notebook_old.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-19T11:31:03.428669Z",
     "start_time": "2018-05-19T11:31:03.424179Z"
    }
   },
   "source": [
    "# <center>Geometric Methods in Machine Learning<br/><br/>Poincaré Embeddings<br/><br/>Zakarya Ali et Samir Tanfous</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous étudions l'article [Poincaré Embeddings for Learning Hierarchical Representations](https://arxiv.org/pdf/1705.08039.pdf) de Maximilian Nickel et Douwe Kiela."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prérequis\n",
    "On fait l'import du code et des données nécessaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.157551Z",
     "start_time": "2018-05-20T20:44:18.982612Z"
    }
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "import os\n",
    "import numpy as np\n",
    "import csv\n",
    "import random\n",
    "import time\n",
    "import pickle\n",
    "import logger\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.161219Z",
     "start_time": "2018-05-20T20:44:19.158965Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wordnet_mammal_file = 'data/wordnet_mammal_hypernyms.tsv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.170537Z",
     "start_time": "2018-05-20T20:44:19.162688Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_wordnet(wordnet_path):\n",
    "    network = {}\n",
    "    with open(wordnet_path, 'r') as f:\n",
    "        reader = csv.reader(f, delimiter='\\t')\n",
    "        for row in reader:\n",
    "            if not row[0]==row[1]:\n",
    "                if row[0] in network:\n",
    "                    network[row[0]].append(row[1])\n",
    "                else:\n",
    "                    network.update({row[0]:[row[1]]})\n",
    "    return network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.174549Z",
     "start_time": "2018-05-20T20:44:19.171828Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def poincare_dist(gamma): \n",
    "    \"\"\"Distance de Poincaré (équation 1 de l'article)\"\"\"\n",
    "    return np.arccosh(gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.181841Z",
     "start_time": "2018-05-20T20:44:19.175861Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_gamma(vec1, vec2):\n",
    "    euclidean_dists = np.linalg.norm(vec1 - vec2)\n",
    "    norm1 = np.linalg.norm(vec1)\n",
    "    norm2 = np.linalg.norm(vec2)\n",
    "    alpha = 1-norm1**2\n",
    "    beta = 1-norm2**2\n",
    "    gamma = 1 + 2*euclidean_dists/(alpha*beta)\n",
    "    return gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.188345Z",
     "start_time": "2018-05-20T20:44:19.183122Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def deriv_poincare_theta(theta, x, gamma): \n",
    "    \"\"\"Dérivée partielle par rapport à theta de la distance de Poincare (equation 4 de l'article)\"\"\"\n",
    "    alpha = (1.0-np.dot(theta, theta))\n",
    "    norm_x = np.dot(x, x)\n",
    "    beta = (1-norm_x)\n",
    "    gamma = gamma\n",
    "    return 4.0/(beta * np.sqrt(gamma*gamma - 1))*((norm_x- 2*np.dot(theta, x)+1)/(pow(alpha,2))*theta - x/(alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.194694Z",
     "start_time": "2018-05-20T20:44:19.189609Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def update(emb, error_, learning_rate):\n",
    "    \"\"\"Equation d'update (equation 5)\"\"\"\n",
    "    update =  learning_rate*pow((1 - np.linalg.norm(emb)), 2)*error_/4\n",
    "    emb = emb - update\n",
    "    if (np.dot(emb, emb) >= 1):\n",
    "        emb = emb/np.sqrt(np.dot(emb, emb)) - EPSILON\n",
    "    return emb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.202140Z",
     "start_time": "2018-05-20T20:44:19.195945Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_negative_candidates(network, vocab, vocab_weights, u, num_negs):\n",
    "    \"\"\"Retourne une liste de mots qui ne sont pas liés à celui donné en entrée\"\"\"\n",
    "    negs = []\n",
    "    while (len(negs) < num_negs):\n",
    "        neg = np.random.choice(vocab, p=vocab_weights)\n",
    "#         neg = random.choice(vocab)\n",
    "        if not (neg in network[u] or u in network[neg] or neg == u): # neg ne doit pas etre lié à u et vice versa\n",
    "            negs.append(neg)\n",
    "    return negs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.209313Z",
     "start_time": "2018-05-20T20:44:19.203386Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def initialize_embedding(network, embedding_size):\n",
    "    \"\"\"Comme dans l'article, on initialise la position des noeuds entre 0.001 et -0.001\"\"\"\n",
    "    emb = {}\n",
    "    gap = 1e-3\n",
    "    for a in network:\n",
    "        for b in network[a]:\n",
    "            emb[b] = np.random.uniform(low=-gap, high=gap, size=(embedding_size,))\n",
    "        emb[a] = np.random.uniform(low=-gap, high=gap, size=(embedding_size,))\n",
    "    return emb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.216294Z",
     "start_time": "2018-05-20T20:44:19.210813Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_vocabulary(network, embeddings):\n",
    "    vocab = list(embeddings.keys())\n",
    "    vocab_weights = np.zeros(len(vocab))\n",
    "    for key in network:\n",
    "        for u in network[key]:\n",
    "            vocab_weights[vocab.index(u)]+=1\n",
    "    vocab_weights = vocab_weights / np.sum(vocab_weights)\n",
    "    return vocab, vocab_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.252001Z",
     "start_time": "2018-05-20T20:44:19.217584Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_poincare_embeddings(emb, epochs, save=True, show=False):\n",
    "    fig=plt.figure(figsize=(15,15))\n",
    "    names = []\n",
    "    positions_x = []\n",
    "    positions_y = []\n",
    "    for key in emb:\n",
    "        names.append(key)\n",
    "        positions_x.append(emb[key][0])\n",
    "        positions_y.append(emb[key][1])\n",
    "\n",
    "    plt.scatter(positions_x,positions_y, s=1)\n",
    "    k=1\n",
    "    plt.text(positions_x[k], positions_y[k], names[k] + \" \" + str(positions_x[k]), fontsize=10)\n",
    "\n",
    "    circle = plt.Circle((0, 0), 1, color='g',fill=False)\n",
    "\n",
    "    plt.axis([-1,1,-1,1])\n",
    "    ax=fig.add_subplot(1,1,1)\n",
    "    ax.add_patch(circle)\n",
    "    plt.axhline(y=0, color='k')\n",
    "    plt.axvline(x=0, color='k')\n",
    "    plt.title(\"Poincaré embeddings pour \" + str(epochs - nb_burn_in) + \" epochs, \"\n",
    "              + str(num_negs) + \" negatifs, \" \n",
    "             + str(nb_burn_in) + \" périodes de burn-in\")\n",
    "\n",
    "    if save:\n",
    "        plt.savefig('poincaré_embeddings_epochs_' + str(epochs - nb_burn_in) + '.png')\n",
    "    \n",
    "    if show:\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Entrainement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:44:19.268099Z",
     "start_time": "2018-05-20T20:44:19.253456Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Initialisation des variables\n",
    "network = load_wordnet(wordnet_mammal_file)\n",
    "\n",
    "embedding_size = 5 #dimension des vecteurs d'entrainement\n",
    "\n",
    "#epochs\n",
    "num_epochs = 250\n",
    "#burn-in, nombre d'épochs\n",
    "nb_burn_in = 10\n",
    "\n",
    "#nombre de nodes negatives pour l'entrainement\n",
    "num_negs = 20\n",
    "\n",
    "#learning rate\n",
    "lr = 1e-2\n",
    "#learning rate pour le burn-in\n",
    "learning_rate_burn_in = lr/10\n",
    "\n",
    "EPSILON = 1e-5 # evite l'overflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:21.232231Z",
     "start_time": "2018-05-20T20:44:19.269521Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1, temps d'exécution: 6.22 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #2, temps d'exécution: 6.24 seconds\n",
      "Epoch #3, temps d'exécution: 6.17 seconds\n",
      "Epoch #4, temps d'exécution: 6.22 seconds\n",
      "Epoch #5, temps d'exécution: 6.22 seconds\n",
      "Epoch #6, temps d'exécution: 6.21 seconds\n",
      "Epoch #7, temps d'exécution: 6.19 seconds\n",
      "Epoch #8, temps d'exécution: 6.19 seconds\n",
      "Epoch #9, temps d'exécution: 6.15 seconds\n",
      "Epoch #10, temps d'exécution: 6.22 seconds\n",
      "Epoch #11, temps d'exécution: 6.20 seconds\n",
      "Epoch #12, temps d'exécution: 6.19 seconds\n",
      "Epoch #13, temps d'exécution: 6.17 seconds\n",
      "Epoch #14, temps d'exécution: 6.17 seconds\n",
      "Epoch #15, temps d'exécution: 6.19 seconds\n",
      "Epoch #16, temps d'exécution: 6.18 seconds\n",
      "Epoch #17, temps d'exécution: 6.25 seconds\n",
      "Epoch #18, temps d'exécution: 6.24 seconds\n",
      "Epoch #19, temps d'exécution: 6.20 seconds\n",
      "Epoch #20, temps d'exécution: 6.23 seconds\n",
      "Epoch #21, temps d'exécution: 6.21 seconds\n",
      "Epoch #22, temps d'exécution: 6.23 seconds\n",
      "Epoch #23, temps d'exécution: 6.24 seconds\n",
      "Epoch #24, temps d'exécution: 6.26 seconds\n",
      "Epoch #25, temps d'exécution: 6.20 seconds\n",
      "Epoch #26, temps d'exécution: 6.20 seconds\n",
      "Epoch #27, temps d'exécution: 6.21 seconds\n",
      "Epoch #28, temps d'exécution: 6.30 seconds\n",
      "Epoch #29, temps d'exécution: 6.13 seconds\n",
      "Epoch #30, temps d'exécution: 6.22 seconds\n",
      "Epoch #31, temps d'exécution: 6.19 seconds\n",
      "Epoch #32, temps d'exécution: 6.19 seconds\n",
      "Epoch #33, temps d'exécution: 6.17 seconds\n",
      "Epoch #34, temps d'exécution: 6.24 seconds\n",
      "Epoch #35, temps d'exécution: 6.25 seconds\n",
      "Epoch #36, temps d'exécution: 6.19 seconds\n",
      "Epoch #37, temps d'exécution: 6.22 seconds\n",
      "Epoch #38, temps d'exécution: 6.19 seconds\n",
      "Epoch #39, temps d'exécution: 6.19 seconds\n",
      "Epoch #40, temps d'exécution: 6.27 seconds\n",
      "Epoch #41, temps d'exécution: 6.18 seconds\n",
      "Epoch #42, temps d'exécution: 6.24 seconds\n",
      "Epoch #43, temps d'exécution: 6.19 seconds\n",
      "Epoch #44, temps d'exécution: 6.19 seconds\n",
      "Epoch #45, temps d'exécution: 6.24 seconds\n",
      "Epoch #46, temps d'exécution: 6.24 seconds\n",
      "Epoch #47, temps d'exécution: 6.30 seconds\n",
      "Epoch #48, temps d'exécution: 6.21 seconds\n",
      "Epoch #49, temps d'exécution: 6.17 seconds\n",
      "Epoch #50, temps d'exécution: 6.19 seconds\n",
      "Epoch #51, temps d'exécution: 6.25 seconds\n",
      "Epoch #52, temps d'exécution: 6.24 seconds\n",
      "Epoch #53, temps d'exécution: 6.23 seconds\n",
      "Epoch #54, temps d'exécution: 6.20 seconds\n",
      "Epoch #55, temps d'exécution: 6.27 seconds\n",
      "Epoch #56, temps d'exécution: 6.27 seconds\n",
      "Epoch #57, temps d'exécution: 6.24 seconds\n",
      "Epoch #58, temps d'exécution: 6.26 seconds\n",
      "Epoch #59, temps d'exécution: 6.17 seconds\n",
      "Epoch #60, temps d'exécution: 6.26 seconds\n",
      "Epoch #61, temps d'exécution: 6.23 seconds\n",
      "Epoch #62, temps d'exécution: 6.17 seconds\n",
      "Epoch #63, temps d'exécution: 6.19 seconds\n",
      "Epoch #64, temps d'exécution: 6.24 seconds\n",
      "Epoch #65, temps d'exécution: 6.21 seconds\n",
      "Epoch #66, temps d'exécution: 6.32 seconds\n",
      "Epoch #67, temps d'exécution: 6.19 seconds\n",
      "Epoch #68, temps d'exécution: 6.17 seconds\n",
      "Epoch #69, temps d'exécution: 6.21 seconds\n",
      "Epoch #70, temps d'exécution: 6.20 seconds\n",
      "Epoch #71, temps d'exécution: 6.20 seconds\n",
      "Epoch #72, temps d'exécution: 6.24 seconds\n",
      "Epoch #73, temps d'exécution: 6.20 seconds\n",
      "Epoch #74, temps d'exécution: 6.26 seconds\n",
      "Epoch #75, temps d'exécution: 6.22 seconds\n",
      "Epoch #76, temps d'exécution: 6.23 seconds\n",
      "Epoch #77, temps d'exécution: 6.21 seconds\n",
      "Epoch #78, temps d'exécution: 6.21 seconds\n",
      "Epoch #79, temps d'exécution: 6.27 seconds\n",
      "Epoch #80, temps d'exécution: 6.20 seconds\n",
      "Epoch #81, temps d'exécution: 6.20 seconds\n",
      "Epoch #82, temps d'exécution: 6.24 seconds\n",
      "Epoch #83, temps d'exécution: 6.22 seconds\n",
      "Epoch #84, temps d'exécution: 6.18 seconds\n",
      "Epoch #85, temps d'exécution: 6.23 seconds\n",
      "Epoch #86, temps d'exécution: 6.17 seconds\n",
      "Epoch #87, temps d'exécution: 6.24 seconds\n",
      "Epoch #88, temps d'exécution: 6.21 seconds\n",
      "Epoch #89, temps d'exécution: 6.28 seconds\n",
      "Epoch #90, temps d'exécution: 6.17 seconds\n",
      "Epoch #91, temps d'exécution: 6.23 seconds\n",
      "Epoch #92, temps d'exécution: 6.23 seconds\n",
      "Epoch #93, temps d'exécution: 6.22 seconds\n",
      "Epoch #94, temps d'exécution: 6.23 seconds\n",
      "Epoch #95, temps d'exécution: 6.22 seconds\n",
      "Epoch #96, temps d'exécution: 6.26 seconds\n",
      "Epoch #97, temps d'exécution: 6.22 seconds\n",
      "Epoch #98, temps d'exécution: 6.21 seconds\n",
      "Epoch #99, temps d'exécution: 6.20 seconds\n",
      "Epoch #100, temps d'exécution: 6.22 seconds\n",
      "Epoch #101, temps d'exécution: 6.21 seconds\n",
      "Epoch #102, temps d'exécution: 6.22 seconds\n",
      "Epoch #103, temps d'exécution: 6.24 seconds\n",
      "Epoch #104, temps d'exécution: 6.21 seconds\n",
      "Epoch #105, temps d'exécution: 6.24 seconds\n",
      "Epoch #106, temps d'exécution: 6.22 seconds\n",
      "Epoch #107, temps d'exécution: 6.27 seconds\n",
      "Epoch #108, temps d'exécution: 6.23 seconds\n",
      "Epoch #109, temps d'exécution: 6.26 seconds\n",
      "Epoch #110, temps d'exécution: 6.25 seconds\n",
      "Epoch #111, temps d'exécution: 6.19 seconds\n",
      "Epoch #112, temps d'exécution: 6.23 seconds\n",
      "Epoch #113, temps d'exécution: 6.23 seconds\n",
      "Epoch #114, temps d'exécution: 6.23 seconds\n",
      "Epoch #115, temps d'exécution: 6.22 seconds\n",
      "Epoch #116, temps d'exécution: 6.20 seconds\n",
      "Epoch #117, temps d'exécution: 6.21 seconds\n",
      "Epoch #118, temps d'exécution: 6.21 seconds\n",
      "Epoch #119, temps d'exécution: 6.26 seconds\n",
      "Epoch #120, temps d'exécution: 6.18 seconds\n",
      "Epoch #121, temps d'exécution: 6.23 seconds\n",
      "Epoch #122, temps d'exécution: 6.24 seconds\n",
      "Epoch #123, temps d'exécution: 6.21 seconds\n",
      "Epoch #124, temps d'exécution: 6.21 seconds\n",
      "Epoch #125, temps d'exécution: 6.22 seconds\n",
      "Epoch #126, temps d'exécution: 6.17 seconds\n",
      "Epoch #127, temps d'exécution: 6.23 seconds\n",
      "Epoch #128, temps d'exécution: 6.27 seconds\n",
      "Epoch #129, temps d'exécution: 6.23 seconds\n",
      "Epoch #130, temps d'exécution: 6.21 seconds\n",
      "Epoch #131, temps d'exécution: 6.24 seconds\n",
      "Epoch #132, temps d'exécution: 6.17 seconds\n",
      "Epoch #133, temps d'exécution: 6.25 seconds\n",
      "Epoch #134, temps d'exécution: 6.17 seconds\n",
      "Epoch #135, temps d'exécution: 6.25 seconds\n",
      "Epoch #136, temps d'exécution: 6.18 seconds\n",
      "Epoch #137, temps d'exécution: 6.25 seconds\n",
      "Epoch #138, temps d'exécution: 6.23 seconds\n",
      "Epoch #139, temps d'exécution: 6.20 seconds\n",
      "Epoch #140, temps d'exécution: 6.21 seconds\n",
      "Epoch #141, temps d'exécution: 6.25 seconds\n",
      "Epoch #142, temps d'exécution: 6.21 seconds\n",
      "Epoch #143, temps d'exécution: 6.21 seconds\n",
      "Epoch #144, temps d'exécution: 6.19 seconds\n",
      "Epoch #145, temps d'exécution: 6.24 seconds\n",
      "Epoch #146, temps d'exécution: 6.26 seconds\n",
      "Epoch #147, temps d'exécution: 6.31 seconds\n",
      "Epoch #148, temps d'exécution: 6.20 seconds\n",
      "Epoch #149, temps d'exécution: 6.23 seconds\n",
      "Epoch #150, temps d'exécution: 6.21 seconds\n",
      "Epoch #151, temps d'exécution: 6.24 seconds\n",
      "Epoch #152, temps d'exécution: 6.23 seconds\n",
      "Epoch #153, temps d'exécution: 6.20 seconds\n",
      "Epoch #154, temps d'exécution: 6.27 seconds\n",
      "Epoch #155, temps d'exécution: 6.22 seconds\n",
      "Epoch #156, temps d'exécution: 6.24 seconds\n",
      "Epoch #157, temps d'exécution: 6.24 seconds\n",
      "Epoch #158, temps d'exécution: 6.27 seconds\n",
      "Epoch #159, temps d'exécution: 6.18 seconds\n",
      "Epoch #160, temps d'exécution: 6.15 seconds\n",
      "Epoch #161, temps d'exécution: 6.20 seconds\n",
      "Epoch #162, temps d'exécution: 6.24 seconds\n",
      "Epoch #163, temps d'exécution: 6.19 seconds\n",
      "Epoch #164, temps d'exécution: 6.19 seconds\n",
      "Epoch #165, temps d'exécution: 6.27 seconds\n",
      "Epoch #166, temps d'exécution: 6.27 seconds\n",
      "Epoch #167, temps d'exécution: 6.23 seconds\n",
      "Epoch #168, temps d'exécution: 6.23 seconds\n",
      "Epoch #169, temps d'exécution: 6.20 seconds\n",
      "Epoch #170, temps d'exécution: 6.19 seconds\n",
      "Epoch #171, temps d'exécution: 6.23 seconds\n",
      "Epoch #172, temps d'exécution: 6.24 seconds\n",
      "Epoch #173, temps d'exécution: 6.24 seconds\n",
      "Epoch #174, temps d'exécution: 6.19 seconds\n",
      "Epoch #175, temps d'exécution: 6.20 seconds\n",
      "Epoch #176, temps d'exécution: 6.24 seconds\n",
      "Epoch #177, temps d'exécution: 6.28 seconds\n",
      "Epoch #178, temps d'exécution: 6.29 seconds\n",
      "Epoch #179, temps d'exécution: 6.20 seconds\n",
      "Epoch #180, temps d'exécution: 6.26 seconds\n",
      "Epoch #181, temps d'exécution: 6.24 seconds\n",
      "Epoch #182, temps d'exécution: 6.26 seconds\n",
      "Epoch #183, temps d'exécution: 6.24 seconds\n",
      "Epoch #184, temps d'exécution: 6.27 seconds\n",
      "Epoch #185, temps d'exécution: 6.24 seconds\n",
      "Epoch #186, temps d'exécution: 6.24 seconds\n",
      "Epoch #187, temps d'exécution: 6.27 seconds\n",
      "Epoch #188, temps d'exécution: 6.26 seconds\n",
      "Epoch #189, temps d'exécution: 6.18 seconds\n",
      "Epoch #190, temps d'exécution: 6.18 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #191, temps d'exécution: 6.26 seconds\n",
      "Epoch #192, temps d'exécution: 6.21 seconds\n",
      "Epoch #193, temps d'exécution: 6.23 seconds\n",
      "Epoch #194, temps d'exécution: 6.17 seconds\n",
      "Epoch #195, temps d'exécution: 6.15 seconds\n",
      "Epoch #196, temps d'exécution: 6.23 seconds\n",
      "Epoch #197, temps d'exécution: 6.29 seconds\n",
      "Epoch #198, temps d'exécution: 6.23 seconds\n",
      "Epoch #199, temps d'exécution: 6.22 seconds\n",
      "Epoch #200, temps d'exécution: 6.23 seconds\n",
      "Epoch #201, temps d'exécution: 6.22 seconds\n",
      "Epoch #202, temps d'exécution: 6.26 seconds\n",
      "Epoch #203, temps d'exécution: 6.24 seconds\n",
      "Epoch #204, temps d'exécution: 6.25 seconds\n",
      "Epoch #205, temps d'exécution: 6.18 seconds\n",
      "Epoch #206, temps d'exécution: 6.24 seconds\n",
      "Epoch #207, temps d'exécution: 6.22 seconds\n",
      "Epoch #208, temps d'exécution: 6.19 seconds\n",
      "Epoch #209, temps d'exécution: 6.19 seconds\n",
      "Epoch #210, temps d'exécution: 6.27 seconds\n",
      "Epoch #211, temps d'exécution: 6.21 seconds\n",
      "Epoch #212, temps d'exécution: 6.22 seconds\n",
      "Epoch #213, temps d'exécution: 6.21 seconds\n",
      "Epoch #214, temps d'exécution: 6.23 seconds\n",
      "Epoch #215, temps d'exécution: 6.23 seconds\n",
      "Epoch #216, temps d'exécution: 6.20 seconds\n",
      "Epoch #217, temps d'exécution: 6.21 seconds\n",
      "Epoch #218, temps d'exécution: 6.19 seconds\n",
      "Epoch #219, temps d'exécution: 6.26 seconds\n",
      "Epoch #220, temps d'exécution: 6.28 seconds\n",
      "Epoch #221, temps d'exécution: 6.19 seconds\n",
      "Epoch #222, temps d'exécution: 6.21 seconds\n",
      "Epoch #223, temps d'exécution: 6.22 seconds\n",
      "Epoch #224, temps d'exécution: 6.21 seconds\n",
      "Epoch #225, temps d'exécution: 6.24 seconds\n",
      "Epoch #226, temps d'exécution: 6.21 seconds\n",
      "Epoch #227, temps d'exécution: 6.26 seconds\n",
      "Epoch #228, temps d'exécution: 6.26 seconds\n",
      "Epoch #229, temps d'exécution: 6.23 seconds\n",
      "Epoch #230, temps d'exécution: 6.24 seconds\n",
      "Epoch #231, temps d'exécution: 6.18 seconds\n",
      "Epoch #232, temps d'exécution: 6.17 seconds\n",
      "Epoch #233, temps d'exécution: 6.22 seconds\n",
      "Epoch #234, temps d'exécution: 6.17 seconds\n",
      "Epoch #235, temps d'exécution: 6.25 seconds\n",
      "Epoch #236, temps d'exécution: 6.25 seconds\n",
      "Epoch #237, temps d'exécution: 6.24 seconds\n",
      "Epoch #238, temps d'exécution: 6.22 seconds\n",
      "Epoch #239, temps d'exécution: 6.23 seconds\n",
      "Epoch #240, temps d'exécution: 6.23 seconds\n",
      "Epoch #241, temps d'exécution: 6.25 seconds\n",
      "Epoch #242, temps d'exécution: 6.14 seconds\n",
      "Epoch #243, temps d'exécution: 6.24 seconds\n",
      "Epoch #244, temps d'exécution: 6.21 seconds\n",
      "Epoch #245, temps d'exécution: 6.23 seconds\n",
      "Epoch #246, temps d'exécution: 6.66 seconds\n",
      "Epoch #247, temps d'exécution: 7.04 seconds\n",
      "Epoch #248, temps d'exécution: 7.05 seconds\n",
      "Epoch #249, temps d'exécution: 6.97 seconds\n",
      "Epoch #250, temps d'exécution: 6.51 seconds\n",
      "Epoch #251, temps d'exécution: 6.21 seconds\n",
      "Epoch #252, temps d'exécution: 6.27 seconds\n",
      "Epoch #253, temps d'exécution: 6.25 seconds\n",
      "Epoch #254, temps d'exécution: 6.26 seconds\n",
      "Epoch #255, temps d'exécution: 6.27 seconds\n",
      "Epoch #256, temps d'exécution: 6.24 seconds\n",
      "Epoch #257, temps d'exécution: 6.26 seconds\n",
      "Epoch #258, temps d'exécution: 6.20 seconds\n",
      "Epoch #259, temps d'exécution: 6.25 seconds\n",
      "Epoch #260, temps d'exécution: 6.19 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAANeCAYAAABK68ByAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3U/4OdsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c2cbaca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAANeCAYAAABK68ByAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIiBRRKghFRERERESKqP8H9nh5unrNbiwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c2c43a320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAANeCAYAAABK68ByAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xd4FFXfxvHvSSOQ0EEB6R1EpERp\nSkAEKUEsRAldEFFELNjwsffyyPPaQZEiSJBQlCIIKmBBkSBIE+kI0gkgBCSQnPeP2eASkhAgyWSz\n9+diryQ7W+6dmR3mN+fMGWOtRURERERERPxPgNsBRERERERExB0qCEVERERERPyUCkIRERERERE/\npYJQRERERETET6kgFBERERER8VMqCEVERERERPyUCkIRP2GMecIYM8rlDI8aYz4xxuT4tscYU9kY\nY40xQdn0eluNMddnMK2VMWaH199rjDGtsuN9xV1pl61kzhgzxxjTx+vvF40x+40xu93MdSGysr0y\nxlQ0xhw1xgRewOvnyrrl2Q5Wv4Dn9TXG/JATmbL4/hc8b0Xk/KggFPExnsLkuOc/yj3GmDHGmPBz\nPc9a+7K19s7cyJgeY0wHoBFwh7U2xa0cucFae7m1dqHbOXKLMaa1MWaBMeawMWZrOtObG2N+McYc\nMcasNMZck2Z6d2PMNmNMojHmc2NMiVwL7xJjzCPGmNWeebLFGPNImumVPfP0mDFmXUYHI9xkjHnW\nGDPB+z5rbQdr7TjP9ArAUKCutbbMRb7Xh8aYP4wxKcaYvulMf9AYs9uzDo42xhS4yPfL0vbKWvun\ntTbcWpt8Me8nZ9O8Fck9KghFfFNna204zg7LVcCTLudJl3frnLV2jrW2m/5z920ZtLgmAqOBR9JO\n8BR3M4A3gGLA68BMY0xxz/TLgZFAL+BS4Bjwfo6Ez1sM0BsoDrQHBhtjunlNjwWWAyWB/wBTjDGl\ncz3lxakEHLDW7s2G1/oNGAT8mnaCMeYG4HGgDVAZqAo8dzFvlpXtVXb1PvAHmlcieZsKQhEfZq39\nC5gD1AMwxpQzxswwxiQYYzYaYwakPtb7aL5Xd8o+xpg/PV26/uP12EBPF9NNnhaMZZ6j/Rhj3jLG\nbDfG/O25/9o07zHFGDPBGPM30NcYE2CMedzzWgeMMZMzawEyxkQZY1YYYw4ZYxYbY+p7TdvqaVlZ\n6WlN+tgYc6mnm9oRY8zXqYWGl37GmJ3GmF3GmKFer5VpLmNML0+r1QHveeOZVtAYM9YYc9AYsxan\nKPeefrp7qWeeTDZO17MjxulOGuH12EbGmOWeaXHGmM+MMS96ppUyxszyzIsEY8z3JoPua57lOcQY\ns9mzPN9Ifaznsz7p+Tx7PVmKeqad1W0tnfxnLNO0722t/cVaOx7YnE605sAea22ctTbZWjsB2Afc\n4pneA5hprf3OWnsUeAq4xRhTOIPPWdsYM98zP/4wxtzmNW2sMWaEZ/oRY8wiY0wlr+nNjTFLjdOK\ntNQY09xrWgnjtLbv9CzXz9O871DPvNtljLnD6/6Oxpi1nvf7yxjzcHq505lnr1trf7XWnrLW/gF8\nAbTwvGZNnIM9z1hrj1trpwKrgFszmCdjjTHvGWNme3IsMcZUy+I8K2mMmWmc7/NS43Tx/MFrerrf\nd2NMe+AJ4Hbj9Fb4zXP/QmPMnZ71Zz5QzjN9rDEm1LMeHfCs00uNMZdmcX69Z639Bvgnncl9gI+t\ntWustQeBF0hnPfXkS9323WXOc7vg9dz+xpg/gW9Nmq7pJvNt8Lm2G+WMMVONMfuM02o8xGva1caY\neM9y2GOMGZ7RvDLONnKX5/P1SzOtgDHmv8bZ7u/xfF8KZvRazlPMO57vzDpjTBuvCWd0ozfp/x+T\n3rxK9/+dTJZV6rxdaIx5wRjzo2c9n2eMKZVJdhHJIhWEIj7MOEVaR5yWBHBaFXYA5YCuwMve/4Gn\n4xqgFs6R9aeNMXU89z8ExHheuwjQD6flBmAp0AAoAUwE4owxoV6v2QWYgtMa9CkwBLgJiPTkOgi8\nl8HnaYTT0jQQp2VkJDDDnNn961agLVAT6IxTED8BlMLZpg3hTK2BGkA74HGvHZgMcxlj6gIf4LRa\nlfNkKe/1ms8A1Ty3G3B2SDNzIzDJM09mAO963icEmA6MxZmfscDNXs8birM8S+O0nj0B2Eze52Yg\nAqeY6IKz3MDZOe7rmRdVgfDUDFmUdpmeD+O5pb2vnuf3y3FafwCw1m4CknCW75lPMiYMp8iYCFyC\ns46+b5xWxlQ9cAqCUsCK1LyenfrZwNs4y3M4MNsYU9LzvPFAIU+eS4D/eb1mGaAocBnQH3jP/Hvg\n4WNgoLW2sOczfXuuGZLO5zLAtcAaz12XA5uttUe8Hvab5/6MxOC0ihUHNgIveV77XPPsPZwW3jI4\n63HadTnd77u1di7wMvCZp1vfld5PstZ+DXQAdnqm9/W8dlGgAs4yuBs4nunMyZoz1iHP75d6Ldv0\nnPd2wUskUAfnu59WZtvgDLcbxjl4M9OT/TKcbfIDxmn9BHgLeMtaW8Tz/MnpfShPof4wzjayBpC2\nq/FrON+tBkB1z3s9nd5reTTBOdBTypN/mjm/Lt3pzauM/t/Jiu7AHTjrcgjOZxWRi2Wt1U033Xzo\nBmwFjgKHgG043esK4uxkJQOFvR77CjDW8/uzwATP75VxCovyXo/9Bejm+f0PoEsW8xwErvR6j+/S\nTP8daOP1d1ngJBCUzmt9ALyQ5r4/gEivz97Da9pU4AOvv+8DPk/zGWt7TX8dpyUh01w4O0iTvKaF\n4RQp13v+3gy095p+F7AjzTK63muefO01rS5w3PN7S+AvwHhN/wF40fP78zgtR9WzsBxsmkyDgG88\nv38DDPKaVsvrs7byzp5B/u/O9f6ex14PbE1zX0mcdTUGCMbZCU4BRnpluzvNc/4CWqXz+rcD36e5\nbyROSxo4hbX3cgvH+U5UwCnuf0nz3J9wCuWynkzF03nPVjhFS5DXfXuBpp7f/8Q5gFHkIr7Tz+EU\nAgU8f/cCfk7zmJfwfJfTef5YYJTX3x2BdeeaZ0CgZz2o5TXtReCHTLKm/b5PSDN9IXCn17zz/l70\nAxYD9S9iXv0A9E1z36Y0634wzvehcjrPr8yFbxdSn1s1ndcL4tzb4Ay3GziF159psg4Dxnh+/86z\nnpQ6x/wZDbzq9XdNT77qOAdiEoFqXtObAVsyeK2+wE7O3D79AvTy/L4Vz3Yi7fpwjnmV7v87mSyr\nIK9160mv6YOAuRe6Lummm27/3tRCKOKbbrLWFrPWVrLWDrLWHsc5Ip1gz2xV2IZzBDgj3iP/HcPZ\ngQZnx2ZTek8wTte53z1diA7hHPH37razPc1TKgHTPV3EDuHscCXjtHilVQkYmvpYz+MreD5bqj1e\nvx9P5++0A+x459nm9VqZ5Srn/TxrbSJwwOt1zpjued3MpJ3PoZ5uUOWAv6y13q1+3q/7Bk5rzzzj\ndAV9/Bzvk9FnLZcm4zacHdgsddfj7GWaZdbaAzgtjA/hLKv2wNc4rSjgHNwokuZpRYAjnK0S0CTN\n+tEDp3XrrKzW6YKagPP5084D+Pf7UQHnu3Mwg49xwFp7yutv7+/KrTgF2DbjdFFtlsFrpMsYMxjn\nXMJO1toTnrvPZ56kyui7nNk8K42zHngv3zOWdRa+7+djPPAVMMnTnfF1Y0zwBb6Wt7TzK/X3zObX\nhWwX0nuut3NtgzPbblTC6V7rvZye8Hrf/jjF3TrjdLWNyiRDRu9RGqcVfJnXe8z13J+RtNsn73mV\nFenNq3TXVeN0LU69Vczg9TJaz0XkIqggFMk/dgIlzJnnXlXEaW05X9txuiWdwTjnDz0G3IbTmlIM\nOMyZXQLTdmncDnTwFLCpt1DrnP+Y3vu+lOaxhay1sRfwGVJV8Pq9Is58OleuXd7PM8YUwmnpSnXG\ndM/rXohdwGWeLoNn5bXWHrHWDrXWVsXpHvvQOboAZ/RZd+LscHpPO4VToCXi7CQCzvmjnL2DmFk3\n1XOy1i6y1l5lrS2B0/pVC6dlAJxukqe7GxpjqgIFgPXpvNR2YFGaZRZurb3H6zHeyy0cp6vjTs6e\nB/Dv92M7znen2AV8tqXW2i44Xdg+J4OufOnxnN/1OE6LlPd5nGuAqmm+y1fyb5fS85HZPNuHsx54\nd4f2nn/n+r6f13phrT1prX3OWlsX59zSKJxi+GKdsQ55ft/jORiRkQvZLqTK6HOfaxuc2XZjO05L\nnff7FrbWdgSw1m6w1sbgrGev4QwyFJZOhszeYz/OQbPLvd6jqHUGKMtI2u2T97w6Y9vBmQdmUmV5\nHfGsl6m3P7P6PBG5eCoIRfIJa+12nO5Yrxhn8Ib6OEeVz/ecL4BRwAvGmBrGUd9zPk5hnB3IfUCQ\nMeZpzm7JSGsE8JLxDO5hjCltjOmSwWM/Au42xjTxvG+YMaaTyWCAkSx6yhhTyHPO1B3AZ1nINQWI\nMsZc4znP73nO3F5OBoYZY4obY8rjdFW9ED/htD4MNsYEed7/6tSJxhlgp7pnh+xvz2MzG6X1EU+m\nCsD9Xp81FnjQGFPFUySlnvt1CqfwCvXM52CcEWvPa8h+4wzEEYrTVc941r8Qr+kNjTHBxpgiwH9x\nusl95Zn8KdDZGHOtZwf3eWBamlaWVLOAmsYZ8CfYc7sqzTlIHb2W2wvAEs9340vPc7t75vXtON13\nZ1lrd+Gci/q+Z/4FG2NaZuFzhxhjehhjilprT/LvMkqdbk0G16M0xvTAWQ5trbVnDMZjrV2Pc/7j\nM555eTNQH6eL9PnKcJ5ZZwTNacCznu9Ibc4s0M71fd8DVDZZvK6ocS5PcoXnoMPfOF0xkz3TnjXG\nLMzkuSGedcwAwZ75kvq+nwD9jTF1jXNu55M43WgzcyHbhUxlYRuc2XbjF+BvY8xjxhl8JtAYU88Y\nc5UnR09jTGnrXALjkOc56W0LJuMM5lXXcyDrGa98KTjb2P8ZYy7xvO5l5t/zFNNzCTDEs95E45wP\n+KVn2gqgm2daBM45kyLig1QQiuQvMTjnXezEGazkGWvt/At4neE4OxbzcHbcPsY5T/ErnB3n9Thd\nh/7h3N0J38IZSGWeMeYI8DPO+TJnsdbGAwNwBjw5iNNdsu8F5Pe2yPM63wD/tdbOO1cua+0a4F6c\nQTR2ebJ4t+A8h/P5t+DMo/EXEsxam4Qz2mZ/nJ28njg78KldB2vgdK88ilM8vm8zv77hF8AynB21\n2TjLDZzzisbjnIe0BWe53efJcBjnXJxROC0ZiWk+a1a0xGl5+BKnBeE4znxJ9ShO68R2nHOyTg+c\n45nXd+PsNO/FKUIGpfcmniKxHdANZx3fjdNa4l3ATsTZCU4AGuN0j0ztuhqFM1DPAU+mKGvtfs/z\neuEUKOs8OR7I4mfvBWw1zgisd+MsQzw7/EdxRgdNz4s4rc5Lzb/d5EZ4Te+GM0DQQeBVoKu1dl8W\nM52WhXk2GKcb6G6cdSSWf9e/c33f4zw/DxhjzrocRDrK4Bxs+RunK+YiIPU6hhWAHzN57jyc9ao5\n8KHn95aezzgX5zzABZ6c2/AqhDJw3tuFLMpsG5zhdsNTnHfGGexlC873ZRTOsgGnq/UaY8xRT8Zu\n1tqzRly11s4B/g9ncKONnD3I0WOe+3/2rLNf47TYZ2QJznZoP855rF29Wl6fwulJctDz2SZm8joi\nkoeZM7uGi4iIm4wxS4AR1tox5/k8C9Sw1m7MmWR5nzFmLE7ro+vX5TTG9MTpmjfM7SznwxjzGlDG\nWtsnl993BU7X2cy6eWbH+1TGKbiC05wXKiLit3ShUBERFxljInFGUt2P05pVH2egB/Fh1rneYp7n\n6SYagtOSeRVOa/WduZ3DWtsgt99TREQc2dJl1Bgz2jgX7F2dwXRjjHnbOBdpXWmca42lTutjjNng\nueXqEUkRkTygFs4lBw7jdGfs6jmnTSQ3FMY5jzARp5v4mzhdj0VExE9kS5dRz8n3R4FPrLX10pne\nEed8lY44ffHfstY2Mc7FTeNxzpOwOOe+NM5k6G8RERERERHJJtnSQmit/Q7nBP6MdMEpFq219meg\nmDGmLHADMN9am3r9p/k4J06LiIiIiIhIDsutcwgv48yRyXZ47svo/rMYY+4C7gIICwtrXLt27ZxJ\nKiIiPsFaS7JNJtkmk5KS4vy0KaTYFJJTkk//ffpnitffXo8//byUFIzXJdcMBuef133GkLzXGe0/\n8JJAsGA9l1pL/ZnefYEmkAAT4PwMCPj37wCv+9ObnsHjzRmX/hQREX+0bNmy/dbatNcOPm+5VRCm\n9z+XzeT+s++09kOcoaaJiIiw8fHx2ZdORETyhBSbwoFjB9h9dDe7ju5i15Fdp3/uTtx9xt9JyUkU\nLlCYwiGFCQ8Jp3iB4oSHhBMeEn76vtSf4SHhFC6Q8X3hIeGEBYcRGBB4zoytWrUCYOHChVn6TEnJ\nSRxNOsrRpKMcOXHk39+TjmR8X9KRs56Tel9iUiLFCxanbHhZyhYuS5nwMs7vnr/LhnvuK1yW8JDM\nrjkuIiK+zBizLTteJ7cKwh041xhKVR7nGj07gFZp7l+YS5lERCSXnEw+ye6ju89Z6O05uofwkPDT\nhU3ZwmUpE1aGSsUq0bR80zMKoCIFipzRopdXhQSGUKJgCUoULJEtr5ecksy+Y/uceec1PzcmbOT7\nP78/Y/4GBQRlqXAsWbCkT8xLERHJfrlVEM4ABhtjJuEMKnPYWrvLGPMV8LIxprjnce0An7pmk4iI\nOJJTktl2eBvrD6xnw4ENbEhwbusPrGfH3zsoVajUGUVe2cJlaVCmwemiJLVAKRBU4Nxv5scCAwIp\nE16GMuFlMn2ctZa/T/zNrqOewtGrCF+9b/VZra01StagRgnPrWQNapasSY0SNShVqJSKRRGRfCxb\nCkJjTCxOS18pY8wO4BkgGMBaOwL4EmeE0Y3AMeAOz7QEY8wLwFLPSz1vrc1scBoREXFRik1hx987\n/i34DmxgfYJTAG49tJVLwy89XUjUKFGDdtXaUbNkTSoXq0xIYIjb8f2KMYaioUUpGlqU2qUyP+/+\n8D+H2Ziw0SnmEzbwzZZvGBE/gvUH1pNiU04Xi6eXrefv4gWLZ/q6IiKS92XLZSdym84hFBHJOdZa\n9iTuYf2B9We19m1K2ESx0GJnFAapv1crUY3QoFC34+e48z2H0NcdOHbg3+LfUzCmtvwWCCzgLH+v\n1sXUv3X+oohIzjLGLLPWRlzs6+RWl1EREcmDrLVsObSFX3f9esYtxaZQq1St0zv5MfViqFGyBtVL\nVNeOvp8pWagkJQuVpGn5pmfcn3rgIPWAwfoD65m8djIbDmxgY8JGKhatSKOyjWhYpqHzs2zDbDuP\nUkREso8KQhERP5Gcksz6A+v/Lfx2/8ryXcspXKAwjco2olGZRtx71b00KtuIcoXL6bwxyZQx5vS5\njNdWuvaMaSeTT7Ju/7rT69rM9TNZsXsFJQuVPL2uNSzrFIrnOhdSRERylrqMiojkQ0nJSazdt/aM\nVr+Ve1ZSJryMs0PuuTUs05DSYRd9CSO/4m9dRrNLik1hY8LGM9bJ5buXUyCwwFnrZMWiFXVAQkTk\nHNRlVEREAKf4W75r+Rktf7/v+50qxaucbo2JrhtNgzINKBpa1O244qcCTAA1S9akZsmadKvXDXC6\nnf55+M/T6+5Hv37Er7t+JSk56fS626hsIxqXa0y14tVUJIqI5AAVhCIiPub4yeP8vONnvtv2HYu2\nLWLpzqVUK16NiHIRNCrbiH4N+1H/0vqEhYS5HVUkU8YYKhWrRKVilbi5zs2n7991ZBfLdzsHOT5b\n8xlD5w0lxabQslJLIitFElk5kjql6qhAFBHJBioIRUTyuKNJR1m8ffHpAnD5ruXUu6QekZUiebj5\nw7So0EItf5KvlC3sXK+yY42OwL+DH6V+B95Y/AZHko6cLhBbVmpJ/UvrE2ACXE4uIuJ7VBCKiOQx\nh/85zA9//sCibYtYtG0Ra/auoVHZRrSs1JKnWz5NswrNNNKn+BVjDFWLV6Vq8ar0bdAXgO2Ht58u\nEN9f+j57E/dyTcVrTheJDcs2JChAuzkiIueiLaWIiMsOHDvA939+z6KtTgG4IWEDV192NZGVInnt\n+tdoclkTCgYXdDumSJ5SoWgFetTvQY/6PQDYfXS3UyBuXcS438bx5+E/aVa+2ekuphHlIggJDHE5\ntYhI3qOCUEQklx08fpCvN399ugVw26FtNK/QnMhKkbzb8V3tuIpcgDLhZbjt8tu47fLbANh/bD/f\nb/ue77Z9x+AvB59xoKV15dY0q9BMLYgiIqggFBHJcdZa1u1fx6z1s5i9YTa/7vqVaytdS+vKrelz\nZR91bRPJAaUKleLmOjefHqzm0D+H+PHPH1m0bRH3z72fbYe30b56ezrV6ET76u0pUbCEy4lFRNyh\nPRARkRxw4tQJvtv2HbPWz2LWhlmcTD5JVM0oHmn+CK2rtKZQcCG3I4r4lWKhxehUsxOdanYC4K+/\n/+LLDV/y2ZrPuHvW3TQo04ComlFE1YzSCKYi4ldUEIqIZJPdR3fz5YYvmbV+Ft9s+YbLS19OVM0o\npt8+nSsuuUI7mCJ5yGVFLmNA4wEMaDyA4yePs3DrQmatn0WHTzsQYAKIquEUh5GVIwkNCnU7rohI\njjHWWrcznLeIiAgbHx/vdgwR8XMpNoXlu5af7gq6IWED7aq1o1ONTnSo3oHSYaXdjig5oFWrVgAs\nXLjQ1RySM6y1rNm3xmndXz+LVXtX0bpya6JqRtGxRkfKFS7ndkQREQCMMcustREX+zpqIRQROQ9H\nk47y9eavmb1+NrM3zKZwgcJE1Yjitetf45qK1xAcGOx2RBG5CMYY6l1Sj3qX1OPxax5n/7H9zN04\nl9kbZvPo/EepWrwqUTWj6FSjE43LNda1D0XE56kgFBE5h32J+5iydgpf/PEFP27/kSaXNSGqZhSP\ntniUGiVruB1PRHJQqUKl6Fm/Jz3r9+Rk8kkWb1/MrPWz6P15bw4eP0inGp24te6ttK3aVgeERMQn\nqcuoiEg6jpw4wufrPmfi6oks3r6YjjU6ckvtW2hXrR1FQ4u6HU9cpC6jkmpTwiZmrp/J5DWT2ZCw\nga51uhJzRQzXVLxGLYcikuPUZVREJJv9c+of5myYw8TVE5m3aR4tK7Wkd/3exEXHER4S7nY8Eclj\nqpWoxgNNH+CBpg+w5eAWJq2exOAvB3Pwn4N0u7wbMVfE0LBMQw0oJSJ5mgpCEfFrp1JOsWDLAiau\nnsgX677gyjJX0r1ed0ZGjdR1yUQky6oUr8Kwa4cx7NphrN67mthVsXSd3JWQwBBi6sUQc0UMNUvW\ndDumiMhZ1GVURPyOtZafd/zMxFUTiVsbR8WiFYmpF8Ntl9/GZUUuczue5HHqMipZZa3ll79+YeKq\niUxeO5nLCl9GTL0Ybq93O+WLlHc7noj4OHUZFRE5T6v2rGLiqolMWjOJ0KBQutfrzvd3fK+BYUQk\nRxhjaFK+CU3KN2H4DcNZuHUhsatjqf9BfepfWp+YejF0rduVkoVKuh1VRPyYCkIRydc2H9xM7KpY\nYlfHciTpCN0u78b026dz5aVX6rweEck1gQGBtKnahjZV2/Bex/eYu3Eusatjeezrx7im4jXE1Iuh\nS+0uOl9ZRHKdCkIRyXeOnTzGZ6s/46NfP2Jjwkai60YzImoEzSs018h/IuK6AkEF6FK7C11qd+Fo\n0lG+WPcFsatjGfTlIDrX7MzAxgO5puI1OmglIrlCBaGI5Btr9q5h5LKRfLrqU5pXaM6wa4bRvnp7\nXRtMRPKs8JBwetTvQY/6Pdh/bD8TVk7grll3EWACGNh4IL3q96J4weJuxxSRfEyHykXEpx0/eZzx\nv43nmtHX0G5CO4qFFmP5wOXMjJlJ51qdVQyKiM8oVagUDzR9gLWD1vJ+x/f5ecfPVHmrCn0/78vP\nO37GFwcCFJG8Ty2EIuKT1u1fx4fLPuST3z4holwEQ5sNJapmlApAEfF5xhgiK0cSWTmSfYn7GLti\nLL2m96JQcCEGNh5Iz/o9KVKgiNsxRSSfUAuhiPiME6dOMGn1JFqNbUWrsa0IDQpl6YClzO05l5vr\n3KxiUETyndJhpXmkxSP8MfgPhrcbzoKtC6j0f5UYMGMA8Tt1CS4RuXhqIRSRPG9jwkY+XPYh434b\nxxWXXMG9V91Ll9pdCAkMcTuaiEiuCDABp0cp3X10N2OWjyE6LpqSBUsysPFAYq6I0QilInJB1EIo\nInnSyeSTTFk7hbbj29L84+ZYa/nhjh/4uvfXRF8erWJQRPxWmfAyDLt2GJuGbOLF617ky41fUvF/\nFbln1j38tvs3t+OJiI9RC6GI5CnbDm3jw2UfMnrFaGqWrMndje/mljq3UCCogNvRRETylAATQPvq\n7WlfvT1//f0XHy//mM6xnSlXuBwDGw+kW71uFAwu6HZMEcnj1EIoInnCr7t+JWZqDI0+bMSxk8f4\ntve3LOq7iJgrYlQMioicw2VFLuPpyKfZcv8Wnmz5JFN/n0rltyrzwqIXOHDsgNvxRCQPU0EoIq6x\n1jJ/03zajm/LjbE3ElE2gi33b+F/7f9HndJ13I4nIuJzAgMCiaoZxazus1jYZyFbD22lxjs1uH/O\n/Ww9tNXteCKSB6kgFJFcdyrlFLGrYmn0YSMe/OpBel7Rk833b2Zo86EaSl1EJJvUKV2Hj7t8zOpB\nqykYXJDGHzamx7QerNi9wu1oIpKHqCAUkVyTmJTI20vepvrb1Rm5bCQvtn6RlfespE+DPhokRkQk\nh5QrXI5Xr3+VLfdvoWGZhkRNjKLd+HZ8vflrXexeRDSojIjkvL2Je3n3l3f5IP4DIitF8lnXz2hS\nvonbsURE/EqRAkV4uPnDDGkyhImrJnL/3PspEFiAR1s8Ste6XQkK0G6hiD9SC6GI5JiNCRsZNHsQ\ntd6txd7EvSzut5gpt01RMSgi4qKQwBD6NujLqntW8Xzr5/kg/gNqvFODd395l8SkRLfjiUguU0Eo\nItlu6V9LuS3uNpp93IwSBUuw7t51jIgaQY2SNdyOJiIiHgEmgKiaUSzqu4jYW2NZsHUBVd6qwjML\nnmFf4j6344lILlFBKCLZwlpvTghgAAAgAElEQVTLnA1zaD2uNV3jutKiQgu23L+FF697kUvDL3U7\nnoiIZKJp+aZMvW0qP/T7gd1Hd1Pr3VrcO/teNiVscjuaiOQwFYQiclFSbApxa+K4csSVPP7N49zZ\n8E423reR+5veT3hIuNvxRETkPNQsWZORnUey9t61FAstRpNRTeg2pRu/7/vd7WgikkNUEIrIBbHW\nMnv9bBp/2JjXfnyNV69/lRUDV9Cjfg+CA4PdjiciIhehTHgZXmrz0umRSSPHRtLn8z5sPrjZ7Wgi\nks1UEIrIeVuwZQEtRrfg0a8f5amWT7F0wFI61uiIMcbtaCIiko0KFyjMY9c8xob7NlC5aGWu+ugq\n7pl1D3/9/Zfb0UQkm6ggFJEsW7JjCdd/cj13zryTQVcNYuXdK7mlzi0qBEXOU0JiEiMXbSIhMcnt\nKCJZUjS0KM+1fo4/Bv9BeEg4V3xwBQ/Pe5j9x/a7HU1ELpIKQhE5p5V7VnJj7I10jevKbZffxrp7\n19Gzfk8CAwLdjibik+Lit/PKnHXExW93O4rIeSlVqBRvtHuD1YNWc/zkcWq9W4unFzzN4X8Oux1N\nRC6QCkIRydD6A+uJmRpDu/HtuK7KdWy4bwN3Nb5L5wiKXKToiAoM61Cb6IgKbkcRuSDlCpfjvU7v\nET8gnj8P/0n1d6rz6g+v6jqGIj5IBaGInGXboW30/6I/zT9uTr3S9dg4ZCMPNH2A0KBQt6OJ5Asl\nwkIYGFmNEmEhbkcRuShVildh7E1j+a7vd/y661eqv1Odt5e8zYlTJ9yOJiJZpIJQRE7bfXQ39315\nH40+bESZ8DJsuG8D/2n5H10+QkREMlWndB0mR0/my+5fMm/TPGq+W5NRv47iVMopt6OJyDmoIBQR\nDhw7wGPzH6Pue3UJCgji93t/56U2L1G8YHG3o4mIiA9pWLYhs7rPIvbWWCaumkid9+oQuyqWFJvi\ndjQRyYAKQhE/djTpKM8vep5a79bi0D+H+O3u3/hf+/9xSdglbkcTEREf1rxCc77t8y0jOo3grSVv\n0WBEA2b8MQNrrdvRRCQNFYQifshay6crP6X2u7VZt38dS+5cwsjOI6lQVANciIhI9mlTtQ0/9f+J\nl657iWHfDKPdhHas3bfW7Vgi4iXI7QAikruW7VzGkLlDOHHqBJOjJ9O8QnO3I4mISD5mjKFzrc60\nr96eD+I/IHJsJN3rdefZVs/q1ASRPEAthCJ+Ym/iXgbMGEBUbBT9GvTjlwG/qBgUEZFcExwYzJAm\nQ1g7aC0nkk9Q5706jIwfSXJKstvRRPyaCkKRfO5k8kn+99P/uPz9yylcoDC/3/s7/Rv1J8Do6y8i\nIrmvdFhpRkSNYE6POUxYNYGIjyL4ftv3bscS8VvqMiqSj3218Sse+OoBKhWtxHd9v6NO6TpuRxIR\nEQGcEUm/6/sdn635jB7TetCiYgtev/51nc8uksvURCCSD21M2MiNsTdy75f38vr1rzOnxxwVgyIi\nkucYY+hWrxu/3/s7NUrUoMHIBrz43YscP3nc7WgifkMFoUg+cjTpKMO+HkbTUU1pXqE5awatoXOt\nzhhj3I4mIiKSobCQMJ5v/TzxA+JZvns5dd+vy7Tfp+kyFSK5QAWhSD5grWXCygnUfrc2fx35i5X3\nrOTxax6nQFABt6OJiIhkWZXiVZh621RGdR7F0wue5vrx17N672q3Y4nkazqHUMTHxe+MZ8icIZxM\nOUlcdBzNKjRzO5KIiMhFaVO1DSvuXsGI+BG0Hteabpd347nWz1GiYAm3o4nkO2ohFPFRexP3cueM\nO+kc25k7G93JkjuXqBgUEZF8IyggiMFXD+b3e3/nVMop6rxXhxHxI3SZCpFspoJQxMdYaxm9fDT1\n3q9HsdBirLt3Hf0a9tNlJEREJF8qVagUH0R9wFc9v2Liqok0/bgpq/ascjuWSL6hLqMiPmTLwS3c\nNesuEo4nMK/XPBqUaeB2JBERkVzRoEwDFvVdxKhfR3HdJ9cxKGIQT1z7hM6XF7lIalIQ8QHJKcm8\n9fNbXPXRVbSt2pYldy5RMSgi2SohMYmRizaRkJjkdhSRDBljGNB4ACsGrmD57uU0/rAxS3YscTuW\niE9TC6FIHrd231runHEnQQFBLO6/mJola7odSUTyobj47bwyZx0AAyOruZxGJHOXFbmML7p9weQ1\nk+kyqQvdr+jOC61fICwkzO1oIj5HLYQiedTJ5JO8+N2LRI6NpFf9Xizsu1DFoIjkmOiICgzrUJvo\niApuRxHJEmMMt9e7ndWDVrM3cS/1R9Tn2y3fuh1LxOeohVAkD1q2cxn9ZvTjssKXseyuZVQsWtHt\nSCKSz5UIC1HLoPikUoVKMeGWCcxeP5u+n/flhmo38Ea7NygWWsztaCI+QS2EInnI8ZPHeWz+Y3Sc\n2JFHmj/C7O6zVQyKiIhkQaeanVg9aDVBAUHUe78eM/6Y4XYkEZ+gglAkj1i0dRH1R9Rn6+GtrLpn\nFT3r98QY43YsERERn1GkQBE+iPqAT2/5lKHzhtJtSjf2Ju51O5ZInqaCUMRlf5/4m3tm3UP3ad15\no+0bfNb1My4Ju8TtWCIiIj4rsnIkv939GxWLVuSKD65gwsoJWGvdjiWSJ6kgFHHRlxu+pN779TiV\ncoo1g9ZwU+2b3I4kIiKSLxQKLsTrbV9ndvfZvP7j60TFRrH98Ha3Y4nkOSoIRVyw/9h+ek7ryX1z\n7mNMlzF8dONHOvldREQkB0SUiyD+rnialW9Gow8b8cHSD0ixKW7HEskzVBCK5LK5G+dS/4P6XBJ2\nCSvvXkmbqm3cjiQiIpKvhQSG8GTLJ1nUdxGfrPyEtuPbsvPITrdjieQJKghFcsmJUyd4cO6D3DXz\nLmJvjWX4DcN1AV0REZFcVLd0Xb6/43siK0XSaGQjjUQqggpCkVzx+77faTKqCdsOb2PF3SuIrBzp\ndiQRERG/FBQQxNORTzP1tqkMmTOEe2ffy/GTx92OJeIaFYQiOchay0fLPqLl2JYMumoQU2+bSomC\nJdyOJSIi4vdaVGzBirtXcOD4Aa4edTWr9652O5KIK4LcDiCSXyUcT+CumXexIWEDi/ouom7pum5H\nEhERES/FQosRe2ss434bR+txrXk28lkGXTVI1wEWv6IWQpEc8N2272gwogHli5RnyZ1LVAyKiIjk\nUcYY+jboy4/9fmTMijF0mdSF/cf2ux1LJNeoIBTJRqdSTvHUt09x+5TbGRE1gv9r/3+EBoW6HUtE\nRETOoWbJmizuv5japWrTYEQDvtn8jduRRHKFuoyKZJMtB7fQY1oPChcozPKByykTXsbtSCIiInIe\nQgJDeL3t67St2pben/emV/1ePN/6eUICQ9yOJpJj1EIokg1iV8XSZFQTutbtypwec1QMioiI+LC2\n1dqyYuAKVu9dTYvRLdiYsNHtSCI5RgWhyEU4cuIIfT/vy7OLnmVuz7k81OwhAoy+ViIiIr6udFhp\nZsbMpHf93jT7uBnjVozDWut2LJFspz1XkQu09K+lNPqwEUEBQSy7axmNyjZyO5KIiIhkI2MM9zW5\nj296f8Pri1+nx7QeHP7nsNuxRLKVCkKR85RiU3j9x9fpNLETL133EqNuHEV4SLjbsURERCSH1L+0\nPksHLKVogaI0HNmQn7b/5HYkkWyjQWVEzsOhfw6dPjq4dMBSKhWr5HYkERERyQWFggvxQdQHfL7u\nc2767CaeuOYJhjQZomsWis9TC6FIFq3Zu4arPrqK6sWrs6DPAhWDIiIifuim2jfxc/+fGbNiDH0+\n78Pxk8fdjiRyUVQQimTB1LVTaTWuFU+1fIq3OrxFcGCw25FERETEJVWKV2Fx/8WcSjlFi9Et2HZo\nm9uRRC6YCkKRTCSnJPPEN08wdN5Q5vaYS+8re7sdSURERPKAQsGF+PSWT+lZvydNP27Kgi0L3I4k\nckF0DqFIBg4eP0j3ad3559Q/LB2wlNJhpd2OJCIiInmIMYaHmj1EgzINiJkaw2MtHuOBpg/ovELx\nKWohFEnH6r2rueqjq6hTqg7ze81XMSgiIiIZuq7Kdfx85898svITek7vybGTx9yOJJJlKghF0ohb\nE0frca15rtVzDL9hOEEBakgXERGRzFUuVpkf+/2IwdBidAu2HtrqdiSRLFFBKOKRnJLM418/ziPz\nH2Fez3n0qN/D7UgiIiLiQwoFF2L8zePpc2Ufmo5qytebv3Y7ksg5qSAUARKOJ9BxYkeW7lxK/F3x\nNCzb0O1IIvlGQmISIxdtIiExye0oIiI5zhjDA00fIPbWWHpN78Wbi9/EWut2LJEMqSAUv7dyz0qu\n+ugqrrjkCr7q+RWlCpVyO5JIvhIXv51X5qwjLn6721FERHJN6yqtWXLnEiaunkj3ad1JTEp0O5JI\nulQQil+btHoSbT5pwwutX+C/7f6r8wVFckB0RAWGdahNdEQFt6OIiOSqikUr8sMdPxAcEEzz0c3Z\ncnCL25FEzqKCUPzSqZRTPDr/UYZ9M4z5vebT/YrubkcSP+UP3SlLhIUwMLIaJcJC3I4iIpLrCgYX\nZNxN4+jfsD9NP27K/E3z3Y4kcgY1h4jfSTiewO1TbgcgfkA8JQuVdDmR+LPU7pQAAyOruZxGRERy\ngjGGIU2GcOWlV9JtajcebPogjzR/RNcrlDwhW1oIjTHtjTF/GGM2GmMeT2f6/4wxKzy39caYQ17T\nkr2mzciOPCIZ2XJwC80/bk79S+ozp8ccFYPiOnWnFBHxH5GVI/nlzl+YtHoSA2cN5FTKKbcjiVx8\nQWiMCQTeAzoAdYEYY0xd78dYax+01jaw1jYA3gGmeU0+njrNWnvjxeYRyciyncu4Zsw1DL56MG/e\n8KbOF5Q8Qd0pRUT8S4WiFVjUdxF/Hv6TLpO6cDTpqNuRxM9lRwvh1cBGa+1ma20SMAnoksnjY4DY\nbHhfkSybs2EO7T9tz7sd3mXw1YPdjiMiIiJ+rHCBwsyMmUmZsDK0HteaPUf3uB1J/Fh2FISXAd5j\nie/w3HcWY0wloArwrdfdocaYeGPMz8aYmzJ6E2PMXZ7Hxe/bty8bYou/GPXrKO744g5mdJvBzXVu\ndjuOiIiICMGBwYy6cRRRNaJo9nEz/tj/h9uRxE9lR0GY3tmwGV19sxswxVqb7HVfRWttBNAd+D9j\nTLqjKlhrP7TWRlhrI0qXLn1xicUvWGt5ZsEzvPLDK3x3x3c0q9DM7UgiIuIifxjVV3yLMYZnWj3D\nky2fJHJsJIu3L3Y7kvih7CgIdwDeoyGUB3Zm8NhupOkuaq3d6fm5GVgINMyGTOLnTiafpN+MfszZ\nOIef+v9EzZI13Y4kIiIuSx3VNy5++7kfLJKL+jXsx9ibxtJlUhem/T7t3E8QyUbZMarGUqCGMaYK\n8BdO0XfWRd2MMbWA4sBPXvcVB45Za08YY0oBLYDXsyGT+LEjJ47QNa4rIYEhLOizgLCQMLcj5XkJ\niUnExW8nOqKCBjcRkXwrdTRfjeoreVH76u35qudXdI7tzF9//8V9Te5zO5L4iYtuIbTWngIGA18B\nvwOTrbVrjDHPG2O8Rw2NASZZa727k9YB4o0xvwELgFettWsvNpP4r51HdtJybEsqF63M9NunqxjM\nIh01FxF/oFF9Ja9rVLYRP/b7kffj3+fheQ+TYlPcjiR+wJxZn/mGiIgIGx8f73YMyWPW7ltLx087\nclfjuxh2zTBd7PU8qIVQJOtatWoFwMKFC13NISL5V8LxBLpM6kK5wuUYd9M4QoNC3Y4keZAxZpln\nLJaLki0Xphdx23fbvqP1uNa80PoFnrj2CRWD50lHzUVERPKOEgVLML/XfKy13DDhBg4eP+h2JMnH\nVBCKz5u8ZjJdJ3fl01s+pdeVvdyOIyIiInLRQoNCmdR1EhFlI2gxugXbDm1zO5LkU9kxqIyIK6y1\nDP9pOP+35P+Y32s+V5a50u1IIiIiItkmwATw5g1vUqFoBVqMbsGs7rNoUKaB27Ekn1FBKD4pOSWZ\nh756iG+2fMPifoupUFQjxomIiEj+9EDTB6hQpALtxrdjwi0TaFetnduRJB9Rl1HxOUnJSXSb2o2V\ne1fyQ78fVAyKiIhIvndr3VuZfvt0ek/vzYSVE9yOI/mIWgjFpxw/eZyucV0JDghmbo+5FAgq4HYk\nERERkVzRomILvu3zLe3Gt+PYyWPc1fgutyNJPqCCUHzG0aSjdJnUhUvCLuGTmz4hODDY7UgiIiIi\nuapu6bos7LuQ6z+5nuMnj3N/0/vdjiQ+Tl1GxScc/ucwN0y4gcpFKzPh5gkqBkVERMRvVS9RnUV9\nF/Hu0nd5+fuX3Y4jPk4FoeR5B44doM0nbWhUphEf3fgRgQGBbkcSEREgITGJkYs2kZCY5HYUEb9T\nqVglFvVdxISVE3jy2yex1rodSXyUCkLJ0/Yc3UOrca1oU6UNb3d4mwCjVVZEJK+Ii9/OK3PWERe/\n3e0oIn6pXOFyLOq7iNkbZvPQVw+pKJQLor1rybN2/L2DlmNbEl03mlevfxVjjNuRRETES3REBYZ1\nqE10hEZ7FnFL6bDSfNv7W37a8RP3zL6HFJvidiTxMSoIJU/aemgrLce0ZECjATwd+bSKQRGRPKhE\nWAgDI6tRIizE7Sgifq14weLM7zWf3/f/zh1f3EFySrLbkcSHqCCUPGfLwS20GtuKh5o9xMPNH3Y7\njoiIiEieV7hAYeb0mMNff/9F7897cyrllNuRxEeoIJQ8ZVPCJlqNa8WjLR5l8NWD3Y4jIiIi4jMK\nBRdiZsxM9iXuo/d0FYWSNSoIJc/YmLCR1uNa88Q1TzDoqkFuxxERERHxOQWDC/JFty84cPwAPaf1\nVFEo56SCUPKEDQc20Hpca55q+RQDIwa6HUdERETEZ6UWhYdPHKb71O6cTD7pdiTJw1QQiuv+2P8H\n131yHc9GPsuAxgPcjiMiIiLi80KDQpl++3QSTyYSMzVGRaFkSAWhuGrd/nW0+aQNz7d6nv6N+rsd\nR8Sv6SLjIiL5S2hQKNNum8aJ5BPcPuV2kpK1fZezqSAU16QWgy9d9xJ3NLzD7Tgifk8XGRcRyX8K\nBBVgSvQUkm0yt8XdpqJQzqKCUFyx7dA22o1vx8vXvUyfBn3cjiMi6CLjIiL5VYGgAsRFx5Fsk+nz\neR9dp1DOoIJQct2eo3toO74tQ5sNVTEokofoIuMiIvlXSGAIk7tOZueRndw35z6stW5HkjxCBaHk\nqkP/HKL9p+3pfkV37m96v9txRERERPxGweCCzOg2gyV/LeGpBU+5HUfyCBWEkmuOnTxG59jOXFvx\nWp6JfMbtOCIiIiJ+p2hoUeb2mMuUtVN4c/GbbseRPCDI7QDiH5KSk+g6uSuVi1Xm/9r/H8YYtyOJ\niIiI+KXSYaWZ12se1465luIFi9OvYT+3I4mLVBBKjktOcU5gDgoIYvSNowkwapgWERERcVPFohWZ\n13Merca1olhoMW6pc4vbkcQlKgglR1lruW/Ofew6sos5PeYQHBjsdiQRERERAWqVqsXs7rNpP6E9\nRQoU4fqq17sdSVygphrJUU8teIolfy1hRswMCgYXdDuOiMgZEhKTGLloEwmJui6XiPinRmUbERcd\nR8zUGJbsWOJ2HHGBCkLJMcN/Gs6UtVOY22MuRQoUcTuOiE9QgZK74uK388qcdcTFb3c7ioiIayIr\nRzKmyxhunHQja/aucTuO5DIVhJIjxiwfw9tL3mZ+r/mUDivtdhwRn6ECJXdFR1RgWIfaREdUcDuK\niKShA2S5K6pmFMPbDaf9p+3ZcnCL23EkF+kcQsl203+fzn++/Q8L+iygQlHtZImcj9TCRAVK7igR\nFsLAyGpuxxCRdKQeIAP0Pc0lPer34OA/B2k7vi0/9PuBMuFl3I4kuUAFoWSrbzZ/w8BZA5nbcy61\nStVyO46Iz1GBIiLi0AEydwy+ejAHjx/khgk3sLDPQooXLO52JMlh6jIq2WbJjiXETI1hym1TaFS2\nkdtxRERExIelHiArERbidhS/82TLJ7mu8nVExUaRmJTodhzJYSoIJVus2buGLpO6MKbLGFpWaul2\nHBERERG5QMYY3rzhTWqUqMGtk28lKVnnceZnKgjlom09tJX2n7Zn+A3D6VSzk9txRERE5AJoEBfx\nFmACGHXjKEKDQuk1vRcpNsXtSJJDVBDKRfn7xN9ETYxiaLOhdL+iu9txRERE5AJplGNJKyggiEld\nJ7HzyE6e/PZJt+NIDtGgMnLBTqWcotuUblxb8Vrub3K/23FERETkImgQF0lPaFAo02+fTpNRTahd\nqja9r+ztdiTJZmohlAv28LyHSUpO4u0Ob2OMcTuOiIiIXIDUrqKABnGRdJUqVIqZMTN5eN7D/PDn\nD27HkWymglAuyMj4kczdOJe46DiCA4PdjiMiIiIXSF1FJSvqlq7L+JvHEx0XzeaDm92OI9lIBaGc\nt282f8MzC59hVvdZujaN5DkaFEFE5PxER1RgWIfa6ioq53RD9Rv4z7X/oXNsZw7/c9jtOJJNVBDK\neflj/x90n9adSV0nUb1EdbfjSD6Q3QWcjnSLiJwfXe9PzsfgqwfTqlIruk3txqmUU27HkWygglCy\n7MCxA0TFRvHydS/TqnIrt+O4Qq1P2S+7Czgd6RYREclZb3V4ixSbwkNfPeR2FMkGKgglS5KSk+ga\n15Wbat1E/0b93Y7jGrU+Zb/sLuB0pFtERCRnBQUE8VnXz5i/eT4fLP3A7ThykXTZCTknay2DZg+i\nSIEivHr9q27HcZWG5M5+qQWciIiI+I5iocWYFTOLFqNbUL1EddpWa+t2JLlAaiGUcxr+03Did8bz\n6S2fEhgQ6HYcV6n1SURERMRRrUQ1JkdPpse0Hqzbv87tOHKBVBBKpmb+MZPhPw9nRswMwkPC3Y4j\nIiIiInlIy0oteb3t60RNjOLAsQNux5ELoIJQMrRyz0r6zejHtNumUbFoRbfjiIiIiEge1LdBX26t\ncyu3TL6FpGQNvOdrVBBKunYf3c2NsTfyTod3aFK+idtxRERERCQPe+X6VygeWpx7Zt2DtdbtOHIe\nVBDKWY6fPM5Nk27ijgZ30K1eN7fjiIiIiEgeF2ACmHDLBH7d/Sv/Xfxft+PIeVBBKGew1tJ/Rn+q\nFK/C05FPux1HRERERHxEeEg4M2Nm8taSt5jxxwy340gWqSCUM7z242tsOriJ0TeOxhjjdhwREcmn\nEhKTGLloEwmJOt9IJD8pX6Q802+fTv8Z/Vm7b63bcSQLVBDKaYu2LuKtJW8x9bapFAwu6HYcERHJ\nx+Lit/PKnHXExW93O4qIZLOrLruK165/jei4aBKTEt2OI+egglAA2HN0D92ndWfcTeMoX6S823FE\nRCSfi46owLAOtYmOqOB2FBHJAf0a9uPqy67mntkaZCavU0EoJKck031ad/o37E+7au3cjiMiF0ld\n8cQXlAgLYWBkNUqEhbgdRURyyHsd32P57uWM+nWU21EkEyoIhecWPQfAM5HPuJxERLKDuuKJiEhe\nUCi4EFOip/DEt0+wYvcKt+NIBlQQ+rmvNn7F6OWjmXjLRAIDAt2O47PUIiN5ibriiYhIXlGrVC3e\n6fAO0XHRHP7nsNtxJB0qCP3Y9sPb6fN5HybeOpFLwy91O45PU4uM5CXqiiciInlJt3rdaFe1Hf1m\n9NP5hHmQCkI/dTL5JN2mduOBpg/QslJLt+P4PLXIiIiIiGRs+A3D2XZoG28vedvtKJKGCkI/Neyb\nYRQPLc6jLR51O0q+oBYZERERkYwVCCpAXHQcL33/Ej/v+NntOOJFBaEf+mLdF0xZO4VxN40jwGgV\nEBEREZGcV6V4FT7q/BG3T7mdA8cOuB1HPFQN+JnNBzczYOYAPuv6GSULlXQ7joiIiIj4kS61u3Bb\n3dvoNb0XKTbF7TiCCkK/8s+pf4iOi+bJlk/SpHwTt+OIiIiIiB96uc3L/H3ib1774TW3owgqCP3K\nQ189RNXiVbnv6vvcjiIiIiIifio4MJhJXSfx9i9vs3DrQrfj+D0VhH4idlUs8zfPZ1TnURhj3I4j\nIiIiIn6sfJHyjLtpHD2m9WD30d1ux/FrKgj9wLr96xgydwhToqdQNLSo23FERERERGhXrR13NryT\n7lO7k5yS7HYcv6WCMJ87dvIYXSd35ZU2r3BlmSvdjiMiIiIictrTkU8TYAJ4duGzbkfxWyoI87kh\nc4bQuFxj+jfs73YUERERySYJiUmMXLSJhMQkt6NINvLH5RoYEMint3zKmBVj+Hrz127H8UsqCPOx\nmX/M5Nst3/Juh3d13qCIiEg+Ehe/nVfmrCMufrvbUSQb+etyvTT8UkZ3GU2/L/px6J9DbsfxO0Fu\nB5Ccsf/YfgbOGshnXT+jcIHCbscRERE5LSExibj47URHVKBEWIjbcXxSdESFM35K/uDPy7VdtXZE\n1YxiyJwhfHLzJ27H8StqIcyHrLXcM/seul/RnWsrXet2HBERkTP4aytIdioRFsLAyGoqqPMZf1+u\nb7R9g8XbFzP99+luR/EraiHMh2JXx7J231rG3zze7SgiIiJn8edWEBHJWFhIGONuGkfXuK60qNiC\nS8IucTuSX1ALYT7z199/8cDcBxh/83hCg0LdjiMiInIWf28FEZGMtajYgj5X9mHgrIFYa92O4xdU\nEOYj1lr6z+jP4KsH06hsI7fjiIiIiIict+daPcemhE2MX6nebrlBBWE+MnLZSA4cP8Cwa4a5HUVE\nRERE5IIUCCrAJzd/wtB5Q/nz8J9ux8n3VBDmExsTNvLUgqf45KZPCA4MdjuOiIiIiMgFa1CmAQ82\nfZB+X/Qjxaa4HSdfU0GYDySnJNP3877859r/UKd0HbfjiIiIiIhctEdbPEriyUTeX/q+21HyNRWE\n+cCbP71JcGAwQ5oMcTuKiIiIiEi2CAoIYtxN43h24bOsP7De7Tj5lgpCH7dqzyreWPwGY7qMIcBo\ncYpcqITEJEYu2kRCYjIYcBAAACAASURBVJLbUURERMSjZsmaPBP5DH0+78OplFNux8mXVEH4sKTk\nJHp/3pvXrn+NysUqux1HxKfpQtkiIiJ5071X30tYcBiv//i621HyJV2Y3oc9v+h5yhcpzx0N7nA7\niojP04WyRURE8qYAE8CYLmNo9GEjOtXoxJVlrnQ7Ur6iFkIftWTHEkb9OoqPOn+EMcbtOCI+TxfK\nFhERybsqFK3Af9v+l17Te3Hi1Am34+QrKgh90LGTx+j9eW/e6fAOZcLLuB1HRERERCTH9b6yN1WL\nV+XZhc+6HSVfUUHog4Z9PYzGZRsTfXm021FERETSpYGaRCS7GWP4sPOHjP1tLIu3L3Y7Tr6hgtDH\n/LT9J+LWxvFux3fdjiIiIpIhDdQkIjnhkrBLeLfDu/Sf0Z+kZB1wyg4qCH3IqZRT3DP7Ht5s9yYl\nCpZwO46IiPig3Gq5i46owLAOtTVQk5yTWpPlfN1S5xaqFa/Gfxf/1+0o+YIKQh/yzpJ3KB1Wmm71\nurkdRUREfFRutdxpoCbJKrUmy/kyxvBOh3cY/tNwthzc4nYcn6fLTviIHX/v4KXvX2Jx/8UaVVRE\nJAsSEpOIi99OdEQFFSVedIkVyWu0TsqFqFK8CkObDeW+OfcxM2am9o8vgloIfcSDXz3IvVfdS82S\nNd2OIiLiE9TqkD613Eleo3VSLtTQ5kPZfHAzn6/73O0oPk0thD5gzoY5LN+1nPE3j3c7ioiIz1Cr\ng4hI/hYSGML7nd6n9/TetK3WlvCQcLcj+SS1EOZxx08eZ/CcwbzX8T1Cg0LdjiMi4jPU6pA+DeAh\nIvlJq8qtaF2lta5NeBFUEOZxL3//8v+zd+9xUdX5/8Bfh8uoXFRARBEKBARBEQ3NSCUFN80y19bK\n3ZL61lrf1jK3XTfL3bbWcrsvXTb11w1rq2/utlqWXQS8oinlJSUIEBQvIc54AUYdGD6/P2DGmXEG\nBpjhnDPzej4ePkZmzpzzPhfxvM/7c8FVg6/C9fHXyx0KERF5ADalJSJP8/zU57F632rsr90vdyiq\nxCajClZ6qhQrvluBvfftlTsUIiLyEGxKS0SeZmDgQPxt8t/wv5//L7bevRU+EmtencGjpVBCCDzw\n+QNYOnEphvQdInc4RETkIdiUlog80W+v+i2MLUa8vedtuUNRHSaECvXBDx/g9IXT+N2438kdChER\nERGRovlIPnhjxht4vOBxnNKfkjscVXFJQihJ0jRJksokSaqQJOlRO5/fJUlSnSRJe9v+3GvxWY4k\nSeVtf3JcEY/anblwBn/85o9YMWMF/HzYqpeIiIiIqCOjB4/Gr0f8Gou/WSx3KKrS7YRQkiRfAK8D\nmA4gGcBcSZKS7Sz6f0KItLY/b7Z9NxTAEwCuBjAOwBOSJIV0Nya1ezz/ccxMnImro66WOxQiIiIi\nItV4avJT+Lrya2w7sk3uUFTDFRXCcQAqhBCHhBAGAB8BuNnJ714P4BshhE4IcRrANwCmuSAm1dp9\nbDc+Kf0Ey7OWyx0KEREREZGqBPcKxsvXv4z719+PJmOT3OGogisSwiEALMeuPtr2nq1bJEnaL0nS\nvyVJMg1t5ux3IUnSfEmSiiVJKq6rq3NB2MpjbDHi/s/vx3PZzyGkj9cXSomIiIiIOu1Xyb9CVN8o\nvLzzZblDUQVXJISSnfeEzc+fAYgRQqQC2AggrxPfbX1TiFVCiHQhRHp4eHiXg1Wyf+7+J/r26os7\nUu+QOxQi8hKcpJyIiDyNJEl4/YbX8dz253D4zGG5w1E8VySERwFYTmYUBeC45QJCCK0Q4mLbj/8P\nwFXOftdbnKg/gae2PIV/3vBPSJK9PJmIyPU4STkREXmiuNA4LLx6IRZ+uVDuUBTPFQnhbgAJkiTF\nSpKkAXA7gE8tF5AkabDFjzMB/Nj2968A/EKSpJC2wWR+0fae11mSvwT3jr4Xw8OHyx0KEXmROenR\nWDI9iZOUExGRx1l87WKU1JXgqwqvTC+c1u05DYQQzZIkLUBrIucL4G0hxEFJkp4CUCyE+BTAQ5Ik\nzQTQDEAH4K627+okSfobWpNKAHhKCKHrbkxqs+/nffiy4kv89OBPcodCRF7GNEk5ERGRp+nl1wvP\nZj+LP238E7KHZsPXx1fukBTJJfMQCiG+EEIME0LECSGebnvvL23JIIQQS4QQKUKIUUKIyUKIUovv\nvi2EiG/7844r4lGbxRsX48+T/oy+vfrKHQoRERF1gWV/XPbNJVKOWUmzEKgJxPv735c7FMXirOcy\n+7rya1SdrsL8q+bLHQoRERF1kak/ronp76zAE8lLkiQ8P/V53P7v23Fryq3o499H7pAUhwmhjFpE\nCxZ/sxjLs5bD39df7nCIiIioi0z9cC3743pT31xdowFrimswJz0aoYEaucMhspIRnYGxQ8bilW9f\nwZ8m/EnucBTHJU1GqWve3/8+AvwDMHv4bLlDISIvxuZtRN1n6o8bGqix+ru34IjFpHTLs5bj+aLn\ncUp/Su5QFIcJoUzON53H0oKleH7q85xmgohkxRs5IuoujlhMSjcsbBhuH3E7lm1ZJncoisOEUCav\n7noV6ZHpuPaKa+UOhYi8nKMbOVYOichZ3lgVJfX5S+Zf8P7+91Gpq5Q7FEVhQigDrV6L54uex/Ks\n5XKHQkTk8EaOlUMiIvIkAwMH4uHxD+OxgsfkDkVROKiMDJZtWYZbk29F4oBEuUMhInLI3iAZRERE\narZo/CIMe20Yvj36La6OulrucBSBFcIeduj0Iazevxp/yfyL3KEQEbWLTcCIiMjTBGoC8eR1T2Lx\nxsUQQsgdjiIwIexhjxc8joevfhgRQRFyh0JERERE5HXuSrsLWr0W639aL3coisCEsAftPrYbWw5v\nwe+v+b3coRAREREReSU/Hz88m/0sFm9cjOaWZrnDkR0Twh4ihMAfv/kjnrzuSQRqAuUOh4iIiIjI\na92QcAMGBQ3C23veljsU2TEh7CGfl3+OOn0d7kq7S+5QiIiIiIi8miRJeC77Ofx101/RYGiQOxxZ\nMSHsAc0tzVj8zWI8m/0s/Hw4sCsRKYea5hpUU6zuxmNBRNR9Y4eMRWZMJl4selHuUGTFhLAHvLPn\nHQwMHIgZCTPkDoWIyIqa5hpUU6zuxmNBROQaz0x5Bq/segU/N/wsdyiyYbnKzfRNevx181+x9ra1\nkCRJ7nCIiKyoaa5BNcXqbjwWRESuERsSi5xROXhy05N448Y35A5HFqwQutnK4pUYHzUeY4eMlTsU\nIqLLqGmuQTXF6m48FvJgU10iz/TYxMfwfwf/DzVnvbPVBRNCN7rQfAEv7HgBSyculTsUIiIi6iY2\n1SXyTAMCBuCe0ffg+aLn5Q5FFkwI3eidPe9g9KDRGD14tNyhEJGHY+WCyP3mpEdjyfQkNtUl8kCP\nZDyC9/e/75V9CZkQukmTsQl/3/53PD7xcblDISIvwMoFkfuxqS6R5xoUNAi/GfkbvLTjJblD6XFM\nCN3k/f3vIyE0AddEXyN3KETkBVi5oI6wikxE1L7F1y7Gm9+/Ca1eK3coPYoJoRs0tzTjmW3P4M+T\n/ix3KETkJVi5oI6wikxE1L7oftH4VfKv8I+d/5A7lB7FhNANPj74MQYFDcKkKyfJHQoR9SBWYEjJ\nWEUmIurYoxMexRvFb+DMhTNyh9JjmBC6WItowdNbn8bSiUs57yCRl2EFRl28LYFnFZmIqGNDQ4bi\nhoQb8Pqu1+UOpccwIXSxtaVrEeAfgF/E/ULuUIioh7ECoy5M4DvH2xJo8g68rsmeJROWIPfbXDQY\nGuQOpUcwIXQhIQSWbVnG6iCRinXn5sCbKjCecBPFBL5zmECTJ+J1TfYMDx+O62Kuw8rilXKH0iP8\n5A7Ak3xR/gWMwoibEm+SOxQi6iLTzQEA3JcZJ3M0yuUJx8mUwJNzTIkzE2jyJLyuyZGlk5Zi2vvT\n8MDYB9DHv4/c4bgVE0IXEULgb1v+hscnPg4fiYVXIrXizYFzeJy8DxNo8kS8rsmR1IhUjB0yFm/t\neQsLxi2QOxy3YubiIgVVBThz4QxuGX6L3KEQUTd4U7PP7uBxIiIiT/f4xMfx3PbnYDCqt3uEM5gQ\nusiyrcvw2MTH4OvjK3coRERERF3iCf2DiVxl3JBxGB4+HKv3rZY7FLdiQugC245sw+EzhzF3xFy5\nQyEiIiLqMg6yQmRt6cSlWL5tOZpbmuUOxW3Yh9AFlm1ZhkcnPAp/X3+5QyEiIiLqMvYPJrI28cqJ\niOobhQ9/+BB3jrpT7nDcghXCbtp9bDcO1h1EzqgcuUNRLTZPISIiUgb2Dya63J8n/RnPbHsGxhaj\n3KG4BRPCbnpm2zNYnLEYvfx6yR2KarF5ivfhQwDPwPNIRETeICs2C/169cMnP34idyhuwYSwGw6d\nPoRtR7bhnjH3yB2KqnFyaO/DhwCegeeRiIi8gSRJWHztYry882W5Q3EL9iHshtd3vY670+5GgH+A\n3KGoGucA8j7so+IZeB6JiMhbzEyciYe/fBjfHf8OV0VeJXc4LsUKYRc1GhqRty8PD4x9QO5QPAqb\noHkH9lHxDDyPRETkLfx8/PDA2Afw6q5X5Q7F5ZgQdtH7+9/HhCsmIKZ/jNyheBQ2QSMiIiIiJbp3\nzL1YW7oWdY11cofiUkwIu0AIgdd2v4YHxz0odygeh/0JSYlYuSbyLPw3TURdMSBgAGYPn403v39T\n7lBciglhF2w+vBnGFiOmxE6ROxSPwyZopESsXBN5Fv6bJqKuWjBuAd4ofsOjJqrnoDJd8OquV7Fg\n3AJIkiR3KEQeTddoQF5RFQAJORkxsj0o4OAp1BN0jQasKa7BnPRoPhRzM/6bJqKuGjN4DKL7RWNd\n6TrcknyL3OG4BCuEnXTk7BFsqt6EeaPmyR0KkcdbU1yD3PwK5OaXy/okn5Vr6gmsWvUc/psmou54\ncNyDHjW4DCuEnfTG7jdwZ+qdCNIEyR0Kkcebkx4NvaEZgMQn+eTxWLUiIlKHW4bfgke+fgT7a/cj\nNSJV7nC6jQlhJ5xvOo+39ryF7f+zXe5QiLxCaKAGi6Ymyh0GUY/gnKxEROrg7+uP+666D6/teg2r\nbloldzjdxiajnfDRgY+QHpmOhLAEuUMhIlIkjt5IRETeYP5V87GmZA1053Vyh9JtTAidJITAq7te\n5VQTRETtYD84IiLyBoOCBmFGwgy8s+cduUPpNiaETtpxdAfqDfW4Pv56uUMhIlIsziXqXqzAEsDr\ngEgpHhz3IF7f/TqMLUa5Q+kWJoROenXXq/jd2N/BR+IhIyJyhKM3uhcrsATwOiBSinFDxiEsIAxf\nlH8hdyjdwkFlnHC8/ji+qvgKK2askDsUIiLyYhyJlABeB0RKIUmSeQqKmxJvkjucLmO5ywkri1fi\n9hG3o1/vfnKHQkRETvLEZnWswLqH2q4VXgdEynFbym3YV7sPpadK5Q6ly5gQdsBgNGDV96uwYNwC\nuUMhL6W2GxUipWCzOnIWrxUi6qpefr3w2zG/xWu7XpM7lC5jk9EO/Lvk30gJT0FyeLLcoZCXMt2o\nAOAcZaRKukYD1hTXYE56dI9WNNisjpzFa4WIuuP+9PuR+kYqnsl6Bn179ZU7nE5jQtiBFcUrsGj8\nIrnDIC/GGxVSM12jAY98vBeFZXUAevahBid6J2fxWiGi7ojqG4UpsVPwwQ8f4P70++UOp9PYZLQd\nh04fwo+nfsSMYTPkDoW8GPuKkJqtKa5BYVkdJieG86EGERF5rLvS7sJ7+9+TO4wuYYWwHe/vfx+3\np9wOjS9vxImIusKyws2HGkRE5Kmuj7se93x6Dyp0FYgPjZc7nE5hhdABIQRW71uNeaPmyR0KEZFq\nscJNRO3hwGnkKfx9/TF3xFy8t099VUImhA7sOLoD/r7+SI9MlzsUIiJV4w0fETnCEV7Jk+SMysHq\n/avRIlrkDqVTmBA6sHrfasxLnQdJkuQOhSz01I0lb2CJXIc3fKRk/H0vrznp0VgyPYl9jMkjpA1K\nQ5AmCNuPbJc7lE5hH0I7LjRfwJqSNdh73165QyEbPTUFA6d6IHIdjpRLSsbf9/LiCK/kSSRJwrzU\necjbl4eJV06UOxynMSG047OyzzB60GhE9+PNi9L01I0lb2CJXIc3fKRk/H1PRK70m9TfYMQ/R+DV\n6a+ij38fucNxCpuM2rF6PweTUaqeGqCCA2EQ9Sw226P2uPP64O97InKlyOBIjB0yFuvK1skditOY\nENo42XgSWw9vxezhs+UOhYjI4zi6sWc/Q2oPrw8iUpN5qfOwet9qucNwGpuM2vjwhw8xM3EmgjRB\ncodCRORxHPXXsmy2p2s0YE1xDecuJDM26yQiNZmVNAsLNizAzw0/Y1DQILnD6RArhDbYXJSIyDld\nacbnaETB0EAN5qRHY01xDfKKqlkN8nK21xabdRKRmgRqAvHLpF/igx8+kDsUpzAhtHDg5AHUNtRi\ncsxkuUMhIlK8zjbj66jyd6l6KDgMvZdjE1EiUrt5o9TTbJRNRi28t+893JF6B3x9fOUOhYhI8Trb\njC+vqBq5+eXQG4xYNHWY+X1TopidHGFeHytB3o1NRIlI7SZdOQmnL5zGvp/3YdSgUXKH0y5WCNsY\nW4x4/4f3cWfqnXKHQkSkCp1vxidsXluZqkEbS2odro+jkHoXNhElT8XfZd7DR/LBnal3qqJKyISw\nTX5VPgYHDUbKwBS5QyEi8kg5GbFYMj0JORmxVjdFjvoVWmITQiLyBPxd5l3uTL0THxz4AM0tzXKH\n0i42GW2zet9q5IzKkTsMIiKPZTlB/crNlVajjdqbuN6yzyGbEBKRGtn2nebvMu+SOCARV/a7Et9U\nfoPpCdPlDschVggB1F+sx/qf1uP2EbfLHQoRkcfTNRqgNxixMCve7k2RqXqYV1RlfpLOJoREpEa2\nFUH+LvM+OaNysHq/spuNMiEE8J8f/4PMmEyEB4bLHQoRkUex119mTXENcvPLsf/oWfMyL39Thpe/\n+cn8NL21eihxtFEiUjVnmsSTZ7ttxG3YUL4BZy+clTsUh9hkFMCakjW4Y+QdcodBRORxTMndzkNa\nvHhrGkIDNchOjsD7Ow+jsKwOKzZXIixQg9z8CgBAgMYX2ckR2HlIi5lpkYgLD3JJHJzsnojkYNlU\nnrxTaJ9QTLpyEj4v/xy/HvlrucOxy+srhPUX67H18FbckHCD3KEQEXmcOenRmJwYjsKyOnOTqY0l\ntag5fR4AcPDYWYyNCUVMWABuTY+CtuEinlh3EIVlddhYUuuyODiQAxERyWVW0iysLV0rdxgOeX2F\n8MuKL5ERnYF+vfvJHQoRkUdKjeqH1Kj+mJMeDV2jAdqGixhzRX/4+/rgqVkjsGx9Caq1evj6SKis\nawQATE4Md2kTKw7kQEREcrlp2E34/Ve/x4XmC+jt11vucC7j9Qnh2rK1mJU0S+4wiIg8Umt/wQos\nmZ6E0EANVm6uxKqtVQCAuPBAAMDSG5MBlODBKQnY/NNJABJyMmJc2rSTzbaIiEgu4YHhSI1IRUFV\ngSJbJXp1k9EmYxM2lG/AzMSZcodCROSRbAdUyE6OwLVxYYgO6YPKukYsW1+CkAANxg8NQ8yAQCya\nmohFU4eZk0FO4kxERJ5Ayc1GvbpCuPnwZgwLG4bI4Ei5QyEi8kimypxpJNHvDp/G9kotro4NwZVh\nAVh6Y7LFqKKtcxJW1jXgiXUHMXRAAA6d0mNbxSnzZ53FwWSIiEgJbk68Gc9tfw5vzHgDvj6+codj\nxasTwrWlbC5KROQulsmYqekoAIQG+uPbqtOYnBiOkACNVfVw5eZKbC2vw7YKLba1Lu50f0J7yZ9t\nsklERCSHuNA4DAwciG+PfYuM6Ay5w7HitU1GhRBMCImI3MiUjOUVVUNvMGL+xFhMiB8AXWMT4sID\nUVhWh7yiKqwprkF2cgSWrS/B8g2lSB7cDzFhAQCAmLYqojPVPXsjiXIOMCJl6alm4GxuTko0K2kW\n1pWukzuMy3hthfC7E98hUBOIpAFJcodCROSRTPMJnjc0Y9XWKiyZnoRX5sabE8BP9x7Dd4fPYFvF\nKew8pEVhWR3iwgMxbcQg9NH4orhah+2VWmwsqUVcZsfzEdobSZSDySgXm/N6p56q2rN1ACnRzYk3\nY+5/5uLv2X+HJElyh2PmtQnh2tK1mJXI6iARkbtsLKlFYVkdUqP6m6t0lglagMYP2ypOYUJ8GBIG\nBqPJ2IJtFVq8WlCOwrI6LMxKwKRhzk8/weRPXXjD7p16agoYTjVDSjRm8Bicbz6P0lOlGB4+XO5w\nzLw6Ifx/N/0/ucMgIvJYtjdkttUg0/t6gxG5+eWYP3EoACA6JAALsxKQkxFj93udwSqUcvGG3Tv1\n1IMbPiAiJZIkCbMSW0cbZUIoswpdBbTntbg66mq5QyEi8liWI4w+8vFeFJbVYechLV68NQ2hgRqr\nzwGB7w6fbhtMRosJ8QMACAAScvPLAXStiqTmKpSnJ7O8YScibzQraRYeK3gMSyYukTsUM68cVGZd\n6TrMHDYTPpJX7j4RkUs4O2jDmuIac//AwrI686Avpu+bbKtonY5iQvwAbKs4hdz8CpxuvIi48ECM\njQntUmzZyRGqHVTG3iA5RESkbpOunIQKXQWOnTsmdyhmXpkRrS3j6KJE5JkcJWnuGHHPmYRF12iA\n3tCMhVkJWDUv3So5M33/kY/34nxTCwDA39cHT96cgoVZCViYFY9Dp/SorGvEqwXlXYptY0kt7suM\nU+VE9xwhlYjI8/j7+uOGhBvwadmncodi5nVNRk82nsQPtT9gSuwUuUMhInI5R00k3dF00pk+YKb5\nB5dMT0JceJDVaKFz0qPNo4umRvXD5MRwFJbVYWNJLRZNHQYAbZPUH0DCwGBzEpdXVA1AICcj1mFT\nSkexqakJqSc1qfT05q9ERJ0xK3EWVn2/Cv879n/lDgWAFyaEn5V9huvjr0cvv15yh0JE5HKOEiF3\nDODhTMLS3nZDAzV48dY0c6JwWm8AUILs5AjzMnHhQZiYEI7lG0oRFtSaSJj6FAZo/Mzbt004HMXG\ngUzkoaZEnIjI3a6Pvx53r7sbZy+cRb/e/eQOx/sSwrVla/HrEb+WOwwiIrdwlAjJVW3qaLuWA8ss\nW1+CwrI6jB9ae1kl0fJVbzACEFZJnW3C4agiZRsPK1fuYXtcmYgTEV0SpAlCZkwmNlRswO0jbpc7\nHO9KCBsMDdhcvRnv/fI9uUMhIvI67SVfpoFnJidePu+gbRJnak5quU5TVdG2fyLQfkWKlSvX0zUa\n8NCHe7Ct4hT0BiMWTR3mUc1fiYhc4ebEm7G2dC0Twp72deXXGB81Hv1795c7FCIir9Ne8mVZQepM\npc7ROp2tSLFy5XorNlVgW8Wptp+ErLEQESnVTcNuwh++/gMuNl+UvSubV40yuqF8A2YkzJA7DCIi\nrzQ2JtThFBKmClJnm23aG4mzM81Au7pdZ6hpRNP2dHY/9h09CwCIDumDnIxYd4ZGRKRaEUERSBqQ\nhKKaIrlD8a6EsKC6AFlDs+QOg4jIK71aUN6lKSTaYy+hU8r8fUqJo7ucnV7ElDSOim5thTN95GD2\nyyTqIZ7yAMrbTImdgoKqArnD8J4mo9VnqtFgaEBKeIrcoRAReaWlNyYDKGl7dR+lNANVShzd5cx+\n5BVVITe/AnpDM+7PjEOYxUAyROR+7A+tTlNip+CJTU/gb/ibrHF4TUJYWFWIKbFTIEmS3KEQEXml\nuPAgvHP3OPPPukaDU3MKdpZSBjAxja6p9lFMnTme55tazK9KOf7k3bxtBGFPeQDlbTKiM7Dv532o\nv1iP4F7BssXhkiajkiRNkySpTJKkCkmSHrXz+e8lSSqRJGm/JEn5kiRdafGZUZKkvW1/PnVFPPYU\nVBdgSgwnoyciUorWSevLkZtfofpmlY6YntrnFVV5bHMuXaMBB4+19hvs4+/DpmukCJ7SZNtZ7uwP\nTe4T4B+A9Mh0bDuyTdY4ul0hlCTJF8DrAKYCOApgtyRJnwohSiwW2wMgXQihlyTpfwE8B+C2ts/O\nCyHSuhtHe4QQKKgqwF8z/+rOzRCRi3jbk11vNSc92u6cgp7Ecu5E2+Zcar/OWyu8Vdh5SItvq05j\nQnwYcjJi2XSNFIEVM1ILUz/C6QnTZYvBFU1GxwGoEEIcAgBJkj4CcDMAc0IohCi0WH4ngDtcsF2n\n/aT9CX4+fhgaMrQnN0tEXcQbSu8QGqixmlPQFWyTLLmTLtNTe12jAQEaX6ubU7Vf53lF1cjNrzD/\nfNWVoZyEnhSDTZdJLabETsHCLxfKGoMrEsIhACzr8UcBXN3O8vcA2GDxc29JkooBNAP4uxBirb0v\nSZI0H8B8ALjiiis6FWBBVQH7DxKpCG8oqatskyylJF32bk7tXedyJ7DO0jUasKOyda7ByH69kZ0c\nAUBA12jwqBtxtZwPIlKvcUPGoVxbDt15HUL7XD4tU09wRUJoL8uyOxOtJEl3AEgHkGnx9hVCiOOS\nJA0FUCBJ0g9CiMrLVijEKgCrACA9Pb1TM90WVBdg5rCZnfkKEcnIk24oqWfZJllKfbjgKNFQSgLb\nkTXFNdhVfRoAcPzsBVSebMD2Si0CNH6Kjruz1HI+1IjJNlErja8GGdEZ2Fy9Gb8c/ktZYnBFQngU\ngOX/tFEAjtsuJElSNoDHAWQKIS6a3hdCHG97PSRJ0iYAowFclhB2VYtoQWFVIV6+/mVXrZKIiBTK\n9mGCUh8uOEo0lJrA2spOjsDW8jroDUZ8f+QMUob0w6Rh4YqPu7PUcj7UiMk20SWmfoRqTgh3A0iQ\nJCkWwDEAtwP4teUCkiSNBrASwDQhxEmL90MA6IUQFyVJGgDgWrQOOOMyP9T+gLCAMET1jXLlaomI\niLrMUaKh1ATW1saSWmyr0GJhVjyuTxnksVUetZwPNWKyTXRJVmwW5q2dJ9v2u50QCiGaJUlaAOAr\nAL4A3hZCHJQkBKZAaQAAIABJREFU6SkAxUKITwE8DyAIwJq2fnxHhBAzAQwHsFKSpBa0ToHxd5vR\nSbstvyqf000QEZEsHDWLU3OioWs0QNtowLVxYZiZNgRx4UFyh0QqpOZ/A0SuljYoDSfqT+Dnhp8x\nKGhQj2/fJRPTCyG+APCFzXt/sfh7toPvFQEY6YoYHCmoKkDOqBx3boKIiMiujprFqbEf1ZriGqza\ncggAsGx9CV68NU01sRMRKZGvjy8yYzJRWFWIuSPn9vj2XTIxvVI1GZuw9chWXBdzndyhEBE5Tc6J\nvTmpuGtlJ0dgcmI4xsaE2j2uaps8u7KuAVt+qsO88Vfi2rgwFJbVqSZ2IiIlmxLT2o9QDi6pECrV\ndye+Q0z/GIQHhssdChGR05wZbMFdlSXLbc9Jj1Zd9UppNpbUorCsDgDMr2ocRAZovebmry5GZV0j\nNH4+ePXXY8zXBxERdc+U2Cn4x7f/kGXbHp0QFlQVsP8gEamOM0mCu0bos9w2RwHsPtPxzE6OwPih\nte0OIqP05qNrimtQWdeIuPBALL0x2ak+YErfJ+oenl8i10kOT0aDoQHVZ6oR0z+mR7ft8QnhwqsX\nyh0GEVGnOHOj7a7KkuW21VS9UirL4xmXefngK5Y31PYScCXdcFteD87GwocKno3nl8h1JEnClNgp\nKKwqxN2j7+7RbXtsQnih+QK+PfYtMmMy5Q6FiLpJSTfFStETI/RxFED3s22ia/lq+7nc56Ir1wMf\nKng2nl8i15oSMwUF1QVMCF1l59GdSAlPQd9efeUOhYi6SUk3xUSuZNmk1N5DD7XfcMv9UIEPk9xL\n7vNL5GmmxE7BXzf/FUIItE3V1yM8dpTRzdWbkXklq4NEaqdrNEBvMGJhVrxqb4qJTGxHcTXdUG8s\nqbU72qjpcyYzXaO2UVyJyLsNDRkKH8kHlacre3S7HpsQ7j6+G1dHXS13GEQAOJR/d6wprkFufjkC\nNH68KVYAXsvdY0pQVmyqwN3v7EJlXYPdhx48zq4xJz0aS6Yn8WESEamCJEkYN2Qcdh/b3aPb9ciE\nUAiB4uPFGBs5Vu5QiADwKXV38IZOWUzX8iMf72Wy4gTbxM50PZecqEdhWR2WrS+x+9Cjp39neGoC\nygorEanN2Mix2H2cCWG3HT13FAICUX2j5A6FCACTmu7gDZ1ymCpZE+I5IbmzbBM70/X85M0pmJwY\njgenJEDbcBET4gcgOznC/L2e/p3Bh1ZERMqQHpmO4uPFPbpNjxxUZvfx3RgbObZHO2MStYcd78kT\nmCpZC7PiMTEhnA84nOBoUJi48CC8eGsaHvl4r3nC+o0lteapKXr6d4baB68hIvIUVw2+Cnt+3gNj\nixG+Pr49sk2PTAiLjxcjPTJd7jCIiDxKV+ah83a2iZ3tvIOFZXUYFxMCjZ+vVYVQ7jiJiEgeIX1C\nMChoEEpPlSJlYEqPbNMjE8Ldx3fj4asfljsMIiIiK/bmHdQbjMjNL7eqEBIRkfcy9SNkQthFpgFl\nWCEkInKtvKJq5OaXY2t5HV6ZO4ZVwi6wrbLelxkHXaMBARpfq+aanD+PiMh7mfoR3pV2V49sz+MG\nlak8XYlgTTAiguRrekNE5JkEAGBbhZaDj3RRaKAG2ckReOjDPXjm8xLoGg1WzTVNI31ykBciIu/V\n0yONelyFsPh4McYO4XQTROQ8VmOck5MRC0ACIDj4SDcsW1+CbRWnsK3iFMKCepmTQXvNSXmciYi8\nz+jBo3Hg5AEYjAZofN1/X+JxCeHuY7uRPpjNRYnIeZY34hxYw7HQQA0WTR0G4NK8dUyiO2/pjclo\nMh5E8uBgzEmPhq7RgBWbKrHv6GnMnziUSSARkZcL0gQhtn8sDpw8gDGDx7h9ex7XZLT4BCuERNQ5\nnCey89iksetCAjSYmDAA918Xj9BADdYU12DV1kP4tuo0vi75GQCPLxF5LtMDRV2jQe5QFG3skLHY\nfaxnmo16VEJobDFiz4k9uGrwVXKHQkQqYurDJWelS23/QXpzEt1sFDh+5nyXz1VeURWWbyhFXlEV\ndI0G6A3NGHNFfwBAtVaPvKJq6A3NWJiV4Lbja+96U9s1SETqxAdezkkf3HMT1HtUQlimLUNEUARC\n+oTIHQoRUaeo7T9IJSTRcjlZfwFHdPpunCvJ/LqmuAa5+RWYmDAAC7PisTArAYBAbn4FAjS+bju+\n9q43tV2DRKRO3vxAsTPGDum5gWU8qg/h7mO7Od0EEamSWgYRsR2AxxsH5BkY3BtA189VTkYMTCO2\njo0JxeTEcMxMG4K48NY5CFunofBz67Vg73pTyzVIROpmObIyOZYakYqftD9B36RHgH+AW7flUQlh\n8fFijI1k/0EiUh+1/AdpOwCPNw7I4+crIbJ/ny4nwKGBGgRo/LB8Qyn2Hz2DwrI6pEYdN89F2BPX\ngr1tqOUaJCLyBr39emN4+HDs+3kfrom+xq3b8qiEcPfx3ZiTMkfuMLyKN1YHiLyZbRXJ9pW/E5xj\nOl7ZyREYP7QWekOzrIm15XkDwHNIRKQA6YPTsfv4biaEzmoyNuGHkz/0yNCsdIk3VgeIvJltFcn2\nZ/5OsGZKtLKTI7CxpNb8Oic9GnPSo62SMHc3E22vua/leQPAc0hEpABjh4zFlsNb3L4dj0kID5w8\ngJj+MQjSBMkdildhnxMissTfCdZMidbOQ1oUltVZvaZG9UNufgX0hmZzMujOilx7zX3b61NIRETy\nSI9Mx4s7XnT7djwmIWT/QXmwzwmRe6i16SV/J1izbRqanRwBoKSt32B/LJmeBL3B2CMVufaa+9qe\nN8vqpZquPyIiT5ISnoIjZ4/g3MVz6Nurr9u24zEJ4Q8nf0BqRKrcYRARAeh+Qseml54lJOBSwvXi\nrWmXNd0EAL2hGbpGg9sSsI6a+1ri9dc1an2QQ0TK5O/rj+EDhqOkrgTjo8a7bTseMw9hmbYMSQOS\n5A6DiAjA5XO6OZr029H7nKfJM1heB6ZzDVyqwJkSwACNL3LzK9w+B6Czk8/z+usazuVIRK6WOCAR\nZafK3LoNj6kQlp4qRWJYotxhEBEBaL2h1huM5qqPo4qLo/fZ9NIzWF4HeUVVyM2vANBaDTT1H1w0\nNbHH+l46W/nj9dc17ENLRK6WGJaI0lOlHS/YDR6REOqb9DjZeBIx/WPkDoWICADMVZ/lG0qtRo+0\nvVFsTRiaoTcY3dpckORheR0szErAkulJyE6OwGOf/AAAON/UYl6uJxIwJizuxUSaiFwtaUASPjrw\nkVu34REJYbm2HHEhcfD18ZU7FCIis/YG7TCxnKQ8QOOr+JtJ9pHqPNsk7JGP9+LbKh0AoI9/z/bc\nYMJCRKQuiWGJKNOyyWiHyrRlSBzA5qJEpCyObr5tkyo1VW042Ejnma6DyroGzF9djMq6RoQE+OOm\n1MHIyYh12XaYrBMReZ6EsAQcOn0Ixhaj24pfHjGoDPsPEpGSdDSATF5RtdXAE6ak0DTISHvrkBsH\nG+m6ZetLUFnXiD7+Pjitb0LN6fMuTdw4oAkRkecJ8A9ARGAEqs9Uu20bHpEQlmnLmBCS11FqwkCO\nb8wvVdfEZUmV7XeUenNvqna5swKl9mvbUfxLb0zG5MRwvPGbqzAhfgASBga5ZB9N28tOjmCyTkTk\ngRIHuHdgGc9ICE9xygnyPkpNGMhxFc30fk5G7GVJVXZyBCYnhrdNXA6MjQlFXHggEiOCVZ0cdYXa\nr+28oios31CKvKIqALCabmL80DCkRvfHxIQBWLW1yiX7aDpeG0tqra4rtSfWRETUyt39CFXfh1AI\nwT6E5JXU1O/M27Q3gIyjfncbS2pRWFaH8UNrEZcZhFcLylFZ14i/fd7azHDnIS1evDVN0X3DXNWH\nTf3XtmT1appuoqD0JL6t0kFvMCInIwaAa/YxOzkCOw9pzQ8TTNjfs+vYH5OIlCQxLBH7a/e7bf2q\nTwiP1x9HgH8A+vfuL3coRD2KowV6Ftt5C5femAygBA9OScCrBeUoLKtDXlE1AjS+ir1JdVUCovZr\nOycjxnyedI0GfHf4DACgydg6xcR5g9Gl+2j7MMHEMrH2hASnu/vQme8zmSYiJUkakIQ1JWvctn7V\nJ4TsP0hEnmL/0TMoLKtDgMYP92XG4Z27xwEAXrw1DXlF1Siu1mF7pRaAvDepjm6s1V/Zcw3LQYL0\nBiO2VZxCXHggRkT2xfdHzqCPxrW9NRwdd8ukc+XmStUnON1N0jrzfV7LRKQk7u5DqP6EkP0HiTye\nJ1Q3OrKmuAaFZXWYnBhudRNq2ndAYHul9rLPbZfriWPk6MZa7ZU9VzIdo2vjwjAhfgC2VZzCjamR\nbhn0xZnj7gkJTnf3oTPf57VMREoyJHgIGgwNOHvhLPr17ufy9as+IeSUE0Sezxuab9lOYm9i2veF\nWQnmZMI24dM1GvDIx3tRWFYHwP3HyBOSC3ebkx6NreV12FahxfyJQzExYYCsDzTcneA4+0CiOw8u\nursP3pDkecPDMyJvJEkShoUNQ5m2DOOGjHP5+lWfEJZpyzA1bqrcYRCRG3lDAuLoZtVRomjJUXXR\nXbzhxrq7QgM1uOrKEGyr0KKPxgf3ZcZZTQ+xsaTWo27anX1o4w0Pd+TE40vkuZIGJKHsFBNCu9iH\nkMjzeXMC0tnmgJ6SYHiCnIxYBGj8zOfHdLO+85DW6WquWio+zj60aR08qRl6gxG6RoOi90mNvOHh\nGZG3cufUE6pOCM83nceJ+hOIDYmVOxQiItkoNWFWSzLjLpbnRddogN5gxMKseMxMG4LxQ2udumk3\nJZF6g1HRI8xaDqTTXoyhgRoEaPywfEMpAjS+irxu1UypvwuIqPsSByTi3yX/dsu6VZ0QluvKMTRk\nKPx8VL0bREQeic3XLiXFeoMRufnlWDI9CXHhQVbTQ7THlDTqDc09diy7msg7e75ZxSIi6rykAUlu\nqxC6duzrHlZ2ihPSExG1x9RvTddo6PFtz0mPdsuomu7iimNlu45LSZLo0rEwVXxyMmJ77FiaYm4d\n3dYx23119nyb9qmrlU45r2kiIrkkhCagUlcJY4vR5etWd0LI/oNERO1y9ubeHWznwVP6DXxnjlWz\nUVy2T6bRXk3rMDUTnT8xFoDUreae3U2iOsM2sXOUgNkeL2fOt6N1dSbJk/OaJiKSS6AmEAMCBuDI\n2SMuX7eq21rWnK3B6MGj5Q6DiEixlNA8Ty1NRztzrE7WX7hsn2xHe80rqkJufgViwgJQrdWrps+c\nbT80R+evdYAYI/SGZqsBYmyXt2yCavmZZZ9DThpPRNSxmP4xOHL2iMvHT1F1Qni84ThmBM+QOwwi\nIsWSe5AJy8FU5qRHe8xAMwODe+NOm+aRl4/2KgEAqrX6HpsSxB0cJWCtA8T4tg0Q42e+zmyXt00C\nTa+O3u+I3Nc0EZFcIoMjcbz+uMvXq+6EsP44IoMj5Q6DiIgcWFNcYx5MJTRQg5e/KUNufgX0hmYs\nmqqsJv+dqVL5+UqXLWObqORkxOB8kxHF1To0XmzGaX3H0ywoMWFuLwGzTOQsY7etJJpeLdeVnRyB\nnYe0yE6OYJJHROQEdyWEqu5DyISQiEjZ5qRHY2FWgrlZ4XlDCwCYX5U0QIg7BsEpr63H90fOYFf1\naSxbX9Lh8mrrH2fZt9FR7I76P24sqUVhWR02ltT2ZMidpqRr1BPweBJ1HSuENppbmqHVazEwcKDc\noRARkQO2zQr7aHwBwPyqpP6F3a1S2Vb38oqqUFhWh9QhfXHuQjMenJLQ4TrU3D/ONvaOqp2dXV4u\nSrpGPQGPJ1HXRQZH4rsT37l8vapNCGsbajEgYADnICQiUjjbG/8AjS+ykyOwcnMlspMjrD5TM8sb\n3ezkCKzb2/oUt28ff+w/dg67q3UYc2VIu+tQatNJZ5I1Zwej6ery3Y2vq9ScpCsRjydR17FCaIPN\nRYlIiZRa5ZCT7Y3/fZlxWLm50uOqBJZ94p5YdxDVWj1iwgLw5M0jsLGkVtU3wF1J1iyPh0l7/z66\nkyi4s+qk1CRdrXg8ibqOCaENJoREpERsDuUctVQJOpPgm/rEpUYdR+PFJgDApIQBiAsPQlxmUE+E\n6zZdOV+m4wGU4MVb06z6GQKX//voTqKgluuJiKg7BgcNxvH64xBCQJIkl62XCSERkQvxxrTrlFhd\n7cr8eNoGA/bUnG17V4Ku0YC8oioAEnIyYhSzb13l7Hmakx6NnYe0KCyrw5riGtyXGWf334crzjur\nTkTkDYJ7BcNX8sW5i+fQr3c/l61XtQnhsfpjTAiJSHF4Y+oce4mWEqurXZkf7+VvyszvldXWt029\nUQEAqpmc3pbluQHgcOJ5y4QuNFCDB6ck4IhOj7Exoeb3bPdfKeddiQ8kiIhsRQZH4lj9MSaEQGuF\nMCM6Q+4wiIioC0wJlmlwmTnp0YqsrnYlwc/JiMWOSi12VZ/GqKh+mJMeDb2hGYCkqH3rDNtzozcY\nzVOJ5BVVIze/HFvLT+GVuaMRGqgxv79u7zFUa/V4taAc79w9zql1y0UpiSkRUXtM/QiTw5Ndtk5V\nJ4SsEBIRqZMp0bIdXMYTbsRDAzVYcWe6VbVp0dREucPqFtvE2HIqEUAAALZVnMKKTZUIC9JAbzAi\nN78cABAXHogHpySYE3/b6ptSqupKSUyJiNrjjoFlVJ0QDgkeIncYRETUDZ50E15Z14Bl60uw9MZk\nxIUHmZMcT2yKaHvevjt8GtsqtCg5cRbbKrSYEB+G+ROHoo/GBzkZsaqoviklMSUias+Q4CEuTwh9\nXLq2HsQKIRGR+pluwl2RKOkaDVi5uRK6RoMLIuv8+patL0FhWR2WrS+xet+UDK0prnFJXN3R0T45\ns8+2CW5ooAavzB2DJdOT8PupiYgLD8S2Ci36aHzbKoitieOS6UkekfgTEcmJFcI2QgjUG+oRFhAm\ndyhERKQQrq5CdXZ9S29MBlDS9nqJkqqgHe2Tvc8tE0AAeOjDPdhWcQp6gxGLpg4DYN0EuLKuEZMT\nwwEIj2sOTEQkt8jgSGw9stWl61RlQtjU0oRBQYPgI6m2wElERC7m6sTL2fVZJkz2Bk5RUlPEjvbJ\n3ueWSaLe0IxtFafaPhFOfF8yDz7jKc1lXckTmxMTkXuxQtjGYDSwuSgREVlxdeJluz7TzXuzUcDP\n99KEwKaESW9oRoDGTzU3952ZTxBoHRH2iXUHAQAT4sOQkxF72bLtDT6jlKRYSdTQt5KIlIUJYZum\nliYmhERENlhtcC/Tzbum/gIi+/cxv285FYPSb+7bm0/Q9nPTe5bNQbdVnMLkxHC8eGuaeXqJ9q45\ny4ohr8/LKak5MRGpw+DgwTjRcAJCXN5Ko6vUmRAamxAZxISQiMgSqw3uZbppf297b6v3TQmTrtGA\nAI2vom/u7SUg9v5ubx8sPzMldB1dc6GBGsxJj8aa4hqrqSh4fbZSUnNiIlKH3n69EaQJgva81mXr\nVGUnvCZjEwYHD5Y7DCIiReFIju5lunm3bC5q73MlV78sY7QXr2WCYjvaqO3yukYD9AYjFmbFt3vN\nXUoaxWXXp6tHhiUi8gaRwZE4du6Yy9anygphs2hGaJ9QucMgIlIUVhta2Y6KKUczRbU3j7zUL9Jo\nrnra7sea4hrk5pdjyfQkp/ogOloHq9pERJ0T0jsEZy6ccdn6VJkQGluM6Nurr9xhEBF5PDUmNh31\nk+vpGJSY6OgaDcgrqgYgkJMRa1X1W1Ncg+zkCOgNzfjusA7bKlqbJdlOQ5GdHAGg4/5v7T2oYB86\nIqLOC+4VjHpDvcvWp8qEsEW0IFgTLHcYREQeT+mJjT0d9ZOTKwYlMVX3TBZNTTS/bzrfARo/bKvQ\nYnJiuMNpKLp7TbCqTUTUecGaYNRf9PKE0CiMCO7FhJCIyN2Umti01yzUNsmQI+FQeqKTnRyBD3cd\nQbVWD+BSn8j2kumVmysxJz1asdcEEZG3CNawQghji5EVQiLySj3dhFOpiY0SmoWqsTmtycaSWlRr\n9YgLD0TmsHBzsucomV65udLqGCvxmiAi8hbBvVghbG0yygohEXkhNTbhdAclNAtV27mwTGDnpEdj\n5yEtCsvq8Md/70NlXSOAy/fDmf6Cak6MiYjUiBVCtDUZZYWQiLyQ2prruStZsJzfbk56tCwJmdrO\nhe3IoUtvTAZQgsKyusv6Cdp+B4B5rkXLaqK9ZYiIyL2CewXjeP1xl61PlQlhSwsrhETknZTahNMR\ndyYLciciajsXpoRPb2g2H7cXb01rN2G3TXrtHfP2EmNWD4mIXI+DygBoQQsC/QPlDoOIiDrQURWt\nOwmDEit0rk6AnFmfvWXsvWdKYHWNBgRo/Oz2GbRlWYk1TUWxMCvBfMw7ik/upJ2IyBP17dXXpU1G\nfVy2ph7kI/lAkqSOFyQiIlmZEo7QQI25uaGu0WD+3JQwrCmusfqevWXbW7dSrGgbfGXF5kqXrM/R\n8eloGdv3dI0GvPxNGV7+5icAaPe42R5707qWrS9Bbn4FAjS+lzUXdRTfnPRoLJmepKiknYhI7TgP\nIQBfyVfuEIiIqJPyiqqRm18OvcGIRVOHAXBc5VNrZengsbNWr93lTBW0vQF2LJt65uZXAAACNL4O\nB48xVQMtj71pHdnJERg/tLbd7dhSW7PajrRXEWXzWCLqKWwyitYKIRERKYPzN8ICAHDe0Gw1MInl\n4DCm7yuxOWh7TMfgkV8kQlNQ3jZgS/c5k1DZW8byPV2jAXqDEfMnxgKSBL2hGZV1DdhYUms+5pZJ\noO2xt1xXXGZQp+PzJHlFVcjNr4De0IxFUxOtPlPrQwwTJrRE6sEKIQBfH1YIiYiUwtkb4ZyMWARo\n/KA3GK2WN31/5yEtXrw1ze7k8q7kjhtf0z4smZ6Ed+4e55J1ukprdbAcS6YnAWids3H9/hNWU01Y\nJoHeluR1jmTzeonaHmLYckVCy6SSqGewQgg2GSUiUhJnb4StBzXxtfqeaU68NcU1Dm9GXXWz6Y5K\njpKTAdvYTMfacqoJJoHOycmIsbp2Lan9GLriGlZ7lZRILVxdIZSEEC5bWU/pP7S/OHPojNxhEBFR\nJ3S3/9XKtgFblkxP6tbNZncTy+uuuw4AsGnTpi7HICdWcchdeG0R9YxGQyPCnw/H+aXnvxNCpHd3\nfarsjMcmo0RE6tPeiJS2o5G+/M1PePmbMqtRRuekR2NhVgL0huZ2Rx+1ZG+0Ust+i86upzPrVzp3\njM7aneOgxmPo7RydMyWO/EvkiQL8A3DReNFl62OTUSIi6hHONkkz9XkDgACNn1U1cP/RMygsq7vs\n/fbWZTlYiu1ImnpDs9WcfJ3FJnKt7I0g66zOHENWoJSB1z2RvCRJQpAmCOdwziXrU2WFkKOMEhGp\nj7PVA1MlcGFWvFXymFdUhcKyOlwbF9ZhUmmqYGQnR5jnwbOsUJrmxzvf1ILlG0qRV1TdpX2ynGdP\nqZWumJgYnDp1yuq9zsSal5eHhIQEJCQkIC8vz/z+448/jujoaAQFBcE0guyl17afhMBDDz2E+Ph4\npKam4vvvv79s/XPSo/Hroc3IfeBmxMfH46GHHoKpO4tOp8PUqVORkJCAqVOn4t3CA1i+oRSv/mcT\nrrnmGvTq1QsvvPCCeV0XLlzAuHHjMGrUKKSkpOCJJ54wfzZx4kSkpaUhLS0NkZGRmDVrFgDg7Nmz\nuOmmm8zfeeedd6yOUfWJUxgyZAgWLFgAANDr9ZgxYwaSkpKQkpKCRx991LyNw4cPIysrC6mpqbju\nuutw9OhR82e+vr7m7c+cOdP8/muvvYb4+HhIkmR1nv71r38hNTUVqampyMjIwL59+8yf5ebmYsSI\nEUhJScE//vEP8/u33XabeRsxMTFIS0tzcFa7h/NLEskvWBPsupUJIVT3JzIxUhARkXfQNlwUKzZV\niKc/LxFX/mm9eOnrsss+0zZctPrOik0V4so/rRcrNlW0u+xLX5e2rbPU6XgyMzNFZmbmZe/b26YS\nXHnllaKurs7qPWdj1Wq1IjY2Vmi1WqHT6URsbKzQ6XRCCCF27Nghjh8/LgIDA4W24aJ46etS8dLX\nZVbH9/PPPxfTpk0TLS0tYseOHWLcuHF2tzN27FhRVFQkWlpaxLRp08QXX3whhBDij3/8o1i+fLkQ\nQojly5eLBxc9IlZsqhDfHjwkHl3xiVj0hz+J559/3ryelpYWUV9fL4QQwmAwiHHjxokdO3Zctr3Z\ns2eLvLw8IYQQTz/9tFi8eLEQQoiTJ0+KkJAQcfHiRfMxmnxLjpg7d6743e9+J4QQorGxURQUFAgh\nhLh48aKYMGGCOd5f/epX4t133xVCCJGfny/uuOMO8zYDAwPt7vv3338vqqqqLjtP27dvNx/rL774\nwnzsfvjhB5GSkiIaGxtFU1OTyMrKEj/99NNl6/39738vnnzySbvbJCL1S3otSQAoFi7IrVRZamOT\nUSIi72FuniaAyYnhyBwWbq5umT7LK6qyqni1VhnjoTcYze/Zq1DmZMRiyfQk5GTEdjqu6upqJCUl\n4d5778WIESOw8Z9LMStci7cfvRMJCQnYtWsXAGDXrl3IyMjA6NGjkZGRgbKyMgDAu+++i1mzZuGm\nm25CbGwsXnvtNbz00ksYPXo0xo8fD51OB6B1EJtFixZh0qRJGD58OHbv3o3Zs2cjISEBS5cuNccz\na9YsXHXVVUhJScGqVavajX1OejTq370PlV++gzFjxmDkyJEoLW1tAmhZPfzqq68wdepUoFcQ3i2u\nQ8Twsfj32vUAgPHjx2Pw4MHmYxug8UNufrlVH9F169Zh3rx5kCQJ48ePx5kzZ3DixAmrWE6cOIFz\n587hmmuugSRJmDdvHtauXWv+fk5OTuu5ysnBV59/hvsy47D9mAEfVmlw8OcGq3VJktRWsQSamprQ\n1NQESbKeIqK+vh4FBQXmCqEkSaivr4cQAg0NDQgNDYWfn5+5ctkfevziF78wfz8gIACTJ08GAGg0\nGowZM8aJ5JvPAAAgAElEQVRcCSwpKUFWVhYAYPLkyVi3bl275wEARo8ejZiYmMvez8jIQEhIiPlY\nm7bx448/Yvz48QgICICfnx8yMzPx3//+1+q7Qgh8/PHHmDt3bofbJyJ1cmWFUJUJoe0vdyIi8lzZ\nyRGYnBgOQKCwrA4vfVNmbuZpbvppaDEnhgDakj4Jufnl7TYH7e4gGBUVFVi4cCH279+PQxU/4dzB\nTdhZtB0vvPACnnnmGQBAUlIStmzZgj179uCpp57CY489Zv7+gQMH8MEHH2DXrl14/PHHERAQgD17\n9uCaa67B6tWrzctpNBps2bIF999/P26++Wa8/vrrOHDgAN59911otVoAwNtvv43vvvsOxcXFeOWV\nV8zvO9rv4N5+iI6MwPfff495//Nb3PvIX6yS7DXFNTh27Biio6PN/Tp/POePVV/uttvU1F4zQtP3\nTaKionDs2DGr7x07dgxRUVF2l6mtrTUnnYMHD8bJkyfblnI8H6DRaERaWhoGDhyIqVOn4uqrr7b6\n/L///S+ysrLQt29fAMCCBQvw448/IjIyEiNHjkRubi58fHzQv48fij54Gbkvv+jwOJ45cwafffaZ\nOQkcNWoU/vOf/5i3U19fbz4PFy5cQHp6OsaPH29OeJ311ltvYfr06QCAESNGYMuWLdBqtdDr9fji\niy9QU2M9UNPWrVsRERGBhISETm2HiNSjt19vl61LnQmhnf8AiIjIM20sqUVhWR36aPywZHoShoa3\nVoDOG4zmEUP3HW2dimjnIZ05WTlvaDa/uqt/X2xsLEaOHAkfHx+kpKQgKysLkiRh5MiRqK6uBtDa\nR23OnDkYMWIEFi1ahIMHD5q/P3nyZAQHByM8PBz9+vXDTTfdBABW3wdg7nM2cuRIpKSkYPDgwejV\nqxeGDh1qTgZeeeUVjBo1CuPHj0dNTQ3Ky8uhazSg/kIzTjvY79mzZwMAtL2H4LuDP1n1r5yTHg0h\nBPQGI/SGZsyfNBRXhAagsq6xw5FiTYSdqa1sH+raW6a5RWDl5ko4mhgrJyMGC7PiAQD6tvNs4uvr\ni7179+Lo0aPYtWsXDhw4YPX5hx9+aFU5++qrr5CWlobjx49j7969WLBgAc6dO4d//vOfuOGGG6wS\nWqsYm5sxd+5cPPTQQxg6dCgA4IUXXsDmzZsxevRobN68GUOGDIGfX+v4fUeOHEFxcTE++OADPPzw\nw6isrHSwd9YKCwvx1ltv4dlnnwUADB8+HH/6058wdepUTJs2DaNGjTJvw9E+EpHnceWYKqpMCImI\nyHuYEpScjBjclxmHkAB/AEAfTet/YXlF1fi2qrV55bdVOjzy8V7oGg3oo/FrW86v3SkvuqNXr17m\nv/v4+Jh/9vHxQXNza6Ly5z//GZMnT8aBAwfw2Wef4cKFC9A1GlBYehLCx6/D71tux3IZy+U2bdqE\njRs3YseOHdi3bx9Gjx6NCxcuYE1xDc6eb8Kne62rcrbrnToiEtH9e5lH7zQldlFRUdi2txS5+RXo\n4++DwOZzmDE+2eFgIraJd1RUlFX16ujRo4iMjLT6TlRUlLk5pK7RgFUbdqOywR/LN5SiT99QcxPT\nEydOYODAgeYqJgB8W6XFvpqzduP4v31aXJ0xEV9++aX5fa1Wi127dmHGjBnm99555x3Mnj0bkiQh\nPj4esbGxKC0txY4dO/Daa68hJiYGf/jDH7B69WqrAWTmz5+PhIQEPPzww+b3IiMj8cknn2DPnj14\n+umnAQD9+vUzfwYAQ4cOxXXXXYc9e/bYPYaW9u/fj3vvvRfr1q1DWFiY+f177rkH33//PbZs2YLQ\n0FCrSmBzczM++eQT3HbbbR2un4jUiwkhERF5DdvKk6nf38y0IVi5udJcCQSAmLAAFJbVYU1xDWam\nRWJyYjhmpkVe1pyxuxXDZqPAv3YehrHFUQ3rkrNnz2LIkCEAWvsNAq39Ij//4QQqTja0803nnT17\nFiEhIQgICEBpaSl27twJoDWZ7tfHHzPThrT7/X59/DEwuPdlczNef/31OLx/Bx6aMBj6+nPYvX0T\nRl9zncMmtnlF1Vajts6cORNvvfMuVmyqwFeFW9GvXz9zE1CTwYMHIzg4GDt37sTHu4/g3x99gHMR\naZicGI45s2eZRzbNy8vDzTffbDHlgYQJ8QMwKrqfeV11dXU4c+YM1hTX4OlP9+E/n21AUlKS+fM1\na9bgxhtvRO/el5paXXHFFcjPzwfQ2kS1rKwMIRFRmDT/Kez9sQLV1dV44YUXMG/ePPz9738HACxd\nuhRnz561GuETAE6dOoWWlhYAwPLly/E///M/AIDTp0/j4sWL5mW2b9+O5OTkds/JkSNHMHv2bLz3\n3nsYNsx6Kg9T09kjR47gk08+saoGbty4EUlJSVbNcImUTqmjNCuZKxNCVc5DyCajRETey5Qgrtxc\nieUbSs1TVAASZqZFYmNJrXmaicKyOowfWov7MuOs5kvr7jxqJ+sv4I3NlRDnmzpcdvHixcjJycFL\nL72EKVOmAGhN1IpGDob/6Qud3rY906ZNw4oVK5CamorExESMHz8ewKW+giFtCdwNN9yAN99887Iq\nnWmflm8oxeHSH3By93q8+eabCA0NxRN/+QueefhWtAiB39y/CHdNGWnerw8++AB6vR5RUVG49957\n0e/a1sTkvKEZKzdX4leZ2Wh+80M8+MuJGBjSF+s+/pd5e2lpadi7dy8A4I033sBdd92FRr0eV43M\nwOzf3o6bR0dhbdiv8Z8X/oCXXluBpPhYrP3kP0CvIJzV1uH5u6agof4ctvn4YNU/X8O23Xvx1oZd\nWJf7OIxGI4yNF/Gr38zFjTfeaN7mRx99ZFXlA1oruHfddRdGjhwJIQSeffZZFFSfd3h9HD16FE8/\n/TSSkpIwZswYAK39EO+9915s2rQJS5YsgSRJmDRpEl5//XUArQPB3HffffDx8UFLSwseffRRc0L4\nyiuv4LnnnsPPP/+M1NRU8zl66qmnoNVq8cADDwAA/Pz8UFxcDAC45ZZboNVq4e/vj9dff908+Ixp\nH9lclNSGc1t2nisTQsle232li02JFVUHq+QOg4iIZGRqOpidHGFOAi0rV+193tkJzi2Xnz3jF2g2\nCty57G1FTZBub586s5/tLevsekzL6Q1G5OaXW80B2dljZUr4JyeGo7CsDkumJzm8UdQ1GvDIx3ut\nluvOJPbd+a4rvk/kbfhvpvOmvT8NX9351XdCiPTurosVQiIiUiXbSiGAyxIBe59bftdSezcklk+v\nAcDPV1LcU2x7T9g789Td3jExySuqRm5+OfQGIxZNHWZ3Gct1tDb7ah2Qxplt22Nq3pudHIHxQ2vb\nnQTdVA2+Ni7MPNVIdyoO7R0LZ7DaQdQ53f035418fVw3DZ9LEkJJkqYByAXgC+BNIcTfbT7vBWA1\ngKsAaAHcJoSobvtsCYB7ABgBPCSE+MoVMRERkXcwJQqmV9ubcdvPHXF0E69rNEBvMGJhVjzmpEfj\nQ5fvgWvY209n971jwua1faZ5CZdvKEWAxrdLN3qWN4hxmUHtLmvaP1NlMkDj68J97zw5t01E3kFR\nfQglSfIF8DqAqQCOAtgtSdKnQogSi8XuAXBaCBEvSdLtAJ4FcJskSckAbgeQAiASwEZJkoYJIYzt\nbpMVQiIiamP7ZNn2ZtzZJ8/2buJ1jQY89OEebKs4hYVZCYpuymRvP1311D0nIxYBGr9OJTg9mRRZ\nViZNyaCcFQdWO4jI3RTVh1CSpGsA/FUIcX3bz0sAQAix3GKZr9qW2SFJkh+AnwGEA3jUclnL5drb\nZp/APuLqsVe3twgREVG3NBsFyk/W42zbwDGD+vZGzIBA80AoaWlpcobXLc1GgZP1FxASqMHpRgMG\nBveGny8fthIRqcWBkweg/VHrkj6ErkgthwCwnNjpaNt7dpcRQjQDOAsgzMnvAgAkSZovSVKxJEnF\nxuZ2C4hERETd0mwUqKhrMCeDAHCq4SLON3nG/z8n6y/giE6Pw1o9juj0OFnvmtFO7Wk2Chw/cx7N\nRvUNYkdEpFSubDHpij6E9qKx/a3vaBlnvtv6phCrAKwCgLiUOLFp06ZOhEhEROQ800A0UT4Smi3m\nGkxODEefvMUAADX/P9TRCK2uZDqWd7YzSigREXXOzR/djE/nfuqSdbkiITwKwLKDQBSA4w6WOdrW\nZLQfAJ2T372McLJTOxERUVfMSY/Gx8U1qKxrRG8/HwwdEIDgPhosvTEZ9+TJHV33OTNgi6uGgecA\nK0RErtciWly2Llc0Gd0NIEGSpFhJkjRoHSTGNl39FEBO299/BaBAtHZe/BTA7ZIk9ZIkKRZAAoBd\nLoiJiIioUyrrGnD3O7tQWdeA0EANVs1LR0xYAC40t6Dk5wZMSRqIuPD2R7t0J12jASs3V7ZN6eB+\nplFX1xTXWB2bzjIln0oekIeISG1cmRB2u0IohGiWJGkBgK/QOu3E20KIg5IkPQWgWAjxKYC3ALwn\nSVIFWiuDt7d996AkSR8DKAHQDOB3HY0wSkRE5A7L1pegsKwOQAmW3piMZf+fvXsPi7LO/z/+ugFH\nBY8gmgcKBYWwNSssM8vyUNq6am1YbaUddjuu69cO28/WbQ+1a3twy2rbtW23tLNYq25lpUiamSWV\nWZkoeAgPGYIagjaC9+8PGBpwOAzMzGcOz8d1eYEwc9/vmUG8X/P+HF7bpAsGJGrH+zs1PDXBeIcr\n0HvbuXf2XJu+S5v09A1n+/3cAIDGBVUglCTbtt+Q9Ea9r93v9vlRSVkN3PcPkv7g1fkYMgoA8LFZ\n4zPkHgZz84s1PDVBM8el+3WOXUPqD9n0xdBLb4aBug8rdX9ufDWUtLn1zl+7XZKlqcOSA/YaBPIx\nAkBLBF0gDLTWbpUBAEB9KYkdartf7gHI1DDR+Wt3aG7OVlU4qzRjzACf7G3X0i6j+3PjWiTG22O0\nRHZekebmFEhS7Qb3gQhrge7GAoC3qo77blBlSAZCAAD8wT1smBga6X7+7xfd9t2boO5dxpYGq9Z0\nKuufs6kasjKTVOGslGTVns8V1iqcVXU2ofd0/JZiIRwAwS7iO4QAAPiD6c6Q6/yrtxRrYO/Omj4q\nVVOH9T3hdt4GKxf3LqM3nb76x2/pc/N9mKtUrCNGFc7K2g6gp2PGxzk0Y0xana+5QlqFs1Kzl23W\nu1v3K6NnR7V3xEiy6xyvpQHRF91YAPCniA+EzCEEAPiD6c5QVmaS1m0rUW5+sd4rLNHMcekeg0z9\n4NqSIDs6o4fe3bpfJYe/U2m5s8556gcpXwXl78NclWYv26zpo/rXztFsLldYKy13auOuQ8rNL9aa\ngv2SpJvP76eL0hI1OqOHJPMBHwD8hUDIHEIAgB+Y7gzFxzk0Z/Lg2oVUGgpK9YNrS4Lsik37tKZg\nv9YU7FdCh7a1j7u03KlpL3ys9wpLVOGs1IwxaR6P7765/dINu9WchV/cw1z94Z7e+v652qEjzsqa\nDqGUm1+sof32KWVEB+MBHwD8xVnluy2IQjIQVrEzBQAgTHkaJunpNu7BtSVBtnp+XpUku05gys4r\n0nuFJTV/sxo8/vy12zU3p0CrtxS73V6aMWZAk+f2VfCufq6+P5970PTleQAg2JQ5y3x2rJAMhMeP\n+65FCgCAvwV6G4PmnK9+mHLJykxSyeHvtGlvmSYM7lXnmPPX7pBk18xrrA6LA3t3lqSaUGh2BA8B\nEECkKPsuwgMhHUIAQCgJ9Fy21pwvPs6hhA5ttaZgu1Zs2qeumdVzCCucVZqbs1WSFOuI0dRhyXW6\nca4Ayh5+AOB/dAh9OIkSAAB/C/RctqbO15ztHlwfXeFy+qhUTR/VX64hpvW7cS1ZvRQA4D3btukQ\n0iEEgPAUrt2lQA9ldD+fp+e0qQ6i+/3dw2FjK5G6sJALAPjX0cqjio6KVpV8k4mifHKUAKs6TiAE\ngHDkCirZeUWmSwkqpeVOzVtVqNJy71eV8/ScZmUmNXu7B1c4rB/Q56/dodnLNtfMK2z69gic1vy8\nAAh+Zc4ydWrbyWfHC8kOIUNGAUSiUOqetbRWukueeTsn0P359/Sc+qZjadd+rF5wpnqrjKa2noD/\nsf8iEN7KvitTR0dH7dd+nxwvJAMhQ0YBRKJQushzr9U1D6054ZBVIj3zNijX/1lxf0599cbC1GF9\nFeuIqX195+YUSJJiHdFNvoah9OZGKOKNFSC8lTnL1LFtR58dLzQDIUNGAUSgULrI87QoSYWzsjZA\nRGIIaE0Iio9zeBWsG/tZ8dUbC/XnGVY4KyVZzVrIhp8J/+KNFSC8uTqEvhKSgZAhowAikT8u8poT\nUloSZNwDzOiMHpKkCmdVyHQ4/aG1Qcyb+zf2s+IKbKMzemjeqkKfhLHqPQ3TGvx+ablTdy3coNz8\n4jo1NPQzQQcRABpGh1DVQ0Zt25ZlWaZLAYCQ1pyQ0dIgU/9+peXOOvvWRZrWdnh90SH21KWT/B/Q\ns/OKlJtfrIvSEjU6o0dtDZI8/kx4qo2QCADV6BBKsmTpaOVRtW/T3nQpABDSGgsZroVCjhw7rumj\nUr0OIvWPHenD2Fr7+H3x/NWf2+n+0Z/czzV/7XbNzSlQhbNSM8akeXxMnmprKMASFAFEmjKnbwNh\nSG47EW1F69vvvjVdBgCEvMa2CHAtFPLk6m2KdcS0aN4b2w/4ji+2EnDfbiKQr0/dc7lG93w/yqf+\nY3MPv66vN7RVRlNblbAFQ2jj9QNOVPadb4eMhmQgjIqKUpmzzHQZABDWRmf00PDUBN18fr8TLsJ9\ncZHmjwu9cLt4dH88Ld2j0f0YnoJWoLjqmDC4l2aOS9fUYcm1X5u/drvHx+b+mBuqPSszSdNHparC\nWeXx8bC3ZWjj9QNORIdQ1R3Csu8IhADgK64L88Liw7UX2ys27dOaghIldHCc0EVqyUVa/bDmjwu9\ncLt4dH88jW0m7/7cNud5NvE8uc65YtO+2m7h98NALY+PzfWYXQvgeArG8XEOxTpiNDdnq8fH09jz\nhuDH69e0cHsjDE3zdYcwJOcQRll0CAHAl1wX2eu2ldRZCbLCWaUKZ2Vtd8mlse81dQ6peg6YP+aw\nhdLWHM3h/ngam0Po/txKavJ59tXz1ND8PU9fb6oOTz9D8XEODUmOV9Y/16q0/FizjuPpGJE8dzXU\n8fo1LZT2qIVvlDnL1K9rP58dLyQDYXQUHUIA8CX3rQiG9ttXe4Ee64iuvdBw3y/O/XtHjh3X1n1l\nmjU+QymJHWqP6VqURrI0dViyRmf00LptJRqd0cMvC4F4OmaoLzjS3IvhxkKSp2P46iK7oQtRT19v\naR33LPpUpeXHFB/XpsHuaCi/xkBrhdsbYWga206oZsgoHUIAjeAisWW6xta9QG9svzjX91ZvKdZ7\nhSX6qjRP2bcOq32+XYvSSNVbC0hSbn6xhvbbJ0lev6Pd1GvqKYREyjvn9YNVoB5rQxeivrxA/csV\np+ueRZ/qL1ecrvg4h+atKqzzmkbKaww0hC5q5GHbCdUMGaVDCKARXCR6p6Hny3Wh4WkPQdfm8yXl\nTu0+eESFxeW6a+EGzZk8uPZ7Fc5KSZbHzpXk3eboTb2m/hwaCc8auhD15QXqmad0Vc5dF9b+vf5r\nymsMINLQIZQUExWjg0cPmi4DQBDjItE7TT1fDV3gZ+cV6cnV2zR9VH9t3HVQufnFys4rql00ZMaY\ntDq3r9/Fqt/t8XWNvHNeLZw65vVfU15jAJHm4NGD6ty2s8+OF5KBsE1UG+09vNd0GQCCGBeJ3vH2\n+XIFjNEZPSR9H9JcoaO5vAnuvKYtR8e8Yd6E5XAK1gBC196yverZsafPjheagTC6jfaU7TFdBgBE\nLFfAqHBW1c4PbAlCnm80FVTomDfMm7BMsAZg2rGqYyo5UqLucd19dkwCIQDAa98vNlPZ4HYHgRCO\nHZuWPKamggrBu2HehGWCNQDT9pXvU/e47oqJ8l2MC8lA6IhyEAgBwKC6i83ENLhoTH2+DnDh2LFp\nyWMKhaASrOHdm7BMsAZg2p6yPerVsZdPjxmSgbBNdBsVlhXKtm1ZlmW6HACIWN5ud+DrABcKQchb\nLXlMoRBUwjG8A0CgEQhrRFlRahPdRoe+O6Qu7bqYLgcA0EzhGOB8LRTCXUvw2gNA6+0p26NeHXwb\nCKN8erQA6tWxF8NGASDEuMKOr4YMurpO2XlFPjleOCotd2reqkKVljuN1uHr1x4AIpE/OoQEQgAh\nKVgucgMtUh93Q7IykzRzXDpdp0a4QvNdCzfwcwMAIY5A6IZACES2SO0MRerjbghdp8aVljtV4azS\neSkJys0v5ucmyPAGDwBvMYfQTa8OBEIgkkXqfKRIfdxomey8Is3N2arpo/rrggGJ/NwEGRbaAeAt\nAqGbXh17qaC0wHQZAAwJ14U3mhKpjxvec3UHp49K1dRhyXRRgxBv8ADw1u6y3QwZdenVsZf2HKZD\nCABoWiQOzXN1B2MdMYTBIBVpQ54j8d8h4EtHK4/qsPOwEmITfHrckA2EvTv1Zsgowgr/UQL+E4lz\nL5u74I7rd09h8WF+B8GvIvHfIeBLe8v2qmeHnoqyfBvhQnrIKIEQ4YS5JID/ROLQvOYOL3b97lm3\nrUS5+cWS+B0E/4jEf4eAL/lj/qAUwoGwZ4ee+vrw1zpuH/d5SgZM4D9KwH+8mXtZWu5Udl6RsjKT\nQnooX3MfR1ZmkiqcVTrirNKgPp3r/A4Kl+cCwYE50EDr+CsQhmySahvTVh0dHVVSUWK6FMAnIm0u\nCRCswmVYW0OPo/7w9Pg4h2Id0Xry3W0nzDcMl+cCAMIBHUIPXMNGE+MSTZcCAAgT4dKtb+hxeBqe\n3tBtw+W5CGd0cYHIQSD0wBUITz/pdNOlAADCRLgMa2vocXgKeQ3dNlyei3DG/HMgcuw5vEcDuw/0\n+XHDIhACABAolVW2vik7qtJyZ0h2ZAh54YUuLhA5mEPoQZ9OffTVoa9MlwGgBltnIBJ8U3ZUX5VW\nMK8OQYH550DkKDpUpD6d+vj8uCEdCPvH99eW0i2mywBQIxwWoCDUBq9geW26d2ynk+Nj6cgAAALG\nWeXUV4e+Ur+u/Xx+7JAOhGnd0rR5/2bTZQCo0dyNsINZOITacOV6be5auMFoKIyJttSrS3s6MgCA\ngCksLVRS5yQ5on3/f09IzyFMS0jTlpIt7EUIBIlwmJvEfJzglZWZVLt5enZeUcj/rAEA0Fz5JflK\nS0jzy7FDOhB2btdZndp20u5vdyupMxdvAFovHEJtqCotd2r+2u2SLE0dlnxCBy4+zqE5kwfXLrEP\nAECkyN+fr/Ru6X45dsi31dIS0pRfkm+6DABAK2XnFWluToHm5mw1PmQ3WOYrAgAg+bdDGB6BcD+B\nEABCXVZmkqaPStX0Uf3rdADdw1mg5ngylxQAEEw279+stG4MGfUovVs6C8sAQBiIj3NoxpgT/7Nz\nhbN120o0a3yGKpyVqnBW+XUfQOaSAgCCCR3CRqR1Y8goAISzrMwkDU9NUG5+sZZu2K1YR0yrh5U2\nNSTUfS4pQ0cBACbtr9iv4/ZxdY/r7pfjh34gZA4hADQplOfExcc5dNYp8ZKkj3Ye1OiMHpo+KlUl\n5U49vDzf68dUWu7UXQs3NDok1PV8/fOdAs1etrlmsRsAAAIvf391d9CyLL8cP+QDYXKXZH1T/o0q\njlWYLgUAglaoz4mbMLiXUhLjtKZgv1Zs2qdYR4yeXL1Nc3MKvH5M2XlFys0v1kVpiQ0OCXU9X5v2\nltV8xT//CQMA0BR/zh+UwmAOYXRUtFK6pmhryVadftLppssBgKAU6nPiVmzap8Li8toQd6DCqXe3\nFiujZ2evH5P7c9HQHETXbUZn9NCKTftOOEdlla15qwobPUY4cS3oE8qPNxweA4DI5M/5g1IYdAil\n6nmELCwDAA1zzYkLtgvh5g5lzcpM0sxx6ZozebDi4xxasWmf1hSUKKGDo9HH5On4zXkuXLdJSezg\n8bbflB0N6Y6rt0K9wyyFx2MAEJn8HQhDvkMoMY8QAEKV6yJdUu0iLp64L/IiNb/j2dzjN4erw1RZ\nZat7x3a6blx6yHZcvRXqHWYpPB4DmkYnuOV47oLX5v2b/bYpvRQmgTC9W7reKnzLdBkAAC+5X6SX\nljtrFm+xNHVYcrM6eN4cX/Lugsf9tpJ018INys0vlqPsqLp3bNechxc2mvt8B7NweAxomi/fBIo0\nPHfB6VjVMe08uFOp8al+O0dYBMK0hDQ9+sGjpssAALRCdl6R5uYUSJI27jpYOzy0NeqHAPc9DZs6\nvvvFkaTahWi2vteudsioxIUTEEzoBLccz11w2nZgm3p36q22MW39do7wCIQ1exHatu235VgBAK3j\nqTvnHrqyMpNU4azURzsPKDe/WNl5RY2GrZYMb8rKTNK6bSXKzS/WXQs3NBoKPV0cZWUm6fL5VsQN\nGQVCBZ3glguW546hq3X5e/6gFCaBsEu7LoptE6s9ZXvUu1Nv0+UAADyYv3aH5uZsVYWzSjPGDJB0\n4oqfM8aknTBUsyEtGd50oMKpY1XHdU7f+CZDZ/2Lo2C4UAKAcMfQ1bo2799MIGyu9G7pyi/JJxAC\nQNCy6330/I50S+cHNqW03KmbF+SpsLhcw1MTNLMVHT6GjAKAfzB0ta78/fk6u/fZfj1HWGw7IdWs\nNLqflUYBIFhNHdZXM8ela+qwvpKav+WE63aFxYfr3N7brTSy84pUWFyulMQ4/W7iaV5vw+Gqw7XK\naGsCJQDAs2DdJsmU/JJ8v25KL4VRh3Bg4kB99s1npssAADSgfufP0xDS+krLnbWre7rm/kkt68qN\nzuihddtKNGt8hlISO9Q5R3Pmq7iGMTnKjqpXl/Z0BgEAfmXbtr4o/kIZiRl+PU/YdAgze2Uqb0+e\n6TIAAM1WPXT0iLOqwU5hdl5R7eqes8ZntKort2LTPuXmF2vFpn0nnKM5G5ZnZSZp5rh0dY1zaM/B\nI2njrfkAACAASURBVE12NgEAaI2C0gJ1attJ3eO6+/U8YdMhHHzSYH1R/IWcVU45omkxA0Cwmzqs\nr2IdMapwVtaZj+fesau/6EzKiA6NHbJRDc1Lae58FVeHc065U1+VVjS5CioAAK2xfs96ZfbK9Pt5\nwiYQxjnilNI1RZ/t+0xn9TrLdDkAEPGa2mjeFbBKy52KdcTUBrL5a7drbk6BKpyVmjEmzWehq6HF\narxdat21KT3zBwEA/pS3J09Deg3x+3nCZsioVD1sdP2e9abLAADo+43m5+ZsbXQ45okLCFj1PgaX\nmGhLvbq0Z8EDAIBf0SFsgSG9hjCPEACChGujecnyqps2dViyYh3RGp3RQ/NWFbI5MQAg4lQdr9KG\nrzcEJBDSIQQA+IVro/kZYwZ4FehcHcMVm/Y1a7GX1mju1hfuKqtsFpUBAPjVl/u/VM8OPdWlXRe/\nnyusAuGgHoO0tWSrKo5VmC4FANBKrlU9/TlXr7krjLr7puxo7aIyAAD4Q96evIB0B6UwGzLaNqat\nMhIztOHrDRqWNMx0OQCAVvB2sZeWaO4Ko+5YVAYA4G/rd68PyIIyUph1CCXmEQJAqGjJcE1fO3FB\nm6axqAwAwN/y9gauQxh2gZB5hAAQGpoarhkMgREAgEBzVjn1+Tef64yeZwTkfGE1ZFSShvQeor++\n/1fTZQAAmtDQcE3XxvQVzirNzdkqSUY3gHfVw2qnAIBA+Pybz9W3S191cHQIyPnCrkOYkZihokNF\n+va7b02XAgBoREPDNV2dw7wdpZo+qr/xuXotWXgGAICWWr97vYb0Dsz8QSkMA2FMVIxOP+l0fbTn\nI9OlAABaICszSRelJeq9whJJtrLzirweNurL4aaBWO0UAACX9XsCt6CMFIaBUJIye2aysAwAhKj4\nOIfmTB6smePSJVkt6s75qqvHcFEAQKAFcssJKQznEErV8wiX5i81XQYAwAv1w9ctI1JUWu5UrCPa\n6+5cS7aT8MQVLKXmzWMkQAJoDL8j0JSKYxXaUrJFp/c4PWDnDM9A2GuI7s+933QZABAWAnUB4wpf\nFc6q2hDY0r0IW7uHoesxj87oIan5wdLbAAkgsvA7Ak359OtPdWriqWob0zZg5wzLQNg/ob9KjpRo\nf8V+dYvtZrocAAhpgbqAcYWuCmdl7fmyMpOMvJve0sfsq84kgPDE7wg0JdDzB6UwnUMYZUXprJ5n\nsbAMAPhAoBZViY9z1JzD0vRRqbVh0MQKny19zC3Z6B5A5AiW3xHs8xq88vbkEQh9JbNXpj7c/aHp\nMgAg5Pn7Asb9wiQ7r0hzc7Yq1hFTGxBNrPDpOndLVjgFgGDHdjrBa/2e9QFdUEYK0yGjkjT85OF6\n7MPHTJcBAPDAfV6i68Jk3bYSzRqfIen74VStnQvY0trmr92uj3Ye1JqC/ZKY6wMgvDB0NTjtO7xP\ne8v2amD3gQE9b9gGwhGnjNA1r16j7yq/C+ikTABA09zn6GVlJmndthLl5hdraL99xsJXdRDcobwd\npTV7IEoXpSVqdEYPzVtVyKqAAMKGiTfb0LTcHbkakTxCMVGBjWhhGwg7t+usjMQMrdu1TiOSR5gu\nBwDgxvWu9OiMHsrOK9Ks8Rka2m+f0XerXcNVJem8lARlJsdr6rBkzV+7Q3NztqrCWaUZYwYYqw8A\nEN5Wbl+pUX1HBfy8YRsIJWlk8kit3L6SQAgAQcb17vTDy7dobs5Wvbu1WI9efaZXHThfb4eRlZmk\nCmeVJFtTh/V1O6Zd7yMAAL63cvtK/eKcXwT8vGG7qIwkjew7Ujnbc0yXAQCoUX9luyPOKknSmoIS\nzV+73atjNbUogrer6MXHOTRjzADNGJNWJ2BOHdZXM8ela+qwvo3en1X7AAAttfPgTpU5yzQwMbDz\nB6UwD4TnnXyeNny9QYedh02XAgDQiSGuvcP9vyHL430aClpNrUDqq1X0mrvK6vy1OzR72WbNX7uj\nydoBAHCXuyNXFyVfJMvy/H+hP4X1kNHYNrE6q9dZWvPVGo1NHWu6HACIeK5hmRXOSpWWO926bpam\nDkv2eJ+GNolvalGElq6i5xqKOjqjh1Zs2ufFkNQTh5a2dIN7AEBkWbl9pUb2HWnk3GEdCKXv5xES\nCAHAvPg4h2Id0Zq9bLNiHTG6ZUSKZoxJa/Q+9YNdc+cOeruKnuu4Fc5Kzc0pqF35VGpemJs6rK9i\nHTF1AihLuwMAmmLbtlZuX6n7R9xv5PzhHwj7jtSMt2aYLgMAIpprSwfJ1oTBvSU1PyTVD3b+6rq5\njjt9VH/NHJeu0Rk9alc+dQ+hza2zoa8BAOBua+lWRVlRSulq5v+LVgVCy7LiJb0sKVnSDkmTbds+\nUO82gyX9Q1InSVWS/mDb9ss133tG0ghJh2pufr1t2xtaU1N95/Q5R/kl+Tpw5IC6tu/qy0MDAJrJ\nfUsHV2ewpfzVdasezlpZ+3l8nEMpIzpIkuatKqwNoQAA+JJruKiJ+YNS6xeV+X+Scmzb7i8pp+bv\n9VVImmLb9kBJYyU9YllWF7fv32Pb9uCaPz4Ng5LkiHZoWNIwrdq5yteHBgA0oP5iKlmZSZo+qr+m\nj0qtE+S8WXTFddsDFf5ZoKV6OGuM5uZsPWEhmqYWsKlfI4vIAACay+T8Qan1Q0YnSrqw5vP5kt6R\ndK/7DWzb3uL2+R7Lsr6RlCjpYCvP3WyueYST0icF6pQAENHqD+t0benQ1O2khucIum7r7dw+b3jq\nPjZnzqJrSGzejlK9V1jil9oQOXy9xyaA4HXcPq7cHbmac/EcYzW0NhD2sG17ryTZtr3Xsqzujd3Y\nsqyzJTkkFbp9+Q+WZd2vmg6jbdvfNXDfmyXdLEknn3yyV0WO6jdK1y++3qv7AECk8dVFaGm5UxXO\nKo/dwPrH9xTAGpoj6LqN+9w+X9def85fablTdy3c0GQAdR8S2zW2jYYkx3NRjxZjdVogcnz+zefq\n2q6rkjqbW3ysyUBoWdYKSSd5+NavvDmRZVk9JT0raapt28drvjxT0teqDolPqrq7+HtP97dt+8ma\n2ygzM9P2dJuGnHHSGdpdtlv7Du9Tjw49vLkrAEQMX12EusLRzHHpHjt87sf3tOhK/a0pXPfNykyq\nva1rbp+va/f0WHLzi3VRWmKjw0WzMpP07tZirSko0YGKY3ps5VYN7ZfART1ahNVpgchherio1IxA\naNv26Ia+Z1nWPsuyetZ0B3tK+qaB23WS9LqkWbZtr3M79t6aT7+zLOtpSXd7VX0zRUdFa8QpI5S7\nI1dXnXaVP04BACGlud26lnAtzlLhrFJpubNZx69fj/vWFJIaDVaeOpK+6s6519zYcNHsvCL9buJp\nevnDIm3ae0jTRvbXqi3Fmj4qVaMzeujh5fly7bVItxBNYXVaIHKs3L5S1w661mgNrV1UZqmkqTWf\nT5W0pP4NLMtySPqvpAW2bWfX+17Pmo+WpEmSPm9lPQ0a2bd6HiEA4PuOmvviKa6L0NYGlur7W5qb\ns1X/XFXocZGV+ouvuNdTP+A1taCLqyMZ64iprd3T42vpY2nqOXGda8Wmfbr1whSd3z9Rq7YU19a0\nYtM+zc0p8LhYDcIDiwkBaInK45VavXO1Lky+0GgdrZ1D+JCkhZZl3STpK0lZkmRZVqakW23b/qmk\nyZIukJRgWdb1NfdzbS/xvGVZiZIsSRsk3drKeho0su9IPfrBo/46PACEFP8PSase2f/2F19rR0mF\nKpxV2rjrYO1cPKm661fhrFKsI1qjM3rU1uNpyGn9bklh8WE9+NomzRqfodEZPbR6S7FKyp21HclA\nDrlzP9f3exmm1gmx1dtZWAwBDFPM+QPQEh/v/Vgndz5Z3eMaXYbF71oVCG3bLpE0ysPX8yT9tObz\n5yQ918D9AzZgdmDiQJU5y7Tz4E6d0uWUQJ0WAIKSv4ekTR3WVxt3HVJufrGSE2K1bluJPtheqovS\nEjU6o4eWbtit6aP6S7JPuJBuTph78LVNNeFykwb16aL3Ckv0XmGJEtw6eoG6MHedq7qzWanpo/rX\nGRpaWu5UrCNGozN6aP7aHZJsTR3Wl6GjYYQ5fwBaImdbjvH5g1Lrh4yGDMuydFHyRQwbBYAAiI9z\naM7kwbooLVE7Sipqw+Cs8Rm6f/HnmptTIFcwcnXSXMPuJDU5THPW+Iza47m6kcNTE4xekFd3NgsU\n64iuU/v8tds1e9lm/WbJ55qbs1VzcwoYOhpmfDXcGkBkWbnD/IIyUuuHjIaUi1Mu1rKCZbrhjBtM\nlwIAEWFQny5K6tpe2/ZXaNb4DC3dsKd2n74jzuN1OnnzVhU2e9hd11iHhvZLqPmbpemjUjV1WN/a\n45jY6qHhLpElSTpWZevmC/qpfZsoOkkAEOHKvivTB7s+0KKsRaZLiZwOoST9aMCP9Hbh2zpaedR0\nKQAQ8ppaSMM1F7DowBGtKdivB1/bpL0Hj9R+/9NdB1RYfLj2GE0tHlP/2LOXbdaDr22qs6CMrxaT\naYn6XSLX8zNiQKJSEuP0wfZSJcQ5NGNMGp0kAIhwbxW+pWFJw9S5XWfTpURWhzAxLlE/6PEDrdy+\nUpf2v9R0OQAQ0hpbSMN9pdAJg3tLqp7z16V9m9rbfLD9gNtcwOpj1N8Uvv7WEa6vpfXoqJTEOE09\nN1mD+nRRyeHv9PDyLZowuJek4JjL5Xp+UhLjVFhc3uRehgCAyLF482JNSp9kugxJERYIJWlS2iQt\n2byEQAgAreQKN6MzetQZplla7tRdCzcoN79YM8elKyWxg+ZMHlz7tVPiYzUiLVFdY9towuDeGtpv\nn7Iyk1RYfFi/WfK5Mnp21q0XpngMnK6vJSfEakdJhZ5as11nndJFT767XZIU64gOmlUeszKTtG5b\niXLzizU8tZv6d++o+Wu3s6AMAES4Y1XHtKxgmf485s+mS5EUgYFwYvpEDf/PcP1j/D8UZUXUiFkA\n8CnXEEn3uX9ZmUm1wc+9IxYf56hZAKZ6q4iUxA61x0kZUf35XQs3aE1BidYUlCihg+etI1yf7z5Q\noR0lXymjZ0e55ugNT+0WVB0418I689fu0Lpt+/Xku9skSbGOmKAJrQCAwFu9c7VS41PVq2Mv06VI\nisBAmBqfqsS4RH2w6wOdm3Su6XIAIOTV34evuiOWoEF96s6LWLFpn3LzizWoz27FOmJOWPhl1vgM\nHauq7hA2FOzct3jo3TVWWZlJOlDh1MZdBzVrfEbQdd7i4xyKdUTrg+0HJEnnpZhdCRUAYN7izYs1\nKS04hotKERgIpepho4s3LyYQAoAPuK8U+v1G7FW1i714+p6nuYcpiR303E+H1v7d1Xl037x+xaZ9\ntR9dgdIVQof221fbbTTNff5jVmZS7cb07vsTAgAij23bWpy/WG9f+7bpUmpFZiBMn6SrX7laD41+\nSJZlmS4HAMKGewdPkkoOf6c/vvGl2reJ0tRhfWu/F+uIrt17cP7a7XKFJUl1gpQkVTgrNXvZ5tr5\neK6PUnWgDMZNwevPf5wxJk3S9yuPmtgWAwBg3sd7P1Zsm1ild0s3XUqtiAyEZ/Y8U0cqj2jz/s06\nNfFU0+UAQEjztBqoa6jk3Jyttbd75aPd6t21ve4d+/1/gq7N3KvZ2rjr0AmrjlYHyBiNzuihof32\n1fn48PItOuKsUnuH+TnhrudhdEYPVTgrNX1U/xNCamMrswIAwp9ruGgwNaUiMhBalqWJaRO1ePNi\nAiEAtJIr5NQf2jkkOV7DUxPUL7GDVm8p1o6SCu06eET3LPpUhcXlqnBWSZJuPr+v2juq/ztyLUbj\nWrl0SHK8Hlu5VbPGZ6hrbHXY7Bpb3YV8ePmWOoHT9GItrufB1cGcOS79hC5gMHYzAQCBszh/sZ4c\n/6TpMuqIyEAoVQ8b/dXKX2nm+TNNlwIAIa3+0M53txZrTUGJhqcmaE1Bic7vn6h/Xz9EM1/ZKMuy\ndO/YdK3fUaoKZ6Xm5hToorREzZk82O2ItpZu2KO5OVtr9/A7VvW52kRH1Q4ZnTU+Q3k7SiVJ5/SN\n19B+8cZDlvs2HK6tNOpzn28JAIgshaWF2l+xX+f0Ocd0KXVEbCAcccoIbS3Zqj1le4JmyVcACGUj\nBnTXxl2HlBQfqzUFJTpWdbx22GR8nEP/vC5T2XlFSu4WpzNP6arScqc+2nlQufnF+sWLn+jRq89Q\nrCNas5dt1vRRqZo5Lr22Q9i/e0c9+e42pSTG1Qwp3aT3Cktqw2QwzMdzD3vui9t4GlILAIg8S/KX\naMKACUG39V3EBsI20W10af9LtTR/qW7NvNV0OQAQslxDJS9KS1RufrGmj0qt/TzW8f1/M/PXbtfc\nnAJVOCs1Y0ya4uMcyujZUWsK9mtNwf7a0CSpTnh6+oazVVruVEIHh0Zn9NDSDXt0xFmppK6x2ra/\nXAcqnEEdtJg3CACQqucPzhwefKMTIzYQStXDRp/6+CkCIQC0QkNDJae98LFy84s1f+12TR3WV+u2\nVQ/xfHfrfrlWFXXNHXRtKh8f56jdz7D+IjXfhylbT767XckJsdpRUqEHX9ukp284O6CP2RvMGwQA\nfFP+jTbu26iRfUeaLuUEER0IL0m5RDcuuVGHjh5S53adm74DAKBBXWPrhrnM5Hi9V1iiddtK9X5h\niT7cUb05+8dfHdTHXx2s3UzetQWFK/w13VGrXpntgv6J6tutQrPGZwTk8bUU8wYBAK9teU0Xp1ys\ntjFtTZdygogOhB3bdtT5p5yvZQXLdNVpV5kuBwBCknuAk1T7+dRhydq462DtNhKSdE7frjq9T1dt\n2vtt7Wby9TuCjXXUqvc3tDV9VH82eQcAhIzFmxcHbd6I6EAoSZPSJgX1CwQAwa5+gKtwVqnk8Hea\nv3aHpo3sr2NVx9WvWwd1jXPUhjj3hVbqdwQb66i59i30tKUDAADB6LDzsN7Z8Y4WXLbAdCkeRXwg\n/FHaj3TP8nv0XeV3QdnCBYBgd2KAq57jJ0kbdx2s3XrC/Tbu9/Fmjl1WZpIqnFWqcFaqtDy4F5MB\nAECS3i58W0P7DFWXdl1Ml+JRcK15asBJHU7SwO4Dlbsj13QpABBUSsudmreqsGaYpjeq5/gNT+2m\nWeMzNHNceu1G8/WP1dCWDK5zFxYfrnO/+DiHYh3RmptToLsWbmhBbQAABNbizYs1KX2S6TIaFPEd\nQkn68ak/1stfvKyxqWNNlwIAQaOxxV0a21tv6rDkOgvFpIzooHmrCjV72WZVOCt15NhxfbH7kH4/\n6TSt2LSv9uuxjpja+7jOvW5biXLzi1XhrKo9ZlZmUu3Xs/OKWLAFABC0jhw7ote2vKbZo2abLqVB\nBEJJV592tR544gE9Pu5xxTniTJcDAEGhsaGcjYXF+kNIS8udqnBWavqo/pKkJ1dvkyQ9+NomzZk8\nWFL1vEP349XfyqLCWVnn+3MmD66zbyEAAMFoaf5SZfbKVO9OvU2X0iACoaSeHXtqaJ+hWrx5sa4Z\ndI3pcgAgKLiCnWv4pns30Jt5f+4LwWRlJunIsSp9sfuQpo3sXyfUuTqA7ueWpJQRHVRa7qztINb/\nPgAAwWrBxgWacvoU02U0KuLnELpMPX2qFmwMzpV/AMAfmjtH0NUNzM4rqv2aK5A1tahLdXewStNH\npdYGyvsuPVXP/2yo1u8orT1uU8dr7vkAAAgWXx/+WmuL1uqy9MtMl9IoAmGNiWkTtX73eu3+drfp\nUgAgIDwFPU+yMpNqu3stOcfcnK2KdcScEOayMpM0fVT/2hVDAQAIJy9+9qImpU8K+ilpDBmt0b5N\ne11+6uV64bMXdM9595guBwD8rrnDPlszPLOxc7hWDJ29bLNiHTEMAQUAhJUFGxfobxf/zXQZTaJD\n6GbK6VM0/9P5sm3bdCkA4HeBGIbZ1Dma231s+RYYwSncHg8AoK6N+zaqpKJEI5JHmC6lSQRCN8NP\nHq7yY+Xa8PUG06UAQERobiht7vDWUNHY4yEsAkDoe/bTZ3XdoOsUZQV/3GLIqJsoK0rXDbpOCz5d\noDN6nmG6HADwu8b2Ewwm3qxqGgpauqUHACD4VR6v1POfPa+VU1eaLqVZgj+yBth1g67TC5+/oGNV\nx0yXAgB+Fyqdt3BbZbSxx9OaRXwAAOat2LZCSZ2TlN4t3XQpzUIgrKd/Qn+ldE3R24Vvmy4FAPyO\n8OF7rR3yGW7hFwAizYJPF2jKoODee9AdgdAD9iQEECkIH03zNuCFStcVAOB73373rd7Y+oauOu0q\n06U0G3MIPZg8cLLuXXGvDh49qC7tupguBwBgkLdz+sJtviMAoPkWbVqkkX1HKiE2wXQpzUaH0IOu\n7btqTMoYLfxioelSAACGeTuslq4rAESuBZ8u0JTTQ2e4qEQgbNCUQVO04FOGjQKAO5NbIpSWO/Xw\n8nw9vHxLQM9PwAMANMeOgzv0RfEXurT/paZL8QqBsAFjU8dqS8kWFZYWmi4FAIKGyflx2XlFmptT\noLk5W5mfBwAIOs9++qyuHHilHNGh9QYicwgb0Ca6ja4+7Wo9u/FZ/fbC35ouBwCCgsn5cVmZSapw\nVkqymJ8HAAgqtm1rwcYFev7y502X4jU6hI2Ycnr1sFHbtk2XAgBBweTwyfg4h2aMSdOMMQMYvgkA\nCCrrdq1TtBWtIb2GmC7FawTCRpzZ80y1b9Nea75aY7oUAAAAAEFq/qfzNeX0KbIsy3QpXiMQNsKy\nLP3szJ/pH3n/MF0KAAAAEDJMLkIWaN9+960WfrFQU0+farqUFiEQNuGGwTfozYI3tbdsr+lSAAAA\ngJBgchGyQHtmwzMakzJGvTv1Nl1KixAIm9C5XWddddpVmvfRPNOlAECjIundWABAcPN2D9dQddw+\nrsc/fFzTzp5mupQWIxA2wx1D7tC8j+bJWcVFFoDgFUnvxgIAgluk7OH6duHbim0Tq/OSzjNdSoux\n7UQzDOw+UBmJGXpl0yu6+gdXmy4HADwyuSUEAACRyNUdDMXFZFzoEDbTtLOn6bEPHzNdBgA0yNfv\nxjIEFQCAhhWWFuqD3R/oJz/4ielSWoVA2Ew/GvAj7Snbo4/2fGS6FAAIiEANQSV4AgBC0d/X/103\nDr5R7du0N11KqxAImyk6Klq3D7mdLiGAiBGoBQHcgyfhEAAQCg47D2v+p/N1+5DbTZfSaswh9MJN\nZ9yk1MdSVVxerMS4RNPlABGvtNyp7LwiZWUmhf2kdRNcQ1D9zX3uoyscSgrIuQEAaInnNj6nC065\nQKd0OcV0Ka1Gh9ALCbEJujz9cv3r43+ZLgWAWFUzXLjPfYyUZcoBAKHLtm09/uHj+vmQn5suxScI\nhF6ads40/SPvH6o8Xmm6FCDiER78w+SwzUhZphyAZwwbRyjI3ZErW7ZG9h1puhSfIBB6afBJg5Xc\nJVmLNy82XQoQ8QgP/kHnFYAp/P7xDYK1fz324WP6+ZCfh/RWE+6YQ9gC086epsc/fFxXZFxhuhQA\n8Dn2MwRgCr9/fIP52P6z8+BOrd65Ws9e9qzpUnyGDmELXJZ+mQpKC7Rx30bTpQCAz9F5BWAKv398\ngykV/vOPvH9oyqAp6uDoYLoUnyEQtkCb6Da6NfNWPf7h46ZLAYAGhcKQoebU6KvbAECkIFj7x5Fj\nR/SfT/6jO86+w3QpPkUgbKGbz7pZ2ZuyVXqk1HQpAOBRKMzFaU6NvroNAACt8eLnL2pI7yFKjU81\nXYpPMYewhbrHddf4AeP1n0/+o7uH3W26HAA4QSjMxWlOjb66TSCxRyYAhBfbtvXYh4/pjyP/aLoU\nn6ND2ArTzp6mv6//u6qOV5kuBQBO4D5kKFiHVDZnWJOvbhNIdCwBILy8V/Seyp3luiT1EtOl+ByB\nsBXO7n22Tupwkl798lXTpQBAowgogcWCDgAQXua8P0fTzp6mKCv84lP4PaIAu2/4ffrDu3+Qbdum\nSwGABnkbUIK1oxgqgq1jCQBouc/2fab3i97XTWfeZLoUvyAQttL4AeMlSa9vfd1wJQDQMG8DSmMd\nRcIiACCS/HHNH3XnuXcqtk2s6VL8gkDYSpZladYFs/TA6gfoEgIIG411FBl+CgCIFPn785WzLUe3\nZd5muhS/IRD6wOWnXq6y78q0YtsK06UAgE801lFkfhwAIFLMXjNb086epo5tO5ouxW8IhD4QZUXp\nvvPv04PvPmi6FADwu/phkSGkAIBwtP3Adv1vy/807ZxppkvxKwKhj1x12lXa9e0urd652nQpANBq\n3oQ8hpACAMLRn977k24961Z1adfFdCl+xcb0PhITFaOZw2fqD+/+QReccoHpcgCgVVwhT5JuGZHS\n6G09bQrPxuwAgFC269tdWvjFQuX/PN90KX5Hh9CHppw+RV8Wf6kPd39ouhQAQSJUh1N6M0/Q03xD\nuoYAgFD217V/1Y1n3KjEuETTpfgdHUIfckQ7dO959+rB1Q9q6dVLTZcDIAh402kLJq6Q11KeuoYA\nAISCfYf3acGnC/TF7V+YLiUg6BD62I1n3Ki8PXna8PUG06UACAKRuiInG7MDAELV397/m37yg5+o\nZ8eepksJCAKhj7Vv0153nXuX/vjuH02XAiAIRFIwCtXhsQAAuJRUlOipT57SL8/7pelSAoZA6Ae3\nZt6qVTtX6cviL02XAgABw7xBQjEAhLpHP3hUl6dfrpM7n2y6lIAhEPpBnCNO08+ZrtlrZpsuBUAI\nC7VwEanDY90RigEgdB06ekh/X/93/b/h/890KQHFojJ+cseQO5TyaIoKSwuVEh86C0kACB6htiBN\naxeiCQcspgMAoevv6/+ucf3HRdy1O4HQTzq366zbh9yuh9Y8pH9N+JfpcgCEIMJF6CEUA0BoKneW\n65F1j+id698xXUrAMWTUj6afM12vbn5VXx36ynQpAEJQuC9IE2pDYgFEJn5XRYZ5H83TiOQRykjM\nMF1KwBEI/SghNkE/PeOn+st7fzFdCgAEHebbAQgF/K4Kf0crj+qva/+qWefPMl2KEQwZ9bM7zsab\n+AAAIABJREFUz71TGU9k6K5hdym5S7LpcgBApeVOZecVKSszyWj3kSGxAEIBv6vC3xPrn9CQ3kN0\n+kmnmy7FCDqEftajQw/9fMjP9evcX5suBQAkBc+73eE+JBZAeOB3VXg7cOSAZq+ZrT+OjNw9xOkQ\nBsDdw+7WgMcH6JO9n+iMnmeYLgdAhOPdbgAAqs1eM1uT0iZpYPeBpksxhg5hAHRs21G/GfEb3bP8\nHtm2bbocABGOd7sBAJB2Htypf3/yb/3uot+ZLsUoAmGA3HTGTdr17S69VfiW6VIAAACAiDcrd5bu\nGHKHenXsZboUowiEAdImuo0eGv2Qfrn8l6o6XmW6HAA4AUurAwAixSd7P9HywuW6Z9g9pksxjkAY\nQBPTJqpT205a8OkC06UAwAmCZbEZAAD8ybZt3bP8Ht0/4n51bNvRdDnGEQgDyLIs/fXiv+rXub9W\nxbEK0+UACFH+6uRlZSZp5rh0FpsBAIS1twvfVtG3RfrZmT8zXUpQIBAG2NA+Q3Vu0rmau26u6VIA\nhCh/dfJYbAYAEO6qjlfpnuX36KFRD6lNdBvT5QQFAqEBfxz5R815f46Ky4tNlwIgBNHJAxDsmJOM\nYPXsxmfVsW1HTUqfZLqUoEEgNKB/Qn9dfdrVemD1A6ZLARCC/NHJc128FRYf5iIOQKsxJxnB6Mix\nI/p17q/1lzF/kWVZpssJGmxMb8j9I+7XqX8/Vb845xdKjU81XQ6AMFFa7lR2XpGyMpO8Coyui7d1\n20qUm189euGWESn+KhNAmHONYGAkA4LJ3A/m6pze52hY0jDTpQQVAqEhiXGJuvPcO3Vfzn1amLXQ\ndDkAwoQr2EneBTrXRdvojB4a2m8fF3EAWsU1kgEIFsXlxfrr2r9q7U1rTZcSdAiEBv3f0P9T2uNp\nWrdrnYb2GWq6HABhoKXvyrtfvKWM6ODzugAAMOnB1Q/qqtOu0oCEAaZLCTrMITQotk2sfn/h73XP\n8ntk27bpcgC0QLAtnMBKoQAA1FVQWqDnP3te94+433QpQYlAaNiU06fo4NGDWpq/1HQpAFqAhRMA\nAAhuv1r5K80YOkPd47qbLiUoMWTUsOioaP159J81460Z+uGAHyomipcECCUsnAAAQPD6YNcHeu+r\n9/T0xKdNlxK06BAGgbGpY9W7U289+dGTpksB4CWGaAIAEJxs29bdy+/W7y78nWLbxJouJ2i1KhBa\nlhVvWdZyy7K21nzs2sDtqizL2lDzZ6nb1/talvVBzf1ftiwrIq+oLMvSw5c8rN++81t9U/6N6XIA\nwLhgm5sJAAg9z258VkeOHdH1g683XUpQa22H8P9JyrFtu7+knJq/e3LEtu3BNX8muH39T5Ierrn/\nAUk3tbKekDWoxyBNOX2K7ll+j+lSQhIXj0B4YW4mAKA1So+U6t4V9+qf4/+p6Kho0+UEtdYGwomS\n5td8Pl/SpObe0bIsS9JISYtacv9w9NsLf6vc7blatWOV6VJCDhePQHjJykzSzHHpzM0EALTIfTn3\n6fL0y5XZK9N0KUGvtSuY9LBte68k2ba917KshpbuaWdZVp6kSkkP2ba9WFKCpIO2bVfW3GaXpN4N\nnciyrJsl3SxJJ598civLDk4dHB30yNhHdNvrt2nDrRvkiI7IEbQtwsIeQHhhU2sAQEut27VOS/OX\natMdm0yXEhKa7BBalrXCsqzPPfyZ6MV5TrZtO1PSTyQ9YllWiiTLw+0a3IzPtu0nbdvOtG07MzEx\n0YtTh5bL0i9T36599bf3/2a6lJDCwh4AAACoPF6pW1+7VX8Z8xd1adfFdDkhockOoW3boxv6nmVZ\n+yzL6lnTHewpyeOKKLZt76n5uM2yrHcknSHpFUldLMuKqekS9pG0pwWPIaxYlqXHxz2uIf8aoisH\nXqm+XfuaLgkAAAAICY9/+LgSYhP0kx/8xHQpIaO1cwiXSppa8/lUSUvq38CyrK6WZbWt+bybpPMk\nbbJt25aUK+mKxu4fifp27as7z71Tv3jzF6p+mgAAAAA0Zve3u/Xg6gf1xKVPqHq5EjRHawPhQ5LG\nWJa1VdKYmr/LsqxMy7KeqrnNqZLyLMv6VNUB8CHbtl0Deu+VdKdlWQWqnlP471bWEzbuHna3CkoL\ntCSfjAwAAAA0ZcZbM3Rb5m1K65ZmupSQ0qpFZWzbLpE0ysPX8yT9tObztZJ+0MD9t0k6uzU1hCtH\ntENPXPqErl9yvUb3G60Ojg6mSwIAAACC0psFb+qjvR9p/qT5Td8YdbS2Qwg/uqjvRbrglAv0+1W/\nN10KAAAAEJSOHDuiO964Q4+Pe1zt27Q3XU7IIRAGub+O+aue2fCMPtv3melSAMCj0nKn5q0qVGm5\nMySOCwAIL7PXzNYZJ52hcf3HmS4lJBEIg1yPDj30+4t+r9tev03H7eOmywGAE2TnFWn2ss3KzisK\nieMCAMJH/v58PbH+CT0y9hHTpYQsAmEIuPmsm3Xs+DE9s+EZ06UAQYHOUXDJykzSzHHpyspMConj\nRiL+zQAIR7Zt6/Y3btevzv+V+nTqY7qckEUgDAFRVpT++cN/ambOTJVUlJguB/C7pi5e6RwFl/g4\nh24ZkaL4OEeTt/UmmHhzXDSOfzMAwtGLn7+okooSTTtnmulSQlqrVhlF4JzR8wxdNfAq3bviXj01\n4amm7wCEMNfFqyTdMiLlhO+7OkZ0jkJPU68t/IN/MwDCzcGjB3X323frlcmvKCaKSNMaPHsh5IGR\nD+jUv5+q9756T+edfJ7pcgC/aeri1dU5QughmJjBvxkA4WbWylkaP2C8zk0613QpIY8hoyGkU9tO\neviSh3Xr67fKWcU8EIQvhgqGL15bAEBrrd+9Xos2LdJDox8yXUpYIBCGmKyMLCV3SdYDqx4wXQoA\nIEyw6AyAUHHk2BFNXTxVD1/ysOLbx5suJywQCEOMZVl6cvyTevLjJ/Xh7g9NlwMACAMsOgMgVMxa\nOUundT9NV512lelSwgZzCENQz4499di4xzTlv1P0yS2fqH2b9qZLAgCEMOZ2AggFq3as0ktfvKRP\nb/1UlmWZLids0CEMUZMHTtaZPc/UzJyZpksBAIQ45nYCCHZl35XphiU3aN74eeoW2810OWGFQBjC\nHr/0cS3atEgrt680XQoAAADgN3e+dacuSr5I4weMN11K2CEQhrD49vH614/+pRuX3KhDRw+ZLgcA\nAADwude3vK4V21fo4bEPmy4lLBEIQ9y4/uN0ScolmvHWDNOlBAQr4QEAAESOkooS3fzazXp64tPq\n1LaT6XLCEoEwDMy5ZI5W7Vyl/+X/z3QpfsdKeAAAAJHjjjfu0OSMybow+ULTpYQtVhkNAx0cHfTM\nxGd05aIrNbTPUCXGJZouyW9YCQ8AACAyvPT5S/p036d6euLTpksJa3QIw8T5p5yva35wjW57/TbZ\ntm26HL9hJTwECsOTAQAwZ0/ZHk1/c7oWTFrAFmt+RiAMIw+MfECb92/WC5+9YLoUIOQxPBkAADNs\n29ZPl/5Ut551q4b0HmK6nLDHkNEw0i6mnRZctkBjnxurC5MvVO9OvU2XBIQshicDAGDGUx8/pX3l\n+zTrglmmS4kIdAjDzJk9z9S0s6fpxqU3hvXQUcDfGJ4MAEDgbTuwTfetvE8LJi1Qm+g2psuJCATC\nMDTz/Jk6cOSA/pn3T9OlAAAAAM1SdbxK1y++Xveed68Gdh9oupyIQSAMQzFRMVpw2QL9OvfXKigt\nMF0OAAAA0KRH1j0iSZoxNDL21w4WBMIwld4tXb++4Ne6fvH1qjpeZbocAAAAoEFffPOFHnrvIT0z\n6RlFR0WbLieiEAjD2LRzpskR7dDsNbNNlwIAAAB4dLTyqK7977X6w8g/qF/XfqbLiTgEwjAWZUXp\n2cue1RPrn1Du9lzT5QAAAAAn+L83/0/94/vrZ2f+zHQpEYlAGOZ6d+qtBZct0LX/vVZ7y/aaLgcA\nAACo9fzG57Vy+0o9NeEpWZZlupyIRCCMAKP7jdbNZ96sq1+5WpXHK02XAwAAAGhT8Sb931v/p0WT\nF6lT206my4lYBMIIMeuCWWoT3Ua/yf2N6VIAAAAQ4cqd5crKztKfRv9Jg3oMMl1ORCMQRojoqGg9\nf/nzWrBxgZZtXWa6HAAAAEQo27Z12+u3aUivIbph8A2my4l4BMII0j2uu1788Yu6YckN+urQV6bL\nAdBKpeVOzVtVqNJyp+lSAABotqc+fkqffP2JnvjhE8wbDAIEwggz/OThuuvcu3TloivlrOIiEghl\n2XlFmr1ss7LzikyXAgBAs2z4eoPuW3mfsrOyFdsm1nQ5EIEwIt017C4lxibq3uX3mi4FQBMa6wJm\nZSZp5rh0ZWUmGagMAADvHDp6SFnZWXp07KNK75ZuuhzUIBBGoCgrSvMnzdfi/MV69ctXTZcDoBGN\ndQHj4xy6ZUSK4uMcBioDAKD5bNvWTUtv0ph+Y3T1D642XQ7cxJguAGZ0bd9VC69YqB++8EMN6jFI\nqfGppksC4IGr+0cXEAAQyh794FHtOLhDz1/+vOlSUA8dwgg2pPcQ/WbEb5SVnaWjlUdNlwPAA7qA\nAIBQt27XOv3h3T9oYdZCtY1pa7oc1EMgjHC3D7ldAxIGaPqy6aZLAQAAQJgpqSjRlYuu1L9+9C/1\n69rPdDnwgEAY4SzL0lM/ekrv7HxHz218znQ5AAAACBPH7eOasniKsjKyNDF9ouly0AACIdSxbUdl\nZ2VrxlsztKl4k+lyAAAAEAb+tOZPOnj0oGaPmm26FDSCQAhJ0qAeg/Sn0X/SFQuvULmz3HQ5AAAA\nCGGrdqzSox8+qpeveFltotuYLgeNIBCi1o1n3Khz+pyjn/7vp7Jt23Q5AAAACEG7vt2ln7z6E82f\nNF99OvUxXQ6aQCBEHU9c+oS2HdimB1c/aLoUAAAAhJhyZ7kmvDhBvzj7F7o45WLT5aAZCISoo32b\n9lp85WI99clTWvjFQtPlAAAAIEQct4/ruv9ep0E9BumX5/3SdDloJgIhTtCzY08tuWqJ7njjDn24\n+0PT5QAAACAE/CrnV9pfsV/zxs+TZVmmy0EzEQjh0eCTBuvfE/6ty1++XEWHikyXAwSV0nKn5q0q\nVGm503QpAAAEhfkb5mvhpoV69cpX2Xw+xBAI0aAJaRM0/ZzpmvDSBB12HjZdDhA0svOKNHvZZmXn\n8WYJAADv7nxX9yy/R/+7+n/qFtvNdDnwUozpAhDc7h52tzbv36xrX71Wr175qqIs3kMAsjKT6nwE\nACBSbTuwTZMXTdazlz2rjMQM0+WgBbi6R6Msy9I/xv9DB48e1MwVM02XAwSF+DiHbhmRovg4h+lS\nAAAw5tDRQxr/wnjNOn+WLkm9xHQ5aCECIZrkiHbolcmv6JUvX9EzG54xXQ4AAAAMqzxeqSsXXamR\nfUfqjrPvMF0OWoFAiGZJiE3Qaz95Tb9c/kut3rnadDkAAAAw6M637pQtW4+MfcR0KWglAiGaLb1b\nup6//HlNzp6swtJC0+UAAADAgCfWP6EV21bo5SteVkwUS5KEOgIhvDImZYx+M+I3Gv/ieB08etB0\nOQAAAAig5YXL9ftVv9f/rv6furTrYroc+ACBEF67bchtGtNvjCZnT1bl8UrT5QAAACAAviz+Ute8\neo2ys7KVEp9iuhz4CIEQLfK3S/6m6KhoTV823XQpAAAA8LP9Ffv1oxd/pD+P+bPOP+V80+XAhwiE\naJGYqBi99OOX9M7Od/T4h4+bLgcAAAB+4qxy6scLf6wrMq7Q9YOvN10OfIxZoGixzu0667WrX9Ow\n/wxTanyqxqaONV0SAAAAfMi2bd362q2Kbx+vP476o+ly4Ad0CNEqfbv21aKsRZry3yn6cPeHpssB\nAACAD92fe78+3fepnr3sWUVZRIdwxKuKVjvv5PP0n4n/0YQXJ2hT8SbT5QAAAMAH/vb+35S9KVtv\nXvOmOjg6mC4HfkIghE+MHzBecy6eo0ueu0Q7Du4wXQ4AAABa4elPntbcD+bq7eveVmJcouly4EfM\nIYTPXDPoGh04ekBjnh2jd294Vyd1OMl0SQAAAPDSf7/8r+5beZ/emfqOTu58suly4Gd0COFTPz/7\n55oyaIouee4SNq4HAAAIMTnbcnTLa7fo9Z+8rrRuaabLQQAQCOFzsy6YpZHJI/XDF36ocme56XIA\nAADQDB/s+kBXvXKVsrOydWbPM02XgwAhEMLnLMvSnEvmqH98f12RfYWcVU7TJQEAAKARX3zzhSa+\nNFFPT3xaI5JHmC4HAUQghF9EWVF6asJTahvdVlP+O0VVx6tMlwQAAAAPth/YrrHPj9Wci+do/IDx\npstBgBEI4TcxUTF66YqX9E35N7rjjTtk27bpkgAAAODm68Nfa8yzY3TveffqmkHXmC4HBhAI4Vft\nYtppyVVL9PHej/Wrlb8yXQ4AAABqHDhyQJc8d4mmnj5VPz/756bLgSEEQvhdx7Yd9cY1b2jx5sX6\ny3t/MV0OAABAxCt3lmv8i+M1MnmkZl0wy3Q5MIhAiIDoFttNb1/3tp7Ie0L//vjfpssBAACIWM4q\np3688MfqH99fcy6ZI8uyTJcEg9iYHgHTp1MfvX3t27pw/oXq0q6LfpzxY9MlAQAARJSq41Wa8t8p\nahfTTk9NeEpRFv2hSMdPAAKqf0J/vf6T13X7G7dreeFy0+UA8LPScqfmrSpUaTnbzwCAabZt6443\n7tA35d/opSteUkwUvSEQCGHA4JMG65XJr+iaV6/Rul3rTJcDwI+y84o0e9lmZecVmS4FACKabdu6\nL+c+fbz3Yy25aonaxbQzXRKCBG8LwIjhJw/X/EnzNeHFCVp69VIN7TPUdEkA/CArM6nORwBA4Nm2\nrftz79f/tvxP71z/jjq27Wi6JAQROoQwZlz/cXpm0jOa8OIEvV/0vulygIgRyGGc8XEO3TIiRfFx\nDr+fCwBwItu2NWvlLC3JX6LcqbnqFtvNdEkIMgRCGHVp/0u14LIFmvjSRL331XumywEiAsM4ASAy\n2LatmTkz9drW17Ry6kolxiWaLglBiEAI48amjtVzlz+ny16+TO/ufNd0OUDYy8pM0sxx6QzjBIAw\nZtu2frn8l3qr8C2tnLKSziAaRCBEULg45WK98OMXdPnCy7VqxyrT5QBhjWGcABDebNvW3W/frZzt\nOcqZkqOE2ATTJSGIEQgRNEb3G62Xr3hZWdlZemfHO6bLAQAACDm2bWvGWzO0aucqrZiyQvHt402X\nhCBHIERQGdl3pBZmLdTk7MlauX2l6XIAAABChm3bmv7mdK0tWqvl1y0nDKJZCIQIOhcmX6hFkxfp\nykVX6s2CN02XAwAAEPSO28d1++u364PdH+jt695W1/ZdTZeEEEEgRFC64JQLtOSqJZry3ylavHmx\n6XIAAACCVuXxSt2w5AZ9UfyFll+3XF3adTFdEkIIG9MjaA1LGqZl1yzTD1/4oY4cO6Krf3C16ZIA\nAACCirPKqWtfvVYHjx7UsmuWKc4RZ7okhBgCIYLaWb3O0oopK3TJc5eo4liFbjrzJtMlAQAABIWj\nlUeVlZ0lS5aWXr1U7WLamS4JIYhAiKB3WvfTlDs1V6MXjFbFsQpNO2ea6ZIAAACMKneWa9LLkxTf\nPl7PXfac2kS3MV0SQhRzCBESBiQM0OobVuuRDx7Rn9b8yXQ5QKuUljs1b1WhSsudpksBAISgb7/7\nVmOfH6veHXvrhctfIAyiVQiECBnJXZL/f3v3HR9Vlb9x/HMSCIQQSqiht1BDCxGQDlKkI02KCAoI\norKLuorr6q77s63KqqugdEQFFCw0wUU6CkLooYbeQi9SQklyfn9kyI4YICHlTjLPm9e8ZubeO5Mn\nOZyZ+c45915WDFjBlM1T+PvSv2OtdTqSyD2ZGXGYtxbsZGbEYaejeD0V5yKS2ZyNOUvLqS2pXrg6\nkzpPwtfH1+lIkslpyqhkKsXzFGf5gOW0+rwVZ2PO8sGDH+iFUDKdHuElf3ctzrlZnAMMaVre4TQi\nInd2+MJh2k1rR5vybXi31bsYY5yOJFmARggl0ykcUJjlA5az7dQ2us/sTsyNGKcjiaRIUIAfQ5qW\nJyjAz+koXq9HeElealtZxXk60iisSNrYcmILDSY1YEDNASoGJU2pIJRMKV/OfAmHVs4ewANTH+D0\nldNORxKRTEjFefrTFGmR1Fu8bzEtp7bkvVbv8VyD51QMSppSQSiZVo5sOZj60FSalm5Kg4kN2Hdu\nn9ORRETkFhqFFUmdzzd/Tp9v+zCr5yweDn3Y6TiSBakglEzNx/jwVsu3+HP9P9NoUiPWHV3ndCQR\nkSwtpVNANQorcm+stby18i1eWfoKS/svpUnpJk5HkixKB5WRLGHYfcMoHlicdtPaMbnzZDpU7OB0\nJBGRLOmzXw7w4eIorlyPY0Srik7HEcmSYuNjeeaHZ1h9ZDW/DPyFYoHFnI4kWZhGCCXL6Fy5M/N6\nz2Pw3MGMjRjrdBwRkSzK3nItImnp8vXLdP2qK3vP7WXFYytUDEq60wihZCn1StRj5WMraftlWw7/\ndpj/a/5/2vFaRCQN9W9Qllx+2bRPoEg6OHn5JB2mdaBa4WqM6zBOJ5yXDJGqEUJjTJAxZpExJsp1\nnT+JbZobYza5Xa4aY7q41k0xxux3W1crNXlEACoEVeCXx39h0b5FDJg9gOtxOtS5iEha0T6BIukj\n6kwUDSY2oG2FtkzqNEnFoGSY1E4ZHQksttaGAItd93/HWrvUWlvLWlsLaAFcAf7rtslfbq631m5K\nZR4RAAoFFGJp/6Wcv3qe9tPac+HqBacjiYiIiCRpzZE1NJnShJGNRvJa89c0u0kyVGoLws7AZ67b\nnwFd7rJ9d2CBtfZKKn+uyF3lyp6Lb3t+S0hQCE2mNOHob0edjiQiIiLyO9/v/J5O0zsxqdMkBoUN\ncjqOeKHUFoRFrLXRAK7rwnfZvhcw/ZZlbxhjthhj3jfG5LjdA40xTxhjIowxEadOnUpdavEavj6+\njG43mj6hfWgwqQGRJyOdjiQiIiICwOi1o3nqh6dY0HcBbUPaOh1HvNRdDypjjPkJKJrEqpdT8oOM\nMcFAdeBHt8UvAccBP2Ac8CLwz6Qeb60d59qG8PBwHdpMks0Yw4uNXqREnhK0+KwFX/f4mmZlmjkd\nS0RERLxUvI3npZ9eYvau2ax6bBVl85d1OpJ4sbsWhNbalrdbZ4w5YYwJttZGuwq+k3d4qp7Ad9ba\nG27PHe26ec0YMxl4Ppm5RVKsb42+BAcG03NmT95v8z59a/R1OpKIiIh4mZgbMQycM5BDFw7x8+M/\nUyBXAacjiZdL7ZTROUB/1+3+wOw7bNubW6aLuopITMKes10AzeeTdNWibAuW9F/CK0tf4S///Qux\n8bFORxIREREvcejCIRpPbky8jWdRv0UqBsUjpLYgfBtoZYyJAlq57mOMCTfGTLi5kTGmDFASWH7L\n4780xmwFtgIFgddTmUfkrkILh7Ju8Do2n9jMg188yOkrp52OJCIiIlncsgPLqDehHr1CezG923T8\ns/s7HUkESGVBaK09Y619wFob4ro+61oeYa0d5LbdAWttcWtt/C2Pb2GtrW6tDbXWPmKtvZSaPCLJ\nVSBXARb0XUCd4DrcN/4+Nh3XGU9EREQk7Vlr+XDNhzw862GmdpnK8w2e12klxKPcdR9CkazK18eX\nf7X6F3WK1aHV56348MEP6VO9j9OxREREJIuIuRHDkHlD2HJiC2sGrtHBY8QjpXbKqEim17NaT5Y8\nuoRXl77Ksz8+q/0KRUREJNUOnj9Iw0kNiY2P5ZeBv6gYFI+lglAEqF6kOmsHr2X7qe20/rw1py7r\nXJciIiJyb5bsX0K9CfV4pMYjfNn1S3Jlz+V0JJHbUkEo4hLkH8T8PvOpX6I+942/jw3RG5yOJCIi\nIpmItZb3V79Pn2/6MK3bNJ69/1ntLygeT/sQirjx9fHlzQfeJCw4jDZftOHfrf9Nv5r9nI4lIiIi\nHu7KjSsMnjuYHad28OugXymdr7TTkUSSRSOEIknoXrU7S/sv5Z8r/smfF/6ZG3E3nI4kIiIiHurA\n+QM0nNQQg2HV46tUDEqmooJQ5DZCC4eydtBadp/ZTesvWnPy8kmnI4mIiIiH+WnfT9SfUJ8BNQfw\n+UOfa39ByXRUEIrcQX7//MztPZeGJRty3/j7iDgW4XQkERER8QDWWkb9Mop+3/VjRvcZ/Kn+n7S/\noGRK2odQ5C58fXx5vcXrhAWH0fbLtoxqPYpHaz7qdCwRERFxyJUbVxg0ZxC7zuzi10G/UipvKacj\nidwzjRCKJFPXKl1Z1n8Zr694nWHzhxFzI8bpSOKlzl6+ztjlezl7+brTUUREvM7O0zu5f+L9ZPfN\nzqrHVqkYlExPBaFIClQrXI21g9dyJuYMdSfUJfJkpNORxAvNjDjMWwt2MjPisNNRRES8hrWWCRsm\n0HhyY4aFD2NK5yn4Z/d3OpZIqmnKqEgK5cuZjxndZjBl0xSaf9ac15q9xpPhT2q/AckwPcJL/u5a\nRETS17mYczwx7wl2n9nN8gHLqVqoqtORRNKMRghF7oExhsdqP8bPj//MxI0T6fJVF05fOe10LPES\nQQF+DGlanqAAP6ejiIhkeSsPrqTW2FoUy12MXwf9qmJQshwVhCKpULFARVYPXE3FoIrU+rQWS/Yv\ncTqSiIiIpIHY+FheXfoqPWf1ZEy7MXzY9kNyZsvpdCyRNKeCUCSV/Hz9eLf1u0zqPIl+3/Vj5E8j\ndSJ7uS0dEEYkaeob4kkOnD9Ak8lNWHNkDRue2ED7iu2djiSSblQQiqSR1uVbs3HIRiJPRtJwUkP2\nnN3jdCTxQDogjEjS1DfEU8yInEHd8XXpVqUbCx9ZSHBgsNORRNKVDiojkoYKBxRmbu+5fLz2Y+6f\neD+jWo+iX41+OuCMJNIBYUSSpr4hTrt47SLDFw7n50M/s6DvAuoUq+N0JJEMoRFCkTQDD1buAAAf\nx0lEQVRmjOGZes+w+NHFvL3qbR757hEuXL3gdCzxEDogjEjS1DfESRHHIggbF4YPPmwYskHFoHgV\nFYQi6aRGkRpEPBFBHr881B5bmzVH1jgdSURERNzE23je+fkd2n3ZjjdavMHEzhPJ7Zfb6VgiGUpT\nRkXSUa7sufikwyd8t+M7Os/ozPC6wxnZaCS+Pr5ORxMREfFq0RejefT7R4m5EcO6wesona+005FE\nHKERQpEM8FCVh1j/xHp+2v8TD0x9gMMXdNAEkbvRUSdFJL3M3TWX2mNr06hkI5YNWKZiULyaCkKR\nDFIiTwl+6vcTrcu3Jnx8OF9s+QJrrdOxRDyWjjopImntt2u/MXTeUJ5Z8Ayzes7i783+TjYfTZgT\n76aCUCQD+fr48tfGf+WHPj/w7i/v0n5aew5dOOR0LBGP1CO8JC+1rayjTnoJjQhLepu/ez6hY0KJ\nt/FsGrqJRqUaOR1JxCOoIBRxQJ1idYgYHEGDkg2oM64On6z7hHgb73QsEY+io056F40IS3o5feU0\nj3z7CMMXDmdKlymM6ziOfDnzOR1LxGOoIBRxSHbf7Pytyd9YPmA5U7dMpdmUZuw+s9vpWCIijtCI\nsKQ1ay0zImcQOiaUIgFF2DJ0Cy3KtnA6lojHUUEo4rCqhaqy6rFVdKvSjQYTG/DOz+8QGx/rdCxJ\nBU19E0k5jQhLWjr621G6fNWF/1vxf8zuNZtRbUYR4BfgdCwRj6SCUMQD+Pr48qf6f2Ld4HUs2reI\nehPqsfn4ZqdjyT3S1DcREWdYaxm/fjy1xtaidtHabHhiA/VK1HM6lohH02GVRDxI2fxl+e8j/2Xy\npsm0+rwVQ+oM4W9N/kaObDmcjiYpcHPKm6a+iYhknL1n9zJ47mAuXr/IkkeXUL1IdacjiWQKGiEU\n8TDGGB6v/Tibhm4i8lQktcfWZvXh1U7HkhTQ1DfJaJqmLN4sLj6Of6/+N/Um1KN9SHtWD1ytYlAk\nBTRCKOKhigUW49ue3zJr+yy6ft2Vh6s9zOstXie3X26no4mIh7k5TRlgSNPyDqcRyTiRJyMZOGcg\n/tn8WTNoDRWCKjgdSSTT0QihiAczxtCjWg8in4zk3NVzVP+kOov2LnI6loh4GB2hU7zN9bjrvLbs\nNZp/1pyBtQeypP8SFYMi90gjhCKZQIFcBfisy2csiFrAoLmDaFm2Je+1fo/8/vmdjiYiHuDmNGUR\nb7Du6Doen/M4pfOWZuOQjZTIU8LpSCKZmkYIRTKRtiFtiXwyEv/s/oR+Esq0rdOw1jodS0REJN2d\nv3qeEQtH0HF6R/7a6K/M7T1XxaBIGlBBKJLJBOYI5ON2HzOzx0xGrR5FkylN2Bi90elYIiIi6SIu\nPo4JGyZQZXQVLt+4zNYnt9K7em+MMU5HE8kSNGVUJJNqULIBawetZfKmybSb1o6OFTvyRos3KBRQ\nyOloIiIiaeLnQz8zfOFw/LP5M7/PfMKCw5yOJJLlaIRQJBPz9fFlUNggdjy1g9x+uak6piofrPmA\nG3E3nI4mIiJyz47+dpS+3/al1ze9eO7+51j52EoVgyLpRAWhSBaQL2c+/t3m36wYsIIFexZQ89Oa\nOhqpiIhkOldjr/Lmyjep+WlNyuYry46ndtCneh9NDxVJR5oyKpKFVClUhYV9FzJ391yenP8koYVD\nGdV6FOWDdPRBERHxXNZaZu+azXP/fY4aRWqwdvBayuUv53QsEa+gEUKRLMYYQ6dKnYgcFkm94vWo\nN6Eef138Vy5dv+R0NBERkT/Yfmo7rb9ozctLXmZsh7F89/B3KgZFMpAKQpEsKme2nLzU+CU2D93M\n4d8OU/njyny55UudpkJERDzC+avn+fPCP9N0SlM6VuzIpiGbaFmupdOxRLyOCkKRLK54nuJ8/tDn\nfN3ja95f8z6NJjdi/bH1TscSEREvFRcfx7j146j8cWVibsSwfdh2htcbTnbf7E5HE/FK2odQxEs0\nKNmAtYPXMnnjZDpM70D7kPa8+cCbFA4o7HQ0ERHxEqsOrWL4guEE+AWwoO8CagfXdjqSiNfTCKGI\nF/ExPgwMG8jOp3aSN0deqo2pxqhfRnE19qrT0UREJAvbf24/fb7pQ59v+vBCwxdYMWCFikERD6GC\nUMQL5c2Zl1FtRrHysZWsPLSSkI9CGBsxVucvFBGRNHXs4jGGzR9G+PhwKhaoyI6ndtArtJdOIyHi\nQVQQinixygUr832v75nVYxazdsyi8ujKfL75c+Li45yOJiIimdjpK6d5/r/PEzomlIDsAex6ehf/\naPYPAvwCnI4mIrdQQSgi1CtRj0X9FjGx00Q+Xf8pNT6twTfbv9ERSUVEJEUuXL3Aq0tfpdLHlbhy\n4wqRwyJ5t/W7FMxV0OloInIbKghFJFGzMs1Y9dgq3m31Lm+sfIPw8eEsiFqgwlBERO7o8vXLvL3q\nbSp8VIFDFw4RMTiCMe3HUCywmNPRROQuVBCKyO8YY2gX0o6IJyJ4qdFLPPvfZ2kypQkrDq5wOpqI\niHiYa7HX+OjXjwj5KIQN0RtYMWAFU7pMoWz+sk5HE5FkUkEoIknyMT50r9qdyCcjGRw2mAHfD6DN\nF21Yd3Sd09HEy5y9fJ2xy/dy9vJ1p6OIiEtsfCwTN0yk4scV+XHvj8zvM5+ve3xNlUJVnI4mIimk\nglBE7sjXx5dHaz7Kzqd38lDlh+jyVRce+uohIk9GOh1NvMTMiMO8tWAnMyMOOx1FxOvF23imb51O\n1dFV+WLrF0zvNp15febpFBIimZhOTC8iyeLn68fQ8KH0r9mfMevG8MDUB2hZriWvNXuNCkEVnI4n\nWViP8JK/uxaRjGetZc6uObyy9BX8s/szpv0YHij7gE4fIZIFaIRQRFLEP7s/zzV4jj3P7KFygcrU\nn1CfwXMGc+jCIaejSRYVFODHkKblCQrwczqKiNex1rJo7yLqT6zPq8te5fUWr7Nm4BpalmupYlAk\ni1BBKCL3JDBHIK80fYXdz+ymYK6C1Pq0Fo/NfoxtJ7c5HU1ERFIpLj6Ob7Z/w/0T7+fpBU8zov4I\nNg7ZSKdKnVQIimQxKghFJFWC/IN4q+Vb7Bm+hwr5K/DA1AfoOL0jKw+u1OkqREQymZgbMYyNGEvl\n0ZV555d3eKHhC2wftp1eob3wMfrYKJIVaR9CEUkTQf5BvNzkZZ69/1mmbp7K43Mep2CugvylwV/o\nXKkzvj6+TkcUEZHbOBtzlk/WfcJHaz8ivFg4EztNpHGpxhoNFPEC+qpHRNKUf3Z/hoQPYedTO3n+\n/ud5e9XbVBldhXHrx3E19qrT8URExM2hC4cYsXAEFf5TgT3n9rD40cXM6zOPJqWbqBgU8RIqCEUk\nXfj6+NKtajd+HfQr4zuOZ/au2ZT9sCxvrnyTczHnnI4nIuLVNh/fzCPfPkLtsbXJ5pONLU9uYXLn\nyVQrXM3paCKSwVQQiki6MsbQtExT5veZz6J+i9h9Zjfl/1OeZ398VkcmFRHJQNZaluxfwoNfPEjb\nL9tSvXB19g7fy7ut36VEnhJOxxMRh6ggFJEME1o4lCldprB56GZ8jA+1Pq1Fv+/6seXEFqejiYhk\nWbHxsXy97WvuG38fT/3wFD2q9mD/n/bzYqMXyZczn9PxRMRhKghFJMOVzFuS91q/x74/7aNaoWq0\n+aINbb9sy9L9S3VkUhGRNHLlxhXGrBtDpY8r8Z9f/8OrTV9l27BtDAwbSI5sOZyOJyIeQkcZFRHH\n5MuZj5GNRjKi/gi+2PIFT85/ksAcgQyvO5zuVbvjn93f6YgiIpnO4QuHGb9hPGPXj+X+EvcztctU\nGpZq6HQsEfFQGiEUEcflyJaDgWED2f7Udl5p8grTIqdR8v2SjFg4gp2ndzodT0TE48XFxzFv9zw6\nTu9IrbG1OBdzjuUDlvN9r+9VDIrIHWmEUEQ8ho/xoVOlTnSq1In95/YzfsN4mk1pRuWClRlSZwhd\nq3TVNCcRETfHLh5j4oaJTNg4gaK5izK0zlBmdJtBgF+A09FEJJPQCKGIeKSy+cvy5gNvcmjEIZ66\n7ykmbpxIqQ9K8cKiF9hzdo/T8UREHBNv4/lxz490/aoroWNCOXbxGLN7zebXQb/yWO3HVAyKSIpo\nhFBEPJqfrx89qvWgR7UeRJ2JYvyG8TSY2IAaRWowNHwonSt1JrtvdqdjioikuxOXTjBp4yTGbxhP\nfv/8DKkzhM+6fEZgjkCno4lIJqYRQhHJNEIKhPBOq3c4POIwA2sP5OO1H1Pqg1L8dfFf2X9uv9Px\nRETSXLyNZ/G+xfSc2ZPKoyuz99xevu7xNeufWM8TdZ5QMSgiqaYRQhHJdHJky0Hv6r3pXb03O07t\nYNz6cdw3/j7uK34fQ+oMoUPFDmTz0cubiGRep6+cZsqmKYxbP46c2XIyNHwo4zuOJ2/OvE5HE5Es\nRiOEIpKpVSlUhfcffJ/DIw7TJ7QP7/3yHqU/KM3fl/6dwxcOOx1PRCTZrLWsOLiCPt/0IeSjECJP\nRvJZl8/YPHQzw+4bpmJQRNKFvkIXkSzBP7s//Wr2o1/NfkSejGRsxFhqja1F7aK16R3am65VupLf\nP7/TMUVE/mDHqR1Mj5zO9MjpZPfJzpA6QxjdbrRes0QkQxhrrdMZUiw8PNxGREQ4HUNEPFzMjRjm\nR81n2tZpLN6/mOZlmtM7tDcdK3UkV/ZcTseTTKpZs2YALFu2zNEckrkdunCIGZEzmLZ1GqeunOLh\nag/TO7Q34cXCMcY4HU9EMgFjzHprbXhqn0cjhCKSZfln96d71e50r9qdC1cv8N3O75i0aRJD5iXs\nZ9g7tDety7fWUUpFJEOcvHySmdtmMj1yOjtP76Rrla588OAHNC7VGF8fX6fjiYiX0gihiHidE5dO\nMHN7woeyXad30a1KN/pU70Pj0o3xMdq1Wu5MI4SSEr9d+43vdnzH9MjprDmyhnYh7ehTvQ+ty7fG\nz9fP6XgikolphFBE5B4VyV2Ep+s+zdN1n+bA+QPMiJzB8IXDOXPlDA9Xe5g+1fsQFhymaVsick+u\nxl5l/u75TI+czqJ9i2hWphkDag3gm57f6KTxIuJxVBCKiFcrk68MIxuNZGSjkWw7uY3pkdPpOasn\nvsaX3qEJp7aoXLCy0zFFxMPFxseyeN9ipkdOZ/au2YQFh9E7tDfjOo4jyD/I6XgiIrelKaMiIrew\n1rLu2DqmbZ3GV9u+Ijh3ML1De9OtajfK5S/ndDxxmKaMyk2x8bGsPryar7Z9xcztMymTrwy9Q3vT\ns1pPigUWczqeiGRxmjIqIpJOjDHULV6XusXrMqr1KJYfXM70rdO5f+L9FPAvQIeKHehQsQMNSjYg\nm49eRkW8ybmYcyzcs5B5UfNYuGchJfOUpHvV7vz8+M9UCKrgdDwRkRTTJxkRkTvw9fGlRdkWtCjb\ngrF2LBHHIpi3ex4jfhzB/nP7ebDCg7QPac+DFR6kQK4CTscVkTRmrWXn6Z3M2z2PeVHz2Bi9kWZl\nmtE+pD3/avkvSuQp4XREEZFU0ZRREZF7dPS3o/wQ9QPzouaxdP9SahatSYeQDrSv2J5qharpoDRZ\nlKaMZn3XYq+x/ODyhCJw9zxi42MTZwY0L9Mc/+z+TkcUEdGUURERpxXPU5zBdQYzuM5grsZeZdmB\nZczbPY8O0zoAJH6AbFamGTmz5XQ4rYjcSfTFaH6I+oH5UfNZvH8xoYVD6RDSgdm9ZhNaOFRf8IhI\nlqURQhGRNGatZdupbczfPZ95UfPYcmILzcs0p0PFDrQLaaeDTWRyGiHMGuJtPBujNyZOBd1zdg9t\nyrehfUh72oa0pWCugk5HFBG5I40Qioh4KGMMoYVDCS0cyouNXuTMlTOJB6F4YdELlM1flnYV2tG8\nbHPql6hPruy5nI4s4hWiL0az/OByftr3Ez9E/UDenHlpH9Ked1u9S8OSDcnum93piCIiGU4FoYhI\nOiuQqwB9a/Slb42+xMbH8svhX/gh6gf+tuRvbDmxhZpFa9K0dFOalm5Kg5INCMwR6HRkkSzh0IVD\nLD+wnOUHEy5nY87SuFRjmpdpzshGI3VUUBERVBCKiGSobD7ZaFK6CU1KNwHg8vXLrD6ymhUHV/Dm\nqjdZf2w9VQtVTSgQyzSlUalG5MuZz+HUIp7PWsvec3tZcXBFQgF4YDkxsTE0Kd2EpqWbMrzecEIL\nh+JjfJyOKiLiUVQQiog4KMAvgJblWtKyXEsArsZeZe3RtSw/sJwP1nxA7296UyGoQuIIYuPSjbVv\nkwj/Ox3E8oPLE4tAILGvvNToJSoVqKSDwYiI3IUOKiMi4sGux11n/bH1iR96fz78MyXzlKRp6aYJ\nIx9lmlI0d1GnY3oVHVTGGfE2nsiTkYlTQFccXEGAX8D/+kLpppTLX04FoIh4jbQ6qIwKQhGRTCQ2\nPpZNxzex/MByVhxawcqDKykUUIhGJRtRp1gdwoLDqFGkhg5Uk45UEGaM45eOsyF6AxuiN7Du2DpW\nHlxJwVwFE6dTNyndhFJ5SzkdU0TEMSoIVRCKiCSOmqw6tIqN0RvZcHwDO07toGz+soQFhxFWNIyw\n4DBqFa1F3px5nY6bJaggTFvWWg5dOJRY/G04voGN0Ru5Fnct8f9wnWJ1aFyqMcGBwU7HFRHxGDrt\nhIiI4GN8qFGkBjWK1Ehcdj3uOttPbU/8gD1z+0y2nNhC0dxFEz5guy61i9amUEAhB9OLt4m38ew5\nuyfx/+bG4xvZEL2BHL45Ev9PDqo9iLB2YZTKW0rTP0VEMoBGCEVEvEBcfBy7zuxKGEV0G4XJkyMP\ntYNrJ44khgWHUSywmD6I34FGCJMnNj6WHad2/K7w23R8EwVyFUgs/m5ea+RPRCTlNEIoIiLJ5uvj\nS9VCValaqCp9a/QFEkZr9p/bn/hhffS60ayPXo+P8aFW0VpUDKpISIEQQoJCqFigIqXzlSabj942\n5PcuXb/EnrN72H1mN1Fnoog6G8WO0zuIPBlJyTwlE79w6FixI7WDaxPkH+R0ZBERcaN3dhERL+Vj\nfCgfVJ7yQeXpXrU7kLA/19GLR9l8fDNRZ6PYeXonc3fPJepMFMcvHad0vtJULFCRkKCEQjGkQEKx\nWCJPCZ3fLQuLuRHD3nN7Ewu+qDNR7D6bUACev3qe8kHlE/9PNC7VmEFhg6hZpCaBOQKdji4iIneh\nglBERBIZYyiRpwQl8pT4w7qrsVfZe3ZvYkGwIXoDX237iqizUZyLOUe5/OUSCsRbRhaL5i6qKaiZ\nwPW46+w/tz+xfaPORiWM+p2N4sSlE5TJVyaxfesUq0Ov0F6EFAjRlwEiIpmcCkIREUmWnNlyUq1w\nNaoVrvaHdTenDd4sJFYdWsXkTZOJOhNFTGwMFYIqUCJPCYJzBxOcO5iiuYsSHJhwOzgw4b6fr58D\nv1XWZ63l4vWLRF+MJvpSNMcvHU+8HX0pmuiL0Rw4f4Ajvx2heJ7iiYV8lYJV6FSpEyFBIZouLCKS\nhaXq1d0Y0wP4B1AFqGutTfJIL8aYB4EPAV9ggrX2bdfyssAMIAjYAPSz1l5PTSYREcl4uf1yU6to\nLWoVrfWHdeevnmfP2T0c/e1oQjFyKZpNxzexYM+CxALlxKUTBOYITCwQE4tGt/s3C8dAv0CNOJKw\nD+jpK6f/V9y5F3y33PcxPn/8e+YOplqhagQHBlMqbynK5S+nolxExAul9uu+SKArMPZ2GxhjfIHR\nQCvgCLDOGDPHWrsd+BfwvrV2hjHmU2Ag8EkqM4mIiAfJlzMf4cXCCS92+wOhxdt4zlw5k1jI3Cxq\nDpw/wOojq383smWxBOcOplBAIQL9AgnMEUhuv9zkzp77f7f9chPo53bbtdx9WYBfgCNTHW/E3eDS\n9UtcvH6RS9cvJdy+5nY7qeU3/nf7wrULHL90nFOXT5E3Z94/FHrl85enYcmGvyukc/vlzvDfU0RE\nModUFYTW2h3A3b6prQvssdbuc207A+hsjNkBtAD6uLb7jITRRhWEIiJexsf4UCigEIUCCv3unIpJ\nuXjtItGXojl1+VRi4eReSF28djFx3e+KK7f1l65f4sqNK+TKnut3BWMO3xz4GJ/bXnx9fNl6cisA\n7b5sR7yNT7zE2bjf3Y+38cTFxxETG5P4My9ev0hcfNxti9TAHIHkzv6/28GBwX9YH+gXSNHcRSmS\nu4hG9EREJNUyYoeA4sBht/tHgHpAAeC8tTbWbXnx2z2JMeYJ4AnX3WvGmMh0yCppoyBw2ukQckdq\nI8+nNsoAl13/TnAipQ8tuOCRBffcPhdc/yRdqQ95PrWRZ1P7eL5KafEkdy0IjTE/AUWTWPWytXZ2\nMn5GUsOH9g7Lk2StHQeMc2WKSIuTMEr6UPt4PrWR51MbeTa1j+dTG3k+tZFnU/t4PmNMksdvSam7\nFoTW2pap/BlHgJJu90sAx0j4xiGfMSaba5Tw5nIRERERERHJABmxN/06IMQYU9YY4wf0AuZYay2w\nFOju2q4/kJwRRxEREREREUkDqSoIjTEPGWOOAPcD840xP7qWFzPG/ADgGv17GvgR2AF8ba3d5nqK\nF4FnjTF7SNincGIyf/S41OSWdKf28XxqI8+nNvJsah/PpzbyfGojz6b28Xxp0kYmYaBORERERERE\nvE3Gn4BJREREREREPIIKQhERERERES/lsQWhMaaHMWabMSbeGHPbQ94aYx40xuwyxuwxxox0W17W\nGPOrMSbKGPOV64A2kkaMMUHGmEWuv+8iY0z+JLZpbozZ5Ha5aozp4lo3xRiz321drYz/LbK25LSR\na7s4t3aY47ZcfSgdJbMP1TLGrHa9Fm4xxjzstk59KJ3c7n3FbX0OV5/Y4+ojZdzWveRavssY0yYj\nc3uLZLTPs8aY7a4+s9gYU9ptXZKvd5K2ktFGA4wxp9zaYpDbuv6u18UoY0z/jE3uPZLRRu+7tc9u\nY8x5t3XqR+nMGDPJGHPS3Oa86ybBf1ztt8UYE+a2LuV9yFrrkRegCgknW1wGhN9mG19gL1AO8AM2\nA1Vd674Gerlufwo86fTvlJUuwDvASNftkcC/7rJ9EHAWyOW6PwXo7vTvkZUvyW0j4NJtlqsPOdw+\nQEUgxHW7GBAN5HPdVx9Kn3a57fuK2zbDgE9dt3sBX7luV3VtnwMo63oeX6d/p6x0SWb7NHd7r3ny\nZvu47if5eqdLhrfRAODjJB4bBOxzXed33c7v9O+U1S7JaaNbtn8GmOR2X/0o/duoCRAGRN5mfTtg\nAQnnda8P/Opafk99yGNHCK21O6y1u+6yWV1gj7V2n7X2OjAD6GyMMUALYJZru8+ALumX1it1JuHv\nCsn7+3YHFlhrr6RrKnGX0jZKpD6UIe7aPtba3dbaKNftY8BJoFCGJfROSb6v3LKNe9vNAh5w9ZnO\nwAxr7TVr7X5gj+v5JO3ctX2stUvd3mvWkHCeY8k4yelDt9MGWGStPWutPQcsAh5Mp5zeLKVt1BuY\nniHJBABr7QoSBlJupzMw1SZYQ8K53YO5xz7ksQVhMhUHDrvdP+JaVgA4bxNOeeG+XNJOEWttNIDr\nuvBdtu/FH19M3nANc79vjMmRHiG9XHLbKKcxJsIYs+bmlF7UhzJCivqQMaYuCd/k7nVbrD6U9m73\nvpLkNq4+coGEPpOcx0rqpPRvPJCEb9FvSur1TtJWctuom+v1a5YxpmQKHyupk+y/s2vKdVlgidti\n9SPn3a4N76kPZUvTaClkjPkJKJrEqpettck5Sb1JYpm9w3JJgTu1TwqfJxioTsK5KG96CThOwgfc\ncSSck/Kf95bUe6VRG5Wy1h4zxpQDlhhjtgK/JbGd+lAKpXEf+hzob62Ndy1WH0ofyXn/0HuPc5L9\nNzbGPAKEA03dFv/h9c5auzepx8s9S04bzQWmW2uvGWOGkjDi3iKZj5XUS8nfuRcwy1ob57ZM/ch5\nafo+5GhBaK1tmcqnOAKUdLtfAjgGnCZh6DSb69vbm8slBe7UPsaYE8aYYGtttOvD6sk7PFVP4Dtr\n7Q2354523bxmjJkMPJ8mob1MWrSRayoi1tp9xphlQG3gG9SHUi0t2scYkweYD/zNNS3k5nOrD6WP\n272vJLXNEWNMNiAvCVN7kvNYSZ1k/Y2NMS1J+OKlqbX22s3lt3m90wfZtHXXNrLWnnG7Ox74l9tj\nm93y2GVpnlBS8lrVC3jKfYH6kUe4XRveUx/K7FNG1wEhJuFoiH4k/KedYxP2qlxKwn5rAP2B5Iw4\nSvLNIeHvCnf/+/5h7rnrA/DNfdW6AEkeRUlS5a5tZIzJf3OqoTGmINAQ2K4+lCGS0z5+wHck7Ccw\n85Z16kPpI8n3lVu2cW+77sASV5+ZA/QyCUchLQuEAGszKLe3uGv7GGNqA2OBTtbak27Lk3y9y7Dk\n3iM5bRTsdrcTsMN1+0egtaut8gOt+f3sIkkbyXmdwxhTiYQDk6x2W6Z+5BnmAI+6jjZaH7jg+qL4\n3vqQ00fRud0FeIiEKvcacAL40bW8GPCD23btgN0kfDPxstvyciS8Ee8BZgI5nP6dstKFhP1lFgNR\nrusg1/JwYILbdmWAo4DPLY9fAmwl4UPsF0Bup3+nrHZJThsBDVztsNl1PdDt8epDzrfPI8ANYJPb\npZZrnfpQ+rXNH95XSJiO28l1O6erT+xx9ZFybo992fW4XUBbp3+XrHhJRvv85PrccLPPzHEtv+3r\nnS4Z3kZvAdtcbbEUqOz22MddfWsP8JjTv0tWvdytjVz3/wG8fcvj1I8ypn2mk3Bk8Rsk1EMDgaHA\nUNd6A4x2td9W3M7IcC99yLgeKCIiIiIiIl4ms08ZFRERERERkXukglBERERERMRLqSAUERERERHx\nUioIRUREREREvJQKQhERERERES+lglBERERERMRLqSAUERERERHxUv8PVkKN1t3TlH4AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c2c423358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# random.shuffle(vocab)\n",
    "emb = initialize_embedding(network, embedding_size)# embedding of nodes of network\n",
    "\n",
    "vocab, vocab_weights = get_vocabulary(network, emb)\n",
    "\n",
    "# On ajoute les noeuds non connectés\n",
    "for a in emb:\n",
    "    if not a in network:\n",
    "        network[a] = []\n",
    "        \n",
    "\n",
    "last_time = time.time()\n",
    "for epoch in range(num_epochs+nb_burn_in):\n",
    "    if epoch < nb_burn_in:\n",
    "        learning_rate = learning_rate_burn_in\n",
    "    else:\n",
    "        learning_rate = lr#/(epoch - nb_burn_in + 1)\n",
    "    for u in vocab:\n",
    "        if not network[u]: # une node\n",
    "            continue\n",
    "        \n",
    "        #traitement des positifs\n",
    "#         positives = random.shuffle(network[u]) \n",
    "        v = random.choice(network[u]) \n",
    "#         gamma_u_positives = [] # distances entre nodes qui ne sont pas liées\n",
    "#         dist_positives = [] # distances entre nodes qui ne sont pas liées\n",
    "#         for v in positives:\n",
    "        gamma_u_v = compute_gamma(emb[u], emb[v])\n",
    "#             gamma_u_positives.append(gamma_u_v)\n",
    "        dist_p_u_v = poincare_dist(gamma_u_v) # distance between the related nodes\n",
    "#             dist_positives.append(dist_p_u_v)\n",
    "        \n",
    "        gamma_u_negs = [] # distances entre nodes qui ne sont pas liées\n",
    "        dist_negs = [] # distances entre nodes qui ne sont pas liées\n",
    "        negs = get_negative_candidates(network, vocab, vocab_weights, u, num_negs) \n",
    "        for neg in negs:\n",
    "            gamma_u_neg = compute_gamma(emb[u], emb[neg])\n",
    "            gamma_u_negs.append(gamma_u_neg)\n",
    "            dist_neg = poincare_dist(gamma_u_neg)\n",
    "            dist_negs.append(dist_neg)\n",
    "                \n",
    "        loss_den = 0.0\n",
    "        \n",
    "        # eqn6\n",
    "        for dist_neg in dist_negs:\n",
    "            loss_den += np.exp(-dist_neg)\n",
    "#         loss = -dist_p_u_v - np.log(loss_den)\n",
    "        \n",
    "        # derivative of loss wrt positive relation [d(u, v)]\n",
    "        der_negs = []\n",
    "        # derivative of loss wrt negative relation [d(u, v')]\n",
    "        for dist_neg in dist_negs:\n",
    "            der_negs.append(np.exp(-1*dist_neg)/(loss_den))\n",
    "        # derivative of loss wrt pos1\n",
    "        der_p_u = deriv_poincare_theta(emb[u], emb[v], gamma_u_v)\n",
    "        # derivative of loss wrt pos2\n",
    "        der_p_v = deriv_poincare_theta(emb[v], emb[u], gamma_u_v)\n",
    "        \n",
    "        der_negs_final = []        \n",
    "        for (der_neg, neg, gamma_u_neg) in list(zip(der_negs, negs, gamma_u_negs)):\n",
    "            # derivative of loss wrt second element of the pair in neg\n",
    "            der_neg1 = der_neg * deriv_poincare_theta(emb[neg], emb[u], gamma_u_neg)\n",
    "            # derivative of loss wrt first element of the pair in neg\n",
    "            der_neg0 = der_neg * deriv_poincare_theta(emb[u], emb[neg], gamma_u_neg)\n",
    "            der_negs_final.append([der_neg0, der_neg1])\n",
    "        \n",
    "        # update embeddings now\n",
    "        emb[u] = update(emb[u], der_p_u, learning_rate)\n",
    "        emb[v] = update(emb[v], der_p_v, learning_rate)\n",
    "        for (neg, der_neg) in list(zip(negs, der_negs_final)):\n",
    "            emb[u] = update(emb[u], -1*der_neg[0], learning_rate)\n",
    "            emb[neg] = update(emb[neg], -1*der_neg[1], learning_rate)\n",
    "    print('Epoch #%d, temps d\\'exécution: %.2f seconds' % (epoch + 1, time.time() - last_time))\n",
    "    if(epoch%100 == 0 and embedding_size == 2):\n",
    "        plot_poincare_embeddings(emb, epoch, save=True, show=False)\n",
    "    last_time = time.time()\n",
    "pickle.dump(emb, open('data/train.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Résultats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T20:27:19.356628Z",
     "start_time": "2018-05-20T20:27:19.354725Z"
    }
   },
   "source": [
    "### 2.1 Taxinomie"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T10:43:02.447932Z",
     "start_time": "2018-05-20T10:43:02.312283Z"
    }
   },
   "source": [
    "emb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Word Reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:21.289053Z",
     "start_time": "2018-05-20T21:11:21.233794Z"
    }
   },
   "outputs": [],
   "source": [
    "def evaluate_mean_rank_and_map(vocab, network, embeddings, max_n=None):\n",
    "    \"\"\"Evalue le mean rank et le MAP pour la reconstruction.\"\"\"\n",
    "    ranks = []\n",
    "    avg_precision_scores = []\n",
    "    for i, item in list(enumerate(vocab, start=0)):\n",
    "        if item not in network or item == \"mammal.n.01\":\n",
    "            continue\n",
    "        item_relations = []\n",
    "        temp = list(embeddings.keys())\n",
    "        for relation in network[item]:\n",
    "            item_relations.append(temp.index(relation))\n",
    "            \n",
    "        item_distances = all_distances_to(item, embeddings)\n",
    "#         print(\"item\", item)\n",
    "        positive_relation_ranks, avg_precision = \\\n",
    "            get_positive_relation_ranks_and_avg_prec(item_distances, item_relations)\n",
    "        ranks += positive_relation_ranks\n",
    "        avg_precision_scores.append(avg_precision)\n",
    "        if max_n is not None and i > max_n:\n",
    "            break\n",
    "    return np.mean(ranks), np.mean(avg_precision_scores)\n",
    "\n",
    "def all_distances_to(item, embeddings):\n",
    "    \"\"\"Retourne une liste des distances d'un mot avec tous les autres\"\"\"\n",
    "    all_distances=np.zeros(len(list(embeddings.keys())))\n",
    "    \n",
    "    for i, elem in list(enumerate(embeddings)):\n",
    "        gamma = compute_gamma(embeddings[item], embeddings[elem])\n",
    "        distance = poincare_dist(gamma)\n",
    "        all_distances[i]=distance\n",
    "    return all_distances\n",
    "    \n",
    "def get_positive_relation_ranks_and_avg_prec(all_distances, positive_relations):\n",
    "    positive_relation_distances = all_distances[positive_relations]\n",
    "    negative_relation_distances = np.ma.array(all_distances, mask=False)\n",
    "    negative_relation_distances.mask[positive_relations] = True\n",
    "    # Compute how many negative relation distances are less than each positive relation distance, plus 1 for rank\n",
    "    ranks = (negative_relation_distances < positive_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n",
    "    map_ranks = np.sort(ranks) + np.arange(len(ranks))\n",
    "    avg_precision = ((np.arange(1, len(map_ranks) + 1) / np.sort(map_ranks)).mean())\n",
    "    return list(ranks), avg_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:42.059021Z",
     "start_time": "2018-05-20T21:11:21.290477Z"
    }
   },
   "outputs": [],
   "source": [
    "mean_rank, map_ = evaluate_mean_rank_and_map(vocab, network, emb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:42.070477Z",
     "start_time": "2018-05-20T21:11:42.060463Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WordNet reconstruction\n",
      "Paramètres :\n",
      "Période de burn-in = 10\n",
      "Epochs = 250\n",
      "Learning rate = 0.01\n",
      "Burn-in learning rate = 0.001\n",
      "Dimension = 2\n",
      "un nombre de nodes négatives de 20\n",
      "epsilon = 1e-05\n",
      "\n",
      "Résultats :\n",
      "Mean Rank = 775.560379089 \n",
      "MAP = 0.0104918942534\n"
     ]
    }
   ],
   "source": [
    "print('WordNet reconstruction')\n",
    "print('Paramètres :')\n",
    "print('Période de burn-in =', nb_burn_in)\n",
    "print(\"Epochs =\", num_epochs)\n",
    "print(\"Learning rate =\", lr)\n",
    "print(\"Burn-in learning rate =\", learning_rate_burn_in)\n",
    "print(\"Dimension =\", embedding_size)\n",
    "print(\"un nombre de nodes négatives de\", num_negs)\n",
    "print(\"epsilon =\", EPSILON)\n",
    "print()\n",
    "print(\"Résultats :\")\n",
    "print('Mean Rank =', mean_rank, \"\\nMAP =\", map_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Link Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:42.085074Z",
     "start_time": "2018-05-20T21:11:42.071998Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_root_and_leaf_nodes(data_file):\n",
    "    \"\"\"Return keys of root and leaf nodes from a file with transitive closure relations\"\"\"\n",
    "    root_candidates = set()\n",
    "    leaf_candidates = set()\n",
    "    with open(data_file, 'rb') as f:\n",
    "        for line in f:\n",
    "            nodes = line.split()\n",
    "            root_candidates.update(nodes)\n",
    "            leaf_candidates.update(nodes)\n",
    "    \n",
    "    with open(data_file, 'rb') as f:\n",
    "        for line in f:\n",
    "            node_1, node_2 = line.split()\n",
    "            if node_1 == node_2:\n",
    "                continue\n",
    "            leaf_candidates.discard(node_1)\n",
    "            root_candidates.discard(node_2)\n",
    "    \n",
    "    return (leaf_candidates, root_candidates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:42.131472Z",
     "start_time": "2018-05-20T21:11:42.086857Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_test_split(data_file, test_ratio=0.1):\n",
    "    train_filename = data_file + '.train'\n",
    "    test_filename = data_file + '.test'\n",
    "    if os.path.exists(train_filename) and os.path.exists(test_filename):\n",
    "        return (train_filename, test_filename)\n",
    "    root_nodes, leaf_nodes = get_root_and_leaf_nodes(data_file)\n",
    "    test_line_candidates = []\n",
    "    line_count = 0\n",
    "    all_nodes = set()\n",
    "    with open(data_file, 'rb') as f:\n",
    "        for i, line in list(enumerate(f)):\n",
    "            node_1, node_2 = line.split()\n",
    "            all_nodes.update([node_1, node_2])\n",
    "            if (\n",
    "                    node_1 not in leaf_nodes\n",
    "                    and node_2 not in leaf_nodes\n",
    "                    and node_1 not in root_nodes\n",
    "                    and node_2 not in root_nodes\n",
    "                    and node_1 != node_2\n",
    "                ):\n",
    "                test_line_candidates.append(i)\n",
    "            line_count += 1\n",
    "\n",
    "    num_test_lines = int(test_ratio * line_count)\n",
    "    test_line_indices = set(random.sample(test_line_candidates, num_test_lines))\n",
    "    train_line_indices = set(l for l in range(line_count) if l not in test_line_indices)\n",
    "    \n",
    "    train_set_nodes = set()\n",
    "    with open(data_file, 'rb') as f:\n",
    "        train_file = open(train_filename, 'wb')\n",
    "        test_file = open(test_filename, 'wb')\n",
    "        for i, line in list(enumerate(f)):\n",
    "            if i in train_line_indices:\n",
    "                train_set_nodes.update(line.split())\n",
    "                train_file.write(line)\n",
    "            elif i in test_line_indices:\n",
    "                test_file.write(line)\n",
    "        train_file.close()\n",
    "        test_file.close()\n",
    "    assert len(train_set_nodes) == len(all_nodes), 'Not all nodes from dataset present in train set relations'\n",
    "    return (train_filename, test_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:42.140435Z",
     "start_time": "2018-05-20T21:11:42.132709Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_unknown_relation_ranks_and_avg_prec(all_distances, unknown_relations, known_relations):\n",
    "    unknown_relation_distances = all_distances[unknown_relations]\n",
    "    negative_relation_distances = np.ma.array(all_distances, mask=False)\n",
    "    negative_relation_distances.mask[unknown_relations] = True\n",
    "    negative_relation_distances.mask[known_relations] = True\n",
    "    # Compute how many negative relation distances are less than each unknown relation distance, plus 1 for rank\n",
    "    ranks = (negative_relation_distances < unknown_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n",
    "    map_ranks = np.sort(ranks) + np.arange(len(ranks))\n",
    "    avg_precision = ((np.arange(1, len(map_ranks) + 1) / np.sort(map_ranks)).mean())\n",
    "    return list(ranks), avg_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:42.159493Z",
     "start_time": "2018-05-20T21:11:42.141665Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate_mean_rank_and_map_prediction(vocab, embeddings, items, relations, max_n=None):\n",
    "    \"\"\"Evaluate mean rank and MAP for link prediction.\"\"\"\n",
    "    ranks = []\n",
    "    avg_precision_scores = []\n",
    "    for i, item in list(enumerate(items, start=0)):\n",
    "        if item not in relations['unknown'] or item == \"mammal.n.01\":  # No positive relations to predict for this node\n",
    "            continue\n",
    "        unknown_relations = []\n",
    "        for elem in list(relations['unknown'][item]):\n",
    "            unknown_relations.append(vocab.index(elem))\n",
    "        known_relations = []\n",
    "        for elem in list(relations['known'][item]):\n",
    "            known_relations.append(vocab.index(elem))\n",
    "        \n",
    "        item_distances = all_distances_to(item, embeddings)\n",
    "        unknown_relation_ranks, avg_precision = \\\n",
    "            get_unknown_relation_ranks_and_avg_prec(item_distances, unknown_relations, known_relations)\n",
    "        ranks += unknown_relation_ranks\n",
    "        avg_precision_scores.append(avg_precision)\n",
    "        if max_n is not None and i > max_n:\n",
    "            break\n",
    "    return np.mean(ranks), np.mean(avg_precision_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:42.163313Z",
     "start_time": "2018-05-20T21:11:42.160664Z"
    }
   },
   "outputs": [],
   "source": [
    "train_filename, test_filename = train_test_split('data/wordnet_mammal_hypernyms.tsv', test_ratio=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:42.167238Z",
     "start_time": "2018-05-20T21:11:42.164461Z"
    }
   },
   "outputs": [],
   "source": [
    "relations = {'known': defaultdict(set), 'unknown': defaultdict(set)}\n",
    "data_files = {'known': train_filename, 'unknown': test_filename}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:42.182958Z",
     "start_time": "2018-05-20T21:11:42.168395Z"
    }
   },
   "outputs": [],
   "source": [
    "items = set()\n",
    "for relation_type, data_file in data_files.items():\n",
    "    with open(data_file, 'r') as f:\n",
    "        reader = csv.reader(f, delimiter='\\t')\n",
    "        for row in reader:\n",
    "            relations[relation_type][row[0]].add(row[1])\n",
    "            items.update([row[0], row[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:47.120559Z",
     "start_time": "2018-05-20T21:11:42.184359Z"
    }
   },
   "outputs": [],
   "source": [
    "mean_rank, map_ = evaluate_mean_rank_and_map_prediction(vocab, emb, items, relations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T21:11:47.131640Z",
     "start_time": "2018-05-20T21:11:47.122033Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Link prediction\n",
      "Paramètres :\n",
      "Période de burn-in = 10\n",
      "Epochs = 250\n",
      "Learning rate = 0.01\n",
      "Burn-in learning rate = 0.001\n",
      "Dimension = 2\n",
      "un nombre de nodes négatives de 20\n",
      "epsilon = 1e-05\n",
      "\n",
      "Résultats :\n",
      "Mean Rank = 813.371761658 \n",
      "MAP = 0.032392950844\n"
     ]
    }
   ],
   "source": [
    "print('Link prediction')\n",
    "print('Paramètres :')\n",
    "print('Période de burn-in =', nb_burn_in)\n",
    "print(\"Epochs =\", num_epochs)\n",
    "print(\"Learning rate =\", lr)\n",
    "print(\"Burn-in learning rate =\", learning_rate_burn_in)\n",
    "print(\"Dimension =\", embedding_size)\n",
    "print(\"un nombre de nodes négatives de\", num_negs)\n",
    "print(\"epsilon =\", EPSILON)\n",
    "print()\n",
    "print(\"Résultats :\")\n",
    "print('Mean Rank =', mean_rank, \"\\nMAP =\", map_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}