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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can comment out after executing\n",
    "# !unzip processed_celeba_small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
    "    transform = transforms.Compose([transforms.Resize(image_size),\n",
    "                                   transforms.ToTensor()]) \n",
    "    image_dataset = datasets.ImageFolder(data_dir, transform)\n",
    "    print (image_dataset)\n",
    "    return torch.utils.data.DataLoader(image_dataset, batch_size = batch_size, shuffle=True)"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset ImageFolder\n",
      "    Number of datapoints: 89931\n",
      "    Root Location: processed_celeba_small/\n",
      "    Transforms (if any): Compose(\n",
      "                             Resize(size=32, interpolation=PIL.Image.BILINEAR)\n",
      "                             ToTensor()\n",
      "                         )\n",
      "    Target Transforms (if any): None\n"
     ]
    }
   ],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 128\n",
    "img_size = 32\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# Call your function and get a dataloader\n",
    "celeba_train_loader = get_dataloader(batch_size, img_size)"
   ]
  },
  {
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xe7XT8819L7Pn+hkwns3cp88Xm0z90UfanPoJKGD1hiRAm9e28jIdK06PJKV5daY+fHmo85wMuFGw2q0DOduddVEfP7ipjcKug2622l4cs72tjaqq2Mz161uie//ujyUL+5On2kzr6VNsxHqka16DXGy9qX5rYsNtboo9HoEeh36OQA//k+/9dl7e2LruvhJ4ziUZhdkvyNN0SAWbutbxD60q5hZoiCXILUqQM7VA6/Q80ex3dkU/PDyUXICmPHQFOd9lvgLKf9ERDLIw8GPn4BXOQTecDGb4XfTO+orq2wSddQyqc4JNd33Qq+cFCw9sLni+0eTFm/X/TEjimRueLOJlra5GW1+XNCwda9weHot6urKpTTsroJxPsHnej34iecTeoc7z8Dk2UZ6LZtp9qPlybUN1KEO6eN6blRI34UTsm2AD11jxLsbYGk8oJ1F/VsqXbIJLlwNubI/z0OGvB4lE1NV4dcsxMvocJuOxe/jpIpYe7Ckuks4/hAymElI2erGsZAI52QzrYiOihAwbekL2E0FPtdXExs74ZFMpL2Jgt6W+rWIDwRrWpEZTNOBSWbHzk4cHeflHn0pGPUZaMcCusANQsGuQRnBT6mpNf0vJ3nyCGDBZvizO9/x8g/AYMsAhN0aHrqUMGVwZ45YbqEYY22ttranRuvq8Wld7lrCxdQCHLMZQ2nSdu2/UKD2A9HS8ovY+amke9PuKIwUjpUDt+vzjH+Zlv3UnL9+6L/p5QWEKKUqCjfOLLnNqD270vEwEge+ajUXuQEclbohO2dLhEdzkEB9SxP4SNiK/vSVq/9ffvpuXuZlwiD7qYnNlH2N+70gx+NHjxdzZOVDsPoDMv4/xPp/DwINykhSb8kJiMcZ6WXROo1se3QU15374o5/k5Xff1AaqdbpYnuvULrPt/BkQeJ7rZDL3KmJ/CX0S4H6Z/3joKx+yiZCOjGVuGq5+m3DDfUohcXyjxDwKm82WFn87Rx40RfyKsWl8WpCecQPjy5w0r6agajyZuD//5GPnnHO/9V1JqHoDxVRuNtxBzh8iuFTQXHUcT7dDSoEr2Hy8UoMcHe3YG8GAAc+O5z2QcFd6jDXK4uZTxY6HD/UsdXKiZ75335IMkc5vZU/1vbmumPIY0q2PsPntGw/0jNNq6p4GMDTYexZk9aIs6cvCc5537oJIlzA4bc00zo9OJQdjDlPC89rDT/9cv1NCh/7ndiaHh8ozqkxiJmr/jY7WznMp+MG+8lmu0Vsbisl7e0/zch/bdawjb5yNldM1q1i7IWEt1zd1TFfSqsM91d3n8xUekmqILwVvnS+4RhoTx2AwGAwGg8FgMBgMBoPhCsBe4hgMBoPBYDAYDAaDwWAwXAG8lpzKd86VMvkRXRL6p8d5OU1AoSL1DZTgKeRUM9CK2lVV5+Z1yXg210W3JqV5CFoeHYSmoM6eO7eMcJ1JIpoS5ScOEoFzxwDnnDvrSxYwBYW/gvujE0V/KCrs1qaoVaMZ6WjYrR71KWF3fX+6oGZeRrP8WTCeTN1PniwoZHunou/6kKmtrkhKsYLy81251Dzbl2xq51jUxFNIqMqgir69Lnrar7xzLy9/6x1Rku+izzewI3lUW9DWogYlTuqr8eBrefnb772Vl/+v7/1xXv4/f//HefnxgWh4vVP128GKKHzXupLkbLZF+euAqumBLhgkcOiCa9Nv/sv/w30V8Jzo1D7mRMEBJtDvLVD2bqAvroFaeHIk6t9sqr+dYZ6lgcZnuy3648kp6KmgEXuwvwkzOnklAlUddGnSYwsUaczRKeeCp7pMR6IxB3CiazZ0r16qY0oYP1On+4vh4kL2uZfJApaopnIumbv5cEGrr3dI31Rb9o9ET336SFLVo2PF3K3V1bw8oVsB6OHHhzrPFDKcMzhLHYPCf3Sk82+va4w0s7noo+1ncG/pnaotJxhDFdBjAx9OTXSGgIQkolPMJe5UI8jIxmgzuv/0EbvT9KuRcEwnE/fks0+dc0W5A9QTBcleGGl8nvbUF6fDHZ2zMPgwn5zkN4MzuOAkio1gZ7vNNcWulZYkPZ3mgp690tVv5brmsw9pMdU8UF64HuL+k4r6N53rD0YhJMee+ncIadXeK5UrkKUcw9GC8b6SufIlyfLWxSRJ3KC/qEeQqK8qkLuVsEZWIbetwHWoWW9eWK5D8kSXirBE2RRlpurbOcY87/lcqRTBSixEmQ4sW3CWHPS1PlFaNUJuczbUvDnqibI/Gypn8Ap1QZC+xGCDoXs+XV7f/UUE5+5bHiX2lMErxp/2NfYSLDRQE7o15AL/6X/4q3n5V//238jL/TO1aaejObWyotg8GCrvePrkcV7+8Y8Xc/rJU0lbXx4rvzqD2ynjSzJVXBjBIfYVYvDOGfNe3dNlrT+FXHX3QGtAb6Dx0OjA/Sdr42UKVUPfc51MtlBi/gBZqYc5EVHehX4Osa1CKVD8SJBvzyClqVEqAwdFutLV8IxSg5yqWlvU00fu4V6p/Z6fam5N07+6tTyMxSS9mnKqfu/Mffe3/q1zzrkj5JYlSOXpUliBFG4D0ppbm3oG4VYAPvWEaK+ALnjIKQLExiHi2wDbXJw/O87hmjyFq+UYZR/XiXH9Xl+51o9+KNfad9/Rswnzm0EIt8qu2sMf6/4+/ky5wVv3sN1FV1tMfPR8Icukc/SXhe/7rlJZrF18tvfhJLyK3PXb3/pmXu4g33j1BE6S2MqgGul+Z4jLL55Lnt2oKYehm/BmR2OkhS0U9jJX7E5Z/b2+oTh89OpJXt7ZVbsGTmv06nW5jQ1ONXZj5NTXbt3Jy+Op7ml7S32yvaHtNJ5mOaJzzp0eSOq1gfabH+sZ5fQLulIbE8dgMBgMBoPBYDAYDAaD4QrAXuIYDAaDwWAwGAwGg8FgMFwBvJacyvM8V8ooZAkkSzPQ0RwotgF3YQcNOwUlrVMVVerNm5IevXFPMptuR3R+MqjnqANpxgmoloPBgsJ6BKnAMaivA9DmxpCIQX3iRtjJnLS9CO4H85kocad9UDHhcpU+F122XhftcnVVtLDuiiho80zS9TH+7stiGs/ds2y37zEkaM1IVLINSFD2dkQle/hKtOpdyGf6Q91vJVXDffOuKNz/+W/8Ul7+5Xe1Y3tnVVK5EPTzoKxxkWQUOspq4jGcl2rqQ7qt3FoVDfN6S2Pof/1tURz/9JXu79UxJHGgMJ9CknEDu6Bf74pWHIIe3qrDQekAbm1LhO97rpFpp0pOFMM29FQB3IE4niOWQc8tsX3hahE40RJJq07IZsUxztFNgxTMrL6QUNXRqR3MiVZd54MSx6W4Zg+Oc7uHoqrvDTUXPdCoKe2iU0CjQK+GFBOs1DSXwyxPkhMGgdvIqOkxqJMDyBoGcH7zGUNH6rfBkSiYdG9qgk4KJaI7i3RjM/weQ5LjoXMniH/NyqINqzj3cKBrUs4XYSf/sKwyXVJIGw/hSJheIslIQNynaw/HWQJniyHcvb5KKrqX3VMLYzgBs34ywpoA6i1Uu24CGc8MjhhBDNr2TG10dqxx0oWb150b0lNtb6ndV+GU06gv1tpqEzGsgrXHwe3B1zVnY82zelX0YKgM3BiU4wDrXxlOeG3IiII53EoQy+pYA0i1PjedWaaEI00l9ylBQtVpqi3XV7WeUTLTQhtWMc5DyKP8kPII9X/BMQhgnlOUJH3+bwPMA8pkYCDiIlDSGxW6Yaqfh4ibLUgSvRPEREgF4omO930NAKQALi3cHtYUtM0ykSaJG2VSiRAumD7cUQ7P4M430xqyCdnU335f0odfev9t/f7LH+TlVlv5RbIld5JyDXMK0skGOrVR1/hZzZzDDu+J2n/UV0yHQtYFnsbX2Yn6YudAOeLHL5XTfPhQ53y0I4nWGDlyim+5XN3OBhoDI+Q3HmQkudR/iZPR9/zchSjE6PextpXCCxyyXPGrdIj1PqJjLeJsAsl5wUkthOQDf1vHGlVFO1QySVcIx1IPEyGeq69e9rSezhzXQlXxkmlzldRUbjKZuEefLdypGswFICUqQ0K1jXz6m1/T898dOHHWyuphD4ks84UgUB+VS3S5VHmK9YRbcfhZvkjX1EFfOdgQkkg+c05RpiPcS7r4Pn6k+7ihrSPOZjr+9ETPqVEJrpQVtc0U83JW1rjys3jspct1bzyvHaaTa8FV8b13FB+7LcXQR3ChegEp0Z1tSZXoQroLh2TOrVUkF996W8+Oq3hWfrnzIi/72bP4dA7n6YHa9R6kVbNjxcoy3CHX6xqvazU9o3a7KveQvA16GiNHU8XiVlvP0rdu3MrLhwe6101sadHDu5HTgdaAvwzGxDEYDAaDwWAwGAwGg8FguAKwlzgGg8FgMBgMBoPBYDAYDFcAryWnck4OMnQkSSFH8C+xJuAxFdDgbm+JnvTWvdv6/dbNvNxZlcwqKokWHINaPJ9BIgB5V5z9fgbHoKMT7Rp/AneG3giuRZAQ9EChS3F/pQJVT7+34QTRaIu6dYydpz99+ElefnUoyc0WKO/VXEa0PK5qHCfupJfR7kATXQUNLoGc4+UuJFSQbZyO6MykftjALuG//p138/LPvy8a3OamqIQeKPxxSbKyBHIXdy7dgJbGA/HXg/NL4CB9WhWV7R/+4vt5+Qx0/8Pf+oO8/OxQ9PDhGJTIWGOr11fbHJzpWnc2RbuvNXQf1yGVWya8NHVBtlt8Gzv512GJMwUPe5bofvr4/RBOFoOe7qcHl7EjuMRMIGEc4Dwp+qAEer8X0KFoUW6U9O+bHc3nB3c05+9sa86vdnV/s1jtv7svKuRTyOL+8Keiyz+Dw8YYczeEU1ENEoi0ofocYr7O87G3PFeVNEncNJP79Puqf6Mq6n+EOq+Cvn/jtujG63CQm+G+SMdswWHtJ588zMsvXsGpBhItUmcpy7q5uYjXSQypCOQ+jbLmf0zaOqS1s4IkRNehVCqBdowtTvVJwfFwpnExgQ0Lj1liGC3A93xXyxwc6OREGvYIRgMDxJYZqNcpVuMQGkL2xQByRj9U+daaxsDWBuS5HfQNNHXVTNoUQjpEh8JyqPOFoc5XKutGNjblvNCsiDrNL0ODARzk4BBZ6ur8TUgU6GA1hnPXYAA3Gm9xfLpcQZWL54trNFe1Jt28rZzk+rXtvFyBDDzAwPIw/zzKM+iMBkccOuWk6cWScP7OnMrLNAQJYjudrCiNiSBhZR0DnLBacJDD3ML6t3Mih8pxX3E2qihvKWheHWUviBn+VzMZk8S54ShbZygDhGzxAJKFAHKOX3hPrpn/yX/wK3n5/k3lK7WGYnPgYcJGGg9eoDk1x3j24aLUbKstqpm8ZAtzYgy3qSnylfmEjoOSNKw04MhTQl8z7mJs7CCXG+H8CfpuNIa8HHGHjmrVbFz5/vK+B/sezouxH/pwL/QLDxd5EU3sQm594HP8c17iNJgLnH8B8waepyBzSrOqQNrYUZlbDvQhVTyecf5fNieukIYKCHzftTJXvjJyLDo/dSCnuX9DOd+tDchV4QJWxrOjj7yEDn1lrGkVSGRKWGc8xCWf5Wwcz5FoxHM9wyWIhZRw081sTCk6nrF+hLzr40dyJ7p/W9LNWkt1L88ky1mBc+TJkeLui4eQhiaZNH+pJpyp87LxR3liDW1cRdsf7Ei++fGP/zQvb7UVq25u6FmJ69UrSLUpubq5qr99cEs5R0RZJyTZN9YXY+fVriRLp3j+f/+NO3nZHyoODgbqz3tbGovDiep1DTK4jx/JQesULrJTBIadPfXzahdy7A299xj56sPqMbel+GIwJo7BYDAYDAaDwWAwGAwGwxWAvcQxGAwGg8FgMBgMBoPBYLgCeC05VepSN4sXlKOzM+x0P4Z7E+jBAXYhr1VEa+ti9/57t0RPun3zRl7e2NLv7XVRq1oroiTReWYyEp3qZF80tHEml6rg2EYVdRmqLj1Qq85QPsHO06dwDfBB59ralhSk0hAVkA5QN6+p7m24HP3kE1HR9w9Fy2pGC8panCxRwpE6N8/YnK226lnG7vGHZ6rDyUBUsiF2dCcltQ0XiG+/oXv8pfdEE1yDs4dXlcwpDdQOHiRpdBI6l0vFHqm1kH6Azjf31J/Ok9xnTew19+237uTlP/n0s7y9Mn7vAAAgAElEQVR82nuqMiRlIzgv0f1nOFXbHGPH+NOBxuI3H2hcLBOe5+UU+RkkTj24SMzmkBuORQM9HWnuHsBlLAItmeM2TlSmkwVlbOmc7jG6bquiObKRyRS+8Y5o6x984+t5+fYNUSUbVbT5FDK3nuRRa6CYbsJRLQCFvfSJ4tFnB+qvPpytVloag52a6PJnfVLaF/+9lPH8MyBJEjfJJJy1suqwtSmnkxKkD8lcbXLv/ht5uUwZBNr7FqR1N24rtr5xT9LGDz9U7HkKF7wZaN5VyN9KmeMGCd6rdbVZAieBGcLWaALHqLn+YQI50YxxzrvYeYJMYbpT1dFvlJ/EWI/C8LXVw18IiUvdJLunCM4YLlSfToYaS3M2nk+qvu6H7gwx4tgclmkhrMBu3ZCcpdPU31ZLkFkEkKhmkqR5KhmIDz0X5RFhCW47kFlt3NA46nY/zMtt9PVkrrXz+akozcdnmtM1uJKcwaEiCdSnlZrmd5w14DLlVL4XuFplEU/WsFh0IW8uYY56cGNKMN4oJ6eEKgbdvkjDR2zFWKXOMCU/nnIqdy6nAvU/LRyg6+gyLon1PxNIGyhPp7Sxifl/DLlyD9Kqekd95fmcixd/J4whi10mZvHc7R0v8pd5DCkzXKKGWAvpmnMf+dn1NY23UhlxA8HIg4wtTJivQK4DUySulyFcpsLGQoKaUiI0huQL+fXcqe/GFUgaUG5AonINc+v+DclCyogLT3c1L4cT5DdwX3nxUn39wTuSGEbZtbwly+MCL3New3im9I/Ot2F4sUwvYgyjzgrzbM6tIPCMkBZ0uyrOMadDdG4pkxfXIC1eQS52va058aKnsXh6jNh+aXKxvPz/3yd833eVzGEwpksp+rHb0hxt1SAhTqF/pJoUZT9RW0cYD2WsnSxHeB5lnPaRa5xvF0I31xjyyBhrZIK4TKfkEmL9DJX/xhvKwQ5OtP493dfcevudB3l50tdae3KqZ7LDWGNpUpJ8ffvGYvuK6EeSNH1ZpGkqOa2HOYH1qQ8H1RM8E5XwPHfvtp7h+cz/6PGzvNwo6/i/+yu/mJfjoWJhPFUsrCO/2urqWbaUxfRaWWPrsKZ+uw65dOldbfnx+Inqksy5PuF5pq1zbsGR8MkL5c5HcMJ68Upyqh62qyhDjh3BaW8FbpFfFMbEMRgMBoPBYDAYDAaDwWC4ArCXOAaDwWAwGAwGg8FgMBgMVwCvJ6dKEzfJ6Eyk4ZLiHYByRTpjpyn60Paa6Eyb69qput0WhbVcE20pqoAWWwb9G24a1UiUt8lEdZtl9YywM3mlrL/zHJw3ID+oYOfzEqinJfx+cCRpx9GhytWpaGGHR3Kd8TxRCssN0aZu3RDN7qis8/T2F85Q6RJ3p/ec58qZhKlaEdV2jF3Xh3OVR3PRBBNQW8vo57dvaifvv//z7+TlN1fVt6VE7ZbMSEmGy1kqqlziQF3P2Ilz0tanqBeor1M4UiTYmd6rYudx1Pedm6JQ/+mjx3n5DA4CM7iqJHTtAP12DFnIYKTdxp/viga5VHieC7Mxn4DaOB7TkUqHTyAD83A/dJiqgtaXoq8p5+NbXxgFuFqo41cwtu9ty9HlV//W33LOOfc3fuFb+W+tFcigcL7RULKKHpw0yKetoQJdUHHfvKZxPUpE45xFon0+2RXlMaH7B6QGBfOJXMizvLk4n8/czs6CSvvgtiimq6twtNuVc93Ghu6lCpcUdooHORPd57pw+GuU1ebdpqQjL16I+jkciu47hlPfOY3dh2TOh7RkDEeTwUjj7BjSNIfyfEqZCVo8uZhaHpC2j7lYijSOPSdKL6WGHpxFlok0dW6UUasHfV0vDLUmeQHdthRbPFKUMUenQ50n9i6mgUdYr6II9HBIWPwEMpdYa2ea0f5RLedD4uIl6n/WN4Ujz9qaxtRWV7H+dO9FXr69oVjQn2r+9SA5PaNMItZ9t2q6j1OMpXG2JhVcm74kgiBwndYiL6lD7k3JHh1rKOyjSw2PSSAhiHFfdNKMsdYWbEUKMkqMYYafTH7BahX+Ge2TOownUMXp6lY0NaH8BO5hqc5zcqh4sQYpbAiau0c9EaVmyVItVHLM5jO3c7SoVzyT1KBSlyxugny1ASn2Wgv5J3KHGK5I1EfR1YaySEp6KJEqyEIxaJJM8hFHkKFhXHiB2tzBtY8Lpo/8NkBeEkHS0Cyrvu06c2edZzItCFbz0hQSESrkzuWq6fKWRefS1MVZHxXD/cUOU15B+QSJG2RWdLIpSKXiiyvOvqIrTwnnnGF+B4GX1ZHyWJ2jzrZHfhSeaa2aw9WIDVrMQ64OkiRxo0y2zhhWhww4oswNMbIQH9AAAdq/jGe+KKBzGZ7dwosdrOhOVWzrrB8LslSsAZBE4mcXOj4n4TkGsb6GWPAA8qJ//b0f5OX9U8XRG9vadqCxoTx6JdHzrUN9yv7iWZb3+WWRpmku+T0f484VJcTHJ8rVJ8jbb6xrHX3rwZ28vLmpWHx2qpzgOhxpv4bjP/yB2ufhQ21/8d5b2lKgVoPDbCZp5/zbgHsnHe+uwSWqDinTwanm5QjxogmXrU04tL5x65auNdbzynl+75xzP/mxti4oQcZ7a1u5f7Nk7lQGg8FgMBgMBoPBYDAYDP+/hL3EMRgMBoPBYDAYDAaDwWC4AngtOVUcz12/v5CK9M4k+yGf2A9IPdQhK5A7bGDH/HZXcqYKXKNIeRyP4aBwJqpSFbTniHRWOgicn4bOR9jV3IHC6IOaVkpV30Yqmuuc9YKDz+GJdvh/taedqk+HomUNIEtotkX1Xe2Iir7SktSsmdHoS6F2zf6yCILA1TuLNi9DBkEHIrAa3WxOrrb+YaOpfvv1b72dl9+7L+eCKh2G6AIwgyMH6PlzXgvSMz9YtCFppRPQez2UQ9JvcfwUkoQWKHEfvK36/j8//igv7/fRHlP9bYKxPgePPQZt0vMhb2JjLhFxnLiT3mIuVEAzpnxkTlo3/rYEqmgdbPd6IRromBJo2xVwFJugMK5vaAzfvXMnL3/n576Tl99///3FdeBIECeg+UPOwXYuUGtTOrhgTBWUFXS0EGpw8Oh2NAZIBz1Dv88LzPLl0/8T57mpW9CA2T+PH8slbdxT/Ni6/bW8nML1IEYccnB1CEE3DugkBlpvHdTua5uScU1Gap8JqNDDPIYxbgqlkmJ1ECre0TVnADmfB5kMZUBfRCjjc90Bp57yUw/HxF+RhCNNnYvni/ad4N5msdYqStsYxwI6qGC+TnGetODKgvmNtejZS8k2Szj+7rbm2gakdi6LtXGoOeenkBmjL6AUdR7kHJW65Fk3bsiF73jvUV5ugTa8geOnkLT04WbVhhNSCIlvCe13mq3vy3Sncp7nvIx6P8M4YTyNUecEEpd5QRKHeI/jKaeiU0tCORViYUFOxTpgzclzJOQ7lDbGoI0njLOIEeyHwvxgDuVBqgNrtbNDScWnE8njwjIcIjkXMb7n6Wulnl8YQRC6dmdB129WNfYDOItRHowQ6coVxFSqLRK6tmFd8i4+nnojj//gMcfCGMgkvGGK9Y8OewX1DxwncR8J9T8olyGtKJcoR0J+xdhZkFHrPH3ksaUSc/xKdo4lzsU0dWk2XxLIbVJeo+BeirWI+itK1jAOeU4PmpgUY9svxFy4okKqFkSIl9lcLCEu0J0HTe+aaL8a8insEODiwqrKHPzqCKqSJHHD8WLc0E0sRS7CNWwGOWlScORVuSA/Zvy/QGbq3F+QFlMKifrEjLV54eLcmb3CEU83v6Cgb2fepftYaypGbnUk43z65LGOuSaZDbcDKRccEFmH7LflGsW5/E6xP8MZ8tLpjOuG8r/34FT84J5kYit1Pfu++6YkY5UOtkpBLG61dO8jOAVyXZwniu9nB4tciFK9TldSrRAy9EZNc7hSkcwrgLQ1gdy0iXx5igl757qcqvYOIH9G7nw0QvwfKtavNPTsNP8ZnOiMiWMwGAwGg8FgMBgMBoPBcAVgL3EMBoPBYDAYDAaDwWAwGK4AXovTOp/N3MGrhfPEyeF+/rt3CautjJ2WG3VRW7sr2il6pS35EHfVpiwrBbVuDinOFC5U3O1/Dlqwl+1KH5bEm6XLkYcdpr0CbRbngFyFO/83G6KFzWJShUG/nOqepjPtxP38uZwdTk/0exdyqrWVBc0qWSKFMnVp7lRBqvtpT3UgrZp0R3SJ215RPd+/o52511D/ABTAFJS0OWimSWHsgPrIzemz9vRQlzQmxZLUWoDOK+AYVkA5/9b9B3n5H3xHY3r34Lfz8gtQ4X2MUTrlJJA/TEGh878iP4HEpW6UtQHHRwXt7Bcox2qvNlrpNiiMNzY1nq+viO7ZXdHvTbgidbqax2vXRZfcvqNd4zeuSWYRlhbX4lz1wBWPMIdiUGtLaELSvcccp6A8F/xQ6AozkbynhrEfp7q/s76kVSkkCEtnqDrnAj90zdqCwtnra/w8faod+Lc6amPeC91LfEwiKNJcCqlOClcHOpn4cO0IQQ+fTki3h8w0kyUEPt0+1A+UKgQ4RxAytoJinDLO0xHii7R4emGRVGwvYN2hnVgi4iR1vf7kc79PQfdNKcfjfEW/hKTWw/2RcpYqJIF0U3z5SvF7cCxpi+drfrfX5IhxbpZSaGaPzmZ0cIQsltKaSLH+3jvfzMv9nuTEL59IGtiu6G+HVbXBBE1D58gJZIIFWa+3fClOmiRuNl6023yKvqQMCa5OXFumlHhSTgWZGONWklwslfLoeEUpFGnjMzpenTsDYc5xXlJCgjlBCStlVvOCbIsyKDplwckINPoJpOLVJuU5kH9xrU++mnWxXCq5u5lTyHpT47NH2SbiYg1jsob44FEG5ti+kE1QwwupeWHdKEx79Mf8AoeygjsPc144Tk74d3T2Iw2fMQXxIoDLWCFpR7zEfbOHOBc9zL/w3LlrmXIqzzmXSX4D5IohElDK5imxYf2ZF1GGSKlSesm6Uci5kfNT0uXj+HLm8jZDHX3E9gB6qhJytDJiuDdFH7I7r46CqoDULdZG54r9yLhEp74EOV9BqsS+w5hPQsTakFtkIB9h7k6Jvn8xfyHIJDiU2TFecw5z8KQp5aqoI+M47q+McXJ9Uw5Jjz/8JC/3TuWgugr3x8Dx+YmSwfNxtdxs1cty6wBSZzw2u1PIioLC1iqIp1hDqlj7y9gSpdZSrtuEI/W7D97KyzfhWl2CPNvvKNavZLIvTNtc9ulcUZrGvJicls4GHNSwL0wUqT9HSLZX4XKd+OqrwrYHKA+hon6+K1ky8+QvCmPiGAwGg8FgMBgMBoPBYDBcAdhLHIPBYDAYDAaDwWAwGAyGK4DXlFNN3f6LhVNSMhGViBTGADtCh5FOX6+K+rTa1i7Q1ZLkGam7mKrG3fM9jzTTi91sYlB1/YxT5UFO5UDh80PRt0mP9Gak4YlaVa3oPqqgdoWRfneg4s1wH2d9uZWc9VQ+OJTT1wi035NsB/AJHCS+LALfc42MQtys6b76PVCAC9I0/W2IdtiAw9M6JDaNmsqUsEUl0dkcfi/QGukw5n1+H3i66vgB6fUXuxCkqHxM2Rzuo1NVff/Btz/Iy3/2UDKAnR+I4khHiBCykFJN9zefgLYZs++WyItNPZdkzmoxZnEZjmAed/tHeQM7rL97S3Pxaw+0G/69a3Iq6q6IHliDhLDS1K76lZaOr61oJ3gPFO7JOUUfshHOrQLFHtz7EPTHOcaODzp/4sNlB0MDDNCCnKqPGABTCueB5sp5cC7HS5bYh2mSuulkUdlXfcYASWmur9JRBLReUPYrZfVJ4hTPHGJbgDb34NIQhFOUMZA80kzZL9mYiy+WmlFuSikTHVYc5nyANaLgkpJcTIsvyKZIy4aEkZciRZ7HLBNpmrrJdNE3lGrQhYP18C9zzCrYoQkB+r0Nx503boiG7UaSUE1GWltO4Jo4HMhR4typja4hQaDxEmLeBugXunA4SDVW1m/k5QdvfyMv+zNInudyWaxgPdg/U780IYE5PpXj1nSo89Qqi3hUXCO+JNLUucwxKKAbCmjyDg6OBflITNchSmmoCeb6QzcpjBHMqYSyAYwdymzOpVCk7KcX2ZU49xfcWyAzARV+zDwDbRsip6PDC2VeE/QPpc5pwZUGp1+m/AYIAt91srysXlNOliAWVSFnubauObTWoWQBwIKSoB/9S2+B0hEedPHakUs3MBbY/hzlBfMl5si4DnPUyQzSBUrBcZ6IKZjPuU65CLcU0Lw/l2t5S/we7HnOlTL5UQkVRbe5ML14nWZ7z7CGTJALniLXnUIiOYPEbYz1NUS71VBuoG7VLEdJAp1jiLWgihymCdleB/LRk77yk8I6d8mUvgo4Xy8K0k44szGniTBHA84FurPF8YXlomsXxy2lUBfLuH3GN//zkqSCKdoF8de5vzBXLnHKpLSUz7HdttZ0H+c82NX2DutdxSk+v/iUBmbXWmZk9Tw5aEbYgqAgTYMD2HxC6a3OM0Z+4oVa40t1PX/V8JwRNTdwDO69qmeOWaBxVIJbciVzBIwh4fJT5RtBgGdRuF0Vt3DRISklf3Otc62W6lKuw+XKV/n6muru/1RbqKSQqJ/gXUp0SQ74l8GYOAaDwWAwGAwGg8FgMBgMVwD2EsdgMBgMBoPBYDAYDAaD4QrgteRU8Xzmeoe7zjnnPFKLSesEDY70pFZT8ptWQzSk0ANFi7vq4zwR6G5eYWdzuFfQqWZKevPi+AR8/tmMsgS6DYBuGmA37bKOabdV39UNUb7KkFY9eiYpzvc//DAvc4d/sPLc8Zl2Ie9DSnHu/jCPl0eiLEWRu30tk83AAYVORpQ+eInaPgJRLwK3NaiJPpaCHudXIJUrQbYGqj4p8SlfKaLPZxlFvWCkgvePRWo2uY/4lQ4ejtDv623JUj54IIel7/5U/XnQw9gCVZPOPlVQ+9Ip3FbQz18WnnMuSBZ3QncgD+UINPAQ/bW1qvrd2oQsblXhoNHVOK+tiJ4dQRYZsH/Rp5RclEAjPh9XPhxU6PhSkESiPWclXdOraXCGCF/JqaQiMVyuCuMLbXN2JupyAtlRAppt0UDl/DzLJDSnziULqubBiaQjlZrGYVRGG1PmiLGdYvf8cqjjvUKfXCwzDILibNBBkFxQqpRRmD2MrQiU8II8iFMRawGlXfzd+wK88aKpCqRSdAVaopvfF0HqZFZEd64SYh4lN64gf8F4Kww4tAsCoxdjPIOG26zq/Cst9fv2lqjLVcyp6NxhxqPkDe4TWFvTmdwTJujfaawYUcZ6XWmIFt1e0Vq/3lO9um24f8zlrAVFlxuVIKn0RWkuZRJA31veXPQ9z5UyqURB7naJJJV0f499xXNijlLyVJAE0KUIrlh0yEoLY5u5y+KclDxTklBQgtERC3WZzehapeOpVKNEi/K7FHJ2uiYVJF+USKNBvMvizpeE7zxXyc5NaX8Vss21lta/t+/dzctd0OMLCRqDEeW/hfQZ84gum475MO+ZMSDJ/nuxzJTxNQG1v4rf28g5uP5OkXOGlLqiJmVojksljZPxVP3+/MVOXv7oo0/z8ne+/nZW9+XFXN/3Xa26kDyEkKYESEwpH2OfJJAyjDAxpuirMp5F/JnygPlIcah/IvnH6aEcK0uQm223tdbe6CzOWYPcOwjg2gMXnEZVco5uQ3XZOcZ8ovtqwSlNuAqmVbn0h469iC21ivqLbsZF8PmPeT/KPnMKSGQg+aWzrF+QEX9exsW2DQOMQaYotD+ijNpnnobrYH2PE/V1HVsx0H3y6OBQ50FaRRdLV8iH0s/V/csiTWWa1x9orniFZyu1a52yubGeLyuQMFU62nqhvrqel0s1lecl5RAe5ZvYtsGlmqMp2vPcFZVuiGGo+EjnzRgdyq0UIsSUIMRWKWOMoTLi0UwxollT43zj/pt5+d/88cd5eQDnyhlyOspZvyiMiWMwGAwGg8FgMBgMBoPBcAVgL3EMBoPBYDAYDAaDwWAwGK4AXktOlcaxm5y7qIAeTvps0YRE9KRGXXSmcrmK40FDo/MBqGdDODmNJnJx4ebkpK6T2TnLKF2ziahXpJg60DWnY9wTzl2FbKTVFj2dO4Zv3tjOy3fu3cvLFchPJpPf1WVx3+WyqGancLA6p1Qu08kh8D3XyHbHPxlI3kM5DHfsdzEp7aCNY1fvE2xD/ujFbl6O0M+Vmtqt0ZRchK5lPD+px+d00oJLDejhpHjHoNxO4J4xR5muGjGkdSNQ6FbRz2twhDk6E+V2DucpH/T3EJqAekt02ZMlyqmcczmV8jIHno1V0cMjUHs72Em9DIZ3BDphGKmPfJRjX2N1xnfAoP9OQLv0sQv7OQcz9jHGL5EQTMYoQzuUJJBiUU7lgUIb1fC73HnIAaVsaoh5P4QsIMYx53GK8pcvi2ql7N57+75zzrlXOzrv0bFiVTIG7RIUTNLX53O2ISjkcAqYQh5DF5wYsXA6oTxCVNjpGNKzjBZdqcF5DnO1D+eJgutDwfmKEguWdUhcUB5Q34nf6ZpEKwRKxxK22Vf3zSLN5BHF0UE3Cro50J1KRxccvDCeU0h3BmP97fMdUXgf3NLxb71xPS9fg+S3CufA85Axxlw9OlB8erkvKvfpkVwVfE9jqtGWI9Xa6s28HNGhBXOrhjETQxZS8iCpnGvsX9+Qc954Irmk82dZXZZIHPe83IWGUh+orQvrj0eXQkglCo5F5JzD+YlzazJU+4+HusfZWO3AqUCnsPPTc1xPU8xz2O7NMC8ncAyb0ikGktsKZNERZA4pnBdjrKMp3bToTkWZQUFe+dXMRd/zXC2rL6UJHazDtzdEyb++oRyOErkp8gIP91aitCK6WMbtFa1N8iIl/dS6Jcm5PBBnCy+WanA8huiXBuRUyG5dua98JYJrFR0fo5DOVrrWueOec849f/4iL//gT3+Yl9+5ezO7h9d3VbkMnvNclOWjlIdzfQghyU6wDcIY0oQIeebtO5LHz7CNwLOnD/PyMNbvjbbkHOVmJy9Ph8pdpiny5Gzu0AVnFup8p8htDk4Vt0dTSFSQKnGsJJflHP+eZcOvDc9zXjaoOd1XsW3B9qYkNI06nwsRZyihoptwqLb24A7sw/E4gvsspf2UzHJtDrJnAEqimGuxJyiddpivc24pwpzDw/NIovXAw1ofQfJ8dKxn3QnWSw8SMY/rQRablm38d+56xVyFQ4+SR8aSFly31uHS1Oiqz/2KxsKcMuOh8nY+T5UYCx3yWMrVs5w2LLS91tPYQz6Lc3CbhwBjy0d88VAXr65nxPkQeW9J57y2pjiyihi9g2dBL+F7D/faMCaOwWAwGAwGg8FgMBgMBsMVgL3EMRgMBoPBYDAYDAaDwWC4AngtOZXzUudllLAg/KvpZhXsnl+rie42ntClCVQ27Mg/OxP9aTgC5ftIFLMI1LqNjc28XIVEZzRcnGcwEK10BjnB/sG+zn0gF44y6kLZ1AQ0W7rpVECV6qyIBv7OW2/l5VcvRUsfDkSdLoEWS1nYLONWPdvVPX9ZxHHsBr1FW9TRJ15fdM8R7nEOKUMNLjgTuGr81h99Py9HYLWN8bdTjIxmWzKft9+Q9Ozr91W+sQkZQCY3i0FTHg5Ea93fU789hZzr+Z4kAX2MoTkcCRwkUZ2GaLOksK411Lef7kKCxvuD7CVN1LdJgdy8PKSec3GwaI+AMjdQAlstURW9qcZ/NaLECX061jvdgxei/J6eqI0iOHuQfl8GVTXEu+FSBLeGTHaQhBe323Ss33s9jcfnr/by8tGx5msp1Hgowy1pCmojx3IE2SLZ6hOMpRklAoXwuGSOqlvQ99u1Bc1/68HX8t8/+uiR6jNQHPTpjAKK/9mhxv/+rsq7iBu9vs5DJ4cy+ifC2CmhgWLEy0lGC09ADY4xDwZDXWeOsTVFfScozyD/owtfQXlAGUZBilWw0FER5/QpOxuB/r5keBdIeyhhKThMQWYzv8yFhPfpQfIGeWuC8TmH5HA60H3GkOWkNZUHmTzpxQutST/95JO8PISEbnNd8frmrWt5eWVV86nZ1roVwlGt4d/RNQ9V9xno0uurur+nr/R7G5LWFThaHvUW97FMNYHv+66SyU0CjH2P1HjMD0oIqYOZpxdL/FLIFlPmP2PF1mkfMqs+ZBagfIcUrGfuXHSto6plAvnlBHMidpCoII+rBJAh0L3Fp6AL4/ISVy7mg2nBuRTyymVz/s+v51KXuPN1Ub/X8T834JhWg56YcenkldaZOWJqvaQx32jC+a2utbaCfKEU0fGU7kq0DluMhxh6mhh1GWHsDCGRG6Kvd0+1hj169jwvf/b4WV7eO0VsRt5dqcAdBxKhM1919LD2lODAlLrlyagIL1sA6CJU+OSMdSul3CKC2+at+3m5ARecZ8/UJq9O1LZPninn4XrZhJvV1orkEZtttVu9uSgPTyiVUmwfQFZKl7AOxkp3Tlmy8syCm667QkileC5j/q221Z68f2pJzrCFRg/PSuEpJIQNxc5WS2O1Nrz499Yl2zgUZFbnfUOHQqzjM8hiB3wuhTyG5cNDuY4OkcvRlXVOWRiu2x9wGxFdq1RBfQvx1X0l8LM5nmAChkjKuPVFWFXd7jyQ818ZLmS7zyTNnExp4YhcPdLvfO5sIvYkGFOU7fpZbKM8a8LtBBCz4oKrtaoynsAdWT+7qAoHXzxfVSO6H+svGk31rcNa68MRsMLnN/StU/f/pTAmjsFgMBgMBoPBYDAYDAbDFYC9xDEYDAaDwWAwGAwGg8FguAJ4LTmV73mumtGJZnA+CUPu6iw6UKsl2iqdMSo1UejqTUmPZqA2nZxICjAFta5I2wUVagaqPKhb57vmn2KX/tO+6G67u5JqRHCZWIUkqrMuKmYZjgenoFw+fCgJxLXror61W6Lcvv/1d/PyzivR2I9Rtxp2aD86WVDLA3/Z1ONFm9Rr6k+drhcAACAASURBVB/Pw677OHLmgxoNOvAYVMLAE2WsBdpq/0hUwt0jUZI/fiw63ZPP1G4nr3by8m/82q/k5dpWJpWDbOP585d5+bd/7/fy8oefPcnLz+C2EsKZodsRDe7WlmRb40M4GUFy1V0R/ZOUaA/UZg/0wtkMblFnX5AT95rwnHNhVpcqpUygNp6e6v7XG3RvUnkfMrreC91/PIJTzaHm4gD0Tc7jlbrGQLem829vavzfvLEYG35JbfXihSRvnz4Shf3jxxo7z/Z0fQ/jcb2juXhvW+OONPrDU/QpUJCugKNcgkTMw07xs4ya6S2R0Oz5gQsrizjTqYn2u9rWvc8qastyXfdLSejejubCFK5e9aqOH40k1dg/hFTuVLEnneuGV7uSFrZBf55kzh4eYmVvJProHsbKEHTyHVxz71jX7KFeqcd1hI4NkHlBbkp5RkDXAtBoKQUJI8pClgfPLRxVnPuLEh/KTf7qGE73o5D3hjEXhHArA/381StF7TqOb1cU41dW5Mozy2QKc8gnrq/AVa+iPq+DNlxwysK6NRgq7k7n6tMq6M8dXD+u6TxnWEv2jjROjg+1NnuQmpzXJwj0718Wvue5aubaWIYMnFLnkLIlyJPGY93vGLT3EWQAZ5BZnJ0otk4gp4ohs3I4f0RpHajgaSbD4bieYO0ZYF5O6GDIMiTK5YrGQnuo3+tt1TH0NS5qtYudX4LgYip60cvpq5FTeb7vomolu4bmQQtuml+/f0fHw21tF3L6Tx9qPM+Rf1XhdsNtAXpwrWTe+9YDuSK9cUcObq0a4lhWhykkTmNI4V7uaF18+FhSqT//7HFefgJZJOUcA0hIe5AZxxxfZfRdwamHZcZgrfXnMWuZMh/POXeeZvlwzKTEJcHvMdaiBNKwASq1v6O+fYyc408fKed8+ERS/CFcYjeRL0LB7b5+93Ze7mb5z/RMsWA4gIQKjkLXNrS2tiAnHyZaO/dOIMmHk1lMl0P31xueS52f1ZIxrIp4FSDJGiMW0mWVeaZDv+8fK7d7hOeBESTdZeQLLcjibm/LTfju7Tt5udtd5GMhco4TrElPnyguPH2p55idQ42pISRX9Yby3611bflBid4IikRuQRJD3zPBGhPjbwtyw/PzLNO00Xn52lsO1Fc/t30rL88QWw/PtCZX0M9//Ed/mJePDyjzRz5DaT3WuY2O7vfONeX5Lfxehdy6GS3GyxTnOzrR/O8NFasPjjSGXu1pzvdhhulw3901xf/1jqSV17q6/i24U9ebcGhG33I9+jvf1FYKLThy/ov/7V+5LwJj4hgMBoPBYDAYDAaDwWAwXAHYSxyDwWAwGAwGg8FgMBgMhiuA15JTeb7nShklKAhFFSy4DuD49TVRn1YgTypB2nIK3lIflLGzU1HY9kFz7UMesdIWzXHVE7WJVLU0owsP4NLxDDS4CBSnFcimVtbkSBWDQr57IKnDYKi6R2JiusFQtKy33nwzL9+5JQratz54Py//4ff/JC8PQctz8ez8Jtyy4Hueq2T0WcrHZlPQw+meAflQpa62qoCmeLinvvroidrnBFKJEiRuqS9q6WQs2hylHRwLLtsxPgBNcdpTf46wG/0ZnFnGCXYAD0SzPTzTMZVIffVzcMdKY9VlbSI6XamkNhvDKSaAc0m1jvHn4PZwukxpVeqCbFxEHB4zOjypjbbhsDaEA1MPO+BXRqIK9tBGx3CN2j1VW48guWrAHepWF3TZ9zXm795Z1OFctrCoo67/Z3/+MC9/tqvrpBVRUjsNSHtSjdPHT0U576583hHLOedKkfqrhN831iXzGM903WM45MUYy8tCnPquly7mRQtSwQgSji4ouKWG7msOxwQe34DbA93GwM53o6muNcL8mySggkOW1V1X7F5pLcZIEKguDx8+zcufoHwMh50xZB4J3ERcoLrTmWGOfphAKkupUAPzrAznsQjSjgiyrBocKZYKz3Nh5pQYQ8pFiWURdKHieVRMcMx0Bhc4NF2Cfneh+v3gVHU4OFGfbiHu1rpbzjnnbtVF7e89w7zcl8zg+SvRn0+c2vngTHPOQWZQKen62yuq171tzbN2Q/fU7ijubN/QMYkTRfnFvuJEI3OCDMMlyuM85/xMBlQuSPZ0CJ1JRpBE0T1zBFev/QOt5S939/G71sg5nIcCjJcAMqc61s4GJJKNTHodO7XDPuS7x5B2DKbqkyGcOuhaVa2oH7amcNCZ6p7qFR1fX5c8qHLJ3PIukRR+ReZULk3T3DkzRJzzYo1bP4YzDHKr3gCuJagrZZ7DFI57yAv3++rTnzz8cV7+0Ud/npf/5reV833n/a/n5XqWj/WQT77Y1dj5N9/9A53708d5+RRyubCqXDiAFLIHl6tCTgVXvDnaw8dYo/sY49oUi0kuAV2mVVyaOu98/CPHSjHfU8THCbZhGKEND15IYvPZM8WzTx5Jwu8g93QttdvJqdq/NkU7eGqrANqqJMtFPDhPjdFmSKFcjLY/hvStD5lHCo13coUkVITnyZCHcmc6+/m8I4yhJrahaOHZMUR/+XB+oyz1EI7D/R7bV7Hx6XPlKRFkP1HmhMTtP856euY8ONS5hyM4h6IuK22tqY2mygEd1ZC7liLNuW5Hx0ehJJJzxOw5ntUCrFWB91VwMnwXeIu26KpJ3D/6m7+Ql19O1D6/9Wcqc0U4xnNtb1fHvDjUmN89Uv8M4ci2uaLYdoZnzc0ttdUKctRWZXE81+ujA8m8dvdVfoXrP95R7BhOIP9DiNg81Doyx1Yc9eR6Xp7c0DNPEzFlFe8lnIaO+7tvKMffvC73z3/hvhiMiWMwGAwGg8FgMBgMBoPBcAXwmhsb+66cbdA1HOltVpzqjVezqTeo16/p7VRU1nu5F/gq9fSZ3jbunehrxsEh2Dc9vX1r4it+FN5QGW/AfbzRTbI3mD28kT3DhoybNb0FO/+a6pxzx9iU8NWLT/Ly0Z7qxY2Ya9HFGz7W63p9+fVVfWX8xgfv5eVDfMXZ+X216zD7+p8ky3v/7nnOnX/QmE7Ub6dgRMyH2FQMbbnVxCaV2ID5sz3V+emRXjGubqr/9/f1NXetgU1w+QYVG6hyd1o/e9scj1RfD18z6g29qW03dL7dkd7+Pt1VG3Nz6hf76s9koq8s33lLY2sbGxt3sdFsf4SvCxiXNTAmUmxWuFwmjueSeHH9SYK3+tik0McXD3wUKmyaWuHXjIG+1IE44epttWnV6atsZ0P9eHtTfVBPNF9r2HxsZWMx16rY+DiINLe2ruk6dXyRf36oek17mpdt9EsNX6ta+GLgg60wxzabUQQmF74spvgynYDtkubvu9GQXxKxS91R9iVqHRu3ek518CO1cYgwEPo6vowNyg8O1Pbf//DjvLx/pPi31tUXhAE2qJ9gfqW49wbm5YM37zrnnHv8RHH7+aHG9eMjMM96Gis+NjBcX9f5WvhqNZvqb6f4QsevdWRGeKB/BPhc4mF+++jb4Kv8jJlNNX69TgtDBRUHy6KwZ32qY1KcJ8QX35v4+lMKdczuS1FBz3y1480xvuxVEWu7951zzs3Liou9l/pyPZiIrRpW1V83b2rd2vmxNnmcgVG7vaJrVuswH0B9vbLO2e5ojJf3dZ6TU837a2DGttcWx5dLr5W+/KXwPC83ZfD5xR+bijIPOUEO0wdj7zE2of0QZgf7uJcBcosQY7gFFkQXG8V72Ey401W5kW2meL7ZuHPOTRHoT840/46we+YJ1v3hDBtmzjQWXuzra+mN64ov19d1/J01bQ4alBV0E+/iueul+AIffDXfD5M0zVl/7TLYXviyf62r8iE2vGxg3PbRRp+CXThK1UcHR/iKjH5vNHRMrYZN/Pc0v742vKtjSou17vRU/fW9H/xU5R9rjZzgi3y1LJbbCLF7BAbWCIzKGIt6GblBmnDN46bwOp7zwAfrOM1j1nKpVRo3jKdgvmB94ubHFWy63AOb5hDMiyoWgm9955t5+eNPxQSeHIi5cw3jYh1Mif5A/R+PFnXzsUh3u+qfDz/SuQ9Havs+2arIPzk7Ug+Mw3RJ+cdfTRL90vA955oZTTwKsVE/cpoAz1wh7jPEo+kp2BInE/ULN/afjDA+wWis17AhfhW/V3St+Vx9MM0UG9zc3gNrJsJY43PEtKT4tw8G/J8/0jyugxXcrqqhN7ew4XldY6YG1hE3Ip8gTyqaCi0/pnq+c0EWR8seNuF/rrVtPtC6eK2q+q+vaN04musZ8RBKjxE20B97WtueIi6PE92vl2hd4obsNfRXkG1sHNPsAiY0U9Dijk907hEYd69O1YcrbT3rNjQU3bCsvDe8BRZtWdcNqxrr6109I91BHLnbAsP+COzmLwhj4hgMBoPBYDAYDAaDwWAwXAHYSxyDwWAwGAwGg8FgMBgMhiuA1+Ijp2nqknhBRZpjc1RuMLgC2uoafNTBIHcvseHYyxeibY9AkQRrrXDODuRJLWxy2qyJxjUGtbt3sqBfzYeiTZUhRVhtYkNk0Ma9EjfHFFVrio0IR9hwE3vDuVe7ovw9evw4L3ex0fPtO9r86BtffzcvP3n2LC+fnCzuw/OXR1X1PM9Vso20Dnqi3e71sakUqLOVSO/5ttfUD7/2njbmi53a57s/1KZ+m9d1j8OBKIsrddHHVhtq53fu38nL7Yr60882BEt9Udw6oES/ce+27i8S9e3ugzfy8nPIuXxQEB99/COd5+ZWXv7aHUnBnu+LZr6Kjaqen2BjM8o8IEFLk6/oPWnKTQU1uWqgiW6vgQbfUZvf3tAcWsFmW2FJbd4HPbXckoTDldXu3Ky8W9d1T/ZEHQ4gvwozGm0ASvoKNmT8hXdV39Lanbx83Ne5R6ea241Ak67ki+fYbmm+HJzp996nijvEABuT9hEnhqBgzrPNLVPIgr4sEufc+d6bpbruvVxXm8QYS3GschnyvTb64ayv+j94435evnFT/VkD9XdvT/TNU0hYu02Nhdt3NL82ry02XtvBZresVwiqdLOleVbBptJ1bL5MmvV4ILruDGtBpYJNi7E5oIeN/KDgcAkkJdgfz6XYjHmZSNPUzTKZg1/gqaOcUpqAMvjrhW160e9QPrgq4vHmhii5057G+RAyGi9UPKy1tf5E9Wzd48b+tzRexgP0L+S8naaO/1sfvJWXoSx2m6BRN6o6PvK1xsTYAHh3R1KHV5C9nvUgb/22Ynl3dXFPpXCJcirfd1FmuMANqWcjyAOxmTHlx7t7km08eqp7SdFumxtq+4MDSEQwQDkWYqRm9Y7m9xbMEc7lxzNsWDtUs7rdA9Ux6WutarU0Jiqp5sQAsZJSkZevFHObkCSMsLHnFLKdiFKXwubTmK9fkZzKOeeS+WJ+UXnCWHH9mtZ5H9KaaktxN0Icq0DW0J9gbb+nc+7sSM7YaCq+lpg/bUkS2EJOu7qx+P05Nrz+6SdaQ7nR9nXICu9hLPTHiu87J5IcfPYYUjDIkaqQhSOVd1OseQliUKWs49c3JHX2z3PTJaqpPE/5FI0J4oQxFAEf4y2e6t4r6Kv3bmjbhChC3DiWzGMj1uT55g3N1yqGcB2S7BjGDpNMelZDsL6PdnryUvF0F0Ytk1jHJ776Gfv0u2CueQZVzV97REHgrnUXa9QYEqAapM9VlKGOcdOh2vn4QG03xNoZVTQXDnbVj3dvKV+5saXYORrouayK8UwJ/TAz+iijMvFceUmthg3SYdCz1VBfNyBpn/f1tzeuaQwGc91THYYxkVfF74ojzBm4yTgTn3Zm9BMEhUziS8HzUhdFi/UFynt3hAeeB7cVT8O55t/6lmJckignGG9qPal1lAtue4q/4ce6Bw9r5OGx2u3oUEHnzg09x69fz8YFDDR6eL4dPNYz9gDr4uY6tn9ZVX3v3NH9Xa9qXHSwhK1t6Bmp01ZdEoyjN25p0+KbOObB+5LT7SD/+aIwJo7BYDAYDAaDwWAwGAwGwxWAvcQxGAwGg8FgMBgMBoPBYLgCeD05VZK6SeaYRDeoElwiauABnst2FmXRiu7eEn1oc11UpTFoa4Mh6E9nomhxF+q710V/8kG1pOtSnNHcQ9BpayVR1kK4BzXhoDWc6RzrK6LK1XBPkxgOLqAFbm7p+JUuJWWgS4MCurEqyt/bb4rS/ur5giIYLpEel6apm0wX9MFXx6KSHQ7hUgFqrAdHkRW4St0HlX9zU/TF63CbegnHhhmchBo1teGNW+p/9mcV0ofk3DXEh2xvRVS9O5AQTbHD/86uaKvVFVETpxhn3/r1X8zLv/jND/Jy14dT01j0vw6celJfFM4UrkVzSO7Sr4j+mnrOJZkjU0JZCdooItWyon5cbeiYN+6p/TduyDFjCnr2aZ9uSaIZ1uBI4iaarwHmaAra5XS4aCPf0/lakPY0ypDcYMf2G5hPs6na2YNUC0x456ais44+RV9gXCd0NpiqPmM4eMTgiCdLpIsTSUZxDyCPunv/QV5+uftY9UH84FyogOL75gNJXBgHn78UlXj3haj/dMJaw9y9f+9OXr5+Uy40YUbt97F0lEFbrVfVn/NY56aTEmUwlD5VIb0pxzjGo6sN3NfwDaLgYAWmfRmyzJVN3ccysaD/Ly5KKU6B8u8Vj78IKZyKfOiTKqnueXSomDNyikvdglxNk2FrXS57JciF5+exAeOuuSG69/xMUo3TV8/zcg9OjSW44zQgnW7XNAaCkuKFB6nGaKx40QeluQdJXaMNR6Gbois3s/UjXKacynkuOl9nYo4lOBw1NT9Koe6x3NJauLKtMTbD2B6MdL9Hhzp+TEdAuHbUa8pFbt3WOdc2JSGoZrLkdKbzDYe65gYc6Tw4NdG9zSGfiWe6vwRxMAw0j69tqp8rocblDLGmoK1LWcQ8hkR9mfCc50rZuWPIh44ghWOOQufPMsZT57rmQretMbwD6Rzj2/aq+itAPNzeVq67vraKY1TnSiaboO9QiP0Efunn5KB0/4Yo+QGufwyXozRV+RSS1qSmuFDCdgF7GCejEeRUmAc37mj+3bmj8ZjHuCW6GqXOuTTTwnmJGiqgSyscORNu7YAY08Tc7TDnh4R3hlYP4CrW2paciuO5XoKDIK57Pr/4VXwdMryfwxr6Yk/tfbQneciMbqJMVgptC4c/HpK+Zgfw8K8otwkDP3eiPUNcopSphLhUhW6NzkytJtctnd+vYEsFSFVWIT9tVCmLUp40n2rtjNGP8ywGx4jFAdzY6g3Fgi62AkjgLlpDbNtoaJ3gclXx9bdpqHs9mWoEtdqQF2G+tuFsdPOGxtXW+mJtKJWXF1vTNHFx5n44xnzaQ974zW3lCrOx6r+2CtfgCM+IHcWS3pn64fBUa8iar/FyipzH9zQvb24qnt7a0LNje3MRc33M+Vunei5M4IjVaWs9nafqoBWss8w9bt/UulBb1VpY5nuPku7bQ4y+j3Vn+kr5+Gike4ohs/qiMCaOwWAwGAwGg8FgMBgMBsMVgL3EMRgMBoPBYDAYDAaDwWC4Ang9OZVLcxlCBfQh7vxPimEMim0FXLLtTVGSxpBHDKd0WRAlab4mGlIdsqwK6Id0y6KzRyNzQul2dY4BJDejoajcI7gzVOGgUoYL1goo/2FVbbAG9wnSn9c39TtdBiZDXSvAzuOroJBvZk4E0RJp43Hi3Gm2a/8nzyU36g1BDU3xbo/yEkjMqmUdcwM7+V97U7S5IajxY1CbKb/zIYVykLV4Q/XhOKOzUioVBXRpEa2uXVdfHcNVaoIdzsug+69vih7ZhGtZeiwJQYC610Hz5BvQBJTbOenk3vKkcH8R5zKqKfiwB9gN35urvbaaOubuJpy/IHeqYTyvrKodO3PIVnzQxmHUNDgAXXGge57N4SgxWlBU56F+I1U1wtxugNpcX0OsoQ1Yqt+TVNfv7UtONQMtmc4bM8aLmDRt1c0DXznIpC7xEnnjnktdmLldJLH6rd3RmOwPoBObw8kGMY5OLwEcLgLMs/VrooeWIYNY3xRVlDH9GhxcyphT00xOO5/q+oGn61Qr7BMMENLfcR8BqO2sVwDa+hxjJOZ8Ar0+xLU6kKJ02rq/tU1RrpeNczkXnQTp7kJ3iaKBFcZzAkc4uOZsNhRzNmo6z0ZF5TldzOpaQ67jngPEt+T8WvgtrCqOd268o7rURPGNJ6I/+5B8+Ji7Huoe+5A6UJ441XgPMDbSmco3b4kiXa5rDIwzinKyTA2HS52XyWxTOMYE6J821uYWyyuit/fhutXrSQZ+eoT7nWqOziCDK5XUzt1VrWnrG5q7bUgLzmnzMVxdOqjXDUhC6oinI6zjCWSZZUhbm3DtbLfhZggXwjkkAQHGH9dFHzE0xdwNoq9GTpW61M0zGdtYQ8n14J7Vwz+c9DSe2131YxkORtUV3WetosbuD/S3c+YIkC2uQIofQRLBHOE8ls8GykXvXlf8vX9T8+AtyEaSiY7fgSPOLKb0UNccwoVx91CyMCyXLkXvhVhXNjY0Huo4p1z3lqmnSvOtBxK4QXpYp2PECbpTlRF/I0iluOZMEcNGkL4ncFCq4Pwe7i2a0gkUOUrmAuhDwp46nW8bjnA/d1fPPzunavyHkJYMmZ7EkMt7HDduOVhmGAVKQeC2M5efEMki0pLCNgRcGCuQfNYh2w/x/OUjR/CRg5TwXBhCW53MdMwI0pk5/jbI8sUUwyvw4YjpU2YKSXukcgNrYQfPGjGczTyMownqMsSz17UtxYB7b72dl7eu6/myVVcuf+4g53vL42YEzneNcJHTcS4+7enZyq/KqbgyUVuFc7VJq67n71ZFfTJsa440m3r+v9XR2uV7uvcyczs8r0Q1xdyou2i32orWzRDy/E5T+fU30NHjnuJpMlMdax3F/1U4fM7xTiFBnye6DRfC3bLEQYV9NmZDjftPsQZ8URgTx2AwGAwGg8FgMBgMBoPhCsBe4hgMBoPBYDAYDAaDwWAwXAG8nk4ndW4+X9CASJufQwbj5ipPIVWagcpZjvS3VdBWPZ87PINaD4oiz5+A8jibicOUgh+4tr6gP3WuicJ49t3v6pqQC3lwBymBKl6rixLXqkMe1YC0BO1RQ5mUu/EI9Nup2iOeqByg7ue0QG+J28cPJ1P3/YfPnHPOPTnULvlz7DzuFVQQqs/RsRw2Rti9PfEoVdK9l+CGMIWshZTTSSJq93QCiRkogefuA3NQ1SPQkSPs7l5bEcVtvQtKXghJTsR3l5ABncFBpK/fT88guYOLlwf3kTnG0QiSn0qNtknLROpctkN8QskQ3BxORqrfzrHupz8GnRGuFoNj8qpBSa2JfhiU9HsKWv5krrYbw6mKUzfKqK0BKdI4YAZa9Bzn9uACV4HDyRyuYf2+XB4GJ5IxDEFP7PcYj+imQgsVyHXoKFSg/S4JaeL82aL9e6eSNroV0eHXu3Dmwvj3UtXHp0sFy5Bh1iCzqERwkILMMILEoQ4JaYJlYpLFg8mE7aHxTumn51Nigd89yvN4DNyZ3MUyDNL96awVQrZRq8CJBC6KLbg6LBO+5+VU5lJF872HeBKDqp9Sfox1hpKUWqTy3ZvqiwfXdG9VX+c83tfYLrXhjrgqSjHb8VxCSNfG1ANtHM4fpQ5owFPFUXLOE1DS55D0RXDKCkaM9SrPIUtpYDD39+Wi9vQR5HWZfSLltV8WyXzuBscLinjkgyYNpxG6EdEBzYdUjm4ovq9+q4Juv9FR//A8EeTWIVzDQqxdUaD+9zLZl4c1tAoZQqclqn0TjpARzk2XqBLcPCoVxQgqWOdjSMSQw7j4YvmJK8xjzHW4zC0TaZLm46IW6v4HkM/u95RnQNXgppDcJIivzOFqdfVFHbR8D+40JcScALnRdIb2gozt3KGwAinvRktjZ9xXm5cjOHgi1tCtMEL+2UYcf7an+fRqX+sNFM+O/bUFmfo33pZragKZ9jiL2cnStD2LOnhZnKcMKkVF55Bjcv1jXxUke+gTD9K6MdZUR6kocrsA5yzjPDW4LLWyZ4HBWPnhmM8TFR37DuRxhwM4jP3wo7xMd1zKupfVyt4lFomv7XL1lyBNExdnOdoqxjPX+QQSYrrX0cGSzsZlxK4oQhzD+hNCfl3I9PG8yOcRxqVzjzjWi46vhZwQMaISsi5wP0tYxpx3kJ3DDtjHc0QXzzJdrBnVitoyDCltdEuH56WukuXrE8jXnp5IjtkfcZsNrRtnh4pbTcgxPTzD+9ieorqm+/JWIL2mJC7EGEE8KGErAD8bOzNu1bChnBrKRufjvYEPR17Psa+Q3zrE+T7GcRXbjmBcDKZaa7ya7unOB5KgTWvKS3/rt3/HvS6MiWMwGAwGg8FgMBgMBoPBcAVgL3EMBoPBYDAYDAaDwWAwGK4AXtP2KM23RCcZLwTf1oc0gRKqyQASizLkFKDz1mqk8OqcMZxbxthNnlRkuoKUCjTKxXk2tiSn+n/Ze5NYy7L0Om+f/vavi9dEl22xGiZLrJLKZZAQSUMSBEiGJ/bE9kwwYMMTjwzZgAwDmnhoAzY0MTzyyLZmEgxCzUQWBBJkkSyxusysbCIz+nj97e89nQf3vLO+U7xPjMi4QfIR/5rkzhPnnmY3//73eWvtdQtuDyePX+gaoLaHkNzEoHCVcNBaDPVcEaQgy774WpfguGWgboWObi26Dh2g8op2Vm6QJzdPM/fh4xWVdgoaXxlQsgDaKn774lzt+eipdif/JdLmuJN8InpcAEp4g/cHd7ISEq0I1H6/OicFDS8HlTvAjugBXEzCmBQ7nUOnGDo8pJfqoy/wTl9CZvT4Qu+d4j0K0CZ9KE2CjDW4WQTVDucBdrSP4TZDWmfTkUN04uGZ3nOYPNPF0RbxQH0y6UtmEWC8pKCKX44kbeph1/iwkgtE2O0/CDRWL8eq5z7kcjmlalNQoUeidA6HooqP+E6nuuYUTiSUa8ZQVpWQF+WUJtXfuyEteU0URelmFbX7i8d6J1gsQQAAIABJREFUzsVY77UDecZuD04HpKTSPQPy1BzUb8qWSriN5XBbiEBtDUDTpXDqyuGIEr6A8gG4UFBOxYmmQU/G1X2UaebE/6EUk8fzAn0R4yEA9XiwJ0rtJhHHkXvnnXvOOedOL9THUrgQLfMFyng5OFNwPkvwP4Oeyrt7aqMCsoB2V+985x25V8SQAmeYm73Fqv94eC4PfdvPNVcFsFvwPDhJUSoTQEKcKEb4kSQnCQJjuy8KcYS5586OfssYP4EUpP5to5O8JsrSlemqLtK55oQ8xjwNd5wA/Z9OicwhWihHkBn7cLVxiHMLSERGU8WwMKJVisZlp3KnitDfO5AbxIgFJ5i3djBedyBzSCDFIhYz1cfiUvTwWaZ+0aElB6UKiE1lYxy/GTlVEAS1hIj5ZNhWTnYGWe0M0qq9PfXJHch4KCfzKctpxD062Oj8FH2Ykv8Y8+KVXP5wX/LvyP+4Lj98+GVd/vY336vL7T5cJjGGIjwXx1YGidAU0oE5pLFvv3W/Lv/tv/lbdfnuAWSiF3DCqmQMeb5BuXFZuvxKKgF5T4G5ueEciX5OWUXYkFhAqujpeAlpuQf5eQY5DaVvlPBQ8uhdSThSPVeyBYc3uMrtLXTtGWRAP36iGHf8pdYlueN8jefdUPhryLU2KKcKA9/tbFX9pq2x6Hg/vAMlgQHiXIRcJEE7RhhzIfLegPkF4mtKiQzyhSXibnjlFoi5ykd8Z/6RY83HW8Z4dh8SqgBjZEk5PGTJIZxb9/c05hprbMiip1OsF5eruJZtcCyWZekW1fqb7TOBDPB3P/qkLv/dv/H9ujyaah0wOocjLVxlS8hNQ9RJwKwT0vKGih9bH/gdrO8q2dwC7pB0Lw0h8S4z9ae81LzgQarFdi4wdkO2/1z9KYuxfkZ+9bWvf6Mu93b1PeJf/Jsf1eV/+ft/6F4VxsQxGAwGg8FgMBgMBoPBYLgBsI84BoPBYDAYDAaDwWAwGAw3AK8kpyrKsqaepaA2tqP1FF46cngNeQR2swaFP8Fu0mfnov8+e/q4LvfaomL1u6KTJtjBnDTK07PVdTzsdv7dv/Kduvx8TxKShw++qMudnq69hFRrDsetDLv0t/AsrTbo0pRTQTblRZQFgGaHOptMV9T8It8cbTwvnBvWtFHsig7JmB+QXqnfPgcN+aOHon7+ygtRP5MWaNI9PXfZAg0U9yrg9pRBfud3VYfBYnUOKXZLys7gQNQGlT+nMwRkU/lC9ywgXbl4+qQuPzqRe8PnJzpngjZnPeXQ3uS0vAig1dkwymr4cpf8GHTbNiRDczjInQ0l+RhO1W9HQ9EPA9A3A7jJeR2dH4VwSil1r/EEshhQZ4twRSku4UJ3CVrmEFKpfUrVSsjoCp0zQ1tMQW2cUDo2RFuDTnvnriQnkyeifb6YiV6/zmNlkwYAeencZfUKI/T9k9PjuvzWrqifvUqy45xzASnAoIqSPu9B4jef6volnNeGE71vAsnBLqj9pDZfxaKScirQnf0AbQXHmvIaOrCP/kGlVE6uOOjBOd7VI+O2q3G/uy9ZxLvvf1CXk503I6dyrqwlsRcX6kthQ9oIKQ7qgs4HdNjqYb4KIH9bjFC/6Od0HNvaloSJymK/RCyqJKqLlDEMjolzSSa8VLGTMdijhARjK8B7h5BC5rhOb6AYsH8o2UGQKzZRjhcPNA7Oqzm4KDY3L3qeVzuf5As619F1gpZUaDe+L+jzJRyRcsz9JfrzguMeMpURnLduv/WOfos2LIsr98qG9lDvhGfMIAV79PBpXU4X+u32FtyOKIvEewQZZZnIVSbq9/2cUgE8D6WTdErbIALfd1vVHEUXrhA5KqXjKebtyxHk/xhbIWS1LZ8uNIhdAecr1d0CdbeEdLw9gIyyku7HkLbNUG+Pn8MJBnPkTl/tRYPFJeLuHPL/C8z7p2cai3P0gdtwcf361yStyqbqmwX1Ym/AEWd1k2qewfij6xnnOTpSUe7WkFsgP0kwXgfIw4NI7bkoVc8ZJhq/kedhHFc5aB9SDQ8uiXM428XIkTs9Beh+S8/bhZtdxjFHN0Hqb1FP10miePxNSaiIKIrc7aPVnHuB/HOG3KVx6/KaOQSSe0q3sdR0qC7nY72WZ2hTSNDnWL9CIeO8ynEsainPZf4zm8FBlW6qWN/G2MYBS4RmPfuQ6CAuYmnkOnArnEPSmnSQv11qXI6rbQzS5eYk/3np3GXlSLpL61OsHf/NR5/W5a99IBe7d4/UV9tww4s6ytW8EOc4bK2CNiyx/qWD4BTPsHMA17Jq24EYUkUH17gM6z8P83KQMj+CCzbalm04hzw2xRq0bENyG0KunCgv/eix8u7/87f/VV3+4pli/cvCmDgGg8FgMBgMBoPBYDAYDDcA9hHHYDAYDAaDwWAwGAwGg+EG4NXcqcrSZVdUTVCr5qAqhRF37AfdlLxLyD8mYzkCPX8qGcGXX0ratIAU5v13363LKaQqGahyi4thXX70dEUdfvhEsqntW3KnirHb+ZOnOmcOWdPbd2/r2UG5msBxa4T3mIOidYTd/re2RHMv4PtE9wNS7dOKirtxumO1M3sIGnaJ3bjpDNbGrvIZqH4/eSjp0QdfPqjLg46oh318IwxIwc1V5xO4Qzx78kiPeKg+0q/63GSidl2ivkPI8NKO+iKMd1xGavtY15meqs0fnUi29/BEcrHLsWjId2/t6TpLVcjTC7hQYGyMQGfeLHxXVvTDwsM4APVwAHp8y9c55xM90yXo2VNQTCNQ5WNImFrkbRdwBPBEP336TG1zfK76fTG5cjbSfT798Cd1+c5t1e234GZWQIaRztR2dLwbo/6fn6q9np2rnIMKT2eR8UTnMDaBcez8arwWG+SPl853aUW3zD1IWTz11fES8QCOCiH6c5aKPjub6l3mkN6ML1Vvo2NRNk/OVX77fTmf7O3t1uXA13gt8iunOGiZUE8exjnlLnQVTEBbD8P1zg/NmIc4BdeQAvTr2/c1L9z9+l/R9RPF3JTaog0ijmN3/+1VnF9AwvbsyQucVa4vQ3oRoMNRZlxO0VdPVI85eOARNB+XoC63OphzfFCaqzbtbIvu6+ACmU4kZ3Zwnos5V4VwZwjp3IRznGLEYiG55mIu+U2rp4vuHep5csxJC9TN9u6KlhyGm5PkFGXp5tXcHqEPewWlDJBGB5SUUDKtw0tQrNO56pDSxoszjcvnp6rz9o6o172vKY4H6F+zal6im9YZXKiGmLcc5Annl5DnQCK9f0s093ZbddsqOXfr+BJ5wgJxZID37sRwUPIZW9/M3w89z3NRJUUsIOuixKAsKAnU2BoONZ9MxnqHOOiiDGlNqMb2EARzxD0q/paYX8/hhOknlUMhxu1TOGJOQe2/gMPibcToOZzqTs81th6/QH7zVPHoYkSZseqgDwdED9I9blfgQ1J9JZf2N9ieZVnK7Qrjjy6MrG9WskcHOcqskCMWMGGLBpI8tSBrycfoO1xbZJDuQ6IVVE5xs7li3MPPH9Tl/btv1eUSiekL9APKiW9jLKZYzyzhKtZMRTgJl3/6OW792myTK43SlXW+VCKvajiOIadhuWy8DyTijtI5bN3QkI7CzRTj4uJCa40l+xW2C5hWc+pwDKfckGtdvofuM0FM57xEZy0OkYLbDOiwy3PGlPV5D7cjibFlwtHhSgoZRZtz/itK5xaVnHsxh4QRMsBLrP//8W//y7r8V78Bp7tvKT/rQe7p4VkzT2NxiTqcYy1yMVY9b90+qsu7yGPKShKXzlB/GNsFZFAXn/y8Lu9hLighZ19w2xH2UUgr0xi5GNa3H36qNeUffaTy//cjuQ/+7o8lR0uXrx5HjYljMBgMBoPBYDAYDAaDwXADYB9xDAaDwWAwGAwGg8FgMBhuAF5JTuX7vut3VlzEJXb9HoPiBLavi2LQk0pRqBagBI6mooadPJec6vRYNNB79+Qkk7REVXqGc8agvy6wa/zjpytZzIsxXGfA1IuwrfnFmSjB07nOH3RFt+uB5g7jDTeZQaKykBTHQa7V5q7lcGsKWw3dRo1RJTXi7tivC8/9grSpQu6pfQK02/uQWJTY1fvhmai/P/pMMqiDbVG/34U7RJ/uYbEo/gvQuf/4xz+qyyfnov7e2Vk55Xz06Sf1sRGokd/57l+ry1t7otjlkAEUoLPPxqKcP79Qm38JCdXJUNcPItH8fuVXvlGXuzvqf2c//LAup6RcFqSwbg5eWbqgoqhmoJuOUvWrY1D1D7fgGleoPJ1qLE7w3C3Qz2eZrtMuReFOfLnKlKHa+smx6u6Pf/5lXf63T1YU8YLUc9DGuy1RiONQlG0XiE6ZFnqu6UzU8stz0ZjP8N7HF+pfnx2DlrnUfYfcWR4D0INcp7yi8W5Q2ugHoUu6qzrM4WQRZhpnC0rJKLeBZDODtHBW6LfDF6qHp8/RV8/U/+/ek+NVp6NnuDjVuGjNRVWdVA5mM7h4LdFXigVo6HQmgsOYB/mfh2Cc+etlCBmo2KQbF3R/aivulB2VU5wTbNDNiGi1O+5bv/Jd55xz/S25ev3+4g/q8vEx7WNA1XYq97twvoFkoeGqsdD5S7rggc6dfahY5FLKP3T9USXHO3xHNOe9W9s4VzFiDsllAap+SO0QZCYB5B+u4RQCySCdy+h+hvlyij7GOSmsKPqbjKyl82opnE+HzZIdEe8IWRHloVO4TY2HGn8zzCfHZ4o9x4hbdM+7uyVp6ePPNb+OppBWXMUASEhO4eo5Qp9Il5Aw4PwMrieLBajqPfWVTqC8JffgeAh5Ygwp0nwoOU+7pz5VQotXBG9mXnTOubzKb4qULi2Ki0vEdbpc+pDvTSCx7cFtNIvoVAS6Phx3CvYTCFQ+/OijuvzoRO3kt1YSpt//g5/Wx/745w/qcoL+9eJMedG925p/Z5BIn51Bon6J8hB9E3Gh05GE6hBy8RLnFA1tMXUhb8bZ6OrO1zmlMoeIIIPgVgBUPIaU6iLGUMJaUk4KR8B0hniGbRByxPFJtQZaok/sQJrmY2777KFkFUPI2vZ3kU8lOn6CdhtCIkgZXN6QKkKiS/UPpK2UpnVDnXSRbrA9Pd95lcNStOR2A3QvxTvgpykdKRGCC3QCOpfx/MUc7chFGvoMZaQNyVUVxy4PISGGS9QQ7pP7e2qvFhzHJhNdr821Hfpa6pjrsL30fnglF6KPtzBeB1xjV1s3cAxsAlfz22wGGRcUWx7G6EfPtC4cTzQ+vvu21mXdvvLJEHt05Bgjc0phkfN1OpqLdiEn7WKNNp9UbYctB1qsEjzvMdYQ5xjPLbhg5/hGMEe8Cz3Nkd2+5jmXqH1+5ye/V5f/93/8z+ryGebjFI6W3ldYXxgTx2AwGAwGg8FgMBgMBoPhBsA+4hgMBoPBYDAYDAaDwWAw3AC8mpzK81yropZRkTMsQeeHbIVUZNKMU7hqXI50/AQUflLou5Do+KC2bh2Kul509AzD56J0TSra2hSUOdKGSQUjC+0SdNqHkCIc7eueGXYMH2OH7hS0TP+ZqngX9LukBfo/bjxd6DnHVV3mm5QBeJ7z4xV9y6PbDU7pgsrPXdcdKfBoH9bPpw9Fa9vaEm2u01a5Fal8a19OYe994+t1+cXJUz3D45Ukpwv63Ltf17l37tzRe4gF55ZwGEshS5nCwecCdOMRKO8Fvm9GXT3vvffe13VwTvwz7TAe0uUJtNUF2d2vidKjLHA95ficDhuQlby9LeofXYbY9zpLtWMX1MYSTlW+pz6/ta3rf+e7kpx5iY7PKipigJ3n22+rTb/xDbnAJQn6PNnbdGdBHBlBFnAJaeMU9OAzunLNuXO9rlmC8tpw1Kvpr5ukj3u1a4wHymYA169eqHZwidoh43OC7r2YKg4+eyEHufOhZBv335dTxte/8U1dHlLRc8g8Lp9LwvHk2Upy+ORYlPCzS8lgL6eU2OgZY8hgc1KfM1oZXeMEBCkuf0upTpxo4NP5pnxzqo0aYRi6vf2VDOHZU9X5b/zGr9Xln3+s+PDhR3ImiBH7l3Cye3yiuXD7UON1v696hBGgW+T67WyqthsONRcevaXYtX93JaMKt+DUiDbyMs0BdIKZo61DtEtAyS9krMtAfYpSrAD08KRF9x/IISD/WiJOffzZA+ecc4uF5tnXRVmWtYPRoK0xF3NipMMh4ybmyDPIQ89PRb0/O5Z85vhC5yzQn4+OFP8COLg9fPygLj87Jl19dV9W/RxOVUvIiZKWKN5bA8hw8E6PTvS8F1PV/QDt4Fjuqt06kKtPRup/PeSDia8xGr4hdyo6GxWU+9E1j7GFciBoBC4gt+hCZhpHeucghjwC9RjCFamFmNqFzPPy/LO6/OjFSib+6SeSzZ2cKObu9XT/F6eq5+eQ5WXYouAC8uBzSnHGGi9LxN3Dvt7pYF9yqoaJXqPMfKOoTt2ka6NkMyFknX5DaoD5j1Ivym3Wzt/N/CMKmVyo/ZcLnYOmdRGdkjDw0kpyGkUaZ2Gsc58/U9x8+kSx4NGx2vD2vuK8l2nsxrRZbTh0CT4nOm/d2U3QZfD2ltr/4mS05uyvCM+5ohoLISRJHvI2ykeYb2WQ0CwgVWRcZFszHpfUXyG324Y7MHOdOeRXe1urNpghdsTIMw52lK/2IIH3IdHMsJ3H3ClfJjKsjekuynWHg9QsTiC3h9w3xjYVcZVrB8GGY2vlTsiYwToLEuRbaJMx5ucLzANepG0TfEpyI7VJhHfYRm7MeHD5TLnW6JlkpsFV26JuKMNvY1Lv9RWTC67hKZtCDhNAqhihD8VY5yzQX04hMx7O1c4Z12x01+OgfsmQakwcg8FgMBgMBoPBYDAYDIYbAPuIYzAYDAaDwWAwGAwGg8FwA/BKcirnxGBvbIANaliciB4VkmIEFwDSx5aQNo1HopBu74paSEoqd8Nvgc52pytaVL8vytvu7kr+9OiBXHLozrJIRXejPMqBNjWEywTpXO2WnisDXXOGd70EzfjsUnTlnT3RLj1IRE4hY5hX9VRs0BHH850Lqq26w5y7Yuu9UtDgnr0QTW0wAH0a7TwHrfTLZ3AVuy9Xsb1bkqElkOQkA1EcP/ir363LR2dv6/qVPOn2wUF9rEueO+RrOaUXJenDuudoojYZw9mnDapst6f+dP5I7fbsj/6oLn/8yYO6PJuKiueTuhtsUn4DlM4V9bjjYITbD44v8BhQe7llShq26iXpQbaYapwtF6L/jjzSWfXO739NlOxuV+3kVe4IpBkXmajfe7fUF8YTyUnoDpCD0jmlLBNySTpvLFEHKSiveYOOzTZCGTGAMW6zCKq7wgnLg7uJTycHOP3g/IbRC2JYlOicb337W3V550Bj0UWqnznkkhGkNRkkH5NqvFxcQrK6gAzR0+98P8Rx0NxJzYeks8B753BtKh2lb5CA0vmvrdhUXtOcbwpFUbhZJWOZQs729fe/VpfPjkWnj0kPhqwohJyjgBT0KWSR7+xLkpIgBGYYU70d0ZXvIwY3XC36K0lN2dNY9SEDcaAQt7c4huEmMlMsSHM9bwA6seerAeh45V2TelBClWF+OsW8cutgJTsKo4drr/FVMJlM3e/94IfOOed+7fvfq48f7qkuSRtPEbcoA3/y5AWOq35OzzSvp+jzt44O6/L+HcyXe4qFW5nmos6Wnufh49UYPIZ8PICeeKena2xDQhvBJZMS2mO4cw4RWyeR5rYE4yyGFCUb6/1mEzhowf2D1H/vDbpT+ZXUm3Iayi2WoK/P8Z7jElJduIecICdLIkrLVMzgrUM5+hKymHfelYy1O9BY/KMfrVw525Dxtb+QtMrL9VxjOKh88URumiHkNCcjxeOnLyBzm8DZD3Fxe1f9JEYcSXEvH3K0rCGvXl1zgynqCt6flF43DGYwD/iIoU357HpJNvueh1jlINUJO8jzwx7OR4yEO81VVdGdqoCsprjUOAtTXeNyqPHRbmt8JJCTNMzAkJP4Jctwn0N84Riga1UbdfAWXLF+tkE5VVF6blLFcM+D2w8kRh5zZTQFHXkXcOL0l5AwYhwXqHf2E5/1hT7chRS0D0nNVf68pGybDmbXSAy5dUijjJyK60tukZFBupU51RNl/tsd9UFKkRnXgmo96m3Ut9FzZZU3w5DaLfEuHcqBUCeU6r6APJVbpQyQo/Z6kKpCMsa8JMd9J2f6dvD8ubbfGFVbZPhwvtrB+vP9b9BhWHLygnkm5VR0QYVDl498NQxVORNIt07HGt90ES7hsuszp8Ucma1X4v0JGBPHYDAYDAaDwWAwGAwGg+EGwD7iGAwGg8FgMBgMBoPBYDDcALySnMrzsBN8yV2aRQEagsp5Drefg4HoYD6kMKRHkZLa6YpuOgfld4odpCnhIEU4ikVL7fgryv3OjiiDOah6owmdtUT5Kko9Fx18zi4krbl1S1SsEO/kZ/o2loNzN53ROQLSraVoVk+fidI8r3ZlLzcpp/I80fnRhiUdLiCnilt6tm24aw18tcmgh53eF7rQo2O9y63boFhHaqsYVMUkUZvfvSspQlntzO5DNlWi3ZYzPcsS1MscNMUZ+s0YkocSfe5wW+83gf6oDHS+h352fon2XFICAwlB9Ia+k3plLffxqQACJbzwST3VOQXPARVyhHqJLiVV2QW1fzaGLO1cNP6sAM240FjY35ZcIK7kVJ6vdpnnGqsLjIkpXBvikPIrOFJBZjKEy90C45XjL7/G3eL60dWo2F/82UZw1f1Jh48g+4ohLwlKvW8J+Qqr3oNl0UEg+WHS1thagBKa57pvAPkTmKIux5gOq5vdOZQj3DiHq9VYNNEFpATsZyntdFihDdoyYhOp4ui7fkh3ADpFQKJF+vOmG69+vqJ20LjA/PDTH/+oLn/0MzlSTSGxdaDqbm9J8tKGdHUykhRrDqp8QocL/D1mNtc5l08Vd7MWxt1odX5yqDG/fQvONOC2F6jEqC3quQfKcwpZMprI+Y0xBBeLgvI6XYfGGi2kJ3FbtOQrl0Rvgw5H0/nC/fBnnzvnnJth/v7173+nLt87wPyA+eQYUrlLuMBdQoY9hcQ6bkO+QpdKuJHMcX7JeQ/1eSUz3UZuw3HmQ4fhIydqmCRCksjxtIDkfYo5sowxLhGvC8wdo0vJ1TPI5fOUjmcIWhuE58paNkIpTtpwrFHfHsPZ75OPH9Tl7vd+tS7HmMPPhxrfYQsyHtQ12z30KAfSfbf6ksV97zsrGfnde6q3bUheHz/WPDudQU71WHKqFiSaQ0iLT840Z8wXaDvE2qZzoK6PLuA8uu8gfufVPFRu0kHVaa7mc9L1izkP/xTto809BJNGft6irBN1AqmO34actAOHPThYlSWkn7PVA+XIj6IUsd3XON9C3ApzyS8fPtUYasFJaebWy8Wazk7ANXMeZf5dSNTvbKsvbhJ+FLvu0TvOOedCyJPmmZwas0JxNEBbR5TQ0wWMDrmQsGRYr82RF+aQBDoqpFCmpLldufgFGE9cf86XiHmUf0FHFHf07K2u+ggdfctc56eYMFO6n3mQV1POyLUjcoBF1d83uf0GwayNd4jQDiXySebhjxCHRlgrDRzqM1if8y3H2KoC9dNJNO/1EmzLklZyKgSJI2zFsYM8y0Psm5a8T110EWTgIeRfCzh0MSbi9dwUubMLEDvw3YPzAreOmL+k+6YxcQwGg8FgMBgMBoPBYDAYbgDsI47BYDAYDAaDwWAwGAwGww3Aq7lTla7ekpt0pyQRZezZsaifv/N7P6jLf/u3fqMu92I6WIGq1NXxz58/rssffflZXd7dlYTp3Xfeq8ukTqaQXLyoJB8TbPV8MhXddTIVTTulqxScpCZw7dmFpOj+u3JQaoHGSRlBUwoAWiAocTBIcJ9+Khet08rdIstIZHtNlKXzr3bGBjeT1HivIVnQe7UhWYhABxx0RE87RBuOQS19+kL1+ckXklktQP0vJtjJHQ5DQUX3DCnhQ510QH29ct5yzrlOV/S1LpxFMlAyB1uieA8ilX/2hWiuHbh8DO7KYeIHP/hJXWad0SGIcgnnXo4e97K4ogI3vKkaVFpQNkFnTSllAE0zTUUnHI5E7Z2MQHnNNRamU/ZtyD/Q7gvInLZbg+pc1f8QdPu4L8lPbwD6s4ed7TOdf36pcTybwwkC1G5Scel4QOkOqchlQ2bl45yrd90sbfyqDUvQfgM4hNx/7+s6efhTlWeKsx7orAHcU4IAY/ECLiW4V7evsZvA2calatvZWHXei1ay2N2u6mG7TbcXyF489ZslnOIKjF2YjTm/0SaU/EEShymrNVAsDiDF5G+9Jv/cvQmEYeT29lbyh/fe1Zz0g9/53bo8vJA8kE4aKeSfLyA/Zf/swynl6UR1GkM6B+MZd/xEkp7Jc51/dKg4FrZX0o3o05/jmNq/BceaBDFye1913h1IIhAm6Dt4do4WtiObIk7UHzzIskLEpu09yUuevVjJSDbZmlnh3PF49bSPf/eHOo5n+Lt/49fr8hxU6hTzfQLXxu0d1WERa1wsIAP/4qEctp48kSPR3kBti6nZLfHbq5iXYaymGFB0wVoWPAf5CZw02rjRoE83FIxLUMs5p5SUK0NONRupL7a6qo8SMq5NoixLl1XvhzTQZYgDeai6ffRMUrgf/uijurzV17Pufv+X6/IYLpQ7kA4s55A5faEcbgz3oYtT5UCMAVH1PMcXOvfLY+Ufw4n62nCmWLy3o3G5d0tj8QxOMMMJ5mI8L6XW55eaG2ZTnd/uQr6EcdBw+qo0V5uU/JcOcQOxhO52js5E/DF1EI0yrk+nRMYqynkxz8ynkDNifMUBnITGqzopnsMRdY6tInBPHzY/uy2Ns0+GavMZ1isz5nQey4ibDVsuOm+i/uDU1EPOvAOnpk2i8EI3j6v8AvFy7msdUaTqqyliW8MFjNcsdJ0EufUCW1WcwqlofI6tEBAPO7HyhW6i9y9aq/7+p+OFAAAgAElEQVTso+9cbWvhXNN5OKU7WKwO1kMu3MZ2FD7ytDljNqSlKdw9IzhEFmjTAK6AXsOJ7M3Ixa9y3/Iah9EQa8QQkqEp9JgPnmkeOB7qeL+rdks6cMHFuvzzjxRPh6c67hX8fAHJf1C1Ldyxh+f692eBcrF5oXg6LdRXDo7kcJ3sqsxxRhcyh3gOxZ2bQtqaoX8HlDpzK4WvMC8aE8dgMBgMBoPBYDAYDAaD4QbAPuIYDAaDwWAwGAwGg8FgMNwAvJKcqnTOFZWMJs8p8WlsD18Xv3yq3fMvsGN4/0BUtq0t7Yx+F3Sw/kDHT09Fz211RFV792u/VJejlqhYAVwWDsf3nXPOZYVoTf/2h6J/XYDmfnIiicL5mcqDnqiq77/zbl2+f+9uXZ6NRcV69lwUXUqhSKHNIEeiBO34VLSzuHpX71wU3teF7znXrnb5p1wkw87/HqyMApBVt9EmLey8fzbU831wT641Ra46yed639ATDfWzTz/XM5yL1tvBruHe+Vn1vKAmol4XS92/vy8a9Hvv36/LEShrCZwKEkjEFkvd85MnapMxnBnuoP8lkAWSuUsiqP+GvpN6pWh4lE153nrJQoa+N4F+Ly1A68tVnkBOdXGhPulACbw4g0TxxTNdc6lzOnAim1TUxQB9LQNFenKqcbO9jV3oe3heJ/rjcKLyMiV1OsRxnXP9rv3XWF00KMpXjm4bFHGUpSsrOc3pservCSRmf+27ovL39m/X5dml+mc4g8QG75LR+Q+U4AguOAvQ51OcP4dsZ3Su9r84X0nYPv1Csf3Bif59yL7l03lA4zUCVTqBhKOEzRZrvsD480CDPrj/jo5DztNw5GgYkr0ZOZXneS6KVvHw6Lbi39ZAsejFU7VvC/LjhowOjhiUHl1ORCF+dKzz77yte5WQq6V0TaBMFtdPKolW31P8m0JmMxuBtgzqfcPZjnJcyC08OEhwLFJxCpa/8yiNQLlEjtHqau65//YqBsfJ77iNwfOcq6SIS0iDPvpcEqfvHCsPaUMSSpo8nWx6cJjpLiSbWML9KghU/zPE3AgeUm1ItEirX1RuYyO4JM4xX2eQmM6nOsfDoBj0dP8t0NkTOI6yX3jBNQ5aUAovQIWfjRUbuku5n9EhaJMonXNpJRGj81SO/jmEa+offyg54fFYL/EHP5Gb3DffU9xt7Wv+H44k5221FH9uH8kJZdpXLD/alyQwgVvYlTzpCDKowXPJXD9EjjR8ghwJMpjCqb2eIh7PKV3FuGQ/OkXOeTFUH9xCnMrRryiju5IIbNQQx5PblwfZQemuizcoB4w9cC+ClDqg9AJzEd9hCRewJw+e6LcpJKdwaA2L1bzkq0u4OdYWF6nq9XSm+i59yOoGeoC5r3b2kN/610hmioYLICVUKKM++pBLtuBcuUmkWe6eHq8qJEWutsB6YSdUW0wgVWy3NRZb2HIjg0aSDpYDuB9Hwb26PNvBVgDIzLtJb235yqmK186wDQa3ylhgDqA7VtjWWAzw7POFnmWG5fMMOfjcMU4pjl7O5Oj19ltadybIe8aT1TNnjbX566KsZXsNwV5DBg85FaVVISSmcF1+8Eiy0sOOLtSnlAimYnQNTjD+Hn4p+eHTZ8qvRtPV2KG7V6v9RV3uQQbe7qt9tvcVk2PEnR4cJH1IcSmzC6P16w+kXM0yvldEOD+jJeBLwpg4BoPBYDAYDAaDwWAwGAw3APYRx2AwGAwGg8FgMBgMBoPhBuDV5FRl6dKanqnvP4ulaIUO1Ko5tml+fiIK1d0D0UpjUDZ3Ia06PDhSeU/01BPs8P/kgXatLrjDd0a3lNWz+YGOnT3V7uWLhShRHVCFv/W+pFq9vmir+wdyx+qAQnsKKdYMO1JH4FClBWl5Kn/06YO6XEailN3aWdXHpy/A0XxNeJ7nkkrC4kMONEYbJnD68kixn4juW4Siwf3kierz62+r3d7ZFQ2tG+mat+HqdeeW2nb8XHT1nS3RiQc7q93BM0gMxpeiqmbgcu/e1k7ifqQ2H55I/uFnosT5GAI/e6Zr/uEncg3ZgePMHLvg+6CwgkXo2hHolMGb+k7qOVfJTLhrfHHNBvUw+3JncBsaz0Gr7WksZrnqdAru5/6u3u3t+xoLh3CTinyNi1u7otC346o/gJ76Yqg2P6NEKNT980DPezLVby/hvFGgHReohAucQyekhlKqwQWHQ4KjvMer/rs5lK5weVXPL55KtvHooeLa/w2a7n/6H//Nurx994O6PH4oV5UWpFh0DPECSCi2NC5boIqGcEYIQUWdQFrz+ecrWuokV92cgQ48RnwOQd/2IQnhpONjTBekSqO/5tDhHEDCun0omUMG+ite1QVlw7vkjYCOOE+fiXp/cqr+vLOnuEQpwxmkiq2Wxl8rVvn4hWIXx+J0Djp5S+01uAXJWaw5tQNqfVRJHqNtjdU7t+S81ztS3UbbcFlCnEtzja0yp/MCXDvoytLQULGMIubgHP+QQ1LX7q1ilu9vUJJTlq6o5IQF+szZueaEx09E2X73QOOGOQx/G6HPBzFlIaBqw/kw9hRPC+QlDm4XdKeaVrK5QQKnS0ihpwvN3aOZ5Bl+hHmLNHC6wCFX8RLIXzEuF7R/wuMWyPsWkHFRZhVBordJlGXp0kqSNwXdPUMM+fgzyZOenKh9U7idnMGRcYr3mcwVX589V3+4f1fj5ehIcx7p8SNIlc5Odd/jk5XU4HSoPG84kvygdDreHai9ZgvV7bMzxZHn56rnJd1U4E5UIk+/xLueQIp1hHHPObLhdJlejZkNulOVZS0JYfRuKGMZP/z1ccVv5F54d+S0PCcM1us99/Y1LuNCsbXlKbb6RSWP2NLvxnARKi51/AlkNY/PJSGP76i+F5jHCyQuZcMdB4MRdeA1JOHMUeH2tyuJih/DWXCDKPLcjSpJWb5Q/SeQsBXoS1PIwimVLz3FihDBcIJ67LS1RgsQdyPKfjCHhNiuwY/5PKsfNPozcosQ8+wCcSFr2GyqyLXoGG5lswLSIbhjBZHmFW65MZ0qdpxj2wE6517lSXm+2ZynrHNfzG1owwCOVBxCSazzLyd6/o8faL34K/c1ns7OtRbYwxYmtw/UVw+7yqM+eOtrdXlyqZj38PFq7TbBOO/BYeoWYnXS1bhJC2wbEUGKvNS1C6wdp3Bi7UR6jxAOsV24k8XMwbE1yfZAa91nj7XufFkYE8dgMBgMBoPBYDAYDAaD4QbAPuIYDAaDwWAwGAwGg8FgMNwAvLI7VV5RVXNIFpaL9e4FpA0/gTvHt96TnIYmBT6cNCgL4c7/h9jhv1iSYKnfzmaieY8qVtS8EPVp0BdVaxaKkpc2XBiwaz2Zm3jGJSjH55D3XEm4nHMuCLULNd/p7FLUrQ8/lkPCArqXZSXL2iRV1XPOBcHqQUI4AwWZqMcJ3t2HVIIU1hKUxc9ORf39wc8/q8tv/9p36nIEd5osV10N9iTnmYx1/Q+/+ETP8GB1LxoP3IJU685bh7pPGy4KGVx7IOfyQXc7gSPWv/7hT+vy588lCyl2dE704EFdXoDO3O2qj/awIz3p15vHqkIKjxIgaoZUBDPTjeBIdAkXoi3sdH/l8OGcc5cj9O1UdZdAKpHNVb8Xl3rni2NJg5JoRf1cgip8NlE9B+C+9nqQvHVUn1OM7QneIwzUj0aQUI0gP8kgj6KjV9CQVql43W78G0NZuixdPesEVPpipvr54e/9fl1eLtQP/4v//D+ry+0779fli0d/WJdbkLV40PvlcFXgGFkgBsxRt6cnork+ePTAOefcw+dwNIF7UgmnngDxxYP8Eo/lPNCpczoTQakRIf7ffesdHYerxhKtFXikloOK7t5EIzrnPOeuDFKOjhSLDg4lFT1DjJxCbosqct2OYurb9yUba2EsPPz5g7r8dK74k2xDogPHj+VCNOZgBklbubpmCR1Mp6d/TzqctzAvU6KH4x6u48P9xacllaOkCKAsALGMTiBkq3tX7pOUVLw2SucVafUIkIvAae+SLn37ijcJ5sJljhwCEocIeRHMdFwUqKO3qMlFPjNP4bIESv58vOpHCzjC0YXQg9ypAyp3EGOsYK6awtWMkr8MbTKcQuYB+VUAZ8Mc982QCy3x27QHCf4GUXrKs1LEkws4f33+haSrS8jP6H60gzzzl775rbp89uBD3Ywy0pHeLYkVyymnpkRtCln2aLyK6ykm6RCujiHcNGeYO1F0j57r/SZz9cGCnY1DEXIqz+f4pvscTqfrGd4jKzYr3XBupQC6ciGiNCT3KSsqmz+4OgfzSQTHLqKAzG4GuRv7bbuvuaULl0G/gOQ1gJQlXdXn9Fxt700x5uF86s/gcIO6bC11fop8ak4ZHF/EW19uREVKiyELSqGLfHp87N4EPFfWEhIfUuF+R1KSHBL6JSSkE8QKqHVcG314gTwv8SHPxDvTfSzD2irD3JJzvVONl8Zah85scOTLkC81pOsYaLMpXCMXeDDIuTpbkgtlkfrUAOsUGF65CAkUY0YcreZs3980N6OSUzUUY+qrPvMAT/UaYgsTH4nOGPPMDL21xHoKyn4XYM0dlFh/n2vtMIg1H3/w3koWHsFhsYQbVIaPDr4OuzmcbBcZythGZIm2zWGhFcZqtyDRfbcHcH/s0UEVjpzoOwXiwcvCmDgGg8FgMBgMBoPBYDAYDDcA9hHHYDAYDAaDwWAwGAwGg+EGwCtfQarjed6xc+6LN/c4hmvwdlmW+3/6aX86rA3/XGHtePNhbfiXA9aONx/Whn85YO1482Ft+JcD1o43H9aGfznwUu34Sh9xDAaDwWAwGAwGg8FgMBgMfz4wOZXBYDAYDAaDwWAwGAwGww2AfcQxGAwGg8FgMBgMBoPBYLgBsI84BoPBYDAYDAaDwWAwGAw3APYRx2AwGAwGg8FgMBgMBoPhBsA+4hgMBoPBYDAYDAaDwWAw3ADYRxyDwWAwGAwGg8FgMBgMhhsA+4hjMBgMBoPBYDAYDAaDwXADYB9xDAaDwWAwGAwGg8FgMBhuAOwjjsFgMBgMBoPBYDAYDAbDDYB9xDEYDAaDwWAwGAwGg8FguAGwjzgGg8FgMBgMBoPBYDAYDDcA9hHHYDAYDAaDwWAwGAwGg+EGwD7iGAwGg8FgMBgMBoPBYDDcANhHHIPBYDAYDAaDwWAwGAyGGwD7iGMwGAwGg8FgMBgMBoPBcANgH3EMBoPBYDAYDAaDwWAwGG4A7COOwWAwGAwGg8FgMBgMBsMNgH3EMRgMBoPBYDAYDAaDwWC4AbCPOAaDwWAwGAwGg8FgMBgMNwD2EcdgMBgMBoPBYDAYDAaD4QbAPuIYDAaDwWAwGAwGg8FgMNwA2Eccg8FgMBgMBoPBYDAYDIYbgPBVTt7qtMrDrb5zzjnPefVxjyeVJf9n7Tmeh9/iH3hNXiXH/2RZVpeLolAZF/LDqC7Hrc7qv+1OfSxIYp0bqAoa70F4eIDy2rPeGB588cCdnJxs5MbdTlxub63qYrFcrj0nRJ24Mse/FDiuOsnVJM7zA10nVJm/zfGDGG2RJK21z7NcrJ4zS1PcXtfz0FuiWG0fhbq25+N7ZaMm11drsxvjf3A6+1+eq57KRr/XD758cnlSluX+2hu+Inr9Xrm3t1fdUM+RoU2nk0ldni8WeD6OP9WL1xh1go+68/31456/DXE++8DVT71renLZGOd6p2Wq/pI32kL3aT456t9rPqXuVaCsM/iu/G1c9avxZOrmi+VGxmIvjsu9Tnt1r0G/Pj6bqw0vLi/rcpah/+Md+SW+EVtxfH3L/mIsZvzVL8o1Py7WHXSuER95vSRJ6nK/36vLvb7em33r3xGNXwLrxx/x8cc/39hYDIKgDINV/+AreJg3eJx9LAhUZjxh/2RdcP7Lc8RAjmPcqyzLteX6/OtiIZ8XZ4R43ijSPMH3uH7MYc7Asy+XeqeMcRTXaXfUZ6J4NU+MhhduNptuZCwO+rvlwf69X3xk50o823JelxczjctlqvGaFZwHdCHGRF6+kcOwfni8QBvit1fVE4Zqhwj3WWbXxE0Uw2tysUajl4wLQlawj/K5rolB+DHfe75cbmwsbm1tlYeHB9X9dEPeL0UeEQbMUfis6+vc57tdM1f4QSMi47c6upyrL9XzN+chPFcYK3byPXjPolzfFoG/fo5kHbBhfORv5TUxPgr/5DnPXhy7y8vhRsaih8DZXDc0znqJC60vB9fkDY01jbe+zzdiKI9XZ5UYq1w3cAwTZfMB1uO6yfvNYGNj0fO88qrRvGvyCe+aZJDnx+hv3Y7WCO2WxkUYYh7FHJIxNi91vHFfjqN6fOn+AdZDnPM8jNEccZ9zGMEx3YgX6I+MR+wzOca3f80a6+r4i5NzNxxNNjIWA98vw+q6zAlfrk+uP8lz6zt98zvCNZe85q3WNycTIRQxLpk3OdRrhLVoFGEded3i5Ro04si1dbZ+/fr0yeOXGouv9BHncKvv/tHf+0+cc87Fvn7qN1Zfmhw8LNYjzJWxz0QQHRKVuHAqX851ndOLi7o8mUzrcornae3frstv/fKvOuecu/vBd+tju+++W5fbOzu6fyP5wKToM/vAi/CDzhv8tvO9f/97G7vW9lbH/Vd/7zedc8598eUX+Be1ya2dvbqczc7rcuBU33mqOhld4qNMe6su793axvWVtFxcHtfld955py6/997XcL7q+YvPV8958vSZ7r/Us/hOgfre/bt1ef/oTl2OWvqIx+DLAM3vVVm2PigzQZtM9ZFkiMV2gQsFuNd//T/8U1b4a2Fvb8/9g3/w31UPqPd/8ejLuvyHv/97dfmjTz+ty4uci2t83OSHGPTnVktBrNtROcK4CDGJ7VYfJpxz7mBnUJfb1QQYReuD+BJ96vxSdfvF4xd1eYiJ2LUUaPEd0eX46hvhgy4jPRdeKdq321F9JJiw799fLfD+yT//V25T2Ou03X//13/dOedc6+/8Vn38hz9TG/6T//e36/KLF4/rcoY37qCPtRAHvXB9IsoPqiHqJ8D7MmlYYJF9FeqXBT8mICEpMRHiI+r7X3u/Lv/mb/31uvwbv/WbdbndVr/xG18iXia4XvPHg5KLNF3nb/2tv72xsRgGkbtz9I5zzrlWpL6U+OpjnUT37uE9B/igNZsppi3wwYAfxl+8OKnLw6HGSNJC3aE/cLG2XGA+jlYJcBBjzmMsRP23kCDv9TU+7t2+VZf7Pd0/StSnSqc+kGb6kHyJ8f3wkeaDExzPfF3ng+/+Wl2++9Y3nHPO/T//1//hNoWD/Xvuf/6f/unqvuwyqea/s4c/rsuf/dt/Vpe/PNZ4PZ+M9FPkCjuonxBJ5Gw2q8sLfKy+GKv9x3PVG/+gdfXh7HBP8+x+X/H2ybHq9ZJ/sEFqs406jmMs5ruohEztP8cDnE3xIQJfKBIsLIKSMVc3nuBD9Yeff7axsXh4eOD+t//1f3HONT94TqcaW0+fPq3Lu8j/Gn8MwW+5MG/hg0rcVjnCXJR01db8uNlCHX3584/qsrdctW+eKnZ0dnbr8tH9t+vyaKTxwYR/luOPNHje7Z7iCz8MPn3yRMcb85/OX+J5GI8P0N/KfBVf/sv/5u+7TSLwVrHI9/XMUaBn4McpV+JjGo4X+K0fMv4qhqXI80KMhShW3OJiOsX6ho9w1V8YYz1f1+YfTLnGZ5twndH45lrg/Tb0QafxMYrLG1dubCw6z3NhlQN4Dh+3+QdYj3/k03vGns5/60Ax7fvf+UZd/uCbWsft72IeHZ3V5ePHGusnT7CW8dTWfqTydD6unlH338FYPDg8qsvtge55Ph3W5bORyjnGTa+vtVGEObqDmLK3rXvxj3lDzBMtjOmdXa3Vep2uc865//Yf/iO3KYR+4O5srWJk8w8Lbm2Z62YPfwDxPYxFnBNw7DLPu/YjDj6uYUyTL3D1hyYfbZwjXy0xzrxEsdprK67de/ebdfnwrtaUIb5XND8w88O9v/Z4Y0w3vmSqfPuuvl38w//x77/UWHyljzhe6VxQJfQeK6LxlVv/E+GvrxH/gtf4eoi/djOhCBREW239dtDRX24ZUAsGg44GS1JNRIsTLQQnWKjF+OIWIBF2QaOJVPJeoqP9BUZRFm5ZLRAi/AVvsKVkJsOijYu8vZ2Dujy6VLJ6coJkcaJyf6B63tlRm9y6peT/8EjXbLW6ddnDQHjr7VUS047Un+YImiECftJWe3JRcomPLP2BJoUkZiKmPtdocwRc/nV1gARw/0jBPSUbBsnjJuGVpfOrv2aPzvVh8+nDh3X5Eh88kau4koEFwa0d6z332mr3Xld1NOipjXawoOvgA0An4gSl41cfSX0ErQj/zrq91Vc7bnX0LE9O1Y7DhcY/cic3xYTR+NjQ+OiqOshTJcALTJaBp2d48mSVDDT+gvmayMrSnVYf4H70L/5lffx3f1+LxRO0rUNS2m6pTvqIYQk+nGQl4iMXKPyAVejdi4x/qQL7in9RrCafEos5n2MF8bxA4vb5g8/q8v6BEo+/9u99X+/UVt9qkt+uY+jwr+XXBOM/g+/snue5JFpdvczJbsQfNAr188ZfE8FETBeKFd0tJKVTXTNGu+z0FMe6SOyum5tHw3Fdvlp0ePwL5pp2rv5Hz7hUf5ljrAyQ0Aah4kWAebz511Iktz1dZzRTnWVYRF6c6ePV0e13VoVr2AdfBX5Qus7W6nppzsW/4s3J08/r8nB0Wpcn+IieIUHd7ageuvhr3myq910u9I5LLNZSlHMmumjPq77TRru1cP8ecpgxYuIMbTvEvBCraV2EnLSFBLmLnI7fuubLFMfV1/FdvmYzOudcGbxS6vnSKMuy/iixAPv0CT5a8EN9G/nCYq52YW5EMI9l3MtwrxgfMTsdjdEhctAQvw2qfLjE/BSHnBfJCuD9hQI5Bz9AJChn/KMq/6pa8I9OWEg7jlf9do6Pilv9Vcwmu/B14XnOReGqv7b5oQyL7SX62wKTf8EPT3hF/rEux7yYF+n648gJGswyjCl+mLn60MN4FyG2Mxfls3sZ5wvEbXwALvCHiLzx56p1v3x1lOun1A2humBjHl6/nvIQl7po97uHWiPcwdqBHxO7bdVRpu+cjQ/m79zXAnlnC3+obuSIq/YouOjHNNNGLtpGjry1fViXd5da65yNtE7yMS92Osp1QsR3/gGGc/oSdbNssO2VM8TVWq28hiH51VDWH2b4gYbMUgYifuwN8RElQO7KpTW/BURUxTQY3fh2gPVCjHIS65xOxdCKI8X2HN8KZvgD2Xip5xotwf7COyXXsDXzxkeZ9XXeZD+vH2g8+vTRY/eqsD1xDAaDwWAwGAwGg8FgMBhuAOwjjsFgMBgMBoPBYDAYDAbDDcCryalc6ZKKHsR9cEg24h4U3HAvIG0KFM/GRqL4phT43HtD12ljb5OGRICbPPK1qn0G5s9Fp52AYtfritYWQ+9Y+uulNcQr7nH0FwJlUbrFYkUbbmFjsBh074dPntflb7x/ry4fHoq+2I4l87g4w14OZ+Iybm3p/KPDQxwXxXh7W+dkuShvKSQfe7urvZ0iT884vJS+1RWixyWgsLZBmybd++JCv53N9LytBs0akjtftDzK78KQ+z2IxjvOqK3fJLURKEtXVhs+P8feRucvtG8Qac9egHGJyySeaKOHkL/dH2hc9CH/2MIeHn1Q0RPubQW+ZEPHXlG1QzxLczNb1WEPHP4+9uHZ25Gc8uRc8pDjc9FWj8fqjxcz0MChhCpI0STVGZT6GNTyvGrHYoNU1fF87v71x6u9Ef7gUnKRbIZ9AFA9YaI6GXCPDe6TAZ2wj5ZmPZct9lvUz2y59vzIJ5179WwRad18RmxBxGchFfejn0ou9s9/W3v+/N3/8D+qy9vboiRfv5ndNfvm/NlsVYZ7FM53q7FWYG8Kypp6kAHvYDNnblCyu6UxF6HuihnkCy1SsiGnIj2b4w+6mHFP4/XsbLVvQIH5N8eYyBobInNfCjz6tZuDY97318/pZVvPeLivvfvGeNfZhSSzoyFi9nQ11jc5FpNW4N7+pVUdLqd6hi+xv8Fs+KguL1JIokCHbyVqkz7o3pfY7+gU0t4F5wpkUtPletlmFzLKK5npXl/1eggZaseX3Lc8VwM9nejZOUdMuYEyVB7cw7ATaa7tQB7VwVxYMI9DDphyL4T4zfz9sCzLWkZ11cedc66FvGB3BzkHaPZhYxNgSLSRrzrkmZRQeRHbBZu1g3OfUaqLsdOuci/25wT5GCUWlKRlmKs6jVxb75GgD+aQKlLSUCDW83iCWINXdUv0mbSq1+s2Qf4qCHzfbVf5Rwv7h+WQIHDkc/9CBxlMzI3RkfN3EJ9SyAwbm/lzb0/ci+WA+4JU75/mGufZjBvQKrcKsX9SijhSQtrs55CyQU41g/Qtb5h7NHYiX1P6d+HN7AvhOYfN/ddvJMw5vIV+fgtGD3dvaZ+Ytw6xFcOuYm2OejxfKEYd7SoG3j/SWqaH+dJhO4arHIvr2BTbS6SI13P0kQXHM7YfSCCpHSMB7WDrjoj5sKO0iqYvus5spLx3Mlb5aq7NNzgvep5Xx5zGZsw8id8CMM5i5DBcHyTYtiGBJG3QU5tv9xWjW9i3s4/tHGLkw5T/XsXWkuYokFBNEMzOsPfco1Pss4q1aGPPX4zF4iXG2XXGEk3zFR39KjmNMXEMBoPBYDAYDAaDwWAwGG4A7COOwWAwGAwGg8FgMBgMBsMNwCvKqbyacupdQ1nn7tSkgXJnbh/UWwdqI33CgmsoZgXpj7CB82n9B8qrS1cUKf8S1DdQLkeUU0EeEu2KtldCTtCg59Ob76VscP/8UZSFm1c03Fab9railS3mKm9v76CsHd1DtNvRHVEZC0+yEDqWdLuiPkahqIRghzYooQ16fiVr6UOeRaeCDE4CSUJasa6xs6Nrn5/JQYvymfNzOY7QGjyBhGobdv63mY0AACAASURBVIO93hbOhz0laNl089gksiyr3+PsRDaKBWiDdN5KYBPigSp4dxsSKtg03gKNuQtadRsuVG3UUdhwM9JzhqCFxxX12vOwC33DeUO/i3A8amk8d/q6+Bb65mAgeV/8Qi4g2XMdH4EGTqfyhuMI+t0MsoOkktptkDXuZlnmfvR89awT7N7fgayiBT1Vv6u63OrCyQ91H6KvLvCSCawUF3SnAhU9TUAVRl1BZeGKKra2EPs4zrdBg96mYxFi6NmlKMA/+DeybD+EC8Vv/tZ/UJcjuNI15Xd/OjZJ878ORVm4xWL1TjHqpQtJ5g7kYYxdtN4kxTpbSObZRRwbHCoGHx3er8ttUI4jyClovTmGJOm4muuGM1hiY3ajGwZlyyHdMyDLS/BOYUAZH+VUehaaOfR6+odbe4qvl1PVwWKq5xxfrmQyeb7eqeWroCxLl1cSpuUczotPP6nL+VLynBT1Q7r1bl/5BGnvzyGhojuGTxcOuONsgx8+gGvbrW2Nqf2qfHRLxw7gWJZBSnq0q7nz4an6wROUT+F0cgHK+Tkkbsep3qkFaU8P8xxltl3YbZe0nw3XO368LrI0daeVtTqdIe/ckTMNjVXojjKFDLeDOa/h3oTwk+N9dpCbJOj/Y8i+S7jK0JWuXUmhKINIEAsSjLMCc/ccspFOF7kr2oJOWdOp2q4DSc/SWy+nYhxxJaSQ8MieVTbztOF+Xfi+71rJVZ+HJAr1QKv3KaRvAZ5jB3WyhXIf1wlDSFgx5iiFLSA5Zo5Kqfy0yp8v0Sajma5xMUaOHOierbbmhflc7ZM76BmxXgm4AinoBHmNPfmfwfx3Pco6vyuvEaXxaNLSuNnfU73cxpy3f6D5oQWp4Pml6rcdqG/cu6MtMnYGcC3mOiH4k7bVHnKwQl3H5ZBHpuhrE6xBZnRoxvrSR3yh1J3W9hxH3IKkg60elinkXXBjm1euh5uUGXue57xq4qYteoxnY5hIMMkzP9i/pVz9YF/t2cf6Y29Ha+6tvs5vx4qFHaxLSsTC2QQS5Uklt8baLsuwtQbqb3cX8q+u8o3T2TVSKY/fAljP68ef1zi+vtyQqX2FPVqMiWMwGAwGg8FgMBgMBoPBcANgH3EMBoPBYDAYDAaDwWAwGG4AXklOVTrnsoouTlp1CGqVR2kCnapiuPqAHuXRzgS7WTc4TNzN/Brakk8XnJw/XtHN4gLORxNJLIZffqprgKq1Bzqr34WbCOn8XuMh3U1AURRuUslEwkhUvwIStBwyNcphPEioItTPLuhxdAmLIL2JcJ08+5MUQOecixL0HVDV8koql7REG6ej0AzOH1HDPkW0uQRSkYiOGaDcJehbOSQnc1DLT+AwUezqeBc7q0eQC3aiVxpiL40sS93x6cqJ6uRUbmI5KNsk+yXgPG63RTe9v6M63cVu723uII93IIU+5FhvjHuUIbPwqj7QkMRgCFEFSRlGDJoz3a4SUPVbPdV5d6DjMaQI6edy7np2JskE+2OIZ1uCRn1lz1FukKqalc5dVC4UdAkKEQfv3RIF+N23NM4Odta7hEVtui6o7gP0yRL3GlJCMZQTz+k5YuRIdZVVdXK0JynPW/fu1GXKSToYizkcHo6HuucffPx5Xf7jP/yduvzNb32zLt+9+5Zbj2uo4tdKW98Qtbwsa5cbUv7pwteDI5XPuEhnGNC6Q4ybO6Ai727Lyelw/25d7nbUT2LEN99jTIMrw9lK9vrwiep/vFBsm6GfLzPFFEo+KD+g205IKSTeI4csIEEcmS50fID+M+iJAn2GPnN+sop3ebbewemrYDnP3IMPV9KXkxeSUD3+Qs5/84WeYQZK9jb0bpHTOc9GlNLo/C3kPHtwmdvD+1KG20F5D31qb2fV5v2ertdFnlWmapPbe+pDv3RbTpEvIKd6eiFK+qNzHf/kWLHg0alixHgJSVmMMTdXHQSw8EkQm0rEpk0izTL34sWqf9y9q/FB18oAc8gS0vGmbGq9EwsdV1t0bRyoXcZD1SPlzREdkpBr1hIZuimhL4TUK2AMuVJlzlsJJFRhtH4rgKghh4C0Cv3Ud6T548eN33q/+OivjaJwbjxdPQfbqiHZhiPprY6e5204zB7B7a+H3AZmlw0JN+ddOmUWkG16cKLLEAPmi1WdTzPJ6qaprvcErq1fPJdsajyHrhuOVDlkG46KfDhXek1bm7pYvvJahOdveI4s19wB/wOVteuhYfYPMOchB2rB7WkBN810qvLRLUkndwdqjxguc5T2NspXQxFL4yCi9Ern0hWU24WEWJhybVxgnM0hUeU6ltJl1lNDRoQU9BRxelnl/puU0JUOqj1UFGWafawnthG39ncgiTvSnHPvntpnC06BLXwXaMfY5iSkIzAaAHKqEXLa6VVvw/NyDM8hBw2wzcTM13ukQ1275HXopk1J1DVjyFu/1Ll2lPneq/NqjIljMBgMBoPBYDAYDAaDwXADYB9xDAaDwWAwGAwGg8FgMBhuAF5N6+E5511Ry8CDIz3cb8ijdLyE/MWDFCcC/TdoUKUoW1CZUoyAghHu6g1qYepVu+eDSh6AzLSYiip88egzPRd2Mt+5967uA3p4GZBOdTOkVUVeuNFoVSdhqDqh81O7CyovaO8Ou6iX6DoeKGakLfsNVRt3+8au+h7opDiftLmaNg9JVLcrOQkdA+jAkLTxjLhNQmkX++tSFDqqoKKYkgDIDGaiyHK39iDWOZt0bSCWy4X74ssHzjnnzkGpTLlzvUfKtrDb0vtsw/2oizHagSyEZdKwA7rgUMIR8RxQr6vjQUM2qXKZUaoIGjXqH8ZWDWO7qKVnaSOmJHBgK9BP0vxhXX52Jvp77nAv9OtZJTUh3XUTyIvK7Q8Sxg5cqL71niQBv/y+ZEs7fTqGwe2IlOGQ74J4DWcPKk8z9NUZZHnDIZyBxqvydgfuZaDT0p0pgBytIXzBuPzmO3qnnzyU3O3jj35al4+O4CxDuulLcPj/TNw5PMn/Yjr2II5SPhRyvkTQiyEdwLB0PbhcHR3IwWsLTkR0HwvhlNdQQWAsDCr5VQJJ1KPncrmbwSlpmasvzCA/pVMDZY6UogSYr9kSPvojz4kRO/qgZl/A0WxUu1MhqL8m8ix3k4uVVOjs2ZP6+AwxfpGCYo1YstUR9fv4ufrwCO52HfTDb7//Xl3+5ruSJe5tQZILCjcl6i3kTq2K0s665/hg7bQwL3TgoNPf0vV2LvQeuy8UR7bRXzuIiZ9AIkKVqd9wjtQ/xDHkd3Co3CQC33e93qqfDyBxomMLZRXTyRjHIT+9TmGC8qAPJzBIDumaQglVgNwoCZlLVbEDjliDPpxDEUczOG5FDVkyZY4YW+gPgU95gXpHC8+VLuDySnMqyiVDyuRX7+F9BRnAdShdWbu/teFMmEI23/E0tn7pXUk13rutPLaHIAqlfiPGcFsID06BrM8SEraGnBrjIqvqYb7UPWeIF33IbbqRjn/8SP3volTekpWQ4TRigdqN7rxsLK9grv3niNJzrpKfletTO4fpxw16ev+9bcWibUjHqTKbnKnuIsgz97fgYNXYDgLbcjTSCI6RVZv6jUQTbUFnLbyUz7mIronIf7uQxVHqmmNuyDHvMnehW24L0qoQUvdptdXDZnOe0pXVuPCx3qW71xYk0LvXOJV2IL+KUJ+RT8c5Xafd0m/pFHadO3SIOHG1dUeR0SUT6wluXQBJagvP2O3ptwuqWRuu3Ix56+u80e/56BvUnxoTx2AwGAwGg8FgMBgMBoPhBsA+4hgMBoPBYDAYDAaDwWAw3AC8kpzK93wXX7mc0DEK9KgSx5egHvqUVYB+nHRFSaWTBXeTziFzaVCScP0CDjOUNgXe6nnDVNRftwQlPJekaHx5XJfPn3xZlwe3tON94xn5LO5mIM9LN7xcvf8yPauP9yCn6oMGl5FKirfM1yvZXEQJB74RFtl6TnKeqf5JsU8g4VkuVlRl3xN9sgX6XL8n6VsDvvpNRmcDukAkonC2sTt6CucTSrvSjK5VoqVPZ3o20vwzOhxtEGmauedPV9T96XiK4zrHg6vJdlv1OYg17Ht4/1ZL8oUYtP0A1wnQ/0O0dWMHf451UNevHHfoasVd3csIfQQDKgD92Q8o4wTNEnTlJMTu83jGyZ1bdfkYTizHkGqMITsIKRHZsIzKudUr1k8NauhgS3V2+0B9ew8OXIkPeju1ggXGE2i1SQS3PV/ncxynEGAkoKJv7Soe5P1VfynRr4uGYwodXhCf8X4J6vUAbjtnU42nzz+TQ9C3v/0dnX+gWOwK9KPrAnDjH95MlPY8z8VV3+5DZtaBPCJhH6Z7G2aRCDTfDhwZtluaL/ttzJfo84FPWQClTbgX9Ift6vqHB5LzXMIxo5iJpp0EeqcE8SJDnKNEj9Rvuo/QNasAL5kyltlC8bXfQ54AqvNktJI9FRuUU/l+4eLW6p3jQBLrLFefnM3xbKC0z2YKuo+PFUuKpZ7ve7/6fl3+9e9/ty4f7SsmtVqqW+ZUGen2GLBXDHG6qxSUp6LtKQNJU+U/YVdzhw9HQuZWPuRBCzzLJZyMzuaU4UAqhT69gMtjL3kzcqooitxR5VBEWQFlaDneZzJRXniwJxkGnYfYb+lsOWlIsdT/W9H6egwo16IWuJKCJJClNkxQMamH6PNeQx4FVymP0hpdp4O8ewFZVhtxik6NBdy6clyf0qpO9cybdKfyA9/1tlZjIcDcUkzVVvfvyb3o3SOVt9p6kBYdu+iwCflYK1lf53QqY5mOeCWOp5WbaXsBN7Y5yoj5rUh5Fg1uP3qq8bGYQZaF6ZVtzhVIwwWH7k/4h6bKhi6/b0Z0VXrOFZX+ycOL+uiUlBDfGqhediCt6qN/LuFmOx0qTt+GhCqhc3Jj+m/YGauIbSKCYHWvsGlbhmvQnQiXwxiFEXIjdifc9sGtP4fgOqIhB8XzcExPEV83Bo8yQ8z3bENUTxfzdA/bNtCLsGRdId4EqB86/JZ0hMP441YV7PNXp+fcQ4JrDkpcC0rCkYtBOpYh3hWNtnoJF9SXOOV1x58xcQwGg8FgMBgMBoPBYDAYbgDsI47BYDAYDAaDwWAwGAwGww3AK8mpyrJ0ebaiFjXcgxq7oYM+mtCxB65FlFiA5urorAK+kQfaLilsGejNhVtP7y+vKMIeOImpKNIlKGh0Z7l8JqeO6T3JjgZwRSrJY74Z5lQuLwo3nqzeeTQRNa3V1fveuyNq4hSOAKRGU6aSNSjeavMYLkE8zupJwVpdzNUWpDP6FT9uCuozpRQ7W6Kk93uSfswWQ10b7TMZ6zrjC1Eyd3b26/IA9EwP1LoCcpEEcqo5XFsux5Lq9OFgsUmUReGW01V9LZeq/wz0QKiKXARabRdStR7omBHca6JY1NYIdFbKoyKOY7rvgO4Zwh3gSmbhN6RtpPXSbQH0WzovYMyVtCoAPT2MQYXPKIHQM/Y7eCdQrXO4MmWIO1eb8W+SeOw7uWbEkLttbanu2y06jaCdSesG3dMncRUyq6bcFOPYI60Y00FB+jMuWcXTZaG4sKTrFynmKBeUuPqkvOueR3t7dfmj5xqXP/3Zj+vy7q7GegjK7cvt9v9mArPvebVcqgdHpTgiPZgcd0rhdE4Md7gWyl1IqEjFp5wxDDjm4FYG2njoUxayqru4oxh9F31k+fjzupyVissR6NLLlC4ckCVAWkJbPA/U+SWkRp2O3qnh6ARpWr+n2HQ5PHXObVoGUDjPX80LfiEpWZ4xHuiZ+9t6sZ8/0tx5CTnYB5Bv/tVfliPVIdxW2oibnC/pBBk2lMiQUFS5Vs58J0c/o/FjQ2aFuZtxE+NpG+6cOcbuEG4ityEjW5xqrmWzlPwzoae6CRwk7RuE5/uuVbm5MQ9MMFednCi2sO8x50jRjo3UDpLf5UzzfwdSOCbVVzIb55xrQ7pDda5fzZd0eU2RTyzhQBMh14oxj2eQcDGO0DaM77e1rfZdIu+KMV9PkV+z/tjfskqatmlHHD9Y3SOdS7K211O/vXeg/Kzf0vvGiK2UJ7YQk7qYUzttnRNdU28F5HfpUnXFrQDyeDW+5nCe8pDDhHCtCgLV671bepbhXOXzqXLIMfK7hstvQxL11WXDDUm725xEdRWhVtfm9EyVSwf9ag+ObDtwdqTb8HysceFBbtRvc/yhL1IGiLyjsR7Bw/lVLtOQBNNlmRJGzH8NuRju79Opig52mGsbuRzaIuM5cCXrcl7E1heT+Son8/3NcTM8J8k3ZaJ95KuUULHcQTxrOOlRJsatJxqSTUqSsHUL8ihKhLkuu/oGwfZmG3N0BCXXGchRcT2f+TWOF3ABfan4d43LIVF8hThqTByDwWAwGAwGg8FgMBgMhhsA+4hjMBgMBoPBYDAYDAaDwXAD8GpyKldql3p8/vFAAYrgakJJFGlIlFAsC1HMfDgYBdfQ3UgbdzF5u6I85XC+8CuaoZeDEldi52vulJ1jR+qh6MGj42d1uXf7UNcORPkjncrjp7GGFgGPe90G1m9mo/jGfefVe/qQCT1/rneMSshRcM6tW6KHd0FVpXtJGuvlu5TqgKbr440bDjYlqOvYkj+5kvOUkPDNRWWeBOd6XjwX4YE2nmDX9LGvew4vdB32uQiuEaTT0nmCz0s3tXEhWuwmUZalSyuHoCWetQCVMgE9sI1x2YGEkXLGYI2TlHPORXCkCSmtgkNYzN+CZhiCQu5VDg0Nyi5kOyGpjRwUOE65JqmYAR0kKJ1EXAhwrzaeMQFd1oejGdRUbrksf/Fyrw0v8Fw0WD3H3pbaYbeNcQOZSgkpA10nfMhUQsp2MLbKXP284V6E8VrQESenIweppVU98J4YzyHidgq6MQwbGrTVqNS/bGHothGDvvz8QV2efE/vMUBMaRLIX8ISYMO4ag+6Fzac1AI+E10Q2HaUosLZClTxxnVKSiUgxYLLHuNuuPbZdL3bMP4aTTX/nY5e6B+g1isaLjwYQ6TnszviGTO4tfC9GwrJTOcMIEsNgtNffPTXRpbm7uTJSrpxeSb5SjlTPbTQnrO5JLmnlzq/jbn/G3dVoVsDSQX43Ckp5EvNaXFMmjfGKLpwTb1G7KMitcjXx8oCDnZ0mWPQDSAz2qab3Fzn7/cVp54Pdc0lnSgxR7qA+eDm3f5WtyvdsHK5u31LfSanIykqcQuunMUcTl2wmFks9NsW3dmYC+Sqr4KWUBmo+C3GRl0/CIvqVPQFSJzyqfpXSHkU0vcQzpJ+QlmW4iXlrQ5tSnkUETYkqsjNIWkeV9JxOlm9LjzPuauhxnC3C4nNgA5TiCVJAnkq3NYS5C1RQjn3eplHifanMxfnVL+RpKwCY4Q5tB1zzaFrL7D+6SG3ubunuP3sQn3xYoGxC5ll4iFXwdql8FQ3BebXRnC4pm03i7J2NvIa6zm4m0FCOuipjdptOBnCTS+FE2s3gFsn5jl3nWtR4zUpreI5efU7OlLhBLryNvrL+j5CSR37kUfHowW3BREyxI68kQfqnBb6dbta+zT75evBd57rVbLtbdT3LuIgx2UPEk+6hNF5s6Q7Md3nEEMyuvqxKbidAvfiQN+Oq7YNPdQZajaDnJF9JUY7c66fF+vHB+VaZcFOxO8P6/dZYR00VFZfoemMiWMwGAwGg8FgMBgMBoPBcANgH3EMBoPBYDAYDAaDwWAwGG4AXklO5cqy3kmb9CTS8wvQ9gs6KUB6of20m1Ksgq5RpNtSKoXzKVvJQQsuQUMLKi4W5VklXBh8UKs8UA890F2n53KnKuA24IEK+DLuKBvdwP8ronRoF/DyUlDTRkNRxR8/Bh0XdMBv//IH+C1p/aKEJi1RyFuRqNclaN4RqPRFgfaH5GZe0Q29BpUVVFU4k+Vow5y7l5PWjU+Xg21Rrr1C7RlDQtUDlX90KXnU5elpXU7hVLUALTuN3tx3Uq+iH1JBQ0q2jzqMgvXyhQBSIrpd+KCzeqCB+6AI+5BTuJBlUB5pReBf/UeUYBq8NamH6x3BClAb84bjCiQHkGFkc0iK4PIQ0yEJ79F4HprPXcmIvM0N4jAM3f7BypFpt6e639lShIzgXkMqadCg+67fyd/32f9JD9fpJaUPkNuQMl805GzVj8FeDjLIMJq6qbqYQUoRUvIHGmoH7bwFl6cLuNItIWEonWLK9XjztHHP81xc1V1ECdU1jgh8jOAaKRplGzHmq6YMl7IADjRqmHAOeMlBNV55jU6iOj/Yk2x4nsEpx6n+m/2LlHPkA5TOImYXIccx3wP9Dh01htNFr6JvBxt04Ujz1L04X0mK55jjZwt16AgylZOzYz0nzrm7I9ec927L7bADWWnDEY7ceEoiIGEMMHZ4zpU7B8UwnPPo9tGUm9OpTqB7mRcihyrpIKMx10GM8Br3QvtTZoP+EryhsegHvhtUji1NpyXNyYOB5vOMcnrWEZxkmKNQHkG5Dp2C6HDY6KMNCSylk6vnzHh/SCyShvQDMm/UM+UqUAS7fIr3QP+dznQ8hsvdDJI+SjVmyG+YPxTzSn6ywfb0nHNB9W6ULLQg20g6dMnEfIK40oIWC0a5jVx9gXVDhvUExx9lMGUjvxKuHBdzSpjhKFR6lN5QzgGHM8ypdw/0P8cTyekuc8RKuu+w/pEPeI25m+Vr5qZNYrXYqP6HawSd0oUsro+cO2buRSk42qu7ozypISHibzktUebCPS8wL/pXYwGxo0BczLBOoitvSTdPzukcu/n6NW0zv9UplFwmBWMT4jfiSLdyTqYj5evC95zrVA1GR6pt5GeDrtZ8LTRcwG8EKJeQyheUmFHm73O9gvoveR1UFjtx1ReukyY12gd5dIuxg3LmFGOFt+RuLhxPzPsY/4v16xvvNceiMXEMBoPBYDAYDAaDwWAwGG4A7COOwWAwGAwGg8FgMBgMBsMNwKu5U5VlLXkqQOuLQMekO07qiXo9n4nOusRu/xGobKQh5TmpjaBwk6LFXcghC0iX3IV/dTyM4MKDnfxJ2e6WepYlKGnLoSQ0y6mo/a2u6Hze65AS/yxlVmXpyorinIFvlqK8XJJKqno4PZOs7Ec//kldHr4Q3XN3sIvyQV2Ot0XD9ikDwG77ZDjmoJxGFW05Qz+YU8KVwjkBFOcQu6mPl2q3ooBsyFdfCEDZj0HzJJ16G24Wi/G4Lqcz9ekA1M7uNW5Zr42ydF5F7Y3R9QJQiKnkCkPKKtbLqZKOxoifkCouUPIUeuupvQElJfjtFRWRLjV+Q7O0frd5SkjKhtsbZQmQ2kEOQWlEAHlZgjjSqD9ck7FMtOTNfff2fc91Kqe0HuV7HThpQCtXgL9Z4tnY5pR7eR7PQZzFmCsc39Fbez7r/EodQecSDzKs0ke7OTpS6Dj7zRRSlBRShU5X42aRrY8RTT4rd/5fe/iN0cY9J4kYJRyNjksXNsrJIDNuweGtDalugjJjWgiZVaO9GnIWPcISdmtXapkAdUh3xi6ksIOOYt5wrrmwBP04y0iLhovgNdRiyh8ztEwbEo7RSPGVw36rkvRsUk7litzly4vq4fQuSzjDhJhbhni2GM9/Z0/z38Gu6rCNfhFj/IUBXUfo4CHQqYMU8rUSFrQnWfUN6nmD2k7pxXrZDs0EO5B33gKNPobMZM5bYRJqGLSl1F1uDlEQuv29Heecc7MRXCvpKoXnGw0lie4idi3hCOXBKa5AXtpCjuBTZoO6o2sj50XWy5UkooBUlIKIAHO3V6yn3lMWWSJ/S8fKS5JSddBCvFgir+Jc4lOCna+XjkXVwNxobC29WmYYNdwzsSUCJIaMA3SqopSFGuIlxgglMZwLI7oDoj3LDPMVJHrzyi21oAMbrkf5YAiNuY+2ClDeRf510FV5dLFefsI8Iacs79rFxZ/NoqN+KrqqYdU5gESnB7lfTOklXduQR3SRM3mNtHC9O+J1eW8jH63itAc3KOaZHmTpdLylQ6BPZyWUI/RBn7I7BNiisQaG1A/9jtItDxNjkrSra29uXgwD3+32Vte9vas8YK+n9VzCmEFpGCSpfiOHRP5Jd8RGH0FbIQ5lWOs11gUNh95K2ojnogyK81mIMZrQ7Yy7FUBm2VCq838olWK8cDy83qnqdYOnMXEMBoPBYDAYDAaDwWAwGG4A7COOwWAwGAwGg8FgMBgMBsMNwKvJqYrczWdXshRwgEB5jOHCUOaQ6GB3+ww74AfX0NYoa4jhbATGvQtBjyuwa/jSwSGrpv+vdxhoUJ9Adzs/lXRofKzyrW9/uy4nt+Q+0eB7k9vXcBP5i4FyjTSE7iYBqGx0u+h0t+ryyalo9TO4Wc2HopmTzvrNr3+rLu/vSWZFSrIH/nfpQaJxRdEDzX0COvt0JFprlOi5en3R/+JQVOLdLd0/T/Xs48mFjoNiPB6R8qdiq93C+ZJlwZxio25GfwLVpUNKmSiP4cOyn2PclCGlMHCPwW180uzLa+7FtrtGNnG1az5dcho7yFM2gL5DiqkrQFGmpAj1/P+392ZNkhxZdqaq7b7HmhG5YC8AtXQ3ZSgy0sLhIuT/pQhf52lkyIfhCDnNbrLZXQsSQCZyjYzdd1t1Htzc7meEB5GB9KhilNzzUloOT3dzU9Wr6h7n05PDUl3y3oSsKbBdY1xZICU+xuD62R+ETW6QX9uDYzBdCRLA6MBnMXGwmLJNJMYVrHnxxrYHJCeAhZx2Y89nClFtz2fEFd4/x71MMSZmldj655ggV3N5HCEAJkZd6EXEy2jr/1+losp8YZJXEBCbkfvJpLgQ9zlEXzCRio+3ESpgZsQAGWbERAwmP21IDWGQELGtflfSfDJgrjnaBTKSaH/mHK1o80a7xJhN0Net9BdYowd1Sg1xjw+Wq0yQrdaU5VLWhJA1Efc1Q9IdKFzz0WNZW/oj2bdYxq0wTjAkboYaSuTphvZ6/EcV+p51k6SqNNvoKe4htmumtNKfuZX9Wog6+wApeiMkSPDm2wAAIABJREFUG46xHgeoNSHqeLzNvoOcq0xe4/rcz4WoJ69/eNW0W6laPj7zUj7zsCtruyPmSxQAKSc+akCC/TDRYT/c0DmYqpw3EeY5e5KoInfyaSb3v8ikvlZGsIDuUFA/onZMqCx4XAEujkmc66MR3BajV50xZl22urge10I/mY4rH77EoJ8DPSsxFhZI2yNz2O1LnesMd5v2Xl/uVQAWI53KXnMyXqF7yxxHRaB/OjFSAEsZT0lX5lDxdty0x6nUoB7WiADpgKz5UYIEn6U8p2olUkF/BJrKWkELuVKHxKYTGdsRETlD9AgJlthzxzH3NKh7GIs++tcHuhxgTgVAXb368dbeAvOD2zHuc4necx01TOIlWsx0KtSgvNxc63kcyaAv38N87tnWK+Z7JCW/r0I/MMd14uIeEKoOj55gkhTW9fbxKED8gCeFwMSWGLdTHFuS5YK2LrA2p0uZL67AmtOwU0AbSbajfyzqhW2lEwJJxW8LTHAlz9XCj1v3/4bvYFusl+rEUalUKpVKpVKpVCqVSqW6B9IfcVQqlUqlUqlUKpVKpVKp7oFuhVNVlTNZbTOl3cwnboHnh8SdYBmbjcUGZXEJOHja+EhwoJ3bh52cfuEKp4Z7Pq1Kq/ctkVLjcC3zudiwnr1+3rS/eybtPBYb2dHLH5r2zqefyeegbY83gSdoMwkCT9mm/e2n5IwzVY2tVEAfCpwGX+I0+DDqoS020LOLl02757OviMeJPe7FD9/Ie8Hi++jhk6Y9GEg/Gyv9fHW1sqp+//zb5rHzs5OmHQGTiYH2PTh62LSPjo6atofPPbkWq57D566YfAQvXgoUhHhO2IUlE9ZZJktsU9baBk8KPFpmeTI67McYhwhcMTOkA12fSlKHB7v3CP1iB2JnJfIYesSygOP5P7YZWp7MjwvjncphVZ/OxEI5QVKcg82RyT4+kh0ypr/EMjb6AxnXAyQheecyNj2mADQeTIJmHybfs6bfWd23DsZSgnvG4VPByg+3ZxtPQ58jjMgsUP9YQpkU0TWwGBNbJebTXf2DEukyDlbZbC7vc3kl/XY1k5ofIoFoB7Z+Jqyc43WyCljbe9XKPy5m5YwxRT0+WolBTDdj0gvt28B1fLRp926hrn6wsd1O3iDGFW58ToPR4HIj2n0xRzuoqXGGlB8mq/A90SZCggBJU2LPwNQGJvjwXhKd7tSW+vcbC++ryphytV5Nx5JqFMbyWS6nSNhExNHuQMbzZx8dN+0e0iu5JphWChWs+q26iDpDrIXu7PphIqNctzgPHBNxMM5CvLaPeVx4QNJh5Sd6w0S9wx1ZI15OZE0NUMsSprIFLU50a6qqymR1alCCJK2Tt7JfmGD/+ehAsJnxybumzb1rhHQ4ztHQARVm+hzGOdFJJrjxGIGqRgoiosdEQjAPLO8b+pqokSsEd6oy6YtlhhoBvC8E0rLA/iZEole6JHYgl7BOl91uxXWmqOeFD6wzJI6G8eyAE05SubgZ7vf1Qu7JbIE0LuwbYrlVpocUqP2h/NvdLhMEMUb29lf/W8lzY3wPKcEqzrEWlzOpEYNK2n4q49X48vzYR1IO1hHigty/F8DIWgQHx4u7w/WySS8jZiz92OkAy8H9bAUsob51OrJvC8LNR3GUre+jm8ct04qYcrxOS3JIiuPdcS0MCvUS9TrD4zke95gQKy/fWsdaKY+49gWx85TjV+7BGsXaJqoT+L45GK7wrT72ASFrFZPxUBMrwGc5kOl0hrmYyVElV2MZ2+n3grxeXgFbnMiRFwUShxMcR7BTY8y7u4Kd7fZkn8nvLQWua7aQuXJ+Kcd1XKfSJ90dJPi29ktNs4UwtnRTUifr+8+Yi+rEUalUKpVKpVKpVCqVSqW6B9IfcVQqlUqlUqlUKpVKpVKp7oFul07lnEnTlbUvDplCReuv2dyGxWt8KXbldCFWwRkSgWg/tLD5x0gZ6tFah5+jOrF8rLVVO4Wd0sAKfXp+2rTPL+S6RjuSbFR2xIo1nYgV18D65iL682B5pbWN7T8iQkVZY42tbW4VjukmquGQTDEcid34/Ew++zskdn18vNe0A6Bnhw/2m/ZoINa2IpU+vwYes7cnaV8hTgGfTle2ud0d+e/DPqynuJWdjtjdhiO5rp2h9Kfv0T6JfnMy/piKsJiLtY78QaubmTgTC3Zm77Cf16/NxJ4QyUZMUOGJ/ZfX0o9FCQsjrIU5eB0iDkSrjh9IEsvxIVJZgMiEPakTZZ1y5HgiPeblOcbU2xOxE19eyxjh/WdqyKAr/R4g/YUISQ+Y1R5s9A8fCQLxhwsZA7MZPK930I2B75vDvdW4HOHahj1aNuX5tMkyAKECVsq0owhziLbeGVIAJhdyz31YZHu4t8ORzN0kWdXfCuODyR/TVGy/S6QQ9oBQhcCAArwOsRouTBE+04d0wx+n4gIzbiG2QJ9o8WaCCvDAJSz/QQXbMKzfIAFaSF0rzA113WB+e3VfM82IVm5yO0RLiDX5reS5zSlOjhgz/q1lzofb2DQFxkaGmmG9W21b3k/OmbLGbGdI39nDmrO8kDqU4XP1E1mLqpw4oWBICdI8ugnqExAC529gpYxp/anNbUifKuHNL1kZWq7uzWOxlaNBXBP3OwOXSSSO60IX85spH0RgiJeEwd3MRmuNCeoJMLkS6/3T3/+uaX/22RdNm/2VL6XdSp5EfQ2A8RAnvERC55D3AsO8QCpLFMk9XSOPAev7Qupoiv0Y+dc55jNRrAzpVAb1mEjDxMq1D/dlLWSiV4V6wH1VCeQqrhFlYpMfKucEA6qAtSRA7InmLdBvc4zVCzxu8H0lHMpepcAMOJkgqfSlHKdgUQ9GwH+OH8i6+Pjxqk4cH8r+KACydj2Wfnj6Ur5zvDuXvrq8lHlzMZZrXxRMNZI+jBNZo+MOFgPu6YDLVy1U44aE3i2iOC2hnnGP2ukwEVOeztS8FOPQwx7dXsk+NsB4YC3qYR4zKdXZm8br6vUr7F1y1Ig5UusWmP8zzO2stXe2G9v83hEDV7dIVm7t/VCPU+CtcQfJeU2fbjH5yLNNIlgMfC9GH1ocBVAQN2Ot5L4IqWIhUFUvlHaFMx9S3M+XrwSzur4U/NVVMi7WWN7Roazdv/7yy6a9j++Zk7n8u++fv23av38tczTYk3rRHcmxHPaGRGquf60xx42RueF3gZ/Rd+rEUalUKpVKpVKpVCqVSqW6B9IfcVQqlUqlUqlUKpVKpVKp7oFu7UtepzB5Hq3RsPMjDYO4znIqSEqaj9EW61E0EAyjPxSronNiJVsAv5rPxN4ctZAPnPjeX+E9FpbUK9hsiVl98smnTbuLtIUpTtw+ODho2q0ciPewk7bQmj8VTmU9k9Q2zBA2rm5H7k+cMMVEhsj5ueBm1Q30mPEYfSO2uQRJHabD08HlPkSwBu7uisW3W4+L/QeCvYwvJUlpfCVISIxT+qNIxlOWAltAOllZIJlhKXbaijif479FagSschFObu905bNyPmxT1kjyBa36va5YEmcWdmLMxQgcxqPHj5t2AAxsvpQ5d3Et8+XdudzrkzeCPF3sy7z46hefN+3dY3nc1lbkElbSd6/lNZ5+J8lv1zOxH+/sC5Z3dEykTq4XzmWTePJZF7C5MsUrQSLH3p6MtX5PPt8sxxwt61S+Lc5b3/caDKwfYh6gtnp0V+K9ia94GGNM86A12mul+wDXQt3KgfA4WJhzWIiDJm1CNIfd/AroYQY8o+fJuCRmO0P9pds7B2YU9WUt4Fy07+E8tS2r6vaSxVrvYayJ6j7w0UesDwHT7sxmW3UGdGAOC30FRGBOzCLdnCSzAwS210O6YGvMrHEqUQ5MwiDBb7qQ9XoxlQiXCuiOj9WQt7xVa7FoWCDP5K+I6DDlKs0xTqrtW/6dc6as739VoN9wPVwfCif3qteT8TnD/uT3z2H9Ppe9Sow19ehQattHj8WqPewDMwBqackc1HPBw/wrYP2fwio+mwNhXQJTWxIZBdqIkbFAmkheyPgrsS9qWcvZzy1cEpfeAkK3KOdMVSfnvXguSAyvadiTdePNs++bdooURKKHLkRS11z2HedY/64uZd347Mmjpp0MgPliYoywvx3V+wUL3GI+lTk3AcKfYsIyebGDaNeLs9fynkhf5fwLOkyZkz7t7cl4THP5t4MdQdNLoOzzNdpxI55yezlXmXK5uqYIiZKPgX1hSpglkTGUBiLe+4cyt4JE9oUz1LzTC+nbZV/e18ecSjziTMCQ6uHMYyAmE5lz//hb2dv84zNJdp2lMp/jQL5zZCn3bsDXWug89tfAklJ8LyLSa3Dshb0Bp9q2mj2I5d5L9ijcr3IvwhI/n8k4vOB4dvJ9xEf92cd3t8dHsl8c9eTzJ27zXmCdFpjNN8+/00vZC59eyV5nXsq1DzFX9jGfAvadI2ZKBF7qMZOTIvzbxUzetztgAiK+e21L1pqgRjhDpP0xtdHhLJM8l/V+icm4xNgjDZ0BSTt7K/Pi1clZ0z5FauD4Qh4PjMyv4Ujma1TvLQqwv5VjCraMlacvZI3+m//2W3k+EOmvv0Lt8Ph9jqmQN+1J7MbmTfo5M1GdOCqVSqVSqVQqlUqlUqlU90D6I45KpVKpVCqVSqVSqVQq1T3QrXAqa21jcQ5xonaCU9IDJKJksOQWsFXTKhiH8m8P9sX+eAArJLGAKbCsi1OxPxJzCYHxxNHaZiXvWRa0KiI161qs4r97/qxpn0zFwvevHn/UtL+4wdbtbvh/douW058ra63xa/seHHomjoFewDI6n4uVOsWJ4RGsex6SJi4nYm39z3/z9027g+cPemJ3/PXXfymvCSSpB5tgUJ+E7mANZR9eXspJ4jlOcfdgsQxwgjpt43xNR+QDtjnP0uYplusIp6nDRWuGfUnC6nfls25T1koiW4zP1uVp/LANdnBi/2go9zYHqjFeim10BiviEjbXzoFYRQXUaKN5i7nco+4SNsd6LvLkeVp8j2B53j+S0jSFRfObH8QqvlxILYgwBp8cCcJ1eCQI3jBBKlKJ1AtgGx3M1xGt0/UteL1N57FzxtQpHD7qJoNbAp56D2yqxO/vBZCcfCnzr0JNtEDo5ikTWeQ+hMS1YA8lZtOtU6uYiDZHTc6Y8IIkswrv/+Zc6jbjbmIkUrSD/FrxPPK42fjwj/7f5n+xPVlrTVz3X4Sx1EqDwvMDJjXgUrtIaugDLaVtPkKyA23wV1dioZ9cMfEPCX0jqUthfZ0lbOVZKmNhMZe18OJaLMyFh1SNAVAtXDuXxQzYERPyAiCgGdAaphLSvl0BDcrz9Eev98GqnKlqnDDBOhAAE0uXsg9wqA0LzL9/+E4QntcXYgO/GANVBOL5YCD9/FdfftK0/+LLT5s2kYAuUMg1VjZD8s0PL8Ue/sMbWRffXkp/niI1K8c93NmTdeHxI9l/fQqcNfGlz+dAIeOEWASTRSngHO/DQv4MlWVhrq9XmMU1EKdPPvqYT2qaE6DYBnXRWpln+bWsZ1djwSlOXmEtSoEeAfO9fPFt0+4iWerTjz9t2uHhqjqEQOhmGGvf/vBMHgdm2dmTtS0Go/D0D4KIJUAAQf+YLq4xBY50gJrV3ZN1lAhjAkRzWq83W+3NypmqXpcOPpJxOAQyZJzcHw+pbvtDQa729qX2uVzm3NXJi6b96lTmyMmpzNcB6uzRjqBvB/js3Z6saZ1639XBGnaOOTe+kL3Vg/2HTbv05PUsNpFhKGvnDPucf3gu4+8aX99aqVKo6ZyLnI1MubuzRCq8pYe1qot+jJBy1Ar7wfXxeIJRX+7XxUTqz3rOG2OMQcpYB6mlHKW8L0z2Wif0FViTLPY6MVI7PSKqE6nvy0vp6xIJTUcYjwnWyyyX+Uf8jXg1U0cXSKeaIN13gKM+tiVrJFUrQJJihO8TxLgc9uoX76S2vjiReXY6lvE8vpI5MgW228F3qCUQunOkqY6QfPuwK599v8YuD7FuHR/LnCPa9Q/fPG3aZ0DFP338WdPu92Uusn8c1rCyhedt3pm2kGMeC1B9GOb/p/9VQaVSqVQqlUqlUqlUKpVK9ZO6lRPHOdf85TbAQUEVf+IPmQEvv1r18NdEY+SX0hjPz/CXnec4cO56PMHjz+Rl8APWr77+qmlHuJ7ezuqXWB+H+fVwMJu18mvnqCd/+bvCr507Fr+441ezAgd3+o6n+/IAsZt+ZcPvZ62/SrkfP2eLf0B2zpmsPmzQh5vFxjy8Vx4/P5Nfeiv8BasLVwMPo+Rhl/zoKf7i3w3kF+xuj4cfy33m+Y3r83kTHILmjPxS672Vf/eHb37XtHfO3jbtAX5NLfBL7Bi/Cg/g/nl0LAf+xrg3S7gS+JcTuppmcCb0B3Kd25ZXO4qc4a/3OGwL959OqCiUv068xV9lf4dDhpOO/PWJTqvZWP7i8XBHDgjceSS/dBf8wRnXm9QOuTyU/o9QIzr4a9+Lc/mLEw8W/xa/6E8mcp95EO+bU/m3/3Igf5nb7cv4sYYHPeNgaB+uAPwR53F9WN3vXuMvtx+oqqrMsj60z4ZyL0P82h/BfWMjuaDCyjUXOBxugYOFUzhYLuAmfPq9HCD35ZMnTfsIh8MlXbmGHH/FLOv7xoP2jJWBdoCDWn0csPsKf536+x/k/Y9GOOAT/dDHwZKGrr/WQeGsoXgY9dTyLyTubv5m4VnPxPVg4SH/PEy8RJuH4NP9xL/ynNFZg8NyDVx3PMxxRBcj1tosl7pbYb1K4tVfNDMcYJzhgNMlDrNmfYHpxCzxlzMff4Gjm6bA4YYODsgCh3XyL1pcRX2O8Upev1rX4A/8CxZVuspMaycOD230cXhiO5hA/iL7CjXp+Ss40fCX1/3Pv2jaZ6izFziw+RmcxUdw39Ch3MPCWJpVf81w8PQEh/NWgdS706XUxLf4HNdYzybfyp6r+1zm6NcP5f3/+je/bNqP9uQaH86lfwaYxwHWDroNB3DubFNFUZiz+vDLwUhq6t6BXOvkUtawAA5xuskciv9zuJtevxO3hgenWBeHmS7x2a5eSj324ZK9Rl8f1AdjO+ypKsztFDVlhpruwwF/gr96v72UWu/jr/wJDuj9xODQ8I48/+pExmYQ4i/s+KwMHenXBw/7W3SZW2NMUP+luoPEAt+TuUj3SA+173AHARpw275+J/f7Ff76f3Yt/XN9Jffq8Vey/+t34dQo5RpMJf21dguEEQIBUHtx+8wRwiS+eS1j8eVLmX/7AxxsHnJfLJ87nXH/A1dAufkwcR/rNA93dXd04H99Bav3hoOzD8dZQKMMDsjlFZEAeHMpe/qz6yWeJZ//zYn09QzfO778TPY6HuZa5OD4rPssy7k+yX/nwb02kL39Ipf3/PapuDsCI89/uC/P/8Un4gwcPpDagbPNTYGxFqKWV+jUGVzvnWXtUtriumiMHILNvU0AEifuijuqh0PjPTh/czxnyQP/4WD67HPpn3/zr/9N0z5BTfp3/+7fNu0BDo3/5GNxzhwdrN73GKEqnz35tGlfgBbx4Sg6/gKv8bG4YgPsOblX4XbSIoiDlZA0RFtwhX3gweLqxFGpVCqVSqVSqVQqlUqlugfSH3FUKpVKpVKpVCqVSqVSqe6BboVTGWcan5ureEgdsBIc7NjFAaqAbEwO+/98Brt3IZbfDuxXO7BvvovFQjUciF326IFYfunsTGs7aQU7exeHXXUTHNKWij3x4b5YsR4AY+DBpw4HQfJ+tA8Z48HG5k8va01QoxBVDtslhgIPib66Flt/4Mv99nGAWwSv6P6O2JYfH4ltNCiAEHhixXuAfks6POxL7u3aeha3LNhyjYcP5IC/Fz/IoXW//0bsqWXBQ5nldR4+FAzo+KFc7w7s1wEwgA4OHDVO7Jw8eGwKm+sPxP+2KGvlcFALnG0CnKYsaMcUtKWbSH+dsn9hYuXhwB5srlewN/dhm09hy8+BSFWYF36NkeTwjDpMikUKPAO4zsfolwz9Pr4WjCDB83d7Ml8PdqV2xDhQL5vC6ozP8dGh3KdeKOPqk6PVOP0//1GsoB8sZ5pTYFuHgfKndeAzET5XgMNXx5fShxYHXPZQt+KhjNuylNc8PhD8aSjT0nQSuc+TqViC1weKL4ESVrCY9nCAcYhr2YPl/Zc81BOYzBAHDPoYoyXmHMcF6+lNrlU+XN1VAbZy+F+Bw1GLzuZ5YIBTgWYyk6lYo9+eyWHCOQ7q7wylBneBpywwL4Y4fNNDnZ4BHenWDzv0YwmcMcDBnpjyZo6DUlO8XrWUWtPB84sFrMtAa4ICiBwGPIe+5aGAWEfXh2rbLfZnWZbmarbCLEY8kJsHXOLiBlirHu2JbbyLgy9Hx1I/Hh7Ioe0vffQnauXxrvzbBGuUh7XFB1q4PnC8Qo0Y4lpKYKtfwY7/r78SJOofvv+uaf/2979t2hXq+cmp2Nn/8Q9N0/T+4tdNewBUdQfYc4R60MVuswdEZJtyzpi83tc8eCBrOzmFJfZ5pEKHQLsvsYa/eyH7iBn2rjbmfkUe7+OIAN/I59zfAWrKfeQa+6lkvzzsSPsYe5HZidSFBdDmd68FM0knso/uhvK593AQ+Q5w1Qj1OxvLmrqYSDtHPwbAHDs10tU6tPNDhXoaYWyjbLYOcu5hPx8R7SdVApR0fyT39hdfCOY4m+GwZOydXCaPl5U8PsVh5b16fbWV9H0vlvsdA8mIEZqx05NrOQei0gcq9+odEPKp7JGQOWJKpiHw8NXWNy/RT0MeHy5njHH1AufjIPhOxBAHHDCMeePA7V4AV724lHnZwyHWHRz4frqU54T4RER1Cxwuny9lHStqXCoDBhy0znaQQchav7sjr7dzKX3aj4GIE5cEKuVh382AjuCGPWGM/eoSh5LPpqt9INHtD5UzxhT198EC+4MK+BCDhHb3pVZ9/KV8h/ir/03G9vVY5tPf/e3fNu3vvpFD4P/ub/5T0+ZnHGEf+9WXnzftf/Yv/o+mndQ1Iwjlpo32pPYO9mX8/fKrr5v2Jfp8H88nRlZxhrTCNzYfFO7c7dC2nzP/1ImjUqlUKpVKpVKpVCqVSnUPpD/iqFQqlUqlUqlUKpVKpVLdA90+napOKqHFKIOFvELyT7cj1u8OrJxxIo8Tp/JhT41ht82ALYW0XsNuNkQSQYVT2BeLlf3btqxg8t89Q2u7WNx8pN0kdP4i5aVAOk68j+e4m0xRt7Scuv/hf7ch55r0KTjtW5bBEjb5MU7vH41gmd4BYgZMhVZ+2oA7vvzbADjV3i5OZscFFeiLtTuUKVghrJf7+2JV/+u//pdN+8svJbGBr5fgRPK9PaQXIU2EaU5FJghBhOSXdCljgSk4SSj3cjyVJIRtynq+STqrex2Ecm+nSCEpM/HbekgH6MRyH7/Gif0HSEQJPCQIYAJ8dCh2/QSYVcTUNiJdSEpYI4e4FNODDTYC/tODxdVhbP7m44/ktVEXerCtEqHqM9HMymumMNcS1XhyLOPhAPb3bneFWATB7QjU/5msNSasaxh/Ta8wlkraMfGkDuzwKLkmhrXVAIssrHzeLx/JPYxhc44M0NYFxjwKULR+PuzOAZCcJdCuANhWnMm9PwIW6WBPP9iTfhsjHcIAXfFbtn0WRnvL9vbkWWui9bhg3yHJgvOAyHG/I/UywTw+fiLpFUGE5/TFThyiXjokiHE8E1FcIv1xUScxWVxXupD+T5DUM2SCGJILC2AYJSzhOWptDkSgwL8tyb+1Bj/SP7AO+WQj7gCLc86Zoh5ztPsTpejgQgcj6ZN/+rkkWTx6+Ej+Lfo5AoLy+KtfNG0meEZIxot8zDnUPx91y6vbPlCX/lCuMQbXdgj80kc9/xrY7KAUtCQk8ookseN9GQvEUuYYW4dDuTdMOLJOxkWFft6mrOeZTj2nQqwny6VY8i3G4QB7gT6TkDA+/9lvJPl0DlJhugRCjMf3gOMlQ7SRVON7SEGsOSEP/dwHzvgA1n6DcTRFTY2QfBMfyT6mG2PMonZ2iSlhHU+BnbM2GNSXJRI9/RoNctX2NqmetSap0ROmV1qsVQExY+y3HGpGgcSm/ZH0Q4jvGbv78vi0K/fnAilkBcYtE54SpNj1u6t2gP9ugbL1E7net8+FSTx4KIk4nS9kL7acyz7uBOMmAwZUlPK5vdZ8NfJ89Ke76XvJNr9fQNa4JgW0g31zF/WKdTEBZsos4y8/lrVw9BeyP8uYlAjk5uuP5IiGDmpdAHYyJCKTYZ9Ut6sStQDjax/fF0IgxET1H3H+AekZ4DkYvmaBFLUS6Dr31zm4a+L/C3zuRY0DV9vEqSpnFvWYi7HnyzHfC+wzOfaCDWuVMcaMuvJd/Z/8+lfyOPrqxQ/PmvbVldTZ//2f/mXT/iX+7ZPPZIx49T4vx+8SOfCvGO/zl7/+TdP+9o3M+RK1snU3ifC7G9AqPv0GzIq6KZv6faVOHJVKpVKpVCqVSqVSqVSqeyD9EUelUqlUKpVKpVKpVCqV6h7odjiVcaaorVoBjD9ZIbalNIM1DEiSgz016YuFMerSBsgT+2FFz3DKdB/pHEhrSWCRmgMFKOpT6WPYuplyRJyL1qchrG99pGA5WNWKyQyPb06n2uah/dtQWZVmOlkhD8O+2EF9WHnzVGyCGSy7k6mkFTz5SBAmJjNZ3AeePN+yhOOk/j76k0Y022qvxDHB6IEuTvL3R0gpGhHVgi0XVkYma0WwVhM1o508hhV3OQdO0EIniOvdkVfVGVNWdcoY7IxLWIgNrLQBMMQEFs8e7v8I1m96CC3ukYd7FMEqXyKF5joVBKGCJXuN6Vl4SXs7eE+kNvTwmTK0yQ556Bd+pgidF8IKzdP1Wx5JjKsebM+dgdSXefXjf/ahskaQmAJ2dfabQQ0tgca4jjzeR7+lQAg4RyzwGA/zzzIlIZf7tpgiVQhz164tvhhnPsbWBLXXMNEE2Fw/lvnqwXLeSt45lffUe4BxAAAgAElEQVTfxRj1gxZ7gzazJ6g/Dk7VresC0yU8wzae7+T/dXAvhj3pxxgpjImhJRz1CvPSRqwBWI+BHHrEkOq5UGKtqtBmn/dhf+4Ag8kwdogALirpuxy++BwJhXMgLSnwKybqRYijCTB1q/rxbaZTGWNMUdcWYhsZ9zDozwT4wgHqxKcPBUn1gMTkxEqJcjJVxso9sVhPLDAApvmVtb2diHmIPVFeyeNcF9KlII8HiIw6+ERQMN77ENjIABinBeZxiQF+gAQRhxShDNilC+5mLvq+b/o1OsPUxuW1JDmZmdSoHnCOPsb8DtDxElhaBrR6mQGtRskOkBrHEpUtsR6HqNM1Xl6hSvjYrw6558X+Ywq0PwU27CGJiVhcjM8Xoo46YJFzpJJNMfYrYDz8eHm+WpNuRHV+hqw1Jlyv53YzbMDkuooIFeZoDDx10BcMMCLigP6MS2nvJJhnFhgM6m/SRxJjsrpeYoI+NpqHe4Ievjt93rTzsSS/7XXl9SbY5+z00W+YaFWK+4EjCrKKaVpMytnMfLi72qMaY/y6/4j+xkjPSrDnC7kuouY9OhI8agd7HaLmZSX9y7Uow3gmTpz4cr8camO5vqet6Eska+Eogk6PCYKYFKjHPq6FSVkl9qUWdWGG+Rrhy2MBXtPHup/gGIH1XLz5OI/bq6wqM52vxlOEPWScyL2MsPefA7XsdmXM94asT3J9A+wLP/9YUmj3MLcWSMSM8N39EOhyvy/v5dffS9KZXBfnCr8GPDmSBMMJ8LjzmdzvBVAsZ7nn/PF3VGPa8+ymPcqN9fJnLIvqxFGpVCqVSqVSqVQqlUqlugfSH3FUKpVKpVKpVCqVSqVSqe6BbplOJRa2glgR7JspTssej8W2y+QpE4ndNurAWs/DnmEnxkH+pgvL/S7STBx8q0ytSetUghyfNApgCUYiRAKrVgcWYh/W4hlfewZsBNZTH7hIKz+F1lB3N3bin9LqxPjVvQpg3aPri6f6M23MoZ/hVG4hMyksixnaXUsrnvRhD+ksvD0W98etX9+JNdIBOfFhG4+BdnmwNVp8VnSnieAN9mDzJDZnYKcLYXNuWaVxEjqRm2qLqQ1UWVXmarIa24VlAg0Sm9BJCdK8iFYRi+S9ixO5LxVOw3ewchbogwUwGo6Tlu25xnscOwADL2TiBGyiCcYgcT2ETRlSNqA2TIVBVaX4B5ivAfpoiCQQP5E68Z//2++NMcYsMf8/VJ7vmd5gVf8C3DNXMHYBqEkATCWS2pPA+t8bbcYTac8nfodwKOMqH4/DSg88J6h/90fZbM3FsmKCASz4Vt5/ABsya+V8Lp9pgeSXh1wj7GYLa2vtMDepuvG/fIistSao7dm7uP8hBigt25Z4IPqa95SITIK5SCs1EVFiu/NcxkmJVIv2+lPW/w73k+mAtPtivsZAVz0kiFW4t8SjLOpR4IARkYwtNmOyg66813Ioa/0l1pVtyTlj1iWf632O+8NSzo3TsCu1dQCsxQcS54AeOeCMTPVqUZR4HEOhhW2v75uHfvWAKbHm97G2ZYxSIv7MeuG45snreEjNyvE6gAlMD+tFhfclrlnd0Vz0PM90u6v7PoedfnohSZW9ObCKAPsSYItMyimxuIQVP7OM7Qj12/jEJrAHsUCoMAaKOsXP5cAZkSrFBB+m2nSHUmuKDlBUHGlgKu7Z5LMy4SYrkP5KNBv7W2KXgx6SYJs92Rb701rj1zhNeEMaJMsT96g+0qx6SEodDKR+JCiWFZKBpsAZY6wi/gA1N+DclXu7rGsu984RcOKdHUk1Oj4U5OQSmN/RQLChEHVkjGSt7gkQ1gm+iwB3Y3JQa/t54xp5U+Ljh8kaSf9NsP9I0KdBKx0YcwvfERIg14GRPuKa7+O6+X00a+1dRAmPAsAavD4ChN9vPPSzjzpOB4TjGoba2VpyiQPiP7B2hlhv4lY6FddjaXaR3DmeYDO3JVVVZabz1d4+wnfvEPXDx1EG8bU8Ttw7QL1LkFrt4/ot2GsfxyxUJZ6Djokxjy1qQLBG3oB/pUzGBH7ZB36805P9xhgI19JtRqhaHdH6Oo/ntxDGzROQPwX8HBJOnTgqlUqlUqlUKpVKpVKpVPdA+iOOSqVSqVQqlUqlUqlUKtU90K1wKs+zJqoTF5hkwWSVdCEWzOVk0rTdjiQFdXDydMAEE5xUnuGk+ACn6vdgM4wjpD/A2p3y1P7aEuo6SLVpISHyeAT7WgBLdcX0DKZDwJbVzkBh4oQ0mW6x5WCN95a11sQ1KkZ8pY1TiX3QA4Y0IiqAe0gLGNNFFhgLw0TscQOkkzmgSjlOaXdMIVofGE/kBPbRFrKERIAg2pxY08q3wdjNmQQEnCRbii275axDykgO22YILC9wd2Mbrypn5jVqM0d/EQPbG4qduAOEsaJ9FPfRh227xF0qOZyJKqGvHeyKPm2DFa2qdVIcrKzsO57kz4QNIlTsL1rFLWz7PucZbz+s4pYpOLiesCe26zkSDJ6+fmOMMWaZb8+y6nme6dYpeHYp1mjawyuiD2hXwFZLtC0TfZBYYYEn0XrKFIgc1n+3YDKJ/FtTjxGHecZ7bFsJPsAAMLdioIcmQz0dM+0PtmJgbTeU1nYiwB3Zw2+U9YxfoysJ8FBTCLbhEfdFPxZIAiwjjK0YYwBlLIC1vML4zzGPl0wxA+rqca7VNYBTNSQiZ5DqBoQwghW+A1wIRKuZL+X904mgA0zl8sE8ZnPsH1hH8V572D9cZWerht3i36CcYG4OtaFdK2GB7yDJEilUMZA0YquRhWkeiU20yafAOZatf7sBLTbGFHU6GNH2GDW8QzwVST1lAPyR04N7Hs4nYpboH67RhuOMax7rL2pAvkUstSXnmvp2ef6ueXh2fta0E1yTP5B1MUC9D3wZ2z7WQuvB8g8UJwJaxSSyJWKrmCwWetIfSY2a+hhfARbdCPti9jXxjAj4P9ucIyFwqhB1fzKX1NESc3cJvHU8k3m8uy9zMYxWe8KtplMZY2xz3Rj7RBCI0qBOJMAzen2pxaPdnaYdYFAu51ivStnf8rtFACzRYJ9xCkRvka1eMybCCISKfQLwxlz9l79r2mUq/TAayLU/MLJf3nkjfeJOpE9SYuuYf63jHMyfQqsr6CHtt4PvDmHrmAOM1YBzcfMRCcR4Ssw5iglh3g1zJMMxDeve4bqYRKjF/E7RQrtbDL+0WzQN+ghjkBhrxydGJvcsw6hJmX4MvCio29vs56oqzbzGqUIcPRGGQOLwKwJTYkOfx1MgTZhJcdh7c23zW1s4jGfuneayX5zac3nNuoZ5qJtkljxweD1gqKO+rAXhleypPX5vwOtUrE3o6HYlvIlhvKGYKU6lUqlUKpVKpVKpVCqVSvXnKf0RR6VSqVQqlUqlUqlUKpXqHuhWOJUxxni1hYhpP0QmiLmkwKnml5JU1emJtTHqySXw5OkQmJWlzQrPSWFFvzgTu+z1OznxPaytSn4o9thuHzZww1PK5Rppp2vZg3nyuN3cpvj4Ta6pP7bNcf1+ccRz0WEbr5C0BdvtaGcgj8M2ju4380zsnhP0/yePvmjaOyOxmbZwKlqPmR5U30Na8hZzJPXguUkH6AX6LYD9j+/JnihgD0+BJGRLpDRkwBzg4JxO5HpGN6SvbVPOSApWjvtClCjCifBs0/pc4kO0LKk+Exl4Ej2SR4BQ0WZvMH5yoDjrdsSkOmJ8JdOMNk8WH9baimMQJ9VHdPMzLYnYGd6rxL+dwMZ+PhOL/GSe/ej1PlS+55lhnaAxpo29hTPiH3Beol1ifLLIVGEXz4cNGbZlWvwz4KEL4IQBLqKo7cwRsNYKuKFFIlUHczFmfQE6lmHMLYBY+KFY2zle2rbVzVXU3lBRvbtCq6xnbJ2EUMLC6wxSlIDlEFdtFc9WPBH7Gsgv0zxQ34oM8xhrcInXDFp29dU8aq1zmAfLFiIt/dsheggsiBhfhBQqv9z8dyKLeUZkKUW9YKJZjKSJVnTIluSsM3kdg7ko5PMuUCcy3O8EeEDSkT63rEOwn8e4ZCYWZbmMedZfx8QpYFEVk49qWzixOqKYbSu/tGnTJ27TQmi5RqB/KqCQTPsjfsz1xcG6zs9X5IRKtqcyL8z1ycpa/+zZt83jIdDuvZ7gKXlAnI1JcehrjDcfWJpz0r95IXNheSX7SCLiAfegwC47NX7d2vNineskgh9cXcl4OTuVPe+gL/VyhCQmvg7X7gjj1APayETBjGmBQAynM9nX7dV7abvF2uqcMUW+uv9ZBqy3wrhtcbVot9Y2s7mNhKkwkX4IgPAwkYhHCjCdtsA+sttd7Wm5JjHd0mHeFhhnQQTEzsf4QyRvl8hHlymj8pkKfh1rkRpMRMLjLRSE2iIWZ40Ja7ym25XPEAFPCoLNCKePGsXvbvze0YqnAjZVtZL95D4yLSwCAmuJK9dLduuYBdRopqaGGEdMPI4jfL/kdxakKhYZUOtWEiASYrnW4qNmTANlnarrCNfiD1XlnElrFH/qC74XY21L0IcLXH/o8bgOrHnYnzD5kKi4c0CaS6Z8EvNHHzE1er7a91okDBrieUC+EtTE4wcHTfvNtbze5anUcx6hUbXOSnGbmjdTUzc8R3EqlUqlUqlUKpVKpVKpVKo/U+mPOCqVSqVSqVQqlUqlUqlU90C3w6mca9KHAlqaLS1OsOdPcXr0pSBOQYwED5zGHSG1KOSJ5LDBLRZ4zbnYu14/ey6XgySWdeJANgOuEADFgd1tMrmSx1vJDrDN0p7q33BSecvN/0dIR7mFrLWNZY+IUQGrMy2A+3uCPu3vCwaXgFlJp3Jv81wePz2TE8OD3/BkeLG5uRbCJP0Wez+2zDN1JUUfL5HOQ/topyu2Rg82TNv2lTYq8P45kI8Ctj2iGj7QCYtUkCVs5nG4Ge34cFkZaN7/8HitHJZQWtn5GZgI4AGdS/qCzkU9ptAAlVrKXCyAQdDlGuBer63+TEeCq9tkuOfGp60UdlqMWeMx5YqJTkiqg/U0JRoB/GuMOvXt95Jo8vSHt/KcGtFkstCHyvd9szNY3efppdSeJVPSYA2uMrk/QcE2sBY6jJGI46FN5I79RpurM7TYAwmobbFhInOYgBlTkjqwsCYYFHNY83NYZWe4lmAg/5Y433txqK17cFNqwPbknGsQuCXGtg+btGvV182+WtfCdvH4EklRldQ6okdXE0k2mWBsZPjUYYIaPFqtwa41P1Ffp3htpEDwJvaBM0cx7MpMFgN+wutt8Q1EarF+WCAIZFeL6g5qqnOmXM87piACL2G6SAd7EuI2AfE41N8p+nACW/0ca1cUMwWFKCwS5FDD1olQHskkzLMZMLgMdSuB9T+B9T8EV8DVN2/hscQ4pT9LJs4ReSDGjPXF3QESZ4wxWZqZl9+u9oKnb980j/dxTYuuYKaTXJChagG0A2iLwf6vxGWPx4IVTabSrhz3rkBaUTMdsAyvRk3CruyLPSA0UYL6PpdrmQB3ujiTpKTjg6OmvTOSJCm/j4tHF6U50XSpHUvM+wp1YjGT983qsdyqaR8oZ4wp60SYCuhhyXmPsepQS7iP5RrPPWLsy32OgDwFFRDhktgU9hPEBoGTzper6+U+a/pK+mSO5Kn5WL4LDXf2m/YA6a8l9jBMxWMKXC+RMZLiWqaY9++nu9mjWmsbdGrQl3vbwqmAtmTY91iPa770ddlaN1CLMP6IGy2wRoXAsvndJ8e65+rdDIkkHq1ggQ4ljGSssF+NiIIjYYpJhzm/oyLpqQzxOJJjA+yZUZqIG9q78GQ4mdus/Uvg/wvUpEXA6+dRKUCLWSti7ouYAkhEGGte6ztN60Y0zaLeJwddwUoDouILvEaK73Ox9FsXxwX4gdTBkmstj0oxm79TUu+xXf1ZUieOSqVSqVQqlUqlUqlUKtU9kP6Io1KpVCqVSqVSqVQqlUp1D3QrnKpyrklTiAKxicIR17IfV4aIiViSJldyqr4fyXMS2M1i4CklrFKTpdhWS6TjMEGoWMjjbyYrPGIOO/NwIIhQ2HL1ioVrf0+sWD2kWc2QuHKAk+Xp9CReQAu2dTewAC2f1d1mVVlrTFBb86wPm3SOVI1IsKnDA9hxjVhMLVJHmPoyB/Jx+vaHpv3k0R/kvWBX93DjCuBx/UTGV7e7uh4v3Gw8YyLAeCy21QDWPp9pS/RKwtpX8uRzWDUrBw8dkgpCoAJdWPe8kIlndwNxWGtMVFtCPXj8UthtL3EC/jTdnJQTMBGAIQ9o+/gPpAWyJe4LfOYBTqL3gfGsEQQf88zcYDGdwcrdieXxBGlfIRNrYPUs8s1W6CITmzETcc7Gcm/+7//vvzbtq6nUlF5tx6+IvHyoXGUqt7rWFPMmBqbXaaWbwQKMuZJgOBeop5aW7IhFWvqzBL6S+LA8A2G7ngrqlU5W/TKGTXlygeRBpj5wEMG+XAJtKArWIKBYsD77+FtDC1FqcavAj1rOVtju7fZQOKpyzixrm/35Qvql58v7HdBmjJqTw56fY+4SS5vjPi6QBJYCPyTmkRZcL2UM50AKelHdT5irxDPGM/kcwxgpK8BoJ9eotcCpiNP4sMUXBngPVkmmn+VL+axXTubl9Vgwx0Wxeq8tBsUZY2TlDVrINMaqQZ3lPgeoUoHUxLmHdB+gN2dzaTN+7sHeYdMOgN6EATAPvFe1WLUTJOyUWFsvLwXnsMBSOkjtGAFhZ5oc00fLVrKafL4C+y/WL2LRIeoBkwVD/272OUWRmbN3L4wxxnSBYXCdaWGbE9QNpGOaRJ6zAPK/wHyaAcONsBZxXZohFWuKBJU59sOd3qo27O6Ind+gVk0nMhdnSIYaDAV5vkLtfPH6hbwP5vRoX/YocUf6YjaXeTydSi1f4uiCsCvvlc7k8bS+H26L62JZVmZcr8nPgCR1kSQWPpDr6RMJxesUxKqXcg894LkgC00BhC5HDV2gzk6RNrZg0a3HOTHLSyCur0/k/QvU1oNd6XOHOc/vNgtgi5cLuRam/yxyrClM4cS6WN703cIS4938lJ8jz1rTqdf9USJzgph1ifXnciq15SHGc4U9HLE9XiqPxciA36SoV9dTHN1QAGlFf6zHTDeSvvCY+MpbiDVgNkP6GNLeOvjcfL7DSGUSYWsEY04RgW+3gb2v667b7neO9fRiSlSGGs96P8X61zpiBGOVtZ/frRzuG0s3U4mZTuWIZeFIl0VdIzs8luBKEMZX7wSzjbFf7nbkd4HzGdY/3O82yQ30u1V58J1/46P/M92+79SJo1KpVCqVSqVSqVQqlUp1D6Q/4qhUKpVKpVKpVCqVSqVS3QPdMp1KMIRWwk/rlGbaxOQpBS1usHVnwAJ8nDhfAdeZw85Ygd0aITnp8SefN+3xudj/d45Wr/ny1avmsctLWFI7YncbjgRdiJBgkMPuhgwGE3TECtlKqnofS5S76f/cMU5ljAlri3iMBIwY9sGdodhuGQZEex/TmCJYSJdz6edLJJL9P//xPzbt759/37S7sFt/9fHHTbv/5KOmndaJCYkn3tfhSK6xgn3w+lrswHNY+zwiPsSpaDeFXdDzaAWkfVH6udMRG3vLTAwLfrZN/IayVhI+4AJnSsgc3r8r2LqJvCUZUjI8sZiWDrZqpFClQApKJFlUSJKZAIXKjYyHIF5dz/UU8wl2bx/jKEA7BxaWAQuLaWHHZyXS2cKpgKLQipnBijxNgQZWqEf1w610hA9Umqbm+2+fGmOMOTuTOrg7lLp2/OhR094/lMfHp6+bdr6QPvHRDyVsqD7GIVFBWm891Lk+EFKLOASvTkqZw6ragQ08AUqYI8UkQJIGyx1Tc5jsNAJGdlMKYFs/XXPvqrI6Y5pZ8ebtSfP4YVc+8+EesFTcoxSpS5yXESz/KTDWqmCNkjoTx3K/Rki5ofV6AqTn1fOXqwb6nOl/Q6TT7QwEuSGimmFuXQHzsEwTw/vPMS9T7hOwyFyi1vxwdirXhvrVGazmRLVF27gzQtyUmfRJiMQMdJU5wb18fSX7DQtUoEBhXlwLFpLB+h2h3+YzqbkZ5miG18mYbDVfjQuLfmAij0MtQKiKmS1R22fYK+0gkTBGIhMSU9IcKZ+l1NMZUNUZrnEH44gJgkx52aaccw1OyBpPLI7XscAewQFb4TwrUJdYr3o96bsYNdALYL8Hdnp+JscIuFMkRNbXmSFViAFfORAuH+hLH8lKFnvhF9hff38u+95HTj7fYEdqxBjjbob3SrEfioDpESleZOt0qu3tc6z1TFhjKGdIbfyuwr49knVxD5+lxCQlWpgDsQ4WgjlZw+RNfOfAPuryWtbm8TXwVNSDqMb1Z0C4cq65QAkv5sDG8ys8R9a5GH9fv0CazvkEqUAYrxnr1C2Twm6PfLzn63q2wasTJkNxbQPa+Oat7GmOdnfRRpoxrtaifwt85grzNcT3xQXWkAXmGvHZdVoYH/OQlFRijDCRiCmiTE0uCuJveD7Q6VZ9AUbNNCjXQquI62Adrb9juy2ui57nmU79Hdm1kjSR+sp9C84nCdEmmhmh9lsmkjHJ0OdvCrgeHv/ABDPsC88vV+j15bffNY9djaVPXp4ITnXwQBDmB0fy/XMWyPhziex/PMzLzQCVMXc3ozZLnTgqlUqlUqlUKpVKpVKpVPdA+iOOSqVSqVQqlUqlUqlUKtU90K1wKs+zplMnVVja2oinODZpcUPaD9NjmCqF98phDyyQrNDbPWjaPuykuw+fNO3h3nHTXtvTOrCE5xks5DiZ3HpiZbMxrGA44dwF8ngE27rXsvwzBYUWNDzF/Gnk+Z7p1zZgnpzOdKoF7IAZUIou8KEEGFQBe3gMDGYwkP45vzhv2p0+UIkd6ZcIaFunJ4+P9lZ93gmQAAX7XF5Iv/UR8JACn1kugeEgSYU2xRJ2Y6JVrdPGgfzFMZETGaPTVMY07e/blDPGFPWAKnGSv0PaUAr75hjPYUpGL+Ax8DhhH3ZJixQGnhrvow920KcFML0C9uZpbWctEyk7/YHY7bsYjxlstrZlZ5fH2aehIU4FpIin37dSxuSDFPzcSHaoSiSn1djJNg/+d86ZRV2fEFJkJkjFurgWu/Wjj6XGDYZSB6c4eT8O5fojK31OttXRq4/HF8BplsAmfPRXkKz6doh0wgDlbgrMxMfQTyv5PxXG5RI3NAPy2OVE5vpyQyIVZW94fMuhDa2ryOoxmgKfMKgzFe5z1JM6GsJansFivSw2J3JYjNuQGDPmS4J51N3db9oHu4J0zZq+RkoUXo+JHHz/MVCNiut7JNeVob609gN4nQhI2RI1eIZ/u8Q+gfsN19yn7XWoNbaxZ+dIsgSR1EodfHst4/y33z+Ta2PaHjCo5UwQjs6+pD/2gad5qHOLsSAuZ8DKxlfyOqZ+fhfpVEmPayjwcFjVc8z/qzNJ/UpT+Uy9Htc2ICdA4qZIYhvDLp+imBU5Mc67x6mqqjKzGpWYTeX6jvdl7IdA3iIuf1gjU+BsDn/rHHbFfu/B8s9USOKnYV/6Yw9zN0MSz8Xbt8YYY86ByHaxF3p4fCTXO5C+zpD4YpDm2R/KZ50D73x3LnswGwMzwZxjys8FxuAQ+71dJLqk9Xq8TYTDesYE9XvsI73pybG0+325D1wSiHoVRFawR6yw/18QC0EK2eVM7skEOFNhuP9Dsm6NH/KoCJdLeziQ/pxMpX11BQz1rfTPXl+u5RQpaNcLolhMywPmc0NX3EwiE13ZIopjrIlqLIlHGPjAbBZA5Sf43nE5lTo3Wch4Nrj/AdDCFp6EvRSTUgf4vsb0zTCQsR3X2BeTL6sSODNSwzjWmIJJhJ9YXsR9V4E9ENZ64lTsLmJEbBNHqtZjfJs4le+Z/mBVW3iPW+tia0sGyAi1hCmkSxyPwnTgoML3Mtwrh7FDFC/AESBd7Kk+763qxEn3ZfPYcCJziJhzgN8QHNYFfqh2wrS5U9kbJ+nNUieOSqVSqVQqlUqlUqlUKtU9kP6Io1KpVCqVSqVSqVQqlUp1D3TrdCqxTcJ6DZuoh1O9fVifaD0l1pADPbHAkJwVa1PYE/sxT7Be0DeH6/HxXl6Ni/R7gm14A7FhCYhgzDwT++hkKda+3Inlz4vk1GobML2GJ4Zv9lx573VoNf3n24euAs8zoxpV4Gnpc6AUC9hHRyOxqcWw1EawstFixoQLC5vg82di6/VglTt+LFbh3QNBRBywpbXF06JfLdOm8FtkBzZkHyfTM91kDswqoK2bp9DDTt1CB/G+YShWeNrDp7CWL5DUtE0550yR/9g+aXFvmao2ha0TxJDJYCGHI9QETCJjSlMrtQtoIazIfkfuS4iUk7hGrpgUxhSGVmoYxv7kAskuwEZKfJAEdacCosAxQxs9varENlrJTUBNZDxsL4WjdMZc18lYV0isKDJ5jznqY4H+GRyKrf8aOFWBlJicqIDP68Z/oPsV89WDzZX95Rare+6YfIVEL+IkTC0gskarcokEhtCTmkKckugbEyHa1m/WzRvS/u7IC7tKxFnNo8MjQXn7sNsviBNjnPdgA7awx9NWGyHZo8LjU6DIIdZO9lcYIFmsI/d3dLCyqFuMtSUSHApYnpfoxynGF2veAJ+j05XrLVrpfPL+C6TJXeC9nC9z98HR46Zd4jnXk9W/dVteH9ejbEnctAP7Nj7vZC6f69VbSR3qom+XsOEXSNv8ckfWPN+JnZspjyESjjpMJOoJUrJGePyIiSDAs7A/AdVkCtRBryPz7N3J26btTgXj7AOtiiP5HDPsGa6BfJRYuz2ukcArtxhm1FLlnJnXa8QyJR7dyjhpWgGuj5FQFkiwAc6fYomqkLjqMT2G8w81oDOSeZEcYj9a/9sC++IE86DflXaA9awwRIc24999pKwu5oLucB11uN4K7TnQ5QHujY9xZe+AUa3KyswmK5wmPJCxt78n3wN6XXx1IbaB/UyOuZhj35J5wAmWdnUAABJbSURBVECZnodJkmO8hKi/Hcw19tEaRfUw5qrWdyS53r2hfI5sAXwba+F0LHX2aiJ1/npJhArYeOvYhs3Jwa1kJ3P3ssaYsK6qEeZZCCx1PpO6wRRYC+Rqjj5NUH+YzEQMfjnHegIkPvJlLJVINuoAzevFq3mZOXnPGbD9inWETJFPtIoJzewjPM6+A37FdFHi0jlqkI8jCrhPMyWP8diOPGtNguS9tSzeNyQqx70aagOPp8iA6nN/7irpE44FjmGLvT1RqBBHW3Tr7/r9Lr7nI7F6gb32GOh06VBTMF+55+T+66btp7nh4fd4+s+SOnFUKpVKpVKpVCqVSqVSqe6B9EcclUqlUqlUKpVKpVKpVKp7oFvhVJVzZlmnbxCVoj0+jMR6yBQg2sNDWK78GxAZWluZPjQ/lTQFx6QO2J8i2HlNbZei2zTLBZt68/ZF057h8aAjhqfOAGkGSB5wtNPdaNV3G1p/wnQqzzO92mJbONi9gaaFsMuORmLfHiJJyIMdsduFxbGk1U/+7TXSPHxY/Hmaf7pA2xPLXdGvExBi2vBgG4fd3NFSiOHE5KuUVmKfPQGEBEPIA37CNBnP22ytq5j4Et6OWHxvOWdcfZJ9K50B18T0mBlSDRysmY62QVi/HfqX1s/xtaQGcE75Hiz6eH1ajm2/novoo0tYVV0E+yjQtvNzSWeh/fLBkSBFHSSntTELuXZiB7Ol3I/zKdFJ2JstLcpF/WrbM0LO0sz8zTevjDHGxJncs18cCbIZAEczwNRsCJwxQepJDsQG1mNT8rNgvgJrIdo0vxab8zJlUsvq+QVsqLNruX8LJLwNd8XK3xvK9XLOzTAXw6H0Z9JjOhVxKrOx3aqnnJZ3FUkFVVVl0hrX9HfkukNgKJcYw0vYw0PauntyjxJiGEjSuJ7I/Ht3LpjhEv21E+K+x9Lm9Zg1ugOcqsJczICcXkwF15tWsmbsPxL8NcE6sYN1osRasgAKMJvI658i0axAPRruyjyYXYgdOr1e/dvt9qxrcJYFsIpRT+paF4k+p1dyH8a5rC0e9j/HQ7n+s7Hc2/Mz6cPrE2kfP0QKERCaPnCOYSQJjWucPCuAgwIrWACHYWJUjucwHWcyBx6EBA8/Ak6OvVWKGnAN/GOGOkKEgOlURGG3Kedcs18k4k58q0AtLFAsiFYRA8uYAlTK57yeINkUe9cO08JSIMfAJkqLOpmsxlgH/VxgHT/HvzOhfJDJVPruDCljFvuhBIheFzilA5LRSvZhQh6Scjzse4j9rdfjnxGqcqOSKDRfffrQGGPMgz25lwHGkm1hssAGM+w/DdYWfJ9YBMQ25GUKJK+RP5yPgcQjxXVuZIxUdVKcw76iIoYKRJNoSYJkpE4ktXq+kL69mMh1LbDnahGC2Kt4wFu4/BHzYfG0d/a3fNvsBX3U9RYpgwvkcQ0xMMCKe1T0dYDHl6hd797IuuiQAtXtSO1MEiCSheyZXLAab7NS+vwCqW4Vai2PX/BDeZ84kXExGGLN9YjrSEd66LuYnxVzMV8gubCV9IwkxXpObPO7pbXWBPUaznHF9EqmWrI2sH/4b1mXM+63gaeGQJd5TzyfmCv3ruiLullhbzM+lT1M10qdvUZpLZGOVVl5H+KmpeNc+WkmuHXMyg1F8kP7S504KpVKpVKpVCqVSqVSqVT3QPojjkqlUqlUKpVKpVKpVCrVPdCtWA9nTGOyzSue5C5tpjMEtC3CksRkI1pYrZXn0/J6dvqmaZ9fXOL5SKTiCdmMYlj7D32xUE2Q6nExBp4Vy3t+/OWn8nqwsxvauWD5uvFA+Ft7vu/qDOta1jYnf+fAJGLY4I6OxSa/tyvW+G5XrK194CshsLLJpWADIU6Y73Xl9RPa0k+kb/+v7wVtGyRiz9+tUwkODuW6usAtIuADAeypvaHYJ0ew5vvR5tQ0D/ZYWyJhg9ZBn5ZIaS6JIsDuyKSTbcr3PDMarO5BUYol3zIxA/bNOU7sp919x5N5QfTLg7XbpMAZr8RCfnomc/Hw4ZOm3RvtNe0p3mtcIxElUsN6mP9dJIJcYVzMges8+VQSa3b2pE+ZtFaUSFmi1RpY2CUSxN6dI6MO9tsEGE9cX/McNvgPVZpV5tvXK9v0rx4/lPcdypw7fChpR3FX6lBFbBB1iMlyMcYhx7NDAleJGnB1IsjK1bnYua0n/VLUdtIql/dPp1JPK6Tq5Bg3RS7XWCAF4B0woHcXgioMP37dtL+IJc2j26NFt2m21oK2hZxpVnekqjJlvaZ887vfNg9/58m9DdBfD3dk3B4NpEZ1A1jIaY8PYb/vyIfeA8J0MQF+OgWimEnf+aXURq9GWksmQmLdTGeIEsKN2zvcb9o7B8D+usT70EcYpkR2J3Pp62+f/9C0f/fDs6bdS5A0mMOW3K2xuy2ics4YU9ao9BI1wCJtbQc2+T8AN/phLOP/s5nUh198+lHTPjyUe5Jlct0nb8X6H+9K3ewdIJETe6EcSXDr9KmOASoFhMNaWPY9zEWgAtzPfPH1l027g1Qus5S6cH0maMEC9+l8Bls/UkNoOae1HGVnu7KS0NjpoF5iqDB5KEUdq7CJC4FHdIDIRSXSUYCRj/H30N3PftG0jz7+vGmTvk8vZc3JaxQzTGT+Dw6l/8Md2Wtdn8n+6g///j/Ia8/khn6BNbIyXAuBkWGf4AM1ynB0AYG3qMMkGNnLuQaH2B7EEQaeeXSwWgNHQIWJl5SYlwbfG0qkhKVMPgzQzySUsRf08Yk97CF84HQFsI0c+NX6K1DB+4e5yATM+VTmPJMX45H0+SXw6rOpXEvGecPvHDwKgPiHYVoQU5PwOne0MDrnTFHfGH5HZB0ImNSIMebj+4jD98iCR2UAv/K6QGsiqVcjpHgeP/ykaXew7hLX8uvXDGfyPn0kT+XAjItM6mhRIMGKCD+Tm/FZixYNiP+D/ZNFJ7VObsA6ms5l7cnqfV21zeg/K9fN7/M8viTAPjmJmbCI7/+tpDbsydDmfePvBQy4zPA9ZnEJnBX1oFys+uLld982jz39RtrLDLX98WdN+/BAvsPk5Cxbk4WPE0/kcQWb2xT3q+296+0nozpxVCqVSqVSqVQqlUqlUqnugfRHHJVKpVKpVCqVSqVSqVSqe6Bb41Rpfaq9AyYSLnkCvtgNQ1ic/JZtanObJ6zPgKfMl2Kb8p1YybIFrMPAMvYHYvPe2x+unlvK+1wvBRXoDWFD7Mvn6I3kNZawcg9jseF5wKzaFv6fPpF6m1bw22p9TURNRkhVGQyk3e2JxfdgX3CmQVeeM52IPbSCbdJUQNzQ/2UhlkSiUPTeT64Fp8mWK+txkUq/DYdiPd/fFxRl51j6bRf2VKYrGFxLEMOeWTD9AKedyzNaqVU5xuIcNsvJXMbr+SWss1uU53mmk8R1Wx5PcKq6heVxBjziBGkwO3syhgPgNwEwi7gvttW9xw+a9gLpFTnmYrQn77v7hSAFXp205OE+u5lYUtMZ+he43qMD6evRQ2lXrYQBJEvwhqDETXAPXr4TO/s5ElqSALUMY6Zbp4KcedK3Hy7PVG51/8P+sHmU9/jJp2IBTlBv8gyJUEiJGacYw4n0YY8WW8aKIU0jRq16ABQxCOR91/hBiXleIo3lYi73cjmX++0jeSyDPfnZi7Om/f8+Fazmv/z375r2r371V037n/+Lf9W0v/5KUIVBH0glktLuGE41xqys7GG9LmZAdWcpxjas9U9fyud8jJraAX6aMMkCiTRliCSLPSRCYcyXSDMxQNcCJH70eitcoUKdW8yB2Qzl3/VxKV4PCT5AHXK8zryQMZXPZaxdTuT1v33xsmn/9unTpp0COwhwL/shUi96q7put9yjawyBqE+BWtXDWmGRuvUD6sfvXgue/evPBWt5eCjrUo4UshKYY4k0jR7WrnAPqZBYjSJv9ToOc3t2JSjBEomQBfCdLvDL/QfyPj2k3zmkMM1zqTVLcElnuN4XF4L0PjoW5M7Hnq6C/XxWbQ+/oTzPM506idLH/qNAvcrQjjiEgFktWRexFoH4Nfuoxwn2iD4S5ALsBfYfPmradp/1dXUv+thrOSKyQCYusV4+GMr8f/hE1owOMC8ibxnWiRKpNhZjKse+tDuSNak/kuuNgFqv0Vxvi/FU1jgT1hhYaJEYgyWMaPSy3IznEE1gImCJvo2AsPnAsgY9IM0jGc8kKAokrK1xoeuJzDkmeUaFfI44lBo3QZqkAzY7RbLc1Xwz8ufxewZroSOeIQ/flNp4V19FnDFmWd+jGda/AjeRx2nE6FN+nyuBJC0wCCK0AyCccV/Gqg1wXACwSKZfBl1gu3WCaWTw3XIJFA57Xos1mmla/QH21EhEZSKfw5yz+HwF0ppyoJ5tlFCuIc2QClgn/VYVQcgPlDOmrPslBGLEfovQDoHBESVrlQe28V0sxOv4GMMZcP3n3z9v2i+ePpPnYK2L1sMC9W6ZAjeP8P0WRz9kuPYM/FqAceljsrzfXd6MTW1T6sRRqVQqlUqlUqlUKpVKpboH0h9xVCqVSqVSqVQqlUqlUqnugW6FU1VVZZbZyubHtJE4EBtUigSVbgdWRaAdtPLRPlbyFGjY0B5+LLbkENami1Ox4s+RdsFkj0Vto5pNaUkVxMXryXMHQyBUOIV+jkCaJztiWw0joEAfdDr/HxetsrXltIO0qRi20iFstMdIqhoB+YiALEzFyWbSlFZ+GSM8tTzHyf+0rj9C2tBuX06Vj8KV3XF/90iucSgW1zCS69rdEXtcAhs0qAKT4v0rJAx46IaqpD0VYxR22mUpNsvxTKyz75AgwbSHbapylVnWFlWL8d6NkAyC+ZQCTXiO5KejB3KPejGtf3gZzumhjJMHO2LFnz2T1JLZ6R/k374WG3//aDWWfHT6fCz3rQI20tmR99zZo5UbSUlWnk87d1HBFplK312ei7X9+2ev5Hppncc8QBiNccXq/jmkrXy4rDFu9Sa0937y1RdNu9OXx5ketECq0xVO6Sem4CGlyLYsobAh32DZLZjwZ4hFrq6XdnyiQguknphWSA2wmpnU6ufAT6ZIWJktZIy+eSNj62//69837V/9UlJg/slf/kXTPj4SbOHRE1k7HhxJTdmmrDEmqm9RiISNDCls7y7lvjx9LpbgX38q6QijRMatNdJmmoOPBLkoRMrjDhI8OrB8E0EARjcuV9iNRz4UyVcR5nzrTz24FksfPub0Au0x1oPXl4L6/P13glCdTq+b9hCJW0PcjwEm4zzI1xdgtiXf80y/rp1nVzK3pqgNBgk3tH7PUqmtT1++bdrfvZUxfHgs61IJ5KMbo84hNSN/cdK00wWs4rDtV9Wqz4kHza9QTxcy/wPE2kRARhMmNcIgXhDRmkp9maNefHci69wrpIY+PJZ5FgM7Y8TK8m6c5TVOVSOqQIuvT+X6MiARBcYVU1aIU3WRbLrg8zH8uBMskHZ4OfnvTduh1nm87zU6Ne8Cheb2A7W4s5Da+emxJBeCNjSFk+cQac+ZSAVUmI+zRgx3ZN1lWhBXhiaxZps4lTUmbi4PKBFDZ9GHUYGbFW7+u3SJf2zxb+dTmeuttR/ImB8ycYcpj8D/axQ1xbrIRDTW6hhrfTmRcTbLpH15LX2YppsTcdopuD8NDrs/8vcMZ4wp6vec47MVuNYY45DzyWKdq3DPs5zJTFLfEnwfiUu5X1Ok/72eyr3rD2VP0R0Al+ysXmd5Lf9uDBSV6WMxE3exX2WKU1GVG9u2AmqEukjktMQAIopFnIqpVesjK7aJ7TjnTFHf8yokps60KX6WVmSotL3NWBH3NhHum4c57WG87AJzXA6Je8r3wYG/us4IvNMcNe4EGHCwL+vyGLWjRAHmbw68ltY3gRsSpm4+TWV7c1GdOCqVSqVSqVQqlUqlUqlU90D6I45KpVKpVCqVSqVSqVQq1T2QvY2tx1p7aox5/pNPVG1bnzjntsICaB/+SaX9eP+lffjnIe3H+y/twz8PaT/ef2kf/nlI+/H+S/vwz0Pv1Y+3+hFHpVKpVCqVSqVSqVQqlUr1p5HiVCqVSqVSqVQqlUqlUqlU90D6I45KpVKpVCqVSqVSqVQq1T2Q/oijUqlUKpVKpVKpVCqVSnUPpD/iqFQqlUqlUqlUKpVKpVLdA+mPOCqVSqVSqVQqlUqlUqlU90D6I45KpVKpVCqVSqVSqVQq1T2Q/oijUqlUKpVKpVKpVCqVSnUPpD/iqFQqlUqlUqlUKpVKpVLdA+mPOCqVSqVSqVQqlUqlUqlU90D/P79uLeLwvJWwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13f6694da0>"
      ]
     },
     "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=20\n",
    "for idx in np.arange(plot_size):\n",
    "    ax = fig.add_subplot(2, plot_size/2, idx+1, xticks=[], yticks=[])\n",
    "    imshow(images[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Pre-process your image data and scale it to a pixel range of -1 to 1\n",
    "\n",
    "You need to do a bit of pre-processing; you know that the output of a `tanh` activated generator will contain pixel values in a range from -1 to 1, and so, we need to rescale our training images to a range of -1 to 1. (Right now, they are in a range from 0-1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
    "#     min_val, max_val = feature_range\n",
    "#     return x * (max_val - min_val) + min_val\n",
    "    return x*2-1         #it's always called with -1,1 so for speed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-0.9059)\n",
      "Max:  tensor(0.8588)\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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper conv function\n",
    "def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, \n",
    "                           kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\n",
    "    \n",
    "    layers.append(conv_layer)\n",
    "\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "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",
    "        self.conv_dim = conv_dim\n",
    "        self.conv1 = conv(3, conv_dim, 4, batch_norm=False) # (16, 16, conv_dim)\n",
    "        self.conv2 = conv(conv_dim, conv_dim*2, 4) # (8, 8, conv_dim*2)\n",
    "        self.conv3 = conv(conv_dim*2, conv_dim*4, 4) # (4, 4, conv_dim*4)\n",
    "        self.conv4 = conv(conv_dim*4, conv_dim*8, 4) # (2, 2, conv_dim*8) \n",
    "        \n",
    "        self.classifier = nn.Linear(conv_dim*8*2*2, 1)\n",
    "\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",
    "        out = F.selu(self.conv1(x), 0.2)\n",
    "        out = F.selu(self.conv2(out), 0.2)\n",
    "        out = F.selu(self.conv3(out), 0.2)\n",
    "        out = F.selu(self.conv4(out), 0.2)\n",
    "        \n",
    "        out = out.view(-1, self.conv_dim*8*2*2)\n",
    "        out = self.classifier(out)\n",
    "        return out\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(Discriminator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator\n",
    "\n",
    "The generator should upsample an input and generate a *new* image of the same size as our training data `32x32x3`. This should be mostly transpose convolutional layers with normalization applied to the outputs.\n",
    "\n",
    "#### Exercise: Complete the Generator class\n",
    "* The inputs to the generator are vectors of some length `z_size`\n",
    "* The output should be a image of shape `32x32x3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    layers = []\n",
    "    layers.append(nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, bias=False))\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
    "        self.conv_dim = conv_dim\n",
    "\n",
    "        self.fc = nn.Linear(z_size, conv_dim*8*2*2)\n",
    "        \n",
    "        self.t_conv1 = deconv(conv_dim*8, conv_dim*4, 4)\n",
    "        self.t_conv2 = deconv(conv_dim*4, conv_dim*2, 4)\n",
    "        self.t_conv3 = deconv(conv_dim*2, conv_dim, 4)\n",
    "        self.t_conv4 = deconv(conv_dim, 3, 4, batch_norm=False)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: A 32x32x3 Tensor image as output\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        out = self.fc(x)\n",
    "        out = out.view(-1, self.conv_dim*8, 2, 2) # (batch_size, depth, 4, 4)\n",
    "        \n",
    "        out = F.relu(self.t_conv1(out))\n",
    "        out = F.relu(self.t_conv2(out))\n",
    "        out = F.relu(self.t_conv3(out))\n",
    "        \n",
    "        # last layer: tanh activation instead of relu\n",
    "        out = self.t_conv4(out)\n",
    "        out = F.tanh(out)\n",
    "        \n",
    "        return out\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(Generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the weights of your networks\n",
    "\n",
    "To help your models converge, you should initialize the weights of the convolutional and linear layers in your model. From reading the [original DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf), they say:\n",
    "> All weights were initialized from a zero-centered Normal distribution with standard deviation 0.02.\n",
    "\n",
    "So, your next task will be to define a weight initialization function that does just this!\n",
    "\n",
    "You can refer back to the lesson on weight initialization or even consult existing model code, such as that from [the `networks.py` file in CycleGAN Github repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py) to help you complete this function.\n",
    "\n",
    "#### Exercise: Complete the weight initialization function\n",
    "\n",
    "* This should initialize only **convolutional** and **linear** layers\n",
    "* Initialize the weights to a normal distribution, centered around 0, with a standard deviation of 0.02.\n",
    "* The bias terms, if they exist, may be left alone or set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import init\n",
    "\n",
    "def weights_init_normal(m):\n",
    "    \"\"\"\n",
    "    Applies initial weights to certain layers in a model: convolutional and linear\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",
    "    isConvolution = classname.find('Conv') != -1\n",
    "    isLinear = classname.find('Linear') != -1\n",
    "    if (hasattr(m, 'weight') and isConvolution or isLinear):\n",
    "        init.normal_(m.weight.data, 0.0, 0.02)"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "def build_network(d_conv_dim, g_conv_dim, z_size):\n",
    "    # define discriminator and generator\n",
    "    D = Discriminator(d_conv_dim)\n",
    "    G = Generator(z_size=z_size, conv_dim=g_conv_dim)\n",
    "\n",
    "    # initialize model weights\n",
    "    D.apply(weights_init_normal)\n",
    "    G.apply(weights_init_normal)\n",
    "\n",
    "    print(D)\n",
    "    print()\n",
    "    print(G)\n",
    "    \n",
    "    return D, G\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Define model hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
      "  (classifier): 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": 17,
   "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def real_loss(D_out):\n",
    "    '''\n",
    "    Calculates how close discriminator outputs are to being real.\n",
    "    param, D_out: discriminator logits\n",
    "    return: real loss\n",
    "    '''\n",
    "    labels = torch.ones(D_out.size(0))      #D_out[0] is batch size\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    \n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    return criterion(D_out.squeeze(), labels)\n",
    "\n",
    "def fake_loss(D_out):\n",
    "    '''\n",
    "    Calculates how close discriminator outputs are to being fake.\n",
    "    param, D_out: discriminator logits\n",
    "    return: fake loss\n",
    "    '''\n",
    "    labels = torch.zeros(D_out.size(0))     #D_out[0] is batch size\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    \n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    return criterion(D_out.squeeze(), labels)"
   ]
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "lr = 0.0002\n",
    "beta1=0.5\n",
    "beta2=0.999 # default value\n",
    "\n",
    "# Create optimizers for the discriminator D and generator G\n",
    "d_optimizer = optim.Adam(D.parameters(), lr, [beta1, beta2])\n",
    "g_optimizer = optim.Adam(G.parameters(), lr, [beta1, beta2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Training\n",
    "\n",
    "Training will involve alternating between training the discriminator and the generator. You'll use your functions `real_loss` and `fake_loss` to help you calculate the discriminator losses.\n",
    "\n",
    "* You should train the discriminator by alternating on real and fake images\n",
    "* Then the generator, which tries to trick the discriminator and should have an opposing loss function\n",
    "\n",
    "\n",
    "#### Saving Samples\n",
    "\n",
    "You've been given some code to print out some loss statistics and save some generated \"fake\" samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Complete the training function\n",
    "\n",
    "Keep in mind that, if you've moved your models to GPU, you'll also have to move any model inputs to GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from workspace_utils import active_session"
   ]
  },
  {
   "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",
    "            # ===============================================\n",
    "            #         YOUR CODE HERE: TRAIN THE NETWORKS\n",
    "            # ===============================================\n",
    "            \n",
    "            # 1. Train the discriminator on real and fake images\n",
    "            d_optimizer.zero_grad()\n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "                \n",
    "            D_real = D(real_images)\n",
    "            d_real_loss = real_loss(D_real)\n",
    "            \n",
    "            z = np.random.uniform(-1, 1, size = (batch_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\n",
    "            \n",
    "            fake_images = G(z)\n",
    "            D_fake = D(fake_images)\n",
    "            d_fake_loss = fake_loss(D_fake)\n",
    "            \n",
    "            d_loss = d_real_loss + d_fake_loss\n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "\n",
    "            # 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_images = G(z)\n",
    "            D_fake = D(fake_images)\n",
    "            g_loss = real_loss(D_fake)\n",
    "            \n",
    "            g_loss.backward()\n",
    "            g_optimizer.step()\n",
    "            \n",
    "            \n",
    "            # ===============================================\n",
    "            #              END OF YOUR CODE\n",
    "            # ===============================================\n",
    "\n",
    "            # Print some loss stats\n",
    "            if batch_i % print_every == 0:\n",
    "                # append discriminator loss and generator loss\n",
    "                losses.append((d_loss.item(), g_loss.item()))\n",
    "                # print discriminator and generator loss\n",
    "                print('Epoch [{:5d}/{:5d}] | batch: {:5d} | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
    "                        epoch+1, n_epochs, batch_i+1, d_loss.item(), g_loss.item()))\n",
    "\n",
    "\n",
    "        ## AFTER EACH EPOCH##    \n",
    "        torch.save(G.state_dict(), 'g'+str(epoch+1)+'.pt')\n",
    "        torch.save(D.state_dict(), 'd'+str(epoch+1)+'.pt')\n",
    "\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",
    "        print('---------------Epoch [{:5d}/{:5d}]---------------'.format(epoch+1, n_epochs))\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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# G.load_state_dict(torch.load('g.pt'))\n",
    "# D.load_state_dict(torch.load('d.pt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   20] | batch:     1 | d_loss: 0.8111 | g_loss: 1.2661\n",
      "Epoch [    1/   20] | batch:    51 | d_loss: 0.9952 | g_loss: 2.3659\n",
      "Epoch [    1/   20] | batch:   101 | d_loss: 0.9237 | g_loss: 2.7058\n",
      "Epoch [    1/   20] | batch:   151 | d_loss: 0.9340 | g_loss: 1.3858\n",
      "Epoch [    1/   20] | batch:   201 | d_loss: 0.9927 | g_loss: 1.6645\n",
      "Epoch [    1/   20] | batch:   251 | d_loss: 0.8794 | g_loss: 1.3428\n",
      "Epoch [    1/   20] | batch:   301 | d_loss: 1.0240 | g_loss: 1.8747\n",
      "Epoch [    1/   20] | batch:   351 | d_loss: 0.8578 | g_loss: 2.3406\n",
      "Epoch [    1/   20] | batch:   401 | d_loss: 1.0471 | g_loss: 1.4166\n",
      "Epoch [    1/   20] | batch:   451 | d_loss: 0.8953 | g_loss: 2.2885\n",
      "Epoch [    1/   20] | batch:   501 | d_loss: 0.8606 | g_loss: 1.5577\n",
      "Epoch [    1/   20] | batch:   551 | d_loss: 0.8905 | g_loss: 1.9201\n",
      "Epoch [    1/   20] | batch:   601 | d_loss: 0.9602 | g_loss: 2.0931\n",
      "Epoch [    1/   20] | batch:   651 | d_loss: 0.9449 | g_loss: 2.2206\n",
      "Epoch [    1/   20] | batch:   701 | d_loss: 1.0528 | g_loss: 1.5514\n",
      "---------------Epoch [    1/   20]---------------\n",
      "Epoch [    2/   20] | batch:     1 | d_loss: 0.8771 | g_loss: 1.3734\n",
      "Epoch [    2/   20] | batch:    51 | d_loss: 1.0019 | g_loss: 1.2820\n",
      "Epoch [    2/   20] | batch:   101 | d_loss: 0.9900 | g_loss: 1.6812\n",
      "Epoch [    2/   20] | batch:   151 | d_loss: 1.0659 | g_loss: 2.1863\n",
      "Epoch [    2/   20] | batch:   201 | d_loss: 0.9917 | g_loss: 1.6778\n",
      "Epoch [    2/   20] | batch:   251 | d_loss: 0.8652 | g_loss: 1.5668\n",
      "Epoch [    2/   20] | batch:   301 | d_loss: 0.7992 | g_loss: 2.3568\n",
      "Epoch [    2/   20] | batch:   351 | d_loss: 1.0523 | g_loss: 1.2216\n",
      "Epoch [    2/   20] | batch:   401 | d_loss: 1.0096 | g_loss: 2.4254\n",
      "Epoch [    2/   20] | batch:   451 | d_loss: 1.1680 | g_loss: 1.1488\n",
      "Epoch [    2/   20] | batch:   501 | d_loss: 0.9187 | g_loss: 1.2972\n",
      "Epoch [    2/   20] | batch:   551 | d_loss: 0.9080 | g_loss: 1.9679\n",
      "Epoch [    2/   20] | batch:   601 | d_loss: 1.0048 | g_loss: 2.4279\n",
      "Epoch [    2/   20] | batch:   651 | d_loss: 1.2464 | g_loss: 1.1955\n",
      "Epoch [    2/   20] | batch:   701 | d_loss: 1.0181 | g_loss: 2.1282\n",
      "---------------Epoch [    2/   20]---------------\n",
      "Epoch [    3/   20] | batch:     1 | d_loss: 0.9845 | g_loss: 3.0135\n",
      "Epoch [    3/   20] | batch:    51 | d_loss: 0.8247 | g_loss: 2.0062\n",
      "Epoch [    3/   20] | batch:   101 | d_loss: 0.9052 | g_loss: 2.0246\n",
      "Epoch [    3/   20] | batch:   151 | d_loss: 0.8806 | g_loss: 1.8005\n",
      "Epoch [    3/   20] | batch:   201 | d_loss: 1.1608 | g_loss: 1.1466\n",
      "Epoch [    3/   20] | batch:   251 | d_loss: 1.0030 | g_loss: 1.7211\n",
      "Epoch [    3/   20] | batch:   301 | d_loss: 1.2835 | g_loss: 1.8969\n",
      "Epoch [    3/   20] | batch:   351 | d_loss: 0.8584 | g_loss: 1.6190\n",
      "Epoch [    3/   20] | batch:   401 | d_loss: 0.7295 | g_loss: 1.6601\n",
      "Epoch [    3/   20] | batch:   451 | d_loss: 1.0188 | g_loss: 1.8397\n",
      "Epoch [    3/   20] | batch:   501 | d_loss: 1.2436 | g_loss: 2.5559\n",
      "Epoch [    3/   20] | batch:   551 | d_loss: 1.0364 | g_loss: 2.0659\n",
      "Epoch [    3/   20] | batch:   601 | d_loss: 1.0645 | g_loss: 1.2833\n",
      "Epoch [    3/   20] | batch:   651 | d_loss: 0.8505 | g_loss: 2.2852\n",
      "Epoch [    3/   20] | batch:   701 | d_loss: 0.9450 | g_loss: 1.7768\n",
      "---------------Epoch [    3/   20]---------------\n",
      "Epoch [    4/   20] | batch:     1 | d_loss: 0.9740 | g_loss: 1.1168\n",
      "Epoch [    4/   20] | batch:    51 | d_loss: 0.9093 | g_loss: 1.2597\n",
      "Epoch [    4/   20] | batch:   101 | d_loss: 1.0745 | g_loss: 0.9085\n",
      "Epoch [    4/   20] | batch:   151 | d_loss: 0.7689 | g_loss: 1.8919\n",
      "Epoch [    4/   20] | batch:   201 | d_loss: 1.2617 | g_loss: 3.2608\n",
      "Epoch [    4/   20] | batch:   251 | d_loss: 1.0825 | g_loss: 1.3975\n",
      "Epoch [    4/   20] | batch:   301 | d_loss: 1.0497 | g_loss: 0.9826\n",
      "Epoch [    4/   20] | batch:   351 | d_loss: 0.8368 | g_loss: 1.8344\n",
      "Epoch [    4/   20] | batch:   401 | d_loss: 0.8927 | g_loss: 1.4510\n",
      "Epoch [    4/   20] | batch:   451 | d_loss: 0.8737 | g_loss: 2.1484\n",
      "Epoch [    4/   20] | batch:   501 | d_loss: 0.8620 | g_loss: 1.7640\n",
      "Epoch [    4/   20] | batch:   551 | d_loss: 1.0488 | g_loss: 1.1829\n",
      "Epoch [    4/   20] | batch:   601 | d_loss: 0.9488 | g_loss: 1.2433\n",
      "Epoch [    4/   20] | batch:   651 | d_loss: 0.8552 | g_loss: 2.1256\n",
      "Epoch [    4/   20] | batch:   701 | d_loss: 1.2086 | g_loss: 1.2018\n",
      "---------------Epoch [    4/   20]---------------\n",
      "Epoch [    5/   20] | batch:     1 | d_loss: 0.6779 | g_loss: 2.0733\n",
      "Epoch [    5/   20] | batch:    51 | d_loss: 0.9240 | g_loss: 0.9773\n",
      "Epoch [    5/   20] | batch:   101 | d_loss: 0.8876 | g_loss: 1.7137\n",
      "Epoch [    5/   20] | batch:   151 | d_loss: 1.0030 | g_loss: 2.4516\n",
      "Epoch [    5/   20] | batch:   201 | d_loss: 0.9148 | g_loss: 1.8731\n",
      "Epoch [    5/   20] | batch:   251 | d_loss: 0.9607 | g_loss: 1.9129\n",
      "Epoch [    5/   20] | batch:   301 | d_loss: 0.9252 | g_loss: 1.8296\n",
      "Epoch [    5/   20] | batch:   351 | d_loss: 0.7578 | g_loss: 2.0952\n",
      "Epoch [    5/   20] | batch:   401 | d_loss: 0.7759 | g_loss: 1.5163\n",
      "Epoch [    5/   20] | batch:   451 | d_loss: 0.8367 | g_loss: 1.9044\n",
      "Epoch [    5/   20] | batch:   501 | d_loss: 0.8096 | g_loss: 1.8013\n",
      "Epoch [    5/   20] | batch:   551 | d_loss: 0.7723 | g_loss: 1.8913\n",
      "Epoch [    5/   20] | batch:   601 | d_loss: 0.7605 | g_loss: 2.4772\n",
      "Epoch [    5/   20] | batch:   651 | d_loss: 1.3507 | g_loss: 1.2231\n",
      "Epoch [    5/   20] | batch:   701 | d_loss: 0.7732 | g_loss: 2.0357\n",
      "---------------Epoch [    5/   20]---------------\n",
      "Epoch [    6/   20] | batch:     1 | d_loss: 0.8181 | g_loss: 1.4688\n",
      "Epoch [    6/   20] | batch:    51 | d_loss: 1.3336 | g_loss: 1.3665\n",
      "Epoch [    6/   20] | batch:   101 | d_loss: 0.8859 | g_loss: 1.9091\n",
      "Epoch [    6/   20] | batch:   151 | d_loss: 0.8380 | g_loss: 2.1336\n",
      "Epoch [    6/   20] | batch:   201 | d_loss: 0.8269 | g_loss: 3.3221\n",
      "Epoch [    6/   20] | batch:   251 | d_loss: 0.9188 | g_loss: 1.9334\n",
      "Epoch [    6/   20] | batch:   301 | d_loss: 0.9195 | g_loss: 1.7430\n",
      "Epoch [    6/   20] | batch:   351 | d_loss: 1.2432 | g_loss: 3.4875\n",
      "Epoch [    6/   20] | batch:   401 | d_loss: 0.7508 | g_loss: 1.7305\n",
      "Epoch [    6/   20] | batch:   451 | d_loss: 0.9282 | g_loss: 1.9829\n",
      "Epoch [    6/   20] | batch:   501 | d_loss: 0.8910 | g_loss: 2.0563\n",
      "Epoch [    6/   20] | batch:   551 | d_loss: 0.7907 | g_loss: 1.6141\n",
      "Epoch [    6/   20] | batch:   601 | d_loss: 0.8585 | g_loss: 2.6222\n",
      "Epoch [    6/   20] | batch:   651 | d_loss: 0.7723 | g_loss: 1.0951\n",
      "Epoch [    6/   20] | batch:   701 | d_loss: 0.8480 | g_loss: 2.0792\n",
      "---------------Epoch [    6/   20]---------------\n",
      "Epoch [    7/   20] | batch:     1 | d_loss: 0.8413 | g_loss: 1.3066\n",
      "Epoch [    7/   20] | batch:    51 | d_loss: 0.7940 | g_loss: 2.6432\n",
      "Epoch [    7/   20] | batch:   101 | d_loss: 0.8089 | g_loss: 1.8077\n",
      "Epoch [    7/   20] | batch:   151 | d_loss: 0.9595 | g_loss: 1.6319\n",
      "Epoch [    7/   20] | batch:   201 | d_loss: 1.0729 | g_loss: 0.9551\n",
      "Epoch [    7/   20] | batch:   251 | d_loss: 0.9064 | g_loss: 2.4957\n",
      "Epoch [    7/   20] | batch:   301 | d_loss: 0.7105 | g_loss: 2.1476\n",
      "Epoch [    7/   20] | batch:   351 | d_loss: 1.0778 | g_loss: 2.9749\n",
      "Epoch [    7/   20] | batch:   401 | d_loss: 0.9246 | g_loss: 1.3092\n",
      "Epoch [    7/   20] | batch:   451 | d_loss: 1.0882 | g_loss: 0.7023\n",
      "Epoch [    7/   20] | batch:   501 | d_loss: 1.0619 | g_loss: 1.6870\n",
      "Epoch [    7/   20] | batch:   551 | d_loss: 0.8053 | g_loss: 2.0815\n",
      "Epoch [    7/   20] | batch:   601 | d_loss: 0.7979 | g_loss: 2.2491\n",
      "Epoch [    7/   20] | batch:   651 | d_loss: 0.8395 | g_loss: 1.9564\n",
      "Epoch [    7/   20] | batch:   701 | d_loss: 1.0466 | g_loss: 1.3503\n",
      "---------------Epoch [    7/   20]---------------\n",
      "Epoch [    8/   20] | batch:     1 | d_loss: 0.6671 | g_loss: 1.4386\n",
      "Epoch [    8/   20] | batch:    51 | d_loss: 0.9737 | g_loss: 1.9353\n",
      "Epoch [    8/   20] | batch:   101 | d_loss: 0.7338 | g_loss: 1.6522\n",
      "Epoch [    8/   20] | batch:   151 | d_loss: 0.8512 | g_loss: 2.1538\n",
      "Epoch [    8/   20] | batch:   201 | d_loss: 0.7490 | g_loss: 2.2567\n",
      "Epoch [    8/   20] | batch:   251 | d_loss: 0.8791 | g_loss: 2.4892\n",
      "Epoch [    8/   20] | batch:   301 | d_loss: 0.7260 | g_loss: 1.8245\n",
      "Epoch [    8/   20] | batch:   351 | d_loss: 0.8386 | g_loss: 2.1277\n",
      "Epoch [    8/   20] | batch:   401 | d_loss: 0.9722 | g_loss: 2.4713\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    8/   20] | batch:   451 | d_loss: 0.6980 | g_loss: 2.2593\n",
      "Epoch [    8/   20] | batch:   501 | d_loss: 1.0766 | g_loss: 3.4284\n",
      "Epoch [    8/   20] | batch:   551 | d_loss: 0.9441 | g_loss: 2.4267\n",
      "Epoch [    8/   20] | batch:   601 | d_loss: 0.9615 | g_loss: 1.1553\n",
      "Epoch [    8/   20] | batch:   651 | d_loss: 0.9098 | g_loss: 2.4200\n",
      "Epoch [    8/   20] | batch:   701 | d_loss: 0.8843 | g_loss: 1.3202\n",
      "---------------Epoch [    8/   20]---------------\n",
      "Epoch [    9/   20] | batch:     1 | d_loss: 0.7078 | g_loss: 1.8200\n",
      "Epoch [    9/   20] | batch:    51 | d_loss: 1.0578 | g_loss: 1.2410\n",
      "Epoch [    9/   20] | batch:   101 | d_loss: 0.8762 | g_loss: 1.8029\n",
      "Epoch [    9/   20] | batch:   151 | d_loss: 0.8846 | g_loss: 1.5970\n",
      "Epoch [    9/   20] | batch:   201 | d_loss: 0.9776 | g_loss: 3.1247\n",
      "Epoch [    9/   20] | batch:   251 | d_loss: 0.7748 | g_loss: 1.2802\n",
      "Epoch [    9/   20] | batch:   301 | d_loss: 0.7308 | g_loss: 2.0371\n",
      "Epoch [    9/   20] | batch:   351 | d_loss: 0.9546 | g_loss: 1.3827\n",
      "Epoch [    9/   20] | batch:   401 | d_loss: 0.9979 | g_loss: 2.7320\n",
      "Epoch [    9/   20] | batch:   451 | d_loss: 0.8290 | g_loss: 1.6430\n",
      "Epoch [    9/   20] | batch:   501 | d_loss: 0.8173 | g_loss: 1.2279\n",
      "Epoch [    9/   20] | batch:   551 | d_loss: 0.7015 | g_loss: 1.6330\n",
      "Epoch [    9/   20] | batch:   601 | d_loss: 1.2070 | g_loss: 2.3610\n",
      "Epoch [    9/   20] | batch:   651 | d_loss: 1.2740 | g_loss: 0.5588\n",
      "Epoch [    9/   20] | batch:   701 | d_loss: 0.8654 | g_loss: 3.2853\n",
      "---------------Epoch [    9/   20]---------------\n",
      "Epoch [   10/   20] | batch:     1 | d_loss: 1.0506 | g_loss: 1.6972\n",
      "Epoch [   10/   20] | batch:    51 | d_loss: 0.8623 | g_loss: 1.2557\n",
      "Epoch [   10/   20] | batch:   101 | d_loss: 0.7753 | g_loss: 1.7687\n",
      "Epoch [   10/   20] | batch:   151 | d_loss: 0.6656 | g_loss: 1.5877\n",
      "Epoch [   10/   20] | batch:   201 | d_loss: 0.9033 | g_loss: 1.9987\n",
      "Epoch [   10/   20] | batch:   251 | d_loss: 0.8301 | g_loss: 1.5547\n",
      "Epoch [   10/   20] | batch:   301 | d_loss: 0.9235 | g_loss: 2.9890\n",
      "Epoch [   10/   20] | batch:   351 | d_loss: 0.8235 | g_loss: 1.8149\n",
      "Epoch [   10/   20] | batch:   401 | d_loss: 0.6793 | g_loss: 2.3275\n",
      "Epoch [   10/   20] | batch:   451 | d_loss: 0.7816 | g_loss: 3.2800\n",
      "Epoch [   10/   20] | batch:   501 | d_loss: 0.7944 | g_loss: 1.7540\n",
      "Epoch [   10/   20] | batch:   551 | d_loss: 0.9119 | g_loss: 1.2870\n",
      "Epoch [   10/   20] | batch:   601 | d_loss: 1.1382 | g_loss: 2.2872\n",
      "Epoch [   10/   20] | batch:   651 | d_loss: 0.8099 | g_loss: 1.2067\n",
      "Epoch [   10/   20] | batch:   701 | d_loss: 0.6867 | g_loss: 1.9326\n",
      "---------------Epoch [   10/   20]---------------\n",
      "Epoch [   11/   20] | batch:     1 | d_loss: 0.8320 | g_loss: 1.1754\n",
      "Epoch [   11/   20] | batch:    51 | d_loss: 0.6943 | g_loss: 1.6403\n",
      "Epoch [   11/   20] | batch:   101 | d_loss: 0.9558 | g_loss: 3.0082\n",
      "Epoch [   11/   20] | batch:   151 | d_loss: 0.8052 | g_loss: 2.7568\n",
      "Epoch [   11/   20] | batch:   201 | d_loss: 0.7848 | g_loss: 1.9918\n",
      "Epoch [   11/   20] | batch:   251 | d_loss: 0.8150 | g_loss: 1.4829\n",
      "Epoch [   11/   20] | batch:   301 | d_loss: 0.7332 | g_loss: 2.1810\n",
      "Epoch [   11/   20] | batch:   351 | d_loss: 0.8433 | g_loss: 1.8746\n",
      "Epoch [   11/   20] | batch:   401 | d_loss: 0.6758 | g_loss: 1.3094\n",
      "Epoch [   11/   20] | batch:   451 | d_loss: 0.8459 | g_loss: 1.6590\n",
      "Epoch [   11/   20] | batch:   501 | d_loss: 0.8478 | g_loss: 1.3339\n",
      "Epoch [   11/   20] | batch:   551 | d_loss: 0.8105 | g_loss: 2.5290\n",
      "Epoch [   11/   20] | batch:   601 | d_loss: 0.8847 | g_loss: 2.9163\n",
      "Epoch [   11/   20] | batch:   651 | d_loss: 0.9158 | g_loss: 3.0656\n",
      "Epoch [   11/   20] | batch:   701 | d_loss: 0.7829 | g_loss: 2.7488\n",
      "---------------Epoch [   11/   20]---------------\n",
      "Epoch [   12/   20] | batch:     1 | d_loss: 0.7352 | g_loss: 1.9948\n",
      "Epoch [   12/   20] | batch:    51 | d_loss: 0.9262 | g_loss: 1.6865\n",
      "Epoch [   12/   20] | batch:   101 | d_loss: 0.8878 | g_loss: 2.6026\n",
      "Epoch [   12/   20] | batch:   151 | d_loss: 0.7256 | g_loss: 1.7047\n",
      "Epoch [   12/   20] | batch:   201 | d_loss: 0.8756 | g_loss: 1.5276\n",
      "Epoch [   12/   20] | batch:   251 | d_loss: 1.1679 | g_loss: 0.8444\n",
      "Epoch [   12/   20] | batch:   301 | d_loss: 0.6936 | g_loss: 2.6643\n",
      "Epoch [   12/   20] | batch:   351 | d_loss: 0.8501 | g_loss: 2.4405\n",
      "Epoch [   12/   20] | batch:   401 | d_loss: 0.7047 | g_loss: 2.2575\n",
      "Epoch [   12/   20] | batch:   451 | d_loss: 0.6888 | g_loss: 2.6093\n",
      "Epoch [   12/   20] | batch:   501 | d_loss: 0.7325 | g_loss: 1.3854\n",
      "Epoch [   12/   20] | batch:   551 | d_loss: 1.0565 | g_loss: 3.9395\n",
      "Epoch [   12/   20] | batch:   601 | d_loss: 0.7159 | g_loss: 3.6967\n",
      "Epoch [   12/   20] | batch:   651 | d_loss: 0.7333 | g_loss: 1.4379\n",
      "Epoch [   12/   20] | batch:   701 | d_loss: 0.7963 | g_loss: 1.4610\n",
      "---------------Epoch [   12/   20]---------------\n",
      "Epoch [   13/   20] | batch:     1 | d_loss: 0.6865 | g_loss: 2.8950\n",
      "Epoch [   13/   20] | batch:    51 | d_loss: 0.9207 | g_loss: 1.5573\n",
      "Epoch [   13/   20] | batch:   101 | d_loss: 1.2883 | g_loss: 1.4500\n",
      "Epoch [   13/   20] | batch:   151 | d_loss: 0.8684 | g_loss: 2.6025\n",
      "Epoch [   13/   20] | batch:   201 | d_loss: 0.7481 | g_loss: 1.6527\n",
      "Epoch [   13/   20] | batch:   251 | d_loss: 0.8783 | g_loss: 1.3452\n",
      "Epoch [   13/   20] | batch:   301 | d_loss: 0.7375 | g_loss: 2.0968\n",
      "Epoch [   13/   20] | batch:   351 | d_loss: 0.6422 | g_loss: 2.5757\n",
      "Epoch [   13/   20] | batch:   401 | d_loss: 0.7280 | g_loss: 1.3058\n",
      "Epoch [   13/   20] | batch:   451 | d_loss: 0.7422 | g_loss: 1.4346\n",
      "Epoch [   13/   20] | batch:   501 | d_loss: 0.9727 | g_loss: 3.5498\n",
      "Epoch [   13/   20] | batch:   551 | d_loss: 0.6053 | g_loss: 2.1011\n",
      "Epoch [   13/   20] | batch:   601 | d_loss: 1.5844 | g_loss: 0.5962\n",
      "Epoch [   13/   20] | batch:   651 | d_loss: 0.5554 | g_loss: 2.5573\n",
      "Epoch [   13/   20] | batch:   701 | d_loss: 0.6035 | g_loss: 2.5171\n",
      "---------------Epoch [   13/   20]---------------\n",
      "Epoch [   14/   20] | batch:     1 | d_loss: 0.7339 | g_loss: 3.6615\n",
      "Epoch [   14/   20] | batch:    51 | d_loss: 0.8485 | g_loss: 2.3583\n",
      "Epoch [   14/   20] | batch:   101 | d_loss: 0.6495 | g_loss: 1.8807\n",
      "Epoch [   14/   20] | batch:   151 | d_loss: 0.5651 | g_loss: 1.4821\n",
      "Epoch [   14/   20] | batch:   201 | d_loss: 0.7518 | g_loss: 2.2164\n",
      "Epoch [   14/   20] | batch:   251 | d_loss: 0.7359 | g_loss: 1.7076\n",
      "Epoch [   14/   20] | batch:   301 | d_loss: 1.4818 | g_loss: 3.6353\n",
      "Epoch [   14/   20] | batch:   351 | d_loss: 0.8072 | g_loss: 3.1063\n",
      "Epoch [   14/   20] | batch:   401 | d_loss: 0.6912 | g_loss: 1.5440\n",
      "Epoch [   14/   20] | batch:   451 | d_loss: 0.7894 | g_loss: 1.2850\n",
      "Epoch [   14/   20] | batch:   501 | d_loss: 0.8157 | g_loss: 3.7143\n",
      "Epoch [   14/   20] | batch:   551 | d_loss: 0.8888 | g_loss: 4.6283\n",
      "Epoch [   14/   20] | batch:   601 | d_loss: 0.7662 | g_loss: 1.5938\n",
      "Epoch [   14/   20] | batch:   651 | d_loss: 0.8177 | g_loss: 1.5437\n",
      "Epoch [   14/   20] | batch:   701 | d_loss: 1.0911 | g_loss: 1.2381\n",
      "---------------Epoch [   14/   20]---------------\n",
      "Epoch [   15/   20] | batch:     1 | d_loss: 0.6332 | g_loss: 2.4432\n",
      "Epoch [   15/   20] | batch:    51 | d_loss: 0.5178 | g_loss: 2.3152\n",
      "Epoch [   15/   20] | batch:   101 | d_loss: 0.5655 | g_loss: 2.4855\n",
      "Epoch [   15/   20] | batch:   151 | d_loss: 1.4360 | g_loss: 4.9096\n",
      "Epoch [   15/   20] | batch:   201 | d_loss: 0.6065 | g_loss: 1.9198\n",
      "Epoch [   15/   20] | batch:   251 | d_loss: 0.6617 | g_loss: 2.7955\n",
      "Epoch [   15/   20] | batch:   301 | d_loss: 0.5642 | g_loss: 2.5870\n",
      "Epoch [   15/   20] | batch:   351 | d_loss: 0.7548 | g_loss: 1.6740\n",
      "Epoch [   15/   20] | batch:   401 | d_loss: 0.7114 | g_loss: 1.2314\n",
      "Epoch [   15/   20] | batch:   451 | d_loss: 0.7264 | g_loss: 2.8277\n",
      "Epoch [   15/   20] | batch:   501 | d_loss: 0.8249 | g_loss: 1.9137\n",
      "Epoch [   15/   20] | batch:   551 | d_loss: 0.6711 | g_loss: 1.9008\n",
      "Epoch [   15/   20] | batch:   601 | d_loss: 0.7163 | g_loss: 1.7807\n",
      "Epoch [   15/   20] | batch:   651 | d_loss: 0.7018 | g_loss: 1.8669\n",
      "Epoch [   15/   20] | batch:   701 | d_loss: 1.2824 | g_loss: 0.8433\n",
      "---------------Epoch [   15/   20]---------------\n",
      "Epoch [   16/   20] | batch:     1 | d_loss: 0.7795 | g_loss: 1.9912\n",
      "Epoch [   16/   20] | batch:    51 | d_loss: 0.7561 | g_loss: 2.7653\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [   16/   20] | batch:   101 | d_loss: 0.9359 | g_loss: 1.5608\n",
      "Epoch [   16/   20] | batch:   151 | d_loss: 0.5931 | g_loss: 2.3988\n",
      "Epoch [   16/   20] | batch:   201 | d_loss: 0.8050 | g_loss: 1.1277\n",
      "Epoch [   16/   20] | batch:   251 | d_loss: 0.4762 | g_loss: 2.5125\n",
      "Epoch [   16/   20] | batch:   301 | d_loss: 0.6604 | g_loss: 2.3707\n",
      "Epoch [   16/   20] | batch:   351 | d_loss: 1.1260 | g_loss: 1.0176\n",
      "Epoch [   16/   20] | batch:   401 | d_loss: 0.5498 | g_loss: 2.5035\n",
      "Epoch [   16/   20] | batch:   451 | d_loss: 0.6702 | g_loss: 2.3546\n",
      "Epoch [   16/   20] | batch:   501 | d_loss: 0.5987 | g_loss: 2.7054\n",
      "Epoch [   16/   20] | batch:   551 | d_loss: 0.6563 | g_loss: 1.9455\n",
      "Epoch [   16/   20] | batch:   601 | d_loss: 0.4369 | g_loss: 2.3982\n",
      "Epoch [   16/   20] | batch:   651 | d_loss: 0.6192 | g_loss: 1.6578\n",
      "Epoch [   16/   20] | batch:   701 | d_loss: 0.8847 | g_loss: 3.6035\n",
      "---------------Epoch [   16/   20]---------------\n",
      "Epoch [   17/   20] | batch:     1 | d_loss: 0.8319 | g_loss: 1.7466\n",
      "Epoch [   17/   20] | batch:    51 | d_loss: 0.8202 | g_loss: 2.0495\n",
      "Epoch [   17/   20] | batch:   101 | d_loss: 0.9077 | g_loss: 2.2762\n",
      "Epoch [   17/   20] | batch:   151 | d_loss: 0.9110 | g_loss: 1.5152\n",
      "Epoch [   17/   20] | batch:   201 | d_loss: 0.6959 | g_loss: 3.1359\n",
      "Epoch [   17/   20] | batch:   251 | d_loss: 0.6086 | g_loss: 3.1093\n",
      "Epoch [   17/   20] | batch:   301 | d_loss: 0.9669 | g_loss: 3.7573\n",
      "Epoch [   17/   20] | batch:   351 | d_loss: 0.6061 | g_loss: 2.2916\n",
      "Epoch [   17/   20] | batch:   401 | d_loss: 1.3833 | g_loss: 0.8206\n",
      "Epoch [   17/   20] | batch:   451 | d_loss: 0.7337 | g_loss: 1.2652\n",
      "Epoch [   17/   20] | batch:   501 | d_loss: 0.5545 | g_loss: 2.2966\n",
      "Epoch [   17/   20] | batch:   551 | d_loss: 0.4312 | g_loss: 2.7339\n",
      "Epoch [   17/   20] | batch:   601 | d_loss: 1.2815 | g_loss: 4.5698\n",
      "Epoch [   17/   20] | batch:   651 | d_loss: 0.5700 | g_loss: 2.5264\n",
      "Epoch [   17/   20] | batch:   701 | d_loss: 0.6704 | g_loss: 3.2834\n",
      "---------------Epoch [   17/   20]---------------\n",
      "Epoch [   18/   20] | batch:     1 | d_loss: 0.4970 | g_loss: 2.2410\n",
      "Epoch [   18/   20] | batch:    51 | d_loss: 0.6295 | g_loss: 4.3854\n",
      "Epoch [   18/   20] | batch:   101 | d_loss: 0.6976 | g_loss: 4.3022\n",
      "Epoch [   18/   20] | batch:   151 | d_loss: 1.0637 | g_loss: 2.3074\n",
      "Epoch [   18/   20] | batch:   201 | d_loss: 0.6216 | g_loss: 1.5955\n",
      "Epoch [   18/   20] | batch:   251 | d_loss: 0.6220 | g_loss: 2.0259\n",
      "Epoch [   18/   20] | batch:   301 | d_loss: 0.4838 | g_loss: 2.7805\n",
      "Epoch [   18/   20] | batch:   351 | d_loss: 0.7369 | g_loss: 3.7731\n",
      "Epoch [   18/   20] | batch:   401 | d_loss: 0.6132 | g_loss: 1.4443\n",
      "Epoch [   18/   20] | batch:   451 | d_loss: 0.5376 | g_loss: 3.1501\n",
      "Epoch [   18/   20] | batch:   501 | d_loss: 0.4992 | g_loss: 1.8216\n",
      "Epoch [   18/   20] | batch:   551 | d_loss: 0.9730 | g_loss: 3.3499\n",
      "Epoch [   18/   20] | batch:   601 | d_loss: 0.4792 | g_loss: 2.8586\n",
      "Epoch [   18/   20] | batch:   651 | d_loss: 0.6218 | g_loss: 2.6634\n",
      "Epoch [   18/   20] | batch:   701 | d_loss: 0.6697 | g_loss: 2.0219\n",
      "---------------Epoch [   18/   20]---------------\n",
      "Epoch [   19/   20] | batch:     1 | d_loss: 0.5197 | g_loss: 1.8495\n",
      "Epoch [   19/   20] | batch:    51 | d_loss: 0.5086 | g_loss: 1.6420\n",
      "Epoch [   19/   20] | batch:   101 | d_loss: 0.6986 | g_loss: 1.1413\n",
      "Epoch [   19/   20] | batch:   151 | d_loss: 0.6020 | g_loss: 1.8165\n",
      "Epoch [   19/   20] | batch:   201 | d_loss: 0.9451 | g_loss: 1.4040\n",
      "Epoch [   19/   20] | batch:   251 | d_loss: 0.4777 | g_loss: 3.1983\n",
      "Epoch [   19/   20] | batch:   301 | d_loss: 0.9183 | g_loss: 4.0350\n",
      "Epoch [   19/   20] | batch:   351 | d_loss: 0.4961 | g_loss: 2.3933\n",
      "Epoch [   19/   20] | batch:   401 | d_loss: 0.8045 | g_loss: 1.3214\n",
      "Epoch [   19/   20] | batch:   451 | d_loss: 0.5569 | g_loss: 2.1318\n",
      "Epoch [   19/   20] | batch:   501 | d_loss: 0.5449 | g_loss: 2.9328\n",
      "Epoch [   19/   20] | batch:   551 | d_loss: 0.5860 | g_loss: 1.8094\n",
      "Epoch [   19/   20] | batch:   601 | d_loss: 0.5079 | g_loss: 1.9517\n",
      "Epoch [   19/   20] | batch:   651 | d_loss: 0.5289 | g_loss: 2.2257\n",
      "Epoch [   19/   20] | batch:   701 | d_loss: 0.5177 | g_loss: 1.4699\n",
      "---------------Epoch [   19/   20]---------------\n",
      "Epoch [   20/   20] | batch:     1 | d_loss: 0.8772 | g_loss: 3.8187\n",
      "Epoch [   20/   20] | batch:    51 | d_loss: 1.0291 | g_loss: 4.1244\n",
      "Epoch [   20/   20] | batch:   101 | d_loss: 0.4135 | g_loss: 2.1966\n",
      "Epoch [   20/   20] | batch:   151 | d_loss: 0.4319 | g_loss: 2.7543\n",
      "Epoch [   20/   20] | batch:   201 | d_loss: 0.5398 | g_loss: 2.8416\n",
      "Epoch [   20/   20] | batch:   251 | d_loss: 0.4549 | g_loss: 2.5017\n",
      "Epoch [   20/   20] | batch:   301 | d_loss: 0.6065 | g_loss: 2.1791\n",
      "Epoch [   20/   20] | batch:   351 | d_loss: 0.3795 | g_loss: 3.5890\n",
      "Epoch [   20/   20] | batch:   401 | d_loss: 0.6528 | g_loss: 1.6973\n",
      "Epoch [   20/   20] | batch:   451 | d_loss: 0.5359 | g_loss: 1.7315\n",
      "Epoch [   20/   20] | batch:   501 | d_loss: 0.3617 | g_loss: 3.3402\n",
      "Epoch [   20/   20] | batch:   551 | d_loss: 0.5715 | g_loss: 2.3122\n",
      "Epoch [   20/   20] | batch:   601 | d_loss: 0.5038 | g_loss: 2.7294\n",
      "Epoch [   20/   20] | batch:   651 | d_loss: 0.4265 | g_loss: 2.0100\n",
      "Epoch [   20/   20] | batch:   701 | d_loss: 0.5678 | g_loss: 1.9734\n",
      "---------------Epoch [   20/   20]---------------\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 20\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "# call training function\n",
    "with active_session():\n",
    "    losses = train(D, G, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Training loss\n",
    "\n",
    "Plot the training losses for the generator and discriminator, recorded after each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f13f4acc128>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13f29f6be0>"
      ]
     },
     "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": 26,
   "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": 27,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13ca785748>"
      ]
     },
     "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",
    "The training data does not have the complete face. Features like chins are not visible. As a results, the generated images miss chins. Data should preferably have the complete face.\n",
    "\n",
    "Obtaining higher resolution images. I would like to study NVIDIA's face generation paper where they trained HD faces and can even merge two faces to crate a new one.\n",
    "\n",
    "A larger model size with more hidden dimensions would work better but take more training time\n",
    "\n",
    "Training for longer will be ueful as the discriminator loss is steadily decreasing anf the generator loss curve is jagged indicating, it is imagining new features well, many of which are incorrect, but that's a step in the right direction."
   ]
  },
  {
   "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
}