{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Hashing (Hashování, Rozptylování)\n",
    "\n",
    "Při programováním často potřebujeme ukládat a číst různá data. Toto lze dělat různými způsoby, můžeme je uložit pomocí stromů, seznamů, či polí. Pochopitelně bychom chtěli zapisovat a číst co nejrychleji. Nabízí se tedy použít pole. přístupy i čtení z pole jsou přeci O(1). Jenže přímé indexování pole má problém, co když naše data nejsou kladná celá čísla? Nebo co když jsou větší než je rozsah pole? Jak bychom vložili číslo 145 do pole o velikosti 10?\n",
    "\n",
    "Zde přichází na řadu hashování, hashovací funkce vezme prvek a přiřadí mu kladnou celoučíselnou hodnotu, kterou lze přímo indexovat pole (hashovací tabulku). Zajisté si pamatujete metodu hashCode z tříd v Javě. To je hashovací funkce pro vaši třídu.\n",
    "\n",
    "S použitím hashů ovšem přichází i problém. Hashovací funkce nám může přiřadit stejný hash různým prvkům. Nastávají tedy kolize, které musíme řešit. Prvkům se stejným hashem říkáme synonyma.\n",
    "\n",
    "Ideálně chceme, aby hashovací funkce:\n",
    "- Byla rychlá\n",
    "- Vedla k minimu kolízí\n",
    "- Rovnoměrně využívala adresy (i blízké klíče na vzdálené adresy)\n",
    "- Umisťovala prvky cca \"náhodně\"\n",
    "\n",
    "Od hashovací funkce zároveň požadujeme, aby byla deterministická. Pro stejný objekt musí vždy vrátit stejnou hodnotu."
   ],
   "id": "fd21afa18e4c1d30"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Nejzákladnější hashovací funkce je zbytek po dělení celým číslem (modulo, v Pythonu %), nejčastěji ji používáme tak, že bereme modulo velikostí tabulky. Tím zajistíme, že se do tabulky vejdeme.",
   "id": "93ff0d2820174bc3"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:38.857585Z",
     "start_time": "2025-07-25T07:09:38.849405Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def basic_hash(key, modulo):\n",
    "    return key % modulo"
   ],
   "id": "c85acae26d71c103",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 1\n",
    "Do nejprve prázdné tabulky s rozptylovací funkcí h(x)=x mod 6 byly vloženy následující prvky v uvedeném pořadí a celkem nastala jedna kolize.\n",
    "- a) 6, 12, 24\n",
    "- b) 24, 6, 12\n",
    "- c) 1, 7, 6\n",
    "- d) 5, 6, 7\n",
    "- e) 2, 3, 4"
   ],
   "id": "8ae821d9e9a9db55"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:38.888606Z",
     "start_time": "2025-07-25T07:09:38.882243Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from functools import partial\n",
    "\n",
    "POSSIBLE_KEYS = {\n",
    "    'a)': [6,12,24],\n",
    "    'b)': [24,6,12],\n",
    "    'c)': [1,7,6],\n",
    "    'd)': [5,6,7],\n",
    "    'e)': [2,3,4]\n",
    "}\n",
    "\n",
    "for option, keys in POSSIBLE_KEYS.items():\n",
    "    print(f'{option}: {list(map(partial(basic_hash, modulo=6), keys))}')\n"
   ],
   "id": "5b6e566868977921",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a): [0, 0, 0]\n",
      "b): [0, 0, 0]\n",
      "c): [1, 1, 0]\n",
      "d): [5, 0, 1]\n",
      "e): [2, 3, 4]\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Hashování je velmi důležité pro implementaci slovníků (dictionary v Pythonu, HashMap v Javě). Ve slovníku ukládáme dvojice (klíč, hodnota) a používáme hash klíče. V tomto notebooku si postačíme s Hashovací tabulkou pouze s klíči bez uložených hodnot, k vysvětlení principů je to postačující.",
   "id": "b35dc478736d8896"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.282063Z",
     "start_time": "2025-07-25T07:09:39.274493Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import copy\n",
    "\n",
    "class HashTable:\n",
    "    def __init__(self, size, hash_function, cell_size=5, empty_cell = '_'):\n",
    "        self.EMPTY_CELL = empty_cell\n",
    "        self.size = size\n",
    "        self.key_table = [copy.deepcopy(self.EMPTY_CELL) for _ in range(size)]\n",
    "        self.cell_size = cell_size\n",
    "        self.hash = hash_function\n",
    "        self.occupancy = 0\n",
    "\n",
    "    def insert(self, key):\n",
    "        idx = self.hash(key)\n",
    "        collisions = 0\n",
    "\n",
    "        if self.key_table[idx] == self.EMPTY_CELL:\n",
    "            self.key_table[idx] = key\n",
    "            self.occupancy += 1\n",
    "        else:\n",
    "            collisions += 1\n",
    "\n",
    "        return collisions\n",
    "\n",
    "    def insert_keys(self, keys, verbose=False):\n",
    "        total_collisions = 0\n",
    "        for key in keys:\n",
    "            collisions = self.insert(key)\n",
    "            total_collisions += collisions\n",
    "            if verbose:\n",
    "                print(f'Encountered {collisions} collisions while inserting {key}.')\n",
    "                print(self)\n",
    "\n",
    "        return total_collisions\n",
    "\n",
    "    def get(self, key):\n",
    "        idx = self.hash(key)\n",
    "        collisions = 0\n",
    "\n",
    "        if self.key_table[idx] != key:\n",
    "            collisions += 1\n",
    "\n",
    "        return collisions\n",
    "\n",
    "    def get_keys(self, keys, verbose=False):\n",
    "        total_collisions = 0\n",
    "        for key in keys:\n",
    "            collisions = self.get(key)\n",
    "            total_collisions += collisions\n",
    "            if verbose:\n",
    "                print(f'Encountered {collisions} collisions while getting {key}.')\n",
    "\n",
    "        return total_collisions\n",
    "\n",
    "    def __str__(self):\n",
    "        row_separator = '-' * ((self.cell_size + 1) * self.size + 1) + '\\n'\n",
    "        output = row_separator\n",
    "\n",
    "        for i in range(self.size):\n",
    "            output += f'|{i:^{self.cell_size}}'\n",
    "        output += '|\\n'\n",
    "\n",
    "        output += row_separator\n",
    "\n",
    "        for i in range(self.size):\n",
    "            output += f'|{self.key_table[i]:^{self.cell_size}}'\n",
    "        output += '|\\n'\n",
    "\n",
    "        output += row_separator\n",
    "\n",
    "        return output"
   ],
   "id": "ebf1eea1c2053780",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Kolize při hashování lze řešit různými způsoby:\n",
    "- Chaining (Zřetězené rozptylování)\n",
    "- Open-address hashing (Otevřené rozptylování)\n",
    "- Coalesced hashing (Srůstající hashování)"
   ],
   "id": "8da53827820bb0f3"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Chaining (Zřetězené rozptylování)\n",
    "\n"
   ],
   "id": "9f155ed935cd2632"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Na každé adrese tvoříme spojovaný seznam klíčů se stejnou adresou (hashem).\n",
    "Abychom zajistili rychlé vkládání, tak v zřetězeném hashování vkládáme prvky na začátek spojovaného seznamu."
   ],
   "id": "82104bf0af4d1dc5"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 2\n",
    "\n",
    "Pro danou rozptylovací funkci h(k)=k mod 5 zvolte velikost tabulky a nakreslete stav po vložení prvků následující posloupnosti při vnějším zřetězení prvků.\n",
    "\n",
    "20, 9, 0, 17, 22, 15, 23, 18, 8, 7"
   ],
   "id": "cac178dd67c6f77"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 3\n",
    "Doplňte implementaci zřetězeného hashování."
   ],
   "id": "a16f77878e1a150b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.300682Z",
     "start_time": "2025-07-25T07:09:39.294288Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class ChainingHashTable(HashTable):\n",
    "    def __init__(self, size, hash_function, cell_size=5):\n",
    "        super().__init__(size, hash_function, cell_size, empty_cell=[])\n",
    "\n",
    "    def insert(self, key):\n",
    "        idx = self.hash(key)\n",
    "        # Následující řádek bude smazán a zadání na cviko bude: TODO: Add code for adding the key to the hash table\n",
    "        self.key_table[idx].insert(0, key)\n",
    "        self.occupancy += 1\n",
    "        return 0\n",
    "\n",
    "    def get(self, key):\n",
    "        collisions = 0\n",
    "        idx = self.hash(key)\n",
    "\n",
    "        for k in self.key_table[idx]:\n",
    "            if k != key:\n",
    "                collisions += 1\n",
    "            else:\n",
    "                # Hurray, we found an entry with the key. If we had values, here we would return the value associated with the key.\n",
    "                break\n",
    "\n",
    "        return collisions\n",
    "\n",
    "    def __str__(self):\n",
    "        row_separator = '-' * ((self.cell_size + 1) * self.size + 1) + '\\n'\n",
    "        output = row_separator\n",
    "\n",
    "        for i in range(self.size):\n",
    "            output += f'|{i:^{self.cell_size}}'\n",
    "        output += '|\\n'\n",
    "\n",
    "        output += row_separator\n",
    "\n",
    "        max_chain_size = max(len(chain) for chain in self.key_table)\n",
    "        for d in range(max_chain_size):\n",
    "            for i in range(self.size):\n",
    "                if len(self.key_table[i]) > d:\n",
    "                    output += f'|{self.key_table[i][d]:^{self.cell_size}}'\n",
    "                else:\n",
    "                    output += f'|{\"\":^{self.cell_size}}'\n",
    "            output += '|\\n'\n",
    "\n",
    "        output += row_separator\n",
    "\n",
    "        return output"
   ],
   "id": "b59f7a5268d9a1e0",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Správnost naší implementace můžeme ověřit srovnáním s výsledkem 2. úlohy.",
   "id": "7ee3365f711f7be6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.390377Z",
     "start_time": "2025-07-25T07:09:39.385205Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from functools import partial\n",
    "\n",
    "TABLE_SIZE = 5\n",
    "KEYS=20,9,0,17,22,15,23,18,8,7\n",
    "HASH_TABLE = ChainingHashTable(TABLE_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n"
   ],
   "id": "dd5802bc3c944da2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------\n",
      "|  0  |  1  |  2  |  3  |  4  |\n",
      "-------------------------------\n",
      "| 15  |     |  7  |  8  |  9  |\n",
      "|  0  |     | 22  | 18  |     |\n",
      "| 20  |     | 17  | 23  |     |\n",
      "-------------------------------\n",
      "\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Open-address hashing",
   "id": "21d848a0f9b4866c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Vše vkládáme do stejné tabulky a při kolizi zkoušíme pozici o i⋅k polí dál, kde i je počet kolizí daného klíče a k se určí pomocí:\n",
    "- Linear probing - k je konstanta, tedy zkoušíme vždy pozici o k dále (modulo velikost tabulky);\n",
    "- Double hashing - k=h2(x), čili máme další hashovací funkci h2, která nám určí (povětšinou) rozdílná k pro rozdílná x."
   ],
   "id": "1043bcb3bdc6044c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 4\n",
    "Vložte následudící posloupnost prvků do hashovací tabulky o velikosti m=11, pomocí otevřeného hashování.\n",
    "\n",
    "5, 6, 1, 10, 13, 18, 14, 30, 0, 8, 16\n",
    "\n",
    "Vkládejte najednou do 3 tabulek, ve dvou uvažujte linear probing s inkrementem 1 a 3, v poslední provádějte double hashing. Hashovací funkce:\n",
    "\n",
    "h1(x)= x mod 8, h2(x)=(x mod 7) + 1"
   ],
   "id": "1173b50bd5e90817"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 5\n",
    "\n",
    "Doplňte implementaci otevřeného hashování."
   ],
   "id": "e2d7c258ecfebb9a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.450240Z",
     "start_time": "2025-07-25T07:09:39.444143Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class OpenAddressHashTable(HashTable):\n",
    "    def __init__(self, size, hash_function, probe_function, cell_size=5):\n",
    "        super().__init__(size, hash_function, cell_size)\n",
    "        self.probe_function = probe_function\n",
    "\n",
    "    def insert(self, key):\n",
    "        collisions = 0\n",
    "        idx = self.hash(key)\n",
    "        probe_offset = self.probe_function(key) # k\n",
    "        first_idx = idx\n",
    "\n",
    "        # Následující while cyklus bude smazán a úloha na cvičení bude doplnit posun indexů. TODO: Add code moving the index by k in case of collision, self.EMPTY_CELL indicates an empty cell, self.size is the size of the table\n",
    "        while (self.key_table[idx] != self.EMPTY_CELL\n",
    "               and (collisions == 0 or first_idx != idx)): # Prevents looping when table is too full\n",
    "            collisions += 1\n",
    "            idx = (idx + probe_offset) % self.size\n",
    "\n",
    "        if self.key_table[idx] == self.EMPTY_CELL:\n",
    "            self.key_table[idx] = key\n",
    "            self.occupancy += 1\n",
    "\n",
    "        return collisions\n",
    "\n",
    "    def get(self, key):\n",
    "        collisions = 0\n",
    "        idx = self.hash(key)\n",
    "        probe_offset = self.probe_function(key)\n",
    "        first_idx = idx\n",
    "        while (self.key_table[idx] != key\n",
    "               and self.key_table[idx] != self.EMPTY_CELL\n",
    "               and (collisions == 0 or first_idx != idx)): # Prevents looping when table is too full\n",
    "            collisions += 1\n",
    "            idx = (idx + probe_offset) % self.size\n",
    "\n",
    "        # if self.key_table[idx] != self.EMPTY_CELL:\n",
    "            # Hurray, we found an entry with the key. If we had values, here we would return the value associated with the key.\n",
    "\n",
    "        return collisions\n"
   ],
   "id": "5af02b9896e75c76",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Správnost naší implementace si můžeme ověřit srovnáním s výsledky úlohy 4.",
   "id": "8c5acadfa33cafc0"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Linear probing s inkrementem 1",
   "id": "3abdb179260454a1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.488876Z",
     "start_time": "2025-07-25T07:09:39.483859Z"
    }
   },
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 11\n",
    "KEYS=[5,6,1,10,13,18,14,30,0,8,16]\n",
    "LINEAR_INCREMENT = 1\n",
    "\n",
    "HASH_TABLE = OpenAddressHashTable(TABLE_SIZE, partial(basic_hash, modulo=8), probe_function=lambda key: LINEAR_INCREMENT)\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')\n"
   ],
   "id": "c7dfcc1446e76df2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------\n",
      "|  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |  8  |  9  | 10  |\n",
      "-------------------------------------------------------------------\n",
      "|  0  |  1  | 10  | 18  |  8  |  5  |  6  | 13  | 14  | 30  | 16  |\n",
      "-------------------------------------------------------------------\n",
      "\n",
      "In total we encountered 22 insert collisions.\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Linear probing s inkrementem 3",
   "id": "5a94ca2247c9c5fa"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.531269Z",
     "start_time": "2025-07-25T07:09:39.525817Z"
    }
   },
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 11\n",
    "KEYS=[5,6,1,10,13,18,14,30,0,8,16]\n",
    "LINEAR_INCREMENT = 3\n",
    "\n",
    "HASH_TABLE = OpenAddressHashTable(TABLE_SIZE, partial(basic_hash, modulo=8), probe_function=lambda key: LINEAR_INCREMENT)\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "2c4acc9856139659",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------\n",
      "|  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |  8  |  9  | 10  |\n",
      "-------------------------------------------------------------------\n",
      "| 18  |  1  | 10  |  0  | 30  |  5  |  6  |  8  | 13  | 14  | 16  |\n",
      "-------------------------------------------------------------------\n",
      "\n",
      "In total we encountered 22 insert collisions.\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Linear probing s double hashing",
   "id": "d283e3d2940d1942"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.573723Z",
     "start_time": "2025-07-25T07:09:39.568453Z"
    }
   },
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 11\n",
    "KEYS=[5,6,1,10,13,18,14,30,0,8,16]\n",
    "\n",
    "HASH_TABLE = OpenAddressHashTable(TABLE_SIZE, partial(basic_hash, modulo=8), probe_function=lambda key: key % 7 + 1)\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "778470b793b14982",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------\n",
      "|  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |  8  |  9  | 10  |\n",
      "-------------------------------------------------------------------\n",
      "|  0  |  1  | 10  | 16  | 30  |  5  |  6  | 18  | 13  | 14  |  8  |\n",
      "-------------------------------------------------------------------\n",
      "\n",
      "In total we encountered 15 insert collisions.\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Coalesced hashing (Srůstající hashování)\n",
    "Hybrid mezi zřetězeným a otevřeným hashováním, vše vkládáme do stejné tabulky a v případě kolizí vkládáme odzadu. Každý prvek má navíc ještě ukazatel, který ukazuje na pozici dalšího prvku v případě kolize. Máme různé metody tvorby ukazatelů v případě kolize. Jednotlivé metody můžeme dále dělit podle toho, zda je na konci pole tzv. sklep, tedy část pole kterou nelze adresovat pomocí hashovací funkce (lze do ní vkládat jen při kolizích).\n",
    "\n",
    "- Bez sklepa\n",
    "    - LISCH - Late Insert Standard Coalesced Hashing - na pozici právě přidaného prvku ukazuje poslední prvek se kterým byla kolize (poslední v řetězu)\n",
    "\n",
    "    - EISCH - Early Insert Standard Coalesced Hashing - na pozici právě přidaného prvku ukazuje první prvek se kterým došlo ke kolizi, přidaný prvek ukazuje na ten co býval druhý v řetězu (pokud existuje).\n",
    "\n",
    "- Se sklepem\n",
    "    - LICH - Late Insert Coalesced Hashing - jako LISCH, ale nejprve vkládáme kolize do sklepa (protože je na konci)\n",
    "\n",
    "    - EICH - Early Insert Coalesced Hashing - jako EISCH, ale nejprve vkládáme kolize do sklepa (protože je na konci)\n",
    "\n",
    "    - VICH - Variable Insert Coalesced Hashing - dokud vkládáme kolize do sklepa, vkládáme je podle LICH. Jakmile je sklep plný, vkládáme těsně za poslední prvek v řetězu který je ve sklepě. Když žádný z řetězu ve sklepě není, vkládáme podle EICH."
   ],
   "id": "4688cbc4424fcf1c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 6\n",
    "Uvažujte hash funkci h(x)=x mod 9. Vložte do hashovací tabulky čísla z posloupnosti:\n",
    "\n",
    "9, 11, 18, 27, 29, 36, 43, 45, 50\n",
    "\n",
    "Jak bude tabulka vypadat pro EISCH a jak pro LISCH?"
   ],
   "id": "531ac17c6eae3ca"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 7\n",
    "Doplňte implementaci metody get pro srůstající hashování."
   ],
   "id": "f2c90757abbded8b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.634279Z",
     "start_time": "2025-07-25T07:09:39.628891Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class CoalescedHashTable(HashTable):\n",
    "    def __init__(self, size, hash_function, cell_size=5):\n",
    "        super().__init__(size, hash_function, cell_size=cell_size)\n",
    "        self.next_ptr_table = [self.EMPTY_CELL] * size\n",
    "\n",
    "    def insert(self, key):\n",
    "        raise NotImplementedError('This method must be implemented by a subclass!')\n",
    "\n",
    "    def get(self, key):\n",
    "        idx = self.hash(key)\n",
    "        collisions = 0\n",
    "\n",
    "        # Tenhle while budou studenti doplňovat. TODO: Add code for finding the key in the hash table\n",
    "        while (self.key_table[idx] != key) and (self.next_ptr_table[idx] != self.EMPTY_CELL):\n",
    "            idx = self.next_ptr_table[idx]\n",
    "            collisions += 1\n",
    "\n",
    "        return collisions\n",
    "\n",
    "    def __str__(self):\n",
    "        row_separator = '-' * ((self.cell_size + 1) * self.size + 1) + '\\n'\n",
    "        output = super().__str__()\n",
    "\n",
    "        for i in range(self.size):\n",
    "            output += f'|{self.next_ptr_table[i]:^{self.cell_size}}'\n",
    "        output += '|\\n'\n",
    "\n",
    "        output += row_separator\n",
    "\n",
    "        return output"
   ],
   "id": "ad0b1e0aeaae5a4e",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 8\n",
    "Doplňte implementaci metody insert pro LISCH hashovací tabulku."
   ],
   "id": "9eb0f9d88d5f436"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.662939Z",
     "start_time": "2025-07-25T07:09:39.657659Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class LISCHHashTable(CoalescedHashTable):\n",
    "    def __init__(self, size, hash_function, cell_size=5):\n",
    "        super().__init__(size, hash_function, cell_size=cell_size)\n",
    "        self.next_free = size - 1\n",
    "\n",
    "    def insert(self, key):\n",
    "        idx = self.hash(key)\n",
    "        collisions = 0\n",
    "\n",
    "        if self.key_table[idx] == self.EMPTY_CELL:\n",
    "            self.key_table[idx] = key\n",
    "        else:\n",
    "            collisions += 1\n",
    "            while self.key_table[self.next_free] != self.EMPTY_CELL:\n",
    "                self.next_free -= 1\n",
    "\n",
    "            self.key_table[self.next_free] = key\n",
    "\n",
    "            next_ptr = idx\n",
    "\n",
    "            # Tenhle while a řádek pod ním budou studenti doplňovat. TODO: Add code for setting the pointer to the next entry in the collision chain\n",
    "            while self.next_ptr_table[next_ptr] != self.EMPTY_CELL:\n",
    "                next_ptr = self.next_ptr_table[next_ptr]\n",
    "                collisions += 1\n",
    "            self.next_ptr_table[next_ptr] = self.next_free\n",
    "\n",
    "        return collisions"
   ],
   "id": "13f2fd92c7103b64",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Správnost naší implementace můžeme ověřit srovnáním s výsledky úlohy 6.",
   "id": "2edeea4e54232237"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.703764Z",
     "start_time": "2025-07-25T07:09:39.699161Z"
    }
   },
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 9\n",
    "KEYS = [9, 11, 18, 27, 29, 36, 43, 45, 50]\n",
    "\n",
    "HASH_TABLE = LISCHHashTable(TABLE_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "1a303f3b692c7e10",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------\n",
      "|  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |  8  |\n",
      "-------------------------------------------------------\n",
      "|  9  | 50  | 11  | 45  | 43  | 36  | 29  | 27  | 18  |\n",
      "-------------------------------------------------------\n",
      "|  8  |  _  |  6  |  1  |  3  |  4  |  _  |  5  |  7  |\n",
      "-------------------------------------------------------\n",
      "\n",
      "In total we encountered 17 insert collisions.\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 9\n",
    "Doplňte implementaci metody insert pro EISCH hashovací tabulku."
   ],
   "id": "64cbc96bf7a73d33"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.759732Z",
     "start_time": "2025-07-25T07:09:39.754839Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class EISCHHashTable(CoalescedHashTable):\n",
    "    def __init__(self, size, hash_function, cell_size=5):\n",
    "        super().__init__(size, hash_function, cell_size=cell_size)\n",
    "        self.next_free = size - 1\n",
    "\n",
    "    def insert(self, key):\n",
    "        idx = self.hash(key)\n",
    "        collisions = 0\n",
    "\n",
    "        if self.key_table[idx] == self.EMPTY_CELL:\n",
    "            self.key_table[idx] = key\n",
    "        else:\n",
    "            collisions += 1\n",
    "            while self.key_table[self.next_free] != self.EMPTY_CELL:\n",
    "                self.next_free -= 1\n",
    "\n",
    "            self.key_table[self.next_free] = key\n",
    "\n",
    "            # Tyhle dva řádky budou studenti doplňovat. TODO: Add code for setting the pointer to the next entry in the collision chain\n",
    "            self.next_ptr_table[self.next_free] = self.next_ptr_table[idx]\n",
    "            self.next_ptr_table[idx] = self.next_free\n",
    "\n",
    "        return collisions"
   ],
   "id": "6e1c50ced5805209",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Správnost naší implementace můžeme ověřit srovnáním s výsledky úlohy 6.",
   "id": "c7e9e4d5f3fa8a41"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.787007Z",
     "start_time": "2025-07-25T07:09:39.782008Z"
    }
   },
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 9\n",
    "KEYS = [9, 11, 18, 27, 29, 36, 43, 45, 50]\n",
    "\n",
    "HASH_TABLE = EISCHHashTable(TABLE_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "c7175809e5b0551d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------\n",
      "|  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |  8  |\n",
      "-------------------------------------------------------\n",
      "|  9  | 50  | 11  | 45  | 43  | 36  | 29  | 27  | 18  |\n",
      "-------------------------------------------------------\n",
      "|  3  |  7  |  6  |  5  |  8  |  1  |  _  |  4  |  _  |\n",
      "-------------------------------------------------------\n",
      "\n",
      "In total we encountered 7 insert collisions.\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 10\n",
    "Uvažujte hash funkci h(x)=x mod 7 a sklep o velikosti 2. Vložte do hashovací tabulky čísla z (stejné) posloupnosti:\n",
    "\n",
    "9, 11, 18, 27, 29, 36, 43, 45, 50\n",
    "\n",
    "Jak bude tabulka vypadat pro EICH, LICH a jak pro VICH?"
   ],
   "id": "6fac33abdad26a51"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 11\n",
    "Co musíme změnit na implementaci LISCH hashovací tabulky, abychom dostali LICH hashovací tabulku?"
   ],
   "id": "5f8ae55a73a891ef"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.820117Z",
     "start_time": "2025-07-25T07:09:39.816105Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class LICHHashTable(LISCHHashTable):\n",
    "    def __init__(self, table_size, cellar_size, hash_function, cell_size=5):\n",
    "        super().__init__(table_size+cellar_size, hash_function, cell_size=cell_size)"
   ],
   "id": "159f4016fc18f132",
   "outputs": [],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Správnost naší implementace můžeme ověřit srovnáním s výsledky úlohy 9.",
   "id": "bf4586ddf268e801"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.846741Z",
     "start_time": "2025-07-25T07:09:39.842339Z"
    }
   },
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 7\n",
    "CELLAR_SIZE = 2\n",
    "KEYS = [9, 11, 18, 27, 29, 36, 43, 45, 50]\n",
    "\n",
    "HASH_TABLE = LICHHashTable(TABLE_SIZE, CELLAR_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "19cf1124291c2c32",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------\n",
      "|  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |  8  |\n",
      "-------------------------------------------------------\n",
      "| 50  | 29  |  9  | 45  | 11  | 43  | 27  | 36  | 18  |\n",
      "-------------------------------------------------------\n",
      "|  _  |  7  |  _  |  _  |  8  |  0  |  _  |  5  |  _  |\n",
      "-------------------------------------------------------\n",
      "\n",
      "In total we encountered 7 insert collisions.\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 12\n",
    "Co musíme změnit na implementaci EISCH hashovací tabulky, abychom dostali EICH hashovací tabulku?"
   ],
   "id": "74701e99755ae748"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.880004Z",
     "start_time": "2025-07-25T07:09:39.876874Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class EICHHashTable(EISCHHashTable):\n",
    "    def __init__(self, table_size, cellar_size, hash_function, cell_size=5):\n",
    "        super().__init__(table_size + cellar_size, hash_function, cell_size=cell_size)\n"
   ],
   "id": "24271c94ca979598",
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Správnost naší implementace můžeme ověřit srovnáním s výsledky úlohy 9.",
   "id": "1f1521b59c9df79c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.909088Z",
     "start_time": "2025-07-25T07:09:39.903547Z"
    }
   },
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 7\n",
    "CELLAR_SIZE = 2\n",
    "KEYS = [9, 11, 18, 27, 29, 36, 43, 45, 50]\n",
    "\n",
    "HASH_TABLE = EICHHashTable(TABLE_SIZE, CELLAR_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "9967f7b6e876a3b4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------\n",
      "|  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |  8  |\n",
      "-------------------------------------------------------\n",
      "| 50  | 29  |  9  | 45  | 11  | 43  | 27  | 36  | 18  |\n",
      "-------------------------------------------------------\n",
      "|  5  |  0  |  _  |  _  |  8  |  7  |  _  |  _  |  _  |\n",
      "-------------------------------------------------------\n",
      "\n",
      "In total we encountered 4 insert collisions.\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 13\n",
    "Projděte implementaci metody insert pro VICH hashovací tabulku."
   ],
   "id": "e43a7e1937d7d6f0"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.946075Z",
     "start_time": "2025-07-25T07:09:39.939899Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class VICHHashTable(EICHHashTable):\n",
    "    def __init__(self, table_size, cellar_size, hash_function, cell_size=5):\n",
    "        super().__init__(table_size, cellar_size, hash_function, cell_size=cell_size)\n",
    "        self.table_size = table_size\n",
    "        self.cellar_size = cellar_size\n",
    "\n",
    "    def insert(self, key):\n",
    "        idx = self.hash(key)\n",
    "        collisions = 0\n",
    "\n",
    "        if self.key_table[idx] == self.EMPTY_CELL:\n",
    "            self.key_table[idx] = key\n",
    "        else:\n",
    "            collisions += 1\n",
    "            while self.key_table[self.next_free] != self.EMPTY_CELL:\n",
    "                self.next_free -= 1\n",
    "\n",
    "            self.key_table[self.next_free] = key\n",
    "\n",
    "            next_ptr = idx\n",
    "            while self.next_ptr_table[next_ptr] != self.EMPTY_CELL and self.next_ptr_table[next_ptr] >= self.table_size:\n",
    "                collisions += 1\n",
    "                next_ptr = self.next_ptr_table[next_ptr]\n",
    "\n",
    "            if self.next_free < self.table_size:\n",
    "                self.next_ptr_table[self.next_free] = self.next_ptr_table[next_ptr]\n",
    "            self.next_ptr_table[next_ptr] = self.next_free\n",
    "\n",
    "        return collisions"
   ],
   "id": "e7e10a0e2e5975e5",
   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Správnost naší implementace můžeme ověřit srovnáním s výsledky úlohy 9.",
   "id": "db577f3f3c076dec"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:39.987270Z",
     "start_time": "2025-07-25T07:09:39.981852Z"
    }
   },
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 7\n",
    "CELLAR_SIZE = 2\n",
    "KEYS = [9, 11, 18, 27, 29, 36, 43, 45, 50]\n",
    "\n",
    "HASH_TABLE = VICHHashTable(TABLE_SIZE, CELLAR_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "6f8a8c523c6a9b94",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------\n",
      "|  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |  8  |\n",
      "-------------------------------------------------------\n",
      "| 50  | 29  |  9  | 45  | 11  | 43  | 27  | 36  | 18  |\n",
      "-------------------------------------------------------\n",
      "|  5  |  7  |  _  |  _  |  8  |  _  |  _  |  0  |  _  |\n",
      "-------------------------------------------------------\n",
      "\n",
      "In total we encountered 6 insert collisions.\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Benchmarky srůstajícího hashování",
   "id": "f8a9b8d1be15fd52"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:40.132855Z",
     "start_time": "2025-07-25T07:09:40.019739Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "\n",
    "def generate_insert_data(key_range, num_keys):\n",
    "    assert num_keys <= key_range\n",
    "    return np.random.choice(a=key_range, size=num_keys, replace=False)\n",
    "\n",
    "def generate_successful_read_data(keys, num_data):\n",
    "    return np.random.choice(a=keys, size=num_data, replace=True)\n",
    "\n",
    "def prepare_data(key_range, num_keys, num_data):\n",
    "    keys = generate_insert_data(key_range, num_keys)\n",
    "    read_data = generate_successful_read_data(keys, num_data)\n",
    "    return keys, read_data\n",
    "\n",
    "class Benchmark:\n",
    "    def __init__(self, key_range, num_keys, num_read_data, address_factor, repetitions=5):\n",
    "        self.key_range = key_range\n",
    "        self.num_keys = num_keys\n",
    "        self.num_read_data = num_read_data\n",
    "        self.address_factor = address_factor\n",
    "        self.repetitions = repetitions\n",
    "\n",
    "    def run(self):\n",
    "        insert_collision_results = {'LISCH':{}, 'EISCH':{},'LICH': {}, 'EICH': {}, 'VICH': {}}\n",
    "        read_collision_results = {'LISCH':{}, 'EISCH':{},'LICH': {}, 'EICH': {}, 'VICH': {}}\n",
    "\n",
    "        for _ in range(self.repetitions):\n",
    "            keys, read_data = prepare_data(self.key_range, self.num_keys, self.num_read_data)\n",
    "\n",
    "            for load_factor in np.linspace(0.5, 1, 51):\n",
    "                total_size = int(self.num_keys/load_factor)\n",
    "                table_size = int(total_size * self.address_factor)\n",
    "                cellar_size = total_size - table_size\n",
    "\n",
    "                tables = {'LISCH': LISCHHashTable(total_size, partial(basic_hash, modulo=total_size)),\n",
    "                          'EISCH': EISCHHashTable(total_size, partial(basic_hash, modulo=total_size)),\n",
    "                          'LICH': LICHHashTable(table_size, cellar_size, partial(basic_hash, modulo=table_size)),\n",
    "                          'EICH': EICHHashTable(table_size, cellar_size, partial(basic_hash, modulo=table_size)),\n",
    "                          'VICH': VICHHashTable(table_size, cellar_size, partial(basic_hash, modulo=table_size))}\n",
    "\n",
    "                for label, table in tables.items():\n",
    "                    collisions = table.insert_keys(keys, verbose=False)\n",
    "                    insert_collision_results[label].setdefault(load_factor, []).append(collisions/len(keys))\n",
    "\n",
    "                    collisions = table.get_keys(read_data, verbose=False)\n",
    "                    read_collision_results[label].setdefault(load_factor, []).append(collisions/len(read_data))\n",
    "\n",
    "        return {'insert_collision_results': insert_collision_results, 'read_collision_results': read_collision_results}\n"
   ],
   "id": "1dc2a8cb6aeacf90",
   "outputs": [],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:09:41.289166Z",
     "start_time": "2025-07-25T07:09:40.145689Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('bmh')\n",
    "\n",
    "\n",
    "def get_average_probes(collisions):\n",
    "    return {label: {load_factor: 1 + np.average(colls) for load_factor, colls in collisions[label].items() } for label in collisions.keys()}\n",
    "\n",
    "\n",
    "def plot_probes(probes, title):\n",
    "    plt.figure(figsize=(10,10))\n",
    "    for label in probes.keys():\n",
    "        plt.plot(probes[label].keys(), probes[label].values(), label=label)\n",
    "\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Load Factor')\n",
    "    plt.ylabel('Average number of probes')\n",
    "    plt.legend()\n",
    "    plt.show()\n"
   ],
   "id": "7ad5a80a30e3e25a",
   "outputs": [],
   "execution_count": 22
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 14\n",
    "Spusťne následující kód a srovnejte, která z metod srůstajícího hashování vyžaduje nejméně nahlédnutí do tabulky."
   ],
   "id": "37690d0e82ef21"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:12:57.386074Z",
     "start_time": "2025-07-25T07:09:41.303888Z"
    }
   },
   "cell_type": "code",
   "source": [
    "KEY_RANGE = int(1e4)\n",
    "NUM_KEYS = int(1e3)\n",
    "NUM_READ_DATA = int(1e4)\n",
    "\n",
    "BENCHMARK = Benchmark(key_range=KEY_RANGE, num_keys=NUM_KEYS, num_read_data=NUM_READ_DATA, address_factor=0.9, repetitions=100)\n",
    "RESULTS = BENCHMARK.run()\n",
    "\n",
    "plot_probes(get_average_probes(RESULTS['read_collision_results']), title='Comparison of probe counts of successful reads under varying load factors')\n",
    "plot_probes(get_average_probes(RESULTS['insert_collision_results']), 'Comparison of probe counts of inserts under varying load factors')"
   ],
   "id": "faa6b7b369c35672",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ],
      "image/png": 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g/vOf/9Ra3tC3sv/73/8EAPHWW2/Vua+0tFSUl5dbre1ip0+fFjqdTnh6eoqjR4/Wuu8f//iHACDuvvtuqz9HY0eXLtbYkaZ+/fqJvLy8WvXHxsYKrVZbK2vTp08XAMT+/fvrvMbFf+NC/L3tLt2mFRUVYsKECUKj0Yh9+/bVqRGAeP/9963+HA39HcrUVp+WHGny9PQUBw4cqHXfTTfdJADU+ubbbDaLrl27CgBixYoVtdZ/6623rH6jnp+fL3x8fIS/v3+dswMOHjwo3N3dRd++fWstr9n+oaGhIiUlpUk/vxB/72dmzJhR575vv/1WABCPPvporeWnTp2qs67JZBK33XabACB27NghVdvrr79u9T1OiL8ze/z4ccuyho401exXLv57zM7OFt7e3sLb21tUVVVZlte8J8fHx9d6P9br9Zb3mZYeaZJ9/6msrLR61KawsFD07NlT+Pr61vrZat5fHR0d6xzNf/jhhy35auxIU42G/ibS09OtnuGwdu1aodVqxb333ltnOQARHR1t9QjsxT9nY/t42feMxj7X2GJ/3lGxaeoAunfvLgCI3377Tepxd911lwAgPvjggzr3HT9+XGi1WhEdHV1rec0fsLU3lujoaAFA/PHHH3XuGzlypHB0dBRGo9GyrGZHcmljVKPmDWXTpk1N+nmmTJkinJ2da71x1OxcgoODre4QL36di/n5+YmoqKh6H1Pjyy+/FADETTfdVOc+g8EgoqKiBIBazVHNm6y1JveTTz6p9w32Unq9Xri5uQkPD49aHxJr/Otf/xIAxHPPPVdreUuapksbIyH+/j2OHDmy1nIAQqfTiezs7DqPaUn2Lm1Ohfj7zfDi02Bq3lRXr15t9WeaNm2acHBwsNqEX6pXr14CgPjrr78aXTcuLk5oNJo6jYMQQnz88ccCgJgzZ45lWUuaplmzZtVZPyUlxeoHxaY0TdZ+H0314osv1vsmnp+fLzw9PYWLi0utv6nWaJqsnRb073//u85pWDWNycUfWK3Jzc0VDg4Oon///lbv379/vwAgnnjiiTo1XvpF0cWa0jQ1VltDWtI0Pfvss3XW37BhgwAgHnvsMcuyrVu3CgDiyiuvrLO+0WgUsbGxdZqmmmZq8eLFVuuq+bu9uKGqya61D4GNiY+PFzqdrs4+cvLkyQKASEpKatLz7N271+r+tLHacnNzLaceX+zYsWMCgBg1alSt5Y01TSdPnqzzGjUN3cWn5995550CgPjss8/qrF/ze2tJ09Tc95/6vPHGG3W++Kp5f73tttvqrF9YWCi8vb1t1jQ15LLLLqvzflTz5d6PP/7Y6OMb28fLvmc09rnGFvvzjorzNFG9/vrrLwCwegpFfHw8wsPDkZqaiqKiolr3+fj4IDY2ts5jai6uvPzyy+vcFxYWBqPRiHPnztW5b/jw4dBq60a15jD2vn37ai3/5ZdfMGXKFISEhMDJyQkajQYajQarVq2CXq9Hbm5unedKSEiQGmp91qxZOH36NHr06IGnn34aa9asqbMdgIa3oaOjI6688kqrPwMAy+lcF4uIiAAAFBQUNFrj8ePHUV5ejoSEBPj5+dW5v6Yma6/dXCNGjKizbNiwYXBwcLD6OlFRUQgKCqqzvLnZc3R0xJAhQ+o8xlpW/vzzTwBAYmIiFi5cWOdfTk4OTCYTTpw40cBPDJSVleHQoUMIDg5G3759G1y3pKQEp06dQmhoqNXBCmz9O2lphmpMnToVHh4emDdvHmbMmIEPP/wQhw8fhhCiyc/R0O/U19cXffv2RWVlJY4dO9bk52yOpm6TWbNmAQAGDRqEe++9F8uXL0d6enqdx+7evRsmk8kyN9Sl/7799lsAwNGjR+s8duDAgc36GZpaW2tp6jas+Z1b2y84ODhg2LBhdZbX/F0mJSVZ3Z41f4+22p6zZ89GVVUVvvnmG8uy7OxsrF27Fn379q1zildeXh6eeuop9O7dGx4eHpb3l5r3tYyMDKuvU19t/v7+uP7663Ho0CFs377dsvzDDz8EANx7771N/lm8vb0RFxdXZ7m1303NPsba72Dw4MFwdGzZ1RvNff85fPgwbr/9dsTExMDV1dWyfR977DEAtbdvQ/ny9va26RxLQgjLKXWBgYFwdHS01Hbw4ME6v/cdO3ZAo9HUOrWzOVrynlHf5xpb7M87Kl7T1AGEhITg6NGj9e7M61PzgbS+oU1DQkKQlpaGwsLCWudi1zeyT81O2Nr9NfcZDIY699Vc43KpmvkTLv7gvGjRIjz88MPw9fXFuHHj0LlzZ7i5uVkmaE1KSoJer6/3uZrqv//9L2JiYvDpp5/i1VdfxauvvgpHR0dcddVVeOONNyxvXE3ZhkD1ud+XsnYtUc12MplMjdbYktduLmu/K0dHRwQEBCAnJ6fOffVt9+ZmLyAgwOp1DNaykpeXBwB4/fXX6/txAAClpaUN3l+z/S49n92atv6dtDRDNSIjI7Fr1y4sXLgQa9aswY8//gig+sPY448/jgcffLDR51Aij9Y0dZtMnz7dMqLoJ598gg8++ABA9Zc+r7zyCsaNGwfg7xzt3r0bu3fvrvd1reWouXPANLW21tLUbVjzO29sH36xmu350UcfNViDrbbnbbfdhvnz5+Ozzz7DfffdBwD46quvYDQaMXv27FrrFhYWYsCAAUhNTcXAgQNx2223wc/PD46OjigsLMSiRYusvr80Vtt9992Hzz//HB988AGGDBkCvV6Pzz77DEFBQbj22mub/LPUd/2p7O/GwcGhxde0NOfvfceOHRg9ejSMRiPGjBmDqVOnwsvLC1qt1nK90cXbtzn5aq5HH30Ub731FkJCQjBhwgSEhYXB1dUVQPWEw2fOnKm1fmFhIXx9fS3rNFdL9pv1/fy22J93VGyaOoBhw4Zhw4YN+OOPP3DnnXc2+XE1H0bPnTtn9chRVlZWrfVaS3Z2ttXlNUelal7faDRi4cKF6NSpE/766686O5mabzCtuXSAgsY4ODjg4YcfxsMPP4ycnBxs3boV33zzDb777jscPnwYhw8fhrOzc61taE1rbkMlXjs7O7vOwAlGoxG5ublWB9aob7s3N3u5ubkwmUx1GqdLs3Lx/xcVFTU46Edjaj6oNOVLieb8TmqOshqNRquPKSwslBqso7m6d++O5cuXw2g0IikpCb///jvefvttPPTQQ3B3d29033Lxz96zZ88697fV/kTG5MmTMXnyZJSVlWHnzp1YvXo13nvvPVx99dXYt28fevToYan3kUcewZtvvin1/LL7HdnaGtJQrmzVuNZsm8b24dYek5SU1OQL+Ws0Z3uGh4dj9OjR+P3333Hs2DF069YNn332GZycnHDzzTfXWvfjjz9Gamqq1UlI//zzTyxatKhZtQ0aNAh9+/a1DAjx22+/IS8vD//85z9bPBhDfWr2ednZ2YiJial1n8lkQl5eXpO+CKpPc/Z1L774IioqKrBx48ZaAyIAwCuvvIKVK1dafQ2ZfDVHTk4O/ve//6FXr17Yvn07PD09a92/bNmyOo/x8fFBXl4eKioqWtQ4teR9vKHMtXR/3lHx9LwOYM6cOXBycsIPP/yAI0eONLjuxd/i1JxqtGnTpjrrnTp1Cunp6YiOjm71D2xbt26F2Wyus7ymrpo6c3NzUVhYiCFDhtRpmEpLSy2H8m0tKCgI06dPx7fffovRo0cjOTkZhw4dqlWbtW1oNBqxZcsWALA6ElRLde3aFW5ubkhKSrL6IWjjxo02f+3ExMQ6y7Zu3QqTydToqWsXa272jEZjrVNcalyaFaD6FBQAlt9Bc7m7u6NXr17Izs5u9LQ6T09PxMbGIiMjAydPnqxzv7Xfia+vLwDg7NmzddY/deqU1dNCm6Om0WzsCJSjoyMuv/xy/POf/7R8WFixYkWjz9/Q77SwsBD79++Hi4sLunfvLld4G3B3d8fo0aPx5ptv4plnnkFVVRV+++03ANWnXWm12hbn6FIODg5NOhrYUG0NaShXtprgvCbH1vYLJpMJW7durbPcVn+XMm6//XYA1SOY7d+/HwcOHMCkSZMQGBhYa71Tp04BAGbMmFHnOaz9jDLuu+8+VFZW4vPPP8eHH34IjUaDuXPntug5G1Lz92jtd7Bjx456v6Rpqua8/5w6dQp+fn51GibA+vZtKF9FRUXYv39/84q/REpKCsxmM8aPH1+nYUpPT0dKSkqdxwwePBhCCKxZs6bR529o39uc9wwZzd2fd1RsmjqAqKgoLFy4EFVVVZg8eXK9b4hr1qzBpEmTLLfvuOMOANXf/pw/f96y3GQy4fHHH4fZbG6TbyNOnjyJd999t9aylStXIjExEXFxcZYhx4OCguDm5oa9e/fWOnXDYDDgoYcesnotU3Po9Xps27atznKDwYD8/HwA1RNsAtXDePv5+WHZsmXYsWNHrfXfeustpKamYuzYsa0yga5Op8OsWbNQUlKC+fPn17ovOTkZ//vf/+Dk5IRbb73VZq/5wgsv1DpvvrKyEk8//TSA6ua9qVqSvaeffrpW85+fn28Z7vniGu6//344OTnhkUcesXrdUlVVVZM/uNWcznDPPffUaWLMZrPl28Can00IgSeeeKLWm2Rubi5eeOGFWj8/AHTr1g1eXl5YuXJlrVMcKyoqbHoaRc3pOGlpaXXu27t3r9XmrOYb3pq8N+SWW26Bk5MT3n77bcuHzxrz589HcXExbrnlFqlrC1vT5s2brX5wvPRnDgoKwqxZs7Bnzx688MILVj/4JCcnIzU1Ver1/f39cf78eVRUVDS7tob0798fWq0WX3/9NcrLyy3L8/Pz8eSTT0rVWp8hQ4aga9eu2Lx5c52jBIsXL0ZycnKdx8yZMwc+Pj547rnnsGvXrjr3m81mq413S0yfPh1eXl748ssvsXTpUgB/N1IXqxkC+tLX37dvH1555ZUW1XDzzTfD29sbr732GhITEzFu3Lg6R4Bs6bbbbgMAvPTSS7X+tquqqvDMM8+0+Pmb8/4TFRWF/Px8HDhwoNb6S5Yswdq1a+u8xjXXXANfX198/fXXdT7XLFy40GZfKNX83mu+AKxRWlqKu+++2+rf4gMPPAAAeOyxx6yehXDxsob2vYD8e0ZjbLE/76h4el4H8cwzz8BoNOK5557DgAEDMGTIEPTv3x8eHh7Izs7G5s2bcfLkyVoX+A4ZMgRPPvkkXnvtNfTq1QszZ86Eu7s7fvvtNxw6dAjDhg3DE0880eq1T5w4EY899hh+++03JCQkWOZpcnFxwSeffGI5zUSr1eLBBx/Eq6++issuu8wyR9TGjRuRn5+PUaNGWb6VaYmKigoMGzYMcXFxuPzyyxEZGYnKykqsX78eR48exdSpUy3flnt4eOCTTz7BddddhxEjRuC6665D586dsXfvXqxbtw6dOnWyXI/QGl599VVs2bIFixcvxu7duzFq1CjLPBklJSVYvHix1Tmimqt79+7o2bNnrXmakpOTMXnyZKnmrLnZCwkJgV6vR69evTB16lQYDAZ8//33yMrKwn333WcZeAOobkY++eQT3HHHHejZsycmTpyI+Ph4GAwGpKWlYcuWLQgMDGzSwAR33XUXtmzZgi+++AJdunTBNddcg8DAQGRmZmLDhg244447LKfzPP744/jtt9+wcuVKJCQk4KqrrkJ5eTm+++475OTk4Mknn6x1cbaTkxMeeughvPDCC+jbty+uvfZaGI1GrF+/HqGhoTabvX7MmDF4/fXXcffdd2PGjBnw9PSEj48P7r//fnzxxRf44IMPMGzYMMTGxsLX1xfJyclYtWoVnJ2da80lVJ+aOcDmzZuHfv364frrr0dgYCASExPx559/olu3bvjPf/5jk5/FFh588EFkZGRg6NChiIqKgk6nw969e7FhwwZERkbixhtvtKy7ePFinDx5Ev/+97/xxRdfYNiwYQgODkZmZiaOHj2K3bt3Y9myZVJ/a2PGjMHu3bsxceJEXHnllXB2dkZCQgKmTJkiVVt9QkJCMGvWLHzxxRfo06cPJk+ejOLiYvz666+48sorbTIYiUajwZIlSzBu3DjMmDGj1jxNf/zxByZOnFjnm3h/f398//33uPbaazF48GCMGTMGPXv2hEajwdmzZ/Hnn38iLy8PlZWVLa6vhqurK6677josWbIE7777Lvz9/TF58uQ669122214/fXX8fDDD2Pjxo3o0qULTp48idWrV2P69OlYvnx5s2twc3PD7Nmz8b///Q8Aas1f1BpGjBiBuXPn4sMPP0TPnj0xY8YMODk5YdWqVfD29kZoaKjVAZhkyL7/PPzww1i7di2GDRtmmf9wz5492Lp1K2bOnInvv/++1vN7eHjgww8/xA033IDhw4fXmqfp0KFDuPLKK7F58+YW/QxA9bVBN954I7755hv06dMH48ePR1FREdavXw8XFxf06dOnzlGt8ePH41//+hdefPFFdO/e3TJPU3Z2NrZu3YrBgwdbGvSuXbsiLCwM33zzDZycnBAZGQmNRoNbb70VkZGR0u8ZjbHF/rzDUnLoPmp7R44cEffff7/o2bOn8PT0FE5OTqJTp05i4sSJ4uOPP7Y6POWyZcvE0KFDhYeHh3B2dhY9evQQL774oqioqKizbkPD5FoburuGteGOLx6Gc/v27WLMmDHC09NTeHh4iHHjxoldu3bVeR6DwSDeeOMN0b17d+Hi4iKCg4PFLbfcIk6fPm31NS6eObs+l9ZdVVUl/vOf/4iJEyeKiIgI4ezsLAICAsSgQYPEe++9J/R6fZ3n2LVrl5g2bZoICAgQTk5OIiIiQtx7770iIyOjSdvC2jZpqoKCAvHkk0+KuLg4odPphLe3txg7dqxYu3at1fVbMuR4ZWWlePbZZ0VUVJTQ6XQiOjpaLFy40GquYGVI40s1J3uFhYXivvvuE6GhoUKn04lu3bqJRYsW1Zon5GIHDhwQs2fPrjUDfc+ePcXcuXOtDo/fkC+//FJceeWVwsvLSzg7O4uoqChx8803i71799Zar6KiQrz00kuiZ8+ewsXFRXh4eIihQ4eKr7/+2urzms1m8corr4iYmBhLfp544glRVlbW4JDjMsNJC1E9rG+3bt2ETqerNeTwjh07xL333it69+4tfH19hYuLi4iNjRW33357rWGMm2Lt2rVi3LhxwsfHR+h0OhEbGyueeOKJWnPFNPXnsKaxIcetsfY6y5cvFzfeeKOIi4sT7u7uwtPTU/Ts2VM888wzIicnp85z6PV68fbbb4srrrjCMgdNRESEGD16tPjvf/8rcnNzG63xYqWlpeLee+8VYWFhwsHBodZ+Sra2+lRWVorHH39chIWFWeYse/nll4XBYGhwyPGLhwiv0dC+dM+ePWLChAnCw8NDeHh4iDFjxojt27c3+nzz5s0TcXFxwtnZWXh6eoquXbuKW265Rfz000+11m1on9lUW7ZsEbgwt839999f73qHDx8WU6ZMEYGBgcLNzU3069dPfPTRR/X+/DK11QxPHxISUmfuuhoNDTle3367vu1sMpnEm2++Kbp27Sp0Op0ICQkR9913nygsLBQeHh4iISGh0Zpr1LdPkX3/WbVqlRg0aJDw8PAQ3t7eYty4cSIxMbHBfcG6devE0KFDhaurq/Dx8RFTp04VR48elc5FQ69RVlYmnnnmGREbGyucnZ1FeHi4uO+++0Rubm6D+5ZffvlFTJgwQfj6+gqdTifCw8PFtGnT6ry37Nq1S4wePVp4eXkJjUZT5/cl857R2OcaW+7POxqNEBxjkOzTpk2bMGrUKKsX3ZJ9GTlyJBITEzlkKRFRMy1duhRz5szBv/71L8tpV0o4efIk4uPjceONN1od5ICoo+I1TUREREQKMhqNePPNN+Ho6Njqp+bVOHfuXJ1BlsrLyy2nZ8kMd07UEfCaJiIiIiIFbN26FYmJidi0aRMOHjyI+++/H+Hh4W3y2m+99RaWLVuGkSNHIiQkBOfOncMff/yB9PR0TJo0Cdddd12b1EGkFmyaiIiIiBTw+++/47nnnoOfnx/uvvtuvPbaa2322uPGjUNSUhLWrVuH/Px8ODo6Ij4+Hg8++CAefvjhFs0jRtQe8ZomIiIiIiKiBvCaJiIiIiIiogawaSIiIiIiImpAh7umyWw2o6qqCg4ODjxfl4iIiIioAxNCwGQyQafTNTipc4drmqqqqrBt2zalyyAiIiIiIjsxdOhQuLi41Ht/h2uaHBwcAAC9evWy/L+SSktL4eHhoXQZpCLMDMliZkgG80KymBmSZU+ZMZlMOHToUKN9QYdrmmpOyXNwcLCbpsnb21vpMkhFmBmSxcyQDOaFZDEzJMseM9PYZTscCEJhRUVFSpdAKsPMkCxmhmQwLySLmSFZaswMmyaFNXTBGZE1zAzJYmZIBvNCspgZkqXGzHS4yW2NRiMSExORkJBgF6fnERERERGRMkwmE5KSkjBixAg4OtZ/5ZL62rx25tixY0qXQCrDzJAsZoZkMC8ki5khWWrMTIcbCKIx5eXlKC8vB9D4BWG24Orqiry8vFZ/HXtRc2DTzc0Nbm5uClejTmazWekSSGWYGZLBvJAsZoZkqTEzbJouUlRUBI1GA39//zab+NbT0xM6na5NXsteCCFQUlKCoqIiuxs5RQ24zUgWM0MymBeSxcyQLDVmhqfnXcRgMMDLy6vNGiYAHfK6Ko1GAy8vLxgMBqVLUSU17mhIWcwMyWBeSBYzQ7LUmBk2TRdpy2apRlVVVZu/pr1QYnu3B2lpaUqXQCrDzJAM5oVkMTMkS42ZYdNERERERETUADZNCuto1zNRy0VERChdAqkMM0MymBeSxcyQLDVmhk2Twkwmk9IlkMqUlpYqXQKpDDNDMpgXksXMkCw1ZoZNk8Js0TTNmzcPt9xyi9X7EhIS8N5771luHzp0CDfffDPi4+MREhKChIQE3HHHHTh//nytx/3888+YMmUKIiMjERERgWHDhuG1115DQUEBAODrr79GVFSU1df08/PDL7/80uKfi6yr+R0QNRUzQzKYF5LFzJAsNWaGTVMHkpubi2nTpsHX1xfff/89duzYgcWLF6NTp06WuakA4MUXX8Sdd96Jvn374ttvv8W2bdvwwgsv4NChQ1i+fLmCPwERERERUdvjPE0Kc3V1bbPX2rlzJ4qLi7Fo0SI4Olb/6iMjIzF8+HDLOnv37sWbb76Jl19+Gffee69leefOnTFq1CgUFRW1Wb1kXY8ePZQugVSGmSEZzAvJYmZIlhozw6apEfNWHENBubHVnl9AQIO6Q2/7ujninWndbPpaQUFBMBqNWL16Na655hqrQ35/99138PDwwJ133mn1OdQ4rn57c+LECcTHxytdBqkIM0MymBeSxcyQLDVmhk1TIwrKjcgtbx+TsA4YMACPPvoo5s6di8ceewz9+vXD8OHDceONNyIoKAgAkJKSgsjISDg5OTX6fMXFxaoc/UTtjMbWa+KpfWJmSAbzQrKYGZKlxsywaWqEr1vrbqKGjjS1hn/961+47777sHnzZuzduxdLly7Ff//7X/zyyy/o0aMHhBBNfi4PDw9s2rSpzvL+/fvbsGK6lJeXl9IlkMowMySDeSFZzAzJUmNm2DQ1wtanyF3KZDLBwcGhVV/jUn5+fpg2bRqmTZuG+fPnY8SIEVi8eDHeffddxMbGYufOnTAYDI0ebdJqtYiJiWmjqqmGn5+f0iWQyjAzJIN5IVnMDMlSY2Y4ep7CqqqqFH19nU6H6OholJWVAQBmzpyJ0tJSLFmyxOr6HAhCeadPn1a6BFIZZoZkMC8ki5khWWrMDI80tRPFxcU4ePBgrWW+vr61bq9duxY//vgjpk+fjtjYWAghsGbNGqxfvx6LFy8GUH1q3YMPPoj58+cjKysLkydPRkhICFJSUvDpp59i8ODBtUbVIyIiIiJq79g0KawpAy40xdatWzFixIhayy6d8LZr165wdXXF/PnzkZGRAZ1Oh9jYWCxatAg33HCDZb2FCxciISEBS5YswaeffgohBKKiojB16lTcdNNNNqmXmi8sLEzpEkhlmBmSwbyQLGaGZKkxMxohc+V/O2A0GpGYmIiEhIQ61xLl5eXB39+/TetpyrVD7ZUS27s9yM7ORnBwsNJlkIowMySDeSFZzAzJsqfMmEwmJCUlYcSIEZZ5TK3hNU0KU+OQi6SsvLw8pUsglWFmSAbzQrKYGZKlxsywaSIiIiIiImoAmyaFubi4KF0CqUy3bq07DD61P8wMyWBeSBYzQ7LUmBk2TQrT6/VKl0Aqk5KSonQJpDLMDMlgXkgWM0Oy1JgZNk0K62DjcJANKD23F6kPM0MymBeSxcyQLDVmhk2TwrRa/gpIjoeHh9IlkMowMySDeSFZzAzJUmNm+IldYR11uHFqvqCgIKVLIJVhZkgG80KymBmSpcbMsGlSGK9pIllqPA+YlMXMkAzmhWQxMySjsMKAYyeTlS5DGpsmIiIiIiJqE29vT8cr+6qwKblAVdf2s2lSGE/PI1khISFKl0Aqw8yQDOaFZDEz1FQHskqwJbUQBVXAu3+mo8JgVrqkJmPTpDBbdNjz5s2Dn59fnX8zZ84EACQkJOC9996zrH/o0CHcfPPNiI+PR0hICBISEnDHHXfg/PnztZ73559/xpQpUxAZGYmIiAgMGzYMr732GgoKCgAAX3/9NaKioqzW5Ofnh19++aXFPxvVZTQalS6BVIaZIRnMC8liZqgpTGaB93dkWG7P6R8CN52DghXJYdOkMFvtaMaMGYOjR4/W+vfxxx/XWS83NxfTpk2Dr68vvv/+e+zYsQOLFy9Gp06dUF5eblnvxRdfxJ133om+ffvi22+/xbZt2/DCCy/g0KFDWL58uU1qpua5tLklagwzQzKYF5LFzFBTrDuZj1N5FQCAUDcNxsf7K1yRHEelCyDbcHZ2RnBwcKPr7dy5E8XFxVi0aBEcHat//ZGRkRg+fLhlnb179+LNN9/Eyy+/jHvvvdeyvHPnzhg1ahSKiops/wMQERERUbtUVmXC0j2ZltvTox3hoNUoWJE8Nk2N2D7+DujP57X56zoH+mPIuk9s/rxBQUEwGo1YvXo1rrnmGmg0dQP73XffwcPDA3feeafV5/D29rZ5XdR08fHxSpdAKsPMkAzmhWQxM9SYb/afQ0FF9dlVw6J8cNWACIUrksemqRH683nQZ9n/Yee1a9ciIqJ2AB955BE8+uijtZYNGDAAjz76KObOnYvHHnsM/fr1w/Dhw3HjjTdaxsxPSUlBZGRkkwapKC4urvO61LrOnDmD2NhYpcsgFWFmSAbzQrKYGWpIZrEePx6q/iztpNXg7oGhqswMm6ZGOAe27vmWQgirR3tkX3fYsGF44403ai3z9fW1uu6//vUv3Hfffdi8eTP27t2LpUuX4r///S9++eUX9OjRQ2pwCg8PD2zatKnO8v79+0vVT03Hub1IFjNDMpgXksXMUEM+3pUBg7n6s+X0y4IQ4uWMI+nqywybpka0xilyF9Pr9XB2dm7x87i7uyMmJqbJ6/v5+WHatGmYNm0a5s+fjxEjRmDx4sV49913ERsbi507d8JgMDR6tEmr1Uq9LrWcm5ub0iWQyjAzJIN5IVnMDNUnKbMEW09XXwvv6+qImxKqr79XY2Y4ep7C7GGeJp1Oh+joaJSVlQEAZs6cidLSUixZssTq+hwIQlmcD4NkMTMkg3khWcwMWWMyC7xXa4jxUMsQ42rMDJsmhdnqkLZer0d2dnatf3l5dQewWLt2Le655x6sXbsWp06dwsmTJ/H2229j/fr1uOqqqwBUn1r34IMPYv78+ViwYAF27dqFs2fPIjExEbfffjuWLVtmk5qpeZKTk5UugVSGmSEZzAvJYmbImrUn8pCSXz3EeJy/K8bH+1nuU2NmeHpeO/HHH3+ge/futZZ16dIFO3furLWsa9eucHV1xfz585GRkQGdTofY2FgsWrQIN9xwg2W9hQsXIiEhAUuWLMGnn34KIQSioqIwdepU3HTTTW3yMxERERGR+pRVmfDpnizL7X9cEQ6tlWv41UQjZK76bweMRiMSExORkJAAB4fasxDn5eXB379tJ9oyGo2W+ZI6GiW2d3vA7UaymBmSwbyQLGaGLvXhzgx8fzAHAHBltA/+NSa61v32lBmTyYSkpCSMGDGiwc/kPD1PYR2sZyUbYGZIFjNDMpgXksXM0MUyivRYcfjCEOMOGtw1MLTOOmrMDJsmhRmNRqVLIJXJyclRugRSGWaGZDAvJIuZoYt9uCsDxgtDjM+8LAidPOuOEq3GzLBpIiIiIiKiFtuXUYI/z1SPsuzn5ogbLwwx3h6waVKYLeZooo6lS5cuSpdAKsPMkAzmhWQxMwRUDzH+/o50y+07+ofC1cnB6rpqzAybJoUZDAalSyCVSU9Pb3wlooswMySDeSFZzAwBwG/H85BaUAkAiA9ww9gufvWuq8bMsGlSmNlsVroEUpmKigqlSyCVYWZIBvNCspgZKtUb8dnei4YYHxzW4BDjaswMmyaFabX8FZAcFxcXpUsglWFmSAbzQrKYGfpq3zkUVVYPbjYyxgc9O3k0uL4aM8NP7ApzcnJSugRSmYiICKVLIJVhZkgG80KymJmOLb2o0jLEuM5Bg7sGhjX6GDVmhk2TwvR6vdIlkMqcPHlS6RJIZZgZksG8kCxmpmP7cGcGTBemXbqudzCCPHSNPkaNmWHTRERERERE0vamF2NHWjEAwN/NCdf3DlK4otbDpklhjo6OLX6OefPm4ZZbbrF6X0JCAt57771ayw4cOIDbb78dXbt2RUhICPr374+HHnoIp06dAgCkpaXBz88PBw8erPN8U6ZMwdNPP93imqn5goLa7w6JWgczQzKYF5LFzHRMJrPA+zszLLfvHFD/EOOXUmNm2DQpTNPAyCKtYe3atRg/fjyqqqrwwQcfYMeOHXj//ffh5eWFl19+uU1roebh4CEki5khGcwLyWJmOqZfjuXizIUhxrsGumF0nG+TH6vGzLT8MAe1iMFgsMnRpqYoLy/H/fffj3HjxuGLL76wLI+MjET//v1RVFTUJnVQy5w7dw5+fvXPfUB0KWaGZDAvJIuZ6XjOlehrDTF+3xXhDQ4xXufxKswMm6ZGfPHOdpSVtN5gDUIIq0eb3D2dceu8ITZ9rQ0bNiAvLw8PPPCA1fu9vb1t+npERERE1L4UVxrxzJpklOhNAIBRsb7oHuSucFWtj01TI8pK9Cgtbh8j3KWkpAAA4uPjm7T+xIkT6xw+raioQK9evWxeGzVdbGys0iWQyjAzJIN5IVnMTMehN5rx73UpSC+q/mwc7u2MeVeESz+PGjNjV03T5s2b8frrr2Pv3r3IysrCTz/9hGnTptW7/u23347PPvuszvIePXrg8OHDNqnJ3dPZJs9THyEAa0czW+N1hRBS6y9ZsqROgzV37lxblkTNcO7cOURGRipdBqkIM0MymBeSxcx0DCazwKsbT+NIThkAwM/VES9PjIWXi3w7ocbM2FXTVFZWhoSEBNxxxx2YPn16o+svWrQIr776quW20WhEQkICrrvuOpvVZOtT5C5VUVEBV1fXVn2NGjVd/YkTJzBw4MBG1w8LC0NMTEytZW1VK9WvrKxM6RJIZZgZksG8kCxmpv0TQuD9HRnYdqb6+ndXJy1enBCLTs38kl+NmbGrpmnSpEmYNGlSk9f39vaudR3OihUrUFBQgDlz5jT62JKSklqnnjk7t+4Rpfq05eh5o0aNgr+/P95+++1aA0HUKCoq4nVNKqBUVkm9mBmSwbyQLGam/fvuYA5WHjkPAHDQAPPHRCMuwK3Zz6fGzNhV09RSS5YswdixY5t0uK9Xr14oLy+33J4zZw4eeeQRmM1m6PXV52k6OTlBCAGj0Qig+hdsMBhgNpuh1Wrh5ORkWdfR0REajQYGg6HOuhqNBs7OzqisrLS6rtlsbvK6Op0OJpMJJpMJGo0GLi4uMBqNKCgowF9//QWtVmtZNzg4GEIIGAwGVFRUwN3dHa+//jruuece3HjjjZg7dy7Cw8ORn5+PX3/9FWfPnsW7775reW29Xo+Kigo4ODjA0dERer0eZrPZ8pw128XFxQV6vR5CiDrb5dJt6OLigqqqKpjNZlRUVKCqqsoyP1RwcDAAIDs7GwAQFxeHjIwMVFRUwMXFBZ07d8aJEycAAIGBgXB0dERWVvXILTExMcjOzkZZWRl0Oh1iYmJw7NgxAIC/vz+cnZ2RmZkJAIiOjkZubi5KSkrg6OiI+Ph4HDlyBADg5+cHNzc3pKenA6geWbCgoADFxcVwcHBA165dcfToUQgh4OPjAy8vL6SlpQEAIiIiUFJSgsLCQmg0GnTv3h3Hjx+HyWSCl5cX/Pz8cPr0aQDVR/EqKiqQn58PAOjevTtOnToFg8EADw8PBAYGIjU1FQAQGhqKqqoq5ObmWrZLcnIy9Ho93N3d0alTJyQnJwMAOnXqBLPZjJycHABAly5dcPbsWVRWVsLV1RXh4eGWWbiDgoKg0Wgs2zs2NhZZWVkoLy+Hs7MzIiMjG9zeOTk5KC0ttbq9XVxckJFRPXdDVFQU8vPzUVxcXGd7+/r6wsPDA2fPngUAdO7cGUVFRSgqKoJWq0W3bt1w7NgxmM1my5ckF2/v0tJSFBQUAKg+LffEiRMwGo1Wt3dlZSXy8vIAAN26dUNKSgqqqqrg4eGBoKAgyzV/ISEhMBqNOH+++s0hPj4eZ86cgV6vh5ubG0JCQizbu+Zv7OLtnZ6ebslsREREre2t1Wpx7tw5y/Y+d+4cysrK4OzsjKioKBw/fhwAEBAQAJ1OVyuz58+fR2lpKZycnBAXF4ejR49aMuvq6mp1e9dkVq/X48iRI/Dx8YGnp2et7V1cXFxvZn19fXHmzBkAQHh4OMrLyy2ZvXh7e3p6IiAgoFZm9Xq91e3t7u6O4ODgBrd3WlqaJbNhYWHcR0BuH9G1a1ecPn262fuIyspKHDlyhPuIDrSPuDizzdlHCCFQXFzMfQTa5z7ih93J+ORIBWrcEOsIt+J05OY2fx8RGRmJtLQ0u9hH1GzDxmiE7IUubUSj0TR6TdPFMjMz0blzZ3z99de4/vrr613PaDQiMTERMTExdY40lZaWwt/fv6WlS7HF6Xnz5s3DsmXL6iy/5ZZbkJiYiHvvvRf/+Mc/LMv37duH//73v9ixYwdKSkoQFhaG4cOH44EHHkBMTAzS0tLQp08fJCYm4rLLLqv1nFOmTEGvXr3wyiuvtKhmAMjLy2vz7d0eHDlyBD169FC6DFIRZoZkMC8ki5lpv/ZnluCZNckwmqvbhdmXh2BW304tfl57yozJZEJSUhJGjBjR4DRA7eZI02effQYfH58mN1menp5wcKg9a3FpaWkrVNb63nnnHbzzzjtNXr9v3774/PPP672/c+fO9Xbdq1atkq6PiIiIiNQlNb8CC9enWBqmq7r54+Y+wQpXpRz1TcdrhRACn3zyCW699VbodDqly5HSVhPbUvsREBCgdAmkMswMyWBeSBYz0/7klFbh2TXJKDeYAQCDIrzwwJAIm12Lr8bMtIumKTExEadOncKdd96pdCnS2nIgCGof1PbFACmPmSEZzAvJYmbal1K9Ec+uTUZuefU18l0D3fDM6Cg4aG33mVWNmbGrpqm0tBT79+/H/v37AQCpqanYv3+/5YKtp59+Grfddludxy1ZsgSDBg1S5aSrNYM2EDVVzYWoRE3FzJAM5oVkMTPtR5XJjOd+T8WZgupBwUK9dHh+fAxcnRwaeaQcNWbGrs4N27NnD0aNGmW5/eijjwIAZs+ejaVLlyIrK8vSQNUoKirCDz/8gEWLFrVprURERERE7YVZCPxf4hkkZVVf4+/t4oiXJsTB19VJ4crsg101TSNHjkRDg/ktXbq0zjJvb+9aQ4erjRrHqSdlRUdHK10CqQwzQzKYF5LFzLQPH+/KxKaUQgCAs4MGL4yPQZh363xOVWNm7Or0vI6Ip+eRrJq5KoiaipkhGcwLyWJm1O+nQzn4/mD13ExaDfDsmGh0C3JvtddTY2bYNCnMbDYrXQKpjFqHxiflMDMkg3khWcyMum1JLcT7OzIstx8YGoHBnb1b9TXVmBk2TQrj6Hkky8mJ5xaTHGaGZDAvJIuZUa/0okq8uuk0ai6OublPMCZ3a/3hwNWYGTZNCuM1TSQrLi5O6RJIZZgZksG8kCxmRr0+2Z0Fg6m6ZRrXxQ+zLw9pk9dVY2bYNCmssrJS6RJIZY4ePap0CaQyzAzJYF5IFjOjTkdzyrD1dCEAwNfVEfcPCW+zM6DUmBk2Te3AvHnz4OfnV+ffzJkzAQAJCQl47733aj3mwIEDuP3229G1a1eEhISgf//+eOihh3Dq1CkAQFpaGvz8/HDw4ME6rzdlyhQ8/fTTrf+DEREREZHNCSHw0a6/r2O6tV+Izediam/sasjxjsjR0Ta/gjFjxmDx4sW1ltV36t/atWsxe/ZsjB49Gh988AGio6Nx/vx5rFy5Ei+//DI++eQTm9RErcPPz0/pEkhlmBmSwbyQLGZGfXaeLcahc2UAgHBvZ0zs6t+mr6/GzLBpUpitDoM6OzsjODi40fXKy8tx//33Y9y4cfjiiy8syyMjI9G/f38UFRXZpB5qPa6urkqXQCrDzJAM5oVkMTPqYjILLNmdabk9p38oHLVtOzCZGjPDpqkRz3x2CwrL8lrt+YUQVhsnH3d/vDz7S5u/3oYNG5CXl4cHHnjA6v3e3q07xCS1XEZGBn9PJIWZIRnMC8liZtTl91P5OFNQfU19t0A3DItq+9+dGjPDpqkRhWV5yC/NUbqMRq1duxYRERG1lj3yyCN49NFHay1LSUkBAMTHxzfpeSdOnAittvalbxUVFejVq1cLqiUiIiKitqY3mvHZ3izL7bsGhnL6myZi09QIH/fWPcdTALAWVdnXHTZsGN54441ay3x9feu+nhB1ljVkyZIldRqsuXPnSj0H2VZUVJTSJZDKMDMkg3khWcyMeqw8fB65ZQYAwKAIL/QO8VSkDjVmhk1TI1rjFLmLVVVVQafTtfh53N3dERMT0+h6sbGxAIATJ05g4MCBja4fFhZW53nVeB5qe5Kfnw83NzelyyAVYWZIBvNCspgZdSiuNOKbpGwAgFYD3DEgVLFa1JgZDjmuMJPJ1KavN2rUKPj7++Ptt9+2ej8HgrB/xcXFSpdAKsPMkAzmhWQxM+rwTVI2SquqP3eO6+KHaD/lvgRXY2Z4pElhtjqPVK/XIzs7u9YyR0dH+PvXPs3P3d0dixYtwpw5c3DzzTdj7ty5iImJQV5eHlasWIH09HQsWbLEJjVR63Bw4DwKJIeZIRnMC8liZuxfTmkVVh45DwDQOWhwa78QRetRY2bYNCnMxcXFJs/zxx9/oHv37rWWdenSBTt37qyz7lVXXYU1a9bgv//9L+bOnYuSkhKEhYVh+PDhePbZZ21SD7Werl27Kl0CqQwzQzKYF5LFzNi/z/ZmwWCqvq59Ws9ABHm0/NKQllBjZjRCdmQAlTMajUhMTERCQkKdLjcvL6/OkZnWVlFR0WGvEVJie7cHR48erdMgEzWEmSEZzAvJYmbsW2p+Be798RgEAE9nByy9vgc8nZU9bmJPmTGZTEhKSsKIESPg6Fj/duE1TUQq08G+5yAbYGZIBvNCspgZ+7ZkdyZqfkM3JgQr3jAB6swMmyaFqfGcTlKWj4+P0iWQyjAzJIN5IVnMjP1KyizBrrPVgy4Eujvhmh6BCldUTY2ZYdOkMDZNJMvTU5k5FUi9mBmSwbyQLGbGPgkh8PHuTMvt2ZeHQOdoHx/91ZgZ+9hyHVhVVZXSJZDKnD17VukSSGWYGZLBvJAsZsY+bTldiOPnywEA0b4uGBPnp3BFf1NjZtg0ERERERG1I0azwKe7syy37xwYCgetbaa56ajYNClMp1N2yEdSn86dOytdAqkMM0MymBeSxczYn9+O5SKjWA8ASAjxwIBwL4Urqk2NmWHTpDCTyaR0CaQyapxFm5TFzJAM5oVkMTP2pcJgwpf7zllu3zkgFBqNfR1lUmNm2DQpjE0TySosLFS6BFIZZoZkMC8ki5mxLz8czEFBhREAcGW0D7oFuStcUV1qzAybJiKVsbdvi8j+MTMkg3khWcyM/SioMOC7gzkAAAcNMKd/iMIVWafGzLBpUpirq6vSJZDK2MsM2qQezAzJYF5IFjNjP77edw4VBjMA4KpuAQjzdlG4IuvUmBk2TQqrrKxs0eNvuukmzJw50+p9f/75J/z8/HD48GH4+fnh4MGDte7/+eefMWXKFERGRiIiIgLDhg3Da6+9hoKCAgDA119/jaioKKvP7efnh19++aVFtVPzHD9+XOkSSGWYGZLBvJAsZsY+ZBTpsfpoLgDAxVGLW/p2Urii+qkxM2yaFCaEaNHjb7nlFmzatAkZGRl17vvqq6/Qt29fqxOIvfjii7jzzjvRt29ffPvtt9i2bRteeOEFHDp0CMuXL29RTdS6eB0cyWJmSAbzQrKYGfuwdE8mTBc+Vs68LAi+bk7KFtQANWbGUekCOjoHB4cWPX7ChAkICAjAsmXL8Pjjj1uWl5aW4ueff8Zzzz1X5zF79+7Fm2++iZdffhn33nuvZXnnzp0xatQoFBUVtagmal1eXvY1bCjZP2aGZDAvJIuZUd7m1AIkphYCAHxcHDHzsiBlC2qEGjPDpqkRuW+Mhqk4u81f18ErGAGPbWh0PUdHR9xwww1YtmwZHnvsMcuFdStXroTJZMKMGTPqjFDy3XffwcPDA3feeafV5/T29m5x/dR6fH19lS6BVIaZIRnMC8liZpS14VQ+Xks8Y7l9S79OcNO17Ev51qbGzLBpaoSpOBvmoqzGV1TQrFmz8Pbbb2Pbtm0YNmwYgOrrkaZMmQIvL686TVNKSgoiIyPh5NT4Ydvi4mJERES0RtnUTGfOnEGPHj2ULoNUhJkhGcwLyWJmlLPuRB7e3JIG84XT8ibE++Hq7gHKFtUEaswMm6ZGOHgFt+rzCyGsDrso87rx8fEYOHAgvvrqKwwbNgwpKSn4888/8fTTT9f7mk3l4eGBTZs21Vnev3//Jj8HEREREdnWb8dy8dbWs6j5VHd1twDcPzQcWhUO560GbJoa0ZRT5FrCZDK1+LomoHpAiKeeegqvvfYavv76a0RHR2Po0KFW142NjcXOnTthMBgaPdqk1WoRExPT4vrIdsLDw5UugVSGmSEZzAvJYmba3s9HzmPx9nTL7Wk9A/GPwWGqmf9IjZnh6HkKM5vNNnmeadOmQavV4ocffsA333yDWbNm1fuHM3PmTJSWlmLJkiVW7+dAEPatvLxc6RJIZZgZksG8kCxmpm39eCinVsM087IgVTVMgDozw6ZJYUaj0SbP4+HhgWnTpuH5559HdnY2brrppnrX7d+/Px588EHMnz8fCxYswK5du3D27FkkJibi9ttvx7Jly2xSE7WO/Px8pUsglWFmSAbzQrKYmbbzbVI23t/x9zQzNyUE4+6BoapqmAB1Zoan57Ujt9xyC7788kuMGzcOISEhDa67cOFCJCQkYMmSJfj0008hhEBUVBSmTp3aYMNFRERERG3vy33n8Pnevwcnu61fJ8zq20l1DZNaaURLZ1dVGaPRiMTERCQkJNS5ligvLw/+/v4KVdbxcHsTERERNUwIgc/2ZuHr/X9PgTOnfwhu6tNJwaraD5PJhKSkJIwYMQKOjvUfT+LpeQqrrKxUugRSmRMnTihdAqkMM0MymBeSxcy0HiEEluzOrNUwzR0UpvqGSY2Z4el5CutgB/rIBmx1HRx1HMwMyWBeSBYz0zqEEHh/RwZ+OnzesmzeFeG4pmegglXZhhozw6ZJYbYYbpw6Fk9PT6VLIJVhZkgG80KymBnbMwuBxdvTsfpormXZQ8MiMLmb/U9c2xRqzAybJoU1dO4kkTUBAe1jh0lth5khGcwLyWJmbMssBBZtPYvfjucBADQAHr2yMybEt5/rwNWYGV7TdBElTpXT6/Vt/pr2gqcmNk9qaqrSJZDKMDMkg3khWcyM7ZjMAv+3Oc3SMGk1wBMjIttVwwSoMzNsmi7i5OSE4uJifphvZUIIFBcXw8nJSelSiIiIiOzGh7sy8PvJ6jmMtBrg6VFRGNvFT+GqCODpebV4e3ujvLwceXkXDoe2wbj3BoNBlbMiN1dNQ+rm5gY3NzeFq1Gn0NBQpUsglWFmSAbzQrKYGdtYdeQ8fjpUPeiDgwZ4dkw0hkX5KFtUK1FjZtg0XaKtP8xnZ2dzriKS0pFP6aTmYWZIBvNCspiZltuTXox3/ky33H5waES7bZgAdWaGp+cprOaoFlFTMTMki5khGcwLyWJmWuZ0QQVe/CMV5gtXh1x3WRAmtZNR8uqjxsywaSIiIiIiUkBBhQHz16ag3GAGAAyJ9MadA9V36lpHwKZJYd26dVO6BFIZZoZkMTMkg3khWcxM81QZzXhufSqyS6sAAHH+rvjnyEho2+CaeqWpMTNsmhSWkpKidAmkMswMyWJmSAbzQrKYGXlCCLyxJQ1HcsoAAAFuTnh+fAxcnRwUrqxtqDEzbJoUVlVVpXQJpDLMDMliZkgG80KymBl5X/x1DhuTCwAAzo5aPD8+BgHuOoWrajtqzAybJoW5u7srXQKpDDNDspgZksG8kCxmRs6GU/n4ct85AIAGwDOjohAX0LGmYVFjZtg0KSw4OFjpEkhlmBmSxcyQDOaFZDEzTXf4XCne2JxmuX33wFBcEemtYEXKUGNm2DQpTI3ndJKymBmSxcyQDOaFZDEzTZNVrMfC31NhuDC2+KSu/phxWZDCVSlDjZlh00RERERE1IpK9UbMX5eCokojAKBvqAceGBoBTQcYKa+9YNOksJCQEKVLIJVhZkgWM0MymBeSxcw0zGgWeHHDaaQVVgIAIrydMX9MNBy1HbdhUmNm2DQpzGg0Kl0CqQwzQ7KYGZLBvJAsZqZ+Qgi8uz0df2WUAAC8nB3wwoRYeDg7KlyZstSYGTZNCjt//rzSJZDKMDMki5khGcwLyWJm6vfT4fNYfSwXAOCk1WDhuBiEejkrXJXy1JgZNk1ERERERDb255kifLAjw3L7keGd0auTh4IVUUt07GODdiA+Pl7pEkhlmBmSxcyQDOaFZDEzf8suqUJSVgkOnivFppRCiAvLZ/XthLFd/BStzZ6oMTNsmhSWlpaGmJgYpcsgFWFmSBYzQzKYF5LVkTNzrkSPA1mlSMoqxYGsUmSXVtVZZ0SMD27r10mB6uyXGjPDpklhlZWVSpdAKsPMkCxmhmQwLySro2RGCIFzJVXVDdK5UhzIKkFOqaHe9Z0cNBgR44uHOLR4HWrMDJsmhbm6uipdAqkMM0OymBmSwbyQrPacGbMQ2JRcgN3pxTiQVYrzZQ03ST2C3NE7xAMJIR7oFugOnSOHD7BGjZlh06SwsLAwpUsglWFmSBYzQzKYF5LVXjMjhMCbm9Ow7mS+1ft1Dhr0CHZH7xBP9O7kgW6BbmySmkiNmWHTpLBTp06hR48eSpdBKsLMkCxmhmQwLySrvWZm1dHcWg2T80VNUkKIB+ID3aBzYJPUHGrMDJsmIiIiIqKLHMkuw/sXDRf+8LAIjOviByc2SR0WmyaFBQcHK10CqQwzQ7KYGZLBvJCs9paZ/HIDXvgjFUZz9YDh03sF4qpuAQpX1b6oMTNsl4mIiIiIABjNAi9uSEVeefWAD707eeCugeq7/oZsj02TwrKzs5UugVSGmSFZzAzJYF5IVnvKzEe7MnDoXBkAIMDNCc+OjoKjlsOF25oaM8OmiYiIiIg6vI3J+fjp0HkAgKNWg/ljo+Hr5qRwVWQv2DQpLC4uTukSSGWYGZLFzJAM5oVktYfMpOZX4M0tZy2377siHN2D3BWsqH1TY2bYNCksIyOj8ZWILsLMkCxmhmQwLyRL7Zkp1Rvx3O8p0BvNAIAJ8X6Y3M1f4araNzVmhk2TwioqKpQugVSGmSFZzAzJYF5IlpozYxYC/9l0BpnFVQCAOH9X3D8kAhoNr2NqLYYqE0pLy5UuQxqbJoW5uLgoXQKpDDNDspgZksG8kCw1Z+brfeew82wxAMDL2QELxsbA2ZEfj1vT5rXHsf3XbGSdLVS6FClMhcI6d+6sdAmkMswMyWJmSAbzQrLUmpldZ4vwxV/nAABaDfD0qCgEe+oUrqp9O5uSj31/pqGkqArfLtmNygqD0iU1GZsmhZ04cULpEkhlmBmSxcyQDOaFZKkxM5nFery68QzEhduzLw/B5eFeitbU3lXpjVjz40HL7WHjusDFVT2jE7JpIiIiIqIOo9JoxvO/p6C0ygQAGBrpjRsTghWuqv3bvPYEivKrr3/zDXRGvysiFa5IDpsmhQUGBipdAqkMM0OymBmSwbyQLDVlRgiBt7akISW/EgAQ7u2Mx0dEcuCHVpaWnIf9O9IAAI5OWoy8Og4alU0azKZJYY6OjkqXQCrDzJAsZoZkMC8kS02ZWXkkFxuSCwAArk5aLBgbDXedg8JVtW/Vp+Udsty+ckI8/AI8FKyoedg0KSwrK0vpEkhlmBmSxcyQDOaFZKklM4fOleKDHemW249d2RmRvq4KVtQxJK45juKC6tPywqN80XdwpGoyczE2TURERETUruWVG/DiH6kwXRj54freQbgy2lfZojqAtOQ8JO08CwBwdHLAxBmXqe60vBpsmhQWExOjdAmkMswMyWJmSAbzQrLsPTNCCPx3SxryK4wAgD6hHpjTP1Thqtq/Kr0Ra374e7S8KyfGw8ffDYD9Z8YaNk0Ky87OVroEUhlmhmQxMySDeSFZ9p6ZHWnF2HVhAlt/Nyc8MyoKDio92qEmib8dR3Fh9YAbEdF+6Dvo7/m87D0z1rBpUlhZWZnSJZDKMDMki5khGcwLybLnzOiNZrx30XVM/xgcBh8VzQ2kVmdO5SJpV/VpeU46B0yY0avWaXn2nJn6sGlSmE7HmadJDjNDspgZksG8kCx7zsx3B7JxrqQKQPVpecOjfZQtqAPQV14yWt7ErvDxc6u1jj1npj5smhSmxnM6SVnMDMliZkgG80Ky7DUz50r0+Cap+jQwBw0w74pwzsfUBhJ/O4aSmtPyYvzQZ2BEnXXsNTMNYdOksGPHjildAqkMM0OymBmSwbyQLHvNzPs7MlB1Ybi8aT0DObx4Gzh9MhcHdlefDumkc8CE6b2sjpZnr5lpCJsmIiIiImpXdp8txvYzRQAAP1dH3NIvROGK2j99pQFrLzotb8SkuqflqRmbJoX5+/srXQKpDDNDspgZksG8kCx7y0yVyYx3//x78Ie7BobBXeegYEUdw6Zfj6OkqPq0vM6x/kiwclpeDXvLTFOwaVKYs7Oz0iWQyjAzJIuZIRnMC8myt8z8dOg8Mor1AIBewe4YE8dJbFtb6onzOLjnktPyGrh+zN4y0xRsmhSWmZmpdAmkMswMyWJmSAbzQrLsKTPny6rw1b5zAACtBpg3hIM/tDZ9pQHrfjpsuT1yUld4N3L9mD1lpqnYNBERERFRu/DhzgxUGs0AgKu7ByDWv/1cU2OvNv5yzHJaXmScP3o3cFqemrFpUlh0dLTSJZDKMDMki5khGcwLybKXzOzPLEFiSiEAwNvFEbMv5+APrS3l+Hkc2psBANA5N35aXg17yYwMNk0Ky83NVboEUhlmhmQxMySDeSFZ9pAZo1ngnYsGf7hjQCg8nR0VrKj9q6wwYN1Pf4+WN/KqbvDyadqw7vaQGVlsmhRWUlKidAmkMswMyWJmSAbzQrLsITMrD5/HmYLqU8S6BrphQryfwhW1b1V6I9avOIzSCwNuRHXxx2X9w5v8eHvIjCy24ApzdOSvgOQwMySLmSEZzAvJUjoz+eUGfPFXFgBAA+D+IeHQcvCHVlFeVoW/tp/Bvj/PQF9pBADonB0x/tqmnZZXQ+nMNIf6Km5n4uPjlS6BVIaZIVnMDMlgXkiW0pn5eHcmyg3Vgz9M7OqProHuitbTHpUUVWLP1lQk7UqH0WCyLNdogPHTejb5tLwaSmemOXh6nsKOHDmidAmkMswMyWJmSAbzQrKUzMzhc6X4/WQ+AMDT2QF3DAhVrJb2qCC3DGt/PISP/i8Re7edsTRMWq0GvS4Pw5yHh6FbgvyAG2rcz/BIExERERGpjskssPiiwR9mXx4Cbxd+tLWFnMxi7ExMwYlD5yDE38sdnbS4rH84BgyPlj66pHZMlsL8/HihIslhZkgWM0MymBeSpVRmfjmWi+S8CgBArL8rJncLUKSO9iT9dAF2JqYg9fj5Wst1zo7oe0Vn9BsSCXcP5xa/jhr3M2yaFObmxknXSA4zQ7KYGZLBvJAsJTJTWGHA0j1Zltv3XxEOBy0Hf2gOIQROn8zFzk0pSD9dUOs+V3cd+g+LQp9BEXB2cbLZa6pxP8NrmhSWnp7e+EpEF2FmSBYzQzKYF5KlRGY+3ZOF0qrq62vGxvmiZyePNq+hPdBXGrH8o134YeneWg2Tp48LRk/pjrlPjMCgETE2bZgAde5neKSJiIiIiFTj+PkyrDmeBwBwc9LiroFhClekTkIIrPnhYK1myS/QHQNHxKB7QggcHHhs5WJsmhQWGRmpdAmkMswMyWJmSAbzQrLaMjNmIbB4ezpqxia4tV8I/NxsexSko9i77TROHs4GADi7OGLC9F7o0iMYmjY4zVGN+xm2kAorKChofCWiizAzJIuZIRnMC8lqi8wUVRqxJbUQr206g+PnywEAkT4uuKZnYKu/dnuUfroAiWtOWG5fdV1vxPfq1CYNE6DO/QyPNCmsuLhY6RJIZZgZksXMkAzmhWS1RmZK9EYcyCrFgaxSJGWVICW/ss469w0JhyMHf5BWVqLHqmX7IczVx+sGjohGbPegNq1BjfsZNk0Kc3BwULoEUhlmhmQxMySDeSFZtshMWZUJB8+VIimzBElZpUjOq4CoZ12tpvq0vL6hni1+3Y7GbDJj9fIklJXoAQARMX4YNrZLm9ehxv0MmyaFde3aVekSSGWYGZLFzJAM5oVkNSczRrPA/swS7L/QJJ3MLYe5ni5JAyAuwBUJIZ5ICPFAr04ecNep70O3Pdj2+ymcTckHALh7OuPqGxKgVWDABzXuZ9g0Kezo0aPo3r270mWQijAzJIuZIRnMC8mSzcyhc6V4a+tZpBXWPeWuRoyfKxJCPdAnxBOXdXKHhzM/srZU8tEc7ExMAQBotBpMuTEB7p4tn6i2OdS4n2ECFSZEfQefiaxjZkgWM0MymBeS1dTMlFWZsGRXJlYfy61zX6SvC/qEeCAhxBO9Qzzg5cKPqLZUmF+OX787YLl95YR4hEf7KVaPGvczTKTCfHx8lC6BVIaZIVnMDMlgXkhWUzKzNbUQi/88i/xyo2VZ10A3zLwsCL1DPODrymHDW4vRYMKqr/dDX1m97bv0DEb/YVGK1qTG/QybJoV5eXkpXQKpDDNDspgZksG8kKyGMpNbVoXF29Ox/UyRZZmLoxa39w/BNT0C4cDR71rdhtVHkZ1ZPVqdj78bJs7oBY1G2e2uxv0M52lSWFpamtIlkMowMySLmSEZzAvJspYZsxD4+ch53PX90VoN08AIL3w0ozum9wpiw9QGDv2VgQO70wEAjk5aXHNzXzi7KH9UT437GR5pIiIiIiKbOV1Qgbe2nMWRnDLLMh8XR9x3RThGxPgofpSjozifVYLfVxy23B57TU8EhnCY9uZi06SwiIgIpUsglWFmSBYzQzKYF5JVk5kqoxnLkrKxPCkbxovGD58Q74e7B4ZxcIc2pK80YOXX+2A0mgEAvQeEo1e/MIWr+psa9zNMr8JKSkrg6cmun5qOmSFZzAzJYF5IVklJCVJLNXhraxrSi/SW5WFeznhoWAT6cBLaNiWEwJofDqEwrxwAEBzqhdFX29fw3mrcz/CaJoUVFhYqXQKpDDNDspgZksG8kIxSvREf7D2Px385aWmYHDTATQnBeH96NzZMCtiz9TROHs4GADi7OGLKzX3g6GRfkwGrcT/DI00K43m9JIuZIVnMDMlgXqiphBCYvy4Fh7PNlmXdAt3wyPDOiPZzVbCyjis9NR+b156w3L7q+t7w8XNTsCLr1LifYdOkMLXNhkzKY2ZIFjNDMpgXaqpj58txOLt6sAdXJy3u6B+Kq7sHcFQ8hZSV6LHqmySIC9eTDRoRg9huQQpXZZ0a9zM8PU9hx48fV7oEUhlmhmQxMySDeaGm+uNUvuX//zE4HNf05LxLSjGbzFi9PAllJdWnSEbE+GHo2DiFq6qfGvczbJoUZjKZlC6BVIaZIVnMDMlgXqgpDCYzNiUXAACctMDwaB9lC+rgEtccx9mU6ibW3dMZV9+QAK2D/X7MV+N+xn63ZgehxhmRSVnMDMliZkgG80JNsSe9BMX66g++fYOc4a6zr4EGOpKDe9Oxd9sZAIBWq8GUm/rA3dNZ4aoapsb9DJsmhfn5+SldAqkMM0OymBmSwbxQU1x8at74rgEKVtKxZZwpuGQC2x4Ij/JVsKKmUeN+hk2Twk6fPq10CaQyzAzJYmZIBvNCjSmrMuHPtCIAgLeLI3z15xWuqGMqLqzAyi/3wWSqHvih7+DO6D1AHZPGqnE/w6aJiIiIiJpsc2ohDBc+qI+M8eXgDwowVJmw4st9KC+rAgB0jvHDyMndFK6qfbOrpmnz5s2YMmUKQkNDodFosGLFikYfo9fr8eyzzyIyMhLOzs6IiorCJ5980vrF2khYWJjSJZDKMDMki5khGcwLNWbDRafmjYnzZWbamBACa344iJzMYgCAt58rptzcBw52PPDDpdSYGbvaumVlZUhISMA777zT5Mdcf/31+OOPP7BkyRIcP34cy5YtQ9euXVuxStuqqKhQugRSGWaGZDEzJIN5oYbklFYhKasUABDu7YyugW7MTBvbuSkFxw+eAwA46Rxw7a394OqmU7gqOWrMjF1Nbjtp0iRMmjSpyeuvWbMGiYmJSElJsVxQFhUV1UrVtY78/Hx06tRJ6TJIRZgZksXMkAzmhRqyIfnvo0yj4/yg0WiYmTZ06kg2tq4/WX1DA1x9QwICgj2VLaoZ1JgZuzrSJOvnn39G//798dprryEsLAzx8fF4/PHHm9S9lpSUoLi42PJPr9e3QcVERERE6iSEwB8nCyy3x8Ta/yht7cn5cyX45dsDltvDx8cjtnuQghV1LHZ1pElWSkoKtm7dChcXF/z000/Izc3Ffffdh7y8PHz66acNPrZXr14oLy+33J4zZw4eeOABhISEIDk5GQAQHBwMIQRycnIAAF26dEF6ejoqKirg4uKCiIgInDxZ3e0HBQVBq9Xi3Lnqw6WxsbE4d+4cysrKLNda1cx+HBAQAJ1Oh8zMTAghUFFRgfPnz6O0tBROTk6Ii4vD0aNHAVQPyejq6oqMjAwA1UfS8vPzUVxcDAcHB3Tt2hVHjx6FEAI+Pj7w9PTE2bNnAQCdO3dGcXExCgsLodFo0L17dxw/fhwmkwleXl7w9fXFmTPV4/qHh4ejvLwc+fnV3yD16NEDJ06cgNFohKenJwICApCamgoACA0NhV6vR15eHgCgW7duSElJQVVVFdzd3REcHIyUlBQAQEhICIxGI86frx5ZJz4+HmlpaaisrISrqyvCwsJw6tQpy/YGgOzsbABAXFwcMjIyLNu7c+fOOHHiBAAgMDAQjo6OyMrKAgDExMQgOzsbZWVl0Ol0iImJwbFjxwAA/v7+cHZ2RmZmJgAgOjoaubm5KCkpgaOjI+Lj43HkyBHL9nZzc0N6ejoAIDIyEgUFBfVuby8vL6SlpQEAIiIiUFJSUu/29vPzs4wWExYWhoqKCsv27t69O06dOgWDwQAPDw8EBgbW2t5VVVXIzc21bMPk5GTo9Xq4u7ujU6dOlsx26tQJZrO5VmbPnj1r2d7h4eG1MqvRaCzbOzY2FllZWSgvL4ezszMiIyMb3N45OTkoLS21ur1dXFysZvbS7e3r6wsPD49amS0qKkJRURG0Wi26deuGY8eOwWw2w9vbG97e3rW2d2lpKQoKCupk1tr2rqystJpZDw8PBAUFNZjZM2fOQK/Xw83Nrc33ETWZbck+AgCOHDnCfUQH2Ud07doVp0+fbvY+QgiBI0eOcB/RgfYRTf0c4RQYiTOFldV/fz6O8HIw4siRZAghUFxczH0EWm8f4ezkht9/TIWhqnpurNju/ugc72p53bbcR9jic0R0dDTS0tLsYh9Rk9nGaIQQoklrtjGNRoOffvoJ06ZNq3ed8ePHY8uWLTh37hy8vb0BAD/++CNmzpyJsrIyuLq61nmM0WhEYmIiYmJioNX+faDN2dkZzs5tPxHYyZMn0aVLlzZ/XVIvZoZkMTMkg3mh+nywIx0/HKpuXh4cGoGru1fPz8TMtC6TyYzvPtmN9NTqLwCCw7xw49xBcHJS74TC9pQZk8mEpKQkjBgxAo6O9R9PUvWRppCQEISFhVkaJqC60xZCID09vcFfhqenJxwclA+bwWBQugRSGWaGZDEzJIN5IWtMZoGNydUf2p20GlwZ7WO5j5lpXRtWH7U0TO6ezph2Sz9VN0yAOjOj6muahg4diszMTJSWllqWnThxAlqtFuHh4QpW1nQeHh5Kl0Aqw8yQLGaGZDAvZM2+zBLkVxgBAAMjvODl8vf37sxM69m/Iw1JO6tPPXNw0OCaWX3h6e2icFUtp8bM2FXTVFpaiv3792P//v0AgNTUVOzfv99y7uHTTz+N2267zbL+zTffDH9/f8yZMwdHjhzB5s2b8cQTT+COO+6wemqePQoMDFS6BFIZZoZkMTMkg3kha/6oNTeTX637mJnWkZaShw2rj1puj7u2F0I7+yhXkA2pMTN21TTt2bMHffv2Rd++fQEAjz76KPr27Yt///vfAICsrCxLAwVUd6nr169HYWEh+vfvj1mzZmHKlCn43//+p0j9zVFzkR5RUzEzJIuZIRnMC12qwmDC1tNFAAAPnQMGdvaqdT8zY3uF+eVY9fV+mM3VQw/0HxaFXv3UNyFsfdSYGbu6pmnkyJFoaFyKpUuX1lnWrVs3rF+/vhWrIiIiIuq4tp0ugt5oBgBcGeMDnYNdfefe7lTpjVjxxV+oKK++7icqPgBXTuyqcFXE1CssNDRU6RJIZZgZksXMkAzmhS518YS2Yy85NQ9gZmxJmAV+/fYAcrOrr9f3C3DH1TckQKvVKFyZbakxM2yaFFZVVaV0CaQyzAzJYmZIBvNCF8srN+CvjBIAQLCHDj2C3eusw8zYztbfT+LU0eo5kpxdHDHttn5wcXVSuCrbU2Nm2DQprGaiMaKmYmZIFjNDMpgXutim5AJcuKwGY+J8odXUPeLBzNjG4X0Z2LmpelJfjQa4+sYE+AXUbVLbAzVmhk0TEREREVnV0Kh5ZDsZZwqw7sdDltujJndDdLz6Rphrz9g0KaxrV17YR3KYGZLFzJAM5oVqnC6owKm8CgBA10A3RPhYnx+ImWmZooJyrPhyH0ym6kN6CQMj0PeKSIWral1qzAybJoWdPn1a6RJIZZgZksXMkAzmhWr8carA8v+jY33rXY+Zab4qvRE/ff4XKsqqr/HpHOuP0VO6Q2PlNMj2RI2ZYdOkML1er3QJpDLMDMliZkgG80IAYBYCGy6cmqfVACMbaJqYmeYxmwVWf5NkGSnPN8ANU2/uA4cOMKS7GjPT/n8rds7dvX1e4Eeth5khWcwMyWBeCAAOZpXifFn1PEH9w73g28AIbsxM82xecxwpx88DAFxcnXDtbZe3y5HyrFFjZtg0KaxTp05Kl0Aqw8yQLGaGZDAvBNQ+Na+xASCYGXkH96Rjz9bTAACtVoOpN/dptyPlWaPGzLBpUlhycrLSJZDKMDMki5khGcwL6Y1mbE6tbprcnLS4ItK7wfWZGTlpKXlYv+Kw5faYKd3ROdZfwYranhozw6aJiIiIiCx2phWh3GAGAAyL8oGLIz8u2kpBXhl+/mo/zBcmv+o3JBIJgzorXBU1Bf8KFKbGw5OkLGaGZDEzJIN5od8l52ZiZpqmssKAnz7/C5UV1deKRcUHYOQk9Q29bQtqzAybJoWZzWalSyCVYWZIFjNDMpiXjq2o0ojdZ4sBAAFuTugd4tHoY5iZxplNZqxath/558sAAP5BHphyYwK0HWCkPGvUmJmO+ZuyIzk5OUqXQCrDzJAsZoZkMC8dW2JKAS7MsYpRsb5w0DY+XxAz07gNvxzDmVN5AABXNydce1s/OLt0jJHyrFFjZtg0EREREREA4I+LTs0b26XxU/Oocfv+PIP9O9IAAFoHDa6Z1Rc+fm4KV0Wy2DQprEuXLkqXQCrDzJAsZoZkMC8dV0ZRJY7mlAMAYvxcEO3n2qTHMTP1O30yFxt+OWa5PX5aT4RHsxlVY2bYNCns7NmzSpdAKsPMkCxmhmQwLx2XzNxMF2NmrMvLKcWqZfshLoyUN+DKaPS6PFzhquyDGjPDpklhlZWVSpdAKsPMkCxmhmQwLx2TEAIbkqtPzdOg+nqmpmJm6iotrsRPn/8FfaURABDXPQhXjo9XuCr7ocbMOCpdQEfn6tq0Q99ENZgZksXMkAzmpWM6mlOOzOIqAECfUE8EuOua/FhmpraU4+fx23cHUFFePbR4YIgnrrq+NzRNGFSjo1BjZtg0KSw8nIdpSQ4zQ7KYGZLBvHRMtedmavpRJoCZqWE0mrFl7XHs3XbGsszT2wXX3toPOmd+5L6YGjPD0/MUdvLkSaVLIJVhZkgWM0MymJeOp6DCgMSU6uuZnB00GBblI/V4ZgbIP1+Kr9/7s1bDFNMtELfePwRePuo7qtLa1JgZtr1EREREHZTJLPDqxjMo0ZsAAMOjfeCmc1C4KvUQQuDQ3gz8seoojIbqbejgqMWISV3Rd3BnaDQ8Ja+9YNOksKCgIKVLIJVhZkgWM0MymJeO5at957AvswQA4OfqiLsGhkk/R0fNTGWFAetXHMbxg+csy/wC3XH1jQkICvFSsDL7p8bMsGlSGL+BIFnMDMliZkgG89Jx7Ekvxlf7qj/wazXAM6Oj4OfmJP08HTEzmWkFWL38AIoLKizLeg8Ix8jJ3aDT8eN1Y9SYGV7TpLDs7GylSyCVYWZIFjNDMpiXjiGntAqvbjwNceH27f1D0DvEs1nP1ZEyYzYL7NiYjGUf7rI0TM4ujphyUx+Mv7YXG6YmUmNm+JslIiIi6kCMZoGXN5xG8YXrmAZFeOH63sEKV2X/Sooq8eu3B3A29e+RBsMifXDV9Qnw9uVgD+0dmyaFxcbGKl0CqQwzQ7KYGZLBvLR/H+/KwJGcMgBAsIcOT4yIhLYFp0t1hMycOpqDNd8fRGVF9dxLGg0weFQsrhgVC60DT9ySpcbM8LessKysLKVLIJVhZkgWM0MymJf2bWtqIX48dB4A4KTV4F9jouDl0rLv0NtzZoxGM/74+QhWfPGXpWHy9HbB9XcOxNCxXdgwNZMaM8MjTQorLy9XugRSGWaGZDEzJIN5ab8yivT4v81/zyN0z+AwdA10b/HzttfMmM0Cq7/Zj1NHcizLuvQIxvjpPeHqplOwMvVTY2bYNCnM2dlZ6RJIZZgZksXMkAzmpX3SG8144Y9UlBvMAICRMT6Y0j3AJs/dHjMjhMCG1UctDZOjoxYjJ3dDwsAIVY78Zm/UmBk2TQqLjIxUugRSGWaGZDEzJIN5aZ/e2Z6OlPzq0d4ivJ3x8DDbTbzaHjOza3Mq9u9IAwBotRpMu7UforrYpskkdWaGJ2Iq7MSJE0qXQCrDzJAsZoZkMC/tz7oTeVhzIg8A4Oyoxfyx0XDTOdjs+dtbZo7sz8SWtX//TBOm92LDZGNqzAybJiIiIqJ2KjW/Am9vO2u5/dDQCERxeOx6nTmVizU/HLTcHja+C3r2C1OwIrIXbJoUFhgYqHQJpDLMDMliZkgG89J+lFWZ8MIfqdCbqqewvaqbP8Z28bP567SXzORkFmPlV/tgvrC9EgZGYNCIGIWrap/UmBk2TQpzdORlZSSHmSFZzAzJYF7aByEE3tqShvQiPQAgzt8V9w0Ob5XXag+ZKSqowA+f7UXVhQl/Y7sHYczUHhz0oZWoMTNsmhSmxnHqSVnMDMliZkgG89I+rDySi8TUQgCAu84B/xoTDZ1j63zsU3tmKsqr8MPSPSgrqW4wQyK8cfUNCdBq2TC1FjVmhk0TERERUTtyNKcMH+7MsNx+/MrOCPVS3xDPbcFoMGHll/uQf74MAODr74Zrb7scTjYcKIPaBzZNCouJ4bmyJIeZIVnMDMlgXtStuNKIlzakwmiuvi5n5mVBGBrl06qvqdbMCLPAr98dQPrpAgCAm7sOM+b0h5s7J65tbWrMDJsmheXk5DS+EtFFmBmSxcyQDOZFvaqMZry66TRySg0AgJ7B7rhjQGirv64aMyOEwMZfj+HEoWwAgJPOAdNnXw4fPzeFK+sY1JgZ9V2F1c6UlpYqXQKpDDNDspgZksG8qFNeuQEL16fg+PlyAIC3iyOeHR0Fxza4LkeNmdmz9TT+2n4GAKDRajDlpj7oFO6tcFUdhxozw6ZJYTodDwGTHGaGZDEzJIN5UZ+TueVYsD4FuWXVR5icHbV4dnQUAtroNDO1ZeZYUhYSfztuuT1+Wk/EdFXfENhqprbMAGyaFKfGczpJWcwMyWJmSAbzoi6bUwvw+qYzlrmYAt2d8Pz4GMT6t91pZmrKTFpKHn77/oDl9pAxcbisf+sMxU71U1NmavCaJoUdO3ZM6RJIZZgZksXMkAzmRR2EEPjyryy8+MdpS8PUI8gdi6/p2qYNE6CezJw/V4KVX+6D6cL2uqx/OK4YHatwVR2TWjJzMR5pIiIiIlKRSqMZbySesczDBABju/jh4WER0Dnw+3BrSooq8cPSPdBXGgEA0V0DMe4aTl5LTcemSWH+/v5Kl0Aqw8yQLGaGZDAv9i23rAoL1qfgZG4FAEAD4M6BobjusiDFGgB7z4y+0oAflu5BaXH15LXBYV6YcmMCtGwwFWPvmbGGTZPCXFxclC6BVIaZIVnMDMlgXuzX8fNlWLA+Bfnl1UdLXJ20eGpkFK6IVHbUN3vOjMloxoov9yE3u3q0Nm8/V0yffTl0zvwIrCR7zkx92GIrLCMjo/GViC7CzJAsZoZkMC/2aWNyPh5bfdLSMAV76PDWlHjFGybAfjMjhMCaHw/ibEo+AMDVzQkzb+8Pdw9nhSsje81MQ9hmExEREdkpsxD4fG8Wvt6fbVnWq5M7/j0mGj6uTgpWZv+2rjuJo/uzAACOjlpce9vl8A1wV7gqUis2TQqLiopSugRSGWaGZDEzJIN5sR8VBhNeTzyDraeLLMsmxvvjgaHhcLKj63HsMTP7d6ZhZ2JK9Q0NMPnGBIR29lG0JvqbPWamMfbzF9dB5efnK10CqQwzQ7KYGZLBvNiHnNIqPLr6pKVh0mqAewaF4ZHhEXbVMAH2l5nkozn44+cjlttjru6OLj2CFayILmVvmWkKHmlSWHFxsdIlkMowMySLmSEZzIvyjp8vw7/XpaCgovr6JTcnLZ4ZHYWBEcpfv2SNPWUm62whVn2zH6J6KiYMGB6NvldEKlsU1WFPmWkqNk0Kc3Tkr4DkMDMki5khGcyLsradLsSrG/+esDbUS4fnxsUg0tdV4crqZy+ZKcwrx4+f/wWjwQwA6Na7E66cEK9wVWSNvWRGhvoqbmfi4/nHTHKYGZLFzJAM5kUZQgj8dPg8PtiRgQsHSdCrkzsWjo2Bl4t9f1yzh8yUl1bh+6V7UFFWBQAIj/bFxJm9odFy8lp7ZA+ZkWVfJ8V2QEeOHGl8JaKLMDMki5khGcxL2zOZBd79Mx3vX9QwjYr1xauT4uy+YQKUz4yhyoSfvtiLwrxyAIB/kAem3dIPjo78mGuvlM5Mc9j/XyIRERFRO1VhMOHlDaex8+zf13jM6tsJt/XrBI2GR0kaYzYL/PJtErLOVg+Y4e7pjBm3Xw4XDsdONsamSWG+vr5Kl0Aqw8yQLGaGZDAvbSevzID565JxKq8CAOCgAR4Z3hnj4/0VrkyOUpkRQmDj6qM4dSQHAOCkc8CM2ZfDy8d+r/+iamrcz7BpUpiHh4fSJZDKMDMki5khGcxL20jNr8C/1ibjfJkBAOCuc8C/x0Sjb5inwpXJUyoze7aexr4daQAArVaDa2b1RVColyK1kBw17md4sqfCzp49q3QJpDLMDMliZkgG89L69qQX45FVJywNU7CHDm9N6aLKhglQJjPHkrKQ+Ntxy+3x1/ZEVJeANq+DmkeN+xkeaSIiIiJqI78ey8X/tp2F+cKID10D3fDcuBj4ufEanKY6m5KP374/YLk9dGwcel0ermBF1BGwaVJY586dlS6BVIaZIVnMDMlgXlqHWQh8uicLy5OyLcuGRHrjqVFRcFH5KG9tlRkhBNKS8/Dz1/thujCP1WX9wzF4VGybvD7Zjhr3M2yaFFZUVKTK8zpJOcwMyWJmSAbzYntVRjNeTzyDxNRCy7LpvQJx98AwOLSDeYRaOzMlRZU4/FcGDu3NQGF+uWV5dHwAxl3Tg6MMqpAa9zNsmhRWVFSEsLAwpcsgFWFmSBYzQzKYF9sqqjRiwboUHMkpAwBoNcB9V4Rjao9AhSuzndbIjNFoRvLRHBzcm44zJ3MhRO37QyK8MeWmPtA6qPsoXUelxv0MmyaFabX8Yyc5zAzJYmZIBvNiO5nFejyzJhmZxXoAgIujFs+MjsLgzt4KV2ZbtsxMTlYxDu3JwJH9maisMNS+UwNExvqj1+VhiO/ZCQ4qP62xI1PjfkYjxKW9e/tmNBqRmJiIhIQEODg4KF0OERERtUPJeeV4Zk0yCiqMAAA/N0e8MD4WXQLcFK7M/lRWGHB0fyYO7c1AdmZxnfu9fF3Rq18YevYLg7cv52Ai2zKZTEhKSsKIESPg6Fj/8SQeaVLYsWPH0K1bN6XLIBVhZkgWM0MymJeWO3iuFP9el4KyKhMAINLHBS9NjEWQh07hylpHczIjzAJnkvNwaG86Th7JgclornW/o6MWXXoF47LLwxER7QdNO7j2i/6mxv0MmyaFmc3mxlciuggzQ7KYGZLBvLTMn2eK8NKGVFRdGN2te5AbXhgfCy+X9vuRSzYzVXojfv56P06fzK1zX3CYFy67PBzdEkLg4sph2NsrNe5n2u9fsEp4e7ev85qp9TEzJIuZIRnMS/OtO5GHN7ekWeZg6h/uifljouHq1L4vB5DJTHlpFX78fC/OpRdZlrm6OaFH31D06heOwBB1TvBLctS4n2HTpDA1hoaUxcyQLGaGZDAvzfPdgWx8tCvTcntUrC8ev7IznDrA6G5NzUxxYQW+/2QP8nOrRxJ0dnHEuGk90aVHMAd16GDUuJ9hQhWWlpamdAmkMswMyWJmSAbzIkcIgY93ZdRqmK7pEYh/jozsEA0T0LTM5GaX4uv3d1gaJg8vZ9w4dxC69Q5hw9QBqXE/06wjTSUlJSgsLERERIRlWWZmJt5//33o9XrMmDEDAwcOtFmRRERERPbGZBZ4a2sa1p7Ityy77fIQzOoTzAlXL5KZVoAfP/vLMoS4b4AbZs4ZwJHwSFWa1TTNnTsXqamp2LFjBwCguLgYgwcPRnp6OrRaLRYtWoQ1a9Zg5MiRtqy1Xbq48SRqCmaGZDEzJIN5aZoqoxkvbzyN7Weqr83RALh/SDimtKNJa5uqocykHD+Pn7/eD6OheiTB4DAvzJjdH27tdCRBaho17meadTx069atuPrqqy23v/zyS2RmZmL79u0oKChA79698eKLL9qsyPastLRU6RJIZZgZksXMkAzmpXFlVSY8sybZ0jA5ajV4ZnRUh2yYgPozc2R/JlZ88ZelYeoc648b7hrIholUuZ9pVtOUm5uLsLAwy+2ff/4Zw4YNw+DBg+Hp6YnbbrsNSUlJNiuyPSsoKFC6BFIZZoZkMTMkg3lpWEG5AY//chIHzlV/6HNx1OLFCTEYEeOrcGXKsZaZvdtO49dvD8B8YSjB+F6dMH325dA5cwwyUud+pllNk4+PD86dOwcAqKiowJYtWzB+/HjL/Y6OjigvL7dNhURERER2IKtEj0dWn0RyXgUAwMvZAa9PjkO/MC+FK7MfQghsWXcCG385ZlmWMCgCV9+YAEcO+EAq1qx2f8iQIXj33XfRrVs3rFmzBpWVlbjmmmss9584caLWkSiqX48ePZQugVSGmSFZzAzJYF6sS82vwNNrTiG/3AgACHR3wiuT4tDZx0XhypRXkxmzyYz1K4/g4J50y31XjI7FkDFxHBiDalHjfqZZTdN//vMfjB8/HjNmzAAAPPbYY+jZsycAwGQy4bvvvsPEiRNtV2U7duLECcTHxytdBqkIM0OymBmSwbwA5VUmpOZXIDm/Ain5FUjJq/5/g6n6VLPOPi54eWIsgnhtDoDqzMREx+KX5Qdw8kh29UINMPrq7uh3RaSyxZFdUuN+pllNU1xcHI4fP44jR47A29sbUVFRlvvKy8uxePFiJCQk2KrGds1oNCpdAqkMM0OymBmS0ZHyIoRAdmmVpTFKudAkZRZX1fuYroFueGlCLLxceG1OjYryKny/dA/SU6uvU9E6aHDVzN7olhCicGVkr9S4n2n2X7yTk5PVxsjT07PWqXrUMC8vngdNcpgZksXMkIz2nBeTWWDb6UIcPFeGlPwKpOZXoLTK1KTHhnjqMCDCC3cOCIWrk0MrV6oeZSV67N6Qi8LcSgCAk84B18zqi6guAQpXRvZMjfuZZjdNxcXFePfdd7Fx40bk5OTggw8+wMCBA5Gfn4+lS5di6tSpiIuLs2Wt7ZKfn5/SJZDKMDMki5khGe01LxUGE17deAZ/phU1uJ6zgwZRfq6I8XNFrL8rYv1cEeXnCncdG6VL6SuN+HbJbkvD5OrmhOmzL0dIhI+yhZHdU+N+pllNU3p6OkaMGIGzZ8+iS5cuOHbsmGW8dT8/P3zwwQc4c+YMFi1aZNNi26PTp0+r8mI4Ug4zQ7KYGZLRHvOSX27A/HXJOJlbUWt5gJsTYi40RjH+1Y1SqJczHLQctKAxwizw67dJyMup/vzn6e2CmXP6wz/IQ+HKSA3UuJ9pVtP0xBNPoKSkBPv370dQUBCCgoJq3T9t2jSsXr3aJgUSERERNdfpggrMX5uC7NLq65TcnLR4eFhn9Avz5HVJLbDtj1NIPnYeAOCk0+L6uwbA199d4aqIWk+z9hbr1q3DI488gh49eiAvL6/O/TExMTh79myLi+sIODQ7yWJmSBYzQzLaU172ZZbg+d9TUXbhuqVAdye8OCEW0X6uClembicOncOOjckAAI0GGDutKxsmkqLG/UyzZhmrqKhAYGBgvfeXlJQ0u6COprKyUukSSGWYGZLFzJCM9pKXdSfy8MxvpywNU5y/K/43tSsbphY6n1WC374/aLk9YlJXBIRwriqSo8b9TLOaph49emDz5s313r9ixQr07du32UV1JNaO1BE1hJkhWcwMyVB7XoQQ+GxvFv5vcxouTKuEQRFeeOPqLvB3d1K2OJUrL6vCT1/+BcOFRrRH31BcPjRK9ZmhtqfGzDTr9LyHH34Ys2fPRu/evXHdddcBAMxmM06dOoXnnnsOf/75J3744QebFkpERETUkCqTGf/dkoY/ThVYlk3tEYB/DA7n4A4tZDaZsXrZfhQXVA+mERzmhXHTekKj4XaljkEjhBDNeeBLL72EhQsXQggBs9kMrVYLIQS0Wi1efPFF/POf/7R1rTZhNBqRmJiIhIQEODgoP3xozbYjaipmhmQxMyRDrXkp0Rvx3PpUHDhXPZqbBsDcQWGY3iuQH+xtYMPqo/hr+xkAgJuHDrfOGwJP7+rT8tSaGVKOPWXGZDIhKSkJI0aMgKNj/ceTmj1szLPPPotbb70VP/zwA06dOgWz2YzY2FhMnz4dMTExzX3aDiclJYXzWZEUZoZkMTMkQ415ySrW49m1yUgv0gOonmvpn6OiMCzKR9nC2omDe9MtDZPWQYNrZvW1NEyAOjNDylJjZlo01mbnzp3xyCOP2KqWDqmqqkrpEkhlmBmSxcyQDLXl5WhOGf69LgVFlUYAgLeLI54fH4PuQRzNzRYy0wrx+4rDlttjp/ZAWKRvrXXUlhlSnhoz06Km6dChQ/j1119x+vRpAEB0dDQmTpyIyy67zBa1dQgeHpwEjuQwMySLmSEZasrL1tRCvLrpNKoujPgQ4e2MFyfEIsTLWeHK2ofS4kqs/GofTBe2b59BndF7QESd9dSUGbIPasxMs5omvV6Pe+65B1988YXlOiag+vzEp556CrNmzcLHH38MnU5n02Lbo0snBiZqDDNDspgZkqGGvAgh8MOh8/hoZwZqLsxOCPHAv8dGw9OZE9bagtFgwsqv9qGspPqUx/AoX4y6upvVddWQGbIvasxMs67A+uc//4nPP/8c//jHP3D06FFUVlZCr9fj6NGjuPfee/Hll1/iySeftHWt7VJKSorSJZDKMDMki5khGfael9yyKjz/eyo+vKhhGhPni5cmxrJhshEhBNavPIKss0UAAE8fF0y9uS8cHKx/bLT3zJD9UWNmmrV3+fLLL3Hrrbdi8eLFtZZ37doV77zzDoqLi/Hll1/irbfeskWNRERE1MGZhcDqo7n4ZHcmyg1my/Jb+nbCrf06cYQ8G9r35xkc/isDAODopMW0W/rBzYNnD1HH1qymyWAwYPDgwfXeP2TIEKxatarZRXUkISEhSpdAKsPMkCxmhmTYY15OF1TgrS1ncSSnzLLMx8UR9w8Nx5XRvg08kmSdOZWHjb8et9yeOOMyBId6NfgYe8wM2Tc1ZqZZp+dNmDABa9eurff+NWvWYPz48c0uqiMxGo1Kl0Aqw8yQLGaGZNhTXqqMZizdk4n7fjpeq2Ga1NUfH8/szobJxgrzy7Fq2X4Ic/WJjwNHRKNb78Y/3NpTZkgd1JiZJh1pys/Pr3X7hRdewPXXX4/p06dj3rx5lnHWT548iXfeeQdnzpzB8uXLbV9tO3T+/HkEBgYqXQapCDNDspgZkmEveUnKLMFbW88io1hvWRbu7YyHhkYgIdRTwcrapyq9ESu+/AuVFQYAQHR8AIaNi2/SY+0lM6QeasxMk5qmgICAOucKCyFw8OBBrFy5ss5yAOjZs6cqu0giIiJSTnGlER/vysSaE3mWZY5aDW5ICMZNCcHQOTbrJBlqQFmJHr/9cBC550oBAL4Bbph8QwK0Wl4nRq1EiMbXsTNNapr+/e9/8wLLVhIf37RvcYhqMDMki5khGUrlRQiBTSmFeO/PdBRW/v2la48gdzw8PAJRvq6K1NWeCbNA0u6z2LL2BPQXtrnO2RHTbukHF1enJj8P9zEkoyp1JwLXPANT+Jdw8FbPtU1NapoWLlzYymV0XGfOnEFsbKzSZZCKMDMki5khGUrk5VyJHm9vS8fu9GLLMjcnLe4cEIrJ3QOg5Re3NpeTVYz1Kw5bhhUHABdXJ1x9YwL8g+QmHuU+hppCGKtQsvY1lP3+FiDMKFr2AHzv+U41B2ZsMqFBRUUFAMDVld8CydLr9Y2vRHQRZoZkMTMkoy3zYjIL/HT4PD7bmwW98e9hxIdFeeO+K8IR4M5hrm2tSm/E9j9OYe/2M5YBHwCgZ79QjJjYrVlDi3MfQ40xnDuOwi/vhTE9ybLMrC+FqCiGxs1bwcqartlNU1paGhYsWIBff/0Vubm5AKqvfZo8eTIWLFiAyMhImxXZnrm5uSldAqkMM0OymBmS0VZ5EULgv1vSsO7k34NNBbg5Yd6QcAyN8mmTGjqaU0ey8ceqoygpqrQs8wtwx9hpPdA5xr/Zz8t9DNVHmM0o3/oxilctBAwXcqd1hHHwPeg0cyE0WgdF65PRrKbp2LFjGDZsGAoLCzFu3Dh0797dsvzzzz/HqlWrsHXrVnTt2tWmxbZHahynnpTFzJAsZoZktFVefj6Sa2mYNACm9AjAnP6hcNep50OUWhQXVmDDqqM4dTTHsszBUYvBI2Mx4MpoOLZwcA3uY8gaU2EmCpc9gKrjGy3LHIPj4XPLBzAHdVNVwwQ0s2l66qmnoNVqsW/fPlx22WW17jt06BDGjBmDp556Cj/99JNNimzPkpOT0aNHD6XLIBVhZkgWM0My2iIvB8+V4v0d6ZbbT42KwqhYzrlkayaTGX9tP4Ntv5+C0WCyLI/q4o8xU3vA19/dJq/DfQxdqmL/ChR9+yhEeaFlmdvwufCasgAanSuOHDmiusw0q2lKTEzEY489VqdhAoBevXrh/vvvx5tvvtni4oiIiKh9yS2rwot/pMJ04XKa6y4LYsPUCjLTCrF+xWGcP1diWebmocPoyd3RtXcn1Vx8T+pirihG8Q9PomLPt5ZlWu8Q+Nz0Npy7jVawspZrVtNkMBgaHPTBzc0NBoOh2UV1JMHBwUqXQCrDzJAsZoZktGZeqkxmvPBHKgoqqoe37hvqgTsGhLba63VElRUGbFl7Akm7zwI14zxogD4DO2PY+C5SQ4k3FfcxBAD6U9tQ9NU/YCr4+yiyS59r4H3dm9C61/5iRI2ZaVbT1LdvX3z88ce466674O1de8SL4uJiLFmyBP369bNJge2dUOHkXqQsZoZkMTMkozXz8v6fGTiaUw4ACPJwwjOjo+HACVRtprS4El+/vwPFhX8P9BAU4olx03oiJMKn1V6X+5iOTRj1KPnlJZRtescyaa3GxRNeM1+H6+XXWT2qqcbMNKtpeu655zBx4kR069YNc+bMsUxqdvz4cXz22WfIy8vDO++8Y9NC26ucnBwEBAQoXQapCDNDspgZktFaeVlzPA+rj1WPtuvkoMG/x8bA28UmM58QAIPBhBVf7rM0TE46Bwwb1wV9B3eG1qFlAz00hvuYjsuQeQSFX8yFMeuIZZkubhh8Zr0LB9/weh+nxsw0a281evRo/Prrr3jiiSfw6quv1rqvT58++OKLLzBq1CibFEhERETqdvx8Gd7edtZy+6GhEYgP4DDVtiKEwNofDuFcevVEtZ4+Lrhp7iB4+XD+TLIdYdTDlHcGxrwzMOWmwph9AuU7vgRMVdUrOOjgOflZuI+cB422dRt1JUg3TQaDAUePHkW3bt2wb98+nDt3DmfOnAEAREZGolOnTjYvsj3r0qWL0iWQyjAzJIuZIRm2zktBhQHP/Z4Kw4WJVKf2CMD4+ObPCUR17UxMwbEDWQCqjzBde2u/Nm2YuI9pH4QQEOWFMOalwpSbWt0g5abClHsaxrzTMBdlWk6/u5RjSA/43PoBnEJ7Num11JgZ6aZJq9Xi8ssvxxtvvIEHH3wQnTp1YqPUAunp6YiOjla6DFIRZoZkMTMkw5Z5MZkFXt5wGrll1YND9Qx2xz2Dwmzy3FTt5OFsbF130nL7qut7IyjEq01r4D5GvYRRj9J1/4fKI7/DlJsKUVks9wRaB7iPuBeeVz0LjZNLkx+mxsxIN00ODg6IjIyEXq9vjXo6nIqKCqVLIJVhZkgWM0MybJmXj3dlICmrFADg5+aIf42JhlMrX1/TkeRkFePX7w5Ybg8f3wVderT9qGTcx6iTubwQBZ/chqpTWxtdV+PuB0f/KDgERF/474X/D46Hg4f8tUlqzEyzrml64IEHsHjxYtx5553w8/OzdU0diotL07tyIoCZIXnMDMmwVV42Jufjh0PnAQCOWg3mj4mGv5vth7vuqMpK9Pjp879gqKqetLZ7QggGjohRpBbuY9THmHcG+R9cD1POhaOUGi0c/CLg4B9laY4c/CPhGBANB/8oaF1te/RSjZlpVtNkMpng7OyM2NhYzJw5E1FRUXXmbdJoNHjkkUdsUmR7FhERoXQJpDLMDMliZkiGLfKSkleBNzenWW7/Y3AYegZ7tPh5qZrRaMbKr/ahpKh6pLxO4d4YP72XYhPWch+jLlVpf6Hgw5tgLq3+UkPrEQjfu7+GLvLyNqtBjZlpVtP0+OOPW/5/yZIlVtdh09Q0J0+eRI8ePZQug1SEmSFZzAzJaGleSvRGPPd7CvSm6gvGJ8T74eru6hpa2J4JIbB+xSFkphUCADy8nDHtlr5wcnJQrCbuY9Sj8uCvKPj8bsBQfXqcQ1AX+M1dDseAqDatQ42ZaVbTlJqaaus6iIiISOVMZoFXN55BVkn1EMTxAW54YEiEYkdA2qM9W0/j8F+ZAABHJy2m3doPHl7qO9WJ2l5Z4gcoXvGMZQQ8XexQ+N7xObTuvgpXpg7NapoiIyNtXUeHFRQUpHQJpDLMDMliZkhGS/LyxV9Z2J1ePfqWt4sj/j02GjpHDvxgK8nHcpC45rjl9qSZvdEpzFvBiqpxH2PfhNmE4hX/QvnmDyzLXC6fCZ+b3obG0VmRmtSYmRZNxZ2fn4/ff/8dp0+fBgBERUVhzJgx8Pfn/AtNpW2Hk39R62JmSBYzQzKam5dtpwvx9f7s6ufQAM+MjkKQh86WpXVoudkl+GV5EnBhmpwhY+LQ9TL7mPKF+xj7JarKUfDFPdAf/MWyzGP8Y/CY9IyiR4DVmJlmN00LFy7Ef/7znzpDj+t0Ojz55JN4/vnnW1xcR3Du3DmOQEhSmBmSxcyQjObk5WxhJV5PPGO5fdeAUPQN9bR1aR1WeVkVfvr8L1Tpq0fKi+/VCVeMilW4qr9xH2OfTCU5KPjoZhjS/qpeoHWA9/Vvwm3wrcoWBnVmpllN0wsvvIDnn38ekydPxv3334/4+HgAwPHjx7F48WK89NJLcHJywvz5821aLBEREdmXgnID5q9LRrnBDAAYGeODGZep79Qbe2UymvHz1/tQVFB94X5wqBcmzbwMGi2vE6P6GbNPIP/DG2DKq/4yQ+PsAd85S+HcbbTClamXRogLV4NJCAsLQ//+/bFy5Uqr90+ZMgV79+5FZmZmiwu0NaPRiMTERCQkJMDBQbmRZmro9Xo4OytzPimpEzNDspgZkiGTl/IqE5749SRO5lZ/oI/2dcFbU+PhquBIbu1J9Uh5h3FgdzoAwN3TGbfcdwU8ve1r4AfuY+yL/tQ2FCy5BaKiCACg9QmF39zlcArtqXBlf7OnzJhMJiQlJWHEiBFwdKz/eFKzTigsKirCxIkT673/qquuQklJSXOeusM5d+6c0iWQyjAzJIuZIRlNzYvBZMbzf6RaGqZAdye8ODGWDZMN7fvzjKVhcnDUYtotfe2uYQK4j7EnFXu/R/57MywNk2PYZQh4eJ1dNUyAOjPTrKZp6NCh2LlzZ73379y5E0OHDm12UR1JWVmZ0iWQyjAzJIuZIRlNyYtZCPzf5jT8lVH9BamnswNenhiLQHcO/GArp0/mYuMvxyy3J07vhZAIH+UKagD3McozVxaj5LdXUPjFXMBUPeS/c7cx8H9gNRx8QhWuri41ZqZZ1zS9//77mDhxIh555BHMmzcPMTExAICUlBQsXrwYO3bswJo1a2xaaHtlL4cmST2YGZLFzJCMpuTlo50Z2JhcAADQOWjw/PgYRPq6tnZpHUZach5+/np/zXQ6GDQyBt372N8H3xrcxyhDCIGqlD9RseMrVCathKgqt9zndsVseM18HRqHFg2U3WrUmJlmXdPk6ekJs9mMyspKAH8PG2g2V18E6uzsXOecQI1Gg6KiopbW22L2dk2TyWSyizpIPZgZksXMkIzG8vL9gWx8uKv6mmWtBlgwNgZXRCo/V1B7cXBvOtb/dBhmc/XHs7juQbhmVl+7HviB+5i2ZSrMQMXu5Sjf+TVMuSl17ve8egHcxzxo15NK21NmmnpNU7PazxkzZtj1L0JNjh8/jh49eihdBqkIM0OymBmS0VBefj+Zb2mYAOChoRFsmGxEmAW2rD+BXYmplmXRXQNx1Q297bphAriPaQvCqEflod9QseMr6I9vBIS51v0aFy+4Xj4TblfcBqfw3gpV2XRqzEyzmqalS5fauIxqmzdvxuuvv469e/ciKysLP/30E6ZNm1bv+ps2bcKoUaPqLM/KykKnTvYx4RsREVF7sCe9GG9s/nsuptsuD8GkbgEKVtR+GAwm/PbdAZw4lG1Z1veKzhh1VTdoHdQ3CSjZjiH9IMp3fYWKPd9BlBfUuV8XPwJug2bB5bLJ0Oh4imxrsqsTHcvKypCQkIA77rgD06dPb/Ljjh8/Di8vL8vtoCD1zA8REMA3HJLDzJAsZoZkWMvL8fNleP73VJgunNA/pXsAZvUJbuPK2qeyEj1++uIvnEuvvoRBowFGTe6OfkMiFa6s6biPsS1zWQEq/voe5Tu/gjH9QJ37HXwj4DroZrgOuAmO/p0VqLDl1JgZu2qaJk2ahEmTJkk/LigoCD4+PrYvqA3odBxpiOQwMySLmSEZl+Ylo6gS/1qbgkpj9elAw6J8cN8V4TxN3wbOnyvBT5/vRXFh9TXiTjoHXH1jAmK7qefLX4D7GFuq2PMdir59FKLqktHlnFzg0vtquA2aBV3ccGi06j4CqcbM2FXT1Fx9+vSBXq9Hr169sHDhwiYNd15SUmIZwAKoHrxCiZE8MjMzVdvwkTKYGZLFzJCMi/OSX27A02uSUVRpBABc1skDT42MhIOdX2OjBqknzmPVsiRU6au3rae3C669rR+CQrwaeaT94T6m5YTJgOIV81G+5cNay50694ProFlw7TsdWrf2c/2gGjOj6qYpJCQE77//Pvr37w+9Xo+PP/4YI0eOxM6dO9GvX78GH9urVy+Ul/89NOOcOXPwwAMPICQkBMnJyQCA4OBgCCGQk5MDAOjSpQvS09NRUVEBFxcXRERE4OTJkwCqj3ZptVrLZF2xsbE4d+4cysrK4OzsjKioKBw/fhxA9SFJnU6HzMxMlJSUoKKiAufPn0dpaSmcnJwQFxeHo0ePAgD8/Pzg6uqKjIwMAEBUVBTy8/NRXFwMBwcHdO36/+yddXhTZ/vHv/E0dXf3AhXciw93hg6bM9cfYy7vhDkwZ4wNtwEb7u5QipQadfe0aRo/vz8Cp2QUaELak5M8n+va9b69z0nybfrlOec+z/PcdzRu3LgBiqLg4uICR0dHFBYWAgCCgoJQX1+Puro6cDgcxMbGIiMjA1qtFk5OTnB1dUV+vn59ekBAAORyOWpqagAAcXFxyMzMhEajgaOjIzw8PJCbq9+c6ufnB6VSierqagBATEwMcnJyoFKpYG9vD29vb+Tk5NB/I41Gg8rKSgBAVFQUCgoKoFAoYGdnB39/f2RnZ9PfNwCUl+vXdEdERKC4uJj+voOCgpCZmQkA8PT0BJ/PR2lpKQAgLCwM5eXlaGxshFAoRFhYGNLT9f0t3N3dIRKJUFKi37wcGhqKqqoqNDQ0gM/nIyoqCmlpafT3LZFIUFSkbyYYHByM2trae37fTk5OKCgoAAAEBgaioaHhnt+3m5sb8vLyAAD+/v5oamqiv+/Y2FhkZ2dDrVbDwcEBnp6eBt+3SqVCVVUVAH2J0Zs3b0KpVMLe3h4+Pj60Z318fKDT6Qw8W1hYSH/fAQEBBp7lcDj09x0eHo7S0lLI5XKIRCIEBwff9/uuqKiATCZr8fsWi8Uteva/37erqyscHBwMPCuVSiGVSsHlchETE4P09HTodDo4OzvD2dnZ4PuWyWSora29y7Mtfd8KhaJFzzo4OMDLy+u+ns3Pz4dSqYREImn3MeK2Zx9mjGhoaEBaWhoZI2xkjIiOjkZeXp7JY8Rtvzi4euDjExUoa1DrveUiwuPRPGRnppMx4iHHiPTUCqSdr6VLiju5CTF8chT4Ig39u7fnGPGw9xENDQ2or68nYwRMGyMK0y/B/sB7EJQ1L8VTRQyD38T3kNPA03/fNQ1wg8Aixghz3EdotVoUFBRYxBhx+zt8ECaVHG8POBzOAwtBtERycjKCgoKwatWqFo/fLjkeFhZmETNNTU1NsLMjG/cIrYd4hmAsxDMEY2hqagJPKMI7e2/icokMAODlIMD3Y6Lhbi9gWB270ekoHN2djosnmwtqRMZ5Y8SjnSAUsvc5NhljTEeVcwa1K+dDV69/oAaeEM6TPoddrzlWvQTWkjzT2pLjrVoQ6ebmhs2bN9M/f/TRR7h27drDq2wDunfvTj9xuB+Ojo5wcnKi/2OqydbtJycEQmshniEYC/EMwRjKKyrw5dF8OmFyEvHw6fAIkjA9JCqlBtvXpBgkTN36hWLsjERWJ0wAGWNMgaIoNB7/DdXLxtIJE9fZF+4v7ICk91yrTpgAdnqmVf9KZTKZwVK2Dz74ABEREejYsWObCTOVy5cvw9fXl2kZrUYmkzEtgcAyiGcIxkI8Q2gtFEVh9TUpjpVqAQAiHgcfPxKOIBcxw8rYTYNUga2rLqGipB4AwOFyMHRcHOK7BTKszDyQMcY4KJUc0o2voenCBjomjOgLlzm/g+foyaCy9oONnmlV0hQeHo7NmzejX79+dGnvxsbGB64BdHNzM0qMTCYzmCXKzc3F5cuX4ebmhqCgILz11lsoLi7GX3/9BQD47rvvEBoaig4dOkChUGD58uU4dOgQ9u3bZ9TnMolAQJ7cEYyDeIZgLMQzhNay5VolnTBxOcA7g0MR62XPsCp2U1FSj7//ughZvRIAIBLzMXZGIoIj2Fdy+V6QMab1aKryULtiNjQlzSu27Ac+B8fR74PDY/eMozGw0TOt2tO0atUqzJs3D8Zuf9JqtUadf69mtXPmzMHKlSsxd+5c5OXl4ciRIwCAxYsX49dff0VxcTEkEgni4+Px3nvvtfget7m9pykhIQE8Hs8ofW0BRVFWPwVLMC/EMwRjIZ4htIbSBiWe2HwD6lvNmF7rH4RHotwZVsVuykvqsf7Xs1Cr9PdDTq52mDi7Czy8HRhWZl7IGNM6FDcOoG7VU6DkdQAAjtAeztOXwC5pArPCGMCSPNPaPU2tLgSRkZGBI0eOoLy8HB988AEmTJiA+Pj4+77m/fffN051O2BpSVNaWhri4uKYlkFgEcQzBGMhniG0hk8O5uJYbh0AYFJHTzzdM4BZQSxH0aTGqmWnIK1tAgD4Bjpj/GOdYe/AzB7qtoSMMfeH0ukgO/ANZLs/w+2SiTzPcLjO/wsC31iG1TGDJXmmtUlTq+cBo6OjER0dDQD4448/MGfOHIwdO/bhlRIIBAKBQGCUa2UyOmFyEACzOrNnb7AlQuko7Np0hU6YfAKc8egT3SEQMP+wltC+6JrqUbf6GSiv76Fjoo4j4TLzR3Dt2NeTy5YxafHk7brvhIfH2H1fBALxDMFYiGcI90NHUfj5TDH985QYZ9gLyc39w3D2WA5y0vXVwewkAoydkWjVCRMZY1pGXZqG2hVzoK3U90AChwOHEYvgMOQVcLitKmBttbDRMybvONNqtVi9ejV27txJNz8MDg7G6NGjMXPmTItY+sYGLKVGPYE9EM8QjIV4hnA/DmXXIrNKXyE31FWMR6LZdzNjSeRnV+Hkfn3zT3CAUVMT4ORi3f8GyRhjCKVVo/HYr5Dt/hyUqhEAwJG4wPWx3yCKHcywOsuAjZ4xKc2VSqXo06cP5s+fj3379kGtVkOtVmP//v2YN28e+vbti/r6enNrtUpudzsmEFoL8QzBWIhnCPeiSa3FivMl9M9P9/RHWUnJfV5BuB8NUgV2rE+9vW0FfQZHICTSeqrk3QsyxjSjzDqOqi/7o2H7u3TCxPfvBI/XDpOE6Q7Y6BmTkqa3334bFy9exNKlS1FZWYlLly7h0qVLqKiowLJly3DhwgW8/fbb5tZKIBAIBALBjGy+WoEquRoA0CPQCZ39yR4LU9FqdPhnbQqabn2foVEe6DkgnGFVhPZCW1eM2pXzUfPDOGjKMvRBDgeS3vPg8dIe8N2DmRVIeGhMWp63detWLFiwAAsWLDCICwQCPPvss7hx4wY2b96MpUuXmkWkNRMSEsK0BALLIJ4hGAvxDKElqhpV2HilAgDA4wBP9fAHQPxiKkd2p6O0UAoAcHIRY+Sj8eBwLaOkcltjy56hNCo0HvkJsn1f0TNLACAI6gynyYshDOrMoDrLhY2eMSlpqq6upivptURMTMwDG98S9NTU1EAikTAtg8AiiGcIxkI8Q2iJFRdKodToAABj4zwR6CIGQPxiCjdSS5ByugAAwONxMHZmEuwkQoZVtR+26hll+iFItyyEtjKbjnHt3eE45j3YdZ9p88Ue7gcbPWPSXzMiIgL//PPPPY//888/CA8nU9Ktgez9IhgL8QzBWIhnCP8ls1KOA1n6h5uOIh5mJvnQx4hfjKOqvAF7/75O/zx4bBx8/J0ZVNT+2JpnNDWFqF0xGzU/T25OmDhcSPo+Ac9F5yDp+RhJmB4AGz1j0kzTggUL8Pzzz2PkyJF4+eWXERUVBUDfAHfJkiXYv38/li1bZlah1gqpMkgwFuIZgrEQzxDuhKIo/HSmiP55VpIPnMTNtwPEL61HqdBg+5oUaNRaAECHzv7o1NX2mgLbimcotQKyw8sg2/8toG6i44LQ7nCetBiCgHgG1bELNnrG5KSpoqICn3/+Ofbu3WtwTCAQ4L333sOzzz5rFoHWzv2WORIILUE8QzAW4hnCnRzPrcP1cv3eiwBnEcbEeRocJ35pHRRFYe/fV1F7q1y7p68jhoyNA4djG/uY7sQWPKO4vg/1W9+Ctqq5VynX0QuOYz6AXbepNvl3fxjY6BmT+zR98MEHeP7553HgwAGDPk1DhgyBh4f1l9c0Fzdu3EBsbCzTMggsgniGYCzEM4TbqDQ6/HauuaT4Uz38wf9PsQLil9Zx8WQ+Mq+VAwBEYr6+ga2NNgW2Zs9oKrJRv/1dKK/fMUnA5UHS9wk4jngLXDtScdIU2OgZk5MmAPDw8MC0adPMpcUmoW43cyAQWgnxDMFYiGcIt9l6vRLlMhUAIMnPET0C777hI355MEW5NTi6J4P+ecSUeLi62zOoiFms0TPahgrI9n4J+amVgE5Lx4XhveE0aTEEfnHMibMC2OiZh0qaCA+Pi4sL0xIILIN4hmAsxDMEAKiVq7HuchkAgMsBnunp3+KSIuKX+9PYoMS/61NB6fQ3fT2SwxAR68WwKmaxJs/olI1oPPIjGg8tBaWU0XGukw+cxn0EcedJZCmeGWCjZ0jSxDCOjo5MSyCwDOIZgrEQzxAA4M9LpZCr9SXGh0e7I9TNrsXziF/ujU6rw7/rL6OxQQkACApzQ58hEQyrYh5r8Ayl1aDp3Fo07P4cuvoyOs4R2sN+0AuwH7gAXJEDgwqtCzZ6htRDZJjCwkKmJRBYBvEMwViIZwi5NU3Yk1ENAJAIuJjT2fee5xK/3Jvj+7NQlFsLAHBwEmHUtARweeRWis2eoSgKiut7UfVlP0g3vNycMHF5kPSZD893LsBx+JskYTIzbPQMmWkiEAgEAsGKoSgKP58pxq3VZJie6ANXiYBZUSwk63o5zh/TV07jcjkYMz0R9g4ihlURHgZVwSU0bH8fqpsnDeKi+NFwGvUu+N6RDCmzfjQVNQDLtoUZnTQplUrs3bsXISEhiI8n9egflqCgIKYlEFgG8QzBWIhnbJuzhfVIKWkAAPg4CjGhg+d9zyd+MUSl1ODqhSKcPJBFxwaMjIZ/sCuDqiwLtnlGU5WHhp2fQJHyt0FcENINTmM/hDCsJ0PKrB9Kq0X2V7+jdNkaBG5dBteunZiW1GqMTpqEQiGmTJmC77//niRNZqC+vh4ODmTKl9B6iGcIxkI8Y7uotTr8eraY/vmJ7n4Q8u+/nIz4RU9jgxKXTucj9WwhFE1qOh7dyQdJvYIZVGZ5sMUzusYayPZ9hcYTvwPa5r8pzzMcjqPfgzh+NCny0IaoaqRIXfA+qo+cAwBcfuJt9Dm8GkJXdpRtNzpp4nA4iIyMRFVVVVvosTnq6urg5+fHtAwCiyCeIRgL8YztsuNGFYqk+qIFHX3s0S/E5YGvsXW/1FTKcOFEHq6nlECr0Rkci+zgjUcmdiQ31v/B0j1DURSazq5B/bZ3QCnq6TjXwQMOw/8Pkl6zweGRJattifTyDaQ88TYURbf3jHEQ8uRUCFzYUxDCpD1NixYtwquvvoopU6awsqOvJUEGXoKxEM8QjIV4xjapV2iwOqW5CtgzPQJa5QVb9Utxfi3OH8tFdnoFcEcLGS6Pg9gEP3TrFwIPb/bc4LUnluwZnVKG+k1voOnCBjrGEUpgP2AB7Ac9D66YHbMcbKZwzT9Ie+trUCr97J7QwxWui55A6IwJDCszDpOSpjNnzsDd3R0dO3bEgAEDEBISAjs7w9KlHA4H33//vVlEWjNs64ZMYB7iGYKxEM/YJqtTytCg1DflHBLphihPSateZ0t+oXQUsm9U4PzxXJQU1BkcE4r4SOgeiM69g+HoLGZGIEuwVM+oS9JQu3IetBXN+9Hsuk2H4+h3wHO+dwVJgnnQNimRtuhrFK/bQcdcunZE4m//g9j3/nsrLRGTkqZly5bR///gwYMtnkOSptaRkZFBZusIRkE8QzAW4hnbo7BOgX/TKgEAIj4X87u2/gbRFvyiUWtxPaUEF07korZKbnDMwUmELn1CEN8tACIxWbLVGizNM/rleKsh3fJ/gFoBAOCIHOA89TvYdZ7IsDrbQF5QistPLEL9lQw6FvT4ZMS8/wK4QoHFeaY1mJQ06XS6B59EaBVarZZpCQSWQTxDMBbiGdvj17PF0N5aYvZovBc87IWtfq01+0Wr0eHCiVxcPJkPeaPK4JiHtwO69gtFbLwveA8olkEwxJI8o1PKIN34GhQXN9Exvn8nuM5dAb5nOIPKbIfKQ2dw5bkPoK7V7x/j2onQ8auF8Jv0CH2OJXmmtZA+TQzj5ETW0hKMg3iGYCzEM7bFqfw6nC3U36x4SASY3MnLqNdbq190Wh12rE9FVlq5QTwwzA3d+oUiNMrDovfmWDKW4hl1yXXUrpxvsBxP0vdxOI37GBwBWWLZ1lA6HW5+uxLZX/0OUPqnNpLQACSt+AyOsYYJq6V4xhgeKmk6c+YMDh8+jIqKCixYsACRkZGQy+VIT09HVFQUK8pPMo2rK+nzQDAO4hmCsRDP2A5Nai1+OFVE//xUD3/YCXhGvYc1+oXSUdjz9zU6YeJwgKiOPujWLxQ+Ac4Mq2M/THuGoig0nVkF6d8LDZfjTfsedknsKjbAVtR19bjy3IeoPHiajnkN74dOS96FwOnufIBpz5iCSfPPKpUKEydORJ8+ffD2229jyZIlKCws1L8hl4thw4aR/UytJD8/n2kJBJZBPEMwFuIZ22HVpTJUNuorVHUNcERymIvR72FtfqEoCgd33EBaSgkAgMfjYNLcrhgzPZEkTGaCSc/oFA2oW/00pBtephMmfkA8PF4/QhKmdqL+agZODZvfnDBxuYhc9AySVnzWYsIEsHOcMSlpevfdd7Fjxw789NNPyMjIAEU11+YUi8WYMmUKtm/fbjaRBAKBQCAQ7s/Najn+vlYBABDyOHi+dyBZbgbgxL4sXD5TAADgcDkYPS0RIZEeDKsimAN1yXVUfT0Yioub6Zik7+PweGkP+J5hDCqzHYrW78SZMU+jqUD/UELg5oJuG75D+IuzweFa195Ak5bnrVu3Ds8++yyeeuopVFdX33U8NjYWmzZtauGVhP8SEBDAtAQCyyCeIRgL8Yz1o6MofH+iELpbzzBnJPrAz0lk0ntZk1/OHrmJs0dz6J+HT+qIyA7eDCqyTtrbMxRFoen0n5BuXWS4HG/6Etgljm9XLbYIRVGoO3cF+X9sQdm2A3TcOSkOicv/Bzv/B/8bY+M4Y1LSVFFRgU6dOt3zOI/Hg1wuv+dxQjNyuZyVm+EIzEE8QzAW4hnrZ1d6NdIr9dfdQGcRJscbV/zhTqzFLymn83F8X3NBgMFj49AhyZ9BRdZLe3pGU52Php2fQHFpCx3jByTAdc7vZHapjWkqLEXxpj0o2bgL8rxig2OBsycg9uOXwBW1rlInG8cZk5KmwMBApKen3/P4yZMnERERYbIoW6KmpgY+Pj5MyyCwCOIZgrEQz1g3tXI1fj9fQv/8Ut9ACHmmL4uxBr9cv1SMg//eoH/u90gUknoGMajIumlLz2gbKqDKPA5l1lGoMo9BW1NgcFzS70k4jfsIHL5pM6uE+6NplKN851EUb9iJmpOX7jrOd3JA7Mcvw3/qSKPel43jjElJ04wZM/DNN99g0qRJiIqKAgB63fRvv/2GjRs34vPPPzefSgKBQCAQCC3y89liNKr0PU+GRroh3teRYUXMknmtDHu2XKV/7jEgDD2SyQwEW9A11UN18ySUmcegyjoGTemNFs/jiB3hPG0J7BLHtbNC64fS6VBz+jJKNu5C2b+HoZU3GZ7A4cC9X1f4Tx0J7xHJ4Elso5w7h7qzikMrUalUGDNmDA4dOoTY2Fhcv34dnTp1Qk1NDYqKijBy5Ehs374dPJ5xZU7bA41Gg6NHjyIhIcEi9REIBAKB0FouFddj4e6bAABHEQ+/T46Fi52AYVXMkZtZia2rLkF3q7NvUs8gDBoTSwpiWDCUWgFV7jkos45BlXkU6sLLgO4ejU/5IghDu0MY2R+SblPBc2XfvhhLRp5fjOKNu1GycTeaCkvvOi4JC4T/1JHwmzy8VfuW2IJWq0VqaiqSk5PB5997PsmkmSahUIg9e/ZgzZo12Lx5M7RaLZRKJeLj4/HJJ5/gscceIwNUK8nMzKRn6wiE1kA8QzAW4hnrRKXRYenJ5p5MT3T3N0vCxFa/FOXVYvuaFDph6tDZD4NGk4SpPTDWM5RWjaZLf6Pp/Hqocs4AGmXLJ3K4EAQmQhjZH6Ko/hCG9gBHaGcm1QRAX9ShbPtBFKz8G7VnLt91nO/kAJ9xg+E/dSRcunQ0278nNo4zJje35XA4mDVrFmbNmmVOPTaHRqNhWgKBZRDPEIyFeMY6WZ9ajuJ6/c1mB297PBLlZpb3ZaNfyoql+PvPi9CodQCAyA7eeGRCR3C4JGFqD1rrGUrVBPnZNWg8tBTa2sIWz+H7REMYmaxPksL7gCshvbTaCmVlDa6//jkq9p4wPMDlwiO5O/ynjoDXI/3BszP/fjE2jjMmJ02Afjrr4sWLyMvLAwCEhoaic+fOZNmbETg62vbac4LxEM8QjIV4xvoorFNgQ2o5AIDHAV7sEwiumZ4As80vVeUybPnjAlRK/U1YSKQHRk1NAPchimEQjONBntE11UN+4nc0Hv0ZOlmlwTGea8CtmaRkCCP7gefMruIAbKVi30lce/VTqKpq6Zh9ZAj8Hx0Bv8nDIfb1bNPPZ9s4AzxE0rRy5Uq89dZbqKiooJvbcjgceHp64tNPP8X8+fPNJtKa8fAgDfYIxkE8QzAW4hnrgqIoLD1VCPWtpkyTO3kh1M18S5bY5Je6Gjk2rTiPJrkaABAQ4opxM5PA55OEqT25l2e0DZVoPPoz5CeWg1I0GBwTxQ6Bw5BXIAjrSZZQtiOaRjnSP1iKolXb6ZjQ3QUdvvo/eA3v325/CzaNM7cxaVT55ZdfMH/+fPj6+uLHH3/EwYMHcfDgQfzwww/w9fXFk08+iZ9//tncWq2S3NxcpiUQWAbxDMFYiGesi4PZtbhcIgMAeDsIMbOzr1nfny1+aZAqsOn382hs0C9R9PZ3woTZnSEQktUu7c1/PaOpLoB085uo+CgBjQe+bU6YOFyIkybA4/WjcHt6I4ThvUjC1I7UXbqOU0PmGiRMnkP7oM+R1fAekdyufwu2jDN3YtJM0xdffIF+/frhwIEDEAiaN50OHDgQjz/+OAYNGoTFixfjmWeeMZtQAoFAIBBsnQalBr+cbW4q+XzvAIhtcFalrkaOv/+8CGmtvhSyu5cDJs3tCpHYdisHWgLqsnQ0HvgeTZc2G1bA4wlg120aHAa/CL5nOHMCbRSdWoOb361Eznd/gtLq/y48OzFiPnoRAbPGkcS1lZiUNJWVleG1114zSJhuIxAIMG3aNLz55psPLc4W8PPzY1oCgWUQzxCMhXjGevj9fAmkCv3enb4hLugRZP5N8pbsl0aZEmcP5+DyuQK6Sp6zmx2mzO8Kib2QYXW2i4+uAjXLP4Xy2i6DOEdoD0nvObAfsAA8F8v1lTXTeLMAV57/CNKUNDrm3LkD4pe9B/uwQMZ0WfI4cy9MSpqSkpKQmZl5z+OZmZlITEw0VZNNoVTeo8wmgXAPiGcIxkI8Yx1cL5dhV3o1AMBOwMWzvfzb5HMs0S9KhQYXTuTiwok8qFXNMxiOzmJMmd8NDk620VzT0tDWl0O66XUor+40iHMkrrDv/xTs+z0Jrr15qjoSjIOiKBT+tQ0ZHyyFtkkBAODweAh/ZS7CXp4D7n36EbUHljjOPAiTvrGlS5di1KhRCAsLw1NPPQU7O/0G1KamJvz888/YuHEjdu3a9YB3IQBAdXU1vL2tp0EYoe0hniEYC/EM+9HoKCw50VyieW4XX3i20cyKJflFo9Eh9WwBzhy+SRd7AAC+gIeufYLRtV8oxDbczJdJmlK2QrrpdVDy5uprXGdf2A98DpJes8EVOTCozrZRVtbg2iufovLAKTomCQtE/LL34NK5A4PKmrGkcaa1tCppio+PvyvG4/Hw6quv4s0336Sn2EpKSqDRaODr64u5c+ciNTXVvGoJBAKBQLBBtl6rQG6t/mlxhLsdxsa1bTlgptHpKKRdLsHJA1loqFPQcS6Xg/hugeg5MIzMLjGErrEG0s1vQpHyd3PMzhWuY9+DXbdp4PDN39OH0HrK9xzDtVc/h7qmjo4Fzp6A6PefB9+eNAZ+GFqVNLm5ud21Sczd3R2RkZEGsZCQELMJsxViYmKYlkBgGcQzBGMhnmE35Q0q/HWpDADAAfBS30Dw2rBpK5N+oSgKN9MrcXxvJqorZAbHYuJ90XdoJFzcJQypIyiu74N0w0vQ1ZfTMXHCWDhO+hJ8J+tO5C0dVVUtMv73E4rX7aBjQg9XdPx2EbyG9mFQWcuw8brUqqTpyJEjbSzDdsnJyUFERATTMggsgniGYCzEM+zmx9NFUGp0AIAxcR6I9rRv089jyi+FuTU4vjcTJQV1BvGQKA/0HxYFLz+ndtdE0KNT1KN+69toOruGjnEkLnCe9CXEnSfi5s2biCBJEyPolCrk/74ZN79bCU1984MGr+H90PGrhRB6uDKo7t6w8brE7C4wAlQqFdMSCCyDeIZgLMQz7OVkXh1OF0gBAG52fMzr2vYVp9rbLxWl9Ti+Lwu5GZUGcd9AZ/R7JApBYe7tqodgiDLzGKTrnoe2toiOiWKHwHna9+A563uEkTGm/aEoCuU7DiPj4x/RVFBCx3n2EsR+/BL8p4+26FLibPTMQyVNBQUFyMnJQW1tLSiKuuv4xIkTH+btbQJ7+7Z9YkiwPohnCMZCPMM+VBodtqVVYm1KGR17pmcA7NuhcWt7+uX88Vwc3ZMB3HEL4eZpj36PRCEi1suib/qsHUolR/2/H0F+/Fc6xhE5wGn8J7Dr+ZjB34aMMe2LNCUN6R8sRe3ZO2oHcDgImD4aEf/3JMTeHsyJayVs9IxJSVNBQQHmz5+Pw4cPA0CLCROHw4FWq70rTjCEbZVDCMxDPEMwFuIZ9qCjKBy5WYs/LpSiXNb8JLZrgCOSw1zaRUN7+SXlTAGO7s6gf3Z0FqP3kAh0SPIHtw33bBEejCr3HOrWPgdt5U06JozoC+fpy8B3D7rrfDLGtA9NxeXI+uxnlGzeaxB369sFMR++CKcOkfd4pWVBURRqVEUIooJY9WDEpKRpzpw5OH36NBYuXIgePXrA2dn8zfVshZycHMTFxTEtg8AiiGcIxkI8ww5SSxrw67liZFU10TEOgGFRbni6h3+73Vy0h1/SUkpw8J/mZps9BoSh18Bw8AVtP5NGuDeURomG3V+g8dASgNLvo4NADKfR70PS70lwuNwWX0fGmLZF0yhH7rLVyP1pLXSK5ocp9hFBiH7vBXgO7c2a5KOirhh/HFiMlJwTeHbkB0juOIZpSa3GpKTpzJkz+L//+z98+OGH5tZDIBAIBIJNUVCrwPLzxThTUG8Q7+LviCe6+yHcyqrFZaWVY/eWq/TPPZLD0G9YFIOKCACgyjsP6YaXoSm9QccEwV3gMuNH8L3ZMYNhbVBaLYo37EbW579AWVFNxwWuToh47XEEzpkAroAd5Qk0WjV2nF+Fv08th0qjb2y7+vB36BY5ABKRI8PqWodJ33RAQABcXS2zGgfb8PX1ZVoCgWUQzxCMhXjGMqmVq7HqUhl2ZVRBd8cq9zA3MZ7o7o+uAcxUi2tLv+RnV2HHusugbv3CiT2D0HcYuSFnCkqrhiL1XzQe+wXqvPPNB3gCOA5fCPtBL4DDe/CtIhljzE/1iQtIf38pGq5n0TGOgI/g+ZMR/spcCFzYU00yreAift//GYqrc+mYs8Qdc4e8Djshe5ogm5Q0vf7661i2bBmeeuopSCTW9QSsvdFoNExLILAM4hmCsRDPWBZNai22XKvEpivlaFLr6Li7RIB5XX0xOMKtTfswPYi28ktJQS22rkqBVqtPmOKS/DB4dCxrlhVZE1pZFeSn/oT85AropKUGx/h+HeEy6ycI/Dq0+v3IGGM+Gm8WIP3DZajcd8Ig7j1qAKLeWQD70ACGlBmPtLEGa458h2PXd9IxDoeL4Z2nYmDMZAT5hzAnzgRMSpqefvppaLVaREZGYvLkyQgICACPZ7gOmcPh4JVXXjGLSGumsrISnp6ktwGh9RDPEIyFeMYy0Ooo7M+qwZ8XS1EtV9NxOwEXU+O9MbGTF8T8lveMtCdt4ZeK0npsWXkRGrW+QFREnBeGT+wIDin40K6oi66i8dgvaLq0Bbi1ROo2fN9Y2Pd/CnbdpoPDFxr1vmSMeXhUtfW4+c0KFPyxBZSmuZCaU3w0Yj54EW69kxhUZxw6SodDqVux7tgyNCqalx2H+3bAE8MWIdQ7BmlpaYA/gyJNwKSk6dq1a1i8eDFKS0uxdOnSFs8hSROBQCAQCHouFNXjt7PFyK1V0DEuBxgV44FZnX3gaidgUF3bUlPViM0rLkCp0M9GBEe4Y/S0RHB5zCeItgCl1UBxbRfkR3+BKue04UEOB6IOI2Cf/DSEEX3JrB8D6NQaFP65Fdlf/w51bXOCIfLxQNSiZ+E3+ZF7FuCwRPLKM/D7/s+QVdK8b9Fe5Ihp/Z/H4IQJ4HLZW+zFpKTpqaeeglQqxS+//EKq5z0kUVFk8yvBOIhnCMZCPMMcebVN+PVsMS4UNRjEewU74/FufghyETOk7N6Y0y/1dU3Y9Pt5yBv1Fb/8glwwblYS+BYwo2bt6BprIT/9FxpPLIeurtjgGEfsBEmvxyDp+wT47sEP/VlkjDEeiqJQeeAUMj5ahsasfDrOtRMhdMFMhC6YCb69HYMKjaNJ2YiNJ37GnkvrQVHNy477xo3ErIEvw8XesEk1Gz1jUtJ0+fJlfPjhh3jyySfNrcfmKCgoQFhYGNMyCCyCeIZgLMQz7U9tkxqrLt5d5CHaU4Inu/sj3tdyNz+byy+NDUps+v08GqT62TVPX0dMnNMFQiE7qn2xFU15FmRHfkDThU2AusngGN87CpL+T8Ou6xRwRebzIBljjKPhxk2kf7gU1UfOGcT9Jj+CqEXPQuznxZAy46EoCmczD+LPg1+hVlZJx/3cQvD40IXoENytxdex0TMmjVyhoaHm1mGzKBSKB59EINwB8QzBWIhn2g+VRoet1yux7nIZ5HcUefByEGB+Vz8MCHcF18KXQJnDL4omNTb/cQG11XIAgKuHBJPndoXYipchMg1FUZCf/AP1WxcB2uZePuBwIIobBvv+T0EYNaBNluCRMaZ1qKpqkbV4OQpXbwd0zeODS7dOiPnwJbh0Zlevq8Kqm1h9+Duk5p6iYwK+CBN7PYEx3R8Dn3fvf+9s9IxJSdOHH36I119/HdOmTUNgYKC5NdkUdnbsmXolWAbEMwRjIZ5peyiKwtGcOvx+vgTlsuYbVomAi2mJ3pjQwQsilixJe1i/qJQabFl5AZVl+iWJjs5iTJnfDfaOInPII7SATlEP6YZXoEjZSsc4IgfY9ZgJ+35Pgu/Ztk/0yRhzf3RKFfKXb8LN71ZC09BIx8UBPoh+9zn4jB3Eiv1kao0KN4ouIeXmCaTcPIGyukKD40lhfTBvyP/By+XBFR7Y6BmTkqZjx47BxcUF0dHRGDJkCAIDA1usnvf999+bRaQ14+/PstIhBMYhniEYC/FM25JW3ohfzhbhRoWcjnE5wPBod8zp7AtXCbtmVx7GLxq1FttWp6C0UAoAkNgLMeXxbnByYd8NEltQF19D7cp50FbepGOS/k/BceTb4Irbp2koGWNahqIolO86ioyPlqEpv4SO8+wlCHtpNkKemgqe2LIfJtTKKpGScxIpN0/gat5ZKNTyu85xc/TG3MGvo1vkwFYnf2z0DIeiKOrBpxnCbUUVDw6HA61W+8Dz2huNRoOjR48iISHhrkSPCdLS0hAXx67pWAKzEM8QjIV4pm0oa1Di9/MlOJpTZxDv7O+Ip3v4I9SNnYmCqX7RanX4d+1lZN+oAACIxHxMe7IHPH3b58bd1qAoCk2n/4T077fo8uEcsRNcZiyDOH50u2ohY0wz2iYlpKk3UHf+Cir2nUTd+eYqcuBwEDBjNCL/7ymIvNzv/SYMoqN0uFl6XT+blHMCueXpLZ7H5fAQHZCIrhH9MThhIsRC4/q2WpJntFotUlNTkZycDD7/3vNJJs006e5Yh0kgEAgEgi3RqNJi/eUy/H29Empt83PHIBcxnurhh24BTqxYamNOKB2FvVuu0QmTQMjDpLldScLURuiUMkg3vgrFxc10TBCYCJc5K8D3CGFOmA2irKxB3YWrqD17BbXnr6D+SgYo9d3Nft36dEbMhy/CqaPlVY1rUjYiNfcULuWcQGrOKUjlNS2e5yRxRWJobySF90V8SC/Yt9NMpqVAStgwjLe3N9MSCCyDeIZgLMQz5mNvZjWWnyuBVNF8U+Qs5mN2Zx+MjPEAzwqatRrrF7Vai/1bryPtsn75EY/HwfhZneEX5NIG6gjqkjT9cryKLDom6fcknMZ9BA6fmaVetjLGUBSFxux81J2/itqzqag9fxXynML7vkYSHoTodxfA65F+FvcwRd+EdhvWHV2CRmVDi+eEeEUjKbwvOof3Q7hPnNn6LLHRMyRpIhAIBAKhFWy5WoFfzjb3uxFwOZjQ0RPTE31gL2R+uTcT1Nc1YfuaFJQX65tycrgcjJmeiOAIy1x6xGYoikLT2TWQbvk/upQ4R+QA5+lLYJc4nllxVoyqug7FG3eh5vRl1F24CnWN9L7nS8KD4NqtE1y7x8OlWyfYRwRbXLIEAAWVWVi+7zNkFqcaxEUCO8SH9EBSWF8khvWFm6MnQwotD5OSJi6X2yoDWOKeJkujvLwc7u7k4kJoPcQzBGMhnnl4DmbXGCRM/UNd8Hh3P/haYUW41vqlKK8W/6xJoRvX8gU8jJzSCRFx7HuCbOnolI2o3/wGms6vp2N8/05wnftHm1fGaw3WOMY0FZcj76e1KFzzD3RNyhbP4QgFcI6Phku3eLj2iIdr104Qeri2s1LjUKiasOXUb9h1YTW0uub79N6xjyC54xjEBXaBgC9scx1s9IxJSdN77713V9Kk1WqRl5eHbdu2ITo6GqNHt+8mRAKBQCAQ2oILRfX46mg+/fOsJB/M7uLLoCJmoSgKqecKcejfG9Dd6tzr7GqH8bM6kz1MbYC6LB11K+dBU5ZBxyS958Fpwv/AEYgZVGadyLLykLtsNUq27AWlMXz4L3B10idIt2aSnBJiLL763Z1czD6GPw4sRlV9KR3zdQ3G48MWomNwdwaVsQOTkqYPPvjgnsdKS0vRs2dPREVZ3kY3SyQiIoJpCQSWQTxDMBbiGdNJr2jERwdycbvew6gYdzzW2YdZUW3M/fyi0ehw6N80XDlfRMeCI9wxeloC7CRt/3Ta1pCfW4/6za+DUunLPHNEDnB+9FvYdZnEsDJDrGGMkV6+gZylq1C+6yhwR2Fprp0IgTPHImDWODhEhYDTigrSlkZNQwVWHvwK5zIP0jE+T4DxPedjbI85EDKwF46NnjH7niZfX18888wz+PjjjzF9+nRzv73VUVxcjNDQUKZlEFgE8QzBWIhnTKOwToF39+VAodFXjO0b4oznewda5P4Ec3Ivv8jqFfhn7WWUFNTRsa59Q9D/kShweey7kbRkNNUFkO3+DE0XNtAxvm+cfjmedySDylqGrWMMRVGoOXkROUtWofrYeYNjfGdHBM+fjOAnpkDo7sKMwIdEp9Nib8pGbDz+E5pUzU11OwR1w+PD3oKfWzBj2tjomTYpBGFvb4/c3Ny2eGuro6mpiWkJBJZBPEMwFuIZ46luVGPRnpt0lbx4HwcsHBBiFdXxHkRLfikpqMP2NSlobNDv7eDzuRg2oSPikvzaW55Vo6nIhuzAd2i6sBHQNVdotOv5GJwnfg6O0DJ7f7FtjKF0OlTsPY6cJasgTUkzOCby9kDI09MQOHsc+A72DCl8eHLKbmD53v8hp/wGHXOSuOKxga+ib9wIxh/+sM0zQBskTdeuXcOSJUvI8rxWIhaT9cgE4yCeIRgL8YxxyJQaLNqTjXKZvsBBmJsdPhwWBiHfNmZT/uuXqxeKcGD7dWhvrVF0dBZj3Kwk+Pg7MyHPKlGXpkG2/1soUrYCVHMvTI7IAU5TvoKk66MMqnswbBljdGoNSrfuR+6y1ZBlGj7cl4T4I/S5mfCbMoJV+5T+i1wpw8YTP2HvpY2g7vDSoPgJmJH8AhzsLOPfLVs8cycmJU2hoaEtZqh1dXWQSqWQSCTYtm3bw2qzCYKCgpiWQGAZxDMEYyGeaT1KjQ7v7c9Bbq0CAODtIMT/hofbVEnx237RanU4vDMdl88U0McCQlwxZkYi7B3Ye1NpSagLL6Nh/zdQXtlhEOfYOcO+/9Ow7/80uPaWXY0NYMcYU7HvBNIWfQNFUZlB3LFDJMJemAXv0QPB5bO3E4+O0uFcxkH8eehr1Moq6XigRzieGLYI0QGJzIlrATZ45r+Y5I7k5OS7kiYOhwNXV1eEh4dj2rRpcHNzM4tAayczMxNxcXFMyyCwCOIZgrEQz7QOrY7CZ4fzcK1Mv/bfWczH5yPC4S4RMKysfcnMzERwUDj+XXsZRXm1dDypZxAGjIoBj+xfemhUuWch2/c1lDcOGMS59u6wH/gcJH3ngyt2Ykid8VjyGKNTqpDx8Q/IX77JIO7aIwFhLzwGj8G9GF+q9jBodRqcTt+P7Wf+QGHVTTou5Iswqc9TGNV1Jvg8yxvDLNkz98KkpGnlypVmlkEgEAgEAnNQFIUlJwtxKl/fuNJOwMX/HgmHvzP7lpA8LNJqFVb/exoNUv1sG4/HwZBxHdCpawDDytgNRVFQZR2HbP/XUGUdNzjGdfKB/aDnIek1B1wRe/fRWBqNNwuQ+sx7qL+aScfck7sh4tX5cO2RwKCyh0etUeHotR3459xKVNQVGxxLCuuDeUP+D14u/gyps07YOw9pJXh6kk7LBOMgniEYC/HMg/nrUhl2Z1QDAPhcDt4bHIooTwnDqtqf9CulOLOvnN6/5OAkwriZSfANdGFWGIuhKArKGwcg2/cV1HmGFdp4rgGwH/wyJD1msLrnkiWOMcUbdyNt4VfQyvUFB7giIWI+fBGBcyawemZJoZLjYOrf2HF+tcEyPACI9OuECb0eR1JYX4v/HS3RMw/C5KSptrYW69atQ05ODmpra0HdUdMe0C/X+/333x9aoLXDZ/H6WQIzEM8QjIV45v78k1aJNSnN+xzeSA5GlwD2LI0yFylnCnDw3zTg1uXcL8gFY2ckwsGJvTfzTKOtK0btH3Ohzr9oEOd5hMFh6Cuw6/ooOBa4dMpYLGmM0TTKkfZ/X6Fk8x46Zh8ZjMRfPoZjHPt6A91GpqjH3ksbsOfiOjQ0SQ2OdQrpgfE95yMusIvFJ0u3sSTPtBaTFO/duxeTJ09GY2MjnJyc4Op69yZFtvzRmKa0tLTF749AuBfEMwRjIZ65N8dyavHDqeZGrc/29MfAcNv6riiKwtmjOTixL4uOdezijyHjOoBvIxUD2wJNZQ5qfpwAbW0hHeP7xMBh6GsQJ44Dh8e+m8Z7YSljTP3VDFx++j3Ic5q/c//poxH7ySvg21tmufYHUSerws4La7E/ZRMUarnBsW6RAzG+5zyE+3ZgSJ3pWIpnjMGkf7GvvfYafHx88Pfff6NTp07m1kQgEAgEQpuTUtKAL47k355YwbQEb0zo6MWopvaGoigc3ZOBC8fz6Fh4Byc8MrEjefj5EKhL01Dz0yTo6ssBADy3IDiO+xjiTqPA4ZJE1NxQFIX83zch46MfQKnUAACegwQdFr8Jv4nDGFZnGhXSEuw49xcOX9kOtVZFx7kcHnrHPoJxPeci0COcQYW2h0lJU3Z2Nr788kuSMJmBsLAwpiUQWAbxDMFYiGfuJrtKjg/350Ct06dMj0S5YV5XX4ZVtS86HYX9267j6oXmmbb+w6MQ392PJEwPgSr/Imp+eRSUXF95kO8TA7dnt4DnbL3+YnKMUdVIcfXl/6Fy3wk65hQfg4RfPoJ9KPuKl8gU9Vh9+Fscu7YTOkpLx/k8AQZ0Gosx3WfD24V9v9d/YeN1yaSkKTIyEg0NDebWYpOUl5cjODiYaRkEFkE8QzAW4hlDrpQ24JODeZCr9Y0fewY54eW+QTaVKGg1OuzceAWZ127t5eIAw8Z3QHy3QOTn5xO/mIgy6wRql88ApZQBAARBneH29EZw7a27DQtTY0zN6RSkLvgAytLmggghT09D1NvPgitk316xwspsfLX1NZTXNT/IEAnsMDRxMkZ2nQk3R/YVT7gXbLwumZQ0ffLJJ3juuecwY8YMhISEmFmSbdHY2Mi0BALLIJ4hGAvxjB6NjsKqi6VYn1pOL8nr4G2PRYNCwePaTsKkUmnwz5rLyMuqAgBweRyMnBKPmHj9TAjxi2koru9F7R9zAY0SACAM7wPXJ9eCK3ZkVlg70N6eobRa3PzuT2R/vQLQ6R9+CNyc0en7d+A1tE+7ajEXZ9L346fdH0Kp1lf7k4gcMKLLDAzvMhWOdi7MimsD2DjOmJQ0HTx4EJ6enoiNjcXQoUMRGBgIHs+wWzqHw8H3339vFpHWjFAoZFoCgWUQzxCMhXgGKJYq8PmRfGRUNm+kTvB1wLuDQyG2oWIHiiY1tv51EcX5dQAAvoCLcTOTEBrV/ASb+MV4mi5uQd2aZwGdBgAgihsG17l/gCNkZ/EBY2kvz1AUhfrLN5D+0Q+oPZ1Cx916d0b8D+9D7Mu+mRidTot1x37Av+f+pGOh3jF4dfxX8LTiJZ1sHGc41H9rhbcCbis2MXI4HGi12gee195oNBocPXoUCQkJdyV6TKDT6Vr1fRIItyGeIRiLLXuGoijszazBj6eLoNDon0jzOMCcrr6Y0snbpmaYGhuU2LLyAipK9cvrhSI+Js7pgoAQwwpWtuwXU5CfWgnppteAW7dT4qSJcJn1k1WUEm8tbekZSqtF7fmrKN95BOW7jkJRXN58kMtFxOuPI/yl2eBYwD2dsTQ01WHJv4twNe8sHevfYRSeGLYIQhb37WoNljTOaLVapKamIjk5+b6l0E2aadLdmgolPDzp6emIi4tjWgaBRRDPEIzFVj3ToNTguxOFOJ5bR8f8nUR4a2CIzTWura9rwqbfz6O2Wj/TZmcvxOR5XeHtd3c/Klv1iynIDi1Bwz8f0D/b9ZoN5ylfg8Nl3w38w2Buz+jUGtScTkH5jiMo330Uqsqau84R+3kh/of34dYryWyf257kV2Ti662vo0JaDEBfFW/2oFfxSOepNrG/ko3jjPU0CSAQCAQC4RapJQ344mg+qhrVdGxEtDue6ekPO4Ft3dBWV8iw+Y8LaJAqAACOzmJMmd8Vbp4ODCtjLxRFQbbrU8j2f03H7Ac+D8exH9rEDW9boFOqUHX0PMp3HkbFvhNQ19bfdQ5HwId7367wHj0AvmMHg+9oz4DSh+dE2m78uudjqG7tf3OSuOLlsV8gLqgLw8oI94MkTQzj7u7OtAQCyyCeIRiLLXlGrdXhr0tl2HhHsQdHEQ8v9w1Cv1AXJqUxQnmxFJv/uIAmuT55dPWQYMr8bnByufdeG1vyiylQOh3qty6C/PivdMxh5NtwGPqqzSZMpnpG09iEqsNnUL7zCCr2n4RWJr/rHK5YCI8BPeA9agC8hvWFwJm9hTW0Og3WHlmCnRfW0LFwnw54ZfxieDj5MKis/WHjOEOSJoYRiURMSyCwDOIZgrHYimeKpAp8fjgfmVXNN16Jfg54IzkYnvbs23T8sBTm1mDrX5egUuqLE3j5OWHS3C6wd7i/H2zFL6ZAaTWQrn8JTefX0TGniZ/Dvv9TDKpintZ6RitXoO7SddSeTdX/d/4KdE3Ku87j2UvgOaQXfEYNhMfgnuDbs385bb28Ft//8xauF5ynYwM6jcX8oQsh5Nvevzk2jjMkaWKYkpISuLi4MC2DwCKIZwjGYu2eoSgKe24Ve1DeUexhXlc/TI73AtcGn/7fvFGBf9ddhubW9+Ef7IqJczpDJH5wcQJr94upUBol6v56EoorO/QBDhfO05dC0n06s8IsgHt5Rl1Xj9pzV1F79jJqz6ZCmpoOSq1p8T0ELo7wHNYPPqMHwL1/N/DE7Lupvhe5ZTfw9bbXUVWv74vG4/IwZ/AbGJo42WZnJ9k4zpCkiUAgEAispV6hL/ZwIq+OjgU4i7BwYAiiPNj/dNpYNBodju/NwMWT+XQsNMoDY2ckQSC0rb1c5oCiKGircqHKO4+mM6uhunlSf4AngMvs5bBLGMOsQAtDUVaJ2jP6WaSaM5chS8+hqwq2hMjXE55DesNn1AC49ekCrsD6bkuPXd+J3/b+D+pb+5dc7N3x8rgvEBPAzgIWtoz1uZNlhIaGMi2BwDKIZwjGYk2eoSgKJfUqXCltQGqpDBeLGyBVND+5ttViDwBQVS7Dzo2pqLxVUhwAYuJ9MGJyPHhG9KKyJr8Yi07ZCHXhZahzz0GVdx7q/AvQyaoMTxLYwe3xVRDFDGJGpIUhTU2H8rdNOHr+KpryS+57rn1EEFx7JMC1RyJceyTALsjXamdaZE1SbD75C/Zc2kDHIv064ZVxX8LNkX39pMwNG8eZh0qalEolLl26hIqKCvTp0wceHh7m0mUzVFVVITAwkGkZBBZBPEMwFjZ75r9J0pVSGark6rvOcxTx8Eq/IPQNcWl/kQxDURRSzxbiyK50ejkej89F8vBoJPUKMvqmlM1+MQaKoqCtKaATJFXeeWhKrgG6e/eY5Dp4wHX+XxCG9WxHpZZLxd7juPzUu9ApVXcf5HLh1DHyVpKk/0/k6db+ItsRiqKQVngRh69sw9mMg1Brm7+XwQkTMXfwGxDwbW9/ZUuwcZwxOWlasmQJPvjgA0ilUgDA/v37MWjQIFRVVSEmJgaLFy/G/PnzzSbUWmloaHjwSQTCHRDPEIyFTZ5pbZJ0GzGfix6BTni6pz88bLDYg7xRhb1/X8PNGxV0zN3LAaOnJcDTx7QqY2zyi7FQGiXkp1dBmXkU6rzz0DVU3Pd8jsQFwpBuEIR0g/DWfxyh7S37bImSv/fh6gsfg9Lqk0yOUACXznHNM0ndOrG2JLix1DVW49i1HTh0ZRvKagsMjvF5Aswb8iYGJ0xkSJ1lwsZxxqSk6Y8//sDLL7+MadOmYdiwYQbJkYeHBwYNGoT169eTpKkV3K/zMIHQEsQzBGOxdM9odRSO5dbiXGE9UktlBr2V/ouYz0VHH3vE+zogwdcRkR4S8LnWubznQeRnV2HXpqtobGiuPpbUMwj9R0RD8BDLEy3dL6airS1C7R9zoS641PIJHA743tEQhHanEySeZwQ43NYvbbQVCv7cirSFX9H7lRyG9ESv3z4Dz856ijc8CJ1Oiyt5Z3HoyjZczD4C7X9mKB3EzujXYRSGJk2Gn1swQyotFzaOMyYp/vrrrzFu3DisXbsW1dXVdx3v0qULlixZ8tDibIGoqCimJRBYBvEMwVgs2TMaHYXFR/JwJKeuxeMiPhcdvfVJUqKfbSdJt9FodDixLxMXTuTRMTuJAMMnd0J4jNdDv78l+8VUlJnHUPfXEwb7kzhiJwiCu0B4K0kSBHcF186JQZXsIGfpKmT+7yf658DZExD3+Ws2k1xWN5TjyJXtOHx1O10N7046BHXDoPjx6BY10CZLibcWNo4zJiVN2dnZePHFF+953M3NrcVkinA3aWlpiIuLY1oGgUUQzxCMxVI9o9bq8NnhPJzIk9KxO5OkBF9HRHmSJOlOqitk2LnxCipK6ulYSKQHRkzuBHtH89ygWapfTIGiKDQeXoqGfz8CqFv7vdyD4TLzJwhCutvMjb45oCgKWZ//gpzv/6Jjoc/NRNQ7C3Djxg2r8UxLaLRqpOScwKHUrbicexrULS/dxtneHQM6jsHA+PHwcWXXPh2mYOM4Y1LS5OLigqqqqnseT0tLg4+PbXU2JhAIBELrUWl0+PhgLs4W6m/+BTwO3ugfjL6hLiRJagGKonDlfBEO77wBjfrWzT+Pg/7Do9G5VzA45Du7C52iAdJ1L0CR+g8dE8UMhstjv4Jr78qgMvZB6XS48c53KFixmY5FLnoG4S/OZlBV26PWqLD/8mb8e/ZP1DYa3vdywEFiWG8MjB+PzuH9wOc9uAcagd2YlDSNHDkSv/76KxYsWHDXsevXr+O3334j+5laiZubdVeSIZgf4hmCsViaZ5QaHT7Yn4OLxfqNwEIeBx8MDUPXALI0qiWa5Crs+/s6stLK6Zibpz1GT02Al5/5vzNL84spaMqzULviMWjKM+mYw7DX4TD8/8Dh2l45+odBp9Hg2iufoWTTbjoW++lrCJ4/if7ZGjxzJ1qdBsev78Lmk7/ctQTP3dEbA+PHY0CnsfBwIhMEpsJGz5iUNH3yySfo0aMHOnbsiDFjxoDD4eDPP//EihUrsGXLFvj6+uK9994zt1arRCIhVXgIxkE8QzAWS/JMk1qL9/blILVUBkBf2OHjYWFI8DOt0pu1U3CzGrs2XYGsvrnYQ0KPQAwYEdNmzWotyS+moLiyA3VrFoBS6j3GETvBZdbPEHcczrAy9qFTqpD67Pso33VUH+By0em7t+H/6AiD89jumdtQFIXzWYex/tgPKKnJMzjWNSIZgxMnISGkJ7gk8X5o2OgZk5ImPz8/XLx4EYsWLcKGDRtAURRWrVoFR0dHTJ8+HZ9//jnp2dRKioqKWLemk8AsxDMEY7EUz8hVWryz7yaulTUCACQCLv73SDg6+DgwrMzy0Gl1OHXoJs4cuQnoC5TBTiLAIxM7IiLOu00/21L8YiyUTouGnf9D48Hv6BjfNxau8/8C3zOcOWEsRdPYhJT5C1F99DwAfUnxxJ8/gvfI5LvOZatn7uRq3lmsP/YDbpZdN4gnhvXB1H4LEOodw5Ay64SNnjG53p+XlxeWL1+O5cuXo7KyEjqdDp6enuCSTZUEAoFA+A8ypQZv772JGxVyAIC9kIdPh4cj1ss2+rgYQ4NUgZ0bUlGUV0vHgiPcMWJyJzg4iRlUZrnoZNWoXfUkVBlH6Jg4aSKcp30Proh4zFjU0gZcnPU66s5fBQDw7MRIWvk5PJK7M6zM/GSXXsO6o8twveC8QTzaPwHT+r+A2MAkhpQRLA2zFEn39PQ0x9vYJMHBpHY/wTiIZwjGwrRn6hUavLUnG1lVTQAARxEPn4+IQKQH+5ZntDU5GZXYvekKmm419OVwOeg7NBLd+4W2W7EHpv1iLOrCy6hdMRva2iJ9gMuD09iPIEl+BhwOKZBhLMrKGlyY/goarmUBAPhODuiy+iu4do+/52vY5hkAKKy6iY3Hf8T5rCMG8SDPSEzr/xySwvoS/7QhbPSMSUnTRx99dN/jHA4HYrEYAQEB6N+/P/z9/U0SZwvU1tbC3p48BSO0HuIZgrEw6Zm6JjUW7s5GTo0CAOAs5uOLEREIc7djRI+lotXqcGJ/Fs4fy6Vjjs5ijJ6WAP/g9q30xqYxRn5mNaSb3wA0+j1fXAdPuMxdAVFEH4aVsRNFSQXOP/oiGrMLAABCdxd0Xf8tnDpF3/d1bPJMhbQEm0/+guPXdxmUDvd2CcCUvs+gd+wj4HLIqqm2hk2euY1JSdMHH3xAZ9/UrW7Qt/lvnMfj4cknn8SyZcvI0r0WqK+vf/BJBMIdEM8QjIUpz9TI1fi/XdnIr9MnTG52fHwxMgLBriRhuhNpbRN2rL+M0sLmflXhMZ4YPrkT7CTCdtfDhjFGW18O2e7PIT/9Jx0TBHeF67yV4Ln4MaiMvTTmFuH8lBehKNJXixP7eaHrhu/gEBnywNeywTP18lr8feo37L+8BVqdho672ntgYu8nMTB+HCkb3o6wwTP/xaSkqaioCKNGjUJSUhJeeOEFREREAACysrKwdOlSXLlyBRs2bIBMJsN3332HX375BX5+fnjnnXfMKt4a4PFIBRaCcRDPEIyFCc9UNarw5q5sFEn1MwAeEgEWj4pAgDPZk3Mn2Wnl2LPlGhRN+uV4XB4HycOj0bl3MGNLgyx1jNHWl0OR+i8Ul7dDlXMKuOOhraTPfDhN+B84fPM0+LU1yvccw7VXP4O6Rp+4S0ID0G3j97AL9G3V6y3VMwCgo3Q4cmU71h5dCpmi+cGEvdgJ43rMxSOdH4VIQB7ktDeW7Jl7waH+O1XUCsaPHw87OzusW7euxePTpk2DRqPB5s36JmgjR45EdnY2MjMzWzy/PdFoNDh69CgSEhJY+QcjEAgES6e8QYU3d2WhtEEFAPB2EGLxyAj4OpEb2ttoNDoc25OBS6fy6Zizqx1GT0+Eb4Azg8osC620DIortxOl0waJEgCAL4LzlK8h6TGDGYEsRytXIP3DpSj8cysdc4gJQ7eN30Pk5c6gMvOQX5GJ5fs+Q1bJFTomEogxsutMjO72GOzFpNUBAdBqtUhNTUVycjL4/HvPJ5k003To0CEsXrz4nseTk5OxcOFC+ueRI0fi9ddfN+WjrJ4bN24gNjaWaRkEFkE8QzCW9vRMab0Sb+zKQoVMP3Pi6yjE4pGR8HZs/2VmlkpdtRz/rr+M8uLm5SlRHb0xbEJHiO2YXx7E9BjzwEQJAM8zHOLEcZB0nwG+ZxgDKtlPQ1o2Up95H7LM5n103qMGoOPXCyFwMa5pMtOe+S9ypQybTvyCPZfWG+xb6hs3AjMHvARXB1LAjGkszTOtwaSkSSQS4ezZs3jmmWdaPH7mzBkIhc0XSI1GAwcH0oejJUyY6CPYOMQzBGNpC89QFIV6pRblDSqUyZQoa1ChvEGFk/l1qJHr9wsEOIuweGQEPOxJwnSb9Cul2Lf1GlRKLQCAx+di4MgYJPQItJhKXUyMMVppafPSu9wz90iUImCXOA7ixHHg+3WwmO+LbVAUhfzfNyHz4x+hU+png7l2IsR+/DICZo416Xu1lOsSRVE4k7Effx36BrWySjru5xaCx4cuRIfgbgyqI9yJpXjGGExKmqZPn44ffvgB7u7uePbZZxEaGgoAyM3NxY8//ojVq1fjueeeo88/fPgw6xpYtRcuLi5MSyCwDOIZgrGY6hmZUoNymQqltxKisgYVym8nSDIVmtS6e7422EWML0ZGwE3C/MyJJaBWa3FkZzpSzxXSMVd3CcZMT4SXn3FP9dua9hxjVLln0bDj43vPKHlFwi5xLMSJ48H3jSOJ0kOirKzBtZf/h8qDp+mYY8dIJPz0YasKPtwLS7guldYU4I8DX+BK3hk6JuSLMLH3Exjd7TFS5MHCsATPGItJSdPixYtRXl6Ob775Bt9++y1dFU+n04GiKEyaNIlevqdQKNClSxf07t3bfKqtCCcny7pYEiwf4hmCsRjrmSM3a/Hr2WJU3eoVZCzxPg54Z3AIXCxgqZklUFcjx/Y1KagsbaBjsYm+GDquA4Qis7RLNCvtMcZQOh0aD36Pht2fAjqtwTF9ojTuVqIUSxIlM1F15CyuvPAxVJU1dCz46amIXvQsuKKHmw1m8rqkUiuw/exKbD+7Ehpt85jVObwf5g5+A14upO2NJcLGexmTRmuxWIwNGzZg4cKF2LNnD/Lz9RtZg4OD8cgjj6Bz584G57733nvmUWuFFBQUkFk4glEQzxCMxRjP7MmoxrfHC3C/hRMCLgdeDkL4OArh7Xjrfx1E8HEUwsdBCBc7PrnRvUXBzWr8u+4y3ayWL+Bi8Jg4dOzib7HfUVuPMdqGCtStfhaqjMN0jOcZAbvOE/RL73xIomROdEoVMj/7BXk/NxfvEnq4otOSd+E5qKdZPoOp61JKzkn8ceALVNQV0zEPJx/MHfwGukYOaHc9hNbDxnuZh3rElZSUhKSkJHNpIRAIBAKD/JtWiaWniuifY70kCHaxoxMjHwchfBxFcJXwwSU3tfeFoiiknC7A4V3poHT6FNTVXYKxM5Pg6WO7FbuUmcdQt/pp6OrL9QEOBw7DXofDsDfA4VnerBvbkWXn48qz76P+anP1Yo9BvdDp+7ch8nRjUNnDUVVfhr8OfYNzmQfpGI/Lw+huj2FCrycgFpIS4gTzQ0YohgkMDGRaAoFlEM8QjKU1ntl6rQI/nWl+Wjupoyee6mG5syGWjEajw4Ht13HtYvP3GRrlgVFTEyyiOt6DaIsxhtJpIdu7GLJ9X9F7l7hO3nCZ9TNEUclm/zxbh6IoFK39F+nvfAdtk765NEcoQPS7CxD8+BRwbm2rMBftcV3S6jRIK7yEsxkHcPz6LijVTfSx2MAueHzoQgR4kEqKbIGN9zImJ027d+/GN998g0uXLkEqlbZYBUOr1bbwSsKdNDQ0wNHRdp86EoyHeIZgLA/yzMYr5Vh+roT+eVqCN+Z19SUJkwnI6hXYviYFpYXNTTS7J4ei79AocLns+D7NPcZo60pQt+ppqG6epGPC6AFwmfUzeI5eZvscgh51XT2uvf4Fync0L3+0jwxGws8fwalDZJt8ZltdlzRaNdIKL+JM+gGczzqMhqY6g+POEjfMHPgy+sWNJOMVy2DjvYxJSdOWLVvw6KOPokOHDpg2bRp++uknzJgxAxRFYfv27YiMjMT48ePNLNU6qaurg5+fH9MyCCyCeIZgLPfzzJqUMvx5sZT+eVaSDx7r7ENuQEygtLAO21anoLFBCUC/f2n4xE6ISfBlWJlxmHOMUaTth3TNAugaq/UBLg+OIxbBfvBLZp/tsHUoikLZ9oNI/2AJlGVVdDxw9njEfPAieBJxm322OT2j0apxLf88zmYcwPmsI5AppHedI+SLMKDTODza71k4iNlXUIDAznsZk5Kmzz77DN27d8eJEydQW1uLn376CfPnz8egQYOQl5eHnj170mXICfeH3JgQjIV4hmAsLXmGoij8dakMa1LK6Ni8rr6YnujTntKshqsXi3Bg23VotfpVF44uYoyf1RneFlZOvDWYY4yhtGo07PwEjYeW0jGuix9cZy+HMMw8xQcIzcgy85C26GvUnLhIxwQujujw9VvwGTWgzT//YT2j0apxNe8szmQcwIXso2hU1N91jkggRlJYX/SMHoLEsD4QCyUP9ZkEZmHjvYxJSVNaWho+++wz8Hg88Pn6t1Cr9ZWBQkJCsGDBAnzxxReYPXu2+ZRaKWzrhkxgHuIZgrH81zMUReH38yXYeKWCjj3V3Q+T473bWxrr0Wp1OLorA5dO59OxgBBXjJ2RBIkDO5v6PuwYo6kuQN1fT0Cdf4GOiToMh8uMZeDas7f4gCWiaZTj5jd/IO+X9aA0zVsiPIf0RofFb0Ls1z7LH03xjEarRmruaZy9lSjJlbK7zhEJ7NA5vN+tRKk3RAJS4MFaYOO9jElJk0QigVCovxi4uLhAJBKhtLR5eYe3tzdyc3PNo9DKycjIQHR0NNMyCCyCeIZgLHd6hqIo/Hy2GFuvVdLHF/QKwPgOnkzJYy3yRhX+XXcZhTnNfW8SewZh4KgY8HjsXXr2MGOM4soO1K17AVTTrSVVPAGcxnwASfIzrHyybKlQFIXyHYeR/v4SKEqaH37YBfoi9n+vwGtY33bVY4xnKIrC+azDWHtkCcrqCu86LhZI0CWiP3pED0ZiaG8IBW23rJDAHGy8lzEpaYqOjkZaWhr9c2JiIlatWoVZs2ZBo9Fg7dq1CAoKMptIa4YUyyAYC/EMwVhue0ZHUfjhVBH+vdG83+HFPoEYHevBlDTWUlnagK2rL6G+Vl/Bi8vjYMjYOMR3Y19FqP9iyhhDqRWo/+cDyI//Ssd47sFwmfM7hEGd7/1CgtE03ixA2tvfoPrIOTrGFQkR+twshL3wGHh2onbX1FrPZJVcxerD3yKjONUgbie0R5eIZPSMHoL40J4Q8tv/dyC0L2y8lzEpaZowYQKWLFmCr776CiKRCG+//TbGjRsHFxcXcDgcNDY2YsWKFebWapWwsSMygVmIZwjG4uTkBB1F4fsThdidod+QzwHwav8gPBLlzqw4FpJxtQy7N1+FRq2/6EschBg3Mwn+wa4MKzMPxowxlEYJ+ZnVkB34Frq65gqM4oSxcJ62BFw7Ml6ZC01jE3KW/IncH9eCUmvouMegXoj93yuwDw1gTNuDPFNeV4T1x37A6fR9BvHYwC4Y3W0W4kN6QsBn53JWgmmw8V6GQ7VUK9wEjh8/jr///hs8Hg+jRo3CwIEDzfG2Zkej0eDo0aNISEgAj8djWg7kcjkkErKZkdB6iGcIxtIga8RPF6twIEu/jIzLAV7vH4whkWR/iTFQFIWTB7Jx5vBNOuYT4IxxM5Pg6Gw9S4haM8bcK1kCXwSnCZ9C0nsuWY5nJiiKQsXuY7jx7ndQFJfTcbG/N2I/eRlew/sz/l3fyzOyJim2nv4dey5tgFbXnOj5uQVjRvJL6BLBvHYCM1jSvYxWq0VqaiqSk5PpWg0tYfRMk1KpxN69exESEoL4+Hg63q9fP/Tr1880tTZMXl4e4uLimJZBYBHEMwRj0OgofHIgGylVOgD6hOmtgSFIDrOOWZH25OT+LJw5kkP/HJfkh2HjO4AvYP4BnDm53xhzz2QJ+mIPjqPfgcCXjE/mojG3CDfe/hZVh07TMY6Aj9AFMxD24hzw7S2jMMJ/PaPWqLAvZRP+Pr3coBKek8QVk/s8jUHx48HnWX6jZ0LbwcZ7GaOTJqFQiClTpuD77783SJoIBAKBYFmotTp8djifTpj4XA4WDQpB3xAXZoWxkIsn85oTJg4wYEQMuvQJtpmn5PpkaQ1kB75pOVka/iYEgYnMiLMyVLX1qDt/BVVHz6Fw1XZQKjV9zD25G+I+fQ324Za5b5yiKJzNOIC1x5aioq6Yjgv4IozqOhNje8yBROTAoEICwXSMTpo4HA4iIyNRVVX14JMJD8Tf359pCQSWQTxDaC0/nC7Cibw6AICAy8G7Q0LRM8iZWVEsJC2lBId3ptM/Dxodi869ghlU1LbcOcaQZKntUZRWovbsZdSeSUXN2VTIbty86xyxnxdiPnoJ3qMGWGSi7u/vj4yiy1h95DtklVyl4xxw0K/jKDza91l4OJEecIRm2HgvY1IhiEWLFuHVV1/FlClTWFcu0NJoamqCszO5iSG0HuIZQmvIq23C7nR90QcBF/hwWBi6BrBv4y3T5GRUYs+W5pvAXoPCrTphAvRjjJO9mCRLbQBFUZDnFaP29GXUnr2MmjOX0ZRfcs/zOQI+Qp6ehvBX5oJvbxn7P/5LWW0h/tj7JVILThrEOwR1w6yBLyPUO4YhZQRLho33MiYlTWfOnIG7uzs6duyIAQMGICQkBHZ2hutqORwOvv/+e7OItGZqamrg40OevhBaD/EMoTX8dbEUt6v8DA/kkYTJBIrza/HP2hTodPpvMrFHEHoPjmBYVdtCaZSQnfgdnGvrSLJkJhpzi1B16Axqz6ai9sxlKCuq730ylwunjlFw7ZkA1x4JcOuZCKG7S7tpNQa5Uoatp3/HrgtrDYo8BLiHYeaAl5AY1sciZ8UIlgEb72VMSpqWLVtG//+DBw+2eA5JmggEAoEZMivlOJGnby7qZsdHPx/2NlplisqyBvz950Vo1Pr9YNGdfDBoTKxV3wSq8s5Duu4F2JdnQndHnCRLpqFpaETW4t+Q//tmQKdr8RyuSAjnpFi49kyEa48EuHbtBL6jfTsrNQ6dTosj1/7FhmM/QCpvbuzsbO+OKX2ewcD4seBxTbq9JBAsGpNcrbvHP36C8cTGxjItgcAyiGcID2LlxeYZghlJPkggzWuNoq5Gjs1/XIBSoX96HhzhjhFT4sHlWmfCRKnkaNj1KRqP/gTc0YWEJEumQVEUynccxo13v4OyzHD/N89BAtdunegkyTkxFjwxexq53ihMwV+HvkJuefMePwFPiFHdZmJcj3mwE1l2wkewHNh4L2NRjwKOHTuGL7/8EhcvXkRpaSm2bt2K8ePHt+q1J0+eRHJyMjp27IjLly+3qU5zkp2djcjISKZlEFgE8QzhflwpleFCUQMAwNtBiBHR7sQzRtDYoMTmPy6gsUEJoLkPE59vnbN1ypunIF33IrRVzaXUtV6x8Jq1DMKgJAaVsRN5fjHS3vrGoEQ4106E0Gdnwmt4PzjGhYN7nz4wlkqltBRrjnyPMxn7DeI9ogdjZvJLkFbKScJEMAo2Xpce6l/umTNncPjwYVRUVGDBggWIjIyEXC5Heno6oqKi4OBgXFnJxsZGJCQkYP78+Zg4cWKrX1dXV4fZs2dj8ODBKC8vf/ALLAi1Wv3gkwiEOyCeIdwLiqKw8kLzLNNjnX0g4HGJZ1qJUqHGlj8voq5aDgBw87THxDldIBSx7yb3QeiUMjT8+xHkJ5Y3B/kiOI54CwXeAxAQRFqKGINOpUbuz+tw85sV0ClUdNxzSG/EfvoaJEG+DKozHYWqCf+cXYl/z6+CWqOk48FeUZgz6HXEBXUBAFSVpDElkcBS2HhdMulKoFKpMG3aNGzfvh0URYHD4WDMmDGIjIwEl8vFsGHD8Morr+Dtt9826n1HjBiBESNGGK3nmWeewYwZM8Dj8bBt2zajX88kxiaWBALxDOFenC+qx7XyRgBAoLMIgyPcABDPtAaNWoutqy6hokTfiNPRWYzJ87pCYi9kWJn5UWYcgXT9S9DWFtIxQWh3uExbCr53JBwKChhUxz5qTqfg+ptfojErj46JfD0R+8kr8B6ZzMp9cBRF4WTabqw9uhQ1sgo67iRxxaN9F2BQ/Dhwuc1NnckYQzAWNnrGpKTp3XffxY4dO/DTTz9h4MCBBmXHxWIxpkyZgu3btxudNJnCH3/8gZycHKxevRqffPJJq1/X0NAALrd5uYVIJIJI1P7rij09Pdv9MwnshniG0BI6isLKC6X0z3O6+oJ3aw8O8cz90Wl12LEhFUW5tQAAO4kAk+d1hZOL3QNeyS50TfWo3/4ums6sag4K7OA0+l1I+j0Jzq2bYOKX1qGqrkPGxz+geP3O5iCXi+AnpyDyjSfAd2DncrXs0mv48+BXBv2WeFwehneZjkm9n4BE5HjXa4hnCMbCRs+YlDStW7cOzz77LJ566ilUV99dOjM2NhabNm16aHEPIisrCwsXLsTx48fBN3KNcMeOHSGXy+mf582bhxdeeAG+vr64eVPfWM7b2xsURaGiQv+UJTIyEkVFRWhqaoJYLEZgYCCysrIAAF5eXuByuSgrKwMAhIeHo6ysDI2NjRCJRAgJCUFGRgYAwMPDA0KhECUlJWhoaEB8fDwqKyshk8kgEAgQERGBGzduAADc3NxgZ2eH4mJ9Z+2QkBDU1NSgvr4ePB4P0dHRuHHjBiiKgouLCxwdHVFYqH96GBQUhPr6etTV1YHD4SA2NhYZGRnQarVwcnKCq6sr8vPzAQABAQGQy+WoqdFXwomLi0NmZiY0Gg0cHR3h4eGB3NxcAICfnx+USiX9t4+JiUFOTg5UKhXs7e3h7e2NnBz9+nhfX19oNBpUVlYCAKKiolBQUACFQgE7Ozv4+/sjOzub/r4B0EssIyIiUFxcTH/fQUFByMzMBKD/x8bn81Faqr9JDAsLQ3l5ORobGyEUChEWFob0dP1GVXd3d4hEIpSU6JcthYaGoqqqCg0NDeDz+YiKikJaWhr9fUskEhQVFQEAgoODUVtbe8/v28nJCQW3nsoGBgaioaHhnt+3m5sb8vLyAOibujU1NdHfd2xsLLKzs6FWq+Hg4ABPT0+D71ulUtENpSmKglgshlKphL29PXx8fGjP+vj4QKfTGXi2sLCQ/r4DAgIMPMvhcOjvOzw8HKWlpZDL5RCJRAgODr7v911RUQGZTNbi9y0Wi1v07H+/b1dXVzg4OBh4ViqVQiqVgsvlIiYmBunp6dDpdHB2doazs7PB9y2TyVBbW3uXZ1v6vhUKRYuedXBwgJeX1309m5+fD6VSCYlE0u5jxG3PPmiM2JlagOxqfeGCICc+3OQlyMioQHR0NK5cuQJHR0cyRrQwRlAUhazLcmSn6bXy+ByMmtYRcmUtytMKWDlGREdHIy8vz2CMyD+8GvbHF4PbqP+eAUDtmwSv2T+jpIkHRXoGPUbc9gsZI1oeI3y8vVG+ZR8Kv1oBXX0j/X0Ko0Pgt+gphA7ohUwLHCMedB9R21iB03k7cD7nEO4kzr8bBkZOgYejHyQixxbHiIaGBsTGxlrlGHHbs9Z0H9HSGNHe9xFarRbOzs4WMUbc/g4fBIei7iiV00rEYjGWLVuGJ554AtXV1fD09MSBAwcwaNAgAMAPP/yAN954wyApMRYOh3PfQhBarRY9e/bE448/jmeeeQYA8MEHH2Dbtm33LQSh0Whw9OhRhIWFWcRMU1paGuLi4tr9cwnshXiG8F+0OgpPbbmBQql+z8Enj4She2Bz00DimXtzbE8Gzh3T31hweRxMnN0FIZHWU21Q11iL+m1vo+n8ejrGETnAceyHkPSaAw737gIXxC/3piE9B2kLv0TtmVQ6xndyQNSiZxD42DhweLz7vNryKK7OxbnMwzifeQg55TcMjvm7h2L2oFeRENr7ge9DPEMwFkvyjFarRWpqKpKTk+87CWPSTFNgYCCdDbbEyZMnERHRtg0AGxoacOHCBaSkpOD5558HoC+FTlEU+Hw+9u3bRydxLeHo6AieBQxufn5+TEsgsAziGcJ/OZhdQydMHb3t0e0/jWyJZ1rm/PFcOmECBxg5Jd6qEibFlR2QbnoduobmPSmimEFwnvodeK4B93wd8YshWrkCdZeuo2LfCRSs2AxKo6WP+U4YipgPX4TIy51Bha2HoijklN3AuaxDOJ95GCU1eXedYy9yxOS+T2No4mTweYJWvS/xDMFY2OgZk5KmGTNm4JtvvsGkSZMQFRUFAPRGx99++w0bN27E559/bj6VLeDk5ISrV68axH788UccOnQImzdvRmhoaJt+vrlQqVQPPolAuAPiGcKdqLU6rLpURv88t6vfXRvPiWfu5tqlYhzdnUH/PGRMHGLi2Vnh7E4oioIq+yQaDy2B8sYBOs4RO8Fpwqew6z79gYUJbN0vamkDas9dQe2Zy6g9mwppajootcbgHEloAOI+fx0eyd0ZUtl6dDot0osu04lSdUPLVYZDvWPQPWoQBidMhJPE1ajPsHXPEIyHjZ4xKWl6++23cebMGfTv3x+xsfoO6a+88gpqampQVFSEkSNH4pVXXjH6fWUyGb0uFQByc3Nx+fJluLm5ISgoCG+99RaKi4vx119/gcvlomPHjgav9/LyglgsvituyVRVVcHLy4tpGQQWQTxDuJPdGdUol+kvPl0DHBHve3dFIuKZZnRaHU4fvokzh2/Ssd6DI5DYM4hBVQ8PpdVAkbodjYd/gLrwssExUYfhcH70a/CcW5cU2ppfFOVVqD2Titqzqag9cxkNN24aNPm9E45QgLAXHkPYC49ZdFNatUaFq/lncT7zMC5kH0VDU91d53DAQXRAIrpHDULXyAHwcjb9yb+teYbw8LDRMyYlTUKhEHv27MGaNWuwefNmaLVaKJVKxMfH45NPPsFjjz1mUonNCxcuYODAgfTPr776KgBgzpw5WLlyJUpLS+nNWwQCgWDrKDQ6rE25Y5apC/uWO7QndTVy7Np4BSUFdXQsqWcQeg0KZ07UQ6JTNKDp7Bo0HvnJoIQ4APBcA+A4+j2IO09iZdnrtqKpsBTVJy/RSZI8t+i+59tHBMG1RwJceybCvX83iL0tdwlno6IBG47/gOPXd6FJ1XjXcR6Xj04hPdAtcgC6RCTDxZ4dywoJBEvApEIQbOZ2IYiEhASL2NOk1WotQgeBPRDPEG6z8Uo5lp/TV3PqG+KM94aEtXierXuGoihcTynBwX/SoFbp96NwuBz0HhSOngPCweGyL6HQSkvReOw3yE/9AapJanCMH5AAh4HPQ5w4Dhye8c9GrdEvmkY5yv45hOL1O1F7NvXeJ3K5cOoQAdeeifpEqUcCRJ5u7Sf0IbiQdQS/7/sMtY1VBnGRQIzEsD7oFjkQncP7tlgy/GGxRs8Q2hZL8kybFoJ48803MX36dCQlJZkskKAnLy8P4eHsfcpJaH+IZwgA0KjSYkOqfm8CB8DsLvdeemXLnlE0qbFv63VkXmuekXNxk2DU1Hj4BrowJ8xE1KU30Hh4GZoubga0aoNjorihsB/4PIQRfR9qZsla/EJRFGrPpqJ43Q6U/XsYWnnTXedwhAK4JMXRM0kuXTtC4MSuppv18lqsPPAlTqXvpWNigQTdowehe+QgxIf0gFAgblMN1uIZQvvBRs+YlDQtXboUX3/9NcLCwjBt2jQ8+uij6NSpk7m12QRKpZJpCQSWQTxDAIAtVyvQoNTPmgyOcEWI670bsdqqZwpuVmP35qtokCroWMcu/hg0OhZCkUmXP0agKAqqrONoPLzMoLgDAIAngF2XKbAf+BwEvrFm+Ty2+0VRUoHijbtQvGFXi0vvHKJC4TNuMNx6J8E5Kc6i9ybdD4qicDp9H/44sNhgz1JSWF888cgiuDt6t5sWtnuG0P6w0TMmXTUqKiqwdetWbNiwAYsXL8ann36KmJgYOoGKjo42t06rxd6enR3DCcxBPEOQKjTYck1fRprHAR7rfP8N/rbmGa1GhxMHsnD+eC5wawG62E6AoeM7ILqTD7PijICiKChSt0N24HtoigyXlHHsnCHpMx/2/Z4Ez9m8vxMb/aJVKFGx5ziKN+xE1ZFzdxVy4Ds5wHf8UPhPGwXnpFjW7/GqaajE7/s/w8Xso3TMQeyMuYNfR5+4Ee3++7HRMwRmYaNnHnpPU11dHbZs2YKNGzfi8OHD0Gq16NSpE6ZNm4aFCxeaS6fZsLQ9TUqlkpGmugT2QjxD+PVsMTZf1SdNo2M88GLfwPueb0ueqa6QYdfGKygvqadjQWFuGDElHo7ObbtEyZwoM4+i4d+PoC5MMYjz3IJgn/ws7HrOBFfUNsvI2OIXiqJQfyUDxet3onTrPqjrGgxP4HDg3q8r/KeNgveIZPDsLP93ehAUReHI1X+w6vA3kCtldLxn9BDMHfImY4Ud2OIZguVgSZ5p7Z4msxaCqK6uxqpVq/D+++9DJpNBq9U++EXtjKUlTZbUEZnADohnbJuqRhXmbkyDSktByONg5aNx8LAX3vc1tuAZiqKQeq4QR3alQ6PWAQC4PA76DYtC1z4hrCn2oC5MRf2OD6HKOGIQFwQmwn7g8xAnjDWpuIMxWLpfdBoNSrfuR95P69CQln3XcbsgP/hPHQn/R0fALpD9vbduUyEtwW97P8HVvLN0zNneHY8PXYjuUYMYVGb5niFYHpbkmTYtBPFf1Go1du/ejQ0bNuDff/+FTCZDYOD9n3wSCAQCwXjWppRDpdU/6xob5/nAhMkWaJQpse/va7iZXknH3DztMWpqArz9nBhU1no0Vblo2Pk/KFL+Nojz/TrCcfS7EMUOYf2SsodFp1KjZPMe5Cz5C/K8YoNjXDsRfEYNhP+0UXDrnQQOl8uQSvOjo3TYd2kj1h1bBqW6uZhF/w6jMHvQa3Cwc2ZQHYFgO5icNGk0Guzbtw8bNmzA9u3bUV9fD19fX8ybNw9Tp05F7969zanTavHxYc/6eoJlQDxju5TWK7E7Q19O2E7AxdSE1m30tmbP5GRUYs+Wq5DLmrvLJ/YIQvKIaAiEzK8meBDahgrI9n4F+amVgE5Dx3luQXAc+ba+x1I7JwCW5hedUoWidTuQs3QVFMXlBsecO3dAwIzR8B03BHxH9u2ReBAlNfn4Zc9HyCi6TMfcHb3xxCNvIymsD3PC/oOleYZg+bDRMyYlTY8//ji2bduG2tpaeHh4YPr06Zg2bRr69+9v80/CjEWn0zEtgcAyiGdsl1WXSnFrkgmTOnrBWdy6IdwaPVNSUIdTB7ORl9Xck8bOXojhkzoiPMbyu8zrFPVoPLQMjUd+AnVHE1KuvTschr0OSZ+54PCZWe9vKX7RNilRuGY7cn9YA2VppcEx935dEf7KPLj1ts7WJxqtGjsvrMHmE79ArW1+IDAkcRJmJL8ISRvtZzMVS/EMgT2w0TMmJU3btm3DhAkTMHXqVAwaNKjFvUG1tbVwdXV9aIHWTkVFBTw8LLe7OMHyIJ6xTfJqm3AwuxYA4CjiYVKn1icG1uSZ0sI6nDyYjbxMwwaeoVEeGD6pE+wdLWNj8b2gNErIT/4B2b6voWuspuMcoT3sBy6A/cDnwBUzu6SQab9oGuUo/HMbcn9aC1VljcExj0G9EP7qXLh2td42JzcKL+H3/Z+jqOomHfN2CcBTw99Fh6CuDCq7N0x7hsA+2OgZk5Km8vLyFjdKKZVK/PPPP1izZg327NkDhULRwqsJBAKBYCx/XSy9XT0bU+O9Yc+CpWfmpLRQP7OU+59kycnVDr0GhaNjZ3+LXulA6bRourgZst2fQVtT0HyAy4ekzzw4DHsNPEfLnyFrS9T1MhSs2Iy8XzdAXSM1OOY1vB/CX54L50Tz9KKyRKSNNVhz9Hscu7aDjnHAwciuM/Bov2chEty7FxuBQGh7TEqa7kyYKIrCwYMHsWbNGmzduhX19fXw9PTEjBkzzCbSmomMjGRaAoFlEM/YHpmVcpzI099EutnxMbaDp1GvZ7NnSgvrcOrQTeRmGC7PcnIRo+fAcHRI8gePb9mb/jVVuaj760moCy4ZxMWdJ8Fx5CLwPUIZUtYy7e0XdV098n7biPzlm6CR3lE2nMOBz+iBCH9lLhzjItpVU3ui02lxMHUr1h9bhkZl8+8f5hOHJ4YtQpiP5SeKbB5jCMzARs+YXAji4sWLWLNmDdavX4+ysjJwOBxMmzYNzz//PHr27GnRT/wsicLCQoSFhTEtg8AiiGdsC7VWh5/OFNE/z0jygdjIJIGNniktkuL0wWzk/CdZcnQRoxdLkiUAaLq8HdL1L4JSNN8MC6MHwmn0exAEJjCo7N60l190Gg3yf9uI7K9XQCuTNx/gcuE7YQjCX5wDh2jLSijNTU7ZDfy+/zPcLL1Ox+xFjpjW/3kMTpgALpcdM8psHGMIzMJGzxiVNOXk5GDNmjVYs2YNsrKy4O/vj5kzZ6J79+6YOnUqJk2ahF69erWVVquELGEkGAvxjO2goyh8dawA18v1hQK8HYQYEW1880o2eaasSIpTh7KRk353stRzgH4ZHhuSJUqjRP329yA//hsd43mGw3nKVxBFJTOo7MG0h1+kKWm49sYXaLiWRcc4fB78Jg9H2IuzYR9m3W1LGhUN2HjiJ+xL2QSKat4Q37/DKMwY8BJjTWpNhU1jDMEyYKNnWp009erVC+fOnYOHhwcmT56M5cuXo2/fvgCAmzdvPuDVhHthZ0fWKBOMg3jGdlhxvgSHb+qLP4h4HCwaFAIBz/iEgQ2eKS+W4tTBbINeSwDg6CxGzwFh6NglgBXJEgBoqvJQ9+fjUBem0DFx0kQ4T/0WXLEjg8paR1v6RSNrRNbnvyJ/xRbgdvUsDgcBM0Yj7MU5kAT7tdlnWwIUReFk2m6sOvIdpHcUAglwD8P8oQsRF9SFQXWmw4YxhmBZsNEzrU6azp49i9DQUHzzzTcYNWrUfTvmElpPQEAA0xIILIN4xjbYfr0SG69UAAC4HGDRoFDEepnWh8aSPaPTUTh1MBtnDhs+fHN0FqPHrWSJz5JkCQAUV3agbu3zoBT1+gBfBKcJn0HSew5rlq23lV/Kdx9F2qJvDMqHO8ZFoMNX/weXzh3a5DMtieLqXKzY/zmuF1ygYyKBGJN6P4WRXWeAzxMwqO7hsOQxhmCZsNEzrc58li1bhrVr12LChAlwc3PDpEmTMG3aNAwYMKAN5Vk/WVlZiIuLY1oGgUUQz1g/J3Lr8OPp5n1Mz/cORK9gZ5Pfz1I9o2hSY+fGKwZFHhydxeiRHIaOXdmVLFEaFer/eR/yY7/QMZ5HGFznroAgIJ5BZcZjbr8oSiqQ9vY3qNh9jI5x7USIeO1xhDw9DVyBdT+EVaqb8Pfp37Hj3Cpo72hg3C1yIOYMfg0eTr4MqjMPljrGECwXNnqm1SPVggULsGDBAuTm5mLNmjVYu3YtfvvtN/j4+GDgwIHgcDiseYpGIBAIlsq1Mhk+O5JHlxefnuCN0bHs6mXRGirLGrB9TQrqqvUFADgcoO/QSHTpG8qqZAkANNUFqPtzvkF1PHHieDhP+47xnktMQmm1KPjjb2R+/otBoQePgT0Q9/nrkAT7M6iufcgtu4Fvt/8fKqTFdMzT2Q9zB7+BLhH9GVRGIBCMxejHO6GhoXjnnXfwzjvv0BX0NmzYAIqisGDBAuzevRtjx47FkCFDIBaL20KzVeHlZdt9OQjGDVAfxwAApOpJREFUQzxjvRTUKvD+/hyotfqUaUikG+Z2ffin0JbmmYyrZdiz5SrUKi0AwE4iwOhpiQiOYNfmdwBQXN2FurXPgWq61VeIJ4TThE8h6TOPtQ8SzeGX+utZuP76F5CmpNExoYcrYj5+Cb7jh7L2uzGGE9d34Ze9n0CtUQIAeFw+xvaYg/E951ldzyVLG2MIlg8bPfNQc+JdunRBly5d8NVXX+HQoUNYvXo1NmzYgOXLl0MikUAmk5lLp9ViCxcOgnkhnrFOquVqvL33JhqU+kSis78jXukbaJa/t6V4RqejcGJ/Js4dzaVjXr6OGDcrCc6uEgaVGQ+lUaFhx0doPPIjHeO5h8B17h8WW0q8tTyMXzSNTbj59Qrk/bIelFZLxwNmjkHUO89B6Gr9M29anQZrjyzBzgtr6FiEb0csGPkh/NxDmBPWhljKGENgD2z0jFkWEnO5XAwZMgRDhgzBzz//jO3bt2Pt2rXmeGurp7y8HO7u7Hu6SmAO4hnro1GlxTt7b6JcpgIARLjb4b3BoSZVymsJS/BMk1yFnRuuIC+rio7FJvpi2PiOEAjZ0YvmNpqaQv1yvPyLdEycMAbO05aCa8f+pMBUv1QeOoO0//sSTYWldMw+MgQdvnwTbj0TzajQcmloqsP3/7yFa/nn6NjATuMwf+hCCPhCBpW1LZYwxhDYBRs9Y/bdl2KxGFOnTsXUqVPN/dYEAoFgdai1Onx0IBc3q5sA6HsxffxIOCQsSyTuR2VpA7atuQRpjf535HA5GDAiGp17B7PuaaPi+l7UrXkWlLxOH+AJ4DT+E0j6PsG638UcUDodKg+cRv5vG1B9vLkqHEcoQPjLcxH23ExwRdabLNxJfkUmvtr6GiqlJQAAHpeHOYPfwNDEyTbpDQLB2rDukjUsIDw8nGkJBJZBPGM9UBSFb48XIKWkAQDgKOLhf8PD4S4xb+lhJj2TfqUUe7Zcg0bdvH9pzIxEBIWx6wkjpVag/p8PID/+Kx3juQfDZc4KCIOSmBPWBrTGL5pGOYo37Eb+8o2Q5xQaHHPr3RkdvnwT9uFBbSXR4jidvg8/7/4QSrW+YaezxA0vj1uM2EDr8sa9INclgrGw0TMkaWKY0tJShISEMC2DwCKIZ6yHPy6U4kC2vnmtkMfBR0PDEORi/gI6THhGp9Xh+L4snD/evH/J298J42YmwcmFXZvg1aVpqPvrKWhKm4saiOJHw2XaUnAlppeCt1Tu55emojLk/74ZRWv+gabecN+yJMQf4a/Mg9+jI2xmZkWn02L98R/wz9k/6ViYTxxeHf8lPJx8GFTWvpDrEsFY2OgZkjQxjFwuf/BJBMIdEM9YB/+mVWJ9ajkAgANg4cAQdPBxaJPPam/PNMlV2LE+FfnZ1XQsLskPQ8d3gEDAnmWHFEVBfuJ31P/zHnBrBgECMZzGfgRJ38etNjH4r18oikLdhWvI/3UDyncdNSjwAABufbsg5Kmp8BzcCxwee/6+D4tMUY+l/y5Cau5pOta/42g8MfQtCAW2VT2YXJcIxsJGz5CkiWFEIhHTEggsg3iG/ZzKr8MPdzSvXdArAH1DXNrs89rTMxUl9di2JgX1tfr9S1wuBwNGxSCpZxCrkgytrArSdS9AeX0vHeP7xsJl9m8Q+LKrIaOx3PaLTq1B2Y5DyP91o0HpcEC/Z8lv4jAEP/konDpEMiGTUQqrbuKrv19FeZ3+3zGXw8Njg17B8M7TWOVzc0GuSwRjYaNnSNLEMMHBwUxLILAM4hl2k1beiE8P5UF3q3vto/FeGNfBs00/s708k5dVhW2rL0Gj1gEAJPZCjJmRiMBQt3b5fHOhTD+EurXPQVdfTsck/Z+C05gPwLGBGQRfJxfkLP0L+Su2QFlaaXBM6OGKoLkTEThnAkSe7Pq7motzmYfw4873oVDrn5Q72jnj5bFfoENwN4aVMQe5LhGMhY2eIUkTw2RmZiIuzrqfWhLMC/EMeymsU+DdfTehutW8dlC4K+Z382vzz20Pz1SWNeCftSl0wuQT4IxxM5Pg6MyeJIPSKNGw42OD3ktcBw84z/gB4rihDCprHyiKQu6yVcj6agUopcrgmGOHSIQ8NRW+44fYTDW8/6KjdNh84hf8fXo5HQvxisarE76Cl3Pb/zu2ZMh1iWAsbPQMSZoIBAKhjdHoKPybVom/LpWhUaXfD5Lk54DX+geBawVLeRplSmxddQmqW415I+K8MHpqAvgs2r+kKc9E7V9PQlN8lY6JYgbDecYy8Jy8GVTWPuiUKlx7/QuUbNrdHORw4PVIXwQ/ORVuvZNsbtmZTFGP8toilNcVoryuCNfyz+F6QXNZ9d6xj+Dp4e9CJGBXYRMCgWAaJGliGE/Ptl2WQ7A+iGfYRUpJA348XYT8WgUdC3MT470hYWZrXvsg2tIzGo0O/9yxh8nb3wmjHmVPwkRRFJpO/wnp1rcBtf53AE8IpzHvQ9L/aXC47fM3YhJVjRQp899C7ZnLdCxo3iSEPD0VkpAA5oS1MRRFQSqvQXldEcprC1FWq0+OyuoKUV5bBJlC2uLrOBwuZia/iFHdZtlcInkvyHWJYCxs9AxJmhiGzyd/AoJxEM+wgwqZCr+cLcbx3DqD+CNRbniyuz/s27F5bVt5hqIo7Nt6DcX5dQAABycRxs/qDAFLGvPqGmtQt+FlKK/soGN87yi4zF4OgX9HBpW1H405hbg463W61xLXToTwz19D+NTRDCtrG0qq87D97ErkV2SirLaQ3pfUWpwlbnhu9MeID+nZRgrZCbkuEYyFjZ5hn2Iro7S0FK6urkzLILAI4hnLRqXRYePVCmy4XAblrb1LABDtKcGCXgGI9bJvd01t5ZlzR3OQllICAOALuBj/WGfW7GFSZh5D3ZpnoZOW0jFJn/lwGvcROEIJg8raj5ozl5EybyHUtfUAAKGnG7r8tRjF7Ctq9UAUKjn+Pr0cO8+vgVanue+5HHDg5ugFH9dAeLsEwts1AD4ugfB2CYC/eygEfNvc03U/yHWJYCxs9AxJmggEAsEMUBSF0wVS/HymGGUNzZvoncV8PN7ND8Oi3Kxi/9JtMq+V4fi+LPrnkVPi4eNv2Y1eKYqC6uYpyPZ/DVXGETrOsXeDy7QlEHcayZy4dqZk8x5cfeVTUGp9AuEQE4Yuq76EXaAvitPSHvBq9kBRFM5k7Meqw9+hpqG5GiKPy4Onkx+8XfXJkM8d/+vp7Ach3wozRwKB8FCQpIlhwsLCmJZAYBnEM5ZHYZ0CP50pwoWiBjrG5QDj4jzxWGcfOIiYHWrN7ZnyYil2bWoumNB3WCSiOvqY9TPMCUVRUN44ANn+b6DOPWtwTBiVDJeZP4Ln7MuQuvaFoihkf/U7bn69go55DOyBhF8+hsBJ31zZWsaYoqoc/HFgMa4XnKdjfJ4AY7rPxrge8yAWkgIO5sJaPENoP9joGZI0MUxFRQWCgoKYlkFgEcQzloNcpcWalDJsvV4Jja55KV6CrwMW9ApAqJtl3JSZ0zMNUgW2rroEjVpfKS8uyQ89ki3z4kfpdFBc+ReyA99CU3TF4BjPPQQOQ16GXY9ZNlHsAQC0CiWuvfoZSv/eR8cCZ09A7KevgHvH/gK2jzFypQxbTv6KPZfWQ6vT0vGksD6YM/gN+LgGMqjOOmG7ZwjtDxs9Q5ImhpHJZExLILAM4hnmoSgKB7NrsfxcMWqamvdHeNoL8HQPf/QLdbGoqlrm8oxapcW21Zcgq1cCAPyCXDBsQkeL+l0BgNKq0XRxC2QHvoW2IsvgGN8nGg5DXoU4aQI4PNu5BKqq63Bp3kLUnbuVPHI4iH7/eYQ8Pe2uvx9bxxiKonAybTfWHPketY1VdNzL2R9zBr+OLhH9GVRn3bDVMwTmYKNnbOeKYaEIhWRDKcE4iGeYpV6hwf8O5SGlpHkpnoDHwaPx3pia4A0x3/JmLczhGUpHYdemKygv1hcNcHK1w7hZSeBb0O9LqRWQn1uHxoPfQ1tTYHBMEJgIh6GvQtRxpM3MLN1Glp2PS7NehzyvGADAsxMj/sf34T0iucXz2TjG5Fdk4Y8DXyC9KIWOCfgijO8xF2O6z4ZQwI4CJWyFjZ4hMAsbPUOSJoZh45pOArMQzzBHsVSJd/fdRJFUScd6BTvjmR7+8HWy3I3j5vDMyQNZyLqu30gvFPEw4bHOsHewjN9Zp5RBfupPNB7+Abr6MoNjwvDecBj6KoTRAy1uRqw9qD55CZcffwvqOn2SL/L2QOe/FsM5Ieaer2HTGNOoaMCmkz9j36VN0FHNS/G6RQ7AYwNfhZeLP4PqbAc2eYZgGbDRMyRpYpj09HTExcUxLYPAIohnmOFamQwf7M9BvVJ/Y+Yi5uON5GB0C3RiWNmDeVjPpKWU4MyRHAAAhwOMnpYITx9Hc8kzCYqioCm7AcXl7Wg88TuoxhqD46KYwXAY9hqEYbbbT6d4wy5ce/1zukKeY1wEOq/6Enb+3vd9HRvGGJ1Oi2PXd2Ld0aWQypv/9j4ugZg75E0khvVmUJ3twQbPECwLNnqGJE0EAoHwAA5m1+CbYwVQ3yr2EOwixsePhMHH0TJmWtqS4vxa7P27uVLegJExCItmppM7pVVDdfMUFNf2QHl9D7TV+YYncDgQx4+Gw5BXIAhMZESjJUBRFLK//B03v7mjQt6gXkj89SPwHdq/T5g5kStlOHL1H+y5uB4V0mI6LuSLMKHXExjdbRbpo0QgENoEkjQxjLu7O9MSCCyDeKb9oCgKqy6VYXVK85Kvzv6OeHdwKOyFPAaVGYepnpHWyrFtdQq0t5r0JnQPROfeweaU9kB0cimUNw5AcW03lDcOgFLU330Slwe7zpNhP+QlCHzuvezMFqB0Otx45zsUrNhMx4LmTULMxy8ZVMi7H5Y4xlTUFWPPpQ04fGUbmlSNBsd6RA/GYwNfgYeTbZSNt0Qs0TMEy4aNniFJE8OIxWRzKsE4iGfaB5VWh2+OFeDQzVo6NirGHc/1DgSfy669MaZ4RqnQ4O8/L6GpUd+oNyjcHYPGxLbLviBNVR6U13ZDcX0vVDdPATrN3Sdx+RBG9IG44wiIO40EzzWgzXVZOjqNBtde/RwlG3fRsZiPXkLIU1ONeh9LGWMoikJG8WXsurAW57OOgKJ0BscTQnthbPc56BDcjSGFhNtYimcI7IGNniFJE8MUFxfD2dmZaRkEFkE80/ZIFRp8uD8H18r1T7Q5AJ7s7odJnbxYWUzAWM/odBR2bEhFdYW+JKyrhwRjZySCx2u7qnOqghQoruyA8tpuaMrSWzyHY+cMUdxQiDsMhyh2CLh2lr+frL3QKVVIXfABynce0Qe4XHT6dhH8p440+r2YHmM0WjXOpO/HrovrkFOWZnBMwBehX9xIjOw6AwEe7NtIbq0w7RkC+2CjZ0jSRCAQCHdQWKfAu/tuoqReP8Mi4nOxcEAw+oS4MCusnagsbcChnTdQmKPfXC+2E2Di7C4Q2wna5PN0jbWQ/r0QioubWjzOcw+BuOMIiDqOgDCsBzi8ttHBZrRyBVIefwtVh88CADgCPhJ++hA+owcyrMw4GprqcDD1b+y9tBG1skqDY672HhjW+VEMTpgIJ4krQwoJBIItQ5ImhgkJCWFaAoFlEM+0HaklDfjoYC4ablXIc5Pw8dGwcER5SBhW9nC0xjONDUqcPJCFqxeKQOm3MIHL5WDsjES4erRN8QDF9b2QbngZuvry5iCHA0Fw11uJ0nDwvaNZObvXXqjrZbg0+w3UnkkFAHDtREha8Rk8B5peNbC9x5ji6lzsvrAOx67vgEqjNDgW4hWNkd1monfMMPBJwmyxkOsSwVjY6BmSNDFMTU0NJBJ235AR2hfimbZhX2Y1vjtRCM2tCnlhbmJ8NCwcXg7sr8R1P89oNDpcOpWHM4dvQqVs7nPj7GqHIePiEBRu/s26OrkU9VsXoen8OjrGsXOG48hFECeOA8/Ry+yfaY2oqutwYfqrqL+iX87Id7RH51Vfwq1n4kO9b3uNMcXVudh04mecyThgEOeAg66RyRjZdSZiApJI0swCyHWJYCxs9AxJmhimvr6FSlAEwn0gnjEvOorCnxdKsS61ebaje6ATFg0MgYRFFfLuR0ueoSgKWdfLcXRPBqQ1TXRcKOKh58BwdO4VDL7A/L+/Im2/fnZJWkrHRHFD4fzot+C5+Jn986wVRVklLjz6MmSZuQAAgZszuq779r5Na1tLW48xFdISbDn5K45d32lQ3EEskGBg/Dg80nkqfFwD21QDwbyQ6xLBWNjoGZI0MQy/lSVgCYTbEM+YD6VGh6+O5eNoTh0dGxfngWd6BoDHsgp59+O/nikvqcfhHTdQlNdcGZDDATp1DUCfIZGwb4P+U7qmetRvextNZ9c0f6bYEU4TPoVd9xlkNsEI5PklOP/oi2jKLwEAiLw90G3j93CIDjXL+7fVGFMnq8LWMytw4PIWaO+oiOgsccOY7rMxKGE8JCJmmyYTTINclwjGwkbPsE+xlREVFcW0BALLIJ55eLQ6Csdy67D+chlyaxUAAC4HeKZnAMZ3YKZxa1ty2zOyegVO7M/CtUvFANV8PCjMDQNGxcDLt22q0SnTD6Fu/YvQ1ZXQMVHMIDhP/Y6UCjcSWVYezj/6EpSl+kIJdoG+6LZ5CSTB/mb7DHOPMbImKf459yf2XFxvsGfJXuSIsT3m4JHO0yAW2pn1MwntC7kuEYyFjZ4hSRPDpKWlIS4ujmkZBBZBPGM6cpUWezKrsfVaJcplKjpuJ+Bi0cAQ9AhiV/nT1nL1yjU01tjh7JEcqFXN+5Zc3CUYMDIG4TGebTLTo1PUo377e2g6/Rcd44gc4DT+E9j1fIzMLhlJ/dUMnJ/6CtQ1dQAA+8hgdNu4BGJf8yb65hpjmpSN2HVxLXacW2XQkFYksMPIrjMwuttjsBeTmSVrgFyXCMbCRs+QpIlAIFg9VY0qbL9eiR3p1Wi8I2kAgAh3O7zWPwjh7uzakNoaKIpCxtUyHPu3FE2Nzb+3SMxHr0ERSOoZBB6/bXovKTOPQrruBWhri+iYMCoZztOWgO9G9qsYS+25K7g463Vo6vW9s5w6RaHrum8h9LC88tsqjRL7UzZj25kVaGiqo+N8ngBDE6dgfM95cLZ3Y04ggUAgmABJmhjG1dXyLngEy4Z4pvXk1jRh89UKHL5ZS1fFu033QCdM7uSFBF8Hq5vxoCgK+dnVOLE/C2VFUjrO4XKQ0C0QvYdEQGLfNlUBdUoZGv75APKTK5o/V2gPx3EfQdJ7rtV91+1B1dFzSJm7ENom/VJSl+7x6LLqSwic22aWxtQxRqNV48jVf/H3qd9QI6ug41wODwM6jcHE3k/Aw8nXXDIJFgS5LhGMhY2eIUkTwzg4ODAtgcAyiGfuD0VRSClpwOarFbhQ1GBwTMDlYFCEKyZ18kKIq3XuoSjOr8WJfVkozK0xiIdEumPAyBh4eLfdcih14WXU/jEX2poCOiaM7KefXXIPbrPPtVY0skaUbt2PtLe/BaVSAwDck7shacXn4Nu3nX+NHWMoisLp9P3YcPwHlNcVGRzrHfsIpvR5Br5uQeaUSLAwyHWJYCxs9AxJmhimsLCQdWs6CcxCPNMyaq0OR3PqsPlqOXJqFAbHHEU8jI7xwNgOnnCXWGeDzIqSepzYn4WcjEqDuKePI4JjxUge0rlNZ3kUaftRt3IeKJUcAMARSuA45gNI+swHh9s2SwCtEXW9DJX7TqBsx2FUHT4LnbJ57533yGQk/PQhuKK27R1mzBiTVXIVfx36BlklVwziXcL749F+zyLYi32bvQnGQ65LBGNho2dI0kQgEFiNVkfhn7RKbLpSgSq52uCYj6MQEzt64ZEoN9i1Qc8hS6CmUoaTB7KRcbXMIO7qLkGfIZGI7uSDG+k32jRhkp/+E9JNrwM6/b4pQUg3uMz6GXwP85TAtnbUdfWo2HsrUTp6jp5VuhO/KSPQ8du3wLWQMr2V0lKsO7YUp27sNYh3COqKaf2fR6RfJ4aUEQgEQttgGaOvDRMURJYsEIyDeKaZGrkaXxzJQ0qJzCAe7SnBlE5e6BPiYlX9lu5EWtuE04eycf1SMag7tms5OovRe3AEOiT5gcvTz/C0lWcoioJs92eQ7fuKjokTx8Nl5o/gCMRt8pnWgqpGioo9x1G24zCqj58HpdbcdY7Iyx3eI5PhM3YwXHslttt+sPv5Ra6UYfvZldh1fg3U2uZZMD+3EDw28BUkhvUh+9ZsEHJdIhgLGz1DkiaGkUqlrFzXSWAO4hk9F4rqsfhIPuoUzTebvYKcMTneCx297a32xq2xQYkzh28i9XwhdNrmbEliL0TPgWGI7x4E/n8q4rWFZyitGtL1L6Pp/Do6Zj/wOTiO+ZAsx7sHqqpalO85hrJ/D6HmxCVQWu1d54h8POAzeiC8Rw+Ea7dO4PDaf4a0Jb9odRocvvIPNp34CVJ58345RzsXTOn7NAbFTwCfZ51LXwkPhlyXCMbCRs+QpIlhpFIp/P3N15SQYP3Yumc0Ogp/XijBhivN1bncJHwsHBCCRD/r7fnSJFfh3LFcpJzOh0ato+MiMR/d+4ciqXcwhMKWh3Rze0anqEftH3OhyjiiD3A4cBr/P9gnP2O2z7AWtE1KlO85iuINu1B97AKg0911jtjfG96jB8Bn9CC4dOnAeNL5X7+k5p7G6sPforDqJh3j8wQY0WU6xvecT3otEWz+ukQwHjZ6hiRNDMMlT2QJRmLLnilvUOGzw3lIq2hulNktwAlvJAfBxc66nnJr1FpUlNajtFCK0kIpcjIqoVI2z6oJhDx06ROCrn1DIH7A725Oz2ilpaj5ZSo0Jdf0Ab4ILo/9AruEsWb7DLZDURSkl66jaP1OlG07AE1D413n2AX6wnv0QPiMGQjnpDiLmhm97ZeiqhysPvIdLuecNDjeM3oIpie/AG+XACbkESwQW74uEUyDjZ4hSRPDxMTEMC2BwDJs1TMncuvwzfECyG41p+VxgPnd/DCpkxe4FnTDaQqUjkJtdSOdIJUW1aGyrMFg+d1teHwuEnsEontyGOwdRK16f3N5Rl16AzW/PApdXTEAgCNxhdsTayAM62mW92c7itJKlGzejeINu9CYXXDXcbtAX/iMGwyfMYPgFB9tUYnSnfgGemH5vk9xKHUbdFTzEsJwnw6YPehVRAckMieOYJHY6nWJYDps9AxJmhgmPT2dlcYhMIeteUal0eGXs8X490YVHfNxFGLRwBDEeNkzqMx05DIVSovqUFooRdmt/1Uq7i4EcCd8ARdxiX7oNSgCjs7GFVkwh2eUWSdQ+/ssUIp6AADPLQhuT28C3zvyod6X7WgVSlTsOY7iDbtQdfTcXcvveBI7+IwZCP+po+DaM4HxpXf3Q6NVY/fFddh84lcoNU103N3RG9OTX0Dv2EfA5ViufgJz2Np1ifDwsNEzJGliGF0L69sJhPthS54pqFPg00O5Bn2X+oe64JV+QbAXsq+E+JXzhTh7NAfSmqb7n8gB3Dzs4RvoAt9AZ/gGusDD2wE8nmk3rA/rmaZLW1C35jngVrU0QWAiXJ9cB56T90O9L1uhKArSlBso3rATpdsOQCNtuOsc115J8J86Ej5jBoJvL2FApXFcyz+HPw4sRnF1Lh0TCyQY13MeRnWdASGphki4D7Z0XSKYBzZ6hiRNDOPs7My0BALLsBXP7MusxtJTRVBq9AOrkMfBs70CMDLa3WKXNd2Py2cLcGB7WovHJA7C5gQpwAU+AU4Qic23R8tUz1AUhcbDy9Dwz/t0TBQ7BC5zV4ArYlfVI3OgVShRuHo7iv7aDllm7l3HxQE+8H90JPynjoAkmB0bnKsbyrHq0Lc4k7GfjnHAwcD4cXi077NwcfBgUB2BLdjKdYlgPtjoGZI0MQwbTUNgFmv3jFylxbJThTiQXUvHglzEeHtQCELd7BhUZjqZ18pw4J/mhMkvyAV+QS50ouToLG7TRNAUz1A6Leq3LoL8+G90zK7nY3Ce8jU4PNu6dFA6HUq3HUDWZ7+gqbDU4BjXTgSfUQPhP20U3HonWfTyuzvRaNXYeWEN/j61HEp188xnhG9HTOvzAjqGdWVQHYFtWPt1iWB+2OgZ27ryWSAFBQWIi4tjWgaBRVizZ7Kr5PjfoTwU1yvp2PAodzzbyx92AvYtxwOAwpwa7Nx4BbhV06Fb/1AkD49uVw3GekanlKFu9bNQXt1JxxxGvAWHYa+zcpbvYag+eQkZHy1DfWq6Qdy1RwL8p47SL79zZNfeuit5Z7DywGKU1OTTMUc7F0xPfgEDOo1F+o30+7yaQLgba74uEdoGNnqGJE0EAoFxblQ0YsvVCpzIq4PuVnIhEXDxUt9ADAx3Y1bcQ1BRWo+tqy5Be2uJYYfOfuj/SBTDqu6NTlEP+fHlkB35EVTjrQamXD6cp34HSY8ZzIprZ2SZecj4+AdU7jcst+2e3A3R7z4Hp46W+3e8F1X1ZVh1+BuczThIxzgcLoYmTsKjfZ+Fgx37nvwSCARCe0GSJoYJDAxkWgKBZViLZ7Q6Cqfzpdh8tcKg7xIARHrYYdHAUPg7t66ktiUirZVjy8qLdG+l0GhPDJvQkZGZmgd5RievQ+OxX9B49GdQTVI6zhE5wHXeSohiBrW1RItBWVGN7K9+R9Gaf0Fpm8ttO8SGI/q95+A5kH3l1dUaFXZeWI2tp3+HUt1cVCXSLx7zh/4fQr0NK1hZyxhDaD+IZwjGwkbPkKSJYWQyGRwdSTd1Quthu2ea1Frsy6zB1usVKKlXGRxzEfMxvoMnJsd7QWhipThLQC5TYfMfF9DYoF9m6BvojDHTE0yufvew3MszusYaNB75CY3HfwWluKMCHIcLceeJcBz+f+B7hrejUubQNDYh7+d1yP3h/9u77/CoyrQN4Pf0yUx67500Qgm9h95RQEUUFXEV+9p2XV3Xhq5+lrWsay+g2OlI771DIJBGeu89mT7zfn8EJoyhHUjmzEme33V5ad5zZubJeHNmHs457/sTzJr2e3wU/t7o9Y/FCJo3DSKJ8C4RPZN/CEt3vIeK+vZ1o1xVHrg7+a8YkzjzslOIC/0YQ+yPMkO4EmJmqGniWX19PQICAvgugwiIUDNT22rEuvRqbMysQbPebLMtzF2JuX18MSHKA3KpcJslADDoTVj9w0nU12gAAJ4+asxdOBByOX+H2z9nxtxcjdbdn0Jz4FswwyVn+cQSOA2aB+dJz/aYZomZzSj5dSNy3v0G+sr2tcAkahUin7wH4YvnQ6IS3nTbVQ2l+HHPRzh2fpd1TCQSY0rSHbhj1KNQK6/8ZUWoxxjCH8oM4UqImaGmiRDSpXJrNVh1rhp7cuthunjD0gVJgS64vY8vBgW7dIsJBswmC9b/nIKKkrZL3JxdFbh90SA4qeQ8V9bG3FiO1l2foPXQ98AlM6ZBIoPTkLvgPOFpSL3DeavPnhhjqNl5GFlvfIqWrPbpw0USCULuvRVRzz0AhY/w7qcrrMrGH8e+x6GMbbCw9r+ciA3ujwcm/gNhvsK7F4sQQhwBNU08E9rMIYR/QsgMYwzHS5qw6mw1UspsF/6UikUYF+WBuYk+iPJy/EU/rxezMGxZfRYF2bUAAIVSitvuHwRXd/6nSY8NcEXjyuehObIcMLXPTAiJHKrh98J5wlOQeATzV6Cd1R1KQc5/vkXdwVM2477TxiDmpUfhHB3GU2U3LqM4BeuPLkVKnu3EFW5qLywY+xRGJ0y/7r+YEMIxhjgWygzhSoiZoaaJZ+fPn0dMDP3NH7l+jpiZVoMZubUanK/RIrtGg4yqVlQ0296v5KKQYEacN25N8IGXuvMWbnUUe7dkIeN02xo+UqkYc+4bCB9/fq/XNtUUoHXnx2g9+hNEFlP7BpkS6hH3Qz3+SUjchHV5xI1ijKF27zHkfrQM9UfO2GxzG9Absa88Ds9h/fkp7gZZmAUpuQew7ugynC+1/Z1cnNwwdcB8TBt0F1QKbjl0xGMMcWyUGcKVEDNDTRPPTCbTtXci5BJ8Z6bVYEZ2jcb6T06tFiWN+ivuH+gqx9xEX0zq5SnYtZau5fj+fJw4UAAAEImAmfP7ITjcg7d6jCWpaNn5MXSn1wHMgovnF0RyNVSjHoB63OOQuPjyVp89McZQve0Acj9chsbTGTbbVOFB6PXiI/C/ZbygLg81mY04lLkN649+j5KaXJtt3q7+mDn4XoztcyuU8hs7y8n3MYYID2WGcCXEzFDTxDNXV1e+SyACY8/MtBrMOH9Jg5Rdo0VZ05UbpItkYhES/NS4tbcPhoe6QSIWzhdSrtJSSrF3c5b150mzeyM6wc/udTDGYMjeh5adH8OQtcd2m1wNl+RHoE5+BGJnL7vXxgdmNqNiwx7kffw9mtNzbLape4Uh6qmF8J89EWKpcD4G9UYtdqeuw4bjy1HTVGGzLdg7CrcOvR/D4yZBKrm5M7n0uUS4oswQroSYGeF8WnRTnp7Cu9GY8MsembEwhtVnq7DsZDkMZnbVfWUSESI9ndDLS4Ve3k7o5a1CmIcSMgFPGX698rKqsXXVOevPIyf2Qt/B9l17glnM0J1Zj9Zdn8BYfNpmm9jZG6oxD0M88C6ovQLtWhdfLCYTyldvR95/v0drTpHNNpfevRD19EL4zRgLkVg4+WzRNmJryu/YcvJXNGsbbLbFBPXDrUPvR1LUqMtOH34j6HOJcEWZIVwJMTPUNPGsoKBAkDfDEf50dWZqNUa8t7cQp0qbO2yTX2iQor1V6OWtQoy3E8I8nCDtxmeSrqS8uAHrfz4Ny4UZAfsPC8WwcZF2e31m0EJz/Fe07v4fzDX5NtskXuFQj3sCqiF3QSR3Qnp6OhK6edNk0RtQumIz8v67HNqiMpttbkkJiHpmEXwmjRDMZXiMMeRWpGHfuQ3Ye24D9JfOdgggKXIUbh12P+KCkzr9telziXBFmSFcCTEz1DQRQqyOFDXiP/uK0Khrv9Z4Ui9P9A1wRi8vFUI9lD2yQfqzuuoWrP7+JEzGtimdYxL9MX5mvF2+kFs0DdAc/A6te7+EpaXaZps0uC+cx/8Vyn63QCTpGYd3s1aPkp/WI/+zn6Arq7LZ5jGsP6KeXQSv0YME0yzVNlfiQNom7D23AWV1BTbbxCIJRsRPxqwhCxHm24ufAgkhpIfqGZ+qDiwoKIjvEojAdEVm9CYLvj5WivXp7Yt7eqqkeD45DAOChHfdcVcxmSxIOVyIo3vyoNMaAQChkZ6YPq8vxF3cTJobytC693NoDn0Ppm+x2SaPSYbzhL9CHjP2ss1BdznOmFpa0ZyZh+b0XDSnZaM5IxfNaTkwt2ps9vMaOwRRTy2E5/DOPwvTFXQGLY5n78a+cxtwrvAYGGwviZVLFRjb51bMHHwPfN27/v9ld8kLsR/KDOFKiJmhpolnOp0Obm5ufJdBBKSzM5Nfp8VbuwtQWK+zjg0PdcOzY0LhpqRDBNC2BlNmajn2b89GU337ZVK+AS649Z4BkEpv/F4SZjHD0loLS1MVLE2VMDdVwtJc1fZv639XwVxbAFw6bbhIDGW/WXCe8BRkIf2v+hpCO84wiwWawjI0p+egOS0HzRk5aE7Pgbaw7KqP850yCpFP3Q/3AY5/yYeFWZBZnIJ9aRtwJHMHdEZNh33igwdgTOJMDI2dAJXC2W61CS0vhH+UGcKVEDND34h4VltbCz8/+8+0RYSrszLDGMP69Bp8dawUxguTPcglIjw8NAgz470FczlTVyvKrcXezVmoLGtqHxQBvZMCkTwtDgoOjaUh/xg0x36GpbH8QlNU1XaJncV8/QVJFVANuRvqcY9D6nN991A5+nHGojegbPU2NJw8h+b0XLRk5sGs0V77gQCUQX7wHN4f4Y/eDdfejn/JWkV9MfanbcS+tI2obuzYBPq6ByG590yM7j3DLmeVLsfR80IcD2WGcCXEzFDTREgPVK814oN9RTha3N4IRHoq8cK4cIR73NjaLt1NdUUz9m3JQv75Gpvx8F7eSJ4aC58AbguGao7+jMbfnuLWIF0gUrpA4hYARd+ZUI9Z3K3WWKo/egbn/vYOWrMLrrqfxEkJ5/gouPSOhkt8NFwSouASHwWZG78LCF8PC7PgUPoWbD+9Ell/WoQWAJzkagyPm4QxiTMRG9Sf/sKCEEIcEDVNPIuLi+O7BCIwN5uZEyVNeG9vIeq17Zd6zentg78MDoT8Ji4z6y6aG3U4uCMbaadKwS65tcQ3wAVjpsYivJc3p+djjKF1x4do3vim7QaxBGIXX0hc/SB28YXY9eJ/+9n8t8TVFyK56qZ+J0c8zhgbmpD1789Rsnxdh22q8CC4JES3NUkJ0XBJiIYqLFBQ04RflF12Fst2vIfcijSbcZFIjL7hw5CcOBODopMhlyl5qrAjR8wLcWyUGcKVEDNDTRPP8vLyEB0dzXcZREBuNDMGswVLj5dh1bn2GdfclVL8LTkUQ0KEdV1xV9DrTDi+Lw8nDhbAZLRYx13clBg1uRcS+gVCxHGyB2Yxo2n1i9Ac+MY6phr9EJyn/B1ilafdmgBHOs4wxlCxbicyXv4Ihuo667hbUgJiXnoUbv3jIHVW81hh56hrrsLPez/BgfRNNuPB3lFI7j0TIxOmwdPFh6fqrs6R8kKEgTJDuBJiZqhp4pnBYOC7BCIwN5KZonod3t5TgNza9vtEBgW74O9jwuChknVmeYJjNllw5ngxDu/MgVZjtI4rlFIMHRuJAcPDIJVJOD8vM+rQ8OPD0J35wzrmMvMVqCc8ZffLrxzlOKMtLkf6C++jeudh65hErULMiw8jdNFciCTc32dHYzDqsPHET1h75Dvoje2TqwR7R+G+8c+iT9hQh7/8zlHyQoSDMkO4EmJmqGnimbOz/WZEIt0D18wcK27EGzvyob8w2YNMLMKDQwJxa28fiB38y1tXy82swu6NmWiobZ+5TCwRIWlYKIaNi4KTSn5Dz2vRNKL+2wUw5B66+KRwm/9fqIbc1Rllc8b3ccZiMqHwmxXIeedrmLXtjYTv1NGI//ezcAoS1s3Al8MYw7Hzu/Djno9sJnhQK10xb9SjmNh/LiRiYXzk8p0XIjyUGcKVEDMjjCN4N+br231u6Cb2wSUzubUavLmzwNowhbor8eK4MER53dw9Mt3BiQP52LMpy2Ysrq8/Rk2Ogbvnjb8/5oZS1H05D6byDACASK6G+6KlUMZPvKl6bwafx5nGM5lI+/s7aEptf68V/t5IeOs5+E1P5q2uzlRYdR7f73wf6cUnrWNikQSTkm7H7SMXw8XJnb/ibgB9LhGuKDOEKyFmhpomnuXl5SEhwfHXFCGO43ozU6cx4pVtedCZ2u7PGRnmhn+MC4eyh0/2wBjD/q3ncWxfvnUsOMIDydPiEBB8c/d2GSsyUffFHbA0lAIAxM7e8Fj8K+ShA27qeW8WH8cZU6sG2e9+jcKvVwCWC/eIiUQIvX8uer34MGSuwvtbxj9r0tTj9wOfY+eZNWCs/T64xLAhWDj+OYT4COt6/Yvoc4lwRZkhXAkxM9Q0EdIN6U0WvLo9D9WtbffoxPqo8MK4cCh6eMNkMVuwbW0azp0stY6NmBCN4eOjbvo+E0PeEdR9czeYpgEAIPEKh+cjK697LaXupGr7QaS/8D50pZXWMee4SCT+5wW4D0zksbLOYTIbsS1lBVYd/Aqt+mbruK97EO4b9ywGRic7/H1LhBBCuKGmiWcBAQF8l0AE5lqZsTCG9/cWIqu67T4dH7UMr0+K7PENk9FoxoZfzyA3o6ptQARMnJWA/sNCb/q5dakbUb/8IeDCjf/S4H7wfPg3h1lPyR7HGYvegLojp1G8fB0qN+y2jouVckQ/9wDCH7kbYpnwP3LO5B/G9zvfR1ldgXVMKVNhzoi/YPrAuyGT3th9cI6EPpcIV5QZwpUQMyP8TzCBM5lM196JkEtcKzM/nqrA3vwGAICTTIw3JkfBs4fPkKfTGrF2+SmUFNQDaJvsYca8fojt43/Tz916cCmaVv4duHB5ljx2LDwWfQ+x0nEWXe2q44ymqBw1uw6jeudh1B04aTPJAwB4jRmM3u/+Harw4C55fXvSGTRYvvtD7Dyz2mZ8bJ9bcOfox+Dh7JjTh98I+lwiXFFmCFdCzAw1TTyrrq6Gj0/3+bAlXe9qmdmZU4cfUyoAACIAL44LR6SXkx2rczwtTTqsWnYS1RVtl1HJ5BLMvicJYdHcFqn9M8YYWja/jZZt71vHlAPvgPtdn0DkYGcbOus4Y9EbUHf0DKp3HkLNriNozS687H4yTzfEvf5XBN4+tVtcpna+NBWfbnwZlQ0l1rFegX1x/4S/ISqgN4+VdQ36XCJcUWYIV0LMDDVNhHQTaZUt+GBfkfXnxUODMCy0Zy9aW1/bipXfnUBjfdv6VE4qGW67fxD8b3LCB2Y2oXHFs9Ae+dE6ph7/JFxmvmq3BWvtxXo2adeRtrNJGu1l95P7eMJ73DD4jB8GnwnDIXUR/gK1JrMRqw59jbVHllonelDIlLh33LOY0G9ut2gICSGEXB9qmngWExPDdwlEYC6XmYpmPV7bng+jpW1q8WmxXpibKKy/welslWVNWLX0BDStbQvouborcfsDg+HpfeNf5pnFAn36NrTs/AjG/GPWcdfZ/4Z67KM3XXNX4Xqcac0tQvGP61G94xBaswsuv5NYDPdBifAZPwze44fDNbFXt2oYS2vz8emGl5FXmWEd6xXYB4/PeAP+HiE8Vtb16HOJcEWZIVwJMTPUNPGssLAQUVFRfJdBBOTPmWk1mPHytjw06tquD+4f6IwnR4b06L8FL8qrxdrlp2DQmwEA3n7OuH3RIDi7Km/o+Sy6ZmiP/YLWfV/BXJPXvkEih/uCz+A0YG5nlN1lrvc403gmE3mfLEflxj0AYx22X3o2ySt5COQerl1QLb8YY9ia8jt+2vMxjCY9AEAiluC2EYtx67D7BbNA7c2gzyXCFWWGcCXEzHT/o7+D0+v1fJdABObSzJgtDG/tKkBhfdsN+MFuCrw8IQJScc9tmM6fq8DG387AfGFB36Awd8y5byCUTtwnwzDVFkKz7ytojv4Ipmu22SbxjoTbnR9B0WtUp9Tdla52nGGMoe7gSeT9dzlq9x233djNzyb9WV1zNb7Y/BpSC45YxwI9w/D4jDcRFSCs9URuBn0uEa4oM4QrIWaGmiaeqVQqvksgAnNpZr46WorjJU0AABeFBG9MjoSLouf+sU49Xozta9OsJ0kiY30w667+kMkl1/0cjDEY8g6jde8X0J/dZJ0V7yJ5TDLUyY9AET9JMA3E5Y4zzGJB5eZ9yP9kORpPZ9hsU/h6IWzxnQhecEu3PJt0OUcyt+PrbW+hVddkHZsy4E7cnfwkFLKeNZkKfS4RrigzhCshZqbnfrtyEEKcp57w62Jm/kivxpq0agCARAS8MiECQW43dvmZ0DHGcHRvHg5sy7aO9R4QiMlzEiGRXF9jw0x6aE+tRuu+L2EqSbXdKFXAadA8qMc8DFmg8M44XHqcsRiMKFu5Ffmf/YjWnCKb/VThQYh4fAEC75gGiVJh7zJ50aprxtId7+JA+ibrmIfaG49MfxX9IkbwWBl/6HOJcEWZIVwJMTPUNPEsNzcXCQnC+xJG+JObmwutazA+Pdw+/fFfR4WiX6DjrAtkT82NOhzelYPU4+3vx6DR4UieGntd93WZm6ugObgUmoNLYWmustkmdvWHatRfoBqxEBLnm5uinE+5ubmICQtH8fJ1KPjyV+jLq222u/aJQcQT98J/5liIJNd/Vk7o0gqP47NNr6K2udI6Nix2Iv4y+UW4OLnzVxjP6HOJcEWZIVwJMTPUNBEiMBUaCz45UYALE+Xhjj6+mBbrxW9RdsYYQ3F+HU4fKUJ2ehWYpX3SgjFTYzFkTMQ1n8Oib0XzH69Dc/gHwGyw2SYLSYJ67KNQ9rvF4dZc4spQ24CGZeuwd/0eGBts78vyHDEAEU/eA++xQ3vUxCE1TRXYcOwHbDn1m3VMpXDGAxP/gZEJ03rUe0EIIeT6UNPEMz8/P75LIALSqDPhu2yGVkPbrHDDw9zwwOBAnquyH4PehLSUMpw+UoTaqhabbRKJCBNn90afgcHXfB5jRSYali2CqSKrfVAsgbLvTKiTH4EsfEi3+OJc+N0qZL3xP1i0tjfc+k4bg8gn7oH7wESeKrM/C7PgbMERbEtZiVO5+63rLgFA79BBeHT6a/B2Fd7lIl2BPpcIV5QZwpUQM0NNE8/YZab1JeRyDGYLXt+Rh6rWtqnFo7yc8MLYMEh6wEx5NZUtOH20COkppdZpxC9SOcvRd3AI+g0Jgct13NOlOfozGlf+HTC2LdIqkquhGvUXqEc/CInHtRsuIWAWC86/+TnyP/vJOiaSShB42xREPLYAzrHXPhPXXTRrG7Dn7HrsOL0KlQ0lNttkEjnmj3kc0wbdDbFIGJN62AN9LhGuKDOEKyFmhpomnlVVVcHbW7j3ShD7WXq8DOcqWgEAnioplkyOhJOs+95/YjZbkJNehdNHilCcX9dhe1CYB5KGhaJXbz9IpNf+wmvRt6Bp5fPQHv/VOiYNSIDH/d9B6ie8RfauxGIw4uwz/0b5qm3WMedZyRj46lNwCvbnsTL7YYwhp/wctqWswJHM7TD+6fJLD2cfTOg3F+P7zoGnS89eBPpy6HOJcEWZIVwJMTPUNBEiAFUtBqxPrwEAyMTA65Mi4aMW9r02V9LSpEPq8RKkHi9GS5PtZWVSmQQJ/QPQf1gofAOufypsY1k66pctgrmqfXY91fCFcJ3zFkTy7jOdtKmlFSl/+Sdq915Yb0ksRsL//Q0tg2J6RMOkM2hwIH0LdpxeiYKqrA7b+4QNxaSk2zEwekyPWKSWEEJI56FPDZ716tWL7xKIAPyUUgHjhckO5vT2QayPmueKOp9Bb8LOPzKQcboMFovtaXsPbxX6Dw1F7wFBnBapZYxBe2Q5Gle/ABjbFgAWKZzhNu9DOA28rVPr55u+ug4nFzyHptS2ZkGslKPf56/Db1oyjEYjz9V1rZKaPGw/vRL7zm2A1tBqs02tdEVy4ixM7H8bAj3DeKpQWOhziXBFmSFcCTEz1DTxrKSkBBERPef+AsJdaaMOW8/XAgDUcgmGeQhvFe1rMRrMWPPDKZvL8EQiICreF0nDQhEa6QURx3u3LLpmNK54DrqTK61j0sDEtsvxfKM7rXZH0JpXjBN3PQNtYRkAQObuggE/vAePIX0BdM/jjMGow7Hzu7AzdS0yik922B7l3xuTkm7H8LhJPW5x2pvVHfNCuhZlhnAlxMxQ08QzrVbLdwnEwS0/VWGdXvz2Pr4Qmzre3yNkRqMZa5a3N0xyhRRJw0PRb0gIXN1v7MuusfQc6pc9AHN1jnVMNfIBuM5+EyJZ91oAuDElHSfv+RsMtQ0AAGWQHwb9/IHNZA/d6ThTUJmFXalrcTB9M1r1tlOoy6UKjIifikn9b0dUgLDW/3Ak3SkvxD4oM4QrIWaGmiaeKZXd6wsc6Vz5dVrszq0HALgppZjT2wcVJRqeq+o8JqMZ635MQVFu25k0uUKKO/4yGAHBbjf0fIwxaA59j6Y1LwKmtjNyIoUz3OZ/DKekOZ1Wt6Oo3nUEp//yT5i1bZceOsdFYtAvH0IZYDu5gdCPMxp9Mw6kb8Ge1HXIq8zosD3AIwyTkm7HmMSZcFZe/71u5PKEnhdif5QZwpUQM0NNE89CQkL4LoE4sOWnynHx7p47+/pCJZd0m8yYTRas//k0CrIvTHAhl+D2RQNvuGGy6JrQ+Nsz0KWssY5Jg/vBY+G3kPpEdkrNjqT0t00499zbYKa2Kdg9hvXHgO/fgczNpcO+QswMYwyZJaexO3UNjmTtgMFke1mqXKrAsNiJGNd3DuKC+3eLdbUchRDzQvhFmSFcCTEz1DTxLDs7GwkJdBkJ6eh8jQYHChoBtE0xPiuh7exBd8iM2WzBH7+eRl5WNYC2hum2+wchMNTjhp7PUJSChh8egrkmzzqmGv0QXG9dApFU0Sk1OwrGGPL/txzn//2Fdcxv5jj0/d8rkCgv/7sKKTMNrbXYd24DdqeuQ3l9YYftkX7xGNd3NkYmTIFK0bFBJDdPSHkhjoEyQ7gSYmaoaSLEQS07UWb977v7+0NxHWsRCYHFbMHG384gJ70KACCViTH3voEIDufWMDGjDrrUDdAc+h6G3IPWcZHSFW53/RdO/W7p1LodATObkfHyxyj6rn1yi9AHbkf8G09BJBHuml2MMZzOP4RdZ9bgVO4+mC22CxirFS4Y1Xs6xvW5FeF+sTxVSQghpCejpolnvr6+fJdAHNC5ihacKGm7yd3PWY5psV7WbULOjMVswaYVqTh/rhIAIJWKMefegQiJ9Lzu5zBWZEJ7+Adojv8Gpqm32SYLHQD3+76B1Du8M8t2CGadHqlPLEHlht3WsZiXHkHEE/de89I0R85MRnEKft77MbLLznbYlhAyEOP7zsaQmPGQd7MJPByZI+eFOCbKDOFKiJmhpolnYnH3OHtAOg9jDMtOlFt/vmeAP2SS9pwINTMWC8OWVeeQmVoBAJBIRLj1niSERXtd45EAM2igPb0emsPfw5h/tMN2iU80VCPvh3rUgxBJu9+iv8bGZpy6/wXUH04BAIgkEiR+8CKC7px+XY93xMyU1ubjl72f4ETOXptxd7UXkvvcgnF9boW/h/Cuee8OHDEvxLFRZghXQswMNU08q6iogKfn9f8tO+n+UsqakVrRAgAIdlNgYrRtPoSYGWZh2Lr6HNJPt11yKJaIcMuCJETE+Fz1ccayNGgO/wDt8d/AdE22G6UKKPvdAtWIhZBHDu+2EwE0p+cg5aF/QZNbBACQqJzQ/5t/w2f8sOt+DkfKTH1LNVYd/Bq7UtfCwtovwwv2isS80Y9iYPQYSMT00cQnR8oLEQbKDOFKiJmhTyZCHAhjDEsvOct074AASDgu6upomIVh+7o0pJ0qBQCIxSLccld/RMVd/tS8Rd8CXcoaaA7/AGNhx0VLpf6xUA1fCKdBd0KsvrGJI4SAMYaSn/9AxksfwKIzAADkXu4Y+OP7cEsS1s2zAKDVt2LD8eXYcHw59EadddzD2Qd3jHoEyYkzqVkihBDisOgTimdRUVF8l0AcyJGiJmRVt63DFOGhRHKke4d9hJQZxhh2/JGO1OMlAACRWISZ8/shOsGv474GDZo2vAHt0Z/A9C22G2VOcEqaDdXw+yALH9JtzypdZGrVIv0f76Fs5RbrmGvfWPT/+k2owoI4Px+fmTGZjdiVuharDn6FRk37wsxOcjVuGboQ0wbeDaX8xhYxJl1DSMcY4hgoM4QrIWaGmiaeVVRUICwsjO8yiAOwMIbvT7bPmLdwUADEl2kOhJIZxhh2b8jEmaPFAACRCJgxry9iEv077GtuLEf9N/fAWJxiMy4NTIRqxEI4DbgdYtWNrd8kNC1Z+Uh58CW0ZhdYx0Lvn4vY15684pTi18JHZhhjOJ69G7/s/Z/N1OESsQST+t+OuSMegquq+54pFDKhHGOI46DMEK6EmBlqmnjW2trKdwnEQezPb0BeXdtlS7E+KgwPvXyTIITMMMawd3MWTh2+8GVZBEy7vS/i+gZ02NdYkoq6r++CpfHCZYkyJzgNvB2qEQshC0nq9meVLlX6+2ak/+M9mLVtOZA4q5D4/gsImD3xpp7X3pnJKjmNH/d8jOyyVJvxYbETMX/MEzTBg4MTwjGGOBbKDOFKiJmhpolnCkX3WniT3BizheH7k+33Mi0cGHDFZsHRM8MYw4Ft2ThxoKBtQARMnZuIhKTADvvqzm5Cw/LFYIa2SxIlHiHweOgXyAKFd8/OzTBr9ch46QOU/PyHdcwlIRr9v34T6qjQm35+e2WmtrkSy3a8h+PZu23G44KTsGDsU+gV2McudZCb4+jHGOJ4KDOEKyFmhpomnoWHh/NdAnEAO3PqUNKoBwAk+qsxMMjlivs6cmbMZgt2rm+/hwkAJs/ujcSBwTb7McbQuvsTNP/xOsAYAEAWNggeD/4IiYvw1m64GS05hTj90L/QkpFrHQteMAvxbz4LiVPnfKjYIzOncvfj802volnbaB0L8orAXWOexMDoMT3qjKHQOfIxhjgmygzhSoiZEd4k6d1MVlYW3yUQnhnNFiw/VWH9+f6BgVf9gumomdFqDFi59IRNwzTxlgT0HWx7KRYzGdD461/RvP41a8OkHHAbvJ5Y3+MaprI123B4yl+sDZPESYm+/3sFif95sdMaJqBrM2MyG7F894d4d9XT1obJXe2Fh6a8hHcX/YpBvZKpYRIYRz3GEMdFmSFcCTEzdKaJEJ5tPV+Hypa2KaUHBrmgb4AzzxVxV1fdgtU/nEJD7YXL7KRiTJmbiIT+tpfkWVrrUP/dQhhyD1rHnKe+AOcpf+9RX6zNOj0yX/kvin9YYx1zjolA/6/fhHNsBI+VcVPVWIb/rn8ROeXnrGODopPxyLRX4ezUMybuIIQQ0jNQ08Qzb29vvksgPNKbLPgp5ZKzTIM6TpTwZ46WmYLsGvzxy2nodSYAgEotx+x7kxAYajszmqkyG3Vf3wVzTV7bgFQB97v/B6cBt9m7ZF615pfgzOJ/oenseetY4LzpSHj7OUjVXTP1dldk5tj5Xfhi8+vQXJgeXiKW4p5xT2PqgPk9qgHujhztGEMcH2WGcCXEzFDTxDO5XM53CYRHGzJqUKsxAgCGh7kh1kd9zcc4UmZSjhRh14YMMEvbZXY+/i6Yfe8AuHnYfvnXn9+L+qX3g124fEvs7AOPB3+EPHyw3Wvmi8VgROmKzch67ROYmttmDRIr5Uh4+28Ivmtml752Z2bGYNLjpz0fY+up36xjvu5BeGrW/yEqoGdN4NFdOdIxhggDZYZwJcTMUNPEs7KyMri7u/NdBuGB1mjGr2cqAQAiAPcPvPZZJsAxMmMxW7BrYyZOHymyjkXF+2LGvL6QK2wPK5rD36Nxxd8BS9uZKGlAAjwe+gVSz54x7bSpuRXFP65DwVe/QV9ebR1XR4ei/9f/hkt81y/w11mZKa8rwsfrX0BBVfu16MNiJ2Hx1JegUlx58hIiLI5wjCHCQpkhXAkxM9Q0EcKTtWnVaLxwSVtypDsiPLvm0qzOptMa8ccvp1GYU2sdGzw6AqOnxEAsbr8si1nMaF7/Klr3fGYdUyRMhvt9X0Os7P5fsPVVtSj8ZgWKlq2GqanFZlvA3Mno/e7fIXW+9plFR3EwfQu+3vpv6Ixt963JJHIsnPA3TOg3ly7HI4QQ0u1R08SziAjh3PRNOk+L3oQVqVUAALEIuO86zzIB/GamvrYVa74/hbqaC5eXSUSYNLs3+vxpSnGLrhkNyxdDn7bVOqZOfhQuty6BSCyxa8321ppXjPzPf0bZ75th0RtstvlOG4OIxxfAY5B91yu6mczojVp8v/N97Epdax0L9AzDU7f8H8J8YzqhOuJo6HOJcEWZIVwJMTMONeX4vn37MGvWLAQGtk25vHbt2qvuf+DAAYwcORJeXl5wcnJCXFwcPvzwQ/sU20mqq6uvvRPpdlaerUKLwQwAmNTLE8Fuyut+LF+ZKcqtxU+fHbE2TE4qGeY9MLhDw2SqykHtf6e3N0xiCVzv+ACuc/7drRumxpR0pPzln9g/cj5Klq+zNkwimRRBd83EqP0/Y8DS/7N7wwTceGZKavLwr+ULbRqm0b1n4K37fqSGqRujzyXCFWWGcCXEzDjUmabW1lb069cPDzzwAObOnXvN/dVqNZ544gn07dsXarUaBw4cwMMPPwy1Wo3FixfboeKb19LScu2dSLfSoDViTVrbwUIqFmFBkj+nx/ORmdTjxdixLh2WCxM+ePk6Y859A+DuqbLuwxiD9vD3aFr7LzBD2yVcIqUrPBYtgyJ2rN1rtgfGGGp2H0X+pz+i7uApm20SZxVC75uDsMXzoPT34anCNlwzwxjD3nN/YOmOd6A36gAACpkSiyb+A8mJs+hyvG6OPpcIV5QZwpUQM+NQTdO0adMwbdq0694/KSkJSUlJ1p/Dw8OxevVq7N+//5pNU3NzM8Ti9hNtCoUCCkXnLSZ5vWQymd1fk3Q9o9mCqhYDypsNqGg2oKJZf+G/9ShrMkBrtAAApsV6wd+FW+7smRmLhWHv5kycPFhoHYuI8cbM+f2hULYfPswtNWj89Snoz222jkl8ouH54I+Q+nW/MxIWkwkV63ch/9Of0JyWbbNN7uOJ8MXzEHLfHMjcHOPerevNTJOmHgfSN2PP2fUoqm7/vYK9o/D0Lf+HYO/IriqROBD6XCJcUWYIV0LMjEM1TTcrJSUFhw4dwptvvnnNfRMTE6HRaKw/L1q0CE8++SQCAgKQm5sLAPDz8wNjDFVVbfee9OrVCyUlJdBqtVAqlQgJCUF2dtsXC19fX4jFYlRUtK25ExUVhYqKCrS2tkKhUCA8PNy6+rG3tzfkcjnKysra/nZeq0V1dTVaWlogk8kQHR2NjIwMAICnpyecnJxQWloKoK0xrKurQ1NTEyQSCWJjY5GRkQHGGNzd3eHi4oLi4mIAQGhoKJqamtDQ0ACRSIT4+HhkZWXBbDbD1dUVHh4eKCxs+zIcHBwMjUaDuro6AEBCQgLOnz8Pk8kEFxcXeHt7Iz8/HwAQGBgIvV6P2tq2iQDi4uKQl5cHg8EAtVoNPz8/5OW1rcUTEBAAk8lkPQ0bExODoqIi6HQ6ODk5ISgoCDk5Odb3GwAqK9tmlIuOjkZpaan1/Q4NDcX5821r2/j4+EAqlaK8vBwAEBkZicrKSrS2tkIulyMyMhKZmZkAAC8vLygUCpSVlQFou462pqYGzc3NkEqliImJQXp6uvX9VqlUKCkpAQCEhYWhvr7+su+3q5sbqkwKnCsoR42OQS9RoaxJh8oWIxoNALtGBmViYJC6Cenp6YiPj0dOTg6MRiOcnZ3h4+Nj834bDAbU1NRY38Pc3Fzo9Xqo1Wr4+/tbM+vv7w+LxWKT2eLiYuv7HRwcbJNZkUhkfb+joqJQXl6O1tZWGPViSOGCEwdzUFOus9YcHueCuAFOYDChqKgMLS0tcCo/AdWet2BprrLuJx10N6r7LEJNrQnhLhprZv/8fnt4eMDZ2dkms42NjWhsbIRYLEZcXBwyMzNhsVjg5uYGNzc3FBW1zdgXEhKClpYW1NfXd8isq6srPD09UVBQAAAICgqCTqe7bGadnZ3h6+t71cwWFhZCr9fDSSYHDqUi96PvYSq3vbRAGuyH6CfvhXlob+gsZhTXViNEpbzpY8TFzN7MMcJoNCI9Pf2yx4j6hnqczN6HM8X7cb4qBeYLsxxelBQyFlMT74Gr3BsVFRV0jOBwjHB3d4erq6tNZpubm694TP5zZrVarfX95nKMiI2NRUFBwQ0fIwwGA9LT0694jNBoNFAoFAgLC7vq+11VVYWWlpbLvt9KpfKymRX6MUKlUtn9e8TFzPL5PYIxhqamJvoegZ5xjLjW94jrOUZERESgqKjIIY4RF9/DaxExxq713Y4XIpEIa9aswezZs6+5b3BwMKqrq2EymfDaa6/h5ZdfvuK+JpMJe/fuRWRkpEOcaUpPT0dCAq1tIjT1WiOW7MhHWmUrp8eJRYC3WoYAFwXmJPpgRJg759fuzMwwxtBQp0FlaRMqShtRWdKEyrImGPS2X5zFYhEm3JKAfkPapwlnBi2a/ngNmv1ft++n9oLbXf+FMvH6zxgLgUVvQMlvm5D33x+gK6mw2eaWlICIJ+6B39TREEkc856ty2WmtDYfe87+gf1pG9DQWtvhMdEBiZg15D4MjZ1grzKJg6DPJcIVZYZw5UiZMZvNOHPmDJKTkyGVXvl8Urc407R//360tLTgyJEjeOGFFxAdHY277rrrqo9xcXGBxEG/4BDHVlivxcvb8lDRbLjsdleFBP4uCgS4yOHvIoef9b8V8HWWQSbhZ/4Vxhga67XtDVJpEypLG6HXma76OCeVDLPu6o/QKC/rmLEkFQ3LF8NUed46pkiYBLf5/4XE1a/Lfgd7M+v0KPl5A/L/txy6siqbbV5jBiPyqYXwHJEkmHt8NPoWHM7chj1n/0B2WWqH7W5qL4xOmI6xfW6hS/EIIYSQS3SLpunitIV9+vRBZWUlXnvttWs2TY7C09OT7xIIB6dKm/DGzgK0Xpj5zkslw22JPvB3bW+M1PKubca5Zqa6vBkHtp9HaWEDdFrjNfd3cVPCL9AVfkFu8A92RVCYh3XBWmYxo3X3p2je9G/AfOG5ZEq43voGVCMfEEzzcC1mjQ7FP61D/v9+gr6yxmab9/jhiHr2fl5mwbsRFmZBraEYn278FUezdsBg0ttsl4glGBA1BmP73IJ+EcMhlQjvOnPSuehziXBFmSFcCTEz3aJpupTFYoFer7/2jg7CyUkYC5oSYFNmDf57sBgXJpBDtJcTlkyOhLdabtc6uGQmPaUM29aeg+nCxBN/5uyquKRBcoNfoCvUV5iYwlxfgoafHoMh54B1TBrcF+73fAmZfyy3X8JBmVq1KP5hDfI/+xmGattrnH2njELU0/fDLckxLie4lrLaAhxI34wD6ZtR1VjaYXuoTzSSE2/BqIRpcFML78OLdB36XCJcUWYIV0LMjEM1TS0tLdab+QAgPz8fp0+fhqenJ0JDQ/Hiiy+itLQUP/zwAwDg008/RWhoKOLi4gC0rfP0/vvv469//Ssv9d+I0tJSuLm58V0GuQoLY/j2WBlWnG2/PGtYqCteHBcOJ5n9L/G8nsyYTRbs3piJ00eLrGNOajkCgt3gF+QK/6C2fzu7Xt/6UNpTq9D4+3Nguqa2AZEI6vF/hcu0FyGS2rdp7AqmllYULV2N/M9/gbGuwWab34yxiHp6IVz7OH5j2NBSg0OZ23AgfTPyKtI7bFcrXDAyYSqSE29BpH98tzkzSDoXfS4RrigzhCshZsahmqYTJ05g3Lhx1p+fffZZAMDChQuxbNkylJeXW2e8ANrOKr344ovIz8+HVCpFVFQU3nnnHTz88MN2r510TzqTBe/sLsDBwkbr2NxEHzw0JAgSsWN+4Wxu1GH9zykoL26vuc+gYEyYFQ8pxybPom1C06rnoT3xu3VM7B4E93u+gCJ6ZKfVzBdjUwuKvluJgi9/hbG+qX2DSAT/WeMR9cz9cImP4q/A66DVt+J4zh4cSNuEs4XHwJjtWUURRIjwScTMYXdjUK9kyKX2n/CGEEIIETqHnT2vq1ycPa9fv34OMRGERqOBSqW69o7E7mo1Rry6LQ/na9qmpheLgMeHB2NWAr8LlV4tM0W5tfjj1zPQtrZNUiGRijHxlgT0GRR8Xc9tbqqEqfQcjKXnYCw7B0POQVia2meLUw64DW63vw+xSlh/O3QpQ30TqrbuR+XGPajddxwW/SUTeojFCJg9EVFPLYRzbAR/RV6DyWxEasERHEjfjBPZezrcpwQAEX5xGJUwDcPjpkApUdNxhlw3+lwiXFFmCFeOlJkeNXuekNXV1TlMaEi7vFotXt6Wi+rWtskOVDIxXhofgcEhrjxXdvnMMMZwfH8+9m89j4t/DeLq4YRb7u4P/6CODQ4zG2GqyoapNA3G0rMwlp2DqTQNlpbqDvsCgEjpArc7/gOngbd3+u9jD/rqOlRu3ofKjbtRd/AUmMlsu4NYjMDbpiDyqfvgHB3GT5HXwBhDdtlZHEjfjMOZ29Csbeiwj49bIEYlTMOohGkI8mpv+kpKSug4Q64bfS4RrigzhCshZoaaJp41NTVdeydiV8eKG/HvXQXQXpg8wddZhjcmRyHC0zFuWvxzZvQ6I7asPIfs9ErrWHiMN2bM6wsnlRyMMRgLT8BYePJCg5QGU3kmYL78lOk2JDIoYsfB9fb3IPUMufb+DkRXXo3KjXtQsXEP6o+eASwdJ8NQ+HnDb8ZYhD00D+qI6zsbZ08WZkF22VmcyN6LY+d3orKhpMM+zko3DI+bhFEJ0xAT1O+y9ynRcYZwQXkhXFFmCFdCzAw1TTxzhEsESbv16dX47HCJdYa8WB8VXp8UCU+V40zDfGlmqiuasf6nFNTXtl1CCBEwfFwUho+PhlgsgrmpEo2/PgV9+rZrPq9Y7QVpUCJkgb0hDeoDWVAipL69BDXRg6aw7EKjtBuNJ9Muu48y2B/+M8bCb+Y4uA/sDZGYn3WzrsRg1OFs4TGcyN6DU7n70ajpuFK5TKrAwKgxGN17+nVNE07HGcIF5YVwRZkhXAkxM9Q08Sw21vFn5OoJzBaGL4+WYm1a++Vpo8Ld8fzYMCiljvWl+mJmMs6UYevqNJiMbZeaKZRSzLizHyJj2+650p5eh8YVz4G1/ulLt0gMiU8UZEGJkAUmtjVKQYkQu/oLcjY1Q30TSn/ZgPI129B09vxl91FFhbY1SjPGwrVvrMP9ns3aBpzK3Y8T2XuRWnAYeqOuwz4ikRiJoYMxMmEqhsSMh0rhfN3PT8cZwgXlhXBFmSFcCTEz1DTxLCMjA/Hx8XyX0aNpDGa8vbsAR4vbTxXf2c8PiwYFQOxgX64BIO1cOirzRTh1uNA65hvgglsWJMHdUwWLpgGNq56H7uRK63axsw+cp/wNstABkAXEQyQX1nXEl9NyvgCF36xA2YrNMGs7NhnO8VHWRsk5LtLhGqXKhhKcyN6LE9l7kFl6usOsdwAglyrQL2I4BkYnY0DUaLiqPG7oteg4Q7igvBCuKDOEKyFmhpomnvWwyQsdTlplC/6zrwgljW2zj0lEwF9HhWJarBfPlV1eS5MOR7ZXoL66/X6k3gOCMPHWBMhkEugzd6HhlydhaSy3blf2nQnXeR9A4uzNR8mdijGGmj1HUfjV76jZfaTDdtd+cfCfORZ+M8ZBHel492BVNZZhd+panMjeg+Ka3Mvu46rywICoMRgUnYw+4UOgkN38vXR0nCFcUF4IV5QZwpUQM0NNE8/c3d35LqFH0hrNWHqiHOvSqnHxj62zXIKXJ0YgKdCF19ouR6c1IuNMOQ7vyoGm5cJ04hIRxs9KQN/BwWAGDRpXvArNwe+sjxEpXeF6+7twGniHw51l4crUqkXZyi0o/OZ3tGYX2myTqFUIvmsGQh+43SEbJQCwWMzYfPJX/Lb/08tODx7gEYZBvZIxKDoZvQL7QCzu3Gu96ThDuKC8EK4oM4QrIWaGmiaeubg43hf07u5UaRM+3F+Mypb2szWxPio8nxyGEHclj5XZYoyhtKAeqSdKcP5sBUym9su3XNyUuGVBEgKC3WDIP4qGnx6HuSbPul0eOxbud30CiXsQH6V3Gm1pJYqWrkLJj+tgbGi22eYUEoCwB+9A0F0zIXO9/vt77K20Nh9fbF6C7LJU65gIIkQH9rE2SpdOD94V6DhDuKC8EK4oM4QrIWaGmiaeFRcXIyEhge8yeoQWvQlfHS3DlvO11jG5RIT7BwZgTqIvJGLHOBvT2qxHWkoZzp4oRv2FhXUv5ROoxB2LRsBJwdC04Q207vwYuHg/jMwJrrcugWrkA4I9u8QYQ8PJcyj86ndUbtwDZrZdU8ljeBLCF8+D7+RREDnw7Dtmiwkbj/+EFQe+gPGS6d2nDrgTs4c9AHc7Xi5JxxnCBeWFcEWZIVwJMTPUNJEe4XBhI/57sBi1GqN1rK+/M54ZHYogNwWPlbWxWBgKc2qQerwEuRlVsFhsr/VVKKVISApEn0HBqKkvgawxGzU/PgJTWfu02rKwQXC/53NIfaLsXX6nMDY0oWrbQRQtXYXGlHSbbSK5DAGzJyH8oTvg2sfxZ9wprs7BF5uXILei/f+Pv3sIHp72KuJDknisjBBCCCE3gpomnoWGhvJdQrfWoDXis8Ml2JPXYB1TycR4cEgQpsd58T47XlODFudOluLsyRI0N3ScAS4kwhN9BgejV28/yGQSMIsZkpQNqNn5H8B8oQGUyOAy9QWoxz8JkURYf6S1pZWo2rIfVVv2oe5wCpjJ9qyS3NsDIQvnIHThHCh8HXNyjkuZzEasP/o9Vh36GmaLCUDbVOHTB92NeaMe6ZRJHW4EHWcIF5QXwhVlhnAlxMwI6xtWN9TU1ARnZ8e9H0OoGGPYk1ePzw6XolFnso4PDnbFU6NC4OvM34KtFrMFORlVSD1RgoLsGuBPE8ionOVIHBiEPgOD4eGtBgAwow66c9vRsvMjGPOPWfeVBsTDfcEXkAX3seevcMMYY2jJzEPVln2o3LwfTamZl93PpXcvhD00DwGzJ0Ki5P9M4PUoqMzCF5tfR0FVlnUs0DMcj05/Db0C+f3/Q8cZwgXlhXBFmSFcCTEz1DTxrKGhAYGBgXyX0a3UtBrw34PFOFLUvu6Si0KCR4cFY0K0B6/3+pQV1WP7unRUl9tOaiASARExPugzOBiRsT6QSMSw6JqgPbUKutQN0GfsBNO3XPIAMdTjn4TLtBcgkjp2U8HMZjScOIfKzftQtWUfNAWll93PKTQQvtNGw3/GOLgP7iOYe7JMZiNWH/oG644uhdnSdqZMLJJg1pB7cdvIxZA7wP8fOs4QLigvhCvKDOFKiJmhpolnQvliKASMMWzJqsVXx8rQami/zGt0hDueGB4MD5WMt9q0GgP2bz2P1OMlNuOu7kr0GRSM3gOC4OruBHNLDfTHf2prlLL2ApdMIHCR2TUIvvd/DXnkMHuVz5lZq0ft/uOo3LwP1dsOwFDbcNn9XPvGwnfKaPhNGwPn+CjB/XnILU/HF5tfs1lzKcQ7Co9Mew1RAY5zg6vQ3lfCL8oL4YoyQ7gSYmaoaeKZ0FZDdlRVLQZ8sL8Ip0rbz+B4OEnx5IgQjIpw560uxhjSU8qwZ3MWtK3tDZBPgAvGTIlBeLQ3LI2l0J35HrWpG2HIO9w+E94lRCoPKBOnQdl3JhRx4xzy7JJZo0P1rsOo+GMXqrcfglmj7bCPSCKBx/D+8Js6Br5TRsEpJICHSm+ewaTHqoNf4Y9jy2FhbQ26RCzB7GEPYM7wv0Aq4a9Bvxw6zhAuKC+EK8oM4UqImaGmiWdZWVmIjXX82cAcFWMM27Lr8PnhEmiM7c3GxF6eeGRoEFyV/EW8tqoF29eloSS/3jomk0swalIv9InQw5C2HLV/bISxOOWyjxe7BUDZdyaUfWdCHjncOsmDI2XGrNGheuchVPyxG9XbD8Ks7TiZhcRJCe/xw+A7dTR8Jo6E3MOVh0o7R0V9MfacXY+9Z9ejvrXGOh7uG4tHpr2KcD/H+P/yZ46UGeL4KC+EK8oM4UqImaGmiWfmP61BQ65frcaIj/YX4Whx+71L3ioZnh4dgiEhbrzVZTSYcWR3Lo4fyIfF3D7LQ0yiH5KHSGHe9Q/Urth+2cdKfKKsjZIsJAkisbjDPnxnxtSqRc3OC2eUdhy6bKMk83Btu+xuejK8Rg+GxMnxzoxdL4NJjxPZe7DzzBqkFR232SYRS3H7yMWYNeQ+hzu7dCm+M0OEhfJCuKLMEK6EmBlqmnjm6ircv3Xny8WZ8f53qATN+vY/dJN6eeLRYUFwVvAX69zMKuz8IwNN9e2Xprl5OGHC5EB45X4LzWffARaTzWOkwX3bGqU+MyD1j7vmdb58ZMbUqkX1jkNtjdLOQ7Bo9R32kXm6wW/aGPjPGg/PkQMhlgn78FJcnYNdqWuxP20TWnSNNtskYgkGRI3BvFGPIMQnmqcKrx8dZwgXlBfCFWWGcCXEzAj7W0034OHhwXcJgtKgNeKTQyXYn99gHfNwkuLpUaEYHsbf2aXmRh12bchAdlqldUwsEWHIqFD0le+F5rdF0Gga2re5B0Kd/CiUfWdB6sVtrQJ7ZcbUqkX19oNtjdKuw1dulKYntzVKIwYIvlHSGTQ4lLkNu1PXIrvsbIft/h6hGN93Nsb0ngF3Z28eKrwxdJwhXFBeCFeUGcKVEDMj7G843UBhYSESEhxnli1HdqCgAR8fKLZZdyk50h1PjAiBG0/3LlnMFpw6XIiDO3JgvGTGvpAIT4xPrATbfT9aq7Kt4yK5Curxf4Xz+Ccgkqtu6DW7MjMWvQE1e46ifO0OVG3Zf/lL7zzd4TfjYqOUBLFU2IcRxhhyK9Kw68waHMrYBp1RY7NdJlVgaMwEjO87G/EhAwQ54w8dZwgXlBfCFWWGcCXEzAj72w7pEZr1Jnx2uAQ7c9onVHBVSPDkyBAkR/L3NxUVJY3YuuaczZpLTmo5JoyQwef8+zD8tsNmf6fB8+Ey41+QuDvWugQWkwl1h1JQvmY7KjfthamxucM+ci93+M0YC/9Z4+ExvL/gGyUAsDAL9p79A5tP/oyi6pwO20N9emF8vzkYlTANzkrhXUZACCGEkM4j/G8+AhccHMx3CQ7teHETPthfhFqN0To2PNQNT40KgSdP6y4xxpByuBB7Nme1T/QgAgb0d0V/yxro/1gGg6X9rJMsYghcZ/8b8rCBnfL6nZEZZrGg4cQ5lK/dgYr1O2Goqe+wj8zdBX4zxyHg1ondplG6qLyuCF9teQMZJadsxpUyFUYmTMX4vnMQ6R8vyLNKl0PHGcIF5YVwRZkhXAkxM93nW5BAaTQaQd4M19VaDWZ8dbQUm7NqrWNquQSPDQ/CxGhP3r7M6nVGbFl1zubeJT9/J0wMOwXR0U+gv+S+JYlHMFxmvQZl0pxOrfdGM8MYQ/O58yhfswPl63ZAV1rZYR+JWgW/aaPhf+tEeCcPgVjuuDPC3QizxYRNx3/G7we/gNHUfo9Wr8C+mNBvDobFToTyBi+bdGR0nCFcUF4IV5QZwpUQM0NNE8/q6urg7+/PdxkO5WRJEz46UIzKlvbFYAcGueDZMaHwUct5q6uytBF//HIGDXUX7nlhDMlxZQgr+RLm3Tm4OLm4SK6GeuLTcB77GERyp06vg2tmjE0tKPz6d5Sv2YbWnKIO28UKOXwmjkDA7InwmTACEpWyM8t1GIVV2fhyyxLkVaRbx3zdgvDQlJfQJ3woj5V1PTrOEC4oL4QrygzhSoiZoaaJOIwzZc1YfqoCqRUt1jGlVIzFQ4MwI86Lt7NLjDGcOVaM3RsyYL5wOZ6rvAXT3H+DNGUnLl1pwGnIXW33LbkF8FLrn9UdOY3UJ5ZAV1JhMy6SSuA1ZggC5kyE39QxkLqoeaqw6xlNBqw5/C3WHV0K84XLJkUQYerAu3Dn6Meg7ILGlhBCCCHdi4gxxq69W/dhMpmwd+9e9OvXDxKJhO9yCC7fLAFAX39nPDcmFAGu/C2MatCbsG3NOWSmtjcdfd1S0afhS0Dbfh+QLHJY231LoUl8lNmBxWhCzvvfIO+THwGLpW1QJILn8CT4z54I/xljIfdy57VGe8guO4svNy9BSW2edSzIKwIPT30FMUF9eayMEEIIIY7AbDbjzJkzSE5OhvQq92/TmSaenT9/HjExMXyXwYsrNUvBbgrc3d8f46M9IObxRvzq8mas/yUF9TVtl+PJWQsmu/4Ot/L2WfHEzj5wnft2p9+3dDXXykxrbhHOPPYams5kWsc8hvVDn4//BVVYkD1K5J3OoMXvBz7H5hM/g124cFIiluCWofdj7vAHIZPyd5knH3rycYZwR3khXFFmCFdCzAw1TTwzmUzX3qmbuVazNC7KAxIxf80SYwznTpZi5/p0mExtZ2lCxakYaf4G4soa637KfrfA7Y7/QOzsZdf6rpQZxhhKflyHzFf+a11fSSSVIPr5hxD5+AKIesiZ1XOFx/DV1jdR1VBqHYv0i8fD015BmK+wDtCdpSceZ8iNo7wQrigzhCshZoaaJp65uLjwXYLdnClrxo8pFThT7pjNEgAYDCbsWJeO9JQyAICUaTBK+huCmrdb9xGp3OF2+3tQJs3l5T6ry2XGUFOPc8+9jaqtB6xjqqhQ9Pv0Vbj1j7dnebzR6Jvx4+6PsSt1jXVMJpHjjlGPYMbgBZCIe+7hricdZ8jNo7wQrigzhCshZqbnfotwEN7e3nyX0OWE0CwBQE1lC/745TRqq9rq9DOnYQy+gby5fWpuRcIkuN35Ea8TPfw5M9U7D+Ps0/+GobrOOhZy3xzEvvoEpOruP8lBXXM1zhYewa/7PkV9S7V1PC44CYunvoxAzzAeq3MMPeE4QzoP5YVwRZkhXAkxM9Q08Sw/Px8JCQl8l9ElzlW04PuT5Q7fLAFAWkoptq9Nh8lohoTpMdD8G3oZtli3ixTOcJ3zbzgNvYf3BU8vZsas1SPrjU9R9N1K6za5lzsSP/wnfCeP4rHCrmNhFpTW5iOr5AyySk8jq+Q0qhpLbfZRylS4e+yTmNj/dohFYp4qdSzd+ThDOh/lhXBFmSFcCTEz1DSRTme2MPxwshy/nLFdPNXRmqXGei1yM6uQk1aJory2szTe5vMYZfkKamOZdT959Ci43fU/SL1C+Sq1g6Zz55H62OtoOZ9vHfOZMByJH/4TCl/73mPVlQwmPfIq0pFVegZZJaeRVXoGrbqmK+7fL2IEHpz8T/g4yJTvhBBCCOkeqGniWWBgIN8ldKraViPe2l2As5dM8uAozRKzMFSUNSE3owq5mVWoLm+2bhMzI/oaVyHBtAEiXJiiW+YE15mvQDX6IYjEjnHGglkswObDOPzR92DGtpsoxUo5Yl95EqGL+LnHqjNp9M3IKE5BZslpnC89jdyKdJjMxivuL5PIERXQG7HB/ZEYNgSJoYMF/x50he52nCFdi/JCuKLMEK6EmBlqmnim1+v5LqHTnCxpwv/tKUSjru3LvEQEPDA4EHMTfXlrloxGM4pyay80StVobbZ9vyVMD39LGgaYf4erqcg6LgsbBPcFn0HqG23vkq9IX1WLM4+9hroDJ61jLom90O/T1+AcG8FjZTePMYbdZ9dh+a4PoDW0XnE/Fyc3xAT1R1xQf8QG90eEX1yPmz78RnSn4wzpepQXwhVlhnAlxMxQ08Sz2tpa+Pn58V3GTTFbGH5MqcDPKRW4uFKyt1qGf42PQIKf2u71tLbokZdVjdyMKhRk18JkNNtsd7GUI9B8GhGyNHjqz0FkMbRvlMjgMu1FqMc9AZHEcf54mFo1OHHXs2hOy24bEIkQ8djd6PWPxRDLZfwWd5MaW+vw1dY3cTJnb4dt/u4hiAnuZ22SAj3D6UzSDegOxxliP5QXwhVlhnAlxMw4zrdCIkh1GiPe3l1gM9nDkBBXPJ8cBlelfeN1/lwFThwoQFlxA6zdG9rOJvlZ0hFsSUWI+CyU+gv3K/3pqi9pYCLc7/kcssDedqv5ejCLBalPLLE2TBJvDwz4Ygm8Rg3kubKbdyJ7L77a+gaaNPXWsRHxUzA0ZgJig/rB3Vl4s+sQQgghpPuhpolncXFxfJdww1LKmvF/uwtQr71wb40IWDQoEHf09YXYzmcDzp4swdZV56w/t51NOoNgpMLXnA7xpWeTLiF2C4AiYRKU8ZOg6D0ZIonjnbXJfucrVG3eBwCQuqgxZNUncI2N5Lmqm6PVt2L57g+wK3WtdcxV5YHFU/6FQb3G8lZXdyXk4wyxP8oL4YoyQ7gSYmaoaeJZXl4eoqMd576Z62G2MPxyugLLT11yOZ5Khn+OD0eiv7Pd68k6W4Ftq8/BzVKCaNNOhOAM1KaKy+8slkIeOQyK+IlQxE+ENCDeoS/3Klu1FXkf/9D2g1iMfl++gSqJBa78lnVTskpO49NNr6CqoX2q8IFRY/DQ1H/BXd19Zv5zJEI8zhD+UF4IV5QZwpUQM0NNE88MhsufAXFU9Voj/m93IVLK2meeGxTsgueTw+DuZP+zNHlZ1dj4+xn4ms5hrP59SNHx/RS7BVibJEVsMsRKYbQcDafScO7Zt60/x73+JHzGD0N1ejqPVd04k9mIFQe/xPqj34OxthkKFTInLBz/HMb1ne3QzavQCe04Q/hFeSFcUWYIV0LMDDVNPFOr7T9Rwo1KLW/GW7sLUKdpvxzvvgEBmN/fz+6X4wFAcV4d1v+UAh/DnxomsRTyiKFtTVLCJIc/m3Q52tJKnFr4D1j0bb9T8D23IOzBeQCElZmLimty8emGl1FQlWUdiwnqh8emvw5/jxAeK+sZhJgZwh/KC+GKMkO4EmJmqGnimRBmDrEwhl9PV+KHU+WwXLgez1MlxT/HhaNvgAsvNVWUNGLN8pPw0p/FWP171oZJkTgN7gs+h9hJGGeTLsfUqsWphc/DUN224K7H8CQkvPWctfETQmYusjALtpz8Fb/s/QRGc9v/I4lYgjtGPYJbhiyEWCzhucKeQUiZIfyjvBCuKDOEKyFmxjFW7OzB8vLy+C7hqpp0Jvxray6WnWxvmJICXfD57DjeGqbqimasXHoCHprUDg2Tx/1LBd0wMYsFZ//6BprPtc2U5xQWiKRv37KZVtzRM3NRTVMF/v3bY/hh13+sDVOQVwTevOd7zB72ADVMdiSUzBDHQHkhXFFmCFdCzAydaSJXlFOjwes78lHZ0vaFVwTg3gH+uKu/P2+L1TbUarBy6Qm4tpyxbZh6T4XH/UshEvhCpznvfYPKjXsAtM2UN/CH9yD3dOO3KI4YYziYvhnf7XgHGn37VPTTBt6Nu8Y8DrlMyWN1hBBCCCHcUdPEs4CAAL5LuKwd2XX46EARDOa200vuSileHB+OpEB+zi4BQHOjDr9/dxzqhhSM0797ScM0BR6LhN8wla3ZhtwPl7X9IBaj3xdL4Bwb0WE/R80MAFTUF2PpjndwJv+wdczTxQ+PTn8NfcKG8FhZz+bImSGOh/JCuKLMEK6EmBlqmnhmMpn4LsGGycLw1dFSrE2rto7F+ajw8sQI+Kj5a0o0LQas+O44lDWnMM7mDNMUeCxaBpFUwVttnaHhVDrOPf2W9ee4V5+Az4Thl93X0TIDAAaTHuuOLMP6o8usl+IBwMj4qVg06R9wFsiMhd2VI2aGOC7KC+GKMkO4EmJmqGniWXV1NXx8fPguAwBQrzHizV0FOFtxySVVsV54fEQw5BL+bn/TaY1YuewEpBUnLpxh0gMAFAmTu0XDpCurQsr9l8yUd/cshC2+84r7O1JmAOB03iEs3fEOKhtKrGOeLn64f8LfMCRmPI+VkYscLTPEsVFeCFeUGcKVEDNDTRMBAGRUtWLJjnzUaowAAJlYhMdHBGN6nDevdRkMJqz+/iRQfLRjw/TA94JvmEytWpy6/x/QV9UCADyG9UfC//1NEFOk1zRV4IddH+DY+Z3WMYlYgumDFuC2EQ9BKVfxWB0hhBBCSOehpolnMTExfJeAjZk1+OxQCYwXpsfzVsnw8sQIxPvyO4e+yWTBuh9TYMo/jLE2DdOkbtEwMYsFZ596E02pbWsXOYV2nCnvcvjOjMlsxOaTv2Dlwa+gN2qt43HBSfjLpBcQ4iOsFb57Ar4zQ4SF8kK4oswQroSYGZpynGdFRUW8vbbBbMGH+4vw8YFia8OU6K/Gp7NjeW+YLGYLNvx6Gpqsgxirfxeyiw1T/ER4LBJ+wwQAOe9/h8oNuwEAEmcVBvzwLuRe7td8HJ+ZyShOwQvfL8BPez62NkyuKg88Nv11vHrX19QwOSg+M0OEh/JCuKLMEK6EmBk608QznU7Hy+tWtxrwxo58ZFZrrGOze/tg8dAgSHmaTvwiZmHYsvocGs/uwzj9O7YN0wM/QNQNpqwuX7sDuR981/aDSIR+n78Ol7jI63osH5lpbK3DT3s/xr5zG6xjIogwsf9tuHPM4zTRg4Pj6zhDhInyQriizBCuhJgZapp45uTkZPfXTC1vwZs789Gga5u5RC4R4elRoZjYy9PutVyKMYaK0iacPJCP2pQ9tg1T3ARBN0zMYoG2uBwtWflozshF7odLrdtiX3kcvpNGXvdz2TMzFosZO8+swa/7/odWfbN1PNIvHn+Z/CKiAnrbrRZy4/g4zhDhorwQrigzhCshZoaaJp4FBQXZ7bUYY1ibVo2vjpbiwvJL8HOW49WJEYj25u+m/eqKZmSmliMztRyNdVr4mdMwTv8f24bpL8sF0TAxsxmawjK0nM9Hy/kCtGTlofV8AVpyCmHR6jvsHzR/BsIfuYvTa3R1ZvRGLXLKziGzJAXHs/egoCrLuk2lcMb8MY9jYr/bIBZLurQO0nnseZwhwkd5IVxRZghXQswMNU08y8nJQUJCQpe/TrPehM8Ol2BnTr11LCnQBS+ND4er0v4xqK9tReaZCmSmlqO2shkerBDhpmMIMR+DGyuz7qeIG++wDZOpVYvafcfQkpnX1iCdL0BrTqF16vBr8ZkwHL3f+TvnmfI6OzNNmnpklZ5GZslpZJakoKAyE2aLucN+o3vPwIKxT8Fd7dVpr03sw17HGdI9UF4IV5QZwpUQM0NNUzdntjBszqrFshNlaNK3fxG+s68v7h8UCIkd719qatAi62xbo1RZ0gAvSy5Czccw0nwcLqyqw/5tDdOPDtcwMbMZpb9tRvb/fWmdKvyqxGKoIoLhEhsBdUw4nGMi4BwbAZeEaLtPLc4YQ3VjGTJLUqxNUlldwVUfE+oTjYUT/o7eoYPsUyQhhBBCiIOhpolnfn5+XfbcZyta8NnhEuTWtk8L7SQT47kxoRgT4dFlr3up1hY9zp+rROaZcpQV1MLHkoUQ8zGMMB+HitV1fIBIBFnEUDglzYFq+H0ON0te7YGTyHztv2g+l91hm0gqgSoiBM6XNEbOsRFQR4ZArJB3Wg1cM6MzaLE/bSPSi08isyQF9S3VV90/yCsCsUH9EReShLig/vBxCxTEulHkyrryOEO6H8oL4YoyQ7gSYmaoaeqGqloM+PpYKfbmNdiMj4vywINDAuGj7rwv8FfS1KDFrg0ZyEsvg685A6HmYxhqOg4nNHXcWSyBPHoUlH1nQdl3BiSujvcHqTW3CFlL/oeqrQdsxn2njUHgnMlQx4S3NUfXWGPJnizMgn3nNuC3/Z9dsVGSiCWI8ItHXHAS4oL7IyaoH1xV9mmoCSGEEEKEgpomnlVWVsLLq3PuEdGbLFhxtgq/na6A/uJMDwCivZzw2PBgJPo7d8rrXA1jDKnHinFo40nEa37DXNNBKNDacUeJDIrYsW2NUuI0iJ0d8z4ZQ30Tcj/4DkVLV4GZ2i9vdO0Tg9jX/gqvkQPsXtP1ZCat8DiW7/7QZhIHAFDKVIgJ6ovY4P6IC05CdEBvKGTCm8GGcNOZxxnS/VFeCFeUGcKVEDNDTVM3wBjDgYJGfHW0FJUt7ZMQuCmlWDQoAFNivOxy71JDnQbbVp2FJHs9php+gvLPZ5VkSijiJ8Kp7ywoek+B2Mlx1/axGIwoWrYauR98B2ND+1TbCn9vxLz4CALvmAqR2PHWhi6rK8TPez7GiZy9NuODe43FrcMWIcIvDhIx/bEnhBBCCOGCvj3xLDo6+qYen1+nxWeHS3CmvMU6JhYBtyb44J4B/nBRdP3/YmZhSDlahJRNezCg9Rv4W9LaN8qcoEycBmW/WVDET4RYoe7yem4GYwxVW/cja8mn0OQVW8fFTgpEPLYAEY8tgFTN75mZy2WmRduIVYe+xraU321mvgv3jcW945+lSRx6uJs9zpCehfJCuKLMEK6EmBlqmnhWWlqKiIgIzo9r0pnww6lybMiogaX9SjwMCHLBo8OCEOZhny/29bWt2LbiFNxyl2OKcR0kMFq3KfvdAte5b0PiFmCXWm5W09ksZL76CeoOnbIZD5w3HTEvPgxlgA9Pldm6NDMmsxHbUlZg1aGv0aprP7Pn4eyD+WMex+jeMyAWOd4ZMWJfN3qcIT0T5YVwRZkhXAkxM9Q08Uyr1V57p0sYzRZsyarFspPlaL5kCvEAFzkeHhaE4aFudpnpzGJhOHWoEFmb12Kg5mu4sXLrNrFHCNxufw/K3pO7vI7OoC2tRM67X6P0980Aa+9APYb1R9zrf4Vbvzgeq+tIq9WCMYYTOXvw0+6PUdHQfkZMLlVg1pCFmDXkPijldK8SacP1OEN6NsoL4YoyQ7gSYmaoaeKZUnl9axC1GszYlFmDNeeqUaNpP5ujkIpxd38/3JboC7nUPmcUaqtasOv3gwjI/wLjze33zjCRBM7jHoPzlOcd/jI8AGhMSUfBV7+h4o9dNpM8qMKDEPvKE/CdNsYhp9qu1ZZhya8fIaP4pM34mMSZmD/6cXi6+PJUGXFU13ucIQSgvBDuKDOEKyFmhpomnoWGhl51e63GiLXnqvBHRg00RovNtvEXphD3tsMU4gBgMVtw4kA+Sjd/h0G65VCi/T4qachAuM//ELKgRLvUcqOY2YzKzftQ8NVvaDiWarNN6uqMqGcXIeyB2x1q6nAA0Bk0yCo9g4Ppm7E/bRMY2s+IxYcMxL3jnkGkfzyPFRJHdq3jDCGXorwQrigzhCshZoaaJp6dP38eCQkJHcaLGnRYmVqFnTl1MF560xKA4WFumN/PD/G+9jubU1PZjH2/bEFE0ccYZkm3jjO5C9xueRWqEQshEkvsVg9XpuZWlPz8Bwq/WQFtcbnNNrmXO0Lum4OwB++A3MudnwL/pFXXjMySFGSWpCC9+CTyKzJhYWabffzdQ7Bg3FMYFD3WIc+IEcdxpeMMIZdDeSFcUWYIV0LMDDVNDiatsgW/p1bhcGGjzbhMLMLEXp64rY8vQt3td0pTrzPh5L7zaNnxIYYY1kECk3WbvN9suM99CxI3f7vVw5WmsAyF365Ayc9/wNyisdnmHBuB8IfnI2DuZEiUCp4qbNOsbUBG8SlkFKcgo/gkCqvO25xNupRSpsK80Y9ictIdkEoc64wYIYQQQkh3RE0Tz3x8fGBhDEeLmvB7aiXSKm0XglXLJZgZ743ZvX3gpbLfF+TmRh1O7UtHy6Gf0Uu3ERGs0rqNuYXAc/5/oIyfaLd6uGCMoeFYKgq++g2Vm/cBFtvLGr3HD0f4w3fCa8xg3s7QNLTWIqP45IVG6RSKa3Kvun+wVyTiQwYgPmQAglxiEBYsrBlnCL98fBxj5kciDJQXwhVlhnAlxMxQ08Qjg9mC/SVabNyTgeJGvc02b5UMcxN9MC3OG2q5/S57qypvwpkdRyBJ/RFRxl2Qo/3sDBNJoBr7ONymPQ+RXGW3mq6XxWhCxYZdKPjiVzSdybTZJlbKETRvOsIenAfnmHBe6jMYdTh2fhd2pa5F+p8mcbiUCCKE+vZCfMgAJIQMRFxwElxVHtbt9fX19iiXdCNSKR3qyfWjvBCuKDOEKyFmRngVdwMWxrAytQqr06pQpzHZbAvzUGJeX1+MjfSATGKf2fAYYyjMrkHW1g1wL/gNCebjEP/p0jBR+Eh4z3sHskDHvP609uAppD3/LjS5RTbjCj9vhC6ai5B7Z/N2v1JhVTZ2pa7BgbRNaNU3d9guEokR4RdnbZJig/vDWel6xecrLy+Hh4fHFbcT8meUGcIF5YVwRZkhXAkxM9Q08UAsEuFocZNNw9TX3xnz+vlicLCr3S4ZM5ssyDxdhNKtPyKwdg36WvJttlvEcij63wa3iY9BFtjbLjVxZahvQtaS/6H0lw024659YhC2+E4E3DqRl5nwNPoWHMrYht2pa5FbkdZhe6BnGAb3Gof4kIGICeoLlcLZ7jUSQgghhJDrQ00TT+b19cW5ihYMD3XBnf0D7DoTnl5nxNl9qWja9y3CW7agDxpstpuV3nAe8xe4jH4AEhfHvOaUMYbyNduR+fJHMNQ2WMfdB/dBzIuPwGN4f7vfr8QYQ3bZWexKXYvDmVuhN+pstsulCgyLnYhxfecgLvjG64uMjOyMckkPQpkhXFBeCFeUGcKVEDNDTRNPBoe4Yum8BBjrKxBmp4apqUGLtK07wU4uRajhAIJgtNlu9kqAx5QnoRowByKpfdZ+uhGawjKkv/AeanYftY5JXdSI+ddjCLn3VojE9rms8aImTT32p23C7tS1KKnN67A93DcW4/vNwcj4qVArXW769SorKxEWFnbTz0N6DsoM4YLyQriizBCuhJgZapp4IhaJEOiqQHpJ67V3vgkWC0NBRilKd/wCl6J1CDen22xnEAPRk+E17UnII4c59Ho/FpMJhV/9jpz3voFZ234Wx2/GWMT/+xko/e17VqygMgvrji7D8ezdMJltG1AnuRqjEqZhfN/ZiOjkRWdbW7s2M6T7ocwQLigvhCvKDOFKiJmhpolncnnXnNFpadIha9cu6I//hMDWfYiGbTjNEmfIB90Nz8mPQerl+KsyN57JRNrf/g9NZ89bxxQBPkh4+zn4TR1j11r0Ri1WHPgSm0783GHB2djg/pjQdw6Gxk6AQubUJa/fVZkh3RdlhnBBeSFcUWYIV0LMDDVNPOvMazqZhaEwPR/l25fDpXg9Av80sQMAGNQhcB33CNxG3QNxJ1wq1tVMrRpkv/s1Cr9e0b7ekkiE0AduQ8wLD0PqYr97wQDgTP5hfLvtbVQ1llrHXFUeGNN7Jsb1vRVBXl2/fpIQrwMm/KLMEC4oL4QrygzhSoiZoaaJZ5mZmUhIuLlpvFuadcjZ9geMJ3+Gv+YQwv98r5JIDkv0VPhO+gsUvUY59CV4l6recQhp/3gPutL2hXWd46OQ+J8X4D7AvrP5NWnq8cOuD3AgfZN1TCaRY+6IBzFz8L2Q2fEesM7IDOlZKDOEC8oL4YoyQ7gSYmaoaRIoxhiKUzNQuX0Z3Eo3wI9VdNhH5xoHl1H3wW/UXRCr3Hio8sboKqqR+ep/UbFup3VMrJQj+rkHEP7I3RDL7Bdbxhj2p23E8t0foFnbaB2PDxmIh6a8hEBPYd3ESAghhBBCuKOmiWdeXl6c9jcaTMha+xNMKT/DR3sSwbDYbpc4wxJ7K/ynPQRlSN/OLLVLaQrLULVlHyo370P9sdT2S/EAeI0ehIR3n4c6ItiuNVXUF+PbbW/jbGH7LH1qhQsWjHsa4/rcytsZO66ZIYQyQ7igvBCuKDOEKyFmhpomnikUiuvet+DIIbSs/ge8DB0XS231HAT35IXwH3EbRDJlZ5bYJRhjaE7LRuXmfajash/Nadkd9pF5uiHutb8i8I6pdm1QTGYjNp34GSsPfgmDSW8dHx43GQvHPwd3Z2+71XI5XDJDCECZIdxQXghXlBnClRAzQ00Tz8rKyuDu7n7VfZqq65Dz3b/gV74CXmifrU0v9QbrfRsCpz+EAD/Hv6HOYjKh/miq9YySrqTjJYUAoIoKhd/0ZEQ8PB9ybw+71phbno6vtr6Bwqr2Wfq8XPzwl8kvYkDUaLvWciXXkxlCLkWZIVxQXghXlBnClRAzQ02TAzObzMhc/QOUh99CAKu1jmtl/lDPfB1ho+dCJJbwWOG1mTU61Ow7hspN+1C9/QCM9U2X3c8tKQG+08bAb+oYOMeE27dIADqDBr8f+AKbT/4CxtouDRRBhKkD5+PO0Y9BKVfZvSZCCCGEEOIYqGniWUTE5aeoLktNRc2vf4OP5oR1zAwp9H0fRMSCf0GscOwv8c2Zecj7+HtUbtkHi1bfYbtIKoHnqIHwmzoGvlNGQxlg34VpDUYdKhqKUVZXiPK6Quw8swY1TeXW7aE+vbB46r8QHZBo17qux5UyQ8iVUGYIF5QXwhVlhnAlxMxQ08SzmpoahISEWH/WNDUj67sl8C74AT6XTB3e7DEYgYs+hnNoHB9lXreW8wXI+c+3qFi/C2DMZptErYLPhOHwnTYaPuOHQ+bWtetEWZgFdc2VFxqjIpTXFaCsrhBldYWobaoAA+vwGJlUgdtHPIQZg++BVCLr0vpu1J8zQ8i1UGYIF5QXwhVlhnAlxMxQ08Sz5uZmAG0TI2RtWg3xrlfhby6zbtdJPKGY9jp6TbjboddXaskpRO4HS1G+ZrtNsyTzdIff9LbL7jxHDYRE2XU3/hXX5OJwxra2Jqm+7QzSpRM5XEvv0MF4aMpL8Pdw7D/EFzNDyPWizBAuKC+EK8oM4UqImaGmiWdSqRTVuTko/f55+DXtsY5bIIam112IuP9NSNWOu8ZSa34Jcv/zHcpWb7OZJlzu5Y6IJ+5B6MK5kKi6fja/PWfX4+ut/4bZYrrmvk5yNQI9wxHgGYbAC/8Ee0chyCvCoRvTi6RS+mNLuKHMEC4oL4QrygzhSoiZEV7F3Yheq4N2129QZH4BP2it4y3OCfC592MExQ7ksbqr0xSWIveDpShbuRXM3D6jn8zTDRGPLUDootsgVTt1eR0Wixk/7f0vNh7/0WZcLJLA1z0IgZ5hlzRH4QjwCIWb2ksQzdGVxMTE8F0CERjKDOGC8kK4oswQroSYGWqaeJK3ZzsMG1+ArzHfOmYQOwNjXkD0LY9AJBbzWN2VaYrKkffRMpT+vgnMdEmz5O6C8McWIOyB2yB1VtunFn0LPvnjn0jJO2gdm9T/dkwdOB9+7sEOe0/SzUpPT0dCQgLfZRABocwQLigvhCvKDOFKiJmhpokHJp0O+OMxuJnbpxFvCrkF4X95Fwp3Xx4ruzJtSQVyP/4epb9ssGmWpG4uiHhkPsIenAepi32aJQCobCjBe6ueQUltHoC2M0v3T/wbJifNs1sNhBBCCCGkZ6CmiQdSpRKS8f8Ctj+DZkU4POd/gNiksXyXdVmGukbkvPcNin9cB2Zsv19I6qJG+MPzEfbQvC6fBe/P0otO4sN1f0ezthEAoFa64plb30Fi2BC71sEXT09PvksgAkOZIVxQXghXlBnClRAzQ00TT0Kn34dSqRi+g6fD3dOL73I6YGYzin/6A9lvf2GzIK3EWYXwh+5E+MN3Qubuave6dp5Zje+2/x/MlrazXYGeYfj73I8Q4Blq91r4olI59hpdxPFQZggXlBfCFWWGcCXEzDjmjTM9gEgkQvCUe1FWUcl3KR00nErD4WkPIf35d60Nk0TlhMin7kPysVXo9Y+H7N4wmS0mLNv53oUZ8toapn4Rw/HGPd/3qIYJAEpKSvgugQgMZYZwQXkhXFFmCFdCzAydaSJWhpp6nH/rC5T8/IfNeMDcyYh95XEo/X14qatV14z//vEizuQfto5NG3g37hn3FCRiijAhhBBCCOla9I2TZ2FhYXyXAGY2o+j7tch+5yuYGtsXG3OOj0LCW8/Cc3gSb7WV1xXhvdVPo6yuEAAgEUvwl0kvYny/ObzVxDdHyAwRFsoM4YLyQriizBCuhJgZapp4Vl9fD7XafrPOdXj942eR/uL7aD6XbR2TuqgR/fyDCF10G8Q8Lj52tvAYPlr3D7Tq2i4RdHFywzO3voeEUMddv8oe+M4MER7KDOGC8kK4oswQroSYGWqaeNbU1HTtnbqAvroOWW98hrLfN9mMB94xDbEvPwaFL7+TU2xL+R3LdrwPC2u7fynYOwrPz/0Qvu5BvNblCPjKDBEuygzhgvJCuKLMEK6EmBlqmngmkUjs+noWkwlFy1Yj591vYGpqsY679O6FhLefg8eQvnat5yLGGKoaS5FedBKncvfjePZu67YBUaPxxMw3oVI481Kbo7F3ZojwUWYIF5QXwhVlhnAlxMxQ08Sz2NhYu71W3aEUZPzrQzSn51jHpG4u6PWPxQi571a7XorHGEN5fREyik8io/gU0otPoa6540yCs4bch7vGPAGxWHh/uLqKPTNDugfKDOGC8kK4oswQroSYGWqaeJaRkYH4+Pgue35TqxYV63agaNkaNKVm2mwLumsmYv75CBQ+Xb/AGGMMZXUFSC9qa5Iyik+ivrXmivs7ydW4f+LfkZw4q8trE5quzgzpfigzhAvKC+GKMkO4EmJmqGniGWOsS563OSMXxcvXoWzFZpiaW222ufaNRcLbz8F9YGKXvPZFVQ2lSMk7aD2b1Kipu+K+CpkSMYH9EB8yAAmhAxHl3xsyqbxL6xOqrsoM6b4oM4QLygvhijJDuBJiZqhp4pm7u3unPZdFb0DFht0o/mEt6o+e6bDdtW8sQh+4HUF3TIWoC68lragvxurD32B/2iYwZrnsPkqZCrHB/ZEQMgDxIQMR6R8PqUTWZTV1J52ZGdIzUGYIF5QXwhVlhnAlxMxQ08QzV1fXm36O1vwSlCxfh5JfN8JY12CzTeykQOCcyQi5bzbc+nftadCqxjKsOfQN9p7bYJ317iInuRpxwUlICBmI+NABiPCLo4Vpb1BnZIb0LJQZwgXlhXBFmSFcCTEz9K2VZ0VFRUhISOD8OIvRhKptB1D8wxrU7j3eYbtzTARC7puNwDumQubm0hmlXlFNUwXWHvkOu1PXwWwxWcfVSldMG3gXBkaNRphvDE3m0EluNDOk56LMEC4oL4QrygzhSoiZoaZJYHRlVSj+cT1Kfl4PfYXtRAoiuQz+M8ch5L7Z8BjaDyKRqEtrqWuuxrqjS7HzzGqYzEbruErhjBmDFmDaoLugUnRtw0YIIYQQQkhXo6aJZyEhIdfchzGGugMnUbRsNaq27Acz/+nSt7BAhNw7G8HzZ0Du7dFVpVo1tNZi3ZFl2HFmFYwmfXsdcjWmDbwL0wcvgLNSeKddheJ6MkPIpSgzhAvKC+GKMkO4EmJmqGniWXNzM1xcLn82xtjYjNLfN6H4+zVozSmy2SaSSOA7ZRRC7psNrzGDIRKLu7zWJk09/jj2A7ae+g2GS5olhcwJUwfciZlD7oWLk3uX19HTXS0zhFwOZYZwQXkhXFFmCFdCzAw1TTxraGhAYGCgzVjT2SwULVuN8tXbYdbqbLYp/LwRfM8tCFlwC5SBvnapsVnbgA3Hf8SWk79Cb9Rax+VSBSYnzcOsIffBTd31az2RNpfLDCFXQ5khXFBeCFeUGcKVEDNDTRPPLt53ZNbpUblhNwqXrkLjybQO+3mOGIDQ++fCd9oYiGVd+7+NMYaSmlyczjuE0/mHkFmSYjPBg0wix8T+t+HWoffD3dm7S2shHXX1vWqk+6HMEC4oL4QrygzhSoiZoaaJZ2EqN2S9+RlKft7QYbpwibMKQfOmI3ThHDjHRnRpHRp9M84WHMOZ/EM4nX8Ydc2VHfaRiKWY0G8uZg9bBE8X+5zlIh0JbQVtwj/KDOGC8kK4oswQroSYGWqaeMAYQ83OwyhathrVOw8Df1oV2Tk+CqGLbkPgbZMhVau6rIaCqqy2JinvEM6XpnZYW+kiH7dADIpOxozBC+DtGtAl9ZDrl5WVhdjYWL7LIAJCmSFcUF4IV5QZwpUQM0NNE0/Ov/UFmtNzrD+LZFL4zxyH0Pvnwn1I3y45bdmia0Jq/hGczj+I1PzDaGitvex+MokcCaED0S9iBPpHjECAZ5ggT6N2V2bz5ZtbQq6EMkO4oLwQrigzhCshZoaaJh6IRCKE3D8X6c+/C5mfF8IfuB3Bd8+CwqfzJ1NgjOF8WSq2p6zAkawdNuspXcrfIxT9I0egX8QIJIQMgELm1Om1kM4hxFW0Cb8oM4QLygvhijJDuBJiZqhp4kngbZOh8PWE84gkqF07f8pFnUGDA+lbsP30ChRWne+wXSFTonfoYPSLGIF+EcPh7yG8+fJ7Kk9PmqmQcEOZIVxQXghXlBnClRAzQ00TT6RqFfymjkF6ejoSEhI67XlLa/OxPWUF9p7bAK2h1Wabs9INo3tPx4Co0YgN7g+5VNFpr0vsp6CgoFMzQ7o/ygzhgvJCuKLMEK6EmBlqmroBk9mIkzn7sC1lBdKKjnfYHh2QiMlJd2BY3CRqlAghhBBCCOGImiaeBQUF3fBj65qrsSt1DXaeWY36lmqbbXKpAiPjp2JS0h2I9BfetI7kym4mM6RnoswQLigvhCvKDOFKiJmhpolnWq0Wbm5u170/YwzpRSew/fRKHM/eDbPFdvYRf49QTE66A2MSZ8JZKbyb7Mi1cc0MIZQZwgXlhXBFmSFcCTEz1DTxrK6uDv7+/tfcr76lGvvObcDu1HWoaCi22SYSiTEoegwmJd2BxLAhEIvEXVUucQDXmxlCLqLMEC4oL4QrygzhSoiZoabJgZktJqTkHsTus+uQknugw+KzbmovjO87GxP6zYW3q7CCRwghhBBCiFBQ08Sz+PiO9xuV1xVhz9l12HduA+pbazpsTwwbgvF9Z2NIzHhIJTJ7lEkcyOUyQ8jVUGYIF5QXwhVlhnAlxMxQ08SznJwc9OrVCwajDkfP78Ku1LXIKD7ZYT9PZ18k95mFsX1ugZ97MA+VEkdxMTOEXC/KDOGC8kK4oswQroSYGWqaeFZUk439BatwIH0zNPoWm20SsQQDosZgfN/Z6BcxHGKxhKcqiSMxGo18l0AEhjJDuKC8EK4oM4QrIWaGmiYeMMaw/fQK7DqzFgVVWR22B3qGYVyf2RidOAPuai8eKiSOzNnZme8SiMBQZggXlBfCFWWGcCXEzFDTxAORSIRDGdtsGiaFTIlhsZMwru+tiA3qD5FIxGOFxJH5+PjwXQIRGMoM4YLyQriizBCuhJgZmpuaJ+P7zgYABLpF4sHJL+Hzx7bi0emvIS44iRomclX5+fl8l0AEhjJDuKC8EK4oM4QrIWaGzjTxZGjsBIT5xqK1xoiEhAS+yyGEEEIIIYRcgUOdadq3bx9mzZqFwMBAiEQirF279qr7r169GpMmTYKPjw9cXV0xfPhwbN261T7F3iSFzAlhvr0QGBjIdylEYCgzhCvKDOGC8kK4oswQroSYGYdqmlpbW9GvXz98+umn17X/vn37MGnSJGzatAknT57EuHHjMGvWLKSkpHRxpZ3HYDDwXQIRGMoM4YoyQ7igvBCuKDOEKyFmxqEuz5s2bRqmTZt23ft/9NFHNj+/9dZbWLduHf744w8kJSV1cnVdo6amBr6+vnyXQQSEMkO4oswQLigvhCvKDOFKiJlxqKbpZlksFjQ3N8PT0/Oa+zY3N0Msbj/RplAooFAourI8QgghhBBCiAB1q6bp/fffR0tLC+bNm3fNfRMTE6HRaKw/L1q0CE8++SQCAgKQm5sLAPDz8wNjDFVVVQCAXr16oaSkBFqtFkqlEiEhIcjOzgYA+Pr6QiwWo6KiAgAQFRWFiooKtLa2QqFQIDw8HFlZbVOMe3t7Qy6Xo6ysDIwxaLVaVFdXo6WlBTKZDNHR0cjIyAAAeHp6wsnJCaWlpQCA8PBw1NXVoampCRKJBLGxscjIyABjDO7u7nBxcUFxcTEAIDQ0FE1NTWhoaIBIJEJ8fDyysrJgNpvh6uoKDw8PFBYWAgCCg4Oh0WhQV1cHAEhISMD58+dhMpng4uICb29v60wngYGB0Ov1qK2tBQDExcUhLy8PBoMBarUafn5+yMvLAwAEBATAZDKhuroaABATE4OioiLodDo4OTkhKCgIOTk51vcbACorKwEA0dHRKC0ttb7foaGhOH/+PIC2qSqlUinKy8sBAJGRkaisrERrayvkcjkiIyORmZkJAPDy8oJCoUBZWRkAICIiAjU1NWhuboZUKkVMTAzS09Ot77dKpUJJSQkAICwsDPX19Vd8v11dXVFUVAQACAkJQXNz8xXfb09PTxQUFAAAgoKCoNVqre93fHw8cnJyYDQa4ezsDB8fH5v322AwoKamxvq+5ObmQq/XQ61Ww9/f35pZf39/WCwWm8wWFxdb3+/g4GCbzIpEIuv7HRUVhfLycmg0GigUCoSFhV31/a6qqkJLS8tl32+lUnnZzP75/fbw8ICzs7NNZhsbG9HY2AixWIy4uDhkZmbCYrHAzc0Nbm5uNu93S0sL6uvrO2T2cu+3Tqe7bGadnZ3h6+t71cwWFhZCr9dDpVLZ/RhxMbM3c4wAgPT0dDpG9JBjRGxsLAoKCm74GMEYQ3p6Oh0jetAx4ma/RzDG0NTURMcI9IxjRGd8jwgLC0NRUZFDHCMuvofXImKMseva085EIhHWrFmD2bNnX9f+P//8Mx566CGsW7cOEydOvOJ+JpMJe/fuRWRkpEOcacrNzUVUVJTdX5cIF2WGcEWZIVxQXghXlBnClSNlxmw248yZM0hOToZUeuXzSd3iTNOvv/6KBx98ECtWrLhqw3QpFxcXSCSSLq7s2vR6Pd8lEIGhzBCuKDOEC8oL4YoyQ7gSYmYcava8G/HLL79g0aJF+OWXXzBjxgy+y+FMrVbzXQIRGMoM4YoyQ7igvBCuKDOEKyFmxqHONLW0tFivSwXaVgs+ffo0PD09ERoaihdffBGlpaX44YcfALRdkrdw4UJ8/PHHGDp0qPU6YCcnJ7i5ufHyO3Dl7+/PdwlEYCgzhCvKDOGC8kK4oswQroSYGYc603TixAkkJSVZpwt/9tlnkZSUhFdeeQUAUF5ebr15CwC++uormEwmPP744wgICLD+89RTT/FS/424eOMdIdeLMkO4oswQLigvhCvKDOFKiJlxqDNNY8eOxdXmpVi2bJnNz3v27OnaggghhBBCCCE9nkOdaeqJhHh6kvCLMkO4oswQLigvhCvKDOFKiJmhpolnFouF7xKIwFBmCFeUGcIF5YVwRZkhXAkxM9Q08ezi4mGEXC/KDOGKMkO4oLwQrigzhCshZoaaJkIIIYQQQgi5CmqaeNarVy++SyACQ5khXFFmCBeUF8IVZYZwJcTMUNPEs+LiYr5LIAJDmSFcUWYIF5QXwhVlhnAlxMxQ08QznU7HdwlEYCgzhCvKDOGC8kK4oswQroSYGWqaeObk5MR3CURgKDOEK8oM4YLyQriizBCuhJgZapp4FhwczHcJRGAoM4QrygzhgvJCuKLMEK6EmBlqmniWnZ3NdwlEYCgzhCvKDOGC8kK4oswQroSYGWqaCCGEEEIIIeQqqGnima+vL98lEIGhzBCuKDOEC8oL4YoyQ7gSYmaoaeKZSCTiuwQiMJQZwhVlhnBBeSFcUWYIV0LMDDVNPKusrOS7BCIwlBnCFWWGcEF5IVxRZghXQswMNU2EEEIIIYQQchXUNPEsKiqK7xKIwFBmCFeUGcIF5YVwRZkhXAkxM9Q08ay8vJzvEojAUGYIV5QZwgXlhXBFmSFcCTEz1DTxSK/X45NPPoFer+e7FCIQlBnCFWWGcEF5IVxRZghXQs0MNU080uv1WLp0qeBCQ/hDmSFcUWYIF5QXwhVlhnAl1MxQ00QIIYQQQgghV0FNEyGEEEIIIYRchZTvAuyNMQYAMJvNPFcCWCwWqFQqWCwWh6iHOD7KDOGKMkO4oLwQrigzhCtHy8zFGi72CFciYtfao5vR6XQ4ePAg32UQQgghhBBCHMTIkSOhVCqvuL3HNU0WiwUGgwESiQQikYjvcgghhBBCCCE8YYzBbDZDLpdDLL7ynUs9rmkihBBCCCGEEC5oIghCCCGEEEIIuQpqmgghhBBCCCHkKqhpIoQQQgghhJCroKapi3366acIDw+HUqnE0KFDcezYsSvuu2zZMohEIpt/rjaLB+meuGQGABoaGvD4448jICAACoUCMTEx2LRpk52qJY6AS2bGjh3b4TgjEokwY8YMO1ZM+MT1GPPRRx8hNjYWTk5OCAkJwTPPPAOdTmenaokj4JIZo9GIJUuWICoqCkqlEv369cOWLVvsWC3h2759+zBr1iwEBgZCJBJh7dq113zMnj17MGDAACgUCkRHR2PZsmVdXidnjHSZX3/9lcnlcvbdd9+xtLQ09tBDDzF3d3dWWVl52f2XLl3KXF1dWXl5ufWfiooKO1dN+MQ1M3q9ng0aNIhNnz6dHThwgOXn57M9e/aw06dP27lywheumamtrbU5xpw7d45JJBK2dOlS+xZOeME1Lz/99BNTKBTsp59+Yvn5+Wzr1q0sICCAPfPMM3aunPCFa2aef/55FhgYyDZu3Mhyc3PZZ599xpRKJTt16pSdKyd82bRpE3vppZfY6tWrGQC2Zs2aq+6fl5fHVCoVe/bZZ1l6ejr75JNPmEQiYVu2bLFPwdeJmqYuNGTIEPb4449bfzabzSwwMJC9/fbbl91/6dKlzM3NzU7VEUfENTOff/45i4yMZAaDwV4lEgfDNTN/9uGHHzIXFxfW0tLSVSUSB8I1L48//jgbP368zdizzz7LRo4c2aV1EsfBNTMBAQHsf//7n83Y3Llz2YIFC7q0TuKYrqdpev7551nv3r1txu688042ZcqULqyMO7o8r4sYDAacPHkSEydOtI6JxWJMnDgRhw8fvuLjWlpaEBYWhpCQENx6661IS0uzR7nEAdxIZtavX4/hw4fj8ccfh5+fHxITE/HWW285xArbpOvd6HHmUt9++y3mz58PtVrdVWUSB3EjeRkxYgROnjxpvRwrLy8PmzZtwvTp0+1SM+HXjWRGr9d3uLXAyckJBw4c6NJaiXAdPnzYJmMAMGXKlOv+HLMXapq6SE1NDcxmM/z8/GzG/fz8UFFRcdnHxMbG4rvvvsO6devw448/wmKxYMSIESgpKbFHyYRnN5KZvLw8rFy5EmazGZs2bcLLL7+M//znP3jzzTftUTLh2Y1k5lLHjh3DuXPn8OCDD3ZVicSB3Ehe7r77bixZsgSjRo2CTCZDVFQUxo4di3/+85/2KJnw7EYyM2XKFHzwwQfIzs6GxWLB9u3bsXr1apSXl9ujZCJAFRUVl81YU1MTtFotT1V1RE2TAxk+fDjuu+8+9O/fH8nJyVi9ejV8fHzw5Zdf8l0acVAWiwW+vr746quvMHDgQNx555146aWX8MUXX/BdGhGAb7/9Fn369MGQIUP4LoU4qD179uCtt97CZ599hlOnTmH16tXYuHEj3njjDb5LIw7q448/Rq9evRAXFwe5XI4nnngCixYtglhMXzmJsEn5LqC78vb2hkQiQWVlpc14ZWUl/P39r+s5ZDIZkpKSkJOT0xUlEgdzI5kJCAiATCaDRCKxjsXHx6OiogIGgwFyubxLayb8upnjTGtrK3799VcsWbKkK0skDuRG8vLyyy/j3nvvtZ6N7NOnD1pbW7F48WK89NJL9EW4m7uRzPj4+GDt2rXQ6XSora1FYGAgXnjhBURGRtqjZCJA/v7+l82Yq6srnJyceKqqIzradRG5XI6BAwdi586d1jGLxYKdO3di+PDh1/UcZrMZZ8+eRUBAQFeVSRzIjWRm5MiRyMnJgcVisY6dP38eAQEB1DD1ADdznFmxYgX0ej3uueeeri6TOIgbyYtGo+nQGF38SxrGWNcVSxzCzRxjlEolgoKCYDKZsGrVKtx6661dXS4RqOHDh9tkDAC2b99+3d+X7YbvmSi6s19//ZUpFAq2bNkylp6ezhYvXszc3d2t04jfe++97IUXXrDu//rrr7OtW7ey3NxcdvLkSTZ//nymVCpZWloaX78CsTOumSkqKmIuLi7siSeeYFlZWWzDhg3M19eXvfnmm3z9CsTOuGbmolGjRrE777zT3uUSnnHNy6uvvspcXFzYL7/8wvLy8ti2bdtYVFQUmzdvHl+/ArEzrpk5cuQIW7VqFcvNzWX79u1j48ePZxEREay+vp6n34DYW3NzM0tJSWEpKSkMAPvggw9YSkoKKywsZIwx9sILL7B7773Xuv/FKcf//ve/s4yMDPbpp5/SlOM90SeffMJCQ0OZXC5nQ4YMYUeOHLFuS05OZgsXLrT+/PTTT1v39fPzY9OnT6d1DXogLplhjLFDhw6xoUOHMoVCwSIjI9m///1vZjKZ7Fw14RPXzGRmZjIAbNu2bXaulDgCLnkxGo3stddeY1FRUUypVLKQkBD22GOP0RfgHoZLZvbs2cPi4+OZQqFgXl5e7N5772WlpaU8VE34snv3bgagwz8Xc7Jw4UKWnJzc4TH9+/dncrmcRUZGOuTagSLG6Pw6IYQQQgghhFwJ3dNECCGEEEIIIVdBTRMhhBBCCCGEXAU1TYQQQgghhBByFdQ0EUIIIYQQQshVUNNECCGEEEIIIVdBTRMhhBBCCCGEXAU1TYQQQgghhBByFdQ0EUIIIYQQQshVUNNECCGkW9izZw9EIhH27NnDdymEEEK6GWqaCCGE3LBly5ZBJBLhxIkTfJdy3S7WfLl/XnjhhU59rbKyMrz22ms4ffp0pz4vIYQQ+5LyXQAhhBDChyVLliAiIsJmLDExsVNfo6ysDK+//jrCw8PRv3//Tn1uQggh9kNNEyGEkB5p2rRpGDRoEN9l3JDW1lao1Wq+yyCEkB6DLs8jhBDS5VJSUjBt2jS4urrC2dkZEyZMwJEjR2z2qaurw9/+9jf06dMHzs7OcHV1xbRp03DmzJkOz1dSUoLZs2dDrVbD19cXzzzzDPR6fafUWlhYiMceewyxsbFwcnKCl5cX7rjjDhQUFHTYt6GhAc888wzCw8OhUCgQHByM++67DzU1NdizZw8GDx4MAFi0aJH1EsBly5ZZH79ixQoMHDgQTk5O8Pb2xj333IPS0lKb17j//vvh7OyM3NxcTJ8+HS4uLliwYEGn/K6EEEKuD51pIoQQ0qXS0tIwevRouLq64vnnn4dMJsOXX36JsWPHYu/evRg6dCgAIC8vD2vXrsUdd9yBiIgIVFZW4ssvv0RycjLS09MRGBgIANBqtZgwYQKKiorw17/+FYGBgVi+fDl27drFqa7GxkbU1NTYjHl7e+P48eM4dOgQ5s+fj+DgYBQUFODzzz/H2LFjkZ6eDpVKBQBoaWnB6NGjkZGRgQceeAADBgxATU0N1q9fj5KSEsTHx2PJkiV45ZVXsHjxYowePRoAMGLECABt91YtWrQIgwcPxttvv43Kykp8/PHHOHjwIFJSUuDu7m6ty2QyYcqUKRg1ahTef/99aw2EEELshBFCCCE3aOnSpQwAO378+BX3mT17NpPL5Sw3N9c6VlZWxlxcXNiYMWOsYzqdjpnNZpvH5ufnM4VCwZYsWWId++ijjxgA9vvvv1vHWltbWXR0NAPAdu/efV01X+4fxhjTaDQdHnP48GEGgP3www/WsVdeeYUBYKtXr+6wv8ViYYwxdvz4cQaALV261Ga7wWBgvr6+LDExkWm1Wuv4hg0bGAD2yiuvWMcWLlzIALAXXnjhqr8XIYSQrkOX5xFCCOkyZrMZ27Ztw+zZsxEZGWkdDwgIwN13340DBw6gqakJAKBQKCAWi62Pq62thbOzM2JjY3Hq1CnrYzdt2oSAgADcfvvt1jGVSoXFixdzqu3TTz/F9u3bbf4BACcnJ+s+RqMRtbW1iI6Ohru7u00dq1atQr9+/TBnzpwOzy0Sia762idOnEBVVRUee+wxKJVK6/iMGTMQFxeHjRs3dnjMo48+yun3I4QQ0nno8jxCCCFdprq6GhqNBrGxsR22xcfHw2KxoLi4GL1794bFYsHHH3+Mzz77DPn5+TCbzdZ9vby8rP9dWFiI6OjoDo3J5V7jaoYMGXLZiSC0Wi3efvttLF26FKWlpWCMWbc1NjZa/zs3Nxe33XYbp9e8qLCw8Io1x8XF4cCBAzZjUqkUwcHBN/RahBBCbh41TYQQQhzCW2+9hZdffhkPPPAA3njjDXh6ekIsFuPpp5+GxWKxWx1PPvkkli5diqeffhrDhw+Hm5sbRCIR5s+fb9c6LnXpWThCCCH2R00TIYSQLuPj4wOVSoWsrKwO2zIzMyEWixESEgIAWLlyJcaNG4dvv/3WZr+GhgZ4e3tbfw4LC8O5c+fAGLM523S517gRK1euxMKFC/Gf//zHOqbT6dDQ0GCzX1RUFM6dO3fV57rSZXphYWEA2moeP368zbasrCzrdkIIIY6B/tqKEEJIl5FIJJg8eTLWrVtnM2V3ZWUlfv75Z4waNQqurq7WfS+9FA5om5L7z1NwT58+HWVlZVi5cqV1TKPR4Kuvvuq0mv9cxyeffGJzuSAA3HbbbThz5gzWrFnT4TkuPv7iWkp/brgGDRoEX19ffPHFFzZTpW/evBkZGRmYMWNGZ/wqhBBCOgmdaSKEEHLTvvvuO2zZsqXD+FNPPYU333wT27dvx6hRo/DYY49BKpXiyy+/hF6vx7vvvmvdd+bMmViyZAkWLVqEESNG4OzZs/jpp59sJpAAgIceegj/+9//cN999+HkyZMICAjA8uXLO20a7pkzZ2L58uVwc3NDQkICDh8+jB07dtjcVwUAf//737Fy5UrccccdeOCBBzBw4EDU1dVh/fr1+OKLL9CvXz9ERUXB3d0dX3zxBVxcXKBWqzF06FBERETgnXfewaJFi5CcnIy77rrLOuV4eHg4nnnmmU75XQghhHQSXufuI4QQImhXm74bACsuLmaMMXbq1Ck2ZcoU5uzszFQqFRs3bhw7dOiQzXPpdDr23HPPsYCAAObk5MRGjhzJDh8+zJKTk1lycrLNvoWFheyWW25hKpWKeXt7s6eeeopt2bKF05TjV5omvb6+ni1atIh5e3szZ2dnNmXKFJaZmcnCwsLYwoULbfatra1lTzzxBAsKCmJyuZwFBwezhQsXspqaGus+69atYwkJCUwqlXaYfvy3335jSUlJTKFQME9PT7ZgwQJWUlJi8xoLFy5karX6qr8TIYSQriVi7E/XIBBCCCGEEEIIsaJ7mgghhBBCCCHkKqhpIoQQQgghhJCroKaJEEIIIYQQQq6CmiZCCCGEEEIIuQpqmgghhBBCCCHkKqhpIoQQQgghhJCroKaJEEIIIYQQQq6CmiZCCCGEEEIIuQpqmgghhBBCCCHkKqhpIoQQQgghhJCroKaJEEIIIYQQQq6CmiZCCCGEEEIIuYr/B8YNeIxi+qeJAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## HashTable objektů\n",
    "\n",
    "Naše hashovací tabulky zvládnou více než pouhá čísla! Stejně jako v Javě i v Pythonu mají třídy metody, které vytvoří jejich hash. Pythoní ekvivalent metod hashCode a equals je __hash__ a __eq__."
   ],
   "id": "d68e3a5f79df4ad1"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 15\n",
    "Doplňte implementaci metody __hash__ třídy node."
   ],
   "id": "bc5d26bcdba60a16"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:12:57.419710Z",
     "start_time": "2025-07-25T07:12:57.414612Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class Node:\n",
    "    def __init__(self, left_child, right_child, value):\n",
    "        self.left_child = left_child\n",
    "        self.right_child = right_child\n",
    "        self.value = value\n",
    "\n",
    "    def __hash__(self):\n",
    "        # Nasledující řádek bude smazán a bude pro doplnění. TODO: Write the hash method for the Node class. Hint: use the Python hash function, which calls the __hash__ method of its input.\n",
    "        return hash((self.left_child, self.right_child, self.value))\n",
    "\n",
    "    def __eq__(self, other):\n",
    "        return isinstance(other, self.__class__) and (self.left_child, self.right_child, self.value) == (other.left_child, other.right_child, other.value)\n",
    "\n",
    "    def __str__(self):\n",
    "        return f'{self.value}'\n",
    "\n",
    "    def __format__(self, format_spec):\n",
    "        return f'{self.value:{format_spec}}'"
   ],
   "id": "69831ccbf3018d77",
   "outputs": [],
   "execution_count": 24
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Ještě musíme upravit funkci basic_hash, aby využívala hash klíče místo klíče samotného.",
   "id": "2baf4a0484bb8faa"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:12:57.474412Z",
     "start_time": "2025-07-25T07:12:57.471521Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def basic_hash(key, modulo):\n",
    "    return hash(key) % modulo"
   ],
   "id": "748387a9120f0d0d",
   "outputs": [],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "A nyní ověříme funkčnost",
   "id": "2935c46d293c6631"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:12:57.506813Z",
     "start_time": "2025-07-25T07:12:57.500791Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from functools import partial\n",
    "\n",
    "TABLE_SIZE = 7\n",
    "KEYS=[Node(None, None, i) for i in range(17)]\n",
    "for i in range(1, len(KEYS)):\n",
    "    KEYS[i].parent = KEYS[i-1]\n",
    "    KEYS[i-1].left_child = KEYS[i]\n",
    "\n",
    "HASH_TABLE = ChainingHashTable(TABLE_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)"
   ],
   "id": "b9747bf2a000a768",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "|  0  |  1  |  2  |  3  |  4  |  5  |  6  |\n",
      "-------------------------------------------\n",
      "| 12  |  6  |  3  | 16  | 14  |  9  |  7  |\n",
      "| 10  |     |  0  | 15  | 13  |  4  |  5  |\n",
      "|  2  |     |     |  8  | 11  |     |     |\n",
      "|     |     |     |     |  1  |     |     |\n",
      "-------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Úlohy na procvičení:",
   "id": "3620285e9fb593dd"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 16\n",
    "Pro danou rozptylovací funkci h(k)=k mod 9 zvolte velikost tabulky a nakreslete stav po vložení prvků následující posloupnosti při vnějším zřetězení prvků.\n",
    "\n",
    "12, 19, 24, 17, 4, 21, 5, 16, 11, 2\n",
    "\n",
    "Výsledky zkontrolujte pomocí implementace zřetězeného hashování."
   ],
   "id": "e959ebcb187fcd8b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-25T07:12:57.854806Z",
     "start_time": "2025-07-25T07:12:57.548794Z"
    }
   },
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = None #TODO\n",
    "KEYS=12,19,24,17,4,21,5,16,11,2\n",
    "\n",
    "raise Exception('Remove this line when you want to check your results.')\n",
    "\n",
    "HASH_TABLE = ChainingHashTable(TABLE_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)"
   ],
   "id": "b3db4da1e58e172e",
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Remove this line when you want to check your results.",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mException\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[27], line 4\u001B[0m\n\u001B[0;32m      1\u001B[0m TABLE_SIZE \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;66;03m#TODO\u001B[39;00m\n\u001B[0;32m      2\u001B[0m KEYS\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m12\u001B[39m,\u001B[38;5;241m19\u001B[39m,\u001B[38;5;241m24\u001B[39m,\u001B[38;5;241m17\u001B[39m,\u001B[38;5;241m4\u001B[39m,\u001B[38;5;241m21\u001B[39m,\u001B[38;5;241m5\u001B[39m,\u001B[38;5;241m16\u001B[39m,\u001B[38;5;241m11\u001B[39m,\u001B[38;5;241m2\u001B[39m\n\u001B[1;32m----> 4\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mRemove this line when you want to check your results.\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m      6\u001B[0m HASH_TABLE \u001B[38;5;241m=\u001B[39m ChainingHashTable(TABLE_SIZE, partial(basic_hash, modulo\u001B[38;5;241m=\u001B[39mTABLE_SIZE))\n\u001B[0;32m      8\u001B[0m HASH_TABLE\u001B[38;5;241m.\u001B[39minsert_keys(KEYS, verbose\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n",
      "\u001B[1;31mException\u001B[0m: Remove this line when you want to check your results."
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 17\n",
    "\n",
    "Do rozptylovací tabulky velikosti 10 s otevřeným rozptylováním a s rozptylovací funkcí h(k)=k mod 7 vložte následujících 6 klíčů.\n",
    "\n",
    "Použijte strategii Linear Probing s inkrementem 1.\n",
    "\n",
    "12, 23, 15, 29, 22, 14\n",
    "\n",
    "Výsledky zkontrolujte pomocí implementace otevřeného hashování."
   ],
   "id": "a743f2b462aa61fd"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 10\n",
    "KEYS=12,23,15,29,22,14\n",
    "LINEAR_INCREMENT = 1\n",
    "\n",
    "raise Exception('Remove this line when you want to check your results.')\n",
    "\n",
    "HASH_TABLE = OpenAddressHashTable(TABLE_SIZE, partial(basic_hash, modulo=8), probe_function=lambda key: LINEAR_INCREMENT)\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "af65b50997a15bd9",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 18\n",
    "\n",
    "Do rozptylovací tabulky velikosti 11 s otevřeným rozptylováním a s rozptylovací funkcí h(k)=k mod 8 vložte následujících 6 klíčů.\n",
    "\n",
    "Použijte strategii Linear Probing s inkrementem 3.\n",
    "\n",
    "10, 16, 15, 31, 23, 14\n",
    "\n",
    "Výsledky zkontrolujte pomocí implementace otevřeného hashování."
   ],
   "id": "a37cd9782956ab3d"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 11\n",
    "KEYS=10,16,15,31,23,14\n",
    "LINEAR_INCREMENT = 3\n",
    "\n",
    "raise Exception('Remove this line when you want to check your results.')\n",
    "\n",
    "HASH_TABLE = OpenAddressHashTable(TABLE_SIZE, partial(basic_hash, modulo=8), probe_function=lambda key: LINEAR_INCREMENT)\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "bd0cb126d0938827",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 19\n",
    "\n",
    " Do do prázdné tabulky velikosti N, vkládejte klíče:\n",
    "27,23,2,28,17,7,14,30,12,21,11,1 Určete počet kolizí v uvedených variantách:\n",
    "\n",
    "1. N=13, Linear Probing s inkrementem 1.\n",
    "2. N=17, Linear Probing s inkrementem 1.\n",
    "3. N=17, Linear Probing s inkrementem 5.\n",
    "4. N=13, Double Hashing, h(k)=1+k mod 3.\n",
    "5. N=13, Double Hashing, h(k)=1+k mod 5.\n",
    "6. N=17, Double Hashing, h(k)=1+k mod 5.\n",
    "\n",
    "Výsledky zkontrolujte pomocí implementace otevřeného hashování."
   ],
   "id": "56395a5d360048ed"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 13 #TODO\n",
    "KEYS=27,23,2,28,17,7,14,30,12,21,11,1\n",
    "\n",
    "LINEAR_INCREMENT = 1\n",
    "\n",
    "linear_probe = lambda key: LINEAR_INCREMENT\n",
    "double_hashing_probe = lambda key: 1 + key % 3 # TODO\n",
    "\n",
    "raise Exception('Remove this line when you want to check your results.')\n",
    "\n",
    "HASH_TABLE = OpenAddressHashTable(TABLE_SIZE, partial(basic_hash, modulo=8), probe_function=linear_probe)\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "5314448a43f9741a",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 20\n",
    "\n",
    "Uvažujte hash funkci h(x)=x mod 10. Vložte do hashovacích tabulek čísla z posloupnosti:\n",
    "10, 12, 20, 23, 32, 39, 40\n",
    "\n",
    "Jak bude tabulka vypadat pro EISCH a jak pro LISCH?\n",
    "\n",
    "Výsledky zkontrolujte pomocí implementace srůstajícího hashování."
   ],
   "id": "c0374ebfec2d1e66"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "EISCH",
   "id": "bd3d46cc44c02510"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "TABLE_SIZE = 10\n",
    "KEYS = [10,12,20,23,32,39,40]\n",
    "\n",
    "HASH_TABLE = EISCHHashTable(TABLE_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "raise Exception('Remove this line when you want to check your results.')\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "aead2731a01d7a05",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "LISCH",
   "id": "5f2ad2824e412bdf"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "TABLE_SIZE = 10\n",
    "KEYS = [10,12,20,23,32,39,40]\n",
    "\n",
    "HASH_TABLE = LISCHHashTable(TABLE_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "raise Exception('Remove this line when you want to check your results.')\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "158b8a94311a6d2b"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 21\n",
    "\n",
    "Uvažujte hash funkci h(x)=x mod 8 a sklep o velikosti 2. Vložte do hashovacích tabulek čísla z (té samé) posloupnosti:\n",
    "10, 12, 20, 23, 32, 39, 40\n",
    "\n",
    "Jak bude tabulka vypadat pro EICH, LICH a jak pro VICH?\n",
    "\n",
    "Výsledky zkontrolujte pomocí implementace srůstajícího hashování."
   ],
   "id": "8d2aa09372633d45"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "EICH",
   "id": "10e6e8da110afa2"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "TABLE_SIZE = 8\n",
    "CELLAR_SIZE = 2\n",
    "KEYS = [10,12,20,23,32,39,40]\n",
    "\n",
    "raise Exception('Remove this line when you want to check your results.')\n",
    "\n",
    "HASH_TABLE = EICHHashTable(TABLE_SIZE, CELLAR_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "3394144027438636"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "LICH",
   "id": "b8018934d89bbac2"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "TABLE_SIZE = 8\n",
    "CELLAR_SIZE = 2\n",
    "KEYS = [10,12,20,23,32,39,40]\n",
    "\n",
    "raise Exception('Remove this line when you want to check your results.')\n",
    "\n",
    "HASH_TABLE = LICHHashTable(TABLE_SIZE, CELLAR_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "d73904d9664da93e"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "VICH",
   "id": "cf20db1d195537b1"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "TABLE_SIZE = 8\n",
    "CELLAR_SIZE = 2\n",
    "KEYS = [10,12,20,23,32,39,40]\n",
    "\n",
    "raise Exception('Remove this line when you want to check your results.')\n",
    "\n",
    "HASH_TABLE = VICHHashTable(TABLE_SIZE, CELLAR_SIZE, partial(basic_hash, modulo=TABLE_SIZE))\n",
    "\n",
    "COLLISIONS = HASH_TABLE.insert_keys(KEYS, verbose=False)\n",
    "print(HASH_TABLE)\n",
    "print(f'In total we encountered {COLLISIONS} insert collisions.')"
   ],
   "id": "8e94dbbff7250970"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Úloha 22\n",
    "\n",
    "Předpokládejme, že v tabulkách získaných v příkladech 1 a 2, (případně 3 a 4) vyhledáváme pouze klíče v nich uložené a každý klíč stejně často. Která z těchto tabulek je nejvýhodnější?\n",
    "\n",
    "Odpověď můžete ověřit pomocí metody get_keys(<vložené klíče>) po vložení klíčů do tabulky."
   ],
   "id": "c39bf8b6e0a0fbe8"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "7337d3c646377e1f"
  }
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