Section_5_sarsa_complete.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "CqXtasNJqBFG"
      },
      "source": [
        "<div style=\"text-align:center\">\n",
        "    <h1>\n",
        "        SARSA\n",
        "    </h1>\n",
        "</div>\n",
        "\n",
        "<br><br>\n",
        "\n",
        "<div style=\"text-align:center\">\n",
        "    In this notebook we are going to implement a method that learns from experience and uses bootstrapping.\n",
        "    It is known as SARSA because of the elements involved in the update rule:\n",
        "</div>\n",
        "\n",
        "\\begin{equation}\n",
        "\\text{State}_t, \\text{Action}_t, \\text{Reward}_t, \\text{State}_{t+1}, \\text{Action}_{t+1}\n",
        "\\end{equation}\n",
        "\n",
        "<br>\n",
        "\n",
        "<div style=\"text-align:center\">\n",
        "    This method follows an on-policy strategy, in which the same policy that is optimized is responsible for scanning the environment.\n",
        "</div>\n",
        "\n",
        "\n",
        "<br>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Setup code (not important) - Run this cell by pressing \"Shift + Enter\"\n",
        "\n",
        "\n",
        "\n",
        "!pip install -qq gym==0.23.0\n",
        "\n",
        "\n",
        "from typing import Tuple, Dict, Optional, Iterable, Callable\n",
        "\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "from matplotlib import animation\n",
        "\n",
        "from IPython.display import HTML\n",
        "\n",
        "import gym\n",
        "from gym import spaces\n",
        "from gym.error import DependencyNotInstalled\n",
        "\n",
        "import pygame\n",
        "from pygame import gfxdraw\n",
        "\n",
        "\n",
        "class Maze(gym.Env):\n",
        "\n",
        "    def __init__(self, exploring_starts: bool = False,\n",
        "                 shaped_rewards: bool = False, size: int = 5) -> None:\n",
        "        super().__init__()\n",
        "        self.exploring_starts = exploring_starts\n",
        "        self.shaped_rewards = shaped_rewards\n",
        "        self.state = (size - 1, size - 1)\n",
        "        self.goal = (size - 1, size - 1)\n",
        "        self.maze = self._create_maze(size=size)\n",
        "        self.distances = self._compute_distances(self.goal, self.maze)\n",
        "        self.action_space = spaces.Discrete(n=4)\n",
        "        self.action_space.action_meanings = {0: 'UP', 1: 'RIGHT', 2: 'DOWN', 3: \"LEFT\"}\n",
        "        self.observation_space = spaces.MultiDiscrete([size, size])\n",
        "\n",
        "        self.screen = None\n",
        "        self.agent_transform = None\n",
        "\n",
        "    def step(self, action: int) -> Tuple[Tuple[int, int], float, bool, Dict]:\n",
        "        reward = self.compute_reward(self.state, action)\n",
        "        self.state = self._get_next_state(self.state, action)\n",
        "        done = self.state == self.goal\n",
        "        info = {}\n",
        "        return self.state, reward, done, info\n",
        "\n",
        "    def reset(self) -> Tuple[int, int]:\n",
        "        if self.exploring_starts:\n",
        "            while self.state == self.goal:\n",
        "                self.state = tuple(self.observation_space.sample())\n",
        "        else:\n",
        "            self.state = (0, 0)\n",
        "        return self.state\n",
        "\n",
        "    def render(self, mode: str = 'human') -> Optional[np.ndarray]:\n",
        "        assert mode in ['human', 'rgb_array']\n",
        "\n",
        "        screen_size = 600\n",
        "        scale = screen_size / 5\n",
        "\n",
        "        if self.screen is None:\n",
        "            pygame.init()\n",
        "            self.screen = pygame.Surface((screen_size, screen_size))\n",
        "\n",
        "        surf = pygame.Surface((screen_size, screen_size))\n",
        "        surf.fill((22, 36, 71))\n",
        "\n",
        "\n",
        "        for row in range(5):\n",
        "            for col in range(5):\n",
        "\n",
        "                state = (row, col)\n",
        "                for next_state in [(row + 1, col), (row - 1, col), (row, col + 1), (row, col - 1)]:\n",
        "                    if next_state not in self.maze[state]:\n",
        "\n",
        "                        # Add the geometry of the edges and walls (i.e. the boundaries between\n",
        "                        # adjacent squares that are not connected).\n",
        "                        row_diff, col_diff = np.subtract(next_state, state)\n",
        "                        left = (col + (col_diff > 0)) * scale - 2 * (col_diff != 0)\n",
        "                        right = ((col + 1) - (col_diff < 0)) * scale + 2 * (col_diff != 0)\n",
        "                        top = (5 - (row + (row_diff > 0))) * scale - 2 * (row_diff != 0)\n",
        "                        bottom = (5 - ((row + 1) - (row_diff < 0))) * scale + 2 * (row_diff != 0)\n",
        "\n",
        "                        gfxdraw.filled_polygon(surf, [(left, bottom), (left, top), (right, top), (right, bottom)], (255, 255, 255))\n",
        "\n",
        "        # Add the geometry of the goal square to the viewer.\n",
        "        left, right, top, bottom = scale * 4 + 10, scale * 5 - 10, scale - 10, 10\n",
        "        gfxdraw.filled_polygon(surf, [(left, bottom), (left, top), (right, top), (right, bottom)], (40, 199, 172))\n",
        "\n",
        "        # Add the geometry of the agent to the viewer.\n",
        "        agent_row = int(screen_size - scale * (self.state[0] + .5))\n",
        "        agent_col = int(scale * (self.state[1] + .5))\n",
        "        gfxdraw.filled_circle(surf, agent_col, agent_row, int(scale * .6 / 2), (228, 63, 90))\n",
        "\n",
        "        surf = pygame.transform.flip(surf, False, True)\n",
        "        self.screen.blit(surf, (0, 0))\n",
        "\n",
        "        return np.transpose(\n",
        "                np.array(pygame.surfarray.pixels3d(self.screen)), axes=(1, 0, 2)\n",
        "            )\n",
        "\n",
        "    def close(self) -> None:\n",
        "        if self.screen is not None:\n",
        "            pygame.display.quit()\n",
        "            pygame.quit()\n",
        "            self.screen = None\n",
        "\n",
        "    def compute_reward(self, state: Tuple[int, int], action: int) -> float:\n",
        "        next_state = self._get_next_state(state, action)\n",
        "        if self.shaped_rewards:\n",
        "            return - (self.distances[next_state] / self.distances.max())\n",
        "        return - float(state != self.goal)\n",
        "\n",
        "    def simulate_step(self, state: Tuple[int, int], action: int):\n",
        "        reward = self.compute_reward(state, action)\n",
        "        next_state = self._get_next_state(state, action)\n",
        "        done = next_state == self.goal\n",
        "        info = {}\n",
        "        return next_state, reward, done, info\n",
        "\n",
        "    def _get_next_state(self, state: Tuple[int, int], action: int) -> Tuple[int, int]:\n",
        "        if action == 0:\n",
        "            next_state = (state[0] - 1, state[1])\n",
        "        elif action == 1:\n",
        "            next_state = (state[0], state[1] + 1)\n",
        "        elif action == 2:\n",
        "            next_state = (state[0] + 1, state[1])\n",
        "        elif action == 3:\n",
        "            next_state = (state[0], state[1] - 1)\n",
        "        else:\n",
        "            raise ValueError(\"Action value not supported:\", action)\n",
        "        if next_state in self.maze[state]:\n",
        "            return next_state\n",
        "        return state\n",
        "\n",
        "    @staticmethod\n",
        "    def _create_maze(size: int) -> Dict[Tuple[int, int], Iterable[Tuple[int, int]]]:\n",
        "        maze = {(row, col): [(row - 1, col), (row + 1, col), (row, col - 1), (row, col + 1)]\n",
        "                for row in range(size) for col in range(size)}\n",
        "\n",
        "        left_edges = [[(row, 0), (row, -1)] for row in range(size)]\n",
        "        right_edges = [[(row, size - 1), (row, size)] for row in range(size)]\n",
        "        upper_edges = [[(0, col), (-1, col)] for col in range(size)]\n",
        "        lower_edges = [[(size - 1, col), (size, col)] for col in range(size)]\n",
        "        walls = [\n",
        "            [(1, 0), (1, 1)], [(2, 0), (2, 1)], [(3, 0), (3, 1)],\n",
        "            [(1, 1), (1, 2)], [(2, 1), (2, 2)], [(3, 1), (3, 2)],\n",
        "            [(3, 1), (4, 1)], [(0, 2), (1, 2)], [(1, 2), (1, 3)],\n",
        "            [(2, 2), (3, 2)], [(2, 3), (3, 3)], [(2, 4), (3, 4)],\n",
        "            [(4, 2), (4, 3)], [(1, 3), (1, 4)], [(2, 3), (2, 4)],\n",
        "        ]\n",
        "\n",
        "        obstacles = upper_edges + lower_edges + left_edges + right_edges + walls\n",
        "\n",
        "        for src, dst in obstacles:\n",
        "            maze[src].remove(dst)\n",
        "\n",
        "            if dst in maze:\n",
        "                maze[dst].remove(src)\n",
        "\n",
        "        return maze\n",
        "\n",
        "    @staticmethod\n",
        "    def _compute_distances(goal: Tuple[int, int],\n",
        "                           maze: Dict[Tuple[int, int], Iterable[Tuple[int, int]]]) -> np.ndarray:\n",
        "        distances = np.full((5, 5), np.inf)\n",
        "        visited = set()\n",
        "        distances[goal] = 0.\n",
        "\n",
        "        while visited != set(maze):\n",
        "            sorted_dst = [(v // 5, v % 5) for v in distances.argsort(axis=None)]\n",
        "            closest = next(x for x in sorted_dst if x not in visited)\n",
        "            visited.add(closest)\n",
        "\n",
        "            for neighbour in maze[closest]:\n",
        "                distances[neighbour] = min(distances[neighbour], distances[closest] + 1)\n",
        "        return distances\n",
        "\n",
        "\n",
        "def plot_policy(probs_or_qvals, frame, action_meanings=None):\n",
        "    if action_meanings is None:\n",
        "        action_meanings = {0: 'U', 1: 'R', 2: 'D', 3: 'L'}\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
        "    max_prob_actions = probs_or_qvals.argmax(axis=-1)\n",
        "    probs_copy = max_prob_actions.copy().astype(object)\n",
        "    for key in action_meanings:\n",
        "        probs_copy[probs_copy == key] = action_meanings[key]\n",
        "    sns.heatmap(max_prob_actions, annot=probs_copy, fmt='', cbar=False, cmap='coolwarm',\n",
        "                annot_kws={'weight': 'bold', 'size': 12}, linewidths=2, ax=axes[0])\n",
        "    axes[1].imshow(frame)\n",
        "    axes[0].axis('off')\n",
        "    axes[1].axis('off')\n",
        "    plt.suptitle(\"Policy\", size=18)\n",
        "    plt.tight_layout()\n",
        "\n",
        "\n",
        "def plot_values(state_values, frame):\n",
        "    f, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
        "    sns.heatmap(state_values, annot=True, fmt=\".2f\", cmap='coolwarm',\n",
        "                annot_kws={'weight': 'bold', 'size': 12}, linewidths=2, ax=axes[0])\n",
        "    axes[1].imshow(frame)\n",
        "    axes[0].axis('off')\n",
        "    axes[1].axis('off')\n",
        "    plt.tight_layout()\n",
        "\n",
        "\n",
        "def display_video(frames):\n",
        "    # Copied from: https://colab.research.google.com/github/deepmind/dm_control/blob/master/tutorial.ipynb\n",
        "    orig_backend = matplotlib.get_backend()\n",
        "    matplotlib.use('Agg')\n",
        "    fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
        "    matplotlib.use(orig_backend)\n",
        "    ax.set_axis_off()\n",
        "    ax.set_aspect('equal')\n",
        "    ax.set_position([0, 0, 1, 1])\n",
        "    im = ax.imshow(frames[0])\n",
        "    def update(frame):\n",
        "        im.set_data(frame)\n",
        "        return [im]\n",
        "    anim = animation.FuncAnimation(fig=fig, func=update, frames=frames,\n",
        "                                    interval=50, blit=True, repeat=False)\n",
        "    return HTML(anim.to_html5_video())\n",
        "\n",
        "\n",
        "def test_agent(environment, policy, episodes=10):\n",
        "    frames = []\n",
        "    for episode in range(episodes):\n",
        "        state = env.reset()\n",
        "        done = False\n",
        "        frames.append(env.render(mode=\"rgb_array\"))\n",
        "\n",
        "        while not done:\n",
        "            p = policy(state)\n",
        "            if isinstance(p, np.ndarray):\n",
        "                action = np.random.choice(4, p=p)\n",
        "            else:\n",
        "                action = p\n",
        "            next_state, reward, done, extra_info = env.step(action)\n",
        "            img = env.render(mode=\"rgb_array\")\n",
        "            frames.append(img)\n",
        "            state = next_state\n",
        "\n",
        "    return display_video(frames)\n",
        "\n",
        "\n",
        "def plot_action_values(action_values):\n",
        "\n",
        "    text_positions = [\n",
        "        [(0.35, 4.75), (1.35, 4.75), (2.35, 4.75), (3.35, 4.75), (4.35, 4.75),\n",
        "         (0.35, 3.75), (1.35, 3.75), (2.35, 3.75), (3.35, 3.75), (4.35, 3.75),\n",
        "         (0.35, 2.75), (1.35, 2.75), (2.35, 2.75), (3.35, 2.75), (4.35, 2.75),\n",
        "         (0.35, 1.75), (1.35, 1.75), (2.35, 1.75), (3.35, 1.75), (4.35, 1.75),\n",
        "         (0.35, 0.75), (1.35, 0.75), (2.35, 0.75), (3.35, 0.75), (4.35, 0.75)],\n",
        "        [(0.6, 4.45), (1.6, 4.45), (2.6, 4.45), (3.6, 4.45), (4.6, 4.45),\n",
        "         (0.6, 3.45), (1.6, 3.45), (2.6, 3.45), (3.6, 3.45), (4.6, 3.45),\n",
        "         (0.6, 2.45), (1.6, 2.45), (2.6, 2.45), (3.6, 2.45), (4.6, 2.45),\n",
        "         (0.6, 1.45), (1.6, 1.45), (2.6, 1.45), (3.6, 1.45), (4.6, 1.45),\n",
        "         (0.6, 0.45), (1.6, 0.45), (2.6, 0.45), (3.6, 0.45), (4.6, 0.45)],\n",
        "        [(0.35, 4.15), (1.35, 4.15), (2.35, 4.15), (3.35, 4.15), (4.35, 4.15),\n",
        "         (0.35, 3.15), (1.35, 3.15), (2.35, 3.15), (3.35, 3.15), (4.35, 3.15),\n",
        "         (0.35, 2.15), (1.35, 2.15), (2.35, 2.15), (3.35, 2.15), (4.35, 2.15),\n",
        "         (0.35, 1.15), (1.35, 1.15), (2.35, 1.15), (3.35, 1.15), (4.35, 1.15),\n",
        "         (0.35, 0.15), (1.35, 0.15), (2.35, 0.15), (3.35, 0.15), (4.35, 0.15)],\n",
        "        [(0.05, 4.45), (1.05, 4.45), (2.05, 4.45), (3.05, 4.45), (4.05, 4.45),\n",
        "         (0.05, 3.45), (1.05, 3.45), (2.05, 3.45), (3.05, 3.45), (4.05, 3.45),\n",
        "         (0.05, 2.45), (1.05, 2.45), (2.05, 2.45), (3.05, 2.45), (4.05, 2.45),\n",
        "         (0.05, 1.45), (1.05, 1.45), (2.05, 1.45), (3.05, 1.45), (4.05, 1.45),\n",
        "         (0.05, 0.45), (1.05, 0.45), (2.05, 0.45), (3.05, 0.45), (4.05, 0.45)]]\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(7, 7))\n",
        "    tripcolor = quatromatrix(action_values, ax=ax,\n",
        "                             triplotkw={\"color\": \"k\", \"lw\": 1}, tripcolorkw={\"cmap\": \"coolwarm\"})\n",
        "    ax.margins(0)\n",
        "    ax.set_aspect(\"equal\")\n",
        "    fig.colorbar(tripcolor)\n",
        "\n",
        "    for j, av in enumerate(text_positions):\n",
        "        for i, (xi, yi) in enumerate(av):\n",
        "            plt.text(xi, yi, round(action_values[:, :, j].flatten()[i], 2), size=8, color=\"w\", weight=\"bold\")\n",
        "\n",
        "    plt.title(\"Action values Q(s,a)\", size=18)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def quatromatrix(action_values, ax=None, triplotkw=None, tripcolorkw=None):\n",
        "    action_values = np.flipud(action_values)\n",
        "    n = 5\n",
        "    m = 5\n",
        "    a = np.array([[0, 0], [0, 1], [.5, .5], [1, 0], [1, 1]])\n",
        "    tr = np.array([[0, 1, 2], [0, 2, 3], [2, 3, 4], [1, 2, 4]])\n",
        "    A = np.zeros((n * m * 5, 2))\n",
        "    Tr = np.zeros((n * m * 4, 3))\n",
        "    for i in range(n):\n",
        "        for j in range(m):\n",
        "            k = i * m + j\n",
        "            A[k * 5:(k + 1) * 5, :] = np.c_[a[:, 0] + j, a[:, 1] + i]\n",
        "            Tr[k * 4:(k + 1) * 4, :] = tr + k * 5\n",
        "    C = np.c_[action_values[:, :, 3].flatten(), action_values[:, :, 2].flatten(),\n",
        "              action_values[:, :, 1].flatten(), action_values[:, :, 0].flatten()].flatten()\n",
        "\n",
        "    ax.triplot(A[:, 0], A[:, 1], Tr, **triplotkw)\n",
        "    tripcolor = ax.tripcolor(A[:, 0], A[:, 1], Tr, facecolors=C, **tripcolorkw)\n",
        "    return tripcolor\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "ykuSUp-6qM0x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4FlUGb-qBFL"
      },
      "source": [
        "## Import the necessary software libraries:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XZ5pjEI9qBFM"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPOLSChNqBFO"
      },
      "source": [
        "## Create the environment, value table and policy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n8I0R27cqBFO"
      },
      "source": [
        "#### Create the environment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vKz0yD5hqBFP"
      },
      "outputs": [],
      "source": [
        "env = Maze()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XinBDNSqBFP"
      },
      "source": [
        "#### Create the $Q(s, a)$ table"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sXX5YdTnqBFQ"
      },
      "outputs": [],
      "source": [
        "action_values = np.zeros(shape=(5, 5, 4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nB_aaY8WqBFQ"
      },
      "source": [
        "#### Create the policy $\\pi(s)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zcG4n5L8qBFR"
      },
      "outputs": [],
      "source": [
        "def policy(state, epsilon=0.):\n",
        "    if np.random.random() < epsilon:\n",
        "        return np.random.randint(4)\n",
        "    else:\n",
        "        av = action_values[state]\n",
        "        return np.random.choice(np.flatnonzero(av == av.max()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbwXSKQCqBFR"
      },
      "source": [
        "#### Plot the value table $Q(s,a)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RPJAHlTRqBFR",
        "outputId": "fc731e0f-0b9d-4821-931d-69d9bf8146ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 2 Axes>"
            ],
            "image/png": 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YMmWKvv3tbysYDCZ/1qtXL5199tl66KGH1L9//853RNIFF1ygn/3sZ/rsZz+r/Pz85Pl+/fpp/vz5yccXDlQ5T3wXn36kBjARi6TMxAAVRvnXv/6ldevWpd3s++KLL1aPHj20ZcuW5H+cPy0xcD3hhBO6ddXz3//+9+T09/e///3kQOnTPv/5zycrYR0NMr///e+nPX/BBRdIat1fs7GxMaP4Et9jYiC6v8T0/rRp01RUVJTRtc8//3xJrdW87vLvf/9ba9euVU5OTocV7sQvNM8//3xK1bqkpESSDvi4RSYS1c5evXpl9LnS0tKUzyd89NFHKT9PJ9GP3bt3d+vuFomcV1RUdHjfROyJvgBAJhigwigPPvigbNvWqaee2mZxTHFxsaZNmybpkyrrpyUGrf/5n//Z1WGmSEwh5+TkdDi9+7nPfS6l/f6CwaCOOeaYtD/7dBUs08VSX/ziF1VUVJQylZ+wc+dO/fnPf5bUtmKd8K9//Us33nijxo4dq5KSEnm93uQisvPOO09S66Kj7vLXv/5VUuu2Tscdd5zKysrSHuecc44kac+ePSkDwMQ/H5dffrluuOEGvfDCCxkP+g+3/RfQ7d69W5JSqqL7mzx5svLy8rRp0yadeuqpuv/++/X+++8flnh27typBQsWqLy8XL169VJOTk4y58cdd5yk1op/R/8sJmJP9AUwlWU5+CYpi2FYe/hmYIx4PK6HHnpIUvuDpcsvv1xS60KO/RcMJRaDHH300V0XZBq7du2S1Fox8vv97bZLPG6QaL+/jqqXn17FHY1GM4ovPz9fF154oaRPqqUJicVTZWVlyQH0pz3xxBM67rjjdOutt2rjxo0KhUIqLCxUnz591LdvX/Xs2VNS6yCwuyQqcvF4XDt37mz3qK6uTn7m0wPQn/70pzrzzDPV0NCg2267TWeccYaKi4s1btw4LViwQDt27Mg4pkTldP9K6IEkYkx8jwmJlfcd/fP0H//xH7rvvvtUWFioiooKff3rX9fQoUPVp08fTZ8+XU899ZRs284oHqm1Mjp8+HD98Ic/1Kuvvqra2lr16NEjmfNPV3U7ynuPHj1S+gIAmWCACmM899xzyUrc17/+9TZbPlmWlayKNTQ0JJ83TTjQyulslhjwr1u3Ttu3b0+eTwxYv/zlL7d5NKGmpkZXXHGFmpubddZZZ2ndunVqbGxUKBTSzp07VVVVpRUrVnRfJz6WmFbu27evbNvu1PHpanxJSYnWrl2rl156Sd/+9rf12c9+Vjk5OdqwYYN++MMf6thjj834Wd9EVTGTxWLhcFj/+te/JEnDhg1L+VliwHugavmMGTP0wQcfaOnSpZo+fboGDhyo3bt367HHHtO0adN0+umnKxwOdzqmlpYWXXrppaqvr9fo0aP17LPPKhwOKxKJJHP+6a3FOhoA19bWpvQFADLBABXGSDdtn0n7srIySa3b9nSnPn36SGqthrW316n0yTR4on13OuOMMzRw4EDF4/Hkgq133nkn+b70dBXrxOCkZ8+eevrpp3X66acnq2IJmW5hlJCoCHdUXQuFQmnPJ/JcXV19SJXbU045RT/5yU/08ssvq76+Xk899ZROPPFE7d27V1/72tc63M5sf5MnT5bUOqhft25dpz7zxBNPJAd4+++t27t3b0mfDPI6EgwGdc0112j58uXatm2b3nvvPd10002yLEsvvfRSyl6+B1JRUaEPPvhAXq9XK1eu1Lnnntumst/ZnCdiT/QFMBWLpMzEABVG2L17d3Lbmscff1yRSKTd4/XXX5fU+szp1q1bk9dIbEn19NNPZ3Rvj6f1/wYHMx0qSePGjZPUWn3qaEud559/XlLrHpHdzbIsfeUrX5H0SdU08efIkSM1atSoNp9JVFqHDRuWspr70xJ9ylRiSvvT1dz9JVay7++zn/2spNZKamc2je+MvLw8feELX9Af/vAHSa0D50wWfl155ZXKy8uTJP3whz884D9Lzc3N+slPfiKp9dGOL33pSyk/T1Rkd+/enfHet//xH/+hRYsWJTfLb297sXQS+ejdu7cGDBiQtk1nc554HjbxQgIAyAQDVBjhN7/5jaLRqAKBgD7/+c+rsLCw3ePkk0/W8OHDJaVWUWfOnCmpdcufTLaSSmzUX19ff1Cxjxw5Mjmg+NGPfpR2ZfOzzz6bHHBdeumlB3WfQ5Wokr7zzjt64403kpXU9p73DQQCkqR//OMfaSudmzdvTr5QIVOJAfH69evTDlLffffd5GBxf8cee2yy4vi9732v3UprwqerkC0tLYrH4+22/XSFOPGLS2f06dNH3/nOdyRJf/nLX/S9732v3bbxeFzXXHNNcueHb33rW8kV+QmTJk2S1+tVPB5vd1FdR9V66ZO+ZNKPRM4Tz/Hu78MPP+z0ixkS/7x3dl9YwCm8ScpMDFBhhMRA84ILLujUxuL/9V//Jal1b8/EayLPPPNMXXLJJZKkWbNmad68eSmry6urq3XfffclB7IJJ5xwgqTWZwL3f661sxLVsJdeekkXXXRRsnoUjUb1yCOPJAelkyZNSu5E0N2GDx+erPZ+4xvf0Pbt2+X1epOVtv2dffbZ8ng8qq2t1YwZM5KLh/bt26fHHntMZ599dsbbUiUkfgmJRqO6+OKLk5XwaDSqp556SlOmTFFBQUG7n//5z3+uwsJC/eMf/9DEiRP11FNPpQyid+zYod/85jeaPHlycuAotQ6wjj32WP3oRz/Spk2bUl4x+ve//z1ZZS4oKMh4YDV//vzkDgGLFi3Seeedp5deein5C0s0GtWf//xnnXbaafr1r38tqXUP03nz5rW5VlFRkcaOHSup/UryrFmzdPHFF+v3v/99ysK7hoYGLV26NLnvbWJbqE9LPNO9/6tmTznlFBUUFMi2bV188cX6xz/+Iam1Wv3cc8/pjDPO6NSz3lVVVclX5jJABXAwGKDCca+++qreeecdSZ8MPA8k0W7nzp165plnkufvv/9+felLX1I8HtfixYs1cOBABQIBlZSUqHfv3rrqqqu0YcOGlGsdc8wxyWcIp0+fruLiYg0ePFiDBw/WkiVLOhXPf/7nf+q2226TZVl68sknNXToUPXs2VOFhYX6yle+onA4rBNPPFErVqxIu09qd0lUSxNVuSlTpqhfv35p2x577LHJDev/8Ic/6KijjlJJSYkKCws1ffp0FRYWHvRrTgOBgJYsWSLLsvTqq69q+PDhKi4uVmFhoaZNm6ZBgwbphz/8YbufP+GEE7Rq1SqVlZVpy5YtmjZtmgoLC1VaWqr8/HwdddRRuuyyy9q8oUlq3TbrBz/4gU466STl5eWpV69e8vv9GjVqlNatWyefz6eHHnqowy2e0vF4PHriiSd0/fXXy+v16k9/+pNOO+00+f1+9erVS3l5eZo6dWpym6xLL71UTz31VMoODZ+W+KWmvTc2RaNRrVixQhdddJH69u2roqIi9ezZU0VFRfrGN76hffv26ZRTTumwmru/QCCgW265RZL04osvatiwYSoqKlJhYaHOOecchUIhPfjggwe8TiLm0aNHM8UP4KAwQIXjEtXTQCCgs88+u1OfOfHEE5P/4fv0NH9+fr5+//vfa+XKlfriF7+o/v37q6mpSTk5ORo5cqS++c1v6t57721zvccff1xz5szRZz7zGUWjUX3wwQf64IMPMpr2nzNnjt544w195Stf0cCBA9XY2KgePXpo4sSJuv3227V+/fqM3+pzuF166aXKzc1N/r296f2ExYsX6+GHH9b48ePVo0cPRaNRHXPMMfrud7+rTZs2HVJ/Zs6cqWeeeUZnnXWWiouL1dLSos985jNavHixXnjhhQ4rqFLrs6iJV5OedtppKikpUX19vbxer0aMGKGvfOUreuSRR1J+yRgwYID++Mc/as6cOZo4caL69eunhoYG5eTk6LjjjtO1116rt956q80btTorJydHt99+u9555x19+9vf1kknnZSMK/Fogcfj0ZNPPqlly5a1+2yv1LqlWl5enl555ZW0+5v+4Ac/0J133qkvfvGLGj58uHJyctTQ0KA+ffroc5/7nB544AGtW7euzff46W20Jk6c2Oa6//3f/61nnnlGZ5xxhgoLC9XS0qIBAwZo9uzZ+tvf/qYTTzzxgN9D4vGRa6655oBtAaexSMpMln2wK0MAAJ22ZcsWffazn1Vtba2mTJmilStXdrjPqSR97Wtf04MPPqibb75Z8+fPPyxx/Pa3v9VXv/pVDR06VFu2bEn5heVw+Pe//62hQ4eqqKhIH3744UE/BgJ0tXA4rEAgoNfOP02FuelnMrpaQ7RFE555UaFQKLkeAq2ooAJANxg+fLiefvpp9ejRQ88//7wuvvjilGdg05k/f778fr/uuuuuw/YyhMRjDwsXLjzsg1Op9Xls27Y1b948BqcADhoDVADoJpMmTdLy5cvl9Xr1xz/+UV/96lc73FVg8ODBmj17tnbv3q277777sMTwl7/8Rccff7xmzJhxWK73adu3b9cDDzygQYMG6frrrz/s1we6gmOvOf34QHoZ1bQXLlyom2++OeXcsGHDtGXLlsMaFAAcqb7whS8csHL6ad/73vdUWFh4wGdyOyvd86yHywcffKB58+bpzDPPTO4LCwAHI+OHLo4//viUjZrbW4EKADh0JSUlWrBggdNhdMopp5yiU045xekwgIw4uViJRVLty3h0mZOTk3zVIAAAAHC4Zfzwwz//+U/1799fQ4cO1YwZM5KbMQMAAACHQ0YV1AkTJuihhx7SsGHDVFlZqZtvvlmnnnqq3nrrrXZXazY3N6e8ki8ej6u2tla9evXq1BtJAADAkce2bUUiEfXv3z+jV/IebkzxmymjAeq5556b/N8jR47UhAkTdPTRR+uxxx5r8/rIhEWLFrVZWAUAACC17v5w1FFHOR0GDHNIK5xKSkr0mc98Ru+99167bebNm6e5c+cm/x4KhTRo0CC9/NJLKiwsPJTbAwAAl2poaNApp57q+H65VFDNdEgD1IaGBv2///f/9NWvfrXdNn6/P+3bUgoLCx3/hxIAADiLx/2QTkYPfdx444164YUX9O9//1uvvPKKvvjFL8rr9erSSy/tqvgAAACQZTKqoH744Ye69NJLVVNTo969e+uUU07Rq6++qt69e3dVfAAAAF2mdYrfmUVaTPG3L6MB6vLly7sqDgAAAEDSIT6DCgAA4GaWx5LH69AiqRgV1PY4t/EYAAAAMnL33Xdr8ODBysvL04QJE/T666932H7FihUaPny48vLydOKJJ+rZZ59N+bllWWmPn/3sZ8k2gwcPbvPzxYsXd0n/EhigAgAAuMCjjz6quXPnasGCBdq4caNGjRqlqVOnateuXWnbv/LKK7r00ks1c+ZMbdq0SdOmTdO0adP01ltvJdtUVlamHA888IAsy9KFF16Ycq0f/vCHKe1mz57dpX1lgAoAALJWYh9Up45M3Hbbbbrqqqt05ZVX6rjjjtPSpUuVn5+vBx54IG37O+64Q+ecc46+9a1vacSIEfrf//1fnXTSSbrrrruSbcrKylKOp556SmeeeaaGDh2acq2ioqKUdgUFBZl/2RlggAoAAOCgcDiccnz6FfEJ+/bt04YNGzRlypTkOY/HoylTpqiioiLtdSsqKlLaS9LUqVPbbb9z504988wzad8OunjxYvXq1UtjxozRz372M7W0tGTSxYyxSAoAAGQty+NxcJup1vsOHDgw5fyCBQu0cOHClHPV1dWKxWLq27dvyvm+fftqy5Ytaa9fVVWVtn1VVVXa9r/+9a9VVFSkL33pSynnv/nNb+qkk05SMBjUK6+8onnz5qmyslK33XbbAft4sBigAgAAOGj79u0qLi5O/j3dGzi7wwMPPKAZM2YoLy8v5fynX1k/cuRI+Xw+XXPNNVq0aFGXxcoAFQAAwEHFxcUpA9R0SktL5fV6tXPnzpTzO3fuVFlZWdrPlJWVdbr9Sy+9pK1bt+rRRx89YLwTJkxQS0uL/v3vf2vYsGEHbH8weAYVAABkLbcskvL5fBo7dqzWrFmTPBePx7VmzRqVl5en/Ux5eXlKe0lavXp12vb333+/xo4dq1GjRh0wls2bN8vj8ahPnz6djj9TVFABAABcYO7cubr88ss1btw4jR8/XkuWLNGePXt05ZVXSpIuu+wyDRgwQIsWLZIkXXfddTr99NN166236vzzz9fy5cv1xhtv6N577025bjgc1ooVK3Trrbe2uWdFRYVee+01nXnmmSoqKlJFRYXmzJmjr3zlK+rZs2eX9ZUBKgAAgAtMnz5du3fv1vz581VVVaXRo0dr1apVyYVQ27Ztk+dTC74mTZqkZcuW6fvf/76++93v6thjj9WTTz6pE044IeW6y5cvl23buvTSS9vc0+/3a/ny5Vq4cKGam5s1ZMgQzZkzJ+W51K5g2bZtd+kd9hMOhxUIBLR50yYVFRV15607lJubq9LSUvnz8mTbthoiEdXU1KRt6/f71au0VH6/X7FYTOFwWPV1dd0ccfYgN2YjP+YiN+YiN1IkEtHoMWMUCoUO+PxlV0iMR968/DwV+XK7/f6SFNkX1Ym/ftax78BkVFA/VlZWJm9Ojupqa+Xz+xUoKVHctlVXW5vSzuPxqKxfP0lSTXW18gsKFAwGFWtpUSQScSL0Ix65MRv5MRe5MRe5ATrGIilJPfLzlevzaW9jo0KhkKp375Zt2wqk+W2msLBQXq9XkUhE4XBYNdXVkqTiQKC7w84K5MZs5Mdc5MZc5MYsiX1QnTqQHt+MWqdaJCXfimDbtmKxmDxer7xeb4dto9FoynkcXuTGbOTHXOTGXOQGODAGqIfIsjJ7jy66D7kxG/kxF7kxF7lBtuAZVH3yG2lOTuvXYVmWvF6v4rGYYrGYLMtSYi3Z/m0TfybO4/AiN2YjP+YiN+YiN2bJdD/Sw31vpMcAVdLexkZFo1H1yM9XIBCQz++XZVmqD4eVl5en/gMGqKmpSR/t2KGGhgb1DAZVVFSklmhU+QUFkqRwKORwL45M5MZs5Mdc5MZc5AY4MKb4P1ZVWanm5mb1DAaVn5+vUCjUZjWl1PrWhqqqKkWjUfUqLZXP51NdbS2rKbsQuTEb+TEXuTEXuTEHi6TMxD6oAACg25myD+o7V13g6D6ox/3qKfZBTYOhOwAAAIzCM6gAACB7WVbr4dS9kRYVVAAAABiFCioAAMhaluXgNlNUUNtFBRUAAABGYYAKAAAAozDFDwAAspaT+5GyD2r7+GYAAABgFAaoAAAAMApT/AAAIGtZHgdX8Tt0XzegggoAAACjUEEFAABZi0VSZuKbAQAAgFEYoAIAAMAoTPEDAICsZXmcW6xkUSZsF18NAAAAjEIFFQAAZC22mTITFVQAAAAYhQEqAAAAjMIUPwAAyF4eT+vh1L2RFt8MAAAAjEIFFQAAZC3LsmRZDi2Scui+bmDUADU3N1elpaXy5+XJtm01RCKqqalJ29bv96tXaan8fr9isZjC4bDq6+q6OeLMubWPbo07E27uo5tj7ww398/NsXeWW/vo1rgzkQ19xJHJqAFqWVmZvDk5qqutlc/vV6CkRHHbVl1tbUo7j8ejsn79JEk11dXKLyhQMBhUrKVFkUjEidA7za19dGvcmXBzH90ce2e4uX9ujr2z3NpHt8adiWzoI45MxjyD2iM/X7k+n/Y2NioUCql6927Ztq1AcXGbtoWFhfJ6vYpEIgqHw6qprpYkFQcC3R12RtzaR7fGnQk399HNsXeGm/vn5tg7y619dGvcmciGPh4Olsfj6IH0jPlmcnNzJUktLS2SJNu2FYvF5PF65fV6O2wbjUZTzpvKrX10a9yZcHMf3Rx7Z7i5f26OvbPc2ke3xp2JbOgjjlzGDFAPRTY8ZOzWPro17ky4uY9ujr0z3Nw/N8feWW7to1vjzkQ29BFmM+YZ1MRvazk5rSFZliWv16t4LKZYLCbLsmTbdtq2iT8T503l1j66Ne5MuLmPbo69M9zcPzfH3llu7aNb485ENvTxcOBVp2YyZoC6t7FR0WhUPfLzFQgE5PP7ZVmW6sNh5eXlqf+AAWpqatJHO3aooaFBPYNBFRUVqSUaVX5BgSQpHAo53IuOubWPbo07E27uo5tj7ww398/NsXeWW/vo1rgzkQ19xJHLqCn+qspKNTc3q2cwqPz8fIVCoTYrDSUpHo+rqqpK0WhUvUpL5fP5VFdb64qVhm7to1vjzoSb++jm2DvDzf1zc+yd5dY+ujXuTGRDHw+Z5fnkbVLdfVhGDcOMYtmJ+n43CYfDCgQC2rxpk4qKirrz1gAAwBCRSESjx4xRKBRScZqdBbpaYjzy/rzLVZTn6/b7S1KkaZ+GLPq1Y9+ByRi6AwAAwCjGPIMKAADQ7RxcJCUWSbWLCioAAACMQgUVAABkLcvyyHJosZJT93UDvhkAAAAYhQEqAAAAjMIUPwAAyF4ey7nFSiySahcVVAAAABiFCioAAMhalscjy+PQIimH7usGfDMAAAAwCgNUAAAAGIUpfgAAkLUsB98k5dgbrFyACioAAACMQgUVAABkL8uSnHqjk0UFtT1UUAEAAGAUBqgAAAAwClP8AAAga7FIykxUUAEAAGAUBqgAAAAwClP8AAAge3k8rYdT90ZafDMAAAAwChVUAACQtSzLkuXQfqRO3dcNGKB+LDc3V6WlpfLn5cm2bTVEIqqpqUnb1u/3q1dpqfx+v2KxmMLhsOrr6ro54uxBbsxGfsxFbsxFboCOMUD9WFlZmbw5OaqrrZXP71egpERx21ZdbW1KO4/Ho7J+/SRJNdXVyi8oUDAYVKylRZFIxInQj3jkxmzkx1zkxlzkBugYz6BK6pGfr1yfT3sbGxUKhVS9e7ds21aguLhN28LCQnm9XkUiEYXDYdVUV0uSigOB7g47K5Abs5Efc5Ebc5Ebw1ieTxZKdffh1CtWXYBvRq1TLZLU0tIiSbJtW7FYTB6vV16vt8O20Wg05TwOL3JjNvJjLnJjLnIDHBhT/IeIB5zNRW7MRn7MRW7MRW4OP94kZSYqqPrkN9KcnNbxumVZ8nq9isdiisViKf9C2L9t4s/EeRxe5MZs5Mdc5MZc5AY4MCqokvY2NioajapHfr4CgYB8fr8sy1J9OKy8vDz1HzBATU1N+mjHDjU0NKhnMKiioiK1RKPKLyiQJIVDIYd7cWQiN2YjP+YiN+YiN8CBUUH9WFVlpZqbm9UzGFR+fr5CoVCb1ZSSFI/HVVVVpWg0ql6lpfL5fKqrrWU1ZRciN2YjP+YiN+YiNwaxPM4eSMuybdvuzhuGw2EFAgFt3rRJRUVF3XlrAABgiEgkotFjxigUCqk4zQ4GXS0xHtlxy/Uq7uHv9vtLUnhvswbcuMSx78BkTPEDAIDs5bFaD6fujbSoLQMAALjE3XffrcGDBysvL08TJkzQ66+/3mH7FStWaPjw4crLy9OJJ56oZ599NuXnV1xxRfJ1r4njnHPOSWlTW1urGTNmqLi4WCUlJZo5c6YaGhoOe98+jQEqAACACzz66KOaO3euFixYoI0bN2rUqFGaOnWqdu3albb9K6+8oksvvVQzZ87Upk2bNG3aNE2bNk1vvfVWSrtzzjlHlZWVyeN3v/tdys9nzJiht99+W6tXr9bKlSv14osv6uqrr+6yfko8gwoAABxgyjOoH91+g6PPoPafc2unv4MJEybo5JNP1l133SWpdRHdwIEDNXv2bN10001t2k+fPl179uzRypUrk+cmTpyo0aNHa+nSpZJaK6j19fV68skn097z3Xff1XHHHaf169dr3LhxkqRVq1bpvPPO04cffqj+/ftn2u1OoYIKAADgoHA4nHI0Nze3abNv3z5t2LBBU6ZMSZ7zeDyaMmWKKioq0l63oqIipb0kTZ06tU37devWqU+fPho2bJi+8Y1vqKamJuUaJSUlycGpJE2ZMkUej0evvfbaQfW3MxigAgCA7JVYJOXUIWngwIEKBALJY9GiRW3CrK6uViwWU9++fVPO9+3bV1VVVWm7VlVVdcD255xzjh5++GGtWbNGP/nJT/TCCy/o3HPPVSwWS16jT58+KdfIyclRMBhs976HA6v4AQAAHLR9+/aUKX6/v/seObjkkkuS//vEE0/UyJEj9R//8R9at26dJk+e3G1x7I8KKgAAgIOKi4tTjnQD1NLSUnm9Xu3cuTPl/M6dO1VWVpb2umVlZRm1l6ShQ4eqtLRU7733XvIa+y/CamlpUW1tbYfXOVQMUAEAQNayPB5Hj87y+XwaO3as1qxZkzwXj8e1Zs0alZeXp/1MeXl5SntJWr16dbvtJenDDz9UTU2N+vXrl7xGfX29NmzYkGyzdu1axeNxTZgwodPxZ4oBKgAAgAvMnTtXv/rVr/TrX/9a7777rr7xjW9oz549uvLKKyVJl112mebNm5dsf91112nVqlW69dZbtWXLFi1cuFBvvPGGZs2aJUlqaGjQt771Lb366qv697//rTVr1uiCCy7QMccco6lTp0qSRowYoXPOOUdXXXWVXn/9df31r3/VrFmzdMkll3TZCn7pEAeoixcvlmVZuv766w9TOAAAAEhn+vTpuuWWWzR//nyNHj1amzdv1qpVq5ILobZt26bKyspk+0mTJmnZsmW69957NWrUKD3++ON68skndcIJJ0iSvF6v/v73v+sLX/iCPvOZz2jmzJkaO3asXnrppZTHDB555BENHz5ckydP1nnnnadTTjlF9957b5f29aD3QV2/fr0uvvhiFRcX68wzz9SSJUs69Tn2QQUAAKbsg1p513cc3Qe136yfOPYdmOygKqgNDQ2aMWOGfvWrX6lnz56HOyYAAABksYMaoF577bU6//zz22z+CgAA4CoeS/J4HDosp3tvrIz3QV2+fLk2btyo9evXd6p9c3NzyhsRwuFwprcEAABAFsmogrp9+3Zdd911euSRR5SXl9epzyxatCjl7QgDBw48qEABAACQHTIaoG7YsEG7du3SSSedpJycHOXk5OiFF17QnXfeqZycnORrsT5t3rx5CoVCyWP79u2HLXgAAIBDYlnOHkgroyn+yZMn680330w5d+WVV2r48OH6zne+I6/X2+Yzfr+/W1/ZBQAAAHfLaIBaVFSU3DsroaCgQL169WpzHgAAwHSZvtHpcN8b6fHNAAAAwCgZr+Lf37p16w5DGAAAAECrQx6gAgAAuJblaT2cujfS4psBAACAUaigAgCA7GVZzr3RiW2m2kUFFQAAAEZhgAoAAACjMMUPAACylmV5ZDm0WMmp+7oB3wwAAACMwgAVAAAARmGKHwAAZC+Pg6v4nbqvC1BBBQAAgFGooAIAgOzFm6SMxDcDAAAAo1BB/Vhubq5KS0vlz8uTbdtqiERUU1OTtq3f71ev0lL5/X7FYjGFw2HV19V1c8TZg9yYjfyYi9yYi9wAHWOA+rGysjJ5c3JUV1srn9+vQEmJ4ratutralHYej0dl/fpJkmqqq5VfUKBgMKhYS4sikYgToR/xyI3ZyI+5yI25yI1BLMu5V47yqtN2McUvqUd+vnJ9Pu1tbFQoFFL17t2ybVuB4uI2bQsLC+X1ehWJRBQOh1VTXS1JKg4EujvsrEBuzEZ+zEVuzEVugAOjgqrWqRZJamlpkSTZtq1YLKacnBx5vV7FYrF220aj0ZTzOLzIjdnIj7nIjbnIjWE8ntbDqXsjLb6ZQ2RRnjcWuTEb+TEXuTEXuUG2YICqT34jzclpLShbliWv16t4LKZYLJbyL4T92yb+TJzH4UVuzEZ+zEVuzEVugANjil/S3sZGRaNR9cjPVyAQkM/vl2VZqg+HlZeXp/4DBqipqUkf7dihhoYG9QwGVVRUpJZoVPkFBZKkcCjkcC+OTOTGbOTHXOTGXOTGMOyDaiS+mY9VVVaqublZPYNB5efnKxQKtVlNKUnxeFxVVVWKRqPqVVoqn8+nutpaVlN2IXJjNvJjLnJjLnIDdMyybdvuzhuGw2EFAgFt3rRJRUVF3XlrAABgiEgkotFjxigUCqk4zQ4GXS0xHtn5yE9UnJ/X7feXpHBjk/rO+I5j34HJqKACAADAKAxQAQAAYBQWSQEAgOxlWQ4ukmLbsPZQQQUAAIBRGKACAADAKEzxAwCA7GVZzk21M8XfLiqoAAAAMAoVVAAAkL08ntbDqXsjLb4ZAAAAGIUBKgAAAIzCFD8AAMheLJIyEhVUAAAAGIUKKgAAyF6Wx8E3SVEnbA/fDAAAAIzCABUAAABGYYofAABkL8vBfVCZ4m8X3wwAAACMQgUVAABkL7aZMhIVVAAAABiFASoAAACMwhQ/AADIXuyDaiSjBqi5ubkqLS2VPy9Ptm2rIRJRTU1N2rZ+v1+9Skvl9/sVi8UUDodVX1fXzRFnzq19dGvcmXBzH90ce2e4uX9ujr2z3NpHt8adiWzoI45MRg1Qy8rK5M3JUV1trXx+vwIlJYrbtupqa1PaeTwelfXrJ0mqqa5WfkGBgsGgYi0tikQiToTeaW7to1vjzoSb++jm2DvDzf1zc+yd5dY+ujXuTGRDHw8Zi6SMZExtuUd+vnJ9Pu1tbFQoFFL17t2ybVuB4uI2bQsLC+X1ehWJRBQOh1VTXS1JKg4EujvsjLi1j26NOxNu7qObY+8MN/fPzbF3llv76Na4M5ENfcSRy5gBam5uriSppaVFkmTbtmKxmDxer7xeb4dto9FoynlTubWPbo07E27uo5tj7ww398/NsXeWW/vo1rgzkQ19xJHLqCn+g2VlQYncrX10a9yZcHMf3Rx7Z7i5f26OvbPc2ke3xp2JbOhjksfBN0k5dV8XMOabSfy2lpPTOma2LEter1fxWEyxWCzl/yz7t038mThvKrf20a1xZ8LNfXRz7J3h5v65OfbOcmsf3Rp3JrKhjzhyGVNB3dvYqGg0qh75+QoEAvL5/bIsS/XhsPLy8tR/wAA1NTXpox071NDQoJ7BoIqKitQSjSq/oECSFA6FHO5Fx9zaR7fGnQk399HNsXeGm/vn5tg7y619dGvcmciGPuLIZUwFVZKqKivV3NysnsGg8vPzFQqF2qw0lKR4PK6qqipFo1H1Ki2Vz+dTXW2tK1YaurWPbo07E27uo5tj7ww398/NsXeWW/vo1rgzkQ19PFS2ZTl6ID3Ltm27O28YDocVCAS0edMmFRUVdeetAQCAISKRiEaPGaNQKKTiNDsLdLXEeKTqj79UcUGPbr+/JIX37FXZF65x7DswmTFT/AAAAN3Oshx8kxQV1PYYNcUPAAAAMEAFAACAUZjiBwAA2cvyODjFT52wPXwzAAAAMAoVVAAAkLWc3O6JbabaRwUVAAAARmGACgAAAKMwxQ8AALIXi6SMxDcDAAAAo1BBBQAA2cuynHujE4uk2kUFFQAAwCXuvvtuDR48WHl5eZowYYJef/31DtuvWLFCw4cPV15enk488UQ9++yzyZ9Fo1F95zvf0YknnqiCggL1799fl112mT766KOUawwePFiWZaUcixcv7pL+JTBABQAAcIFHH31Uc+fO1YIFC7Rx40aNGjVKU6dO1a5du9K2f+WVV3TppZdq5syZ2rRpk6ZNm6Zp06bprbfekiQ1NjZq48aN+sEPfqCNGzfqD3/4g7Zu3aovfOELba71wx/+UJWVlclj9uzZXdpXy7Ztu0vvsJ9wOKxAIKDNmzapqKioO28NAAAMEYlENHrMGIVCIRUXF3f7/RPjkcrnHlZxQX6331+Swnsa1W/qZZ3+DiZMmKCTTz5Zd911lyQpHo9r4MCBmj17tm666aY27adPn649e/Zo5cqVyXMTJ07U6NGjtXTp0rT3WL9+vcaPH68PPvhAgwYNktRaQb3++ut1/fXXH0QvDw4VVAAAAMPt27dPGzZs0JQpU5LnPB6PpkyZooqKirSfqaioSGkvSVOnTm23vSSFQiFZlqWSkpKU84sXL1avXr00ZswY/exnP1NLS8vBd6YTWCQFAADgoHA4nPJ3v98vv9+fcq66ulqxWEx9+/ZNOd+3b19t2bIl7XWrqqrStq+qqkrbvqmpSd/5znd06aWXplR0v/nNb+qkk05SMBjUK6+8onnz5qmyslK33XZbp/uYKQaoAAAga5nwqtOBAwemnF+wYIEWLlzYrbFEo1FdfPHFsm1b99xzT8rP5s6dm/zfI0eOlM/n0zXXXKNFixa1GUgfLgxQAQAAHLR9+/aUimW6QV9paam8Xq927tyZcn7nzp0qKytLe92ysrJOtU8MTj/44AOtXbv2gM/DTpgwQS0tLfr3v/+tYcOGddj2YPEMKgAAyF6JN0k5dUgqLi5OOdINUH0+n8aOHas1a9Ykz8Xjca1Zs0bl5eVpu1ZeXp7SXpJWr16d0j4xOP3nP/+p559/Xr169TrgV7Z582Z5PB716dOnU1/xwaCCCgAA4AJz587V5ZdfrnHjxmn8+PFasmSJ9uzZoyuvvFKSdNlll2nAgAFatGiRJOm6667T6aefrltvvVXnn3++li9frjfeeEP33nuvpNbB6UUXXaSNGzdq5cqVisViyedTg8GgfD6fKioq9Nprr+nMM89UUVGRKioqNGfOHH3lK19Rz549u6yvDFABAABcYPr06dq9e7fmz5+vqqoqjR49WqtWrUouhNq2bZs8nk8mxydNmqRly5bp+9//vr773e/q2GOP1ZNPPqkTTjhBkrRjxw798Y9/lCSNHj065V5/+ctfdMYZZ8jv92v58uVauHChmpubNWTIEM2ZMyfludSuwD6oAACg25myD+qO53/n6D6oA6Zc6th3YDIqqB/Lzc1VaWmp/Hl5sm1bDZGIampq0rb1+/3qVVoqv9+vWCymcDis+rq6bo44e5Abs5Efc5Ebc5EboGMMUD9WVlYmb06O6mpr5fP7FSgpUdy2VVdbm9LO4/GorF8/SVJNdbXyCwoUDAYVa2lRJBJxIvQjHrkxG/kxF7kxF7kxiGW1Hk7dG2mxil9Sj/x85fp82tvYqFAopOrdu2XbtgJpyu2FhYXyer2KRCIKh8Oqqa6WJBUHAt0ddlYgN2YjP+YiN+YiN8CBMUBV61SLpORru2zbViwWk8frldfr7bBtNBpNOY/Di9yYjfyYi9yYi9wAB8YU/yGyKM8bi9yYjfyYi9yYi9wcfrY8si1n6nU2dcJ28c3ok99Ic3Jax+uWZcnr9SoeiykWi6X8C2H/tok/E+dxeJEbs5Efc5Ebc5Eb4MCooEra29ioaDSqHvn5CgQC8vn9sixL9eGw8vLy1H/AADU1NemjHTvU0NCgnsGgioqK1BKNKr+gQJIUDoUc7sWRidyYjfyYi9yYi9wYhkVSRqKC+rGqyko1NzerZzCo/Px8hUKhNqsppdbXilVVVSkajapXaal8Pp/qamtZTdmFyI3ZyI+5yI25yA3QMTbqBwAA3c6Ujfo/XLtCxYUObdTf0KijzvovNupPgyl+AACQvSxLcmiRFFP87WOKHwAAAEahggoAALKWbVmyHapkOnVfN6CCCgAAAKMwQAUAAIBRmOIHAADZy/I4uEiKOmF7+GYAAABgFAaoAAAAMApT/AAAIGvZsmTLoVX8Dt3XDaigAgAAwCgZDVDvuecejRw5UsXFxSouLlZ5ebn+9Kc/dVVsAAAAXcq2PI4eSC+jb+aoo47S4sWLtWHDBr3xxhs666yzdMEFF+jtt9/uqvgAAACQZTJ6BvXzn/98yt9//OMf65577tGrr76q448//rAGBgAAgOx00IukYrGYVqxYoT179qi8vPxwxgQAANA92AfVSBkPUN98802Vl5erqalJhYWFeuKJJ3Tccce12765uVnNzc3Jv4fD4YOLFAAAAFkh46H7sGHDtHnzZr322mv6xje+ocsvv1zvvPNOu+0XLVqkQCCQPAYOHHhIAQMAABwutmU5eiC9jAeoPp9PxxxzjMaOHatFixZp1KhRuuOOO9ptP2/ePIVCoeSxffv2QwoYAAAAR7ZD3qg/Ho+nTOHvz+/3y+/3H+ptAAAAkCUyGqDOmzdP5557rgYNGqRIJKJly5Zp3bp1eu6557oqPgAAgC7j5H6k7IPavowGqLt27dJll12myspKBQIBjRw5Us8995w+97nPdVV8AAAAyDIZDVDvv//+rooDAACg+1lW6+HUvZEWtWUAAAAYhQEqAAAAjHLIq/gBAABcy8FFUrxJqn18MwAAADAKA1QAAAAYhSl+AACQtWxZsuXManqn7usGVFABAABgFCqoAAAga/EmKTPxzQAAAMAoDFABAABgFKb4AQBA9rLk4KtOnbmtG1BBBQAAgFGooH4sNzdXpaWl8uflybZtNUQiqqmpSdvW7/erV2mp/H6/YrGYwuGw6uvqujni7EFuzEZ+zEVuzEVuzGHLI9uhep1T93UDBqgfKysrkzcnR3W1tfL5/QqUlChu26qrrU1p5/F4VNavnySpprpa+QUFCgaDirW0KBKJOBH6EY/cmI38mIvcmIvcAB1j6C6pR36+cn0+7W1sVCgUUvXu3bJtW4Hi4jZtCwsL5fV6FYlEFA6HVVNdLUkqDgS6O+ysQG7MRn7MRW7MRW6AA6OCqtapFklqaWmRJNm2rVgsppycHHm9XsVisXbbRqPRlPM4vMiN2ciPuciNuciNWWzLku3QIimn7usGVFAPkcU/XMYiN2YjP+YiN+YiN8gWVFD1yW+kOTmtX4dlWfJ6vYrHYorFYrIsS7Ztp22b+DNxHocXuTEb+TEXuTEXuTELb5IyEwNUSXsbGxWNRtUjP1+BQEA+v1+WZak+HFZeXp76DxigpqYmfbRjhxoaGtQzGFRRUZFaolHlFxRIksKhkMO9ODKRG7ORH3ORG3ORG+DAGLp/rKqyUs3NzeoZDCo/P1+hUKjNakpJisfjqqqqUjQaVa/SUvl8PtXV1rKasguRG7ORH3ORG3ORG6Bjlp2YR+gm4XBYgUBAmzdtUlFRUXfeGgAAGCISiWj0mDEKhUIqTrODQVdLjEe2rn9JRYWF3X5/SYo0NGjYyac69h2YjAoqAAAAjMIzqAAAIGuxSMpMfDMAAAAwCgNUAAAAGIUpfgAAkLV4k5SZqKACAADAKAxQAQAAYBSm+AEAQNayZcmWQ1P8Dt3XDaigAgAAwChUUAEAQNZiH1Qz8c0AAADAKAxQAQAAYBSm+AEAQNZikZSZqKACAADAKFRQAQBA1rLl4CIp6oTt4psBAACAURigAgAAwChM8QMAgKzFIikzUUEFAACAUaigAgCArGVbloNvkqKC2h4qqAAAAC5x9913a/DgwcrLy9OECRP0+uuvd9h+xYoVGj58uPLy8nTiiSfq2WefTfm5bduaP3+++vXrpx49emjKlCn65z//mdKmtrZWM2bMUHFxsUpKSjRz5kw1NDQc9r59GgNUAAAAF3j00Uc1d+5cLViwQBs3btSoUaM0depU7dq1K237V155RZdeeqlmzpypTZs2adq0aZo2bZreeuutZJuf/vSnuvPOO7V06VK99tprKigo0NSpU9XU1JRsM2PGDL399ttavXq1Vq5cqRdffFFXX311l/bVsm3b7tI77CccDisQCGjzpk0qKipK+Vlubq5KS0vlz8uTbdtqiERUU1OT9jp+v1+9Skvl9/sVi8UUDodVX1fXHV04JG7to1vjzoSb++jm2DvDzf1zc+yd5dY+ujXuTJjcx0gkotFjxigUCqm4uLjL7tOejsYj3SXT72DChAk6+eSTddddd0mS4vG4Bg4cqNmzZ+umm25q03769Onas2ePVq5cmTw3ceJEjR49WkuXLpVt2+rfv79uuOEG3XjjjZKkUCikvn376qGHHtIll1yid999V8cdd5zWr1+vcePGSZJWrVql8847Tx9++KH69+9/OL6KNoyqoJaVlcmfl6e62lo1NjYqUFKinsFgm3Yej0dl/fopNzdXNdXV2rdvn4LBoGP/gGXCrX10a9yZcHMf3Rx7Z7i5f26OvbPc2ke3xp2JbOhjtti3b582bNigKVOmJM95PB5NmTJFFRUVaT9TUVGR0l6Spk6dmmz//vvvq6qqKqVNIBDQhAkTkm0qKipUUlKSHJxK0pQpU+TxePTaa68dtv7tz5gBao/8fOX6fNrb2KhQKKTq3btl27YCaX6jKCwslNfrVSQSUTgcVk11tSSpOBDo7rAz4tY+ujXuTLi5j26OvTPc3D83x95Zbu2jW+PORDb08UgRDodTjubm5jZtqqurFYvF1Ldv35Tzffv2VVVVVdrrVlVVddg+8eeB2vTp0yfl5zk5OQoGg+3e93AwZoCam5srSWppaZHU+tBuLBaTx+uV1+vtsG00Gk05byq39tGtcWfCzX10c+yd4eb+uTn2znJrH90adyayoY+HQ+sqfucOSRo4cKACgUDyWLRokcPfivOOiG2mrCzYpsGtfXRr3Jlwcx/dHHtnuLl/bo69s9zaR7fGnYls6KNJtm/fnvIMqt/vb9OmtLRUXq9XO3fuTDm/c+dOlZWVpb1uWVlZh+0Tf+7cuVP9+vVLaTN69Ohkm/0XYbW0tKi2trbd+x4OxlRQE7+t5eS0jpkty5LX61U8FlMsFkv5P8v+bRN/Js6byq19dGvcmXBzH90ce2e4uX9ujr2z3NpHt8adiWzo4+Fg25ajhyQVFxenHOkGqD6fT2PHjtWaNWuS5+LxuNasWaPy8vK0fSsvL09pL0mrV69Oth8yZIjKyspS2oTDYb322mvJNuXl5aqvr9eGDRuSbdauXat4PK4JEyYc5Ld+YMZUUPc2NioajapHfr4CgYB8fr8sy1J9OKy8vDz1HzBATU1N+mjHDjU0NKjnxw9vt0Sjyi8okCSFQyGHe9Ext/bRrXFnws19dHPsneHm/rk59s5yax/dGncmsqGP2Wbu3Lm6/PLLNW7cOI0fP15LlizRnj17dOWVV0qSLrvsMg0YMCD5iMB1112n008/XbfeeqvOP/98LV++XG+88YbuvfdeSa2/tFx//fX60Y9+pGOPPVZDhgzRD37wA/Xv31/Tpk2TJI0YMULnnHOOrrrqKi1dulTRaFSzZs3SJZdc0mUr+CWDKqiSVFVZqebmZvUMBpWfn69QKKS62to27eLxuKqqqhSNRtWrtFQ+n091tbWKRCIORJ0Zt/bRrXFnws19dHPsneHm/rk59s5yax/dGncmsqGP2WT69Om65ZZbNH/+fI0ePVqbN2/WqlWrkouctm3bpsrKymT7SZMmadmyZbr33ns1atQoPf7443ryySd1wgknJNt8+9vf1uzZs3X11Vfr5JNPVkNDg1atWqW8vLxkm0ceeUTDhw/X5MmTdd555+mUU05JDnK7ilH7oAIAgOxgyj6oGze9qUKHxiMNkYhOGnOiY9+ByYyqoAIAAADGPIMKAADQ3WxZsuXMrgVO3dcNqKACAADAKAxQAQAAYBSm+AEAQNZiit9MVFABAABgFCqoAAAga1FBNRMVVAAAABiFASoAAACMwhQ/AADIWkzxm4kKKgAAAIxCBRUAAGQt27Zk2w5VUB26rxtQQQUAAIBRGKACAADAKEzxAwCArMUiKTNRQQUAAIBRGKACAADAKEzxAwCArMUUv5mooAIAAMAoVFABAEDWooJqJiqoAAAAMAoDVAAAABiFKf6P5ebmqrS0VP68PNm2rYZIRDU1NWnb+v1+9Sotld/vVywWUzgcVn1dXTdHnD3IjdnIj7nIjbnIjTlsOfiqU6b428UA9WNlZWXy5uSorrZWPr9fgZISxW1bdbW1Ke08Ho/K+vWTJNVUVyu/oEDBYFCxlhZFIhEnQj/ikRuzkR9zkRtzkRugY0zxS+qRn69cn097GxsVCoVUvXu3bNtWoLi4TdvCwkJ5vV5FIhGFw2HVVFdLkooDge4OOyuQG7ORH3ORG3ORG7PEZTl6ID0GqGqdapGklpYWSZJt24rFYvJ4vfJ6vR22jUajKedxeJEbs5Efc5Ebc5Eb4MAYoB4iy+K3H1ORG7ORH3ORG3ORG2QLnkHVJ7+R5uS0fh2WZcnr9SoeiykWi8myLNm2nbZt4s/EeRxe5MZs5Mdc5MZc5MYs7INqJgaokvY2NioajapHfr4CgYB8fr8sy1J9OKy8vDz1HzBATU1N+mjHDjU0NKhnMKiioiK1RKPKLyiQJIVDIYd7cWQiN2YjP+YiN+YiN8CBMcX/sarKSjU3N6tnMKj8/HyFQqE2qyklKR6Pq6qqStFoVL1KS+Xz+VRXW8tqyi5EbsxGfsxFbsxFbsxh25ajB9Kz7MQ8QjcJh8MKBALavGmTioqKuvPWAADAEJFIRKPHjFEoFFJxmh0MulpiPPLChvdVWOjMeKShIaLTxw5x7DswGRVUAAAAGIVnUAEAQNay5dxipW6dwnYZKqgAAAAwCgNUAAAAGIUpfgAAkLWcXE3PKv72UUEFAACAUaigAgCArMWbpMxEBRUAAABGYYAKAAAAozDFDwAAshaLpMyUUQV10aJFOvnkk1VUVKQ+ffpo2rRp2rp1a1fFBgAAgCyU0QD1hRde0LXXXqtXX31Vq1evVjQa1dlnn609e/Z0VXwAAABdxpYUd+jgTVLty2iKf9WqVSl/f+ihh9SnTx9t2LBBp5122mENDAAAANnpkJ5BDYVCkqRgMNhum+bmZjU3Nyf/Hg6HD+WWAAAAOMId9Cr+eDyu66+/Xp/97Gd1wgkntNtu0aJFCgQCyWPgwIEHe0sAAIDDKrFIyqkD6R30APXaa6/VW2+9peXLl3fYbt68eQqFQslj+/btB3tLAAAAZIGDmuKfNWuWVq5cqRdffFFHHXVUh239fr/8fv9BBQcAANCVeJOUmTIaoNq2rdmzZ+uJJ57QunXrNGTIkK6KCwAAAFkqowHqtddeq2XLlumpp55SUVGRqqqqJEmBQEA9evTokgABAACQXTIaoN5zzz2SpDPOOCPl/IMPPqgrrrjicMUEAADQLXiTlJkynuIHAAAAutIh7YMKAADgZiySMtNBbzMFAAAAdAUGqAAAADAKU/wAACBrxe3Ww6l7Iz0qqAAAADAKA1QAAAAYhSl+AACQtVjFbyYqqAAAADAKFVQAAJC1eJOUmaigAgAAwCgMUAEAAGAUpvg/lpubq9LSUvnz8mTbthoiEdXU1KRt6/f71au0VH6/X7FYTOFwWPV1dd0ccfYgN2YjP+YiN+YiN+aw7dbDqXsjPQaoHysrK5M3J0d1tbXy+f0KlJQobtuqq61NaefxeFTWr58kqaa6WvkFBQoGg4q1tCgSiTgR+hGP3JiN/JiL3JiL3AAdY4pfUo/8fOX6fNrb2KhQKKTq3btl27YCxcVt2hYWFsrr9SoSiSgcDqumulqSVBwIdHfYWYHcmI38mIvcmIvcmCUuy9ED6TFAVetUiyS1tLRIkmzbViwWk8frldfr7bBtNBpNOY/Di9yYjfyYi9yYi9wAB8YA9RBZFr/9mIrcmI38mIvcmIvcIFvwDKo++Y00J6f167AsS16vV/FYTLFYTJZlyf74Seb92yb+TJzH4UVuzEZ+zEVuzEVuzMI+qGZigCppb2OjotGoeuTnKxAIyOf3y7Is1YfDysvLU/8BA9TU1KSPduxQQ0ODegaDKioqUks0qvyCAklSOBRyuBdHJnJjNvJjLnJjLnIDHBhT/B+rqqxUc3OzegaDys/PVygUarOaUpLi8biqqqoUjUbVq7RUPp9PdbW1rKbsQuTGbOTHXOTGXOTGHIltppw6kJ5l29379YTDYQUCAW3etElFRUXdeWsAAGCISCSi0WPGKBQKqTjNDgZdLTEeeeLlXSoo7P77S9KehrC+eEqfLvkOamtrNXv2bD399NPyeDy68MILdccdd6iwsLDdzzQ1NemGG27Q8uXL1dzcrKlTp+oXv/iF+vbtK0n629/+psWLF+vll19WdXW1Bg8erP/+7//Wddddl7zGunXrdOaZZ7a5dmVlpcrKyjodP1P8AAAAR5gZM2aosrJSq1evVjQa1ZVXXqmrr75ay5Yta/czc+bM0TPPPKMVK1YoEAho1qxZ+tKXvqS//vWvkqQNGzaoT58++u1vf6uBAwfqlVde0dVXXy2v16tZs2alXGvr1q0pg+4+ffpkFD8DVAAAkLVsWbId2o+0q+777rvvatWqVVq/fr3GjRsnSfr5z3+u8847T7fccov69+/f5jOhUEj333+/li1bprPOOkuS9OCDD2rEiBF69dVXNXHiRH3ta19L+czQoUNVUVGhP/zhD20GqH369FFJSclB94FnUAEAAI4gFRUVKikpSQ5OJWnKlCnyeDx67bXX0n5mw4YNikajmjJlSvLc8OHDNWjQIFVUVLR7r1AopGAw2Ob86NGj1a9fP33uc59LVmAzQQUVAADAQeFwOOXvfr9ffr//oK9XVVXVZko9JydHwWBQVVVV7X7G5/O1qXr27du33c+88sorevTRR/XMM88kz/Xr109Lly7VuHHj1NzcrPvuu09nnHGGXnvtNZ100kmd7gMVVAAAkLXitrOHJA0cOFCBQCB5LFq0KG2sN910kyzL6vDYsmVLt3xvb731li644AItWLBAZ599dvL8sGHDdM0112js2LGaNGmSHnjgAU2aNEm33357RtenggoAAOCg7du3pywoaq96esMNN+iKK67o8FpDhw5VWVmZdu3alXK+paVFtbW17a6kLysr0759+1RfX59SRd25c2ebz7zzzjuaPHmyrr76an3/+9/vMB5JGj9+vF5++eUDtvs0BqgAACB7OfgmKX183+Li4k5tM9W7d2/17t37gO3Ky8tVX1+vDRs2aOzYsZKktWvXKh6Pa8KECWk/M3bsWOXm5mrNmjW68MILJbWuxN+2bZvKy8uT7d5++22dddZZuvzyy/XjH//4gLFI0ubNm9WvX79OtU1ggAoAAHAEGTFihM455xxdddVVWrp0qaLRqGbNmqVLLrkkuYJ/x44dmjx5sh5++GGNHz9egUBAM2fO1Ny5cxUMBlVcXKzZs2ervLxcEydOlNQ6rX/WWWdp6tSpmjt3bvLZVK/Xmxw4L1myREOGDNHxxx+vpqYm3XfffVq7dq3+/Oc/Z9QHBqgAAABHmEceeUSzZs3S5MmTkxv133nnncmfR6NRbd26VY2Njclzt99+e7LtpzfqT3j88ce1e/du/fa3v9Vvf/vb5Pmjjz5a//73vyVJ+/bt0w033KAdO3YoPz9fI0eO1PPPP5928/6O8CYpAADQ7Ux5k9SKF6qV79CbpBobwvqv00sd+w5Mxip+AAAAGIUpfgAAkLXishR36E1STt3XDaigAgAAwCgMUAEAAGAUpvgBAEDWsu3Ww6l7Iz0qqAAAADAKFVQAAJC1bAffJOXYG6xcgAoqAAAAjMIAFQAAAEZhih8AAGStuN16OHVvpEcFFQAAAEahggoAALIW20yZyagBam5urkpLS+XPy5Nt22qIRFRTU5O2rd/vV6/SUvn9fsViMYXDYdXX1XVzxJlzax/dGncm3NxHN8feGW7un5tj7yy39tGtcWciG/qII5NRA9SysjJ5c3JUV1srn9+vQEmJ4ratutralHYej0dl/fpJkmqqq5VfUKBgMKhYS4sikYgToXeaW/vo1rgz4eY+ujn2znBz/9wce2e5tY9ujTsT2dBHHJmMeQa1R36+cn0+7W1sVCgUUvXu3bJtW4Hi4jZtCwsL5fV6FYlEFA6HVVNdLUkqDgS6O+yMuLWPbo07E27uo5tj7ww398/NsXeWW/vo1rgzkQ19PBxsWY4eSM+YAWpubq4kqaWlRZJk27ZisZg8Xq+8Xm+HbaPRaMp5U7m1j26NOxNu7qObY+8MN/fPzbF3llv76Na4M5ENfcSRy5gB6qGwrCP/NxC39tGtcWfCzX10c+yd4eb+uTn2znJrH90adyayoY8wmzHPoCZ+W8vJaQ3Jsix5vV7FYzHFYjFZliX74+Vu+7dN/Jk4byq39tGtcWfCzX10c+yd4eb+uTn2znJrH90adyayoY+HQ1wO7oPqzG1dwZgB6t7GRkWjUfXIz1cgEJDP75dlWaoPh5WXl6f+AwaoqalJH+3YoYaGBvUMBlVUVKSWaFT5BQWSpHAo5HAvOubWPro17ky4uY9ujr0z3Nw/N8feWW7to1vjzkQ29BFHLqOm+KsqK9Xc3KyewaDy8/MVCoXarDSUpHg8rqqqKkWjUfUqLZXP51Ndba0rVhq6tY9ujTsTbu6jm2PvDDf3z82xd5Zb++jWuDORDX08VIl9UJ06kJ5l29379YTDYQUCAW3etElFRUXdeWsAAGCISCSi0WPGKBQKqTjNzgJdLTEeeej5OuUXdP/9JalxT1hXTOnp2HdgMqMqqAAAAIAxz6ACAAB0N151aiYqqAAAADAKFVQAAJC14raluO3Mvq9O3dcNqKACAADAKAxQAQAAYBSm+AEAQNZikZSZqKACAADAKFRQAQBA1qKCaiYqqAAAADAKA1QAAAAYhSl+AACQtWxbijPFbxwqqAAAADAKA1QAAAAYhSl+AACQtWzbku3QK0eduq8bUEEFAACAUaigAgCArMU+qGaiggoAAACjMEAFAACAUZjiBwAAWSvu4D6oTt3XDaigAgAAwChUUAEAQNZikZSZGKB+LDc3V6WlpfLn5cm2bTVEIqqpqUnb1u/3q1dpqfx+v2KxmMLhsOrr6ro54uxBbsxGfsxFbsxFboCOMUD9WFlZmbw5OaqrrZXP71egpERx21ZdbW1KO4/Ho7J+/SRJNdXVyi8oUDAYVKylRZFIxInQj3jkxmzkx1zkxlzkBugYz6BK6pGfr1yfT3sbGxUKhVS9e7ds21aguLhN28LCQnm9XkUiEYXDYdVUV0uSigOB7g47K5Abs5Efc5Ebc5EbsySm+J06kB4DVLVOtUhSS0uLJMm2bcViMXm8Xnm93g7bRqPRlPM4vMiN2ciPuciNucgNcGBM8R8iy+I9uqYiN2YjP+YiN+YiN4cf20yZiQqqPvmNNCendbxuWZa8Xq/isZhisVjKvxD2b5v4M3Eehxe5MRv5MRe5MRe5AQ6MCqqkvY2Nikaj6pGfr0AgIJ/fL8uyVB8OKy8vT/0HDFBTU5M+2rFDDQ0N6hkMqqioSC3RqPILCiRJ4VDI4V4cmciN2ciPuciNucgNcGBUUD9WVVmp5uZm9QwGlZ+fr1Ao1GY1pSTF43FVVVUpGo2qV2mpfD6f6mprWU3ZhciN2ciPuciNuciNOVgkZSbLtrv36wmHwwoEAtq8aZOKioq689YAAMAQkUhEo8eMUSgUUnGaHQy6WmI8cseTIfUo6P77S9LePWFdNy3g2HdgMqb4AQBA1orHWw+n7o30mOIHAACAURigAgAAwChM8QMAgKzl5GIlFkm1jwoqAAAAjMIAFQAAAEZhih8AAGQtpvjNRAUVAAAARqGCCgAAslZcUtyhSibboLaPCioAAACMkvEA9cUXX9TnP/959e/fX5Zl6cknn+yCsAAAAJCtMh6g7tmzR6NGjdLdd9/dFfEAAAB0G9u2HT2QXsbPoJ577rk699xzuyIWAAAAoOsXSTU3N6u5uTn593A43NW3BAAA6BS2mTJTly+SWrRokQKBQPIYOHBgV98SAAAALtblA9R58+YpFAolj+3bt3f1LQEAAOBiXT7F7/f75ff7u/o2AAAAGbPjUtyhDUltNkJtF/ugAgAAwCgZV1AbGhr03nvvJf/+/vvva/PmzQoGgxo0aNBhDQ4AAKArsUjKTBlXUN944w2NGTNGY8aMkSTNnTtXY8aM0fz58w97cAAAAMhcbW2tZsyYoeLiYpWUlGjmzJlqaGjo8DNNTU269tpr1atXLxUWFurCCy/Uzp07U9pYltXmWL58eUqbdevW6aSTTpLf79cxxxyjhx56KOP4Mx6gnnHGGWk3mj2YmwMAAODwmzFjht5++22tXr1aK1eu1Isvvqirr766w8/MmTNHTz/9tFasWKEXXnhBH330kb70pS+1affggw+qsrIyeUybNi35s/fff1/nn3++zjzzTG3evFnXX3+9vv71r+u5557LKP4uXyQFAABgqrjdejh1767w7rvvatWqVVq/fr3GjRsnSfr5z3+u8847T7fccov69+/f5jOhUEj333+/li1bprPOOktS60B0xIgRevXVVzVx4sRk25KSEpWVlaW999KlSzVkyBDdeuutkqQRI0bo5Zdf1u23366pU6d2ug8skgIAADiCVFRUqKSkJDk4laQpU6bI4/HotddeS/uZDRs2KBqNasqUKclzw4cP16BBg1RRUZHS9tprr1VpaanGjx+vBx54IOWVrRUVFSnXkKSpU6e2ucaBUEEFAABw0P5v2TzULTqrqqrUp0+flHM5OTkKBoOqqqpq9zM+n08lJSUp5/v27ZvymR/+8Ic666yzlJ+frz//+c/6n//5HzU0NOib3/xm8jp9+/Ztc41wOKy9e/eqR48eneoDFVQAAJC1Eqv4nTokaeDAgSlv3Vy0aFHaWG+66aa0i5Q+fWzZsqVLv68f/OAH+uxnP6sxY8boO9/5jr797W/rZz/72WG/DxVUAAAAB23fvl3FxcXJv7dXPb3hhht0xRVXdHitoUOHqqysTLt27Uo539LSotra2nafHS0rK9O+fftUX1+fUkXduXNnu5+RpAkTJuh///d/1dzcLL/fr7KysjYr/3fu3Kni4uJOV08lBqgAACCL2XFbtkOrpBL3LS4uThmgtqd3797q3bv3AduVl5ervr5eGzZs0NixYyVJa9euVTwe14QJE9J+ZuzYscrNzdWaNWt04YUXSpK2bt2qbdu2qby8vN17bd68WT179kwOqsvLy/Xss8+mtFm9enWH10iHASoAAMARZMSIETrnnHN01VVXaenSpYpGo5o1a5YuueSS5Ar+HTt2aPLkyXr44Yc1fvx4BQIBzZw5U3PnzlUwGFRxcbFmz56t8vLy5Ar+p59+Wjt37tTEiROVl5en1atX6//+7/904403Ju/93//937rrrrv07W9/W1/72te0du1aPfbYY3rmmWcy6gMDVAAAgCPMI488olmzZmny5MnyeDy68MILdeeddyZ/Ho1GtXXrVjU2NibP3X777cm2zc3Nmjp1qn7xi18kf56bm6u7775bc+bMkW3bOuaYY3TbbbfpqquuSrYZMmSInnnmGc2ZM0d33HGHjjrqKN13330ZbTElSZb96b0BukE4HFYgENDmTZtUVFTUnbcGAACGiEQiGj1mjEKhUKemtw+3xHjk5ofrlJff/feXpKbGsBZc1tOx78BkrOIHAACAUZjiBwAAWevT2z05cW+kxwD1Y7m5uSotLZU/L0+2bashElFNTU3atn6/X71KS+X3+xWLxRQOh1VfV9fNEWcPcmM28mMucmMucgN0jAHqx8rKyuTNyVFdba18fr8CJSWK27bqamtT2nk8HpX16ydJqqmuVn5BgYLBoGItLYpEIk6EfsQjN2YjP+YiN+YiN0DHeAZVUo/8fOX6fNrb2KhQKKTq3btl27YCaR5YLiwslNfrVSQSUTgcVk11tSSpOBDo7rCzArkxG/kxF7kxF7kxSzxuO3ogPQaoap1qkVrfsiBJtm0rFovJ4/XK6/V22DYajaacx+FFbsxGfsxFbsxFboADY4r/EFmW5XQIaAe5MRv5MRe5MRe5OfxYJGUmKqj65DfSnJzW8bplWfJ6vYrHYorFYin/Qti/beLPxHkcXuTGbOTHXOTGXOQGODAqqJL2NjYqGo2qR36+AoGAfH6/LMtSfTisvLw89R8wQE1NTfpoxw41NDSoZzCooqIitUSjyi8okCSFQyGHe3FkIjdmIz/mIjfmIjfAgVFB/VhVZaWam5vVMxhUfn6+QqFQm9WUkhSPx1VVVaVoNKpepaXy+Xyqq61lNWUXIjdmIz/mIjfmIjfmSEzxO3UgPV51CgAAup0przr9/v21jr7q9Eczg7zqNA2m+AEAQNaK27biDpUynbqvGzDFDwAAAKMwQAUAAIBRmOIHAABZy463Hk7dG+lRQQUAAIBRGKACAADAKEzxAwCArGXLVjfvuJlyb6RHBRUAAABGoYIKAACylh2X4iySMg4VVAAAABiFASoAAACMwhQ/AADIWrbt4CIpXnXaLiqoAAAAMAoVVAAAkLXiduvh1L2RHhVUAAAAGIUBKgAAAIzCFD8AAMhadtyW7dBcu1P3dQMqqAAAADAKFVQAAJC1bLv1cOreSI8KKgAAAIzCABUAAABGYYofAABkrXjcVtyhxUpO3dcNjBqg5ubmqrS0VP68PNm2rYZIRDU1NWnb+v1+9Sotld/vVywWUzgcVn1dXTdHnDm39tGtcWfCzX10c+yd4eb+uTn2znJrH90adyayoY84Mhk1QC0rK5M3J0d1tbXy+f0KlJQobtuqq61NaefxeFTWr58kqaa6WvkFBQoGg4q1tCgSiTgReqe5tY9ujTsTbu6jm2PvDDf3z82xd5Zb++jWuDORDX3EkcmYZ1B75Ocr1+fT3sZGhUIhVe/eLdu2FSgubtO2sLBQXq9XkUhE4XBYNdXVkqTiQKC7w86IW/vo1rgz4eY+ujn2znBz/9wce2e5tY9ujTsT2dDHw8G2bUcPpGfMADU3N1eS1NLSIqn1H5hYLCaP1yuv19th22g0mnLeVG7to1vjzoSb++jm2DvDzf1zc+yd5dY+ujXuTGRDH3HkMmqK/2BZluV0CF3OrX10a9yZcHMf3Rx7Z7i5f26OvbPc2ke3xp2JbOhjgh1vPZy6N9IzpoKa+G0tJ6d1zGxZlrxer+KxmGKxWMr/WfZvm/gzcd5Ubu2jW+POhJv76ObYO8PN/XNz7J3l1j66Ne5MZEMfceQypoK6t7FR0WhUPfLzFQgE5PP7ZVmW6sNh5eXlqf+AAWpqatJHO3aooaFBPYNBFRUVqSUaVX5BgSQpHAo53IuOubWPbo07E27uo5tj7ww398/NsXeWW/vo1rgzkQ19xJHLmAqqJFVVVqq5uVk9g0Hl5+crFAq1WWkoSfF4XFVVVYpGo+pVWiqfz6e62lpXrDR0ax/dGncm3NxHN8feGW7un5tj7yy39tGtcWciG/p4qOK27eiB9Cy7m5eQhcNhBQIBbd60SUVFRd15awAAYIhIJKLRY8YoFAqpOM3OAl0tMR6ZdWul/D26//6S1Lw3rLtu6OfYd2AyY6b4AQAAupuT2z2xzVT7jJriBwAAABigAgAAwChM8QMAgKwVj9uKx52Zanfqvm5ABRUAAABGoYIKAACylm23Hk7dG+lRQQUAAIBRGKACAADAKEzxAwCArGXbtmyHFiuxD2r7qKACAADAKFRQAQBA1rJtW3HeJGUcKqgAAAAwCgNUAAAAGIUpfgAAkLXsuIOLpHiTVLuooAIAAMAoDFABAABgFKb4AQBA1mKK30xUUAEAAGAUKqgAACBrxe3Ww6l7Iz0qqAAAADAKA1QAAAAYhSl+AACQtVgkZSYGqB/Lzc1VaWmp/Hl5sm1bDZGIampq0rb1+/3qVVoqv9+vWCymcDis+rq6bo44e5Abs5Efc5Ebc5EboGMMUD9WVlYmb06O6mpr5fP7FSgpUdy2VVdbm9LO4/GorF8/SVJNdbXyCwoUDAYVa2lRJBJxIvQjHrkxG/kxF7kxF7kxh23bsm2HKqgO3dcNeAZVUo/8fOX6fNrb2KhQKKTq3btl27YCxcVt2hYWFsrr9SoSiSgcDqumulqSVBwIdHfYWYHcmI38mIvcmIvcAAfGAFWtUy2S1NLSIqn1N5pYLCaP1yuv19th22g0mnIehxe5MRv5MRe5MRe5AQ6MKf5DZFmW0yGgHeTGbOTHXOTGXOTm8IvHpbhDi5XicUdu6wpUUPXJb6Q5Oa3jdcuy5PV6FY/FFIvFUv6FsH/bxJ+J8zi8yI3ZyI+5yI25yA1wYFRQJe1tbFQ0GlWP/HwFAgH5/H5ZlqX6cFh5eXnqP2CAmpqa9NGOHWpoaFDPYFBFRUVqiUaVX1AgSQqHQg734shEbsxGfsxFbsxFbszCIikzUUH9WFVlpZqbm9UzGFR+fr5CoVCb1ZSSFI/HVVVVpWg0ql6lpfL5fKqrrWU1ZRciN2YjP+YiN+YiN+hqtbW1mjFjhoqLi1VSUqKZM2eqoaGhw880NTXp2muvVa9evVRYWKgLL7xQO3fuTP78oYcekmVZaY9du3ZJktatW5f251VVVRnFb9ndPHwPh8MKBALavGmTioqKuvPWAADAEJFIRKPHjFEoFFJxmh0MulpiPPLV7/9LvjxnxiP7miL6zY+Gdsl3cO6556qyslK//OUvFY1GdeWVV+rkk0/WsmXL2v3MN77xDT3zzDN66KGHFAgENGvWLHk8Hv31r3+VJO3du1eh/ar3V1xxhZqamrRu3TpJrQPUM888U1u3bk3pU58+feTxdL4uyhQ/AADIWkfim6TeffddrVq1SuvXr9e4ceMkST//+c913nnn6ZZbblH//v3bfCYUCun+++/XsmXLdNZZZ0mSHnzwQY0YMUKvvvqqJk6cqB49eqhHjx7Jz+zevVtr167V/fff3+Z6ffr0UUlJyUH3gSl+AACAI0hFRYVKSkqSg1NJmjJlijwej1577bW0n9mwYYOi0aimTJmSPDd8+HANGjRIFRUVaT/z8MMPKz8/XxdddFGbn40ePVr9+vXT5z73uWQFNhNUUAEAABwUDodT/u73++X3+w/6elVVVerTp0/KuZycHAWDwXafBa2qqpLP52tT9ezbt2+7n7n//vv15S9/OaWq2q9fPy1dulTjxo1Tc3Oz7rvvPp1xxhl67bXXdNJJJ3W6D1RQAQBA1kpM8Tt1SNLAgQMVCASSx6JFi9LGetNNN7W7SClxbNmypVu+t4qKCr377ruaOXNmyvlhw4bpmmuu0dixYzVp0iQ98MADmjRpkm6//faMrk8FFQAAwEHbt29PWVDUXvX0hhtu0BVXXNHhtYYOHaqysrLkqvqElpYW1dbWqqysLO3nysrKtG/fPtXX16dUUXfu3Jn2M/fdd59Gjx6tsWPHdhiPJI0fP14vv/zyAdt9GgNUAACQteKyFXdoP9K4Wu9bXFzcqVX8vXv3Vu/evQ/Yrry8XPX19dqwYUNyALl27VrF43FNmDAh7WfGjh2r3NxcrVmzRhdeeKEkaevWrdq2bZvKy8tT2jY0NOixxx5rt9K7v82bN6tfv36dapvAABUAAOAIMmLECJ1zzjm66qqrtHTpUkWjUc2aNUuXXHJJcgX/jh07NHnyZD388MMaP368AoGAZs6cqblz5yoYDKq4uFizZ89WeXm5Jk6cmHL9Rx99VC0tLfrKV77S5t5LlizRkCFDdPzxx6upqUn33Xef1q5dqz//+c8Z9YEBKgAAwBHmkUce0axZszR58mR5PB5deOGFuvPOO5M/j0aj2rp1qxobG5Pnbr/99mTb5uZmTZ06Vb/4xS/aXPv+++/Xl770pbTbSO3bt0833HCDduzYofz8fI0cOVLPP/+8zjzzzIziZ6N+AADQ7UzZqP+Sb/9DPr9DG/U3R7T8p59x7DswGav4AQAAYJSDGqDefffdGjx4sPLy8jRhwgS9/vrrhzsuAACALmfbtqMH0st4gProo49q7ty5WrBggTZu3KhRo0Zp6tSpbbYzAAAAAA5GxgPU2267TVdddZWuvPJKHXfccVq6dKny8/P1wAMPdEV8AAAAyDIZreLft2+fNmzYoHnz5iXPeTweTZkypd33tDY3N6u5uTn591AoJKl1Dy0AAJCdEuMAp6e57biteNyZGGyH7usGGQ1Qq6urFYvF1Ldv35Tzffv2bffVWosWLdLNN9/c5vwpp56aya0BAMARqKamRoFAwOkwYJgu3wd13rx5mjt3bvLv9fX1Ovroo7Vt2zb+gTRMOBzWwIED27xyDc4jN+YiN+YiN2YLhUIaNGiQgsGgo3HYcduxSiYV1PZlNEAtLS2V1+vVzp07U863955WqfV9suneKRsIBPgXhqE6+8o1dD9yYy5yYy5yYzaPhx0v0VZG/1T4fD6NHTtWa9asSZ6Lx+Nas2ZNm/e0AgAAAAcj4yn+uXPn6vLLL9e4ceM0fvx4LVmyRHv27NGVV17ZFfEBAAB0GSf3I3V6gZjJMh6gTp8+Xbt379b8+fNVVVWl0aNHa9WqVW0WTrXH7/drwYIFaaf94SxyYy5yYy5yYy5yYzbyg45YNsN3AACQZcLhsAKBgC785lvK9Rc5EkO0OaLf33mCQqEQz0nvhyeTAQAAYBQGqAAAADBKl++DCgAAYKq4g2+Scuq+bkAFFQAAAEbp1gHq3XffrcGDBysvL08TJkzQ66+/3p23RztefPFFff7zn1f//v1lWZaefPJJp0PCxxYtWqSTTz5ZRUVF6tOnj6ZNm6atW7c6HRYk3XPPPRo5cmRyE/jy8nL96U9/cjospLF48WJZlqXrr7/e6VCy3sKFC2VZVsoxfPhwp8OCgbptgProo49q7ty5WrBggTZu3KhRo0Zp6tSp2rVrV3eFgHbs2bNHo0aN0t133+10KNjPCy+8oGuvvVavvvqqVq9erWg0qrPPPlt79uxxOrSsd9RRR2nx4sXasGGD3njjDZ111lm64IIL9PbbbzsdGj5l/fr1+uUvf6mRI0c6HQo+dvzxx6uysjJ5vPzyy47Gk9gH1akD6XXbM6i33XabrrrqquSG/kuXLtUzzzyjBx54QDfddFN3hYE0zj33XJ177rlOh4E0Vq1alfL3hx56SH369NGGDRt02mmnORQVJOnzn/98yt9//OMf65577tGrr76q448/3qGo8GkNDQ2aMWOGfvWrX+lHP/qR0+HgYzk5Oe2+Hh1I6JYK6r59+7RhwwZNmTLlkxt7PJoyZYoqKiq6IwTgiBAKhSRJwWDQ4UjwabFYTMuXL9eePXt47bNBrr32Wp1//vkp/+2B8/75z3+qf//+Gjp0qGbMmKFt27Y5Go8dtx09kF63VFCrq6sVi8XavG2qb9++2rJlS3eEALhePB7X9ddfr89+9rM64YQTnA4Hkt58802Vl5erqalJhYWFeuKJJ3Tcccc5HRYkLV++XBs3btT69eudDgWfMmHCBD300EMaNmyYKisrdfPNN+vUU0/VW2+9paIiZzbLh5nYZgpwiWuvvVZvvfWW489r4RPDhg3T5s2bFQqF9Pjjj+vyyy/XCy+8wCDVYdu3b9d1112n1atXKy8vz+lw8Cmffpxs5MiRmjBhgo4++mg99thjmjlzpoORwTTdMkAtLS2V1+vVzp07U87v3LmT51CATpg1a5ZWrlypF198UUcddZTT4eBjPp9PxxxzjCRp7NixWr9+ve644w798pe/dDiy7LZhwwbt2rVLJ510UvJcLBbTiy++qLvuukvNzc3yer0ORoiEkpISfeYzn9F7773nWAxOTrUzxd++bnkG1efzaezYsVqzZk3yXDwe15o1a3heC+iAbduaNWuWnnjiCa1du1ZDhgxxOiR0IB6Pq7m52ekwst7kyZP15ptvavPmzclj3LhxmjFjhjZv3szg1CANDQ36f//v/6lfv35OhwLDdNsU/9y5c3X55Zdr3LhxGj9+vJYsWaI9e/YkV/XDOQ0NDSm/vb7//vvavHmzgsGgBg0a5GBkuPbaa7Vs2TI99dRTKioqUlVVlSQpEAioR48eDkeX3ebNm6dzzz1XgwYNUiQS0bJly7Ru3To999xzToeW9YqKito8p11QUKBevXrx/LbDbrzxRn3+85/X0UcfrY8++kgLFiyQ1+vVpZde6lhMccUVt+OO3RvpddsAdfr06dq9e7fmz5+vqqoqjR49WqtWrWqzcArd74033tCZZ56Z/PvcuXMlSZdffrkeeughh6KC1LoZvCSdccYZKecffPBBXXHFFd0fEJJ27dqlyy67TJWVlQoEAho5cqSee+45fe5zn3M6NMBYH374oS699FLV1NSod+/eOuWUU/Tqq6+qd+/eTocGw1g2u8QCAIAsEw6HFQgE9J/XbFSuz5kdBKL7Ilr5y5MUCoVUXFzsSAymYhU/AADIWnbcucVKDj1Z4Ard9qpTAAAAoDOooAIAgKzFNlNmooIKAAAAozBABQAAgFGY4gcAAFnLtm05taERGym1jwoqAAAAjMIAFQAAAEZhih8AAGSteDyueNyhV506dF83oIIKAAAAo1BBBQAAWYt9UM1EBRUAAABGYYAKAAAAozDFDwAAspZtx2XbzixWcuq+bkAFFQAAAEahggoAALIWi6TMRAUVAAAARmGACgAAAKMwxQ8AALKXg1P8Yoq/XVRQAQAAYBQqqAAAIGvF7bjiDm335NR93YAKKgAAAIzCABUAAABGYYofAABkLfZBNRMVVAAAABiFCioAAMhath2XHXdmsZLNIql2UUEFAACAURigAgAAwChM8QMAgKzFIikzUUEFAACAURigAgAAwChM8QMAgKxl23HHVtOzir99VFABAABgFCqoAAAga8XjUtyhxUoObb/qClRQAQAAYBQGqAAAADAKU/wAACBr2XEHX3XKHH+7qKACAADAKFRQAQBA1uJNUmaiggoAAACjMEAFAACAUZjiBwAAWYs3SZmJCioAAACMQgUVAABkLRZJmYkKKgAAwBGmtrZWM2bMUHFxsUpKSjRz5kw1NDR0+Jl7771XZ5xxhoqLi2VZlurr6w/qun//+9916qmnKi8vTwMHDtRPf/rTjONngAoAAHCEmTFjht5++22tXr1aK1eu1Isvvqirr766w880NjbqnHPO0Xe/+92Dvm44HNbZZ5+to48+Whs2bNDPfvYzLVy4UPfee29G8Vu2bVNfBgAAWSUcDisQCGjC1GeUk1vgSAwt0T167bnzFQqFVFxcfNiu++677+q4447T+vXrNW7cOEnSqlWrdN555+nDDz9U//79O/z8unXrdOaZZ6qurk4lJSUZXfeee+7R9773PVVVVcnn80mSbrrpJj355JPasmVLp/tABRUAAOAIUlFRoZKSkuQgUpKmTJkij8ej1157rUuvW1FRodNOOy05OJWkqVOnauvWraqrq+v0vVgkBQAAslasZY/j9w6Hwynn/X6//H7/QV+3qqpKffr0STmXk5OjYDCoqqqqLr1uVVWVhgwZktKmb9++yZ/17NmzU/digAoAALKOz+dTWVmZ3lhzsaNxFBYWauDAgSnnFixYoIULF7Zpe9NNN+knP/lJh9d79913D2d4jmGACgAAsk5eXp7ef/997du3z9E4bNuWZVkp59qrnt5www264oorOrze0KFDVVZWpl27dqWcb2lpUW1trcrKyg461s5ct6ysTDt37kxpk/h7JvdmgAoAALJSXl6e8vLynA6j03r37q3evXsfsF15ebnq6+u1YcMGjR07VpK0du1axeNxTZgw4aDv35nrlpeX63vf+56i0ahyc3MlSatXr9awYcM6Pb0vsUgKAADgiDJixAidc845uuqqq/T666/rr3/9q2bNmqVLLrkkuYJ/x44dGj58uF5//fXk56qqqrR582a99957kqQ333xTmzdvVm1tbaev++Uvf1k+n08zZ87U22+/rUcffVR33HGH5s6dm1EfGKACAAAcYR555BENHz5ckydP1nnnnadTTjklZS/SaDSqrVu3qrGxMXlu6dKlGjNmjK666ipJ0mmnnaYxY8boj3/8Y6evGwgE9Oc//1nvv/++xo4dqxtuuEHz588/4B6s+2MfVAAAABiFCioAAACMwgAVAAAARmGACgAAAKMwQAUAAIBRGKACAADAKAxQAQAAYBQGqAAAADAKA1QAAAAYhQEqAAAAjMIAFQAAAEZhgAoAAACjMEAFAACAUf4/9S716u/qeS4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_action_values(action_values)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ldE0EH4kqBFR"
      },
      "source": [
        "#### Plot the policy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "uTMuUFlYqBFS",
        "outputId": "886e3344-3842-47ec-8ca9-6ed5afaa31e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_policy(action_values, env.render(mode='rgb_array'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOJiawyuqBFS"
      },
      "source": [
        "## Implement the algorithm\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FrfUBijoqBFS"
      },
      "outputs": [],
      "source": [
        "def sarsa(action_values, policy, episodes, alpha=0.1, gamma=0.99, epsilon=0.2):\n",
        "\n",
        "    for episode in range(1, episodes + 1):\n",
        "        state = env.reset()\n",
        "        action = policy(state, epsilon)\n",
        "        done = False\n",
        "        while not done:\n",
        "            next_state, reward, done, _ = env.step(action)\n",
        "            next_action = policy(next_state, epsilon)\n",
        "\n",
        "            qsa = action_values[state][action]\n",
        "            next_qsa = action_values[next_state][next_action]\n",
        "            action_values[state][action] = qsa + alpha * (reward + gamma * next_qsa - qsa)\n",
        "            state = next_state\n",
        "            action = next_action"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EhRr4G7pqBFS"
      },
      "outputs": [],
      "source": [
        "sarsa(action_values, policy, 1000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lB7zVHxDqBFS"
      },
      "source": [
        "## Show results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VafpyTWkqBFT"
      },
      "source": [
        "#### Show resulting value table $Q(s,a)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i-uQ6vb-qBFT",
        "outputId": "8509bd0f-ab2c-4b77-a8c2-f87ca2e13552",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_action_values(action_values)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsXpPsKnqBFT"
      },
      "source": [
        "#### Show resulting policy $\\pi(\\cdot|s)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mZKL4RXvqBFT",
        "outputId": "8f16c8ec-c149-4100-9c84-7eddd2e34c79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_policy(action_values, env.render(mode='rgb_array'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AoJbX_79qBFT"
      },
      "source": [
        "#### Test the resulting agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ckonhYqBqBFT",
        "outputId": "fbb9e830-fc03-4abd-b1d5-a04ae8c2d20b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
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              "  Your browser does not support the video tag.\n",
              "</video>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "test_agent(env, policy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmJOa0huqBFT"
      },
      "source": [
        "## Resources"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "joHvXmxXqBFU"
      },
      "source": [
        "[[1] Reinforcement Learning: An Introduction. Ch. 6: Temporal difference learning](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
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