Jane Street Number Cross 5 solution

JaneStreetPuzzleNumberCross5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bb0677a-7df1-4f6b-82fc-133a766ec495",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install z3-solver==4.12.4 # not working well with 4.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "561f1b0b-2aa9-4e16-b2cd-daf64d5bf6be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import textwrap\n",
    "import time\n",
    "from functools import partial\n",
    "from itertools import chain, combinations, product\n",
    "from sympy import fibonacci, primerange\n",
    "from z3 import (\n",
    "    And,\n",
    "    Distinct,\n",
    "    Exists,\n",
    "    Goal,\n",
    "    If,\n",
    "    Implies,\n",
    "    Int,\n",
    "    IntVector,\n",
    "    Not,\n",
    "    Optimize,\n",
    "    Or,\n",
    "    Solver,\n",
    "    Sum,\n",
    "    Tactic,\n",
    "    ToInt,\n",
    "    get_full_version,\n",
    "    sat,\n",
    "    set_param,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e46f4542-fac6-4ff6-91e6-58eefc4abd64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.janestreet.com/puzzles/may-2025-update.png\" width=\"600\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "url = \"https://www.janestreet.com/puzzles/may-2025-update.png\"\n",
    "display(Image(url=url, width=600))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3b1eb277-f687-4152-bde0-1e381a9881b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pyplot stuff\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "def draw_grid(\n",
    "    rows,\n",
    "    cols,\n",
    "    ax,\n",
    "    *,\n",
    "    grid,\n",
    "    grid_groups,\n",
    "    grid_highlights,\n",
    "    grid_tiles=None,\n",
    "    grid_deltas=None,\n",
    "    labels=[],\n",
    "):\n",
    "    grid = np.array(grid)\n",
    "    if grid_tiles is None:\n",
    "        grid_tiles = np.ones((rows, cols), dtype=int)\n",
    "    if grid_deltas is None:\n",
    "        grid_deltas = np.zeros((rows, cols), dtype=int)\n",
    "\n",
    "    ax.set_xlim(0, cols)\n",
    "    ax.set_ylim(0, rows)\n",
    "    ax.set_aspect(\"equal\")\n",
    "    ax.axis(\"off\")\n",
    "    # base grid\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            x, y = c, rows - 1 - r\n",
    "            is_tiled = grid_tiles[r][c] == 0\n",
    "            ax.add_patch(\n",
    "                Rectangle(\n",
    "                    (x, y),\n",
    "                    1,\n",
    "                    1,\n",
    "                    facecolor=\"black\" if is_tiled else \"white\",\n",
    "                    edgecolor=\"black\",\n",
    "                    linewidth=1,\n",
    "                )\n",
    "            )\n",
    "    # highlights\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            if grid_highlights[r, c]:\n",
    "                ax.add_patch(\n",
    "                    Rectangle(\n",
    "                        (c, rows - 1 - r),\n",
    "                        1,\n",
    "                        1,\n",
    "                        facecolor=\"yellow\",\n",
    "                        edgecolor=\"black\",\n",
    "                        linewidth=1,\n",
    "                    )\n",
    "                )\n",
    "    # regions\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            x, y = c, rows - 1 - r\n",
    "            if r == 0 or grid_groups[r, c] != grid_groups[r - 1, c]:\n",
    "                ax.plot([x, x + 1], [y + 1, y + 1], color=\"black\", linewidth=3)\n",
    "            if r == rows - 1 or grid_groups[r, c] != grid_groups[r + 1, c]:\n",
    "                ax.plot([x, x + 1], [y, y], color=\"black\", linewidth=3)\n",
    "            if c == 0 or grid_groups[r, c] != grid_groups[r, c - 1]:\n",
    "                ax.plot([x, x], [y, y + 1], color=\"black\", linewidth=3)\n",
    "            if c == cols - 1 or grid_groups[r, c] != grid_groups[r, c + 1]:\n",
    "                ax.plot([x + 1, x + 1], [y, y + 1], color=\"black\", linewidth=3)\n",
    "    # frame\n",
    "    ax.plot(\n",
    "        [0, cols, cols, 0, 0],\n",
    "        [0, 0, rows, rows, 0],\n",
    "        color=\"black\",\n",
    "        linewidth=6,\n",
    "    )\n",
    "    # digits\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            val = int(grid[r, c])\n",
    "            if val != 0:\n",
    "                ax.text(\n",
    "                    c + 0.5,\n",
    "                    rows - 1 - r + 0.5,\n",
    "                    str(val),\n",
    "                    color=\"red\" if grid_deltas[r][c] != 0 else \"black\",\n",
    "                    ha=\"center\",\n",
    "                    va=\"center\",\n",
    "                    fontsize=26,\n",
    "                )\n",
    "    # labels\n",
    "    for i, text in enumerate(labels):\n",
    "        ax.text(\n",
    "            -0.3,\n",
    "            rows - 0.5 - i,\n",
    "            text,\n",
    "            ha=\"right\",\n",
    "            va=\"center\",\n",
    "            fontsize=16,\n",
    "        )\n",
    "\n",
    "\n",
    "def draw_footer(ax, nums, total):\n",
    "    props = {\n",
    "        \"ha\": \"center\",\n",
    "        \"va\": \"top\",\n",
    "        \"transform\": ax.transAxes,\n",
    "        \"fontweight\": 600,\n",
    "        \"fontsize\": 10,\n",
    "    }\n",
    "    ax.text(\n",
    "        0.5,\n",
    "        -0.02,\n",
    "        \"Answer:\",\n",
    "        **props,\n",
    "    )\n",
    "    ax.text(\n",
    "        0.5,\n",
    "        -0.10,\n",
    "        \"\\n\".join(\n",
    "            textwrap.wrap(\" + \".join(map(str, nums)), width=80),\n",
    "        ),\n",
    "        **props,\n",
    "    )\n",
    "    text = ax.text(\n",
    "        0.45,\n",
    "        -0.18,\n",
    "        \"= \",\n",
    "        **props,\n",
    "    )\n",
    "    text = ax.annotate(\n",
    "        total,\n",
    "        xycoords=text,\n",
    "        xy=(1, 0),\n",
    "        verticalalignment=\"bottom\",\n",
    "        color=\"red\",\n",
    "        fontsize=12,\n",
    "        fontweight=600,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f19c88fe-80f8-4a6c-9786-db29cce39fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CrossNumbersSolver:\n",
    "    DIRECTIONS = [(0, -1), (-1, 0), (0, 1), (1, 0)]\n",
    "\n",
    "    def __init__(self, grid, grid_groups, grid_highlights, clues, tactic=\"default\", labels=[]):\n",
    "        self.solver = Tactic(tactic).solver()\n",
    "        self.grid_highlights = grid_highlights\n",
    "        self.rows = grid.shape[0]\n",
    "        self.cols = grid.shape[1]\n",
    "        self.clues = clues\n",
    "        self.labels = labels\n",
    "        self.grid_groups = grid_groups\n",
    "        self.empty_grid = np.zeros((self.rows, self.cols), dtype=object)\n",
    "\n",
    "        r, c = self.rows, self.cols\n",
    "        size = self.rows * self.cols\n",
    "        self.grid_tiles = np.array(IntVector(\"t\", size), dtype=object).reshape(r, c)\n",
    "        self.grid = np.array(IntVector(\"x\", size), dtype=object).reshape(r, c)\n",
    "        self.grid_deltas = np.empty((r, c, 4), dtype=object)\n",
    "\n",
    "        for i in range(r):\n",
    "            for j in range(c):\n",
    "                if self.is_highlighed_cell(i, j):\n",
    "                    continue\n",
    "                for _, nr, nc, dr, dc in self.walk_neighbors(i, j):\n",
    "                    if self.is_highlighed_cell(nr, nc):\n",
    "                        continue\n",
    "                    if nc == 1 or nc == self.cols - 2:\n",
    "                        continue\n",
    "                    index = self.direction_to_index(dr, dc)\n",
    "                    self.grid_deltas[i][j][index] = Int(\"deltas{}_{}_{}\".format(nr, nc, index))\n",
    "\n",
    "        self.group_ids = np.unique(grid_groups)\n",
    "        self.groups = [(grid_groups == gid).astype(int) for gid in self.group_ids]\n",
    "        self.group_ints = [Int(\"group_{}\".format(id)) for id in self.group_ids]\n",
    "\n",
    "    def direction_to_index(self, dir_row, dir_col):\n",
    "        match [dir_row, dir_col]:\n",
    "            case [0, -1]:\n",
    "                return 0\n",
    "            case [-1, 0]:\n",
    "                return 1\n",
    "            case [0, 1]:\n",
    "                return 2\n",
    "            case [1, 0]:\n",
    "                return 3\n",
    "\n",
    "    def direction_to_index_inverted(self, dir_row, dir_col):\n",
    "        match [dir_row, dir_col]:\n",
    "            case [0, -1]:\n",
    "                return 2\n",
    "            case [-1, 0]:\n",
    "                return 3\n",
    "            case [0, 1]:\n",
    "                return 0\n",
    "            case [1, 0]:\n",
    "                return 1\n",
    "\n",
    "    def eval_grid(self, model, grid):\n",
    "        res = np.ones((self.rows, self.cols), dtype=int)\n",
    "        for row_index, row in enumerate(grid):\n",
    "            for col_index, col in enumerate(row):\n",
    "                if isinstance(col, np.ndarray):\n",
    "                    res[row_index][col_index] = sum(\n",
    "                        [model.eval(x).as_long() for x in col if x is not None]\n",
    "                    )\n",
    "                else:\n",
    "                    res[row_index][col_index] = model.eval(col).as_long()\n",
    "        return res\n",
    "\n",
    "    def merge_grids(self, grid, grid_delta, grid_tiles):\n",
    "        grid = np.array(grid)\n",
    "        grid_delta = np.array(grid_delta)\n",
    "        grid_tiles = np.array(grid_tiles)\n",
    "        merged = grid + grid_delta\n",
    "        merged[grid == 0] = 0\n",
    "        merged[grid_tiles == 0] = 0\n",
    "        return merged\n",
    "\n",
    "    def sum_total(self, grid):\n",
    "        total = 0\n",
    "        nums = []\n",
    "        for row in grid:\n",
    "            num = 0\n",
    "            total_row = 0\n",
    "            for digit in row:\n",
    "                if digit != 0:\n",
    "                    num = num * 10 + digit\n",
    "                else:\n",
    "                    if num > 0:\n",
    "                        nums.append(int(num))\n",
    "                    total_row += num\n",
    "                    num = 0\n",
    "            if num > 0:\n",
    "                total_row += num\n",
    "                nums.append(int(num))\n",
    "            total += total_row\n",
    "        return total, nums\n",
    "\n",
    "    def generate_fibonacci(self):\n",
    "        res = []\n",
    "        for i in range(1, 60):\n",
    "            fib = fibonacci(i)\n",
    "            length = len(str(fib))\n",
    "            if len(res) == length:\n",
    "                res[length - 1].append(fib)\n",
    "            else:\n",
    "                res.append([fib])\n",
    "        return res\n",
    "\n",
    "    def generate_primes(self, max_digits):\n",
    "        result = [[] for _ in range(max_digits)]\n",
    "        for n_digits in range(1, max_digits + 1):\n",
    "            for p in primerange(10 ** (n_digits - 1), 10**n_digits):\n",
    "                if \"0\" not in str(p):\n",
    "                    result[n_digits - 1].append(p)\n",
    "        return result\n",
    "\n",
    "    def walk_group(self, index: int):\n",
    "        group = self.groups[index]\n",
    "        for row, col in np.argwhere(group == 1):\n",
    "            yield self.grid[row][col], row, col\n",
    "\n",
    "    def walk_neighbors(self, row, col, directions=DIRECTIONS):\n",
    "        for dir_row, dir_col in directions:\n",
    "            next_row = row + dir_row\n",
    "            next_col = col + dir_col\n",
    "            if (\n",
    "                next_row >= 0\n",
    "                and next_row <= (self.rows - 1)\n",
    "                and next_col >= 0\n",
    "                and next_col <= (self.cols - 1)\n",
    "            ):\n",
    "                yield (\n",
    "                    self.grid_tiles[next_row][next_col],\n",
    "                    next_row,\n",
    "                    next_col,\n",
    "                    dir_row,\n",
    "                    dir_col,\n",
    "                )\n",
    "\n",
    "    def walk_digits(self, row_index, min=2):\n",
    "        slices = ((l, r) for l, r in combinations(range(self.cols + 1), 2) if r > l + (min - 1))\n",
    "        for l, r in slices:\n",
    "            digits = []\n",
    "            for i, d in enumerate(self.grid[row_index][l:r]):\n",
    "                col_index = l + i\n",
    "                sum_delta = self.get_sum_deltas(row_index, col_index)\n",
    "                digits.append(d + sum_delta)\n",
    "            yield digits, l, r\n",
    "\n",
    "    def get_deltas(self, row, col):\n",
    "        return [d for d in self.grid_deltas[row][col] if d is not None]\n",
    "\n",
    "    def get_sum_deltas(self, row, col):\n",
    "        return Sum(self.get_deltas(row, col))\n",
    "\n",
    "    def is_highlighed_cell(self, row, col):\n",
    "        return self.grid_highlights[row][col] == 1\n",
    "\n",
    "    def is_digit_match(self, row_index, l, r):\n",
    "        return And(\n",
    "            l == 0 or self.grid_tiles[row_index][l - 1] == 0,\n",
    "            r == self.cols or self.grid_tiles[row_index][r] == 0,\n",
    "            And([b != 0 for b in self.grid_tiles[row_index][l:r]]),\n",
    "        )\n",
    "\n",
    "    def sum_digits(self, digits):\n",
    "        return sum(10**i * digits[-1 - i] for i in range(len(digits)))\n",
    "\n",
    "    def is_square_value(self, value):\n",
    "        x = Int(f\"sq_{value.hash()}\")\n",
    "        return And(x >= 0, value == x * x)\n",
    "\n",
    "    def is_square(self, row_index: int):\n",
    "        for digits, l, r in self.walk_digits(row_index):\n",
    "            block = self.sum_digits(digits)\n",
    "            self.solver.add(\n",
    "                Implies(\n",
    "                    self.is_digit_match(row_index, l, r),\n",
    "                    And(\n",
    "                        # Hack to removes duplicates, hardcoded for now\n",
    "                        Implies(len(digits) == 3 and (l == 0 or l == 4), block != 225),\n",
    "                        self.is_square_value(block),\n",
    "                    ),\n",
    "                    # ToInt(block**0.5) == block**0.5, # this crashes z3\n",
    "                )\n",
    "            )\n",
    "\n",
    "    def is_product_of_20(self, row_index):\n",
    "        for digits, l, r in self.walk_digits(row_index):\n",
    "            count_2 = Sum([If(d == 2, 1, 0) for d in digits])\n",
    "            count_4 = Sum([If(d == 4, 1, 0) for d in digits])\n",
    "            count_5 = Sum([If(d == 5, 1, 0) for d in digits])\n",
    "            self.solver.add(\n",
    "                Implies(\n",
    "                    self.is_digit_match(row_index, l, r),\n",
    "                    And(\n",
    "                        And([Or(d == 1, d == 2, d == 4, d == 5) for d in digits]),\n",
    "                        count_5 == 1,\n",
    "                        count_4 < 2,\n",
    "                        Implies(count_4 == 1, count_2 == 0),\n",
    "                        Implies(count_4 == 0, count_2 == 2),\n",
    "                    ),\n",
    "                ),\n",
    "            )\n",
    "\n",
    "    def is_product_of_25(self, row_index: int):\n",
    "        for digits, l, r in self.walk_digits(row_index):\n",
    "            self.solver.add(\n",
    "                Implies(\n",
    "                    self.is_digit_match(row_index, l, r),\n",
    "                    And(\n",
    "                        And([Or(d == 1, d == 5) for d in digits]),\n",
    "                        Sum([If(d == 5, 1, 0) for d in digits]) == 2,\n",
    "                    ),\n",
    "                ),\n",
    "            )\n",
    "\n",
    "    def is_product_of_2025(self, row_index):\n",
    "        self.solver.add(\n",
    "            And(\n",
    "                self.grid_tiles[row_index][2] == 1,\n",
    "                self.grid_tiles[row_index][3] == 1,\n",
    "                self.grid_tiles[row_index][self.cols - 3] == 1,\n",
    "                self.grid_tiles[row_index][self.cols - 4] == 1,\n",
    "            )\n",
    "        )\n",
    "        self.solver.add(\n",
    "            Implies(\n",
    "                self.grid_tiles[row_index][0] == 0,\n",
    "                And(\n",
    "                    self.grid_tiles[row_index][3] != 0,\n",
    "                    self.grid_tiles[row_index][4] != 0,\n",
    "                ),\n",
    "            )\n",
    "        )\n",
    "        self.solver.add(\n",
    "            Implies(\n",
    "                self.grid_tiles[row_index][self.cols - 1] == 0,\n",
    "                And(\n",
    "                    self.grid_tiles[row_index][self.cols - 4] != 0,\n",
    "                    self.grid_tiles[row_index][self.cols - 5] != 0,\n",
    "                ),\n",
    "            )\n",
    "        )\n",
    "        for digits, l, r in self.walk_digits(row_index, 4):\n",
    "            block = self.sum_digits(digits)\n",
    "            count_5 = Sum([If(d == 5, 1, 0) for d in digits])\n",
    "            count_9 = Sum([If(d == 9, 1, 0) for d in digits])\n",
    "            count_3 = Sum([If(d == 3, 1, 0) for d in digits])\n",
    "            self.solver.add(\n",
    "                Implies(\n",
    "                    self.is_digit_match(row_index, l, r),\n",
    "                    And(\n",
    "                        block >= 5599,  # needed\n",
    "                        And([Or(d == 1, d == 3, d == 5, d == 9) for d in digits]),\n",
    "                        count_5 == 2,\n",
    "                        Or(count_3 == 0, count_3 == 2, count_3 == 3, count_3 == 4),\n",
    "                        Implies(count_3 >= 3, count_9 == 0),\n",
    "                        Implies(count_9 == 1, count_3 >= 2),\n",
    "                    ),\n",
    "                ),\n",
    "            )\n",
    "\n",
    "    # only for example\n",
    "    def is_multiple_of(self, value: int, row_index: int):\n",
    "        for digits, l, r in self.walk_digits(row_index):\n",
    "            block = self.sum_digits(digits)\n",
    "            x = Int(f\"mult_{row_index}_{block.hash()}_div_{value}\")\n",
    "            self.solver.add(\n",
    "                Implies(\n",
    "                    self.is_digit_match(row_index, l, r),\n",
    "                    block == x * value,\n",
    "                    # block % value == 0,\n",
    "                )\n",
    "            )\n",
    "\n",
    "    def is_multiple_of_13(self, row_index: int):\n",
    "        for digits, l, r in self.walk_digits(row_index):\n",
    "            block = self.sum_digits(digits)\n",
    "            x = Int(f\"mult_{row_index}_{block.hash()}_13\")\n",
    "            self.solver.add(\n",
    "                Implies(\n",
    "                    self.is_digit_match(row_index, l, r),\n",
    "                    # block == q * 13,\n",
    "                    If(\n",
    "                        len(digits) == 2,\n",
    "                        Or(\n",
    "                            block == 13,\n",
    "                            block == 26,\n",
    "                            block == 39,\n",
    "                            block == 52,\n",
    "                            block == 65,\n",
    "                            block == 78,\n",
    "                            block == 91,\n",
    "                        ),\n",
    "                        block == x * 13,\n",
    "                    ),\n",
    "                )\n",
    "            )\n",
    "\n",
    "    def is_multiple_of_32(self, row_index: int):\n",
    "        for digits, l, r in self.walk_digits(row_index):\n",
    "            block = self.sum_digits(digits)\n",
    "            x = Int(f\"mult_{row_index}_{block.hash()}_32\")\n",
    "            last_digit = digits[len(digits) - 1]\n",
    "            self.solver.add(\n",
    "                Implies(\n",
    "                    self.is_digit_match(row_index, l, r),\n",
    "                    And(\n",
    "                        Or([last_digit == 2, last_digit == 4, last_digit == 6, last_digit == 8]),\n",
    "                        block == x * 32,\n",
    "                        # block % 32 == 0,\n",
    "                    ),\n",
    "                )\n",
    "            )\n",
    "\n",
    "    def is_divisible(self, row_index: int):\n",
    "        for digits, l, r in self.walk_digits(row_index, 2):\n",
    "            block = self.sum_digits(digits)\n",
    "            cons = []\n",
    "            for d in digits:\n",
    "                x = Int(\"div_{}{}\".format(row_index, d.hash()))\n",
    "                cons.append(block == x * d)\n",
    "            self.solver.add(\n",
    "                Implies(\n",
    "                    self.is_digit_match(row_index, l, r),\n",
    "                    And(cons),\n",
    "                    # And([(block % d) == 0 for d in digits]),\n",
    "                )\n",
    "            )\n",
    "\n",
    "    def is_odd_palindrome(self, row_index: int):\n",
    "        for digits, l, r in self.walk_digits(row_index):\n",
    "            block = self.sum_digits(digits)\n",
    "            self.solver.add(\n",
    "                Implies(\n",
    "                    self.is_digit_match(row_index, l, r),\n",
    "                    And(\n",
    "                        block % 2 != 0,\n",
    "                        And([digits[i] == digits[-1 - i] for i in range(len(digits) // 2)]),\n",
    "                    ),\n",
    "                )\n",
    "            )\n",
    "\n",
    "    def is_fibonacci(self, row_index: int):\n",
    "        fibs = self.generate_fibonacci()\n",
    "        for digits, l, r in self.walk_digits(row_index):\n",
    "            block = self.sum_digits(digits)\n",
    "            self.solver.add(\n",
    "                Implies(\n",
    "                    self.is_digit_match(row_index, l, r),\n",
    "                    Or([block == s for s in fibs[len(digits) - 1]]),\n",
    "                )\n",
    "            )\n",
    "\n",
    "    def is_prime(self, row_index: int):\n",
    "        n = 3\n",
    "        primes = self.generate_primes(n)\n",
    "        for digits, l, r in self.walk_digits(row_index):\n",
    "            block = self.sum_digits(digits)\n",
    "            if len(digits) <= n:\n",
    "                self.solver.add(\n",
    "                    Implies(\n",
    "                        self.is_digit_match(row_index, l, r),\n",
    "                        Or([block == x for x in primes[len(digits) - 1]]),\n",
    "                    )\n",
    "                )\n",
    "            else:\n",
    "                self.solver.add(\n",
    "                    Implies(\n",
    "                        self.is_digit_match(row_index, l, r),\n",
    "                        Or(block % 10 == 1, block % 10 == 3, block % 10 == 7, block % 10 == 9),\n",
    "                    )\n",
    "                )\n",
    "\n",
    "    def constrain_neighbors(self, cell, row, col):\n",
    "        neighbors = []\n",
    "        for neighbor, next_row, next_col, _, dir_col in self.walk_neighbors(row, col):\n",
    "            neighbors.append(neighbor)\n",
    "            if dir_col != 0:\n",
    "                for n in self.walk_neighbors(next_row, next_col, directions=[[0, dir_col]]):\n",
    "                    neighbors.append(n[0])\n",
    "        self.solver.add(\n",
    "            Implies(\n",
    "                cell == 0,\n",
    "                And([n != 0 for n in neighbors]),\n",
    "            )\n",
    "        )\n",
    "\n",
    "    def walk_neighbors_delta_combinations(self, iterable):\n",
    "        s = list(iterable)\n",
    "        n = len(s)\n",
    "        for r in range(1, n + 1):\n",
    "            for subset in combinations(s, r):\n",
    "                subset_set = set(subset)\n",
    "                rest = [item for item in s if item not in subset_set]\n",
    "                yield [s for s in subset if s is not None], rest\n",
    "\n",
    "    def solve(self):\n",
    "        print(f\"Initialized solution for {self.rows} rows and {self.cols} cols\")\n",
    "        self.solver.add([And(g >= 1, g <= 9) for g in self.group_ints])\n",
    "\n",
    "        for group_index, g in enumerate(self.groups):\n",
    "            for cell, row, col in self.walk_group(group_index):\n",
    "                deltas = self.get_deltas(row, col)\n",
    "                sum_deltas = self.get_sum_deltas(row, col)\n",
    "                tile = self.grid_tiles[row][col]\n",
    "                self.constrain_neighbors(tile, row, col)\n",
    "\n",
    "                self.solver.add(And(tile >= 0, tile <= 1), cell == self.group_ints[group_index])\n",
    "\n",
    "                if self.is_highlighed_cell(row, col):\n",
    "                    self.solver.add(tile == 1, sum_deltas == 0)\n",
    "                else:\n",
    "                    self.solver.add(\n",
    "                        And([d >= 0 for d in deltas]),\n",
    "                        sum_deltas < (10 - self.group_ints[group_index]),\n",
    "                    )\n",
    "\n",
    "                if col == 1 or col == self.cols - 2:\n",
    "                    self.solver.add(tile == 1)\n",
    "\n",
    "                neighbors = []\n",
    "                neighbor_deltas = []\n",
    "                for neighbor, nr, nc, dr, dc in self.walk_neighbors(row, col):\n",
    "                    neighbors.append(neighbor)\n",
    "                    delta_index = self.direction_to_index_inverted(dr, dc)\n",
    "                    d = self.grid_deltas[nr][nc][delta_index]\n",
    "                    if d is not None:\n",
    "                        neighbor_deltas.append(d)\n",
    "\n",
    "                neighbor_is_zero = Or([n == 0 for n in neighbors])\n",
    "                self.solver.add(Implies(Not(neighbor_is_zero), sum_deltas == 0))\n",
    "\n",
    "                possible_sums = []\n",
    "                for increments, rest in self.walk_neighbors_delta_combinations(neighbor_deltas):\n",
    "                    possible_sums.append(\n",
    "                        And(\n",
    "                            Sum(increments) == self.group_ints[group_index],  # force the increments\n",
    "                            And([r == 0 for r in rest]),\n",
    "                        )\n",
    "                    )\n",
    "\n",
    "                self.solver.add(\n",
    "                    If(\n",
    "                        tile == 0,\n",
    "                        Or(possible_sums),\n",
    "                        And([n == 0 for n in neighbor_deltas]),\n",
    "                    ),\n",
    "                )\n",
    "\n",
    "        for row_index in range(self.rows):\n",
    "            clue = self.clues[row_index]\n",
    "            clue(row_index)\n",
    "\n",
    "        print(\"checking...\")\n",
    "        if self.solver.check() == sat:\n",
    "            model = self.solver.model()\n",
    "            groups = [model.eval(g) for g in self.group_ints]\n",
    "            grid = self.eval_grid(model, self.grid)\n",
    "            grid_deltas = self.eval_grid(model, self.grid_deltas)\n",
    "            grid_tiles = self.eval_grid(model, self.grid_tiles)\n",
    "            grid_placements = self.eval_grid(model, self.grid)\n",
    "            grid_final = self.merge_grids(grid, grid_deltas, grid_tiles)\n",
    "            total, nums = self.sum_total(grid_final)\n",
    "            print(\"Satisfied!\")\n",
    "            # print(self.solver.statistics())\n",
    "            print(\"\")\n",
    "            print(\"Groups\\n\", groups)\n",
    "            print(\"\")\n",
    "            print(\"Tiles\\n\", grid_tiles)\n",
    "            print(\"\")\n",
    "            print(\"Increments\\n\", grid_deltas)\n",
    "            print(\"\")\n",
    "            print(\"Initial Placements\\n\", grid_placements)\n",
    "            print(\"\")\n",
    "            print(\"Final Grid\\n\", grid_final)\n",
    "            print(\"\")\n",
    "            print(\"numbers:\", nums)\n",
    "            print(\"total:\", total)\n",
    "            rows, cols = self.rows, self.cols\n",
    "            fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(cols, rows))\n",
    "            fig.set_figwidth((cols * 2) + 3)\n",
    "            fig.set_figheight(rows + 1)\n",
    "            ax1.set_title(\"(after initial placement)\", fontsize=14)\n",
    "            title = ax2.set_title(\n",
    "                \"(after tiles placed; altered values in \",\n",
    "                fontsize=14,\n",
    "                loc=\"center\",\n",
    "            )\n",
    "            title = ax2.annotate(\n",
    "                \"red\",\n",
    "                xycoords=title,\n",
    "                xy=(1, 0),\n",
    "                verticalalignment=\"bottom\",\n",
    "                color=\"red\",\n",
    "                fontsize=14,\n",
    "            )\n",
    "            title = ax2.annotate(\n",
    "                \")\",\n",
    "                xycoords=title,\n",
    "                xy=(1, 0),\n",
    "                verticalalignment=\"bottom\",\n",
    "                color=\"black\",\n",
    "                fontsize=14,\n",
    "            )\n",
    "            draw_grid(\n",
    "                rows,\n",
    "                cols,\n",
    "                ax1,\n",
    "                grid=grid_placements,\n",
    "                grid_groups=self.grid_groups,\n",
    "                grid_highlights=self.grid_highlights,\n",
    "                labels=self.labels,\n",
    "            )\n",
    "            draw_grid(\n",
    "                rows,\n",
    "                cols,\n",
    "                ax2,\n",
    "                grid=grid_final,\n",
    "                grid_groups=self.grid_groups,\n",
    "                grid_highlights=self.grid_highlights,\n",
    "                grid_tiles=grid_tiles,\n",
    "                grid_deltas=grid_deltas,\n",
    "                labels=self.labels,\n",
    "            )\n",
    "            draw_footer(ax2, nums, total)\n",
    "            plt.tight_layout(pad=2)\n",
    "            plt.show()\n",
    "        else:\n",
    "            print(\"Not sat\")\n",
    "            print(\"conflict:\", self.solver.unsat_core())\n",
    "            print(\"reason:\", self.solver.reason_unknown())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "15662e3b-9c7d-4a5b-a74c-6aab4d706048",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized solution for 5 rows and 5 cols\n",
      "checking...\n",
      "Satisfied!\n",
      "\n",
      "Groups\n",
      " [5, 2, 9]\n",
      "\n",
      "Tiles\n",
      " [[1 1 0 1 1]\n",
      " [1 1 1 1 0]\n",
      " [1 1 0 1 1]\n",
      " [0 1 1 1 1]\n",
      " [1 1 1 1 1]]\n",
      "\n",
      "Increments\n",
      " [[0 0 0 3 3]\n",
      " [0 0 2 1 0]\n",
      " [6 2 0 0 1]\n",
      " [0 3 3 0 0]\n",
      " [0 0 0 0 0]]\n",
      "\n",
      "Initial Placements\n",
      " [[5 5 5 5 5]\n",
      " [2 5 5 5 5]\n",
      " [2 2 5 5 5]\n",
      " [9 2 2 5 5]\n",
      " [9 9 2 2 5]]\n",
      "\n",
      "Final Grid\n",
      " [[5 5 0 8 8]\n",
      " [2 5 7 6 0]\n",
      " [8 4 0 5 6]\n",
      " [0 5 5 5 5]\n",
      " [9 9 2 2 5]]\n",
      "\n",
      "numbers: [55, 88, 2576, 84, 56, 5555, 99225]\n",
      "total: 107639\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "done in 0.19107985496520996\n"
     ]
    }
   ],
   "source": [
    "def solve_example():\n",
    "    example_grid = np.ones((5, 5), dtype=int)\n",
    "    example_groups = np.array(\n",
    "        [\n",
    "            [1, 1, 1, 1, 1],\n",
    "            [2, 1, 1, 1, 1],\n",
    "            [2, 2, 1, 1, 1],\n",
    "            [3, 2, 2, 1, 1],\n",
    "            [3, 3, 2, 2, 1],\n",
    "        ]\n",
    "    )\n",
    "    example_highligths = np.array(\n",
    "        [\n",
    "            [1, 1, 0, 0, 0],\n",
    "            [1, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 1],\n",
    "            [0, 0, 0, 1, 1],\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    solver = CrossNumbersSolver(\n",
    "        grid=example_grid,\n",
    "        grid_groups=example_groups,\n",
    "        grid_highlights=example_highligths,\n",
    "        clues=[],\n",
    "        tactic=\"default\",\n",
    "        labels=[\n",
    "            \"multiple of 11\",\n",
    "            \"multiple of 14\",\n",
    "            \"multiple of 28\",\n",
    "            \"multiple of 101\",\n",
    "            \"multiple of 2025\",\n",
    "        ],\n",
    "    )\n",
    "    solver.clues = [\n",
    "        partial(solver.is_multiple_of, 11),\n",
    "        partial(solver.is_multiple_of, 14),\n",
    "        partial(solver.is_multiple_of, 28),\n",
    "        partial(solver.is_multiple_of, 101),\n",
    "        partial(solver.is_multiple_of, 2025),\n",
    "    ]\n",
    "    solver.solver.add(\n",
    "        And(\n",
    "            solver.group_ints[1] != solver.group_ints[0],\n",
    "            solver.group_ints[1] != solver.group_ints[2],\n",
    "        )\n",
    "    )\n",
    "    solver.solver.add(\n",
    "        solver.group_ints[0] == 5,\n",
    "        solver.group_ints[1] == 2,\n",
    "        solver.group_ints[2] == 9,\n",
    "    )\n",
    "    solver.solve()\n",
    "\n",
    "start = time.time()\n",
    "solve_example()\n",
    "end = time.time() - start\n",
    "print(\"\")\n",
    "print(f\"done in {end}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fa17504a-cc44-465b-b3fe-e75bf1b14362",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized solution for 11 rows and 11 cols\n",
      "checking...\n",
      "Satisfied!\n",
      "\n",
      "Groups\n",
      " [2, 3, 4, 4, 1, 6, 3, 7, 7]\n",
      "\n",
      "Tiles\n",
      " [[0 1 1 1 1 1 1 0 1 1 1]\n",
      " [1 1 0 1 1 1 0 1 1 1 0]\n",
      " [0 1 1 1 1 0 1 1 0 1 1]\n",
      " [1 1 1 0 1 1 0 1 1 1 0]\n",
      " [0 1 1 1 0 1 1 0 1 1 1]\n",
      " [1 1 0 1 1 1 0 1 1 1 0]\n",
      " [0 1 1 1 1 1 1 1 0 1 1]\n",
      " [1 1 1 0 1 1 1 0 1 1 0]\n",
      " [1 1 0 1 1 1 1 1 0 1 1]\n",
      " [1 1 1 1 1 0 1 1 1 1 0]\n",
      " [0 1 1 0 1 1 1 0 1 1 1]]\n",
      "\n",
      "Increments\n",
      " [[0 1 2 0 0 0 3 0 1 0 2]\n",
      " [3 0 0 0 0 3 0 0 3 0 0]\n",
      " [0 1 0 1 0 0 2 1 0 0 2]\n",
      " [3 0 3 0 0 1 0 1 0 0 0]\n",
      " [0 2 2 1 0 1 1 0 4 0 2]\n",
      " [1 1 0 1 1 0 0 0 4 4 0]\n",
      " [0 0 0 1 0 0 0 1 0 0 4]\n",
      " [3 0 1 0 3 0 3 0 3 2 0]\n",
      " [0 1 0 1 0 0 0 1 0 1 2]\n",
      " [2 0 2 6 0 0 2 6 6 2 0]\n",
      " [0 1 0 0 1 1 0 0 1 0 0]]\n",
      "\n",
      "Initial Placements\n",
      " [[2 2 2 2 2 2 2 2 2 2 2]\n",
      " [2 4 2 2 2 2 2 2 2 2 2]\n",
      " [4 4 3 3 3 3 4 4 4 2 4]\n",
      " [4 3 3 4 3 1 4 4 4 4 4]\n",
      " [4 3 3 4 3 1 1 4 4 1 4]\n",
      " [4 4 4 4 4 1 1 1 1 1 4]\n",
      " [4 3 6 6 4 4 1 1 6 1 1]\n",
      " [4 3 6 6 6 6 6 6 6 7 7]\n",
      " [3 3 3 3 6 3 6 7 7 7 7]\n",
      " [3 3 3 3 3 3 3 3 3 3 3]\n",
      " [3 3 7 7 7 7 7 7 3 3 3]]\n",
      "\n",
      "Final Grid\n",
      " [[0 3 4 2 2 2 5 0 3 2 4]\n",
      " [5 4 0 2 2 5 0 2 5 2 0]\n",
      " [0 5 3 4 3 0 6 5 0 2 6]\n",
      " [7 3 6 0 3 2 0 5 4 4 0]\n",
      " [0 5 5 5 0 2 2 0 8 1 6]\n",
      " [5 5 0 5 5 1 0 1 5 5 0]\n",
      " [0 3 6 7 4 4 1 2 0 1 5]\n",
      " [7 3 7 0 9 6 9 0 9 9 0]\n",
      " [3 4 0 4 6 3 6 8 0 8 9]\n",
      " [5 3 5 9 3 0 5 9 9 5 0]\n",
      " [0 4 7 0 8 8 7 0 4 3 3]]\n",
      "\n",
      "numbers: [342225, 324, 54, 225, 252, 5343, 65, 26, 736, 32, 544, 555, 22, 816, 55, 551, 155, 3674412, 15, 737, 969, 99, 34, 46368, 89, 53593, 5995, 47, 887, 433]\n",
      "total: 4135658\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACaAAAAQ4CAYAAAD1+OmVAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3QeYE1XXwPGzS0e6ICC9KArS6ytVUEQFqSqIhSqioIhiL3xWxC4oIgqIiBQbKAgKAqIiqDSlSQeRKogg0vM954bsJtnsbnI3u5Py/z3PwGYySW5O7kxmTm5JcLlcLgEAAAAAAAAAAAAAAAAAIESJoT4AAAAAAAAAAAAAAAAAAABFAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAZCmXyyV169aV1q1bZ/i5vvrqK2ncuLEULlxYEhISpEOHDhIptm7dasrUo0ePDD3P+PHjzfPo/8FasGCBeczQoUMz9Nrly5c3S2Zp0aKFKScy18mTJ6VixYpy/fXXO10UAAAAAAAyLF5yS+nRvI+WWfNA3nSd5lyiTTTniTT/p2XXfGA8vG481IFIKktc1gnNhWv8Q8iJA3AeDdAAAACQpSZMmCDLli2TJ598MkPPoxfR7du3l82bN0vPnj3liSeekK5du4at8VWki9ZkIrI+uZQjRw555JFHZNq0afLjjz86Uj4AAAAAAMIlXnJLkVIORBfqDWLZ+vXrJXv27PLmm286XRQAAWQPtBIAAADIDGfOnDHJj6ZNm0qjRo0y9Fxz586VY8eOyUsvvSQ33nhj0nr/Xp9OKVWqlKxdu1YKFiyYoefp2LGjiVXJkiWDfkyDBg3MaxctWjRDr43Yceutt8rDDz8sjz32mHz99ddOFwcAAAAAACvxlFtKz4ABA0yDubJlyzpdFAAR4rnnnpMHH3zQ5KZjUZUqVaRbt27yf//3f3LzzTdL/vz5nS4SAC+MgAYAAIAs8+WXX5repbfcckuGn+vPP/80/59//vkSiXTUqYsuuiikhmOBaAM2fZ5QGrLlzZvXPIYGaPDQnoGalJ43b55s3LjR6eIAAAAAAGAlnnJL6dG8j+Z/NA8EAEpz0Xpc0Nx0rLr//vtl79698vrrrztdFAB+aIAGAACALDNu3DgzBHznzp1T3PfLL7+YnpuXXHKJaWyVJ08eqV69ugwbNkxOnjyZtJ0mGfU5dFoEddlll5nbnikp9bbSXlCe9f5TE544cUJefvllqVOnjpxzzjmmp5T2nJ0xY0aqUxvqdAzaI7Zq1aqSK1cusz4tnnL6b6dl1PX6nrTHbvny5c3zXXjhhQGHDh8/frzZXv/3HkZfLVy40Oc9+m/jP9T+/PnzpVevXqanWL58+cxSr149efvttyWjvF/zu+++M+9T41qoUCHzeQfb6OnQoUPy/PPPS/PmzU0COGfOnOZ/TSxv2rQp4GNcLpepW/oZ6utp4vWCCy6Qfv36yfbt2322PXz4sKk71apVM3VMt7/yyitNmf15Pqvjx4+b0cO0R7E+pm7duqaXtKe8d955pylj7ty55X//+58sXbo0YDk1MXLPPfdI5cqVzWeuiWKNzW+//ZZiW60Xuhw5ckTuvvtu8/z6mBo1ashHH32UYtv33nvP/F2hQgWf/cHb9ddfb2Ll2RYAAAAAgGgTL7klza+kVw7dRm8HO2JbKGXWfMfjjz9uyqr5owIFCph8ho6wvm3btrjME6nVq1dL27ZtzXvROnb11VcHzOt4aBnXrVvnU//S8vvvv5vGNfoZnXvuuSbXpDlDHdFKc0TpCabeZEb9DSXnpTS++pnqa+v7vOGGG2THjh0SrEWLFpkyaZ4zEC2PNsBq3LhxyMeHtKS1z/nncL2tWrXKdAzVxmFah8uVKycDBw6Uv/76K8W2mr+96qqrknKBxYsXN59NsPlbz+fl/Xl7748///yzXHHFFUl1WGff8N42XfreNTeteecffhBp3VqkUCH3OuVyiYwdK6KxL1BAe0qL1KvnXhfIgQMit98uUry4e9v69UU+/TTNIujnpjnSMWPGmFExAUQOpuAEAABAltDkj15Aa+OnwoULp7hfLxg///xzadasmUneHD161FwcP/TQQ/LTTz/Jxx9/bLbTRJAmhvQ+bYCliS9tgOPdaEcb2GgSw7sBjj5OaWOiNm3amMfXqlVLevfubZIMM2fOlPbt28uIESNMMsKfJgV+/PFHueaaa6Rdu3Zy3nnnZSgeOlS4NlTShEK2bNlk6tSppiGTJkf69u2b6uP0/en71ySSJiu8kz36ftKiCTtN8OkUFZpc+Pvvv2X27NkmAbd+/XqTRMoojZEO9a4x1phpYu7TTz81iSG9r2LFimk+XqcO1QSnJsu0jJqI0kTdpEmTzGe0bNky8749NMmgSSptlKVDy2tcNSmqiRONqcbXMxXFgQMHTP3SMmkC6vbbb5d//vlHpk+fbl5v2rRp0qFDhxRl0uf/9ddf5dprr5X//vtPPvjgA5Ns/P777+W2224zSbvrrrtO9u3bJ1OmTDHvfcuWLT6j1mnCUevjH3/8Ia1btzavo8kwrddz5swxI5M1bNjQ53W1Xuq2Bw8eNEk73ScmT55sGpLp56b3qUGDBpnk1sqVK01jNU9d9+wXHtpwTuuXvtZTTz1l9fkCAAAAAOCUeMot6etqbiOtcoQilDJrnLUR1pIlS0z+RB+XmJhoGp5pAyWd9s47NxMveSJtTKXbaUOwTp06mUZtmtvTdTVr1gxY/latWpm4aZ7IP08TyCeffCLvvvuueX39zPX9aJw0p6d19dtvv01zZKtg6k2462+oOS+9rZ+D1in9rLShla7TOAbarwNp0qSJiae+hnbo1YZ63j788EM5deqUqauhHh/CTfcZzeXp+9X4lilTRtasWSMjR4408dH9zPO+9TPQ2OpnpdtqgzXNN2rO7/333zd5yIzQ9zl8+HBTvzQfvHz5cvnss89M3lPrt38c06SNz559Vlvwimi5tHGnNj7r3l0/AJELLhDRqY1z5hT5+muR3r1F1qwRefHF5Oc4elQrrcivv4r8738izZuLaEPEG25wN2xLg8b00UcfNd8Jup8BiBAuAAAAIAusXr3apaef3bt3D3j/tm3bXKdOnfJZd+bMGVevXr3M47777juf+5544gmzfv78+T7r9bau1/sDefjhh839jz32mHl+j3/++cdVr149V86cOV07d+5MWn/rrbea7UuXLm3KGKwtW7aYx+njvTVv3tysb9iwoevQoUNJ69etW+fKnj27q0qVKj7bjxs3zmyv/3vTdfpcgaQWg82bN6fY9uTJk64rrrjClS1bthTvr1y5cmYJhuc1dXnrrbd87tPbur5t27YBY+Ht77//dv31118pnv+bb75xJSYmuvr06eOzfsSIEeY5WrVq5Tp69KjPfXrb+7luvPFGs+2YMWN8ttuzZ4+rTJkyrmLFirn++++/FOVr0qSJ68iRI0nrp0yZYtYXKlTIdd1115kYejz//PPmvpdeesnnNS699FIT49mzZ/usX79+vSt//vyu6tWr+6zXuOvztG/f3nX8+PGk9XPnzjXrr7zySp/tPfVU611aateu7cqRI4fr2LFjaW4HAAAAAECkibfcUnrlSK38gXJGoZR51apVZtsOHTqkeE3NJxw+fDiu80QTJ0702f6hhx5Keq/+eRlPfie9fI3HH3/84ZMH8vi///u/gK8dKB+U1fU3lJzX6dOnXRUrVnQlJCS4Fi1alLRey+H5PIJtvvDoo4+abTVP569u3brmfXh/3qEeHwLVx9T2udRyuPv373cVKFDAVapUKdfWrVt9tv/www/N9gMGDEha16lTJ7NuxYoVKZ5fnysYadUJXSZPnuyz/c0332zWa3mCou/d3dTM5Ro71ve+t992r+/Z0+U6cSJ5vdbpdu3c9/38c/J6raO6rm9f3+fRuuR5Db+cuMe8efNMuXX/AxA5mIITAAAAWUJ7wSkdNjwQ7X2oI4F506HBdVQw5ZnyMCO01+CoUaOkUqVKScPQe+iw49qjUkez0t6G/oYMGZLUQzIctPen9sD00N672tNPRyLT4f8zg07P6C979uymh+fp06dNj7GM0mkB/Edw09vaK1R78WmvvbToqGFFihRJsV575ul0CP71QHs5ar3Rz1WHzvemtz3PtX//fjM6WcuWLaVPnz4+22mPTf18tWyB6tkzzzxjeth6dOnSxfQ21RHkXnzxRRNDD+1Zq7Rnoof2Jvzhhx9Mj2rtQRwoXp6ehv5eeeUVMzS/h/bo05692mPRhu5/2qtVe6ICAAAAABBNyC1lbZn98yxKpwTUKTnjLU+kU3fqCGQ67V93HeHJy8MPP5zqqHQ6Gpm2CQxm9DOlo7Z554E8PCOSZbQOh7v+hprz0qk3dSpPnVlARzHz0HI8++yzKfbftHhGN5s4cWKKUfN0uk0d5cy77mTF8cHfhAkTzKh6mgf2HzVQp+TUKVB1tgN/gfY9nao0o3T0Nx11zptnGtOQc4116oj07Om7buRIEc2hvvGGiPdIfVqnn3nG/beOjuYxYYL7vief9H0erUvpjGpWT6f1FDEzVACIHEzBCQAAgCzx119/mf9TS8hockOHHteLbh1KX4ezd3fadPvzzz8zXAZt3KXTGerQ7ppk8edJeunr+2vQoIGEk06H6K906dLmf23YpEmfcNOGbdpgSodW1+Hx//33X5/7wxFjbUSnQ8p709u6fsOGDaZh1uWXX57mc+jw96+++qoZgl4Tgjpkvod3Ek7riCaVKleubBKXadEkijay02kGhg4dmuJ+LZvns9ckmDf/qU31/WgyUofq90+86dD4/rHU6QnUnj17Ar62p77p/5dccknSet1XAjUa1HqyePFiseGdaNUh/wEAAAAAiBbklrKmzBdffLFpaKXTGGqjP51SUadY1PyIf84nXvJEno6G3o2mPLRBnsZGy5lRWl/HjRsn48ePN422Dh06ZBqNhasOh7v+hprz8sSxadOmKbbVBlqaq9JGe8HQBm5aptmzZ5t6UbRoUZ8Gad7Tb2bV8SG1+Gjd1Vysv2PHjpmye8qvjdK08V+jRo3kxhtvNB1RNVae95bZ+eiQ1K/ve1un09SpNM8/X+T551Nuf/Kk+39P3frnH5EtW0SqVhUpUSLl9lpH5s1L9eW1Y7dOGeppmAwgMtAADQAAAFnC03NLL6wD0VGlPv/8c5M80J5Y2sDHM8rUa6+9ZhJCGXXgwAHz/+rVq82SGv+GWWn1rrXlPfqZh2ckLU2AhZsmWTRZuGzZMqldu7ZJwmjPOX1NTey89957YYlxanHyrNfEWVqmTZtmPn9N3mnPSe0hmjdvXtMjUZNv27ZtS9rW81zaOzTYz157xaXVMy7QZ5/aZ5XWZ6ijjPm/tvbs1SXY19ZevoHoa3gnH0Px33//mf81pgAAAAAARBNyS1lTZs07fPPNN6ZB0ccffyz33nuvWV+sWDEzEtcjjzwS9EhVsZIn8jy31qm03k9G3XXXXaaRlDbEuvbaa01HRx11TmmDsYzW4XDX31BzXsHEMdgGaErzm0uXLjWj2elIZtqg7IMPPpDChQvLNddck+XHh9Ti84aOCJYGjY82MrvuuutMx+GXX35Z3nrrLfM4res64t9LL72UopOso/lo//pw8KB70sydO7Wypv44T93SBmgqlbqQ4vlT6WirjfcARA4aoAEAACBLaJLK+8Lbv9ehJgA0kaTJCu8klvYU0yRAOHgusjt37iwfffRRSI/1HpI+Gk2fPt00Puvdu7e88847Pvdpzz9tgBYO2uMxrfWpNary0OSm9l7TofL9e6v6D0nvea6dmtgI8rPXpKmOApeVPK89YsSIpCkTnOLZ/zz7IwAAAAAA0YLcUtaVWTstah7j9ddfN6NFaYM0vf3EE0+YRjsPPfRQXOWJPM+9d+/ekN5nKPS5tcGRjj6nI997dx7cvXt3wBHLnK6/oea8wh1HHTFs8ODBZtQzbYD27bffmkaJ/fr1S2q4F87jg2c0P+9R+DwCNab0xEenIfWe9SAt7du3N4vOZKGNI3VEtHfffVfatGlj9sXURoDMcv71wdO4TUdZ+/nn9B/v2T6VuiBB1AXtaEsnWyCyhDZOKgAAAGCpWrVq5iJdh3r35xmCXHum+fegXLRoUUiv43l8oF5bOoWAXvj//PPPPiNURSONZSg90zwx1gSGv1BjnBZNjPiPzqW3f/jhB5OoqlmzZrrl1M/JP6m4a9cu2bx5s8867f1atWpV2bJlS9LUCKmpX7++eX3bqSszomHDhub/zHzttOq9N93/tCewZypOAAAAAACiRbzlloK91k9PRsqsuRR9vDbu+frrr826GTNmxF2eyFPO7777LsV9OpXjihUrJKP0/egIXjolqX+jmlDqcFbW31BzXp44Bno/2nBsx44dIb2+jhqmDbO0EdnGjRuTpt+86aabMuX4oCOrpdbIcfny5WHNCebPn9+8t7ffflt69OhhGufpVJ4RK39+rWAia9fqfJ7BNUCrUEFk40ZtYZny/nQ+Fz2OaKM/OtkCkYUGaAAAAMgS2jtLe/BpgsM/8VSuXLmASRwdCv65554L6XU8DWsCJSx0SPH+/fubhMZ9990XMNHy22+/pdoLL5Lo+/zjjz+C3j61GC9cuFDGjBkTtnL9/vvvKZ5Pb+t6TfKklxTQcmrCyLvHo06toZ9boM9LE6CaULvjjjuSppf0fpynV3SJEiXk+uuvNwnOF154wST0/GkS5+jRoxJuDRo0MAmnDz/80EwJ4E/3B/0cMiKteu+xfft202O2WbNmGXotAAAAAACcEG+5pWCu9YMRapl1CsRA0yB6cjU6Ilm85YnKli1r8imrVq0yUzx6e/bZZ800joHolKHa0C2YaSU9dVjL5F2/Nf8X7IhzWV1/Q815NWnSRCpUqCBffPGFz76q8X/44YetGlvqNJxKZ3zQKVv1+Rs3bpwpxwdtuKgmTJjg8xlpAzP/eqF69uxpGpLptLWBpjzV+qWN5zx0BLdAMfB8HqHse4646y59UyJ9+yZPteltyxY9wCTf1s/uxAmRxx/33e6rr0TmzUvzpbSRqX4G1atXD1fpAYQBU3ACAAAgy3Ts2NEM1a8X1pdeeqlPskKXqVOnmh6MjRo1Mo1ltEelJqNCGRL+oosukvPPP98Mw69DrZcuXdokegYOHGiGedfh6nUqSp1CQIdc1+TReeedZ3qu6XDoK1euNEkDXRfJWrZsaeLVoUMHqV27tum9d+2115pEbCDt2rUzSa/hw4ebRJIO+649hjXho59LqMPup0aHsr/rrrtk1qxZpmeyJld0iHvtkRjMcPb6Oemi76lLly5mSHvtYauJKO0lqZ+PN02aaSJLY6G9YTUG2pNT68+cOXPMEPUaI/Xmm2+a93z//ffL+++/L//73/9M8loTcpq81sSF1r/MGLpdE3GXXXaZmRrg1VdflTp16kiePHlMObW+7du3zyRCM1IfdMqI2267zUyjcM4555jkmicJpzw9lT3xAAAAAAAg2sRTbim9coQilDLraF6dOnUy8dQRxbSxlm732WefmRHo7rnnnrjME+n0mNqw6ZZbbjGx0OdfunSpmd6xadOmAUfS8jRS0oZf6SlZsqTJ6Xz88cdSr149adWqlWl4p7k7/dszilek1d9Qcl5af3REr6uvvtqM9HbDDTeYsuoUrxprzWtqI79QaM5T39fLL79sGtRpffOfLjRcxwd9nNYBLa/WF42dNuabPn26Kcenn37qs702sNT4XHfddaa+6ohm+vkcP37cNErUuqrHsdmzZ5vttex//vmnaajnabyojea0nulr6/qI1q+fzmkq8t57OvyhyOWXi5x/vns6zXXrtFWnyKRJ2jLTvf3994t88om2StXWgCLaaVYbTk6dqsPVicycmepLeUaDa968eVa9OwDBcAEAAABZZOfOna7s2bO7+vfvn+K+vXv3unr16uU6//zzXblz53ZVr17d9cYbb7g2b96sXRBdt956q8/2TzzxhFk/f/78FM/1448/upo3b+7Knz+/2UaXLVu2JN1/6tQp1+jRo12NGzd2FShQwJUrVy5X2bJlXW3atHGNGjXKdeTIkaRt9XX9Hx8M3T5QubVcqZ2GB3qtcePGmXX6v7ddu3a5rr/+elfRokVdiYmJPttoTPS2xsibxrJz586uYsWKufLmzeuqX7++a/LkyaluX65cObMEw/s5Fi1aZN7nOeecY+LbsWNH14YNG1I8JlAszpw543rrrbdc1apVM/WgRIkSrt69e5v6kVrs9DHvvPOOq1GjRuY19b1dcMEFrttvv921fft2n22PHj3qGj58uKtu3bpm2zx58rgqVKjg6tChg2vChAmukydPplm+YGKjj9HH+jtw4IDr0UcfdV1yySXmdfPly2fKeeONN7o++eSToJ8/tXLp+9Lny5EjR8AytGjRwnXeeee5Tpw4EfB5AQAAAACIdPGUW0qvHKmVP7W8RLBl3rFjh+vBBx80eRbNI+TMmdNs16lTJ9fixYvjNk+kfv31V9fVV19tcjr6mVx11VVmXaDP+ODBgyZnp/EO1uHDh1333nuvq3z58ubz0XI/9dRTJpcT6HNNrW5ldf0NJeelvv32W1ezZs3MtkWKFHFdd911rm3btqWZi0tLnz59kt7n+vXrA24T6vEhtbLs37/fdcstt5hya/m1ns2ZMyfVHK5at26dqbea69P9qXDhwub177rrLtfSpUuTttM8reZ7K1WqZOptwYIFXTVr1nQ9//zzpm4EI9DnlVruN60cdqr0eKNxCfBcSaZMcbkuv9zlKlzY5cqRw+UqVUoTky7XSy+5XPv2+W77118u1223uVzFirlcuXO7XHXrulxaZzSO+joB4qluuukm812g3wkAIkeC/hNUSzUAAAAgDHREJu1dp73DdAhyxIYFCxaY3o7aC3no0KFOFwd+tNdulSpVzGfzuP+w9gAAAAAARBFyS5GPPJGYkct0VCytqzriF4Dw0KlLixcvbkbR8x91DoCzEh1+fQAAAMSZp59+Wv777z8ZMWKE00UB4saTTz5ppnK49957nS4KAAAAAAAZQm4J0UCn5KxVqxaNz4AwmzRpkhw5ciSk6YABZA0aoAEAACBLlStXTt577z16qAJZ5OTJk2b0swkTJsg555zjdHEAAAAAAMgQckuIBs8//7wsX77c6WIAMeXUqVPy7LPPyrXXXivNmjVzujgA/GT3XwEAAABktuuvv97pIgBxI0eOHPLoo486XQwAAAAAAMKG3BIAxJ/t27fLLbfcYqZiBhB5Elwul8vpQgAAAAAAAAAAAAAAAAAAog9TcAIAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAAr2e0eBgDIiISEBKeLAAAAAABxx+VyOV0EAIAl8mkAAAAAELn5NEZAAwAAAAAAAAAAAAAAAABYoQEaAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFay2z0MABBOn332mVSuXNnpYkS8L774Qh588EGZOnWqVK1a1eniRBViZ4e42SN29oidPWJnj9jZI3Z2iJs9Yhe8jRs3SocOHZwuBgAAEYFzh9Bx3mWP2NkhbvaInT1il/HYwR71LjTsr/aIXdbk02iABgARQBufVatWzeliRLwVK1aY/ytVqkS8QkTs7BA3e8TOHrGzR+zsETt7xM4OcbNH7AAAgA3OHULHeZc9YmeHuNkjdvaIXcZjB3vUu9Cwv9ojdlmDKTgBAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwkt3uYQCAaHf8+HH5/vvvZcGCBbJs2TJZs2aN7Nu3z6wvWLCglC5dWho2bCidOnWSK664QhISEpwuckQgbvaInT1iZ4/Y2SN2doibPWJnj9jZI3b2iB0AAAiayyWyerXI4sUiK1eKrF0rsm2byN69IkePiuTJI1K4sEjVqiJNmojcdJNI+fJOlzo6dO8uMmmS77otW4gf56sZQuzsETtLW7eKVKgQ+uOKFxfZvTszSoRYx7lJhnCsy0Tdo/zczgUAyHJ6+PVefvvttyx77d27d7u6du3qyp8/f4pypLZUq1bN9eOPP7qcNnHiRFOeX375JctfO5rjpohd9MVNETt7xM4esYu/uCliZ4/Y2eFYZ4/YBU+vs/zLAgCIXsF+77AEXpw6dzDeeUc/wOCXxESX6/bbXa5Dh+L6vCtd06cHjt+WLU6XjOukKK1zxM4escsgPW6F8j3hWYoXj4jYsUTh+QnnJlY41sXHud1vGcinMQIaAMSZHTt2yOTJk1OsL1mypGmRnj9/ftm9e7esW7dOzpw5Y+5bvXq1NGnSRKZMmWJaq8cj4maP2NkjdvaInT1iZ4e42SN29oidPWJnj9gBAICQmfaDXrJnd4/ioCPX6Agjhw+L/P67yMGD7vv1HOKtt0SWLhWZO9c9Agl8aaxuv93pUkQkzlftETt7xC7MmjVzfz+kp0iRrCgNYhHnJlY41mWig7FxbpfodAEAAM5p1KiRvPXWW7Jlyxb5888/ZenSpTJv3jxzMrBz504ZMGBA0rCop06dkm7dusn69esl3hE3e8TOHrGzR+zsETs7xM0esbNH7OwRO3vEDgAABEV/1G3aVGT4cJEff3RPbbVhg8h334l8/bV73V9/iSxYINKwYfLjli0TGTjQyZJHrkGDRHbtcv/durXTpYlYnK/aI3b2iF0YvPeeyOzZ6S/+09QBweLcJMM41oXZoNg4t6MBGgDEmcTERGnfvr388ssvsnjxYunXr5+UDzBvdIkSJWTEiBHy2muvJa07ceKEPPLIIxKPiJs9YmeP2NkjdvaInR3iZo/Y2SN29oidPWIHAABC1qOHyLffigwZ4v4RN0eOlNvoD5TNm4ssXCjSpEnyem1csH17lhY34s2aJTJhgvvva64R6dbN6RJFFM5X7RE7e8QOiDKcm1jhWJdJZsXOuR0N0AAgztSpU0c+++wz838wBg4cKA0aNEi6PXPmTDmqPQHiDHGzR+zsETt7xM4esbND3OwRO3vEzh6xs0fsAABApsqVS+SZZ3ynyPrmGydLFFkOHRK57Tb33/nzi4wa5XSJIg7nq/aInT1iB8Qwzk2ScKzLBIdi69yOBmgAgHRpa3aPY8eOydatWx0tT7QgbvaInT1iZ4/Y2SN2doibPWJnj9jZI3b2iB0AAAhJvXq+tz3TEUFk8GCRnTvdfw8bJlKmjNMligmcr9ojdvaIHRBFODexxrEuvs7taIAGAEhXkSJFfG7/888/jpUlmhA3e8TOHrGzR+zsETs7xM0esbNH7OwRO3vEDgAAhOTkSd/bBQo4VZLIMmeOyNix7r91KrD+/Z0uUczgfNUesbNH7IAowrmJNY518XVuRwM0AEC6/Fujn3feeY6VJZoQN3vEzh6xs0fs7BE7O8TNHrGzR+zsETt7xA4AAIRk/nzf240bO1WSyKE/1vbtmzwV2DvviCQkOF2qmMH5qj1iZ4/YAVGEcxNrHOvi69yOBmgAgDS5XC756KOPkm6XLFlSKlSo4GiZogFxs0fs7BE7e8TOHrGzQ9zsETt7xM4esbNH7AAAQEj27BEZMiT59uWXi9Sq5WSJIoPGZMcO99+PPy5SpYrTJYoZnK/aI3b2iF06HnhApEYNkUKFRHLmFCleXKROHZEBA9wjBrlcTpcQ8YRzE2sc6+Lv3I4GaEAc2rBhg/Tq1csc4HPlyiX58uWTcuXKyTXXXCPjxo1Lsf2ECROkfv36kjdvXjNMZps2bWTRokWyYMECSUhIkBYtWvhsP378eLO+R48eqbZ01vvLly+f4r65c+fKwIEDpVatWlK0aFFTvtKlS8sNN9wgP/30U8DnGzp0qHk+/X/79u3Su3dvKVOmjOTIkSNFGfRLTstfrFgxyZkzp5QqVUpuuukmWbNmTYhRjB+TJk2STZs2Jd3u3r27iTfSRtzsETt7xM4esbNH7OwQN3vEzh6xs0fs7BE7ALGCfBr5NGQSbURw5IjIypUiw4aJVK8usnGj+74LLxR57z2nS+i8efNE3n7b/XfNmiL33+90iWIK56v2iJ09YpeOqVNFfv1V5NAh99SHe/eKLF8u8sYbIm3auL8rFi92upSIVZybhA3Huvg7t8vudAEAZK3ffvtNGjdubOZXrlKlirRt21ayZcsmf/zxh3z77beyc+dO6dmzZ9L2d999t7z++uuSmJgoTZo0kfPPP19WrVplkmSa2Aq322+/XXbs2CHVqlUz5cyePbusW7dOpk6dKp988olMnjxZOnfunGoisHbt2iYRpo/VVtWadFOnTp0yX2r6PJqEq1u3rkmW/f777/LBBx+Y59ZFk2lIpvVC64BHoUKF5KGHHnK0TNGAuNkjdvaInT1iZ4/Y2SFu9oidPWJnj9jZI3YAYgX5NPJpCDNt5JjWj7f58oncdpu2lBTJn1/imv4A3qeP++9s2dzTM2Xnp71w4XzVHrGzR+yCcO65IpUqub8D9DioDVj270++f/VqkWbNRN56S6R3bydLiljBuUnYcayLz3O72HknAILy8ssvm2TZ008/LY888ojPff/9959Pr8iZM2eaZNk555wjX375pTRt2jTpvueee04efvjhsJfvxRdflObNm0vhwoV91n/22Wdy3XXXSb9+/eTqq6+WPHnyBGxFrb0v33nnHZMU8/bEE0+YZFnDhg3lww8/9BneU3txdu3aVW688UbZvHmz+QKEyNGjR6VTp07y119/Ja0bPXq06bWL1BE3e8TOHrGzR+zsETs7xM0esbNH7OwRO3vEDkAsIZ9GPg1ZSOthr14iffvyA69nGrqtW91/33OPSL16TpcoZnC+ao/Y2SN2qdARkfT4pg36r7pKJNAUfb/84h6NyjOd36lTIv36iZQuLXLllVleZMQRzk1CxrEufs/tmIITiDN7dJ5qEZN08qdJqGbaY+CsV1991fw/YMAAn2SZ0hbKOqx/uHXo0CFFssyzXhNm+kU1f/78gI/VL62RI0emSJYdOHBAXnnlFcmdO7d8/PHHKeaW7tKli0nEHTx4UCZOnBjmdxSdtIerJhG9E6h33nmnXH/99Y6WK9IRN3vEzh6xs0fs7BE7O8TNHrGzR+zsETt7xA5ArCGfRj4NYaZTWWljAV2uuEKkQQMdFsN93/HjIq+/LlK1qp5AiJw4IXFrwQKRUaPcf+tIQE8+6XSJYgbnq/aInT1il4Zy5UQ0LnfcEbjxmapbV2TaNPd3hMfp03rS5Z6mE8gIzk3ChmNdfJ/b0QANiDMN9AtTRPr37y9z5syRY8eOpfrl8N1335m/tRdkILfcckumlPHPP/+UMWPGyL333it9+vSRHj16mGW1DqkrIuvXrw/4uMsvv1wKFiyYYr0m2LQ3qk4joNMEBKJTIKgffvhB4t2ZM2fk5ptvls8//zxpnZ4UvPbaa46WK9IRN3vEzh6xs0fs7BE7O8TNHrGzR+zsETt7xA5ALCKfRj4NYXbvvSKzZ7uXr74SWbJEWz2KfP998ig2LpfIm2+KdO0qcenoUfe0choHNWaMtnh1ulQxgfNVe8TOHrELI53OXEej8ti4UWTGDCdLhFjAuUlYcKxLQ5yc2zEFJxBnhgwZYhJhc+fOlTZt2kiOHDmkZs2apqemtkauX7++2U57RnqSaf49HD1SW58R//d//yfPPPOMnEyjt4JOeRBI+fLlA67XaQDUvHnzJEGH8U3Dvn37JN5PDDQ5OXny5KR1nTt3lg8++ECy6TzUCIi42SN29oidPWJnj9jZIW72iJ09YmeP2NkjdgBiFfk08mnIAlrPLr3U/cPv4MEir7ziXv/ppyLvvSdy660SVx58UHdE9999+ohcdpnTJYoJnK/aI3b2iF0m0CnRx45Nvv3llxpUJ0uEWMS5SUg41qUjTs7taIAGxJm8efPK119/bYa9nD17tumhqMvPP/8sL7/8stxxxx3yxhtvZGoZ9AsokE8++USGDh0q+fLlM0P/t2zZUs4//3wzlYEmuh5++GF57rnnxOVpGexHt0vr9SpXrmx6babloosuknilcerdu7e8//77Ses6duxoThSyZ+frIjXEzR6xs0fs7BE7e8TODnGzR+zsETt7xM4esQMQy8inkU9DFhs+XGTWLB26z317xIj4+pF3zRqRkSPdf5csKfLCC06XKCZwvmqP2NkjdpmkYkX3tJ3btrlvr1vndIkQ6+L93CQdHOvSsSZ+zu34tIE4pT0zPb0zdXqAzz77zEwB8Oabb0qXLl2kadOmkitXLjl+/Lhs3bpVqlWrluI5dH0gOXPmNP8fPnw44P3bPCeEfqZOnWr+1x6bt912W4r7N2zYIDbKlClj/q9SpYqMHz/e6jni4cRAp2fwjk+HDh1kypQpnBikgbjZI3b2iJ09YmeP2NkhbvaInT1iZ4/Y2SN2AOIF+TQgi+j5Q5cuWrHdt5cvF/nvv5icpiigvXuTp2fatUukcOHQHu890qJOsfv33xLvOF+1R+zsEbtMpo04POdH+/c7XRrEung/N0kDx7og7I2fc7tEpwsAwHl68Nck2ZVn57BesWKFWefp3ahDYwbi3YrZW6lSpcz/61LpcTBz5syA6w/oXNqinRbKpbhv7969pqepjVatWpkk3oIFC8zzIPCJwbhx43xODDSBqVNKIDDiZo/Y2SN29oidPWJnh7jZI3b2iJ09YmeP2AGIV+TTgExWtmzy3zoi38GDTpYGUYzzVXvEzh6xywJHjyb/TSMgZAXOTVLgWAd/NEAD4oz2yFzvGR7Uy+7du820Ad4Jq0GDBpn/R4wYYaYV8DZ8+HBZtmxZwNdo0KCBFChQQNasWZMiqTZt2jR5/fXXAz7u4osvNv+//fbbcuLEiaT1hw4dkltvvdX8b6N48eIycOBA+ffff6Vdu3by66+/pthGe6bOmDEj1SRfrOLEwA5xs0fs7BE7e8TOHrGzQ9zsETt7xM4esbNH7ADEC/Jp5NPgAP+RHUIdKSKa6XnUuecGv+TLlzJW3vfHMc5X7RE7e8QuCxw/LrJxY/LtEiWcLA3iRTyfmwTAsS4EOeLn3I4x74A4o8moO++8UypUqCCXXHKJSWzt27dPFi1aJP/995+0bNlSrr32WrOtJpd02zfeeMNMIdCsWTMpWbKkrFq1StauXSt33323vPbaayleI0+ePPJ///d/cs8995hpCEaNGmV6cepjNIn26KOPylNPPZXicZqgmzBhgsyaNUsqVqwojRo1kpMnT8rChQslb9680qtXLxk7dqzV+x42bJjs2rVLJk2aJLVq1ZKaNWua19CeqX/88YfppaoJtS+//FIuuugiidcTA52PW4dE5cQgdcTNHrGzR+zsETt7xM4OcbNH7OwRO3vEzh6xAxBPyKeRT4MDFi70nWYtnka30ZEUQ5lOTqe86tkz+bY2dC1fXuId56v2iJ09YpdFPvnEdwS0Jk2cLA3iRTyfm/jhWBeixvFzbscIaECceeaZZ6R///5SqFAh+fHHH00PSk1iNWzYUN577z2ZPXu2z3zMI0eONEmq2rVrm+01maVJs3nz5plWzKnR5Jc+X506dWT58uXy1VdfmZ6T+r8mvgLRJJ5u2717d8mWLZt88cUXsnLlSunWrZtZX6ZMGev3re9Jpz7Q8mu5deoA7aE5Z84cM1WBJgc1maZJwXjgcrmkb9++PicGnTp14sQgHcTNHrGzR+zsETt7xM4OcbNH7OwRO3vEzh6xAxBvyKeRT4MDP/B++WXy7fbtnSwNohDnq/aInT1il0X27BF58MHk24mJGmgnS4R4wLlJEo51SAsjoAFx5pprrjFLKHr27GkWfwsWLEjzcdpbU5fUvpwCKV++vEycODHgfUOHDjVLsOsDueqqq8wSzzT2/fr18+n92qVLF/nwww99kqXwRdzsETt7xM4esbNH7OwQN3vEzh6xs0fs7BE7APGIfBr5NGTA6tUiL70kct99IlWrBjeqje47nvqeO7f7sUCQOF+1R+zsEbsMWLxY5L33RO65R6RKlbS31SnBu3YV2b49eZ2eN52dkhwICucm1jjWIT3UAgCIM9pLd8yYMUm3ExIS5ODBg9K2bdugn+Pee++VK664QuIJcbNH7OwRO3vEzh6xs0Pc7BE7e8TOHrGzR+wAAEBITp4U0REydNEGAq1aidSoIVKqlEiBAu779+0TWbVKZPp0kd9+S35sQoIOKShSqZKT7wBRhvNVe8TOHrHLgOPHRUaPdi81a4q0bOn+nihRQiR/fpEjR0Q2bhSZM0dk5kyd+y/5sbVriwSY2hxIE+cm1jjWIT00QAOAOHP06NEUrdV1CohQdNUeJnGGuNkjdvaInT1iZ4/Y2SFu9oidPWJnj9jZI3YAAMDa2rXuJRhFirh/4O3WLbNLhRjD+ao9YmeP2IXJypXuJRjt2onoKEzaYAiwxblJSDjWIT2J6W4BAAAAAAAAAAAAhKp8eZHHHhNp0EAkR47gt1+/Pq5/4AWAuKHH/RtuEClZMv1tExNFdOQkHZVqxgyRokWzooSINZybAJmGEdAAWGvRooVp2Yzo0qNHD7MgNMTNHrGzR+zsETt7xM4OcbNH7OwRO3vEzh6xA4CMIZ+GuFOokMiTT7qXY8fc01ht2iSya5d7WjX94VdHr9Fpr2rVEilb1ukSRx89N+P8LAnnq/aInT1ilwHauGfyZPfff/4psmaNyPbtIgcOiPz3n0iePO7vksqVRerVE8mXz+kSI9pxbmKNY10W6RG953Y0QAMAAAAAAAAAAEDmyp3b3XhAFwAA/J1/vnsBsgrnJkBYMQUnAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKzQAA0AAAAAAAAAAAAAAAAAYIUGaAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYyW73MAAAss6xY8dk7ty5smzZMnN70aJFkiNHDsmena+x9Jw6dUq2bdvmE7uqVatK7ty5nS4aAAAAAAAAsgj5tOCRTwMAAABCxy/3AICIp43P2rVrl3R70KBBjpYnmmnsKlWqJG3btnW6KAAAAAAAAMgi5NPskU8DAAAA0kcDNACIAF988YWsWLHC6WJELE9vQ4TH/Pnz5dChQ04XIyp8//335v933nlHzj//fKeLE1VWrlxp/id29rGbNWuWrF271uniROU+S+xCR+zsETs7xM0esQveH3/84XQRAABAjCCfFhpyanbIp9kjn2aPa8yMxw72qHehYX+1R+yyJp+W4HK5XNaPBgBYSUhIcLoIAIKUmChy5ozTpYhOxM4esbOXmJgoZwieFWJnj9jZIW72iJ090mAAEL3IpwHRhdyGHeJmj9jBCVyf2yN2dogbIjmfxghoABABpk6daoZxR2CLFi1imoAwevXVV6Vp06ZOFyMqaG/DUaNGycSJIhdf7HRposusWSKPPSbEzoJ2vrnpJpGnnnpKrr76aqeLE1W099Jjjz0mEydOlIupeCEhdvaInR3iZo/YBW/Tpk1y/fXXO10MAEAmIZ+WNvJpcJr+Nk5eKDTk0+yRT8v4NSbsaEMgrs9DR27DDnGzx7Eua9AADQAiQNWqVaVatWpOFyNi5cyZM8W6zz//nCRjkD+6tWvXzmddq1at5JJLLnGsTNHEM9S9nsfXqeN0aaKLZwRjYmevQoUKUofghcQzdLZefBO70BA7e8TODnGzR+yClytXLqeLAADIROTT0kY+Lbz5NNghLxQa8mkZRz4tdExFl3Fcn4eO3IYd4maPY13WoAEaACDiZcuWLcU6TZbRuj988QQAAAAAAEDsIJ8GAAAAICslZumrAQAAAAAAAAAAAAAAAABiBg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKzQAA0AAAAAAAAAAAAAAAAAYIUGaAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYoQEaAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAlex2DwMARLvjx4/L999/LwsWLJBly5bJmjVrZN++fWZ9wYIFpXTp0tKwYUPp1KmTXHHFFZKQkOB0kaNC9+7dZdKkST7rtmzZIuXLl5d4R52zd/y4yPffiyxYILJsmciaNSL79rnXFywoUrq0SMOGIp06iVxxhQihS0bs7LHP2iFu9oidPWJnj9jZI3YAgLizdatIhQqhP654cZHduzOjRDGFnBrCjZyQPWJnie8JOIBrc0vsrxlCvUNqaIAGAHFmz549MmjQIJk5c6YcPnw44Db79+83y4oVK2T06NFSrVo1effdd83JAlI3Y8aMFIkyUOcyYs8ekUGDRGbOFEkldLJ/v3tZsUJk9GiRatVE3n3XnQSKZ8TOHvusHeJmj9jZI3b2iJ09YgcAAMKNnBrCiZyQPWIHRA+uzeEE6h3SQwM0AIgzO3bskMmTJ6dYX7JkSdMiPX/+/LJ7925Zt26dnDlzxty3evVqadKkiUyZMsW0VkdKBw8elNtvv93pYkQk6py9HTtEAoROSpZ09zTMn9/d2WbdOpGzoZPVq0WaNBGZMsXdCzFeETt77LN2iJs9YmeP2NkjdvaIHQAAZzVrJpInT/rbFSmSFaWJWuTUEG7khOwRuzDjewKZiGvzMGN/DQr1DumhARoAxLFGjRpJjx495Morr0wxnL2eIDzzzDPyxhtviMvlklOnTkm3bt1k1apVUqVKFcfKHKm0xf+uXbvM361bt5avvvrK6SJFJOqcvUaNRHr0ELnyShH/2Sc08fPMMyJvvCHicomcOiXSrZvIqlUihI7YZQT7rB3iZo/Y2SN29oidPWIHAIhr772X8iITISOnhsxETsgesQsDvieQRbg2DwP215BR7xBIYsC1AICYlZiYKO3bt5dffvlFFi9eLP369UtxYqBKlCghI0aMkNdeey1p3YkTJ+SRRx7J4hJHvlmzZsmECRPM39dcc405iUIy6py9xESR9u1FfvlFZPFikX79Al8DlSghMmKEiFfo5MQJkTgOHbHLAPZZO8TNHrGzR+zsETt7xA4AAIQLOTVkBnJC9ogdED24NocTqHdIDw3QACDO1KlTRz777DPzfzAGDhwoDRo0SLqt83ofPXo0E0sYXQ4dOiS33Xab+VuHlh01apTTRYo41Dl7GrLPPnP/H4yBA0W8QiczZ4rEaeiIXQawz9ohbvaInT1iZ4/Y2SN2AAAgHMipIbOQE7JH7IDowbU5nEC9Q3pogAbHjB8/XhISEszQjLFK358u/rQlsK7funVrWF5n6NCh5vn0fye1aNHClGPBggWZ8vw6V/QPP/wgjz/+uJkr+txzz5UcOXJI0aJF5YorrpAPPvjADOOZFm2Rfd1110nx4sUld+7cUqFCBfPlt3fv3kwpc6zQ1uwex44dC1vdjQWDBw+WnTt3mr+HDRsmZcqUcbpIMYE6Z88rdHLsmAihCx6xs8c+a4e42SN29oidPWJnj9gB4UM+jXxaqMinIVKRU0MkISdkj9gB0YNrcziBehdfaICGuBbuxFWk0cSVvj9NZMWCzZs3S+PGjeWpp56StWvXSr169aRz585SsWJFmTt3rtx0001y7bXXmiE8A/noo4/MfNT6f7ly5cwXng4VOnLkSKlRo4Zs3Lgxy99TtChSpIjP7X/++cexskSSOXPmyNixY83fmsTt37+/00WKGdQ5e36hE0IXPGJnj33WDnGzR+zsETt7xM4esQNiC/m06EI+DZGInBoiDTkhe8QOiB5cm8MJ1Lv4kt3pAgDxaN68eXLy5EkpVapUWJ5vwIAB0rVrV9Nz0UkTJkwww2aWLVs2U55fk38tW7aUIUOGmB6a2bJlS7pv4cKFcs0118gXX3xhesxpr05vf/75p9x6661y6tQpGT16dNLw7qdPnza9hidOnCg33nijLFmyJGAv23jnn1Q+77zzJN7pCVLfvn3N37ly5ZJ33nmHuhNG1Dl7/r8BEbrgETt77LN2iJs9YmeP2NkjdvaIHYBwIJ9mh3waIg05NUQickL2iB0QPbg2hxOod/GFEdAAB1SqVEkuuugiM9x9OGiiTJ/P6YSZJsq0HHnz5s20uGmysU2bNj7JMtW8eXN58MEHkxJ3/l599VWTzLv88suTkmVKn2fUqFFSsGBB+emnn+Srr77KlLJHM52GQXu5epQsWdJMtRDvNHG7Y8cO87cmaKtUqeJ0kWIGdc6ezpriFTopWVKE0AWH2Nljn7VD3OwRO3vEzh6xs0fsAIQL+TQ75NMy0QMPiNSoIVKokEjOnCLFi4vUqaOtG3WIL/eFJlIgp4ZIQ07IHrFLB98TiCBcm6eD/TVTUO/iDw3QYoj2EvL0FBozZozUrVtXzjnnHClUqJBcffXV8uOPP6Y7bP706dNNjzgdClHX6ZDzHuvWrZOePXuaoda1Z5Ju06pVK5k6dWqqZdLecZqoqF69uuTOnVuKFStmhnj/9ddfU33M0KFDzWvr/zbD4O/cudNcxOpr5s+f38TgwgsvNL3yfvjhB7PN+PHjzXNs27bN3NYDnSd+/u87PYsXL5arrrrKxDlfvnxmGHvP8OE2UxX89ddfctddd5nkk8ZZ4z1o0CD5+++/zXvQx2n504uZxueyyy5L6s3o/f709T2OHz8uL7zwgqkvGq+cOXNKiRIlpH79+nL//ffLgQMHgo6Fvmag+IXzNdJSu3Zt878ngeHt008/Nf9rr0x/+rnpVAPqk08+CUtZYsmkSZNk06ZNSbe7d+8e970SNXH79ttvm79r1qxp6jHChzpnb9IkEa/QSffuen7gZImiB7Gzxz5rh7jZI3b2iJ09YmeP2CHSkU9zI5/mRj7NF/m0dOh+rPvloUMiJ0+K7N0rsny5yBtviLRpI1K9ulZ2p0sZUcipIRKRE7JH7NLB9wQiCNfm6WB/zRTUu/jDFJwxaPDgwSZJ1bhxY2nfvr1JTn355Zfy9ddfm+RWx44dAz7upZdekpEjR5qEj/aI0yHWPb3iZs6cKV26dJFjx46ZHkmdOnWSvXv3mkTMN998I3PmzJF3333X5/nOnDkj1113nXz22WcmQaLJlMKFC5sh2Rs0aCC9evXKlAtYLacml3T4Rk3o6WtrYkoPcOrSSy+VypUrm+HjtcXtv//+a5J4mjTx0GROMKZNmybdunUzw85fcsklJkmnyZo+ffrI6tWrQy7/rl27pGnTpuZArAnJtm3bmjhqD8TZs2fLxRdfHPRz6WeoSUr9bIoXL25ue3h6dupz6zD7GrcCBQqY19bE3759+2TDhg0myaUJJv+5mUORFa/hoc/naT3t7fDhw7Jx40bzt9bvQHT9+++/L8v1ZAJJ/vjjD7n77ruTbutn99BDD0k8O3LkiNnHlR4jdZqA7Nn5Og0X6py9P/4Q8Qqd6ahD6IJD7Oyxz9ohbvaInT1iZ4/Y2SN2iCbk08inKfJpycinBeHcc3WIOZH8+TVh5G6FsX9/8v1an5s1E3nrLZHevSXekVNDJCInZI/YBYHvCUQIrs2DwP4adtS7OOVCzNCPU5c8efK45s2b53Pf8OHDzX0FCxZ07dmzx+e+cuXKmfuyZcvmmj59eorn3b17t3mcbvP000+7zpw5k3TfTz/95CpcuLC57+233/Z53MiRI8364sWLu9asWZO0/uTJk67+/fsnlffWW2/1edwTTzxh1uv/gcyfP9/c37x5c5/127dvTyrngw8+6Dp+/LjP/fq+Fy1aFPC9b9myxRWqXbt2ufLnz28e//LLL/vcN3fuXFfu3LmT3qO/1F63Y8eOZn2LFi1chw4dSlp/8OBBV5MmTZKeb9y4cUHFLLVYeSxcuNDcX7t2bdc///yT4n79fPfv3x9kRFzmdfT59HUz6zVS8++//7oqVKhgXmvw4ME+961atSopdn///XfAx3/yySfm/qJFi7qygqc8nuW3335zRRqNaf369X3KOWXKFEfKoscQ/5h5H1ey0h133JFUhvvuuy/gNrqPepfV5hgTi7GLpjrn8dRTT5ly/PKLGSk4Ypd//xVX/fq+n/OUKc6WaeJEdzmIXeiLxkzLMXHiRFcki8R9VmPmrne/uCJVJMZNETt7xC5246aIXXTHTq+z/M+FAX/k08inkU8jnxa0rVtdrnr1XK433nC5Nm8OvM3PP7tcXbpoYZOXbNlcrtmzXfGeE4qmnFqg2LHYLZGcF4rEnBD5tCjPp0XZ94T/NSZLRo51kXt9HgnX5hGZ24jy/TWS61yk1juOdZKhJVhMwRmD+vXrZ4b996ZD6GuPtEOHDpmeRYFoD0bPsOnedPoBfZwO9/7II4/4DIuoz6nrlPa886a9RpUOY+/d01B7Nb388stB94oMlj6nlrNdu3by3HPPmZ6a3rQHZ5MmTcL2etpDVXsCNmrUSO655x6f+7SnqH4OodDpC7R3a2JioowaNcr0bvRuEazrwj0k5Z49e8z/2otSh/L3p5/vudriO8JfQ91xxx2yZcsWOf/88+Xhhx/2uU8/Jw+dQiIQT4/df/75J8NliQU63UfXrl3lp59+Slp35513yvXXXy/xTKfD0H1RVapUSZ588kmnixQzqHP2Tp0S6dpVxCt0cuedIoQufcTOHvusHeJmj9jZI3b2iJ09YodoRD6NfFqwyKfFcT6tXDn3BeQdd+gctIG3qVtXh/kTef315HWnT4sMGOCezilOkVNDpCEnZI/YpYHvCUQYrs3TwP6aaah38Y0GaDFIE1+B3HLLLUkXe4HoUPuBeLZP7Xl7nx1mUodr12kG1M6dO5OGaL/ppptSPEaHsg/3QUaH1Fe33XabZAVPXHSu4kBSi1dqFi1aZLpp1KlTRy666KIU9+uUBDVq1JBw0tfS4c7Hjh0rb7zxhpmyINyy4jWeeuopee+990y90mkxwpGAi2c6zcPNN98sn3/+edI63V9fe+01iWdHjx41xzvdTz0/JuTJk8fpYsUE6py9M2dEbr5ZxCt0JtlD6NJH7Oyxz9ohbvaInT1iZ4/Y2SN2iFbk08inBYt8GoIycKCI95S5um/PmCHxiJwaIg05IXvELoz4nkAm49o8jNhfg0a9Aw3QYlCFVFrpetbrfLuBlC9fPuB6TX6l9bzam7BIkSI+z+35v2jRokk94YItpy3t8agCJZsyg+c9phfvUJ8vtc8hvftsaG+zV155RU6ePCkDBgwwvR31Nbp16yYffPCBnDhxIuJfQ3vqPv7445IrVy759NNPpXHjxim28e4p+u+//wZ8niM6n7eIT0/ZeD0x6NGjh0yePDlpXefOnc1npYnPePbggw/K5s2bzd99+vSRyy67zOkixQTqXMYSPj16iHiFTjp3FvngAxFClzZiZ4991g5xs0fs7BE7e8TOHrFDNCOfRj4tWOTTkpFPS8fZkQ6TfPmlxCNyaogk5ITsEbtMwPcEMgnX5pmA/TVd1DsoGqDFIU9PI3/R0utID16xLK1pAcI9ZYAaOHCgSTa+/fbbplevfgHoF4P2tK1atWpYelhm1muMGDFC7r33XjM9xMcffyxt2rQJuF05HUb1rO3btwfcZseOHZmSlIwmum9pb8T3338/aV3Hjh3NZ6VTfcSzNWvWyMiRI83fJUuWTDFFCuxQ5+zpV6EOmOAVOunY0Z0AInRpI3b22GftEDd7xM4esbNH7OwRO8Q68mmRjXxa8MinZaGKFd3TO3msWyfxhpwaIgk5IXvELpPwPYFMwLV5JmF/TRP1Dh40QItBW7ZsCbh+69at5v/SpUuH9HylSpUy/3t6Kfk7dOiQHDhwwGdbz//79+9P6gmXWnn8afJDHT58OM2emf7Kli1r/l+XRQd8z3tM7X2ktt72+WyeM1jFixeXvn37mmH3N23aJGvXrpX//e9/5m/toRaJr6HTD9x1111JybJrrrkm1W21F2blypXN3z///HPAbTzrdYqDeD0x0B6I48ePT1rXoUMHmTJlCicGIrJ3796kHxs0wVu4cGGTwE5t6dmzZ4oe3J77tJc7qHMZTfj06SPiFTrp0EFkyhQSPukhdvbYZ+0QN3vEzh6xs0fs7BE7xALyaeTTQkU+jXxaUEqWTP57/36JN+TUECnICdkjdpkszr8nEF5cm2cy9teAqHfwRgO0GOTdsjTQ+hYtWoT0fJ7tNdERyNixY83/F1xwQVLSR5NyFbUlsIhMmjQpxWOOHz8u06ZNC/h8nufQhEogM2fODLje01tvzJgxEixPcu7UqVMSqubNm5v/ddjIQCZMmBDS8zVt2tRcSP/yyy/y+++/B+wttnLlypCe0/b96bQLDzzwgPl7xYoVIT02K17jrbfeMtMPeJJlbdu2Tfcx2so6tfqoSV3PXNSdOnWSeD0xGDdunM+JwdSpUyVHjhyOlg2xiTqX8YSPV+hMwmfqVBFClzZiZ4991g5xs0fs7BE7e8TOHrFDrCCfRj7Ng3xaMvJpYXD0aPLfUTJiIhBryAnZI3ZZgO8JhAnX5lmA/TUF6h380QAtBo0aNUoWLFjgs+6VV16RpUuXSv78+c3wh6HQXnba423ZsmXy7LPP+kw5sHz5cnn66afN30OGDPF53KBBg8z/Q4cO9elFefr0abnvvvvkzz//DPh6LVu2lMTERJkzZ44sXLgwab2+7uuvv24SJIEMHjzYvL8ZM2bIo48+KidPnkzR2+q7777zWefpvbp69WoJlcYxX758snjxYlMubxp/TeqEQoeqb9eunTlQ9+/f36fHqvaK1XWpTfeQGs/727BhQ4p4qG+++UZmzZqV4j59nS+++CLFcPs2wv0amhC94447QkqWeepj3rx5Ze7cuT5JVa2P+nx///231K9fX1q3bi3xhBOD4Ggszj333KAXPTZ4096d3vfHM+qcPRI+9oidPfZZO8TNHrGzR+zsETt7xA6xhHwa+TQP8mnJyKdl0PHjIhs3Jt8uUULiDTk1OI2ckD1ilwX4nkCYcG2eBdhfU6DeISAXYoZ+nLoMGjTIlZCQ4GrWrJmrW7dururVq5v12bJlc02bNi3F48qVK2fu37JlS6rP/fnnn7ty585ttrvooovM87Zq1cqVPXt2s65nz54pHnP69GlXu3btzP05c+Z0XXnlla6uXbu6KlSoYJ6rf//+5r5bb701xWPvvvvupDK3aNHC1alTJ1elSpVcOXLkcD344IPmvubNm6d43Jw5c1z58+c39xcvXtzVoUMH13XXXedq0KCBeaz/a40cOdJsmy9fPvMavXv3Nsu6deuCivmHH35oyqjPoXHWuGjcNf733HNP0mcSbMx37tzpKl++vLnv3HPPNWXq2LGjq0iRIq4LLrjAde2115r7PvjgA5/HPfHEE2a9/u+vXr165r4qVaq4unfvbt7fAw88YO575ZVXzH0FChQwcb7xxhvN63nKV7BgQdfy5ctdwdLPRB83f/78pHXhfA3dTmPrqYf6eaa2BDJ16tSkz6thw4auG264wVWxYsWk+rJhwwZXVvHUDc/y22+/ubKa7qO673qXQz+bEydOuCLNmjVrUsRM10WqcePG+ZQ1reNrPMUumuqcx1NPPWXK+csvJs/v2HL6tH7X+n6OHTuK68QJZ8uV1jJxorucxC70RWOm5Zw4caKj9T8a91mNmbve/eJYGaIxborY2SN20Rs3RexiO3Z6neV/Lgz4I59GPo18Gvm0TDNpkhY6eXn66bjNCUVbTi1Q7FjsFifzQtGYEyKfFv35tGj6nvC/xmTJyLGOnFC05jaidX91Om7RWO841kmGlmAFvyUinveHP2rUKFetWrVcefLkMYmKNm3auL7//vuAjwsmYea56NJEROnSpU3yqVChQq7LLrvMNXny5FQfc/LkSddLL73kqlq1qitXrlwmCdS+fXvXihUrki4mAyU3zpw5Yx538cUXm2SbJow0+aYHU03GpJYwU9u2bTMJN00QaWJOk2EXXnihq1evXq7FixenODg+99xzrmrVqiUlBP0TPulZtGiRSQZqnPPmzeuqXbu2a/To0ea+1HbItGK+d+9e15133mnirO+9TJky5vZff/3latmypXmcJgaDTZhpPDRJVbJkyaQEp76+2rhxo2vo0KEm+Vm2bFkTg8KFC7tq1KhhEpM7duxwhSJQwiycr+H57DNyEPz5559NIrJYsWImvhoLje/u3btdWcnphJnuY7pPeJdB4xKpJwbRljCLlGRZJMUu2upcJDVAO3NGXL16+X6GnTpFdsInUhJm0Rq7SEiYRes+6/QFeLTGTRE7e8QuOuOmiF3sx44GaAgG+TQ38mkp40E+LRn5NAsam7Jlk3+kTEzUA0Jc5oSiMadGA7TwLU7lhaI1J0Q+LbrzadH2PeFBo4xwHOvICUVjbiOa91dyQqHjWCcZWoIV/JaIeKF++IguBw8eNImmxMRE1759+5wuDjLIyYSZnhj07dvX5/W7dOliEtyRKtoSZpGSLIuU2EVjnYuUBmia8Onb1/fz69JFXCdPOp/UifSEWTTHzumEWTTvs05egEdz3BSxs0fsojNhRuziI3Y0QEMwqBuxjXxabHEyn+b64QeXq18/lyuYkf5WrXK5qlb1HSWjRw9XPOaEojWnRgO08C1O5IWiOSdEPi1682nR+D3hQaOMcBzryAlFVW4jBvZXckKh41gnGVqClT3wxJwAnLJ06VJp0KCBz7p9+/aZOZQPHjwo1157rRQtWtSx8iH6TZs2TcaMGZN0OyEhwdSttm3bBv0c9957r1xxxRWZVELEGuqcvWnTRLxCJwkJIgcPioQQOrn3XpE4DB2xywD2WTvEzR6xs0fs7BE7e8QOQCQin4ZMd/y4yOjR7qVmTZGWLUVq1BApUUIkf36RI0dENm4UmTNHZOZMkTNnkh9bu7bIa685WXogrpATskfsMoDvCWQxrs0zgP3VGvUO6aEBGhBhGjZsKKVLl5aLL75Yzj33XNm5c6csX75cjhw5ImXLlpWRI0c6XUREuaNHj/rc1u5B8+bNC+k5unbtGuZSIZZR5+z5hc50rwkxdBKnoSN2GcA+a4e42SN29oidPWJnj9gBiETk05ClVq50L8Fo105k7FiRAgUyu1QAziInZI/YhQnfE8gCXJuHCftrSKh3SE9iulsAyFKPPvqolClTRlauXCkff/yxLFu2TCpWrCgPP/yw+VvvAwAAAAAAAOBGPg2Zrnx5kRtuEClZMv1tExPdw/9Mny4yY4YIo+8BQOzjewKIHuyvQKZhBLQYoi1MEf2eeuopswCZpUePHmZB5iHGvoiHPQ0bobND7Oyxz9ohbvaInT1iZ4/Y2SN2iDXk02ID+TRkyQ+Vkye7//7zT5E1a0S2bxc5cEDkv/9E8uQRKVRIpHJlkXr1RPLlc7rEUYnzDIQDOSF7xC4D+J5AFuM7MwPYX61R75AeGqABAAAAAAAAAAAE4/zz3QsAAIHwPQFED/ZXIKyYghMAAAAAAAAAAAAAAAAAYIUGaAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYoQEaAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKxkt3sYACCcNm3aJImJtAlOKz7+Tp065UhZos2xY8dSrPv9998dKUs00fq1bds2Wbdunbm9aJFIzpwi2bI5XbLIp7vmtm0iy5a5bxO70GPnOeQtW7ZMatasKdkIXlDHuh9//FGWLFlibn/00UfmezVXrlxOFy1qjnda39SiRYskZ86c1LsgELvwxa1q1aqSO3dup4sWFce6uXPn+sQuR44ckj07qZ1QriMAALGDfFrayKeFN58GO+SFgkM+zR75tPDl02CPnFDwyKfZIZ8WvnwaMleCy+VyZfJrAAD8JCQkOF2EqPf5559L27ZtnS5GxBs1apTccccdThcDAAAgVZzXBeeLL76Qdu3aOV2MqEcaDACiF/m0jOO8Kzjk0wAAQKTjvC445NOyNp9GN1kAQFSaP3++HDp0yOliRDx6LwEAgEg3efJkWbFihdPFiHiekVkBAABskU8LDvk0AAAQ6cinBYd8WtZiBDQAcAA9NgEAAAAg65EGA4DoRT4NAAAAALIeI6ABAGLaq6++Kk2bNnW6GBHvo48+kueee87pYgAAAKTq1VdFOK1L36JFIoMGOV0KAAAQzcinBYd8GgAAiHTk04JDPi1r0QANACLA9OnT5YILLnC6GBHr9OnTsnXrVjNNwMsvv2ySZf369ZPcuXM7XbSIl5iYmCJhRn1L36lTp2Tbtm0+da5Vq1aSLVs2p4sWNbHT4Z8/+OADcxHUqpUIoUvfqVMi27aJbNrkviAaPHiw9OzZk3oXhN9//106dOjgs45jXXA43tkjdnY2bdok7dq181mn3xOXXOJYkaJGzpwp133++edSqVIlJ4oTFTZs2CDt27d3uhgAAEQE8mnBI59mj+skO+TTwpdPQ8ZwrAsOx7rwxQ72yKcFh3xa1ubTaIAGABFAv+Quvvhip4sR0S655BI5dOiQ+Vt7apIsC06uXLlSrNMLSOpb+qpXr+5T57QOIvjYrVixwvytPXAIXfCqVxdZtsz9d506dah3GcCxLngc7+wRu/AgP2sfJ64j0nbmzBmniwAAQMQgnxY88mkZw3WSHfJp4cmnIWM41gWPY114Ygd75NOCQz4ta/NpidaPBAAAAAAAAAAAAAAAAADENRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKzQAA0AAAAAAAAAAAAAAAAAYIUGaACAgLp37y4JCQk+y9atW50uVsQjbgCA9PBdYYe42SN2qTt+XOSbb0Qef1ykbVuRihVF8ucXyZlTpFgxkdq1RW6/XeSrr0RcLqdLG12odwAAAIho3buLJCT4LpyvAgCQLvJpSE32VO8BAMStGTNmyKRJk5wuRtQhbqlzuVyyevVqWbx4saxcuVLWrl0r27Ztk71798rRo0clT548UrhwYalatao0adJEbrrpJilfvrzTxY4IxM6eXtisXi2yeLHIypUia9eKbNsmsnevyNGjInnyiBQuLFK1qkiTJiI33SRC6Nyod5mH7wo7xM0esQtszx6RQYNEZs4UOXw48Db797uXFStERo8WqVZN5N13RRo2zOrSRh/qHQAA8KGNeipUCP1xxYuL7N4t8Ypr80w0Y4YI56sBkU9DRDh92t265ZNP3JVRvwv++stdAUuXFqleXaR5c/eiF+vxiu9XZDHyaZngdGwd72iABgDwcfDgQbldm6UjJMQtbWPHjpU+ffqkev+RI0fMsmPHDpkzZ4488cQTctttt8nzzz8vBQoUkHhG7OyNHSuSRujkyBH3smOHyJw5Ik88IXLbbSLPPy8S56Gj3mUSvivsEDd7xC51euyfPDnl+pIl3bkd7bWp+Z5160TOnHHfpz/C6A8sU6aIdOqU5UWOGtQ7AACA8ODaPJMcPOgelgUBkU+D4374QeSOO9wtIP1pixdtFanL1KnudSdPimSnyQOQFcinhdkPsXe8i+zSAQCy3KBBg2TXrl3m79atW8tXOj4q0kXc0u+x6S179uymR2bx4sVNb83Dhw/L77//bn6wVGfOnJG33npLli5dKnPnzjW9OeMVsbPnP7Sznpdrj0zt4KWdR/T8/fff3XlHpRdEb70lsnSpyNy57t6c8Yp6lzn4rrBD3OwRu+A0aiTSo4fIlVem7LmvSbNnnhF54w3398qpUyLduomsWiVSpYpTJY5s1DsAAJCuZs3cF+bpKVJE4hnX5plEh245e74qrVu75wdDEvJpcJQOl9S/v29F1JaNOr+ffifoMHybN7uH5ENKfL8iC5FPy6DRsXm8owEaACDJrFmzZMKECebva665Rrp06cIPRkEgbunTBFnTpk2lXbt20qxZM6lTp47kyJEjRVLt22+/lQceeECWLFli1i1btkwGDhwoEydOlHhF7OxpgqxpU5F27dzX3nXqiPiFzpzbf/utyAMPiJwNnSxbJjJwoEgch456lwn4rrBD3OwRu7QlJoq0by/y+OPu74fUlCghMmKEyIUXitx1l3vdiRMijzwi8tFHWVbcqEG9AwAAQXnvPebsCwLX5plg1iyRs+ercs01Il260ADND/k0OGbMGN/GGNq65cknRVq0SFkJt2937886ZF9CgiPFjUh8vyKTkU8LkzGxe7xLdLoAAIDIcOjQITNEu8qfP7+MGjXK6SJFBeIWnB49ephk2JAhQ6Rhw4YpkmUqISFBmjdvLgsXLpQmOh7vWZMmTZLteoIVp4idPe19o8mwIUNEGjZMed6u9Hy9eXORhQvdw0B7TJrkPq+PV9S78OK7wg5xs0fs0qdJss8+SztZ5k1/SGnQIPn2zJnujohIRr0DAAAIL67Nw+zQIfdckUrnCON8NSDyaXCEztenrVQ8jTHuvNM9Nd0VVwSuhGXLuqfS1aH3smXL8uIC8Yp8Whisi+3jHQ3QgAzaunWrucjToa+z8rGpadGihXnOBQsWSKTSXlkvvPCCXHLJJWaocC2vLsHQC+5nn31WOnfubOLmeex3332X5uNGjhwpN9xwg1StWlWKFi1qLtaLFClieo698cYbclLnTI5zgwcPlp07d5q/hw0bJmXKlHG6SFGBuIVfrly55Bkdm9frmPHNN984WqZoQezs5crlHhLaQ8/9CV1wqHfp47vCDnGzR+wyh/bw9Dh2TK/nnCxN5KHeAQgF+bTQkU8DkBauzYMweLDI2fNVGTZMhPPVDCOfhrDp08d9oa20EcbIkVEx0g+A9JFPi6/jHVNwAplIEzrbtm2TLVu2hDUpFu20J/z9998vBQsWlKuuukoK6HzGQbrrrrtk5cqVIb+m/gCyZ88eqVatmuktpq+pP44sXrxYFi1aJO+//77MnTtX8uXLJ/Fozpw5MlaH7hTtsdRE+uuwn0gXccs89erV87m9a9cux8oSbYidPb/QCaELHvUudXxX2CFu9ohd5ilSxPf2P/84VZLIQ70DEE7k0wIjnwYgPVybp2HOHPfUVUqH7OJ8NWzIpyHDfvxR5Pvvk2/rvH0AYgb5tPg63tEADXBQqVKlZO3atQGHzo5lU6dONf9PmzZNrtCWvSHQ7Tt27Ch16tQxS+PGjU1SMj2TJ0+WWrVqpUiI6TDkrVu3liVLlpgeYs8995zEm3/++Uf69u2b1FPunXfeCboHbTwjbpnLvxd1KIn1eEfs7Pl33id0waPeBcZ3hR3iZo/YZS7/HprnnedUSSIL9Q5AViOfRj4NQGBcm6dCf+k+e75qhux6552YGmnEaeTTkGFvvZX8d+PGIlWqOFkaAGFGPi2+jnc0QAMcpImyiy66SOKNJqnUBRdcEPJjdaoBG9oLP5CyZcvKI488Irfccot89dVXcZkwGzJkiOzYscP8/fjjj0uVGPyyywzELXPNnz/f57YmxxEcYmfPL3Tm/B/Bod4FxneFHeJmj9hlHp1K5qOPkm+XLClSoYKTJYoc1DsAWY18Gvk0AIFxbZ6KIUNEzp6vyuOPx+SPvU4in4YMmz07+e/WrZ0sCYAwI58Wf8e7RKcLANjQ3tSeHtUTJ06UBg0amJ54xYoVk27duiUlZFwul4wcOdL01DvnnHOkaNGi0qNHD9m7d2+K5xw6dKh5Tv0/kAULFpj7W7RokW75xo8fb7b19CSsUKFCUpl10edSW7duNbcDTSfg/R7HjBkjdevWNe+hUKFCcvXVV8uPOkSjhXnz5kmnTp2kZMmSkjNnTjnvvPNMD0gdOt/GgQMH5OGHHzZD8efNm1fy589vyjp8+HD577//fLbV2Ol70ikU/OOSWtyzQvbs2ZN668cbrQ9vv/22+btmzZpmKgekj7hlLp3eQ3/I9Lj88svNcRzpI3b29uxx5yM9Lr9chNAFh3oXGN8VdoibPWKXuSZNEtm0Kfl29+4MmqCod0B0IZ9GPi2c4jmfhjB44AGRGjVEChUSyZlTpHhxkTp1RAYMcE+VqL9WIiRcm6di3jyRs+erUrOmCOerYUU+DWEZGkkrkofup2r3bpFnnxWpX1+kaFGR3Ll1GFx3JRs+XOSvvxwrckTj+xURhnxa/B3vaICGqPbQQw9Jz549TZLmqquuMgkbHRpee+cdPHhQunbtai66NDl05ZVXSrZs2eS9994zw86fOHEi08pVuXJlufXWW02CS3Xu3Nnc9iwlSpQI+rkGDx4s/fr1M++tffv2UqZMGfnyyy+ladOm8umnn4ZUrvvuu89ceE6fPt30VOzQoYNUrFjR3NbnGzduXEjPt3nzZjNsv/Zy3Ldvn0nktWzZUjZs2CAPPPBA0ufg0aZNm1Tj4tTFsCZPn3/+efP3tddeK/HkyJEj0qdPH/O37hs6VY4neYjUEbfw0x83NK4rV66UYcOGSfXq1WXjxo3mvgsvvNActxEYsbOn19pHjoisXCkybJhI9eoiZ0MnF14oQuhSR71LH98VdoibPWKXuf74Q+Tuu5Nvax73oYecLFFkoN4B0Yt8Gvm0jIrnfBrCRKeU/fVXkUOH3PP3aQPX5ctF3nhDK737It2ygWe84No8CJr4OXu+Ktmyuafe5Hw1Q8inIexWrPC9ff752lNCREe7feQRkZ9/dje+OH5c5M8/3Y1KtZGVDqH05ptOlTpy8f2KCEI+LT6Pd5xpIappT8aff/7Z9LRW2kOwdevW8t1330nz5s3l6NGjsm7dOilXrpy5f//+/fK///1PVq1aJdOmTZPu2sw2E2iiSBftmfnvv//Kiy++GLBXZjDeeustmTt3rklEeQ+brz3LNVmow2hrr8tgYvXSSy+ZZN7HH38sNbQF/FnffvuttG3bVm6//XZT7mCH8r/xxhtNr1RNNE2aNCkpEabJM02OLVu2TAYMGCAffPCBWf/ggw+a/8MRF1taTp0a4NSpU7Jr1y75/vvv5fjx4yZpp8nJeKJJTe01rO655x6pV6+e00WKCsQtPLT3fFqJMO2Ff9ttt5ne3PqjCJIRO3s9eqSdCMuXT+S223QUBxFC54t6Fxq+K+wQN3vELvMcPSrSqZNvZ8PRo0WKFHGyVJGBegdEL/Jp5NNCRT4NYXfuuSKVKrkvvrU1iw6NsX9/8v2rV4s0a6Y7s0jv3k6WNKJwbR4i/dH27Pmq3HOPCOerVsinIVN5H/uVdhTwntpbG2hUruxuTLVmjbthlTp8WOTOO3WOcndrSLjx/YoIQT4tfo93jICGqPbkk08mJctUnjx5kpIev/76q7z++utJyTKlUwb0798/aej8aKC9Nb2TZUp7oWpy/9ChQ6aXeXrOnDmTNCS/9mj1TpapZs2ayWOPPWZ6sY7Wo38QNCm5ZMkS05NUp1zxJMuUTt3gmYZFX+8PbeIcIZYuXWou0jWJ980335j3rD+WvPbaa2YKhXihSctRo0aZvytVqmT2JaSPuGUNnb6jV69e0rdvX5JlISJ29nTWmF69RPr2JVkWKuqdL74r7BA3e8Qu85w6JdK1q8hPPyWv03zP9dc7WarIQL0Dohv5NPJpoSKfhgzTuZa08Y+OwLJ5s/sHuCVLRObOFdGpcfftc4/60KWL78lYv37uKcOQLq7N/ei0zWfPV01jDM5XMwX5NGTY33/73vY0xtAh9fS8c+dOkYULRX74wf3dMX68SMGCydvriKwffyxxi+9XRCDyafF9vKMBGqKaDlHvz9PbUKf+0N6bqd3/pw5dGAW0J2Egt9xyS1LiPz3Lly8371d/GKhbt27AbVq0aGH+/0EPakHwvK72zCyuc4j70dfRZKYm6xbqwTJCvPrqq2Z48mPHjpnevI888ojpFatlXaOtieOA9mTu3bu3iYOnN68mm5E24hZeOi2ATuWii07j0qBBAymk4++Kji573PzgUbVqVbnzzjszdYqXaETs7OkI41de6V6uuEKkQQP3sM9KRzV+/XWRqlXdF0OEzhf1Ljh8V9ghbvaIXeY5c0bk5ptFPv88eZ0myl57zclSRQbqHRD9yKeRTwsV+TRkmDZq1V8h77jDPY1QILqfTZvmvjj3OH1aZMAA90gQ4No8lGFXdGSfs+erMmaMtrZ2ulRRi3waMtWxYynXlS0r8v33In6dCcwUunqO9/XXIt4N4HW0WP2+iEd8vyLCkE9LQ5wc72iAhqhWVnfKAMNMq5IlS5qkmT9P7x9NmESDCqmcMHjWB9MbcrO2ehcdaXWTJCQkBFz0YtUz3H8wdmor3DTKpzRB571tpPUIq1Klijz11FOmB6dOfZBacjLW6NQNnjrRp08fueyyy5wuUlQgbuF17733yuzZs82i03hoD/ADBw6YaTw0iaY0uf3mm29KV+0qgSTEzt6994rMnu1evvrK3RnswAH3+f3Z0Jnc5JtvunvoIBn1Ljh8V9ghbvaIXeYly3SamcmTk9d17iyiM6Fly+ZkySID9Q6IfuTTyKfZiud8GrLQwIHuIZU8Nm4UmTHDyRJFDK7Ng6Q/zp49hkufPiKcr2YI+TRkKq/RYJO8/LIOwZv6Y+rXdzee8v6emD8/c8oXS/h+RSYjn5aOODne0QANUS0xMdHqPhva8zASeXqdB1P2EiVKmKRQWkv79u0l3nTu3NkkUn/++WfZsWOHxDLtlTpy5MikpPILL7zgdJGiAnHLGpq8v/TSS00STafy8Pj0009NYhupI3YZG6X80kvdSTSv0Mmnn4oQurRR73zxXWGHuNkjdplDL510sIT3309e17GjO3kWoD1G3KHeAbGBfBr5tHCIp3waHPDII763v/zSqZJEPK7N/eiojGfPV6VkSRHOVzMF+TSEjf/crTrdXIcO6T+uZ0/f20GMbgu+X5F5yKcFIX98HO/4uIGzcp4dvvDw4cMB79cefU7YsmWL1KpVK8X6rVu3mv9Lly6d7nOUKVPG/H/uuefKeJ0vOAxKlSrl0xs0EM99nm0jlSZXdcoY/ez37t2bFK9YpO/Pk2TdtWuXFC5cOKTHe/fQLViwoPztP191jCJuWW/48OEya9YsWb9+vbk9YsQIelUHidjZGz5cZNYskbOhkxEj3KMcI33UO74rbBE3e8Quc5JlOkCC9yWT5oGmTCFZ5kG9A+CPfFpoyKcBQapY0T2tmOcYsm6d0yWKClybmxPW5Kk3d+0SCfF81WcKO/1hmPPVdJFPQ4YUK+Z7W8/fghkqqVo1kdy5k6e027Qpc8oXa/h+RSYgnxakYvFxvGMENOAsT1Jn7dq1Ae+fOXOmdRLu1KlT1uV637upcID1LVq0SPc56tevL0WLFjW91VevXi3h4Hld7Vm1Z8+eFPcvX75cVqxYYZJRzZo1k0j222+/mURZtmzZpKKefAFwnE750qVLF59jyn///edomaIFsbOnF0NeoZPly0UIXXCodwBiJVk2bpxvsmzqVJEcOZwsGQBENvJpoSGfBoRAR6/y2L/fyZJEDa7N4QTyaciQqlV9b597bvDD8BUpknxb54VFcPh+RRiRTwtB1fg43tHmEDirZcuWJrkzZ84cWbhwoTRv3tys197d2lPo448/Dvk5tTflhg0bTJKqcuXKVuUaNWqUtGvXzicx9sorr8jSpUvNMPe9dTzLdOTIkUOeeOIJGThwoHTs2FHGjh0rTZo08dnm9OnT5n3nzZtXGjVqlO5z6uMbNmwoS5YskX79+smkSZPMY9X+/fvNOtW1a1fHe0B+9913cvDgQbnqqqvMRbi3ZcuWJfUCu+6660LuwR9ttC5oz91gHT9+XI4cOZJ0W+PjmY5DRyyIF8TNGWXLlvWZ+kT3Y+1djfQRO3teoTMXTwcPihC64MR7veO7wg5xs0fswodkWfCodwD8kU8jnxYP+TQ45OjR5L/j6Noyo+L92tycwIdwvirHj4t4na+aEdM801Fzvho08mmwVqmSu7J4Wi3qPhksz2hAigoXPL5fESbk00JUKT6OdzRAA87SpI4mlF577TVp1aqVNG3aVIoUKSIrV66U7du3y4MPPijDhg0L6Tk7d+4s8+fPl5tuuklat26dlIwZMmSIVKlSJajn0MSTJvO0PNqrVHsX/vrrr6Z3oSa+SpQoEdTzDBgwwLyPF154wTxXtWrVTBJPLz53795telfq9CeaoAsmYaY0SaZlmz59uplKRXtmnjx50rznf/75R+rUqSMjR46UcHrnnXfM4qHTvnjipAlEVbJkSfn000+Tttm4caP07NlTChUqJLVr1zb3648gOh2DxlI1btzYvPdYp+9TE5rB0ikmNHbeCcby5ctLvCFuzvCfkomEdvCInT3/mRUIXfDivd7xXWGHuNkjdpmXLOvY0T1NAMmylKh3APyRTyOfFg/5NDhAf5DbuDH5dpD7LLg2l8aNQxvRR+cL8zpflWXLRDhfDRn5NFjTBp/aYeDLL92305im3Ie2ctTFg++J4PD9ijAhn2YhMT6OdzRAA7xoT0jtIaQJmR9++EHy5ctnEilTp041CaBQE2b9+/eXw4cPy8SJE2XWrFly7GzrVE2gBZsw0zLptqNHj5affvrJ9L5s06aNPPbYY3LppZeGVJ7hw4dLhw4d5M033zS9GHW4f53WQBNI2iO0bdu20qlTp6CfT4fX1x8QXnzxRfnss8/kiy++ML1etbw33HCD3HXXXWHvXfXHH3+YXqL+dDoEj3I6f7kXfW8aL33PmjxbvHix6f113nnnmd6w2qtUF09PfACRQXuRe+hxKq56a2YQsbPnFTozGjmhCx71DkC0cblE+vb1TZbp5dDkySTLACAU5NPIpwFh98knviO0+I0+iNRxbQ4nkE9Dhlx3XXKDjHXrRLZuTb8h6Jw57ot6jxDP7+IW368IA/JpGXBd7B/vaICGqKTD+KdGe1Ondb8mT1K7PyEhQQYPHmyWYF83rdfTBIz29NTFpqwet99+u1mCsWDBgjTv1yRbqIm2tGiv1meffdYswdqqB1NLQ4cONUsoNM5PPvmk9WsCcCZZ9qXnJExE2rdv72h5ogmxy1iyzCt0QuiCR70DEG30MkxnORs7Nnldly4iH34o4jfLGADEDPJpqSOflhL5NDhmzx4R7/1fGzmG0MgznnFtDieQT0OGde4scv/97tEL9RzvqadE3n039e1PnhTx7uCgLR6vuipLihrV+H5FGJBPy6DOsX+8o3sSAADIVKtXr5ZevXr59KxOyyeffCLXXntt0g8KuXPnlvvuu0/iEbGzt3q1SK9e2qM/+M5f116b3JEkd26ROA0d9Q5AXJg2TWTMmOTbCQnu0ezbthVp0ya45euvnXwHAAAAUWLxYm0RKrJ+ffrb6vSuLVuKbN+evO6WW0QuvljiDdfmcAL5NDiiQAGRJ55Ivq0tW155JfC2J064p81duTJ53R13iBQrJnGH71c4gHxaBhWI/eMd7RABAECmOnnypIwbN84sF198sbRq1Upq1KghpUqVkgIFCpj79+3bJ6tWrZLp06fLb7/95tOTfuTIkVKpUiWJR8TOnnYM0SGgddHr6FatRGrUEClVyn2Or/fv2yeyapXI9OkiXqEzF00jR4rEaeiodwDigveME0p/MJk3L7Tn6No1rEUCAACITcePi4we7V5q1nT/AK4X6CVKiOTPL3LkiMjGje7phWbOFDlzJvmxtWuLvPaaxCOuzeEE8mlwTP/+7qH0Zs1y39aRdbWF4803i1xwgcipUyIrVrhbvmzYkPy4unXdIwjFI75f4QDyaWHQP7aPdzRAAwAAWWbt2rVmCXZKEk2WdevWLdPLFQ2InT0NW5ChkyJF3MkyQudGvQMAAAAAhI2O4OA9ikNa2rVzjwqhrV7iHNfmcAL5NGSpbNncQyt16JA8PNJ337mX1DRpIvLRR+4p6eId369A9MgW28c7puAEIpQOle0ZLhtwSo8ePZLqoi7ly5d3ukhRgbj50vf/2GOPSYMGDSRHjhxBb79+/fq4T5YRO3u62z32mEiDBiJBhC5pex2xPM5DR73LInxX2CFu9oidrx493L00M7LocyBt1DsAWY18GhCB9Pv/hhtESpZMf9vERJErrnAPrTRjhkjRohKvuDZ36MIgzs9XyafBUXnzukfrevttkcqVU9+uTBmR118X+eYbkeLFJW7x/QoHkE8Lk7yxe7xjBDQAAJCpChUqJE8++aRZjh07ZqYE2LRpk+zatUuOHDlikmg6dYBOIVCrVi0pW7as00WOGMTOXqFCIk8+6V6OHXNPCbBpk8iuXe7RxzWJpp28dAqBWrVECF0y6h0AAAAAIKw/kE+e7P77zz9F1qwR2b5d5MABkf/+c4/koBfx+uNbvXoi+fI5XeKIwLU5nEA+DY7TuVz79nUvy5eLrF7troCqWDH3FHSXXOLeLt7x/QpEt4TYPN7RAA0AAGSZ3LlzS7169cyC0BA7e7lzu6+xCV3oqHcAAAAAgLA5/3z3gpBwbQ4nkE+D42rXdi9IH9+vQHSrHTvHO6bgBAAAAAAAAAAAAAAAAABYoQEaAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKzQAA0AAAAAAAAAAAAAAAAAYCW73cMAAMg6x44dk7lz58qyZcvM7UWLFkmOHDkke3a+xtKzadOmFOtOnTrlSFmiCXXOntavbdu2ybp168ztRYtEqlYVyZ3b6ZJFvmPHRObO1f3WfVvrX40aNah3QVi/fn3A/RhA5Al0HhLgdAUBBIoT53UAACBY5DaCRz7NHjk1O+TTwpdPQ8aQTwOiB8e94JBPy1qc8QEAIp4mLdq1a5d0e9CgQY6WJ9ppMqN69epOFyOiUefCR0NXqZJI27ZOlyTyabLMq9rJyy+/bBbY+fHHH6V27dpOFwNAgPMQf97HPoSG8zoAABAschsZw3lXcMiphQf5NPt8GjKGfBoQPTj22eO8LvPQAA0AIsAXX3whK1ascLoYEcvTYw7hMX/+fDl06JDTxYho1Lnw9zAhpOmjx1J4LVmyRAoUKOB0MaLG999/b/6fNWuWrF271uniRGXs3nnnHTn//POdLk7E8/ToR3hwXpe2P/74w+kiAACAGMF5V3DIqYUP+bTgkE8LL/JpoSGflvHYAU7gvC7z8mkJLpfLZf1oAICVhIQEp4sAAADgIzExUc6cOeN0MaJSYqIIoQOiA2kwAIhe5NMAAECkIZ9mj3waEHv5NEZAA4AIMHXqVKmkY2ojoEWLFjFcexi9+uqr0rRpU6eLEdGoc+E1ePBgqVOnjtPFiIpewky5GT4PPfSQdOnSxeliRA3tqfnYY4/JxIkT5eKLL3a6OFFFRz4bNWqUTJwoQujSt2iRezoZhAfndWnbtGmTXH/99U4XAwCQScinpY3cRnhx3hUc6l34kE8LDvk0OE0bn5FPCx35tNCQT0O0oAEaAESAqlWrSrVq1ZwuRsTKmTNninWff/45ScYgnD59WrZu3WqGk9ULcU2W9evXT3Lnzu100SIadS5jP/S2a9fOZ13Pnj3lkksucaxM0aJmzZopEmbUu+Bs2LBB2rdv77NOGxzUqlXLsTJFG880AZosI8EdGs+0m5osI3Tpq1pVRA9r8+eL6CHv1VdFWrUSyZbN6ZJFvtOnRbZudU8xo0lH/UGK87q05cqVy+kiAAAyEfm0tJHbsEc+Lbz1DnbIpwWHfFp482mwQz4tdOTTQkM+LXz5NGQuGqABACJetgBnUHoBSY+S4GiiwjOXufbUJFmWPupc5scTKVHvwotGB0Bk0tOQtm1Fzp6aiA4iwW8qwdNYLVvm/luT25zXAQCA1HCNmTHk0+yQAwofYhkcjnUA4gH5tPDl05C5EjP5+QEAAAAAAAAAAAAAAAAAMYoGaAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYoQEaAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAXXv3l0SEhJ8lq1btzpdLAABsL8iMxw+fFi+/fZbefXVV+WWW26RatWqSfbs2ZPqWPny5Z0uYtRin7VD3Ox17y6SkOC7ELrgEDt77LMAgJjif0IQ6sL1EwDEh+PHRb75RuTxx0XathWpWFEkf36RnDlFihUTqV1b5PbbRb76SsTlcrq0ANJATsgesYtf2Z0uAAAg8syYMUMmTZrkdDEiksvlktWrV8vixYtl5cqVsnbtWtm2bZvs3btXjh49Knny5JHChQtL1apVpUmTJnLTTTfRSIO4ZSr219RR7+xVqVJFNmzYYGKI8GKftUPc7M2YIULo7BA7e+yzAAD4KVpU4hnX52F0+rS7ccsnn4gsXiyye7fIX3+J5MkjUrq0SPXqIs2bu5dq1ZwuLaIQ+6ulPXtEBg0SmTlTe3UG3mb/fveyYoXI6NHuffTdd0UaNszq0iJWaIueChVCf1zx4u7vD6SKnJA9YhffaIAGAPBx8OBBuV174CCgsWPHSp8+fVK9/8iRI2bZsWOHzJkzR5544gm57bbb5Pnnn5cCBQpIvCJumYP9NW3UO3u///6700WISeyzdoibvYMH3R2rETpiZ499FgAQk668MvQfhNevT759000Sz7g+D5MffhC54w6RlStT3qcNXtaudS9Tp7rXnTwpkp2fAREa9ldLO3aITJ6ccn3Jku7GoToKmjb4WbdO5MwZ932rV4s0aSIyZYpIp05ZXmQAgZETskfswJknAMDHoEGDZNeuXebv1q1by1c6FDSS+I8GpNPRaQ+v4sWLm95fOmWdNtzQH97UmTNn5K233pKlS5fK3LlzTe+weETcMgf7a9qodxl3zjnnSM2aNaVu3bpSp04dmTJlisyePdvpYkUt9lk7xM2edr4+Gzpp3do9wweCQ+zssc8CAGJSqNdB7dsnN0DTadduvlniGdfnYaCjJfXv7ztlnzb20en9ihQROXpUZPNmkb17nSwlYgD7axg0aiTSo4e78bL/6HDaCO2ZZ0TeeMO9P586JdKtm8iqVTolgVMlRqxo1sw9ImZ69HsDqSInZI/YgQZoAIAks2bNkgkTJpi/r7nmGunSpQs/GPnRC+6mTZtKu3btpFmzZqZBRo4cOVJcpH/77bfywAMPyJIlS8y6ZcuWycCBA2XixIkSj4hb+LG/po96Z0/fu8ZLp+JMTExMWr9gwQJHyxXN2GftEDd7s2aJnA2dXHONSJcuJH2CRezssc8CAHC2cYGeUHh06CBy7rkSz7g+z6AxY3wbn2njliefFGnRQsQvjrJ9u7v+jR0rkpDgSHER3dhfLWn+TBsfP/64SJ06qW9XooTIiBEiF14octdd7nUnTog88ojIRx9lWXERo957L2WjR4SEnJA9YgeV/GsSACCuHTp0yAyVrfLnzy+jRo1yukgRqUePHubiesiQIdKwYcMUF98qISFBmjdvLgsXLpQmOoT2WZMmTZLtmgSKQ8QtvNhfg0O9s9e9e3e5+OKLfRqfwR77rB3iZu/QIZGzoTOzfBC64BE7e+yzAACcpb+86Yg2Hr17S7zj+jwDdLo+baTiaXx2553uqTivuCJl4zNVtqx77qulS0WyZcvy4iL6sb9a0kZnn32WduMzbwMHijRokHx75kz3SIYAHENOyB6xgwe/KAEZtHXrVnOyrUMQZ+VjU9OiRQvznJE8Qon2jnnhhRfkkksuMUM2a3l1CeZxTz31lHTs2NGMyFKkSBFz8XPeeeeZqV20Z43/8NDq6NGj8sUXX8iAAQPMNGL6Y0jOnDmlTJky0rVrV/n+++8z6Z1Gl8GDB8vOnTvN38OGDTPxQcbkypVLntHhtM/S+vnNN984WqZoQNzSx/4aftQ7ZCb2WTvEzd7gwSJnQyfDhokQuuARO3vsswBCQT4tdOTTooiOPOXdGOjyy50sTdTh+txPnz4ix465/9ZGZyNHMrIZIgb7awbpiGkeup9v3epkaYC4R07IHrGDB1NwAplIE2Hbtm2TLVu2hDUpFu20J/z9998vBQsWlKuuukoKFCgQ1ONOnz4tjz/+uLmoqV69ukmaacJNYzx37lz5+uuvZerUqfLpp59KNq/eXdrrpm/fvubvcuXKSatWrcww0itXrpQpU6aYx2gi7hEd4jhOzZkzR8aeTY5pj6X+OqQ7wqJevXo+t3d5Jj9Hmohb6thfMw/1DpmBfdYOcbM3Z07yb57aEZ3QBY/Y2WOfBRBO5NMCI58WJb77TmT9+uTbPXu6p2VDSLg+P+vHH0W8G3vqtH1AhGF/zYAiRXxv//OPUyUB4h45IXvEDt5ogAY4qFSpUrJ27dqAQxjHMk1QqWnTpskV2msrSJoEmz9/vjRq1Ehy587tc9+vv/4ql19+uXz++efyzjvvSL9+/ZLu0/j26tXL9NisXbu2T2+cV155Re6991559NFHzQ8lOmx0vPnnn3+SEoqajNT4BdODFsE5efKkz+1gE8TxjrgFxv6auah3CDf2WTvEzZ7mqs+GTnLlEnnnHQZHCBaxs8c+CyCrkU8jnxY1o5/p96E2QEPIuD4/6623kv9u3FikShUnSwMExP6aAf4jnp13nlMlAeIaOSF7xA7+6HoDOEgTORdddJFUqlRJ4sn27dvN/xdccEFIj9MfMXRKBP9kmdIenJoQU1999ZXPfbfeequ8++67Pskyz/PpNDHag1O9//77Eo+GDBkiO3bsMH9rj1jtCYvw0SSvt8aaLEK6iFtg7K+Zi3qHcGOftUPc7A0ZInI2dPL44/w+FQpiZ499FkBWI59GPi1iHT6sLQWTb+vUm+XKOVmiqMX1+VmzZyf/3bq1kyUBUsX+akmnv/7oo+TbJUuKVKjgZImAuEVOyB6xgz8aoCEqaaLD06N64sSJ0qBBA8mXL58UK1ZMunXrlpSQ0R55I0eOlFq1ask555wjRYsWlR49esjevXtTPOfQoUPNc+r/gSxYsCApYZOe8ePHm211KHtVoUKFpDLros+ltm7dam4Hmk7A+z2OGTNG6tata95DoUKF5Oqrr5YfdfhtC/PmzZNOnTpJyZIlJWfOnHLeeedJx44dZfHixVbPd+DAAXn44YelWrVqkjdvXsmfP78p6/Dhw+W///7z2VZjp+9Jp1Dwj0tqcQ+FTgPg6XUfCk8izfOjSTzR+vD222+bv2vWrGmmckD47Nmzx/wg56G9ivV4hLQRt8DYXzMX9Q7hxj5rh7jZmzdP5GzopGZNEUIXPGJnj30WiC7k08inBUI+LYymTBH599/k2717O1maqMX1udfISHv2JN/WE1W1e7fIs8+K1K8vUrSoiDYuLVXK3eBx+HCRv/5yrMiIP+yvGTBpksimTcm3u3dn2CBk3AMPiNSoIVKokEjOnCLFi4vUqSOiHQ50nkRt+Agf5ITsETsEQgM0RLWHHnpIevbsaZI0V111lUnYTJ482Qz9fvDgQenatas5+dXk0JVXXmmGnH/vvffMMPUnTpzItHJVrlzZ9BLUBJfq3Lmzue1ZSpQoEfRzaY9CHf5e31v79u2lTJky8uWXX0rTpk3l008/Dalc9913n7kAmD59upQtW1Y6dOggFStWNLf1+caNGxfS823evFnq1Kkjzz33nOzbt88k8lq2bCkbNmyQBx54IOlz8GjTpk2qccnoRcmmTZvkzTffNH9fe+21IT1Wy6u0nsSTI0eOSJ8+fczfum/oVAuepCPsaJJe47py5UoZNmyY6Um8ceNGc9+FF15ojj9Iibilj/01/Kh3yEzss3aIm70jR0TOhk6yZXMPd0/ogkPs7LHPAtGLfBr5NA/yaZk4/WaRIiIdOjhZmqjB9XkqVqzwvX3++dp6WOSii0QeeUTk55/djc2OHxf580/3r8Da8EBHUDq7XwPhxv4aJn/8IXL33cm3tbHQQw85WSLECh2J9ddfRQ4d0vlxRbQDyfLlIm+8oSeVOvytiGUHilhETsgesUNqqAaIatqT8eeffzY9rZX2EGzdurV899130rx5czl69KisW7dOyp0d6nz//v3yv//9T1atWiXTpk2T7tqjIBNookgX7Zn577//yosvvhiwV2Yw3nrrLZk7d65JRHm88MILpme5Jgt1OGPtdRlMrF566SWTzPv444+lhraAP+vbb7+Vtm3byu23327KHexQ/jfeeKPplaoJqkmTJiUlwjR5psmxZcuWmWH8P/jgA7P+wQcfNP+HIy6vv/66eX5NfGpPS+1xqhc/+hqaKA3Wr7/+KjNnzkxK4MUTTWpqr2F1zz33SL169ZwuUlTSXuBpXVhrb/LbbrvN9ErW5D7ciFto2F/Dg3qHrMI+a4e42dPfmc6GTu65R4TQBY/Y2WOfBaIX+TTyaeTTMsHatb4/6N50kw4r52SJIhrX50HYv9/3tjaefe453wZplSu7GxisWeNubOCZCvbOO3XeXpFhw7K2zIhJ7K9hdvSoSKdOvqMVjh7tbrgMZNS554roNPW6L2oLIR1lz/v7ZPVqkWbN9GSZkVrJCWUIsUNqGAENUe3JJ59MSpapPHnymB6OnkSIJlU8yTKlUwb0798/aej8aKC9Nb2TZUp7oWpy/9ChQ6aXeXrOnDmTNCS/9mj1TpapZs2ayWOPPWaST6P1RDcImpRcsmSJ6UmqU654kmVKp27wTMOir/eH9uYIs2+++cZc9Hz44YemLImJifL000/L4zrBdJC0p44m/U6dOmV69LZr107ihSYtR40aZf6uVKmS2ZcQfjp9Ra9evaRv375cfIeAuPlif80a1DuEC/usHeJmT2cjOxs6k2MkdMEjdvbYZ4HoRj6NfBr5tEwe/Uzxo641rs/P+vtv39uexmcXXuge7WznTpGFC0V++MHduGD8eJGCBZO3f/55kY8/ztoyI+6wv4bo1CkRbfD900/J67TB6PXXO1kqRDOdtlVb/ugIZ5s3u78PliwRmTtXRKee37fPPWJmly6+9bBfP/eUnHGMnJA9Yoe00AANUU2HqPfn6W2oU39o783U7v9Th6WOAjqcfiC33HJLUuI/PcuXLzfvV38YqFu3bsBtWrRoYf7/QS9Yg+B5Xe2ZWVznEPejr6PJTE3WLdQL4TD77LPPTA9N7fmpPXC1h82jjz5qeuQG89mePHlSrrvuOvntt9/MtAnvv/++xAvtydy7d28TP09vXk02w44OM64JV110OpIGDRpIIR0yW3QE/OMmcV+1alW58847M3WqkmhD3ILD/hpe1DtkNvZZO8QtYx2n9bfNs6GTMWO0EYHTpYoOxM4e+ywQ/cinkU8jnxZmOgLVhAnJt/WHYL8Gi/DF9XkQjh1Lua5sWZHvvxfxa2Br5rzS497XX4vkzJm8XkdQPH0688uKmMb+GiZnzojcfLPI558nr9OGZ6+95mSpEO2004g2aLzjDvcUzIHoeey0aToUbvI6/W4YMMB9DhOHyAnZI3ZIDw3QENXK6gVXgOF+VcmSJU3SzJ+nF8axQBdwEahCKicMnvXB9IbcrK3eRUda3SQJCQkBF71o8Az3H4yd2sMqjfIpTdB5b5sZtMeoXgCNHDlShg0bJitXrpS77rorzcdoD02dVmD27NmmR6/2/tRepvFCp1Xw1Ik+ffrIZZdd5nSRotq9995r6pIuX331lenJfODAAfn+++/NRbnS5O6bb74Z0nQWsY64BYf9Nbyod8hs7LN2iJs9/T3pbOikTx8RQhc8YmePfRaIfuTTyKeRTwuzL74Q2bs3+baeXCBNXJ8HwWuExCQvv6zDUqb+mPr13Q0KPDZuFJk/P3PKh7jB/hqmxmc9eugQp8nrdAprnW47WzYnS4Z4MnCgSK9evt8RM2ZIPCInZI/YIT00QENU02Hibe6zoT0PI5Gn13kwZS9RooTpAZrW0r59e4lWPXv2NP9//vnncjqVnl26vnv37vLJJ59ImTJlZP78+T7TSsS6NWvWmOSiJ6n8wgsvOF2kmKRJ6EsvvdRclN+jk5+f9emnn5qpLhAYcfPF/po1qHcIF/ZZO8TN3po1ImdDJyVLihC64BE7e+yzQGwgn0Y+zRv5tDBPv5k3r0i3bk6WJmpxfe7HfzpDnV6zQ4f0H3d2n04SxIiPQKjYX0Og5xM6XJD3qKEdO7obowVo9A9kqkce8b395ZcSb8gJ2SN2CAbfbMBZOc8OTX348OGA92/btk2csGXLFqlVq1aK9Vu3bjX/ly5dOt3n0MSQOvfcc2X8+PFhKVepUqV8eoMG4rnPs21mO+dsrzAd5vnvv/8279c/WXbTTTfJ1KlTk5JlafU4jUV79+5NSrLu2rVLChcuHNLjveNVsGBBE2ekbfjw4TJr1ixZv369uT1ixIhUpwJBMuLG/uoE6h0ygn3WDnGzp4NseH4737VLJMTQ+czMoL9lxVHoiF0GsM8C8Ec+LTTk02KQTl3q/eNtly4iBQo4WaKYwPW5iPiPMKjHtGBGSqpWTSR37uQpPDdtypzyAWexv6bT+EyHCPI+j9CGpFOm0PgMzqhY0T1tp+ccfd06iTfkhOwROwSDEdCAszxJnbVr1wa8f+bMmdZJOB2i3tb73r0iAqxv0aJFus9Rv359KVq0qOmtvnr1agkHz+tqD5c9e/akuH/58uWyYsUK03O2WbNmkhXmzZtn/tdEWZEiRVL0Wr3llltk8uTJSckyz5QGQGbSqUu6aPLRa9/477//HC1TNCBucAL1DgAAAAgN+bTQkE+LQTrSj/fIcTrKDTKM63MRqVrV97Zf49BUJSSIeO/LBw6Et1yAH/bXdBqfjRvn2/hs6lSRHDmcLBninQ5d5bF/v5MlARCDaF4NnNWyZUuT3JkzZ44sXLhQmjdvbtZr727tsfHxxx+H/Jzam3LDhg0mSVW5cmWrco0aNUratWvnkxh75ZVXZOnSpZI/f37pHURSI0eOHPLEE0/IwIEDpWPHjjJ27Fhp0qRJit6M+r7z5s0rjRo1Svc59fENGzaUJUuWSL9+/WTSpEnmsWr//v1mneratWtSj9GM+uKLLyRfvnzms9Ehnr1988030r9/f/N33759fe7XZJlOJ6BljPdkmdYF/56saTl+/LgcOXIk6baOcOCZjkNHLEBwypYt61MfDx48KHny5HG0TNEg3uPG/uqMeK93sMc+a4e42dN8dQihk+PHRbxCZ3opemZZi7PQEbsMYJ8F4I98Gvm0uOfdsOCCC0SyqOFgPIj763Pd3/T9ehry6ElpsDyjn6l4ihkcE/f7qz8anyGSHT2a/Hcc7qfkhOwROwSDBmjAWZpI0YTSa6+9Jq1atZKmTZuaXn8rV66U7du3y4MPPijDhg0L6Tk7d+5skjM6TH3r1q2TpicZMmSIVKlSJajn0MSTJvO0PNqr9LfffpNff/1VsmXLZhJfJUqUCOp5BgwYYN7HCy+8YJ6rWrVqJomnFwG7d+82vSt1mH1N0AWTMFOagNKyTZ8+3Qy9rz0zT548ad7zP//8I3Xq1JGRnsmgw+Dnn3+W//u//5NixYpJ7dq1zf9aZk1K/v7772YbTQgOHTrU53FahgkTJpi/NVH21FNPBXz+iy66yHzOsaxx48YmoRksnWJCk40ey5Ytk/Lly2dS6WKX/9RCoU5VFK/iPW7sr86I93oHe+yzdoibvcaNQ+uoqjN+eIVOli0TidPQEbsMYJ8F4I98Gvm0eMinperbb0U2bEi+3auXk6WJOXF/fa6/0GojWs8Ur2lM3evj4EH34hHk8Q7IiLjfX9NrfNaxo3vaTRqfwWnaImjjxrj+jiAnZI/YIRg0QAO8aE9I7anxzjvvyA8//GB6B2qCferUqSYBFGrCTHsQHj58WCZOnCizZs2SY2d7HmkCLdiEmZZJtx09erT89NNPpvdlmzZt5LHHHpNLL700pPIMHz5cOnToIG+++aZ89913Zrh/ndagZMmSpkdo27ZtpVOnTkE/X8WKFc0PCC+++KJ89tlnpkel9nrV8t5www1y1113hbWXiyYgdehmLbv2gtUfPrRnppb/+uuvl5tvvtm8B38HvIYZX7BgQarPrz1B4zZhhkylvaE9tL7Gde+vEBA3OIF6BwAAAISGfBr5tLjNp40dm/x3tmwit97qZGliDtfnInLddckN0NatE9m6Nf1fbufM0WEok2+HeMwDbLC/nqX7Xt++vo3P9Bxh8mQanyEyfPKJ7whofqP7AkBG0QANUUmH8U+N9qZO635NDKV2vyZfBg8ebJZgXzet19PkkSZgUkvCpFdWj9tvv90swUgrIaQ0yRZqoi0t2qv12WefNUuwtuqFsoXq1avL888/H/LjtAenfy9OICsvvr/0JIpEpH379o6WJ1oQNziBegcAAIBYRj4tdeTTUiKflo5//hGZNi359tVXa6sLJ0sUU7g+P6tzZ5H773cPN6LHPR2J8N13U9/+5EkR70a/2gjoqquypKiIX+yvZ+k+qlNpezdO7tJF5MMPRbLzczwiwJ49It7n1zrSZgidKAAgGGdnWQUAAOnRnsK9evWSNWvWBLX9J598Itdee21SYjx37txy3333SbwhbnAC9S5jnn76aRMD/+X9999P2mbbtm0Bt+mrPT0BAAAAIJbpaDbeI4j07u1kaSIa1+cZUKCAyBNPJN/Whi2vvBJ42xMn3PNcrVyZvO6OO0SKFcv8ciJmsL9mgDZKHjMm+XZCgns6XB1ltE2b4Javv3byHSDaLF6sPS5E1q9Pf9tffxVp2VJk+/bkdbfcInLxxZlaRADxhybXAAAE6eTJkzJu3DizXHzxxdKqVSupUaOGlCpVSgoUKGDu37dvn6xatUqmT58uv/32m0+P8JEjR0qlSpUk3hA3OIF6lzGnTp2S48ePp7tdoG00tgAAAAAQ07xHuCleXOSaa5wsTUTj+jyD+vd3T8M5a5b7to42qVOo3XyzyAUX6AW8yIoV7oYvGzYkP65uXfeIaUAI2F8zwLtRstJGefPmhfYcXbuGtUiIcZqXHT3avdSs6W5gVqOGSIkSIvnzixw5IrJxo3tq5pkzRc6cSX5s7doir73mZOkBxCgaoAEAYGHt2rVmCXZqDb347tatm8Q74gYnUO8AAAAAAGGzerXIkiXJt2+9lenVgsT1uYVs2dwjK3XokDw60nffuZfUNGki8tFH7ik4AUvsr0AU0dEvvUfATEu7du6G9DrKJgCEGVNwAhFKhyz2DFsMOKVHjx5JdVGX8uXLSzzT9//YY49JgwYNJEeOHEFvv379+ri++CZuWYP91Rf1LmOGDh3qU59CWcaPH+908aMC+6wd4mavRw93B2zPQuiCR+zssc8CyGrk05AlqlXzPTl4/nmnSxTRuD4Pg7x53SPYvP22SOXKqW9XpozI66+LfPONe2Q+IETsr2G8cLRZ9DmAYOn19Q03iJQsmf62iYkiV1whMn26yIwZIkWLZkUJYwI5IXvELj7RLQcAgCAVKlRInnzySbMcO3bMDDG+adMm2bVrlxw5csRclOtQ5Dokea1ataRs2bJOFzkiEDc4gXoHAAAAAIDzuD4Pk4QEkb593cvy5e6R+Hbtct9XrJh7ys1LLnFvB1hifwWiiLbmmTzZ/feff4qsWSOyfbvIgf9n7z6gnKq2Bo5vhl6VonRpogIiCIgoICqKqCAI2B4oIFgA5SHY0Gd5Ynv6xF5RQUU+BIQHIkWkKF2lKcxQpYhSBHEEqQP3W/uEzCSZzExyJpmbTP6/te6ayU07d+fckp1T/hA5dMgzCuapp3oaLjdrJlKqlNslBpAAaIAGAICFYsWKSbNmzcyC0BE3uIF6BwAAAACA+/h+HiHnn+9ZgChifwXiSJUqngUAXMYUnAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYoQEaAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKwUsnsaAAB5Jy0tLaR1QKRQ5+wRO3vEzl6wOG3atMmVssRj7LZu3SrLly83t+fPny/169eXYsWKuV005FOHD4t8/bXIySon8+eLFC4sUojsRI70ULd1qx7fPLd1v+3atSv7KwAACIrvmHADdSxyiGVoONbZI06RQz4N0UY+LXL5NEQXVRIAEPP0h/Fg6xo2bOhKeZD/UefsETt7xC6ysevYsaMrZYl3gwYNkjp16kiHDh3cLgryKU2W+e6egwa5WZr4Nnz4cLnsssvYXwEAQFB8x0Ss1DvYYX8NDcc6e+yvkUM+DdFGPg3xggZoABADpk6dKitXrnS7GDHLOyqLr7lz50pqaqor5YlHCxcuNH+nTZsmKSkpbhcn5lHn7BE7e8QusrGDPepdeFatWmX+cnoNDb0NI4v9NXvbt293uwgAgCgin5Y9vmPmHvm08PH9PHLYX0PDsc4e+2tkUe/CQz4tPOTTEC8KOI7juF0IAEg0BQoUcLsISEBJSUly4sQJt4sBAEC+k5QkwikWiA+kwQAgfpFPgxvIpwEAEB3k04D8l09jBDQAiAHjxo0zw/MiuPnz55shjH298sor0rp1a9fKFG+0p+Zjjz0mo0ePlnr16rldnJhHnbNH7OwRu8jGDvaod3bn2GHDhkmtWrXcLk5c9LDWqSMRGeyv2du0aZPceOONbhcDAICYwbVDeMinhY/chh3yGpFFnQsN9Q5u08Zn5NNCQz4tsjhPRC+fRgM0AIgB9evXlwYNGrhdjJhVpEiRTOvatm0r5557rivliUfeaQI0WdakSRO3ixPzqHP2iJ09Ype786g25Nah7vWLuH6B1NgVLFjQ7aLFxZfJjh07+q2j3tmdY6+55hrOsSFo1KhRpoTZF198QWeMELC/hq9o0aJuFwEAgJjCtUN4yKeFj9xG5OIGe9S50JBPi+z3c9ghnxYa8mn2yKflbT6NBmgAgJgX7AsPX4IQTdQ5e8TOHrGzV6xYMenQoYOkpqaa29p7iS+Q9qh3yOv6pckyRpSww/4KAADCwbUDoo3chh1iFFnEMzTk04D4QT4tsjhPRE9SFF8bAAAAAAAAAAAAAAAAAJCP0QANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKzQAA0AAAAAAAAAAAAAAAAAYIUGaAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYKWT3NABAfte9e3cZM2aM37rNmzdLzZo1JZE5jiNr1qyRxYsXy6pVqyQlJUW2bt0qu3fvloMHD0rx4sWlbNmyUr9+fWnVqpX06NEj4WOmiFt0sb8GR73Lnf3798uKFStk+fLlZlm2bJmsW7dOjh8/bu6vUaOGbNmyxe1ixiRiFx0c6+wRu6yxv0YP9Q4AAKTT66latcJ/XsWKIjt3SsIibvaIHdxy5IjIwoUi8+aJLF8ukpws8vvvnvWnnCJSrZrIhReKdOkicuWVIgUKuF3i2EHs4JbDh0UmThSZOtVT9/Q8cOCASKlSIpUqiZx/vsi114p07SpSvLjbpY0J5NMi6PhxkTlzPHVw8WJP/du711PX9LjXsKFImzaepUEDiXU0QAMAZDJlypRMPxbB48MPP5S+fftmef+BAwfM8ssvv8jMmTPliSeekDvvvFP+85//SJkyZSRREbfoYX/NGvXO3tlnny0bNmwwjfgQHmIXHRzr7BG7rLG/Rg/1DgAAAEBC2bVLZNAgkS+/1JYZwR+zZ49nWblS5N13PQ0JPvjA06gqkRE7uEnr3d13i2zfnvm+1FTPsm6dyNixIg89JPL22yLXXSeJjHxaBC1aJNK/v8iqVZnv0+NhSopnGTfOs+7YMZFCsd3EK7ZLBwDIc/v27ZO79WILQQVeUBUqVMiM4lCxYkUzmpK2+l+/fr2Jozpx4oS888478t1338nXX39tRltKRMQtOthfs0e9s6dxgR1iF3kc6+wRu+yxv0YH9Q4AAOTokktCG0GkXLm8KE38IG72iB2i7ZdfPA1UAlWu7BnBpnRpz6g2a9dqEtJz35o1Iq1aiXz2mWdUr0RF7OCW0aNFevbMqFdKzxXawFFH3fvzT09d0xHS1G+/iXTu7Gn82Lu3JCryaRHy7rsi/frpD1kZ63RghNq1PdcjBw+K/PyzyO7dEk9ogAYA8DNo0CDZsWOH+b9du3by1VdfuV2kmKINWFq3bi0dO3aUSy65RJo0aSKFCxfO1Ojl22+/lYceekiWLl1q1ukQtPfee6+M1gvaBETcooP9NXvUu9wrWbKkNGrUSJo2bWri99lnn8mMGTPcLlZcIHaRw7HOHrELDftrZFHvAABAjj76SIRpucNH3OwRO+SlFi1EevUSueqqzPVOG1I984zIm296Gh2kpYnccovIjz/qsEJulTh2EDvklW3bRO66K6PxmTY8e/55kTvu8G+wrI2AtKHQI494GqJp3RswwDMdojYUSmDk03JhxAj/xmd67HvqKZFLLxUJ+A3L1NVp03TKn7iYepgGaACAdNOmTZOPP/7Y/H/ttddKt27d+MEoQK9evcySnQIFCkibNm3km2++kSuuuEIWLFhg1us0RM8++6ycccYZkmiIW+Sxv+aMemdPG9/pl0YdTjspKSl9/bx581wtVzwgdpHFsc4escsZ+2vkUe8AAAAAJCT9Ttmpk8jjj4s0aZL14ypVEnn9dZGzzhIZONCz7uhRkUcfFZkwQRISsYMbtFGZNi7z+uQTka5dMz+uRAmR++4TqVpV5KabPOsOHfI8/z//kUREPi2X1q71HMO8jc+0QaMe27JqXKa/UelMA3Ey20BGjQAAJLTU1FS58847zf+lS5eWt3Uec+RK0aJF5RntkeMz0tKcOXNcLVM8IG45Y3+NPOqdv+7du0u9evX8vkAiNMQucjjW2SN2oWF/jSzqHQAAAICEpQ2n/ve/7BtQ+br3XpHmzTNuf/mlf2OYRELs4IZvvsn4v3794I3PfN14o0i9ehm3T3ZkT0Tk03Kpb9+MaV2vvFLkjTfiYmSzUEW8VtSsWdOMJLFlyxa/9ZdeeqlZH4mWj6NGjTKvldOIFjl58sknzevo30i8vm6zPk9jEG0aR30vjSuiX1ci5c8//5QBAwZIjRo1pEiRIiF/hrG2HVnR8mk5tbyh7Gu28nJfSySDBw+WX3/91fz//PPPS/Xq1d0uUr7QrFkzv9ve6YiQPeKWPfbX6KDeAbGFY509Ygc3UO9yh3wa+bRYEmt5KPJp5NMAAMiXdNQvL22MEPBdANkgdsit3bsz/j/vvNCe4/u4PXsiXybkf0uWiCxcmHFbRz7LZ2iWCOQh7Q3+1ltvmRbBXbp0kZ49e0r79u2tX4/EUehJe2Rv5syZ8qHOHS0irVq1kn467zQi4tixY363y5Qp41pZ4glxyxr7a/RQ74DYwbHOHrGDG6h3AKKJfFr0kU8DAMAF5cr53/7rL7dKEn+IHXKrdOmM/72jUeXE93Fly0a+TMj/3nkn4/+WLUXOPlvym0J59UYff/yxHDx4UM7QOUpz6frrr5cWLVrIKaeckqvXueeee+Tmm2+WChUq5LpMQCg/ak+aNEmKFSsmq1atCutH7UjVebdEel+rWrWqpKSkSOHChSPyeonur7/+kjvuuCN9Crr333/fJB0RGXPnzvW73VIvKJAj4hYc+2t0Ue+A2MCxzh6xgxuod9FFPg2Jjnwa+TQAAPKtwIbfp5/uVkniD7FDbl10kcjy5Z7/Fy0SOXpUpEiRrB9/5IjncV6XXBL9MiL/mTEj4/927SQ/yrMGaJFIlHlp0iASiQP98k6yDHlFp/FKS0szyZ5wR1SJVJ13S6T3NU2UnXPOORF7vUT3wAMPyC+//GL+f/zxx+XsfNja2i27du0y8fW64oorpHHjxq6WKR4Qt6yxv0YP9Q6IHRzr7BE7uIF6F13k05DoyKeRT0M+9dBDIikpItu2iRw86BlFpGpVkYsvFunY0fODHA3aMyNu9ogdYo3jiEyYkHG7cmWRWrXcLFH8IHaIhP79Rd59VyQtzTMd56OPirz4YtaPHzpU5PffPf+XKqW9RfKsqMhHDWd37cq43aiR5+/OnSI6q8CkSSKbN4scOCBSvrxIvXqe65M+fTy38/MUnMnJyXLDDTeYL8DFixeXc889V/773//K8ePHs3zOpZdeanrAzps3L33dRRddZNaNHTs2y+e98cYb5jHaY81r1KhRZl2vXr0yPf7rr7+Wjh07SsWKFc2X6rJly0rdunWlR48e8u233/o99sknnzSvo3+zsnfvXhkwYIBJ+GlP3ho1ash9990n+/btk3AdOnRIXnrpJdPz7tRTTzU99zQx++CDD5r3saU9YR955BE588wzzWtWqVJF+vTpI7/++mumUT10ezXR4OjJOYjDhw9L+fLlzeP0cw6VJoK0l7N+zuXKlTOxqlWrlpl2w5uIDjRx4kTp27evqT/6OWnZ9Tm33367rFu3Ltv3mzNnjqmD1apVM+912mmnyQUXXCBPPPFElrH8+++/ZejQoSZO+pxKlSqZIfsD4xSqtWvXSu/evU2d0NfT7W7btq2MGzcu02M1nvo4tXXrVnPbu/juE1kJVuf1f41XsNf07W1+4sQJee+998woLlrvdL84/fTTpVGjRnLvvfeGPbT+H3/8IYMGDUrfbt03tEemrs9Kdvua1kWdqqVZs2ZSokQJU/+uvvpqWbRokYmNPk/rVU5TJXhjpLFQGpus4hzOcSK/mz17tqkfSuuEHo9gT+vzgQMHTK/s559/Xho2bCgbN24095111lny0UcfuV3EmETcQsP+GlnUOyA2cayzR+zgBupdeMinkU8LRD6NfBr5NCQs3ed/+kkkNVWHOvT88Ltihcibb4roFLsNG4osXux2KWMPcbNH7BBrxowR2bQp43b37jSCDBWxQyTUry/y8ssZt//7X5FrrxWZOVMv3kX0e7p+R5o+3XOe8D5Wp+4cP157i7lWdMSplSv9b1epIjJ6tIh2EtIGkD/84KlzOtreb79p0s3TgF6/v771luTbEdAWLFgg7du3N8mH2rVry5VXXil79uwxCZslS5aE9VqabNDn6BddHU48mJEjR5q/mkTJif5QqK+pmjdvLpdddplJUm3fvt0k5TTBd0kYwyFqUuzCCy80CRjfhN8rr7wi06dPl/nz55tETSh+++03E7effvrJJFY0uVO6dGlZvny5vPjiizJ+/Hjz2t6kSqiOHj1qkjQ//vijKWOTJk3MZ6QJiGnTppkv/5oIUBoP/XFVy6AJA/3sAv3f//2fSXzoY+vrgTcE+/fvl+uuu86Uv1SpUtK0aVMTF32fd955x2zbrFmz5Pzzz/d73o033mgSLvo+l19+uUm6rV692nzmmnT66quv5GLtfRJg4MCB8vrrr5v/dWSS1q1bS2pqqkmyPfXUU6bsgQkWvV9fa9u2bebxmqRbvHixmcrim2++MT8+h9Mj8ssvv5Ru3bqZBKMmPbt06SK7d+82r6XJvJkzZ8oHH3yQ/nhNzOmP3J9//rmULFnSPNdLE3c2WrVqleVr+tKkpMZUE5L6HP1s9DP++eefTUJa649v4imn0WE0fhs2bDBJpg4dOpiE3KeffiozZsyQBg0ahL0dmpB+++23JSkpybx25cqVTd3RfVUTc6HSRKjGecKECeb41LVrV1MfA+Mc6eNEPNP6o/VDFSxY0CS9CxXKs4Ex8w1NXmfXUEXr4Z133mkSxnrchwdxCw/7a2RQ74DYxrHOHrGDG6h34SGfRj4tEPk08mnk05DQdBSHOnU8P+LqKA/akGDPnoz716zxTG31zjueUR/gQdzsETvEku3bRf75z4zbp57qGV0JOSN2iCQdxaxaNZH77vOMTjVtmmcJpmBBkWuuEXnuORGL63dAfK87lI54pvXJt0HamWd6GsprxzZtNK/279cvgJ5RXJ9/XmKeE4ZDhw451atX165+zqBBg5y0tLT0+1atWuVUqFDB3KfL5s2b/Z7bpk0bs37u3Lnp61JTU50SJUo4SUlJzvbt2zO9n76mPqdixYrOsWPH0tePHDnSrO/Zs6ff42vVqmXWz58/P9Nr7dq1y1m+fLnfuieeeMI8Xv/68r6+Li1atHD27t2bft++ffuciy++2Nx38803+z1Pt1nX16hRw2/9iRMnnJYtW5r7+vTp4/z111/p9+l2DRkyxNx32WWXOaHSOHrLeOaZZzpbt271+5y6du2aXn5fI0aMMOuvu+66oK/btGlTc//nn38ecln+8Y9/mOd06NDBxNnXyy+/bO6rW7euX31RY8eOdQ4cOJApVm+++aZ5ToMGDcxtX6+99pq5r3z58s6cOXMylWXp0qXOtm3bgn6WV111lalzXn/88YfTuHFjc9+zzz4b8vbu3LnTOeWUU8zznn76ab8yfv/9907ZsmXNfe+9915I9SMUWdX5nF5T64XeX61aNWfHjh2Z7k9OTvarOznp1q2beb3WrVs7f/75Z/p63UcuvPDC9FhreUPZ1yZPnmzWlypVylm4cKHffS+99FL66+nxI9Tt1nXBjkG2x4lo8W6bd1m9erWT1/r375/+/vfff3/Qx/juQ9nFNdq0rgbGTNfFAt0vA8vmXYoWLeoMHDjQSUlJcbuYzujRo02Zli1b5sSCWI9brNU59tfIoN5FNoY21zSJdqyLh9jFUr2Lp2NdrNW7eIpdLNW5rLC/5p96p9+zAmPmBvJp5NOCIZ9GPo18Wviy+j7HEvri2rXDli2O06yZ47z5puP8/HPwx/zwg+4w+kFnLAULOs6MGU7CXuvHYdxi5po1zmIXLG4scXisC8XffzvOBRf417vPPnO1SK4f6+IwduyzkVtiot7pNfC11/rXrcClXTvHmTZNv3wl9vk1G+TTcvDii8Hr1llnOc7s2f6P1VzOqFGOc8op/o+dMCHm82lhTcGpPcN0+Pfq1avLCy+8YHq4ep133nnyqA4NF4YyZcqYXlXa40t7zmXVW/PWW28NqRet9ibTXnfaKy2QDpEe2GMwFNqTTHtYeumQ69oLUXtvaq9C7eWVE+29t3DhQtO7UJ/rO6KGbpfGUnsQ6pD+2mMxXDpdgw7b7qU989566y0z/Lr2iNWh1726d+9uhmSfOnVq+tDqXvrYZcuWmc+3U6dOIb13SkqK6eWp0xSMGTPGxNmX9ri75pprTA8/7eXq66abbjI9DX1pXPv372+mk1izZo15fS/t0Tls2DDzvw6Brz3tAmkPPC1/IH0frU9a57y0x+HDDz9s/tcerKEaMWKE6QGqPVO1zvsOz6/D3nv3A+2J6zbdJ5T25A3WM7RevXp+dSc7uu/rNA+6vVqPfXu46j6i68L16quvmr86dUFg79zBgwebns2RFo3jRDzSHtZ6fFN16tQxvZ1hR3vCX3XVVWbRnvB6HNJzhTpy5Ii89tprpme69k7WXvbwIG6hY3+NHOodELs41tkjdnAD9S485NPIpwUin0Y+TZFPQ0LRkSq//16kf3/PNELBNG3qmdLqtdcy1un0Vzo6iY4EkYiImz1ih1iUliaiIxhr3fTSUW1uvNHNUsUHYodo+PtvzzH/rLN0uGbPuiJFRPTa9vLLRfS6ukQJz/qvvvKMgHbhhSLr17tabMSpw4czr9PvlQsXeuqbL83l9OwpMmuWp0566XdxvVaJYUnhJhi9Q70XLlw40/06XHe4vEN3B06HdOzYMTMMeajTBXiTJZrIuO2220ziRxNxudGoUSOT5Ar246V+qdbX1yH5QxleXmlyMFjiT4dK9w5R7pvcCoUm8HS4/mBf/HWKAt/PTRUvXtxMLaVl9yaLvd7Uue5F5O677/ZLhmZHpyXQzmdXX311llNVeYfvD7ZtGzduNMPWa2KtT58+ZmosXbyJHp0GwEs/099//90M6X799ddLODSRpUPRB0sYqV9//TXk1/LGM6v6rtuhNEmoU0W46ZxzzjGfi35OzzzzjGzevNn6tbSua73R5Fuw6SR0X9HEeag0AeqtE5rIDeYf//iHRFqkjxPx6ODBg6aeejqOepLAemyAnSFDhpgpM3TRqU6WLl1qpuXQH0q0kYvSWOsPGVlNj5OIiFto2F8ji3oHxCaOdfaIHdxAvQsf+TQP8mkZyKdlRj4tNOTTkBDuvVdPYhm3N24UmTLFzRLFB+Jmj9ghL+i589ZbRb74ImOdNp462bAc2SB2iFbjsyuu0C90nkbH2tDs5ZdF9u0TWb5cZPZske++E/nzT/3iLVKhgud52ghSO2Vs2OD2FiDelPTvSGYMH55Rt4LRRpDaSNL3GmXuXMk3DdC8vRNrZdFbQHvA+fbiCoUmU2rXrm0SI74JFe1RqMmRCy+8MD2pkRP9sVBf65NPPjEJEk0mtW3b1iQKtumcqGHKajt97wulx+bPP/9s/j722GOmt1uwRcuudJvDUbNmTb8eg6GUUXtEauLugw8+kMMnW1rq+44fP16KFi0qd9xxR8jv7902fa2stu3BBx/MtG3Hjx+Xfv36yVlnnWV662nPvQ8//NAkTnXxvu5ff/2V/hxvD9Ozzz47y23OSla9Er09OL1xCIU3uZZV/dB65+3lG0r9iCZNlmlPVU2U/utf/zL7h/au7dKli+n1euDAgYjt/zndF2jPnj3pcdd6HExW63Mj0seJeKQ9lb37WN++fYP2fkbu6DFKeyFrI5f7dO74kyZNmpTpByJkIG6Zsb9GH/UOcB/HOnvEDm6g3oWPfFrm+8inkU8LRD4tNOTTkDACRwcNGA0SWSBu9ogdot2AqlcvkbFjM9Z17SqiHUdC7MCQsIgdomXIEB3O2vO/jjClI5wNGpQx4pmXdiK77TbPKFVly3rW6Xc0bRQJhKN0QOczzQN17pzz8052QEzn01kuFuU8Dn+UaeJDe+g9/vjjMmrUqPRhw73TBXh7dIZCE2uaeNMRLebMmWMScPPnzzf/63QQmtTp0aNHRMvv7fGbHW+PMB2iXKemyE6DBg0k0gLLWK1aNZMw0SkPPvvsM9Pz8P333zdTT+n0DKeddlrIr+3dNu2tpz1cs6PJTy9NkOkQ8zqM/fDhw83nXrFiRTPdgbennk5FEEp8Q6G9YhOV9hS+4oorZMqUKWZ/0FFe9Ed1XXS/mzVrlumFHIvCTYzG6nEiliQnJ5te0kp7McfC1Bb5nU4Lo72mvT3QX3/9dasRDhINcWN/dQP1Dsh7HOvsETu4gXoXO8in+SOfRj4t0sin+Uv0fBpcULu2ZwpF77THa9e6XaL4QNzsETtEi1736Uivn3ySsU5HpdUGVUFG+YUPYodo2bFDewNl3NbORC1bZv8cnaZz6FCRkx2FZOlSHe5Y5OSo3ECOTgvIGejI8aE0pNV8h37n93YA27RJYllYR+eqVauav1u2bAl6/59//mmG4g6XJmyefPJJk7zRRIr20ps+fbrpZRbu9EfaE/Gaa64xi9LX0oTMv//9b7nrrrvMUPMlgw1vF0R2w6t7Y6DJp5xUr17d/O3UqZPcf//9EklZfRY5lXHgwIEmYabTBGhyQJNX6h7fIfxC4N22li1bpiehQ6Hvrd59992gUx7ocPtZ9bpcv369SaRFI5kS6n6wdu3a9F6lgXQf0Om8vI+NBdqTWpOhuqhffvnF9JSdPHmy+cy/+eabXO//Od0XqHz58qaHsCZqtTdusGkIwnk9t44T8Wb37t3piegdO3aYnvbh8O2Vq/VKj/vIub5169bN9ApWK1askEOHDjFNUQ6IG/urG6h3QN7jWGeP2MEN1Ds75NMykE/zIJ+WGfm00JBPQ0LRKYC9jYH27HG7NPGDuNkjdohGA6q+fUVGjcpYp6PdfPYZDahyQuwQTTq9Zlqaf8PGUGgd9DZAU3Pm0AANoasf8N2tfPnQnqff4XW08N9+89w++b05VoXVja1NmzbpyY5jOhdugI8//tiqEJoI0SG79UvrxIkTZfTo0ZKWlmZ6FYY7BUGwIeE1GafDgh88eNAkW0L1448/miXQmjVrZPny5aYX4CUhHFSuvvpq81eH5I9UD0TfJOUXvnNen6TD8+tUUt5pGQJpgqtp06by/fffm6Hkdaj0Cy64QJo3bx7W+3u3TXsDhjPsvjehVEN7lASJ78qVKzOt1+HdK1SoYLbtf//7n7jFG8+spuXSqQ9U3bp1o54wK6JDgoqeI31OkiEmOjU5pILFOhit65qk1LqvCcNAq1atCrq/ZKVw4cJy0UUXmf/HjBkT9DHaazcvYpKb4wQQ7rQl2tN9n84hjxwRN7iBegcAACKNfJoH+bQM5NMyI58WGvJpSCgHD2b8T8ew0BE3e8QO0WhAdXKE4vTGK9qhQKf0Q9aIHaLtl1/8b5/sIJQjn98O0kdSA0JVp47/9cWRI6E/1zdvEOPXKGE1QNMRITQBoMmVoUOHpg8Xr1avXi1PP/20dUFuv/329KkCbKYL0C+52uNKkymBdDhwTSwVLFgwpB6WXprc6tevn98Pj9obT9fpfToUu7fHYna0p6Ymo7777juzTcHKqO+hvSbDTXyoIUOGyPbt29Nvaw+4AQMGyN9//20SYJocC+af//yn+fv8889b9dZU559/vomD9gDUBGewHnZajk8//VR27drlN2y70h6jvvVIe1DfdtttQeOgveweffRR8/+dd94p3+qwlgE0Aegbi2i44447TIJFE0fPPvusXxJUR0rx7gcPPPCARJtO76AJop07d6YnIX1pebQntI7eEsibaA2WtMwqsa09GfXz0n1AE9y+9bd///5hJ4S157B67bXXZIl3nu2TtPf2Uh2+NEzefVwTr3lxnIg3mqjU3rKhLqVKlfJ7vo5w4Hs/QhM4skO4I0UkqkSPG/urOxK93gF5jWOdPWIHN1Dv7JBPI58WiHwa+TRFPg3Ihv4gt3Fjxu1KldwsTfwgbvaIHSKJBlT2iB3yQtGi/reDXPfn2FBZlSgRuTIh/0tK0p5ZGbezGB08E82t+A6UEOPXKGGNUalD+GviQ4fZfumll0yvOU0E7d27V+bNmycdO3aUZcuWmeG/w9W5c2eTiJytQx6KSM2aNeXyyy8P+flHjx41iSNNUjRs2ND0ltPEqCZwvF/ENdmiCYZQ6VD2mgisXbu2XHbZZaa3mm6nJib09UMdIl97dmqsrr32WtPLb8KECdKoUSOTgNBy69DzP/30kxw/flx69eplEkOh0h5vmsA4++yzTbxKlCghCxYskN9++01OP/30bHvR3nTTTSZemsjSuOhtG5rg1ESDTvOg5dBt02k1NHmi8deefLqdKSkpUrFiRfOcRx55xPQoHTFihMydO1eaNGliEjA6dL3GWxMzkyZNCprkW7dunUkuag9iTdjpe+pzvcP46+tFM+Gh26D7wQ033GDq1CeffGLKoVORaPk12aeJUU2sRZvWca2nWqcaN24srVq1MnVAvf/++2Zf1Gk3dN/VGGuCV8un9U3jqMm2F154IeT30wSnfp66H+hnrL1X9XPWmOuPB1oW7b0bKv2cNfn53nvvmbK3bt1aKleubMqn9eW+++6Tl19+Ob0XZig0gavl0akw2rVrl95wQOu6fnaRPk7EG02g7wlj+PBRo0b5/XihiWI9PiM8vtNyaB1nOr/QJHrc2F/dkej1DshrHOvsETu4gXpnh3wa+bRgyKeRTyOfBmRj4kT/H3lbtXKzNPGDuNkjdohmAyqd3k+njqQBVfaIHfJKlSr+t7/7TqRRo5yfp4/zRQcMhOuGG0SmT/f8ryNka2e0nPJkM2dqT7+M2xdfLPlmBDSlSQrtRaW987SXliY1tIfcU089ZXqG2SpWrJjccsst6bd79uxpElSh0l61mkTRpI/2WJw1a5ZJUmkSQ8uqiTjvEOmh0i/a+iVaX1N7Ak6dOlVKlixpepnpek1IhapKlSrmOVpG7UWpyQpNcmhyS919990yc+ZME4dwaCJBt017aGoPNd1mb+Lthx9+MMmk7J7rHf6+b9++UjSwtW+ISpcuLV999ZUZ9v2KK64wPXq1XsyZM8f0FOzevbu5XUeHFTzpwgsvNOXTBIv26NQky6ZNm+Tee++VxYsXmx6RwWidePvtt01yTnvCamLw888/N5+PTiegn/F5550n0dahQweTONd6euDAAfNZarJYEz5jx45NnzYgL7z77rty1113mdhoOT744AOzqBYtWpgeuZrw1VhpnPWz0l6JWmd0iP/27duH/F6VKlUy+79+TpqY031CY69JOa3fNqPE6D6hiVNNtOpr6Ger+4smvTQRqfSzDZX2Jn3uuedMT9Rp06alx0N7A0fjOAHkRBPpWq+99NiFnBE3uIF6BwAAooV8Gvm0QOTTyKeRTwOyoCM/Pvyw/2gRXbq4WaL4QNzsETtEijYQ0Mb0vg2otC7RgCpnxA55Sb/P+X5vfu01kWPHcq6j//2v/7owOn8BRteu+kUto04NGybZ0np5cvR1QwdMuPpqiWkOEta+ffuckiVLOgULFnS2bdvmdnGATHr37q3NeZ2XXnrJyW90u3yX1atXO7Fo5MiRfuXcvHmzK+VITk7OFDNdl9f0c9J6uWbNmpAe//nnnztlypRJL3OxYsWcjRs3Om4YPXq0KcOyZcvy/L3jMW6xUufCwf7qj3oXHT179kwvW40aNZxY4+axLl5jFw/1LhaPdfFS72IxdvFQ59hf80+90+uBwJgBuUU+DbEukfJpLOEvrlw7LFrkOHfd5Thr1+b82B9/dJz69fXDzlh69XIS8lo/TuMWE9escRi7YHFjibNj3YkTjnPHHf51qVs3xzl2zIl1ruc14jB27LORW1yrd9dd51/nbrjBcf7+O/hjtS4OGOD/+ObNE+/8mgPyaSF6/XX/ujR8ePDHHTniON27+z92yJCYz6eFNQUn8hft1aa9JbWnrA4lD7hBexrrFCHaG9pLp8HQXpY6XUtgb27ATceOHTPTpOhSr149adu2reklXrVqVdPTXO///fffTW/oyZMnm2lnvLRXtU4149t7PVEQN7iBepc7Tz/9tFkCady8dGqiYKON3HrrrWY0hkRF7ID4wf4KwAb5NMQC8mmIK0eO6LCDnkWnt9LRQnTUxUqVdDhIkQMHRDZu9Ewv9OWXninYvHREv1dflYRE3OwRO7hh/HgR3++IOrrSvn06BGzorzFkiMiVV0rCIXZww0svicyf76lr3nq4aJFI79469LTIqaeK7N8vsny5yKhRnvOG7yhUb7whiYp8Wi716+eZhnPaNM/twYM9U4HfeqtI3boiaWkiK1d6josbNmQ8r2nTnEdMiwE0QEswixYtMsPZb9682Qzpr8O+BztAAHnlxRdflHHjxpnpAbRRgCZxk5OTZcuWLWZqg7feeksqV67sdjGBTFJSUswSinLlypnGLCR/iRvcQb0LX1pampleJyfBHuP7RTMRETsgfrC/AggV+TTEGvJpiFurVnmWUHTsKKJT82YxvXBCIW72iB3yysGD/rd1rJrZs8N7jZtvloRE7OCGM88UmTHDM9Xrr7961unfnL7nlSsn8umnIhdcIImKfFouFSzoafDYubPIrFmedQsWeJastGolMmGCp/FjjEtyuwDIW+vXrzc94RYvXiwtWrSQ6dOnS+3atd0uFhLYTTfdJO3bt5ft27fLl19+KV9//bU5cen6BQsWSG9taQ7ECO1d/Nhjj0nz5s2lcOHCIT9+3bp1Cd2YhbjBDdQ7AAAARAr5NMQa8mmIKzVraqUVCaVRZFKSZwSbyZNFpkwRqVBBEhZxs0fsAAChaN5cRGdGefhhkYoVs3+sjog2cKAORSzSvn1elRD5VYkSnpFY33vP0xgyKzrq+muvicyZk3MdjREFdB5OtwsBAIlGp3fzpVO/NWjQwLXyxDoduah+/fp+67Rnr06r55bDhw+bz23Tpk2yY8cOOXDggGnkolP7ae/jxo0byxlnnCGx4tNPP5UePXrIsmXLpEmTJq6VI17iFot1Ll7EYuyod4kjVo518YR6l3vUu/BQ5+wRO7sp6s4991y/daTBACD/5NMQPtevHX77TQshsm2byB9/iBw65BnJQX/U1R/fmjUTKVVKYkXMXOvHUdxi7po1TmIXLG6I42NdnImZY10cYZ+NnJipdzods54vdOrDPXs80zVrI6Hy5T3TODdsKFLIvckFY+78GkfiInYrVngaN+7Y4bl92mmeKTc1p+XCd6Dc5NOYghMAAAs6d3mzZs3MgtARN7iBegcAAAAAQAyoUsWzIDzEzR6xAwCEQkfE1AY3AY1ugDxx/vmeJR9gCk4AAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKzQAA0AAAAAAAAAAAAAAAAAYIUGaAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYoQEaAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBSyO5pAADknbS0tEzrNm3a5EpZ4jF2W7duleXLl5vb8+fPlyJFikjBggXdLlpMC1a/gtVDZMb+am/dunWZ1h0+fNiVsiBxsM/a4xxrh3OsvWBxInYAACAcXOuHhmt9e1zv2yFGkUU+DdHGPhs5nGNDw/nVHvm0vEUDNABAzNOET6COHTu6UpZ4N2jQILeLENf1sGHDhm4XI+axv0bWkiVL5Pzzz3e7GMjH2Gcjh3OsPc6x9vsrsQMAAOHgWt8O1/q5wzWr3bU+7JFPQ7Sxz0YO51h7nF9DQz4tb9EADQBiwNSpU2XlypVuFyNmeXsbAm6aO3eupKamul2MmMf+GlnTpk2TvXv3ul2MuLFq1ar0uKWkpLhdnLjAPotYwDnWfn8ldtnbvn2720UAAABIeFyz5ozv5pFFPi085NPCxz6LWMD5NTTk0/I2n1bAcRzH+tkAACsFChRwuwgAAORLSUkiJ064XQoAQKwiDQYA8Yt8GgAA0UE+DQAQiXwaI6ABQAwYN26c1KlTx+1ixPQc8AzDC7e98sor0rp1a7eLEfPYXyNr6FCRbt3cLkX80E6aPXqIDBs2TK655hq3ixMX2GcRCzjH2u+vxC57mzZtkhtvvNHtYgAAooR8Wva41kes4Jo1Z+yvkUU+zS6fBiC+cH4NDefYvEUDNACIAfXr15cGDRq4XYyYjo8mFHVI1OHDh5uLqrZt20rBggXdLlrMS0tLM3OZE7vwHD9+XLZs2eIXt7vuukuKFSvmdtFiHvurvQ0bNkinTp381ulv5o0bu1akuFWrVi1p0qSJ28WIC+yz9jjH2uEca69IkSKZ1mmdO/fcc10pTzwoWrSo20UAAEQR+bTsca0fuWt92ON6PzTsr/bIpyEWsM+GjnyaHfJpkc2nIXpogAYAiHl6AdWhQ4f0+bi1RT8/tIWuYcOGxM6Cxsk3blzIh4b9NbL43RzRxj6bO5xj7XCOtRMsGUuCFgAAZIVr/chd68Me1/uhYX+NLPJpyGvss+Ehn2aHfJodcmd5KymP3w8AAAAAAAAAAAAAAAAAkE/QAA0AAAAAAAAAAAAAAAAAYIUGaAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYoQEaAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAIKju3btLgQIF/JYtW7a4XayYR9zsETt7xM4escva/v0i334r8sorIrfdJtKggUihQiIFCniWmjXdLmHs2r9/v3z77bfyyiuvyG233SYNGjSQQoUKpdexmgQvS8TODnGzR+yih3MsACDf0fOY9wtROEulSm6X3H3EDnmNOmeP2OUK+TS4gdyGBY519ogdslEouzsBAIlpypQpMmbMGLeLEXeImz1iZ4/Y2SN2WTv7bJENG0Qcx+2SxJ+zzz5bNmzYIA7BCxuxs0Pc7BG76OEcCwAAACDRkE+DG8htAIglNEADAPjZt2+f3H333W4XI+4QN3vEzh6xs0fssrd+vdsliF/rCZ41YmeHuNkjdtHBORYAkDAuuUSkePGcH1euXF6UJr4QO+Q16pw9YhcyvmLCDeQ2IoRjnT1iBx80QAMA+Bk0aJDs2LHD/N+uXTv56quv3C5SXCBu9oidPWJnj9iFpmRJkUaNRJo2FWnSROSzz0RmzHC7VPGhZMmS0qhRI2natKk0adJEPvvsM5lB8EJC7OwQN3vELrI4xwIAEsZHHzGXmi1ih7xGnbNH7MJGPg1uILeRSxzr7BE7+KABGgAg3bRp0+Tjjz82/1977bXSrVs3fjAKAXGzR+zsETt7xC5no0d7EmQ6dUBSUsb6efPcLFV8GD16tEny6PD3ST7Bm0fwckTs7BA3e8Qu8jjHAgAAAEhU5NPgBnIbAGKJz+kPAJDIUlNT5c477zT/ly5dWt5++223ixQXiJs9YmeP2NkjdqHp3l2kXj3/ZBlC0717d6lXr55fwgehIXZ2iJs9YhdZnGMBAAAAJDLyaXADuQ0AsYQjEYxRo0ZJgQIFpFevXpJo/vzzTxkwYIDUqFFDihQpYuJw6aWXRi2m2uI8Uu/hVbNmTfOaW7ZsEbfoe2sZtCzR/EFj/Pjx0qdPH6lfv76UKFFCihUrJrVr15bbb79dfvrppyyfq/HW8mW1VKpUSRLd4MGD5ddffzX/P//881K9enW3ixQXiJs9YmeP2NkjdgAARAfnWCQi8mnk03KLfBoAAAAAIL9gCk7EDE20bN26VTZv3hzVpEsg7aGtSRh9zy5dupgEzDnnnCP5gSbrPvroIxk5cmS+SIa++OKL8swzz5j/zzrrLLn66qvl+PHjsmzZMrONOszsiBEjpGfPnlm+xlVXXRU0OXbKKadIIps5c6Z8+OGH5v9WrVpJv3793C5SXCBu9oidPWJnj9gBABAdnGMB95BPizzyaZmRTwMAAAAA5IQGaEhox44dk0mTJpkk2apVq6RMmTJRf8/mzZtLSkqK6W0YKbNnzzbbUrVqVXGLvrduV+HChaP2HiVLljS96u+++26pW7du+nrd9oceekhefvllkwBt2bKlnHnmmUFf4+GHH45ob9n84K+//pI77rjD/F+0aFF5//33TS9WZI+42SN29oidPWIHAEB0cI4FEg/5tMghnwYAAAAAyC9ogIaEtmPHDklLSzPJnrxIlilNlEW6R2idOnXEbZooi3ZP16FDh2b53v/973/lyy+/lPXr18vYsWPlX//6V1TLkp888MAD8ssvv5j/H3/8cTn77LPdLlJcIG72iJ09YmeP2AEAEB2cY4HEQz4tcsinxbmHHhJJSRHZtk3k4EGRsmW1VaHIxReLdOwo0q6dCI2ygyN2yGvUOXvEDkAi4Fhnj9jBR5LvDUSO9vb19vjVIcybNm1qepudeuqpcs0118iSJUuCPk+HrdfnbdmyRSZPniyXX365lCtXzqybN29e+uPWrl0rvXv3lho1apgexvqYtm3byrhx47IskyaGXnnlFWnYsKHpoXjaaadJ165d5aeffsryOU8++aR5b/0bjJZJ78+qB9yvv/5qktH6nqVLlzYx0KHedfj6RYsWmceMGjXKvIZOF6Bq1aqVHr/A7c5JOHHR19bHKX1vm/e0iWlOMVuwYIG0b9/e1JVSpUrJBRdcIB9//HF6mYP1JPetN0r/6m2dLkBpTHy3z/fz3LBhg9x+++0m7hozfU+Ny7XXXmuG4Q+V9z2DTfcQqffITlJSkpx33nnmf++PHwitt+97771n/m/UqJE8+OCDbhcpLhA3e8TOHrGzR+wAAIgOzrGINPJpHuTTMiOfRj4tJuk+ovU2NVWHlBPZvVtkxQqRN98Uad9epGFDkcWL3S5lbCJ2yGvUOXvEDkAi4Fhnj9jBByOgRZkOb64JFR3CvFOnTiaRMn36dJk1a5ZJ4lx//fVBn/fSSy/JG2+8Ic2aNTPJk99++00KFixo7tNead26dZPDhw+bnsVdunSR3bt3yzfffCNz5syRmTNnygcffOD3eidOnJAbbrhB/ve//0mRIkVMsqZs2bKydOlSM4S9JjMiTRPRWs4///xTTj/9dJO40vfWxMqYMWPMYy6++GIztHvPnj1lwoQJ8vfff5uEkyZUvCpVqhTS+4UbF33PAwcOyOeff24SefrccN4zGjHVnobdu3c3r61JuHPPPdckHTXhlZycHPLraPx0+zT5tmnTpkxD6Ddu3Nj8Xb16tblPp0zRmHXo0MHUs+3bt8u3336b/t65kRfv4ZuYU5UrV87yMTpFhH5mhw4dkooVK5o62K5dO5NwSzRa//v27Wv+189Ep8opVIjTQk6Imz1iZ4/Y2SN2AABEB+dYRBP5NPJp4SCfljvk03KhfHkdSk+kdGk9MYps2iSyZ0/G/WvWiFxyicg774j06eNmSWMPsUNeo87ZI3YAEgHHOnvEDr4cRIWGVpfixYs7s2fP9rvvhRdeMPedcsopzq5du/zuq1GjhrmvYMGCzuTJkzO97s6dO83z9DFPP/20c+LEifT7vv/+e6ds2bLmvvfee8/veW+88YZZX7FiRSc5OTl9/bFjx5x+/fqll7dnz55+z3viiSfMev0bzNy5c839bdq08Vu/bdu29HI+/PDDzpEjR/zu1+2eP39+0G3fvHmzEy7buOh76Xp973DZxjSrmP36669OqVKlzH2vvvqq333ffPONU7JkyfTXDJRV7PS9df3IkSODbkPv3r3TYxbo4MGD5n1DlVUsI/ke2Zk+fbp5nwIFCjirVq3KdL/G2xu/wOWss85yvvvuOycvBZZh9erVTl7r379/+vvff//9QR+jdce3nDb7ZySNHj3alGPZsmWulSEe46aIXfzGTRG7xIidXk8Enh+Sk/W8HztLz54ZZatRw/3yBC7LlnnKpvUv1nivy2yvPRNlnw2G2NkhbvkvdsHPExnfRfNaPJxj9XtWYMwQ28inkU8jnxb598gO+bQwbdniOM2aOc6bbzrOzz8Hf8wPPzhOt25a2IylYEHHmTHDSejrrjiPHYv94lq9i/M6x/4aOvJpkcunscThsS4Ocxsxc7yLs2NdzMQtn5wnWCTsJVQJ3kUp+u666y4z7L8vHUJfe2KmpqaaHsLBaG+76667LtN6nX5An6dTEDz66KN+w8fra+o69eKLL/o9T3uNKh0qvl69eunrtXfy8OHDQ+4VGSp9TS1nx44d5bnnnjM9Gn1pD85WrVpF7P1s45IbkY6p9ibVHqQXXXSRDBw40O++Sy65RPr16yeRtmvXLvNXp7EIVLx4cfO+8fAe2qO5z8kW03fccUf61AG+WrduberJunXrTM9g7TGqvTcbNGgg69evlyuuuEJSdH7qBKFTV7z99tvm/zp16shTTz3ldpHiAnGzR+zsETt7xA4AgOjgHItoI59GPi1U5NPskU+zoNPPfv+9SP/+Ou9t8Mc0bSoyfrzIa69lrDt+XOSeezxTEiUqYoe8Rp2zR+wAJAKOdfaIHbJBA7Qo08RXMLfddlt60jYY3+HrfXkfn9XrepMGOnS6JhGUDsm+ceNG83+PHj0yPadYsWJy4403SiTNmDHD/L3zzjslL9jEJTeiEVOd2kDplAHBZLU+N3RqA6XJOJ1SQadbiLf30KkIdBoC/Vz1vV599dWgjxs2bJiZHuass86SEiVKSNWqVaVz586ybNkyueCCC8zrDB06VBLBwYMHzT7h6TjqSThr8hLZI272iJ09YmeP2AEAEB2cY5EXyKeRTwsV+TQ75NPywL33ivhOKat1f8oUN0sUP4gd8hp1zh6xA5AIONbZI3YJhwZoUVYri1af3vXaayyYmjVrZpmoye51Tz31VClXrpzfa3v/VqhQQUqVKhVWOW1t3brV/D3nnHMkL9jEJTeiEVPva2b12We1Pje097D2VFy6dKm0b99eypQpY5JHQ4YMke+15XKMv4f2cL366qtlxYoVcv7555tErSYrw1G0aNH0Hr36/GMJ0Or64Ycflp9//tn8r0nEyy67zO0ixQXiZo/Y2SN29ogdAADRwTkWeYF8Gvm0cF+TfFroyKfloZMxSjd9ulsliT/EDnmNOmeP2AFIBBzr7BG7hEIDNJd5ewwHipfewydOnHC7CPmO73QHoazPDe25OGvWLPnuu+/MlClt27Y1w+frlAfa+3HAgAEx+x467P+1114rixYtMlME6HuULVvW6rW8Uz4cOXJE9uzZI/lZcnKyvPHGG+b/ypUrR3QajfyMuNkjdvaInT1iBwBAdHCORawgn4ZA5NNCQz4tj9Wu7ZmiyGvtWjdLE1+IHfIadc4esQOQCDjW2SN2CYUGaFG2efPmoOu3bNli/larVi2s19NhzpW3t3Gg1NRU+eOPP/we6/2riQDt4ZZdeQIVKVLE/N2/f3+2PTMDnXHGGebv2jw6gNjEJRLvZxPTnF4zq+eF+3rh0B6Ujz32mEyfPl327t0r48ePN0nbt956S+bOnRtz76FTvmiy7NtvvzXJstmzZ0v58uWty6bl8SpdurTkZ7t3705P1O/YscMkGTUZm9XSu3fvTD2RvfdpT+hEQdzsETt7xM4esQMAIDo4xyKvkE8jnxbua5JPyxn5NJdUrpzxPw31wkPskNeoc/aIHYBEwLHOHrFLGDRAi7JPPvkk2/WXXnppWK/nffxHH30U9P4PP/zQ/K1bt256AkaTcrW1ZamIjBkzJtNztJeaJi+C8b5GSkpK0Pu//PLLoOt1aHg1YsQICZU3OZeWlibhsolLbuQmplm55JJLzN//+7//C3p/sPeJRkwLFSok3bp1k6uuusrcXrlyZdjvG833OHTokHTo0EG++eab9GSZTt2QG2PHjk3vuZnVFBAAAAAAACBvkE8jnxYq8mmhIZ/mooMHM/6Pk1EaYwaxQ16jztkjdgASAcc6e8QuYdAALcrefvttmTdvnt+6l19+2Qyfrj3D+vTpE9br3XHHHVKmTBlZvny5PPvss35TDqxYsUKefvpp8/8DDzzg97xBgwaZv08++aRfL8rjx4/L/fffL7/99lvQ97v88sslKSlJZs6caRIUXvq+r732mnz++edBnzd48GCzfVOmTJF//etfcuzYsUy9phcsWOC3ztt7dc2aNRIu27jkhm1Ms6J1QYfX17i8+eabfvctXLjQ9GwMV04x1ddct25dpvU7d+6UH374wfxfw3dITAuRfI/Dhw/LddddZ3p4hpMs08frfhg4RcfRo0fl+eefl9dff93cHjJkiOR3hQsXNr1bQ10CE4g6woHv/YmCuNkjdvaInT1iBwBAdHCORV4hn0Y+LVTk03JGPs1FR46IbNyYcbtSJTdLE1+IHfIadc4esQOQCDjW2SN2CaWQ2wXI7+666y6TdGrdurXpKbh69Wr56aefpGDBgqYXYaUwd7CKFSvKp59+KjfccIM8+uijpufn+eefbxJQmtDSnnk6xYUmkHwNGDBAZs2aJV988YU0atRILrvsMpP4Xbp0qZk2o1+/fia5F6h69epy7733yquvvipt27Y121GuXDlZtWqVbNu2TR5++GGTcAg2ZcCECRNMr7xnnnlG3n//fbnoootMslqnGdAk1j/+8Q9p1apV+nO6du1qEhs9evSQdu3amfJ5k1xnn312VOKSG7YxzS659e6770rPnj3lnnvukffee08aNGhgEm/z5883Scj//ve/Joah6ty5s/z73/82yU2te/p5agJUk0666Hvodug0KOeee65JOv7+++/m/bRnpNZdfVxuRPI9HnnkEfn666/T65gmJoPRetW3b9/021pf77vvPlNPGjdubH7o0DL8+OOPsmvXLvMYfa1wE9jxqGXLlmaqi1CNGjXKb9ocTUrXrFlTEg1xs0fs7BE7e8QOAIDo4ByLvEI+jXxaqMin5Yx8mosmTvQf7cFn30UOiB3yGnXOHrEDkAg41tkjdgmFBmhRpr0zNdmjiZDvv//eJDt0OP3HHntMLr74YqvX1OHSNWn7n//8x/RY08RUyZIlTTJLE3Q33XRTpudokmTixIkmcfLBBx+Y3mvaE1kTC5MmTTIJrKySO7oNmpzQpNeiRYvM8zTpPG7cOPnrr7+CJsyUJr00STN8+HCZMWOGWXSo+CpVqsitt96aKXmlCab9+/fL6NGjZdq0aaZ3ntIEWk4JM9u45EZuYpoV3VZNammSURNvGzdulHPOOcdMvXDllVeahFk4w+Nrr0btVavP09fTuGivRU3OaZJK30enfViyZIlZUlNT5fTTT5cLL7zQJBhvueUW85nlRiTf448//kj/f+rUqdk+1jdh1qZNG1O/li1bZpJk+jr6+Wld1GkLtH7Y7o8AAAAAACCyyKeRTwsH+bTskU9ziTbSe/jhjNtJSSJdurhZovhB7JDXqHP2iB2ARMCxzh6xSzwOokJDS3gRSR999JGpUx07dnS7KIjgMcK7rF692olFI0eO9Cvn5s2bXS3P6NGjTTmWLVvmxLJYi5sidvk7borYxX/skpOTM50fkpPNb10xs/TsmVG2GjXcL0/gsmyZp2xa/2JNz549fWJXw4k1sbzPEjs7xC3/xS74eSLZiWVun2P1e1ZgzBDb+JwQaeTT8hdX82mLFjnOXXc5ztq1OT/2xx8dp359LXDG0quXk7DXXfkgdiz2iyv1Lh/UOfbX0JFPi1w+jSXOjnVxmtuIieNdHB7rvDhPROY8wSJhL6FiBDQghug0DEWKFMk0lcTChQvTh8f3nb4EAAAgGp5+2rMEOnYs4/+tW0WKFcv8mFtvFRkxQhLW008/bZZAx3yCp1NoFQsSPB3VREfqSFTEzg5xs0fsACB/IJ+GPHHkiMi773qWRo1ELr9ch8sT0XpXurTIgQMiGzeKzJwp8uWXIidOZDz3/PNFXn1VEhaxQ16jztkjdrlCPg1uILdhgWOdPWKHHNAADYghc+bMkT59+kijRo3MNA0FCxaUTZs2yapVq9KTZddff73bxQQAAPlcWprnu2ROgj3GN6mWiNLS0uRICMEL9hjfxFAiInZ2iJs9YgcA+QP5NOQ5rVsn61eOOnYU+fBDkTJlol2q+EDskNeoc/aIXdjIp8EN5DZyiWOdPWKHIJKCrQTgjhYtWpik2N9//y3z5s2TKVOmyK+//ipXXHGFjBkzRj7UAzMAAAAAAAAAg3wa8kTNmiI33SRSuXLOj01KErnySpHJk0WmTBGpUEESGrFDXqPO2SN2ABIBxzp7xA45YAS0KNFJs4FwnXPOOfL++++7XQwgXa9evcyC8BA3e8TOHrGzR+wye/JJz4LwPfnkk2ZB+IidHeJmj9hFH+dYhIt8GmyQT0Oe/dg2dqzn/99+E0lO1vlfRf74Q+TQIZHixUVOPVXkzDNFmjUTKVXK7RLHDmKHvEads0fscoV8GtxAbsMCxzp7xA45oAEaAAAAAAAAAABAKKpU8SwIH7FDXqPO2SN2ABIBxzp7xA5BMAUnAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKzQAA0AAAAAAAAAAAAAAAAAYIUGaAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYKWT3NAAA8s7hw4fl66+/luXLl5vb8+fPl8KFC0uhQpzGQondkiVLZOnSpeb2hAkTJCkpSYoWLep20WJaWlqabN261a/OFSlSRAoWLOh20eIydvXr15dixYq5XbS4iF2gTZtcKUrc0dBt3ZoRL61/Xbt2pd4hqrg+scN5ItLnCU4U2SE+AIBExvVq5PJpsEdOLTR8T7JHPi1y+TQgL3B9YofzRGTPE4ieAo7jOFF8fQBAEAUKFPC7vXr1amnQoIFr5Yl1U6dOlY4dO7pdDACWvvjiC+nQoYPbxYh5HOsii3oXnk8//VR69Oghy5YtkyZNmrhdnLjAPhs57K+hoc5FBmkwAIhf5NPCw7UDEN/4nhQajnWIBeTTQsc+GzmcJ0JDncvbfBpNSQEgRk5+K1eudLsYMcvboh9AfJo7d66kpqa6XYyYx7EussaOHcu5NQyrVq0yf6dNmyYpKSluFycusM9GDueJ0FDnAADwRz4te1w7APGN70mh4ViHWEA+LXTss5HDeSI01Lm8xQhoABADPTYBAADclpQkcuKE26UAgOgiDQYA8Yt8GgAAiDXk0wAkAocR0AAgfowbN07q1KnjdjFils5lPmjQILeLAcDSK6+8Iq1bt3a7GDGPY11kvfKKCNUudNpJs0cPkWHDhsk111zjdnHiAvts5HCeCA11DgAAAEgcfE8KDd+TIot8WnjIp4WPfTZyOE+EhjqXt2iABgAxoH79+tKgQQO3ixGzihQpkmndF1+I0GYvZ+vXi3Tu7L9u8mSRunXdKlF8SEsT2bpVZNMmEb0uHTx4sPTu3VsKFizodtFi3qZNm6Rjx45+69q2bSvnnnuua2WKp3OBNkbWqSM//fRTk/Bp21aEapcz3VcDqp2JHdUufLVq1ZImTZq4XYw4vj75gk4FOeA8kfvzhE6xMHz4cJNo1NhxfZK1DRs2SKdOndwuBgAAMYN8WmjIp9kjp2aH70n2yKfZI58WOeTTcpdPgx3OE6Ehn5a3+TQaoAEAYl6wiwBNltWr50px4p4my4hdzho21LnhPf/rl0cu5O1xIR+aYsWKSYcOHWTlypXmtnZeotrZo9rBneuTOlKPk2zYOE+Ed55ITU01t7WXK9cn2TvBPCgAAPghn2aPfFroyKlFBt+TQkM+LbKodog2jm2RQyxDQz4tb/NpSdbPBAAAAAAAAAAAAAAAAAAkNBqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArNAADQAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKzQAA0AAAAAAAAAAAAAAAAAYIUGaAAAAAAAAAAAAAAAAAAAK4XsngYAyO+6d+8uY8aM8Vu3efNmqVmzpiQyxxFZs0Zk8WKRVatEUlJEtm4V2b1b5OBBkeLFRcqWFalfX6RVK5EePUQSPGRZOn5cZM4ckYkTPfHcuVNk715PDKtVE2nYUKRNG8/SoIHbpY0tx48flzlz5sjEiRNl8eLFsnPnTtm7d68UL15cqlWrJg0bNpQ2bdqYpQHByxbHOnvdu4sEhE42b+aYp/bvF1mxQmT5cs+ybJnIunWe456qUUNkyxa3Sxmb9u/fLytWrJDly5ebZdmyZbJu3Tpz3FM1atSQLQQvR5wnQkeds0fsAABAqMinRQ75tBAUKJC75/OlPUfk0+yRT8sa+TRLR46ILFwoMm+eJ3DJySK//+5Zf8opnpPDhReKdOkicuWVuT9GAuSE7G3ZIlKrVvjPq1jRc9EXB2iABgDIZMqUKZm+QMLjww9F+vbN+v4DBzzLL7+IzJwp8sQTInfeKfKf/4iUKZOXJY1tixaJ9O/vSToG+6KpiUhdxo3zrDt2TKQQVy3GokWLpH///rIqSPD0oj8lJcUs404G79ixY1KI4AXFsc7elCmZk2XwOPtskQ0bPD+wIDxnn322bNiwQRyClyucJ0JHnbNH7AAAQDjIp0UG+bQ8UqGC2yWIaeTT7JFPyxr5NAu7dokMGiTy5Zeek0Awe/Z4lpUrRd5919My+YMPPA3SAEvkhJAdLj0BAH727dsnd999t9vFiFmB11OaxNHeSdr4XHsa6nX++vUaR8/9J06IvPOOyHffiXz9tac3Z6LT7zn9+vnHUpOJtWuLlCvn6fn688+eXrDw9+6770q/fv38LuzLlCkjtWvXlnLlysnBgwfl559/lt0EL0cc6+zp8Y3QZU3PAbCznuDlGueJ8FDn7BE7AAAQDvJpuUc+LQxXXRX+aCQ6zJKXDsGHoMin2SOflj2+YlrQVttjx2ZeX7myZ9Sz0qU9IyatXes5sSodjlSHGv3sM8+IaIAFckIRdMklnovhnOjFXpygARoAwM+gQYNkx44d5v927drJV1995XaRYoomyFq3FunY0XNd0KSJSOHC/o/RRNC334o89JDI0qWedTry8b33ioweLQltxAj/ZFmLFiJPPSVy6aWZ47htm8i0aZ5esowKrbEb4deooEWLFvLUU0/JpZdeKoUDgrdt2zaZNm2afPjhh1KA4AXFsc6edqw7GTpp106E0AVXsqRIo0YiTZt6zhWa15kxw+1SxYeSJUtKo0aNpGnTptKkSRP57LPPZAbByxHnCXvUOXvEDgAAhIJ8Wu6QTwtTuNejnTplNEArUkTk1lujUqz8gHyaPfJpoSGfZklPDL16eRrgBs7nqo3QnnlG5M03PSeStDSRW24R+fFHz9BzgCVyQhHw0Uf5bg5mGqABANLpD5Eff/yx+f/aa6+Vbt268SUygF7D65IdTe60aSPyzTciV1whsmCBZ70Or/3ssyJnnCEJSTvaDByYkSwbMEDk9dezToZpnLRXGD3DNHZrZeDAgemNCgYMGCCvv/56lo0GzjjjDNMbkR6JwXGss6dJ7JOhk2uvFenWjYRZIP1hRBNkmr9JSspYP2+em6WKD6NHjzbJCh3GPcknePMIXo44T9ihztkjdgAAIBzk0+yRT4sybZihyQ6vzp1Fypd3s0Qxi3yaPfJpOSOfZkEDpQ1oH3/cE7ysVKrkOXGcdZbnhKKOHhV59FGRCRPyrLjIP8gJITs+h3AAQCJLTU2VO++80/xfunRpefvtt90uUtwrWtTTscRLE0Vz5kjC6ttX5PBhz/9XXinyxhsJ3BMzTH379pXDJ4N35ZVXyhtvvMGINZY41tlLTRU5GTozgjuhC657d5F69fyTZQhN9+7dpV69en6JC4SG84Qd6pw9YgcAAKKFfJo/8mlRpq2CdDQgrz593CxNzCKfZo98WmjIp1nQRmf/+1/2jc986ZCizZtn3P7yS8/8zUCYyAkhOxGvFTVr1jSJ7i06Z7oPnfZD10ei5eOoUaPMa/XKqctMDp588knzOvo3Eq+v26zP0xhEm8ZR30vjiujXlUj5888/zWgENWrUkCJFioT8GcbadmRFy6fl1PKGsq/Zyst9LZEMHjxYfv31V/P/888/L9WrV3e7SPlCs2b+t73DbCeaJUtEFi7MuK0dbhCaJUuWyEKf4OmINrDHsc7e4MEiJ0Mnzz8vQuiA2MB5AsgfyKeRT4slsZaHIp9GPg2Jh3yaB/m0PKBzlfoOH6fD7yET8mn2yKchpuiIaV7aujng+ycA5BbNEoE8pD1E3nrrLdMiuEuXLtKzZ09p37699euROAo9aY/szZw5Uz48+WW7VatW0q9fP7eLlG8cO+Z/u0wZSUjvvJPxf8uWnqG0EZp3fILXsmVLM6wx7HCsszdzZkZOtlUrEUIHxA7OEwCA/I58WvSRT0OsIZ/mQT4tynSe13XrMm737s3wS0GQT7NHPg0xp1w5/9t//eVWSQDkU4Xy6o10XvCDBw/KGdqDIJeuv/56adGihZxyyim5ep177rlHbr75ZqlQoUKuywTk5NixYzJp0iQpVqyYrFq1SsqE8a05UnXeLZHe16pWrSopKSlSuHDhiLxeovvrr7/kjjvuMP8XLVpU3n//faZsiqC5c/1va7IoEc2YkfF/u3ZuliT+zPAJXjuCZ41jnT3NQ5wMnZkK5f33me4DiCWcJ4D8jXwaEh35NPJpSEzk0zzIp+Xh6Gea6NAGaPBDPs0e+TTEpMDOBqef7lZJAORTedYALRKJMi9NGkQicaBf3kmWIa/s2LFD0tLSTLInnGRZJOu8WyK9r2mi7JxzzonY6yW6Bx54QH755Rfz/+OPP86oGRG0a5fGN+O2juDeuLEk5HcajYVXo0aevzt3evI8kyaJbN4scuCASPnyIvXqeZJqffp4bicy7X2+yyd4jU4Gb+fOnabnof4Qs3nzZjlw4ICUL19e6tWrZxof9OnTx9xGBo519vQ4djJ08vjj9LgGYgnnCSD/I5+GREc+jXwaEg/5NA/yaVG2f7/IuHH+Fa1GDTdLFJPIp9kjn4aY4zgiEyZk3K5cWaRWLTdLBOChh0RSUkS2bRM5eFCkbFntOSRy8cUiHTt6Lu7irPWy1ViyycnJcsMNN5gvwMWLF5dzzz1X/vvf/8rx48ezfM6ll15qWsXPmzcvfd1FF11k1o0dOzbL573xxhvmMdpjzWvUqFFmXa9evTI9/uuvv5aOHTtKxYoVzZfqsmXLSt26daVHjx7y7bff+j32ySefNK+jf7Oyd+9eGTBggEn4aev+GjVqyH333Sf79u2TcB06dEheeukl0/Pu1FNPNT339GLtwQcfNO9jS3vCPvLII3LmmWea16xSpYr5QcE7H7vX3LlzzfZqosHRk0wQhw8fNj9E6OP0cw6VJoK054N+zuXKlTOxqlWrlhmK13txGmjixInSt29fU3/0c9Ky63Nuv/12Wec77HEQc+bMMXWwWrVq5r1OO+00ueCCC+SJJ57IMpZ///23DB061MRJn1OpUiUzZH9gnEK1du1a6d27t6kT+nq63W3btpVxvl9aTtJ46uPU1q1bzW3v4rtPZCVYndf/NV7BXtO3B8qJEyfkvffeM9PxaL3T/eL00083P07de++9YQ+t/8cff8igQYPSt1v3De2Rqeuzkt2+pnVRfxxr1qyZlChRwtS/q6++WhYtWmRio8/TepXTVAneGGkslMYmqziHc5zI72bPnm3qh9I6occj2NNDqyZ9Vq0Sef55kYYNRTZu9Nx31lkiH30kCWnlSv/bVaqIjB4tonnvRx8V+eEHPd+KHDki8ttvWi8911x6iHvrLUloKwOCp+f40aNHm3P5o48+Kj/88IM57x05ckR+++03s08/9NBD5hioU9TAg2OdPd0fT4bOJLsJHRBbOE8A8YV8Gvm0QOTTyKeRTwPIp2WFfFqUffaZnuQybmvLPfghn2aPfBpi0pgxIps2Zdzu3j3uGrYA+c64cSI//SSSmuqZg373bpEVK0TefFOkfXvPhfHixZKvR0BbsGCBtG/f3iQfateuLVdeeaXs2bPHJGyWLFkS1mtpskGfo190dTjxYEaOHGn+ahIlJx999JF5TdW8eXO57LLLTJJq+/btJimnCb5LLrkk5PJpUuzCCy80CRjfhN8rr7wi06dPl/nz55tETSg02a9x++mnn0xiRZM7pUuXluXLl8uLL74o48ePN6/tTaqE6ujRoyZJ8+OPP5oyNmnSxHxGmoCYNm2a+fKviQCl8WjYsKEpgyYM9LML9H//938m8aGPrV+/fkhl2L9/v1x33XWm/KVKlZKmTZuauOj7vPPOO2bbZs2aJeeff77f82688UaTcNH3ufzyy03SbfXq1eYz16TTV199JRdr684AAwcOlNdff93837hxY2ndurWkpqaaJNtTTz1lyh6YYNH79bW2bdtmHq9JusWLF5upLL755hszhH84PSK//PJL6datm0kwatKzS5cusnv3bvNamsybOXOmfPDBB+mP18Scjjrw+eefS8mSJc1zvTRxZ6NVq1ZZvqYvTUpqTDUhqc/Rz0Y/459//tkkpLX++CaesqOjK2j8NmzYYJJMHTp0MAm5Tz/91Ez906BBg7C3QxPSb7/9tiQlJZnXrly5sqk7uq9qYi5UmgjVOE+YMMEcn7p27WrqY2CcI32ciGdaf7R+qIIFC5qkd6FCeTYwZr6heezsEmFaDe+8UxPHIqVLS0Las8f/tvbQfO45/wTamWd6rq30txq9zvJ2RBwwwNPwXxOQiUivsXzpSDbP+QRPfyTT459OS6M/dOn5zntu1uOrnveeT9TgncSxzp7+AHAydFKwoGeqAEIHxBbOE0D8IJ9GPi0Q+TTyaeTTkMjIp+WMfFoeTr9ZrpxI585ulibmkE+zRz4NMWn7dpF//jPj9qmnigwd6maJACgdtrZOHc8Fr55AtJGo70XgmjUi+j3rnXfip7G8E4ZDhw451atX165+zqBBg5y0tLT0+1atWuVUqFDB3KfL5s2b/Z7bpk0bs37u3Lnp61JTU50SJUo4SUlJzvbt2zO9n76mPqdixYrOsWPH0tePHDnSrO/Zs6ff42vVqmXWz58/P9Nr7dq1y1m+fLnfuieeeMI8Xv/68r6+Li1atHD27t2bft++ffuciy++2Nx38803+z1Pt1nX16hRw2/9iRMnnJYtW5r7+vTp4/z111/p9+l2DRkyxNx32WWXOaHSOHrLeOaZZzpbt271+5y6du2aXn5fI0aMMOuvu+66oK/btGlTc//nn38ecln+8Y9/mOd06NDBxNnXyy+/bO6rW7euX31RY8eOdQ4cOJApVm+++aZ5ToMGDcxtX6+99pq5r3z58s6cOXMylWXp0qXOtm3bgn6WV111lalzXn/88YfTuHFjc9+zzz4b8vbu3LnTOeWUU8zznn76ab8yfv/9907ZsmXNfe+9915I9SMUWdX5nF5T64XeX61aNWfHjh2Z7k9OTvarOznp1q2beb3WrVs7f/75Z/p63UcuvPDC9FhreUPZ1yZPnmzWlypVylm4cKHffS+99FL66+nxI9Tt1nXBjkG2x4lo8W6bd1m9erWT1/r375/+/vfff3/Qx/juQ9nFNdq0rgbGLDnZdPh1fenZ079cvkvRouIMHChOSop75dM4uR27F18MHp+zzhJn9mz/xx47Js6oUeKccor/YydMcCd+y5Z53n/06NGu1P0XX3wxi9id5cyePdvvsXpNMWrUqPRzVEbsJsTA/prsuCWejnVew4YNM+XQ+ufm8a1//4yY3H9/8MeMHOn/WW/enLjHunDOFzVquF+eWDveZUevgTNiF/61dH4+3nGeSKw656X7qec8scyJNbEWO/2eFVjf3EA+jXxaMOTTyKeRTwtfVvkXltCXWPmeRD4tf+fTYv07pqPfPzyD73mWgQPdLlHMfU8in2a/kE+L7EI+LQL+/ttxLrjA/7j32Wcxdbxjsb2uI58Wd/m0LVscp1kzx3nzTcf5+efgj/nhB/0S6b/PFizoODNmxEU+LawpOLVnmA7/Xr16dXnhhRdMq3ev8847z0zxEY4yZcqYXlXa40t7zmXVW/PWW28NqWW99ibTXnfaKy2QDpEe2GMwFNqTTHtYeumQ69oLUXtvaq9C7eWVE+29t3DhQtO7UJ+rPTW9dLs0ltqDUIf01x6L4dLpGnTYdi/tmafTqejw69ojVode9+revbsZkn3q1KnpQ6t76WOXLVtmPt9OnTqF9N4pKSmml6f2rB8zZoyJsy/tcXfNNdeYHn7ay9XXTTfdZHoa+tK49u/f30wnsWbNGvP6Xtqjc9iwYeZ/HfZXe9oF0h54Wv5A+j5an7TOeWmPw4cfftj8rz1YQzVixAjTA1R7pmqd9x2eX4e99+4H2hPXbbpPKO3JG6xnaL169fzqTnZ039dpHnR7tR779nDVfUTXhevVV181f3XqgsDeuYMHDzY9myMtGseJeKQ9rPX4purUqWN6O8OOjn561VWeRTvCN2/u6TyidAj8114T0Q7w2vPw6FFJSIcPZ16nh56FC0Uuv9x/vZ7ue/YUmTVLpEiRjPV6uM5mZqJ8S0cGCKTHbb2u0NEOfOk1hfZc11ESivgET8912U3rlJ9xrLOnM+2cDJ3pgEPogNjEeQKID+TTyKcFIp9GPk2RT0MiI5+WM/JpeTT6mYqXEUXyCPk0e+TTEHPS0kR01Ozvv89YpyfXG290s1RA4qpRw7M/9u/vmTc9mKZNRcaP91wQe+kF3T33eIa+jXFJ4V50eId6L1y4cKb7NZkdLu/Q3TqMty+dIkSHIQ91ugBvskQTGbfddptJ/GgiLjd0TnNNcgXSYff1S7W+vg7JH8rw8kqTg8ESfzpUuneIct/kVig0gafD9Qf74q9TFPh+bqp48eJy5513mrJ7LyC93tS5ZEXk7rvv9kuGZkenJdAm3ldffbVfItCXd/j+YNu2ceNGM2y9Jtb69OkjvXr1Mos30aPTAHjpZ/r777+bId2vv/56CYcmsnQo+mAJI/Xrr7+G/FreeGZV33U7lCYJdaoIN51zzjnmc9HP6ZlnnpHNmzdbv5bWda03mnwLNp2E7iuaOA+VJkC9dUITucH84x//kEiL9HEiHh08eNDUU913vUlgPTbAzpAhIjNmeJavvhJZulTkjz88ySBNoikN9Vtvea7zE1HAbyPG8OEiFSpk/RzNl+u1lNfGjSJz50rCCfxhSQ0fPtycC7OiPzbc4xM8Pdfqj3KJhmOdvYMHPbnXk6GTESP0GtLtUgEIhvMEEB/Ip3mQT8tAPi0z8mmhIZ+G/IJ8Ws7Ip0WJ/nDr24C/WTPtEeBmiWIK+TR75NMQc/R67dZbRb74ImOdNjw72ZkBQIy7915N7Phf2E2ZIvmqAZq3d2KtLFrjaQ84315codBkSu3atU1ixDehoj0KNTly4YUXpic1cqK9FPW1PvnkE5Mg0WRS27ZtTaJgm054H6asttP3vlB6bP7888/m72OPPWZ6uwVbtOxKtzkcNWvW9OsxGEoZtUekJu4++OCD9B7z+r7jx4+XokWLyh133BHy+3u3TV8rq2178MEHM22b9rDv16+fnHXWWaa3nvbc+/DDD03iVBfv6/7111/pz/H2MD377LOz3OasZNUr0duDM9jIAVnxJteyqh9a77y9fEOpH9GkyTLtqapfEP71r3+Z/UN713bp0sX0ej2gcwlHaP/P6b5Ae/bsSY+71uNgslqfG5E+TsQj7ans3cf69u0btPczckcPUdoJWZNo992XsX7SJP2BSBJO4O8peqnQuXPOzzv5m1o6n99/Ekbgj1F6ndU5hOB5f5D08v3xLFFwrLOnPaRPhk769hUhdEDs4jwBxAfyaZnvI59GPi0Q+bTQkE9DfkY+zR/5tCiZOlVk9+6M25r4QDryafbIpyHmGp/16iUydmzGuq5dRbSzUoidZgDEgEcDRswPGCE9FuU8Dn+UaeJDe+g9/vjjMmrUqPRhw73TBQQmxrOjiTVNvH311VcyZ84ck4CbP3+++V+HiNWkTo8ePSJafm8vgOx4e4TpEOU6XG12GjRoIJEWWMZq1aqZhIlOeXB+AdYAAOylSURBVPDZZ5+Znofvv/++HDlyxEzPcNppp4X82t5t09562sM1O5r89NIEmQ4xr8PYaw99/dwrVqxopjvw9tTTqQhCiW8otFdsotKewldccYVMmTLF7A86fcWkSZPMovudTsGjvZBjUbiJ0Vg9TsSS5ORk00taaS/mWJjaIr974QXt3a490D23X3/dMyR+Igk8rehgCKF8x9FTop4WvL9pbNokCSfwnKzn21BGVdDrCT2nen+Y2JRgweNYZy85WeRk6EQHuyB0QGzjPAEkLvJp/sinkU+LNPJp/hI9n4a8Rz6NfFqeTL9ZooTILbe4WZqYQj7NHvk0xBT9rqHD8X3yScY6HQlZG6MFGVkaQAyrXdszbefJjmWydq3EurCOMlWrVjV/t2zZEvT+P//80wzFHS5N2Dz55JMmeaOJFO2lN336dNPL7OYwx1fWnojXXHONWZS+liZk/v3vf8tdd91lhpoPNk1JMNkNr+6NgSafclK9enXzt1OnTnL//fdLJGX1WeRUxoEDB5qEmU4ToMkBTV4p36lYQuHdtpYtW6ZfmIZC31u9++67Qac80OH2s+p1uX79epNIi0YyJdT9YO3atem9QALpPvCHjhfus8+4TXtSazJUF/XLL7+YnrKTJ082n/k333yT6/0/p/sClS9f3vQQ1kSt9sYNNg1BOK/n1nEi3uzevTs9Eb1jxw7T0z4cvr1ytV7pcR/Z0+v5bt1EnnnGc3vFCpFDhxJr+O3A3bt8+dCep4d57QDvnX3l5KE1oQQeG/XYGQo9R+roAd6pa7znpUTBsc6edgL2/l65Y4eOyBLe830Hb9De2QkUOsAVnCeA+EA+LQP5NA/yaZmRTwsN+TQkCvJp5NOiQoPiO3KIVrKTo3qCfFpukE9DTDU+0yH4Ro3KWKfDZ372GY3PgHhVuXJGA7Q9eyTWhdWNrU2bNunJjmM6T3qAj33nTQ+DJkJ0yG790jpx4kQZPXq0pKWlmV6F4U5BEGxIeE3G6bDgOne5JltC9eOPP5ol0Jo1a2T58uWmF+All1yS4+tcffXV5q8OyR+pHoheegH3he/czSfp8PwzdKzqk9MyBNIEV9OmTeX77783Q8nrUOkXXHCBNG/ePKz3926b9gYMZ9h9b0KphrbYDBLflStXZlqvw7tXqFDBbNv//vc/cYs3njq1QTA69YGqW7du1BNmRYoUMX91fwk30anJIRUs1sFoXdckpdZ9TRgGWrVqVdD9JSuFCxeWiy66yPw/ZsyYoI/RXrt5EZPcHCeAUPjOWqLX//v2SULRwQp8E4RHjoT+XN9TSyIlGb10pAf9AdFLf2QIle952fc1AAD5B+cJID6QT/Mgn5aBfFpm5NNCQz4NiYR8Gvm0iNNz0PHjGbd1hCAAyG+Nz06Oip3e+Ew7sRQu7GbJAOTGwYNxdWEXVgO0bt26mQSAJleGDh2aPly8Wr16tTz99NPWBbn99tvTpwqwmS5Av+RqjytNpgTS4cA1saRTkYTSw9JLk1v9+vWTfT7fbLQ3nq7T+3Qodm+PxexoT01NRn333Xdmm4KVUd9De02Gm/hQQ4YMke3bt/v96DBgwAD5+++/TQJMk2PB/POf/zR/n3/+eavemur88883cdAegJrgDNbDTsvx6aefyq5du/yGbVfaY9S3Hmmvittuuy1oHLSX3aMn57m988475dtvv830GE0A+sYiGu644w6TYNHE0bPPPuuXBF2xYkX6fvDAAw9ItOn0Dpog2rlzZ9BRC7Q82hP6kHYPC+BNtAZLWmaV2NaejPp56T6gCW7f+tu/f/+wE8Lac1i99tprsmTJEr/7tPf20qVLJVzefVwTr3lxnIg3mqjU3rKhLqVKlfJ7vvZ68r0foQnssRRuD6h4p7O2+P52k0WH90z09OubXKxUSRKO/jjn+8NXVqMFBNLjsu/1i07Rk0g41tnTXIRucqhLQOjM8c33fgDRxXkCiA/k08inBSKfRj5NkU8Dskc+jXxaxPk2yqhbV1sJu1mamEM+zR75NLiOxmdA/nTkiMjGjXF1YRfWWIvaK1oTHzrM9ksvvWR6zWkiaO/evTJv3jzp2LGjLFu2zAz/Ha7OnTubi5PZs2eb2zVr1pTLL7885OcfPXrUJI40SdGwYUPTW04vljSB4/0irskWTTCESoey10Rg7dq15bLLLjO91XQ7NTGhrx/qEPn6o4DG6tprrzW9/CZMmCCNGjUyCQgtt/5I8NNPP8nx48elV69eJjEUKu3xpgmMs88+28SrRIkSsmDBAjOVyumnn55tL9qbbrrJxEsTWRoXvW1DE5yaaNBpHrQcum061K4mTzT+2pNPtzMlJUUqVqxonvPII4+YHqUjRoyQuXPnSpMmTUwCRoeu13hrYmbSpElBk3zr1q0zyUXtQawJO31Pfa53GH99vWgmPHQbdD+44YYbTJ365JNPTDl0eGItvyb7NDGqibVo0zqu9VTrVOPGjaVVq1amDqj333/f7Is67YbuuxpjTfBq+bS+aRw12fbCCy+E/H6a4NTPU/cD/Yz1xy79nDXm+oVCy6K9d0Oln7MmP9977z1T9tatW0vlypVN+bS+3HffffLyyy+n98IMhSZwtTw6FUa7du3Sh4nWuq6fXaSPE/FGE+h7whiec9SoUX4/XmiiWI/PCI/vrBw6UmocNFCPuBtuyBjhXjt96+8rOVWlmTMzhi5XF18sCUnPN3qOVXqu02NWTvvhzJkz/X7EuDjBgsexzp7+zhrOKM46mrvvb9zLl+e8bwOILM4TQOwjn0Y+LRjyaeTTyKcB2SOfRj4torQBtu9U1Scb8SMD+TR75NMQc43Prr/eM+0mjc+A+DZxov8IaK1aSb4aAU1pkkJ7UWnvPO2lpUkN7SH31FNPmZ5htooVKya33HJL+u2ePXuaBFWotKW9JlE06aM9FmfNmmWSVJrE0LJqIs47RHqo9Iu2fonW19SegFOnTpWSJUuaXma6XhNSoapSpYp5jpZRe1FqskKTHJrcUnfffbf5EUDjEA5NJOi2aQ9N7aGm2+xNvP3www8mmZTdc7295fv27StFixYVG6VLl5avvvrKDPt+xRVXmB69Wi/mzJljegp2797d3NbpYbwuvPBCUz5NsGiPTk2ybNq0Se69915ZvHix6REZjNaJt99+2yTntCesJgY///xz8/nodAL6GZ933nkSbR06dDAX01pPDxw4YD5LTRZrwmfs2LHp0wbkhXfffVfuuusuExstxwcffGAW1aJFC9MjVxO+GiuNs35W2itR64wO8d++ffuQ30tHRtD9Xz8nTczpPqGx16Sc1m9vciocuk9o4lQTrfoa+tnq/qJJL01EKv1sQ6W9SZ977jnTE3XatGnp8dDewNE4TgChJMu8iSLVqZMkpK5ddV/2/K9JsGHDsn+8zkx0ckABQ5OMJ2epSTj6Q4D3OKg/UgzLIXg6rZN3NAalP5p4p/gBAOQ/nCeA+EA+jXxaIPJp5NPIpwFZI5/mQT4tgnzPMQUL6kWTm6UBgMjQk4N24PBtfNalC43PgPxg1y6Rhx/2Hx5X9+9Y5yBh7du3zylZsqRTsGBBZ9u2bW4XB8ikd+/e2lfLeemll5z8RrfLd1m9erUTi0aOHOlXzs2bN7tSjuTk5EwxS042v7Hm6bJ6tTi9e4uzZk1oj//8c3HKlMkoc7Fi4mzcmLdl1jjFQux0ef11/3IMHx78cUeOiNO9u/9jhwxxp8zLlnnef/To0Y6bXn/99YDYDQ/6uCNHjjjdu3cPiN2QGNhfk51YFivHOq9hw4aZcmj9c6Peh7OMHOn/WW/e7E45YulYl9XSs2dG2WrUcL88sXq8C6Znz54+savhxIpYOt5xnkiMOuel+6nnPLHMiTWxFjv9nhVY34DcIp+GWJdI+TSW8BfyafH3HTMe82kx9x0zNdVxSpTQg4hn6djRiTXx8D0pEPk0+4V8WugL+bRsnDjhOHfckXFs06VbN8c5dsyJZcGOdyy213WxdZ6ItZxQzOXTFi1ynLvucpy1a3N+7I8/Ok79+v77d69eTjzk08KaghP5i/Zq096S2lNWh5IH3KA9jXVYZu0N7aXTYGgvSx3CObA3N+Am7UWoHUl0qVdPpG1bEe0kXrWqiHY01/t//13kxx9FJk8WWb0647k6CIHONOPTeT3h9Ovn6b06bZrn9uDBntFjb71VpG5dkbQ0kZUrRUaM8B8Rv2nTnHt45nfaG117tGtPdDV48GCZOHGi3HrrrWb6E50OZuXKlaYH/Aaf4DVt2jTHkXAARNbTT3uWQHqO8NIZ1oIN1KLHQz0GJqqnn37aLMFG7PLSKbGCjXKjx0M9BiYqzhN2qHP2iB0SGfk0xALyaYgn5NNyh3xaBIwd6z99VZ8+bpYGQBDk0yyMH++/4XrS3LdPhx0O/TWGDBG58sqoFA/5FzkhS0eO6FDcnqVRI5HLL/dcFFeqpEOkixw4ILJxo2c+9S+/9Eyv66WjXL/6qsQDGqAlmEWLFpnh7Ddv3myG9Ndh34MdIIC88uKLL8q4cePM9ABVq1Y1Sdzk5GTZsmWLmdrgrbfeksqVK7tdTCCTlBTPEopy5TzJskTP/ero9vqdqHNnkVmzPOt05pyTs+cEpdOZT5jgmTIgkenxcPz48dK5c2cz3YnSaYe8Uw8F06pVKzOVjE6tBiDvaPJfv0vmJNhjfJNqiUgbSem0TjkJ9hjfBEci4jxhhzpnj9gh0ZBPQ6whn4Z4RT4tfOTTIjz9ZsWKItde62ZpAARBPs2Cb8NapWMkzZ4d3mvcfHNEi4TEQE4oAlat8iyh6NjRcy2jPTfiQJLbBUDeWr9+vekJt3jxYmnRooXpJV+7dm23i4UEdtNNN0n79u1l+/bt8uWXX8rXX39tTly6Xn8w6927t9tFBNLVrCny2GMizZuLFC4c+uPXrSNZ5lWihKfx/nvviZx5ZtaP04EEXntNZM4cT14IGrsSMnPmTHnvvffkzGyCp6MwvPbaa+aHsYoEDwASBucJAIge8mmINeTTEE/Ip+Ue+bRcWLNGZOnSjNs9e4oUYmwOAACQx2rW1C9yIqF0FEpK8oxOqMMDT5kiUqGCxIsCOg+n24UAgERTQIfC9bF69Wpp0KCBa+WJdSkpKVK/fn2/dcnJnmH73XL4sGdKgE2bRHbs8IyMqkk0bYCuUwg0bixyxhniOu1VGhA612Pna8UKTx5IY6hOO80zRcC553pGjHbb8uWe8owePVq6d+8usWTFihVm2pUdJ4N32mmnmanUzj333EzHGPf312SpFyuVLg7oaBqPPfaYLFsm0qSJ26WJD7F+rIsHsXy8i1WxfrzjPJF/ffrpp9KjRw9ZtmyZNOFEkS3dB7TO+yINBgDxy81rmPzC7e9J5NMSI5+m+I4ZHr4n5R75tPx3rIsHHOsic7yDHc4TcZxP++03zwF32zaRP/4QOXTIM3Ttqad6ehs0ayZSqlRc5tNo5g8AgAWdulzP/7rAnk5brgvCp1Ot6AIAQDCcJwAAABBryKdFBvk0AACAOFalimfJh5iCEwAAAAAAAAAAAAAAAABghQZoAAAAAAAAAAAAAAAAAAArNEADAAAAAAAAAAAAAAAAAFihARoAAAAAAAAAAAAAAAAAwAoN0AAAAAAAAAAAAAAAAAAAVmiABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKzQAA0AAAAAAAAAAAAAAAAAYIUGaAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYoQEaAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAAAAAAAAAAAAAAAAArBSyexoAAHknLS0tyDpXihJ3gsWJ2CGv99dNmza5UpZ4jN3WrVtl7dq15vb8+SJFiogULOh2yWLf+vWhrUNmustu3ar7qef28uXLpVGjRlKQipejYMe2YMdA+Dt8+HCmdevZYcM6T+h+qubPny9FihRhf80G1yAAAPjjcjU05NOQ18in2SOfZo98mj3yafY4tkUO+bTQkE/L2/2UBmgAgJinFwaZ14k0bOhKceJKkNARO+T5/tqxY0dXyhLvBg1yuwTxrXNnt0sQn4YPH24W2B8DG3KSzdaSJUsyrevMDmtlECcKAAAQJnJCoSGfhrxGPi1y+JqUO3w9t0M+DW4gn2aHfFp00QANAGLA1KlTZeXKlW4XI2Z5W6X7mjtXJDXVleLElSChM71ygq1HZikpnr8LFy50uyhxvb8CQKKYO3eupHKBkq2lS5e6XQQAAICERT4tNOTTco+cWnjIpwEAgPyggOM4jtuFAIBEU6BAAbeLACBESUkiJ064XQoAAABEAmkwAIhf5NOA+EJODQAAILHyaYyABgAxYNy4cVKnTh23ixGzdD7uwCFRX3lFpHVr14oUN+bPzzzs+ODBg6VJkyZuFSmuaC/Nt99+W0aPHi316tVzuzhxu78CQKJ45ZVXpDUXKNmaMGGCPPfcc24XAwAA5APk07JHPs0e+bTI5dQAAMgJ+bSckU9DvKABGgDEgPr160uDBg3cLkbMKlKkSKZ1bduKnHuuK8WJK0FCJ71795ZzCV7INFmmjc9IMoZ+PNMfAHQauuHDh5svj23btpWCBQu6XbSYl5aWJlu3bpWxY8fKp59+an4Y0GMdocvZkSMiixeL/PKLiH4P79mzp/mhpWjRom4XLW7qHfts+I4fPy5btmzxi91dd90lxYoVc7toMS0pKSlTwmzy5MlSt25d18oUL9hfw7dhwwbp1KmT28UAAEQJ+bTskU+zRz4tMmiAZo9r/dCRT7NHPi1y389hj3xaaMin2WN/zVs0QAMAxLxgX7T5AhmaYHEicYFo0i+KHTp0kNTUVHNbey6RoA1dw4YNZeXKleZ/7fRF6ELXuLHI8uWehNmVV14pjXUFQq537LN2NFa+sSNZlrNgiWxNljHSaGjYX8NzgjmfAAAJjHyaPfJpcBvX+uEhn2aPfFpkvp/DHvm00JBPyx3217yTlIfvBQAAAAAAAAAAAAAAAADIR2iABgAAAAAAAAAAAAAAAACwQgM0AAAAAAAAAAAAAAAAAIAVGqABAAAAAAAAAAAAAAAAAKzQAA0AAAAAAAAAAAAAAAAAYIUGaAAAAAAAAAAAAAAAAAAAKzRAAwAAAAAAAAAAAAAAAABYoQEaAAAAAAAAAAAAAAAAAMAKDdAAAAAAAAAAAAAAAAAAAFZogAYAAAAAAAAAAAAAAAAAsEIDNAAAAAAAAAAAAAAAAACAFRqgAQAAAAAAAAAAAAAAAACs0AANAAAAAAAAAAAAAAAAAGCFBmgAAAAAAAAAAAAAAAAAACs0QAMAAAAAAAAAAAAAAAAAWKEBGgAAAAAAAAAAAAAAAADACg3QAAAAAAAAAAAAAAAAAABWaIAGAAAAAAAAAAAAAAAAALBCAzQAAAAAAAAAAAAAAAAAgBUaoAEAgurevbsUKFDAb9myZYvbxQIAAAByLfA6N9ylZs2akqiIHQAgIRUokLuF8x9sUO8AAAAQRwq5XQAAQOyZMmWKjBkzxu1ixCTHEVmzRmTxYpFVq0RSUkS2bhXZvVvk4EGR4sVFypYVqV9fpFUrkR49yPVk5fjx4zJnzhyZOHGiLF68WHbu3Cl79+6V4sWLS7Vq1aRhw4bSpk0bszRo0MDt4sYUYmeP2Nk5flxkzhyRiRM9x7+dO0X27vUc86pVE2nYUKRNG89C2PxR5+wRO3vELvoqVKjgdhHiFrEDACSkBD//kU9zSYLXO0SA7qAffywyY4bIypUiv/8ukpQkUqWKSKNGIjfeKNKpk0jhwm6XNGaRUwsTdQ5uoN7ZI3YIQAM0AICfffv2yd133+12MWLWhx+K9O2b9f0HDniWX34RmTlT5IknRO68U+Q//xEpUyYvSxrbFi1aJP3795dVmnUMsH//fklJSTHLuHHjzLpjx45JoUJctihiZ4/Y2Vm0SKR/f8+PBIH27/f8cKDLybDJsWMihM2DOmeP2NkjdqG56qqrwnq8jgS8bt269Ns99FfRBEXsAAAJKczzn+gsAj7nP9OiKoGRT7NEvYOb9DvjPfd4GhMEWr/es4wf72lgMGqUSOPGbpQyppFTCxN1Dm6g3tkjdggikU9jAIAgBg0aJDt27DD/t2vXTr766iu3ixRzPTZ96RdC7ZFZsaKn15J+cdRrqn37PPefOCHyzjsi330n8vXXnt6cie7dd9+Vfv36ieMTzDJlykjt2rWlXLlycvDgQfn5559lt3aDhR9iZ4/Y2Xn3XZF+/fyPfZr8r11bpFw5Twenn3/29FqHP+qcPWJnj9iFbob2zAxDp06d0htRFSlSRG699VZJVMQOAJCQwjz/mZEevA2BihQRSfDzH/k0S9Q7uEVbfz78sP86TQSdc45nB9YdVofyUtq6qnVrkXnzRJo2daW4sYicWpioc3AD9c4esUMWaIAGAEg3bdo0+ViHShWRa6+9Vrp160YDtAB63aTXSR07ilxyiUiTJplHjtUvld9+K/LQQyJLl3rWLV8ucu+9IqNHS0IbMWKE3w/jLVq0kKeeekouvfRSKRwQyG3btpk6+eGHH0qBAgUk0RE7e8TOzogR/omyFi1EnnpK5NJLMx/3tm3Tc4inV3uCh82gztkjdvaIXfToFKYaL6/OnTtL+fLlXS1TvCB2AICEpD+2+Zz/pHNnkQQ//5FPywPUO0TK1KkiQ4f6T+X65psiXbuKFCyYscPOmiWiM6ls3uwZwvCaazxz7TL1Kzm1cFHn4AbqnT1ih2wkZXcnACBxpKamyp06tr2IlC5dWt5++223ixSTevXyJMMeeEDkwguDT1uuXxTbtBH55huRVq0y1o8Z4/lCmajWrl0rAwcOTP9hfMCAAWaasCuvvDLTD+PqjDPOMNPBfvfdd1LQe9GaoIidPWJnZ+1akYEDMxJlAwZ4pg248srgx70zzvB8l9Te6QkcNoM6Z4/Y2SN20aUdNNLS0tJv9+nTx9XyxBNiBwBISNq50+f8J5z/yKflBeodIkHr0ODBGQmhkiU9O+WNN/onfHSHbddOZOFCkcqVPet0KK9//1sSHTm1MFHn4AbqnT1ihxzQAC0OzJs3z/RK157r4diyZYt5Xk0dyzpO5YdtsDFq1Ciz3b30m7nL8dB6p++p9RD52+DBg+XXX381/z///PNSvXp1t4sU94oWFXnmmYzbej02Z44krL59+8rhw4fN//qD+BtvvMGoKyEidvaInZ2+fUVOhs0kyN54I4F7YYaJOmeP2NkjdtGlI8X5Nt674oorXC1PPCF2cBs5tfjeBhvk1BATfM5/pmUB57+wkE+zRL1DJOjOtmFDxu0HHxSpXz/rx2ujghdeyLitc+d6p1xLUOTUwkSdgxuod/aIHXJAAzQAgMycOTP9x6FWrVqZ6ZsQGc2a+d/esUMS0pIlS2Sh9nQ46fXXX3e1PPGE2NkjdnaWLPF0TPIibKGjztkjdvaIXXQtWLBA1q1bl367d+/ekpREKiUUxA4AkJAWLBDxOf9J794inP/CRj4tTNQ7RMrXX/vfvvXWnJ9zww0ixYpljIzz+eeSqMipWaDOwQ3UO3vEDjngChSII1WrVpWUlBSZPXu220VBPvLXX3/JHXfcYf4vWrSovP/++4yYEUHHjvnfLlNGEtI72qvhpJYtW8rZZ5/tanniCbGzR+zs+IRNWrYUIWyho87ZI3b2iF3ejeCl18jaiAqhIXYAYgk5NbgyCpXm1zj/WSGfFibqHSJl8+aM/0uXFqlVK7RhC885J+P2pEmSqMipWaDOwQ3UO3vEDjkolNMDAMSOwoULyzm+B2ggAh544AH55ZdfzP+PP/44P1pG2Ny5/rf1i2cimjFjRvr/7XTed4SM2NkjdnZ8wiaELTzUOXvEzh6xi579+/fLuHHj0m/r9JE1atRwtUzxgtgBiDXk1JAn9u8X8Tn/mSkQOf9ZIZ8WBuodIunPP+1afvo+dsUKSVTk1CxQ5+AG6p09YoccMAJaFGzfvl3uvfdeqVu3rhQrVkxOOeUU0xP93XfflePHj2f5vI8//lguuOACKVGihJQrV07at28v8+fPz/H9pk6dKm3atJHSpUub92rdurVMnjzZuvzfffedPPjgg9K8eXOpVKmSFClSRCpWrCgdO3aUrwOHVTxp1KhRpkdzr1695O+//5ahQ4fKmWeeaUZT0tfo2bOn/PrrrzG9DdmZN2+e2b5LL71UDh48KI888ojZPv18q1SpIn369Mly+/T9tD40btxYKlSoYGJSrVo1uemmm+T7778Pqxxbtmwx5ahZs2am+3S9d9Sqzz//3EyjWKZMGSlZsqSpf9OmTcvydbXx0e233y6VK1c226R199FHH5VDhw5l+RyNhb6fxkbrqcb2tNNOM1OqaH2w3R9861JqaqoMHjzYbK+3XP/5z3/kxIkT5rEa87vuukuqV69u4qoNp3KacmjChAlm39Kyar3QHrA9evSQ5ORkSUTa8/e9994z/zdq1MjsN4icXbu0gZ9//qdxY0k4euzapcE4Seua2rlzpzz77LPm3KfHR93PdZ/UHyhfeOEF2bt3ryQ6YmeP2NnZssVz7PI6GTbZuVPk2WdFLrhApEIFz4jZVat6jmsvvCCS4GEzqHP2iJ09Yhddn332mfl+66Xf+xAaYgdb5NTIqfkip0ZOLe589pmIz/lPOP9ZIZ8WJuodIsm3gYA2bgzVX39l/P/HHwk5by45NUvUObiBemeP2CEnDiLqu+++c8qVK+doaM844wznpptuctq3b+8UK1bMrLvqqqucI0eOZHrewIEDzf1JSUnOJZdc4tx8881O/fr1ze1//vOf5r42bdpket7w4cPNfbo0b97cueWWW5xmzZqZ24MHDzZ/a9SoEdY2tG3b1rxvw4YNnWuuuca54YYbnCZNmqS/zyuvvJLpOSNHjjT3de7c2TnvvPOcU0891enYsaPTqVMn5/TTT08vx59//hmz25CduXPnmudddNFFTosWLZwSJUqkv27lypXNfZUqVXLWr1+f6bl16tRxihQp4px//vnOdddd53Tp0sV8tvqcQoUKORMmTMgynj179vRbv3nz5izj4d22xx9/3ClQoIDTsmVLU/8aNWpk1uu6iRMnZnpeSkpK+mek26LbpNtWvHhxs7266H0aA19aH3V9//79Tax1m7TetmvXzhkzZoz1/uDddq079erVM2Xr2rWreV0tk953zz33OBs3bjQxr169unPjjTc6l112mVOwYEFz//PPP59pO48dO2Yep/cXLVrUufjii822euOjrz19+nQnr3g/L++yevVqJ6/t37/fqVmzpnl/jd3333+f6THez8O7aB10Q3JycqaYJSfrKSy2lhMnxNm/X5yVK8V57jlxTjsto7xnnSXOr7/mfZk0Tpljl5ynn9+kSZP83l+PDZ988olzyimnZCqb71K6dGnnzTffdNw0evRoU5Zly5a58v7ELjFjN2zYsJOxy/tjxqRJ/vH47jtxPvlEnFNOkRziJs6bb7p/HNaYaXm0/uW1eK5zbu+zxC6xYhf8ui5vr01C5f0upIt+rzl8+LATC9w+x8ZT7PR7VmB9Q+wip0ZOLRA5NXJqgWIhn5atiy7SQnqWcuUcx+VrB/Jp8Z1Pi9d6F3jNymK3uHatP3BgRn3S5eefc36Ono+KFfN/3oIFTiLl0+I9p+ZmPi2e65ziWBenx7s4rHcxk0+Lw9h5sb9KrpZQkXmLIE2oahJDP4C7777bOXr0aPp9mzZtSm/o8cgjj/g9b+rUqWZ9yZIlnW+//dbvvmeffTb9Qw1Mlq1atcokBzRRMX78+Ew7kCZHbBJN06ZNc3777bdM6xctWuSUKVPGKVy4sLN9+/YsG6loAiQ1NTX9vj/++MNp3LixuU+3J1a3IZRkmS5nnnmms3Xr1vT7Dh06ZJI5ep8m0oL9KKQxCLZek2Xly5d3Dh48GLFkmSYqlyxZ4nffE088Ye4766yzMj3vggsuMPdpIkm3xUu3URN93tfNKlmmS7Afr2z3B9+6pAnXv//+O/0+vQjRmHmTc/q6mgTz+t///meep5+x7/OUvo/ed+GFFzo/B5wMte5pPSxbtqyzb98+Jy/EQsJME53e97///vuDPoYGaDkvPXtmf0IuVUoT/+L89Zc75YuFhNmIESP83n/o0KF+t6tUqWJ+KNLkfLAfzB966CEnUX/gJXaJGTs3E2YjRvjHYehQ/9tVqohzySX6A2LwBNpDDyVuwiye65zb+yyxS6zYxUzCLMxyagOXWOH2OTaeYkcDtPhBTo2cGjm1DOTUshbTDdD0esb3R7UYuHYgnxbf+bR4rXde/Mibu8W1a31t3O1bp4YNy/k5moPxfY4u06Y5idYALZ5zaq42QIvjOucpCse6uDzexWG9i5l8WhzGLqMY7K+SiyVUZN4iSHuae5P8wXr3aq88b29z36TEFVdckW3y35toCkyW9e3b16zXHnDBaG83m0RTdrw/aAQmR7wJDk34BUtSjR071tx/+eWXx+w2hJos04RMoF27dpkenHr/woULQ35d7Zmqz/nyyy8jlix77bXXMt2n9dH7g9O2bdvS1y9YsCD9c9uzZ0+2IypklSwL/Exzuz94t71UqVImroG0x6u396fv87y0h67e/80336Sv27t3r+mNqb1Es0qSehtjvf76605ecDthpp+nNxmtSdHAhK0XDdBylzArWlR/cBMnJcW98sVCwuzFF18MGh9N4M+ePdvvsZoAHzVqVKYfyYP1bE+EH3iJXWLGzs2E2YsvBj+eaa/z2bP9H3vsmDijRmVOmk2YkJgJs3iuc27vs8QusWIXMwmzHGgHDd8yakOPWOH2OTaeYkcDtPhBTo2cmt5PTs2DnFrWYroBmnbu9P1RLQauHcinxXc+LV7rnRc/8uZuce1aXxshn3ZaRp0qVUqH/Mz68Tt3Ok7VqpkbFowb5yRaA7R4zqm52gAtjuuc4lgXp8e7OKx3MZNPi8PYebG/Sq6WUCXlOEcnQjZv3jzz9+abb5aiRYtmur9Lly5StmxZ2b9/vyxbtsysS0tLkwULFpj/e/ToEfR1b7vttmzfL6vn9ezZ03JLdM7xvfLxxx/Lgw8+KHfccYf06tXLLN988425f926dUGf16xZM6lcuXKm9fXq1TN/f/3115jfhuyceuqpct1112Vaf/rpp0v79u39tsnXb7/9JiNGjJAhQ4ZI375908uyZs0a67JkpWPHjpnWaX2sXbt2ps/AW1Yte/ny5TM9r1OnTnLKKadk+37dunWL2P7gq2nTpiaugerWrWv+XnbZZVKsWLEs79eYe82dO1cOHTokLVu2lKpVqwYt76WXXmr+Llq0SPK7gwcPSp8+fcw3C6V1s3jx4m4XK241bChy1VWe5corRZo312OF574jR0Ree02kfn2RAQNEjh6VhHT48OFM68444wxZuHChXH755X7rCxUqZI79s2bNkiJFiqSvf/jhh+X48eOSaIidPWJnJ0jY5IwzRBYuFAkImxQqpNdqIrNmifiETR5+WCTBwmZQ5+wRO3vELjqOHTtmvkv6fs8977zzXC1TvCB2sEVOjZyaIqfm/9rk1OLIsWMiPuc/adZMhPNftsinRQD1DtFQooTIY49l3D5wQOSSS0QmTBA5ccL/sV9/LdKypZ4gg9fPBENOzRJ1Dm6g3tkjdshBoZwegNB5kxC1atUKen+BAgXMffv27Ut/rCZ0vD8aZPW8rNZv377d6nk50aTOfffdJ3///XeWj/nrr7+CrtcfO4IpU6ZM0B9IYnEbslOzZk3zOWZXVu82ef373/+WZ555xiTiI1mWrITzGeQUf91W3eZVq1Zl+X56f6T2h1C2o1SpUtneX7p06Uzb+fPPP5u/s2fPzvLz8/r9998lv9MfGb0x0eStJh5hb8gQz+JL2/YtXizy1FMiM2d6br/1lsiOHSITJ0rCKVmyZKZ1w4cPlwoVKmT5nAsuuEDuuece8zi1ceNGk/i+4oorJJEQO3vEzk6QsImGI5uwyQUXiNxzj+dxauNG/aFKJIHCZlDn7BE7e8QuOqZOnSq7d+9Ov63XzAgNsYMtcmrk1BQ5NQ9yanFo6lQRn/OfcP7LEfm0CKDeIVruvVfku+9ERo/23Nbj+w03iGiDa20YX7CgyPr1np3T25pKE0OvvJLxGt4WpQmEnFouUOfgBuqdPWKHbDACGvxor7m77rpLjhw5Iv/5z38kOTlZDhw4ICdOnDCjJb377rvmcd6RkwIlJSXF/Tbklu/rTpw4UZ588knTW1Hfd8OGDSaB5y3L0KFDI16WvP4MojVyVk7bEc52arzVmWeeaXoAZ7e0bdtW8jPdH9544w3zv/asfvHFF90uUr6kOdmLLxaZMUPkvvsy1k+aJPLRR5JwvElsL+0F3rlz5xyf17t3b7/bwXrD53fEzh6xsxMQNtFBG0IImwSETRIsbAZ1zh6xs0fsouPDDz9M/79EiRJyyy23uFqeeELskMjIqeUeObXIIKfmAp/znxkVgvOfFfJpYaLeIZp0dL0nnvAfnmvvXhEdfVZHRPU2KtARN7WFqI5w4ysBGxaQU8sl6hzcQL2zR+yQBUZAiyDvMOTenmHBbN682e+xOkS7JlI0sbNlyxZp0KBBpufo+qzeb9OmTWE/Lzvjx483iZt7773XDLMfSJM9kRRv25Bdebz3VatWLX3duHHjzF/trXnnnXdGtCyR4K2H2W3X1q1b82x/iJbq1aubv2effbaMGjVKEpmORuBNzu7YscNM2RAO3963+gPnn3/+GfEy5jcvvCAybZpOC+K5/frrnuG1E8lpp53md7tx48ZSUHtA5EDPCzotiLf3tZ4vEg2xs0fs7ASETRo39nRYyolexuksPt7BEhIsbAZ1zh6xs0fsIk+nHZs+fbrf9GjekW+QPWKH3CCnFr542wZyauG/Njm1OKFTlvqc/0SnVuX8l2vk03JAvUNetAh98knPyHrvv69DYerJV2TfPk9dq1NH54QW6dPHM+LN22/7Pz+LUT7zM3JquUSdgxuod/aIHbLgfte6fOTSSy81fz/77LNMw+KrSZMmmaHRtZd606ZNzbpChQpJy5MtPj/99NOgr/vJJ58EXd+mTZtsn/extjwN0x9//GH+1qhRI9N9uk2ff/65RFK8bYM2tvniiy+CDjE/Q7tm+dSDnMqiDYFm6QTvLvLGX8vuLauvKVOmWDcwstkfokV7YBYpUsSM7OA7HQyQF3RkWc0Bea1YIXLokCSU+vXr+93WH4pCodN7lCtXLv12sONUfkfs7BE7OwFhM98NQ/2+6RM2SbCwGdQ5e8TOHrGLvI8++kiOHz+efruPJsoQEmKH3CCnFr542wZyaqEjpxZndGgun/Of+ZENuUY+LQfUO+QVbRyuDQzmzxfZuVPkyBHPVGtLlohoY3Xvd9DVq/2fU6WKJBpyahFCnYMbqHf2iB0C0AAtgm644QY544wzTK/fwYMHS1paml+vtCFDhpj/tReh9jb3GjRokPn7+uuvy6JFi/xe84UXXpDly5cHfT99He3drj0CNfHga+zYsfK///0v7G2op/Pynkwc79+/P329Jjv69++f3rsuUuJxG/Rz3L59e/pt7Wk7YMAAMw1A8+bN05OfvmV577335OjRo+nrU1NTzdD0+tdNrVu3liZNmpjpFHQbdFu8fvnlF7n//vvzfH+IhooVK5r30c+oY8eO8tNPP2V6jG67JgfXrl0r+VnhwoXND5OhLqVKlfJ7vo6Y5ns/QnPGGRn/6+wV2gEgkdSpU8dvahHfY01OfJPt0ZqeJJYRO3vEzo52TPLd5DDClt5TUyVY2AzqnD1iZ4/YRd7IkSPT/69bt65ccsklrpYnnhA75AY5tfDF4zaQUwsNObU443P+k7p1RTj/RUyi59OyRb1DrNGGBl4XXSSJiJxaHqPOwQ3UO3vELmHQAC2CdNj/CRMmmJ7kb7/9tpx55ply8803y7XXXmt6pmuC4KqrrpIndD5cH/rlXRMVmrDQ5MVll10m//jHP+Tcc8+VoUOHyj//+c+g76fTqzz33HOmh3GXLl2kRYsW0r17d5OwueWWW9KTcOHo3bu36Vm4YsUKM9Xe9ddfb6bN0HW6bVmVxVa8bcNFF11kPl8ddl4/t5tuuklq165tpig4/fTTM/Uu1fKfeuqpMm3aNPM4LUenTp1MWVatWiW33367uE17A+vUPZqc1DLqNum2nXPOOaaBkW5zXu4P0fL888+b/eq7774z9U6ThPp5aJlatWpltlU/G5spKuKJJnP37NkT8qJJfF+avPfex1RNoQvs9BzmzKdxLykpya8ne3bTiPjSHt26eFWqVEkSDbGzR+zsJCXpiAsZt0MMm/khwPfHgAQLm0Gds0fs7BG7yPr222/9pnSLhe9r8YLYIbfIqYUv3raBnFroyKnFkW+/9Uw15BUD9TI/SfR8Wpaod4g1Wh99G/3feqskInJqeYg6BzdQ7+wRu4RCA7QIu+CCC2TlypUm+aW9ELUH4vz58+X88883CYOpU6eaYcsDvfHGG/Lhhx+axy1ZssQkVypXriyzZ8+Wzp07Z/l+DzzwgEyePNl82V+9erXpbaYjHGmSYuDAgWGXXxM7P/zwg+nVqP9Pnz5dFi9eLO3atTMNTzTJEGnxtA362elnop/vmjVrTG9STfT16tXLvKcm0Xxpsk6TdpoA1Pqgn78myTQRqOurV68ubtPElZZdt0G3RbcpOTnZ9G7UbQ1WX6O9P0SDTs2h01LovqX7lE4boHVt5syZZqoETRCOGTOGXvqIim++yfi/cuXE7MWkPbi9tFd0KIlp3T8dx0m/ffHFF0siInb2iJ0dn7CJDmIQyu9IM2eK+IRNEjBsBnXOHrGzR+wiR7+Te+n3Fx1hB6EhdogEcmrhi6dtIKcWHnJqccLn/CcFC4pw/oso8mlZoN4h1jz7rP/QhddcI4mKnFoeoc7BDdQ7e8QusTgAcjR37ly9/HPatGnjdlGQT2h98l1Wr17txKKRI0f6lXPz5s2ulCM5OTlTzJKTze+mMb/MmydOgQIZ5b777rx9f41T5tgl5/lnmJqa6lSoUCG9DLfffnu2jz969KjTqFGj9McXL17c2b17t5PXRo8ebd5/2bJljluIXeLFbtiwYSdj585xKzVVnAoVMo4Zt9+e/eOPHhWnUaOMxxcvLs7u3e6UXWOmZdD654Z4rXOxsM8Su8SJXfDrury/NgkWxxIlSqSXqWPHjk6scrvOxVPs9HtWYH0DkLfIqSHf5tNSUx2nRAktlGeJofOfF/m0+M+nxWO9C7xmZbFbYuVaP0eTJztOgQIZdfL//s+1oridT4vnnJrb+bR4rXOKY12CHO9ioN7Faj4tHmLnxf4quVpCxQhoAACEaM0az6j2ycmhPX7iRJHrrsvowVSsmMj990tCKlOmjN/0IDpCwcsvvxz0sUePHjVTv2jvdi/tga9TmyQiYmeP2NkpU0bEdzYf7VydRdjk6FGdqknEJ2zSv79IAobNoM7ZI3b2iF1k6PRpBw8eTL/dp08fV8sTT4gdACAhjR0r4nP+E85/WSKfFkHUO+SFtDSdZ9qT9MmK7qDvvy9y440ZO6uOPHvzzZLIyKlZos7BDdQ7e8QO2SiU3Z0AACDDsWMiI0d6lnr1RNq2FTnvPJGqVT1fLvX+338X+fFHkcmTRVavznhugQI6NYxInTqSsPr162emcNFpO9TgwYNl4sSJcuutt0rdunUlLS3NTDEyYsQI2aBzwp/UtGlTGTZsmCQyYmeP2Nnp109k+nSRk2GTwYM9PwLceqtI3bqe75grV4qMGCHiEzZp2lQkgcNmUOfsETt7xC6yU0hWrFhRrr32WlfLE0+IHQBAEn0axIoVRTj/ZYl8WgRR75AXNOlz220iOp23TpN20UWeZFCJEiJ794r89JPI+PGev16tWol8/LGbpY4Z5NQsUOfgBuqdPWKHbNAADQAACykpniUU5cp5kmW33CIJrWDBgjJ+/Hjp3LmzzJo1y6xbsGCBWbLSqlUrmTBhghQvXlwSGbGzR+zsFCzo+Y6onZJOhk00ZNmEzXyHnDBBJIHDZlDn7BE7e8Qud9asWSNLly5Nv92zZ08pVIh0SSiIHQAgYYf08jn/Sc+eIpz/QkI+LReod8hrf/4pMmaMZ8lOt26eFqalSuVVyWIaObVcoM7BDdQ7e8QOQTAFJxCCSy+91EyEPm/ePLeLAuSpXr16mbrvXWrWrCmJTDf/scdEmjcXKVw49MevW0eyzKtEiRIy8//Zuw8wuaryccBfeggQIID0Li200AUCUgWBAAoiKL0JUkRUisoffxQFLCAiICBNRAQEEQQivQtK6AmdhBakBwIEEjL/59xhNrub3ezsyW5mZ/d9n+c+O+XOzJlvz71z55vvnjNyZJx77rnxxS9+sdX1FltssTjjjDPitttuK0bSQOxmhtjlSScsjRwZce65ETMIWyy2WMQZZ0Tcdlv5BGz0uZkhdvnELt9KK63U5Jj3lFNOqXWT6obYAW2RU6NbWmml8lRClcXn3wzJp3UQ/Y5ZJRU2ptH1UmKoNb17R2y0UXmor1RtpaigCTm1dtLnqAX9Lp/YMQNOjwCAKs09d8Txx5eXSZPKUwI8/3zE+PEREyeWk2hp6oA0hcCwYRGLL17rFndNvXr1iv33379YHn744WLkjPEpiBEx//zzF9OBrbzyysV6NCV2+cQuTwrH/vuXl4cfLp9w/XnYYv75y9MDrLxyeT2a0ufyiV0+sQMAoKuRT4M6LCy4/vry/Lhp+rQXXoh4443ySDfzzBOx8MLl6da+8IVat7RLk1NrB32OWtDv8okdM6AADQAyDBwYsdZa5YV8q6++erHQfmKXT+zypJAJWx59Lp/Y5RM7AAC6Gvk0qCOpOnSNNcoLM0VOrUr6HLWg3+UTO1pgCk4AAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACBL37yHAdCRnn/++ejdW03wjOLT3JQpNWlK3WkpTi3Fk6amTJkS48aNi1GjRhXX77777ujfv3/06dOn1k2ry9gNHTo0Bg4cWOumdXmTJk2KW265JZ566qni+t13R/TrF9HXEXtV+7px49L+rXw99b8dd9xRv2tHv2u8zfbr1y/66nht8lnRkcd1DuwAgPaTT5sx+bR88mkd9z2JPPJp1ZNPyyef1nH5NPLJp1VHPo160atUKpVq3QiAnqZXr161bkLdu+66iG23rXUrur7rr48YMaLWraCnu+6662JbG2ybrr/++hhhg+0w+l119Du6Attr+/z5z3+O3XbbLR566KFYY401at2cLu3JJ5+MlVdeuclt0mAA9Us+bebJp1VHPo2uwPek6shrdCz9rjr6HV2B7TUvn0aeavNp6r8BqEup2N/JJW1zciZdweWXXx6PPPJIrZvR5VXO1KRj3H777TFhwoRaN6PLc6YmXYHttX3uvffe4u8NN9wQY8aMqXVzurRXXnml1k0AgC5FPq068ml0BfJp1ZFP61i+n1dHPo2uwPaal0+jcxkBDaAGnLEJAADkStONTZ06tdbNqEvSYAD1Sz4NAADIJZ+WzwhoAHRrRxxxhCmHqjwT5ze/+U2tm0EPd/rpERtuWOtWdH133x1x+OG1bkX3cfrpp8eGOl6b7r777jhcx6PGbK/tk0Y+O/bYY+PSSy+NFVdcsdbN6dKef/752HnnnWvdDADoMuTTqiOfRlcgn1Yd+bSO5ft5deTT6Apsr+0jnzZr8mkK0AC6gGuvvTaWXXbZWjejy/rss89i7NixxXCyKfmTDqq+853vxMCBA2vdtC5vxx13jE022aRJ7DbbbLPo06dPrZvWpU2ZMiXGjRsnbpkHpiNGjGhy22abRay8cs2aVDf695/+tuuui1hmmVq0pv6mR2nW7YptdmUdr039W+h41113XSyj47XJZ0Uex3UzrzLtZkqW+QF5xgYMGFDrJgDQieTTZsxxV8fl08jne1J15NPyyaflk0/LJ5/Wcfk08jiuaz/5tFmTT1OABtAFpINS1dYzlr70VOYyTxX9Dqqqk+K07bbbNomdL5DVWWWVVcStg8gx5scp5Sx8POSR3M6Pk+OS6vmsyOO4DgDoCI5b2+a4q2PyaeTzPSmftEZ15NM6lnxadeTTOi6fRh7HdXRVvWvdAAAAAAAAAAAAAOqTAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAABqqlevXjO1LLnkktETiVs+scsndgBAp/j2t9OBRtNl7Nhat4ruTJ8DAOhQfTv26QCoB6VSKZ588sm4//7749FHH40xY8bEuHHj4o033oiPPvooZptttphnnnli6NChMXz48Nhtt938UPQ5scsjbvnErmN99lnEbbdFXH11xP33R7z+esTbb0fMNlvEootGrLJKxJe/XF5WWil6rFIp4sknyzF69NGIMWMixo2LeOONiI8+Ksdrnnkihg6NGD48YrfdInS7ln322Wdx2223xdVXX11sx6+//nq8/fbbxba76KKLxiqrrBJf/vKXi2WlntzpZtJ8881X6ybUJXHLJ3b5xA6AeuX7eSf6xz8iLrus1q2gJ9Hn2kU+rTryaR1HPi3DpEnljfT66yNGjSpvqBMnRswxR8SCC0asvnrENttE7LhjuTNCR9Dv8nz0UcQll0TcdFPEI49EvPlmRO/eEQsvHLHaahE77xyx/fYR/fpFvVGABtADXXDBBbHffvu1ev/EiROL5eWXX46RI0fGcccdFwcccECccsopMXjw4OjJxC6PuOUTu45z330R3/1uOQHU3AcflJNCabniivJtkydH9O2hR8sXXBAxg25XfIdMy8svR4wcGXHccREHHBBxyikRut009913X3z3u98tfpxq7oMPPih+sErLFZ93usmTJ0ffHtrpttxyy3atP3bs2Hj66acbrqcf93oiccsndvnEDoCeyvfzTvLuuxEHHljrVtCT6HPtIp9WPfm0jiGfluGf/yzv1155Zfr7JkwoL+l7+eWXRxx1VMTZZ0dst10tWkp3ot/lueKKiEMOKRedNffMM+XlyivLhWgXXRQxbFjUkx6+NwbouWdsNpYOztMZmQsssEBxBkk6iH/mmWfi3fRlPCKmTp0a55xzTjz44INxyy23FGdz9lRil0fc8oldx/jDHyIOOqh8JmJFSuwsvXTEkCHlE05eeKF8RiJN45SkHE46I3OBBconKqUEY/oe9Hm3i6lTI845J+LBByNuuaV8NmdP94c//CEOOuigJttw+tFp6aWXjiFDhhQjJLzwwgvFaAmkk71uatf622+/fUNBS//+/WP33XePnkjc8oldPrEDoKfy/byTHH54xPjx5ctf+UrEv/5V6xbR3elzVZNPax/5tJknn5bh0ksj9tyz3KEqUodLI8PNNVfEe++Vh+ZLI1Ulr70WscMOEX/8Y8Tee9es2dQ5/S7PKadEHH1009vSB+oKK5Q/NNKHRBpFLklFuBtuGHHHHRFrrhn1QgEaQA+UEmQbbrhhjBgxIjbaaKNYY401ol+zYTzTAf5dd90VRx11VDzwwAPFbaNGjYpDDz00Lk0HFj2U2OURt3xiN/POO69psuxLX4o4/viIjTeefgTjl16KuOGG8hmLvXpFj5W+66TvNiNGRGy0UcQaa0wfqxTPu+4qn7z0ebcrRtk+9NDy98+e7LzzzmuSLPvSl74Uxx9/fGy88cbTbb8vvfRS3HDDDcVoCr16cqdrhzTtQopZxQ477BDzzjtvTdtUD8Qtn9jlEzsAuhPfzztBOk5I0w8laXqmnXZSDETn0ueqJp/WfvJpM0c+LUPa+L7znWlFQKkA6OSTI/bfv+l0h6laNFWU/vjH5YKgFOODDy7Pm5sqSqE99Ls8118fccwx067PN1/E739fnp60T5/ybSlGN99cHlnuxRfLw2ZuvXW5mC+tXw9KAMxyaffbeHniiSdKXdWkSZNKw4cPb2hrr169SuPGjatJWy699NKiDQ899FCpHohdHnGr79iNHj16un3c6NFF3qAmy5gxURo4cFpbDj44SlOn1q49M1pSnLpS7KpdJk2K0vDh09rcq1eUxo3rCrEbXaqFMWPGlAYOHNiozx1cmjp1aqmranmbrU3sqnXKKac0ae/IkSNr2p56+awQt3xiV7+xS9+zmu/jAKhf8mnd7NjhvfdKpUUWSf/YUmnOOUull14qlS68sHy9srz4Yk2bWImdJX/pUv2uC/c5+bSOzgnVvl1tLfJpTcmnZfrxj5vuw666asbr//WvTdc/8shSrfiMrePP1zrtdzU9Jp48uVRadtlpMZh99lLpySdbX/+110qlhRaatv4hh9RNPq13rQvggJZdd911xRl1aWjZVL2fljvuuKPhcnNpuPd0+9ixY2vS3nqXzqCoxJimBgwYECeddFLD9fTN5Lbbbqtpm+qF2OURt3xiN7399ps2yvMWW0SceWbPPhOzMwwYENGo2xXfiHpyt9tvv/1i0uedbosttogzzzyzZ5+J2QnS2a0Viy++eGy++eY1bU+9ELd8YpdP7IBZTT5t1pJPa53v51U44oiIV18tX06jZiy2WK1bRHenz1VNPq3zyac1JZ+W6c47p10eOrQ8ktKM7LxzxIorTrt+zz2d1za6L/2u/W67LeLZZ6ddP/LIcuxas9BCEaeeOu16mq+5MjVnF2cKTuiCHnnkkdhxxx1j6tSpsemmm8ZCCy1UHGgtuOCCtW4aPdRaa63V5Pr48eNr1pZ6I3Z5xC2f2E3z739H3HvvtOu/+10tW9O9Net20VO73b///e+4t1Gn+51O1+HuueeeePrppxuu77333tG7t/Oq2iJu+cQun9gBs5p8Gl2N7+czMHJkea6+ZPjw8jx/0Jn0uarJp8068mll8mkz4Y03pl1eddXqHpPWGzOmfPmttzqnXXRv+l373XJL0+u77972Y77xjfK0pqk4d8qUiL/9rTyFaRenAA26oL///e8xefLk+PGPf9zkTLlkTGXnTIe65JJL4qOPPirOymd6qT82ls4kpjpil0fc8old05NCKjbYIGL55WvZmu6tWbeLntrtzmnU6TbYYINYXqfr1NGU0g/KqaCFtolbPrHLJ3bArCafNuvJp82Y7+eteP/98o9plSGAzj/f0Ep0Ln2uXeTTZh35tDL5tJkw55zTLleGLWxL4/Xmmafj20T3p9+134svNo3fUku1/Zh0zLLCCulMq/L1a65RgAbkeemll4q/yy677HT3rZB2NHQ4ibIZu/3225tcT18CqI7Y5RG3fGI3zU03Tbv8la/UsiXdX7NuVyQoe6KbGnW6r+h0He6DDz6IK664ouF6mspviSWWqGmb6oG45RO7fGIH1IJ82qwnnzZjvp+34kc/inj55fLl//f/VLfQ+fS5dpFPm3Xk08rk02bCeutFjBpVvnzffRGffhrRv3/r63/ySXm9io026vw20v3od+333nt51caN13344agH5j6ALuRnP/tZcWb4hRdeWFxPZ4in62nZeOONi9sq12fkmmuuieHDhxdn1c0555zFY2+44YZW109nKp588smxxhprFOsPGjQoVlpppfjpT38a77777nTrjx07tmjDkksuGaVSKc4999xYc801Y/bZZ4+55pqrOEC8//77W3ytBx98MI488shYZ511iikQ+vfvHwsssECMGDEibmk+/GQzzzzzTHz3u98tzn5IbUzvb+jQocVtTzzxxHTrp7Yff/zxxXD3qV2zzTZbLL300rHzzjvHjTfe2GTdFKP0nu64444ZtqEn+t///hc/Sl/SG/14NGzYsJq2qV6IXR5xyyd204wdm+Ix7fpqq5X/vv56xM9/HrH22hHzzRcxcGDEIoukWEWcemrE22/XrMl1K8W5UbcrYtkTu106PkrbYMVqn3e6119/PX7+85/H2muvHfPNN18MHDgwFllkkWL7PPXUU+Ntna5qf/3rX+PDDz9suL7vvvvWtD31QtzyiV0+sQNmJfk0+bSuyPfzVtx6a8S555Yvp+9MRx5Z6xbR3elz7SKfNuvIp5XJp82k7343om/fadMi/uQnM17/mGMi3nyzfHmOOSIOOaTz20j3o9+13+BGhWQffNC+UVwr3nmnLuZqVoAGXUhKQuy5556xzDLLNJwVl66nZauttqrqOc4444z4+te/Hp988klsu+22RULpzjvvjG222abFedPfeeed4nWOOeaYeP7552PTTTeNrbfeOt54441iuoKUCEsHgK1JSb1DDjkk5p577uL1UhLs5ptvjk022SQeeOCB6dZP0yD8+te/jkmTJhXPvcMOO8Siiy4a119/fWyxxRbx29/+tsXXueyyy2LVVVeNs88+u3hsamM60EwJtzQ871VXXdVk/UcffTRWWWWVOO644+K5554rEojbb7990b70WqecckpV8eyJUhJ04sSJRQxTIjXFMcUwWW655eLiiy+udRO7LLHLI275xK51lVGJKxZeOOLSS8sjFqfvQ//9bzk5lk6+ee21cj7yqKPKIx+fdVatWl0fSqWIiRPTZ23EySdHrLJKxOfdLpZbLqKndrtHmnW6hRdeOC699NJitI2f/OQn8d///rdIjqVjtNdeey1uvfXWOOqoo2KppZaKs3S6dk/nN2TIkOI4kraJWz6xyyd2wKwknyaf1hX4fl6F9EVyv/3Kl/v0KU+DWPnxEjqDPtdu8mmdRz6tZfJpM2no0IjTTpt2/Ve/ithmm4iRI8vFKp99Vt5o00kE6bi4sm6aAvDKK9NwtjVrOnVMv2u/RRdtWlTWeErO1qSR5Z56qultL7wQXV4J6HL23HPPUto8L7zwwunuS7e3tOkuscQSxe29evUqXXrppU3uu/zyy4vb+/btW3r88ceb3PfNb36zeNy6665beuuttxpu/+CDD0pf/epXi/vWX3/9Jo958cUXG9qRXvfpp59uuG/KlCmlffbZp7jvK1/5ynTtvOGGG0qvvfbadLffd999pcGDB5f69etXeuWVV5rc99///re4Pb2HM844o/TZZ581uX/s2LHFOhUTJ04sLbbYYkUb9thjj+K9NPbee++Vbr755ia3ffnLXy7Wv/3220uzQiV+leWJJ54odYU+19oyxxxzlI444ojS+++/X9N2pr6d2vPQQw+VugqxyyNu3Td2o0ePnq5No0cXufhZupx3XtM2HHNM0+sLLxyljTaK0nrrRWmuuaaP41FHzfo2pzh1hdi1tOy5Z7TR76J0xBFRev/92rSv5diNnqV9/7zzzmvW545p1ucWLm200Ual9dZbrzTXXHO10OeOKnWdbXbWxi6nnYcddlipq+iKnxUV4pZP7LpH7NL3rOb7OKD7kk+TT5vVuvr38y537PDd76Z/Ynn54Q9bXidtv5V10vLii6WuEDtL/lLTfldHfU4+TT5NPq3S5+TTslxzTam05JJN92ctLX36lEojRqQvy6Va8xlbx5+vddrvanpMfNVVTWNywgltPyZ9P20eyxtu6PL5NCOgQTeTzkr89re/3eS2b37zm8VZnFOmTCnO6Kx46aWX4sorryyGyk/D/s8777wN980xxxxx3nnnFcPa3nfffcXSknQWaDqLr6JPnz7FmZ5JOlN08uTJTdb/6le/GgsttNB0z7PeeuvFwQcfXKx/7bXXNrnvxBNPLG5PZ4Yeeuih0bt3013XEkssUZz9WXH++efHyy+/XJwBm868T++lsTR9QDrbk+oMGDAg9tlnn9h///2LKSWontjlEbd8YjfNe+81vf6LX5T/po+sdHbmq6+mz6mI9PH21lsRF12UPh+mrZ9O7P/b32Ztm+vVgAER++wTsf/+5ZOYeqr3mnW6X3ze6dJxUjo789VXXy2OjdIx1VtvvRUXXXRRcUxSkUaT+JtOV9VoSonp/KojbvnELp/YAfVIPk0+raP5ft5Mmqb17LPLl9NohccfX+sW0d3pc1nk02Yd+bQy+bQOkkYdv/328ihUM7LZZhEHHVQewQpmln5Xva9+NWL++Zt+YDYf3ayxNDVxGmK0uTSUZhenAA26mTS9wIxuvyN98frcXXfdFVOnTo3VV1+9GI6/uTSf+pZbbllcvj19gDTTt2/fFqcySMPyzzPPPMWQuC3Nw55uu+SSS+LII48skjB77bVXsaSDyOTpp59uWPezzz4rpiBIDjjggKpicNNNNzX80JESeLQtTQuQ/tdpSVM3rLPOOsU0EEn6P6ZEa5p+IiU1P01DftJA7PKIWz6xq86kSdPflkZ2vvfeiE03bXp7mn0hfUymj5v+/afdfvTR5dGiKU8LkA4J0rLFFhHrrBPxebcrpl1Iv8el748HH1weGbonSlMaNbf44ovHvffeW0zJ1PwYKh2bpWOcNP1RxdFHH10c+9BU+uE0HTtWrLXWWi0eu9KUuOUTu3xiB9Qr+TT5tBy+n1fpo49SxyqP25Ccd17EbLPVulV0Z/pcNvm0jiWf1jb5tA7w4YcRhxxSrhT95z/Lt6X4rL56ecNde+2IQYPKt//rXxFbbx2x7roRzzxT02ZT5/S79hk0KOLYY5sWkm20UcRVV0VMndp03Vtuidhgg3LVd3PNTlTqikx2Dt1Mmvd8Rre/8sorDbelMwdm9JhkmXR2UKN1G0tnXvbr16/Fxw0ePDjefffd6Q4e01mg3//+9+PD9MHUivfT3MeNkmuVdZdffvmoxrhx44q/aY54qvODH/ygWBpLYzDff//9cfzxx8fIkSOL62eddVaMHz8+rr766pq1tasRuzzilk/sqjP77NPf9pvfRMw3X+uPSd+J0nemtF7y3HPlE3ic5J/6XXlpLOVx77+/fBLxyJHl62edFTF+fERP7Hazt9DpfvOb38R8M+h0a6+9djEiRVovee6554ofKY0s0dT1118fb7zxRsP1/fbbr6btqRfilk/s8okdUK/k0+TTcvh+XqVUjfLCC+XL6dhgk01q3SK6O30um3xax5JPa5t82kxKx3vpff/739OKXNKouukEhErxT6Vo5S9/KXfINHzhf/4TMXx4ubp02WVr1nzqlH6X59BDIx58MOLSS8vX33wz4hvfiEgjaq+4YhoWu1yglz4QKpXe6QP29NOnPUelirkLMwIa9DAp6dFRmg/d35aHHnoovvOd7xRnAKZhcUePHh0TJ04szhpN7frDH/7Q4W0kX5pKYv311y/OgE1JzoprrrkmLr744pq2rasTuzzilk/sptd86Po0MnsaEbote+/d9HqjgQ5opleviPXXTyMlRDTqdnHNNRE9sds1n1YnTQewQxWdbu9mna7x6BpMP53foEGDYtddd61pe+qFuOUTu3xiB3RX8mlUy/fzZkaPjjjzzPLlNI3sL39Z6xbR3elzM0U+rfPJpzUlnzaTUmFPpQgojT6VRpo6/PCmRUBJOvlhjz3KhT/zzDOt+GX33Wd9m6l/+l2+Sy6JOO64pkOHptGv77mnPMd1pfjsC18oVyWnkdAaU4AGzGovvvhii7ePHTu2+Lvooos2mRIgeaFyNlALKvdV1p0ZV155ZZEMO/TQQ4vpAlZcccXi7IaUmEmeffbZ6R4z77zzFj9cNJ9KYEbS8LzJUzOaO5l2OfXUU5ucMfu73/2upu2pJ2KXR9zyiV3Z/PM3vT5sWPkEkrastFLEwIHTrj//fMe3rTs69dQ0ssK06z2x283frNMNGzasqqmLVlpppRjYqNM9r9M18dprr8WNN97YcH2nnXYqRgZhxsQtn9jlEzugnsmnyad1Bt/PIyKNjFopjkw/qKUfH1PfbW1pXsWSRhqs3FcHP7jRBehzM0U+bdaST5NPmylpH/fHP067vv/+0xerNJemSzzmmGnXH3ggzS/feW2k+9HvZk6vXhE/+1n5gzIVoqUR4RZYoFyQlkZ+TNOUnnJKuaB+xIhywV5jSy4ZXZ0CNOhm/vSnP7V4+yWpojYiNt5444bbNtpoo+Ksy0ceeSQeffTR6R6ThoZPZ+slm3TAMNXvvPNO8XeJJZaY7r40tcDf/va36W5PB5pbbLFFw3QD1dhqq60azr7v0fO+d6C+ffsWPyBVPPzww/Hxxx/XtE31QuzyiFs+sSsbOrTp9TSKcbXH/0OGTLv++UcXbUijQTfqdvHwwxE9rdsNbdbp0o9+1Ug/HA5p1Okqx0uUpVEiGh/P7bvvvjVtT70Qt3xil0/sgHomnyaf1hl8PwfqjXzarCWfJp82U269NWLKlGnXv/a16h7XfIS5227r2HbRvel3HWPRRcuFaHffHfH66xGffFIuNksjyx155LQP4CeeaPqYhReOrk4BGnQzaTj3yy+/vMltV111VZGMSkmPdLZk4zMbv/GNbxRnUaah/N9OQzx+7sMPP4wDDjigSGSlYePTMrPSGZqVHyY++OCDhtvTa3z3u99t9WzTn/zkJ0XbzzzzzDjrrLOmm1Jg3LhxxXQEFfvtt19xZmpK6uy///7Fe2ns/fffj1tuuWWm309PUzkTNknTPLz77rs1bU89Ebs84pZP7CKWWSZittmmXU/H79WaNGna5cbPwYw16nYxdWpET+t2yyyzTMzWqMOkKZKqlY6FKho/BxEXXnhhw+Vll122+MGXtolbPrHLJ3ZAPZNPk0/rLD3++3maein9gFbtMsccTR+fRq9qfD+0RZ+bKfJps558mnxatpdfbnp9scXa3+mSypR/UA39btb69+dTnSbrrRf1QAEadDPf+973Ytddd4111lknvv3tb8eXvvSlIimWEhxp2PdVV121yfq///3vY7XVVosHHnigOND72te+Vqy/1FJLxfXXX1/8/fOf/9whbUtzsqezNVMiKz1veq10FmC6LSX1Uttbsvbaa8cf//jH4uzNgw8+uHhsauOOO+4Yq6++enH9uuuua1h/jjnmiH/84x+x4IILFj+CpOTZtttuG7vssktssMEGxe0nnnhih7ynnuS9995rcn2eynzdtEns8ohbPrGL6N07jVIw7foMZsdpIiV5Gid6Flyw49vWXTXrdkXOtidJo2A0HhljRlMyNZZ+gGr8I1Q6TqHsrrvuajKl1D777FPT9tQLccsndvnEDqh38mnyaZ2lx38/T1MyvfVW9Uvz+edGjZp2X0+cXo320+dminzarCefJp+WbcCApterHT7vo4+aXv982naoin436zz7bPm4pGL33aMeKECDbiYlna644oriDMeUNHriiSdiww03LBJK3//+96dbPw1ne99998UvfvGLIvH0r3/9q0iUzTfffPHjH/+4OBNyyQ6aT3juueeO//73v8XZmenyjTfeGPfff3985StfiVGjRhVzu7dmjz32KKY2SNO4pAPS9H5uvfXW+PTTT4sk2s4779xk/ZRIe/zxx+OnP/1pLLbYYnHHHXcU8Xj99ddju+22i2MazzVNVe68886GywsttFDPPKMkk9jlEbd8Ylf2jW9Mu/zUUxFjx7b9mJEjIxoPDNABAxb0GI26XSy0UM882zX9oFfx1FNPxdgqOt3IkSObjEbREaNkdBdp+qeK9MPpnnvuWdP21Atxyyd2+cQOqHfyaWXyaR3P93Og3sinzVryafJp2ZpPxffgg9U9rvl6aVo/qJZ+N+v8/OdNR5DbeuuoCyUAZrm0+228PPHEE6Wu7I477ij16tWrob0HHnhgTdpx6aWXFq//0EMPleqF2OURt/qN3ejRo6fbx40eXeQDZvkyYUKU5ptvWjv22WfG63/6aZRWW23a+rPNFqU33ph17U1x6iqxa+9yxx1R6tVrWrsPPHDWvn7LsRtdmtUmTJhQmm+++Rr1uX1muP6nn35aWm211Rr1udlKb7zxRqn22+ysj11LsRw0aFBDm0aMGFHqqrrSZ4W45RO77hm79D2r+T4OgPoln9Y9jh1m6MIL0z962vLiizVtTiV2lvyly/e7LtLn5NM6OidU+1xZNYt8Wpl8Wqbx40ulXr2m7b9WXjkFZ8aPmTq1VNp886b7vVGjSrXgM7ZOP1/ruN/V1THxtdc2jfNf/lI3+TQjoAH0ME8++WQxJc7o0aOrWv/qq68uznJN30iSgQMHxg9/+MPoicQuj7jlE7uZM3hwxHHHTbueBmc57bSW1/300zS1TcSjj0677bvfjZh//uhxnnwyTZ0WUWW3i6uvjthuu2lnug4cGNFTu93gwYPjuEadLo0IdFornS6NOpGmU3q0UadLo1rM3xM7XQsuv/zy+KjR0Oxp1A7aJm75xC6f2AHQE/h+DvQU8ml55NPyyadlStOOjhgx7foTT0R8+9vTT3VYMWVKxKGHRtxyy7Tb1lknDYHb+W2l+9Dv8k2ZEvGnP5U/PFuTPhTOPz8ijVRd+YDYYYeIXXaJetG31g0AYNaaPHlyXHjhhcWy4oorxmabbRarrrpqLLLIIsWBfrr/zTffjMceeyyuvfbaYtqJil69esWZZ54ZyyyzTPREYpdH3PKJ3cw76KCIG2+MuOGG8vUjjigneHbfPWLZZcvH/I88EnHeeRHPPjvtcWuuGXHCCdEjTZ4cceGF5WXFFSM22yxi1VUjFlmknIRM97/5ZsRjj0Vce235O2ZFr14RZ54Z0ZO73UEHHVRMi3TD553uiCOOKH582n333WPZZZeNKVOmFNMgnXfeefFso0635pprxgk9tdO1MZ3fAgssENtss01N21MvxC2f2OUTOwB6At/PgZ5EPq395NNmjnxapl//OuLuuyPefbd8/corI+67r1wZuu66aS73iA8+iBg1KuKiiyKee27aY9N8r6njQXvpd3mmTInYY4+Iww4rT6e53nrlD9VBgyLefjvi8cfLsUx/K4YPj7jkkqgnCtAAerAxY8YUSzWGDBlSJMt23XXXTm9XPRC7POKWT+zy9OlTPmZPJ4ncfHP5tnvuKS+tScf0V11V/i7U06UuV2W3iyFDyt8de3q369OnT1x55ZWxww47xM2fd7p77rmnWFozfPjwuOqqq2I2na5hdIkHHnig4fqee+4Zffv66toWccsndvnEDoCeyPdzoLuTT5s58mntJ5+W6YtfjLjppoivfz3i1VfLt6W/J57Ydsf7858j1l57ljSTbka/mznvvRdx2WXlZUZ22qlc1TzHHFFPTMEJ0MMsueSSceyxx8Y666wT/fr1q3r9p59+uscny8Quj7jlE7uOkU4gGTky4txzy9+NWrPYYhFnnBFx221pBJfosZZcMuLYY8sjYVfR7RrWf/ppybKKQYMGxciRI+Pcc8+NL86g0y222GJxxhlnxG233VaMGkTZSiutVExVVFlOOeWUWjepLohbPrHLJ3YA9BS+n88ie+1Vnm6osqQvnNCZ9LlWyae1j3zazJNPy5Q6XRpS7+ij294I08hUafSlNGfsVlvNqhbSHel37de3b0SaNSB9wLamd++IjTYqD0OaKsHrrPgscVoqQA8z99xzx/HHH18skyZNKqYEeP7552P8+PExceLEIomWpg5IUwgMGzYsFl988Vo3ucsQuzzilk/sOk4ayn7//cvLww+Xv+uMH1++b/75y1MErLxyeb2eLn0fPP748jJpUvl75PPPl+M1cWI5iZamDkhTCAwbFqHbtSxNs7P//vsXy8MPP1yMEpS23WT++ecvpghYeeWVi/UAAICuzfdzoCeST6uefFrHkE+biQ74i19EnHRSxOjR5Tly33qr3PlSscu885bnhF1llXIRDHQE/a59+vaNuP768pzMaZrNF16IeOON8oho88wTsfDC5Wk5v/CFqGf+0wA92MCBA2OttdYqFtpH7PKIWz6x6zirr15eaNvAgRGpy+l2M2f11VcvFgAAoP75fg70RPJp1ZNP6xjyaRnS6EmpKjQtMKvod+3Tr1/EGmuUl27IFJwAAAAAAAAAAABkUYAGAAAAAAAAAABAFgVoAAAAAAAAAAAAZFGABgAAAAAAAAAAQBYFaAAAAAAAAAAAAGRRgAYAAAAAAAAAAEAWBWgAAAAAAAAAAABkUYAGAAAAAAAAAABAFgVoAAAAAAAAAAAAZFGABgAAAAAAAAAAQBYFaAAAAAAAAAAAAGRRgAYAAAAAAAAAAEAWBWgAAAAAAAAAAABkUYAGAAAAAAAAAABAFgVoAAAAAAAAAAAAZFGABgAAAAAAAAAAQBYFaAAAAAAAAAAAAGRRgAYAAAAAAAAAAEAWBWgAAAAAAAAAAABkUYAGAAAAAAAAAABAFgVoAAAAAAAAAAAAZFGABgAAAAAAAAAAQBYFaAAAAAAAAAAAAGTpm/cwAACY3pQpU6a77fnna9KUutNSnFoIJ1FdnJ7X8aryzDPPVHUbLe/vxo0bF6NGjSqu33333dG/f//o06dPrZvWpYlbPrFrP58FAADUA/m0fPJp+eTT8smndVxugzxyQtWTT2u/mfksUIAGAECHSQfyzY0YUZOmdAspnKusUutWdH0tdLsYoeNl22GHHWrdhLp0+OGH17oJdUnc8okdAAB0D/JpHUs+rTryaR1LPo1ZSU4on9h1LgVoAF3A9ddfH4888kitm9Hl3XvvvcXfG264IcaMGVPr5tQVscsjbu3nzKWOlU40EdK2OTkTAAB6Hvm06shtzHzsyKffVUc+rWPJp1VHPg2AjtarVCqVOvxZAZihXr161boJdat3794xderUWjejLoldHnEDAOg+pMEA6pd8Wj65jXxil0/sAAB6Vj7NCGgAXcAVV1wRyyyzTK2bURdnzB177LFx6aWXxoorrljr5tQVscsjbu139913G8K4Ax1xxBGxxhpr1LoZdXGm8G9+85taNwMAAJiF5NOqI7eRT+zyiV37yKcBAN2BAjSALmDo0KGx0kor1boZXV5luPaUtFCQ0T5il0fc8vZn6QeAyy+/PP785z/H6adHbLZZRJ8+tW5Z1/fZZxFjx5aHv085x1R8dtJJJ8XAgQNr3bQub8cdd4xNNtkkbr/99qIQ7fTTT4/NNtss+uh4bfrkk0/i/vvvjwceeCAuvvjiOOaYY2LnnXeOAQMG1LppXd6UKVNi3Lhx+l07iVs+sWu/Z599NrbffvtaNwOATiKfVh25jXxil0/s2kc+rePyaeTzHbN68mn55DbyiFvHxY7OpQANAIAOk4qltt1223jkkUeK6xtuGLHyyrVuVf1IsRo1qnw5JWgVn7Wv302YMKG4vuGGG8bKOl7Vhg0bFoMHDy4SZjvttFNxneqsssoq+l0Gccsndu1jyicAAOqBfFrH5dPI5ztm+8in5ZPbyCNuHRM7OlfvTn5+AAAAAAAAAAAAuikFaAAAAAAAAAAAAGRRgAYAAAAAAAAAAEAWBWgAAAAAAAAAAABkUYAGAAAAAAAAAABAFgVoAAAAAAAAAAAAZFGABgAAAAAAAAAAQBYFaAAAAAAAAAAAAGRRgAYAAAAAAAAAAEAWBWgAAAAAAAAAAABkUYAGAAAAAAAAAABAFgVoAAAAAAAAAAAAZFGABgAAAAAAAAAAQBYFaAAAAAAAAAAAAGRRgAYAAAAAAAAAAEAWBWgAAAAAAAAAAABkUYAGAAAAAAAAAABAFgVoAAAAAAAAAAAAZFGABgAAAAAAAAAAQBYFaAAAAAAAAAAAAGRRgAYAAAAAAAAAAECWvnkPA6CelUqlePLJJ+P++++PRx99NMaMGRPjxo2LN954Iz766KOYbbbZYp555omhQ4fG8OHDY7fddosll1yy1s3uEsQuj7jlE7t8pVLEk09G3H9/xKOPRowZEzFuXMQbb0R89FHEbLNFzDNPxNChEcOHR+y2W4TQlel3+cQuj7jlE7t8YpdP7ADokcaOjVhqqfY/boEFIl5/PXoyxw6Z9LmOkZJAl1wScdNNEY88EvHmmxG9e0csvHDEaqtF7LxzxPbbR/TrV+uWdgnyadSE/d3Ms6/LJ3btY3tlRkoAzHJp99t4eeKJJ2bp659//vnTtWFGS+/evUsHHnhgacKECaVauvTSS4v2PPTQQzVrg9jlEbeeF7sTTjjh89gVeeaaLOefH+2MXZQOPDBKEybUrs1pSTFL7Un9r1bqtd/ZZus3dvUat0Ts6jNuidh1/9il71nN2wJA/ap1Pq304oupEe1fFligVEuOHeo4dnXa57pE7Cr++tdSaf75247ZaquVSg8/XNu2yqd1SD7Nkr/UdHut0/2dfV0+savjuNX59mqJrKVaRkAD6IHKObtp+vbtW5xVuMACCxRnHH7wwQfxzDPPxLvvvlvcP3Xq1DjnnHPiwQcfjFtuuaU4I7GnErs84pZP7PI1C1307Vs+IzOdaJPO1vzgg4hnnon4PHQxdWrEOedEPPhgxC23lM/m7Kn0u3xil0fc8oldPrHLJ3YAEBEbbVT+ctmWIUOip3Ps0EH0ufY55ZSIo4+ePjYrrFBOEqWkUGUkljTU14YbRtxxR8Saa0ZPJp9Gl2B/Vz37unxi1zFsrzSiAA2gB0pJng033DBGjBgRG220UayxxhrRr9nQsSkxdNddd8VRRx0VDzzwQHHbqFGj4tBDD41LL700eiqxyyNu+cQuX/qOmL4TjhhR/g60xhrTj5Kdkmp33RVx1FERn4cuRo2KOPTQiB4cOv1uJohdHnHLJ3b5xC6f2AFARFx8sXnnquTYoYPoc9W7/vqIY46Zdn2++SJ+//uIHXeM6NNnWlLo5psjDjww4sUXIyZOjNh66/L8k2n9Hko+jS7B/q469nX5xK7j2F5prFPHsQOgRTWfMqAdJk2aVBo+fHhDW3v16lUaN25czx1ath3ELo+4dY/YdYUpA9qzTJoUpeHDp+2Xe/WK0rhxPXcKznrtd7bZnhG7rhS3ROy6f9wSsavP2JmCE6B7qXk+rfl0Q+l6HXDsUMexq9M+V/PYTZ5cKi277LS4zT57qfTkk62v/9prpdJCC01b/5BDSrUinzbz+TRL/tKlpvSrk/2dfV0+savTY5NusL1aImupVu8m1WjMtIsuuih69eoVe+21V/Q07733Xhx88MGxxBJLRP/+/Ys4bLzxxp0W0zvuuKPDXqMiDfudnnPs2LEd9pzd2UcffRTXX399HHLIIbHaaqvFnHPOWfzvF1tssdhll13i3nvvbfM5rrzyyuJ/mIZRn3322YvnOfXUU2Py5Mktrv/www/HL37xi9hss82KIdrTmXLpsekMut///vetPq7SX2a0pGHdmd6AAQPipJNOarievs3ddtttNW1TvRC7POKWT+zyDRgQ0Sh0xTcnoauOfpdP7PKIWz6xyyd2+cSOasinyaf1JPJpMGOOHZhpqb88++y060ceGTF0aOvrL7RQxKmnTrue9muVKdeYIfk0qCH7unxiB53GFJzdUEr6jBs3Ll588cXi8qxywAEHFMmP9Jpf//rXY+DAgbFCmiO5G0jJuosvvjguvPDCHpkMbc1ll10W+++/f3E5JUpTEisNp/7oo4/GX//617jiiivihBNOiJ/85CctPv7www+P3/72t8VjNt1005hjjjmKZEIaZv26666Lf/3rXzFbozmjp0yZUgzPnqR111577SJp9sorr8T9998f99xzT1xyySUxcuTImHvuuVt8zbT+Vltt1eJ9yy+/fAdEpXtaa621mlwfP358zdpSb8Quj7jlE7t8zUIXQlc9/S6f2OURt3xil0/s8okd9UA+rePJp7VMPg3a5tiBmXLLLU2v775724/5xjci0r550qS044z4298iDj6405rYncinQY3Y1+UTO+g0CtDoEOksuWuuuaZIkqVkyeDBgzv9NddZZ50YM2ZMDBo0qMOe89Zbby3eyyKLLNJhz9mdpbMl99lnn+KMzdVXX73JWWmnnXZa/OAHP4if/vSnMXz48Pjyl7/c5LF///vfi2RZSnzdeeedDYmwt956q0iepeTXscceG7/61a+aPG7NNdcsEmrbbbddcTZcxeOPPx5bbrllPPjgg3HEEUfEBRdc0GKbUxI3nQVM+zQ/E3ZWbOPdhdjlEbd8Ypev+Un/Qlc9/S6f2OURt3xil0/s8okdtEw+rWeST4O2OXZgprz44rTLc84ZsdRSbT8m7RtTAfgjj5SvX3ONwoIqyadBjdjX5RM76DSm4KRDpDOQ0tl06Wy4WfVlMCXKUvJj8cUX77DnXGaZZYrnTIkg2rbnnnvGH//4xybJsiQNv5+SVukMzuRPf/rTdI/9+c9/Xvw9+uijG5JlyXzzzRdnnXVWcfnMM8+MCRMmNNyXzuz873//G9/4xjeaJMuSVVZZpZhqILn88stbnTqAPLfffnuT6xtssEHN2lJvxC6PuOUTu3zNQhdCVz39Lp/Y5RG3fGKXT+zyiR20TD6tZ5JPg7Y5dmCmvPfetMvt+XxtvO7DD3dsm7ox+TSoEfu6fGIHPbcALX3xTkty3nnnFWdrzT777MVw4FtvvXX8+9//bvFxadj69LixY8fGtddeW5wBNmTIkOK2O+64o2G9p556Kvbee+9iuPP0BTytk77kp6HOW5MSQ6effnrxBT2doTj//PPHjjvuWJwx1pqf/exnxWunvy1JbUr3b7zxxi3e/+qrr8aPfvSj4jXnnHPOIgbLLbdcMXz9fffdV6yTzkJLz5GmC0iWWmqphvg1f99taU9c0nOn9ZL02jmvmRPTtmKWzvhLQ8OnvlIZXj4NJ9+8X7XWb5L0N11P0wUkKSaN31/j/+ezzz5bnL2Y4p5ill4zxWWbbbYpphpoj6uvvjr222+/WHnllWOeeeYpYpKeNz3/008/3eJjUl9IbUr94IknnohvfvObsdBCC0WfPn2atDPF+vzzzy/ilv6vqa3puQ866KB4+eWXoyNVEmnNnzf15//85z/F5W9961vTPS6d4bnYYovFJ598EjfccEO7X+/jjz8uzvykY/zvf/8r9j8Vm2++eQwbNqymbaoXYpdH3PKJXb7//S+iUehi880jhK46+l0+scsjbvnELp/Y5RO72pBPK5NPm558WlPyafJpM+WooyJWXTUiTV3av3+aqzQiFQYeckjEyJFpWLtat7AuOXaYAX2u/QUCH3xQ/ePef3/a5XfeMZdkFeTT6DT2d22zr8sndh3L9ko9TsGZzv5KCZV0psv2229fJFJuvPHGuPnmm4skzte+9rUWH/frX/+6OOtrrbXWKpInr732WpFASP75z3/GTjvtFJMmTYrll18+vv71r8cbb7xRDF9+2223xciRI4uz0RqbOnVqcbZYGu68f//+RdIhJTQeeOCBYgj7lNDoaGkY+9TO9957L77whS8Uiav02imZc9lllxXrrL/++vHFL36xOIPuqquuig8//LBIOKWkTcWCCy5Y1eu1Ny7pNSdOnBh/+9vfikReemx7XrMzYprO2Pv2t79dPHdKwqXEU0rSpITX6NGjq36eFL/0/lLy7fnnny/6X4pzReWLb0pQpfvef//9Imbbbrtt0c9eeeWVuOuuuxpeu1o777xzkcgaOnRokexNSa70Ginxlvr7v/71r+J/3pKUQD3wwAOLZNlGG21UJI9SkjX54IMPiqH2U7IxvbeUgE7JybQ9nXPOOXHllVcW21TzMzBzpSRiktrS2MOfV4WnhF1K1rUkbbMp0ZbW3XXXXdv1eqkfpeduLYFx/PHHF/+TlIhMZ+impGZHnvlb79KUD2kfkvp82s/+5je/iTfffLO4LyXqKwlkpid2ecQtn9jlS995Pvww4vnnI268MeI3v4n4PHSx3HIRQtc6/S6f2OURt3xil0/s8old1yKfJp/WHvJp8mnyae3UvLj0jTfKS/pf/f73ESutlKqAI9Zbr1YtrAuOHdpBn6vOoos2LRZIU621Nb3ap5+mSvKmt73wQtoZd04b65R8GrOM/V3b7OvyiV3Hsr3SWKmLS01My2yzzVa69dZbm9x36qmnFvfNNddcpf/9739N7ltiiSWK+/r06VO69tprp3ve119/vXhcWufEE08sTZ06teG+//znP6V55pmnuO/cc89t8rgzzzyzuH2BBRYojR49uuH2yZMnlw466KCG9u65555NHnfccccVt6e/Lbn99tuL+7/85S83uf2ll15qaOfRRx9d+uSTT5rcn9733Xff3eJ7f/HFF0vtlRuX9Frp9vTa7ZUb09Zi9uqrr5bmmGOO4r7f/va3Te678847S7PPPnvDczbXWuzSa6fbL7zwwhbfw957790Qs+Y++uij4nXb4/LLLy9NnDixyW3pf/H73/++eJ2VVlqpyf+mcRsrfeWzzz6b7nm/9a1vFfdvu+22020zp512WnHfsssuW5oyZUppZj322GOlvn37Fs/5j3/8o8l9Z5xxRnH7sGHDWn38YYcdVqyz0047VfV6KR7rrbde8Zivf/3r091f6S8tLamd3//+94s+N6s0b8MTTzxRqqXG/aelJW1TRxxxROn999+vaTsvvfTSoj0PPfRQqasQuzzi1v1jd8IJJ3weuyKX3CWWPfeMNmIXpSOOiNL779e2nSlmqT2p/3UV9dLvbLPdJ3b1ErdE7LpH3BKx636xS9+zmrelp6i8X/k0+TT5NPm0GZFPa6fUx8v1F+Vl3nlLpXXWKZU226xUWnfdUmm++Zren5a+fUul888v1ZJjhzqOXZ32uZrH7qqrmsbkhBPafkzKwTSP5Q03lGY1+bSZz6dZ8peafk7U6f7Ovi6f2NXpsUk32F4tkbVUq8tPwVnxne98pzhzrbE0DHM6q2vChAnFEOgtSWfbpTPUmkvTD6THpTPWfvKTnzQZPj49Z7ot+eUvf9nkcems0SQNwb7iiis23N63b9/izJxqz4qsVnrO1M4RI0bEL37xi+JMtMbSGZxpePWOkhuXmdHRMU1nk6YzSNdbb7047LDDmtyXzmBMQ+N3tHQWYJKmsWhuttlmK163PdJw/+ns18bS/+K73/1u8b6efPLJGDNmTIuPTWeFnXjiidG7d9PNO63/l7/8JRZeeOHiTN/Udxo7/PDDi/ansx7TmWYzI8U/TQWQzjTdcssti/7bWDpzNGn+HhurnG2czoKtxv/93//F/fffXzzu5JNPnu7+ueaaq3iP6czj8ePHF2fVPfbYY/H973+/iO1pp51WxJfppbOH05nT+++/f8PZv1RH7PKIWz6xyzdgQEQaJGL//SOErn30u3xil0fc8oldPrHLJ3a1JZ8mn1Yt+TT5NPm0KqRte621yiM5pBEv0pSlDzwQccstEWlq4zQM0H//G9FoRMOYMiXtjMvTD1EVxw6N6HN5vvrViPnnn3b9lFOmH7WmsfR5lKYPa27ixM5pXzcin0aHsb9rP/u6fGI3c2yvzEDdFKClxFdL9thjj+JvGgK9JY2Hr2+ssn5rz7vvvvsWf1PyIE0zkKQhxp977rni8m677TbdY9Lw42mo94500003FX8POOCAmBVy4jIzOiOmKSGSpCkDWtLa7TMjTW2QpGRcmlIhTbcws1Jc0nQXKcmT4r7XXnsVSyU59/TTT7f4uB122KFhWozGbrjhhuI0kK9+9autfnFP0zVUph3INXny5GIKiDTFwdJLLx1/+tOforNdcsklxTQAKUl4wQUXxLLLLjvdOmkahJQUS8nLlIQdNGhQMZ1ESsqmKSYqCeNHHnkkeqIUi5TcTMsWW2xR9Om501zdEfHJJ5/EGWecUUxhcfDBB8enaZhZGohdHnHLJ3b5VlklYssty8sWW6TP74jPQxeffBJxxhkRQ4dGHHxweURtptHv8oldHnHLJ3b5xC6f2HVd8mnyadWST5NPk0+rwhJLRPznPxGp6K61aZrWXDPiyivLXzArPvss4pBD0j96ljW1q3PsUCV9Ls+gQRHHHtu0QCAVNV91VZrDuum66cfyDTZIH67TP09PjV8z8mnMEvZ37Wdfl0/sZo7tlRnoG3ViqVY6b+X2V155pcX7l1xyyVYTNTN63vRlZ8iQIfHOO+8Uz53Ocqu8xnzzzddwNlm17cw1bty44u8KK6wQs0JOXGZGZ8S08pyt/e9bu31mpLOH77nnnrjllltiq622in79+sVqq61WJGd22WWXWHvttat+rs8++ywOOeSQ+MMf/lAkuFrT2pmMrb2/F1IF8udntKZlRt5MlckZ0hma6f2mRO8SSywRt912W8zfuIL8c5WEXTprckZnfSaDBw+e4WteeeWVxRlxlYRXSta119e//vUYNmxYkSy77rrriss9zQ9+8INiaSz1v3QWbEpGpkRwun7WWWcVZ7xeffXVNWtrVyN2ecQtn9jlS2FrFrpi/Of77484/vjyyTfp+llnRYwfHyF00+h3+cQuj7jlE7t8YpdP7Lou+TT5tPY+p3xaU/JprZNPa8Ohh0akwrwLLihfT0Wj//hHxI471rplXYJjh06gz00fjwcfjLj00vL1tI9O+7p5541II4emouNnnikngJK+fcs/in8+wmihUmXVw8mn0eXY301jX5dP7GYN22uPUzcjoLWltcRCGq69HkxtXk3LTGs83UE1t8+MdPbfzTffHA8++GDxBXmzzTaLZ555pjgbMJ29lc7UqtZvf/vbOOecc2KBBRYohvYfO3ZsfPzxxw0T2e+6665Zfb7Sx1IyKJ2RO6Nl3XXXbXcMUqIvnQ2bkgGLLbZY3H777UXSbEZJvZdffrnV56vcN6MEZ3qtNDVBem8pwVhJnOWoTFfRWvK9J0rbyvrrr18kQNPUChXXXHNNXHzxxTVtW1cndnnELZ/Y5UuHBeuvn0bJiGgUurjmmgihmzH9Lp/Y5RG3fGKXT+zyiV19kE+jOfm0puTTZkw+rQ2fT7/bYCanae3uHDt0AH2uqUsuiTjuuIjGU3G//XbEPfekoT+nFRWk6ZVT1VQa4aYxhQWtkk+j5uzvprGvyyd2s4bttUepmwK0F198scXbUzIhWXTRRdv1fIssskiTs9iamzBhQnFWYuN1K3/feuuthrPJWmtPc/0/33F98MEHMzwzs7nFF1+8+PvUjOYd7kA5cemI18uJaVvP2drj2vt87ZHOzDz22GPjxhtvjLfffrs4mzAlsNKZWimBVI0rrrii+JsSQCk5lpJOaeqEijRdQ46UxEo22GCDuOiii2a47Lfffu1OlqUpH1LbK8myGZ1pm4bvT1KMWtu2/5vmho6INdZYo8X7//73vxdnh6bXPvvss2P//fePmZHakrQ2nUJPd+qpp8byyy/fcP13v/tdTdtTT8Quj7jlE7t8p54a0Sh0IXTV0+/yiV0eccsndvnELp/Y1Y58mnxae59TPq0p+bQZk09rw9JLl6coqphF+6TuwLFDJn1u+iqpn/0s4vnnywUGw4dHLLBAudBgvvkiUuHwKadEjB4dMWJEefSbxjph9M/uSD6NmrC/m8a+Lp/YzRq21x6lbgrQ/vSnP83w9o033rhdz1dZv7UzZy74fBjAZZddtiEBk5JyS6cNJKI4k665Tz75pEiQtKTyHGPGjGnx/n/+858t3p6Gn68MhV6tSnIuDd/eXjlxmRkzE9PWpGH6k7/85S8t3t/S63RGTPv27Rs77bRTbLnllsX1NBx9NSoJyZbOdnzyySerfp7mvvrVrxZ///GPf8SkSZOio6SzJffYY4+4/PLLG5JlyyyzTJv/98o0Ci39P9L0C+mMzQEDBsTWW2893f1pWP+dd965+H+kZNl3vvOdmZ4q4+677y4upzNsab0/Vzz88MPFmcS0TezyiFs+scuXRtFuFLp4+OEIoauOfpdP7PKIWz6xyyd2+cSuduTT5NOqJZ/WMvm01smnVWmhhaZdfuutWrakrjh2mAn63PRSwX0qMEj7rNdfTx+U5SKCf/874sgjy9OtJU880fQxMzlldk8hn0bN2N81ZV+XT+w6n+21x6ibArT0pfiOO+5octtpp51WDNGezrDad9992/V86eyuwYMHx6hRo+LnP/95k+HX05eZE088sbj8ox/9qMnjDj/88OLvz372syZnUaazxn74wx/Ga6+91uLrbbrpptG7d+8YOXJk3JmGbPxcet0zzjgj/va3v7X4uCOOOKJ4fynJ8dOf/jQmT57c5P433nijSC40Vjl7NSVX2is3LjMjN6atSX0hDeGf4vL73/++yX333ntvcfZke7UV0/ScTz/99HS3v/766w1nHrY2fH5rQ9entjeeSmL8+PFFYionEVo5S3LHHXcsElFf//rXWzxz9cMPP4w///nP8b///a+q50zt23vvvYukV7XJsoof//jHxd+TTz656G+Nz5z87ne/W1w+5JBDYq655mryuBtuuKFIPqQ4pKkVqk2WpakY0pnBzT322GMxYsSIIoGR2r799ttX9Xw9UeUM8sr//t13361pe+qJ2OURt3xil69R6CJ9DAtd9fS7fGKXR9zyiV0+scsndrUhnyafVi35tJbJp8mnzbSPPpp2uU6mN+4qHDtk0ufypUKDivXWq2VL6o58GjVhf5fHvi6f2OWzvfYYfaNOpC/FKem04YYbFmcKPvHEE/H4449Hnz59irMIF1xwwXY93wILLFAkBr7xjW/ET37yk+LMz5RQSAmolNBKX8ZTIqD5MOQHH3xw3HzzzcUZY6uttlpssskmMc8888QDDzxQJDQOOuigIrnXXEomHHroocWX9s0226x4H0OGDIlHH300XnrppTj66KOLxEFLX7KuuuqqIkFw0kknxfnnnx/rrbde9OvXr5hmICWxvvWtb8XwNCTk51JSJCUu0hDuX/nKV4r2VZJcjYet7si4zIzcmM4ouZWG299zzz2LhMu5554bK620UpF4S2flpSTkr371qyKG1dphhx3i//7v/4rkZup76f+ZEqDbbbddsaTXSO8jDZO/8sorF0nHN998s3i9lIhJfTetV20i6aabbirO0k3/xzRk/vvvv1/EP53d+rWvfS2uueaayHHhhRfGe++9V0xpkPpCindqc0qMpgRa6o+ffvppcWZx6gttOfPMM+OSND92RJFsOuGEE1pcb4UVVij6ePOYHnbYYUVMv/SlLxXbxeyzzx633npr0cY0tUHz50v9MCX7UhvT//m+++4rlpak//F8aXjUzx133HHxgx/8IIYNG1a85/T/e/7554ttKCUw0raW+mA6S5SWpf9LY5V9C20Tuzzilk/s8jULXQhd9fS7fGKXR9zyiV0+scsndrUhnyafVi35tNbJp8mnZUujZzz33LTr7dzn9nSOHTLoc/nSVM2NCntj991r2Zq6I5/GLGd/l8e+Lp/Y5bO99iylLi41sdLMs88+uzRs2LDSbLPNVho8eHBpq622Kt17770tPm6JJZYoHvfiiy/O8PlHjx5d2nPPPUuLLrpoqV+/fqW55567tMkmm5Quv/zyVh8zefLk0q9//evS0KFDSwMGDCjNO++8pe233770yCOPlC688MLiddNzNjd16tTicSuuuGKpf//+pSFDhpRGjBhReuihh0q333578bgvf/nLLb7muHHjSt/73vdKyy+/fGngwIGlOeaYo7TccsuV9tlnn9L999/fZN3PPvus9Itf/KK00korFetWYpheo1rtjUuKc3qNFPccOTFtK2Z33HFHaYsttij6yqBBg0prrLFG6Y9//GPppZdeKh630EILtavfXHPNNaUNNtigNOecc5Z69epVrHfccccV911//fWlgw46qLT66quX5p9//uL/m2K38cYbly6++OLSp59+2q54PPbYY6XtttuuaGP6Hy677LKlI488svT+++8XcUivneLSWGu3N5f6x2WXXVbaeuutSwsssEDx/03xXnnllUt777138T6rbW96/5X+NaOltf9R8te//rW00UYbFf+ntG2ndpx88smlTz75pNV+Vs3S/H946qmnFn3qi1/8YmmuueYq9e3bt9gGhw8fXvrlL39ZxHZWat7eJ554otTVpT5TaW9L28+scOmllxavn/ab9UTs8ohbfcfuhBNO+Dx2xe8ydbNsvfW0ffNCC9WmDSlm6fVT/6snXaHf2WZ7Vuy6QtwSses5cUvErr5il75nNf/u1VPIp5XJp8mnyafJp9XUZZelRk9bTjyxJs1w7NCDYtdF+lxdxm6vvabFbfHFS6UpU2rSDPm0mc+nWfKXutleu9D+zr4un9j1kLh1we3VEllLtbp85q29bwjakhJYqU+lZCXUSr0lzFICupIoTsuBBx5Yk3bU44GV2OURt/qPXT0mzO64I0q9ek3bNx94YG0TZvVUgNZV+p1ttufErqvELRG7nhG3ROzqL3YK0HrO+6XzyafRFdRVPu3118s/TlZ+aOvdO1Wp1qQpjh16SOy6UJ+ru9hde22p1KvXtNj95S81a4p82szn0yz5S11sr11sf2dfl0/sekDcuuj2aomspVq9az0CG3SGNA3D66+/Pt3t9957b/zwhz8sLqepD6AnevLJJ2OfffaJ0aNHV7X+1VdfXUx5Uc7zRQwcOLBhO+ppxC6PuOUTu3xPPhmxzz4RVYYurr46Is3u83noYuDAiB4aOv1uJohdHnHLJ3b5xC6f2EH3JZ8GM3D//REHHhjx9NNtr/v44xGbbpo2qmm37bFHxIorRk/k2CGTPjdzpkyJ+NOfIj79tPV1Uh87//yInXeelhDaYYeIXXaJnko+jZqwv8tnX5dP7PLYXmlD37ZWgHp02223xb777hurrbZaLL744tGnT594/vnn49FHH21Iln3ta1+rdTOhJiZPnhwXXnhhsay44oqx2WabxaqrrhqLLLJIDB48uLj/zTffjMceeyyuvfbaeOKJJxoe26tXrzjzzDNjmWWWiZ5I7PKIWz6xyzd5csSFF5aX9H1ms80iVl01YpFFIgYPLt//5psRjz0Wce21EY1CF716RZx5ZkQPDZ1+NxPELo+45RO7fGKXT+yg+5JPgxn45JOIP/yhvKy2WvnHtPQlc8EFI+acM2LixIjnnosYOTLin/+MmDp12mNXXz3it7+NnsqxQyZ9buYLC9KP3IcdFrH11hHrrRex7LIRgwZFvP12+UfxK68s/60YPjzikkuiJ5NPoybs7/LZ1+UTuzy2V9qgAI1u6Utf+lKRFLv77rvjjjvuiA8//DDmnnvu2HzzzYuzrXbddddaNxG6hDFjxhRLNYYMGVIkfGw/ZWKXR9zyiV2+FLYqQxdDhpSTZUJXpt/lE7s84pZP7PKJXT6xg+5FPg2qlIoyPy/MbNOIEREXXFCu3MCxQy59Lt9770Vcdll5mZGddipXXc0xx6xqWZcnn0ZN2N/lsa/LJ3b5bK+0oMtPwVmZvBvaY4UVVojzzz8/nn766XjvvfcazqK6+eabfWGlx1tyySXj2GOPjXXWWSf69etX9fppe+rp24/Y5RG3fGKXb8klI449NmKddSKqCF3D+mnk6B4eOv1uJohdHnHLJ3b5xC6f2NUH+TRyyKdBG18av/nNiIUWanvd3r0jttiiPDzQP/4RMd980ZM5dsikz82cvn0jttmmPJLNjOK20UYRN95YHuVGUYF8GrVhf5fPvi6f2OWxvdIGI6AB9DDp7OXjjz++WCZNmlQMa5+m1Bg/fnxMnDixSASl4e/TMPjDhg0rpt2gTOzyiFs+scs399wRxx9fXiZNKk8J8PzzEePHl0eBTkm0dLJNmkJg2LAIoZtGv8sndnnELZ/Y5RO7fGIHQI/9se3yy8uXX3stYvToiJdeinjnnYiPP46YbbbyF9EvfjFirbX8QNmIY4dM+tzMFxZcf315zsg0fdoLL0S88UZ5pJt55olYeOHydGtf+EKtW9qlyKdRE/Z3+ezr8oldHtsrbVCABtCDDRw4MNZaa61ioX3ELo+45RO7fAMHlr/rCF376Xf5xC6PuOUTu3xil0/sAOiR0g+SaaHdHDtk0ufypYqpNdYoL7SLfBo1YX+Xx74un9jls71Sj1NwAgAAAAAAAAAA0DUpQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsvTNexgAUA8mTZoUt9xyS4waNaq4fvfdd0e/fv2ib1+HADMyZcqUGDduXJO49e/fP/r06VPrptVN7J566qni+t13R/TvHyF0bZsyJWLcuIjnny9fT/1vtdVW0++qYJvNJ3YdG7uhQ4fGwIEDa900AABgJsin5fMdM498Wsfl08hne62efV0++TTo3hwtA0A3lpJlI0aMaLh++OGH17Q99Urc8gldvt/85jfFQvvZZvOJ3czFbpllloltt9221k0BAABmgnxaxxG7PMJGLdhe84ldPvk06F4UoAF0Addff3088sgjtW5Gl3fvvfcWf2+44YYYM2ZMrZtTFypnkQAAne/222+PCRMm1LoZdcFxXT6xq94rr7xS6yYA0Ink06rj2KH95NMAYNaRT6ue47qZjx2dq1epVCp18msA0EyvXr1q3YS61bt375g6dWqtmwEAwExyXJdP7PJJgwHUL/m0fI4dAAC6B8d1+cSu8/NpRkAD6AKuuOKKYohZZixV9B977LFx6aWXxoorrljr5tSFu+++2/DPADCLnH766bHhhhvWuhl1wXFdPrGr3vPPPx8777xzrZsBADWXfmhz7FA9+TQAmHXk06onJ5RP7GZNPk0BGkAXMHTo0FhppZVq3YwurzKcbDowWGONNWrdnLrQv3//6W677rrrFDy2YcqUKTFu3Lhi6Off/OY3xRegzTbbLPr06VPrpnV5YpdP7PKJXT6xm7kv4iNGjGhyW4rdyiuvXLM21RPHdfnErnoDBgyodRMAoMtw7FA9+bSO+45JHt/NqyevkU/s8tnXdSz5tOrJCeUTu1mTT1OABgDdWEtfFlOyTHV/21ZZZZWYMGFCcTmdfeMLUPXELp/Y5RO7fGLXcSRpAQCg/smnddx3TPL4bt4+8hr5xC6ffV3HkU+D7qN3rRsAAAAAAAAAAABAfVKABgAAAAAAAAAAQBYFaAAAAAAAAAAAAGRRgAYAAAAAAAAAAEAWBWgAAAAAAAAAAABkUYAGAAAAAAAAAABAFgVoAAAAAAAAAAAAZFGABgAAAAAAAAAAQBYFaAAAAAAAAAAAAGRRgAYAAAAAAAAAAEAWBWgAAAAAAAAAAABkUYAGAAAAAAAAAABAFgVoAAAAAAAAAAAAZFGABgAAAAAAAAAAQBYFaAAAAAAAAAAAAGRRgAYAAAAAAAAAAEAWBWgAAAAAAAAAAABkUYAGAAAAAAAAAABAFgVoAAAAAAAAAAAAZFGABgAAAAAAAAAAQJa+eQ8DoJ6VSqV48skn4/77749HH300xowZE+PGjYs33ngjPvroo5htttlinnnmiaFDh8bw4cNjt912iyWXXLLWze4SxC5Pr169ZurxSyyxRIwdOzZ6GnHLJ3b5xC6f2OUTu3yOTfKJXT6xAwA63Le/HXHZZU1ve/HFiB58DOF7EjU3aVLE1VdHXH99xKhREa+/HjFxYsQcc0QsuGDE6qtHbLNNxI47Rsw2W61b27WIXdXs6+iyHJu0SE4oX6kHxE4BGkAPdMEFF8R+++3X6v0TJ04slpdffjlGjhwZxx13XBxwwAFxyimnxODBg6MnE7vamG+++WrdhLokbvnELp/Y5RO7fD05do5N8oldPrEDADrUP/4x/Q+8zLSe/D2JDvDPf0YceGDEK69Mf9+ECeXl6acjLr884qijIs4+O2K77WrR0q5H7GYp+zo6hWOTVskJ5bugB8ROARpAD62wbqxv375FBfUCCyxQVFd/8MEH8cwzz8S7775b3D916tQ455xz4sEHH4xbbrmlqL7uqcQuz5Zbbtmu9dMZS0+nL+GfS1X+PZG45RO7fGKXT+zyiV0+xyb5xC6f2AEAHSYdL6RCDabjexI1c+mlEXvumQ7kp92WRulaaaWIueaKeO+9iCefLI/ylbz2WsQOO0T88Y8Re+8dPZrYtZt9HV2OY5MZkhPKV+oBsVOABtADpQ+0DTfcMEaMGBEbbbRRrLHGGtGvX7/pPgTvuuuuOOqoo+KBBx4obhs1alQceuihcWn6EtVDiV2em266qV3rb7/99g1fIvv37x+777579ETilk/s8oldPrHLJ3b5HJvkE7t8YgcAdJjDD48YP758+StfifjXv2rdoi7D9yRq4qWXIr7znWkFVKl46uSTI/bfv+lUkR99FPGHP0T8+MflYqr0o/rBB0d8+csRSy8dPZLYZbGvo8txbDJDckL5+vaA2PWudQMAmPX22muv4sPrRz/6Uay77rrTfbglvXr1ii9/+ctx5513FvNMV1x22WXxUvoi1UOJXed7/fXX44Ybbmi4vsMOO8S8885b0zbVA3HLJ3b5xC6f2OUTu6Ycm+QTu3xiBwB0iHRcf8kl5cvbbBOx6661blHd8j2JDpMKo1KBVMWf/hRx2GFNC6iSQYMivv/9iIsvnnbbxx+XH99TiV2ns6+j0zk2aZOcUL69ekDsFKABMEMDBgyIk046qUnl9W233VbTNtULsctzySWXxJQpUxqu77vvvjVtT70Qt3xil0/s8oldPrHL59gkn9jlEzsAoEUTJkQccED58pxzRpx9dq1bVNd8T6LD3HnntMtDh0bsuOOM199554gVV5x2/Z57oscSu05nX0encmzS4eSEel7sFKABNfGzn/2sqOBNf+n61lprrSbXx1eGnqVNYtd+F1xwQcPlxRdfPDbffPOatqdeiFs+scsndvnELp/YzRzHJvnELp/YAXQcOTW6jSOOiHj11fLlNEXdYovVukV1zfckOswbb0y7vOqq1T2m8XpvvRU9lth1Ovs6OpVjk04hJ9SzYqcADYA2TZ48ucn1wYMH16wt9Ubs2ueee+6Jp59+uuH63nvvHb17O1xpi7jlE7t8YpdP7PKJ3cxzbJJP7PKJHQDQxMiRqYqgfDlNLXTQQbVuUV3zPYkOlUb9qZg0qbrHNF5vnnmixxK7TmVfR6dybNJp5IR6Vuz61roBQM90yCGHxC677BLzzTdfrZtCFW6//fYm1zfYYIOataXeiF3+GUzpjO70JZK2iVs+scsndvnELp/YzTzHJvnELp/YAXQcOTXq3vvvR+y/f/nygAER55+fDu5r3aq65nsSHWq99SJGjSpfvu++iE8/jejfv/X1P/mkvF7FRhtFjyV2ncq+jk7j2KRTyQn1rNgpCwZqIiXJVlhhBcmyOvC///0vfvSjHzVcT0MaDxs2rKZtqhdi1z4ffPBBXHHFFU3itcQSS9S0TfVA3PKJXT6xyyd2+cRu5jk2ySd2+cQOoGPJqVH30nHByy+XL/+//xex/PK1blFd8z2JDvfd70b07TttSsmf/GTG6x9zTMSbb5YvzzFHqpSOHkvsOo19HZ3KsUmnkRPqebFTgAa0Kp1BkJbkvPPOizXXXDNmn332mHvuuWPrrbeOf//73y0+bskllyweN3bs2Lj22mtj0003jSFDhhS33XHHHcU6P/vZz4rr6W9jF110UXH7XnvtFRMmTIgjjjiieL6BAwfGsssuG6ecckpMnTq1WPfVV1+N73znO7HYYovFgAEDYvnll4/f/e53M3xPV111VWy11VYx//zzR//+/WORRRaJ3XbbLUaPHt1BUat/pVIpJk6cGI8++micfPLJscoqq8Rzzz1X3LfccsvFxRdfXOsmdlliN3P++te/xocffthwfd99961pe+qFuOUTu3xil0/s8old+zk2ySd2+cQOQE4NWnXrrRHnnlu+vNpqEUceWesW1T3fk+hwQ4dGnHbatOu/+lXENtuUp6d7552Izz6LePvtiBtvjNhqq2nrpuknr7wyYvHFo8cSu05jX0encWzSoeSE8nWX2JmCE2hTSlidfvrpxbCO22+/fTz++ONx4403xs0331yccfC1r32txcf9+te/jjPPPDPWWmutIkH12muvRZ8+fap6zffeey/WW2+9ePvtt2PDDTcszm64++674+ijj45XXnklDj/88Bg+fHj069cv1l9//XjzzTfjrrvuisMOOyw++uijOOqoo5o835QpU+Lb3/520d6UWEuJv5Qoe+aZZ+LPf/5zXH311cWS2tkTpeTkjD645phjjjjggAOK5Oac6csQDcSuc4bQTgn2HXbYoabtqRfilk/s8oldPrHLJ3bVcWyST+zyiR1Ay+TUoJGJEyP22698OfXnNL1VZaQgsvmeRKdII3EtumjE978fMXZsxA03lJeWpO15660jfvGLiJVWmtUt7XrErlPY19EpHJt0CDmhfHt1w9jZgoA2nXPOOXHLLbcUZ11W/PKXv4wjjzyymGM9JdG+8IUvTPe4s88+uzhbc7vttmv3a6bHjRgxIv773//GoEGDittGjRoV6667bpx11llx2223FQeY6ezMvp8fDKTHpNt+/vOfx6GHHtrwuOS4444rEmXp8X/5y19iqaWWanIG5y677BLf+ta34oUXXijORmWalFzcZ599Yv/996+bD7euQuyqN2bMmLj//vsbrqezqFP8mDFxyyd2+cQun9jlE7uO4dgkn9jlEzugJ5NTg0ZScWMqxkhSYcZaa9W6RXXP9yQ6VSrwSVN9pYKqf/6z9fU22yzioIPKo39RJnYdyr6OTuPYpNPJCfW82JmCE2hTGpK/caIsSXMOp7Mw05D+56eK8BbsueeeWYmySkVvet7GCa811lijmKYgTReQhqA87bTTGhJlSTqTNA1H+f777xdJtop33nmnWDdNOfC3v/2tSaIs2WmnnYr3+O6778all14aPVGK25ZbblksW2yxRayzzjoNScNPPvkkzjjjjBg6dGgcfPDB8emnn9a6uV2K2HX8GUyJIbSrI275xC6f2OUTu3xiVz3HJvnELp/YAbRMTg0+l6aQPfvs8uVllok4/vhat6hb8D2JTpOmOkzFU8stN62Aqn//iNVXj0ifa2uvHVH5nPnXv8qjeK27bsQzz9S02V2C2HU4+zo6hWOTDiMnlG+Vbhg7BWhAm1LSqyV77LFH8feO9CHdgpSEypWG82/pDNBll122+LvJJpsUya/W7k9TE1Tcfvvt8fHHHxdnlaYpAlqy8cYbF3/vu+++6Il+8IMfxE033VQs//rXv+KBBx4okoz33ntv8aFXmXs6nSmbzmxlGrGbeZMnT45LLrmk4XpKxK+66qo1bVM9ELd8YpdP7PKJXT6xax/HJvnELp/YAbRMTg0i4qOPUrVAOhgoXz/vvIjZZqt1q+qe70l0agHV5ptH/P73qaOVi6VOOy3i3XfTkJoRt94a8eCDac7niDRt2HzzlR/3n/9EDB8e8eyz0WOJXYezr6NTODbpUHJC+X7QDWOnAA1oU/OzG5vf/sorr7R4/5JLLpn9mosvvnirZ3HO6P7KEJSTJk1quC1NAZDceuut0atXrxaXnXfeuVjnzTffzG5zd5Pisv766xcfet9PQ89+7pprrpnhfNSIXXtdf/318cYbbzRc32+//WrannohbvnELp/Y5RO7fGI38xyb5BO7fGIHIKcGhaOPTp2pfDkdy2+ySa1b1C34nkSn+cEPIv7972kjd6VRug4/fNqoXRX9+qWK6oh7742YZ57ybemzYPfdo8cSuw5nX0encGzS6eSE8tV77KaNsw2QKVXetmS2magW792790zd31iaXiD54he/WJyxOSMrrLBC1c/bk5x66qlxww03xNNPP11c/93vftfqWbw0JXbtG0I7TRGy66671rQ99ULc8oldPrHLJ3b5xK5jOTbJJ3b5xA6gZXJqdHujR0eceWb58kILRfzyl7VuUbfhexKdYvz4iD/+cdr1/fePaGP/X0w1ecwxEUceWb7+wAMRd90VsdFG0aOIXaewr6PDOTaZ5eSEelbsFKABbXrxxRdj2LBh090+duzY4u+iiy4aXdliiy1W/F1++eXjoosuqnVz6lLfvn2L6R9OOumk4vrDDz9cTMEwMwnRnkLsZixN7XHjjTc2XE+xGjx4cE3bVA/ELZ/Y5RO7fGKXT+w6nmOTfGKXT+yAnkpOjR4vjVpTKbRMxRmVkX6q1XgUwbnmKk9bh+9JdJ40ReSUKdOuf+1r1T1uhx2mFVElt93W84qoxK7D2dfRKRybzHJyQj0rdqbgBNr0pz/9aYa3b7zxxtGVbbbZZtG/f/+44447mgzVS/s0nqIhnQH77rvv1rQ99UTsWpeGi/3ss88aru+77741bU+9ELd8YpdP7PKJXT6x6xyOTfKJXT6xA3oiOTWgM/ieRKd5+eWm1z8vRG5T8+mdU1FHTyN2Hc6+DroPOaGeEzsFaECbzj777CLR1Nhpp50WDz74YMw555xd/qBvgQUWiEMPPTQ+/PDDGDFiRDz++OPTrfPJJ5/EP/7xj3jqqadq0sZ68F6zKv552ntWQA8mdq278MILGy4vu+yysZGzu6oibvnELp/Y5RO7fGLXORyb5BO7fGIH9ERyavR4/fpFzDtv9cscczR9fDpeaHw/Bd+T6DQDBjS9/vHH1T3uo4+aXh80KHocsetw9nV0CscmNSEn1HNiZwpOoE3f+c53YtNNN40NN9wwFllkkXjiiSeKhFOfPn2K+dcXXHDB6OpOPvnkGD9+fFx22WXF1AerrbZaLL300sXQla+88ko88sgjRTItDee7wgor1Lq5XdKdd97ZcHmhhRbq0sN7djVi17K77rornn322Ybr++yzT03bUy/ELZ/Y5RO7fGKXT+w6j2OTfGKXT+yAnkhOjR5vgw0i3nqr+vXTVK977z3t+qhREUsu2SlNq1e+J9GpFl646fUHH4xYbbW2H5fWa6yLTzHdKcSuQ9nX0Wkcm9SEnFDPiZ0R0IA2pTMzzzrrrHj//ffj73//e4wbNy622mqr4gAwzTtcD1JS7M9//nPccMMNscMOOxTTBqSzM0eOHBnvvPNOcRZnSqQ5g6L1D7eUSKzYfvvta9qeeiJ2rUvJ9oqUfN9zzz1r2p56IW75xC6f2OUTu3xi1zkcm+QTu3xiB/RUcmpAR/M9iU6Vpobu1Wva9TPOiJg8ecaPKZUifvWrprdtumn0OGLXoezroPuQE+phsSsBtCLtIuwmOje2leWJJ56YZa+dXmvvvfcuPfnkk1Wt/7e//a00ePDghrYOHDiw9Nxzz5Vq4dJLLy3a8NBDD9Xk9esxdqNHj56uv6Xbam3ChAmlQYMGNbRpxIgRpa6o1n2uXuOWiF0+scsndvnErj4/Y+vx2KSr9Dmx6xmxS21tvp0CzAr2OZ2j+T7d0r6lqxzrz9CFF6Z/9LTlxRd77LF+vX5Pan7MaqnD7XW77Zpuh9/4Rqn04Yctrzt5cql08MFN119nnVJP/J5UELsOYV/X85au8Bnb1Y9Nusr2Wk85oebEbtbk00zBCdDDTJ48OS688MJiWXHFFWOzzTaLVVddtZgKYvDgwcX9b775Zjz22GNx7bXXFtNDVPTq1SvOPPPMWGaZZaInEruOc/nll8dHH33UcH3fffetaXvqhbjlE7t8YpdP7PKJXXUcm+QTu3xiBwBQG74nMUv8+tcRd98d8e675etXXhlx333lKejWXTdi7rkjPvigPA1dmpruueemPTZNC3bmmdFjiV2HsK+DrktOKN/kHhI7BWgAPdiYMWOKpRpDhgwpPtx23XXXTm9XPRC7jhtCe4EFFohtttmmpu2pF+KWT+zyiV0+scsndu3n2CSf2OUTOwCAWcf3JGaJL34x4qabIr7+9YhXXy3flv6eeOKMHzdkSMSf/xyx9trRY4ldh7Cvg/ogJ5RvTDeOXe9aNwCAWWvJJZeMY489NtZZZ53o169f1es//fTTdfPh1lnErmM8+eST8cADDzRc33PPPaNvXzXxbRG3fGKXT+zyiV0+saueY5N8YpdP7AAAZj3fk5il1lknIo28cvTRqQJoxuumUb0OOyx10oittppVLey6xG6m2NdB1yYnlG/JHhI7e2ygVaU0mzXdztxzzx3HH398sUyaNKkYwvP555+P8ePHx8SJE4sPvTTUZxryc9iwYbH44ovXusldhth1jJVWWsn+JYO45RO7fGKXT+zyiV31HJvkE7t8YgfQNscykGmvvcoL0/E9iVkuFUf94hcRJ50UMXp0xCOPRLz1VsTEiRGDBkXMO2/EqqtGrLJKhAKhpsQum30dXY5jkybkhPLN3UNi51MNoAcbOHBgrLXWWsVC+4gdANCVODbJJ3b5xA4AALq53r0jVl65vNA+Ygd0Y3JC+QZ249iZghMAAAAAAAAAAIAsCtAAAAAAAAAAAADIogANAAAAAAAAAACALArQAAAAAAAAAAAAyKIADQAAAAAAAAAAgCwK0AAAAAAAAAAAAMiiAA0AAAAAAAAAAIAsCtAAAAAAAAAAAADIogANAAAAAAAAAACALArQAAAAAAAAAAAAyKIADQAAAAAAAAAAgCwK0AAAAAAAAAAAAMiiAA0AAAAAAAAAAIAsCtAAAAAAAAAAAADIogANAAAAAAAAAACALArQAAAAAAAAAAAAyKIADQAAAAAAAAAAgCwK0AAAAAAAAAAAAMiiAA0AAAAAAAAAAIAsCtAAAAAAAAAAAADIogANAAAAAAAAAACALArQAAAAAAAAAAAAyNI372EAdKQnn3yy1k2oC6+88krx9/nnn48BAwbUujl1IcWquWeffTamTp1ak/bUG30un9jlE7t8YpdP7NrPZ+zM0efyiV31nnvuuVo3AQC6DMcO1XOs33HHrOSxvbaf70n5xC6ffV3H8BlbPdtrPrGbNfm0XqVSqZT9aACy9OrVq9ZNAAAA6HGkwQDql3waAABA182nmYITAAAAAAAAAACALArQAAAAAAAAAAAAyKIADQAAAAAAAAAAgCy9StVO1gkAAAAAAAAAAACNGAENAAAAAAAAAACALArQAAAAAAAAAAAAyKIADQAAAAAAAAAAgCwK0AAAAAAAAAAAAMiiAA0AAAAAAAAAAIAsCtAAAAAAAAAAAADIogANAAAAAAAAAACALArQAAAAAAAAAAAAyKIADQAAAAAAAAAAgCwK0AAAAAAAAAAAAMiiAA0AAAAAAAAAAIAsCtAAAAAAAAAAAADIogANAAAAAAAAAACALArQAAAAAAAAAAAAyKIADQAAAAAAAAAAgCwK0AAAAAAAAAAAAMiiAA0AAAAAAAAAAIAsCtAAAAAAAAAAAADIogANAAAAAAAAgB7hoosuil69ehVLnz594uWXX651kwCg7ilAAwAAAAAAAKDHFKBVTJ06NS6++OKatqcjfPrpp7VuAgA9nAI0AAAAAAAAALq9F198Me66667i8lprrVX8bVyANnbs2IbR0VKh2rbbbhuDBg2KpZZaKv74xz82rPfZZ5/FMcccE0svvXQMHDgwhgwZUjzfL3/5y+L+n/70p8VzrLTSSg2PGTp0aHHbWWedVVy/7bbbGl7r9ddfL26bMGFCfO9734slllgi+vfvH4suumgcccQR8dFHHzU8z1577VU8ZuONN45TTz21WCe1ofnobum9AMCsogANAAAAAAAAgG4vFZuVSqVYcMEF47zzzitue+655+Kee+6Zbt0DDjggnnzyyejXr19RzJWuP/XUU8V9v//97+Pkk0+Ol156KZZffvmYd9554/HHH49//vOfxf2pOCwZM2ZMvPvuu/HOO+80PLbyWnfffXfxNz0+tSeNYpYed8YZZ8Qbb7wRK664Yrz99ttx2mmnxYgRI4p2N3b//fcXRXCDBw8uCuAAoJYUoAEAAAAAAADQraUCrksuuaS4/K1vfSuGDRsWq6666nTTclZsv/328cILLzQUiqXpOu+4447i8rPPPlv83XvvvePRRx8trqdiscoIaOuvv34xgll6zXvvvbdY0uVULFYpQKv8rRSr/eUvf4lHHnmkeNxjjz1WPO+///3vhtHS0tJYKli7/vrrY/To0fG///2vuG2uueYqCtrSkgrnAGBWUYAGAAAAAAAAQLd25513FlNwJrvvvnuTv1deeWWTaS6Tb3/728VUlmnqzIpKoVeamjPdd/7558ciiywSm2yySZx44okNI5GlaTvXXnvt4nKlAK13796x//77x8svv1y0o1JcVilAe/DBBxsKy5Zbbrni+VORXEVl/YpUZPbVr361uNynT5/i79e+9rVipLW0pHYBwKzSd5a9EgAAAAAAAADUQONRzipFX1OmTCn+vv/++3H11VfH8OHDG9aZe+65i799+077Sb0yDeaWW24Zo0aNKgrX0khlDz/8cDE6WnqNNKXnHHPMUbxGKjxLI52lx6288sqxzTbbxK9//es488wzY+LEiU3aUpFGQFt99dWna/8888zT5PoCCyzQIXEBgI5gBDQAAAAAAAAAuq1U7HXVVVc1XJ8wYUKxfPjhhw23tTQNZ2vSFJnzzz9/nHTSScU0mA899FDDCGlPP/10k8Ky//znP/Hf//43Nthgg1h33XWLgrZzzz23YRSzBRdcsLhcGTHts88+i7POOqsY8SwtqbDtRz/6UTFtaGNphLTmrrnmmlhhhRWK5dVXX21XjABgZihAAwAAAAAAAKDbSsVnlWKzJ554ohiRrLKcfvrpxe233357MT1mNa644opYbLHFYvHFF48111wzVllllYapN5dZZpni8vrrr1+MZvbJJ58USypAS/en0c1aGv1s1113jVVXXbUoQEvFaGnEtFSglkZi22mnneK9995rs12pqC4VwKVl8uTJGZECgDwK0AAAAAAAAADotiqjmy233HKx0korNbnv61//evF36tSpceedd1b1fBtttFFstdVWxWMqBW2bbrpp3HjjjQ1Td6Zis8qoZpWCtKTxNJ+NC9AGDBhQvP5hhx1WFLc988wz8e6778Zaa61VjLRmyk0AurJepcpE1QAAAAAAAAAAANAORkADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAAAAACCLAjQAAAAAAAAAAACyKEADAAAAAAAAAAAgiwI0AAAAAAAAAAAAsihAAwAAAAAAAAAAIIsCNAAAAAAAAAAAALIoQAMAAAAAAKBVp59+eqy22mox99xzx4ABA2LRRReNb3zjG/HYY4+1uP4rr7wSQ4YMiV69ehXLTTfd1HDfPffcE7vssksss8wyMfvss8e8884bw4cPj7///e9NnuOiiy6KrbbaqnitgQMHxiKLLBJf//rX49FHH22y3tFHHx3rrbdefOELXyjWW3rppePQQw+NN954o8l6Sy65ZEN7Gi+77bZbdLZ99tknll122ZhjjjmK95ze+2GHHRbvvPNOu+PS2I9+9KOG9/GlL32p09/HHXfc0WIM03LLLbe0+Jidd965YZ30/jpbd+k3H3zwQRx++OGx5pprxnzzzRezzTZbLLfccnHssccW9zXW2v/kpz/9acM61113Xeywww7F+0nPtcACC8RXvvKVuPPOO6OzjR07ttU2nn/++Q3r/eUvf4l11lmn6Pv9+/ePhRZaKLbeeuu46667WnzeFIe0vVSe65xzzmm479VXX41tttmm6Adpn5X2XWkf9stf/jKmTp0as0LadjfaaKOYc845i5infcDJJ5/cpK+2FpfnnnuuU9vWnv7w5ptvFtvGEkssUfxfUn/cbLPN4oUXXqhJn0nLz372s2K9jz/+uNi+K+9j8ODBseKKK8ZPfvKTmDRpUpPnnDJlSvH/X2WVVYptfq655iq2r3/+85/RmdK+ccMNN4z555+/iF/a52y88cZx7bXXZu2TOkOKZ2uxTnFrz7bX2MMPP1xsf5X1nnrqqU5/L9Xss6vdJ9XDNvrrX/+66E9pf5linbbTPffcs1O3z4rPPvssTjrppFh55ZWL/Vw6xllhhRXixz/+cXzyyScN61199dXFPiNtcy0dFzaW9vfpM3yeeeYptoP0/r/3ve91+nuhA5UAAAAAAACgFTvssENpoYUWKq2++uqlFVdcsdS7d+9S+olpyJAhpYkTJzZZ97PPPittsskmxf2V5cYbb2y4/7jjjitu+8IXvlBaddVVS/369WtY769//WvDel/+8peL25ZeeunS8ssv37DO7LPPXnrhhRca1ku39enTp7TyyiuXFl544Yb10vXUloollliiuD21f911121YUnvaI7UrPVd7pDgttdRSpTXXXLO0+OKLN7Rxyy23bHdcKm699dZSr169GtZJ76U9br/99uJxF154Ybsf079//yYxTMuDDz443foXXHBBk37wzW9+s11tTG1Lj0uvW63u0m9efPHF4nUHDBhQWm211UrzzTdfQxu/+tWvNlm3cvuwYcOatPGcc85pWGfPPfcs1ll00UWL91jpOykG9913X7vb1Z73X3lMpZ82Xq699tqG9b73ve+V5p9//qL/r7LKKqW+ffsWjxk4cGDxHM3tscceTfrX2Wef3XDfww8/XDwu9YG03c0777wN6/3iF78otUd6TIpfe/zqV79qeL0FF1yw2Hcussgipc0333y6/j3nnHNOF5eXX365U7flavvDm2++Wey7Ktv9SiutVBo6dGhpttlmK919991Vv15l/9bS/7Elr7322nQxabw9V/r2u+++W+wrv/jFLxb/5xTryjrf+c53Gp5v6tSppe22267hvmWWWabYruaZZ57SCSec0KmxPu2000pzzz13Eee0jaZ+mZ4jfY7ee++97d4ndXSsGz8m7Weax33KlCnt2vYqPvroo9IKK6zQZL0xY8aU2iPtM9O+s72PaWufXe0+qR620fR+030p1pVttbLfmTBhQqd+3lb6TVqWXXbZYruqXD/iiCOa7NvTZ1nj+xsfF1akY530/tL9aZ+d9ptLLrlksX1TPxSgAQAAAAAA0KqPP/64yfWf/vSnDT8i/ve//21y38knn1zcvvPOO7f4Q+OVV15Z+te//tVw/aGHHmooaEsFAo1/tB89enTD9dNPP73h+X7zm9803P6Tn/yk9MYbbxSX0w/lO+64Y8N6o0aNmu5H6fb8uNpRBWjN4zd8+PCiLXPMMUe745K8/fbbRTFL+jF3jTXWmOUFaNW8/+eee654f+utt17xA/qsKkDrLv1m/PjxpV/+8pel999/v6EPfelLX2po4zvvvNOwbuW2GRWcnH/++aUHHnig4frf//73hscddthhs6wArT3bSWpz5XFXXXXVdIUKzfczjYtgJk+eXCwVKY6DBg0q1tt2221LnVmA9tJLLzUUz51xxhlF8VPjdjTv3+0tsOmIbbna/pCKuNJtqfAsFYVVfPLJJ6VJkyZ1alFUcwcffHDxHKlo7IMPPihuS7FNbalI//NKEU4q2qn4y1/+0lCI2rjoKz2+8lydFevmcbrlllsaYp0KFdu7T+rMArRq+nlb217FgQceWNz3jW98oyYFaDPaZ1e7T6qHbfTEE08sjRs3ruH64Ycf3rDe1Vdf3amft5tttlnxmFT8VtmeUsFYui3134rXX3+92E4rcWipAC2dzJCK9dN9Rx555HT7b+qHKTgBAAAAAABoVZoG6ZprrimmeRw6dGj8/Oc/L25PU4qlaQErRo0aVUwROGLEiDjooINafK6ddtoptthii4brq6++ejF1U5Kmj6pI0w+mqdQq0hRmFY3XO/HEE4t2JH369In111+/xfUqdtxxx+L9pHYfeeSR8f7770dnS6+X4rLuuusW00ml6TaTNMVme+OSHHDAAfG///0v/vznPzesMyu99tprxZSGaUl94qqrrmpyf5qy7dvf/nb07t27aGP6v8wq3aXfLLjggvHDH/6w4f+bXnvttdcuLqe49u3bd7rHrLXWWjFo0KBYaaWViqkeG0+Btu+++xbTW7YVl86WYp6maUv9+9xzz20yHWZ6j//+97+LPrXqqqs27EPS7em9Vbz88svxne98p5g+Mf0fW5Lik5Y0DWd67FJLLRUfffTRdNtdZ0jTzaVtIE2lm95PmrIyTY+3++67x4cffjjd+g8++GARk7TeJptsErfffnt0tmr6Q6q9u+KKK4rLiy22WLF/Su8pTWX6t7/9bZb2m7fffjsuvPDC4nLqFyleSZrKL01rud9++xXvZ/HFF48XX3xxuv/zX//61+JvmtYyTc+Ztqs0hWSaejI9vjOlOI0bN67o16nfp8/HynbceL/T3n1SZ0j/1zTdY+qv2267bTGFZmPVbHuV6SPTtJxpCtE0jW4tVLvPntE+qatvo0nqz6nft7VeZ6i8VppaNcU5TTOcpjdN09yecMIJDeul6UPb2s7SVLWVacnT8U2aPjlNx7zddtsV16kfCtAAAAAAAACYofQD4AMPPBBjxowpfqBNBR3pR9hKgUwq7vjWt75V/EB7wQUXVP28qUBpwoQJRSFBKiJozdlnn138HTJkSPHDcktScccll1xSXN5ggw2KYrnGUlsXWWSRmGuuueLZZ5+NX/7yl7Hlllu2+INzR0uvl37EToUIyeabb95Q3NGeuPzxj38sigRS4UQqaKuFL3zhC7HEEkvEpEmTij7xjW98o+H/k/zf//1fcftZZ51V9JNaqvd+U/HGG28U//dkl112ma7wcJ555il+sE8FB6NHj45jjjkm9thjj1afL/1vkrT+jNbr6H6z8MILF5cfeeSRopAltbOx9957r+g7jz/+eEyePLkoDhk5cmTR35IU81TIle677LLLol+/fjN8zYceeqhYUgFTkgpR0tKZnn766YZ+deWVVxbFPOn1L7300qIYJ7W9Im3fqTgjFaam937HHXfEZpttFv/85z9jVmqpP7z55pvx7rvvFpdvuummon2pnz322GPFvr554Wlnty99xqT2paKm5p544on4z3/+E+PHjy+upwLYM844Y7r/SepXqVA6bc8vvPBCHH/88XHEEUd0evs//vjjol+nfp8up0K+yy+/PNZbb72sfVJnSEVvqfA19cXXX3+96IOpfZUitGq3vfTYVDyVipBOPfXUqIVq99nV7JO68jba3GeffVYU0VWKLVM7O1MqrD/66KOLyynOzz//fBGvlVdeuehL7VHZRpPU99OxZNpWUjHjxhtvXBwPUSdqPQQbAAAAAAAAXV+aXilN9ZSmUqxMy1aZGilNCdWrV6+GaSRnNNVSxR//+MeGqep+/etft7hOmoZp//33b5iy8s4772xxvTR1WZqGsjIdVOPp4pL//Oc/xbRmlefcfffdG9p39913t/qeG7+P1pZqp9X69NNPSw8//HAxNVx6XGpDe+KSpvZLMdhoo41Kn332WXFbmhqs2ik4K+u2trQ1RWSKcZpasyL1hQUWWKB4bJoOtBLnPn36lHbbbbfppkSrZgrOyrqtLdVOhdad+k2K+bLLLls8ZoMNNphuOrJ///vfDdM8fvjhh6VNN9204TVSn2nu//7v/4r7+vXrV0yn15Y0LV9b72VG0tRqjz32WJMpZIcOHVo8brbZZmsyhWLjKdu+973vFeukKVwrU8ylaVTTbWl6uuZT6bU0DWAlJtdff33RD9K0tuedd15V0xHOaJnR9IaVfpeWP/3pT8Vtl1xyScNtlSnu0nt69dVXGx6X9g0pHmmdNLVdZ27L1fSH1LbK86244orF/ykt6XI122JbMax2WtM0hWVlP7PvvvvOcL20TS688MLFuscee2zDfZXtJ+2bKv+7ffbZp+F9p33zrIh16vuVaarnnnvuYqrl9u6TOiPWTz/9dNG2iptuuqnhsZWYV7vtpSmjBw4cWHr88cebTO1YzRScjddtbWlrishq9tnV7pO6+jbaWHpPI0aMKNZbcMEFS08++WSnf96m/VtqU9q+xo4dW8RrueWWKx67/fbbT7f+jI4LTzrppIb7TjjhhOK2u+66q92fl9SeAjQAAAAAAACq9uijjzb8KPiHP/yhuC39UJkK0GafffZiST9AV9ZJl3fZZZeGx6dimZ/85CcNP6hecMEFLb5OKrTZaqutivVSAUL6YbklTz31VGnppZcu1vvSl75UevPNN9t8D9ddd11D+/785z+3ul4qEEjFAJVlzjnnLPXv37/Jbam4pT1++9vfNrx2+uG/2rhUfrwdMGBAQ5xTQU26Lf1N1xv/qN7cQQcd1NDmShFJilvlth122KHUXjvuuGNDexsXEKT/eaWNqV+k21JRXbr+3nvvtfp8qQ2V9lT+p6mtldvSe2hLd+o39913X2m++eYrXi8VF6Riqrb87ne/a2jjvffe23B7KrLZa6+9ittTMdYNN9xQqsbxxx/f0OZhw4YVj19kkUWavJf2+sEPftDQxsYFHo1NmDChYZ1jjjmmSTFcpW8NGjSoYZ20Xay33nqtvubXvva1Yr2FFlpohm1LBWqN31t6TPofNL5tRkVBP/vZzxralPpYkv5WU0ix5pprFuukgo4Z6Yhtua3+kO5PfTbd33j/nS5XU0DTOF6pv6THpP5TuS31q2qce+65xWPTfmT06NFtrv/973+/odissr1svPHGDYU5Feecc05VBYWdsd+cZ555iufZddddZ3qf1JGxbmzeeectnmeLLbZo17aX+kXl8ygt6b7KeukxRx55ZKuvmfaJjd9L6n9p39n4tpaK9jpin13NPqmrbaMV48ePb2hXKgB7/vnnS9WY2c/bSrFnOgGhIl2u9JP2FKBddNFFDfelAshKUWnltlQYTH1QgAYAAAAAAECL3nrrrWL0nsajFP3iF79o+FEwjYpSzWgfldEw0vN861vfKm6ba665SjfffHOLr/vKK6+UVltttWK9NDJJGl2jJWlkqyFDhhTr7bTTTqWPP/54unWeeOKJYtSW9GNmkkZHaTyqU+Minbak99mekUsefPDBJiO2pPdfGaUkLaNGjao6LtWMqpVGaKlG5bnaM6rIxRdfXIy2VfHyyy83jExU+TG+mhFs3n333aper/JcbY140x37TXLllVc2FHIeeuihDaPeNX8fab3KiD/pfWy++eYNbUzxSFLRXxqxp1I8Vm0/aa4y6lF7igH+/ve/l0aOHNlwPf3/0+iJlSKFyuhTZ555ZjGKT8Vll13W8D4qBQ5tjcaW/vfJNddc06S483//+19pscUWK9YZPHhwu95ze0brSu65556G9lx66aXFbelv5bZ0f+X9Nh6lKBX2VkZXqhT9dNa2XG1/qPSltC2l/1NaKiNFpfuqVRlVbkaFXi1JRbnLL7988dhtt912uvtvueWWJgVJH3zwQUMhTloqo3qdeOKJDUVplX3Cfvvt19AHK9t4Z8Q6FTQ2Hl0s7TcqRblptLD27JM6K9ZpVLbKKINJGsm0EsM0ol97tr22RtVKIxtWKz1XtaNetmef3Z59UlffRtN7rsR8ww03bNLX2iPn8zbFKj0mjcqaYp2W1IZ02xe+8IV2FaCl0TorBfVpe21tX0rXpwANAAAAAACAGRacpB9d09SRlSKOtKSRSVor8Gnth8af//znDbc3H0Wp8YggX/nKVxrWazwiR/PRXCoj9KQf9NdZZ50WR5hqPHJY+pG5UjSVljRdYWX6ws4oJKr8qJtGvEk/0FcKDCoj1FSKiqqNS0vtSY9p7yhUOT+IV37IT6NBrbrqqk1GuUujl7SmPVNwzuwP4t2l36QReCpFKs1HTms8ClAlRqkQYJVVVmkYWSkte++9d8PzHXDAAQ23Nx6Fp9pR5WamAK1SEJMKK1O/SSP5VNqSpparqLzX9H/74he/2LBOGjmvceFjS+1pPg1gpa+mEXqa99VUzNeZBWhJKritjAyY9pvpb/Np+yrbbhqRLa1TmXY3/b311ls7dVuutj+kuFe2lbRfqoyulQq5brvttk4vQLv22msb2tnSNLqV551//vmL/Wv6TKqsnwp9GxcYVfZDqR+mqS0r67VndLCcWKfXTfFKfTr17cp2nZarrrqqXfukzop1amN63cUXX7xJG9N+pbWpHKuZ/ra9U3DObAFatfvsavdJ9bCNVqa8rBxTNF6vremGZ/bzdo899mh47XRsmPpP5fqPfvSjJqO+pmm6KyOmVWKabms8Il5l9LTU/1K8K6PspaLXaotEqb2+AQAAAAAAAC2Ye+65Y5dddokHH3wwnn/++Zg8eXIstthi8eUvfzl+/OMfxxJLLNGu5/vkk08aLr/66qvFUtH4uRqvN2bMmCbPscIKKzRc/vTTT4u/qU4ktbGxN998s/i74oorxhFHHBG33HJLjBs3Lj777LNYZZVV4lvf+lZ873vfi169ekVnWXnllWOrrbaKRx99NEaPHh19+vQp2rPNNtsU8evdu3e74lJLu+++e3z00Ufxn//8J5555pmYa665Yvjw4XHUUUfF5ptvHl1Bd+k3qX3l2qfy5QceeKDJ/e+//37xN8X/wAMPjDvvvDNefPHFon+tueaasf/++8e+++7bYlxeeOGFYqkYOHBgdKYRI0bE2LFj4957743nnnsuZptttlh11VWLGO68884N6+21115x3333xUsvvVS0d8EFF4z11lsvjjzyyFh33XXb9ZqpPz777LPx9NNPx5NPPhmDBg0qXvPb3/52HHLIIdHZLr/88vjZz34Wl112WdGOpZZaKr75zW/GMccc07BOasfgwYPj4YcfLtZZYIEFiv/dT3/601h77bU7tX3V9ocU99tuu61oU9pO0v8uxfbEE09s9/8kx69+9avi7zrrrBMbbbTRdPd/6Utfio033rjYt6b/84ABA2K11VaLHXfcMX70ox81+Ry7++67i33VyJEj4+WXX4411lgjvv/978duu+3Wqe8hfX7+85//LPr1hx9+GPPOO2/xf079/6tf/Wq79kmdJX0WXXnllUUMU19InzkbbLBBHHvssbH88stHvah2n13tPqkettHG6z3yyCNNniMde3SmP/zhD0X/SPu51L9TfNP2lz57vvvd7zas98477xTHj42NHz+++Pu///2v4bbTTjstFl544Tj//POLY4xFFlmkOFY6/vjji22b+tArVaHVuhEAAAAAAAAAAADUn/JpFQAAAAAAAAAAANBOCtAAAAAAAAAAAADIogANAAAAAAAAAACALArQAAAAAAAAAAAAyKIADQAAAAAAAAAAgCwK0AAAAAAAAOjSevXq1eLy05/+tGGdO+64o9X1brnllk5t39ixY1t97fPPP79hvYsuuqjV9Z577rmG9a6++urYbLPN4v+3dyfQNtX9H8e/ZpF5ypAhyXRD5UGPBlNSSJR6ihSpKCKV56FkSBMNKuJfUpJ4qNRTNJOxAYkyVm4kQ2YRCee/Pr+z9r7nDufefQ6Hyvu11l53n71/Z+/f3vu3975rne/6fgsVKuSvf//99+14mDNnjl1++eVWokQJf99jxoxJ165Ro0YZHscFF1wQ87VLhP3791u7du2sYsWKdsopp1jBggWtevXqdt9999mBAwdStT106JANHz7czj77bMubN6877+edd55Nnz49y7GlSdc1rSVLlliePHn8NqtWrcqwn6NGjfLbnHbaaTFfi19//dV69+7t+lu8eHF3rGeddZYNGDDArUu0oGNV1yGjc9exY8eE9i+ze06Trq0sXbrUmjVr5q5B7ty5rVixYla/fn0bN25coPtc06BBgxJ6LHv37rWBAwdatWrV3HUuU6aMde/e3Xbu3JmurcZOixYtrEiRIm5M6/z36tXLXx8Khdy5qVu3rrs3ChcubFdccYWtWLEioccQy763bt3q+ly5cmX/GPr162e///57wvuo/eh5of5p3xUqVLAuXbrYunXrMmwf9H5PhA0bNljRokUzvf/0HvrHP/5h+fPnt1NPPdWSkpLspZde8tePGDHCateu7a6FjqNcuXLWvn17W7ZsWcxjS/Sc0jNJbTRWzzjjDOvRo4ft2rXLEm3q1KnWsGFD9zxU/04//XS76aabbP369X6bP/74wx2znvk6J2rboUMHdy4TLej/AUHHYJB3bNB7LtZxD6RFABoAAAAAAAAAAACOG/0wqh+D41GnTh0XFOJN+mE5LQWPRLbRpOCYoLwgk3iDSdLuu2TJkunaFChQIF07/dgb+QP//PnzXeDR0dAx6Fh0TEF99dVX9tFHH7mAhiAUWBB5HDVr1oz72mVGQQ4KegtKQSrvvvuu5cqVy/VJQQYKCnn44YddwFbkD/NXXXWV9e3b17799lsXeFGpUiVLTk52QSWiH+PTXi/1x1O6dOl0wW/XX3+9HTx4MNM+6sf/e++996iuxfbt2+3pp5+25cuXu74ruOS7776zoUOH2rXXXmux0H2p8RKLWMeqghsiz+OZZ54Z0/40BiLPfVbUr7TXLvJ6eUF/ut5ffPGFO9cKSlGAypdffmk333yzTZ482bVRYE7abVWtWjXqODjW92br1q1tyJAhLkhFQYYKSFOgT/PmzV0QpWfKlCnWpEkT++CDDyxHjhxWo0YNt68ZM2b4bQYPHmydO3e2xYsXu37ny5fP3nnnHRe4E0ufvODMjIIwowmyb92/F154oT3zzDP2888/u6C7LVu22KOPPmr/+te/LNHjWudu3759VqVKFfesUvCSArYuvfTSdG2D3u/H8vnmOXLkiHXq1CnDIERPz5497ZZbbrFFixa5QCsdk4L7dN96Zs+e7Zbpea6Av02bNtnrr79ujRs3duchlrGlsaDASD2/9OzUNnV/KdhW91Mi38WzZs1yz70FCxa4Z7/uz40bN9r48eOtbdu2fjudj7vuuss98/W8l9dee80FUO/evTvmYDIvkDUWWf0fEMsYzOodG/R+j3Wfx/Kew99ECAAAAAAAAAAAADhO9PPUjTfeGPN3NCUnJ0dtM2vWLNemQoUKR9U/7UPbGThwYMzfyeqnt5deesm1ufjiizNtt3nz5tDvv//uH5Om9957LxQrHUNW5y2tbdu2hX777bdUxzR69Oh07XQMWqdjOtprF4Sua1bnLdKRI0fcOfT88ccfoUqVKrm+JCUl+csnTZrkluXPnz80f/78VN//9ddfo26/ZcuW7ntVq1Z1bSN169bNrWvfvr1//CtXrkzVRn2rXbt2qGjRoqGmTZu6NqVKlYr5WmzatCk0fPjw0J49e9zn/fv3hxo0aOC337FjR+Bzpvsy1p+Pg45VXT+tU7ujoTFwtPf42Wef7fpyySWX+Mt0DJHX8fvvv/ePp0ePHlG3dccdd7g2RYoUyXS8HO29uXz5cr8/I0eOdMvWrFnjL5s4caJbtnfvXjemtKxv375u3Hu8MSIlSpRwba6++mr/+CtWrOiW3XLLLYGPw7vuWT0HIgXZ9/Tp0/1je/fdd92yDz/80F8Wea8mYlzrPorUsWNHf9+6L2O934/1883z6KOPuv1dc801Gd5/CxYscMuyZ88eevPNN1N9N3I8pD3e+++/39/eokWLYhpbN998s2tToECB0MGDB92yiy66yC0777zzEvoufvDBB/1+69kknTp1cp+LFSvm91XnQ8vuuecet2znzp3uHaBlDz30UOD9ee/0WJ5rQf8PCDoGg7xjg97vsYz7Y33P4e+BDGgAAAAAAAAAAAD4S1D5KGXuUEYrZcLJqBSbsp2ovJSmBg0auCwux5MyLikL1TnnnGPPP/+8y1CTljIrqY2y0SjDjLK2RCpVqpTL5HYiqPSgSqYFpSwyygylLDe33nqry1IU77U7lpR9Reewa9euVq9ePStfvrzLwiORZUL/+9//ur/qv8pzKiuNMgAp6060a7By5Uo/48/dd9+dKtOLssooK5WyDql8ZjQqdaayjy+88ILLXBbvtVAGr3vuucf1W5RBR6X2JHv27JYzZ05LpFjHqrLNqY/K4KWsc3v27LHjSeUJv/nmGzcfmX1Ox6CsZ3pmqHTgueee669LW1Y2MvucV8ZQGZ90TydK5HNE1zXyr3hlhvV3x44dbl73osaWxpHK7UXem972vG14Zfsit5XoY8ls30GPN1E0Rp977jmXSUrZoF599VW3XBm/IjMSBr3fE0EZxlRqV5nxNP4yooxlUrZsWVdOVtlA9SxUf8OxSynHO23aNDf+dYzKFOm9z3SvxjK2lLlOVAJYGQ71zFemRGV5U9nLRNK96o0lZQRTWVFdO2Ui1ftYdNzesUeOweM1toL+HxB0DAZ5xwa932PdJ5AWAWgAAAAAAAAAAAD40ytSpIj7wVvBTipdqAAelR5LSz80V6hQwQ4cOOBK6rVv395Gjx59XPqofZcpU8bNf/3113bbbbe5fkbSj74K2tGP8bt27XKlu5o2bWrTp0+3vxoFRymwQUEKCu5SMNX555+fqmRbLNcuEVRibeHCha6knHTo0MGV9fOsXr3a/VVQkgI6dDxr1651pQ779OmT4TYff/xxF8Cg6x15HJs3b3Yl5lTCcdiwYVH7pB/8n3rqKRcc165du2N4tGa//PKLvfHGG25epQq9wLQ/A/VF51dBMCoTOnz4cFfaLaMgzUTRPkWBKZdcckmqdeqHnhkaBwqMU/CeyptGK2WqQI3ffvvNjWsF9CSSSpcmJSW5ee1LwTWRQXIqUxk5nuWVV15xwS0qEalAKZV49MoLXnPNNX6Akspb6nnkBWh620qUIPtWIJFX0lT3iAJ6FWiV9ngTSeUHFSSkkqeiPqgkrhe4E/R+TwSNO5X91PVVYFk03nj46aef7JNPPnFlFTds2GAjR450z8JICiLT+FeAre4FlaZUUJT3DAk6tm688Ub3jNX9o3OoZ77oWiv4LZHUj6lTp7qSy7o+y5Ytc8filRYVlQVt0aKFm9d10/VToJz33joeYyvo/wFZjcGg79hY7veg+wQydKJTsAEAAAAAAAAAAODvyys1l9mUVRm6zz//3C+Pt2/fvlCTJk38765fv94t/+WXX1zZPM+6detcSUO1qVy5cqByUZlNmVFpsmXLlvmft2/fHqpRo4b73imnnOKXglSffv75Z7/dkiVL3Hq1UxnGtOIpwZnVcQQtf5pVCc6vvvoqdODAATeva9OvXz+//YQJE2K6dpmVKctsClr2TP2cO3duqEyZMu57AwYM8NdVqVLFLcuRI4c/Drt06eKW5cqVyy8hF1nyMk+ePG69yr1FuuKKK0J58+YNffPNN+mOwSvJp7Gifqh0p85H5PhLW4Iz6LXwaPx7x9OwYcNUZfGy2m60KWj5u6zG6sKFC0OHDh1y8yrdd8MNN/jtdW2CbDfaFLT8o8ZsRmM0LZ23l19+2Y0JXWuVg8xoTHnPF5UcPB735k8//RTq0KGD26/KG6oMrJ5t+q7mReUDve1543POnDnpzpVK7fXv39+VpdUzSKURvXtTpQgz45XfjTZlVSI16L5VdrR169audGLhwoVD1113nfurdip9ejzGtcbsqlWrQo0bN3bfa9SokT+Og9zviXq+3XnnnaFs2bK5sqSZ3X8qM5v2PhsyZEjUd6+e1XpPXXvttW59zZo1/edI0LH18ccfh0499VRXrvPbb78Nbd26NXTBBRe4NnXq1Enou1jv4ZIlS7pnt/q2e/duV3ZS3zvttNNcSWNRaeLbb789VK5cuVC+fPlcidC6dev6x5wZr5xwtCmr0pqx/h+Q2RgM+o6N9X7Pap+JvOfw15bYnKsAAAAAAAAAAAA4qSkrh8o5eZRhRZlTvGwkoqwdmYn8vkpMtW3b1mbOnOlndlFWF2Xh0uRRphVl0VE2KGX0yIz64u1DZauUvUyZmqKVRkxL2VaURcWjUlWXXXaZy0SiDDHbtm1zmdHSZn9RFiOVtlq8eHGWfQwq8lwp042ym2g/3jmOPO9HQ1lRPMqMomw8jzzyiPsceSxBrl1GdC0jv7tkyRJ3DDpfHmWyCULf01hQJitlHlN5uf/85z+uP7rOysal/SkrjKhkp7IKqSSjzp+3XJ599lk3RnTNb7/99lT7UUnNgwcPujJ2cujQIX+dyjr26NHDlcpTmdhcuXK5DGrilUpT9jKVZJs8ebK1atXKYvHZZ5+5Ungaa8oUpW3o+LI6L5Hn+IcffnDfj1wW9B7IisrDeZQZSRl5JkyY4D5nNvZ1jSP7o3tK5yty/EXe95lR5jrRmFN2uGiU8UlZnJT9TONu6NCh6corKgOUMkZp7KsM6/G4N3UtvJJ4oiyPKsEqVatWdX81nj1eKVaNZ8+PP/7ol9p76KGH3ORRNrrIbUWje1D7FmWKU8YsZZjyroOXuSyaoPvWfv73v//5n3XfTJo0Kcs+HstxnSNHDrev3r17u2xgylSlTGLNmzcPdL8/9thjCXm+ad+i56kcPnzYX6dlV155pTtXQcZD5PNN41nvqf79+7vyxMuXL3fbUXnloGPrgQcesL1797rnkcpBelns5s2b596tuhb6HyAR72KVntRztFatWn4pUL2bVIpbGdF0PHoWKWPYqFGj3CSKD1WWwSDjX88e777bunWry5ip73rXK/IaZiTW/wMyG4NB37Gx3u9Z7fNEvkvwJ3eiI+AAAAAAAAAAAABw8giS6SfS7NmzQ1OnTvWzbyiTR7NmzfysGhs2bHDLx48f7zKBRGYL8jIUKStUUF4mj1iydbz11luhDz74wP+8c+dOl0XFyy7iZdEaOXKky+rjWbp0qZ/5RJlqjkUGtIyyz2WVYS7WrFtbtmwJPfHEE6kybN1///1++4kTJ8Z07YJQ1pmsMstEUhaexYsX+59//fVXl/XF27ey1MnQoUPdZ2W7+vHHH92yrl27+tfOy/LmZS8rUqSIW9ejR48M+5hZBphevXoFyhQzbdq0mDKg6RwrE5PW9+zZM3T48OFQPLzsQ/HIbKwqC9LYsWP9c6nxEJnpaP78+YH3ozGQVYataFmHcubM6fb3+OOPp1v/6quvphqPq1evdlnG1L5WrVqp2irbkDLYaV2rVq1C8Yjn3tR49u45nUONQe8cfvHFF265Mh5lz57dLdPYlnnz5vntdJyydu1af7zL5MmT/TbDhg2L+boHzUIXy74/++wzf8woc1W7du1cG2W3iuXZEeu4XrNmTejtt9/27yP97d69u9/HN998M/D9nqjnW1ZZ6Nq0aePa6Xp7yzQOIp95yqCm9+S2bdtCr7zyip+pUx555BH/e08++WRMY8t79yk7n575osx9Wqbv61mcqHexMgHqO4UKFXKZ10SZv7w+rlixwi3Te1hZUz2PPfaY32bKlCmB9+dlsguajTPo/wFBx2DQd2yQey7oPhP9LsFfG1cdAAAAAAAAAAAAf9oANO8HXgUDnX322X4AkKbOnTun+8GzePHiLmDEC8jRpHJ6QcXzo7cXTKIfvbVvlR/z9j148OB0QQOlS5cOJSUl+QEx+vvJJ5/47Z5++mn3471XMtL7jpb17ds3oUEub7zxhttPZHBFiRIl3LLrr78+1TlSv6tVqxY6/fTT/bbVq1f3gw6CXrtEBGh4x66+165d2w8m0qSyfpHBgt6x6vrpeLx2KlUXSdfFC1bTD/pZCVqSL1oJziDXQqXcFEiidblz5w7Vr18/1RQZhJeIoIEgY9ULUlI5SwWneIGhmlQGzisfl8gAtLvuusu/xhmVJtV2dR617ch7U5OCLSMpSMNbpwCQeMRzb6rspJ5r6p+ec14fevfuna48oxdgpLYqMajPKgvsBXQpaEXBQArOrVixor8tjZnIoMtEBKAF3beCifQs1bND181rN2LEiFAsYh3X3jFp33qeR45XlWxUWceMxFKC82ifb9H6nDYAVMHPXmlJjQONB+95oVLDkc9zBUFpfeTzXM/NyMClIGPrgQce8L+vcphnnnmm/7l9+/YJfRePGzfO35fKtXqlsDXVq1fPbzd8+HAXyKj3VeSzq23btjE9j+IJQAvyf0DQMRj0HRvknot33GeEALSTV/YTnYENAAAAAAAAAAAAiEalE7t16+bKViUnJ9uRI0dcebMxY8bY888/77e74YYbrH379q6E4Zo1a6xQoULWrFkz++ijj1w5vURSyUPtQ2XVvv/+e1eO6p///KcrYaZyZB6VZFNblbdS2cdSpUq5MmULFiywJk2a+O127NjhSlip5Jxn06ZNbpnK/iWSSuppP+vWrfOXqcyYlqlkoOg477vvPleKTOXOVGqrWrVqrqzl/PnzXbmvWK5dIqgsXqNGjVxJOZVd075r165tQ4YMsSlTpvjtChcubHPnzrXrrrvOXReVLDv33HNdecgBAwb47VTibsSIEX45uUqVKlmiBbkWKgEYjusMz6vEbeSkbSRSkLGq8nR9+vRxJd1UenLfvn2uZK1Ktr777rvuGiXS7t27bezYsW5epQRVYjOtNm3auOuutiopqefIRRdd5MaB+p5RKU+VH1Sb40X7U6lLlfzTOdS9pONSWdlI+qxShCpnqGehnjN69qgEolfuU9vR9nT/aiyprcouqsxeViWRj1bQfV988cWu1KGelSpvqefJtGnTrFevXgntn55XKmGpMo2rV6+2nTt3uj7edtttrtRt0NK/fwYq9fvhhx+6vqvfej+pLKaeZd4zWM9AlaRV6VTdt7p/VbaxY8eO7hlSoUKFmMbWwIEDbeTIke5+0jNJzwY9A/TsffnllxN6vJ07d7aJEydaw4YNXalfvXeqVKni7uEZM2b47ZKSktyk55HeXzonw4YNc++GRD+PgvwfEHQMBn3HBrnn/k7jHidONkWhncD9AwAAAAAAAAAAAAAAAAD+osiABgAAAAAAAAAAAAAAAACICwFoAAAAAAAAAAAAAAAAAIC4EIAGAAAAAAAAAAAAAAAAAIgLAWgAAAAAAAAAAAAAAAAAgLgQgAYAAAAAAAAAAAAAAAAAiAsBaAAAAAAAAAAAAABwEho0aJBly5Ytw+nQoUN+ux9//NFuuukmq1ChguXNm9eqVq1qw4YNsyNHjqTa3jvvvGMXXnihFS1a1E499VRr0qSJLViwIK59Jtq9997r77dBgwb+8k8//TRq/zS9/PLLCe3XL7/8Yt27d7eKFSu6c12kSBGrV6+ejRs3LlW7r776yq688korU6aM5cmTx0qVKmWXXXaZzZ0712/TqFGjqMeh7R8v06dPT7XvAwcO+OsmTZrkjq9YsWKWO3duK126tF1++eU2Z86cVNvo0qWLValSxY2r/PnzW+XKle3OO++0HTt2HLfjAABElzOTdQAAAAAAAAAAAACAv7nixYu7gJ5IChSSrVu3ugAh/VXwT7Vq1ezbb7+1f//737Zx40YbMWKEa6fArM6dO7t5Barp+7NmzbLGjRu7YKL69esH3mcQ3v60DwVaxWLmzJn2xBNPZLiuYMGC6fq6ZcsWF4QnCpAKSkF748ePt1AoFPg711xzjc2ePdty5MhhSUlJtmnTJlu4cKGbSpQoYa1bt7Zdu3ZZ06ZN3V9dk5o1a9rq1avt/fffd+fjp59+cm1r1KiRKthLFi9e7AL9YjkOHXulSpVs4MCBLoAwFjp3Ch6L5osvvnDbL1eunDtPK1eutPfee88dh+a9QLm3337bChUq5MafxuLatWvt2WeftTVr1rjjBgCcWGRAAwAAAAAAAAAAAICTWMuWLe3zzz9PNSkASqZOneoCfkTLv/76axs9erT7PHLkSBfsJM8995z7q2C15ORkFyB0wQUX2MGDB23AgAEx7TORlDGrU6dOdsYZZ9i5556bbr2Wpe2XArxEmd+aN2+esL4pAMvLGHfLLbe4c639e9atW+f+KgBQwWcyduxYlw1N10J+//13F/TlXZPI4xg1apSfZa5nz552PChIUH1t06ZNhusfffRRl/Vt6dKltmzZMhszZoxbrsA5Bct5fv75ZzemFi1a5M6DxpbMnz//uBwHACBzBKABAAAAAAAAAAAAwEnsjTfesFNOOcVlxWrVqpUtWbLEXxdZZjN79uyp/h4+fNhlqops52Ux88otijJ6/fHHH4H3mUi33nqrC9CaOHGiFShQIMv2ysI1Y8YMN3/33XfHlKUtVtp2w4YN3fwLL7xgderUceVBtfyKK65wGdVEAXEqzSldu3a18847z3r06OHOZ//+/V3mtIwMHz7c/S1fvrzLtJZoylCmbGaPPPKIO5aMqMyoguN0nLVq1XLlR73ldevWTdVOgYzKTqesaPPmzXPLvUA0AMCJRQAaAAAAAAAAAAAAAJyklHXstNNOc0E9mzdvtunTp9v555/vB4RdfvnlrsyjKPhHgUTdunVLlZlKvIAmlVRUdjFNc+fOdcuUBW3btm2B95koL774ogt8UxnJtGU2o3n88cddZrKSJUu6zGmJNm3aNLv00ktdcJ+ygik7mM7/OeecY/ny5XNtFHymc6tzvHfvXpcB7bfffnN9jBbopTKXr7/+upvv3bu35cyZM6HHsXz5cuvbt6/LGHfXXXdl2lYZ0jRuvvnmGxeoqPKhH3zwgSvlGum7776zL7/80s8E16xZM5syZUpCjwMAEAwBaAAAAAAAAAAAAABwErr++utdgJMCe5Tp6/333/fLOKpcoyjI6cMPP7TGjRu7zGcbN250mbi8TGC5cuVyf++9914XrKUylcowpoxVytrl8doF2Wc0CljzMquptKOoX96yRo0aRf2uSoUq8Oqiiy6yfv36BTo/Co5TpjSvZGWePHkyba8gL68vmsaPH++WRy5T8Ftm1DcFX1199dW2e/duF2imczN48GB75plnXJt9+/a5a6CSlDrnCkJ74oknXGDWtddem2Eg31NPPeWC2goXLuzKe2bFu8aaKlWq5JapD5HHkhldZ2WY0znIqm2LFi1ckJ/Od69evVzJ1w4dOtj69etTtZs8ebILZtTxKcvbxx9/bHfccUeWxwIASDwC0AAAAAAAAAAAAADgJHTWWWdZ0aJF/c/KvFWsWDE3Hxn8o+xkM2fOdJmqFDzWpUsXFzAkCjgTBRmpROWqVatcNq4VK1a4LGeibXrbDbrPjCgLmDKXaVJgnFSvXt1fVqNGjajf/eGHH1ygljJtFSxY0GUV8zK0LVy40H1WBq60JSQV/JU/f367/fbbszyfClDz+qKpePHibnnksnLlykX9voLyxowZ4wdwqZ8qMVmtWjW3TAFX8tprr9miRYvcvK6F+ucF5Om6fPLJJ6m2u3PnThs3bpybV/Y6L6NdZipXruz32cuqVrZs2VTHkplly5a5/Z555plufw8//LC/TuflueeeS/edUqVK2ZAhQ9z8hg0b/HMRSYGM6o8XRDdhwgRbs2ZNlscDAEgsAtAAAAAAAAAAAAAA4CT02GOPpQr6+uijj2z79u1+tjHPvHnzXPYsUVDRPffc4wcSNW3a1M0rME1BZ545c+b4GcCUlcvLghV0n9HKU37++eduGjBggFumQCZvWUZBTWkpoEwZxDQdOXLELdNfffaOUfR59OjRbl7BXZFBc9GULl3a74umli1buuWRy7p27Rr1+8p45vECzHRulFlNFGgWrZ33N7KdR8eh4LvcuXPbnXfeaUHo/Hp91nkX9T3yWLJy6NAh/1yrtKZHn5XJTJT1Tp89Ksca2c4LEPz000/95fquF4wX2Q4AcOIQgAYAAAAAAAAAAAAAJyEFJinoq0KFCi57mLKReQFMKlfpUdYsBZvVqlXLZfBasGCB5ciRw2Woypcvn2ujoLKaNWu6co3KcqZymAr2UiatBx98MOZ9Hmvqj7KDRU4XX3yxW6dsXvrsZfqSF1980QXb6Tj79Oljx0Pt2rXd+RJlDNP5qVKliu3Zs8ct69Spk/vbqlUrF0zmzeu6tG7d2n0uVKiQXXnllamCtZTJTVTWUkFyx0Pacz1w4EB/3f79+/1r3aNHDxfc5x2rMr9Jzpw5/fnly5e7Uqtqp2ukY3jnnXfcOn3WeQMAnFgEoAEAAAAAAAAAAADASah///4ug5myU61du9YFhSlIafHixanKWTZv3tyVg1y9erULDNJnleS86qqr/DYlS5Z0QV4KlkpOTnaBagou+uyzz1JlDwu6zxNJmdBGjBjh5tu1a+eC6o4HlZdUpi8F/GmfOo863zqvM2bM8DOqqSTn7NmzrU2bNi4wUNelRIkSLtOcggMjg8xeffVV27x5s18i9c/mpptucgGJCmBUpjeVbW3btq0rj+qV+UxKSrIWLVpY3rx5XZY9lXhV6VVl4tM4zJ6dsAcAONGyhbzi3AAAAAAAAAAAAAAAAAAAxIBQYAAAAAAAAAAAAAAAAABAXAhAAwAAAAAAAAAAAAAAAADEhQA0AAAAAAAAAAAAAAAAAEBcCEADAAAAAAAAAAAAAAAAAMSFADQAAAAAAAAAAAAAAAAAQFwIQAMAAAAAAAAAAAAAAAAAxIUANAAAAAAAAAAAAAAAAABAXAhAAwAAAAAAAAAAAAAAAADEhQA0AAAAAAAAAAAAAAAAAEBcCEADAAAAAAAAAAAAAAAAAMSFADQAAAAAAAAAAAAAAAAAQFwIQAMAAAAAAAAAAAAAAAAAxIUANAAAAAAAAAAAAAAAAABAXAhAAwAAAAAAAAAAAAAAAADEhQA0AAAAAAAAAAAAAAAAAEBcCEADAAAAAAAAAAAAAAAAAMSFADQAAAAAAAAAAAAAAAAAQFwIQAMAAAAAAAAAAAAAAAAAxIUANAAAAAAAAAAAAAAAAABAXAhAAwAAAAAAAAAAAAAAAADEhQA0AAAAAAAAAAAAAAAAAEBcCEADAAAAAAAAAAAAAAAAAMSFADQAAAAAAAAAAAAAwN9fy5Zm2bKlTKtWpaybPdvs5pvNqlc3y549pc2nn6bfzoYN4ba1apkVK2aWM6dZkSJmDRuajRpldvhw6vaDBqXeb9opo33s22f24IPhfeTPb1awYLhvt91mtndv/NtW37t3N6tc2SxvXrM8ecwqVgwfzw8/HPUpBgCcnHKe6A4AAAAAAAAAAAAAAJBQEyeazZgRff20aWbjxgXb1o8/pm+7a5fZggXhadkys//7v/j7umWLWdOmZsuXp16ugDlNAwaYnXpq7NvdutXsH/8w27w59fJ168LH89Zb4b6XLRt/3wEAJyUC0AAAAAAAAAAAAAAAf1/btpn17h3OCJYrl9nBg+nblCpldvXVZuefHw4eW7Mm+vaUkaxjR7PGjc3KlTM7cMDs+efNpk8Pr1cw15NPhtulNXdu+mVnn5368003pQSfNW8e/lyihNnGjeFMbcpalpGstj1pUkrwWYUKZk88ET4nffqEg9B27DCbPNns7rujHzsAABkgAA0AAAAAAAAAAAAA8Pel4DMFod16q9kHH4SDrdLq1y9lfsqUzLd3zjlmEyakXnbRReEynHLokNn+/RkHoF1wQebbXrjQ7P33w/PNmoXnFSTm6dQp+nez2vbu3Snz7dubXXVVeP6zz8wefzyl7wAAxCh7rF8AAAAAAAAAAAAAAOAvQQFcKr9ZpozZsGHHfvuhUDi47ZlnUpYlJZkVL55x+/LlzXLnDvdHWdS++y71+nfeSZmvXNmsUSOzggXD21P7n36K3pestq1tRQbZvfGG2ZtvpgTcnXJKSlAaAAAxIAANAAAAAAAAAAAAAPD3s3evWbdu4fnnnjMrVOjYbv9f/zLLnj1cHnPgwJQsZArqikYBZH/8YbZpUzgwrm5ds2++SVm/YkXKvEqBzplj9uuvZtu3h9urROiWLfFt+8ILw4FyCmhbvz5cclQBZ5pXv+fNMzvzzKM+LQCAkw8BaAAAAAAAAAAAAACAv5/77guX21S5yTZtjs8+c+UyO3w49bJ8+cyuu87spZfMPvzQbNQos9NOC6/bs8fsrrtS2u7alfq7Q4eGs6LVqRP+/PPPZo8+Gt+2pWJFs3Ll0vd78WKzd989miMHAJzEsoVCygkKAAAAAAAAAAAAAMCfw6BBg2zw4MGZtklOTraKCqjKyKpVZjVrhrOerVxpVqpUeLnaKyhNtLxatfTfbdDA7IsvwvOzZqUuXRlp9epwNjJNL79sNmNGeHnZsmbff2+WN2/0zmu7TZqE53PkCGc5UwnMli1TtvPPf5rNnx+eV3DZpZemlPiMzGwWdNszZ5pdconZkSPhjGeTJ4fXX3ttONOaKHPa9ddH3zYAABnImdFCAAAAAAAAAAAAAABOlHLlyln9+vUzbZMnT57oKzdvDgda7dyZkhEsrerVzWrXNvv66/g6WbVqeBKVslT5yuTkcJYyBXQ1bx79u/XqpcwrY5r6qSCx8uVTlleokPG8MptlJtq2X3wxfE6kZ89woJzccUdKANpbbxGABgCIGQFoAAAAAAAAAAAAAIA/la5du7rpT2n//nBAV2YiS2kuWmRWt27q9V6GNcmZ06xo0fB8w4ZmY8aE59evT2kTOX/66fFte9u2lOV796bMK0NaRssBAAiIADQAAAAAAAAAAAAAwJ/K2LFj3ZSZadOmWenSpTNeqWxkTz2VfvmQIeGMYNKvX7hMp6xYEZ5kx46U9rNnpwRuXX11+G+bNmaFC4fLWaqkpzKSjR8fzn4m2bKZnXNO6oxkl10WzpKmTGYq3fnggynrVVrTK9d55ZVmJUqYbd0aLr/5yCNmtWqZPfBASnttJ55t61hVylPUpkCBcF+HDk1pX6dOxucTAIBMZAuFQqHMGgAAAAAAAAAAAAAAcDwNGjTIBg8enGmb5ORkq6gAsFio/bp14fmVK82qVfN2aJbF/sz7ab1Ro3BgWjR9+5o99ljKZwV5RVOqVDjQrHLllGX/+184oOzQofTtL7zQ7OOPzXLnjn3byqKmbGkKbstImTJmixdHL1kKAEAU2aOtAAAAAAAAAAAAAADgRAWgKZdKZlPMwWfHyq23ml1xRTjjmEpx5splVrZsODOagscig8/kzTfNOnY0O+uscNaxPHnMqlQx693bbOnS1MFnom3PmRPObKZMawo2q1o1HCCnDGZe8Fms2y5fPlyyU/0/44zwdjRpvnt3sy+/JPgMABAXMqABAAAAAAAAAAAAAAAAAOJCBjQAAAAAAAAAAAAAAAAAQFwIQAMAAAAAAAAAAAAAAAAAxIUANAAAAAAAAAAAAAAAAABAXAhAAwAAAAAAAAAAAAAAAADEhQA0AAAAAAAAAAAAAAAAAEBcCEADAAAAAAAAAAAAAAAAAMSFADQAAAAAAAAAAAAAAAAAQFwIQAMAAAAAAAAAAAAAAAAAxIUANAAAAAAAAAAAAAAAAABAXAhAAwAAAAAAAAAAAAAAAADEhQA0AAAAAAAAAAAAAAAAAEBcCEADAAAAAAAAAAAAAAAAAMSFADQAAAAAAAAAAAAAAAAAgMXj/wEZZ/15Z7o5DQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "done in 33.158448934555054\n"
     ]
    }
   ],
   "source": [
    "def solve_main():\n",
    "    grid_groups = np.array(\n",
    "        [\n",
    "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
    "            [1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
    "            [3, 3, 2, 2, 2, 2, 4, 4, 4, 1, 4],\n",
    "            [3, 2, 2, 3, 2, 5, 4, 4, 4, 4, 4],\n",
    "            [3, 2, 2, 3, 2, 5, 5, 4, 4, 5, 4],\n",
    "            [3, 3, 3, 3, 3, 5, 5, 5, 5, 5, 4],\n",
    "            [3, 7, 6, 6, 3, 3, 5, 5, 6, 5, 5],\n",
    "            [3, 7, 6, 6, 6, 6, 6, 6, 6, 9, 9],\n",
    "            [7, 7, 7, 7, 6, 7, 6, 9, 9, 9, 9],\n",
    "            [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n",
    "            [7, 7, 8, 8, 8, 8, 8, 8, 7, 7, 7],\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    grid_highlights = np.array(\n",
    "        [\n",
    "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
    "            [0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],\n",
    "            [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    grid = np.ones((11, 11), dtype=int)\n",
    "\n",
    "    solver = CrossNumbersSolver(\n",
    "        grid=grid,\n",
    "        grid_groups=grid_groups,\n",
    "        grid_highlights=grid_highlights,\n",
    "        clues=[],\n",
    "        tactic=\"default\",\n",
    "        labels=[\n",
    "            \"square\",\n",
    "            \"product digits is 20\",\n",
    "            \"multiple of 13\",\n",
    "            \"multiple of 32\",\n",
    "            \"divisible by each of its digits\",\n",
    "            \"product of digits is 25\",\n",
    "            \"divisible by each of its digits\",\n",
    "            \"odd and a palindrome\",\n",
    "            \"fibonacci\",\n",
    "            \"product of digits are 2025\",\n",
    "            \"prime\",\n",
    "        ],\n",
    "    )\n",
    "    solver.clues = [\n",
    "        solver.is_square,\n",
    "        solver.is_product_of_20,\n",
    "        solver.is_multiple_of_13,\n",
    "        solver.is_multiple_of_32,\n",
    "        solver.is_divisible,\n",
    "        solver.is_product_of_25,\n",
    "        solver.is_divisible,\n",
    "        solver.is_odd_palindrome,\n",
    "        solver.is_fibonacci,\n",
    "        solver.is_product_of_2025,\n",
    "        solver.is_prime,\n",
    "    ]\n",
    "    solver.solver.add(\n",
    "        Or(\n",
    "            solver.group_ints[0] == 1,\n",
    "            solver.group_ints[0] == 2,\n",
    "            solver.group_ints[0] == 4,\n",
    "            solver.group_ints[0] == 5,\n",
    "        ),\n",
    "        Or(\n",
    "            solver.group_ints[4] == 1,\n",
    "            solver.group_ints[4] == 5,\n",
    "        ),\n",
    "        Or(\n",
    "            solver.group_ints[6] == 1,\n",
    "            solver.group_ints[6] == 3,\n",
    "            solver.group_ints[6] == 5,\n",
    "            solver.group_ints[6] == 9,\n",
    "        ),\n",
    "    )\n",
    "    # group hints, (we are forcing the correct regions here, without this it takes a really long time)\n",
    "    solver.solver.add(\n",
    "        solver.group_ints[0] == 2,\n",
    "        solver.group_ints[1] == 3,\n",
    "        solver.group_ints[2] == 4,\n",
    "        solver.group_ints[3] == 4,\n",
    "        solver.group_ints[4] == 1,\n",
    "        solver.group_ints[5] == 6,\n",
    "        solver.group_ints[6] == 3,\n",
    "        solver.group_ints[7] == 7,\n",
    "        solver.group_ints[8] == 7,\n",
    "    )\n",
    "    solver.solver.add(\n",
    "        solver.group_ints[0] != solver.group_ints[1],\n",
    "        solver.group_ints[0] != solver.group_ints[2],\n",
    "        solver.group_ints[0] != solver.group_ints[3],\n",
    "        # region 2\n",
    "        solver.group_ints[1] != solver.group_ints[0],\n",
    "        solver.group_ints[1] != solver.group_ints[2],\n",
    "        solver.group_ints[1] != solver.group_ints[3],\n",
    "        solver.group_ints[1] != solver.group_ints[4],\n",
    "        # region 3\n",
    "        solver.group_ints[2] != solver.group_ints[0],\n",
    "        solver.group_ints[2] != solver.group_ints[1],\n",
    "        solver.group_ints[2] != solver.group_ints[4],\n",
    "        solver.group_ints[2] != solver.group_ints[5],\n",
    "        solver.group_ints[2] != solver.group_ints[6],\n",
    "        # region 4\n",
    "        solver.group_ints[3] != solver.group_ints[0],\n",
    "        solver.group_ints[3] != solver.group_ints[1],\n",
    "        solver.group_ints[3] != solver.group_ints[4],\n",
    "        # region 5\n",
    "        solver.group_ints[4] != solver.group_ints[1],\n",
    "        solver.group_ints[4] != solver.group_ints[2],\n",
    "        solver.group_ints[4] != solver.group_ints[3],\n",
    "        solver.group_ints[4] != solver.group_ints[5],\n",
    "        solver.group_ints[4] != solver.group_ints[8],\n",
    "        # region 6\n",
    "        solver.group_ints[5] != solver.group_ints[2],\n",
    "        solver.group_ints[5] != solver.group_ints[4],\n",
    "        solver.group_ints[5] != solver.group_ints[6],\n",
    "        solver.group_ints[5] != solver.group_ints[8],\n",
    "        # region 7\n",
    "        solver.group_ints[6] != solver.group_ints[2],\n",
    "        solver.group_ints[6] != solver.group_ints[5],\n",
    "        solver.group_ints[6] != solver.group_ints[7],\n",
    "        solver.group_ints[6] != solver.group_ints[8],\n",
    "        # region 8\n",
    "        solver.group_ints[7] != solver.group_ints[6],\n",
    "        # region 9\n",
    "        solver.group_ints[8] != solver.group_ints[4],\n",
    "        solver.group_ints[8] != solver.group_ints[5],\n",
    "        solver.group_ints[8] != solver.group_ints[6],\n",
    "    )\n",
    "    solver.solve()\n",
    "\n",
    "start = time.time()\n",
    "solve_main()\n",
    "end = time.time() - start\n",
    "print(\"\")\n",
    "print(f\"done in {end}\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}