connectxrl_constraints.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copia di connectxrl.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbWIV879UzYE"
      },
      "source": [
        "# ConnectX and constrained Reinforcement Learning\n",
        "\n",
        "The notebook shows the training and the evaluation of different types of techniques used to apply constraints to reinforcement learning agents playing the ConnectX game.\n",
        "\n",
        "The environment of the game is taken from this [Kaggle competition](https://www.kaggle.com/c/connectx). The objective of the player is to get X stones in a row horizontally, vertically, or diagonally on the game board before your opponent.\n",
        "\n",
        "You can play against the agents developed in this notebook by running play.py\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ba66idGhR18B"
      },
      "source": [
        "## Preparation\n",
        "Import libraries, download the project and update the paths."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YayXzhljIyP"
      },
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlW2Dywip1_-"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "from getpass import getpass\n",
        "import urllib\n",
        "\n",
        "user = 'FiorenzoParascandolo1'\n",
        "password = getpass('Password: ')\n",
        "password = urllib.parse.quote(password)\n",
        "\n",
        "cmd_string = 'git clone https://{0}:{1}@github.com/alomb/{2}.git'.format(user, password, 'ConnectXRL')\n",
        "\n",
        "os.system(cmd_string)\n",
        "del cmd_string\n",
        "del password"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MclgkwjRskWd"
      },
      "source": [
        "!pip install kaggle-environments webcolors\n",
        "!pip install torchviz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsZPvV-UpoIa"
      },
      "source": [
        "sys.path.append('ConnectXRL')\n",
        "sys.path.append('ConnectXRL/src')\n",
        "sys.path.append('ConnectXRL/src/connectx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLBfeJaFP1az"
      },
      "source": [
        "from random import choice\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "\n",
        "from kaggle_environments import make, evaluate\n",
        "\n",
        "from src.connectx.constraints import ConstraintType, Constraints\n",
        "from src.connectx.environment import ConnectXGymEnv, convert_state_to_image\n",
        "from src.connectx.evaluate import get_win_percentages, interactive_play, play\n",
        "from policy import CNNPolicy, FeedForward\n",
        "from dqn import DQN"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cutclmjRSH4F"
      },
      "source": [
        "# Environment\n",
        "\n",
        "Game setting is the classical 6x7 board where X is 4 (Connect4). In particular a [gym.Env](https://gym.openai.com/docs/) subclass, called ConnectXGymEnv, is defined to represent the game and the rewards during training. The game shows multiple symmetrical properties, but still there is a design choice to consider on the color to associate to the 1st and 2nd player. In particular, we left the possibility to choose whether the color of the game reflects the player identity (e.g.: me = red, opponent = blue) or the order of the game (e.g.: 1st player = red, 2nd player = second player)?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "keU1FGLTSHjp"
      },
      "source": [
        "hyperparams = {\n",
        "    # ----\n",
        "    # Game\n",
        "    # ----\n",
        "    'board_columns': 7,\n",
        "    'board_rows': 6,\n",
        "    'stones_to_win': 4,\n",
        "\n",
        "\n",
        "    # --------\n",
        "    # Environment\n",
        "    # --------\n",
        "\n",
        "    # Rewards\n",
        "    'step_reward': -1.0 / 42,\n",
        "    'invalid_reward': -1.0,\n",
        "    'victory_reward': 1.0,\n",
        "    'lost_reward': -1.0,\n",
        "\n",
        "    # Episodes\n",
        "    'player_2nd_episodes': 625,\n",
        "    'player_1st_episodes': 625,\n",
        "\n",
        "    # Rendering\n",
        "    'render_env': False,\n",
        "    'render_waiting_time': 1,\n",
        "    'update_plot_freq': 100,\n",
        "    'reward_avg_roll_window_size': 100,\n",
        "    'plot_performed_actions': True,\n",
        "    'plot_mean_reward': True,\n",
        "    'plot_actions_count': True,\n",
        "\n",
        "    # --------\n",
        "    # Training\n",
        "    # --------\n",
        "\n",
        "    'batch_size': 8,\n",
        "    'gamma': 0.99,\n",
        "    'eps_start': 0.99,\n",
        "    'eps_end': 0.01,\n",
        "    'eps_decay': 1500,\n",
        "    'memory_size': 1024,\n",
        "    'target_update': 10,\n",
        "    'learning_rate': 1e-4,\n",
        "    'epochs': 1,\n",
        "    'device': 'cpu'\n",
        "    }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GAWPfuh5PzSj"
      },
      "source": [
        "The library provides some predefined agents:\n",
        "- random agent\n",
        "- negamax agent\n",
        "\n",
        "We can show a match between the two"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30ATRck4Pyku"
      },
      "source": [
        "env = make(\"connectx\", debug=True)\n",
        "\n",
        "play(env, 'random', 'negamax')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KjZYOEg2X_23"
      },
      "source": [
        "## Observations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jANPHgT6YFV-"
      },
      "source": [
        "In its simplest form, the observation of the environment is an array with length equal to the number of cells of the board. Each cell can assume 3 values:\n",
        "- 0 if the cell is empty\n",
        "- 1 if the cell contains the first player's stone\n",
        "- 2 if the cell contains the second player's stone\n",
        "\n",
        "\n",
        "However, we have decided to transform the board in a RGB image for two reasons:\n",
        "  \n",
        "\n",
        "1.   In order to represent the problem from a more realistic point of view (e.g.: a bot asked to playing this game given picture of a real board).\n",
        "2.   Because we wanted to investigate image-based methods of **Explainability**.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONJDBtUM5kqP"
      },
      "source": [
        "![plot.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyrFgVBGeIQw"
      },
      "source": [
        "## Actions\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCajiKUhnFS4"
      },
      "source": [
        "The action space is the same size as the number of columns: for each observation on the board, the agent must choose which column to place the stone in."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LlD83sAU7ujU"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MwkboKHX8oi"
      },
      "source": [
        "## Rewards\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLwsTHLrX-c0"
      },
      "source": [
        "Rewards have been selected after a trial and error phase.\n",
        "- **Invalid_reward** when an invalid action has been performed, namely the player has put a stone in a full column, the match is concluded.\n",
        "- **Victory_reward** when the player has won.\n",
        "- **Lost_reward** when the player has lost.\n",
        "- **Step_reward** is the reward at each step. Default value is $\\frac{-1}{rows * columns}$.\n",
        "\n",
        "The game can also ends in a draw, in such case the cumulative reward of the agent is $step\\_reward * \\frac{board\\_columns * board\\_rows}{2}$.\n",
        "\n",
        "Choosing rewards is a crucial step in RL, in this scenario the hardest choice was setting the **Lost_reward** and the **Invalid_reward**. We didn't include reward shaping, namely modification of the reward function to penalize or stimulate particular conditions, since it generally requires prior knowledge about the task domain and a robust hyperparameter optimization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D64eDUL3kOxW"
      },
      "source": [
        "## DQN\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vj58MDnG1yrh"
      },
      "source": [
        "\n",
        "All the developed agents are based on the Double Q-Learning algorithm. The classical Q-Learning algorithm is a model-free reinforcement learning approach to learn how to map an action in a particular state to its value. Instead of learning and storing all possible values into a table, Deep Q-Learning proposes to train a neural network to predict the Q-values associated to an input state. The double here stands for using two neural networks, the policy ($\\theta$) will be used to make decisions and create experiences and the target network ($\\theta'$) to estimate their values. This reduces common overestimations in the Q-value prediction.\n",
        "\n",
        "The training phase involves 7 steps:\n",
        "1. Preprocess and feed the game screen (state $s$) to our DQN, which will return the Q-values of all possible actions in the state\n",
        "2. Select an action using the epsilon-greedy policy. With the probability $eps$, we select a random action $a$ and with probability $1-eps$, we select an action that has a maximum Q-value, such as $a = argmax(Q(s, a; \\theta))$\n",
        "3. Perform this action in a state $s$ and move to a new state $s’$ to receive a reward. This state $s’$ is the preprocessed image of the next game screen. We store this transition in our replay buffer as $<s,a,r,s’>$\n",
        "4. Next, sample some random batches of transitions from the replay buffer and calculate the loss: \\\\\n",
        "$loss = smooth\\_l1\\_loss(r + \\gamma*max_{a'}Q(s', a'; \\theta'), Q(s, a; \\theta))$ where $Q(s', a'; \\theta')$ is 0 if $s'$ is a final state. $r + \\gamma*max_{a'}Q(s', a'; \\theta')$ follows the Bellman equation while the difference between the two terms of the loss is called temporal difference.\n",
        "5. Perform gradient descent with respect to our actual network parameters in order to minimize this loss\n",
        "6. After every $C$ iterations, copy our actual network weights to the target network weights\n",
        "7. Repeat these steps for $M$ number of episodes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63Z0SCoSyUIa"
      },
      "source": [
        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J9pAkFbtA02pnlT6IEGPxcnou0pOKMupoi0pQ+KKQAFqfcIMqtKsydNG5sCLYA3S0HENB9lEAfS25DISN0r9ABeVEkkWhQx2kPa6W1e9JwtRLQ0AQ8i/OyKqgAUxGgO5zMSfjijthFVtaJE8Gir8pDToGnkmsaUVU4DKCJCBIn0MCaBn+P3QN0TDz+HPJqDiFK0FkQ5pSDSBt3PlCc5DCKai5QK3RhBzjW0J7NO72Kkghg5BbUAs0yLJrAkBSl1RCjW41YHoU4FXdQBNUoBqBDewHbqXJocYMrJcioC3kx9Ji7FlwCmfgmJgDiJNQTCoyahCCBlhF+RBhByRE4TcEf853/+5y9+8Yt79uwplUqFQiEyNEV6zrkAXfxOn6VLl77zne+84YYb2tvbVfIDkSJCiR5WpTDo2CEi+lNB6KyCayAIQJEKCcw6P95AnVy6N7MEcDmQaALAlAZw9IKzxOFMqWs5+RGBqD0dUUUXDSRAaLUugDr9SOlRF20+JfFLKGoOCcwACRRxpmkDgMmI1P1yjJhMES3k0qUoDTOmHSPNiNAR/boAS+plF7hERZplSEcY0pMBiVDIGX17qCd74xI0S/E6zUoBVVUioS7OTdLGydQjj1anjhi1UCdnSkO6C5GpuEZ5osh2rUIRHArgDQDbyT2BDmg+RCqsUgfEk17MZQEgVydTbkgpQANStdMjRboI4W1SxAFvBqJ2YhY/oBbaeF7IWVILdwLo6NRLIE3OkSPQyWmQP+Lw+PmIkQFRHpbUhRKdtDH17b4FoIIIjgkgGquBgpwkRDRExGqZzOnTp7/zne/87d/+7cDAAKZJ/98K7AF3yvnz599xxx2333477pcQ/fnhPgEdIKoQuT3oiORMAVUF2gDqspHLDMKUGyD3Bg/oiDOZuUmkLNFJEoF+NYoQTP0EXwVUasp9CvizGorTuE474HUAKaKfDB55IrCqCCUy+3ZwpgAIlDQhmCKCexKKBiqEDBEHol6miNFDyWq6kZEpEJ1YEl4DSBl1er1NVcJ7EFECAfzj4nUQgLonSqkgMuWWyCMPOCP3IAVVEXEABIDCkjhIWhGJzkmEWTyacy4chArSaBpLaQ4kpxhADwkc3iQriDgNgEQp6qVOhZACcDmIjAS7AF+SgSXtk6ABOgkVpOQESzKEMWYggR4ZAOg8iTRAUVUK4XWlTT2KJPQT4HrMmNIDeB2AzicGxTBsajgB7lsADKFBk0kiG8AqI3Dq1KnPfe5z//N//s8zZ87g4yOWRsu5AxMSFoCUm9chEFRIqCAtFotnz5790pe+9IlPfAL3aZYQAR0CUx5d6LODiqqAV8DhB2EkyMOAqfMjUKHouSb4gfKAKFJBZCqOSFD0kM6xTAFycziCUdFmiNBhHr8u4P0qEVpOqTeoEQaVaAa30Y3GCNDZyBbOYUuoG6AwpROQmSmgRT00jaltIoAlkghpUYsCnMaBImHktFPnEQ1UF3maAJzjRU9YReSiUtToIQ8iCcFdRXuLwF51yaxVUAKnATE9Tb0AzZ7DT1BRiWQWoJejovkJczskuATBpZOkAYiA34lvIUcUqMcHrBrHYRm+nAERRxVORuo0UOT+qGugStBlRslXqSDSAwWAIi4Dojz0IwKRjalaSGAG4R6o0BNxD7Y3tWmOiDhtvjeaz/0D9CCiyw9k1fNoMiKrWhqir9JAESSar1HWOR2HDx/+r//1v953332Tk5P8+4vsivwcS0C3ZRpnGASK3xu5V4hoJyDU8WkSuOmmm975zneuX7+eoiC/oDmAFvK2aGlfmskWtXj4km8HmEZzoNATzWQ602YA9oIggoPMtCUBEwC/enoJxPSKJNEOAa/4OWmnoJL3zDQHOJdRHk3HRgcSLUFE0+ABoPy8G5gF3q/5TAWIiNwe/Ij0nMs20ItGppqTboQCHUjyFNSiUWmzF9MGKenVibRBRKX0WCLtbAraZtoA4aucBgWRY6kgglMh4nHBb2hinb42S37fOhJxpT4SsxwJ4c08EnLpUkDEMVYc8JxAOzwc0rTKSIiH4hQoAkw1xw/UKooRIffnQalEDgG8mN42AJE2cpojZ1rnfPIIR48e/fSnP33PPffg5OMGyZawlxj0A0keFE98CXMUPSInCIEd5vP5H/3oR/g0uXfv3kRN2SJgPgCikyazlJlAJ23kHCX4daP5vpTu9UtH23jeXYUDMohTnwXYwCwrEn6ONk+S9s+06Cyb0RDvmWVRliSmMVMJenobNEctTLkxlUCoIHq/5+kTEsGbAe/XfI9oIRhwCBRnWis9h6mO3Vc1SmMjRGuReCd5WokgMb26J0C0ltB0LBE5ZwJt6WsAmH17s4CeJstzGUW8RAY5BvtpoydyogSPbP4+B/gSFXpAqCN6PxA6EhFRVYiIfr6fIzF0TBvoQXMa0tEr7s3RUaMUKcJMS2AyNw8kUhjLORTRy3YSQGbaAOpK/TTApqdGhUqyAXKK/f39n/vc577zne8Er60FAyK4J6FoIGc7QH8keqhEAmgPvgRAB8rlMu7Wn/zkJ8+cOYOSB9sFPA3QxZIIdBJCKedTIfe9stFD0EA/FYAcNrYgAj6VWaP8fIGKpgVtKlULYlT18Ir8EtUIsErAoJL3sDG9iu8lPxcPQJsvAVqFS6MKaCAIUpUoAhQ1UCW//9mhFsBzIoxsbsCiXJdRkBjpgnQOx1Z1CIDfOUUfPdKK7xW4CpDkDaTbCeiYA39k4ISZuthCj9C0BSmdAEvaNhSSNOgHktxB85tWBdlEuGdyQqmtFDjjNB/68RITDMlrjU9JeEhaAIQ2cFapsxRxRkDDESHSoDkAPBRBaAh9CWcqsxo5jSXPUWIvRYCKqjJ4QkQpwOXSOhREibQxIlVVjTIgpTI7/IraAzjEKA2WKfg9+Cp0RJbkAUZHRz/+8Y9/5StfGRsbw2c4tbA9GhKlacxiUAkEURwk6kJarVaxwze/+c1vectbFi5cCNHvmfBzBNl0vP7AAaRoiboANqq9KbAioPkkEv1kikqBmSbTBsJ2iTCrJMyyPZmjrmhXKkWjVJLedC0vRoaZBgIqQaTOEjiibGnMvhzncCBjUpsOvzo8amQ1gszRcoQ2rOXShIjSCKwy+v1AQSQnNCca6HWg6VZhYBWEHDEpN+DFpocciUzRBR5NazofgA6kJzcFnIico6XTG/MlmNPryoCBqvrhBG22v8ZZmloGkkZQYaeZGrsJLSbSI2cEeQhxElb9TJYI2ZimwRKHCBABHhsH0kAO0BN1aQ8EbR6hNRFF0MWHgelM8Db0goiHenIIiFLSmKnEI1WVBxJWaLIrHab8JDCzhFSeyclJfGLDJ8jx8fFCocAqIodHCB3TSmmnV4J9mgHDESX6qhcB3K1hvvPOO++6667h4WEo2rMgs4dE+NkCxfeCN22kxzsBng0PGfyQpr1KOQSppqUJAANmEtEoYSZ9JmB+tA0hGsUqIlanQszSFTl9GnX5kt9PZEujaePPCzZqLaVNV/83rzLTwJlEAhwrkgDROSSSvBloUJc3kyNqvq82hY7dO/2WBIhen2UySjI/7wYA79F+0u1UAH/2PGTwsH1M17XEFOFvQHo9RLzKUwHCtERHhN+nItH+IFJHZImpBxR/OwHAmXpQiUo0J4kbDoISCCPTpk7Cp2whaapHvQTESI9SnkyJJNbTUDDfn/CmgMFvQ73ngsisVAOBZ5999mtf+9rJkyf9pQZCrihEqQe2ysMhAZJCAyixXYRcUQh1u2pPnTqF+/dTTz1FRcDwhDlOkj5FTU+an5AGq+lVmo6i6HcOUFQpaM0BT3ozTZeLbEwlzm4mIk9TyJM2p4/RrxL5fRrNjJyY2XS3gq/649U0iOSzINp5wgJmmk8SQYsy1djI71M/n4iGkKRtQOQRfGqPSuoa8wbwpqdIYtOlPWRIDyG4gfQ2hFlKAjyYryWQsiu9qPeQRIcgA8nsB+jnT90j8QLENpS5FUAiOAgAQg9KGiQdhO3gqiIlJ0EkYMYQ6BCpk9OJGIElOQnPMRCRNg5HlUsoNV8DSAWkmh+KBnF/XID3cFGCBioyE0ixAc6RgUST6TF3AzRDB7ioDocpCGDWBmhOkulgiwecaNeix44d++xnP/vII4/om3QQSTjTTwZn1YtIuaVKpQLdA2I1/HRWgqK6RCgielFAunPnzk996lPHjx8H1wQ9uFEjUqxFnWDJAyI8nEAzEO0QVXm8Qk4zliOhTkV7UCO4elGlSCIPu8jpRIs4IwwSwZX6UX4VRbUrEtABG9Q4IRD9NPZ6hbEp4ExYALsIW2D6qaZIDyI4Vg+uJvMh0sOq349mcvMeMPgtIUUje6OtIlU7bOIwcxUPTvC697OKgd4AMdoeq4j0S/E2Vr1H2wYRhwFdAGwUoagKqB1EXEAjzSBoVC+c4MGSTEBUKchTgBMiwZS98LNEETEypAGdngj0o+QPjYCIzYPwWMLsqeFMAdgQ2e4jq+KIU0cO4nfsd8ZxEpmK+BRRCiJWoshqWgSoJ4nbhhAZCIkgPB5yRpTUgpSKUhKANiqMtCHyLBPyYCHqUsR9TOvcoXpD3RCZyQF6qGgCU0Qq3uNBZ1pXl4/UsTEqX/ziFz/+8Y+fPXuWfwxJEeD+vaKBJIgkFOfMmdPV1YXY3t6OUQAmlEol3Dj5L2cB5XIZ6wLws0tDwOHnrqD4EoBSoVB473vfe/vtt7e2tlLnENnYS0VzVCJB5GRwEYAGpoicrMhSBH/O1RUqU6k8mpMeKLMfwlPBqvdrIAkURtoUIx2EYAlxpuOlSHAPMlAhiZznAj9fM7kEI0tcQk5AjSwh5a7ImXonIriqIAANfiHaBDlDU5PzQ05CDtAGQpHt8kTLSQfY4idoCURw9lJXFfAzxdXlOYm4VwANjAAP4JdOCm5m097oYAHYAOhQyKnTBp1i6JgRnCOOiFQiifcAEsGpR2ulSxSDJfEk/QALINGRUKROhWeNREuC+ANmi4dEOjlBIghSckApIs1Q2IjoDSSINJMQMlDkxsgFtWhX1L0tPV9E3CsEB5KjCjAFoRj5PaKtMiUX4RykfiEAujyAqraDUFKVKSPS/v7+3/md38GHSN4g057I74FVcP9D49KlS88///ytW7eC9PT0dHZ24n4GHV2TAVjlxIkTzz777MMPP4yPrbhx8jNrNFNrgaskEffXq6+++n3ve9+6deuke4N6ASkykLA0k8g0MswENfJsIwV42v35pweRgBKNjR5KgjaUEEmigdEcP8QvGtmYRqIwkxmcuuezYJYjElfKJXyJfCbizYBPZQD3JBQNkRmRKc8z06grOrFN28mZeg/HMvU6wJQxsiGCe//zcnV5UdzPp4ioNA12aQ79iOnH1EN+ckTf7tOIzI6mY6Mh0s3k4G2I3qCHVSWZiWQoQXfkSA8lNFrnXQqALs4BlPozSzMieFqXHxEihzSFFhVBixY1R6M9GhVVCYiA9pPeHuFt8GiCH6UDAWf0NiiI2nMEzgSY2mKN5dgYiQRLiODqFfxa4Ih+IO5VX/rSl/78z/88min4ySQCUtwFV69efdttt1155ZWrVq1qaWmpVqu4k+GmiLX4Lam4WUIHwNFy6NChe++997777vvpT3+K+2sya1bwoNALYM6b3vSmt771rXPnzoWooxOBR36/Z4oC/FAkRjZO0ygp5IIMvpreCRGlzwv6EUnOvZf+JJmOpkchoEoSeWYaOMtCEWZyPu9+0KVGb44GNp0zy/DZd56unsuRajmYEemHiOi3jehHQQFgQIyWmGX/gtrJtShI02l+5rnMPxdPGtrJTIAhYQGzm4GmR+RXAWcKeD2Ncz+i+PUOkMIFmHJJiQJWQsRi0YaQ0j/TI8GjjTwERUZ6OD8Up3bFUrSoZnodSItUEJuuDsisXhCKIAAbeSDmmz6fxwtbkjcmkOjwo9UJVBFVgo2TQcjZC4U6wVKwTI0FJwG8GVAJ+jPPPPOhD30In+30J5ERYKYuQg7gdnX55Ze/8Y1vvOqqq3BfPH369L59+3ALxOdFYGJiArdA3CDb29t7e3txB12+fPmyZcuWLFmCOc8999wnP/nJH/7wh7Bx7OyrE0hx3928efO73/1uLE0FkR6aef6R+geIJaWC5oOkPZzgFQLOhM16bjmQKRClzwuOQuTllB4YQVWdgecFl2AXOaFV/LreTPhqIs0AbSk9hEgPoRPRHwtFEviBdCMgW7pEzH6K0u1UZprm9wAuMyAeirauUkYYIiKkFSASNZ+EHDFKATZG7UCkpG3giEw995B/9nYCSiSmW6iwRMVzEv8gktMmQ3oIFaazgJ6k2UOPH0CFoK4dEOQQtVEQjY3MHtA1n0QTUFKVBODqgGaqRN16Gq8jSNnCgQDblQK0sYWKEE1gSvjlyGUO8hT86kLUBaQbgaYzMVAKiEal4UvqSpu1Cm5s3/zmN//qr/5qcHAQe246VjP9cHD0Xnzxxe95z3u2b99+9OjR+++/f8eOHU8++eSpU6fGx8dxJ6NfmDNnzoIFC/ChE584r7nmmqVLl+L2/JGPfOSBBx7Ah1HMhIfDPfyiAFIcV1dXFz5Hvu1tb+PXcqHTQ7NaWAIoklARZI44MVMX9oAIPV3yi5LwgYDe9KpoqguoIkajopQc4KjII0BvugFEmskJtUMUn2ly5Gl6OPJoCBRA5nQjqiRsBNhCDnCObxSnTY0ASohywuCraaQnzNQCPSopTftREomO14M2tkdnxpc8uCijFESk6XPr4VuA9By0gzPVzFCZgjzeTKAF8BuAB5E2DQRBDPWpPbCEqEOINkPidQ1nhAdgL/2M1jMz6Mn/4R/+IXMMVTNSTdFiSLEGN0FFYKr9IQVk5hzqJACnkSCyhIiUvRIBmqkjAlRoplMetpipMc1DJdoAph7SfdR8giIJSkr9+aFC0CORHOAOQRD9HJE02OXNHl4Hx56RUolK5Ii4n/3TP/3Trl27aEaJHo+ZdBwvPkG+7GUvO3PmzO/93u99/etfxw0SnN/XioF+AzDj8yI+aO7Zs+dnP/sZ7qlr1qzZuHEjPD/5yU8mJyd1t4vAdl+CglEYiBst7rthheTQAHCYqYRKkhL0RIAuD1JwgDohkdyX6KSCSA/BFBHHCIJovhSo43CYslFA6tfVHE3WQuJRC8BUelQFfCpzkk+visOmFFEpOaKZApo+L8j9OWGjuIg4wJSQohgRcYKK3yeIX1ElKeKCV3SYXhTXw5EGdIKp5kQcIPcKgJS2aMMAuFI/StcMlYiQpwE9MpADTP0S4FyFBqTUASq8DKhH16oaETmTXESQzjkgfOKYL8BzAKkUEioEUi7HaYR3Tl2d2CJ3SaCMVM3s91uhQtBAPwAFBpYAcj3/AXrSoB5mGMI6yZxEagynzg1TJMgVYUMkEZchQlMRoM52KoJKTAGdw5mmCTKAPK85Avz+wfJ43lFpw8GDB59++mk+QKyGs5WAKXSmgPU0xEKhgA+FSHFrxGdB3LegFItFbA83PJpDkwEKq62trSMjI/jQ+dhjj8GwbNkyfoeqzJ4DWMhvAGAV93X/Q1w9UEULOXvFSTy8kwSIJjBqjgd0IZGazeQTxCtEZCCoEyxRESdkADSfaLpVwtuAMMPAFI2AUiGtABQVSaR4ApBHpbRIKI2IhxebGoCmE8ijUiQCngu+y3MpALh/mACKhDyzwHu8Wbzp4xs5CXKJJEBUIryTXArguTCTKF3EX1ckjDwWKSSERMUIEHWqZzIkzCE9UBzE8+avswB9gFISHYwUf/OLwDOiSDG0To2V7kGDIj1+gjgIUiok1AmmfO0gZ2wKlfzdguByM/VqMzMBjd6TnkyINx0IMTLMcuYJ+elEqqVZYnz88cfxkY63NOmCuQOS3IFiW1sbSE9Pz9y5c6sBXAXRL0c/gf3A1tvbO2/ePKS6m9JP8/MCD+vZs2cfeughfsuPHjWUQDwkaj8eqqrRK/5iQEo9DV+KPL4EnPujJrDFN6b3k+6KlMjpq+BKSbwCRFw7Ca44FSchkOqsMhWHzsMhWAKUknjIL2dTGyGz7yIBsPkww0BFpCmiqk/JpfiDel6k55AA/nH3oI0eKgB4dDIZSXiwoZKUPCHYbg0B5NyDFMA/6CSA50CwJ5AiPVKYzo7QN+XHVkmkNAVtkYEKoHPClKAHQG+Te6RMGo0XI4E6ELzTTrrOGht9CQCHDhGRo8hRQhTo5EKAZgKocixTEIAiFRAaEKFjCAj09FjaoCMilYEp9waiKsBRVAhwigRSziHYBYir1/MI0jkNIFcKgHNd7BOpzNaWOjRCB06oC7FcLv/oRz9CpK6SgJRI8sZOQJJCwJYtW97//vdfe+21uFkixX0L4C0TwHygVCohohe31euvv/7d7373lVdeyXZOw+Y5OaxghIhSACn8uLk+/PDD/EHnetQAVnkqSBDFrT/Ai4joIhFH5FhyAqkiCaoCBxKq0gmAYyBi4gjwXSx5A1vQq0aaoXB4cCXwKQwicLKrKdAFyE/4UYDaQbATcgA2bQwTQOhUO4lSVrkWRBAenUoQiaZzBFbPBRrlgUUhcvWoCpH7AZouShEcBPAij4Wc0HCJHO49gN+D5wCckTmNqEVb0mYQIepUExrr2+EhQZUGRIrsZZQNoA1DSMQBEPjZAoBApMIqInXP6YHCFJElRkF7oBOIDE3BgQT8/nhRAkAAKNySqXQQ3sQSCSINBPdEUX4dpEQRRVYFlZhGXQBm4hjUDuiQwBGpMwUngUeboc4IJIfdmAyDPORA0xQRnMRXPXwp2rl0cKUskYRi0kUOyKlp4DIDMoiQI0Y6CUsE9ZMnT952223j4+O431D38F30CxAB7OqjH/3oVVddBWVycvLw4cO7du3CfQsEH/L4swK4eX5ra19f34oVKzZt2nTJJZcsWbIEN0uM3blz52/+5m+ePn1aHyg9sApFECryYPjixYs//OEPb926lQrNvkUpt4FIRedZHnB2UVTKFqYcYo6Gh+00UAHolBjqzU/jLPB+DglyAu4EOhAdCzk3AGjPqmpviBRtSqPKCEghUYQijwdL6oo81LE0uC9R9wqhA4xKXtExQgR0HgDZRNKISkgT1uwkzAQa0qPSk6n4PVPx8E5yQk6eFpCZquBANIck4gBSwJ83AApJek6Qp5aLAEN6IXURfqYnUSMi4C8AEc/N1OCqEtBF0nN0GonITA6YWTWCqRZT1S/PhyFJ3FAQQCUSmVGSqFRbZ0poiHRELaEJAkU6iSgVqANaNykESNF1Rg6zTmjoTtqtx61FP+GPWo2IMhO+lyTyAywhaibhexHBAfZ66FhQZaSNKQyPPPLIL/7iL/JehZRRoA0RnCVygsM/9rGP8R6JEhRgeHh4aGgIEcCNEx8ocYPEEnPmzJk7d25XV1dnZ2cx/KPNaMFmnnrqqd/4jd/o7+/HPVKHgDnkIlpam8QnVIz9j//xP7785S+nSDOcnEwbRZV4RBoCnRxRa1GkLrOcRFMxmg9CXR5WPSFkYC/HgmvnjPLQJj8Iu+iRQVETQmWqZOMaQxDB/So0eCfgdURyblgiwLVA/BBVmZKLAP4QoEdHTSIdCgCClDpAD3RxwM9RL1PYxIlI4RIgfoiHH4UIGwg5oe0xBUGLxrKdVfayJN2s0/cgGxSACiNSjhKBTQpt5IAmAN5DjkgDU+qAhviqUoKLAl4EoGgOUlQBTvN7o04PYrQ0qyBIYYgUpWGSgUqSTD8ukHQaXI29MQG4GMACFd8jXTtQFU4QlhDppwgFKRFxQGZGgjqhsZgGwEZFgEjCndAgD1NFTmBJHj+BkRsAQYlLIyIF0E4FBIACp4gAD0QSRK0rIj9SKoq2jDsWiIjcA5R0C6sUQaAAIr5Lik+fffZZ7YpAqnYCVW+IMDY2RgIPbnK4+c2bN2/16tUXXnjh1Vdffe211954442IuI9edNFFq1atQrWlpcUPLJVKXBEiCI+a++SpoAhCgBMw4KPkoUOHMAEcQJUR7ToKf8iKmuB1OgkqGAKi7QFqpIgW6gQnUAQhR2SViicEOKel94y1wBUhckvskkepQIU7US/AUeIksjHlNlCVAQCnrrVAZOAEgEMYqYDIRsVDJRAAjVIiwOwjbIhaDhwwX+MUkVChDWCJvQR1EK5LhQa/BCAC0IxIqAtRoxgBNLJXCsfKD0iHKDN1RCnsUiRQQoRCgghwFKDh4iBKBSg8XZigVCWYpWsJPxwI3uSkERIBclUxHByipokAMHA5ACk5WtJm6JgDQGHkUYRWQ1pRF0hoTbbElFEtU3viAiC0UvGpTXJnliIIQc655BoIqFFR8J4waWo5RCrg3AwIdXJErkLOGEFHgb0hesXKAZxAnamt2jgEr5OgZA3uPKQ9AD0gnEOCGGYbzORsFMkJ+LXtNOTkWKTahghAG6LgxSNHjnC+VkGv2mkjIfdmRNycnnvuOZYYPYngDd5z7NgxfNxMklRVG/MiOUr4kHrq1CncKakL3gzMlIJgiJYQeAZQBfG98gOJFADd2wTOmeV8kmOaPAJXkQ0gl5MGgopAhVET/GZkeF6Ph5YW0gpAETM1VoTwKcxR1aPpfI+mS2jnUTs80d4YCW/mhFlW1wSANn+6wP1kAmJTPUya2gymiVh5BjStcj4XQmpz3WTGWaClAUyIllDJe0g80uJM69ouG/ukco6Any3aoRaFzrEC9TR4sJGBA/2Q+CxDQuTyjFQEiF73ka/mgDyCTyNOgGsIOVNGgMPhlBKBVRICnIrXOZ8pCUBDU6gUtWsm0zS4YXFEb44aOS1t4xBUJfpqtAdfSgNmP4e91Wq1v7+fyiygWYjSBx544PDhwyDYLUsz7cSvTieuyIGBgbvvvnt0dBSfQSFGvbRB9DpTAobTp0+Pj48ntZlBc5L8PNCuOMFWdZvho+ahqrcBfDSTxJ1GvysYok2yxcc0qEeNTed4QKGYLhHSZ7dFCwkz6UR6Gvx+IRn8GZYIIk5E6SxI9wJYnRtATKSA9OPrwVHsTaQZ5hNRKbJhSKQQGt60KrAazQd8O0CiOMsBRgeVMAdvEPxwD+np0jlCjX5CmGdI8unV50XUS3hx6h6ph0dlKADOIAlezqCD0ACwFLrNTIPOOAhS62y0UAcoAtQBcJYwhBOoiPP2DqdEEpW8R130i0CHBykbEakA6iLAaUCJHICHKacRtIlrDpwSOR8cJQAKuXphoA0cThnYSNBJsJc6UnCm5DSYL4A6IqfRQBGfAvEhjOsSYdIU/ExxEMxn2tLSgs+Rf/Inf/LYY49F33EDDxDGJEgK4XgBVPEp9gMf+MCjjz5aLBZpwATuBykMtEFklaKNcLvCXRa3WHCV2MJeKgIVDkQEqCBqCfM1nEgxBzYqIr5KXSVGPwecTqUwMyWgMHIIoDmI1hwgDgInuebbDoLILprpZEoniHQSchIAIiAzQREEOkCRBDoJIm0EZ4prCaQkmsMSzySq0gnpSe72gKixILZSariAEjzQSQCKTOVRlJMKEM2EQgKPOBA5oxIiqgA4U7YDPFIq5m6ANhKCKUA/FXE/nFFEKUC/rkMtTY+fTPhd+V54vI3gHkAQNRMROktaVyWApYiwCoI51DlQJcAGNcASqwTW4ublJOFAEJnZq4WAJs9SEgA+gSUSginNnvPIradhlkcxQmTQuQM4BIpvhMiUTnBAHnHamPKwqVPhZEaJ4ISq1GlgCeA0EIpsjDxpoIouNgIc6xclKGoUiZxMyTWKkBMRJaUijL6rHH7sOKZRRPQEAI8ONppQKBR++Zd/ecmSJR/60Ic++9nP7tq1a2RkhCWMTU8mcG9+5plnvvCFL3zwgx+cM2fOO9/5zt7eXi6E+VwCQHu0HBClwNjY2MTEBEhosioaQ2WKaCwVjRVBBKcinSVwikp5XAQU7hMEoMJI+D0ghqZkTpCngcNB4JE/VAyhdaqXUUckg1aMEOls8YCiOd5MkdHrsCUsVMPiU9vz1abAKD9ZQGNTXUAjt8EIpCeoFCHSo036/acnQNFCcKoXojgRpRFCa+xverAQZx/uN0mOarRPEiCao16u4kcBkdmDSyTJDGC7tiGCxqg3LB6fVR+JtDLTHuAhOBmAKBKBQ9KjoHDI1M+iA5B74iOABcSJ9JLpCSKMbGFKQo7I+ZoZKlMlAJtWVJVQioiqxIgQ3qlTw5nUZ4okaU4CkIdiQhSJUJkmphVAioiiCA9TJZF0KqKo3uHh4W9961v8hlLzBegEAn4VrxNQUHrzm9/8ile8orW19etf//rdd9+9c+dO3Cb57axcCwAfHx8fHBzcu3fvQw899I1vfOOLX/ziwYMHr7jiire//e34MPrd734Xtzo+uMn0AKUi0WZwZ507d+7111+/YMEClpoCflS9QZxk9qh2gCIBLkRL+CpLiHziJaobrusQiAweiRrAFJHDKQJNud7o+Eji27VPpuAqiQBpfZbIowPRHlT1SJdA/H50ijjHR+ogBLg/BBIAXDonUyQhwOmRjohVeGWCR43RBuTRbj0HrGc6ksL0FRGjySLRQIDbk58eRXpAAHoAKKhSB6GTOkUf/U4An5KjPb0l6FHJc5QQ/ShfEpBqb4AM1MkBepRiLKaBRB5w6BIRtQERRoJdSSegoyKBjnHqBPxoElTFRWCjkykjnTwLNECkjigPDdGiFAEOsTZu3XmoI7IXsDWmbxgA5ypaC/Ac0IRI10CI5Kz6gTSg1xoCoCBlROrNTG0l5xfk8eB8Ls2ZTW0eWhrw65KcPHnyXe961+7du/FxUDbfQngFHJEp+V//9V/zRwGcOnXq8ccf37Fjx1NPPYU7Ijy49QLcLRYl2tvbV6xYcdVVV23btm3+/PlofOKJJ9797nfzVh0tDXB1v64H7r5r1qz5sz/7swsuuAAp5iNGhwmCRvUyBcFMGtJgr5ZWr+ceWguYyQOglLBwLHQ27QUBqIMgaqYvAb5d8B5vSJuliKCx6d7SvQScjKjO5PGAB5MBcPaSe/1c4Ncl0RwoUQpCUPeRIkBb1D47aPNLqNGL0R486Eckp01DIqQX8k5UEWGIlqNnpl4/4VwAPzD7Pj3Sx04FEb1sn2VOWC1ZTmjq10BVm9qaikR6q0Dy2AAkaOZKVNDgdZKmKSLXpp+cqUQQRFVJqMjpRUIlbQyAooMBp4Ep/eRNQadvIXwXh3gDV6eOlCW1+NR7OES99JCQk0BRCxXB69EcK7sWTmZkSl2pnALuarhH7tq1iz9hlWLa6RWOZVqtVkF0jySwSXxePHPmDOLZs2f5k8dx88MHze7u7r6+PsTOzk58doSZk/3PEEhvg0fNdaMqIvawevVq3SM9NAEEUC+hqhApSBGhYAPpXXmznOTATCcTqSI8JN6gtVACyCEiaiY96V4PnjRV02YqgBcJzUdJMam5w1R1Jj+dRKR4GyIPjSlAGz1UWPIDgei0yM/9MLJE0MCoPUOnSCKbP6ioFymBlEokIkJhSaKUSAf8bpGyJI8QlZiyHZGTmQbXFFRlCu6PjjpnejTVIQJaAjw9QSmrFAHqAEVvA/y2fSltRvQPLkuAqooUWSUgsur1tJMe8mnbgko3YDNSU4DIo+hTEUb5BZZwnGad3u4JQB7tCil1QiK5F4E0T+8HgCidzjRm0tUIQk8YNrWfmRqBWUoCRwEwz+THyUzYdMDPkoYAMuNG1dbWxpLtOMBzIqoqBcF8EioArt3e3t5169Zt3779uuuue9nLXvbyl7/8pS99Kfill16K+1lPTw+/QwcIw+xlTsdF3YMiDVQi+Bv8uWOmMylgMo8OUecw2qeI19mlEnUCJYEpdYEKlwPXI4UDFE93pQGPbNEeAI1qCjaqHdCEcEDJoSkS0XLmc1XPsbrMIOQUyWVuuk9VvTmaCVEpoS6SqEpEpaglAkQgPccrahQBPAfoj+b4PRA+JaGS3oBKQnoJcD/Bt8x+bQCa47vSi54L/JY0AYR8lkefjd5AnfBjI3ibLeMWRWy64tQrC19l/PETUAgaMIVPV4owgNPJLqYoRetBSVgD/nWNZg5RI1s0mctRZwngciDaHkAnCBRwEkSNoo4oINUZoJNDaIbCKlNEziS0hD8icNlgAKjLrAlSQDifgIgUoE7CUjrV4VMhaOBOwFHlTA5ECZ/t8JFOoygKSCMFiEQMHB4eJqeOiM92iFxLCIsbaCaY4rMmWkC0DYAEEeCGWY0Avaurq6OjgykGWvP0HSI27fVgC83gIDxvSNEL2NCGhzYCoj/54N4MTg+iB3U/B5xdACdApI0pV2HKRurgTAX2QgQhNJCixpKEAYkZBApFRBhA/NK0kbDKIQCUiKAK0AmA0A8C0IOUkSIJFSBYEk49uBLQIKK1QjGB9bst4Vi0B0QAIiOAEokOOcyYWpcGRvWyBCCloiUITENkNQ2VtDoUcOqYw4HiJKgqBbiiUoDtGgXQAALdcyJYpi4zAAauyxIVRA00k9sVAJFcVZpBZLPOoCBq2xLpQeROqFMk/OEo8gyT08YWpACPgqLGMqUBIjwcwi2xxUrWNwM4ywOdagbQj5QcsKUaCmLUziqQ5NMVdomThKJBM+WhDtJ0kyQ8YIAKUpFQnwJHCX4hAkrao8lMSYiZdA9f4paabsMjMqSRPjQPVCMDfyYOrwmkiBEkpqtUyuXy7t27+e05ErkKUxKKAImqiFh9z549/of1qOSJ4Evk+GCqe6R0gKeLy3k0PY0UfTtPi1fIeRQEU/QCJMH4PA8EQKe3RS0cTnAtDQfS1SRxkEiiSCJw59JV1X78xmQTZlI8qOuQQQjqghS1NAWqdEZnDKIabdXp6yoFPI/A7SVJgMaSkIsQUQvAarRDoqlI+FJk03JaayZDBPjpBFFvhJm2BH/TsfTPtGIEP9xvINrMuUzjfohEOjfQzxWjdQFWNROECzFtcmp8g3wEUgGpdgwCyBC8Bt5NqdADhMo0qAWEHpviXhTAPaEHoAiQI8oJBEuiI/Ilj5zwVUC3CijRij4VkPIAqZMQTCMxgo1wBnCtC0LOFFElQlwGRh2CRALc6wAGguPyXbBgQVjNACVhAcE+TQG8CIJR999//44dO5CCQ+E5YZUI3gRMGeHHBvbu3fu9732PfwVFVSFqESQC8+fPbwv/PpceYu6EkanMgOcCPIRPyTmBQMpVyCNosghs/kEBojTiEZJCCr7qOeEVcu6HB6K9EUiloKoY2SiSRKAOKI0IEOoJlJJwA8GVpIqeADTQCURcKYgGAlFJUMorFqCCXiJ0NGlniZyIUgAektBnVREtB1AkIoUcMezFpkknqBNyygMeXNYlTtBDEYQiQJ1cV6kiiRQSzzEwWgiQoqpvISLukagNj1cwjSS9VS5EmCMgrG8tviqwlCQBTBHje2TUr5SPK6NW0np4ddNE6SQQqcNDUOGBERoLgMAjgqpaCCqhz4azSy00MALsBcES3kOEVgNTLaQuRDTSwJSQEs3kgUSgqDkgfpSHNgCDzBAZWaKOSJ0iCDltTIFQNESc7eBYaO3atVqO0BJepCet8CevfuITn/jqV786NDTE7+JBVRuOgCqBbQwMDNx3330f+chHxsfH8UEwGh4RgQrMIIj4KLxixQp+BxAX1dJyIkL0J4GQAqKFeH5AGNNdcMpDQGE7InROA/GRcxB15hE1BETcD6fOXoBEVcBzDzq5CgkBHRFdjEixn1BJnBAJ8Gg5EqWsPi+0E4Ki5wC41mIJoA5E22gK+uGBGdA1ALDkEY2SGY3RBgCeHxCJapdCAqBEoIuETkxWhC1oBttrED3YhSg/gRQiSHCZgToAni4hMgWQ8kA408emgJlVDvGjCK0l0EMdkZAIkGhRKUyBpvvxIs1QMJZnGKkeIKRUyKNp8PCgQAAYEJUCsrGkUVNXEkBVNRBxgBxDdfrCZEOoT2v3jQBSKZ4D4BiYJmYKCC4TSQDpjOrCTkJlWgsjSyRWCzobyUkIpd4DIGVJQwBv8JygH1DJ984C+NEbDdQ0QqNg805wlCKFvYokwOrVqwuFAghaBKah3kQXkGLya17zmu3bt991111/93d/d/fddz/zzDPDw8O8WaYBHdVDhw796Ec/+uxnP/vlL3958+bNb3zjG+fMmYNp2lXibkCiJzyK1tbWJUuW8BDSgAFmRHB/Qggp0bnifIpay2oNaCcRoANo1LpsRPTTIsjDyPZQmRqYLhFe8SU2JkkDnJAkAVEKaKDWFaCgRCBN9zZF2ha2ZgDXtERqdn4o0uZBRToJlnvejaUN6MUqz9tIG/cTQdtoCk5m9M5ZVvRrsSUyz54CVDSEoIgY6WnQQz770RFNBzbdVeTUcJl9V9MJiM+7pXQjAJFLM3pPtA2lycsHwDsqOQDOGyEbWKUZikpUVBJB1DRfRQT3qQBRBl8ix4raOiBdTuqawE0iJQ/FpIseP5A6IqGSCOGd6ZKfQNiSQfclKIhpcxrsBeGBUATxS8vzvPBDCKanT59+1atehfuWv81wLFtoo6LNy4CdfPSjH92yZctPf/rT73//+ydPnsQHu+XLl+PWu3jx4nnz5vFf3YK/VCrhg+bhw4cPHjx47NgxfHaE7eKLL37Ri14E5Td/8zf19yNh1gFyCURwEG5AKJfLS5cu/Yu/+ItNmzYl0nSwHV1+ZpQ2BQwJC5Bf+5m9HQYcCLlOF9M0tEk5m/oparIMMkdd6SFpZ9MDmeXo0CgSedJd6Q0QXufAprYIM83XtJmWmwW+F+B8iWnMfma4GfCm7RobeZouh4UgSp9l3XOHH/LzDmy6SWKWkhAtF7Wcy4QIaEH0XT/v0WlRmTkTiPTkQSX8wuAATCRQwNFGAwCOqBIi4ffHKqI3eNCAOUw5kwrnwMB2pXQCdLLF+6mnAQMiqhpIJ1MabND0dq6osTIDfgIiZypSj/yIYQXbLTjNoWigEwoMUaO6SLyedpKToMoUoBlEIhrB3/Oe9+D2hnsbFLXTE+0wAjzw4x559dVXIx0dHd2/f/9TTz21Z88e3PBGRkZQxW2PZsyBv6urq6+vD7fPSy65ZP369byDPvnkk7/xG78xy7+xTMISFyWZnJy87rrr/vRP/xRjgyWBzgxTcKDp6SKh4lPY2M4SOSJHKUUEKJIDtKnKlETnUwQI3ckFTMUDXX4CzeBN/RI5nE4AjUzTveDexo013Ql12830bSAyJdcEOv1AENgkQpFfemRjikjFE0AG7/QiRyEFtC4nSCeQsuR1DiGhwirbuQ2/HAki4FOui8hGcLYzSk/7wQFUaUtXpZhvesqudIqFADgRofhpRFSCEnkoAtRpZkrdT6BCsBElRLYA3iMDPZwDUNdygKpaBanfACL9UaSBq7NXHJE2EkTw5NgA+bgDEEANADlK4Ijk8LAULAYo9EScoMIuT1hiSkWiJz41X2OIInblNyN4v1JxEnISgKIOGaBTqcBTNMu6voVDtE+tGI3luhTZEokeMgDcDG3QWQJYJaiDsPSP//iPH/nIRyYmJrCltFOKWsQZP/axj+EeqSMC8BkRH0zHxsZwm8RHPX7dtbW1taOjo7u7e86cOeA0syu6R3IsEe3HAzZ8Nn3f+973pje9SXdiiNohAIJUColAA6J2rqOw5hlOHRUQKoS3yRNxQF0QyRGxIiIJqx7QYebGEMGBpDYzNBCR6UyNLCVJgH8om4Iz2UXuUxBvSENVfzhsDPUEfht+rEfUMvu6EdIrql0lEW6VVenpCYCGMAW8jVVEHppK6XOuElsA7/RVDY+AEqs0aAmIiDN1CexNEge2A6zSNpM5AvaA6LcBpBv92dB8cE9C0eCX9lWuBa5qGhqOqL2BhKaka9qjAqCgZWQCpEfQkaQhP4jnJARX8aJXFEkAlESoA1JIBB5zYgo27/GjSADaPFD1SNTpYInLRUi3UJHfOgNCcQpU5AnaFIngt22znG2mFtoA8PPPP3/ZsmU6RUIwTnM2TUW0jfb29oULF65evXrLli3btm279NJL8akRfO3atfgQiaouG7bbuOkDqQhpBcDJwbStW7fqBimglJgCKIoIrAJJHhQchT8VTR9WemggkkKAT8GjCcFuBhJAOkkE6jNVPbhtwuZOXwVAmj4c6j8XNE0cUKo4E1DlVr3Nb57wVY2NwKrQVJwJTdsRo9MoQihV9NCFl+QBPiWXkiZCpETOWRoFXaLpKzBhs2IWW5g6bQNKZ0fom2okmM4EXSrA855eDzqBJG8GX5VZhJi6w0H1Fwc4wZczVkOvEe6VhH5AnIR+RMDrAEtIWZLBpjc2Rw8hBdGviL2xKhGpbHxHIJ2EQKrVPdCidVmlk1WWQHwveLDP9gf+MGsI0dQPm8aCyCMR8AZxPwpd0BGVikcHIn3VqlXbt28vFArQ4RE4P0kaveRaGp8RBwYGQKAArBJUIiS1AKboHR0dxZwwL9kkzUAwJk4P+HHUl112Ge7EbIQHkQfFEwJOcXboPBB2Xlxj9BBoFX/OPaIVsdXo0UlYA1RsyVTJg2MxahZbtDQQKUjRjohdMQVIQj1B+tDkJxF8GoZNzUnv0xuwhDeDQ9EqFJseqVa0WQFMAc89pEcGpogeWFTrIpXBk4gTTHnqopJH6Jt6eupwpBC0gcDAElPAO2kTErWh6ySzxc+JlpsJNDMSkZIuiSBKAcSjpb1H8Feg37bXBZYQAZg133OCHiBKaZOfUZi2JGvRmaXCVAogTqKVBM7hKEBXAwhK6ceeZgA6EYlMSdJASaSpjbonBFOAnCIgRQ8MZlKkAg5QBILFwBRI8gANSWoBSkkUAa0FmCPA61SI4DJ4noY/EBIALV1dXbhHzp8/n18UFVBlFCIdES07duzATU4KCRC2NgUqiHIiYkuVSgUT+I9++KoHFInkGNXT04Nt808iwwrTTgu5Dpkol0rHTw89e6R/eLTJv8msLkQ2SiGJ0FQE6AfAtWGCIqMQpYC3MepAkFKJOJBeS1VxEjiZEhSTZAbQoEal5IJPZRNRFJSaKYApwVQ6olZkJBEHmKbBkjxNUymA5xFUIkmnIookkQLoIWNKQoXwx0viDUpJAM8BzQcSqeGREooGzwFWhUR1vYyCT8UjZ9MWxJngbSI+RiQCdUDci0BwTWv3nJj2CsIaI04uESoGzwWKYezU3SISmZIA1jZ9GrlXBEwQEffwOonmMFU1mo80UgTNUS9v8Eo9IKZ1L2oVLzZF2FG8JbRQZK8MwZuccIAiifeQzI58Pr9169YLLrgANzxNIJAmrBngxGv3/ffff88995TLZXCIavFzCCqR/uCDD955552Tk5Pg6ZY04CHWr19/8cUXSyEJFkNDsX+W69Tg6FPP9d/16IH/fueuP/7qo/947zNjE+XQNA3ceZI0BuqEiCDqeqDISMhJeAMiCeDfMiJKBzwX5Acig1K/eYpQ2KgSSJpLmQUYSJuWAyFnDMOmGQjtXAaCHBF+8VAx2OjGHH/sAsyEn68WD+iIKHkDUxBEgjwUE+JTgQpAPsuKTaEuNs7khM6SIokaCepCoqbGMo1EP6cpYEALIxWcanClwEwcCEZTuFBUTQM2OiOw0bfLRqJSegk/EBwGQo2EDARTYeqiwfHzvSoJTweqegMrQESJNrbTyRJ7KcrGaM2NfauFCqtRiUSRBpBoGgACnSUS6rCxysgUVSngakEqUARB5HLws5e63wPAkieAGqUAFDnf64CfKS6/UjVislkDqMgsj4d2UqngXljjN7IKd9999/vf//6RkRH+8R7mwA/41QnNpwHp4sWLb7755je96U34MAonS3CCsIWgCEBH18mTJ++7777PfvazBw8ehMgl2MIlzOoeAkYouO3NmzfvXe961ytf+UocBca6hbCf+nipcurM0O7jQ/tPjR0aLB84NX5saKx/uHJmorqsM/d7t6x95VUbcpksRidNDXBdrcgNI/rVRcRhoAeROxEhB9ITvAcKIqvQGVFlo1IQcumA34+QFtNKNAdcS/xcaLqBWTC7n9Wfd6YHD0QT0iSNcCamTkVT+HZxdDFGvdT9+YTyvKc33QVE2/ZL0znLcQG0wYNIf4SZ2qMNN91/0w0/L2bf8CzQHvxmomk+lW2Wwxdm2lXyWuYhK0pAus3r4ohIsQkq4N7g98cqUiwEAhtXpM6S9/gSCP02zp0jRC6nEpzsog2cugx+CKsg4qhqXaQUvS0C/SBqIdconyICzzuTSNvOpQWRnnTj8OjYtx45MFkq/9L1F2bdxoaGhv7zf/7P+ETIby7l4YMgAko5SucHCjhIa2vrpZdeeuWVV27evHnlypVdXV382TcR8HFzeHj49OnTO3bswA3yiSeeAOf5wSjNtyUby0kksByG3HTTTb/927+9fPlyivVqeXCsfHJo7PDp8V1Hzu7tnzh0auTYSHlosj5ZqZWruERy5Uy+o5h50yULfuvmjb1d9iML0sCiiFiRqzP1CtM0IicJFTXOpEfwfhl0FZ0jorWa4ufdz8+LqNGn/+aZwOz7RAT/Nw+fBdG6z7tcU3+SzICZpqnXD5nJHEEt/jnL0uyInE0bz3EPaZzL8DRkS/ubTvCi70Wc3SyYyAaPSEm3AVrGEznxYIBTlAFgVfA2xaTWgEpJPn1RKj71M0MxAVK+xMgMghiK00Bd1SidCf4lLN0L6OrUDmHwXH5yDfSTAbb4gRS9x8PPBPoHR/76zqe+vWuwXKn9+Ws2XLt1DUScEnq+//3v/5f/8l+OHz+OD2d+IIcgMgXnin4DAG6uuDXio+SyZcsWLVrU19fX09PDv/IxMTExHnDs2LETJ06cOnXq6NGj/Nl1XIhzGAFM5nApAm6QbW1tH/jAB2688cbQW//JMyd/+PSJff3DJ86WTo7Vz47XS+VKBTduvAVEg43A7/WJWvaqZS3/6eUbL924JFNv8iES0BF5NBXTkI2EpyjSAX9iI8jpAZGRJ5yin09EvXRG09SS3ltT0NbUIzE9U0hvSWl6ptZiml5RoCdt4Mz05Jng5zSd6bcUlSACTU+OlPQJmQWROVpaM6PlkALRKholMwgj9HPZ1Uwezmm6k6aYxeNHAbPsaqYhaiGh7Vx2BX/kYdq010T8r4QEAOeqANsQmUa7UWQjQZs/bCgAnRL9QaIESGSEGQp0P8p3IbKLEYBogxq3IpbYEraQHA51RC0RNAPNalFJCjii5/DQBq5Uc5hCh4E6FRlkg0cK52hdktA31QhRfkZbvtkZBkrl8kNPH/mbe/bft384V8+MZjsuWZT949ecv339MhqAycnJvw8YHR1Fo5ZDiXPSa8HAFRFp0B9qcgKJqiSIuKFCBziNIqJfUQQRgAHD8fH0Va961Xvf+177nBp6/sfdz3zo+4dGJipF3BNzeRuRrduXUjkxl83Uqrhfzm3JvP+Wla++akO+UOCijHBxn0ittfFQ2pLTrw2KUrQ96RzIEknTOb4KeA7CVApSEeoAUg6MVqQe7OZXVQZEGNhLUKGfqVqAqEvVyEbIRt50DiLWggIbiZYmvNMr5DCjEQrbo0Zb241lyirBEkTOpAdKtARi1JgGWzSHYnoyEaXwIIWoRQnatBmNohORNoDtnCk/QZvMUYktrJIL0Rwh2i1tPtImwEkzq0y5llakOPscADpa1CWbJ6hyPhDNifywgcuGiJREXRxlm2sc79Q4gAUQjQPQQAKoOckb5xoRJTo9UVU64BVyEoLc29IGKCyB+xKgVMcM8HC8k+0kEacNkSlLQLQQSxD9uQK4rpSoKg7CJRjZxcgqoyfaAGcikmgCIqu0McXv+H9yYuIf79v9x9/Zd9+haqUwp1pozWcq5Wp5ZLwMH80APvPhw9lVV12FW5FEDiTnHrQTEEDbIMetCx9DAX7NFkCVflQLAfycyhLbOY02QlcwqjTwUr7kkkte+cpXYhUTw/y1i7rmd2TzOfuZPsVMvVivoQf+bLhHY2fYfWuu/qJ1XdduWY4bZL1uzysORwS4hHYyO+SBP2rBWBvkThp1gDoAztUFr7ORIqJSTWYKkEuPDEwRwamDR68FJPSonYQlpfQAOi7ZEEkA2QBxGtCIqC6CXDMZ4cE+2Y6UCgkBnVV/LEA0XNU0aBZH9KMiAoATShHZEu0N0GQiUafD740KCRVV/RGJpOdTkYGgyFHREO+UEolMSfyRhroBokrBZWAJoE4PCHq5HwE6qxzr58gAsJEcAGeJBNBCiNAVCfXSQCBlOyINjAI9apnqZAOQ5I1ZjEhD0VKWgiXZBBURRpaYEp4T3uCr6I2I4BVyRraDMxXCCslklGSTQsJX5FlAp48EBkYpCUS/ltdJZgINM80kmQUyA+Fhzp4dHvn0D3b/5Q+OPNmfqWay9VpmslzbsiD3Oy/deNmGJYm1MXzFihW33377smXLKpUKUkzjQJIIs+gEZmIPiCTkSS0gaZiOpBaQSOE7g8rl8qJFi2699dYNGzagpFEreluWdOZwYOisU0PEHDw0oPjsWqsv787eunVJX4/9g9KhdWob4ih5PThnBC8YeiLn7I0AVpHHr0ikL0WYfcss0DQQcb+Wbj8AdQ6nAoD7hchpOJfniO99Xvh1AfT65YS0DQogP0EdhLEpQt+Uh3F2aBVGrwCaQ0S6h0reY4Ma6SzQNJlBIPpHRCWaEUk8pESLMk37hVlK0SjCi7aPRrs2LEXk50LUheWoRLpSvx9xVGmQDUgrU/dIPnkAjADIAZYQNdq/0pFIsYYwgSl0imFkMpMcOksACSI9oWKLop3O0DHtIGUDVOI+EUkImSlyqwB0KOQgaZ1mjQLRQgDNIJxPGzhTlgCNSkcSAC2RiMiB5GYKgNPvx5eQcmnbQQA4dvSTp4+857M//cgPjh0fy+brpXxlsjsz+patHX//q5fdsG1te1srRnBOmJrBh7xrrrnmne9859KlS/Vp0hZoACmd4KySizAKaocO+Gl0UmFVokAdwGZ6e3uxsVtuuQWfdynyJCyd33nh4vZcrQR3LVuo4ZHEyAzeDNiH6Fyt0tGWf/W2JTduWarRbMfSPG/gSaFxJgFw2qAA3oMqr22CoicAPCQUAc3kEr5EQNFkpgDM5Ijs4hyJJBqIyJQ2RD8QHIpmgvgDEbxHkU6NNV8j9RwR8IpEcoIGgGdDIiJTib6RBrSAQGeUzlTKTLBiY4eE7WH6Q8x+ngRwBJ46iCBJGWljb+T+ZHKTrIaWBKDWNb2ULBTmsjEBj6WxCucHakWNtUM2YWpLoIkCoNyYyZOjI2Cv5tBAXREKIM49cJMqqYoITh0AlxOcvfTwkMVJQnFqDsBGQnpwTbOR0IxpIFHVc0R6qBO+BKLq1CPqd08SVjGIg/jpEglxTYhAA2JkkO7h9wqOGLwGn4r4mTgW6TouEZRYpU2N1CFSp6JqRGhQysjeICdQCgM9hE+9DtgmUqdIgI7Tkq5iIYqI+A1hdHzyWw/t+72v7Lxrz9hkLVu0c1Zb2l14702rfv+OS5f09bJRQDlhmcxNN930pje9acGCBfw0aTMDZuKEFE+ESJ+JAJ4DSHGD7OnpueOOO3CDxF0cCsHT29Hacv7y+YWCffU1V6/Us7m6fbNO6MxkK5lcd1tuWU+bfcycfpge0nkmuS5BhQYfmwKl6EH3k8k5EESl9EAqiJpAHopN/BqeLqWhOdpqGvSkVyRhI/gsEyI03Ty4dADT0pe396OkRdNLe6fQTJz6ji2uhVF4/aaSoKEbbZTcxgJBqjmNCfJE25vW6xoJbxZHSzJz+qiAae1yTpvcIH5vALUpW/jjmJmgsxcRbTI6TKLp4UQPBHQofpoMIhHYkiQOFBF5dI1jnILv4lre46saQo449QT2oA+RVfVQTHOAKZ3WNv3Jw7sdQJsvyUyisVDQRR0inzkAuziQVRDqcqpLkR6fCmkbYuSkyMkkVOhRNKuDF2cxqKSdg0tnKsVzdhHaG6aifnJg+MsPHfyHh048NzCRz+fwpiZbz120sPD2F6269dJVRfubkWbzKwJ+4ODg4Be/+MVPf/rTpVKJf7II0S8abQCQEhFEcJKmBk/SHPfpzs7OV7/61W9961tx26Ye4cfPnHjH3z82iPcC2Uw134o33vlqJVcv4RrBhEIus3pey61bFrzh6nUL5ncnPWF4wgK0aFNwV2oB8Q8WRQCKStQ9QYzMKgGNR9CANGEBsFEhAWB2D7oBIqpenInTmfYTVFhNJLdVVEHIvUdzokYPenyEGWAjAI7oh3M5erSEB20y+1F+DrWzg6PHhyYmylV8bAwCFgsVIko9Znf6dBYO+C5iplFp4jFT1/MBrpp9G1sGT4ru1sKC3jlz2tug4yxZtXEmg9eghwk8dT6fH00fEQEKMMtDFj3iSKEDkQ4/CbsIDYk44RVNgwhoMnU7I8xZRhuj+gHtjB6KMkDhUE7UeuySAVEtgA1qKCBsIfwcRpojkWbojJHuORAGJEcHXZxpYgo26oxQRDzUCwJA0XIgVAhw2qAzjUR0qYWRoEEEkTq5SuKMJtaqO/af/Pj3n/3RgfGhcrZgf0yXbc/XXnnRgl9+4ZpNK3phDa1TS3hA0bGMjY195jOf+cY3vnHs2DHoOlFRo81qtvkoJrXUIXiilArR1dX1lre85bWvfW1vr332hZJuee74mV/9u0efPTlZzxexexx0Hi+C9XoVnynDd/aU6/nWzMTVK1tfe9mKF29e1ts99fcjbY3GVZFIjcORwjQNnSs6ZePDrZnQPZHN86bgfPbKzMiUJRJE71cVkSIN1KFwk0ip0CA/iAAFUVX6pXgRUXvwVUTfRSL4rkRywwmWEKEDJNLBeTja+fRt1A/3D3374b3/uuv0vrOZcq2e4VcV/o8EzghOWD6T6WqtbVzQ/rorl79w0/K2VvsyTPrEAhQBnU9FEnpoBiRGBDHUDdT9g44o3rSLIkBdHJE2RAxEBFjlfF4YBKsw0w8FxB8XCZ1snNoBa+moEgh3wJQKOJA+d6wSTUUur0aKHuoCB+HRRmafzlICmEoEkUiFoA5AJBfxgMhTQc45LEVmOrlzpjQz+hKrJAANIvIQLGkPhsBGxsbve+rIJ354cMfR0Vq2UM/lC/Xqgq7Cqy9e8KsvWd87txPGaJQHSthPkgQMDQ1997vfxQfK/fv3o4RFAO0N0DSvkyACSNGIrdJj1gByngERNSLyT0OXLl162223vfnNb+7o6OA0gL3ePDo2/s7PPHrfs6cr+fYcPknWa5VsMZ+tdRdreDU4O4HGKt5rVzP5BR2Zm8+b+9YXrV27uLdYKGASx3IUp4VFEgUEMTotadAJqF1HxHYaOFMez4lIYYrodRIojEgBpoDnqoozam+0USdPt5AggkeEYNo00kAzRQBLYwOqEkzpJCIDSxxChQY/isPFSVAcGpn48Hef/tpjJ4cm7HrI2Tc2Ty30fxrC2+ZcNZvHGcrWKlsW5H79+rU3b19byNvfm+K1ARtOIM9h9EBED0HaA4UpCaIUITJ4JeJC2uBTv21VBT+KZhIp7EIkkQiS/8M//MNgm1qGpmCO3eSEr4oEi4GcXVFVowjovMo1kATQYdNG0ECFMT1QCj3RHB9JPNBLszyMgpmcolQkLRJMfSSJFCA6QEUCXIYpZDKnz45+4b5nPnHvoSdPVfCpr4p6rbZ9aes7Xrji9desnds1B13BmAxJA7r3gLe1tW3YsAE3queee25gYID3LRmAyE8yE9JLNAUeLywE2wUXXPC2t70N98g5c+xjHxQ8NBoSvMnMlmLhp/sHdh4ZruWK+UwtW69WMvl5LZVfumTeDRfM6x8c7R8pV/GWIZsZLdefOj6268hgvVzCu4euDvtHnjmEUSTIBm6YK4IEi8Er5nNDeEkTbAe8WQaZQeCkGZwgV4zSWUREEsCvRSj1hBEb0JMFoJIkAXKSEBKjSIArFdGzjIqiYCYHijo/jB5NdaS1ev3hZ479zQ/3909kWqDk8lgbT5D/Y3/Vc/gomcNJyOONQjZ7Zqw+Oj558fKeed0dyVkL541n0kcSXE567DwPdQN49DD5SMKLihGpL3kuBaAIRBzgculrw88X/NPBEx89mbpHYhyPVusx1UTpEJUS7ALQwlQelsAxh4SRThq4kCA/dEYqgAwqIYVOhZx6sE+dDhB6OCqMMTMiFKUEFNpUEpGByxE0J0kAbSDpzSDSwKgdWtt0aA+sBpcRvzqVUqn8xIFTf/29p//uodMnx7JFWHL57rb8TRvm/N6t571wy4rWFvtGUJrDmGQOiTbGrUIhyAuFwpo1azZt2oQUt0l8svR7ZvSg4s9JRKIWL6KL21iwYMELX/jC3/7t37766qv1M+1Q1VhEgiUI/WeH7903XMKzHo8VflUrL9/S81u3Xnj5ecu3r+wqZqqD49WhCXyarNdyhcNnyw/v7d91eKB3Tn7enJbWYlF/nKOZIlyUKfcAIEWEAoI9B+O0XoAGPVjyKxLiJBxORZCIyOVACF8ya4BXvA4gpcJGRD+BirdRwVFIRyRYMt/0JQS16JoRqPuZ0XwdJnspek80UzohcXyy/J0dR+7aebqQK5gMKVzmxrB+yPDoU6EYyiD2KyhNflG3aF+8DEuFz6ZgdonlMKVu9yI8+MFj1tASfoU/AMjaX1WyPxs0L1iYgw95OdzBGhuz30xPnOEE8GkaVpz6xYcCHl+yfkuN2VdMQokp9hW2lq1kMpOl+qYlnRuXzYVg1fAbTi9fEDxHiVVyHwlswvYRdsMrP+KAF5nyoWQjUg8pqLIFXJeECKAlmALgSuH0ikgEiH4mMHWPBFDAJkgUoUj3olIZsEXpgvw8HdwoD4Y6RICN5IDmJLnbMbn8nIBIhaJKUSqOSMyiKyVJRwLtOhxCAwnyyCZDNMoSV2IVkTMRkWoOa6Vy+TuPHvzkPQfu2TM4USsU8OTM1FfNa33ttgVvf/HaDSsW4A0JjBzOnXFa6LZIREcRATctfKpbtmzZ2YByuYzeCNFYxihF9AQtWJcKOIBb8iWXXPK6173ujjvuwL2ZOiJAG6OAKpVKqfSD3aeGJmr2Y3Uy+cVdud9/xXkrF89HqW/unAtXdK+d3zY2UTo5Ui3VMoVsdaKW33O29vTRoaGR8a62/IK59oESZs33C3kR4LkiJwnFhGhLHt7jj8ibwQE9EL4EeH6OJRIo4k0JV1Qj/YAMADmiF4WmYjSnqQeYfXuIUgCJSrXtCNJxj7xnd/9PDo7m8/gEmfSjbrcfu1lMDVFLuJWEUtgcxCTVL+uGOXjxf2BEMIe5OLENR/jFQviFhWjlGQoNYSBdGbyjp4znLvdgv9uO8TnY9k4kYxFtkN0BzYs8uQUbwu2XPNynQ0sSkYZRLbna5Wu6t6yYlxhtYPBMTz3SioASq97juSBxFmdUUkqiFBDX4+gR+dMGgr2sksf3yIQFyMfIVB4RvyEtALBRT3goiBTJlXqgpNu4bAL9XAUE0XNzONEPlwgkkludL9N+VJooRkiLXhH3RDyC6aFED5CILpIkSjYzNDrxrZ8e+Kvv73/y5GS1hudPtpDLb1zY+q4XLfuFK9cstu/h9P6kmb/7UeIAT4tXAIhz5sxZt27d5s2b8cFudHQUHyhLpRLf9NCAiC6d1Qi+5D2YUA0oFotLly699tprf/3Xf/3yyy/v6elJHNO3FG0MoNKSz923+/ihgUm4i/nMa7YueN0LNuDouVRHW+v6xd2bFne0ZUoDg6NnJ2qVXDGfzZwenXzy2OgzxwZ727OLuttbilN/sQRIHw4ULkdoY4QXSSSSzO6JoOFMva0p10wqjF7EqcalnlaYShc0OXJ6yEOkUyrSo4OKAB0LqUoy04aRogTIHyOUxkvlB/aefeTgSB6fz8JNCe5wj7MbT+DB2YiJEoAFLDLxCP3UVVWKHkTpCTQUEG8Q3izVQjlJpxrtcGx+Y1fy18Jm8FTk9a5pQUlspifU0GCQ6235+tXrey9cOd88AdFZ1YPCpQFf9WYSRIBdfMiSPbsucDb6ds8BVsUBzgS8zjjTJjUzqgKzdAlTKxFIAZgAEoxgiccJhTYSVslJUJIiWxp+LAn3iha/EG300ADCJQgoAP0wyEkiPbimbYkiU8Tgr0+UygdPDh/tHxwdn+A19f8YuGUN9MOjksdMJTwj8tnxcv2BvWf+5enBwVKumKvhFaBeyyzuzL3ivPbta+fDUQ0/p1RP5ibg13jy+XmdrSsW9i6e15lvPByAzhWgU0ocPHjwRz/60UMPPfT0008PDAzwY2XjNBrUCJGpTjV1DCTBHbe7u3vt2rW4O1566aX8GTqAGhH1YOnxRdRDHGz2b2J96FtPfupHh3C812+Y+/u3b165aF747oTGhQEnhlQrOw+c+MIDB3+09+yJ4Wopm69lC5VyZXFH5cYti1+5dckl6xa2t9pXd9nFbSOlEm2DBgApRUHVJA9Aql6NAoeCFAiuBDZ3+lqeCGxvakM7uIaAcFGSaLlZlhBm8jD1VURwLhQsBuhMJcLANHIKM+kEqoB2om2ASzw7Mv6Xdz77ifsOteXzWVTD4tlaeGYYsrjB2E/3RXuDYLC94bTfYcOnOvsaJtzYpWmQ7D+7T5pm/9kxhD6TsLwlftthjnXakYayKfZbg5qO/7AohyUNfO6GYLDx0wcHiQdk38/d8HN7SHDG8TCA2YGHpZLzEoBmPHF6Wurve+naN75gQ+iwzegc8pTyPHvFmtEbHh2vRCefoI0TEFmNlqASlSR6hSIGAkybwk/j6mzRQMX0UQjWy30TkTVdQuREKEzF6WEVoKISAJGEiq0dCKL88oBE24UCwEY/FabiOmAgIvDIKc4UnPHM8PhdOw59Z8fRA/2TE+US1GBJnAFQkEZxFlgvRuMyzNftWReEIOMR9xzA4SZPEMIue7uuExm/swGXMfebqWQKg+V6pWbfjWZ/CA9jrdZRqPYUaxX4a1UsXM0UwOwJk7QDU0tgPCYVctnutvymZXNff+WKS9ct0l/ST5aZATjbhw8ffvzxx3/2s58dOnTo5MmT/f39k5OTlUoFJfu6VjPg8yIiPjJ2dHTMnTu3r69v/fr1mzZtuvjii/EhlZ400jtpurcv/3jvf/rqrr6ulve/4vybty4PLw7ND+FY/+APnjxy51Onf3JwbKyEiyRbzWRxpi5YWHzt9iUv2bRozeK5eEjQDrOf0HQgbUDkTNh0nfBzonaVIk96CDF7iQQGchD/1AvFnwOzrBWBq8AfPZEBDlEkSWqpXUXVCKqCUOFM3zI4MvGXdz79yXsPtxZy9VwBNbjDV0HRgl980oXzE56gjUn2lHOF0IDJuIbDx9Gc/UEelHw2g0s6W8thOhCeUnU85XDYaLT5OBE5u3dVa3hja/uromovCtlcvlbF8rj28PKPU2b7tsVsoG01U8MzGqtU8B7YXkOqYfO8b9rzK8zEipiRR3seTy/721723Wm2f3sFwH/YCJLG7S/8liCMwnZ6Wuvvu2UN7pGo29HasU6DHXhKBOzc2NCkRBtFQLouOYAGpU3xvAYg8vilm/ZGJZ9ie+mrVJg6HkDL+EGa4kVOhEiFqdopAmoBNIEeQNsC98NJqDBVC7i60gcGA53gINweCBDqCWhLkgbGJya/+ciBv/nh4X0Dk7VMARcSRutGdo7g1htHYn8wht/xMSVnhXDd41U7vPTCWgvUPvqFSx5u2xRHGMAg2DVsHdZr71aDXrecPDwJbHL4A30cG95K4qkWdm6r2Mu/FawNMM4nTLRarYZn4bWr237rlo0XrbN/CUSnMZRnBO55Z86cOXXq1IkTJ/CZ8siRI8ePHz969Oj4+Djul/h8CQ8eCNx37QeOF4v4yDhv3rzFixfj1rhq1aolAT09PbPcmGfaiXeSP7T76Ds+89MbL1j4n151YXfn1DfpEdHk8YnS3hODX3rw4Hd3njo+2vhGiExmcUf9mpXtr71y5dXnr8jMcKdvuqVoPuGd6Ss2Dfg5B9BThvAcoE0c0Rt8FbChjWccCGI0TfCbhDOyRYeQNgAUuQrgDU2H06kWiudyrjzYi5juwj3yv9359MfvO9qez9byuIXApuHoSrYH0f70D+8qw8btyWN3vjxuY9iTfZbE1Y4Povaqgidj4qjlWgrVSdww7aZYs9se3qjm7Ms3uP/h7lXDjqr2FR547YD5fMedzG6dYOElKnxMxetBFe96wfOZCqzhmYthFbjK+ZZcrYp7pH1UDKNQtn9VznaLFbl6BbdZTM7XypW8fY9e2Cr2jbt4OKDktUKwOzm68dnTPkcm98j47GE5/wim4Q161CACeizAEfkQK7J07uBwjaIIUAEgwgOidYGmF1I0KtpP1JJcl0B67QjRIEE6iLiUqAUKIkTtMm3wCmxIIXLTTGmIjsRDQ/w0cKaIWpoR1Z/tOfZn33r6wcPjKNlTxU6NPQfssrKbCs8UnEZtmLWhzjuR/Q5wN/ZsMobrGx32dLLbZZhib1/DV3GsgDuo0fA0yWTxjLK/v2c0zMIK6LF2Pj/DWqGKnWAeEXR+lcUWyNlHw6CHAoCNmGJ/9GJuVu3ubNx+wyQbaI31lmzt9RfPfe9tF/d0Tv0Ve55zEEYPnT0C98tKpYK7I26ZAwMDo6OjExMTuFPC1tra2tLS0tnZOX/+fNwj8SESStOHL1oOvQBTEF0GIP5iQAnk+MDQR77x6L+7ecu6JfOpoCTQT12l0fGJ+58+9u0dJ3747MDZSbzk2AtKrlZe3JG5/ZKlr9i+fMOS3mJLUdNAABC/ee6HXAZgJg8ADwdqV6iG1mnbQxXRD6cZMSoBVNhLj1K/im+kLj+5QCfAvYHIgFQKRSqcL5GA4vcJwKkuEG/gon57NJAEyzSEZZMLA6ATBJFjG/fIYx24yeSLcASrPY9xADgpljVgnxFtA/aZD5tAB26T9rQxG25FeNNULdYmcSsqZ/O4O1ZyHflMCX48o/Ekwx2uAGc407XwnMO48Ld189VcoaU2gYHlbAvmZDPloLdWsgXMKWYq2Cue2PbFn0whX68kP1UxV8hWa3ncIXH7RCX84QE2Y3/BCUeSKWYzebxu2B2zXq9kirhN5uularbVXn7sJOBs4E4Iktxd7eTYEeMpj/3ZiwaQfK31mg04ZNR43qDrHEpU9I+FuM1yF5J6yWUDgQ0E0RvIVUKEAgKQSGeJJNSTOSAcrrUA7RmcVaZpMdiTIVMc/ytBTC9AqMpUgA5E+wOn0x+2SoDmeLNP6aeiRiqYCULuoRbPfWM0imlypuq1r//kwIfu3H98uIJbZPg6jC0UjHDW7B0dnlLIMQe/BWY5LjK7t+FWh4cw/DBtyOFrJvgfNx6keFIl7Vi6WrFrNV9Eau8Qw1vUet7+9CuLtFqu54r1fMG+ZGLvCus1+068Ap4g9hYzrGK/Mln7amayEdus/Y/DwSxb0Qo46/amFjDF3i7igMLbVZ4T6w2Hht8aTxQ4soVLl7f+2e3nnbdiAc9PWIKnYtpFiRjaDf4hDvV/OzjEP8RQpFORiJQPHzgJPrcePnF23bI+OxPNwEaAozgWvx8/M/ytnx765mPHnj1dHqsWcEjVaq07X962tO1lFy966dbVfXPbcfbRwr1xQqM9WR0E0BIgXqRTPGrRHBBy2cDh9F20QWGJZkW1ixNsV5WNHIg08oNEIrlfjgCHgX6knoS6getSod+bxaWQE9yhJ4D3+HabFVIAZrZM3SML9WqucY/EoxneMuLgkaELzwM8p/AM6WnL9nbkcJ8ZHCuNjJdrhfb2Qq0TNyN73mVGShn7m7bZTN+c/NyW6ngtd3wEt9N6R2s+U6zlq9nyZP1spdbThvtVtlqvT1Qy46VaWzHfUshOTJRK9UIhn+1qqY+VaxO1fGs229VaK5XKw5UCntrzW+xzIG51uA3j6Xq2lGlvwS6rI5P2XrmzWJvfideK3NB4+ew4XiLwylHobKtVJ0sjpSL2NKcVd+TK8GS9vSXXM6fYUigMjZSGJzJ47eCDjEMIv0CM2xvr8Aj0FGv880iclmAzgODsIfLc4mQi8gGirhJ1EogkUPSQSYxa/GMq7hdShBKl8gDg6uXe5AH8KjQzCqjKY+PcMxf6tJ8hQEL4lBMjgwAd8PsAZBYhgjeeAwXt0kl8CmAPUgBy35XeJDjTdIwIWh977ux9+86O4yYVbnThjoYbWXgm8b/ghZL8Cr+FKy1RwwbCLco+E9bac9XlvYUFczKVSr1Sw/Vc68hX57XbXWqyYj1t+frcjjzml/AetJ5pzdV65+TzePdYrc9pyeNZ2tGKd5D1iVIVz8Du9nyhpVCuVO1zbD3bXqzP68h0tuba2wpYsFLla1A4//iQaBuxvYVbYuABoAG2WVMBZmbGXPD6/DnFF2+ct7jX/g0p1kkInvD0qRYopg1USAim8kARB0lzGUh8pIhYyOfndXWghynAqkC/Wgikne1tm5b3rOxpLdbLh8+ODdvP8LM/Njo0VHni8MjI8HB3W7avux2fK9DoJ9jQACg6OaEe740ecOmMnqAUPZm9M/L7lGZBJUxjyZNQmYLa/Rwveh1gGs2RGDkT4nlj8540kAjma0Cp1yOulJxgilirVR/ce/rh54aLSLFte7uKE2F1e6rYc9G4/VevXrqi47VXr3zJefMvX9O7Yl7r08eG8Sb4RRu6X3/VikvXzd+2urMlWzs+XF6/oPiWq5dfc8GiS9b0Hjg1umxu8fVXLn/JBT3owpPu2NDom65Z8dLNfZet6VnU03pmaHR1X+uNF/adHpo8NVrfuLj95VsXjJcqJ4brl63s+MUrF3V35Pf2lzqKtdddsfjmixZfvWHeC9Z1r1vafaz/7A0XLVg9v+3JQ0Mr5rXdfsmil1285Iq1PeuWdJ4eHjs9PLFxcdsbr166oqf1wOlJvHC9bGvf4u7CscHJGy9c8KrtS69ejydy24ETZ0fLOBXhlcye6OHNnjtFtXq9o1jHIW9e2UdFYGqnp5nuQQ+Q5DP3Ct4AiIdiCqEggwiut1A2RJweAkr6FgNEVyzAlBNom5qFEYjoAeETgEPpI9TPVE5AEwHq6fZZgHagaddMc/yKEVjiNrzNc1bJ8TT4/I8P/vld+86MlrL5vF01ODG4lsKZSp5IOD3hE6bfjx/CN6V491fN5Oa2ZN7+omUXrujGR7cf7z3z+R8fHZnMvGbr3Fdctvq+3ac+cfdzhUL2hevnv/XFa352YOCz9+4fnyzfsGnha1+45r6nTnzjoYMvv2T5DVuWDE9WhsdKX3/4wK7DZ1//ovX4kPeFe54drRWzteoNWxa87qoV5UptrJb51qMnfrjjCN562pMAnxnDPrA7+wKqPcD2VRbs03Zv32pgB8s9m2hFKPbyYI9tvXbh0vY/vv38i9cs8oc27TAbqY8Q7USFy8OLIDxXItbfAFsiJ0VyzfEGLxJNhwOckyQhZfQzp4+qnx0ev+fJI1986Mijh0fHKrksPuJXS+358rq+9hsumP8LV6xZvrAHJy2xNyZoDpfgouBcS9vzVb80dIiyqeojbYDXQ7cNhA4CkR7pKhHgTUVEDkRJikC/1gWnQXsII5P9hw5TaA5ZvVqplSsV+zHiGGUdFhIrJgUGJfyBgF22NXvK8YOdXa7BZ7Cv2NhwENZxoiA1touLf/rObXI9c2p44u/vP/RPPzuBT1l1+wH/KNhOwnbDH1HU7R/lLmXyq3qyH/qFi0arla88eGjtws5XbF/2Dw8d/c5Dz739hcuu3bLsaz89snZR99J5c/76e3tefEHvJavmf/mRo9Vq5Sd7z162uuc9Lz3vu4/uffLYxLMnS6fHxj/19iv7j/U/duTsi7av+e7DB585Pvyum8/7+sNHvvzTE2+4avmtFy/8r//81K4Tpd++ae0rLu579sTof/nugb3HB69Y3bVt7bw3vmDddx7Z98O9w/sO9v/BG7f3D5f/+EuP/rubN122fv437t+DQ7xu28rTo5Mf+dbOC1fO/cPbt05Wqh/+9lMP7Rv+g9dsPtk/8JXHTv3Ba7c+u+/IT/acLbfNuf/pE6MT4StIeP7bg4RXB/v+IKR2xuyFot6Vq9yxdd6LNvZBsVNu59gousIf5CTRThxeC60lPFT2CCQlnnhL7fegNNLkT3ZoC9WkKzDzN0p4LJL2EK03m53b2bZqUW9vZ0s+X+CFh8jrMLiwH9sZL0VecrosEXWtigMoaRQVgNez4pTZm8g5mqA7SUKKKg0wy5luJOgHQYz8kVmT06OoABTVm3YCrPqS/BH3sHvkg4f+4q59/SOTOXy0M9gXUe3P2m2YXRdQw+/hvWfYETnG2S0USlgN/+PT3gvP6/7dV17wwJNHnjw6MThZfWDPMD41/smrN2xe3nt6tPyOT/349Hj+NVcs+8CrNz12cPCPvrar/+zwb738/NdduvIrDx/8y7v2/PubNl65qufORw9cs37B0fH6x+7c9Y6bLyhmcn/wT48NV1oy9fIvv2D5O6/f8Dff3z0wVn7q6DiegZlCkbvCEYLgmrOXJNujpTxonhbsk394aUXKYf9Wq1e3Lmr5wKs3X7R2kVVCC12eALrmAOqITHl5JbMd/LUEA4kfTpET/PWAGLzJhOiiAvEeiSD0s4oIcANUgt2AFFFKpVLZd/zsdx49/OUd/UfP4pN7Ho9xrl7uLtSvXjPn9ktXXLFxcU/XtB+Jrt7AEZMMV42Gs2RqUp1aMbybQWYp/UCUAprMaUEz+HQ6t4CUjSoFgnMYzkxYAqo/saFlykwO6OwBEMnl8Q8ZRZTHJkuPHhh46sjgsTMjpXI5vJhmSxW8hsFjq9sLIa7VrP0TZvYdK/Yn7TXcTivVsLdsBl7eUTE1edtnX/M0iiqepKDV5F0eHrvk5R3TraOeqWbzpXqmf6R6dqyELdnPYQsjeCOww+BtNpuZLFVvvXDe+3/h4j/7ysPffGxw6fz2//yK89oL2Q9/86mXbV923eZF3/jxsxuXzp3f0/3X39t/4+YFL9q08H/cd+THu4+eGKnhnvcfbtr4jUf2PHps8tljI3hP8Ldvv2LH04e/u+fEu1924b07T3z1Zyfee+v6E8PV//GDve+4YW13e/6DX32qt7vzfa8470j/0PL5Hfft6v/Cg0cnStVL1/Z85K3b/uxrO771+NCStupfve3SE0OTf3PX03/0+u27D576k3/eUygU33zlkjuuWP3Br+9oay388gtWFvLZ586WP/OD/b96/frjAyNf/unJD9yx+eiZkc/cf2j3kcHRWqt9rxIOEs8FnHicKXtocBLsBmVfdw5/utndlm8r2OODD9N2iv5fgmy2WCxuWNhxzZquWy5etnj+XGh8Xje53hrQlYwIDqJLl9Ff7VTAAW8gB+KfIQAkSQORopQjlHobl/QRYIkKRaTaqGwg6QPgEUohoZkcxOsisoFI8cMT1OtPHB768d6B8cnkGkIDfuedwwTTGsOT1x1DeKKh3UrJfQesVlneU3zJ5qVHBifv2Xn88cOjE5XMpgW5Wy5d+c0f7z9vZe/hM6PPHh8/f0XX1iVzhscn9/ePoefaLYsK1dLBgYmH9p++ZENfMZv5+oP71i7oWDC/+6HdJy5dNRc37B88ebxUt+/t3raye9uqed967NixwdKRgfGJkp0j20hy7RPYEfbE1M4Az4Pd+AMLBwWhYQey9SVz8tdesGBRr/30c4ImT4A0RxRRBHiuPPGQAkIDFUXfFXadiCRBnmbzVYqKFBUFpF7BxTa/u+OKDQvW9xYmRkdOj1fHawW85S5VM3sHao8fGhgcGlnW09o7pzV80WratMDDuAAqIghRlfApORClgLKUPq0rYcHPlJpKDTHEoAD+/AB8jWAa6lOnUTqIr1pbAzScHhr/0gPP/tXdz33/6TOPHh7B+8Wdx8d2nhh/8vjErpPju05OPn1y8qmTk7tOTjx9wvizJyeePTX+bP/knv7xfWcm9+NX/+RzpycPDFh87szkwQE8QUrPnZ08NFA+PFg6fLZ0ZLB8dLBydLh6dKhydKh8YqR6YqSCeHykFnj11EhlYLQ6UamGA7Ovr2B3/LxqQth6DW+DcrlKqfzi83q3r+753P0H+wcncPwXrZi7fH73fXsH8DR8wfmLFs7tXL1w7t5Toz/YffK5gfFNy7qv29S3bH7HYweHVvW2vvC8vsXzu1Ys6jx5duT4UPVl21esXdy1bfX8iXLtnx46sb9/dGlv+5rFc88ODl+5vu+n+888sPfs1ecvunpd9492HFg5t3Vhd8v9eweGJzNL57W+dOuSe58++fTx0tyW+su2Lh2ZqOw+evbGrcsefeb4g/uHq7nWxT0dV583/2cHztSzueXz5vx498ltq/pas+W+7raByeoPn+zPVSev2rjwJZsWnhmr7O+fyNZw28Oxh3Ngz/vwAIXHDFK4G2QnKrWRUnWoXBss5YdK2f89vzJDpVxKnOVXZnSifGa8tv9M+bEDA/1Dw9tW9XS0tmRTb7VFeIxIecsQwuNsHkamTW8rJMDUTDHcOfxcppGYBg0Ywjls4RpIKXI9ReoAG6FoFfayhalWVwtTggbf7ucAUVfaiarZGp8jT41O5u3H4bt2u6Lsiz618FXLevgagd0NrdHedtkbX7xY4kjtyQapXq1mWgrFN17ac8u2pfVs4XP3773r8VP//to1N21b8vFv73zDS84/MDDxZ1/bddOFC95y1dLdR86cGqseHxi9bP3CaqUyXsn99zufftcNG27duuyJo4N4If76z45/52fHf/cV6wu5wn/+0o6RakumVvqla5b/9i3nP/jMEfR+/sEjOw5PtBbz2AuONXyByg4N+8LZx0HgCJLjxR7tD0bDF13t+4oajwcOKLwrz2Sq2xa2/NGrN120drHJ7kQJdk7CKUUJKU8US+nTK64uKCCI6KKoCdRZSk8g9y0RpwEcfkSa0agNQMNnhsP9Q3uPDQ6MTYLT5mGPoP1u396Uq1eP9w/+cP/YA0ft+yMKtUolmytniu250rZFxRfhNbF3DgfYcJ6//69hTmt+eW/72qXzOtracBThLE07mTxFOIeIPI0ASbBMnXASKyHN5Q6dHPj0D/d/6fHBkdHxPMTw5+ToCQ8MW+03Unsnl8vYP3OKSzE8q6wQndCkq9GXeuxMtIvdhoTU/nYFNpoLjypuEbYL+wsR9mXG4LInvi2M5TKZ8XLttovn/f5rtvzel5+887GTS3pa/+BV53W2Fv70m0+/etuCyzYu/Mi/7FnT1/LGF6z96LefvOupwb6ezpdunvvGl2z41PeeqVcmf+3GzZ/83s4HD5ZOjlXnFnOfevu2o6dOT05W5nR1ffDbe/YdHb76vHlve9Hq8sREsa39L7+7++iZ8d+7ffMVG/v2Hz+9dG4HXjF+/X889LNDE1etm/uXv3Tpn3x9x7d2DC1rr/7VL20/NjT5ie89/YE3XIpP5B/6xlNtxdwbrlj2xmtWfvCrT7Z1tN26benf37Pn+vPn3bB5ca1YuHfX8f/+7WfLmfzyvo5fe/HS89Ys/d1/+OnOw2N5+ydu+EjZ6xfOlJ1v+9Gw4VGx1w77+qe99NmpS5/b/0eQflBnA9x1+7s62KT9CVIuU/3Fy+a966ZNi+Z1hUfPDgc2XZbhGkwuQkWOmoWzVzomIJXHJrIMgCAlQUzzCPT7dq5H0bcTXidY1RCWuEuKVAil3uw9nvPwdPwg1OlhdIq9dH4B98g78R6xlMdro317W/hDyeCzEfbUs0XtWWw3QlxPdjuyi8puojyEcM+0mymOodCWndywoPUdN2wstLb85T/v+v1XbV4xv/XJY6Or5nVOlKvv+9ITmxfNfdUVS//18eeuPm/RaGnymWMjfZ1z2mD+l93vvGH9C9b2fvWhA7tOjT96cLxWqf7RHZux5vu/+LOz1bZCpvr2F638pRev/Y2/feDkaPXMZGGimilgJ7ZVO6aw2QQ4XAS9pCTHEjZs+7c7qBVDDVdiddv0r7VGsO7kmWYt4aiTxwJRqR5EpOB8IMAFlKJRUugkp+49iBqoEnW1yC8FACmXK9977OBXHjmyu39iosSvsCVVgrsPf+csU8tl8aYDT8uJWmF40l5I7CSGE2tfasDdpVhvS/7l6dD4/0XUM8VCfuGc7HXru9/wwg3hC1m4dpOj4bEh4jQCFHHmcXpJEHmGGZkigk+Wy5+664m/+/HJ4xOFPD7F1OvFTK1oP6fBbk46k3i+5PL2E7vxWoh5ubxdl5hn/yYFauHLIOFv3WOqIXwMtCXsjhcoIh4xGiDhkjYWHr1sIY93OrgB4vnYP1I6NVwONlw5U699YPVcPl+3byOfqLeunZ//89dfMFKq/8M9u1ct7f2FK9d85YHnvvLQwV++ds2NFy752kMHlvbN2bam7xP/siuTsT8b6+nM/uJLzvvbH+wtVyrvfumWux49sPNk+ZkTw0Nnhz/5jmu+t2P/D54ceM8rN/9sf/9ffXcPPvz91o2rb7xo6bceO/7h7+y+cHnvf3jZ+h37T9zz1NHl83vedv15//Nfd//Dj09csrbnv/3ypX/2lUe/8ejg0jm1v/qVy/AR+YP/+PBv3HLeJeuWfPGenTj/t16+dni09KFv7rpwXd9t25f+xbefyeczf3THxZes7v3MvXu+9pMDF61ZcPDk0HWb+67atPx3P/vIzw5PFpO/6YtXLBy8MZw5PNQ4JxBastXVc/OLu4v2Zpm2/y0Ij9nPgVy5nj06VD4wiAsLr3/Vvs7Ce65b8QvXrC8W7PzrekMk5/Wpq1QlKME+5QGJOMEWAOZwjeD6CW1eogIOsB+DaKCOyJSce2JKD8FUolJWvciUQyhSIdF8lpAisqSqJwDMQFOdE6QQUHCP/PM79+K5lMeTswExrA1uOwgRzeG+YjcYnKDpKR7L8tXn9axd1HNyYOy27Ytb24t3P7Lv12/e9Pc/Ofrwnv41fR3vvvm8j37vGezjpktWfe5Hu37l2o1dbW3//btPXblufkdH60e+s/sdN5+/uqf4h//06IHBbDXf3pcf+6M7Lti4pPefHzk0UCs8vOf4S9YvfNfNGz/5w71nx2qPHx7ceXi48eqg7U1x7BC1pNxIiUDwQhROrr1A1bYubvmgu0dGp5cnMJzdqceLoJ4kAd5JBUMoIsrAEgGFniRv5vEDwZX6rfoJAJJKrXrvrhN/+q3d+05PVOzFOTFgNBijwU6G/Y4tYm1KeETtfRPOpb1qW7lqHz7sr1/Tq26AiSRfC+ZpqXcSkZ+8qZPwpch/LsB1i0PqyldevW3Ru248b7G9Q8fZS15ocHzR+dcDBCBliZxg+5OHTr/vi4/tPjpazrdi/nnzCrduWbh8Xivvv3yG4dmC96O4SYfHIpsr5IrhWwEwLJ/LF8DtkbV7JNTkiBrPTbtFBsrfmYQPSPTZQxU+OppzZKL0hQcP/uMjp1ryOB68tobLo3FQdvXYq1y9gs1kai9YO+f2K1cv6+sanqw8sa//7+87UpoYv3nrolu3LS9PTA6V6j989uyO505ft2XxTVtX5OuVvcfO/s339y2b3/6GK1d1FjPD5fq/7u7/8ZNHfuvWCx7dM/StHUduv3zxhcvn/fX3D/WPTLxsS88tW5d+82fH79p5+trNfS/Y2PPNhw89vH+4b07hrS9ZNTxZ/cyPjqyY3/aOm9f+0737Htg3Ma8t8+9vWjc4Vv7b7z+7dmnXK7cvuXTjEpz/vUfPfvnHh548MnbpurmXb+j55oPPHRrOvGrb4lduX3zXzpOP7T31jpsvWDq/a6hUueuhvd/ecWq4Er6hz84ifwunK5wC+5XN9OZr73jR0ldcsgJnxj/c//8FLgw87fadGPy/7zrw+DH7Ocx45K5c3vqnr9u2fon9MGecCkReqKHDrgReuiwhlUIPYpoDsFl/YwKiqlNnxIxBZRrN5QimAErcBP2eAzaoMUocUcsDTOkX5Il6fdVzOuX3iLYk0BwG2wQO1Nda+4dL2YKVeZLsKWdvfMNaoGi3bwTDM9Xebttw+0YsvO3F7/DY30msZrOVeu6F6zt/+aqlXZ2tZ0ZKn//RU3N7ei/fuPhj/7L7UP/k/I7qb77s/P3946dOD69bteA7jxy8bvNC3CO/+dCxy9d3t7fmvvXIkVu3L5k7p+3zDxwZHK/UcsU5+fJrr1hy9YbFrfVMOVP9zI/34sb5hsuXd+BjTrXy1Z+d/M7jp/J53NztTaBdWNi2vfCEBwtPiLB57B1vz+33cBT240DsK1F2BOHtNs4MrLXwtdb4e3aiM0wxSRqn1Ec7ueFSURfTcFKt16eAb+RyLEmnAnCUJyohBTQEER4AHN7Dp4Y+/K2dX3lisIhXTXxgwcnC9vAw0mInDb2Idup45gKxWXZy7DSaEL7Lwb5mB+DkWQkyNmknOXwhwS4FFHmGzYjfGn9rHReWXVFhIEp1+4KuVXgRhUY4MMYEs0JNjoO7wgbDhWdfMbfrLezBvksxbD7cgsxlY+3htSUhhizs0DbPr7bbTPPjwHLVSnVee/3dL1n+ppecXyziLsKTFizh/IPofAI6wzzbJHKOjE98+l93/+U9R+GoZLJXrWz9DzdvvOL8pTgctv8vww4xodN4c4yMTfzlvzzz1/cd7bC/mV/ASbNHx11yNiPcX/E5t1LPt2cm27Nj5VpxIlvEc7lgP4vG/gK//U3nbG7SvqOljqdba85+mMBYtTiR62irjuaz+Cxsj0epXsQqLdlKvpIds7/bVZ9TK5fruUn7G1tjrZnaRK6lkil21MaLtbHxXEc515qrV9tqo/g4O1roydarc2pDE/nOUqatUC+3ZkZRHc71YtuFzGRroZrDtGp+PNuBR7ylXsrXSvYDDfBepFYv2HVsP/0gn6m1ZMrY6UQtX8u14sHKZyvZcEXYQ24XD36ziwv/YdPzWmq/89J1r3/BxnDC/l+H+548+Kff3rPzVAVbb8lUfueWVb96wxZ74OxSn3oFQApRhNFfoojyALwAFFEiJNJvFzoLTcFxIhyEZkROMVOAnOSITGUToTOaHIkEUkQpvhqtHsE3RpN9OiW6e6T9FUW7kMwZXjTxy15gbB5ULA0Jv9krZnhVChXzmGDnKFcr4TWoe05bR6E+WqoNjJbaW4t4Zzw2UarWC3iVmttif/HRXuwKeC7WO7Jl3N9GKm3tBbykgBS6s+OZfGGo0oInAF6AsUBHoVws5qrZtnw9MzlRymSrLXhS5DrwYE6Wq+Pl8DUrbDlsxTaHQwiBCKcjvA4Eiwnh0EMKlV9yBWrbFk27RxIo8EQROPlIqdiEBqCwpJRV6iB4yDjK6xAZKdIgDyGRHJEGRkDtXrfOoCD+dO+J3//qk3g3aj+JhH/N0UrJGTJv48SggLMRzhV0O5wwLpwyq+OlJbwWhvbgt6qNCK4wwGTck2xn4eUX/9llg4ItFqJpwY2Fwg3VKujgw2HT+Lpti+MWaL2h3R4q25oNwEnFk8CON0wNR2NfQLOa+cJME8OuccHZopgUjia4QjMu42o+n331hT3/6bYt/JbdIIdtNEAlSRqgQc9Eep4+dOoDX9t594EKXvvbc+X33bLu9Vet7WhvQRkLBthko419h5gsF9a1a95zrGxVDGg80I0qxKlHDqmuBBoQB0fH/9u/PPM39x9ttx/nUbD3H3aPRMmq9jzEuwh7d2MNtap9QRjvU8Ipy9oPWAKzHwZgPysVH0PtB6GGJc1exU3TvlZcrNuts5JtwY00X8VT3P7Cch5t4Uf0FHDbwmOKN2a1SjXTgisDLyi2U7zjxYNUR2MBzyr7USH2r4BjRTyLWvOQ6xW857a/bZ1vsx++WitjLXvPY2+z7K2tfbeRXWCYjiXqOLpC1b6kXLE/e7Gf2IPDsgfeLiX7AjaO194hYVW76Ox40Y71et3PorMjnv5YI+VDDCKFD4md4vAsNnejhX4Tg6JpPuUolSQmvoaB1fHxyY/e9fSnf3JqfLJarVVfuanz479yDV7y2KWFCHJfAvf7p8JoDQFeASGnmFzcAoaS0CFOAqAnImxh6jkBzvaoEZFmVv0SM0ETYBZ/XsCs4SIAuPZAWJVPxYYTIWmBmUkjJhcX6/Z/6AxZNV+czLQeH808N1DpH8vi+h6p5AYncW3aP8ZUqJdws5ycLE9WauMTdo8dqbYMVgp4jz+Gu10Jb07HJ0qVkUlcdvwmbHtGDZdbBsby/RP1k+O5oWxxONNyaqKtf7x+ajw3VsHDmKzOCxwJn8eBG7GTxX1StW2G3xoHklwRIQtuaFNXggg5zhsJI4lEn1Jh6gnBKqBeXyWXIo/gFU1Oe+y3em10ojJaRo6Xk3DNwwcnvPaLLUEJu2YVoAoNv4XK1OliCiCFFE5WSJNTb1nyK1GSXvxKuqY8oRflJNg2GqUgmhYIXtmM2EgrNSZzcYt2iLwWTVFXaAjFUEhKJprdvt0rkzlbLkxWgzWMCpZpjyOQnM9AIOruCCCFWKvWDvWPPne2jApetFf0tFy9YX5He9Ec1gIxmY8U9357rxhiEGxmeEoagRIiRSNwWGNwNqo8E6SGKTYFWuQPL8Qhxy87/pAhYBs4FZiNz4mZXBE3s8lCrlzI1fKtGbwfzbTgeVbNFWvhJ4wX8UTP2zfE4r5Ssc9qeJsB5Or5At784h1SudBiOp6/+UKp0I7GSqEVHzazWfsAWsjiDbHdz/Gqic+39offWfuRc5iczeFOXClUS0HHzEKuXg4/rguPVKaAjWVbK7k2bNR+uF74y532MNgfrNaqhTb7R9/QDilfqOaxhyJuveH4wvGGVwNGACc3eVSmA+dKJ1lEHABBD1NyVMEZidCRtCDGL7YpaA6RqJlMe3vL9tXdvW1Z+zOOfOGZkxMjE/aDoGnjfHHEKJVBVRBshjpTEkAecsRplzjAi579SGnSiOhuHLSEUOTbB6XaB1IqHjBLhI0egAoAznadXBoA6UzJIwMiFBLNh0IbFTsir8IffuFyxK9AqPC7dfBkNQsuMLyxhGKbsHdwkMIO7dtectVMsZCZbMtM1vP2oaVYLRfq1ZbquP2A/Xoev2DEs6Wca8EmCtVKsV6CLV+r4A1iFZd9xp5dtrNqCddEFW8a8XYVz1s8R+ujbfXxFvs0mm2vleZUxlpqJVzgeJGxt4f2XtfuAWGrNXsFCn+TDKcGv/D0s7eadtT21lWHic3Ym0A7BeHdpR2FBb5/4okKZ2LqEQeiK0GPO3VENjK1ftermYAMjCixNxhN4SVEm9c9UZUpopYwEyV7bOw9B16BcIB8fNFkRZSxrik4Acnjjl/2Hpw2nC4zBR1e/BcSvDqG33kSbB3zh2LyVdsAXrvJnFANbmsIPJSQ2SpWsHXDNsLOICfL4T/MtEMNCPOTB8BS7iMQSNibPTdMCKbA8bgDth+bjAb7mGwSLi4Y8PGaIwLCXqwPp5eAghSAjscFCjhEPfUQy9XqkcHJoUn7c7+WbO3CZZ2rF9g/YgoPDWwREZByZpIHTzrCJiAlUOJmEKUgJp5wF0BCxVZC1RT7MmN4bIMLxE4ILuVqS2WypTqZxdO2Ui9Wq/nqZK4+YT8W1T4UTuTxxMwXyzncqOyM5etlRPspd1Yt5WolTK1mi9l6qa0ygic8zlZbdRSlsASyfBWfF8NJq4Q5tl0cPR54fArFgdgjVanm7daLzdtn33q2nMvbF1RzGFvO17AHvALla7it2hHhv7y9BOHww10TDy+WCD/NvB5+9CuufDsJfOh57HYSwkHbwQuNRwdnUk89f6rFSRBpEyAiQudDqXZE6uSqIiXUK4JIP7J1fZ0LOvP2eTyTOzGePTM6IQ/AgeIiGgVOiINw50rpJPGY2qLABkR/AEIYmGwCEBdhr58AQk5CmxAdmzeonfu25sABHB4UECgs+fnRWkxB4KdCQhH/2+sPoq0X7hIY2yChyNem8Lyyt3L1jkKlq1idU6y15qptuWpnoWT3PUzL5TvyeGmotmSqbfl6V6HagivNng25YkuxK1/uLE7CgGPAO8a5ubG2ov2M5BzujnX7l+068hX7U41spT1XB+8olNtyZVzirUVMK9kWa/gkWi0WqoVauYrX/Gyxqy2DS6cT7yxtZqWjxXbVibePODGZOuYv6Craz1BDigUwwY7Hbor+MMPBE8ERikJylkKMoAcI0ElmFJRyutKmZoJjCS0R8Qh+LOFFQ6B4CQlf/cJ9K3l8bVfhUQ51u4WR48yYHh56kmSEIZwmnENUwy9U8XzHGW78sha7aYX5YaR92kApRBvGaviVVDFBtsavsIFgtuVs0dAMMXAz2O92G0YMfqtzb7IHc7gT45QY5f92jLlMFR+N8M4M56ZYmeQxhj2z3Z5fgvU0qv4xoo1KpVYbGCuV7GuNeJHPrl3U3dbWoioAEoYZqBBM8byWTsLILiwEHtafIgRfEKLeKdhJwEnDJz3MCqfPTlcyIXkpgcmC/VsfeLtgf8KMhwDLwonnj2X2m72TsD/YAMPTyP6gGWPZbktk0Yh2TKzhnNbwViFs1UTez4ILm7D5eGtSq+IDIgxQ+fVSXAFG8IjYg2K7xR0Oqj1CuEnjQHEqsBP76nzV3h7bV25thbAHLAqCudgqbrTV8H4Zb7vDdwzbaUgeYKO2FR64fSkfGzAdJJw6RD1SSAl4CZ/CxpMPiBDohRLNUbsUIPL4Enn3nNZuvMrVaoVsaaJcGRmZdo9EO1Mu50WAE2QmINIPzhIUOsnln/bzWgGZxEH8wkAwTluPkJMGACkiS/QAac4NaVteFCEnwL3ICEBExAZIAHkiDoBrCMjOw0MP7DkzVrZP8wbcYcJLU7h4bXK42uzK7murvu7yhS+5YP62tT1dnS1Y7tpNveO13MmzY5uXtb/4gq7x8uT5K3pfvrXv6nWd6xZ3nR6ZHJ+cfPXlC2/eMn/rut61K7oOnRjcvKzrNZcsuGxt79rFXSOT9dMjpQ2LitdftOD0aHXB3Pbbti98wcaei9bPy9TKlcmJmy9ZetHKuUdOjQyWChcuLbzkwgX7T46P1luvXjf3F69acsPWZSvntR0fmlgxv/gLly28am33tlVdk6VKIZt5+fZFr7t8+YUru4ZKmSPDeH7V7Wcl25952CHxMPl/Q6gv6sxfe8HCxY2fIYCTQwLwXInzJPtTHcV0CtiZbDxAqir13E8GR2QacUQZRCQGl+HgqeEfPN1/ZtzuU6EWHmEwDKHDJHvdpBxeP+w1BWvAZJm9ihgaF4hdH2Y3PwQz2X9TIxnNxy2FF2WoeFra740yEAYzNLYVVdFqh5a4eBICg9FIctQ2NYzGJsP2IIZl7Y+1gyWclvBCzDNlhvC5OrO+r/XaTQvntNk/q8RTxwgXvWZvgCINeECpIOLCe+jZ/kcODeNzTlsh+6KNfdvXzLeGhoEgTysRAbAE5vOFhStyOUIeckRxArxUrjyyt/+hg8MF+/sP9tef7RSFM43zAAODSXbqGo9WgqDa/+HNDeqs4nee4Ma6FJMEv+FhtkE5RKyCrkbJrJbgd9sp5tos89rvNscOgTOphDq8duXaLwu4hsJXBUJfkOzxtXn2/o/zk83idTgYrBODrcd8JoTrFWe0tSX34o3ztizvZc26HJjyzHjFAwqQnL0AO4pGiZwkFGcbyAcaxFcnJkt3Pn5030AZ7/7xeft1ly1e2NulJQCamw4EMBMKV0T0SxDiwZ600zN1tQEcQYAzRUxflEkSAAMiPP5WCo4oJ0loTdZmF6EJIH4tDgHUjiq5bOyV6HXyaCfQwdWVVEMJ14td2PbNiHhDZ2/HaoHgrW2YjmcU3hflFnbmX3vl+m2rF+BVoFqtjZfqm1f2veUFK69ZP+cd1y7fsGzBZLnwgg2LbsMNsKP95m0r3nXLxtXdpTuuWP2C85cV7WdXZhCvXtd7+1Ub++a03HTBgne/7Pyl3bmNC9ted+WGFXPbLlzU/pYrV66e34m3iBP14tw57W+4dMX7Xrrh5Rf3tWbKFyzpfO0Va/EJ9eql1d9/9abu7u5dh4bxbrI1W71oee9tl62f39U+Um/NF3K3bFv6ikuWHzt+amBwuD070VoZs2Ox728Px4tjsq9chDcxOB3hPPAxSc5GA/6MIRIq8SQjSkfEKUMLuyjaSQwETlYlAjKzXZGi/ODUAYiRognkiFgLJMDEUAzfsoDjtlcLsyO3N+Pg9mYaFfTal6bDqwlEWO2y4GR7ax5ennAKsWsuH3Zhw+0/LmOfMSxiDnYfjgAtvJSs2XS7vGx5XHFJO36ZMQy0BpSs175JAyvY8doGYbF3a6aE9qCEZYOIA7OvEtim7dtq4bWThwxPdWRBtz7rtuvZ+tlreycwMDll7hyGLdhC5AB1AKKqmFQFCYPx2Thvf50jGQWwkZyNiFQYeTlJ0U60Cg3QwWlAZBUAgQGASBuaWlsKK3paO3NVE2FEtDOIw0+e/naKoOLc2GMIChl6sFrJfrOXAGs3E4q2bfziBzD7z1pQhsVG27aR2KXFqVZItm1Fk7B6OIW2/+DCUPtlGwoZfrPr0sajEYdtKyAiswvRNgO7HYD9jw+W5keTPb7cDzdtXWCwhgZ70QsbalzwYH1t9SXdbVzdxoQNkBC2QABTEnoQecIBf+bpBwdAWKJim3NveughocE63VqmmMXOWnCHwwkiuwiaFSmyHSlWJKEiQAFIVPLELriZIB9HeISxJtoId4JYQiTnBCqzQAuxS9AQTvBRUIuGCE3XhQ0nS+ZAGja8GuA+iHm4NPHSgCJeZ0DwWmLvxex7z8IXTguT5drTR0fu2XH8qb39B08OffeRIyt7W3/z1i19nR3ffvjQ8YGJQqZ2sH/08/fvf2j3iTULOtraO8ar2X0nR+7ZefL+nceHJnP5Wv304MQ/PXz4/mdP9XUVO9sLuKJz1Wq+Vq7W6kOVzE/3nv3hjmP7jwxW69mh8cozx0Zu2LZyy9KWjP0TWvb9by+8eHlpsvQ3d+36xN37P3nP4f2n7Vk0MDz20DMnfvj4sYOnxttyWdyMHzk8/vkHTz50YKKcs68I2XMXR4ZDxINmX0myN6N4CIOos5KcEJxAnkN/QcMEUNfJV+oNBES2swrIT6fSUEx0KeiKFHIphJ/DEnkSTYVuNXtpDO+1cQqQhadAOApbyN6iYrbxsKxdDnaKbIAdQN6+op2xr7YFewLrQrTZBoy2ZyMV3CTyuILCoSMt5Oo2ILwO20bRYl9Rs9esXLjorB2Tsa3wFwOxffsQYDcbeO3lDrvLZzMthVz4UGTLFPJhUzxA7BQTwkcL+5QU9mM9VrNtWAWrYOXwiFu/9cIAFtJwpCQ8q4RtNiAciEGiDOE3GxhuEa43VNUIUOejk0gBFLGBJA+gR5HEw2+JMzGBIk5Ia7F4ydq+Cxa25OsV3BeqydkJbzDszoJp9qBDRQznAKubngwI8yy1G4s1JqK1W4vVw7nG7wlCu3XZLzOylx4uZ6M4B2LYDzh/sb2xGZvD/YTlLMX/HIhf2k+gSUz2w4WsPLVcIEhtODjuuJ0t9StXzTl/2VwsoDOZhm3EtmDXFIk4UxIqjHwo+YhQhE2vBlDCjKnJhOeA2gFsGkBgigJnGg87B2GL+RyoCzJLB+GWqIgAiZqGfAAmAknSDDDTMIvND7SVp6dNG2dfNEJk5vymE8LiU2cBgSZsonFRmkSKiCpc4Z9Ftfd79i9AFvPXrO97+w0bL968MlNs3XFk+OTA+CUre589PvLYwRG8+lVrmSXz2l9/zerL1s578rnBU2PZQj1z8Yq5v3L9+bdfsTqHJ2smu7iz8O9evOYlF/Q9vPf04cFqJduG95NYqpLJtxaKr9y+7G3Xrr9oaScuw/F6/t6njmHCbduXFPOZ8Zq9gC7v7Tw+NHFicCRfn5jEQ14vVzP5eT1dd1y59m0vXLmwp+P+PQOHB0vvuOn8X3zBihVzwycbO65qOKAEdmiBhOPVMdtvOHWEP10eKJGwiig/UlVFAHo0zTdGKSJT6SSEUka2+8mEdDvO5Fej14Il+AU5PKFNSn5vdATY4I5iZtvyOTde0Hv9pr7LV3eum1e8YnX3kp6WSi2zuLv1qrVdizsza/paXnxez/UXzLt8Zdu8VnxKqG5c2Hrj+T03be55yfk9i7pyvW21G7bMv+WCXvgXduFOUu7rarlkzdyejhxep65aM+fGzfNu3Dx/y9KO+R11rHLl2u6uQq1arS6Y03LV2u6WvP0jovNa6y8+v+eOy5fesmn+kq7c4u7Cizd0vxSrXDBv3YI23EaxX9t02DqPTsdinx8CYY7AlFUBB2zHHE4mOYg/7eR6Yx5k93DYp2y8TaCfbxcMU4bGQHLE1NvWhJDbDgLEqTOSADJItNRofePyea+7bNl583MFPE1qGTxqtVq1Hn6B+F9SfJU8SauzGbyoElvSpZl+yRn5JUa6fkmnh6lIowSQ13FpdeRKL17b9dor1y7qTX5MP08miaIn4RFIHjUqgk4+QA85SGgykNPAEhBcUwjGGaqQp16nDXQS4GwB/PXpp4lLV0qQy5A0EOSMuqnq8kVECfAlRKUAzfSD+5JHukRFA/0crEgyO/xMTYtWiaDlcN18/sGDf/Eve/vt30OyQ8WydufEXdG+JBWe8PbPjpcms4ULFhT+7zdftHP/6S8+ePD0aPnkUGXb+gXvuG7N8u7ioYGJP/v2ngOnxt/78g23bO6795lTTx8dvvvJM0PjI3/3zqv7T4985t7nTo5mjgyPvPPFK199yZpHDvSv7mv/u3v3f/Vnp27duvRdN6z90Nefmt/V+vYb1n/9wb2P7B88dKY6v7fj/a867xsP7MsXMm9+4dr9R0/O71v4u59/9NduXL96Qef/9bnHjgyM1QutbZnqG65eedvlS/7hR88+e2jo4HB1aLy2sDP7xqtW3rh1xTcffu6T9x7HR9JivWLfU2cvYgTOrv2GN2P2Rat6Zuvi4gfDz2v155APgTigE+tPMm1elNnbwO2hbbwyAmz0XYjUATU2Bbv8fCASccu778kj7//arn1nSrmC/U3rINpKVrbPfXi3yC8+hZ3gfbh9gMO7d3tngfO2dn7+vbdtWTe/dV//6L5jw4/sP3P7ZatPDE7gbL/6ytXrFs/92L88deOFi1+xffnhgbGutsIj+85+7F/3/rvr191+8dIdxwZHJqtfvG9vV3byL37txj3PncK19Nyp0U99f/eKxXPfft2Gv/r2U4MTtQ/csbmYqR0fnvzJnlO7njv9f922ddHcjj//9s6vPHLyldsWvv36tb/6iYcy+cLbX7LiotUL+4dL9WrtSz/ZN6+z7Tdu3HB6qHx8dPJfnzzxnUdP1DMFOyT7/GnHaIcaHm/j4cLGf/bZlCY7cLxw4g1g9ubz5v7Rqzct7O3CeUOT9YVHkxEiHxGcUv+40KyTPzQ28dd3PvW3Dxwby7T2Fqvvu2nFL75kE8wocU4dt9CwKcHPb3im/HwQuQqVSEfKzdADiJMggpdL5cf2HfvBrpPPni7bV0D1DPg/ETg5uN5zHYXahcvm3LptxbKFPTivOlfmcKcXKU8yzzMiPRJBALVTAWG7StB9laBIcEVGKSRnB4d/87OPfH/feEuuXshlv/nO7ZvWNHmNUkQL1wWhh4tGq8uMqHUpIiaEDppIANbEEdnD3VAHIFIHSCgieuV/EdGiPy/S29AOdQbxSvj5B5/DPfL0WPg5ZQH4Db/gCF+TsJdLPLFK2fyWBfW/ePP2Z46Ofv3x46cHh86OlH7puvM6ioX7dx+//bKVO48N/+P9B37pxWsuWDL3j76y85mTk6VM6/yWkc+/68rB4coXHjnaP1HbfejMr7xgzYs39f3+Vx5/2UVL+nraP/qdnZuX9bz9pi0f/vrOpV2Ff3/jxm8/fuKJw8N7Tp5tL7T8was2f/6hg/c8eex3b7/wpRcs2H1s9D2f++nVG3p+7WUXfeH+A088dyZXzO87NnLTRYveePXSrzx06Jnj48cHR/u62toylZ7O9l94wfqHdh/7b987Usrki5kKTgeOiY8u2BQxVr94Ee6R9jMEdNp13eg0qsQzCbCkU+11ij+vk4QKDYA49SiyRA9S6kwh3Pvkkd//2q69A+VcfuotAq8qcKawJlPA0Y+DxO0j3CPXzS/87qsvHDk7+MUHDg5MZI4O1269eMErti0+cPTkooXzv7+z/1uPHv+1G9duW9r6sbv23XLpqm2rev/DZ+//hWs2Xray+wNfebRUb9nfP3HFiuLvv+VF//Wrjy6dN+e2y1b+xT8/3ZKtv/OmDf/lm08NlDK/f/t5jzx58J7dA8dGM50d7X9yx6al3fmdxyf+8EuPXbWi9Vdu3faWj953zYb5v37Lef/j+7u/t2dybmv2zODQdRctfus1K//x/v07Dg/1j2dPDFULuNnbt0TafdAeuQAcKQ5Ep1LHG4BPFPbV15vPn/tHt9s9EocOlSeQp4EKwPOJ+9xMX4IaHZ/82J1PffqBw2P19p5i5XdvXvXmF1+Q1JItuGh/KGarJPUw368FDvBiA3QpIkKngdEeq+kv6OQUG0Pqo2MTg+MlLN74mAtwOZ+KTwPUZGfGzRas2C79vlF8Sgwt+N+21Wg02U0gpnr53kbvcCClzIRaZsd0Wz1TzGd7O9taWuxvreiMgTDytIOQk9BDnRyRJxmAyCo4YugwRVXAP1JE5EHkA80NEI175ERL3n6cEO+RLLGFHLA1wkAuREJR8+kHt4aAplUq8b+NBcnvDGADCduYAukSIlJygFUgahSik0VolEpNe88dUTs3KYS1Mk8eHnxwz9nRcmM/4TisBAMsmUzePlLYzwfraqltWTl34dyW1Us6c4WWWjZ3/tK5333ixA+eOpPJVtcumnN0YLSztVCp1X/8zMDYeAVnEx8BL17V0ze3Y83COcv62nbsO9XbNae9JfudnadOD41vXN59drw+WarP7259aP8w3tOvXdi5bH7b+mVdg+MTxwfKa/o6dh07+/jRyWppYuPizr39o/c/O/DcqbFlfR2XrurGy/G8rpbdR4eLLcXzl3ct7ZmzenHX8Ojomr45N1+y6vzlPSdHJ+9+4sSRU8M13CLx4OJoG2cERw7Cw2S+pDN/7aaF/Lex6Eo/HIDnQFSSGVEkFBOSTv18306FiAyMkQiAIKXOCBw8ad/XOjBhdwOTg4g47VIICtEwJNdBb3vuJecvWNXTXmwt4jVl99GxIwOjm1d2v3zbqieODH3toUNDY+XL1/ct62157uTw5uVz84XCnY8duWhlz0XL57XmKwt6OvedGp/fnr1l26qDxwcW93YU87nv7zo7p7W4bd28+3f3T1Sqt2xeNLe92NPdPlqqTpYrV5238Mn9J5b2dU9MTJbKE9vXL/7WQ8+9+OIVq+Z1fOKu3QcHasPjpZFKZtPyris3LGor5OZ3F0+PVo6fneR3/9ufu4ajs6Nt/CIkNmACsL6v7doLFsxptx9dBrAWcRI8bbHDSrVarlRLpTL2N45fE6VyuTIwMvng/jOPHx2tZgqt+ewFi9qWzS2eGhw7dXa0H3Fw7MTZ8YNnxg/1D2eqpe7ODrcNA5bAw4eXIK0FUCQh94SRTjVKiYDHrqujDb+6O1qn//JKuspfLTPY0mQmkamfQ5L2eBJ5InNTZaZfzjmntbO9NW9/rB2+C6hx0nR6AYrRGfbnnyIhRXcQ7/QEJUYRqzldfnME4EnwXfu+1mo+h8sj84ZLlyxI/ft90XBEKFYOQCoFhCJAPlM04qd48A4M8KQAIBAV2ahBBEQoNFABp4cTqCBqDpXI74eTM2VJkY3kdAIgfiwHsh0RoF/VEOv/+OBzf/4ve06NVdEabozBHN4wh6aM/USo8JeOiplq35xMoWjv7yZK2EmhUK+enKiXK7ViMbOwIzNWyePG2ZqrnR23L/nXMoVqPr+ko9KaK2DjeM9+dLTcmcu0F3P2ryLUSnjprOQKlVJ5Tkvm9GShkK134cNjroQXuzOTbaOlWl9baayUGyoX22oj8+a2V6uVUxOFyWq+s1BdPb+lq63l+PD4ycFqa6HW1WE/IBK7PDtRKeRyi3rb8VHy+ODkqVF8HsKTAQeH05F8UrZzxTNjBCleXOvbFrf88as3b1ljP4uOJyd4pziJP4fiPMNMQRDF06CfVdiA8EAkq8zSyBKd0RAZNErifU+Fr7UOlHJ4w2JfYJyCPdqNyFQKbBhQrdfX9hX+0+1bWuqV7z55+vDAxI/3nFk0p/CeW9a+aOOiu58+/vF/3XdooPLbt6x71bZFz5wcPzY48a87jt+958zv3bbp+vW9n7l335lS7b6nBy5clP2bX7tu94GTvV3tX75v78fvPXz9hYvedf26D39z18Bk7YN3bDlw7Myjh4afsD+drn/wjou+cN+e7rnt129esnPvoasvWPWrf3P/m6/f+IKNC//dp396+EytpYBrq/ILVyz+5RdvvHd3/67jQzsOje49OW4/Pg0HHj5H8hDwfMPh8OgAPrF5BsCnvtZ6fvcf3b6ZX2vVCUQ0syM4JcOj4197cM9jR+0fT6nVcKesVbAVXOm5zEQle+Bs6dhgGSPz2dqCjsz8lky5Vp+sZe1vvWfqlUx+slq/eGH+N65fd9mm1WEXTa40v7p/iAGmKjFqAgjScA6SIVEj0+DBhCknS0HntacrAv4pJzl1QmYuxwgzS5ym+Q1/gsZaRjUBSWMI14Jn6ljM2ugKBgycarFyA26rNiEQA/SoBQQR3BNEbZ6cKZcGoJMj0kNR7YhQ1EsDAE7CdtqYAqhKZy9S97XWTCGXfI5kI1u0qBrVTo8X6UGU4lMQgttINp0Gy4w6JIJrANRVFWEXAS4ghQftAAmVYExAEWaRIE/B++nxCOskLawiqiUiivhs1duW7WzBibMc/+s3IIyr40Talz3sZxbnDw/nD5wpHDidOTGSOTFWOTpmP8yxkK9Xa/kjw9nB8ergeP3kiP10YfsLvPadi/ZFsOcGKvvP1PYPZEq1wtnJzHF8YqxW8Xb72Fi+f7Q+XM4eHi2UqpnJyeqxs+UDZ2r7zmSGxmq1SuXoaG6wUsxla+Vcy8ER82NzrZlyqZZ56njpof1DB89USrXccCl7aCBzcAALVYcnsmfG608eGX/kSAnttUwRHyJxIDwiRHvjGD4l8RcOLXwNJzevPT+3I/zksACcHJ4lnUYQXEbSBZx2GRBpIELdFJI0UGI7HzvrCWZGgToAruUYiVBMRnEbDdih2h3DPjYHmFG9VjAaUjhowgFbDUnd/j44xh09PfrTPaf3HR2eW8jcunVx35zWzz+wf35n23VbFs5pMd+h/pFPfe/ZD//zsz/adSpTxWNWHxgv/f/Y+w84Satq+xuv7uru6Yk9iWESTGDIAww5MwQRUFRUUDFfFcWcr6Jcr+l6uZgVxYDXAAYUVIJKzjkNaWCYnPP0xM5VXf/vOeup1Wee6h7w/j7v5/97X+5y3Ky99tr7nOep8FR1qH54/rr5S9b0dHQVi5U127t/c+/KO+Zt2nuPlj1GNdXFD9wMn4TCY7hUXram9dEFa1dt7iwVGnrq67vKdbc8tbatq3TmrGmDGoo9lfqVazY3NzQcO71l4pjBe4wdOro5bLWnu2f+8g1PLli3ta09PBWEW4q9xKPgGhZ/thIlatlxcSzx2LLHv47X//Wp0/OFbxoRugY3Ne3WMuT5VVv++OSG657ecOPzW/++oP22Re23LWi7f8n2NZs7GRRedPXWrdlReHZj+YXW8uKtheXbKyu29a7YWtpzRPFtx02btc9kdlC9CXQDhOjl0tVtkOInMjiRVKJ0uHQiXcAK0BycmifdBk3Ao/t4cIQ06wpS5AJKtStL00jJ02STLiLIA6iQUlJa9ciclaLSNyHG8C9tUQQoZNV1M1HQBRJiM7c4kJIrAbjmC5laNYN0dc+xQVXtJIcwJZkDpADPBCpFhK+QAA4opgGanO5BROc2XVcpEWRScjPl/JC4ePyRhVpoAYFUMYddiC5pjni/fpB6BFIp1qUAi4qCeXBEKBV8LFkebYjm4T/1dXvvPuyA3ZuKle7wM2/xnRXPjHp+DOZ4m4Q0/NZAeBbiLT+X8TiXuxyRZcJbuPrwKRvhsxLD/znpWAvl+kpPoVgfPnOjoVJs6A3f2uT9Xvht2PDMjYXX8uwyvE3lRT1jw0c3FuOfpyhXQsY7P94IckvWNxRK8We69QsHFVbByJsAlmIT7KoYft8gPApYnxV468rzO04G884iHloACv+vq5R5Z4terqvr7q0bViwfMXXEuJa+n3ALndVzJeRSIJuIOZAzLFYljsBO62qHiKel2ignXE5Duqs7IdxUmgsPvwYRf1KHWxVzfNMVfsK+ry3M4T/hZg6nvqO3MHPv3T981j5vPnbCkXuPmDlt9INLtlz1yIanV7cfvd/4vScM6SlXNrTzMqiztS18+kkD75m6eyaObP74qw987ysPnD5pbGehob2rZ8Wmtpuf3TRq5PATDxjX2FBoK/E6qRJeXhWLpxw69SNnH/yqWeNamrrbe8ql+iJXlDueWTV4+JAe7naV+oeW7Hhi2eY3nzD186+e+r5Tp02dMKq9t2HIkKZzj5324VcfeNyMlsbwkWnhLhoONBxm+Bfvt6wR78P8Jz6LhePkLhKPNj7xhGMVYjU7DeGMJWcYncdDQ2PDaYdM+ehp06a3hB/D5n7L1b6hIdxpeYoM91s6sIb7NvdG7qCNvMRr4JT3lvYcXv7QKVNPOWiPxoYGnsYZ7rWEdDkjbCgiyyPUK9EltbtkIDqKpIqiiJFLgRWtkhpy3GnKgVMRIJ2BmumokmOqiKQIzTsfsvSUA6VGv0q6kKMRTFUdp4m6DJWkh7YIdBN5RCSmqUWTnRCf/zJeA09ICYjFPNJS3Gx27EpFdvp+ZO7qCqS4DbiTEo8HcYmK6aWYRnPBaerRS4+c00CXx6lIDrUTpAhSROzUzMArhZHDBo0f3ripdfPaHeWuSiMXDozxWQSn/gkpF7i9wgkKnycXfi8p/CkAHOHHPeriL4pEQ/QFxG8UaUg6yjxHnIKUA6epIY36J6Qk+afnzfAzHqWxzb1vO3zMW0/cuyX+5YdgjeDgdMaARGJ4KowlR5dEUriEUzy9k4DcjavhTgV6pQPvx0sLfTdoFdEVrn/6fmRrZ7h1bCFwM8fLI1I4Feqvjo0/0hbTcm/v2tYd81dvX7ahjVFrtvcsWNd237yN67b1rGptW9vavmFrz8qN219Ys3XZlp7we/vhlVTTlrbS8yu3LmgtLV+/bcHGzk07SivXbn5uXcfizZU161tbt3cu21R6fm37gnU7tnWUVm1qn7uuffn6toXr21e2dq3YsH3B6m2bOwsrN7XPX735kfnrFm7oXNdWnLty87b2nh1dPUvWbXt+Tefa7aXl6zbPX9exZGPHovUdG7fHv2MM4r1N/+GsczTZKY5pOAXh6CN09HV1M8YMqn4/MsicTA6cqEe6zgkQbygW9544cvchdas3d6zfUQonL57ZMEkvNzh74RSiFFkyfKBaXd1+45o+cdq01xw5o9gQ/ig4HR5OzLqrMdUhUlQS0hTiNI3Ac6wL4kSqUvrdRqxk8BxASVWJ7s1NSBEaqtvGrxSoBUiXQXpoi5BfThsQReyEi8gp2C+urlyLRME2DZGiUtolAxxig+Y4TXXMfuBrvog8dnqmCKBU/X5kSb8K/JYjJowbNdxVmdMWQ2NdghDh6aJpyaNUClBi2EfMpCgCzTXsMclxoAdAluwSfqjA0wlAIvBy4jlzupa2URtdFUHRbYYSVu/tXbBy4+3Pr1uwoaurO3z1NLw1fHFwKQxPQqVyZeHG9uWb2svheTd8fatQ7m5pKhw4aeSIoc3csuwg6/i/CTydc+SjmguH7jni9JnsdQhierpqoZOm80b0bQen6tMLpABEGawI0mlnCKlIOg14Obh0eVQFMmRJ1aMognbf3NVf+uvzi1p7wt9UoIKL3cW3WOFiEbfMDsJbRi4hLAGPnbHKFF7yxKV5RPUdH+/YuBbQEd/Khy+u15XrectUjj8Q2xS+2ljh/XlDY29PKfw8bbgwxA/qpLXcWz8ovKLq7S4UGhhbrHSzEKPip2Mzt6cYfpm1yFWdjbDR+KeAmF3iBU2h2MSVuFRoZPn63p7w0ajhGBpYIvv9RB2SLoT+FaZ4YYzOeMzhvxDeGYY9hd/9eOMB40YOj40BYUFtdudbXFVQKpXuf27Vd29d9NiaHtZmCfYfF9CXc2UNXwEpVSr77z7kgpP2OPvQPZoHhU9wjQMykKY3KGmqKIVYSe8SujP4PoBTRICrHQ/RoghAlEc8tQmqeo4UAFGqCFJdolJFID26shUlalHZOBCiDFKAuWJq1rETg696ZmQg+nDkN1IxbQfeAMAGZFNqIh2kLUQRVeEQKyJErWhPDv3q+n7krfHnWrnT//XCWTOnTfRMbUBHkbbLYILBB+uSzE6BzUqzrRukWpKoZkGj05gVqui3pOHpHKPWiS3n90wpivZAgIfA3Qgn2i9zv1WGR81npNLZVdrR0R1+0xhUW+iJ/+l78R2eX8L/g8Jt1tbV/ejCDb9/ZOWTq7t4PuJpsadSnNjcec5Bo885avrYkUPCcweFDEyJvX2xFrlqXDrbRgJ2GLTUKTF1VredueJ/PC2eiEFNxWHNTY3hr+MhB92ny7eUz6FOWnrmiZREpMjpIbmZwB4R38opbBCHyJyWINphrmoOIWTXyM0lXjKGC1AsaEr4f3yrH57Z68NHv4VrJDMZQjWuzqxe5PB3iMrxNytoyvTwxepwLugOrYzmGkWV5kjKXDt5fcSVrb63Sd+hLpZ7erkuho/K4drBdbSeCyErxD9/HL5QyZ2+HL7yHr7Hzf2mXB/+4kH80mR9UCrh29jhHsV2w89lhFdp4VDDNzfDngkxhcYjDV8rDpfB8HufMY2HFT3hCLlGconnGjlC18jcLR5PYIDOpyFbT6l0x1PL/vOmxYtb2WYxnJ/wC7igEgZzUgrFUm/v7kMqn33l1LMPmzp8aDOHHIdlN6huuzgyqCkXAaxFTBVsYUT11ifC3SugECVqlZQHRxWaA6FE9LRc6oGekM6hKptSE+kQLQE0VsitBQFSFBFD/84bSEkuArUoSlFqWJHBUSWQlkyky6PU54cI17HAFQ37pWtOjtOrswp8Kmzb6Xc/6gvXXXjogdMm2JyuW8vxKMrp4QCiVLp7gQievsM2pNiag9fIIZ3j3oFGpUPCJpLDyEWZIeK1sK22ywcfmqseIiBFN1c13dU/hc3b2q59aPFfn17/wvqubp6FeA7orcycOPRdx0w8c9akkcPCO7MBwH76P67/Y2jyPzM/nh2dCiM9J+kphaTRBrV7CArRXebAt5SgXuCBOYJfLUQPSUsSIURssR6AQnLPc6svvm7BktbuYvhwLl3+QHgaj3ea8E3eOIgt8sROOXwjOFTDCMQYw3WJy1h478gIRsezG990hmnVayQ6/80yEPfEwDAsXhK1eJjIy6nw5yCijjnsm6W5EKJxscHAKdYqVU/MqtD5gjBTd2hOYfyvfoS5vjf8bYpgCJfJ8LWSsG/p4b+sXHONjOYwJN4g4fTAiempJiIGTyj1/v3xJT+5c9lT63sawiuGhvB+vFJX7O3hrWZXYfC4IZUPHD/hPafu29gYfuqWmb5xgThRy2lpkC4nRR5xzQEQKQainWoPUxI/kC3a+zxKreeGiMRKRlTVTInYtH83qupDEFK/uCAzUUPUpW14SJq6VyQ1QDwtZ3O7VxERF/EQYPL/R3R09nzgl4/csriruSH89MbNHzli70mjstr/kwjnwafP8HlUmgM9Po8GLektIe5og6o5bmJRsEIUT6MMitqPq2mvOJEU+IYXj/XsbmFb1Ha6i4B+dQhPCIvWtP73vctvmbtuW1tXV8PgcqU4pKH3pKlDLpg9fdb03QY1xb9kXt0ba4mnc1JdKVF+RaW0yCY95wE6kDg5FPm/qhAh5ZRM0PstWZTHuiGDdBmARCI7CaZqSQRAnNpvMYUM4iK+sdI5Tj0KqKQJqNk1clNP+GGrYNPe4pco4/XQ44ISLnvhO8lcPcLlNC6CIa4TfpQLd0zDbzuEa2ucxSUsdPMvjM8un6jhP+F6GvJsTtxpeAcZco6IHcQZrBn2EGUfOx3ZFZ2oITpSDQgXwlgKh4Q1c4RyED2Hkr5RGqfRwv0kXsi5ocJf/u27Rmbr7gzNcdQdWBx0dnXfO3flT+5e9sjKrobw993CqeHyXy5Xpgwvve3oSeefMGPk8KHZniLUniVxtygQ6UpB+kgZCDbQpVQPFoueA1B0z/R9yV1EkdoWKTIATZBClFMeT8spOaATafQ2JErJDSQC2xR9gEBdrspMlGK40R7By3kz4kT0G264oVQqpX7p4tg2btw4cuTIYjH8fpzmpGYgZRc6ve3t7T09PS0tLeXw53J3spG2t3f+6dEVT67c3tm+fdzYsR84cfLk3UaWojMdC+ns7Ozu7mYOm5EIvBC8oaFh7dq148eP56BUdTuQE7Cf888/HyVrq0WtrkEaIQWkt40NijrdVkDthLQdoKuqc41SO0cGkHaJCNblFAdqSRsBXPNtVtVpirQXXu7tfWjeql/du+zOpV09PeHHFnrqirsNKrzqwJHvPnH6PpNGY7IfeCbRByikXDetne5SS7oxpeJAqRQOKp2DCJEhWCPMfQaI8pgDlSAD7VlmEy2tmPqBUxnciJ4bAhClEz1KCkjbc5BT1WwC18i5q/7try+E30EOP1kV6vFyE6z8C5ehvqV1UQpj9D4yy4MhXFaCiyR82TR7X0c9vHcMZVK9wwzQVuIlmN1qdvz6K/3xOALTt65jNTaGERFxl2FI+DpuOI5MDscUBwQ5DAxfcYXG74uGJWMWvr7KSYs/BM2lESXcImGH1HlLSWPcKKv28sKOq9oZ+7Z8+Q0H7F79/UiW8s0UthqRbaG6F0E6T3D3PrfqqzfMX8RJrisUC71ddY0tjYWPnDD2vOP2Hltz6aXLNyvQEopSRFBAmtqmGKwR8HRgWgKUiD4uV9Mh4kAHntoAHIO4iQxq9ChV4RJThahUXATY4EVRcntQVSKpjkUTZPNYQTwdKBGIG2ljWoWwys0338wlJ9dijBgx4n3ve99nPvOZPffcs1S96vyzaG5uvv766xcuXPjZz362ra0tUxNwHx85bMi9d9/5pz/96Tvf/V5HT29PKfzdzRyampruvvvuJ5544l//9V/7nQOGDh36lre85Zprrtm6dWsm1YBL/gknnLBp06ZwZtIj1w0gHmrJyfIZNGSWwU7bPEq3kHUjbXSUDuj1TWubdHlcAv1uG6RimqZbcomoqnTNRCR1BB6ydVv7tY8u+80jGxZv6q4r9IQf26vU7T1u6AdOmnjqzEmjR2Q/GqqusFj1/q1UPI3S1SIDPLRVRZcARBxRuudIAR4Ll1mNtalBqhaQM5hDPBloXXQTAVvGBoDmaKx6FVEkygYhFRfxcmH56j7drpJSGSCMefD51Rf/Zd788D6yyPgwOLwB5E1l+CT58NXM+N260BQuX+F9Vvx+Wnw3qcsQisfG7bAXLkHsIL6DCyJS6M8uaqQU9NXO8MYUNZsfLrCMRGAW248CLeHPWQV/vIQSYk/0xy/8hq+ahsiK4UyE94UcAG6UcOyQeGDxq7phfNh5uBiGUxAbw/LhkhkvzHEK7t5yIfy60pn7jfzy6/ffrfr9SO2s5kyGHYe1q7eFqplSKd8yZ9mP7lj65LpSb2957KDKO48Zf8ErDhg2pFlmoobA8YOUpItqILBZJRFxeWQWJ1JKl0jT3NIyQ1TyHPHULB3gJ1pxL7pGARE1iuDUTNJclzm6iPyhs6ZXykDEEyBM1hBxLUTUqDTKJoOIbERP2LJlC9cMyEDg/dZ3vvOdCRMmZPn/CNddd93cuXO/8IUvZHl/uPPOO3/605/+4Q9/yPL+wDXy3nvvvfjii7O8P5xxxhlc+LNkAEyePHnlypXhRGVCFTo1Jmk00tOn02qRKMCV+nRbNFIl5ZpMo1JKqkqBy2DYadigXtJUEdH9A4TnqgiXRGwIIyLCQrG0aHXrD2+e+9M7FizeuL1Qz8uZ+mKx6ZhpI7509l6vP3r66BFDsobqQEMik9mPqkRxQ05t2GkKbywcVfW4NFMcqDF1EuP4gNy5BeIS49T8OQHRGDZPOxGennlSYq5X6JdrwxCieezLEF3521oeieKGDlbTlKa2lmHNY4c28k4rXnbCYP7DZSP8L1yHNIqjokY7I8L/oqSIL44N1QjGhksblyBYFEOvSkGNlXDhDbZM0btN2cPIuDcWDHJoC5WM8v/4R1oi5TLZW4qX0HCEYXS4cAauExT2zVUxbjIeGI44O66qu1dMiPyjI6QhhgJD6pvqKxNGNDQ3h4+PYGTsCLdRMFVvBYlG6oQHc13x5JmTP3Tq1Jm71Q9tLLzmkN3eduLeXCDlSYfA1auoGwsiA1FQSRxDSijZmZaASrV3TkZpISkWlUoXJ9ocjRk0M6wRzTZIJAJS6XAIUR56bRaUKkpXVC88JcGRKMR+CcjZlFqBENmMbYpAuwWUgJXu7u4oDwhsL+p5UZQismQA9PT0hI9d2SVeisdH+qLg5OSvLoZPnyAxhU4iUDX1UErTftsRpRN34Q+mqk2K1xWUppuBg5xfacpDGv5Gec/mbdu5VyPmPIJHZQ+5urrnl6z5+l+e/N1jGzZ0NBZ5p1AuN9f3vmnWyC++Zv/j9p9UjH/WT0OiPfTmohDKVdHEyznm9pOWiELKgVcHoWdnp9JcNIKpqpsQtTGlUkC6t1QH/XKRtFQLqkKOAy+XisB7S5F6IBNaBh85ZWhDpae73FsuhY8ILIVPUKtU/5VLpUhKkcR/PUophX9VJ3rwVMUqCWLmDJ9imtlIQ4zzY3u1Ny4Rqpqg4TRmKY9y0nJGuItxqePaF77s45MSvuXHFbW3VCh19ZbDZ1/0xL/61NNbCf9KcRvVReO6YVoUlYbYUy509RY7e8oTh5SO36tleHNTmBzPmCLw3Sl3/o3U39TUNPvAyZ955V6fO2Xie0+eMW7UMF2O3QsB6RC4IE+qmIvEYhiVKsTcQM8R0rGKABFYsQ7MISm3YqgEzAcSB4L3AERiR58iLiIzcNWRknmKXAsQ79ev1FGIlZcvwgnUyRK4uuZefAErcJ0ykxTRm5ltyA0UVE1jVnjJSOcTWSJdiCoRA6KWkAHRzljCUz93xcYbHl36uiOm7L/nbupVY+004tYdHTc/uewHty9dtiX8QhqmukJx+ojKe4+feN7xew8aNAg73mSJgHSmRG1Jw5VKIWotmaWjyCyolHJ3QRSVEqUEa/VADOnpNIAHoEhPI7qHC54AAR5lP1A7oieomprTdjyIgsUsT8xZXj1A2RzR1aVqNILKps3bL79t3q0vbGnv6m1qKPrrB//XInyJN3x9tVwqFDd01nV29YQvE8dtF3vDnwouNjRxWQufMdhbLlRK4Udkw/vLXqqcPw5Zc3aB7vh99GmjG95/yvSTZ06pCx9mm91VOHW+OXySpRB9wmV46fCN0u+tRtUGLaptEJXKnEvTCWmVqDkaC1cqXaLStIuSdiKzFHEiuqsSMXgbGpgq1tVLVTw1S0yVODvbKiJQqphOjsbsWLwQsE1V9aaNEq1QldlVGcD69evHjRtHaSC8+c1vvvTSS6dMmZLl/yNce+21c+fO/dKXvpTl/eGWW275+c9//qc//SnL+8Ntt912//33//u//3uW94fTTz/91ltvzZIB4K+1ZjcYEOGM6NRYyUEGESny2wzXWVYKPFAIq1ZvbCs7qmhra2tvb+dNN1WuOs3NzcOGDRs+fPjQoUOHDBmSzhHS4bULpdsA2iQe7QHvvXNXfv0vzxy+54hPv27WmJZh8Q1ldkfRKFrit3/q1mzYfPntC26Yu3lTe6m3YRDPP0PKpaOmNb/7pKmnHDC52NCQ24kIkB7m1DxsVFIVYFZqg9tVElcVBUJUSUSw0zNFhDSFaJS4iKehAKW5IbWwoV9nrUjqY0yBghnAfR7cniMAAzalRKDTUttI3NHRtWTtlu0d3YPC74Jy4Dudt//rwP2OvdeFP5//xMK1f3hk1fzWukpDU0Ndqch7wfqG0YPrX3/giFP2nzC6ZTBHHd5r6tKYP6kDoFJp7+oqFip77DZiwti+P0Ov8ymL0vRkOsogdHd388gFW7du7ezq7Ozo5I0tryMHD2oYOnR48+AhI0YM54HMwzlriDe0okYper4NAAV4V0TZwhFXG3cB+dVCCuFwVEqPFGim/KkO0nYpNojoLGlLErWQGt0l5FpkkKiq5kAAJVchmmlFomwqSRwIOLUuPEzZeXteBSKd+L/XyOzsiOi8pMTwKYPXeqynNiGXGtu3b1+6dOmiRYtWrFixfPny1tbWTZs26SeAdRcpFotNTU08tMaMGTN27NipU6fuueee06dPhzQ29n3uNniJ66IQEav3ksqNDy+55G9z23vrPnv6fm88dmpTU3apIwLdk8DiVeu/94+5N7zQ1VOoG9Tb01nXMKS+91V7j3r/K6btv+fY+mLokhNoUU3QKOm6B6eKiPyCuLYns05F6gEeIo9EQOp7v3SRNEp0RElFeGpOd+Jom4h6U4Uu6UpFclVSJvc7P9cC1KVDgxDVpRIEqEtOIpBIRFdJ5P+NKJdLj85b9ct7F927uH1rb2MDxxKOp3ePYZUz9hv5pmOn77PHrp7FXhQ6S0SfWKXpadT5T8GzJ4/cJUuWLFy4EM7jd/PmzV2gp9RTKRYLvUMaehsHDR46bPioUaMnTpzA884ee+yx9957T5o0yQ/hdBWl/epWxAUU7cpdghol2pCbD1JeW0UBUtSuqDRa+jy5kkQpaeo7PCIRrvuzkHapqlQxvedLkc2NnpzCTiPXK1jRlkilgP+9Ru50TtMTmp5KEd3AUoC4TqhSGVKbpwmUeLH5zDPP3HvvvU8//fSqVat478hFEacM8ntdRw3UJZPL5LHHHnvMMcfstdde6StTo3ZdQyXtkHWvvGvJ9+9Zvq69NHNM8yfP2POsQ6fEhbLVmdHV3XPD48t+etfS5zf01NXzbjH82MOEluLbjxj79pP2bYmfD6AdGt48hAh0TkhTxcSnSwQxjsnmgIF6VVWqKhHFJAcvlBo8B6TtaVTV20j3qapsMkh0Y7r5nN8eYJuqxFouqMtrAaW5U5drrJ0TgBgUSo7/NLSfLElOspEz7Jxqw+m6A20j6DvaOu6Zu+Kyu1Y8tyH8UZnwcYmV3sbenmmj6t513OSzDpsaPo+++kMGWkgHDuA+S96Aqi5582nqOeoqlUpcCJ999lmesIi8tO3o6EDUqNheiR//0xD2HL7qWx9/ECm0U+UhPGrUKJ6AePyedtppEyZM4CHs/YB06X73k0btCo4ndaYtGDRftjT1nNqowxHwuwSnBEdPRelE6VJi906pxnq4dM+xAsmNzQ0BbtFmgOZQgqtFJE3TLjmBpkE0QdHp/14j++4KgJSzqZhJVTHl6kpFp7VmcV5cLl68+KmnnnrkkUc4Bt5E8jaxoSF8/0O3B07fNoLHho3GORBEXVN333332bNnc7Hcf//9eVkaf1gmawFeF6TbEKTsaO+87OZ5v3xkQ1dXmTNx2OTBX37t/rP24t4QzZXKyg1bf3v/kt88unFLR09jQ32lUD+ovjxr4uAPzJ522sGTC/XhEzTjvAxexSuK1EaViFY4NFIOX7pKIsAicIsiSm0qW3AnvbVVl4DarYukt4huBaCqNgxRFbgrFd2VO0AU+0EqShcnpnsASikphZBqFaWxdafhgpUcyUV0VWUw0aIgrYpDtEMIUXMARAbgHRJVIqpkjxVgA1FL27ZszYZf3rfi7oVbVrZ2dnOZLBR6Sr1D60snzxjxlmOmHL3P7sOHDIqNWa+W9gRieg5TXakUoj0q8Zjl/eKcOXN4fnn++ee7u7t5L0hJx55fSMNh5tUIuKYCHrZ6sXvYYYfxLKxnA4YQQbq6CDFMS1CryOyWFFI8Voq2rZQuIL2WiKeKiEQpnqaSiKoGSupJDSpJ93zDOki3rSNSr1N70FVNFTW6lNqAqkDKy/kaGU5aJkSQZ6zKFXNnLWMJZKCkaq1n9erVf/nLX/7jP/7jsssuu+OOO3h0DR48WA8wD3evkeoCZq6FvPDk1ejGjRuvu+66b33rW9/85jeZyQNYBkVPMBFPSVep3NpWKpW56/DiuzhnbfmPjyzfuqMjWnqfW7rmO39//oqHN23t7m0q8tCtDC2WXrH38C++5oDTDtmTC2S09SE9EEGiiCFbTjTQMQiZlADRY6WQqkWpoFQlIU1tkOg0HSIdeFS/aS0wgJzBCoQIF1EUXBIHcKcuGfJkSUwx5ESgrnSOiKepSzyFW2RWtAKiayfYkOMGqXRgknrgTjE4tZnnMviUCbt96lX7f/HMaSdMG9pYz1u13ua63q66QTcvLn/1b4t+dcfzC1dtpEftmqOoIbWgKohLNGhk3SVLlvz0pz/92te+RnzmmWfQeQjzYORpNyyTLBF5+E9kYSJiWtX3UIYMGcJD+MYbb+QhzDPszTffzFtSDAzUTDjQfkilkEpJxbQEYrEPUog5m1NiyoVo6TtpaQkodRdEinUrBmlOwSMiXS2KIip5VJSzCRC3g1wJeBpK6oQ7ldOcSEkikA709mMXoOtFPS8K3e5ZMgBYxZsfCC9lzosOMTgPO/1tLHKm6zUF0GnKjVMa630veQT80uGO27Zt+/vf//7LX/7y+uuvX758OS8eWUJVAZ7eHgKiX9pkUoQUYjwPYc6OHTt49D7++OMrVqzggcebS70alVkEpwhQu+KmbV03PrVucWu7Xgb3lgvL1m8b0VCaNKr5jqdXX3rLwjuWchmtNPV299Q1Thle+MCJE9936r57TxrD+0yerJgAaCV6n+LWa48i3QBpytM0bQE2AHh6GxElivtG0QQfOwRFkdR+EaAScEmNThWZL90DlaYeRUSZ4RJlFjdIPVMlc0dDhlSUoskinpA60xZ0AZ7TiSgSlQo+TAGDqujmsa+PpzYiTg5TMRqzkmyIpIIOAVGN0RvMjmBQU+P0CSOPnDp6ZFN5W3v3pnbOIOXeze09T6/c+vSKrXW9PZNHNjcP4mWoNpYdjoZ7M+K54SbC0qVLeTHK6/fbb79906ZNNOohRiltRPeG+W92XPG3qrSWqtLT2NHRwRKPPvoob1JbWlpGjx7NA1nV1K/58NpR1UWzo4MDGYRaHUW7skiUqBbZiCJUo5xBKaXQnKwFZ4hKIq4qBTYLaVU8juxbThOkiEhRSVFwVU4iY6VDVJUupH5BBpmtrFu3btCgQV1dXby3qQU31h/+8Ifjjz9+xIgR+hy4/wG4+D355JNr16499dRTeUrP1J3Brp577rk5c+a8+tWv5jrS737wPP/888uWLRtoTk9PDxv+9a9//c53vrOjY0ep1N3vv8bGpu9///uf/OQnwxmoPWUmKqVnUMjZRAD7091OOocxf/58Hl28QtyyZQs2SsTaXojTtJQitRFRTEC5XGb4xIkT3/CGN3AGuVKm5lwjgBOfXbbpC9fOe2rVtvCLHNy3e3tLhbo9R9TP3H3QM2valu2I96/e3uaGyozRjR85beoZh06tDz8c7zEZlOvAgYZbVxonZU+OKsmp1IqGWKGqKAXAdZKVaqYbRdAh6WSibNaVprqXlpLqiACuqrhL4tZFnJqnkA5Sm1I/ScE9DYg7FdRom6sogO1pWm1VZCDg1Il1SnRX2p7zAFdzXSDnIfXNp9QkmKpQSgmIyynGw/6Z5ZuufnjlnfO3bG7rLsVPSe2tFMY29559wKjXHLHHQXuOHdLcFL4hGGekE/pGVZcgShThKY9nrl/96ldPP/00zzg8kfnOIKjXE3JIq/06nUL0e99Tpkw544wzzjrrrOnTp0fLTh6g2zQdAmpn2qNU0Wk0Bk70QCBFBkRzRVUFz3FXKqZIRZF0/2oHfiilurigNNfoKFGc+UyzrtSLpn5DXdLFtR/hqKOO0q2f5TujsbHxhRde2GOPPbj2MDxT/0mw3ObNm9vb25nDhSNTdwYe3nGtX79+xowZ7DBTdwaerVu3sttdzGHDc+fO3XffA0ulnkyqASepvr5n7tznw+lKj0qnxgonq98Tmp5unUqIuE8u6Z133vn73//+iSeeYK86v+nAHHGallKkNiJK2gUBPMx4HXryySe/6U1vOuigg2JfKIkAtYC4ycqDz6/5wl9eWLSpO/zlhfDnFzgwfXhmQLEQ/gbgkMb60/cb+Y5j9zxqn/GFuuwAqTJKt5OOWsPjIhnBo6hUfqLSsEBSsl+pONFr5WJqS51KQWqTQgrXHqTbUNsoT3pcqU0cxNPYdyxEG1KS09NUjd4VUNUlcetCTpHTYq4KUJyqKqSieNprrqo9ioLPgNJapAOVeohvXJ3n2luQKEPaEosByd2psmFL281PrbrxqbWPr+zoKiP1lurqBxcr+49teO2s3V97+NTdRw9jpLq8UOzNloCIA62yceNGHsLXXHPNvHnzMEi0U1BvbXwpVZAzE3kIDxs27Igjjnjf+9534IEHaqupB57eW1JITD1AYyUqAhSdc6U6J7aJEAUrIuLAe0vXUgRUvU9FQzaI1rVfOpESxGNTm5Q4Zie4KyVyijAE7l2l1XQPqmqCe2+88cbhw4eT9gtur89+9rMf+MAHJk2apBc6/wPwPvWWW25ZunTphRde2NGh73blwTX4/vvvZzNf+cpXdAhZIQGeBx988JlnnvngBz840JyRIwcfe+yHOjp+ViiEb88NgOGHHHLuk08uDmcgXQkOOFNw3TASdb5S+GaDq8V+0Nraevvtt19xxRVr1qzxV5A9RF0+SBGnaSlFaiOimBgobIPIo4tzffTRR3Pqc34iiFut/OOJ5V+5fv6q7eXGYj0vvcMP44WP9OIiGfz1hfKkEQ3nHbrbW0+YPnbkiCDrZ3lq7txWVEX0ikQ4VSKIHdk2FHNdtZOBRJdQcEqP9eyZGkhRKn+UA9KZEE/wNux31QYIUQaqpIpKNQGoHRtRoKRGpTm/q+haKJfi0Uwgv20ocGK6aK5RLegyxL4sFTAo2kwUkZMuzzfZdZUIl5LqamE4ipZQdLvNUohpGpasHr6GWIfEUnnxmtZrHl15y3ObV2wpdfUWwh/Y6u0d1FA/e9+RbzhswlH7jBs9bHCcly2tFVMeqwHLly+/8sorb7vtNh7LVP0eArOIQEntihLtSZXUKRE4lVOcnfBUe8ghh5x//vknnnjiiBE89DJQInosRGdAhP3LoIMCOiKNVUw9RHGlnsA0FLWrC1hMFbg8Ip6QNkLUC7RPkVTxKHGJpBjUrmnyQDREq0iUIliHe5p1iKK7RLSKYmpG4Z0Z7z3gA+Gtb33rd7/73d133z3L/0e44YYbnn322YsuuijL+8Ndd931k5/8ZNef13rPPffcd999u/7c11Gjztyy5aYsGQCzZu0xZ84KSN+jwmdEKeeIiGhIF1Q1qLqRB9XVV1/985//fNWqVekXZzQEOE2JU8HTjNRGHIiwHOBc//CHP+SFSWdnp291z5TC9XRre1dHT1kft5J9EmudLoOhPHpIwwdO3OOC0/aPF8hQjl0BPigTrwLkSTk2K24xVNL2HEUo+d6PYu5ptgkSgbiVWMweACLSIUBbSkU84oYfRUrVpdQDIdqh0vRIbRAsimu4RR8mgMujaBtgvicIcIlaOm0UUg5IcwrQcUn3chZFUBRFvA2IWqSIA+0HuErEoC6QngFDxw7cLpJOkwEgzpi024dO3++zZ05/1f4jxjTxkr6+0tjcUWm46fmt3/jHoh/cNP/5ZRt79cfD4+q0EJWmqy9YsOAXv/gFz1mbN2/mIewLpGCnVlcqLj0FonRXa9ulmLOrhoYGHsI8h/z1r39tb2+XX5AHwD1TZ4+UXojuPzo6QX7BXDaAk6gJrmqO2lOoJI8aRSg5tZMos1KviCgdhS5zCE7bwpSd5xhpqsYsqa5lRb0gzKpy6UTpUpSKew+gq6srYwOA1zQ802bJ/xT6ZmGWDAA8L/pW9aXM4WVYxnaF7CbLblTBZypFv6LOb+1Zhlx11VVc5zds2MDbXt11KP1ToOWf6qo18xhbuHDhL3/5S14Ia4dEbU+EHLqlvdRVQmFCMIUfVK9UL5KFQqncO2xQ3eDB4cfofS+MW8uGDMSBOJEu8XQP1i0KKuWALrO4o4igFKRpqgPSdI6qIkA8FjPkRAjtIKenMxUNUvnlUVTJsOI5QF0QlSRaUSrEelBoSScIngNSIojbIyKuKJJTgETBiomgFJhDvMNoCbAubqAIcNuElGMwkTxi6OBXHjL5M6/e94ITJ0wb01BXVwp/sa1QWbKl93ePb/iP656+8bHF27Z3xNnZcIgjWLRo0W9/+9tbb72VZz0/fQvamKKJhpiLCCkXUoMIsd92lua97O9+9zt24sukSuKGWrIkghQxS6qIrX1H6oVAaiZNSawHSBSUusvV2kWBqmFElRDl1B6ISoE9OV2QKAIgTiGpKN2wDlG0biitFf8XfddInRHOoF5BmKD75KIIMhNNiLwl5+Xnr371qy1btkihxRMgUtwCF2xQqmrOA0ilEKkK4hZdgvBy+NJLL/3b3/4mRUAPnrq6nlJlw45SR/gV5/rwGZhMrg//wmcE8ACtr2/tqPz4rpVzFq5Vl9qFuJ3sZaO4xkJkUAmgwK3LZsU6RGfVJXgaESkBmaW4qpJgg6BUJd9wKPKIuAUiLgLoClMipIgoaqY8ShU9DeLjUgnkJkjRoqlTnCjA07EisZKdCiCPdHskEgGpETuCTQYDUWalOIm5o5CYtosTgT1EGiGqShGRGQIg6PIIcEFzUBSBFItOMcvA2749xo16/+kHfue8/d5z+Mg9Wxq5heoq4Ted7l3aedFfF37x2qcenLdqR0eXllC7sHTp0h//+Mc8ZLgsaUtZ4SVA+xGh0SkI24rI8qpZ81MdpOnq1asvv/zyv/zlL23VvwXI+RTC1mO7I43SIWw+9cgAEDWfKAWkK6YcKJVNRJPhOvPiIG2U2QpEiriiCLq4DJqmVIpFICcpJQiK9iBn7MiIU2yOhgZCNM0pEWiybf+L7AEm6FTqvAMTwPnSSUwNVgAPquuuu443kaVSibdxEgXfTgC/iAYCFN0YiFIAaa0S+8IE/OKCDFlSTbE1Nja2trZy2X7wwQf1M07S8RC7SqVNbeFXO8L7R8T4zpopWOJ1sq6+WP/spt6f37Fg1brN2bvLKtI5JmHtCKrsEIR9RGfOA7EOUgUemzJYBwxUakUkjuyDDAaG9PYC6c0q0EXUfCCbiEogzg7QcbkqDmGsU0eATkvojEBPIUURc7T0wVuCyyBFh0Cqkpa2R9xVtYh4ghRS2RRNKBF9olwFKrkXkOKEpEcavX2PGqBNissmUYoRR4ZN2iwFKA3Tq5DiyfJoCw0NjYfvPemjZ8789OnTT5o2vKWxFP7QckPz1p7i9c9s/sb1z/3irvkvrNzQWy7HO34YywXpj3/84/3338+DJf36qhdiPlxRolNVBbhOMjwadzpXRCmpTSJcqSPPJGvWrLnmmmvuvvvu3Bz5fQI9SkRmOPD5EVe7okHJG9gF8OCkF4TRcS0TQcvJQ6oSCtDqgp2hp3o4LkkRV2SOSNolqIQoneghmL03V4kY1AU0MAeq2m1sfVmjn4eoCWfKXLpJLbgO8XJv8+bN6QUSPWMDQx47X0rLQEh74dzGTU1NvDS+8sorFy5ciAJ0D4C0d3Zt2tJeqStml79woeQaWSjGv7RQVylzx2koVO5ZvO0vjy7t6Ap/Hc2TPUSiScpTRcQtEOuKIkJqM08NOQ7kFKRk5WQakC5F3FGAOxX3QCAFpDydD1dqIp6LRjBFJTeQVFEpECGmTsMegEEeYtoibpthsyE9TEx2IsBdJYUoWgdwAR6bsoGCdCFXEtQCsvylwSsK5qNGDDn7yCn/euaMdx41btLw8Kf1wjHU1z+3tvtXD6y65O/zH3thhb7B09HRcfvtt99yyy2Q9MkROzxMj4hb64MNNgMusd0RLOdndlDrN1AEcaJs7AQsX7786quvXrBggZ0yEKWICLmqQJpTDPQ4Mhsip1NgXksEUinqFaTHeiCpKD2nAHH7IYJEEzWax0pfSamQpubqTSFROlE2IDFLXq7gnOx0jdQZSU+Nz5pOJdGvSgBcVe6+P/nJTxYvXpy+/HRVoFfQBIh0HgPYiFZqgcHVMKKKdD5jGZIuqi7i448//pvf/Eaf4oGCDbKtrbRuW0elUu4Nf5Kzt4yvl9hbKtSX6oqVunrGFet6t5Sbf/3Ixj8/+EKl+r1ibRiSbkBrpUDUkSraIKLdeg7RHutKIZitC6TAnBLAqagWEaoiKegS0RARQyVBA3OeWMmglCiPtyEiKBWkxDGhJacAFOlKKYmnNkh6gPZLj5bMD+BahSj4cIjiBimgpJipVaSjpGBLCToeIdVTEnYQodSgBTF3XOa54zIRNE29cbYfCHWNDQ2HTB/34bNmfvO8/d5wwLCxzb2VcqGnWLeurf7xlT3bu3rqG8Io3qjxdm3Dhg16jUuvIqMgmoktLRGFuFBYHQwePHivvfY68sgjjzrqqBkzZgwdOjQ8uuJmiJqmOUAp0AQIUZAO4SmFCU899dTvfve7FSvCzxm6qrFKLYJoCanma10URREaBVJi7MuGuConkQmKqqoFIp42QqyIIKajgFIRgAdAZJCiSIqYnjcAsZMIB+Iu5XptBpSAjkUKCCPiECK6U3e9bBHORkYjfNZ0apTmzpTPHYDj2bZt2x/+8IcXXniBe3M8t9nMlKfIrQKsgJRrlSzpD7WNinQ5ZQ+8pL3jjjtuuOEG3dcjKkMGNZy0z+jT9xp2xB7Ne49pnDC0fsygyojGcmP4uZ3wh9x7y+VSpZ532mvbypffueyhBWto61svQquwBCS31Xj02eF7PwCx34NClC4C4HSB2jmq5qCSYJudaUs6MBeFtEtQCphsJdVBbqzgXeXMSrlFIDloCdnSdomqei2Aojlw9ORW7oO65FE0LKbwukBKjqRpqigFniASvQESU55Cx1Vb0v4h/UbBCxHFfXqHNDedcOCeXz73kM+eMfWwiXVDKpWm+tJZ+w456cApdYX6RYsW8RBeunQpq3vbKTzT59n7EeA8/PfZZ58PfvCDX/va17761a8S/+M//uMjH/nI/vvv39TUlN4ouSXoFeAaK4MUwK7gXMV5p7tjxw6JILWZsxBcLSqJa7IUkOvV9mQAwVGFUgzpzLQ9jSmCNQKz/EYqwok5A4it2eFoe+JpNZcCuPcJSOmCIALPCT0R8kRvtn8rDKdF4ssZfafA5yg970qtCOg2Q+6JyHWJiJsItQNJ7ZQSO/oU+5XmiLiHyEwUAdzS7e3tf//73+fPn08abXUTxgz/yFkHff3cg75yzoFfOnvGRWfs+YlX7HnBiXu+5Yhxr9pvxPF7Nh8wtjh+eP3gQk+lp+v5zfWX3bF0xYbNzGWCh8fFA8whA6Hfar+ihhPjyL7hUlQVSDNW7RLMU1HQkCxJDGGBms1IxOMuRUF6lvSH2J3500Zxt3uIdaWx3sdFapHaapGbSZrjMhCVqgSU5hQhy6tjbVMpNRj2/LPQNPfm5oTdRMClixPlVMrzJPXdRg0/99gZF79m/9fPGn3KtOa3HD25sampo6Pjrrvueu655+x3bzofaAkAV1Ug5QI5c+bMD3zgA29605sgkydPnjRpElfHN77xjRdeeOGsWbMwxBkB6konxKnZ0iBTq6tDaN+yZcstt9zCPkllJtoDxIkWxQ0pKgG4JpgQASWloNYmRVFKSpwCe4D1VBRQck7S3LQcUYucgkuqmkjMtYvEvuAUAS5JdHyZY6drpM+gQKpXLjkdcNWRuHDhwr/85S+bNm3Sm0hVdWZru9LXLwADTkFm8VKp1NMTvgAKEOlSI6XQFhX7c6sodVWRFPBi+frrr29ra4MjcgQjhw3ec/yog6aNm33QHuccM+NtJ+73wVce8LnXzPzqGw+69M0Hf+f8Q37wlpnfe/MBXztnv0+ePHHcoMqTL6zQrwFpAkTc8IqCtm1YwelzBUekpFSHBlAcZTN3KkKEG6QMAdIdAWK0BFgnergUbF4IwFVSVVEGicAckiIVOWTxVNQcAS4PgGu36YEI3h5EgHs/wVEljrkUc9QCUNSbRuCq5hsY7BHXHnKi2sU1QRyiVIpIHBDgFTkPRBk0KhqzXpeIlHxfkq5eUnXJIz9oamw8ct/Jn3ntwV96/SEHTd0d19NPP83LR/2mB4awWHU5kPZ6VLT0bQlMmzbtXe961+zZs3nLqJIi6Yknnvjud7976tSpejjTrhagVGYAZw+ar5J1gPL888/fcMMN+u03H6ZKgg4BhS4fjpRas2xZElNDqcXcNEZpdVUBOhHFy6WTgUrag8wiHgtkcy/RNnEtKoXoElCXd6UqUBdEG3BJfkWA4p3IQyodNO78x3prgflFPS+KhogsGQAYtM9dAA8XoywZAJyVjL0E7HRb+gSlIvCZFWwDP/nJT6666iouPGzdogyprV9gAOI4uRUBc4YPD3+yHL5161Z9nlB6XuCU8INdL6GqliDSxWvbT33qU6eddprFOIYJpP3OCXqlt9zVU2rv6qn0VlqGDeY2oFG9OIjiEtPtiUjESSq/REUpqsZKVkJRNQcb0gnWTQCLiqCk1WgPUMkxU6uQqCpgexoIp6rd2hY7+oCiQ04hm/1qB2m7RBRXASnQidUeEFNnrGfHS5o7k94qaRiXLG2ikszpEpqQS+UXEdRlIj/ImT0nx+kiKgWUgAcS00MzR4eEtp2nKbUh7ZVYtdESPq768ssv57UjHr/Vo0ZXurREzzfBo9K55577kY98pKWlxU6IbVyAf/zjH//6179mCYuC0rBAcp/RYeY4hKvsyJEjv//97/PGFEVLEKMxgwZq83ClQGZzIik2i2AXZmA/is6PTo50exwpyUmMxb6xImmUATglegmJQFzVaO8bBfd+rEdL4CKCdA33EdkMpECIeG6//fYRI0bkhhiUuOk/9rGPTZ48mbc3mfpPorm5mRdqi9ev//jnP+/f88lh0KBB99x2219//etLLrmEtN/94Ln33nufeuqpT3ziEwPNGT162AGHXNDTfkOhsDWT+sHIQ2Yd+uScpeHMeCUI4IyIS0xPn4lOLmT16tWf+cxnnnvuOdL0FAv2DwQZiAzk3s+Q3Xbb7YQTTjj++OPHjRuHyJvU++6774knnti8eTNOLk5EtdAuQtS0geBVABPOOOOMz33uc1yG0zsHsfbAfZio5FEOVZ4Wcot6CJwWTVYJqGpOxJO2GFKANwNckqL52cbibdHvcIAOMEiMlmwzRDhEIpAiYl2iB3pp6UCjAERdKhFtky7RHpBuiUjJxKJBms4hql1cVeAuKcR0t8C6UriIFMw6n7KB3PECV7O82gvkd8nOXLsIoqpAt5FKaQrUK5LGVPSiUiSCdDMCJaJPHdU777zzO9/5zooVKxBllgeeI+JENUqBA658vPR861vfCpcO4LZB/vGPf3zpS1/iORRuPfW8KIcQu7u7ecP6oQ99aOjQoVIEPP0er26+3DTH1GOiUeZEN0LSqtvTpd1ikoMmyC8DE8TVkjamtpwuTsxtQECXYickd1xWiLEpe7CrKgIeeOABXpqQypMD18gLLriAC8EuPkb8RcE18oY77lj44Q9/ZsqUtupmchjE3XXTpj+ddNJ3//M/ObB+98M18q677pozZ85nP/vZga6Rw8eMOmfGQd9qG9l/OWJoofz+WcMWznk2nASvJMIZcQp04wGdRJ1BCDburNdcc813v/vdnp4edDe+KDwBwliujly69t13Xy6Nxx133AEHHDB4sD5VMnzE0fbt23lRwGE/8sgjK1euRGQtAKFdQ17i0jgBbyX/7d/+7eijj0bx/UMD5REBjKVE1HxKUtSCoi7zlBDtjwP6zrPEnB9RJdmI6BA5paiqkluUEpUKnu9eRTmjJRsI8RwrBilDIGqUQaLnp4bY1A9oUa+iWoCHwF2yqIGIRBSIuXptSHvNZVYVeBVxDQFugUsUtyhFAwG6RM0RAZTEidZtcztKugpIDdLTRohLKO4CcJXk15B0VDrZ04AUwNu7b37zm9dd99dyOWsBIlorXTFViCgqMY3H78UXX/y6171Ouqsyi99///08jfoXS+RRSURIe01SP8/CU6dOveiii3gIowMfoNP0VDjSa4LZfnFEuEoSFaUrTbliOt9mYqoT7VcVrlTcnrQd4pJ0IB3II102Imk6NhoznioQ4O1RAumKSon2tLa2jh49Ohr7x/nnn//tb3974sSJWf4/wl/vuWfu7NlfzLL+cUeh8NP3vvfqK67I8v7Ayz7eWfEkn+X94cyRI27auosPNA/YY9YhK+Y8Gc5JJuwMCkTOmlIA9wkV1q1b99BDD3V1dfnrJ45CdPWJuZQluLhy2T/44IM/8IEPfP3rX3//+99/+OGH84KCErcNF0hsvH6ZPXs2j66vfvWr55133pQpUxD9agWnB+aQiuLaP9t++OGH029pEJljoANxH7LuNCieBuSB2CaCIr840Y2CVrSiVFxQiSFuj3I2J+2FeG9SfETiRHGJ4kRPkCLUcrq0hP1AoxSlQ4CqQqqIG5oJ4Go3PJNIGtzxziBFUAskdgSIpwNTj7hhg2Cl9hhJmSCoJC7DQEi7MilOE0HUZKVAJcVUh+vmM7yl1Aacquq1IDooAa4twWVYs2bN/ffe09PT9yddifbkFkoNqQ5Q9HFxEFVlQ7Fz69atehOpFIgPFFMC4ErZ6vr165944gn/YIEjVROJPgNhRARielbFUwNI01xvDlQ1XxyPh0uxTgRUUZSK5IDoLQGJAO7JIOUgNxboPiAuomlKZbOBdisppENe9N0hTj2j/p+AJ/zst+sGBvvIHXstGPPinkrfuR0I+ssWIHs4AU5H7kylKdxEfNGiRUuWLLEeTvZLALvnGLiyct7322+/T3/601/72tfe/e53z5gxQ3+jQ9MYq8etuoYOHcrl8+Mf//hXvvIV3tePGzeOxyRvZJkmw0sEA3ntzDtxnh20Z+1fVUAqRdHI6UqBFfVCrKQpVXHBCwEpOZ4jgrrcq5kQRyuQlBNFUr9IColGpkY4zelCtA+oA3FHoVYRUFLR2wYpT8VcKu4opLqJU1VB2gJIgZYAVoAMIMurkJKLKfE0QQpR3CQWd1o6hRTiQAZBJXsgQDrxscceW7FqNQ80iSmsxI6AVBSc8ljmqWD79u0aK8QFs6V5yM+fP58HLJxSOgeewqKJhigF7JZnj3nz5ulP0oKwTIRSQ10qme8iGlpU3AZBYm6akIpCv3PS1AaiEIsZNBBYryWaIDhVCaQTUj2FJ8DtcWrl5Yy+a2QKTg3njisQJL0m65oE4U7/1FNPrV271g8wnc30nMKBbgN3EUeMGHHyySdfeumlv/jFL970pjdNnz5d7x3lUcwRwNJcKQ899NAPfvCDv/zlL3lnyVVz8ODBetUgj/wCi4qgm0PYMI9nLpM6OkQIopAbQiljketA7EmJSqkItDEIk8WxaY4m2+ydEBGla6b84kCjqEIQ4VaUElUlCtY1AcgjmMsAkUcTPDk4IlBkgKOrJL9Eg1I0BohHe98qcDmBRMB8ICepD1MKcGPqhHiCCKIIJR0LKTw4EiDKpv1rmhRFIEVrQcRz50dEVbcAlYBSIFvOXysCD5EBYEAUkVmpPDarESLA3Wjwxu7mm2+GoMtvg7mJDWmM3qz0yCOPPPTQQ6yi02ioeu+99952221KDVJNSCGdCHe7ooh0rrj65CwpgKVliMYAduKjliInimyO3jZcBplTolFAqYfLQBSsBGv1XgEhiudASY2AVFF3LaU2mGhd21yS6BTIoNQlKSZE4KOLfQFK+93zyw3ZjWFwXog+RxDfYMC8tbWVa6S/E7kLYOZEcyVj1Pjx41/5yldedNFFvHc888wz9Zfh0OPIALeYC/ZAwOTJk9/znvdccsklvLM8+uijW1paWEI3p/wmIMfZcFtb2wsvvKDv6EoUmKw0FQ0duzYgQpTTXHsgNReRQWBOOoFo4nVFNEF+oFONaBuiIyJEJRuEVBfUYpAKKgHp2oAgTtR58BCbgU+ROcBJRBQRcu06KC0BAcGUAMULmSDq5IhLBJ6sPdiT425BgfsAldoGxCUSgXRgrslOdUTiIGeTorUMGUxkEJcBpHPMpRPh6kobzYG6AHzp0qUrVqzQz8G5VyVHkC5qIrgKGPWb3/zm7rvv5jmBlIUE3mJygbzqqqvWrFlDKrN6c0RIRYHU2zPZuHEjD2G4Eb19+wEcuKJEIsjcyUK2KSKmNgFFE2QQpBCzvKpAOFKIqsB7sE2enCICMMMxSDRRFXiaIqk8sZiNApqjQ4NoV7GStbhRcFUgTae9PBFOUUZrMNAJksgDbOXKlfIACJDBQOHm4UECdtttt9NPP52r42c/+1mujqNGjbI/16iBghUit6UV3ee44r7xjW/8whe+8LGPfezYY4/lXSaPT72txFO7H4DIHDw8pLdvD9+zRfHYFIgqmXiguFOAsosowEHa6KpEII9EgRQ9SyJSg7gac0RVKwZpqsBtti7uXkcBni4k7tR+pfDUIKTckCdnTokgbptWzMF6roruKMggPzFXUmoCoqXPk8I2G6I3U4xd60RvKTWIU/IqhkTx2hIx9EfIaSxfvlzf0hPkB2oJ/Tv3EiXKkyrFYpEH3TPPPPODH/zgt7/97ZNPPsnji/mQ3/3ud9/73vf0ZRs/RyvmoFVSAqwI5jzS586dq++B4UltUsxTuCq/uIhKNigKobyz0zpcLYb01GDF3FCvxZSY1yK3ouCW2plWBOvmQHsDHm5F6csZ/VwjdY3RHdqnLAceD/r2Q3oexWkHXBe5FPHgmThx4jvf+c7vfOc7X/nKV04++eQxY8b4oYINwhARKYsWLeJV57/9279deuml99xzj7/rIIOidgXnVfDUqVO5Uv7Xf/3Xf/zHf5xxxhn6e9msrplEOSFAG4asWrVq3bp1GmVRRJySrsqkRA3JiVIU1SJQ0jRgRVyNgDmpxySdbF2iUqKqRE1gFClcRCVi6KyOgsRKKAFS9UoJvuTVrjglSGwKcAme6uYyKCpNdRGNJQLtNm1RBOqV2bBoG1AvoqpAA00MVQ0rsjlKEWwQJ2pLcoqrZHiIb1+JAlwi3I0pF5Sm8+URV0pMFYg8RJYGuZKrUri6LF68uL29PVcFGLQ06DfFKSglYiDyYOfN4h//+MfLLrvsRz/60eURV1999dq1a2XGBnhaUApM4vgwGYNmOhqkGMQhCxcu3LRpkzjH228LUEm6lkuJAE9LijRCFF3qdyH2TAl4DhHdS8fiTooO06BqmwgR3dwpkV4pgnQBsyZDtJb3r6qHiMjvUhoR6RV/OYMzkD2rAp07ny+noVaDJUuWdHd3+yQSDRmGDx9+2GGHvfvd7/7Wt77Fe8eDDz5Yv9GhgZk1Ik259H7iE5/48pe/fM0111xxxRW8QeTBxqtd3d5yKqJ4daDvcV5yySVf+9rXuGTut99++ohIGyCCujZv3qzfJDHQifawnFYEOc7YdOkUEvG4GsYlTq3iaYIMqSgb0D0+14Vf3DYUcwD3Q0VOIsjtRCV7IOIAgiJdSOcD29SSqclYoBJRKbBZNnFiOsSiV0yrEj0T4ltZXMR+YHMtck4gxVAvxFWntSeEiEF7EM81psR+AFfKTI+1Ux4IHqrMl5noY4eoFBqqwwFVeByw05Fu27aNh7AvV0TE6MoWFVJd7VJEpBCHDh165JFHvu1tb/vSl770/e9//9vf/vZXI775zW/+8Ic//PrXv37hhRe+4hWv2G233dgkfhBHBsA1JE4NMKdqYg7gXI95jauXufKnk6Mr6HCiUqDzE107ldSLksJmeQBcJ1zIlYjSnaZAEZRqRYgGwtUopEMU8YdChJ2QFChMS51A87MkglQzITpGKWqEmNf2vgwRzkZGE/i8DHSCeAfJKzias3xnoPNm8bzzzvviF7/4nve8Z+bMmTwUeWMXzn1E5tt5vvhTTz3Fy0MeclxQ9VE7t99++5w5c9LrcS2YyRK6f5x44okf+chHLrroIh6TjY2NXi7tRezo6NCPtgpUw852PiKltYvarFJqMIekegqNBRjEFeVPu8KImiFa16sPBFfDiMhpUReAqCrIICLkeM4vUJIe5lb3Y1LbiDJQKfRHZHkCiUR1WUlJHJxNllMpkCdVhDStLWmIe72QgAIg0okmoVwDTXCLuZBWc7Ao4ghoiZWAVBEBEFJxe4I7gteIGzZs0GNKsE1RjYIUiaE5wqU99tjj/PPP5xH3wQ9+8NWvfvXee+89cuTIhgheKE+fPv3UU09917ve9a//+q88MI855hge3Xq0ag5gcsZ23gDEMYU8vBXmEGwG5jKAVJcoIo8iSHWJoblmsqJIDoiyiecUiFIrwKmiuqzkhijmkDYShVgJyFWjlhHNtwhS7rVS8eWMfq6Rhk4lhDu07tM6fVwjeR0aHLGkaJCOHTuWd3V77bUXlzpacpe3zFcFCh5N3meffejCHL6HGX8ph4sx70R//OMfP/LII1zYKAH5Ae1EbFpCvKWlZdasWbNnzx4yZIjmAznDevHFEQ+wVatWwVUF6gWI8DSNa4Y3armSCVWlGOQXD9YqSGUWnIrIbB4r2fycyHBzSM4jEtZL9oZIBBKJ8LQkMbirR5FrJ5Xi+ZqmyJlRVYALTkW0EBFojoja4Y42A7erRZtxu6q5RlUF32pK3Q6cqqoJAFGr0yuPiGyCnCnPnQTNF9C1K9lQxLWKnFo9uCPcIm4/RE7706g9AK0i2OmFiFxduEzKTOooArC5kahecSu8meOV6Oc///n3ve99XAv1YBe0ipyA6+W4ceNe85rXYL7gggt4fuAyqZJsQpp6dRCNgWusOM8PukaSplsVSaGzoV6iCDZxiI4utvYNUYneXLtvAnQ1QuSHu6pURCAFmMWJGgKHxGIgqeJUPI4JXVLEBaUAopSotbRJQYYcsV8pXWpx70v6vNahQ7PkfwruJIMyOiAGh81kJ2QgDB784p/XynnJ2EtA3xmH6Hz5rAG4SAre6vGqcOnSpXCb5YQDLk4HHXTQOeecc+CBB/J48FdZQe1Ai0QuXY899tj999+/enX4tS3iihUrtm7dyumbOHHi6aefzpWPRyOvT71u2g5va2uj6/HHH7/++uv1k2+qpsAGjj/++P/6r//iVa1SlhPBr1FySvcQ6SKUzInuMhHk5N4mv0hqSKFetRC1LkSllIsYtYsS1SKCUjvBokCqdUWkqAQ8RNEHkuqyUVKKwQcuT5wUUNsoMW3RHNtkiMa+/WsPEgG6N6Yqogxqia7AiWkKt0HVFKqaK2q4lbRdO4/FbFp6LKmetmjb9qSGFCg+QFLZBJS03STlxH/84x+XXnopl0kfgnS1SzEXcTsE8JDk8fixj31s9913T0tplOgJOgNcHa+99tpf/vKX69at0+o5Mxgo1Ryl7e3trM6bVykq2cxknU9H2bSiPCIeKwVYURdAhKtEVDvchChIAVoU4pYcEdJR2pu75JQhVeBpl4nagbYtPU0NzVFUCQ4gtQpj77jjjhEDf14r70k+9IlPfOLkkyeNH1+Km+8HvOHZc0/ePBUG+JyA5ubmW+6/++4Pfu6thRmdhf49jYX6OYUN95xw2Ife+1X2yGubrJCgsXHQ44/f29Hx1Mc+9sn0z6gZHN3Q0aPfdfDBv21ry37UpQYcf0uhcPqsWUv1K4I+cojOTo7XYu7cuZ/73Oe4FIXTGc818J1P4JHA1fHggw8++uijjz322AkTJugjAoBvS4FN+M6hRbu6uvTTBCtXrnz44YfvvPPOBQsWcPFjwt57733ccceddtppXCm5EqftnBEu21xfaZk3bx5+Xk1QYkWNNVDY3hFHHME1csyYMXFTO91pRHBqOKka05LEVJGY8yhVVRz4eL09RdmARaUuSVHqA097Zci1p6uQQtLjUinV4RBS4GoK2dINELHlFKJtcKXSNRkuuFel3BwZRFJgI1JKW4Dng3QmUX4puaiS21MC0vlCalPE4FUARF1yyiad6HaB1Eqtng4hejOxmJ1VpSLAPJ0GlF599dU//OEP/Z1+TbbB3KsoFVideNJJJ33qU5+aMmUKDyVSujCrBPcQDcfjUTwqWfdPf/rTr371Ky7SpBhUsgdI9Bxxn17Ezs7O97znPZ/+9KelyxCN2aJS3CKuJSRKIWoJCFFEE2rHiqQtXiutKnppUNsrpL3m7pLZVZF+OdGi/VYsAolEcSKpFMWcCHhS9afV14LSBRdf/Jkrr5wcPwdnQHz2s4UPfpCn6SzdGVwj7733xve+d02hcHGh0L8HV6FwS6Hw34XCd9g7N2Mm7wTei979yU/O+djHPrt9+4DXyPP32edPA18jwahC4fhZs5bNmRNOQnrkvutIhEjPYc6cOZ///Oc3bNggs0T3wmkH+mIpbyJ5N3nCCSccfvjhegsY7dmTjhdSCkfREHEeXUuWLLnrrrvuu+8+rnxcCHkBu88++/AQPeaYY/bbbz9O7rZt23jL+GAEZtaNGwnQEkDDgfaJwq54Kc3FOxVTm3qlE1MFA4RoXS2hs/rMotRcXek0VUmlSLQCcRepeolpCqI3mCUK6BoF1xAgHuvZuuKpWVCKQV1CKipqmkelivxqp4rikua4agMx51QVqCVLdoZKNGp12SxqoKrSiXDp9kdjOAS34wG5gSkB6iJFlKLJEEQIqXR1yeY55oZEdXn1uEjfUVjUWjnFQ1Ag8mgaUEn817/+9eWXX64PvgEakkaN0mS450N6enpmzpz5hS984aCDDtIFkle0PAzTr8jRq8lwWkgBZmwqbd269cc//vEf/vAHrpFAotYC3oBEp0QrLPqOd7yDF+uqCpQw4ExbAKm4zfLkDERP0EKKGJzCIZqj9tQDPNmpPWmXRCBFRJ50oeCr6rJJ1CjgaTKY5JxegmgbEWAD6kpFOVFe/PNaP/OZ8HmtWTYAfvazwgUXZLw/3HPPX2fPnlsovPgntvIaL8v6x53f+tb9n/4019oBccbIkTdv3cUf/QiYPGvWyniNzM4I0EkRsSJiSOG+XlvyyRVIecxw7+dN4WOPPXbFFVfwpu0Xv/gFb/J4eMjAEOBbLvZlBJ2bh4iN9456MFxwwQVHHnkkDzPeyF511VXf/OY3GcsL0ssuuwz++9//fv78+XTpUeeBILc3wGSOgucIuO4H8mtRiFrgSq14LDpcUYogEYJfLeKyeRpA8XJuiZW+VUDKc5Npj3K2zxTaBpABwD1fuj05nRRSO8G7TbeEqJQSxKNE3K5UZolEwVzrEoEOTTotWtqgpKoJ8K60BHBJkAGiqOWAdC1KCpfB65JCgEisB2ghEXEiKcSwLVoyP2sRQbQEKLXBMSVeGptEFDhRBlelixNVShVd29JGYu1pF0GRKKWpqem8887bf//9tQpPoDfddBMPczdqCICIY+NtH690V6xYodLIkSPPOecc/XkfeQA2ANEckTRVVYqIYANgmm05v057agZe0fcH7QeiEhE4lRJ3nW07LcHT0ygRYPB8pdJJPSf1gHSIZ6qRmFYlokBU0hw7xQVVrQvoaRTBIK53O7sAL39e/NNaB/gqqxHvky/iie9Us10NjDKvxzI6ADjyjL0YOA/ZY1WIZ7jvfkAZKE0hT78lQQYiNwbgYskj5LnnnuN6xsuNn/3sZ0888YR+8ZEqUaPSgfQKGNC5LvKW8S1vecunP/3pd77znXp88t7xj3/8409/+tPrr79+2bJlKLzFxJ91RmiUZgqkXkir1wKDIJ4qhuYItoUlEz2HdBoxh35LaQpnuIh1EUVVnQZTQoRY36kryi+O2BeQckFKdOUhAzDP6YIUlYQ0jZaALE8gcaCSbou0CpeeikCpYtrl6HMLUFzKESEVjZwykMG6t2FdiMUApybiRPUKuWoKnwdHkPYCpXbydMmbyBNPPJGXoSg8rm+77ba//e1vsXWnVSB0qVGP4r///e9//etfN8e/c0fKo/iYY46RIeeHK1UpBXrG4mcXiEh0KTRHKGWOOUhLAC6D4EVzXdZls5JGlUCqEPslIFirZnFBpTSKWBGk5zYQKwGp4pgj4jmnEIv/i3Aa+y4SOi9EVADxJUSvOIjopHqDKJ7CSnqKddEigvb29nnz5l155ZVf+MIXLrnkkhtuuGHDhg28fFAjXWoUQQRaGtA+bNgwrpRnnXXWIYccMmjQIKo8RHkviHj++efvvvvuccHsDhe7+3aIImKRl8NA3FCVqAlWQNhEMlaRXcViADwtyUyU4l4RNRIF+QW4IL8JNiIGR0ETIBKV0iVRioZYhFgxcUyJ/NqV5niHHqUooj0oBWox1OKYpgCDCBCPTQHymHsPINpDCV2pomxEQQYhdgTkusTTQ5A5jYKqELXYoF6l0qMxEO+ZkmxeSH4AMafqlCib2sOUnbskigNvAwRHBFyNgIePShKDL75TQcntCmIRA69WTznllN12203bmzNnzu9+97v169fz8jTMTfag1EDBds0119x5551ScJ522mm8oWSsW8QhRJaIxr4NaMmVu2IAAP/0SURBVFGJYPDgwYhZUn0EQeyHa444w1VVao4BG8RmpRg0UETQBuRUSQiDqo2eYz/cUVWiS7WjRDDAbSCtdUoh2klUKVpCFxBPS5pGdApUBU7/F31ngTOS3gDiOn0+axJ5gHGZFCe+RGgI17kDDzyQR9Stt976rW9965vf/CZXSt4R9sSPeQSZuwqJYPv27bz7vOqqq+i65ZZbuNxSnTx58rve9a4vfvGLb33rW1taWvQVJDXuAkzDxgOMBzzELdIFDtYPVwGOQU4p8suTKwFXJcqQkjQiCvJDEHNAV4mo1GKsZyumCBOjXwQgkvq4LCpK10B1eTgwh6S6IHM6ze3R3jcTIm6CKCiVAmc/3iqAo+uZTiDFLO6unCjQm7FqCWg4fkGKiHQRtRCjKztMAd1DgjVChiyp7ie4I9Jp4krFFSlFOcCKiKCUCEjFiYI8AC4PJD1vQ4cO5TGIIrNJipyiCaNHjz7ggAOkdHV1ccFbvnx57hWznKkiYOMyybvJbdu2qTphwgT9wKSBrl4iBtnQQ3+EqjKPGjVKnChgEPGNktuJxgIZgKrozJECnFINK9Wcao01EGNfqApwdFbxiojiqorr/gbgIgYlzwFqkV+6QWqPgJLz6Hjl1By4IqCEKF02iUpf5tjpFHBeOIM6QSLSUw64RurqEo39wKWch2vY9OnT/+Vf/uUTn/jEa17zmiFDhnC1+9GPfvTd7373z3/+86pVq/CzFjcMgAh0Pfvss7z7/N73vvff//3f999/P48r3juyjalTp5599tk8aHlHmF4ga/eWUxjL1Vq/lKISEVHRHqUiUoippxa5FiCz0tooAtQiSPeQ2mmOtWa3iCsVF3FJPFqykiHFUchxwdMg6TRxlQynVHMloC6iiBRzAS44JeJRFMlBeoqsUG00lDIT4slKjWjcFfCoRanmCHCXxKUD+yHmIOUCihpzpdBWRSYlsCgyfPhwfREl1RnrKMXwVnnQ6U8R8AjdunXrM888468DAfWmfriIwIV57ty5ixYtUrp27dodO3a4S1DJE4BEl0TYQPqDJIhpi5C2UBXggg2I0ZXZUjE0VElwRI8Nacm6iETDInAVoujUXKlIChlq/USnRClAtlyjYJ1onVRQ+r/gVORfJvBSQqcS+GRJhCjyAOMCo8sY0TpcoqBUOpHH0h577HHuuefOnj37tNNO++QnP/mf//mf73jHO5j26KOPcv3j7eDPfvaz+fPnd3Z2Yi6VSjwIH3vssW9/+9sXX3zxFVdc8dxzz40fP/6d73znN77xjTe84Q16nLMThrPD3DZAvxzCQfGSdrfddtPTBKI2rJKiCNBkAVE2QzbpGi74NHoOPBfx1w400kZx7dNHSgonAhk8itSQIpDiASIWRYC4PFIEUlYxweYUREsAuiYougSRX1FVzVEV4vOMDpSiy2OQhiWrUIqfkhR5ojcgFTVZOmlqE1C0LjZV6YUoVS/EA4nAO5dHLVIEtwDMcIHU+0kVEcUUA+maCaFXAzXEOkQ6qZQxY8boUmcRg/apCNKqxU2bNj344IP6FJHbbrttyZIlYcnqUQDZxNMDB3Cuke3t7Zdddhkvea+66qqf/vSn/h1Nz8FGo3o9wVEEcLXWD6VLwZnjHDWEmR6FaDjFRlSjbNoGgPgcAs2BEIEaNUdROkSLCnBEiKIUR/cKoRwhsxRFzYGnGwYqiRBTXSWvax2RIYqqEoEMQCJEa72cwanou3ODeKLCmQpntHqHAOltALg46RUcSnoS4bGv78YADJHCkOOPP/6EE07QlamlpUV/DJIr5Uc/+tG99tqLqyPXyM985jNf/vKXL7/88h/+8IdcNb/whS9cffXVGzduPOiggz7+8Y9j/tCHPjRr1iz2EBfp2xXwckK/XDbeB++5557qAqr6HqPjBSmnJCVN00gpJToz6SgiIkpuCKkUEQzoKqVVQxOAiBbSyafLRyGzRKJIqsC1H7WQ2glHJwIpIG0XIWp1Q1tyLyTlahHgglLNBFK0E1pEVDJCW3JcKFpFKboX1fZSxduQU6KgVC32hEIyX6mQSwVE6+ZWaufoKIhaXVwl2bxzYK6bDANAjN3ZXc6wmZJSDQS8QBw7dixEJc2JlV0Bc1tb269+9auLLrro85///E9+8pOuri7El9ILtBaXySeeeOLHP/4xV8rnn38eUTpDcvvfBXCOGjVq3LhxWZ7A0zheHbKWAPA0ZQgGiER1KY2TMuRSEJqrryoEKVm5OkdcB6U0ujIdjyKK/a4C6RB7xGMxABFkSdIrIqdPKaLmWExHCVQN0lrDyxN9Dz9BZyclQnq+uMhx7+StGOc6ns/s7JsQa8FjY9KkSSNHjmQCKb3EwYMH77vvvm95y1t41L3//e8/4ogj1q5de+211/IQ4nrJC1Uek1xZuS7ymDzvvPO4lHJ5i/PCfhRTqLQLyMO7z4kTd/p9HkR2niXJ4atFByVi0cTVVMmlgkQTVdNo2ANia0gtYk67UkMOwZ3o5iKxmBFHIVYyOE316OrT2YnTdG85yEYUkaJSrj0lhltyJdJ0mquIQirKDKwAiyDVSYlS0mqOiAvRlR0LMLdNSsoFKdGVHQ5RYtqiVDxY+5uTwqII4E3k+PHjITJoWhpFUo6ZyIVh69at999/v95N1v7snvwiapcOYiUb29nZyfWVadLlVClHclGjSqXSHnvswWXS7SJOiYZLQE4PFFca6wHSpagKYqVvCUeQqyolkgIRe5RCJAoq2QykqOQU1CqGSyJEEcGpCJCuUbUDM1My4eUJzkDfNVIXPHGIr38Qc1XBgQcemH5LUqKI09QP5269ZMmS9evXk+qxIeBn1MyZM9/+9rf/27/921e/+lXeL5577rlcOD/5yU9+7Wtf462kfh9LK4J08ktEbjO8iPZzhKK3RFUiRDyFPFlSfQnP+YGnLeLoMiuiQCSK0K4hUnJQi6Au9wJEL0T0fIg4Eb+ISqTw1CzANdm6bAKK4DStClLsAVK8YQiQgRLQHJ0BpSoppmnaBTRHIhFFVetS0hTuCRBxShCVFIEMlLQxeSRqoFLgFi1BBIi+S+RKIqGzStQOSDOWHFFuuVjs40TvUEoKKxCAx9MEXptOmzaNl612GvKD2JqfA3SAgqopWEhdqqYe6Z4jqCTIIJjLQ1Qj8wHX5oMOOkiftAVQdDbshKQT4ErFUz/cenDHRlVVkgLwoCslqoVoHqzVqlMZAApgphV1oRDhDPeKQCJEiwL5SaXDgYgavW2iPIoCXKurChB9RILmiIsQBw16kQ9SLTY0vPintTa/yGeoNjc3slSWDIjBrJbRAdHEHTujA6Ch/2fcnaATxxnou2lroZJOpRF66uoWL178qU99imuedyNdXMgp5XK5paXlsMMOO/HEE0866SR9qQePbTZzNe2Jfz21sbFR8/Eo4pHtlltuufTSSw855JCLL76Yl5Pr1q37yEc+smDBAh48VGvhVXQvOfPMMz/96U/7g+jQVRWRkkIikE1Rqe5VwPc2TyAiYlBEl99p7MsDPW2XqLFaK0ypbsAeleAoKslJVGoOSVfXhJxC9FiRdKC4YE7JVZA64SLpQhLFgQwComyq1pZ0mEAGRcG6q0AkNxMSvYGIK8ofKxm3P20kgl3s004rIrn9p54U8mMm6rypSyURTXBEAdhUkg6UKqoK7rnnnn//939vbW3VlVITUrN4Cuupk41NnTr16quv5plUKTN5l8nbRB7m+jRXzG1tbR/4wAfmzp0LV6/axXNkIF2EJXjkfuYznznrrLPsN9EB+nRJzE2WTTqp/EREiG0oIMdFUEA6wSVBXUTtJ62KewOChwA3KvWu7IkdAXCZVZUtLUGsA0TrBqkO37oUpcQ///nPQwf+yPLhw4d/8VuXnP2Xu8YXBocp/aOt8O4PF171qkJnZybsjMbGQU88dee3L3lqXOFtvYX+PXWFxp7C462FR6qfxbPTCa+iqVB46L3vnfvud3+gra0903bGsFEjP3Dy6V/pGNx/OWJwofT5WRMXznkmnC7+n8nJGYfrBKVckKe9vf0rX/nKbbfdptS6PEJOIRV49Xf44YefccYZhx566MSJE/EgZqaIXJcJusyrVq3iZvv9739/3HHH/bPXSC7AbOC9733vO97xDh7VvnPIYJuJuO7o6f1GulqARZHUaa5UXYh+DLgqyAMhal1xddlsmzglSG6fhtpl07oSxdEd5QdOa3WtBclxD5EfTrQI9yHXrgvcYrhRXCQ9BESlKkkkyqx2YL9EIqkI7RBxQSX5bcvpoSHZtjagVDpKuk8gnZjq9ssjHeQM8hBlU0mwbuIUQDRKEwApIlixYsUnP/nJefPm6domg3uJ4ilqdRQOZ8qUKX/84x81B3HRokVf//rXt2zZQpw5c6bG7tix44Mf/OCzzz4L9wSVaslAuggrHnzwwRdddJE+S0RHhC4PEPc5FOCaIK4u2tWSEp8xOWNHBulqN5fTE9SiCCRakQGkukSlzJGNCHJVKym3AvFxEcWVugWkLQCuqIOSCGy78cYbuRCmpRSULrnk09dc84VCYY/4OTgDoa0wwMUvgneQD19U+OT7oq9f4ODy+I7CmYXCRew3/qsF18gHP/CBZ973vg/t2NH/RXD06KGHH/vRUvsfC4VdfBxdyyGzZj85Z0k4A+mRp/cPcUSlqhJ93nnlePnll+tTiaXUwnPUqzm8oWQmL0xmzJhx6qmnzp49m8eYhngzXl1Ejby55J3rfffdd8cdd/A43Lp166te9aovfvGLXCM3bNjw4Q9/eP78+XJqRaIHaicQIi976TrqqKN0aPKoUYps2gAKyI2yKJvXkohHa3mmPUqtkAJswEMsypD2ph4NCbPinj3BNqJ0mU2AbBKlRGP2kCBShaTLKYV4CXGvJWfalRvlXiHXQhSRR8RdMsDtSScTSdNpbgGaBkk9iIJtGgjMRcRpJGqyFGLs65sPVALwdFE4YtrrgfIQraereKYGuss2TUCUR1VIyj0hmCLgPJq++c1vXnnllc3NzV7OUBcxXSurVTegFnh6jaSFx+aXv/xlHp7f+MY30muk30fSpXZxL5HqGg7EibLx7MFzxbnnnnvhhRfywHevbOY0OlXUKEfpKcGvFouk8g+UAgipJ6uUUyAAnaiSRlkRV6/HmkjPVZXmBgqpjVQcDyBNp3lm7AtO2URCWxzCyx39HMlA+Oxn3/qtb327UAg/Zvx/gPt+XDjxgxnvH48VCkcWuIz+PMv7x93f/vZ9n/rUrj73deTIM7ZuvTlLBsCsWZPnzFkZTlEmRHBedMbNOUdwRRSVlPIibtKkSWnJSM26nYgiiNzFGxoaOjo6nnzyyR/84Aef//znf/7zn7/wwgu8w6PKQADR6nTBOzs7eXRxSebF449+9KOnnnoKJZ3f2tqqT4yM62QrajlBCoTIzvfdd19VFSuFypbNW/WrWigGGyDSIqIqG5NIBNqtiJwyeLiJjsiKIO4h4gCzNiyiXqoaDrQBDLaRqld+RDgEQFw10YpEEetAHF1cIGUyioYDRCkyGFpROracgRQDRJFUoyCe5i4RGQAt8ggopETZpMA1WUCURzyN2DCrFwXiqgCXCDxTIoAAFM13CrxP2Yjeg2BnnJQtgUdbSvfvKkB3o+fDIdJzijyk6RCA2NjYeMopp+hXhHMeCIpSbdublzNdeiDUVulCBJ4meAkRNTpqIaKqEC6NvI/0766k0PYETchBYjotyhlRVSdf0UNsQKdXN4Sq6SiVpAAPAZ4gImgIgKsE3C6ga46qTo3QX90nxDZibgPWHTFA8CgVcSpFn2u9C5RKZVxZ8j9Hz4t8LCyOEF70M117yuUXmcSJydhLwE43hs6Lz5FEw6dM4MXjgQcemCovEZrDlVIf1sPV8YorrvjCF77w7W9/+7nnnuP+IWAg4pwzZ84ll1zCO7/f/OY3vETlRqVRJTy8g7z++ut5RbxmzRrEbI0BwHLDhg078sgjc3/qZevmHdf/+oHf//COJfNXS2G4IjagFNRypdYhxFQnShGx0woI7uq9FmRqojslKiVaF1wFsa9vDpDZnjQiihuypXrOJm6kKVwQl1mAyyPRc6QrWgkN1bGkhhRVrYjY328VqJQlEaSpwZAYx2R+cU3wHNkcJSrWEtngOV3EHMBlFuDutaKY00HKqSpNo8l+++136KGH5p5w+4VWGQjpikK/iofkpimtXaJ2CB5eSe+9995sm8e+WzScmKK2ZD/EXLogTpQuA4pSID6QJ4VLoS1yz7HoLnGi0tRp3dFVt6ce664aqWKea0xTV1/O4Az0XVQ4QVxjcqcJiEdL8PCIIoVwmXnlK1+pT+4npaoHmwxpShRRo0W4roXc7xcsWHDVVVddcMEF3/jGN26//XbeYnJp/Nvf/sa184Mf/OA111yjP3qFX5sEND7zzDOf+9znvvrVrz788MM9PT2I6fBaoPMAO+qooyBxk9FfqDxw0zOP3rjoseuW/fzfbrjz+kdbN2wp8N4yDiEK4nT5KIjiSsXZXnBXN4AIZ0sgdMZUJSAzJBXxpLqh9hTy63h1WhBNJAKJpNKVwqUQU8hgXY1SgAhRJXNgbsWLEhGlk6Krl+jTApdBHmAzxCU3pmItwSMuomnq0ooQIlAqA5AfYE6dLplQxQNxr/ZmjsGpCNGKJqQRURxgE7HZRCWl2oA4JB2ihVIzXCVxIg/hU089lTdkapQZiMijEqmJRBuASb9QlWh4CfR0VCxmo9DhqtoDaW5uPuecc/TpAcBVnXx1qSSiiAFEOVsRuESkPRaDH8VpOlBcJXWRpiKcaG4dIi6DlyaKpwSbiEaZe0XNVFoLL4FNHk1Gh4gTozfbQCrKJhIt/4vkfSSniajzlUIlEeBbGuhPKPuzW3M3nlJFDGkVLoUUNEQwZ8eOHb/97W8vuuiiT3/605/61Ke+9KUvXXfddR0dHU1N4cd55Ve7ulavXs3FtVwuY0irWigFCtvmTeQJJ5wwadIk0qDEbS9+bsVDNy/obuse1NS1ZWX7Xy575Krv3vrMwwu6u8Jf12MqHnarIRBxK4J0wTpE58qK74JCrqqdAw8xsQcSF+m7sRSVMlBK9IZ2kRSIgLXSqnohqkoEcK9ikiv5hEsHwdrfHnKHD7wNRaoaa5vmp40QHSaQQUgNAEVV6Y7pSU6JWogeLkUGEzkdrYjUAl1OHYKORXqsh14pRO2Nkk+LoAm1qyglimAA7lUqDjxcEaAcdthh++67r9LoClCXoqaJUEo5hmjvH9hksK3Wn44yEK2oqgg4RTNnzuQ5J2dT1ZySzrZOrCDuXUFAqjjmSoANaCCiQAlRNqVyijsVQRSP3mAQ0QQZDCsQIBGI56ZpV9HYt5AIivesKFHHQozGvh2qJJuADWTJyxg7nRSD85WxyAW4zqYIClemE088ccyYMVJSWOm3JORSbiHeGg4aNIh3hBs3bmxtbeXiR4ooZy3itbVBBiZIBBpoTtQh8CZy9uzZuiuQ1teF+ygX3zG7Dy0OaeqpNNTXFXvby/MeWH7NT++9/bpHN67frDEaEobGxiBFmEuXAYhLT4mQpu61CFd7itwEG9wuDmJ3SEWAeOpUFNIWeVKoFOZWbeZUIf16YmsfpBDlsWKxtkU2we1CWgJWVJWomdYlxnqAUwyCU5dMxFOPeVrKwU5AKiK/oZKH2EA0SVMbDJXUy7Dw/wiI7uRSFOUM5aoCpkyZwqtG/dQiIhEx50y5iTkwV7uAmEsNKVrFSD0yAE+A8Hw9dOhQ3viOHDkynQyUZs3VUfakHCgFwRrNaTWFDEBmibE1O1exmOkmBoaM7czlJGqCeSxmkG6DFTmlE1HEQXQFg6AqhJjjIilCw857kAKy/GWMvmskDyq9auC8wP0YEyH6DEpReuyxx3KZ5ELFhU0/sKpSDrViTvFArchlT9E3kj05pGK/BsBxEVtaWl772tfutddeniZM3XvyWz912mlvPXjMnqMKjZVCXam+t37tgo4br3jyN/9107ynlnR1d7MtmrKGCM0U0SbZMDBXCaINSBFIiT404P1AxKkCcUV1AZeAuHTtxAu5KidItyczopzqivYwAQUihQhCf3WTQFyiPBAdJoRUu1XEkC4HFwEilFQV4PJA5FFV+9dML5TCLYZtRJB2wTVKkAHCBA+xHydceroBYvDtfKIU45hs/7Q4lSKDIkosZlVNVqpGUpXsQfGWZG5v61y9fP3GdVs62rtKpVKlenelJI9SwcqQIUNe8YpXHHXUUUzLeZRahOQ4gNPoXiJczwNEIJEYthQhhQ1AxHOwQdEeng1OOumk0047DcX719LiSonu1a1jg04XnrTkKMCpmgup7lQEoMPTkyDFc1Ki1eEYgHSJpNqVoCq6/OKOuaoniBOF1GZdoog5VTgbEAHqUvVli3BOMqqk+iwQT1F2S6fEwCPniBEjzj///IMOOsg3kuAhJgaK4JSo3lT0nbh29RRuya3u1EN4B8nTAamAmO25UBk3fvQb3nvCOz530iGvmDp4THOlvtjAXaWz5/n71/704r/d+Ot7l72wslTOnhmB7m30xklhlHcLJAKJsqkFxWZ0lURsEDQKok26F8hvRURziFLcBajKIMDxEDGIy0lUrz0SieneQjnytMupFPxyKmqJdLggm0qqwlHg0oH8IoZSV8UFcU3QTLXDU11IS5qjdpHUA2RD1wR7RFQCmkPK0akKdwsRLgR3rAKVpABXIegY3C5zbMpWFHZsb7vjhod/9tW/XPPDW2+9+uHH735+8bwVG9dvbm/vLIefGFcLnYHoBhUmT578hje8YcKECbqkAQzeDESLSiSmIpy4devW+fPnw7mMMWTZsmUbN27csmXLvHnzuru7dSwrV67csGGDWoDaNUdIUxnsJLLhfffdl336GyVenaizBEiBWoDuYNKVEnNmEL3ZQqS1JQCnXYp1ETcC9RJ11CqpKo6uOXLC3ULEIJtSoD2npRTypBO8SRYSAW6UYl2bBFrFqVC73MsQnJN+ToqjiADnzKYKqc7s9OnTX/e61w0fPlw3TwrZXgo0PwW9XlQK2MVA2WzQVkXAtGnTeIANGzZMKaUUKDx17HvwtNdfcNKpb541fq9R3MF4N9ncOKhnc/nea57502V3P3HvC53x10sAY9UVGqtLqAScmoQNRUgMphqbqkK/ulJxMNA0EFaKVUUjNeeikPMLObOIkbaI25ASQangVCSNBqkVLwQxBzbYbMVAUUu09EFVkHKQK2nF0FDVxS0aqqaQ2G9JoMQckRwkRlc4arhiTlG62+5jDj5yvx2bOx64/rlbfvn4tT+87+rv3fGny+646fcPPnr3vEXPr9y8cVtnZzcvDDGHR3547DMgTDv00EN5+Th48GAewkGKIpMNiSJSgDjXxR07dvzxj3986qmnuDQ++eST//jHPyBtbW233HLLww8/vH79ei6Wf/3rX7lGsqh6Ae1eBZKmhpZgV83NzbyD3G+//UixCWoxZCaKoHiCIpAzBSXFnE26ifRUlM3cVXGVRNDtsQ5SEa6oNAfEMKK6utK0UUTc0QaRNMojyCAdQKSknpctOAnFL3/5y0p0jnRqdFfWmaotWYdInzJlCg+AxYsXl+LvOEpMSQ5eAshjp0taBaSG2oG1jYJSIhg3btw73/nOU045xZPpUhUiiA8dNnjKfuMnTh/VW1/avLG9u7OrodBb7qnfuLp78XNrt2/dNmhYY8uY7O+CqVFziH4t5vneBilcBoh0c5shwDalQMOjN0BckTlARDaNEjxKusxS4uBw1OgiEt1lTnSvCIpSiDhRLXCiZ0at75wAOb0fKZqQEu2BFJv2AEJ/skMp8sRKQC6VOTTH4VIEuJeDABrhGq7tqZTGMLQKeSAqKQX2i8jslEiqXakUeqrtEiEqpaJjalMKcI4YNayto2v5vNaejmJXe3nb+q71S7YufmbDC0+snffEyoXPrFy+cN36Fa07tuzgCCuF8DtUmtPU1DR+/Ph169atWLGCN4L6HgcztYGUa1GJBsfCe0eujo888shNN9309NNP6y0pV8dnn3328ccfR6TUU/076oAqSI9OXEQeQMoo9nnyySe/5z3v8R+MZEVvhohTu3IqhZimqUG9Ihpl0Vyp9iMRyKAWiYrWdctqh6rmSnCQtkiUoojoqogNEBlIGagSKREFuEVprPcN9x7EZfY02STaxsudXXwWHbj55mseeuj0QmFXnzPwErD0rMKvj854/1hZKPyicGih8Los7x+LTz99xfHHn5xl/eGSS67s6npnlgyA8eO/c+GFn+Lw+66Rgk+QCSaBqmIt4U7Mu8m1a9fqMeZqv2BmxiJyaTpZJaI9Jkaq1Fa51XnveN555/EmUi+TNV9R9wx1WecJYuz4kTMOmjRsbGPbtrZtWyvlUrG+2NO9o23ZC5uXvrChUlcaPW7EoOYG7o1qYQJRxNwzpUBA7s7naOREiOBUh2AFiBO9NARFkZTIukSlMkg3VyqFKDGdEBxV6KEV3Mk5h0hUzE1IuaviIqqq1/OJ6OKGFbdLhEhPW8LoOFyGFKoSKeV4uqJ0EUGj8Pi2EOQJKyUD0wjUK24C4Ck0gSVM5LETohLwbhubGkaNHbJ6+QZe3nEJrKuvNNQVuLXKnaX21s71KzaveH7TwifXzXt4xQtPLFv43LK6pt5Je+5OIwNHjhw5ZsyY5cuXr1mzRvMFVvFCQNxKLIYj4lTw3pH21tZWFB5EVBE3b96sL73ytOCjUIvaw5QaMQWjDjvssPe97308w6gaOwKRX1GK0tAWq6kig06pYs4g7lRRzlQUFwEWAURmc6eGlRxR9ATPNNARbQDyEHViU4M9QDoEG7cIRJ5Y7IOUVPdZ4uldN+hAuOeev9133xtfwieS7xqt5xV+xgVwF9hWKPy4cGyh8Oos7x+bXve61UcddVyW9YdvfvNPnZ1vzZIBMH78d7lGQnY6WeI6odYhvg2Iqhq2oT/11FM/+tGPHnjgAV6W5myg3/YU6SgRAd2KPQAxLaWQjYcl5I1vfONHP/pR/8FL/H4KliJRc+DSScvl0rIFa+77x/PP3rts+/odlfpCfV0DTz1DRxVnzd7ryFMPmH7ARO49aZeI7lsgd970lCouXQTovgtQgFo0we1SlNbeImlJ3HM8RCIEmKhkqEtibkLaIq6qUxuARD8mpZincBXiKCVdV2Jtu+DbFJtbgHuBDekQ71CiUjtVquU5Ik4k9cC0ar9FG3KibWnq81B7mEQpKlUqvffd8szNv5mzacW2+vpyMfyRhHIl/GhefV2h0ssVM3z8ZG9duVDXVHntR4844w3HaxS9vM+78847L7vsMi51TBYopTsRMXdVRJyYVgXrKZcnF20DPIQPPvjgf/mXfznppJMakj/mkJ5knRzpANE3Yu62sFk82sNaEqVQkq45Su1x1Vyn3U4Brmni2oa4V9GE1Eb0WKdSiKQsRNRa0gGKoJ3Ejp0OxCnQTlQi2pw2CvKgUHrF+ee39/Ro9VoMamp6/pG71iyYVSgMYxeZ2g+2DvxRrIAbt3WvwjN7ZB+m0w+4KzPiqcLUQuEotpapeeBaPWXq4unTZnR39z9p8OCm2257uFA4plDIvnfWH4qzZq2bM2cerO9GEnSiU57eJI6ph+h03rx5F1988aJFi7AJ0oWc2WCgZqYGra5q7ZyBSgIGHl3Nzc3HHnvs5z//ef0ZLHW5hRQSUzJodnShP0Hbjo4597/w6O0LFj+1udTW2VhfKuNqbNx9+sijz9z70OP3HrN7S339rl5k/X8enEmibxFFpUCn3WcbRamJnCmRM62mBkDVBlIIqW472TQkegNcMheRAtyisXF82CoKgMjphVQFadW6pnmOu7wckMctaW9q9ihHiUTxNKULbG7ddtPvH77/+nnljkJDkf2VecBjrQ+fixE/gytcI+uaRze+/2tn7XPwFK0L6O3u7r7uuut+97vfLV26lFSiiDdASosXFRFXKXaE/SulZA/wHKV4iBqoFqVSpkyZ8vGPf5xHMS+7UTRQw7FhiMa+M6xeG6xggGjp1KNUQCSqBaiq6JJFpcR0ONFcJOW5rlCL/tpGIjZNtmKPbE6xaWNwdakkolI6R1HbkF8cqEXEImR+U9O4np6wQH8YWSicWyhcWijwNn+gj4BrKhTeX7jk54VPFgo7MimPwYXCLZcUzuGN2/ZMyaO5ULipUPh9oXAVB8gmM3knsBCeu7/85a984Qs7tvczicMZNWpoS8uZ27bdXihsztR+MHrWrClz5izjVOz0tVadGgD3SU91E5ATQ3+hMHbs2IkTJ66N8C3x0hHnZTfzLnp3UQK0c4EcPHjwWWeddcEFF+y5557S1aXhMQ0hfrW17/sBvPTo7OxCDD+yw1NJXV1TU+PkvcZN3X/3QYMLretaO3dw8zRUeru3b9y+/IXWdWu21TdWRu82lKEs2odSqZdtlEplLtal8H62zOkIF+5QilxJGU4oRb/FQOyJ/jCFORJ7e4NWCSRU1ZCalcaZauL/+uH74ImD+E8YKjFWw4bdG8rZwKBHlnXFySThHIWvNnPoO90cDNT5FCf69CpaFBf8gCcaqUcckQ1A9CwgRU7ZNCdYqyWgUpbsDNmIMsS+TAESiRKFgUZ5aaWy+biASjZ4PkSwnkb1ao6gLsHzIUT8gwcPGjG6ednidZvXdhbp5h9j+E+hEn6mNdxe9Ty2x04bfOKrDxk6fDAtAieW92pTp05taWnRF0ila76jAAdUU1FmxZQYaYvNFqVA2AmHc8ABB7zrXe867bTT2JWq6LkWUh24dRFFIVeCpNz+VBEneniuxdD5B+hU1ZVCLe6ywYQJcB2alxPENUGKudtJ4U5ByoF3CFwSSdcSNC1LIkL6rW+N7uriItbvP9x/iV/9bIlv4vr9B/5eeNNj4f1frtv/GguFdecUfsWbu1zB/3CsLhQeLxTOr5nvf2BFobDm7LNPPeEELgH9AuN//ddv4/cj0/G5f4Xx47/dz9dagc4RUcRcJ9r3UemxI4A0Y/EXoZ544okrr7zy3nvvxbPrL2QbuYH/YzCE5/EhQ4acffbZPMAmT56cTg48PlMoBTztd3d279jaubV1R+u6batXbBw6snjEiQeNHMUtvtPdpW17+7OPLnzk5hcWPbm2s62roa5YqDTWD6ofs2fzvkfsMXRYM89EtGTuAZHzKJUCT6sQ0n8WHjUQtIQM4das8n9uOU7piNFDpuyz+5R9Jhbri9wx0vPMvUV3FUGp7kJ4gMzSxYkiagGkIjkxdydUFR7rGdKSV4HXmuUkeiwRRbtN14LnRE0Td0SUbtGKoRJAh8tj0dCKRHmkqISumdLV7h2C269/7LrLH+tt76LAS8G63upWww1dLBfKM08e/67PvHro8L6fxfAe2tvbb7vttl/96ldcKXk0MVO6oHUBovkukNo0p7Yr1TkKyKxZs97+9refEJ/pSIFORWoW0fycKKR6yonuIvqkAVfFRcRFgPW0N72ZZHY17RWkyJy2pDxFrpojioJElwxNUASeoFRgt2rUsaCQIq5vaRm3bZs8/eLN8X3klCzrHxcWLv8pYVe48weFUz+a8f5xS/yTH3/Ksv5xW6Fw/6WX/vtnP5vl/aGl5fRt227NkgHgv/vRd/fV/U83Kty3PXDJZzBn0xBKMhBXrVp1xRVX3HHHHVu3bsWjM56bqYEAQpreKiJpSVWQLpqKcB7P8AkTJrz+9a9/85vfzMthVROEl6jdXaW2to5tm9rXrty0funmjeu2b1q7fevG7dtbuxuHFM798AlHnHRgY2MDThrSDXB93bS29Ym759/397mbVnTV9fTW13WVC7ybGhTfUO10h9sl6mjCXdfLu7FyJfz1FQaEr4nFbx2FJzKq8Yz0FurKddxMhSKHyj+uxKExkEL47VZMIS1FQb1h26EavgUFr6vUF+PWwv+5baItcJYPs8IMbrv4nywDhPrYkHl3QriJeTtdGjN58BlvPuK4M2f5toDEet8lx9y3mnSiTqxEnWdHVUWMtESUmQlpO1ypibtIHWXw6ukmSYGJPbmqxqort4d0lKoouTnm0t0iXS0eiAjEqYLUqSiP0N7Wcd8/nrr5V093bOupFLm9e0OICDvh/tZUOeWtB7zu3SfFl7DZ9jwW8Er3wQcfvPbaax9++OG2tja9k9PqxH73xgRtQ7p3lbMRpXsgqXTAQ3jo0KFHHHHEhRdeuN9++6lRE3JzwmLVORCiSkA68B68iuag5AhVOY1cr0RBc3LcQ3JruSqSm1a7CgSkXaTw2iokHSX0u1Bsym44RKBq7fYEVSHSX87XyHAq+H+mxVND1NlJkdN9BsVdBWlp/fr1N9544y233DJ//nyda5eAnSJpo5AzSEyR01miqanp0EMPPeecc0466STeSmZ6pZd3im3bO7a07tiysW3bprZNqzavW7m5de2OLes7utpL5R49RHuLjcUTztnnnPedNGTYEIYD3W9SDkqlnueeWPLwzfNeeGBtR1tb+DzX3qb4wxB9Vy9tq5qG/0rn/7HERalc4QIWKlyK6nlvWwyv9Mu99ZX63iKjeBMbn8zK9WEAHvqKXOLCV4CxhwtZHV30hGa2EN4dM4KHVEgZTnM9V1hu4nixq0EQ2TEtTISyavx+edTDf+LxxoMii0p18/Hc816E8zZ+yoiPf/8NY8aOQor2nW5KcZUEleIJD+CsyqN7CFxmd0mM3qwXoIgH985LWCfSy1gUoPmIWlocxAH5+QKpS8Ap0VwtSlNRqdfSNlSSKC4xrRLp1RwdO6IUVQWJImoHpF0dnQvnrnjy/kXP3Ltk+4ZSHfeVeu7fHGy4n4ZbO9wz6oaOanjjx0845rTwxx01WdM00HHRokXXXXfdTTfdtGHDhlwJqFFc0BCJruZIrkstImDatGmnnHLKq171qhkzZljErxZ49AbAdSNaxCMuM/CZ0RCdeafwnL/fkhVDJUFVm6WIu5TuUyIkh5zNMwWlVkiBeAoO0ENAvx6gEoCQQtwopHOovpyvkZC+8wJ81gBEUEpJBKQcA2mqCOjjxo3Tz5Qef/zx+hRWXiTGkQHyAHrT1JAOakuCdG5OxnZ2do4aNep1r3vdhz/8YR5jvkCCZx9bcMOv7r3m8juv/dE9N/z0oVt+OefBvy6Y/8DadQu2dm7t6S0VGurLTcW6Yl3D+Okth522ny6QOiKtInjFhobGAw+fftxZBw0bO6RcaQiPQl1lwvs6PCHGi5dTZonAwgWRqxIXpAb+hRJXse66+vB6v1DPm8VieMfIuPpKMTy3FesqzXWVhnA2iszlPSvzQgzLcmHjylnHlTCCy2N9if1UkKnx5pKF4w8UcXUtcqAF3jXwBpadMJl2ZArl+rreBpRKucjkusZCPVsr9oYLbBgQZsQzEEYGhOsl+2ZuY7Fp45q2lYvXRj3AZ0kpm1IEFtMqnKg0x03MBVLbhFTxzBxR1SQFBpG0qi5HFIhAKkU28TTKQ0x1wQqIw3ZakYiSGhRzRIDLGe4KkWzduu3uvz9+/S8eevTmxdvWhUtjXalU1x2+FckdMPjpCK29Q0Y2jtk9+/uL6lUEWkJxr732esc73vG+973v4IMPRuEhLB3wuKOFNEXYR1VkXEasVM+JUkHTurq64DNnzvzIRz5y/vnn6wMjWcIGIXYEOCV6vhWn5l463QM8WjJuXUhbpLuKoipAFCyaWI/GrF0iSEWATbqgUk4EYfTOAw1Vs6SmCkJ/FDXEBumGDNJle5ljp2sk4OUDd01xnymiRXNF/DqnKrkKIba0tBx33HGf+9znPvWpTx155JGNjY1czzCo6i6IIR3YA3KECDDoi6vDhw8/66yzLr744g996EMHHXQQ12MZhPlPrLnjd/OevnP18ic3b1q6bfum7d2dXeFaHa5QTeGKw5YrhWHjBh376v2n7zuZFpZgkzo0DUERVywWi0NbBtUzoJK9OeZdpK4nwZmQGLMDjBYuTiXe9JUKjd29xe7wtd+eunJdsaehwH9L9b2lYqWHN6UldlVfZgPFnt7e7kqZHVKu6y0XSrwvru8pV7oqhPA90d7e8AFgnIv6EpdIbpf2cm8HHcVykaRc7u7hHV+lEXP8ORzeRzQQe0ul3lIvLxHqy8Uii5Z7y3XdXZXOnt6ecCzhmTcebrZzxexAOB+UKIdDKtV1tYcfoQ6G6j0B+NSl51BVRU6viO4wKVQSaHea3rUEV0UAoswyQOhyis1cIFUvnJhz6m4gxbCHUrolxTRVrzYgURyoBCFqXY0SUGxQYy7dCXV13Z3dTz087w8/vPPmX85dMXdzaXs3r2HK9eUh45taJoyqhJ9m4HBwhq9d9FbKu00ePnZC+E4E0zRTOwEsRGrstttur3/967/4xS/y4Jo6dSrVUvwxsbSrL7K3KAYexHCPVwnILwQ1no1w161UeNheeOGFX/rSl0477TRWtMFHTcSWpoJOmnTBq0BIzQE2uM3uRZQCor1vdQ0R4JSkpL3yK5pEOTtkogi6VhdPiWEnRIonaDmIojxW5Iwd2cYcpdgGtH/SnALS+RZftuAMZOdCMIf4nAZTcgNIJIpIBNh0Y6SwiHn9+vW33377XXfdtXjx4tbW1tSvtcQ1VoRonhJ64Q0NDTycDjnkkNmzZx977LHpdx/VS2TyvTfOufKSOxrqBsU7TLlX332Ll6/4jiiegsamo1+716vffsyoMSO4ynO/ol071IoamO0h/m/ZgjW//cZtq+dvrWvSHKrhq6XMDeUqcYyN/Cd8mZQ3bg1D6yZNaxk/ZRTj1y3etmLB+lFjho6b2sK7t+620oaV4ZujYYsNndP2G9fQNGjFwk1tW3vqGytT9xs3pKWRVxnbt3RsWLqts61ULNZPnDGWU7JuyZaJU8aM2L2Bt6Nd2+pXLWttHlI/btLwjWu3rl+zo7Ghfo+pLdt2dDQPGTFqzOBe3rzyPrBSXL18fXtb114HThoxdvCm9VuWvbChbUupkTex4Su38aqY7TwcdnYgbJoS757LRea86yunHHVy+JZk0OMpErLTtbMoSOQMuwRBFAfuNVdqDnwDAd2XIMRcr4i4WhQlUpXHk9PbXYomEHM6qC3lDkpV6/JDxIk2hIYqlw3Y4FTc/lJPac3yjQ/d8uwTty/avr6rUG6oFEvIDUOaZxwx7sTXzCx1V/74vXt2bOzm7h/ujDwACj1n/sthZ739+MYGXmBluyJ6JvAqPldczBYtWnTrrbfef//9S5cubW9vR1QJW5gQ7i1xn9U5MWNQlmsVEY0dMmTI5MmTTzzxxNe+9rUTJkzwb0CmzpSLZMvtfCqsOCLq6KykMS1J0SjpcMOpT0UKehWxgTis//uJFFA7h5KIWpTC1ZKm4rn23GGiyCaiElzrpp5UD+4IdJAqL/PvR/b97geJok5cinCOq2JKzIVcCjRNcdiwYfvuu+/MmTOnTZvW3NzMDdPW1sYjjaqQ9dRAJfw8SnsiRo4cud9++x1//PHnnXfe6173Ol6Exp/ozSPsr66uq6fr0bufK/QU64pl7krF8Nkj4Xt24SuiXA4L9V2lyu4zWk5/06GTpu5GT7oR2k3YRl9aKGxpbQvf8tnUXhffToZK+J5hdkU0cYxd4UmD+Y3Dmo4+Y+9XnDOrZdTgUWNHNDRwoVp98LFTz3rrsbtNGrHvodPG7j5i6cL17e3dEyYNffOHT5914j6b1mxbtWRj0+Cmt3zk1IOOnTJ69NCZR0wfOXbIupWb2NfZ7zx28oxRC55b/oo3HX70afu2jB48dNigtWvXH3D4Hm+58JTBLc2LF6wdPKjhVW89uq653DJ60MFHTz/2VftP23/8yJYhm7du2e+oPY457YBhI5p3Gztq3erNWze28VAKxxQfUTre6v5jGg42fLczfquzd9YpUydNHR/PTfy/z1I8aeJAejawWkrN5jrViiqZOxWRIgxkIErpN8aOgH51E0QRkOvKWHUCRM8vKnkaxM87OcNAHCIuAlIRtLd3PHb33Nv/NOfpe1a2by7xbjG8S6wvj5489JhX7/fKNx211/6Tx00avW7V+pWLWiu9XCV7C+W6xsGDjjt73z33mhjs/R2XRSlalD2PGTOGx+8BBxwwfvx4Lm88Erds2dLd3R3vFOEFFS9def0HReAI68NXecJ9InzhKF4XeQjjLxaLEydOPOyww84+++xzzz33Fa94BZOZH13Z6SIqDSMSyJYlEekma/2C9LSK2Y1KHTVKxDeZe1UCsgH7gYmgKiT1GKkiW6rAlVLyybEOV1VwmopgINGjaomjSdsllwytflR1v7imUDj9xT6J7sbC2Y8XjsiS/vHin0W3qFB4olB4U5b1j8WFworTTz/5+OOzvD/8H30WHfDdQicove9yL1dEVBVITA3SBZeIvE4cPXr03nvvfeSRRx511FGzZs0i1Y/M8eABtAulUkmEEkNYjsvq7rvvzkP0zDPPPP/889/whjeccsopXCm59GrDmNOlvS7vCZ95cGH7hjLvH3gYV3+CJV6u6oo8ozQOr5z+1oOPmL1/sT58dBddPgPizGC4V4m/FFjY3Nr2NNfIzR3h+3I88cSnheRaCMk2E/+T7Qy1XClMm9ly5puPWPj8+ut/+cDcR5auWLixp62y96GTxkxuueW3DxWKvYccN33Js2s3r287/MR9Z8ya1NXd1djYsGDO8mJd48nnzFq1bN0NV927o63z9Ncfu2N759rlW484bUax2DD3keWHnjh9R9fWv/zmgUVPbty8rnufQ6bsc8T4IS2DN2/Yum1D++Gn7t+6of3hfzzHu9KDj9l79dINN/76Id5ivvbtx23d0vbnqx54/snVbRtKbJG3sxwF506HEY4qAjEcS5R6w3U0fBFP18jsQKvITn7NY0+pIqdUZ1gl3YLyEG0TcUmpbhdVU0i0TaluPqJErQhs1mR5PBYFQFSVmPNoJvBMGZzSmFsF7nYhnW8i2Ol2KeXe8vKFa/7xu/vuufaZdYu2lLp6wjeWe4tNwwsHn7LnWe84+pjTZvLyi1uwWKxvGTt4yfzV29Z2N3KSK4WWMUOOe+3+o8eGv8KofRLhHg4hwoG4QNrY2KiH4THHHHPEEUfwqpeHMDdFVyn+jjkHzn/5p8dzoVzi0ljhUhn+1GtLS8ukSZPoeksEF0ieB7hYMlNLE7WfXOqTCdEJp6oop24Rp4qxI0BHZy6CYptaZDPxhHQUyJV8W6NblMdOSLouXFUgA4BITBUdl1KVtDelRHlSEcVRCwGdNEMlIpABwF0lYhCn9HK+RkL6To2gVKcG6ORCYrHv5CpFl1MpIJUiUSlEXYiK0RvAi8rW1tZNmzatW7duw4YNkG3btnV0dHCNxNbU1MSlkYfWuHHjxo4dy4Nzt912GzFiRO53LjUzd2clKu3s6PzNt/725E2rwk+isDRvgOLWdOxcCibuM/LdF71i8rQJqOhcl3P7VCouUFiyYN1vv3HbmoWbCg2N8dc2mJg1cIGM1xK1hIsxq6rKM1Spt27266eddPbhv/7evcWu7sn7jtq2pWvJM+uPOXPG8a+aOefOhZP2GtvU3PS7b93ZtqPnDR87oVzq3rBy4wFH7vX77961bWPXp77z+kXzVv/5sgcKTYWPffuc1Ytb//7rR9/ykeNLPXV/+OGdb/7oiVP2Gbv4ubVs7ME7nj/m9IP3O2JCW1vnoMb6m/74+KvOP27+E8tuvfLhoSOHf/Rbr1/6wuprL7+3s6v+nPcfftRJ+817es1tf31y44JW3heHr6FWGsLO48U/OxHVQwuxUsd1tL7c2FsoveurJx118iHxePvOm86Yz6RPYFpNU5A+LH3HqzVQsq4bXVGNivKIq10GOJAIpNhvUZyYtpgL9oN0A146tzE5xftVlEonlS4RKJWydfPW+//x1BN3LFy3cFuhp4nXZuVKT6WxMnHGqBNee9DhJ+4zYuRwtRBp4W5+/y1PXnfZw+2tvaVCz7SDxlz4n6+NvwGcerLl0lMBpBN9AtMq6Ozs3NS6afXq1Tx4W3kEb9jY1t6G2NVbrivWNzc1DR82fNTIkfp0ESJvGdMPyE43ANKbCUhPI8BDSec23a1KRDtVUipPylMgujd3BtwiUVXxfseKpzHVc9xE5ljsA4qXS/1OBbjPm6uKqZ6enHRCjhNtAHStq6sLn+o7MM4uFH4QP2dnF3h74bu/LXwiS/rHzZcWztzVV0gLhb8VCj+KMX+mEvwjfs7OJf/+71neH+rqji0UHsySATBt2ojFi7eFU8H/JYWk5mRxC5mr5JMO0DGgwN0uIp4qjrkJIkZ49Zms5ZiDDJopRdASEKIaeyu9f/vNvbf86qnwu4h4Mz8env5LZZ7qm5oPPH7CaW+cNX3fyQ3xJW009B21Z6bi0v/RNTK8Ya5wjTzk2DMP+PV3b9h338mz33RIZ1v5N5fesv8hU854x5E8vXDG//6z+x69ZdmUg8ac/+lXrFu1fvu6bYedctDVP7l93qNrP/2d1y9+Ydlff/BYd13vJ793zvpl22741UNv+fBJpZ7C1Zfdcd6HTtp9yuinH1u2YfnGZx5eNPtVh06atttjDzx3xuuPWrJw9fgpo+c9sPyWP8wZNmrkxy599dIFa/58+cPt7ZVhY3qPnL3/SWce3Nmx49rLb1s5b3Oh2Fwo8K46vPOO+88gXr1GlqvXyNlcI3OnSNDZy5Ka5xeIowzCQEraKF13P42VYqDk7pzSc3PUmxPNQ0MViCgyENO7pRZSqZZ7G4aVMK66ooijqvDYEQAvl3qWvLDqpj889vwDyws9lWKlOfxUV6WneXTx8NOmnvjqg6fMmBi+chLBKuoCO7a3Xf2DWx/5+7K6Su9hZ+55wZfOCT9BnSzhdWsVHw4xbVHMIXjSPSf+FDmRtFbJrQvgPr1K0y5K8DT1BCm100SAbxHgFjuBFVWBiKI8gFQxnSYisxS1SAG5fQLpqU1EpeCorhJ8VZKK6vLM3MlxKSVEeK6LaZ3XXNPc3Y0qMfUrvbdSOaxQCC98kvadSKHr0boT5hVmNESRVirpBGJXYf2xhX/sU2kMP3Ffbd/JU1e3ulxeUC7PbmpiZxRsEAme3t61PT3rjzzy4AMOcImYVav4059u7+k5jSZPAHYSOVujR//lrLNeH3Q7UsJ5EdeZlUIU0YlzxKYqRJEWdRE1JG2Xoi61pOZaUCKmoxRjsW/b6RCIdNmeuGfe1d+6Z3trZ11DXX34pcTwBdfwZVe2QZ2n+0LnmD1HHXbqPie+6qDdxo8K46rHDvFkHwiirpGruUYWG+rrypUwidkcV/hvffh1ifAR0jSFEl3hhyV4C1uulEsHnLjHG993yh3XP/LgzfOPfvW+J5xy6JXfv2n/g6ccMnvGPdc9ftix+2/csPXvV95/5GkHnPK6I1avWN3d0TP9gCkvPLPs+t888tGvvHHpopXXXfHg9Jm7v+PDr7zpT48+dNtz7/rMWT3dpT/86LY3XXhqT13PH398W2FHfXdX75nvOHaP6aP//Iv7Dpu9z2lvPJS17/jjo3f89kmukR/+1quXzV/7lx/fX+lqaBnbtH7bpukH7f7eT73utmueuOcvc8OvitR3F3qL4U03Bxx++pezFG+1uvpK/Cne3mK5rtzIG2NdIynpzPhEpadLuniYV3MvkhNgS/1SgDwqATfCqSoqlY1GRVUh9svgCWF61QMhykxEzJUUrXsI3GPlka45KOIY5HHVEY8a1WuiFrBh3aZ7rp8z5/Ylm1a2x5+nrustV5oGl/acOWb2a2cdfMy+zYN5ZcPe+taCEMUXzlv2sy/dsm1l22s+POvVbzuZ4bnlwhrxuCyqPaxUPViidLXIrKrQWypvWbWmbfUmynXF4vA9dx8+YZxKqV8TPAdA5FFUFS499aeRUlrVfLjmKAXaoQgxNuVvL5fcTtRYD5coIk6UX2JqS3vlSXW4nESl6ESZRXJiWnKqqA2kXNHm3OqKqU0GOQFEYvDA4FRjxM3O4GF/kQgWw7iqk39qj6ItaZRF3kwdyLr+Hw+VF61redsrho0KV2RNVrRNYOeKOka4DlkkOAJItbq6gUoZpxHk/34k0GlyCklLIhZdShURw13J5rLGNKZEYBsi/Q50FeTmALgPhOvHC3NWtW3cEZ7yec6vpxe53NAI4Uz0FgvFHZu7F89dv2LJmmGjBo0cM7yhIbzKxgQYojni4T/hZ3a2P3Pv0h2bOuoay1wweMqiEJ+4OOvh+hto5OE/4Z1lvM7E/7d3bh85tuXo2QdNmjFmr33Dp+U9c/+iMbu3jB0/6uG/Pde2rfvQ2Xs3DKrbe+Yea1a2Xv2j2564ZyEXqAMP22fJouV77zdpz312nzFr0sFHz3j+4eX33/R0T2fp4CP37unpeubxxTMPmzZj5qT9Dp44bcaEjZu37DZhxLARg+c+tGj54g2URo8d8fzTS5Y+u27wkPpZs/fdsmHbvMeW1jVX3v3ZV888dtL0AyfV9zY8evdzm1Z3FuuKlfoSh8M5CEfic1GlxPBOMrzWyH5mB8WwE3DGRASV/PjUbaSSUnEDRU64owiIw3ZaSBP0YHCVkheSopLnpEhFuNJUVGMuWpcHSASI0on2qwpnYz4bKCr5wSxzZ0fnEw/OvfZH9z5++4q2bZVifV38FKjeUZMGnXjuAa951wn7HDS9obHvQ02JtGuC+eixI7u62lcsXn/i6w8eP2k3L0dMlxYXVPU2JKYGwVUKPW0dK6+8df1//r7jzw9sufOJ+nGjRh48A4O6iL4hcqg1hN0kp0skZ1PqqrgMqSg95UT1pqfIBKQeuBArAfB0OID7dpSeRs1xmiuJCHClREpqtBIcVfh2qY1AHhGJELeoZJ4DNh2LPIARqQ8eenFW50sEKLz3kApnjs5vuMPyZBKcKkZ35PErHxL7VhEJ0+KcMm8Qb7x3/Rd+2XXbU12r1g4+4cDG5kGhIVY1jpa4VJBjd0ilcESpgiFGOZWGPUivvkcNjcR+fmZHsEOpmvslRJFaLgJSHaQphFHAh+HJQKTfVDYhFneCq/Du7p6FT6/asGIrpxy1WCnzlm7Y2KZjXrf34NGNG9dt6+0K18S63u7WVdsXzl2/bdv23fcYOXRY+FlZzXEUYQtbNm1/5r4lOzZ3co2N7xoRwyUwLh5Q5dwI2l8I8UrZ2NnZu2r5hg1rt/R09axctunJB5etX9i6Y3vXqhUb1y3bvmntljXrt7RtLq1ZsWX+kyu3re6pKw/avrlt/drN61d2rF/RumzRquVLNj95/5JHbpnftrmTxbdt27p8/qbNa7p2bNu+Ytn6NSu3rFm2ZcOqbVs2daxd0dq6tnP71p51a9Ysmrti8XObOraEX+rYuG3Tinkbtq8r8eays7ejq7Nr85odD9z63NLn1hZLYctx2+EW8eYjwr0nY+HnWovx51qn+BrpcxUMVSKolCURKDaIA3PMekijKEoUIaYEhJ6ILE8eGECl3AaA/SJpmq7l1EgVuKFUem63AmI07uQULNoDVi1f+7ff3Hf3H55av6S9oa6uIXwpoq5+cNM+R+/2mvcce8zph7S0ZJ8GYOjAa1cfOXZY69ZNR588c8jQ8CGomg9UFXeXIJsNKXEqKO3tLO14YG7xgfmD20vFuvohx+w34tB9XBUBjE1vHSH15NbNpQapRhHhmildSEvWUxvRw9NGuEUgj0YBT3CvIO5FrdijRlURAVyidDkVlSpKEZxqjhUgbh1IFFRiuWBNloYrmqTRG3CvzVpISmqzKGJFohQfNZCSJUkvgHOB3PiPh9Z/7y+DlrUWi/WdC1d3b9vRfPjejYObc07vIZOqoxxFBLj9QFzb2Glv6c7E8cVrfoCt6hcB6OEFQjXKk1PStDYyRF1AHNEp6LcrVw1HppcqO9vk0YaJbdvbr//vB+/587PxJzHr68tdTYMHHf+GA1///pPadnQ8dPuzD1z3/MYlbXVcG+rrS+H7bJ3TDh596rlHHnDY1GEjwkf2ME2jRBCWzV9z1X/G348c1FvPG1Emxx9rTfbDxYSrUdxOoL28sQ1XJ5rD/8IPB1Xqe8q9xWKhqSl89muhVGloCl+hLfSEz5oL79NoC98VDF8hLpd7Kw3hr5iWewqMZlahsRA/NqeO61xPfYWrfLFU11NG7A2f0VNkJ1zHgjXeHIWOsMnKoMa68Jl2XYX2xt6mRt4FF0rdlS4uhyxXrGsqhlcRJfZQri/SHN5lhxeF2bnFFn6gtS6c4d5iqb7cyOrx9yP7vtZKzMzJ3dGiUhvQFaWkUe325HoVreCpHYJC1boXQkxtcUZQ0p2g2KmdAA0kBfIItTM9Qe3pWOsQlWxAkR+wWuvGrc8+uPDWP81Zx9WRF2D1hfpyXWOxMGbGsONed/ARp+w7YsQwWjRh595sMyqJR2dva+vWkaNGcAvKlhoc0UUoYXAqki7nLolYe7a3r/jRte1X3NLY1dvd0jzqM2+c/O5X4U5t5gzRKE0jooubSIRoM+KOVIFG9VsV8boekpZ23QsRJ1pnjgamXRZF7KwdTtQ0p6ktdodUionNEpkgXQvlDLUiMxGJOd2RqrpqnXDAckR50l6VUudAc4jaucX0QNwOQQxV5pRKa/94x+af/r1h8Qae03hL0M6TW6F+6OuPH//pNw6eNI41PF+TAQRow+lCgkSIdFJVbVZEgez0+5E4gFLzNHpQKqYEeFWJRJYR16riHqUoqJHoIVQNpcG3M0lHaQmlmkO9vqG4fvWW+Y+vxlrPpaZYt9cRu59x/lEjRg4fNKhpr/0m77HP2O7e7m1bOzs7yoW6Hq47ras6589ZtWnDppHjho4YOZQ5mq/JXOi2b9nxzAOLt2/sDB/oFr7kmCEzhBiImkTZZbii1ZegKPX1XB15VcQ7A643rFsXfoOaWvjz8cwLX2dg2dhf4UmtWNdYx3Mk74PrGrhwNrJmeMfHfYYZ4e8EcpWF81YjfKRcaA4fXRe/ABw+caehwkYbwm9z1HHnqGssDGJ0+CZj8A+qLzQWuSiGVcOHLIS1GRyOIewkfiGCCEIMxxMOJvxkK2zWK6ZPmtL3g2/BUI1C6ItpekuJ+C4hpDd67qa0DoI1QrogXRHdLaEWETojlPrBkKlVPZ2pqm59IE7MhsezIaBQIoZB1VEiimrxQYXXStVp8ghxrUJXZ89zjy+68ZcP3P+X57av6+YOHD69sL7SMmHowa+Y9tr3Hn/ocfty18WvoxAhapS2Ya7V4WhDhgzWBVIKESh1lE7MHZE3LyepFnUXsdzVs/WR50tzFhfLld7mhubjDmiZtXd4NVh9RNNCBO4SSYdYBCaIrppQVYyWoGgh71x7pqTUtpzo7SmN9WyUkM6RQaIPJ3Zk84FSmZVaNKzLpuVQTBBdDQ0RqRLK1TQVo7HvVPi+QSqiKAVIcVWpkUtBrQLUntt8rPRxRa8ohVSbDKZ0k7xa7+jaeMMDrd/9c++qVt7d9IQP3qwfwqv0SqVr/sr2tRubZ81oHDEUbyV8gTS7uXMIo6rbc2oRAkScRmOGfiYC+QTaQFaolkSIqoJY7B9y5hDHBIinEySKCKmYQl21pbQFS0OxOG5SS9OQYqHcy5u1oaOHHnbyvuMmjqaqe89eB+xx9ruPfeU7DplwwLD45cOGxoZi55buR29a/KfL73r4rrnbt2Z/GpSZ4VPFC4Uhw4aMGDOkzDkNA7jCha1Aw2d9xX8xDdP1jzT+Y8vF0MSzCZdGnq0KlXKxu1Tk2sUlkkauZOHtY/gyZnj7WOwtDKpUmiqVRu4cZZ5heC8afsmtrlzgLSBXsPChKmEHdfxjJko5vBCAhz+BxELsJL4ci7/zrw1W2DDvKep7y8EZN1lfLtd3l+o7eSfK3SS8B8bHDqv7j/+UhrOOp1ApcjobhjbsNiH8ZhQqJ8e3o7igqnWnsZh1GdEV4FQEpL0m6aICKRcaHi2UBOkQiUr1cLKtlqgKoj1AIlEeXWxAbakW0uUJaRCyp12LAL56+fo7r3vszz958NkH1nZ3Fhob6hp6K81NTfscNun1Hzz29e8/ccre4df/BQ0RARoijijuCGrPHhCRQpQIcgaikabREpCl4V8MVbjk4UpFHEWElAs2iHiUiDyCUnQgBUisdaaK/TkiDzFMjLBoSDSchgUSp1JBqUW3pKPEiakHEosZsQjsF0khg4irEgUpgiZItFmiuaqxtQ8qRUvAQFzA7zlOgxLvreXuno23PLL+538rrtrcFH8MuyH8oAfPQqFe6K103PzE+p/e0LF6g3eBRxMM6yKCShKJJrlU6DtZQJ08nLhy6DEmRVUrRI3QBQZdtlwjEeCUiGJeG9OqOBGQ7mKaokrpNKcqgTUr1v/k4hvXLdzR2FQ89py9Xvvek4YNC19ETadx8Vu+eM1d1855/v7V27d0cxGJ75fKQ0YW9ztq8hlvPXaPaeN4tpG5VCo9cPNTf/vlYzs29hRKpfDZbdmTAueEddkwqeJOkBrAhS586bXCW7z4RUyeCrk4cVfgAscr71gMXi7aRDWVk+EB3FPif8NVUAj3nZ1e+gSz3PLIEBVOK5rNGgWsUE0NKTAXGwbVHfmqae/4xKvDRTNuifNJ9E2Qcp/nfm81paqmirnmALWY4FcLEVGTuXW2tW7f2trWWw5nEq16DwrQ4lKqG9kJlNwiktqCrqONcDXYd15FE1KeOeMEm/uq9XVrV2544G/Prnpha3d7mMsLZmwcwF6HjD/m7H13mzQSM+dDq2uOdyKESZpWHctdiZeJw0cPDT+MVuQlWrTE8xJOTZVLBxIFlfq9CRz7/HV1Pdval//o2s5f3NrQ1dvTMmjkZ964x7tfRcU3VnRlfvemimwoXlpca+GB2OmIgk3+nC4/IMUAkQ2S9mpv4iJSPE2KRWKqO6I4dS+RsUCEqg3i6ESlbqltTGfGvnyjuMxwNbrFMUysmalebR54ey6Ji8jmIeJEoV+bhoO0RPQe1BjmoJTKa667Z/Plf6tfuLY+3MvD82E9dVxMIhTCH4Sva25sfs1R4z957tBJ4zRKQ7Robi0pRG3G0c6cnlXVLKg/VTTOI1BURRG3rlQkHSLuKFHQJtDTkrlagDyqSoRIlyjkPLnY1dX186/c+PTdq/c6YvTbPnna5Knj0W2ojmJCXVd315P3z3/opueXPr2ppy284ytXejrLnZP2GXPyGw4+4LBp4yaN0ocY9PT0zHlg3txHVm3b2Ma7wdj+T4CjLJcLm9e2t65qq5R6Kw0lntPqewfx/m9IS/246YObBzcykX9cANnjPzn+/0Gwc+6pbG/6Absff9ahQ4eGv5RSLWU3kDjRJ9mKRJFcSeZY6YPF3BCQzlEK2bRhy2O3z33m/oXLnt/Y0829N7yASIeqR0rWvzMouUUkteUaXZWeIV6fwqZiTUMkxv/uhL4qd6PwKfONxYaGumL4pdvw42Dh+SH8SZhSb0+p1MNlE2s6J9I+eHg2lqeSQn1Tc9PkvUYd/ooZR84+YGT4UOLsRknPW/BGLiKDCFHPAxJB7aOSCO/e3r7sMq6R4fuRPS3NvkYKapETeCbc0wREVZ0S9YwhReh3D2oktdklc0cUkxwH7kqXdpdSkE4Tlw7SgXDN0Q4BonvTKD9wWushojuVRwpRgOuWcouWpqQItCVVpQjW4ekcVYEHUrJClMfHmIqCFRHBBh4FvNJqvemh9d+6tn7N5oZi+F58Bh4L4UVjJL11DZVKT6XS3VAYetYREz73liF7hO/4MJP9aHW4xvYNry6NQYo23+9uM9G7hIDao83NAi7FjrAJcSI6fjt3DbfEMWEmjRIV7XFq1CoDwc5rf3bnfTc+96p/OfTU1x1dDH92OECe/Gnq7V2xeN2DNz3z9H1LW1e3FStNXBIq5Z6hYwbvffjE4169/74H7dnUFL8bVC5v397e0barD2oaCOypVOpduWjdQzc9u/ipDZ0dvBetFAvFcqVhyJj6w07b86Bjpo+dMCpuNbwZytr+b0GlsalxeMvQpqbGLK+eTADPzmQERKJvX9+p0hLEN4FEGTxBXbl7l+9vVdv/j7z/gJPtvK470a6uzun2zTkAF7gXOWcQIAiQBEiCIAUmMViWTFHJkm3JT5blsf2e34z83pufrdGTA0VR5lhji2ISKTGBIkHkSGRcZODmnEPn7qrqWfv7n1r99akGpXnzm9/M+3mhse/aa6+9v++cOlWnq2/f7sa3/vShh7/+8sTJmu4zusVoRLzLzW8l0BjZApXQW8n/JtBVGvI3jFIt/q46vqQkXklfsA9/OgfxORgHngb9tDlNxHJhjltzQ2e+0b1s9t2fuPz2n72hmm60nDqdN8FnlVRLplWLM4+OAoGjMyRM895H1n2P1MuajsdDwpmQp34c/SiLuArBz1ouFUsn2CaIO03FqJZKraCqKF4ibhEX2C26IiXxfNuCe4mu2uMDN889eW++CsTAAMlTodSYl0C+gdKc1l4ICma3oCjmXYI9dmpFRS1askFmxiaO3/Pk8T/5ftvL++PLHsXWAtgx6jWxqotqdnZKL5udHQM/e/PSn7+9/+y1UljLwz1ZEOd4S1X86BCLcwdDLlLqL+leQwokjxLxRHMTJUVOxbwFES4is5APVMwn5Mht9ogLpfanH3n5tWf3fOAzNy5eMkQXa8mQN9IljI+Ov/zs9kfveWH7Mydmxtur7XW9fNUr1eVnD1x3+9Yrbtqyct0Snvn/e9CYbRzYffgnP371hYd2Hd05Et8iE18Nq/Uv6Tzn8lVXv+eCi685R59JFe7/S4IzDDiZnE9xSpxSzjkGcSkiShEhPCI2CHlVKTpOK/KHNQ3cs/PA//Rb3xo/1uiMHyTTqMXTSKb43iI8gjYlMf+sAxoO/V+8nPt1vXDa7kFScg5Q4rPdVNbWPLaImRk0h+gNr97yzdbjL13aq41q3NvbdavU7vVWWAcaOyo+jW6OaPYG0Kywh/gLnPZG/GDitrap2uzQmt5f+h/ev3nrBk4gZ0+nl9h6kq2XxJjffJhcrbS36x659z/8xcR/urdjqjZ3j0woLUGXo0qlmSUbKUNaDeIq0S6F5XKuKNCFImKgKBZ5E60ijZ5AtVXMq61QFU8pFuUmGALerkovij2t01AcpTiVrz2dK1KfWKUoaUBMsJibOcmO6MkVNhpRxJkmJ8QlGoXa9Mzhb9x/5k9+WH/zQDX+riH9wzqN0TMgvoQS34vBl9TUUk//QLu73jY926gv6u2948qVv/LBgXPXU9U0EcByID9A6d48O8FpXXzev4+Ug364eyyiCPCS2RyC4lKuw9mWCB4iMzGgUII7Mgfu1IRe0iaZ7R/s3bhl5bIV8WN08kaIIM7qVPU+ae3GledcvK57oDIyMjZ+eqZSa6u218ZOTO145fS+ncc7e2YXLe3v6ozflKwWumgXRAQp6DrdEJeALoKh4cGzL1izZvPSqempMyfGa1MNfe40PVo7uPv09m37xsbGhxb39A/0+nQJjBLxpcDDCbDZI9iWr+7NlGyManWWFNmsiDAEJ6KgPaOXtmczJFeIKIZSe4B1iID4xH0vPP/Xu7u60s1FZze9OVdNz67/TR/q4Pt6/5YfsXjW1Ur+Nh96PtbTL94Wi99Lo/PWvLXPph/klI8l/m2Gt1Wq8W1mutdWKuNj9SXr+7ZcFL/ISOvkZ5WLH+QlG4BtGJyKC/XpmTPp+1rb6w1/X6urIvlYcYiioFRgoIh1IKVgWUsrEcSdMkdQs/Yaiv5Ln7jkHyjpjM77aBVzv6utosmCH/aUYulDoj9KJX1Yb/XkPFccTdI3xxS3qPyc88yVLuJzGE0p+rFwSbDoFIJCCTjFY+f05NSh+58++PnvzB483t7VWevpaHR3zHZVZ3o6al3Vjlp8P2L8a/RG22R/VNs6O2a6q/WujnpvR71ttrbn6OjJM71Xxb+b1GgOAbA3EUdghT1YAeIBmoF9oHTiFPNThsdAZBpVXhYRUawzRJySgIiiWEKpkdROpXDrgpdoijoWzQ8NneW8bs6jv7kxpVMTU2++svfR77706uO7Z0Z13tsblYY+v1m2buCyW8++4qZz129e1Z6+eNtca25jEAFRim2CVoSH3jZ7eP+xZ+5//YWHdh5+80xtujHboc+U2nqHOjZftuya286/6Lpz+/qLXwGWr2VibsTYJPrQSoQNCMleiGoRJwqlc6JoJdULqMQ09Hx4dGbmXGRU7lTMFXGJxHwnIkpF8Bh/8Sc//Os/eamru4uiVksLK4QvvQmL66ARX7gsGiUlz9z3Jqkat5b5errJh+BGhsREbSrV0j0pULyFS1paN7TmTJmDNPW5daPUHBCvYEV3jE0Hw7EoZbmo4QZ8GpIdRThTN7Pi28BmGrO3fPKCn/3Vd3PqFCFFT0LptOcpXDF/gHJMv837SJUwM9DDFblURAR7FP1wO+bLibMHldxVMrgr/tDexsfHdh1sHDrVVquj/LeJuHJ0Zipt7X3dXeuW961b2d5RjdOXThfQSRPyM0zqJyweIo3YFP3AYRCBU4W70aLyydMjh5/YNvHGvvi7AOl6fumqF9rj9ln79lPd24/FPxjXzL97U8eaZZXp+JpLGpRcjdlGb9fyW69ctGmtXqZDam6SJfJ1TdAVeT3BbGdEVIHTgUoK8RWck1QJlBSnLKPU3Asj5kSwX7zU0jpTKaOAnUWeJpcMKCb5fAhOES3kdlJKJ4+fevSvt73w0P5Dr59qzEykb6Tp6OirrL9g8U13Xnrh1WcNDPVrKcyArTJB3JMFbyMZ545uenp656v7Hv/+Ky89sn9iZCr++cdspdpWX7ys+4o7zr38XVs3bF7FX6bKTC9cUfBuEU28k3xLqaNoYXu5iCLuhfIhFgXm58uhC6QMVIpN8CgMTt2CueTBAEjzUuJt3/jij370pZe6urrjs810v9D/eqj0nEtftBQvvqc3voM83Ufi/9DjVqIq/ngJSf9SlG8S1v+xm/RF2/hDBx2l9P3I0RhFEVXT/TVmpFsa60ZRw5MuK187ja54c1gMD1d6pxj/wCbNCW9SgkhLnnjjmO7NWk57SGOaiHL6U1GT40cuFkpsNr4ZdnZW7+5u/sQFn/z7xT1StfyEW/FjQdWR80yaO43We+S6n38/g3InD66gOQJLyyPFS+DnwYVgFsepSCqIMDNZ5gbSK/eZnfuPffex0R8/17HnhDpDSjrQFHFiDuug5DfydpuFt/O0wl1USVudebtbQElvTd2bvjgRF1JbT0f7ljWLP3bzknde3j3Y39Dpmn/qOKVCOqnFGeYBsq7oM2+dXiK6I06qmNHjey7SL0mMK5dNN6F3ICPHT+z8zf/Q9eNX2zrbJ7ra1nzzn6+66Ly63kOUkH5rk3fIchArEHNSb1icPQsiSuf9vFYBrjY4ooAiYIBI1FCLAgqrJnthM0eHoMOFZJnnwVCalnOVOCqXDDyCVxfyB0aQomgDomAPOmeqt69305bV689d3N5VP35kbGqsHj/KfKZx/MD07tePnThxqrunY/Gy4pdZsmeQ719ALBH7Ozo6lq1avOH8lYPLuuqT0yMnJ2ozesDax0dn9r5+at/2AzMz08vXDvPvxwX2JmiOhggQREEpDzmcklMIYt4l+MQyEw6xgpjvAR0iwOUHKIKGKxWxLkVzEGlJxuBWXC3p9IpLe+XZHTueO5K+Fy7eL4YnPhTUrNeG9M0qSlLFN7DkVEhDwxktqavwJh63LlHdoprOiDh0/8MTB5f+ELhHpluvIEMyFkifAMPmnKLpBaw5M+nihRHONlKjB6YVioFaTms1sjmYg+t0bbxo+cXXFL/IiBMIrDgiinMx5JeEH0GcglLx+Frrk3yttfgZAkOXnUsLZhGcpKm1gFIrrKWHFQI8BCilykAhuYqxmCNWKhMnTx/8d9+a+vJD3W8drU5MV8dnKpMz7RPT7eMz7QWZbp9IRFFKob/dB+ZkszKZponTXhDpjvlMTeCjqVCN2NxVHuUUiVWa/rkJ2ZBCT8TtUjISxz4x3ZFsbWcmGjsO197Y3xjo7tm6vtrRofOVTuE8cG45yaQ+8+J+efElgW6Si7kueGYgcQ2p6v9KfLlO/1cjxnO2Nj19+vtPVncc7WqvqnnwU7cOrFiCLX5kStMZrQmMFGF1RCF/sVL0BvJNWtQ0xfLPa8WqSBkryJutm5RKRMQ8hjWB1Iqr1nODSc4dS5MBSmkVQdx+TqhS68kyx7EVaSVuYIuXD647Z3n/cOfYyNjI6Yl6LX6yzeTo5OHdpw7tPN5WbSxZOdTV3UVjjpLCTEUIiku9fT2rNy1bddbi2Wrt5JEzUxONSrXRVp8dOTK9981jp06f6RvsGlo6kC6R6GIUkVSHxlhDJaoLIq9CnOZjLQriKCxU8iMK6IoQYA5ht8lSmPM0n4/o4cAlhbhHPnu4mn4kvZD5RMMGivcRTal5f9Kimh23pZTpvzSUG1VFMaS4m8XloFzPt/TFHnpD1oQoxLvP+PkNTEqzQo3/4t4VUG9Us1a1pOnpD0UONd0ISfUnKX3RHgn2JqBpdI4kJyP3yDSOoaw0B05++DODo9EqiuseefqpV2ee215t/n1k3COLerml9CLD8zFXBHNIvjch9+dObIJmNur1U0+8PPqFe6rHR6vV6my1El+vk1mvvW/3UX17Q6mktODpPb/1v9XH/w8t2UdV7YnM22pz5tvsU4cvkOr1I7JT41OTU92Xnt2Tfu12lGVonkkAt2jih0PRqYHuqmDFJMnlSy7uu02e/mybnpg8fc9P2nYerVTb9aI78LPv7F+xNAypGt/4n14TiDExzSy9sGAgFVB8EQJxDyFG2eAzAkZzv/VdVyIQp9kET6kkuBcRA2g1GxKF3GAFEeQGI/eY5B5zE9k4RxC4IIM4Q8QFXtskL1m26Ja7Lv/kP373DXdfMLCqvzJb08f0eG37C0f+8o8e/+YfP/zaC7tmZmaYwEBxxXxL4jG05U2YQNrT233OxRs//Ll3fvhXr9t8xZKOLr021ur12VNHZh775mtf/h9/dN/XnxwbHddUtXgVbzjNCKDIgKhUnNWJEhVb4Y0J5iJ0CQu2kyqqikEtqRKwiA1Oah3OhgV0OwUMOaRIh+jTPiVxk4rbQXNiQOcoOaMQN6V0s9FdDmG2Ua/Wa22Nmp5v7fHNcw19/hP/6KatFl+1bcyqNqNHoKaX/npjtq4/1F6vNOpaOH5xdjTqv/a6yrV6m5o1fTayeq0iQQvMzs7UavFzehtaJlIVpyszs41aJeZpYPqj1mjM6L/ZuohclZlaoz4d1XglaG/T6rU4qxqsreoOoLZG+rY/Dja9RnIuihfI2EkcZpw9jUiuuRMOMTSZy0lEafIG4BJJ3SUiSMEQpTQVLijDkMfWXsHDBbhE0hy0QNgkoMVVQaJiY3J6fNvOmUMnGro76rKK85POjf6LqyzSOOS4JuK+kXacLr/iGmwinRYdWTo/zVsRaVh1Qop2OmN5ZUmkH60wxJBZxVQC4dTIMCWzt5R42lg8omlpMR1p8SlTaDE4zYz96EN6sz1dEbFPzY01Y1AYVFBVF+L0W/sm3twbP6Ck+UIRQ5svFDqxInDp5iZApYI1J0BQBJmV+iFjLIrXEqIzQRxDDj3GesFN11gB2oUiTwpE7dJLM4G34XXztehCWeD3fpTcpDEjQZyUyG5EiErRjTSj2BncsOIWSK7AhWQMIAJ4rtgpEv1ZSWgVY3TzPIoUaoIVCI9BuGdnq9WO4aWDmy9Yu2R1b60+ffr4VGNal9vszHjtwFsnd71xcHJyctHS/r6B+LH0Qpo3b5+C5igylhKKU6Grq3Pd5hWbzlvZ3Vc9cWJsYnymM/5OrTFyYvStlw4fP3Km0tE2tKSvsyt+NbTAgQh+XLztNC+WoyQFp0BpQWAzz3cLYbKJIacUQSWhUBMQ8xJEG2P/2ETkMXcE7ESQmFVnX3l2147nDlY7NEg3y2IJ/dF86ZmHeF+nD1Xa2xet7B1c3tO3aGBmtm1mqpbeh1YHFnctWtkzMdNWm9bnr139w72LVg70Dfe2d1ZmZqZna52V2c72rrbBFT09g9WpSd39OiqdbctW9/QOd0xNzVSm43Vo8eqB/sX907pC2tuXrhseXNw7vKivs7Oro6t76Yr+gUXdg4u7Boa6qpX27t62JSv6BhcPDQ711Wu19o72JSsWDSwd6hrqma5P1aZjo4PLq2vOXtu/pLPRNjUzxXcXCfEaGScgZfEpQqQ6OpR0AtJZ0svqxotW8D6ShyDE7EFEUYQAcT+1S2aiIFGFxtTMSHytdWf+fa3Y0qSikVSc6yQ3CEoFES0KgSv6cRdcAko9dm5a+pmfpx96ceYnb3Z0dKi/Kac7T3wNIIZGlJTmFfeaqCQh5KjF7SV+HkVcMTTEzSY1hSMdvp2Fh6/iazP6M3alyckQ7VLiSwdRYER0aXzsqzknuQqEmObqv5Rr4xSUx1UcM7TFuAtWOG9pVFGJ7mDRk4bG0iLxR6Oj2n31lsGLzqYCeD7y6ISTpiaB++HAJvCQcf7da0IsQUN4cAXPEUgVxacmJ0/d85PqrqM6g7Vqu95HDqxY6irRq7Arp8DcTkUO0DqiuKJAdd7PNIcY9BhSUmNxDBjoytOoNXdDVUCxGaRKGbkOLy1hwlriRp6Gb34VINLrCXDriIDUfqEo6AbW3bly7fDqzcuqnZXjh05PjOrzML1oNiZOTuzffuL48dO9g51LVw4zQX5iCaVSnrKWPkUcXDyw9uzlS9YOTo6NnzhwplGrznZU6zNtJ/ae2b/j8Oj42OqNS3rSr4mhxcRXMAoPuThAx4BfkIInV4ScwPMoM/6SAcBlsD/JC59t5lDKZ5YUCMj1V57dueO5Q/FzaoRU1P+8cYwsIU/1Vk/vwnoW9d3xs1de+66t51+9fvXZi44fOTp+YqKru+PGD1x084cvG5mY3PfGsa6u6hW3brrt7ssvvGLjpvNXTE2Pnzw8UatXVp3d/+67Lz/n0rV7dx2cPDXdN9hx589de+HVG/btPHnm2Hj3UPUjv3TDBVdsOLD3SLWrfvcv3nTJO8664MqNfYu7h1Z33fzBy8+/4azzr9mw5dL1+jxr2abhd9591UXXbrrg8o2HDp/qHez5wCevkf/8y9cvWd63981Di5b33/axy6+6Zcs5F67WK/Sencfj97PE61wcTrw0R4yvvXKAzZgONB24HPnfRxqcLZ/V/JTmJa4oFEV7FCHxM82fKv99ZNpX4bfZQMmXFvLJRBtAaVT0NLvyVLE+NT36xCu1p7d3VKt6/Y7TEbcUmlVPf+i/dL9M95qkhMCpC7WZpk7EBC1IiY4whDuGBY8NxOi4I+ONNVgv9YQnrNHAOrGDEKItHQ/OeT1pRtjCW8hFqvYYqM74ntCilB74GK3/Y3iaLzQ7Gz2dPTeeP3iJ7pGyhJr8gRiUvYxABEoQRXMIEWDIoyCDuJA7QUnENj05dfIHT7bv5B5ZGUz3SBsgQs5boSrrlhalixKKYLE4eECnffT4Di9EQ/MzfZtJgdO8q1UxNESQwdPQBbh0Py15iU/FYm/mEEGiYMWLQvIWgWNBVAoXZFaUqCg9dcwZ4MSu7u5N56x5/9+5/iP/4B0X3Ly+0hvfxqPFxo7NPHfvrj///97//S8/cvLEGZkZxfoM8ZXHNERsSQ4gyjE0PHDtOy/41G/edtvPXzKwurMRXzBpm5mePPD68fv+6ytf/v37XnjyNb7AW7TMn4+YnwTbBOl4WNr78QS3cGZKtnAktB6RFZZAF0SosiUUiMBMKbSzImYRgaqAAV7sRMX0kbSiVPwRXzotIV7zFPWmbvMFK06cOfPMk69ceOWG699zYb1SW7yq+8IbNi7bMHTduzb3DukNRGP5xuGh5T0vv/Ba36LOW+68sn9xV1t14qyLlq3YtEh3zbMvWd3eMVXtbNt04erzr9608dxl0/XpjRcuv+zmrWdduKp/sK+7v2vdlmX7dh/+8beffOUnu3e8cOK5R97q6e3s6W5/5oFXD7xxbPnyJUtW9j/16AsPfu8nx/YcGx7u2Xjhildf27f7jb03f+CyS67beO4lyy+6evODDzx/33df2rvj+GwtrigdXiMOREdZHHg6A0J8zVg8vkybgB5nKSGU5tnmJCe5eBxTR6HozEv04yIClKIjpnEaJY8e1qI5PSqB1OFsLuUacMqiRCneg6C1SqsjKpJaz2MQ2Rioe4gmcHTxdbtip4Z4LBloxHGoR7SQ0on22UxIlXRZxcmN05+qxcxYPrKiPwZmwIsjVeJBFNFHo8hjy8EiSaGY33ohcxgKjdSRkigAPW2Vxo8D1+mOPKrRogVkozWdpzJ0DlUkKoXnJRE9guZ5VVGiYI7u1FzgQiLFqRiPOJts1PXaCm2rxBfTBGX4FTEnuWjPL4yY01ydi9miFDyORjFH/4NCbo6AM6JwJHBBC1SZQkQkzZcPU5MLOBHN8xLwQqpySMBd+EnzKHiUPRDNFCcV4Mk4Byleztx+cU4OIp7+gd6rb7nw5//pe+78xUuXb1zS6O7RGZydrh57a+yeLz37xf/+u089+MrkxFTyxxx10QiRoo35eDl1gA2jy7dizbIP/8LNn/2X77n6trW9Q20z8aWarsb4zMv37/3y/+fBb3zhxwf3Holvnk4z3cghsG0B0ZBNJTyCt4GTLhGmYZMiG7y0SiyQIJ1R4lQFmzGgiwBKgrj0nBDVBUERMMR6Pi79mT4ZThGiz+HlKXQ99ulvb+IT7njVqMRXVeMvDtsaJw+N7Xr18PTU1ODwQKPSvWHr+tlq455vPL569ZJzzl9XjxtO2+R4/dCekVMnRts7q41qvX+wZ+PZq994Zfdb2/aed/Hm/iWLZhsdU9P1w0dObjh3Wc9QxxU3bT149PSZM2PVtmp8aa19tlqtdnW0T42N7Xvz8MtP7Dp95PSJw6MvPrH3wJ7j8bee1baOTh1jozY1o8d+ptE4cfDU0T3Ha22N9o7u0bFpHe76tcuO7D6it7ZVvYlUC99pq484tDjEOM6CC5yEtvb0NkIE6NRFmiBSOpNUBREMyVgAkRYUIVr0h1bp7NTjFAvplE3X0r6iouuBgfHIpUbm5EMEe8Rj5vxNEvOUsYoCJcGk2Zwa9P+sHgGRdKRJlRYh/oivUcaqcZmFEHJU4rwkObnSR/p0IN1atAs5Ymy6Tek/eVWNUvwps0i654VV/6W60kBhjbXTWgWP3w0kGr8JT+dNj1w8eEVT/Lgv7TQ2m1riGk5bSjE+Ajp/zQsgHUVcDs1DwJeWa1bj68YBFdOukiuB1BEDVREtQ0kKJUBqXQ8NXCUepthfNopYAsPTDttmJiar49PFA9Hd3dXXE47mrphjYjHM8WimOU3de6YKseIu7ZNNzrs6Bc/yREfgoSKYPVRRHDFZAnk1R+7Me4VcKTWyaRHrIqBVtOKUdmBdoJQ74YhwYonAIYNDA+++++qP/MY7LrttY8+ieMp0VDoqM5Xtzx795hcevefPHzkUv8OlABO8qAgoVQV4iOlaqVSqWy7d9DO/8q73/NwVKzYONBpxS9QbhpFjU499582vf/6Bpx58eWJ8khaDEytRMVdEcMJZMa+W8HZivhwej3Up14EURMVcB5RMco+4RYAuofizYGkPmahcH5TifOp86PVIL2DxW8raL73h7A//wo3TU9PbHttVq9e2XLp2dmZ2/Ph4tbO6ccuy7q6a1lmxbvGdn7lx09a1zzz62ujRieVrlyxft6Ren52anFq1fvGqdav0nK7X6nvePLJs1fDmC1ctW7Xkjed3zeqzYH1K01bRG9aN56y5+IbzV2xY2ladbe/Q41utxLe7d7RVOrSR/v7+iy7fcv6V5/Yt6tOLYVd35y0fuOTOn7/x1W27tz21a8e2Y49+/8WLrtj0ib//znOvWNVWqTXid7nFNydydM1D1DGiaMs63HSwOtowFCg9LqAkKkohWjSUAnEZ8MS34vd26dKPFWv1+vEzekXEED2JuAuUJzTXQhGSfc6fK+KK4slYtAtUifqz+F/3G5XiHiObsjDGTSZoccaaxrjXR1RIq6HLL1do3JpYPeSYqY74gUazlfRNJfzrm9SpG0M6+bq9pYYI2olugeLx2UT8DrxYJf7dbWO2rrPIRvXQzrbp7VOlHr8YL35ySays7mKwBsRHJDEz1EjF055xxrZDEdH/7DkQvSEqDynJzY4CGhIHmVpM0CmRCkoV8QikAGdusJjqAftzYi5MHzrVdnJMm4xb12BP1/CQxNIoiLsgilYEnPYnbR4B4nbO+/tIFvCrpGLOXdUu3U8XqWAn0Sn3ZDcKrU64CaJIwePKquhJNzE+MTkxrReyqana1NTMdPMj53m6oKckkprkH1ZKJbekVJuZmZ6eqddmFy8dWLd5SW9/9dDuE9MT8buR9SyYPF3TG5Tdbx3o6GkfHO6bma5PTxZz+MiHty6kD03v6IyfXxhno63SN9Bz1nlr156zdGpm8viRkdpkQy+28c5178hbrxw6deLMqg3D/YO94U2fv6ezGNApFfRYKPo8E3VOTxw71dsXP/QueYuS2t1CF6lKTnHmVS9KyVAKsDlKwSCODQ7BH+UEi4q5X/GVZ+LvI3XTUSYlYa4RkKucXkWqs41q12D3VbdtPXTgyLP3v/rkX7+8c9vJoeXVj3zu5kWD/Rs2rBxcOVCrNba/dnDjuav7l3a+8fKexYsHX39u38FdY5ffsuGSd2xevXL56rXLF63oP3Lw5MEDp658x+YdL+5fsnLRmq3L9WL30mNvbLnsrFefOTA5Xb/42s0Pfff5H37jySN7R2rTld7Bzguv3aBXxm2P727MVM69fO3AYOdffuGB5x/aefr4xKqNQ+dfsfH5h98YXjxw8tT4Cz/e1Rhrf+u1Y7t27r/wqo2r1ix7/aU9U+PpWRq/24PDAXNk3sHrXcjs7Kb0PTucW84ej6ZSnqFCcocCL518eos8QWk84hFnx3cfrD3+WnV8Jj73GO4duOXSzp5uz2Etr4vCuvlYuCIETgtmcYutVaWxULLF30c++frM02/ET0LWk0fO4gIXsnNWgvS0l3DEbRBjhKik96MytMdvs4tvbde7ve56ZUYH1NamT3Z0z4ul0kHrxtlJixLd89KXD+Mf8Lalb5nWTbG9oXSmY7ajMdtVr9Ti95zHIpqT7n9pjHrjXxO1dcZP+Wr+MIsCaWuBgnA25iMGSi5GpRt7opE0eju6brxg0SWbm9V5D4SgkylF51aEKhGCGCc8+UnFXTUpPdBwCBAXPCHOcHznbeP0vc/M/PjF9qkZna6OSzat+OS79R4i6tmcvB2ROdbhgg+kNWKgXbYgUlN79Efe7OdgBNqIpZL8TJHIBImtfrgMC3oUKeHJzQClMdt47YUdj/1w25Edo7O6ErPL/P90pK/ezKYLW2cm/gGWPhM8fuDM5Oh0nAM9UxrVtka13qj1LOpasmpQ10zzoP920LOtvbHy7IEbbr/4gsvPKi6edJZGRsZeemL7j/7s2YNvneqIryBV447WWdtw0dJr77jgqneePzDYx2Prs60o8CAqTY9dfAb75APP79158K7P3Nbd3e2HWAZaYskEi6VHVuBiECxiyD0oOEWY4+sET25AyRfCRq8n2PCNL973oy8919nVRXO8GsmcXhnSa41eGKREymff8Wl7vTG0uv/X/p8feu6RV37wX55r6IVrtnH7py++9e4rv/aF+/bvOnnO5Wtvu/2K7//Xx886f9X6zSu++scPXHvrFr1l/PH3nr/lfVt1I3jo+y9NTdff+7GrZybq93zzJz/3q+996C+fWb15xbs/dd1f/NGPDr124CO//oGvf+GB06fH/8HvffStl/bsef3A4V2jrzy9e2C46xO/cUu9Mfvn/9OPpkfrt/+da66547ynH3h1/FTtpSfeWrF+yUd/9aYv/9v7Gm2dn/nNW3/0tSfGR8aWrFp64vSpG9998eixia/9h/tHT1faO/QKGwenUxBHGUdXHGNC8OLY+Tk7P3shP2eHk8Z5gxNJhfxBEWzIHy8UIbiugdnGyUdePPx//9Pq64fjp7KvWbzqD391+JoLdZGpCyct+WQBsUjeBu76KWbPj+WUtrdPnR45+IffHPv89zu6uurx1VRNaOlNU9UcvaIazu5sTE1aPAmRKKtX2qYaunzqHe3VrkpVd6+RznrHTHwZdHqge2ZianCiPh0vCul3mre3d7ZVembbJ+PlodJT6eiozU7p+drTWdOdbyJ95/RsZUL39Ea9u73aO1udjJ/KX+tttNerlanq7GxPZ+dMpWMyvoe+LT4N5DymTebRiO1yIInMBycyTqMOYFHvot/56OpPvze+F3z+4yJwqtELHnICp1oH1zzt0pSyGKUQ2en8iCFIZoC7pCh+8o09B/6H/1J75JXOWpzJvt+48+zf/lRpZt7iVFEgFaxwKaLQgo4ThZJQ/jk7lEnRBeuuWoRYFMwheRTyOUSBUk7yhdJjXzmw68hX/92PX3nw8NjhxsiJyTMnRkePj420fJw5PoZukn2M/9Tq3AdVRfHR4+PzxfHWCafjY/yUPk5MnFQ8NjlybKquT6Oresjj26J0HeuYdGusTdW1Dbre7mPBvZ0+OrbntTN7dh3ZevnaoUX9nBydpe7urvWbV266YMV028SxwyemJvg2x9mTB6feeOHg0SPHVm9cPLCoT59R0aIoqNE8kbZ9Ow/+1Z88PnZi5uLrN/f0dlMlyqwXRHGnlEDOBaUoJkCcRpYGjLUNYo+IQS9wKW8RuA3wfa0dHfGvJON5GzeOAlGOjwI6TTE4/qx19HQsW95/YPvxw3tPS+3qbDv/0o2H9516/Afbj+2ZmTw1tmi4d3J0ZuzMxPR4Y/tzB48cPrpk5dCigT69KXj1J3veeHL/0QMTbbVKb1/3kb1HB7u6d7586Ojx8d72tifue70+VutfNLBr2159zrRy/dK+vvalq4anxqb3vnVYF8XiJf2nj47vffVAvV4fGB4YGu5dvHxwyaqhAzuPTp2p9Q7273pp3/ZXj/cMVpct7p1uq1989ZbzLtx0bN/II99/9ujesfb4p+K8p2keVvyvCy69i2rKIqkQr1/5v/3Q2Zs7gQkoAJEqKQQxWQK+PFSIz/qrbadf2Vl//UC1Wq2MTbUtGei5aFOHLqrYRrTRG7MyOC9sC6G1qxV47Eybaow++er0T95s74ifF+/7RVQxCek60J+hx//xfAkhvV0Lms5oUNJ0EqbbZztvvWLpp95frdXrh05M6w1hpdJ74dmL/uFHFn/klqFLt+o09d9989CHbhp81xX9t17VddaaxsTUovff0DE0MLvzQNuGFX2fefeyn//AoluuaKwYGt+5vzFT77r5smW/cJfO4cz2fW2Dvcv+zgem+rtnTpzu/8D1K3/hzsH3XjMz1Du170j3xHRzZ+lvVxNNx1BsMgk6Ki4DbSQdURPUMcWR9Hb1vPPioYvifSSGHGFu6gX3h4c0IwQUfCFb6AsZ4OnPILocpyYmDn3xO7V7X+ib0OcSs421i5d89v0DG+JXRdqpI+AiFCGiC5BknHfBK9pGCU6ak7k2gFVR4FNFQTo74PPHZAwoVbSSf56oVMQ6o6R4QmuvuCMlm6enZx763rN/9YePt8106qFuVPSpWliiM6DlirUEsUZbRZ+BicdX/IPIKVZTLZ7B2kXY41VV70fjMoqvf8gQfzPV3haPRFxWusXFRaZUu9c7Vy3T0JvFMLZ1pl69Nsmg92CxgXR5aq309ZxKHbFttrNa13/yzagqv/amZ1ajOqOldA+Nn9gZb4rVFBuO/2MURxSRKWFsVKbaGh/85cvv+swtPplxktN/Y2MTLzz+xn3fePHAm2ca07MdehTiryobGy5Zed0dW664acvw4sGYrSWaD5PJ2Nj49//rEw99+eXFa/r+3v/jjk1b12Lj5McVkF2CgohSCHNQFOkiYlCUhwhxl525nxalItIVKeUiREppjrRvfPH+9D5Sn7LHNynwUMoc/hScCulsq3lWj2u1Z7Iy01HXWwO9D9dj390xHV+mq1f0CFWn27q79e6g3qh1aOxU+0yl1tFV6aw2Zto6a9P1ai19GUxrdrc3phud7e21mbaZjtmB9sbETDV+A4neZUzV67qKeto6tIf4O8v2ml7ldM11pYtuShdYXW8fuquxer29MTU9G1dSV19laqxWq1Q6O7o7azMz3dWejq7u6amxmenxSkdbR61D15Uu3PjtaY12XddxdDrqdP0EnXf46ZuAbpn/PpKqHxrAiRXh5OepYv4QCHB0vW+rTc8c+vp9x/71V7pHp9obbfVNy/t/5X2r7npH10AfK9AVG4pcY+Mij6dRbFXPkGSKv3lKRIivjRS0wGyjMV1PT7LZ+JkLtVoxayq+q1zHpKD3zOkwZmdOj4587aGZv3iivTM+adVlwbg45LRkGpifKZVVDDG9SIRVWjpVcXmIxddI6zO1tUtX/4uf67x8y+R9zx79g29UD5xsu3Lz2n/x98aPHJ165o1Kf//4trfaN6wcuu78RTdcePyvnx595KWZo8fX/YOPjj7w4vHvPLTqNz7as2X96Qef61g8NHjL5Uf+03cnvvrgkl/4wMp/9onR57cf+K3PT584ec5/+t39P/zJ7Lada//ZZ069uH1614G2QyOjDz7beXpcT3J+uWLaeXEosd3YKYcQXNXCkqE4RL2syKqT1dfd9TPX9d90cfoy8P9VoMOoT82ceu6Nxjef6Dg1rrzRUe391M1rf+eTupZk4DqMqy6BND0+xeWahsRjh54rQvSkB9UTxLERUeY9MVzIF8sjttaqRJN8vRz5BEGpIoq4oJmpMgeJGMZGJ77znx9+8CuvdFTj/UFx2WJqgR732UZcPTLNxl8LzLTP9uqKmJ2dUld8Z2GlLhLfLiZNafxkllo8XdKVJjEulPDolqmXvvg+Mj234j5W75htr8ftqlKVWK+0dzbiiyi1+OJp2lNccukn6Md1qlm1Nr1gtrXrxUvP5TivsaReBnWnHqvM9uiTRe1VL5HxSV7shAuZh0SUPaWtxcXemKnNXnnHls/9i/jF7pycWCqBE7Vv1+FHv//Ss/dvP3NoQi+g2kZtttbVXz3/utXv/cS1G85d1dkZP20Avx+pJ+7f9q1//+TogfHeZe0/8xs33vjey+IA0nzBNjd6aUVVY/nmiylcuqJSFPfKwzSnkJJNRGBy7smXAPY347x7pNJ09pInkSxGt4gqGNJ3EcZ78LgGlMVf/Wj1xAO6bJijj7hAhOJyKT5Xj1deObSN+DxINU3Xq3zcqcOtP2Sr6cFOfVpWr7QxU00aEzbZ000ubSdWij/jTGicTka6yKq64NKPYle9mi6ZYLIlFu0xO/rjT2YIKY01Zxr1dzbvkZSE2F7Ta4IhDuftnaQWw5nYyO6De3/nC5XH30znYLaxbKDjmnO7Vi5VSZ+51Sen46jln9b9jSejPsmIx1veRi1+dJGgO9+snm36TKWmT050FyygB0mflHSlT00irzfiiREsfnBR7EFnSgMlaZCURqM6UatMTMezLB6H4oE31DL/WJCLP2JgOipOaQrx94hatvP9Vy3/zPsOPvfyivM2H/0P35p+6rVl//STg++77s2/+3vV7Yfq+mxGz/x6veuOK9f9k0/t/u/+uPbgS51XnLPqv/u5kQeem953RPfII1+/78yffK8y2Lfyn/9cz6Y1u3/nPy79wA29P3NtZ6Vr5L5nj3zpu+f+u986dO9TbW/tX/FPP33krx4+/ef3dp4ab69UO+OKSV+k8v7TYaXdpW0qaq/p4UiHAC2QHaxOfhyT7rhtVV1qNM6BLIakKECc/h8EzdfBaYu1mZpefjvim1Fq9Ru2rPsXP7f04nO8/3ByFC1Aty2HxNLLmgCR4ogy7+fsSNLLEARFsNvjRBBNANyepAXoFVE0B2+nAyszUzOvPb9n78vH2vVJuUT9Fx8AEkpAcxT0Uqy7qV5SG20d8a3TOuMNvbzoutKLVHrd0n+12ao+P5/paNTb9Xl7uz55n45bYHtHXUPida6h1zPN0blUk26RNd019Y5Qn62nkz/bXosXUZ2wdi2mG6WmVat6g6i3GXr7qCWq7bUu3Yunq/XOjrb0brJDDdrCTAzV59xdbX0DPZpQ1/NY14NebbMDiyNqfsEwvYjqZaNt9eZlV77zHBU4z4p6yGSNtNK2aHhg09aVy9YNHDtxcuTUhN67dlT0mjRzcPfkzjeP1OoTy1YP8dMGOFkaPzo69ue/f++xXafSdzRUVmwcPOeiddX0W52Lsc0blRCbSr2U4KQQiU5zgyAuWMxTYI5fIAVU85mkIly0KNnPEIhU/8d5TUinsZgpSTyug0TkSZ+nFBuLkjLdgZIYDwLF9HAE00d7erKkL8aleZCEZE4kjEGiV0SFdKuMD/VEKZaOOWFgmbiLpmXCnfYQ02Ovqsf3QcYjrlp6kYxZsa9oZwNpTkhNBNWxB4sHZd7PawVwRWHBMwwnAis5KaCnwaL+qcnJ2st7Z0bGO6rt7ePTUzsO117aVXth58y23bMv75t9dW/j1X2zbxyovHGg7Y0DjTcPVt461KaP7Ydndxyu7DrSvvNYddex6p7j7XuOV/ed6Dxwyh/ViKfbj4zMHj8ze2Jk9uRom24bpyfa9DEyOTuiOFHVW9j4mKiMTVXHp3U3jZMWV3x6SOJU6o84kDhfwdg4JzMMBle0urkJRaI5OpEDPcMfvqmtu/Po9x4ZumzL7OjU2Gs7F33kZn0+PPLV+7tnq91cVdVKz+a1A++4eOz+F9v2Hm1btWTgnZdO7jnc3lHtu+jsMz96prL9QF2fSp+7fuDCs48/9kLv5rUd3d3H73tm+XuuPbF998DlWyf2Hpr58XNty4dXvO+63vPPmjhysvPI6fj0Kj5zj73qDx1dRI6pEAsWf8QzPSGVFPP/dGBxauoN3YX0OVR7bd5HNX1ArOTp/0EfzNfLdrzE6hNEfaqzetHwP/vEssu3xtfw4zji0ARdovEMSuDqRcl1QXrBEscJB5RodCpSvkdSg+RchIgiaJZuxa7mRBHOWwEURcGj4AJDmIbN1aK30jYzPfPG83t3v3Q0XhcCMbAJOEu06YawYevyuz937RW3nb31yhX9y6qH9o1PzdRWbOr76C/ftOXS1Yd3Hp88NTO0vOu9n7ji5p+57Pyr1q88a+jEyTNjp3WHa7vmti3v++QVXQMzB3eN1Cbbz7pw6Sd+/caVawb3bT89NVnfcNHg3b94/Zbz1+567dimi7TKDZfdcNaFV60ZWtV9yY1n3/jBS65495bL3rF5zdlLR8ZH3/fpK29433mX3nD26rOW79994Ob3X/jez1x18bUbN21Zemps/MzJxupzB+76pWtvuvOSLReuP3ni1Onjo5W6Hvt0NAn5QQZ0Lhqzq8+Zd49ETicyfYbb1tbd07XurJXnXrKuo7ft9MmxiTM6q116/3H62MhrL+w+sOvI8PKBwaHejs74STT1eu3H33r6iW/t1PuuRrWiTxD6hzu3Xr6hp7f4RkQBolW4euASHaWLmNsjHv0JePQQI+InFcEjkDIZyKNI1TrDaUfEIFLcI+MX0MVCnKN4KWgi5zmaenyOZRSn+O3RnD8HKa0LlObIkJTSWnOupEdoTosjpKUQMuSNGWwrqjpAnSuBv4/Mz9uCnKiUkwyxwZEHgjT6E+Ln6p2zbrK7Mrn7wOzp8WqjrbsWt2m909NreXx9Rq/L8ZY6dqkPzQ0lfcFGV4BmxZvF9FmCPuIfx6SPDt2qkh5fA4qbXRPRHW+i6/rEWIb4CwxeKdIZlj994hF3ufibjeLtd3FiFMNEUqD53JqPtNs4j1qrVm8/Z82Su2+unrWia/3K/gs2tff3TNz3fP+VW7vWLT9yzxOV8ck4EP2vs7F5je6Rel84u/tI2+rFg++8dGrXwbYTo31XbJl4dc/Mtl2z3Z19t17WvWLxyPefGNiyvnvFkiN/8t2hS7f0Xntex9KB6Zf3zD647fSLb43uP7L0Pdf2bVk/8cKbjZGxGJ7vcx6PhAcw5UIkKUuKMv0Z7yHjsujQ2+L0SVt88bY4sbH1RAwpQDpVFNvc0uo0FkzzaCiNJ3G10d7R3ztzyYYl//Tj6959rd6PxPlPLwtceFyBOia/GoA41OyBLF2i4jZ7DqKqlIjlf/sBz608MSgpQnIRItDCSrlu4mko9qO4sRR13mZm6q+/sEf3yHY9U5IcnyXpz0D8mdK4Seho152/9NaPXvXaC3t1lu/42LW7Xzty5MDRq2/bct0dFw4t7x07Nr7rjRPDy7tvuesyvXvcv/vIdbde2t7V9cYLe4cW9b3n41et3bJk0bKB7dsOjZ6Y3HTx0ts/c83Q4r63Xtp35tjoDR8475YPX9bV1fnMw29tPG/1VTef+/STr+zbfujIvrGRUzODS/svvGL9jtcPvf7coVNnxu/49LVj45Ov/GTX4V2njx04844PX7ps49Dzj75+0TVbzr34rB0v73rXBy9Zs3XVg9955ui+0TOHx8ZOTehpnA5CIZ7OkaQnuUg62PQ+8pylV77zXM6kzg3nUOB0GYOL+nWbXLN58emx0ZHT09Mzk/Ev2Gc6Du8YeXPbntGx8eHlgwOLel96avv3/tOTU+Nt8Q/1tEC9rauv89zL1g4vLf7ykiUU8wfOYp6Ky4NuBVFRih4ac0UZ8ONEBKV2cSmGUomaJo6ZCOHffnR0xLfWK9fp9MlsLpD8/KFdzOlxnom+67hXPEmFXnSnP5KncFokJD2OpHDHn8V+mrlWxxkPdHoRjhIeSslDA0q+52JpE7dQzocQ9WKw4eIVF18972fRcepAOpGR8hDwkAmIivnjRRRKD5OUjq7O/gvPqmxYPj4+0ajXOrR4V0ejp7PS1dXe29XW09nW013p66r0dlV6uip94j1tfd1tAz21oZ7aop7poe76YK/ITEprQ736qA/3VZYMzC4eaCwdaFu+qG3p0OzSobYVi9pXLe5cvqi6crixerhtzXBl9XBj7XDbuqWNtYsr65e1rV0af5Uymm5axfNLO9ep4Rj0f6GlP+N/GSnEOQ+XYlKp6DW6rdF9y6V9V5x7+sHna0++3piaGrjsnNFnXp8eGRl655Uz8ePmG9Wz1rSNTbWPTbZtXTN4/cVn7nu2vudIRffImy+t7Toy9tRrfRdt7tq0YubMeM8lZy99/w2jT7w8+cDzvZdt7Vi59NQ3H5w9dmb5J2/tWb/izEPbZqdqPZeeM3nwaGXlop6lQ6OPvTR7fDTuFc3PFNIhpB2mR6G4UFItjqEoxjElMR7copT+jy97XbKx69rzus5b17FlXVVx6//5H51b17Wdv3b2qrP7f/bmZX/vjqVXXaDNZgcTkeuTVFBqnl+oELiiSm7MbcScxInCUSTNU6yU1yClfpK8Hfza51H5AnnKzLyal1oXsmFsZPzbf/rIw195tdpRvMtmpaK5meptVq02c8FN6z71a+/5s/94f0d3+y/85m1/8YcPv/jYGx/9+7e29TamZmYqtZ6v/LuHlizu+eQ/fNehQ0dfePzND3ziHbvfOvLNLz5y9nlr7vr5619+ecf5F5390Leefea+V6+4dYsaR0+P/fibz217dMcv/PYdK85dMnJ07Eu/d88FV21+90cv/+FfPnb64PihHRMnDp+65ObNH/756/7qPz/6/MM7+5b2/eN/+5H9u46+8PBbx/ad2r/j1M//7h29S6vf/OMHb7v72jWrh//sf/zhde89b8u1m//6z598/dl9Eyenq+3xxeE46DgqHWRc542I+tAnvjoZjfpM44rbz/vcv7zdZ671lBKbJ3N2784jzz78+nMPvHnkzRFNis/B9WnjQPXcq9ZcefO5z97/xiuP7dfn7rK3z8bvq1i2YeBn/v4Nl99wXmoPaAlFP8RaSxEisIFkDKDDTRDzksAjnrfLAEEhFRfMcyJAwlEMmf3GF++790+2dXV3aHp659LQO4d0U9E55JtA4rMBfYjr3PJXh9EZ7zHiWzHiTERL8SqjoNchGdJjEUhD1KVpxRj9oQdLXUrTWnRHe1qdJhnm1kr+6Eoj5KGFgVGNNdLMJEWa9Cirkkik2n9cNCHGW4L4M7IgqR0xlPDPVmbqjXd+6sJP/uptOm/haZ5AHmVOIyI6nEiJh8xVGvVQoojnqNfrZ3YemHp9z+yBk43pmnYRX33s7Iytq6ujQ+c2tpW+D1lKraO91s0/oNdMienE6NrDH73xG6Fm401lta3aEc+Ojvb2zs54zCpttfRmSIjHLDapJaqzoxMn/+zHE195pKtDV0VMitObbPyhsxSXR5zlOMh0FPFnOETSf2l3ujwkzOopVG9v6/3UrR0bV5/5Lz+YfeNg51XnLP3Vu8a++dj4M68N/t3b+y/fUhufaDs5dvSL36m+daDtHRcs+fTtx/7o2/Xn35zdsnbF333/+AvbT3/30f5bLlt05/XVni69rk+Njp/8/F+1vXmg96Pv6ty85ti//fPu9urg735i8MrzD3/+O9WR8eFPvVt3+vbG7OnHXhz/q8c70y0/zkyBeHzSIacHizROXRyjnvJxBOlodQg6SZijK95912cW9w38+gdWfvjmsEctnQChRByFVt0pyEWh1WZdsDI/xqXW09HZ3xsvj9qY5HSMcQCJCyKkhqtO/fJlxb0Cr0XoRK5n0nS2EkTUQ0ShEyXnVEH+DEEB2OjKo1exB5tiaX7uGRsZ+/afPso9Ug6uC5XzrYS7MTuje+SN63/+H7/n4N7jlbaO7p7O//lf39Pb0/6Jf/y+J+57vqev5+Jrz/lf/s2P6qNTn/onty9fOzx+aqzWaHzrjx/Zve3QbT975XlXrLr320/f8sFrD+04/q0vPnLpDZvf/3evPbT38JkzjacfevOjn7n+xJkzg4MDX/rXP9h69Vl3f+6mI7sOnz4++dC3X3zz+T2XvnPrB3/++u/+50e3Pbi7Z3H1t//g4+2dHScPnNn2kx33f+e5v/uPbz/vmk1H954YXNx9z58/9sz3d67cOPj+X7hxxbqlb76w996vPnn60Mxse6eOIb5OlI6s+Xodl0pc7LO6lnWP3Pq5f3mHzo3OTH5iw9+8X+anUZicnNz1+oEf/vkzO148MjOanmjxWjXbt6hzenxmNn6bRPx9S3x/22yjf7jrvT93+bs/ch29rUvkKfC6Rf63hvfpsUoRc12KoBROSUBUZDOU7v+rx7/6/3qsq6tDBxjfiT9bjW/21HNNvfEqqlMZ14/ObcwLEmc2idwv4+VSopzJbCIqUnytf36XHy/EuDDTZJFYIj2C8aKdDMUo7scMTM4YIlu+LEwz04tWQC9gMscFkuakmbFWauOerf8lFkukTwskpm940dHPttfb6nf9/avf9/EbOV2Kgk+sTnhMaD5DhZ/yuFiXAuGBYAhcYFC9Ueew4gxETEcUIdm4Wc2HlGITCU4X1Gk3EWyeHh078Ad/MfYfv6+3tnUONW4vKnEy5dSRx81VB9GIvwtOG416bFWnPz3q+r/4Fiw9UaTXlw3pjHQeG2mv1Wp6f7xiuDo2pTd/s4O91bVLZ3u72s9M1Q8cnZ2cmRnsbh8eqB4daZ+Ynu3prC4eqE9O10+PNjqqHcuG25cNVmqN+rHT6tUOaov7Zno7ew6e7phuTKwa6B4aahw9PVtvdKxbMtvT3TY6Xtl/sjoxXWvXK2Gjqkeh2Os8cFFy4eg/HbHORlwt8dvU0hFLjhDPCR3OzHD8+8i1n3xvtahFFErEUWjVnUKEEi/ZSh4rAqJQeJoXZ1x/zUvRFyExzPNfGbgCMSS5uDIFhohQKs0vzZz395HKcb9dxIMZKLWSl+B0wR0lhiPzhyPjEME8vtb6/J49Lx2Lt9Z28kcTSvWI68CWbRq8+Lqz33r5wCuP77/vm08f2H/mmtu3nnf5el1nS1csWrF2+NjBkVNHxi+85qzR06NHDx6fnm68+NiB3v7qO++8oG+4t6+3a9nq4UVLFr/23JGhpV2bL1j94tM7zr1k/cp1g2eOzhw7Orpi5dALD+1YvXbJyrV93/jjHz/98O6TByZq9dlVG4fPu2L968/tO7brTGd/+9XvOX/bUzu/+2dP7n7l+MzE9JU3b2rr6HjthT2Ll/U9fv+bJw+MT43WX3r+QL0yc/UtW2emK7vePBq/SFCfKqcjS+dIhxpHm15hQ9Ojlr7Weo7OjE+jH84cGPB0dnYuW7X4vKs3DC7tmpiYHjk5GT8gOH7dTKOtXkl//52e+WnkbL1t6Zr+C645i09FeAiYBhfRikx2FRLfsTg5efLkyQMHDuzdu3fPnj1vvfXWjgSlBw8ePHbs2Ojo6PT0tPzVavy+cXUxgfnGgjqKYr56aTN9Qz0v/uTNyfjbZYn66NQLcirH/0FgsgfnxAbXy4l4vKDEq0mY4vUjvYooSzewdEb0TAulcKY7UPD0AKSX2eYqqUWloj3ZUhIfmiRzOu2xHJuJqPZEQkhiDEtpDNcq3CDpUoVNhjHZmo1zE/QR50ev8/GdbI3lG/vv+PTVixYP6XRx3ngU8tNrkj9AKIrqkq5RVpKlIBjmKcriWyZ1ZcfP3FOUrj+KqlaBJR6HGbesUPywwoMkKYT5S8QTI4lNSyo1q7WJqdEnX515+q341jrdCrV0cTHrf8fUQgZPIWpNJXYaS+mER7U6Olk9MxGL6ylUb1ROjtYnZxp6Nk3NVI6cbjtwonZypC53tb1rst55ckxv5vTus61Wb1fX1HRd74x05zs1MXvwRNvRU5WRqbYOba69c2yy59SkHq56R6VzZLJ6bLQyU2ubnmk/fLpy6ETb8VF9qjwb/yo23bmLzaWzkraoo097TqcsnVsu4CRGSJcL3pCjqv+6O7tvOH/oks3NSzENaoEG6PgjJsTwZEMvtcRqxXmOvRQkPrsokPcyDV0oekoPaNMgosuPJ76jRGAPjSiOiPhFENFzUXBavkcWLBuHqAjEpYsQncIFeMlTQrIsrBdsPp+Zrvke2dqoXRZSukeu0D3y6nPu+eozzz64c/z0yPDS3lvvvur0kTOP3/vyob1nhpb0DQz0Hdx5dOsV6/e/deip+17deuH6wWX9XcPd5160dttP9rz27IGRE6Nnn792/66jHdXaOResf/rhNzaes/Lsc9c8+oPXujo7Vq1f9PzDO5atG9hy+dqRUxMDQ4MzMzNjZ86s2bBk62Xr33hh34HdJzsG2m+4/cLZSr02M9PX233mxPRl1501MVX77n99ZuXqZRdcueng/uPnX75u2ZrhSkdj1bolB3Yc3/3GkUZ8n6yufI4mnopxGlKSjlJHN7v6nOV8z45SnQqdYQgRxUSiAOnp6V539vI1Zy2Zmpk8c2pkZrwRv4RcT9r01anmOazoedyzqHrR9Wd3dXWF0ARDGG4Cn5qaOnXq1K5du55++umHHnro/vvv/9GPfqT44IMPPvzww48lPP74408mPPfccy+++OL27duPHz+uG6radQvv4FdZZXteECwnYMv9VgYG+2dqtf27z4yP1vQphz71j99TLOgprGdivFUOIl0xskAodf7dhUZxIoIFidF1lcOT/DTGWzWi0uZw6fEYKVWpRaRd/4eimCZYDxFdq3l7isxJXCQmWzdJ/8BBeSjzP1JvvaqHVa7+VdVb7r7s8hu2pjM57+LhQB0BpZJBKOkiwOk8km4spPkEL1fYuKLkTqkOhxcpGfAIOlaIlNIrmj1OmSOlPqV75GvcI2WKDelBxpjuGEBTFVNfakxSFIrhuhzI4zbLLvU+Lr7NrhLf81IpfkdZQPc/raWq8viKf9zwolwtPleRljYfH+26icbTXp8yanL87B4dWHs9boBtnbPxVeX0SUR7pSO+b0m9ug2nDcT2+DMBxmlosngxaaJ5c9RBFiJ/pAekHr8bi59FVxTjBKYV4GmpbFh6CKzkBJijCzkXPN86BN2pIKX0WItjy/cgwF1dMAWtYl4ttZfvkRRyaH/qUSSNp2JySrRZnDQXSakiqlcEviAwmLsx3SN3p3tk/IWaRqbPGwXNj0u76NElNjvb09fZ3d3x5gv7x09O6YoaHOpfsWHxtkd2Pf/orqN7T81M1bq7Oo7sO9nWNXt018hbLx7WHW7x0qH2tsbp4xNP/Gjnjpf2ndg/0jvQO3Jq/MyJUX229/JTB+ozjcmxmafufbmjvW16qrHjpYPtnZX+4b5FQ72LlvSeOT56Yv+Zvp4+zdz5ypGR41N6SJcu76tWOxYvW6S4d8eR3sHqqeOjO587eOLImeWr+0+dGFm6amjLxWuHhvv3bz++7eE3Rk5PV/SmpzgaHV08YfS00eteeicUtzOde3/PTjqRxQmH+EpS5GFCUSmdHb2P7ViyYtG6LcsO7Dl2ZNeYnq3py4rxyKbnf9jiWwY7Zy+4dt3QcHzbDvM9gYGGbo264f3gBz+45557vvvd74o88cQTr7322r59+44ePao3lGcS9N7x9OnTSvU+Um8o9ebyhRdeePbZZ7lf7t+/f3x8fECfufT1MVYLKbIoFx5Hx+WhiGiPiBS2qnTVhqWDS7v1WOgFpnewo39Rx8Bwb/+irvTRDelLHykNZWC4e3C4V4NqU/FbU/SyFl+M1mWmrL2xeNXAwOKiMX1EV7M9YuJO5/G+OWK91GU9CKJiPjPxfJX4cNpC5n1oA31D3cPLejZcvPidd19yza0Xdvd0Nx/PefAJ5GRa8bMSkQfCUYqqNsBxIqLjxEC0rjkQdC9nTikGZdcDZnH7PSF8CXEnaku/P/LJV3yPTJ8TariCZqbbhp4EisWDzvtaPe/ipqKnEfOiGM0B/RnfXhf3vPg6RHwvbQyTwox4DiVWST+UNZ7AGh83xvTUTs9rMW1XhZiqAwhZT65oj78j0FvK+FfV7fHTLVKXVooTJ18cU0NZzCj2VuwRFGIsyYnRzOhOUhxzekXhhq8YYvpt2DdeMHjJ2Wmt8inNz7+Qc9kE+Xlc4LQo9RxBYvIWaeuDBej1EhDZPAoFTz4EnRQukpdKkKh2xrJnzOIY0IO4H5NUdZJSkii0GhYEQ1sNTGOOojgkFQvYYGDTozYe37Pz0ENfeaMaP02Kf02sJeSWoWjR0LgapHRUunvbpsbjLZHM1fbO6kBbY2y2NqMXvEZ7V3tnd3ttqlLpbrTV2msTtUpXpbenS0dWq7dNj2vNKV03Hf1dmteoNTq72qbHZtu7dINpnxqfqXbHtw3URKqVjr64Y+iyrU201Sbr1c729t7Z2mTbrG52ldmefj17Ombbq3WNHZvq7K1X2qqNUV2hbd1D1dhMe3vfUPzQlfGR6ZmxGVWlxFE0T54OTIcXR8SlPNteq9WveN9Zn/vndxbi/IeDE6gIQSydUuGNbbu+8gePHn7jZKW9pu3pHGlWmh+febTVK71LKp/+nVsv07uNeI0oHlDNFGGaPqvQu0C9a9TbxJdeeunIkSN6K0lVTjxel8YcKJosyNbd3b148eKtW7fecccdl1xyycqVK/O3sPmxwNWVHzJEsE2Qfub02KnDI7WZ+OFKKqInqIU0SBx4ZXZyYuqtbfuf+eHuk/tHdN3ouOMHt7S3L1reteXq1de/55LOLj1SNMWDMX9gC8JjQzxSWSo0NzBvFCJrZFkR8YinyegFciupEI9mEijFL8gcXNI/vGSQs9R6DtHFhfxMCpxzG9RICs/90ZxSRaX5EoiQkihu0cTQWorotAiIAhsQQVSEK3qt+Hmtf/AXo398T2dXl6zxxk7rxtaiUfYUlUQBSShUkJJUUtANMv5Bs0Sd4mrRFyYdjAzaRBjTTVGRTWiXjejjZSr+TI9i+vvOqMe49uxpyC9si1E6yenOTWe6nUZ75GLar2gaEZClGJxCyrQInlADqmipJMigDdbrM/p0/3c+turT74lvP8YUmwxo++aCOOdc4PTmQESH60HhMlCqSImHz3NEeOzgNoC8HYgzB+45EtHFqeY2DHBFcSs5z8E25h2/kKee5ZRZbHpBqIrNXUCiI2cEErUmaCyS+RgbGf+r//zww195qyM+c6vFlRT/kmreztPVLb1YSfuONN00s8mFoelLrmZPermMimK0RCVK+hQs1VPb3J8xFqqm1BZKOvLI9JlJ09hcJT5/jMs9NSaIMCDtrziioqmYGeAPvXjPNC66Ze0v//c/09Ee/8ZfYFTqLlZXFDjJae68h3/0zNh/+f0fvPzQobba9Gz8BX0XP7RFU9KpEmu0dc7e9SvX3/rhKzs6OzwkdQcOHjzIl0/1LlDvI3ODotcSSfYFxJJBBCxfvlx3ymuuueZ973vfihUrqGq+rxN58mlG6p67LNOW4rPtvw3UuG/X4Rcf3/nUD988tvd0vT5b1afu9Xq1u+3cq9de+Z4t512+cfHSRYX7///BuRLROSydRoGSok6mog1+FHi4RVyFCCKCFDgiBkUgnj+aEAFnCRg8SgSFvSVLgJJifikC9OmRsYP/7psj//671a6uhj4RTa976VnGEPuVxlOdfG4BIRvZ9BQ7UCWkyDUz7lnzGot60xBsviOaCqGwhlckdUWqF6b0Z3bICbzMFC8gSCmN7QlFL4GRc7BfLH1Nfkn/0t/9+IqP31bcp317yM4n51yR808KeEwR8StyzfiC8Rwh7xVnoFsQHVGAGzHDMbhXKE0DiIqCUrhI7hEkqgRBmfe1ViF3qJmUiC7Rh41OSsy7bBNEgEdBqAKneSOo1+t73jy647kjegjbK43Z+FUzcWTpAtOfce9Rf/qsKh7i9ko1LkiRsAjxfi8e+vjgCzBt8bOg4wv20RVCfCFECK7h1aTznQUQp7Fmc07qIg0Sf79X8DCKs1bcZSvxt39prdDiSyXi8VeC6RvcC3NaLi7TyJrPnbTJ9MN9dLBTk5NtnY3BRb398Qs90jrNUy1I8UPgKkSxMVv/yQMvP/rN1xvjs9X2Wvw2ujgNqZ42EbvXp6q12eG1/ZsvXtcdb6+LCcLo6Ojzzz//pS996dvf/vYrr7wyPj6uhVTlx16wYr40kQcaUbCBXnHaBQ3cs2fPCy+8oNuwxCVLlvT0xI8EYoJaIHAdMqlIzPUxpieGaHw63hQB60rxwNHR8WcefP3erzz13I93jRycjN+ZogntlaWb+q+9Y8v7/871Wy/d1Mtvc222C/TGG4PsuIR8LcHL/S35gpBBVUcprWZ00BxbPIPyFkR4Hn0miYCzClFUiapEQdwPn1JxxFQsxuYi0e05GKLIQlKcYhA0iqqBjhOQWqQ9lHiSt4/tOTz5yMtVaVpO3nh26Y94DuqlIy6WuDXElPhKDp8Ux+ezMVHeZItniEqqi2pmPE/je471pFWMw43V0lHHpHhtied1Wp+toaX1ReK4UiaqXI4Q1RovaVoQSf81XwZ4yYh5saqWSB3RIJK2xfLxBjZeO7RCzE5imp5OR7hiXNp2Gl7R54Wrhwfuuq5v4+p4BUhgx9qEzmScnfQwcSSxZDrhPjBSRVIjV+BWSiXBHBtECinb0IpWMAvYcqBQFcl78ROtcHXhtCIig/i8eyRWUEgJhZRgpUQ4X6ThS6AESvuGQ1wizXXxOJhqZWZ65rUXd06M6k1kuv+lG4mQYuEXdJE107jqISl6N7p+KERIXKSoKqXKE1QEhdtVUw+zh+Nv9glEQbbg0Zn86uUSlNJoDqSESJr83m1T1JlI/yhsfLS+67VD+7cfauuordqwLL9oiKSKKBBF4c2X9/z1f336+J4Jnc/Z+AaCeMarFs+cdIT1evxZm50dWN55zsXrhhb1e+Ybb7zxta997c/+7M+efvrpiYkJvitVJQ9nJ0aecm1AFNWS79ARzMzMvPXWWy+//PKBAwfWrVu3ePFiiTLYSWNpDlzwWpLtUUxKcaIErfLGi7vv/+bTD3/z9QNvnKrPzOhVRs+LnoHK1bef895PXnXtey9alL4y6Xb3muj5ouVEqAq2QRQFpYjhyPZvlFIh91O1RwopVYsoijy9rYg3zYWIgkhEUTRBtB+U/LxcZCe86LXiXuuKNrdCNlXz3reLgmwlLtArOJUjfshiT/X0th3tB0/pNimrnoa64j0ufZ1TnN5Q9D+Xr56bYQ0h7k8So4wnLrAIiTTNBYlnE2lS0m6KziZLnrgr4kliU48bmLI0pPho7oc/vBOyCL7/xYeOPS2PmnYeB5HWCPjFsVKbbQx0d7z/qmV3Xt/V15u04lbB45VD55MHNw0KQGJgghXZRPwEoUQUUOxEFGiBe5qQ81QMmJdETyb6WCCuwks6CmmyBFngfaQiU+zLDwMwzpwISrohHcW6iRfF4yhg0Ab6B3unahMnj41MT0+3tU+3VeOHp7ZV9SlQvT1FcZRKtd4IMX5Aq8UmKQwWm574kB6/k77a0MCkFJ742VbFR2GTQmMi8s9VF/oIQ75Q4g316kNblcKcpsELeWytvUPvAuptNd0nq7XJxslDo7tePzJVnxgc7h8c6tVTwOfKJIfO58iZ0fv+4qk3nzwQvw5ET/vZjuY3T4rHrznv7G1ftKp3zeZFZ1+64vyrN2w4Z2Vv+smuuob0rvGP/uiP7rvvvkOHDumxyK+HBZczfnq1FVoOnDlzZseOHXpDOTw8vGrVKt2SC8dC0H7Ukq8FlwiHCOz81InTj/71cw9888U3nz40cXoy3t832hqdbWvOH77xZy5814evXL95VWdnh0bo/NDIfEUIYg7pBWsCJXUUvbmY78pAUUlAAU5F6CX1NCJVOMAJUSxxQylwyYRzm1yFzTwnLMTplaJUHBGDI5OBlLyk6Dm5KNBFRBFYDuS6uFO1aFbH8ECjozp56Hjt5Ghluq4Xzijo7YXIXOTbiYMjpg9sakml+AZi2VyVM02Lj0KXEl11SPhzcyll7LwPNcbvTSjS5vD07c7N9kLhI31PMx9NvZg55wk+J8bPkU9L1NU82NPx3suW/tx7BjetiZt2OnucZ84hEVgEskEkukVAUUTMp9HiFA4BmIukeSXkEwS4RQHRJcFzvGKJO9qZc1J4qEiCOCZq3GC5EMV9ReJBIdKoEoqqSkuNUhgL8jQfRbvgOTIwYXRkfP+OIyePnVapOMT/ZlCtVg7tOfHi/TuObB+b0SnpiLelnQPtG85ffP37LrzypvO7u+OXPgqcRk5vOod6kOOz0ge/9+x3vvjE2JGp+KS5PT797+hu7+qtDizpW7t50fINQyvXLl26enhouK+/v6envzt+BWNbRW8ZH3zwwa985SsvvPACj2npxOePFJEHUSVxCDZAKtjWSrDV6/EPvrds2fKRj3zkQx/6UFdXlwZK90JuySdIFyDYIIIMkxPTr7+48/F7tr3+9P6p+LlDMjSqs5W+RR1X3n7utbdfuHbjchbCn48VQYfnKG2DXjsVSyUrRMRWvRRVFXBaJBWUQlpF78dzUPKqRC/BcOniKjGQXuuIIDWFk1GKNpPip5e0VXGjSF5Sip9UOkuXiP1OiUzT9Nrk1Mhru08/+UrtzQNtNd1Y0vvI//agM80jF/+GZKhv4IJNi2+8uGfN0vb2qp6unFJOeH4a4xw2RQw61TFk/kMsLiTjvMc3JzLkM/GjUIoRLdcb0VXBJdskElt7U8fctomkNkuBsIpJjNAfQt4gLuAQwVBaiehBQKlEV0tRBhGciPAc2AQ3lpb4bxb6JHDnq/se+s4LLz22f/zETPoVXJXG7HTv0u5rbj/v6tu2bty8ukPvfponTVGgd89b+/7nf33P8X3TfUN9Q8v7h5f2L17ZvWLdouWrF63asHR42VC1U3fh8knWDfLrX//6N77xjd27dyv179ViCYGUVVhRRJCuVP41a9b098cXbI8ePXrq1KlarVZcds0H135zlzRBEF+yZMlnP/vZ97///SJKqZacSjVZhPkoKgkigpY+uOfY4z96+bkHt5/aN1Gtd7ZXZmtt0z2LO8+5ZPUVt5572Y1benrm/uqRxpwQS/PRJeKU7hUF/HlvzpNlDhKZoFJpPlyRNNeFvESa20pmhhNVoip40RxU82hdsVTKDYJFcXRzCIpbck+eQjhd3jPEo0q9OVQqDlaf3ukTL82Ymgm1WDZ9KbIgldn0Bdj0lVUgl7i9xdcnnQrNLkdEIboSiaAqJL5wm8xRaRpIm1/+pDEfG8X5RDT2phj/F71FY4mAlAaaSvrSazV+LmAqxBY4n28XZYKHPUYHyRUeIyF/mJSKoIt4VP4IegKlVhGu+ZTci0EopSWoiiGP0hXZs4hg3Sj8ViGKbEWQg5JJK7Rd/OL5GrQwJI9UhQVb7MlbOCO5wTY7IYrWdUeJv+RuMaSLJUtBEtNl0hTmr5ViyHSVqlLQCzSXEKy/rXkhLGhLExonj5157uE3n/zBK3teORX/KrnSHt/CNFhZd96iq99z4dW3nt/bE28o1SuoRZicmHr+yZe3v3BwyYolS1cvXrpqcHCor3egq6u7q7Orylnyau6amZm55557Pv/5zx8+fDgZ5h21Uj/uhqt6C7h27dp3vetdN9xww+DgoB5B3WUfffTRxx57bGRkhG/SKXrSWD/EOTxN1XXr1n384x//2Mc+1tvbK0XwxY3HLb56laokiOhm/+wjrz314zd3bzs2PTrd3uiIX8Q4OzO0uuvmD11w6U1bl69Z7LeP9NKuiKg9ILIuqc0iAjrEUIrIHCm0sM/cnNtyD1yw7i6lcJcAumOhJuR+IgY7MQApSl0CuV8RxWnuzKsoVHNdQLQtJ4pCcoWiU+TH3TYDP2ZzbDxeLJxCQA4x1kAyhwjwPJZQagRO7c9LrXPyIaC1N2+EtHYJJY/R2ltUs5PfGpuWhblgs3iuI6ILetS4vNGlKIpDkiXgqolALwSFy0CG3GNuAhiFQby0DRN7UlMxhOrcVmimUOIi7I/NtUZPVOpG+11VzBHLp01A4NJFFN2rVFyKU2KMSEMEleCKNjCBSJUubHS5CkqL4jenJW/HSRr9CfgZrhSiKF5S8i5QWguOiF+8NlPbv+vIA9957sl7Xp2d0A2nGoPbq/3DPZfdtv6GOy46a8taTdI02hv1+sTYlN4pdnZ2xC/gbNeOiz2rik0xX043yB/84Adf/OIXd+3a1dEx97skiaSeYIV29Z533nm/9Eu/dN111w0MDKgk6K6pt5Lf+973NHNqaop2H5EnQAApUZNXrFjxmYS36xJhA3mvDm7/nmP3/cXT2x7ZO3a83lavaS+a1zVY3XL1Op2r8684u7uni8/N1c5wegUGNkcVD7oITkVxgJ5aiwsDIpEhQmm+SkQgsWAJSjGL0+sWD8SGqGgw37FQm70a6wkLzk/eABNE6MrFfA+uegiA50Nc9boC7dgUBa+lkqJTwWvhLynhyA4z9yhi81dZKeGxE5LPFDyqVRePlP+aioii/UoZTkkcg0G19SIUUaoqLY5hzmbKoyd0Jf2VCu8N3JVvQFFmiODh4q7a7AkSKUHyvUnJF8IjULLNeyCFyEAjvIRcF/FatFvMOVXbIF4ijyoJ7hKX7giw4S9/zw4qawApSiVSUhS8Dys2A9bO5wCVCpa4twjHL84eFJWyRGkP0d+yem5AFFejJpMqehWvpYghN3uIIANOIdfh8lMl2lCaoNRDSs68vVQSpJAyQdD7sOGlgxddc/bKTYtOnxkfOTVZn6l0VNpmRif2vHb0lWf3T9emVqwd7unp0lmTX1G3geIGGRMX2GTYmpCod5Bf+MIX9uzZ4/d8OjMlImBGsai3er/927/9nve8p7u7W1VBJXn0hvLSSy89cODAzp07dctETH0BeqXkhKiqiN6Avvbaa7pTbtq0KX71VXZt4BF3e0ysVI4dPvHAd5751h8/9vrjx+oj09Vwt1e6OtZsGb7zF6+9/ZPXb9i8Ov21a7zC0M5Y9aYx87YhoDsaFnMCUn3uPDMfXdGpgAGQogPtoVUXZzJRCgPtocRxISqaYBbwwAVK2Egd82eK2+mlhOKW1iUgduajJLpFhGcWcJqvK0hXtOJp+UwRRSn2KNpA1bZkCVDNSyUDCoQ3ZZqpTSIK7mJpEZQSMAOnIjKLqFecYw93Uwf2kLYalCou2AKRgSVyEc4oQQSuqJIITnQrqa8AOsTc8xHFBaoiniCldC1hg0jBbA+NLmGjZCBatwfYw0yXSMs/iy4nnmieR4mt0VAqyNmaiiDmcyxasRmbibtsgEN+ShQ8CsVdTs0hQm5QpAWnuUvYnMJzHSg1SBXzOQt6IJ6mK2P1hmUbti6rdrcdPXByYnS6Pe5n1bEz03vePHryxOmuvo4lK4byKy/fCTyHREXpuhV9/vOf37FjhxS1J/u8TSoKniwgiihecMEFn/3sZ3Wn1BVGOx5B6fT09IsvvqgbnnR3lTjEPBUD4+Pjx44dW7t27Zo1azSKEk5Aqlir1994aed3/5dHf/KDnaePTsUPE2qr1huNwRWdV7x703s+dc2l123pTl9cFWjxtBJXhAjSWRdRQMwVcZsVEVEgRFV9CKUWxdRUhkolIpR4nhoSWU6cJRBTcQ62KQpSnJYM5qVREPcScyewArHiqknO3YIi5AoExTOTaw4oXghEW7aKkBtcElr1ZA+/AKcKUPIukNuo4kQRci4oZT4csVVZEK6KqKX10cyJwFjriBBiXio5PaSEBc3AXAQuj4hHIYJkmUuF/FKE2ABxCpQyv8gTlFrP1zWfu0dK5bZpwgkVsUFQSbz1eUIKEeCqloYI4syHWywpAjwfgqKoNDfASYmlYxEkUs2VfGzOOUCLRFLFXBFktgIRWg9cQAHiUiCKcJ9eT7AH3TZFPUSLFg+ee8m6pWv7T5w+efLYeKVW6ay01Sfre3ecfuvlg9Mz4+s3L+/s7GSCoAn0MtAzSaUfPnz4T//0Tx944AGlehMpXaINIOcGouK73vUuvYlEzMEorf78888fOnSodWwJGErk6NGjk5OT55133vDwsEVFzfHJOXHs1L3fePKv/8sTu1481TalN9jts/X4/R+rzx+685euueXOK1avXy4b+6FlwW1wriA20GLQi8E2duIWp/RCgFJFel2iEeKIknvyRaUIPLLy5KBUImpxV3IVYJrg4RJFsCmmYuhaCw86kdNVmqkUyFywZEiTiiWkwB0liuBMHQVRFPJGdpIfOxOUEiGYPTkZAxLdq6oQDQmlsYIUpfIwQZzVpZAKEMyKgBaiZ7pXaR4FVYkoTqOWiId7LKkgg7ukk8JzgiE3t4oQFEEEoKOkSkEU8514IR2pUnGqEKcyeBp6TuCK2AT3MpZGRQytZqXAJVKIoh8RIo3iYO4eyThqKBCn4KeIAkNQcp6KQcxzSPSeCinBulNHSOnYSsidECHnGm4indQGpfn5tQHRLU7xKJZgp6LMPK45JNKOJxeVgrxUQkdHx9pNKy+8elPXYPvhAyemR9Nft8zOjp4cf/O5A/t2H+3uryxZMcjv1tDq+WQmiEiRPjU19Zd/+Zdf//rXa/O/AdWEFkVIrpCq6/rrr7/66qvFXQXRU6noDvfUU0/t3r3bJYjMcLpMkmVuRe1///794lqCrwNzRFSnp6aeeeL1L//bH7/woz2TJ9o72jrrs/V6W33phv5bPnbehz9749aLzurs7kwj51bkEWGCwLo+Sy6RugsRBS64CxspJeAue+Ai2gZEyOdYDF/WhSjIbOIhuYEuRc6VjxedkmCzSV4VStVSZGk2U+oyqBaFbJS3pEgvTjZsQ9rOAvuRiB9YVMz345KiwRIYbKNXkZK4gEEkF91rgu5pRAEzZ0miQEtpmqJSeXAqjeZmKiRjAO4IxGkPR+J5uiC0qAyy5avnLdYhip6p6H2SEjF4lEC7IgbgVLFkJi0tDfDkHAMPZX7evCKpuYEBv+BGgDi3sIC71OOUBcSdQgy3k+ZcgBPdSAtcUIrBwJCLyVJ05dEeDCZud0SBREO2HxMgj6LEksG91kH4mrpJqsxD61grjoZKVIF4bhDXNSGyZNnwnZ9+x8/+1js337isEt/72eistnfUO195YP9Xf/+B7//ZoyePn5aNS42B6hXg6C+++OK3vvWtkZERpa5CVBUgjjkB8uut3oIeRUHDT5+OnQDryTjPmSsm2tjExMR3v/vdZ599VgrbE6k3Gvu2H/iLP7rvK//vB/a+dKbSHj/RtlaZ6h6a3Xrd8k//9jvv/Ds3rVi9jBbOGI0+QMEiUEkRxTYUSoCUEiRvVCwRqrkHwJOrIEkOmHshOESQqBQlbxQoFcn8LkGpDLS4hFLqAqQlP6QkgjQpdKLhFpGcp+Kc2SJKHlshJ6W8xdypiKJB6uhGokVzRVKgNNfhAlUBrpiMRS8iioCCxyUUgVSxRMzlgVsRxGkXrCjmaV4VQYEjCjlngm0lBRHFaS4qumpFEcXRnpyLAESISyVOZAIQB0XerLY6BYlSiErL37MjqCboxUgvJSa8htLjia3EkMKcPC1xIr35BLiqOO23DufzBQg2QSUgRXvGTDUnyRvISxwjiktCLgq8wsosLt0liM9VSZeCyK6siHAgpBgQmSMopQtFaW5TtLJ6/bJzL1nbO9R95ODJ6TP12Ua90j47NtLYvu3wgT2Huvral6xYFN+lEt8FVxwXAxX1Du+P//iP9SaPyYiu4jTJUztBvV6/6aabBgYGSi2CnI8++ui99947Pj5OlyJVr1JSTCiJaHv8A5KLL764v79f4tTk9NMPvfr1f3/fa4/vmx2rdFQ7Zhu12uzsqi2Dt33q8vfF9+as8TQBrshMwAknsgERRW8JW6kxTzHTlQMx96CTakVKgj0gF/N279AeXy2kVO0RVJWnSJqQs2DZWAHdCgMtiqBQ9bolDyUWtQJXFGwuzUGElNLco2jdoiARkpfMgRtFWF1pzqmSilBFEc/PtiLAo4iIX9FDBPNcBHkXqZB73IITYGM/ipQU5ZSSigEaUSgpUpJOI1yRkjiA0+XUiiKp2xVtzkflhjzmsFJqV2QmJFnKozA4JsvcJrGR5pyYvKHkUTpnZoF7pFSQ91txtF4SSzFPcyJ4u0LpcUJE8TZKupBz4adUW7kjOxdMJFL1odkGD1Ozmp8iHQVckI6ZwxQ8gVQlIkr0JLOG0NjqyRVEE4v9A33nXLx27ebFjbbpUycnJifr2kh7rXp0z8hrL+w+ffLU0lWL+vp7vG2Tn/zkJ1/60pd0p9TtByUtOLdhIV+oRLyZU6dODQ0NnXfeefzon2hr4tChQ3w3EItKsUGE4cwxAckyz3z8+PHVq1efc845nLFXntnxzL1vVOpd7W0d9Wpt0YruS9658RP/6N2XXnNub3/xUyg1UFA7UYojlx9cVRHGigCqBvt3lyCSc6JGlY6UpUkF1jIoCeKU8FPKRY1FAfZYtAeFKMU6o1J9gROCTRySR4l4bBaYIKAkSwC/lJIBgoiS2xQFGzREEZ0oeA6w2cDAtkVoMVCw4UEvAQPENg5KUMrexPNoHb8IUKqokq8ctwtUiRB0A13Ag5LryRWAe6AVk1azUUrVzhxgEYKoo8AmWM+fHUIrLymldqpSIESI4BVdQhcQBT8E1ukiFbAJlOB0wUVUmnePVK6Ym4BTDAJKaVYutpaIgsScA7cDuGNeFYFLJE3yPOT+Eld0KgI3gUNozHVAKpRSYKVEStNAnubVnAuteokI8DAFaV+6anjDeasHlnYf3XdyKv7VfFu1rTo9Xtm//dTeHXt6FlVXrYvvWxHY2MmTJ7/85S8/++yznZ3x13WUAKkjRF2+BEsXjO6yUq655ppFixZZZ5Vt27ZplampKRkk0giUEk0cF0xZ5YILLhgeHtZNfdHSvh2v7B851Jhtb2y4YtF7PnX1LXddvnzlYnVoafkF9kC7CXzBCClxgCKYQ1JxXpU0jyVinp9GtiqiyBlGF6IhAY+jq+KIAiKghA5BVFSqJRSTcR6SvZgPF/I52IRWkVSghEJEcWxdveQUF1nQkxMMSgVx64JLpJxS23IiqFTyk+ZiyQCUShcgKJQEKxjgue5UBLFVh6PnHojwdjyHdMHrts5J9WKfJtLtFFCIC6Z0eYhEz1FklAdahwA4HnHa84huYpvTnBMh6IKJROsiRKXzvq+VCFFNhIhikFIiLX0annMRoDRX8qe9IF3AgFOgxHB0p5RyHeKqdQEbXKDkKmlJBNokA9lt7oFTVXQJkOY2cRRgg8VcAZ6JQYB7GgZFbQ8inVhpq/QN9J51/tp15yyptc2cPjUyPT4Vv2FrqnLqxJnV5y4+58L1/FtjjvHJJ5/86le/Ojo66u9lFVjLx65ogpgfl/0dHR1333331VdfrfeLTzzxxIEDB95444233npLbyvXr1//yiuvSGGJvCtfRVwEBeB3lKIW7XbdunXnn3++0v7BvkrH7LHjJy67ddPH//5tWy8+q6c3/nUmfgbK5lW8uqBREAEb8AakE2nkqA3mm0MEdMxMyEHVUQoeT5De2uUW9pyLcImeJqJoJxGRIe4SEE0cBQy0iHBEebs4fgEiMbXO9ZqD5C3miyAyUwrEqYABOF3QILFgTdgGh+Qt4j4olByqqh1Oe+mUKgJWUZStNBAxLwlUnbqXOYhCjG4OF+SXBy5IL3wZbBZQWIvhJqW1IFYwpEqxB4uKiKlYiErp9XCqQt5iGxGDkCoBUh9mayMpXJBTnOXEKeUrOgIaiUpxigssmqcyzPu+VqkiRKdWIIJHQNiWgAcozQeCvARBETyTVMTtOZJlXntuS8XgrdFQqpa8q8QhgkTrAikGc4gg0b05oSRgk8JjmeuK6DwkLhm5meEopIqkBjpEbyi3XLZ+6fqBU6dGx85MzszMXP6eze/9+HX9/X3uPXPmzF/+5V/+5Cc/IfXlBeAWReTxfG9b4Aq79NJLf/3Xf31kZOSP/uiPvvCFLzz44IM//vGPH374Yd07r7jiisWLF7/++ut625qWKo4dIlhkOVZBx4aCZ2JiQvfd6667ji/qrli3ZMvla6579yX9A3Fo0daEUu+f3py4mpeUWrcIkYhBEcCpCnDighPcSwkulJ5KtuFBNCh5aUWtBUn1ubHyqOThgqoeLjily70WBR+IV8GQioF8dXEISN65aSK5GULVHkXDHkVPznVzVbkI4YieKQUuiMAt4qcUjqYCxCmRYhBIcx3uKtHDFSECTk9WFIcIOKnmFwZR8KJCaT5z4IJ1T8iJnKwixV0opJScwt0isJwgRch3i4gNUQp+dEQp8GQM5NxVw2uVptkmRdw2wdVW4naLuTJ3EeTIl2SxHLmCUxEiRdGEqFJY54MWgTQ1hd8KUEoJFGqGkkh7zJ0/h3bB3M5UL+YQLYrgBIiglMbEhCJvGlJfcWasRLkFpaoIIC1Ber4ctmQvNkyqi1VE94xr3nnJhz530+XvOXfjFctv+MDFw0sX8ZNBwOHDh/X2jp99k89kjkUIIlXSPC5btuxDH/rQ0qVL9cb0ueeek3NqaqpWq+l+9uijj7744otXXXXVLbfcMjAwoL3Jn88nJQKXgFNFPTG04ZdeeokfJ6uunp7u9ZvW8D4YpKY50CtdJK/+9FQgpREFlFJBHsyUWrmJV1HMDYoQQTrIFRO4IlUTOIhZ83uJOTBYd1pSjDzF1rqEFQiRKsh14F6ISobT8CXggZgL9iC6RHsJrsaI5p7RBTgp3EpO4I6C290IgZPmCgSQJte8dQWl8FScA6VcD3cCuhshNlA1cUmpFetSiFIsCk4VU9/cJlMl4C7j7arSgbmIDdaJOVgXknus5ylRCoCXROYoCnP3SDt48cKUt0kXRCiJ28OdHE5J0Sk2YM6nBsB+0Lq04DTfQ05y5L34842Jqwsl14EHeghbzf2KQr60JyDmJbWznCJEIilcBCXczeEi0n1igVbBLNiT7IXHM9lzMbkRa229eOOHP3vT3/m/vXfLheu0oUb2W+lfffXV7du3s0+lgBRwdCIaTsp8EZZT1B2rq6vr5ptvvu2221577TW9MT169GhnZ6ecum+pS28fv/a1r6nlfe97n95rqgUwRwaWYLJ0L+fUNkFEY/fs2fP888/bTAniRgaiIJaQi3B3+UilCDYYMrArV0sGIW8U18AFPZTMRWTTcAi6IvCEvKSIHw5KhlQsQ54c2ka+rpAmFVsyR08dhe3tgD/vhRjJVVwGxNxDVWjl+JOrKJU2o5SzShRsxul24KVFFKMhIeeUUBQ12cN9CFSZBvJ92qDGklnc0/J2I0Y0e1FYUQSdlCpmcXvEpcCJXhcOxJ1SEkwYIoLTXAPFWQ7d20CBA3PrrGVFYIcA3dXWPZCqhTlsA+Q2RSAuEcURm+BTVJxlobQkYBnc4ngwOEIoKbqEkx0rBV6CKiV1QeAlT0nHLG5gwOkuiBS2BBhSikBVuiCYFeXB5hKQIl1EEXMyFjtHETAIuZN2uKKAIogIiAKNcBOccIlOLQKlaeX4GaVKtfiixf3rNq7o6IpfceVp4g8//DD/JlKpH2uR1B3ARok054ry683i+eef/+EPf1h3yi9/+cvbtm2TyCoyQB544IEf/OAHa9as+fjHP673mpQAA1lUhBKRjQmQPNWN+aGHHhodHVWqJWgRYTk4kw0pshGVKpLCk6U4dRLRFaUIVNmkRE+mkUhLqd0GT1YKXEWUQQSOH0LJNhQghbGKeAC6iGJMbDYKpEIyzk1DweAUAsxFZGN+riiiQIBslHB6P/nGqHpplQxEAS5gFnDmaQ6cAiU8aWTAOqAKUfQqijKb0AjRBUBqRVBjDGpONqFKmuqxEybA0UWY4A2IuFqaY6KILrgLEW5RsI0oSLQZRYCjQ+CKDJHoRrgIHI+iOKmiFEUZlKLYnHMT81xX9OoWRcKUYEU2QcRVGnNRXFGiUkUhT8VFyt/XSlmcCGFoaTQpHpCnyTgvLdj8kkk+TcRpiad6wDwXAQpdrppId6pI2soFES+dc0U4REBMrrk5Jop5FZJ3ieceIa8CDHksCgkLliDWUSCGFN1g/v2///djY2N66EtVgRZHiHRFvY1buXLlxRdffMkll5x11lmDg4N33XXX9ddf/8wzz/zpn/7pxMQEP3k879V9dHJy8uyzz77ooovGx8dXrFixdevWxYsX65VCG8DGukApy70dVNW23/GOdyxZsoQUHThNi88bBZdICpFoG4RzYhvIFYifNTmkCDLbTwSpGFWiRQhKsoTiCXCIFNsUrQNa3JhXreQEnuuK7jIPX3OsCR5A6kYT0KrjZ5TPNmJutoJoDyIKXEMsEgWbhZyXDB5iHUinJJRKgkvGT1EgpTQngpcQkU5EARgQc27QIuQTiIUjtbhXpHSp5yU7gVJBhJIgAscgwK2o6ogIN7GYp60oVUWktJo9LeekcMVSV+sQIH3ewYvbqpctc+mcQQiKIga4UyDRVYGuEvgcKo9FIWsv8b89PFa93kAuYqPEEtIR82qeQmjPeWmHpa68Ku6dKJpjoJpvQ5AokIoQ8yECXaWSUhFaREqpCOLrr7/+sY99jC+Hlkr2l6KGy3/LLbe8+93vvvDCC3V/mpqa4qeNHz169A/+4A/uvfdevZtkJ3RB9Lavr6/v7/29v/fpT396enqaOadOnXrttdd+mMAPzLOfbXhdpSVowtDQ0L/6V/9Km1GK0y2qSkShKqBA2KEhv8+kAJHTp7R0RCUDCgSDQNW9AJsUVQVzunJR0Qopo5SyNAbBXFXPf7uFBLULMuREem4gCsyhXREdIiAKstFOaiJ4bJHP3wlRYCERRYnJGJCCx6kiy7FzdHvopSpi0YqIIlyQQaCdaJtTRSn5EIAOXKVRQEdsnWCbuyR6uLtozCdQRTQnlYeBStPsuVOhWFLS7HmPvrtKJekCIpyBEEVK8Fz3ojQSKRmI+SqknuMhIh4LwSyoCnFJcFUQl67IHES6iIgsFzX2ABNsBeYiNGi0IGJuAyklFIhQKgml3TNckWqqlGE9NyR7wKmJl2CsFgpf00Aa20qAY7YhV9xolEZBFBmlLlIUHxolEUpezgTQa1F+ccEzxfMhAkQGPKwl4gixQjv8rbfe4hc6klpfEDTqxnbTTTf95m/+5u23375hw4aBgYGlS5fqHaGI3j7yo+Y0xJs0dI8877zzdFvVhOHhYd1cFy1atHHjRs353d/93VtvvVV6a5eR9liGVtyzZ48mi3MqcCqmczN33jCQ5magdaUoqmSPgC1/2lAVShME/ILmCCjMxADw8HSFS8xbLAIpAhydFJ1G6aSuCoiqKsbE5qJ40CH4xU1ctU4sEYF2Rc1vHSURrpi2EKkiipATqiJw4JJiMhYrWvGimA2q2OBypkoQXiVUtUFgFDbppH49EeilGoPmgxYG0usWTxPx0lQVmanoBwhRHhuYIFjJuRoZK4UlJDoVyZezQirko9yomHflBsG9+RwTDKQWTdRiJFdAvOSBWAG5k5LWynU4KSWgcyJRRHo6W8VfmuAUsJnQWyQCeT6R5iJJKKUCa0CsKEoUsWjY/HYlkNsAeo6iML9Emm9JPEeYElLr3MMskFqBoACl8qRiAG4dD6IiBIVSSSd1RIHkVXEIyNPcBmkt2e8SonXdYMQpGVRxOoWL6L6ot57r1q1Lljnomtu0adMHPvCB3t74ibF5F416m3jHHXdceumlfBk2x7Jlyz7+8Y+vXr2aC1dQYz4BXoL0Wq12+PDhmZkZlgCxXkKJG6TWF4w52APIq7luYGDDKII4aR5BqsydKMFVSKrP+ReEDJrAEBO4e/FYdGrF3FBqGxEljxBXhdyvqgieVqiETfAcgCg4zYeY507E3EY1DSjDVUip10pYm6vDhWQp2qXjJEo0AeIgTyGpdd4qjgAPiggikGhQElG037oiCGuzlENKSSSlS5xeE+CWt6sCcRtSpQzrtgFEIeeCUjmF5JqDSoh4TKInAS6PuSJpDitMm7tHKhFMBOvpjjv3mZSBzS9qUsSluAoBrGeOPwe9XihfsXWmqrrbK81LEllFUVWWsEHAA4kdzDeUUjtRlApwhgMUII4ojocqerLMW86wgdjaBbyuDYr2WEchegiRg4KopLdf/NsJAUMOpgml0mWXXXbRRRcVyXzo7nj99devXbtW89XlCYKUlStXXnXVVfyQVSkyeLLIBRdcoHeZbDhvFLBJNNBFVDp69KjeTZISOUz2QK/wU7giXTrDVmwQvKIQnVmphLyqLkEp7da1UK5EQ/IgAg4BYBMkorsq4lGCqx6VO2WQ7iqRFC5DWOdvTBC32YoJJSBuHZAK4mpnOXiqFzNFFBHD1KwCp5DSBIEJEEEGPKSCuPxFkoDHQxBFJEIUc4MgJU8FzMC8qDXhkmIhJeBEzLuss5ZSQyI6JNnDKaiqqxci5DoQZ6BI6ismMw1FiJ6EIs8gMwSDGpkgoEPopcqK6OFrOgVKRGCbovWSLc0ohsAFVgRKIfnktN9AkbegtA3A6+28e6SjoJqjRQ1CAeICD0wxbv6bbkGiFSBPbgOaLFGEWCIls+DJeclOxdZ1DZUUZRbgBqkihONVZHsem+rFfJVEBBGlzCQlYsapKKAIpAI2OKSkKEURNBZdkEgU8h2i26nUoiCPzCJ6+3XkyBG9qxOnN9XD4BROKsis95F9fX0qMQRdBNvSpUtXrFjhtQytpXun3imKMw0/vQJfd5VCmqYW28htAooAP3HixNjYmHUOVqkiD4cURc6PdLaNU0SwQVFAVIourgjP0+QKWNdMEVYhVWQsKVWIFEXNUWouIlgB4h4CSlsF7lLUKIAIEVRFEZzGoOb5kZOqYF0xL3mCQBdOAUVEZlJBhBZFzHAb1MIO6VWECCxa6oLLoyoGwQZBxDoopZiBuQaqV05HiYqenFzFKEVBJXUpFSHFLy7iFibYw9G55IMVEAU5IQx0CV0xNwB5LNKCSNV63itQwiM99QWown2MRGw0CiJhatoAIh5FiIATMVcERIHtETUTIoNKOAUpRq7QTosiJQNPidgMNIGSuOLcIQE3x04TpEAQqbbCjTlysdTbOio359swz8nffiet7ULJI7RWtQSAI7pkTipuT2vqCKhaNyglS8AKZEGkvgK5Alcv7YIJJaD7lu4uJfF/D7SKL7JWpO3EASqWPErfrvQ3YnR0dGpqqkje5rwpFUQQWc48HFmLFYgb8wlCKS2hVfd8EXNSiJScEyHoTg1S63n6U2CnlzOsJ2MAXfjpaQ5KJeQ6NkBaEo1SVcRcIDUs5jFHqgecWiyRPP0paPU4pVQ6kxBE0uSam2AxT43W1CBFB0q9EDBfUBTgihZzksMKlxBKDqpCkTdhBZKnoMQXLKEbuWIDBLiUxxytijHvHpm/uvnI81MQHU2D4JT7LaQU81SekgjE/YlVrqMQDRvynQjoiGpJWyt4lOeDUk6E1DGng9IoPlFiwyKKKilV9ELJHjCHOHKwcMQS0AU4Yg4OlnUFca9uwg4FPCVQmp6e5ht2xNkS7SYWIUK9Xt+3b9/k5GT0Nw2C09OnT+u9qVcXJCpWq9WDBw+qZEVRcK/u1rt374ajC5DcBqKzeaQ6Cv4+Eg/+VCnAwySiaJJHiKGUM2xI0Uw/cAKcEsApeHWILw+1eCxbEkSUwgVxedyiKO51U2uAFJjnouB2uBulwCmJhLuFtKYAkeFKmYYosFCRJOBExyyeiyL5JkkxAInocMxwz0ERGIIn2pqPAgQPQDd3BHAZ2DOK/SaCeN7u1I2IC/K8kVhKFXOzeY40b+60CF6aCJIrQOrlpOB3SUBZcA8QIT9A4DTfAMSNXgvk22AhQSm6U5CvLlClS6lbUnEBUIqeJtxLZII3T3XeCxmqHIo8dd1Dm5ATVYHMgpzSSUWUgpIozhARRXGViChAnF4iBJsVg8a8XWAV1hU8jQkyYwAocHaiKgolRUGpOAaidZwYBFJBcxRxeiY2KRCBzeARaDGxMzqz3coAsWKPiER02RAFpUBcNzxBRDZK+CFA3AZB/qeeeuqFF14QxylQ0ioTExMPPvjgnj17lNKiKkM6Ojp0j3z88cf5J//AY4Vnn3325Zdf9jTBvR5lESRXsQ2XiEo5OXqYfJaAuCAPp8UpnAk8uFKsM4eU2AoZZFNVoJ0oSPFyriLSQokqa0Vb8iCSCpRsEGiEEPO1RBgiYi6DCBDHL6iEUwoEKJUTxbqd5hABMY9Cflz2C/m5la4UPZ+DQQoReEv2iFgXUBQRbVOKU1wEniveKl2uAnGqjGI5b8YEMV+idSYKHNCuDdggAhdEaLEuiDNTxFw6EYhbJ8UslEQR9qBU8GQpnBZK3qEgAifyCHqyCApVhksUxCGCWxS9AUXMTvEkeW6awD4pmYO83aTkEaR4OKlicS0KVinEpIRUnEN+XjCTElFSsVigROAQQZxzByyaCPn83Cx4J4L9ArxkFjwKWAF0URKUOgKqyRtmRIFGQ0oePScVi1VyMY9G3iIoBaRusceEB0gpZrcI0Z9Q5E1YKU1TNMEjovn79+//+te/rhueRAxU9U5R97/nn39ed0pOPgY8irq/3nvvvaWfe5fqwZ988knNpFGKV4QzCtE6xCKkFCHYSmACVYggPY8Qr0I0Un2e7pSZJWIu2GyFqqJ1oZSae5RIDqoC3EpOzJlGCi8NREF0lLggMMBzAsQx5MDDTKUenq/iriJvKsRcAUpzsmDM09beVJ9HAFx+dIMSxLtSNKHFiiAFZw5EnERgHp0ZUPIWiCARxSlVIdeBUhtIBSt5r+AWcwwWlQJeiyihCPajE3NQNRfgQmqaWwiQ0pXHPM17DUpEpSUbUSjfn+wzIQLuscBViBeLWhMLekiBB4orKoUIbECREy2g57AogllcRPDkUiM2ixCZWZqSQFUg5YWbFC5/riuFSFREZyAiQBdRzHXx2HRz27aJSITnuoBOKq4oTnupSgmYY+js7Ozq6pLI4UuxIdoSSAXzmZmZl1566dSpU6zLJpXu27dvyZIlN954Y39/vwbmM3HqHqkVe3p6pqen/Z2oIseOHRM/++yz+/r61BVrNJFvwJAI4NoAZ8ZgXURsUoTUFCklKd6kQFUlRcxwVxWlsENSRRCONNCpCaukerExgQ3kwAwRZMCs3nwbAqlH5SWgkmAdM3uAoAh5b869YYBfxBEzKYCrUQQgAg8XV1QqoNNCFdFKHkHOhTRm3kMm0R5EiE+4FBvgdDnmVUipVIqCSq7CReCKuQ7nIQhHtgrAJoJHYL4UiKJA6ugrJBXnVdFl0EAh1ecGAnFSRZYQyc+YoyYoyiPAgVJF5sOBuBRERSmOIOf2GFL84BbS/AkqmQupPgeq9ih6zyIeW5pfwtwTFVN+XjjgkqjIksDT8cBL547dCFbg0unKF6LKBGwoJkIuAq+uRnqlQATMAqso4lcUlw43AblIFPBrMh4RFGZ6Qu4HUtDF0ZXaIKJRrlqXIpDS7jT3c6RK5WFvjjGluRZ+cUXmdHd39/b22iCIm3gtIkQz1XXHHXds3br12Wef/ZVf+ZV/+A//4a/92q99/OMf/53f+Z233nrrzjvv/OAHPyiz2pkgiKhx8eLFn/nMZzZu3Pid73znk5/85K//+q//1m/91sc+9rHf+I3fUKO6br/99vzHCLAiYCDEHAwMDPDrsdBLEd2nQkhN8TBRslmKICcGiyISISiKEICZKEghpQShxDakqB3udkEiJDXNXXWCbKRUPZltKAIUHyyKwP5J0RXhirlOarMaMUOolghD8kUBJemCuIBYAmvJKY4/PzpHJjNKBhG30K6Ijs2iIkTwNMFOuijhhEOkkCoK7spFwUuL0wLsgSgKsaH00JNaFyiJuMoORTwEIhsruiogwiWi26YqqTgkfAkukQJEhkCkILqEmOohKmW4eL4TiJ25Ii6nAFeECPmhCdZJRVDoVUQHSmmPcVlV3I1E2VQVyQ34o4pq4Mv7LaJDFoRsoMgTlKqrdZTAqRRcNaQo0ksKAZittPai+KFK8jy4haojxPPfbiFKgktuFFCMXLGhNTJTEYKiKKDDIQu2EH1WBSkQ+/NU6OjoGBoa0ts7lwRxwyklrpsLL7zwrrvu0vs/fuvkvffe+8gjjxw6dGjHjh3f+973pH/4wx8+//zzZXajVtRaugVec80127dv/8EPfqD40EMP/fCHP9yzZ8+rr7761a9+Ve8v77777nPPPTf/BhwIvEQEDmRwcFB3epHWw8RGCvAQW1FqsU2KGy2al4iQ6gXy1E9aiyWzga1IEnAqmiS5gHUgHofRvDAE6xDDij0iiET0GNGcFqas0TYRuAlIrcUQRZVMotwEqUrWcToiCjkHGKwzH0gkxZCXSlyQQZxIKkBcFYmGrAXApcNJiSY0Ck5Tca4LbkhRtMHXTyoGXCIKvAKgyJmbxaVbgVihpURaRaA0n5yj1VmwJlejAHeVabmOkiNX8kZ0E0HV/Fz9lJkgtc4NyRvn3SN9pxUpPRgmhkcIkCQvsA+luWjiFiHnMuAh0pvDJaKRioEiT8jbUSB5yup+TVeKIuQ2QXoeKUE4aVZyQ3TOTxVLtxA4ntwswtjS9iTa5jlATkWXkn1u21RBZ2fn8uXLdY/EA6TnRFXdtKYTpqam9KZNN0i9ifzrv/7rBx98UB69h9McQXfHH/3oR7rzXXrppXpbuWTJEil01Wq1Sy655KMf/aj4t7/97W3btun9YleC7p1a5a/+6q+efvrpiy66SI1aTjZWFPf2Ysfz9yboiJYuXdrf30+VU4GhhPyE+zzgtB+SD7EC4XkhIpjYL/iJQ0o1P+cW7THQkytscEW1O307qIpB0ZtEyYlgnu+qFZ5m5DMVvYoIijeJYriXmAO/QK8n4GQJIoqrEvELIn5wHQVKirnOUZOaWIfTSCru4UBK6QDZoTzYFEurLIh8lejMuAlcThTgrYJ8jp1WhDRmzpBziBR4Ms6taN2QwtnI5wgoreaCJajKedMEO0Ww2UxJ6YLnUGmu2Mwcp4r5WfIqcGL+IKqKwdFk3u/9EOgHKjsVAam32AEEUSRVgsAV5SntgwiBM8e2vCQwgaOl1DoT3XvzfoxSKuCxU0RRYz0EkYWU2onHLclbVFE8RBwDxH4iKRD3WJcgbCDXiZB8Tr5uHiFwPMxEUdy9e7feBepGFdak8Igwmb9B3LRp00033XTVVVetW7fu2muv/chHPnLkyJF/82/+zcGDB/nSqGeeOXNm165d8px33nkSdeu67rrrdENdu3at3lxK//rXv/61r31tZGREjbQoyjk+Pn706NHLL79cq2jsOeeco8b169dr+NjYmG6xcgq+HuD03nzzzVdffbUOQSLbZj9EREXSfLfoEImkKgFxRLhLtHsIEYiXRuW6/RDDuiI2Dy+lefRAOB5EUs4SVbcouktwiT0LXosqc5gpRQSOjtlwL0sjtnZJAYgQFEGKUkblh+DhrdFDBLjA9rwTRDwirAJJlaLkVaRTQrFISimPeAS4uwQbbDZBTK4gOZfBPF8UwPGoKp68xTSnHiLA6TJPlQJ5bynm/lz00uIiiCbS2blSt1hULD1GgmdCmEbJ7XkJMMHmpBVdQH4Iva4yUKmJRaJQ1IC5SD7FIwRxSiilkmLeyylAyS9WquhuB67moJdS7peSD7EhJ/YjClIQURS9T4t4fGYxwLEpyimQmpgr0uXhiIqIOcfgXutKURwRWcigWiQp9XxFSFHLtkf66KOP/uIv/iJ/K5nbBN0gV61a9d73vveDH/zg5s2btSW9q6P3z/7sz/7wD/9QHomYBdrl+exnP/vLv/zL4tqG3mXKrzeUWkJ31t/4jd944403dIOkkWmKgm5yv/7rv/7pT39aE8QFEd1x77nnnm9+85u6+8opxEpNaIea/0/+yT/5mZ/5Gc6hAGFs4ctOESXBXKT1IXD0Cc+5Svn+ReAlBbhF8N4otaK0E0U4kOJ260rZmEA1bxdKYokols4YjfAc6AKbJCqll5JQGsJYx9JWfbxW0owCVnKSKgGbvRMhH4JZKYvCUyVQGuUUj1LBY0G++UJKQHm7KPhEYU5NASsQc0dEV4XcLIi3btJKfuClzYskS0ywCCjlNveii9gPR8yV3IwukCoWeWbOo3R7SHPgyQ/TZqE0RMhPPsAjooiz1JUb5k6uloSUekiF/JHwOHNFQal4aRRQO84ib8K9kEKdD7oY2zoB5O04nZbGKtVm5GeUzzU2RSmU3KiUmYoSU2thwJOLAF2wE+IIvJZAi1MhVxzztTwWJxHkXJBNikVzvUfk21DTyDlIGRwc/MxnPqMb3pYtWzhFXV1duieJL1q0SFxK4U6IuWny6Oiobm+6Kfb19cmsO+LAwICi3ixOT0/bSZdAl/w9PT1aV1E3SOl6C6ulP/e5z33qU5/Sipx8zKrCtfkNGzawPUFEeg47BRE7BevsQZDiR8RVIXeK535FtZAK+XLidpoI+BXzxpwLuZ+BUuQp6UqpljxKUVxqjYZSK3mJOV6RElGr6EyyZwziKglOXSJlmjixRHJOr+B2EURVmUPMVxFniNtJgbjT3OAlhNIcITUVUMqi6Kk+j5REp0ZJUZqvrhQDBFASvLQ3L0hkAmKpRSJPB1KcpHBSQJpPM3cVOBWRJzejpGJAJQzSbRPHpigFTglPaSDR8Ew7JRJpsVIiQB4T6fn5gaRiEJwic68XclsVyZdXKkAskornJUGKX4YsCvmxlcDSIooebljJxwI4Md+VnFrIfhPBG8Zv7hSFfYpLN7cBvyDFw2VDxC+Ck6pAqljkyUN7rqvd3BMkAnGODt0GEcHTsKErAilKqSpCli9frvuQ3pAxHKikW9rVV1995513Dg8P4xTw6AZ2++23q8o0wfsRWbNmzYc+9KGhoSHMbACb3ox+4AMfUAkFg6I82sDNN998xx136L6okhSq4rrR3nXXXVqOL88KVOnScrpHSkShV6kIx8scwwYBv4AuSOH0ukrJIqCUK/QK4px5RAHu7YlDkn1uDku4VzyamzaBKmZFi4r5ihCJigyhao/9eBzTakW7gJJ3QRDzVBDBSaqq0zA1p0FUYtvWpdCFB0O0pUOA4Ck5TRAFnIpeAoWqbAIGAUWRVFzRZgHFQGSyYIWqIM7A1lXQ4ZogUFWKR34BmxTasUVzc2CaUSxNlIdGpVaUYsjnCIiYiVLcS5fABKqKGiIihbXQRZSGO3kQFRFJ8YtgZiYcXalXV4qTVFCaVyFSsOVVAV3EE0QEPOaKeaNSRXRFdG1PkVXmdgNoFkSEQm3uT5ERLoUp2zogFeAYcAo2QHI/KOm0KJKWoBJOPDlP9YBTEQERs0Ap9+eKIiQHoiDOBEXzZJkHe3InJcHcHoEUHdFK7rFoqCpAUBaEJ+jtoG4/vj6ALhG9X7zgggsWL15cSGmaWrjQ9e7t2muvlUd3KUo2nH/++brpiiMKXHAYrrjiipUrV6ZiAFEGNV5zzTV6s4ietyiuWLHisssu01ZLeq1W0yZ1F1fK4QBx0jxC3Cu0VhXRF6wK1lEgJcgDscEKiEHN4RDgRyEXDUSin7mtTpTSigCxFCFAvaX2vJpzgRR/a5pDJel5u20lHbQqIF8i53kEqjoVx2yUlFJqSCzNVJQilPx2igCnedXckEKkRJSiCOD2kGJDESya25/rpCjWDRQ3GiXFc/LU8BARc+s5ct3VVnNuUISUgOidLOgBacBclVQo8uYQj4LMu0fyUiWiqE5bxXltKsFi3qjIqqWW3GDgVIQApxBBrwj5WhCgasES8MNxoghwXlzYMLoIHIOiUkWBCRhEkr04LRBzRSCFV7pSNeeg1EWkUeuyQ0G6JwjmebugtFWRzRySZswB/aqrrtJNSHc7PIL0np6es88+m1RHxEHhB+edd57e88E1yketN4u6d0qUgi6uEkQ3SK3FHBQa1XLuuecqVQvRUKp3kJs2bdIbSjkFtQg6RbpV33TTTSyHU1W3yyAO8vMJ7CR1VbpILJONKpE8zblXZIi5Ih6QZgcwtMK7FZTmZikLrgKJhqYCSGmh6lESdRoRBUSAB5HNOM2dOc93CBHcwlqI+UB0K8CjQGky5pynYtgEpwa6kJri8B0FDFaUaiYx1QMxpTlHxKJb0FM9Lk6JakfEAEGBCyyEDicV1xBGKWJQtME2GjFQFZQCi3m7QFWkNBY/MVkCpN6qOQQPqSFFXTQaMvtwlCpKwcMFkFwBN9q5YATihtJ8juAlqC5IBPHSbvNqcdUKmFhDDu0bUdwNrpLiMZEufy6ikBpuF8SBzBough8x783HlnQ5IZpASi8tihC4dNoVcfo8IkKkK7qRAwdwlUTgzIllkp8hTKAkiBss7SiFOYIUcY9CgchJC7wU5RfxHKVyYkYkyoMNIkjUzemSSy7hK5lp5Vhavdw1bWagIFHgHWQhZQeo93aKtEAUZXAKgbMWQHcVOPUtXKPYkvhFF120detWEaUATmTR2FzzFq5GcXuA/aQ4ZSvpAB0wTVG6U0Gpl1NMfQGlgJTe3CDQLkgXbEBHzI8FhVK0zd+JgA5xquhUYBWngjg2xdKpQxcXwtpEKRUwA4bAc6c4VYgVOERI3mLnMkMUZfYpymM+DSiVU5F2KTitKPq8qYTIcKBUJRQRt5AapCrR4ghRKU+FfKAg7v1Yz7uslJYAGPJdibBPVVFwCiyUK4a6AClrkTJEAxVRiJSSK1K1KEWXwgRWTJZiewI6oqpS2D+pF0reOGo8VB0h+XwROMSQwnw4kYEQQaI9MRFQsIMyRChVc+DEhiK8HX87yMPhCfkSMfRv0Q48oRUeUppGqhVFvK44uiOGVCzMeQqxAlkweo45BAWIA6etXea2CUqBxFwnzSFPrg8PD1988cX5z4GTYWJigt/CweWLnuPNN9+cmZlhFAb4nj171KsUXUj2SBUPHz589OhRFENLTE1Nbd++XVw2DiG1Fr3T09MaOzk5SUnQVvVO97LLLuMLrblZIGU/hdTcQKrMiQBRgOcxJ8Cp58NzP9wEkApF3uy1kopzkJIvQQT5cgAFMY+QEhfsB3kVggJKZkO6S7KZAFKhyJtj1WKeI3nLc0qE5UiFPPVOcrgFKAUSBRHE1gis5PDA3GCbCMAmoggvreg5drpq0hoNpUBcEwSLDCQmbxioEq0LTi1ayYESyzS3nes5EVgOJO/CngV1IddLJSPXbVPMl/YSqT7nxyMFkrcImAXSt72plMBiipBWxKzmPdyRTwGinDjkp0N+euFuz7GguCD8uUBri/Q0PnTx/E4AKaUCfkGKRYFVckVwWtIFKSynKFgURDS/VQGIqRjIxdyJEo4ERFYEKIKqSru7u6+44or169f7vZreU+qmtW3btuPHj8umXkU5VUp9lbGxsccee8zfpEoJ8sorr7z22mvYAMdIfPrppw8dOpS8Aa4KebS0BrIcpdRa4ODBg88884zukfnpWrNmjbbNt9cCiT7MQsqgOYoYUErAANKykZp4LGlONFMH4r1Jz9NWIsBj9PzhiJDc76qQm/Hk1QWBgagWEZYWUj1gjkHIh6uKaCTv3NIyp3nFEIifgIqMheMESlE4aeK0CyLsAaAIeIjAXYq+qKKQYGdOSsBfIvC3gz0iKPjzIRbDN98p+DpBd1XIz4aQjxK3DtDTyIIoch6A9XzOgsCjCBC9HDN9hrVD9olZ4HBEFJkjAocIFgV7LIqUuEAqyJ+PArmndNTWhZyX9gnPDUCl8s8QEPIdcPBwEZUcEQV4biOCXIny/JOi4ylV85JPvZR8UXe9HWRWL1FpmjE3HzDQcFXRR21dCFMCjd4bq5jL6XYAtyLCrnJIBPRygN4/aQ63sBmlnokIZxpAFMGJYr5kyRLdgZ566imtFZ3pHB47dqyjo2PVqlUDAwNSmCbDkSNHvv71r3//+9/XjU0KYCfCyMjI6dOndcddvHhx/p2ouq0+8sgjX/rSl06ePKlU09iAN6a3mBq4YcMGvvEVqKr3nV/+8pfvv/9+v4+UqDeRd9555+233y4iRTpITfMeAhHvTUDEgJ6n4mFKoiM6W03FAjZj8DQRmV1FUURxCVuyFNC5ZQjgccdDiyLbcCM6EC+lEO+HXsTkDcCJpbUUIUauwA1NRlRkTn4sQmqa61owbT00hrB/UhF7XCXFAylxeRhOSqMhhWgbegnY6IVD8IuYl+YrVQmR+ZgFDCKITiECfpFcFBfytVAEeJiaoki+AYiix8IVoydbUSiVhJx7oCMKsOKFHCE5h+TIDUJpV4LHAkqCOHu2yMuaiLfnFGc+2QRx7h4pN4SyUwEunQUQTQxVMSh6iyhwYAWSV10q6SBXWqslvJ1ZSxSsqZcWFRDRFe0kRSml7hKBK5JisGLuNOdORayAkgcixbyEmPI3zQFSdDNbtGiR3uTpvqiUh09vJffu3XvmzBm90dTNUu8aR0dH9R7xm9/85ve+9z3dCP0o51C77nZ6s6iZuoHNzMzo3rZr16777rvva1/72s6dO9VVauQotNzu3bs1dtmyZbqmtZy4lvv2t7+t5bQNdckmf61W27p168/+7M+eddZZUvLDcYoTMN8cA5xIF8TOnAviMkBQBFpEwprtBLHVbyDalvuFBdtFSgbDE+AQ+60YuRmeTwBuFyAYSjbBE8zpVTQJX4ZcKVWdMjCfY7HkEazkcNVEEE/z5oBINQcloVTN03xCrsM9QSgpEEX0PEosVQ303FzCgn7PEQGpWEBpPpDUHpdKXShUc6BgLnVRcovb7XFqZ/5C8XbmEjcQBQ803CXA3Yszb5lj3GnZE5/AJnluJZU80USQmS6JQm5jDqJtXsg2o6Tkk0vOnw4a1SXOBmhHZwOC96MqNgEDETFX6DKhRRxnGlN8+i+RElUijYKHCDaLENHdDqiaC0wQUXSJyRhQchtR5twP0Xu4/5QwMTHBN6xi031u06ZNZ599dm9vr0o7duzQDU/Eja3QipqwcuVK3cn45xy6+QnHjx8X98Zaocaurq4LLrhgzZo1/f39uiWrS8vpPajuuGrUfuRR/LVf+7VPf/rTvMHlAAUZ8MARXWXDSl0SMPvh8Nmj5GPEZrOibCJSStNQBK+LAUVRBqUMwcwopbSI2wl3xGMDE5yiiCsK6OZyCtZlznvN8QsuidOIDUURswiKSoK6cu6B6AIz3UL0WuKUXBWSPUqk+OHqorekQFAYSLsXsi6UnAJmpShKUVphD0OYj64W70rwkHwP6BBAVRFd0YqjnTExm4lC1TrEqaKdiGxVBHgUqRdlw3J6iI/RHqKquQebIIWSPego2EqGaEiQgl88bxFQKDlVFPKWvGQe/YkzP4+qFmb+ECCKtFlnBKCac2wQSlbClOAlW3tzm0Cv4SoT4PbkvYil+XD1ijs17BERODVWFEstEmNK85VOiiKiq8lYtJtIFywC+xV9dBIFzlVyzQGz9NZRLpkzEHM4WuA5AgfOBg4cOPD7v//7esOHgSGqKqpKi1LdrvAzHzNRVThQFYirRSVBqT3MwSBR7eJ6m4jOxqRrRRmwSbz88st/7/d+T/fR6MyAhyXgpLlegkpCqUSLIweFrsiukrFAruTTWrkiaT6qJObIS3AIEQK3DSLkw9HxE5NlbocQHykpHEhRzKsyi5eWAFKE0jkhWoEApsnPBgQMHkIqHW6CWCKtoCS83bT8wPFYsW4zHhQDm8BpUYqCzVXBoxhCyQdOSURRYDMWzUkFlisdF9WwZqKizN55bnCLGxdMSzrT8moJ0hXtd6QKmEM70Qb8iD4/XtTAJuKSiCIiMfeICPk2SB1LS8xbTJBDMW8gTcWCKFopoVWXwhBirhh05bFE/EgQIT8F+fzSIRjSGSUCh1ASpIgTrZciVdKSPwc6RNEcHSKgE4FEItU8YhBScc4GZMBpLpAKpK29q1at+tCHPnTOOefwzTt4dH8CehQU9QZRIi3MSa1zMwFmg58tJ5Gq/EQaIQJVvZVUi8xaS408+tJZYsOGDXfffXfrDbIV+EUYWxIBum1JK9JkDJQ4+8lFK0BKKUJA2ktxtfjQQDIWwJA6ikcqN6CLSIQLlEqIuU2PUkdIiQvitBR5s8XAIIItmhMomYS1RSG2QjozDae0lKoGOlFO5pgkS1RRSIWcC0rlwW8zaY5cwbMgiUHzna3EHiIKBjgliBRKgFQliEVFxFyxDs+vNxEgrpKdEMOlXM95juQtSpAFU1YXhygCcXRACTGHq38jbNOc1haUmJXZIIDS3HPbZQht3JBzoJvI7MaYlz3YQn4/B3jgeSMkZmVABBrlXj3Spar9srGoieD5VoAHqhciA6OARK1lD+0iiiXkk2NoeruTZkSXUs1BkYEoMdnnpfBWeGnZFLExmZJQGihIaUVuEKQoZYg2qbdoH/zgB/v7+3WbxC/gzIHo6oIRkgPRyBW4FRE2SQq0pYGBgfe+97033HCDSgJnwNynRVE6rwgLAg/tXE6eA0h9ohjV6gGehi6gux3kqaoFm99oYPC69ovku02VuSMt7UQluAirQxxRgLjntFalCLliGyVEECMSSKmS5ntTVMncqeFGc0NzSmaJROk6kOQqkDuVOmpIqoeBjeGk6lSwAmzjjCmNKQmkQmmgQapoKHULkV4BxV044RyjeH6wlBSZA1EURwfJG8AjomhOau4NoECM1h0imgPzfP+siGI4NcEmqCs357oAQRHEeXRaNyNQzUWmoeR87nt2pDIRMEJEexJU9eZQBHGmFHnTQKNKuSh4CcR8Jsvle6AdHlOakE5J5twDkiWUEhHg6hKhERHkG1OUAdFLiLBDG1wi5pBiUcROoFQxWeZKiIZE1gK5hy6BPXuTeXQLBnGnQKknQFiuu7t706ZNIyMju3bt8j/twBxtiZfSVi6Spzn+RjEfIqKIru3pPeWHP/zhT3ziE/5pdlRzpyJpqs+hJCpV5PBB6guIy9l60khV8pzcQ4RgaFXgik4FqiiKJbOvN0XpWpEqO8dMFdHcoxQtCnBFNzoKC15yrIXigW4XvC6RVNA0RbcDSiLoHiikejEkJwI2Io2kVAWX3AVxtNlEoquKpbF5ieEihlJQ5C1DdOziNFKCCPk0CCI6kTmUEK0AxCLJhgtKY1aq0iWel0SoCoiCnOjha+6KkoBIVSmHRqRkT7LPnQfBHqAUj+AJ4hCqbMa6pynmsMcE7piTfC0Bki8E8OQrzp33fEQ0NUkqFiTJxWEomjhiEIFDjNKhkqqxVTEHUhCFWCDbDKJgg9DKHdVCTMWAUkEkF1tBY5E0V5eiqJRSDMo8Bk4RxdQXsJPUXDEvEVHCly2awy1AKbaS07acyMPLGeZFixbdfffdeq9WrVb9okn17YCBUTazBLwVeUncac6BUran97gf+9jH3u6vIReEJ+Qe79MoeZSWZtpfagSIuQeQgkJqOsHb7YqqeW5zCSLkYgnWTcxLoCQ4NQHi3oa3mixBUECyFKAlrwpSShEiyEkLyEvAKSXBHB14SKlECvDAXS0pBqJRqE0U6vyFmL/gKoBUsIfoElxVKwC9BOnMyQ356syB5B6Q+kJUxGB/qhRQShUsaFCkl9SwDsLdhHRBpLVUQmvVCoQlWlOUEjC0lpI9ID53HxJwK0Jy7jYrkDRwbiW/pAq8rmGzAhHsd1UgzRV4LnpFiHSPheRRcK8VgQkocGzm6DYA8Vw37MdTuvcrYjDQhZIuoGiCZya5GO6ZgjzaBiJwSbyVCK0GTWAt9uzjOvfccz/72c/efPPNUgT7hRg0Py1YhgVF8Df6zSGK7Eo3yF/+5V/evHlzfhT2KEoXMMOlK5JaBKQ0Gp6T6+I+1YWUUEplo4tokttcWhBy2pwvZwJyj22a/Ha9ShFR2INTAT8tihhsQ/fOk7FwKpYMHguhlMNdjlLyLq5GK4JSeWgkqiriLQnwvJoTOESQiO4JpYE5V4mqIiXgqqLMrhJp8ZAS3A5xr/x5F7qAbuJUESRXgdI0RMFjS+35zgV2boXU6yZLAJ3eVtArPy3EktkDMQs8y0o2pTYILJr32g9xSVFcSMXyHpTCmZb3OjXKP0NAJu2V0e4xUcQm8KqaKzIQMVtxasVgiM0Anh8PzxxSlcwhqSnAQoY9RZ6AaHCw1jFDFCnBSXNinS7F/HDEeQBQTNwlKMVmzhAUCKKqTIPjFxfgwKMA1RyaRlRVxFGQmbVQli1btnXr1kOHDh05coSfOSeoZIOiU8EGuPRSaq5ItdWjiEIqwhnQ3fpXfuVXrrzySinuhYCSrlTgYEmt2w9yzuELiIqsLo6IjoKoKBGCgk2EaeLWSx5xi8x0KpLbUCCGtyFgVhTHWdq2EL75VadCvpx4PlxQynDBLQaNntDqFNGZRE+WwqxUxMMhiipxJaMLNuc835UIXVSFEnHEQ+qBAhOsiLATQQqiIgZAVSD1BItO6YLkwCaiyOFIEfJNEvEo+qilc1ZFXI2e5qJSDClqLJLmTIC/SBKoKgpaQo0+sYjJVYZ1TWM5FOsi0olKfSBCNGQtAofG0hhcQoczxDMNr9VaEpjgg8qBnxZFpfOeCULeg8MciEtEhwjw8LVALS7lnpLIZIBSMihCGAiHCOHLWnKURNJ8V+ICBEVQVSkRFIWFtipYhBBpT3JA3CmN+SoelT9yJjnwm5OiiCs6BaSCOTpwi1Nxrsuzzjrrc5/73Lve9a7u7m5+UjmIJVOXIygppRIpJEculgz1er2zs/Paa6/VO8iLL75YivaGx052TkrVJSDRuiBuEQMiqbhiqWQiQPIoYDYBpVRA8cw8GkplEOCIBjrRhBaIuaFUYpE0UXKWDPnYEheSJWBOe16FS6fRIgQ/QAx3Ii6hCBY9yjEfDpRaESlNE/EoQCkc6dUWgqJYmBIoFcn8nQCLOSSamAty2pxzAacVUgGbgC5IVJRCFW4SjsxjDmyw3yTJhVnRNyHrpKBUgggWDZQ85pAipNmBXIEIuTMnrbChdVSqF6UkzxulmLeEVRcGfwAKvD4CK1xAvozsweDlvUAeIflYIF1i3iIoxWkFGynRBiCuLuYInpmLgnVgnouCB0KsCLkzFs4MAI6YQyK9PoGCxNKRGpgdcSq2OgElPIXUbKfEuiIS4SwtSAHW803qzvSrv/qrd999Nz9nx0sUPdkJKfL5O0+VOSKEY75nwVQL6a7c1dV1xx13/KN/9I8uuOCCavqxdqzuzYP8WBQ5TBSBFkipUbCio865R6EwEyh1zJ0g3AnwaGheS3kUaMTgVGjdpCDRA62Qtu7WaanFRFApXwinkHsEeazYbyWtUJwZFO+KyFiJvqjyIbSTojuFlDwlnq+Crqg5cOks6hWlI9IYPdlJgOQz4RZNGGunILM40WCCzGxJMCkcCQuKStUrwvBYo7kHARFugiEZ55F8FJDCohLRIXYKJkClvAoQzSGcRqcloGtOaVSO1lUUcxGFhQzbIIoQIT9YkKdu8RLRmU4Rl8q8k0JBER8lCFViqeoWV9FtQGmdDEpd7EzcW8z10kxgMTeXPKQyCMy32USxdVERKSVdRKCFCDAYOIukZQnBvXaauApRdFfrfmxjwygluAoXaCdVCcVRuohWwfONb3zja1/72vbt23FKIRruWhCeLO7hJcWi7o66I65cufKXfumX7rrrLnF0jpqj8MYEdEYpUiIVWAUdkeVyEYIYPQlwdI6aUUQMripKJ5b2gw4RIHlVYL5SN/6v7Z3Nym1X1a2thPiTQMQfxCiIRcGaJZEPC6l5B7kH786KVStiyZoWRCtWoogoYjQa4XvmeOZqq71jznfrBfQHTqeN1lvvY6y1d7IMHM7xaOW4hfu1+ityoMa06zGm46/5ObJ/uyUko053032L0DUTUxFfEe1XwdEwcKTi+DYfbMCkgcwa3lrHQH1YzdxlrImZGBwr1njvATRdhBW2lyDS6kHMBBDRYvc81Cqwla5+8oh8tDyVqkMFndRAntqmI2oEm8lQ49jNR5Y23XbkHg+Io9DMUYca06P3tolY8QO0bzMMBjzG79k40XGeqyOcUeiAk51RQL6m61TjhtapCvyYxqSda1fwT1XvVysc1NeM9iuD2ymPW4sjbH/qq3+I/PFsrYbWa3+EitdelW7+ikQk0H8c4BKn1Ak4ArSoHA1oghn4z3/+88tf/vInP/nJT3/6048//pgNgJ+MT3IKNF1lV4FjzJaxtIAfyHfffff73//+j370ox/+8IdJiquooNPdQBefajgmNe900CTk8Y7gZDDoI7obE3oP9O1p+YBtpJNNmyR7W98LOppUnHwQtFdnCvqdHK3HfG2QI73y+lZ9hLfoQI50dZrN50jN2qupIwY0+zvc8jERkI/JiH7HmoTX3LltC3N0T38bigSomh4hAc3GFrgWTAq6R9D51KJeC55TmPGt/XXFF03qsaKemtatqWBnNhvgmKSDaug9COjv0Ax41I+jMJYNPYjO1bbaMZCdLeD2/e0//+/s9HxXTQMxoddt3bRacCWitS1rRC7NbAK9JGAykoUZyTtX6kDTZJYLJg7CDNWjTsIZyZ6EbV0vFU266Dy1j4JJ1b+yLY+mOqKT8ez3ecDRQLQfE4eYvrSp5j/mvv71r3/ve9/78pe//Kc//ekvf/nLJ598YvKKPrWX6EiOthC8hF9HxPvvv//jH//4ww8//M53vmMMTFLBY3/n+Q7NIKjREU1iVjLLPsEJthDxdbhUk2M0RPBCP3u61PxZBAJUuiZbgC3/mDyqyeiAGci4a9EI37D6O1mejBviq6E3oMFMBNWP1qZ+TJabQdvtpIF2AO0zXsNVucLlfoomO/MGj02baHAh+SzUVEf0s81Hb0dqP0/tnizpmLM60LrBdDZ5d0IvAdfawu8j3RZ0acUEr7DqO04FM7Y8RueK+B4NeJGOYDqVWTK2RP/6QvWxtJ6n0InpwpiQEQX1KXCPyEJtT3Tc204PIo5Fa4q7o4MfxuqRel3oMbTvFdbVPOCoo6m25tiiyZNodayfigP9TpOQj4CpQ0udbcs+/XQhR5Nby+vsbs9okwzC2YwcKxY961EftiXXAGTW/Thb4KOPPvr5z3/Of1P+5je/+dvf/mYSf4vBtkEdPHrd5z73ue9+97v/t/jmN7/Zgaw12R8N6OIkg/9aBseAGVuhw9TeCa1J9sLgBrs6Cp32r7Ph+nKqx4x7pGZJO2rY8gGfWxS0UiNoObJ1/U4grSQFJyOntUhYPOqYbN3gx1Sb9CXtQ74NP11/FcEp/dYB0z8CdItcB95ohf96kZiPADOdRHup1YytdKltXnEK0P1y/N6MQ+s1EXpcrcjmmNHXK64ZteBsI/1sxBbuPeCswq4ta8K9RDD7ImidwReT28uodqk+Qp1W63xCiInYTNCxlaTO0V5sGb+FfBcxqWA4+WhFpsBBTUVi10Ec6FkqXcUbWm7gCqrP1omv2S27HuOYcSfgv6YV5hG04Fj0aIEO1WNa8VNjduCq+a/JX/ziFz/72c9+9atf8Z+V//znP/kPwQQS2yAAav7b9O233+Y/TL/97W//4Ac/+OCDD9577z1bYphVkKlohM72RcGWp+bL2bT71ZAjGYR6uw6Mbb5mlkckvI3oIKRjcRTxu7r/6jty9an6TDGLVtjKU6kuQYNa07BHcfZWG95M6rH08s9O4wis6RcfR2H1s6Pb95h3Um8zTWc2Qe1bFK+xXdF5NE9yVWLQ33mOSaLBPdu2q04MnTw1fwoRBiDvseJ3xhiaitZUezSM058iMVseTSIkukfIuEeyQXHVZ27Bsb+xxDyGPINKBpjqj4yzgi9eePTSALXVVg/3ZxB88H1wrKuX6XQ9Gw80/XZuvyNEd5OnAkfDgqMwQNeFveoYu9MudyQiMUTgSJe6tRzB9OgexHbFah6m45CYGasxWuCgojM4PYjeRI4m3XAsenw6SBLdomPqzadyNebHH3/861//+re//e3vfvc7fiz/+Mc//vWvf/3Xv/7l/3OvjlMNU/ldfOutt955553333+fn0bgPx+/9a1vbb+OcNxUb2YW8mCPJj2qEYQR1pU6HECsyIEmgurHdLMmEMhaM7bipOaFPiwVJ2Gvsyri9DamEBkHA9RsPiYfbwMzdkmGLEl3q2QMQPwc6SKojh83Pb6xRhO2ANqF6AhJHm1MYQsBZq5TvtCjWpEMlXEfbIuKNuBdcNxRnyviWLemcFJxHKEa6ONt4Nj1WGtL02osI31XWkDXDRH6kCvUfew9OFT1dkUcp3o2MYXhY3u9xyVUyBEclN6jAz0OHtWYXmeM6nUe9aPjZHC7LjrCVZBZBZiBHB20Pu/2zHFboViR/XuRXke1lVnh2C3DtsR7Ed3S7MGYCHzN+Ml4VPdCVwHdBKwSHxzM7fpU9PWTph5bHhep42c2muoqtJAE/LQ6bytCTUCRo7d7VGQJ4KCbbENEZwTHGFoh8cMf/vAHfiM/+uij3//+93/+85///ve//+Mf//j3v/9Ni9/Fz372s5///Oe/8pWvfG3xjW9840tf+pK3BK9wc3+HgM7nytus/enQfp8K81TQTwuhbyAOVeeILjhS24m2RWUnFWhRMRHG2OwL8x4fkBFa4ogO1YCtgNMxBNXHe4R8lh7vbT0bc42+cAAdU0F9LbBd4cc0bybobCNJJo+IY0ZuPwvieqMmFYwdMwU+Jih00mIW4R9Wf6tr4lxlnqNXqzHjI+KbtwVZ6yMRZHpPLoUOZATSyhKylcvEAAAWEklEQVRBxzGGMOySDCLiHJMvMU91223Soz4gCOO4v8k2tGJNnOMrcsKG7IFcmg3be+JTwSQgckRYc9yEYZ1T6NpOz4rpo4/sMtXpynVwC/g5NxPa6e8iZJvHbdy3XaegN191FnqMg4gZYas1Qu3RB6QVvYFp11m1nzpf8rYwzhY2kKnEAJGFYhezM5nS7K6+gopWRFOhRyQx+PTTTz/55BN+HYH/oMQhz88kvP322/7/k2WSJeh8qLX7WK5ekfNJiamvATUkILbaCdnTGzIev2dbhzzsPD8u7Q+SnaAvzOYWMzoIqnmPoKaywZitlTpjVFqKmKKDsPabEXlVZ7IzydYK9+QitL5Otxp8b1zx5560Ng1ebcXsr66THcMX9bZ2+6rVIQ6xvg6H44qcbC9R5xhyTL7X9tuSVNvl2BeBR2NUdftWjmpIMhlwvzFYwTMJdnN06jrepuO9RK5OuA2fauHCvtcRqyIxteE4iDV3kPyGfneff9JY/R0pQG3LTHfFu1O3gMc2yRgObj4PRWIZJ4bpMzZTOFqlNXgLZmaPmYXH+AjNtPx7kED+WlCTaZIEtMfjphVmsP/mbRD2M6IR0XEQ4DjVI7rRhOPK+shx/AgbdqO5C7E9wPEjUY5JBL+CX/jCF9577738V+NXv/rVL37xi++88w4/k2Yavwex61pBB1o6eY/oA8IPpdNHajIKnHw6u8bEgFV6JOQlVHA5x3yolXqBvnh0llVoB03C9f2QwSNRfo5OqSUbEOmi7YIO5EgsvmZuBFupPj6OdEZaQ7497ooGVgFH0Af8CInp0ZE4VAV+PjIkBgiOaj+C2NJJGNA52qWyHGHVv44A/mk9yEWg0GlsQcKpgAl+OgMbPg+Rbm+gSmvwuHY/p9DUfIoj93iGxMkgR6c0+5s5oguPcJ5f7lybjlVWIBANaKeAoxV8pz6oDaPz9yGmxFQcrVwWQbs1NUc4ZhZ+WtDUNxMBPtS/PfHRik7C5ntEv+FPpUk+QnMb6W6OIXl9Xp4/13YQku8anN1e2wshC+NvxFc4wjHPAITHOD0FiExtwi6QN4xPzaqNDIIxKjrjimRE/zzUXwOfbTcBjhH6fWlwiQFEpgTTcapC064L04of8BW0Nm1VxKfmkX4oMNYXrfjBdtywRUatcITqLQhImJovhCpqfZ/RpntaG5DeA1lCzZ5eiAZ0jlRR+wBIhp1qzZU9L4LcBXHUhh1HYCpyvDoIaodBM06EvnUFT3ByKSRDjQmZajPYTctjVuVTG+jrDOSYb8+jaLqz/7Ca3KJ2g6LDvTn+lkG/4cFX0urH90LAdBvVVl4Lmg5Cx7JNkUycLabmJQi1AvR1ktd8PgXcIujtuFU+RgJXEfKObqmv4VuMsQe2Ja8dO4lWgOYKHsRZuw9iQhyqZoQj1BxbBMevgay1atrd/BZHaIFuH/RDt9ym0FyRA7W+x6AT4lxb7kccY69gUshvN2paFZpWhaAJREfEj1borNT58SVhq6zUCUfz6k2EOPHjbON9O8QHWgmrEWLXFtg9Zh6+jl2OEbYUmuAR1KnQe8LKnpxWjYCt0Bs8psZUxwH0cfdi9c9AQyvhdK9Ob2jW7ucG8KiWHBWpkFlAr9TZ2kTWWq90Ukdwwmk9ON0FRwfP88OJSAshmLd+0BePmykre25r54pdk+20bjTtBltwnl8uudJdhbVfGxHaQfiLFkfY8OLnOj97frzMwPGJizVxxiR+m7Ad/Q1f2QPNkIvUVHSE5H8FHM+qz4PmwQQ8RiQP5ompJUm0Qo7LXrZ01AkjBG3eCpjR4L1XsrPDwW4HtpoWnxStD/GP4YcfrYD4bR7zRUwy3JIRnSNRmeDC8/DADQjzgHanOqa1hZn8tdSMVoiZ24APjnMkHn9J+i8GwuvUYcuoIxjJ86K3TMSV+GbEZ0SnKuIotnG1lYB/QyCPzMc50g9sZadfjj41H8qAOmgKR2b7ivjWNgGdo9rA7RXnYdEBtMeYhKPhtfE2k6F16/uhNgi7ytrfW5ZgRhuLoMK22TB+BKzOSbYloAZ1bkwr5raK4+YAzvYnGHG0H3Bsx8+u03k05DOuoXMh1Yxa0GbC5vTRS5lCZ1yHihkN/YaYyUD8ZxtOa+W8L0erdAwNuQ8dn3qdgmvgNgw44Evy5xSfIy18HFtUfeuGgQgHYS07n3EVVLQXUTsABvoI21N1cgS6rrJlODU+VQe2nerVeX5ku53cprZMvyEf7QqBbqEJK/TXyjND9ZgrEHa9AhxRyJo4Po4vUWjGcdaYs1mObnMFj6SOggrGPMbU2W6xQj9AHx22oyObCZr6boPE6PYHAVrG9D2i7QJm8vpxfPBx2XrwihxwpOrYUneeY+tgsjGgnzBHtBvUMVskA2+ICUeqSY/5TtKiurADaDCmabfzsL0WPCro5vvpbvIIN2chjjEcRcz418dQYxrrkejukqf2QlBfZxXX2pnWCCrbFFagC7lax6lUk6FNdGreaQVjVD9F304GEDrbEQznSDfCL0cnUwj1Sh2oMa0eqec3K5xtmPMoq7+TmMc34x44zxfc1mxhAtDma7p5w1oEvBZYt50tHf9QwaO+2po8bNpjx7pmCeB4vI6EFTzwuHUDfloJIzR1/AvUpLumj8eY9Lgiz206ZsAMwq4iZvs4tuJEqK+OehtZzfNejxFH6KXTenME7dGaroKqtiqu6B+L1kgGQwd0zHhE5N8OK3IQfaQX2bn5zFJ1bMlqvpjyaD7kXo9r9LncI5znlfcYP07TDtqMxFR37aPEjAa0z4a8P+DAeXhMaapjGoAcFVuyZyNgjZ6BCP2j/RD5J66XRL+ZJK19i6A1qWFrqSPMIGIaSw2r+RwBHYi+mlkrmJsDV8d/BAiHs/F4UrQiYU2OIaaiMYBIS+GU7P9+FBqnWmz/FNHN3nXF/ce7+k0Hor3I2l3/N0WO3YrT2JV8lrP3stuQNLzd5TF7XsOA+xnxnwSPmT0ueLnHY/x14YFHHWq6yztYqWfrmH+MgF1NMMmr9KMRBnpEoZOqgF4CmfUI3UXkIvW67cQMqP2fIAiTCqskmRjCZDDZflaBjuPSvtDF6Xo2ajnoXG+n2u3/8aG/vURB2A/uFEToGwbDdmNmVofa9wa722sB35Y1yxXJr9RBluezrMvPBwPaJFoBmBn0SEVHNFcHtitOteAYh1nHU215RPOMfKjrpwOPa+UBx9RNmIdk8hnN9JFq8pbsyZMcAXxMKs5KPeku+rjyMdXYbfqDU53KeOejEXS9K2byeQN1rXmxsDmGF+f5QSfVHdv2ePTrJZOPc8VWj2enghYcN12eBFxxdPk/nhVE84E9KkA/G5O3yxHOpY/rI8CPhEjLLrCZrvs1DeQlTpnpPWAAn9otx3E4Qlrmo9txSY4Rkqs32GAl7whJ9XbFdoyDyFPRHfZJ/XiXc9ShletiAiKmy1fzfBvC2SRX8yCBmG/IgOK4cj0PvM4jrRzjUNHH8Bo8ZuqT2sJZ/WOcmg2Ivh0nWjhinofHnh4B90Q7YgChhm0KOrYi56CP1MkULW/RiUkFj4CjiYPokfjq29phZ/V9Eg5HlyBs2d0uMmnAJU5RPbafizzCtduYxPdeY5AphC/R1FmR44Wa1mNRdSFm9us7CFu4fWAQbdVZzRN8nKzNY6BbVp34avIGODprAN0YsKujNqymZg/g5FVegV7ZFzFwVq3AsXJUN5jxqX40/d7sMV3qMXY3CPpUdJZQb3VWqe1SCVBdi0OFHGmBZmLQezB9cyomATCAsIVW3PxtJnQeHnszHGFVtNn5PuaihM3r2O0Ngs/RMOBQccSMGIiJTlVseY/XJVbz6b6mZZvSVNjaSKzDPSuZjUggYQUVbIlHa5ZDtzQVOEHHMKy5Fw+DjCgMrLnj2H+pbK0dzyXoDCoEnVjEsbTef+uIphqxZdbQiQH9ZOIYyD88yztQtwNtZhVH2XZCnJDWmnjqOKDJLDobEK1t6XhsBzgq4iD8RyxHRcBxSWbFpPW45uWzgVYyoO9XqnOEFmoqmdU5sSu9Fi06YBhnmwJjx7ri2jrWLWyJLUUfg0emEFZ9SAsQfnC5Lkk4jlo/rODpqDMSAWhbmmh9sIU4hmsV1bBVYWZb0oIWnyuDOgYQsILnXaIDduMk39UROBKPI4Ju7k0XEqDSNWBtAflZ6WRmNVfwTO7/OoMkIiRH/8j7bzygCUT3MYO2OEL+3mj6sdWCk6MjinbAPWpboccVcH226POlZOGZeLBS56Aa0MkrgrE2dY75C7nOZMCH8/CAWP8OEehbHIHzvMhmBC3yW0CuvnfF5GjGPVShxZHWeX78oUdQ4wQ/BSIBdGLUvKfDOlZNBwHt0S7oe8zzbEF/Xn2qzrHiIdbQc8qMXVHzmMTyLXlE4CB6ClOOXY+7bIlHAh4jZOvmiOhqF3ySvg7CDOgodAB9DUSLTuMnBfeDR7tqUNNFWMV8pqB9P0V/vYitbviXBHpEDQgdq4+npkvNBtlGtgqMKOL4dyPgeFGO1IQlDpWwG9DNMXwZhHbUJlnivcfwBQNr6Mgbxk8e066P0dexm2NDWJ/q7eB4j/QXghkf0EmuziH8Tlb/nAKPyYgtzTwAbb4FKI7E40nPj62g5vMDaTS1HwQG7OpcMyE+ghjoN6/NQl+haOhC9meJU7cjQNfWmn6OH49bvquMpeIjbHW97qE67ohmHCqmqwzo2KK6x6Mxu7I5vS0muCqOIma/Rz8BhTUvTAu2iwImmW0D4CjEDYj+QvQhDt1+ZByTVkx9ajKIvHDLAKYZjwS2jEcCZqg4Oaqpvi0XaSa/vbPD2zGxwBL39Dt9G+Akz7FnW3uFAackDnVrcTzVA7v6rSGPb5JUmMkVySeA6M+ICWq/n9Q192KQmvBqHsc4DnJMS39Fnm+zlXcevYegrtETHAVhaj9JAdsGHaoavCgtyB5aan0zVLSmjibVvI+B3qPAyVTfC+07aABhNeDVmtEbtqDHIfkE1Ab6urQSg3wVxvQT1rEVcPzIfVRQt1lavgGdPR7tUk1Sz2/2ipOGchTMHBOA7YkK8K3A1O0gxA8dcHAbARzI/gjAdydCB1b84DxfblFg9nUKu3yzOt51ewU1q6C7wJEu+IcEmFYFZCTbtiVXh6NPik/GI7QGu4oQZ0X25ZsDOK6F7Peo6BECEN+jrYaWqDUBbb4HV/CZCQlv3QyGW2dtPQc5ChpTYZXN6VnxSABBjdBUXLHlbPKi3hxIWFM8JmxSTSvhmBATgb9STyc4YvUYnaSBkCNC+BubmE7A73+cg3lg5Hb2uOBlq7UVcvVGm9kTsmqLUdNqRxH62FN9VFCBo6zIYeb42vuB2KkWxtbciT70N7wufH4uhE60PiQTcwUPevl1VQ+ixZamoG0p0oqmNu1nyo+GWKPPkf96PFW1nt/R9gK3x6HimAF/KgDflkczITEheaoHBMzQUqRmg1P6LSQ7t0CmWLLsg2gF3f5b0l3HddCImH0jOtdFe2xhUge2LngR4ET7PGcF0646cMyU//B0wCUIM9mZ2Bb26IgOFRNyi5lwXegxLYRH/XYgV0RTExM1NRAwDIbBYwQYy8cBtXnf0HdlVe9sDflQVpeId+H7EXK0a5Jjb/A9oMAR9JaXXOcV6pBtaSHUuXQjIyEj5jOoD5vvhrNXT+obbeHk2TpggGRfpNm0ueaef2rguGJrbTjrM0Anr8pgWh4hGuFdG232oH4cjmqfGl/t3xmd7TGKoBOY6r8SzLrHJY4AOvtBB9oEj3bX3CGyKsvVCMHsJEeflEzfgt/0HtBUu9ZLjXUX1NnQLUmrl4B6W5XMQXodwu8tzhjwzy9/EjGjm97QR3GPoltqRlrkGPIZ8h50LrrmwWSqgWuSY+/ESXgjSbQZYArsAsI9MUki3A89ztFqN7MINb7joJ+ufszckjzk2Puvd207Cajx0wKndJzNBsN2MUENWwuxgbmCz4uuVzfmc4VHRnx2rkjLI/RI9Orsb9PHQVw3g9pv0kxG8vU2W0ahzzFOhP6KHHBkXGGLyrEz/wvOKnSgHTRcu/krsbznX6d+htqvy+oquwZ07BqmqreW9CwtqkfD+tGSDVnYU7R0Yga6jgDidB9TihxZgkZ00kEr9DOoZvR9pEuO+2rQ2FWn4thK5lZQgybVS7Mk749OiwocfaqO+6/L9dEdyKCOrTXxRJOKNr8JW9DjmhmMrwPXlmhu+80cY6t7NCRbqGqrpAuu09eBHrcqxCMZtBXco+j9kmNPUYNTClrmj2uWwKfeQiDftTH02vS8SBF9LF3EvFa61D6aVyuEIxUnQjNO1yvXbpan5SqPOsIHTyBTsh0DYardNfRk64o6f7jdCpi+Ab0JcCd4hOstYGsTq3OiD+e5iJ+7QB0nl27Y3TRhiNP6yL38UAqd+JJkYsfwIseMbEnZFm5HwOmRLNG0ZgqhbrE6L5JqBazsecv2T7emTmp80Gmzq2QWrVBTO4b2GAGdiXY8/pF+6A2STnUAHd+jPhy5emFaHttMDI7EJdwC6Mp5fjhkFIDpiFoTNE0ipI9Zgqb2ko4lILc6YhuMvqW7+Ve9z5AEFDnCm18raFuaCgepCs3nb2TcPuZfqbI6J7Zg8yFOMvCa/h/pW3wkS65Xvxnyr13tquMbWX8eausmJHmP17XeRcBWki3UEdmpcDDvAXdGr7kn+tDH9oOz5+GSOYYftBMdB9B+zDYljh8nx02nKsRjiOnH98ZgqwW02X6zZSBHBeiDOnUTEtOjxJToFtESRwHqVEVrBajbgS3ZXb9SxDYC3cqfcv9pKkBNfY3uovtv7zH/6Lo8mmo3L9GJIAN0E07LB6sVoDZM9diBjWRgE6mSB8iRexwRvjB+o5mqCDqQb8AqV5362nVN3uyxhToO3JqQY4ugA330f0CrI2Az/Qj5c4TrFEI8Nu13QN3O9d/5fjPy4s91GIZhGIbw/O/IYRiGYRia+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGe+Y0chmEYhnvmN3IYhmEY7pnfyGEYhmG4Z34jh2EYhuGOz3zm/wEoCDexYfehHgAAAABJRU5ErkJggg==)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PsmxyZnvRhM5"
      },
      "source": [
        "# Agents\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmvX0836r2xr"
      },
      "source": [
        "Six different agent have been developed to solve the task. Everyone have been trained trained using the Double-DQN algorithm in order to evaluate the contribution of the constraints and the different ways of injecting them.\n",
        "- **DQN**: The agent is trained with the classic DQN algorithm.\n",
        "- **LOGIC_PURE**: An agent able to leverage single choice constraints (put here to win / don't lose) both at train and test time. The agent does not store constrained choices in the memory.\n",
        "- **LOGIC_TRAIN**: Similarly to LOGIC_PURE, it supports only single choice constraints, but stores the constrained actions and does not perform any logical check at testing.\n",
        "- **SBR (Semantic Based Regularization)**: The loss is penalized when the chosen action are not coherent with the computed constraints.\n",
        "- **SPE (Safe Policy Extraction)**: The agent performs a sort of post-processing phase to discard invalid actions at test and train time. They support multiple choice constraints.\n",
        "- **CDQN (Deep Constrained Q-learning)**: It expands SPE to restrict the action space directly in the Q-update to learn the optimal Q-function and the corresponding safe policy (https://arxiv.org/pdf/2003.09398.pdf)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laE9IkZRQTR5"
      },
      "source": [
        "agents = {\n",
        "    'LOGIC_PURE': {\n",
        "        'constraint_type': ConstraintType.LOGIC_PURE,\n",
        "        'non_local': False,\n",
        "        'train_first_player': True,\n",
        "        'n_episodes_as_1st_player': hyperparams['player_1st_episodes'],\n",
        "        'n_episodes_as_2nd_player': hyperparams['player_2nd_episodes'],\n",
        "        'train_save_freq': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'], \n",
        "        'train_weights_path': './logic_pure/',\n",
        "        'test_weights': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'],\n",
        "    },\n",
        "    'LOGIC_TRAIN': {\n",
        "        'constraint_type': ConstraintType.LOGIC_TRAIN,\n",
        "        'non_local': False,\n",
        "        'train_first_player': True,\n",
        "        'n_episodes_as_1st_player': hyperparams['player_1st_episodes'],\n",
        "        'n_episodes_as_2nd_player': hyperparams['player_2nd_episodes'],\n",
        "        'train_save_freq': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'], \n",
        "        'train_weights_path': './logic_train/',\n",
        "        'test_weights': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'],\n",
        "    },\n",
        "    'DQN': {\n",
        "        'constraint_type': None,\n",
        "        'non_local': False,\n",
        "        'train_first_player': True,\n",
        "        'n_episodes_as_1st_player': hyperparams['player_1st_episodes'],\n",
        "        'n_episodes_as_2nd_player': hyperparams['player_2nd_episodes'],\n",
        "        'train_save_freq': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'], \n",
        "        'train_weights_path': './dqn/',\n",
        "        'test_weights': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'],\n",
        "    },\n",
        "    'SBR': {\n",
        "        'constraint_type': ConstraintType.SBR,\n",
        "        'non_local': False,\n",
        "        'train_first_player': True,\n",
        "        'n_episodes_as_1st_player': hyperparams['player_1st_episodes'],\n",
        "        'n_episodes_as_2nd_player': hyperparams['player_2nd_episodes'],\n",
        "        'train_save_freq': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'], \n",
        "        'train_weights_path': './sbr/',\n",
        "        'test_weights': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'],\n",
        "        'sbr_coeff': None\n",
        "    },\n",
        "    'SPE': {\n",
        "        'constraint_type': ConstraintType.SPE,\n",
        "        'non_local': False,\n",
        "        'train_first_player': True,\n",
        "        'n_episodes_as_1st_player': hyperparams['player_1st_episodes'],\n",
        "        'n_episodes_as_2nd_player': hyperparams['player_2nd_episodes'],\n",
        "        'train_save_freq': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'], \n",
        "        'train_weights_path': './spe/',\n",
        "        'test_weights': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'],\n",
        "    },\n",
        "    'CDQN': {\n",
        "        'constraint_type': ConstraintType.CDQN,\n",
        "        'non_local': False,\n",
        "        'train_first_player': True,\n",
        "        'n_episodes_as_1st_player': hyperparams['player_1st_episodes'],\n",
        "        'n_episodes_as_2nd_player': hyperparams['player_2nd_episodes'],\n",
        "        'train_save_freq': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'], \n",
        "        'train_weights_path': './cdqn/',\n",
        "        'test_weights': hyperparams['player_1st_episodes'] + hyperparams['player_2nd_episodes'],\n",
        "    },\n",
        "}\n",
        "\n",
        "# Opponents and relative epsilon value used in the following order at training\n",
        "train_opponents = [('random', 0.99), ('negamax', 0.01)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7rc3Ev2xk3r"
      },
      "source": [
        "# Remove old weights folders\n",
        "import shutil\n",
        "\n",
        "for a in agents.values():\n",
        "    if os.path.isdir(a['train_weights_path']):\n",
        "        shutil.rmtree(a['train_weights_path'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yquKq4TUQztg"
      },
      "source": [
        "# Create weights folders\n",
        "for a in agents.values():\n",
        "    if not os.path.isdir(a['train_weights_path']):\n",
        "        os.mkdir(a['train_weights_path'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAheQLbAxAai"
      },
      "source": [
        "## Metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fuOsqJ4rz99"
      },
      "source": [
        "In order to evaluate and monitor the performances and the goodness of the training phase we have decided to show the following plots:\n",
        "- **Loss evolution** represents the smoothed loss of the last $n$ episodes.\n",
        "- **Epsilon evolution** where the epsilon greedy value representing the probability to choose a random action.\n",
        "- **Episodes performed actions evolution** represents the smoothed number of performed actions of the last $n$ episodes. However, it should be noted that the duration of an episode is an ambiguous metric: longer matches lead to lower global rewards, but they can represent the ability of an agent to block a strong opponent.\n",
        "- **Rewards** represents the value of the cumulative reward obtained in each episode with green star (victories), no symbol (invalid actions) and red points (losts) label. This plot also shows the number of wins compared to the games played.\n",
        "- **Action distribution** is a histogram that represents the distribution of the actions chosen so far.\n",
        "- **Reward evolution**. represents the smoothed global cumulative rewards obtained in the last $n$ episodes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ntCtE_NjouF"
      },
      "source": [
        "## Baseline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-YuFjAM3WAh"
      },
      "source": [
        "def training(agent, opponents, hyperparams, cnn=True):\n",
        "    policy = None\n",
        "    for i, o in enumerate(opponents):\n",
        "        env = ConnectXGymEnv(o[0],\n",
        "                             first=True,\n",
        "                             invalid_reward=hyperparams['invalid_reward'],\n",
        "                             victory_reward=hyperparams['victory_reward'],\n",
        "                             lost_reward=hyperparams['lost_reward'])\n",
        "        \n",
        "        init_screen = convert_state_to_image(env.reset())\n",
        "        screen_shape = (init_screen.shape[1], init_screen.shape[2], init_screen.shape[3])\n",
        "        if cnn:\n",
        "            policy = CNNPolicy(env.action_space.n, screen_shape, non_local=agent['non_local']).to(hyperparams['device'])\n",
        "\n",
        "        else:\n",
        "            policy = FeedForward([hyperparams['board_rows'] * hyperparams['board_columns'], \n",
        "                                        32, 32, hyperparams['board_columns']]).to(hyperparams['device'])\n",
        "        if i != 0:\n",
        "            policy.load_state_dict(torch.load(agent['train_weights_path'] +\n",
        "                                              'weights_' + str(agent['test_weights']) + '.pt'))\n",
        "\n",
        "        dqn = DQN(env,\n",
        "                  policy,\n",
        "                  batch_size=hyperparams['batch_size'],\n",
        "                  gamma=hyperparams['gamma'],\n",
        "                  eps_start=o[1],\n",
        "                  eps_end=hyperparams['eps_end'],\n",
        "                  eps_decay=hyperparams['eps_decay'],\n",
        "                  memory_size=hyperparams['memory_size'],\n",
        "                  target_update=hyperparams['target_update'],\n",
        "                  learning_rate=hyperparams['learning_rate'],\n",
        "                  epochs=hyperparams['epochs'],\n",
        "                  constraint_type=agent['constraint_type'],\n",
        "                  notebook=True,\n",
        "                  keep_player_colour=agent['train_first_player'],\n",
        "                  sbr_coeff=agent['sbr_coeff'] if 'sbr_coeff' in agent else None,\n",
        "                  device=hyperparams['device'])\n",
        "\n",
        "        stats = dqn.training_loop(agent['n_episodes_as_1st_player'],\n",
        "                                  agent['n_episodes_as_2nd_player'],\n",
        "                                  save_path=agent['train_weights_path'],\n",
        "                                  save_frequency=agent['train_save_freq'],\n",
        "                                  render_env=hyperparams['render_env'],\n",
        "                                  render_waiting_time=hyperparams['render_waiting_time'],\n",
        "                                  update_plots_frequency=hyperparams['update_plot_freq'],\n",
        "                                  plot_performed_actions=hyperparams['plot_performed_actions'],\n",
        "                                  plot_mean_reward=hyperparams['plot_mean_reward'],\n",
        "                                  plot_actions_count=hyperparams['plot_actions_count'],\n",
        "                                  avg_roll_window=hyperparams['reward_avg_roll_window_size'])\n",
        "        \n",
        "        if i == len(opponents) - 1 and i != 0:\n",
        "            return stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HbOenswCnLUm"
      },
      "source": [
        "#### Random Opponent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kFf1q13lNCOS"
      },
      "source": [
        "In our first experiment we trained the classic DQN agent against the random player provided by the competition. This experiment has two objectives:\n",
        "\n",
        "\n",
        "1.   Compare two different architectures to justify the use of convolutional neural networks and the proposed board encoding.\n",
        "2.   Verify the logical correctness of the proposed solution.\n",
        "\n",
        "\n",
        "As we can see from the graph DQN - CNN performs better than DQN - Feed Forward.\n",
        "However, the result was used to  rather than to obtain a robust and skilful model, infact the model manages to win most of the games after a small number of episodes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyv_UhZIpHCI"
      },
      "source": [
        "##### DQN - Feed Forward"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAz83o7utSMc"
      },
      "source": [
        "*   Fully connected layer with $in\\_features = 42$ and $out\\_features = 32$\n",
        "*   Fully connected layer with $in\\_features = 32$ and $out\\_features = 7$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "coJBX4QGtfdF"
      },
      "source": [
        "![feedforward.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3RcV_SncZNm"
      },
      "source": [
        "_ = training(agent=agents['DQN'], opponents=[('random', 0.99)], hyperparams=hyperparams, cnn=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnJopOWGpEJq"
      },
      "source": [
        "##### DQN - CNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kca-kLeGtCWV"
      },
      "source": [
        "\n",
        "*   Convolutional layer with $kernel\\_size = 3$ and $channels=64$\n",
        "*   Convolutional layer with $kernel\\_size = 3$ and $channels=128$\n",
        "*   Fully connected layer with $in\\_features = 768$ and $out\\_features = 64$\n",
        "*   Fully connected layer with $in\\_features = 64$ and $out\\_features = 7$\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xt_Gh5Kgsx1q"
      },
      "source": [
        "![architecture.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqUnd5NKGhvd"
      },
      "source": [
        "_ = training(agent=agents['DQN'], opponents=[('random', 0.99)], hyperparams=hyperparams)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crtpnwPBnkU2"
      },
      "source": [
        "#### Negamax Opponent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NHEUCbWVEh6"
      },
      "source": [
        "Training against negamax turns out to be more interesting. Negamax search is a variant form of minimax search that relies on the zero-sum property of a two-player game. As we expected, this turns out to be a really tough opponent. The agent loses more than 90% of the episodes played."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dcq21IgXvOFC"
      },
      "source": [
        "_ = training(agent=agents['DQN'], opponents=[('negamax', 0.99)], hyperparams=hyperparams)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sk1xGT1koJCF"
      },
      "source": [
        "#### Mixed Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHTImZ1tXUNh"
      },
      "source": [
        "*Humans and animals learn much better when\n",
        "the examples are not randomly presented but\n",
        "organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones.* [Curriculum Learning](https://ronan.collobert.com/pub/matos/2009_curriculum_icml.pdf)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQEgh-xlYr6b"
      },
      "source": [
        "We have therefore developed a training based on curriculum learning (very simple).\n",
        "1. The agent is trained against the random player with $start\\_eps = 0.99$. \n",
        "The goal of the first step is to allow the agent to explore the environment to \"understand\" its general rules.\n",
        "2. After the agent has had some experience he is trained against negamax with $start\\_eps = 0.01$\n",
        "\n",
        "Making the agent play against negamax after having him accumulate some experience allows him to avoid the trauma of receiving many negative rewards in the first steps that compromise the training phase ([Stress affects instrumental learning based on positive or negative reinforcement in interaction with personality in domestic horses](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0170783)). \n",
        "This approach has been used for the training of all the agents we have developed \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFJtZCvAn0Gu"
      },
      "source": [
        "_ = training(agent=agents['DQN'], opponents=train_opponents, hyperparams=hyperparams)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9A9VG-gyjyDC"
      },
      "source": [
        "## Constraints\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hBKRs9zSk6E3"
      },
      "source": [
        "The constraints we have developed are applied (differently depending on the model) in 4 different situations:\n",
        "1. When you are the first player and the board is empty it is possible to demonstrate that the best move is to place the stone in the central column.\n",
        "2. When it's your turn and only one stone is missing to win to end the game with a win.\n",
        "3. When the opposing player is missing a single stone to win and it's your turn to avoid losing.\n",
        "4. When a column is full to avoid making an invalid move.\n",
        "\n",
        "However, these situations can also occur at the same time. In general, if it is our agent's turn and situations 2 and 3 are verified, priority is given to 2."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pTw9Cr5W2O0K"
      },
      "source": [
        "### LOGIC_PURE\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LfMfbpnU2w-o"
      },
      "source": [
        "In **LOGIC_PURE** a logic strategy is used to decouple the logical and the learnt actions. The agent encapsulates a logical process to solve particular situations, during training and testing. It only supports single action decisions, namely the logic is not able to decide or help deciding between multiple possible actions (the network is used in that case).\n",
        "This lead to a different scale of the loss function, since the neural network is never exposed to positive rewards for example. Comparing the plots of the loss and the rewards must consider this aspect. The training part with Negamax clearly shows how the model is not able to learn a proper strategy and deal with a stronger model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyE1S39uvVd_"
      },
      "source": [
        "_ = training(agent=agents['LOGIC_PURE'], opponents=train_opponents, hyperparams=hyperparams)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7RP9Ir821pY"
      },
      "source": [
        "### LOGIC_TRAIN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOMpV8KtVSP3"
      },
      "source": [
        "On the other hand **LOGIC_TRAIN** aims to train the model on critical situations inserting the in the experience replay the actions performed also when the logic is involved. The logic process is active only during training, in order to help the agent learning a better policy. At\n",
        "inference time the agent will only use the learnt policy. It only supports single action decisions, namely the logic is not able to decide or help deciding between multiple possible actions (the network is used in that case). Oppositely from **LOGIC_PURE** the model is able to deal also with stronger agents."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpjhgwZd28eb"
      },
      "source": [
        "_ = training(agent=agents['LOGIC_TRAIN'], opponents=train_opponents, hyperparams=hyperparams)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0Dm8cZ99BdY"
      },
      "source": [
        "### SPE (Safe Policy Extraction)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PWeu6YvzqCH3"
      },
      "source": [
        "In **SPE** The logic is used to create safe action sets which are used to restrict the actions (action masking) at execution time (Safe Policy Extraction). This approach can lead to non-optimal policies under the given set of constraints. The mask also allows you to avoid invalid moves (this does not happen in **LOGIC_TRAIN** and **LOGIC_PURE**). As can be seen from the graphs, this additional aspect is decisive in terms of performances"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpgdS1wJvasQ"
      },
      "source": [
        "_ = training(agent=agents['SPE'], opponents=train_opponents, hyperparams=hyperparams)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8iXNdRF8z9F"
      },
      "source": [
        "### SBR"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gjpqJMOWtHpV"
      },
      "source": [
        "In **SBR** the logic is used to produce action masks used to regularize the loss function. For this purpose we have built two regularizers:\n",
        "1. The first with fixed weight of the regularizer\n",
        "2. The second exploiting Lagrangian Duality to get a criterion for choosing the weight of the regularizer\n",
        "\n",
        "However the loss function of this approach is: \\\\\n",
        "$loss = smooth\\_l1\\_loss(r + \\gamma*max_a$'$Q(s', a'; \\theta'), Q(s, a; \\theta))+ \\lambda*Q(s, a; \\theta)^{2}*a\\notin{S_c}$ \\\\\n",
        "where $S_c$ is the set of safe actions according to the constraints."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmSIBoI8vdux"
      },
      "source": [
        "stats = training(agent=agents['SBR'], opponents=train_opponents, hyperparams=hyperparams)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1bt2QdfqvktZ"
      },
      "source": [
        "plt.plot(stats.dual_losses)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tHDAlQtnvne_"
      },
      "source": [
        "plt.plot(stats.sbr_coeffs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAsxt9oSvqVv"
      },
      "source": [
        "agents['SBR']['sbr_coeff'] = 0.15\n",
        "_ = training(agent=agents['SBR'], opponents=train_opponents, hyperparams=hyperparams)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qcNK1iW29KXV"
      },
      "source": [
        "### CDQN (Deep Constrained Q-learning)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BjzuDrrrtPE0"
      },
      "source": [
        "The logic is used to create safe action sets as described in [Constrained Deep Q-Networks](https://arxiv.org/pdf/2003.09398.pdf). In this scenario the sets are used to constrain the Q-update by allowing the agent to perform only the limited portion of legal actions. \n",
        "hence it acts as an **SPE** from a constraints point of view, but the loss is computed as follows: \\\\\n",
        "$loss = smooth\\_l1\\_loss(r + \\gamma*max_a$'$_\\in{_S}_cQ(s', a'; \\theta'), Q(s, a; \\theta))$ \\\\\n",
        "where $S_c$ is the set of safe actions given the state $s'$ according to the constraints. This approach avoid the extraction of sub-optimal policy among feasible alternatives."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHKfJgW_vtjQ"
      },
      "source": [
        "_ = training(agent=agents['CDQN'], opponents=train_opponents, hyperparams=hyperparams)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rcec3ezTRVj9"
      },
      "source": [
        "# Testing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2yVmhImtAFlm"
      },
      "source": [
        "In conclusion we have tested all the agents to analyze their performances. \n",
        "More in detail we have developed two types of tests:\n",
        "\n",
        "\n",
        "1.   In the first we had each agent play against the random player and against negamax for 100 episodes each\n",
        "2.   In the second we made the agents play each other for 1 episode as player 1 and 1 episode as player 2\n",
        "\n",
        "The second test is justified by the fact that the models developed are deterministic policies, therefore, if for example the CDQN agent wins against the SPE agent for one episode then he will win in all games. \n",
        "These two tests helped us choose the best model and draw up a ranking. At the end of everything we took the best model and trained with a curriculum following the ranking drawn up previously.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjMK2oAuREL5"
      },
      "source": [
        "for name, a in agents.items():\n",
        "    a['model'] = CNNPolicy(hyperparams['board_columns'],\n",
        "                           (3, hyperparams['board_rows'], hyperparams['board_columns']),\n",
        "                           non_local=a['non_local'])\n",
        "    \n",
        "    a['model'].load_state_dict(torch.load('ConnectXRL/models/' +\n",
        "                                          name.lower() + '.pt'))\n",
        "\n",
        "    a['model'].eval()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdKNIEjnB3Ur"
      },
      "source": [
        "All agents perform very well against the random player but not with negamax, as expected. The final ranking is:\n",
        "\n",
        "\n",
        "1. **CDQN**\n",
        "3. **SPE**\n",
        "4. **SBR**\n",
        "5. **DQN**\n",
        "6. **LOGIC_TRAIN**\n",
        "7. **LOGIC_PURE**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64f-NL6ivyOl"
      },
      "source": [
        "show_percentages = True\n",
        "\n",
        "for a, a_c in agents.items():\n",
        "    for e in range(1):\n",
        "        print(f'{e}) Evaluating agent {a}:')\n",
        "\n",
        "        # Define function used to represent the agent at testing time\n",
        "        def dqn_agent(observation: dict,\n",
        "                      configuration: dict) -> int:\n",
        "            \"\"\"\n",
        "            Agent trained using DQN and trained on the images of the game.\n",
        "\n",
        "            :param observation: turn's data (board status, step number, ...)\n",
        "            :param configuration: environment's data (steps, board, timeouts, ...) and weights file path\n",
        "            :return: the column where the stone is inserted\n",
        "            \"\"\"\n",
        "\n",
        "            # print(observation)\n",
        "            # print(configuration)\n",
        "            configuration['c_type'] = a_c['constraint_type']\n",
        "            return a_c['model'].predict(observation=observation, configuration=configuration)\n",
        "\n",
        "        env = make('connectx', debug=True)\n",
        "        # print(f'{list(env.agents)[0]} VS {list(env.agents)[1]}')\n",
        "\n",
        "        if not show_percentages:\n",
        "            env.run([dqn_agent, 'negamax'])\n",
        "            env.render(mode = 'ipython')\n",
        "        else:\n",
        "            get_win_percentages(dqn_agent,\n",
        "                                {'random': 'random',\n",
        "                                 'negamax': 'negamax'},\n",
        "                                player_name=str(a_c['constraint_type']),\n",
        "                                n_rounds_as_1st_player=50,\n",
        "                                n_rounds_as_2nd_player=50)\n",
        "\n",
        "        print('='* 50)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtJAtcTmHy8x"
      },
      "source": [
        "The ranking in the championship among the models:\n",
        "\n",
        "*   **win = 3 points**\n",
        "*   **draw = 1 point**\n",
        "*   **defeat = 0 points**\n",
        "\n",
        "1.   **CDQN 13**\n",
        "2.   **SPE 11**\n",
        "3.   **LOGIC_PURE 10**\n",
        "4.   **LOGIC_TRAIN 4**\n",
        "5.   **SBR 2**\n",
        "6.   **DQN 1**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qnLn5J8qv2CK"
      },
      "source": [
        "show_percentages = True\n",
        "\n",
        "agents_list = list(agents.items())\n",
        "i = 0\n",
        "\n",
        "for a1, a1_c in agents_list:\n",
        "    for a2, a2_c in agents_list[i:]:\n",
        "        # print(f'{e}) Evaluating agent {a}:')\n",
        "\n",
        "        # Define function used to represent the agent at testing time\n",
        "        def dqn_agent1(observation: dict,\n",
        "                       configuration: dict) -> int:\n",
        "            \"\"\"\n",
        "            Agent trained using DQN and trained on the images of the game.\n",
        "\n",
        "            :param observation: turn's data (board status, step number, ...)\n",
        "            :param configuration: environment's data (steps, board, timeouts, ...) and weights file path\n",
        "            :return: the column where the stone is inserted\n",
        "            \"\"\"\n",
        "\n",
        "            # print(observation)\n",
        "            # print(configuration)\n",
        "            configuration['c_type'] = a1_c['constraint_type']\n",
        "            return a1_c['model'].predict(observation=observation,\n",
        "                                         configuration=configuration)\n",
        "\n",
        "            # Define function used to represent the agent at testing time\n",
        "        def dqn_agent2(observation: dict,\n",
        "                       configuration: dict) -> int:\n",
        "            \"\"\"\n",
        "            Agent trained using DQN and trained on the images of the game.\n",
        "\n",
        "            :param observation: turn's data (board status, step number, ...)\n",
        "            :param configuration: environment's data (steps, board, timeouts, ...) and weights file path\n",
        "            :return: the column where the stone is inserted\n",
        "            \"\"\"\n",
        "\n",
        "            # print(observation)\n",
        "            # print(configuration)\n",
        "            configuration['c_type'] = a2_c['constraint_type']\n",
        "            return a2_c['model'].predict(observation=observation,\n",
        "                                         configuration=configuration)\n",
        "\n",
        "        env = make('connectx', debug=True)\n",
        "        # print(f'{list(env.agents)[0]} VS {list(env.agents)[1]}')\n",
        "\n",
        "        if not show_percentages:\n",
        "            print(f'{a1_c[\"constraint_type\"]} VS {a2_c[\"constraint_type\"]}')\n",
        "            env.run([dqn_agent1, dqn_agent2])\n",
        "            env.render(mode = 'ipython')\n",
        "        else:\n",
        "            get_win_percentages(dqn_agent1,\n",
        "                                {str(a2_c['constraint_type']): dqn_agent2},\n",
        "                                player_name=str(a1_c['constraint_type']),\n",
        "                                n_rounds_as_1st_player=1,\n",
        "                                n_rounds_as_2nd_player=0)\n",
        "\n",
        "            get_win_percentages(dqn_agent2,\n",
        "                                {str(a1_c['constraint_type']): dqn_agent1},\n",
        "                                player_name=str(a2_c['constraint_type']),\n",
        "                                n_rounds_as_1st_player=1,\n",
        "                                n_rounds_as_2nd_player=0)\n",
        "        print('='* 50)\n",
        "    i += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ux_sSmk6cvwk"
      },
      "source": [
        "## Final curriculum"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fO7HoAqoc0q3"
      },
      "source": [
        "On the basis of the results obtained we decided to train CDQN against the following opponents:\n",
        "1. **random**\n",
        "2. **LOGIC_PURE**\n",
        "3. **LOGIC_TRAIN**\n",
        "4. **DQN**\n",
        "5. **SBR**\n",
        "6. **SPE**\n",
        "7. **negamax**\n",
        "8. **CDQN**\n",
        "\n",
        "The order is based on the skill (from the poorest to the strongest), with a decreasing $start\\_eps$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sU8BbYyfB18f"
      },
      "source": [
        "for name, a in agents.items():\n",
        "    a['model'] = CNNPolicy(hyperparams['board_columns'],\n",
        "                           (3, hyperparams['board_rows'], hyperparams['board_columns']),\n",
        "                           non_local=a['non_local'])\n",
        "    \n",
        "    a['model'].load_state_dict(torch.load('ConnectXRL/models/' +\n",
        "                                          name.lower() + '.pt'))\n",
        "\n",
        "    a['model'].eval()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFN8xfLY-8py"
      },
      "source": [
        "agent_to_train = {\n",
        "    'constraint_type': ConstraintType.CDQN,\n",
        "    'non_local': False,\n",
        "    'train_first_player': True,\n",
        "    'n_episodes_as_1st_player': 156,\n",
        "    'n_episodes_as_2nd_player': 156,\n",
        "    'train_save_freq': 312, \n",
        "    'train_weights_path': 'curriculum/',\n",
        "    'test_weights': 312,\n",
        "    }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xz0KuusdNALk"
      },
      "source": [
        "# Create weights folder for curriculum\n",
        "if not os.path.isdir(agent_to_train['train_weights_path']):\n",
        "    os.mkdir(agent_to_train['train_weights_path'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjWOcciVM-HT"
      },
      "source": [
        "# Opponents and relative epsilon value used in the following order at training\n",
        "train_opponents = ['random']\n",
        "eps_opponents = [0.99, 0.85, 0.70, 0.55, 0.40, 0.25, 0.01]\n",
        "agents_list = list(agents.items())\n",
        "\n",
        "for a1, a1_c in agents_list:\n",
        "    print(f'adding', a1)\n",
        "\n",
        "    # Define function used to represent the agent at testing time\n",
        "    def dqn_agent(observation: dict,\n",
        "                  configuration: dict) -> int:\n",
        "        \"\"\"\n",
        "        Agent trained using DQN and trained on the images of the game.\n",
        "\n",
        "        :param observation: turn's data (board status, step number, ...)\n",
        "        :param configuration: environment's data (steps, board, timeouts, ...) and weights file path\n",
        "        :return: the column where the stone is inserted\n",
        "        \"\"\"\n",
        "\n",
        "        # print(observation)\n",
        "        # print(configuration)\n",
        "        configuration['c_type'] = a1_c['constraint_type']\n",
        "        return a1_c['model'].predict(observation=observation,\n",
        "                                     configuration=configuration)\n",
        "        \n",
        "    train_opponents.append(dqn_agent)\n",
        "\n",
        "train_opponents = (list(zip(train_opponents, eps_opponents)))\n",
        "train_opponents.append(('negamax', 0.1))\n",
        "train_opponents[-1], train_opponents[-2] = train_opponents[-2], train_opponents[-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJM93zYHDs7F"
      },
      "source": [
        "_ = training(agent_to_train, train_opponents, hyperparams, cnn=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NeoKXc6w8LdD"
      },
      "source": [
        "agent_to_train['model'] = CNNPolicy(hyperparams['board_columns'],\n",
        "                                    (3, hyperparams['board_rows'], hyperparams['board_columns']),\n",
        "                                    non_local=agent_to_train['non_local'])\n",
        "\n",
        "agent_to_train['model'].load_state_dict(torch.load('ConnectXRL/models/curriculum.pt'))\n",
        "\n",
        "agent_to_train['model'].eval()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SzaKU6IV8ZZK"
      },
      "source": [
        "print(f'evaluating curriculum')\n",
        "\n",
        "# Define function used to represent the agent at testing time\n",
        "def dqn_agent(observation: dict,\n",
        "              configuration: dict) -> int:\n",
        "    \"\"\"\n",
        "    Agent trained using DQN and trained on the images of the game.\n",
        "\n",
        "    :param observation: turn's data (board status, step number, ...)\n",
        "    :param configuration: environment's data (steps, board, timeouts, ...) and weights file path\n",
        "    :return: the column where the stone is inserted\n",
        "    \"\"\"\n",
        "\n",
        "    # print(observation)\n",
        "    # print(configuration)\n",
        "    configuration['c_type'] = agent_to_train['constraint_type']\n",
        "    return agent_to_train['model'].predict(observation=observation, configuration=configuration)\n",
        "\n",
        "env = make('connectx', debug=True)\n",
        "get_win_percentages(dqn_agent,\n",
        "                    {'random': 'random',\n",
        "                     'negamax': 'negamax'},\n",
        "                    player_name=str(agent_to_train['constraint_type']),\n",
        "                    n_rounds_as_1st_player=50,\n",
        "                    n_rounds_as_2nd_player=50)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}