{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Deep Otello AI\n",
    "\n",
    "The game reversi is a very good game to apply deep learning methods to.\n",
    "\n",
    "Othello also known as reversi is a board game first published in 1883 by eiter Lewis Waterman or John W. Mollet in England (each one was denouncing the other as fraud).\n",
    "It is a strickt turn based zero-sum game with a clear Markov chain and now hidden states like in card games with an unknown distribution of cards or unknown player allegiance.\n",
    "There is like for the game go only one set of stones with two colors which is much easier to abstract than chess with its 6 unique pieces.\n",
    "The game has a symmetrical game board wich allows to play with rotating the state around an axis to allow for a breaking of sequences or interesting ANN architectures, quadruple the data generation by simulation or interesting test cases where a symetry in turns should be observable if the AI reaches an \"objective\" policy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Content\n",
    "\n",
    "* [The game rules](#the-game-rules) A short overview over the rules of the game.\n",
    "* [Some common Otello strategies](#some-common-otello-strategies) introduces some easy approaches to a classic Otello AI and defines some behavioral expectations.\n",
    "* [Initial design decisions](#initial-design-decisions) an explanation about some initial design decision and assumptions\n",
    "* [Imports and dependencies](#imports-and-dependencies) explains what libraries where used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## The game rules\n",
    "\n",
    "Othello is played on a board with 8 x 8 fields for two player.\n",
    "The board geometry is equal to a chess game.\n",
    "The game is played with game stones that are black on one siede and white on the other.\n",
    "![Othello game board example](reversi_example.png)\n",
    "The player take turns.\n",
    "A player places a stone with his or her color up on the game board.\n",
    "The player can only place stones when he surrounds a number of stones with the opponents color with the new stone and already placed stones of his color.\n",
    "Those surrounded stones can either be horizontally, vertically and/or diagonally be placed.\n",
    "All stones thus surrounded will be flipped to be of the players color.\n",
    "Turns are only possible if the player is also changing the color of the opponents stones. If a player can't act he is skipped.\n",
    "The game ends if both players can't act. The player with the most stones wins.\n",
    "If the score is counted in detail unclaimed fields go to the player with more stones of his or her color on the board.\n",
    "The game begins with four stones places in the center of the game. Each player gets two. They are placed diagonally to each other.\n",
    "\n",
    "\n",
    "<img alt=\"Startaufstellung.png\" src=\"Startaufstellung.png\"/>\n",
    "\n",
    "## Some common Othello strategies\n",
    "\n",
    "As can be easily understood the placement of stones and on the bord is always a careful balance of attack and defence.\n",
    "If the player occupies huge homogenous stretches on the board it can be attacked easier.\n",
    "The boards corners provide safety from wich occupied territory is impossible to loos but since it is only possible to reach the corners if the enemy is forced to allow this or calculates the cost of giving a stable base to the enemy it is difficult to obtain.\n",
    "There are some text on otello computer strategies which implement greedy algorithms for reversi based on a modified score to each field.\n",
    "Those different values are score modifiers for a traditional greedy algorithm.\n",
    "If a players stone has captured such a filed the score reached is multiplied by the modifier.\n",
    "The total score is the score reached by the player subtracted with the score of the enemy.\n",
    "The scores change in the course of the game and converges against one. This gives some indications of what to expect from an Othello AI.\n",
    "\n",
    "<img alt=\"ComputerPossitionScore\" src=\"computer-score.png\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial design decisions\n",
    "\n",
    "At the beginning of this project I made some design decisions.\n",
    "The first onw was that I do not want to use a gym library because it limits the data formats accessible.\n",
    "I choose to implement the hole game as entry in a stack in numpy arrays to be able to accommodate interfacing with a neural network easier and to use scipy pattern recognition tools to implement some game mechanics for a fast simulation cycle.\n",
    "I chose to ignore player colors as far as I could instead a player perspective was used. Which allowed to change the perspective with a flipping of the sign. (multiplying with -1).\n",
    "The array format should also allow for data multiplication or the breaking of strikt sequences by flipping the game along one the for axis, (horizontal, vertical, transpose along both diagonals).\n",
    "\n",
    "I wanted to implement different agents as classes that act on those game stacks.\n",
    "\n",
    "Since computation time is critical all computational have results are saved.\n",
    "The analysis of those is then repeated in real time. If a recalculation of such a section is required the save file can be deleted and the code should be executed again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import os.path\n",
    "import warnings\n",
    "\n",
    "\n",
    "%load_ext blackcellmagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and dependencies\n",
    "\n",
    "The following direct dependencies where used for this project:\n",
    "```toml\n",
    "jupyter = \"^1.0.0\"\n",
    "matplotlib = \"^3.6.3\"\n",
    "numpy = \"^1.24.1\"\n",
    "pytest = \"^7.2.1\"\n",
    "python = \"3.10.*\"\n",
    "scipy = \"^1.10.0\"\n",
    "tqdm = \"^4.64.1\"\n",
    "jupyterlab = \"^3.6.1\"\n",
    "torchvision = \"^0.14.1\"\n",
    "torchaudio = \"^0.13.1\"\n",
    "```\n",
    "* `Jupyter` and `jupyterlab` on pycharm was used as a IDE / Ipython was used to implement this code.\n",
    "* `matplotlib` was used for visualisation and statistics.\n",
    "* `numpy` was used for array support and mathematical functions\n",
    "* `tqdm` was used for progress bars\n",
    "* `scipy` contains fast pattern recognition tools for images. It was used to make an initial estimation about where possible turns should be.\n",
    "* `torch` supplied the ANN functionalities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import abc\n",
    "from typing import Final\n",
    "from scipy.ndimage import binary_dilation\n",
    "import matplotlib.pyplot as plt\n",
    "from abc import ABC\n",
    "from tqdm.notebook import tqdm\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from scipy.spatial import Delaunay\n",
    "from KDEpy import FFTKDE\n",
    "from ipywidgets import widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants\n",
    "\n",
    "Some general constants needed to be defined. Such as board game size and Player and Enemy representations. Also, directional offsets and the initial placement of blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BOARD_SIZE: Final[int] = 8  # defines the board side length as 8\n",
    "PLAYER: Final[int] = 1  # defines the number symbolising the player as 1\n",
    "ENEMY: Final[int] = -1  # defines the number symbolising the enemy as -1\n",
    "EXAMPLE_STACK_SIZE: Final[int] = 1000  # defines the game stack size for examples\n",
    "IMPOSSIBLE: Final[np.ndarray] = np.array([-1, -1], dtype=int)\n",
    "IMPOSSIBLE.setflags(write=False)\n",
    "SIMULATE_TURNS: Final[int] = 70\n",
    "VERIFY_POLICY: Final[bool] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The directions array contains all the numerical offsets needed to move along one of the 8 directions in a 2 dimensional grid. This will allow an iteration over the game board.\n",
    "![8-directions.png](8-directions.png \"Offset in 8 directions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1, -1],\n",
       "       [-1,  0],\n",
       "       [-1,  1],\n",
       "       [ 0, -1],\n",
       "       [ 0,  1],\n",
       "       [ 1, -1],\n",
       "       [ 1,  0],\n",
       "       [ 1,  1]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DIRECTIONS: Final[np.ndarray] = np.array(\n",
    "    [[i, j] for i in range(-1, 2) for j in range(-1, 2) if j != 0 or i != 0],\n",
    "    dtype=int,\n",
    ")\n",
    "DIRECTIONS.setflags(write=False)\n",
    "DIRECTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another constant needed is the initial start square at the center of the board."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1,  1],\n",
       "       [ 1, -1]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "START_SQUARE: Final[np.ndarray] = np.array(\n",
    "    [[ENEMY, PLAYER], [PLAYER, ENEMY]], dtype=int\n",
    ")\n",
    "START_SQUARE.setflags(write=False)\n",
    "START_SQUARE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new boards\n",
    "\n",
    "The first function implemented and tested is a function to generate the starting environment as a stack of games.\n",
    "As described above I simply placed a 2 by 2 square in the center of an empty stack of boards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, -1,  1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1, -1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_new_games(number_of_games: int) -> np.ndarray:\n",
    "    \"\"\"Generates a stack of initialised game boards.\n",
    "\n",
    "    Args:\n",
    "        number_of_games: The size of the board stack.\n",
    "\n",
    "    Returns: The generates stack of games as a stack n x 8 x 8.\n",
    "\n",
    "    \"\"\"\n",
    "    empty = np.zeros([number_of_games, BOARD_SIZE, BOARD_SIZE], dtype=int)\n",
    "    empty[:, 3:5, 3:5] = START_SQUARE\n",
    "    return empty\n",
    "\n",
    "\n",
    "get_new_games(1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_number_of_games = 3\n",
    "assert get_new_games(test_number_of_games).shape == (\n",
    "    test_number_of_games,\n",
    "    BOARD_SIZE,\n",
    "    BOARD_SIZE,\n",
    ")\n",
    "np.testing.assert_equal(\n",
    "    get_new_games(test_number_of_games).sum(axis=1),\n",
    "    np.zeros(\n",
    "        [\n",
    "            test_number_of_games,\n",
    "            8,\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "np.testing.assert_equal(\n",
    "    get_new_games(test_number_of_games).sum(axis=2),\n",
    "    np.zeros(\n",
    "        [\n",
    "            test_number_of_games,\n",
    "            8,\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "assert np.all(get_new_games(test_number_of_games)[:, 3:4, 3:4] != 0)\n",
    "del test_number_of_games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation tools\n",
    "\n",
    "In this section a visualisation help was implemented for debugging of the game and a proper display of the results.\n",
    "For this visualisation ChatGPT was used as a prompted code generator that was later reviewed and refactored by hand to integrate seamlessly into the project as a whole.\n",
    "White stones represent the player, black stones the enemy. A single plot can be used as a subplot when the `ax` argument is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_othello_board(board, ax=None) -> None:\n",
    "    \"\"\"Plots a single otello board.\n",
    "\n",
    "    If a matplot axis object is given the board will be plotted into that axis. If not an axis object will be generated.\n",
    "    The image generated will be shown directly.\n",
    "\n",
    "    Args:\n",
    "        board: The bord that should be plotted. Only a single games is allowed. A numpy array of the form 8x8 is expected.\n",
    "        ax: If needed a matplotlib axis object can be defined that is used to place the board as a sublot into a bigger context.\n",
    "    \"\"\"\n",
    "    assert board.shape == (8, 8)\n",
    "    plot_all = False\n",
    "    if ax is None:\n",
    "        fig_size = 3\n",
    "        plot_all = True\n",
    "        fig, ax = plt.subplots(figsize=(fig_size, fig_size))\n",
    "\n",
    "    ax.set_facecolor(\"#66FF00\")\n",
    "    for x_pos, y_pos in itertools.product(range(BOARD_SIZE), range(BOARD_SIZE)):\n",
    "        if board[x_pos, y_pos] == -1:\n",
    "            color = \"white\"\n",
    "        elif board[x_pos, y_pos] == 1:\n",
    "            color = \"black\"\n",
    "        else:\n",
    "            continue\n",
    "        ax.scatter(y_pos, x_pos, s=300 if plot_all else 150, c=color)\n",
    "    for x_pos in range(-1, 8):\n",
    "        ax.axhline(x_pos + 0.5, color=\"black\", lw=2)\n",
    "        ax.axvline(x_pos + 0.5, color=\"black\", lw=2)\n",
    "    ax.set_xlim(-0.5, 7.5)\n",
    "    ax.set_ylim(7.5, -0.5)\n",
    "    ax.set_xticks(np.arange(8))\n",
    "    ax.set_xticklabels(list(\"ABCDEFGH\"))\n",
    "    ax.set_yticks(np.arange(8))\n",
    "    ax.set_yticklabels(list(\"12345678\"))\n",
    "    if plot_all:\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "plot_othello_board(get_new_games(1)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_othello_boards(boards: np.ndarray) -> None:\n",
    "    \"\"\"Plots multiple boards into subplots.\n",
    "\n",
    "    The plots are shown directly.\n",
    "\n",
    "    Args:\n",
    "        boards: Plots the boards given into subplots. The maximum number of boards accepted is 70.\n",
    "    \"\"\"\n",
    "    assert len(boards.shape) == 3\n",
    "    assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
    "    assert boards.shape[0] < 70\n",
    "\n",
    "    plots_per_row = 4\n",
    "    rows = int(np.ceil(boards.shape[0] / plots_per_row))\n",
    "    fig, axs = plt.subplots(rows, plots_per_row, figsize=(12, 3 * rows))\n",
    "    for game_index, ax in enumerate(axs.flatten()):\n",
    "        if game_index >= boards.shape[0]:\n",
    "            fig.delaxes(ax)\n",
    "        else:\n",
    "            plot_othello_board(boards[game_index], ax)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_duplicate_boards(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Drop boards that follow each other and are duplicates will be dropped.\n",
    "\n",
    "    Args:\n",
    "        boards: A set of boards to be reduced.\n",
    "\n",
    "    Returns:\n",
    "        A sequence of boards where boards that where equal are dropped.\n",
    "    \"\"\"\n",
    "    return boards[~np.all(boards == np.roll(boards, axis=0, shift=1), axis=(1, 2))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find possible actions to take\n",
    "\n",
    "The frist step in the implementation of an AI like this is to get an overview over the possible actions that can be taken in a situation.\n",
    "Here was the design choice taken to first find fields that are empty and have at least one neighbouring enemy stone.\n",
    "This was implemented with element wise check for a stone and a binary dilation marking all fields neighboring an enemy stone.\n",
    "For that the `SURROUNDING` mask was used. Both aries are then element wise combined using and.\n",
    "The resulting array contains all filed where a turn could potentially be made. Those are then check in detail.\n",
    "The previous element wise operations on the numpy array increase the spead for this operation dramatically.\n",
    "\n",
    "The check for a possible turn is done in detail by following each direction step by step as long as there are enemy stones in that direction.\n",
    "If the board end is reached or en empty filed before reaching a field occupied by the player that direction does not surround enemy stones.\n",
    "If one direction surrounds enemy stone a turn is possible.\n",
    "This detailed step is implemented as a recursion and need to go at leas one step to return True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1],\n",
       "        [1, 0, 1],\n",
       "        [1, 1, 1]]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SURROUNDING: Final = np.array(\n",
    "    [[[1, 1, 1], [1, 0, 1], [1, 1, 1]]]\n",
    ")  # defines the binary dilation mask to check if a field is next to an enemy stones\n",
    "SURROUNDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.11 ms ± 144 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "920 ms ± 10.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[False, False, False, False, False, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False],\n",
       "        [False, False, False,  True, False, False, False, False],\n",
       "        [False, False,  True, False, False, False, False, False],\n",
       "        [False, False, False, False, False,  True, False, False],\n",
       "        [False, False, False, False,  True, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _recursive_steps(\n",
    "    board: np.ndarray,\n",
    "    rec_direction: np.ndarray,\n",
    "    rec_position: np.ndarray,\n",
    "    step_one: int = 0,\n",
    ") -> int:\n",
    "    \"\"\"Check if a player can place a stone on the board specified in the direction specified and direction specified.\n",
    "\n",
    "    Args:\n",
    "        board: The board that should be checked for a playable action.\n",
    "        rec_direction: The direction that should be checked.\n",
    "        rec_position: The position that should be checked.\n",
    "        step_one: Defines if the call of this function is the firs or not. Should be kept to the default value for proper functionality.\n",
    "\n",
    "    Returns:\n",
    "        True if a turn is possible for possition and direction on the board defined.\n",
    "    \"\"\"\n",
    "    rec_position = rec_position + rec_direction\n",
    "    if np.any((rec_position >= BOARD_SIZE) | (rec_position < 0)):\n",
    "        return 0\n",
    "    next_field = board[tuple(rec_position.tolist())]\n",
    "    if next_field == 0:\n",
    "        return 0\n",
    "    if next_field == -1:\n",
    "        return _recursive_steps(\n",
    "            board, rec_direction, rec_position, step_one=step_one + 1\n",
    "        )\n",
    "    if next_field == 1:\n",
    "        return step_one\n",
    "\n",
    "\n",
    "def get_possible_turns(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Analyses a stack of boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards to check.\n",
    "\n",
    "    Returns:\n",
    "        A stack of game boards containing boolean values showing where turns are possible for the player.\n",
    "    \"\"\"\n",
    "    assert len(boards.shape) == 3, \"The number fo input dimensions does not fit.\"\n",
    "    assert boards.shape[1:] == (\n",
    "        BOARD_SIZE,\n",
    "        BOARD_SIZE,\n",
    "    ), \"The input dimensions do not fit.\"\n",
    "\n",
    "    _poss_turns = boards == 0  # checks where fields are empty.\n",
    "    _poss_turns &= binary_dilation(\n",
    "        boards == -1, SURROUNDING\n",
    "    )  # checks where fields are next to an enemy filed an empty\n",
    "    for game, idx, idy in itertools.product(\n",
    "        range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n",
    "    ):\n",
    "        position = idx, idy\n",
    "        if _poss_turns[game, idx, idy]:\n",
    "            _poss_turns[game, idx, idy] = any(\n",
    "                _recursive_steps(boards[game, :, :], direction, position) > 0\n",
    "                for direction in DIRECTIONS\n",
    "            )\n",
    "    return _poss_turns\n",
    "\n",
    "\n",
    "# some simple testing to ensure the function works after simple changes\n",
    "# this testing is complete, its more of a smoke-test\n",
    "test_array = get_new_games(3)\n",
    "expected_result = np.zeros_like(test_array, dtype=bool)\n",
    "expected_result[:, 4, 5] = expected_result[:, 2, 3] = True\n",
    "expected_result[:, 5, 4] = expected_result[:, 3, 2] = True\n",
    "np.testing.assert_equal(get_possible_turns(test_array), expected_result)\n",
    "\n",
    "\n",
    "%timeit get_possible_turns(get_new_games(10))  # checks turn possibility evaluation time for 10 initial games\n",
    "%timeit get_possible_turns(get_new_games(EXAMPLE_STACK_SIZE))  # check turn possibility evaluation time for EXAMPLE_STACK_SIZE initial games\n",
    "\n",
    "# shows a singe game\n",
    "get_possible_turns(get_new_games(3))[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides the ability to generate an array of possible turns there needs to be a functions that check if a given turn is possible.\n",
    "On is needed for the action space validation. The other is for validating a players turn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def move_possible(board: np.ndarray, move: np.ndarray) -> bool:\n",
    "    \"\"\"Checks if a turn is possible.\n",
    "\n",
    "    Checks if a turn is possible. If no turn is possible to input array [-1, -1] is expected.\n",
    "\n",
    "    Args:\n",
    "        board: A board where it should be checkt if a turn is possible.\n",
    "        move: The move that should be taken. Expected is the index of the filed where a stone should be placed [x, y]. If no placement is possible [-1, -1] is expected as an input.\n",
    "\n",
    "    Returns:\n",
    "        True if the move is possible\n",
    "    \"\"\"\n",
    "    if np.all(move == -1):\n",
    "        return not np.any(get_possible_turns(np.reshape(board, (1, 8, 8))))\n",
    "    return any(\n",
    "        _recursive_steps(board[:, :], direction, move) > 0 for direction in DIRECTIONS\n",
    "    )\n",
    "\n",
    "\n",
    "# Some testing for this function and the underlying recursive functions that are called.\n",
    "assert move_possible(get_new_games(1)[0], np.array([2, 3])) is True\n",
    "assert move_possible(get_new_games(1)[0], np.array([3, 2])) is True\n",
    "assert move_possible(get_new_games(1)[0], np.array([2, 2])) is False\n",
    "assert move_possible(np.zeros((8, 8)), np.array([3, 2])) is False\n",
    "assert move_possible(np.ones((8, 8)) * 1, np.array([-1, -1])) is True\n",
    "assert move_possible(np.ones((8, 8)) * -1, np.array([-1, -1])) is True\n",
    "assert move_possible(np.ones((8, 8)) * 0, np.array([-1, -1])) is True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moves_possible(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Checks if a stack of moves can be executed on a stack of boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A board where the next stone should be placed.\n",
    "        moves: A stack stones to be placed. Each move is formatted as an array in the form of [x, y] if no turn is possible the value [-1, -1] is expected.\n",
    "\n",
    "    Returns:\n",
    "        An array marking for each and every game and move in the stack if the move can be executed.\n",
    "    \"\"\"\n",
    "    arr_moves_possible = np.zeros(boards.shape[0], dtype=bool)\n",
    "    for game in range(boards.shape[0]):\n",
    "        if np.all(\n",
    "            moves[game] == -1\n",
    "        ):  # can be all or any. All should be faster since most times neither value will be -1.\n",
    "            arr_moves_possible[game] = not np.any(\n",
    "                get_possible_turns(np.reshape(boards[game], (1, 8, 8)))\n",
    "            )\n",
    "        else:\n",
    "            arr_moves_possible[game] = any(\n",
    "                _recursive_steps(boards[game, :, :], direction, moves[game]) > 0\n",
    "                for direction in DIRECTIONS\n",
    "            )\n",
    "    return arr_moves_possible\n",
    "\n",
    "\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.ones((3, 8, 8)) * 1, np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(get_new_games(3), np.array([[2, 3], [3, 2], [3, 2]])),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(get_new_games(3), np.array([[2, 2], [1, 1], [0, 0]])),\n",
    "    np.array([False] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.ones((3, 8, 8)) * -1, np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.zeros((3, 8, 8)), np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reword functions\n",
    "\n",
    "For any kind of reinforcement learning is a reword function needed.\n",
    "For otello this would be the final score, the information who won or changes to the score.\n",
    "A combination of those three would also be possible.\n",
    "It is probably not be possible to weight the current score to high in a reword function since that would be to close to a classic greedy algorithm.\n",
    "But some direct influence would increase the learning speed.\n",
    "In the next section are all three reword functions implemented to be combined and weight later on as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191 µs ± 2.27 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "33 µs ± 1.4 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
      "33.8 µs ± 345 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def final_boards_evaluation(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Evaluates the board at the end of the game.\n",
    "\n",
    "    All unused fields are added to the score of the player that has more stones with his color up.\n",
    "    This score only applies to the end of the game.\n",
    "    Normally the score is represented by the number of stones each player has.\n",
    "    In this case the score was combined by building the difference.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of game bords ot the end of the game.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    score1, score2 = np.sum(boards == 1, axis=(1, 2)), np.sum(boards == -1, axis=(1, 2))\n",
    "    player_1_won = score1 > score2\n",
    "    player_2_won = score1 < score2\n",
    "    score1_final = 64 - score2[player_1_won]\n",
    "    score2_final = 64 - score1[player_2_won]\n",
    "    score1[player_1_won] = score1_final\n",
    "    score2[player_2_won] = score2_final\n",
    "    return score1 - score2\n",
    "\n",
    "\n",
    "def evaluate_boards(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Counts the stones each player has on the board.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    return np.sum(boards, axis=(1, 2))\n",
    "\n",
    "\n",
    "def evaluate_who_won(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Checks who won or is winning a game.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        The information who won for both player. 1 meaning the player won, -1 means the opponent lost. 0 represents a patt.\n",
    "    \"\"\"\n",
    "    return np.sign(np.sum(boards, axis=(1, 2)))\n",
    "\n",
    "\n",
    "_boards = get_new_games(EXAMPLE_STACK_SIZE)\n",
    "%timeit final_boards_evaluation(_boards)\n",
    "%timeit evaluate_boards(_boards)\n",
    "%timeit evaluate_who_won(_boards)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute a chosen action\n",
    "\n",
    "After an evaluation what turns are possible there needs to be a function that executes a turn.\n",
    "This next sections does that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InvalidTurn(ValueError):\n",
    "    \"\"\"\n",
    "    This error is thrown if a given turn is not valid.\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89.6 ms ± 3.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def do_moves(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Executes a single move on a stack o Othello boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of Othello boards where the next stone should be placed.\n",
    "        moves: A stack of stone placement orders for the game. Formatted as coordinates in an array [x, y] of the place where the stone should be placed. Should contain [-1,-1] if no new placement is possible.\n",
    "\n",
    "    Returns:\n",
    "        The new state of the board.\n",
    "    \"\"\"\n",
    "\n",
    "    def _do_directional_move(\n",
    "        board: np.ndarray, rec_move: np.ndarray, rev_direction, step_one=True\n",
    "    ) -> bool:\n",
    "        \"\"\"Changes the color of enemy stones in one direction.\n",
    "\n",
    "        This function works recursive. The argument step_one should always be used in its default value.\n",
    "\n",
    "        Args:\n",
    "            board: A bord on which a stone was placed.\n",
    "            rec_move: The position on the board in x and y where this function is called from. Will be moved by recursive called.\n",
    "            rev_direction: The position where the stone was placed. Inside this recursion it will also be the last step that was checked.\n",
    "            step_one: Set to true if this is the first step in the recursion. False later on.\n",
    "\n",
    "        Returns:\n",
    "            True if a stone could be flipped.\n",
    "            All changes are made on the view of the numpy array and therefore not included in the return value.\n",
    "        \"\"\"\n",
    "        rec_position = rec_move + rev_direction\n",
    "        if np.any((rec_position >= 8) | (rec_position < 0)):\n",
    "            return False\n",
    "        next_field = board[tuple(rec_position.tolist())]\n",
    "        if next_field == 0:\n",
    "            return False\n",
    "        if next_field == 1:\n",
    "            return not step_one\n",
    "        if next_field == -1:\n",
    "            if _do_directional_move(board, rec_position, rev_direction, step_one=False):\n",
    "                board[tuple(rec_position.tolist())] = 1\n",
    "                return True\n",
    "            return False\n",
    "\n",
    "    def _do_move(_board: np.ndarray, move: np.ndarray) -> None:\n",
    "        \"\"\"Executes a turn on a board.\n",
    "\n",
    "        Args:\n",
    "            _board: The game board on wich to place a stone.\n",
    "            move: The coordinates of a stone that should be placed. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
    "\n",
    "        Returns:\n",
    "            All changes are made on the view of the numpy array.\n",
    "        \"\"\"\n",
    "        if np.all(move == -1):\n",
    "            if not move_possible(_board, move):\n",
    "                raise InvalidTurn(\"An action should be taken. A turn is possible.\")\n",
    "            return\n",
    "\n",
    "        # noinspection PyTypeChecker\n",
    "        if _board[tuple(move.tolist())] != 0:\n",
    "            raise InvalidTurn(\"This turn is not possible.\")\n",
    "\n",
    "        action = False\n",
    "        for direction in DIRECTIONS:\n",
    "            if _do_directional_move(_board, move, direction):\n",
    "                action = True\n",
    "        if not action:\n",
    "            raise InvalidTurn(\"This turn is not possible.\")\n",
    "\n",
    "        # noinspection PyTypeChecker\n",
    "        _board[tuple(move.tolist())] = 1\n",
    "\n",
    "    boards = boards.copy()\n",
    "    for game in range(boards.shape[0]):\n",
    "        _do_move(boards[game], moves[game])\n",
    "    return boards\n",
    "\n",
    "\n",
    "%timeit do_moves(get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE))[0]\n",
    "\n",
    "plot_othello_board(\n",
    "    do_moves(\n",
    "        get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE)\n",
    "    )[0]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An abstract reversi game policy\n",
    "\n",
    "For an easy use of policies an abstract class containing the policy generation / requests an action in an inherited instance of this class.\n",
    "This class filters the policy to only propose valid actions. Inherited instance do not need to care about this. This super class also manges exploration and exploitation with the epsilon value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GamePolicy(ABC):\n",
    "    \"\"\"\n",
    "    A game policy. Proposes where to place a stone next.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            epsilon: the epsilon / greedy value. Should be between zero and one. Set the mixture of policy and exploration. One means only the policy is used. Zero means only random policies are used. All mixtures inbetween between are possible.\n",
    "        \"\"\"\n",
    "        if 0 > epsilon > 1:\n",
    "            raise ValueError(\"Epsilon should be between zero and one.\")\n",
    "        self._epsilon: float = epsilon\n",
    "\n",
    "    @property\n",
    "    def epsilon(self):\n",
    "        return self._epsilon\n",
    "\n",
    "    @property\n",
    "    @abc.abstractmethod\n",
    "    def policy_name(self) -> str:\n",
    "        \"\"\"The name of this policy\"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"The internal policy is an unfiltered policy. It should only be called from inside this function\n",
    "\n",
    "        Args:\n",
    "            boards: A board where a policy should be calculated for.\n",
    "\n",
    "        Returns:\n",
    "            The policy for this board. Should have the same size as the boards array.\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def get_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"Calculates the policy that should be followed.\n",
    "\n",
    "        Calculates the policy that should be followed.\n",
    "        This function does include the usage of epsilon to configure greediness and exploration.\n",
    "\n",
    "        Args:\n",
    "            boards: A set of boards that show the environment where the policy should be calculated for.\n",
    "\n",
    "        Returns:\n",
    "            A vector of indices. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
    "        \"\"\"\n",
    "        assert len(boards.shape) == 3\n",
    "        assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
    "\n",
    "        if self.epsilon <= 0:\n",
    "            policies = np.random.rand(*boards.shape)\n",
    "        else:\n",
    "            policies = self._internal_policy(boards)\n",
    "            if self.epsilon < 1:\n",
    "                policies = policies * self.epsilon + np.random.rand(*boards.shape) * (\n",
    "                    1 - self.epsilon\n",
    "                )\n",
    "\n",
    "        # todo talk to team about backpropagation of score and epsilon for greedy factor\n",
    "\n",
    "        # todo possibly change this function to only validate the purpose turn and not all turns\n",
    "        possible_turns = get_possible_turns(boards)\n",
    "        policies[possible_turns == False] = -1.0\n",
    "        max_indices = [\n",
    "            np.unravel_index(policy.argmax(), policy.shape) for policy in policies\n",
    "        ]\n",
    "        policy_vector = np.array(max_indices, dtype=int)\n",
    "        no_turn_possible = np.all(policy_vector == 0, 1) & (policies[:, 0, 0] == -1.0)\n",
    "\n",
    "        policy_vector[no_turn_possible, :] = IMPOSSIBLE\n",
    "        return policy_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A first policy\n",
    "\n",
    "To quantify the quality of a game AI there needs to be some benchmarks.\n",
    "The easiest benchmark is to play against a random player.\n",
    "The easiest player to use as a benchmark is the random player.\n",
    "For this and testing purpose the random policy was implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RandomPolicy(GamePolicy):\n",
    "    \"\"\"\n",
    "    A policy playing a random turn by setting epsilon to 0.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float):\n",
    "        _ = epsilon\n",
    "        super().__init__(epsilon=0)\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        return \"random\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        pass\n",
    "\n",
    "\n",
    "rnd_policy = RandomPolicy(1)\n",
    "assert rnd_policy.policy_name == \"random\"\n",
    "assert rnd_policy.epsilon == 0\n",
    "\n",
    "rnd_policy_result = rnd_policy.get_policy(get_new_games(10))\n",
    "assert np.any((5 >= rnd_policy_result) & (rnd_policy_result >= 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting the game simulation together\n",
    "Now it's time to bring all together for a proper simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Playing a single turn\n",
    "\n",
    "The next function needed is used to request a policy, verify that the turn is legit and place a stone and turn enemy stones if possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.03 s ± 19 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "990 ms ± 29.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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gPCV0QwkZqIelFGO2aW3y4BXJxptaby2ZJM2hpDGz9fF9lyZHL0kWXZLMOWP/mw+U6/ickbPzgZycJWlmOEkyIzMy/D8fL8mleTBfy625IuvzvSnJoKOA0ZTQDSVkAMpTQjeUkIF6efoe+9RsJg9cntx0ZtJ3c5JmqyiaQ//z/e0fN5ONNydfe02rWJrN/WM+ULaz84FckrU5KYszIzMyM12Zma40dvl4Rmbk5Lwpl+T2vD4XT+p8HQWMpoRuKCEDUJ4SuqGEDJTBUop9eujK5K4Ptj5ubtv7dbd//85LWrfbH+YD5Xp9Ls7/yuVJkpk5YK/X3f7983LFpC6mdBQwmhK6oYQMQHlK6IYSMlAGSyn2atPa1p1/PO68JNl8+/SeD5Tr+JyR83LFuG57Xq7I8Xn1hDPoKGA0JXRDCRmA8pTQDSVkoBwTWkp94hOfSKPRyPvf//5JikNpHrwiaYzzlccaXa3bT+f5TF/6af93dj6QoTwzrtsO5ZlJOVtKRzEe+mn/V0I3lJCB6UlH7d9K6IYSMlCOcS+l7rnnnvzN3/xNFi1aNJl5KMiWja0XnNvX6ZR70tyW/PxryZZxvuFV3fOZvvTT/u/QzM/JWbLPp+ztycwckEV5cw7NvHFn0FGMh37a/5XQDSVkYHrSUfu3ErqhhAyUZVxLqS1btmTZsmX57Gc/m0MPPXSyM1GIdWt2vgPCeDVmJOuumZ7zmZ70U2d4VS7Y8S5749XMcF6VC8d9ex1Fu/RTZyihG0rIwPSjo/Z/JXRDCRkoy7h+HZYvX55zzjknr3/96/d53cHBwQwMDOx2YXroXz85P2dgw/Scz/SknzrDkTl+En5KM0fkuHHfWkfRLv3UGUrohhIyMP2MtaP00/RVQjeUkIGytP1Mzi984Qv5wQ9+kHvuuWdM11+5cmU++tGPth2M+j3z1M635Byv5lDyq3H+d6ru+Uw/+qlzPCezM2OC79UxIzNzUHrGfXsdRTv0U+cooRtKyMD00k5H6afpq4RuKCEDZWnrEX1fX1/e97735frrr89znvOcMd1mxYoV6e/v33Hp6/Pkz+nigNlJY+bEfkZjZnLgOP/OV/d8phf91Fn+O09leIJP3xvOUH6Z8T+i0VGMlX7qLCV0QwkZmD7a7Sj9NH2V0A0lZKAsbZ0pdd999+Xxxx/PS1/60h1fGxoayu23356rr746g4ODmTlz99+w7u7udHd3T05aKtU7Gc+OSdIzzmfH1D2f6UU/dZbHMxnnfjfyi4z/3G8dxVjpp85SQjeUkIHpo92O0k/TVwndUEIGytLWmVKve93r8tBDD+X+++/fcXnZy16WZcuW5f7773/WAyqmt4UXJM2JnYiQ5nCycJyvI1z3fKYX/dRZ7siaNCb49L1GZuSOjP9VMnUUY6WfOksJ3VBCBqYPHdU5SuiGEjJQlrbOlJo9e3ZOOumk3b52yCGH5PDDD3/W15n+Zi1IFixJ+m4e31t2NrqSBW9KZs2fnvOZXvRTZ3kifXkoN+WkLM7MHND27YfyTB7K1/NEHhl3Bh3FWOmnzlJCN5SQgelDR3WOErqhhAyUZYJvxsj+7pRLxlcWSesF6BZ9YHrPB8r1rVw+roVU0nqR81tz5YQz6ChgNCV0QwkZgPKU0A0lZKAcE15K3XbbbbnqqqsmIQolmnNGctrl47vtaZ9q3X46z2d600/7t/X5Xr6c8T0q+Yd8MOvzvQln0FGMl37av5XQDSVkYPrSUfuvErqhhAyUw5lS7NPJF+8sjcY+nvC5/funXd663f4wHyjXrblyx2JqKM/s9brbv//lfGBSzpLaTkcBoymhG0rIAJSnhG4oIQNlsJRinxqN1imSS9e2nr+bRuttOLe/leeOjxut7y9d27p+o7F/zAfKdmuuzOU5Iw/l6xnOcIayLUPZlmaGM5RnMpRtGc5wHsrXc3nOmNSFVKKjgNGV0A0lZADKU0I3lJCBMrT1Qud0tjlntC5b+pJ11yQDG5JfDSQH9rTeknPhhVP7gnN1zwfKtT7fy/p8L4dmXl6VC3NEjstB6ckvM5BfZEPuyDUTelHzsdBRwGhK6IYSMgDlKaEbSshAvSylaNus+cmpH+nc+UC5nsgj+Xo+VmsGHQWMpoRuKCEDUJ4SuqGEDNTD0/cAAAAAqJylFAAAAACVs5QCAAAAoHKWUgAAAABUrtFsNptVDhwYGEhvb2/SSA6ZW+Xklqc3J83hpDEjOXhO9fNlkKG0DHXPT5Ktm5I0k/7+/vT09NQTIvX3U1LG8ag7Q93zZZBhpBI6Sj/JUMp8GcrKoJ9aSjgWMshQyvxSMoy1n+pbSgGMUMxSCmAURfylD2AU+gko1b76qavCLLtzppQMMhSRoe75yc4tejH8S1/H/07KIMOuiuoo/dTxGeqeL0NZGfRTSwnHQgYZSplfSoax9lNtS6mDj0qWPVL93OvnJVsfbR2YOubLIENpGeqenyTXzW0VZynq6qekjONRd4a658sgw0gldZR+kqHu+TKUlUE/tZRwLGSQoZT5pWQYaz95oXMAAAAAKmcpBQAAAEDlLKUAAAAAqJylFAAAAACVq+/d95i2tmxM1q1J+tcnzzyVHDA76T0+WXhBMmvB1M8/NPPzqlyQI3N8npPZ+e88lcezPndkTZ5I39QHAIpVdz+VkKHu+cDoSrhvegwFjEY/USdLKcZs09rkwSuSjTe13loySZpDSWNm6+P7Lk2OXpIsuiSZc8bkzz8+Z+TsfCAnZ0maGU6SzMiMDP/Px0tyaR7M13Jrrsj6fG/yAwDFqrufSshQ93xgdCXcNz2GAkajnyiBp++xT81m8sDlyU1nJn03J2m2yqo59D/f3/5xM9l4c/K117TKrdmcvAxn5wO5JGtzUhZnRmZkZroyM11p7PLxjMzIyXlTLsnteX0unrzhQLFK6Ke6M9Q9HxhdKfdNj6GAkfQTJbGUYp8eujK564Otj5vb9n7d7d+/85LW7SbD63Nx/lcuT5LMzAF7ve7275+XK5QWdIC6+6mEDHXPB0ZXwn3TYyhgNPqJkrS1lLr00kvTaDR2u5xwwglTlY0CbFrbKqDxuPOSZPPtE5t/fM7IebliXLc9L1fk+Lx6YgGYNvRT56m7n0rIUPd8xk5HdZYS7pseQzFW+qmz6CdK0/aZUieeeGI2b9684/LP//zPU5GLQjx4RdIY5yuPNbpat5+Is/OBDOWZcd12KM/YpHcY/dRZ6u6nEjLUPZ/26KjOUcJ902Mo2qGfOod+ojRt/zp2dXXlqKOOmoosFGbLxtaL3mWczx1ubkt+/rVkS18ya377tz8083NylmTGOJ9lOjMHZFHenEMzL0/kkXH9DKYX/dQ56u6nEjLUPZ/26ajOUMJ902Mo2qWfOoN+okRt/yasX78+c+fOzbHHHptly5Zl48aNU5GLAqxbs/NdGMarMSNZd834bvuqXLDjHRjGq5nhvCoXTuhnMH3op85Rdz+VkKHu+bRPR3WGEu6bHkPRLv3UGfQTJWrrTKnf+I3fyJo1a7Jw4cJs3rw5H/3oR/PqV786P/rRjzJ79uxRbzM4OJjBwcEdnw8MDEwsMZXpXz85P2dgw/hud2SOn4TpzRyR4ybh51A6/dRZ6u6nEjLUPZ/2tNtR+mn6KuG+6TEU7dBPnUM/UaK2llKLFy/e8fGiRYvyG7/xGzn66KPzpS99Kb/3e7836m1WrlyZj370oxNLSS2eeWrn24KOV3Mo+dU4/zv1nMwe92md283IzByUngn9DKYH/dRZ6u6nEjLUPZ/2tNtR+mn6KuG+6TEU7dBPnUM/UaIJ/TY897nPza/92q9lw4Y9r0pXrFiR/v7+HZe+vr6JjKRCB8xOGjMn9jMaM5MDx9kX/52nMjzBUzuHM5Rfxt+4OpF+2r/V3U8lZKh7PhOzr47ST9NXCfdNj6GYCP20/9JPlGhCS6ktW7bk3/7t3zJnzpw9Xqe7uzs9PT27XZgeeifjzMokPeM8s/LxTMb5pY38Ip6b0on00/6t7n4qIUPd85mYfXWUfpq+SrhvegzFROin/Zd+okRtLaUuueSSrF27Ng8//HDuuOOO/NZv/VZmzpyZt73tbVOVjxotvCBpTmyJneZwsnCcr0F3R9akMcFTOxuZkTviVXw7gX7qLHX3UwkZ6p5Pe3RU5yjhvukxFO3QT51DP1Gitn4bHnnkkbztbW/LwoUL89u//ds5/PDDc+edd+aII46YqnzUaNaCZMGSpNHWK4/t1OhKjl46/rcLfSJ9eSg3ZSjPjOv2Q3kmD+ar3iq0Q+inzlJ3P5WQoe75tEdHdY4S7pseQ9EO/dQ59BMlauvX8Qtf+MJU5aBQp1ySbPza+G7bHEoWfWBi87+Vy3NK3jyu287IzNyaKycWgGlDP3WeuvuphAx1z2fsdFRnKeG+6TEUY6WfOot+ojQTO2+O/d6cM5LTLh/fbU/7VOv2E7E+38uXM77m+4d8MOvzvYkFAIpVdz+VkKHu+cDoSrhvegwFjEY/URpLKfbp5It3Fte+TvXc/v3TLm/dbjLcmit3lNa+TvPc/v0v5wM26NAB6u6nEjLUPR8YXQn3TY+hgNHoJ0piKcU+NRqt0zSXrk0WvClJo/VWoNvfTnTHx43W95eubV2/0Zi8DLfmylyeM/JQvp7hDGco2zKUbWlmOEN5JkPZluEM56F8PZfnDGUFHaKEfqo7Q93zgdGVct/0GAoYST9RknG+xBmdaM4ZrcuWvmTdNcnAhuRXA8mBPa23BV144dS+YO76fC/r870cmnl5VS7METkuB6Unv8xAfpENuSPXeME76FB191MJGeqeD4yuhPumx1DAaPQTJbCUom2z5ienfqS++U/kkXw9H6svAFCsuvuphAx1zwdGV8J902MoYDT6iTp5+h4AAAAAlbOUAgAAAKByllIAAAAAVM5SCgAAAIDKNZrNZrPKgQMDA+nt7U0aySFzq5zc8vTmpDmcNGYkB8+pfr4MMpSWoe75SbJ1U5Jm0t/fn56ennpCpP5+Sso4HnVnqHu+DDKMVEJH6ScZSpkvQ1kZ9FNLCcdCBhlKmV9KhrH2U31LKYARillKAYyiiL/0AYxCPwGl2lc/dVWYZXfOlJJBhiIy1D0/2blFL4Z/6ev430kZZNhVUR2lnzo+Q93zZSgrg35qKeFYyCBDKfNLyTDWfqptKXXwUcmyR6qfe/28ZOujrQNTx3wZZCgtQ93zk+S6ua3iLEVd/ZSUcTzqzlD3fBlkGKmkjtJPMtQ9X4ayMuinlhKOhQwylDK/lAxj7ScvdA4AAABA5SylAAAAAKicpRQAAAAAlbOUAgAAAKByllIAAAAAVM5SCgAAAIDKWUoBAAAAUDlLKQAAAAAq1/ZS6tFHH83b3/72HH744TnooINy8skn5957752KbABt0U9AyXQUUCr9BNSlq50rP/HEEzn99NNz1lln5eabb84RRxyR9evX59BDD52qfABjop+AkukooFT6CahTW0upT37yk5k/f36uueaaHV875phjJj0UQLv0E1AyHQWUSj8BdWrr6Xtf/epX87KXvSznnXdejjzyyLzkJS/JZz/72b3eZnBwMAMDA7tdACabfgJK1m5H6SegKvoJqFNbS6mf/exnWbVqVY4//vh84xvfyDvf+c68973vzbXXXrvH26xcuTK9vb07LvPnz59waICR9BNQsnY7Sj8BVdFPQJ3aWkoNDw/npS99aS677LK85CUvyf/3//1/+T//5//kr//6r/d4mxUrVqS/v3/Hpa+vb8KhAUbST0DJ2u0o/QRURT8BdWprKTVnzpy8+MUv3u1rv/7rv56NGzfu8Tbd3d3p6enZ7QIw2fQTULJ2O0o/AVXRT0Cd2lpKnX766Vm3bt1uX/vpT3+ao48+elJDAbRLPwEl01FAqfQTUKe2llJ/9Ed/lDvvvDOXXXZZNmzYkBtuuCF/+7d/m+XLl09VPoAx0U9AyXQUUCr9BNSpraXUy1/+8tx44435/Oc/n5NOOikf+9jHctVVV2XZsmVTlQ9gTPQTUDIdBZRKPwF16mr3BkuWLMmSJUumIgvAhOgnoGQ6CiiVfgLq0taZUgAAAAAwGSylAAAAAKicpRQAAAAAlbOUAgAAAKByllIAAAAAVM5SCgAAAIDKWUoBAAAAUDlLKQAAAAAq12g2m80qBw4MDKS3tzdpJIfMrXJyy9Obk+Zw0piRHDyn+vkyyFBahrrnJ8nWTUmaSX9/f3p6euoJkfr7KSnjeNSdoe75MsgwUgkdpZ9kKGW+DGVl0E8tJRwLGWQoZX4pGcbaT/UtpQBGKGYpBTCKIv7SBzAK/QSUal/91FVhlt05U0oGGYrIUPf8ZOcWvRj+pa/jfydlkGFXRXWUfur4DHXPl6GsDPqppYRjIYMMpcwvJcNY+6m2pdTBRyXLHql+7vXzkq2Ptg5MHfNlkKG0DHXPT5Lr5raKsxR19VNSxvGoO0Pd82WQYaSSOko/yVD3fBnKyqCfWko4FjLIUMr8UjKMtZ+80DkAAAAAlbOUAgAAAKByllIAAAAAVM5SCgAAAIDKWUoBAAAAUDlLKQAAAAAqZykFAAAAQOUspQAAAAConKUUAAAAAJVrayn1whe+MI1G41mX5cuXT1U+gDHTUUCp9BNQKv0E1KmrnSvfc889GRoa2vH5j370o5x99tk577zzJj0YQLt0FFAq/QSUSj8BdWprKXXEEUfs9vknPvGJvOhFL8prXvOaSQ0FMB46CiiVfgJKpZ+AOrW1lNrVr371q1x33XW5+OKL02g09ni9wcHBDA4O7vh8YGBgvCMBxmwsHaWfgDroJ6BU+gmo2rhf6PwrX/lKnnzyyVxwwQV7vd7KlSvT29u74zJ//vzxjgQYs7F0lH4C6qCfgFLpJ6Bq415KrV69OosXL87cuXP3er0VK1akv79/x6Wvr2+8IwHGbCwdpZ+AOugnoFT6CajauJ6+9/Of/zy33npr/vEf/3Gf1+3u7k53d/d4xgCMy1g7Sj8BVdNPQKn0E1CHcZ0pdc011+TII4/MOeecM9l5ACZMRwGl0k9AqfQTUIe2l1LDw8O55pprcv7556era9yvkw4wJXQUUCr9BJRKPwF1aXspdeutt2bjxo256KKLpiIPwIToKKBU+gkolX4C6tL2GvwNb3hDms3mVGQBmDAdBZRKPwGl0k9AXcb97nsAAAAAMF6WUgAAAABUzlIKAAAAgMpZSgEAAABQOUspAAAAACpnKQUAAABA5SylAAAAAKicpRQAAAAAlWs0m81mlQMHBgbS29ubNJJD5lY5ueXpzUlzOGnMSA6eU/18GWQoLUPd85Nk66YkzaS/vz89PT31hEj9/ZSUcTzqzlD3fBlkGKmEjtJPMpQyX4ayMuinlhKOhQwylDK/lAxj7af6llIAIxSzlAIYRRF/6QMYhX4CSrWvfuqqMMvunCklgwxFZKh7frJzi14M/9LX8b+TMsiwq6I6Sj91fIa658tQVgb91FLCsZBBhlLml5JhrP1U21Lq4KOSZY9UP/f6ecnWR1sHpo75MshQWoa65yfJdXNbxVmKuvopKeN41J2h7vkyyDBSSR2ln2Soe74MZWXQTy0lHAsZZChlfikZxtpPXugcAAAAgMpZSgEAAABQOUspAAAAACpnKQUAAABA5SylAAAAAKicpRQAAAAAlbOUAgAAAKByllIAAAAAVK6tpdTQ0FA+/OEP55hjjslBBx2UF73oRfnYxz6WZrM5VfkAxkQ/ASXTUUCp9BNQp652rvzJT34yq1atyrXXXpsTTzwx9957by688ML09vbmve9971RlBNgn/QSUTEcBpdJPQJ3aWkrdcccdOffcc3POOeckSV74whfm85//fO6+++4pCQcwVvoJKJmOAkqln4A6tfX0vVe96lX59re/nZ/+9KdJkgceeCD//M//nMWLF09JOICx0k9AyXQUUCr9BNSprTOlPvShD2VgYCAnnHBCZs6cmaGhoXz84x/PsmXL9nibwcHBDA4O7vh8YGBg/GkB9kA/ASVrt6P0E1AV/QTUqa0zpb70pS/l+uuvzw033JAf/OAHufbaa3P55Zfn2muv3eNtVq5cmd7e3h2X+fPnTzg0wEj6CShZux2ln4Cq6CegTm0tpT74wQ/mQx/6UH73d383J598cv73//7f+aM/+qOsXLlyj7dZsWJF+vv7d1z6+vomHBpgJP0ElKzdjtJPQFX0E1Cntp6+9/TTT2fGjN33WDNnzszw8PAeb9Pd3Z3u7u7xpQMYI/0ElKzdjtJPQFX0E1CntpZSS5cuzcc//vEsWLAgJ554Yn74wx/myiuvzEUXXTRV+QDGRD8BJdNRQKn0E1CntpZSf/VXf5UPf/jDede73pXHH388c+fOzR/8wR/kIx/5yFTlAxgT/QSUTEcBpdJPQJ3aWkrNnj07V111Va666qopigMwPvoJKJmOAkqln4A6tfVC5wAAAAAwGSylAAAAAKicpRQAAAAAlbOUAgAAAKByllIAAAAAVM5SCgAAAIDKWUoBAAAAUDlLKQAAAAAqZykFAAAAQOUazWazWeXA/v7+PPe5z02SHDynysktTz+WpJmkkRx8VPXzZZChtAx1z0+Spze3/vfJJ59Mb29vPSFSfz8lhRwPv5MyyLB7hgI6Sj/JUMp8GQrLoJ+SFHIsZJChkPnFZBhjP1W+lHrkkUcyf/78KkcC00RfX1/mzZtX23z9BOxNnR2ln4C90U9AqfbVT5UvpYaHh7Np06bMnj07jUaj7dsPDAxk/vz56evrS09PzxQklGG6ZKh7vgyTl6HZbOapp57K3LlzM2NGfc8q1k8y7E8Z6p6/P2UooaMm2k9J/cej7vkyyFBaBv20U93HooQMdc+XQYbJzjDWfuqaSMjxmDFjxqRs8Xt6emo7ODKUlaHu+TJMToY6n7a3nX6SYX/MUPf8/SVD3R01Wf2U1H886p4vgwylZdBPO9V9LErIUPd8GWSYzAxj6ScvdA4AAABA5SylAAAAAKjctFtKdXd358/+7M/S3d0tQ4dnqHu+DGVlKEEJfw4yyFDKfBnKU/efRd3zZZChtAx1zy9JCX8WdWeoe74MMtSVofIXOgcAAACAaXemFAAAAADTn6UUAAAAAJWzlAIAAACgctNqKfX9738/M2fOzDnnnFP57AsuuCCNRmPH5fDDD88b3/jGPPjgg5Vneeyxx/Ke97wnxx57bLq7uzN//vwsXbo03/72t6d89q5/DgcccECe//zn5+yzz87nPve5DA8PT/n8kRl2vbzxjW+sZP6+cmzYsKGS+Y899lje97735bjjjstznvOcPP/5z8/pp5+eVatW5emnn57y+RdccEHe8pa3POvrt912WxqNRp588skpz1AaHaWfRuaoq6Pq7qek3o7ST8+mn/TTyBz6yWOoUugn/TQyh37qrH6aVkup1atX5z3veU9uv/32bNq0qfL5b3zjG7N58+Zs3rw53/72t9PV1ZUlS5ZUmuHhhx/Oqaeemu985zv51Kc+lYceeii33HJLzjrrrCxfvrySDNv/HB5++OHcfPPNOeuss/K+970vS5YsybZt2yrNsOvl85//fCWz95XjmGOOmfK5P/vZz/KSl7wk3/zmN3PZZZflhz/8Yb7//e/nj//4j3PTTTfl1ltvnfIMPFund5R+enaOOjuqrn5KdFSJ9JN+GplDP+mnUugn/TQyh37qrH7qqjvAWG3ZsiVf/OIXc++99+axxx7LmjVr8qd/+qeVZuju7s5RRx2VJDnqqKPyoQ99KK9+9avzi1/8IkcccUQlGd71rnel0Wjk7rvvziGHHLLj6yeeeGIuuuiiSjLs+ufwghe8IC996Utz2mmn5XWve13WrFmT3//93680Q53qyvGud70rXV1duffee3f7PTj22GNz7rnnxptqVk9H6ac95ahLnRl0VFn0k37aU4666Ce200/6aU856qKfqjdtzpT60pe+lBNOOCELFy7M29/+9nzuc5+r9aBs2bIl1113XY477rgcfvjhlcz8r//6r9xyyy1Zvnz5br+k2z33uc+tJMdoXvva1+aUU07JP/7jP9aWoVP853/+Z775zW/u8fcgSRqNRsWp6PSO0k9sp6PKo5/0Ey36qTz6ST/R0sn9NG2WUqtXr87b3/72JK1T6vr7+7N27dpKM9x0002ZNWtWZs2aldmzZ+erX/1qvvjFL2bGjGr+GDds2JBms5kTTjihknntOuGEE/Lwww9XMmvXY7H9ctlll1Uye285zjvvvCmfuf33YOHChbt9/XnPe96OHH/yJ38y5TmS0Y/D4sWLK5ldmk7vKP20uxI6qo5+SsrpKP20k37ST7vST/X3U6KjttNP+mlX+qkz+2laPH1v3bp1ufvuu3PjjTcmSbq6uvI7v/M7Wb16dc4888zKcpx11llZtWpVkuSJJ57IZz7zmSxevDh33313jj766CmfX/rpes1ms7Lt7a7HYrvDDjusktl7y7GnrXYV7r777gwPD2fZsmUZHBysZOZox+Guu+7a8eCiU+go/TRSCR1VUj8l1XeUfmrRT/ppJP30bB5D1UM/6aeR9NOzdUI/TYul1OrVq7Nt27bMnTt3x9eazWa6u7tz9dVXp7e3t5IchxxySI477rgdn//d3/1dent789nPfjZ/8Rd/MeXzjz/++DQajfzkJz+Z8lnj8eMf/7iyF4EbeSzqUkeO4447Lo1GI+vWrdvt68cee2yS5KCDDqosy2j//x955JHK5pdCR+mnkUroqLoylNJR+qlFP+mnkfRT/f2U6KhEPyX6aST91Jn9VPzT97Zt25a///u/zxVXXJH7779/x+WBBx7I3Llza3nHte0ajUZmzJiRX/7yl5XMO+yww/Kbv/mb+fSnP52tW7c+6/t1vn3sd77znTz00EN561vfWluGTnH44Yfn7LPPztVXXz3q7wHV0lEt+ontdFQ59FOLfmI7/VQO/dSin9iuk/up+DOlbrrppjzxxBP5vd/7vWdty9/61rdm9erV+cM//MNKsgwODuaxxx5L0jq18+qrr86WLVuydOnSSuYnyac//emcfvrpecUrXpE///M/z6JFi7Jt27Z861vfyqpVq/LjH/94yjNs/3MYGhrKf/zHf+SWW27JypUrs2TJkrzjHe+Y8vm7ZthVV1dXnve851Uyv26f+cxncvrpp+dlL3tZLr300ixatCgzZszIPffck5/85Cc59dRT647YMXTUTvrp2Tl2paN0VNX000766dk5dqWf9FPV9NNO+unZOXalnzqgn5qFW7JkSfNNb3rTqN+76667mkmaDzzwwJTnOP/885tJdlxmz57dfPnLX978h3/4hymfPdKmTZuay5cvbx599NHNAw88sPmCF7yg+eY3v7n53e9+d8pn7/rn0NXV1TziiCOar3/965uf+9znmkNDQ1M+f2SGXS8LFy6sZP6uOc4999xKZ+5q06ZNzXe/+93NY445pnnAAQc0Z82a1XzFK17R/NSnPtXcunXrlM/f0///7373u80kzSeeeGLKM5RAR+2u0/tpZI66Oqrufmo26+0o/dSin3ann/TTdh5D1U8/7U4/6aftOrGfGs1m4a+uBgAAAMB+p/jXlAIAAABg/2MpBQAAAEDlLKUAAAAAqJylFAAAAACVs5QCAAAAoHKWUgAAAABUzlIKAAAAgMpZSgEAAABQOUspAAAAACpnKQUAAABA5SylAAAAAKicpRQAAAAAlbOUAgAAAKByllIAAAAAVM5SCgAAAIDKWUoBAAAAUDlLKQAAAAAq11X1wOHh4WzatCmzZ89Oo9GoejxQoGazmaeeeipz587NjBn17cr1EzCaEjpKPwGj0U9AqcbaT5UvpTZt2pT58+dXPRaYBvr6+jJv3rza5usnYG/q7Cj9BOyNfgJKta9+qnwpNXv27B0fHzyn6unJ048laSZpJAcfVf18GWQoLUPd85Pk6c2t/921H+pQdz8lhRwPv5MyyLB7hgI6Sj/JUMp8GQrLoJ+SFHIsZJChkPnFZBhjP1W+lNp+SufBc5K3b6p6enL9vGTro8khc5Nlj1Q/XwYZSstQ9/wkuW5uq7TqPuW77n5KyjgedWeoe74MMoxUQkfpJxlKmS9DWRn0U0sJx0IGGUqZX0qGsfaTFzoHAAAAoHKWUgAAAABUzlIKAAAAgMpZSgEAAABQOUspAAAAACpnKQUAAABA5SylAAAAAKicpRQAAAAAlWt7KXX77bdn6dKlmTt3bhqNRr7yla9MQSyA9uknoFT6CSiVfgLq1PZSauvWrTnllFPy6U9/eiryAIybfgJKpZ+AUuknoE5d7d5g8eLFWbx48VRkAZgQ/QSUSj8BpdJPQJ3aXkq1a3BwMIODgzs+HxgYmOqRAGOin4BS6SegVPoJmExT/kLnK1euTG9v747L/Pnzp3okwJjoJ6BU+gkolX4CJtOUL6VWrFiR/v7+HZe+vr6pHgkwJvoJKJV+Akqln4DJNOVP3+vu7k53d/dUjwFom34CSqWfgFLpJ2AyTfmZUgAAAAAwUttnSm3ZsiUbNmzY8fm///u/5/77789hhx2WBQsWTGo4gHboJ6BU+gkolX4C6tT2Uuree+/NWWedtePziy++OEly/vnnZ82aNZMWDKBd+gkolX4CSqWfgDq1vZQ688wz02w2pyILwIToJ6BU+gkolX4C6uQ1pQAAAAConKUUAAAAAJWzlAIAAACgcpZSAAAAAFTOUgoAAACAyllKAQAAAFA5SykAAAAAKmcpBQAAAEDlGs1ms1nlwIGBgfT29iaN5JC5VU5ueXpz0hxOGjOSg+dUP18GGUrLUPf8JNm6KUkz6e/vT09PTz0hUn8/JWUcj7oz1D1fBhlGKqGj9JMMpcyXoawM+qmlhGMhgwylzC8lw1j7qb6lFMAIxSylAEZRxF/6AEahn4BS7aufuirMsjtnSskgQxEZ6p6f7NyiF8O/9HX876QMMuyqqI7STx2foe75MpSVQT+1lHAsZJChlPmlZBhrP9W2lDr4qGTZI9XPvX5esvXR1oGpY74MMpSWoe75SXLd3FZxlqKufkrKOB51Z6h7vgwyjFRSR+knGeqeL0NZGfRTSwnHQgYZSplfSoax9pMXOgcAAACgcpZSAAAAAFTOUgoAAACAyllKAQAAAFA5SykAAAAAKmcpBQAAAEDlLKUAAAAAqJylFAAAAACVs5QCAAAAoHJtLaVWrlyZl7/85Zk9e3aOPPLIvOUtb8m6deumKhvAmOknoGQ6CiiVfgLq1NZSau3atVm+fHnuvPPOfOtb38ozzzyTN7zhDdm6detU5QMYE/0ElExHAaXST0Cdutq58i233LLb52vWrMmRRx6Z++67L2ecccakBgNoh34CSqajgFLpJ6BObS2lRurv70+SHHbYYXu8zuDgYAYHB3d8PjAwMJGRAGOin4CS7auj9BNQF/0EVGncL3Q+PDyc97///Tn99NNz0kkn7fF6K1euTG9v747L/PnzxzsSYEz0E1CysXSUfgLqoJ+Aqo17KbV8+fL86Ec/yhe+8IW9Xm/FihXp7+/fcenr6xvvSIAx0U9AycbSUfoJqIN+Aqo2rqfvvfvd785NN92U22+/PfPmzdvrdbu7u9Pd3T2ucADt0k9AycbaUfoJqJp+AurQ1lKq2WzmPe95T2688cbcdtttOeaYY6YqF0Bb9BNQMh0FlEo/AXVqaym1fPny3HDDDfmnf/qnzJ49O4899liSpLe3NwcddNCUBAQYC/0ElExHAaXST0Cd2npNqVWrVqW/vz9nnnlm5syZs+PyxS9+caryAYyJfgJKpqOAUuknoE5tP30PoET6CSiZjgJKpZ+AOo373fcAAAAAYLwspQAAAAConKUUAAAAAJWzlAIAAACgcpZSAAAAAFTOUgoAAACAyllKAQAAAFA5SykAAAAAKtdoNpvNKgcODAykt7c3aSSHzK1ycsvTm5PmcNKYkRw8p/r5MshQWoa65yfJ1k1Jmkl/f396enrqCZH6+ykp43jUnaHu+TLIMFIJHaWfZChlvgxlZdBPLSUcCxlkKGV+KRnG2k/1LaUARihmKQUwiiL+0gcwCv0ElGpf/dRVYZbdOVNKBhmKyFD3/GTnFr0Y/qWv438nZZBhV0V1lH7q+Ax1z5ehrAz6qaWEYyGDDKXMLyXDWPuptqXUwUclyx6pfu7185Ktj7YOTB3zZZChtAx1z0+S6+a2irMUdfVTUsbxqDtD3fNlkGGkkjpKP8lQ93wZysqgn1pKOBYyyFDK/FIyjLWfvNA5AAAAAJWzlAIAAACgcpZSAAAAAFTOUgoAAACAyllKAQAAAFA5SykAAAAAKmcpBQAAAEDlLKUAAAAAqFxbS6lVq1Zl0aJF6enpSU9PT175ylfm5ptvnqpsAGOmn4CS6SigVPoJqFNbS6l58+blE5/4RO67777ce++9ee1rX5tzzz03//Iv/zJV+QDGRD8BJdNRQKn0E1CnrnauvHTp0t0+//jHP55Vq1blzjvvzIknnjipwQDaoZ+AkukooFT6CahTW0upXQ0NDeXLX/5ytm7dmle+8pWTmQlgQvQTUDIdBZRKPwFVa3sp9dBDD+WVr3xl/vu//zuzZs3KjTfemBe/+MV7vP7g4GAGBwd3fD4wMDC+pAD7oJ+AkrXTUfoJqJJ+AurS9rvvLVy4MPfff3/uuuuuvPOd78z555+ff/3Xf93j9VeuXJne3t4dl/nz508oMMCe6CegZO10lH4CqqSfgLq0vZQ68MADc9xxx+XUU0/NypUrc8opp+Qv//Iv93j9FStWpL+/f8elr69vQoEB9kQ/ASVrp6P0E1Al/QTUZdyvKbXd8PDwbqdvjtTd3Z3u7u6JjgFom34CSra3jtJPQJ30E1CVtpZSK1asyOLFi7NgwYI89dRTueGGG3LbbbflG9/4xlTlAxgT/QSUTEcBpdJPQJ3aWko9/vjjecc73pHNmzent7c3ixYtyje+8Y2cffbZU5UPYEz0E1AyHQWUSj8BdWprKbV69eqpygEwIfoJKJmOAkqln4A6tf1C5wAAAAAwUZZSAAAAAFTOUgoAAACAyllKAQAAAFA5SykAAAAAKmcpBQAAAEDlLKUAAAAAqJylFAAAAACVs5QCAAAAoHKNZrPZrHLgwMBAent7k0ZyyNwqJ7c8vTlpDieNGcnBc6qfL4MMpWWoe36SbN2UpJn09/enp6ennhCpv5+SMo5H3Rnqni+DDCOV0FH6SYZS5stQVgb91FLCsZBBhlLml5JhrP1U31IKYIRillIAoyjiL30Ao9BPQKn21U9dFWbZnTOlZJChiAx1z092btGL4V/6Ov53UgYZdlVUR+mnjs9Q93wZysqgn1pKOBYyyFDK/FIyjLWfaltKHXxUsuyR6udePy/Z+mjrwNQxXwYZSstQ9/wkuW5uqzhLUVc/JWUcj7oz1D1fBhlGKqmj9JMMdc+XoawM+qmlhGMhgwylzC8lw1j7yQudAwAAAFA5SykAAAAAKmcpBQAAAEDlLKUAAAAAqFx9777HtHVo5udVuSBH5vg8J7Pz33kqj2d97siaPJG+KZ+/ZWOybk3Svz555qnkgNlJ7/HJwguSWQumfDxQsLr7KdFRwOj0E1Aq/USdLKUYs+NzRs7OB3JylqSZ4STJjMzI8P98vCSX5sF8LbfmiqzP9yZ9/qa1yYNXJBtvar21ZZI0h5LGzNbH912aHL0kWXRJMueMSR8PFKzufkp0FDA6/QSUSj9RAk/fY0zOzgdySdbmpCzOjMzIzHRlZrrS2OXjGZmRk/OmXJLb8/pcPGmzm83kgcuTm85M+m5O0mwVVXPof76//eNmsvHm5GuvaRVbszlpEYCC1dlPiY4C9kw/AaXST5TCUop9en0uzv/K5UmSmTlgr9fd/v3zcsWkFddDVyZ3fbD1cXPb3q+7/ft3XtK6HbB/q7ufEh0FjE4/AaXST5RkQkupT3ziE2k0Gnn/+98/SXEozfE5I+flinHd9rxckePz6gnN37S2VT7jceclyebbJzSeaUw/7f/q7qdERzE++mn/p5+YznTU/k0/UZpxL6Xuueee/M3f/E0WLVo0mXkozNn5QIbyzLhuO5RnJrxNf/CKpDHOVz5rdLVuT+fRT52h7n5KdBTt00+dQT8xXemo/Z9+ojTjWkpt2bIly5Yty2c/+9kceuihk52JQhya+Tk5S/Z5SueezMwBWZQ359DMG9ftt2xsveDdvk7n3JPmtuTnX0u2VPOGERRCP3WGuvsp0VG0Tz91Bv3EdKWj9n/6iRKNaym1fPnynHPOOXn9618/2XkoyKtywY53YRivZobzqlw4rtuuW7PzHRjGqzEjWXfNxH4G04t+6gx191Oio2iffuoM+onpSkft//QTJWr7pLkvfOEL+cEPfpB77rlnTNcfHBzM4ODgjs8HBgbaHUlNjszxk/BTmjkix43rlv3rJ2F8koENk/NzKJ9+6hx191Oio2iPfuoc+onpqJ2O0k/Tl36iRG3tKPv6+vK+970v119/fZ7znOeM6TYrV65Mb2/vjsv8+fPHFZTqPSezM2OCb9A4IzNzUHrGddtnntr5lqDj1RxKfuW/kx1BP3WWuvsp0VGMnX7qLPqJ6abdjtJP05d+okRt/Ubed999efzxx/PSl740XV1d6erqytq1a/N//+//TVdXV4aGnv3btWLFivT39++49PV58ud08d95KsMTPL1zOEP5ZcbXGAfMThozJzQ+jZnJgePvTKYR/dRZ6u6nREcxdvqps+gnppt2O0o/TV/6iRK19fS9173udXnooYd2+9qFF16YE044IX/yJ3+SmTOf/dvV3d2d7u7uiaWkFo9nMs6tbOQXGd+5lb2TcXZpkp7xn13KNKKfOkvd/ZToKMZOP3UW/cR0025H6afpSz9RoraWUrNnz85JJ52029cOOeSQHH744c/6OtPfHVmTJbl0Qj+jkRm5I+N7FbqFFyT3TWx8msPJwvG/Dh/TiH7qLHX3U6KjGDv91Fn0E9ONjuoc+okSTfB179mfPZG+PJSbMpRnxnX7oTyTB/PVPJFHxnX7WQuSBUuSRtsvx9/S6EqOXprM8jR32O/U3U+JjgJGp5+AUuknSjTOX4WdbrvttkmIQam+lctzSt48rtvOyMzcmisnNP+US5KNXxvfbZtDyaIPTGg805x+2r/V3U+JjmL89NP+TT8x3emo/Zd+ojTOlGKv1ud7+XLGd6//h3ww6/O9Cc2fc0Zy2uXju+1pn2rdHtg/1d1PiY4CRqefgFLpJ0pjKcU+3ZordxTXvk713P79L+cDk7JFT5KTL95ZWvs6zXP790+7vHU7YP9Wdz8lOgoYnX4CSqWfKImlFGNya67M5TkjD+XrGc5whrItQ9mWZoYzlGcylG0ZznAeytdzec6Y1MJqNFqnaC5dmyx4U5JG621At7+V6I6PG63vL13bun6jMWkRgILV2U+JjgL2TD8BpdJPlGLCrylF51if72V9vpdDMy+vyoU5IsfloPTklxnIL7Ihd+SaCb3o3b7MOaN12dKXrLsmGdiQ/GogObCn9ZagCy/0gnfQqerup0RHAaPTT0Cp9BMlsJSibU/kkXw9H6tt/qz5yakfqW08ULC6+ynRUcDo9BNQKv1EnTx9DwAAAIDKWUoBAAAAUDlLKQAAAAAq12g2m80qBw4MDKS3tzdpJIfMrXJyy9Obk+Zw0piRHDyn+vkyyFBahrrnJ8nWTUmaSX9/f3p6euoJkfr7KSnjeNSdoe75MsgwUgkdpZ9kKGW+DGVl0E8tJRwLGWQoZX4pGcbaT/UtpQBGKGYpBTCKIv7SBzAK/QSUal/9VN+77zlTSgYZishQ9/xk5xa9GP6lr+N/J2WQYVdFdZR+6vgMdc+XoawM+qmlhGMhgwylzC8lw1j7qbal1MFHJcseqX7u9fOSrY+2Dkwd82WQobQMdc9PkuvmtoqzFHX1U1LG8ag7Q93zZZBhpJI6Sj/JUPd8GcrKoJ9aSjgWMshQyvxSMoy1n7zQOQAAAACVs5QCAAAAoHKWUgAAAABUzlIKAAAAgMrV9+57TFtbNibr1iT965NnnkoOmJ30Hp8svCCZtWD/n19KBuDZSrhvlpABKE8J3VBCBqA8JXSDDJ3LUoox27Q2efCKZONNrbeWTJLmUNKY2fr4vkuTo5ckiy5J5pyx/80vJQPwbCXcN0vIAJSnhG4oIQNQnhK6QQY8fY99ajaTBy5Pbjoz6bs5SbN1J20O/c/3t3/cTDbenHztNa07dbO5f8wvJQPwbCXcN0vIAJSnhG4oIQNQnhK6QQa2s5Rinx66Mrnrg62Pm9v2ft3t37/zktbt9of5pWQAnq2E+2YJGYDylNANJWQAylNCN8jAdpZS7NWmta073njceUmy+fbpPb+UDMCzlXDfLCEDUJ4SuqGEDEB5SugGGdhVW0upSy+9NI1GY7fLCSecMFXZKMCDVySNcb7yWKOrdfvpPL+UDOybfuo8Jdw3S8jA9KCjOksJ3VBCBqYH/dRZSugGGdhV24fhxBNPzK233rrzB3R5rfT91ZaNrRd7yzifM9vclvz8a8mWvmTW/Ok3v5QMjJ1+6hwl3DdLyMD0oqM6QwndUEIGphf91BlK6AYZGKntp+91dXXlqKOO2nF53vOeNxW5KMC6NTvffWC8GjOSdddMz/mlZGDs9FPnKOG+WUIGphcd1RlK6IYSMjC96KfOUEI3yMBIbR+K9evXZ+7cuTn22GOzbNmybNy4ca/XHxwczMDAwG4Xpof+9ZPzcwY2TM/5pWRg7PRT5yjhvllCBqaXdjpKP01fJXRDCRmYXvRTZyihG2RgpLaWUr/xG7+RNWvW5JZbbsmqVavy7//+73n1q1+dp556ao+3WblyZXp7e3dc5s93ftt08cxTO98Oc7yaQ8mvxvnfqbrnl5KBsdFPnaWE+2YJGZg+2u0o/TR9ldANJWRg+tBPnaOEbpCBkdpaSi1evDjnnXdeFi1alN/8zd/M//t//y9PPvlkvvSlL+3xNitWrEh/f/+OS19f34RDU40DZieNmRP7GY2ZyYE903N+KRkYG/3UWUq4b5aQgemj3Y7ST9NXCd1QQgamD/3UOUroBhkYaUKvYPfc5z43v/Zrv5YNG/Z83lp3d3e6u7snMoaa9B4/OT+n57jpOb+UDIyPftq/lXDfLCED09e+Oko/TV8ldEMJGZi+9NP+q4RukIGRJvTyXlu2bMm//du/Zc6cOZOVh4IsvCBpDk/sZzSHk4UXTs/5pWRgfPTT/q2E+2YJGZi+dNT+q4RuKCED05d+2n+V0A0yMFJbS6lLLrkka9euzcMPP5w77rgjv/Vbv5WZM2fmbW9721Tlo0azFiQLliSNcZ5P1+hKjl46/rfJrHt+KRkYG/3UWUq4b5aQgelDR3WOErqhhAxMH/qpc5TQDTIwUltLqUceeSRve9vbsnDhwvz2b/92Dj/88Nx555054ogjpiofNTvlkqS5bXy3bQ4liz4wveeXkoF900+dp4T7ZgkZmB50VGcpoRtKyMD0oJ86SwndIAO7ams3+IUvfGGqclCoOWckp12e3HlJ+7c97VOt20/n+aVkYN/0U+cp4b5ZQgamBx3VWUrohhIyMD3op85SQjfIwK4m9JpSdIaTL27dYZN9n+K4/funXd663f4wv5QMwLOVcN8sIQNQnhK6oYQMQHlK6AYZ2M5Sin1qNFqnJy5dmyx4U5JG6y0wt7+N5o6PG63vL13bun6jsX/MLyUD8Gwl3DdLyACUp4RuKCEDUJ4SukEGthvnS3vRieac0bps6UvWXZMMbEh+NZAc2NN6O8yFF07ti73VPb+UDMCzlXDfLCEDUJ4SuqGEDEB5SugGGbCUom2z5ienfqRz55eSAXi2Eu6bJWQAylNCN5SQAShPCd0gQ+fy9D0AAAAAKmcpBQAAAEDlLKUAAAAAqJylFAAAAACVazSbzWaVAwcGBtLb25s0kkPmVjm55enNSXM4acxIDp5T/XwZZCgtQ93zk2TrpiTNpL+/Pz09PfWESP39lJRxPOrOUPd8GWQYqYSO0k8ylDJfhrIy6KeWEo6FDDKUMr+UDGPtp/qWUgAjFLOUAhhFEX/pAxiFfgJKta9+6qowy+6cKSWDDEVkqHt+snOLXgz/0tfxv5MyyLCrojpKP3V8hrrny1BWBv3UUsKxkEGGUuaXkmGs/VTbUurgo5Jlj1Q/9/p5ydZHWwemjvkyyFBahrrnJ8l1c1vFWYq6+ikp43jUnaHu+TLIMFJJHaWfZKh7vgxlZdBPLSUcCxlkKGV+KRnG2k9e6BwAAACAyllKAQAAAFA5SykAAAAAKmcpBQAAAEDl6nv3PQDYD23ZmKxbk/SvT555KjlgdtJ7fLLwgmTWgrrTAZ1MPwFQGkspAJgEm9YmD16RbLyp9fa7SdIcShozWx/fd2ly9JJk0SXJnDNqiwl0IP0EQKk8fQ8AJqDZTB64PLnpzKTv5iTN1l/2mkP/8/3tHzeTjTcnX3tN6y+HzWaNoYGOoJ8AKJ2lFABMwENXJnd9sPVxc9ver7v9+3de0rodwFTSTwCUru2l1KOPPpq3v/3tOfzww3PQQQfl5JNPzr333jsV2QDaop+o2qa1rb/AjcedlySbb5/cPJRNR1El/UQ79BNQl7ZeU+qJJ57I6aefnrPOOis333xzjjjiiKxfvz6HHnroVOUDGBP9RB0evCJpdO37DITRNLpat/f6LZ1BR1E1/cRY6SegTm0tpT75yU9m/vz5ueaaa3Z87Zhjjpn0UADt0k9UbcvG1osGZ5yvvdLclvz8a8mWvmTW/EmNRoF0FFXST7RDPwF1auvpe1/96lfzspe9LOedd16OPPLIvOQlL8lnP/vZqcoGMGb6iaqtW7PzXazGqzEjWXfNvq/H9KejqJJ+oh36CahTW/+5+tnPfpZVq1bl+OOPzze+8Y28853vzHvf+95ce+21e7zN4OBgBgYGdrsATDb9RNX610/OzxnYMDk/h7K121H6iYnQT7RDPwF1auvpe8PDw3nZy16Wyy67LEnykpe8JD/60Y/y13/91zn//PNHvc3KlSvz0Y9+dOJJAfZCP1G1Z57a+bbq49UcSn7lsXxHaLej9BMToZ9oh34C6tTWmVJz5szJi1/84t2+9uu//uvZuHHjHm+zYsWK9Pf377j09fWNLynAXugnqnbA7KQxc2I/ozEzObBncvJQtnY7Sj8xEfqJdugnoE5tnSl1+umnZ926dbt97ac//WmOPvroPd6mu7s73d3d40sHMEb6iar1Hj85P6fnuMn5OZSt3Y7ST0yEfqId+gmoU1tnSv3RH/1R7rzzzlx22WXZsGFDbrjhhvzt3/5tli9fPlX5AMZEP1G1hRckzeGJ/YzmcLLwwkmJQ+F0FFXST7RDPwF1amsp9fKXvzw33nhjPv/5z+ekk07Kxz72sVx11VVZtmzZVOUDGBP9RNVmLUgWLEkabZ1zvFOjKzl6qbdb7xQ6iirpJ9qhn4A6tf2fqiVLlmTJkiVTkQVgQvQTVTvlkmTj18Z32+ZQsugDk5uHsukoqqSfaId+AurS1plSAMBOc85ITrt8fLc97VOt2wNMBf0EwHRgKQUAE3DyxTv/4revp8ps//5pl7duBzCV9BMApbOUAoAJaDRaT3NZujZZ8KYkjdZbqW9/O/YdHzda31+6tnX9RqPO1EAn0E8AlG6cL38IAOxqzhmty5a+ZN01ycCG5FcDyYE9rbdVX3ihFw0G6qGfACiVpRQATKJZ85NTP1J3CoBn008AlMbT9wAAAAConKUUAAAAAJWzlAIAAACgcpZSAAAAAFSu0Ww2m1UOHBgYSG9vb9JIDplb5eSWpzcnzeGkMSM5eE7182WQobQMdc9Pkq2bkjST/v7+9PT01BMi9fdTUsbxqDtD3fNlkGGkEjpKP8lQynwZysqgn1pKOBYyyFDK/FIyjLWf6ltKAYxQzFIKYBRF/KUPYBT6CSjVvvqpq8Isu3OmlAwyFJGh7vnJzi16MfxLX8f/Tsogw66K6ij91PEZ6p4vQ1kZ9FNLCcdCBhlKmV9KhrH2U21LqYOPSpY9Uv3c6+clWx9tHZg65ssgQ2kZ6p6fJNfNbRVnKerqp6SM41F3hrrnyyDDSCV1lH6Soe75MpSVQT+1lHAsZJChlPmlZBhrP3mhcwAAAAAqZykFAAAAQOUspQAAAAConKUUAAAAAJWzlAIAAACgcpZSAAAAAFTOUgoAAACAyllKAQAAAFC5tpZSL3zhC9NoNJ51Wb58+VTlAxgzHQWUSj8BpdJPQJ262rnyPffck6GhoR2f/+hHP8rZZ5+d8847b9KDAbRLRwGl0k9AqfQTUKe2llJHHHHEbp9/4hOfyIte9KK85jWvmdRQAOOho4BS6SegVPoJqNO4X1PqV7/6Va677rpcdNFFaTQak5kJYMJ0FFAq/QSUSj8BVWvrTKldfeUrX8mTTz6ZCy64YK/XGxwczODg4I7PBwYGxjsSYMzG0lH6CaiDfgJKpZ+Aqo37TKnVq1dn8eLFmTt37l6vt3LlyvT29u64zJ8/f7wjAcZsLB2ln4A66CegVPoJqNq4llI///nPc+utt+b3f//393ndFStWpL+/f8elr69vPCMBxmysHaWfgKrpJ6BU+gmow7ievnfNNdfkyCOPzDnnnLPP63Z3d6e7u3s8YwDGZawdpZ+AquknoFT6CahD22dKDQ8P55prrsn555+frq5xvyQVwJTQUUCp9BNQKv0E1KXtpdStt96ajRs35qKLLpqKPAAToqOAUuknoFT6CahL22vwN7zhDWk2m1ORBWDCdBRQKv0ElEo/AXUZ97vvAQAAAMB4WUoBAAAAUDlLKQAAAAAqZykFAAAAQOUspQAAAAConKUUAAAAAJWzlAIAAACgcpZSAAAAAFTOUgoAAACAyjWazWazyoEDAwPp7e1NGskhc6uc3PL05qQ5nDRmJAfPqX6+DDKUlqHu+UmydVOSZtLf35+enp56QqT+fkrKOB51Z6h7vgwyjFRCR+knGUqZL0NZGfRTSwnHQgYZSplfSoax9lN9SymAEYpZSgGMooi/9AGMQj8BpdpXP3VVmGV3zpSSQYYiMtQ9P9m5RS+Gf+nr+N9JGWTYVVEdpZ86PkPd82UoK4N+ainhWMggQynzS8kw1n6qbSl18FHJskeqn3v9vGTro60DU8d8GWQoLUPd85Pkurmt4ixFXf2UlHE86s5Q93wZZBippI7STzLUPV+GsjLop5YSjoUMMpQyv5QMY+0nL3QOAAAAQOUspQAAAAConKUUAAAAAJWzlAIAAACgcpZSAAAAAFTOUgoAAACAyllKAQAAAFA5SykAAAAAKtfWUmpoaCgf/vCHc8wxx+Sggw7Ki170onzsYx9Ls9mcqnwAY6KfgJLpKKBU+gmoU1c7V/7kJz+ZVatW5dprr82JJ56Ye++9NxdeeGF6e3vz3ve+d6oyAuyTfgJKpqOAUuknoE5tLaXuuOOOnHvuuTnnnHOSJC984Qvz+c9/PnffffeUhAMYK/0ElExHAaXST0Cd2nr63qte9ap8+9vfzk9/+tMkyQMPPJB//ud/zuLFi/d4m8HBwQwMDOx2AZhs+gkoWbsdpZ+AqugnoE5tnSn1oQ99KAMDAznhhBMyc+bMDA0N5eMf/3iWLVu2x9usXLkyH/3oRyccFGBv9BNQsnY7Sj8BVdFPQJ3aOlPqS1/6Uq6//vrccMMN+cEPfpBrr702l19+ea699to93mbFihXp7+/fcenr65twaICR9BNQsnY7Sj8BVdFPQJ3aOlPqgx/8YD70oQ/ld3/3d5MkJ598cn7+859n5cqVOf/880e9TXd3d7q7uyeeFGAv9BNQsnY7Sj8BVdFPQJ3aOlPq6aefzowZu99k5syZGR4entRQAO3ST0DJdBRQKv0E1KmtM6WWLl2aj3/841mwYEFOPPHE/PCHP8yVV16Ziy66aKryAYyJfgJKpqOAUuknoE5tLaX+6q/+Kh/+8Ifzrne9K48//njmzp2bP/iDP8hHPvKRqcoHMCb6CSiZjgJKpZ+AOrW1lJo9e3auuuqqXHXVVVMUB2B89BNQMh0FlEo/AXVq6zWlAAAAAGAyWEoBAAAAUDlLKQAAAAAqZykFAAAAQOUspQAAAAConKUUAAAAAJWzlAIAAACgcpZSAAAAAFSu0Ww2m1UO7O/vz3Of+9wkycFzqpzc8vRjSZpJGsnBR1U/XwYZSstQ9/wkeXpz63+ffPLJ9Pb21hMi9fdTUsjx8Dspgwy7Zyigo/STDKXMl6GwDPopSSHHQgYZCplfTIYx9lPlS6lHHnkk8+fPr3IkME309fVl3rx5tc3XT8De1NlR+gnYG/0ElGpf/VT5Ump4eDibNm3K7Nmz02g02r79wMBA5s+fn76+vvT09ExBQhmmS4a658sweRmazWaeeuqpzJ07NzNm1PesYv0kw/6Uoe75+1OGEjpqov2U1H886p4vgwylZdBPO9V9LErIUPd8GWSY7Axj7aeuiYQcjxkzZkzKFr+np6e2gyNDWRnqni/D5GSo82l72+knGfbHDHXP318y1N1Rk9VPSf3Ho+75MshQWgb9tFPdx6KEDHXPl0GGycwwln7yQucAAAAAVM5SCgAAAIDKTbulVHd3d/7sz/4s3d3dMnR4hrrny1BWhhKU8OcggwylzJehPHX/WdQ9XwYZSstQ9/ySlPBnUXeGuufLIENdGSp/oXMAAAAAmHZnSgEAAAAw/VlKAQAAAFA5SykAAAAAKmcpBQAAAEDlptVS6vvf/35mzpyZc845p/LZF1xwQRqNxo7L4Ycfnje+8Y158MEHK8/y2GOP5T3veU+OPfbYdHd3Z/78+Vm6dGm+/e1vT/nsXf8cDjjggDz/+c/P2Wefnc997nMZHh6e8vkjM+x6eeMb31jJ/H3l2LBhQyXzH3vssbzvfe/Lcccdl+c85zl5/vOfn9NPPz2rVq3K008/PeXzL7jggrzlLW951tdvu+22NBqNPPnkk1OeoTQ6Sj+NzFFXR9XdT0m9HaWfnk0/6aeROfSTx1Cl0E/6aWQO/dRZ/TStllKrV6/Oe97zntx+++3ZtGlT5fPf+MY3ZvPmzdm8eXO+/e1vp6urK0uWLKk0w8MPP5xTTz013/nOd/KpT30qDz30UG655ZacddZZWb58eSUZtv85PPzww7n55ptz1lln5X3ve1+WLFmSbdu2VZph18vnP//5SmbvK8cxxxwz5XN/9rOf5SUveUm++c1v5rLLLssPf/jDfP/7388f//Ef56abbsqtt9465Rl4tk7vKP307Bx1dlRd/ZToqBLpJ/00Mod+0k+l0E/6aWQO/dRZ/dRVd4Cx2rJlS774xS/m3nvvzWOPPZY1a9bkT//0TyvN0N3dnaOOOipJctRRR+VDH/pQXv3qV+cXv/hFjjjiiEoyvOtd70qj0cjdd9+dQw45ZMfXTzzxxFx00UWVZNj1z+EFL3hBXvrSl+a0007L6173uqxZsya///u/X2mGOtWV413vele6urpy77337vZ7cOyxx+bcc89Ns9msPFOn01H6aU856lJnBh1VFv2kn/aUoy76ie30k37aU4666KfqTZszpb70pS/lhBNOyMKFC/P2t789n/vc52o9KFu2bMl1112X4447LocffnglM//rv/4rt9xyS5YvX77bL+l2z33ucyvJMZrXvva1OeWUU/KP//iPtWXoFP/5n/+Zb37zm3v8PUiSRqNRcSo6vaP0E9vpqPLoJ/1Ei34qj37ST7R0cj9Nm6XU6tWr8/a3vz1J65S6/v7+rF27ttIMN910U2bNmpVZs2Zl9uzZ+epXv5ovfvGLmTGjmj/GDRs2pNls5oQTTqhkXrtOOOGEPPzww5XM2vVYbL9cdtlllczeW47zzjtvymdu/z1YuHDhbl9/3vOetyPHn/zJn0x5jmT047B48eJKZpem0ztKP+2uhI6qo5+ScjpKP+2kn/TTrvRT/f2U6Kjt9JN+2pV+6sx+mhZP31u3bl3uvvvu3HjjjUmSrq6u/M7v/E5Wr16dM888s7IcZ511VlatWpUkeeKJJ/KZz3wmixcvzt13352jjz56yueXfrpes9msbHu767HY7rDDDqtk9t5y7GmrXYW77747w8PDWbZsWQYHByuZOdpxuOuuu3Y8uOgUOko/jVRCR5XUT0n1HaWfWvSTfhpJPz2bx1D10E/6aST99Gyd0E/TYim1evXqbNu2LXPnzt3xtWazme7u7lx99dXp7e2tJMchhxyS4447bsfnf/d3f5fe3t589rOfzV/8xV9M+fzjjz8+jUYjP/nJT6Z81nj8+Mc/ruxF4EYei7rUkeO4445Lo9HIunXrdvv6sccemyQ56KCDKssy2v//Rx55pLL5pdBR+mmkEjqqrgyldJR+atFP+mkk/VR/PyU6KtFPiX4aST91Zj8V//S9bdu25e///u9zxRVX5P77799xeeCBBzJ37txa3nFtu0ajkRkzZuSXv/xlJfMOO+yw/OZv/mY+/elPZ+vWrc/6fp1vH/ud73wnDz30UN761rfWlqFTHH744Tn77LNz9dVXj/p7QLV0VIt+YjsdVQ791KKf2E4/lUM/tegntuvkfir+TKmbbropTzzxRH7v937vWdvyt771rVm9enX+8A//sJIsg4ODeeyxx5K0Tu28+uqrs2XLlixdurSS+Uny6U9/Oqeffnpe8YpX5M///M+zaNGibNu2Ld/61reyatWq/PjHP57yDNv/HIaGhvIf//EfueWWW7Jy5cosWbIk73jHO6Z8/q4ZdtXV1ZXnPe95lcyv22c+85mcfvrpednLXpZLL700ixYtyowZM3LPPffkJz/5SU499dS6I3YMHbWTfnp2jl3pKB1VNf20k356do5d6Sf9VDX9tJN+enaOXemnDuinZuGWLFnSfNOb3jTq9+66665mkuYDDzww5TnOP//8ZpIdl9mzZzdf/vKXN//hH/5hymePtGnTpuby5cubRx99dPPAAw9svuAFL2i++c1vbn73u9+d8tm7/jl0dXU1jzjiiObrX//65uc+97nm0NDQlM8fmWHXy8KFCyuZv2uOc889t9KZu9q0aVPz3e9+d/OYY45pHnDAAc1Zs2Y1X/GKVzQ/9alPNbdu3Trl8/f0//+73/1uM0nziSeemPIMJdBRu+v0fhqZo66Oqrufms16O0o/tein3ekn/bSdx1D100+700/6abtO7KdGs1n4q6sBAAAAsN8p/jWlAAAAANj/WEoBAAAAUDlLKQAAAAAqZykFAAAAQOUspQAAAAConKUUAAAAAJWzlAIAAACgcpZSAAAAAFTOUgoAAACAyllKAQAAAFA5SykAAAAAKmcpBQAAAEDl/n/5yCv6wQ736AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def single_turn(\n",
    "    current_boards: np, policy: GamePolicy\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Execute a single turn on a board.\n",
    "\n",
    "    Places a new stone on the board. Turns captured enemy stones.\n",
    "\n",
    "    Args:\n",
    "        current_boards: The current board before the game.\n",
    "        policy: The game policy to be used.\n",
    "\n",
    "    Returns:\n",
    "        The new game board and the policy vector containing the index of the action used.\n",
    "    \"\"\"\n",
    "    policy_results = policy.get_policy(current_boards)\n",
    "\n",
    "    # if the constant VERIFY_POLICY is set to true the policy is verified. Should be good though.\n",
    "    # todo deactivate the policy verification after some testing.\n",
    "    if VERIFY_POLICY:\n",
    "        assert np.all(moves_possible(current_boards, policy_results)), (\n",
    "            current_boards[(moves_possible(current_boards, policy_results) == False)],\n",
    "            policy_results[(moves_possible(current_boards, policy_results) == False)],\n",
    "            np.where(moves_possible(current_boards, policy_results) == False),\n",
    "        )\n",
    "    return do_moves(current_boards, policy_results), policy_results\n",
    "\n",
    "\n",
    "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "VERIFY_POLICY = False  # type: ignore\n",
    "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "VERIFY_POLICY = True  # type: ignore\n",
    "plot_othello_boards(\n",
    "    single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))[0][:8]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulate a stack of games\n",
    "This function will simulate a stack of games and return an array of policies and histories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x4800 with 61 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def simulate_game(\n",
    "    nr_of_games: int, policies: tuple[GamePolicy, GamePolicy], tqdm_on: bool = False\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Simulates a stack of games.\n",
    "\n",
    "    Args:\n",
    "        nr_of_games: The number of games that should be simulated.\n",
    "        policies: The policies that should be used to simulate the game.\n",
    "\n",
    "    Returns:\n",
    "        A stack of board histories and actions.\n",
    "    \"\"\"\n",
    "    board_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 8, 8), dtype=int)\n",
    "    action_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 2), dtype=int)\n",
    "    current_boards = get_new_games(nr_of_games)\n",
    "    for turn_index in tqdm(range(SIMULATE_TURNS)) if tqdm_on else range(SIMULATE_TURNS):\n",
    "        policy_index = turn_index % 2\n",
    "        policy = policies[policy_index]\n",
    "        board_history_stack[turn_index, :, :, :] = current_boards\n",
    "        if policy_index == 0:\n",
    "            current_boards = current_boards * -1\n",
    "        current_boards, action_taken = single_turn(current_boards, policy)\n",
    "        action_history_stack[turn_index, :] = action_taken\n",
    "\n",
    "        if policy_index == 0:\n",
    "            current_boards = current_boards * -1\n",
    "\n",
    "    return board_history_stack, action_history_stack\n",
    "\n",
    "\n",
    "simulation_results = simulate_game(1, (RandomPolicy(1), RandomPolicy(1)))\n",
    "plot_othello_boards(\n",
    "    drop_duplicate_boards(np.reshape(simulation_results[0], (-1, 8, 8)))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.48 s ± 330 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical examination of the natural action space and result\n",
    "As for many project some evaluation of the project is in order.\n",
    "\n",
    "1. What is the expected distribution of scores\n",
    "2. What is the expected distribution of possible actions\n",
    "    a. over time\n",
    "    b. ober space\n",
    "\n",
    "The easiest and most robust way to analyse this is when analyzing randomly played games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 100, 8, 8)\n",
      "(70, 100, 2)\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(\"rnd_history.npy\") and not os.path.exists(\"rnd_action.npy\"):\n",
    "    rnds = RandomPolicy(1), RandomPolicy(1)\n",
    "    simulation_results = simulate_game(100, rnds, tqdm_on=True)\n",
    "    _board_history, _action_history = simulation_results\n",
    "    np.save(\"rnd_history.npy\", _board_history)\n",
    "    np.save(\"rnd_action.npy\", _action_history)\n",
    "else:\n",
    "    _board_history = np.load(\"rnd_history.npy\")\n",
    "    _action_history = np.load(\"rnd_action.npy\")\n",
    "print(_board_history.shape)\n",
    "print(_action_history.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 100, 8, 8)\n",
      "(70, 100, 2)\n"
     ]
    }
   ],
   "source": [
    "print(_board_history.shape)\n",
    "print(_action_history.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 100)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "__board_history = _board_history.copy()\n",
    "__board_history[1::2] = __board_history[1::2] * -1\n",
    "poss_turn = np.sum(\n",
    "    get_possible_turns(__board_history.reshape((-1, 8, 8))).reshape(70, -1, 8, 8),\n",
    "    axis=(2, 3),\n",
    ")\n",
    "poss_turn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_possiblilites = np.mean(poss_turn, axis=1)\n",
    "plt.title(\n",
    "    f\"Mean turn possible per turn {np.prod(np.extract(mean_possiblilites, mean_possiblilites))}\"\n",
    ")\n",
    "plt.plot(mean_possiblilites)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcb93d3e5e0b4c5ea594ad05ce99838d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=35, description='turn', max=70), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(turn=(0, 70))\n",
    "def poss_turn_count(turn):\n",
    "    plt.hist(poss_turn[turn])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 100)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.046875,  0.046875,  0.046875, ...,  0.046875,  0.046875,\n",
       "         0.046875],\n",
       "       [-0.046875, -0.046875, -0.046875, ..., -0.046875, -0.046875,\n",
       "        -0.046875],\n",
       "       [ 0.046875,  0.046875,  0.046875, ...,  0.046875,  0.046875,\n",
       "         0.046875],\n",
       "       ...,\n",
       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
       "         0.      ],\n",
       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
       "         0.      ],\n",
       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
       "         0.      ]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculate_direct_score(board_history: np.ndarray) -> np.ndarray:\n",
    "    boards_evaluated = np.reshape(\n",
    "        evaluate_boards(np.reshape(board_history, (-1, 8, 8))), (SIMULATE_TURNS, -1)\n",
    "    )\n",
    "    direct_score = boards_evaluated - np.roll(boards_evaluated, shift=-1, axis=0)\n",
    "    direct_score[-1] = 0\n",
    "    return direct_score / 64\n",
    "\n",
    "\n",
    "assert len(calculate_direct_score(_board_history).shape) == 2\n",
    "assert calculate_direct_score(_board_history).shape[0] == SIMULATE_TURNS\n",
    "print(calculate_direct_score(_board_history).shape)\n",
    "calculate_direct_score(_board_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36fb809b8d9e42b79512d6d788b2008f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=35, description='turn', max=70), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "@interact(turn=(0, 70))\n",
    "def hist_direct_score(turn):\n",
    "    score_history = calculate_direct_score(_board_history) * 64\n",
    "    score_history[1::2] = score_history[1::2] * -1\n",
    "    # print(score_history[turn])\n",
    "    plt.title(f\"Histogram of turn {turn} by {'white' if turn % 2 == 0 else 'black'}\")\n",
    "    plt.hist(score_history[turn], density=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_final_evaluation_for_history(board_history: np.ndarray) -> np.ndarray:\n",
    "    final_evaluation = final_boards_evaluation(board_history[-1])\n",
    "    return final_evaluation / 64\n",
    "\n",
    "\n",
    "assert len(calculate_final_evaluation_for_history(_board_history).shape) == 1\n",
    "print(calculate_final_evaluation_for_history(_board_history).shape)\n",
    "_final_eval = calculate_final_evaluation_for_history(_board_history)\n",
    "plt.title(\"Histogram over the score distribtuion\")\n",
    "plt.hist((_final_eval * 64), density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y9fXVjBkzqozJGFNlXrxR03zVVceOHTV8+HA9//zz+uGHH6rcvnfv3pNuo/LjlyeeeEL9+/dXUFCQuz07O1uffvppnT6iORVXXHGFjh8/rueee87dVl5eXqfH3f79+6u0RUVFSZL7uXGq9/OJ/vGPf+jgwYPuv19//XX98MMP7udR5T6r+0iy8hyOyvN0pJ/H+sILL5x0v3V9HqN548gIrOrTp4+GDx+u6OhotW/fXp9++qlef/11TZ061d0nOjpaknTnnXdqxIgR8vX11ZgxYzRq1ChddNFFmj59unbs2KEBAwZo5cqVWrp0qf7yl7+4XyCjo6N13XXXKSMjQ/v27XNf2vv1119Lqtu/5AMDAzV06FA9+eSTOnbsmMLDw7Vy5Upt3779NNwrVQ0YMECJiYl64YUXdODAAQ0bNkzr1q3TSy+9pNGjR+uiiy7y6H/FFVeobdu2uvfee+Xr66vrrrvO4/Zu3brp0UcfVUpKinbs2KHRo0erbdu22r59u9544w3dcsstuvfee+tVa03z5Y25c+dq8ODB6tevnyZPnqyuXbuqsLBQOTk5+u677/TZZ5/Vun737t0VFhamrVu3epwwOnToUN1///2SdNrDyKhRo3ThhRdq2rRp2rFjh/r06aMlS5ZU+2Z+opkzZ+q9997TyJEj1aVLF+3Zs0fPPvuszj77bPf33nTr1k3BwcHKzMxU27Zt1bp1a8XFxVV7vlNdtG/fXoMHD1ZSUpIKCwuVkZGh7t27a/Lkye4+0dHRysrKUnJyss4//3y1adNGo0aN0nnnnacLLrhAKSkp2r9/v9q3b69Fixbp+PHjdbqf6vI8RjNn4QoeNBOVl/N98skn1d4+bNiwk17a++ijj5rY2FgTHBxsWrVqZXr16mUee+wxc/ToUXef48ePmzvuuMN06NDBOBwOj0sgDx48aO6++27TuXNn07JlS9OjRw/z1FNPmYqKCo/9lpaWmilTppj27dubNm3amNGjR5utW7caSR6X2lZeYlndJZXfffedueaaa0xwcLAJCgoyN9xwg9m9e3eNlwefuI3ExETTunXrOt1P1Tl27JiZMWOGOeecc0zLli1NRESESUlJ8bj89dduvvlmI8m4XK4at/nvf//bDB482LRu3dq0bt3a9OrVy0yZMsVs3brV6/oq1TRflZf2PvXUU1XWOfE+NMaYbdu2mfHjx5uwsDDTsmVLEx4ebq688krz+uuv16mOG264wUgyWVlZ7rajR4+agIAA4+fnZ44cOeLRv6ZLe6u79PzEy2Nrsm/fPjNu3DgTGBhogoKCzLhx48yGDRtOemlvdna2ufrqq03nzp2Nn5+f6dy5sxk7dqz5+uuvPba/dOlS06dPH9OiRQuPbdY2ZzVd2vvqq6+alJQU07FjR9OqVSszcuRI96XglQ4dOmRuuukmExwcbCR5XKK7bds243K5jNPpNKGhoeaBBx4wq1atOumlvcbU/XksyUyZMqXKmE58XUHT4zCGs37w27Rx40YNHDhQL7/8sm6++Wbb5QDAbxbnjOA3obofIcvIyJCPj89Jv/kUAHB6cc4IfhOefPJJ5ebm6qKLLlKLFi309ttv6+2339Ytt9yiiIgI2+UBwG8aH9PgN2HVqlWaMWOGNm/erEOHDul3v/udxo0bp+nTp6tFCzI5ANhEGAEAAFZxzggAALCKMAIAAKxqEh+WV1RUaPfu3Wrbtm2DfwUyAAA4PYwxOnjwoDp37iwfn5qPfzSJMLJ7926ueAAAoInKz8/X2WefXePtTSKMVP54Un5+vgIDAy1XAwAA6qKkpEQREREeP4JYnSYRRio/mgkMDCSMAADQxJzsFAtOYAUAAFbVK4zMnTtXkZGR8vf3V1xcnNatW1dr/4yMDPXs2VOtWrVSRESE7r77bv3000/1KhgAADQvXoeRyp+PTktL0/r16zVgwACNGDFCe/bsqbb/K6+8omnTpiktLU1btmzR/PnzlZWVpQceeOCUiwcAAE2f12Fkzpw5mjx5spKSktSnTx9lZmYqICBACxYsqLb/hx9+qAsvvFA33XSTIiMjdemll2rs2LEnPZoCAAB+G7wKI0ePHlVubq5cLtcvG/DxkcvlUk5OTrXrDBo0SLm5ue7w8e2332r58uW64ooratxPWVmZSkpKPBYAANA8eXU1TVFRkcrLyxUaGurRHhoaqq+++qradW666SYVFRVp8ODBMsbo+PHjuvXWW2v9mCY9PV0zZszwpjQAANBEnfaradasWaPHH39czz77rNavX68lS5Zo2bJleuSRR2pcJyUlRcXFxe4lPz//dJcJAAAs8erISEhIiHx9fVVYWOjRXlhYqLCwsGrXeeihhzRu3DhNmjRJktSvXz+Vlpbqlltu0fTp06v9elin0ymn0+lNaQAAoIny6siIn5+foqOjlZ2d7W6rqKhQdna24uPjq13n8OHDVQKHr6+vpJ+/sx4AAPy2ef0NrMnJyUpMTFRMTIxiY2OVkZGh0tJSJSUlSZLGjx+v8PBwpaenS5JGjRqlOXPmaODAgYqLi1NeXp4eeughjRo1yh1KAADAb5fXYSQhIUF79+5VamqqCgoKFBUVpRUrVrhPat21a5fHkZAHH3xQDodDDz74oL7//nt16NBBo0aN0mOPPdZwowAAAE2WwzSBz0pKSkoUFBSk4uJifpsGAIAmoq7v3/w2DQAAsIowAgAArPL6nJHmJnLaMtsloJnaMWuk7RIAoEngyAgAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAqnqFkblz5yoyMlL+/v6Ki4vTunXrauw7fPhwORyOKsvIkSPrXTQAAGg+vA4jWVlZSk5OVlpamtavX68BAwZoxIgR2rNnT7X9lyxZoh9++MG9fPnll/L19dUNN9xwysUDAICmz+swMmfOHE2ePFlJSUnq06ePMjMzFRAQoAULFlTbv3379goLC3Mvq1atUkBAQK1hpKysTCUlJR4LAABonrwKI0ePHlVubq5cLtcvG/DxkcvlUk5OTp22MX/+fI0ZM0atW7eusU96erqCgoLcS0REhDdlAgCAJsSrMFJUVKTy8nKFhoZ6tIeGhqqgoOCk669bt05ffvmlJk2aVGu/lJQUFRcXu5f8/HxvygQAAE1Ii8bc2fz589WvXz/FxsbW2s/pdMrpdDZSVQAAwCavjoyEhITI19dXhYWFHu2FhYUKCwurdd3S0lItWrRIEydO9L5KAADQbHkVRvz8/BQdHa3s7Gx3W0VFhbKzsxUfH1/ruq+99prKysr0xz/+sX6VAgCAZsnrj2mSk5OVmJiomJgYxcbGKiMjQ6WlpUpKSpIkjR8/XuHh4UpPT/dYb/78+Ro9erTOOuushqkcAAA0C16HkYSEBO3du1epqakqKChQVFSUVqxY4T6pddeuXfLx8TzgsnXrVq1du1YrV65smKoBAECz4TDGGNtFnExJSYmCgoJUXFyswMDABt125LRlDbo9oNKOWXzLMIDftrq+f/PbNAAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCqXmFk7ty5ioyMlL+/v+Li4rRu3bpa+x84cEBTpkxRp06d5HQ6de6552r58uX1KhgAADQvLbxdISsrS8nJycrMzFRcXJwyMjI0YsQIbd26VR07dqzS/+jRo7rkkkvUsWNHvf766woPD9fOnTsVHBzcEPUDAIAmzuswMmfOHE2ePFlJSUmSpMzMTC1btkwLFizQtGnTqvRfsGCB9u/frw8//FAtW7aUJEVGRp5a1QAAoNnw6mOao0ePKjc3Vy6X65cN+PjI5XIpJyen2nXeeustxcfHa8qUKQoNDVXfvn31+OOPq7y8vMb9lJWVqaSkxGMBAADNk1dHRoqKilReXq7Q0FCP9tDQUH311VfVrvPtt9/qP//5j26++WYtX75ceXl5uv3223Xs2DGlpaVVu056erpmzJjhTWkA8JsQOW2Z7RLQDO2YNdLq/k/71TQVFRXq2LGjXnjhBUVHRyshIUHTp09XZmZmjeukpKSouLjYveTn55/uMgEAgCVeHRkJCQmRr6+vCgsLPdoLCwsVFhZW7TqdOnVSy5Yt5evr627r3bu3CgoKdPToUfn5+VVZx+l0yul0elMaAABoorw6MuLn56fo6GhlZ2e72yoqKpSdna34+Phq17nwwguVl5eniooKd9vXX3+tTp06VRtEAADAb4vXH9MkJydr3rx5eumll7RlyxbddtttKi0tdV9dM378eKWkpLj733bbbdq/f7/uuusuff3111q2bJkef/xxTZkypeFGAQAAmiyvL+1NSEjQ3r17lZqaqoKCAkVFRWnFihXuk1p37dolH59fMk5ERITeeecd3X333erfv7/Cw8N111136f7772+4UQAAgCbL6zAiSVOnTtXUqVOrvW3NmjVV2uLj4/XRRx/VZ1cAAKCZ47dpAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYFW9wsjcuXMVGRkpf39/xcXFad26dTX2ffHFF+VwODwWf3//ehcMAACaF6/DSFZWlpKTk5WWlqb169drwIABGjFihPbs2VPjOoGBgfrhhx/cy86dO0+paAAA0Hx4HUbmzJmjyZMnKykpSX369FFmZqYCAgK0YMGCGtdxOBwKCwtzL6GhoadUNAAAaD68CiNHjx5Vbm6uXC7XLxvw8ZHL5VJOTk6N6x06dEhdunRRRESErr76am3atKnW/ZSVlamkpMRjAQAAzZNXYaSoqEjl5eVVjmyEhoaqoKCg2nV69uypBQsWaOnSpXr55ZdVUVGhQYMG6bvvvqtxP+np6QoKCnIvERER3pQJAACakNN+NU18fLzGjx+vqKgoDRs2TEuWLFGHDh30/PPP17hOSkqKiouL3Ut+fv7pLhMAAFjSwpvOISEh8vX1VWFhoUd7YWGhwsLC6rSNli1bauDAgcrLy6uxj9PplNPp9KY0AADQRHl1ZMTPz0/R0dHKzs52t1VUVCg7O1vx8fF12kZ5ebm++OILderUybtKAQBAs+TVkRFJSk5OVmJiomJiYhQbG6uMjAyVlpYqKSlJkjR+/HiFh4crPT1dkjRz5kxdcMEF6t69uw4cOKCnnnpKO3fu1KRJkxp2JAAAoEnyOowkJCRo7969Sk1NVUFBgaKiorRixQr3Sa27du2Sj88vB1x+/PFHTZ48WQUFBWrXrp2io6P14Ycfqk+fPg03CgAA0GQ5jDHGdhEnU1JSoqCgIBUXFyswMLBBtx05bVmDbg+otGPWSNsloBniNQunw+l6varr+ze/TQMAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACr6hVG5s6dq8jISPn7+ysuLk7r1q2r03qLFi2Sw+HQ6NGj67NbAADQDHkdRrKyspScnKy0tDStX79eAwYM0IgRI7Rnz55a19uxY4fuvfdeDRkypN7FAgCA5sfrMDJnzhxNnjxZSUlJ6tOnjzIzMxUQEKAFCxbUuE55ebluvvlmzZgxQ127dj2lggEAQPPiVRg5evSocnNz5XK5ftmAj49cLpdycnJqXG/mzJnq2LGjJk6cWKf9lJWVqaSkxGMBAADNk1dhpKioSOXl5QoNDfVoDw0NVUFBQbXrrF27VvPnz9e8efPqvJ/09HQFBQW5l4iICG/KBAAATchpvZrm4MGDGjdunObNm6eQkJA6r5eSkqLi4mL3kp+ffxqrBAAANrXwpnNISIh8fX1VWFjo0V5YWKiwsLAq/bdt26YdO3Zo1KhR7raKioqfd9yihbZu3apu3bpVWc/pdMrpdHpTGgAAaKK8OjLi5+en6OhoZWdnu9sqKiqUnZ2t+Pj4Kv179eqlL774Qhs3bnQvV111lS666CJt3LiRj18AAIB3R0YkKTk5WYmJiYqJiVFsbKwyMjJUWlqqpKQkSdL48eMVHh6u9PR0+fv7q2/fvh7rBwcHS1KVdgAA8NvkdRhJSEjQ3r17lZqaqoKCAkVFRWnFihXuk1p37dolHx++2BUAANSN12FEkqZOnaqpU6dWe9uaNWtqXffFF1+szy4BAEAzxSEMAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWFWvMDJ37lxFRkbK399fcXFxWrduXY19lyxZopiYGAUHB6t169aKiorSP//5z3oXDAAAmhevw0hWVpaSk5OVlpam9evXa8CAARoxYoT27NlTbf/27dtr+vTpysnJ0eeff66kpCQlJSXpnXfeOeXiAQBA0+d1GJkzZ44mT56spKQk9enTR5mZmQoICNCCBQuq7T98+HBdc8016t27t7p166a77rpL/fv319q1a0+5eAAA0PR5FUaOHj2q3NxcuVyuXzbg4yOXy6WcnJyTrm+MUXZ2trZu3aqhQ4fW2K+srEwlJSUeCwAAaJ68CiNFRUUqLy9XaGioR3toaKgKCgpqXK+4uFht2rSRn5+fRo4cqWeeeUaXXHJJjf3T09MVFBTkXiIiIrwpEwAANCGNcjVN27ZttXHjRn3yySd67LHHlJycrDVr1tTYPyUlRcXFxe4lPz+/McoEAAAWtPCmc0hIiHx9fVVYWOjRXlhYqLCwsBrX8/HxUffu3SVJUVFR2rJli9LT0zV8+PBq+zudTjmdTm9KAwAATZRXR0b8/PwUHR2t7Oxsd1tFRYWys7MVHx9f5+1UVFSorKzMm10DAIBmyqsjI5KUnJysxMRExcTEKDY2VhkZGSotLVVSUpIkafz48QoPD1d6erqkn8//iImJUbdu3VRWVqbly5frn//8p5577rmGHQkAAGiSvA4jCQkJ2rt3r1JTU1VQUKCoqCitWLHCfVLrrl275OPzywGX0tJS3X777fruu+/UqlUr9erVSy+//LISEhIabhQAAKDJchhjjO0iTqakpERBQUEqLi5WYGBgg247ctqyBt0eUGnHrJG2S0AzxGsWTofT9XpV1/dvfpsGAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVtUrjMydO1eRkZHy9/dXXFyc1q1bV2PfefPmaciQIWrXrp3atWsnl8tVa38AAPDb4nUYycrKUnJystLS0rR+/XoNGDBAI0aM0J49e6rtv2bNGo0dO1arV69WTk6OIiIidOmll+r7778/5eIBAEDT53UYmTNnjiZPnqykpCT16dNHmZmZCggI0IIFC6rt/69//Uu33367oqKi1KtXL/39739XRUWFsrOzT7l4AADQ9HkVRo4eParc3Fy5XK5fNuDjI5fLpZycnDpt4/Dhwzp27Jjat29fY5+ysjKVlJR4LAAAoHnyKowUFRWpvLxcoaGhHu2hoaEqKCio0zbuv/9+de7c2SPQnCg9PV1BQUHuJSIiwpsyAQBAE9KoV9PMmjVLixYt0htvvCF/f/8a+6WkpKi4uNi95OfnN2KVAACgMbXwpnNISIh8fX1VWFjo0V5YWKiwsLBa1509e7ZmzZqld999V/3796+1r9PplNPp9KY0AADQRHl1ZMTPz0/R0dEeJ59WnowaHx9f43pPPvmkHnnkEa1YsUIxMTH1rxYAADQ7Xh0ZkaTk5GQlJiYqJiZGsbGxysjIUGlpqZKSkiRJ48ePV3h4uNLT0yVJTzzxhFJTU/XKK68oMjLSfW5JmzZt1KZNmwYcCgAAaIq8DiMJCQnau3evUlNTVVBQoKioKK1YscJ9UuuuXbvk4/PLAZfnnntOR48e1fXXX++xnbS0ND388MOnVj0AAGjyvA4jkjR16lRNnTq12tvWrFnj8feOHTvqswsAAPAbwW/TAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrWtguAGiuIqcts10CADQJHBkBAABWEUYAAIBVhBEAAGAVYQQAAFhVrzAyd+5cRUZGyt/fX3FxcVq3bl2NfTdt2qTrrrtOkZGRcjgcysjIqG+tAACgGfI6jGRlZSk5OVlpaWlav369BgwYoBEjRmjPnj3V9j98+LC6du2qWbNmKSws7JQLBgAAzYvXYWTOnDmaPHmykpKS1KdPH2VmZiogIEALFiyotv/555+vp556SmPGjJHT6TzlggEAQPPiVRg5evSocnNz5XK5ftmAj49cLpdycnIarKiysjKVlJR4LAAAoHnyKowUFRWpvLxcoaGhHu2hoaEqKChosKLS09MVFBTkXiIiIhps2wAA4MxyRl5Nk5KSouLiYveSn59vuyQAAHCaePV18CEhIfL19VVhYaFHe2FhYYOenOp0Ojm/BACA3wivjoz4+fkpOjpa2dnZ7raKigplZ2crPj6+wYsDAADNn9c/lJecnKzExETFxMQoNjZWGRkZKi0tVVJSkiRp/PjxCg8PV3p6uqSfT3rdvHmz+/+///57bdy4UW3atFH37t0bcCgAAKAp8jqMJCQkaO/evUpNTVVBQYGioqK0YsUK90mtu3btko/PLwdcdu/erYEDB7r/nj17tmbPnq1hw4ZpzZo1pz4CAADQpDmMMcZ2ESdTUlKioKAgFRcXKzAwsEG3zc+8AwB+63bMGnlatlvX9+8z8moaAADw20EYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFbVK4zMnTtXkZGR8vf3V1xcnNatW1dr/9dee029evWSv7+/+vXrp+XLl9erWAAA0Px4HUaysrKUnJystLQ0rV+/XgMGDNCIESO0Z8+eavt/+OGHGjt2rCZOnKgNGzZo9OjRGj16tL788stTLh4AADR9DmOM8WaFuLg4nX/++frb3/4mSaqoqFBERITuuOMOTZs2rUr/hIQElZaW6n//93/dbRdccIGioqKUmZlZp32WlJQoKChIxcXFCgwM9Kbck4qctqxBtwcAQFOzY9bI07Ldur5/t/Bmo0ePHlVubq5SUlLcbT4+PnK5XMrJyal2nZycHCUnJ3u0jRgxQm+++WaN+ykrK1NZWZn77+LiYkk/D6qhVZQdbvBtAgDQlJyO99dfb/dkxz28CiNFRUUqLy9XaGioR3toaKi++uqratcpKCiotn9BQUGN+0lPT9eMGTOqtEdERHhTLgAAqIOgjNO7/YMHDyooKKjG270KI40lJSXF42hKRUWF9u/fr7POOksOh6PB9lNSUqKIiAjl5+c3+Mc/Z4rmPkbG1/Q19zEyvqavuY/xdI7PGKODBw+qc+fOtfbzKoyEhITI19dXhYWFHu2FhYUKCwurdp2wsDCv+kuS0+mU0+n0aAsODvamVK8EBgY2ywfYrzX3MTK+pq+5j5HxNX3NfYyna3y1HRGp5NXVNH5+foqOjlZ2dra7raKiQtnZ2YqPj692nfj4eI/+krRq1aoa+wMAgN8Wrz+mSU5OVmJiomJiYhQbG6uMjAyVlpYqKSlJkjR+/HiFh4crPT1dknTXXXdp2LBh+utf/6qRI0dq0aJF+vTTT/XCCy807EgAAECT5HUYSUhI0N69e5WamqqCggJFRUVpxYoV7pNUd+3aJR+fXw64DBo0SK+88ooefPBBPfDAA+rRo4fefPNN9e3bt+FGUU9Op1NpaWlVPhJqTpr7GBlf09fcx8j4mr7mPsYzYXxef88IAABAQ+K3aQAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVc0+jDz22GMaNGiQAgIC6vwtrsYYpaamqlOnTmrVqpVcLpe++eYbjz779+/XzTffrMDAQAUHB2vixIk6dOjQaRhB7bytY8eOHXI4HNUur732mrtfdbcvWrSoMYbkoT738/Dhw6vUfuutt3r02bVrl0aOHKmAgAB17NhR9913n44fP346h1Ijb8e4f/9+3XHHHerZs6datWql3/3ud7rzzjvdPyhZydYczp07V5GRkfL391dcXJzWrVtXa//XXntNvXr1kr+/v/r166fly5d73F6X52Nj82aM8+bN05AhQ9SuXTu1a9dOLperSv8JEyZUmavLLrvsdA+jRt6M78UXX6xSu7+/v0efM20OvRlfda8nDodDI0f+8iu3Z9L8vffeexo1apQ6d+4sh8NR64/SVlqzZo1+//vfy+l0qnv37nrxxRer9PH2ee0108ylpqaaOXPmmOTkZBMUFFSndWbNmmWCgoLMm2++aT777DNz1VVXmXPOOcccOXLE3eeyyy4zAwYMMB999JF5//33Tffu3c3YsWNP0yhq5m0dx48fNz/88IPHMmPGDNOmTRtz8OBBdz9JZuHChR79fj3+xlKf+3nYsGFm8uTJHrUXFxe7bz9+/Ljp27evcblcZsOGDWb58uUmJCTEpKSknO7hVMvbMX7xxRfm2muvNW+99ZbJy8sz2dnZpkePHua6667z6GdjDhctWmT8/PzMggULzKZNm8zkyZNNcHCwKSwsrLb/Bx98YHx9fc2TTz5pNm/ebB588EHTsmVL88UXX7j71OX52Ji8HeNNN91k5s6dazZs2GC2bNliJkyYYIKCgsx3333n7pOYmGguu+wyj7nav39/Yw3Jg7fjW7hwoQkMDPSovaCgwKPPmTSH3o5v3759HmP78ssvja+vr1m4cKG7z5k0f8uXLzfTp083S5YsMZLMG2+8UWv/b7/91gQEBJjk5GSzefNm88wzzxhfX1+zYsUKdx9v77P6aPZhpNLChQvrFEYqKipMWFiYeeqpp9xtBw4cME6n07z66qvGGGM2b95sJJlPPvnE3eftt982DofDfP/99w1ee00aqo6oqCjzpz/9yaOtLg/i062+4xs2bJi56667arx9+fLlxsfHx+MF87nnnjOBgYGmrKysQWqvq4aaw8WLFxs/Pz9z7Ngxd5uNOYyNjTVTpkxx/11eXm46d+5s0tPTq+1/4403mpEjR3q0xcXFmT//+c/GmLo9Hxubt2M80fHjx03btm3NSy+95G5LTEw0V199dUOXWi/eju9kr61n2hye6vw9/fTTpm3btubQoUPutjNp/n6tLq8B/+///T9z3nnnebQlJCSYESNGuP8+1fusLpr9xzTe2r59uwoKCuRyudxtQUFBiouLU05OjiQpJydHwcHBiomJcfdxuVzy8fHRxx9/3Gi1NkQdubm52rhxoyZOnFjltilTpigkJESxsbFasGCBTCN/P96pjO9f//qXQkJC1LdvX6WkpOjw4cMe2+3Xr5/7W4MlacSIESopKdGmTZsafiC1aKjHUnFxsQIDA9WiheeXKjfmHB49elS5ubkezx0fHx+5XC73c+dEOTk5Hv2ln+eisn9dno+NqT5jPNHhw4d17NgxtW/f3qN9zZo16tixo3r27KnbbrtN+/bta9Da66K+4zt06JC6dOmiiIgIXX311R7PozNpDhti/ubPn68xY8aodevWHu1nwvzVx8megw1xn9WF118H39wVFBRIkscbVeXflbcVFBSoY8eOHre3aNFC7du3d/dpDA1Rx/z589W7d28NGjTIo33mzJn6wx/+oICAAK1cuVK33367Dh06pDvvvLPB6j+Z+o7vpptuUpcuXdS5c2d9/vnnuv/++7V161YtWbLEvd3q5rfytsbUEHNYVFSkRx55RLfccotHe2PPYVFRkcrLy6u9b7/66qtq16lpLn79XKtsq6lPY6rPGE90//33q3Pnzh4v7pdddpmuvfZanXPOOdq2bZseeOABXX755crJyZGvr2+DjqE29Rlfz549tWDBAvXv31/FxcWaPXu2Bg0apE2bNunss88+o+bwVOdv3bp1+vLLLzV//nyP9jNl/uqjpudgSUmJjhw5oh9//PGUH/N10STDyLRp0/TEE0/U2mfLli3q1atXI1XUsOo6vlN15MgRvfLKK3rooYeq3PbrtoEDB6q0tFRPPfVUg7yRne7x/fpNuV+/furUqZMuvvhibdu2Td26dav3dr3RWHNYUlKikSNHqk+fPnr44Yc9bjudc4j6mTVrlhYtWqQ1a9Z4nOQ5ZswY9//369dP/fv3V7du3bRmzRpdfPHFNkqts/j4eI9fYR80aJB69+6t559/Xo888ojFyhre/Pnz1a9fP8XGxnq0N+X5O1M0yTByzz33aMKECbX26dq1a722HRYWJkkqLCxUp06d3O2FhYWKiopy99mzZ4/HesePH9f+/fvd65+Kuo7vVOt4/fXXdfjwYY0fP/6kfePi4vTII4+orKzslH9MqbHGVykuLk6SlJeXp27duiksLKzKmeCFhYWS1CDzJzXOGA8ePKjLLrtMbdu21RtvvKGWLVvW2r8h57A6ISEh8vX1dd+XlQoLC2scS1hYWK396/J8bEz1GWOl2bNna9asWXr33XfVv3//Wvt27dpVISEhysvLa9Q3s1MZX6WWLVtq4MCBysvLk3RmzeGpjK+0tFSLFi3SzJkzT7ofW/NXHzU9BwMDA9WqVSv5+vqe8mOiThrs7JMznLcnsM6ePdvdVlxcXO0JrJ9++qm7zzvvvGPtBNb61jFs2LAqV2DU5NFHHzXt2rWrd6310VD389q1a40k89lnnxljfjmB9ddngj///PMmMDDQ/PTTTw03gDqo7xiLi4vNBRdcYIYNG2ZKS0vrtK/GmMPY2FgzdepU99/l5eUmPDy81hNYr7zySo+2+Pj4Kiew1vZ8bGzejtEYY5544gkTGBhocnJy6rSP/Px843A4zNKlS0+5Xm/VZ3y/dvz4cdOzZ09z9913G2POvDms7/gWLlxonE6nKSoqOuk+bM7fr6mOJ7D27dvXo23s2LFVTmA9lcdEnWptsC2doXbu3Gk2bNjgvnx1w4YNZsOGDR6Xsfbs2dMsWbLE/fesWbNMcHCwWbp0qfn888/N1VdfXe2lvQMHDjQff/yxWbt2renRo4e1S3trq+O7774zPXv2NB9//LHHet98841xOBzm7bffrrLNt956y8ybN8988cUX5ptvvjHPPvusCQgIMKmpqad9PCfydnx5eXlm5syZ5tNPPzXbt283S5cuNV27djVDhw51r1N5ae+ll15qNm7caFasWGE6dOhg9dJeb8ZYXFxs4uLiTL9+/UxeXp7H5YTHjx83xtibw0WLFhmn02lefPFFs3nzZnPLLbeY4OBg95VL48aNM9OmTXP3/+CDD0yLFi3M7NmzzZYtW0xaWlq1l/ae7PnYmLwd46xZs4yfn595/fXXPeaq8jXo4MGD5t577zU5OTlm+/bt5t133zW///3vTY8ePRo9HNdnfDNmzDDvvPOO2bZtm8nNzTVjxowx/v7+ZtOmTe4+Z9Iceju+SoMHDzYJCQlV2s+0+Tt48KD7fU6SmTNnjtmwYYPZuXOnMcaYadOmmXHjxrn7V17ae99995ktW7aYuXPnVntpb233WUNo9mEkMTHRSKqyrF692t1H///3MVSqqKgwDz30kAkNDTVOp9NcfPHFZuvWrR7b3bdvnxk7dqxp06aNCQwMNElJSR4Bp7GcrI7t27dXGa8xxqSkpJiIiAhTXl5eZZtvv/22iYqKMm3atDGtW7c2AwYMMJmZmdX2Pd28Hd+uXbvM0KFDTfv27Y3T6TTdu3c39913n8f3jBhjzI4dO8zll19uWrVqZUJCQsw999zjcVlsY/J2jKtXr672MS3JbN++3Rhjdw6feeYZ87vf/c74+fmZ2NhY89FHH7lvGzZsmElMTPTov3jxYnPuuecaPz8/c95555lly5Z53F6X52Nj82aMXbp0qXau0tLSjDHGHD582Fx66aWmQ4cOpmXLlqZLly5m8uTJDfpC7y1vxveXv/zF3Tc0NNRcccUVZv369R7bO9Pm0NvH6FdffWUkmZUrV1bZ1pk2fzW9PlSOKTEx0QwbNqzKOlFRUcbPz8907drV4/2wUm33WUNwGNPI12sCAAD8Ct8zAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwKr/D3LbmxAbufQvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_who_won(board_history: np.ndarray) -> np.ndarray:\n",
    "    who_won = evaluate_who_won(board_history[-1])\n",
    "    return who_won\n",
    "\n",
    "\n",
    "plt.title(\"Histogram over the win distribtuion\")\n",
    "plt.hist(calculate_who_won(_board_history), density=True, bins=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def history_changed(board_history: np.ndarray) -> np.ndarray:\n",
    "    return ~np.all(\n",
    "        np.roll(board_history, shift=1, axis=0) == board_history, axis=(2, 3)\n",
    "    )\n",
    "\n",
    "\n",
    "plt.title(\"Share of turns skipped\")\n",
    "plt.plot(1 - np.mean(history_changed(_board_history), axis=1))\n",
    "# plt.yscale('log',base=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 100)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_gamma_table(board_history, gamma_value: float):\n",
    "    unchanged = history_changed(board_history)\n",
    "    gamma_values = np.ones_like(unchanged, dtype=float)\n",
    "    gamma_values[unchanged] = gamma_value\n",
    "    return gamma_values\n",
    "\n",
    "\n",
    "get_gamma_table(_board_history, 0.8).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'calulate_fina_score' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[126], line 25\u001B[0m\n\u001B[0;32m     20\u001B[0m         combined_score[turn \u001B[38;5;241m-\u001B[39m \u001B[38;5;241m1\u001B[39m] \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m values\n\u001B[0;32m     22\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m combined_score\n\u001B[1;32m---> 25\u001B[0m np\u001B[38;5;241m.\u001B[39mmax(\u001B[43mcalculate_q_reword\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_board_history\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgamma\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0.8\u001B[39;49m\u001B[43m)\u001B[49m, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n",
      "Cell \u001B[1;32mIn[126], line 16\u001B[0m, in \u001B[0;36mcalculate_q_reword\u001B[1;34m(board_history, who_won_fraction, final_score_fraction, gamma)\u001B[0m\n\u001B[0;32m     12\u001B[0m combined_score \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mzeros_like(gama_table)\n\u001B[0;32m     13\u001B[0m combined_score \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m calculate_direct_score(board_history) \u001B[38;5;241m*\u001B[39m (\n\u001B[0;32m     14\u001B[0m     \u001B[38;5;241m1\u001B[39m \u001B[38;5;241m-\u001B[39m who_won_fraction \u001B[38;5;241m+\u001B[39m final_score_fraction\n\u001B[0;32m     15\u001B[0m )\n\u001B[1;32m---> 16\u001B[0m combined_score[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m] \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[43mcalulate_fina_score\u001B[49m(board_history) \u001B[38;5;241m*\u001B[39m final_score_fraction\n\u001B[0;32m     17\u001B[0m combined_score[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m] \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m calculate_who_won(board_history) \u001B[38;5;241m*\u001B[39m who_won_fraction\n\u001B[0;32m     18\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m turn \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(SIMULATE_TURNS \u001B[38;5;241m-\u001B[39m \u001B[38;5;241m1\u001B[39m , \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m):\n",
      "\u001B[1;31mNameError\u001B[0m: name 'calulate_fina_score' is not defined"
     ]
    }
   ],
   "source": [
    "def calculate_q_reword(\n",
    "    board_history: np.ndarray,\n",
    "    who_won_fraction: float = 0.2,\n",
    "    final_score_fraction=0.2,\n",
    "    gamma=0.8,\n",
    ") -> np.ndarray:\n",
    "    assert who_won_fraction + final_score_fraction <= 1\n",
    "    assert final_score_fraction >= 0\n",
    "    assert who_won_fraction >= 0\n",
    "\n",
    "    gama_table = get_gamma_table(board_history, gamma)\n",
    "    combined_score = np.zeros_like(gama_table)\n",
    "    combined_score += calculate_direct_score(board_history) * (\n",
    "        1 - who_won_fraction + final_score_fraction\n",
    "    )\n",
    "    combined_score[-1] += calulate_fina_score(board_history) * final_score_fraction\n",
    "    combined_score[-1] += calculate_who_won(board_history) * who_won_fraction\n",
    "    for turn in range(SIMULATE_TURNS - 1, -1, -1):\n",
    "        values = gama_table[turn] * combined_score[turn]\n",
    "        combined_score[turn - 1] += values\n",
    "\n",
    "    return combined_score\n",
    "\n",
    "\n",
    "np.max(calculate_q_reword(_board_history, gamma=0.8), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rewords\n",
    "evaluate_boards(boards).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 1000\n",
    "\n",
    "\n",
    "class DQLNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(BATCH_SIZE, 64)\n",
    "        self.fc2 = nn.Linear(BATCH_SIZE, 64)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if isinstance(x, np.ndarray):\n",
    "            x = torch.from_numpy(x).float()\n",
    "        x = torch.flatten(x, 1)\n",
    "        print(x)\n",
    "        x = self.fc1(x)\n",
    "        print(x)\n",
    "        x = F.relu(x)\n",
    "        print(x)\n",
    "        # x = self.dropout1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        # x = self.dropout2(x)\n",
    "        x = torch.reshape(x, (BATCH_SIZE, 8, 8))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQLNet().fc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ones = np.ones((1000, 8, 8), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQLNet().forward(ones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
    "torch.flatten(t)\n",
    "torch.tensor([1, 2, 3, 4, 5, 6, 7, 8])\n",
    "torch.flatten(t, start_dim=1)\n",
    "torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DQLearningWinner(GamePolicy):\n",
    "\n",
    "    # network =\n",
    "\n",
    "    @property\n",
    "    def policy_name(self):\n",
    "        return \"DQL-Winner\"\n",
    "\n",
    "    def _internal_policy(boards) -> np.ndarray:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQLearningWinner(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_simple_rewords()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sources\n",
    "\n",
    "* Game rules and example board images [https://en.wikipedia.org/wiki/Reversi](https://en.wikipedia.org/wiki/Reversi)\n",
    "* Game rules and example game images [https://de.wikipedia.org/wiki/Othello_(Spiel)](https://de.wikipedia.org/wiki/Othello_(Spiel))\n",
    "* Game strategy examples [https://de.wikipedia.org/wiki/Computer-Othello](https://de.wikipedia.org/wiki/Computer-Othello)\n",
    "* Image for 8 directions [https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281](https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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