loss: 0.5019 | g_loss: 0.5399\n",
      "Epoch [    1/   50] | d_loss: 0.4064 | g_loss: 0.5478\n",
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      "Epoch [    1/   50] | d_loss: 0.4517 | g_loss: 0.7551\n",
      "Epoch [    1/   50] | d_loss: 0.4085 | g_loss: 0.7184\n",
      "Epoch [    1/   50] | d_loss: 0.4155 | g_loss: 0.3415\n",
      "Epoch [    1/   50] | d_loss: 0.4496 | g_loss: 1.2192\n",
      "Epoch [    1/   50] | d_loss: 0.3697 | g_loss: 0.3836\n",
      "Epoch [    2/   50] | d_loss: 0.4525 | g_loss: 0.6267\n",
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      "Epoch [    3/   50] | d_loss: 0.4093 | g_loss: 0.9002\n",
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      "Epoch [    3/   50] | d_loss: 0.5008 | g_loss: 0.8197\n",
      "Epoch [    4/   50] | d_loss: 0.2400 | g_loss: 0.4223\n",
      "Epoch [    4/   50] | d_loss: 0.3010 | g_loss: 0.8603\n",
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      "Epoch [    4/   50] | d_loss: 0.2413 | g_loss: 0.8199\n",
      "Epoch [    5/   50] | d_loss: 0.2098 | g_loss: 0.8068\n",
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      "Epoch [    5/   50] | d_loss: 0.0998 | g_loss: 0.9814\n",
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      "Epoch [    6/   50] | d_loss: 0.0744 | g_loss: 0.6189\n",
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      "Epoch [    6/   50] | d_loss: 0.2220 | g_loss: 0.8005\n",
      "Epoch [    7/   50] | d_loss: 0.1587 | g_loss: 0.8458\n",
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      "Epoch [    7/   50] | d_loss: 0.1771 | g_loss: 0.6612\n",
      "Epoch [    7/   50] | d_loss: 0.2149 | g_loss: 0.3388\n",
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      "Epoch [    7/   50] | d_loss: 0.1864 | g_loss: 0.5911\n",
      "Epoch [    8/   50] | d_loss: 0.1571 | g_loss: 0.8343\n",
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      "Epoch [    8/   50] | d_loss: 0.1279 | g_loss: 0.5256\n",
      "Epoch [    9/   50] | d_loss: 0.1807 | g_loss: 0.7903\n",
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      "Epoch [   10/   50] | d_loss: 0.4158 | g_loss: 1.1455\n",
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      "Epoch [   12/   50] | d_loss: 0.0658 | g_loss: 0.5162\n",
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      "Epoch [   12/   50] | d_loss: 0.0459 | g_loss: 1.0836\n",
      "Epoch [   13/   50] | d_loss: 0.1028 | g_loss: 1.3850\n",
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      "Epoch [   13/   50] | d_loss: 0.1835 | g_loss: 0.7795\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [   13/   50] | d_loss: 0.0730 | g_loss: 0.7264\n",
      "Epoch [   13/   50] | d_loss: 0.0631 | g_loss: 1.1748\n",
      "Epoch [   13/   50] | d_loss: 0.0447 | g_loss: 1.0189\n",
      "Epoch [   13/   50] | d_loss: 0.0532 | g_loss: 0.6730\n",
      "Epoch [   14/   50] | d_loss: 0.0534 | g_loss: 0.7295\n",
      "Epoch [   14/   50] | d_loss: 0.1308 | g_loss: 1.1982\n",
      "Epoch [   14/   50] | d_loss: 0.1112 | g_loss: 0.9359\n",
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      "Epoch [   14/   50] | d_loss: 0.2725 | g_loss: 1.5960\n",
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      "Epoch [   15/   50] | d_loss: 0.1494 | g_loss: 1.2316\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch [   39/   50] | d_loss: 0.0675 | g_loss: 0.9121\n",
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      "Epoch [   40/   50] | d_loss: 0.0740 | g_loss: 0.7785\n",
      "Epoch [   40/   50] | d_loss: 0.0581 | g_loss: 1.1142\n",
      "Epoch [   40/   50] | d_loss: 0.1385 | g_loss: 0.5922\n",
      "Epoch [   40/   50] | d_loss: 0.0282 | g_loss: 0.7447\n",
      "Epoch [   40/   50] | d_loss: 0.0354 | g_loss: 0.7885\n",
      "Epoch [   40/   50] | d_loss: 0.0433 | g_loss: 0.8961\n",
      "Epoch [   40/   50] | d_loss: 0.0848 | g_loss: 1.3848\n",
      "Epoch [   40/   50] | d_loss: 0.0716 | g_loss: 0.7566\n",
      "Epoch [   40/   50] | d_loss: 0.0655 | g_loss: 0.3331\n",
      "Epoch [   40/   50] | d_loss: 0.1822 | g_loss: 0.7275\n",
      "Epoch [   41/   50] | d_loss: 0.1029 | g_loss: 0.5351\n",
      "Epoch [   41/   50] | d_loss: 0.0613 | g_loss: 1.1506\n",
      "Epoch [   41/   50] | d_loss: 0.0707 | g_loss: 1.1504\n",
      "Epoch [   41/   50] | d_loss: 0.1127 | g_loss: 0.9214\n",
      "Epoch [   41/   50] | d_loss: 0.2119 | g_loss: 0.7527\n",
      "Epoch [   41/   50] | d_loss: 0.0465 | g_loss: 1.4255\n",
      "Epoch [   41/   50] | d_loss: 0.0373 | g_loss: 1.2849\n",
      "Epoch [   41/   50] | d_loss: 0.1941 | g_loss: 0.7334\n",
      "Epoch [   41/   50] | d_loss: 0.0427 | g_loss: 1.1167\n",
      "Epoch [   41/   50] | d_loss: 0.0541 | g_loss: 0.9213\n",
      "Epoch [   41/   50] | d_loss: 0.0767 | g_loss: 0.6903\n",
      "Epoch [   41/   50] | d_loss: 0.0303 | g_loss: 1.3968\n",
      "Epoch [   42/   50] | d_loss: 0.0916 | g_loss: 0.6181\n",
      "Epoch [   42/   50] | d_loss: 0.1242 | g_loss: 0.5949\n",
      "Epoch [   42/   50] | d_loss: 0.0909 | g_loss: 1.2835\n",
      "Epoch [   42/   50] | d_loss: 0.0472 | g_loss: 1.0355\n",
      "Epoch [   42/   50] | d_loss: 0.1423 | g_loss: 1.2335\n",
      "Epoch [   42/   50] | d_loss: 0.0648 | g_loss: 0.7836\n",
      "Epoch [   42/   50] | d_loss: 0.1621 | g_loss: 0.8923\n",
      "Epoch [   42/   50] | d_loss: 0.0380 | g_loss: 0.8404\n",
      "Epoch [   42/   50] | d_loss: 0.0656 | g_loss: 1.1703\n",
      "Epoch [   42/   50] | d_loss: 0.1760 | g_loss: 1.3609\n",
      "Epoch [   42/   50] | d_loss: 0.1024 | g_loss: 0.8234\n",
      "Epoch [   42/   50] | d_loss: 0.0564 | g_loss: 0.3408\n",
      "Epoch [   43/   50] | d_loss: 0.0329 | g_loss: 1.1638\n",
      "Epoch [   43/   50] | d_loss: 0.0982 | g_loss: 0.6419\n",
      "Epoch [   43/   50] | d_loss: 0.0388 | g_loss: 1.1614\n",
      "Epoch [   43/   50] | d_loss: 0.0509 | g_loss: 0.8126\n",
      "Epoch [   43/   50] | d_loss: 0.0876 | g_loss: 1.1614\n",
      "Epoch [   43/   50] | d_loss: 0.0598 | g_loss: 0.6436\n",
      "Epoch [   43/   50] | d_loss: 0.1614 | g_loss: 1.1004\n",
      "Epoch [   43/   50] | d_loss: 0.1693 | g_loss: 1.3669\n",
      "Epoch [   43/   50] | d_loss: 0.0824 | g_loss: 0.8135\n",
      "Epoch [   43/   50] | d_loss: 0.1674 | g_loss: 1.6732\n",
      "Epoch [   43/   50] | d_loss: 0.0966 | g_loss: 1.0803\n",
      "Epoch [   43/   50] | d_loss: 0.0282 | g_loss: 0.7919\n",
      "Epoch [   44/   50] | d_loss: 0.0286 | g_loss: 1.1358\n",
      "Epoch [   44/   50] | d_loss: 0.0330 | g_loss: 0.9293\n",
      "Epoch [   44/   50] | d_loss: 0.0353 | g_loss: 1.2658\n",
      "Epoch [   44/   50] | d_loss: 0.2089 | g_loss: 1.2355\n",
      "Epoch [   44/   50] | d_loss: 0.0776 | g_loss: 0.7196\n",
      "Epoch [   44/   50] | d_loss: 0.3197 | g_loss: 0.5859\n",
      "Epoch [   44/   50] | d_loss: 0.1546 | g_loss: 0.9410\n",
      "Epoch [   44/   50] | d_loss: 0.0477 | g_loss: 0.6940\n",
      "Epoch [   44/   50] | d_loss: 0.0712 | g_loss: 0.9568\n",
      "Epoch [   44/   50] | d_loss: 0.0945 | g_loss: 1.2202\n",
      "Epoch [   44/   50] | d_loss: 0.0794 | g_loss: 1.1865\n",
      "Epoch [   44/   50] | d_loss: 0.2244 | g_loss: 0.6788\n",
      "Epoch [   45/   50] | d_loss: 0.0276 | g_loss: 0.8158\n",
      "Epoch [   45/   50] | d_loss: 0.0682 | g_loss: 1.1122\n",
      "Epoch [   45/   50] | d_loss: 0.1028 | g_loss: 0.9248\n",
      "Epoch [   45/   50] | d_loss: 0.1040 | g_loss: 0.6585\n",
      "Epoch [   45/   50] | d_loss: 0.0502 | g_loss: 0.6539\n",
      "Epoch [   45/   50] | d_loss: 0.4367 | g_loss: 1.1138\n",
      "Epoch [   45/   50] | d_loss: 0.0273 | g_loss: 0.6737\n",
      "Epoch [   45/   50] | d_loss: 0.1066 | g_loss: 1.1326\n",
      "Epoch [   45/   50] | d_loss: 0.1614 | g_loss: 1.3444\n",
      "Epoch [   45/   50] | d_loss: 0.0355 | g_loss: 1.2528\n",
      "Epoch [   45/   50] | d_loss: 0.0926 | g_loss: 0.9688\n",
      "Epoch [   45/   50] | d_loss: 0.0663 | g_loss: 1.0168\n",
      "Epoch [   46/   50] | d_loss: 0.0954 | g_loss: 0.5706\n",
      "Epoch [   46/   50] | d_loss: 0.0425 | g_loss: 0.8073\n",
      "Epoch [   46/   50] | d_loss: 0.0721 | g_loss: 0.9911\n",
      "Epoch [   46/   50] | d_loss: 0.1725 | g_loss: 0.7792\n",
      "Epoch [   46/   50] | d_loss: 0.0743 | g_loss: 1.3191\n",
      "Epoch [   46/   50] | d_loss: 0.0473 | g_loss: 0.8722\n",
      "Epoch [   46/   50] | d_loss: 0.1216 | g_loss: 1.3248\n",
      "Epoch [   46/   50] | d_loss: 0.0535 | g_loss: 1.1578\n",
      "Epoch [   46/   50] | d_loss: 0.0857 | g_loss: 0.9949\n",
      "Epoch [   46/   50] | d_loss: 0.0902 | g_loss: 1.0000\n",
      "Epoch [   46/   50] | d_loss: 0.0723 | g_loss: 1.2710\n",
      "Epoch [   46/   50] | d_loss: 0.0617 | g_loss: 1.5353\n",
      "Epoch [   47/   50] | d_loss: 0.1150 | g_loss: 1.2651\n",
      "Epoch [   47/   50] | d_loss: 0.0282 | g_loss: 0.5296\n",
      "Epoch [   47/   50] | d_loss: 0.0485 | g_loss: 0.6765\n",
      "Epoch [   47/   50] | d_loss: 0.0283 | g_loss: 0.7019\n",
      "Epoch [   47/   50] | d_loss: 0.0939 | g_loss: 1.1783\n",
      "Epoch [   47/   50] | d_loss: 0.0686 | g_loss: 1.1719\n",
      "Epoch [   47/   50] | d_loss: 0.0622 | g_loss: 0.6497\n",
      "Epoch [   47/   50] | d_loss: 0.0237 | g_loss: 1.2146\n",
      "Epoch [   47/   50] | d_loss: 0.0462 | g_loss: 1.2053\n",
      "Epoch [   47/   50] | d_loss: 0.0727 | g_loss: 0.9677\n",
      "Epoch [   47/   50] | d_loss: 0.0491 | g_loss: 0.7910\n",
      "Epoch [   47/   50] | d_loss: 0.0410 | g_loss: 0.9670\n",
      "Epoch [   48/   50] | d_loss: 0.1658 | g_loss: 0.7566\n",
      "Epoch [   48/   50] | d_loss: 0.0345 | g_loss: 0.9387\n",
      "Epoch [   48/   50] | d_loss: 0.0252 | g_loss: 1.0891\n",
      "Epoch [   48/   50] | d_loss: 0.1549 | g_loss: 0.6764\n",
      "Epoch [   48/   50] | d_loss: 0.1253 | g_loss: 0.5434\n",
      "Epoch [   48/   50] | d_loss: 0.2176 | g_loss: 1.8316\n",
      "Epoch [   48/   50] | d_loss: 0.0519 | g_loss: 0.8194\n",
      "Epoch [   48/   50] | d_loss: 0.2199 | g_loss: 0.8121\n",
      "Epoch [   48/   50] | d_loss: 0.0977 | g_loss: 1.0481\n",
      "Epoch [   48/   50] | d_loss: 0.0699 | g_loss: 1.3408\n",
      "Epoch [   48/   50] | d_loss: 0.1795 | g_loss: 1.1939\n",
      "Epoch [   48/   50] | d_loss: 0.0193 | g_loss: 0.8048\n",
      "Epoch [   49/   50] | d_loss: 0.0941 | g_loss: 1.1553\n",
      "Epoch [   49/   50] | d_loss: 0.0373 | g_loss: 1.2267\n",
      "Epoch [   49/   50] | d_loss: 0.0232 | g_loss: 1.1205\n",
      "Epoch [   49/   50] | d_loss: 0.0258 | g_loss: 0.6994\n",
      "Epoch [   49/   50] | d_loss: 0.1216 | g_loss: 0.8222\n",
      "Epoch [   49/   50] | d_loss: 0.1814 | g_loss: 0.9450\n",
      "Epoch [   49/   50] | d_loss: 0.0287 | g_loss: 0.8222\n",
      "Epoch [   49/   50] | d_loss: 0.0482 | g_loss: 0.8094\n",
      "Epoch [   49/   50] | d_loss: 0.0959 | g_loss: 0.9068\n",
      "Epoch [   49/   50] | d_loss: 0.0637 | g_loss: 1.0103\n",
      "Epoch [   49/   50] | d_loss: 0.0198 | g_loss: 0.9342\n",
      "Epoch [   49/   50] | d_loss: 0.2725 | g_loss: 1.1447\n",
      "Epoch [   50/   50] | d_loss: 0.2082 | g_loss: 0.7420\n",
      "Epoch [   50/   50] | d_loss: 0.1833 | g_loss: 0.6354\n",
      "Epoch [   50/   50] | d_loss: 0.0791 | g_loss: 0.6799\n",
      "Epoch [   50/   50] | d_loss: 0.0619 | g_loss: 1.1187\n",
      "Epoch [   50/   50] | d_loss: 0.0176 | g_loss: 0.7437\n",
      "Epoch [   50/   50] | d_loss: 0.0797 | g_loss: 0.9393\n",
      "Epoch [   50/   50] | d_loss: 0.0815 | g_loss: 0.9280\n",
      "Epoch [   50/   50] | d_loss: 0.0805 | g_loss: 0.4580\n",
      "Epoch [   50/   50] | d_loss: 0.0689 | g_loss: 1.3235\n",
      "Epoch [   50/   50] | d_loss: 0.0715 | g_loss: 0.7801\n",
      "Epoch [   50/   50] | d_loss: 0.0591 | g_loss: 1.0334\n",
      "Epoch [   50/   50] | d_loss: 0.0627 | g_loss: 0.7467\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 50\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, print_every=500)"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa4c6e75dd8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4e843a320>"
      ]
     },
     "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": 24,
   "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": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4e846c438>"
      ]
     },
     "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:**\n",
    "As mentioned in the factors already. The result propagates the bias of the dataset. The dataset includes faces of mostly \"White\" celebrities and this bias is picked up while training and shows up the in the generated results.  \n",
    "Training the model on a larger and varied dataset should certainly help with generation of diverse faces and add variety to individual facial features. Also, it seems the model performs slightly worse on generating side poses as compared to front view of faces which can be solved adding more side pose images to the training dataset.  \n",
    "\n",
    "My model contains around 4 convolution layers. Certainly adding more layers with help as the depth of convolutions increase, models can learn more features. This means training time will also increase considerably.\n",
    "\n",
    "Adam is usually a good optimizer for most cases and a standard learning rate of around 0.001 or 0.0005 works well. The original DCGAN paper recommend a beta-1 value of 0.5 but I noticed the generated images improved with setting beta-1 to 0.1  \n",
    "Higher epochs should help with learning although at one point the learning would stop as the generator and discriminator become really good at their individual tasks.\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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}