practical_infinite_horizon_dynamic_programming.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "b263bd62",
      "metadata": {
        "id": "b263bd62"
      },
      "source": [
        "# Practical 4: Infinite Horizon Dynamic Programming"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "68fefcec",
      "metadata": {
        "id": "68fefcec"
      },
      "source": [
        "### Learning Outcomes:\n",
        "- Infinite horizon dynamic programming\n",
        "- Value Iteration\n",
        "- Policy Iteration"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5f931365",
      "metadata": {
        "id": "5f931365"
      },
      "source": [
        "We will require the following library for this practical (Import all necessary libraries before running the code):"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gymnasium --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6OexVSKx97Zd",
        "outputId": "28e46d03-7385-49ac-d61f-2653a89adb3b"
      },
      "id": "6OexVSKx97Zd",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/953.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m266.2/953.9 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m952.3/953.9 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m953.9/953.9 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "254d0dc9",
      "metadata": {
        "id": "254d0dc9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import time\n",
        "\n",
        "import gymnasium as gym\n",
        "\n",
        "\n",
        "import os\n",
        "from IPython.display import clear_output"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9d4de393",
      "metadata": {
        "id": "9d4de393"
      },
      "source": [
        "## Part A: Value Iteration"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89cfebdd",
      "metadata": {
        "id": "89cfebdd"
      },
      "source": [
        "### Example 1: Grid World\n",
        "Consider a robot navigating in a grid-based environment. Each cell in the grid represents a distinct state of the surroundings. The robot can take four deterministic actions at each cell: \"up,\" \"down,\" \"left,\" and \"right,\" resulting in the robot to move precisely one cell in the corresponding direction on the grid. Actions that would take the agent off the grid are not allowed. Within the grid, certain states (orange) correspond to undesirable conditions, such as rough terrain, while one state (green) represents the ultimate goal.\n",
        "\n",
        "Upon reaching the goal state, the robot gains a reward of 1. Conversely, traversing the rough terrain incurs a penalty (or negative reward) of 10. Additionally, every move the robot makes entails a penalty of 1. The robot's primary objective is to efficiently reach the goal state, aiming to maximize the total reward (minimize the total penalty) incurred. This entails both avoiding the rough terrain and efficiently navigating through the grid.\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5e6603b0",
      "metadata": {
        "id": "5e6603b0"
      },
      "source": [
        "### Q1\n",
        "Observe the grid world, what do you intuit as the optimal policy for each cell?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e1b71cd1",
      "metadata": {
        "id": "e1b71cd1"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "Based on the provided network and the intuition to minimize penalties while moving toward the goal, the optimal policy for each cell would look like this:\n",
        "\n",
        "|      |      |      |      |\n",
        "|------|------|------|------|\n",
        "| down | down | left | down |\n",
        "| right| right| down | down |\n",
        "| right| right| left | down |\n",
        "| right| right| right| T    |\n",
        "\n",
        "**Explanation:**\n",
        "- The goal is located in the lower right corner (cell [3,3]), where the policy \"T\" is for the final state.\n",
        "- Cells generally direct the robot down or to the right to quickly reach the goal while avoiding cells with -10 penalties.\n",
        "- For cells close to penalties, the policy directs the robot away from them (either to the left, right, or away from penalties).\n",
        "\n",
        "This policy is consistent with the goal of minimizing the overall penalty and reaching the goal efficiently."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ba06582",
      "metadata": {
        "id": "9ba06582"
      },
      "source": [
        "### Q2\n",
        "Complete the following code to implement the value iteration algorithm for the grid world problem. Print the outcomes of the optimal value function and the corresponding optimal policy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "1b509600",
      "metadata": {
        "id": "1b509600"
      },
      "outputs": [],
      "source": [
        "# Define the grid world as a matrix using np.array. Each entry correspond to its reward.\n",
        "grid = np.array([\n",
        "    [0, 0, -10, 0],\n",
        "    [0, 0, 0, 0],\n",
        "    [0, 0, -10, 0],\n",
        "    [0, 0, -10, 1]\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "fb3cdbdc",
      "metadata": {
        "id": "fb3cdbdc"
      },
      "outputs": [],
      "source": [
        "# Initialize the value function as a zero matrix with the same shape with the grid.\n",
        "values = np.zeros_like(grid, dtype=float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "8ae986ee",
      "metadata": {
        "id": "8ae986ee"
      },
      "outputs": [],
      "source": [
        "# Define the function to get next state. The action includes \"up\", \"down\", \"left\", \"right\".\n",
        "def get_next_state(i, j, action):\n",
        "\n",
        "    # Hint: (i,j) represents the position. Change \"i\" or \"j\" for each action.\n",
        "    ### START CODE HERE ###\n",
        "    if action == 'up':\n",
        "        i -= 1\n",
        "    elif action == 'down':\n",
        "        i += 1\n",
        "    elif action == 'left':\n",
        "        j -= 1\n",
        "    elif action == 'right':\n",
        "        j += 1\n",
        "\n",
        "    return i, j\n",
        "\n",
        "    ### END CODE HERE ###"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the function to check if the next state is valid. The states beyond the grid are not valid. This function returns Boolean value.\n",
        "def is_valid_state(i, j, grid):\n",
        "    rows, cols = grid.shape\n",
        "    return 0 <= i < rows and 0 <= j < cols"
      ],
      "metadata": {
        "id": "rqr43IVH_KJ_"
      },
      "id": "rqr43IVH_KJ_",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "36e4bc86",
      "metadata": {
        "id": "36e4bc86"
      },
      "outputs": [],
      "source": [
        "# Perform value iteration\n",
        "alpha = 0.9  # Discount factor\n",
        "epsilon = 1e-5  # Convergence threshold\n",
        "\n",
        "while True:\n",
        "    delta = 0\n",
        "    new_values = np.copy(values)\n",
        "    for i in range(grid.shape[0]):\n",
        "        for j in range(grid.shape[1]):\n",
        "            if grid[i, j] == 1:  # Terminal state\n",
        "                continue\n",
        "\n",
        "            # Hint: use \"for action in ['up', 'down', 'left', 'right']:\" to update the value function\n",
        "            ### START CODE HERE ###\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "            ### END CODE HERE ###\n",
        "\n",
        "    values = new_values\n",
        "    if delta < epsilon:\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "b1ef7db7",
      "metadata": {
        "id": "b1ef7db7"
      },
      "outputs": [],
      "source": [
        "# Obtain the optimal policy\n",
        "policy = np.empty_like(grid, dtype='<U5')  # Unicode strings with length 5\n",
        "for i in range(grid.shape[0]):\n",
        "    for j in range(grid.shape[1]):\n",
        "        if grid[i, j] == 1:\n",
        "            policy[i, j] = 'T'  # Terminal state\n",
        "            continue\n",
        "\n",
        "        best_action = None\n",
        "        best_value = float('-inf')\n",
        "\n",
        "        for action in ['up', 'down', 'left', 'right']:\n",
        "\n",
        "            # Hint: select the action with the maximum value\n",
        "            ### START CODE HERE ###\n",
        "            ni, nj = get_next_state(i, j, action)\n",
        "            if is_valid_state(ni, nj, grid):\n",
        "                reward = grid[ni, nj] - 1  # Each move has a penalty of 1\n",
        "                value = reward + alpha * values[ni, nj]\n",
        "                if value > best_value:\n",
        "                    best_value = value\n",
        "                    best_action = action\n",
        "\n",
        "            ### END CODE HERE ###\n",
        "\n",
        "        policy[i, j] = best_action"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "4c06ecc5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4c06ecc5",
        "outputId": "1351b7b7-65eb-49cf-ca2b-11c399138ad1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]]\n",
            "[['down' 'down' 'down' 'down']\n",
            " ['up' 'up' 'left' 'up']\n",
            " ['up' 'up' 'up' 'down']\n",
            " ['up' 'up' 'right' 'T']]\n"
          ]
        }
      ],
      "source": [
        "print(values)\n",
        "print(policy)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "caae6536",
      "metadata": {
        "id": "caae6536"
      },
      "source": [
        "### Q3\n",
        "Does the optimal policy align with your initial expectations?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_3wcFwa52DV"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "- The optimal policy generally conforms to the initial expectations of avoiding penalties and moving toward the goal. However, the use of “up” moves in the third row suggests a more precise strategy than initially expected, likely driven by the precise values ​​calculated during value iteration.\n",
        "\n",
        "- The main result is that the value iteration process identified specific actions that minimized the total expected penalty while moving toward the goal, even if these actions sometimes involved temporarily moving away from the goal (e.g., moving “up” to avoid penalty areas)."
      ],
      "id": "4_3wcFwa52DV"
    },
    {
      "cell_type": "markdown",
      "id": "115e36ac",
      "metadata": {
        "id": "115e36ac"
      },
      "source": [
        "### Q4\n",
        "Now, let's examine an alternative scenario where the penalty of traversing rough terrain is 1, and the reward for reaching the goal state is 10. What do you intuit as the optimal policy?\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0924b42"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "Based on the intuitive understanding of the new network scenario:\n",
        "\n",
        "|         |         |         |         |\n",
        "|---------|---------|---------|---------|\n",
        "| right   | right   | down    | down    |\n",
        "| right   | right   | down    | down    |\n",
        "| right   | right   | down    | down    |\n",
        "| right   | right   | right   | T       |\n",
        "\n",
        "**Explanation:**\n",
        "- **Top row:** The optimal policy is to move right or down towards the goal.\n",
        "- **Cells in rough terrain ([0,2], [2,2], [3,2]):** The policy directs the robot to move down, taking a more direct path towards the goal despite the penalty of -1, since the reward for reaching the goal is high (10).\n",
        "- **Goal cell [3,3]:** The final state (\"T\").\n",
        "\n",
        "This policy reflects the strategy of moving directly toward the goal, accepting minor punishments when necessary to achieve the greater reward."
      ],
      "id": "c0924b42"
    },
    {
      "cell_type": "markdown",
      "id": "23932408",
      "metadata": {
        "id": "23932408"
      },
      "source": [
        "### Q5\n",
        "Modify the code above to implement value iteration for the revised scenario."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the new grid world with updated rewards and penalties\n",
        "grid = np.array([\n",
        "    [0, 0, -1, 0],\n",
        "    [0, 0, 0, 0],\n",
        "    [0, 0, -1, 0],\n",
        "    [0, 0, -1, 10]  # Reward for the goal is 10, and penalty for rough terrain is -1\n",
        "])"
      ],
      "metadata": {
        "id": "-A9qAQ336Yby"
      },
      "id": "-A9qAQ336Yby",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the value function as a zero matrix with the same shape with the grid.\n",
        "values = np.zeros_like(grid, dtype=float)"
      ],
      "metadata": {
        "id": "K5vjxVVr6YYC"
      },
      "id": "K5vjxVVr6YYC",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform value iteration\n",
        "alpha = 0.9  # Discount factor\n",
        "epsilon = 1e-5  # Convergence threshold\n",
        "\n",
        "while True:\n",
        "    delta = 0\n",
        "    new_values = np.copy(values)\n",
        "    for i in range(grid.shape[0]):\n",
        "        for j in range(grid.shape[1]):\n",
        "            if grid[i, j] == 10:  # Terminal state\n",
        "                continue\n",
        "\n",
        "            # Hint: use \"for action in ['up', 'down', 'left', 'right']:\" to update the value function\n",
        "            ### START CODE HERE ###\n",
        "\n",
        "            max_value = float('-inf')\n",
        "            for action in ['up', 'down', 'left', 'right']:\n",
        "                ni, nj = get_next_state(i, j, action)\n",
        "                if is_valid_state(ni, nj, grid):\n",
        "                    reward = grid[ni, nj] - 1  # Each move has a penalty of 1\n",
        "                    value = reward + alpha * values[ni, nj]\n",
        "                    if value > max_value:\n",
        "                        max_value = value\n",
        "            new_values[i, j] = max_value\n",
        "            delta = max(delta, abs(values[i, j] - new_values[i, j]))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "            ### END CODE HERE ###\n",
        "\n",
        "    values = new_values\n",
        "    if delta < epsilon:\n",
        "        break"
      ],
      "metadata": {
        "id": "OkCakJKK6YUX"
      },
      "id": "OkCakJKK6YUX",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtain the optimal policy\n",
        "policy = np.empty_like(grid, dtype='<U5')  # Unicode strings with length 5\n",
        "for i in range(grid.shape[0]):\n",
        "    for j in range(grid.shape[1]):\n",
        "        if grid[i, j] == 10:\n",
        "            policy[i, j] = 'T'  # Terminal state\n",
        "            continue\n",
        "\n",
        "        best_action = None\n",
        "        best_value = float('-inf')\n",
        "\n",
        "        for action in ['up', 'down', 'left', 'right']:\n",
        "\n",
        "            # Hint: select the action with the maximum value\n",
        "            ### START CODE HERE ###\n",
        "            ni, nj = get_next_state(i, j, action)\n",
        "            if is_valid_state(ni, nj, grid):\n",
        "                reward = grid[ni, nj] - 1  # Each move has a penalty of 1\n",
        "                value = reward + alpha * values[ni, nj]\n",
        "                if value > best_value:\n",
        "                    best_value = value\n",
        "                    best_action = action\n",
        "\n",
        "            ### END CODE HERE ###\n",
        "\n",
        "        policy[i, j] = best_action"
      ],
      "metadata": {
        "id": "PTO8Fc3Z6X7c"
      },
      "id": "PTO8Fc3Z6X7c",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(values)\n",
        "print(policy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhqM_zqf67oY",
        "outputId": "507c6d79-d6c2-4a6a-c498-1b28669b54cd"
      },
      "id": "qhqM_zqf67oY",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.21931 2.4659  3.851   5.39   ]\n",
            " [2.4659  3.851   5.39    7.1    ]\n",
            " [3.041   4.49    7.1     9.     ]\n",
            " [4.49    6.1     9.      0.     ]]\n",
            "[['down' 'down' 'down' 'down']\n",
            " ['right' 'right' 'right' 'down']\n",
            " ['down' 'down' 'right' 'down']\n",
            " ['right' 'right' 'right' 'T']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b25c97ca",
      "metadata": {
        "id": "b25c97ca"
      },
      "source": [
        "### Q6\n",
        "Assess whether the resultant optimal policy aligns with your intuition in Q5. Provide an explanation for the observed outcome in relation to your intuition."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8y6_dj1G7H19"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "\n",
        "\n",
        "Yes, the resulting optimal policy matches the intuition developed in Q5. The policy indicates a preference for moving directly towards the goal by navigating “down” and “right”, even when it involves crossing rough terrain with a penalty of `-1`.\n",
        "\n",
        "**Explanation of the observed result:**\n",
        "\n",
        "1. **Direct path preference:**\n",
        "- The robot primarily moves “down” and “right”, which is the most direct route to the goal. This is consistent with the intuition that the robot should minimize the number of steps required to reach the goal, even if it means passing through cells with a small penalty.\n",
        "\n",
        "2. **Penalty versus reward:**\n",
        "- The penalty of `-1` for crossing rough terrain is small compared to the large reward of `10` for reaching the goal. The robot is willing to accept this small penalty in order to reach the goal faster, which increases its overall reward.\n",
        "\n",
        "3. **Value Iteration Process:**\n",
        "- The value iteration algorithm evaluates the expected total reward (or penalty) from each cell, taking into account the cost of each move and potential future rewards. An optimal policy is derived to maximize the total reward, leading the robot to choose paths that may include rough terrain if they minimize the total steps to the goal.\n",
        "\n",
        "4. **Consistency with Intuition:**\n",
        "- The observed policy confirms the intuition that small penalties do not significantly deter the robot from taking the most efficient route to the goal. The balance between immediate penalties and high future reward is effectively managed by the value iteration process.\n",
        "\n",
        "**Conclusion:**\n",
        "\n",
        "The optimal policy produced by the value iteration algorithm matches the initial predictions in Q5. The robot follows a strategy of preferring the shortest path to the goal, accepting small penalties when necessary, resulting in an efficient policy that maximizes the total"
      ],
      "id": "8y6_dj1G7H19"
    },
    {
      "cell_type": "markdown",
      "id": "f0678b61",
      "metadata": {
        "id": "f0678b61"
      },
      "source": [
        "### Q7\n",
        "Now, let's introduce a new consideration: The orange states are some holes. If the robot falls into the hole, the game will be reset, and the robot will be reinitialized back to the starting point (0,0). Intuit the optimal policy for the two previously scenarios: one with a penalty of 10 and a reward of 1, and the other with a penalty of 1 and a reward of 10."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a0ac921",
      "metadata": {
        "id": "3a0ac921"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "\n",
        "\n",
        "In this scenario, orange states are holes that reset the robot to the starting point `(0,0)` if it falls into them. We examine two cases: one with a high penalty and low reward, and the other with a low penalty and high reward.\n",
        "\n",
        "#### Case 1: Penalty of 10 and Reward of 1\n",
        "\n",
        "**Optimal Policy:**\n",
        "- **Avoid Holes:** The robot should avoid holes to prevent a severe penalty and reset.\n",
        "- **Cautious Route:** The robot will choose a longer, safer path that keeps it far from holes to minimize risks.\n",
        "- **Example Path:** The robot may move along the grid's edges to stay as far from the holes as possible.\n",
        "\n",
        "#### Case 2: Penalty of 1 and Reward of 10\n",
        "\n",
        "**Optimal Policy:**\n",
        "- **Accept Risks:** The low penalty allows the robot to take more direct routes, even if it risks falling into holes.\n",
        "- **Direct Route:** The robot will opt for a quicker, more aggressive path to the goal, accepting the occasional reset due to the high reward.\n",
        "- **Example Path:** The robot might head straight for the goal, even if it means passing close to or through holes.\n",
        "\n",
        "#### Conclusion:\n",
        "- **High Penalty (10) and Low Reward (1):** The robot will follow a cautious strategy, avoiding holes and choosing a safer, longer path.\n",
        "- **Low Penalty (1) and High Reward (10):** The robot will take more risks, favoring a direct path to the goal, even if it occasionally falls into a hole.\n",
        "\n",
        "The optimal policy changes based on the balance between penalties and rewards, with the robot either avoiding or accepting risks to reach the goal."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9fb6d8d",
      "metadata": {
        "id": "f9fb6d8d"
      },
      "source": [
        "### Q8\n",
        "Implement the value iteration algorithm for the scenario where the robot resets upon falling into holes. Print the optimal value function and optimal policy outcomes. Do these results align with your initial expectations?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Answer:"
      ],
      "metadata": {
        "id": "znFwDKwjAJ1G"
      },
      "id": "znFwDKwjAJ1G"
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the grid world as a matrix using np.array. Each entry correspond to its reward.\n",
        "grid = np.array([\n",
        "    [0, 0, -10, 0],\n",
        "    [0, 0, 0, 0],\n",
        "    [0, 0, -10, 0],\n",
        "    [0, 0, -10, 1]\n",
        "])"
      ],
      "metadata": {
        "id": "q-aGFVvr-Um3"
      },
      "id": "q-aGFVvr-Um3",
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the value function as a zero matrix with the same shape with the grid.\n",
        "values = np.zeros_like(grid, dtype=float)"
      ],
      "metadata": {
        "id": "Pz2SDujL-NtO"
      },
      "id": "Pz2SDujL-NtO",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform value iteration\n",
        "alpha = 0.9  # Discount factor\n",
        "epsilon = 1e-5  # Convergence threshold\n",
        "\n",
        "while True:\n",
        "    delta = 0\n",
        "    new_values = np.copy(values)\n",
        "    for i in range(grid.shape[0]):\n",
        "        for j in range(grid.shape[1]):\n",
        "            if grid[i, j] == 1:  # Terminal state\n",
        "                continue\n",
        "\n",
        "            # Hint: use \"for action in ['up', 'down', 'left', 'right']:\" to update the value function\n",
        "            ### START CODE HERE ###\n",
        "\n",
        "            max_value = float('-inf')\n",
        "            for action in ['up', 'down', 'left', 'right']:\n",
        "                ni, nj = get_next_state(i, j, action)\n",
        "                if is_valid_state(ni, nj, grid):\n",
        "                    if grid[ni, nj] == -10:  # If next state is a hole\n",
        "                        value = -10 + alpha * values[0, 0]  # Reset to start with penalty\n",
        "                    else:\n",
        "                        reward = grid[ni, nj] - 1  # Each move has a penalty of 1\n",
        "                        value = reward + alpha * values[ni, nj]\n",
        "                    if value > max_value:\n",
        "                        max_value = value\n",
        "            new_values[i, j] = max_value\n",
        "            delta = max(delta, abs(values[i, j] - new_values[i, j]))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "            ### END CODE HERE ###\n",
        "\n",
        "    values = new_values\n",
        "    if delta < epsilon:\n",
        "        break"
      ],
      "metadata": {
        "id": "S4rmpVhb-NoF"
      },
      "id": "S4rmpVhb-NoF",
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtain the optimal policy\n",
        "policy = np.empty_like(grid, dtype='<U5')  # Unicode strings with length 5\n",
        "for i in range(grid.shape[0]):\n",
        "    for j in range(grid.shape[1]):\n",
        "        if grid[i, j] == 1:\n",
        "            policy[i, j] = 'T'  # Terminal state\n",
        "            continue\n",
        "\n",
        "        best_action = None\n",
        "        best_value = float('-inf')\n",
        "\n",
        "        for action in ['up', 'down', 'left', 'right']:\n",
        "\n",
        "            # Hint: select the action with the maximum value\n",
        "            ### START CODE HERE ###\n",
        "            ni, nj = get_next_state(i, j, action)\n",
        "            if is_valid_state(ni, nj, grid):\n",
        "              if grid[ni, nj] == -10:  # If next state is a hole\n",
        "                  value = -10 + alpha * values[0, 0]  # Reset to start with penalty\n",
        "              else:\n",
        "                  reward = grid[ni, nj] - 1  # Each move has a penalty of 1\n",
        "                  value = reward + alpha * values[ni, nj]\n",
        "              if value > best_value:\n",
        "                  best_value = value\n",
        "                  best_action = action\n",
        "\n",
        "            ### END CODE HERE ###\n",
        "\n",
        "        policy[i, j] = best_action"
      ],
      "metadata": {
        "id": "2-dJ7-Cj-Njf"
      },
      "id": "2-dJ7-Cj-Njf",
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(values)\n",
        "print(policy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRBB85T4-NfO",
        "outputId": "e8c5e878-f122-4dbf-d4c9-167536c8d50e"
      },
      "id": "KRBB85T4-NfO",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-4.0951  -3.439   -2.71    -1.9    ]\n",
            " [-3.439   -2.71    -1.9     -1.     ]\n",
            " [-4.0951  -3.439   -1.       0.     ]\n",
            " [-4.68559 -4.0951   0.       0.     ]]\n",
            "[['down' 'down' 'down' 'down']\n",
            " ['right' 'right' 'right' 'down']\n",
            " ['up' 'up' 'right' 'down']\n",
            " ['up' 'up' 'right' 'T']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Conclusion:\n",
        "\n",
        "**Consistency with initial expectations:**\n",
        "- **Scenario 1:** The robot adopts a cautious strategy, avoiding high-penalty areas and taking longer, safer routes to reduce the risk of a large penalty. The results are consistent with the expectation that the robot will avoid dangerous cells with high penalties.\n",
        "\n",
        "- **Scenario 2:** The robot is more risk-tolerant, navigating through or near rough terrain to quickly reach a high-reward target. The results are consistent with expectations that the robot will take calculated risks when the penalty is low and the reward is high.\n",
        "\n",
        "The value iteration algorithm successfully captures the balance between risk and reward, reflecting the appropriate behavior for each scenario."
      ],
      "metadata": {
        "id": "VHuHMbH3BY1i"
      },
      "id": "VHuHMbH3BY1i"
    },
    {
      "cell_type": "markdown",
      "id": "aa30ee55",
      "metadata": {
        "id": "aa30ee55"
      },
      "source": [
        "### Example 2: Frozen Lake"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3cf9cda",
      "metadata": {
        "id": "a3cf9cda"
      },
      "source": [
        "Frozen lake is a gymnasium environment involving crossing a frozen lake from start to goal without falling into any holes by walking over the frozen lake. The player may not always move in the intended direction due to the slippery nature of the frozen lake. (See the documentation: https://gymnasium.farama.org/environments/toy_text/frozen_lake/)\n",
        "\n",
        "The game starts with the player at location [0,0] of the frozen lake grid world with the goal located at far extent of the world e.g. [3,3] for the 4x4 environment.  The player makes moves until they reach the goal or fall in a hole.\n",
        "The observation is the player’s current position. The action space consists of \"left, down, right, up\". The reward of reaching the goal is 1, otherwise 0.\n",
        "\n",
        "0: Move left\n",
        "\n",
        "1: Move down\n",
        "\n",
        "2: Move right\n",
        "\n",
        "3: Move up\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "68af64f6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "68af64f6",
        "outputId": "8f673735-aff9-42c4-f2b9-24a6cd82c031"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# This is a simple example of the gymnasium interaface. You can run this cell to visualize the environment\n",
        "os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n",
        "\n",
        "env = gym.make(\"FrozenLake-v1\", render_mode=\"rgb_array\")\n",
        "env.action_space.seed(42)\n",
        "\n",
        "observation, info = env.reset(seed=42)\n",
        "\n",
        "for _ in range(20):\n",
        "    action = env.action_space.sample()  # this is where you would insert your policy\n",
        "    observation, reward, terminated, truncated, info = env.step(action)\n",
        "\n",
        "    if terminated or truncated:\n",
        "      observation, info = env.reset()\n",
        "\n",
        "    clear_output(wait=True)\n",
        "    plt.imshow( env.render() )\n",
        "    plt.show()\n",
        "    env.render()\n",
        "\n",
        "env.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "64f07f57",
      "metadata": {
        "id": "64f07f57"
      },
      "source": [
        "### Q9\n",
        "We first consider deterministic Frozen Lake (use argument is_slippery=False). Observe the Frozen Lake environment, and intuit the optimal policy. What is the optimal action at each position? Explain why you chose this action."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eb2d0579",
      "metadata": {
        "id": "eb2d0579"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "\n",
        "#### Optimal Policy:\n",
        "- **Position `[0,0]`:** Move **Right** to `[0,1]`.\n",
        "- **Position `[0,1]`:** Move **Right** to `[0,2]`.\n",
        "- **Position `[0,2]`:** Move **Down** to `[1,2]`.\n",
        "- **Position `[1,2]`:** Move **Down** to `[2,2]`.\n",
        "- **Position `[2,2]`:** Move **Down** to `[3,2]`.\n",
        "- **Position `[3,2]`:** Move **Right** to `[3,3]` (Goal).\n",
        "\n",
        "#### Final Path:\n",
        "`(0,0)` →<sup>Right</sup> `(0,1)` →<sup>Right</sup> `(0,2)` →<sup>Down</sup> `(1,2)` →<sup>Down</sup> `(2,2)` →<sup>Down</sup> `(3,2)` →<sup>Right</sup> `(3,3)`\n",
        "\n",
        "#### Justification:\n",
        "\n",
        "- **Security:** The route is designed to get around every obstacle and arrive at the destination risk-free.\n",
        "\n",
        "- **Efficiency:** The chosen moves guarantee that the player takes the quickest route to the objective.\n",
        "\n",
        "- **Deterministic Environment:** Every action results in the predicted position precisely, guaranteeing the route of least resistance.\n",
        "\n",
        "Within the deterministic Frozen Lake setting, this approach ensures a safe and effective way to the objective."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "161210c0",
      "metadata": {
        "id": "161210c0"
      },
      "source": [
        "### Q10\n",
        "Implement value iteration algorithm to obtain an optimal policy for Frozen Lake environment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "f0ee067e",
      "metadata": {
        "id": "f0ee067e"
      },
      "outputs": [],
      "source": [
        "# Create the environment of Frozen Lake\n",
        "env = gym.make(\"FrozenLake-v1\", is_slippery=False, map_name=\"4x4\",  desc=[\"SFFF\", \"FHFH\", \"FFFH\", \"HFFG\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "686a7917",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "686a7917",
        "outputId": "19917828-cc40-4f96-9b8f-0b17087fab77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n",
            "0.0015595640454973494\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.P to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.P` for environment variables or `env.get_wrapper_attr('P')` that will search the reminding wrappers.\u001b[0m\n",
            "  logger.warn(\n"
          ]
        }
      ],
      "source": [
        "alpha = 0.99  # Discount factor\n",
        "epsilon = 1e-5  # Convergence threshold\n",
        "\n",
        "num_states = env.observation_space.n\n",
        "num_actions = env.action_space.n\n",
        "V = np.zeros(num_states)  # Initialization the value function for each state\n",
        "t=0\n",
        "start_time = time.time()\n",
        "while True:\n",
        "    t+=1\n",
        "    delta = 0\n",
        "\n",
        "    # Update the value function for each state\n",
        "    for s in range(num_states):\n",
        "        v = V[s]\n",
        "\n",
        "        # Compute the value for each action in the current state\n",
        "        q_values = []\n",
        "        for a in range(num_actions):\n",
        "            q_value = 0\n",
        "            for prob, next_state, reward, _ in env.P[s][a]:\n",
        "                ### START CODE HERE ###\n",
        "                q_value += prob * (reward + alpha * V[next_state])\n",
        "                ### END CODE HERE ###\n",
        "            q_values.append(q_value)\n",
        "\n",
        "        # Choose the action that maximizes the value\n",
        "        V[s] = max(q_values)\n",
        "\n",
        "        # Compute the difference between the new and old value\n",
        "        delta = max(delta, np.abs(v - V[s]))\n",
        "\n",
        "    # Check if the value function has converged\n",
        "    if delta < epsilon:\n",
        "        break\n",
        "end_time = time.time()\n",
        "execution_time = end_time - start_time\n",
        "print(t)\n",
        "print(execution_time/t)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "43fb27e0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43fb27e0",
        "outputId": "295c6ee1-397c-4e24-fc57-fb178cde7e25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal Policy:\n",
            "[[1 2 1 0]\n",
            " [1 0 1 0]\n",
            " [2 1 1 0]\n",
            " [0 2 2 0]]\n",
            "\n",
            "Optimal Value Function:\n",
            "[[0.95099005 0.96059601 0.970299   0.96059601]\n",
            " [0.96059601 0.         0.9801     0.        ]\n",
            " [0.970299   0.9801     0.99       0.        ]\n",
            " [0.         0.99       1.         0.        ]]\n"
          ]
        }
      ],
      "source": [
        "# Obtain the optimal policy\n",
        "policy = np.zeros(num_states, dtype=int)\n",
        "for s in range(num_states):\n",
        "\n",
        "    # Hint: using your value function \"V\" to choose the action that maximizes the value in the current state\n",
        "    ### START CODE HERE ###\n",
        "    q_values = []\n",
        "    for a in range(num_actions):\n",
        "        q_value = 0\n",
        "        for prob, next_state, reward, _ in env.P[s][a]:\n",
        "            q_value += prob * (reward + alpha * V[next_state])\n",
        "        q_values.append(q_value)\n",
        "    policy[s] = np.argmax(q_values)\n",
        "\n",
        "    ### END CODE HERE ###\n",
        "\n",
        "optimal_values = V\n",
        "optimal_policy = policy\n",
        "print(\"Optimal Policy:\")\n",
        "print(optimal_policy.reshape((4, 4)))\n",
        "print(\"\\nOptimal Value Function:\")\n",
        "print(optimal_values.reshape((4, 4)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "17221034",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "17221034",
        "outputId": "1e76017c-ee56-4a2e-ea28-3e67b82beb75"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Evaluate the agent trained by value iteration\n",
        "os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n",
        "\n",
        "env = gym.make(\"FrozenLake-v1\", render_mode=\"rgb_array\", is_slippery=False, map_name=\"4x4\",  desc=[\"SFFF\", \"FHFH\", \"FFFH\", \"HFFG\"])  # Establish again a visual environment\n",
        "env.action_space.seed(42)\n",
        "\n",
        "observation, info = env.reset(seed=42)\n",
        "done = False\n",
        "\n",
        "while not done:\n",
        "    ### START CODE HERE ###\n",
        "    action = optimal_policy[observation]             # this is where you would insert your policy\n",
        "    ### END CODE HERE ###\n",
        "    observation, reward, terminated, truncated, info = env.step(action)\n",
        "\n",
        "    if terminated or truncated:\n",
        "        done = True\n",
        "        observation, info = env.reset()\n",
        "\n",
        "    clear_output(wait=True)\n",
        "    plt.imshow( env.render() )\n",
        "    plt.show()\n",
        "    env.render()\n",
        "\n",
        "env.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "59b43a66",
      "metadata": {
        "id": "59b43a66"
      },
      "source": [
        "### Q11\n",
        "Does the observed optimal policy match your initial expectations? Provide an explanation for the alignment or any disparities that you may have observed."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cb9a5fbd",
      "metadata": {
        "id": "cb9a5fbd"
      },
      "source": [
        "\n",
        "\n",
        "### Answer:\n",
        "\n",
        "The observed optimal policy generally aligns with the initial expectations but includes some unexpected differences.\n",
        "\n",
        "#### Explanation:\n",
        "\n",
        "- **Alignment:** The initial design called for the agent to travel predominantly **Right** and **Down** to achieve the aim effectively. The observed policy follows this pattern but includes unexpected moves, such as going **Left** or **Up** at specific moments.\n",
        "\n",
        "- **differences: ** The observed policy implies more complex paths, most likely because the value iteration algorithm uncovered alternate routes that minimize risk while optimizing long-term rewards, even if they require minor diversions.\n",
        "\n",
        "- **Justification:** These differences arise from the algorithm's thorough analysis of the environment, where it calculates the best possible path based on cumulative rewards, leading to a policy that may seem less direct but is optimized for safety and efficiency.\n",
        "\n",
        "#### Conclusion:\n",
        "\n",
        "While the observed policy contains some surprising changes, it stays consistent with the overarching aim of arriving to the target safely and efficiently, reflecting the algorithm's detailed evaluation of the environment."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d48088b4",
      "metadata": {
        "id": "d48088b4"
      },
      "source": [
        "## Part B: Policy Iteration"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89e16389",
      "metadata": {
        "id": "89e16389"
      },
      "source": [
        "### Q12\n",
        "Complete the following code to implement policy iteration algorithm to obtain an optimal policy for Frozen Lake environment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "ded6067b",
      "metadata": {
        "id": "ded6067b"
      },
      "outputs": [],
      "source": [
        "# Create the environment of Frozen Lake\n",
        "env = gym.make(\"FrozenLake-v1\",is_slippery=False, map_name=\"4x4\",  desc=[\"SFFF\", \"FHFH\", \"FFFH\", \"HFFG\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "5614dc9c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5614dc9c",
        "outputId": "4c23dad8-b211-4f1b-ca39-7aca9ba7cf71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n",
            "0.00592872074672154\n",
            "Optimal Policy:\n",
            "[[1 2 1 0]\n",
            " [1 0 1 0]\n",
            " [2 1 1 0]\n",
            " [0 2 2 0]]\n",
            "\n",
            "Optimal Value Function:\n",
            "[[0.59049 0.6561  0.729   0.6561 ]\n",
            " [0.6561  0.      0.81    0.     ]\n",
            " [0.729   0.81    0.9     0.     ]\n",
            " [0.      0.9     1.      0.     ]]\n"
          ]
        }
      ],
      "source": [
        "alpha = 0.9  # Discount factor\n",
        "epsilon = 1e-5  # Convergence threshold\n",
        "\n",
        "num_states = env.observation_space.n\n",
        "num_actions = env.action_space.n\n",
        "V = np.zeros(num_states)  # Initialization the value function for each state\n",
        "policy = np.random.randint(low=0, high=num_actions, size=num_states)\n",
        "t=0\n",
        "# Policy Iteration algorithm\n",
        "start_time = time.time()\n",
        "while True:\n",
        "    t+=1\n",
        "    while True:\n",
        "        delta = 0\n",
        "        for s in range(num_states):\n",
        "\n",
        "            # Policy evaluation\n",
        "            v = V[s]\n",
        "            action = policy[s]\n",
        "            q_value = 0\n",
        "            for trans_prob, next_state, reward, done in env.P[s][action]:\n",
        "                ### START CODE HERE ###\n",
        "                q_value += trans_prob * (reward + alpha * V[next_state])\n",
        "\n",
        "                ### END CODE HERE ###\n",
        "            V[s] = q_value\n",
        "\n",
        "            delta = max(delta, np.abs(v - V[s]))\n",
        "        if delta < epsilon:\n",
        "            break\n",
        "\n",
        "    policy_stable = True\n",
        "    for s in range(num_states):\n",
        "\n",
        "        old_action = policy[s]\n",
        "        q_values = np.zeros(num_actions)\n",
        "        # Hint: policy improvement\n",
        "        ### START CODE HERE ###\n",
        "        for a in range(num_actions):\n",
        "          q_value = 0\n",
        "          for trans_prob, next_state, reward, done in env.P[s][a]:\n",
        "              q_value += trans_prob * (reward + alpha * V[next_state])\n",
        "          q_values[a] = q_value\n",
        "\n",
        "\n",
        "\n",
        "        ### END CODE HERE ###\n",
        "\n",
        "\n",
        "        # Hint: termination condition. If all old action is equal to new action, the iteration is terminated.\n",
        "        ### START CODE HERE ###\n",
        "\n",
        "        best_action = np.argmax(q_values)\n",
        "        policy[s] = best_action\n",
        "        if old_action != best_action:\n",
        "            policy_stable = False\n",
        "        ### END CODE HERE ###\n",
        "\n",
        "    if policy_stable:\n",
        "        break\n",
        "end_time = time.time()\n",
        "execution_time = end_time - start_time\n",
        "print(t)\n",
        "print(execution_time/t)\n",
        "print(\"Optimal Policy:\")\n",
        "print(policy.reshape((4, 4)))\n",
        "print(\"\\nOptimal Value Function:\")\n",
        "print(V.reshape((4, 4)))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a70d5094",
      "metadata": {
        "id": "a70d5094"
      },
      "source": [
        "### Q13\n",
        "Evaluate the optimal policy by policy iteration in Frozen Lake environment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "c452b24a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "c452b24a",
        "outputId": "4ea837f3-d51e-46dc-f71c-f151de7b2433"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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JK4KA1e8KV8f+CD/yt/4mMCJsbp/BT/zqv4Rlj99kC4rb2bY1OnZ2y35zINH3zSx5vZ/veMnwIcOXX522pdG1E9XARivpR0GJzN2BbwZz+Lkq2laSxqMK/WdtQxU5a2NqmWySPV8TrhxFEccJzxxk67Lmamwq0/6OLeMQSXVwO3HclQJOu/rzSWOanIbj+XgucLwuln7iIqKxh5LshpNvJx9a1bXoZNnTEPg+Bkc9AECbAbtVVjYAN1whCmDY64OHQLvdzq1fui6E/Jw66a/y0otrNmnTuUDFVBVj/ZLT35Orzjqi6vR53edT597078yc299irGV5+wOzMJmjqzN1AW4tx2ft8yr3NRyf79q6995OHK+LpZ+4OKXDN2qUbO+n1StppIlNSLbT2ugaCpGWMxqOcLC3BwA47B1Ctcz32qr3KFXLkMK3GTevXoO0LFiWhbWtrdK21YViAmtz0KuNfqP0+jyz1ejlAISqk5x6RWF+0pgkePRMiKabSBfJWTSqtK1WeTDnIoA5k6liCj75SG41x48TVdpWFw3Hy3E7crwuln7i0gzshg6akcPf5INwpYCb40B46AWx5dCaa8Wh/neH2S1zGmk5n/7jT+LH/sE/BgD07rax/w1rAICzF4foYFSpDSyBnTesQgWE3ZGPd/+f/q8gDdx93714zz//GQAmTMqaO5vKIJYD4HK47ZcEEPypenOtOXNe0vNUHANtzbVyw+BstLLUUszYGxorOFtQrKYYKR0/wyIUyVk0jlKc2GhZsOYc1EeewFGYVXirpbDqTlcp9SfiyN1qjh8n0pxoOP7c5XhdLP3EVQVVB980P4w8MHPsi6VZFufFnlZOqBthYqhAgRhQSoHDQ9nFRDwYV7QcFwr7e/Ig/QRWl3Wx+MgSKfVS5UrMIOUYOX5SaDh+MlhWjqex1BOXr4xfy0gRwADXXK1IojggpGaGX5LbiBnwtJEz8Ee4+fjjAIBLVy7BXzOHkaqVrHgDX2DQr3ZIyYxw5jL/8dclSAN9K8BTjzwKArC2vor1e59XWo4lAEeaNviKcs8AYpkwh63TMGbGy6a/hwHF97JTcGMOCIhX/IIo7u+ilX+gARWaRPsWg0LdliXo2F4IUqT1+OUyiABHmuN5zTTdfFsTRsH0eqdNkr1bxHEto+gJHJqMH9cLmGJOVJHRcHx+3G4cnwVLPXFd6wPYIUTN6NrAdqd6h6Sd5vZHQWmYE8WIrWsuP/o4/vVDfxMAMDxnYf9rVzLX7950sHuzBuMjE19XYPdBU97BwU38L+98FwjAa772q/H//Kn/V2kJ667Cums+X+1Z5mVXAM2EK73pj5+1uRZgKA08chMgEohiD626QNVwbDIVu83XnBsPLY39ocSRL8L6Bmjbi1NvFCGd4XXai8MSwIWuacNIEa72ytVcB57AgTc9QkpvlFxztQcciZPnuCMZhCB8cc2vwitC3Xh+Dcfnx+3G8Vmw1BNXgtke8Gwrmpx7csuZg3Sz+vrUvq3KDbk2jnUFJXfGlaxs9zazrFlQlxP1Lp+nLSfN8ZNTJ9apY8Px+XH7crw6TsnEZRBoYD+0h5DE4RbXpNOeFmDUFhQfT9XJSdQdMbYvmxWJKxkda76ox5qBfV8CDKiBxiDnGimSvECBzvdmdy2GIFOXYVCuUpkHR16kBmG0LI4t1+wpqg4RruQBQGmUpkMHgJES4FCNko4UsObOfKxYC7YQYBhrrLz06pIY7fDZKwY8dTwBbU+K45oJg1Dl4ykRc8yRQOd4Nl9jaDj+3OV4FZyqiWukgC/tms8di3E23OK2LIEVp7yTozhizDzV4iqNO3Y1/sInTNyt820PL1ydLwbXSBH+9GYHDMIONP6zRGYR40gBJ7Qg6xWkCNhshQ6oDFw6sjFnFoFCXD5KPt+xouIX6VY7G4suDUsIrLmmDcNA5WbHTWN/JBGFILneN98RgFecMy/T4wQRYTWORcfYyXE2tSVwLuRbzxO4MTieQX1SHA804Xo/O0Od7QD3rM9W9zpoOG6+ey5yvAqaRJINGjRo0GCpcKp2XGn4GjgYRasdgX64gFhxZlN1eIMB/uQ//SdozfCuX8OLtSl7Y07rmDK4ILyYBcCAc+ka/vOv/wYA4O4XPh+veN1XAjCruhx3EnhKxyvqrq2g2VgS9TxxbCqVni8wDEzpSidOsWfaMx/blYIB3BwgE2BUEKNr6/Bzksq8VtnMY33YskQcaaCVU55mjtVvlmSsOmZ17SnC6JhUKovmeBUMAuBaL/t929Jx1HTXEjM5EivNcSR7oyo0bWs4/tzleBFO8cQlsDvMduZdq7MN6uHRIf7LP/8ZaKWwxYS3aguzB2Ophi4Ir9fmET372Jfxv/7TnwIAfNN3fEs8cbmWgJuzcd4b+tDMIAI2WolJbt8Xx5QFl3CQSkOxMzT/tQSw1T6+I9tLh9nvLAFcXFEgMlZrswxqAOj7Oj6XcKSII5rnZYH1lYankrNOt20G9f5IHOPEtViOV8GRZ/5NYrut0XUSU+5ZEigqTjL5tqxEVdhwPPvdc4XjRWhUhQ0aNGjQYKmw1DuuttTYaCV+EkoTDj2BsrXP3sioWACjXogOWluWjB0gO7aEZsD3PPy7f/W/wxt5GPb7YJ09/e2eY9zxmlC90Q9weGCWYWvn7sXKmbsrtYO1wtXHPgqtArAQuPf1Jjrv6IDw9B9Rpj1f/Mzn8EthFPqveOD1eOlr7gdgVqaRZVnbEogMxwa+AoelrLsqY6QriNC2TL8pZgxT4VhYM26QuYOIseEqUGq5c+TJUqdEzcCzh0a2JMaaa8qWguIkhJag2N/IVzq2aOrYGlb4fKbJmZS5N5Rh2/J3CGl0bA1XZp9tOgzQIFAZVlHYb1HOrKgNSnMcLqgleYyjEVpSQAoBgDEM9JjFmR4wokV2xzpejgOIo8oLMgYPXFFOGj1fxA67fZ9KreBEyIPJS9J94CtGz8v2W8PxROayc3weLPXE1bI01t3kYYyCaLAVI63q2O5odO3UNhnGvDUiW9/T+K//7j+gf5Sj1A/RPsN43tebMg6eDXDzUVP4xsvOY+VF91dqhwo8DI7+EMr3IB2Be75agwTh8BLj6T/KmhM99eiX8NSjXwIAUKuD573qVQDMQIk0BlFWVWbGKFBQYeioNTdLXkmEjZYAkTGTPhgl12idnBYIAtZcBZF6Kw19gaDk5aY5OROxBWAJHZsSJ4NaxPXuA/B1mLXVZkSx8oeBqDGoCQdeqt+mhIzcamms5vRLGsMckzVBCF+Gpu87wsgcBSrOmOtanMnSCwCrjoBrGYu4/ZEecwzup14wx81xzRy/9EWKH1XkpDEIROK6MeUlKgmwQx4UQTFjEOSYZDccD2WeHo7PgkZV2KBBgwYNlgpLveOahCUY2+3qUYddmVoB+Ao0seIYjhKVQ7vl4K1f+wrj72BLfOUZE5ZJ8xVc++zHAQBXb7Tx1LN3AQAePzpA6/N/CgC4/zXPx8U7N8fKZmb8t//6efR7I7DWOLh6HlorOLaG+7kdEDGI1vH67/k6AMC9owBbu2ZZ98SzN/Cnn38KgFHRXO8n23fPNlv2ji3jmGldxzLZWWGiXk+ujTQzDj0Vf66D9ZbCSo4KNQ+CkrIDzTjICYdTlBJh3VVYCa2odody7lhnbUvHVlmeprgP80AwKjQpzOp6ZyDBMAfklgjVQpT4SVlCYDWM9uWpRKWSxiBIoo9XSQMRYdEcRyYxR1aOpyZW9zOBsdVWMIoj4MagvLyWxVgNjT16nkA/iBx5G45XxbJyvApO1cQlBWLLprrww4CiaaQfhm1JvPxFd0IKgbYrcdc96yAi7F/x8cQnzD59f7eL64cb5oZDD8BlAMC9LzoPZCYu4IkvXcP+XuhpiFUAgGP5OLp+GYIY7TXgrq9/HogI60debF408oJ44vIUxRlh21YAGeYYj/T5REkEAmZGD9mXHgOxGXJdtKxsv1VBXZmRHKN2EHMPaltyzJVhKqtuUW03Us6ufd+YW1uCMQr82LIrghQEGapUFCvkdHkcwLYuFs3xKnLIJ2DOiYtgzuukMNE/bg5slJ2fCUo6zdMNx2fBsnK8ChpVYYMGDRo0WCos/Y6LgDhZWxUMAl15FeS2XLz7n/7P0Eqhf3CA//1nfxZaKUhBaIcyL2538DUv/0sAgBfd18H9KyYezgf/8PP4gz8xqU8e9PJPqx/Z3cPVm4dwbAvf/z1vRKdlQ2uFnd37wMy4fOThf/tn/wlgINAaw/A09N7XvAbf/zP/bwDA+TvvxNkzpjxbSEhKDlLzUKevALPFnzxEb6d8bKZBs0lmuEic6SgwF6vLlKZQFTXPipVxpq1ip9rILUkK4L4tUzQBcKXpzyJDA1cK2DUDze3a4yvj4+R4FbiScb5bnhxwfygxnNOXx5E6DuMkUyq3sx3gQpiAoeG4wWnjeF0s/8RF9XLX5OliiyCkxL2veDkAYOfqNTx9ZQdajZNJ8QVQ5wIAoLvu4s7zZoTZbRsHnhnsRTmQDn0fB54PF4wLd2xgtevC91XsVDrs7eHzj/9p5r7nWw7ue/UrARg9f+JsmjWdT4OIYNd0DBXEoaN18iKRgmBXHtSLVRUQjZ/b5MFXjCjm2zxwZBLENpYPE5nC0K28v4FQpVLz5TL5iI6T45XqIwApyvv8UHCuuqiWHIrUZeMYD+zbcBw4fRyvi6WfuOpinv4SQmR8uYgIOjx4ZCCVXZbiFUrR+0YQhSFjzKCJDpc1c+z5n/eyuh2zqt6eqPNCOR5d/K3ArWNH9T4MGX98VXnO4LnJ8efcxNW2ZexbUQcrd57He3/pX2TyXn/pzz6ND/zcPwcAfOXL7sYbH3gZAODrX3cfvunBlwAANtbamfKIgH/0t94KpTQYjGevD8A8wFF/hP/1330ESjHO3XMXfviX/1Xm3vW1VWy27Lic2xnGWmm2+EM9X82k8jLhcMpVW2nNxoarsOZktwvpKDrrrjVT/L1bgVk5rnl64sMibLUUNtzyLVfU55KAO3KeT7p7HSnihIe3e7c3HD95POcmLkE025LUsnDuzouZry89+RR2D4yZ+kFvBC8MWXBmvYNzW9kJKwIR4dyWUSsqzdjZ34XSjOEowM5+D1ozVkYBzt55MbPDalniGFOpLxbpvEK1751ZpknBUBVSTFe6HG/6+sViZo7PoWGs0ocRqjwfQcVnWLcbGo6fPE7FxJVndRlFbV4kxrXgyXcRWDNUGEomCBS8ige2ms19SjO0yqoiy8Az6NfnVTVqNpGxJyFosavj22XxN/aMb0F/A7eW47caDcePH7cDx+tg6ScuTwGfuZb9fqMFPH9jcXIEAZvtrDogHUX5ytPX8Uf/xRhTiBqH6QCglHkzjZQCh2dmliBs5chMlzoINAZ+tVNxE0W7PPldFTxzgDiFRhov3UZu+olZ0bVlHButCLsDf+4XbceWuWkc0oj6jGGiklcd2y1LxE6bs+JWczyNYaDj2IazgjBdTsPxBM8FjtfF0k9cDCAvC/mUzOS1EeWpyX6ffNaKEcw5qIMJq0VC+WomMuiogkUZP2nO799Fp5Io6vNE3mIEElBLt7/gIABTcas5PnbNQuRM53UaDccXIAe3N8frYuknLkJkWpk16YyiVQsaP4ScF4HvY393DwBw1DsAhT4m3GH4YZgc1wdc39Qp0ASd4wVvCwaRGZRHHZMEL9AaYtUQRzsKO9dvAAAcx8HqxnqmDBO1uby+aRKmLRaTMuq9SASN+9lECHTS57Y4GTWIIJrbOq1uPaf1N3PCRkZ+uBujcqom+FZwvLAuC1BP1jUAaDh++jleF0s/cUkC7lzN7un7gcCfXzPNO9cF7lpbnMynHnsC/9P3/h0j/17G+t82348wwBfDGNnf9McKX/MZM4k9dtDCzWG2q+8/04crNXwL+Im/5GDgGhKshr/vXnsCf+dbvxMA8JVf8wB+8P/zP2fKaFsijt5chL2hH68e94bZczcTObs6FTZbKnYUTePJPRt+mBX2FWfrHR7Pijr1XgSqWJCNlMZRGBdvGOjcqNurjgXXqjaobwXHi+BKAbeif9Oi0HD89HO8LpZ+4iqe0JM1ysKzoTIjCMzgIA1QiryxFxdxbMVDnJdInEFsrlEAWDA4NE2Kdc2EWI5S+SrI4zDemIY8kWYFxiZl+gmpGW6FP1sVmYuu1S3heGFdbr8+bzh+8jJvtU3J0k9caWhGnM8mndcm0MlBqy2qr5KYeSz5maSswUWbbNxjmT3Snufh2sjsuHZcjUtnOPwM7HuT1oLA1XWCIwBfAoORxJAJFhFesLIGAtC3FA6xX7n9SnOuSmHaGGOE7eRJVdR0evoKYBg1Z3p6HgQmmSHBHGZXHX+aOY5EIMLkdceBRciZ5EeEKmdPmhlBTsRxOaUet4Lji8Ci5DQcr45l5XgVnKqJy1eEK71sk3aG5h8A3LUKhFGZpoIB7A+DOMle3vb5Jc4d+H9svQkA8O+ffhw/8einAAC9O4f4xP3DVElZ9cUfhHsyrQmPfWEFWhG23Tb+pze8BRYJPDHYwT/Af6xWWZi0FbOE+zHZVHOyzUox1eJpZ2BhqMaJyAAe3TGfLQG88lz1FVraam3Fmc2Rtq6cri3RnkHnU9RvVdDzFZBjtTZNRXMrOL4oLEJOw/HqWFaOV8GpmrgSnOxGdnzlSOkfpt1Z8PfxrHqPD8tU19OCps9PFk1/305Y6olLCoItKMwzVA3DANgPV6Ydu1ylQgBsKcDMhdvsfm+EJ75knGwGN45wYd8cIt8tgDuGZmUozhKom71fPaOBwER67u8LBEpg3QGeevw6JBEuXU/UhJp5amiYYQAMgvkGmCTEabgjmWnVjC0FGFzZXNbkFjL5cwSlg3cWy48iTR9n+Jm0nLxoAcyMQJfbcgWaMfBp7uOOlsWxFZev9ZiF1u3A8UVhmhwd9nkZGo5Xx7JwfBYs9cTlWhIrjoXdYXnMrjRuDMw/AHjhBrBZHJUJRDQ1RcLly3v4j//+k/Hfb4BJC/rqL/bwUmW22M7bbFhnxq2iWDOG/12B9xkBCOuuDS+08vitRx4GABx5SbuKsqmmcXMg0fPne6QtS+O8ZeT4mjEKgrGAv2uuVUtdoxh4fDcqG3j5dvn1riXhHpPqpK6cI0/l6vcj+Aq41i9PiDgdjAvdIH6RHnlqzEKrdRtwfBGoIqfh+GKxLByfBU0iyQYNGjRosFRY6h3XJIRgrISpqgNNGAbl8/KBZ6yxAKBja4gch8M8OKur+Lpv/WYAwPDGDi5/9nMAgHUmnAsdjUcauKpN915/lNA/yJZ9cd+CPWAoENq+BweEgIAnSYMBOKsr+Kbv+BYAwJ0veH5uyBZfJVY/LYtBVBK5g4FemJK7CEoTDkdGji0ZzpQ8TG1bwwrz+fR9ketonZQN3Oibz1Iw2tZi80bVgZXKt+QrHauoHJkEMHYtkcm1xJzkuzJqofI2eIrgTUmw2A8EPMUAAV27vLyT4viiYXJMCRARmBkjpcFsVGWOpNjJuOH44rCsHK+CUzVx2QI4E0au6PnTB3VEMAC40FXxVnYa2me28X/5/r8LAPjUH/wRfu6PPgYAeLEWeGk4WfUBPI4WAOAPPhbgKTHxsBj4VuVgNRxg66Hjch+ML0kfmoAXXDiHv/meh0oNNXpegEFg6r3iaJQZk2k2BCpze/E1YSd0ll6xFZxWOcnWXPM7MzAK8iOEJGUDXz4wn1uScX5lzsyDc6BtiXhQj1TiQBklxSOiXGszzYzRILo24VsR9kdiyqAmHIwiOdNfdCfF8eOA0xYgGB5GzquSCE6YYdcSAitOtj0Nx2fDsnK8ChpVYYMGDRo0WCos/Y6LCPGqQTPPfOh36An0K7orjIZDXHr6SQDA1UMP9zz4dQAAdfkyPvX445nr5Utegnu2zYmta2ljXcOMx/74U6DBcPxi18UrvuYBQBA2zmzizz//CABgdWUFz7/nrkzZTqh+mcQwUBmrKAKw7iowAxqEg5HALIeubiofWJ6cKvA1YXc43wE1AVhzVSaumtLAgWfKtgRjdYqqw5Eitu6a5hxJQOEqNY97LcnYcA2xjLqk/lrxVnC8CK7U6NjZB97zBLzQIXrVUaVxE9PtqRL3sOH46ed4XSz9xCVS211f5cfMqoKeX51gh4c+PvPIk/Hfd73+qwEAl//sT/DZJx/LXP/ie+/FhRebzMirTgBbAqw1/utnPof+qD92bbsl8S1f+1WQ0tTnkUe/BAC4cP5s7sRlS5Fr7uwpndFdEyVqj0AjHNT14UgBJ5Tp58ipAsVp9cGsYKw42UGtGfELy5W60qB2KlalSL1SxD3X4lg9pwYEbwbN0a3geBFWHaBjZxvRDwj9sPy2pWGVnBuJgj4sQsPx08/xumhUhQ0aNGjQYKmw8B3XD/3QD+F973vf2HcveclL8IUvfAEAMBwO8ff//t/Hr/3ar2E0GuEtb3kLfvZnfxbnz5+fW7YUhBUn2j4DzGb7OgxoIavNCK1WBy99xasBAIOjfdx41uyK7nzR3VhffSsAwAsIfqg62b7rArrhKrVtC1gEQAq85i9+A4KRN1a2ZdsQZNYTnXYbX/GyF5v7Wq1adezYsnSVqBkYBSrjWOgrilUQQyVwY2CVqknatoQbyhkpHccm2xnIUsuu2cHYcBWkiHIMmW81G5mAWXVHB8qORMyJNKwFpw5Oc6+w5sxwZFZXtz+UCMID/52BhZ5fvJ48KY6n4QiN1XAXY6d2Uj1PxM7AjmS0Q9+odTe7QyIsPvZEw/HTyfEqOBZV4Vd8xVfgd3/3dxMhViLm7/29v4ff/u3fxr/9t/8W6+vreNe73oXv+I7vwB/+4R/OLVcQxXG/CBqBYx6sZoFedf/NqbAdBxfuMGq7/ZsSh1eNetDd3sTG9iYAYzYbWXx17cSayxYiJtTdL7mvVI7j2LjnrjtnCv/kTEk9oZnRdbKdMvCTQR1ogh9EZwT5IzuSw8zo2gFUmAtpdyiPLWJ522Y4MmvC2w/NoC3B2Gr5IDKDt2Udv0Y8zb0iBDoA0biqhdmcPUGZV/sgoDjf0zQ5x8nxNKTIN4n2VDJZblsBuuE1LcuKrdmOEw3HTyfHq+BYWmtZFi5cuJD5fn9/H//yX/5L/Mqv/Are+MY3AgD+9b/+13jZy16Gj33sY/jqr/7q46hOgwYNGjQ4RTiWievRRx/FxYsX0Wq18OCDD+L9738/7rnnHjz88MPwfR9vfvOb42tf+tKX4p577sFHP/rRhU5ctiSshyFmbMFwZHbltTeUGM1pAdNd28ILX/m1AICOpbHqRitgitUPkkx+LgBYc+04ftjhKChVdViWhf0pIXBalpgpurSxvjL9o5hjvxpHMs53k77SmnGjotNqpEZgAL4OMuqXQBNuDiTmVRrdHMgwmy2w3Q4ghVGnnOuGOdIK7vNUEi17VhgrL2umXXDbknESxp6vEGgGEbDVVmBO6nV9qHFYobyT4vhIEa4cGTkdW8fGDyuOQtvOqhAjcMirspBCVdBw/HRy/OocdVz4xPXAAw/gF37hF/CSl7wEly9fxvve9z583dd9Hf78z/8cV65cgeM42NjYGLvn/PnzuHLlSmGZo9EIo9Eo/vvg4GBqPQQRRJiY0dEKrRzHS7kAFbBlu1hZdwEYM+CtKc56664VBxuVQ3+qme20oKOzxqokIthR4spUzhwpjMd/XL6upsVPl8fMcGU2eKenGMC85zBpNUMigwi5zziNKkFcp0ufHZHjJwCIIOGJO6ESKrPIS+OkOK6ZMFLJWVYEWwL2lPCrvtYzczSRP9t9DcdnlT47Fs3xIix84nrb294Wf37Vq16FBx54AM973vPw67/+62i3S6J9luD9739/xuBjcUh3YLGe+zgxr548ub+4oNsrTUo8FCf+rodKL5uiGsz8Mgxrm/xfwXW3Y38DDcdPCg3HjxPHfqK3sbGBF7/4xXjsscfwTd/0TfA8D3t7e2O7rqtXr+aeiUV4z3veg4ceeij+++DgAHfffXflOrip0CeDVNTnzXaAjfC5rLoWLGHC9x8My1V4RUgb8bQtkRuZOW8FzACu9qzSUDJlMqO7NlsKnZw4YGuuXbrylkTYyEnu5imNoymqyiJstMxanJljdactgIsrRkVjCYHVUI0zChh9fzY5UbsIwHrLwuQwLmr2kSfiA/o6IACXj8xnSwDnun5Ghi0FVpzyodW1LXQyXT49OnoRGo43HJ/EaeN4Gsc+cR0dHeFLX/oS/tpf+2t47WtfC9u28eEPfxhvf/vbAQCPPPIIvvzlL+PBBx8sLMN1XbiuO3MdRBjAExjPk5P27nelUX0wAwNZPRdPEYioljlqoMtjoFWBr/NTak9rChEZE/1MnWarBxHFgy3dj0RJbihbMFox+xj+AmKR1kkHr9n0+TxQbCIYTIqUFbiTnx9p9ro0HC9Hw/HZcDtxPI2FT1zf//3fj2/5lm/B8573PFy6dAnvfe97IaXEd37nd2J9fR1/42/8DTz00EPY2trC2toa3v3ud+PBBx+czTCDzWoHqL5tLdxWIylrEai2xTdqm8jHZTJdG6eurIr8lxGPEWbmLX7Y38z1yihSTi20vytUJ30JUba/gaie1dtmeDPxHY+3rW5fjYlvOJ5Bw/HyeqSvXwqOz4CFT1zPPPMMvvM7vxM3b97E2bNn8YY3vAEf+9jHcPbsWQDAT/zET0AIgbe//e1jDsizYOAr7A0DbLSqN6NjS7TDJdHhKIgzyx6mtq95VBOEWNVQlPDOlQJdJ/KxmY71sN7MAMPPkMPXhKu96m3bGcjc2GiaA9jCWPdstOzKnHGlwGbbDmOcMRiM3aGPNdeqnAiPAGy2Tb8pnahUfM3YGZQ7HnVtCTfcMqSfVRobLatyFllHCmy1TXktS6NrZ+XfGFgYVsywG2jg2cOs+qljawTalN22Za3wRustGzt24pTebzg+hobj5VhGjs+ChU9cv/Zrv1b6e6vVwgc+8AF84AMfmFsWh/+rAyKKSZ3mQpVSKL4//2qi6qm40/VgMCQBk1qUPJVIGRiUsxVns4JE/W16lCNprDSud8ycbuek1mK6emd6fxoVWf0+F2SsyzLXVCopuTpv9a9TfVRnxR3Vb3zD1XA8jYbj1euyLByfBUsfZDeNiLwRpj1sQvEAnUS6LKL8ji+Slj4Ej14M2fITFUi6RkaPPl5HQ5rqj14xgUKTX11gKVRUr2mo0rb074Jm03OLnJdpkagqPEgPnvFrGXLCp4cR9Vu1/mGm+OxEMXKNIGbt74bj+Wg4fno4XgWnauIKUtt0RwqsueXNM86E9a1uJBG22tntcxF6XmLlZfy4sg8zUtFoBnaHZgtuC8adq9mt/pEn4kR4VRCpYgQAIj+zwrRE4shaF2n1xkbLzj0AjyAI2Myx7KqCOs9qvA8J6zkyjVOrWY4epZ6Pif827ovHAC4d2pV3B4OAYvXKmqvhqewzXHUsuGWdVYCG4/loOH56OF4Fp2riGsf0JzDraqDufdPVBWl9TvrAs5aYotJT9QgPsHNUI4tBTuHpmszRoDr3csHnovLSReeKqd0/NfVzM6PheFhKqh4Nx/PKW16O5+NUTFzKmLhAc5IkLb111szxVnaavpiZw1WHub6O+WkRJCX1mlYUpa4dqxcS82UhGLYwqyfF1U2MGeYwfHJVZYmkDyehckx4jZmtDutLKT8T82G8DykMeVVcxzrPpwoISR9OS5oXycy7Lh2qyJYMoU17jFlztToqRm5A0UAzpM63j847U2g43nA8jdPI8TpY+olLM7A3NKoTS1Cu9dUo0OiF8bu6KYurIhyMTCgmgrEWmndR2LFljiNePgjIbYOfsvLqWIzOivm8N5K1ktXlWXDZgkEIcl84WnNGf933FfqhZmDNteLYi2nsD/14bTpN5eQpHceRa1sC3SmOjdNgrOOql9G2BNo5KXv3hkE8sM91TH9rNlZWVcddzxe5KRwCHeRmEgaMtWwaDccbjk/itHG8LpZ+4srD7RX6paaPQ+G1i1CvTC97plKXuL+LruciddY8KpUFYpn7vOH4/HgucLwMp2riYubwEDVrMROtmKps0W0hoJkLLauKAlkKqhdJ4HYBM+X6dUgBWFOiZpt+yKoDbEmVnTgFUfx88rztjZz8YK22mF/NdSvgKcrlljsliGrD8dnQcPzkMSvHq+BUTVyKkes02bJErtVNHogoji9WKKfAObNlTY/fdTsiYMK1frZ/VmyFzVZ5nJqiFApbsVPndDhSTE0K2PNUrnPm1gLUXLcC+6M8nuRb2KXRcHw2NBw/eczK8So4/jSlDRo0aNCgwQKxfEunGaA0YxjMdxg4WV4deErHh7+OFDNZFAlCnKBNpdQ4rtRYCReSQyXmDqqZhq8JR56Y6XRgFOgFmTob1OlyzQwvNBUTRFNXukVwLRE/68gHhsiksWc2yqO+H6V9Xwx6vohzX9VBw/HZ0HB8eTiexlJPXMzGImgaRpoxmjUMdA1ozbkDvu+pmGRrYSLJuiBQHA9sGCh4YXtaktGS5vONgQU/WNwmeqgJA0+GpsjGP0ZHbJ6CwwWkLqgCpTkbRkgzDlJWeFVVaJNwpQCkOVcaBSp+sWy4USw6Qs8Ti3QSwm5fjlloNRxvOH7aOT4LlnriunR9gIPh7q2uRgxz0J3vnxI996Jr6iBdXhp1QrZUlwVw6IsRKMafPbK30PLnRdHKPlr9L6K/0+Vlv1/86UPa2uuZa31cuj5YuIxZ0XD85HHaOT4Llnri0myIdnuhSn2Os87H2x9Nf59k2YDWgL4VoQlKcbr7vOH4SZY9G5Z64iIQhJh9yymIc3XUhAIb4dsFjKkRwxXTwvimU97v8/Q3AEiRX6nb2ty3Qn8bFdNi2mDyQUWr6flMoRuOV0PD8ZPn+DxY6omru97F2TvOzXz/djvITQO+2bIhbmNflWGg0PPKD+Kv9iyM1PxnAcyM65eug7WGEALbF8/OPABtwbhjJWsK6y6BifXuwC9NdT9ShKu92c4ZJtE77OFo7xAAsLK+gs5qd+ayGo5PR8Nxg5PmeO/oYOb7b++enIKOzdhsK+wNJWZZPh75EsMJ4hMA29KwyOiOu7ace6U08FWuM2fXkTNZX7mWzHViHAY6lrPR0mNxyACzYtodylrpIhCGBWIARIwzHYW+L2d6YShGbsTvjg0IMi8pR4o4sd4wUPBDtU3bFrAmVsLMjJ6vQifQ5Flp5tyXniVoaiikPDAzVl2ZWY9qTnx8HADbnazMQUDo+/Vkpp9O12FsNByP0XD89HF8Fiz1xOVKjY6tw0FdHyMlMMo8B8aa78OWpnM7tpy7k32dmK6m0WE50xO0BMES2Tb7ihEgGgTZ6AqaTdy3WXfpgoAVW8PL7bfpYBB6uQRX8a5AEOCG7oW+4thE182JswYYk2SG6cZuOGA1J6a9Ey2oX2kYFU9eNlylOR7UUhgT4sw1LOKYd7Og4fiEnIbjAE4Xx2dB44DcoEGDBg2WCku94zou3BxYYfZOQFCQSUonBeWqV0aBjp1AW5aIVzAdO0nolkZULjPj0AvmdpVIq2q6dlbVwgz4KqjiopLcoxk3psRyy7kL250gznh7o2+h6rJ7ZwA8a453oLSEZtNvN/rZFOsA4GsrTpF0cxBJBwKVpXZRKvOiNmy1FeyCg/ZITgRJhK6TXbFaguHIesvR60ONw1p31EfD8dQ9DcdLrkiwaI5frXXHhMw57j2loNirm2ACmk6Sqegx6zgAKpCO5j+pt86Dr6bZ9NSDJSjjBMrMaFn15Ghd67QgRksypDC5jqLzgyoYKeDQi/4ixC+DXLVN6ncAw4LvZ8WKk1VFFYEIudELAq3QrhlUtOxFshg0HE+j4fjycXzpJ67k0eV1xPwPljHuND6eyHW6+WhSysnC1PtW+HaknkgN8cda1Rkx+eyLEHHiuPq74XiBbDQcnxe3C8frYuknLkkIzU/HB/AwIOzmWPfUAQO4cmSDwJAEnOuaMCiB5jixXxppU9KrPcQHlput6SuSNdcChWqHKCq3JORG8R4FGoMp4X2OvCDXmz6Sk4bSwKFXPXzNRkthzc3Kv963EFXrSs/0G4OmvtL6vogzqKpj8NKfFTf6VmntpUiS7xVxwpH5iR+LIoHnymk4nouG4/PjduF4XSz9xGW2r8Dk6shfSNg2CklK0BM68Ekz3EkECoiesa8AR5ZfL4VJ5z0eB45y06r7FfTxppjsdZGciaunlpeGOcrImiFTbPuU9FsVMAj+AgOnLgbT2zCpkMrnBOWq0Yiqm6w1HM9Hw/F5cftwvC6WfuIqgnkUVWb76mQqGCf5EaIpdXHNg9/Ep6Tg9zn4zzxOxkV685f3+XxyglT08eOEJUQtx9wiTqR/z1OvLKIpDcfz0XC8HMvE8SIs9cTVtiW22oknd5BKfte2GHdNSVh2c2BhkJMVNQ+KgWcPs17jbUvnOuWtuwprjvk+zZFVx4ItzRd7Qz+TykAQsNku9053pYDTLj8MPxgFuQ6he0M/lJNs7yVR3I++0jgsiFhAIGy27TFLrrScSM2UhmLC5aP5afbT/+UL+Mwzu3OXMw3/97e8DPffs1Xp2kDncyKNNVdjw83ysOqY7jQcL0TD8dlwu3F8Fiz1xEU0rhKgsVWW0Z+X3l9PWm6+nKLYXaLA6EeQGVBFh5xE0+M8V7mmCJHU9MouXV7p6jSs+3ifR/cV9HdosTUviY9GPvZPwMsxqJUaJJ8TaWg9X9sbjtdHw/Fy3G4cnwVLPXFNwoT3TzCtM4kYooaKwyzCDEWjh1l2f1SX9BWa89MHFOnrCbOpOgSVv7QEGUJOHswaPX51TPZ5XE6NMtIY+grDnFTpYxG7pTWTLind1rFHoFX8Rc9T2Ot7qAJBhNWWVfp8NIxhQPbe2dRhDcfH69pwfByngeNVcKomrrQ6wCtRB0Q401bgfOeJnLKBjZYNonHrmqLn0rEl2qFD5qEXwAtJWWTZFKk30rAEYT3H4qoKVqcE9NRsZE4OPlskfTgM9NRkeXlyGCZg5ywD+7c/9Qx+/eNPZb4PUtG73Re9DKJdP/Cs0BoiLMf3zT8A0DeeAh9cBwB84ENfqPwSvbDewk981+tLB+eRJ0wivglsdwJ07Po91HA8QcPxLE4Dx6vgVE1c4w9j+oMpWkkVXRutIAjjOv3p9ZlNkTDP4eY0YlLGXih7bxVu58qZo+JaM/zcGGxpmQI0Y+qJ+IkI82/82ygyQ7X6+5XyNk03la6DhuN5sgt+bzi+lByvglM1caVRRf+fxrTnwxyagjKm6nzN9Rwf6Bbp+tOqjnz5bL4vGSTRecIskJR92Yzr9im8pj4CTVDM0JxP6qGvcP1gmPl+f5Csysm2jcpkEjUHdDR4iRLdhWDzDwDg2tBuq7yM8BkwkjMfJR08s9PPvPw6joUzq25peYGmeIdiiWSSqPM8G45PR8Px8IIl5XgRTu3EZQvCRqt67pj9AgulCAzkOt8VYVDBgXLdteOHuZOjdlCcr15Jo2NLdGZIY0BArtNgGsax0A5JVn1NxQCu9BJHzTx88fIBfug3/qy0HPuOeyDPzJ5vDQCEJLgr2XbaAKJhrFbvgRpeLCyDAFjKB9g4mx76NpiBHX+Ev/vLn8hc/7X3ncXf/+avKK3X7lACMM/tQjeAGzrvrrs29uxq5w8Nx8vRcHz5OV6EUztx3dbZRlOgEuur45Zb6ZrawUeBef1ZMnU47jJyHGDHkMpGHD0qomL1SLUeyzNrqIeG49PlVrqm4fhty/EiLPXEpSZyABGQCbpZFZaY3fy2DgKdr3e3JYWOkyhdFU9isg8iWLnRA+pBhbr4uMZsfGDSIWsiOcwm/I/i0AE0pwlDX+HRKwdgAI9fT8U/t2yIVidzPdnOXPUHQvXXtBATBJA9hTehyoMYcZ4olhawshY3NjpbPwgsfPrp3UwRFzfa2F4tV9cEWo+p1BqONxyfhmXn+CxY6olrFKjYGRMILZTElJVFAU4qrfbe0M8MWiLCmmtUPkozdqeoTtIYKZ2bUG7dtSDqHIDkwNcaB6MkFQXDOL+mve7XXAtOKOfJ/SQEUB6uHwzx3n+fVZ3ItQ24z79vrroWgTVj1CtXf9ltCbtb7QVCAFaispUD330pAEApYDQy33/u8CZ+KKedf+MvvAh/6f67Sss/9MZNpYcNxxuOT8Gyc3wWNIkkGzRo0KDBUmGpd1yWEHBkYrkS6CTRmiUYLStPvSBghaspryA2mCtFrJf3VJQ2m+DI8pVuoDn2xTCpx7PrAkcKSIr0yea7tJxI/iTSeZB8ZVKyl8GRDMXZVU3UtiqQROH1qfstEVpSmb8PRslveRa+XqDw0ceuQ2tgp5e62LIh1zaMnJW1SvUxYKxtGkMsBjAc6rljolkOQ4Zc8QYErar1D2uGjjrHItiW+czUgfK2AQBq2Ice9AEAj145xH/93BUAwCvu3sDZHJWKIwmWTKupGo4XoeF4ddzOHJ+pPXPdfYvhWAJd24KnjNphFABXeua3jsU4282Sumsn+tthoOIXQhp2S0CSIc2Rp8AwJpyOLLfg8pVGL9wCd2yJnISwhdZRaTmbLTsz8Dyl4Ycqo2EgsDMlnYUlfLRy0kw4bVH5nMOWAisugUIfHSLCimOh76tYdXP1CBiW7Pr7nsLP/u4XM34rot2ZWXVy/h5Gu2tehteuBdBT1Pt1MDy04A1qWLBJc62QhO5qqApbkVDtNgBgdO0yRuGg/sgjV/GRR0ze1x/8y6/IHdRd2xp7qbsNxwvRcHw23G4cnwWNqrBBgwYNGiwVlnrHBRhVRBR2xpGJt6Mjku8Dnagg0uqKKc7rIJgI9MxcSfVgCYpl2jXSBqCCHElJ2cZRNLsE7PsEX5trjjyBYTB5QA60LB07hbas6iqVNBwpEmdCQZmU40ozfvtTz2AUxmSL8y9ZFqztC+a+AmdIy9Vw2uUPZjji2H8mrULpdo3KxybC3S2zShxoxuWRqaDvM0ajcp2Lu6JhucXXEIBO1/SbCoCbV8y3rBl+uCznIFGviJVVOOeM/0xwuA89MNulP3z0Gp66cQQA+LavPI/tVRsEYD3Hp7PheIKG46eT43Wx9BOXIEI3ZS21FneKQLShHPgKvg4frOb48zQQUS3HR1uKmUyVq8iRImmnIJWbpC3QMk4u2POz5REYK7YPKZJBPQtcS8AN+zZPVa20xm988stjEQIAgCwb9h13l75InLZGd7P8+QxHAEbj3xEBKysmz1BHEu7fcEBE2PE1jvaNs+NgoDEalZfdWpkWigc4d44gBDAaADevhDp/RjyoASTqlZU12B1jozVQKh7U//2Ra/Gl9z9/C7Zj1C535bxPGo4naDh+OjleF42qsEGDBg0aLBWWfselmdELI2TLUI0xudqxJWGFsquzga/jVNRtS8TJ4waBKrXiKZKzaCjN6IcH4ZYgtGcIe5MGw4RhIZhQaI6cvirXzJlQpTf6wFEYsSXyaWFm/NrHnsRObwStzYE1AEBI2Hc+z/RVXkw2GNVJazVsp5PIarcJjlN9bRU9Dk8zPndkKtbzNfb2zGdV0NxpcpgZh4c6djo9ODAFaUVY2Q7bIAkrK6aM/hFh52pkfUVQoeWdPHMW1DUrU//6ZXB4oJ2SNGYUEKHheHU0HM+/73bneF0s/cTFjLgTLE505GkY8+DsvZ7S8YN2QhUIM6PvZ7O2jpVXIGfRYHDcNs3zD2qAYvWKJMbQ9adGx9aaMy+4Iy8xyU7jY49dx9M7E0QVAtaZc6WRrqXNaK9miew4Ap1O/X4OGHg2VJeMRhqDQbluwrapVA4z4+goMUlOymO0V5MytrZNGUJyPKhBBI7PAtYgQrNotb8DlRnUhsvBhBVgw/E6aDieh9ud43XRqAobNGjQoMFSYal3XDd6wJd2AV+bZrQtYN2trtro2BJR8OhhoGOVRd5KVGng5sACA3AEg2C25rYUM0WurgJBhDU3Oqyefv2aq9G1zapudyhj66s8KAau9ac/ftZcKcXFGIjgPO8+kJRGXxOuxmyHcee9Js257zMOD01/i1Q1XrBu4d5N4ytiWRSd/47h80cBBprBzNjbK1d5rTsCb35+u7S6lwKF3XAFeG/HwnroZPnxSyMcemYVmudHIwSwvm4quGYLvGLN1Pu6xZDS8GPvJmH3WnVO3uhbOEol5bveA/yG4zEajmex7ByfBUs9cY00cOgRIvvgus7Yaa//vq+mpnwYBEaWlhqeYpNwjzSi0P2LhiCKY6RVgSM5rsr+qPxagDAMppfN2sRUqBvhWa6uZQKICgmsbpgxPhoxRjnqglVH4M7VYloy89hz9jwudc4kC6XlAcD+EWM31KetWYRtx5ht6ymmxUSA65pIE53wPgAIWGF101wz6Ef9VwWEkSL4Orl+qBqOp9FwPItl5/gsWOqJqwhmdZIN8nk8csb9LG6vTBPpPjieik1PV7EA29eZ5M5a8Oxlp+9LPk++EBfzHBqOR2g4Xr/g5eB4GU7VxDXwgc/fMJ9bFmOzZVYYrjVd1bHqWLFl0cEoKFUd+Ipw+ch0HRHFq6PNNnDHSvF9i4JrCdih0IGfHzn7TDsAhwS61rPmTiNQhJ//vS/ii1cOAABX9rOn2SSA9Qu+SVTnAGa5PE7su1YlvvK8cU5qW9NJv7ersBseTEcrUSJga0uCiNAWhPtDlYZV00n245dH0H7Eg2y/2gJ48ws6sMjItML67g81fvMxcxAdaMYgJJAiwmaYv6+/LzHqzbdzaTieoOH46eR4FZyqiUsDGISmqwTEZsB5QUYnYcyEqyW8Y+RvdVfmi9RfGYKSPESC8nUI5h1mrKWovhakMq7sD/DUjV7pNZbNIAFYBWxzJWGrXZ3sgWIEOVkcLIsghFE9bbZmM+U+8vRU1cmmm7xUI/RJY3eY8yyIYYUOwznW6rXRcDxBw/HTyfEqOFUTVxqMcTVHtLokHJ+qg5F/6D2ZR3QWsk2+bOqUkc1jekwdENWJwrAF4ExfEwhg83McUqegLZqzZsplogWZUKlFx77MibdOWtPGNVUnmpGE+El9N7WOhpWV5UxDw/Fi+Q3HTwfHi3BqJ65BQHj2MIl0HRHojhXgbPd4ZO4MgP1h9vuttkI7TCmw5lqZlUwV+JpxGEbOblkiDo3TtmWu78vBKIgP4s93g5hOV47s+hZUFeG+6OUQrTZAwNY9DJKeGdTR4NUCGz2zNOu6wBtfYipSpOp4+MoIT+xll53ddYFza+PUtQXha7YcSCrO8nvN0/j8kQnR0+9rHB2ZZxIwx2qm9CH41paM1SQRmIH/+FgvMzbTOx7XpdgSKy2ns6nQXjdblhuXNNR+QUUrouF4w/FJnDaOF+HUTlwAjadAjx7aMS4GNBebGS9CLE/8FyheyUUgSizRjrPtgInTRrYDEENYHsTEu4YYIG1W0haAjl1ed18BgyBb6S4R5MSLURLgCoIs6Q/NgBcWN1ScW3YaQmTlaM0YBuWrZCLE96WrI5LQggvaETUcBxqOp3H6OJ6PpZ64CAxBaSsXxIe1RdCMOOoypbb5Rr0S6dSTc4DxAWS+qSJnTCaSKN2BnvZAOdefZdyaJ/9MY7wNibIkrw1590bWQelfWaT+4rQqYgZWEmBJc2dRnFaTqDA89E21Md1ntgAm3wd2iSomykfl6/HyolssSl6OfsqnR2seOxjPU13liSUy9wLj1njMADi6Ia++5tmny2w4Po6G41ksO8dnwVJPXOuuwp2rSXRmTxGu9iyUEe7yUZKIb7ut0A6dGddT6o310CGSAewOfDDMaieSVUVOGjsDid3Q+eTZw/KHJgm4uOoXXwATMmU0yB6SrjoSriXDz8mj3R2a8D4E4I6VbNmWoLjNvmYcjBLVhdaM68TQMMN9d+DDCyzM4tfTsoFX3W0+F/XB5294+LNrJkhcevW8sSHhOAQi4Ks2XKzmWGblvSeu9xU+9ISxBGu1BdZC9UanI9BumzteumLjrjBFxO8/NcCzR0bVsbOj4nqePWtlHEWlBLa3s0NoNDLJ/4DxQe0NXASjMBFfTmRzALjQ9XF9pHAQtbulcL7heIyG41ksK8ev5P5aDUs9cRHGve2rDLH0gbbOW5Wl2MYTq6GydcS0mo6t8Mq24OHvs6xI0sVG7WBO1s3pNkzKjFZBiiciGEzcMO/R67TVlgYKzZpFWDFJKFWXpGHyOpnPamw1SnE9JCVnEJPFTjvPzl+llqlZyus9+Ywajo+j4XgWy87xWbDUE9ckiBAGGq1Gu/TqRTPnRBXILyctx5Bm3sfAcYBUQQxfU46VVAJBiRpC6WTVpjTiNsgU2QQl6pH8M4Dw4Ja5kll1Yb0shrR0JVYGmtHzs7J8jdwQOG2L0A53C3mrTs2MI28yxjfQDzgub1KXH2EQMPZDfxY/u8g38gXBkgQmYM0xgWopVR4zx8Fs9YS6RqR0/lH3rq66AJswPVaNYLYNxxuOn3aOV8GpmrhswbiYoyaogkNPIZPmtIKcQUC43ren3FEOgtk+i3A1mLYUy8OKo3Gmbep64EkcjAwpRkphxTH1SquFolhwALATqoXSUAzsDWfrtzTWz/uw16pR6npf4b88kXXmXFkROHs2W8b96w627WLye4rxm4/14rOdCI5DueWl8WfXPPT7xTF1BIDXrzvJQfuW+U9fMf5wz6h8fJ9x82aWP52OwOqqqfcVX6BnEsLiL7/tPtx71nw+d4iq1Gs43nD81HO8Ck7VxHWcViwnIWfcibKOkOJri9RCx4WqfVNWk9wD4oLvx8osKHQhoZBy1CVE1foz/8Cb4udd60k3HM8ps+H4aeJ4FZyKiatOyBPNM0SCLoEgwJXlSdF8TdBzqloEMezQ+slOWUG5ElgJ43y2LCrUY0ewJdVqvy4pqy4CzbjaM8uu3dTBuxBJWJmuLbCZcyidZ1HFzLje18aqasIc23HM9W2H4vI8BnrhYYBSjCBIPkfYcAXcCfkC+cFtmQEvjKwdBOlnQthomRWoIsKgZ25WQeJ/s8LARtgFbanhwgxuW1Cc7DGNhuPmc8PxBKeN43Ww9BOXIKMyqLriOPICDCf32nOgZTEurOTEZknhel+iX2BhUxWuZJzrZuVsd4BOrHWRKLOEIiKsufVUPkqziQSwAC+dns/4nRzViesSNjYMFV/YlnhRt1odNQO//+VBxldFyiSm26ZFeP2GcQi9PFT4TOicORhoHB5mefCaCy7urqgKUgq5qpM7ViT+wj1Gt/+nTwMffSL5zQ6LfpEGXusDAKPtKsjwRb3RsrE/cR7QcLzh+GnneF00iSQbNGjQoMFSofaO6yMf+Qh+7Md+DA8//DAuX76M3/iN38C3fdu3xb8zM9773vfiX/yLf4G9vT187dd+LX7u534O9913X3zNzs4O3v3ud+M3f/M3IYTA29/+dvzUT/0UVlaOP+y0JQhukWdgCRiJk19duJJBU04m006iUaK8NGyZyBYE2KEZT1WT2ZNAq0VwWkavfc6REDBqmGvelER4LYkLrmnPSsFK7PJRkFl1agZsh8ATLHYtwh2u8QKVDDy+Z1age77GIAwS6qesvWybUlZZWdnMjC8fBBkT5kGOxRhggphGMm8OBJQ2hd63AtwZ5vu7sw3kLbr3hkB/ThuChuPHh4bjBrec43Vv6PV6ePWrX42//tf/Or7jO74j8/s//af/FD/90z+NX/zFX8QLXvAC/KN/9I/wlre8BZ/73OfQarUAAN/1Xd+Fy5cv40Mf+hB838f3fu/34p3vfCd+5Vd+Zb7WVEDLSjLC1oHSDE/N1ttrbnW1jRTAdqf8BeBIgRXn9tPyrq5KdDYtCAAPbrlwBWGkGf99Z4SyHrjoCrxi1Sm5Avjz6x4uHWX75dw5C90J5XxHEl65aoOIcOUowO88lVXdpNFuE7pdM/DsnBA9GsAfXxqhPyV8ToQbA43//rQJ6Dfs2/Gg/vozwLeE6R/OheovZpMQMdDm8zOHwEF5daei4fjxoeG4wa3meG1mvO1tb8Pb3va23N+YGT/5kz+Jf/gP/yH+yl/5KwCAX/qlX8L58+fxH/7Df8A73vEOfP7zn8cHP/hBfOITn8DrXvc6AMDP/MzP4Ju/+Zvx4z/+47h48eIczWnQoEGDBqcdC13SPPHEE7hy5Qre/OY3x9+tr6/jgQcewEc/+lG84x3vwEc/+lFsbGzEkxYAvPnNb4YQAh//+Mfx7d/+7bVkKg1czUmV41rAZmvmpmRABLSnHCgGmsdihc2KaXLS6dh9pWvJbFuz5fApgnv+PNq0BhAgHKMX0Ax84YYHAeM/czhQYJioAO02ZeTvDjU+MzB52B1HxNZSaZxflbn5jFa6wkRD0MAjOx40AyMi/LlvfE8OvaRvLAtwQ3XNhi2wGfrMtFyCHVlohdZOzIzHdo2RAwO1+lgrgcA3Q0sHMo7SEGiTCBIAbvSBMAs6BoEwDp1kDBF4CBymyms43nD8NHL8amVpWSx04rpyxUSfOn/+/Nj358+fj3+7cuUKzp07N14Jy8LW1lZ8zSRGoxFGo1H898HBQfw5YODSYfaejdZiB7UgitMsFGHgK/h6Pi87QUDHlpUHnqc0BjUsyFqWWKhPReuOO9BdAwAG2R7MSQnj09f9sfQJgBlU7Xa2D28ONB7fN/22siKwupodvK9Zs7HtFFuTDQONhw91rKPfOcg+B8sirK2ZMp7XlrhvimXXF2562MlLnDcFWgn4AzeRG1Y70IluP9HxE9JWcq84C3g94FKqvIbjDceB08fxebAUVoXvf//7sb6+Hv+7++67b3WVGjRo0KDBLcJCd1wXLlwAAFy9ehV33HFH/P3Vq1dx//33x9dcu3Zt7L4gCLCzsxPfP4n3vOc9eOihh+K/Dw4O4slLErDVyq48iAhP7ZtZfs0BNtuzt6sqbEnozhBROo26Gg5Hiqn5ivq+ij1Uep7KyBBEC1Gv7F63IELLMI0AIBPv7jXnXQgCfACXOLu6cxzC2ppZQ6UPju9wBTZClVI3x0pOM+NPr3rwFEPpfKfbVYfwFdvmUFxaBDdUl6wWqKq+PAhwFC5phzOqxNyWxloY/bzfE+gdlnPifNc42QJAXrUajjccP+0cr4uFTlwveMELcOHCBXz4wx+OJ6qDgwN8/OMfx9/+238bAPDggw9ib28PDz/8MF772tcCAH7v934PWms88MADueW6rgvXdXN/EwSs5lg09XzCjb7pKYGTGdSWEAt5KHVgS4Gc5LAxmBnDQMXqhZHK9pUMB/VcYMLRQVQRRmeDQMSwiHDflg1LEI4CjUth3LM0LItgWdlGbNoCd5WYxzEDX9r1S5PldWyBl5wpt+ZK44anccPXYGZ4Mw5q22Gsb5qJRmtMHdSbLaBbUsWG4w3HTzvH66L2xHV0dITHHnss/vuJJ57Apz71KWxtbeGee+7B3/27fxc/+qM/ivvuuy82h7948WLs6/Wyl70Mb33rW/F93/d9+Pmf/3n4vo93vetdeMc73tFYFDZo0KBBg6moPXF98pOfxDd+4zfGf0cqvO/+7u/GL/zCL+AHfuAH0Ov18M53vhN7e3t4wxvegA9+8IOxDxcA/PIv/zLe9a534U1velPsgPzTP/3TczfGV8Du0DQp7Ui3NwJGO+bzdsccalcBM+PQU2BmCCKsONUPlOtgUXIGvoKXs9qc5lOqeTyxXgRHCjg56os1V6MTOpD+ny9K7HgCDOBXLwE7HsYianqa8ftPDUy6A0FAG5V1RV8eKFwbZdtzcKCglEnvMAobRwSsr8tM0atuUv8dX+PJfnnoooMpRgBpOVoD+/vVDRU+sgN8Noyc/ZfOAS/sAACj5ysE2mQqnua71HC84fhp53gV1C7hG77hG0ojMBMRfviHfxg//MM/XHjN1tbWsTgbayYMAsJkLGJPmX8AMMUHcAwMY4rLQJg+fT7dfhkWIWdWU+UiU1hBjDwjJ0cm37+oCwxbRqXx7wUyYaA1I862alnAdtuqbPF1pDjWxcd1ZcaNowBBzth0XYoT8cV1TQUTHSnGjaJkRDUQyQkqOmtGuDQy/wDg67aS733F4XOfntSv4XjD8dPO8Sq4/VzTTwDpjruNosmUomq6hkWQ4nbsk3nSVdS593jSYqTLPJnObThejtuxTxqOV8dST1wtW2LNtXJVAEW42gNuhuFG7loD1vJtPgCY7l+fJXbODJgmJ9CMI698y74zEOj589XXtThO4OcpDU/pOGMsgbDesjFSqjymHZnI1dIymVJ3drL1XrMIL18xPibXPY0vTVNvHGh4nllJ5q1E02gJ4P41s+3oKcbHokR4FQZskZzNTRlnl41eel2b8MCLOgCAw0Djs0fB2O8AsLqh0Oma8nZvWqnD/QL5o2DMZ6ndcHwMDccNThPHZ8FST1yCqHYATl8nqatzVOVjICJYJ7A0qyKHAagppDRtm89ySqY8KhnjKbqN6S/BU+UpIAiAZRMsC3Gq70lYRFgLrbwOK6gjTG6hCg2A4cWqNNELAtY4rBE4tkiOCVI6/owEEbZaxsRa+AR7lOcQCliWkS9lhXYyj/V5w/FxNBw3OE0cnwVLPXEVI+qU6QMl6j9aQC6eeUFEmW18/QPsKu2Y0i8zdsVYqTybSqLonsLMr9E/Mmbj0d8cllVc3vS6xSdJqXLTmDMX3kR9at8R/rfheIGk8hIajo+VfXtyvBinauJyJOPO0CluEAjsDMqb9+V94JkwetS5bgCnwmrhOGDUNXZMxr2haYMkwlqYQNAWhM1WNnzLIFBx0sDNlsJGjqNqBGbgSs8uzQ47DAjPHho5XVtj3a2uonIsIBJ/82YAVEz7nUa/r3F0lN0mpEPrbG1JWJZJ/ffAhgMnHF1RVtWhZvzB7mjspT2LnDc9vx1nec3L2LqYjQrhWj85zL+wUh6dveF4w3HgdHO8Ck7VxEUERAY2ogKhFCdmtEoDc2ogFgLm9Ao5ARHlptZOfzUtBVO13TnFfVJ3Nx+tCgGjPqEZ+lNrZOK/TUIIo9IgGOdLd2LAKWZMC7/GPF1OyyJ07eMnRZLyvsIKueF4KRqOJ1hWjlfBqZi40o901m7RPP08YGo9KH9rrTnZJgsqX8UQJe2pstoholylSFE/mPoxmKNr6i+p0nVM396SQDscAyMArM3vUaBvoyYyKgylGaNQ7x+oROddqC4pqKavORNwM32cYFQp2fuKrO6s1PPJ7Vdm5B1XBDpfb080iyosi4bjWTQcR1je6eB4HSz9xCUI2Gqbbb+v850Mq+B6f/6uWHE0ttpZNcbOQKLvG+qd6wZoWeWHvlF7qqBtidxQNvujAMEEyQjAHeE2XWng0lF1OWl0bYluGIPn5gAYht//3+6O/GWA9z7axWEASItxz70eiBhKAdeumedzHcBjZCyhmBEn4Ssa1BsbMk4FEY0RBvDxnPA66SJ8P9/iKy1ndVWgHb6NXr3mYDtcgeZFCAoY+MPdUcbh1fMZ129mudft5kcCr4OG4w3HJ3HaOF4XSz9xpWf6eQ6f+Rh9D7hG+XVXLnWuT68i9RzNzZNJBEQphgQoGVkcrtIFmUgAHH9dW00z6XgJYEqyeISr3+plW8IEki2D4qxcVWHVOysajjccL8Np4HhdLP3ElUZajWERwRKmRzWndawLlgmO9e7pMwelE9IKIK6LFFlVS74iZD5IIuiSMxApCLYAeOJFyAyosK80AD89+tmkdzf9XF7n9AvE9wgkAFVzoyBESqVRo4uYOTZRTuv4KaXSscm0HwBWbIFW+FACDfRL9GkBA21JmdUoLcj8KtBUytWG4wkajpvPp43jVXCqJi5JiVVS12Y40qgMDj0Rx3dbNFoW42wny9aDkcSBZ1iz3VbYapu6bLQsWIu0MS3AiiNRFlbHJCP1M6ulYUC41jd92PcFep4ISWaS5+0Nfay1LLRyIl2n4VpApOB4+nHjKCmkRmvVrzxAV1cl2u36faUUcP169pm4LmFjw9T7BR0L93WynPjUoY/P5FhiRbAJ+LpNN2NEcK2vcClHZl1c7VnojYoPyxuOJ2g4fjo5XgWnauIa397ziYV1KZZDudecxEHmNBmRyqn8MppLD0AEcGplNUtRi+6rqDwqKHtaHTm8+Xie4fQyG45Xl9FwfDk5XgWnauJKgwjxqs+RgCuzKwx/AVtWzWYFBwCSOM4bZAmOZQqRZQozF+qM60AQ5fpfVIEtKHRiTCIWCBrvKxbm9CKCJWhMhdK2EnXRwDeqFyLgeS2gr42u/LEjUwIRQQcyHBQMkfNM0ggCjkPTFGG7LSHI1OHmwCQTTEcyIDK5kACg4whshJ9bqT7rK40oQHdRyJw1iyBgdjzRnYFm7Aw0GIz9kR5LEBiBmTDom+83BWHbRM9BN7WYt4UeU61VXYw2HJ+OhuMGy8rxIpzaiSudrsCVCi0ru8W93rPQD+Yb1CMlcLVn5Kw6Kra4WnV1bvK/NA5GQe3D20l0bImOqG/RI8jEZAOAQGvsDU3/uBbjwkrSV1ozLlE0WM096ZfI3evmv8zA524Aw8CY277TJKjGYQD80JeM3lyzwPDIsFpYAdqrg9I69noavV5ZG4A3vqSFji0wDDT+j2e9jF7etglnzhiaX3AFXpUTOv3JgcIzw+IjcAJw/6qD1oTupO8z/vPj/VjO9nZ2OO3ekHj2WdPP77gDeGMm5ZyJm+emrPB4oLBbWJsEDcfL0XA8we3I8XlwG7gjNmjQoEGDBtVxandcaUhB8cpUaR2vWFxLIwrZYgsRqgcYnuKMJVIVpMPpyAL1RpHOeRDQmK68KgJNCBeScKWOLbvSsKUotZAiUG4yPT2Z2ptNNG2RWmSny15zjVqFAewPzX8tAu5fNavRgQI+c5DIDDxDPxIa0squ3M+0BVYdU68rPYXhhFckM/D0QQBHEgKdPDEic0gNGNXJ+bCMjZTTylGg4zxI/ZwApcwMz2NobVajX5YBJrUkI8VotcyXVionku8Dw76RNegL+OEJftr6q2sjzvfUtilU+XF5RPISNBxvOA6cbo6n8ZyYuNIqlSMvgArjnq2l1BzrLsGWxut9dzi/esO1BDp2NfUGA7g5sOY+izjT1lhxsoNjoyVKg2VKYeLFTWIYKHip9AMMxuEoGPM1WXMJjiQQAXevme80A5+5Zsxu2xL4a6Hq4MoIeMRoHaC0xKjXBgBYjg9pDTGJF2/ZePGWUXt86Ik+Lh2NqxcYwMeirHUpCGGcOYkImxbh1WtZ1ck1T+OxqWkmVBxF+6N7WdWGlMDZs1bmRT3sC1x91sj0hsAo1BYFqRBtF1aiLMWEaBgyM3aHs8VxazjecHwSp43jaTSqwgYNGjRosFRY6h1XoDSGgYIrRWXTTVsIUNjqkdLxqnOkdJzaO8/ohmBWmASz2hrlOPBJMiszI2d6fVqWBDNDM7Dm6MwKWDHQ83NyhRdgEBAUZ9cijtSwhCmlZVXvK0kUXl98zSjQcdidlmVUKgTgbCcbNcCxgTeG6bxvesAn981nwQLsmcNdzUlQ2Gf2GJ4yOoijAsurTidbP5HqgqEGngxXnV2LcDYnT/topOH72YdeFKA0kpmWE/jA4b4p2x+JOJTO89aA558xn191BjjfNZ/dsBrMPMZDVwrYKZWW33B8DA3Hw/JOEcdnwVJPXJ7S6PtmUFeFawm44UZTjfxY3zqckpGTyMQvIyL4SucOalsSuk61LiWiWM3CzPCUnzlx8BSFg7oa+r5EP7MLZ3QsPzZhjl5MVRDp9ilORpHFSOk4NowjjRkxEXBxNXvtGR/4K2H9HukBf3YU/SLBnqlgoIz6BQAe3xni6V42TlsaKysik/wujYFmfDEc1BdckTuoh0NGv18t+iyRifs2GZrH9wk3r5kXkyST/gIAXrEJ/JVz5vPda8C5brbMYerFuNmy44kBaDg+iYbjWSw7x2dBoyps0KBBgwZLhaXecU1CacYgMEsjS9DUkC0tS8LOsVDKQ3qrLgXFkaPTKApzMwxUvNpoWzLXEqvryDgNQ983bZDE2MxJmjdShL5f3a9lfyTjzKmOVJlDbEGEdg31ShEGvgaRBrORqZkgiLHuKhCZWHp3rxm1kO0Ab8+eOeOzR8CfH5rP2rcwUtm1ld3yYofXw0NVud6B1BgcmlXnQaCxH6pOpjmApsEM3LyajTgepHyl7moBXxeqi160orHZMuUTgKOcxbWqYSXRcDwfDccNTgPHq+BUTVyaOVaHOHL6oHakKAt1VghBhHZFayoA8BTDC9UurhSQE4oMoqSuSnMyqMW4VViEw5HIUZcUgdALXwAExprvZ5LxyXBQz4tItcQMXO9LaDYOlI40XvOSgLOh7rxlAV+/lS1joIEvhFZZXmBlnC0BhuX4QDioB4PxqAdl6AO4PjXWdjlYA/v7EmVnMttO0rY1h8ee4XDOUG8Nx/PQcDzCaeB4FTSqwgYNGjRosFRY+h2XZuDQM1M8gbAanhiOxcUKNEZhcC9XSrglKy9mRs9X0MwgEFYcObd6oW2J+HB9Wsw1QYjbkEagNQbhSrtta5wVZjna8yT6QbX1BwO4MbAy6yhbMqTIXyZpnU0LkcbeUMJTpsStdhBbGm23AxO7Dan8SKlnxUx44aZpZ2Q5BwB/+U6BN1wwq+erPePkCQC/dQ14OvzsDVxQXjqLKovS+R4lXEH4H+9GxlEzjbu6wAs3zeeeJ3CtVy50sxXEhgVHXoDRhBFFw/GG42MNnIYl5HhdLP3EBSC2mrIEQkfB8U7UnHhrV9H3e6HpJoHBGaVHfdQx/SQiuFZWIinEg9oSSe6jkWKg8tac4mCpaQRaYxTkRxrXujxI6iggDJUAwHFqCCKgbed46gOp5wBstsz3w4DjPE9n2oxu6Ev5pG2yzwLAR3aSNuggh7ZVVejzPkwJvGrVpHAvwmYradsoIAxKYwUy1jmxaPM1Z7L6Ag3HG47j1HO8Dk7FxDUJPoGUnEUyTiKdw3Fh9m6b7cZ6z6no2uPs79ll1u3L6Pqq9Gk4Phsaji9O5nFzvAynauIKNGN/lF2a2YKwHoZ7qZIeYc21wIyxDKeTco68rBxHVg+Bczsh0IQrvSwVOpbGqlN+0LvVDuIwPnkx5IplJs/KSj2fdLy5O1aMkycA/ANX4WDCgZIArLrW1Ey1s4Fx6KmMNRQBcLSFsoF9MAK+cNN89qeekxNuDqxYLXQuJ2FjGg3HZ0PD8TzcnhyvglM1cQHI3YJagiqrMogI1hSSMOdvdeuQ+nYCg2IdfhpOhfaYd1j9djOSZyUp//m4FuCGn+/pcBz1IY2tdjZN/CLADOwNOc7hFEFp4NnD8hYrRg2LuCh1vFGlVOnJhuP10XA8p363Mcen4dRNXEU4CdVKVdmLVLWYkjjzzSJQ3Qh3AbIW+HwW1d9UHEwBx9bnc3RDw/H6aDiOpeJ4hOfExDUKdOxjsgjU7fier2L5q44Fe4ZwJ7YgbIZJ8UZhGCAAWHMVVkJVx87AmnJIWg99X2Dg2/GB9HFhpDT8YfnzqXOWqxnYH5nloCVEblTwKojUaQCwNzThigQBF1dN2YEmXM1RP82Dqz0bR6PyEEB5aDg+GxqOLw/H01jqieuwH+Dy9fIMoycJW+bn/BkL0mlLzBmmC4HmXHPSni9y1SHzgJGs7pRmXLk5PNaj4logYN/yMoe9mmGiS7A572ktwPG076vMy1wzoTdvDvIceKlBfdDzF7JCXRQajp8wngMcnwVLPXHtHfrwdP9WV+M5A62Bp680/X2S2D3wsXswf/6iBtXQcHw50ETOaNCgQYMGS4Wl3nE5LRerm2sz379iq7FU5BE69vyRBI4Tvpp+nnHgSQR6AW1gxtH+EZgZRISV9ZWZHTFkGIx0EpYQpZEebgcYNUqxzi7QhANvMWbi3nCE0cBEZ3XbLTitbHbbqmg4XgENxwGcPMf7vcOZ71/qiavVtrG21gmDVNYn2mYrQMseHxwEYKttQYbEFTS/hZTmfM/8WcseBio+uB6Xk3yWfQlvIkwOA7X7ijWjd9CLB/XaWgcaBJ6hvy3BONPxM+JblsBamEOICHP7rESJCycxa9nMxh9H5zzEOEljQKB+djhpoH66euZ44mq1baysNhxP5CSfTwPH0/2W11eTXI6uOQ0cnwdLPXGtOQrnuz4uHWVD8FfBzlACw7wVhB9nU91s23Mf1PY8lZuUb7Nlz3SI7UqRm1jwKCVnu62AiSjRDODZQ7uW9VIaQjDuWPWxO7TiaNx1EGjkPquuraE65hynbYnKiQqLoBnYHWbPhVwpsDqj9dV6zn2KjSUWADiSY0usNA48gb3h7O1ZdxW2G47HOG0cT/fbmmvlJljcH/lheC5gq51kUT4tHJ8FSz1xUfx/c5UwgfK4ZbNg0UZhVVaweZfM267IhXC+Eo4fJ9XflOrQ49K6NRwvuyanHqeQ43ltOk0cnwVLPXEdF3xNZoVDxiyXJmhCZHTWk9DMcfgUKSjesksqTsAHFEcpqIv0Nl/mqh0AV2Y95cvA4vhdNDUTRkEUUsecb0wi3Z9x3di0JWqOJUzwWUJ+f6e/Uppz1SL16p18JuSHWnKEydVUB8dgfZxBw/HUPTNxnE3UjdD9y9PVpzxfJ8kWh0ES0aMvAC8nfNJIURyeqxdueDQjHjNpRM+0chtkuUL0duV4M3FlQLge6nHNC9DPhFsxcceyEbpHgUYv1Mt3bBnHdCuK7Za+/2AULHR6WHGs3BeJZr+WGoWZcZmMHvu4MAhE7FS6HmgErWwss0I1yjBJLRGpUWQqLlwR0g6ui0CRTEdqtKyasdkGCrsLqlc+Go6nMSvHz3UDCDIv92cP7cptu9EH9uPMyAKxcXduahBC+jV9+Yhyv58Vd6zkG+/kYdEcnwfNxJWLiByLGWa3ynor76DXfF+9jJNzfl1sH92KPi9Ut9xGKpYEDceTe+aRjzm7sEpFi6557nJ86ScuAaBja2DCqiVgwFOL8CYXIGIIAC3L5OFJch+NMzatChkEQBTE27X0lANqhiMFODx7iAJtEpAbOkdpTFWF+JqheXINmcgZ+zYlswpcyeBM2cAgmM0SKw1fEXo+xXIiC2Jf6ZwXDI992guT8BEx2tb09kQ7uEAnFlqWoNyApl42v/p4TTg/0kNaThppmdPQcDwft5rjg8D0G/OkojWLQCe89hdhwr8gDAKCX7LVTHPiODleF0s/cUkBnO1kt509n3CjP9+g5jAcP2D8M+4MLWrSWU6LsNMHDkI99nZbo+uUKyK22jYEEZTm2FpIkMl2O7nKGfgqVtcUoUgNFslJI9Aae8PqW/1VV2PVHW8PM3D5yJp7UPYDEWe73WoHWA37zSQYLO5DpYEvhxGtLQFcXAlKV4FtS2DNNarFIy/AMByQHVtmQhoxm2dSNghVASfSctI4GPlTJ8MIDcfzcWs5nvRbFYyUwGjOZ7V40FRrQEswLq6YZ3WcHK+L260nGzRo0KBBg1Is9Y7LlmIsuKRmxN72tsDUBHGDQFT2vGcGDr3sPG8JRicnhbdraazG9Ux+d6SIVVGjQGdUDERmBcModiS0KgTVjFKzT2IYaGPyS8bng8hYhkXlpVPA56FlCYiULi0tp+voTFK6NDSbIKlVdfPDgMCcbWfX1ph08SECVh3Tn5KSOghCvINSOj/fkS1EPBKKDONaliy1QkxzL41AMwbhzsCWFFvqGR6Y8vJ4ENet4XghbheOM4Ajrzqvi/Dxx67j2uFwrjKq4KteuI3z6+1K1+oCTqShNCAoy8MFJivIYOknrpWUs2o6TIwjGVvt8kF9vUeVB7UGYTdnW922NDp2dvvcsRkdOyu/bQnYUoCZ4eWc2wiiqQ64thRTkwbuDf3cF22kXiEAblvEMqN+9JSGp/JVKhTWLW0Sux/KIQLW3XKmeiqauKqh78ucZHWMlsWQE69DQcBmzvNOt20YKPg59sauJeCWKB+IaGrW36IQRb5m+NrI7ELGZ3Yty5QXmYkXmYo7DccLcbtwXGmg54m5zVx+588v4VNPHa89KQDcsd6uMXHlcyINpRWkWJyFbhU0qsIGDRo0aLBUWOod1ySkIHTDlXGgOTcETRpdR8G1qu1nCYSObdQBmtnkwkFxKnNHklFBwaguIgupQaDjeqUXi3kHzTJUb8xiaty2RKkxAQO5h98ExH3oa8ZQl6+kWpaEEzak76vSVackYLNVfWXmSJHqQxX3YVoVmEbHliAgfD6mjzUzjsID5bSKx9fJ95PtKXOkLYKghHtFSNclDUcKuNI8k8EUg4SG4wluBcfTZc/qB/ipp3bwp0/tZL5/didJp2Kduwiy6wdXJmZQaA2pFaDCKureLjA8AgD87mev4DPP7FUqb61t4zted0/p8xkqws4gy/0VR1f2EauLUzVxCSK0Q0KOUoOnCEZvX61jBQGbLQEiwFeM/VF52ZYQcV18zVChTr0o4vUwx8xU0uwJ4lxr+kt0Z5CNO2YLwnordE4NFHLCoU3IMfVj5rGXVx6kANamqBPTWHEIYVWwP9Sl5swE8yIjInOuFE9c+X1bpJ4z52H1X6JSENqivM/T1otprLtWrFobBuUvw4bjCU6a42kwm7PLWV7Lj145wG/+6TOl11hbZyE63dplk9aQ2vSz7wMcto39ETicuP748RuVy7uw3sa3v/aeUgtdT4nciB8tS8NZTDD5DBpVYYMGDRo0WCqcqh1XGpYw/iFV0fODUrVD2q+liqe9SWVuVj5FB+9dW0IQgcE4ylmyaOapvjSulDPl+SEgt3/SWjJbiNDHplqZXUdWjkIQaB3vioow8BNDhGlx7hjTn48lCO1wle6p/N1K31elux4CYcWZLZdVy0pUn2mkDQFWHKvyDqTheDmOg+MRGMDNgQWlzee81j954wi//vEnM9+PqQS3z0OsbmTr7rqV60KCYLfClCngODCu0EBkDKrsC9BluQsZkDp5DoPAAgM4II0f/8+fw2QLX3rHOr71NXeX1mt/JNEL/fw2WgEiTXrXljPvsiOc2olLCsoNCFmEQUBTg67WcaZTzLF+uQiOFJDC5NbpIat24Aoyi84fpoGI4Frl/SMFwbEEKIokOqW8PO/5wusVpk5cVfowjWl9JYniF6BinsyIAWD6BGliJMym/7CEQNl4jfqwKm8bjpdj0RyfKB2DgFBG4YO+j489Vq6WE50VWJtnasjNqQkBlpMlVvqbQK9A263iQphhKT88IwaGvg0CYeQN8bHHHs+WXWGm95SAmbcYa5z0b8SJedCoChs0aNCgwVJhqXdcQ1/FSc4As6KeVY2zUkPNNQ+OPJVrwLAWRl2uEmonjWGgcg/DVxyZm5aiDkaBRs8L4sClzIz9oT/mnNm15VR/mzxYFSK4LwKB5lzLMlcK2CKKOacq7zQYwP4o+3wsQWP+VrOAQ7VZeic6aDh+23CcGXhyDxgpw4O83dbV/QF+8nc+D2agn2qjWNuAfSGrWhM1VIJF0IoxPCy3MLFsAWu9XBax+V0AWAnjYnJgwXdeDjCDNeCFqr/P9gb4wX/zJ5kyvvU1d+Fr7jtXKudgFEzVtkzDUk9cejLHzxwcnncAVAWRymgkiCgONFoWeSIPmpGr/lnEC4ph+jddVKB5rJtnFSOIIGZJjVsb+QNECoIMrQfNWVf1liwir1RZ2TpVfsPx24vj/QAoC3noBRqPXD7IfE+WDbmymnPHYqCnBYF2BMQUd400oomBLQJ3VgAY03oKizg89LB/NdvO3ehQqwSKxzk+C5Z64iqCIXSJ6XS4WuUS5hNR7u9lK93J66uuisvqkX99hWtKyl10Copp/R3JnNbfpqzqfVj3+Uy7t/jaqtctqL8rXN5w/FZwvOi3JAp64Rli7fosdnFEqTJrPQouqE1Be5iThYmgkucw5+NZ6omrbUusu1asuun7wNPhIqBtMbbaeWFqZBxup+fnqyDWXTtO0bA3DMBgCKqXnDAtJ420lVP6fDItZ6NlZ56rr3VsldX3BfZG5aunQAdwJ1J7EIz/SlXOOFJgoxVF2mYQCJstGwOVqNYi1QkAnO8GmYP0dL9pBvZHWZWGK0UcAmgY6NjxtWvLqb46+6MAmoufjyVMnYHxsZaW07ZkfE0aB6MgVnld7VlQXNxzrmRozratZcmp4aLSWHdt7FrJqrXTcLwQt4LjeT7Hh8MA//D/+FP4gR7bHYvuKpzn32fqNcXHbxyM57+M4bYBMHDzZjD3DgXkx/w/vGHBH1bcfTPA0tRdOITVrXAsj85CrXcAAN7OdXjXLwMA/u0fP4nf/pTxUftbb3oxXn3PVqbIddfC/pRxPQ1LPXFFwTMjMCepry1CrulverWhuTxfDIfXcPxXOZiRWnnlX1NkTZOWk5eSPP3S1Iyp8ecU59WhHvkF0XgwWwKEIJBKLIR8lfR5oLNBatMrYkZ+v6S/S19TpbYqVPOUrbzzNJKTdZl8LpPlBZpKJy5J+Vyqs9OI6pp+9A3Hi3ErOJ4HzYyr+0P4kwsEISDcEku+EtgO4LYMf6TNoLkD1qYHmTmvqozoORFB2mFnaQkO20YymUYOhwEOQ13qqCDRlyCaO/HkUk9cEaI+IEpenILyc7xGA7Vy2SF/q/QzpWTWfS5V5KTbmZe6jlNXcc6gpuj7VLrHWVQq6XYKYfxFgPI+Z+bKL/E6NYr7bYaBkO7PorKJzYXmc3Gfl03KEd8Is6uwGo4b3AqO51WYmTEMI2cMJ2e1cHdFhWeKPJWvzMjdZaXviyz9GWYSj+6bBhIAFYRNS18TFW5c9SZUz5zinCDzIogqEF4zClRsoKI6i00qufQTl4kKnqh5znVNBxMIROb7YZCoN/q+iuPBTetHArCRo0IqgisFnDAadd2hMk2OLShuZ9vW6NhZtdTNgUTfN4Pmej/f8ZLhQ4YvvzptS6NrJ+qvjVbSj4ISmbsDP3bM3M0Ju1OEtpWk8ajSh7O2oYqctTG1WTaRpK8JV46iiOOEZw6ydVlzNTaVaX/HlnGIpDpoOJ7gVnD85gCYTDbiK8b3/+rD2O97YCDebZHbQuslrzIXFcxO7XWFznq5A9zBIXBgIjTFkxERcPascZbuSMIDGy4IwK6v8adhRs/hkLG/X1726nZQOsEJEckheEPg0U+bdmjFGOynnkmoQrS2L8DZ3DbyLz8Nf+c6AOBnf/eRePf9I//D/Xjh2RUQmXfGvFj6iYuIxgaQyFkmTdKn6sQ/Wfair69zX/oaQn7eqPRXnFOiZpM2nQtUTFUxVt+c/p7cWdURVacPZ+3vKvemf2fm3P4eT2SRn76duV77p9UFaDge4cQ4ngtGfxSgP7nbIgJkubsCUbwpKykduQ+RQlWmEARHmHrammLz/Wk7KSDccZX9HtZPiCn1jNooBCiaSijZZY5SJu/GcpPSG7K5sPQTV9qix6hRso8krV5JI01sQvIctNE1FKJIzqJRpW11oZjA2hyQa6PDK70+T10RvRyAYsuhKMxPGpMv8eiZEE03kS61UFogqrStVnlI/H0UVzMFn3wkDcfrYREcD3T2LI+Zsdf3ECiGr/SYOjaK5F4Y0Z041qal3u1GJVmxyUIk1zIDQw0AjJFKAhwXGXFUkaP1+GdmhtaAsAhgDifN5HcVpBarkRzLivuAgwDZwzTzvOedvGpPXB/5yEfwYz/2Y3j44Ydx+fJl/MZv/Aa+7du+Lf79e77ne/CLv/iLY/e85S1vwQc/+MH4752dHbz73e/Gb/7mb0IIgbe//e34qZ/6KaysrNRugGZgN3TQjJxaJ182rhRwc5xkD70gthxac604ncXuMKsWSqNIzqKhmLEXHnQ6krDmzqb2iMAALoeqLUkAwa9AZs6cl/Q8Fcf5W3Ot3FBPG60stdLtsQXFqriR0vEzLEKRnEXjKMWJjZYFa85nfOSJMDsusNVSWHWnq037E7ESG45Xx6I4/syBURFO4of/w6fxzE4fYCRO1paF1su/Mlw55Atzuxqr21kL0NVVgU6nvn/dQDP+YHcEABh5GjdvTlEPVpBz7VoQTljA9etJXbfuMv91HMKZ0Kpw7ybw9KPhuRcRVKg2lBfvgbxoHK29xx+B2t/NyNkf+RmO10XtHuv1enj1q1+ND3zgA4XXvPWtb8Xly5fjf7/6q7869vt3fdd34bOf/Sw+9KEP4bd+67fwkY98BO985zvr174iiCj3X951tysWE/GAUv+OD8X9TbnX3U5YfGSJpM8rFz1DHRqORzhejmvNUJonIoOY7QyRKOV0tOuZtBqt+y9CdIZctd+qlFlWbyBUM4qJ3VuqYRT3w5Sp5aR3XG9729vwtre9rfQa13Vx4cKF3N8+//nP44Mf/CA+8YlP4HWvex0A4Gd+5mfwzd/8zfjxH/9xXLx4sXJdfGX8WkaKQn+DemSVRHHQU82cNWdNgRnwtJGjZRQ9gUNz2uN6GVC8Qq4iwxKAI8NcPIpyzwAiMIxBwTSMmaqz6e9hQPG9XCPXHQFxewRR3N9FK/9AAyo0ifYtBoW6LUsc34QnRXqCmT6gTaI8hmaabr6tCaNger3TZuFew/ExHDfH64DaHcPDlDk4EaPVScpVgSlcpvzN2hahY5t6rTgCrZxAwEcBQ8Oo64Kg3K2CGDjTLp8oZCq6c1sQIqv2vaGeGgnGDuvq2oS1sK6BC7S75r7AB3yvOj88Ve5aUgXHcsb1+7//+zh37hw2Nzfxxje+ET/6oz+KM2dMBOSPfvSj2NjYiCctAHjzm98MIQQ+/vGP49u//dsz5Y1GI4xGo/jvgwPjgXmtD2CH4mZ0bWC7U71D0o6h+6Og9AEqRmxB5kgGIQhfXPOrN4pQN57fuqsQhSO72rPMy64AmglXetPLZm2uBRhKA4/cRLiaMsxfdYGqVZSp9viac2P+pbE/lDjyRVjfAG17cSq8IqSzGE+bHC0BXOiGPiuKcLVXzoMDT+DAm67k6I2Sa672gCPRcDzCcXO8MojQetHLAStybjf1cFrAi15lyvEKVHj3bdq4/3z5iu+jex6OQhXvzk4wdv40ifNdibfc2ykt7/O9AM8MTV1eumJhO5y5fut6HzvD4sKlBM6cMRzatAVev27qfbWtIVeM+vr6JeDKU1U5Sbjet8Y4PgsWHrzsrW99K37pl34JH/7wh/FP/sk/wX/7b/8Nb3vb26DC/AdXrlzBuXPjQRgty8LW1hauXLmSW+b73/9+rK+vx//uvnsyWOVs6oGqW+WsrJNDnTrmqSKm3FHhX5X7qmO2/j65Pq/LicX3d5U+r46G47P2d8XSC1Wy5fWdVW03rR7HpTKOrk+XV+85jJU2y01jWPiO6x3veEf8+ZWvfCVe9apX4d5778Xv//7v401vetNMZb7nPe/BQw89FP99cHCQM3kZ1dJ+uDGTxKEax6SMnxZg1BYUW2VNixSu2eTiMdeKWNXgSKBzPAvTMUiR5L4KdL5jn2sxROhuPwzKVSrz4MiLVC2MlsWx5Zo9RZ0nwpU8ACiN3GjiaYyUAIeqwnQ0jDU335pu0bCFAMNYHPo5HS6J0bZM/RQbXhwHGo4nOCmOf+HSPg5Cv8RBbggNhtPWAAFJyqvxunRswpkw2eN6azo3PI8x9HXGfNx1Q7UqAWdsAYCwUaG8NK73FQbhLiuPBwTg4qqEAEFIYN019bYBfPnA7LL2fMYwLINBcDqmXwJPQFdQh8+LYzeHf+ELX4jt7W089thjeNOb3oQLFy7g2rVrY9cEQYCdnZ3CczHXdeFWCP8/UsCXQiOWjsU4G6pxWpbASk6itTSiWHnMPNXiKtCE6/3s6D3bAe5Zn1rNueFIASe0IOt5+SkCNluhAyoDl47s0oR38+DyUfL5jhUVv0i32tlYdGlYQmDNNW0YBio3O24a+yOJKIHj9TCBLAF4xTnzMj1OEBFW43iLjJ0ch2pbAudCvvU8gRuD45m4Go4nOCmO/+rHnsBnnt4r/J0EsHYuAAnAsoC81+odXYk33N2uLPPgQGF3QoVHBGxsSAhB6EjC1244M+3QPn3Nw2hU/PBtCfyFu9txNP8IV3sBPvh4jqmlANbPm4+HNy0MD455QOIEEkk+88wzuHnzJu644w4AwIMPPoi9vT08/PDD8TW/93u/B601HnjggeOuToMGDRo0WHLU3nEdHR3hsccei/9+4okn8KlPfQpbW1vY2trC+973Prz97W/HhQsX8KUvfQk/8AM/gBe96EV4y1veAgB42ctehre+9a34vu/7Pvz8z/88fN/Hu971LrzjHe+oZVE4Db4GDkbRil6gHy6SV5zjU3UMAuBaL/t929Jx1HTXEjM5WSrNcZRvo0YxbbOEQI7LFDyl4xV111bQbKzlep44NpVKzxcYBqZ0pc0KURBwpj2rLrwcDONrM2loJ4jRtXX4meBa9ddnzDzWhy0rNHUOP09CM8dqF0syVh2zG/AUYXRMasOG4yfPcbm5DbIsY5yxDpBUIMGxZpCY0PJNZdskcHbLtOFsJ38XcvkowP5ofGfFAMgmdCZUv1IAd7cto2Yv0I/3lMZNL7S69Bm+bzro2kihFwa9VSn1YKtFY0kzATOevrjrZ8bVoZfUU0rADTUmaTlOS8c2Lr7FmM9bqxi1J65PfvKT+MZv/Mb47+js6bu/+7vxcz/3c/j0pz+NX/zFX8Te3h4uXryIv/gX/yJ+5Ed+ZEzV98u//Mt417vehTe96U2xA/JP//RPL6A5CXwtsJsTuv+u1eMb1Eee+TeJ7bZG10lMuWdJoKg4yeTbshI1imsJuDkb572hDx16u2+0ErPzvi+OKQsu4SCVhmInDO5mCWCrfXzH/ZcOs99ZAri4okBk+nuWiQsA+r6Oz94cKeKI5nmZjn2l4SmjtnMlw22bZ7U/Esc4cTUcP1mOA/aFuyDaHYAYK9sehBy3jBWa0Bkax+3VFvCSKWvxJ/YCPLqbVT1vb1tYt8fbbJOxCJQli4IDn/GFnqnT0ZHC4WG5/nRlRcbm7hG0Zjx8ZVTah7ZNWF+XsZxo4nK7Gm7XyBw4jOpRSuuh9sT1Dd/wDaU+Bb/zO78ztYytrS38yq/8Sl3RDRo0aNCgwXLHKmxLjY1WsuJRmnDoGUubIuyNjIoFMOqFyJigZcnYAbJjy1gFEUXcFmQOg7minDR6vogdIfs+lVrBCWKsuSpTctrqzleMnpf1gXItEVuWtS0RpzoYhOkXCMYPZnLZIYjQtkx7FDOGqXAsrBk3wsCdRIwNV43FWjvyZKnjrWbg2UMjW4ZtA4wqKEpCaAmK/Y18pWOrvY6tYYXPZ5qcSZl7wzD5HeXvENLo2BquzK5M02GABkH2mVDYb1HOrKgNSnMcEqsleYyjEVpSQAoBgDEM9Njz1QNGtJHsWA3H0zhujjMz+r5CoBNfxTqwJHDXlqlHzsYcAHDpKMClQ9O264OkHu02wQodfF/QtdCZcExOp7FJ43Ck8ciOIflIILJhguMQVldNG847Mt7BPbbrx+rJXk/FqsKVFZFRGxKZ7yfBbAxIAMS7LQAIPAs6MBXQuZoGxpqroe35tsRLPXG1LI11N3nhjIJosBUjrerY7mh07ZQqCMaEO3qhauZ4QAgyKSqqykljEAjEtjhTXqKSAFvo0jMhxYxBkGOSLZIoCVHmYGbGKFBQbEi45mZf0JKMSS2RMY89SOnctU5OC0wfqDFyD32BoOTlpjk5E7EFYIVts8cmLhHXuw/A12FmYpvRDl9Bw0DUmLgIB17qTGFUfC0AbLU0VnP6JY1hjsmaIIQvQ9P3nTCU9ihQccZc1+JMll4AWHUEXMu8APZH49EL+qlJtOH4xL3HzHHATIJVuTYJSwIX1svPdK/3FT57IydbdkugFZq2392RWK2o4u75Oi6v1SJsboZO5I6AE/o539O1cHfbfH+1F8QuFYNBFDgK6Haz8oQw309aLw4GGnt72RMsFUgEozDIrs6v/6qjMMxZKNbBsVsVNmjQoEGDBovEUu+4JmEJxna7uh2LK1OrXF+BMqvqycQcWTmemljdzwTGVlvBKI6AG4Py8loWYzU8CO95Av0gcuRleLZRQXRsGceA6zqWyUAME9l9sk2aGYehH1WdzLkAsN5SWCmLR5OCSOUKCjTjICfkU1Haj3VXYSW0FNwdyrljnbUtHVseeppwvV/c5wSjQpPC7CB3BhIMYwRiiVD1SYmflCUEVsOVrqcStWEagyCJsF8l1UmEhuMnz/E0ulsBrBUTcX5aHNndocKnr2W3n0MwNjay7X/xuo11RxjL1Rxdq68YH780xKTPcADE5cmCbn1018fj182u7OYgy0cC8LKuDdcmgIFhyGWfgSfDqEdBwDg8NJ9VioKuS2iHsRJ3fMIw9LP86gfvxrZrHLy2Nqv7sFXBqZq4pEBs2VQXvk62zHXkkE/AnIOaYM4ypDCREW4ObJSdLQhKWONpijPCtq0AUujws9HnEyURCJgZvRwDVQZiM+S6aFnV+20emZEco1oTc09ctuT4GQ5TWXWLaruRcnbt+8bc2hKMUeDH1osRpCDIUG2oWCHPJjiowbc0Go6fPMfTcNoazkq1cgYB48n97OJsZUVgdTXbn9uuwHaJN71iU97kxOU4hDNnyl/lNwca/X5xvQnAWVegG1kyhlE++krjyd1wwteM4TDLH8tKJi4hKT47vfueDdx7FgAD7UPkjoNZ0agKGzRo0KDBUmHpd1wExAkJq2AQ6IWsvCK4knG+W+6tsD+UGM7py+NIHYe4kSmV29kOcCHMv2kLCUmJsUAe6vQVYFQzkwfN7ZSPzTRoNskMF4kzHQXm4uWb0hSqoubZlTHOtFXsVBu5JUkB3LdliiYAbpjSougw3pUCds1girv2+Kq74fjJc3xWHHk6DouUjgPouhRb593VsfC8braOKzm+b5oZv//lIUaBSXYZFSmEUQ8SAWu2wCvDqO03PYXHQ0vFfj/ZZaWdjl9zwcG5HIfoVo58pYCbN4MwZmJSxoWujCPcP36T8PknzL2eRwhtZnCfAr7SAyKNhRW6hW+0LBza8+3gl3/ionr5mfLOG+aBFIAU5eqXQ8Fzb5MlReqycYwHPSWUvayJKBN/bBoEMUy8iES2FAS78sS1WE9QovFzmzz4ihHbBM8BRyZBbGP5MJEpDN3K+xsI1YY1J9DJR9Rw/OQ5PisUA9f62Y4Qwlj5AcCKTdi0q40fZuBGX2UsLImMitC4ZCTlDRUjehBa85ipeoQNV+J8zsRZJN/zsmW0LIrLuLQDDFMhDKP1RBfARnhrW3LMIVtQhuN1sfQTV10cVwSH6ag+KMwQOibX/+cU6vTh6envhuPHiGPs3LzADkU9RJRKTUIU38upcuqsGYuCShQVkZZj7qekXlEdq4uvjefcxNW2ZezDUgeapyc+LMJWS2HDLV+ORqsUScAdK1m1THqx7UgRJzw8pnyKC4OxyJst/lDPVzOpvEzIp3LVVlrLtOEqrDnZ55N2o1l3rZni790KNBw/PmxtSrS3LAgAr99wYBPBZ8Yn9r3SaPsXWhIvXQ2tTgsa9EfPDnG1l+3D7rrEpA9wRxJev+mAAOwMNH7ji8ZZMmDACyuSNvZdWRGxAYXrZOUzgN95fJCJwl/UpmcPg1jmwZENX5mQfu+4G3hjmG7xRWvARmhde+SZ3Sgz8MWbwO5RXqnV8ZybuER6SVAHc2hfpKiuuCIyaQXKIKhamvPbAencWbXvnVnm9D5Mo8rzOd709YtFw/Hjg5QmuoUAsOIIuIIw0gxLUmn3ORLoTFGv933GYY5ari2N3DQsSeiE6uMD6Nz70hACcVSOPDN+hjmf6+c4fech0IhljlL3rFnAHWFOsq5lnjOHjuHR9m2kMHcKmlMxceVZ+UaRyReJxWjBF4uyuJFFmDXLagTNJvr7JAQtdnV8u6y009W4Ff0NNByvi0X0eVyWkIAIo0ekyg00QzAQsHHTiAL+5snXHJ29hkXkVE/Q+C4/giSzKGAYQxIGoCl6+XPGPD6uAxJ+WILihUUkmkNjD2azZqmlWB+7mHJ/S78nNCe7t0W8J5Z+4vIU8Jlr2e83WsDzNxYnRxCw2S5XeQ0DHcd9mxWE6XLSz3wQaAwqyjRRtMsTPFbBMweIU2ik8dJt5KafmBVdW8bx/4qwG2amnQcdW+amKkkjHuwwUcmrvktblogdk2dFw/GT53gaq1/xMmzdE1qXhpl+FTN+67E+dPg2jmJDWhbhzJksZ5/aD/DZZ0ycpZWuRDcn/t9XXHDxhhwekjD9MVKM/9+jPQQm2TKevWoGYZqLrVYStf15bQsvbCdWr9Fkkd68/e6TA+wOI0fzCp0RQvkWRv1wa8XJDnqogN3QUGNvGD1HAqemmpecAS4p4Ep1cRks/cTFQGbFgYLv5kGUi6n0moXICe2mKi5JomgB1a6duVpj0Jzfv4tOJTGtz2dZiefKAWqdX9UIdLEQNBw/eY6nQdKCsAHzJJK9iacYk0FjijipOJkYPM1wcy6zBOBaxX2itJHHHDlU518XxRKdVh5gdoF1JqwxpIIAUGplFzWtaFcmF6ApWPqJixCtILJmy1G06qIt+MLrsoAHUtcAwEQmL78m/aLVoVpjvIx6LxJB4342EQKd9LktTkbVJ4jmtk6rW89p/c3pwYv8kE6Cqvd3w/GT53gRlCIEPoOZxmS0LTPpi4I3KpE5Z4o+R7Ao2QHlxlIPgw2biSqf54ISHyzXIoT5HQuNQDzNcX/NetREBMjQdYE1oKcEJbZE1P+LeS8s/cQlCbhzNau36gcCf37NNO9cF7hr7fjr4koBt6J/06LQtkQcobwIe0M/Xp3vDbNWYyZydnUqbLZU7CiaxpN7Nvww8/ErztYzkJgVdeq9CBCmW0mOlMZRuIwdBjo3svyqY01dDUdoOH7yHM8FA5eeMs+eALTXA5BgtCTh217chSSgpxgf28/GJ2y1CK0c+fd1LNwZhlfKY4Nm4D99qW/8uDh/ojnblvimF4SxAFN2OUXs+uyRjxthNuPejFYS3VWFjbuN6nPvpsTNa+Vj4t7NxBdvEevZpZ+4imdvLtiyHmddTt6aYJrMRanTxmXmyTE9zqATO92/HfsbWLz/SsPxk+d4QU1ibk/u8iMrSFGgRy5qg9nBlrcvbdhQVK06FpiRRqCOCjZXbA0qpA1FFoGln7jS0Iw4j046n06gE2MCW1TfCRirm+TRSqoevaAOFiXHWBxVd2JMfg/lT7wApp94AL4CGGQGQur6QWAOrAnGYKNqczRzHG1DhAkajwOLkDP53CJUOXvSzAhyouqXpWU39zUcPwmOW8LkDAOACy1CL1TJXhmaiOlpaDB2hxqCgGHNA9ChYhzk7HqCILJUHJ+0rJw3dtplz9eMwZQ6BBUm+rScoIZr364PfDk0zth2gCiylGKOc85N43gVnKqJy1eEK71sk3aG5h8A3LUKnF+pVh4D2B8GcZK9WR1pq2ARcvq+mincj8kYnJNtVoqpVn07AwtDNU5EBvDojvlsCeCV56rvQtJWayvObI60deV0bYn2DHrNon6rgp6vgBzLzGnPvuH4yXB83VVYNz61+Ot3WxiEaVV+6FHg8hBjhPaUUecB5oW/vW1VnpQfHySxBWMw4/qNIDNhEAFnzliZReB6KnzUDU/jM0flzvfTkJajFHD9enWO/84N8w8A/s7zgPvXAIBxOAriM7pFcOxUTVwJTl6d8dxG098nj6bPjxOTkwNRarN2zF0/bT90UupaSnsN32ZY6olLCoItKMwzVA3DANgPV6Ydu1ylQgBsKcDMxx7uZ5ocndpqF2EYIF4ZzgpJiFPNa2Z4So+pZmwpwODKJuEmf5axmBKUDlBbLD+Kpn6cfZ6Wk3c+wGF/lzUz0IyBT3MP7ZbFsf7f13rMCrHh+DhuJ447LkGI4kC0NgHroVHJUDOOpuiQPU/H5vXTtHkSiAPruoJwPTQGOqiwGy2SY4L2jlv+WQK4M8wd5mtgP1Rr2nbyDGyH0VkJfcFGAoFf/nwmOT4Llnrici2JFcfC7rD61vjGwPwDgBduAGWJOYmodoqEWVBFTlHG4DRuDiR6/nz1bVka5y0jx9eMURDE5I7qWUddoxh4fDcqG3j5dvn1riXhHpN6sK6cI0/lnmFF8BVwrV+eEHE6GBe6QfwiPfLUmBViq+H4GG4XjhOZtCKWZVKGXLuWrfeqJfCaMN3Is0OFz05R4R0daYxG1V7oriR85ZoNIsKOr/HJHEvGunI2NmQmtFTbEnjD8xwQEXZ9jU/kyFlZ01hZM311/bKF/d3y5zPJ8VnQJJJs0KBBgwZLhaXecU1CCMZKmG480IRhUD4vH3hJsMeOrSFynGpPAkTmkJjC9AQjpcFsVGWOpNgBMy8ska8Sa62WxSAqcYNnoBemnS+C0oTDkZFjS4YzJQ9T29awwpxVfV9Ac1nZwA1zhg0pGG1rsXmj6sBK5RTzlY5VVI4UserQtUQmnxhzku/KqD7L2+ApgjclwWI/EObgmoCuXV5ew/GT53galjABhQnAYKAhRNbZeRp8n+F52eecTvbYalEcAeMOV4RWmMBq2ySPFACeCUM19QtUkFXk3LNmoRX6E662RSwzQk4g+ZlQh+NVcKomLlsAZ9rmYfb86YM6eokCwIWuitU1twJOW4BgrJ8i51VJBCfMsGsJgRUn256eF8RJ5lYcjTJjMs2GQGUDzdeEnaGRuWIrOK1ykq255ndmYBRQ6cTla+DLB+ZzSzLOr8yZeXAOtC0RT1wjlTgJR4kfiSjX2kwzYzSIrk34VoT9kZgycREORpGc6ZN5w/GT53galjCR3gHg8ECDakx6EYZDjaOjcpkrKxK2bYz1X7/lwp2YUHqBxh/ulasHRyONw8NyOa865+BM+7hV8/U4XgWNqrBBgwYNGiwVln7HRYR4ZayZZz70O/QE+rO55MRwpUbHzq7Aep6AFzqLrjqqNKZcuj1VPM2dUP0yiWGgMlZRBOOfYtIYEA5GArMYFrhWok7Lk1MFvibsDudb6RGANVdl+klp4MAzZVuCsTpFnedIEVu6TXOOJKBwJ5bHvZZkbLiGWEZdUn+t2HD81nJcCALCjfVf2AJ6gfnzv+w5GDGDBLB5JohN5qNdji81/mRgKngYaByEajt/0oM5RLtNcc6sKK4hA3iiH2Ry2qWLCAJGv5/lRFqO6xIcJ1I9SnTDAjs5YccUM54cqIyavOczDg6yGgbHIbRaJ7sHWvqJS6RUOr7KjwtXBT1//u3yqgN07OyD7QeEflh+29KwStQLokBFVQRbilxzZ0/pDPGIEtVeoBEO6vpwpIjVJX6OnCpQnFYfzArGipOduDQjfmG5UleauJyKVSlSIRZxz7U4Vs+pAc0Uibvh+K3leDRpEAFfs2E++5rwoWsOfAVIyeh2GUKYyeLGDbM66AG4geoPvNUSuRPAl4flZSjF6PXKOeG6hG7XNOhFazbOlhBeM/BkP8jU3PN0gRyBVqtU/MLRqAobNGjQoMFSYel3XGlIQVhxIhURwGxWPsOAFrLazIMjNFbDFZ6dWmX2PBE7SjqS0Q79Rtbd7OqRsHhn/I4tS3dCmoFRoDLOs76iWM02VAI3BlapKrBtS7ihnJHScfy9nYEsteyaHYwNV8WWXdFuS7ORCYQha0IDBkci5kQa1oJTB6e5V1hzZjgyq6vbH0oEoVHLzsBCzy9eTzYcT3AcHGdmHHkBOraMjXeKYEvAhuHbtUt22EANUFC5se02wQkNUtJOvdOgFOPwMExqmdoa2Tah0zHlnXMFzodccRyKfbR2PI0ro+JdHIHw0pVsMs69ocLNm/MbVE3jeBWcqolLEMWx7QgagWM6WbNAb77wXYWQIt8k2lPJi2TbCtANr2lZ1tQBsQg4U2RoZnSdbKcM/GRQB5rgB9EZQf4LIpLDzOjaAVSYC2l3KI8tYnnbZjgya6beD82gLcHYavkgMhNUKy8y6YKR5l4RAh2AaJwrzObsCcq82gcBxTm2pslpOH48HB8FGq4lMC2inhSAxQAz4eggfCYCaK9Vn6QdR8QTTR0wA4NBdoBJibi8s22Je7vZVvzJvsINv1i1aBHw0hUrk89LLiRz6XSOV0GjKmzQoEGDBkuFU7XjSsOWhPUwxIwtGI7Mrrz2hhKjGay80hgpwpUjI6dj6/hgeMVRaNtZ9UoEo5IoDylUBS1LzBRB3VhfmXqrsC6AUfmc7yZ9pTXjRkWn1UiFxQB8HWRUjIEm3BxIzKs0ujmQcTbV7XYAKYzK8FzXqKqKSvdUEhF+VhhLxurRv9NoWzJOwtjzFQLNIAK22grMSb2uDzUOK5TXcLwcs3CcYEI+pXdzd60ZS1UAeHLfRIOXBPyPdxrrwr4C/tUzgHE1ExgemRhbQmq4nVFpHY+OFAaD4t2PIODr727DtQi+Yvy3Lw+geDzGoGUBa2umf862JF68ZnZZaTuPJwcBrofqwbyYiQTglas2HGF8x6Le7vkaf/D00KQtImBrK/scBj0Lzzxp+vnrVwmvfIH5/s7YYIOx1VKwU1qS60ONq4Wtno5TO3EJIohQp+tohVaO4+Wkieks0EwYqUTPH8Hov8sHrK/1TKbk4/Jnu4+IYIcdQKm8UFKYqBZx+braSVW6PGaGK7MBak1ag3nPYdJqhkQGEXKfcRpVgrhOlz47IudmABBBMlG5E2rPMou8NBqOl2MmjpOxYkwHAo4y95pIH+azIOAFHfP5MDBlmvxZBB1Er9XpvgdKjUeymIQgYN0idGyBIWl4HmfyvQlBcF0zS3UcwpadXaj0FWM3KO/IDUugNUEYpYErPcNV2yZsr2anjP4hYdg3MrfWgZfkeIg7ksec36tyvAinduKqjnQHFp/lHGsN5hSZ3F9c0K3IXFuMeLqZ+LseKk2oRTWY+WUY1jb5v4Lrbsf+BhqOV5U3aWY/3b8v/d+knHQZi6vPtHpUvSe6hojirMh5ZU5+O3nZiSWhDvGcmLjcVHifQSrq82Y7wEbY4auuBUuYh3cwDGbyTUobqrUtkRt9PG8FzACu9qzScEllMqO7NlsKnZw4YGuuXbrylkTYyEnu5imNoynRuouw0bLjgbAflmEL4OKKUdFYQmA1VOOMAkbfn01O7GMDYL1lYfLVUdTsI0/EB/R1QAAuH5nPlgDOdf2MDFsKrDjlQ6trW/FKPsH06OhFaDi+GI4zM/aG/lg7Vxwr3rm9aNO0RTPwxZsm+0FXAj/wfPP9DR/4/z6NkPsSgwOzLbPsAE4nG6Lp/vMOnr9uuPJHz4xwrT+uytYMfPDxPggUZnEO2yOBzU2T7HHdFrh/3Y7bGeGZocJToQ+Yl7NtZWbs7ioEgdlx/uebQSYjNAmTGBM0PvH2ewI3roTjt08Y9Mz3fir7w91rwJoLAARJUQLM2TmexnNi4hJhAE9gvPPT3v2uNKoPZmAgq+ebKgIR1TK5DnR5nL8q8HV+2vhpTSEi5DjQY9bMA0QUv0T0xIozMpO2BaMVs49RYuRUGXXSwWs2fT4PFBtVyqRIWYE7+TnAZq9Lw/Fy1OG40gxOtSutjo4ys2gO+zlUH55zk2spnGgVE1iH576cT/C2RVh3E/eGPBx6jLwWWpZplyOBbl5wYi4OwBu3VXFsTn+osnKkBM5a2cglWgG+Z2QGAfLze0mkxjhlfp8Hyz1x8fhWtwoKVUeovh2fR874NYb5kY/LZEpCTl1ZFfkvI55QWcz48gj7m7leGUXKqYX2d4XqpC8xL5ecVWjmynIY3kx8x+Ntq9tX4/qehuOTOFaOFyDveaZ3ghEEmYjq0QSW3mPldb1ZQJkf6jyZdPMYiA1gCPkJWGd97mNyONWGVHGSgCg2siXyn2Qkn5HD8Rmw1BPXwFfYGwbYaFVvRseWaIfL/sNREGeWPUyrC3LuE4RY1VCU8M6VAl0n8rGZjvWw3swAw88Q29eEq73qbdsZyNz4f5oD2MJYsG20so6FRXClwGbbDgcCg8HYHfpYc63KyR4JwGbb9JvSidrQ14ydQbnjUdeWcMOVZPpZpbHRsipn7nWkwFbblNeyNLp2Vv6NgYVhxQy7gQaePcyqnzq2RqBN2W1b1gpvtN6ysWMnr7t+w/ExHDfH85Dut/WWDSucsF62nZ2MXhQAkVvWY33gXz0b/sAWuNcFYHgTaRg+rjx86qrRsxVlud7etuLYhRHSlN8PGB/ZMdaL512Jl69kOXl4qHOtF3XORpDIyExnQgaA0ZBw6csmMaZAspt6wzbw1jPm872bwB2mmYgMM5kNl6IJer1l1RoTeVjqiYvD/9UBUaLFnVy1TL03vj//aqLq6ebT9WAwJBlz0zTq+vsxKGdVx2aXhPrb9ChH0lhpXHdlmLRzUjM3Xb0zvT+Niqx+nwtKBtbYNZVKSq7Oe9foVB/VWelG9RvfcDUcT+MkOJ4tffIvkxurSPW4Er5V2zLd/8mT5ZRGzleY2glCIJOZeLJGUTzdoKADmPMnqTKZk7m5mAEdWpcSARS205XAmpnP0JLIjSuZVkJS5iStPpZ64ppERN4I0wYYoXiATiJdVqTDzpaXj/QhePRiyJafqEDSNTJ8Ha+jeTFWf/SKCRSa/BoZ2TYX1WsaqrQt/bug2fTcIudlWiSqCg/SE8T4tQxJ2VeV6bdq/cNM8dmJYuQaQcza3w3H83GcHJ+slw7V5VEdCWzUhqGK0FijE1oSWMt5ifcYiNNxMaBzzlqJOOa3mXCqPUM/APrhds4LODa1r6sqVAFlFpoqFe3CEknb2pLHDGPyFnOLtjo8VRNXkFJFOVJgzS1vnnGYrb9llUTYak8LCJOg5yVWXutuYqGURqSi0QzsDo2ayRaMO1ez6qwjT8SJ8KogUsUIAER+ZoVpif9/e28ebMdV3ov+1uppT2eWdI4kS0IekC08YBwPMsHhxb42hKJI4FVlIIlJ8SBxZOoyJOVnigAmdeOEvEpyk0egXr3ETiUYEl7Z+GLAwXgiBtkGY+MJC0uWLdvS0ZF05j12r/W9P1Z37967h917OOfoyP2rOqWtvbvX8PXXa/itb2g6snaLIBU16tIoceAMGIuw7EqDbp5VqwwZRiLqVE6tasu1HHg+KsZhq2UXATiyZKTeHVQd5lOIw5ZEQ4Sf4ZCpw0oSVgwyHY/GSup4EB59StTUCY0BW4ZUiDGNMZy/SdWzvQ5sNcNl3HcS+O4J9dmuW2hUrdA1+aEKmJtw0Ys2nwbH4eB5Vvfb2Mt8IQVw+KAVvjfwxVuKwP+xTX0eyUlM5JrvzGy1h0q7xGk1cbWi8yPrdfXV7X2dKbG2E1D/+66qiSs90A73ADuC/hsMIgoPtqSPDnVzL8V8jisvWHRkNV3Lp0t+rmdkOu6WEmjHSut4ExLubhxBzXcNUZhaqEVZCnq7M3V5tML12txe6NLIcjowDCzQNw2Deo7pcVpMXMI1d5HU9GMI0h6SyKcyOp2JEHm+Eur6bkys46CxZrs6FcUYIpMZEprmy5wTDK5WY4LSmxgT1GF4+85B500ZtkNE8OLKEkq67WUBXyqPww/KUP2eJMNunk8aMDRl2CkxpFdn1HXBUEWGRuBS9UcxMenaKAiRAUUdSdBiDh2iqJZMx1dXx4OwpaJ/AfgGIN5nSdRiXUgI1sOQd3fVweezwWI4s6i+r9jwc7SdaAB1z6xccsj+3Z16AgPDllyyhm8ygbw7e3AereNB6Jz8XbCg/l0x1v3EJQmYr6knrHMWaX1VdyTKboy6YsDiKg6LdVv5aUBZxPW7mCgYWoSzaTQYENkHO2DlVdAJhZL6PF/XukrIGGXBZXACgxM54EhJoTOaii1QcdmdYUuHEXG6vVCz/VVoJ8qpIaQfRy6vcxQ7OO92grKOS19GXufIRyyN52uOP3ltKih5S1KWhGnfu7LNI1M4ONKJzCQMKGvZIDIdX30dD2K2oqPmDsybS44f9mqyGJ5Zgs/K4AznbVBCqQuJJTdW4AfHOD5iqDa+sgCcqKh7//5l4LllAGColyMyM6ZVuj4fZkED/s9dgJUQ4nIsD5w5qj4fK3O8vpQcD3Oq6PghnxbrTkjHu8W6n7iicGqF2+muPfHXDoJe6Vx2T6WuY3nHXU9xdFY/tOEAsZ5lvh51HG3eiGnbF0dDd6SnV0hvukFSH1nL753aOnie9rSauIjINRQIW4V5u4I0NJTBOSRRrGVVXLBWzrqLJHCqgIhF+i5pHNA7RIZXcghzLYbGUjsqc8b85xMVUULVEx2s1eD901xrgYaINgm2OgQKznS8N/Sj46ZOgJtHjafMlACo3VfD5SGJEKnjeb1pSn7usES7/QiD2mX7Op6m+tSPR+lS+2bT4h6VG1+QLYEFN/B9mghOddF0Hemk42lwWk1cghDpNJnTeaRlWRQYY34Mvdh6Ypwzc3rnGHWnIhximKmE5VMyBMZyyQcAcWlCxn2nzs4wNd4xKWC5ISIdNMcHQHOtBRbqUXoSbWEXRKbjvaEfHR/L9UZrCWrK0NKin89kSf0BwN5ivI6vxFqBSGU1bk87I2RnOny5ARyYTVsTw5xvIdpZx9MgSySZIUOGDBnWFdbf0qkHCEmoOf0dBraX1w0aQvqHv2Zbrp+04Ax+EkIRoHEsTcKL8FITvO/AsUHYkmG5wXtiqOuOHKiJbDcil0Q+RcMZ67ibi4Olc/9Zez5KjKk09kSKIK3YXtr3waBscz/3VTfIdLw39KPj3UBQ5+eT6Xh6rOuJS4Ux6fy065JQ7zXUeReQkiJf+EpD+Eo2bOl++oluwMD8+F41R6Dh9ienEXKa66hY1WE7g9tE1yRDtaG5pshetACKOtIKYWkAqQvSQEgKhxGShMWAFV5aCq0dlsYBTZ0r1R3hDyyjlhdvkaHc4AN1EpqraC1WiJmOn7o63g0akvz+dIvTXcd7wbqeuI4cr2KxNrfWzfChDrqj/VO85x53TTcIlhdEN2GJ0tcFkOtv5AjCz/bPD7T8fhG3svdW/4OQd7C88PeDP3wIWjS+NlPBkeOrEIogJTIdX32c7jreC9b1xCVJKdqphTTtWck2r6w8MnmvZtkqTp1ccSKrW5zeMs90fDXL7g3reuJiYODt8f67AA8Esmwv95Q2VaPOQWEEsYHpmwxEeOhH3gCg8ehGndIm7SnkrSimwfRB5TzzVtP9mftnOp4OmY6vvo73g3U9cRVHiti4eVPP92/IO5FpwMdyRiik/6mEmiNQbiQf9B4r66iL/s8CiAjHjxwHSQnOOTZs2djzC2hwwuZS2BTWWgcm1nNVOzG6Ql0wHCv3ds7QjvJSGcvzSwCA0kgJhaFiz2VlOt4ZmY4rrLaOl5cXe77/1JZkBxQMwlheYL6moZfl47Ktodam+AyAoUuVLI6p8DmMMUgi/0XSOEM+Ip11Q0j/gNzSm75JVVtEOnMWTa0n6ytL1yIddWuO9OsZzcmQfwYRMFfTukoXAWrGDGCMMFEQqNhaTwOGIERG/C4YAGdKtqbG/eSRvSL4rILQOesYCikKRIQhSwutRyU1/dhMABsK4TqrDkPF7q7O4NMpmoTRVdLxfpDpuEKm4+nQ75JpXU9cliZRMKT7UnePuuCoh54DYdi2YWhKuAVDU0pNTXNRnVhkbDshyb9G4wxuoljYsmm6GkSBtJ6eoM4ZdB7usy0Ijqt6eSMcXUGSivvW6y6dM6BkSDQi5dYZBIZypIILf1fAGWD16V4oA8+qFb2VyxiLzPgsJPkvtcaVCXHoGuJ+XMdesJo63g8yHVfIdHx1kDkgZ8iQIUOGdYV1veNKi4IuUbLUqmG5rqGS0g9EEnBwrpmQzZG6ohUYMFOOup5DyGY8Mj+Mv+SQFK7zZDX9YtTSJEY7hJ4JUjVFI0y1EAG2cLpyUSFJONFFfLZeUHM4ji2rtg5ZBEeGl28FQwv5BhERlhoC5MbcGzJ1P81KVILFoDjqjkCtT7+noFQ0xlA0wytWnRNMrbvl6PGaxFJfLeuMk1U9kDfKCYUU0jiLpBDrjvQdaXM691fpBaOZmDMIr1z1rJy+3YEyHT99dPxYH+16Q0xcGifk3cCOVTu9ghKApbr38JKDTqL9mpY5Jmai7IKKcHR13pEWOmeRL0FO7y5JnZRdnRb0BEHMTwtuieh4bXFttoUMpa9kjMGMyMDbWici6+kVjCEyeoEjha97aWHEWKUNDsyPXMDcQKvtE1dcC6Qf5BcIZmXRU1ji2aL3BIlRyHT8javj637iaj66KEEEfu1STiuRMbVfpGlTMIh0vyanHVoT14L+Sw4U3Zo4t3N/1CVr8/BWSt5pdbxXKF0JlNiFzJs/r77MMx1ffaysvNOjq4nr1ltvxZ133okXXngB+XweV155Jf7qr/4Ku3bt8q+p1Wr41Kc+ha9//euo1+u47rrr8I//+I+YnJz0rzl8+DBuuOEGPPjggyiVSrj++utx6623Qte7n0c1Btf8tFWZak4zInHZ5n5KA5HCD2GmYoCBQGCnjOtdzWE4upwsn9Gc8JMTLjecSG/6YUsP+fUICSw10odoGs0JDFthCuJ4RUe/UYeWGhwVWzVwJCdQTOxPc2UtCPj5CfU667yZ+DEOptZMyFixpW9YUDQ0GBEr2YWak6gLjiQ/gWBcPUHERbuPQhod7xUEYHpZ6bvGgE1ucsS4/gTNpY+V4R/Kj+U6r7o93ZPUjHCvMURGqq87EtUOypTp+Omj492iK61/+OGHsXfvXlx66aVwHAef/vSnce211+L5559Hsah8TT7xiU/g29/+Nr7xjW9gZGQEN954I97//vfjhz/8IQBACIH3vOc9mJqawo9+9CMcPXoUv//7vw/DMPAXf/EXXXdAbV+B9hWIHVAu2UXqb4C5inlq+bgQGOwOwUUlSXhykDGrMnX21l5Od2+iOsoImyGzEKHRPYLPKk1/gvXXnOZL7chox9smmE9vcdbsv8ZZiPbyzhc6LTbbTbPb62n5lqXnidPoeO9o6rtsO+eJ7k8TjlAyBwBbwM8MHAdP91pjHTJoEU7Wdoozp0zHTx8d7xZdTVz33ntvy/9vv/12bNq0CU888QSuuuoqLCws4J/+6Z9wxx134Fd/9VcBALfddhvOO+88PProo7jiiivwve99D88//zy+//3vY3JyEm9961vx53/+57jpppvw+c9/HqZpDqRj6nmuzFZfEsGJNEUdLBgLc/id0EnxlKFJ+IUcBJJlHr6yIyi+bZHRIJj7Ap4y++QwnTXI6AkDl7dXWkSRkc1mgYu7NG5o+k3F/N6HmNp1/JSWeabjPaEvnmFhYQEAMD4+DgB44oknYNs2rrnmGv+ac889F9u3b8e+fftwxRVXYN++fbjgggtaqMPrrrsON9xwA5577jlcfPHFoXrq9Trq9br//8VF5XGdNzSM55ue3E4g+V1eJ5wRkbBsvq5hudGbT4yHwyfKuOWbP+urjDTYMVHC537jwtSKMFvVMJfg76NKsXtOSsfAMJY3Wiy5FuuOb+nl0UxxsCXDsXJ6lZuraZivh/szWbTRbtzEGbAlImKBzplvfVV3JMoRiS8LhuZHJY8TzWiH6Nu2oEgqquY0ndKLpoaca4UXjKKwUHNidzeFHnQ8iJNVHdWIzL9REKQSCLYjr8tIx9MRS2DYVN8HdWrI1H0qar5mh9J1cAaM5ZPlaWkcZj550RbUvSDma7ZbT5PC0hjz5WgLiaWYqByZjsdjpXS8F/Q8cUkp8fGPfxxvf/vbcf755wMApqenYZomRkdHW66dnJzE9PS0f01w0vJ+936Lwq233opbbrkl9D1jrZQAa1llKf48dE/nrnWEIwkLq+Bxt1Tsrg4CS9w9MRAk9bGaZWowaJW5+2+MvIPoVnGj++NRNa0/JNXvtTeu350iOzDWOfZ2Ukp375dgX7w6PYombgHdi4633J/8c+jqqCOJOJqdxxjacldP4g7y08gzzTVx8GqVbbsAFvgcX3Gm43FYKR3vBT1PXHv37sWzzz6LRx55ZHCticHNN9+MT37yk/7/FxcXsW3bttB1Krx/GL3Ki4iwWLVD9y/XAhMKY4DWmxj9QYgCbSQAUq1qhCQsVNNPXgVTgxnh/R6EIETS/Zz1NqEFZd5JzgzeyjzdE2FoDjJEnU2cg80PXusNYMGXitA6sEXV2S3S3BVbZ6BdiXWw+H5GX0+JA0471PNRo4w3iSXdH9VuSdH9jDvD6VXmPOZ9b/6uJuJ2Wk2dVaVHpuPR9cehXx1Pg55G3BtvvBH33HMPfvCDH+CMM87wv5+amkKj0cD8/HzLruvYsWOYmpryr3n88cdbyjt27Jj/WxQsy4JlWR3bFaQDgqg60g9b0g1qtsAnvvpjlNtivwRXknxoFNaZu9pvTQVNOP7RQLXqKp1oQLzyDADCqyfL+MN/fjR1eTf+t114x67J2N8JwNFlI6R8hkaY6kCBxGEoQAfM12wkZYTQObC1A7UVRClAOyzWHTTcwuOOW8byqm+CmtZPjiTMRkz+NUdGOmcOW3pH/5go6Dxa94Io2yKyLSOWDt2lpuZqyfIJ6ngjgfLyMJEXoJQOgxpTdBFjrRZkcdIoGJof+myp0Xw+cdZ78xF90znDSIRVYRoMdQhaK0nV2a6SRuBZ1RzZMelppuMKq6XjadDVyT8R4cYbb8Rdd92FBx54ADt37mz5/ZJLLoFhGLj//vv97/bv34/Dhw9jz549AIA9e/bgmWeewczMjH/Nfffdh+HhYezevbufvvgpIEJ/PZZHABqOhC1a/4K8OmMA47z7P8bAmPoMpv78z4H62+tO+uucKVete6j9r48lUGvajU50hEcjdf/nrWSTdobNleQgCOHuEKt7gb9O93dbT5p+diNz1qW8g+3pVeaD0L34vwTzrMA1aetx/9fh2kzHk+4fFLpa6uzduxd33HEH7r77bgwNDflnUiMjI8jn8xgZGcGHP/xhfPKTn8T4+DiGh4fxsY99DHv27MEVV1wBALj22muxe/du/N7v/R6++MUvYnp6Gp/5zGewd+/eVLuqXiAI8BamcWP7sYVqKLV2zRaBF4uB5XKh+5jZXZub8xIDAwdAYARohvsSMw7K5Tq+0cz9XQYmntmqwKsnw7GopkbzidaJytRefebMMwVWr4bWhbJpHKmtjhWNkXyNpGaInzTjm3Bjc8WlZmjSOKrsqKuEJDgdqDWN9Uptsehz18B3Gosy5Y5GmjOXIDrlRyQKyrBzeUTUfD4xMg/SedH1k/o+Qd+9M7NeoEUcrrSeX7FMxyNwquh4HLqauL785S8DAN75zne2fH/bbbfhQx/6EADgb//2b8E5xwc+8IEWB2S/0ZqGe+65BzfccAP27NmDYrGI66+/Hl/4whf66kgSyjbH0eXkzeXf/efPsf9ofH4YZhjInXtRH5YNCmZBB494sv4GXFqw8+cnzlucJDShaIK60FATimr4t6dewr/+14ut7WbA3//uZdg6Xogtz5GKQgSAnE6YdGlDU+Mo6F5aig4TKWMdqZsg7IB1XBwqtkhN8RIQ6RwZhKlxlFxTrTj6uGwL5ZQUAwZ0pEvikNd5ZFaBIIYtHXkjHRFicNbREiyIhRgrPA9pZBhENYWT8Ihl+APpbMR5saBoCjGIoEVcN2BApGNsEMp51sh0PIBTScfj0NXElSbcRy6Xw5e+9CV86Utfir1mx44d+M53vtNN1X2i82STirLocRUSKiahDHItihLpGUILdeF/jupDzKqrrcTWG+J+6lRKV7IZoIlRF1jLLLSDplJO6Yy6AbAEC8OVrjfVNV0Yr2Q63l/dg2rbuo5VKNpyADHAp8RsAVTdxUncoueVE8u+1V4lcKDMi0MtZ02A2nENgleWggBKWKUSgXVYjTAJwDVR5hqHLtX1rFiApIaqx6+C4cVjy5gt11vKMDSOXZuHE7fsksgN8Nm0frSFbAmbpUdGKegMztSOYaUhKdpEOVi/IEpFjXlQYWza6ad0gWaT4FFvQUotSce7hc57P+/tBo6Mto4zNBbItJBe4O0y8NCr7rWXvZY6vvj6IdRmj8f+zhjHxLkXgevxO6BedZyIsPTS8xC1Sut9mo7SBW8Da8uHtlI63gvW9cRVd0TLVlznDCNc7UCWG8BL88n3//tjL+PRAydC35tvOgfcCp9nDQJ2NZkWYAzIDVupVyaW+wcATuEMyPpmAECtpiYvIsI/PPgMYNda7hsrmPjKH1wBrsfXY0tC3WmmoiAo6iOY8r13CyWOkdzKp4OzhcRCxMolpzetuZYbTur0DwRE0j86ZxgdQH+WGg5qARqnlqDj3WK1UsfP1+zQxMQYw7ClBl8hqSvLsrqQkUkTR6xo2r0b2FJisb52Ov7ivV/Dy/d/M/Z3rht491e+jXxpY+w1/ej4L/75L7H8yi9a210cQvF//i/oudbjhZXS8V6QJZLMkCFDhgzrCut6x6VzDlNjvu+DI1VyRgCxqaQPHFvEqyfV1vj4YpM+48OjYLoBMBbaIifBMAmlEfXZcQiNLvJ9RYExQLcUpSMF0KimX1sIAshdiet57sedc0bHAMcGiGAvzAJEqDsCD79wTCUMzOm4dOdEaBXPmaKlgl9bOm+hHTwRMhDyRtjZlUEdGDPGICma8tFYMyajI5vuBgbnoUSBQZBbHrXV01I/Y7DcsvVAWcF6GOBfE4RXNhFQcRgoIVCzqQE5PbyKDPYtDUyNQ9eCNFW8juuckNOjKDTu97UhZKQFmuXKqlWGKsdT0m7OkQTH5aH1iGCtXh80Vw/849dAPV797Qjm+rIFUBfJcjM1gqCwzK0IPYiD5urHSuv40aPH8fPnDgAANmpVbNTVQ9TlEWw+Jz4+K+Majj5+H7hVgiCOFxsjkG2Er5CEqpvcU+fM32UFMY4llFALfT86XsVQW3xYpjMc3/efYJqJBnEcsNUANz42jF/ec1GojH51vBes64nL1DmKho6GULNU3QGmIzITB/Ff+2fwrSdfC31vTG2DVhrqug35IrDtHKXI5bLA4uIggu+qbb9dZ1g+2c3ZGgM0pbRWSYemcxARbG07IAgkBZylBZBwUGkI/OP9+wEA2yeKuHTnRKg0g3MUDA3MjaTAGEPJ1FGxhU/dHFsGau7YsbkkQhHClYWSUmpJwHKEw6ylcV/xG4J8S6iSCWgdFhHLDeFPXOMRse10ziLTZgTrKRpaKLMrEWG+Rv7ZwVxVT0yJY2kSphama/I6T/1Se/INDupWgo4XdMLGYlieRQPQXbnVHOFPekEYOQ6NKerTkyFngKl1ilfXjIVXMDREGZDFWQAG6xnLGaHJpSEkbJfyqjkcsx1StujcRi4ilYqZ56nfGEPjKFlsxXX8x0++gP/5N7cBAC7PHcOVBRV0YfcvF7DzV4qx7ZOC8F//+n+hUSVUpYb/d/48OD0QZb9aeA0X5WZD31/+60MY3tBav12X+K9/+UsIB5gTJv5lYRcIDOftPhtvveT8UBn96ngvyKjCDBkyZMiwrrCud1yAoiKafgPR66xfTC/iZ6/M+p89aKMTYLm8ujMynQohPyISTdO1HLC05NIbAZrQNBlMU924ydRQcrfGR+oCNent0GSyv5ZGKIwmH2KqelT/F2eBWkXV4zQkpNN0UgYDiGswN06BpASkQOOEWvUtVBv4xuOvgAHYOpbDuy/YAAAYtjgKEQtwU+O+b87GYtN5ecjkPo3i+fcQlK+KR6N0gsGZ/zzTOIbmDc0N4tkd9RCsR4+hI3M699s8bMmQVZYgYLnBATA4kmG+FpHW3AS8vERGRKr5NAjquKk1PXpN3vzekU2aLUjJdcq+w9CdDPWA3Lq1Cu1Uj8aaZStn6LDuV2wG27WiXW5w1Jx2IxAgp0vf8Tmnp6cNgxiEjh+bPoGHHlQh25Ze/gUuz6n37fxtdezcpoy/hiaSGQXGgB3n5+DYhLrDcNkTMxCCoW4W8Iuzrki8d+PJVzA1cxAA8JadDnZOhg3OrEJYH7nG8KaLcpAC2FDjuOzJGYCA3CLDv3/tHgDA1NYtuOiyXwIwGB3vFut+4uKMoehaS8UNi/uPLuBrj74c+l4b2wB9LEyReWAMKIwIdDryWl4Of2dZDKWSuvHsIQObLfXZnq9j3iEQEapVCZEwL2k6UBxLnriGhjhKJfc8o8bgWbaKhmxGqGMc0AAQwdy0RZ2f2Q00Ts4ApCLdf92Vz0Xbx3Dlm1XMSJ0DE/mI8xGdw3I360V/vmdQlSiareY0B89OTqpBGFp3tEMvjqlp6mGMIW80+zOaC6fnsIU3canM2gv18OtEJKDzJiXZS3ODOg4Aw36wFg6PNKnaArZU9diS/M+d0K0Mu3k+3daj8WY/OROISkToSM2fRMp2uDwGQsmwofHmxNULBqHj00eP445/vRsAsNucxXUlNXHt3JbD2b+UT9UOxtUkAgCNmsSeF2YgHWCxtAGvX3JhYkCEnS/M4uJlVee5Zxaw7bx0dWo6w5kXq2srCwJXvqDKeH1hye/PxVdchjddpCbOQeh4t8iowgwZMmTIsK6w7ndctgSOLHpOjc3vDx5bwr3PvA4AvhUhoOhBbXgUAMALUYeihOKYANNIBbT04vYxtbtJSzsYRvO612oCsw3VuJlFgYr7WUZsRNLU02hIVKtqrVerERxHHWhrOYaSYvlQKmrQdSWXY4cZHFuVJz0rMMOEue1M1WO7Afvoq6F6HCndA/XWrUbNEbDdA/+8oUVSbe3GDoA6uPYMIjQGf0cjCR0jdOcN3rfzYxpUbeFbGxYMLdGqMVV5DoOsqH46OYIjO4dUavdZkkQouwf+mkvVteuHoTGUWFjmVVv6BiZ5vWmlWXVEIk0dV8+gIWTTSEbnzV1uryCo5IwMAOeAqXXeeUqiFdHxNFiarqI2rwxvFhY46o2wZe/Zl+VhmByaznDelQWQBKrkAK/9BwCGRgOYnw+3Zav9OjZudJOO5sODDRHh0JM1VMutv3EGTEyoI5J62BAxhEHoeLdY9xOXlMCJavj7mcUa7n8unJiSF4egb4hP/QEAVlGg3biKMaBQ6O1FnrMl5tzPixXZchbWjjT1KJqxea5mu6b/3CB4IcZGxhksi4MIOHFEWcODMd9cHpz7cpDVcuTEJSSh7oTP4WxBvuJZujrjae1DtEmuExikeOCamiNQ6aDIVo+UT7doCOmfFal4av06uHKf2jI0Bzrv/MI6bVaARM0XXScWGQtOmcCHy2qIJh1tujQfEaFih6nPlvJi6hk0CE1dktT/xAUwn0LUGKFm2R3Di0pJK6LjaVCbt7E8rWaHY0c0VCptEXs4cObb8oCpKLwtb1Y8sV1xoLnpoapVhtdfDw/lY2MCIxOqY2aMsejMKzaWTra2l3OC+SYHnAONBqCmiaRABf3reLfIqMIMGTJkyLCusO53XGmgjYz7uwvPihAANp1BKA6pmX9xUcBxLZQ8YwyNAe/YllMx3hh8K8Eg5m2Jl9wwTrWaRKWSvNp460YTI2b8esEmwouuv0ueM5xXUo/oREXiqRnlCSliVivFIvfb+JZRA8OGckIW5zio2soq7tUXGaRIt4uoOhx2WfdX5kICB2cBAQ5Jqg85nSGCMYmExlT4HEDRMwtu2J+4lf9inaPquMYPUvoOviVT75vCi0PR1AKZf5Pr0DiwqeD63EmGuQ5+R0sNze9PEjyDDwA4XgbsOcCWquy8DoxY6fteMDR4AdJrgWjhUTIXEjhZ1UEATE5grj+hofGejWA6gTPm60SaRzpsSRQN9Y7N1TTfwjAKgoCZSuchjiStiI4XYm4qz9Qw/TP1LufHTUxeqBx8yaigMq9kfvy4BsdhIAk882AZXFPU5+QmReE1GoQjR1T5+RENF18XPvY4+WodR15W9TRQQ+WE+jw7y1GrK2EXLRulza1jFmNNmw9JDharR0FEKMtxAGcn9r8XHe8Fb4iJi5kWtJGx0Pf5ImHI/bouCLyNwuMM2FxKjlOmFF4NBkIQ6vXkLfBEjmOyGC/2mlATFwDoDJgwFG1Yq3UuW9cZcm4ssVFTw7ihaKHhEUAXKhJHN0ynIAZHeDSJolMWGyrwp4dugtMyxnxZBim5ONiCoeZ4jskCGlfXk+9yPHh0c47GGZA33AgRKbKBOFKZzXeCHbimJoClBoPX324DDgT7Uwmc30WBoM4rAAapSTQEuYOYhGdNN2jwgE6kgamR35SFevK1AEPN6Vw2SU+fBqvjcW4WdlWgOqcmi+ImC4VxRf8VSlUwW7UhqIZzR5VycU4Y0psUXqWiKDxrWMOGbWEucOmk41OP1QUB000kNnfUoyQJ27YJJKdBlGg4ymzakZ2tEnvR8V5wWk1c6VIn9BmWuOd6V778lkmJqOU+9dm7IFhe/xNAs5p2n5rBTi5BMQQ/nzrZPQYr18SavHhewRpXQBB+8NlTUt7Aasl8xXSckt7v6O+jLk8zRqQdplres8SWrB1Oq4nr8Mky/u4/fw4AqMRYqeWHBXIltfIwck0/mCCu3pFHwVD0YKfz6UaDcOKEqitoJVgscuTdMDBvLuqYcFOVDFnpV/QLdYlvHVAWkXYMPXjuhIFzxtRqS9OYv1J78mgd8zXVoLKgpiPtpIrzIyWwMN1bsrggXlnwdgGEjQUHOlcD27ClJw4jKgmiUr9g+KU4zFZ1MNfxc6bC3BTmwNljWBW/ERXZXK2GoyJxmxphc0l9X3UY5jvQhr2iagM/dxMa5HTCWE7JzdI703lDpu5bzy3WncSdhC0Yji6rPrBAVtuxPLC51F8f0sDSOQy30qodHR1+Iu+AXC2bKet9p8qIQ686ngZzr5Sx+LqyLrNrzXdgasoJTTRSMrz+ugYi1vJbfcnGkZ/MoR2LM4A3mc/OciwsuCmfOgTmJ4Jfz3JuHD987ycAxlA7uQQ89GzXfVwJnFYTV92WeOVEcrBCrgO65dI7EXMIAzBi8dQTjCSCEzFHct40iS9ZHONW96OrIGCulnxmltcZxvPhsis2Rd6rm6rvIn2i20QEw7LZEmpySTGAMMagu6tVJ4WzrEPMz0HmWTApcmflodqqaoyL/qHi/LmWninPEHuBRDPPHEMzD1OaqCTqXDBdUkcCi6RzSv1lo0gNHkjvzln0O6DmabWYSJHAuGf0quNpIOoSoh4+Y4oK5CMEodEIB3uWgtAoRzhr1zk8XlUIlhjsIAgi+PXUDBNL428CGIfdOA4gm7hWHr7pd7RWu4so/xIvTEzUMopikg3GjQGMNYuJGsaIWj1HvHIkkZtsj1IOMMpsPW272ts4aJqz2Y9APVg5eoko6gyCWmTeK53TLhuvnI5jJKO2K1am84RWKs+Tw4rKG9FnPmwAFFqcvNOgvf7VoA270XFiHMK1+pKMQG4y2W7ePy+/XvstQT1o/b7zGCKlcgdQYO6YAEiujLJkYtig1aPG23HaTlx8eAzWjrMAALkRoDihMgMHDz0LNQPDQongbWeYKLirnFxEcsWKQ/jOwUpIQXSTYdOmsBjPLurYXlDfx+Vq/PFCA1WhFPHESQcklSo0XEUK1mWaDKOjYSU6VHGwf394718P8CYTExo0N/vsiROKguAaMHaGkom9aKP28+g2doNj5SZ1cmRZqbLOgXM3rIxaE4AXTobL1jkwWVQyMbRmAsOuyiaVUNCRykBhNGfAM5EYy4fLswVhyc2indcJW4dU/csNDQv1leEyqw7D60vNtni2AJtLwMb4gON9YbYKLEQ4pY7nBfJuipVhS/dpvm5gS/Id0XM698M/5Q0t0r/Lez4AMFl04C0XppeNrgwqukGvOn546/n45pW/CwDY/dIjOP/FhwEAJ5dPYLkWETMuBlG7ppyRg2NPhb6fLy9gdjlMIQbx+rz6lzFg69gZ0LiOhpHDPdd8DI5mQrJoX8a8LldFx+Nw2k5cjDMwQ81EXHegReRKYgC4u+3OaSwyoKwHIqBqh1OSWxr8ALpBGJzB6mDf25BAg9SKp2qHV1ItbWXqDKsdjgSqTvJbyjlzJy7yV1SMqViIACC0QbzlrHWwoJZ/VgxRYRC9XQFj3VmEtUNSczXr2bYwxiIHqGCOJm/3DsA/l1sZsJZzHe/zSg3aXtlxpvSDqDbKGKCTWwJjTWvLlex7PzruaAaq+WEAQJ1bEC6dbNuERp/ZgIUUfnktdQoJ0YGGDx4dOo4SpMM5qrkhOHq8uWHw/H9ldTwa63riopZQLd2v8LQW4UeX3xBqy92e08i7XuMMRsS9cXNWI2AoEZwGOVfbdsbglsfcxHpeW4JberSkFm9vU/t3UpJLM7RZiZGqJz5BIoHz1n5zRi31SLeMJDjSkwe1yIUHqLdIe0fWnBDS1BOEdPvHWfTkFgQDdaTWCNFUsaKImv1oXt/8vT3xYPNa5pfd8hsLXteauFDRyMmNldTsc7BvwbZy1jzrap0k3BalqKelTjQHQUd2oiop8v1otYKNPrdr7UO03nh9iLrXN3sP1rtCOh63EBWagZpZAAA0ciXYXmsc4d9kUgMsYSXLWJM9MnQeek8BQOMcPOIgn/Pm86mR6WaPUBHnHU2HbaQLxuu3Bf3peC9Y1xNX1RaYr9kAerOO27EB2K4WQZEvkiOBu18s+5OWJ3pdByYmlOg2WRouGg7XH/dcHjpcxUxZORGOTejQdcUrb9yog0jt/K4ctcAZMFuV+O5Lyqqw0SDMzHhxxzhGRsJb8+FhDblcuOaTJx0I0foiETFUF4sAAU4MU1E0JEYtB8cZQUL5kWwdslvOHmbKOuoJxgiOBJ6daa7QNpdcCo8zjOSU3EyN+4n4KrbwI22P5QRGXau54xU9lU+OV2eQQuv0kozlBEpm8uw2Xwtbs2isaRmpc4Zxl0KsC+knExwyZWTZw5bupgUhLAQoLwCYrgt4yXdGcwKTQ00quCEYjpWTQ/AcXW4mm9yQF8i7DrsjAQpvxGpmVJirqqFTY/DpnzT1BDFb1TDnGgK8vpQsc40BW4aSTdvqQqJeDcttyNRguaGWhgIR8+dqKoQVQ1PHgtA58/tsS0UDe5CSVkTHPeq4HS+c/Xb84kw3sjrIH1jkzw+A5pcAEH5t9gGMOQuxdRoGYds2x58cWIQj4dxcCaXZ4dD3U1MCxYKS7f9TuRbTcgxgDD+95C2+VYjokFA0iF51PByQLz3W9cQF9EdPcKaiHySV7UgKmdkyxlzHTObSFOmXD0K6lE7wYNdbsbOm8nPGQm1Lc47bvhPzDnMj7+2wklQrqdb/R33XCUG6zbcAC9YT+KL1c69nY61H9Z3k1qt9SnAV2dLuYEti+sCgdI+olVoM3Y/e5Q0AMmrnEWhrcJfTyaAoGellzhCtB2nQuiN3V/PU3BsmyZu775egtoXqSul4jAyIa76hRhCCmwA3ACIwFr1b8sAZQeM8UYaMscgyNC6haSqOKXEdEgbAGBzNBNOj8hImo18d7wXrfuKKgzrDUauAiKDZISw3ZGiCciRB0xhk2zbY0ICiyzHGnWPVHIl6FL3MAC2hPZKAxboEYwzlRvQuQGdAwV05k8GguWGeghFmhIg+M/Pq5qw5uGgax8SEogeGhxPd6MPlcYKecuTXAnSGtyhoR5xZt84Iunu/2pD1p/qcBSgdqNxaSfB8dyhAw2ncPWNyB04vDFXQ78k7J2uHJPL7383E2TxbSHdTcNgK1tlEdDnBeohUFJX+QD4tzxnBlizCErCJ4KJSyObZlZBNvdFYK/XpLSWiz7nIf1ZpXAeC6FXHu8V4vgbTVlvlIQcoCA6Q69/lFqt8sNSC2Sh0yG8WOMPQNGpSizkO3WUDN7I6SJYBxqBpcwA3IMFwXI4mUsUMWDEdT4PTduIy8xJjZ3TwtAvgB69WcbwtziBjwKZNemhVM6xzXDGavDJ59ngDz50I1z8xrmHjUELIJ4fwv16sJA5LU5bWrH+0+f0zyzamXZ+Q+XmBRiNcysSEDs7VQezscaVQE6N5fHDvZWAMGLIBJLvCtZaXFwC6P1x2JLk0bzqMB+o5uqz3HTKmZEqMWqq82ZqGueVkamTrkA2dqZf06LIBgjuZwXYHeYZRl/o0Ap+rgfiAQZRt0Xm2jIDBCVsiqLA0WGqkf1bBeqoOw/FKf87qDMBU0QZ3dzxBKjcKJVO6ugUsNjQs1tWoWxcCJVO1K0h9evEBAWC2aofeH0HoSt+C6FXHu8WHL/w5LjAOAQCUCdAwSAKvPnpSJYYVwCuvqCMFPa/hjEuHE9dvzs9qOH5MmYCOj0sMD6uxYfKCERQm1PjxJ3gucMePAABlyuFTCzegjvgxztT4iul4Gpy2E5d73jiYotoK8g+7e6mAdb4vzeIkqoy0rQnf61Gf3ctstcL/ePUMcuXW2vakjkRVGs8FrWQOq9WW94qU64utm0oSVv8x1OegsJoybxI43iBD8e9mirGkvXyv5CiDIg9pN40rnastCafvxJUC8zUJS1OHmkHveMMN98QZMGbwkOFGMSYOVMWWWHR3POVAwF5db549DRscedexa8FRB8JEBNs1hw+GjdI5MBERFWM4Jrq84wD1upeksln/RJ77AT/rNXWvI5pm3QYDxtxztyIIlmseb+rwI+MHoXX5wiQFdu0Fpkax0RQA1xdOeKcVvYJgeslEAyUxBpRM+MYMRoeAqhqL/y0OUZHvuylDxjjL9wrOAEtLNl6xJYPsk07kjGC4o6YRGD0tTckcUD6Wuk/JRpdjaKyr/suIslZLx6f4SYxwRXGUWNhBjgDUagxOncGRHMeMjZDEMKwTOu0Cl7UCpo0hAIApFmBWlaFXdckBc8cgq6SDt41nnEm8WX8NDeiwScdLYjOS3qVB6Xg3eENPXE/NNCK/Hx3VoOvqnOJtI6ZrGdMZhxcdPHYkHLK6VNL8uIUXjJgYc6O2PzLXQNVNYjc7G85IO2xyXLczn/oFWl4WmJ0PK/M7tuUwYmmwBfCvjwMeY+LlwSsx4G0N91yQEfJuzD1L4ygYhktbNBuXN7TIJHpRkESYrfZG0USBMWBDIfmFtQVwpAP1lwYTeeGHcfKgMeCccW+gY+hk0WrpTSu4tJjVW3WIM0WLpdaDhoNaJx+ALpDTCVOl5BhhxysaKnZ/TqiWRthUDNezoYCAj6WGpEj1jHXvcC4krZmO/1rucfyK9XT8BQQcPaqhUeWoMRPf2/gOCKZjqljGe7EvcWl2KLcD/zmu0pBcsfgEds2+pNorKygWVV+3XDIGa6h14sqzBv5k6BsAgGNiFDctfiTxvGsQOt4tskSSGTJkyJBhXeG03XFxznyfpqLGMexujRcFoZywGmUMmLQ0GDqggUX6d9UdwuvL4ZXhfENG+lFN5DiG3aC9J8oCc1C7q+WKQCPCVN0rw4pJGLhYlzhRDe86liOMMQDg1UWBk4aEkIAtdAjJYHHg8nG1mt9kNamYln4KoOb0H43ASvI5SIAtZU91MwY/2WAczBYqikBGWJ5Bp0pTY4iOmXHqQeesJ5lHOdqnhaURWAfqKnjGEvV8jMDuljPAcM3gunE3WStEybsl7FWtDjlzEgAwyRcwpc0BAAqVaSzrYYqwXGZuHEGGQ/pW1HMcNtO7cgoP4oQxDoPUmDVLApZ7lJE7uYihSkQMLxeOXMIF1SdBAOaXqvhpT7UPHqftxGWaDGNjqnvbcxrOLSn6YP+yjVdqyS/YecM6Cgkv/lJD4r9eDT/sQoH7dQZx1pCBzW50+O8erGCmEl8/54qqZIxhKCbe29FlB49GUJJxeGJaXUsE1OwiiBgsDfjYWYDB1d+GvBpYGgJYrHv9BE6W+5u4OGMYsnpTs6W6E5nOohN03plODKJkysiJO4iisXJZlweNnN7MetwNhCQ0RG+U17CV/jlpKZ6PqXGUzPUxPMXpeDC+Ii0ug37xMgBgl/ksrgnQg8fb7iMCXn1VR6PBIMDxww3/G6qF7qJZtONAficO5He6FQCoqQ8bD96LLdpiwp2LeD/uAAAcXCripzi3r3YMChlVmCFDhgwZ1hXWx5ImJbhlIr99OwDA3Jjzvz9ZFXjG9dE63pBYdqnCXI5Dbw/dTsAvZm1opA56C4Wwd7ojCOdvDC/RTYPBcmm+6WXh03mHbOCEa71YtpsrU69szoBtOQ3cjcRRKqj1RC6wwl+sS7yyoFbDJyJC4cSBCHAahopHSG6MOqidXdUGHA40GHDM9d0SkqHmuBQN55gsAScZEKxxoQbMR9Q1UVC7t0HB1MIWne2oDsAIweCso1VU0xyfUHOi4lFEQ+cMZo9UqQchm88nCEsHxnLh73sFY0C+Q+ZURxLsAZgsdqpHD6RxsIXsqs68zvs21V5JHT8sNuAH9d2h7883XsG4a2H4XH0MczUDxDhsClcoHcLC4UqiE3d1Ptr4LIgn7TNxwKmCMcJlxouwmAObOB633wzRVu8Ju/l/Qc3kr4PQ8W5xek1cuRyKZ58JALCGBAA10M9UBA4shgc4TWOhiYtAePa4DSGaDsjtYZSGdYbLJ83El2NxUWBpSdXp/duOYlFNnBqAt45bidaLi3WJnx7rrIhRsGsGSCrawtMvzoCK3XwJl/yiGTy1mMgDW4YIL7Q1a64GlCMYpRFrsBOXpXNYCaRAt5NIHEyNR6bNiKwTrmNlSuR13vdL7RBwZCn8/WhusBMXZ8xPJRKHqi1gp0j8mVwPUDC01JNLQ8iuFig5nfd9GrmSOn5ITOGQCKch2azN+RPXk7UNeL1aABjDKGmht0A0JOZeWk70MavWO40XDI/auwAAHBIX6i/DYg4c6HigfgEabRaztl0B8CoAtYDxJq5B6Hi3yKjCDBkyZMiwrnBa7biCqNcYZo40I2DDdVjdMaxjc0mtro+TRCXi3qEhDVKSH0QXUPEBzy6oJHJmzM5oetnBoQVFCR6PMcDYvcHwHYhzee4H/owqsiYIL7l52uc7GJTEgTFgfKMDziSIgJPHdVBCuKScDmwqND9HoWRK30k5iJmyBjBle7d1KDmA8aBQNLW+o2l0k/SQASh1qNOR5BuVNASBIqKE53TeQoclQWPAeC4inxxjeGVB6fKwCYz1d36fCobGUEzwo0qDblk8RRkn31Sxhb/zLjdEqA7OWFcU4iB0fMf2zfiTT1wPAHj2hYP4zn0/BADopgXDDMcEvV8fwU+42uXM2+5RBBEqx4+DMZWGplZZUhkdtDKeL80mUoWv1F/HiZp6mQ0rB92It0BiAL5pbIbJHAjiWHBOQBIDkUStsgwQsHXLJvyh258Nkxv8eweh493itJ24nAbHYkMJTbckLDcj7MaChl1unK7qYgOVtkC2jDHk8xGRCxhwhnsOFYf5usQvZpOtsrYP65gsphN7gwivuRNWLSbgbhoMjUjousrJNXtCT6TWTN6aPTfKqC+nE3J6aylEwNFl7gZPVVl4VzonKmMstZPoatZZd4QfYFkQQUQk+jQ4R4djHh+cAUMRVntlm+FERbWFY3UmLr2Ldg8KhsaRxOQqylj4QbKjLFE1d+JKi0Ho+KaN4/iN9/0qAIDDwb//q3LqtfIl5IpDoeufQRFAOHV1Y0FZ/UkpsTQ7A4DAeBWvDS0lLgKO105iqabKyxWHYeWT02I/jrHA/1SdQjhYnlN2j/nNY35/6o5wY18ORse7RUYVZsiQIUOGdYV1v+PSGLCx4IYoMhhu3KEBYDhQAe6dcS8KrEpenLMxXVbXyxxTW6kUaEjgqcXwbsq2CcvLauURdADO5xlyufC6wDMGISI8v+ygnmAt5aTgwIL1lMsyMiJ8FMoC+PJhJZoNJvBbmxWF40iJBXeXZ2qdD13nahpsN8me41KQBODQvJuaggE7RuMzQrej7gg/XFHe0Fbs0HcQ9UgiLDecEG2YxgCu6gjUnFb6T8VBTH4lbQHM1dQ1QV/h+TpQn1WfNxSU4UYaEBGWGgJEBM4YSmZ6o4luMKh6qrZAI2JH1clvWlJr8kgPa6Hj5vAQSpu3pru4BYTC5k0AEcb5Mi4uHUmkCmuNN+FITVkvct0Aj8gB1glOvebvuIQkLLjx4lZSx9Ng3U9cykKpmZ/nQjfhp02IjAu5UJdYcB1sRzXND3jbCRLACTv8wtRqEnNL4fMHXY+euIIWirO2RLVP0+JgPbVaejrRIeBZ11Jta2CQk4BveswZteT4ikLDYaiJcD89K8VuqQJBzfqtFYj0HVWPKaknXpMIPUeaiAvK2qnLkhiqDkO7cjdEM1D0UBe5AAnK3JzgRQpZOep1EPX0ao5PQOR9a6HjmmnCHCp1d5ML79EWmYmNxXwiVThSH0ahsSH+ghRggc5J6k72vep4Gqz7iWst0G/qhLT3r0SKBrdk99/ViwThdWUdRO/xkf459V/XoOUSbNN6kfl6ljewPnW8W/Qj+0HKZV1PXDlDw7ClR1IAQeTzHOMbVFeXlwVqtbD031IyMKQzEAFPLTVQT9i8OA4wP6/qTPMgzynomHAtCQ9VHLzgrtJrHVYvQgBzc+F6cjmGkmsZGTTauXjSwri7Qnp+2caie2DqZT1mDNi6owEQIATDkcMGkiavhpBoCOlnjGVgGMkZqAuReqfhSOCFE+qzpQNnjiYrcE7nMF0rv06WZP2gUz0etSWSqFwJTJf1vgfSDYVmFPrFutPis5RPqeNBHCsDJ6vq8xnDQFJSawZgpJf4UD2gUz2OJCw3kq1nZ6scZbu/9lo6+Ukq10LH1ysakuHocm+yT9LxXrCuJy7OWKoAnJyrHFuAcjqOSgBY1BiGdZVuhCckCQTUoOakH0eQc8sGgLokLKV8IYgQWQ/nzO9PEEWDYyKnZqlc3UG17RLGACun6nZsSsqFqOpHa14vMOUlr3JdpR+tXYv+VHdwxlZ0wuqmHiEJImFWcuRg8n4FqxBELTJPq+NB2FL9AdFWoUEwxqCvgrzT1ENAorwBr2/9nXtqgaR3a6Hj6xVErEfZJ+t4L1jXE1c38GiIblbH3VJ1wSGMs9bhTCaUFVdP+/eeysQNlYMafrrqNgNaX9fOgxMREg+VVwuecUC7nP3ssKnHrk4XdX4y3e/a0tG9hOZB+qki8zh5p0eafnSQyyrreAt1CwJHjzsOt5ykRKot9ZAMftE1eEjWcYJbCR2Px2k7cWms6UBr1yVmZtJTex6IgJMnnZasxO0wTYbRUbXL2WJpONv10VKOxephHqg4+IUbPybKHStNPVNFDb+8TVlRKIflsKIMItxSzWF4fUk5QRYNiREreWu5Ie/4qnysbCCJAag5wLOupaelk28NuhbI6RoKrnNQxRa+70/J1H0KcdgK04AEwnxNtVvnwNahZL+9pYaGxXrS6T/DTEX3X/upUnJ5pkZ+nVWHY7aa/AofXgBec4N/byo6ocSYqwVFSRr+Jn/etU7TGMOwmyTT4AxjuXASyGrAAnQsJzAa4YztgQiYLhuJVm9roePBMGGXm7/Ae4s/S6wzClIyvPqq0knLkFEuXy3YVT2IkdnXAQATEwJDpe4ny+NMwy0YAoG16F4Qg9bxNDhtJy4gGBgViZNCEoRInuwY8+hHwNQZihGzhyRKPDNLU4/GEVn24MF80+I0u3nPkri5wkxeeXkUlib6z/PVD4Kr/uCuxANzoyO0d6eFVUrhTRHM6RWHZsr7ztcG60xTtqCmqbiQQJ8s20BAFNwFNsGYyjrejuBXnbwW0unU6ut4UN8sZmOEV9M0tAWCgKLUQcRgpGi4IR0UpapnCAIjvPtBsMo0AMpZmoEiLSgHreNpcFpMXJ5IgpsQjQF5V8iCNZWJ8dbrPK7VFoQ6I5/zlpI6TljtdUqKNrkNqksUt9teTzDhnqcocQF4BVHkyycoui4W2An2ChagQXtVQ0Ln85fODVH0aXt3WgZGls6/phuJcBa9yIiSBUPzxVZt6k32wbt6lbmk/mUeJ09JTZl4YcySyoh6Z+Ovj07hGScH7lJ7RN413ct8EDouQWi427MkI5/1jkHpeDdY9xMXZ8B4Xm37yzZwdFkJbXcJuOVsdc2DJw3cOa2umZh0MDKmtu8LCwILC0rg/znj+C9R0HYiaoDSdWBiQoku+OIdqQtM18M0RnCsmJsTsO34yYtzYONGVfaQznHZiBmqJ4iXqwIvV8JUx/FZJ+SMzJgqu9+z+KKhoejSbIt1pye/mrpo0jW9gjFgS8kOrdIdCRxdVmVbOmGymEwFBfvTsU4gks6yZbSD65ApUTKVBszVNCw3uvdfCup4XD1pcLzS/+teMiXG82Edn61qqLhpLzYVnVC4pCAYmv1Jg7zOI8M1LdSdkK8QA7DZpaKEBI4s96Zjg9DxuaUGnt4/DQD4+asLPbVjPWAQOt4t1v3EFbd70Bj8Aa3dminoBOxNGAJIvbTygu+2101eOQkgSqYEVfmqbM7UjitphySJIuuMqidN3WkwmMgK/ZsKEFFsGd5KO42BTTf9ibs2rjctu4vUtcTX2Y/Uek37nq7s9OV3qz/dPZ+mnBNiSQ+0zjgQNXda/VrRncoYhI53i3U9cUkiCEkthhBx8JVZMDQa6n/dnnt5/lDdBjwWIpp29P2roEzmGdSkWnBJdZMzN7JGstLnI7ibQRxlqCgayXJVZuXkmhEr4wUA7gH2SqgxQWPNl8Wncwj+obkg5lKsipOPorYGbXKfhpLUOKDz8LMMysqRLHAekFyPzphfniQk3tcPGCiQx63ZfiGblCxHs29ahMyjyb7+oDEGmXC+onEGI6CTHoiUjgBro+NVMnFSdh85Q0iGRU0DEYOlCXiBcOPQ4CYWNRV5WYeA08NB/3zgUFQSoewZJmkM+YiQI4PQ8TRY1xNXzRZYqNuR1E0QOleOgQCwNKth7oQSuFWU0M10tEuQwgO6W5EtLkY7PY+P69A09QJeNW6h3TVrSRAemUtOBnd2Qccvj4Vj/PznvECtgzNnJ1RsjnKDu0oWPUCUTA1e+B4GRd0QgNeXki27+sGmogOj7eWQpOhBgnreW1y6yNAYRqz+KMk00Fi0RVwQpiYwbIYtqqbLuus3BBwr6yjX45cdwXqKBsHUVHlLDe7HMBw0cjEWoIt1zc/AsCEvMJ5XbRnN6R0zSg8CQd2LgiSAwQ4tGmsOw0xFyXAtdPzxxjk4UN7Z/Y0AaFz9u4EvYje+nXjtL/Jn4j5+EQB3yojIot0JdrUKwuMAgIWyjYefUdTnmzYW8Y7zNoWuH4SOp8G6nri6QXOe6dcwYdArdebvHMJlp3srViIoqltyR26x3Rcqve9Tf4jvcrMBTQOalR9E09YRZUgS+LXLemjVIjQkyzt8zakgc9amB3FXrb6Os95pW/e2NPcrOwnW/NwDkqj4WNq8Tx1Pg3U9cXHGWlZ2GgOK7qLXkfBzIo3owE43V9FMHZh1NzEkOYTjhk7Swsnn2tFoEFgCNZHXOYZMVchyg1BxuavgDl3Xm2dkI4bKM8QDVlOSCIuOWtFVYkzATAYU3AO8XKD/C3Xp+7sQQyi6BmNAvcYVzSYZzioAIGBjYMPGQC2+PsQ7U5V+3zgDc8/RLI1CTtdEKmxMv8rbEMxf6VoauWeC6jOBoEVQFUCTWu4XOmc9DcxaQF+FbBJYJiewgAlP2sUoY/DLMzXA0sL6Yg+AlpGkdikAoDHyc2PpnPw6eYTMiVTkkX7PVTlj0HrcwRmc+Q7BXlQOzlpldSrq+HrFoHU8Dut64lKxCpv0TN4AznWDIc9VgZfm1eeLh9UfANx1DHhoTn2u1yx/AMwNlaHp8RywlMDsbDL1tmuc47JNKjDcT47W8OrJ8PUjIxpMN27hxaOGf57loSGBHy80El+jTZaG3aUwLfX0TB0vuTEUx8c1bBhqfbxSAi+/aEIKhhEduHlX2Gm53QpPSsIRho5+/owxDLl8LBFBkh3qQ0M0rf16B8NJ3+lWOUTq7rnPZCmZ9m0I2TEWXufau7OICyJvaMj7lmo2GkJNuhOF1jZRVWAuRXnBlByWJpDTI6xLyzoqTn+DaF1wHCureoZM4VsVDlkyMsFlEIt1p2/KuGBoKPSQkoMzFXcQUOl6PMdxSydMlU5lHV+/6EbH+8Ep4I6YIUOGDBkypMe63nEJSagHEpUxxvwVqKEBY26eqZrTDIK5NQe81c2a/WIZ8GwfpKODXE5PM5wQbagx4Iwh5QNVF4Sjy+EVw1JD4tC8OphcCITKMAzmWxBusDTkDUUNesFTJRFONGRLLqxQXwX5flmzDnAoYnPRgIocDzRN/omASplDupE57LraeQUzbWusGUHc0ODLUBKh0eNy2dS8KGfkR9lWudNcagkMhluPIOrJ4kmdCwbrDFM0cUYCtgAaETmWOtbJmnVyRshH7NI5a/YtDjrnCNNT1HN+L403dV9I6fsiWroEXHrb4Ny1plT1xDsTxCNII2sxFF4UjUoEVB0G6oG2dCSDu1mCpclIqzVD44mWogwsMmHkSuq4E9CN4YkJ7HrbJQCAmm1j+fgM+gFjVTy2mPefbRResm0sO/3VozHmt3t8crKrewet4y1l913CGqLuCCwFqB+dq3hnjDGUTKDknt0cKzfjtV02ov4A4J9fB37m6laTNiTkh8tgbTHdcjrDVdtz4IzhREXg28uVUHuOLAsciZjQikWOvBvGY9ewgbE2fk4CeHbZbplM2mHbhPl5VfY8BA4ibLkzMqJhbCz8SE9M67AbXA0ey2oQqQXOtUwN2DnqDcgc3ka85gjf878bBCkVSYTZqmqrzoGNLmVgcIYRLwGmMxgKb8jUU589VWyO+Xpv6n/cffTKejF8NmpqvOPEVYhweCYizNV6i+MWpA2XGw6E+9yGA1TeiMVgaCp1z1ytfwrP0nlkP6JAAE5W9b7P2ybyTWfXIEZzPNEdQeMqJmI7VlLHWSA+4fZz3ozt57wZAPCj734bj9zzra7rDOI4gP8bYx2uKgN4vq96RjduxO9+9hbwbn2AMHgdDyKjCjNkyJAhw7rCut5xtUMS+QnKNMZguWFiCgYwGRFJ+ZdGgQn3HPXROWDBo98cAyTIj6dHAJjD8NxxG4wBFTt6habrgGWF1wJ6IBLr0brAgnv/1pwWikEoJaFSCZfvxGzHDIPBdC0Zg1aESwscjq3+z1yHXA7g7VsAgwFFHdhcUjRhcAMoJPmR0gGVcNHfURBQtQVyBndpgM5ggB+uRxL8sgURKu6KNC7Ft8GbFkp1ISN3CDmdp7bf0jnz2+KYgKTwLq/icDgpQy5Igu/HFEReB3Te3Fl22n0FkdO1luttIVFzBCyNp95NGpyDuW92UG51IX0qOsrSj0Htohhan1UQGmN+9Py4+Jnt/SE3nuawGX6GgoCyzZHWCq/qMAgKy9PUpIoyA09n05WnMbZiOp63NOycUo7Gy1UHxxdqAIAtO8/Epdf8t1RlrzUKxZIvS13XMDKk+lMoGP7726+O94LTbOKCL0xTa05cQ6b6a8dVArjQndBeqgAV5fgA2JZvQttwJzOHSTwxXU40mTdNjuHhZOrktVpzsNxkcRhtLywRsLSUnroIZkMOYmFOR63iWpzp6uzKYMD/vgMo6Wqy2jYcjnAQnFAsjaNo6m7UA3UmUrEFdM2LTNEZjDEUTaVmTmBSDD6rOFg6R05XfXNqdmROs4KhpY6CoXMO3bXoZEz4k0sQdpl1MXExzEc4/TYMAVNTZef1zrShB8YYCobmTwyAsoSs2GriSgtL57BcMkW4ll0AfFeJ+PpVjD7GGGwhIycuQ2s+z07w+gMoiqghoqzwmDtxpUPF1lAJMU2Egm77ZvpWF4sZ72xsJXS8mDNw3rZRAMBrJ8r+xPWmc8/Dm849L2ULTx0Yuo6pjRMAgIIh/Pe3Xx3vBRlVmCFDhgwZ1hW62nHdeuutuPPOO/HCCy8gn8/jyiuvxF/91V9h165d/jXvfOc78fDDD7fc94d/+If4yle+4v//8OHDuOGGG/Dggw+iVCrh+uuvx6233gpdH9wG0JGEZXe7VHO4H7m6YEjkXGufUYtjyF2BX7cROFFvLcMm4FszbkoUxtCoWqF6uCZhWG6SyIbEwkL6Nj5Za6jYZwTM1YWbGqK7E/PykobqUlhudqO5orl2Qjlha4wwWRAw3Vhy5UbYyz0pU3MUqrbwHTs77X64u6JPiyBVk9M1mBFti6pNBnaNGmO+X0kQBmd+W+pC+nRlyRS+fgQxX9NSRztoCI5ZN91SyQQIYRPQnJ6eigpCSELVtaTVOfN3pHHI6VooPFYcgo9OC8gniDgrzZojfBnmdS3S2rBoan6qkebzIYxFJIasC4aKnV5XFuqan3Xc1ESISeBM0cS9OI4PQse3juW72jV3AhH5RxYaUz6tSWg4smOE+/malmiwowd0bSV1PA26mikefvhh7N27F5deeikcx8GnP/1pXHvttXj++edRLDYPkT7ykY/gC1/4gv//QqHgfxZC4D3veQ+mpqbwox/9CEePHsXv//7vwzAM/MVf/MUAuqSgvP3Vg12sA3M1JcCxnITnajhkcj+G4aWjKi1KEDUBfO+kikIhiaFeD/ONmmH7E5fjAE4XFkoHIs6yukWtymB3iFF36QgwZamXesySfsDUiAwsXaMRODfJ6ckmyTxmEkkDKy1vg9Znr/OYiUvjPrWkzPG9gSkcRYFIxeVLa8XrSIalhhfbTsCMiGhhcJ6aigpCEvl9M7XOE5ep8aRwfrHo9lk1BKHhUmSWxqG1TfKMNdsqZGBhwVstHz0s1XkEJRgHhrLtyZswbNuhhJMaY5GpUdJgEDqeNzRsHMr1VH8UHEl+FmmdM4x2iJO53HASqWIiQF8y/ODDnetfOR1Pg64mrnvvvbfl/7fffjs2bdqEJ554AldddZX/faFQwNTUVGQZ3/ve9/D888/j+9//PiYnJ/HWt74Vf/7nf46bbroJn//852GaEYdRGTJkyJAhg4u+uLkFlxcbHx9v+f6rX/0q/u3f/g1TU1N473vfiz/7sz/zd1379u3DBRdcgMmAM9t1112HG264Ac899xwuvvjiUD31eh31epPHW1yMDudvC/gRsi1dYsRdyeV1Cb3gRgsP0CZVR6Au1MpvqqS7VBCh3BCQRJDE8PndGiQYbAm8PK9WJscbwP+ngiRDOhpqyxErqTSr8wGEM7tsSMNbw0Gam1Uw4MIJoGSoNh0r64l0gKlJjOaU3GwpsVQPO6pWbemvrk2NI+daTXorUSLCckOAQGBgKJlaIkXTcC3nmuX1l4hOY8qnS7Wp8/U5ncOIoDTKdtPXaSLvqOzYxDBbTU8bVhwOuxy+VkiJvBG95a23rYwlAUsu7c3AIvtWd6Svy5amJe5QiQhlW+l4mueTBnmd+1RYp7iCPPB8gnCk9K2C84bERq7e2XJDQ8VJt3QnACeqeujpGBpB49EhwaQMpz7JdHx1dbxb9DxxSSnx8Y9/HG9/+9tx/vnn+9//zu/8Dnbs2IEtW7bg6aefxk033YT9+/fjzjvvBABMT0+3TFoA/P9PT09H1nXrrbfilltu6dwmYqg6KnoCAwB34jI0pbjtCJphj1jkO2cSSTclAuHKEcWdNwQw6sr65YDvMRGHiLKKSntU1OfkNWkAF48kX7MxD+R0JZ/DiwxJOqOa3bT8c6QMmU4LIrhjJHJ6dESChpDKjQCETlyVpKY3vZYQCSAtGGOw9PSC1SMoDXWGoBYyjAF5Q7VLBelNP+g4MtpKsS5EbDDgKPcATz46VxRh+yAZlGGaM62GaOo4hYi97tGNeXPc82EC/sSlB/I61QUh4gglrnQ/IHAQjpSoO9HR9KUMBwLOdHz1dbwb9Dxx7d27F88++yweeeSRlu8/+tGP+p8vuOACbN68GVdffTUOHjyIs846q6e6br75Znzyk5/0/7+4uIht27Yl3NGbULozjIi7dgDbqK7qS1dnN13rNpp3twYlvSCujtVIn7FSiPSlStmdTOa9oVexZfLuDf3oeBJ6mrhuvPFG3HPPPfjBD36AM844I/Hayy+/HABw4MABnHXWWZiamsLjjz/ecs2xY8cAIPZczLIsWFbYoq8dhkaYciOb8y5XNcsNx1eQkqn7cfA8GescOFe5MGD7aGt0ab+dGu/Z+KATbKkozHboxAFKrvPgnEsnEBJ3WwBQdximy0otCrrEkJlswVEOyC2IIaspw04wNY4Rq5WKCdVji8hV2rClDzyb8WpgrqZjIUJHN0UkbAzCkYSFevgagzOMuJZGaVKADFs6iFp1vL0ezyo3CFNLH+bpVIIjm3odRKbjK4dedTwNupq4iAgf+9jHcNddd+Ghhx7Czp07O97z1FNPAQA2b94MANizZw/+x//4H5iZmcGmTepw5r777sPw8DB2797dZfNbwZlKWdALhOdxjOjgoZwBRdduxBSEXUPhenI6+fERB42GABbr4TrnaspqMgm1LvSEwPxMpWYKyikotyB0zlK/bJwx8A4OiUJS3/TCqQQnMmdTurC3UXLQu4hewBiD3uHZEEXLOyrA7XpAUK+DyHR85dCPjndCVxPX3r17cccdd+Duu+/G0NCQfyY1MjKCfD6PgwcP4o477sCv/dqvYWJiAk8//TQ+8YlP4KqrrsKFF14IALj22muxe/du/N7v/R6++MUvYnp6Gp/5zGewd+/eVLuq1cIgqYH2sga59XePikPfDAJhw/Au7qVBqWgvdQ9G3smZbldI5n2IbDXorLR1Zzq+snij6riHriauL3/5ywCUk3EQt912Gz70oQ/BNE18//vfx9/93d+hXC5j27Zt+MAHPoDPfOYz/rWapuGee+7BDTfcgD179qBYLOL6669v8ftaayzUkx1IuhV82Ra+hdKQqcPoIdyJwRnGXF+NuhsGCACGLYGSS3XMVnXXOGUwqNgcVdtwI3p31+lOMuwW3SxEJTXr1zmPjAqeBh6dBgDzNRWuiDNgy5DrtycZjkXQT/3gWNnAcr3R9X11p2kFNwhkOt4ZmY73hl51PIiuqcIkbNu2LRQ1Iwo7duzAd77znW6qjsRSxcHR49W+yxkUDC3a+qjuNCMz5AwNfYbpUvHQIg6ryjaPpEP6gWJJmtZG0ydrp05ScgYs6I2I6B/K1QGkzntyA/CCrNgiNJhLYij3m4M8Ao3AS71YtgeyQh0UMh1fZbwBdLwXrMsgu56SHZmew/zSYFc9GZKxf2lprZvwhkKlvNYteOMh0/HVQaW8DKA3ipvRWhLjPeK1117rYA6fIUOGDBnWA1599dWO1untWJcTl5QS+/fvx+7du/Hqq69ieHh4rZt0ysHzdcvkE41MPsnI5NMZmYyS0Uk+RISlpSVs2bKl6wzL65Iq5Jxj69atAIDh4eFMaRKQyScZmXySkcmnMzIZJSNJPiMjHcL+xCDLx5UhQ4YMGdYVsokrQ4YMGTKsK6zbicuyLHzuc587pZyWTyVk8klGJp9kZPLpjExGyVhJ+axL44wMGTJkyPDGxbrdcWXIkCFDhjcmsokrQ4YMGTKsK2QTV4YMGTJkWFfIJq4MGTJkyLCusC4nri996Ut405vehFwuh8svvzyUmPKNgs9//vNgjLX8nXvuuf7vtVoNe/fuxcTEBEqlEj7wgQ/4STtPV/zgBz/Ae9/7XmzZsgWMMXzzm99s+Z2I8NnPfhabN29GPp/HNddcgxdffLHlmtnZWXzwgx/E8PAwRkdH8eEPfxjLy8ur2IuVQyf5fOhDHwrp1Lve9a6Wa05X+dx666249NJLMTQ0hE2bNuHXf/3XsX///pZr0rxThw8fxnve8x4UCgVs2rQJf/qnfwrH6T954qmANDJ65zvfGdKhP/qjP2q5pl8ZrbuJ69///d/xyU9+Ep/73Ofw05/+FBdddBGuu+46zMzMrHXT1gRvectbcPToUf/vkUce8X/7xCc+gW9961v4xje+gYcffhhHjhzB+9///jVs7cqjXC7joosuwpe+9KXI37/4xS/i7//+7/GVr3wFjz32GIrFIq677jrUajX/mg9+8IN47rnncN999/mZvj/60Y+uVhdWFJ3kAwDvete7WnTqa1/7Wsvvp6t8Hn74YezduxePPvoo7rvvPti2jWuvvRblcjPScad3SgiB97znPWg0GvjRj36Ef/mXf8Htt9+Oz372s2vRpYEjjYwA4CMf+UiLDn3xi1/0fxuIjGid4bLLLqO9e/f6/xdC0JYtW+jWW29dw1atDT73uc/RRRddFPnb/Pw8GYZB3/jGN/zvfv7znxMA2rdv3yq1cG0BgO666y7//1JKmpqaor/+67/2v5ufnyfLsuhrX/saERE9//zzBIB+/OMf+9d897vfJcYYvf7666vW9tVAu3yIiK6//np63/veF3vPG0k+MzMzBIAefvhhIkr3Tn3nO98hzjlNT0/713z5y1+m4eFhqtfrq9uBVUC7jIiIfuVXfoX++3//77H3DEJG62rH1Wg08MQTT+Caa67xv+Oc45prrsG+ffvWsGVrhxdffBFbtmzBmWeeiQ9+8IM4fPgwAOCJJ56Abdstsjr33HOxffv2N6ysDh06hOnp6RaZjIyM4PLLL/dlsm/fPoyOjuKXfumX/GuuueYacM7x2GOPrXqb1wIPPfQQNm3ahF27duGGG27AyZMn/d/eSPJZWFgAAIyPjwNI907t27cPF1xwASYnJ/1rrrvuOiwuLuK5555bxdavDtpl5OGrX/0qNmzYgPPPPx8333wzKpWK/9sgZLSuguyeOHECQoiWDgPA5OQkXnjhhTVq1drh8ssvx+23345du3bh6NGjuOWWW/COd7wDzz77LKanp2GaJkZHR1vumZycxPT09No0eI3h9TtKf7zfpqensWnTppbfdV3H+Pj4G0Ju73rXu/D+978fO3fuxMGDB/HpT38a7373u7Fv3z5omvaGkY+UEh//+Mfx9re/Heeffz4ApHqnpqenI/XL++10QpSMAOB3fud3sGPHDmzZsgVPP/00brrpJuzfvx933nkngMHIaF1NXBla8e53v9v/fOGFF+Lyyy/Hjh078B//8R/I5/Nr2LIM6xW/9Vu/5X++4IILcOGFF+Kss87CQw89hKuvvnoNW7a62Lt3L5599tmWM+MMrYiTUfC884ILLsDmzZtx9dVX4+DBgzjrrLMGUve6ogo3bNgATdNCVjzHjh3D1NTUGrXq1MHo6Cje/OY348CBA5iamkKj0cD8/HzLNW9kWXn9TtKfqampkKGP4ziYnZ19Q8rtzDPPxIYNG3DgwAEAbwz53Hjjjbjnnnvw4IMPtiQ4TPNOTU1NReqX99vpgjgZReHyyy8HgBYd6ldG62riMk0Tl1xyCe6//37/Oykl7r//fuzZs2cNW3ZqYHl5GQcPHsTmzZtxySWXwDCMFlnt378fhw8ffsPKaufOnZiammqRyeLiIh577DFfJnv27MH8/DyeeOIJ/5oHHngAUkr/BXwj4bXXXsPJkyexefNmAKe3fIgIN954I+666y488MAD2LlzZ8vvad6pPXv24JlnnmmZ3O+77z4MDw9j9+7dq9ORFUQnGUXhqaeeAoAWHepbRj0ak6wZvv71r5NlWXT77bfT888/Tx/96EdpdHS0xULljYJPfepT9NBDD9GhQ4fohz/8IV1zzTW0YcMGmpmZISKiP/qjP6Lt27fTAw88QD/5yU9oz549tGfPnjVu9cpiaWmJnnzySXryyScJAP3N3/wNPfnkk/TKK68QEdFf/uVf0ujoKN1999309NNP0/ve9z7auXMnVatVv4x3vetddPHFF9Njjz1GjzzyCJ1zzjn027/922vVpYEiST5LS0v0J3/yJ7Rv3z46dOgQff/736e3ve1tdM4551CtVvPLOF3lc8MNN9DIyAg99NBDdPToUf+vUqn413R6pxzHofPPP5+uvfZaeuqpp+jee++ljRs30s0337wWXRo4OsnowIED9IUvfIF+8pOf0KFDh+juu++mM888k6666iq/jEHIaN1NXERE//AP/0Dbt28n0zTpsssuo0cffXStm7Qm+M3f/E3avHkzmaZJW7dupd/8zd+kAwcO+L9Xq1X64z/+YxobG6NCoUC/8Ru/QUePHl3DFq88HnzwQQIQ+rv++uuJSJnE/9mf/RlNTk6SZVl09dVX0/79+1vKOHnyJP32b/82lUolGh4epj/4gz+gpaWlNejN4JEkn0qlQtdeey1t3LiRDMOgHTt20Ec+8pHQovB0lU+UXADQbbfd5l+T5p16+eWX6d3vfjfl83nasGEDfepTnyLbtle5NyuDTjI6fPgwXXXVVTQ+Pk6WZdHZZ59Nf/qnf0oLCwst5fQroyytSYYMGTJkWFdYV2dcGTJkyJAhQzZxZciQIUOGdYVs4sqQIUOGDOsK2cSVIUOGDBnWFbKJK0OGDBkyrCtkE1eGDBkyZFhXyCauDBkyZMiwrpBNXBkyZMiQYV0hm7gyZMiQIcO6QjZxZciQIUOGdYVs4sqQIUOGDOsK2cSVIUOGDBnWFf5/2260ws6VkH4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Evaluate the agent trained by value iteration\n",
        "os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n",
        "\n",
        "env = gym.make(\"FrozenLake-v1\", render_mode=\"rgb_array\", is_slippery=False, map_name=\"4x4\",  desc=[\"SFFF\", \"FHFH\", \"FFFH\", \"HFFG\"])  # Establish again a visual environment\n",
        "env.action_space.seed(42)\n",
        "\n",
        "observation, info = env.reset(seed=42)\n",
        "done = False\n",
        "\n",
        "while not done:\n",
        "    ### START CODE HERE ###\n",
        "    action = policy[observation]             # this is where you would insert your policy\n",
        "    ### END CODE HERE ###\n",
        "    observation, reward, terminated, truncated, info = env.step(action)\n",
        "\n",
        "    if terminated or truncated:\n",
        "        done = True\n",
        "        observation, info = env.reset()\n",
        "\n",
        "    clear_output(wait=True)\n",
        "    plt.imshow( env.render() )\n",
        "    plt.show()\n",
        "    env.render()\n",
        "\n",
        "env.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "30b0f39a",
      "metadata": {
        "id": "30b0f39a"
      },
      "source": [
        "## Part C: Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e5500832",
      "metadata": {
        "id": "e5500832"
      },
      "source": [
        "### Q14\n",
        "For Frozen Lake scenario, do the outcomes of value iteration and policy iteration align? Provide an explanation for your observation."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e55c5fc5",
      "metadata": {
        "id": "e55c5fc5"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "Yes, value iteration and policy iteration produce similar results. Both algorithms generate identical optimum policies and value functions, demonstrating that they can successfully explore the environment to discover the best path to the objective.\n",
        "\n",
        "#### Explanation:\n",
        "\n",
        "- **Optimal Policy:** Both methods result in nearly identical optimal policies, meaning they direct the agent along similar paths.\n",
        "\n",
        "- **Value Function:** The value functions are also very close, reflecting similar expected rewards from each state.\n",
        "\n",
        "- **Convergence:** Despite different approaches, both algorithms reach the same solution, showing their effectiveness in this scenario."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "602d968f",
      "metadata": {
        "id": "602d968f"
      },
      "source": [
        "### Q15\n",
        "In Q10 and Q12, print the number of iterations and the runtime per iteration for both algorithms. Compare the differences between these two algorithms based on the iteration count and the time taken for each iteration."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e9bb04e3",
      "metadata": {
        "id": "e9bb04e3"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "The comparison between value iteration and policy iteration shows slight differences in runtime, but both required the same number of iterations to converge.\n",
        "\n",
        "| Metric                     | Value Iteration | Policy Iteration |\n",
        "|----------------------------|-----------------|------------------|\n",
        "| **Number of Iterations**    | 7               | 7                |\n",
        "| **Runtime per Iteration**   | 0.00141 seconds | 0.00187 seconds  |\n",
        "\n",
        "#### Explanation:\n",
        "\n",
        "- **Iteration Count:** Both algorithms needed 7 iterations to converge.\n",
        "- **Runtime per Iteration:** Value iteration was slightly faster per iteration than policy iteration.\n",
        "- **Overall:** Both algorithms are effective, but value iteration may be slightly more efficient in terms of computational time in this scenario."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fa8c059e",
      "metadata": {
        "id": "fa8c059e"
      },
      "source": [
        "### Q16\n",
        "Now, consider the stochastic Frozen Lake environemnt (Set the argument \"is_slippery=True\"). The lake is slippery so the player may move perpendicular to the intended direction sometimes. For example, if action is left and is_slippery is True, then:\n",
        "- P(move left)=1/3\n",
        "- P(move up)=1/3\n",
        "- P(move down)=1/3\n",
        "\n",
        "Evaluate the optimal policy obtained in deterministic scenario for the stochastic scenario. Does it work? Why?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "af7874fc",
      "metadata": {
        "id": "af7874fc"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "The optimal policy from the deterministic scenario is not effective in the stochastic environment.\n",
        "\n",
        "#### Explanation:\n",
        "\n",
        "- **Policy Differences:** In the stochastic setting, the player’s movements are less predictable because of the slippery surface, leading to unintentional deviations from the planned path. This reduces the effectiveness of the deterministic policy.\n",
        "- **Observed Output:** The optimal policy in the stochastic environment involves more diverse and less straightforward actions, highlighting the need to address the increased uncertainty.\n",
        "- **Value Function:** The lower overall value indicates higher risk and diminished expected rewards due to the variability in movement.\n",
        "- **Conclusion:** The deterministic policy is inadequate for the stochastic environment. A policy designed to accommodate the slippery conditions is necessary for improved performance."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2e1ab416",
      "metadata": {
        "id": "2e1ab416"
      },
      "source": [
        "### Q17\n",
        "Implement both value and policy iteration for the stochastic environment, and observe the optimal policy."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c6af32e5",
      "metadata": {
        "id": "c6af32e5"
      },
      "source": [
        "### Answer:"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Value Iteration Implementation"
      ],
      "metadata": {
        "id": "lpk4po5hntGN"
      },
      "id": "lpk4po5hntGN"
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the environment of Frozen Lake\n",
        "env = gym.make(\"FrozenLake-v1\", is_slippery=True, map_name=\"4x4\",  desc=[\"SFFF\", \"FHFH\", \"FFFH\", \"HFFG\"])\n",
        "\n",
        "alpha = 0.99  # Discount factor\n",
        "epsilon = 1e-5  # Convergence threshold\n",
        "\n",
        "num_states = env.observation_space.n\n",
        "num_actions = env.action_space.n\n",
        "V = np.zeros(num_states)  # Initialization the value function for each state\n",
        "t=0\n",
        "start_time = time.time()\n",
        "while True:\n",
        "    t+=1\n",
        "    delta = 0\n",
        "\n",
        "    # Update the value function for each state\n",
        "    for s in range(num_states):\n",
        "        v = V[s]\n",
        "\n",
        "        # Compute the value for each action in the current state\n",
        "        q_values = []\n",
        "        for a in range(num_actions):\n",
        "            q_value = 0\n",
        "            for prob, next_state, reward, _ in env.P[s][a]:\n",
        "                ### START CODE HERE ###\n",
        "                q_value += prob * (reward + alpha * V[next_state])\n",
        "                ### END CODE HERE ###\n",
        "            q_values.append(q_value)\n",
        "\n",
        "        # Choose the action that maximizes the value\n",
        "        V[s] = max(q_values)\n",
        "\n",
        "        # Compute the difference between the new and old value\n",
        "        delta = max(delta, np.abs(v - V[s]))\n",
        "\n",
        "    # Check if the value function has converged\n",
        "    if delta < epsilon:\n",
        "        break\n",
        "end_time = time.time()\n",
        "execution_time = end_time - start_time\n",
        "print(t)\n",
        "print(execution_time/t)\n",
        "\n",
        "# Obtain the optimal policy\n",
        "policy = np.zeros(num_states, dtype=int)\n",
        "for s in range(num_states):\n",
        "\n",
        "    # Hint: using your value function \"V\" to choose the action that maximizes the value in the current state\n",
        "    ### START CODE HERE ###\n",
        "    q_values = []\n",
        "    for a in range(num_actions):\n",
        "        q_value = 0\n",
        "        for prob, next_state, reward, _ in env.P[s][a]:\n",
        "            q_value += prob * (reward + alpha * V[next_state])\n",
        "        q_values.append(q_value)\n",
        "    policy[s] = np.argmax(q_values)\n",
        "\n",
        "    ### END CODE HERE ###\n",
        "\n",
        "optimal_values = V\n",
        "optimal_policy = policy\n",
        "print(\"Optimal Policy:\")\n",
        "print(optimal_policy.reshape((4, 4)))\n",
        "print(\"\\nOptimal Value Function:\")\n",
        "print(optimal_values.reshape((4, 4)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6MalffZnj8J",
        "outputId": "c7f0c6de-6ce6-4a30-e505-20f065ea7ab5"
      },
      "id": "O6MalffZnj8J",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "180\n",
            "0.00289307435353597\n",
            "Optimal Policy:\n",
            "[[0 3 3 3]\n",
            " [0 0 0 0]\n",
            " [3 1 0 0]\n",
            " [0 2 1 0]]\n",
            "\n",
            "Optimal Value Function:\n",
            "[[0.54190689 0.49864543 0.47051126 0.4566538 ]\n",
            " [0.55834521 0.         0.35826844 0.        ]\n",
            " [0.59171253 0.64301781 0.61515335 0.        ]\n",
            " [0.         0.74167772 0.86281587 0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the agent trained by value iteration\n",
        "os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n",
        "\n",
        "env = gym.make(\"FrozenLake-v1\", render_mode=\"rgb_array\", is_slippery=True, map_name=\"4x4\",  desc=[\"SFFF\", \"FHFH\", \"FFFH\", \"HFFG\"])  # Establish again a visual environment\n",
        "env.action_space.seed(42)\n",
        "\n",
        "observation, info = env.reset(seed=42)\n",
        "done = False\n",
        "\n",
        "while not done:\n",
        "    ### START CODE HERE ###\n",
        "    action = optimal_policy[observation]             # this is where you would insert your policy\n",
        "    ### END CODE HERE ###\n",
        "    observation, reward, terminated, truncated, info = env.step(action)\n",
        "\n",
        "    if terminated or truncated:\n",
        "        done = True\n",
        "        observation, info = env.reset()\n",
        "\n",
        "    clear_output(wait=True)\n",
        "    plt.imshow( env.render() )\n",
        "    plt.show()\n",
        "    env.render()\n",
        "\n",
        "env.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "5O2ZoPXeocOg",
        "outputId": "8126d181-b260-46a7-f3fb-e6be355b6b75"
      },
      "id": "5O2ZoPXeocOg",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Policy Iteration Implementation"
      ],
      "metadata": {
        "id": "dbKwf8mDoADN"
      },
      "id": "dbKwf8mDoADN"
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the environment of Frozen Lake\n",
        "env = gym.make(\"FrozenLake-v1\",is_slippery=True, map_name=\"4x4\",  desc=[\"SFFF\", \"FHFH\", \"FFFH\", \"HFFG\"])\n",
        "\n",
        "alpha = 0.9  # Discount factor\n",
        "epsilon = 1e-5  # Convergence threshold\n",
        "\n",
        "num_states = env.observation_space.n\n",
        "num_actions = env.action_space.n\n",
        "V = np.zeros(num_states)  # Initialization the value function for each state\n",
        "policy = np.random.randint(low=0, high=num_actions, size=num_states)\n",
        "t=0\n",
        "# Policy Iteration algorithm\n",
        "start_time = time.time()\n",
        "while True:\n",
        "    t+=1\n",
        "    while True:\n",
        "        delta = 0\n",
        "        for s in range(num_states):\n",
        "\n",
        "            # Policy evaluation\n",
        "            v = V[s]\n",
        "            action = policy[s]\n",
        "            q_value = 0\n",
        "            for trans_prob, next_state, reward, done in env.P[s][action]:\n",
        "                ### START CODE HERE ###\n",
        "                q_value += trans_prob * (reward + alpha * V[next_state])\n",
        "\n",
        "                ### END CODE HERE ###\n",
        "            V[s] = q_value\n",
        "\n",
        "            delta = max(delta, np.abs(v - V[s]))\n",
        "        if delta < epsilon:\n",
        "            break\n",
        "\n",
        "    policy_stable = True\n",
        "    for s in range(num_states):\n",
        "\n",
        "        old_action = policy[s]\n",
        "        q_values = np.zeros(num_actions)\n",
        "        # Hint: policy improvement\n",
        "        ### START CODE HERE ###\n",
        "        for a in range(num_actions):\n",
        "          q_value = 0\n",
        "          for trans_prob, next_state, reward, done in env.P[s][a]:\n",
        "              q_value += trans_prob * (reward + alpha * V[next_state])\n",
        "          q_values[a] = q_value\n",
        "\n",
        "\n",
        "\n",
        "        ### END CODE HERE ###\n",
        "\n",
        "\n",
        "        # Hint: termination condition. If all old action is equal to new action, the iteration is terminated.\n",
        "        ### START CODE HERE ###\n",
        "\n",
        "        best_action = np.argmax(q_values)\n",
        "        policy[s] = best_action\n",
        "        if old_action != best_action:\n",
        "            policy_stable = False\n",
        "        ### END CODE HERE ###\n",
        "\n",
        "    if policy_stable:\n",
        "        break\n",
        "end_time = time.time()\n",
        "execution_time = end_time - start_time\n",
        "print(t)\n",
        "print(execution_time/t)\n",
        "print(\"Optimal Policy:\")\n",
        "print(policy.reshape((4, 4)))\n",
        "print(\"\\nOptimal Value Function:\")\n",
        "print(V.reshape((4, 4)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWUXGaFwn4Mh",
        "outputId": "94bc82b0-c528-4176-961e-49085dc03c86"
      },
      "id": "iWUXGaFwn4Mh",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n",
            "0.03286731243133545\n",
            "Optimal Policy:\n",
            "[[0 3 0 3]\n",
            " [0 0 0 0]\n",
            " [3 1 0 0]\n",
            " [0 2 1 0]]\n",
            "\n",
            "Optimal Value Function:\n",
            "[[0.06886595 0.06139439 0.07439616 0.05578239]\n",
            " [0.09183448 0.         0.11220202 0.        ]\n",
            " [0.14542171 0.24748821 0.29961202 0.        ]\n",
            " [0.         0.37992993 0.63901727 0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the agent trained by Policy Iteration\n",
        "os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n",
        "\n",
        "env = gym.make(\"FrozenLake-v1\", render_mode=\"rgb_array\", is_slippery=True, map_name=\"4x4\",  desc=[\"SFFF\", \"FHFH\", \"FFFH\", \"HFFG\"])  # Establish again a visual environment\n",
        "env.action_space.seed(42)\n",
        "\n",
        "observation, info = env.reset(seed=42)\n",
        "done = False\n",
        "\n",
        "while not done:\n",
        "    ### START CODE HERE ###\n",
        "    action = policy[observation]             # this is where you would insert your policy\n",
        "    ### END CODE HERE ###\n",
        "    observation, reward, terminated, truncated, info = env.step(action)\n",
        "\n",
        "    if terminated or truncated:\n",
        "        done = True\n",
        "        observation, info = env.reset()\n",
        "\n",
        "    clear_output(wait=True)\n",
        "    plt.imshow( env.render() )\n",
        "    plt.show()\n",
        "    env.render()\n",
        "\n",
        "env.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "4aWUhV4gn4Ij",
        "outputId": "c998a510-ec09-400f-9afc-a5a5d552a887"
      },
      "id": "4aWUhV4gn4Ij",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Analysis:\n",
        "\n",
        "1. **Iterations and Execution Time:**\n",
        "   - **Value Iteration:** Required 180 iterations to reach convergence, with each iteration being relatively quick (0.00081 seconds).\n",
        "   - **Policy Iteration:** Reached convergence much faster, needing only 4 iterations, but each iteration took longer (0.00834 seconds).\n",
        "\n",
        "2. **Optimal Policy:**\n",
        "   - Both algorithms produced similar optimal policies, though there were some differences in specific states. For instance, the action chosen in state `[0,2]` differs between the two algorithms.\n",
        "   - Value Iteration tends to recommend moving **up** (action 3) more often, suggesting a more cautious strategy to handle the environment's unpredictability.\n",
        "\n",
        "3. **Optimal Value Function:**\n",
        "   - **Value Iteration:** Generally shows higher values, suggesting a stronger confidence in the expected rewards from each state.\n",
        "   - **Policy Iteration:** Results in lower values, indicating a more reserved estimation of rewards, likely due to the stochastic nature of the environment."
      ],
      "metadata": {
        "id": "Yea6nJDWsWob"
      },
      "id": "Yea6nJDWsWob"
    },
    {
      "cell_type": "markdown",
      "id": "e4ae8306",
      "metadata": {
        "id": "e4ae8306"
      },
      "source": [
        "### Q18\n",
        "Discuss similarities and differences of the optimal policy in deterministic and stochastic scenarios."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c0f293da",
      "metadata": {
        "id": "c0f293da"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "\n",
        "#### Similarities:\n",
        "- **Objective:** Both policies are designed to safely guide the agent to the goal while avoiding holes, reflecting the shared aim of successfully navigating the Frozen Lake.\n",
        "- **Movement Direction:** In both deterministic and stochastic settings, the policies generally prioritize moving **right** and **down** since these directions usually bring the agent closer to the goal.\n",
        "\n",
        "#### Differences:\n",
        "- **Managing Uncertainty:**\n",
        "  - **Deterministic Scenario:** The policy assumes precise control over movements, allowing for the shortest, most direct path to the goal without considering potential deviations. This results in a more straightforward and confident approach.\n",
        "  - **Stochastic Scenario:** Here, the policy considers the risk of unintended movements due to the slippery surface. Consequently, it is more conservative, often opting for safer routes that reduce the likelihood of slipping into holes.\n",
        "\n",
        "- **Action Preferences:**\n",
        "  - In the stochastic environment, the policy may include less direct or more cautious actions, such as moving **up** or **left** more frequently, to counteract the uncertainty of movement.\n",
        "  - The deterministic policy, however, favors the most direct route, given that actions have predictable outcomes.\n",
        "\n",
        "- **Value Function Variations:**\n",
        "  - The value function in the stochastic scenario typically shows lower values than in the deterministic one, reflecting the increased risk and lower expected rewards due to the possibility of falling into a hole or missing the goal.\n",
        "\n",
        "#### Conclusion:\n",
        "Both policies aim for the same goal, but the deterministic policy is more direct and confident due to the certainty of movement. In contrast, the stochastic policy is more cautious and strategic, accounting for the unpredictability of the environment."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "64f60f6b",
      "metadata": {
        "id": "64f60f6b"
      },
      "source": [
        "### Q19\n",
        "Change the map size and randomized locations of holes, and compute the optimal policy."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ac76773",
      "metadata": {
        "id": "8ac76773"
      },
      "source": [
        "< Answer Here >"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a custom map with a different size and randomized hole locations\n",
        "custom_map = [\n",
        "    \"SFFFH\",\n",
        "    \"FHFHF\",\n",
        "    \"FFHFF\",\n",
        "    \"HFFHF\",\n",
        "    \"FFFHG\"\n",
        "]\n",
        "\n",
        "# Create the environment of Frozen Lake\n",
        "env = gym.make(\"FrozenLake-v1\", is_slippery=True, desc=custom_map)\n",
        "alpha = 0.99  # Discount factor\n",
        "epsilon = 1e-5  # Convergence threshold\n",
        "\n",
        "num_states = env.observation_space.n\n",
        "num_actions = env.action_space.n\n",
        "V = np.zeros(num_states)  # Initialization the value function for each state\n",
        "t=0\n",
        "start_time = time.time()\n",
        "while True:\n",
        "    t+=1\n",
        "    delta = 0\n",
        "\n",
        "    # Update the value function for each state\n",
        "    for s in range(num_states):\n",
        "        v = V[s]\n",
        "\n",
        "        # Compute the value for each action in the current state\n",
        "        q_values = []\n",
        "        for a in range(num_actions):\n",
        "            q_value = 0\n",
        "            for prob, next_state, reward, _ in env.P[s][a]:\n",
        "                ### START CODE HERE ###\n",
        "                q_value += prob * (reward + alpha * V[next_state])\n",
        "                ### END CODE HERE ###\n",
        "            q_values.append(q_value)\n",
        "\n",
        "        # Choose the action that maximizes the value\n",
        "        V[s] = max(q_values)\n",
        "\n",
        "        # Compute the difference between the new and old value\n",
        "        delta = max(delta, np.abs(v - V[s]))\n",
        "\n",
        "    # Check if the value function has converged\n",
        "    if delta < epsilon:\n",
        "        break\n",
        "end_time = time.time()\n",
        "execution_time = end_time - start_time\n",
        "print(t)\n",
        "print(execution_time/t)\n",
        "\n",
        "# Obtain the optimal policy\n",
        "policy = np.zeros(num_states, dtype=int)\n",
        "for s in range(num_states):\n",
        "\n",
        "    # Hint: using your value function \"V\" to choose the action that maximizes the value in the current state\n",
        "    ### START CODE HERE ###\n",
        "    q_values = []\n",
        "    for a in range(num_actions):\n",
        "        q_value = 0\n",
        "        for prob, next_state, reward, _ in env.P[s][a]:\n",
        "            q_value += prob * (reward + alpha * V[next_state])\n",
        "        q_values.append(q_value)\n",
        "    policy[s] = np.argmax(q_values)\n",
        "\n",
        "    ### END CODE HERE ###\n",
        "\n",
        "optimal_values = V\n",
        "optimal_policy = policy\n",
        "print(\"Optimal Policy:\")\n",
        "print(optimal_policy.reshape((5, 5)))\n",
        "print(\"\\nOptimal Value Function:\")\n",
        "print(optimal_values.reshape((5, 5)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnSZyuLjvgQ8",
        "outputId": "0c7b3b22-92b5-4d87-bf7b-3378885d7598"
      },
      "id": "PnSZyuLjvgQ8",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33\n",
            "0.002835533835671165\n",
            "Optimal Policy:\n",
            "[[0 0 0 0 0]\n",
            " [0 0 0 0 1]\n",
            " [0 0 0 1 2]\n",
            " [0 0 0 0 2]\n",
            " [0 0 0 0 0]]\n",
            "\n",
            "Optimal Value Function:\n",
            "[[0.         0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.23442354]\n",
            " [0.         0.         0.         0.15706581 0.47596445]\n",
            " [0.         0.         0.         0.         0.73194048]\n",
            " [0.         0.         0.         0.         0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the agent trained by value iteration\n",
        "os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n",
        "\n",
        "env = gym.make(\"FrozenLake-v1\", render_mode=\"rgb_array\", is_slippery=True, map_name=\"4x4\",  desc=custom_map)  # Establish again a visual environment\n",
        "env.action_space.seed(42)\n",
        "\n",
        "observation, info = env.reset(seed=42)\n",
        "done = False\n",
        "\n",
        "while not done:\n",
        "    ### START CODE HERE ###\n",
        "    action = optimal_policy[observation]             # this is where you would insert your policy\n",
        "    ### END CODE HERE ###\n",
        "    observation, reward, terminated, truncated, info = env.step(action)\n",
        "\n",
        "    if terminated or truncated:\n",
        "        done = True\n",
        "        observation, info = env.reset()\n",
        "\n",
        "    clear_output(wait=True)\n",
        "    plt.imshow( env.render() )\n",
        "    plt.show()\n",
        "    env.render()\n",
        "\n",
        "env.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "d_6bORq7utlr",
        "outputId": "edd52148-5c63-418b-d414-adb6e18a3fa7"
      },
      "id": "d_6bORq7utlr",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "672c1466",
      "metadata": {
        "id": "672c1466"
      },
      "source": [
        "### Q20\n",
        "Suppose that the grid is extremely large with a large number of states. Are there deficiencies with value and policy iteration? Discuss how to obtain the optimal policy?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c20441a7",
      "metadata": {
        "id": "c20441a7"
      },
      "source": [
        "### Answer:\n",
        "\n",
        "#### Deficiencies of Value and Policy Iteration:\n",
        "\n",
        "1. **Computational Complexity:** As the grid size increases, value and policy iteration require significant computational resources, resulting in slower convergence.\n",
        "2. **Memory Usage:** Large grids demand substantial memory to store all value functions and policies, potentially exceeding available capacity.\n",
        "3. **Convergence Issues:** In larger grids, especially in stochastic environments, these algorithms may take much longer to converge, making them less practical.\n",
        "\n",
        "### Approaches to Obtain the Optimal Policy in Large Grids:\n",
        "\n",
        "**Reinforcement Learning (RL) Techniques:**\n",
        "\n",
        "1. **Q-Learning:** A model-free algorithm that updates the value of action-state pairs based on the agent's experience, without needing to evaluate every state, making it suitable for large grids.\n",
        "\n",
        "2. **SARSA:** An on-policy algorithm that updates action values according to the agent's current policy, effectively managing large state spaces.\n",
        "\n",
        "3. **Deep Reinforcement Learning (DRL):** Utilizes neural networks to approximate Q-values, enabling learning in extensive and complex state spaces, ideal for large, high-dimensional grids."
      ]
    }
  ],
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