M2_SETI/IA/seti_master-master/code/tutorial.ipynb

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    "# Formal verification of deep neural networks: a tutorial\n",
    "\n",
    "The aim of this tutorial is to give a glimpse on the practical side of Formal Verification for Deep Neural Networks.\n",
    "This tutorial is divided in four part:\n",
    "1. Verification by hand\n",
    "2. Small problem verification\n",
    "3. Real use case application\n",
    "4. Image classification\n",
    "\n",
    "The tutorial material was written by Augustin Lemesle (CEA List) with material provided by Serge Durand (CEA) based on a previous tutorial created by Julien Girard-Satabin (CEA LIST/INRIA), Zakaria Chihani (CEA LIST) and Guillaume Charpiat (INRIA)."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Verification by hand\n",
    "\n",
    "This first part aims to give a rough overview of the challenges posed by the verification of a neural network. In the first part of the lesson you should have seen a technique called Abstract Interpretation which can leverages intervals to estimate the output of a network. You should have seen an example by hand of this method. In this part, we will developp a small class that will calculate the output automatically with intervals.\n",
    "\n",
    "### Step 1: Encode the network\n",
    "\n",
    "![image](imgs/network.png)\n",
    "\n",
    "With the above network create a function `network(x1, x2)` which reproduces its comportement. It must pass the tests created in `test_network`. For the relu layer, its function is already implemented here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(x):\n",
    "    return max(0, x)\n",
    "\n",
    "def network(x1, x2):\n",
    "    # apply all the operations\n",
    "    x3 = 2*x1 + x2 + 1\n",
    "    x4 = -x1 + x2\n",
    "    x3p = relu(x3)\n",
    "    x4p = relu(x4)\n",
    "    x5 = 2*x4p - 0.5*x3p - 1\n",
    "    x6 = x4p - x3p + 2\n",
    "    y = x5 - x6\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def test_network():\n",
    "    assert network(0, 0) == -2.5\n",
    "    assert network(0, -1) == -3\n",
    "    assert network(0, 1) == -1\n",
    "    assert network(-1, 1) == -1\n",
    "    assert network(-1, 0) == -2\n",
    "    assert network(-1, -1) == -3\n",
    "    assert network(1, 0) == -1.5\n",
    "    assert network(1, -1) == -2\n",
    "    assert network(1, 1) == -1\n",
    "    \n",
    "test_network()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Create an Interval\n",
    "\n",
    "Following the rules of interval arithmetic write a class representing an Interval by overriding Python operators. A skeleton is avalaible below.\n",
    "\n",
    "Intervals rules:\n",
    "- $[l, u] + \\lambda = [l + \\lambda, u + \\lambda]$\n",
    "- $[l, u] + [l', u'] = [l + l', u + u']$\n",
    "- $-[l, u] = [-u, -l]$\n",
    "-$[l, u] - [l', u'] = [l - u', u - l']$\n",
    "- $[l, u] * \\lambda =$\n",
    "  - si $\\lambda >= 0$ -> $[\\lambda * l, \\lambda * u]$\n",
    "  - si $\\lambda < 0$ -> $[\\lambda * u, \\lambda * l]$\n",
    "  \n",
    "We will also need to update the relu for it to work on intervals.\n",
    "\n",
    "Some tests are available for you to check if the class implementation is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Interval:\n",
    "    def __init__(self, lower, upper):\n",
    "        self.lower = lower\n",
    "        self.upper = upper\n",
    "    \n",
    "    def __add__(self, other):\n",
    "        if isinstance(other, Interval):\n",
    "            return Interval(self.lower + other.lower, self.upper + other.upper)\n",
    "        else:\n",
    "            return Interval(self.lower + other, self.upper + other)\n",
    "            \n",
    "    def __sub__(self, other):\n",
    "        if isinstance(other, Interval):\n",
    "            return self.__add__(other.__neg__())\n",
    "        else:\n",
    "            return Interval(self.lower - other, self.upper - other)\n",
    "        \n",
    "    \n",
    "    def __neg__(self):\n",
    "        return Interval(-self.upper,-self.lower)\n",
    "    \n",
    "    def __mul__(self, other):\n",
    "        if isinstance(other, Interval):\n",
    "            return Interval(self.lower, self.upper)\n",
    "        else:\n",
    "            if(other < 0):\n",
    "                return Interval(self.upper * other,self.lower * other)\n",
    "            else:\n",
    "                return Interval(self.lower * other, self.upper * other)\n",
    "            \n",
    "    \n",
    "    def __rmul__(self, other):\n",
    "        return self.__mul__(other)\n",
    "    \n",
    "    def __str__(self):\n",
    "        return f\"[{self.lower}, {self.upper}]\"\n",
    "\n",
    "    def __repr__(self):\n",
    "        return self.__str__()\n",
    "\n",
    "    def __eq__(self, other):\n",
    "        return self.lower == other.lower and self.upper == other.upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(x):\n",
    "    if isinstance(x, Interval):\n",
    "        lower = max(0, x.lower)\n",
    "        upper = max(0, x.upper)\n",
    "        return Interval(lower, upper)\n",
    "    else:\n",
    "        return max(0, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_interval():\n",
    "    assert Interval(0, 1) == Interval(0, 1)\n",
    "    assert -Interval(0, 1) == Interval(-1, 0)\n",
    "    assert Interval(0, 1) + Interval(1, 2) == Interval(1, 3)\n",
    "    assert Interval(0, 1) - Interval(1, 2) == Interval(-2, 0)\n",
    "    assert Interval(-1, 2) * 3 == Interval(-3, 6)\n",
    "    assert Interval(-1, 2) * -3 == Interval(-6, 3)\n",
    "    assert relu(Interval(-2, 3)) == Interval(0, 3)\n",
    "    \n",
    "test_interval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Run the network with intervals\n",
    "\n",
    "At this point you should be able to run the network using the interval class and see the output reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-7.0, 5.0]\n"
     ]
    }
   ],
   "source": [
    "print(network(Interval(-1, 1), Interval(-1, 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus step: To go further\n",
    "\n",
    "- Reproduce the first neural network from the slides to confirm the results\n",
    "- Implement a class for an AffineForm to compute more precise outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*****\n",
    "\n",
    "## Part 2: Small problem verification\n",
    "\n",
    "We provided a toy problem representative of current challenges in neural network verification. We also trained a deep neural network to answer this problem.\n",
    "\n",
    "The goal of this section for the participants is to formally verify that the neural network is _safe_, using the bundled tools."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem definition\n",
    "\n",
    "This toy problem is inspired by the Airborne Collision Avoidance System for Unmanned vehicles (ACAS-Xu) specification and threat model. \n",
    "\n",
    "![problem formulation](imgs/problem_small.png)\n",
    "\n",
    "Let A be a Guardian, and B a Threat.\n",
    "The goal for the Guardian is to send an ALARM when the Threat arrives too close.\n",
    "\n",
    "The Guardian has access to the following data:\n",
    "* The distance from B to A, $d = ||\\vec{d}||$\n",
    "* the speed of B, $v =||\\vec{v} ||$\n",
    "* the angle $\\theta$ between $\\vec{d}$ and $\\vec{v}$\n",
    "\n",
    "All values are normalized in $\\left[0,1\\right]$, the angle is not oriented.\n",
    "\n",
    "We want to define three main ”zones”:\n",
    "1. a **”safe”** zone: when B is in this zone, it is not considered a threat for any $||\\vec{d}|| > \\delta_2$, no ALARM is issued.\n",
    "2. a **”suspicious”** zone: when B is in this zone, if $||\\vec{v}|| > \\alpha$ and $\\theta < \\beta$\n",
    "   then a ALARM should be issued. Else, no ALARM is issued.\n",
    "3. a **”danger”** zone: when B is in this zone, a ALARM is issued no matter what. When $||\\vec{d}|| < \\delta_1$, B is in the danger zone.\n",
    "\n",
    "  \n",
    "### Solving this problem with a neural network\n",
    "\n",
    "A neural network was pre-trained to solve this task (all files used to this end are available). \n",
    "It has 5 fully connected layers, the first layer takes 3 inputs and the last layer has 1 output. There are four hidden layers: first and second hidden layers are of size 10, the third is size 5 and the fourth is size 2. We used ReLUs as activation functions. \n",
    "\n",
    "The network was trained to output a positive value if there is an alarm, and a negative value if there is no alarm. For a detailed summary of hyperparameters, you may check the defaults in `train.py`. It achieved 99.9% accuracy on the test set, with a total training set of 100000 samples.\n",
    "\n",
    "The specification used to train the network is based on :\n",
    "- $\\alpha = 0.5$\n",
    "- $\\beta = 0.25$\n",
    "- $\\delta_1 = 0.3$\n",
    "- $\\delta_2 = 0.7$\n",
    "\n",
    "We will aim to prouve that it respects these values.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the safety property\n",
    "\n",
    "The trained network is in the repository, under the filename `network.onnx`. Your goal is to learn how to write a safety property and launch different tools on the network.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Visualization \n",
    "\n",
    "You can first visualize the network answer on the output space by sampling inputs,\n",
    "using the function below (**careful, it may take time if you input a big number of samples!**).\n",
    "\n",
    "`sample2d` is faster but sample only on a 2d slice, `sample3d` gives a full representation of the output space.\n",
    "\n",
    "Blue color denotes no alert, red color denotes an alert.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
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0SPgeJEwz5ImJ6gG56v26TpWSToMi4ChHK2HT02AHSJdlWZdnVa7zwcFTZSuXVVcXeX2YdgFzJTrG3h/HBMBXkl/ufmB21mwra9Yo9cADfH1OaI9YKuIrRh+emic7NiIARQA6EcOAYmeRDzFrr+ukTeeyzs3hOjiO3G9l8URJjdJqZR4cbF1gBkiXZVmXJb2qQaXORnNxaLKbqjyArVZY8WcT66qbAKS+vNfp4NMXcfc5oUVZLwqh2ONGiogAFAHoRGgD8pmiwaYcPmft5U56aYlv8wnnMUi5eKL+JsXrhh0gbZZl87KYBkWbjTDFz2NjFPN6WVrKYv12787+hlz2VQr/PCcncfYJQE/+HRKbs3vLbd1FWPWiKMPiWjepjBs5qTxrEYAiAJ0IaUApZZFXKvysnXPpGePFpGa9p3oVqV43bKdJOXmhGOuo8w5U/ebw8OrEwLoyppYDzQT1eXIn/w6NjVe82OeIh8kPrvXKMW5wCraU7EQEoAhAJ0IaUIoJj0PO5LgFhMmLOTdHW+qm7sLlfk7FZ7G4mL327j11JJYuplLnHaj793IHXhebSU1UncoxUpxe4uKrKvl3qHaku47L/ebJk+tsSESgHRyeO+yReHXtzlaw1YWGpGQnIgBFADoR0oCa5kHhxoeAMHkxsUvdCwvZEVLYAZPbU2vqpOvuMxe5deXs6zPvyjW98npKNQdaHa6pbrA2GsojYrqOzfF++UuX8zK15+pCyAkvh+duYQH/DKvGDVvBVmVrIyP4GOBQiABUIgBd6HUPYEh8CQhTp44ViVRBxAW2k666T19ervIrj31rWg4025hK7GAbyiNCyXnpQ/B2Q78UeunStb+nCvry72C+327j86umaCciAJUIQBdixAA2xYPig1gCQpcgmNLJcsdIunoJOHdDm1557FsqaTiwUGIqKYNcqJhe6nWoMaQ6r07x1eSViRhLl64rPpTJXZWdYb9fjGl18SLHsBMRgEoEoAuxdgE3xYNii84rl5KAwHaSH/uYnyUjVy9BKA8gwMrYt1R2AWLJyzs56V4Pw8OnjtxyeXZYbE/XyZ+PKYbUNcYsdWJtvnNt25TJXVXfafN9jv5EPIBhgdgFaDKp5AFM2YNioiwGTDtSq74TS0DEjst0vT7Gq+waA0jt3LmfLefvYeoL6xHDvlxth8NGdX1Ot69MxAq9ca1XGw+ezfdzW+h03FcUJAYwPBC7AE2mF08C4aLTyTofXQB5sbPDeDhD10vsuEyO62NOXuGKgzOJGZc4K0pCapfJEsYLz7lsHMMDWIXJK9+tKxMxJ3ku9YrZxNRurz5W0za8Zf9+dw8gNpE/FyIARQA6IQZkx8IC3VNimvHGyC8V2/vBdX3MRhcOQYMRolX3YBrsdEfSVf0eQDb5cEmsa/LC54JpZgafU9KH7YSy0W5bmciJPclzqVeTgPzIR1bbZjl7ANZe9+51iwG87LLw/beM3yIAnRADouNjl1jM/FKxvR9c18eeBHLZZdl5sMVrtVpKnX66vZhxibPi2LlqM9Bgvc02XhFfu4B922g3rEyUwXjS8rhOX/dcl+MTc706Abltm94Gp6ay377qKlq/bDrLO2Y7KCPjtwhAJ8SAaPjYJZbCCSmxvR+mGC3OLP51QtulE7f1snDYk++BxiYuyoftxLbRJkNJB+TTa8WVlHl2FmeHmFWaYv/K1R5D9d8yfosAdEIMiIaPXWKxl2hyYns/fMfAYYT24ODqhNgYkWEbZ8W5i7lqoKmqU+pzxpZxejruSSCCHmwYhK/JBNcqR6eDP7YQ2244dwHXvRYXeetTKRm/lRIB6IQYEA2XXWJ1M0HOIO1uGiBNS6P5Eg8WbOe+uEivQ1sR7yOPYX6NurjCslfEJKhjx4k2idTbHzauk/uZcq5ycIu04i5in3lFBwb4RbWM30qtAUEIxKZNdt/r68v+3nILQKt16v3lZYATJ3iuvW8fwLnnAlx6KcDVV2d/zz03ez+/1oEDAPfck/1dXibeRECWlwGuuy7rOuvYs2fl/Zn48pdxn/vxjwEuuQTg3e/O/hafVx1btwK026eec5m+PoDR0exzxeeAffYUjh3L6uSKKwCefHLlv/3sZ9mryNGj2Wfr6rHVArj11uy/y/dXZ9e9yL59AFu2rGx/W7bg7TMErVZm0yMjAD/5Sf3nlAI4cgTg4EGe6x48uNoWba937BhPmXIuuODUf9v27xh+/nN9OxMsia1Am4zMIGhgAqqrXlXLiJQlGdczMycmwu9Qc4Eyy8em18HuZLVdardNr8KZpxAg82BS45iwNiYxeNXE3MRlQ+jUMJzX4/YAlhOI+zo/G9vOKMj4rWQJ2AUxoAzK0o0poHpw0JwPCrvzE5svyyZ5b6qDk1K0pRhMp4odNIaH3TeZ1IkkmzOXc3ui1MPiIs9gWEXqS5wxSGETF5XQccec1+PcOFX1XHydn+2jXmX8ViIAXRADsttoUBdfhYlLo3RgGA+Ly4Cf4uCklN0sX9epYgXl+Lh72es2XpieedkTWLULemrK7GV0iWNq8nm3sUhlExeFEHGd5dQvnNdzTZ1kmvza5g1dty5sO5PxW4kAdKHbDIjqoXBZurH1hlB2VWJ+k+N815QGJ6XslmJ0nWrsQRp7/R07sue5uEhLGF6cKLgskcW2gyZ6GGMfp4ilXLfz8265FU0nq9QlNufK5YgRaXVedMzE2kbAPvBA2HbWbeO3DSIAHegmA6J68mIt3XAPGBwCMPbgVAV1lq/rVGPvZKV65TA7c+sGXxvxnIInOMZJOBxMTaUvruvqtio2GCOOdM/KlGvTRozVUfaOU3KJ2joLdAI2dD/TTeO3LSIAHegWA7Lx5MXquLm9US5LwCkMTjqws3zscXGxTjyhPiPXMlHimFKIBW3aJooczCQltrjWlbGvj35+relZ6WJW+/qy9kw5CYQCVtTZTjYwG6FC9jPdMn67IALQgW4wIBtPHvWMSB/l5Zol2m4CyV+uGx980+nUi3VqpxpjJ6ttPJGrcMDmAYy9kzflTRQYTyvmWcaqX0wZBwdpfY3vM7V94zrZwIjMUP1MN4zfrogAdKAbDIjqUaN2Yj46K+5ZIkXQll8cGx84qetguTrVkHFmHOf8utgfx9KXb2LHZ9Zh8hJhy11MNBwa7jJypWAJHXJSTICtO0WEc7IRop11w/jtymnhMw8KIVlezhKEHjuWJercunVl0llsYtD8c6akpEXy5L3cbN8OcO+9WbLjYlna7Syp7vbt9N9bWFj9exi2baN93if79lXXya23Zve4bZveFjDkyXB9g0lmjcEl8W3dvXLev6l9mqC23xDkibTLzy5Pmn3vvQBLS7jfKiYaDg22zm69FeCGG8zPjesZUBMuu9hYVZ9Sh1KnElK7tpFQ/UzPE1uBNpnUZxCYWA2qB4ESkM/hsjctI3HOEn2mXvBNU+PA6uDylvjyfHHYXlX7HB7OdphiSc0DiF2SxsZ1xlzupNggppyuNm3T57hsDrL1wKe4Ka6K1MfvEIgAdCBlA8IKAmpMXcilm9g7G2NufKCQchyYLRznivq6Zw67NA2uExO434m9Q7sM5czolMpdRaeTnUHLJXowz6ou3Uv+okwOXNN02cYrproprkzK43coRAA6kKoBUQUBRehg0mS02+4ddyoerSYc4ZWaF4gDDg/g3Fz979t68DjsEju46spfVaYUJiqUNE0plbsO7mwHtkceFvtWDhszCWyb9peCaKeQ6vgdEhGADqRqQDaCgCJ0uLwXdaTm0Uot8L9MU5LpUuA4V7RuUK6y9aGhbENP3fPtdDKvlc4jhLVLH0frpTJRofY9qZS7DlOWAK5l2fI9z83VX6+8glPVN2Gfw+Rktc1TPfApiXYsqY7fIREB6ECqBmQrCChCZ2JC3xm4dARN8WilIgybUl9UXHcBVwlezG+WvSzUVDSmeqYMrouLeBvD2qNPu7VZkk6lHdVRlyXARfS4pMjJ63B+vj4UwTV5OtUDmJJox5Lq+B0SEYAOpGpAvgWBbw8dtvOanIw3aMSOTyySWhwYJ7Z5AKvsG7v0Wl6Oo4pQk6eVMriWPY6uNhbCbpuwtEslpKfSJfwhr+OrrrL7HjY2HECp9euV+tjH9Ecvpkyq43dIRAA6kKoB+RYEvgWmTQdYHsR8ehVSiU+sKhM2jjNlj0uZYh6y6Wml/vIvs+VRqn1T7Co/dcFGfJrsvtPJyu8ywNvYWEi7TX1p14ZQ7YZjA5StbWFiw019b1NIdfwOiQhAB1I2IJ+zcN8xZ7bxX7ogaq5OKrX4xCLYo5ZS8Vy6YGPfvgdWyrOfnw9znZwYdtu0iYZPKHXBlQLJ9mWKDa+ynSZ6dlMev0MhAtCBFAxI17H4moWHiDnDzj7Lr3Xr3DspXZ2mHm9nsofUPJcuzM+v9qTp7NvnwGpTh7o4Wm4bS91uTTRVTOZHMVKW8jk2QLm8qmLDuTZApUQK43dsIHYBmkxsA8J4c3x0nKFizlziv2zLZarTpu64TdlzaUPdTl5T6hdfA6vtxGpubvXxWpy553KaardKNddrvbBQv4PYNGHQebh9iL7iq2oS0PQJRBWxx+8UgNgFaDIxDSi2NydUoHdRwE5O+uvgivekq9OmdoRNLXcVLraPXXotxgDqBt3BQfcg+PIkzfWUjKpJX1Off+x+zhbM5iHTpKtuBWduzs9ERleeJk8g6hABKALQiVgGFNubkw8o4+OrvRc+A719HqaOrdOlpXA7bjm9t93SgbvYPtajXLULOOSOVhcPe523LE8Z0qSd4rH7OUz5qton9RQNneiuuwa3h9Bkz02dQOgQASgC0IlYBhSzMVYNMMPD+iS6XFCOZqLWC6VOfYiCckevy/FlQ7d04Lb3QUnngtk443tHq42NmbxlExPNSs8Sw2axky7dsjR1omo76aqzy/w5U8pgsuduTDUlAlAEoBOxDCiWNyeF5Rjs0UzUTopap5yiwMYzRaVbOnAb28d4ZIaHszQzupNAQm9CoNgY1luWLx9y2K1vOPs50/OjbNYw9YPj47R+yUXA6jyE2Mny5CTOnrstv6MIQBGATvSSB9DHcozNoGo6mqlYHkonZVOnmPKbPkNNNOwi1LqhA7d5Tk32fmLbCOUem7Kjluu5mTaRUDZrYPpBSo5Hn32maywpti5TnUCYEAEoAtCJ2DGAIb053IModWdfseOr8wIWl7qonZSPOjXdIzVWiEOsNL0Dt3lOtl7DVEQSpixc3rKY912+Nke8LWZZ3FRnxetg+0FdovLiy9QnFeujynvrmk5mYIC+iQnjTU2l7egQAdhlAvD2229X5557rurv71djY2PqoYce0n5+enpavexlL1Onn366arfbanx8XP3yl79EXy+FXcChvDmcyzHUpeQq0bJmzervDw6uFFd5J7S4mL0wcT1cdcq5o9i2nutoSgddB/U5UScvKaUdwZaFY4KGmbD4spu6a9vELeblnJlZvUmt/ButFr7N5feO+ez4eHW5q/oqbH3U3YOub8LmU+Wy75TajgkRgKp7BODs7Kxau3atuvPOO9Wjjz6qrrnmGrVx40Z14sSJys/ffffdqr+/X919993q8OHD6oEHHlCbNm1SH/rQh9DXjG1A1Bghl86bywNIXUqmLJFixaOuQ+LwkGHvcWYGP/hQ69lUvqYIQF2MU9VmpPn56t/AepJSiHPNoZTF1YON8ZT5Gthtrl3XJrlzhxZfuQ1i22dVWQYGshUMG68lts+0qRMO+6baa+w+KPb4nQIQuwBcjI2NqV27dp38/+XlZbV582Z10003VX5+165d6k1vetOK9z784Q+rN7zhDehrpmBAmIbEMSvjWiKlxipROnOqeKzriG28hzb3OD1NH4Rcl/dDz9BdOnpTWauSKNclQq8LzC8OTimlHbEpi60H2zYUgUM0UNIvYfo56u5Xymt6GudVLE4mRkZW/vvICC4UxaZ8pnQyPk/zoNhrKl7CFMbv2EDsAnCwtLSkWq2Wuu+++1a8/973vlddfvnlld+5++671YYNG04uE//gBz9Qr3jFK9QnPvEJ9HWbYECmTlF3ckLdb7kskVKWkm2XSCniUdchV3VUIyOZcOSIx5qZoSV0dR1wQ3u3XDp6bEoT3b1UXb+85Ff0JKW0WcQl3Q3Vg+0SiuAqikOtLLi+MEvFZduzmXz6DAvxad/Y356aSsfD3oTx2zcQuwAcHD16VAGAevDBB1e8PzExocbGxmq/d+utt6oXvehF6rTTTlMAoH77t39be53nnntOPfXUUydfR44cSdqAMJ1iq1W9bFaHaYAxeXywHcXiov3JH1TxWLd0jBFmrvFY2Bidcj1TCe3dchGbWLvV3cvgYP31AZT6zd/MPDpLS6eum1KybGpZXLzW2GuZbDnEfdbhIpy4Xnn7xIrRqr7D5VlUPYOiXWD7Uxv7xpY7pfOERQCq3hWA+/fvV2effbb64he/qP7X//pfat++fWp0dFT94R/+Ye119uzZowBg1StVA6J0itTlYGxcVtWSnGkpeXBw9dIJtSOkdKTljofiTbCJx8o7wnz3XZ2onpuzWz6tej4hvVuuYjPkYF60z6Z6AF2X1Djq21YUc9U5h4itelVtNivacVX+SJfJp82zqGtPtvGQPj2AqbQvpUQAKqW6QwDaLAG/8Y1vVL/7u7+74r3//J//szrjjDPU8vJy5Xea5gGkdIquMy+dxwdg5bLp3Fz9UrJLx1HsCG06pLzjoX6XEo9VfuUDNVdQdJ0YwCan5fBuuQ7qvgbzumdXjgF0jXPlAFuWvC3p7sv1Wi7Pkus+uWKL617luL6BAaXe9z67e+eYfLqGhdjEQ1aVBdsnYZ4jNjF1qOMoRQCq7hCASmWbQHbv3n3y/5eXl9XIyEjtJpDXvva16qMf/eiK9/bu3avOOOMM1UH28KkbELVTdO3EsdfJ0ztUCRVMkmdMR2gzqOUdj60AwcRjYTtwG0winMMGQuSkC72cV7UL2CXOlQtTWaqOC6y7L9trUerO132GELG5F69o17Z27DL5pD6LqrAQm3jIcl3beJUXFvS/jT3FSTyA4YDYBeBidnZW9ff3q7vuuksdOnRIXXvttWrjxo3q+PHjSimlduzYoa6//vqTn9+zZ49at26duueee9Tjjz+uvv71r6vzzz9fXXnllehrpm5A1I7A9zJO1QCWx/pNTir1x3+M/w1dMH8OdRZs6wGsq798593HPqbUunX6+nAdRLFxc1hPS5XQC5WTDjOY6+7F9qXLAxgrWbauLNxL1nXXCnGGMEed24rYujqyrV8bAVbuO+rqY37ePAGz6b+KdW0bv7uwUD2Bz3MepuRhVyr98TsEELsAnNx2223qnHPOUWvXrlVjY2PqO9/5zsl/u/jii9XOnTtP/v+vfvUrdeONN6rzzz9fnX766Wp0dFR94AMfUL/4xS/Q12uCAdXNylwGijI2HjOXWL/JSVxaiPz+MV442xjAuvqzib9xmflS4450HXtV2bFHZRXrz6WjN3mF6kRJ/jrjDPrza9pJID42rVDie7lFMUedY3Z+Y23RxY4xJ4yY2r5tfWDtYnJy9W/bxu/qJtvlviUVD3sTxm/fQOwCNJmmGNDcnH0niCH0kh1VKHU65uPjqryHlDLZJq+2HajLUE4o0A3kHLFDxd9x6ehNosM2yJ3LrmLj4gG0ERcuAi2koC5fa37e3hZt7JjShnx4vVzswua7VNGYioe9KeO3TyB2AZpMCgaE7Vjn52kCiFoG1yDyEJ0ltePBxqwArI49DC1AKB13nc245lLDxEDanKqis2+duA9lV7Gw9VCFTsTLdT0XEelii5TvumYQ4MDFc2njVbYVjbE97CmM37GB2AVoMrENiNqx1n3elNgYWxbb+JuQnaXNzjZduVotpfbsOfV7i4txBAjHsqurJ7fKg+m7o3cVrfkrRnwfB1QPlUt+RpfyuV6PQ0SG8GBS2pBPr5etB95GzKWUP5NC7PE7BUQAOhDTgGw71mJHNjW1OgbPxRNQFwTM8YoZhK8TtuX7xaY6KL8oJ7JQy4odbF3Tr8RYQuUIP5iaCl9uTrAeKtf8jFS4rudLtPqYnFDa0Oys+/V02Hg9bSaSKeXPpCACUIkAdCGWAXF0rD461U7HLYFz+TU9nUYQPmVThO2LawnOZanLVkzFXELlyBmYimfCt4cq9EDNcT2Mh3d4eOWJLhh8LYNT2hD1FCYbbGyKOpFMbXcvFhGASgSgC7EMiCvNBveAji3X8HD9UV2pdhjFjnRxkVfoFjtXTJoHSlkpv2ETyxlj914RDg9gCp6JEHF5oZfqOK6Hfb5DQ7TlZF/L4DYhCSmGH1Ankint7sUiAlAEoBOxDChUol3qwIgt18xMMzuMHJ+7nsu7tX0G51ehey4Aqz2fsZbmc1w2IFEnGr7iGUPF5TXRA0g9VcNUVyGWwV0yCKQE1t7zz42PZ5P7lPoHHSIARQA60VQPoC9PALVc3OkAQu0s4zj4nDKohRbEuueSwu69MjYbkGJsQqgiZFxe6KW60BuTOH/PVQRTd6an4IW2oapdDA1lYjCV/qEOEYAiAJ2IHQNo27H66gQ7HXNs3ODgynJxCQqfA3S5fNj6y0854RCBtgOzy3JwakJPh26Hu6tnwqeHLrRXLrTn3fV6Nh5eDo+i6zI4NR46lThUCqF3lHMjAlAEoBMxDUiXad7U+Hx5AmwEIAe+OqI6UZGfwYqpP87lYqoICJ3vLTY60eoaF+lDnCsVJ4WG70S8VYmYXa5HTU7OEVPIIbgpS8E+PYA+JnOhd5T7QASgCEAnYhmQqUOcmMD/BqcnIEY6AF8dkUlUYs9H5UySTREBTZ+d+4I6GPq26VBtpnzf2KMUqdRNOubm3K63sLDai2tTV6GXwU2nMAFk9zUz48fTrpsEugjDpqZ+KSICUASgEyENKG+sMzNZjIWu0WE7MO7E0DG8GT46IqyonJvDeTaoQeGu92Ajipu25GuDjUfUxaYxdRpCkITyBPuedCwtmfs+TEqY0MvgdacwVb24nkunoz/+EmD1ag3l2rbtIqV+RgSgCEAnQhlQVQfOKRaKiaFdBooYs0IfopNyH5gOzVUAUkUAx2acblsqthUntjZNqVOfgiSUJzjUkiBmww/Gdquez/Cwv7x82D6c65nbnI5DubZNu0itnxEBqEQAuhDCgKjxLzaCR3cdSqdgCnz2kXbDh+jkFJUcR5VRBwRK+Zt0yoItLuLExkNnU6c+4vJCxmmFjq3TxRpjbXdubrVH0acgKa7i6JazXZ6L7XhhunZVCAGlXaQYkiICUIkAdMG3AbmIBx9LnqYOSdcx+0q74WMJzXYwc9kxXPeiJLilln9x0V/8ZEozfVdxYvI6Fb1GrmKTUzSHFGUhwz8wHnWT7cYUJD6zMHCci12+dl17psZBc/czrogAVCIAXfBtQDbiIaTgKWKaeQ4O8qTdAFgdo8i9hIbpSKu8PlWd5Pi4W0f8wAO0shfLbxLFi4v8A1GKM30OcaJbViuK25SC4ymJ2TGxirrPhNzMQhE5VdeLLUhi52GlXBuzGc7kuU6pTRQRAahEALrg24Co55zaDrKuHRKmU263cR0qtYPPB1/uJTRdmh2AlTutTYKVoyOmghHFlOdO2dCgs0/M8hJl4A0ZJjA3Z253MTZC1YG97/JyZNlbi/HohtpdSxU5VfUcW5BwrjAU4TgXu3htbHs27ShPqU0UEQGoRAC6kJoH0FbwuHaInB0q9Z6Lgy/XEhrFA4jpJE1pIHwNQiZRjK1r7Oag0IHhIcMEsIOhD6+qLbYpiIptiuLRDbG7lipyquo5tiCxjSs12TqXBzDfSc3Vr8cW3HWIAFQiAF0IFQOo6yg4cki5DpCcHarNLJZ7yYbSYVE6XcpAXL4nl0TGdd/DPPfBQbwAoNqBy3Ix9buu4oQaV+nbE4YFs2u2rpztNn1Tl+8k05T2VlfPKQgSij1ibR0r+PMjKnWfo4SvmPr10LkXsYgAVCIAXQi5C9jnrNr1OtgOdXraLGBcZrFcHTZFyGA/Oz6+emDMN8yY6tznpgrdcy+WEdNxU9PnuO7MpX63atcnVpzY7Kz23WaxVNnP+vX27czU7nzuAO90zKcN5XVdV8+pCBKMWKbauknwT02dips2/a7t86+7V+yGkVDZA0QAKhGALsTMA8g5q3a9DmbmWV4GrRMwtstW+eDLgQ8PYF3OQFOd+95UkSeMzb0CxTJgD7TPBwDKwOriheFaaqbkfePIreijzWIp5/uktq0Q7Q4DZgcwZsNZKiKdc3NN/lvYs6+XlszpaHThK1ShjOnrQmYPEAGoRAC6EOMkEN8zI9vrUJeadB2t7bIVlweQImS44suq6tz3bsWqDndg4JSXwGZpHzuwuoQNUJemuHJcUp9zSrkQczhThXC3O46yr19vPgkkJzWRXoXLCsPQUPa+6+ao3L5t206RujYRI3uACEAlAtCFphqQr4GpqkO1nUGalig4hVDdvVBjdGw7ybrn4TNWCdPh2l4fM7C67IQ0HQdW/C6niE7Fa+QCZdDPYwBjL5VSy05pD776Qq7fdQmJMdmli7jkFMqx0vI0dfzmBGIXoMk00YB8u9mLHd/0NL3DLn5/cTF7YTw+PgZfiofAtl513/O1W5GS3sHWu6nzau7ff+o0BOpvU1KbuC41Y59Xal4jperrn7LJqrgL2FX0cgii0Lt3XVZDuPpYjOfZdpLtGr7CRaxNOU0cv7mB2AVoMk0zoNBudptdoeWOc2TEHPQ9OBh/SXxhYfWOyZERt52s1Bg8LJQOl9PrhfXq6n6b4rWgfJ4iGlJc2i2iEyCU1D+636OIXi5BFFIoUMpcjq/k7mNNG7Vs6ySVzTCx0vI0bfz2AcQuQJNpkgHFcLPbCA1sh+aj07fFRlhjnke77SZ+Xb1AeYc7MbHay9BqrUyGbVtHVS+duKB6LWy80E3GZIvz8+ZNVlVJ211jgzkEEadg0d0PpcyhQlXqRLhrqpYYsXdlxAMYD4hdgCbTJAOK0ciwHXa+1IgpH7WD8w0mML0qVyP2eaxbp//3OgHI4QUyCXPqBgpqHel+SydgRkdPCR2fg3IqlJfVTfc6N6evP4qwN5WLe9LJ4ZHWtQ1KmW0nrbZ9bJVodU08vrBQPcmkHN3pSixPZJPGb19A7AI0mSYZUCw3O6bDdgl0du1Ubck748lJWjmp8X02926a1c/N8QhzbMfsIw5PZ1P5IfWm6zVp80YdFA9UsZ51Rx1y1YmvSafLkjRX2EWe8NumvXL1sZhnb9pop2snIdtFjA1WTRq/fQGxC9BkmmRAsdzsSpk7bBcxxDE7pC5v2Qy6tgMNdUDBejDm5/mEuclmfEw+6mwqF7eY66W4eYOCrQdqZiZMOIjPSafNkjTGE13Oh1n3ok78KO0FA+bZu4aghPaMh95g1aTx2xcQuwBNpkkGFDvgV9dh23oAOWaH2GDvYpJV246/WG5Mig3dcl7dgEJd3uUQ5pOTervxNfkoLn1OT5/6i7nW9HTzl31tJyGh4iJdUv342GTDsdJQtHmbds/Rx2KfvW6jTUyHgI6QG6yaNH77AmIXoMnENiAbz1VoNzsGjDgdHOSfHWIDoF08frpXvmOw7nmYAvarBhSq14VLmOsGG5+TD9tnEytmlAsbMZPX88xMmDqyee4+01Rh28ZLXmIuMzb2rq5PcQH77BcX3euibAOp74CnEHv8TgGIXYAmE9OAOPPODQ9nS2cxwYhTzs4HuwRiCph3eeXnxmKOgsOKds6ZPWbDBXaA8zH5cNk53vRdv7ZhE5xL+xgoz51rR6prYvX163F9EXa5uNymXeFYWrexAd85ZEMjAlCJAHQhlgFhgvx1Qml+fvXyYgoNOWQMCLYDxJ464SJCTMKWUi/c3ra6ARz72+UcaVzP12UJ1GfeyFDYeADz+w4dDoKxX66YNNMOX2x7xtgqNo6XO9yAQ8BTbSCFdDHciABUIgBdiGFAmIGvnLOtKO5Sb8ihlhg4d+GWXx/7mN0pF1z1wu1toy6z5gNP1fdGRrKBM+YRWd0gACne2bpnEzIcxGS/HKIGk7KIkjfPVOZOR5+n01dcNZeAx9pAihtGOBABqEQAuhDDgGxjf4oxZd3WkG3gDAivqr+FBf2z4Bpg6wYpbm9qp4MPfM+XtusmGgDuItBVwDd9CVgpmne2+GyK30/lWDvXZU3MxJgSu0dJSRSinVddl0PAY2zAd8hArLhCEYBKBKALMQzIduCz3VVaR9ODgTGzaGx9VXW8IRKsmmJyuJ8RJfic4jH0eSwYVUjEwOU52XpnOa7NiavIoNomp3eeQ0jbPAcuAW+6ts90PjHjCkUAKhGALjTFA8g9OHZLMLBpFo05Nquq48UsRXGVver3fXkesEtPIXZI2i6BmoREaDjaUn4ihG5TQurefddlTezO5pkZe++ZTihxi3isDYQQ8D4TescMRxIBKALQiZgxgL52plY15HIgP9cgbnPv3J2d7S7c/PWud2WDb16WEPEyPq+B2ZBiGjxtvNS2nhfqEmhKQoh7AEw1zRMWl/JT8j/m16J4z3xNemOLIAw+Ng2lEFcoAlAEoBOxdwHbiMChIVpDpiwx+Wy0Pr2ONrtw68oRIsUG9RpY4YytY9Pg6eKltvEilMuSL72nLIR8DYBUYZPKEnCO7bImxQOYQ2kXVJGG+e0URBAW7slFComoRQCKAHQitTyA5d2/VZ1JntcO05Bt86xxN9oUZsmdDs77GeLMZco1KKKOUsem5bCQ56RWlSWlDQ5V+BwAuQV/aGxEqa/6tBFp2HpNQQRR4GxTsc6mLyICUASgE7ENqNxRYs53VYonL1eoRpvKLBlbDmz82/S0vdcFO3DkJ42YRJ2POp6YsLMdzsEuNe9WEc4TW2xIYVJFAZOSBbML2FdbK6fXwdRrCiKICpcdpiB+Y4/fKSAC0IEUDQg7S+PKy+W70abQUVDKYdplCKDP04gBE5OTnzWMEXXcdWzrOW61lFpasng4DYRS59yeulQmVVjq7r+c8J6yuoGFItKo9ZpK3xYDH3GFVFIcv0MDsQvQZFI1II5ZWqhAfq5y+J4lU5ddKTGaNgOUKSYHe0rB/v205LgmXDzH3TrYVYEdAOfm+Gwmp0nCgzKZaLczzzPn0j+lrmxic2OLoJjE3rSU6vgdkjUgdB2tFsAllwC8+93Z31aL/hubNtE+39eX/b3lFrvruZaDWl6f5di+HeDeewFGRlb+W129KJX9HR8HWF7GXafuGu129v4FF+B+5+hRgLvvxn0WUwcHDwI8+STu96o4dsz+u02i1QK49dbsv/O2k5P//2c/C/A7v1P9fRubycHW8dGjtN+1ZXkZ4MABgHvuyf7m97O8DHDddafu1cTRowA33wwwPQ2wfz/A3r3Z38OHs/Ziw9atWZsqP6Ocvj6A0dHsc9h6zT+HsQFKf1pXjzZw/lYdpj7M9pkJBGIr0CbTzTMIaroZXwH2qcySbcpR9MTefLMfr0udtxfrjcCmzxgextWxnNBBQxeyQfHiUsDaxvCwfy+MbnnbJgzFR3+A9VTZelY5NldwhgmE3hwkJ4HEQwSgA91uQJhlxhCNNvZSgWs5Fhb0SXqLL66lbKxgxabPGB/HXdc2djS15a6Qg1LVtTodfzaDndz5bl+mDRPY0AQOUYwpK3bjnCk2d3FxtV25JpLm2tDTtM1BLnT7+I1BBKADvWBAqaTTaGo5qBsiFhf5hAdGsHLHg9kkKk9tcEkhPQpFSFOejSmhu60wpwoYzIYJylGMrqIYA+YedW0OYPXxkK52xbmhp2mbg1zphfHbhAhAB3rFgFJJp1FXDs7yYRO4Yq5H2RDR15cNDtzCwyRYfSyxUwfBlPLzpeIBwS6lDwzgnk1dwux169xFpo1gpixD2+won5qi1rgZl/yKVeeCc9gV5wSuSZuDOOiV8VuHCEAHYhtQKsLMhM9yphz74pJKh1N4YE464V5i1wlPDnvwYVMpeUAouR5N6EQt1g7rPGq2ghkrcMfHaTvqi9fnFOtV9jwyUh8GU7TPxUV8SiYqnFkSuH6rKeNS7PE7BUQAOuDLgLBLDbGXqXJ05bUpp88jmurw4fmheHHqPAShhIePJXZfA4Ev20/JA4JZSh8cdF9qxb6q7tlFMLvmQcQIQK42gw3jqLNBn3aVmgcwpXHJhAhAJQLQBR8GhGlAqSxTmcprU05sB9KE2Bdsh+prhzCVJszcfdp+Kjknc3T5JLH36uqF5hJxZaihB2XbvPHGMG2GGsZR9Vx82hVnCIfrb9m0zZh9jghAJQLQBW4DMs005+fTWqYyLS1RvVqUDiS1mW8V3DtxUzoSKga+bT8lD2BO3YQIuwOfkpaHGgLgKmxcQg9CiXWqgK6yQd92halH6qoK9ZnYtM3Y3kIRgEoEoAucBoSZabZaSu3ZE3eQyjuSmRm3XXrlclI7kBRjX6qIsRM3BXzM7H3XUyo5J6vKVdy9W44n0w2alFhCaggAR55CW4Ebqs3YpqIpXjeEXelCOKhCyyYchPo8UljFEgGoRAC6wGlAHBsGXMWKCZtYHGw5qR1IEzyAunor78TVeUsBcPFeqeBrZh/C65NKzkld2SiDJkV8UET7woL5OdikkKkSoroQEN+iiqv/DWFXVc/PVmhRJ3A+z0z2hQhAJQLQBU4Dcj1BgUus1EHNZ0ctJ3VwTyn2BYOuQ01FAHJ47bhm9lVlCeX1SSXnZBGXQZNbfGDj4nxvxPIpqlw3z1TZYGi7Cim0KG0zlRUPEYBKBKALKXoAbWbdpsGeayehrpw2nQLnABDT85NCh8jhteMacOrKMj8fbomWKoZ9B7O72gin+OBMUZNjazu+RJVLf6yzwZCbHkL2K5Tnl8pmKxGASgSgC6FjAKsalY1YoQ723MvTVeW09cJxDgCxPD+xO0Qurx1XGgldWSYm7IV609LSFOGwEa7792GvrjuKOZ9rp6PU5KR9/zYx4a9sFEL3KxMT+j4/bw8pTHiVEgGolOouAXj77berc889V/X396uxsTH10EMPaT//i1/8Qn3gAx9QL33pS9XatWvVBRdcoL761a+ir+djFzC2k7EJ2s6vQR3suZen68rpsgONq5ON0WHH7BA5l4lcBxxsWebm6LbvS6SFCmZPZdD0VZbYk6AcjjjnvL3E3uUa0mZMIUJlUZzCZisRgKp7BODs7Kxau3atuvPOO9Wjjz6qrrnmGrVx40Z14sSJys8vLS2p173udeptb3ub+uY3v6kOHz6sDhw4oB555BH0NX0Y0Px8ttsXMyDbLFPZDPacHsDJSXNweWrxV77BeH99dYgpbaahfL9s+0tL9W3BV34yzHNrt7OTIFwnFKkMmhxliRnfWXd9pXjjnKem9PUToj8LZTO2KWB09Se7gMMAsQvAxdjYmNq1a9fJ/19eXlabN29WN910U+Xn//RP/1Sdd9556vnnn7e+pi8Dmpurb0gunYdtJ2vqSACU2rCBrwOPuWwSC93yCcDKGTQnnJ4X1x2ntmXReVp85iezmRi5eIBS2qFsW5a6us29ur7Fiun61OdZ9xoY0P97qF39IWzGNn5b91kRgGGA2AXgYGlpSbVaLXXfffeteP+9732vuvzyyyu/85u/+ZvqPe95j7rmmmvUWWedpV75yleqT3ziE6pDaJU+DciHJ8xlsDd1JKE68BRxFazd4gFUCjfg6HK/2QwkOu8e9Tcp3kJsAm/Ogde3h5yaCoZSFp/xnRh016c+R45XcZOMz0mvjc1QymObwUHXRmQJOAwQuwAcHD16VAGAevDBB1e8PzExocbGxiq/8/KXv1z19/erf/fv/p36H//jf6jZ2Vk1MDCgbrzxxtrrPPfcc+qpp546+Tpy5IhXA+LuFHzvJEzJQxEKjjifmPFd8/Pm61I7Y52dmETA4CB+EoEZSEyemOLgRB2YpqftBn7XAS6lzSzYsmDrNt/pTRErGLgyGXzsY+aJrimlU/4aGMjKNT+/Oqk+d6wgxWbm5pQaGsKXh9p/pRLPKgJQ9a4AvOCCC9To6OgKj98f//Efq5e+9KW119mzZ48CgFWvphgQR0yIqSPppRg+1w0AeV3u3o0XKZxgB8W5ObvfLtsJRgTkgydmEsEZm0rJTzY9nd2LjQcw5ABHwfdmFpf4zpB56jDlM010sV5nAKWuukrfHkL3m9idvEqdek75qVDYcSWVDT8iAFV3CECbJeB/9a/+lXrzm9+84r37779fAYBaWlqq/E5oD6AO204yVkb6bsN1GcNmtyG3YAg9E6fkj8NMIrADycAAbnCi7HYfGsIfyxhrgMMSYkku9qDvmsmgXAdV7XdgILPdZ5/lW1YOGTYzO4svD7b/cpm4iQfQPxC7AFyMjY2p3bt3n/z/5eVlNTIyUrsJ5Pd+7/fUueeeq5aXl0++d8stt6hNmzahrxnLgFyXHZvipUtZSLp0YtTdhr5iYkIPytjrjY/jnj1FUGImPTZeope8xH5wn5xMw65DDMixB33Ks8VOjjudzLbKYQbl5VPXVwhP8fy8UmvW0NoT5rNV40oqO9pFAKruEYCzs7Oqv79f3XXXXerQoUPq2muvVRs3blTHjx9XSim1Y8cOdf3115/8/A9/+EO1bt06tXv3bvW9731PfeUrX1FnnXWW+vjHP46+ZgwD4lqqSVlcKRU/h5YJW/FEjUXyGT9JEVAhrzc8jLNHykCCmfRgdrv7eMW26xATgdiDPvb6lBhEzrQxvuodAyX/LIA5rnZ4OFsW1o0rKcSLiwBU3SMAlVLqtttuU+ecc45au3atGhsbU9/5zndO/tvFF1+sdu7cueLzDz74oLrwwgtVf3+/Ou+885LaBVxFKrunfBMquS6GOqFs69GgeplCnBWKGcS4QgPKwe51r8VF3ASFMpBgJj11v6d7vfjFbgN87E1Sobxz3IM+dRKLvT5XDkiul08PoK/70JU5r9/x8dX9QciVKBGAqrsEYGhCG1DsZZQQpCRyMfnlqB4NrLdl924+z6xuQMN4MTjrfHwcd/9lL4POS8Yd0mATn+m67Bdz8hbSO8f1rGxXCLiuz7kBSffCesNtod7H+vW4z9V5Lavqf2go6xdCr0SJAFQiAF0IbUBY8ZC731Nd3tWRisjFeCFtPBqh7w8zUNrk37PFduA0eYm4Qxo6HVqal/Fxuucwhl3XEXJJzvVZce2+N3n4dJ/hPh6z7jU/T6sbKtT7wG58osQ+x/KAiwBUIgBd8GlAVR0QJYZKN+CnTAqH3lO8kFSPQmhvC6bDDbkZxGXJKbSXrNPBe/by9CDle8MueXPWsS1N2BwWYoUAM2my7YtHR/ETrquucq4uI5QJ2cREduQixqbLwjWllZ0cEYBKBKALvgyorgPKA5SpXobYMUYUfCSrrhLAOpFILYOvWCQXKB1uDK+ki7cspJes7ljG8qBeTE5dtAVqrsDY4Rupbw7zbavYSRN2Ild1RjUm9jZPEu0bTFnWrMlSxFBCI8piLpWVnSIiAJUIQBd8GJDtcUmmV18f38H0PnHxkGE7b5NIxHrEJift69C3t4WaeDf0Ds2q+8eeoBDaS2ZKjjs/7z6ZyAf9xcV022UKwpCSSogK1UvlMpEzTYJCxsSZyjI/b7fjuSjmYueBrEIEoBIB6AK3AWGWx4aHlfrgB1cvTVGXmsqiJyVsOlZs5z03ZxaJlEHbpQ59DqrUDjdGWoby/S8u0geWqt/xMWhWHdc1OpqJQ91kwia1TGrtMqWUTJS26WtjR9H+XCZyVd9tteLUs+4+bMM2imJOPIBpArEL0GS4DYgaID88fGqmGONgeldMu1MpHSu27nQxXcVlG+ygHbsO6wg9mHHQ6Sg1MmJ+PiY7sRk0bTYGYCYTeRkpXvuUbIojcJ9ToGPFiI3X2naHvsv9FVOixLYF15RXur4ldh7IKkQAigB0gtuAqDuybL1WsRueUriBm9Kxcu7KywP6sYN2zBQedVA63GI9Ly7GCxNYWDAvA09NrV6Ccx00bUSkzXJh+fM620rBpjgC9314DymJiykeJWofyuWdS3GDRBGbcamqvCkkfy4iAlCJAHQhtgew2NiefdZuGdimo3TFRzoAzrxcxWVRytJH7AD+MpgON5XlPUqMUbuded84Bk1bW7TxsBaFNjbFjKtNuXinODZk+Ur7gc0nSYkpoy7XcwmXGMujFLug9K2mOom9ylBEBKASAeiCrxhAm92RroloQwXf+prtYjxeWIFcHrQnJ9OqQwq6DtdmgPYRb0eNMaK0D92g6WKLrkHtIYLiXcW9Sxld6hZjY75Ek81yvat3LvQGCapdUMYljJhLZUORCEAlAtAFn7uAbVNk2L5Cea98znZNHi9TGp26zhy7OWFxMft8Kh1cTlV5bAZoX95CTu8tZdB0sUVXO3b5Psa+OLxvMeqHksbJV0wZ1fPv2n9y9InYPsfWLkwe+ssuS6OvoyACUIkAdCFkHkDb19BQFlifSvCt79muaYnBJg6FIgBTWVI1QR10fC7n+TxVQTdocni4bNuV7fexsbMcXnZqGYsixMZrTrUxnzFl+b3s3u23v8qv5WJLVNFsaxemdEip9XEmRAAqEYAuhDgJZGYmW7p08QhOTaUTfBsi3sU0G6bGoWCFwmWX1Q9g+XNIxStIyXWY26CroKjDhwcQUyauGDfbdkX9vk4gAZzKCGCbTseljLaT1rwMtuIkpXyaLtjaEkU0u3qdfSzpx1wtEQGoRAC6EMqAXJeF9+5NJ/g2lXQAPoKg16zBP5PYXkEfost2EHSJfc1thjJoYq9ru8uV0q6w36fESQ4M4PsFjjJSNvDU1a3vJXFbQvZXVFuiijIXj7ePJf3YqyUiAJUIQBdCGlBVY6Fuauh0Mu/A5GT2inXqgM1sN+ZMsdNx32RT1TlzeF9t68VVdLkIiipsg+/zuE7KoFmVyw9ri3XxlC62ybnpgfKiCPa6MlI38NTVbYonReSETF/iY2KaP2cXkW3zfEwea5/9IgYRgEoEoAuhDajcOfy//6+5QZpykcXyRFFmuza71rjFIjbtBHUgdPEeuD5P7g1HHMtg2CS/xUECm8ewrr6qTvOossWY7YczTpLTa2UjTKvqNkYqFAqprKAUoYqyTsecZ3P9+izkY//+lecYU9MW2UwMfNinDhGASgSgCzENCNvA5uayz/sM4ne5B+7djE3cqeqy49n1eXJsOOLssKs8dOXyDQysTAitu5ficpOuvnTn+XLWty1c9sddXkosqemUlRRCQ3SktrufKpoxArD4Kh9LV/7/8qt4ljWHvfoW+yIAlQhAF2IaEKXx+8q95xtquamDNKVDxwxQpg6y7kVd1uJ+nja7N30JirryTU2tjm0rCnvTcpNu4MPu2I3ZfmyX7Mufb7UyjycXnJ67kEut3QBVNPucxJbbJceKie/lfhGASgSgCzENiOL+xzb86em0RKBPkWvjKTQNUBMTdkuqxcERI0o5d3na/nb+CrEMhvHecaRN8pGzz0c9uC7Zc4opbs9dikutKUMRzVxhBKaJbiohJSZEACoRgC7EMKBcIGA9NbmYwDa62LtTi/gQufv3uy3nYXZEYsWIjShdWODf5VkEKwA/9rEwy2AYYe9yBCKmvlLaoJDakn1eJp0AXFjQbyLh3lTTa2BFM6cH8Oabzf1Qq2UnBiUGMBwQuwBNJrQB2YoLSsNPabkFW+58ZzPmszMz7st5pgGq+O/YHIwYUUpNt2Ezg05J7CgVbtlKV1+peABzcvtyXWbjLO/ERHXM2MQEbfON7wloyuLSpWyY73Lu/KeEilT1fbrPyi7gcEDsAjSZ0GlgKCkyyrskqYecpxATiFleGhykeUSou9k4MM3QMV6udtves0jBJrDc54Dq85QQbH2lvEGhyrawgf6cZ8tS031wCACq7aWUBSFW2bjCCLACcHx89X3plpBDLveLAFQiAF0IZUDULfV1KSyoDT9WyoUiuhgXm0F+Zsb/4Ehd1uL2crnMoCliJ8Sgha2boSHzRMFlg0HKGxTKtuUzRrTq2lzHVlbZWB1U24u9i1tH6LK5hBHkz4ZiY1iPddXOfp+IAFQiAF0IZUDYQXByUj8Tpjb8GElXq6jr7LGejmJH6ns5z0YUcXq5BgfdBwyM2Ak1aGEFKSahs+sGg6ZsUAjpsYyRHslmt3+oXdzUyR/W+1+X25KjnHVhKrr6pdpYCjvpy4gAVCIAXQhlQJxxWZ1OnGVQV2y9HGVRxD04ljtSU8dZBecgurjoXtdK6cVO6M4c633DCDTXJeuUY8iKhPJY+lyir+rPbGwvVAxn3XJ8eaJanBDatH1fS8OmpdpyW1pYwPd3qcXRKiUCUCklAtCF1DyA2MaTckwTFsrAU+6MuAZHlx2/RTDPI48BDPnM6sROjM4c631rikDD4nI/ITyWoT2ANrYXYmOTbYy2jYD2tTRctrXiSSB1ydGrVmGqViFS21ymlAhApZQIQBdCxwByDv4pxzRhoO5sLtcPx3KgTSC1aVkLs+wa+5lxe6Qpybi7SdyZ4Iix9F1noWMAbWzP94TFpg6osXQcfT4nmNQ/RSi5aEO1bxGAIgCdiLELuG4zxPh4mh4CX9ikNCgnXM7Tx0xOnjrCiHJtm45bJ4owzyOFZ8Y1oC4sKDUysvI7IyPNsD/fpLxpoUidF6jYN5X/G/NyDZcot3Wf3nMXL+jioltqFl0b8yX+bZbhMf11ecnZ9w5tEYAiAJ1IIQ+ga6NpsleF6oXLxZerZ8WlwzeJImw+r5jPjGNArYsfMgmAXsB3jCWX/ZjaX74UaJOqpu75Y22vvHw5P+/Pe+4SB7l3r1tqlroJpc8d+rYTQOp9+p7siAAUAehEzJNA6rbUuzSa2MLChoWFLAUItkPi8KzYxu2kHldJwWU5utMxC4DBwe6pKyo+lyy5hAHGC95ur9wFmvctudfLtq2YbK8uwXTV+xzec44JoW1qliob8O09dgkBwTgxQvWbIgBFADoRy4B8eAhSTpJqYmlJfxxY0SvAUW/UDj+1ZTsubJejsXFPXDuam4avgHlOYeAiUjkEbp3t5edx193j3Bz/JNcmHKUqTUoxJOWBB+y87CF26Ls+v+JkIGZGChGAIgCdiGVA3B6CpsQb6cAsKXLVG7XDTzmuMkZaFOwpApOT4e4jJXx4ALmFgYtI5RK4VbtWbe6Rw3Yoy5uY9EW5x5LqZQ+xQ58zpjLm7mARgEqdBkLjOHaM73PLywDXXZc1szJKAfT1AYyPA2zbBtBqkYqZHFz11moBfPazAFdeufrf+vqyvzfeCHDBBQCbNgFs3Zpm3e3blz37J5889V67DXDrrdnzPngwqwvdPbRaAJdcEqzIlejuY/v2eOWyZevWrPxHj1a3y76+7N+3btX/zvLyqWd44sTK+imjFMCRI9nnMc9z0ybzZ+o+5/LdImXbO3CAfo9ctrN9O8C9967+rcHB7O/Pfrby92+5JfvOvn0AV1yx+jkfPQpw880Av/u7APfcs7p8+ffLcI4NdbRaWf1ccUVmi8Wy5/3fLbfg+jwuWxAsia1Am0w3eABTTNBJBevd4DoiSxevk7K3r4jO6wugT17rCucScDd4r6twTfljG1OG9bRgvODDw5lXjvpd22VKqjfJh+1QTgLB9lumfHxFQvbnHBkJYuakFQ+gkiVgF2LHADbdBc8FttMzpVzA7l7VDXrz88Fu2xrbvGVcgoprEwj3sib3MrLr79kOsLY5KqnCALPsWTdx8JHTkiJ+UjiazOdSfyhB5XP5XHYB+0cEoAMxDYir0YSKGfEZn0URsa67V2OeKcqF7a5FzvvjSANja7tVdavbBGXzLKp+b2CAfuA99dq2OSptn63J06hrV9w5LSniJ4WVD9+bfUILqhyu9uJ7NUUEoAhAJ2IbUBNc8CF2F1M7c0y9VXVi3INGXUfpu85cz2/lGhRd7xN7HzMzp+p5amp18mlTImPqcjg2P54PbMS9qzBYWtKnYtL1IeX0MIuLYbxJKax8hE73EyI8xaVNh97IFXv8TgERgA6kYEApu+BDxWfZiNi83mZmslQEuUjQeYPqci/aDBpV1xgeVuqyy/wM0kVc8pZxD4ou9ou9D2yeSA7BRPHA+RiMbcS9qzDwldbFdtKDET8peAB9T759C6ry78/NNSseN4XxOzYQuwBNppsMyNdyjG4Q5YxFsRGxlBMKKDFVmE0kNjFaVbnDbDp4m7xloQZFCq734fKqs1+KuPYRY4a9PueZq67etFCbMcr/HmvzQZHYy7W2pJTQ2ZZuGr9tgdgFaDLdZkCcM8YYM2yKiLUVYa2W26BhG6NVrjNXj4nN8VMpduK6AZRD6FHtl+qB4xbTGFFcPKGDA5e2HnMzRiriK9ZyrS2hNhn5ptvGbxsgdgGaTC8ZEFUcxoqxwZSTQ4TZDhquy68A2VI0h8eEkiokZY9E3XK6az1jXjMzK8tCfb4+YsxM4p47BtHFmxZ7KTYV8dWUROaufWdK2SR6afyuA2IXoMn0igHZeJtid+w6XEXY+Lj9oOG6AcMkbqgek/l53DVT9kgotXoAnZnhFXp1r6Gh1ZuHKAOkT2GjC2fgFvO23rQUNmM0RXyFxmUjXEr9fR29Mn7rgNgFaDK9YEC28TmpxNhUwbEL1nbQcOlA+/rwni1MR4sRKwMD2c7Mpg2KHJ5Wymt8fOUmItMSmW/773RW73b2fX0bb1rKE8VexnUjXEr9fR29MH6bEAHoQLcbkEt6B6XSibEpEzMPnu3GhbzOOHcid/Pg2+lk4pVSx2WPWf7/lGeVe8ZDe+DKxHq2lJMw8s+nOlHsVUynBNn0mymGj3T7+I1hTchj54TmsG9fdubkT39a/xmlTp2tWUV+PubIyMr32+3s/VjntOZnrebnVmKgnnFZR36OZvE3MeR1tm0b7vOYszNDnBsai1YrO5cVw/AwwMJCdl7u/v0Ae/dmf0+cyN4fGsJf9+jR7IxUgOz7U1MAAwMrP1O0/+Xl7Azbe+7J/i4v46+lI9azzc/nffe7s79f/jLAli0Al14KcPXV2d8tW7L+Jf98XXvI//+zn836GO46ElZjOhseIHtmur6r3D/G7u8FDbEVaJPp1hkEdZeXydsUMsYGey3T7tGy94Y7Bg5zggLAyqXF/P64PCbd7AFUCnfkXPm82ir7ocYTYtP1+Ez4ncKzpYSP1G3k+chH/CeSbyo++lXK6kjdys78fDNiKrt1/KYgAtCBbjQgm11eqQgE6oCqi1kKIVrza4yPr15q1wlOrqX1Xlh+cxUh7XZ2egilPWDahe8k6bGfrU16l/l5XIyrzyXFpmwI8TV5wMZHu2yES4VuHL+piAB0oBsNiDoDTEUguGxWSaHDp5bDFHDP4QlNMW7HhomJ1UlqW63s/RyT/QwO0mOgqjzjnU62qUYXn8jVrmI+W5vjGWPnpAxxbCUHPicPlOeWSt9pSzeO31READrQjQZE2SGbikCImUw2JtSlxbk5/OebNpuvQycscvvF2I/NhpCyB5CSd7Hq+7b3H/rZdjpKTU7iRbJLbjnOc6l9emS58N3XxfYch6Qbx28qIgAd6EYDws4Ah4fT6RRTiHdKBYonpejdaPpsvgrsYLm4iKuvqSlzTGHdIGlzegJX7ruQz9ZG5Lqk7OGooyZNIG37OooNYD3HTe8zunH8pnJarM0nQprkO2SPHs2afRXDwwBPPgmwdm3YstXRzbtZKeh28FWR71jNd+hdconX4gXn4MHMTutQKtvFfuAA7vd+8QuAn/9c/5m+vux33/Wu7Ppbt2bvU55LDmYnN4Z8Z65v9u3L7Al7n+12Vj9zc/bX5KgjrJ0cPMhXj8vL2e8dO5bdw9atuOwCNn3dvn2Z/RXvcWAge++GG1ZfN8/eUP5Ou51lQdi+vfo32+1sR7dpt6/tvQseiK1Am0y3ziBMO2Snpuxmfb5mjL3qASzXJ9aTlap3gxtsOMM734n7nC4nZv4qxxrabCJp4jOxWcbNj6Sz9QAODvLUUejTSFxiDbljK3XHAupCTGyXy1OKs+zW8ZuCCEAHutmAqhrq4ODqJTBs4/XZ8HspbiWnqj6piY+rBoxugus0EMoJLFXfpX4+pZgzLDZ1nd/r3JxdcvSpKZ5J5VVXhWsjrrGGmKMb874OK8ptMgfofquur00tzrKbx28sELsATabbDajYuU5Nuc36fDf8btnNihnQbOLJTK+UDmnHgKknTB5ArEixPQKL+mrq5hvb4xVzwTA/X91+616Dg9l3XCeVS0urvbZVrzPPdJ9AusYaYgXd3Fz2eYoo9507NMU4y24fvzFA7AI0mV4xIJfGG7LhY3Y8ci1Dm463srnGwsLq81tHRlaX33bHJKXDThmsN5lDAOb24/ts4Ze8JOyZy9zhGK71s38/Pg8gQJbCR3dcGTZMZXoaX0ZXYe4aqkL9PlWUY/oA2+XyFMN0emX81gGxC9BkutWAbGPLqhovR8OnDFa6z3ItQ+t+x/YaCwu4wYdbiMRcHrcRIRRvsktdTU7STmDheIXy/PkIx3Ctn6rEwuvXrw5rGB09tWSM/W3dve3ejf8d13biGmtI/T7V/n2eHx46zhJDt47fFCB2AZpMNxqQS2xZVeN1bficoo1jGdrmoHRMMmqTpyoPeMfWJ+aZxVwet3muVG+y7bJknT3WhRlgXyYbCSHEudpBlXh3rZ+6MpW9eVRho7s3igcQwM1DhS334qLb9/MyUlcLMPdmG28tHsA0gdgFaDLdZkCusWXcHkDOwYpjGdpl+VV3DayHdXGRNogUB+iqeKlY8Wa2z5VqSy4ewLqBqE642pwUEnoAdA3lKMYDUzzgphg73b9XlclG2NfdGzYGMH+5eKiwXtK6iZCN+DKtLJieexU28dYpbtTrtvHbBohdAE5uv/12de6556r+/n41NjamHnroIdT37rnnHgUAatu2baTrdZMBucaWDQ8rNTNTHQ9n0/A5Ywe5Zp8cy69V18CemjA5iRtEhoezga3qGRdF4dISz9I6BZfnSvUm2yxLYuzKp/fL5xKYbTvAJHcuDv7l+qnb4EGpq2KZuIX9xITb9ylg7EQnpGzE18JC/QqD7SqAzQkzqW3U66bx2xaIXQAuZmdn1dq1a9Wdd96pHn30UXXNNdeojRs3qhMnTmi/d/jwYTUyMqK2bt3a0wKQM7asPIO1aficSwZc8ScuS4r5a3x89e9SBKCuPnXPoAxlCZYzZszludp8lyLMXAeiqnqipo/x6QG0aQeUVQFTCpAqwYDdXV0sk0u8YV0b/93ftb83KlWbvTjq0hQ+MTVVHVNpa++2MbyprER00/htC8QuABdjY2Nq165dJ/9/eXlZbd68Wd1000213+l0Ouqiiy5Sf/Znf6Z27tzZ0wLQd2wZteFzBg2n5AEEWH3PlCVgXX2ankHxu9glWO4UPi7P1dabjM1pyTEQlQfFmRm8XfheAvMdQ2ZqR1WCwcUraeNxrUpPkpfp93+f3pZscdlYV1eXGFI4vi2FMijVXeO3LRC7ABwsLS2pVqul7rvvvhXvv/e971WXX3557ff+4A/+QL3zne9USimUAHzuuefUU089dfJ15MiRRhlQ3vBmZrLg5+KSLTW2bGZG792oGowpDZ/TA8gVf8KxE7SuXrCbQIosLZlPp2i3q5fkMeXzkcLH9bnaLiNV2V6IgYgyafDtBaG2A9sJD2UZ26VtYpamdb8TcmJQxsbzyU0qQiwWIgBVdwjAo0ePKgBQDz744Ir3JyYm1NjYWOV3Dh48qEZGRtRPfvITpRROAO7Zs0cBwKpXEwxI11m22+Zs/LaDg+2SFnfQMFf8ie53KINklUeDKg6wz2Bqiv6d/fv9PGOO55rKMhJmAMVMGlqtU8l7fWNqB8UdtxTvpUubd2mb5c0p2N8xebZtj7vE0OngjhV06T9N+EgF1DREAKreFIBPP/202rJli7r//vtPvtfNHkBMHE9f36nkqpgONEReJ+6gYS7hoPsdl5k9tVOmxCTmv0F5br6eMcdzje29oMZQ6pYrBwbCDrxYzxc1ftElTi5E28zx4dmmgJ1YDQ/7KQN3WEdTEQHYJQKQugT88MMPKwBQrVbr5Kuvr0/19fWpVqulvv/976Ou69OAQu26LHd62HQhofI6cXt7OOu16ndc64VSPmwcUV5n1Jgrn884FS+eDTYDqI+dmC6UPWd15aKIP9d78N02c2LnpMNOrKo2jLkSW/ymhAjALhGASmWbQHbv3n3y/5eXl9XIyEjlJpBf/vKX6rvf/e6K17Zt29Sb3vQm9d3vflctVeXQqMCXAcXYdVns9DiWtjg7ktjeHgqc8Ya6e6bEP5WfLbZ8vp9xk55rju0A2unod37GGnhd0z81TbwrFf9UijrBHUKAxha/KSECsIsE4OzsrOrv71d33XWXOnTokLr22mvVxo0b1fHjx5VSSu3YsUNdf/31td9PZRdwrF2XNp1eanmdUsG1XkwTANuE3fmzpZRPnvFKbAdQmx24IcQxtlzlmLV222+cnE9iiiCs4C5v3sq/62oTNrk0Oe0wpUmfCMAuEoBKKXXbbbepc845R61du1aNjY2p73znOyf/7eKLL1Y7d+6s/W4KAjDmrkvbTq/JS3k+sa0X0wSAeg5q3bOtE5lVgzr3M15aynah796d/UU63JPA1ntE+V7IAH1sufKMASkM3K6EXL0oY7Nxqy6Hn41NUMQvtx2mtvFEBGCXCcDQcBuQz12Xpt906fRSmtWlBLVeMBMAamC+7tmW48B0nTPXM56YWH30VquVvc+Bb1v07QHMd7JWPUMfHleXPqfJ7T6WZ5s6geCOHcWK37k5XjtMceOJCEARgE5wG5DvXZcmkdDrXrvY2OZdMz1X07MN1TmbjtxyFYEhPAy23iPM99rt8AH62Ani/PzK7/ms61DCMsbqBdUDh2nfVJswid+qjYAu10x144kIQBGATjTBA5gzP1/vPZIl2zTgOGqO+mxDdc5LS6s9f+VXq2W/HBzSw2DrPcLk4PPV/nXMzeHsqBgS4KuuQy8ThvZiYicQS0u0UA/O0B3ucSjVjSciAEUAOuErBpA7NqWqsa9fr9QHP9i8pZtuhpIfTGcjw8MrT3nhuKZr5zw9jbvO9DT9t2N4GFxiPOu+F2t3KsUGfNZ1isuEPsBMIKirATY2USd+ue0w9q7rOkQAKrUGhGRotQBuvTX7776+lf+W//8tt2Sfw7JvH8AVVwA8+eTK9595BuC22wB+/nPa7wn+2LoVoN1e/exz+voARkcBPve5U/9f/ncAgM9/HuA97wG45BLzsz12DFc27Ofq+MEPeD9X5ODB1fZdRCmAI0eyz9myvAxw4ADAPfdkf7dtA3jiCYD9+wH27s3+Hj4MsH27/ne2b6//3qZNuLJgP4eFYgO+6np5GeC667LvV/0mAMD4ePa5prN9O8C99wKMjKx8v93O3t++nd7ebGyi1cr6iHe/e2Vfgf2ts87iLRu3XQtmTotdAGEleedw3XUrO9p2OxN/pgGmCKZTve66bDCrEgrLy1lHfuxY1ji3bhWx6JtrrgHYs6f635Q6ZQNcNhKqcz7/fNznlMpEFsXefIvYffuq6/rWW6vr2tRu8oG3TD4BOHq0us329WX/vnWr3X3UQbEBX3WNFZYHDmT1d/QowE9+AjA8nAmppvVN27dn/W6dnVDa2+gor02Y7DDnfe/L2oDuPjC/19cHMDSU/fuBA817lo0mtguyyaR+EohNyoEcaixOk3cEpgA2sXP5SCvXOg+VEgMTA1h+YWO/fJ9YQlmWdI1hi7E7lWIDvjIVTE7ifrecCsWmjpsA5gzp/Nn4uG/T8YX5tQFW71KuehaY3wv9LGUJWEkMoAsxDQgz+NucFatU+EHPN6mLU2xiZ1+75UKJDtMuYKy9lfElYqnxblwxbL52p+raAdYGuOva5kSb0GKIG2x/ZBJNg4N+j95bWNCfXkNtt9hnHSrmUwSgCEAnYhkQVnBRAonLx3+FHvR80QRxanOkGzehUmJU5QE0DQYYQWFKmeHzGEUfmyO4Jy2YdoC1AZ2QpwpdzMSH8kr9HFtqf1T1+YGBbNXG5T6x5aCcN46x99yuZ2ZWny7j0l5sEAGoRAC6EMOAKIKLKi72709nR2DouoqFTe4/X7vlQnlKiyeB7NqFtzcd3AKQsiyZ1xfHffiA2meYvEI60YbN5ch1BnEqdYzBtj/yMRnAlsM1NVXds0ihvYgAVCIAXQhtQDaCC5NMtDiQjY93x6CXujjNselgUx3gbOBIEcH9rKnLkvng7HofPuCsG4xow/4WZeJTF/eXSh1jSKU/opYDm5uS+ixSaC8iACUNTKOwScGwfTvA1BTu9886C+Duu3Gf9bkjkIMQqUE4oOz2y9PAcO8CjQnHLmTOZ12XNqmK4vNINdUFZ92YfgsA/1vYPmFyEmBuDvfZnBTTiaTSH1HKsbwM8MUvul2v7lmk2l56DRGADcJWcN1ww+qcU0XygQwgS61gYng47UEPIG1xWiRPkYCFmgcydbC5D3WiF/sMv/ENfR45XdqkqnIBnHoeHPfhA852wPlb2D7hzW/OUubo6rZI6Dou54ess69U+iPOnI86TPaeanvpNUQANghbwdVqAfzJn2SNSpdg+sc/xv3+e97DM+hhO08K+W8eOoT7fOwZZquVJWI1MTp6KklsN+Ga/Hx5GeDECdy1Pv5xgC1bMi9fFZQB78wzAW68McuBBuAniTsHnJM0zt+i9B3FutXR1xe2jvfty+zp0ksBrr46+1tnX6lMln3kfCyDsfdU20vPEXsNusnEigG0TcFg2uVnE9Pnci4q9+5cSuxWKjGAmLiq4WH7M3Kbgs0uZJsUIjq7tInHtN1Jawt1QwBn2hbOGECl6H2H7nmHPs+cuqEjVL5NEz5yPpZ382KeRf774+Orz6gP9SwlBlDJJhAXYu4Cts3ZphtAbDsp6qA3P08fnLH14ioCfFJV9ylvpAkNRdy4pBCps2ObHdk2O2ltsZ00ceZ5NOVyxO4C1t2Tru8ophGZnlbqL/8y+4s9+5oD2w0dMZJ8V8Gd83FpiWbvVc98aCgTgyHztIoAFAHoREp5ALlmTbadFHbQm5vT54GzmQlTU0pw1RVVsFQN3pRd10IGVwqRsqjGnr7AYbNUXFMacfQZ3B7A4u/q2lHdv8fK8ekyaQuVb9NEVTmGh1eLMG7RmlJqLhGAIgCdCGFAdZ2fz5xtpk7K9tqUlDQUjxe2Q56c5KsryuCj6/R81EdqcNuqjaeu6jU+vvq3KUdWVT0jzL3aLuO6ClDX5xDDW13XziYm4gkJ1xQmqZxMpFuGLfZlXKI1lVQ4OSIARQA64duAYp5iwT3rpnptKB6v0DmluJNxt1rxY4N84cOGXZPTFl91gt3Gwzg+bnfahqk+UgkTwNb75CSPuLFd5vfdZlJ5Hhxg+zIO0ZpavYkAVCIAXfBpQCm5yjnKRPXa+PAAcnQs1Fks5b5jxwZx48uGbY+nqnrVCYXigDc9bf/7xXu1rY8UkuYqZed5tRX7HMv8voREKhs6XAntkUvFjnNEACoRgC74MqDUXOUcZaJ4bWxjAEN0yFSxib3vKu9RjNggLnzZMMY719dHOz3CJBQw9mWKa223lRoZsauPVDwnNjGStptkOJb5fQqJVDZ0uBDarlKx4xwRgHISSJKkkjWes0yU/FbU/E8+ckrV5SikJnTF3ve2bQBPPAGwfz/A3r3Z38OHm5v3z4cNY07pyJ/3ddfhf9f0TE32pZQ+h6VSWZmPHtV/pq4+Ukmam9eDUvjv5J8dH8/qCJs7jyMhss+cetu3Z3k5ywn22+3m5Ou0TU6Nzd9a/txFF6Vhx0KB2Aq0yfiaQcR2lVfN0DkCn03eg1YrSxGDLVMZrmBlXZwWdRbbLctFOdhYIG4bxi4J5s+J4nHGehzq7Au7k9ulPmJ6nIrP3OVs2Kkp/BK4iwfQpU3ZbNBJYUOHDbZ5XzExrKbNOyl4TsUDqGQJ2AVfBhTTVV6Xo+mKK9zLZNphOTeHL1NdbJFrh2yK05qbowu6blguUor2HLhtGPt7i4u0zw8P08MNbHM5crSf0GECthtiql66Zflyu8EuN3O2qZib7mJAnZxiY1hNn5uYSCPcRQSgEgHogu8YwNBeIx/JdauuQWn8ITfDYOPW5ufpgi6V/F+2xD75YGYGZ4czMyuvb/p83aSDAuZe8xhA1/oI6XFy6Q9sX5RThjiFRIqb7nxR9uhi+jJs37i0hP9cbM+pCEARgE6E2AUcymvksuuOWibsIBZ6MwzFa2Uj6Jq6XETp/Iv3ZyOU68DuxJ2ePvUdk4ChnlqhA9Nem+QJ5kq2nd/f4CDus+UlcGpOUhthkeKmO19U1efg4OrnU+7LsH0jtp2mkCJHBKAIQCdi5AEcHq6Pk3PBZRnLlycr9FI4NW6tqYKOCvY5lM8EzWN+OLw0VA9gTsg2hJkU+PQEc9oj17J2Lm6xsYNVbRl7X7rTLRYXs1fVb3D1M776A67fNSWkn5qqvwa2b9y9m9aHxkQEoBIB6EIIA5qbqx5YuQWXTXJdzpM1XMrE1ZmklqaAC9ujtnJsEy8X4yZjJpENKdR9nASCgTt+zfaZl9Ph5OLWd1gLdbm6WDcc/QxX/ZdtY36e73ddvJziAexOIHYBmkwID2CouBSbGX8qiWe5OpNu27GrlHlgwgxcrt6g4eFsac4FzJJk054NFz76Ceozz681P18vbn0tgdssVxev6drPcNU/dsONTX253iO2b8xjAJvQh4oAFAHohE8DCh2XYtOJxk4866MzaVKclgnMbjzMwGWTALhKBLrWnc7L07Rnw4WvfoL6zLHL2D6WwG0nKByihav+qR5M6mYKLi8npm9sSh8qAlAEoBM+DSjGciQlx1eoWVyMzqTpO3aVwg1MptMrqlJAuIhAjucV8tk0IcbTZz+ha3sA+pgxHdz16no2dL6py6af4ah/lw032PAgLjvBtr8m9KEiAEUAOuHTgGIkg6Z0pCEbcowdt00Y/HVwBfGbksAOD9MEIMfEIcSzaUpOOB8Jt4t1m+e9THkgd7X1vXuz+56aWp2r0HSvHPXPmUeyTrByrqZQsjik3IeKAFTqtLDnjghYsMcYHTqUHbOzdSvtqDOXa05NhT3qaPv27Ki0gwezY4k2bdLf77592VFgxSPD2u3sGCtsuVstgEsucS56NDiO0ir/TtVzuOgigPPPz445U0r/W0qdOu5MV7fLy/pn7fvZ5EfOle/n6NHs/ZSO+sK22RMnsnrV9RF17WZ6GmBoCNf2YpAflYexwSr+z//JjqMr3vfAQFYXN9ygv1ds/es+x9VWAbL77+vLjt7btu1U2fNj/K644tTxhTnU4zKx7a/pfWhPEFuBNpmYMYDlF4d3AhP3026nN5Mr0ksJXZWqn2X78ADWQY1fCrGb0pam5YSjxOrp6rHp7cYmREGXnxB73xyeNU4PoKntUldTUvfiuSAeQCVLwC74NqC5OVpnxhVj1YQA3iqaNni7ostxhxmYdDGAANly2OIirr4WFlbHI1FFZQoipImpgLDix7Q82PR2g91FW6wLXYJq6gYO2z6TY5NV1Ut3LrttXkWO1Dap2JEIQBGATvg2IJtUDBwddRMCeKto4uBtC+aUC8xRWhjhgO30l5b0MYEhdlO6EiP2lgNKCpFyPXZTu8nFxvi4fkIyOuqWnLqMa59J8WBi425dnpfP1DapxNKKABQB6IRvA7Ld3cbRUac6a9PR1MGbCuWcW9PAhBEOlE4/5m5KDnyXg9KuqG2w07FLxNut7aZYf4uLSj3wQJa8fnIy+3/s6TK2G2iofWZdWy0nUveda893apu8L7DdRc6FCEARgE6k5gFsakfNRUwREVIwY+9zeDgrB+YkkMXF1TsgbQcVG29IKiLE527Jqh21dd4QW8+JTT2mIr594rKDPeR9U5ZnfYXqxEhtE8MrKAJQBKATvg3INjakyR21C7FO8gi9zEHxDGNtgVsEUAVxSiKEY3B1OdXBdvnN1gPYjSfgFNHVp+nZpHzfvkJ1YqS2iRFjLgJQBKATIQyIurttcBDfYcVe5vV1JmrITSwxNi5QOlesxwzb6Y+P89+PUrjJDmVTiisug6vtqQ65t9Zm+c0lBrBY5iZu/tJhm2S5Kfftow/lmIzZhC+FFtwiAEUAOhHKgCi727ACMHZwrs/rh9rEEmvjQqfjvuO2DEVU+rIR7GQnlJ3aDK4upzrs3283+GIFp0nUNHXzlw5sfZbbU9Pv24XYqW1CrWCJABQB6ERIA7Jd3qkidroNlyUuXwH0NsRatlxY0KevKAolijcYG27gc5Zusykltie7iMvAt3cvffmNIjgxoialuuQAW58zM91xugUXMVPbhIphFwEoAtCJ0AbEEZsRO90G5xLX8HC2JIndIcndccfYuEBZXhwcpIn5hQV8J+1zlk7ZlDI/7+5J5rQNl3NpbTyA2M9PT3evWNGBrZ+pKfNvxV41CU3I1Dah+pYiIgBFADoR2oA4PE6xg+19LXENDWW7LKvw1XGHrkvq8qKNR3d8HPfbpkmGq6By8aS5btZwsQ2bclfFAGKX31LZPZ0qWE+UyV5ir5rEwrUtz8/Tzgtfs0ap2Vkfd7IaEYBKBKALoQ0I05kND2d5ouqIPWD4XOICyJIbF/HZcYfePekqLjivoTvNg0NQuXjSsG3Bh21Ql77ya83Pnxpop6bwy2+xJ3RNAOPZbkKS8qZR1ResX2/Xj/tABKASAehCDAPCuNV1A27sAcPXElfxNT+ffTdEx62LlQHAL1FjcF1exNDpmOML6zYacQoqFw9g8TU8XH1dH7ZRPIWiaAO61+hoNtiVyzI4uPo5VC2/+ZyENC3eTVdel1M/QvSZTatrE6bUO2vW4PtxX4gAVCIAXYhlQKZAed2Ai/FQUFLJFH8XG0TtY4mrPOjn5bHpuKmdcdXzKJ+zG3LJuepV9KiakkKbBOC6ddXf4xRULkHkVdcu1z33oI6xgTxmsZwcWjdQYk5L8JHCpWnxbqbyuqx8+F41aVpdm3DZCV/Vj/tCBKASAehCTANaWtKnAtENuJglEermAUoHRhmwbEVPPshSO27bzrjs/al6Hr6XnE31gbk326B5H14Sk3cV+6pqC5yDuilGtc4LzCmaOVO4NC3ezVT/uei2tU+fHsCm1TUGLu+9bZ1iEQGoRAC6ENOAXDolk5eHOvDovHmuOcdsRU8+6FLqyLUzjrnkbLqmztNUvDesMBoY8Ceoyvdbdz4q1S6KbQFrG4uL+vK5PHNsGSYnw+10x3hvUop3w5S31co2FtgulftaZu/W2EKO+F3b/oKCCEAlAtCFEAZU16m7DLhcM9pOR6mREfvBAjtg2aQTyDcAYAczjs44VHwl9ZixqlQpdfdGmb3bCCqbe6+zE+qJG8W2gJ1YmLy/LmEGk5O0wTDEsmDsGGEqFHudmLBfKjf1QVNTdKHWtLrGwukBNE3AXBABqNQaEJJl3z6ALVsALr0U4Oqrs79btmTvb9qE+42qzx07hvuu6XOf+ATA0aP6zxw5AnDwYPW/tVoAl1wC8O53Z39brerPbdsGcOONAGeeaShwgZ/8BODBB7Pf1vFv/2123YMHAZ58sv5zSunvBYCvXk1s3w7wxBMA+/cD7N2b/Z2fB2i3V36u3Qa4916AoSH8vW3dCjAwgCtH8T62bs2u19dX//lWK3suVOrsZPv2zC6wFNtCqwVw663Zf+vKfPQowBVXZG2uCptnnrfrj38c911sWTjA3s+Xv+yvDBQobWl2FmBuDmBkZOX7eTvZvr3+u9u3Z58pfzdnz55TfTMWn/3F8jLAgQMA99yT/V1epv+GLaa+oK8PYHAQ91vve59fe+95YivQJuNzBmFajsy9OthliaIXheNEEUrS4JkZt3ooe69OOw1/XYwHcGkJ743RnYUbe0bP5S223TGJ8chxxzVh7023c9nkxc7txGUZ1xRmgH35XhbE3o/vAH0sVG9T3i5sl8o7nfr2QY3b89VfpLCpBBPnPTGBs3dfsZDiAVSyBOyCLwPCLkfmcV2mJQ3MDkXdIFPuMDFLq8XX9LRdPbgOllihiz1XN3/pjqrzscPaFepA4xIjOj9Ps61Q96Y76WFx0X4gpsSHce2OrCsLB50O/znTPqHWqWtMGWfcno/YwpQ2lWDivOfnzfbma9IjAlCJAHTBlwFRBmxTI6OKKIx4pGR2B7DzALoMlnmHMTPDM9hSOiPuHdYc2Aw0dfdhGkhinY7iIrqxdlJnx9hd7ZyxUT6D4zlOg7HFxjsX8ghDH+mDuFL4pLipBPM8XSZgLogAlBjAJMHGfHzjGwBLSwB33QWwuHgqHuzw4SxmZXkZ4LrrsuZTRznurhgPs29fFnNUjh+jxnHVxc3oMMXk1ZHHndxyi911TSiljwXctk0f39LXBzA+HjYmRxfvVqyvoi1s3w6wsLC6DkdG9PFSoeIgczCxfF/4QnV8aR4n9bWv4a5VZ/d18WHl2DKuewbAxwDbsG1bnDLoYp51bN+excDWxRADZLYxOprFp9mQ28rCAu7zpmed/97SUhbHahOXWIYjjpkbTJz3j3+M+y3O9iP8M7EVaJOJ7QEsvqpiPLC/Mz29eobGtVzVbmczPGq8DdYLMTCw8v+L3k/OZMJY70fsOEAd1FxxNrFEse6f495ML5Mn2+TtwNbN0BB/yhEKPk8XqYNj6XJurr68LsufNrZiip8u/97ICC7pt47Yx3zaEqvPEA+gkiVgF3zHALos3SoVJlWM6VWOJTMJiE4nE4zr1uF+f3FRP+japJBx6YxCdMJLS5lo3707+6s777YMNfUOdTDFioelJf6jr1zvzfcAxB3b6xMfp4vUkWpC7Pz3qP2wKUTEV4xeypNPHTEmHEqJAFRKdZcAvP3229W5556r+vv71djYmHrooYdqP/uFL3xBvfGNb1QbN25UGzduVG9+85u1n6/CpwDE7sTUNRSXDsE1medLXlJfxrqOjjrTxu5E5IhjxHZGlATDNgJoYmL1JotWi/fwdNcB2SQeqs6+5dylqBOCtp5tjgFoYaF+cw0m9tZFyNiWN0QZuIULR0Ls/HcotoKdHNm2K2x5Y3qPbVmY66hLYL96N+xVF8N+tQY6qDp1QQSg6h4BODs7q9auXavuvPNO9eijj6prrrlGbdy4UZ04caLy81dffbW644471MMPP6wee+wx9b73vU9t2LBBPfnkk+hr+jAgm+WGuo7SpUOgLFcV/39gQKk9e+gdnY1XRpeSpUzdTmZXL2vVdUx1PjhoJ4BMaRMuu4zHm8YxINeJhzwZr03dYjAtW9t4trnKZdqoUnUKTlnIcIkbLCGul+rSJdVWqs56LtZXCA+db8+tF3uoaLQ/hLb617DgddIjAlB1jwAcGxtTu3btOvn/y8vLavPmzeqmm25Cfb/T6ah169apv/iLv0Bfk9uATIPEu95F7yhtOwSXpTybtCM2otf1VIncy1pVN/mgXO7gFxbMnaDNGbam57G0pE+vUid4bOAakKnpg1w9FJjlNapnm2MAwth3u22+7xTyu/kg1aVLrK3s3p2VLT+esO75UH/PpR348Nxa25+uw6xptC/886szO+dWaA0iAFV3CMClpSXVarXUfffdt+L99773veryyy9H/cbTTz+tTj/9dPVXf/VX6OtyGhBmeQC7bFmVoNemQ7AVj1QBQZ1p2wqFqnoYHKQJPVMnmH9nfHz1746M2OfXw+Y0xDwfE74GZJ8DPXZ5DZtyAnv+bqj75o4dC+1JNJUlxaVLajou0/Oh9nMu4p77+Vrbn67DxMyMWq3MreoBEYCqOwTg0aNHFQCoBx98cMX7ExMTamxsDPUbv/M7v6POO+889ctf/rL2M88995x66qmnTr6OHDnCZkCUDPw2HaVth2AjHqkDHsUrYzvgmTowzA48029UxbYVXy4Jdnfvpg0eLoOmrwHZ51IfJfbSdPKH7c51X/eNEbeUMmMnMSHFoW6zVqiNL2UoqyDYE4coYSchN/1g6oHc12A6XWyH5qESRAAqEYBKKXXTTTepM888U/3d3/2d9nN79uxRALDqxWFA2EFifDz8DkHqgEAVEJSZsc1SBkfwNecpDjZCgOIBLL5sl818xBL59ABS2o/OCwuwegOT6zKr631zpYVSym4SE2qZuW6TTFV8ZCgw7cDGU0gRgbE3bljZL6bTLefw0r08VIIIQNUdAtBlCfgzn/mM2rBhg/rv//2/G6+Tggcw70Ri7xA0QREQ2NMcFhft+gAO4cGVFse2HJQYwOLLJXCe2858LvX5fD6uk6ulJX34hum+bXbk69pZ6PvHsrAQ9/q6cunaAWXyUfd7Ln2Tb6w82D4aJHMliABU3SEAlco2gezevfvk/y8vL6uRkRHtJpD/+B//o1q/fr369re/bXVNHzGA2MGRY5nG91IPRUD43L3GsfTomhYHO9DqhADm8HTuPtNXLBH3c8ZMImwENPbZ6O7X5Agx3bftWOriaee6fyy2XvpQy9W661DCd8r9Nza0I2byZqsJNLbDpHgBmStBBKDqHgE4Ozur+vv71V133aUOHTqkrr32WrVx40Z1/PhxpZRSO3bsUNdff/3Jz3/qU59Sa9euVffee686duzYydczzzyDvqavXcAhlndD7SikdNC+PJtN8ABin3FdGpW6vjXJnF8edylSd19TXxRBjUlthLlvjLjFlJljEuPLE2XTRlPZFd3p2Mf3proDuoiV5x57Y5Q4QPEAsgOxC8DJbbfdps455xy1du1aNTY2pr7zne+c/LeLL75Y7dy58+T/n3vuuaoqnm/Pnj3o64XKA8i9vMu9o5ATHzN6jqVH10G4/CoPGJRn/PWv4/vWVPHlualrPx/8IM9zKzsh6u4Ds9w6PIw/xYUaO1ZVZo5JjC9PFNVLn1ofhj26spziJdUd0Cf5ZwNfGP8b1QcvqL6+F3D1TbmxuTmzex6TJ4mICEDVXQIwND5PAvG1rOE7G32qcHhXXQbh4vXa7UzETU5mL2psI0aMrl+fnV0bO8UHFp3N22xCKn/edhNN+YX1QPnw7NgmiS/n23SxX1+TCkp9ufRhvvpWlxQvIVd+6qisl5LBLcC/Vu3W0RVl1E5cKTc2P6+vMA87gUQAigB0ookG1IQlB19weFfrfiNfmtUNrvm/Uc9HrisHVoymnixYJ6S4lvlmZuxFT5WoMHmgsB4hqketOFDnKW0o3iPXSYzPTANYh5FtH4axJYpALD8LlxQvMTf2VdbL4D+pBdi+quAdaKn9cInaO/4QTkBTA8FNiVIZK6SJ4zc3ELsATaaJBpTqsUs5rjN00/d9bp4xeWiw58BiwXqEXDwJITYK1Qkpzvtx3QBRvB7GA2WbtN22/ijeI90kBlMXvlYIsPdi04dhloyr6mVoKBPzZduv+mzevikikHtjn22dryobLKs+WFYL8K/djQB7Y5i4CUbja+L4zQ3ELkCTaaIBpewBdPX2+Ehw67L8uLh4Kjlv7iHgHljz683MuKUaqcJ3kL1LbkWbMQhzLd3JMDnYNjQ0FCa2y8Z7VGfX2Jh8X/0D5l6ofRhGsJtyQxZt3zRpwfxW7L4WVS+wrEbhH1QH1tAKbqtkAw9OTRy/uYHYBWgyTTSgVIOOXYO6fSS45RRAHH0bRyoKbN8ZIsieY1OCza7cOtufmsrqVLexg5K6I2TSdi7vUQorBBgvvq9E87pX/txMq5T5qSypp3hB9xlwMb7gLp0mNk5jZobl/ps4fnMDsQvQZJpqQCkEHRdx3Zhi60kyLZNxCiDXgdXUr3IO3NwbheoGdI60JNTB0zbWyjZ5L6d3LgQprxAUofRhIXJ4VtVP6nWJ7jPg3+IK7tppYndqTU+z3H9Tx29OIHYBmkyTDShm0HEZ147SNb6rLGZ87JTGlnFxcfV3Mf2qbR1WiQ3OgYtjpyz34EkVWJh8fjr7cM2FadqosLTEJxhTXSGoAtuHcXkAKa+9e4OHtJGx9gD66jTFAxgciF2AJtN0A4rpaSji6r3iTnDrY+aOTcExMrJ6sMf0q6aD5ut2hFaJDewO1slJve2YhOvcnH1aklBChOpddvGi225UKKdQc417TW2FQAfm/jjS39j2J6aNNRMT8fpho9ivigGsMwKOTlNiAIMDsQvQZMSAeIjpAcxfRXHpK4UHJQVHHo9GqRvKwG2z+1b3qvJSYYTr/Dy+TsovbiHi4g3NX7ZedK6NCsXnbRP3mpPSCgEHprYxOMgjEIsTE8zkYXAw7mkm2nqBF9TC4G/hjIAjBkV2AQcHYhegyXSrAYWekS4t0XawVi2Buc7wizsGbY91wkCJJRsZoYtRzMCN6WdbLfqyZ1FkUoWryatVNXBy2qWrN7R8wkOxrjFtKdQyJcWLl8oKARe6toFd5i8KcdNEy/aZhva0avsMbgM2dZoB3c/dOn5TgNgFaDLdaEC+U39grqdr93Xlq0vEbOrUbXcMFg92p7K4yD+wF/tVU4yYj+uX65LqECiWGRsLbhpLsGMXhze0qiyUthRyo0JKcXyh0dkEpS/CTLRcnmnZk+hbiDtfgzN4NJD7uRvHbyoQuwBNptsMaGHB3PFxX880yBbbvU2qF90pHS47BsfH7e+bOjDovHGmfrWqLx0YoF2bOni57oDkWE3Cii9Xb2hd/VM3RMbYqBB7J2+K5EJofHz1qkSVJ10nmjie6dRU3CViEpzeuwCqt9vGbxsgdgGaTDcZUKdjzm+FncBRArN1nd/wcOa9wny+uBmi6trcOwZdBk9bDxy1X6XuXtVdc3w82/SB+U5xB6SNcHV9BhTxRRmksfVvsyEyxkaFWPnnmoKNBil+x3RUn0ubTG0zzkkaFDzaTeO3LRC7AE2mmwyI6wQArOeFOshzCDOuHYOuy2c2AnB8nNav2uZG1AkWbLnzZ2DrEMA8g4GBrDx1SYJN90LNRfiSl5hPCMmxtVWTB55rowK2LQs0qvo+6vFw1DaZ5DJ+Q4JHu2n8tmUNCD3P8jLArbfiPnvsWP2/7dsHcMUVAE8+ufL9o0ez9/ftw/1O1fWon6+i1QK45BKAd787+9tqVX8mr4u+vpX/1teXvW65pfq7WH78Y/p3tm0DeOIJgP37Afbuzf4ePgywfXv15w8eXP0cbFEK4MiR7L/b7dX1ktPXBzA6CrB1a/b/27cD3HsvwMjIys+129n7dWXXPYOcn/8c4C1vAdiyZaVdme47v5eDB7P/37Sp/rNF/u//BfjZzwCmpsz172Krg4Or3xsYyOrrC1/I/r+uTnIw/158TsvLAAcOANxzT/Z3edlUcqFMXd/3859nfwcGVr7fbmfP2vSs6ijbMTsuRoHpaIU0iK1Am0y3zCAoy2B1XgOq5yWGB5CCz5UM6rKjzUzfx6aCvXuztC06L1VV/dg6BDA7psvXtdl8gl16xT4LG1vVLdebNkGV4zQpca+hN311I5i+Lz8ertgGdB5ySptkp0eMolvGbxcgdgGaTLcYEHbQHBjgywKAXaKcn1/5eY5NZlh8rWRQRYdNv+tjU0FVQHpRdPgYHzqdbODUbVwpPntu8WX6bl2ZKbZKnTxhTwIxTWJcT+4SMlwmp3XPyDokR9dpYTq0HjKKbhm/XYDYBWgyoQ3IlyDBdmBTU/W/YbNzc27O/PniwNekEwpMYJJCu4gqzk0Fxdxnda89e/yF+lAGWNuJwsICfnc0xutCsVWf3u26PsNmo4pQjeuu9apnZGXHOs8d9ozBHjIKEYAiAJ0IaUA+vfIYsWBKvGsziNl6axqyycxI1b0MD2cbPnSbVLCTAK5dwBgByGmPZagDrO1EgbrJxQTWVjnS3lAJHVLRzfiqS5Id2ySzLP9QjxmFCEAlAtCFUAYUwiuv80hhrmEzebQd+HxvMgu5iY0q6KiTAOxS0uRk9RIvZTkKaytUQk0UfIQZYJ5vjHE3huhsEpR26TM8BWXHLlv+i4XrMaMQASgC0IkQBhTSK+/qXdMdfO6S/DbkhDPV+GfbSYDLiRz5wEfZUOIrFtPkoW61spCC8veoQj5GLtsY8a2ubQ+bUqkB2UBWYdMH+AxPMdYjR8Cva/Z2TgIZjghAEYBOhDCg0G3SZcemboCemKi+VuiBz+YeYscYukwCOOzHZnzhHiMwy9mc3nDXMAOqiAgd3+rS9jD3Fmsi5aodXPqAaOEpHFv+XbO3cxHQcEQAigB0IoQB+fTKc020XARKKhs7Uo5/dhFxHH26zYYSH6tE8/P6o+k4n5FL27AVEaEFhE3bMwnx+fl4EylX7cDRB0TxenJ5AJWK2yEHNhwRgCIAnWiyB5BzouVaxhQ2dqSy+lGF6ySAo0/nTpViQ8rPKMdVRHBOynS/k/875sxb7L0BZAIdm7KHEw7t0AT7qsRly3/VA7HZncZ1DwENRwSgCEAnQsYAcnrlKZ0lZkAaH3cTKNjr+CRlTyvHwMS1rDkyQh9PuGhCjHoKIsI0uav696Eh8/jOmVuymA/Utd1zaYcm2FctNlv+depYN0PwsSQboeGIABQB6EToXcAmD06nk6WymJzMXq5npWJTRw0NxR/0XEnZ04qZ4A8PKzUzszKPWFVuMY7Btm5XsO9VohTElYnYIsI0uctPCLF5dpyny+zdy7cKwWUXTbAvLZRklgDmGWDIJdkIDUcEoBIB6ELsPIDlrP5VudoGB+2Szk5N4do+9veGh9PcBZiLopkZvZD17WnF/hZmkj84uNoWuCftVI8iVXzaJMfN7awohEMTU0RgJncuMZScHkBs/4KBSzt0Ovp8lzHjgNFgk1lOT+MCf0NVhngAowCxC9BkUjkJZGHB3G7yDpVy7Bum7WN/b3w8SBWRqBIxdfdrkwKEq//UrcZgX9R74EzzYbMjVnegAVYIx0jfE3MjJZdA06V+sU03V7z/dlsfSkCtIy7tQOlHk4XLAGOknwjccEQAKhGALqRgQNhOud0+NWBzDBJ522/qsgklZMZmQwrnoFQVr/XBD9LFILYP5dwgRPWCYj7vU7hz4LLpxmWZnmuJVucpw4gk0/OwPudWU2dcO9115TGdhpQMHLu+YsQyBN6BnML4HRuIXYAmE9OA8oFichLfAecDiussvtj2MctyqS2bYLxzrkuJHP2nTgy5PDfTZhGupTmqF5Ty+eLSvU4Ix1q2q9tIOT9P+86GDUrNzuKu6dsDmINJxzM4WB8i4ENbuGqHpk5ka3Hd9RWrQgKmhBABqEQAuhDLgLAekLoOdX6ed6BIJZcflhB9m+s1OIU6dmDlDvuh1oFNnaU8cM/NrY4rrfOkmjzS27aZr4fxhHHlUZybq/+NvM3XeTNDbrjCaofYm3e84OJOjhnLECglhAhAJQLQhRgGZLPb33agpQwUKeTywxKis3ftPzmX6rEDK/fATE0PZPNcUh24qamWMGL/Ix/BX7duMpbvAuaYrNm2eZ/awlY7pDyRiEbTZvZERAAqEYAuxNgEYusVymMAlaKf7Ypt+7Fz+WHLEqqzd+k/OVNuYAdWTjHV6eBjFLvNA0j1pGLvYc0apZ591tzGMBkDuCZrtm0+NW0R0+EVHd1DbNLMnogIQBGAToQ2IBevkG0qmHLbHxzM3k+5IzRtYgjZ2dv2n9weQMzAyimmbNIDmdJw5PZXfC6+nqXLZIZajxSxv2FDvV1Typ/CZC01bZGaKD2Jz4eFTfYa21g8IAJQBKAToQ3IxitUzgOoFG3QzBP/ltPCmHaFxuozsEtvITt7m7rAbK6hvEIvzdmkB7IRgErxP0vXXdBUT6qL2I8uThyhtI0QfUrx2a+BjroY9qtdA3vV/zfFdEHqTXBuya/6ba4dXw1EBKAIQCdS9QDu2FF/EkgOdtC06SN89lk6qEtvscqJhZLzrnyf7Xb2/H0vzXEG+rt4IF29ScVci65Ci3ofnc5qzx71eXft8uQ/E6ytdjqqs7hf/fCKcfXsOuQOHiw2CTF9CbTQiZ4TRASgCEAnYsUAci13mQZNmz4i5qSSOvAuLKxOSDsyko4AVKr+GXEG82OvWZWzr248s7FV1xhEl3g0bF5BTPuyuffZWXsBWLbrbiNYn2IyBJcLUm/Ct0BLMXg2MCIARQA6EXMXMNfAz7lZIvakkiIgmrT6UfeMfMZQmcSUqf7m5uhnBtuOSS5Lgza76jFjok073bbNTQA2KkUJkmB9CtYQbC5ocxO+BVqq2+cDIgJQBKAToQyoPMDNzZk9dxyxMr5imXxNKrHXX1zsntWPGLGWmN3ounxzdSLVxnPmsjRou6seOybaCPSPfCTb7Vv8Tvn/fbSrVOP8g/QpNoZAuSD2fN7ib/oWaLE76wQQAajUaSAkzb59ANddB/Dkk6fea7cBpqcBhoYAjh0D2LQJYOtWgFar/vO33gqwfTvt2ps20T537Bju89jPUVhezl4DAwA//3n1Z/r6sroAWFk/ZZQCOHIE4OBBgEsuYS8qK61W+DIePKivP4DsWVQxNQVwww1Zucu0WpmdXnFF9qyUOvVvfX3Z31tuOfXdffuyzxY/BwBw9Gj2/r336m0ecx9VYNvF9u0A27Zl1ym30zpuvhngk58E+NznAH7wA4Dzzwd4//sBXvay7L7K9wpwyq63bjWXaXl5dXm+/GVcn1H1Xd29cBCkT7ExBOwF9+0DuOYa+m+edRbuO9jPldm6NXvIHEYlNJfYCrTJ+J5BUJcpuZc1MTtRbfKZcU8qMTFcxTropdUPH54d2xyFWM8qxnPGsTRIvY+YnmGO0I+qeq3bdV21GSzGhqkgfYqNQVPiAGx+E+s1XFy0v+9k896EQTyASpaAXfBpQNQBzlesjKkPm5hYXeYQ+fWw5ctfw8PZDs/9++1WZJqIr0HbNUchpl5NwpVDGFDuI4Ux0SXm0ybWMW+v8/Px4mWD9ClUQ6DsBLL9zZkZ3Hf/8i8dblyll4wxICIAlQhAF3waEHWA8zlTnpjQ913FviJ0fj1TH/uSl1Sfxzo4GFaohiZEBgmqoMhfHJ5VDi9up7N6F3jdK5X0QC45JW2eFYD+RJcQbcV7n0IVa5y5gOp+c3oa990NG/ABr2XDyd+bmcmuNzOTVgCo58BUEYBKBKALPg2IOsD5Wta0TQUTYlJp64kqDiTduPoRYuekbY5CgPpJCKW/55rw1O1ULr9cVtpM+B6HuU+VsalnV7z2KQsL5gzk1AtiO+SBgerfxHoA8wat2wFYt/ZfvudUZjlKBYk5EAGoRAC60AsewBipObC4nJfb15f1f2UPUDesfoSKxazqo3W7f3XCk9rfcy0Nxo4H1cWvco13Ps6VDlU/Rbz0Kaa18fXrT8WOUC5ISUng8v2isc/P44M8634nhdlvoBxdIgCVCEAXQsQAYgc4X7EysQdIHRyejcXFNNNf2NLpZKfAhHpm5UE5jxejeFZt+3uOpUGsDU1NOVRSDZi4PI7xzsVTrlv+Lb4aGS+LWfptt+06BdcOeWkJX/mcr9jxLwGTyYoAVCIAXQi1Cxg7wJmW5aam6O0m5XRRrrFoXCIoFbAnWvh+ZlUnrNR5s1z7e44j4DA2FCN+tXg/LuNdp7P6LG+MDsgTeofe2MUOx3mFNu5H2xkKtSH7eMVS9AEHHBGASgSgCyEMiDrAmfoO6rJSjJ29FFxi0WL2c9xQd3naOjawZcEu53L0965LgwsLOEHEaedUr5yrnWJjHav6mGSzhWAevM4Yscsb4+P28Wg2HbjLjJbrFWtmHHDJSQSgEgHoQqyTQDAZCKjHcOlIdgAolK+qf+72nb45Nrs8Bwf9PDfqcm4qIQZYgcQ1YaDG5bnef6ejDwfLl3vrNqBEzxZSFWtgEmUmY6SqYtvOD9uBu27X5nyJB7AngNgFaDKpGpCPMAqXASDEhpC6zW/YHIYpw5UTz3b8opQztSNPsYQWoqE9gErhxXmdvUU7Lg67JFq8EYwxttvm9X/bXU022DZkG4Hn655cjSTgklOq43dIIHYBmkyqBuRrULUNg4lxgkAOJYdhbLAZHMr15/tkDiw2dpdKiEFoIUqJX+W8f9NELnZ7rSwwReTkBoPN9j41Vb+8gb1mlVFQvH7557C7t8oPr+pweNsOATMT0D0rDuMJtOSU6vgdEohdgCaTqgGlsqwWaDd/LQE3lDnjckxXiJM5MNjaXegQg7qcuCmeYhPq/nXliRbq4bIkStkKX6eKx8ftDRojhGw3e0xPr54lUtK95J1L+TsuMwFu4wkQc5Dq+B0SiF2AJpOqAaWwrJaC+EqhHjDYxH0X6y+FkzmUcqvvUDFmunEtRqyrTgOEjLFLob2uwmVmgxWAuTFWqWIbg8YKIdvNHq1W5vEzXU/3et/7Vp4E4joT8GU8chKIdyB2AZpMSAOitIUUltVSEF+peEJ1uMZ95/Xnshs61LKmye58x5hhxrUqQTY8vHLM5cb3SSB113PVOt5xyWC9uOjeCdomYzUJoaUlt0aPjXOsew0M2G9ESTmQl4gIQCUC0IVQBmQTWmESBL49CymIryb0S67Lt8X64zyZw5ZUd4xTxrX5+dU5eFM6JcuFur7EdrXTK7abIvIHyWGMlI4Uu6sYe86v6R6xcY6UTs+m00yhs7dABKBSa0BImn37AK64AuDJJ1e+f/Ro9v6+fdXf274d4N57AQYGVv/b4CB/Octs2sT7ORu2bgVotwH6+qr/va8PYHQ0+1wsjh1z+36x/rZvB3jiCYD9+wH27s3+zs5m91mug/z/b7kFoNVyK0OR3O5GRla+325n72/fznctCgcPrm5DRZQCOHIE4BOfALjySoCf/GTlv5vaWxPQ9SW33IL7DZ/tdRWmBlymbNQcxojtSPftA9izB1fOH/wA97k6cmM9cMD+N+o6HmyHVPxcCp29YEdsBdpkfM8gOE5JiBXUncIytFLpeqRyXI7pwtZfjBxudRstqPksuZaEsU4K3YkZ3DYbMq0KZrWw1YrfXldBiW2oM2qOTOG6jrQqJ6Hu5eoBzF82u4arPHhFbDyAS0v6pYbcuJaWaPXuGfEAqu5aAr799tvVueeeq/r7+9XY2Jh66KGHtJ+fm5tTL3/5y1V/f7961atepb761a+SrufbgFyWMFMI6k5FfEVPYqvBZgOHTf1Fy+H2z9hsKuRMR+K61I4ZOymETrdCuf/Y7XUVdQ14bi5MglFTR0o5s7cYA+iaw88U52jT8dvM3JsQa1OBCEDVPQJwdnZWrV27Vt15553q0UcfVddcc43auHGjOnHiROXnv/Wtb6lWq6U+/elPq0OHDqnJyUn1ohe9SH33u99FX9O3AbmEVqTSJlMRX7EFkA6dUAbQZ2toAlRPtA/PNWZcw2bScA1liuGZdzn1LAl7i9WAOWcOAKt3AdepbewxRhQPKdbAqDN3iQFsLF0jAMfGxtSuXbtO/v/y8rLavHmzuummmyo/f+WVV6q3v/3tK9678MIL1fvf/370NVP2AKbUJlMWX6mgE8pNrj+qJ9qn59o0roU4Di6WZ57SlzTZ3thx2Ylcfk1NrfxtXaOniLC6JKIuM0fKzD0VbwMREYBdIgCXlpZUq9VS991334r33/ve96rLL7+88jujo6Nqenp6xXt/8Ad/oF796lejrxsqBtAmLqehbbKn6caBl2qHvu3WJLR9x63GapepxOQ2DuwDGxrSe+Ha7erK1TV6igjjCLqllK38uQYalwhApU6LtfmEk5/+9KewvLwMZ5999or3zz77bPj7v//7yu8cP3688vPHjx+vvc7S0hIsLS2d/P+nn37aodRmWi2AW2/Ndu719WWtKce0izPfQHf06MrvFb/fbsfdASuspNUCuOSS2KXghbqp0GYTIoXt2wG2bct2BR87lm1M3Lr1VBuybW9YfN9fHS59SU+D7Uj/+I8BrrqqvnJvvbW6cnWN3mSsmN9x6VCwHZIYV2ORNDAEbrrpJtiwYcPJ1+joqPdr2mYyyNskQLgUIIJQhpohIkRGiXxce/e7s7/FNuA7jU3MjBmppuhJGmxH+m/+jZ/K1RlrSohxNZI+paqmNc3i+eefhxe/+MVw7733wjvf+c6T7+/cuRP+8R//Eb785S+v+s4555wDH/7wh2F8fPzke3v27IEvfelL8Hd/93eV16nyAI6OjsJTTz0F69evZ7ufKpaXcRPBMvv2AVx33crcX6OjWZ8lbVLwzfIywJYtZgfK4cOZPVM/77PcNu0N87ux78/XvXU12I601yu3Qff/9NNPw4YNG4KM36nSFQIQAODCCy+EsbExuO222wAA4IUXXoBzzjkHdu/eDddff/2qz1911VXw7LPPwl/91V+dfO+iiy6CV7/61fD5z38edc2mGFCD2qTQheQJiAGqV4fKDgLq55tGt99f1yIdaVfRlPHbK3FDEPmYnZ1V/f396q677lKHDh1S1157rdq4caM6fvy4UkqpHTt2qOuvv/7k57/1rW+p0047Td18883qscceU3v27EkuDYwgdAvUdECppA/yRbffnyCkjozfSnWNBxAA4Pbbb4fPfOYzcPz4cXjNa14Df/InfwIXXnghAABccsklsGXLFrjrrrtOfn5+fh4mJyfhiSeegAsuuAA+/elPw9ve9jb09WQGIQh4qA6Ubne4dPv9CULKyPjdRUvAMRADEgRBEITmIeO37AIWBEEQBEHoOUQACoIgCIIg9BgiAAVBEARBEHoMEYCCIAiCIAg9hghAQRAEQRCEHkMEoCAIgiAIQo8hAlAQBEEQBKHHEAEoCIIgCILQY4gAFARBEARB6DFOi12AJpMfovL0009HLokgCIIgCFjycbuXD0MTAejAM888AwAAo6OjkUsiCIIgCAKVZ555BjZs2BC7GFGQs4AdeOGFF+BHP/oRrFu3Dvr6+th+9+mnn4bR0VE4cuRIz55RGAqp6zBIPYdD6joMUs9h8FXPSil45plnYPPmzbBmTW9Gw4kH0IE1a9ZAu9329vvr16+XjiUQUtdhkHoOh9R1GKSew+CjnnvV85fTm7JXEARBEAShhxEBKAiCIAiC0GOIAEyQ/v5+2LNnD/T398cuStcjdR0GqedwSF2HQeo5DFLP/pBNIIIgCIIgCD2GeAAFQRAEQRB6DBGAgiAIgiAIPYYIQEEQBEEQhB5DBKAgCIIgCEKPIQIwEnfccQds2bIFTj/9dLjwwgvhv/23/6b9/Pz8PLziFa+A008/HX79138d7r///kAlbT6Uuv7iF78IW7duhTPPPBPOPPNMeMtb3mJ8NkIG1aZzZmdnoa+vD975znf6LWCXQK3nf/zHf4Rdu3bBpk2boL+/H172spdJ/4GEWte33HILvPzlL4czzjgDRkdH4UMf+hA899xzgUrbTP7mb/4G3vGOd8DmzZuhr68PvvSlLxm/c+DAAXjta18L/f398Gu/9mtw1113eS9nV6KE4MzOzqq1a9eqO++8Uz366KPqmmuuURs3blQnTpyo/Py3vvUt1Wq11Kc//Wl16NAhNTk5qV70ohep7373u4FL3jyodX311VerO+64Qz388MPqscceU+973/vUhg0b1JNPPhm45M2CWs85hw8fViMjI2rr1q1q27ZtYQrbYKj1vLS0pF73utept73tbeqb3/ymOnz4sDpw4IB65JFHApe8eVDr+u6771b9/f3q7rvvVocPH1YPPPCA2rRpk/rQhz4UuOTN4v7771c33HCD2rdvnwIAdd9992k///jjj6sXv/jF6sMf/rA6dOiQuu2221Sr1VJf+9rXwhS4ixABGIGxsTG1a9euk/+/vLysNm/erG666abKz1955ZXq7W9/+4r3LrzwQvX+97/fazm7AWpdl+l0OmrdunXqL/7iL3wVsSuwqedOp6Muuugi9Wd/9mdq586dIgARUOv5T//0T9V5552nnn/++VBF7Bqodb1r1y71pje9acV7H/7wh9Ub3vAGr+XsJjAC8KMf/ah65StfueK9q666Sr31rW/1WLLuRJaAA/P888/D3/7t38Jb3vKWk++tWbMG3vKWt8C3v/3tyu98+9vfXvF5AIC3vvWttZ8XMmzqusyzzz4Lv/rVr2BgYMBXMRuPbT3/4R/+IZx11lnw7//9vw9RzMZjU8//5b/8F3j9618Pu3btgrPPPhte9apXwSc/+UlYXl4OVexGYlPXF110Efzt3/7tyWXixx9/HO6//35429veFqTMvYKMh3ycFrsAvcZPf/pTWF5ehrPPPnvF+2effTb8/d//feV3jh8/Xvn548ePeytnN2BT12X+w3/4D7B58+ZVHY5wCpt6/uY3vwn/6T/9J3jkkUcClLA7sKnnxx9/HP7rf/2v8J73vAfuv/9++P73vw8f+MAH4Fe/+hXs2bMnRLEbiU1dX3311fDTn/4U3vjGN4JSCjqdDvz2b/82fOxjHwtR5J6hbjx8+umn4Ze//CWcccYZkUrWPMQDKAg1fOpTn4LZ2Vm477774PTTT49dnK7hmWeegR07dsAXv/hFGBoail2cruaFF16As846C77whS/Ab/zGb8BVV10FN9xwA3z+85+PXbSu48CBA/DJT34SPve5z8H//J//E/bt2wdf/epX4Y/+6I9iF00QKhEPYGCGhoag1WrBiRMnVrx/4sQJeOlLX1r5nZe+9KWkzwsZNnWdc/PNN8OnPvUpWFxchFe/+tU+i9l4qPX8gx/8AJ544gl4xzvecfK9F154AQAATjvtNPje974H559/vt9CNxAbe960aRO86EUvglardfK9f/Ev/gUcP34cnn/+eVi7dq3XMjcVm7r+/d//fdixYwf81m/9FgAA/Pqv/zr80z/9E1x77bVwww03wJo14m/hoG48XL9+vXj/iIhFBmbt2rXwG7/xG/CNb3zj5HsvvPACfOMb34DXv/71ld95/etfv+LzAAB//dd/Xft5IcOmrgEAPv3pT8Mf/dEfwde+9jV43eteF6KojYZaz694xSvgu9/9LjzyyCMnX5dffjlceuml8Mgjj8Do6GjI4jcGG3t+wxveAN///vdPCmwAgP/9v/83bNq0ScSfBpu6fvbZZ1eJvFx4K6X8FbbHkPGQkdi7UHqR2dlZ1d/fr+666y516NAhde2116qNGzeq48ePK6WU2rFjh7r++utPfv5b3/qWOu2009TNN9+sHnvsMbVnzx5JA4OEWtef+tSn1Nq1a9W9996rjh07dvL1zDPPxLqFRkCt5zKyCxgHtZ5/+MMfqnXr1qndu3er733ve+orX/mKOuuss9THP/7xWLfQGKh1vWfPHrVu3Tp1zz33qMcff1x9/etfV+eff7668sorY91CI3jmmWfUww8/rB5++GEFAOqzn/2sevjhh9U//MM/KKWUuv7669WOHTtOfj5PAzMxMaEee+wxdccdd0gaGEtEAEbitttuU+ecc45au3atGhsbU9/5zndO/tvFF1+sdu7cueLzc3Nz6mUve5lau3ateuUrX6m++tWvBi5xc6HU9bnnnqsAYNVrz5494QveMKg2XUQEIB5qPT/44IPqwgsvVP39/eq8885Tn/jEJ1Sn0wlc6mZCqetf/epX6sYbb1Tnn3++Ov3009Xo6Kj6wAc+oH7xi1+EL3iD2L9/f2Wfm9ftzp071cUXX7zqO695zWvU2rVr1Xnnnaf+/M//PHi5u4E+pcQ3LQiCIAiC0EtIDKAgCIIgCEKPIQJQEARBEAShxxABKAiCIAiC0GOIABQEQRAEQegxRAAKgiAIgiD0GCIABUEQBEEQegwRgIIgCIIgCD2GCEBBEARBEIQeQwSgIAiCIAhCjyECUBAEQRAEoccQASgIgiAIgtBjiAAUBEEQBEHoMUQACoIgCIIg9BgiAAVBEARBEHoMEYCCIAiCIAg9hghAQRAEQRCEHkMEoCAIgiAIQo8hAlAQBEEQBKHHEAEoCIIgCILQY4gAFARBEARB6DFEAAqCIAiCIPQYIgAFQRAEQRB6DBGAgiAIgiAIPYYIQEEQBEEQhB5DBKAgCIIgCEKPIQJQEARBEAShxxABKAiCIAiC0GOIABQEQRAEQegxRAAKgiAIgiD0GCIABUEQBEEQegwRgIIgCIIgCD3G/w8C6qah3B79zwAAAABJRU5ErkJggg==",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' 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       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c73225e37797417c9071d0a431d9e5cf",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from visualize_outputs import sample2d, sample3d, plot2d, plot3d\n",
    "from onnx2pytorch import convert\n",
    "\n",
    "%matplotlib widget\n",
    "\n",
    "n_sample = 1000  # number of points to sample\n",
    "frozen_dim = 0    # which dimension will have a constant value for the 2d plot, (0: distance, 1: speed, 2: angle)\n",
    "frozen_val = 0.9    # constant value to give to the frozen dimension\n",
    "model = convert(\"network.onnx\")\n",
    "dim_1, dim_2, colours = sample2d(model, n_sample, frozen_dim, frozen_val)\n",
    "plot2d(dim_1, dim_2, colours)\n",
    "dim_1, dim_2, dim_3, colours = sample3d(model, n_sample)\n",
    "plot3d(dim_1, dim_2, dim_3, colours)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On this plot we can already start to see some tendencies of alarms. As always the difficult part will be at the decision boundary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Write some safety property\n",
    "\n",
    "Let us say we would like to check the following property:\n",
    "\n",
    "\"No input above a distance .95 output an alarm\". \n",
    "\n",
    "If our neural network correctly follows our specification, it should respect this property (since the distance is above 0.7, no alarm should be issued).\n",
    "\n",
    "The basics for formulating properties to our various tools is the following:\n",
    "\n",
    "1. Write constraints on inputs as defined in the specification\n",
    "1. State the contraint on outputs you want to check\n",
    "1. For tools using SMT, write down the negation of this constraint\n",
    "   (remember in the course section, VALID(F) is equivalent to ¬SAT(¬F)). \n",
    "\n",
    "For this tutorial, we wrote a simple API to make easier the writing of properties to check. This API is detailed inside `formula_lang.py` in the repository.\n",
    "This API allows to define linear constraints on symbolic variables and real values.\n",
    "* To define a new variable, use the constructor `Var(x)`, where `x` must be a string\n",
    "* To define a new real number, use `Real(r)` where `r` is a python number\n",
    "* A constraint is a linear inequality between two variables or reals. To create a new constraint, use\n",
    "  `constr = Constr(var1, bop, var2)` where `bop` is either `'>='` or `<`.\n",
    "* You can create multiple constraints\n",
    "* Finally, once you are satisfied, you can add your constraints to a formula. A formula is a conjunction of constraints\n",
    "* `f = Formula()` creates an empty constraint, and `f.add(constr)` add the constraint `constr` to the formula.\n",
    "  `f.add(c1)` followed by `f.add(c2)` is equivalent to adding a conjunction of `c1` and `c2`\n",
    "\n",
    "\n",
    "Here is how to use it:\n",
    "#### Variables creations\n",
    "1. Create a new variable with `var = Var(str)`; `str` should be    \n",
    "  either `'x0'`, `'x1'`, `'x2'` or `'y0'`, respectively the first, \n",
    "  second and third input and only output. For convenience, they \n",
    "  are already defined when executing the cell below as\n",
    "  `distance`, `speed`, `angle` and `output`\n",
    "  \n",
    "2. Create a new real value with `real = Real(r)` where `r`\n",
    "  can be an integer or a float (all variables will be converted\n",
    "  as real values). For instance, `real = Real(0.95)`\n",
    "  \n",
    "  \n",
    "#### Creating constraints and adding them to a formula\n",
    "1. Create a new constraint between a variable `var` and a\n",
    "  real value `real` with `constr = Constr(var, bop, real)` where\n",
    "  `bop` is either `'>='` or `'<'`. For instance, `constr = Constr(distance,'>=',real)`\n",
    "2. Create a new empty formula with `f = Formula()`\n",
    "3. Add a constraint `constr` to a formula `f` with `f.add(constr)`. \n",
    "    \n",
    "#### Printing and saving to disk\n",
    "1. Print a formula `f` with `print(f)`\n",
    "2. Write down a formula `f` to SMTLIB2 format at destination `dest`\n",
    "  with `f.write_smtlib(dest)`; similarly for Marabou format,\n",
    "  use `f.write_marabou(dest)` or PyRAT format `f.write_pyrat(dest)`.\n",
    "  For smtlib or marabou the negation of the output is done automatically.\n",
    "\n",
    "A simple example is given below for the property \"No input above a distance .95 output an alarm\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x0 >= 0.95; x0 < 1; x1 >= 0.95; x1 < 1; x2 >= 0.95; x2 < 1; y0 < 0; \n",
      "Wrote SMT formula in file formula.smt2\n",
      "Wrote marabou formula in file formula.marabou\n",
      "Wrote pyrat formula in file formula.txt\n"
     ]
    }
   ],
   "source": [
    "from formula_lang import *\n",
    "\n",
    "distance = Var('x0')\n",
    "speed = Var('x1')\n",
    "angle = Var('x2')\n",
    "\n",
    "output = Var('y0')\n",
    "\n",
    "one = Real(1)\n",
    "real = Real(0.95)\n",
    "zero = Real(0)\n",
    "\n",
    "constrs = []\n",
    "constrs.append(Constr(distance, '>=', real))\n",
    "constrs.append(Constr(distance, '<', one))\n",
    "constrs.append(Constr(speed, '>=', real))\n",
    "constrs.append(Constr(speed, '<', one))\n",
    "constrs.append(Constr(angle, '>=', real))\n",
    "constrs.append(Constr(angle, '<', one))\n",
    "constrs.append(Constr(output, '<', zero))\n",
    "\n",
    "formula = Formula()\n",
    "for c in constrs:\n",
    "    formula.add(c)\n",
    "    \n",
    "print(formula)\n",
    "    \n",
    "formula.write_smtlib()\n",
    "formula.write_marabou()\n",
    "formula.write_pyrat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Launch solvers and recover results\n",
    "\n",
    "As mentioned, we will use three tools in this tutorial:\n",
    "\n",
    "1. Z3, a theorem prover from Microsoft Research [https://github.com/Z3Prover/z3](https://github.com/Z3Prover/z3), used as a state-of-the-art SMT solver; however it does not have any particular heuristics to work on neural networks \n",
    "2. PyRAT, a tool internally developped at the lab, that leverages abstract interpretation to verify reachability properties on neural networks. The source is currently not available, if you want to access it just send us an email\n",
    "3. Marabou, a solver tailored for neural network verification: it uses a specialized Simplex algorithm and merges relevant neurons. See [the paper](https://arxiv.org/abs/1910.14574) for more details\n",
    "\n",
    "You will notice that PyRAT performs a \"reachability analysis\" (given the input range, what is the possible output range?). Marabou does not deal with disjunction of clauses ($a< x1 <b \\vee c < x1 <d$), so you will need to formulate the two clauses in separate properties (one with $a< x1 <b$, one with $c < x1 <d$).\n",
    "\n",
    "It is partly due to implementation constraints, and on such simple problem this should not be a limitation. But the set of expressible properties is different between abstract interpretation and SAT/SMT calculus.\n",
    "\n",
    "Here is a recap about the tools we will be using:\n",
    "\n",
    "| | Z3 | Marabou | PyRAT \t|\n",
    "|----------------------------------\t|--------------------------------\t|--------------------------------------\t|-------------------------\t|\n",
    "| input format                     \t| SMTLIB  \t| Specific  \t| Specific      \t|\n",
    "| technology                       \t| SMT                            \t| SMT / overapproximation              \t| abstract interpretation \t|\n",
    "| specialized  for neural networks \t| no                             \t| yes                                  \t| yes                     \t|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Z3 the classical SMT solver\n",
    "\n",
    "As mentioned, Z3 is not made for neural networks specifically, we are simply transforming the network as a classical problem for the solver to handle. For this, we will thus need to transform the network to the SMT format first before launching the tool. This is done in `call_isaeih` using the open-source ISAEIH tool developed at CEA. The `launch_z3` function will then call the solver directly on the transformed network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "\n",
    "def call_isaieh(fpath):\n",
    "    \"\"\"Convert an ONNX network at fpath to a SMT formula describing;\n",
    "    the control flow. The output will be called fpath_QF_NRA.smt2\"\"\"\n",
    "    !./bin/isaieh.exe -theory=QF_NRA $fpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: ./bin/isaieh.exe: Permission non accordée\r\n"
     ]
    }
   ],
   "source": [
    "call_isaieh('network.onnx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def launch_z3(control_flow, constraints, verbose=True):\n",
    "    \"\"\"Launch z3 on the SMT problem\n",
    "    composed of the concatenation of a control flow and constraints, both written in SMTLIB2\"\"\"\n",
    "    !cat $control_flow $constraints > z3_input\n",
    "    \n",
    "    t = time.perf_counter()\n",
    "    output = !z3 z3_input\n",
    "    output = \"\\n\".join(output)\n",
    "    if verbose:\n",
    "        print(output)\n",
    "        \n",
    "    return \"unsat\" in output, time.perf_counter() - t    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To launch Z3, provide the filepath of the network's control flow in SMTLIB as well as the filepath of the\n",
    "formula you generated. \n",
    "\n",
    "A **SAT** result will be followed by an instanciation of the input and outputs that satisfies the negation of your property, i.e. a counter-example. \n",
    "\n",
    "An **UNSAT** result signifies that it could not find such a counter example and that your property holds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat: network_QF_NRA.smt2: Aucun fichier ou dossier de ce type\n",
      "/bin/bash: z3 : commande introuvable\n"
     ]
    }
   ],
   "source": [
    "res, t_z3 = launch_z3('network_QF_NRA.smt2','formula.smt2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Marabou the simplex-based solver\n",
    "\n",
    "As opposed to Z3, Marabou is tailor-made for neural network. It uses its own property format and can only read network under the NNet format. We already provide a copy of the ONNX network in NNET that was made using a converter `network.nnet`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def launch_marabou(network, constraints, verbose=True):\n",
    "    \"\"\"Launch marabou on the verification problem:\n",
    "    network is a .nnet description of the network (provided here\n",
    "    for simplicity) and constraints is a property file you wrote\"\"\"\n",
    "    t = time.perf_counter()\n",
    "    output = !./bin/marabou.elf --timeout=100 $network $constraints\n",
    "    output = \"\\n\".join(output)\n",
    "    if verbose:\n",
    "        print(output)\n",
    "    return \"unsat\" in output.lower(), time.perf_counter() - t    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly for Marabou, provide the filepath of model and the property you generated in the Marabou format. The output of Marabou is more verbose dans Z3, the important part lies at the end: SAT or UNSAT. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: ./bin/marabou.elf: Permission non accordée\n"
     ]
    }
   ],
   "source": [
    "res, t_marabou = launch_marabou(\"network.nnet\", 'formula.marabou')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: PyRAT with Abstract Interpration\n",
    "\n",
    "PyRAT API provide directly a function to launch the analysis `launch_pyrat`. Remember that a _negative value_ means\n",
    "no alarm issued, while a _positive value_ means that an alarm is issued. \n",
    "\n",
    "PyRAT can directly work on the ONNX network while its input format is similar to the Marabou with only the difference of having the property to prouve instead of its negation.\n",
    "- **True** means the property holds,\n",
    "- **False** means it does not.\n",
    "\n",
    "PyRAT will also output the bounds reached at the end of the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output bounds:\n",
      " [-6.0188885]\n",
      "[-5.915209]\n",
      "Result = True, Time = 0.00 s\n"
     ]
    }
   ],
   "source": [
    "from pyrat_api import launch_pyrat\n",
    "\n",
    "res, t_pyrat = launch_pyrat(\"network.onnx\", \"formula.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With PyRAT you can also use different abstract domains to get a more precise results, avalaible domains are \"poly\", \"zono\", \"symbox\". While they increase precision they also increase computation time. Nevertheless, unless we are in such simple setting the interval by themselves are often too imprecise to conclude.\n",
    "\n",
    "An additional argument `split_timeout` can also help to increase the precision of the analysis. Try it for now and we will see it in more details in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result = True, Time = 0.01 s, Safe space = 100.00 %, number of analysis = 1\n"
     ]
    }
   ],
   "source": [
    "res, t_pyrat = launch_pyrat(\"network.onnx\", \"formula.txt\", domains=[\"zono\"], split_timeout=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving to bigger properties\n",
    "\n",
    "You were able to launch solvers to verify this simple property. We can already notice that Z3 took significantly more time than the others to return a result: it comes from the suboptimal encoding of the problem as well as the lack of heuristics tailored to neural network verification. Using Z3 on more complex properties will likely hang your session; don't hesitate to terminate the cell's execution if it takes too much time.\n",
    "\n",
    "Even so, with all three tools we managed to prove the simple property we created. But this property was not the one we initially mentioned. We should aim to answer the following questions:\n",
    "- With the setting of $\\alpha = 0.5$, $\\beta = 0.25$, $\\delta_1 = 0.3$, $\\delta_2 = 0.7$ can we prove our three initial properties on this network ?\n",
    "- If not for what values of $\\alpha, \\beta, \\delta_1$ and $\\delta_2$ do they hold ?\n",
    "\n",
    "Initial properties of the zones:\n",
    "1. a **”safe”** zone: when B is in this zone, it is not considered a threat for any $||\\vec{d}|| > \\delta_2$, no ALARM is issued.\n",
    "2. a **”suspicious”** zone: when B is in this zone, if $||\\vec{v}|| > \\alpha$ and $\\theta < \\beta$\n",
    "   then a ALARM should be issued. Else, no ALARM is issued.\n",
    "3. a **”danger”** zone: when B is in this zone, a ALARM is issued no matter what. When $||\\vec{d}|| < \\delta_1$, B is in the danger zone.\n",
    "\n",
    "**Write these properties and try proving them with the different tools**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x0 >= 0.7; x0 < 0.3; x1 >= 0.4; x1 < 1; x2 < 0.25; x2 >= 0; \n",
      "Wrote pyrat formula in file safe.txt\n",
      "Wrote pyrat formula in file suspicious.txt\n",
      "Wrote pyrat formula in file danger.txt\n"
     ]
    }
   ],
   "source": [
    "from formula_lang import * \n",
    "\n",
    "distance = Var('x0')\n",
    "speed = Var('x1')\n",
    "angle = Var('x2')\n",
    "\n",
    "output = Var('y0')\n",
    "\n",
    "one = Real(1)\n",
    "delta_1 = Real(0.2)\n",
    "delta_2 = Real(0.78)\n",
    "alpha = Real(1)\n",
    "beta = Real(0.01)\n",
    "zero = Real(0)\n",
    "\n",
    "# Safe\n",
    "constrs = []\n",
    "constrs.append(Constr(distance, '>=', delta_2))\n",
    "constrs.append(Constr(distance, '<', one))\n",
    "constrs.append(Constr(speed, '<', zero))\n",
    "constrs.append(Constr(speed, '>=', zero))\n",
    "constrs.append(Constr(angle, '>=', zero))\n",
    "constrs.append(Constr(angle, '<', one))\n",
    "constrs.append(Constr(output, '<', zero))\n",
    "\n",
    "formula_safe = Formula()\n",
    "for c in constrs:\n",
    "    formula_safe.add(c)\n",
    "    \n",
    "# Suspicious\n",
    "constrs = []\n",
    "constrs.append(Constr(distance, '>=', zero))\n",
    "constrs.append(Constr(distance, '<', one))\n",
    "constrs.append(Constr(speed, '>=', alpha))\n",
    "constrs.append(Constr(speed, '<', one))\n",
    "constrs.append(Constr(angle, '>=', zero))\n",
    "constrs.append(Constr(angle, '<', beta))\n",
    "constrs.append(Constr(output, '>=', zero))\n",
    "\n",
    "formula_sus = Formula()\n",
    "for c in constrs:\n",
    "    formula_sus.add(c)\n",
    "    \n",
    "    \n",
    "# Danger\n",
    "constrs = []\n",
    "constrs.append(Constr(distance, '>=', zero))\n",
    "constrs.append(Constr(distance, '<', delta_1))\n",
    "constrs.append(Constr(speed, '>=', zero))\n",
    "constrs.append(Constr(speed, '<', one))\n",
    "constrs.append(Constr(angle, '>=', zero))\n",
    "constrs.append(Constr(angle, '<', one))\n",
    "constrs.append(Constr(output, '>=', zero))\n",
    "\n",
    "formula_danger = Formula()\n",
    "for c in constrs:\n",
    "    formula_danger.add(c)\n",
    "    \n",
    "print(formula)\n",
    "    \n",
    "formula_safe.write_pyrat(\"safe.txt\")\n",
    "formula_sus.write_pyrat(\"suspicious.txt\")\n",
    "formula_danger.write_pyrat(\"danger.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result = True, Time = 0.01 s, Safe space = 100.00 %, number of analysis = 1\n",
      "Result = False, Time = 0.04 s, Safe space = 75.00 %, number of analysis = 7\n",
      "Result = True, Time = 0.07 s, Safe space = 100.00 %, number of analysis = 11\n"
     ]
    }
   ],
   "source": [
    "res, t_pyrat = launch_pyrat(\"network.onnx\", \"safe.txt\", domains=[\"zono\"], split_timeout=10)\n",
    "res, t_pyrat = launch_pyrat(\"network.onnx\", \"suspicious.txt\", domains=[\"zono\"], split_timeout=10)\n",
    "res, t_pyrat = launch_pyrat(\"network.onnx\", \"danger.txt\", domains=[\"zono\"], split_timeout=10)\n",
    "#res, t_marabou = launch_marabou(\"network.nnet\", 'formula.marabou')\n",
    "#res, t_z3 = launch_z3('network_QF_NRA.smt2','formula.smt2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*****\n",
    "\n",
    "## Part 3: A real problem ACAS-Xu\n",
    "\n",
    "Let's tackle a more complex network of the public benchmark of Airborne Collision Avoidance System for Unmanned vehicles (ACAS-Xu). For an introduction of the ACAS-Xu benchmark, see _An Introduction to ACAS Xuand the Challenges Ahead , Manfredi G., Jestin Y._\n",
    "\n",
    "Formerly, programs such as ACAS were implemented as a lookup table. The neural network approach was partly introduced to reduce the memory requirements for hardware of the original 2GB tables down to 3MB. Neural network becoming more and more efficient, industrials considered using them as a possible replacement. Since we are dealing with critical systems, software safety is a major concern.\n",
    "\n",
    "In _Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks_ , Katz et al. provided a neural network implementation of a ACAS-Xu system, as well as a tooling to formally verify several safety properties. It was then adopted as a common benchmark in the literature.\n",
    "\n",
    "The problem is similar to our previous one, a neural network needs to give direction advisory to a drone when an intruder is detected at proximity. The advisory can be \"Clear of Conflict\" meaning there is no risk of collision, \"Right\", \"Strong Right\", \"Left\", \"Strong Left\" for evasion direction. The minimum score will correspond to the decision taken.\n",
    "\n",
    "![acas](imgs/acas.png)\n",
    "\n",
    "The network takes 5 inputs:\n",
    "- ρ (m): Distance from ownship to intruder.\n",
    "- θ (rad): Angle to intruder relative to ownship heading\n",
    "direction.\n",
    "- ψ (rad): Heading angle of intruder relative to ownship\n",
    "heading direction.\n",
    "- $v_{own}$ (m/s): Speed of ownship.\n",
    "- $v_{int}$ (m/s) Speed of intruder.\n",
    "\n",
    "We want to prove one property on this network: \n",
    "\n",
    "**Property 1.**\n",
    "- Description: If the intruder is distant and is significantly slower than the\n",
    "ownship, the score of a COC advisory will always be below a certain fixed\n",
    "threshold.\n",
    "- Input constraints: ρ ≥ 55947.691, $v_{own} ≥ 1145$, $v_{int} ≤ 60$.\n",
    "- Desired output property: the score for COC is at most 1500.\n",
    "\n",
    "The formulas for this property are already written. Normalised values will be given below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote pyrat formula in file formula_p1.txt\n",
      "Wrote marabou formula in file formula_p1.marabou\n",
      "Output bounds:\n",
      " [-10710.81020574  -9110.01167952 -12047.17947303  -2730.80270361\n",
      " -13194.04888676]\n",
      "[ 5272.51746506  5429.67755395  4817.41682782 10864.69018591\n",
      "  5844.88081043]\n",
      "Result = Unknown, Time = 0.01 s\n"
     ]
    }
   ],
   "source": [
    "from formula_lang import formula_p1\n",
    "from pyrat_api import launch_pyrat\n",
    "\n",
    "formula_p1().write_pyrat(\"formula_p1.txt\")\n",
    "formula_p1().write_marabou(\"formula_p1.marabou\")\n",
    "\n",
    "res, t_pyrat = launch_pyrat(\"acas.nnet\", \"formula_p1.txt\", domains=[\"zono\"])\n",
    "#res, t_marabou = launch_marabou(\"acas.nnet\", 'formula_p1.marabou') # does not finish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result = True, Time = 2.97 s, Safe space = 100.00 %, number of analysis = 281\n"
     ]
    }
   ],
   "source": [
    "# splitting the inputs\n",
    "res, t_pyrat = launch_pyrat(\"acas.nnet\", \"formula_p1.txt\", domains=[\"zono\"], split_timeout=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here that PyRAT performs 281 analysis on this property in total. Indeed after failing to prove the property at the begining it divides the input space and perform subsequent analysis until it proves the property. At the same time we see that Marabou takes too long to prove the property so we might want to accelerate this.\n",
    "\n",
    "In this part, we will aim to facilitate the analysis similar to the `split_timeout` option of PyRAT. For this we will divide the input space into smaller parts before performing the analysis on smaller parts.\n",
    "\n",
    "### Step 1: Divide a formula\n",
    "\n",
    "We will first look at how to divide the input space for our problem. As we worked until now on Formulas it might not be evident to divide them as they are mostly in a textual form. We will thus come back to our Interval class, which we can easily divide, then transform an Interval into a constraint that we can add into a Formula.\n",
    "\n",
    "**Write the following functions.** `create_formula_p1` is already written and should properly create a formula if `interval_to_constraint` is correctly implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def divide_interval(x: Interval) -> (Interval, Interval):\n",
    "    if isinstance(x, Interval):\n",
    "        return Interval(x.lower, (x.upper+x.lower)/2), Interval((x.upper+x.lower)/2, x.upper)\n",
    "    raise NotImplementedError\n",
    "\n",
    "assert divide_interval(Interval(0, 1)) == (Interval(0, 0.5), Interval(0.5, 1))\n",
    "assert divide_interval(Interval(-5, 1)) == (Interval(-5, -2), Interval(-2, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x0 >= 0.6; x0 < 0.6798577687; x1 >= -0.5; x1 < 0.5; x2 >= -0.5; x2 < 0.5; x3 >= 0.45; x3 < 0.5; x4 >= -0.5; x4 < 0.45; y0 < 3.9911256459; \n"
     ]
    }
   ],
   "source": [
    "from typing import List\n",
    "\n",
    "def interval_to_constraint(x: Interval, name: str) -> (Constr, Constr):\n",
    "    if isinstance(x, Interval):\n",
    "        return Constr(Var(name), '>=', Real(x.lower)), Constr(Var(name), '<', Real(x.upper))\n",
    "        \n",
    "\n",
    "def create_formula_p1(inputs: List[Interval]) -> Formula:\n",
    "    output = Var('y0')\n",
    "\n",
    "    constrs = []\n",
    "    for i in range(len(inputs)):\n",
    "        constrs.extend(interval_to_constraint(inputs[i], f\"x{i}\")) # for each input interval we create and add the constraint\n",
    "        \n",
    "    constrs.append(Constr(output, '<', Real(3.9911256459))) # constraint on the output\n",
    "\n",
    "    formula = Formula()\n",
    "    for c in constrs:\n",
    "        formula.add(c)\n",
    "\n",
    "    return formula\n",
    "\n",
    "initial = [Interval(0.6, 0.6798577687), Interval(-0.5, 0.5), Interval(-0.5, 0.5), Interval(0.45, 0.5), Interval(-0.5, 0.45)]\n",
    "print(create_formula_p1(initial))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Iteration algorithm\n",
    "\n",
    "Now we can generate formula from intervals and divide the intervals, we can start working on an algorithm to divide the input space for the analysis. The general idea of the algorithm would be:\n",
    "\n",
    "1. Initial: 1 Interval per input as defined by the property\n",
    "2. Create a formula from the intervals\n",
    "3. Run an analysis with eith PyRAT or Marabou (Marabou has a timeout of 100 it can be changed in `launch_marabou`)\n",
    "4. If property is verified stop. \n",
    "   Otherwise, divide an interval in 2 and come back to step 2 for both subspaces created\n",
    "5. If all subspaces are proven True/unsat we can conclude that the initial space is as well.\n",
    "\n",
    "`launch_pyrat` and `launch_marabou` both return a tuple of `boolean, float` the boolean indicates if the property holds when it equals `True`. The float is the time taken for the analysis. Here do not use `split_timeout = 0` for PyRAT not to do any splitting on its own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-13703.76840556 -11650.49647462 -15399.48471075  -3491.64369193\n",
      " -16873.93784018]\n",
      "[ 6741.31879213  6948.28653093  6161.77284022 13889.18982366\n",
      "  7474.49709356]\n",
      "Result = Unknown, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-3550.55339166 -3023.83749887 -4002.8480418   -902.79341163\n",
      " -4383.57122813]\n",
      "[1745.18966127 1794.98362025 1593.07387949 3605.36711416 1933.38484528]\n",
      "Result = Unknown, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-55.06820859 -46.94232084 -65.63917304 -19.87184717 -73.61251578]\n",
      "[37.9098227  37.86804778 35.40315467 70.01284302 43.81451971]\n",
      "Result = Unknown, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-0.02121775 -0.01879782 -0.01952425 -0.01893638 -0.0185982 ]\n",
      "[-0.01423817 -0.01179154 -0.00823094 -0.01761423 -0.00731985]\n",
      "Result = True, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-0.0207541  -0.01877377 -0.01951269 -0.01891985 -0.01858325]\n",
      "[-0.02016294 -0.01871954 -0.01948663 -0.01888259 -0.01854955]\n",
      "Result = True, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-352.70570723 -303.42657267 -401.7464177   -90.58138666 -436.26610886]\n",
      "[172.19825764 173.99488784 157.01285023 362.83767465 192.71922046]\n",
      "Result = Unknown, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-0.15568065 -0.1716002  -0.20014387 -0.699908   -0.27070714]\n",
      "[0.92424944 1.04669573 1.47032404 0.97590631 1.82164312]\n",
      "Result = True, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [ -7.47190445  -6.81427319 -10.90876595  -6.29717939 -14.23650304]\n",
      "[12.45968295 12.90411493 12.33305946 16.50971771 15.35373532]\n",
      "Result = Unknown, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-0.02050728 -0.01878764 -0.01951936 -0.01892939 -0.01859188]\n",
      "[-0.0042468   0.00676165  0.05240967  0.00147856  0.06137028]\n",
      "Result = True, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-0.05395529 -0.07882239 -0.0844974  -0.09567312 -0.23208993]\n",
      "[0.10340051 0.15111863 0.11976172 0.14357011 0.18966976]\n",
      "Result = True, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-3695.87115685 -3150.16992906 -4167.56277234  -945.01871031\n",
      " -4559.71282389]\n",
      "[1821.48322914 1870.45224624 1662.87037422 3759.04988596 2018.72527643]\n",
      "Result = Unknown, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-282.51928827 -241.91368502 -320.69790512  -73.04203202 -349.31167125]\n",
      "[138.68520869 140.0404299  127.11648591 288.24411521 156.15636524]\n",
      "Result = Unknown, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-1.60438756 -1.94363294 -3.4275154  -3.51762934 -4.9584253 ]\n",
      "[6.10297656 7.01423348 7.20661815 8.34524076 8.29646485]\n",
      "Result = Unknown, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-0.02100108 -0.01882828 -0.01953889 -0.01895731 -0.01863867]\n",
      "[-0.0172831  -0.01226754 -0.01223274 -0.01425342 -0.0132472 ]\n",
      "Result = True, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-0.02078877 -0.01879703 -0.01952387 -0.01893584 -0.01859771]\n",
      "[-0.01966997 -0.01630435 -0.01706997 -0.01716751 -0.01701337]\n",
      "Result = True, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-0.02383511 -0.01882766 -0.03147746 -0.05449972 -0.01881987]\n",
      "[0.08913946 0.19379825 0.13595365 0.15602469 0.15905256]\n",
      "Result = True, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-1.95314583 -2.29833054 -4.66155779 -4.11012113 -6.49521951]\n",
      "[ 6.57163797  8.37302401  7.80644603 11.1525727   9.50623178]\n",
      "Result = Unknown, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-0.02041463 -0.01878185 -0.01951658 -0.01892541 -0.01858828]\n",
      "[-0.02007477 -0.01875068 -0.01950159 -0.01890399 -0.0185689 ]\n",
      "Result = True, Time = 0.01 s\n",
      "Wrote pyrat formula in file formula.txt\n",
      "Output bounds:\n",
      " [-0.02037451 -0.01878399 -0.0195176  -0.01892688 -0.0185896 ]\n",
      "[-0.02005151 -0.01875436 -0.01950336 -0.01890652 -0.01857119]\n",
      "Result = True, Time = 0.01 s\n"
     ]
    }
   ],
   "source": [
    "def analyse(inputs: List[Interval]):\n",
    "    formula = create_formula_p1(inputs)\n",
    "    formula.write_pyrat(\"formula.txt\")\n",
    "    res, t_pyrat = launch_pyrat(\"acas.nnet\", \"formula.txt\", domains=[\"zono\"])\n",
    "    \n",
    "    if(res == True):\n",
    "         return True   \n",
    "    else:\n",
    "        i1 = []\n",
    "        i2 = []\n",
    "        for i in range(len(inputs)):\n",
    "            i_div = divide_interval(inputs[i])\n",
    "            i1.append(i_div[0])\n",
    "            i2.append(i_div[1])\n",
    "            \n",
    "        analyse(i1)\n",
    "        analyse(i2)\n",
    "                              \n",
    "initial = [Interval(0.6, 0.6798577687), Interval(-0.5, 0.5), Interval(-0.5, 0.5), Interval(0.45, 0.5), Interval(-0.5, 0.45)]\n",
    "analyse(initial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can print some statistic like the number of analysis you do or the time taken. **Can you do less analysis than PyRAT?**\n",
    "\n",
    "### Heuristics to go further\n",
    "\n",
    "How could we reach the same number or lower of analysis from PyRAT:\n",
    "- Can we select the interval to split more carefully?\n",
    "- Can we ignore some of the preliminary analysis because we know they won't succeed?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*****\n",
    "\n",
    "## Part 4: Choosing a network for image classification\n",
    "\n",
    "For this section we will focus on an open source image dataset for classification purpose. The images we will use are a subset of the Fashion Mnist dataset ([available here](https://www.kaggle.com/datasets/zalando-research/fashionmnist)). This dataset was developed to replace the MNIST dataset which was overused and presents the same caracteristics:\n",
    "- 28x28 grayscales images\n",
    "- 10 output classes: T-shirt, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot\n",
    "\n",
    "![fmnist](imgs/fashion_mnist.png)\n",
    "\n",
    "We will use a subset of 50 images for the purpose of this TP.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal for this part is to decide which model would be best suited for our needs. Let's suppose we are in a critical system where picking the right class for the cloth might lead to any potential damage (ecological, financial, loss of clients, ..). \n",
    "\n",
    "We propose 5 models trained with different methods:\n",
    "1. **Baseline model**, normal training\n",
    "2. **Adversarial model**, adversarial training\n",
    "3. **Pruned model, normal** training + pruning\n",
    "4. **Certified model**, certified training\n",
    "5. **Pruned certified model**, certified training + pruning\n",
    "\n",
    "We must decide on a model to use. The accuracy of the models is already calculated on the whole test set (more than 50 images). This is already a first criteria of choice as some training lead to less accuracy.\n",
    "\n",
    "|Model | Accuracy|\n",
    "|--- | ---|\n",
    "|Baseline | 90.50%|\n",
    "|Adversarial | 79%|\n",
    "|Pruned | 89%|\n",
    "|Certified | 72.30%|\n",
    "|Pruned Certified | 73.20%|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Local robustness \n",
    "\n",
    "We will look at the local robustness of the five models around the 50 images from our subdataset. All models and images are  available in the `fmnist` folder. We already created two utility functions to read images and to launch PyRAT on an image and a network. `read_images` return a list of `(image_i, label_i)` while `local_robustness` returns the robustness of a network around an image for a given perturbation.\n",
    "\n",
    "An example is given below with a bag image and a perturbation of 1/255 (1 pixel modified)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape: (28, 28), label: 7\n",
      "Robust: True, Time: 0.39942956200047774\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyrat_api import read_images, local_robustness\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "images = read_images()\n",
    "image_0, label_0 = images[0]\n",
    "print(f\"Image shape: {image_0.shape}, label: {label_0}\")\n",
    "plt.imshow(image_0, cmap='gray')\n",
    "\n",
    "res, elapsed = local_robustness(model_path=\"fmnist/baseline.onnx\", image=image_0, label=label_0, pert=1/255, domains=[\"zono\"])\n",
    "print(f\"Robust: {res}, Time: {elapsed}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now its your turn create a function that analyse all the given images for a given network and a given perturbation and returns a robustness score including safe images, unknown images and unsafe images as well as a mean time for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[85], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrobustness\u001b[39m(images, model_path, pert):\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[43mrobustness\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfmnist/baseline.onnx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m255\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# should return [1, 48, 1]\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[85], line 2\u001b[0m, in \u001b[0;36mrobustness\u001b[0;34m(images, model_path, pert)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrobustness\u001b[39m(images, model_path, pert):\n\u001b[0;32m----> 2\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def robustness(images, model_path, pert):\n",
    "    raise NotImplementedError\n",
    "    \n",
    "robustness(images, \"fmnist/baseline.onnx\", 1/255) # should return [1, 48, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With these results you can now try and plot the results to show the evolution in function of the level of intensity for the perturbation. We aim to see if a model is more robust than another to what could be adversarial perturbation. You can use matplotlib to plot these results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_robustness():\n",
    "    raise NotImplementedError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Metamorphic testing\n",
    "\n",
    "We checked with the engineer in charge of taking pictures of the clothes on the condition in which he takes pictures. After a careful investigation we realised that the following issues might be happening in our picture:\n",
    "- Luminosity of the setting varies from -30 to +30\n",
    "- Angle of the clothes might vary from -15° to + 15°\n",
    "- Picture can be blurry \n",
    "\n",
    "In that sense, to test the robustness of our model we will proceed to see if it is sensitive to these perturbations. Following the examples [here](https://opencv-tutorial.readthedocs.io/en/latest/trans/transform.html) use opencv library to implement transformation on the images. You can visualise the image with matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[87], line 10\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mblur\u001b[39m(image, intensity):\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(\u001b[43mblur\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m15\u001b[39;49m\u001b[43m)\u001b[49m)\n",
      "Cell \u001b[0;32mIn[87], line 8\u001b[0m, in \u001b[0;36mblur\u001b[0;34m(image, intensity)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mblur\u001b[39m(image, intensity):\n\u001b[0;32m----> 8\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def luminosity(image, intensity):\n",
    "    raise NotImplementedError\n",
    "    \n",
    "def rotation(image, angle):\n",
    "    raise NotImplementedError\n",
    "\n",
    "def blur(image, intensity):\n",
    "    raise NotImplementedError\n",
    "    \n",
    "plt.imshow(blur(image_0, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To apply these transformation and compute the stability of a network towards we will use a tool developed at CEA called AIMOS which does exactly that.\n",
    "\n",
    "After installing the aimos module (which is provided locally), the function below will call aimos on your transformation and a specified range before plotting the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ./bin/aimos_compile\n",
      "Building wheels for collected packages: AIMOS\n",
      "  Building wheel for AIMOS (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for AIMOS: filename=AIMOS-1.0-py3-none-any.whl size=27865 sha256=b6149cb0febece22984ce1e9319ee6cb64109d5fac6dca16f576ffb60d8673fa\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-mr8hxog1/wheels/70/50/9d/c65b449093c72794e4464fe38d49de849130fb6a47de22aede\n",
      "Successfully built AIMOS\n",
      "Installing collected packages: AIMOS\n",
      "Successfully installed AIMOS-1.0\n",
      "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 22.3.1 is available.\n",
      "You should consider upgrading via the '/home/NEMO18/.virtualenvs/tuto_seti/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install ./bin/aimos_compile\n",
    "from aimos import core"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[89], line 24\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m y\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m core\u001b[38;5;241m.\u001b[39mmain(\n\u001b[1;32m     15\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfmnist/images/\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     16\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfmnist/\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     21\u001b[0m         verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     22\u001b[0m         )\n\u001b[0;32m---> 24\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mcall_aimos\u001b[49m\u001b[43m(\u001b[49m\u001b[43mluminosity\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[89], line 14\u001b[0m, in \u001b[0;36mcall_aimos\u001b[0;34m(transformation, value_range)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21midentity\u001b[39m(y):\n\u001b[1;32m     12\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m y\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfmnist/images/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfmnist/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     17\u001b[0m \u001b[43m    \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtransformation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midentity\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     18\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcustom_load\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload_image\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     19\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfn_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalue_range\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[43m    \u001b[49m\u001b[43msingle_plot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     21\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     22\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m.\\aimos\\core.py:408\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(inputs_path, models_path, transformation, alt_predict, custom_predict, custom_load, custom_global_load, fn_range, out_mode, per_input, custom_comparison, single_plot, save_single_fig, verbose, individual_load, export_path)\u001b[0m\n",
      "File \u001b[0;32m.\\aimos\\core.py:254\u001b[0m, in \u001b[0;36m_loop_over_inputs\u001b[0;34m(inputs, inputs_name, model, predict_fn, alt_predict_fn, out_mode, custom_comparison, transform, transformation, per_input, range_value, verbose, custom_load, individual_load, export_path)\u001b[0m\n",
      "File \u001b[0;32m.\\aimos\\core.py:169\u001b[0m, in \u001b[0;36mcompute_results\u001b[0;34m(transformation, input_ori, predict_fn, alt_predict_fn, out_mode, comparison_fn, export_path)\u001b[0m\n",
      "File \u001b[0;32mC:\\Users\\AL253370\\Documents\\Projets\\AIMOS\\git_aimos\\aimos\\core_functions.py:138\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n",
      "Cell \u001b[0;32mIn[87], line 2\u001b[0m, in \u001b[0;36mluminosity\u001b[0;34m(image, intensity)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mluminosity\u001b[39m(image, intensity):\n\u001b[0;32m----> 2\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def load_image(path):\n",
    "    return np.load(str(path)).astype(np.float32).reshape((1, 28, 28))\n",
    "\n",
    "def call_aimos(transformation, value_range):\n",
    "    def identity(y):\n",
    "        return y\n",
    "    \n",
    "    return core.main(\n",
    "        \"fmnist/images/\",\n",
    "        \"fmnist/\",\n",
    "        (transformation, identity),\n",
    "        custom_load=load_image,\n",
    "        fn_range=value_range,\n",
    "        single_plot=True,\n",
    "        verbose=False,\n",
    "        )\n",
    "\n",
    "res = call_aimos(luminosity, range(0, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, you can run the above function on the different transformations that we want to test to see the different responses from the models. \n",
    "\n",
    "**What do you notice?**\n",
    "\n",
    "### Step 3: Choose a model\n",
    "\n",
    "**With all of these results, what model would you choose, why?**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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