{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# OutilsMaths - Python \n",
    "\n",
    "Bastien DOIGNIES - Outils Maths LIRIS / ORIGAMI - 12/02/2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "Ce notebook / slideshow présente quelques tips, tricks et idiomes pour le language Python. Le but étant de présenter certaines façons d'utiliser le language, afin d'avoir une meilleure compréhension, rapidité d'execution et fluidité dans le processus de développement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "La présentation est en 3 parties : \n",
    "\n",
    "* Native python : présente les quelques idiomes python très utilisés qui sont des fonctionnalités \"natives\" du language.\n",
    "* Tensor libraries : présente les opérations `numpy` qui sont à la base de toutes les librairies manipulant des tableaux (tf, torch, jax, ...)\n",
    "* Performant python : quelques tips & tricks qui permettent d'avoir un code qui tourne plus vite."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Native Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Function parameters and expansions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Les fonctions sont des éléments essentiels de tous les languages de programmation. En tant que language non typé, le python offre de nombreuses possibilités en ce qui concerne les fonctions. Il y a deux types d'arguments : positionnels, et nommés."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Les arguments positionnels: \n",
    "* Se sont les arguments qui doivent être fournis dans l'ordre de la fonction.\n",
    "* Sont toujours 'les premiers' dans la liste des arguments d'une fonction.\n",
    "* Une '*' marque la fin des arguments 'positionnels'. Tout ceux d'après doivent OBLIGATOIREMENT être 'nommés'.\n",
    "* Ils peuvent (ou non) être nommés. En revanche, dès que l'un est nommé, tout ceux d'après doivent aussi l'être.\n",
    "\n",
    "Les arguments nommés:\n",
    "* Sont les arguments pour lesquels on spécifie le nom lors de l'appel.\n",
    "* Pour certains, le nom est obligatoire.\n",
    "* Puisqu'ils sont nommés, il n'y a pas d'ordre !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3 4\n",
      "1 10 3 4\n",
      "1 4 3 hello\n"
     ]
    }
   ],
   "source": [
    "def une_fonction1(arg_1, arg_2=10, *, kw_1, kw_2=\"hello\"):\n",
    "    print(arg_1, arg_2, kw_1, kw_2)\n",
    "\n",
    "une_fonction1(1, 2, kw_1=3, kw_2=4) # tout est spécifié\n",
    "une_fonction1(1, kw_1=3, kw_2=4) # default for arg_2\n",
    "une_fonction1(1, kw_1=3, arg_2=4) # No order when named"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "une_fonction1() takes from 1 to 2 positional arguments but 4 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# 3, et 4 corresponded à kw_1, kw_2 => Ils doivent être nommés\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mune_fonction1\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: une_fonction1() takes from 1 to 2 positional arguments but 4 were given"
     ]
    }
   ],
   "source": [
    "# 3, et 4 corresponded à kw_1, kw_2 => Ils doivent être nommés\n",
    "une_fonction1(1, 2, 3, 4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "positional argument follows keyword argument (1138015686.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Input \u001b[0;32mIn [3]\u001b[0;36m\u001b[0m\n\u001b[0;31m    une_fonction1(arg_1=1, 2, kw_1=3, kw_2=4)\u001b[0m\n\u001b[0m                                            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
     ]
    }
   ],
   "source": [
    "# pos_1 est nommé, tout ceux d'après doivent donc l'être !\n",
    "une_fonction1(arg_1=1, 2, kw_1=3, kw_2=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "En python, les fonctions peuvent nativement prendre \"une infinité\" de paramètres. Pour cela, on utilse (par convention), les notations : `*args` et `**kwargs`.\n",
    "\n",
    "* `*args`: Spécifie une 'infinité' d'arguments positionnels (qui ne peuvent donc pas être nommés). Cela marque aussi la fin des arguments positionnels, tous les arguments doivent être nommés. Il s'agit d'un tuple.\n",
    "* `*kwargs`: Est la contrepartie pour les arguments 'nommés'. Il s'agit d'un dictionnaire. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal args:  1 , bonjour\n",
      "Positional arguments:  (2, 3)\n",
      "Keyword arguments:  {'tata': 12}\n"
     ]
    }
   ],
   "source": [
    "def une_fonction2(pos_1, *args, toto, **kwargs):\n",
    "    print(\"Normal args: \", pos_1, \",\", toto)\n",
    "    print(\"Positional arguments: \", args)\n",
    "    print(\"Keyword arguments: \", kwargs)\n",
    "\n",
    "une_fonction2(1, 2, 3, tata=12, toto=\"bonjour\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Parfois, il se peut que nos arguments soient rangés dans des listes ou dictionnaires que l'on souhaite envoyer à une fonction en tant que paramètres indépendants. Pour cela, il existe les opérateurs d'expansion : `*`, `**`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal args:  [1, 2, 3] , 17\n",
      "Positional arguments:  ({'toto': 123, 'tata': 17},)\n",
      "Keyword arguments:  {}\n",
      "----\n",
      "Normal args:  1 , 123\n",
      "Positional arguments:  (2, 3)\n",
      "Keyword arguments:  {'tata': 17}\n"
     ]
    }
   ],
   "source": [
    "params=[1, 2, 3]\n",
    "kw_args={\"toto\": 123, \"tata\": 17}\n",
    "une_fonction2(params, kw_args, toto=\"17\") \n",
    "print(\"----\")\n",
    "une_fonction2(*params, **kw_args) # une_fonction2(1, 2, 3, toto=123, tata=17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Context managers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "L'utilisation des `context managers` est assez courrante en python, sans que le nom ne soit pour autant pronnoncé. Le but est de gérer des ressources, principalement lors de la création d'une ressource dont la supression est nécessaire. L'exemple courant est l'utilisation de fichiers. Les `context managers` s'utilisent avec la close `with`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Je suis du texte dans un fichier\n",
      " Deuxième ligne\n"
     ]
    }
   ],
   "source": [
    "with open(\"file1.txt\") as file:\n",
    "    print(*file.readlines())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Ce bout de code est en fait équivalent à un simple try/finally. Il sagit principalement d'un sucre syntaxique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Je suis du texte dans un fichier\n",
      " Deuxième ligne\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    file = open(\"file1.txt\")\n",
    "    print(*file.readlines())\n",
    "finally: # Always executed, no matter if an exception occurs or not\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "L'implémentation d'un objet supportant ce type est très simple. Il suffit de surcharger les fonctions `__enter__`, et `__exit__`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'ValueError'>: A random error\n",
      "  File \"/tmp/ipykernel_17413/2219059811.py\", line 16, in <cell line: 15>\n",
      "    raise ValueError(\"A random error\")\n",
      "\n",
      "Closing file now\n"
     ]
    }
   ],
   "source": [
    "class FileWrapper:\n",
    "    def __init__(self, file_name, method):\n",
    "        self.file_obj = open(file_name, method)\n",
    "    def __enter__(self):\n",
    "        return self.file_obj # Returns the 'classic python file'\n",
    "    def __exit__(self, type, value, traceback_):\n",
    "        if value:\n",
    "            print(f\"{type}: {value}\")\n",
    "            import traceback\n",
    "            print(traceback.format_tb(traceback_)[0])\n",
    "        print(\"Closing file now\")\n",
    "        self.file_obj.close()\n",
    "        return True # Execption is handled. False would rethrow \n",
    "\n",
    "with FileWrapper(\"file1.txt\", \"rt\") as file: \n",
    "    raise ValueError(\"A random error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Decorators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Les `decorators` sont des fonctions qui prennent en paramètre une fonction ou une classe, et, éventuellement des arguments, afin d'en modifier le comportement. Ils s'utilisent comme des annotations et ne sont, là encore, qu'un sucre syntaxique. \n",
    "\n",
    "Exemple d'utilisation : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student(name='Toto', avg_grades=5.0), Student(name='Tata', avg_grades=10.0), IsSame?: False\n"
     ]
    }
   ],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "@dataclass # Rajoute: constructeur, egalités, print, ... \n",
    "class Student:\n",
    "    name: str\n",
    "    avg_grades: float\n",
    "\n",
    "st1 = Student(\"Toto\", 5.0)\n",
    "st2 = Student(\"Tata\", 10.0)\n",
    "print(f\"{st1}, {st2}, IsSame?: {st1 == st2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Exemple de création d'un décorateur qui est bien utile ! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param\n",
      "Function hello_world('Param',) {} Took 2.0022 seconds\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "def time_function(func):\n",
    "    def time_wrapper(*args, **kwargs):\n",
    "        start_time = time.perf_counter()\n",
    "        result = func(*args, **kwargs) # Execute la fonction\n",
    "        end_time = time.perf_counter()\n",
    "        total_time = end_time - start_time\n",
    "\n",
    "        print(f'Function {func.__name__}{args} {kwargs} Took {total_time:.4f} seconds')\n",
    "        return result\n",
    "        \n",
    "    return time_wrapper\n",
    "\n",
    "@time_function\n",
    "def hello_world(str):\n",
    "    time.sleep(2.0)\n",
    "    print(str)\n",
    "\n",
    "hello_world(\"Param\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "L'idée d'un decorateur est ainsi \"d'intercepter\" une fonction ou un objet, pour en renvoyer une autre à la place. Quelques decorateurs utiles : \n",
    "\n",
    "* `abc.abstractmethod` : decorateurs pour faire des méthodes abstraites\n",
    "* `staticmethod`: fonctions statiques (comme en C++) \n",
    "* `functools.wraps` : permet de forward les noms, documentations, ... des fonctions retournées.\n",
    "* `functools.cache` : cache les valeurs de retours d'une fonction\n",
    "* `functools.total_ordering` : Etant donner un opérateur de comparaison pour une classe, complète les autres.\n",
    "* `dataclasses.dataclass` : utilitaire pour construire des classes rapidement\n",
    "\n",
    "Librairies externes: \n",
    "* `numpy.vectorize` : Permet à une fonction de prendre un tableau en paramètre (mais n'optimise pas le code pour du SSE)\n",
    "* `numba.jit`, `jax.jit`, `tf.function`, `torch.jit` : Permet de compiler une fonction pour accélerer son execution. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Generators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Les générateurs représentent des séquences qui ne sont évaluées que de manière \"paraisseuses\". Le générateur le plus connu est `range`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for i in range(1000):\n",
    "    pass # Do something with i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Tous les entiers de 0 à 999 ne sont pas mis dans une liste. A la place ils sont 'générés uns par uns'. Cela permet d'éviter une construction mémoire. C'est l'équivalent des coroutines dans pas mal de language (`co_yield`, `co_return` en C++). Ils s'utilsent dans une boucle, ou avec l'instruction `next`. La fin est signalée par une exception `StopIteration`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def fibonacci():\n",
    "    a=0\n",
    "    b=1\n",
    "    for i in range(6):\n",
    "        yield b\n",
    "        a, b = b, a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 2 3 5 8 "
     ]
    }
   ],
   "source": [
    "for fibo in fibonacci():\n",
    "    print(fibo, end=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 2 3 5 8\n"
     ]
    },
    {
     "ename": "StopIteration",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m gen \u001b[38;5;241m=\u001b[39m fibonacci()\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mnext\u001b[39m(gen), \u001b[38;5;28mnext\u001b[39m(gen), \u001b[38;5;28mnext\u001b[39m(gen), \u001b[38;5;28mnext\u001b[39m(gen), \u001b[38;5;28mnext\u001b[39m(gen), \u001b[38;5;28mnext\u001b[39m(gen)) \n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m)\n",
      "\u001b[0;31mStopIteration\u001b[0m: "
     ]
    }
   ],
   "source": [
    "gen = fibonacci()\n",
    "print(next(gen), next(gen), next(gen), next(gen), next(gen), next(gen)) \n",
    "print(next(gen))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### List / Dict / Generator comprehension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Apparues dès le début de Python 2, les compréhensions offrent de puissants outils de création de listes, dictionnaires et de générateurs. La puissance vient principalement de la rapidité d'éxéctution du code: l'interpreteur python est capable de compiler le code pour l'éxécuter plus vite. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 4, 27, 256, 3125]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i ** i for i in range(6)] # Une liste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.0: 1, 1.0: 1, 2.0: 4, 3.0: 27, 4.0: 256, 5.0: 3125}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{float(i): i ** i for i in range(6)} # Un dictionnaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object <genexpr> at 0x7f5f7209cdd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(i ** i for i in range(6)) # Un générateur : rien n'a été évalué !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Il est également possible d'appliquer des filtres et de composer les expressions: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 256]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i ** i for i in range(6) if i % 2 == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{-3: 0, -2: 0, -1: 0, 0: 0, 1: 0},\n",
       " {-3: 2, -2: 2, -1: 2, 0: 2, 1: 2},\n",
       " {-3: 4, -2: 4, -1: 4, 0: 4, 1: 4}]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[{k: i for k in range(-3, 2)} for i in range(5) if i % 2 == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal args:  1 , 111\n",
      "Positional arguments:  (1, 4, 27, 256)\n",
      "Keyword arguments:  {'tata': 97, 'tete': 101, 'titi': 105, 'tutu': 117}\n"
     ]
    }
   ],
   "source": [
    "une_fonction2(\n",
    "    *[i ** i for i in range(5)], \n",
    "    **{f\"t{c}t{c}\": ord(c) for c in 'aeiou'}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Important à noter : les compréhension ré-évaluent les expressions à chaque fois : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10], [10], [10], [10], [10]]\n",
      "[[10], [], [], [], []]\n"
     ]
    }
   ],
   "source": [
    "a = [[]] * 5               # Liste de 5 listes vides\n",
    "b = [[] for _ in range(5)] # Liste de 5 listes vides\n",
    "a[0].append(10)\n",
    "b[0].append(10)\n",
    "print(a) # a est en fait une liste de références\n",
    "print(b) # b est une liste de liste différentes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Tensors manipulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Shape and definition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Un tenseur est simplement un tableau n-d de valeurs. De nombreuses librairies permettent de les manipuler : `numpy`, `pytorch`, `tensorflow`, `jax`, ... Ici, on ne se concentre que sur `numpy`, qui est la plus utilisée et celle qui a historiquement défini les interfaces ainsi que les façons de manipuler les tableaux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 (4, 8, 12, 24)\n",
      "float64 73728\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.random.uniform(0, 1, (4, 8, 12, 24)) # 9216\n",
    "print(a.ndim, a.shape)   # Nombre de dimension, taille\n",
    "print(a.dtype, a.nbytes) # Type, occupation mémoire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Pour information un tableau numpy est limité à 32 dimensions et $2^{32}$ éléments. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Il est possible de changer la taille d'un tableau ainsi que son type. \n",
    "Pas de contrainte entre les types, cela marche \"comme les casts\" en C. En ce qui concerne la taille, il faut que tout soit compatible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 9216 into shape (123456,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [23]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m b \u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m) \u001b[38;5;66;03m# Floats entre [0, 1] => Tableau de 0 !\u001b[39;00m\n\u001b[1;32m      2\u001b[0m c \u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39mreshape((\u001b[38;5;241m4\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m12\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m24\u001b[39m)) \u001b[38;5;66;03m# Change la taille\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m123456\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 9216 into shape (123456,)"
     ]
    }
   ],
   "source": [
    "b = a.astype(int) # Floats entre [0, 1] => Tableau de 0 !\n",
    "c = a.reshape((4 * 8, 12 * 24)) # Change la taille\n",
    "d = a.reshape(123456)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Slicing, strides and views"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Il est utile de savoir manipuler les éléments des tableaux. Pour ce faire, il faut indexer les éléments. On accède aux éléments d'un tableau avec l'opérateur \"classique\".  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,)\n",
      "0.09699281353192157 0.09699281353192157\n"
     ]
    }
   ],
   "source": [
    "a = np.random.uniform(0, 1, (100, 100))\n",
    "print(a[0].shape)       # Première ligne\n",
    "print(a[0][1], a[0, 1]) # Deuxième élément de la première ligne"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Le slicing consiste à prendre des parties d'un tableau. L'opérateur de slicing s'écrit avec des ':' avec la syntaxe: `début[0]:fin[N]:increment[1]`. Le début est inclu, la fin est exclue. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "[3 4 5 6 7 8 9]\n",
      "[0 1 2 3 4 5 6]\n",
      "[0 2 4 6 8]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "print(a)\n",
    "print(a[3:])\n",
    "print(a[:7])\n",
    "print(a[::2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  2  4  6  8]\n",
      " [10 12 14 16 18]\n",
      " [20 22 24 26 28]]\n",
      "[[ 2  4  6]\n",
      " [22 24 26]\n",
      " [42 44 46]\n",
      " [62 64 66]\n",
      " [82 84 86]]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(100).reshape((10, 10))\n",
    "print(a[:3, ::2])\n",
    "print(a[::2, 2:8:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "En python les indices négatifs permettent d'indexer à partir de la fin d'un tableau. Cela peut être utilisé dans les slices :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 8 7\n",
      "[9 8 7 6 5 4 3 2 1 0]\n",
      "[4 3 2 1 0] [4 3 2 1 0]\n",
      "[0 1 2 3 4 5 6] [0 1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "print(a[-1], a[-2], a[-3])\n",
    "print(a[::-1])\n",
    "print(a[-6::-1], a[a.shape[0]-6::-1])\n",
    "print(a[:-3], a[:a.shape[0]-3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Les tableaux numpy sont stockés sous forme de buffers linéaires. Les opérations de slicing et de reshape changent seulement la façon de parcourir les éléments et non la mémoire associée : modifier une slice modifie le tableau originel !!!\n",
    "\n",
    "On appelle `stride` le tableau d'entiers qui permet de parcourir le buffer linéaire."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80, 8)\n",
      "(160, 16)\n",
      "(400, 80, 40, 8)\n",
      "100\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(100).reshape((10, 10))\n",
    "print(a.strides) # En bytes !\n",
    "print(a[::2, ::2].strides)\n",
    "print(a.reshape(2, 5, 2, 5).strides)\n",
    "\n",
    "# La mémoire est partagée\n",
    "a[::2, ::2][1, 1] = 100 \n",
    "print(a[2, 2]) \n",
    "print(a[::2, ::2].base is a.base)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Les tableaux `numpy` implémentent l'interface `python buffer protocol` qui permet de lier les structures de données des différentes librairies. En particulier, les tableaux peuvent être utilisés comme des listes ! Ainsi qu'avec l'opérateur d'expansion... : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9 \n",
      "0 1 2 3 4 5 6 7 8 9\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "for e in a:\n",
    "    print(e, end=' ')\n",
    "print()\n",
    "print(*a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Transpose, swaps and reordering "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Comme expliqué, l'opération reshape ne réordonne pas les éléments; pourtant, cela peut être pratique de les réordonner. Les images sont courrament chargées sous la forme : (W, H, C); mais la plupart des modules de deep learning attendent un input sous la forme (C, W, H)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = np.load(\"img-orig.npy\")\n",
    "plt.imshow(img, cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fc9wRtP1c2ucI8PLycmOtjo8lY0aBbWG5thtHuGkfo+rLy8trJOU25M6DnddzOvbasXQDAH+jTEDYDRQnZ/fHMjwppzQdr8lfWCajsiWwpNFNzr27fn9/cwcm0yL5nomfCUIGGuOmY2V0R2eRsibH2rFvghwjn46MdP1eAjrX3elo2/VMTU2/O3rdJcrx7yR1dLhMU1f1ttsBHT+daZhS/V17o/+pLJ7HMpbK9rM98xttL3VxjY0gYntx5Jw6PW7MEJnoMHvT2WLXHxIZzhv+xr505RBUTV44nzwPGRHmgQR7e3t1dHS0vsWCJLQbnyXgi55Tdhch3rbcebBbciSO3hxe896ejnVTOuZL0OoiC0/a7v9tTmNbBJFr7IjtQJwCc5lkwO4PjZWTmimSTJSDg4P1BoDoiaDMcaEj6vrI8+kIGB13zrOLcp1KfVbp0rUed0d2bLOd1BIo+lquy1VtrptGV9lKvq3tPk7HtVqtrqUmO5vt2sjv24B76VyT06qrp5TQRhNxceydQusilZTbbf5hVoYb4bqNFY6g3P78RmLKOWkAn+ap2812+jjtipugeF3SiklfPnz4cP0wa/pG6i7ldCQ+en/y5K1nX+YWkl8PeUeAHXPXVdcZB7/TuHMuIwwyJF/Lz269i4yu++Ogd06jc9DdGoFBYyqfv7F8EwGnuhjNdfomYw4AkjwcHBysGWLu3Uk7/HSGONROujRRJm63hsPbGPxbjk3pXgNTd85EECgkHiZb6csUhXcAyt88ZrQFbrrK2yWqNlN/01q1617KXuwS0U0y6d7l02ZZh6/NTckEOkY13JThMumYCW4ESmYIkrbk2xtyDevtsifTBjrOmU4Pu5Jcg6L7yj7kO+difiPQxZYC8J5vtsf0L3VfXFzUo0eP1g97zhtn3M7blDsPdn5yNp3JlL6hM+JTC+g87LCm9BINjgu9NGhPAKcBpigk5xpMk2Lw9U6PdL+Z6Zl5rlardfk8zkiCfai6ejXI6enphrPIK1HIxM3KyZ6nFBnHzA7TjLybQDy2zdk+LQvtHAwZ/HRfJG+LIfNPNBLpdrmafMWeq64/ed73prGvJIe7ANySLF3vSHHS9a6AGod9enq6vu7o6Oia3fL8pbKcOSCIZKx4j1l0TCKaet0f2m6ujXR6cJkdMe4iv87WM7+S6iV4ce4xcrVt2o+4TfYHedl2Mjypkzq+B7sbytnZ2YYjtNPk/1XX7wuKU8nuo4gH1UBCR813SzHc5+TqGM3E7junY7DqogqnoFKeJ3ycYgeqZKGcGFzvYFtTZlKXmYC+QZX31PmesZTNSWdxXz3J2bZuYt62MBOwi5CA0UmF8SYFTJ3GhhzVs8zOmRv0cx2fShMbovj/LoXMuqKHzp67qISRUpzsroDHdhuwA3jMFLBspwjZfvYhtjhFgewXy+S8qaqN51XSN0U4l5j2ZFaA5IQg2o2DyTCP236YmpxIZdbTq67W5E3+2Vb+f3l5ufFw8qwVXl5ebixtdDb1rHLnwS7RBJ1BZyxMgxEAM7D5nNIC+aSRGwR8jNfY4GmMNuIuBWY2bEP2dTGwJRZtZp/rfCzHeaMzrzcjDgHg4nTHfDuWm4lFUtIx5oxRtlS7T3ZYtz2xXOYU1TG6yyf7E3DPrmI7P346mjexsg1215HMcGyWxPVyHnQyOVJGt7m2u13BGYvOvqnnAN6bb75Zl5eX63dU8pxO0o84eAPdVJftt1sPq9pckzaRJjlLed79TB9GcpI2sP353X6Dtpbf+NSV1JvNZHzuZ9rtMeoIA4k/38KRMviwim3287Ry58Euj/sx2NFpxGj4FBU+mYCL1zGyKUXAtOcEejy/M4yq6xOYKY4uZZFPAqOjwK5MTpxu0puddX2Nwabubs3M1/F+JrbNTtNRWNigb+LtIpuur2zHbU4m69qRwyQGXd5c7vMonWNnvewznacjrUl/tLklMXmK7NL3JRBl3XGudM45x9K1l4B3enpaq9VqnbLzdZyz3ZsjCMLxFVlrynXWt1OdaRM3dnX96eadSW/KI1F3JEef4fHNtQwCSJIzn7O8wIczpywHDswc0f89efLWs3DffPPN9brx2wFqk9x5sLMiHfYnIqmqDaAz4NGIJmZqBs2JM01CO4NuArsfTCPYWAymdmid89nGjN1eghn7yfIyaRJdMXrOdcnLpyw62ClVRIbI47yPz+zVsuSAb0tuMoHNxOkg+BoW3rg8PfzAIBN92HHuAmSOlDshGWIksBQBuQ6X3RGfzi47e3bUR7Gznu7lsoM20MWm86YDvoWbc75L7bH9JH67LGPwOO19EkaWBFTrzSSNn3xWcPRnYMt333YRPZyentb5+fl6MxofND3NxclXPYvcebDrhCmjyP7+1ToSBy83TzICclQ3DYwnfK7vFp/Nqidhrn9JWG8MvovSOkChECSZvtgm7C//ONm8jlk1Pw/PToztT8qNbNV6cNsYJfx6MMttDj9C2+IuYqZlDw4O6vj4uE2NTQ6k083ULuolIOxrHbVYaOMTOUw5ndBOGa3u4hy79HFHxhxpLUXT9hVdermLyGn3KdNkmPU5k0NA4xiy3IBKzulAklkfb4ZivawrG0/SXwIzN0yRNHD55/Lyss7Ozur09LROT0/X0aHT6B7HqR/PKu9IsLPQEOnME/XFiGkQZDYcaAIKy+XkX0pdepBTV3e92+8Iz+15GgPqJhoBx06iS5GR9cXBWOdesKeep35Ojs8OrgPot4M5Po10Yx1J9Ft1pZekzZYyBSYwtIPOxljfFPH5/A7kuvpZLsvZxQ67iIjEqyvfttmRo6w98XF8XnpwdoFPHPETgZx1iH46oIujZ2q0W27oxslzgUTHevW5Tm+6vSyHOg6w5eZx22vXNvaRT0XhTvSuDNbvMm9D7jzYLbHZDCijq0iMcLpB2YNiA7XTYDRDdte1zYbLTz/lnX3xBhq2tzPqJSY7OTenLegMGKGZ/XriV721EE6m16WcOz1wqzPbTl04zTpN7NuK8DqHvgSm28A248gxT1Zh6g/LZv12mryui4Q7B+Rrp+iB7WJZjCj42zbdc46RhLofnT0Y+HJedOrHiLGcbi57fLNL223NdQTFnO+opiPMbKd1kXnF1Gn6uvSEIacnqZfURZtk3wN2JycntVqt1s/GtI/L+CdDxjkd/XVr+RPBv09j3lC6yMATbIlBMN2W7csxitVqtd7Z5bQmheenTP55EnVlMIduoLTRdSwu3ydmvwvjpgFmEtgZkO1yfclpWv4f1pcdWrxXiWkcOoZMeqeBnLKhdOSDE3yKam4iE4Ho9Luka7btptH4Lm203e1ax3Rely7M8Q5QMpZTKrRz+hOB7NoyRXhVm4+i4zVL5KEjj7Fb36MY2+R8CKnjfaS2DdZhYkBA4tpY9Euiaz12BDzX2ieZbAdYQxDSNtbNMi4uLurs7OzaM367uWVywGPThrlnkTsPdl7E7cCvM/RujWK1Wq3vBYmsVqv1s+J4jEJg64x8us7ndIDKiUKjMwvepZ+ObHncrM+RXhetMppz9Mt+BBCjG97Az++OWPf2ru4Ny3WTMzYAThPpWQCPqWNLB17boqi0h860i5Cna1l39z3XdM42/7Nv24Rg1unA6cYuonHbMh5d5JNzXP8uTpIRlvXWle1+xtYSIeY+SAIL+9dF5DyX/3fSZVaWSCv/78pcWm915J91SUfqAfDseM+9tLwhnH7PY961exfS/bTyjgC7bcbULRT7MwOb3PXBwcH66SxkynFoNGyvA5jZGZwMCh04dO3kcYMf6+lSJ6yTjnkp1WrnzT6lzG2TMpuAuDnI45A2kI0HCHM8dXoSs22TTBHC08hUj5lwp5Mu+s0x3hbTva1+qp/jY0dVtam7JWLVkT/LlML0dSnLEZbbwLoJeFOk0F0z2TDXzbY9pCBj1kWvvt+TfUjmw7s6We7U/ul/Ro20qYlw8rqOpG4jo7aP9Ofx48d1dna2Bjmuz9nfcHyn/ruP92D3FJL1oqr+2X9haVWb7IsbJjqASgqDDsR57JzjJ6ZMay6e9Aa1Tph+iNi5ecL7mg5cWecSC6RjcZS4C/vMhDs8PGwfEcS2Jspj2z3h6Rj39zfXCKbJ5XTM2yGM+iaQIwHwhh7/EfA6YpTIo4uc7Mg78sOy/L0boyk1t6QLlrW3t/mYOksHeJZdxpD6j9PubllhHR3IxUcsEbxcE0DgszonfbC/np8kSbQVZisISJ24jbQzi4GQwJ65GqDLEgT1Q91M64kdweps9jbkzoMd74OhMySgJURnBJbJF0aW67p1oPPz83V5XPQ2wC1Jys4Ax4g5gTu2SfFksGMgKGxzgm5ztxaQdhCY0wdOqqn9bEMcOIGHbchxEgISC0dOHakgMFNuCnQ3Te9tE+rQTjftT3/8+0RUSEB4HW3CzvRpxCDX9W26jm2c7NJtX0pVu3zbs//nU0GmW15yrtPfBgpe70im29E9bSabSKh/39u7/ng3g6bvreOngYzi6JH95E5LA5wlOvF7Oj2GXUaDc/225M6D3eHh4XoTSRhcBsuDTHDL/1VXDiSGmwlSdTWJMvC+4Tt1ccLwXjs7ZrLXTIiOWVqWIi870illESEoTnU6CmWbXU/XHrc9uzZzDkmHgbdLuXUOv9OZHZN1HqDwtdYvnfKuk3KKaCbd0OYuL996zJXvT+rSlF30uAugdenfXSK6TnYFpIy1swKeA1MqzLro6lhqK5/ik/I7MWGjjYdoTY6/y1iwf1NdrMf/d/ZoYmNCSqDj8o7Hnf3iWOS1UHmIM59t2+1s7dqU47bRzj/dg90NJQOW7bJR4vn5+bUdWWaZYSBJV0xRQ8rgI8XsMG3AAbyU36U1eT7Z+pJ0UZwZ81LqgtfT8W+LUG3MrLebvHYe7P/+/v61tx84BehdmvxkfdHZtFuN59ixd+m87v+bAF4nTqMSAKh3Mu38ddGey3bbOwBzdNGJx7Ibu2fpu+3H9XSAZyCI8JwOpFNewI67JllXd12na0e47I8JVddels/zJ+BztLdUro/xWs5RAx37lY0ofBJK1VXKnRvMaFOMbvnWk7Sjy2YwKLlNufNgF2DhOkccx5RnTthNR9JFGgFBsjM/yWCKCKquT0hO4Ay2jaFjnjZ6O89ctxTNde3LdZ6sTiV6EhJMu8nJVHJAhb/zgdJkzHQgU0SZ9mXMSFY6h0O9d79POpnAzc5p2znUZ/pnx5v+xeEcHR1d08fk0P3/tohrl4iM7Tb5q+ptdFsEOIEWj1nvJDJ2jEu65/yII+b9cFM7PZ+cutzbu9qeb1t1G6a2mkCwvW7LRDA41l2UTyHBJ/ikHKYsmb2qqo1brmKvU6aBj2mzn6OOOn9xW/KOADsOJI2aA0Snx0FOlOFUxGp19SQGMkUaJx0/r2N9/quqDdZU1afp3MeUTWEkwGumNZyUxYlktmZmZj07cjTIEpAIMmSTLCvR75RG6vTrlDB/79Ka1GvHwrv03tNGc07ZWa/TQn70wJv3Gf1RloAuKSc+nCB9pr11+qa+Urc3X1E6HXbpaOrDEfsUEbmNBpS0L/qaSEjAbnpOpvWbMcj5ia69yY398yP+OgBKv/mbo9eOqPF7Z49LoM96fK9gACrvnKO98VzO20mcvl96lNxtAxzlzoOdJUrn0ztsNIzwsi0+95KYhdsgeU4kDiJlc2LwXXcdoLjdNnw7lC7yoaF17MuA14G/+9Pl9DMJuPidtixFpIz0SB78u9uTeviEio7tUgdOddLRc0Hf1y85mq5PNxETDToHs/7YrHcC8pxu7EgOojdGvB2hoh3aQTutzPZ3+shv1C1taNqFuaSvpXrZ523nhrh2qX3rhLZPgORDzWnv01ik//m8aeRt0pRy6I+WyIfH0nP0yZMn6/W58/Pza3rIeDm669ofPxfbZf+3AeVtyp0Hu075HQhYAnbZcBI2bCczpcYIhkmTVF05ZD8U1Yvk02PK+L+jJQNQxxQ7fTgdRlA0EfC6kUFtKZoju7ezpOPlumdAiICYvsfhpCyCVerg5Gf91iHHy4SA48L25zt/4zEfn8SRBtNzHieDXa6ZUnB0eoy6nULv0mWst2tvR1acet12HYGuy0DsIhxDt9v2wqWInJNd1I5uObaJuGNzfEMKM0Hut8lHxPY/zdluHk06mNKXnoP2fy6bQMenGhks/eYNzn9nhjifPSYTWbltILzzYFc1L6BzwC00RoKIWbVZG9fyctxPV/EDUv1MybQtQMm2dobM9pklMyKyU/AkSp3UFQ3XQLe0NtexYYudAoHNaULqt4sqGTU4FWmdebMKJ2vHvu2kO8dCcrAUxe4iaWuXQowOaH90IJOds6w4KUYjnVO1E3I0HP2kTV0/3HeTIabEJh1NZLRLC06OM2LAI9k6PDxcdLrRdcgv11a5oSrt8LJIJKlPg4Kjee5sniKmKbqzLXf2nN8c0WUjSu6hY5tSxpTVYVnRbze+u2QFblveEWDXSefAIo7aOLETpXHnZa6J0cfwLbneYOdIMf/z6Q5mYQY5R1qczIx87Lw9iXg+SQJThx3QTZOp03mXomOf4ohTL/8M7N0kIYunc/P4dm3ztZOOSBwYYbmOThe0GR+j2AESVGMn7OMS8LAejmGn52n8XO5Smozt76KKqs209+TQaX+TM+zmMp39JFN7+RudN+daPnMdP/PdY5ZyO4Bze7rsiH/PNRMZ83gZkFx2fATBzjro2tPNE/uPJdCmPjwetynveLCLdE4ljJnsL+DXDSwdUNKVZHvdBDQbTH0BJt7LQieRY0zl8Y/lpZ60PfU5NUHjZKRCJzhNFJY3AcSk+/SF9xWSTNhhsH7XYcefskkiusgk48trOgdjfXUTtGOtnUzHeb1BjNkF3rLC86e68hegI+h20R3tMUTDgN5lAjpQNEljW7alqzxmnXN0lJ/fO0CbhORh2lXdzTO2v9uZbeLTjSn7wXpZt/s5EVfqy3OXbfaxpC9PT0/XL1d1OzpiMpHCbdLNn7dT3hFgt+tAdOyeLJEgxPMMbHREBhYbWMqZWGjSnpE8DcbOopvsUwSRaNGO2yyRGz7opHK+mXAHgF0bOvHaWhxy97gv6s+AzHPTXj+xoVs/6Ryjz+nsyGmbTpaivEk6wIudmFx1fYkYdEOODg8P1/dLOQrh7kQSNKc0DT6dOLpzWzpytlSuMyHpM534Ukp1Ev7OeZ85xnZzzc7rerwuvoF26yiQxNX64aftz/NxImJddMXvnJ8XFxfrt4r7Vg5HtF27lvzYl4LcebBbSqt1Mp1LwPNaT9VVPt9pIZcdo+Y6nqO/nGvj9U6vzmApnZPqyu0MdOmYQX9KJ7LOicF1QNBN7Hx27NKOyuX6iTWUKaVKsFlKZ3bfJ7kJ6HXRAMGOb3g3oHANL9eyr9Eh7yc1QLL/Hhun6+xULZPedtGfoxSvjZL0sdzV6ur9f5m7SzadTRgkcCSRne5sE12UZVBn9ofCtCiPsW8sl2X6t/Srq4v9YVTNnZfeJxCC1PV5GjPqZIlMOgvydsqdB7su2tgmXZSQzy60z7MxA0h8FJnr9JMvkqpkCsVGS9Zkx2Lmtg3weI7TQxZGdmaHdMLdU+M5CTvCseR8upSlv3dg6Eew+dq0N791u+bY12kikoUz7WU7meRpJnh0SAd2cXFRBwcH114gyvKdfsx48LaaACfLoIPn8bTFD2OY7Miptm7npedm2usIlmvZLLu7bYH22a2pdcI5yLY4YuMtB87gsO6cG32HJHqndUfe2LcuddhldGyXHEeTF/YrbzA4PT1d+yLOX0aynifUV+eTrBNK2vTrBXjveLDzhN4WhpPtJGKgY6AzjcF5Ybjbxejorlv3SIqp6w8no5320iSfgCH1TY/sMePl+iaj0Il1W9eMWrrfzaw9ycPku01D0VsXQTtycBujP9/gP8mURqM868SOfgN2FxcX60fhse9pDwGPDjAO7Pz8fN3PqtpYJ+YDGDoCwohrqa8cM6ZIu3lpoEt7CHh22NZ753C7sZnssQM49qPq6q0J3bM16VfSR86DXMOHUDt9yujNutoW2XG8rQtmhy4vLzfW6TzfYyN5ao/XAa3rCeSW/OqvB9BVvQPALjJFWpSlyUEjY5TlKK/qyiCYDpjYJ9MbEUYonpzTzbdOwaQdU99yrHPwZNBsA89l3238bJPJxBQtdOkq69XRJctg+62/ySGzX11UaN3YqXQO3nbWEQnaSSdd2qkD6TjaRGe7bPZIWwIUSclFBx0hWCIGFNqAIwOmw3bdfdlFcwQ66tXzZynS9Pmd0zap7VKaXVQcYfnOIORYdEOw69b2vaGIv01EPr7A6UPbJ9fpOO5M1+bl1BNgLema8yb1/nqBm+UdA3a7Cg3fk8rRidlu1RUTyhMZwqpTZlXvjMlgp7RQBxRmoDTyCNvYOXL3u7vGTpATLvdrEXQ6Z0+n7f4YVBMphlGb0TsKtVi3cYCebNQ59brERDvd8NpdortdJeUw9c30UfrGezt3bX8cWa6l06Y9dtkPAsNEyOzQGYl1EQeBlHOATxxK/xh1Wl8kalPWYhcx2JnQpi5mMKwPzn1nDwJiPNdEhLf58Lylnc/boiy2p3uDAYkJ1+pYvzMfKZNzjBkRywR4u5C1p5U7D3aemJNMjKOLCFkmn4DC91aFedMhkfW6HDoOP/ZqydkkXeinh+S8SSfbJsqSbmLENG7vUs15LocTukvV2kHRgdhxpd/b+ku9TJOUn7uwTzvTqquHNXdMmgTgaYAwGyicNk//+eqpbZFj1ZVNBexYTq7NsZRJoE2fTBLo+OOkuYOY5VA3XgPkPEp7nLmwkNjwQQ1sn0lsF+Exzee+WTexlSlVb5JH0sYyqS/etN6tzbHcbv52JIKSuZBnX/o2A7aHY59+2sbtx+wHOt1OpJfXTED5tPKOADt+drLk3Lrrabh8tmV3w7gna9V8k2bOo0PxPXWM4DKh9/ev3uBAFtYZG+s0y7YRu31utydg1x/2JUa+BHT57uix203Je8PoPB1Rc9y2EZrJTnIt2+FNHdts7FmF2QTqK6nMKcXN6zvHd3h4uEGwAngBWL4Ch+VwjamLunz/p9vg8e7AmXV1ZVEYbe0a3bhezx3PzVzXpZozV6fbZbo2VF0HOts+29G1i/2hzjoSR13xTQZsJ8eQEXTGm7t9pwyS/cU22eazbkPuPNh5ELcJQakTR1UGOObx6dhtFDRWro3QURuUeIzMPu1OXt1rInbELMPOs1uP4fndxKF+2Ub3l9dNE5OTLP3KsWn9Le3qokK2I/dF+Vo7u8lROlKwTq2PZ01ldqSoq4sZBkY/HquOsO3t7a3TmRx/r5Fxw9EE6rRTpi47kJzEtujUufViMkkSuK0uSqfrCVRtr51+Tb74ncAdvXkts5vrk77S5kl/tvf4rbOzs3a9sZu/LrtqBjoTFJ7bLc10hJmftyXvCLDbRXmeiEsGnvO4oO/0CY3QaRFGIzmedT5HUgRCs8w4oUR0nbN3Hxx1sT9mxQacLjKYdO3jTsV0ES9TNl6j6NZuLF37lsY+x8JUvebFtu7tbT45x0C39P/TSEcaqD87o2QZuNMxv00RYcoJOHEs0l/33+PC7/ljCq5r50QmfA5tmWNFcbRFO552ErPv7Ad1S12n39RhpwPX6wcls+xcS4IwRXHWwQSu+bQuPF8S1U2PNfT878gHz+2ALtcT5LYRj8l/3JbcebCruq5Es24zwM4p+DdGGB1QBGyczvFtCN0kXtr51LUl/eEW6C7fbeMleHSRUVdfPqffuyiuAzmTCzpLnu+dbHyME9NGJCBdv7OJpktZUWcdayX4db+bvbp901ia+W5zutbJFOFVbT60oANjSurx7syltruN0UPSorwB2URxivBixwbFiOcOgY6bbPK5rc+drRtwJpvv2uFINMBP0GQ9JrYst5sL0xhQrxTrmeQ4a3UdEJr8RaedTXA8edzEccrK/HrKnQe7bjCo7C4U3xbBUBy1MMedCcIbS8l4GdnR6Ak+jjQ56czC9/b2rq23cFIxQsv5E9u3A3Bayw6YKVunYjqd2uC7a3K8i1adujQbneqhkPSwP6vVagP4WJbHbVt5nZNMuaxjCegcgUSszzj52MG0wE+CxHbxBvWU7edvxhkSePk9dj6l3uj8LCaOPMcRUjdvTTi3icfIqXiWTxt0GrVLWRuworfpd7bHQNfNt0k8JwxMIcV5UkpHXDu7nwjIRFwiHO+lsXm7o7qqdyDYbZsIZnv8f9u1S2zXu6s80bImYjYVJjYZAiceIzWuDTA9GGfSOSO238bXORdGPDyHdXWOoOsD6/ME5XdHTKyDO0TZ7o5Bp/0dOE6OhRM3jr/rS1eWy6HsEvmRrCxd6+jWjtQRan4jUXMkZifHcfCOQY7lFB3m2u7exi4FSRCyuH1TW5fEdk8A4rNDGVHbRmxXIQ4hDyFn+b0Dsqq6tvuxO2epb4yiSBxiP0lhcrcq56i/0166bMeST5wiwOnczpZvU+482FUtK3qaCAY9RyhhSN1rerq1jI6pp1xGdEw1VtUiYHTlrVZXtzwkQmG77Yg8WZcmF+uYnBlBoAONJYZqJ+d+sX2UOGjqKf3Nb0ttoXQpSfdx6Xoe81guRYqMun1OynKE0EUbBIs42i7Ci0Nm3VME0d04nfO9uYLtMBminacMZ1k8T7t0XkeGOsCk7AJ61o/1YGEmw23ly13ZBvsI6p1ZH99H1xEv/29S3+khT97hkscSqGzzj75+KVI0+ObYlAXYtuZ6U7nzYGdj2Wb0nKAGhEjYc7buUsj+4oDJFjtD4GTpcuO5rmPIBgSuX9HozY67hfFJbNg22K5N/t3Og+NCgGKfujZ3KegOiNLv7n44tqMTA6aPTzKVuS2S48SnBPycHqaeSJbyGyN8At5EYlj+/v7VfXFpm8fJzt2bUSZiYDunPrrU/VK0yOv4EtrOJrpo1rrvImbqlsdyncujPg4PD9dEINd2hKHTIdc7txFPtzk2H52wj1VXjzib5rCzCdSTfSL9k22DMhHbye7fLnlHgN02Z07pHKNTOgS6gJ2ZbVKPTFsuRUSsy5Fi+mFnYiPy455yrlOqOZ6JFen0lP5yTSjHO3EK0w/t7SYYHSmZrVNkjFp4f2MedURW640KU8RocfSx1NdtTqhLkXaON+OwlBLKeX4qiUEsfeXDBp48ebJ+5FOXQmdbzbT5O3fvdetr3tiwi74IdFPq0o6W5/M75w513kXMbk+XBt8luqMeY6vRNaM62ifL5aagKYW5JJwXjug8vhm/m0ZMsSPPX9qR50yXdaK+qDeW9XbKOwLs+GknMwmdElMEdv4514blVCbL9XdHO2yfJ/v0HD7eZ5WJm00KNMJMru639IHtNdB1jozt7vL926TTg1mvCUdALikZpmVMGHYdc/Yl+qZzv6kQOMh8O6JhsdOlY4xOoicTrdRHUpCy8sBo6oL2zVQpxyGp8UQMng97e3vXbJN94GeXfSApyqcdJseDJMa3AD1tpGCilHTkRC4tBDy2fVqCcBToTEZHRPg/y/IavyMtkpIuctvWrwl8OVecwu5IkyN3+9m3U+482HmSdWmIXZgbDYmM0o7UN0R34LbUxpTj9RmuDfA6PuYpEz2O58GDBxsOiI/6oTPPhHRUaodEZm2ZmJyNeBtr7QgFX5n05MmTNchloZ0TzbrKZ/d0kc4xTuUsZQecfnMd1gXbMTkESq6ZUs4mWnHQIQIcx6q6FuGx7pTB1Dfrjk3RuVbV2t46YkfbyXixXZk3bIcjUEeAHZh3YzCNG/vlaJV1dsCSc53+pZ6msTSJmyK5zu7c1q7tnquef4wkSRAt0S832HRCoh+f4fVGjskEgveR3S1IN/hej7DYUDsmO0U5BLuqeaMD6+gMPO0kKHs9IkDg19cQIO18yOK7424n9TTpy7q0/gimrsfCPga0ze6796/xk8cdIUWS5nMZlDiTgMcEznTaE+h1zod2yHLMvFkv9ZLIPPrlbRkBEOorbWCazWVbUk4+CTJ8gSydXpe+pa4czbGP1kmXNmWmwfPQYzoRWgMT25Kyp/sZU09sw+VxnKjXnOMU/VRH2uLIzp8mHx0xTfl8+MREYLvfPa72D+zzLsD96wlykTsPdlXXtycvOS5+GmiyVheQ6Ri1024ds+nEDj11RsjccpwLzTZWXk/jW8qlT+1K/6yrTMRufcX69IRYAlZOcLNfkgD2zekQsm5LymAEy752UUIXWfm8Ln3jtyqwPd2Y+bhTymxLvnutrBv3y8vL9XvrwtiPjo7aSGSK3DMeWV/Kg4QDFo42DOJLEXCuMzAymus+uyh46f/uN9oL6wyBmJy01wJtlxwDRnTxFV22YfJNU2rWBLwjEuwDH8SQjEkXwaZsRrhpF4ldB+7sfye/EUBX9Q4Bu6p+EkyRQMQMx8BCIIjTYSonxkcnbOlYPFmyUzRpX5i1Dbtq8/FGBA5HDhSysk5vXfs5AcxqXTbb1dXN73EiTod4PSfnbdMt+0J9LU26Lh3m8U57Q37YPo6Vo1WnECcA8C0DBkQyftbPur31PZt5EpXlaSfbxsWOjLsMA6Lsx64gR32ZkLKsKVV5U8dJW1mK1CcQy3XdtUvl0nZ8ry1BsyNfS0DHVC6jd0rs3aCXB4ATvOivUqafxtMRyZsCV0eiM/75bXoowtPKnQe7DD53p+3COnJtBp3pE08ELjLnj8ZgFroU3pPJetGZmwN4XieZIFz764BvlxtluzSFJyAnaeckyW53mRgdM86nJwEdKq91WVWb9+50KSQ6DIJT1weubbFMOovoynbjeh09OEJ0CruLMtM36oF6TJTJTT2J8PhIO0fmXXvZhsPDw41HTznduA3sKNO5S0Bn/VFMNLo5NwlJVefYPT5dlsPtzP8mThOATmKg88Yh9rmr0xkoEw36yylDQj3tMra2YZLxXTJvzyp3Huyqrqcnl6KBSexQyeIJcklPZGIyrz6BCIEiBhbh+ggZbtrTOUT+HyMnY+ucmCe2Jx+dNgGzAzgCLcue7vXyeJD9encb9RYiwLFZAr0JjKkjs+wOEBPZJJpPFGqCQWLAP+vOdpByXWeXVZj65bGtutqglLqZYs3u3O4pPyx7KcKjw3UbUr+jXZfvNke6FPEkS21Ykg7UTCqcqudxXtPZ3i5kzzqfzmVGgW9ZcTRtm45ws5ptM7/nmNe3uzT7NrLhdqeOnOMxuwe7WxA7xSnlwMiJ0ZXXQ+gg8nggOjs6eLNLM2gaaufIfctDl6brtn9XbebZUz6dYNYROufO8j1xllJT3b1yXdlm6LyuA0hHT7yB1v2m7qlf/ubzGZ1P/XLalnqZQCdCwmLnOTlRE5ccW5LOPvJJZ5l2pF/MUriMzoYdJbidnb0sZSWWrvM5S+ct6aezQbffkbyjJF7DcraBGcvedv4S4MWWeeuNCT314PnNculn4kfySZLbAZ11x3YsSUDU7eS64m3KnQc7h8fdIHQsko6R2/vNyDMZujcL55UrXAi2EHg7YM3/vNYpr5STtORkaDSqOCg/pHqJSbrNTJt1zqNz4BM7ty4ZOTmSzCSZIkSnKhkJbhO3m6A0OScDKvuc6Ic25bZQf3Q2ua4b75xLELR0ANGtt5DZ5/fuWa5T3x355PqOlJm8pV1so9tPosL/3cdndY4doLHvHVB0eu+iIOpvIpK7in0TN6p1ukzbfeuVo1ZvaqEv6tbPlmwz82FbirP7bRegfBq582C3lDawYTvqC8BdXFxsPKUj5RIwfMOvI6ilic0oLmsfjCLN7tl2/2anwHNznlOWXdpqEgIwJ/ISA6WOrft8N9DZyaY/7JsBw2lC9neacG531wdGlwbebX2KzbCPdBK0PbPi7sZ/tjvXd7o38FpfZuUEvEQKXWTe2QtBwfWwPgK9sxiWLiVHB7+3t3dt/NnvbcRmAhzaoNcxbcNT6m46x8C5i1+ysH8BoqrNewRNnHgd20J/5XvueP8k1+2mNrOfBiv7m11lF3J6E7nzYDelH3KM0UwYNY2Jj6Pi7kqnI7r7ZjpH3bFSMrQ8fowOMuIbQWk8XifyGmGOM5ozs4tMTNtGzP8ZuTrKsZP1ZE/bDHJdNOkoeIoeOlmKeLexbYLV0qR3n3jM6T7rL5+OVnaNuHlsyeF3EQqBmfbP/ia96bSn7dzZBc41ko+ksDiOJlD+Hsk1BkKSUZbX6WgJ8LonoZhgLtmMAYfzb0qL+nvX5nwyBc06DGoGOxMtLmFkXjG7EJ+U5Rn7pI4EdEKd7iK7EJabyp0Hu8lo6XDNqDPR+Tbf7r4eOsCJuZjBOvKw4eZYZ/zb0pQEvO5GaDtNRmn539JFHJRtk9P66taCGCWnrIA6dWpGm7Z5ojICT71JK9uJbpO02bZjkOh0knqdyurWJLoIPf3b1l6nk7YJgYaOkhur8lvGISC8v3/1clYSMjv5bVEJCV7axPnn+VFV1zZpeaw78rUtjdZd5/ns8e6IDMUkKZ8mTN3vkSkVal/iJY4ueqceM4YE9ekdhqkv9WwDavbZPqyqNrIFSynn2wa5yJ0HuyX25MjGYMetvS6D0dzEvBmJkInyN9bLNndpIU7cCfDiSDvmStDkgnYeAeVIceoPy5vq4Hf2p4somRqs2rx5ehICXfp0eHi4kY7r0lye8Pyt64edndvUrZVM+pnGzPUYcAhOk17sXJYcfQCDTsnjQ105Qs+j2rrNQyYG7JvJiu8TNcDZ4XcA2fWPt9Ms6XzS3yQms0tpTJfVke2cy8+pHvbTKcylfpqsRrf2Y7a5LsMVUOzGtiP5/p8PI9iFhNy23HmwY8QQ9uoUTNWmIXiSW7xbMLswGbHQoDrpHO1STpvOjtdPkyVtYB1e7+Kf12Y6hmidTG1iOVz/oO6r6lpE2a0LLTnf1B8nTTDgMTpUlj0BkR1fd57Pn4TjTH0zOpmAjE6sSwV3bSbw2plMoJxzJ2du4AnYdK+RIfGzg0+kSGcaktW11WSF68TuR5eaXRpTRrVsP3/jH8vcFtm5XQa56dpd+mDCsOumnJTH2wg4j/zHCNKPJOwIDdtfdd32QqgfPHiwvi/T4mtukq3YRW63tKr61Kc+Vd/8zd9c73rXu+q9731vffu3f3v9wi/8wsY5p6en9fGPf7ze85731Jd92ZfVRz7ykfrc5z63cc4v/uIv1oc//OF67rnn6r3vfW/94T/8hxcfXDrJZGwOx2NAHGQaQz7juHMz7snJSR0dHa1TO9P9Ya7TUQ53Qy611dvxDSq8d4X94p/vzfH6h/VHYb38n8D/4MGDOjo6quPj41YvVdcjrXw3mE7jxDQzX7fE/9NPfidj3UZqTIS61BnHwM7LBCvSpdumiT5FCCw/euGYup/8jdeyfKbTOS6O+rwzj7rh7sDz8/M6Ozvb+OR4hO3T/qfNUtY315q8kWRKAVqHHVCxD7y/lWO0DeAmsO8Azyn8iYB17ePD0P0whPTd0XKu99yv2nwUHH1JSMrZ2dlGPRwLk1P/v7f31prv8fHx2i/4dwYQacNtyq1Hdn/9r//1+vjHP17f/M3fXI8fP64/8kf+SH3bt31b/fzP/3w9//zzVVX1vd/7vfVX/spfqb/0l/5Svfjii/WJT3yifu/v/b31t/7W36qqt9jHhz/84Xr55Zfrb//tv12/9Eu/VH/wD/7BOjw8rB/6oR+6UXucMvNkoPE4bZnzUw7vQzo+Pr4GcmSwYezdxDCTNSOvmu8XYnvy/7bo1Oyvi2hdvs/j7wTgXOto1xPYTLUDDE8eXsPNQlxLZX87Ru611r29zQ0gBhVHh9aJdcByTBg4VqvV5lvTCRh2ah7jHNsFJKNblp1zCYwZsy6CYJrQ0XVnz3beFNofHe5UDp2vHXVXV5cus0z1df2JY0+qLZI57oyNx3pJP52epnZ1fWfbSBgcebrPjvbjm6wfk2j6wsvLy/Wu9P39t97Zx77xO20uQlDrsg+ed0t7E55W9la3XaLkl3/5l+u9731v/fW//tfrX/wX/8X6whe+UL/1t/7W+vEf//H6N/6Nf6Oqqv7hP/yH9bVf+7X1mc98pj7wgQ/UT/7kT9bv/t2/u/6f/+f/qZdeeqmqqn70R3+0vu/7vq9++Zd/ea3oJXn99dfrxRdfrN/9u3/3xtuayQSjdAIdQ3YzplzPiK57KWbVlXGcnp7W6enpmoFR6JDJyPl7PmkUk3M00HVrGmSbZHBL2627thisAvqeNPl9yRmYlbOMqlqPD1/t4wjcERf/Z/qFUTrHyroludkG+BwvtsfjEkfVRQhOj+0SdXZEhuUtHe8cr/XA1Kb7O9ngFC1Vbb6DzuV6HJf6swQQduoT0VuKzkjYSGoz10lWbNsmQK6P9kdw6PqZfuR4ouU33nijvvjFL9b5+fk1u3E/u1Qg/RWvp/7pE0l6jo+P177v5ORkrQ/rwqBLG0ikH59IAuR2/+RP/mR94QtfqBdeeGEcr13lbV+z+8IXvlBVVV/+5V9eVVWf/exn6+Lioj74wQ+uz/mar/ma+qqv+qo12H3mM5+pr/u6r1sDXVXVhz70ofrYxz5Wf//v//36hm/4hmv1nJ2d1dnZ2fr/119/vao2H35qZ8KbxfkwVQJdrovzMyB0wsiiO+a/pRx8B24dgHlS+BrroGs7WVWXvuwYstOwkw7MbqcIkZPDIHF2draRZts1px9g5yfry+6wKbrr6pnIAK+b/o+NTeVWXd+E4Do6QHCf2cfumtRlh0TAYSpzG+DYPjpw7NrCPnf2vq2f/r+7p5X1LEn6yZQvSe9qtdpIr012MNmSZamvJh8hekwJu++RadNcoufumvzvVDX9SZYPcm8eMzgU2lbKzVzL2zYODw83dqG7/qdZtlqStxXsLi8v63u+53vqW7/1W+t3/I7fUVVVr732Wh0dHdW73/3ujXNfeumleu2119bnEOjye37r5FOf+lT9wA/8wLXjHLgMCG+YZM6beWwyfEZBWa+bcu3b2KkZLEG3i0zyne3v9Mzzcm7nqM2ocy7BvANypj7o+HKt0yJTm5yjT8TV6c9gx6jbk3YSjo2Jj9uX+gj4bG8n25xzx6Bd//Q7o54cW7Kvru+7ONNdxODUldsBWIRR1jby1kkH3v4950Rnk7M3IPt3fk4ON/Mk9st+klDb6afsjuw4/ew/ZzeYdXIUbgKTenNOF7Wz38x8pZzMyaR4WVbm8GTvKdt+g2+C5zXTfHsWeVvB7uMf/3j9vb/39+pv/s2/+XZWU1VVn/zkJ+vVV19d///666/X+973vrXTclojIMOUpVNzXDTnBgyCggeHzs+ASYDj9+65dmTWZNeTIdN4vYEgMqU8UjYB3EKg6hyX1xgnEDDAdWx7iQw4rUx9eWIz728nwPP4SR0a6DqApCw5bUc53jCSc7ro+VmkA4FtEdo2mfpJXRugl8Rkzk5xqp/6mupInzvSR/EYuz7eb+j0pPXZjaFJ1UTWTIg5D7JmlqjOUVHV5vMv03b2jYS106PFfWSUmfPzaDjOm464sUwCZdoZSRt/00R2n/jEJ+onfuIn6m/8jb9Rv+23/bb18ZdffrnOz8/rV3/1Vzeiu8997nP18ssvr8/5mZ/5mY3yslsz51iyy8cSx8xt0nTQVCiBKwMXYOMg+v66zrANnPmz0/YNsp0xUrbdjOkodhLW0zl2ThgacepJWzpw93oay0kaiMezJZllWI+O9vb2NtNVna47B0i2OoFetxbXnevyPWZTRDiBZ8DPKaTuWtfVOdOuzZZnBT63zTrp7NW67cqY9MYMA8ugLg20S+nLXfrMTILf+m4x+HaRHz+7453dZ3NWlmu6NOb+/v46tZh53d1PWVXrdH4Huo740n8S9qq37rfMfObuWGdEbIuO5DhGJPc3yTzsIrcOdqvVqr77u7+7/vJf/sv1v/6v/2t99Vd/9cbv3/iN31iHh4f16U9/uj7ykY9UVdUv/MIv1C/+4i/WK6+8UlVVr7zySv3xP/7H6/Of/3y9973vraqqn/7pn64XXnih3v/+99+oPQm5vcvSEoeeASbI0QFOf+w//3KMnwQ6Di7b0kWF28TOdQLMqtmJduUR2BmZOhq283XfHD05TcJrO7CkdFFw1bxWYUDMsW267dasJhCZHDE/I906a9rf3WS9i1OedEFHsr+/+QqpXW1rV3E7pwgqMoHhRFS6cji2tkFHidPYLek3Yx+Smr9ua/wE3h5Dnue1OWdGUi93YRoMu5RoZzfeIe0ojO1ZIgkE/jxdJbbbZUeoi0nsO/NS4NuSWwe7j3/84/XjP/7j9T/+j/9jvetd71qvsb344ov18OHDevHFF+u7vuu76tVXX60v//IvrxdeeKG++7u/u1555ZX6wAc+UFVV3/Zt31bvf//76w/8gT9QP/zDP1yvvfZaff/3f399/OMfb6O3JXnzzTfXkQQZb3evmv+8maFqXqPid67/dSytqjaizRhIB6h2YB2Adbn3qmVAMzB6zdAsLUDHdQOn4ayPnJ9j0btZrJkwy+naZaAjayWQRbedGHCpny5K4gYWX2/9dn2hcKysny7KtbO0TbBuyrYIaena2xQ6Uzu+fHqcPI5dWZFOHxPJcPnbQM7nu+xdotRpjnh87SP4G++R7aKfzh6sU0Z6mYsByo4QWDcTweBSEH1jlzFaEhOA7sbzZ5FbB7s/82f+TFVV/Uv/0r+0cfzHfuzH6t/5d/6dqqr6E3/iT9T+/n595CMfqbOzs/rQhz5Uf/pP/+n1uQcHB/UTP/ET9bGPfaxeeeWVev755+ujH/1o/eAP/uCN22Pm0t30bQbcRW025IT2HiCvBZq1dRGLo0cKUwnsU+rjedv0wL75OtbvqLbqakcab9zu7ke0rugw2E+ThQ6UyKTd186JdQ5gmsRkr0trogbklEM27b4vEQDqOs5hGyjtms7J2HFdOnUvOeVdpbO7XduV6z0OTJNN0fou7ep0tQ3Qp2zGtvOWSKOF9tf5gG3rn/lO0sdxpTDdyvalT4nAvHzDeZ6Ua/cgBKZkl8anA+vO/kxo7EuXMnFPI2/7fXa/UZL77P71f/1fr8PDw2tRXKI2M/wpdcYBM0hG4pTPz8+v7e48Pz9f32/nDSlVu7NrGv0S4/XCPNmWnU9ukvfTTlIGUxbeFblNDHAG0o6pU5+80T9tsQ4IQgFOR2xO2UyOztFx1fU1sClSybkZp/zfjTOBnLpkW6P3LtXNulguf1uKeDoWnt+XxvVpAM/1TFEonVvXZzvJrj3d+GcedHYw9S9iYH7w4MH6XjM/ItDX8VoCuclIVxf7u1q9ldJ79OhRffGLX6xHjx61O7g5z+ivuqUYZ7Gqrm6c973IaYN11s0x339IMjAtq3R+d7V6axPMX/2rf/U3z312v9Hy/PPPb9yETpAys8gkmZxLhE7WaUOCgstlG6o2dzp1jI3RBK9liqIDZgKco9oOePJsT6ZtmWIlwPg5edZLJPWGTXbRDydTruE5viWE9zuxTbzW48dJyHUxXtNFaJNut0UIU9S5S7kZazsIl9ulertUNiOmLpUe4T2SSXMRMKhDArFJR6cH1tlF+Tx/irIjjGy6daqI10Nz7a7pNEtHclL2FGlN5IP94LkGuq4Mzzm3xVHdVJ5tIBFbl62g73BK0kDIICLXmmynPpMU63C1Wq0fBnJbcufBLs484hRaVW983YTh+XYwdM7ZKeUbgll3V2aO3yTis1GyjmnNIUbpSM5rAQYUts1MvHOkdKJxlktpGLfTqRsCXxcdV22+niRtmJxidLEtwlxq43TcgBOh/tKPLmJZqst6TD0dq85vHsucR+fGtVkSHRLE3BBcVRv3P5JIeAzZ7tTlp6iwDc5csD/U17RmbN15nZi62nZthG2l/m1/HBc7+Pxu4ksQ4TymXyHIT/aXnZgmTdQX600/Mx4Z+/TLBMeRoImMf+eyiHXaAbX9y5f8BpUvNXGawdFUVR+OZzC6/HMkhkf23N38bIdKoDDjq1reBeW2RrKpYUrRMJrzrROM5KwTT1JOOveDn3t7e+syvfW6m9zsjxmo6/bDee3UU8ZSOrIbl2kCsn0d2+bk3xUwrWv2m8f4v6OeAAOdSWc7XUqaNkf7Zb94LQmSnXiAi06S7aENXV5eXnu83lJqK5I+dgDi67ZF6JOO0uZIF3ky3dvZfgeA9iFdeY7maZMEu64Mp3+3pZo7Mrzkc9xe2jvtx/bMh3dUbUaHrNeRonV1W3LnwS7SpUPy1wGPz7PT75xkBwhhWjRQA0aOV80bSdyHnEv2xfNplHSONC4/jorlLqVDohuvjXTCCfH48eNr27WdprMuWQ711jnmqR10ANOaEfu5LeLsyu/6dJPypzLd7xybnHLXFoJ7dEHbzjh2WQfapZ904/52aa6US+Lnc9hmO8P8FkD1eSaL3ZwxSXC/+H8XnXseOdpiXzvA68acEc/SOibL5rWTsE1JTXLM2ZclOySImbx4kwozABwn2i3TpN78NvVrV+K4q9x5sOsYUYQpu5xn4Ouca6Rbi2J5LMNgxHLN7ph+6fritILTV7m2SxOlnG4TByOAJV12u03ZftbltJknUeeIuugy9U5j4Uivm2jWJyd/V3c+t7FeS9e+Ka3pPlTVNabuzQh21OlHlzmouv52AztjAw6FZGq1unpzOdvlOrj+N6X0uujDwGzwSN89julrF6FOfVo6ZsJJshibnYhyF9l5vNhuA3ann25Hsm8j8JxJRMj7YzsxcaLfor/p9GXA5NxLWcwAeVx5TUcI7iO7Gwrz2BYOqPPi3DFlUOgGmmX602kJiqOzLufNsm3wBLu0L5+T4RnAzZI7dhyD9QYVM7/JaVAnPDYBiZ2cdebIbkrBEVg7/QcEd2XRnX52kUxqRjlLERnr647R6VZdbSzppFtbreqjGY4LnW7aSOflOREb9hpN9044ikmZiY+jKOqlIyO0764+12ObNWk00E3t6my1s7eUy76wP6zn8vJy3P08ZWA4j01mPdYTKXV7DfxsO7977tGOeB2Bmt+niP825M6DXXZGRjqFEuC6G8Jzbq7rnnoeMahMN68zmvMfB94MmKkPr0e6zQYLAvjSkyvs+B3R8QkOnXR5ebP2Tnd0vmwzHWvHJi10GN1GBDt79rdbxLeOpvJcJnVJx2RC1GUePBaT2MHk2okpmyx1ANP1P1HCBDr5btCiLRjwbA8RzhsTFc9JR7WpNxEo9d7ZnklXN1e7+U3g9zi6fOuZ+t924zXtP+3ivZRL0tmO9d3NJW7+6iJPbi7qSCLJnAHSc8y64nm/aZ6N+aUijx8/HidxBtfb23PbgB16Bi5rT3t7b61DOW0zsZIYfsDS6UcDo6M0fve9cEwv2QBzjEZMJ2IwXLqe6ZQp6klfOsfilFAXXaSsjnxQBx1zN0M2c+/Se3aeBEk6hgnozKRTpseC0UGXSmT/pghzKap0JGbg3caU7Rxd3jbgZRqL450HBXdRZVd/2P4UueR4px9GEo7CbQuWzh4d/ewKdNHb0vpU5nG3cSM6iF4CIOkfAY8EkGNu/+FMi4U2Yl9BG0q7OxKQujoQ9TwhgHfEZ5u93VTuPNhF6KBpqAEKRi908ByUlDMxR14/yd7e1TvVmAY0GHhi8lg3gZgei9B5Jyqz8P6zbY50W6SR6zN5uxc7Tn3s2h3iQVAzAPOazvnlt6paP6mdE85PifFbuvnwgQCW2W5HHmwLTuXYOfpG4y6qnJy8nWTnWLtxs113Esc6rat0dfIcEp4JdB3ppswu4s1v7DvrNtlcIo5sY9cHAofL7vpiQup3PDqlxxu4O3Dl/84IxU4Tbfs+SesqbZueFkWJH+FDpdlvE27qphsn6mpqG+vZZpNPK3ce7MwU+GdgM2OzQ2M5VddfQcFr7NjskLoJ50nZTWqzQLbdhsmtvwF0OyxuC54m3DaGRWeRbeVL24wnMI9kHLr1wUmmiZbyHAnS+eY7wY66pEPpQHaXT7aDbfQE7yIzRzgdmFGcNnK9Bt5OpgjMWZFtv9kG8vs0tzrnO7VlIkrOUDhq6K7rdMGy0u4O6JZSoN2cNhhmXrtdBk+CJUGP13HjyuQzpv52Og9ARQ98l91S5J95le8dSTM57HR2m/KOADveG8NJZkbtyM4TehcH0a1ncGtuymGo75QDJ2n3qB+yuFzL1+aQ3fkJKJNDqqprtwVEzNCsj/zO9/9NQOcy/Ukikv4t3SLBiNaAx/q9e3QCOzqylP3kydXDmek0l/S5FC1nvKZIvDs/DsevR/J57r/baAdFhzrVn+PUgX93FJTzCKwca97EzD5O/XKGZcpkbMussMyU09XLVGj+Tx0dsTK4dJvHWB/vde3009XB8jrQCyljf7qNb25vl0XohPZBcOLvabv9budL7fesz9uWOw923eTgxObgxbE5fdIxs451M6oye2UawQZC1hNDYXQ0bbVmHx11pC/dhptuwpKF2dAIljnXLJZ9WzJU6r+b1Imu2OalNNfSROXYmqVH0o6Ux0nZTWgSJLd/Ii+8lsy1K2uKYKmvCZQIbiQguZ622o1z2tQRPesq57HPE9PPdWnXFMl1Yzmlvtjf1OHbadyHbkyWZBtwdvOAc3YCO/5FHIVO7c53g17Ik22WBLkDwen2KfqtiZD5e+rj/X1Tn+hL6Jfd59uUOw92VdfTGJkYcXBMVXEi07AcXm9zsp1TcTtyLllr6uHrdSI0rikiyksekwLM2wm4mE0ntY1Fsdz0yxPFzJRttZ5Yrx0JU5dst0GPDn1bXt+T0W3xpPO5sYO0jeLxYVndjf7WAfu0za6YTjJhc/1Tei1j5SjJ+uhAKb93jpjlTSlTf+ccWyJIfD2X9TEJo4suouD3boxIKLrxcWSUuUCQI6ExSfYYp43dOnz6v7e3t7FpJBt4Hjx4sJHK5EtZu34bMOnv2P/M805v3Y7lbh5ZN904s++0v12i85vKOwLsOgcS46Fx0IEugdQS82VajADmtEXK4WSK8TpC6iIyOrz8HqDzI8tYj+8Ncoo333nMTjQ6Tfvs4LhNfXKCdpyMiPl+LAOPv0+OkuV342WHMDlPRgxd9EsH5QnPCU5g5rgyzWw7ZRsz1h5zgx715bUbEyQTJeqA9pVzrH9HaN133sOY6zogoL5TBslE1y8/VSU6nlKOBpJtRI/9drREwtdFdAa4iQyyrxwH+oLDw8P1i1s5ZtFn1uk6Ekq9b+t3l/2gXUbnJgCU7jmp7I+JFNsy2dptyTsC7Ko2J7hZgydMN6AcEEdH+d0TmKkAr2UxJcRbEegYY/xcm2N7mV5M+i8RUcDOEyjXUXJOx6ymFKaFv3PiGvBYBvuUunkLCAGwc+ZLkd1S5D391kVaOe4nWKQc9yFtSlqJt5f4fPaXINBFEUxbub1el+3Si+y3nR3r87nuswFxki6C4W+eL1WbT0IhcDDLEvvqxt2kYzqnc/hTf9lvEhxuxOK8dnRmZ57x4sYw/vG8+Ib9/f06Ojqq8/PzjbLOz8+vRVL55E7QCTxiI9yBzLK8RtzNDY4ll01yftenLo1r/VT1b7F/FrnzYEeHW3UFdrzdgBLH0gGZAcGRDsWRTfcXtkNhZNOlUQiEATWCXb5zcwfL7vRDJ9lFYZwEPMeS4/ydBs5zuvQsJwv7sIsDsuM1KOSTUVenh0jHinPcTtVPw9jbu9q23UXuBHtG5XbCWftg+d6EwD55zB3ZWOiYu+jOerC+GF2yTN8Dymuii4jB3WBriSPPhoxuXu7v74+OkrY4ETdHHl3qMsCWzJAfbm0gTb84TyN8j5zJdq43Ic/nxcVFnZ+fb9x315EntsFZni57kvqYup/IDfVveyORSUTOW5PSRt6oTrJ8m3LnwY6gwcHmsSi7EwMUU0mdAU47P8lonOqgc09klvY5nUnnmM9s6LDRTcbSpZUchfFc/xbDtPOKPqxLkgdPGLbXIOe2+lgHmq5jlwmzDVBZbhclTbbjSMrjznscqzZ30TIKjI2mLjsy2xn13YFNJ3SK03kGlrSzc6zT9RbbW9d3nmcQMNhO9Tqa6G70znGmf1l+7onLS1u7HZVdeXT4JJfsQ/qV8rpMCqOsgEWiuzwlKvYR/0Ay7bFjOyy+Xy91Wq/dZjm22z4px0JY+LYWt/Ee7G4oU6Rkp0Ujj0wM0M6xGxTWE9CKgfJGZUY0ecO5nTYNgOcH5KZ3u9nxON3Btk5Ra0DIDo4S/TAKYVmuayl69IYUtmMaO/Z3aUworL+b8JMTsHSgnLZk40AX+aT9cWB2PEsR1hSNbGujj3Gdp5NtUU/+ZxmMQidHSLFN0uk5EuE5sVGD0xRZmGR05DTARvtnGppv8M6foz32yWtnHTBfXl6u711L2xLlRagTRpWHh4d1fn6+TnEmyuPj/PI0KLaLkR31GTG5Xa1W195YQpLS9ds+ktEmxyXldvZ9D3Y3lAABI4ZtDLSqrhlzVe9MzUA6J8VJlj+WxYjO0RWdvgGOazWsj221o+0Aw6kk9pERsNl2rjH4+LOLuDhB2D/uwGQ7zOhT3pKjtlOedDSlLFP3tBHC501lEhDTX6cQmYLq+jwB4RQZpS1LIMg2kol39uSx7L6b6dNOrBfb/yQcR+rLds06ncbs6nT/XadTsPYHjhT9nf2exoGgks0nzP74OraH5ybaTDrTG9To+zyXO5Cx7gh6tH3PC0apjOqcvXE/ogvqxf29DXlHgR2fjuGtrh6A7mbP/EZxGoKTk0yRZVZd35CRY1xv4TFHdEspiFzXTWazYwL/FME6t2+n43Qb01o5vwPfiPvmtk7676ISTq5t6a0l5thFbE6l3ZSBdqAdfSWtUzUDm8vpsgsG9U6HtkE6NPeHt+i4DR3omjiwXa7H5ZmgUGxzBGfP0+55uLchzFwskazJJv0953KeXVxcrDc38TFi1kt0GbBjeQG8qtrYsEb/EmEdXT2OyLqILfXaHvw/I8msUVMnKauz79uQOw92Tl9GurRYhAvsPH+KTBgx8dPOPgNOxuWIjWk8bzhwXwjSBqoufcO2d2ybn6nLqQ+KHalBPaTCLNU32pL5pW6uT7AdBmgLz5smy7ZzHBF2kcE2AmQnFWfAvi6BpKOkSRw1Ltls2uYU1ARAlG7M+Om287ujCZe3i2OLTrmLcSla7KRr63TM7SEp6MCLTp/l5rfLy8tra+skigS8pPaS3uuiffoAEgCnGjOXUj+vNbAsZbq22SvP6+zWeqOPCaFie6Lz25R3HNiZoSylWGzQS9HJxI4cxnOA077VanUt5eB2M/raVaZ0lu/HsbNzW933DiwJQCnb9xWaFXvidn3c29vbYOpd/dRzzunIDaWLVjrpmPqSTOPD6NdsOaSAUS2B3+UsgQOB3KCd3xkVdGl3C0HG6zOdXdHunIHoIrhdxoHtZjnesboEwtvK33bdEmmmk6d9TzuM2QenXnkjvdvluelx4BzKuh7b7Z2enveduG/T9ZOwH26fr2dG5qZjuE3uPNg5pWFnkIlCx00QNKPh8c7h0gjN4r0lnKmd3DjaRRIEprSxkyWnwTLpZDt2ye9sxxLI5f8Ysdc0GN3R8bJN7qf7xvMnwsHx47jcZOJ0ZTs9t02m+mwjHhdftxSh5jo6/l364uvY3m4XXtX1G4RNSOzEUhb7yrnHNri/bLd/py463XURhEkqQYbiiKKLeJciwU5vfBIQlyymJYOq2liPZ5vtU7oMAcciG13yezbzbNs9TAkB7zIAJBhLBJbzMvW4/9Zn1f19dk8lZmQ29AymH2uzC2sx0813GqInKB0DHf/h4eH6KSidYU3R402EgEODTJmd87QuJv1kckafmSCdE3abUi5ZetrkCe1dhJxg1rXHJce3RepLk3FbinRbWi3Eh3q0Q2DfuuPdsa6dliWisO3aHO/KoF3nN+46daaC9XbkjtKltQwwJpnsg+vo+j9FUp4jPMbrcg7Hn2nJ6UWkjnSdUelsuwO49JcknJH24eHhteWQqs0HpHf+IGNnEhTg7NKmnbA9SdFO/rYjQbchdx7sum3zneFyQjH1VnV9gtDYmG+20bFebnhhCJ8/MuYuOnHd/M3n7Wo00wRmu3wT89Qm1te1zXV1+syEI6ObUj9OqxkE2AeOkdt1E0C4aXQ4AYJB2m3x+O8CeF35rpvX7kLkLN6yT/vOeMQB0tapEzvE9I1t7Bymr6F0JMdRMiNOyy5AR//AyIpjwGwNbXkpPTjZGcHHoJc2sW3UHZ+2c3l5tWZnn3J5efWIwfPz82sp5yUf04F09Ox6coz+bcoC7RpkPI3cebAjQ2JE1xlHxJNnCRw5QNnZ5CjEIMc0EAfXW6g9wbsoxwAzfbp/+VyKCnwz+FJKiXqzo5miwIxNmG92jFFIVsyiLYyQ2Kas69AObjqhrPcpuqM9mFgRvFxe2tiVS3Dn9UtA16XvtkU3u0iXZvbuYDL3pXoIDttAkf1mn1hO+kjCmDZ3c48gNQGdy+exlEH7zFilrVMqjhEgJQSzi3byyQwII1wSZ+rQemNfsvtzf3+/nYN7e3t1eHi47ktHth3Nul+M6vjpexZpL7ssFdxU3hFg50lHQ+rWkqo2jXlinBmkw8PDjcmdbb9hXHR8naPhpPfTSczuzCK7iUhg6uraNpFz3RQNsW8di5tStXb2SY94XcftM1BFSA5IGOgQUkaXwpl00km3VnSTqND1JRVrorJErPi5zRnEbg1+SyCcspcAhdd3EXjqTv3eOGJdmdU7G0J9cc27az/HxenUw8PD9Txlaq7bsOG2duNiG/WfwbOzCae89/ev7pnzjdbpF8k6Qc5jRNBjG02IuJEsER6f0mMA4o5SluXgwHqk3fC9l9vA/TblzoNdHu1jZuMIgGJHMoHcgwcP6ujoaAPoyH62OdCq606Abc3vZmdd9GRx36ZzJ+aY7x3TNZO0bp2mdRvoEHxzvPXWTSJHAtSdJ1UHdJND4rb2Jd1OYMBjXD/J/9bf9ASTrlz+39lj1w6DxkRq/H/X5iXyk/7EAWdcEy1wTAluyYRwVzBTtQTA1N3dQ5fUacackR3naQAvpLJ7lVT6061hTUJAoexCKnP9wcFBHR0d1cnJSZ2cnNTR0dE4bgQ8ljsRCqbxTYD4CLT8JaXJMaqqjTHtSGNXt7MhnJt8WLVtaynt/LTyjgC75KzNNKq273aLOBUZwyArMchNjL2TjtEY6NhOG7yv7YRg0zkvluOojf3qHrEUMeB0bch17FvHrk1I2N5ddh4G6Jhq4iQ1gYg4ZThJF/lUbYKEU9Y55r6QOBB8c8ys3vroCMqS/dn5dqmn7lqOa8D68PCwqjYfGsxsiMfAUQuj3FxjmchjgI6RLNfc9/b26ujoqI6Pj9cAEgDIw6T5eK3YTJ5C0umEko0anZ62ASbbe3h4WCcnJ/Xw4cM6Pj7e6EOnDwNJhBHrdJ2zTIzwQgw8/2iLS2TPxz1fSYS71Dv7MQUjTyt3HuyWWDLZbycGODKhGAeF7LWLJjsAWGJ8PGdq52QUKdfskjIxffaD9bg8R3acvEsstgO8rk3WWxfVdfozMHji5lgXKdvhdk6li3a79nMtgtF/zjk8PFw7Yaaw9/f312spqaeLejsHy7Z3YuDNsV0IGcvmODCqYh3p09nZWVVtrr92wFh1fXdg9O8UJtvMc/PH9Nzx8fHGUgPLyJzmuGcZgv2l/aUd/mS5S8DdpS4T0aWtjtambESE84lPTKFeXC/bc3l5uY56s0bH3wz+JlypN+1gBJ/zaWO2ZUb3bPdtyp0HOz4mZwKfzlmSIQbg6LDsWBwVcUcUy4yYHT3LIGddkjI55C6i664js2dk4esYhaWfdPKdc3W76KBcNrcrdzctuwzrhU+hcerFDoS/ex1xm56qrt+sz7YnC0C9xhEfHx+v9Rs74wN8+bktC0E7cl8tT+tMXDYzG/k/Y+Q+ux2JrlIeNyuxP2xzdJp6CUhVtXbaiZi6TWGxtZxLndHpxn90uupsxeRqIrf5TBsdeVLPBDGOr3XjFLCXG9we/r8tdd8RK5J6PhSD56Rs6jRRIglJyukyMLcldx7svJtvMkJGcWRcnDB03hkYgl4HJJzo/OR3s1K2h/8vDX4Hap2T2BYJTNFeVf/W6fzOfHyXEnO/OVk68kHS4O3JrIvle1yZOpucv9NX+Y3s2C/BzTnd5LcO2AdPevYtzvfBgwcbaT+2NdfQydF50gbjeLcBnse/k26+RO8utxtnpjkJCqk7eufvk7PzuLsfJkIGDm9II9hSp3Tkvs2Fx+xfMgbObjh9TtDOeiLTuy6zAznbhyO3iaSxrxMg275ZH+cm39rhdoW8mezkf5OU6JXP87xNufNgF2OuumJ0BC07UDpW3vjotbmUV7XphBjC0xFZJufhB7NGtjkvrllMgJv+8vgSm+M5ZNDd2hGdvB2/ozbWy7SSAc/fXaZTJFP03KUA+Rv/z2f3mKfuMW7OFljPbBMB0+AbPeRVLZnwlK5O/tYB+i7RIIUglLbzhuKuP6wjY2Qb6XZlksUzMpjaPIHulD1wXyKTc89vmYcmHb6G45Pz9vf3NzYe5dMP+c5vfAamgTnn0+a69kzzycSc/cl85iaRqQ4CI/1TggDaSs6jj3Vb88lrYgN5TZHTybchdx7sqjbvs/H9P4xalsDOE6RjjmaG07pPZ6x0lgSGztDNeulgPPEnpmvpooguirM4qpve9+WUU3TEibQ0SaZ2sgwyUp/n692fLgLP5gO+Rbzb2Tnphv13eorRTNbnAnYBvKlss+5tOrJ0zj7H7MAjab9BzGQhesk42iY7u+p2WHrO0Z7zx/M6MJ50sxQlsY6M/+T8+Z1683o9hfPBt0NYn5zrXeQ0EdousktZIW2Tvpgyze+JVrMm61sGcr7fwecMjOcDQZa7YwNy92nMG0oWfDugq9pMgTHq42BuAzpOYkcpHrDJUdmAycxoDDR8g6O3Knd97dqb+jmhunaznKWImN/JblmWmb4nhtu4je1TX26zwdDldKkxO5vcInFxcbEeD6emumgtusjmk5zLNFiecr9ardZPtOAOQfeni6w9RpSJ6FBfHQmgbv14s6nfGUc7y67+/J7ogICx1GaCm7MA1Eln+92YdZEd/cEuxIb6MwGjraTsrNFlc9IUbU5tdL86v0LdT3OhI94UR6Jd1B7iSrAzabV/zWdsftoBe5ty58EuW3qrNrd4V21GVF4b4nl29J5IOc5PS5z3tigvhpQ0Vpj+2dnZtftbyHA7Z8/2EoRTL535UjTgSUZQC+Mz2yPQMSrghhFvj+50bwD25J0iWJ7TpX7t3FwGHVs3IR1dm32nv3EETPd0LJ31kIFPY9E5Kgt1EcfS2e6SUE8eq06vtE1H2h0wxkYcIRHA3Jeu77TJpSdz5Hra/FJkzvU8rulO/e7WVC8vL+v8/Lyqrm6FCtB1BLyzd9drnUz+ir9bF0tASXFWgvrjjfnxBbF5+yaODed3CB/9gTer3YbcebCj0jioBjoOTs71+fzcJo7uOkblyU8DZ1h/dna2wfbZt865dE6NoJd6uuiEjr9j5GTQBjo7Gacvk6JIms5A63GZJtgU4bmfHEuDJOvj9Z2kXoKcn7xhWQIVA14XDewiPI/pQvaLfdtG0uKkunrcpqmtHXljf53ui+1ku33q95oX28HUXs6NM3UWxxmDaew6kpRohVGn+9Dpiik9zjPqt8skbZNp7Pw/53naQF3SP6TuHDPh41ykjyHx4+7ZLtLm+HiJhkAX/aStty13Huyqrq8rOPLI/2aXVddTgl105v9TFx20wY1Ok4vyfNQYDcksz2kbOhOmF9wPts+/ecJPjo91E/TMxOlgCHQBO0d2jrpZnkHOZCTrEBzH1M1rXF93XieOBCfg8HldZOHz0y6TkwjHgQSE/fQGoFzndprU5VjqZ7mdzt02k4oQGxKx/OZnKzry9RM1DFRVV8+aJQBlHnsNLJsptkU8E9AFqKmzrgz3aW9vb23b0SfXaOPc2c8lsOvsujtn6bjtlr6M5xGMaRMRpnXZd2fNUgZ3IhNYvekr59Mn37bcebDjYiwdUNX1rcQd2zMQusycZ6ZLUOvALgNN45/SY9Mk5WfHYicAphObHLev7wDegMv2sI6kY8/OztbpWC/8T5PN36dIgsC6S3SyC5OmZBJyp90EnL5mKXpk5ONUbJeaXYq4c2y63syd0qUlHX3uKtFTF8F6Hl1eXtbR0dH6OgrrdfvYBzpV3ybUzQXO5yUwTJ3OghAk2E4682y62d+/Wr/kfMhct0/yvJxAZykyzf/sY9q+ZIv0Jd05HYmyfdsv5vcJ6FwGge4mNreL3Hmw64w6x61MA5bXWjpQ8AA7cnPq0eLyPJl2ASKvmS1NisnpT46Y4rRpJ2btcRRJxSb1cROWupQ2WpIOIE0SdgU9O4Luui6C6xzGtlTf1NZOZ1OKkvZq5zyVlePecZrv28SRoW8jsO2l3Ox29TnT2Hju5qZ8pkKnaMnkbKlcksMQtOjGa19d9H1xcbEG/qpat5Hti4+J0A4IKExJLumm6yvL8rWdP5hshXYYXfPpM5OdkiQwImc0uKt9PovcebAjq4hCd9nxE4M0+5oMnMJcdLerrqo2HErK5xPZI3ZUdECcDM6H59z0xZNl0tXkUO3scz77YLKQtiaFSaDzBJzEaTLWS0fesUuO37a0rfvbCccq36ljpruWAMpR6DQmLGMaP6/35bocXyIK7ucESq7bUbfbO+m1c7IBu729vY15ScBcan/uh1s6h/3aZm/UOcHb2R/uTu1Sufme9TtGnoeHh+unpXRkme3w2Hb2wjoNuo602Jeck+sMPhPpoD+0rdDXUWdM3VbVxvp+znUW4T6yu6HQETLi6hRplkyQ5HcyMq+BZPIG7PjKDBqPGS/bMK1T7O9fPXE8//tewG4y24lxIk+Tf5sDtnQOMqAfkGMqaJecvMFzYuqRpbWPyRFPqZrOAfn+KddNtt5FKpGsaeX3LnXLiCD1dLsBCbgpI+O77QbtLkroQJVEJ2V3pCfluqwl6YjgkvPsyOb+/v46iiIRmYBhqm8XQsSoxHbc+YoHDx6sHwmX3Zd5fBwBj9mk9NvSERDrxgSKSyYEOh5LxJn63fduTnX15/qOkFMv9J1Oc/Pap83oTHLnwY6PnYmz7bZ1G8w8SFXX16m4NhCJMdG5czPGFKk4MnEkxbaQjXUPGE7ZjgwMuOzPUpTBNG5k2wTwpPLOz26Nyscp26IG6zFip+7dfV1/tgGmmTQBoAPcjv3HyQSUPIZk5SQoZr92th536yDC/zvg4j1TbIPL6SI71t2tsxnMXMfUzq5erqll3nksTQhYzxIZYP0c76WlBq5RBUSOj4/rueeeq5OTk43XGtF3WC+sc4p60m+SMd76wB3dnIMhzEkfB7hJ5qwrg7HbOOmuK9MRMVOcBL/bljsPdomsqEynEKs2N6Pk/6rN3LwXZpnK8OQgk8rx7hmL2+rI77w5mZPNToniSWLnbkO1Q+C5jEYm4HF/ObFSf8eELXTkPGcCoV2iRJa3y/pQ58gsfDo87WcJOAlY3KjA38xwOyfXRTtdfWbejgKX+s4y0gYTuw7gJgDp9BBhvyZ9+zfqKRkEZ1oyfl3WgX2zU881vm2AJIWpa0f2VbWxw5QRHc9j+/jZLWd0QMe3i/OB9Wk3Hz/n66o2d52en59v7KomYY2vdDZrmpuM3Pb23rq1xO3vwI3jcB/Z3VDOzs42nEhVbRhW1eZCriMOGl+O8XgXVaWOTjpHQObOLfSpL5+ZOLyJs2pO33XpmW1io0tfOdG3gYuNOX2agMN97diloxbXtwvgZeItpWgZ8TJNxvb5e87x9Wy/r6HT4e7eTPLOCZu0LYlT62zLNtIzAeQU7bAsRwDb2tnJUsTtpYXU48huGyHzH52udc50MstOZsVLGj4n9fL+u47IdRGvyXHX7rQ9GQLaicfDcytzotNfwDLPqqyqjRdWm9x1UbTTtGy3AwLqKeTlNuXOg10GzyBjgGCqzuctRSOM7rrJz/tSdnFSE6t19OeUJGUCOUexbvPEsKiTKRpgGpQgx0lqMYGgw3cfeP9gB5xd1JHjnW5YBp0nU5FTZNk5Up/fRTzWF9mvHQSdVX6zLXX96ggB9cpsRsTAuNT2XcDLoLctldmVayLH/w0q3VNIDMgdOHQOmL/zvOgluw9Zbwd21B0jxInM2uG7XemTyQ79k98wkDQq7YZpXr5GibtNU97jx4/r9PR0nR1j/1Nf6loiox5nAzH9M6P0e7B7CukckNl3jk3XT+UsMTSyzy7F1bXRk9jv0kt05V2NbCeBatKDwYXX+XtX9jajnibsLmLnHDHg+vxcw8nXAaTTUAa7KVq3uOwc2wZ0HrPUbQdtcsM+eC3EzN91dynqlD9lGlzukljvHaBZOM7sY9X1ta8pZRhd5NNPTHIUxLSk+81ow1FHNz/TjqV7+ui8CYghZwY5k5wJgNOmRJfpv+eGicz5+flGH6KPRIQMDvgQCLY3fQ4J7uaBbYP6nOzN/b9PY95Q8kQFKpFOpGp5EXximBEagSe6J0XVlYF2zCzfmecPO3O6oOr6LRR0UNsiWZ7Pa+woO6dFotCBUZeqcH2+truhOO3uxscAwz4aSKgDR21d+rEjQm5v11afw353E/zy8nK9lmLdc+dl5wS7NtApul1T+7sIo+uDz186x/Ogu4a/MXLlfOkeP0eHPgEf51QXoRnI7GC7yJnldnbEscr3tCmvGeO78wLsboOBzjezd/Pa+iBh4PhE3xcXF+u2+1VSS3WnXkaA6Z99XUf4poxEB+IZy9uUOw92yS+TXVTVNSdoI82nJ1MnNr4OMDJ5Xb8dbsCZ9+Z0jjfXdhHcxFpdL/vTOVLvBOV3M8b0PZO4S2dOzjYMs0sDpi6TBzsas3wTgy766sY0uuy2+XfjuiRTlGynwSg959sBGQAjdLTdGojFTJzrhazPjpj1GaQmQuRyunSpx8p2T/tPBDORUD9YYdKZCUTa4y36aRPtju2hrU3zkMQ0BDbf2Ta2o4voqDeKo6olUmuiHz0HrDpSzf6xvKREWT7Hz2BnMJ9s2uN3m3LnwY6pjThNTsIMAicnhb93AxjJ753z4O8eSAPq0jv0bDQEk12kmyipwzeZGoDYj7RlAsz8LT0Gjekc5v7pSKg31t1NbJMG6oy6Y9st24Bi+m3JubjsjD3XUAJ2XWTM67eB3fSb2+p08GT3vMY2sFptvrCYY0c7sMOkszVpYMqec4VtcBRvsOO74XJe3g3IPlCXJGN0yB2wdtHkNGbUB3dOBvB4QzyjG0c6FGeFOmDy2Jt4ZB0vgJc2MJLO/GN7qQOTLJMo9t0p2A7E2Z9Ev/dg9xSSgZlSZXbSdDoEwiXnP4Elox2DLq/tFrjdTjKq7t41t6kroztG4I/hE4w42XmtgY7M2PcWdkBnx9alhyZddH10ashpVI7Rko6WQG3p+l2FE34aRzL9XGM95njXFoJJ134eW+qL05xs/9I5BKcpMxEJqJHodfW4/Vy3JAjFpmJ7+/v71yLlfHLnoiNsgijts3vSyGSPE+CxvfmdPoiyNF5sh39z9G0dRlec87w+zyxNO1er1cbriVh+J+mHdxF3RI79IfG/TXlHgJ3FoX/VZqqIqTezc25Drrr+jDwCA8+hE3e5ju4iZJg0HE4cTmI6CZbBT9a55OS4jpIyGeHSYLMAz4daE/hiuF26tru5ln1wfRyXKRIziWGfPTa7EoOJNe8qXZRntsvf+H8+fYzlkU1bDExpf5xxR2bYzwlMnfFwpN5t9plAO86UqTFu+yc4JIIzyKQMRmh8+3W++2k+/GPfl9LdtgeTgRzvyqTN34Q4dW1g/R2p94adCKPD7sZvgiIju4ODg/XczTm8r8/kxmniDhzp33jNbcudBzsCVIST1AvaCaG7RWxOpKrZORDYKPv7+2tW53QJ2+tJwt/MEOkMukgtdVgHnGhTtMp+m6WZlT558mTjPXWMgPxuMaZQOFG5DtCtVXSAZ71PQsdAxu0Ie7p2iT2nfZYpUiQY+7wO+Lpz+d3gNZWdvtMpkfkvpbk7sbM2icz8ij0yynYfOO6JsGizjP4Cpo6y0x4CWTIMBj3W5zltYLXz7kgT+zMBB2985xzgdSY3Bkt+dsc4Bl0U3313epztD7AdHx+vryOJqar1O/+syylomMT2fr8b84aS1CMnD5mwUxRVVwut+Z5rqq6nxiI0rsvLq8fkTJORZUamKKUzAm5uYNkGKq/FxdGZhS2louhM6Ux4w6gNm9Fct9GmS9sxal3Sz+SUl0gIj3V9dX0doHUsfErHbBtb9oWRaidLTLdrj51WF9kR5LZlK7o6na1wG0yGHE1RDwR4r+9yznAno9PTe3t7G2/T4HkkhySJrJfrRSRi3CHKNmdtnvdKUtcdEUn/kj7kvKAd5HxGtmyDfZIj6+iUZNNzmASTdtMBonVAXxPiy/lL/bKduZb6oGQ8YgP399ndUDJJImYMdMaMNJzqogMgADklFbHjZLlpB592QLHTJBtPeT6fN4tODorANzko6mWJtboukocuWuLaVAdm3mrPNnUgY5lSRwRjTnAz4K486oNjb2fjNnTi9k9j1JXhCKwD1aW67YyXIrYO6LqILcf5P8kV09jOQrDtXeRqEpR6OBcIAhnTbrPHlK4MCaYD9y5Q+oEpa8AI9vLyciPlR5tkJiEOnc+m7PrtCL/LdMRO0/b0Of7g6OioVqurl6sywmZbUrajNvqtbi52ESSzMFPbqZ98Z9kE+tuSOw92ZpLd8W3pNTIxAp9TTg7XJ4eWMjgp7Kw64OQxTpApF+41B4I+DTjndu20Y5wcfM51lMKHcFPvnCBdjp+Ta5tMkY9TuyzXjN5964hD2myA4YR3fZN0fZ/OW8oiuN1L0jl2gxfT+RPJ6IgSx73b6diRulzrvnF8cs4ueqqqFvD4GeFu3/zvdDttw0ST86O7n427sidSwBu4c9zjEZLGOZXruaQQWa2ubgbP96Ojo/UOzKraeDA0QSWbT7p5bz2zPpMWk4wuGPC17Eeyaru8iu0mcufBLoNJhuZUB3dHJf/MEL2q1saSV2FMOfeOjXSTnGmFLqc9OTCnM+wYHC2ZlVVdn7iOYjvpWFi+kzFPD7tmGbze9+Rsc2ouoyMCLMugvQ3kurom3Zjh7hKB8vxE9vv7/XvnCGoe545dL9VFEuct5l1E30VcER/bNt6MStneLno2gegyDh2Jm5yw05XWH/XQ3fbjucbrp/HmeDlC5HcDhQk2bdzpxq5Ojo8j6idPnmyAyNnZ2TXATAToNCUzS5OumS4miHZEI9d4nOyn73djPoVksLy93WyUA5PBZkQSIMwi+RQFmq0Z0GzgPDZFKVV9VJLPtC1tZb+SYuH1XSRnJ8K+cGJzInh36HRPHfvq6GHK4/v4ko4cbVHY/4x7+jDpYimaNdA8rTiS3AWwJvtwOxydGORIgtwmO9cOiLsxMyGizXdEwREqy065XVrVaeiuH+6Ty2eZ3CmcT9+mRNvt2my9EYSdQaHtcY5O7WUbnK0IUMUHeAwyL+33qq42m7mPuTZzOeMfQsCHWVdd+dds/smzNLulCbbbdaaupcjyWeTOgx1z5d5JyCcGMN1Gxunrc5Ml1/myAOxJEyHgccMMneoS+6m6WoviRJ/AIc6C6yeMEMzyyfydcknddjhOXTBtwfoMKJ0BLwE8+0OHs3QNHU7nlNgHTv5Ol2yzHfTkrLeJo+Ep1dM5PbaLAG6nakdrMsO+sT0pl2DlCMs6W8pKxOl35I5j6vbw3K7P+X1Xp8g2WUfT5+SUp/InUjhlVtKfkGqD9lIEZ53GF+X//GZ/ljlPEpw2McoPWGUHa6K+pEST4UofQnTzLE36h8k+qCv6LEaatyl3HuwODw/XYMZIxKmcg4ODjXc+VfWbHao23xweA/Ejvmh8vC/IufpM3mkHYoROtYt0GK2Q1XMHG0Ep/aHhOVKlTCBFndJxJ5JeEt42YTC1bAM8TvD8xjSQAc5sOr+lLK65UOhQngbwmLrqIiG3hUIdkdh4vNPObhzdDtuG2xTpyAqdmevx2HTXORrjvOvqexqm70gsIOGUnAkB6zfg+RoL5yHPow3mMyTb/mBJunawLwGl/f2rB8bTV3FuZMzzrr2cQ7A7Oztbk/yLi4s6OTmp1Wq19q0ERS5jdLbU2UH6wEwSN83cltx5sGO0EjCLk83gMiXZbZslyHjNJykC7qzK2h7X+JxSctqiS0FWLU/wnOc3IHA9kfckuW902Gx7J5z8BGamLQl0nLB0Cp1jJLB0awKWJcDLZGFETh3T+S3t9jIA53uIjtuz68TMOHEtqYvq2U+2xxGI05WdA6bzYX0EONtF6nRburJvIs4IpB4TuEkPu9YxiXVIu16KMrtyDIwdIWOZ/C39jO+ouh6NL4nJgIlefBpvvk/ZidLi73L+0dHRev53hJrvtaMeAna8vYTSgV7ng+KrcsvB+fn5og5uKssavQX5z/6z/6z29vbqe77ne9bHTk9P6+Mf/3i95z3vqS/7si+rj3zkI/W5z31u47pf/MVfrA9/+MP13HPP1Xvf+976w3/4Dz/V7hyG4LzfhE9TCEs5OTmp4+PjOj4+XrOWquXJE0cRZnN2dlanp6f15ptv1ptvvllvvPFGPXr0aOO9UAHBk5OTjb8Ym1MfE0M3cCadkJu7o6+wNtaVPvoVQgQ9fzdoTcx+KWVp3dkJ0+k61df13aDK3yK8tkvRddFWt97kMWE/uj4t/fG6KYqLzR4fH9fJyUk9fPiwnn/++Xruuefq+eefr4cPH26MJ51Vyk0mIeml8/PzOjs7W/+RkXPDAvXoKNb92BaVd+Ixoe483h0AUpZ+5zg60vJ6+ySdHpzZyXkmH5wvIRe+DSffecM732C/JLQhEjpmmzLPSe4PDw/XdkV/4MwU53/0GZviK4Dic6aNVp4vtE2mLpMi5e7R25K3NbL72Z/92fqzf/bP1j/zz/wzG8e/93u/t/7KX/kr9Zf+0l+qF198sT7xiU/U7/29v7f+1t/6W1X1Fnv+8Ic/XC+//HL97b/9t+uXfumX6g/+wT9Yh4eH9UM/9EM3akPArmNCTFvm7QjHx8drw/PWXAonf8QOMkaStUEb3f7+fh0fH2+s/eU+HTKkjvmnnqRlaOzRIa+N8R8fH19Lh3HSLoGWwYBtJNPtojfqjWLdZodiHFEHdGwbo4NOP2kLx2W6Jr+z/SYeLNvfeaxLf+0SJYThkmiQePA8R61OJ8eZkETQRgy27Ccj7AlMtoGcx2gaS9ZhENyFOLl9BmMK16rdjy4qdh+79HHGz2uoGSNmkzo7pL0lc+A1OJ9nPWa5oruloutLyuf8i27s09JfblRJ++NHeR/lEhlkuR1p2t/fr6Ojow2d3Za8bWD3xS9+sb7jO76j/vyf//P1x/7YH1sf/8IXvlD/9X/9X9eP//iP17/yr/wrVVX1Yz/2Y/W1X/u19b/9b/9bfeADH6i/9tf+Wv38z/98/c//8/9cL730Un391399/af/6X9a3/d931f/yX/yn6wfULqLJNpZ+j1Ooao27qfzvSgEjshS6iODlbDcTzxnXWFWR0dH19idHzFWVRtt5NNeuA6W/p2fn28YfcCXuf6ldTMDXPRlg2SUaTbdldc5gExolhtnTaFTmYCOY9U5zm49lsLImWnRKbJx/V1Z7GPGgLry2u8UdRDUGJWF3DE9yXZPxKEbewPGtgioa+OSTlxP1zanng0yu4gjVafKHYXxNxORacxZFgHRdmw9EPTcXp7j/zPPuAZHPe4SFXXgljKpi8PDw41HrOWcqs0n3mSMbCe0edo6bTRl8F2eeUTZbcnbBnYf//jH68Mf/nB98IMf3AC7z372s3VxcVEf/OAH18e+5mu+pr7qq76qPvOZz9QHPvCB+sxnPlNf93VfVy+99NL6nA996EP1sY99rP7+3//79Q3f8A3X6ktaJvL6669X1fXtsRQaBHdjcvNKyiDYOGqgTM49DjwD7l2d/H54eNjes0KjcLsyKc3Iu+h02nVZNS8gp24CsQFtchgsK5NxaUJ7nBgZWKd+rFu+E/DSrl0jBbfZY07HTLDiROdfp2+n63hel4r0mijJhm/5WAKPTqiXRNXcOFG121oq++VjN2nPUvss1inr68Da62weq+78Ln3vSJJ2xgzHFGFaN1OGoPMxXm+MjmjjBtkO3Nmv2A77ywd6Hx4eXiO3vMakYAoCqJe0l3/57TdNGvO//W//2/q7f/fv1s/+7M9e++21116ro6Ojeve7371x/KWXXqrXXnttfQ6BLr/nt04+9alP1Q/8wA9cO354eLgGl6pNh5mJ7QnOtB8HLymGXdYOO6cfx5g2eHOHwe/o6GjM79Mw6Jj4rExOQAp3k04pxy5FxofqMmXBKJEAEekYfuo0Q5/A0OUSFLxuR/Dv6qTkfIojPzvArowO1BiFdNEIx9B97SJojr8fZMxyngZUDN5pZ/cyzkmmMXsWoFsqf/p9AtfOGXf/x35yjdPzJHkkAwQCZoI4VyIsZ8oUmAzRpp0iTp0sP78z4qKeGCEy9c2UuZcVYh8pI/6K9VA3HDO2tfNJsffcGjERnKeVWwe7f/yP/3H9+//+v18//dM/XScnJ7dd/Cif/OQn69VXX13///rrr9f73ve+jRsgu0X4KJVpksjjx4+vsSizk22OkMc7Zs/juSUhqQOyq25hO2XQsGKcUxTDlFwnnYMi0GVHlh1JnKLL6iI4AwDPpWPwtZ4sXTrHTsATnfqw4+icAceIn92tK47KXFfqyTkELgMa10H8VBrb3DYQuIlQhx2zdrRhm58A3DbXOb6OeFAchXXg1enCduKozqSI42obze98sETV9Xe2UVe2zfS/A7yUlfbRV6Vek+Zc34FprreODdQpw3M7dbJPJljxU0sElX3vCDKJejIVX/KR3Wc/+9n6/Oc/X//sP/vPro89efKk/sbf+Bv1p/7Un6q/+lf/ap2fn9ev/uqvbkR3n/vc5+rll1+uqqqXX365fuZnfmaj3OzWzDmW7KLshDd7V9WGEXDAPCEZzXFy2zjoHKeUhAebnymL4JW8O0GB9+plQpAB5XovUnuCk61OUR2BrgNY6s2sNAac85wO7urtzq+6foO42S/TIdQvIzyOgZlnV2YnBDU/VqoDbjoHO086ouxiy842RnNLj15LmygG8ejPINJFBRHvBjaTj3iccqxrz9I5u8qUynRURvDw71XXMwn+bkfs33Msm81Moruyuno7vdKXdGPT2SnH0tkaznkCaf7n+SyXvpEAG3+Y+pg+35aJMQGY5hwzXbtkE24itw52v+t3/a76uZ/7uY1j3/md31lf8zVfU9/3fd9X73vf++rw8LA+/elP10c+8pGqqvqFX/iF+sVf/MV65ZVXqqrqlVdeqT/+x/94ff7zn6/3vve9VVX10z/90/XCCy/U+9///hu1J0qb2EucTuc4ElIzxdNFBcxdc5DMtlx+B7Axxoijj6qr90cRHNk3RnZmwqy7A5zoLJ9cEyLLZN1pM9Mc1pM3XETvztlP/e5SshxLE5Kuf44GeNz1shyupXKHpNvCzSB2mt6+fXFxUY8ePapHjx6tbwFwivomu9Emm+tSve4r9WbgTnuncpfaYv2wvGcRR3g55oi3I5guw8dMSqqur0mTtJnY5HzW3ZE32i7nDcfdBD31Zxe5SYznA22UpJR66gA67Yz/M7FJPb5FgjphX9l3+hDO6eiMJPs2Ut+UWwe7d73rXfU7fsfv2Dj2/PPP13ve85718e/6ru+qV199tb78y7+8Xnjhhfru7/7ueuWVV+oDH/hAVVV927d9W73//e+vP/AH/kD98A//cL322mv1/d///fXxj3/8xjt0olw6HkZIVTPTDWvjQHTMkINsA7XsAjxdipCs0tETJ12upZPrnEPqnurMsW5zDOth/Zz4bK9TwQTk7lma1N0Su2NdBkpGXdZ75+go+c365rguARHtLN95Xe7HDNhNqWGugbK/qcPt9ffU2R2nI7fD4bVdpNQBJ3/rWLnB6CayLYJ0W1OfHf3UBpMzXjvZUq7rIhEDIgke5wl/447ryJSWDzD6Hj9Gb7G56RaSrg3WyUS6Mm95n7Lb7nFhRoqAPMmUIXsW+Q15gsqf+BN/ovb39+sjH/lInZ2d1Yc+9KH603/6T69/Pzg4qJ/4iZ+oj33sY/XKK6/U888/Xx/96EfrB3/wB29c18XFxfretjgtht1mZv6faUEP6MQOJwOfHKyP04nHmJyKtRNnRJBoi2xwau8kjOi8+9ISQ45+2S9vcGAK1LpcStfZAVlXPM/MlqBlspL2pyw7fzoKvjOQKWae63viUm4AninL8/PzEeyjE7aR9ueIflfbinCnncHcZCbldHpbInY+13NryRa7qJPt6MBsWzsnB2o9M2thcsq+dmn9LjPQ+QOX1UVpJnGxQddBO6UfiPApSvR7aQ8zM26LiYt3/3ZkqKsr7ersncsuHu/blL3VbcPnl4i8/vrr9eKLL9Y3fuM31sOHD9c59qq3lM8NADRwAxMddpfeMNjkOB1h6rQwajALTDlMm/H+OBtVHOjp6ek6JTaxejuaLpXKp274HptOpk0avkfN99bQYbA/E+h1aV1PnNSZTQSdjjuy43Ghc+rG1mlNpmzpSJgOprM4Oztb67dz/raZLj3k9C2PTzrko6ToLNNPRt2dDRmIJj12Tn1i/yxjir6jVz4VpNsp2tXL+Z22e64R9DnOS7tRnc4z4ZnaR8ffbVAiCDoiTNl8JGGyALEv1p9xJgDZj+QYxyg2mT4k/R5fYwJMfbrNnAPezd3Nl5z/cz/3c/WFL3yhXnjhhVb/N5E7/2zMqs2XAAYwqjYXZB15MMVpR59B9foaGd/l5fWHTdOh0PC6HD2FaYC0cSn1mgky5bw9wXmc/XdUZxLgNnZpXkcJ3URylJI1VDNApl14rBMDeBcRsB0Wp19cjgmN2a+ZLJ0Yj7vOmzJa6pzXdynR6JtP8CGod+Nk6SKcrr6nEQIvbTS/pf2Oqrty2L5tKTGO6RQZ+v/JqbNM+wde49uOIpx/JCz0GyZeE6lIXxgNdlFs114DVcpMkGAfNunFt2CQ/PE6Z4TeDrnzYJdBMdvhmlfV9cdWVfVbcauus9yO0eztvbWQ7LUbAixBlEyni0Ti1Lr0RupNezJJuvveujSL9ZV0G9Ns7HdnjF0OvjN4yhSlJQJnSsYOZxuQZ7JTDx1gT6DdpZQ6tszxdJ/JYA0mLJt1LAEeI1LrogP3qit78JNZCJBss1NzLGtJ7xOY7BLRud3RNesmGJFodGtWtFfX4bHnGHbHDGBdHxgNUw4O3nqTyjRfqG9nN3IOU/McJ0aPXeqSbaNw3FmX+9/1ze2diFWuJbiZNC61M2N8kw1au8idB7uqzZvH+Viw7GokY5qiiVxnFhKDNMt0SJ4ynfrqHCKNMWVy3amq3zofI8zaktNdLK/7zusZzfHcyRF3i80py9uyHZUGkFy207UE7MnpciwzBrxJlc6Sk5s6pE3wfJOW9K9L2RgwbiLbgGPqN8eYGQU+7JtkaGLcSynwri12mBRHaGyvIzBfb3vnWmhnx66LdVgMqp5fbmvOsQ44vzmPXVdsiQDRLaF0Ytvk9/g2Z588dxi1ccOU57h1G5/JhzzvGv0yi+G2xR9TvyYety13HuzIhjxBktKJ0+qE1/ChunaWdthkZ54QeTo91yDsJLvowxOf9XOCh+2xHUuTnv3jk9dp3F3E4RRT59C6SdwBlcGwY918awUdxBQ1kagwjcfojMzZDJ3RHNe3eJ4d0JTW2ab/bc5jV0kfua7FdcvoOtH7tMu2syvqtRvDjlDt0pcpYjWxc0TXpR2XymE9dOqO0pcAnvZnsuB0d3dtRwqX6iSZZt9Sb5ZZnAYkcBHUnNlxFMnzDMyMNNlek0+TAPrMDkxzPsc5fuw25c6DHcVPgM/6HR86yscwdcyTUWIcSKKHqitDc0qFEyLn5wWLKYfpikyc6T4YMkU65fzFOLcxpLRz2jTR1V11PX3L8jj5ltJMXTvS5y6CZR9TDlOGdMJOjRh4yG4p3s49Tfop1Tc5fB6zw6RNbpMpoiWA523S3sFLls7ovUsXcV0sbd52u8UUGXXH7CBt1zzP6W7avYGM5bAOAyKjHNe9ROxYhlOLzMqQ+Lg82grtddIT28SIiG3p9MZ5YvLbzcv8T30z4l3Se+qhXgh2zsyk70lXkuQT+G5T7jzYeQLSUJ48ebLxjqeq648VSxmUsJQMMiOpziCqNndN5mbNi4uLjXc3dfl0GwGNjIZh8GO92yR95XvwPIF2ceZM96xWq3XEmvLT3k7MWhlReHIQ+HJt0iVMyU1g1wmdqlOSnIwuM7rwGmuOT/aQcebtDF0E7PVMS5xRAC6vqqL+0gdH7bQztpc6TtRD8rUU+bB/26SLCCwGpaWdkR3xmtKnS2StI3Ys37ZK0jVFI7YJHuMcnoRAZ5B0RsFttz/xeFOv1HfqYhaq0xmPOdtBsY+Mn7DPo05uU+482MUJ0VE5ZZPzci8ejXrKpXPgCKDd2klnAEyBXF5evU+PbIzi9Cad/zZ22EVUdIYBYd+OEelY6VQPJ1KcZWTbgrNTZfweBpj2cet5+sK3RTjSY+TDyeuUGEGTu1En6daPHIFQ3xSe6z53xxzl7u1dPbaKkVyucxRH4F6S/f39jZd5VtW1t1QbqCYg3lXcJvaRUabHy9ewrClSym923hPQmVCmv13Uku/puwFkWuro+tEdcxpyEpbd+aEQAeuRJDJ1+Jh16QxS99B9/rZarTbIS9WVb2D0twtRv4m8I8COEQXTiga8rG9UXd+osC20DuPlbQRVm+yNwvTR+fn5BtsJSHByMepg+yamlU/3O0JQTjREoKPRpaylqM5RaOcE/D8/JyDOOZlA2SWa1DPXpPLH+6/sLD2x2a8ARB7fxfG3eFwNZvnfjNkRY75vA1T+cb2RNlt1lY7udtN2OmZ/ors8ZzbkLww/Mo2j2Xhnv0viTAIBr9NDzrEtLUkXYbFuH5/mD8kqt+J3UZLT726Pl0VYh89litER6hSlsr8mWG6Tdcr2x59YJ07p5rdutynPY7khs9b7bcqdB7uqzRxx1eYEzc3TkRhl3t/kFEHV9fx/jnmAujy/DZRRS/68XsI6+dk5WK/bVc2GY2bqCLRL9XR9t34I5Jk40zbrqV0U6irlE/w4aTh++fP7CVOHU1CxhURD7jP76SiaUTajgC4l7bJ8zM7dUalvpmb7Hcl1ep6AJ3VxviTqPzs727rO57EjOepIgYmQxQ66+z/ndSnZbWnBDhw68bjns7tXkuV3JMVjZhLLdjNi4icByr7CkSv1zXP453YviaND2gKJIVPnk61zd3PnQ24b8O482AU8JodVVRtgV3VlDHkj+jbm2EUnNlKnfTxhCThxmNO6Ao919fBeqm0GE2N1Pn/p/KX/XWfXBqdz2a9JOgcdXe3tXT20u6o2QMGpztRDZ0CmygffmgwwYmVZ+S2f3K1r0GOf7aCoA4J20pTuRxxuUtCMzpeki9ptj4lss5ZL8NzFTigmd5SurG1R4DbyZXGE0123RNzseDnfpzkffZJ80ibSpo78eF2S50c/XUTH66wjnu/2OPMQCQln9sHfp9sHQvim21jo6xgJd/PktuTOg50jFRtoHFPWJFarzbfkxoHyPhOKmTwdX+rsIjCn1RgB8aGwXIex0FCdNslE84I220Am1q3lMC3FKJT1TxI9c5eqUxVL0kUC3EREJ5GymYLLGmjV1ToBnQb/MvnzslxHRvkjANBBRg9k4l1abCImzjwEpL2rknrlZpx8nyK5TjyOrOPi4mJDb74XdFfW7bmReh29uD2TmJB19rcLabPT7aJujqPLc7Tusg1y3QPBYysdKOV75txS9NhFcJ1eprQnfZD1yOiMaX3vfubObd6mk/7HntI2RnLRYfSz7bGEzyJ3Huyy2405c6d4OoPKNQSryfi61BVBj4y8m4RTSsrGOV1joIujjGEa7NNmGnTKoVPNsbTLgOdUXte/7hy246bXc9JGxwGHk5OT9VoTiU13a0LGhGDCFGH0x4ibacKU6XbacZrURMz+Oc5HR0drsI7jSD+4q9JgfFPJODLNSzZOgHtaYbu4NjWd5zWkLrLuZIqiTdC665mW6+x+Sbc5j1kFp567JQnab75zdyJJFSNqEyi2ceqnyfXkU9I2E7ys/zJtSXtkW2MzXFMmgXf7Yw/87bYjusidBzveAxeA6Jja3t7VU1LonAIcBwcHG+s5E+AZtFh3blHgBPJiNtNySwysi+giBGk7mEwoOnCuxbj/Syx6KbLr5CbG7HHqIlOm+ggQVVcbkLp7yjj581YMP2Vkb+/qbfFpx+PHj+vo6Ghdpm2BevXGqKWxikPg/wQ5j9O2dbNtQqfHvw7obgqkS2PLzVu2ZV7POcBonlGWU3uOFNhmA0HO45wjoLBNtkFnYxiBVdWGDfHcrg0UzlOOSYAk/We9k5h4Wc9LBDNjn3XaPAyepJlA1xEFbrjLDumUvaSD9Dl6uKl/2SZ3HuyqrtgDozvnk8moeO+TU00ZhIT1ndCw+d23FXhSs21xrmlHopVdDCB9jaERZOmQydrs1BzRbZPJyXXXdxEkf+uiO56bsYhjyUYUgwOjIE9oOqec53U+M3My8IyZnzbT9b9zdCQx3E1JQkDW3IFc58i2iTMV1PmuER2jEfaz02/qpHS/mezFbvmAgtg1MymscwloO7LEdm9Lf3Z2auBjG0msc3wbaWC0V3VFWg1cBni2z+2h/XkudvOM99xmUxKJALNBXZvSjsePH699Fvc+dHbK492Sy23JnQc7DkIiLe627HYL2XFli7tfseN1PObhaXxxkIzutu3kSrsnwDOrNYCwvkRzNNYwthj0JEtMbJcIzZOQEzpMr3MkUz1M0/reRK5fcU0r/U5f2CdH8wQ5bgzh48J4LEDJlM6kB+sy7Y5N8fmr3nDiyMNAtzQW1L1JW/RmkLPOb+qAnDLncYodMW07gOe+EqgcxUxpR+uH55BUsJ0pt7NPbpnPsar+uZNTVM+oaGp36nBU5wiXGaKprul7fFQItl/tRftwvdFVfA51ubd3tRbMZQH2nboyqVzyPU8jdx7sGE0FOOLEqq52tHEb8LQRJenM4+PjDSP32g0N3OsqTJPROWbwuWZhtpsnrhC8fB6vZc48hhpj5mPBfB2dI8tnO3cVMktGM9sAz/Vm3LIJJZs3GI07Zcm2dsyWkvNiB0lvMs0Zpp0x4v8Eu20Rh9dOGLmmH77vsQMzRzhL0Z2Bjm3b5nAtN7UBt5lt4jG3zenCKbPBOTRFpc7g8DivW+qnlwe68XObCN5d1NXVY+mu5Zzn+BsgvBa2FGEyE+IUvQmR25c5QMCLX/S6JbNI/LSNPm2afpI7D3Y0biqPazTd/S5hJhmwGMDx8fF6A0MA6PT09NpTA7g25h2RqZes3WyMk5Xt5vqbJ82ULqTxxKD9Ulb3nxGpo8aUtS2yc8Rs9pm+bDNqPtCYG1CijzBRvrSWgG29EHzdBxMUTlgCntPKOW6n2kVgHj+mKXND+xQlssxuHYrndREdz2U/d5FdIvmbCOeK027eqFLVr3tS3933ru0+z4/HMyB4bdPOuwM71mXSdlPhNSYEFkftJgxuG9fbYu+ZS8l6ObJjudQbd3+zfC/TxBeanGRe8sH4tyl3Huw4caJcOijuFrJz5hpJ1g8S4WWdKMLtuREaWsqJMXA9yNEAd0d2wMBjSYvaETh9kjodhVb1qcpukrJetnvJCXZRHY/zk8K0Im8Qj84zZn5SCOtbul+J0QTHiv02A82OskR+3GXraI3Rn6MSRlHcdMJHtnVsn2C65AhSp/vvtJHtrpOldNiziEmoU8yT/bmMKVrviEx3LYHP9XJM44QZzTsTxDnn9TzafkewPAe6OckybEsdierIAfufZRyuwXFeOUPiJQnvBUj77BNIKLzD/fDwcONWH4LdfWR3Q+GuxqorxUf29vY27sfKOVXXn9OWCZSB50N3Y9ws21FFjIvGksE3k/HONab+uOGGuwvZdvaPBkZmOjkJSvodYE272adtDjjtJpOn43ZdaaM3oOSaRNOM5NL2qV/5nXU60ukAj33gOkLGkWPPnXjsI1NfrCNjnGjbzokkbVdJHb43j32gM+rWRqYMA3VB6aKmyRa6c2kbcYhMc3dlcD52dXYg1pXj1DOjb5LEkC5uPvHcph4dXe0S3fl6Cm1jim4ttqOcS93GH3Y3jNMnmXxxoxBBMGNJwp4sV+Yr53XKclT44MGDaw/7eFa582BX1d9rE1Catul3g8tQPoaQ9busHyU9SeE2dAIbHSWjpggNJmIWOE0wSjdREtFuc6Rpq42R7eqckgGDaYqlyRmnwmdfcrJkvTFRkAF8ybmxDkeZvsbgl2vNvt2v7B50u5iKZXRAwsPo22keOx5HAtyxSDITh7a05mJb6sB+yU44/tsiKot1S1Ln7ETOZ3u3Ae8EemwnxzC/cTw4btyo1PkJ/jaBHc+dZImwGcQ8psyYdBGlsxEGOW6Ish65rENytxSVUk+JHPNKNW7+S/0kvLcpdx7sJgbL9ZgYtpkKjcRpFw4uAY+gmWs9AZxC4aSw4UyOJ0bRCSdygKIzeK4rsq7OMfBeH7aXfbXOea6jO59bVRtrcgGHvb29NbAlmkvqhSyQkYxJCSc3Iyzv4rR4fDo9d4AeEM45dJq+J9BR9+Hh4bXUEtPpZNx2MGyPo7m3Yw3kJrIEgBOZsG5p95MteW7xOp9LHUVIQphhYPbEkjHubI5tWMo6pJyOJHK+sZ3ddwKh5yGvZ+YoacP4ni47RH1nLS//R0epK8f8ZJXVarW+OT2At1qt6uHDh+u5kKWet0PuPNhVXTcYDpRvXHVeOpLohmVmDSfRSAeaTrHxek5KnrPEjn0NhQbdpejcdxp6l5PPtfnkda7LRKGTOAMCbcoJwIXppaxs2sgmIBMUpkTI/vf29jbS0mTrHgvrt3MuFPafDsx25Yhxb2/zvXMnJycbu2sJxrGpqk3Amm4TYCToY52zX5IpCrlJdLfNFiwTsXCk1qVd+bv/TByn6JaEg0BHMtKRiXzvQIL1dkC2TRe+vqpPb/IcznOX50xFjtHWeA3nddXVU2IyL0kQndp1ZBaScnBwsLH8kLacnJxspIgvLy/vI7unkcnIltIluzCwDDonCtNk3SSgc2SqIeXyt20pGX9nu80CDw8P1zlwA5d1MpXHKJll+/uSk3PqjWm+OP5Ec9ll6Vsk7JTSJzpZEo+u3Y46qZddGPgS0LGNE1DlLzrI8bBmA3Pn4Om843wC8lPUueTs306ZHDDF89Tj0BHOqWxnLDgXPR/zSXsM0NkvGCg6YMqfsyQGvA6cXYdtPt+ji20Rs+d5dECbpM9KRBbdM4tC3e/v76/nZFL3TPGyTuqLczQ7qaveIsK8nYiZl9uSOw92NtKO7WXR1INRdT1CSpl2ckld8SGodnZuT6JDDnAmRFKGNmanA5ku60Cz6moBmMyJ1yYFk2u3pRGYQjWw7Qp4TLd0z4A8OzvbeK0My7fuc43Fbcm5Zul2jmSzdr78665lG82oc4w7atkHPs2GkSvTah3Iuo0dI069cU5dWnNb1EHbpF59fdpEZ8wxNxB09Uz97aQDCgJdFx1WbZImr5vSF7Dcrl4CiI/7/O76+IGpL9QnN4akD+yvyXTXFvuklM+lAGZUeGtV1ebzP0Pgzs7O1n6UY2edMyUce8y1SW2yHbcpdx7s8iT7qn7nmaM7swk6pY4R8resa8XZZLLwdgdK6uyiia7ezkl0k7BLdeTewKOjozo9PV2DmtlrWNoEeJNjt8Olfjvn2EXTZHu8by4ybaXnFmk6AY4Ry5miWkrHqEkSODYTsNvhO8UY4Ekq2Y8eowNiJOtIjwDKlLojOzJx3uPpsZnE9rdLqnIJ1HweSYFJUOpLO7q2deSwq4cZGG8SoqNmpM96XKfBgxETMw3xNSZZXIudIu+UyyiLESTPYfu4CY3j10W51HMe3JDP09PTdYal6gq04r+sA+qs86tJm3YPTlitVutsx23KnQe7RE2+6ZsT1dEdt3zTYTEltpTK6oCTUR7LtsOcJlb+dzt4ng2fEhDOelFeu0HQIVuzI5sML+cYiBKZdtEV6+PC9OXl5cbN7nSqU7/MptMHOgU+2aETO1Oz4JzD+xr9+5LYFghO3e7dKcp0VE5wcBTU2SYdLx9qQP11ERF18DQOyNkSkkueU3X9QcqOVCbAmyKpzqHT1gl0vI7S+Qz/TvDIeFJfnCfWbcCRTt/Egr6C40ibmLIV9jnuo8lY1ZXf9M5hvtg440PAYuaJ+mI7mSqlX2QafwoQnkXuPNgdHR1t5IinCCRGSiYZyaAb7FIGBzID563gTuPwM4bBCdu1gUJjSvTIHVE2egJHQI8skhPGwvZOzr1jiIxOu3Ni7CmX0RwnyVQPdWHhOE4AznFKm9zWTFyWM5EKO+VuDDm5efvLpHfqwunclBkbi0NK+xwB+lo+1cLEL98NgLw157aYN+eSQY7zhu1cEo6ho7MuE0EnHTtx1oF2YB/gdCHbzbFwhiHXxjc5tWwdk4yxvIzhlDpk+0ww7B/YtwBenhr14MGD9Y5oRnLRE3UXO2f5tLdEbvRjtK17sHsKCTOhMdFwY0BOlxFIAhBMAUQyuHSuZmcBS/9etTlhIp5ou4oNmmmSsKaO/S45sF2ALu136qIro2pzY0omhB862/XfYOmI1o6N7SD5MKslKHf6yWdSvI6gWP6kj6rNJ6BUXX/yhuvdpvOUOTF9gp0jwXwyaky9dERuC+/PzDFHKl1EkjZMUaKBLrqiE811na6W7M19pA7s6B11erkiffTxLg1JwmEC5MiL0R11wvoIpiZjti3W4QgvujDwpQ0sm7fp5NagRHMGu460OE0Z+2LWhe3IHHP26jbkzoPdkydP1jv9eNMkjbDq+sK2WTHz+Skjwl1IzvfTEPjb0iaQjmXxM+KUQcfSmB7IHydjrrdjNcPchVH7HE5Wggp3uhHomLrMBGP9dpARR1SWjBkn1xKwW+jUs9Ox6vpLTwkedi50jnRMvB2D11nnBmi3LXXYgVVdf2QZbZUpPOuuA3479Q707Kh4zIDntna6Jznt+unzraNOb5w7BiJHc13Ez7md/1mW9Wkwik7ok3z/rklc2tRFZ5wvXb85bry+u2bK9nSEznro2sI3kKQfuY/WdsP6b/t+uzsPdo8ePVpPai6KVm0yQjvMbuI7x98xIDqVqk1nxuMEp0jH8GlEFDptg6/LZ3THRwNNzr1j9F0b2D72geWkjIBcUqiJtjuQq9p80e5Un+vugC7nce3OQGJG2vWbBIUpNreDoOZNMXSCTg0aqF03j7nPdMTTGLmP3pRhstbplbbo9hHQujQob4y3brpxy3WeZ9T7BNKWSbe51tHc1C5f16UNJ1JoUpo56/m4BOpTBMfjS9kgjlHmIzepsVxG09QDU6bMFHGO2LZSbjae5foHDx5svOuOz8fchVzfVO482J2entb+/tXjmqreMjw/7T8Sx0jHV3W1w4qOghOWhkaj4eRkdMPUGa/pWBvrYAQxPfUjwMxrWDbBL21jGzu2NcnEpH0db6aOs+P9c9Er+73kPCYHl0mT75ycnESMcpyCJtPmZgO2z2lpp7YyRiYiTjkS6LuIaNsY3MQppO7YfxfpUVdVtRUIyeCXADCpUd4P2I0v+8sne9xEbItL13f2EPH8ziftM/13lBthJodrvgQ6P5M3103tZRaA7WOE6r74vNTB21SmzBH7bP8Y8pJzSSZj43k02Gq1Wr9arKrqueee27hRnf27T2M+heT18lVXobMdH8UOoOrKCfG5h4wWcs5kbDQUOpIuipsily6NstQHR3rJg3dOmvI0BtZFHgTYMLhsFsprRAJ2dKR2CC5rKbJj2307Ap122pp1CNpFxjmbZUwqJkafMgOwZuCO3gg8jBjfTqFj70hQfusiwar+bQqTvcfmcy2jiIODg41t7JNkznSAxznURTSO/nyc5fN/1+9rKIxaaK9dP5wxYGrPZMjZpg643CamAOmj2H+Tcaddu3YzTcmsF3dRZwwyd9jHtIVPaMmzbU9PT9ePzSMJXkpTP4vcebCL0qNAPrFjW06Ykzjn8ukUZDdks3QELMeGtzSgHVAa8DjBWJ4nMdtKx96BBaO6rk2OPKhnHicLzL06AY7cPJ3bH1K2o4QuNZtzA1Zug9OIdpiOVAh2YZlpV87nTf90/l2EzMi/6opk+AkwHfAtpYvZf+v7JsIyuqzCtl1w0wadlD0dS/lMozLtuc3BdfNlCagikzOf7Ju/d+SN5zLV5+wN5xKvj60ksiPg0HY70kEx0eWcI3miL6B/4PHJlkjeeYykjmu+KYep2QDewcFBnZycrPt0fn5eb775Zl1eXq4fFVa1mSa97VTmnQc7phE4SHGuXWTjlAVDc94fxTpiBJ2BdGmHKbrrjNOTkH8pd3IEXfqBRsqUwbZU5C6SMlLPyclJHR8f197e3jrK5r063QSkQ+yiHk9qCgGEfXCUkaiuexVOricAclKnrd7oRN2yXN6fFztMv3PPkVOA1A3Tp+7LTceoywiwXyY0ljD3JXJlcSRYVRvzMcLIh456iuqeRSZATjuWgI7SjVuOs685hyBnMmaCyr77t/ixKfpkxGWwyif9X0cmaBcZQ/q5zIlEZdYTAS/RW3Zz7u/vr5/OUlVrH8F2TBtunlbuPNhxPYYMMg7PxsrBp9C5ZAA7Y/REMYtnPd1W6HyfIj+yRpbnDSusj9cRTKZ0XDe5u4huIgqM6BIVMZrz1uyU5XuGLi8vr92j6DYYXJ2mTdk+vwMkb1zyo99SltM/0a2zB/xLPxgJ8u0IToMxzcn/U980TrtIlwKb7GWX6yfJ9YmUu76wzCXn1mVL+Mn6uvZOAOkIqrPzJSDv6uK9iwFtRrHcrMM2uF9uWwd4tL3U2fWL/SH4TkS5i8jpX+JvuGkva3Ox92Rx0t+AHh/aniezrFar9ca1m+p8V7nzYMdccRROhXKBmUJD8GTh8aq65rAYeXSgRaOjc+bnEth6cpK9kSlOMjFGtm26fnIoBp2kLqtq46HOdNh0biYBdBhTvXZGZMMdEGeSxiYI+CEwEUZ9doRm8YxCvCs3+mDfCOzRCftPXXZ93jZGTyO28acF0u76joA5OvR5dtr+vlRfzvX85PEOYG5DXFdVbQBdR4TdD9vzRHwjJCas39cwVZ666E+6CJcS2+52ke7vb67LJX2ZpzUF9PimmJOTk6qqDRKc5Y7bjuqq3iFgF2fGKIgbI6qub+qgU2G0UbW5nuR0JtkPgdKfrCfXUrr0gyfSUtqxA0O2KwbVbRLoAMYpGrc1dTKiq6oNdkcnYwAxqUiElPZ5swr7SPDOuHap2Q5QWEZAidGcI99c60hjSmd1rJgpT4/V+fl5m34i0HYg14HTbTjxXSKnri2dvXTkkHXYJmgL28Cpi0RjBwZO6r3r3xKJMOl1G9w+A52j8y7jY9CZvrvuSS9pB5+HGv+RttEPmsjT7uPvfPtS3liSJzPlnLSB8zP1JwVaVRvvufO78W5L7jzY8TaBDFoirrARsydvOKi6fktC0lG81jn6qs1ILMzejoDA5fREjuVcSsfcDBp27nb0jh62pTA7R5B2xNj5BgO+u4rkYuoD2+V+s+281hOUk9PtI+Dkt+7euw6gOgDl+S6XkR7tKP+bXLGejoFPQNfJ0wBdl+6jDqOPLiLp6iZodWWzjY5ImP7tUnJPKyYRN5WllGhHUKcxrdrU001BrDuPn24XydjUji6yy7lPnjzZWFLgBiP7VD5lJ0DKqI+PBEz0t7e3V2dnZxuBxD3Y3VCyNpMoJuwmDxytqnWu2Ow5A8tJyzQZz+O6TtX1e3QMYDnO+hhNeKB3naCpi/Vxt2jO4SSa0ims247LkrRf0hCJUkgctvWBAJ32ZYJVbT4MgNcQFMmQ/XunU0ZY1hHX16bx7PrF/nJcyaZJTNLHw8PDNQN3RMn+Ggg7eVr7mWSXqIfndVHWLsIIg3pz+c/Sxu6apeMdiPm8rt8mMJ1wHi5FZvxuYrxNujqYYYhdMq1PG03/+bg8+0G/DYFr1KnXgJe5wOyYH9h/m3Lnwa7q+m62OBQaTQAvoGiGQdaS8ngegSN/THnlk5Gc0wURGtxNnRTL80L0FN0xalkCvY7VkoHzXjWmLlOP+2JAdRqWG0jo9NhHp8TcB0bWKZeT2lE9QS73goUs5Pp8Ourp+uZ0JfXHHW4kUn5gQc6lnVGvt8mAd3Uyu4COy2V0G2F/uP28i+ac6nOqbZt47i0R0JvIEiFZAjqe7+yQ27NtnG2fXR2+L9hzrpv7BLrMyWljHR9szvpo3/Q3/K174Md9ZHdDYbhMR1dV68gjkryzn4rAEJ2AR8fIiZkyHBFEnDOPAeztPf0NxlPKk2kGgi9TbLwXrCvTYmAO0DGfzy3qbNfUXuuGwsnFqGdyhEu6YaRP0Mt4Zo0xT3rgI5HIaifxpOa6ReohsMWJhSDw3kO2mbp0SnVypl100o3pNkLlayYw21XYF39fahv7aie9a786cmlC58h9qR+d/rel0ykGui7Vn7qm9nRg7XNpe2xj5v5SajZl8uk3aTv9TOZPosTMNfpPZlFOT083oju2zZt5bkPuPNhxADon+fjx4/XLTDkgXH/LgHFji0N5pp9isAE9RlZLzjhGtwvg7cJ6OBm5Zkmg696nRtmWfuH9atHnrm8vmCavUz8kIKmjqr9/0UIwy+PKeJM7J2nGN29ozhoC65p2kLI+kqWMu9dDGLEeHR3V5eXltQd1O3JkfzuwcyptKUp/2uPP4oDoVHfNWhiIpnZtu7bq+saRqmUbmuyX5zul2QHnJB3QsZ0sz5HoRFx8jvvv5ZZpHNIu2ljIOHeQT3pJGXxbjDMxq9XVG87tj7bp7mnkzoNdnsPGWw6qNp9sEcCrqvW9It06C59vR6OhUXCXJgc3oLiUjqBx+140A8S2lAbbZMCr2rwRnRHONqHD4oaU6DigkbaSEFC21eX0cCaq+2k9OL3FyPPk5KROTk7Wjyhimwl4jOi4ruF7+LwWygid48i1v8vLy3UGgXo8Ojqqk5OTdb+px5zj8ep0QEDwOmN3e00nS9His8i2bIVTym5T9+nrO+G5XSZhW3umsrq1u13t3Glaju3Uf9rVTYGAoN4tB7C9JOiOrGPLnosGxwQJzHLlvGxIif/NrUlHR0dr8uy6b0PuPNjx8V5UnqOnKJ2On4bHUDzSOfIMUhZzHVFyLc8Tg+sJdITdwC9FXJwYjJCcJw9ToyHuuv4RFhZQTzl+MsokS9Gd2SEZIu+/I4A4ujLQ5UHUfi9hQI73DZGwdKnAtLnbxZnzeNwbXVar1RrwWFbuO2Kb2D8SJ9qU7dJ6zPV8Tx1BshvvXe1gSbq5MUUVdv7uj6M6j/ckXQq2i7BZZpcBYlndmDN6mfTp8zuZwH5JeH7XdtqLI0jalO3KhIr+pFtqiA6YmeB6NXWfOXl2draRQSERvY/sbihRfjeJ7NwvLi7WjpWpJxq5bzxmWY7K8r0L+7uUDCcJb1GgI1hiuP6dxknHx348TbogqTc+sdyRSMced2G9TAs7OuXE8aTzhA+j5Isi056AT1Wtd+byEWaZ+LlnqIsgWR6Za8Q2YwBfrVZrICagHx8f1+PHj+vk5GRNwFIPdUmQiz4cFcSODcq7pD99Tpe287gtHZ/Garqmy2AsRZydk98GdLw28z1tvY2owv11VsdAv2t2hWUZRDogdwSZeZFAgFksZrOmtnd6jZ1lrhHwuvEMEQ0BPT09XWdYstxw29mFdwzYVdWGU+ocPCMTGiCvyTk0VC66Vl0ZBvPcOddt6wbUhsFoj0a3CyOnc3c/rJ+uHSYHSUMwQiLQOb1B3SxFC6yH330vI9N/SXd0IGCCkT770VUkA4yAWAejIW+H79KFFpKptJMOIKQhx/gcwbSVb3GIXTEqtLPsnN5S9GdhXUzZTrbia/3/rpFN5/AdidoRh3BsAwr/bn3REXcpSl/bpQRti653VzDzXO+ud/kToXfE7M1ZJFuM0Lp6aBeefy6X49aVsbe3twa209PT9e0I9DG3KXce7MhSkkeumo07YOanpuSanMMbpV2fwYrXdZOe4JP2OqWya1rJk5TGmbUgtm1yIJPT9LPt+Ky/pRQM9e1J2V1nRs9rqWPqyeU6BRgWyfsqPb4ZV6Yzw4TtOFi+25t2MH3IMeITfLrUWtbxKH6aTM6dUnMeQ+uf53fj3fWhi2JZdhcx7hrJdSlOEzfXuVQ2+20bWwLHKYJxuUvp0+68XZcH+Mn2T+dSTBZ8TvTA87j27g11uYaklWQy9pD5krIyl7gbnv2J8PVf2Rz25MmTev75568R52eVOw923EmZydMBjlMYjKY6J81JzZstcz3robE6ncO6pvSAnT1/dz/8f9rC59lZXL6f8sJoKUARh83dg9xW37XHqciltrN/JAQkIp4MXn9xyvnk5KSee+659RPWqRc+qJpreCnXL3glyLP9ZrGxFT6SyQSEdpC2B2A5NlWbj0ObZEqpTrJLtLEL2erscZuN5lh01QGIdWC72CWi49w1aZn6sI0kMErxtRGvl3U6dAp6W7q3I9hu8xQBUsch/5nbTM2TRNL2WV7a7LXz+MDYPiM5/h9Sd3h4WCcnJ+v7Wi8uLuqNN95YZzZuS94RYMeQemmdJ5LzuvuqHLnReJ3vJqMx4FHs7NyeXO9rbip24J70mYycHGl31pd4PxqdPcVPWbhpWzkpOHkydmTqWR9gio2RVSK65557rp5//vl6+PDhejdYdlySCJyfn69fPZL0bPocFtsxXwOt/7cjM5EhO87vAdluHKkrSudQ7bRNrnYBQ657s/3s49NIF9VNpC31dNHsUttZ1zZ7XALoLgvQzdNt7ZpIwdL8z7Fdo8qunImEMnNRtZkt8fi47bFRvsmBkSBtr+tffEt2I2cOTr7lWeTOg12iDTP9KbXQRTV2IDYWOmOXFUZvFto5qU6WIjnL0kSjYyMYsA72tWOD1BmddPrKDSHcfLGtb10UOYEA28Rr6QSi78PDw/WaQG47yIsiuUElgPjkyZONBwucnp5eW+ec0p/8v7Mh6pwpNNtHF2V3kcjSvZHWt1PiU4RisT0ZHJ0ynxz0knDcfA8Y+8NoodtAQj12dXZZlkmW1st4rTf+7BK1sb1L9VO6bMFNZYrMYuvxX8mCOfXryJob72yXjNxS39JaPe+BzcYU++vbkDsPdmHk3XZWG3U3iao2DaVLg7Jcrr0xdUVxVLWUWpkmZ+ecOsfEDQaMSiJ2zJ0QQBjVcOIRFLMu5rSm29Y5XX4a8BxFhxWyLZzAATECXgDYT8UxgKUPfMpO2u20jseDY7ZLlMDyebtBzme6dm9vb33vaKeTThxlui3+Ph0jEE/R47OIMy9VfYpuW0q1mzNeE18av9Q12eeu/ZiAt9Mhb9nxtQbxbVF0l7myP3CUaz9B0tn5RWZSeIx65Pzvolf6osxbv2PyNuXOgx3XP6quM2IOhBlkF/FUzbsjaVCMAnzrg50z052dw39aiTF192uxfE/KyZlwMvDcfDK6W2rTBNR2Qo7WHJUGzAhurMPjyDa43qorspPFegKeX1cSkkAwnvo2HeP6MKMD3qyesfEtFFW18X7AXLskXhOjo+l09CypyV2u7ZYEMs5dtsQgMaX9pmiE5bnuiZRQdgHJjshNZTEVznblu+cRbWJqQ9dHAhrBLHMmD1Lw/Op8F+uZ0rgk1Xxerm3K/pS3LvD325I7D3be0ECH7dRc1bz4O02cLuXgicWyuojIAMfPKcW0TehEaeTpayYCgbYTL1zHEbtfZnCZDEtP7eicQBcNOhrf399fpzyOj4831mVz35xvlucbBdiXlO/ocW9vb/1Eh/SJ6R2ndLu2xhaYYvN5dI6J7MyuGcV2a1y7pnumqIn2Zl1PkeCuGYGbOqzoiztYO0Lp70tRl4GU1zjiuAnJ7M6hjjuy5Tbwe2yS5K5rz5JOmSGyTzMhyzmxORNyzgX7MwcAaaOJymq12phDnC/dHOiI123JnQe7OBA6Y25qiLOfnBedA4EyrIVi4zIL6/L3HYhWbWeGSxIj5DqIIzmnKlwPJ+zEfLudldFRgMSptpTd9YkEogPPAO/h4WEdHx+vn3FJMA7Q+b7CbELJJhOOo1mn62daJed09yNRB/nsnCt/4/9pN+83St3egcq25S3wk4O2rrf9T6EtOZ1lR+z6l5yy50iuzVpQlyrneHq8JnHUvA1sujbuUjbL6uqpuk5i870jSF0bOz17XjMbkN+ZQchf+pbt/lXVvtFgsg0/yo9ldzpxBN/ZasZ6m96fVu482HFbvAeOO+3IarpJakdF5jIN0gQe3WeXnripdKw9T+Vw5JI6q65SvZNj7lJ03USgPkwM0iZHK1Wbj27Lmh9vcGU+P/3xmmDKynkGtExO7sCkTqgXls3rA0J5mzLT0NSJHRb70U1ksl4+VizXkiHzEW0ubwnwqH/Wa1vw95AMj7eZ/66RxyTMMDASY3l09lMdnU129XTEs2uTpUsDe250WZuOLIR00zYmIjL13WScnz7PdbMfzNiQ4KQ8A2iuM2G2r8gnn7bUkeeO6O8SXd9E7jzYJarrnmLCyTQZvkGIzszpwK4MO2M6KD9xIPVRbgKAXVo1ziq7DZ3KS902NqYpAlw8xyQh5aUPBjrqwmlTgkN0mYkUoMuzLXnza66lE6E+3X4CF9/MwFQry3Xa2yDJtcIune1IkYSLDqSLID1GZMjMRPDG84B5+mOytQR0S1GIjy1FkLs4qIm5d066O39XoJvEdts53qncbo7lnF3rJ0lipM750T2px9mArq2cg5x/LI+2wLJC/vPp8glyJHn0IW5X5g/tuyPOEc6RpyFNS3Lnwa5qc1vykgI7xtgNSucADGo+1qXFlibHLk6ju35q//7+1bMeq5bXeWyYdvB7e3trnToNxUiJRh3dd+uDS2CbSC5g5+3p3et+OueVPvA5mNwmnTLy6h/aCyMY9pF9I4iTUBjUcqwjRwbN1GP27mxC3gDN87y+aFuxY5qigC6S6YRjOGVHtomjOQL90lyZfpvawkh6Wwq0q8fZkS71yN/sd0jOSB5JZLgE0aUhtwE0wS72yFdxZYxjOyFJfHB+hL6AZVufng+dLia/xjl2209OibwjwK6qvxmVztepkqrNNaRIx2hy7uS4/N3Oj7Jr6N6x6C7NRKcc8OAaRldu2pg1JDruiTgw2nW0ZF0x/WF9cUcn773hGwvYvwnY+PDlHHvy5MnGS1l5DtOnnLRpF29E9wtWEznHlribcrW6elJEN2ZdhHdwcHDt4dEE5tVqtbFeTMDjOHLddMoedFHUEolask+P566A5znJMiag8zzalpLcVqaB0ZE5x87XmtjR1zh16fReynefHIWxLEd5LpNzMXa/v79/7UXCsXmTJL8pxhkg1sF6u7Fjv60/X+/xvG15x4Bd1c2fvkBWxmisY+T8Tkc+AV1XV8rfFfC2tZ2gQ+bE/DmBj4zK4JXvWe9YqpPX7JLeIdAF5LIJhRGdgY7t5zFukJn65Een8QnsAdiA69HRUT158mTjJb8BMYJyiIQ37kzpG6+LeNzjfDvQvry8vLY7M4C3t7dXZ2dntbe3t5EOncaMbfV3Exrqept0DqvLPDiCzrX5zSk1Ei0/d7Trm+t/VkdqJz71J20l8fMcSZuWohn7hA48LZwL+b9LiTKl2s0LkiiDa47tmlZOfV27mdbt7mO9DXlHgd0uRk7mZGfFlALLNLtc+r5tEG3UdkY3MQKzMC5Ce4HaOvCOxnxy4jLlxHqmlJiPOZrjDeBZp2M6J3UQ6Lirli+PZcqVLDllMTIKYOXZmQ8fPlzfgB49PX78+Fq6MOXleaH5y7kkSGnj/v7+hg45xo7COX7dOlzGkf10hEcdudxdswudQ12aS54jLMPA1YEf599SPdxo0mULdgXlqQ/8zjk8gajnSuaRo7MO2HN+xocPpOjGaNKvJbqxHp2G5D1ueWgBj7vuKZp2m7sxcBTNMqnr247u3lFgV9UbyWRMZrthJk5vbAO6aQ3Ebdpm1LsAXZeqYkqjS5+kb7mWT1txWSzT96ntKplEXSTHtxtbdwRsR5xJvyRFWVUbqU8zR45hAPa5557beKwY7+HL9uyU7QgtYEvQI/AQnOMECQosg/rvCASPJ70aEGGqmulcA6xtwL9tyy5Mtkj2bmfWncvvtKeczzWmSEeuvBY1yVKGhvOQIOH52ZWfMXcUZz1wacHzy888zbh6TNiOLnvSjR3nnOd51VUqnsQxesgmKBJ2Z686HfIa643AzvMy/51evQ15x4FdhE6mA7ZIN5BdmqozTm/Z9nVLQDsdW5rIbrcdSFX/CB73n6nAro03TWcxquD2+aQLc3M4N6B0YG1H4QgvT0zn2kQW5rs+M6rkdn7+nnNWq1Wdn5+vJ2NAhO0gUHOyEpA9+en0cnsDN884DZ3ynH3wOAb0jo6ONtro/ncOZQnobAu7RIZm8W6rwYDnmJw5eopkzBnl5pqlNhskGCVO85znZ9y7jR1LMkU8ARq3aZr/JqqeO06Bd+2PHfD1Z3kQev5fIvVuT9fO7pzuWNrxdmxSeVvu3vu//+//u/7tf/vfrve85z318OHD+rqv+7r63//3/339+2q1qj/6R/9ofcVXfEU9fPiwPvjBD9Y/+kf/aKOMX/mVX6nv+I7vqBdeeKHe/e5313d913fVF7/4xbejuRvO2H9V11lKJgIjDwId02X836zI6c1n/Yuw3d7K66io0wUjAQL1UlrFZZjVckJx40meWZmbxLk+56iN7fZN4R3gcYNN/vemFI7harW69rqfOJ/8z92cLI9tCUPlWl5A0FvAUzcjaUZjBKr0x1Es2xNHzfoTOdMGndq1Pbt9tDGCrUkJx53XM01GWzLgTrY1nWNboN26fs6Fae5NfabtmwixPAJAl6Kd+sl+0J67+0En4uB+RGLDseu8M47p1tjMycnJOo2fiC7XMgrzPa4mcV0fc06nB5b9myaN+f/9f/9ffeu3fmv9y//yv1w/+ZM/Wb/1t/7W+kf/6B/Vb/ktv2V9zg//8A/Xn/yTf7L+wl/4C/XVX/3V9R/9R/9RfehDH6qf//mfr5OTk6qq+o7v+I76pV/6pfrpn/7puri4qO/8zu+sP/SH/lD9+I//+G03eUO2OXIOcHfMDmMyhJu2ZZfraByMKjo2x3P8u9MrU1okZUzRHVMsiXa6++U8CTLRObHdLkaoBLo4/bTLgGQQp3NiFJX6CGpvvvlmPXr0aL2mQV0EsNgmkh+CA8kQU7Nk9GnD9CQarldFwsxTbxfRRjckYHYqJkOOKu2wbGOOvjrSmHYaHLvMA/UcyfhGzyyDbSPAuYwpKiH5YV9StvuR4+kPU6BT+6N3Ak50mQiMaW5HUdOcY7nWb+yM7WW5OZY18+xcpn1eXl6u0/NJ8dNW7EtybPIlE1masg7PIrcOdv/5f/6f1/ve9776sR/7sfWxr/7qr15/X61W9SM/8iP1/d///fV7fs/vqaqqv/gX/2K99NJL9T/8D/9D/f7f//vrH/yDf1A/9VM/VT/7sz9b3/RN31RVVf/lf/lf1r/2r/1r9V/8F/9FfeVXfuW1es/OztbrKlVVr7/+etu+LgJaMkoKB2AplbkUteWabWsiUzsmA/AEpFGbbWbC0lFEGGGknKVUJZ2lJ270lTTldAtBwISOtEtbdm3M9QS7PBYsG0V483jW1bobdwmI+Tw7O6vValUXFxd1enq6fkoJAdzp6idPnmysC9IBMRpInbku9xRW1foRYCYTqTuglYdBM61J0sV0anQSmzBJS3sIznTaPNYRpMlWCHoT6TOQdGXwvrNc47Koc74RI8K5N9VF8mXQY599fsCF+ukAlSDURXjpB/tisDXhdh2xM0fg7gfHlut3BwcHdXJyUoeHhxsZjbQl9t0R7A7wUrcjOcuUcboNufU05v/0P/1P9U3f9E31b/6b/2a9973vrW/4hm+oP//n//z69//r//q/6rXXXqsPfvCD62Mvvvhifcu3fEt95jOfqaqqz3zmM/Xud797DXRVVR/84Adrf3+//s7f+TttvZ/61KfqxRdfXP+9733vq6r+KSVV1wemi9hynEw8ZdJJ+EZQpzAnwNv2x/qXUqFLkWTqolF26acuPbJNJl3FyWaHY1Ii3IlHkGIE5tQNnagjPaZ6GL0xIvL1Ee7K7MAqaUKmCvNkiaRek+7pUq98kknqYVTr+wcDdNQXU6CTY099AXk/QSX1Pvfcc/VlX/ZlGztNY1N8w0Pq5GuRslGHETnbZXuchONpG+xIIsc5333+RI6ik3y6Ts+vyb47okibSnmdD+hStiy3m69sv5cdujnR6TOAyrXj1EnS57Rp0uFJ36dfsdNE3hcXFxv3qzLdznHip793x9y325Zbj+z+z//z/6w/82f+TL366qv1R/7IH6mf/dmfrX/v3/v36ujoqD760Y/Wa6+9VlVVL7300sZ1L7300vq31157rd773vduNvTBg/ryL//y9TmWT37yk/Xqq6+u/3/99dfrfe97XzsBPaE6hlt1fcGfx5gemaK7iYE5EurE4LzE4vxJh89ILhOM/1fVrRmaAZjO3FFUDD0A0jk0ts1CsDNI8pypnWlf2tQ5ssj+/ls7K+NYvcOzajP1mrp5WwLTjowAaAs8bp1GDNoEdktAOIBXVfXw4cMNUGTaKOIIKvWkrrxBIr97M431P9luznHabyn6ot55nglNynXfOtLQtY/j0vmQCXhzTTeGbJPHvpMpLc7IlpHrFC0nwiWwMfVL4MlGm9gv5+2DBw/WQNiNS6cff3aRpaPntwPoqt4GsLu8vKxv+qZvqh/6oR+qqqpv+IZvqL/39/5e/eiP/mh99KMfve3q1pINDpZtQFc1s8ru/G1R4lKEtq1tk3STsZusPMaIx6zP5TlqYrmOAHmtyySz5U3a7CcnWsojUHhDTTc5nA7xrRJsf8eaU04mcM7ho8fSjwBGUjdMbTkyZZ/oXEyk7Kytv6SIshU8azhxXkyDWQfWQxwWI2tGrdyws42kTRHUwcHVA41Tlnf+pd+ddGO3TQjGdKgZRxKofOexLrNjcCERiu5ZnyUk2H2NvjifqDPXnTqcupwIQPc7fQIju45QuG3RIdvKDUYm0JNPsBDM7Evc76exiW1y62D3FV/xFfX+979/49jXfu3X1n//3//3VVX18ssvV1XV5z73ufqKr/iK9Tmf+9zn6uu//uvX53z+85/fKOPx48f1K7/yK+vrbyJLoOLfpmivMyCen+8Ti3WkMDE7pxvpECk0Khu7hazJbafBm6124NGJHTX/OrLAstK2OAoCM9tEwPL1bgcB1X0hMKQ9HNeUwVRj0jfe6JK0Txi7QS3XdKmk/f39tVOOA6GeTFACTtSB2XLKjNBRBvCqNt/qwMemdQ6sSyvaIaWfWRMkW/f4cDxuam8kQ7Z/6iFAYsDjU3VoI6mHJMzHOYechqPt8vq0yVEix6cjc6zbkf60JZ8+wu1w+2xrEY6L5wdT1p2fmbIvXR+oPxKEqtogbUublZ5Gbh3svvVbv7V+4Rd+YePY//F//B/1T/6T/2RVvbVZ5eWXX65Pf/rTa3B7/fXX6+/8nb9TH/vYx6qq6pVXXqlf/dVfrc9+9rP1jd/4jVVV9b/8L/9LXV5e1rd8y7fcqD12fN3vZieddNEgP2/aJn52v1HIKndpJ40szqhzOqlvchwT6+0AhmlB3yfjycfIhsDfsdup3aw7kVBSMC7DKaWpbK7jcU2NUZx3fdJxeG2NayRV1d7LV1UbKdLYa6Lis7OzjVRS1g8ZWXEs8390wnaRSOVm9Ei2ljOiSKTWbdmnw4runzx5so4WQxB8n2cHYNP4dsduyvajAz7JxE7UOzadamffOZ75jYTA/oR2zTFmuSRI7GfaSjL0NNEO7TNtzDESsU7oQ7y2zXOc3VoCOrfLuni75NbB7nu/93vrn//n//n6oR/6ofp9v+/31c/8zM/Un/tzf67+3J/7c1X1Vme+53u+p/7YH/tj9dt/+29f33rwlV/5lfXt3/7tVfVWJPiv/qv/av27/+6/Wz/6oz9aFxcX9YlPfKJ+/+///e1OzF2EDGtX4JjE0cCSbDNOs9ROJgB0PR1jZtrJ0VvHRKdIsYtCCTZJW9Lh+/w4iy6qmgAp/3e64fpXvvOh1YxAWGYmfP64Bld19U48rmvwvjbe9G1mmrZy0nPBnxtimPLi80oPDw/XO0mPjo7WO0EZefj2h5RDssHzw5TpmBPtkRDxVoeUF9BKmR6P1Wq1ES3l5vvoKXZnu6STzDHeoG1bneZHlzVgJJ3fuXGni2C8RNHZX9pBYkViM7WHIEsAdJan8wUGjl3IuUGGbSG4ewPNNF9jB7Z1f7rdHbHn/LDOmf5eysg9jdw62H3zN39z/eW//Jfrk5/8ZP3gD/5gffVXf3X9yI/8SH3Hd3zH+pz/4D/4D+qNN96oP/SH/lD96q/+av0L/8K/UD/1Uz+1vseuquq/+W/+m/rEJz5Rv+t3/a7a39+vj3zkI/Un/+SffKo2UeHbFDhNKh7fFSR3YWFdVNXJEiOu6rdu08nFmTnl09XpCG9qywR0BkaXZRZsfdjJdEzSKZpMYNZPsPNaSn5LOXx6hMEnEUtub6Eu6SCYMuPk5R93NAag6SijE657XlxcrNvPlGHnqLlrkjvo/IQRjkv6bGCiI2IbqUenoNJu7g5lWspZBo4z6yDwGhDpnKl39qUDru7eTN6bGP2xz+xvB8BuH8/vjnf9n/riPoe0TWn6jqTyuEHSER8JGK+jnXFMSFY6O2GaO9+9O95p8m4e35bsrZ4mLv5NIK+//nq9+OKL9U//0//0hmOour4O0SmY5ywNjh1xx8i6824q21gt2xxHnoiAa0td7nxKH3YTL/UQ6JyeczlpJ0EyDt23ali31D1BxsB9fn6+vuE7jj3pyOPj4431wEzi3BrhJ7fEYafOs7OzevPNN+uLX/xinZ6etv3i7QPcDFK1+Sb2RFPdedGZNxadn5/X6elpvfnmm/X666/Xm2++Waenp2snwrHwDlj++TaG6C6p0bOzs43HrdGmWEfnsOLk6NwIcH6PIDdARH8ZI+6w7SKdKXMQ4fgFJNguj5sjYoIeCQltmilsj52JAvXAjIK37Buc3bfYZ3e7zNL1zuB4ftHPESxJCO0LTSi669PHjH3AjsTY152fn9cXv/jFeuONN+rRo0f1cz/3c/WFL3yhXnjhhWvjfFO588/G5EB0wuN0xl1EsUtdkZtwiCm62uUcRnOOlDJR/ZoXG7Pr6SZK6qPDo0PpJkmEZCLtMrC57QY9s1WuWSXN6Gsd6cQZdQvfdH7sG+tnpGzHwaguT5hYrVYbqbz8ziir2+YfwE1Kx1vAycK989WOhI4+kRfLIXDzGAGajD/9Yvq4GzuCbCKRpDgj09pRl52gTCTMY+kIm9emvXx2KoE+uswOb2YjMnYeN5ZrR87+MgvTzWuOB8vP+DGFTp05NTrpJp9MUbLefHc0zB2ZmR/UcdrhiJfzJm1mGbmeG8B4/W3JOwbsuuP+/izgtnTt0m8TKHapnl2kS+m43KWorWsDox0/5ov1MgXW6X1ihlXzpoCqK0fl1GNAjs/DJNtOmxPtVF1tu8/vbFcXaaZ+p4I7HcZxZj0n7WNkHeef+rJGR4fICMEEgs42befN6mb3HA+C3ZTOtJ2QTNk2WA7HfCnaoN6chrXTJGl5WruNXXXrZTy/OxY9mzDTqdNGCD7RDfUa8mPp1rW6tqQegmXamHpCQjy2/Jzsw2PN37uUrsEqYqJKYuFomUCdLEMXNd+G3HmwmxhY1eZE7CK8pf+XgG7X9OW26G9ifix3G8gmuuHGjaVJxd9TNlN0+c4IwC98rLru5ByNOO1pB9mlZDwBDCaMRhlNMtrKdUw12dHmmNMwftMB20sd+pOpq9SbtKEBxQDHlBkZM9NtJCDuI9l56iFZ4TiYqfOa9GNv7+o+Nrfd5MGARXDf39+/tn7V6Yq/d1GUMxBsK6OWaWOF7cxl5RFZ7l8+U1/a4nnp/jMCS12J4icbYt8JdCnPa5ETwe90R9t1xsPjl7527fP5+Z1zjGNiG8u6OF/Rddty58FuMuhtBmEQnMDFoNcx4QkM3aapzK5tBoeqPk05ycTYUx7BiW/wzmTjOoTXX/hJpsu2O+IMY/UaANc3nKbp1kHIkglOqTtb8h0hZhJ6TadLXVLnOY8AQhCgk8/5YbCpL4yX5XI8GZUkHUuG3EV0+cx36u/y8nIDHDlWKY/6XbIhOlcDn+ceo53Um/JDlri27NsWeB3tinbPMc0n+2mH3fkHXsvNPRxT+4VO/9HNVBfbaQLlfvB8LxuwbU6xT36MGYTMcwr76uOcDxOxsXi+sB2Xl5d1dna2fvbstInnWeXOgx1ZRdV18LCTmaI9/s/B2gZsPDaB3AQ6NmjX7YlDtuzrkuvflgYiE45D9HvmDDDsg8HKwEwHSeFk7yYLIzuCLJ04z43uuBkiEzqRLiOmLppw2+MUppROolbrnYDD75nYWa9iWqiz02yo2d/fX6dKnQ624+8ipTirAGfamX7kvKR7eR0dKoGOEQeBzztl+SaGRE28L28ad+uCEpvsosS0i+k93tpAHZLcmAgxRc6U90RMu+if0V906rXPJUl7fZ5T0ktZLJIR3mTP21KW6uf8N6Eh+ejmN20/12QeP3r0aA12Ju23JXce7MjsKB1w0WjMVnxu/ufnNtn1vF2u6YCETogsu4saOvECNP8PcNCx0KFM5bv9vNfJ6T8zYk/aHCOILZVnQExbTYAcGXJLOss20DliIegwutrb21tHx3SkKSOg0DF/jgujOW5SsKPj2NhhsM9sH+vhGiKfoRmdGuw7BxcH7nv7CIwhHRwLptdIFGgjtjuOXa6fzk0/Uy71yrpJrkgaco2jd89FtoG27Do43k5nLglJJm/4Zh0UrilyvsbeSXJTLu2jy3y4/yQ8S0J9Pn78uM7OzurRo0d1dna2YdfbwP+mcufBbmI6VT3QdSDHwZyA72mku97OySnJrl5GgDEgM2s7li5y8K0ETMsx7UF9epLt0rdMvDjEKcqzU43T9bb0tIPMu3OGXdsY6aSOg4OD9eui+OBbrvvRmXZtTz2JjDtAyu/c4eq20e4CEA8ePFjfYsFx53dvpqEe076k5zh+TDUyNeasAB3SNBe6c+IQDWiTXaZ9Jlnpp3VqwMt48mZlz3tG5Iyau4gpfWJd9BkGaOs3OvDN02kj2+qIz7ZAHZPsMaL13M08dzTrMWO7u3lrXRJIHRB0JDg2lWgub0Zf2kj0rHLnwc7heTeZJqDror/u/yVxeqkTApmP04lxorHs/BYgyAIvb5hd2n1Hh8sNKDm3S1taD5OOJ6bnKI7Xdf2mM8vn3t7VC0qZEnJqdSI8jFY47nyPHMGOOmQUmP8tjIxJHLqU2ERiCEAEoi496TVUOjvqw1GtbzNgnYwcvFHG4+6xpf6dafCmqYk8XF5evTuN5XMeTGs8jl4d1ZAMep6aBHuMGZ1M45d6XE7KN2BkXGjH+S196OamMysGOo6Jo+AlX+a5uxRxMTq2X6A90J9kQ0oe1MBxtK+5DbnzYJdIh4ZAEHM0MgHb00Zxu4Ac/+8GuyuDk9GAxKf35zOAxp1O0QVBzhOfC/Qd0G2bMNalQZfM1DozIzQIEkCYznRbOObWZcrkGljV1YYOApPL9k3BqZ9RSth5QMq3Uezt7V1L5bEfjBjSX9qzU2y0KV7P9Fj0QYeYaIN6Zfr0wYMHGxEe9W1xtODf8snndzLiIYFgpGSy5tRgVbU2nHNMEgmA/J3RJnce09HTNq03lp9zaTsmCxaDHIkdyV36S+mAkvV7XDqyQvH8dfvZ5q5Mzzf6KKfIO7Jzm/KOADvfe2LpDHSbLJW37bruO4/RgXWAYLbVfY9DmtIlmeC5R4upHNbPNkQ6QrBNulQwHTT7aAedSco2BLy5TuAnf+Q8TvrUm/8dPcZeqMeU1+mXL3bNebkdIP/TtnirBtNsXYo845J+eFdht4aSOpdS99ZDvjMiJEvn02SyXmgn7SyIbcTjx3FjlJVNMbFRA5qjBvafZMp1O3pnZOP2GQTZVraHdkk7sm9YSs+mD9anCZbT3G4rr8n4eXwMdiYRS2CWMlJPJ0ukl/4o350lsNw28N15sIt4QDvgqFpOuy0x1l2lMzZPFAKMDdKTeTI8RiuROOJMLj6n0ZPT286XDLnTWRcx06lbn9bLNAYsL+BtgtC11+Nt0I1DDFM2EHonJdmp34DAJ0HYEQUgc75ThlVXwMZII+d1u+a6TEV06zXWDnxXq6t10ICs1xGdkmW5nhPW/ZT6ZtsZWeZB0slC8HwT11yXY073OhvACJBRuVOBtC/+39nXlHKjXkIUfDxtzrh6vlfV+o3x3f2s9AcZR4Iz6+na2kWqk21xHnve8neWxbnFtXZHn918fZpgYknuPNgxpeG1OTuNScF0zGaMuW5XELRTd+oy/xNo6HCW2pi2RLiBgSmQ6IMOLUYfkPP9OrwuYmfXAcmSkEluY5V03KvVakMXZOJm0W5Hzk0ZfpdbzvEtA4ze2JZuPBgRcw0tZfnmdLJ7RlQEF0d+ndAxL6XB7agIXNENAcJ6ib3kj9d3URV14pSex9FZhbRvyiykr7Q93kOYMWBqlBFap8uOlEW6tGNH7CJsG+f0Uuqbx05OTtavY+JGs0RvPH+1Wl1bqujsP2ICQKCaInX2tSOq9IUkzcwa5Lf0mXPNJPE25R0Ddh3gcWCfhUU8TSjulFg+u63HBGYba9durufEUSdlmVe6dM7En12ZdEhsu9PAXbucSurONVO0rsgIOwfYpfDYX4IixenanH9xcbFeSO/WqXx7RnSRqJn6Irmh/h1B5NMP2GYUxlToUmQVJ0ld0PEbWOz43C+CVPrh20CcqWCb/GqclGUnH9BKv53SZ3/y6Z2tuTZ1dxFNiIdTll3bLdsIGs+hdMsDJo77+/v18OHDOj4+3mh79OIHOfB7duval5hEdxE/I9AO8HjcyxDsD9OV7DPrsp46oL1NufNgN6WJHIXYoDsmfVPld5Ogi+yqNreLR7rok9eSedLY6dBTRxzwyclJrVartRPncxu9tbvr8y4RrPWVduzvX72dm9HL1Dc6E07MtNHP2qOj6AAybQoAxFFXbe5YJUnge+zSLhIoRgABYTprjp9To6mH45e+8CHb6WvGjWlSp5vtpEl8co13QXbAz01OLMfZATqxbpwIJOmnx5flpa6kGTsboc07TWvbZYqQ16btBBKSEtfp1JyPdSTO9XHMSQxMgC4vL9egzQwAy9vb27sWkbNtVbUxzp1+rFPX4YehM/p2FE5CQuJk2+r0SB126+W3Je8IsOMENUOZgC6ylAawbAOCDujM8CxTfZ5QXSRGBxWgyzsD9/b21vdq8R6qzsC6NYAcT1kdkXCbOEns8Bx9dNFZjpudZnzz6bVKXh9H5rGPnWSCZ0v0+fn5+pqkXEIcnGJkWtMO3hHa/v7++l4+RlF+NQ+dMVOgE3AR6PIZh2/Jud1aEqO2MH6mWzshYcrYsB0GekZRXWQTQsJbH1iGd8Pa2XoeO6LviGSIhX2E63UZU5bI5XuThvtPUrNarerk5GSjfurfKWQez/mc2110xn6zjawrhIzXkEx3GQKLo7XO3912JGe582DH6KgDuZwT6Rz400Z2XVs4yGZASxHVLhGWUx1xlrxRvLumexK7z4ljYkSTuiZG69RhNxYuw8DdMduqzWcpEnS4VZy6I7EwwUi7sjM153Cdbm/vandk6iDgkfG6fwEc7mqMPvkGhoODg/XbyZMCZRTA20vsZEwQTB7Sjui7Ay3qP0490Xhn+5wXTINl3FgHnXXawHrYXqbRaDus08CZ73HMBrXOCU9EK58sh0AybRKZ9EoCSKBjGjpjSPBKdoEElX1kmz1vSBoyFuxnJ+x7+kry1s3xfDoqpOySlpwyPLcpdx7svKnDkyfSMbIO5KYB6waZv03HaOiug5trOifa1U/n6/JygzR3tE0s3cLJMrFDtqXrczdhWJ6vsSO3jrmZxux6Kp/OkOV7/J1WJoEguLJPJgGpO+MSAMs45LPq6kWzfNksdemohrqmA3XaNH3hGKRd6bf17TEg4HdjHkLFcbHuJ3BlP6Z5ylQ1d15OQvub6u3mx9I1Od4RJZKXjvR43CZCRzKT44nMsnZniY5464t/Pzw8XCS0Sz7FJIXl+vjS+E5tJ6Ba7tOYN5SOmXRRRc7lNTzm7109E6h1bDTiiIWT29+XwM5rklWb6YikK+2084R7LtSzXZ14Ypnhu2/8v3sHFtOanf58Tw6ZehhyF811qZqkqfJJHbGdTv94HAhCaVPq6zZccP0uGwxITPKi0Ly9nNv7vS5F/YUIUEeTjXQkxZFXZ8eMLOk00zeDfx7w7NTpUnTlfhngmEb2TmH3l/VwjHheyuHa4JKYdHVOnnOnS9u5DLfFbcw5p6enVVXrXZkmZVVXDzfvdBwb8zqs67MQuCm0H0eL2/S4TTod3pbcebDLhHlakNs19A5DsaPojk15bgJaogEf69pEZ5q2xJF3DjOpsv39/fWbmKtqnCypwxFG2uLUpqWbwEvATaDq2sN1lERpFxcX194a4PIZAUYncTJJX2a9hPpiuQFJ9ivpzIwZ+5djXk9JWpNgyTanbWkLdWLgnYRrZh6LzkFzfFhG/vxOxGQICHZcw8yaG1N1FpNBk4SUzfVYpyW3pclIMnMuga5z6F3aj7qZUqOUibz5nKyp5/mQvp7p7qOjo7Hsbm2WpNmPXdsFmDpfs83ueK5l12htF/3eVO482C1Fc7tGdJRtbMPXMbWR/+M8pi3+XSTasawYMG8Mz1PELy4u1pPHKbtsSz45OamDg4M14BEA7EAmpzWltfjdKR+nQ7km6Ovj2L0G1Dlykgbrk+c4WmQ7mEqiw+W13bbv4+PjtYPk+JEEMApztBYhoeAjlXzzsKNOjhnXEJ0ezDUur7Nbp7Bos9ERd43mj2uTjKInIXiyjSyTf12Ev4ssOeilpQYT1rTB9UefzsxMJDggzqftvPnmm9cyQNF1biHyQyBybcrt0tFpE233puK5dNPznH6fxm6XQONp5B0BdpRpIKZo4Fmki+i8BuNIZ1sUxwmUiZLUV9h33oLNV2YwdUL2x7eP8+G8BrQl4+x+s54nZ0qg8fXUC51cd57LSMTB+uMIcm63buInrzsq5VNTqq6excg3IjDCiRBcAwIBPd+zFobOJ2bQdvjUltSXyGfSuVOrtLHUzecuMv1HHdGZGnAZyfK7y5nmmskOyQbJDskRAYfj1WUG/NvUli79zd9ItLiZZgI6S8rI3I1kXe6NN964pt+Mx/n5+UaUb71u27rP8ejIZaeDzqaWSG7nu0xQ0gf7mS7wuC2582BncS7YrMuplO76JXFZ+SQr9s3AdhJsnyPDqivnll17ZtIBOzpE9+fJkyfrre9h1VnITlvZliUwom4M1IyMHNF1urPTyXhx3SZt6TYfcS2PzsLjbvbr6HsqlxFyBx5ca2LqN3+MKj021FWn84BhXoviJ+SkrYz+HF1OYptj1sG7CfMZHdjBsx3pGz+p047AdBEnwZ767871Bi2DoR02He8222R9dtQkOLZNE6uTk5P1G+bTn/39/fXywptvvrkxBtxYdXFxUQcHB+sHRFBvVW+l1b2+ynOSvp122W4jqksBQ3d+d2wX0NxmszeVdxzYVfU3hU6yjV1M4JZPOg46yvzfscyufEZzAblMFkYLvOm4y9+TFQfwwjL396/W8HgTddX1txnvyrpyHtcUPSm2ORrqwgBghp3zores5TE1Sb26fEfBVdejwBCT6IubSkgO0lY/E9DlJEK3PdIRZjzzokuu5UW/dvLcNMI+TpGW9TBF9Izw+JCA1Mn1y5TDdTeW0xEyg0/IHfXQpQ9Tvx01AXhJJjs0CZvAsovqTFSrauMRYE7NU1dvvPHGBqlgRJ3XUDGjQPGmFEt8yQR47it11M3/bdFh9Mpo3PoxGXradOsk7xiw6xRqsJnWLrqBnECODjPHDHIRGo4Ng2manMN7sPjE/wBd1uK6jSaMGrkWwPak3Eh3P5F1uKt0rJ/OgrqLzkkSHG1wkpBpcrce9chy7TBzbY7zwc5pIwEgtxDkVoGAXdqUawmsLIcMPG3udtBSslnl0aNH9ejRo43nIwZUogs7XIOmx8W2nH77odi83vPI65h8l2LKoG2ZBLLt/PN4c22yI4qOOnxPmq+ZCMY2scNnRM8IMOdGp7m1JDrgLSbUc5YVfu3Xfm3tP/y2CT4GMGWkvug+WZGOWOcc78SeInH2exu4dSDpNPSkV+rsNuXOgx2d3E2u6RiZz3EddMxk0N2CMRlolxZIajGTKE6VT/kgM86rZjomZ7brfsQhpFw6OW/Bn3RixucIipOYKR9HQr7NgADkFGZ0REfXpSIZEbAPTMP5ePpEkGIfk/blMzCpT48ff4+Ts4M04DMlm9T06enpOhp3ZBC9M/JM2YxsO4flTAAjtWmNhySBztPRq8eGZeX86KTbgRyHzHF0dJqyCGAZg85m3f+OmKQM2gp143S6xzrnZoySBVi6V5DHDg4O6rf8lt9Sv/Zrv7bxcOe0Z7Vardfw8hjAroyl5Yy0cenh0elrRwymayYwdOQ8AedUxrPInQe7zgHvImaUNE5PHjonOo0O5LporhvwpKWS4vLTO7zJodvht8SeOoZOB0RHxk9P5K7tXfqC98LFCTvt57cB+M+7V5Paog75N0Uh1EMiNW9m4Rb6gGTOzzjwTRJMWTKi9AaGkInYU0dc0qeMZ9LTeUZn9NGRJGYPbGc5xggr/eO1jG7YFpdBECNIZJw7B2hQ4XZ4PsDZQjDJGKQcgqIdKQnOlC5jP91G65a+hLsiu7FgBJqIjqBiUtD1+eDgoF544YU14LEf0dvZ2Vnt7e3Vw4cP2z4GYHkvn8fn4OBg3babRlSTf+36FPK05DOWjj2LvKPAbukcflK6CI7XPQvQdewyE5npSq7HrFarjWjOESXZptvvPtGpcyceHUsiKAsdT8TnMUJmCsrp0DhMAy8BbhpHjglTX3F0jGa6PpBIdGtHBgve98XUI4lH1VWKknUzreioy2NoW2NEQxAnKNNJ84/ndiDk6JuA17Wfx7qlAP51t5rke8pLtO/doGwb602fGSU4dRs9cqcr685YTsJynQbsgG2SAF0e3tDpPe2d5ODgoN71rnddi/BISnLj+cOHD9e/uQxukvJY5JqsBUamzMFSynMCbra7K/vtljsPdpN4sKrm9bn8ZmHU003QbWsKdiRxpHxkFB0AN5509+h1THFybO4/05mOYDjZWa6drFNVdIj5PyCd717rcptYlh0XAZTRjidQByB7e1droFlDSdsShTLVGGdM55wx4XppgIJEgdGPwcJtcoqMAOi1WEYzBM1ubFjvxKqZGk0/upTkNmGfo9fUYxsiecg48jFqJgexR84Ln8d+MSXeOVy3mTIRJBOgSZK6DNB1hCZ9mPTKKOz555+vR48e1dnZ2cY4p/1JaT733HMt+ETH3T2eGdujo6MNO9tGCJZAzvM4fWHWY1fAvw15x4HdlGfeFtV1x+l0u0jOgNMBnUGO63KOGLmetQRaERqTU3q+LqCRtRGy7ZsY4TRpnRZLHQRYl9OBZXRGHaTt1AnZPUEgkyx6T1THVGGcBtflmHZNXbnvKeulcZjRO9M1YfiRAGW+B2jovBjdeuPKlNLzGJjELDkoR0t0yI4K7bgN7H6VU0DU66x8dF3V1S5gkquMRdr0+PHja1EgSYjXrU2UOkLURa3UTwfU1Hm+5zg3k7Gd/N6Nl+tl+fv7++uHQue2oZyT/mRtN29L6PQQHU3RLts3keZOR7Y7/m5QM7jZx9w0lbqL3Hmws2FvG7xpkHzuNgDwpOmYdkCNEyNt7aI415m+GXS3MVm3LWVzfW21Wq03XgSI/ZisTo+uz6DjiCXndZto6My77fIRb77I712qmM475CI65I34VVdM2BsnUmfAket0XMfhuIXd7+1trpGZCDFyPT8/Xzt3g46dryOclMVbSAimnBdMOfJ/67rTY+yD7aAzM1Fhfx3V+RadtDE3W3dvtCDYZpNG+uJ54P539mTCwbY7/Zzf3B4+1WgCOeup028HvHt7exuA183F7NL064FYhl8AS+F863yICXtnH11fuiiu86VvB9BVvYPAzqmCpe9TeoHHCTD83+V1jDGRAqM4Ahc3K7DcODlOPk9Ogp3748kztZ/s2JtIllIuuZY6oHMj0LFtnb7C5PlkkEzOrmz2hWUxTcr203lVbb56hXXkGEG5qjYiwdTFcXQ5BCCOCfUbO+X4sxw/y5GA5wgkbSSDp25ZL3VHG4/Qfm0rjN4ojKJ8PH8mErT3lJs0M7MAHYAEFKm7JelseIpWHPF213GOccd0p89dZMk/cT04hKhqM+WYG8+5U5hldDrv2jCRZAOer9sWJJAc+XyTjduSOw92BhoeX7qG0g2cI6pODHTeru4t6zFc/nVAZUeXFFrXjmnTSNdW9p/sOmXw5uzuuk5nS21i+jHnkdFzp57JhR09gcI6Jxhy/YgpHa63eU2QwMfolw41bD6/5Y+bM7zJwc84jKPOs0154zhT0hnDLuJku9PG3J+1Wm0+Qq0bry7CoRCwp1taTBY4xtQBbaOzda6p5qkgBNuQinw/Ojqqy8vLOj09Xdc52Sf7wjqtjwhvL+C5jqj5LkLPgWmZoxPXPwFeoneek7r58OgJ8Hi+57UJ/ERkt8kEfrv8dpvyjgG76bdOnJawM/ZvZH8RrqVkouTpJ9l8kmuyMYLrcVN0udTW6beOIXXttRFzjSXHzch4vifMLkJAjXOiA6FOoqtc56eGMAXG9TX+znN8A3nqNLPMNb7Jms4vnxxPRm/c+LK/v7/xmDfaQd6OzhfHMhJjW9k/2gF1FICLcwsod7eX0C5sH9GDN0bZAfJc6oXnM0o0mWC0x/R+Uvxcv6Q++GxXbrLogHUbsHU62eZDAkB8qtFSHR6zLqrO9bZj1uflAdoT1zf99JpISETGgePLLIGDhm1AtE2/1EHnU94OufNgN8muxu40n1NlLI+GwciB2/vztI2q2ogAPInZjslhOyrooqAp4nNO3vXkt9TjNZ7pSR/WKSeO9UVhtMdJkLUxPwKti1DoPOnk036WwYcs+x4k65NRmEkBGT9vCWGEaifiN8cH5HgvXYS3OFD/jPzpXKl3OsAACNfJcj4JRnRrO49uYw9ThLi04ShtpI658zJ1EpRJaFIv3xIQvdKZHx8fr3cseq7YoXbOdduShPu1t7e3sf5rWQJKEogpmuP4cSfwarWqk5OT2t/fr9PT0405zA05fLRYF8nmGj4EnnVPINcRoqmfSzIR59uWdwzYdbli/9YxLUYW3TqAnV/+eFO4owyuD/FvKZpz21JO1dV6U5gcHfUEzpYACx1rN7GZLnS5Uz0EDZbNqKRLo+Ras1cLr3WKK33KZD49Pd1wqhlXRtbUI/vOSNnraYnk+BSb6NSPHyNxyO/ZBeo0Lh8iwPaxb3GAaWPOo712Ok8bPX4kbuzzBHSsk+OZsjpnxzHzvCK4Eaw4to6gEj1X1TrCyrWMdOjgSQa69lEXnuP87fj4eL2JxoBO/bs9fEAAy7bjJyli5GtbefTo0UYan3ruAI/kPOW5/ex7F+U5RU3d0sYnMKO9mIQspaCfRu482HUM1b935xkszL4cEdGZ0FF5sTqOyk6Lk5oDb8OxcaRtvDWBwGmgoSwxLk5ypzM6XRqIJkNlGqtz0HZsjGIcPXdjRr1WvbVjLRMym0pybaIBRnoBHm5f520g2STjW0HYVo5fora0h69UCgB3OzqzeYlAHyD16334yXrTpugz9zVmLdFjmXPt3Okcu3ujDFYd6ZnYuzdIcK0tJKRq8yEJaUeiGpKT9OPFF1/ceLzaTYR26L+0P/o+OTnZaEcX+dDemeam7fAcXm+g4TjQB4X08H141HVVbaQ06VOqaiNtbmDn2rJJQPRNf+R56mUBt6v7vyP8zyrvGLDrDMAgaIDzMTNllhV2FEPMuggXqwludMxsn6M7MkGunbGdjrIcJbnfkxHR+H0umbaZfxyQ67SjICmg87I+PDlSftc2OyJHNiyb6zt5nVHVdbBjipNRTAAuAOVUp/tNkHry5EkdHR2tHWOcFcGRD5hOqu7y8nLtsL0pJg7Ojy1bSi3F+bIc2iBTnE5vMgLgGNEOOQd8/x9147Hy3EuknLF48ODB+tmSx8fHazDM7yZnq9Vq/XYBvt8x6c1tWQKDQRdxnZyc1PPPP9+mkamvHHP63GUa7Dx38hn7pd5DYA4PD9cPCk8ZJKvRFTfHJXWesru1YuqH0Z99URelRUyAlyJrln1bcufBrmqO5vi9iyR4jsvgZMjkZuqSazJMZXW3E9BhOI3gdMLEuGnUHZOKdMzdYuOe0gtMD/k6/+9Jl7LTTjogRstmxpxc7Hc3cRiBdREI1yjyR4BkWUkFXV5ertfXHJFY/6kzuyHDkrv7xahXZhHOz8833jifumJrfK5nF93bXnz7hqMrAh2li2rM8rl+OUX2JE3UbcojGMcOnjx5svFwA6bxPC85X0jEbBeeV9Efx5PnuIyTk5N67rnnrt3awjbke+Y/n8xjwGUdXbSXcYkNkQwlYs81x8fH9cUvfnFjJy+zG9ylmV2/ifYMsCYtJAL0R5xDlA74usj/10PeEWBnmSZaF835fApZFne50ZkxGujuaTEjitDY6NQnwyHgdv2zgXaTnZPMuqnaXEvhEywMIo7yJoN3+bye7aM+WJ4ZL8GAGyXyf/pgsCZI8I/pmwCjH76dchzpczPManV1o3FsJG1NO0kcCLyJCpm+Y/TUORvfLOwIi3ZuYCRIOO1kkOrGrIsEeH3amjpoOxxjRryZPxcXF3V6erpBKDvgy03RT548qdPT0/UjtqyXDsSyLpb2eJPT3t5bD1xOdGly2WVYUmaiJraX85f2zfkam8k5sSPaD4H24OCg3v3ud6/feG5yGwKVaC5RXmwn/e0iO+snNmFyQ+LSBQu/EfKOALuJSd5UnDf3fUB0Pk6NeSKwTRPQ0XnwmB1RjCoO0E6qi1Y5iacoz04x/TZ79y6vtMkTmr93qa0pmki9BDRH5PnOSZZ+Wq/eim0Gn3OWJGOT74zkDRZJXz58+HB9zxNJEM/PRhU/H5Lj76iM3/1euS6KN9h4h2ucXkeObLc85rHtgG6y+ejf9p1yc5M0CSD/+Nbuvb299Qtus+aXsmkbtmMDOn8PwGYzSvTVZXACttEz2xvAM9B7d25AhXWzDKZHaZPsxwsvvLBOaZLwpA0py1mU6Jy7d5fIafTq8SNxsu1MZb2d8o4Au6UIjYPhAe0GNUZweHi4XkPgluPVarWeZGHlU4qN5fpzYpU8j2mafNJwOSEy8RjB5DpGalWbOyENdgbfDhTMblmWjd1RQBcFdpFmtzPWQj3kf6YRWV7aEcfS3QaS3y1eM0sUmPqS8jo5Obm2A9RgxfU5jk33RgC+4422a/C2fvm9W4fO70zVpy2Tc7Jtm1jZJngOQZX9cXu5WcVp07Q1G3AYqTp6ZH9zTf43EWZdzz///Mabwf1cz4BF2p/raPMmYmwn29HZrG8/cIo//WP5zz///DV74hy27XLOkFhUXUV1tjfWmzqc1pwAbwLst0PeUWDn1FrHPMliOBgxkqSjwvCyBTpOLqzcO+aWBjEGMoEe2+7IigbFc9gPGqgdFo85WqHTpHN0FMeIloBK4EoZBjlOVjtB1mF9pR47KDrAXE92zY0huZ5tCNB10b8dQY6lDK/3HR4eriO6bJZI5M1dlekPX8DraN39iZ44HvmdNkJ9Geg4RqnDEaoZe0fYeKxzbh1xsH6XIj/2M3oyITw/P693vetdVXX1MGnqI39TNGmg5++Z79lcxN8IdiRQJEXd3DWJ5O0jnK+M5PidbSVJ5HyLnJycrNd93X6Od/rAaI96y1iSXHAeWq9Oy1MfnQ+ybd121PeOALtObBh22N1aRlgQn9cXA+e9UmRRnkgWOhA7KrI7ttlpIYKdbzugU+6iJrcjawodCHmCUsz4DPQG8tTJ63OMk2hJb1VXmznYV0ebqZ/vrUtU5VROdJD6aSNxrqyPpIOR9oMHD+rhw4friM5pKjuCbn2Ozo1rwSYsjuQcKXV/BHTX1dlI59CmcTGR5G/T/45+uv5N9nd5eblex8qmi278rbuUx99MTPf3r94wznQkx999MMlIG90etp/HOQYkOyZrsZv4G881lh27Z/t8bn5z9Gffwe8ducl1tnHOWdrm1JbbljsPdkvplxgOUwM51w6bLI474MLOz8/P6+zsbIMtP80AMjIx64qwTDJuTgYaVOTy8vIaEOQ4U5vRG7+73hxnnj9lkVk7EuomDP/Pn9PJHWA6EmT7LY5W4iT4oOmMaR47ZRZMndPR2V4ODw/r4cOH9fzzz6937KVugnPa7E1MjkZid3bG/L8jE93/VZtgy3UZRimdMMpj/fmNY8J5Z4fWRVgdMHfRHh0nQfvi4qIODw833vLg8dvfv0pprlabr1hiREOwMclw5iB955hSP9wdS52ZUHONNO1luw3WtGGuS6bNTvlzSYVzgYSO7XPEy3ZxacA6cCbCYMdsiqNF2kFHqJ9V7jzYVW0yLk44O5MYslllN7mrNh/3xYexTo7HwgHlJJtYdgzN/eqExuZt6DZGt4XlT4YXkKPxsi6CJ52PAY3M05Fk6kk5XTrPk9XrBNZRl/pLZJCys0vNTpC2YceTOpO6DNBl8073ZBvu6nSbMoZLhIPOjWPm8aQ9ksREn9lNbAdOiS66lFyEqUbW141ByoxN81yChgGQwJKMSs5/44031qDBtTXa+mp1dQ9fB1IkwBzzZDxIvLrbPGgXuc6AFDC6vHxrQ9JqdfWA7rzzkDozwAa8+IqpqquHH/ChAZeXV7fKkIDk01Ehifzkv0wiGF2adBvUOd57e289VIFvcU+d6eNtyp0HOzJ2Ggy31fKTwnRe1aajDFPqHiS8JJz8XcRCQ6LxxBj5NA+nLLmF3o7SE9LMkUKGSRadNtIg89k5o1zf6cXAzS3ZFJZHB8R2EMjpkOmYzUYNGrYNMnnqaIpuV6vVRoqb78mLs+ucIsuxXgj07m/aYqfcgYsjUD8yyuTHNkodT5Fd9M7fSA7oULtymTr2eKZd3rnqcQ5wRPdsbwgk547TydQHHzydDADnTNWVQ+ZuVvaRQJd72ziHuWObG5AISCw3YMsyA2LdfKqqjY1ytAeOderIvXYhEB0BzRjbX3Yk0r7LKeOqzacTRdL3pSzD08idBzuCmiO76TeG7JycZJNkdV36bIqUaGCsL+0gg6bTmZw828Ft5AY8tintcHTLFI8/u/5MAOD/WS+jOurFUSInYYCQabY8AYV1MRJ2ZENnlR177CPPNSGg0+H53pASsOOmpRAjMnDeN9VFIAYzptLyO8GHbYxuGYWYZVNv0WccL+2T5cZOTJjoOJmuNehZp+xDyudN/qyLOuG4MXWWceCYeFxp57R1Zim45Z91sG3RLcEm/etuReCGNfeTacyMQyT65Pgzfcm0d4QEIOd0EbP7lzFgBMaUesrM2OTePPvI9MXE3f9XVfsWd+rA5z+r3Hmwy8AwTVm1yYoJdlMKpjMyGmaMgJHIlNqwM2D9Vddz6Xt7e9fYpYEof7m2ajNdmf6b/ZNBJZ3gtBqBl5FGnAQZfcrkcx2po4wJ+8v+kDjQkZul59UkucYkxW3hLrbLy8s1ozTZ4TV85JKvZ2SfOn1Ol2pzyty2ZscUR0dnRydPwmYbNGmbdpl2G2aoawMUx4t6ji1xLFm/QXciERaSJeqW9ZLkdESxI3e2T+o01zsqJtH9/9s7/yA9y+r8n91s9t0ETUKkbIglmloqFvFHoaZRtO2QEZGxaJ3OxKZOqoxUhVFqR8UqtKJ8QXQowlipnfHXFHR0RmnrWNoMWJFpDJASFLWIIxXHMWFqjAtJdrObfb5/MNe7n+d6z/3sLnkBffOcmZ3dfX7cP8597nNd59z38zx6o44TD+qbEavmlhNW/WaqW2AkO2UbfL67vROIM/ti5KhjXOPnvGMER2JfVVXtBQkkSxlguziQUQ/0m/2UgQc7vrWCineQc3bEtSf95ot4HTAYjdEQdd4N0sWjJ11D0PNNE6yP/7thZ68v84hUDsEnYuYc6QzZfgc8nuM13k8HUbXHy6KzI1lxcSJBG8j0Q6cu5q5oUm0hGyYguaPzdA6dpkfabj9k7hE5MLJOndOD6k5OqF9uguFYMSPA+zgeGejJ8fs1ThAJaAQ76sGBmiDuYES9u2POiJ/aQX0T7HS/ds8uW7as1v9szlBXTjoJJLyWfXByKP2wrbxec9j9kdum2qo1Oteb2523X22lD+AYSK8+ZyOi9siMBw+83vsloX3TLvopRxXYObC5Y3f2RsZLh+HM2Q3JGaPqyv6nETjo+jWM2ujE3MH4ZCADpGPlS2kZrbmTaGqbrqNOXScRve8d9PPOOv1etosgRGekY2oHgY6PHSiy03Xc3k+9clxUh0iPf8qHDFjHKdwwwHFzuyBYuo3xHn4ZwR2+ZxiyjRRk6xwXj0D5jJePl+YD++9Oi+cYgWbC69hGpZ1pg5wHGkumJmlDJHIsW/YwNDQUy5cvj+XLl9fSe5wrSuvxGUgCPO2EbZjPD+gYj3sE6l8+5zVerj7+m5EqipMaHuP1Hqk5eFFffMmBz8smgpm10wOEfsjAgx1TWKXQmM6NaUo+GO6MMiuDxu0Dl0VDvI8RAtuZRXL8n2Vk18lYncmyfj2kW4o66EwpWcRTIgD8TRbP9tPB0an5+hqdkC+SU8+Zw2aUxeiQE1DHPcJnukb24pGXxDcK0WG4/jLiUkrzqm206RLZYpvpwPkGDrbFf8vZZmlMb5MTEbdp6snbzGiOEQV1mc0dittv6VrahVLPevBf7fJXrjFKy76GQaLXZPel8/qb9id96Pm+EtGkjuS7sjnutlQiHPRHnEtZmQ5qjPy0WYjrk27DJZtQffO9sm+xctSAnTOniOhxYPzJFro52Tk4Gcg522P0U3Ieus4dPo3B2yNh2WT2uk4Ow4XnaLwySO9HBmYefbieKT4O3k5nix6xylnzcQCdd6GTZQrTx9WjfR8f9smjKycn2Xh6SpARht+nY556Zp/0k/XFgZlt93eCZmNLp+tpS4/WaJNZv9leSgbiHK+I+joe++zkz9OhWeTgzpqkR7tn1S6SW80LEUJ9Fy9z/N43OfpMN9l9arfPQfqqzB51r76ZyKxHyXd55kCZDo2nEybaJHXP6xkweKSo61kW16J9bvB8P2XgwU5OxQ1UzsDfY1nKx/M3AcmBzlNfFBmjGz8Hmcd4TtJkxCrbUwjsr0eYpaiN57ytdHwsi3rSdVk5nn5k2q+U/sii0ixSZwSj8sTiGeFw48Dw8HBt+7en/tjPrC/+TGTm3FzvHpWyP55SpdPPIkGvhz8qV45FTs3TtKWxdn0xkuf4qG63YY6REy4niAQoXsd1RTr6zGYJxJl9EAiVBnbd0d7Ud4FJRkA9cqL/cEBzX+LnWE6WamYGSjaglDrJinTrYJuNW0bcqQsS6IyoEIBdf1kb6EO48Y7Rfckmj0QGHuzcseq3ojkt5nLnlA9MNsA+cemUWTfL8XY5iDkb9ehFRkKnyojHjYTG7/qg0ypFmCUdetpBdclQucFC1zFapUMi2DAKojB6Y39dx+yzRwUOEixHTkwpoFIqOnOeTINmNuDjm0V1Kidi7uXC6m8JOOlUJHRU+pvASV3wHq5/8XjE3Pqj7yJ0/ej/7GsJvC7bKu/RmKcz+b7Sw4cP10hJNodKqWWmlfWIiOvPCQTX6TS+DvauC9qYp5GbojwnDZrvbBPfp0qSThv0di32b/3OyEhGbEvgrrHOwI7griBAjyLwTTb9lIEHO3eUXHPh7krPKfN+n7BN6TlP/dFAuFbgQEhnlG2H53VMIUREjX1xIuo6lckIxJ06f9xZe/vI4nSOaRCP1KgD14mnlPV/xiSzscmEdXi7ufmIoDk7O9t1fuq/O0y2KYse6CzZD/Zd1+k3wY9j45GlyqIT9XEqOR2mPBmdqe3u4DPQdILhpIL9EsgyneVRBO/PSAXvlSPkGPk9nC8+x2jTw8PDtbeEsH1sP+3YyREjF087sjyOn2eL6FdYLuer7MkjNUWaBDrvC+d2Nk9c/55+pC5oK+pzBnKsKwNSifsvEnumYfstAw92MgZfl/MdahH1iejO2R1LxtBowO4YHMwyJ+4TtWSkPlG5zsBruP5BQ8oiC0Y9nKDSIfWh+/maIjoXpgxL/WW9vE/CulUOJ1kGhBKlFrUL0yO6DBTIMKljRr+0AY4py89SslkkNTs7W9s8pTZHzLF3buVW+3yDjKeRMyen8VB7RHyy9uneLJoRUNAWfB4s9H8nayVSI0fI/vn4ezbE08okL9zB6lGd2sP0ZSmCdgDKNkplm5skJHkkqGqnznG3a8TcW1smJye7OsiAKtsQNd+YqE6SOOoxI3cZOda1LDvzl7pPtk1Cwt/9koEHOzohvsnA03QZ69Lf/J2xFBc6QY8APKVWmuiZI89SmjpGw/Gowsuj4yOTjIhuyojRb8RcOov9UNqBQE69c+L4zkGepw49Tev9p7PLUo5sH6MLTyu5M1C9JBvakcndee5k9TejRV2XjSfbKR3SOTOqIHDKIfjbOCLq5EX3eypZdfMhZUaS1Jv64mTIX2rAvnBMPQLOhHZfsne1PyK6LyngywoYWXlWhGV66pLj4+NEEMqyHexbRmglHhln9ubiJDUDE31dhSTQSYdHVSXd8lpvD/XA/9XObI7S15TmsNfLvz2r1W8ZeLCTo/KIToOhdIGH1pJSOJ5d6wDi0dl8EVsGGpzQXq6Mvaqq7pet2Qavh+kfOgn1TalIMX89kqD3+nlKI6vHJwX7k/XbgS6iviCfORuOYTYp6Jg59nxlF8uXY2IELP0SPLyfWd+z9nrfh4aGah//pQMWuPKxF7WTJIRlumPh2rM7bP5PkFAkrP/dQWdsvuTIMjCgfnysXI90fhS1kZFdVdXXgz19Kl2zf9RDRpYioieyZd8ym8tAZT49uZB08n7VW1VV7aXO/pPV7+eyeZvZte7ROeqb8yGL4NxW2J6mfnOZpwmgH6sMPNjJWJre+xdR3+VYCr2zwXLnrmOlAXMD0H1yOJ7GzIzTUwsCOxkhAY19ofNS1KK6+fJXsktGLtQpIwJPpTkIqdxsInhqMdNXBoh0OnRufq/K1+uTHDQpWX+kK5cM6Dxa4Bh5VO56FijzBb96e736xc0JdEj+rkXq1NeQFRVoI4nWw3zdUOfdsXuk4tFzliFxEuaO0Z0my/C5lNlJRjIZteo7d7LzDCg4Xg50mV3xHtez28hCwI5tVj8JzBHRffzBgYdzpAQSDl6q0/vOtpP4RETPfFloYFBqF+su7Wfopww82DmjXaiUGF8JCDOWnUUeDracnFwn4o+uzaI1TbLR0dFaio8OidGLJEtlHj58uPa2EW7LLjFdti0TRhuMmHw7ebY2kunMgZF6UX+5xqPUH1/GXBpb1sfxVMTLqHI+IXD67kA5YOpD7eMHgNVP6qykC53zzUO83rewR9TThFn05/3xdmTtcULitst26TgBjSAsh8x55RkKjyJZ5+joaO0LFJkQjH33pdrIDIvEI1cffz9eShV6+tXvJxFivTpHXXj7Mp/n2aysjW6/JKeql21hO7IsSNZutpPzJMsA9EMGHuw48Zjrd0YU0csqKQthKrwuO8/JH9H7lgoya57z8jjh/ZGJLFryCI9lSOjIZ2dnazvW/NksOkS2KzNmn1RkpNwwxPY0RdRi0vq7RCg4vrOzs10w8SiRqUuPUFSHxsajW9bF1JrG0L+ATgLjwM+ojo9huP1Qh3Q8DnYcN6+TgM1xOHToUPrJlZKOs2iMG2c83chy6FSd1BHs+DmcTLL5pr8FdKyPbVe7COT+QgmP9t02fbMX26OXiZeIr+4jGSK50vybnJzskkVGp9l4uM046HjGyK+n/3MAZh0ZcLufIcnJQJXXZBmtko99rNL3ePHw4cNxySWXxPr162PZsmXxrGc9Kz7wgQ/UBqeqqrj00kvjhBNOiGXLlsWmTZvi/vvvr5Wzd+/e2LJlS6xYsSJWrVoV5513XjzyyCOLbo87o4g68PHcQvLrnNRNayNZpMKBdCfojoPRWRaxyTlNTU1134fHzQuMmCjOkGdn59aItMvr4MGDcfDgwZicnKx9RoSOSRNf7cn0wHqynYRyLlkZvuGDUkpz+LUkBP6GHDkyOlVOZK4LZn33vlHPHhVnkSjPefrPAYB60njxsRm/nzpyJ6s2M3phmQJb9smdT+ZofTcdHRh1KMfuu0H9XKfT6f5oXVM7KTVmGje+21R1djqd2voeN/cwEvK56sSL/XdS5FkZtYc7PjMd8Mff7qT6CPTsj0c+TQS9BDIOVm4f/K1xdB2XUvs+RygZqaGOSqRT0wAAW4hJREFUeB/Bvl/S98juQx/6UHz84x+Pz3zmM3HKKafEXXfdFW94wxti5cqV8ba3vS0iIq666qq49tpr4zOf+UysX78+LrnkkjjrrLPiu9/9boyNjUVExJYtW+KnP/1pbNu2Laanp+MNb3hDnH/++XHjjTcuqj0eLTjo6ncJ5EoTnOU7W9V9ZLieXsgGmABUSjVwUmoC+y49Ok0aD/tK4Pe2q3zuFPTHB7xNTIuqjKYIjf9n13r6VpI9NuHlOXP1dSyVr3QiN7P4JNffHGNFwR5xUkeZE8vKcufDOt1meV2TzZbEdUC96Q0cIjZuJ36cenaHm41PKa3FCFFRjb9GyiNirTer3S4CQvbZCZjOcRz1PkcnaszEsK1ZZJZFodQldZN9z43zTXM7++IB7/HouSRZ9EdikvlI7yvtNyMFWaRdijp1nRO7x0v6Dnb/9V//Feeee26cc845ERHxzGc+Mz73uc/FHXfcERGPduaaa66J973vfXHuuedGRMRnP/vZGB8fj5tuuik2b94c3/ve9+Lmm2+OO++8M04//fSIiLjuuuvila98ZXzkIx+JtWvXLrpdZHAOYO44HCAkTQPhA8kdcwQ+GrsDkB8rOXRGd77GQMBxRsm6GD0J3HSeTnF6ejp9h533PZtsqlf1ZefkuHzSeB94rmlC0/mIaFBvEXORl9Zy1AcCIVOQpYlIJ+ggSlaajXnEnCPOdpe6w2XdJWbONlAcJF1Uh8ZabWS0lj2KwPI5xlkaKmPsHHsSNCeCPjd1DW1Z7RwbG4slS+Y+0JqlntVnrt/O5x8yZ+/RmutEj0n4vBkefvQxCI+IaTMCXm5gOnz4cDfy9q+VlISpayclstsSkeX17KuDdhYN+nFmCXws2QefB/2UvoPdi1/84vjEJz4R3//+9+O3fuu34p577onbb789rr766oiIeOCBB2L37t2xadOm7j0rV66MDRs2xPbt22Pz5s2xffv2WLVqVRfoIiI2bdoUw8PDsWPHjnjNa17TU6/SeZKJiYmI6GXIHDwfJE4qGmgGRh7FZMJBzgZP0ZDf79Ggt5kRoK8vMcKggbpxOthxfYHlk0HTifvzW+7QPWXhaT7q2KNelsE6ModdAlCOnfSkOv0ly6V0KcXZtBwZz3P8MjZcahfX3jJH45KBWXZNBuSqg+On/pd2zNI5Zu2kfjienpVgu3SN7450YVqR8yCbf51Op2vHiooynVXV3KM6Ks/Jqeag6taY+suJM/AgiZOw3E6n062TbSAAqy5Gi9nLBrwtqksyNjYWQ0NDMTU1VVs/dALq/oEkIFsvXUwgoPNOXLLMlnyi/u6n9B3sLr744piYmIiTTz65y7Auv/zy2LJlS0RE7N69OyIixsfHa/eNj493z+3evTuOP/74ekNHRmL16tXda1yuuOKKeP/7399znPltBy5KdqwUhbEs1ZE5nNIxOhVPSzFCcjDMGKJHTiXQyfLfTAd5Ss3BVNEf2+CPQLiTZ5szFuksmb+dbbpDpNPjpGGqi46CZZIte5TkenJiQVbL9vnWcwdrrt1kZGMhQj162pU6URv1m2OoMVF57uCoSzo6JyysiwSJ7WJ9VdX7RW2dU9TI9sjeSAqccETMpSI1rt4e1evzLIuonJxxjnM8PVIp+Q7ZIncJMyXp5E1LEXoGk0Ib9F3Wpcce1LclS5Z0o0EHKerEpWRnbHPWbz+fRX7Utc85ZhP6KX0Huy984Qtxww03xI033hinnHJK7Nq1Ky666KJYu3ZtbN26td/VdeU973lPvOMd7+j+PzExESeeeGLX2MjUSuAgccZRivDIdEuAp/MyTDoflstrWScdBhfH1S93MhH1FKru5f8STQQaYxbZ+WvWJHT8dBjUE+sgW/Rr5jNsZ9nUCXew+uugfKMF03TcrJA5W99Z6OOWMWsSIAd/Z7EuPg4eGTSxZ7dN2oKDEa/xnX3cfECbVZ+ydmR6Zru4DunnPCLknOCmHP64rhSlzczMxPLly7ttYnqUDpXnuHOW892zJg50WXoyyzzoXr1mjW3ifFPExo0pHHtvf8n2qEc9uuFpffpClZdFdazL6/Q+ZMez35m/9XtL/x+p9B3s3vnOd8bFF18cmzdvjoiIU089NX70ox/FFVdcEVu3bo01a9ZERMSePXvihBNO6N63Z8+eeMELXhAREWvWrImHHnqoVu7MzEzs3bu3e7+Ldm5lQkPNUhtZBJcJjcMfCSgNjAORH2c7JO4gdY1HSQJZsWO1gxN0IUIWqLaxndzNSOdJXfKYR1+jo6PddnNXJp1XE1nwFCH1wMi1qqrazkqmfiLqXxRgSkd999QYgY195H2esnP7YTv9OK8tOZvFCiMu2V2WbtVYiPXrPr+effX26zruqPX+lIRRCu3Y31vJiI5gSvLJSG12djbGxsZq5zhm/F96cCIi2/GIz0mIdmASiOjUdS/nldtPRHRTrrOzc+t8Iuh8NIZrmbQ3tol2xvU6buyhqC0eELAP9F2e9XFx/WZZKbcNJ9kL9VuLlb6D3YEDB3oaSyWvX78+1qxZE7fccksX3CYmJmLHjh3xlre8JSIiNm7cGPv27YudO3fGaaedFhERt956a8zOzsaGDRsW1R4ahouzs4UCnf7PANIZGYX1ZLsx3eG7Y3eGRwMh48zA0nXgbacenMEp/eLpJHdYWUSisrXWwY0DHoEyAiRgurPLojyCCicpF/JVv8pg2s7Hmm3zcl0ycHJ9ert8jahUdikScnv09hOMdMzbLMBz50Y2r754W5zoqBzuyqWz9ihI468HpXkdx8pJlWcJnHjJJvkKthKpYLuyNVw6dAcBvmg8I2oqn/eyPGYL9MO+Odh5FoN9Z9tYvm+S4tj7MbbTfZtHru4HPHorrfHRl6mNGjthRBYY9Ev6DnavetWr4vLLL49169bFKaecEnfffXdcffXV8cY3vjEiHu30RRddFB/84AfjpJNO6j56sHbt2nj1q18dERHPec5z4hWveEW86U1viuuvvz6mp6fjwgsvjM2bNz+mnZgeBbhhciBKIblHLX5NiY2zXhotJ5mDEuvg5C+tY+lvT6/wWNYmOuMM+MmGVT9TXCqfzJ6vrpLO9I5NtT17aFrGXiIOardPOF1H3ZLBZ2PFcWSdJdByUGVZ8wEdyQQnN4XXEoQ8Yp9P3H6bztGp8DijPfVPOs0+3aR7NJ7MeCiS5jNnno5zckjbdXD3LIdHEbS36enpbkaBwEEn7nOAbScR8ohSQKfn/1QHSR91zl237Pvs7GzPRhrNJx8j6dGJUeavSmDGa7LMhLc9e+SI830+4uURrIMdfcD09HTNR86XGXgs0newu+666+KSSy6Jt771rfHQQw/F2rVr4y/+4i/i0ksv7V7zrne9K/bv3x/nn39+7Nu3L84444y4+eabu8/YRUTccMMNceGFF8aZZ54Zw8PD8drXvjauvfbaRbeHQMe3MXgag9dLOOHIzpw1e30qlyDla0vOeOlMmUJjGUyneH1ZOz2a0PWc6NJJtoHFiUGWKpGDIxt11idDpt7c8RJ0PTXjbdIYHjp0qMvgs+iJbcgmovTh7NLHk+s5ZL7+w3vc7hyEPVqZb3IvNKrzvnlfpCsSGpcSGEs8g0Cnpb4R4AQOtGEH8WycPBpS25VloD5p/wI8RXiy8WzeuI4439RXrt1qHvM1ZD4WJHVMEXvmQo8R0FZYJ9dB1W/ZUhOgRfQ+5ymyIGBWClbzyV8wzrQpy6DtlcYsA1TPTHHcpCOPsvsd4fUd7J761KfGNddcE9dcc03xmqGhobjsssvisssuK16zevXqRT9AXhINLOsnoOiYi08kDiyBj+UyanOwyyIq/mY5fg0dUxa10Bm7s+IkYp0yPF+cd4B0g2Y5NF71k6kZfgCTm2Gon5Kw3Q6AqpsMnOzX061ZFOP94TpNyYlQv/4leV6j8eZDwgJzEhmPpBmlltJjajOPEYCb2s/r2X/qm7bmAEm96nw2jrqG9saIzO2aYMVIWzYknSqSYnnqk673v91xsnyfwxxj/eZuUY2pjxH1QPDQvT6eU1NTXVLgoMR0Hkk57cmXADyK4vthaWOcM5ovAly3Pyd4jOqa7CrzTdlY+fxknRnxPFIZ+HdjRsw5Rg6C7x5zY3GQ0zH9ztabMhAj+PGabIKxvXRqnKxZGk99KxmIs245rSbm5A5B97uO6Fy5oO+7YD0C8LbRYTrYZk5fZSmypCOgfphOVTl06iyPaUM6Zjo76kz95/oQU3oZseH/Hqm5vbEdfpxt8L74OYrG3nXNsaD4a68iohYFkNB5WYyIHNQ9DSZHyt2hHt3wzSizs49u5hgbG+vJJHDO6H2fSmm6Th1YaFfZWKqfngnReX5KinpmdKlzfBk46+XY0HYETvpNn0DdK2Lzcc82vxCkCeaqg1FmKU0rWysBXWnsSfxIbAi8/ZSBBzs6TGc7XIPgYPEncwyMMJie0jWM8BzwHOg8WsyE58k2/SXKDkZNOlGb5ch1XMDphkyRcWapBp84BNZSWpJ9ZB1sV3Ydde7fK9O1dDpNqUydV73udPSMIR2S2uRRioMYmbJHYGoHHaxHJD6mJcbbFGV5n/nMlUf+3jfacUR9k487Y7aXzJ164/8kIwIKzindMzY2FsuWLYulS5d2N7UMDw/H2NhYLF26NCYmJuLAgQM99lZVVUxOTtZSqa539j1zxk4+1E8d5xfmeZ9EhIFRqh6A13H2yf2HdCybYGTn/s3TxSSjw8PDNcKgsXTywrcLOdllgMDfFCeFbnscG5FVAj6/19dPGXiwi+hdH2LqzScGGZ7f69GeJhbfquB1Zazer/WBbWLpZEB8sS0dWlP6iyID51ssmHZRfaW+6FoHDTJnARBZoTNwd9IZ8Oke9k1OgS8F5qTJUm4lcYbvbNvfYyhHrzq4dqi6HTR1P9uWPUumclhPFllSZxlTJ5C4eD881cRjHAeu9ZDwldKu6iPBkyTEiQmJFKMqfnpK0ZHK5NrZwYMHu+Xp9+zsbBw4cCCWLFkSxxxzTK19jNJ0j+YXx8Ntn2lA3cMyfYwUZXK9LNvsJX2yPh8zlausQinK5P8cZ4KmhDtCfW1QY+NRXSYEwfmucf3QVkvk+Ehk4MHOUwMRvY8j0DlGlJ8V0TkvK6L8mAEnkrNuB1JGMk3XsY2+08uZ+XwGQ/BWesMNVY7XF8Z5jTM9RVtsO52YynWn6lGlR1iaqJqscoJay2FdBDs/x36pbLbPo9aZmZmeV0WJhZYisPmcQ5YiUrt0j29qyPRJ5+tkTsxZ19IuVK/qZCSh9jKaYz08p3o5ZionAzGV5zZPZ6f+yvkq+mEakbrtdDqxatWqHhAlkAgIO51OSoLk0D1dRwLEF1XrHrXXbZV6nJycjKmpqRpwDw8P93yJo0R0OVadTieWL1/e1QHXFzMfUyJcGnf1R/1zoKVdeDlN4pEchfPdr8lIYj/kqAG7zIg0iL5+VYru6HS8jJKBqA367em5JmkCQNZVcoARvc8lseyI+gt+PWrSb2f8Xn8Gevyfa3nZpGGKz/Wrtnq7VFbGQsnMS+3KdMFJlk02rrnyGn09miDTVBfXgvS/Pw/oEVWmN7XBo6DMVvyHjJ024zvwJEx70inqf/7tm3xIIn37P/voqS21Q3UfOnSoNg5M7VXVox8wXr16dezduzcOHDhQy3oMDT2aij5w4EBMTU1Fp9PpmScCHwe3iEejR6Ye1Sb2gcSTwK1HIWSvJAce5Wa6d2Lrx50keLTEMaKN+3nq06N9jzD9fo6jXyMQJpH1gIK+Ttmy+fzjYuWoAbuI3oGgaGCd3dMwvBwaLqMN5qBdfM1A5ZXSU24UKkPGUdouz4lIY9VEJIv1OinuPMnY/T4CVpZik7PTD8cmG5MsmtE4aX2Cm1KoL+9D1i+W2SRMb8n5iSQxesy+DOHkJutvE1mg0AExYqUz9wiO9xHMpP+hoaGe6K5pXSsiajauMfdIUOV5pCRn6jqg05PQtvUVgNnZ2Z5HQUgily5dGqtWreqJXJlWFvgwpenESDamOjh/mEJVP3WOXyWYnZ2tESH6CZIcXq8olmlzLjfoS/YaZwdP6sU3omQZAulJRKLT6XTXRn3uexSeRbMUDxIym2Sg4USyTWMuUrKoJjNspshK4TejG3ccBLoMrOgYVEapPbxeZfC3t9udOZ0iN8dEzL2HcmhoqMs2s6jP65SjyFKPKtcjTB2n7pR65JojIx2/X2Uy4uEEzoQ6565QlyZgcb3IKbhjkfMQeeAzde6QPWXJvtBps87susyGuGZEsHPgi5jbMCFnyXI8YnenSN1lztAjgWxsCYil9L50yvlAB8t+0ckrGluxYkVPhC/9VlXV3QRx7LHH1sCKGzYIDCQ2/kJlOmgCmB4v4KYREkWlZjUPOOaM+vQ3Xysm/XD3Kj9mS7sgcdd9Kotgo88Ouf+jfjlubhPZjlzqxyNC/59tzDI8RyoDD3ZkdFnIPTz8aA7dGahYZRY5eehPoMvSQO4APCXggOqO0ndekRXSGDPn7ayKTorboTOmXNKjymF/afR00LpWOiVRWLJkSc/3uXwzBYEli5p8PDMQ5LFs4rqQRGREialAHeO9Tnb4hhk5GQfxUt81FhkrzkiZbINkw7MThw8f7qbx+JqsLItAfbgT9jbRHjRudFyeVtR9TKVTF4p0NGbcTOZvE8nm3DHHHBMzMzNx8ODBHqevOg4ePFh7nEHgU9pQ5Xbu0Yi+9q5P6mSPGxG8+JiC5hdBk7rg+2lJPFX35ORkRDxKZpYtW5YSp6mpqTh48GDP/bwmAyIK2+jEy4GN4vZK4KZf8bL7JQMPdsyJR9SBigAVUX77PiebD4Tu9y2/dDYlICq1N6IeWWQTjqzQwYBlkeFn6U7fwUf2TAPMQFeTWWDvr5PiOzWZGuY1SpdwkmgbNtuZrR34bkvXle5lfVmknh1biHhk42124Mnay355ek/j42OizTI85mNGm2U/GZ12Op3aM1ue+lMbCK60f48mGYWR6ETMbeTwRx48QqPQ/kg+Mlt2kiP7W7ZsWVRV1QUCF63jjY2N9UTfapPaxUiIulX7lWacnJzsviLPs0Hql4COO6o5Rrqf/dVxtsnnjuaPp/dVNt/a4rpkZMV7WJ/+dxvwzVsuGdDRp3G81afMFx+JHBVgR2fhjNojMaYznaUr/OekFtA5Q26KQCglBuUpAI8GBXQEu8zJ0EmwfGfF2bqXp1Qi6u+VZFSolKjXyZ9srYeOWD8eVZVSw/zhhOfXGdS/LAJwkMvIgB/3+jzq0jW8Z2hoqLbxIQPuLCKl/ZFNa13Hdw/678xpcSy0g1WOimSEGQMec/2xT9yYwuuyXYM+/tKb/y1wdsLo6S6m7wj6Ss1FzK37OZGcmZmJycnJGqiUIg5GtDqv/6enp7tAx+gzAx7pgdGu6ifZY9qcqdTMTjgfpDcCifwXozq37YyMOehQd4sRjimjZI8kFxoYLFaOCrBzxyvxnYp0jlmkxokdUU+DZqzcQ3ofxGxgHZD8N43CnbozWzpLf5ZI15RSQUx3zmfUjPTYd7I4RYFcUHf9sM3sk4OJxpGL+PpNh65+N+mYZeq3E4YMzEqi+z0NRtaq3xxP6pv/M2Uqh6UMhJyZ18O0KdcHOfaMYPyVZhHRJVHskz/SwNRiJiQL2SMU6m82V3weUBe0LTpN6dWBmp+Ympyc7MmCKOKhDt3BZ4RZdejc1NRUDUyYOfL+c20wIyXUh0fmtC2PqDQmiu50n0CUUST7xutI6DL7oj68jJIdePSZreXSFh4PwDsqwE6/aahMQRBMyMT4QlJGcxJFdnTQbrxuBJnjZnn8m+WTCYs1ujG7kTI9VjIitlkOTPW7kasvDiAqQ38T/MSE+byU/nYgU3/5fkvqzAkLgSGbRA7k7F8GABS3CTo4snY6TBIr7jYlmHu6kpEs+8poiXWw35ktMS3JSMp33zJSYSpbdRI0VSeJSsTcBotszc1Jkjs8J3JOWLIx8EwL7/PUGMtRylb3Tk5O9tiJAC/Tq/SWRfkEF/+CgWd9ZAvcgenRofTK+a82qU9OKr29JE3eB/ZXZdAOHcR4L/1EBm5NpId94+7TTNi/fspRBXbZRPLUmZyFryVFzO12kmQMMaL3je4OMj5p+L+uc6ZdctZi4NkbDgiUpfSdM7ZMCCRODtg/Bz2yf0YmS5cu7UYUpXQe00nOpqUPv5ftpJR0mI1FSTjGjPzYv6x875d+yLCzlJ7q8jYyvVYaN9crU1i+PsRdgP4oTWmO+A83WHi7JU6cvK+8xolaRh5J7Ji1oNPm2PkjQQcPHuxpk0DLv1PHftHWSZynpqZ65p7vCfBolBGkjyMjPGaOMr2xnz4faKOldKSX4aJ7uSGuNGdYJ49l13kUq/65fvslAw92pTUm/a/fmlh8SJlSej7O01Febzb4bigOTGRsDgTuZLL3Y6rMLH3g/c9SNtl9TL/Q6WXs28v0uquqqqXi3MHyBbRKC3lUl7XBI+tMh02AxPHJokLvD4mJg6xHcxxjstuIuec16eTZFk9HU490oH4dn5WSLrUpgkxf7WCb/fGAqprbOEQHTB3xWv2t30yb0u5oPxxfgS/BqxQtMhrmBhKOPwkKU4i6j2RYKUBGyLqXdVdVVdMndZFFda4HRs0ZODgBj4haGtuFtiYd+uMEJWEWISPWTjyzObFQIsnyXK9eZj9l4MEuYk7pmaPzaMifU4noBToyV0/Dscws5SEhq3GmnAGUs2GmAbkzj+JApWM+yR0kqbOSOOi4vtl/Z+YEZ9WtNVI6BzkS1ee7BF3/TNf5xKfw7QwCXG9nkzQ5XU8TcVzVD+3aY2pRfedGJ5bBzQ6q0x0m01QERSdJIkpuj9y56WMqB83x4FofCZ8TG/WZ2+YV3UqY5uP4OdN3vdNJ6z6Vq+PMkAwNDXW/m7l///7auEu/TjDVX7f1bIMY7cFJpcbI183cN6i+jDC5Tas92Wu+6PNUT5aiZbt1H9vNKJTEyoW+xdOvTfPMfQ8JTz9l4MHOHVFpMmvScJCobN/gUYoOPGqkuDPweh1UPUdOkHKWzL6yfEZE3LjAdrCfdLR0thlwODMlw85YMI9nuyXdKVEXTkp8YlO37EdpfPSbRIGTjXVlkzuL/uRM2NfZ2bnNGfqR06cTkRP2fpMUyaHRaZBYMDKhntQ+jxqpL/ZdIOE7jKUnfzTEdcs2q7/+8gDqs5TpoE34eHvUw8g4Ym7di++y5BgPDz+6sWz//v3dzSk6rvL0BQZ/72pEdN9k4kCX6Zb99DRgZqfqN31OyZ45Xmq7ruVjClkbm8gdfU6W/XEim/kQifrhEW2WyXg85agAO+b9qWB3yE0TUEJDJVuh0PFk6QkHOn4zzB2Xp4XoPJh+YvtpTAI53UcQyyaCn5O4UZZYfJbGKBlz5vQEEEwzeZ10fBnA06G5ONvWdWSg+tsdrPeba5gZk1d9ZNiZA1QbDh061AOeHnnT3kji9GIEByTXFV8kENGb5mdqkePHDTcOeN5/lsFoppR9cPsYGhrqPgPIb745sXGw8/mVEUrPjixdujT27t3bTQ9Kx6prcnIyZmZmul9WEJmYmpqq9Z36og2Q5PhY6n7d6xEl08lccy35Jo212rlkyZJ0M0gpsuJv+jiSFPcP3hbarfpEG3OCzv6yDe6P+yFHBdjx78eqQA4+HRQdfUTvphRno3SinIAOdGJT7gxkvEqDqS2coDJEz7NzHdGjNgdK1q2UlVikzruwDdRzBvysWxNCu+HUjmz9jZOkxKjdwfs5tUnjQTDSegjHLGPEJCB0khlA+4+DF9mzogV+OoZj5x/mHBoaqn2YlOtRIg2ebnTxlx/7bjh3Tvqb2+x9XpGEZA7OnTyBleTPdx27LTnQum4EYorOuNtXdq2XR/ObcpzPArzly5d3N6OwDy6cf7KHLBOja11vXrZHWU3pPScF2fpkVmdGQtgH1e02nAEnAwwSJepBevaMA31kU+T5WOSoALuM/fOcJlaW2mTE4+zGmSb/1iA6iKlOf44vMybWwb/VBk8JeD8JFmqThFEVf6tsd9y+MM52ksHxbxc6KTozlS/nyf6W0m8cM04u36DgOiSoS1dMnTJa10R1vbP/XsbwcO9DuBQCnQMs05v8Rpvq4Zqa60jOXNcINEWOSms6AnaWrfucXHg/OK6ZvfL/zHGR6HG82FZGKiyH89pB2UFR76kUKeCXCyIeffD8uOOOi4cffriWBaHMzMzE/v37U9JFkX7dTnwe0jeUyoiIGlhqfvijItRLFkW6f6IvkvijNCTc3IXJsmnLHkionU56JfQDTpaaUrxHIkcN2M13TUQvc3SDY7ThkZD+1mRi3XRWZKsq39dwFtInd/Lu3Gl8NEiPAL08B9ISm3QmzTQF++zC7e1ZLp+sj2DIvmlysCymdBeSJspSrD7RfbISyLNoLxPaQOYkCHjuaPmFBSc1BFff0ap2yjkqEyBQdKLAKJX99d2lJYDjMddxyWFJL65jd5qZ46bdStwx0444Xuq/fqT7kZGR6HQ6sX///picnKyROIIL7clBw/WXRfJeN+/NHsXROHiWg/3WvM3AiJkiJ+siOD5G2fXUaUYoOKYZ6LHdtGfOZycEC/GFi5GBBzuKA5+HzhpQOgw68ab3UGbAwXNyNP7cje9yY70eRaoM/s/zJbbH6wnSDuzUj3Th6y10Nkyx6Z7MQD2lSKDznZksw7eeqx/c1k1d8jEMApecQUZQ2MYs2qWoTEbrWXl0bPqfDoZ9zBxfBnglwib9i2Q5eOiFxLSF7IH+rFzeQ6fJa5zsSXzDCO9xW6WeOA4kl2xTZusEO94jvTJVyw0nBFzNr4mJiR5b1FixLhcHw5J/cN03jQPnFkk29d2ULnTCyoiV805g4z8+5x28SVqcTEf0voDC9zhk4zifzT9WOarALqJXgXRc2o7NtIoGmg/k+s5MZ5vOYDztwPpklAQERQ/Z5FYdBIymPkkccAhemVER8HW92iq9sH/sf+YE6VAi6umZrJ1y+CQXKmN0dLSWdnFWTyBxFsyJ7NFp9m7FTD9ODNhfB0FG3jyeAa4/QiIbYV9lK7xGfe90Ot3+iBDMzs52dw5K52NjYz1M2sXPOamRHlyvql/zhKDiQhtzJk/nzEcL3BbopH0+yu48/eugwYi3qqo45phjuvPd2+22kYmTah1T+dokJHDKdO3zyAmc7lMb/cejSbbX7U/tICiSYLJdfkz+0ue3+0HV61Fgdh/JdD/lqAE7T6/oGJ2vJoFe9hwx53C4+9HX4XzBlUbF+p3pO3v055VoSB4Nsh+cHE2MzCcgr2W9Ok5m6ODEsr2fbJscPvtP9ukRsZen1O/Q0FD3zRajo6PdCFdt46dSPELIIhQnEfxNYMmctDtw14M7Vl/nlM4Z0WVlclzo+AiAqpdOkZsxli5dWvt2mfoo/TkBy6IxCtvufaSePcrxcWYEXdrgQzsksXMH61GH68PtmoRVbVF5qmt0dLSbBibwkKCyncweePqTduOZiMWk6wj4ETlRVJkcC9p2RG9EyGupI79XcyIrd3h47mO3GksnhGqz38tsRDZu/ZKjAuwc6OhYnEXJWLibTZsnyAjpwMhYWI8bHFNBmYG7w5CzYrSjdrmzcdZGwCtFOhkb1jHuHCNA0Jm4U3TDVh84yUvRDSdKFjFqMnU6ndpuOqWACXZqC6MptYuTlXrNxo71Z+PlYMX/PYKkvUjcUXHy83qCLsukbjluQ0NDtfW+kZGRbmQ3NTXV4zSdoPnYZn0niaF+WYbmS6Yfghx1KSHpcBLC82wPfzyaZj/ZR9aheS8dM53GyCfrs67L2q//Xc8jIyM9G2F8rlJnEkXyTE8T6LzP/O3tzgiK67iJ8JEoELQIdtJd1iaP7NT+hRKAxchRAXYRvWxbQhDS4DB9xl1QXoanzFReRD3V48zc2YwDE78kza3mBLosWnCAcgZHoHFQ8zZmBufHPM3gk0IGTyCjM+J9ZJIRc285cSapH50j2Cn15BMui3bdMbkwPZYxTAKTR8+l9ItHu9QDo3XpzIFRZXsKqvTsHFOo3PGp5/l0X+ZQfezpeDOnKD00kSKPoKWzTM8ERF/n8ftozxnhke2LQKktPu9VxtTUVHcechOV9K+569GdjzX9hXTELI6/ksztUmWorVyHU9vVBo4vgYZArk1Q3l5Gyg5SlIycsR8iViyPunMfSIJH4kk/3E8ZeLBzo4soPzAuo+LaD1MW/KKxG1o2iUvCa5y90oDc2N1BsG2Zk6HQIXs6hoDXpB8vz/uYsURPv3rbPDpwJ5rpUmUw6p6amqqBvRwTmS4nmkcXap+zfXemJDOlqIWTOutnlnrjdWxTiVxI/9xdyLZl5EoOUU6Ruvaow8fDx1a/Sdiqqv4qOKZbGX36jmAnkr5jN7MDRrwlO87u8wiD7YyIrh35OhRtScscrhfpzh8pcJJDGyEASTxS4njQJ/lyBnXDsXGAoW7UD5Jv2nVGRAh0BDu2mWDnETUDBEZ3bH/WryOVgQe7iN5NEyW27s6FkQ8N0FMlEfnEJ4iVIkMHPD5Q6yBIpy2G7lFEBlRutHSGHhVSB85MM8nqI0gzCsuYGvXizpe68onLKEU7DrkuFTG38M5+Mdpk5MdoIHsROB2FgyfHioDIvjiAuj7dIflmGeqZjl7l63raHu1HEQTnAp1v5lTdvksiEOUGGwfOJkdKHdP+PfvhBEHsv0TwqC/WwbFWG5UZ0APj3O3Lew4ffvQZSEbYBNdMT9Qx56FslfZPkkig992VqpOA40CTERhG+WobibWTC/oJ1acy+Cq2bNy4rszPe6kdDrI+ziWf81hl4MHOJ7enslyhnFBMifBFxSxDddCxk5nQcOignH0THLJ2qR7dy/Qq+1IyEI8muYbmaQN3fNQLJXMu7qT4suPMKRAYXAcCOXfCEfXPjpReiUQ27kzUnaiuKUXR3vaMqNB2Mh3qfpXt65gZ2eB4NqWVFG1kNslo0R236pGw3ZwnJCVu4yQjJAkeXbL/1JtHin5cZVGcvOp+1p9lCAjg7Ae/MO6OnA5brxjLvppAyYgO2+7nFQ0y0qFPyfTAucYvLNC+qUuSIpU7MjLS3fCVAZ33iWQwAzoHTV0voKOefAMP51opIDkSGXiwo8PzVEcJHGS8up4TiKmqiF5AI2PWMZ8MmYPNBt3LLQ2+A0d2jGCrcwJuTVyla9g2j2KaUpyaAAQEMu+MPfKYt5d9oT59l6g/csCIxRktozaVQafJe9jfzGm5I2E5XoacmYO+yibbp3P1cfQ0EDeiUAjanjmgUJ/sU5Mu2S+uEckGND8YlXD8PMJ1x89+6x53fuob++gbijKAZDt0Hb9AQXsUAOg4Iy6Bnvrn4j7G56ZnenTc18BK/eGYdjqd9B2ibv9OpgV2nuVw++Q9jNo8+vW6Cdbu49gP6ktzUn3upww82ClCk5PItus60HhaZD7GwUHNIiK1gccoBCBNEDqLrO2ajM6ceR+NmiDt0ZdHSJy81EUJ8OggCCR8XCPrr08OtcWjomyi+Dhljlgg46ke9pmEhpss6Fw8GilFGVmaSff72ioJB1/HJL0xetR91JP6qDqdQEjYP7L7LNPBejhOmWQ2QPDPMgjqn45R37rG08fZfNP/6hfB0wHC57vq1QeEmdJkH9gXzR39r3sZ3WdjPp/d+LVev/fX54uiMkagPp88gtb8py44b933sI0kIwI6f+sL/YC3hfOKfolkj3bYRnaLFLIETu6IOeajCe1pEUkpAvRwXcd0D9l6NnC+EYV1eRqH4CFjIdip3VnU6lGSJjtTT0o1qK3cTKJdjplufFFaOi8BnetVZVA81Uwn6OPAMc3GSPdQn3KCWSoqi9wJzD4h6UwzoJNkET4B1pm7ysrsSmsudOx0/O5c9ONpOeqatj/fLluPohzE6PxIYthW6oX9ZLRGfTQ5PieGWn8jEWQfZOe+CcyjWpXn/SY5YqTsaeEs2nGdSQja7JePDa9l2pKRGa8l6aCOBNC8N8sQ0R+pbl8OYD+zTFWWQaEPdp2USMqRysCDXURv7j6ilym7Yn3wPfLicZ5j5OVRB+9zkKDjJyP2tSmu0+l6XVfqo4SpGNXLtJIYv8rTxykdfCRu9Ipgs8/zZOneEolg2wncBAcHHoJCNkkYXYs4yAE7qaBOVDaZKAE2c4wleytNXtaVgY5siVEGnWwGBAINtZPjIUfnETGJmdtyVr7u8WwE+8MIVeUxYqLdZmPJcfZISfUPDQ11Hz/x5y0zHel+77uXWyJQnC9sc5Y6drskmGf6LBEVB11G6dkmD/7mWETMgR2PZ8DsIBZRJ7dOyEskj76Lr1zMdEybyNLDRyIDD3YcqFKkkQEdf/tCamYEHsFl26BVBhd4fe1CZcrYBTr6zAgfJM2YoPdBIofJdmQOm+sJ7Bv7IufnE823RKu8rD2UhaYr6PzYLl8DoU7oPNTGLBrMSE9pI0sWpbmj9ijB04WMyiLqX3b27AMli3Ddfj1SZN9L9tokBH+3nyb2zUjTI1GJ2uBgKfHx87/lPEVgnIxwXmVzkbY+PDz3BXd+GogRPHUmsGEfNT7cMOIEjG3JdE1y4tkD2jjJa7YmzPpIoEvrZyWbk/hyhY+Nkzu1RyDnBIskTmPhfe+nDDzYOTuMKDMQDhYnthy6GwFByR0M0yKUbP2IbWAIL+CQofA7ZRGRTmRPrdJ4yPad6Uu4e9I3f7jDo8ETiEspR96bpbo4BtRbBji+A9NTja5XskX9n6VbOBEd5Pl/RhAy+9F9qpt1MTqTPpXS07W+oUTXOhnxsulg3dmpLU5KHKC9PB93OuIsBUzipjqlE35cNht3j4oo3ERF/TMa8pRiNufVHieJnHscQydz3i72xdfC3EbcVkiKeF5zkHZI8WUTifshEjfpSGNDXbJd9DkLBR7awOzsbM8LH2hzJEAq30loP2XgwY4pK7LIiN7ctIQTnyk/AieBI6I3D+3pSUWVni6QkAmRQXpEQENyJ8EHeD3t6iBK8Umg+hhFco3G298UUbL8DOh4f8kRuMMRU+QDzFk0J/FUYynNSbIiR+cpxFKf6GxoEyorc2baiOIO3ye/rskYsbfP21DSK/vNPniU4k6O7F7/c25RMlun7U1PT/esNRHsM/B1wuoEjuAgPRDks/t9DPmjfsnmFElm/WSKnVkPj7ikD62Tl14Zxr+dYPkx6jnzQxo3B5LMXkgasudX/V4XEksHOicKWZ+a7PVIZODB7tChQ12HEFFfD4iIHkepaxiuM2XgIsPghGFaRBOIAJqtz9CRZyk2Oh46NpXNdrNMMreM+fkkJxiwH+yn7uMkVkRHx+UTKwO6knhkLAdYVVVMTU31rM1k4+jjRJ3zuBym+uHRXokUUX8kIVk7spQx7VJ99kyB+k2nrY0FLIc699SQ91W27REhJYtWKdyCzijb9Ut7cfBi/WwPI0Jn/D4GTuZojz5uOubEsTS+JChOBF0v1JcyN3oht5MRJ1EiDB69sT6fv/6/dMFj7sOy/jEAcJ81MzPTnWsRvVkGJzI+Lp7CzMDRiTJ9RLtmt0jh7kUZGQeFhsCJ6q+/cZbqjpj/6xiFwBHRuwZBFkXjY7sI1L7+QUfpDs4jQwdObqZhH7neoInhzirrb4mZZalL9Y/9cQepH+lIqRFnyw40WQSXibN9ikdIWbTi5fDeLDJy/aksOmtdwwf93VGWwNUZftY+70Pm9BmpsE98dIC2X4rwXCfUDdtP0FJKl2BHUuF9yjbcuD4c3EhCpWPqhWCTgZzPLaYM9aD20NBQ7SsqbJMT4OzFCAQ62oUT3oxkOLlWmR4Js0yfayKV2SYl6lb3S29Z+e4js3LcP/dTBh7s6CD0Q8bAvHop/HfWI8mMKZOMVTGcJ2tk2VlfaKwepSrdQofZFHFyYtOYPVKk/rIISef1dzYB3ZG4yInS0fh6BHeluqPRdVnkNp9OKdRVFok1TUQ6JupB/c8cttsBox1do3SnIinVk6WjCaQeSXlkLqbuTpa6yGzbyZN//ZuPqZQIBPuodvEBdSdfWUagCejUbk9hZu3JSLATTJZJoKaNaI7oM1TSL9f+XLIlBNbrNqF7CNhuC7SVLPXItmdrtyRaTLE2ZboYjak9vq7uvtP9rXxuNo/7IQMPdmJZ/kyMWB3BLqK+O4sTYCFgV4oCmhwyjc4jK7+uNKnJiJkKZRv5ACgBTLpgCqa0psc20Yn7uQxA52NrDoZMY6nfnDzO+ucjHJnTYrtZP8fZSYjsQjrIUl08RwfL+zwCKgEdz9N2PF1NnfvGC09z+v0q3/XkZIp60r1ZtKt2Uj+ZPvg3nS7nZhZJu6N3nfoYOlioT952kipFl0zrsW7ZY5aNUNv1HGwTUPN/1T1fqp/64NzlmJTqpJ1yw1tGEqkzZro4LtIl+8S5WlXlT2U5mS5lfvolAw92o6Oj0el0usrkhGPEIvHtuZ6qzAzfxbflsr4sullIPpvOIzNMvhhagOCpU/aDLw52J+Rt8766Y+PfPvkoTSkM/TjDVnl8fi/L5ZecCtvuEXYG2H7eJ2c29nS0pcjS9RQRtQiIv718l/n0KOfi45BFsZ4Oz+pSfRlJoF37hha3VY+I2E/9Pz093X0NVwakvMcjMZUrG/LoPBu7jLA6iXIiRgJGG/Kxpi+Rbris4kLdsVxuDmHZXCN3H8NyaF8EO/543SRVWn/MXkkmPek+zdNDhw71ZBJEYpxcchMg2zwfgV2sHDVgR2Pk+gfZfERvKtMnmxtPybl6ZJCxYKYUSmE70xRZlKE6nEkzlUFG5mlbbsXnb3cSZLjSTRZR8Voec91mOvM3MlA3zP+zHI5p5hAl8z0XpL99k41E0ZLbC8tz8eNZ6iei/OYevy5j05njVllDQ3OL/O4Ys/JpO05mCAjZfKBt0NFzzcbnTqn9pbqyNnuU6IBTAnj+cNek9OAZHnfEnG9uq6xTGSX6ASc1TQQtI3aZX2I72BYfWxFHZX8yQuJ1+Xswm6JGX7fNsl6Z/mm7zDL1UwYe7DRIEeXt2HQgDhaZUc3nlCJ619c8vcCJmBmYG6+DYhaNeFTEVwH5A9IEN5bL9maAx7q8DUw9OqObb92CToiOmR9jzXTF/pecIh029ar7eF2TcELTwZeu9bVh1qfxyEA8a7v30wHXMxG81gkIdci0p2R2drbG4OmYPKqiw/c++DVNc8d1n0XUJXKp6zMwITDN1xfqJ0vrsh6VzTksHfic908f8X6mLUskMbMj6sbJs4+Jt12/53uln5aA9KJpzxJlQJrpzgGZ95fan/mXI5WBBzs5FX8g26UpbVliidnE1nE3eoKFp45KzN0nUsbmda2/uohl+W/pwnd/qi5OVIk7H9eJ94fXeOSSAYv6588ElZitxtSdvzP+iPqOW7Jwd3wEd5bpTo2PAvA6PgbhO0UJKLQD1Z1tEnFQc4JWsgmPuhlxsX7W4+8J1ZpVBrQUd/yZnUgnrsdSdNyUCs70w+tol9luQP/JogrXtZM+z6Q4ieTaslKyBDztpmUdS5Ys6doz++Pzi49mSGijWQTFtnEsXLxMfsKHaVOPvtWuLLL2sj2Fyb55ZqBEjB6rDDzY0RAc7Nwpu/L9+EIlAzsd5wRxEGKdbIc77gxk6Zj8fmdeyqlrjY8AU0qVStR2j/TYPxqtkwQHpZL+3DG6HiLqDoTtyKIbT5Fwwooo8IHmrN90OBqHTAdM5ZCxa7ehAy3XT9nGbH2EYyVyQL2VyBzbmAGE9JatH883B9hunzu086x9XleWGue5bH5k7aRj92iedTtISO9uAzpPAPVnPKU7PoCejVcG9LIln6tNz99F9BJHzr8sqvOyXNQuPj6RvUDc6yUJbMq+ONCV5irJe79k4MGOzMzTNzK+jPk7e+ZEKzHtiN4txBQaaIndsHwd45tROAnYF04qtYOGr7KUr+fDomwT66Yj5ORhlML1jtJzO5QsDZKBZcZUWaaDC3Xhk8pZpXThYy+HlTl23ueA7bbDnbUS6l9j6Xr35x893eZ1Z0DC+zlufpzlsX9st49Fk5CQyHGrrbTbUpSWlVeaQ7QJ9it7fMB3Vvp9apc7XerW0/oZIWD//UHqJgLCPnk9Hk2qn7rO50JGLr393kefj/pR6lI/1AdJmBP7pgfBPXjIdts6Ae2nDDzYcZA5AfV3tuDK/0vOocRcXTwNlkWSXi6P0Vm7IyYoS0qpMF07MjIS09PTtahAi+j+jFMWpXk9PuHI7nTc1xmaHJ4zUK5n+fUChwxQM0DwscjSMQ7wWXRNfWRrKP7MYpZmIvtmO/StNG8b9e3tcN3y2T1dQ+fJKN7Fd3IuBJRIkLx/TgQ9QvH55QDLCCuzbfbV20XnmxEpXus7HrPIi7ZdAmLVx1faCewzP1AqI9so5n0v+ZCMJGURl/rJDMTw8HD3GUG+zDqLygh0TUtEbBvbV5qj8k1NwPlYZODBjkzVWXSW+pOUUiklB8NyfHIyReIG4+V66oRApbr4OEDmqDMQzKIMTULW5yzTr1P047riNWK3YtSlZ4eyNqkdnsJRXzOQzcZKbWK9/vxZxoqdZXrE4szUhSDo5WTrGmw7yUdGsjI78DZnxzOwZbu8715PU2oxE49EHPDYX3eQBNssImFEFlF/IJ5tziIZgT5tIKJ3py11pvNNaTpGW/rhx5BVn5O8jCRnwJERnhL46Dw3eTURRrWDERXJL+ePz1cCc4kAuJ5UFzNrWbCR+ZkjlYEHO4X7HtX5wvR8Qtbnk0X1SOgMySzJ5LOJSONVu93ZermsPzNKZ/cRjz6OMTQ0F+Ux3ZKlm9RmOnIem52d+yI4deFg65PS/yfYZu9aVH3ZpGLdJdZM5+7i0T/HhO3g2GbOSg4ji4w4geksWC9/6zpGymyvzmWg62PGezLS4fU56GV6dKG+HFy9joj6B2clXDfNUoyl6IERuBww5wj7k5G1EgGiLtQf6sUzO7peywW+6YTtyYgKy/Hxkx4IEq5Tj5boP+Z7jIEbUjziyp5/pZ6zyC4Dd7Vdz+x5ZiW7v18y8GBHYKMz8cGTlFikO0G/VpKBYRMw6h611Q1K592hlurleQdov1eGxmdudDwDGY9C3JlrwjgoZuVkD/76s35N6RtKKf2UiUeHEb1EgXZCG8h06GXT6WR1ZA7D2a50SOfJ6Iy6bNKLR2065i9R8M0YHl1l/XViI72qXVzXyli/Ax774akxBxfPmOg+fp5KZMnnaxbZqVzaJdvokS5Jg+uHJEbvcNVcYFszUqU+EZT4KTD1lzskm4CUxKNpV6pnu7iWnbWVtuGf7skiatr+kiVLotPp9ACqAz313y8ZeLBT7tkHTpIp1EGGxuxO0QeX1zHd6E6+ZKR0NGyrDDfbkktDbXKAaq8MjekK32EmPWQTxMX1I2etT5iU+uoglYGjA1OJDLjTYD3e/8x5e1msr6rqrzwq6cKdjJ/jg9aZ7oaHh2uMV46KnzLy+9wJuV1mEYs7I40/HTKBwh08y6Kjctt10Co5Maa5SXwc7JwUqj72k4/UqB0cuywDozHj+hTLzTIRTmqyPim64zOWrNPHz+e62sSdkLJFPg7gEbsDFMfU9afzjLJ4f+Y3NZZM1VK/tFVP5+u9oUzVOwFpmqdHIgMPdlJuRK/zz9YEMifmURqdgO7JJoSXSbbkRs+Hvt1pMzWWpQzIxjJgYdudOVVVFUuXLu0yYTJTTwWxP+yX64xMMWuD7nWDV72+wSMbI29PRmT8+iZiw+t8krkDZJ3eTp1zcM4cHp0ZUzsiZxH1tF6JdKkfmSPlcfaZdqzxKqUIS89QsfwsXcl7s9Sm9ML7Od6eTmff9cMUrwMlAck3ujB9Rh04ySlFpf7jQCMQEujRBqRrrotRZx6x+rZ/LhlQH9RbFpEz0mYmgTbqO7s5niqfjy75e3h9fKUb2TofZ1BbnFB7e/slAw92NCQaO8UnVBNgZJKBIUWO3TceeFqPDseNW5JN5lL+3svJ1pFkoJ57d+fufctAjteKffp5lsE2O/HQ1mM6MDf+zLkvVrI0qwv7WQKEpnZlwKcoXbYhsuMP8BLM3Am6k5Nune07ufKIRcc9Dcv1ZTot15MDgttwCTQi8nU7v8ezHN5/B4qmungd+1OSLMLzfmbEgWOYRcaygdJD2xlgUKfZOfY/or5xhuJAl0WBKsvtQEA3NTXVXe8v6UxkxHXjqUvP4Hgw0S8ZeLBjzjuLahYq8wFaSTR42VfEeZ4TPgNjOk4ZQ/aFY3dg3tYM2DMj57mMxboTZRpDjlzOuzThGVEcPny4G72yzzondl4CmyyKZD/9bweP7BqJp1u8v+xT9oFLto3Mm44vWytieVVV9bBoOsamyItA5ezfbU91eaRSsvkM5Hgui+Yei7hd+08pfe1kaqH3l/qh62mnir5JUJYsWdKNYkrLC0rzM1Xp85Fp4Ije186xj7zeU8OuE7YzG18BHDMLEVF7RtfrkT4y4HKS1GQPur40px+rDDzYcdI70PlEdOWWJmspPeAM2o3LDVrnyQAlXIQnqHHyMvWXOcomR+DG7c6NdWS711yyKFDluV7d8Kenp6OqHs3Rj46O1tipgFPvyCwx98yhemoxk+y8jxvL85QNyyitzZQcWNZm2ijXVjXOrI/Xu/14f8jQSSLUL97DCNEf12D93v6MaHEsvS0u3g71LyOmGVFVmzObpa68Dq8nc9xeD6+R7apOZlr0ujC2m/OS62/6zffBkuyxDEZbTpQUgREwuG7JyC5Ln/oOcpar5wcPHTrUQwIyfdPX6G9+DNZ1TKDstxw1YEfnmYX1FGftGUjKWEuOTRPAHV1J5Mw46WQY3O1EtpZFqR55MYXLNImEeuExAqlHDexrFvWxDK4PcIKrH9yByTUNlskdi5rEpd1f1EnJufkE9vOMKpvGy8tjmsbTf6yTfXem6+tJJAYjIyO158oyJ5M5i8yJU5qeg5TzlT5JXji3GPXyftcH25b1IyNodO4kplnanuORiepyUqj2+VoXATgr1wkChUDDdrq9eBozI90l0M2ic45V5oeyVHRWj4+TiDc3s6l8XU9ClRH4DEhL0m/AG3iwk9D5Zo5bkjFY/Z2xSQkH2MulQ8uiLBmAM379zxc2UxzwWBcNmmzaAYV1kvFr6zR1xuiUdWZOXb/pSJXS4a43XZNFuaWNEdQXdeBkQdeUxikTtt+JgwvHQ87Kvy7hduMOgI7DnW4WUXsq0lOkygI4a3cbd/3QSVHXbDNtJAMad2C0PbdDj95Ubim74uLrbxx3J26ZU+d8cnsmUDDdnBGIhaZn3ekr0tI4aWPS6Oho0c9wfLLUofu4EqBz3un/0tqb2qvf3Fnr2QlmDSKiJ3pzUpQBbgmI+yFHBdiVACyi1/lk12UT1MHEHSLLpZNwwPF0BR08gY7HPZJjHRlL9UiQfaQBRzw6AbQA7Q92K0WU6YP99okowOPkIxvMIgLXO49lfXH9+SMUjCocCOgE9JuM3q+n3tkHRRv+iIFHVpnTz4iQjrNOMmaPiHWNol+1y8fD+yCn61Fh1meSlwxEJA6aGbFkneoHx8F/Svbr19CpypGXogTODX+sh/bqjy+wPp/rTaJxJlAoulu6dGl0Op0a+Htb3R5JDD3TQ3/Dx0K8D05mvJ/+t4+BZ52oL7VhZmamZ8e5kyOWuRgisVA5KsAuYs6hZY66iUXQyUb0pgndEdNplQaLDopOixNAQMMdkvzhvTQwHZdwctGxejTEFMWhQ4d6PokkpzE0NFRzpq6/jExkjpbnVVa2JkX9ZH1jZFTSE+9jmpDpRLVF+uRmA48AWB51n/XTwSlz9pkeWU7p8Y8SgNH5lR7/yOy/xKiVyvQ1PpXNbImPscDfxzETgkemB5f5nCHHKIvwvb+Zs9f9GaFtapf6unTp0h7HnYHS0NDcG0w6nU5ERC1KJ5lyAhMx9zWGbJwcPHwssqxUE2h7VE479QiYRJl+w8miriWZno84LFYGHuwyx5OxPB9kZ6bzSVOkUmqXM0SCHh0403POZNXGEnuPiJozF9DKMD1dqsVnAh2dMev3tR5fl8k+GKvokONA5+IARdDyvi/Ugbo+6FAdBOnwGCVTD1kE4+lF1s/rXDzVSH1mzlzOgu3N7ieZySI09c/r8N1+PmekbyeBWXSeEcP5hGMqW3GiMF9E4LrzSLREVj2Vq3sZ9WWgzvbM10+PoLLHiJQOpzDd6eNB3+Htykif2yj14CTASRHJHTfOqUxmXqgXtp+ATb1LMrLaDxl4sHOQiKin0SQZyFH8erIkMpKI8jZ4FxqeRyfcmJKxMmedapO3P7ueRkigE9j5eoazLZZBh6a/mZJy/TlIse9Z1ErD975w3OYbv0z37FdEzkzlcKUrXs9+ZdG2t6NERghSJdKVXd/kDJx9EyTZTrWfhCgDB4rGq6lO/u/RXVVV6UYbgQr17GMvyf4ujTmjC4kvQ7jQrqlDv8avn8/HOMFlRKYymHbMCENGbDMd6Tg/cuySPUDuNuv6dbCijp2EDQ8P1yI6RqlOuuinmenplww82Pl6V0RvalIg4YZCyYydEZfKU9REyRwyHZVHcPw/W3tyh8r2+aTTfewjnQyBThMuA0ZGeR75ZMxd/eB1dGAqvyly9WOc7M7CMwBpGotsPKkrnteEzXbVSa/qgwNEE3CUnGzpfxIP6sGdXBMQMvXo6VvW4c+G0bZlN2xX5tw9M8B6nYyxHfrt9lDSCdvAvvh8dyDiM7guPk7UqafYSiCf/Xayp/nHfkrXC0m9NhEMiUfiHsVxw5iX535mISDE+c35oXU7JzDSC7NJLKdfsui9nbfddlu86lWvirVr18bQ0FDcdNNNtfNVVcWll14aJ5xwQixbtiw2bdoU999/f+2avXv3xpYtW2LFihWxatWqOO+88+KRRx6pXfOtb30rXvrSl8bY2FiceOKJcdVVVy2+d1Ff18lYVxYxZZGEG6del8MfvRSVTrrpx9ld9uMT3ie+A0wWcWb9Ufn87pZPYvaXm1VcV16X60nRol+bRXYe8XmbOH5ZylggRwZJx+2O2SdUpk/vVwZITWPnDo0kwSNejl1pnHWfv2mlKapg3/n8nsY5sy3XqW/3d32wTfxbaTl+I20+Kdk7++E64f9ZPzjWfK7NlyCaIikvS3bIdrAsSUaUs7lMnWn3MttK22bZ2bO6GYiwT6qD9sAfB0bOO+qMeuC50hjQB3F+0H5LkeiRyKLBbv/+/fH85z8/Pvaxj6Xnr7rqqrj22mvj+uuvjx07dsQxxxwTZ511VkxOTnav2bJlS3znO9+Jbdu2xVe+8pW47bbb4vzzz++en5iYiJe//OXxjGc8I3bu3Bkf/vCH42//9m/jE5/4xKI76BPGHV0JFDJHNzMzE1NTUzE5ORlTU1PdH27ooKPOyiFocEE521xBEHDAbhJOJGdLrhuKp23UXraZ7XJy4NGpl+F9JJCWorpSG92hZo6PE5SgUJLsXDaOWRSVgVpGAuh03Llm5MrHzp9XFIDwOHXkG6EY1dBpOtHwPjIK8MdDSLBc9x5ByLlyPddtsYlwuixkPrg4KHtKMwO6khPnWGRt83HP+uFAIRDiD8fZyVuWTmTbM5shmPory5yg+O5xfyDdQZDzLROOr5ZOGNXOd/9jlUWnMc8+++w4++yz03NVVcU111wT73vf++Lcc8+NiIjPfvazMT4+HjfddFNs3rw5vve978XNN98cd955Z5x++ukREXHdddfFK1/5yvjIRz4Sa9eujRtuuCEOHToUn/zkJ2N0dDROOeWU2LVrV1x99dU1UFyocJJLiQ5Kfi2dj45nIBWRr2lkf0u0pqXzWfSVhfpqB+vMogzW6wDm4hP58OG53ZiMFLmW4unaEpBQV3o1Eu9RfaX1SR8T77uvC6o89o1OqKSzUpTHczqv1IxPdNoRz9N+2H7faMBrqANGENIB9ZyBve5j3Ww/wUabDDQ26p/rSA5O19D+ZSd0itSB63F0dLRGolgXy2HbpTcey8gZiViJ3BDcMpArRcXUtcao1Ee1g/dzfGk/TDN6xJbNd5XrtkBQJSCxTj4L6l9OUD2cD7IHzj8nCYzuSJp0jOOgcrhmy+/oSb/ZEsSRSl/X7B544IHYvXt3bNq0qXts5cqVsWHDhti+fXts3rw5tm/fHqtWreoCXUTEpk2bYnh4OHbs2BGvec1rYvv27fGyl72s+7WCiIizzjorPvShD8XPf/7zOPbYY3vqVpQlmZiYiIhexyYFK4XnBhNRNzIyMv3vk83z/wQJiUce7ig8giC7p7jxN+Xa2R9P8fjE4wTO5PDhuWflmI5THzJGr/uYp2e/STpKKULXXQk0+OP6V5sEuNn4uKh/cr4EXOp+vjL8d1P7vV20iQzwCS582F96dZuVPTEyZkrR9e9zQzrkmHD86FwzckLA0Jc2skiH9WYgkF3HKErXZ1FPFr3NF0F4FOURMcGBQK1z+l2KTiU+d2QPBBR/7pYRNgFEwnerarw9Jco+elqXgOfX0XZowyJQah+zMGyvylZd0s188+qxSl/Bbvfu3RERMT4+Xjs+Pj7ePbd79+44/vjj640YGYnVq1fXrlm/fn1PGTqXgd0VV1wR73//+3uOaxJG1B0sowxneZ4+kTjrZpqAgEHQclaTgZL/lCJOd5QyRknJSGhU3gfem+XO5ejdEbsjzJhexNzzNZxEmsAZ2EkyNuj95xjwPr35xZ2mrtVkzHQwHwiWmL9LRqLcMXuKjA7boywCF3/0OSC2SWWyHo4fswVyQlk0p2vZdl3PNrG9dJSZLmRTo6OjPanw+fTppEE2QudZitqrqv4OSbev+aSkH51TXQQUjq/aUIpWMnDP5pTbg/sM2pXawTqyzwoRZD1LQCIjP6L5zFS0zvt7PZn2VNkca2baOJf9un7IwOzGfM973hPveMc7uv9PTEzEiSee2I3eIupMmek0hvIOClm6IqI5siK74TV0MHTgZEWlCeVOIWsv0yGs1+930JABa6OKtiq78CFXLz9jodSVb27wdJCTCOpF4o7CJynHSuBNRqpzesektzvTeaYHZ+yZMGrOhO1itMAISXbk6T7aivrNe+SE3AmWnAcBTG1nql3H6PAcJBm5c9xYpsZbbdVLv6uqqs2XEvi4g/e/3bYpPi94r48z5yn7xbFoAi3fVMIol+WzDZ4qpP27b/E++RgzWuP9flzHOJ6cv4y01E4tSQjUCOa+bsl+MvNAUqlyZTecw7/Uacw1a9ZERMSePXvihBNO6B7fs2dPvOAFL+he89BDD9Xum5mZib1793bvX7NmTezZs6d2jf7XNS6dTqf75gEvmwCn33IUTeyeBuoRlIf1zrx9QjL8zwxbwr854Xh902YLB1Sf2GyzA6V2T7IM9akEwtSPnDDZpf4n46MeCXYsy6/NnIWPgRwNQZ/jwU8INUVnrisSI6ZlPRL0yI0Ep8RYmb4R82a9Xo//poNi/7kxhQ47i9SYgchIHm3bIw93cCXAcZ0yvZaRMEoW4TOtznqzsZRe2G7aRwlc6Qd8l22WBeA40BYyAp2BNZ27Rzm6VpJ9+onnnZC6X2Afs406qrcEyiSZHrG57bEdGRHWdf7Ccf+E2ZFKX8Fu/fr1sWbNmrjlllu64DYxMRE7duyIt7zlLRERsXHjxti3b1/s3LkzTjvttIiIuPXWW2N2djY2bNjQvea9731vTE9Pdz+RsW3btnj2s5+dpjCbRAakNTpuhNAOp4g6c3UQogH4rjc/H9HMMN0R+fXO4J1RE+hYb7belRmtO2BNZIozXtcDoxG1P3PgjFjJhtUWfv7EI11vCxfl9ZvRGsthxEGik012d3QeEfpxJ0heXua8SjvLnNhwTLj+SzvhMabuqDcnY3RmThDYD0Yycj7Z5gWPBtzWGf1lYybR2p3GZ3p6el49UUgEqIOmCIkRjJfvdXu9TfPahWPvIJeVkZFmAjLL1IeNh4eHuzvEdQ1TqBkpdgDV3yXAZzQoYCTQkfBkus+yMV6uRJEjfVo/ZdFg98gjj8QPfvCD7v8PPPBA7Nq1K1avXh3r1q2Liy66KD74wQ/GSSedFOvXr49LLrkk1q5dG69+9asjIuI5z3lOvOIVr4g3velNcf3118f09HRceOGFsXnz5li7dm1ERPzpn/5pvP/974/zzjsv3v3ud8e9994bH/3oR+Pv/u7vHlMnmYaYmprqATMHMhojJ7Y7Dgrvc0eVTSZnYRxYXk8HRnEWFjG/cTgo8G9P8TnIZEDnbfKog18bL7HaUvRJp0ygy3Tp0RVTbHTwct6liZ0JoxuyUI8svUw6q6ZUZont6vjSpUtrpIH6UBkeTdN2s3pc55kN+fhH1NNUTvQYZTlZomRzTik/rhdnkp3LsjB+nnNXdXHMGJE46JBUZKlgj4p1DzMK7g88DUod+sYT3kewY53um5g+nc/WaSMReQrZ/QbHWzqNqD+8XgLVbKet242PS79k0WB31113xR/+4R92/9c62datW+PTn/50vOtd74r9+/fH+eefH/v27Yszzjgjbr755hgbG+vec8MNN8SFF14YZ555ZgwPD8drX/vauPbaa7vnV65cGf/xH/8RF1xwQZx22mlx3HHHxaWXXvqYHjuImGMMepM/ZXh4OKanp7vXcXDo4P0ZFBqSD1pEdMPwjMU1McksqqPz4uSlwauNmYNhmzy6UZ2aJAIKrU/4/QQ6Z2d0PJys1FUJ8LNjNPaFMmqfUBKNhaKzJieQnfNjTKO5Tn2jjRMGFycAEhIrjYn+53WMWrNHGlS32pORJNrGfE6GY1m6x9NXtEu/n/PqsaSumiIlCnWZfaqK46TjJMq+CSYDdRI8kqOM8GQp9cwnODASYGQT6g93WWZzzaN09lPi86M070rRWVaHp0mdTLGOJh92JLJosPuDP/iDRqczNDQUl112WVx22WXFa1avXh033nhjYz3Pe97z4hvf+MZim9cjYlh8Swidkk/AbMeWLzg3gR2didi4R1yZ46eUwJDtKwGIszn/m0bnRs61KLXDr6Fe3YHyEYOSY8/6maXOvC9OJHSsBEyM7tQen1hedlaXp6tZL3WgFI/uzZw2WTjr0/0ZE6ctsW8kFuxjphOPylmWrlf6qCmNy/pIcBixeETiDrGkY6XfSo+fUEjsnBC5vpvsPyN8nl3g8gffBJRFenTi3qYsC0CHz12Tfo/rXNdS90NDQzE6OtoDdJ5NoR5Yn1+TZbnUjiaSyrZm5NjL1Tzycn8pwO5XRaS4AwcO1HYaOuOUMU9PT/fkurWmIOFOudJg+FZ6f82W6mYbZPBcoPW+uCFkToPn/RwdnT/vwnayLdJZRNScoer3XZtsZ7YVnODppGN6ejqmpqZieno6dfCZzMcAnZX7J1PomCkkJ9wSzbroxMm0qT/2Wc6UUWfT7jUnXJ4Cc0frOtUxb28W9XJM3Ik7Uy9FHgRI1pWlu7P6PYWpY7rXdxpnbS8BJfWmfqidbL8/e5ZFdSU9yZdwLpUiNp/v1BHnjNsn7UekRHamdDf7pIwV07T+2JXPR76pxVOcGgfqg1kGj3p1PedARtJ0zElWFvUdiQws2P3sZz+LiIjbb7/9SW5JK6200korj1UefvjhWLly5RGXM7Bgt3r16oiIePDBB/uiqEETPYf44x//OFasWPFkN+eXTlr9zC+tjpql1U+zzKefqqri4Ycf7m5cPFIZWLBTCL5y5crW0BpkxYoVrX4apNXP/NLqqFla/TRLk376Gag8Pi8ha6WVVlpppZVfImnBrpVWWmmllYGXgQW7TqcTf/M3f5O+QqyVVj/zSauf+aXVUbO0+mmWJ1o/Q1W/9nW20korrbTSyi+pDGxk10orrbTSSiuSFuxaaaWVVloZeGnBrpVWWmmllYGXFuxaaaWVVloZeGnBrpVWWmmllYGXgQW7j33sY/HMZz4zxsbGYsOGDXHHHXc82U163OWKK66I3/3d342nPvWpcfzxx8erX/3quO+++2rXTE5OxgUXXBBPe9rT4ilPeUq89rWv7fkq/IMPPhjnnHNOLF++PI4//vh45zvf2fevBv8yyJVXXhlDQ0Nx0UUXdY8d7fr5yU9+En/2Z38WT3va02LZsmVx6qmnxl133dU9X1VVXHrppXHCCSfEsmXLYtOmTXH//ffXyti7d29s2bIlVqxYEatWrYrzzjsvHnnkkSe6K4+LHD58OC655JJYv359LFu2LJ71rGfFBz7wgZ6XWB8tOrrtttviVa96VaxduzaGhobipptuqp3vly6+9a1vxUtf+tIYGxuLE088Ma666qrFN7YaQPn85z9fjY6OVp/85Cer73znO9Wb3vSmatWqVdWePXue7KY9rnLWWWdVn/rUp6p777232rVrV/XKV76yWrduXfXII490r3nzm99cnXjiidUtt9xS3XXXXdXv/d7vVS9+8Yu752dmZqrnPve51aZNm6q77767+upXv1odd9xx1Xve854no0uPm9xxxx3VM5/5zOp5z3te9fa3v717/GjWz969e6tnPOMZ1Z//+Z9XO3bsqH74wx9W//7v/1794Ac/6F5z5ZVXVitXrqxuuumm6p577qn+6I/+qFq/fn118ODB7jWveMUrquc///nVN7/5zeob3/hG9Zu/+ZvV6173uiejS32Xyy+/vHra055WfeUrX6keeOCB6otf/GL1lKc8pfroRz/aveZo0tFXv/rV6r3vfW/1pS99qYqI6stf/nLtfD908Ytf/KIaHx+vtmzZUt17773V5z73uWrZsmXVP/zDPyyqrQMJdi960YuqCy64oPv/4cOHq7Vr11ZXXHHFk9iqJ14eeuihKiKqr3/961VVVdW+ffuqpUuXVl/84he713zve9+rIqLavn17VVWPGu/w8HC1e/fu7jUf//jHqxUrVlRTU1NPbAceJ3n44Yerk046qdq2bVv1+7//+12wO9r18+53v7s644wziudnZ2erNWvWVB/+8Ie7x/bt21d1Op3qc5/7XFVVVfXd7363iojqzjvv7F7zb//2b9XQ0FD1k5/85PFr/BMk55xzTvXGN76xduyP//iPqy1btlRVdXTryMGuX7r4+7//++rYY4+tza93v/vd1bOf/exFtW/g0piHDh2KnTt3xqZNm7rHhoeHY9OmTbF9+/YnsWVPvPziF7+IiLkvQOzcuTOmp6drujn55JNj3bp1Xd1s3749Tj311BgfH+9ec9ZZZ8XExER85zvfeQJb//jJBRdcEOecc05NDxGtfv7lX/4lTj/99PiTP/mTOP744+OFL3xh/OM//mP3/AMPPBC7d++u6WflypWxYcOGmn5WrVoVp59+eveaTZs2xfDwcOzYseOJ68zjJC9+8Yvjlltuie9///sREXHPPffE7bffHmeffXZEtDqi9EsX27dvj5e97GUxOjraveass86K++67L37+858vuD0D99WD//u//4vDhw/XnFFExPj4ePzP//zPk9SqJ15mZ2fjoosuipe85CXx3Oc+NyIidu/eHaOjo7Fq1aratePj47F79+7uNZnudO5XXT7/+c/Hf//3f8edd97Zc+5o188Pf/jD+PjHPx7veMc74q//+q/jzjvvjLe97W0xOjoaW7du7fYv6z/1c/zxx9fOj4yMxOrVq3/l9RMRcfHFF8fExEScfPLJ3Y+hXn755bFly5aIiFZHkH7pYvfu3bF+/fqeMnTu2GOPXVB7Bg7sWnlULrjggrj33nvbj9dCfvzjH8fb3/722LZtW4yNjT3Zzfmlk9nZ2Tj99NPj//2//xcRES984Qvj3nvvjeuvvz62bt36JLful0O+8IUvxA033BA33nhjnHLKKbFr16646KKLYu3ata2Ofsll4NKYxx13XCxZsqRnB92ePXtizZo1T1Krnli58MIL4ytf+Up87Wtfi1//9V/vHl+zZk0cOnQo9u3bV7ueulmzZk2qO537VZadO3fGQw89FL/zO78TIyMjMTIyEl//+tfj2muvjZGRkRgfHz+q9XPCCSfEb//2b9eOPec5z4kHH3wwIub61zS31qxZEw899FDt/MzMTOzdu/dXXj8REe985zvj4osvjs2bN8epp54ar3/96+Mv//Iv44orroiIVkeUfumiX3Nu4MBudHQ0TjvttLjlllu6x2ZnZ+OWW26JjRs3Pokte/ylqqq48MIL48tf/nLceuutPaH/aaedFkuXLq3p5r777osHH3ywq5uNGzfGt7/97ZoBbtu2LVasWNHjCH/V5Mwzz4xvf/vbsWvXru7P6aefHlu2bOn+fTTr5yUveUnPoyrf//734xnPeEZERKxfvz7WrFlT08/ExETs2LGjpp99+/bFzp07u9fceuutMTs7Gxs2bHgCevH4yoEDB7ofhpYsWbIkZmdnI6LVEaVfuti4cWPcdtttMT093b1m27Zt8exnP3vBKcyIGNxHDzqdTvXpT3+6+u53v1udf/751apVq2o76AZR3vKWt1QrV66s/vM//7P66U9/2v05cOBA95o3v/nN1bp166pbb721uuuuu6qNGzdWGzdu7J7X1vqXv/zl1a5du6qbb765+rVf+7WB2FqfCXdjVtXRrZ877rijGhkZqS6//PLq/vvvr2644YZq+fLl1T/90z91r7nyyiurVatWVf/8z/9cfetb36rOPffcdCv5C1/4wmrHjh3V7bffXp100km/ktvqM9m6dWv19Kc/vfvowZe+9KXquOOOq971rnd1rzmadPTwww9Xd999d3X33XdXEVFdffXV1d1331396Ec/qqqqP7rYt29fNT4+Xr3+9a+v7r333urzn/98tXz58vbRA8l1111XrVu3rhodHa1e9KIXVd/85jef7CY97hIR6c+nPvWp7jUHDx6s3vrWt1bHHntstXz58uo1r3lN9dOf/rRWzv/+7/9WZ599drVs2bLquOOOq/7qr/6qmp6efoJ788SIg93Rrp9//dd/rZ773OdWnU6nOvnkk6tPfOITtfOzs7PVJZdcUo2Pj1edTqc688wzq/vuu692zc9+9rPqda97XfWUpzylWrFiRfWGN7yhevjhh5/IbjxuMjExUb397W+v1q1bV42NjVW/8Ru/Ub33ve+tbYs/mnT0ta99LfU5W7duraqqf7q45557qjPOOKPqdDrV05/+9OrKK69cdFvb79m10korrbQy8DJwa3attNJKK6204tKCXSuttNJKKwMvLdi10korrbQy8NKCXSuttNJKKwMvLdi10korrbQy8NKCXSuttNJKKwMvLdi10korrbQy8NKCXSuttNJKKwMvLdi10korrbQy8NKCXSuttNJKKwMvLdi10korrbQy8PL/Aab59ri66U9ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.load(\"img-reshape.npy\")\n",
    "plt.imshow(img, cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "La transposition permet de renverser les dimensions des tenseurs. Dit autrement: (a, b, c, d, e) -> (e, d, c, b, a). Il n'est jamais clair si une vue ou une copy est retournée.\n",
    "\n",
    "La documentation dit :\n",
    "\n",
    "* `np.transpose` <=> `ndarray.transpose`\n",
    "* `np.transpose` retourne une vue \"si possible\"\n",
    "* `ndarray.transpose` retourne toujours une vue\n",
    "\n",
    "La fonction transpose, lorsqu'aucune copie n'est effectuée, ne modifie que les strides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7, 6, 5, 3) (7, 6, 5, 3) (1680, 336, 56, 8) (8, 56, 336, 1680) True\n"
     ]
    }
   ],
   "source": [
    "a = np.random.uniform(0, 1, (3, 5, 6, 7))\n",
    "print(a.T.shape, a.transpose().shape, a.strides, \n",
    "      a.transpose().strides, a.transpose().base is a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Il est possible de n'inverser que quelques axes en les spécifiant à la fonction `np.transpose`, ou en utilisant la fonction `np.swapaxes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 5, 6, 7) (6, 3, 5, 7) (3, 5, 7, 6)\n"
     ]
    }
   ],
   "source": [
    "print(a.shape, np.transpose(a, axes=[2, 0, 1, 3]).shape, \n",
    "      np.swapaxes(a, 2, 3).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Il peut aussi être utile de modifier l'ordre dans lequel sont parcourrues les données d'un axe. Cela peut par exemple être utile pour convertir des données au format BGR vers le format RGB. Cependant, une copie sera effectuée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.79837192 0.6188082  0.23601781]\n",
      "  [0.98645274 0.71349537 0.85564159]]\n",
      "\n",
      " [[0.448892   0.27675856 0.77261651]\n",
      "  [0.15859153 0.66546963 0.90570064]]]\n",
      "------\n",
      "[[[0.23601781 0.6188082  0.79837192]\n",
      "  [0.85564159 0.71349537 0.98645274]]\n",
      "\n",
      " [[0.77261651 0.27675856 0.448892  ]\n",
      "  [0.90570064 0.66546963 0.15859153]]]\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "img = np.random.uniform(0, 1, (2, 2, 3))\n",
    "print(img, end='\\n------\\n')\n",
    "print(img[:, :, [2, 1, 0]])\n",
    "print(img[:, :, [2, 1, 0]].base is img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Il est ainsi possible d'indexer un tableau par un autre tableau, dans ce cas, il faut penser que l'on fait des permutations des lignes et colonnes selectionnées. `a[b] = [a[b[0]], a[b[1]], ..., a[b[-1]]]`. Le tableau qui sert d'index peut contenir plusieurs fois les mêmes valeur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.92843087 0.28977279 0.46622837]\n",
      " [0.44286041 0.28848803 0.86780291]\n",
      " [0.61255275 0.76783121 0.95824636]]\n",
      "-----\n",
      "[[0.92843087 0.28977279 0.46622837]\n",
      " [0.44286041 0.28848803 0.86780291]\n",
      " [0.61255275 0.76783121 0.95824636]\n",
      " [0.92843087 0.28977279 0.46622837]\n",
      " [0.44286041 0.28848803 0.86780291]\n",
      " [0.61255275 0.76783121 0.95824636]]\n",
      "-----\n",
      "[0.92843087 0.28848803 0.95824636 0.92843087 0.28848803 0.95824636]\n"
     ]
    }
   ],
   "source": [
    "a = np.random.uniform(0, 1, (3, 3))\n",
    "b = np.mod(np.arange(6), 3)\n",
    "print(a, end='\\n-----\\n')\n",
    "print(a[b], end='\\n-----\\n')\n",
    "print(a[b, b]) # a[b[0], b[0]], ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Il faut cependant faire attention à la signification de l'indexation lorsque le tableau utilisé est multidimentionnel :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.39837793 0.16433895 0.25441618]\n",
      " [0.16983742 0.78703155 0.55759947]\n",
      " [0.74368042 0.31835569 0.5106531 ]\n",
      " [0.09724865 0.25062777 0.92992606]]\n",
      "------\n",
      "[[0 0 0 3 0]\n",
      " [0 2 1 3 3]\n",
      " [3 3 1 1 2]\n",
      " [3 3 1 1 1]\n",
      " [0 1 1 1 0]]\n",
      "------\n",
      "(5, 5, 3)\n",
      "[[[0.39837793 0.16433895 0.25441618]\n",
      "  [0.39837793 0.16433895 0.25441618]\n",
      "  [0.39837793 0.16433895 0.25441618]\n",
      "  [0.09724865 0.25062777 0.92992606]\n",
      "  [0.39837793 0.16433895 0.25441618]]\n",
      "\n",
      " [[0.39837793 0.16433895 0.25441618]\n",
      "  [0.74368042 0.31835569 0.5106531 ]\n",
      "  [0.16983742 0.78703155 0.55759947]\n",
      "  [0.09724865 0.25062777 0.92992606]\n",
      "  [0.09724865 0.25062777 0.92992606]]\n",
      "\n",
      " [[0.09724865 0.25062777 0.92992606]\n",
      "  [0.09724865 0.25062777 0.92992606]\n",
      "  [0.16983742 0.78703155 0.55759947]\n",
      "  [0.16983742 0.78703155 0.55759947]\n",
      "  [0.74368042 0.31835569 0.5106531 ]]\n",
      "\n",
      " [[0.09724865 0.25062777 0.92992606]\n",
      "  [0.09724865 0.25062777 0.92992606]\n",
      "  [0.16983742 0.78703155 0.55759947]\n",
      "  [0.16983742 0.78703155 0.55759947]\n",
      "  [0.16983742 0.78703155 0.55759947]]\n",
      "\n",
      " [[0.39837793 0.16433895 0.25441618]\n",
      "  [0.16983742 0.78703155 0.55759947]\n",
      "  [0.16983742 0.78703155 0.55759947]\n",
      "  [0.16983742 0.78703155 0.55759947]\n",
      "  [0.39837793 0.16433895 0.25441618]]]\n"
     ]
    }
   ],
   "source": [
    "a = np.random.uniform(0, 1, (4, 3))\n",
    "b = np.random.randint(0, 4, (5, 5))\n",
    "\n",
    "print(a, end='\\n------\\n')\n",
    "print(b, end='\\n------\\n')\n",
    "print(a[b].shape) # (b.shape[0], b.shape[0], a.shape[1])\n",
    "print(a[b]) # [[a[b[0, 0], :], a[b[0, 1], :], ...], \n",
    "            #  [a[b[1, 0], :], a[b[1, 1], :], ...],\n",
    "            #  ...]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Il est aussi possible de sélectionner les éléments avec des tableaux de booléens. Cependant les règles sont légèrement différentes... En particulier, lorsque `x.ndim == idx.ndim`, seul un tableau 1D est retourné !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.67082828 0.87703582 0.5809604  0.12740931]\n",
      " [0.7347204  0.52260261 0.38983942 0.34035182]\n",
      " [0.80333285 0.61066083 0.99357265 0.37331473]\n",
      " [0.23527046 0.78415762 0.30598898 0.91661261]]\n",
      "[[ True  True  True False]\n",
      " [ True  True False False]\n",
      " [ True  True  True False]\n",
      " [False  True False  True]]\n",
      "[0.67082828 0.87703582 0.5809604  0.7347204  0.52260261 0.80333285\n",
      " 0.61066083 0.99357265 0.78415762 0.91661261]\n"
     ]
    }
   ],
   "source": [
    "a = np.random.uniform(0, 1, (4, 4))\n",
    "print(a)\n",
    "print(a > 0.5)\n",
    "print(a[a > 0.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Broadcasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Le broadcasting est un ensemble de règles qui permettent de faire des opérations termes à termes sur des vecteurs de tailles différentes. On dit que deux dimensions sont compatibles si :\n",
    "\n",
    "* Elles sont égales\n",
    "* L'une est égale à 1\n",
    "\n",
    "Lorsque les conditions sont réunies, le tableau est alors \"virtuellement étendu\". Il est possible que des tableaux n'aient pas le même nombre de dimensions. Dans ce cas, le plus petit est \"étendu\" pour commencer par (1, 1, ...)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "![](https://numpy.org/doc/stable/_images/broadcasting_1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "![](https://numpy.org/doc/stable/_images/broadcasting_2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "![](https://numpy.org/doc/stable/_images/broadcasting_3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Le broadcasting permet de faire de nombreuses opérations. Notamment les 'outer product' avec une opération quelconque (il y a alors un \"double broadcasting\") : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.]\n",
      " [10. 20. 30.]\n",
      " [20. 40. 60.]\n",
      " [30. 60. 90.]]\n",
      "[[ 0.  0.  0.]\n",
      " [10. 20. 30.]\n",
      " [20. 40. 60.]\n",
      " [30. 60. 90.]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([0.0, 10.0, 20.0, 30.0])\n",
    "b = np.array([1.0, 2.0, 3.0])\n",
    "# a[:, np.newaxis] => (4, 1)\n",
    "# b                =>    (3)\n",
    "print(a[:, np.newaxis] * b)\n",
    "print(np.multiply.outer(a, b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Reduction et Einsum "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "raw_mimetype": "",
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Les libraries qui fonctionnent sur les tenseurs offrent de nombreuses opérations mathématiques (trigo, hyperboliques, ...). La plupart des opérations binaires peuvent être vues comme des opérations de réduction : `min/max`, `+`, `*`, ... \n",
    "\n",
    "Cependant, il est parfois utile de ne faire la réduction que sur une partie des données. Par exemple, pour convertir une image RGB en nuances de gris, il suffit de faire une moyenne sur l'axe correspondant aux cannaux. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 64)\n",
      "(3,)\n",
      "(64, 64)\n"
     ]
    }
   ],
   "source": [
    "a = np.random.uniform(0, 1, (64, 64, 3))\n",
    "print(np.mean(a, axis=2).shape)      # Moyenne / pixel\n",
    "print(np.max(a, axis=(0, 1)).shape)  # Max par channel\n",
    "print(np.min(a, axis=-1).shape)      # Min / pixel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Einsum est une opération très puissante. Elle tire son nom du physicien Einstein, et de (ce que je n'ai pas vérifié) ses notations utilisées pour ses calculs tensoriels. Einsum est une notation pour représenter les opérations qui demandent : \n",
    "\n",
    "* Transpositions des axes\n",
    "* Multiplications termes à termes\n",
    "* Additions des multiplications (réductions)\n",
    "\n",
    "Ces opérations peuvent être réalisées sur un nombre arbitraire de tenseurs qui sont compatibles avec les opérations demandées. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Les dimensions des opérandes sont représentées par des lettres (par convention : i, j, k, ...). Les opérandes sont quant à elles séparées par des `,` et le tenseur retourné s'indique après une flèche (au besoin). Les dimensions qui restent identiques peuvent être nommées par des ellipses `...`.\n",
    "\n",
    "Les règles sont les suivantes : \n",
    "\n",
    "* Si une dimension n'apparaît qu'une seule fois : elle n'est pas touchée\n",
    "* Si une dimension apparaît dans deux opérandes différentes : les dimensions sont multipliées puis sommées\n",
    "* Si une dimension est répétée dans un même tableau : cela indexe la diagonnale et la somme\n",
    "\n",
    "Premiers exemples : \n",
    "\n",
    "* `np.einsum('i', a)`  : retourne une vue sur a\n",
    "* `np.einsum('ij->ji', a)` : transpose a\n",
    "* `np.einsum('ii', a)` : trace de a\n",
    "* `np.einsum('i,i', a, b)` : produit scalaire\n",
    "* `np.einsum('ik,kj', a, b)` : multiplication de matrice\n",
    "* `np.einsum('ij,kj->ik`, a, b) : multiplication avec transposition de b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6  7]\n",
      " [26 31]]\n",
      "-----\n",
      "[[ 6  7]\n",
      " [26 31]]\n",
      "-----\n",
      "[[ 5  7]\n",
      " [23 33]]\n",
      "-----\n",
      "[[ 5  7]\n",
      " [23 33]]\n",
      "-----\n"
     ]
    }
   ],
   "source": [
    "a=np.array([[0, 1], [2, 3]])\n",
    "b=np.array([[4, 5], [6, 7]])\n",
    "\n",
    "print(a @ b, end='\\n-----\\n')\n",
    "print(np.einsum('ik,kj', a, b), end='\\n-----\\n')\n",
    "print(a @ b.T, end='\\n-----\\n')\n",
    "print(np.einsum('ij,kj->ik', a, b), end='\\n-----\\n')"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Pour certaines personnes, `einsum` est une façon plus clair de voir des réordonnement de dimensions et les opérations. Pour voir la puissance de cet opérateur et son utilisation en deep learning : [https://einops.rocks/pytorch-examples.html](https://einops.rocks/pytorch-examples.html).\n",
    "\n",
    "Pour info : \n",
    "\n",
    "* Le backend de pytorch (opt_einsum) compile les expressions pour les accélérer. Mais peuvent être plus lentes ou consommer plus de mémoire... \n",
    "* Un certain nombre d'opérations sont pré-compilées. \n",
    "* Trouver l'algorithme 'optimal' de contraction étant donné une expression est NP-hard.\n",
    "\n",
    "![image.png](attachment:a3d72f47-062d-40a8-bb82-c5538bd45cf8.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Performant python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Anecdotes (CPython)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Question: Le python est plus lent que le C++. Quelle preuve avez vous ? Avez vous des chiffres à me donner ? "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Petit test : incrémenter un entier. Quel est le coût d'incrémenter un entier ? Quel est le coût d'appeler une fonction ? En C++, on ne se pose pas la question..."
   ]
  },
  {
   "attachments": {
    "6966dfb6-f725-41ce-aea4-5a3a4e40ad9c.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "![image.png](attachment:6966dfb6-f725-41ce-aea4-5a3a4e40ad9c.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "* Incrémenter et affecter un entier (+=) un entier coûte une dizaine de nanoseconde en C++\n",
    "* L'appel d'une fonction (et affectation) est à peine mesurable en C++\n",
    "\n",
    "* Incrémenter et affecter un entier (+=) un entier coûte une centaine de nanoseconde en Python (x9)\n",
    "* L'appel d'une fonction (et affectation) en python coûte 35 ns (x35)\n",
    "\n",
    "Et encore, c'est du python 3.11 ou il y a eu un gain de 60% sur les appels de fonctions. (Voir : EuroPython 2022 \"How we are making Python 3.11 faster - presented by Mark Shannon\")."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Enfonçons le clou : quelle est la taille d'un entier ? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "En fait, ça dépend de sa valeur. Un entier \"petit\" fait 24 octets, ou... 28 s'il référence un objet (oui, oui...). Un entier en python peut contenir n'importe quelle valeur entière et donc sa taille croit avec la valeur. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n",
      "468\n",
      "['__new__', '__repr__', '__hash__', '__getattribute__', '__lt__', '__le__', '__eq__', '__ne__', '__gt__', '__ge__', '__add__', '__radd__', '__sub__', '__rsub__', '__mul__', '__rmul__', '__mod__', '__rmod__', '__divmod__', '__rdivmod__', '__pow__', '__rpow__', '__neg__', '__pos__', '__abs__', '__bool__', '__invert__', '__lshift__', '__rlshift__', '__rshift__', '__rrshift__', '__and__', '__rand__', '__xor__', '__rxor__', '__or__', '__ror__', '__int__', '__float__', '__floordiv__', '__rfloordiv__', '__truediv__', '__rtruediv__', '__index__', 'conjugate', 'bit_length', 'bit_count', 'to_bytes', 'from_bytes', 'as_integer_ratio', '__trunc__', '__floor__', '__ceil__', '__round__', '__getnewargs__', '__format__', '__sizeof__', 'real', 'imag', 'numerator', 'denominator', '__doc__', '__str__', '__setattr__', '__delattr__', '__init__', '__reduce_ex__', '__reduce__', '__subclasshook__', '__init_subclass__', '__dir__', '__class__']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.getsizeof(10))\n",
    "print(sys.getsizeof(10**1000))\n",
    "print((10).__dir__()) # int are objects !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "L'avez vous vu ? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "`sys.getsizeof(10)` vaut 28... Comme si c'était une référence sur un entier ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "a = 10\n",
    "b = 10\n",
    "print(a is b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "a = 257\n",
    "b = 257\n",
    "print(a is b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "a = 257; b = 257 \n",
    "print(a is b) # Can be true or false depending on interpreter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Oui oui... En fait, tous les entiers de -5 à 256 sont pré-chargés par l'interpréteur, et le code fait des références sur ce singleton."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Multithreading en Python - GIL n'est pas notre amis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Le python ne se multi-thread pas.. C'est la faute à GIL ...! En fait, `CPython` fonctionne sur un système de \"reference counting\" et de nombreuses questions se posent sur le maintient d'une telle structure dans un contexte de multithreading. Le principal problème est la performance : un compteur atomique ou un lock sur chaque variable coûte trop cher en temps. Le choix a donc été fait de mettre un lock sur tout l'interpreteur : le Global Interpreter Lock."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "* En soit, il n'empêche pas le multithreading. Mais seul un seul thread peut executer du code python. \n",
    "* Multiprocess lance un nouvel interpreteur à chaque fois en lui faisant passer les données globales.\n",
    "* Les librairies C/C++ utilisant des objets pythons peuvent \"relacher GIL\". Cela n'est pas possible en python en revanche. Cependant, il est aussi de leur responsabilité d'aller le chercher une fois qu'il s'est barré !\n",
    "* Les librairies C/C++ n'utilisant que leurs propres données peuvent utiliser la parrallélisation sans aucun soucis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Il existe une branche de CPython qui s'appelle : \"GILECTOMY\". C'est à dire d'enlever GIL de l'interpreter totalement. Cependant, il se trouve que la tâche est très ardue :\n",
    "\n",
    "* L'acceptation est conditionnée aux performances monothread qui doivent rester identiques (actuellement : dégradation de l'ordre de x20 sur du single thread)\n",
    "* Il faut que la communauté accepte de changer presque toutes les librairires python "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Une façon de faire est de lancer le même script sur des données différentes, via, notamment, les paramètres en ligne de commande. Pour ce faire, je conseille la librairie (native) argparse :  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import argparse\n",
    "parser = argparse.ArgumentParser(\"nom_du_programme\")\n",
    "parser.add_argument(\"-s\", \"--long_opt\", type=int, help=\"help\", dest=\"nom_variable\")\n",
    "parser.add_argument(\"-f\", \"--flag\", action=\"store_true\")\n",
    "parser.add_argument(\"-l\", nargs=\"+\", type=int, required=True);\n",
    "# args = parser.parse_args()\n",
    "# args.nom_variable, args.flag, args.flag, args.l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Les autres interpréteurs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "En soit, le language Python n'est pas \"lent\". Rien dans le standard ne spécifie la façon de gérer la mémoire, les appels de fonctions ou les threads. Il existe de nombreux autres intérpreteurs. Cependant, ils ont tous des spécifités qui peuvent légèrement changer le comportement du code...\n",
    "\n",
    "* CPython : interpreteur par défaut\n",
    "---\n",
    "* Pypy (Jupyter !): JIT. Garbage Collector, support partiel pour les extensions C. Mais pas de multithreading.\n",
    "* Pyston : Pypy légèrement plus performant (LLVM + optims).\n",
    "---\n",
    "* Nukita : Compilateur python vers C (WTF ?!)\n",
    "* IronPython : Python en C# (WTF ?!)\n",
    "* Jython : Python en Java (WTF ?!)\n",
    "* JsPython : Python en JavaScript (WTF ?!)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Comment ne pas faire du Python - Python edition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Never use loops (ou du moins le moins possible)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Sur la plupart des calculs, il n'est pas nécessaire d'avoir une boucle. Le coût est juste trop élevé. Utilisez `numpy` le plus possible. Les fonctions sont parallélisées et vectorisées. \n",
    "Exemple : calcul de la discrépance en python.\n",
    "\n",
    "$D(P) = \\left(\\frac{4}{3}\\right)^d  - \\frac{2}{N}\\sum_{z \\in P}\\prod_{j=1}^{d}\\left(\\frac{3 - z_j^2}{2}\\right) + \\frac{1}{N^2}\\sum_{z \\in P}\\sum_{z' \\in P}\\prod_{j=1}^d \\left(2 - \\max{(z_i, z'_i)}\\right)$"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "![image.png](attachment:6079119b-65c8-4c66-9e25-6867f5a32823.png)\n",
    "\n",
    "* Python vs NumPy : x100\n",
    "* Python vs PyUTK (C++): x1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Pour les personnes qui utilisent `pandas`, voici l'ordre des performances : \n",
    "\n",
    "1. Extraire les colonnes et faire les manipulations en numpy\n",
    "2. Utiliser les opérations `pandas` (+, -, ...) (<=> np dans la plupart des cas)\n",
    "3. Utiliser les namespace `pandas` pour les données non numériques (.str, .time)\n",
    "4. Ré-écrire une colonne avec une compréhension de liste\n",
    "5. Utiliser map / apply\n",
    "6. Faire une boucle sur le dataframe.\n",
    "\n",
    "Souvent, les points (2, 3) offrent les meilleurs compromis temps de développement / temps d'exécution. Si le dataframe est très gros (en gros: 100000 lignes), un groupby est parfois trop lent avec la méthode pandas. En `numpy` : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2030, 1992, 2030, 1997, 1951]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.random.randint(5, size=(10000, 2)) # 5 Catégories + data aléatoires. \n",
    "\n",
    "data_sorted = a[a[:, 0].argsort()]  # Trie les données par leur groupe\n",
    "\n",
    "# Recupère la dernière apparition de chaque groupe\n",
    "group_indices = np.unique(data_sorted[:, 0], return_index=True)[1][1:]\n",
    "grouped = np.split(a, group_indices) # Split le tableau \n",
    "print([g.shape[0] for g in grouped])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### JIT "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "La compilation \"Juste à temps\" est aussi un moyen de ne pas faire du python en python. L'idée est qu'une fonction, lorsqu'exécutée pour la première fois est compilée pour accélérer ses performances. Le première appel est bien plus lent... Mais pour des performances largement augmentées par la suite. PyPy et Pyston sont deux interpreteurs qui le font automatiquement. \n",
    "\n",
    "La plupart des librairies de ML proposent un JIT sous la forme d'un simple décorateur : Pytorch, Tensorflow, Jax & Numba. Cependant, elles ont souvent des limitations sur les opérations et les types de paramètres possibles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "![](https://miro.medium.com/v2/resize:fit:720/format:webp/1*uxacxgB_8V3ouM6R0wI82g.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numba as nb\n",
    "\n",
    "def Test_python(a,b,c,d):\n",
    "    res=0.\n",
    "    for i in nb.prange(a.shape[0]):\n",
    "        res+=a[i]+b[i]*2.+c[i]*3.+d[i]*4.\n",
    "    return res\n",
    "\n",
    "@nb.njit(fastmath=False,parallel=True)\n",
    "def Test_nb(a,b,c,d):\n",
    "    res=0.\n",
    "    for i in nb.prange(a.shape[0]):\n",
    "        res+=a[i]+b[i]*2.+c[i]*3.+d[i]*4.\n",
    "    return res\n",
    "\n",
    "def Test_np(a,b,c,d):\n",
    "    return np.sum(a+b*2.+c*3.+d*4.)\n",
    "a = np.random.uniform(0, 1, (1000 * 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "487 ms ± 18.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit Test_python(a, a, a, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.53 ms ± 101 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit Test_np(a, a, a, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "284 µs ± 49.8 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit Test_nb(a, a, a, a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Bringing C++ into python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "La principale force du python dans son utilisation actuelle est sa capacité à utiliser des librairies provenant d'autres languages, et particulièrement C/C++. C'est ce qui rend le deep learning faisable dans ce language : quelques lignes de python pour les developpeurs , mais des centaines de milliers de lignes \"efficaces\" pour les ingénieurs concevant les librairies. Cela permet une grande flexibilité : avoir de très bonnes performances avec un language très peu restrictif."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Il est tout à fait possible de compiler un code C \"à la main\" vers une librairie (.so, .dll) qui sera lu et comprise par un interpreteur. Cependant, cela est plutôt technique et demande de bonnes connaissances sur la structure même de l'interpreteur. La solution la plus simple est d'utiliser une librairie qui simplifie le travail : `pybind` ! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "L'installation est relativement simple et pose assez peu de problèmes. Sur le site officiel, vous trouverez même de quoi compiler le code directement avec g++, ou, ce que je recommande, avec CMake. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "La déclaration d'un module commence par l'utilisation de la maccro `PYBIND11_MODULE`\n",
    "\n",
    "```c++\n",
    "PYBIND11_MODULE(example, m) { // Nom, variable\n",
    "}\n",
    "```\n",
    "\n",
    "Toutes les opérations seront effectuée sur la variable m, que vous pouvez passer à d'autres fonctions : \n",
    "\n",
    "```c++\n",
    "int add(int i = 1, int j = 2) {\n",
    "    return i + j;\n",
    "}\n",
    "\n",
    "// Ailleurs :\n",
    "// Nom, pointeur sur la fonction, documentation, \n",
    "// nom des paramètres et éventuelles valeurs par défaut.\n",
    "m.def(\"add\", &add, \"A function which adds two numbers\",\n",
    "      py::arg(\"i\") = 1, py::arg(\"j\") = 2);\n",
    "```"
   ]
  },
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   "source": [
    "Les types des paramètres et des valeurs de retours sont automatiquement inférés. Ils sont aussi convertis automatiquement : \n",
    "\n",
    "* Objets Python <=> Pointeurs, Reférences\n",
    "* list <=> std::vector\n",
    "* tuples <=> std::tuple\n",
    "* dict <=> std::map\n",
    "* str <=> std::string\n",
    "* function <=> std::function\n",
    "* numpy <=> Eigen, py::buffer, ...\n",
    "\n",
    "Les conversions marchent dans les deux sens ! Il est possible de désactiver les conversions automatiques au besoin. Il faut notamment faire attention aux durées de vies des éléments, et aux éventuelles copies.  "
   ]
  },
  {
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   "source": [
    "Il est également possible d'exposer des classes C++ en python afin de les manipuler : \n",
    "\n",
    "```c++\n",
    "class Pet {\n",
    "public:\n",
    "    Pet(const std::string &name) : name(name) { }\n",
    "    void setName(const std::string &name_) { name = name_; }\n",
    "    const std::string &getName() const { return name; }\n",
    "private:\n",
    "    std::string name;\n",
    "};\n",
    "\n",
    "py::class_<Pet>(m, \"Pet\")\n",
    "    // Constructor\n",
    "    .def(py::init<const std::string &>())\n",
    "    .def(\"setName\", &Pet::setName) // Fonctions \n",
    "    .def(\"getName\", &Pet::getName) // Fonctions\n",
    "    .def(\"__repr__\",               // Marche avec des lambdas\n",
    "        [](const Pet &a) {\n",
    "            return \"<example.Pet named '\" + a.name + \"'>\";\n",
    "        }\n",
    "    )\n",
    "    .def_readwrite(\"name\", &Pet::name); // Expose une variable\n",
    "```"
   ]
  },
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     "slide_type": "fragment"
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   "source": [
    "Sans s'étendre plus, pybind supporte aussi correctement l'héritage : exposer une classe qui en hérite d'une autre d'une part. Mais également le fait d'hériter en Python une classe C++ et d'utiliser cette classe en C++ (via le polymorphisme). Cependant, cela demande plus de travail (côté C++) mais la documentation est assez claire sur le sujet. "
   ]
  },
  {
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    "editable": true,
    "slideshow": {
     "slide_type": "slide"
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    "tags": []
   },
   "source": [
    "## MERCI DE VOTRE ATTENTION :) ! "
   ]
  }
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