04-generative-adversarial-networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Generative Adversarial Networks (GANs)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Recommended Reading + Notebook Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Required Reading**\n",
    "\n",
    "1. [Goodfellow et al 2014](https://arxiv.org/pdf/1406.2661.pdf)\n",
    "2. [Style GAN](https://arxiv.org/pdf/1812.04948.pdf)\n",
    "\n",
    "**Additional Reading**\n",
    "\n",
    "1. [DCGAN](https://arxiv.org/pdf/1511.06434.pdf)\n",
    "2. [Wasserstein GAN](https://arxiv.org/pdf/1701.07875.pdf)\n",
    "3. [Improved Wasserstain GAN](https://arxiv.org/pdf/1704.00028.pdf)\n",
    "4. [BigGAN](https://arxiv.org/pdf/1809.11096.pdf)\n",
    "5. [Progressive GAN](https://arxiv.org/pdf/1710.10196.pdf)\n",
    "6. [CycleGAN](https://arxiv.org/pdf/1703.10593.pdf)\n",
    "7. [SRGAN](https://arxiv.org/pdf/1609.04802.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Ian Goodfellow Invents GANs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2014, graduate student Ian Goodfellow published this paper where he showed, that if under a certain set of assumptions, you solved the equation below, you could create fake data that was completely indistinguishable from real data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/goodfellow_gans_2014-01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, as is the case for many other interesting equations, we humans have no idea how to actually solve this equation in all but the most trivial cases. However, we have found some algorithms that allow us to compute approximate solutions. Goodfellow published one such algorithm in his paper, and using this approach, generated fake images that look…kinda crappy:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/goodfellow_synthetic_images.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So why are we talking about a 2014 paper that presents an equation for generating fake data that no one knows how to solve, and an algorithm that allows us to compute poor approximate solutions? Well, if we jump forward 5 years to 2019, Ian Goodfellow is now a director of machine learning at Apple, and the approximate solutions to his equation have gotten better. A lot better. Which of these three images do you think is fake?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/who_is_fake-01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/spacer_small.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The answer is…all three. None of these are images of people. Further, clever variants of Goodfellow’s idea have been used to do all kinds of other cool stuff, such as: \n",
    "\n",
    "- Fill in missing parts of images\n",
    "- Transform low-resolution images in realistic high-resolution versions (super resolution)\n",
    "- Generate captions from images\n",
    "- Generate images from captions \n",
    "- Compose interesting music\n",
    "- Synthesize raw audio\n",
    "- Synthetically age faces in images\n",
    "- Make fake videos of real people dancing\n",
    "- Generate training data for other machine learning models\n",
    "- Predict depth maps from images\n",
    "- Find anomalies in medical images\n",
    "- Compute the mapping from the style of one video to another"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty crazy, right? Goodfellow called his approach a generative adversarial network, or GAN. Now, when I first learned about GANs, a couple questions came to mind. First, how the heck to these things actually work? How exactly does solving Goodfellow’s equation allow us to create such realistic fake data? How did GANs get so much better from 2014 to 2019?\n",
    "\n",
    "And secondly, what does all of this mean? What does it mean that for arguably the first time in human history, we can create completely photorealistic images of people that don’t exist? Are there limitations to GANs, and if so, what are they? I find that when trying to understand a new technology, there’s really no substitute for building it. So that’s what we we’re going to do here. Let's start by having a closer look at Goodfellow's equation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goodfellow's Idea\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/gan_simplified-01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generative Adversarial Networks are composed of 2 seperate neural networks: a Generator (G), and a Discriminator (D). The Discriminator's job is to determine if it's input is real or fake. A good discriminator will return a value of 1 for real input data, and value of 0 for fake input data. In practice, discrimators are exactly like the neural networks we've used to solve classification problems! The Generator's job is to transform random vectors into realistic fake data. The way GANs are trained is a bit different than the neural networks we've seen so far in this course. Instead of optimizing a single objective function, here we have **two networks with competing goals**. Goodfellow's equation above is a propossed way to expresses the competition mathematically. \n",
    "\n",
    "Goodfellow's equation makes use of the expected value $(\\mathbb{E})$. Here, the expected value really just means to average over a bunch of examples. Notice that if you remove the expected values in the equation above, we're left with something very similar to **Binary Cross Entropy**, with the minus sign removed. Thinking through each term above, we can see that $V(D, G)$ will be maximized when $D(x)=1$, and $D(G(z))=0)$, meaning our discriminator perfectly classifies our real and fake images. Alternatively, our generator is doing well when $D(G(z))=1$ (and has no control over the $D(x)$ term), so a good generator will minimize $V(D, G)$.What goodfellow proposes with $\\min_G\\max_D$, is that we find a Discriminator that maximizes V, while finding a generator that minimizes V. If this sounds a little conflicting, it is!\n",
    "\n",
    "A more natural way to think about what Goodfellow is describing here is as **a game.** We have two players $D$ and $G$. Each player can change its own paremeters, let's call these $\\theta^{(D)}$ and $\\theta^{(G)}$, but has no control over the other player's parameters. The solution to the optimization problems we've seen so far is a local minimum, but the solution to this type of game is known as a **Nash Equilibrium**. What might our **Nash Equilibrium** look like in the paremeter space of our models? We'll explore this idea shortly, but I encourage you to give it a little thought first - it's pretty cool! For lots more information on the GAN forumlation check out [Ian Goodfellow's 2016 NIPS Tutorial](https://arxiv.org/pdf/1701.00160.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Let's Build the World's Simplest GAN\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start really simple. Instead of creating full fake images, let's start with grayscale images that are only 1x1 pixels - so just one number! I know this seems trivial, but it will give us a sense for how the whole GAN works, and then we can expand up to full images. Instead of actually importing images, we'll make some \"real data\" by sampling from a gaussian distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from tqdm import tqdm\n",
    "from ipywidgets import interact\n",
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Set plotting style and minimum font size\n",
    "from jupyterthemes import jtplot; jtplot.style()\n",
    "plt.rcParams['figure.figsize'], plt.rcParams['lines.linewidth'] = (10,6), 2\n",
    "for k in plt.rcParams:\n",
    "    if 'size' in k:\n",
    "        try: plt.rcParams[k] = 18\n",
    "        except:pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 32 #How many examples do we want?\n",
    "x = torch.randn(n) + 3 #\"Real\" Data for now, in practice this will be our real data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([2.3315, 2.5618, 2.1476, 3.3819, 1.3855, 2.8628, 4.3531, 1.5069, 2.3776,\n",
       "        3.9692, 1.2518, 2.4372, 3.0909, 3.1859, 3.1704, 1.6799, 2.6347, 3.5930,\n",
       "        2.3994, 2.6218, 2.6303, 2.4492, 3.4754, 3.0570, 3.6516, 2.0394, 1.2151,\n",
       "        4.5903, 2.6275, 2.2423, 2.8841, 3.7059])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = figure(0, (12, 4)); ax=fig.add_subplot(111)\n",
    "scatter(x.numpy(), np.zeros(len(x)), marker='x', c='xkcd:purple'); xlim([0, 6]); grid(0)\n",
    "ax.get_yaxis().set_ticks([0]), ax.get_yaxis().set_ticklabels(['Real Data $x$']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create some fake data. We'll start with a sampling our random noise vector $z$ from gaussian distribution. Something we haven't discussed yet is that **we get to choose the size of z**. In this case, since we're trying to build the world's simplest GAN, we'll start with a z dimension of 1. Just as with our real data, we'll sample a batch of size $n$. It's worth noting that z is often called a **latent variable** in the context of GANs, and when we make changes to our random vector $z$, this is often referred to as working in the \"latent space\". This interpretation will become more clear as we get deeper into GANs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 1.2561, -0.7220,  2.1083,  0.8234, -0.9590,  0.7406, -0.3957,  0.2896,\n",
       "        -2.3038,  0.7644, -0.2717, -0.6227,  0.0930, -0.6600, -1.7431, -0.9907,\n",
       "        -0.3737, -0.1735, -0.8092,  0.7771, -2.4389,  0.0740,  1.8695,  0.6293,\n",
       "        -0.1055, -0.8009,  0.4687,  0.8661,  1.9818, -0.5856,  0.4264, -0.0404])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = torch.randn(n) #32 examples of noise z. \n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, to create fake data, we need a generator function. In the spirit of keeping things simple, our generator function will just have one single parameter, we'll call it $b_G$, that we add to your input data. So $G(z)=z+b_G$. Generators in successful GANs are typically neural networks, but the only strict requirment here is that $G$ is differentiable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.b = torch.tensor([0.0])\n",
    "    def forward(self, x): return x+self.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 1.2561, -0.7220,  2.1083,  0.8234, -0.9590,  0.7406, -0.3957,  0.2896,\n",
       "        -2.3038,  0.7644, -0.2717, -0.6227,  0.0930, -0.6600, -1.7431, -0.9907,\n",
       "        -0.3737, -0.1735, -0.8092,  0.7771, -2.4389,  0.0740,  1.8695,  0.6293,\n",
       "        -0.1055, -0.8009,  0.4687,  0.8661,  1.9818, -0.5856,  0.4264, -0.0404])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = Generator() #We can now instantiate our Generator and create some fake data!\n",
    "G(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def vis_data(ax, G, x, z):\n",
    "    scatter(x.numpy(), np.ones(len(x)), marker='x', c='xkcd:purple');\n",
    "    scatter(G(z).numpy(), np.zeros(len(x)), marker='x', c='xkcd:sea blue'); xlim([-3, 5]); ylim([-1,2]); grid(0)\n",
    "    ax.get_yaxis().set_ticks([0, 1]), ax.get_yaxis().set_ticklabels(['Fake Data $G(z)$', 'Real Data $x$']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = figure(0, (12, 3)); ax=fig.add_subplot(111); \n",
    "vis_data(ax, G, x, z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, so we now have some real data $x$, and some fake data $G(z)$. Based on the plot above, how do you think our generator is doing? How good is our fake data? Let's see how changing our Generator parameter $b_G$ changes our fake data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def play_with_generator(b=0):\n",
    "    G.b.set_(torch.tensor(b,dtype=torch.float));\n",
    "    fig = figure(0, (12, 3)); ax=fig.add_subplot(111)\n",
    "    vis_data(ax, G, x, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5047cf881dbf4472a6edafd4dec16cc6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='b', max=4.0, min=-4.0), Output()), _dom_classes=('wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.play_with_generator(b=0)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(play_with_generator, b = (-4.0, 4.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following the GAN approach here, we should use a discriminator function to seperate our real from fake examples. Again, instead of using a full-blown neural network here, we'll start with a really simple function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.b = torch.tensor([1.0])\n",
    "    def forward(self, x): return torch.exp(-(x - self.b)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D = Discriminator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def vis_discriminator(ax, D):\n",
    "    xs = torch.linspace(-5, 5, 100)\n",
    "    ax.plot(xs.numpy(), D(xs).numpy(), c='xkcd:goldenrod')\n",
    "    title('$D$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def play_with_discriminator(b=0):\n",
    "    D.b.set_(torch.tensor(b,dtype=torch.float));\n",
    "    fig = figure(0, (12, 6)); ax=fig.add_subplot(111)\n",
    "    vis_discriminator(ax, D); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6665fb00b7a49e688e9b04e0ff07f4a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='b', max=4.0, min=-4.0), Output()), _dom_classes=('wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.play_with_discriminator(b=0)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(play_with_discriminator, b = (-4.0, 4.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, our discriminator is a really simple bell-shaped curve with one parameter that shifts our curve to the left or right: $D(x)=e^{-(x-b_D)^2}$. Remember that our discriminator is supposed to return the probability that an an input example $x$ is real. In the case of our super simple discriminator, the closer our input $x$ is to our single discriminator parameter $b_D$, the higher the returned probability of x being real is, reaching a maximum value of 1 when $x=b_D$. Now that we've chosen functions for our discriminator and generator, let's think about how whole systems works. \n",
    "\n",
    "$$ D(x)=e^{-(x-b_D)^2} $$\n",
    "$$ G(z)=z+b_G$$ \n",
    "$$\n",
    "\\min_G \\max_D V(D, G)=\n",
    "\\mathbb{E}_{x\\sim p_{data}(x)}[\\log D(x)]\n",
    "+ \\mathbb{E}_{z\\sim p_z(z)}[\\log(1 - D(G(z)))]\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def probe_value_function(D, G, x, z):\n",
    "    bs = torch.linspace(-1, 7, 64)\n",
    "    V = torch.zeros((len(bs), len(bs)))\n",
    "    for i, bG in enumerate(bs):\n",
    "        G.b.set_(bG)\n",
    "        for j, bD in enumerate(bs):\n",
    "            D.b.set_(bD) \n",
    "            V[i, j] = torch.mean(D(x)) + torch.mean(1-D(G(z))) #Ignore logs in value function for now\n",
    "    return meshgrid(bs, bs), V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def vis_value_function(ax, grids, V, bd=None, bg=None):\n",
    "    f=contourf(*grids, V); grid(0); colorbar();\n",
    "    if (bd, bg) is not (None, None): ax.scatter(bd, bg, color=(1, 0, 1), s=50)\n",
    "    title('$V(D, G)$'); xlabel('$b_D$'); ylabel('$b_G$'); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grids, V = probe_value_function(D, G, x, z)\n",
    "vis_value_function(ax, grids, V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def play_with_gan(bd=0, bg=0):\n",
    "    G.b.set_(torch.tensor(bg,dtype=torch.float));\n",
    "    D.b.set_(torch.tensor(bd,dtype=torch.float));\n",
    "    gs = gridspec.GridSpec(3, 2)\n",
    "    fig = figure(0, (18, 6)); ax=fig.add_subplot(gs[:2, 0]); vis_discriminator(ax, D)\n",
    "    ax=fig.add_subplot(gs[2, 0]); vis_data(ax, G, x, z)\n",
    "    ax=fig.add_subplot(gs[:, 1]); vis_value_function(ax, grids, V, bd, bg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "976095b912fe49dbac05db23dd998005",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='bd', max=4.0, min=-4.0), FloatSlider(value=0.0, desc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(play_with_gan, bd=(-4.0, 4.0), bg=(-4.0, 4.0));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\min_G \\max_D V(D, G)= \\frac{1}{m} \\sum_{i=1}^{m} D(x_i)\n",
    "+ \\frac{1}{m} \\sum_{i=1}^{m} \\big( 1 - D(G(z_i))) \\big)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to get a sense for Goodfellow's equation using our interactive plot. Note that we've removed the logs from Goodfellow's equation to make the visual more clear - the logs are mostly here for computational reasons - the logs make computing these values on a computer easier, but do not change the solution to the equation. Remember what Goodfellow's equation is asking us to do two things at once: 1. Find the generator $G$ that minimizes $V$. 2. Find the discriminator $D$ that maximizes $V$. In Goodfellows original formulation, $G$ and $D$ are expressed as arbitrary functions. However, in practice (as Goodfellow goes on to layout in his paper), we fix $D$ and $G$ to be some specific functions (like a specific neural network), and search for the parameters of our functions, instead of searching accross all functions (because we have no tractible way of doing this!). For us, since we've chosen really simple functions that only have one parameter each, we only have two parameters to tune! \n",
    "\n",
    "Notice how changing each of our variables, changes our plot above, especially the value function (the magenta point) in the value function plot to the right. Let's try to achieve what Goodfellow's equation is asking us to do manually. Let's start with the $max_D$ part, if we adjust our Discriminator parameter $b_D$, notice that as we increase $b_D$, our dicriminator curver moves more on top of our data, increasing the value of $\\frac{1}{m} \\sum_{i=1}^{m} D(x_i)$, while moving away from our fake data, decreasing the value of $\\frac{1}{m} \\sum_{i=1}^{m} D(G(z_i))$. The next effect here is to increase our value function. We can see this clearly on our heatmap plot, reaching a maximum around $D_b=3$. Alright, so we've done half of what Goodfellow has asked us to do! For at least this one Generator we've found $\\max_D V(D, G)$ by setting $b_D$ to approximately 3. \n",
    "\n",
    "Now, of course, we're only half way there! Let's now consider the $max_G$ part of Goodfellow's equation. This part asks us to find the valug of $b_G$ that *minimizes* V. If we start fiddling with $b_G$, notice that increasing $b_G$ brings our value function down. This is becuase we're moving more of our fake data beneath the large valued parts of our discriminator, increasing the value of $\\frac{1}{m} \\sum_{i=1}^{m} D(G(z_i))$, decreasing $V$, and \"fooling\" our discriminator with more realistic fake data! $V$ reaches a minimum when $b_G$ is around 3, by no coincidence, right when our fake data ends up directly below our discriminator curve!\n",
    "\n",
    "Ok, so with our \"brute force\" method, we've solved goodfellow's equation! We've found a Discriminator configuration that maximimizes $V$, while simultaneously finding a Generator configuration that minimizes $V$. Pretty cool, right?\n",
    "\n",
    "This type of solution is called a **Nash Equibrium**, becuase we've found a solution where if either player **unilaterally** changes their strategy, they will be worse off. In the parameter space of our value function, Nash equibruim look like saddle points! So while in most machine learning problems we're looking for local (or global :)) minima, with GANs we're looking for something different. We're looking for points in the paremeter space where our functions are **in balance.**. We're looking for Nash Equilibria. \n",
    "\n",
    "Now, a good follow up question is...why? Why do we care about finding Nash Equilibria in the parameter space of this value function? Well, as we mentioned at the beginning of this lecture, it turns out that if you're able to find this solution, to solve Goodfellow's equation, **your fake data will be indistinguishable from your real data**. That is, as Goodfellow goes on to prove in his paper, if you find this solution, the fake data the comes our of your Generator will have the same exact probability distribution as your real data. \n",
    "\n",
    "This is pretty boring in one dimension as in the example above, but in the case high dimenional data, such as images - this result is remarkable. Now, before we get too excited here, there's a couple of cavaets. For one, Goodfellow's proof only holds up for the set of continuous functions, not for parameterized functions like neural networks. But more importantly, **we haven't figured out how to find these Nash Equilibria in the general case!** In our toy example above, we found the solution by hand - a strategy that definitely won't work in many dimensions over 2!\n",
    "\n",
    "Fortunately, Goodfellow also presents a computationally feasible strategy for finding these Nash Equilibria:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/goodfellow_alg_one-01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's spend some time breaking down goodfellow's algorithm. First, in the algorithm description, notice that Goodfellow is proposing we use stochastic gradient descent to find a solution to our equation, just as we do when training supervised neural networks. So the idea is we'll take lots of small steps along the gradients or our Discrimiantor and Generator (with respect to their parameters). Specifically, we start by sampling a \"minibatch of noise\", and a minibatch from the data generating distribution $p_{data}(x)$. In practice, this just means that we need to take a random sample of example from our dataset, just as we do when training an supervised neural network. After sampling our data, we compute the gradient of our value function with respect to our discriminator parameters, and use this gradient to update our discriminator parameters. Next, we sample another \"random minibatch\", and computer the gradient of our value function again, but this time with repsect to our Generator parameters. Notice that Goodfellow has ommitted the $log D(x^{(i)})$ from the value function when computing the gradient of our generator - hopefully this makes sense - this term would be $0$, becuase changing our generator parameters has no impact on our real data $x$. Let's implement Goodfellow's algorithm in PyTorch!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.b = nn.Parameter(torch.tensor([0.0])) #Making this an nn.Parameter now, gradient will be computed\n",
    "    def forward(self, x): return x+self.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.b = nn.Parameter(torch.tensor([1.0]))\n",
    "    def forward(self, x): return torch.exp(-(x - self.b)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_steps=512; lr=0.1 #Let's run goodfellow algorithm for num_steps at a learning rate of lr\n",
    "\n",
    "D=Discriminator(); G=Generator()\n",
    "n = 32 #How many examples do we want?\n",
    "x = torch.randn(n) + 3 #\"Real\" Data for now, in practice this will be our real data. \n",
    "z = torch.randn(n) #Note that goodfellow's algorithm says we should re-sample this each time, for now, we'll just sampel once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 512/512 [00:00<00:00, 1910.89it/s]\n"
     ]
    }
   ],
   "source": [
    "params = {'G':[],'D':[]} #Keep track of parameters while training\n",
    "\n",
    "for i in tqdm(range(num_steps)):\n",
    "    #Train Discriminator\n",
    "    V=torch.mean(D(x)) + torch.mean(1-D(G(z))) #Leave logs out for now.\n",
    "    V.backward() #Perform backprop\n",
    "    with torch.no_grad():\n",
    "        D.b+=D.b.grad*lr #Gradient Ascent\n",
    "    params['D'].append(D.b.item()) #Log Paremeters\n",
    "    D.zero_grad(); G.zero_grad();\n",
    "    \n",
    "    #Train Generator\n",
    "    V=torch.mean(1-D(G(z))) #Leave logs out for now.\n",
    "    V.backward() #Perform backprop\n",
    "    with torch.no_grad():\n",
    "        G.b-=G.b.grad*lr #Gradient Descent\n",
    "    params['G'].append(G.b.item()) #Log Paremeters\n",
    "    D.zero_grad(); G.zero_grad(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    grids, V = probe_value_function(D, G, x, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10be0d310>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = gridspec.GridSpec(3, 2)\n",
    "fig = figure(0, (18, 6)); ax=fig.add_subplot(gs[:2, 0]); \n",
    "with torch.no_grad(): \n",
    "    D.b.set_(torch.tensor(params['D'][i]))\n",
    "    vis_discriminator(ax, D)\n",
    "\n",
    "ax=fig.add_subplot(gs[2, 0]); \n",
    "with torch.no_grad(): \n",
    "    G.b.set_(torch.tensor(params['G'][i]))\n",
    "    vis_data(ax, G, x, z)\n",
    "\n",
    "ax=fig.add_subplot(gs[:, 1]); vis_value_function(ax, grids, V)\n",
    "plot(params['D'][:i+1], params['G'][:i+1], color=(1,0,1), linewidth=3)\n",
    "scatter(params['D'][i], params['G'][i], color=(1,0,1), s=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, as you can see above, using goodfellow's algorithm, we found the Nash Equilibrium in our value function! Further, our fake data distribution has been shifted by our generator to be close to our real data. Let's watch Goodfellow's algorithm in action:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/1d_gan_a.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/spacer_small.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And of course, in 3D :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/1d_gan_b.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty cool, right!? We've built a really simple GAN. As you can see, using Goodfellow's Gradient Descent algorithm, we have found our Nash equilibrium, and our fake data disribution looks pretty close to our real data! Now, this of course just really simple a 1-dimensional example - we could have solved this problem with much simpler methods! But what's really remarkable is how well **Goodfellow's approach scales to higher dimensional data.** We'll explore this next. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](../graphics/spacer_small.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. A Dive into Higher Dimensions\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, so we've seen how to apply Goodfellow's algorithm in one dimension - now our job is to apply Goodfellow's appraoch to much more interesting, higher dimensional data. Let's start with a relatively simple dataset, the MNIST handwritten digit dataset.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "#Let's automate some data loading with fastai vision (a torchvision replacement)\n",
    "from fastai.vision import *\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') #Do we have a GPU?\n",
    "defaults.device = device\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the super handy [ImageDataBunch](https://docs.fast.ai/vision.data.html#ImageDataBunch) from fastai to handle our data. We'll also go ahead and choose a batch size, and normalize our data> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size, im_size, channels = 128, 32, 1\n",
    "\n",
    "path = untar_data(URLs.MNIST)\n",
    "tfms = ([*rand_pad(padding=3, size=im_size, mode='zeros')], [])\n",
    "data = ImageList.from_folder(path, convert_mode='L').split_none() \\\n",
    "                                                    .label_empty() \\\n",
    "                                                    .transform(tfms, size = im_size) \\\n",
    "                                                    .databunch(bs=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(figsize=(8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y=data.one_batch()\n",
    "hist(x.numpy().ravel(),100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you may have guessed, our simple 1-parameter models from our Toy GAN really aren't going to cut it here. We need a more sophisticated generator and discriminator to make fake handwritten digits. We'll use an architecture similar to the architecture Goodfellow used in his original work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.1 Generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our generator may look different than the neural networks you may have seen before. The reason for this is that, unlike most neural networks that map from high-dimension to low-dimensional space (e.g. 224x224x3 color images to 10 discrete classes), our Generator needs to map from a low-dimensional random noise vector to a higher dimensional image. Goodfellow used random noise vectors of length, and for the most part, researchers continue to use noise vectors around this size. The challenge here is that we would like to map from smaller to bigger images, would still like to use convoultions for the speed, efficiency, and proven effectiveness, but the traditional convolution gives us no means of producing a larger image than we started with. \n",
    "\n",
    "Fortunatley, someone much smarter than me has already thought of this, and developed a method for doing exactly this: **transposed** or **fractionally strided** convolutions. There's a [terrific paper](https://github.com/vdumoulin/conv_arithmetic) on this by Vincent Dumoulin and Francesco Visin, I've borrowed an animation from their work below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"width:100%; table-layout:fixed;\">\n",
    "  <tr>\n",
    "    <td><img width=\"150px\" src=\"graphics/no_padding_no_strides_transposed.gif\"></td>\n",
    "    <td><img width=\"150px\" src=\"graphics/arbitrary_padding_no_strides_transposed.gif\"></td>\n",
    "    <td><img width=\"150px\" src=\"graphics/same_padding_no_strides_transposed.gif\"></td>\n",
    "    <td><img width=\"150px\" src=\"graphics/full_padding_no_strides_transposed.gif\"></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>No padding, no strides, transposed</td>\n",
    "    <td>Arbitrary padding, no strides, transposed</td>\n",
    "    <td>Half padding, no strides, transposed</td>\n",
    "    <td>Full padding, no strides, transposed</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><img width=\"150px\" src=\"graphics/no_padding_strides_transposed.gif\"></td>\n",
    "    <td><img width=\"150px\" src=\"graphics/padding_strides_transposed.gif\"></td>\n",
    "    <td><img width=\"150px\" src=\"graphics/padding_strides_odd_transposed.gif\"></td>\n",
    "    <td></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>No padding, strides, transposed</td>\n",
    "    <td>Padding, strides, transposed</td>\n",
    "    <td>Padding, strides, transposed (odd)</td>\n",
    "    <td></td>\n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv_trans(ni, nf, ks = 4, stride = 2, padding = 1):\n",
    "    return nn.Sequential(\n",
    "        nn.ConvTranspose2d(ni, nf, kernel_size=ks, bias=False, stride=stride, padding = padding), \n",
    "        nn.ReLU(inplace = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G = nn.Sequential(\n",
    "    conv_trans(100, 512, ks=4, stride=1, padding=0), \n",
    "    conv_trans(512, 256), \n",
    "    conv_trans(256, 128),\n",
    "    nn.ConvTranspose2d(128, channels, 4, stride=2, padding=1),\n",
    "    nn.Sigmoid()).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([128, 1, 32, 32])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = torch.randn(batch_size, 100, 1, 1)\n",
    "fake = G(z.to(device))\n",
    "imshow(fake[0, 0].cpu().detach().numpy()); grid(0)\n",
    "fake.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there you have it, a fake image! Doesn't look to convincing yet, but of course, we haven't trained our GAN yet!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.2 Disciminator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our discriminator will be much closer to the deep models we've seen before, we'll use 4 convolutional layers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv(ni, nf, ks=4, stride=2, padding=1):\n",
    "    return nn.Sequential(\n",
    "        nn.Conv2d(ni, nf, kernel_size=ks, bias=False, stride=stride, padding=padding), \n",
    "        nn.ReLU(inplace = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D = nn.Sequential(\n",
    "    conv(channels, 128), \n",
    "    conv(128, 256), \n",
    "    conv(256, 512), \n",
    "    nn.Conv2d(512, 1, 4, stride=1, padding=0), \n",
    "    Flatten(), \n",
    "    nn.Sigmoid()).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([128, 1])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D(fake).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from torch import optim\n",
    "from tqdm import tqdm\n",
    "from IPython import display\n",
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_progress():\n",
    "    '''Visualization method to see how were doing'''\n",
    "    clf(); fig = figure(0, (24, 12)); gs = gridspec.GridSpec(6, 12)\n",
    "    with torch.no_grad(): fake = G(z_fixed)\n",
    "    for j in range(30):\n",
    "        fig.add_subplot(gs[(j//6), j%6])\n",
    "        imshow(fake[j, 0].detach().cpu().numpy(), cmap='gray'); axis('off')\n",
    "    ax=fig.add_subplot(gs[5, :4]); hist(fake.detach().cpu().numpy().ravel(), 100, facecolor='xkcd:crimson')\n",
    "    ax.get_yaxis().set_ticks([]); xlabel('$G(z)$'); xlim([-1, 1])\n",
    "    ax=fig.add_subplot(gs[5, 4:7]); hist(x.cpu().numpy().ravel(), 100, facecolor='xkcd:purple')\n",
    "    ax.get_yaxis().set_ticks([]); xlabel('$x$')\n",
    "    fig.add_subplot(gs[:,7:])\n",
    "    plot(losses[0], 'xkcd:goldenrod'); plot(losses[1], color='xkcd:sea blue'); legend(['Discriminator', 'Generator'],loc=1); \n",
    "    grid(1); title('Epoch = ' + str(epoch)); ylabel('loss'); xlabel('iteration');\n",
    "    display.clear_output(wait=True); display.display(gcf())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike our toy example, intead of regular SGD we're going to use the **Adam Optimizer**. We'll use a seperate optimizer PyTorch optimizer for our discriminator and generator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optD = optim.Adam(D.parameters(), lr=2e-4, betas = (0.5, 0.999))\n",
    "optG = optim.Adam(G.parameters(), lr=2e-4, betas = (0.5, 0.999))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need to make a few more changes to our Toy GAN. First, we need to spend a little time on the **Value Function**. In practice, our Toy GAN will struggle to learn on real data becuase of the way our value function behaves when our generator creates very bad fake data. Goodfellow points this out in his original publication. The idea here is that the Generator loss function saturates when $D(G(z))$ is close to zero, and does not provide much gradient to learn from. Goodfellow proposes a Hueristic solution, replacing $log(1-D(G(z)))$ with $-log(D(G(z)))$. Both curves monotonically decrease as $D(G(z))$ increases, but $-log(D(G(z)))$ gives us a much stronger gradient when $D(G(z))$ is close to zero (helping our generator learn more quickly when its predictions are very different from real data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/Hueristic_GAN_Loss-01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, before we train our GAN, we need to be a little careful about how we implement our loss function. Instead of directly implementing our loss function explicitly in pytorch as `torch.mean(torch.log(D(G(z))) + torch.mean(torch.log(1-D(G(z)))`, we'll use PyTorch's [`nn.BCELoss()`](https://pytorch.org/docs/stable/nn.html#bceloss). `nn.BCELoss` is more stable computationally, and in my experience can really make the difference between a model that converges and one that doesn't. To use `nn.BCELoss`, we'll break up the cost function into two parts, on for our real data and one for our fake data, and add these two parts together before taking our gradient. This is where libraries like PyTorch realy shine, just as a results of us adding the two parts of our loss function together, Pytorch knows how to backpropogate errors to each portion of our loss function!\n",
    "\n",
    "It's worth taking some time walking thought the training code below to make sure each step makes sense. We'll visualize our GAN performance as we train. Measing and Visualizing GAN Performance can be difficult, becuase there's not unique measure of how realistic our fake data is (If there was, we could just optimize this directly!). For this reason, it's become popular to visualize generated images during traininng to get an idea of how our GAN is doing. We'll do this for a fixed set of latent vectors, `z_fixed`, visualizing the output of our generator from a fixed set of inputs will make our training animation easier to follow. We'll also visualize the distribution of a batch of our training and synthetic data, and finally, we'll plot our discriminator and generator loss functions. Note that in Goodfellow's original GAN formulation, our Discriminator and Generator loss were the same quantity (The value function)! But our modified formulation means that our Generator loss is a bit different than our Discrminator loss. However, our losses still quantify opposing goals, so a lower discriminator loss will correspond to a higher generator loss and vice versa. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "criterion = nn.BCELoss()\n",
    "zero_labels = torch.zeros(batch_size).to(device)\n",
    "ones_labels = torch.ones(batch_size).to(device)\n",
    "epochs, viz_freq = 1, 50\n",
    "z_fixed = torch.randn(batch_size, 100, 1, 1).to(device) #Let's use a fixed latent vector z for viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yW7K+aSVk+IPs7OzQeXl5oWk7Pfzww0P369dPkvTuu+/Gseeff77Qc2nf5Dm0ye67776haQOlxXHChAmhjzjiiNC0tb700kuhWZ8MlZBYsmmBfeGFF0IfddRRoWnXf/jhh0MzTAMtsxwL6YZjkX2VY5HZDmnjnzVrVuj27duHTuz9tNXm5uaGPuGEE0KzL3z44YehabM96KCDQid9RCqwWEup1uf+/fuH5ljkfMCyH3zwwaGbNGkiKXVe4riknbZ27dqhORbZRxgOobhCENCazbHI8CPUHF+0abdo0WIdzXHLkCKtWrUK3a5du9AvvvhiaPaVSy65JDTDxPB8tj/7E+uZ8yLnIIYmKCxMAPtHp06dQtM+n4SfkFL7Lu3czOKeTjhv0MbNuSJZJ6TUtYptzrk/sa2zzmgp33///UO3adMm9IgRI0Kz/c8+++zQbMMxY8aEnjNnTujDDjssNEN+cD7lGOb1k/AIU6ZMiWMTJ04M3bdv39Cc53k9thUt87ThpxNel+sg1xuOFc6JDCfUsmXL0Oedd54k6ZVXXoljTz31VOgLLrggNEMrPfbYY6E5Dvlcw3mVISVYrvvuuy80Q40MGzYsNMdbUl6p4NmKoUs++eST0JxDGAKB4aI4Lli/XJ+NMcYY89di9erVWrFiRcr/TcmxcuXKv21blC5dOuX3+Q2lxF7AGmOMMcYYY4wxxhizatUqLV26VBkZGSpdurTq16+f1pdf5n/j79wWyUvn1atXq1y5chtcB34Ba4wxxhhjjDHGGGNKjKVLl4azZvXq1crIyFCZMmVSHJtm4+O2yK+D33//fYMTMpfYC1jaq2mPq169emhak4855pjQM2fODD1jxozQiW2zQ4cOcYzWXNr/mb2dFr7TTjstNC155cuXL7QstEcy2zktvkceeWRo2jN5/SSjL62USfZjKdU2Sstwo0aNQieZ56VUKzntjOmE1u7BgweHZlZz3sPxxx8fmpmgaTX/5ptvJKWGAkiysUupts4ff/wx9K233hr6oosuCk0rJa3GtPozBAX7Ba2SDAFx7rnnhma4g8QG3KtXrzhWVKZmWrlpu6WtkzZ2ZitPN5xE2D85FmnlZduMHTs2NEM5JBmtDz300DhGSz/HPG35J510Uuibbrop9J133hm6YcOGoRn2g2X56KOPQtOezTAktMR/9tlnoRO7ee/eveMYQxDQEst7op2bcxevzfAF6YSZ79lvk7AYUuqcQAv+hRdeGJrhMJJxzPZ+4oknQtP+X7Zs2dA8/9FHHw19zTXXhOZYZKgBWsxpb6FlvVu3bqE557Gek3Y555xz4hj7J63UDEXRvXv30LRwf/DBB6EZyiGdbLXVVqFpo+d8yvmP/fOUU04JzbU1sWnz3IsvvrjQ6zFcCsfhmWeeGZohPDgnnX/++aG7dOkSmvMpw/VwLWCbf/7556GTeXnAgAFxjOsJ5w0eZ9gb3lNRIUfSCUNlPP7446FZnwzpwHWFzyEMGZGEFmLIhSeffDI01y9qPqdwrrv//vtDM4xE27ZtQ99xxx2h2W7JvC6lzskMC3PPPfeErlChgiTpH//4Rxy7+uqrQ3PtYcgPrs9vvPFGaI7b4gzLY4wxxpiNT/KSz5hNkVKlSikjI0OrVq3aoF2wf789xMYYY4wxxhhjjDFmk2DlypV/S4u72XwoXbr0Bud0cQgCY4wxxhhjzDpkZWbXlvSNpENy83Imru/8tT67r6TXJd2Qm5dzaXGUzxhjjDFmc6HEXsDSUl+vXr3QDBNAqyft+rSn0Z6Z2BcTG7uUmuWXGbZpFXz66adD095Ma/jo0aND0+pK6y8z/datWzc0bcq8p8zMzHXOoe2W9m1mxWb2X1rgmUWY1uPvvvtOxQHLus8++4SmlTArKyv0l19+GZrhC5i9PLE70qKeZPJe+xoM+cBQELRV0rZJ6yUzYtN6Sbsj6zOxgUqp7UbrZWItZVvtueeeoadPn17oPdEavt1224VmX2EW9nTzww8/hGa/ZfgEWj9pr+/cuXNoZuBO+jP7J0NxMAQDLf28Rp06dULTwstwAAwNctRRR63z/VJqe5CmTZuG5pzy9ttvS0rNxM3QILT4V65cOfSWW24ZmpnrqVnX6YRjnHNbci9San9iiAbWOa3ZyRxNWzMzqHOscPwzUznn06IyrrMOiwqZQLvx1KlTQ3N+adCgQejEzs/wExxztIHTHs2QBhyLTZo0Cc2+lU5Yz5wT2Wdoy+J8wjmPbZi0Bdcs9lOOJfYD2s533XXX0Ax1wJAfDEHBEEAMXbLzzjuH5hiiVb5169ahk3bms0KfPn1Cv/jii6F5z3yGYLvRPs/QC+mE4YFatWoV+tVXXw3NOmFYDK43DOOQPM+wDTmuOJYYfoH1xjAf7O9jxowJzTWGYUS4tjLkEfsI5xaGVUjasFy5cnHsgAMOCD1t2rTQhx9+eGjeK9ekosK8mGLnkvWfsi5ZmdllJQ2RN3sYY4wxxkjyQ5ExxhhjjDFmDVmZ2XtLOkdSS0m7ruf0orhsAz5rjDHGGPOXwy9gjTHGGGOMMQk1JNVXfuiBVZIa/PHpqWRlZjeW9E9Jl0q6Ie2lM8YYYzZzBj/0qB4eMjTlWKlSpbTVVlup4R4N1KNnV+27b4H7qEf3fqpRo7r+e+ctG6V8WZnZandIG1162UXrP/kP+PbbeerV4xj16dtLJ5zYPz2FW4vhT43QFltsofaHZ6//5BKmxF7A0g5ISx6zY9PiSqs/re8MH5BYOGm3o02Rx5nVmvZi2mevuuqq0LTQFZWVmhZCWuhoQWYYBFolE0vmIYccEsdosaMNj3XBa7Asb731VqHfn05WrlwZmtZLtg/rn5bvFStWhKa9PbEd01LLe2FYBto32T6lSpUKzUzmtO/StsnM27S00ybPkAmsZ2aCTvou64I2dlpwWS+0dXIsMBM2QxmkG2ZfZ9mrV68emm1NqzTbhtdJsp8z8zjt3S1atAj93nvvhZ4xY0bohQsXhr722mtDN27cODTbiP2Idupx48aF3nrrrUPTBs26TvoJz2VYE9YR62XOnDmhaVPnZzlHpBNmE2eIEtroaYNm/TAExEsvvbTOtZnJ/t133w3NfsuQBgzXQRs025k2eIYUYN+ihXvixIKwg7Vq1QrNIOgMfZKcw/LyGpzzabFm+AbOBVyLuEalE4431jnt9c8++2xohgihfXz48OGhk/WM1n72x5YtW4ZmyAfO4exPI0aMCM26ZzgEjnPa0Z966qnQDPvCcc75NLk/hothGzJsCPsfw1VwrmZYG4Z4SCfsM1ybGWaJ6zrDb3AuueWWggfrZN1gv9thhx1CM1TLM888E5p1zJAbrCuumzk5OaH5/MKyc37gOGeoD/aRJDQK741hhBhCJDc3NzRDmnBeZ9/m84RJP7l5OZMkTZKkrMzs/pIG/9nPIvTAWElPyS9gjTHGmCLp0qWjtt8h/3e2lStXat638zVp8ks69+wLdeZZp6pL146SpOOO65vyO19xc9bZp2mnnXZY/4nrYeutt9JZZ5+m3Rv+v/6W+//i6adHqkaN6n4Ba4wxxhhjjPnb8C9JdSW1l1Tuj08tHlauXJny4t+sS1I/rqeSx22x6eC2KFlWrlyp0qVLx0aAtf//1yP/vg5u2UKNsdNVkvof20fnnXuR7vzvvdpzrz3UoMGuOjS7Xf6nirk+li1bpnLlyqlT56Pi+zakLSpWrJhyreJhtaTVabl+cv+Ffsvq1Vq1alXKhr+1Wd/84RewxhhjjDHGmA0iKzO7kfKTdp2Wm5fzTVZmdt3/x2dPknTSek/8AzIyMjJ2b9JUs2fPVrlyZdf/AZPifjAli9ti08FtUTKsXr1a9evXT3GtSqnOsL8Sy5fnO4KXLl22ToLYjDIZOu2Mk3Tu2RfpiceG6aKLB6h/v5NUvXo1DbzlOknS4sVL9Phjw/TqK1O18PvvtU3VqmqVdbD69O2VkiB32tTpeuLxp/TZZ1+ocuUt1KjxPup3TG/VrJnvku7f7yTVq7+z2rVrrQcfeFh1d66jy/79T7U/rJNat87UBRedK0lqf1gntWvXWs0O2F9DH3lC8+fPV716O+uMM0/R0qVLdf99gzV37qfacqst1anTkercJT8x7/x5C3Rs/5PVo2dXHdP/6Pj3qaedqDJlymjEM89pwYLvVLt2LfXrf7QOPLB5lP23337TU8Oe0ZQpr+r7hYu01dZbae+991SPHl1Up+5Oca3ke1q3OkzX33CV9mm0l5YvX66nho3QpBdz9d3ChapapYoObnmgju7TU5UqVUwp24knHavFi5do9Kjn1bNXN3XsVJA8nKxcuVJz5879wxewy5YtL/JnUgm+gP3xxx9D047OEADMxEvbHi3AzHyd2NZoS6bVlDY8ZlXnW2pa05mRm5mgFy1aFJpvx2nboz33l19+CU2rKG32iY2wdOnScYy2c94Tr02LO6ENs7i2qrOtaFOl1ZMhFVgPn376aWj+pSKxl9PeSKsp6/j4448PzUmLVspmzZqF5kREWyNt4bSN9urVKzTtvqzPChUqrKNpsaRVlPZ6XpsZ7LnA0A7NfpFuOP44XjjOjjyyYBJi27HuGMqhUqVK61ybFmtmdmc4Aj7w0D5++umnh2aYBoYRYIgH9kFmZef4mz9/fmjavBNbML+f1mK2P7Oss+1osWcdsL+kE44XfjfvkW3IscvQALT+JnN0UVZ3zsknnHBCaLYJxyvnU455hn/gORzHzPrOOmQfTfocob2dbczQCJ07dw5NKzX7OccoQ8CkE9YzrelsH/ZljjfWJ+8hqU/2D65lDK3AccjwG4888kjodu3aFXqdMWPGhOb6xPW0X79+hZZx5syZ65RXKghpwrmddnvO5wxXwbrgtRl2gGtROuH44JzB+2U9MxwEy8SQN8mayD7Ifs0QK127dg3NX17YnxhCiWVkGAnO1az/iy4qiAHGPsXP1qxZM3TycMrQIgxpwPAffN7hGOdczmeBtX9RMSVPVmZ2GeWHKpiSm5fzwP/387l5OfdJum9DynD6+f/eWtKPDRs2VMWKxbPe/lVYuXKlZs+erYYNG67zssNsXNwWmw5ui5JlxYoVKl26dDxPrV69Wr///rsqVKjwhy+8NlfKls2/z/LlyxX6vqZx40babrvamjlzlipWrKhSpUupdEbpOPe6awdq2tTpOqrD4dpmm200+/3ZGv7Us/r996U6b8BZkqQJORN1/XU3q2HDBup/bF8tXLhQz48Zr/ffn62HH7lf5cuXV6nSpfTJ3E9181vv6B/tD1WTJvvGd2SUyVDFihXj+fqdd97T66/P0BFHtteyZcs0YsRz+s+V1+nnn39R8wP2V8vMgzR5cp4euH+I9tyzoRrv20gVKpSP+61YsWL8e/SosVq8eLH+0T5bpUpJI58doxuuv1mPDn0wXg7/+9Kr9ObMt3TYYYeoTp2dNO/b+Ro3foLefGOmHn9yiLatWUNnnX2ahgx+VJUrV1bXbp1Ur/7OqlChgq668npNmzZdbdu11i671NOXX36l0aPG6p2339Pt/71ZFStWSCnLaq1Wh45HqGnT/Yp8f7ZixQo1bNgw5Zl/bZYs+V3SqCJ/7h2wxhhjjDHGmA3hPEl7S2qblZmd/PU5Cdq95ZpjC3Pzcop9K1NGRoZfnvxJXFebDm6LTQe3RcmQvORb+2VrqVKl/pIvYKU19/QH91e3bh29+urUNX+oLyUp/9wlS37XKy+/piOPaq8zzjwlzr/6qhs08YXJOm/AWVq6dKn+e8c92n333XT7f29RmTL5fXq33XbV9dcN1OvTZiiz1cGSSmnBggW64aar1bz5/uuUkWVbuPB7PTT4HtWpm59P5Jeff1FOzkR16dpRZ551qiSpbbvW6tGtr6ZOna5992ssxefXXGvNv7/7bqGGPHKfatfOf1TYaacdde01N2nGjDd1xBH/0K+//qYZM97UMf376Njj+kYZam9XS3fdeZ/mzvlUjRrvrc5dOmjYsGe0TbVtYtft5Ekv6bXXpumEE/urT9+CjW9NmuyrK6+4ViOfHaXeR/eIsvzy6696dOiD2mabgjxVhbZYqVLrnR/WN3eU2AtY7iLiblgmh+HOKO584G4b7ny57778P1XgBiYAACAASURBVJz37VvQQLfddltoJu95/vnnQ3PnHN9mcwcikwlxJw132HCXFnfpFfUGnTuIkuu0adMmjp1//vmhuZOTu16Z+Ig7aIragZdOWD9J3Uupu7S4A+6ggw4Kfc8994Tu1KlT6DvuuEOSdN5558Wxhx56KDR3RXMXK3fucfcvd/swwUuPHj1CM5EbB8y0adNCMxEMEyyxPEmCGO6uvuGGgtwTTKTGnWd77LFHaO6eZdtyd1y6Yf94/fXXQ3N3OeuIY5E7n9iOydjlTl/uOuSOXu7m4o5p3jPblIl4uHtxwIABoTkvFLWDkNdk/0n623XXXRfHjj322NC77LJLaI5zJpRh32SSq+KKe8Mdc0yMxvosKgkVy8Q5LJkj2YaDBxfkYTn11FNDcxce53DWD3f8M1kQy3jJJZeEPvHEE0OzPtn/uAuX4y7ZPduxY8c4xjbkTkHu2OU458MG14jCdtqmA+5G5M5e7l5kG3L3NcfhWWedFTpJJMmkTv/4xz9CH3XUUaHpBGGiJu6WZjKvoUMLsrb27t07NNuNfYTJ1tjPmCSSuyOT/sdxeP3114dmH+buULYh51x+T3E9xLMNuUOUzx5MRsb65Dx4yikFD9J33323JKlLly5x7NJLLw194YUXhmZyUSbhorOAbf7cc8+F5vXZbpzXmMiPzy+c23mdZL7gek7NxKh0E3HnPtdBPitWqVJFZpNjD+X/XrFuNkfprDX/dZI0cmMWyhhjzObP8iklElJ8vZRtuWz9J20AlbbI/70jCVeQUKZMhsqVK6vXp83Qhx9+rAYN8p/LLr2swK305htv6ZdfftVRHY+Il6+SlNW6pRYvXqxatQpcS7Vr1yrk5eu67LHH7vHyVZJq1sp/vm6VVZDYN9m9+uMPP+qPyMpqGS9fJalBg91SPlehQgU9/Oj9qlGjesrnEmfZ8hVFW/0nT85T2XJl1bHTUSnHW2W1VNWqVTT1tdfzX8CuITPzoPW+fE0X3gFrjDHGGGOM2RBulfT0Wse2lfSgpGGShkqavvaHjDHGGFM4v/7yq0qXLp0SHkzKD8V10T8HaODAQTr5xDNUvXo1NWq8t1q0aK7MVgerXLly+vLL/E1j9evtnPLZChUqxE7RhGrVq+nPsPbL0FKl8jdVbVN13ZeXK1et+sNrVa+R+p0Za14SJy+by5TJUNWqVfTcyDF6773Z+vabbzVv3nwtXbr+l95ffPGltqtdW5Urb5FyvHTp0qpVu5YWLvw+5Tg3ZhU3fgFrjDHGGGOM+dNkZWa3ldRQ0rO5eTlf5+blzJI0a61z6q6Rc3LzcsbIGGOM+R8o7p2mmyqffPKptt9+u5QdrAmt27TSAS2aa8aMN/XGjDc1Y/qbenFirhoMf1a33zEwnIhlyqYvKWXpjCLy0vwP5rLk5W1RfL/we514whlauXKl2rRtpf2bNVHdOnX05VdfaeCNt/3hZ1csX6EKRcSCX7JkyR/GcC1uSuybmUBku+22C0277s8//xya1mzahWm3Pe644yRJDz74YBw78MADQ9NqS4s+k0bQhkl96KGHhn7sscdCM2lI8+YFGdtoj6OVmtbot99+e53jDzxQkLeAFkLanmknZBkZ6oB2vuIKQUC7I62+RYVfoK2VdmG285lnnikpNWFVy5YFW9ppP2afYOiA+vXrh6btlX2B4S2YFIWDkffHcAtsW/ad7OxsSamJZ/bfv2ArPxOVrcJfhJhYhlZOnsMkLumG9U+rNb+fdcoxyv7PsAJJ+IKXXipwIvIeaAEfObLAjch6ZnuxP9DKThs0bb7sJ7Rzs35pVea9JmEKaNvmtVkXTFbDe6VtnOUtLpjkhiEtGK6EdcsxyjAdDBmSzLMM+cKQD7TiM6QM7eBHH310aI7Xbt26hZ4wYUJozo+E9UzNtYBhZZI5gwmkzj333NBFLbpM+MOkakz4VFwLNpN+MSERQ8sw8RDnVs4zL774YugkfMSzzz4bx2gRZ99kqA6Gi2HIHSamS9ZbKTVMDNu2qMRinK85//Jek8RuQ4YMiWN9+vQJzbAbTGbFeuH6yD5fXGEkuKZz3eJxho7gXNq/f//QDHmUJI8bP358HGMoFY7rWrUKbFysE4aI4Hcy5BHDeTAEAUMNcC7lcwWfVTjPJ+EuOMaTdVJKnTf4OfZ/rtUMUbFqPbsqzEahr6RjJL0rqfCMsMYYY4z5n3j77Xe1YMF36tW7+zo/mzdvvt5++121aNFMLVseqJYt839vfnjIUA1+6FHNmvWOtt02/znr66+/Ub16deOz33+/SINuu1Pt22frgBbN1rn2psLo0eO0aNEi3XPv7dq9YUFoxLlzP/mDT+Wzbc0a+vCDj7Rs2bKU92KLFy/WN19/+6fCLRQX3gFrjDHGGGOMWYfcvJwhkoYUcry/pP7r+exn+p/2xRhjjDF/X3766WfdfNMgVahYQR07HbnOz7/7bqGuvfpGnTvgTHXoUJCTpkrV/I1iZTLKaP9mTVWmTBmNem6MDjrogNgsNfLZ0cp76WX16Nl1netuSiQbM6oiNuvixUv0/PPj1zl37QeNFi2a68033tKo555X124FeWrGPp+jZcuWKatNZrGU+c/gF7DGGGOMMcYYY4wxxmxEXs57VZ9+8pmkfMf2ggXfafy4F/Trr7/qoosHRFIrssceu2uXXevr9tvu0gezP1SdOjtp3rfzNW7cBNXfpZ723mdPlS1bVn379dLghx7V+eddrCZN99Xnn3+hFyZMUrPmTbXnng3Xue6mRIsWzfTM0yN10YWX6tBD2+rnn3/RpBdzI/FXzviJ2mGH7VWrVk1tXWVrfTL3Uz3x+FNq2661jupwhMaPf0F3/vdefTD7Q+2ya3199dXXGjs2R/vt11itW/8NX8DSus1swbSpMvM1rW877LBDaNq6E7s5bXi8NjMf05pIux+zMzMY75NPPhmaNm1m62Vme4Y4oGWVtjmGXhg0aJCk1FADtN7RNsiy02ZYoUJBnAtaVWm9TCesn9dffz007533wPMZDoJZ0ydPnixJqopAzrSu0orOe/zpp59Cs0+wL/CztBQXZd9m9m/ay5m5+uOPPw6dZFP/4Ycf4hjvg32btlHagzkuCLNrpxtmB2c4AlpC58yZE5qZxZs1K7AtsG8n4RtoU2W73HXXXaFpJR47dmxo9hGek5ubG5rhANgH2C6cAzi+2DcYNiKx4vI72V8Y1qJRo0ahGYKBY5H9obgCfDNTOOdKfjfnAc5VnIdYP0mGdPZJhnD4M5Zu1hXDBTBMDOF1GAKEY4e2doYMOPzww0NPnz59ne9MjkmpcyjnfIa0Yd9mudjP0gn7G0NBFDV/c85lOA2OsyR8BPsHw1Kwf9DenczDUmqf4NzOsAP8LNuKY4JzC8PRMAQLy5PMuU2bNo1jXKsZroBhYVj2o44qyHzK+2Z9pROOpVdffTU01wH2H1rwOcdyvk/msqJC2DD0BkOv8Bp8BuGcddttBfGzGMZi4cKFoRmagPMgw0iwfzF0R1J2hnBh27OfE9YF5x/O6+xzxhhjjDGbK888UxCOr1SpUqpatar22nsP9erVTXvtvWehn8nIyNA1116he+66X6++Mk0vTJikGjWq69Dstjru+GPime2Y/n1UuXJljXjmOQ0ZMlTb1qihPn17qffRPTbKvW0ITfdvogsuPFePPfakhgweqp3q7Khjj++nNm2ydP6Af+ql3Cnq3Pko1apVU716d9dtt9yhwQ89qkaN99G229bQrbfdoAfuH6IpU15Vbu4U1ahRXT16dFW/Y45O+R1lY+MdsMYYY4wxxhhjjDHGbASOPa6vjj2u758+f9hTj6T8u2bNbXX5lf9a7+e6dO2oLl07Fvnzta+bkJuXk/LvseOfTcm1IBV9D/xs7dq1/vDfCTvssP06xw8/4jAdfsRh65x7x39vSfl3VlZLZWW1TDm21VZb6bwBZ+m8AWet8/n1laU4KblXv8YYY4wxxhhjjDHGGPMXp8R2wNKOSFsZMwQz+zIzNNNKnmRKlgrsf7R00wJ70kknFXrtnJyCt97Tpk0LzazZ7dq1C/3mm2+Gpg2UGb95H8OGDQtNSz4zyHfqlB8cmFZo2gxpseTn8vLyQtMmS6sg7dDphPZyWhYZIqJjx4K/tgwePDg0QyrQEplYRmmRTqzQktSzZ8/QzODNLMujR48Off311xdarh9//DE0rbTNmzcPTUsm+wXDETBbdGKHZ//juex/tMDSUs9xwRALxWWZlVKz1idhFKTU0BDMis6x+MADD4TmeDnwwPxMjBwfzM7OccsQCKwLjl1e+7DDCv4KxhAcQ4cODX3jjTeGZv/nObQz33PPPaGTsf7CCy/EsXHjxoU+9thjQzMcAy3uLBftt+x36YTjj3XIcBm07rN8zNDOPp/cJ+uBbXjdddeFpo38zjvvDE2bONvt5JNPDs1649i95JJLQtN+ffPNN4dmSASOxSSswFNPPRXHOKd06dIlNMOOMEsmLdYMX8PQGOmEYRG4xjHkTZJVXpLuu+++0DNnzgx91lkFf+VN2pM/Z//o3LlzaFq9Z8+eHZpzGMchLeDvvvtuoeezndu2bRuacyRDBnBsJSEg+HPa27mGc26fO3duaIaoYLgSro/phOObdXjwwQeH3nfffUM/8kjBjgPOw9dee23o/fbbT1LqHMSQSLfffnvoPfcssKmNGDEiNNuHYRmuvvrqQs/nmL/77rtDs/4nTpwYmm3OdknWaz6nTJo0qdCyc67gOQxFxecwlsUYY4wxxpjNAe+ANcYYY4wxxhhjjDHGmGLCL2CNMcYYY4wxxhhjjDGmmCixEAS0N9PezUzQzBBMC99XX30VetSoUaGTbGZ9+vSJY7T/097NLLunn356oXrQoEGhaTXu1q1b6Icffjg07YePPvpo6DPPPDM0LaRJhmpJql279jpl79evX+jMzMzQtGZnZWWFZtbwDz/8MHSDBg1UHDDjMzNfs0zMcnzNNdeEZp0w83hiu7/00kvjGK2R7BMvv/xy6LPPPjs0rZy0nPOzzJjOumJ4CVrXeX32KerEQkxbJzOs00rJjNw9ehRkIXz66adDsz3Z/umG9l1aW2nNpp02Ozs7NMMR0DaaBOju0KFDHGPm+++//z406/n8888PfeWVV4ZmaATa7Xv16hWalniGb6BVln3w+OOPD80wFMlYP+644+LYHXfcEbpatWqh2Ubs97Tc0s7LkADphLZfhvpghkfW8xVXXBGa/ZUZ1ZP5sn379nGMoUPYJszszpAh7Cu0W1euXDk024GW5M8//zw0LdQ8Z8CAAaFvuOGG0Em/69u3ICj88OHDQzNbPMc/22fKlCmFfv+uu+6q4oBhRubNmxc6CU8jpZaVfZnhF8aPHx+6atWqklJDsXCccH7m3Mf598ILLwzNccJwDUcccURojm2OFZbrggsuCM25lfN1siYyXAnDWNBuz7KzT/B6DPHAUBvphHMpx0eLFi1C89ng4osvDn3LLQXJBBhuIAkRwjHL+Yjryvz580Oz3k477bTQ//znP0MztAzHKsMysY+wzdmn2rRpE/qVV14JnfTXM844I44NGTIkNEPULFu2LDTXRNYLz2/ZMjXRgjHGGGOMMZs63gFrjDHGGGOMMcYYY4wxxYRfwBpjjDHGGGOMMcYYY0wxUWIhCGgj7t69e2hmm/7tt99Cr1q1KjSt0fvss0/oxHrOMAa04fF6H3/8cejELi2l2kBph6ZtkNZCWqAZGoHXYVZmZt+m5S8pD214SSZ5KdXqXL169dCLFi0KzazdrBeGXkgnzEDONqF99tdffw3N0AA8n5mvX3vtNUnS+++/H8eSLNBSahb2vfbaK/Tjjz8emrZ/Zt5mXS1evDg065n9rHz58qGZlbxSpUqh2Z5JKA3a8mn9ZIgKhuCgXZ4Zsvk9/Gy64bVZ1+zPLC/rhdbTpk2bhk5sqGwvhiOgVZbhGFj/tC8zgznHKzPEn3LKKaFp+y9TpmCao62cIQMYbiAJecGwC7QKM+wIrbLs97Qib7vttqGLK3M3v5vzKa3ziR1dKhhnklSlSpXQDK+S1EPNmjXjGMMFMEQAQxAwgzvDfrCO+VlmP6eVndesV69eaFqua9WqFZrzyOTJkyWlzj8MaUErM8PbcN4htLsXVxtyTWzVqlXoTz/9NDTrimF02Mdo00/WlcTGLqVa/tk/OJ8yXADDRTAcAUPAsB3Yhzi2uSYy7BDDQTDUCm34Cf/6179CM+wN56EJEyaETsIYSNKOO+4YmmES0gnXGK4NtO7vsssuoRk6gaGC2CeTZyK2Ffv6Dz/8EJpjfOTIkaE511Ez5A2fj7hu5ubmhmbYIc7P7K8ML5SMQ7ZVz549Q/OeeM8Me8DyMvwH1wpjjDHGGGM2B7wD1hhjjDHGGGOMMcYYY4oJv4A1xhhjjDHGGGOMMcaYYqLEQhDQ1kh7Hm30M2fODE0rK21rtLUntvK33norjtGmSfvaF198EZoZkWnlvPfee0PTHsrMwbT30+LI82lfZyZmZoVP7m/p0qVxbKuttgpN2zUth7SP0xrLumOG9OKC5aPlmzbVsmXLhqY1dezYsaET2zftkPwcra4MI0H7O0NE0GrMNuH5tPgS9kVq2r0HDRoUOrE7r1ixIo5NnTo1NC2+H330UWhmqmf7MxwFw2qkG47FrbfeOjRDDXBMse0YpoD9b9asWZJSrcHTp08PzVALrAuGXeD9jx49OjStzLwmwwt07Nix0HPYB9g32N+S6/Tt2zeOsa/RbsvQIAyrwTHKOYJW9nTC0C1J5nEpda6iJZlWds5JDOWR9GfatTlWGX6BFneGq+DYpZWZ8zJDU7DemjRpUuj3Ll++PDTDFNx4442hk77LccOwC3Xr1g3NUBMMt8B2Yx8trjZk3+cY41h59dVXQ3Pe4JgYPnx46GQu5v0OHTo0NOt+zJgxodu1axeac+6oUaNCc57lvEW7OccEQ7PQbs+QEu+8807oJHQP25710rJly9DsWwxLwjmMtnaG6EkntOXz2YTzKvsbQyTw3h555JHQSTtzXDGEx1FHHRWa45fH+Yz13HPPhWafS+ZsKTU0AucQrpXsi1zzrrvuutDJHM45k6ElGHaAYWMYdoBjnOfw/owxxhhjjNkc8A5YY4wxxhhjjDHGGGOMKSZKbAesMcYYY4wxxhhjjDF/R3755Rc9NWyEpkx5Rd9+M08ZGRnadtvqatZ8f3XqfJRq1661/ots4kyZ8qrmfDxXx/Q/OsXF93ekxF7AMgv9N998E5r2wdatW4em1Zd2S1r4EpsdLbMtWrQo9BrMyE0bHu3zPN64cePQzFpPyyotlrTtvfHGGyoM2vKSsjM0Ai3rtIfSGs16pGWW1ktmAk8ntOXTgki7eu/evUMXVW8sXxLGgZZw1gOtkbSgMlxDXl5eaPYn2k5ZXtogabFs3759aNpXGYKAoQySaybWWSm139D+zrAQvH/W6ZZbbhm6evXqKi5Y17Sj00rMOqJVtqiQBUk2bobIOPDAA0NzrNBuSjtyhQoVQrOtOUZpt6fdnOPokEMOCT1//vzQDD2y++67h65Xr56kVKswxxnrhfZw1iPDOjA0CcdAOmG4BtrN2RebNWsWmu3M+6xTp07opB44PzOkBMOOHH744aFZDwyNQMs6xzQzsbOPMJt9dnZ2aPYRftcBBxwQOrFrsx+wP3Escl6g3Z7X5vyyevVqFQfsY0ndS6kWbGan53GOs1122SV0Ms9w3uR44JrYp0+f0AxRwXPYlzlmirKss825FrPsDCnBEBTJ+s+5b4sttgjNdZ7PClwHOT8wHALrOp2w3pI5UJIWLVoUmuEdCNcYhrlIwiIx9BJDJY0fPz40w4KwzzKMA8cY1zt+f6NGjULz+eWMM84IzfFRVHiJZG3j93C8MTQC52OOVdYpn/3Y/sYYY4wxmyOfzP1UF154qRYtWqTMzIN02GGHaOnSpZrz8Sd65umRGjXqeV1/w1Vq3Lh4wmdtLF6e8qpyxr+gvv1662/+/tU7YI0xxhhjjDHGGGOM2Rj88suvuvjiy7Vi+XLddfcg7b77bik///ijORpw3j91zVU3aOjjg1W+fLkirrTxWbZsWcof0v8u350O/ALWGGOMMcYYY4wxxpiNwDNPj9T8efN11TWXr/PyVZJ23W0X9ezVTY89NkzvvP2Omu6fn6B47txP9OD9D2vW2+9o1cpV2muvPdS3Xy/t06jAYdujez81aLCrunTpqPvufVAffzxXW221pY7qcLj6HXN0yve89dbbenjwUM3+4EOVySij/fZrpP7H9lW9+gVO4p49+mnnneuqffts3XPPA6pXb2ddfc3lWr16tUY9N0ajR4/TV19+pfLlK6j+LjurW/fOatGieZRl/rx8F2q7Nu11TP8+Ova4fEf3xBcm6amnRuizTz9XhQrl1aTJvjr+hP7aYccCZ2uP7v20yy71dNhhh+juu++P795cKbEXsLSv0oJNC2xRFlRm+j3yyCND33PPPZKkc889N45dcskloWkXpmWO2YVpb6Wdjxa+rl27hmaWe1qpaedjGS677LLQvNfknmgDHTRoUGjaCZldmVnGGY6B2ZhZ1+mE30GrJzNr075YlPWRltWkzRm64IILLgjN9mZbrVq1KjTDC9BSzEz2tFLfcsstodm2rGdaPmmJpJU6KXvnzp0LLTutmcwgTXs/raq0+9JWm24YRoDj4tBDDw09bdq00Ay7QIt7/fr1QyfW78zMzDjWs2fP0AMGDAjNzNasO37nbrsVLEpsd17/2WefDU17NC3U7IPMFs9QETNmzJAknXXWWXHs4osvDk17MG2wDIHA8cCQCbyPdEKrPzOkc95k9nO2OTPYs20nTZokKbVu3n777dCcvximgDZh9o8rrrgi9L333hu6efPmoW+//fZCy067M+3R/F5am5M2PO200+IY5/OGDRuGZggGzmkMAcJ7Kq4QBLSsc37gusX1Zocddgi9YMGC0CeddFLooUOHSkq93+uvvz706aefHprzI9dEhhbhGsNs9pw3b7311tDsTwxN071799DDhg0LffLJJ4dOxj/bgWs/w5Jst912oRleg+1WpUqV0MX1V3Ou01999VVorhNJ35RSQydw7t9vv/1CT58+XZLUqVOnOHb88ceHZjglhinYddddQzPkBsvC+rz00ktDn3feeaG5Pr/88suhOZe9//77oTk/TpkyRVLqsxHne4biYfgJhmrh2s7QHAy7YowxxhizuTHpxVxVr15NBx/coshzeh/dQ72PLvgd5oPZH+qccy5U1SpV1LlzB5UvX04v5b6sc86+UFdedZlatiz4XffDDz/Wxf/8t9od0lotDmyuFyZM0kMPPqIddthebdpmSZJeefk1/fuyq1R35zrq3bu7Vq5cpYkvTNKpp5yt2wbdqIZ7FPxOPXfOJ7ru2oFq3z5bTZrm/37y2NAn9cD9Q7R/syZq06aVFi9eohcmvKhL/nm5Bt1+k/ZptLeOO66vRo4co9nvf6AzzzpVe6y55pNPDNc9dz+gRo330XHH99PPP/+iceMm6OSTz9Sdd92qunULftf8+KM5evONt1K+e3PFO2CNMcYYY4wxxhhjjClmli5dqi+++FItDmyespFGkhYs+G6d8ytWrKAtt9xSN910m6pXq6b7HrhTlSrlb7Lr0bOrTjn5LN15xz0pL2Dnz5uvy6+4RK3b5OcdandIG/XqcYxefXWa2rTN0tKlyzRw4CDtuWdD3XLbjSpTJj8XTddundS/34m6994Hddugm+J63323UDfceLWaH1AQtz83d4oaNd5HNw28No5lZh6kk048QzNnztI+jfZW9mGH6M03Z2n2+x+oQ8cjVaZMhr77bmH+i9v9m+jGgddEHRx5ZHsd2/8k3XXnfbrxpmtS6uSGm65W8+YF37254hewxhhjjDHGGGOMMWaTo+wFd5V0EQpl+U2nrf+kQvjll3wHOBM3S9LSpcvUvWufdc5vd0gb9T66h+bO+UTdunfWr7/+muIib9GimYY++qS+/Waeam+X7wCuWatmvHyVpFq1aqpKla314w/5yXvfeONN/bDoB/Xq1S3FiSVJTZruq4kTJ2vFihXh5qxVq6aaNW+act5/rrosJXmqJP2yplzLl69QUUzJe0UrVqxQtx6dU15A196ulpof0EyvvPxaSqzX2rVr/SVevkol+AKW2alpd/zoo49C085HWzntlLTEJTa3J554Io4dddRRoWlloy2Z1kTa82hBZwbxBx54IDTtsLQ9085J2y4zVDNDdJLdmjZNWmpr1qwZmhZL2oBpbaRVlOenE9YhbfS0w3JSoR2Z1nGGCUgydDOzc6tWBRMH7ahsT9q/mW173rx5oRmK4cknnwxNGyjrjeETmH2bYRBo+Uws03fdVbBA8D5p5WXbFpXNuTRSBJYuxnSBtGCzvMxsXqNGjdC0uLP/MRN7UkePPvpoHOvSpUtohnSgxZp9mNZ32k05XnNzc0Mzizbt3CwX4fVpZT/ssMMkpc4jtDtz/mFdfP3114WWkXXH/phOaNllfTIUCstBa3alSpVCcz5N+gLrmP2WWeUnT54cmuOMGd9pn2dfYLgGjl3WMxdmjoWi5u5krA0cODCOMdQB74PWZ86VDDXCeae45lP2pd9//z00w7WwX3MM1a1bN/TEiRND77xzfuymokKecD2aP39+aPYPrnGLFy8OfcABBxT62SZNmoSm9ZzrBccbr8N7TUKwsN/Qss725ucYjqCocBG05KeTOXPmhGabsM/yuYbhP3r16hWa7ZXcJ587aPNnqJqffvopNMNp8DnoxRdfDH355QXxsxhSgmEH2D5cE/jcxjWcz3BJX+Acw/WcbVVU6CA+h3HNN8YYY4zZXEkSavHZWpLKli2ja6+/Mv69cuUqXfav/H9/8Xn++7PhT43Q8KdGFHrdRYsWxQvY6tW2WefnxFF3iQAAIABJREFUGWXKaPmaZ64vPv9SknTXnffprjvvK/R6P/34k6pVz3+m3Gabquv8fNttt9ULL7yoN2bM1Ndffa158+bHy+U/4osv8r971113Wedn221XWytWrNCPP/yobWvm/76TlOGvgHfAGmOMMcYYY4wxxphNjv91p+mmypZbbqmtt95ac+d8knK8dOnSOvBAbnIo2NiWbALp0PGIlDAAZMedCnJElFrPBq7kesf076MGu+9a6DmVt6xc6HEp/w/jF55/id5++11ltc5Um7ZZ2nHHHVS9RjWdcNwft1fyh3fmM0pINhaVKeRnfwX8AtYYY4wxxhhjjDHGmI3AAQfsr5yciZo5c5b23bdRoee8+kqBEyzZDVqpYsWUl7SS9N57szVnztx1wgH8EYmbbpttqqxzvRnT39CCBQtTHLxr8/bb7+rNN9/SKaeeoJ69usXxT+Z+WuRn1v7uzz//Qnvu2TDlZ3M+nqutttqy0B23fwVK7AUsLfW05dJCR/ssOxPflDObcWLfp72R16MNjjbSL7/8MjQtrbQu0xJIex7t9gw1wPN5fdoFmdE3sWrTVklbMbOAc6s67XkZGRmhaWFdO7ZIuqBdnzZ22ldZJ7RGMzM8wzUkGZJpbac18Z133gnNc5KM2VJqX2EZ33vvvdCsE1rHGQKCtmPCe2L/S0IP0JrNeCq0bI4cOTI07Za0BLNcvI90w3LRQkvLOo/zfFp8f/7559BJdm22My3WHH+04TKzNsMOsE05thgagvMF5xR+duzYsaEZMoSLy5gxYySljk/eP224DH1BK3v9+vVDc+xuvfXWKg54j5z/OOexb9PCTHs0x+7NN98sKXVOoq2Z2evZDuwH1F27dg39yCOPhOb8yPmMdnuOBfadAw8sCDTP8Amvv/66pFSbPsty330FNhtmi6eVvUGDBqHZtpzD0wlDN7CfsA0ZuofzE8NpcO5PQkOwLtlWeXl5oVk/nONo+6Z9nqFWGC6Cdc7r0/bP0C1t2rQJ/e2334Z+5ZVXJKXOCewfo0ePDs21YObMmYVem21Y2F/b0wHLwbmfIRpYz5zLmjYtiKnFUDBDhgyRlLq+77BDwe6GZK6VUucBtiHXJLYP1yE++3C8cVyxX3K+Zd/lWjV48GBJqfMN+xPnac7BDFfE9ZFhHTivGmOMMcZsbvTs3U2TJr2km28apFsH3agaNaqn/Pydd97TQw8+HP9u2LCBqlXbRi9MnKyj+/ZS5cr5z/S//fab/nPFtapUqZI6dDjiT3///s2aqFy5cnp+zHi1P/yweD5eMH+BLrv0KjXedx+1Pzy7yM8noQaqVi14hly9erVGjHhunXPXyjOmFgc210MPPqxhTwzXFf+5NH4f/fjjuXr33fd15FHt//R9bG54B6wxxhhjjDHGGGOMMRuBnXeuqwsuPEc33nCrjul7olplHaw6dXbSkiVL9OGHH2va1Ok69NC2+uij/I1AZcqU0TnnnqHLL79ap5x0plq3aaXVq1frxYmTtWjRD7riyn/9v76/SpWtdfIpx+uO2+/W6aeeo4MObqElS37X+HETlJGRobPO/uMwAnvvvYcqV66sO/97r77++huVKVNGr7wyVRkZGcrIyNCbb76ld955T3vvvWf8Ef/eex5Q6zattMceu+vII9tr9OixOvfsC9V0//20ZMnvGjNmnKpV20b9+6+biOyvgl/AGmOMMcYYY4wxxhizkTg0u5122aW+hj35tKZPf1MvTJikypW3UL16O+vSyy5S23atdfPAQVqyJN/h1jLzIA0ceJ0GD35Ew558WuXLl9PuDRvosssvVsM9dl/Pt61Ll64dVa3aNnps6DANffRJVapUSY0a760TTuyv2rVr/eFnq1Spoutv+I/u/O+9GvbkM6patYoOObSt+vTtqfvvG6znnhujD2Z/qL333lPtD8/Wq69O1YhnnlP16tW0xx6769wBZ2q77WtrzOhxenjIY6pceQs1a9ZUx59wzF8q6dbalNgLWNokaUFPMpBLqZZ12sdnzZoV+ogjCrZZJxa+UaNGxTFm9j3vvPNC0+pNCx+txldeWZCBjhbP1157LXRimZekk046KfTVV18dmlbJSZMmhW7WrFnoo48+WlJqxnFmhD/llFNC0+43ZcqU0EXZ9pcvX67igFnCaSOnpfkf//hHaGbBZqbkk08+OXT37t0lpdobb7/99tDXX399aNYDra6J/VhKtT7ShllU2AG2IducfYRtxOP77befpAILuyS9//776/xcSu2LzFrOemEGcdpP0w3HIq2yDBPSsWPH0I899lho2mmPPPLI0O3b59sGaDVmu3To0CE0raScC55//vnQDB3AsA8MB8Cx++9//zs0Qwk8++yzoZ955pnQN910U+hkbnjiiSfiGK3Cxx57bOjs7AJbxvDhw0OzThlKhBbudMI6pKWf81y3bgWxeWjxZZ336VPw18YkHAH7RE5OTmiOW9q7E9v72t/D799nn31Cc35i2WkTP/7440O/9NJLoR988MHQnAuT0BgMEcN5+IQTTgjNsAssL0PQNGpUEJeJc2s6YciNX38tyB669957h+acx77PeYZ11bJlS0mp2eYfeOCB0Iccckih16Z9nWF8Lr/88tC0krNNJkyYEPq4444LffDBB4dmCIpbb7019DnnnBM6CePx1VdfxTHeM+ckhlVg3bHvtmrVqtB7SicMs0LdvHnz0JzX77///tBPPvlk6GuuuSZ07969JUkvvvhiHOMadNFFF4XmnM3564033gjdr1+/0Mccc0xojp+pUwvijXH95fhk+IAPP/wwNOeQFi1aSJIGDRoUx/j8xDbkXDV9+vTQbE+G0uC4NcYYY4zZXKlXf2dd/K8Livz5gPPPTvn3fk0aa78mjYs4O59hTz1S6PGnn3lsnWNZrTOV1TqzkLMLeHLYIym/0ybstfeeuvve29c5fsaZp+iMMwveX9Wps5MeHfpgyjmlS5dWr97d1at39z/87qLuZXPlj1OjGWOMMcYYY4wxxhhjjPmf8QtYY4wxxhhjjDHGGGOMKSZKLARB9eoFWd5oE6WVnTY42uaYlZfW5CQzcJcuXeIYbXW0HdIanWTqXft7brvtttDM/N6jR4/QSaZwKdUGSfv0gAEDQp955pmhGSoh2dJNe/OIESNCv/POO6GZiZgWz6effjo07aG09qWTnXfeOXSSqVlKtdrTGst2ueSSS0LT1pxkOGfoAmY9p12aGejvvvvu0IntVkoN+UAbP+3Qt9xyS2haxNm3aGlnGdhfkmzqtNQyazPPZXvSxs42ZzgChkNIN8wyzdAPu+9eEEeGIR5oJz3xxBNDs60TGDqDGbdpZf3+++9D05L6zTffhP7Pf/4Tmm3Uv3//0AxPwRAAnAP++c9/hmboCfaBJJRH586d4xhDFNB6T+t169atQ9PCzfmNWcrTCbPQF5Wdnvd79tkFVhZmXB86dGjoZNwdeuihcezGG28MzbAwtKRw3uRn77jjjtDMxE4b9MMPF2T6pD2eoQHY52i5vuGGG0Incx7HDef5b7/9NjRDqXBe4PnTpk0LTSt9Otltt91CM0xAYsWXUtcwzv2nn3566GHDhq1zzYMOOiiOcW064IADQjPkQlHr4IUXXhiaoRE4PtnnkjlRSp1D2M5J+B0ptZ2Te+V449zDtmJ4k549e4ZmiBha+JNwRemGYW64JrGf0q5/2WWXhWY9sH6SdSsJRSCltglhGBS2Sbt27UIznArnKYau4Dhnv2DIGc69vH5hoVgGDhwYx5LnNCn1WYZlYYiQ++67LzT7LsNPGWOMMcYYszngHbDGGGOMMcYYY4wxxhhTTPgFrDHGGGOMMcYYY4wxxhQTpZJs0Rub0aNHxxfPmjUrjtOSxqzMtO7SPkp7ZmLrpu2RoQaYCbhJkyahaQ9kZl9a6WfOnBmaFjpmOqYNt3LlyqEPPPDA0LRB7rnnnqFLlSolqSB7tyTNmDEjNG34+++/vwqD5+y4446haSHs06dPqUI//D8wbty4KCwtoAsXLgzNTOa0aH/66aeh991339CJPZP2VloaaXumdZoZxGldZ0ZmZlZmmIJnn302NOufIQvY/xYvXhy6ffv261yffZJ2T2aqT7JDS9LXX38dunz58qFpD6ZttW/fvmlrQ0l6/fXX46aZZZwhNVgvDP3A8VWvXr3Q3333nSRpr732imOzZ88OzT6ZnCultin7P8NXjBs3LjSvz+zarFPagmnzZp0efvjhoZOxTpsy+8iKFStCV6tWLTSt18zczTHAftSiRYu0teOoUaOigRgKgmOBoRuSMAtSakiX0047LfRjj+VnyWzTpk0cY3svWrQoNDOSc27leGVIC4YG6dChQ2iGI/nqq69CV6pUKTT7H+c8WqITCzWt9ByLDFHBfs5rsw0ZgoYhM7p165a2NszJyYk2/OKLL+I41zuuK8uXLw/N8Uk7eGLBp32dYU54DfYJ6kcffTQ0Q8fQGs5QI7x+mTIFUY7YhgxfQ1v7PvvsEzrplzzGOYFjnPXSqlWr0PPnzw/NdZ59N51tyDWRzxuc19lW2223XWiGX2B4haQ+mzVrFsdYD0WtU6xvrrcMoVHU/MBwALxOUW3FuZThg5KQNpwzOfZZXq6lPJ/PgeXKlQvNcFWHHXZYWtdEs/ly+vn/3lrSjwOvukQVKxZPyJ+/CitXrtS7776rvfbaK2UeMRsft8Wmg9uiZEnW/+T5cfXq1VqyZIkqVqwY70pMyeC2yGftPloYS5b8rvMvu1aSqtw58D8/rf1z74A1xhhjjDHGGGOMMcaYYsIvYI0xxhhjjDHGGGNMiZCRkZGSSNyYTY1Vq1apdOkNe4Va9N7ZYoaWQVqzaTGjDY62f9rpGAIgsaHTajh58uTQhVkzpdQs9LTE8ftpX73yyitDJzZdKdX6SItjUXZBZoVO7K4MS0CbNi2hH3zwQWhaqmnl5jm0u6cTWhNpB2Q9047K8AusZ4YGSOzavBdmb69Ro0Zo2h6TrOdSqqWY38/v/Pjjj0PT4lu1atXQtEMfe+yxob/88svQzBadWDi33HLLOEZbKUNR0C5P6z4t1QzrULt2bRUXnESoaUlNwntIqfVLKzfH5dSpUyWl3g/DeNCuT7s1v4f24VGjRoWmPZU2X16fISZ4fbYvLcwPPvhg6MRizjHE8Uz7NMcww6fQqss64mfTCUMAcE6k7X/s2LGh+XDDMjHcxyeffCJJOuigg+IY7cMM0TBy5MjQVapUCT1+/PjQbCuOP4Yb4VjnnM/65HHOrex/yXzJkBq8/6Js7Z06dQrNepwzZ07ookLAbCjsP5yTuCbyfjhvcKzcfPPNoZPxx77MemCIlmnTpoXmfMP+ccstt4Rm2Be2G/sCw85wDaWdniFrWLZkHHLt4xhn+A+G3eAYZxgJ1hHDFKUTrk9cb9hWTz/9dOiiynr33XeHTtqQ/TSZXyXpkEMOCc0QIQwFwP775JNPhv7ll19Cs62SsS+ljk+WkecsWLCg0Osn6/m2224bxziWWS+cexj2hKFIaPfiGmKMMcaYzZ9SpUql/O5ozKbE6tWrtXLlypTfzf4XSuwFrDHGGGOMMcYYY4wx5cuX15IlS5SRkREvZFesWPG3jju6KZC8fPw7tsWqVau0evVqrV69OiWvw/+KQxAYY4wxxhhjjDHGmBKjdOnSqlixosqWLatVq1Zp7ty5DkuwCfB3bosyZcqofPnyqlChwgaHH5BKcAcsLb202vOtMi3bPL+obMpJRmFa2mlTo+V40KBBoWm9LMrWfvLJJ4cePXp0aFop2SDM5s7vpYWUVtqk7LT40QZKi3+jRo1C//RTQWI1ZltnvTDrcjph6AhaoJntnlZwWlZzc3ND169fP3RiK2U9sA3ZPsy8zeOsB5aLltCirKy8p3POOSc0bfcvvfRSaNo8k7AJ8+bNi2N169YNzX7GtqfVgvfN8tJ2n26WLFlS6PezzzVo0KDQstDWS7v7oYceus730PZLW2n37t1D065PCzjbhe3Fv8DRDkBLbNu2bUMzxMETTzwR+sQTTwydhAFhuBBmi2fZeW2ORZ7D/l1cJHZtSdppp51Cc+457rjjQnPOfe+990LXqlUrdDJ2GTqAP+d4ZhsyfAitx+zPtBgzHEHz5s1DM0xHVlZWaIav2XHHHUNz3CdtwXbjnMh+dsABB4Rm/1+8eHFohnqhbTudsN449zO0AvsbxwT7W9euXUMn98DwL1zXOMY6d+4cmvXAcAgc1wyvQkt8jx49Qr/wwguhuVby4Y3zCcMBJWVn+AS2PUOkcIxx3uI57H/peHgqDI4r9jGGX2DYB7YbwxdwTUj6MM/luGIbcr1lG3KMs334WYZc2muvvUIzjFOXLl0KLSPLxhAkST1zbuZYqlixYmiuHxx7HG+cN7j2GGOMMeavRalSpVSmTJn4P38vMxufUqVKuS3ShHfAGmOMMcYYY4wxxhhjTDFRYjtguYuNO5e4Y5CJPbhLibsXuZMwSeLQq1evOHb99deHZtIY7pjcb7/9QnMHC3eZcAfLEUccEfqKK64Izd0n3FnCXTvcyduzZ8/QSYIN7py95557QnPXCHfxcbcbd/hw90txJf7hrtBXXnklNOuHO2+4k/Wbb74JzZ1XgwcPliRddNFFcYyJX/bdd9/QL7/8cmjeI3dAH3300aGfeuqpQo+znpmwjMm2uIOH981dS0kCmeOPPz6OPfzww6GZnIsJd7hDmTt/uAupOHfAMhEMk7gceeSRoT/66KPQTLLG++MOy4ceekhS6o65CRMmhD744INDsz+zXtiH27dvH3rIkCGhH3jggdDXXnttaO6k485jjlFq7ipPzuf9sL/w2kwow7HIXcXcEUadTrgTmHMPdy5zjLJdmGyOyamSna88xmRorAeOD8533D3LHeVM1LPHHnuEZnsyuRAT/rDv8F7Z/klyPO42HDhwYGjuxmYSKDowOLaZ/IfjP53wr8kcb+ynXIc4n3IHIsfzlClTJKX231NPPTU0xzITpvXt2zc0d+ayX7Pu+/XrF5q7yY855pjQXE95HToGGjduHPpf//qXJOn222+PY9dcc01o7u7mjnfOFdxdzbmluP5yzx3i/G66NSZOnBiazzvcaco6TNqQu58vv/zy0NzxzOeaohI3sn9wfJ533nmhuUuWSbjY5lxzOW74rJYkKWSCr0suuSR0x44dQ3O8cYcyn/G4PqYjBpcxxhhjjDEbE++ANcYYY4wxxhhjjDHGmGLCL2CNMcYYY4wxxhhjjDGmmChVXAma1sewYcPii2nLJ0wsQ6sek3IwcUNi8X7//ffjGG2AtDfS9kgb5qRJk0LTpli9evXQtD4yEQrLSPs07a5MdMHzEwsprbxMNsbwArTMMvkI74m2PVr1OnfuXOBV3kBGjBgRbUgLKGH9MCEKLfW8zyQpB0MU0ObN6zGRy/777x+aiV+S5GZSqtWZtnTWOW2QbOfDDz88NMMqsF0SeyRtmkwIQ9t3UbZ7JjapWbNmaNZRx44d09aGkjRy5Mhox6LsxrTO03rKhGu816Rf0jr+xRdfhGZ7sd8yaRKt7EzWsv3224fmeOJ8Qcs465fJohJ7rJTaH5PEbbNmzSr03hiugNZ/Jt3h9zNRFBPKtGvXLm3tyPmU/Y9tyHtgOAweZ9K6xDLOhHmsJ1qT2T4cl2xDjhu2Iedrjif2+Tp16oRmH2X4Gn5X0i5sk6LCaLBfst1YdwzDwDHavn37tLXhM888E23IBEPsY5z7OT/wOEnqmX2Cbci+WVTYms8++yw010T2IfYRhvphGzKZGO3xPId9J5mL+TmuC6wXzk/16tUr9DsZ0oL31KlTp7S14ZgxY6INuW4V9YyTJPyTUuuQ/T0JecEQEXx2YFsxFAVt//wsQxmwPvlZwjZnaADOFWwLkoxP9j/eG0MKcG3ncx3n9aISMLZt2zata6LZfDn9/H9vLenHgVddoooVK6z3/L8zK1eu1Lvvvqu99trLCVVKGLfFpoPbYtPC7bHp4Lb48yxZ8rvOv+xaSapy58D//LT2z70D1hhjjDHGGGOMMcYYY4oJv4A1xhhjjDHGGGOMMcaYYqLM+k8pHmhHp02ZW5ppj5szZ05oWhZ//PHHdc6nvZCZl5nhnfa5YcOGhWa4ANr7R48eHZqZjmnnpJWaNlmeQ4stbZNJVvBOnTrFMdrzWBe0MfM474lWveKiKLsubaSsB1r6WVba/pMM0bQ6MoQC7eRz584NzTZnNnpaPJl9mm3Lsv/www+hGZrgzjvvDN2mTZvQtJY+99xzklJDXRBarfmdX/4fe+cZJlWZbu0HURFQVECRIakEaQkqQYLQNNLQZCQqSZCkoBgxYEABIzrq4DCjooggkoasYBObBiQJKDA0OBjRQUdHUcfBUVu/H5734S6pOvAdd9nd47qv61xnnU111d5vrFO+az379rmuW7eua9rxaXuOmipVqrimVZS8/vrrrtmntDBzvAY7La3mrB7PduOzcezQYspIjVAV3Cx2DPL1HF/8rJEjR7rmPG7fvr3rEA/BtYhzddu2ba6//fZbiwfnOa3xybJs0A6eKH6BNnWOc1qYOeZCRAPjIlavXu2az8JYGI4Pji22IeMdeC+cu1znXn75Zde0J3NNofU93APjBTjnNm/eHPe+OEcZO8AoG8bRRAnHCduEY4w2bVrGL7zwQtfsl1dffdXMYtdQ7omc73z2rVu3xn1vWvq5RvMeuZ8zImLLli2u2bannnqqa86bMP7S0tL8GvuN0RnsQ94j93OOFd5XlDBagXsSP49jn23VpEkT15zD8+bNMzOzOnXq+DXuZfv373fNZ2RbMX6BcUHsE34n4xrH/Y5jJNE86NKli+spU6aYWewakugzOW4zMzNdc6xwPjNeSAghhBBCiIKATsAKIYQQQgghhBBCCCFEktAPsEIIIYQQQgghhBBCCJEk8iyCgBWXaTWlrYxV42nnY+VzWvVClXtaoWllow2PlXg3bdoU9/X9+vVz3bRpU9e02NLm17dvX9e0RL722muuGXcwduxY1zfddJOZxVptWbV76NChrmn9ZOVkWlj5+WyPKKFdmbbGc845xzWt/qw6Tzs0IyVCH61du9avrVq1yvW9997runHjxq6nTp3qmpZ22ls5nmixXbJkiesBAwa4psWSURfr1q1zzTESXs9/p02YdvwwVs3M9uzZ45p9xWiGZMJK8aw4zSiFRHEcrGzfoUMH1z169DCz2OiC9evXu2ZfJGpbzq0GDRq47tatm2vOl40bN7oeNmyYa47TL744VIiQ1l1avkMV9RUrVvg1xl3ccMMNrjm+aK2ltZsxAFz3ooR9yCgUjjNGDfDZ2C/du3d3fcEFF5hZ7PygFZ/V1Pk5nFu0Sqenp7tu1KiRa8bLTJw40fUTTzzhmhbmpUuXuma/cd2pVq2amcVa7+fOneu6d+/ermmfZ5QD7dYcf4kqvv9SeB+0qbdr1841ozg4xubMmeM6NTXVdbNmzcws8R7XtWtX1w0bNnQdInHMYves0aNHu2akQ4g6MDNbtmyZ69tvv901owQmTZrkmrZ5RvB07NjRzGLnOL8HPPDAA64ZR8D7JV9++aVr7pVRwrWMdnna+Nk/nFvTp093PXz4cNfhu8Gjjz7q1zjHBw8e7JrfDZ577jnXjA657bbbXDPOY/Hixa65ZvI7GeMrOKYY88I9L+yPK1eu9GuMiOA8LFOmjGuOUcYs8fNVgVcIIYQQQhQ0dAJWCCGEEEIIIYQQQgghkoR+gBVCCCGEEEIIIYQQQogkkWcRBLR60oJIiyVtdsFKaRZr9aUNLdjWL7nkEr8WKtObxVqBaZm/9dZbXT/yyCOuaX1jdWZaNWmlpCWUVufWrVu73rBhg2tGDATrOW31tF7SGs52YewBq8PzXlgJOkpoTaQF9Pjjj3fN+ADa9Z988knXtLEHeyKr0rPNdu7c6fr77793fe2117oeN25c3PulRT0jI8P1tGnTXDO+gNERtHnec889rmnfDrbW/v37+zXaqBkLQcsm7di0b9O+X6NGjXiPFAkc22zfiy66yHVOTo5rPv/DDz/s+uOPP3Yd5hett4wuoA2Vtvzrrrsu7nvTklqhQgXXnOuMrdi3b59rVhofMmRI3PvhGAs216uvvtqvXXHFFa5pTaf1P1imzWKjQWjVrVWrliUDRiHs3r3bNaMBOP779OnjmlEojFoI63KnTp38GiMi2MY//PCDa9rUBw0a5JprGG38nOu0jzOagPZxWqgZNcH5GvYFxh5wnWF/lypVynXdunVd00JOazmjHKKEfUgbNyN6qGllZ7wLowFCXE+bNm38GuNa+FquN08//bRrxoXMmzfPNdeNOnXquOZcZWQA9y2OEa7dHLthLWIEA++L85DRLZdddplrflfg+KtYsaIlA0YbcfxwveMae+mll7rms3P9CFE03IMYg8IYDsYjjR8/3jX3Ia6TbLeyZcvGvV9+P+M8v/POO12PGTPGNeOFQmRJ27Zt/RrXXe7hfI6ePXu6ZnzCcccdF/dvhRBCCCGEKAjoBKwQQgghhBBCCCGEEEIkCf0AK4QQQgghhBBCCCGEEEkizyIIWPE5VNs2MytatKhr2jBpga5SpYrrkiVLug52OtpFaXunXbhYsWKuaf2jRZj3RXsrrZSsyE77OquFH3vsoWZmHEHz5s1dh6rXtDDSAk1rYenSpV1v27bNNSsEB9uiWay1L0poX6XVmXZh2u5pBa9Zs6ZrVi8PFkdGGtACTdsr+4GRC02bNnVN6z7v5ccff3TNCAqOC0ZQ0JpN6y/HS7B4c2zTvstnovWT7Riqt5vFRmbQehw1fG/a6L/++mvXZ599tmvGLrB9+ZpgQ+VYoB2cNlVaSTdv3uya44IV1Fkhnm3aoUMH1xwnjMrYsWOHxaN79+6uQwwEYyJooS1evLhrWmJpz6ednOtjRyC9AAAgAElEQVRIsuYi+4pjjrZrVjxn/En9+vVdN2jQwHWwpDNqgHOL45ORAgsXLnTdsmVL12eeeaZrzl1GV/To0cM1K9VzLtLCzfgOrilhTWe7nHfeea4ZgZMo3qVy5cquGavC+RoljOLgmscxzj2GY5n7ULly5VyHduMexz2Rn8n2njFjhuurrrrKNduEMQ7c4xg7wbnCvmAsB2N3Lr/8ctd79uwxs9i4oIEDB7rm/KQ1nnsf40r43YJjLkq4rjF+pUiRIq45VziW69Wr55prZdh72PcjRoxwzb7lOF22bJlrxgtwPeLnn3HGGa6HDx/ummsc5wf/lmsso2tCBAajIHr37u2ae+V3333nmtEVfCY+B18vhBBCCCFEQUAnYIUQQgghhBBCCCGEECJJ6AdYIYQQQgghhBBCCCGESBJ5FkFAyyBtaLQylihRwjWt4bTxMqYg2ONoL+draU2kfY12cNoDV61addh7m8VabwktvsF6ZxZrh6bdmtWow3PTastKxLQGM96Atltayb/44gvXtKRGCS2RtJey32j7ZJ/zOqtvB7stn4VVmGmXZt9zDNEymagKNi3I7CvaQGkbZR/SEkv7Z7Amsy1o8WX1dFZkZxV2xjTwmXi/UXPMMYf+OwzbnbESK1eudE0LKyMGaAkOfZPIPs12Zv+zLRi7wQgQfg5jJfbv3++abc31gDZ43g8jJsLcYbVwzlvOf8YhNGvWzDUjMdinfL5kwT6kBZvtRisv5yXbOYxRRr4ksgOzUjljLBhXwbnA9ZTrJtdrVqrn53JtY3vS1h5ev2vXLr/GdZ5rBKNpuP5zbHNNo1U/Srj3cY1h+3BN4H1wLWQ7h/HJ9+BYpv1/8uTJrhkFwPgfRoSwDwnHGe3ofCZGlzAOY+LEia7LlCljZmZ16tSJ+zmch5xv7HPGEXE9Z0RIlLBtuWZzPVqwYIFr7s2Mtpg9e7br8F2Fax3nOKNa1qxZ45rzk/OKeyI/k/EJnCuM2eGazHHG+cG1IPQ5922uMeeff75rxg4wuoLzgt/bChcubEIIIYQQQhQkdAJWCCGEEEIIIYQQQgghkoR+gBVCCCGEEEIIIYQQQogkkWcRBLQ90xZMqykthrS70ZJIG2ao0EzbM/+dVjZep+2UmtZbVn8mVatWjfu3vEdanWkhZaxAaI/SpUv7Ndr0eZ2aljxarUuWLOma7REl/GxWUGY78z5oGaS9l/bE8BpWqa5bt65rWkfT0tJc01JMWyVt9GwfWixpz6Wlme3M96d9lq8JlaA5Vk8//XTXjCZISUlxnSgage3Iz48atgvtprTvsq1pAWf/cgyE56A1lRZgfiZtqITzn+2YKFKDVlzeC/+W6w5trrS7hzHGsRYqspvF9kWLFi1c8/n4mYliOKKE7cm1is9ACzPHItuzfPnyrsO6zHnOWBReT01Ndc3IkEqVKrmmJZpWbb4P13/GzrCd+bfsz9q1a7sOsQJck9lvHB9c52ntZmV3jsVkrafsQ+4rnEN8XrYb43XYbp06dTKz2DZjGzPypnPnzq45TrkmMtKC6ybXCq4JbH++nlZ2zn/OldDmfG+2Bcc2xzDbiJr9z36OEu71HDOMAGjevHnce+LcY2RBGG+cs2wT9me3bt1cc3xwTLA/uccQXqfmfspYHvY5+yLMLe63fE72N/dVfk/ivOCz8roQQgghhBAFAZ2AFUIIIYQQQgghhBBCiCShH2CFEEIIIYQQQgghhBAiSeRZBEHHjh0LHflVv21oVcyPtG3bVn34X0DDhg1/lX5s2LDhr/ExRw2jLY5Eokrs+YUOHTpoLhZwWrZsqT4s4DRr1kx9KIQQQgghhIiLTsAKIYQQQgghhBBCCCFEktAPsEIIIYQQQgghhBBCCJEk9AOsEEIIIYQQQgghhBBCJAn9ACuEEEIIIYQQQgghhBBJQj/ACiGEEEIIIYQQQgghRJLQD7BCCCGEEEIIIYQQQgiRJPQDrBBCCCGEEEIIIYQQQiQJ/QArhBBCCCGEEEIIIYQQSUI/wAohhBBCCCGEEEIIIUSS0A+wQgghhBBCCCGEEEIIkST0A6wQQgghhBBCCCGEEEIkCf0AK4QQQgghhBBCCCGEEElCP8AKIYQQQgghhBBCCCFEktAPsEIIIYQQQgghhBBCCJEk9AOsEEIIIYQQQgghhBBCJAn9ACuEEEIIIYQQQgghhBBJ4ti8vgEhhBBCCCFE/iMtNaOsmf3dzFpmZWcuP8JrTzSze8ysm5mVNbNPzGyzmd2flZ25Ocm3KoQQQgiRr9EJWCGEEEIIIUQ8bv//eO0MM7vJzHaY2Wgzm25mTc1sXVpqRt0k3JsQQgghRIEhz07Arl69+segv/rqK79+0kknuf773//u+oQTTnD9+eefu65QoYLrd99918zMypcv79c+/fRT18cee+hx+Znnnnuu6w8++MB1yZIl417n+7/33nuuzzjjjLivP++881yvW7fOdaNGjQ57Pd+b71G4cOG4916qVCnX//nPf1yfeOKJro8//njX6enphSwi1q5d63348ccf+/UiRYq4/uSTT1yXLVvW9d69e11XrVrV9c6dO83M7IILLvBrO3bscP273/3OdaFChx6lePHirv/5z3/G/cy33nor7vv84x//cF20aFHX33zzjetKlSq53rVrl+uzzz7b9YcffmhmZtWrV/drf/3rX10XK1bM9THHHPpvHxw3bK/TTz/d9bfffuu6TZs2kfWhmdmaNWu8H/n5HEMHDhxwnajt4vVvjRo1/BrbjWOS7cw59/XXX8d97z179riuXbt23Ounnnqq688++8w174fjoWLFioddr1atml975513XOfm5rrmeDnuuONcc77yOtegtLS0yPpxyZIlcfuQ7cm1kOvsl19+6bpcuXKuw3hmf7/99tuuuSZzLiZaw9meHDdcw95//33XHCN8f/bV3/72t7jvHz6Xc27fvn1xP5N9xf787rvvjngvHTt2jKwPV65c6X3IMfbjj345pt24V4R108ysTp06rkN/cf1iH7Jv2Vds4zAOzMxOOeUU1x999JFrruHsQ7Y/27N06dJH/Nywt/IeE+3PX3zxRdz3/uGHH1yzb9mmUe6JW7ZsiTsPuT9xHFapUsU191C2Z05OjpmZnXbaaX6NfXLyySe7/te//uW6TJkyrhO18f79+11z3nIPTfQdg+3J1/Nzw/vzOTluuA9yvef6TbjmcI40a9Ys0j1RmKWlZtQys+vtpx9Pqx7h5eFvaphZOzN7Iis781pcn21mG82st5ltif5uhRBCCCEKBjoBK4QQQgghhAicZmaV7afogT1HeC3/Zp2ZzfzZ9fBfF34wIYQQQojfMHl2ApanHb7//nvXPAXCUyOEJz7CqVezQ6cmeNKOJ+d4eoKndHiqhp/JU388uRZOpJjFngjiKUWeXuSJ1aZNm7rmSZDKlSubmdmiRYv8Gk8P8RQKT4ccPHjQNU8s8jpPmUQJT87xRA5PJvE6T47xRBlPBIXTTqtXr/Zr559/ftz348k1Pm+JEiVc87Q0+/D11193zZNFHFv8W548IzxNFu6T780TWOw33gufifOC7XvmmWfG/fwo4Gkw9gufn+OZJ0p5IoonWcN43rRpk1/j+GS78NnYLjy1xXZh/27efChSjvOVn8X1JTs72zVPfG3ZcuhQTrifrVu3xn0/nuTj/fL6v//9b9ccUzydFyU8gcaTaewfzhGuJ5yXb775puvgLuAY5xjmesexynWNay7vhesm5z/7imOOJ11r1qwZ9x5472FOc27xlCTXRPYJ12rCuZCsPmQ/sD2pOVc53jmWuZ+F0/XsQ57I5glIPiPXVrb3tm3bXPMk5caNG12fddZZrjnm+Bwco3Sx7N6923VqaqqZmWVmZvo1zjE6Lfje7FuOP46zlJQUSwY8gcrxxvWL8FQ6+3/58kMxm2Ff4RpF5wXbmN+H6KxgX/E13Fu5b7ENOZ+5P/BzuZ5zfwify3FDOFYSOaE4dvl9Llnfa8RPZGVnrjSzlWZmaakZ/c3suaP4mywza/I/f1PEzMqYWTkzu8/M/m1mU5Jzt0IIIYQQBQMV4RJCCCGEEEJExQVmth7/911Z2Znbf60Pz83NjYmpEIcT2kftlPeoL/IP6ov8hfoj/6C+OHqO1Eb6AVYIIYQQQggRFbvNrLOZVTKzAWY2Ni0147us7MyHEv1BWmrGEDMb8ks+tHDhwoWr161nOTk5dvzxxx35D0SMq0/kLeqL/IP6In+h/sg/qC+OzLfffve//nue/QBLKxutlLTds4MZDUCrGm17wQbZpEkTv7Z9+6H/4E7LIu3StEPS1ka7IwtBsfAPr/PeaUFmsRRe5/svXbrUzGKtwbSS8rW0LdLezKIftBzS1hsltIJT0w7N9uR12jP5zMH2zTZjISs+7xtvvOGafcJxw3gBfj7HEC2TbEN+LtuT78PrTz31lJmZNW/e3K/R7kl7Ku2btGPzv5gkKkQSNbTRc2yx71g8jn3D+Uer6Pr1Px18YeQG24LPTKs/LaZsC8YU0I4eog7MYsc5532iQlCc99QrV640M7OGDRv6NdpzOdZp5+b6wvWNdm5ayKOE7cl1g7Z/tj/heKYdPNigGQVAizj7is9F+zAjAo6mT3iPLNTHonyLFy92zbblGvnaa68ddi+0SfN+OeZp2+b7JYrpiBKO30SFAblWJSoYyDk5d+5cMzNr3LixX1uzZo1rFuxi2zMKiOOD7bNw4ULXnIdcWxkBwXZj9AH3UPbngw8+aGaxcQG8L66njBpgO7K9atWq5ZrjMkoS9QnXe663fF6ua1xXZs+ebWaxcQF8dsYb8O8YWcD+4XeQyZMnu2ahNs4Vvj/vl7Es7M/69eu7fvHFF80sdv4y6mLDhg2uucZzH+YaxvuiFvmPrOzMA2Y238wsLTVjopm9bmYj01IzHs7KzozbeVnZmU+b2dO/5HOvHjHqZDM7kJKSYkWLnnDE1/+Wyc3NtZycHEtJSYmZ2+LXR32Rf1Bf5C/UH/kH9cXRc/DgN2a2MOG/6wSsEEIIIYQQ4v9MWmpGDzOrbWb3ZGVn+n8JycrO/HdaasYyMxtmZqeb2UcJ3iIyChcurP8H8ShRW+Uf1Bf5B/VF/kL9kX9QXxyZI7WPqhgIIYQQQgghfgn1zewOM6sa59+KmdkPZvZlnH8TQgghhPhNkC8iCGhxo62M1tSdO3e6ptX5qquuch0sbKx2THvzsGHDXLOyLyMQaDVu06aN6y5durimVZM2vxtuuME17cjBmm5m9v7777t+4oknDrsf3vsrr7wS9/PLlCnjmnZC2lZpt0yW7ZmVpxkH0KxZM9es1kwb+/z5813fdNNNroPdlP3Ddho7dqxrVlCeMuVQcV1aFgcOHOi6SpUqrmk7njlzpusrrrjCNW2bWVlZrmmZPe+881w3atTosM/Pzs523adPn7j3wjFK6yVjFZJpt6Q9lvbh6tWru2aUA8c8Lb7t2rVznZaWZmZm06dP92tswxtvvNE17cOcf4ypYBxD3bp1XdOSzLYeNGiQa1qB2e8hasDMLD093XWwy3Jd2rVrl+tevXq5pg2XaxQjUzge2L9RQuszLdglS5Z0Tfs4+5AVyi+99FLXnTt3NrNYyzrvn7ZirueMDti0aZNrrtWcN0uWLHHN2I/+/fu75rhkfAGfY/Dgwa7btm1rZrFr6Kuvvuq6a9eurrlW0x7NfYFrLte9KGHkAeck5yFt2lxDSWpqquuaNWuamdlbb73l19jffO8OHTq4DrZ3M7P33nvPNdfHVq1auWafr1692jX7kHPlz3/+s2vGFPTt29f1ueeea2Zmy5Yti3vvAwYMcF2jRg3XITrDLDZSI1HUUJRwr+X6xfHzu9/9znWIajGLXWO434foAUYUhGgJM7OhQ4e65vcLzlXGf4QxYRYbI8LvVdyTGKnDv+VrEkV09OjRw8xi24L7J6MxQn+bxcaoMG6B0RGctyLfsMrMRphZfzO7NVxMS82oZD/lwWZnZWf+O/6fCiGEEEL896MIAiGEEEIIIcRRk5aa0cLMUsxsXlZ25odZ2ZmL01Iz1pjZLWmpGWeZ2UYzK2c//SB7rJndmPDNhBBCCCF+AyiCQAghhBBCCPH/Q18ze8JiIwc6mtkEM2tsZg+aWR8zW25mDbKyM7cd9g5CCCGEEL8h8uwELO2ItOH17NnTNW2YN998c9zX0N4fKl737t3br9HeyugAWuYyMjJcz5s3z3Wiyu98/+HDh7umhZM2vGeffdY17cu0eQZ7MP99woQJrmm7pp002N7NzFatWuWadlu+Jkpo0Z86dapr2j4zMzNds+I2Le2MDwjv2a1bN79GC/mbb77pmnblUaNGuaaN9S9/+YtrWj9ppf3973/vmtWc165d6/q6665zzagH3k+wR9LezEgJWkJpby5Xrlzc17A/W7RoYcmC7cL2om2W1bVpJR8/frxrWoVDZAJfO2fOHNe0mDKCgO08YsQI17Stsu2C1dwsdgyycvuePXvivj+tyrRQhyryTZo0iXuPifq/e/furmkPp8W6YcOGlgwYUUFr8MUXX+ya84Vr6GuvveZ68+bNroOdmdEZQ4YMcc0oAEZR3HbbbXHfe+nSpa5pH2bsAW3l3CNog+fr+az8rDBeOP6eeeYZ11yree+05HP+s3I9bdtRwjWR+9bpp5/umusT95777rvPNdeN0qVLm1nsmsR24BrOz2ScDvfe559/3vUpp5zimuvprFmzXHPMsZ3vvfde15w3bPMQO8K9YOPGja65VrLvGb/DdYNwj4qS8B3ELHaf5hrLMcv5xLEa77sH22nixImu2Q6MJOI8eeyxx1xz/eIa37Jly7j3WKJECdcbNmxwzX322muvdc3Yj/A96KKLLvJrXJtzc3NdM06kYsWKrhnhwhgGRhaI5JKVnTnZzCbHud7ffjrdymsHzOya//kfIYQQQggBdAJWCCGEEEIIIYQQQgghkoR+gBVCCCGEEEIIIYQQQogkkWcRBKwkTdsnLbCsfM0qurSAXnLJJa5DxW1WzQ0VhM3M3njjDdfVqlVzTYs77fqMA6DFl5V4L7/8cte0XvK+aFOmzY9VxIPFNicnx6/RckibPCMTWDWeltCTTz7ZNau5RwltwW3atHG9YsWKuK+nfZaWzNatW7sOMRHFihXza4xfoEW1fv36rhl1cNNNN7lm/y9atMg1beGsGs736dixo2tWR2dkAG3yoZ85Pjt16uSasRChwrxZ7LOmpKS4ZqV4xmdEDe33iey7rCDOsUWb+IUXXug6jAHeN8cI7bm0zTK6g+sCq4JzjtAGzznHMVilShXXtDDTns95GWzTjF3gXCW0cDNKhWOE45eRFFHy3XffuaZ9lxbwkiVLuqZtmeOf/R9iWlgdnXZwWo3PPvts17QSn3XWWa7btWvnmvOYldsZd0ArPeG9N2vWzDX7M6xNM2fOjPvebJdKlSq55rpUvHhx12lpaa4ZnxElXKc5rvh5jELgehIibMzM0tPTXYc9kbEd3Fceeugh15w/HMusfM/25ucTxltwP2cbMn6He+7QoUNdh3ghrgnNmzd3/dFHH7lmRBDbkdc5Rvm3UUJ7PfcnroOcn2wfrhO09y9evPiw1/J7B683btzYNect+41xAIwUOOaYQ/89nt8lVq9eHff9Fy5c6Jr7LN8/7Plcexj/wHlYr14915zjIUbDLHaccy4IIYQQQghRENAJWCGEEEIIIYQQQgghhEgS+gFWCCGEEEIIIYQQQgghkkSeebhYWff77793Tfs2LXTbt2+P+z6sfB6spNu2bfNrtFLSur9mzRrXtBozjuCFF15w/c4777imVZMxCbTy0gJNqz7vjbEKIUqAlYtp5aU9kZZDWqNZLZjWbFr7ooTW+V27drmmdZ7tzLairZR29PBsHB+0/zOuICsryzUjJRgv8NJLL7lmNfHJkyfHfX9apmnBp3WddsqxY8ce9v607DKuokGDBq45Lps0aeKaFvXdu3e7TmSBjwLaTRnTwefgXGRkQeHChV1zXAYrLsd+sLSbxbYh5wGrmXMMs3I7beKMG+Gc4xikzZX3QJvrnj17XBcqVMjMzOrUqePXOKYYwcAxzQgG9i+fI1mVu9merCxO+y7HM3XVqlVdz5gxw3WIUaB1e8KECa45F9nPjEXhesr35jhnZfPPPvvMNa30XEc4F9m2tLWHOc21j3sI4wUYEcPrjJ1hNEnt2rUtGZx00kmuaa9m+7NtubYwYuDFF190HcY417hE8S5cb2j15/7MKJxEY4ttRcv4pEmTXDOO5NNPP3X95JNPug4xRZzXnLNsF64DHBOEsUeMeomSIkWKuOZ+zHnPfZ3RLoxi+eMf/+g6PA/7hLEdjNDgOsbxFNY0M7PHHnvMNfvn1FNPdc2xVaZMGdfcH7jmcP7zu1KI0uAYYkQE954Ql8G/MzM7ePCga7ZRstZSIYQQQgghkoVOwAohhBBCCCGEEEIIIUSS0A+wQgghhBBCCCGEEEIIkSTyLIKAVlNaPWm9ZFVrWu5oJWzVqpXrYGWl3Y2WclbzHThwoGta3Gix69Chg2taBWmZpw1u69atrocMGeKa9lA+a6lSpVyHZ2UVZVaBZ5VzVmxnhWza+RjlQFttlNAOyPahDZK2e9q1WUGddtDQX7RysjI7qzbTAs12oKZtk9Z5VqhmfAKrY7OdGZmwYMEC17TBhtiE8ePH+7X27du7pv29SpUqcd+bFbLZb7STRg3tqbxHRgOwgjlfw5gOzoUQ98ExQmgTpqWb85xVuTmP2UZcRzgGaYNlNADnBa3srNwdrMO0ZLO//vGPf7imbZf3nqjv+ExRQks/xxP7sEuXLq65zsyaNcs1LfunnXaamcWuX4wrYBuzyjrXuI8//tg113PamhkNQts274tV3/mstDPTEh3iIzhWGEXBaATa4RlTwHFOSzSvRwljAtjmHFd8HkZhcD4xOiOso2xXvjf3SrYD9ziOd1a753znusz4GM4brvOMyeA8vPLKKw+7d45n7pkcZxy37B/uyRy7fO4ooXWf7cCYDa5H/C6zatUq102bNnUd4lQ4Nrnu8L07duzoev/+/a45V/nenAfcB3lfbH/up4wd4DrD7z4hUoYxE4zTYeQMo3g4lzn+krkPCiGEEEIIkWx0AlYIIYQQQgghhBBCCCGSRJ6dgOUJDp6S4gk1FvXh6YglS5a47tSpk+t58+aZmdnIkSP92oMPPuiap2v43rwXnrA555xzXGdnZ7vmSbJbbrnFNU/j8mTHmWee6Zqnt/r06eM6FN3o16+fX3v66add83QST7OwEApPG/JkIJ8pSljMhadOu3fv7nru3Lmu2Z48VcMTkGEsVKhQwa/de++9rnv27Omap5XZxjyNw+vs87vuuss1T9gmOiXIglQ8+TNq1CjXjz/+uJmZXXPNNX5t5syZrvlMPK3MIkBsF76eheKi5oQTTnDNcVO+fHnXLBzD1xMWdAmnR3v16uXXWDCPJ5B5So1jgZ/JuctiLT169HA9bdo01xyDPCnHk288pcrTeTt37jzsMzkuhg8f7jonJ8c1xzcLhfE0N9eaKOGJMZ6I4ylurmGJTmDzpHNozzvvvNOvPffcc64vu+wy16HNzMzatWvnmvOfJxO55nP+PfXUU65ZNJFF0HgKk/OL/RI+l8/GdZMn81kYkWOCpxl5YjNZp5h5uo/zkCcmeYqfr+ec5MnUUKiQBSJZ4IlzfN26da653rGvWBiNfXv33Xe7Zv/TxcGTrjyxynvg/AjrOAtT8qR6165dXbM4GU8Gs/950j9ZrhB+f+Gp4IYNG7pm8Uhe5zrB8Tl69GgzMxszZoxf41jn3/EUK0/88nsHi3axuOHgwYNdL1y40DXHAtfexo0bu+YJ7BNPPNF1GMfsq6FDh7qmQ4SnZLmH0znDk/PJmodCCCGEEEIkC32DFUIIIYQQQgghhBBCiCShH2CFEEIIIYQQQgghhBAiSRRKVkGRIzFlyhT/YNrNaJenVa9atWquacemDTNYRpcuXerXGjVq5JrWOBZ2oH2Ttnpaiml3p2WctkZaY1nYi/ZV2rRpoQtFtmiTp425bt26rvkaWktpw6RVj4XNunfvfuiGfyGzZs3yPnz77bf9Oq2ENWrUcE0LcKJ2CJbRHTt2+LV//etfrkNBErNYazltp3zN8uXLXdN2+u6777pmIR+OBVo1WeCJ98viNmG80HpKWMBk2bJlrhk1wDaiTZ8201atWkXWh2Zmc+bM8X7kuGVMAm3tnHN8PedOmMcrV670a7Qas51pm+V85XtzPBMWyiJs6xBNYmbWokUL1xyzjCAIhWlYbI1jjXEoLLRDuzPnIm3YfP3FF18cWT/OmzfP+5BjiGsPizmxUBPXNt5r6C8WL+RcodX91VdfdU3rM+cl5zFtxYyFYLFBWpwzMzNds/geowlY3CfsKZz/jGihTZ73zs9cs2aNa8Yk0Gbevn37pPQhYRwB25AFlAjbPMwh7rEssMT1mUXAOE4ZxcB7YQHCKVOmxP1bRlpwvrGoXuHChV1zXQ59wegCroOJCuPx+bi2stgb9+0o+3DBggXeh5x7bP9EkUuffPKJa+6PoRge5yGjGjgnOZfYt4kKLbJ95s+fH/fz+Rq2M78f8Zn4WWEsrl+/3q+x33jvbAuOc74fxxyvN2nSJNI9URRcrh4x6mQzO/DI2NutaNH4cUniJ3Jzc23nzp1Ws2bNmHVY/PqoL/IP6ov8hfoj/6C+OHoOHvzGRtx1v5nZKRMeGfPFz/9dJ2CFEEIIIYQQQgghhBAiSegHWCGEEEIIIYQQQgghhEgSxx75JcmBVldaQGnVo2Wc1sdE1vz77rvPzMzOPPNMv0bbbenSpV3TMkdLHmMEaIOjxbJp06auaZnlc9BKyfgEVhFmhfJg0UsUHTB9+vS4z0HbYMWKFeP+LSukRwn7gdZEfni5aO4AACAASURBVDbt0LSds63Yh6GqPO21tFvSFkzb+AcffOD6lVdecc3q9azaTRskrYwcZ7x3Wt1ZFZrRBNu3bz/sfv/5z3+6fvzxx13T6rxo0SLXjB3gXGB8RtRwvrz88suuaT1mG3GM0qpK63MYrxyTBw4ccM25ReszP58WV1pV//rXv7qmrXj//v2uGZlAVq1a5ZoVyBnrsXXrVjMzq1y5sl9j1ATnHOMuOAcY08BIDo6XKGGfcG3jPdEuEuaZWexaXK5cOdcvvPCCmcX2IWMWPvroI9eMhVmxYoXrDh06uOazM4IjWKz/t3vknOJ8YezAnj17XO/cudPMYtciRodwHeHawXUnUbwE14goYTtwvXnvvfdcsw05n2rXrh33eohD4R5EqzujXrhPhPYzi53jjAh67LHHXDOOgm0e5pJZ4tgftnm8WAv2D/dbRgHxPbgmcA3j9wn2Z5RwLeWawjZnPAnXvnr16rnmfA7rDfcA7k1sE343CPuRWWw0Aecz5yrvnX3F9YTrMMdlgwYN4t5DmGdcKzi2uQ9zzWR7cR4yRkT2NyGEEEIIUdDQCVghhBBCCCGEEEIIIYRIEvoBVgghhBBCCCGEEEIIIZJEnkUQ0HpHmzYraFPTzkdrKivnBks/K2NPnjzZ9bhx41yzCvbo0aNd09p38803u77nnntcs8r9X/7yF9e0mLPKN6uM0/o6dOhQ18GGP2vWLL/23HPPuV6yZIlrVpPm/e7du9c1rZy8lyhhv7FNGK1AW+2WLVtc0xLJKsuhP2k1ZD/ccccdrhmB8NJLL7mm/Zy22ssvv9w1IyIeeOAB17TV0so6depU1+zDq666ynVo86ysLL9G/cgjj7jmM9NCSqsmLZmnnHKKJQvGAfDzGZPAe3zmmWdc0x56//33uw59Q2v6nDlzXA8ePNh1r169XHOOvvbaa67/+Mc/um7SpInrzMxM15wX7EdWRWcfcE25+uqrXYdK2+xn2m0vu+wy1xzffD2tz3Xq1HGdrLlImzatx7QVs4J4Ims4rezDhg0zs9hoB7Zf+/btXXOO3nTTTa7Hjh3rmtEBrVu3jvueieIdGEcxe/Zs1yEmwczs7rvvdh3s2rSmP/roo65vvfVW1127dnXN/YJz7uDBg6657kQJ90SOJe5VXHMZbTFt2jTX7IswPrkHZWdnu2abMfKEkQ+MzRkzZozrZs2auaatnPvWFVdc4Zq2+gkTJrjm+tOqVSvXxYoVM7PY+I/ly5e7vuuuu1yzr7gPMqaA0TG8HiVcP/lctWrVcs11lWvc6tWrXd92222uQxvyuwHn75133umaewb35HXr1rkeNGiQa+7VHBfz5s1z/Yc//ME1x/4NN9wQ9zlGjhx52L1z7vM5GSHCWAqORa5PfH2y1lIhhBBCCCGShU7ACiGEEEIIIYQQQgghRJLQD7BCCCGEEEIIIYQQQgiRJPIsgoDWXdojMzIyXNNWTrsb7cWseB/sd6y8PWnSJNe0VS5dutT1s88+67p///6uaW/+8ccfXdO+SqsebeWhgrOZ2cMPP+yatkDaRoNNmvZdxg7MmDHDdZEiRVx36tTJ9fvvv++aleJpK44S2pv//ve/u2Zb0bpNu2vPnj1d0woerMOdO3f2a7Qjli9f3jWf8frrr3fdt29f1+vXr3fNaAraJNmffA3bkzbMRo0auX755Zddh/agxZPjk1Wb2feMMaA1l/2czIrPrGzOCuo//PCDa9r4aeWmxZjxAcESzHbmPKClnHbruXPnuqbF/fnnn3fNeIru3bu7fvLJJ13Tks+q4w8++KBr2nxpoQ6Vvvv16+fXUlNTXQ8YMMA14y6GDBnimvN14cKFrps3b27JgO3J/jnrrLPivp7PPnz4cNeFChVyHSzxvXv39mvbtm1zfemll8b9zIkTJ7q++OKLXT/xxBOuq1ev7vq6665zzTU3JSXFNaMJFixY4Lpjx46uacvetWuXmZn16dPHr9HKHP7dzGzPnj2uuc/ce++9rmmDZ6xJlFSqVMk11yHGDjDGJUREmMVG5MycOdN1/fr1zSy2jbmW8dk/++wz15yrjG7h33Ld4DrAtmXUDON6Ro0a5ZrRJYwaqVevnpnF7ucrV650zWgERk0wIoSRAC+++KLrtm3bWjLgfOMawBgSPgPXR0YSMQojxBcwKoPtxPWTtnyOCe6xTz31lGtGN7APOVcZ18AICM7D9PT0uPcWxh/X0ilTprjmfnPssYe+jjIaYfHixa7Z52xTIYQQQgghCgI6ASuEEEIIIYQQQgghhBBJQj/ACiGEEEIIIYQQQgghRJIoRLv4r8natWv9g1l9l5VwaTWmHXnFihWuaVUL1b8rVqzo12jjp8UtNzfXNa2frKTOKsu0jNM+y4q+iSou0ypHGybtrn/605/MLLZqOKvN0w5etmxZ17TGFi9e3DUriLOKeYsWLQ55jH8hS5cu9T5ktALvg9WXu3Xr5jonJ8f1wIEDXYfYCdqPaeGmxbJatWquGQvAKvDnnnuua0YZBJu5WWxcBC3YtBp/9913cTWt1+PHjzczsyZNmvi1VatWuaZdvUaNGq4Z38BYhzPOOMM12zQ9PT2yPjQzW7Zsmfcj40Boa+f4q1y5smtGLPB+Q3wCK4HT7nzyySe7LleunGtW8WZ8BK3mtGH36NHDNW3TjB3ga1jF+6233nJNO32IQQj2WbNYCzznXJs2bVzTzst25Bjk3zZv3jyyfpw3b573Ie3GjEVhZAdt5bQ704Yc3qdKlSp+LaxTZrHPe8wxh/5bHuNlqBnjwPZk7AwjKBgZwPlCuzn3CK65a9euNbPYvuc8Zz9w3NKyfvrpp8d9b64dXbp0iawPly9f/iO0Xy9atKhr7o+cN4wPYDtv3LjRzGJjYfbu3euaz0v7PKMjZs2a5bpx48au+Z5cfzlGOOcbNmzomnsY14imTZu6DnP47LPP9mu073NvadeunWuOOa5VjHJgNELXrl0j68NNmzZ5o3Af4r7Cz2Y7fPzxx64ZoxAs/ccff7xfe/fdd11zP+KY4HcA9sOIESNcM5KFbU/bP+chv8uUKFHCNeN6uJ9nZWWZ2aE4CbPY6ILXXnvNNWOH+Bw7d+6M+5khrsjMrH379pHuiaLgcvWIUSeb2YFHxt5uRYuecMTX/5bJzc21nTt3Ws2aNZMacyWOjPoi/6C+yF+oP/IP6ouj5+DBb2zEXfebmZ0y4ZExX/z833UCVgghhBBCCCGEEEIIIZKEfoAVQgghhBBCCCGEEEKIJHHskV+SfFgJl1ZP2lFpB6XVn/b6YNGjrY8WaVrjaGWkLZlWa0YE0AZHqxwjC2gzZNQAn4/V5FmJPFiyx4wZ49doWaedkLbR0qVLuw62b7PYNkpW5fUyZcq4pgWZ8Q60lH/66aeuaR8cN26c61BB+8Ybb/RrrG7OGINgrzWLtXXSksn+/+STT1ynpaUd9plmsRXfaZNu0KCBa8ZO9OzZ03Wwe9PqvXv3btccZ7RD057JOIL33nvPNS3BUcNq5rSbcs5Nnz7dNW3dvF/aoENf87XPP/+861atWrlmO/M6rdJz5sw57L3NzOrWreua8RSMT2DESadOnVxzLj777LOuQ/9xbtGyzmiKZcuWub7gggtcc/4x+oRjIEpOPfVU18WKFXPN+cd1aPbs2a6PO+441w888IDrYC/hmvjhhx+65vPSSkwLOKMgOI/ZJu+8845rzh1a1mk3b926ddzXc66H9WDbtm1+LTMz0zUryrOKPONluKbx82vXrm3JgO3GtZz3RPt2okgPrjmh/9lXO3bscN2nTx/XjNlh7AT7irZ6jrkQ+WBm9sEHH7hmO8+YMcP1VVdd5XrSpEmuOUbD8zEWg/feokUL19wLunfv7poxJoxvYKRFlDCWgd8NEo1l9jNjeX7/+9+7DtE5jMrYt2+f6379+rnmvOYey3l7xx13uGbf8rsU24rjb9OmTa7btm3rmnM4RCaYHYp94Hcjro1cSxkJw3v/9ttvXTP+g9/zhBBCCCGEKAjoBKwQQgghhBBCCCGEEEIkCf0AK4QQQgghhBBCCCGEEEkizyIIDh486Jo2PNoXWa03VNM1i62CTXtasKQxLoAV6WlRpSWQNl1WIqYFuWrVqq5ppeVz0Pp7++23u6aFlzY/2jPDc/PfaUllVXVWXqeFjzbD8uXLu6bVOkpoP6ctnPZVWv3ZDrRhDho0yHWINaAtnX3IStDnn3++a/YD/5bVsWldpr2fVbbZt4xuYP/zs1gVOow72l4Zx8CK7exD2ttZEfzcc891zViFqOFnshI6+3TYsGGuGTXA6teMSQjW1hUrVvi1+vXru2YV744dO7rmczIagZECLVu2dL106VLXbGvao2nRpYWZMSF9+/Z1HcYDbfVcCzh2OL5oIa5YsaJr2sOTVTWS1u3ixYu7pjX4zDPPdM01lOOZEQ3B+kwrNeczxznHB/uWlnXOP1qc2W5ci7mepaenx713WrFpSQ+v4ZyrXLmy688//9w1Yy8YZcI4Dq4RtG1HCe3r/AzuiZxDtGOzj3ivYRxyzDK2g3/HecXrwUZuFtuGKSkprtnPjLTgvnXNNde4ZmQAK95z/od1YfLkyXFfy/2H6yn3O1rZOYeTtZ7Sas/1m+3D/ilatKhr7uWcw2FN5vrG99iyZYtrxgJwHjJOg9EyjBdgdAXnDduc7cz3TxTvEOYTo0IS7Xccf/wexHvnPiyEEEIIIURBQydghRBCCCGEEEIIIYQQIknoB1ghhBBCCCGEEEIIIYRIEnkWQXDKKae4pu2/UaNGrl9++WXXtJtv3rzZ9W233eZ66tSpZhZbNZmWvKFDh7qmNZIWt127drmeNWuW6zvvvNN1RkaG6z/84Q+uU1NTXa9atcr1eeedF/f9O3To4DpYYmnrpPWyVq1armm9pD2UbUrLNmMaooRVmdmGtCyyr9g+n3zyiWvagUNMAS3Ho0aNck27OiMNaDWlBbJZs2au//znP7u+9dZbXU+fPt01Ywdob+bz0XrJ6uLBTsmxwmem1Zmf2b9/f9e0Y7PfkmV7Nou1j9J2zWdjNfsQE2EW2y600C5fvtzMYqty0zZ70UUXuWY716xZ0zXXBdrkGUfSq1cv1zfeeKNrxpq88MILrjkvaL/l9TB3abfl2KlXr55rxjE0btzY9bZt21zTZsz2ihLaqxnL0K1bN9dczxo0aOB69erVrmnxXbJkiZnFrsnjxo1zffnll7sOcQU/h5XKaclm/APn+sqVK10zroHrHO+RVnLa41999VUzi7U10wbN+6XdnuOAcSckWfb1ROs0Y04WLVrkulKlSnHviXtIsKczHuSOO+5wzfWRMMKGsR2M9liwYIHrkSNHuuZ6xhgdWty5n23YsME14xGmTJliZmb33XefX2vfvn3c9+DayvnGcUPN+4oSxhCx3bhOTZs2zXXt2rVdM0aEa0/YKxh3Esa3Wex3HM4fjgPGeTB+6fnnn3d9ySWXuA5z3yw2Uoj7NtcFzi2ucSGWh2OC+zn3HsYecC9hTAGjCXhfQgghhBBCFAR0AlYIIYQQQgghhBBCCCGShH6AFUIIIYQQQgghhBBCiCRRiJWXf00WLlzoH0w7KC29rEj9u9/9zjVt0rS7Bwsfq5fTEkjrMG3PtEavXbvWNS3YTZo0cU0rLy15jEnYs2dP3Hunbb5EiRKug1WXVmdWHKfdnzEGtGHSyl+hQgXXrJ7cvn37Q2WafyHTp0/3PmSVbd7rV1995ZrtQxt/vArd7B9aV2m7pWWXFmxaIPn5rP7MPmQbsn9Y0Zo2YNo8+Uyh4jjtm3yPpk2but67d69rVg3nuKGFlHbLVq1aRdaHZmYvvPCC9yPbiJZYxl4w7oE2bbbdmWeeaWZmc+bMOeyaWazFmRXR2Z7sX64RbFPax0866STXtKlzTWF8wjfffBP3/UPVcc5trkW0x3ItomWe1lq2ET+/devWkfXjSy+95H34t7/9za9//fXXrhlRwjnHmAASojw43kuVKhX379asWeOa44NrQZgfZrFzlGsu25DrH9d0xkswboRrRojs4NrBv2MsDO3ZfCa2IyMbWMW9a9eukfXhggUL4m7GtLJzrahWrZprthtt2qHPGRfAvYFtzOgKxml89NFHrrnHcF9jpA/XELY55xPHBccLx2WYQ4n6hOOJ782YBMYR8Lk5J9PT05MyDwnnIe+VkRdcY/m9LHzH4Txhm3AucZ3iGsh9he3AmBf2bfHixV1zb+W9s58ZNcTvSmEefvDBB36NcRlckzivOCb4fPzOxL5t1qxZpHuiKLhcPWLUyWZ24JGxt1vRoicc8fW/ZXJzc23nzp1Ws2bNmD1E/PqoL/IP6ov8hfoj/6C+OHoOHvzGRtx1v5nZKRMeGXNYTp9OwAohhBBCCCGEEEIIIUSS0A+wQgghhBBCCCGEEEIIkSSSV1r9CNDiRjs67aC08dJKyigBWodDZW1GCvC9advjddrjaNuj1XjYsGFxP592OlaKp2WW9lXaJr/88kvXwbbOKvChgrBZrDWTVuv169e7prWQlYNpA40Sft5bb73lmtWME9mh2W+0EW/atMnM4ttozWKtlLSRvvPOO64ZU8DK61OnTnVNez/ts7R+Bvuk2dFV0J4xY8Zhn8m4iGeffdY1YyH4frT1cvwxDiBq2Ba0cXN8sso1q81zjnJ+3XXXXWZm1r17d7/GuABaF2h9ZSwAxw7bhW3KdiSMHeA8YqwB25RtEKzYnDe0xG7dutU1beBcRxhfwjZK1lyk9ZjtyTG8bNky11wjaf1lTERYT9nGtI5v2bLF9aeffhr3Xhidwc958cUXXaenp7vmPA7zySzWeswYhOzsbNchOsLMbO7cuYd9Pvt+9erVrrme0gbO8cyxQutzlHC+027OdZbzkOOa8R8hxsXsUAQIxzefkfsUx/i6detccx2gZZ5tTzs6+5BtzvgEWtkZB8Gol5kzZ5pZbP9wrXz55Zddc98+ePCga/4t9xHuHVHCz+A6xf2Dz07N/mcUy/Tp080sdjxmZma6zsjIiPuZbAf+LT9z8eLFrhmRw+9Hb775Ztx75BrO7y1cc8L44rrB73j8/pQoGoHvl+jzhRBCCCGEKAjoBKwQQgghhBBCCCGEEEIkCf0AK4QQQgghhBBCCCGEEEkizyIIaBFmlWXaJ2l3XLFihetgLzWLtXWH6ruzZ8/2a7QpdurUyXXz5s1dB6vtz//2iiuucD1u3DjXrBb98MMPu6ZltkWLFq5p86MN+PHHH3cdbJi0mL700kuu7777btd8Da3/jHWoXbu260Q27V8K7eKsBk87aOfOnV0zAoD28gsvvNB1sWLFYv632aFYAjOz4cOHu6aldfLkya7Z3i+88IJr2uFXrVoV994vvfRS17QvT5kyxTUrvt9yyy2H6WC/N4sdK8FSaxbbVwsXLnS9fft217Rt0kIcNbSsM46BNvU2bdq4pm2Z93vNNde4DrbZWbNm+bXx48e7pn2Zz8Y24jzPycmJ+5m8zjn6zDPPuGZ8AecLba7t27d3HcYv7zE8j5nZhAkTXNO2zfulhZfjqFCh5BTrptWb1nT2G9cQtg/76LHHHnPdu3dvMzsUj2JmNnLkSNdjxoxxzWdkTMgrr7zimmt7r169XO/YscP1n//8Z9dcHxkNwv7nOjJgwADXIY6A83zbtm2uOc/Lly/vmmsu11mOIdr9o4SxLFyzqdnOjJ/huGrdurXrrl27mllsG7P92rVr55r2bvYD199bb7017t+ynxcsWOD6vvvuc834naeeeiru33IODxo06LD349rLvYCxL4wIYSRA9erVXTOGIUo4D/m9pnHjxq5pu9+wYYNrjjfea48ePcwsNvKDEUP8nCFDhrjmnsU+5P5Urlw51/yOxe9NN9xwg+vU1FTXXO/YnhdffLH9HO7JXIO5f3JPZEwBIzAYL8TIFyGEEEIIIQoCOgErhBBCCCGEEEIIIYQQSUI/wAohhBBCCCGEEEIIIUSSyLMIAla+p92RVZBpdWalX1rxHnnkEdfBbtitWze/Rtstq+ayCjctdqw+zWiC999/33WrVq3ivuf+/ftdb9682fWTTz7pmtEA119/vetgBQyWUbNYGzMteawmTxsmrcRsI9ozo+TUU091TdvnRRdd5HrRokWuaUEPcRFmsRbUUIX9wQcf9Gu0wrNNaMN86KGHXLP/ae/fu3eva44Ljjne19dff+2atk3aZGnnDWPn/vvv92u0PdOyyXgNvvdVV13lmhZLVqePGlqwWcGc45xV0YcNG+aaltdHH33UdbBE33nnnX6NNnLOrf/85z+uOSc458aOHeua1e4ZHTB69GjX//73v12zH3v27Oma85L3E9qDa87EiRNd06bO+Txw4EDX06ZNc717927XtLJHCfuQsQzsQ65tt912m+u2bdu6pq09zDXa9blWc/6zsnqILjCLjTqgTZl2+2A1NzObNGmSa1r9+fo//OEPrmnt5mcFWzYt8B06dHBdqlQp14yFYYzGE088cdj7mcWuF1FCe/Udd9zh+tprr3XNdZ1r2ODBg10zMiLMQ+4rjHbYtWuXa+4x/HzusZyHTZs2dT1ixAjXN998s2vGUXB/euCBB1wzyoLvX61aNTOLXW84PhlBw3Zh7AajQ/h8jL2JktNPP931m2++6ZrrPS34/fr1c821/8CBA65DXFOXLl38GvdHjgPGprDf+B2HMQ6MQ2CEx+9//3vX3BMOHjzomnOF/c81IkQJ8fOHDh3qmhEiXLMvu+wy14xSyMrKcl2zZk0TQgghhBCiIKETsEIIIYQQQgghhBBCCJEk9AOsEEIIIYQQQgghhBBCJIk8iyD45z//6bphw4auaZ/la0h6erprWv6C3Zz20kR2t2CNMzObP3++a1p209LSXNMOTbs9LdOvvvqq68qVK7tmZXFaQWlxDhXZ+Ty0/tKOe8YZZ7g+5phDv6HTjs2K1qxyHyW0SbZo0cI125n3SoswLdNXXnml6zfeeMPMzDIzM/0a+4Q25n379rneuHGja/Yb24Q2elqKO3fu7JqVmGkV5TOxbWmtDLbzE044wa+xIjRt2rSkMqaBlcUZ00Ebe9SEsWdmVq9ePderV692zf56/fXXXVeoUME1x3aw37KP7rnnHte08bPiN63mtMZzfPE92S+0as+cOdM1+5cxFCeffLJrzrVgc+W9cAwef/zxrhlvwD7i/K9atarrRGvaL4XjmTEa7733nmtGQDDGhDZorq1Lly41s0NWcLPY8UmL9ffff++a6yBtxbfffrtr2sFXrlzp+rrrrnO9Y8cO14wamDx5sutatWrFfY5QXZ79zbgMxoFwbHFctm7d2nWw8pslrw+5tl1xxRWuGdHBvY0xN4xX6NGjh+upU6eaWexazTWL+xph/A8jbJo1a+Z69uzZrlmpnus5Y1QYv8G9gOsG52QYf4z56dWrl2tGLfA7BPuHESFct2mlj5KvvvrKNfchzoPSpUu75p7AiAE+M9e7ACMiGKfEfYVjmdcZEcRYEvbPhAkTXDP2J8Q8mcW2f8WKFV2npqa6DnsF18ZLLrnE9TfffOOaUUMc29wfuBZxvgghhBBCCFEQ0AlYIYQQQgghhBBCCCGESBL6AVYIIYQQQgghhBBCCCGSRJ5FEJx44omuTzvtNNes9r5ixQrXtD3T3ksLZajWG6yLZrE2WVod582b55pWwW+//dY1qzbT1sgKyqy+zPen/Y+W4H/961+uaf8L9/6Pf/zDr9HevH///rifycgCWlVZIZo2zChhLALb+bzzznMdLLBmZrfeeqtr2rIffvhh1yGCgvZz2sw5blhhOxGsdE+LI9s+VJk2MytatOhh92IW+0y0W//pT39yHarC85kZ/0ArJW2drCbPitOMTKCNPmpYfZ12cNqd//rXv7pOSUlxff7557tmNMe2bdvMLHZ8Ll++3DWtxIxg4DwPNnIzs507d7qmfZj9smDBAteDBg1yTas07a+ffvqpa/ZZ4KSTTnKdm5vrmpXD+ZmXX365a84H9nWDBg0O+5wo4HynTZjzjO1PCz5t07SeB0sy249zoly5cq5ZnZ42YbbxzTff7JqWbMYOvPLKK67r16/vOsTLmJl17NjRdZkyZVw//fTTroPNmzESjDTgvbOvaA9nBAnXOo65KOE6wPWe7cBnCHPs54wcOdJ13bp1zSw2ooJ7JiNsaBPnWsl7GTdunGvax8ePH++a+zZjIbg+dOvWzTXXYkYZhL6YMWOGX6M1nXOJ6wZjb2jx59rKNSxK2J4c+2xPRgpwjeM85B4TYhy43/H7BfdV7qU9e/Z0zT5nvAHbh2ssv4ewrbgnMbqD84lzOHyf4prNuCBGiHCvrFOnjmvu/4wU4WuEEEIIIYQoCOgErBBCCCGEEEIIIYQQQiQJ/QArhBBCCCGEEEIIIYQQSSLPIghor6c9kpY42hRpEw1Vys1i7dOXXXaZmcVa+Vidl+/NCtesWk+rNasF00bMaspNmzZ1vX379sPuxSzWssxq2scee6j5g22SbUH7Ni18AwcOdE07Ie2ZjGngZ0YJq8FXqVLFNa2po0ePds22ZVzCWWed5Tr0F+21rHDNCAdWguZ1Whlpx6aNuEiRInHvnXb4jIyMw+7LLDaOglXmw3vSJly2bFnXtMmyqjsrwtMyzeiKZFXtNou1ttKCTUs0Laa0p3K8cpz16dPHzGLjHWg3Xbx4sWtWcKeV/uOPP3bdvHnzuPfCOc1q7WvWrHHNivLsr02bNrlmDEmI7KAdmjEeu3btct2pUyfXrODO+ALGWiRrLrLdihcv7poW5759+7pm+3Du1KhRw3WIIKAFOD093TXbh5EPbAdaqdnPtETT+s55wUiBhx56yDXHHMcX4xbC+pfIms453KhRI4sHLeTcI7i/RMmBAwdcc8+gNbxly5au+WxcTzmWQ9tyv6Xmc910002uaSPnvTBe6xbgIAAAIABJREFUgvOZFem5J/JvGaOSaD378ccfXYf1hxEI7Cvun4wF4jMxEoBRR1z/o4TRGvxsPgOt+1yDuDZxDrVr187MYudhly5dXDPaYcCAAa75vFzjuT/yNXwfjkW2Fddhrr2cw4wFCXs7owPYh9xvuE6yD3mP3JO4zgkhhBBCCFEQ0AlYIYQQQgghhBBCCCGESBJ5dgKWJzx4MrJ79+6u58+f75qFJkJRCrPYE1tvvPGGmcWeopo+fbprnrbgCSieSOEJRJ784GnPUNjELLb4SJs2bVzzZBZPvfFk5+DBgw+7z9tuu82vXXvtta5ZcIInT3hKlvfLk588iRMl7EOeqOPJZRb74aljnha+8sorXY8dO/awa8OGDYurWeyFRU54GosnkVkoiCeIeL98fxYcCqcBzWKLwvAeQtGTSZMm+bX27du75iktnqJjO7LAD09N1axZ05JFhQoVXLMfeZJp9erVrvlMLNrFAj2hOFk4vWUWO1d4kpLFjngamSeieNKdc5onrG+44QbX119/vWvOOa4XnDvU4QTzxRdf7NdGjBjhOpzuNYs92cXT6FxHWFgwWSfvihUr5prjlmsV+5CFdXiil9fD3L333nvjvh+LYbF4F0+0c56zCBdP63OMjBkzxjVPzPJUOddinrbnybrwTLzGtZXznydJ+fwsyMjTtTydFyVcB3han3OfBatYhCozM9M1T4iH9Yl7E9cnvgfnPp/3ueeec8025OlNFtt6/vnnXXPf4npWr14919OmTXN94403un7xxRfNLLa4Hec71wSum9zvuPfy5C9PDEcJ237JkiWueXKZBbTKly/vmusH18dQDJRFEVlQj/ON7/f222+75kl0rms8Xc0+4b7Gk84sgskTqDwB26pVK9fZ2dlmFusU4HjidzIWSeP98tQr5yRP8gohhBBCCFEQ0AlYIYQQQgghhBBCCCGESBL6AVYIIYQQQgghhBBCCCGSRCEWvfg1mTt3rn8wC2yVKFHCNYtdsSAKXxPv/mlvpV2VdtQQV2AWa7Fj8QnaGmmxY3wBrb+J7I609tGqyyIiwQZOOzBjBFiIgu3F92Y0AW2YtOc3btw4sipACxcu9MZnjAQLArEYEQuy0IrNuIbQDqtWrfJr55xzjmv2Pe2IbB9a2lk8ibbTdevWuabdMZE1mzZgWi9pGw12ymC7NIsdE7TMsggRLaG0jbL/aXtu06ZNpJWcFi1aFHcu0vrJ6yx2xbgH2vjDs7L4C+cE7cj8HI5nWlI5j1lgjvECLCbFWAXOC851jlnav0M/co1gXzAagesP75EWcrYXC0h16dIlsn589tln/UY4txiRwHWAz7BlyxbXtASHecy1l21MSzThOKfFmfESnDcsQsf3TLRGsC/4t4x6CK/n3zEWguNg3759rmnJZ7twXeBc7NixY2R9yD2R0Qrcb7hn8HlZsI5rbphzXJPq16/vmoXJuJ6yXfn5nCcsTMh+5np6/vnnu+beynvn+/M1oaAV5xh1bm6ua649fH6us3xu2tc7deoUWR+uWrXKb5BznffNPYFjnHsY51x4BrY9/z3RWsc24fNyD2XMDNd4thvHCNdqtj813zOsg7xfFnHkfGPMCGMVuObwvbmGNWrUKDnVDUWB4+oRo042swOPjL3dihZNTlzMfwu5ubm2c+dOq1mzZsyeLH591Bf5B/VF/kL9kX9QXxw9Bw9+YyPuut/M7JQJj4z54uf/rhOwQgghhBBCCCGEEEIIkST0A6wQQgghhBBCCCGEEEIkiWOP/JLkwKrytJjRnkYbL+13tLjTkjlz5kwzi40u4Huzsi8tbu+9955r2vNow2OlbNqkQ6Vms1irPO+Br2GsAe2HwXJP2yLtuHxmWj9Z2ZxWQVqJafGMElofaR2lvZz2XvYnn532/gULFphZrM34b3/7m2vaf/kaxgXQXszxxMrRtEmzfWjlpZ0zkWWe14Pdl2ObcQi0ntK6P3/+fNesMk37bjLh87BfaBulvZ7WfcYB0DYdKqHTMsroBo7VDz74wDWjJ2hvqF69uuupU6fGfQ6+nhEctNZynHL8cK4FKyz/nVZ2PgdjNQiroXMcMbIkSriebd261TXnC/uNfcW5QGv45MmTzSy2HbiusV1psWZcC9c7RoC88sorrhkTwsgKfhbHH2NcuOaybcOc4rOxr7jO0m7PCvVsC8ZncC2OElrQ2Q6871dffdU125Njn+tPiOhg+zFag9EBtKx/+eWXrmn1DrEAZmabNm1yzfWMc4nvn6gPuZ9xnw+REZxvBw8edM21knElXLeqVq3qOtFcjRK2Fdd7RgBwLalWrZpr7mEct/Heg+3N/ZNrKfcethsjGmj755rAfuZY4F7JMcexy+8e06dPN7PYMcm1mXBO8jmaNGnimus3x5MQQgghhBAFAZ2AFUIIIYQQQgghhBBCiCShH2CFEEIIIYQQQgghhBAiSeRZBAFtbbT3d+jQwfX69etd05LGWAFWkL/77rvNzOwvf/mLX6PtvF+/fq5pI6VNklbGgQMHur7wwgtdT5gwwTXtubQC8jqfdfHixa4HDx7sOiMjw8xirZm0+F5xxRWu+fy0M/I6LdCMaYgSxgts2LDBdXgWs1hr8pIlS1wz3qFGjRquhw8fbmax0Qp//OMfXT/++OOu+eyMGqBN9+qrr3bN/qHVmLEDnTt3dt2tWzfXf/rTn1xnZma6vuWWW1wHK/+kSZPivnf37t1d06YZYhd+/kyMA2CV6WRCayujHGh35nxZtGiR6x49ergeMmSImcXGR8ydO9c15yLncLC9m5m9++67rhs2bOi6b9++rtetW+c6xB6YHRpHZmZt27Z1zWrwbFNa5UO1dNrkOdavueYa17Rtc1wwdoCWbPZplNA+zD7s1KmTa0at0O7M+UJ7chjbXIe5nt5www1x72Xjxo1xPyeMCbNYez/n+tq1a13ffvvtrrm2BVu9mdnKlStdt2rVyvXQoUPNLDZ2gWO1WbNmrjm2+HyMb2DcSrLmIp+R84ZRJBz7HMu0hnM9DffN+ZuVleWa85DtN2XKFNeMBeF6Rzs435/W9xEjRrimrZ3jgvOGe2LoI/bxli1bXA8bNsx1iCswi103eI9fffWVa8auRAk/g1EPF110kesqVarEfQ3bjREtYXzyuwnjlBiJxH2Fln62N1/PMb53717XXEM6duzomvsTX5+Tk+Oa8Ql9+vQxs9g5u2bNGtfcY7lW8XsS3zuszWa/3p4ohBBCCCFEVOgErBBCCCGEEEIIIYQQQiQJ/QArhBBCCCGEEEIIIYQQSSLPIghYNZdWOVYwpl332muvdT1mzBjXtAYHq//ll1/u11ipmZY12tpopaVlcs6cOa4ZKUD7Ki2TtPaxcjNt8D179nRNS3Co0Ny6dWu/tmzZssP+3SzWbk97NS3+jGxglfEoOfvss13Tssg+ZKXie+65xzX7k9bLYMNljMHEiRNdf/vtt65/+OEH1+yH0aNHu6aVlrZnxgGMHTvWdfv27V3TyhqslGaxdm+OrzBeBwwY4Nduvvlm16w4nei9acHma1JSUixZ0EbPcdO4cWPXmzdvds3nYz9yToW5du655/o1xkfs3r077t8NGjTI9fjx413Tfsvx36hRI9fsX851jkdGeTzzzDOuGTcQ7L/XXXedX6PdmdZbxjFwbjMOgXEErPIeJbRU8/5ocWa8AMf/ww8/7JpW8hAZwTZmdMj27dtdc/6PHDnSNec8bdBst9TUVNfTpk1zTVs57eujRo1yzb2AYzfsKXzvhQsXxn1v7iH9+/d3zdiREiVKuGZF9ygpX7583HvivsJ2ZhTGo48+6pp9GCzxtHczoqdixYquGUVBazhjHPj5tKNfeeWVrnv37u36ww8/dM2x+MADD7jmnGfEQBjTjEbgHscxwXvnvcyfP98195kLLrjAkkHJkiVdM1KC+yC/73Tt2tX1U0895ZoRSSFmhevu008/7ZprHdub/cAoHPYbSU9Pd80oDu65nGP8XsO1kmtEuB/OQ743owa4Nl522WWu+T2MURtly5aN+xxCCCGEEELkV3QCVgghhBBCCCGEEEIIIZKEfoAVQgghhBBCCCGEEEKIJJFnEQS0ktHqTFshbfy0L9KSSYvz66+/bmaxll9a2QsXLuz6rLPOck2LZZs2bVzXqVPHNa3hvC/abYsVK+aa8QI//vij64EDB7quW7eu6w0bNphZrFWRFa95j7Q50g7PdixatKhrtmmU8H1pE6V9kpZFWpBpgS5TpozrYNNnvACtjrSif/75565ZNZpVwGkLZwQAq1WzD1kVmtZOPgfvt2nTpq5D5XL++1VXXeWaVnhWXn/ttddcs0I2bc+0TEcNLf0XXnhh3NdUq1bNNaMRbrzxRtelSpVyTYt5gNEgbKP9+/fHfW/aVjkXaX2vXLmy6x49erguVKiQa46TEFNiZnbxxRe75poSLL+06tKazjgQzm3GKnBdKleunGtWZY+Sr7/+2nW7du2O+Hm8zrnL9gyxK4xfYVwLre78O1a15zindZ/zmGsVIwu4BjBWgXOEY4TXg82bz8lq7pznnJe0snNMsA+5d0UJLeuMueF4Yz+vWrXKNdc8xlEcc8xP/42Vexb7kJZ1romMGuDcq169umv2Cf926NChrjkPGbGwdetW13zWiy66yHVYCzj++vXr55qRAjVr1nTNWBheZwRHstZTPu/555/vOtE8ZLslWjNC//O5uB4dPHjQNfcMzjH2YcuWLV1zDPG7B+MLOD8YVzN79mzXjCNiH7799ttmFvudgLEL3GM539leHNuMrhJCCCGEEKKgoROwQgghhBBCCCGEEEIIkST0A6wQQgghhBBCCCGEEEIkiTyLIKBVjpZ6alocaaGkbW/Pnj2ug92NdnS+lhWf169f75p2aNr5QiyAWawdmTZQXufnsmo8q4hnZ2e7fvnll10H+x2tmbRjp6SkuKaVn7EKtFfzmX5t2x6r0bP6Mu2ZtGuHfjMz+/77780s1hq7bNky14xw2LZtm2uOFUJ7Pz+Htl5GR9BeTHsk7aTsf8YaBFsr7ZPsQ45h2u4ZY8DX05LMeRE1RYoUcc22oCWUz8y5y6gBzoVgf+X4ZJ9yPLMtOG5pMc7JyXHNOU97MmM/GMfBiIVjjz205HG+ZGVluQ7tzvfj+KINlzZ8vh/XHUZ10IYdJWxn6kqVKrlOFK/BiAaueWG8sgo6X8vYAcZC8HPY3uxDRqfQ7s7xT7t7sNKbxdrHGQ3C+wxzkFEzYW35+eczRoL3y8/k6zmPo4RRDJz7rA7PZ2TfMiKHrw9xPCEexSxxvADbh3sG5zvnHq3pXCvefPNN14yF4PynZZ3Wd46vsJ5w3+CcPPHEE13Tbp+enn7Ye5iZvf/++665zkQJ24G2e64ZZN++fa7ZDmzD8GyMKEg0D9kOnIccN/PmzYv7Gu6PXMsaNGjgmvEijBrg+69Zs+aw9+GzcSwy8obrNNdP3mOifUgIIYQQQoiCgE7ACiGEEEIIIYQQQgghRJLQD7BCCCGEEEIIIYQQQgiRJPIsgoAWM9qIaYOkTZnWs7Jly7pmZe1gyaRlkbY9Wq0zMjJc0zbIz69QoYLr+vXruw62TrPYStm0tffq1cs17YK05/Peg/Wb986qzbRM0sZMiz/vl3+bLBjXwKrNtAjTRkwbL/uW1tvQF7RRsy3Zh4wFoF2YFmG2G9ueUQO899KlS7tOZH3k+9OGGe6d44N9wggMtgXtnrTVJrKMRw3vhXOLYzEtLc01+53PxH6MF3vB92Oldlpc+d60L3ON4NihbbVWrVpx358RILQFlypVyjUtsmGcJLK48l74frThcvzyXtjWUcJ2owWd84XrHC3u7HOOv/A+iWJEaN2nlZ3zg7Zv9ict6Gwr9gNp3bp13Nefd955ca8HWzavMS6CayjXYc4zakYTsJ+jhOvmOeecE/c1tWvXds0+pA2dUTgdOnQws9g1hmsy10fGjzBGhXOZY79atWqu2W+MS+GeyM/l/s81knOSOt5rucfVrFnTNfdwar6e16OEc4JrI8ce1xWu8YRtGNqfexzHL+c7v48kWhO4x3Guci0j3Cu5VrPPGfXB9wzxKxxb7CuuN+wfzj3uvXwOIYQQQgghCho6ASuEEEIIIYQQQgghhBBJQj/ACiGEEEIIIYQQQgghRJLIswiCFi1aFDryq37b0O6XH2nWrJn68L+Apk2bqh+PACNI8iNt27ZVHxZwtJ4emSZNmuT1Lfyv1K9f/7+iDxntIYQQQgghhIgGnYAVQgghhBBCCCGEEEKIJKEfYIUQQgghhBBCCCGEECJJ5FkEgRBCCCGEECL/kpaaUdbM/m5mLbOyM5cfxet7mNmNZlbDfjrosd3MHsjKzlyY1BsVQgghhMjn6ASsEEIIIYQQIh63H+0L01IzBprZTDMrbmYPmNmjZlbBzOanpWa0Ts7tCSGEEEIUDHQCVgghhBBCCGFmZmmpGbXM7Hoza2pmVY/ybwqZ2T1m9qaZ1c/Kzvzmf65PMLPdZjbOzF5Jxv0KIYQQQhQEdAJWCCGEEEIIETjNzCrbT9EDe47ybyqbWXkzmxF+fDUzy8rO/MjMssysZlpqxkkR36cQQgghRIFBJ2CFEEIIIYQQZmaWlZ250sxWmpmlpWb0N7PnjuLPfjSz581sdZx/O8XMCplZ4YhuUQghhBCiwKEfYIUQQgghhBD/Z7KyM98ys/4/v56WmtHWzFLNbENWduaBX+NecnNzLTc399f4qAJLaB+1U96jvsg/qC/yF+qP/IP64ug5UhvpB1ghhBBCCCFEZKSlZhxnZrea2Sgz+9zMrjzC64eY2ZBf8pmFCxcuXL1uPcvJybHjjz/ul7zVb4acnJy8vgXxP6gv8g/qi/yF+iP/oL44Mt9++93/+u/6AVYIIYQQQggRCWmpGS3N7AkzO8fM1plZ/6zszL3/299kZWc+bWZP/5LPvXrEqJPN7EBKSooVLXrCL3mr/3pyc3MtJyfHUlJSrHBhJUPkJeqL/IP6In+h/sg/qC+OnoMHvzGzhQn/XT/ACiGEEEIIIX4RaakZhc1svJkNM7NPzGywmT2blZ354695H4ULF9b/g3iUqK3yD+qL/IP6In+h/sg/qC+OzJHaRz/ACiGEEEIIIX4p4+ynH1/nmdmgrOzMz/L4foQQQggh8g36AVYIIYQQQgjxfyYtNaOkmV1rZtlm1iMrO/P7PL4lIYQQQoh8hX6AFUIIIYQQQhw1aakZLcws5f+xd+bxVo9r/78SUmkgShJpHmnSoNrtxt1uLhqoaJBQMiV0cBDnIY5zOHLOQYooSfO4G3db80CjNCmSoVCd0xEl/f7gvvZ7aa2T3+usZbf1eb9ez+v5+Lb2Wt/vfV/3da9nPffnusxsUnpG2h4za2Y//d8VO8zsluSklGh/9lp6Rtq/f7u7FEIIIYQ4ddAPsEIIIYQQQoj/H3qY2Y1mttHM9phZ8Z+v9/r5f6Ix3cz0A6wQQgghTkv0A6wQQgghhBDiBNIz0kaZ2ago13uaWU/89zNm9sxvdFtCCCGEENmOM7L6BoQQQgghhBBCCCGEEOL3in6AFUIIIYQQQgghhBBCiASRZSUIpk+ffjzovXv3+vUCBQq4/uabb1wXL17c9aZNm1xXrFjR9ZdffmlmZoULF/Zrn3zyieu8efO6/u6771zz9V988YXr888/3/Xnn3/u+vLLL3f96aefuj5+3B/J8uXL5/q8885zvXv3btcXXnih66+//trMzC6++GK/tmfPHtdnn312VF2wYEHXR44ccX3s2DHXuXLlct22bdscFieWLVvmD/zVV1/59VjPe8kll7jmOOfPn9/1vn37zMysTJkyfo3zzfjgcxUpUsT1ihUrXF9xxRWuOVelSpU66fsTjvPHH3/sukKFCq537txpZmZFixb1azt27HDNuf3Pf/7jmvH3739nlkY755xzXOfIkTltTZo0idscmpktXrzY53H//v1RP5/zxbXIsShfvrzrzZs3m1nkWHz22WeuGfthzs3MLrvsMtecl3LlyrkO42wWGSe8zrXLsWac8JmKFSt2wn1yvvicjNejR49GvXfmHb734cOHXTdr1ixu85iRkeFzyPFk3DKHlSxZ0nWYK7PIdbFr1y4zMytdurRf27Jli2uulR9+yGx2zXHbtm2ba+ZNjj3nhPd45pmZ2xNz26WXXur6o48+cl22bFnXIRb4mYwP5ijO4UUXXeSaccOc++OPP7pOTk5OSD6NlR+5JxYqVMg11y3zYthbmae4rvi8zOGc8+3bt7vm2ufrud/961//cn3uuee6Zm7j+gz7tlnkfIV1yPXDPZH7OeMj2lo2i8w5jNd4zuGcOXN8Dg8cOODX8+TJ45rfd2I9G8c57Fu8xvg444zM/z96zpw5XXPP4FqKdS/Rvo/88j05t7z3kCvMImMqxCX3Bu6JvC9+Z+F7cBwZZ3y+1NTUuO6JQgghhBBCJAKdgBVCCCGEEEIIIYQQQogEkWUnYHkahqdReIr0+++/d82TFzwxuHXrVtfhVNf69ev9WqyTHzwNxZNOfD1PgbRo0cL1e++9F/VveSLs4MGDrnPnzu36ggsucM0TROHEB0+H8HQKTwbxRAjhSRiekOEp4XjCeYs1bnzNhg0bot4T5zacmFy0aJFf44k6nmjiqSeebuVJRJ4G5AncjIyMEz7TLPL0GOE88/T0qlWrXIeTee+//75f44mdQ4cOuebJMMYK541xztNe8ebbb7+Nep3zwhODPMXJk4Effvih6zCmPKHIE05cixwXPj/nne/N2Pnggw9c80QgcwfjkffAE19r1qxxfeWVV5pZ5DrnqUue0uRJLXLWWWe55vjyVHE84VzxZBhPL/I0LE99ci44DuF0MU+38b35mXxGntLk6VbGAvPg4sWLXXOcmc94v5xPntRcvXr1Ce+zZMkSv8Y1xLXFeObJZY4LrzMXxxPmFe6PjFmOA0/icvz5mhDjzFPMcXx2rkPmO44DTzTzBCTHh6db+UyxTn5yrdAxEU40L1iwwK9xjcf6TsD4YCxyTLlHxBPuzb9mPnn6l7mB10Msx3J2cB/kSWjuazzBzzFkTmAu5alX7lWMC+Zk3jvjskSJEmYWuQ753swPnCvGMD+fr4nlVhFCCCGEEOJURSdghRBCCCGEEEIIIYQQIkHoB1ghhBBCCCGEEEIIIYRIEFlWgoDWN9rmaCWnfZ12N9qUaQedN2+emUVazWn9oya0V9NCSFvd8OHDXdO+GMveTPss753PTYLNks9DayatxLQ9s5QCywDQkkmLYjzh+PA+eJ3WRzZSYpkIWs2nTp1qZmZ169b1a7RjxmrMQ2sk7bjUbDbEJkR8f1rXee+MEcZlsFiamS1btszMIi2TLD/Bz6Hdl5ZN2oNpt6QNON7wM7n+GM8sk8D1Rdsqry9fvtzMIi2mHFuOBdczLe60rDOmWHakUqVKrteuXeua48VSA7Fs87TFzpgxw8wiG3yxHAGtr7S1h/k3i1yjHLtE2df5eRs3bnTN53r33XddX3311a4ZZ4zz0HCL8cGyKWwKxNIgsfIpx3v+/PmuaUdnjub647onbArGOQ9jwLVIWCaEcxirZALzNuM4njBXsDQAx425h/mPlm1eD3ti9erV/Rr3W9rkuZfFKrnCfDdr1izXjGuWI2Bu59pLT093zTItjKnXX3/dzMyqVKkS9R4ZE4zLWA2+aNvneMUTxj7HmfPJfahq1aquOT4s7RLWCmOZ7xFrj2E5J+ZYxjJzAvM355zvyT2Bz8o1xHwScjKbwHHt8d45XrHKTzH3cq8QQgghhBAiO6ATsEIIIYQQQgghhBBCCJEg9AOsEEIIIYQQQgghhBBCJIgsK0FAixnt2LQa0+JGWxtt0uyC3b17dzOL7IadlpbmunPnzlH/buHCha6DddrM7Pbbb3fdsGHDqK+n9ZF2wsqVK7umXZAdnW+77TbXobM7O4KzdEGzZs1c02obyw5LCyPtj/GE1kBaVmkBbdKkiWvajvm3LAdw4403mlmktZ1/17dvX9e0V7NEA8d48ODBrmk7ZTdv2jD79evnmmNLizW7wl955ZWuwxzRusw5pA2TnahjlZEoVaqUa5a0iDex7M68R1pMN23a5JoW89q1a7sOJTD4bDt27HCdkpLiuk6dOq7Z6ZvrZuDAga4ZX5zrKVOmuA65wCwyvkaMGOGadto2bdq4DjHLkgKcx969e7uOVRKD9nmWLEmU9TlW5/eQV8wi53DVqlWu2Xm+ZcuWrlu0aGFmkfl0zZo1rkN3drPIdcCYyMjIcM1x499ybXH+w+ebmV1wwQWuWWqCe0Hjxo1dh7ITXKvM29dee61rjsucOXOiPlOiyg4Qlheg7Zv7CvM6x4r3R6t9+/btzSyyLMXevXtdt27d2jVzMssIMP+yjAznc+LEia45/yx9wH2LJWg4h02bNnUdcsiSJUv8GkuB9O/f3zXzFuM5Vskfln2JJ8yltMizDArLLLCcyueff+6aJUJC7l+3bp1fo+7Vq5drxhDXLcsX7Nu3zzVLR7AUxMyZM10nJye7Zj5ZunSpa5YVaNWqleuiRYuaWWRpEcYWn5PlP5jDGE8sV/JbrEkhhBBCCCHiiU7ACiGEEEIIIYQQQgghRILQD7BCCCGEEEIIIYQQQgiRILKsBEH+/Pld09KcmprqOpZVbcOGDa5p9Q2vv+aaa/zaq6++6ppddmPZUXkvc+fOdU0b8Q033OB60KBBruvVq+eatlo+E22Bb731lutgyWWZhGDHN4u0GPO9+Zm0pPI1tKTGE1pKOSe0iNNeet1117lmeQeWEgjW0A4dOvg1lghg6QB22L755ptd09Y5ffp017Rb0nL+xhtvuKZlmWNOS/ujjz7qevbs2a5D+YxbbrnFr9E+S3sobbJdu3Z1/corr0S9lzJlyliiYKkPWtMZt7StduzY0TXl1hWuAAAgAElEQVTLaPB9gmW8Vq1afu3tt992zRIEnKPHHnvM9YwZM1z//e9/d81SDox/fj6tqlwXw4cPd92tWzfXK1eudB1KH9DizHvkvASLrVlkjmIpEY4d10w8YbkI2pNZfoH3zfUyZMgQ1yy1ECzRjOeXXnrJNeOWpV7uu+8+1xz7d955xzVzRJ8+fVxznNmJnXm5bt26rll2gjbrYD1v27atXxs5cmTU96Y9nzmKz0RLNC3k8YRzyLI8LO/CzvbMkcOGDXPNbvZhPhs1auTXnnvuOdcsdcCSH4x9ro1YpQMYTz179nTN9c98yvIFTzzxhOt//vOfri+66CIzM7vpppv82qJFi1yzvAnvhZ/JfMY9irb6eBLu2Swy3plLWdokKSnJ9csvv+yaJQBCvPF7Ddc4yzZxj7n//vtdDxgwwHXu3Lldc+9lKZh//OMfrlmugWVkGFPMGyzjFMaD+Z7vzXXI9+Z4xYo/lugRQgghhBAiO6ATsEIIIYQQQgghhBBCCJEg9AOsEEIIIYQQQgghhBBCJIgsK0Hw1VdfuS5fvrxrWj1pvYz1elr4goWSr23QoIFr2vJpX6c1sVq1aq5pyaM9c/v27a5r1KjhmvZI2mTnzZvnml3TQ7d1s8yO7B988IFfo9Wb7007XyxLPruG084aT9hFnp9NayhtwexU3bBhQ9fsdh7si7RJsmM6x4+xwtIO7BpOqykt7ZxDzgNLA3AMx48f75rWR75/iJEtW7b4NVrAWVKDHZ9pq4zVWZ6xG2+OHj3qunLlyq5pKy1durRrjhFtqFwLoTQISydwXtg1neVIGOccux49erimJZVWdsYUrcd8ptGjR7tm3NFyfe+995qZ2UcffeTX+BwsccHO8ezKHTq4m0XaxgsUKGCJgGurfv36rtl9nGuR1zlurVu3dj1ixAgzM1uwYIFf69u3r2uuCZY5ycjIiHovtO6zmz3HMJZ9nGuUdnPeb7t27VyHOWJsVa9e3TVL17B0Bcs0sNQFbd6J6r7OfYvr7YwzMv//pMz35cqVc81cwVIYU6dONbNIqzfninmFa4ljz32wbNmyrhlzhw8fds3yLlyrtOfTbs7naNWqletp06aZWeQz9+rVyzXLfLC8DOeHpYOYW2nVjycss8B74poI5THMMst8mEWWA2CJhjVr1phZ5HNxHlhGgt+ZWGaJr2/WrJlrvif/NlaMMI74PYx7JUtQhFIJ3Euuv/5619yT27dv73rt2rWuzz77bNdckwcOHDAhhBBCCCGyEzoBK4QQQgghhBBCCCGEEAlCP8AKIYQQQgghhBBCCCFEgsiyEgS04dG+yOu0u9F2StscrZLB6svXfvHFF65pQeR7047Ljti0GtPKmCNHDtdHjhxxTfvsnDlzXNPCyXIA7777rutgP6VllxboihUruqZ9llbB/fv3u6a1kX8bT2jLJWeddZZrWo1p76V98Y033nAdbJAcY5ZlYIdvWqo5n9Ts2k6rM0sAsGRA48aNXaenp7tu3rx51PuZPXv2CfeeN29ev8Z5YByw7ABt6bz3YME1M6tXr54lCo5FLJsr7eYs+8F75/2GEhJPPfWUX2P3bz4P7cjsPM+1wuuEVlXGV8uWLV3PnTvXdYkSJVxz7d5yyy2ugxWbc0R7MN9j3759UTXXALuIM2bjSb58+VyzfAct0cwPtCfTej527FjXIRaZH9l9nbmaJVp4ndZjWsZ5v/xbzifjkp3TWd6F4/nqq6+6DpbovXv3nvA8ZpHjQus/X8MyEhwD7gXxhM/CPYwWbOZFrknGJNdZyHm08XNP5Fxxv+HYb9q0yTVji+uWccF7b9q0qetQXsYssiwHc84///lP1yEuWZaEz1G7dm3XtKOz1AtjnqVTuFfHE1rxOQ4sW8N9n3sFSzSE8h9mmeUo+OwsF8ASBPw+dOGFF7pmLn322WejfmalSpVc79mzx3WuXLlcs6QHywfwu0Ao4WKWGUdXXXWVX2Oe5D7A92OZhFjf/fhdQAghhBBCiOyATsAKIYQQQgghhBBCCCFEgtAPsEIIIYQQQgghhBBCCJEgsqwEAe2/tF7SJsguwrS+rVixwnXNmjVdh2697CDNztu0lLNTNG1ttATSas1O1LTz0QpIy/INN9zgmjZcWtLZDfjgwYNmFmnTpU2TnerZzZsWUloR2RU7UV27acvluLFrcqNGjaL+7fDhw1137NjRdZgv2kirVq3qmmUEOM+0DtOqWaxYMdclS5Z0PXHixKjPQbt6t27dor4/LeW0QQYLL++xTJkyrlnSgnHLeOK9x4q/eEMrMT9n9erVrvv37++az0G7MztUB3swS1AwbmlrfeKJJ1zT+kpLOdcNu6bTYszPX7p0qWta1mm3p/U9Wnf3WHZXlkzhPNIqS4s7rdKJWouMOeYK5gSW/WDpiFmzZrnms1WpUsXMIvMa758W/ZSUFNe0QbNcSoMGDVyzC/2CBQtcn3feeVGfo0OHDq65F4wePdo1LdQhnzKHci3Shs/rtGSzHA3zKWMxnrAUAnPCzp07XdPKzVwxYcIE123atHFdtGhRM4vMz8yDXPssf0ObOPN5GFezyH2b8cf1yfXPcjksZcASC/zbUG6B9nbmc44R7fbcE7mPMP4YF/GE656xz/zBOD3zzMyvYIxJ7omhZEGePHn8Gvc+ltC56aabXDMfcc9inuR4srQOS1B88803rvm9ibmXJWK454e/5ZrhGDE/JScnu+br+V2Rc5vIPVEIIYQQQohEoG+wQgghhBBCCCGEEEIIkSCy7AQsT73whChPQfAECZvg8EQET9KFkz08OcuTQV26dHHN01U83cRGEDxtMWXKFNe33nqra57A4ufyZCBPyc2fPz/qvYdGJ3369PFr999/v2ue0uKJJJ7k4em+Y8eOuU7USRGeROPJZTay4knThg0buubpudatW7seNmyYmZm98MILfo0NTNhcKZxU/CU8xcd7+etf/+r67rvvdv3000+7ZoMdnp5iLPAENu8nnN5k07OhQ4e65inBDRs2uGb88ZQiT0fxBFO84ak/rrlOnTq55mlYNk7hacdrrrnG9WOPPWZmZo888ohf69q1q2ue4Bo1apTrFi1auOY8cu2OGTPGde/evV3fd999rnnqlafzeGqPDZ8GDBjgOpyeZaMeNpZhHLP5UKFChVzzxBvXSaJOwPKE45IlS1x37tzZ9eTJk11XrlzZNXNI9+7dXYc1yEZqAwcOjPr5PI3Ztm1b14xzxtlbb73lmvPP/N+vXz/XsU4rc13wBOfMmTPNLPIk4eDBg13zVDJP+PGkO0/jsqkaGy3FE546ZEM5romVK1e6ZhzGOpUf9huuGToKeCqdsXzxxRe75slLnp7kqUeuPc4b74UNxOgc4al3zldovpWamurX2NySeyJPw7LZJveOWKer4wn3CZ70ZM7kyWru03wGxmQ4Acw1++CDD7qONmZmkd9H5s2b5/qhhx5yze8jffv2dc09kade6Szg9xo+B9dhaKrWvn17v8Y9gfszv9dwH+RpWH5nohZCCCGEECI7oBOwQgghhBBCCCGEEEIIkSD0A6wQQgghhBBCCCGEEEIkiByJakZxMsaOHesfzCZItMuz1ABtvLEa7AQLJ5tJHDhw4IR/N4u0+4VGJWaRDU9o66XdnZo2OJZVoJWVVj02MGIDitBwafr06X6NY0FbJa3ZLDvAJh5sPkbLbOfOnTN9oP8j06dP9zmM1UCGc8iGH2wQQmtleJ5Jkyb5NTZh4ZjQFkxoUWYzGdreGSOh/MMv75ExxzIBtFUzRkL5gqlTp/o1Wt7ZfIW222rVqrlm2QM2qmHsNmrUKG5zaGaWnp7u88jPZwxz3GM1GeOchrFgaYrly5e75rrl+LNBEGOYzX9CcyizyAZOzCO0xHIeOabUtMeH62wCxjXM0hDR1rBZ5Bjx3mkLbtq0adzmcdy4cT6HtPLSms5Y5RplDuOYhzlnWQpq2rtpt2ZMcByYN2lxZ8zxfVjSgXmclniuL74+zGGs2OJaZMmGWLZmNnnia1JSUuI2hxMmTPA55FqKleeYE5iTaHcP+Y/vx3ngWDKuWZqAMc7vCyVKlHDNUjOcW44VywgxRpgjuA5DgzDGBMeC5WIYZyyTEBpY/fIzGSuNGzeO2xyOHz/eB4j7MZ+Lz8sSDSxNwOvhmblnEM4D7fpshsXcy1gOjSPNInMmSyCwrAJLN/D9WUYqX758rkMssNQB8y7nkPfOPMTvFvxbfq/p0KFDXPdEkX3pP+jhAmZ24JmhQyx37nNO+vrTmWPHjtnGjRutcuXKEflZ/PZoLk4dNBenFpqPUwfNxa/n8OHvbNBDfzIzKzj8mccO/vLfdQJWCCGEEEIIIYQQQgghEoR+gBVCCCGEEEIIIYQQQogEcebJX5IYChQo4Jq2VlpKadWjVZIWOtqOg32/QYMGfm3//v2ut2zZ4rpGjRquaUenpZ/W92XLlkX9W3bnpsWV1vcpU6ZEvXda9UKXalq9+VradDlGtIfSKkg7aaLgHNKCSGssx422Z3biZofmYDek/Z3PxessHbF3717XtEleddVVrkeOHOmaVnDeF+2OwQJrZrZ27VrXLJnAe3j88cfNzKxHjx5+jfNDuzwtqdOmTXNN6y9jIZFH/RkrLG9BZs+e7Zqd6mnx5ZyGLve0ILMLOW3ftMlTcy445uzozjGitZrW1rlz57qm9Zx2Vq6psWPHmlmktff77793vW3bNtecf8YIy5EULlzYNeM3nrB0BuOfY7Jy5UrXHB/OC63+oZQGS3rwvbluaGUmtDJzrvh65nCOFeFabNasmWvashm7wZLO8ebzZ2RkuGZMsGRBxYoVXf/nP/9xzbwXT7h/7Nq1yzX3gXXr1rnmuqV9fPv27a7T09NPeA/ua/w75tk5c+ZEvZebb77ZdVjjZpFriXsV55xrJS0tzTXji3k8xB+t7rx33hfnmbHCvYiW9USVXmKZD5YeYWxyjXGsuIb5PeSNN94ws8hSNbTrMzdRv/TSS66Z3zgPnH+Wd+B+wzXM72Esl0Q4RwsXLjSzyDnmdzJ+b+C6Wr16tety5cq55nNzDxVCCCGEECI7oBOwQgghhBBCCCGEEEIIkSD0A6wQQgghhBBCCCGEEEIkiCwrQUBrLK16tMddffXVrpcuXeqaNsw77rjDdbDT0dZH23fPnj1dd+nSxfUjjzzimtbIBx980HWrVq1cs6M0rdTt2rVzTcsd733NmjWuu3Xr5jrYLNmteObMmVHvl9boYPEzi7RVsis07afxhPZF2sVplezQoYPrV155xTU7PnMcgjU62MDNIudk4MCBrmn35GvWr18f9fVXXnml69deey3q3w4ePNg1S1nQvkzrJWPq6aefNrPIkgIsr8DSBOwUzjmkxbJOnTquuUbiDdcireG0hD722GOuX3jhBde0AV9xxRWue/XqZWaR9tVQosHMbPjw4a5pZWd5AcZ/sOGaRcb/6NGjXb///vuumzdv7przOHnyZNecJ3ZIDzHLLuac865du0b9u0mTJrnm2NHKnijrMy3YLKlC+27nzp1dr1ixwjVzUvfu3V2HtcPXch6GDRvmmnPCUi8s/0BbO0sH8DWvvvqqa+Z2rjOWeuCzXn/99a5DzuOcvPfee65ppS9VqpRrlsPhPsOyG4zpeMIyB7TuM881bNjQNceN49mmTRvXYf6//vprv3b77be7/uMf/+iaZTMWLVrkmuV0aGXv06ePa+ZH5oq//e1vrllGhOuWayslJcV1yKfcY/nMLVq0cN20aVPXLI3AkgbMM3zWeML3ZVkG5gPmDH4/ef31113//e9/dx3WJPMR18mgQYNcs5zI/PnzXbMsw7XXXuua+xDz56hRo1wzV7PsBV/PXDp06FDX4XsB45N7+5AhQ05679z7OHY5cuQwIYQQQgghshM6ASuEEEIIIYQQQgghhBAJQj/ACiGEEEIIIYQQQgghRILIshIE7GDLTvG0etJ6SLvjnXfe6frtt992HToAP/TQQ37t+eefd00rJe1+99xzj+v77rvPNcsB0BJar14917T6U2/dutX1gAEDXD/66KOuZ82a5bp69epmZtayZUu/RhvikSNHXK9atco1yx6MGzfONa2QtAfHE9oqaQtu3Lixa9p4//znP7umBZj2xWDvffLJJ/0arc7smM4xueWWW1z/3//9n2va3n/44QfXzz77rOt//OMfrjmHtD7SqsuyCiwxEGyeN954o1/jczAOGIu0Wr/88suuN2/e7JrWy3gTyj6YRXaHZ2mO2bNnu+aY0hrOUhuhPAXtyBxbln1gp3LORZUqVVy/+OKLrllSo3fv3q5Z4oKlFLgW+/bt65qdtt955x3XwSpNSzvX7R/+8AfXmzZtcn333Xe7fu6551xzXGrVqmWJoHjx4q5pDWbZDY7JAw884Lpu3bquWVYkxChzKMuy0N5NzTmn1Zy5lZ3S77rrLtecf3ZLZ/kC5vdOnTq5njBhguswhzfddJNfo22bVvply5a5ZjyPGDHCNcvasKREPKFlnVZ75jauT5alYXkTWvAvuOACM4vMPcyhtKAz34wcOdL1Nddc45qlCUqWLOmaOZFjznIULFPDtcX1wRgN8cfvBIwP7mvcq/mszPOLFy92zRIl8YRxtX37dtccQ5ZruO2221zzGaZOneo6jBtzHePxrLPOcn3gwAHXnEP+LXMdX3/ddde5ZrkGlg9gCQKWYuH3KcZfwYIFzSxynT7xxBOuc+bM6ZrjwlIo/I7HPTlRZSSEEEIIIYRIFDoBK4QQQgghhBBCCCGEEAlCP8AKIYQQQgghhBBCCCFEgsiyEgS0uAWbpFmkTZBdbjds2OD6xx9/dE1rXbAy0kr58MMPu2aHeVoFaX1jeQHahWmNXL9+vevatWu7Zpdt2iP5+mDJMzO74YYbTrh3vgdtnbzHChUquGYX8GrVqrkuVqyYa3a5jye0lHOsWHaAttOdO3e6pjWaYx46gbO8QejGbhb5vLTP8nNSU1Ojfs7MmTNdT5w40TW7gtOSyedg53OOLcsNhDnifdGOvXHjRtcsKcD4KF++vOuiRYu6Pnr0qCUKlkOgNZj2YcYz7bGcJ5bPiPYe/HeuUXb3Zldwru1mzZq5ZgywfAKt7LTl0rbNuWndurXrUqVKuQ7zzlig9XnNmjWu2aGeVnba1BkvtNzGE9rKmR94nWVf+AycF9q9Q7wuXLjQrz3zzDOuuT7y5cvnmvHBHNa/f3/XtNgztmnVZmwdPnzY9Wuvvea6dOnSrlkmIpSvYRkDrnOWK2D++eyzz1xXqlTJNXMr95F4wjzdtm1b159++qnr888/3zX3MOYhdrkP5WqYe1i6gPstcxLH4eqrr3bdsGFD15xnrkOWiOA65zpgCYqrrrrKdYsWLVyHe+aeSZs8y96wLAkt8Cyvwc/n88UTllPi57GcUp48eVxzT+Ra4RoOe2L432ZmTz/9tGuWHOE8sKQBy7nE+s7C71u8F+a1QoUKuWY5Au6zXMMh/7OMAf+d98g44PWyZcu65vPx+4cQQgghhBDZAZ2AFUIIIYQQQgghhBBCiAShH2CFEEIIIYQQQgghhBAiQWRZCYIzzsj87ZcWQ1oTac1mx2Xa82hHDVZN2m53797tunr16q5pn6XFkzY/WvLY0ZiW3Xnz5rmmvXjbtm2uv/vuu6jvz+7PwUZYo0YNv8ayCykpKVHfg9ZfWrNpk01U126WguBYXXHFFa5pveQY0t7NLsfBknnkyBG/xrFs0qSJa85hUlLSCe/xy8//+uuvXdPKu2vXLte09dKaT/s242/s2LGuzzvvPDMzmzJlil+jfZMlJ1gOgXPOz2Rn+fr169tvAUst0G7KZ2Is0rbMTthFihQxM7OmTZv6tfT0dNe9evVyTTtwrLXCz6fllmP04Ycfuua6YH5hOYLjx4+7piU+WId37Njh10aMGOGaFm/eCy3r33//fdRnYqmDeMK8SUt/mAezyGdkPuU65loM64627xUrVrhmWQj+Hdcin3348OGuc+fO7Zrzw1xcuHBh1yyZQAszbcj33XffCc8UK1ffeuutrrmHNG/e3DXLS7A0Btd/PMmfP79r5gGuQ+5tkyZNcs05fP75512HGGa5DZb8YB6kHZ3zRh544AHX3O8uvfRS19y3ixcv7prlIPgcjNdx48a5DjG6detWv8ayA+3atXPNUgosmcA9lPfFPSqecA7PPvts14zl6dOnu2b+5LxwPsNeWblyZb/GdUjrPnPgDz/84JrfB7i3skQMcxP/luuA67NcuXIWjTFjxrgOZTX4nHw2rqW0tDTX/K7G7wrcqxs1ahT184UQQgghhDhV0QlYIYQQQgghhBBCCCGESBD6AVYIIYQQQgghhBBCCCESRJaVIKANjt10aVOk1ZdWU1qW2Sm+ffv2Zmb21Vdf+bVYFlxaEA8dOuSa3dNpIeTraXuuWbOma9r8OnXq5JoWS3aF5hgEq+Ynn3zi1+rUqeOa9lBa2Wm9ZCdovjctf/GEY8V5oA0y1rjRbsmOy8GySItlamqq69DV2yxzvs0iS03Mnj076t+ygzLnlvHH2KHtnx3KN23a5JrPF8aDHdbZHZoWfNrVaU9lnNPGztIA8ebYsWOuaSvmHNH2zY7qtOlzvYau9SwdEKsEw5133umapUloSe3Tp49rlhdg13F2y542bZpr2s2ZD/j+7AweymkwL7Rq1co1S1lwjbIMANd5gQIFXLOsQjxhTqRVmSUSuF5Y1oPzfPnll7vu0qWLmUXajkuUKOGa1vB+/fq55jpn3NIqzVz50Ucfua5ataprxhbXIi3JjF12eg9xzDVHuzM/n1Zmzg/zAssk5MqVyxIBYzmUMzGLtIOzlAD3BJazYZmWMF8sT1O+fHnXjPHSpUu7ZumA1atXu+7atatr7tWcE5YAYJyxvAOfj+UlWJoijAf3Fs4h83C1atVcc79lPqPFPlaJhf8VluLhmDCXDxgwwDX3e66niy++2HWIPe4NoUyKWWQ5B64xPiP3R5Yf4fu8++67Ue+dsc+9muucmmVnwnNwjXPPXLhwoWuWNOGzMs4uueQS14nKpUIIIYQQQiQKnYAVQgghhBBCCCGEEEKIBKEfYIUQQgghhBBCCCGEECJBZFkJAnZfpg2P9jXalNlFl51we/To4XrGjBlmFmljnTx5smvaqGm7pXV5/vz5rjt37uz6lVdecU0r9eOPP+6a1kta5QltprRsjxw50szM7r//fr9G6zQ7ka9cudI1rZy0z9NmmCi7Je2dtE+2adPG9ZIlS1zHsvGyNECwQQ4aNMiv9e7d2zVLCtAay27ntM/G6oJOqyvjiTby7du3u+7YsaNrduJmh+idO3eaWWSn8CFDhrhu0aKFa1o8zz33XNcs60DbNS2s8YZrkTRo0MA1O66z7Aat/iy7MWvWLDMze+ihh/wabfwci+XLl7uuUaOGa8Ywr48dOzbqZ9IezRiklZ3zy/XCZw0lLLjmunXr5vqWW25xTWtt48aNXbOLPeeUcx1PWFqB+YG5jeVKeB8sGXLTTTe5HjFihJmZ1atXz6/RpszrnEOW3aCNn2UeGE8sHfDiiy+65jiPHz/eNcs+sBwF5zasxTvuuMOvDRw40DWfnzZs3jvzwgUXXOCa5UviCUvIcN/iemNJD5aaYFkM5r/169ebWWT+4jgwrpmTWAqEMCc9++yzrrt37+76tddei3rvjDOuCZZB4Bp+5513zMysVq1afm3UqFGuWY6BpS4KFSrkmvZ87lcsCxJPuK8xv9BezzV06aWXuj548KBrlsUYM2aMmUV+H+A4cf2w1ATniuWXGPssXcHvRyyTwHHj2DLOWDKhYsWKrsM65z54/fXXu2bJBH4nY/yxzAjLcRw/ftyEEEIIIYTITugErBBCCCGEEEIIIYQQQiQI/QArhBBCCCGEEEIIIYQQCSJHVtm4Jk6c6B/88ccf+3VaBmmd5/U8efK4pm0zdHym/Z+2d9q4aWX78ccfXdPuRms2LXbscE57Hq19vEfa3WkzpJU1vD+7mX/77beuCxcu7HrPnj1R34NjQUtquXLlXLdp0yazTfP/yJQpU3wO2Xma88br+fPnd71x40bX7Mod5oJ2Vdqr2aV90aJFrtlhe8uWLa45Jhx7jm2sDs2c/2XLlkW9X75PiB0+M0sU0PbKshvs7Mx4YmkGxmv9+vXjNodmZpMnT/Z5pB2Yc0C7M++XXeZpAQ8lQ0Ipgl/+XXJysmvGAseI5Tq4LrleafXnvXOOONa0MzNOWMojjHtaWppf47zEKjXAuOd80WbMXJCamhq3eZwzZ47PIfMDx4q2Xq4LzjOvB7sxy0zQMsycyE72HHvOIe+FuZKv4Xpi3uIc8v1ZVoElYML7sJs6LfZc55xD3iPXMT+T3dfjmU8nTZrkc8j9jnZ95nWWzVixYoVrznMor8BSMCwLEfZMs8yyDWaRlnnuSbTVs/zDunXrXDNXcPz5HNy3OLft2rVzHUpAcJ0yPlgugvfFuGRsc43TPt+iRYu4zeHYsWN9DjnOn3/+uesjR4645pgwx7CUwUUXXWRmZu+//75fy5Ej85b53YDfpViKibmR4126dGnXLF3C11evXt01x5n5nHsrc3XIF4w/lhpgaSk+M+dq27ZtrvldgOUQOnbsGNc9UWRf+g96uICZHXhm6BDLnfuck77+dObYsWO2ceNGq1y5ckT+Eb89motTB83FqYXm49RBc/HrOXz4Oxv00J/MzAoOf+axg7/8d52AFUIIIYQQQgghhBBCiAShH2CFEEIIIYQQQgghhBAiQZx58pckBtoXafukbS+WVY+2Odqe58yZY2aRHas3bdrkmlY9WiNp66QFmpbW5557zjU7FNN6S+slbbX79+933aRJE9d87oyMDDOLtCHSDjxhwgTX7AJO2yjHq1SpUq5pD4wnLO9Am+Dhw4dd03pJS2/RouXJbVIAACAASURBVEVd876D1Z8W4dDJ2yyyvAQti+yeHssmze7TtBTTzkmbJ8eNz8r4o937wQcfNLPI7ty0hDJW+X579+51TVslYfmGeEMbMi3dXKNTp051zXXBEibBKmtmNmPGDDOLtN/Tesqu9lx/HCP+Le2u48aNc02bOmOKn7V169aomuUs6tate8K9MY5piWXpC1qpY1n8+RqOaTzh2PMZGTfvvfeea64L3jdLp4Tu67QyM2dNnDjRNdcQbcrMQ1wrtJVzHROOP3P62rVrXTMuaH0O3ddpn2bOnzlzpmvmmtWrV7tmWROWKeA+Ek8YM3xGlq3gM3BNMvY5h8OHDzezyJzEkgLMz3xGvje72nP+582b55rrkKU4vvnmG9e0vrO8C0utcB8JuZj5ieUfWCKEY8SSJlWqVIn6mkTBZ6Gln+tw7ty5rhmfzKUslxH2eO4NLDnB70yh5IRZZHkYlsLh94f58+dHvUe+5+7du10vXbrUNffZkiVLumZ5kZBDGNvMFXwOrmWWi+I+wDzHzxFCCCGEECI7oBOwQgghhBBCCCGEEEIIkSD0A6wQQgghhBBCCCGEEEIkiCwrQUAr4YYNG1xXq1bNNS3CU6ZMcc3Oxm3btnVdv359M4u0b7L77pNPPumaVv9gkzOLtC+2b9/e9V/+8hfXb731lmuWOKDNk3Y+dhFnKYOnnnrKdbBt0q4cSiqYmXXq1Mk1bYa0zLMcQYUKFVxzrOMJx5BWe85bixYtXM+ePds1x/nuu+92HboiL1++3K/NmjXL9V133eWaZQRoh2WZAnbK5vxs3rzZNUsT9O3b1zWtxrRqPvbYY64HDhzoetiwYSc8WyipYGZ23XXXueb80KpKeybtrOz+HW8+/fRT16HzuJlZ7dq1XdesWdM11xfjnx3MgyWZVu8PPvjA9Z133umadnRaT2n1f+CBB1z//e9/d/3CCy+45lpnTNFyO3bsWNe01rI8RkpKiplFrps33njDdY8ePVwzvt5++23X7CJO2N0+ntDezZInjGGWP2HXepYmoB2/cePGZhZpU3/55ZddM/ZpcWY+f/31112/8sorUV//7LPPuuY67tixo+ukpCTXTzzxhGuWzJg8ebLrkCO5bzAWuYbZWZ0We5aRocWapRHiCWOfJXd4fyE2zcz+9re/uV64cKHrG2+80fWgQYPMLNLaz3HgvsZSKMyPLEfQv39/15UqVXLNceY6CJ//y/v661//6pr5/YYbbnAd8jVLDTBuBwwY4JplEhijfH2DBg1c0+4fT1h+g+uQ8Z6cnOyauZTlGrp16+Y6lAjhvHGvZ67j5/D7A9cG82HTpk1db9++3TXns2fPnq5ZfoYlDvhZjz76qOtQcoWlmrhm7733XtcsNcFczjHluuAeKoQQQgghRHZAJ2CFEEIIIYQQQgghhBAiQegHWCGEEEIIIYQQQgghhEgQWVaCgF2QaZ9t1aqVa9qhaT0fOnSoa9rTwutpjaV9cceOHa5pq33++edd04K+YMEC17RnsgQByxqw+zMtnw8//LDrXr16uabNL3QA7tKli18bNWqUa3a/pmWbtvY333wz6nt37tzZEgG7EHMOWYKA5SKGDBni+uabb3Y9evRo18GyyA7LtIKzIztLGtBSTCs8x4Q2VVofQ+kAs8ju00eOHHHNOXzooYdcM0bC/A8ePNivsVM8uzzTGtu1a1fXtPvSjk2raLxhB/MXX3zRNe2e7EJPmzCtpY888ojrUEri9ttv92ssHcCSFbTqMv5pTR45cuQJ721m1rt3b9fseE57avHixaO+D9cFu3FffPHFZhYZL8wzpUqVcs2O5ixxweu0+Ddq1MgSQbhns0gLNp+BufD+++93zdIQq1evdh3KXjA+mSv37NnjmqUzGjZs6JrlODj2fD0ty//4xz9cs+M6S12w7ARj8aWXXnId1hrLRUyaNMk18xVLCoQyNmaR8Ud7OOc/njBOhw8f7rp69equafvmHDKfsaROsIzzub744gvXHFfG+IMPPuialnneF7vTP/30065ZxuKzzz5zzbll2Yk+ffq4Zkmd8Le8d849bfozZsxwzTn/5z//6Xr9+vWu69WrZ4mgYMGCrlkygHsCv3twDjkOLBkQXs/8yjlmqYwzz8z8SseYYLyz5M4PP/zgmnsySyWxLAdzBb+Tcf/lOgulmFq2bOnXWAaGpS4OHjzounnz5q5ZuoTfJypXrmxCCCGEEEJkJ3QCVgghhBBCCCGEEEIIIRKEfoAVQgghhBBCCCGEEEKIBJFlJQi+/vpr17QY0mL20Ucfub7oootcn3322a7bt2/vOnTipb2ZVjba+AsVKuSaNjjasWn7poWP9sA//OEPrufPn++aFkfa0GlB/uMf/+g6lDVYuXKlX2PXZlpFy5Yt65qd4kuXLu2anY6DlTje7Nu3zzXLDrBkwHnnneeadlBaEmlZDt3mWaKCXZjZPZsd6FkKgO8XOrmbmS1btsw1rbzsIs2yF3wm2jZZmoD3E0oi0HbLzvO069Myzrm97LLLXHM+2fU63nz66aeu27Rp45prlEyYMME1S0Vcc801rsMa4XiyBAWtrFyLtLv269fPdc2aNV1zLv7973+7ZrkDlk9gd3nGAMeX9x4stMwFSUlJrtlxnjFCi3OxYsVcx8pv8YRrvFmzZq5pr2fXeo4hSzfcdtttrkM5DJYaoNV49+7drml9JiyBwFhhORjmi6eeeso1Y6RkyZKuab+m3Z25PnRaZxzQBk7rNceINmjmcO45vN94snXrVtfM8YwrzjPXbYsWLVyz/EbYCxmnLPnAvMLSBBzjatWqueaaZLkA5gSWAGDphpw5c7qmrZxzy7ICIXewXAVLBDGfBqu7WWQ5gvLly7tmiQfOeTxhnHAcmHe4ty1ZssQ1S9SwFEvYT5jrHn/8cdfMsSwLxPXD/Zb5aPz48a4Zf/zuwe9NLHewffv2qM/EOQr7wNGjR/0a12mePHlcc13x3ll2iDmZJTOEEEIIIYTIDugErBBCCCGEEEIIIYQQQiQI/QArhBBCCCGEEEIIIYQQCSLLShDQml6gQAHXtKGxNABt5Weckfm78ejRo10HCyXtv7TP0prJbt/s8E5CWQCzSIsjuxizTEKNGjVc02pMqzNt3ddee63rwoULm1mkpZYdxNn5nZ9Jqyrha9itOp7QOk7rZe7cuV2vW7fONW33W7ZsiarD/PPZaY2ljTmUnDCLtA4zhl5//XXXLB1w/Phx17Soh67hZpklBczMvvvuu6j307t3b9ehAzbt6rRS1q5d2zXjkjbpvHnzup43b55r2sfjDTt3b9u2zTXHmqUuaEnev3+/62HDhrkOMcB54Xyx1AEt1owFlpXgXBw4cMA17bG0ZNP6vmHDBte02/Nvp0yZ4jrMB+3Tx44dc8044vwyv7Bkybhx41wzR8QTWo95f1wLK1asiHofe/fudX3nnXe6zpcvn5mZFS1a1K/RMs1yKhx7liZgPHG8eV9cW7HyFvN8u3btXDP+XnjhBdfB7s7SNSwRw/hbtWqVa9rAuc45nyzvEk/CHmAWaelmWRI+A++J6yyUcTHLjOELL7zQr3EdMmY5J5wH2sdZgoLrkGPCdcNcwdgpVaqUa8Yfc0i4H5bFoO2c+ypjjmv80KFDrt977z3XzG3xhGuPsckyC3Pnzo36t5wj5qPwniy3xLJG3INYWoR5lXP7pz/9yXWRIkVccw3Heg7mZ5YsWLt2revhw4e7Dmuen8/7qlWrlmuuQ65bzi0/h6UMhBBCCCGEyA7oBKwQQgghhBBCCCGEEEIkCP0AK4QQQgghhBBCCCGEEAkiy0oQ7Nu3zzU7n9O+3qdPH9fsoMzXsJRBsHDSykabIrsms0Mx7e7sYE8bXuPGjV2zgzZtqiw7QGsdn49WfVryg+WU9k2WHeBn0ppL22LoeG0WaS3l58cTWlB5H7Qas5vz8uXLXdOSSLttsDXTJkt7Ncs/DBgwwDW7WtOGSSs6x5v2fo5PWlqaa1o+Y9mnWUYizC3LW7BrM+2b7FTPsgOxOqFzDOINY5j3wvIR7KLNTthcI+y0HqytXBOcZ3YFp6Wc9lRaX2l3pz1106ZNrlmmgc/UqVMni8Y555zjmmsqzDXfm926mSOYFzhHLFnCz2EeiycsbUJoTW/atKlrllTg8zDmQjkUdl/n2HMd0MrM6yzv0qRJE9eMIe4FvF+O+T333OOaJSVom77uuutch7IGtJ0zn9L2zrIjtNuzNEn16tVdcz7jybfffuua48z5YQf7XLlyueY65L2GOOTz1qtXzzXXW9u2bV0z9kMpCrPI0hWMZZZU4b6wePFi1zVr1nTNsWUscj8vX768mZnt2LEj6mu531WuXNl1jhw5XLOsA/N8ovZEfjbHjXm9b9++rpmnuPdz3wjxFqvESnp6umuWOmBMcP9ivNPez9JOLJfDfYs5hGuYz814Dd+/WGaH5SpYloL5m/s5n4Oxy+tCCCGEEEJkB3QCVgghhBBCCCGEEEIIIRJElp2AZaMsnobjyVGeGuEJF56CadGiheu//OUvZmbWoUMHv3b77be75gkpnrpiMymesOzevbtrnj4JJ3PMIk/+VK1a1TVPlsRqvsRmTUuXLjWzyNMhTz75pOsSJUq4ZtMkNiXhiRCeZuFpo3jCE388pcSTbhkZGa55oo0nkDgO06ZNMzOzu+66y6/x1AtPRe/cudM1x4cnyYoXL+6aDTz69evneujQoa55Ypqnt3iPPNnEE83h9Oyjjz7q12699VbXjAPGB0+dcox4Ypcn8+IN44nNyVq1auU61kklNgXq3Lmz6+eee87MIk97vfPOO647duzomqe7eYKLDZx4ferUqa45vjfffLNrnhDjSV42Wdq1a5drNvkL886448loNo1iHuHJP54O5OnaM89MTMrlHPIkHZ+LDcN4GpTxzLUbGiLdfffdfo3NsHi6lfmG+ZTN25iXGU88Vcd4Yj5lfuGa5snOaE2MeEL5z3/+s2vGE+OPJ5eZTzmmXK/xhPmUJ1A5J2y+xFzFPZG5MJz0ZyMr7oPdunVzzdPCPJXOz+ScjxkzxjXzNU9M8uQjnSZcnzzJeu+997oOewHvd9KkSa4Zc8z5XIds8Mm1z7+NJ8yfPF3ctWtX13yG1q1bu2aO4an9MA5cJ6+99prr1NRU19u3b3fN5lxsjMXXc49jLDBe6D7g6XN+V+OYcw5ffPFFM4t0wrBJHHMCm8pxLbMJHceXa1UIIYQQQojsgE7ACiGEEEIIIYQQQgghRILQD7BCCCGEEEIIIYQQQgiRIHLQ0vVbMm3aNP9g3gOtbGxQQRsv7YbUwcLJcgF8b9rFaV9jM5NYpQlo61y5cqVrWsPZQIZ2R9qR2cCF9sxwb3xv2vBKlSrlms9HOy5t7SyTQCvttddem9kt439kwYIFPriHDh3y6xzDWPZylk6gBTrMP0sU0C7K56VdmU2tWNKC9kVah2mNZqMWQqs5m9/QQsrr4bNo62RsMVZ5nY1taItnjLKMRKdOneI2h2ZmM2fO9HlkPLMEQCy7M+3DbMQSNC3i/Ds2GeI4027NceT6Y3MujhfXCNc6G1TRQs2SFLTThjIkXGd8bzYBZMMpzhdzF630bIbTrl27uM3jnDlzfA752cynXGcsb8GyKCxlEOblo48+8muxrO7Ms/wclg5gg6WiRYu6ZoxQc66Yw6pUqeKaDReZa0L+ZXwS5iLGE8eFDeRoyed4pKamxm0O582b54PIOWFDIu4fzGfcexiTISexFAz3JsbmwoULXbOhHuOJ65brgI2gmCsYC2zCyKZ+fA33v9AsasWKFX6NDeFY0oWND/lMjBvGK8e0WbNmcZvDSZMm+RzGalbI/YwNqzgv3E9DUzWOMdch1wnXL+Hew3jnfLI8CktuEJbIYY7l2uNeFZ6J5YK4tzNWmG8Z58zfLH/D+23ZsmVc90SRfek/6OECZnbgmaFDLHfuxDRM/L1w7Ngx27hxo1WuXDlhDULFr0NzceqguTi10HycOmgufj2HD39ngx76k5lZweHPPHbwl/+uE7BCCCGEEEIIIYQQQgiRIPQDrBBCCCGEEEIIIYQQQiSIxLTk/hXQgk1LJ62XtCZT0z5Ky9306dNP+Hd2dqZ9nfZAWt9iWdzYubds2bKuaefj37KLNe2HtCnTvh46xNNeSyt/LBvw3LlzXZcrV851sFGbRdp94wktiPw82jtpaWcHdXYnp/UydO3mPXOOx48fH/VeWFLg/PPPd01r7iuvvOKaVvd169a5LlasWNRnoq2XFkvGcegKHutYPksd8DM5t+xOzw7etGTGG64L2vJpK+f40kpOyzZLZrB8QKBIkSKuObaE64wxwrWwefNm17QVc0xpj6ZtmZ22GXfsHh7il89JizOfg6UJaLenzZjxQKtuPKE1mPmB1zk+LC/BUiucw2Dvp9WdeZNr5d1333XNsh8ss8I5CfnOLDLn8fW0KtMSzXXMXF+3bl3XIf74zIxzxgTzC+3u7FxPq3iiyvZwvfPzuPaXLl3qumLFiq45h8wzb731lplFzgnjkeuNuYy2dr6eZUaef/551zfeeKNrloDh3DJvzpgxwzX3Td77lClTzCxyjrl+uCb5/WDy5Mmur7zyStdcF4zdeMJ5YEkgwrIYoWySWeQew/GfMGGCmUWWFeKz8zsI8w7XEnPZFVdc4Xrr1q2uubaZb/mdhfmO5SK4X9epU8d12DdY6oAlBViShXHOUiT8W+Yifs8QQgghhBAiO6ATsEIIIYQQQgghhBBCCJEg9AOsEEIIIYQQQgghhBBCJIgsK0HA7twffPCB6+bNm7umnS6UFzCL7KjboUMH1z179jSzyC71tOfRds4yArTD0uJJu911113nmrZNPgetd/Xq1XM9btw417T20TJ76623mlmk1ZvW9PDvZpHlDdihmpZdwnGMJ+ysTMsgrbG0iLNkwcaNG13TDtqlSxczM5s6dapf4/zcc889rmlHnDVrlmvaM8P7mZn98Y9/dM3u6bSfM/4aNGjges6cOa4ZU7z3bt26nXAv/Lu77rrLdVJSkmvat2lDpW00UdZ1s8gSHFu2bHHdrFmzqPfF7vChZIRZpA25VatWZmY2f/58v0bb8eDBg11zHmfOnOl6xIgRru+9917XtLty/b300kuuaY+m3Zz3PnHiRNecm2BbDvZts0hLOJ+T1niWT2HpDa5LWpTjCS3YjO2mTZu65rpkuQTOOecixCjjgyVdaA2+/vrrXaelpblmrqLV/Kabbop6v9OmTXPNOb/88stds5TBkiVLXLMcxrXXXmtmkfNAizVzNaEFnqUuWN6FVv14wr2BHe/btGnjmnmD5UKYc6655hrXd9xxh5lFjivX5NChQ10zxjkny5cvd33bbbe5fvbZZ6O+njHC+7366qtdM//zOZgXateubWaROYG5/ZFHHnHNtcecRLs7y7twvcSTQ4cOueb+3bp1a9fVq1d3zWdj+YvwXcbMrF+/fmZmNnbsWL/2/vvvu2bu4n70+uuvu2b5B5YgSE1Ndc3SEfy+xX2TZTKYV1nqh+swxC6/kzEH33LLLRYNxgfnimUH+H1CCCGEEEKI7IBOwAohhBBCCCGEEEIIIUSC0A+wQgghhBBCCCGEEEIIkSCyrAQBrZ60PtJixm7BtLjSOhytI3qwgptF2htp16dNcuDAga7vu+8+1+yYTmthnz59XPfv3981u0XTEnn//fe7fu2111zv2rXLdbAfsvM7rZ+0qbNbMJ91wYIFrmmTPfPMxEwzOzhzHkqXLu2aFmE+28iRI13Tvjh37lwzi13ygXZEWnaHDRvmetCgQa5ZGoEdvDlub775pusjR464piV0wIABru+++27XLJ8R5og2Xc4bLZu0gNMmznFcs2aN61q1almiYMdtdu6mNZ1W3s6dO7vetm2ba85HKBPCdctyDLSD8/M5tixBQMst7bQsK8KSDXyOWGPNNU2bfbjOEgxvv/2269mzZ7tmHqP1e/z48a45jrRBx5N8+fK5ZgyzgzljtVevXq5ZroGE+ONaHDNmjGuWX1i8eLHr7t27u+Y80FbPXNCoUSPXzO3M/8zFLCvC9XX8+HHXwebctWtXvxbs+L/8fN5jSkqK6w0bNrjmHsX8Fk8uueQS1yzvwrzFMe/du7dr2sRJKDdAaz/jmrbzggULumb5B9rEmU859g888IDrV1991XWePHlc0zLOfMq1xRwRvgtwTpiTt27d6pr7Avfw0aNHWzSYc+LJpZde6po5jrk/7HFmZn379nX9pz/9yTXXU/ieEMpqmJlNnjzZNcvDsOwBS6WwDBLLOBw7dsz1hRde6LpChQquWf6G78+88Morr7hmCYizzjrLzCLnm9+ZuPaY19u3b++aeYPlC0KpGJF4kpNSiprZZ2bWLD0jbd7JXo+/q2Nmy8ysTHpG2vaTvV4IIYQQ4veOTsAKIYQQQgghojHk//cPkpNScprZ/Sd9oRBCCCHEaUSWnYAVQgghhBBCnFokJ6VUMbM7zayBmf1q60ZyUkpDM+tjZo3M7JKTvFwIIYQQ4rQiy36ApWXwqquuck0bGq2XtNmxIzU77garMW2SHTp0cM3r7BZMqyktzSVLlnTNkgK099OeSatxsN6ZRdrsaJtr27at62C9pCWwRYsWFg1ajGmNpb35sssui/qaeMLSBrRfs6QAS0rQhkm7Le2gwdL9r3/9y6/RssjX0hbPkhJVq1Z1Xb9+fdfs1E37Kq2ajEta2vl62ppr1qzpOozH559/HvW9ORbUjHk+X6lSpVxzPOLNGWdkHoRv1aqV61hdplkmgZ3tOe4//vijmUXOOceCdudQrsAsco2y2z3HgqUDOC4sK8G/pSaXX365a5Z4COUOWAKEpRS4Rmnx53NwXHjvzCPx5OjRo645D/v373fNnEBLP7vThzIuZpnWZ5aZqFevnmvGMHMyrdfVqlVzzbXILufFixd3fc8997g+cOCA6yJFirjmGPI92Wn+nXfeMTOzypUr+zVauJk78ubN65q5muuf48K/TRTMMbHyBvM6960GDRq4DnPHcjd8b+4lLMPBsh2MJ8YK92TufdxzuQ9yPvn+HNukpCTXYV9mTmaJEuYhPgct9nxvlo5I1J7I9cbvNYwZlu5hWZA2bdq45tiGOee6Yq7jGPNzypYt65r7FMsksKRArLIg/L7D3Ldo0SLXXOf83hTigqUzWHaB8N6537I0Ar+3JXJPFGZmdqGZlbKfSg/8aGbl/vvLnWJmdqmZ7TCzY2Z22X9/uRBCCCHE6YNOwAohhBBCCCHMzCw9I22BmS0wM0tOSulpZiP/6x9k/t0YMxvz8989YmbRC1QLIYQQQpyG6AdYIYQQQgghxO+CY8eORZzYFicSxkfjlPVoLk4dNBenFpqPUwfNxa/nZGOUZT/A0jZHCzRvuGjRoq5pa6cFmV1xg2WadkR2k6ZNLnSHNossdcCuzez+S0sgLbm0MpYvX94174E2bNrmJkyY4Dp0/K5YsaJfo9WWVlraZFkGINi+zSLtmbTvxhPOGy2GtHbz/mif5d/S+hrGjZbW7dszm+fSzk1ob6Vmx3pa6vn5tD7SSs3PZSzQZjp9+nTXwcr81VdfRb1H2txZMoFjx/ij9TaWjT4ecF4Yt7wX3uP5558f9TWcs/B62oE5z4xVdq+P9Zws00DbLueatnrax8natWtd07afkZHhOowH7cu0/9LCy2fiOme+4GfSnpsoOLacK44P53Pfvn2umZ9C/HF+aNdnmQXmUz4jy3HQBs/PZNzwNbSj832Y/2lPnjVrlv2SBQsWRP075mqWoClQoIBr7jnMBYUKFTrhc+IB9yrGG8vscPxjEcrZmGWuD8Y6x7Ju3bquN23a5Hrp0qWu+bccz3Xr1rnmuDHPcsw5nrTnr1mzxjXt7iFek5OT/RrzOctP8F64PlnqYsmSJa6rVKliiYD7HceB33f4fYP7EHMvyxSE0jmMWeY92vK5Z3Ddcs6ZY5kT+F0qVokcxijjkvvj4sWLXYc8yGdmnDFX8DO5r8TK8SxjJH4fJCel3GxmN/8v75EzZ86c5WvUtM2bN9vZZ5918j8QEf/3gshaNBenDpqLUwvNx6mD5uLkHDly9L/+u07ACiGEEEIIIbKM9Iy0l8zspf/lPfoPeriAmR2oUKGC5c59Tnxu7HfKsWPHbPPmzVahQgXLmTNnVt/OaY3m4tRBc3Fqofk4ddBc/HoOH/7OzKbG/Hf9ACuEEEIIIcTvgOSklMLpGWl7f9YXmFkHMztqZpPTM9IO/Nc//p2QM2dO/R+IvxKN1amD5uLUQXNxaqH5OHXQXJyck41Plv0AS7sZu3PTqkarHG2QtNWyC3mw/PGhaS+nxS8lJSXqZ9I6zM8555zM/0867ZaxbIN8vnz58kX9LNqkCxcuHPEMZmZlypRxTQsjuznTwkh7IK25tCTHE87JRRdd5Jr3Spsgx4q2SZaaCM8cax5owa1du7ZrPiNLTTCGaJOkVZNlDfj+qamprnk/tNXyuUNc0GpLWzo/k6UOaFtlCQ5aSHnviYQxzLGrUaOGa94j1xrHomDBgif8O2Ofne855rTAc1wYI2Gt/PL9uZ74elrGOTe0O3O9BHs+P5+xzjXK0iAsPcF7iZUXEgVLgPC+uea4XpjbOLYhnzDfMd/yPerVq3fC35lF5jCON++F48zcwTUX4umX98v1xXkO48yYow2bMdewYUPXzFGMLVqiE1X7iLmcNno+A/dKjifnjbEXxpPPxbzJ8W7QoIFr5irOOT+HNn7Oeax1yzUfqzQQ7z3EDu/liiuucM154L1wjTNueC98z3jCcWbpH8YsY4ljxVjmPDdr1szMIksBcBy4Pqm5T5UtW9Y11wlzBceT65bxV7VqVdd81lg5MfBr9meuN44L8yc1n0/8RHJSSn4zG2dmjc0sV3JSyrlmttzMLjezHGb2foYLsQAAIABJREFUaHJSSsP0jLRdWXeXQgghhBCnL2ec/CVCCCGEEEKIU5gnzayZmT398393NbOSZtbLzFqaWV4zezxrbk0IIYQQQqgEgRBCCCGEENmbdmY2Oj0j7cGf/zvVzNamZ6S9bmaWnJQyysw6xevDkpNSmphZBTOblJ6RtudkrxdCCCGEON3Jsh9gW7dunePkrzr1adSoUVbfQpZRt27d38Ucnu7Ur1//N5nHxo0b/xYfc1ryW82hSBy1atXSHGZzGjZsqDkUWcn5ZrYB/51kZiPx31+aWRGLHz3M7EYz22hm+gFWCCGEEOIk6ASsEEIIIYQQ2ZtPzKyUmVlyUkpj++kH2bn495pm9lWUv/uvpGekjTKzUVGu9zSznv/l7x4xs0f+fz9PCCGEEOL3in6AFUIIIYQQInvzuv3UaOsyM6ttP514XZiclFLMzPrbT+UHXs7C+xNCCCGEOK1REy4hhBBCCCGyN0+Z2XP200nXA2bWOT0j7QczSzaz+81smpkNzrK7E0IIIYQ4zdEJWCGEEEIIIbIxP//Yes/P/0Nmm9kl6Rlpn/32dyWEEEIIIQI6ASuEEEIIIcTvgOSklMLQF5hZRzNrlpyUUjDr7koIIYQQQugErBBCCCGEENmY5KSU/GY2zswam1mu5KSUc81suZldbmY5zGx3clJKw/SMtF1Zd5dCCCGEEKcvOgErhBBCCCFE9uZJM2tmZk///N9dzaykmfUys5ZmltfMHs+aWxNCCCGEEDoBK4QQQgghRPamnZmNTs9Ie/Dn/041s7XpGWmvm5klJ6WMMrNOWXRvQgghhBCnPToBK4QQQgghRPbmfDPbgP9OMrN5+O8vzazIb3pHQgghhBDC0Q+wQgghhBBCZG8+MbNSZmbJSSmN7acfZOfi32ua2VdZcF9CCCGEEMJUgkAIIYQQQojszutm9mhyUsplZlbbfjrxujA5KaWYmfW3n8oPvJyF9yeEEEIIcVqjE7BCCCGEEEJkb54ys+fsp5OuB8ysS3pG2g9mlmxm95vZNDMbnGV3J4QQQghxmqMTsEIIIYQQQmRjfv6x9Z6f/4fMNrNL0jPSPvvt70oIIYQQQgT0A6wQQgghhBC/A5KTUnKZWV0zK2ZmR8xsc3pG2sasvSshhBBCCKEfYIUQQgghhMjmJCelXG8/lSE438xy/Hz5eHJSymYzuz09I21hlt2cEEIIIcRpjmrACiGEEEIIkY1JTkppYmajzexTM7vdzFLtp8ZbT5pZATObnZyUUj/r7lAIIYQQ4vRGJ2CFEEIIIYTI3jxkZuvN7Kqf68EGJiQnpfyfmS3/+TUpWXFzQgghhBCnO1n2A+yyZcuOB717926/XrhwYddffvml64suusiivb5kyZKuP/74YzMzO/vss/3akSNHXJ9zzjmuv/vuO9fFixd3vXPnTtfly5d3vXnzZtcXXnih66+//jrqPX7zzTeuL7jgAtf79u1znT9/ftd79+41M7MSJUr4tT179rjOly+f64MHD7rm6//9739HfT3HoHHjxjksTsyePdvn8PPPP/fruXPndn348GHXHLft27e7rlSpkuv169ebmVnt2rX92tKlS11ffPHFrvm8pUqVcr1p0ybXpUuXdv3ZZ5n9J4oUKeL6wIEDrs88M3NJHDp0yPUll1zimvEXLRZ4L3zOokWLRr0XvjefifHK+2rbtm3c5tDMbM6cOT6PXHOMIcYzx5RjzecOz8dn5nuff/75rkPsm5ldfvnlrhn/HOdVq1a5rlOnjmuu3QIFCrhm/BcsWDDq6xmDW7duNbPIXMR1zrk444xMEwGf6T//+Y/rc88916LRunXruM1jRkaGz+Gnn37q12PlKuYevp75NIz/pZde6tcYzzlz5nR9/Lh/fMQcfvLJJ645nhyfvHnzumYs8D05znwfvn+5cuVOeCbGKuf72LFjrrnO+R7M1Zzbo0ePuk5NTY3bHC5evNgfmOOTI0fmR3z77beuuW9xXvgMGzf+VPYxVv5ijvnhh8zfa7hOuH6YE8J+a2ZWpkwZ1xw3ziH3BcKcx8/dv3+/mUXuq8whXOPcE5lzmNsZZ4yn5OTkhMxhrO8Asb7v7NixwzXXXFi3XMscM44x44PfDT744IOo17neeC/czznOfCauc8bflVde6TrEX4UKFfwan5PzzfxUqFAh11yrsWKoefPmcd0TszE1zezxX/z4amZm6Rlph5KTUt40s/t++9sSQgghhBBmKkEghBBCCCFEdueY/feDFXnN7Kzf6F6EEEIIIcQv0A+wQgghhBBCZG+WmVmP5KSU/L/8h+SklAL2Uz3Y9b/5XQkhhBBCCDPLwhIEX331lWta6GgZpMX1+++/d01L2rvvvus6WC9pZaMFnu+XJ08e17Tg0vrHv+XrafMjtKnSkkfbJu2ktPaFv122bJlf47jwHmknjWWHpg2U9x5PYlm7aZ/lGHJuea8rVqxwXblyZTPLLEVglmlFNYu0JtLaTRtxvXr1or43Lbu8znmgDfOLL75wzbmgFTTY1c3MkpKSzCwyJnm/tNTT+ksrL9+br6fdM94wnmnlzZUrl2uW7GBsXXbZZa5pZw3xwBIFZ52VefCG80VbKceC8ULLfOvWrV0vXrw46t9Wr17ddax45Nrh+1x11VVmFjn/tDXTnsu4YE7j2mD8crziCXMecyXn81//+pdrjjk17dFh/jds2BD131n+gfPJsgCxcg9zIsczVr5m/NEqf95550X93GD5njt3rl9j2RE+M3My45Xxz1ihlT2eMGZotee64T3Rys3x4fUaNWqYmdnChZmNzxnLzDEcH+5fXHuxSgowD7JMDGOf+ZpzyLzB5w5laLiuY5XZYczz9bT+0z5frFgxSwSx9jjmTOYjapZa4POEPYGxybXBsiH8bsScwLnld4lY+x3njSWduFesW7fONb83MQ+G/XzmzJl+rUqVKq653qkZ55xP7qeJ+l6TzXnIzBaZ2YfJSSmvmtlaM/vezCqYWT8zu8zM7sm62xNCCCGEOL3RCVghhBBCCCGyMekZaavMrLmZfWNmQ8zsbTObYmZP2k/lB3qkZ6RNz7o7FEIIIYQ4vcmyE7BCCCGEEEKI+JCekbbYzConJ6VUMrPyZna2mX1sZiujNecSQgghhBC/HVn2AyxtjR999JFrWrNpn6U9L1aH3qlTp5pZpP2Y3eZpD6RlnhbPWHZldhGmBZU2OHbZJrRb8vlmzZrlOlgUq1at6tdoA6V1mZY8WhFpz6SFjzqe8Nk5PrTS0obJMee88Jlnz55tZpnWRbNIq+uHH37omrbgWPFEa+7KlStdszs674WxResl7ZycZ97DG2+8YWaRpSh477RmxrLG0hLMbtU//vijJQquIdpTeb+8R84vY4720FCGgWuF48J1EKuDNu+FhM7aZpHzzjjne9KWS2st741W9pBHatas6dc2b97smpZ15gveC+eLz814jCdci5wrPi/jnHNImKvC+DPG+Tlr1qxxzTxEazitz8xbjHNa4snRo0ejXuf4Mx/w3oIlnvfCZ4tVUoNzyP2C8crPjyfMN4xNzict+sxh0UpHmJmNGDHCzCJz7LZt21xzX5kzZ45rxj7njdZ9rgmWd9m1a5dr5rlJkya5pmWd98sxeOGFF8zMrH79+n6Nz881Rms855D3wu8Q3NvjCeONJTG4hpib+LyMN+bkN99808zM2rdv79do/+d+x8/nmuTncA6XLl3qmqUjeC98f1K2bFnXLOHCEklh/DknLJHBsgdcv1xv3GMYT8zrpzPJSSmfnPxV/lozs+PpGWmJqYUjhBBCCCH+KzoBK4QQQgghRPbjIzM7ftJXCSGEEEKILEc/wAohhBBCCJHNSM9IS87qexBCCCGEEL+OU+IHWFoQaROkJY4WN1oy27Vr5zpYWWlRpl28UaNGrmnfDNZxs0hr7A033OCaVlpa8qZNm+b60Ucfdc2u9cHSbGb22muvub7jjjtcB0ssny09PT3qa5s0aeJ69OjRrtnRmPZAlgGIJ+yqzg7XtOiyvAMt0BxD2mNDh/v169f7tffee891ixYtXLOkwaJFi1zTdtqtWzfXtB3Hsn6yQ3OFChVcz58/3zUtpP369XMd4njVqlV+jXMYunqbmTVs2ND19OmZPTFopeVaoH063rDjNm3/5cqVc82yHpwPvr58+fKuO3fubGaZJSXMzBYsWOA6NTXVddOmTV2vWLHC9ZYtW1xzLhjnjKkpU6a4HjhwYNS/feutt1zHKgkRrL58Ts4/cw5LJjDXcB5pJ05U526WFKD1mLmVOY8d1Vk+5LbbbnMd7Om0oMfqds9yGVzb8+bNc52SkuKaNmhaiYNl3sysT58+rplT3nnnHde0Kl911VWuy5QpY2aRa37y5MmuhwwZ4vrQoUOuGaPMXaETvZnZ8eOJOWxGqzVjkyUsGMvLly93zTmsWLGi65BnuJZYaqBnz56ub7zxRtcTJ050TZt4nTp1XNNq/v7777vmOA8dOtT14MGDXf/tb39zzTlq0KCB67Cfcs9ctmyZ6y5durjmfst4ZdywpAXzfzxhLmW5BH5/SEpKcs31xBI511xzjeu2bduaWWS5E5b/uO6661wzhjgn/F5xxRVXuGbu5TrgvnXvvfe6ZvmA1atXu2aJng4dOrhm6YXA+PHjXfN7DfMk45nrkPMZ7b2FEEIIIYQ4lUnML3NCCCGEEEIIIYQQQggh9AOsEEIIIYQQQgghhBBCJIos83CxYzptn40bN3ZNizvtkXfddZdrWqBDZ+C7777br9HqTNs3SxoMGDDA9eOPP+6apQloHe7Vq5drWjtjWeyvvvpq10uWLHFNK3uw9vXt29evjR071jUtxrQHtmzZ0jU7GtOmS8t0PKEtl1Zjlgmg1fn666+Pen+0xAYrOC2YLLPQsWNH17Rs3n777a47derkmuUf2E2b99i/f3/XzZo1c80u1rfeeqvrHj16RL23YMFnKQqWvaClf+7cua5ZmoClFzZs2OCaZRriDUs5sHxDpUqVXNPy2r17d9ccO8Z26IrONZeRkeH6vPPOc01b8Z133uma5SP43ux+/tRTT7keN26ca9rE09LSXHPt0gbN9RpikGUXOBccr4ULF7oOVmGzSBs2LfaMgXjCEgosl8A4Zw655557XIdyEWaRpTxCp/oaNWpEfW+uFZZ9YUzQHs05ZBkJvg8t1KEEgllkHrnvvvtcDx8+3PX+/ftdh3m5+eab/Rqtz4w5lmzgHNL6Trs14yKesIM94zF37tyumeM5btu2bXPNnBTWIdfsqFGjXNPWvnbtWte0hrMUBP82xIdZpJWdsc9yLBxz7rmPPPKI60mTJrk+fPiwmZm1adPGr4U93iyydAxLCrBED+OZ3xVCuaJ4w7Ix3L+Z7z788EPXXHssRcNxC6UTuK6GDRt2wr+bmeXNm9c1y+M89thjrpkr+B2kefPmrjm23JP4HYfrkOuMpVtCXuBrOS4sKcAcwnIizLH8vhXKFQkhhBBCCJFd0AlYIYQQQgghhBBCCCGESBD6AVYIIYQQQgghhBBCCCESRJaVIKDFjZ2V2RGZncRpyaxbt67rkiVLug5dfPft2+fXatWq5ZrWP1oF2S2YlnF2K6aN+eDBg65ZSoEWW9pJaR9nB2LaD4O1lPZmdg2nZZ02fD4TO4vXq1fPNTsgxxN26qYdleUd2Cmb9lGWmqDFOXSw3rNnj1+j1XbMmDGuWVKAY0/74g033BD181legN3haY9kN2daWWlBbtWqlf0SWnD577Nnz3YdyhWYRXYZZzdv2rTZZTrecC1ybXEdsds8SwkwRitXruw6zGOJEiX8Gq3BLKPBseA4s5s7Y3737t2uGScsU8J8QbsxY5O2ZZYmCPdQrVo1v8bnYC5gfmH5Bq4Hfj7LAMQT2u+Ze3idJV3++te/umb+5boM65v3z3IZ06ZNi3r95Zdfds0cynIItD6fcUbm/x/w2muvdU07Oq3KP/zwg2vma3ZRD+zcudM1cxFjmH/H/YTxzLUYrPHxJleuXFE/m/nh4osvdh3WmFnkGmLchnFmHLC0CMsOMGZpI2/fvr3rBg0auN66davr/Pnzu+7SpYtrrlXe+8SJE12XLVvWNW3loXxMmTJl/BrXG+32LCNBmzzHhfOcqD2RubRKlSquWYaE65P7OtchLf0zZswwM7Mvv/zy/7H35vFXz/n7/4NsjS1GmkHIkiJKGxX1btEmlZKypDRpse9DiESWzDDIWIpI2tAiLaTevROVJVRChBGGyZAt+3z/mHle3V+cM/x+c85U87nu/8x1e3Xe57xez/XM8byuh67xu8P06dOlud/w9SUlJdLsQ+5JHNccI5MnT5ZmHAajo9iH/G614447RkR2/nKPGT9+vDT3kg8//DDn+3E94fdDY4wxxhhjNgZ8AtYYY4wxxhhjjDHGGGOKhH+ANcYYY4wxxhhjjDHGmCKx3iIIaP+lptWXFlda+1jhnBV6k4WOtnxa+mlZe+ONN6Rpn2QVblb85nvSSkkrLa3ptLjTsrx8+XJpViZO9mB+Pt+b1eTZLqxczOdj1XraWQvJNttsI81q2rQjsmo3X89K9sliGbHOGkqLKK3dbGNWTaZdnlbK++67T5qVqNmHtMmy8jqrUtO2STspIxEqVaoUEVnLNqMRaKnn+Nh8882laTd+//33pYtVtTsiGy/Aauo1a9aUXrhwoTRt/19//bU07fXJEsznnzdvnjRjH2iD5jznnLv//vtzfg7nC9+H78/K7YMHD5amPZ3RE6l/Of84Hvfbbz9p9hct5JyLfA3t1IWEY5uxKLQ7cy7yPhYtWiTNuZiegZZ/WtO5VjN2o1evXtKMlBg3blzOe+fY5vvzOvuZVdxZFZ3zKMUnMPaC6wj7kFZ+jn/a9hmTwCiDYkELNvuT7UmLO+ck51BaZ7necr4zOoLzge/x7LPPSjPCo2LFitJc27iesk9efvllac4V7gvc29I6zjGRLzqAawVjClatWiXNyALuUYWEc4VrAD+P842xCPxucPHFF0un9uSzL1myRJrPy/ZjdA8jGh588EHpFNsUkY3o4XhiNADHAtt22bJl0uzbFAXDOfvMM89IM3aBkRbc5zkWOf65PxljjDHGGLMx4BOwxhhjjDHGGGOMMcYYUyT8A6wxxhhjjDHGGGOMMcYUifUWQUCrXrKLRmTtkazESxsaLY605qfX0N5Nm+SaNWukaeukpZyVwvm3rL5LKyAtrrRYNmrUSJoW5L333luaVtZkiZ81a5au/epXv5KmfZiWQLYLn4Ntx+uFhH1IWzjti23btpWmTXTx4sXStCEmq/9HH32ka7QvMjqCFdtZ8ZnxC6wInq+CM/t22rRp0qzITjsp7ciMXkhWUNo6a9WqJU2LfIsWLaTZdnw+Wkhp5S00vF/aWVlNnFEX7BuOeY6zZB/mXKFlnfOAdlPawfm3bCPanVkBnFEmtC2feuqp0vkiHngPyarMcczYE0YTcOxwLnKMMAagWP3I8Um7OfunQ4cO0rTXc52hNT9VLuc44L+z7TkXOScYHdC6dWtpzn/aphmXwvW6evXq0nvttZc09wKueTvvvHNEZG3NjKVI1dkjsmt4WVlZzufg2C3mXExwLG222bptmu1MGMXDtkp2cEa00I7OaBHGr+Tbn6tVqybN6vScSxz7fI6SkhJprjlPP/20NPf/XXfdNSKyMSfcC6h5X1yfOG/TmIhYN7YLDZ+d3x+4rnNP4DrF/YnrcOXKlSMiuzayLR977DHpCy64QJprHeOU+N5sH/YD4xoYHXPyySdL837y9Ut61rFjx/7kWkR2LeUzsX+++uor6TQmIrLj2BhjjDHGmI0Bn4A1xhhjjDHGGGOMMcaYIrHeTsDyFBFPt/FEBk9w8KQrT4PyNFQ6PdquXTtdO+6446QHDhwoXVpaKs2TjizCUrduXekzzzxTesSIEdI8XccTXjztyRMfd955p/Q111zzk/vp0aOHrl1++eXSLJbC4hc89cc25QlfnuotJDzx+O2330rzlApPffKUUr4TqKlACE9c3n777Tlfy5N5PL3F0218PYtq9e3bV/qcc86R5lh46KGHpHnyjCdy+vXrJ51Oz5500km6xqJGu+++e+SC/cZiXzwBWMwiXDyh+fDDD0s3bdpUesaMGdLdu3eXZlEUzrvU1m3atNG10aNHS/O0Fz+fJw15ApUnu2666Sbpbt26Sf/pT3+S5ilxFmtj4aJUICYiokuXLtKpsBrHIMcO35tFoHgCk2sa4anKQsLCNzztyJPWU6ZMkeYJfZ7C69Onj/R1110XEdm257zhOOcJXBZV4rpAeKqR6yPXPK75bDeefFuwYIE0i/KkfuF6yrnasWNHaZ4C5TxjMT+e2OSp3kLCPuTJ0cMOO0ya843OAZ4Y5EnnoUOHRkR2/eIc5ylN9huLurHtefqcp5i5z/bv31+apy15CpR9yP2Jcy6NhUGDBukai1M1aNBAmiez2T883c325feMQpJOq0ZETJw4UZprxpw5c6S5P7HAFdfBtPYOGTJE19ifZ599tjTnONdVrlNdu3aV5v7EtfTEE0+U7ty5s/Qtt9wi3b59e2megOY4SgW3LrnkEl3jGsLvLxzzHB/sN76e33GMMcYYY4zZGPAJWGOMMcYYY4wxxhhjjCkS/gHWGGOMMcYYY4wxxhhjisQmxbLi/RyTJ0/WB7OQCu3YtH3SYk57L4uPpKIgLDzDYiJHHnmkNAt10G5J+yIt4LSdUxMWtqFVjnZfxgfwOZJdkJZJWrBpjeV71KlTR3r58uXStFrTzldSUlKw6iOjRo1SH7IIFp+dhU9YdIwFlvg8yaafbOAR2fFBqyufl0WdaGWsUKGCNPtt0aJF0qkAWkS2IE+yT0Zk+4q2Whb5SX1IOybjEFichH3Lsc0xRAsp/7Z9+/YFrSAzYcKEnP1IWznHEO/rrbfekqY9OcVT0KbMv+N4pvWWFmQWjmE/sr1on+dYYzwGLay0CC9btkya4yc9K9cIFraibZsW+ypVqkgzgoSRHLTNtm3btmD9OGbMGPUh1zxqRi7wXvO9JsVHjBs3TtcqVaokzTWGr6EFnuOJ6ywLSzFShn217777SrNYD6MGaF/ncxx00EERkS0sxDnMNXnmzJnStISz/zleuY506tSpYH04depU9SHXsHzrGeck1w2+Ju2nXGO5r3Fd4fNyzHJP3n777aU//PBD6TfffDPnffFeuLcyroNRQ/ysFKvAPub7EbYR14d832+4Lrds2bJgfThy5Eh9ID87XxE/xkiweCgjOlJbcT3k/OGawrgItgP3ZK5l7H/up+wTRmDw3vMVGeM+n+A6wP5kRA33Ut4j1wTOQ6697dq1K05VNbPRcdr5A7ePiE9uGDwgypff6mdf/3+Z77//PpYuXRo1atRwUbv1jPtiw8F9sWHh/thwcF/8ctau/SrOv2xIRESFYTdcuebH/+4TsMYYY4wxxhhjjDHGGFMk/AOsMcYYY4wxxhhjjDHGFInNfv4lxYF2YVoZaTdbsmRJzuu02TFWYPbs2RGRrTZP2+PkyZOlaUelBZ22Wtrdhg0bJk0LLK19W2+9tTRjEGi3p321pKREOlmpWR1+u+22k6bFkzZNVjembZE2eFoRC0mKfPjx59EOSxs/LYu0F9Puetlll0VE1ppI2/h7770nzbaifZYVwdk/rHZOCyyrxjP6gO/Dys6sNM3IgkceeSQishZcvse8efOkWcGb9k2Oc1qqaRUtNHxvWk9pm2UMBGMyaDflaxhNkGCkCO2mXAvuueceac5tjvlp06ZJcwzyM3mPXAM412nd5funccJK5G+//bY05xznFu37tDjTfssYhkLCZ3nuueekafvPZ4Nmm3Au/vGPf4yIdXb+iGwf09bONuacr127tjTnAiML8tmKOddpMWesAPu5Xr160g899FBEZK3aXIdffPFFaVaUnz9/vjTXKM4L9m0hYbzD6tWrpdkm3BNpx+feU716demrr746IrLRCpwntL3XqlVLmv3MfYVjgXsSxxbnOa3k3OfXrl0rzX2Olvy053J8bLXVOkvxppuu++/HXMM4/vh+jCXhMxUSxtNwnSKMJ+EYZ9sy6uHWW2+NiIiGDRvqGtuY78HYGK6N7E+uCdzvCL9DzZkzJ+f7cD/nWsn9NEX9VKtWTde41k6cOFGa6xDbiBEI/N7G9zHGGGOMMWZjwCdgjTHGGGOMMcYYY4wxpkj4B1hjjDHGGGOMMcYYY4wpEustgoBWfFrWaDc8/vjjpSdMmCBN++ghhxwinaxqrBZcVlYmfcEFF0jTujp+/Hjpu+++WzpZcCOyVj1afPv16yd9ySWXSJ9wwgnSQ4YMkab1ktbXZHelJfTOO+/M+d60Fs6aNUuaNmBaQln1upDQXkprIO39rLae7IgREffee6/09ddfL33GGWdERDZm4r777pPu3bu3NJ8rRThEZC22559/vjQjHxYvXiw9adIkafY5bb0cf/lsmEOHDo2IiD//+c+69thjj0mzv2mZXbZsmTTHf9WqVaU5bgoN7ayMcqAN9eCDD5ZOUQsRETNmzJA++eSTpevUqRMRWRvu3LlzpQcOHChN6ymvs99HjRolzZgQVrB/8sknpbt37y69zz77SCdrekTE0qVLpTkekxWY/841pUePHtLsL9rXuaa1atVKmlEdhYTzjzZt2uVp2V2xYoU0261p06bSV111VURkn52W4Z49e0rXrVtXmhEN1157rTTjJdiGtNVzLWBMAeMLuP6zzTm/kg2fsQPsz3POOUeaMSV8b8aOcJ4z1qCQMHKEayX3Qa7rjz/+uDTbnBEEl19+eUREjBkzRtcYBdCxY8ecf1daWirNtYcRFYwf+SX749577y1NWzvbnNEzXbt2jYisxZ7r86BBg6S5nnJt53zjey9fvjyKwbvvvivNmJWTTjpJmnZ9ro+ch5eGuheFAAAgAElEQVReeql02k+51l5zzTXSXCc5htgnXONTzE9EROfOnaU5V0aMGCF95ZVXSh999NHSKRohIhsZwDW2SZMmERFx22236dpLL72U8zk4rxipwvWBMQ3FjOUxxhhjjDGmGPgErDHGGGOMMcYYY4wxxhQJ/wBrjDHGGGOMMcYYY4wxRWK9RRCw8vGzzz4rTRsz7Xm05NHW+OCDD0ona3L79u117cYbb5Rm1W5a3C+88EJpWlMZAUCr8emnny7NCueMBpg+fbr0LbfcIt2tWzdp2imTxZWVvGm9pE2d7UVLIO+Xz8p7LyS06NMyy4rHtICeeuqp0ueee6708OHDpZOtlX04ePBgaVq7aVnks/fp00c6XyVqWq3vuOMOaVbKpq13wIAB0ry3sWPHSifb/VlnnaVrtNpyzL388svSxxxzjDQt1XwNK5QXGlbcpm2W1n0+B+dihw4dpGmJThZSzpWRI0dK04a6cOFCaVpfW7RoIc3+pR2dFupkmY/I2u1ZFZ3zm3b322+/XTqN63bt2ukaLbZPP/20NGMaWrZsKc2YBvYpLbSFpHLlytJ/+tOfpC+++GJpRnN06tRJmmsk15lkDU+xIBHZ9mPUCMcHLfOML0h2+IispZ9xBJyLn376qTQr3h977LHStMrPnj1bevXq1T+5d8Yn0G7NNYVjm3Ob45V9XkgYi/PXv/5V+pNPPpF+4YUXpLnOMF6HsRxprnDPZCRG+fLlpWmfpzWdax/jLbj+ci+jxX7LLbfM+UyMo2Bcz7Bhw6Tr168fERFHHXWUrp133nnS3B8Zv8P1lN8PHn30Uem0VhcaRjS8+eab0ozIYcxQ//79pTm37rrrLuly5cpFRESXLl107eabb5ZmRATb5P7775dmFA8jC/bff/+c98K5yrHPNYRrO9dSRhN8/PHHP3k/ruu8X46tBg0a5HwNxygjLYwxxhhjjNkY8AlYY4wxxhhjjDHGGGOMKRL+AdYYY4wxxhhjjDHGGGOKxHqLIPj222+lGzZsKE2r3h577CFNm/Jhhx0mTdsvbbCJK664Qvrrr7+W3nbbbaVpKa5Zs6Y0Lci0wfFe+vbtK82K96wKzvenbY5W2mThZ4wBqzbTGs5IAVacrl27tjTbjrb6QkLb58EHHyxNmyKrdrOaMe3tvXr1kk6WZf4d/502XVZyp12Yz057M23mVatWlWYExeTJk6UPPfRQadowaXFllEEau7z3vfbaS5qWXVZwZtQB753RAJwvhYb2bkZgsB8ZGcKK2m3btpXm+Js0aVJEZO22rATOZz7kkEOkaVnnusC/ZZXtr776Svrqq6+W5hzlvdOSXFJSIt2sWTPpZGV/5513dK1nz57StG1vs8020pyjHDt8fbH6kZZ6WnwZf0CrPZ+N6wnnWlpPOT5oZR86dKh0sotHRHz55ZfSjEhp3rx5zs9npATXU1aF5z0yyuOII46Qpm0+RVlsscUWusb4h7lz50pzjjKyhJ/Jef7RRx9FMWD/0ILNtuL+wKgBRlDQBp/WIkY10NJfVlYmzWgF9iHHU+vWraUZF7JgwQJpxjjki9/gPNxll11yvv/y5csjIrvP0JrPNYT7KtuLawjXUz5fIWEfci3lONx5552labvn9wGuR6nvuI4xqol70w477CA9ZcoUaUbYNGrUKOffMmaFMTtr1qyR5nr7+uuvS/M7Ged8Wqu5lzCi5KmnnpLmmGe0E79LcQ/l3mKMMcYYY8zGgE/AGmOMMcYYY4wxxhhjTJHwD7DGGGOMMcYYY4wxxhhTJNZbBAGtu7Th0W7G6uW08dLizQrNyQY/fvz4nK+lDZBRB7SuslLz6NGjpRlfwMrvrEpNOzbtkbQdsyo0bdLJokcbIKuA07LOe0k2zYislfbll1+WZqxCIWEcAG3EtNHTJrty5UrpChUqSF900UXSyUpdqVIlXeOz0A5NOyRtnRw3Q4YMkWYbsko9Lc20QdIOT/ss2//xxx+XTn23+eab61q+iuCpwnxEdlzS1slnpcWz0Pzwww/SjBdgTATvixXM2e+Mb0j2UFqQaXflmORraGVnTMjdd98tTbv1G2+8Ic0IEL4Pq5Hvtttu0pzrd955p3SqKk5b87Jly6SrVKki/f7770vT+s2+o5123333jWLAMUd7dY0aNXLeB9cttsnUqVOlUx+yHViFnBEtjAv45ptvpGn75pyj7ZwWc1rZGU3BtbJJkybSXMcXL14sndaGGTNm6Br3GbYLo1G23npr6VTBPSJi3rx50oxbKSQVK1bMeZ17H9uBkQFckzhWk3180KBBusYID9reOX+4VnF95N9yPL399tvSKcIjImt9Zxty/nNMcc9NcQuM9mDUDPth1qxZ0owx4H7KPWe//faLYsAxxuif7777TpqxQdy3uN/zO0zqQ66fXHe4Z91xxx3SN9xwgzTnCdf4pUuXSjMmgHOYfcj+51zhOswIiDR29t9/f11jdMCOO+4ozTHEtZ/jj3EI3JONMcYYY4zZGPAJWGOMMcYYY4wxxhhjjCkS/gHWGGOMMcYYY4wxxhhjisR6iyCgTfaTTz6RZlVm2nX5GlrlaM1O1alpa6NN/cMPP5S+5JJLpGlxpw2QkQK0W1LT6ky7I22QtOGy6i+tzMluy3ah1ZpWb1rp88UO0J5arMrrtEHyvmkpp12YFsP33ntPumPHjtLJ/k1LI6unT5o0SZr2WX4+LZC0MbOqOSMACO2OHFu0+DJeonHjxtIpeoH3zngBVshu06aNNO3q7Fs+E22jhYb22HLlykkzdoB2T1qGGTHBeZfmDt+btm/20QknnCC9evVqac5djnPCtmb0Be+d7892pFWalcGT/ZpjlNXnaV/+9a9/Lc31hX3NtuMzFRLa5Wllp/WYtm9Gs3A9YxX12rVrR0TWpszoAK7VjGVhpfI5c+ZI08pMKzHXc1qVaXc+44wzct47+4jPV61atYjIxkywj+fOnSvNivPsT67nnJcc04UkrX0R2ViQVatWSffu3VuaewnnASMl6tSpExER9957r65xb1q0aJH04YcfLs11i2OLaxXnJKMjGB/D5zjqqKOkuc5wLT7ggAOkU8QN+4FrMmMSOOa5LzHqiPstx2gh4f5BGz/XNa5H7Dfuj2nuRUTUq1cvIrJxN4wdYOTOzTffLM2253pUtWpV6TQ+fnzvXONGjhwp3bdvX2mOS34W50qaz4xdYB9z7UlzNiLbt/wcwogHY4wxxhhjNgZ8AtYYY4wxxhhjjDHGGGOKhH+ANcYYY4wxxhhjjDHGmCKx3iIIaA2kHZWW9ZUrV0rTHknrK+3rybJ64okn5ny/k08+WZoVn2kRZqVkWt9YYbtPnz7SAwcOlO7UqZM0IwNYxZlWRFoyL7jggoiIuPjii3WNldmPO+44adpDCSuh77nnntK0BxYSWr6feOIJ6X79+knfdddd0rQ70nacLJYREffff39ERBx99NG6dtppp0mzzVhVm1EQtKDSOk67Iz+T9lxWdua47N69u/Ttt98uzXiEadOmRUTEtddeq2unnnqqNOMKaJmlxZdVtGkH530VGo6nFStWSLONaCHdddddc95X//79pZMV9sILL9S1NMYjsjbop59+Wpr9yDHCec576dy5szTHHccan4n2V1rZaWdN98PPZFVyVl9nNAqt37x3viZflMJ/yj777CPNaIWGDRtKM0qA7TN9+nRp2qOffPLJiMj2yYQJE6TbtWsnTRs024frfNOmTaWHDBkizfX0zDPPlOaat2zZMmmu6YQxCOmZtttuO13jPGccAm34jL1hdAvHCm34hYRzn3sf97PRo0dLcy1k33KdGTduXERk5xvn5BFHHCHNeA5a4Dk/eS8TJ06UPuWUU6Svv/56aUY6cD+tXr26NK36HMfpmU4//XRdO+aYY6Q5ZzkWGW/AvmIf8jWFZOedd5ZmvADHJiNsGMvCuAxGlaQ9cfDgwbrGOcD588ILL0gzEibFM0Vkrf78HsTvL5yH11xzjTT7nOOPY+Skk06STvfcq1evnPfL2JC//OUv0uwfRjgx3qB8+fJhjDHGGGPMxoRPwBpjjDHGGGOMMcYYY0yR8A+wxhhjjDHGGGOMMcYYUyQ2oS3vv8nDDz+sD6ZVjxEArLZ+4IEHStNmR8tiqlS/fPlyXWNlaVYO5nXa9Rk1QJs4rams+Ex7P2MSHnzwQen27dvn/Nvdd99deu+9946IiFGjRukaqxXnq7ZOG96bb74pTbsvX9O0adN12Qj/IVOmTFEf8rNpuf7ss8+kS0pKpF9++WVpVhVPVdZnzpypa7QjduvWTZpRA3xGWorZxrRksp8ZF8FK2azOzUgEWrw5dtJnzZ8/P3LBCt7PPvusNK3+tKfSrp4qgkdEtGvXrmB9GBFx3333qR85d/JFDdAy/Prrr0uzmnyykzIugBZnjgVeZ2QE24i29r///e/SHHecI7TN02LPOUV7+nvvvSedxtKXX36Z87Xsl9LSUuk0diOyUSMcg4yV6NSpU8H6cdKkSepDjiH2G6Mu0noTkbWv00Kd+n/OnDm6xnZgdAXt84x54DrLNubfMl6A1c/zzUVWYmfleM7pdJ8cH7R7MxqB7UK7M9clXue9d+7cuWB9OG7cOPUh42QI4zS4d3Ou0qa92Wb/TBniHstoFY4P2t4ZQcC/5fjgvbAfGC/w6aef5nwfWs+5z3ItTm3wxRdf6NpTTz0lzVgFjvnKlStLM0aCc4/rabNmzQrWh1OnTlWncA2gTn0Ske1DtgPjFVK0Cb8bcA1mnAavc45zr+TY52tmzJghzXih3/72t9JcK7j3vfLKK9Jcq9OcZ/wPxxy/77Gv8kUHce1lO7Zt27age6LZeDnt/IHbR8QnNwweEOXLb/Wzr/+/zPfffx9Lly6NGjVqFC2Wxfwy3BcbDu6LDQv3x4aD++KXs3btV3H+ZUMiIioMu+HKNT/+d5+ANcYYY4wxxhhjjDHGmCLhH2CNMcYYY4wxxhhjjDGmSGz28y8pDrT0s0JvihGIyFrZaZvMZwdNtnXam2l7nDx5sjRte7QH7rffftK0LN5xxx3StO2xci+ttLTqMo6AUQq0HCZrJS3SvC+2Ba28tOHVrVtXmnbSYh0Tp5WQdm1aUPns8+bNk6b18KCDDpK+++67IyIbLcFnfPzxx6VZNZrPzr9lP4wZM0aacRGMDKAFv1GjRtIPPfRQzvenPfPOO++MiGzUBeMiGI3Acf7SSy9Jc0y888470rS3Fxr2Ba2fjACh7Z/2XbYvbcDpWWk15jznvKF9lc/P+cd7HDFihDSri3M8cqwR2plpj+dzP/LIIxERsf/+++sabersF/Yjn4ljlpXYqQsJ73/JkiXS7B+ORfYh/5ZtWFZWFhHZPuGYoKZlnnEBnGdctzkXOf85RrimEM5FQuv5lClTIiJrpeY44xpKyzRfwz2K+wzHYiFhdXpGt6xcuVKa8TuHHHKINCMAeH/Jts71kfsKIxr4vKn9IrL9Rgs45ztjJxhLwjHHvuV44XuyL9Key3gLRlQwcoLPxNgDrs+03jM6qJCw7RcuXCjN9YD9SRs/+5xz8uabb46IbMRKvhgWrmmMI+Dc4D2mPevH15988klprpmMCfjb3/4mzXgdxiekuB6u5fx3rjeMEeJn8jX5+tYYY4wxxpiNAZ+ANcYYY4wxxhhjjDHGmCLhH2CNMcYYY4wxxhhjjDGmSKy3CIJ89vqWLVtK067/8MMPS9PKePLJJ0un+IDhw4frGu3drJ5Niz4//89//rP0n/70J+lBgwZJJ5t8RNY21759e2naZ2nnHDt2rPQFF1wgnWINaCecNGmS9EUXXSRNCzytiLRDs7pysSIIWJ2YVlPaLdkm7HPaMOvVqyd9xhlnRES2wjZjDLp16ybdrFkz6QULFkg/99xz0kOGDJH+3e9+J52skRFZy2yfPn2kGSVw1113Saeoi4istTL10YQJE3Tt+eeflz766KOlafd9+umnpVkFvGHDhtL57NiFgPZqtnvTpk2laUMeOXKkNG3IJ510knR61meffVbXnnjiCek//OEP0gMGDJA+8cQTpRkf0qlTJ+kbb7xR+pZbbpF+6623pDk2mjdvLs25Swttv379fvIc99xzj67Rsn7uuedKV6lSRZqvZ5vSKkubcSHh/a1atUqatuIjjzxSevz48dKMLLjhhhuk01ikBfiZZ56RPv7446VpTeZ4HjdunPTvf/97afY/IwWGDRsmfdNNN0lz/JeWlkoz9oHjItm1Of8YX3LttddK09ZOSzZjGhgBwrW1kDDmYuLEidIcy5wHjDThnsh147jjjouI7HPR/s+YDa7btK9zfLRr1066R48e0pznc+bMkWafMwJg9OjR0o8++qg0YxWuu+66iMjO8blz50o3adJEmmOb3xXef/99acZxMNagkPC7BK32fK7ddttNmnsP117OrWTv53eTzz//XJrxD4zz4J718ssvS1999dXSjFxiv3F+XnzxxdKtWrWS7tq1qzTXH+6hV1xxRUREPPDAA7rGPuSezO8BXEsZvcD1lmPUGGOMMcaYjQGfgDXGGGOMMcYYY4wxxpgi4R9gjTHGGGOMMcYYY4wxpkistwgCVuWlTZXVy2nnozWZVtPBgwdLJ8sfowNYnZfWS9rXzj//fGnaYWlfp9X81FNPle7SpYs0rc7Tp0/P+T7du3eXnjx5snSKMjj22GN17bbbbpNmW9Du26ZNG2laL2mvbtSoURQD2gFpS991112laQGmfTZV547IRiekuIT+/fvr2jXXXCP93XffSdPSfs4550gn62pE1tJMe3+vXr2kaY+kpZlWblpp2f+MuEgW2zPPPFPXjjrqKGnaYdmHRxxxhDSrT9PKXb169SgWKf4iIjtu1qxZk/O+Bg4cKH3hhRdK09ZeqVKliMj2+dSpU6UZ48DK7uw7tiPnAuMQGHHBiBGuF7Ssc72gVbusrEw6VdrmvOU857igtZe24UceeUSa1dpZjb6QMK6F6yPXJI4nPhvH3x//+EfpNI/5WrYfxwfncIoRichGxDAahlXeaVnm59MezbHD/YK2Za5BaUz37t1b12jrZ3QG7dO0Z993333S3C+K1Ye0pvOeGL+wbNky6aFDh0offPDB0lwjd9ppp4iIGDFihK7VqVNHmpZyxvLQgk6b+MKFC6UZQdOzZ09pRgZwPWG8TrKmR2T7n1EJaUxzbee6QRhBwzWHkT+kWDESe+65pzTHUv369aUZHcHxefnll0uzv1L8Bfegww8/XHr33XeXnj9/vjS/B3Xo0CHn57zzzjvSjPyg5ne1+++/X5pxJIzXGTNmjPSmm/7zv/GzvxnRw/bi+GAUCtcf7s9c84wxxhhjjNkY8AlYY4wxxhhjjDHGGGOMKRL+AdYYY4wxxhhjjDHGGGOKxHqLIGDFX1ZiTpa1iGzVYlrwk705IuKss86STvY4Vm1u3bq19Ntvvy39xRdfSDMKgHbLli1bSjNSgKQq0xFZSyarET/33HPStOrRKjl79uyIiFi5cmXOe6EllRbspUuXStOOS4svn7uQsPI2reApRiAi21e0btOaT3tmqqbN8cEK13xeWm1pTWXld0ZQ0OpO6yWreTMagFXBae3k/TRt2lQ6jSNWved707JNOBZZ8blatWo/+7eF4MUXX5SmrZNtUaNGDWnaoPn8bdu2lU5zkdZ0WrfXrl0rTTv4Dz/8IM05RAstoydYWf28886T5pxu0aKF9LRp06Q5X2ibTxZZxguw+jvjGPj8HN8cg2zTzz77LIoB17yOHTtKv/baa9J77LGHNKMzGNfAau2pXzgXGzRoIM11hfb5v/71r9JcQ/m3b731ljTHP9tz1qxZ0nvttZc0q7vz3lmtfcCAAfFjaHW/9957pWlxZxQFI1a4jnEtLiQcG02aNJFmW2277bbSjJzZZ599pPmcKX6DY3nQoEHSnOPUM2bMkGZUB9f5KVOm5Lyv008/XZpjhHOCUQbc5xilkKJh+PxcYxgXxIgWxgswfodrK/eOQsKxwbH/yiuvSHN/4L2ybRs3biyd4jc+//xzXWObMbaB6zTjnBgjwLiItN9GrItwishGAHG93XHHHaUfe+wxabZz1apVpdM688Ybb+S8d+7b7EPGidSsWVOa6wD3cGOMMcYYYzYGfALWGGOMMcYYY4wxxhhjioR/gDXGGGOMMcYYY4wxxpgisd4iCGhZZWXbnXfeWZo2SFqZN998c+nrr79eOlmjt9hiC11j5XlW+2ZVbdrzaKWkxZH3yMrOH3/8sXT58uWlae2jTXrOnDnStCUmax8tdu+++640K4LzvWnZ5nPTBpyqmRcaRiGsXr1amnbYuXPn5nw9q4qzL9Iz0/7PCAdalFmFmTEWfG/aM2mfZF/RUn/ooYdKM3Yi33VavJNVlvfy6KOP5rx32tVZCZo2ZFqwWfW60KQq2xERy5cvl2bsB+9ryy23lKa1mfM1xQcsXrxY1zhXaCullZoWZMZNsLI2rae0u9I+zv6ihZYxCLQCn3vuudJbb711RGTHKz8z33vTDs/24trBWIlCwvWU82LfffeVZvV1WrDZh7zvZKfeZJNNcv4718rnn39eeocddpBmuzGihevTp59+Ks150bx5c+l8sQP8W8bRpHHMGAuut+zD8ePHS7OiPKMj2EZc3woJ2znfeGO0xlZbbSXNscr5lJ6Zz8J24N7EKvSMsOF+wziAfBEwjEvh3so+ZEwB74eflfZ8jgm+tqSkRJprOPccrlWcF4wyKCScE4zi4b7O/uE+99VXX0mn6IiIdXMr37rLNY1RORwT/A7w9NNPS7/++uvSXEMYX9C1a1fpFM8Ske1nxogwyuLvf/97RGT3Eq7TjPPhPs9nYiwT5zvXb2OMMcYYYzYGfALWGGOMMcYYY4wxxhhjisR6OwFrjDHGGGOM2XApadzqtxHxXkQcUVo2c9YveH3HiLgsIqpFxCcRMTEiBpSWzfz03/6hMcYYY8z/OOvtB9hkTYvIWil5ndVyacemDa1OnTrSRx11VERkLW7JThyRrXzNStGs/kvrHSuC8zNpFeW9MzLgsMMOk6bNkLa9Vq1aSSdrJaso0x5K6yetubQEb7rpugPNtGbT+l1IGDvANmHF4y5dukjTUk4LMqtTp5gI3vNOO+0kzeusbs8+pGWRFmT2A+2hFStWlGZV6n79+knTQsnPYmX59D4LFiz4yfNEZK3+tFjSTvrRRx9J047Pey80rFROWzEjLQ444ABp2k0Z5cCIhVSNm33BMUmb8JAhQ6Q5zjlGaA0/+uijpWlbpf33xRdflG7Tpo30l19+Kf3qq69Kd+/eXTqNa45jriOs/t2jRw9pjhGOWdqPizUX2SesNk8rO+MPaHfmGkJSfAGt6RzDXKu4VvJeOLc4hhgNw3WT6wXt9mznKlWqSE+ZMkWaVv1koaeVm33Ccd66dWtp9s/ChQuluZ5zjSgk3Bu4JnIc5luT2J60kqdxm2+Oc76dfPLJ0ql6fUTE2rVrpRnjwpiCFStWSDMiiGOHERFc59jmtWvXlk5RP9xnOIZ4jxwfHJcc5/Xr15dmpFEh4RrEdmCMAuMXXnjhBWnO1SVLlki3b98+IrL7J78bsG979+4tzWfkGOL4YLQRo3V4nTE7HCMcozfffLM097a0Fk2bNk3XGIvCsdWhQwdp9jnHHDXjl0zRGfBLX1jSuFXniJgQES9HxOCI2Cki+kVEnZLGrQ4vLZv53b/7e2OMMcaY/2V8AtYYY4wxxhgTEREljVsdGBFnR8ThEbHvz7w8/c1mEfGHiFgVEQ1Ky2Z+9q/riyJiXEScGBEji3G/xhhjjDEbA+vtB9hf/epX0rNnz5ZmsSGekuNJRp4m4WmoVMCDJ0TbtWsn3bNnT2meruNpD54O4unWDz/8ULphw4bSo0aNkubpHN4jizLxFCBPDKYCOSzwwsI2PL3DkzXVq1eXZgEOnjzkKc1CUqlSJWmexuLz8nQbX89iZJ06dZIeMWJERER069ZN1yZNmiTNE1s8lchTNfxMnrpiXzVu3Fiap+h4qoftzM/Kd2pn9OjREZHtVxYB44kxnorce++9pXmKmSd8OBYLDecQCzXxNFe+wlc8vctTpKlYC080XnfdddLNmjWTZgEdPidPZvLU69133y19zjnnSJ9yyinSPD3MAkG8zmJRPO2bxmaTJk10jSfjW7ZsKc3TZzxtx0J9LIDDcVRIOFZZWKdBgwY574lrAk8Cs1hTKh7G9XTo0KHSnCssjsSicrwXrmE84XbSSSdJX3vttdJsZ54OpUuC60Hnzp2l0+lVrtVXXXWVNE9McgzTUcFiQpyv3CMKCccj1zDOFa5VPI3MduCJ3rTPsU9YBIonl3lyn6cYeXqR44nv07dvX2nOVZ5u5z7PucXrHH8TJ06MiGyxLT4/1xueemXxKc4LFm36/PPPo9iw2BXvdeTIkdLsK17nd5V0invgwIE5/44nv7mvcizTtcE+vOWWW6QHDFh3yJGndLnHcR/g+kwnBPeH4cOHR0TE7bffnvNzuJdyTeA+yOucI+xnUxQqRsTe8c/ogR8iYr9///KIiDgkIvaIiCvSj6//YlJEfBkRx4R/gDXGGGPM/2FchMsYY4wxxhgTERGlZTNnl5bNLCktm1kSEdf+3Ov/Rfp1fwEvlpbN/CYiFkdEzcLdoTHGGGPMxocjCIwxxhhjjDH/CemY+aoc//a3+OcJ2f8K33//febkt/kpqX3cTusf98WGg/tiw8L9seHgvvjl/FwbbVKsgiI/x5QpU/TBtMZ+8MEH0rRYVq5cWZqWWRbHShZ3Fv6g1ZD2YxaKYVEKFpygxZaRCbT304JMqy6t3LQI8vV8z6RZTIVWW1rsafdNxY4ishZo2spZlKdevXrrqmX9hzz44IPqQxZS4ZiiZZF2VxYIYUGWZEmkzZfxDxwH7MN69epJT506VZq2f9pwGRHBPui1IGAAACAASURBVGHRLBZ54d8yvoK21gQtzSyWQpsmx22+4kC0rtMOfeyxxxasDyMiJkyYoA7j+OM90gbKeAhan0myntMmyvHMImiEERwssMVxzr9lsZp8hdvYBxyDy5cvl2bcQrLWchznK7zHdtluu+2kuY5xXeAcbdmyZcH6cfr06bpZzgvahDnPuJ6wnWk9T3OR9m5uKGwTas4Vzl3Cz3/qqaekaTFmBAc1Lc6MaWEcS7rPJ554QtfyRYDwHrlucu2qWXPdwS2uXW3atClYH5aWlqoP2W9sW/btN998I819jmtSWvtZjIzzhHsi34/Py/WMaxhjO7gncS6xPbnP16pVS5rFzlhkKa2zHBN8b8ZSbLnlltLcz7me5htPLVq0KFgfjhkzRn3IccV5z+ehBZ/rGp8zRSQxXoDvx+ijZ555RppRQGwfjifG3zz00EPSnCvUjItp1KiRNL9zsWBimiuPPvqorjFOKl8sBOc4CypyLWc/F7IPzU8padyqZ0TcExFHlJbNnPVvXjc8In4XEXuXls1c+aN/GxURx5eWzcy9+f/zNX0iok++f/8llCtXrly1OnVr9ezaPrbYYvOf/wNjjDHGmALyzTffxshxUyIiKgy74co1P/53n4A1xhhjjDHG/Cek/4rzqxz/tmVEfJTjuigtm3lnRNz5717zc5x2/sDtI+KT6tWrR/nyxcvO/1/g+++/j+XLl0f16tXz/kdx89/BfbHh4L7YsHB/bDi4L345a9d+FRFT8v67f4A1xhhjjDHG/Cek6oE75/i330bEX3NcLwrlypXz/0H8hbitNhzcFxsO7osNC/fHhoP74uf5ufZZbz/A0taWqm3/GFrPWBWX1n1aHO+6666IyNrqaKmmxY3VyGmNpNWZluJx48ZJMxqBtk1a1mmVo037jTfekKaFL1mpaRWk3Zea98V733nndd95aa/mMxUS2stpU6bFlQNw/vz50nwGxgqkStx8Xlaypi2cNm/GAtAay8/ha2iZpA2S/cPXU9NCSjv6vHnzIiJr06XVldZMRgrQVsxxyegItkGhyRevwM+kVXbs2LHSyR4bkX3u1DfsR0YQsA1p1WW8By2mHEe02bJN+RreC+3Rn3zyiTT7jpXBH3vssYjIrjMc37QH8/qXX34pTQs3n2+zzYqz5HJ95HpKmzZfkyIiIrL9zGdO6y//nfOG45PP+MILL0hXq1ZNmvNsxowZ0hzz7B/GwRx22GHS06dPl2b0CNfiFAFTsWJFXWOkAMcK7f4rVqyQ5thmP3OdLSSMvGG0xbvvvivNyA3eB9uB0SFpPWUbs524P7IduH9wLtWpU0f6kUcekW7fvr005yf3Qfb53LlzpRkHwWiKFJnBvuLeQis924tjkW3RsGHDKDZcGzgPuQ4uXrxYmt9fGJfAuXXllVdGRPY7E/c49hvXILZDvnnAucTIAu6DjOjh95NJkyZJN2nSRJrfd6ZM+ed//d9jjz10jeM5X99yHvL7Ftcn7qFmg+HZf/3vIRExO10sadxqs/hnAa6R6+GejDHGGGM2GDb9+ZcYY4wxxhhjTF5KI+KDiOhd0rjV1rh+SkRsGxEP5fojY4wxxpj/KziCwBhjjDHGGPOLKWncqnlEVI+IiaVlM98tLZv5dUnjVudFxP0RMa+kcauxEbFXRPSOiAmlZTPn/pu3M8YYY4z5n2e9/QBLuxmteqzK3LhxY2lWAH766ael+/btKz1o0KCIiJg4caKu0fpXv379nJ8zYsQI6SeffFK6e/fu0ieddJL0hAkTpGnl7dixozTthNOmTZOmhbdTp07Sye5699136xptr71795amPZAVzHkvfD7a9gsJrY+0gLICPWMKGJeQzxqd2nDWrHWFdmlHbNGihTRtmPfff780LZYXXnihNO2O/PyysjLpfv36SbMS88iRI6VZBZ0VqE844YSIWBdFEJG19NNee+CBB+a8d9r0+d4cx4WGNuRVq1ZJH3744dKsWs75xfHKMZr68fHHH9c12pq7dOkiTftqsv9HrLOvRkTceOON0owdoK2ZFbqPPPJI6UqVKkk/++yz0s8//7z0GWecIZ0qsdN6S1ttq1atpGn9fvDBB6U552j5ZpX3QsKICFr3W7duLZ0s3T9+DSuq0x5ct27diMi2A+Myjj32WGla3EtLS6Vnz5YLNfr37y/NtZhtdfvtt0sfffTR0oyjoMWda2u634h1ewfnIteFZs2aSfP5+RrCMcT1qpBw7nMfYvwC74NrC+cZ52rXrl0jImLMmDG6xjnAPZY2cfYb13nGJCRrfETEww8/LM19iPdy/PHHSzO+gPt/7dq1pdN6yQgRjq1rr70252dyHWAEDOfwrrvuGsWA+x3t+ozf4Nh/4IEHpNm2HPvpGbh+cu3q0aOHNPeM6667TprRHuedd54013iOi5dffln6lFNOkWZczdChQ6WHDx8u3a1bN+mLLrooIrLxTHfccYf0vffeK83vNdyfGTXBCIJizUPz/4nuEdEjIpbGv/JfS8tmji5p3OrbiLgkIgZFxOqIGBoRl6+vmzTGGGOM2VDwCVhjjDHGGGPMTygtmzkycuS3lpbN7BkRPXNcHx8R44t8W8YYY4wxGx0+QmCMMcYYY4wxxhhjjDFFYr2dgKUde9SoUdKsskzb2mWXXSbdq1cvadoXk8W2T58+ukYrMiuQL126VJoWu9tuu02aNr9U1T0ionPnztJTp07N+Z6s/n3OOedIn3XWWdLz58+XTta+c889V9cYUUCbPKuSH3fccdKjR4+Wpt2/WFW7+b5sH1rEaW+nlXbYsGHStGomG+bJJ5+sa2effbY0Kx9zfLCNaY1ltW2OuRQXEBHx0EPr6kIwIoIxEpdccon0FVdcIU3ra4pEoNWWY4V2XFpwk004Ihu1QRsmYx0KTeXKlaVpN2dMAm30fL5c8y8i4tVXX42I7Bi+6aabpGlT5TzgfOXYYT+yX4YMGSLNau2MNWAfpZiSiIjrr79emrbYZLNv06aNrtE+f8wxx+S8d1qBGZPCSuyskl5IGAHA9vn888+laXHnmHv00UelGXWR1pyePXvq2j333CO9ZMkSaY6PE088UZrtw7gUrh20g3PNZQQMr19++TonK6MsGLvywgsvRETWys25xdgBjjNa8jnmFi5cKH3IIYdEMaAFnfOQa8iCBQukGYvD/ZE2+BdffDEisvONVnO2A9dTxktwf07vF5GNazjiiCOkGdfCtmWUAeMDaGXn69P7N2rUSNcYjbBo0SJpxjFwn1m8eLE0I2u45xQSRmWwT7hnMDbjqquukh43bpw0+yL1+Zlnnqlr3D8YJ8LYoltvvVW6Q4cO0owy4FjhOGM0QFrLI7LtPGDAAOkLLrhA+sMPP/yJZuQH91u2BfchRqfMnDlTmvES3E+NMcYYY4zZGPAJWGOMMcYYY4wxxhhjjCkS/gHWGGOMMcYYY4wxxhhjisR6iyCgZZ32RVrnadedPn26NC2gtAknyyxtjLTosyIyrXSswk2740EHHSTN6uCsRE177g477CDNOAJa5fis1MmyTAshrfd8b1Zz5ufstddeOTWtn4Xk66+/lmYFcrYnLY60SrL68i677CKdnp82RrYTKyLTsks7LCMqWDWZ1ktGDdCmzErgtK7T7kp7JK3uqTo7K0hz/LGCPO2wHK+MSWCUAytEFxq2dcuWLaXXrl0rzXHJNiopKZFu2LChdLKn09JOy2q+ec55mW88M4JjxIgR0rQy834rVqz4k/v68Xvut99+0qnfGR3AyuFcu2j9p8Wf780xyDFQSDjnqlatKs22yreGcMyxWvtWW20VEdl5k6qaR0SsXr1amvEuM2bMkGbUAduYVnJGEDCmgvEWjB4pLS2V5txt27at9KxZsyIi2z+MzmBbMD6hXLly0vvvv780IxM4pgsJP5vRCpyfHMspZiEiu1dVr15dOo1lrtXsE44PznfGRXBM1K9fX5p9yLF/6aWXSn/55ZfSjDvgve+zzz7SBxxwgHTqI/4do2MYy8I9jnOsRo0a0pyHbNNCwrWB441r/I477ijNfYXjkGts6nPGifTt21eaUTVsK8YWMfqI781oD34+Iwu4lrGfP/nkE2nGF3Aspj2f3704Zzk/ud7yuwLHPN+bkTfGGGOMMcZsDPgErDHGGGOMMcYYY4wxxhQJ/wBrjDHGGGOMMcYYY4wxRWK9RRAQ2uZoZWfFZVoZacWj3fvjjz+OiKzdku9NuxstgazQy4q7tJrSAs73pN2RtlbaAmllZ2V5VrpOlkjeF23UfH7a12mN5XVaLCtUqBDFhvdNmyCfgffHZ6c9MVkc2fbsb9oeWZ25Zs2a0rTPli9fPuffMlKC1/lZrCbPvl2xYoX0/fff/5O/Peqoo3SNVts999xTmuOJ1k/arnkv/PxCw35JcygiokqVKtIcf2wv/i0rjic7M6Mh+Dy0tbLiNythc77S4k5LPPud6wVtzYw1oOaYpf06PR/nDSMraJmnrZltx3ahtZzPVEhoJeeYZwQILbu09DOOgP2c5uihhx6qaykqJSL/eOb8z9f/vJeVK1dK0xJNq/LEiROlGVmRbz1IVnDuD9wXaFmnVZvrM9+b/bbNNttEMWCbcF/ZbbfdpBn7wLHHPme8R2oHvt+qVaukOU84Bw4++GBprk/PPvusdL79hrE4jFFhG3JccJ3lOEp7AWMEpk6dKt20aVNp9i3vi2ObcA4Xkk03XffftGnXp+2fMSjc57jesg1T2y5btkzXuB4y+oX9w/7k2GIbc76zD3lf7B9e51rNdqZO8zlXzFBERLVq1aQ533jv/HyOXUZQGGOMMcYYszHgE7DGGGOMMcYYY4wxxhhTJPwDrDHGGGOMMcYYY4wxxhSJTVg9+79JaWmpPpg2uC233DLn6zfZZBNpvp42tBQNQHseK+vSOkpLJt+br6dNmnZl2sFZ9Zjvv9122+W8X9oSWQ05PTf7g7ZnVgKmBZeWR9pqafekPbN58+brHvY/ZNq0abpZWhxpN2S8AG3P+folPRufhbZbWqrZb/wcvp4RDbSCc5zRekn7Ki2+vHdqjr/UzrTscpzxHjk+2D+E/cy/bdu2bcH6MCJi9uzZ6ke2HduX7cUxx9fTzpz6j/1IWykrq+erxE5LKsc/24v3lW++sL/yzV3O9XTPjBrhc3AM5luv+Bz5xlEh+3HGjBnqQ3425xbbjVZitvNvfvMb6fRs+dYvzi32Icc835t2dN4L/5YREb/+9a9zvob3w6gX9m0aX7S6817Y33w/zl1an9mHfE0h+3DRokXqw3xrEtcE3gefjdEZ6Rm4J3Kt5ljmM3KtZJ9wPOWLd+H442t4D4Ttzz7faqutIiLb96+99po0xx/7inDd5L3w9SUlJQXrw6eeekp9yD2Oawnvg+sB+5PPlr4TcD3muGaf8PsDxwq/vzB+gW3Lz2dMAMcF35P9ybWUa0jaK/jeHE+cs7wXtgv3G36vYZu2aNGioHui2Xg57fyB20fEJzcMHhDly2+1vm9ng+b777+PpUuXRo0aNTJrvvnv477YcHBfbFi4PzYc3Be/nLVrv4rzLxsSEVFh2A1Xrvnxv/sErDHGGGOMMcYYY4wxxhQJ/wBrjDHGGGOMMcYYY4wxRSK3J/C/QCFtf/+rHH744ev7Fv4thbbCm/VDs2bN3I8bOa1bt3Yf5qBmzZrr+xZ+MfXr13cf/gwben82bNjQfWiMMcYYY4zJiU/AGmOMMcYYY4wxxhhjTJHwD7DGGGOMMcYYY4wxxhhTJPwDrDHGGGOMMcYYY4wxxhQJ/wBrjDHGGGOMMcYYY4wxRcI/wBpjjDHGGGOMMcYYY0yR8A+wxhhjjDHGGGOMMcYYUyT8A6wxxhhjjDHGGGOMMcYUCf8Aa4wxxhhjjDHGGGOMMUXCP8AaY4wxxhhjjDHGGGNMkfAPsMYYY4wxxhhjjDHGGFMk/AOsMcYYY4wxxhhjjDHGFAn/AGuMMcYYY4wxxhhjjDFFwj/AGmOMMcYYY4wxxhhjTJHwD7DGGGOMMcYYY4wxxhhTJPwDrDHGGGOMMcYYY4wxxhQJ/wBrjDHGGGOMMcYYY4wxRcI/wBpjjDHGGGOMMcYYY0yR2Gx9ffDzzz//j6Q//PBDXd9qq62kP/jgA+ndd99devny5dK1atWSfvnllyMiYtddd9W1V155Rbpq1arSX375pfTOO+8s/dJLL0kffPDBOd9nzz33lH7zzTeld9ppJ+n3339funr16tKvvfaa9F577SX99ttvR0TEb37zG1376quvIhfff/+99K9+9Svpzz//XJrvw9cfdthhm+R80/8fzJo1S324evVqXd9mm22kP/vsM+ltt91W+qOPPsp5r6tWrYqIiL333lvXlixZIl25cmXpTz75RLpSpUrSb7zxhvQBBxwg/c4770izzz/++GNpthXbn+/P11eoUEH6r3/9a0RkxxnHwbfffiu9xRZbSO+www7Sa9asyXn9u+++k27Tpk3B+jAiYt68eepHPtv2228vvXLlSun99ttPmuOZ8+K9996LiIhq1arpWpqfEdlxy2erUqWKdBoLERG//vWvpble8Dr7d5dddsl573x/zvW6detKp3Vnt9120zWuORwL7FO2F9eXrbfeOnLRtGnTgvVjaWlpzrnIccb1ge3GfqlZs6b0iy++GBERrVq10rX58+dLc73juGG7se3zrZtcZ9nOO+64ozTnOvv2L3/5S873Wbp0aURk11iuC1yj2C68d85drjtcF1q1alWwPnzqqafUh998842u//DDD9JsZ65hnIdsh2XLlkVExD777KNrnFds43zjY8WKFdJ16tSRXrx4sTT3OLYz5wTnOa+/9dZb0lw733333YjItj33Dc6xtWvXSu+xxx7SnJ+bb765NOfFoYceWrA+XLBggfqQazm/13BccbzxOwb7K61TnJtsY75HvvHB17N9/va3v0lzfnLeli9fXvof/9DjZcYOv6txjX399dd/8t58znzzivvqF198kfMzv/76a+kmTZoUdE80xhhjjDGmGPgErDHGGGOMMcYYY4wxxhSJ9XYClqdw0mm5iOxpMZ7m4Kknngh64oknpJs1axYRETNnztQ1nphIp6Iisidj+DkHHXSQNE9q8CTluHHjpBs2bCidTrFGRPz2t7+Vfu6556R5EoT3s++++0bEuhMjEdm24OkdniQqV66cNE/WbLnlltIVK1aMYsD7YJ/wNBLbmSdW+Dw8hZNOF77wwgu6xlNPPBXKE3j8HD47T/7sv//+0gsXLsx5Lzx5w9OwPM2U7+RVOtXFU3kc5zzFzb7adNN1/x2E78dTcBzHhYbPw5OG7NNPP/0052s4nv/+979Lp5OvkydP1jXOYT4PT1vxZCLhKSiemOapV57CSqfnIrKn+TgGefoqnfaMWHcadsGCBbrGE3NcLzbZZN3BK7YRxylPe3IMFhLeE/uB6wP7mSeQ09oTEfHss89KH3fccRERMXr0aF1j/3DecqyyHbhG8PUHHnigNNdZzot0ojwi2268zjm6aNEi6TSPp06dqmscNxzbXF94UpEnndmmPMlfSHgClffHfuNr+Ow8Bck+TO3AE7IcE1yr8p1o5olJtvF2220nzdOwvM5xxnvgeOHz0e2Q9i2euuapTq7zPFHLfZj9yf2U87OQsH/4vWazzdZ91eIJdcJ+4ZhMLh+uUXSI5Ntv+Tk8ufzqq69K83vKY489Jp1v7HPd5nzm+OPeXbt27YjI9j1dMVwr+MwcQ1yz2aYcr8YYY4wxxmwM+ASsMcYYY4wxxhhjjDHGFAn/AGuMMcYYY4wxxhhjjDFFYr1FENDixiIctBXSavrkk09K07ZG6+GkSZMiImt15mtpI6Y1kTZM2u1oDb/nnnukaY9jjAAt5oxGYEENWixZzOjOO++MiIg2bdroGu12tEzSpsvn4GtoeaR9vljQBklrIu3qtLLy/tjmyY7OKABGBMybN0+a/cw+YewAi7o8/PDDOT+fhUV4L7Rz0gJPGzb/NllEOQ7yWfr5fhzzLHjCscv2KDS0vtLuSUs/baNso1TkJyI7F9N8pe2b1lfaTdkXfA2L6dHGz0JNTZo0keb8zvccjAzg+kIL7fDhwyMiW8CJ78fxzTHCdmS8AW27bLtCws/gfGdhG943owEY48D3ueGGGyIiu37lG8+0kfMzOT9omWef5CvEw3nGKAEWI6LdmfeZ1iOuC3zvfBZ43hfnKMc2n6mQ0K7Pe+XYY/tz7eceyvcpKyuLiGyxSo5T7nFcWxlLwYgQtn3abyOyhb/YbtzvaM/PFymUKx6Bdns+J6NFOA85nhhHwM/MVxjvP4VjlmsN+41rH8cV5y3bKrUz45G4xzGWgQX1uKYxIoKxMfxexT7k/OB98d65n8+dO1eabTBhwoSIyK7lv2SP4T7AvmWcEvcNY4wxxhhjNgZ8AtYYY4wxxhhjjDHGGGOKhH+ANcYYY4wxxhhjjDHGmCKx3jxctJXRJkrbHPX06dOlaZ9Nlboj1ln+aC0eMmSI9Nlnny1N2zctzU8//bR0v379pGnPW7FihfQtt9wi/fvf/176+OOPl77yyiulaeejLa9Xr14REbFw4UJdo2X+D3/4g3SDBg2kx48fL017Hu2P+SrL/6fQOspYhGrVqknTDkrbJK3YbNsDDjggIrLWdtonO3ToIE2L+NixY6UZ3ZDeLyKievXq0uz/kSNHSrdu3Trn3zJSglXGW7RoIZ3s8C+99JKu0QLbrVs3adp0P/zwQ2laVatUqSJdTLslIx5or2ZMCC3JCxYskKZVlhW1U7twPFP3799fmtZgvvfEiROlzzrrLOm6detKcy5OmzZNuqSkRJrrCPuGsQacU4ccckhEZK3ctNjWq1dPmrEftO2+++670hyntOEXEo4nWnl5f2wTrqe0dR9++OHSaVywCv3zzz8v3bFjR2mOVVZrZxvz9YxDYLvRBt+3b1/pww47TPrBBx+U5nrKft52220jImtxnjFjhnS7du2k2T98Pt4jn4+2/ULC+JFXXnlFms9F+/ijjz4qvWrVKmk+W1rPXn/9dV1jRXq+99FHHy390EMPSdPWTrs7+5N7zJgxY6Q7d+4szTWX+yzXvCOPPFI6rUWMmeA6fNFFF0lznHOusk25LzAapZBwHnKt4ZpRo0aNnK/hXLnkkkukU5+zj7lOnnDCCdJt27aVvuOOO6T5fat27drSxxxzjDT38ClTpuR8z7Q2RmTHCL9znXbaaT95T0YXPPLII9LcB/g98PHHH5fmfONr+J7GGGOMMcZsDPgErDHGGGOMMcYYY4wxxhQJ/wBrjDHGGGOMMcYYY4wxRWK9RRAki2hE1kbfo0cPaVpABwwYIE2L26hRo6STZfG6667TtTPOOEOalaVnzpwpnap9R0SMGzdO+q677pJu1qyZdJcuXaSHDh0qvXr1amnaBR944IGc98Pq0smqd+aZZ+oaK3/Tbrdo0SLpY489VpqWbVpzaR8vJLRo83lZtTpVso6I6N69uzSjG2i9THEDjJa47777pGnFnzVrljTjH2hrpGWVkQ98f7YVrca0vvbp00f6pptukmaUQopBOPXUU3PeI/uQFvVWrVpJM3aCkQ20sBYazkVGP9CeTGs2basDBw6U/vTTT6VTrABtymwLVv+mDbd9+/bStLUuXbpUmmPgqKOOkr744oulaafmPKMlmvOedt0UJUE7Pu22rL7OscsoDdrwaeFmPEchYYzD5MmTpRmjwTHHdYhr7ujRo6WTVZn9zegC9mFZWZl07969pW+77TZprme0FdNCzegUwvenPZtryuzZs6Vr1aoVERH777+/rjGOgPOckSKci2xHWvgZ9VJI2CaM1qC9ntcvuOACafYnYxxS5MWll16qa5wbl112mTQjDTgPOW4Yb0EbPON9GJ9AHnvsMWmukSl+JyK7nqbYiUMPPVTXOA/Xrl0rzRih+vXrS3MecmxtscUWOe/xP4XzkJELXLMYqcNYEO7fXbt2lU5rFmM4GBHAfZhr7L333ivN/uH4oKWfY2HYsGHSFSpUkOY87NSpk/QVV1whzQiKNM8YHcQ1husno0gaNWokPWnSJOly5cpJpzlujDHGGGPMxoJPwBpjjDHGGGOMMcYYY0yR8A+wxhhjjDHGGGOMMcYYUyTWWwQBKx+zsjJt36xaT5syK/rSlvf1119HRNaOyEratP7RRsyqvCeddJI0beq03rJqfar2HpG127Oa83PPPSd90EEHSacK1RERb7/9dkRkbaiMPaAFnPZJWmP32WcfaVa2/2/Az6atkPdBezvtoLSYpv7faqutdK1x48bSbAdaL1kZnVWba9as+ZP3johYsmRJztczDoP25VdffVV66623lmYfJQv+l19+qWu0NPPeaWOmlZcV2RmZwDiOQsMxxwrZtDvvscce0rSw0uLOOIAUH0CbMCto0ypL2y6ro9Niyn6kpZ/9yLnL8ZXWhYisxZnjju+f3pORCnXr1pVmHALjG2gzbtCggTTbrlj9yHauXLmyNJ+d6wYtzLzXdu3aSad+5vhgH86ZM0f6wAMPlKaVOlngI7IxIYza4D1269ZNmlErXF84jzhfOP7Sekr7PqNjaIPm/sM9ghEcnBeMtCgkjMjh+sC+/fjjj6UZGdC8eXNp9meyjNPGzb2JdvE999xTmlXoGSlz+umnSz/55JPSr732mnTDhg1/8vkR2b2Pc4j3kys+hvPq+OOPz/kevHfGJNSoUUP6hx9+kOYaXUgY11CpUiXpFDEUkbXav/POO9ItW7aUZvxJinRghE3Tpk2lp02bJs15wugjRg1wnWRcBPURRxwhzXnOPZH7edWqVaUZ15O+N/He+VrOce7zjAXhHsu9ghE9xhhjjDHGbAz4BKwxxhhjjDHGGGOMMcYUCf8Aa4wxxhhjjDHGGGOMMUVivUUQ0GJJKzmtrKyaTOvrb37zG2nansixGgAAIABJREFUUZPdnbZU2v9pX+N12pXfe+89aVbtZtRAskZGZO2RfA5WVWd1dEYp0I7M90nQSknbIl/L16xevVqaMQC0HBYSthtjGWiDpMWQdmTashnvkGzS7Fdac2ml5PVtttlGmhZhWhlpO6WtkdEErEpdWloqTfvsW2+9JT1y5Mj4MbQrMyKCsQq05rIK/D/+8Q9pWu1pKy8mvF/OOdqdaRmeP3++NCthp0rk++23n67R4lqtWjVpjlVaxmlxpq19++23l2ZFd1qSOTZnz54tzcgA2oU5L9M84ufw32l3ZqQB753zkuOe614hYcX17777TnrfffeV5njmdY45RhOk9ZRWY1qmaUdmLEWbNm2k2edDhw6V5jhj1AP7hOs15zHXOUaDcF1M/cz+5pxjBA3jcHgvbNMZM2ZIp7FdaNhvrPbOe+Lawyge7j3jxo2TTtbvJk2a6BrXFY5Hxi/w9YwiOfPMM6W5JtGynuJHIrIxIpzDjAbg6xlTkeYT9xPumTvuuKP03LlzpRnHwPdmlEax1lOuGdyfuN+xHdhvjEjgd5/PP/88IrJRHZyHXGMZ7cJxyjlzzTXXSHN+5ItK4T1y/FEzlufmm2+W3n333SMiGy3DOcu4Cu75bEeu6xyLXIeNMcYYY4zZGPAJWGOMMcYYY4wxxhhjjCkS/gHWGGOMMcYYY4wxxhhjisR6iyCgNZB20BdffFH6jDPOkF6+fLk0q9+yenmybdKGxwrXq1atkmbFYVqEaXtm9WfaA2k7TjbdiKwN//zzz5emTZu2RFZGrlOnTkRkrbaMUmDswk477STNqti07dGGSetiIeGz0MrI/uF9rFmzRnrhwoXS1atXl04Vv5966ildox2XVnDaF5PVMSLbD7RP7rrrrtLsN1ovn3nmGen27dtLs684dmm3TmONEQh8ZlqaWbGd78eYhDQmIrIV7AsNP5PWXM6XVq1aSdN6SlsvLem52uKYY46RZuwD+4X9xbWA0SR169aVZvV1Vq2ntfp3v/udNKM8aI/PNV9oaec6whgMjjvadmmx5/Px8wsJ7ei8b87RY489VpqxAhyjtJIn2zItwFx7OCZPOeUUaUY+cKx069ZNmmsbYRxAlSpVpBk1wSgPvj/nV+oX9gn7kJXY+Uz5KtczDiFZwgsNYzMYO8DrnEOMEuCedOihh0qn9uH7lS9fXprjo1evXtJsY+4fbGO2A+cS5+pvf/tbae653KsY6dOzZ0/ptI5zPa9QoYI07eiMReH+w72dfcvnKySMkeCc5Brft29fac5DxrxwrqSIAfb3YYcdJs35xvHBucQIAu637B+Od9479wdGR3AdfPvtt6U5n9LeyvgPzn2ukx07dsz5+Vx/eL8ci8YYY4wxxmwM+ASsMcYYY4wxxhhjjDHGFIn1dgKWJxl40u2iiy6Svvfee6V5qocFH3iqZtCgQRGRPWHSu3fvnK/94IMPpFnEgifwmjZtKs2TVqeddpo0C4RcfPHF0tOmTZNmwRue4LzrrrukU4Eanpw99dRTpbt06SLNU0s8hfaXv/xFmqdGecKzkLAfeEqJhazYtzzRxpNCPD2bTlux7ceOHSvN9ubpR57q4anLE088UXrSpEnSp59+uvSQIUOkeUqQJ6kaN24szdNMPIWbCqfw+c8777yc783CRxxzPAHdoEEDaZ6CKzQsZsNTrzwVynF7+OGHS/NkU//+/aVHjBgRERHdu3fXtQsvvFCap+Q4Fng6jyfWzjrrLOmHH35YmifBuF5wfeEpK84jnjjjqdFUDIYnkHmKnffFccFCbZyjPEHGvy0kPNHNQoIsdsY1iWsCxzkLr6W+HTx4sK5xTLC4ID+f7cA25qk2zl2u18OHD5dm+7OID8fZ5MmTpVkQL42/yy67TNcmTJggzaJ9PB34xRdfSOc7YccxWkhYVIinknnKfs6cOdJsZxY24gnDVHjt3HPP1bVbb71Vul69etI8Ycn9ltf79OkjzXHGwoPct1jsMJ8DhXOVJ9eT64V7AZ+ja9eu0tzPuRfwZC7XXDpaCgn3Mo5Z3jdP/HL/YF9wnViwYEFEZE8o88Qvv+PQWUB3ENuHr7/99ttz3uP1118vzf2B84Pj78EHH5TmafW0lrJAI8cz1yF+x+JpdRY35LzlvmmMMcYYY8zGgE/AGmOMMcYYY4wxxhhjTJHwD7DGGGOMMcYYY4wxxhhTJDahLe+/ybhx4/TBtOvSas3rLKZBuzBfnwoC0YL+0UcfSR9wwAHSLF5FWx2tbywORnsoC0jRXkxLJgt4MQ6A987IgGSVpz3x3XfflW7btq00bepsI9q6aZPlZ7Zu3bpgXvZHHnlEfcjiVSw0xEId+Qrc0A6aIgb4jOx7WqQZBcDXsDAUr9MyyWJr/Hz2G++RBZ5o52Shk1q1akVEtoAYi8bQbvnOO+9IsyAMiwa1aNFCmoVwOnToUNA8gilTpqgfOZ84nmkfp82WbUEberIVM7qAVlbaSmlNpm2W85iWW9psaY9mm7KYFNeA1EcR2dgMvmcq8kXLPu+dY4HkW68aNWoknYrMRUQ0b968YP04duxY9SHHM8cn+5NjkQWauOYlaG/ne7MtaY3v1KmTNOcQbfVcE7kHce3geOLfci1mcUaO0dRHjDpgFAyfkwWsGIfBeBf2P+d0u3btCtaHEyZMUENwzHJcsfAV7fqct1xz031zHnJ95N7AtmdBPf4ti0HSDs52ppX8hBNOkM5XeI9jlPEJaW5zf2akAKNQ3nzzTWnGHixatEiaazv3+caNGxesDx944AH14WeffabrjFngvbI9uZ+xb9N1vh/nD6M6GLPTr18/aUaB8H24NjEuiLEtjElgsS1+3+D3Kc6t9HzPPfecrnG9YfwTxwe/q3HN4XcIjvlCfq8xGzennT9w+4j45IbBA6J8+eIU2/tf4fvvv4+lS5dGjRo1MmuK+e/jvthwcF9sWLg/NhzcF7+ctWu/ivMvGxIRUWHYDVeu+fG/+wSsMcYYY4wxxhhjjDHGFAn/AGuMMcYYY4wxxhhjjDFFYrOff0lxoNWQ1jdaimnDo1WStj1aNVMlXtrXli5dKk0L7jHHHCO9ZMkS6enTp0vTxjx37lxpvj/thLSs8plomaXNk5bY8ePHR0TWUsvPoYWQVZ75fLTkbb/99tK01RYSWg1pF6amHZWWVbYVLbEPPPBARGQrX7NNHn74YWlaV2mLZ0wBrab33XefNK3utD7ySD37kHb4qlWrSjdu3Fh6ypQpEZG15dPezL5nn7AKO22yHK+5rOGFgnZ0Wolp6ea8ZN/tsssuOd8zVafnOGTkRoqaiMjOIT4zrbWPP/64NPuFbUoLL+c0Iw4YD0FrLefahAkTIiJr/eZ90W5Liz1t6iUlJdJsO65dhYSWbrYP+3PLLbeU5rq5++67SzM6YfHixT/5nGXLluV8b1qJuVax/2kfZv/w82mPpvWY78PP5Vxk7Eqai7RJ00rPMczxz/nK92PfFovKlStLM3KEa9LMmTOle/bsKc3YAbbJTTfdFBHZ9YNrGdchRmuwf7jfsB1Gjx4tXaVKFWmOkYkTJ0oz8qNatWrSHDv82zRv2D9bb711zufgXB03bpx0gwYNpPPFGBUStgPjD7hOMNKD6wr7mREtjzzySERkoz3YP9xjOa7Hjh0rvdNOO0lzH+a6mu87FmOR+JoVK1ZIcy3lc9xwww0RkR3bjNnhHs57Ly0tla5du7Y01wGOBWOMMcYYYzYGfALWGGOMMcYYY4wxxhhjioR/gDXGGGOMMcYYY4wxxpgisd4iCGh9ZMV7WoFpE6cN7sorr5RO1v2IddbzZCGOyFbtpU2Xtspkl47IWtxo8WRFdL7njBkzpEeMGCFNWzktyLNmzZI+9NBDpZs0afKTz7nnnnukL7zwQmnaHFkV+vnnn5c+/vjjpWnBLiS0fdJeXq9ePWlaEx977DFp2iaPO+446WQ3TbbLiKyl+ZxzzpGmzXvYsGHSd9xxhzTHB+398+bNk+b4YzRF586dc74nn4PPd+SRR0ZE1ubO6vD169eX5thmrAItqXw97buFhu9N2zmtzEcddZQ0rdxsizPPPFM6Pd/w4cN17cknn5Tm2K9Vq5Y0245zi3ZnVtResGCBNMdJhw4dpNlHHA98//bt20t37do1IrKVwLlG0ArMuA3aw9euXStNG3Sx+pGfx/me1pWIrE2dfci5duqpp0qnKvPdu3fXNdqHb7vtNmlWNme7TZ06Vfqqq66SZuwAxxw1xxzHIvuCa97AgQOlW7VqFRHZtZf31bt3b2na87meMo6A9vx//EOF7gsK35dW/JYtW0qfcMIJ0oxOoR28f//+0s2aNYuI7L6Sonp+fJ3Wce5TjOVhNAz3pxQdExExcuRIae4FJ598sjRjMu6++27p0047TTqtIbTy0zLP92vdurU0xxzbiPOWfVtI1qxZV+iUERqM/0jzKiIbr8M9ibb7a6+9NiIi5s+fn/O1F1xwgTRjja655hp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LU45LuxtTjo+1TTiobcLZlHIB76AkvqyoqfX42z5tD9XjFpRsWyhZu71eYsWCvpLWUAZgpcH0yXq8sPtkzYx9c0/fF1KOLwDb1M8P9bSTcnwemNcz1vmU4OspKcerei7pjLEtpSC9JGkcqTXrdqwfn+0qZSNJ0iDrxDDmMHTZtT8PN0DbhLmU+635wE+AByg1ZZ9cwbV0Aqf/7dO2QT0u6Tr3ygqOL2kcMQArDZi2CetQdvP8a8rxwZ7mPYHUc24Gpb7QxvXzP0cYfxIlsDsDmDFEvb+n69HXYSRpnKm7NV9NufF8CTimbcIFKcc/je3KJEka0aP1+GzKcUF3Q9uE3SgPF58f6uK2CVtRgq+XphxndJ1/4yqsZRplY61uOwHLGCEYLGniWGusFyBpOVtQ/m0uV+id8nR2n/pzfT13dz12spcmDTVwzaD9FmWzrmOH2WylM4YZUZI0jrRN+CBls8XbgCOAMygZObPGcl2SJA2h937kDuAJ4Ii2CRt2TrZNeBNlI6sZKcd+GakdG9Vjb7Zrv1I8I90LLaRkuH6ubcK6XWvZnLI3x8KU479HGEPSBGEGrDR4OhtlLffvM+X4BOUXDtomfB5YCtxTmxfXY2+JAmr/yZSdrz8GHJly/MEwa+iMsXiYPpKkNUjbhGmUDUoeAPavN6gPtU24AjiubcIeKcfbxnSRkiT9v3/U4xltE+alHH/RNmEGcB1wZ9uEaynJI4dTar8eMsJ491Hqw55as16fBvalJME8BezaNmF6yvHmfnN3D5RyfKptwlnAl4Hb2ybcUJuOpjzcPGWlv7WkcccMWGnwPEapI7RN24ThHpLsBNyXcnyxfr63Hrfu7dg2YW3KJiuHAoeNEHwFeE/PmJKkNVjbhLdTMnWeAaanHP/T1TwTeBE4fyzWJknSMK4D7qIEWPcDSDnOpwRNn6QEOY+nPFxsUo79NiR+VcpxCfARyqbDnwFOBP4I7AZ8Bdiyjt137j7jzQKOpSTRnFbH+x2wR8rReylJr5q0bJlvGEuDpm3CPMqroWelHM/u0/5uSomCuSnHo+u5yZT6rz9POR7c1Xddyi8P+wAHpxxveg3z/wzYMeW46er4PpIkSZIkSROVAVhpALVN2ATIwHvrcSHwd+BtlMzX/SgZ7B9POV7bdd1VwEeBzTq1j+prOYdSyg/8ss90N3RnQrVN2IDyNPnKlOMJq/3LSZIkSZIkTSDWgJUGUK0n9AHgZOAA4HRgMqUO0R/q52tSjn/ruXQOcBTltZrr2yZMAqbXtqPqT7dXgN4dPw8C3gBcthq+iiRJkiRJ0oRmBqw0ztTyAeunHPdcyevvBhalHA9cvSuTJEmSJEmaeNyESxp/TgZ2b5uw74g9e7RN2B94H/CF1b4qSZIkSZKkCcgMWEmSJEmSJEkaJWbASpIkSZIkSdIoMQArSZIkSZIkSaPEAKwkSZIkSZIkjRIDsJIkSZIkSZI0SgzASpIkSZIkSdIoMQArSZIkSZIkSaPEAKwkSZIkSZIkjRIDsJIkSZIkSZI0Sv4HfG+FEjLIIzMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1728x864 with 33 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 3/546 [00:12<39:52,  4.41s/it]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-9eb18b44e3cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mones_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m         \u001b[0moptG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl-workshop/lib/python3.7/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m    116\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         \"\"\"\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl-workshop/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m     91\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m     92\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x864 with 33 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = [[],[]] #Keep track of losses\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for i, (x,y) in enumerate(tqdm(data.train_dl)):\n",
    "        #Train Discriminator\n",
    "        requires_grad(G, False); #Speeds up training a smidge\n",
    "        z = torch.randn(batch_size, 100, 1, 1).to(device)\n",
    "        l_fake = criterion(D(G(z)).view(-1), zero_labels) \n",
    "        l_real = criterion(D(x).view(-1), ones_labels)\n",
    "        loss = l_fake + l_real\n",
    "        loss.backward(); losses[0].append(loss.item())\n",
    "        optD.step(); G.zero_grad(); D.zero_grad(); \n",
    "\n",
    "        #Train Generator\n",
    "        requires_grad(G, True);\n",
    "        z = torch.randn(batch_size, 100, 1, 1).to(device)\n",
    "        loss = criterion(D(G(z)).view(-1), ones_labels)\n",
    "        loss.backward(); losses[1].append(loss.item())\n",
    "        optG.step(); G.zero_grad(); D.zero_grad();\n",
    "        \n",
    "        if i%viz_freq == 0: show_progress()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be pretty slow on a cpu machine, here's an animation of our GAN training:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/goodfellow_gan_fail.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the 30 images in our animation are supposed to show our fake data! As you likeley noticed, these look nothing like realy handwritten digits. Our GAN is not creating realistic fake data. Notice that our Discriminator and Generator stop learning after a little while, and our generated images just look black! So, what's going on here? \n",
    "\n",
    "Well, it turns out that GANs, especially the early variants, are notoriously difficult to train. While Goodfellow's paper provides some convergence gaurantees for GANs in theory, these are only true in the space of truly continuous functions, not pareterized functions like our Neural Networks. \n",
    "\n",
    "Fortunately, since the publication of Goodfellow's idea, a large amount of effort has gone into creating more stable varients GANs. One of the first improvments, DCGAN, was published in 2015 by [Alec Radford, Luke Metz, Soumith Chintala.](https://arxiv.org/abs/1511.06434) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. DCGAN to the Rescue\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/DCGAN_overview-01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In late 2015, published what is know known as DCGAN, one of the first GANs that could be trained by a mere mortal. Perhaps their most valuable contribution are the above guidlines for training GANs. Like alot of deep learning research, these GAN recommendations appear to be largeley based on empirical investigations - really \"tricks of the trade\" that work well in practice. \n",
    "\n",
    "Let's apply these Architecture guidelines to our GAN! We'll also borrow the generator architecture that the DCGAN authors use for the LSUN Bedroom dataset:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/dc_gan-01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll change our image size to 64x64 to match the architecture used in the DCGAN paper, this is really easy with fastai. We'll also normalize our data to be between -1 and 1. We're doing this becuase, following the DCGAN paper, we're going to use a Tanh function on the output of our generator, making our synthetic images values fall between -1 and 1 (we used a sigmoid function in our first generator)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size, im_size, channels = 128, 64, 1\n",
    "\n",
    "path = untar_data(URLs.MNIST)\n",
    "tfms = ([*rand_pad(padding=3, size=im_size, mode='zeros')], [])\n",
    "data = ImageList.from_folder(path, convert_mode='L').split_none() \\\n",
    "                                                    .label_empty() \\\n",
    "                                                    .transform(tfms, size = im_size) \\\n",
    "                                                    .databunch(bs=batch_size) \\\n",
    "                                                    .normalize((0.5, 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll add Batch Normalization to our Generator and Discriminator. [Batch Normalization](https://arxiv.org/pdf/1502.03167.pdf) is a simple but very effective technique for normalizing our data as it passes through our network, speeding up training, and sometimes making triaining more stable and improving generalization. Since it's introduction in 2015, Batch Normalization has become common practice in deep models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv_trans(ni, nf, ks = 4, stride = 2, padding = 1):\n",
    "    return nn.Sequential(\n",
    "        nn.ConvTranspose2d(ni, nf, kernel_size=ks, bias=False, stride=stride, padding = padding), \n",
    "        nn.BatchNorm2d(nf), \n",
    "        nn.ReLU(inplace = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G = nn.Sequential(\n",
    "    conv_trans(100, 1024, ks=4, stride=1, padding=0), \n",
    "    conv_trans(1024, 512), \n",
    "    conv_trans(512, 256),\n",
    "    conv_trans(256, 128),\n",
    "    nn.ConvTranspose2d(128, channels, 4, stride=2, padding=1),\n",
    "    nn.Tanh()).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv(ni, nf, ks=4, stride=2, padding=1):\n",
    "    return nn.Sequential(\n",
    "        nn.Conv2d(ni, nf, kernel_size=ks, bias=False, stride=stride, padding=padding), \n",
    "        nn.BatchNorm2d(nf), \n",
    "        nn.LeakyReLU(0.2, inplace = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D = nn.Sequential(\n",
    "    conv(channels, 128), \n",
    "    conv(128, 256), \n",
    "    conv(256, 512), \n",
    "    conv(512, 1024), \n",
    "    nn.Conv2d(1024, 1, 4, stride=1, padding=0), \n",
    "    Flatten(), \n",
    "    nn.Sigmoid()).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, after all that work, we're ready to train our DCGAN. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/546 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-97bb5bbd9afa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mrequires_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mones_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0moptG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl-workshop/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    545\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl-workshop/lib/python3.7/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl-workshop/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    545\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl-workshop/lib/python3.7/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl-workshop/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    545\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl-workshop/lib/python3.7/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, output_size)\u001b[0m\n\u001b[1;32m    785\u001b[0m         return F.conv_transpose2d(\n\u001b[1;32m    786\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m             output_padding, self.groups, self.dilation)\n\u001b[0m\u001b[1;32m    788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    789\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "zero_labels = torch.zeros(batch_size).to(device)\n",
    "ones_labels = torch.ones(batch_size).to(device)\n",
    "losses = [[],[]]\n",
    "epochs, viz_freq, count = 1, 25, 0\n",
    "z_fixed = torch.randn(batch_size, 100, 1, 1).to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for i, (x,y) in enumerate(tqdm(data.train_dl)):\n",
    "        #Train Discriminator\n",
    "        requires_grad(G, False); #Speeds up training a smidge\n",
    "        z = torch.randn(batch_size, 100, 1, 1).to(device)\n",
    "        l_fake = criterion(D(G(z)).view(-1), zero_labels)\n",
    "        l_real = criterion(D(x).view(-1), ones_labels)\n",
    "        loss = l_fake + l_real\n",
    "        loss.backward(); losses[0].append(loss.item())\n",
    "        optD.step(); G.zero_grad(); D.zero_grad(); \n",
    "\n",
    "        #Train Generator\n",
    "        requires_grad(G, True);\n",
    "        z = torch.randn(batch_size, 100, 1, 1).to(device)\n",
    "        loss = criterion(D(G(z)).view(-1), ones_labels)\n",
    "        loss.backward(); losses[1].append(loss.item())\n",
    "        optG.step(); G.zero_grad(); D.zero_grad();\n",
    "        \n",
    "        if i%viz_freq == 0: show_progress()\n",
    "        count+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a faster animation of the same model:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/dcgan_mnist.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not, bad right?! I think the beginning of this training is really really cool, let's watch it a bit slower."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](graphics/dcgan_mnist_slowmo.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, for the first 100 iterations or so, our discriminator completeley \"wins\". During this time, our generator hops around randomly until, around iteration 100 it finds some simple examples that fool the discriminator! From there, the generator and discriminator battle back and forth, making our synthetic images better and better in the process. Finally, here's the same architecture trained on the LSUN bedroom dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"562\"\n",
       "            src=\"https://www.youtube.com/embed/xxXbJ8-XmHU\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x11049cc50>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('xxXbJ8-XmHU', width=1000, height=562)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. GANs Grow Up (Sortof)\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [DCGAN](https://arxiv.org/pdf/1511.06434.pdf)\n",
    "2. [Wasserstein GAN](https://arxiv.org/pdf/1701.07875.pdf)\n",
    "3. [Improved Wasserstain GAN](https://arxiv.org/pdf/1704.00028.pdf)\n",
    "4. [BigGAN](https://arxiv.org/pdf/1809.11096.pdf)\n",
    "5. [Progressive GAN](https://arxiv.org/pdf/1710.10196.pdf)\n",
    "6. [Style GAN](https://arxiv.org/pdf/1812.04948.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. StyleGAN Insanity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As of 2019, some of the most photo-realistic fake images our there are being generated by [Style GAN](https://arxiv.org/pdf/1812.04948.pdf). \n",
    "- Style GAN uses a different formulation for the random noise vector, allowing noise/randomness to be injected at different \"scales\". \n",
    "- Let's explore one **fascinating** way to think about what GANs are doing, by exploring the mapping between the latent space and the image space of a trained generator. \n",
    "- We'll use stylegan for this, but we could use other GANs. \n",
    "- There's a bunch fo dependencies, so we'll do this in a seperate repo\n",
    "\n",
    "~~~\n",
    "git clone https://github.com/stephencwelch/stylegan-encoder.git\n",
    "~~~\n",
    "\n",
    "- Once you have your repo + environment setup, check out `Play_with_latent_directions.ipynb`!\n",
    "- Note that you will need a GPU for this to work!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"562\"\n",
       "            src=\"https://www.youtube.com/embed/kSLJriaOumA\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1104e3490>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('kSLJriaOumA', width=1000, height=562)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. The Unbelievably Interesting World of GAN Variants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [CycleGAN](https://arxiv.org/pdf/1703.10593.pdf)\n",
    "1. [SRGAN](https://arxiv.org/pdf/1609.04802.pdf)\n",
    "1. [GANerated Hands](https://handtracker.mpi-inf.mpg.de/projects/GANeratedHands/)\n",
    "1. [GANs for unsupervised depth estimation](https://arxiv.org/pdf/1808.07528.pdf)\n",
    "1. [Stack GAN](https://arxiv.org/pdf/1710.10916.pdf)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}