R00195552_deep_q_learner.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "R00195552-deep-q-learner.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.9"
    },
    "papermill": {
      "default_parameters": {},
      "duration": 571.103242,
      "end_time": "2021-02-19T00:41:23.588495",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2021-02-19T00:31:52.485253",
      "version": "2.2.2"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pste22/CoinPayments/blob/master/R00195552_deep_q_learner.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38eiVUYNxYrr"
      },
      "source": [
        ""
      ]
    },
    {
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAbDESGvrkG3",
        "outputId": "7c14fe2c-155e-4421-c7b1-0879a31d0f01"
      },
      "source": [
        "# info about current GPU\n",
        "from tensorflow.python.client import device_lib\n",
        "device_lib.list_local_devices()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[name: \"/device:CPU:0\"\n",
              " device_type: \"CPU\"\n",
              " memory_limit: 268435456\n",
              " locality {\n",
              " }\n",
              " incarnation: 3753501318950839759, name: \"/device:GPU:0\"\n",
              " device_type: \"GPU\"\n",
              " memory_limit: 11154422528\n",
              " locality {\n",
              "   bus_id: 1\n",
              "   links {\n",
              "   }\n",
              " }\n",
              " incarnation: 10725280165535478077\n",
              " physical_device_desc: \"device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7\"]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
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        "outputId": "0e33bd5e-151a-4c25-feeb-926dab1cb272"
      },
      "source": [
        "# Ram Info\n",
        "!cat /proc/meminfo"
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQ6Yc7SOxYru",
        "outputId": "c4fe96e4-302a-45c1-cf44-67460ec96d0b"
      },
      "source": [
        "\n",
        "\n",
        "# Set up for installing the Google Football environment.\n",
        "!pip install kaggle_environments\n",
        "!apt-get update -y\n",
        "!apt-get install -y libsdl2-gfx-dev libsdl2-ttf-dev\n",
        "!git clone -b v2.3 https://github.com/google-research/football.git\n",
        "!mkdir -p football/third_party/gfootball_engine/lib\n",
        "!wget https://storage.googleapis.com/gfootball/prebuilt_gameplayfootball_v2.3.so -O football/third_party/gfootball_engine/lib/prebuilt_gameplayfootball.so\n",
        "!cd football && GFOOTBALL_USE_PREBUILT_SO=1 pip3 install .\n",
        "\n",
        "!pip install reinforcement_learning_keras==0.6.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting kaggle_environments\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b9/88/d54f1ced9fa3c64bfc95c9838d23c3926258e82a15f57c4fbe84524e1624/kaggle_environments-1.7.11-py2.py3-none-any.whl (108kB)\n",
            "\u001b[K     |████████████████████████████████| 112kB 5.1MB/s \n",
            "\u001b[?25hCollecting jsonschema>=3.0.1\n",
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            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0.1->kaggle_environments) (54.2.0)\n",
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            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0.1->kaggle_environments) (3.10.1)\n",
            "Requirement already satisfied: six>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0.1->kaggle_environments) (1.15.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->jsonschema>=3.0.1->kaggle_environments) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->jsonschema>=3.0.1->kaggle_environments) (3.7.4.3)\n",
            "\u001b[31mERROR: nbclient 0.5.3 has requirement jupyter-client>=6.1.5, but you'll have jupyter-client 5.3.5 which is incompatible.\u001b[0m\n",
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            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "Cloning into 'football'...\n",
            "remote: Enumerating objects: 2687, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 2687 (delta 0), reused 1 (delta 0), pack-reused 2684\u001b[K\n",
            "Receiving objects: 100% (2687/2687), 26.99 MiB | 19.05 MiB/s, done.\n",
            "Resolving deltas: 100% (1521/1521), done.\n",
            "--2021-04-22 08:05:09--  https://storage.googleapis.com/gfootball/prebuilt_gameplayfootball_v2.3.so\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.204.128, 64.233.188.128, 64.233.189.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.204.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 45163384 (43M) [application/octet-stream]\n",
            "Saving to: ‘football/third_party/gfootball_engine/lib/prebuilt_gameplayfootball.so’\n",
            "\n",
            "football/third_part 100%[===================>]  43.07M  27.1MB/s    in 1.6s    \n",
            "\n",
            "2021-04-22 08:05:11 (27.1 MB/s) - ‘football/third_party/gfootball_engine/lib/prebuilt_gameplayfootball.so’ saved [45163384/45163384]\n",
            "\n",
            "Processing /content/football\n",
            "Collecting pygame==1.9.6\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/b3/0bf5afdcf6ef95d2a343cd7865585a6efe5e3e727c1a4f3385c9935248cf/pygame-1.9.6-cp37-cp37m-manylinux1_x86_64.whl (11.4MB)\n",
            "\u001b[K     |████████████████████████████████| 11.4MB 4.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from gfootball==2.3) (4.1.2.30)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from gfootball==2.3) (1.4.1)\n",
            "Requirement already satisfied: gym>=0.11.0 in /usr/local/lib/python3.7/dist-packages (from gfootball==2.3) (0.17.3)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from gfootball==2.3) (0.12.0)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.7/dist-packages (from gfootball==2.3) (0.36.2)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-python->gfootball==2.3) (1.19.5)\n",
            "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from gym>=0.11.0->gfootball==2.3) (1.3.0)\n",
            "Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from gym>=0.11.0->gfootball==2.3) (1.5.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py->gfootball==2.3) (1.15.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyglet<=1.5.0,>=1.4.0->gym>=0.11.0->gfootball==2.3) (0.16.0)\n",
            "Building wheels for collected packages: gfootball\n",
            "  Building wheel for gfootball (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gfootball: filename=gfootball-2.3-cp37-cp37m-linux_x86_64.whl size=38649863 sha256=aaf4149c35a828ecda1e2887fea046b32fef5a1690d84fcfea1e82d2967c1ac1\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-pwvkumvr/wheels/41/ad/ae/8cf1d92b8694b10187e5daf33e8d5c248ffa5437e234ccbbee\n",
            "Successfully built gfootball\n",
            "Installing collected packages: pygame, gfootball\n",
            "Successfully installed gfootball-2.3 pygame-1.9.6\n",
            "\u001b[31mERROR: Exception:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pip/_internal/cli/base_command.py\", line 153, in _main\n",
            "    status = self.run(options, args)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pip/_internal/commands/install.py\", line 382, in run\n",
            "    resolver.resolve(requirement_set)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pip/_internal/legacy_resolve.py\", line 201, in resolve\n",
            "    self._resolve_one(requirement_set, req)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pip/_internal/legacy_resolve.py\", line 365, in _resolve_one\n",
            "    abstract_dist = self._get_abstract_dist_for(req_to_install)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pip/_internal/legacy_resolve.py\", line 301, in _get_abstract_dist_for\n",
            "    skip_reason = self._check_skip_installed(req)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pip/_internal/legacy_resolve.py\", line 253, in _check_skip_installed\n",
            "    req_to_install.check_if_exists(self.use_user_site)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pip/_internal/req/req_install.py\", line 441, in check_if_exists\n",
            "    self.satisfied_by = pkg_resources.get_distribution(str(no_marker))\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 479, in get_distribution\n",
            "    dist = Requirement.parse(dist)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 3147, in parse\n",
            "    req, = parse_requirements(s)\n",
            "ValueError: not enough values to unpack (expected 1, got 0)\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_1_MvNdxYrw"
      },
      "source": [
        "import collections\n",
        "from typing import Union, Callable, List, Tuple, Iterable, Any\n",
        "from dataclasses import dataclass\n",
        "\n",
        "from IPython.display import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "import tensorflow as tf\n",
        "import seaborn as sns\n",
        "import gym\n",
        "import gfootball\n",
        "import sys\n",
        "from statistics import mean\n",
        "\n",
        "sns.set()\n",
        "\n",
        "# Tensorflow 2.0 preview release which has eager execution enabled by default. \n",
        "# Training keras models in a loop with eager execution on causes memory leaks and terrible performance.\n",
        "#ref : https://stackoverflow.com/questions/53429896/how-do-i-disable-tensorflows-eager-execution\n",
        "\n",
        "tf.compat.v1.disable_eager_execution()\n",
        "\n",
        "sys.path.append(\"/kaggle/working/kaggle-football/\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2QfErvZxYrw"
      },
      "source": [
        "# Q-Learning\n",
        "\n",
        "Q-learners are off-policy temporal difference leaners \n",
        "\n",
        "Ref : (https://lilianweng.github.io/lil-log/2018/02/19/a-long-peek-into-reinforcement-learning.html#model-transition-and-reward) \n",
        "\n",
        "## Components\n",
        "\n",
        " - Agent\n",
        "   - Model, usually a neural network \n",
        "   - Replay buffer (the agent's memory)\n",
        "   - Exploration method, such as episilon greedy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUCqn32AxYrx"
      },
      "source": [
        "## Model\n",
        "\n",
        "Using the GFootball enviroment with the Simple115StateWrapper which returns observations (state) as a (115,) array. \n",
        "\n",
        "Simple115StateWrapper is a simplified representation of a game state encoded with 115 floats, which include the numerical coordinates of all the players, ball positions, ball ownership, game modes, etc.\n",
        "\n",
        "This array will be used as the input to a very simple neural network with 3 layers, which will output an estimated value for every possible action. The agent will either select the action with the highest value as estimated from this model, or randomly (chance depending on epsilon) select a random action.\n",
        "\n",
        "To make learning more stable with deep-q learners, two copies of this model are used by the agent. \n",
        "  - \"Action\" model This copy is updated every training step and is used to estimate the value of actions for the current state. \n",
        "  - \"Target\" model\" The copy is updated by copying the weights of the value model, this is done less frequently in order to keep the training target more stable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "LgdQqEO0xYry",
        "outputId": "d64fc957-c4af-46ab-fe4c-5704a07eeb58"
      },
      "source": [
        "class DenseNN:\n",
        "    \n",
        "    \"\"\"\n",
        "    :param observation_shape: Tuple specifying input shape.\n",
        "    :param n_actions: Int specifying number of outputs\n",
        "    :param output_activation: Activation function for output. Eg. \n",
        "                              None for value estimation (off-policy methods).\n",
        "    :param unit_scale: Multiplier for all units in FC layers in network \n",
        "                       (not used here at the moment).\n",
        "    :param opt: Keras optimiser to use. Should be string. \n",
        "                This is to avoid storing TF/Keras objects here.\n",
        "    :param learning_rate: Learning rate for optimiser.\n",
        "\n",
        "    \"\"\"\n",
        "    def __init__(self, observation_shape: List[int], n_actions: int, \n",
        "                 output_activation: Union[None, str] = None,\n",
        "                 unit_scale: int = 1, learning_rate: float = 0.0001, \n",
        "                 opt: str = 'Adam') -> None:\n",
        "        self.opt = opt\n",
        "        self.learning_rate = learning_rate\n",
        "        self.output_activation = output_activation\n",
        "        self.unit_scale = unit_scale\n",
        "        self.n_actions = n_actions\n",
        "        self.observation_shape = observation_shape\n",
        "        \n",
        "\n",
        "    def _model_architecture(self) -> Tuple[keras.layers.Layer, keras.layers.Layer]:\n",
        "        \n",
        "        obs_shape = self.observation_shape[0]\n",
        "        frame_input = keras.layers.Input(name='input', shape=obs_shape)\n",
        "        fc1 = keras.layers.Dense(int( obs_shape/ 1.5 * self.unit_scale), \n",
        "                                 name='fc1', activation='relu')(frame_input)\n",
        "        fc2 = keras.layers.Dense(int(obs_shape / 3 * self.unit_scale), \n",
        "                                 name='fc2', activation='relu')(fc1)\n",
        "        fc3 = keras.layers.Dense(self.n_actions * 2, \n",
        "                                 name='fc3', activation='relu')(fc2)\n",
        "        action_output = keras.layers.Dense(units=self.n_actions, name='output',\n",
        "                                           activation=self.output_activation)(fc3)\n",
        "\n",
        "        return frame_input, action_output\n",
        "    \n",
        "    \"\"\"\n",
        "    Compile a copy of the model using the provided loss.\n",
        "\n",
        "    :param model_name: Name of model\n",
        "    :param loss: Model loss. Default 'mse'. Can be custom callable.\n",
        "    \"\"\"\n",
        "\n",
        "    def compile(self, model_name: str = 'model', \n",
        "                loss: Union[str, Callable] = 'mse') -> keras.Model:\n",
        "        # Get optimiser\n",
        "        # we can add as many optimizers as we want under elif\n",
        "        if self.opt.lower() == 'adam':\n",
        "            opt = keras.optimizers.Adam\n",
        "        else:\n",
        "            raise ValueError(f\"Invalid optimiser {self.opt}\")\n",
        "\n",
        "        state_input, action_output = self._model_architecture()\n",
        "        model = keras.Model(inputs=[state_input], outputs=[action_output], \n",
        "                            name=model_name)\n",
        "        model.compile(optimizer=opt(learning_rate=self.learning_rate), \n",
        "                      loss=loss)\n",
        "\n",
        "        return model\n",
        "    \n",
        "    def plot(self, model_name: str = 'model') -> None:\n",
        "        keras.utils.plot_model(self.compile(model_name), \n",
        "                               to_file=f\"{model_name}.png\", show_shapes=True)\n",
        "        plt.show()\n",
        "\n",
        "\n",
        "DenseNN(observation_shape=(115,), unit_scale=0.25, n_actions=19).plot()\n",
        "Image(filename='model.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9s1BJxJ7xYry"
      },
      "source": [
        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f96T68scj0l5jVb3yftxKnn/xMYpNf72y6zzZY6x02trtUzmOIvZXNapK13FPigTnlyavN05tPp+b4x1VbzcldDqecO98XR+56n0cYS1D2z0M8exPBfhPtT2mxdsL111x1UqM/Os1hhjZFItV+3Tw29YMH29hE2g3c/d8cgEndM1J9fmCgjTaGFjN9P6bfdoKyXEhwin+Z83LKYtf1c/d5/9tpgejm2BMf2LmMYYfqf//M+9rS4V3++k4W21UUSv9vDntdEd6+jzU0SV53+koicnxY0JeqvO1/Rcd7UKiNPl1S3878whdeoQp3nlE8rZ7flv3/ZOIuJnHHbkys/plOcb5zx9v+8tmve29osTvk0penpE2gEup3nvyyrvvKpmjuGnJ7kx5ntaerfK42/yVsb9uYg+TT1yOvafKnd+rpq3xBJ+rfjubeoZO4a73TRtew1/o3q/SWToYEm4oeJBlt358K/6XRvnUw/D8SiH5dUA9raJU7sm0NV5HX2TcXcuVSNo/qrF4RLJGK0hVYMQs4P4YklPb2KwOT7ZUhLxNg6eIH+RwIEwH1V636XNqe19awIrViRffJrBmA7NnGFkgOdffaNkDG47Kaei0c0DvzpmQy+ekeLGppS8swvy5HxLnrtN8huOeJDAx42bxi9P/ib6BKE4NzaU3djUMeCXEOY1KMf5Y5toebE1ID+7lYY0bE4dJu2NvxxN/2orULPrp1Kp1fkfWn6PpRMlS7KyExcgtsrIZCLf8WCN65zrT4ymzK9R0wlUou3q8nqSq6pjf5yV2Fmq6NJVxhOVX1f9V75qmgf07wobbCsScT1ORcuKLcNXORWNaEA8ifjAqMP2hEAgSdsy9DXTIEJ04dwur/5K+1ZxfK33i1/hSn84slasSbz6mmg7GX/XyWqHlH7YeCina0lcB16+lRpEbEejcpG4/VfFPMo2paj1jeMPttWSatjgN/X6F6X9lLJdk+2U1HG7frnqikyiXfXFFT5VzmF1Q7RjXqW+mKtK+QeMa1o9Qg88kEc3TNHtilr5Z/b7qlxlWkfC8pbntRN0JXMq5cfMpdyn8n60tihqH+v1jxHHXSKt/+qqU9H5c1wig11P+LtgW6TiNsdYIo+7fIWHl/fMqx1H1dtjovfndPR+nLHbVzM9L++ONpjDKON9R1r4sX5DWEUZJ0t5jVL3pb4l659Ykij5chvp+O3oZV1ksvPqyTCrozducZUDTUvd2TqV7xPFL4bMYF2Xt/V1z5qlgVU0H8/vSg2bDPDNUVh3svpTve2qOzreTN/Ve2MMr2z54/9Qk2+9IuIHWv2AWgHhX3ESxG9O4qMZY3TcPOG3J/TiYJa/uyZ2tLdpjLGNDruecTnJ9RsNntmt5f7l/WXxrTqS31/2vUYtYtt/tIGG39GySroH50+JGyo6P5vYqBBmjEklPTOszddclSTy6Zzad2U0/zZljJF8myFMPdqGGP6crjGGB1LiT+ny8bHzok0BavheGj6Z5NnicENlN14qzCGa272J4eRRfb7G+nWLw1ZW7KLRDZonsZZzSNUIVocxRvL9yJt6q5KsGrB9eLiY8C+8tiHHDMcrF3jiZHbY5veK6/20Ob5dSQ1OTIOBhGcnr7YOwvWifo3U8UTXC3eOTRPILPLI5MDucBOt1ji0vJ/R4ZXRor55ob5LOCxPPGRZs07Xvos6oAuWqyj6ryzfSiMaNCf9y65OnwcFtzYyt1OE60+/Zd6pJf/2hJdDyGDNdZpSJk4541+MXOn6pKti+XXVf473/TkdDANJ9DIpZU2XZ1NK973ULbP8hA38DrzcNdSoxVsspC2qLN9FIy9JbIB1SSNlKGzwKOVw2DoxIKqyWnDnmsAKGG/VXuN8mlXiSin5MxcX91uqLNtlJIxhMI5gfeMwnL5/0CtvqvB2esIu3fpnt5+1oZ2SOh7UpWoX/PpX+ohWt1LnHF2vrC+1jdeUhesQfz646h7fSj/Wt984Hd5GKp3oNj+6jnR7abNMrfxIWdTXdN/nmOw6x6yj9LHqPf+kUUlmj7u0vP47V50K52/XES27e/XlhVMb6d6GzUkbp1Xc9piA43GXr/Dw/nyoT3YcVW6PYytDw9pjlZpi7P+xTEsl40TzB41wfdnjsfDyShSs+5H7p0j54jy4ygX/kBZ9rMPjs7Dxr4yLXOVAE1R3tk7ley/PCcbXvCWySUHyFakSn3nlNPlHLnO+w7rz90tuRmY8mbyvNcYY2b4SzJzfsJDplTFE39OygX7fMBd+3kmF/10ZaJIZHMxJfHKDAOduP436nYpbr/7RuTbjE6NBImOMabzRJxaFMzPjkvjZAbHLAGE+U7KejK8u0QYKLbt9F2aMiZ4ekRnWLiNmXpKz18YQybcpr4u7+b2693NNNln3v584fQl7/sgi6nzRRXRRhv5+23NZJAfcMhkwGyeRia/SUJm/MqvvVYMlkxWZNLl+zZdn9mDbTMcdp3qaSEaRT5sHXQ2pq+M/RSsKG8UnW5wS+xJpFsEIwJNZvQ8+mAt+wnIlWrmgOoUCmhtbFiv8XJ1YtA5E5Ki6XpLJLCmEycrvi65dV21gUfpwTbKEnQ6rUw3eufRKdOGHQ1S8aqE62vmWHnHfN7qsckzJ9S/lzpUXeab1pWTRg9OgtP4nrnKqQrj07X9XbXGyDXR2mLDPUYwxycpCWPkNyz/Hp3WqWAlD11W3FYm58r7+z7avorkzJ9Pgu26hm7opHwA6Lc3UfsbfMAtX+vLMHKR9WfSYt0ya/TvcN3k17T+afFmSlCH/9g1TMyp/emAfXi4kLl3mzHj891G5+N+ST0omrQP1nHXuYhitvum+RLfVkp47z5Jf0YXrqlkomfVniVtPfqSPqw3tVBhLlprLpFnupL2NVrdS55xIFk1R3/FkKa9gKh3wVp6piSuXb78PLDWxNFfKJMqHak/0JCm6jmSyHzRcpFZ+dP7kLtX3o9S5ZO2qpM3huAwkH3fJG/bVVaei1xGOLVwG/tZuIxLHHWw3wsPbOeHPrjKanfZY5y1sfCIGjSsG6S3pwiqsbTPHHq68SJ7tuh+1f5KyGt7PSAqucqFXn7vaWHkmbSvLFDb+FTkkrKTquoZxSBQ/x8OsuTzpHzJdsRNRRTl9s389vTzzWRo16BbqcktXb+sT50f6AonP3966GYWkkvZjGGOIyNySxP48eGLOjl5tPzIu2hd+3EL3/N96W0uiLU3qfT0przXGmFdnRzTG/I0W/FkZrtIyxkROj8g0NqVnjNEOmatujCEyt7Wl4sCXOy1ZGeNffWSXqp/pxx9/zODfz3YCzs/SeJqNkxkweiejOtc/PSV+RJRfgJ2e7xftI8CMO3ivGkGz4+IwiWQMyudqSN0dvzK+9CW1n5e3A/0+dLWGKSt3Fv5fLs1v1T3LVZuMMVH0IiyDA0A7f26eXp6bDKVFxUHdq3IgvmXU+1EnF3bq+nNQDtlrzE52n5j5LC1dz/vcN9OEfk18hjeOI5n+pdwNm6/8dphlWe71hDwoi5bTdXecVgxs7FyhYk9MXG/LKglzZZcrnOsZl197om2Hy2T55bhV2RAHn4pVlDIpg37X4O582Tq6m/0vte1GhWNn0czlyvfQ+hl3BIwGiQZ++V0mxHx2uMqt8pcifCrLy+jdpU/S7Z4j8wL687Om3wQJpa9ShsIHyYpFJo0xqXDRkpp3Sia7jLgYplLfpH0Jm7DY6Qm7dOufXZ9qQzvlYika4Prpnxwkblv8dUtPoqNyTiSLyGReue2Rbceso36NpV6rVQBcV2U1rL/ch+ej6jpyT9RZ3tTKj5lDdZ/K+1HrnNSBZH2shAvToT1GCkoffRzE79r8+Vm4DPyt0qVs45TPrnbaXb7Cy0IwL+E6rmp7bK+i8qepZKstxpgo/ZOU1cT54ly6ykVqdSXR+FfkcJUDP+NwnXL8YcYejoPLZTJjDG+l9vy0tbyWeo+YQtNfUr6BSpaOwsoYUxGJJto6nH81gm9lzPbFcSOCOcF2r3IIj4dXwjz0e2VQ4YnyO7GTb+xtMVom8+77+JYmNlD8rsd6OpRw/7j2XcLhbeONyeSmOZ97CSVaAWKGr+rKGHPrUmJDAZG5LSrZqqGwlTGppFceomMGI0Yz5ph8ZYzWfzKfMWZZMjXN97xtqnXMYTOn+8Tb0Zzx1gWfMf6BoZCxDSdqIGYuX5aQ0a92nOrNeAMfX8qvY+Qli375XJ2Nu+MXR6680kmdTpPccS+nzFsuXCdHaKnIc7CabJuSPw63jBxnagMlm2F0vfBS5Wg+KdyyqqXO7mXNJhuixDLxL6/BX9L8MahPfjlk+XBwj7vaFmUPYJPpX5Zihw/KTJn8spjfhN3zoMNVX1z6Dsahtgsk9+9jv6mMQHryb3+vPvM2gaqUX/8v4RK3MnRqZ8WJ9S9vqWs4V677bn8+wW1u7glBrC5XwfcO/wpeOn+g54A3zJDN8svy77Bl8dK2aR8crvZLkZCwyQa6qXDxs5ZPirltHLEZplrfwid37jxnqv7Z9amm2ymmbLMU8nwNM8ZEq1vhkxXRVzK9mrK471X95XaRdWr6veK+kT+fPDiHrmli+7QKr8tV11E4y9TKTzCnqbwftc5F7WOT1RW7LwtK76pT0flzfDLpNbd7SjpqFZR5IqWK29WvuOMJl0XSMK+J6guHi9oei061EclMRe6jbVMy40hFX4nyYtd9b6wZoX+Kli/On6tcEMn7UcY6iWSScpGsj2JJwjhE2aakxxKiM/9Vypztv7J8J7Yp+UiZhgR71YMZcOsT+gSbP47f4zmMrfz5a1pgOHA1J9CpGmM4rd3T1NHD//PqudS/t1r9ccdS4ygSUyDr/vxh7eiWJ+jNb3uNNn9+Mn4a0oWzZbR37XKaul45t/3b+jlxvy3/ecNy2hk77/fMobdp4JXKKPTP/zGLXo0ddZptY0zlz6X0eMwRMDvlnfD2MSr3nPISsezvLp5HCz9QmTaNEf6wP1NZyRqaFcujxzRm1GImbUbv8pwO8/NU0rvwYzHd0yDG5N8n0yNrjnhcK77fQ6Njzo6jGWO0IYmPl95m2VDcZcZStPfxLG2bMDVuBGQGI5bsp2/OxoDRz3Ti0730wsh1PifBpgEqV09T8hs7hI1q1M1BATu/5Um03QDKG8mvwTj5HZnQ2KctiPNCv3yuzia842eZ2Qq/ObbfPZHjXpGfOxx2XuxyYCZhEu1pFgeo/hOmwmW0B6qcRiqdf1S9CE/bsZzkSV/dAy85bjloDNFvyh0PXl1l5cKpbZ5vjqoYY8Kc+om+zLIqciTWv9rK5Jrwy/v6Gq4/HcZ/l85pShzT6Q+meacDXN5nXuQjj/kXzKcvTn6aUlXLb16rCbTLOt5W+JunT0Utk+G/uCqjkr1CrbLiEM3t0SRgzFN1yNwWqHTxy/453ukPtoNEv6Z4yXPsz/hC9sdrQ4rxZfy2nLaMc52exgHYaWIn69QmV/ulIotmjEmNS1xM340qy8km7anWNxkgB5eXqzyby/6VOJmpf3b7WdPtFOctbCLC39kTMqkDUeuWMqQWWCcbqR8ImublB1bFhdUNX5GwPnD7ffl9i2nO0EZkrs7juJo3nUrzFnbzGWnU6+FtZDo6CmeZSvmxMuh9jPp+9DoXtY9NpX93Se6edEfnz3GyDLylY8Cir60k1KEC/tPmlAGDT3v7xnCcHtYeS5l2GW+sxLyPAR2n0R7zgQhBJ9L+VNnBv8uJOodSDnz9p0Omoq9AXoyk7bofuX8i0Umy8Xf6bS3Lz+2I3WeyQYzZcpmJaozhI8X1Vn0F4vzh8PG1OPA1xxIGvvjtzulNHdv8FKM6v01p6cLP6ceffoxP5uNUHDdRjTGn3l/mW43Ak2/++/8aqyvfp2uMMR3FcnymMcQheuDRmU/XhR4NLfLqlSv+Cb18b16HrdGnNWTbGMOZOVWy2MlYZDJXJH26YnbcmCTfy1XnMbiKhMOI0S0T6f1b4wlxOZKvjCHiU6xETttxcnRjDNP6noqeTHw0uS2POD52bQ9pAf8AACAASURBVE0LFKYaeCAD/LBfd1PpZMTIYB7hyts51ix7hgbfOi/pEbAygAhOmstp1/RO3q/Qcrw2HyM9oOOfaczYu6q8MoZxc8PfrXE3+kOnZpSKLyDVIeeTefTe5lUzraOtg8dfy9HWwRUNqQ+U/EYoKTyqszUZpqIXOU7UPC64eP1Sejx2tLVKRaXBA5p5W/dQ8apl9M7f1DcuLnyM7/Nj7qXH14qTUl4Wu5b6Xdrcfwz6qknUq90gGj/hzsBkWnLnv9rM1GoR+2j1Lt1GUqFjmxLHlUz/0vmbRyTyMZjLX3qKBj6k94zL4DLKIETywEaKTnmyTU6eqomaPWDQ3/rvvn1noufHhI+z5OW4C17nLVUs30yaPPYB6uGTkTy/Dq40/bGqT66ykKz83nzvfXRDG300uxxtb5f36GXS1rGW1BswB46k7kF8xK1tzJN2ru1987zjq1cs+yup0qgMIjwo1Md0lhCX+aeG96EFMZ/r/P7t7QbFj3vfuUMd2c4T5N3WSVtaQnXnymv8aOvAiR2qbtmGEI5J8pCsjKXCxZZVfVbMbRmCk4jU6psM4v/01BqvjL64Nu4wi3hyz5OfCav30I4tz1NR7KtM1D9O165PNdtOVc0YE7Vu8WlR429s6h2lLkejr543mDrdNp4e62c63VfalnIVrBvu0sFPRZd2PVM/nNxI7a5zOewPr8vp6IjfteUQyaOXH3nDf436fip1ztWu2n1sKuMuv8TyydWOpMafZWCu/e+9gmTcJe0et5fDVuu5CqfKPy7wJP2q2FHtXhvXqzWNGTvaoZ/wMYTkwLzaOs50e8z5WjDmXt/R1osHXhJytHUw76noy86LmU/bGCMG+2T9E8fh6mfsfHG49NtaNj4ymwLqN1Ft/1H9WTu6b/Jk72CDZH0Uy5Go3XG1z3K0dfIfC9UWdC6LXQoXx7Ygr6dpd7ajB4c9GOgLgrpz1R1TS5m9T+oz5p1Zz9GYmSto6owofy/RmJX6aONkokY1xnA8x3csozuvU8aX/6fls/TQC6X09x2Z2aak5NTHLfOEvdmg4vhKjmT5kO8rz39HxStepUH9nqEGsdOJvLiun069hr5Gf/2qXIJyEaQj76ynYfdOo+Yxo9JvWk6mXkPX0eavfjLCEVWHMYYTrPhuL814cCa1baGNXC3/i1nvom/0YTOebN8f2ErjDNn/rfFk6nj7Alr6kd+Zoak30xiTWnrManlc/5xWj8ffpoMH36KuseOibeOHD2Dsg7kdTY7qlnCpGWPUWycObacXRr1I17XWvFi2P3SbS48v2W+Un89pahe12qoq5UpkzOZVGsRMGGNYzgunD9KqSYPp+jbKQs7OLrvfNZIWbT1K/hLiypVqBE1DgoSqrDhO22breNvcNoHWfvGLY4WIqyENH4SII9eGTYKTYkk77Hp0+wIafFeXmFOwArq6+xCavvErXz7PHd5G0wfdQle0VEcVX9y+L42cX0zHztmxhsvoGqgGOxCJz80wFb2cPLiOnjBkvrzz3TRy/n4yFzycL9tEj/Zs7XVsl7T3r4ZgLoW9lCNVngRd3qkHPTB5NX0UPzlKyVpxfBfNGd47xuZKuqlwCm2M6TRscC25VNcgM45z+qCbYjq5kvqMeJU+Ov2td7S2q1xF0X/Fsd2GnPmej5I/FU6hNfvMDAVl8cvq+qQms7YR0KVv19vyjHX7+oyHqXenNp4+mDkPlK7q1oOGvujXG69I4V8vv5OXk1xTLr+rj9GJD5ZQYaxssD+XByYXBY6Y5GSjlclwrsE2YSS9tu+M98ulq/x8WfQEdW/N7VIBdRq3PV6e+Re9D9dNogEdhR+z4zKv62ll+QGaP/yueLvG+eo/bnmgTIfhvHCujDbNHhx3Js3tYp8Ri2jHEbtVdLVfKlZpq5MNdFPlEpRZMU9ujCFKrb6VU+ncgfH6PmCJ/sX9wrmD9NxfrvEmc3lt+9KqL7RU6da/sPpUk+1UahOyWB1IoW7Zeuk7bg0dPve91w7aemXSYXVDa8F/J6tVg22qctzrqn+JDNbp6CgRS5Y6Wvnx58/8FOX9VOtcsj421f7dlFfdu9qR8LbUxV9kOHChjDZO+kus3hZQuz4j6ZUSs++T1Mvp4KonqbvnTyufLmk/iOaXnAhdBZZoDCExytXWcdrtcWyc2rldK6/P5Pb8zhGL6KMfJUXe+nScSpY+afSraozi7/dVeGEVxcePnRedomtVnNqClax/kjikT02Ur/TbWsXmnXnD47pmH33TNnxN5469Rn0bRVsZwzInaneOfbCEHrtTxtf5xGlMXb2PYhtKJMsh12BZnLH1mFcWbcN8UHeuuhOSTAYeJzXGZCCNnInik0XT4ysnhm/4IWfkhqDRCRycr3TMW5U26h/oo0dQhZA/vbMg7hB65l570F2FCPFKFgiopchhxxhmIUFEWasI1Kz+2YfApddPpQPV0DyoJfKtAtsXMqOO8IF+ZuJHLCBQXwmgbtVXzddkvoOT1JqUBmmDQN0kAGNMXK96Zcxv8l6h3eYilngY3OQ6gcqf98d9zdw4vjTu1yd7+TpIT3dQq2LajN5TDellLyd1OWa1PzWa4966zKG+5q2m9V9ZUeb9Wp1sD3T6+lFb/a40juNMP04zBkwYTRq4B4HMEUDdyhxLxBSVAIwxUUkhHAhUnQCMMR678/TJUu0H5fZF0Rz3Vh073qxJAuJwmZ3vztybZKN/moLunjXFW23FjppLA1tS0owcr2eMAG/bYM/sURz3ZixRRFRrCNQG/Xt+CVp0o1mlv2aNC/spuL7lIHo75tsn8wlhwph5pogRBJgA6hbKQfUTgDGm+pkjxfpHoJ4bY0pp1H/T/j7Yp0mLv7xNZfWvHCDHIFDvCJw/tomWvL6HXp99B12a15GeLcneJLjewc2BDEP/2VASJozZoIo4QQDGGJSBmiAAY0xNUEea9Y0AjDExYww7zy2cWUrH5YTi+lYSkF8QqGcE5EhWdhT5zEb/iQD1DEW9zC70nw21wxiTDaqIEwRgjEEZqAkCMMbUBHWkWd8I1HNjTH1TN/ILAiAAAiAAAiAAAiAAAiAAAiAAAiBQ0wRgjKlpDSB9EAABEAABEAABEAABEAABEAABEACBekUAxph6pW5kFgRAAARAAARAAARAAARAAARAAARAoKYJwBhT0xpA+iAAAiAAAiAAAiAAAiAAAiAAAiAAAvWKAIwx9UrdyCwIgAAIgAAIgAAIgAAIgAAIgAAIgEBNE4AxpqY1gPRBAARAAARAAARAAARAAARAAARAAATqFQEYY+qVupFZEAABEAABEAABEAABEAABEAABEACBmiYAY0xNawDpgwAIgAAIgAAIgAAIgAAIgAAIgAAI1CsCMMbUK3UjsyAAAiAAAiAAAiAAAiAAAiAAAiAAAjVNAMaYmtYA0gcBEAABEAABEAABEAABEAABEAABEKhXBGCMqWF1VxzfRdP/cg01zcunKwYV0ckalgfJV4XAcVrUvzFdO2V/VV7GO3WIQPl7E6hhk/tpw990plzP9LdVvzu6aw7dfk0rapCXT8PW5UrLUUqjGzRHXYmpfefE31LjplPpYNWLQYQ3FfNha3KljETIki9IdssU+mgf7Ax8iF4eKysO0KwuLaj9gxgbZQB8tUVRPe1aNrITvWxmI/XaEOf5Y69R30b5VHf7i+SUS+d2o4vbDqW3jXFc8reSh8hWvMlTTiVEavOZipObaOgVrajvnP30SyrJZCBsXeofYIzJQIFIFkVY48YFacqNTal557G0bOseWvvGHhhjksGM+L0w54mq/Rc2+TlXtpsWjHmAOrdTE1z1XgFd3X0MvXMiUcKpNV6JYsJ3uU3AZXhxPUs3l78cfI6ua5xP3R99lbbtWE8bdiYsoOkml8H3sztxzqCg1RJV9Uxa6voEI3tlqqp9dMUPB+n1GQ9T705tfP3P5Z160JJ9umix/rmfuTLhDzHu/KXzrpagJu6il8fK8lKaCmNMTSgprTSrp11LS8SQl6OXzZAIcv6xjJ3rrzGmnHbNyoYxJlvxZrrIpTafOX9sLd3duoaMMXWof4AxJtPl2BFfWOP2ywfTqGleAc0udbxUzx+dP7yJHr9/RJUt08K818Qi2rmjxPr7is4YfCsrTtOuObd5q5Mubt+XHprwLC1dv4f++vpCmjZzMg3uPTKJHKk1XkbSuK1jBFyGF9ezdLO9c3pTyiuYSgcupBtTdb/vnli6pSijtaMfoNEb6uqKDqJMTlrC28y6PsFIpUy5S1rY06r00V+ue8wzlDZu240Kx86iBa8XU/H6pTRt5rM0atBNtMDo78Wg0iCvgEZvOhsihjt/6bwbklA1PU6/PIaX9Uxn4RTtXjCc+r+IVa+pkM1ku5ZKuumHTb9spi9DzcYgY+coxpjqq4fZYIK67aZaG+czdX8sCGOMuzRm9GlY43b8zWGBLQ0ZTTiHI0t3AhvGPIiknHZO7EQN8q6k4cs/ozMVwRDJn9TGxiu51AiReQKucut6ll7Kp+iNIQ3psoFrcnAlnXti6eZR9wfGmZy0hJezus4xlTLlLmlhT1Ptow+tHOD9wNJ74nY6GaEvYf03ajKUHhx2KeW1mkC7/+GSxJ2/dN51pVJ9z9Ivj+FlPdO5QN9eFaKZbNeqkn7V30m/bFY97drxZvSxM1H11cNssEHddlOtjVzqfr2EMcZdGjP6NKxxO7JyAIwxIaTTbeTDmNvJnX5vAl2VV0DDVh+zv0rhc21svFIQH0EzRsBVbl3P0ktQlTcYY9KjWBvezuSkJbyc1fWBjMpfNnx2pdJHS5/Tc0r0vfOi/33fraN+lzana5/Y7th3785fOu/WbNlPvzyGl/VM5wx9e1WIStnMri+sqkiW7J30y2ayFGr799KORVkZU331MBvUULfdVGsjl7pfL5MYY7RSLpw+SEvG9KYrWrIPjiupS+Fi+tjc6xHT6oVzh2jVpMF0fZvm3l7oyzsPoWkbvvYNMKSyu7bn8ODH79NDy3D2s5X0aM/WXrzmuxXHdtOc4XfF0+TlwXeOWETvBdwoaIXyO9MH3USX5uVTo5bXUt9xRfSd9UsWO+4z4+UtLI9vSH3SLvmVxk0+275M5HtXBTEbvaO7FlBhr/bethrO6wOTi+jwOf9bwvHjc4c8vXE+zcFqFD0RldMn6ybRPV1VWkrv8wJ6P/vZOnoixpLD3FQ4hTZ+4Xfl5JN/+/M0oKPaS89MJ/mYKh3ZbFKdeArjREyJTtGKwkbUoter9E1a2z10GTW1UFlxnN6ZNzzuN0CVszVxXZ0//Ap1bZxPPeZ+Zr4Wuy+nLeOaU/Mb5tEXhmym7jm+Po5ynkj3Lpn6jCii7xwS2I+Obl9Ag+/q4tUZXlbfrs94WmvpOVHaHF+0cmenrD7zAM9dDnS91m8mK7uud9TbZlnV8RFFyb/rXf+zMprbo4mn10Nm5LH7U8XjqGleR1r4seNLImK+dt2wHQafPMj18ZZYW51PF7fr6WwjUuOpeUVpO1l6JYdqY7ld6DPiVfro9K5IDnxZtmT5jNLus2+dqxrn05TiX31ApX2w6975stfo1kb+8Knoff3RE7Rt9kCPvVlWuf80+8U2t42k1/adoa0Tf2f1d0Sp9zvJ2sxEutPtkQkonXrK8WS2T9CSpVOmJBa7DeSy2bn/SHqlRA8YpHzYZTBRf/L+rA4JVrdI6v6rOWlVq2o60rMl/rJKpPRn9t8cSzrv+qXQn+y2g8dv8w0uHFLas7CyzmH8dSafOJ4FpTJgTKU82n1rsrKu8hKln+SQvEX5Q98Yh/u1kbTpiLut5fIgY89U2s9kzKRMdo85ZOdx0cj5ewOrq1JvGxQPaX/EHx6PG4fOsePXrCOP91No15Qk7v8TlxeibMkflMYom974X895XONafj+q7oJpqTLmn+9IKK0LecJX1a7qvt1fr1TIVNpu5i5zCR5PDpq8nY58szKCA99o9dBuT8LGImYe7fso/bx6J7ruXOMos27LOFYbE7U+PIfusflOXls9fzGfM8vCQP0K6pvbBbuP0Z8L6OnNeutqKnq1GfJnKafaj1mw31Pv6bz601Rto9lP2tzNdCW9ZG0av2PXbzW3UfOSxGNBLSvHo8bPBTR1p92HElWW76KRlzSnW57X862oMkapd2beq3IfyRjT9tEpNLZrDxq7vNjzvbF56ShvTzRPYs1J3IVzpTTGc0g7hBZu2OOFfX32Hd7krZcx4ZSBjnRqpuBhleDyB0fSyAdepc+lP4+9dPq96dSpRT617D4ynubmVZPottbNqXGr+2n9UTN2VVnvnjiZbr/mbnpRZJzRyzNsXPf03rjRiD1E33dpc7qq/zza4PkceZeWTvoLdZ2V+t5hya8M5CrLy+j9HSW0avzN1LBJT5oWk2P/Ub8Bw5RcOvP5rz9DXbrpvK6eN9jTRdv71viMScyRlz8PeeRBWvmpH1pUPb0/pyM1atUzrvfidQup8NYhvpNieGUJOxJl45zitJ6m3XEFNWjSjV7+VOdAyT+Axj9zL3WIM11PE25rQTyx15XnFH22o4TeWVDoY7P7w6Nx3ehYw+9s5q6QleVbaUSD5nTnkq9dX6fwzN8gyIvciOS1vZumrvbXG+2sURmD2OBiT8wvnNpI9zZsTve8qo1/sgS+38TVtG1HCe3YspSGdW1JjVs9Qu/+KKmqBt+te97ecrEnk5R9rit3tJud9DQX1WlcSQMmG2l3bObVMdPjfCbKnc6J/y6VwW/ysqs7bn8qerJhn4jEk7Zk+Zd6ar9rGkz+vnYQNcjr6KsfSga1/chuV035zpV9QDt3rKexNzelS3pMjdW5D+lYuQp1aPX9Xnt7Vf8ptGqLaoNXzxvutZH5nafSx8Y2iFR4yoQwStvJkki7oOXg9vNOuqxTV6+9sCeWZh75/ofPdtOOHQuof6N80n6fdD6jtvueA9b2zegG66Qz3n7Cg55Le75K3xiJ8/NGTSdQSawpjlrupX17cNhdNHmr38eNtLe+tsDro7rSHzo18xljqtbvJGszE/d79moMkbd558T9uIHNdyu6z1yfoKKXeKtapjgWztv4Li29fm3k/De9McqOLato3J1XeP2QrJBMvY8+RHO7N6FrnCtbfHh8H7gOymSssqLMO5Wv2fVTqdSop1L37DqTzrs+IWIfVJ9ZQD0e0n251z836UYv7tOD20RlnX/AUdt+zXhKiNugSXHfT6mUR7tvTVbWiaL2k8x78cBLvB8XpV3fuUON8xbuIzLb2tYPLo77nfv6BwUslfYzETORo1Grvr6xAo9rrxm+Je7frmptA8uqGJpj5NdjY95bZ5mruFS4VMf7Udo1V3lTz6KUl+zI75ZJlc3BL6/25jzSRvDYd0BsvGPOKaLqzp1WcHKuw9nlnkg57S+gW0cv98Z/PA6YM7w3jVit+5tU2m6pJ2Z9nzP0T14fzT9gyHxFy2TeRaiHKYxFzJjN+6j9vHonuu6S1e2weSjXjXE9+8fmjrG5Tl5HmrH1XRp7Y9f4XEnqlznvZRnteC/8cCjerohvy+1FM7wfac0Vlqno1eQn91H7PRVelb12E1fT4oHXGGPeVTSuVwtvbjerVPcH0j+Z5SWVesE/gPGqULMd4T75yXvnevOSxGNBfz2R8Z6rHz61bRw1bNKXXj+ichlVxij1Tjinc41kjOGJtR8+0YGXb7UmFNyoXkfBgQTRoeX3UKMmj1BxzCYgE+VUjDE8udxsTDg50+xpf0yHZmQbIvg7KbythxTFOzMpNEEjTTkVjW7uk7FsNW8hGmAZc4jIWj0TBb7k1yys/B5XTHOSliguNSnI91YG2Ct4uJDx0dimA0COmycbj7ypLasq/qh6KqWnL25OHSbt9YvF+Y8xEP7dJmkjFgfmQs5HPbc1BhIiv62rC2e3etZKMyzHoQYv/iOC/YIk/iTMtZVZn6okepAw9q/miWN2fetvECTEnjVL46tg5NnH87v6ylXYSgiesJtlXmQd+po2znCcF87t8uqA+Qt/mO4rjq/1fvEoXOmPQ/QpMrquv3ywjlZZq2B+/Ww+dWycT2Z8YWmrQXq09sGVPj+LPvhNXnalLZCyYKbpKntR8+96137mstBz+hUn13rGh2CdNaXje1XezJUX/FTKiNmJy5vS4Zm/CkTnybGogU6UtlPaBbuucyxla4d5bZU9sRQ5/VeVpq2jRPG72v2dE5tZK5GU0WvAsBF0dZ7ZxnA/8Htfu5WK3rmtMfmqvKj2NsiNV8Cs9Qz+Mhn3+KTR79jlTLOMrrt066noJtN9gsSbXpkK14UYEcw2l/lF76MV41QN+6ZBhdPjesrHyvonySpuu86k864uG/ru/LFNtLxYT+z4G+6fhzT0jwO4nLnLOhEbM/lQAjFq6djNu1TKo7tvDSvr0gZG6SfZYO8a25qSSltrs+cwqbSfiZhJGTMn+Rz/qV0T6GrDaF/1MelxKlpWbK2yKaeiEQ08Q+AH8ZW3irWLSdh4P2q75meqP0UrL9mQX8vgv1Nlk8fltj5krNruMW0gi6o7fxr6E79vtv/6m2C5Z6f9HFbrKxY6PieJOrZPPFZ4f1ZHr37b/a6WTd8lq4dRxyI6Rn2XqM139fMyRkmkO/88I8hYUg/qRYXlyfyqrySUnoPyD8r+Ns/9Q2swXh2XulNzUf8PZ9H1ascmcfLc3FVX3f1erB3IC67SrKw45K3svvi/Fhs/Yqk6Y5aXqPVCTiv059eVi2AaKlRQh+/P6eCby6twQT+LUWVMXu9c8qb+LJIxpuDONb4VMJyMLP+eVaISTbTKQJZ+i/FFOkz5bIodLKwKtksGNkLwL8yJlvRzxdwcX32sFBowMPD0xnOmq40vstzJ7tRNWaPeS37NwsrvSmEwf0UPi1M6c7fh4ABNad/MZzhRcev8SLzR9aS2U3Aled+/sEaiImVp7OlrnOTLQ8v7+ToZkV/Ki4TjrULsjNTWb1gjr99LfCfM9a/q+kQlWYEkYVzlMHHs9rfBBsEOIZ+Fg6RZWVFKo62lczIANFdqHXi5q+8Xe4mPr1ue/p1v+0647tUyPdeExowv+r2qT+ZANTzt8FVIdvsQln70wW/ysisdt10nOe3oZS+Yf9e7rmdiIDC3oDE7duJZGr5ALoZGlTfbGOOVEYfRWnhymk0K9Cqo6Dw5huhtZ/lOnqy52+XKimjblJTMKk1bR6m2+6ot1+2U1wY27Ekrv/jQq3tixJa6mNwY5tY7T1Dt9k3yGzTSqBxy3TUH4+n0O65yZnJ09XuqDdcGqej9g4rZ/j9bfUImypTkLUwX0h8MW6cNEqo903zs/OrP7rKqv3ff2QYVDsU/XvGEWK9GCZY3DpfOu25pXE+DbQ2XM1dZl37L/wOYK06VnyjlUeI0+xiOMaysR+0npa6HlQUtdXjfnkr7Gc4sfEWVtB3S/qXTNuj86Ltg2VZ5tcdi/EZgvB9rx8P42e2aTtW8U+klLy/mO/o+Hfl1LPadKpuuX9Y5JE/0tEEkuu7sVOQz58Fs/+W5q9x/saSn1y48W2L/wKrekvbNZRC2x1g8PrcNz5K2hJVyJ89d14T1MIWxiCvuVPt5GaNE0x2nGF63g3oJqxvqBxxzNa3kRTHWixD4eTBeCa2uvFqpWcDgk97YWcpFWF0N9nsqr/aqYZHUnisLd11eotcLLj+JtuVLmsE05JugDsX9gzmOC/7QGV3GZPVOJEn3GskYY3eEnGhcgWvUwEU+cycd9ifKkrAyITUzESysCrar4+awrkog8akOsIDm7pMn4QMm6SxFJl7dsfaxm71fcS9pP4imrt4X3wogsUW9Sn4l//Iey5/KyhjnSh0vMsXInJwJR9uKLrKE6Yifi5xn9s/3tnspfw+LaPtXfqsMp5EoHjNvYY02i+8aVCYKL/wSXSWfkhdX2NCVIq7ACZ8FGwQveEU5fbN/Pb3sHWl6C3W5pWvM34red87hPF8DxjHFqjHxH3nOjBKxNjv0MN1zWl8WPeZtWeE9mfdNXk1imEqYvdiX58oO0pplM2ny2Aeoyy094j6azPYhLG3RR6I8JNIVi5DK4DdZ2Q1v3MMH+lHy7yq3rme/7J/jOY6W9oZIGZDs7TRuvQTru/AJ60D5e9aNaZxOhWcyXqxXyQunE94uuyeW7ny62+vE8Sv98S9V0u7LYETKFw/yFCc1kJLBG084/CtllFTR9R40fku5dxvRg21fOv2Oq5ypHCiO5go24c3vmLoTeataT1k3id6tap+QWOfRypTkLUwXUsb/aGxF5nRNmYVb8BqyGi8Y0PfE1ffFf33sLP7C3PlL512fEMYHXjZfvGohTZnwMPW5pQeJjxFzbKHKWbCsCz/XZNBIIm7YjVIewyZMYWWdmSQqf9JPJi8LInFI355if5SMWSKZpX9Np21g3zifbV9Fc2dOpsF33UI3dVN+AP1lOzyvwkvaUPkcVpdcZVOI6qsq18nLi/Ltk0n5tQz2XXhbySH97YEKG0V3dirymeOTMinP1DWoiwvnDtJsdgGQl0/sc2zR1q98p3+KThLJI/pj/YSNFSQeCeuXy/8pUT0Mi59jEI76h3J/vBImfBwR7Oel/XG1KxJf1PIe1EtQHyJxWFmXPJo/tgfjlVjUqsh+LewVNnquHUWvOjZ9J/oMq6vCTfd7Kq+uOTfHyjpnWWS8J+/r8hK9XjCPRDrWuVBx6jTkG5de1DPTyMvp+H/ojC5jsnonkqR7zbgxZth85R9D9r+ZV5n4SeHQytTZCBZWF2wVPpkiVaHRg/JgodHpBguY+u5c2W5aMOZezyEj+0950bdXTr+f6E7yaxcklt/fOITHEtboqTcUI3MJXpCjChmXJYKe+A3l4G4GFcYcJ7d/UDs69vg3GUqLivWqE1PfO3doHw+J5Hc1ZonCh1PS38TzGTMW6m/MO7WiyKy05rfR74NlNL5Hs+W11HvEFJr+kvK1UrJ0lNWQyQppTQAAIABJREFUEYnxRfzm8K97tuNeZpTfZULMR4iL91fx7Xhhupf8sD+Ed5c+Sbd7zgIL6M/PmvvGJZR5Ladd07t7y8/ZueHEmYs8fyS73l/iLWGXwSK/EZZ2XB8Ry52ZutwzA3NSIM/D6nWishv2DscZLHvR8x981xUfp6KML7L66ddDr1DHxu7VJDqfcqfKm82C+aQyAEqNZ1hnGOycpQzYhmAlvYrHLDOSq+DVnabX7hh+Xez3WAemMUZW30n7yCuEZFsf/8qTFzOE8i9Zfn7p6Z3lknJvr5gRmVkHrsF4VfodV9lT6bg58neKlR5YibxR+nHJg3n1dJOFPiETZUryFn1QqicN5oDazK++Vys87XZbf+++C9M/twfs3F35HHDXmXTedUkj/qbYKegTM5+lpevZ59RmmtCvia/dTVbOwiZDOs3o5bEqxpgo/aSUhbB6qWUN9u3yXSrtZzJmf3qqKOA7QsZSJV/4fwRLtW04X7aO7mYfim27UeHYWTRzufKVtH7GHdb4MzyvwkvGsPI5jF9Y2RR26qrKQbLykg35/XKYn8LLJodS7ZusdlBhU9GdmZLE52r/w8o9v3Ny/zp6Zngf74e9vLZD6e2YX0zRSZS2m/XjWgHF8cfjSTh2VjkJK9ccv78vVeHlf+bIc59sGGOkjEpacvXrjp+Gl3fpb1wOfCU+uYaVdcmj2XcE41WxyHYd7U9SYjf0UcWxs+gzer8XzoWlUmMG0+ehXWei1wvh4R4ragbh43W3rH43D2qs7V8ZFF1GkSKs3sn36V4zZowRh6NRrNxSOMzlwJIRHij7Gyc3bA4fZRmb6asmXKFSwPSgVOSRK3t7fq5/XnzgLs+jXOP5tRo3V2UNi09VAP9qCQkrv/zKBIOfSyHXjYkKnYqeJH65frvpCe9kEmns1HI1vfxfwrmuYY02h3U1ZonCu+K3n4Uxt8PJEjTbJ5IdLvHnYBkV3dr7jtVye7ucqf2lPDEvr1AGItNxL6ft7YNMMAE15QvTvRmG79lYUTp/oDdxdRlGJbywtLfsyTJqc2IdlnY65U7k4HJibrOR5yKflEt5bl7tsittgf41QIcW3UknKvFHyb+r3LqecWrcYYi1nldHJXLcq6XjO1XebGNMlG1KprPo1HjaHa6WiPNn/lKSqF2QtsosMzom+86dZurtvtqGyr/A7PrHBzT6khvjzpNVueTPZ7ztkmb/la7eOTeJy71amePv7/wMUul3wsqZlHVX/bB1l1hev2yuT4l0b4cPlzfYJySKN2qZknD+QZmWSvQt29b4G7st0KGDd7KNxO87IBjOfOLq++R79mmifBTsdp5Als67koZcZduO7euHjcaL+uZFNMaovkuMnhJ38Oqu1xzOLo/S1tntRVjZidpPSjk3x0xBOflJsG+XcKm0n2HyEilmsjpP4o5yjdo28Jja7cuxX5WNMcLPbC+1zMnbNRU2WnnJhvxaVvtOlU33ClWVL92HVl13kqq0L7ZRQuqjXe7lPb6yj6dHOjSL183EOjHfTDyeVD8M6RXy/jf9n8LKdapjEX+s6lPq/XwquuM0wut2cBwbHjasHRbdyjiSUwzGy0/ZJ0wnZx3lb1PRK4e3/6Xe77nHlxKv0q0YJPmp3Z5Hrxfcr7sPs5DU5GqnIc/depE885iHy3OnPO24V70ZXUZJSa52vZPn6V4zZoyRY4Jdjb4tpIAKnOIQc+bqH5y6YXOc4gjT5QNDHDyZ3uiDhUZLFhgExJ1i6TCuPYD62/A7GeTZg2FXZQ2LReRjZmcs2TznatbpRe5Kz7GriX8UPdmOXWXyLQMY2YcXHMAFcxHWaHNIV2Om8muuagrGmehJGHP7HXYSNv7GpsQnGSzc5/8Fyg4b/jlYRt1On7gzV0e+28YPz5LbdAJtfXciNXPstfW2tTTOJ9sg4JIpVPdcbqyyI/uDzQmIHecvH0zztuvZv4JxZ8mOo80BQ2jaqZQ7W4DYZ+4EbAdq/JVyLmgNHqx82mWXSO0ZtY81ly0C5oq1VPLvKueuZyy31J9ntr7r+S4x97iGIIg9dneWslc2kdM807t/SjwDHa6WUNVVbWCUuudqFz6ez47f/WVGx2TfqQ7YNpil3u7Lr0sFNP2lF6nD9bL9g9NTLO99+WUa0UAbafibdPWucqPid7W37ACbVz/4+jur3HIcUfud8DYzbCDjmvym0D+oDPr+lzLt0r0vYGzibdYz83u7T8hMmSqnLeNcpyxyympA7DqZLkxGU151f5xW8Ok8TbrRkxssJ+nBwN4TO59mMPm1tOCeKTSuXxNfO8vh0nnXTIfvw7bsygod0/Ab1p5xPDIWSfzjRirlMdi3cjphZT16P6n6YrdTS5OOSv+KQUWkPQmp71NpPxMxY0M8lzH7hxtTCu++Sm0Dl8nGgVUQrn5O2kKzPxcZpP7pMWyK7ZpEZF2Tl5dsyW8JEv+oyib/SLLLGg5KXTD70Mi6i8fvv1EGhwKyVy24xlb22I1jYt882jgUve3m8shjN3s8yT/QyRhV69ovs/kprB6mOhYx45T71Pv51HQn5d1Vt4PjWHc7xLKGtcMch913BOMVR93BQ3KEQ0pzNv2ScZdqv6fyymPtZV8Y0cQcuvPx0Mnm1VHrhTjFds3h/Skr3dpjQdFhsM1SeeZ+a/3z18YNlmacUWVMXu/MWKt+n0FjTGzPm3VEFR8VuPylp2jgQ2Znxsu/O3m/yMsxberouD/TmLF3+QenCayXnG057tI8tk+Otg56aI4+COBKc+19+njY4nUveP5TtAFJ5cFbJvi3xAoIdmQqvKuyhsWkOvOedPe9XXzHbc8fpfza2BMwV6WXuHkCbh8lFtRTKY25oi/J0X78PadlO5dSx+Plkz5ulI9cXkXPj7mXHl+rhy+JBiOuxkyYtb1vnneM34plf/UGQ8qvQ/LVOPJ+lA6l4vg2GtulpTdJ5L24z8xXW4r++vpCmjrzabqn20gyj28WjvoabKjVr6TWkd93tqMHhz3oW0UgcaiOpxX9sdONJFtX5Dt1VZMF3n6hjyIsoeL1S+mp4X1oQakOHaZ7ZnJ7O/Z/JFsJ1dF8PPDY7TtKVcfFd9Ip2kfdXv/QCO/0H7MhDEub44lW7vxpm5/OH1sbL7fqaO53acn4XnTduKe97VJa19HKLp9OwQMSOb7eO+K2V2saM3a0rxNNJf+ucu56pvKlOoxLO3WldpEc9woNVd7MCZJ846qPcrS13eFF58mxR287eWKr2nd/+edjM3tNnhzY2iayB68qnzxhmrd1DxWvWkbvxNra1Np9jlnFxYYg+1dPnlQ1ue5GatdqKh2InyySWrkP1zHR6Q+mecd5m/WHddKl+1R6amQjX3/H9SdxvxOkJE+kzbPbzNR0l349dZXB9PuEzJQp+ZGGtxxL3+bVez7a2nFqJOvDHlALb9f1wrlD9FLh5V4bz77mnpi53Ntayu30NM9fRxdfW+3q+8x4pey4DJjpvGumoe7VUm77eOUu3UZSYeRtSuokRddx0ctmPBw42lq311oarkfmKjupt2Yfw6HDy3r0ftJVFvi4YN6Wzkdbyz9emcFlY8LqPbRjy/NUFPsulfYzUfvglqOE1ix7hgbfOs873pVlqWrb4K3+zSugfhPVuGbHlqU0rGsPenDYXVbZDo5jhIHwNnUmZTNKuybx2Ff2g5OsvGRLflsW9Vn1czffex/d0Obu2BHGJbR56SjP1549p4iqO3da6jQe9WNgz/ixyKvnDaZOt42nxywD7M6JLanPiEXxreoi04AlX8ejjz7GihmOfeViFY27sx3dN3ky3drI+nErnoL/RspFsM+RI+b9c4OwsYg/Vv0ptX4+Nd1xKmF1OziODa8bYe2wq++w45U25IrCxbHjyv0uCL7+QbGIrlfNzrxzldPwfk/ltdU999HtrXW5lDlw0IAdHBu60uNtl3abxjK6dMz95eOxo61VPpRMwbFguF7YMH91Xjf6Q6dmjlOF9YnL5ljAJWOUemeyrup9Ro0xLETFsd00Z3hvz8cKd6q8T/VPhVNozb74kUaerJUVx2nb7MFxJ6BtbptAa7/4xbGMKxy2ZPrc4W00fdAt8TQvbs9GhGI6dk5CyDVYaOQbexBw5uArNPiuLnGHq5d3vpvGL99vHA9YE8YY/vXkDJUsfYi6t1YrLHh/97QNX5N9AItd6SWfck2up+O0cdLguAM/dvj6p8J5tOOInRLR0e0LqLCXcgjHOr+8Uw96YPJq+shQeaLBSFhj9mXRE7F8FlCncds9nyicL/1LgOQmeJVOwhw8BEPpJ+JfxNQ55+Xidj29fH9s/Uqi3+Q7Vxktp4OrnqTunl+WfOKB+YytxxzLsHVMbKllY4v4jtHfqDsl4yQa0LGNN2DlsFd1uztQ1sN0X1l+gOYPvyte57hu9h+33KcnO035fPazlfRor3ae8YLLwqDJ2+kYnwTVoLnvF9uwtCWe5OVOQrqvthyFc/bSiZgcWtdRy25QR/NLTng6sidgdrph+XeVc9czyZ38kus2wEko+6rKm8sYwyGPfbCEHrvTbLuGeA7ITzp+WbXz5ebJsUZvO5W05fTJOl1WVZusdWVPruwcyufzZZvo0Zi/qkva+3+tjN7uq9jY6MJ1xl6VJieF2EYafsvmk4reJQ8qnnX0xKCbvP6E6w8Pqt87EfxVLXm/Y8YavHe1manrLno/HpRAPclOn5CZMnXhXBltmu3v21gfrr6N2zO7LQjLs/n86PYlNMoYk8hYqPtdI2nTER0yrO/TIbQRyq4z6byr49d3Fcd30fRYGVVO+1+lj05/S4v6N/b9spioPePYeGz3zrzh1LuT6qfU2GEKbf1Kxg6ptCWuvlXJ7C7r4udOtz1h/STHwlt9VhnjHO4T7xyxiD76UXNhB47P/eUar+/La9uXVhm/FtvtQ1j7mYyZyHF9GzWuY2ZcVhZtPRof21W1bQiOtUfSa/vOEBtN/WU7nHXYeOrsZ9HaNU0zeJesvGRT/qA0sbK5+hid+GBJ3Fcil4sHJhfR4cCcQpehRLoLpqOf2PWu77g1dPjc9169M+v8t+9MpHu6yji7gK7uPoTmGeVDYow6xhLuMkaVucS5Y69R34jGGE4zrB7yd6mMRUR++xq9n6+C7kLqdnAcG143wtphV9/hj/cUvTEi/MAb7jPMsUpUvdr85HP0fk/n9fzhbfRErO0zxy0Sp7q62/MobZrEc/IgtyN6Ds/z7ZHz98f9YHI491hQyypxydVbWdq+WdwVgDw3r1FkjFrvzHircp/EGFOVKPFONggk68yzkWbti1Pt2ZVtUrVPvvQkYmMMG5rM447TixFv13YCynof1XFvbc8N5AMBEAABEAABEACB6ibgNgpUtxT1Lz1wz4TOYYzJBMVqiAPGGIbMTpda+qzF1YC+WpKQrTC2495qSRyJ1BABtU0puuPeGhITyYIACIAACIAACIBArSUAo0BNqEZWzSXyO1kTcuVamjDG5IjGYIxRvgx6tfT7dcgR9SUV038UW9LgCFAHCIiz0+iOe+tAppEFEAABEAABEAABEMgoARhjMoozYmQ8d/Fvd4z4IoL5CMAY48NRez/AGFN7dVN1yUpp9QvFcedwtmf7qseLN2szgT1rXqCtnhPFlp4vhu8cvlxqs/yQDQRAAARAAARAAARqDwEYY6pLF7sXjKJlW/fE5y63ztof921VXTLUtXRgjMkRjcIYkyOKSknMUnr64ubEjrEemP8ZGrOU2OVuYD72nJ1KXnffYvo8wSlWuZtDSA4CIAACIAACIAAC1UUAxpjqIs1H0l8aO6Bn6Jy9xsE21SVB3UsHxpi6p1PkCARAAARAAARAAARAAARAAARAAARAoBYTgDGmFisHooEACIAACIAACIAACIAACIAACIAACNQ9AjDG1D2dIkcgAAIgAAIgAAIgAAIgAAIgAAIgAAK1mACMMbVYORANBEAABEAABEAABEAABEAABEAABECg7hGAMabu6RQ5AgEQAAEQAAEQAAEQAAEQAAEQAAEQqMUEYIypxcqBaCAAAiAAAiAAAiAAAiAAAiAAAiAAAnWPAIwxdU+nyBEIgAAIgAAIgAAIgAAIgAAIgAAIgEAtJgBjTC1WDkQDARAAARAAARAAARAAARAAARAAARCoewRgjKl7OkWOQAAEQAAEQAAEQAAEQAAEQAAEQAAEajEBGGNqsXIgGgiAAAiAAAiAAAiAAAiAAAiAAAiAQN0jUAuNMcdpUf/GdO2U/TlP+8jKAdS46VQ6mPM5QQZAAARAAARAAARAAARAAARAAARAAAQyRQDGmEyRdMQDY4wDCh6BAAiAAAiAAAiAAAiAAAiAAAiAQD0nAGNMFgsAjDFZhIuoQQAEQAAEQAAEQAAEQAAEQAAEQCBHCcAYk0XFwRiTRbiIGgRAAARAAARAAARAAARAAARAAARylACMMVlUHIwxWYSLqEEABEAABEAABEAABEAABEAABEAgRwkkNcZUHN9Fc4bfRde3aU4N8vLp4vZ96fENx+LZDTc4hDvirTi22xdn47bdaOiL++mMF6t+78Lpg7RkTG+6omU+Nci7kroUzqP3TsSTTnjD766aNJg6t2vlye2lMWcvnazwv5Ysf/7Q/k92Go1aXkt9RhTRd7FgLjaVFcfpnXnDqXenNp5c/E7fcWvo8Dl/3K5wZtwc+uxn6+iJQbfE+OTT5Z2H0IJSRdEfm/9TlPcqK07Th+sm0T1d21PTPOZfQO36jKRNR3RcrjBXdx9C0zZ8Tb/oYN6dsPj43CFPp5fm5fucNCuZbiJ+zrq+qXAKbfzCjqWcPvHJpMrEx8mzbEmDjyAAAiAAAiAAAiAAAiAAAiAAAiBQcwQSGmMqTm6i+y5tTlf1n0cbdpTQzh3v0tJJf6Gus/RJRzLJDp4YpI0qZvZOvzeBrmucTy27j6SFG/bQzh0lVLxuIQ0bV0QnvYDqvbaPTqGxXXvQ2OXFXpjNS0d577Xo9Sp9c8GM0XWv4jDTeH1GL8+ocOus/XFDQZT8uWLnZ+fLXqN+lzanvLZ309TVSsYdW1bRk/fOjZ+e5GKzc+Jvfe9Ivq4cJPnn2E/RG0Mu9sK9GGO0edUkuqPd7Hjcvxx8jq5rXEC3jl5O2zzdrKc5w3vTiNWKYpjcUd6rrCijxQMv8YwiAyavjsWvdL9wn4qZw6wovMwKs57mDP2Tx7nnFM2Z32AWjZoMpSGPPEgrP/VbT6RMdClcHCtn62naHVdQgybd6OVPdU7en9ORGrXqGS8TXG4Kbx1CG/6mw+AOBEAABEAABEAABEAABEAABEAABGo7gYTGmLLVA6hhkwG0/qiVDWN1icvgoEIHjTFi/Gh73xr6zojDH7t6jyfis0p/9X114OVbqUFeR98E3Rcg/uE4FS0rtlbBlFPRiAbeUdMfxIw5UfIXj9K4qaw4QFNubEr5nafSx/8wvrBuXWz2rFkaWAXz8fyuPs4Vx9dS30b5VLhSr0DyojaY7Zze1JeXeNJGmPgz4ybKe2z0cPE3ovGMK2FhDq0cQE3zOtLCj/UbzIJXVj3y5ln9kIgqy0tpTIdm1G3S3riRjAOwsYePOG87fEtsxVQpPX1xc+owaa/vfeL8Jsmz/wV8AgEQAAEQAAEQAAEQAAEQAAEQAIGaJZDQGHOqeBw1zSugoa9ZRgFDZpfBQX0dNMZwWF4dsflHI4LArXqv4M418e0+EuTXQ69Qx8b5NLtUnqR25fQbNrk/vpIiSv5cKZS/NyFgbHCFC2fjD83xsaFC8lVZvotGXtKcEhmtvljS0zOYPFviN274Yw5+SvZeZUUpjb6kOd3y/GfBl+NPDtHc7k2o9ZCimKEk/oV3I3H80VpB5TLsndo2jho26UmrvvLHwZ8OLe/nGZzUqqsymtujiWcAe9+/sCb4Ip6AAAiAAAiAAAiAAAiAAAiAAAiAQC0mkNAYw6sT1j52s7ft5JL2g2jq6n10rNyfm3CDQ9AYw1t0Lu35Kn3jj8L6pN4LrIDgrUHHXvNWjAxbk3grDkfI/kw+276K5s6cTIPvuoVu6qZ8n5jGmCj5s4TzPnpGpaYTqMR2aWIFdrKpKKdv9q+nl2c+S6MG3UJdbuka85OijTEczZdFj1GnFvnEPmXum7ya9h/1J3bh3EGazVt58vKpzW0jadHWr+hMhBUiyd4TxlOK/auS/FkrpdENmtOdS772P45/Ujq8wth6JSxkVZIE5eech7A/U19n9s+n21qz76Irqc+IRbT9K1hlhCOuIAACIAACIAACIAACIAACIAACuUMgoTFGsnGubDctGHOv5yiWfXa8aGwfkkl2FJ8xbIxxrXiRdNQ1aMSR78VQkMwYc75sHd3dujmx097CsbNo5vI3Pb8z62fc4VsZI/Emyp+EMa+SZ9uwYIbhewknbC6cK6XxXVp6BpbeI6bQ9JeUP5aSpaN8K2MknsryMnp36ZN0+zXshLiA/vys3w8Lhzu5fx09M7yPZ9DJazuU3ra3lElk1jXsPWE8q8R6wfex6sYYYSHRMSNeLbWomH0Suf4+9BkAldPgGVTYs7XHrP2DRQm2vEkquIIACIAACIAACIAACIAACIAACIBA7SEQyRgj4vLpQc/1z6O8gql0IOZ3hSfTvHphs3XKkWxVuXaKdvb7/pwO1KjJI1SccEFD+saYnRObUbPrp1Kp5c+Ft72YKy0kX3J15U++M6/H3xwWyXeNbYwRVrYPnvKd/m1KZlp8zwaI0vkDPYOMbGWyw5w/toke6dCMLhu4JuYI2Q7h/my/d+HURrq3YXPqMTf9bUpmHDYLkYZZhm1TkjBh1283PUFXNc6nZMa5sPfxHARAAARAAARAAARAAARAAARAAARqgkBiY4xj2wsbNEyDCvv84FUb9rYWfs5HIpvGGPb50rVxPtkn7fgznq4x5jitGNg4sAKnsuKQ53PEZ4yJkD+/bOrThbNbk/p04ZC2AcLtPLecikarY8PjhhaWy5KNT2+6tVE+jd4U8xFjfc/pbXn6d9T8hnl0yCW0PEv6npKncav7g46bJQ4iEt8ztpNlDqIc+PpPQrJZSFQVJ9dS/0b5AQe+8r3vasleWbHL2y5lGn184fEBBEAABEAABEAABEAABEAABEAABGohgYTGGJ5AX3vfFFq1RY6gfsHz2XHtE9vjJ9/waTjjb2zqO3J49bzB1Om28fRYvyY+YwznX03U8+mq/jrezatmBo62No04wk220CRbCeEZCvIKqN9EtQ1ox5alNKxrD3pw2F2+lTFR8idp21c5jtk8Prt4/VJ6PMHR1sphcD75jnC+sx09OOxB3zYlzuft7dhHjzoye+cOddRzXqsJtDu22mfnxJae3xR15HgJ8RHZ7GNmQKgfF5WDKO/xdqoxMZ2OnK+2eLEMvFXNPNo6ePy1HG1dQMNW+50+hxljWCpXmeBjwp8fcy89vlb8A5XSmCv6kpbnXZo/6mZq1qRv3Pnv+WNrqV+LAuo7J7idy9YfPoMACIAACIAACIAACIAACIAACIBATRFIaIw5c/AVGnxXl7iD2cs7303jl++3jowmqji+i6YPuikW7krqO24NHT73vXc0scuocnT7AiPeArq6+xCavvGrmIEn3ZUxvK3nOG2bPZiub6NWnLCD29f2nfEm/ebKmKj5C1POyYPr6IlBt3i+dNgBLfMZOX9//IShoAGinA6uepK6ez5g8omdIs/YeoyCpykdoPnD74rLz75v+o9bTh8ZW8G+fWci3dNVOSXmlUnMcN7Wo3EjWZjMUd/jLVurJg2mzu3YX02+53/nzhGL6CPjJCzmXLL0SerdqY0Xhh3r3lQ4hdbsMwSNCRJk4ZeQy0RhL8lPPl3eqQc9MHm1kefjtNGQhx0b/6lwHu04oh0bwxjjZ4pPIAACIAACIAACIAACIAACIAACtZNAQmNM7RQZUoEACIAACIAACIAACIAACIAACIAACIBA7hKAMSZ3dQfJQQAEQAAEQAAEQAAEQAAEQAAEQAAEcpAAjDE5qDSIDAIgAAIgAAIgAAIgAAIgAAIgAAIgkLsEYIzJXd1BchAAARAAARAAARAAARAAARAAARAAgRwkAGNMDioNIoMACIAACIAACIAACIAACIAACIAACOQuARhjcld3kBwEQAAEQAAEQAAEQAAEQAAEQAAEQCAHCcAYk4NKg8ggAAIgAAIgAAIgAAIgAAIgAAIgAAK5SwDGmNzVHSQHARAAARAAARAAARAAARAAARAAARDIQQIwxuSg0iAyCIAACIAACIAACIAACIAACIAACIBA7hKAMSZ3dQfJQQAEQAAEQAAEQAAEQAAEQAAEQAAEcpAAjDE5qDSIDAIgAAIgAAIgAAIgAAIgAAIgAAIgkLsEYIzJXd1BchAAARAAARAAARAAARAAARAAARAAgRwkAGNMDioNIoMACIAACIAACIAACIAACIAACIAACOQuARhjcld3kBwEQAAEQAAEQAAEQAAEQAAEQAAEQCAHCcAYk4NKg8ggAAIgAAIgAAIgAAIgAAIgAAIgAAK5SwDGmNzVHSQHARAAARAAARAAARAAARAAARAAARDIQQIwxuSg0iAyCIAACIAACIAACIAACIAACIAACIBA7hKAMSZ3dQfJQQAEQAAEQAAEQAAEQAAEQAAEQAAEcpAAjDE5qDSIDAIgAAIgAAIgAAIgAAIgAAIgAAIgkLsEYIzJXd1BchAAARAAARAAARAAARAAARAAARAAgRwkAGNMDioNIoMACIAACIAACIAACIAACIAACIAACOQuARhjcld3kBwEQAAEQAAEQAAEQAAEQAAEQAAEQCAHCcAYk4NKg8ggAAIgAAIgAAIgAAIgAAIgAAIgAAK5SwDGmNzVHSQHARAAARAAARAAARAAARAAARAAARDIQQIwxuSg0iAyCIAACIAACIAACIAACIAACIAACIBA7hKAMSZ3dQfJQQAEQAAEQAAEQACc4QDgAAAgAElEQVQEQAAEQAAEQAAEcpAAjDE5qDSIDAIgAAIgAAIgAAIgAAIgAAIgAAIgkLsEYIzJXd1BchAAARAAARAAARAAARAAARAAARAAgRwkAGNMDioNIoMACIAACIAACIAACIAACIAACIAACOQuARhjcld3kBwEQAAEQAAEQAAEQAAEQAAEQAAEQCAHCcAYk4NKg8ggAAIgUJcJ/Prrr3Ty5Ek6fPhwVv6OHDlCJ06cqMsIkTcQAAEQAAEQAAEQAIFaTgDGmFquIIgHAiAAAvWNwLFjx2jqlCk07IEH6KHCwfRQYSENHzzY+/PuCwfTw4P5u0L1XeFgGs5/g42wsWcShsNzGP786MMP04oVK+obVuQXBEAABEAABEAABECgFhGAMaYWKQOigAAIgAAIEO3bt49uaN+BOv22MfX65/9Fvf/5f0b/+6fEYW/51/9Ff2zcjMaOHQvUIAACIAACIAACIAACIFBjBGCMqTH0SBgEQAAEQMBF4MMPP6TO7TrQrObX0F8v+h1tu+j/ZOzv9Yt+R4PzWtCYMWNcSeMZCIAACIAACIAACIAACFQLARhjqgUzEgEBEAABEIhKgI0x/9WuAz1/cQcqvuj3tPOi32bsb91Fv6MhMMZEVQXCgQAIgAAIgAAIgAAIZIkAjDFZAotoQQAEQAAEqkYAxpiqccNbIAACIAACIAACIAACuUMAxpjc0RUkBQEQAIF6QQDGmHqhZmQSBEAABEAABEAABOo1ARhj6rX6kXkQAAEQqH0EYIypfTqBRCAAAiAAAiAAAiAAApklAGNMZnkiNhAAARAAgTQJwBiTJkC8DgIgAAIgAAIgAAIgUOsJwBhT61UEAUEABECgfhGAMaZ+6Ru5BQEQAAEQAAEQAIH6SADGmPqodeQZBEAABGoxARhjarFyIBoIgAAIgAAIgAAIgEBGCMAYkxGMiAQEQAAEQCBTBGCMyRRJxAMCIAACIAACIAACIFBbCcAYU1s1A7lAAARAoJ4SyLoxpnELevzxx+nXX3+tp4SRbRAAARAAARAAARAAgZomAGNMTWsA6YMACIAACPgIZNsY82CjS2nw4MF04MABOn/+PF24cIEqKyt9MuADCIAACIAACIAACIAACGSTAIwx2aSLuEEABEAABFImkG1jzD3/byNq1rQptWjRgsaMGUM7duygEydOUEVFRcqy4gUQAAEQAAEQAAEQAAEQqAoBGGOqQg3vgAAIgAAIZI1Ato0x9/2fplRQUEANGzakZs2a0W233UZPPvkkrVixgvbt20c//PCDt1omaxlExCAAAiAAAiAAAiAAAvWeAIwx9b4IAAAIgAAI1C4C2TbGPNDwErrjjjvo0Ucf9VbHsGHm5ptvpgceeICeeeYZmj9/PhUVFdHnn39Op0+frl1wIA0IgAAIgAAIgAAIgECdIABjTJ1QIzIBAiAAAnWHQLaNMUPyWtAjjzxCn3zyCY0cOZLatWtHf/jDHzwDzbBhw7xVMlOnTqXnn3+eXnvtNdq+fTt9/fXXdPbsWayYqTvFDDkBARAAARAAARAAgRolAGNMjeJH4iAAAiAAAjaB6jDGsK+YX375hT744ANiA0yXLl2od+/enmNf/m7mzJk0d+5cevHFF2nWrFneapmNGzd625iOHTtG586dg9NfW3H4DAIgAAIgAAIgAAIgEJkAjDGRUSEgCIAACIBAdRCoLmMM54VPU3r33Xc9g0yfPn1o4MCBNHz4cJo4cSItWrSItmzZQhs2bKCXX37ZM8rMnj2blixZEl8tc/LkSSovL4dhpjoKBtIAARAAARAAARAAgTpEAMaYOqRMZAUEQAAE6gKB6jTGMK9ff/3VM7iwEYaNMbx16YknnqCnn37aWyGzc+dOOnLkiHcU9tq1a+m5554j3sbEK2Z4G9P+/fu905h4GxMbd3BMdl0ohcgDCIAACIAACIAACGSXAIwx2eWL2EEABEAABFIkUN3GGBaPDSl8mtLDDz9MhYWFnt8Y9hnz7LPPeoaZxYsX05dffkk///wz/fjjj8Qycnh2+PvUU0954davX0+HDh3y4mIDz4ULF1LMOYKDAAiAAAiAAAiAAAjUFwIwxtQXTSOfIAACIJAjBGrCGMNojh8/7m1B4pUxvEpm/Pjx3sqXt956iyZNmuRtXXr77be9VTC8+oVXwbBhZteuXbRgwQLPKMMratiIw9ubvv32WxhkcqTMQUwQAAEQAAEQAAEQqG4CMMZUN3GkBwIgAAIgkJBATRljKioqvNUvL730Eo0ePdpbHTNhwgRavny5txXplVde8bYn8ff79u3zHADz6hf2GXPmzBlvK9PmzZtp3rx5NHnyZC/swoULaffu3fT9998Tx49/IAACIAACIAACIAACIMAEYIxBOQABEAABEKhVBNgY07nDtfRsmz/Spv9+KW3+vy7J2N+Kf7uYCi+5wjO2uDLNhpWPPvrI8wvD24/YLww78122bBl9+umn9M4773gnLPFpS2vWrKGjR4/Go2FjC2934hU2Bw8eJPYv88ILL3hGGfYzs3LlSs+ow05/YZiJY8MNCIAACIAACIAACNRLAjDG1Eu1I9MgAAIgUHsJsDGm0/U30Ng/30HP9+hLL9zy5xT+Eoef3PVW6nftDfToo4+GAmCDyp49e2j69OneH6+ImTJliucjhg0ybGhZvXq1Z6jhlS8lJSXeyhjTRwz7jPnhhx/oq6++8lbGsH+ZOXPmEJ/GxP5neLvTZ599Rj/99BMc/oZqAl+AAAiAAAiAAAiAQN0lAGNM3dUtcgYCIAACOUnAWxlz3fX0zH/1pBWtO9HKyzrSyitiV/Oen8nziPcvFdxA97RpR6NGjUrI5tSpU57fF3bQy0dcszFlxowZ3vWLL76gY8eOecdbz58/33vOznvZ8MIOfs1/7FuGDTO8WoZX3GzatMmLj1fKzJ071zPq8Damb775JmDQMePBPQiAAAiAAAiAAAiAQN0iAGNM3dIncgMCIAACOU/A8xnT/lp6ocX1tPOiJlRyUV7G/t68qAkNa3IZjRkzJiEnNqKwc15eAcN+Y9atW0evv/66txpm6dKlnm+Z06dPe1c+3ppX0fAqmffee89z8MvOfV3/2FjDW5vYAMPvvfjii95JTGzUYX8zvOrmb3/7G507dw7Of10A8QwEQAAEQAAEQAAE6ggBGGPqiCKRDRAAARCoKwRqgzGGWfK2I95qxIaSadOm0Y4dO6ioqMgznvDpSbwShn3M/OMf//BOVOJTlHj1DPuS4ZUu/DzsHxt7+HsOxycv8SoZNujwUdq8LYpPaGKjDW9j4jTwDwRAAARAAARAAARAoG4RgDGmbukTuQEBEACBnCdQW4wxDJINMrwliX298F9paam3PYmNLuwDhv2+yCoYPsp61apVnn8ZNszs3LnT23qUzFkvG2bY4MJxsdNfNsiw02B2EixOf3nbFK+q4bg4PP6BAAiAAAiAAAiAAAjkNgEYY3Jbf5AeBEAABOocgdpkjBG4n3zyibfqhY+1Zie+fLQ1r5Zhx74HDhyIG2TYPwzLz6co8WlM7KyXjTSmc1+JM+zK2584fj5Se/z48TRu3DgvHfFLw4YbGGTC6OE5CIAACIAACIAACOQGARhjckNPkBIEQAAE6g2B2miM4dUv7OeFV62wkeTw4cP0+eefe0dg8yqWvXv3xp33clh22MvOeidPnuz98VYkNrJEMaJwGI6DV8J8//333gqbl19+2Vstw/5r2M8Mx1dWVhY3AtWbwoGMggAIgAAIgAAIgEAdIQBjTB1RJLIBAiAAAnWFQG00xjBbdqrLR1KzQYad+f7973+nr7/+2jOOTJ06lYqLiz2DC4fl7URnzpwhPnmJV8fwqUzse4ZX2HA8Uf/xihr2LcPOhNkAxAYeXp3D/mV4qxTHzcdwsyyprL6Jmj7CgQAIgAAIgAAIgAAIZIcAjDHZ4YpYQQAEQAAEqkigthpjODsnT570TlZig8xbb73lGV/YCS+vXOFnbKzhMPKPty3x6UhsqGGfM+xnht9j57zJfMlIHHLl8Ly6ht/lbUzsX4ZXybBvGXYAzI6D9+/f76UvfmzkXVxBAARAAARAAARAAARqFwEYY2qXPiANCIAACNR7ArXZGMOrT9gYwsdSsxGETz1igwuvWlm2bJlncHnzzTe9lSqyJYmvvBqGfcusWLHC29q0ZMkSzxkwO+atyj9Ok0964q1SbOh59dVXvXjZ2MPbqLZt2+Z9xytqsGKmKoTxDgiAAAiAAAiAAAhklwCMMdnli9hBAARAAARSJFCbjTGcFV51wsdaL1y40DPIHDx40HvGjnr59KPnnnvOW7Xy3Xff+XzEsFGGV8ls3bqV5s2b562S2bhxo3e8NRtXxHiTIi7PGMQ+ani1zIYNG4iP3WYZOA1ePcMnQB05csTbNgXDTKp0ER4EQAAEQAAEQAAEskMAxpjscEWsIAACIAACVSRQ240xnC0+0YhXpfDWIzZ8iDNdNra88cYbNGvWLG8VDBtkbAMIO+blY6x5JQ2vrmFfMh9//LHnFybVrUs24l9++cU7iptX7HD8LJukwU5/v/zyS88pMPuhseWy48JnEAABEAABEAABEACB7BGAMSZ7bBEzCIAACIBAFQhk2xgzNK+ARo0a5a0oqepqFM4WG1V41QmfpvTKK694JyixMeXEiROeXxg++vr/Z+++g+2ozrTR43HAJCNAEsrxHOWIQBHlhAKSEBlEEiCCwCJnBIgMTjgCAzZgsE0Gm2DAGWMbbGN7fD04zVRd35mpmTu3pubWN1/dP99bvzVeTLPZJ2rvI4FWV3Xt1L16rWe93ec8Tz/v2w899FBKWaoVWRz3P//zP+O1115LtWYU+PXoauJNox5drX/q2Xz3u99Nj9r2ZCd90tfXX389HUuRYQLO9uDQjSkuuxQECgIFgYJAQaAgUBDY5REoYswuHwIFgIJAQaAgsHMh0Gwx5uOtU2LTpk3pyUZqqhA/pAlJPyKacIx0VpywL8fJLbfcklKCFO+1r1owP/zhD5NQc88996T0pFpBBuq+46bhYrn11luT0+YXv/hF/Nd//VdDnSsEF04erh2CzE033ZRepVX99re/TTVtjL+4ZXauc6H0piBQECgIFAQKAgWB9y8CRYx5/85tGVlBoCBQEHhPItBsMeasIWNizJgxMXPmzDjrrLPijjvuSA6WF198MT0m2uOoiSqdeSIR4YWQQ9TQjsK53Ca+J6hwoBBZPGnJY7DrtWlb33PZSHu67bbbUoqTosB+a9RCaHEcT2SCsaK/3DKEGY/K5syRxkS4KUtBoCBQECgIFAQKAgWBgkBzEShiTHPxLa0XBAoCBYGCQBcRaLYYc+6w8TFhwoQYNWpUTJkyJdatWxebN2+O6667LgkhBIpPfepTqe6Lpx55FPUbb7yRnCX/9m//FuqtVEUS7zlspCTddddd8bOf/eztVCNPUfrVr34Vn/zkJ5PQ8tZbb6Xf6kFCBNE+UUSdF3VnuGuIJ41c9NexiEb/8i//Ej/5yU9S341bPz0mm6ikDg7nT1kKAgWBgkBBoCBQECgIFAQaj0ARYxqPaWmxIFAQKAgUBLYDgWaLMReOPihOPPHEuOCCC2LOnDmxYMGCOOmkk+Kyyy5L7hZPSfJ4aLVVPJFIEVzOEQKJR0cruOt3Is3Pf/7z4KQhaniqEiHDNsYgBYnwoXaLAr1cL37jgCHS1Fu4V9SS+bu/+7v46le/mo6rH7///e+TA6fePtvzneMRlzyN6c9//nO8/PLLcf/996exGrdx5voy9dKstufYO3pfziVuJfNBkLISztTZIVZVBbcd3dcdcXwuKil0YpfAqL6ReFd8Oqfj7Yh+lWMWBBqJgDh3/ZauWY1zaZ2uxbv6daCRWJe2CgIFgXcjUMSYd2NSvikIFAQKAgWBHYhAs8WYS8ZNj8svvzwR7+uvvz6OOOKIOP744+Pcc8+NrVu3JncIwUXakvotv/vd7xJh5xbxqGhuGSKNWjAEC6vHSRNNuEsuvPDCuPHGG+NHP/pR/PM//3P8x3/8R3K3+GefWGM/Tztqz/FCwOFM+c53vpPaJ+J4TzRBHpqxEGbUuiFG/PKXv4xnnnkm7r333iQiEag8NptIpEBxs/rQjHHlNolJ+q6gsdSx0047LVavXh3z589PKWvS1rxfs2ZNnHHGGammjnnalYQHApV4N9/EyiOPPDIWL14cs2fPjhkzZsShhx4ay5cvT+fLVVddFU8//XRIp3svxkOOi/K66yFADP/Nb36Trm/nnXdeivNFixbFrFmz3o7zww47LMW5vwmu+3/5y1+aIojveuiXERcECgJVBIoYU0WjvC8IFAQKAgWBHY5AT4gxRBjuBwKJGi1nnnlmnH322UmkUYxXmhKR5fHHH08pSkQVd0mlI3niEReJfr766qvJIfPkk08mJwv3C5J6yimnpPZ8fuqpp5LjRMqROi1qtHgC00svvVT3SUt5AogjBBtuja997WspBcr++kw0aeYdW+SaiMSRo0CxAsPZ2fPYY4/Fj3/844QBTHZ2Ig4n6V8vvPBCXHTRRUlYGN46JoaOmRzjZy6KQ5atj5krj0/rtMXrYtyMhTGodXy0jhkXCxctSgKduSPKvF8X6WjIqTS79evXx7iJk2JI67honTo7pi5cHTNXHhezVp0Q05cfHRPnLIsREw+Joa1jY8rUg+LUU09NaW5EGedUWQoCOysCUjNdT4nmhNhRY8enOB910JyYsuDwmLHi2Ji1+n/ifNj4g2Joy5iYdvAhqei76y+x2rW5LAWBgkBBoBEIFDGmESiWNgoCBYGCQEGgYQj0lBijw/6ploYhDYkT4JJLLombb745OVi4XzhS/Mb1om4Mt4p/6Kv/jHNcEE2kdCiAK92FkEPc4bYh7BAyFPFVB+aGG25Izgu/cclIA5Lq5M4r0YDg486twsCEBO1zxEiL0q59CCSs9T1R00U//v3f/z2RGMISd49xwCe7ZYhVtbg0LCC2oyHiAEHJo8MPmTEz+g4aHmNnLIjDTrsoNt32QFx6/0tx3WM/i23P/CpuePrNuOYbr8XF970YG2+6LxafcE4SIwYOa4n5CxYm3KU1NVME246hdntXops6RUSYEUSqsVOT8HLiVZ+OLV98Jq7+2qux7alfxo3P/Cquf/yNuPzB78W5n3k01m/ZFlMWrIr+w0dHy6gxsWXLluT4IhSWpSCwsyFARFdofe26dTFwyPAYOu6gmLPm5Nhw7WdjyxeffTvOXQuuf+KNuEKcf+rrsfbca2PSvBUxYPjomDB5Slx66aXJMcm9WJaCQEGgILC9CBQxZnsRLPsXBAoCBYGCQEMR6EkxJndcvRdCx9VXXx1XXHFFcq6o2aKGgHQdYopHQhMgpCsRHwgwbblCuCi+9a1vJdeNuisEH6k/r7zySqrD4slLUmGkgXjlxrn77ruTu8bxOE/0yXEIIQQapFl9E30g8OifgsBEkJ4SCAhD1aK/nBTEKmldBCJilb4iKlXBKuPck69EJC4i6UjI17Dx0+KYi29P4svtL/8x7njlz+2vL/8pCQ+rN10ZA1vGxfCWUXH++eentJyewruZeJkfc2Xuxo4bHwcOHh6HrjslPv75p+LWF/6+fWwSdn+KG7/5mzjjli/HxDlLY/++A2LJ0mXJgVQEmWbOXGm7qwgQYrhhJkyaHH0GDY956zfGhXc/F3e8/KdOxPmf44an3ozTbrw3Oen6Dx4WS5YsTTWUekIM7+pYy/YFgYLAewuBIsa8t+ar9LYgUBAoCLzvEdgRYgxQiR9qpEgjIo5YOUAIKUQGAonfpRgp5ktsYVknPBBlagk6NwtHjfokhJjqnVSODQUiOW8QfKlNHC/cM1wcUqfs51iEIMKLugWKqBKIpE/53XbPPvtsEmwQg54UQIzB+NWyMQ590V81dbh9iEhtYdPsIIYDR4z6L70P7B8zVxybXDAdCjB1BJrbX/pjbL7r8Zg8b0X0ObB/Kv6s9kxPYt1ovMQqIYYA2LtP3xg0cmycct0Xk7jSHYxufObXseL0S+KA/kNi5qxZKTWvENVGz1pprzsIEM2vvPLKGDp8REqvO/WGu+O2b/++UyJM7bnAJbZi48VxwIEDY8qUqela3JYg352+ln0KAgWBXQ+BIsbsenNeRlwQKAgUBHZqBHaUGAMUBJ7gQljwVCFumG3btqW7/cir9CFFbIksivQSbrwn5NTWy0B4pR0RdLRHfOHWyIvfOQgUQVXDRl0YAgbyoP4GAUddGd9rw7Fsl1cOHrVuNmzYkNKrbOsOcE+TA+MwdqlWXD3uQEvF4v4hGhGzPLGpJxdztWrVquh1QO9YcuLmuO7xN7pFvqpk7PIHvhtzVp8Y++yzT0pBM0/v1YWbSu2fPffcMwa3jI8tX3im2wQ1Y3Tbt/8QR190Sxw4ZGQqiKyeUlkKAjsaAeL2wIEDUy2o8+56fLuvA9ue+XUce+kdse/+vWPcuHHpiWwcg2UpCBQECgLdQaCIMd1BrexTECgIFAQKAk1DYEeKMYQMj+4lxBATuE6IIRwf0oOINQSZ/Fhkv0sZ8jsRx9N3uCYIFBbtcY/kujFqjlT/ceeuQOo9ucnxHnroobS9/Qg33AWEDMdTLNf+nvBEeFFUl5NGUVoOkMMPPzyl5BCP9D87aQhFUou0k/vV6MnTrj5zwuinIpnIvjQmQpR+KqBLsDGmZvXDuODraVkH9usXC4/ZFFc+/MPgbsmiQXdf3U2/9Csvx9QFq5Mg8+CDDyYxrdFYNrs986T+0bjx46N3/8EpLemW53+33fjAVdrSEeddH/2HjIgVK1akOkrNHk9pvyBQDwHXge9///vR0tISo6bOirPvfDhuffGt7Y5zKY7XPfHzOOqCm2KvvfeJY445Jong9fpQvisIFAQKAh0hUMSYjhAqvxcECgIFgYJAjyJAjFm5fHlce/rZ8emN58RnNp4bd2085+3V5+p31fe12+XPeZ+bNmyME1asSrb1tgZFLPC0JI+r5vJQOFc9FOlCHCqerKN+C1JLSFHXRaqONCP1U7yqDSO1iaBi9aQavyHwvq+KEd5rRy0a4oXjOn5VtMl9zYIHZwP3ByeN9ogf+nbOOeekp9tcdtllSSDi7CEEEUOkEjm+9CqiEWFJ37lztNuoxXhgqH85vctxjd9KKIKh36qpW404vuMa29ChQ2PCzIVxwZe+ud2Oj6p4o5aKNhUCnnbwwckR9V5LV/rHf/zHVKdov94HxglXfCpu+tZvt5ugVjG65uuvxZITzo0hQ4clgbER81raKAh0BQHXINc1gmC/wcPi5O1IwavGdn5P3JWyNHPFcbHf/gek1EwidFkKAgWBgkBXEShiTFcRK9sXBAoCBYGCQFMRIMasXrI07lx3dDw297B4/NDlDVvvm704zpo9Lz2uuL1BVAUZAgJxhSNF7Raih0dZZ5dLFlM4YNR0kbZEuPHUIe4URXa5ZQgQ9pWWJH2pdiHIeBIT0YQgw9FSTWuq3b76mZiiTfVkHEOaEHu+YxF5OG8UElaslVikf7mP3DhcLLYhZOgvoUQRYmKJ8XV3IVRwEkmfMh7CFoeRPsIHjlwafq9N8+rOMfV548aNsX+ffqE2BKdGJlCNetXmus1bY59e+yWxQW2g98oinqSStY4ZG5PnrkhPkOpUMeM6tXTawlO60rmfeSxap8yIKVOmpKeE1RMW3yuYlX6+9xDgJPTkt169esW89acl4aSteO3u97e+8FZcdM9zcUC/wbFs+fJ0fXvvIVV6XBAoCOxoBIoYs6NnoBy/IFAQKAgUBN6BQEpTWrQ47pm9OH78oTHx0w+0Nmx9bq9RcdHEaanuyjsOWudDroPCyUG4+NGPfhS/+93v0uOcCSaEDXVd/OOfBQupQP/0T/+URBEChyc0KZJKlCHGED4U/5VmxDlSu3C8KH5LqHBc9Wm6IlLYlkgkJchxpU4RkqQHEWvUoiGK/PznP08Wfi4Z6U6ORaSxj5UY9Oijj6ZHHrP6E6KySENYMuauumlySpb+EX0IMfoHSyKXfsJX+50Voar4ZQdSv379YvKhy+PKh3/UcCEGcXNX3COwB44cG/PmL0hPuKr2Y2d+LzYvvvji6DNoWJy27Z6m4AMjaRxrz7kmevfpk2K50Q6onRnj0rcdjwBx97jjjosD+vYPdWI693SwDp6uVkeQlN7nyUz9Bg5O17Oero2145EuPSgIFAS2F4EixmwvgmX/gkBBoCBQEGgoAv9dM2ZR3DNjUfxkt5Z4fbeRDVuf/0hLXDzhoE6JMQbljr7Cu8QKogERgQPGK4eH9J/vfve7qTZGVZwgDBAViDVPPPFEEmWyMKOmiychcazU++edk8R+BBnCCDw4dbLg0xmwWeYJKMQffdQHtXC0ndNq9Jf4k0Uawg/B6fnnn09OGfsSSwhR+qE/DzzwQCIdhBOikeK8//AP/5DSnbhStEcQ6qiv+iDVi8Dz8ssvJzHGcWCsr9om2hhHPWEmC0J5LDAhbsH4bz74wTjhik82xRWT76ITZBYcfWYMGDw0uY/q9bEz89TobeBhXqu4VI+hqC4BacSk6XH9k79omhgDny1ffCYGDGtJtYx2phSO9vCpYlXe77wIuL60dU10zXZdGjhoUEw6dHlc++hPmxrnmz/9jejVp39ceOGFyVG486JWelYQKAjsjAgUMWZnnJXSp4JAQaAgsAsjsDOJMXkaOEuIL1mQIQYQCzg6pANJAVKLo7ZILtLgO/VZOEEIDlu3bk01O66++uqUliQ9qVa84CTgEuFW4aR58803kxulLZKd+1l9RTrdIX7uueeSIEOU8XhuaTXtuW1yn42Rk4ZIQzhi++eYIcxIM1LfRpv6qCYMEUVtHfMHG0WDHYtIwkmDPNXrv34Scuzn6UsEFalhxB/CEMFGO9pAtPSPGERYIOjkNv/yl7/ESSedFB/da5+48qs/aPy3juoAACAASURBVEjR3iy+1L5K7Tn95vuiV98B6SlXavfsDItYI47BQlxlbHLfzN+osRNiwVFnNI2gZqyu+OoPYsq8FdG3b98k1tXGeO5TT786T+HT6FpJPT2OXfl4xE+Ci5pXBOBqGpzrhGvRhz6ye6z/+A2x7dlfNzXWuWMGtY6PRYsXp5TLXXleytgLAgWBriNQxJiuY1b2KAgUBAoCBYEmIrAzijGGq6iu1CSCClGDywShU2vlk5/8ZBIRpP/4rkoOMlS+46qRQqSmi6dwnHLKKemJTdwp2stig30QDsSa+OFpTerBIB61BDu339YrEURqEpeLJzZ5OhS3D+Le1bb0jwBSFWkQH4KMQsEKHjuGVzhx0hBxpDoRdZAn4gyhwHiJTsaZx60/BCS1cwgHcNUu3LVhf8KH77iLfJfFLKLNnEPnxpDRk1ItlCwKNOv16q/9KPoOHpEEIHjuDIu5kXqmcLP6PLDMcQVbj0fvP6w1TrzqM00lqDC/7ok3YvkpF8QHP/jBFLvtCYA9iZ2UQaKq81BtJOeB+CvLewcBgovrA1FY8XRxnufRtfS6666LD+++R2z5/FOhtkuzrgG53YOXHBETJk9N4vF7B8XS04JAQWBnQKCIMTvDLJQ+FAQKAgWBgsDbCOysYowOEkeQAM4NKT1ZROBi4ejwGGePu/7Xf/3XdoUOTg8ukGOPPTY9lpo4gyS6Y191kGifwwR5vOWWW9Ixs/jwNmCdeMOVgJRzlBBKrNKDqs6STjTT5ia5feTWk6OIJMaXhaQbb7wxpGdZHZvLiEjDcfPb3/42jZEwwyVjzIQD5J3oo8+w1UZu57TTTgtPjIK3Wjz2FTeto8fEQQvXNP1uOBJ224u/T3Vjli5dmu6IE5X0f0eu4krRZmT0+uuvT4IVdxGySgSTSjFg5Ng4+85Hmk5QORKkixFj1CVy/B2JTT62AsY333xzwoirzXnnHMvncptBvov/4JzcGWLcPEp7I9C6noh14qw6XOKc88n1Yfc994rrHn8jGl2gOgsw1dflp14Qw1pGJ6F7Fw+TMvyCQEGgiwgUMaaLgJXNCwIFgYJAQaC5COzMYgxC4p999VS4NggBhAhEjmMDESY2SLHhBPF9vUU7RBWCzpYtW+Kss85K5Jm4wLlCoHD316INogkRiOskiyj12m3vO/1EpqQQPfLII8lto5/Ek7b62V57tb9p37i0hTA5FmGJm0fhWMeRxiRdSw0e+CHFVkKTsXHv+J1IAz9OIiSe24j7BL7nnntuLFu2LNUiOf/881OaGKeSOjMDhwyN2YdvaGq9mCoJGz7h4BjZ0hIbNmxIxJBoRoDaUSs3EhcVgpoFGQIW0UFK3ZFHHhmDR0+K8z/7ZNPFGI/MPv3Gv40PfOAD6RHD5te8i+MduTo/iZ9VfPRNbBFDxW1Z3o2A8+/aa69N5+mOiu98XOKsawbBsTqPHGF+W758eey9735x4zf/rulx7nqw5pxro/+Q4Smu341c+aYgUBAoCLSNQBFj2sam/FIQKAgUBAoCOwCBnVmMAUcWNJBbggLhgIuDCEFgkU4kbYd7RqoIIYFQUbv4zh1eaU5IDiLvsdis9zk1R6FgtVuIHF7VptEu9w07fncWQgmBw2O49ROBQUQ7cvN051j2MU7YwEgqAXHGuI0H+SXSKILMSSOtCZmCK9Ksb2rmEI2M3Xr00UfHokWL0rpixYr0KGsCBKJGjJmz+sQeE2OGjZ8WH9t33xgyZEjMnz8/LrnkkoSp2NgRK7FD7GQxJr+KGfieeOKJSYzxhJmqqNSM98SYjdvuid122y3VjZk1a1YSHs0nV8OOWqtiTCbyUt44tbJDprux/n7dzznLzTZhwoQU51deeWX6TJTeEXFOzCNcZzHGPBLY1LASV2vWrIk99+nVY2LM2nOvjYHDRqbr1/s1Bsq4CgIFgeYgUMSY5uBaWi0IFAQKAgWBbiKws4sxhpVTaIgnyB2HDCcL4UE6kLvIHg1NTOBC4aapd8edSKF4LaKsHXUsOEKIE8gP4opgS9XhEuEwkWZBsFGrRQ2V7iz6qb/SrggxyDpXCncJ4uX3nliITNk9w/1DECLQKJjsiVLqQWSRhuuD2DFjxoyYPXt2LF68ODku1q9fH+ecc05cdNFFMaKlNSbPW9kjaUoelyvlZ+TIkak/xIZTTz01zQ0cpUwQzHpyhRvBKpNTRZaJfeZZfy644IIYMGJMbLrjoaaLMdue+XUce+kdyRlz0EEHxdSpU1N9HUKb4sviWZ96eoWP2jmIvHPOeVp9pHpPxX5PnF+NOoZrF5HZuTZt2rTk5COa/upXv0rXpJ6McceSvkhIJsBYXRs4Bl1nFfY9/fTT4yN77BlbH/tZUwt5ZxFz6YbzY3jrmJSO1yjMSzsFgYLAroFAEWN2jXkuoywIFAQKAu8ZBN4LYgwwsyDjzjDSS5DhgrEgL0gmMYGgQlhRlJYjhABRXXx2R94dXe4YT05S+0M6EdKq/SzKEGt899hjjyXSrxYHYtLdhUuGQwXR0kd3lrlukBp1WHbUghDDlzAk/UstGoTZeOfMmRMzZ84MtVqOO+64JMRcc801SYA4aNrBMahlXFz/1C+bLjZc+dAPUgFfqT+O7/Woo44KT8kioHE11ZvvZmJK0BIjxDoijKd46QPRzyq1Y+CIUXH85Z9oOj5bH389kFQ1Ywgfa9euTfio9YNISy0jRPb0osAxIYGYSUxwrjkPytI+AnAi2EoBct6JedcsRcvFWE+KWIRkrhz9IcIQv117XUv1U2reh3f/aJz/2SeCaJpFk2a9Tl24JiZOmZrOvfZRLL8WBAoCBYF3IlDEmHfiUT4VBAoCBYGCwA5GoNlizIXjpsQVV1yRUnWk63RnRXqt/vHXX2KJuhyIAeHAbzkNx3fcALkuBYJsP0TUdl6txBrbuMv7i1/8IjkqiDTSeBQZ9T0yzW2DzNo2pzdx02jTMTu7EjnyymGTBRmuAcdS34Uoo928ndf8ufY1/+b7zqzucFe3q36ud6edK0hKmDvzkydPToKMNKWzzz47iUjINZLo0daXfeWVuO3bf2gaCVMU9LRt98Z+Bw5Mc8LJZC6OP/74uPjii1MscBspnkss6ylhiyvLk7MIV1Lm8kKI4UASNy2jx8a89Rub7hjwaOuJc5ZF//79E2H3SPcTTjghPv7xj6c6QYQ/cZ1dYz1F5p1PzlnxWhVhvM9Pnsq4ldf/QYDALLa4qzwJjqgmPZCwJR2To42A2hOL4xCBiDCEmSxwZ6chN+JHPrpHrNu8NTi0miXCaHfbM79KAvCSpUvTdbsnxl+OURAoCLx/EChizPtnLstICgIFgYLA+wKBZosxm1vGpbv06pAolovEc4O4Y/7cc8+lV++rK9JY+9l3VvsiJOedd15s3rw5pT5wrkgn8ptaFIinQr1nnHFGqLfg2LaRIpRXhWuJLWeeeWYiOuoi+M7+XjlXPD3EMdjwTzrppFQ/hRtDmg6irU3jsb3V++oq5Sd/9r662tedbqKCfm7atCnd/Xb3GbmRztHRmuu6VF+5Wdpa4Z9X2+T31VfHtqonIyVh+PDhMXTo0Bg1alRIf+GQ0VfEEDYf/NCH4thLbm9qqhKhh6AxYPDQNMeIPbzFADFG/Rrzp37Lgw8+mMh/T7hkEGYijP4Q8qQCeSS71A1im37NmDkz1LrZ+tjrTSOp8Dn/c0/GwGEtsW7duiQ6Ohc8zQmZhwv3kPNCfHGoEEWR6WYv8CFYEvwIfBxoSL3rTn40fU8JQ80ea6PbF8NSM6XjEbRdc1zLzCMHHxEwuwMbfexqe8SXLIZX41zhc440fRw4aFBMnL0krvnGa02N87PvfDj27d0vnfeE+LIUBAoCBYGuIFDEmK6gVbYtCBQECgIFgaYj0GwxZtPQUYnIjxkzJpYsWZLcDJ7Kw3ZP7FDMUy0JzgaOl86uHCUeqequsSf+aMe+iIpVm4i6IqocHIQD+1SP471t/I7weNJQtQ3tILLSPhzDdp4sNH369DQWgg9BRzv5uFJW8nuvHX22jTYIC5wejpHdDNKxchva6e7KeZP3rX2fPxs3FxDHESyNmVA1aNCg5LY48MADo0+fPtG7d+844IADUhHdlStXpmKxSBhnRjMea3v7S3+Ma7/x0+g7aHgsXLQokXjknWtAUVHzYl65eKSpGQ/MpA8RAJrpHuDsILoQr4gc4kAMwc6r1A6x3nfQsDjlui82jaRe98TPY83ZV6W5IfBxBnEImVO1ffSFKChViZDp3CCEItYcPM0SQ7RL+CGA5mLRji2tBUZERKJQWeojQOx76623Ukqga4J45wrj/nM+m1/1rTj9OI2aNY/OIeeXOCcIuU64ZplDjkHfe8LZx3rtH5vverxpqUqKVM9Zc1L06TcgCfNitywFgYJAQaArCBQxpitolW0LAgWBgkBBoOkINFuM4YxRbFURWEVFpbpwVnCsSCfyj7xHTnMUqPuCQLuT7k5sNaXJXeL8Ob9HyDlYiAcIn3395m6x1fZqwnAoIIC2VXRV2353194xuWq0gRw6ttopVpb8vGrXvo5z6aWXBiGCEIT8utOvTe0h6J5i1NVVX6VLIcvIMycDTPQTuUY8rIhRWyvyVrsiadXV7/bXlj4an7HmFC1Ej2sG0UMA+/XrF/vss0/ssccesfvuH42PfGT39Dp27NgkfBBsPrbvfnHqDXcHstToFAVpD4efdWXsuc/HkthhfiwIKPcHZ5GUHE4deOWiukgiDLkHjLcZRBWWUtaQ5Pwkpfwq1qQEET2Gj2yJsYfMbwo+xKpzPv2NGD5+anr6Dlw4GbheOMm4mwifhCJOFEWbxbk+c1lIIxIHOfWk0RccLiECWcbFKxKPzHPJiM2ytI2Ac5/rS4zDzrWMI0Wcc8e4bplHRaNt24w4d/6IHceqzqPYEuccKmomfexjH4s5azbEVY/8qOHXgdu+/fu4+N4XYp/9esdhh61IIlXbqJVfCgIFgYJAfQSKGFMfl/JtQaAgUBAoCOwgBJotxlw84aCUKiGt5OSTT05ijJQfrhL/2BNJ/FNPgEAqiAHuBhMIOiKIiAciIM0G+X7ppZfelXphG2kk6iwgE+7qIqAIhiX/TmThCvn+97/f5kzYFsklkCC6XDerVq1K7gPjQ763J/VD+0QdGCCrCLNaMo7XqIWAoHYMksxJQgyDCXeJeeBWImzAIj0NaMCAJL58+MMfeVuI6dWrV3IGEbqIHeqUjJw4PbZ88dmGkjAE7IIvfTM+tn+fOOigaUn0gpFFbCCk5p17Sfxwp6jHQ2CSPsFZBENzJZ7yvo3CUjvmi5hXJalimmNGjEldgqNH/x55wY1xy/O/ayhG13z9tVhw1JkxcODANI/VsRGnYCLuOVIIbFKFxMAbb7yRPmd8CJnNWIgtzj3xlTGCDyGNCJQXc9OM+cntv1df4edcdU46T82l6x3xVExzO5lD3zuficbNWPSD8Gzu8jw671577bU0b0To1atXx/59DoyTt36+4XF+7aM/jakLVsdH99gznW/O8bIUBAoCBYGuIlDEmK4iVrYvCBQECgIFgaYi0BNijLu6BAX/uHt/yimnpLQcrgZOECkUarUQY5BrK+Lhzi97POcJklGPrCEJBBl3+wkIBJkstGTgEHfEhYvBE5QQGwSdSOM3Aoo21D7g1pFaod22Fv1AJIlGRIA8Hu0SNxynu3ep9QexcafbHXFjQoKIHtrs7KKPtidOuJNO1OF4kd6AmMNYf+GP3EnrkfKQH+XNvSCFy6Ot9957n7eFmL333jvVkFHnhhABa8JR/4GDYvbhG1Ix30a4Yzg+Lv3yyzHm4Lnp+GLEHFYXxyeCIYjix3i8EhY4fwgh6vQgqxwFnFS+b9Ti+MQ7x4VXJqnSk5Blcwl/zpADeveOXn36x+ZPP9oworrt2f92DfUbNDTWrFmTRLbq2My/eUWa9ck5xS2U3Vb6Bi+/51oy7cV9te3OvBcb3F5iDj55dTyCIHycR56MZp7MV1neiYDzGI7mjbuJE4XI6DzlxDPHivm6VjqvpYO5LtVeA9/Zauc/mSOuPXGUU5PEOeE2p5nZxrWUA6tPn74xuHVCnHnbA3H7S40p6u2R2avOvDz22mff5Kp0btX7W9D5UZUtCwIFgV0VgSLG7KozX8ZdECgIFAR2UgR6QoxBwrKAgRAjFVdddVVcfvnliUgTQAgCnBZSFzzpCDkjziAeVrUKFLT1m20QyuxCcaffHX+/Z0Gm9s6p4yPittM2Ykrk8LQlhEYb2nUcx/Ped+0tfueG0U9pV1Yih/FpmzsDSekqwdVX+9hXChcBSR0UTwuqR0TggJgZm7voBAJYINiIsLFyRfhMLJJS8Mtf/jLVFVFbBGFWUNgx8pOokC3zoxbEiBEjkiCy1157pboknrDkTnzGX6qXJ/cMHj4y5h5xWlz6lVe2y/1BiLnw7udi8twV0bffgCTgSU9C+qoL/Lk/9AUxtBqn8cMDhvbjUvG9NdeSqW2r2m5H7xHdP/zhDwlLbRLNxKV+WIlfxD0OJzGhDpBHcffvPyCGjpkc53zikbjxm7/ZLoxuePrNWHfe1hgwYlQsWrw4OV1qxySO9NO8iyFjJ0xxpRC24ENQev31198+1/wm7vLcdoRFvd8dV5xymzmXCAnaJfw5PtGPuCru4MUpJ+7EUVnqI+DaCEviY04jFOfm0FxJTyNqmWfiH4zN4/YshDLxk0Vh1xQ1a/L1ROoSQc/1RLHoww8/PNW9GjRkaLRMnhmbbnswbnvx99sV5woCrz33mug3eFjMnTs39aej6/L2jLnsWxAoCLy/EShizPt7fsvoCgIFgYLAew6BnhJjMjD+keb64EJxp5WzxD/3hACkjdUewUbMCAVcJkgHop3FBS4HooJ6HAiB7RAPd9YRBsQOGXFHFzGsLgiru/KvvvpqIi7aRGKQdqIMIu3OvafyIJTITnuL8Tg+gmQshBhij3aNSTtcKfpWTctor83qb0gzQsQZgmQhRgQaKTr66r275PBw19w2CBmBCMYIODGCs4bApGZIxkqqinbhiSjrs/0QZq4lNWM4YObNm5fcMAr4erKSdAT7VRepXwoQDx81Ng5ZemRs/sxjcesLb3WZiHl07em33B8TZi+NYSNbU4qPO//1xAFzS5QTG+YMuYe38YgLoolt4M5dRXAiTsEFdrWCXXU8bb0XJ+KRaOc44hUhJf4Qy2CPMItpT+E67LDD0tPE9M/2I0aOjBETpsXRF90aUoy64yK67IHvxpITN8fAkaNj0eIl6TxoK7b0i1tMXIrznI5HPMrj9yqe/GY7GHKH+b72/GkLF9/b1j7mQZwSqogwznfnHGec9uHkXFfcWCwtXrw4CQ3msiz1EYANTJ2f4i+73Fwf82Kuid3OB9cB2/jcVfHCNdJ1QmyLc225zviOeMdNJUacT661ip8rzr5gwYLkwJLqOGHylGiZPCOOu+zOuO6JN7oc51IUL7r3hVh4zKYY0jo25s+fn1ySHV2PMxbltSBQECgI1EOgiDH1UCnfFQQKAgWBgsAOQ6CnxRgD9c8+F4fHV7vbi6BJo0A2vGe55yrxzz+BRNqO1BOiBBJJhLE9ooD8cqEgksgD4on4Sh3hTHDHuB6h1CYig5hrg6DB5YLQII1IBlLtuLWOg9rJQhA4IfQBCUeCCCD6g7QQOqQPEIiMoTN1HbKIoP9EFARM2wSSs88+OzkuuAqIC3AwDsTXdsgw8YGghcQhY/rouL7PT0ZBsuxLPMoEyzHU9/AoXU944lpQF4fQ4r3CxR7tzVlTXWDkO083GjN+YrROnR3rNm+NC+95LhWu7ehJS9uefjM9nnnFxotj5MRDYvzEyWmMRIL2FmPjboIF4YmDQDxYjTXPHTzFE3yy6EQkg1Hepq3j2Jewoz0xAXPiF8EiCwjEIoKbWFIjxiOmiQwIqsehiw3bwnv69BkxbMzE5CIiPBFluIHaE2bUmrn8oe/Fhqs/GwctWhdDRrTG2rXrkrAhZttajE3MEYLEohgn2vlcTdsSH4QmpNtvxiFeCU2dSXmxjXPa+QcfooF6JtqEDQy1RYSRznL00Ue/TeDh5PtCtNuaxf/+noPJOZvjHMZWuOcYhiGcs5BiLgnPnXU7EdPEBaElpzBy2xH7zKHFZ/Gh7pf0uIULF6Y4F+sEXjFGiJ4xc1YMaR0XC489K86646vp8e4dxfmtL/x9ctYde+mdMXneihg5enw6f4ynniDbPmLl14JAQaAg8E4EihjzTjzKp4JAQaAGAf/wuGuonkVeEdbaFRn1e/X7/Nlr9ffq97Xb+y3/Xv3N+2ob+be8rdfq79Xv87ZefZ/X6vf5fbWN/F21rerv+fuXX3qpPEmhJm625+OOEGNyfxE1hNg/9v7xR+TEAYKB0HG56B8iUSVqhBQkmrsBYbQdgmkf7UgVQoil2SAVWWSptqEP/rknoogt+yHwBBrHcx4SJgg67gZ3tCBDhBukmxhE3EHOuWYQXAITIkUIYOtHkI0/CyXG5DiIFGcQp0l2MhBykHjCFaHE44o9xYhYoj0ECCHjoqklLMQsIgQChcRlbAlfzi/ilv4Zu37DTg0W2J111llJhOGOkUZG5OGIITZxDdUuyBrxhBA2e86cJBhMmL0k1p13XXK7fPwLT8elX3k5iQqEhUvufykJMBtvui9WnnFZjD5oTowcNSaJGGICFh0tjmmMWfQimCCtSCjBxJxUF9dYceM388FFYJ8q2axuL2YIYjDWpjiBm7ipOg70A5ElKhx77LFvCzEIqs/SOyz6KsY9iWvUmHHRMuGgWHDUGbHh2s/FuZ95NC7+2xeD8wU+lz3wnZSuhcgee9mdMWPF0TFwxKiYOGlyEuS4f7TX0UKEM/fmF9EWC8gyAU985pgxBqKKbcw3Zwt8fCb4+L128R2RKWNKTM2Y1tbngaPzQupblcB7XLzjlaV9BFwvzJlzl9PN/LsmuMa5dmRBRiuuBa4huVYQsdV1xXWm3jzal3vJ9cA1Qqy7HrvO1m7v3HfdEcPiO6/cMerViCd9JVyuXbs2WkaNieHjD0purpO2fiHO/cxjcfF9L8blD34vxbm0xgvu/lZsuuOhOOqiW+LgJetiaOvYmHrQtFS3iuhZ24f2kSq/FgQKAgWB+ggUMaY+LuXbgkBB4K8I+Cdn3NixMX9oSywe0ph1yV/b8bpw5JiYPWVqLGgdG4uHjoolQ1vTunhoa1g7+9l21e2X/vXzwpFjY+6ESTFv7IRYPLwlrUuGt4S1q5/zPnn/pcNbYt6I1lgyY2ZyT5SgaQwCO1KMMQIE1j/byDcixxGDaBBIkGWkkQCASCB9VcJhf/+kI9LOHWPJYo4UqNNOOy0RP0WDEWhEIVvtkRJE2/7IKieLPiA33BNSKxAZhNSdZf3saMlt6QNBAkkiKCCliCgxg+jhN04OAg2Sb/yEF4QJ0UL4EWd4wMA+XC/6SOwgLnG3EJ8IRo4nlScLCsaFPBsrwuY4xmHNriNuG2KRYzqO4xESvEpRkkLiEeRSEAhd2SGEaBlPe24JRJAgwiUzceLEGDx4cAxrHRutU2YEcebgJWvjoEVrY/ysxdE66ZAYPKwlhg4bFmrRXH311Wke22u/dh7EBBFNAVN30MWCORQ7yL+5ri7myXiQTdvAEbbmKAt2Oa7gqg1tE8TEWL1YsL3jIKCbN29OTw1btGhRIqrq6SC51cUcmmfbtLS0xsAhw2Lk+KnpEdhTF66OaUvWxaS5h8WYaYfG8NETY/DQYeFx4pwI+qOvjtmZBT6OZ/4JaeJETJt3bSHOtW0h94RO2CDm8LRdxsdxiVEIPKHJuSNWiavO09r2bO88I0QqDM0NYyXKcF7BrSztIwBTrj04i13zQWRzrYO7866Ku7kyP+bYdUL8uh6Y2zyPtifoiXOitutfFqXFSb3FuWNbDjkCTJ5HNWMIm1nccwzXUcc+9NBDY/jw4TFk+MgkQI6dPj/EOeFl0tzlSYgdPnp8DBo8JMaNG5dqUBHExXlZCgIFgYJAoxAoYkyjkCztFATepwggbqNGtsTjuw2L7+w2LL7b4PXZfabEnSefHk+2HBrf221M/GC30Q1dn+t1cHz+8OPiqzOXxA92G97w9Rt7jYwt0+ckAvU+DYEeH9aOFmMMGKkjDrgjS6TwKFz/8PtHnghBIPAPPVKBaCMJtaJMFThiAecCgqJQsJQIwoyitJmQcoM4JoKIRCPYjseFgtxwThAU3AFGSJGYqhOierza98gNkmQs2iOgOA5iwUHgO+lGUn+ks3BOcLkodEwcIJAQpexXFQiqxzF+1wukiFCgn/bJuBGftEX4QbAQG4IWkQaJI/4g45wwXAlwho0npSg6qyCnmiewd5xMsKp96Og9vIyBmMNdM2vWrFQMeNCgQUmgQc6QNOIFvIlDncW49tjEGyQS4XRMOIgX/YdLJp/V/cwTgQ525gqW4kY8EBSIZ9pTFJpYALf2cEBsxRDxz5yKOzVRCFq1aV25H8iyeCdCrVixIkaPHh3wsarPM2XKlNSOPhB0uouP+DPHYlvci3efxQABqx7xhhl8uI6k7RHyOGngZnvxRLAUf35DxNs7L/3GaWF7opLCxoQYaVwExbJ0jACB1flKOHVemlfz4zwmSNa6kbRoHrlkCGaw597L4q39CcLiwG+uW86dqqhT2yvtSQcU50ccccTb82g+nXu154h5dx0Tb9IfOaHEOZHWI9lznNtfP5wr9QTP2n6UzwWBgkBBoKsIFDGmq4iV7QsCuxgCWYx5drfh8ePdRsRPGry+tPe0uOv0s+LFUfPjp7uNj9d3G9vQ9ZX9ZsS9R2yIx+YcFq/vNrLh6zN7tcZF0w8tYkwDz4u3xZiZi+O13VrjZ7u1NGx9fo/WuHjCtCSCdNRl//wjBki5VByviDGSjTxymfgemUQYOlsDAXmRNoEEKCyJPBIovCfO+Odf2wi7XLhQbwAAIABJREFUVB99kJqkGC8iz0lAzOAOUJukrUX/84qMIPNIEqGBy0Tqj74TXBzXWLTvOIQPqUcIb3YgtEeGqn1AzglF9nUs9WQuvfTSdDyEzW/umCNEiBoxiOilP8YkZYVwg8wRJPRRipJ2fEaw6wkZ1T505b35hI21ke3qA/GEaMepQSwwn8ZqbESEeseDM4GKWGXscISH+DN+LgTOpfZEBsfWDpwJO4S2Sy65JM2/OYCnNjqzOA4iqvg00aOzcdBR22ISATcmTjN9JS7ldCSEvK1jcVLA1HljJd7AyHt4EfZg2NGiHY43eEizMzfOLbFPkCpLxwiYI6Ic3LhjzKtroesY8ZUY2FasZkHEvNnWXJh/10JCLOGtM44012UOPqmgrjXacC0hKBPoasWYeqOyjRh0vRXnZSkIFAQKAj2BQBFjegLlcoyCwHsYgSLGtC/gFDGm8cFNjFm5bFlsO+nUuPfYE+O+Y05o2PqpI46Jk5csT+6UzvQc0fCPufQIJI8rgXhAROBWcNdVzQRP+yFkIIUd3cVFTNyNR0CR7VxfAWmQ6oNIIDZ+0y5SwiHhe8SVaGIloHB4OEerC4KP5KgBI4WAqwUx0RaSQ9TwWOOTTz45CQW2QZiQV4KBcXFH+B5xd0xj4+ogQrXnhPCb4xqbY3FgOJZj6j9CbxsYaEuhXsIEpw8CTazgaNBXKTMIHYcGcUg/kL56AkZ1/N15b57bIv7daS/vg9xxHSH55lffYQ1PogrSWo+o6gsSSphSXDbPl/kQO53BQNzCV6wQYmBMvDPH5sHa2SXj02iM4MPhY76zC8k5lR1FsKq36IfxGY/44WRRL8R5Q6yDXUd9FefiiQgoTYkDS+zqE4wJQ2XpHAKuQa6DnC6uHWJaaqfzl4jovK632M48mgdxztVyyimnJIGaKCLOO5pHIqrrI1cMIcZ1I7sVCd/muaM2ct9sl9f8XXktCBQECgLNRKCIMc1Et7RdEHgfIFDEmCLG9HQYJ2fMypXx+SuvjZdv/1S8csen4zudXNvb1m+PXndTnHfihlSDpCvj4gxggyfGILWeyuI7wgJSS6DhOkE+CB+EBUS7rQXJQBQIJcgku7y79NpEQgg67gq708w5464xMQLhyHVTiClqJBBrYIbQsPsj/r5D+DPpJwj4zTYIrP4hrtIAjAUhqi6ZJCGkxsaNoy3jI6r4DglCXGCgv9omFnCCOD4S5jupVUQjd7/1g+VfnQ445rvhhBlFWW0HD/sSKwhOUpQIU9JGCAnvpQWOar8YC3cTEc8YuIOMCYk077WLu/Q5/YNIQIzi3jBf5o5g0N6CgEp7y4IeHAmFeT/zpm87enEemHux7RwQ+4QUMQIz8VIPH/0Xm84Poh+nl5WrSyogMbK9xXGlyYg/+8CHCEiQzNjsDPi0N4ad6Tdxzcnk3Oeks/jOXLgWcHnVm0cYm3/Yi3NPQxLnUuCcN84V89HWYh45mByX6Oga47PvLdpvb/+22i3fFwQKAgWBnkKgiDE9hXQ5TkHgPYpAEWOKGNPToUtYWLdqdTx18+3xL/c/Ev/3l7/esPW3d90d12/clFJzujouBJeQ4Q4wIUFaRBZcEEjkGSEhyiB5XqUaISH1CAHCYB8klABBbMjb2p7IgRwSOjgEkGtiBbLCBTBnzpyYNGlSTJ8+PdUDkfaEjEhLIbwQWdydRnbc6ScaGQOi75XrgrXfWIgmjle76AdS5W43cSg/5ck+hBcOGClXWQDyiiBzGMCGyGN1Z1wqV37qkkK67qITuKQYSOfRb6QKrsbJLYEkI8uOkbGu7WN3PxO+uDGQcoKRuVPjQuoMLOrNWXeORRggYHF7SE3SLlEEHgQuhJWwZfGbOdcXGMAD5ubRd7mAM8eLArj1BAPzRdgithEGEVvCBmdOve3rjSnPOyzMkVgiAokTcSNuO5P6Ua/t6neOIz6Ifc4Zca5dGIh343d+GJNtLbYXizC1j/OROCjtKsczt1l2yFSP5712HMe23G6cFGINZvXOgdr9y+d3IwBT5zjxTKy51vjOK+eT78SPubP4zTkgnlwzzIHz3hwQKp0rhF/75tTQ2qNqw7a2s79YcU0Ss11Z9Ml1QPyIc3Hnuu2cyy6frrRXti0IFAQKAl1BoIgxXUGrbFsQ2AURKGJMEWN6OuyJMUesXh3fuvXO+H8feDT+90OPN2z98+fui1vOPLtbYgwciC7EDY4WRJAY4R/5TEzdyUUGpBshe7bhfkBoEZO8XcbUZ2KL9pAKBJ3F3rZeiRpIOFeFdtxlJlAQZDgBiDIzZsyIMWPGxMqVK1NKDwKO2MARac4EKB8zv+orUovgI+5ICIECyam3IPL6JGULefWYaY8EVoiYEIRQI1PcDdVxOo7UE4KKNAJ1HNQwgY26H+5kcxzpt8/aISAQljg79C+LGPX61dnv9IkwYpzGqx8K2ireOX/+/LQuX748CVucKPqE7GVi2dnj1G7nuEgnwmisCD8siUscAb4XH3ATO1wEyCuSmp+opA3zCAcx4nfEE2m0XxZZCHy2QYqzkMF1In6y4FPbv+pnIghxSuwSzsSXJ9ModCzOFDdW1JcryxOuxD9hY3tEDH0nchKmOKaMRwzCR2ocNxD8jB85RuoJVTle/I3ShrEj7jD2G3yQ61qXjPONmAV3MemcgldHLowqTuX9uxEg8BI1nbtwz4KmmOZ0cz6JLfPoGkpkgz3R2jWOEG0eCW9iigjpPPU7sbZ6HpprbZhDsUCQsy0h2P7tLVkI4r4iADvXXQeqcT537tx0PZUyxbEjDp1XHbXd3nHLbwWBgkBBoB4CRYyph0r5riBQEHgbgSLGFDHm7WDooTc7sxgDAsSYyIEUExSQBaJGlcx5z7lgm5zahFxUxYpMPJAQjgxklKihTSQRwUDIEUukA7kkyBBakB04ISm+R5oRCE8IImBwrtjX9pwXSBDyIrUDGXJsC3Khn46VU2CQnvy7bZAqfeQ0IDIZL6LPzWNF9okzjidFwXaIPww4ctSecacb8XKnG2HjArIfUUZqgmNrG0FD5hCr3KY71MhXd5dM7Ak90rwILyNGjY3BreNj9LRDY/K8lXHQ4nUxddHhMeHQ5dE6dXYMGDE6Ro0dH8uWL0/9IAbApbsLHDg5YOSamhduFfNrhQlxxvzDCY61Iod5IVYQYWAKJzGGKMJbXHJFmR9pSTnFrSoY5mNXX+0rnhx73bojYsz4iTFwxOhomTIrJs9bEQcvWx/TVxwbhyw9Mj0KfPiEg2PwyDExacrUVBdIv7eHrIpDMehcIcwYpzkn/mUHi/kTz84F23IyEI9qF/gQ0YydaOW8E+OEVG4MYo02CElINqyIYW2JkLXtl8/1EYCf+COOwDeLK+aRm0rsiy/CIPFL7IpV1yXXpOqiLfPoOmc/54d5I7aJVaKcGNCG6wZRxflAxGlvES/m2rV25cpV0TJ6bAwaOTad864DBy87Kg457Kj0GPcJs5fGsPHTYtCIUTF12sFJfBZ/+utvQFkKAgWBgkAjEChiTCNQLG0UBN7HCBQxpogxPR3eO7sYAw9kwT/2XBZEBik3mRxWhQwkE6kmrhAcED8ExLY59YQTApn1u3owhBW1YBAG6RbIBjIjDYDIUSUC+oFgIjgcKuvXr09pPYg/spIJkD4iSUg+twXSQiBAeOyvbeRV2gYSj+wYH3Kln0irMSI/xB/HQ3AJFNrhVkCajc92RBXCTHZo2MdxOW9gwilDdNFnT7FRRFX/PNkJ8eISQtyQauSruwsxw1iR8pmzZkfv/oNjxKTpsfj4c+P0m++Pi+99IbY++pPY9vSbse2pX8bVX/txXHTPc3HK9V+KuUecmsjYgCHDY/GSpQkfZBDmXV30w/iNibMozyEsiClcThs3bkzCD9w6En4QXI4D8QErOBNospuA00rqjnoq3pvnthakGdFdu3ZdDB3RGoNHT4pZq06Ik7d+IWGx9dGfxk3f+m3c8vzv4sZnfhVXPfyjOP9zT8RRF94cU+avjAOHtMSwES2xefN5aYydceDU6wtBUpxyN4hJi7mHG8GOS0GciC1/lzKG9dqCN3yca9ok8iDh4tQcINWIPhIv3vLx6rVVvus8AtwxcDVP3C5ZZBFjxEaFvBX1dq179dVX0zWmvfPJHDt/zZd5c43SrhggOjuXzKlrpbhx/HqLY7iWuf6tWr06+g0aEkPGTolD150SJ1/3pbj43ufjusd+luL81hf+PralOP9hnHfX47H+49tiwpxl0W9oS4ybOCk5xsRSHlu945XvCgIFgYJAZxEoYkxnkSrbFQR2UQSKGFPEmJ4O/feCGJMxQfoIJe4EEyIIJ9wnSCRigJhKk0AoWPVZ4k888cQ44YQTEjHxBBEOEU4WhIPrBXlEZqRSIOXtkZXcD6QFueGM0Tbigow7fzkNuAry3WgiCAJPIEFqCEMcCMQVxBSBJ+YQiQgsiI6xZTeCPtUSYQKUsXLB2P70009PT7ghtmhTuhXyYiVOIcH6wJ0gLYn4tGbNmkS6iTlcMUhWPedDHnNHrwQLY5dONXDIsBjUOiHWn78tiAu3v/zHuOOVP7e/vvynuOzLr8TyUy6M/sNHx4iWUempRDDtzJzU9s9+RDfCkBQc8QELIpRHgCOp3ndFzIAnzIlkYgvZ1b5xE2o8JYgzighWu5gzcykORo8ZG30GDo0FR50RW774TNz27d+3j81fsbv5uf8jzrrjoZg4Z1n06n1gEq3EcHfEDX3MAiKnmbHBTIzARyqJc4PI0ln8tck95nxQCJrYyc0l5gmKzjMEX6yUZfsRMC+ElyzaEnXFgmuR64BzkajmWuga2dl5FKeEb2KxODeP2nPdci2RMufaW28eHUNqmnkfN2Fi9B44LBYff05cev9LnYpx14kbn/1NbLzpb2PCrMXRt/+gWLx4SUqdI6aWpSBQECgIbA8CRYzZHvTKvgWBXQAB/wyPGtkSz+42PH6824j4SYPXl/aeFnedfla8OGp+/HS38fH6bmMbur6y34y494gN8dicw+L13doXVrrze3m0deNPgveKGOOffISWMCENBClA7tjliR/Z8UJ08L2nfXi96qqrErlEKBBEzgUEHJHQJjEn18rwWz0i3RbqiHd2EbhzzGWB9GfSgyhxunBCcM0QPfRJkVzuAUSeKKB2ynHHHZe+V8ejIwIMB+QK8UXE1IZBmoyP2AQTqT6EA6SIYGNbrhrtwyTfNT/88MMT6Ua0tNudxXiJS5xC+/XuG9MWr0sumA4FmDoCze0v/THO+dTXY/ysxXFA7z4JHyJRxrSz/ZMmY8zEMIIFMcB7BJNrgCggVohqXRm3fiCqiiITLOANU7UuiGKEPq6Aan+955bx2377759SNU694e7kDOgORjd/67ex6ozLY/9+g+OQ6dOTkFiPGHeEVRZOOBg4ffTPeHwmDIodY23LAVGvff0QC4TK1atXp3h3XhEY1Q3SJmdYWRqDAIGCMOJ6Z664k5zvhDTXNfPo2kTEdk50dsntqlfkvDZ/xGRxLt2R2Ox6UruIFemfgwYPiZGTpidRxTndnTi/5uuvxYqNl8T+ffrFxIkTU4x2J85r+1g+FwQKArsuAkWM2XXnvoy8INApBIoY076AU8SYToVRlzZ6L4gxyCzxgUiBQCLXiCMniAKnRx55ZBIkiBscCkQR9WK4ZNzxR4TtRwDhREFU2O8JL9omyCDoithyyCAiVTLdFqDIDRGICIJoKvbrLnQmKdrIAhJXjzvG0pIQmmOPPTYVs1XIUl0VggyRhIjSnlvDMTl/kCzCDpcQG78xIrnSB4gORB5kmFikeKa75vrqN0IEjDggiEAcQwi4/bsiTGRcHNscHND3wFh4zFlx7Td+Et0lYEibfd1Jn7ni2OjVq1cif91xf0hzMt48Rk4ZQh4M1aJQj0cscLZ0dhFTxC6iDmcNcUFh5XXr1qWUCnOLFFdFPWKS1Lm99947ho2dktww0jO6Q1DzPvY/5pLbUr0dBX8R8q4u4oxIKHWNoCeWuCoQXu4IMUawcu50Ni7ME3eNcwFOUmSIVOKDU4MQ6ffOttfVMe1q27vGmEcCCBfM1Vdfna6B4tw1h8gqxgk0zodal11beKlJZP4JyNx0RLqjjjoqrYpN++waUjuPth80aFBMnL00zv/sk3Hbt//Q7TjnqLvh6Tfj2EvviP37HBjjx49PTykrgkxbs1a+LwgUBDpCoIgxHSFUfi8I7OIIFDGmiDE9fQrsbGIMcoHcI8tEBDU+csoPYkcwcZcWiZRmQYBRKBbJ45BBTAgwiEgmCtpEwBFpNTGk5iDpyDQhA/GUykLkyYJMZ90Y+urOP/KpH/YnFOl7ttVry51pQhHyTkThaHHnmnhCJECGkR3EXlvGJnVIvRf7G4/rA0HF+K2EHUKSseUxSsWBCaHFmlOz9M2qXUV9ETRtcO7oP9JtP24aY+rsAmPt9u8/IOatPy0uf+h720XA3hYbXnwrLrnvxZg897AkyEjlgkNnF/MpdjiGpGSZc4JUJnLwJNgZM5EOjjle2joGnIlWcCO4iCevUtXUHpK+QYyRpkMwMyeOJ+bGjR8ffQYMSQRVPZg8zu153fbMr2P9+TekoqeHHXZYio+2+l77PXGTG4YAKO7UDxJfxmiBBULuHHGuiauOFgKU+EHIuSjMgTpEhEr4XHbZZek4zj345LnoqN3ye30EzJEYhDWhi+DlPCH6ZmzNpzQ01x3X0VrXVr2WnQscNuJYfHP4udZwyHDKEJ/Fi5Q7oo/F8WzT2toaY6YdGmd/4pFU92h74tu+BJnrnngjjrrgpthn315pjASmshQECgIFge4gUMSY7qBW9ikI7EIIFDGmiDE9He47UoxBFJCJLLxI58l1LAgaBAtk2R12NV6kBblzjwAgFfZFsNWkQAxsr8hkdrzUYom8IKH+mSd0IOEIBwKjDeST2IFMIt3a74ig+1177h5LB0Betcs5QWRR5NUdZsfhPCDIKJ7KPUNc0FdiEAxsS5ThJCBwuJttP8IJMuU7Qo2xEmm4EHL/YElcgJXtjQPhVeAzCz3a5QwiBhGNbG8/x9cnZM2+xqL9TOjgaBtCV/U7+3IYjRw5MsYeMi8+/vmn4tYX32qI0ICIES3cXe8zYGjMnDUrkco83tq5zZ8JYMYFe/EgjswnLJFKAklepFTov7kSX/Cs/p6382rcYianOSGs4oMTipihKDAhxgpfqUswMwdSdvbrfWCceOWnu52a1Baplcqx+MTNMXjosDTOap/rvYcfhwR3CnzMN3yIcdKLqov5JaaoFyImkPy2loy7eHUOEGIIXoQ+cWfNYpUYdJ53x+3U1vF3te/FLiHVtYVI7Zog9pzDVbFCPJsboopzgANQ3La1uD66hjpvXI+c865Trl1HHHFEcn/leRQ32svXEC7F/kNGxCnXfTG2Pfvrhl0HOOWu/tqrMXv1CbHf/gckZ59zqywFgYJAQaCrCBQxpquIle0LArsYAkWMKWJMT4d8T4gxRATkgXCCnHKNcLEgakiz+i9EF6QPkSNYuMuKCLqrKz0JuUPeqmJAxgqBcOdeKgiy7E4xsabetnkfbekLAcjxOScQVOKFVwSHcwUB7UgAcByPTUaM1G5ASFn5FQ/21B7kHLExFuNAXmoXpIkjxfE5B2BGWEGSOVy4L9yVvuSSS1L7XDxwJEy5bnAkcDAYCycG4YHQ47j6BF/kW0qKFKlchBNOSDM3kbY4dcwDQYn4RXTwOzJHYLB9XhzDHXkpBCdf+/ng1GhLNOju90jd6k1Xxj699kuOFPi1tYgvfdZ3OHhPYMhPnOEIEit5yZgbl7QL28OsdjFfiC9HDBFBOhdMnDvSQ8wzB1IWY7wSOcwPUa91zLj0JCRPjelUMeM6tXTawg9RPfczj0XLpOkxZcqUNIf1UlHEMFINA3ElRhFpopu6OeJDTNXuax/nKnJu+3oCin3gTAiwnTYdjyjAQSFuFTfO+PiOk80j4LN7rBbz8rk+ArCGqxpIzlMiifj2nXMd/q6b1WufOCequK753fb14tycSUnTJnFSmzn2XTekhXJSVefRtuZRnEsp5I4jnLQVr939/tYX3ooL7/5WHNBvcCxbvjwJrvURKt8WBAoCBYG2EShiTNvYlF8KAgWBiESqSgHftgWZUjOm8adJs8WYa048OdXTcOcW4ePw4CAhUCDAyAGCiwgSFAgWSADSV0sM2xs9ckyUIMS4A0zQIbYQGdpaMhlHtAkvBJlMRAgX3CMIDhdLW4KMNvTVE4zUYuGCQD6RdCKI1AzjJXIQC2zf3oIkSR/hHiC8WO1P6NFHApZaJ/qpvzAlHDmOfQgL8COUINHEKXVwOIqIVUQY7dkPIecKQs4IMcbhjjOXEMcHDOyrjg1xAa7mCIFG9rQ5cODAmDhrSVz58A8bTsAQNzUnkLt+w0bFwkWLkwBSxQ+e5ljdF2TTuIwJQSQkmDeCkrHCsHYOxJjvspPGflXBxjg5ScSvWCX6IajIrViDOdcIcuwzEkuUyGl0nlzVZ9CwOG3bvU3BB0bXPfHzWHvONdGn74FJhKqNefNF0BQb5tFYzKkYMX7xLTaMT+zULgTJ/Nhq+9WelwQ6cWl/QqDfzYv4I7wQxwg52jA3xCDxC6+upMTV9mtX++z8dK0SY+LcnEl/NL/wNsdESNc+MV27iGPngNjkhHPNrC6ul+bKNuLcPLoeOJ7rkOuPGDJvji3ObetaIQ1t/74D4rzPPh7bWw+pLcFGu3OPOC0OHDAoiT/V87Q6jvK+IFAQKAi0hUARY9pCpnxfECgIJASKM6ZtIcbTl4oY0/gTpdlizJY166Nv375x8MEHx5IlS9ITaAgWUmfY3JEHhJ9DRUFIZM+dXaQY8SByuNPuzrs0Iq4EhBFB5JJAKBE6xNuKGCJ+hANEAelAYtpbEHZtECGQRW4UAgd3CjLprm+tIIOQI/n6xlHhWJwTxI5cqJRYYSxZPEFiECb71lsQfykE9pGOdPzxx6einPrk2oAccRh5r9YNMuTJJpwZMCVwIbpIE4GLIESEgKM+Em8QNSlR8FRThRMJQdd/aTh+My44S3WCIww4GxQHtZ0xIGnm7m8++MFUYLORaQm1ZIz7Y8FRp6dHZhtbJpHwEgMEAOTeakziw295sb3YIgTCrVassK1xI7iECqTUPuJC+4QG+xLJ7Iv4SoVCXJFS8WVb5FA8Or42CDzTDj4kRkyangST2nE16jN8tnzx2RgwrCU8HSsLHDmunQNZbEKs9a0ag8g8XMQ9Ucl+1cVnYp14I9CJ+SzIOOfsQ4ghqBIT4SOO4Sn2iQAwhhM8fXZe65f9y9I+AvB3veNgEnPOY5hXXWpaELPmwO8cUHmOcuvmhXijDfFgTn2nffNFTDZnroPi3JyZa9c112jf5WuQ7V2HzCORrf+AAam+07WP/rRpoiNhdvOnH41effrHBRdckK5DeWzltSBQECgIdAaBIsZ0BqWyTUFgF0agiDFFjOnp8G+2GHPx+mNixMiWmDt3bnjqi6fOeNIPco/kc8cQPhBBq3/8CQC+4+rwHUEB0Ub+kWWkEJkgUnBDcK8gJ8QExJyY4XfCCHcLVwri4W4xoQGRICYgrRwOSCISabUN0uN4BBGPgJZyRNxwftpWG4QWwoa+6aN+IfzIibvH3CfEDMdCPokIiCzxA5HPpNV8I0NEFoIAYgMD7XJcwMJ7IhXiatvsdIADkcArsSXvK61p7dq1ScixL0IMa+2ab6IBoqUthM6TeNwt97vUB4IGvPyGhHmMs+KdCrFy+xiXO+ee3rL7HnvFlQ/9YLuentSRKCG1Z+ON90avvv0Trgi9MegjoUxal9gwJ/XulmeySXRCOOu5P5BScQMDYya+iQ/YiEUxlgUMx/BZe34nwJiT2oVwNWrshFhw1BlNI6gZuyu++oOYMm9F9OnTJ41Pf8Sy8wLBtko1q8Zdtb/IPmyM37kBs+pi7Ii5MSPz2oYD0u9cIfJow4KwO5bzV/0RWOoPjMvSeQTMAYyJn1kkJrYSVGoFxdwqoc25TCQ0j7WLeeSEc10wZ2JXTDjnxbnriGuixbXLNcX5pbaS6579q/NoX9fgD31k91j/8RsaWismx3b1VR2pQa3jY+GiRalPteMrnwsCBYGCQHsIFDGmPXTKbwWBgkAieyVNqW1BpjhjGn+SNFuMuen0sxKJRw64OLg9uDg8kSMLCQgbgiHVhIsDqXanHtEjsBBTuF2QZCIL4QMBJFQgCtohuli5bQg8xBCCz9FHH52EiU2bNqVtiQ1IMnEFGeF6cDdZH4gwjgkTtROIIWoksOAr0iolxfaIjLoJhAn9URskExV3p5EZ1n19QEqJKFZtE2SQHikctkNSESKkHmE2HuSG8IP0EGiMl+CDKDkOjDgRjNd+BCLberUNEUr/iDBeCSmKb3L6mIecloRIIXX6jLgZs33gqY9wso9HZC9cuDC14Y60Y3P5HDp3XgxunZAeP1slTM14f9XDP4y+g0fESSed9HZsiAF4IYwwrBUQas8WcSRmzBcxqnYhIhDSiAgEJ3MHd2KX+csklChBkIAPHMyPeIUnLG1nFR/9h7Wmwr3NwKTapifOLDt5S3zoQx9K/XYO6R98EHkkPfe/dtw+i0Nx79whKtYj+4QBYqd6L84D2zufjF/NJItjiEOiDVHAOSTmYC7eMz71+lC++x8ExCK8uLHydU78EiI7mkcx7BwlLtbbVixri/BGJCa0mNMszuiFc0nKqHkUR4RnceQ6rV/OH20TbF0jPrz7HrFFAe8XGlfAuxrf1feHLFsfEyZPTdft/0GsvCsIFAQKAh0jUMSYjjEqWxQEdmkEijOmbSGmpCk159Rothhzy5lnJ1eGf96lGbmzK93FI4eJBgSO7JDxzz5BoN6d+bZGr10kmEjh/EE6OU+QY0SGiOMY6rio34GkcKd4RboJJhwwuShl7StXzKpVq9IIBOi/AAAgAElEQVQTg7gORowYEZ4aov+IKOGEmKPgMDEHAZJixepPGNJeFmQQUek/HAjGjMRyLkgrQNxhQ2hBxKoLJwdxxzbaRJz03Ti1aUGmCTOEFO0gbdw2RIOcXuS4yLljG7M29B8ZQ6q05c43DJFoeHlk8oQJE2LGjBkxf/78JEwR0tSoaRk1Og5auLrpd8MRsdte/H0MGDEmpk+fnsgfDIgxRId6hLOKX35vjOZdnHFr1FsICcQD4yM8OQZnU16Q1CzIwdv8mWN42lb8Ei20I3YGtoyLcz7xSNOdMdLEjr/sE/E3f/M36bj6ZH7FpPOjMwtBi4BiP+dgPVyNjfjl3FVHRLy5hmQhTBwSHaXMEW44iIiGzkHni1irJ/R0pn+7yjZwdy0Tp3Aj0hIA87neEQ7cec5757v5qrc4F8T55Zdfnq5lRJeq0GP+XdeIx+aXUGNOxbnri++0zZ2msO/ue+4V1z/5i6bHuWsB0XHoyFEJn3pjK98VBAoCBYG2EChiTFvIlO8LAgWBhEARY4oY09OnQk+JMcZFZCAQIGtIsX/ukQD/6CMG/vHPaUuIiO2QhnouhipOyAsyqH2r7bOwgPhJn3AnWNuIJrFECgrCbHWnFxFBut3hR/A5HpBIKT/ScazcMVOnTo1p06bFcccdlwipu9batC1hxzHySrQ48sgj035q5HCbuBtNEFFXhtvk0EMPTQKHfZEfApL+uStNFNEnwoE0JcRJTRAOI6KNcRo7AszNoG2OH5gZF6FFX7iLEG3kCclC7DhobGsfAg9STRQyboISHJA6T4OSXibNjMtIv/XVmPv2HxCzD98QN37zNz1CwoZPODhaWltTqhshQP+RfSkWnVnhS6SQdiXuuF9q9/Od2JTmtWjRojTHBL28HYLrMdYcU94TGLSZBRl4I8JqIG3YsCGGjJkS53/uyabjc9O3fhsbt90bH/jAB2LevHmxZcuW5HLikiAUcmJ1tNqW6wu5Jiz5XLuPtogx0g3nzJmT8BEzeTtYmRspbUQrwicRET6EBbEmzsS58y6LONXzubyP5BJ0zXDOuz52Jc6l7rm2inPpezl2q6/myVwvXbo0XYNcm8xj3sZ+4txcas+1OYvWeR6JydyKa9asib0+1qvHrgNrN2+NAUNHJMdaiZWCQEGgINAVBIoY0xW0yrYFgV0QgSLGFDGmp8O+J8WYPDYCCYGBQIL4IgLEF33hKCEiIGy5XgxXCPKWHymc2+nsK8LHmi9VAglHLLRXdQwQbwgy7tzn9A4CC1JCuCCM5MdnI9lSf7gnMrGUTqS2A6GDmMMhwZVAZOKQ4DIheCC5XDWEDcLKsmXLkvOGc4fdHxaEDiKP8esrYnX66acn0qMIsv0QJf1EhggqyLf9kSyOhOzuIAwQdtT4IEAZM0GMWEOYIVQRdogHxAXzoT0r0sWJ0traGuPHj0+CjGLBiDVsBgweEnNWn9hjJGzY+GnRu0+fGDduXHLsKFBsPnNNlM68wtL8wZMwULsP7KXRLV++PIkx0uq4cGwHG2KUR4MT2mAEhyzG5FdiFZGGoDN49KQ4767He0SMOe2Gu5MYM3z48FiwYEFK0yP8wagzq1iDidj0KHXj9l11X/iINSSeGENs5JKxnd8ICOLTNj5rL5P4jI85cF453zmxyvJOBAjK3HTEQHHovO5KnBNZ7GMOicjmRuxWY928OAdcTxzHeS2W7WveXK/Evke2O7aY5qjLc+jV9czqGrbnPvvGjd/8u6bHOWfMus1bY+CwkUWMeWfYlE8FgYJAJxAoYkwnQCqbFAR2ZQSKGFPEmJ6O/x0hxhgjgYQ4QAggvviHn/2d8OFJP8QPDg134aVOIBMInLvEHq/MHk9Y6OyddQ4SIgRhwh1dpMOdf6lTXCDu+KuX4vvsEPG0FylC+ThIkqKWHBZIJ0EEsUS8tUWQ4T5BMAlOjmd76SzIDPGAwIHUEGU4VohCxp1JDreH8RFUYAEXLoNcPNdnBAl54lrJ4gABQdvEHGlJiJT6KtmpQbgxPu1LQVIDArbGwg1CxOFkgK9tETNtDBo0KPr16xeDBw+O0aNHJwKu/o5jjGhpjcnzVvZImpLH2kr5mTJlShJDCB36AXsOKiKYeexoVZdIDRhzYKzV/fJvxAWxRgQ0L8gpkdB8wT6nIxHd4JjnTiyILfNHuDNnUqs23fFQ00nqtmd+HcdccntKU+JUQOLFBCKu3+Ke00m/rN7XW6W+iQNCI4HUuPN2zgfuCbFPLCT4GTvBR4wbNxcMIcB+8ISx2Led35zHRFjnN4HUOVWWdyLgWmWOXC+kCXK4uAYSis1FRzHud+464qo2nOPay/uZG+eAeXL+iGEOGNc+grXrgWsJ8cb12Squs6hGyLG/a5dzj5AsTWnroz9paiHvXDdm2ckfjxGjxqb4eydy5VNBoCBQEGgfgSLGtI9P+bUgsMsjkMSYlpa4f8yceHrsgnh63MLGrH9t6xuzVsadm7fEw0vWxzOTl8azExY3dH10zuq467Sz48vrjo1vTlvQ8PX+gw6NcxcuSQR1lw+WBgGwo8SY3H2OFO4MRI4ogTwSCZBHqTVqwSDM0nSQRCQZGbYtYki0IIIQdjojzEjtQUwILtwNSAdiiSQiLwSQfOx66VFZREIokUsEhUMCkSFiIKVSi/K+xBnkh8tAahNipWgml09OMzJG40d4jE3b+qioMHFF36QhEY44ivyGCBNLVq5cmQgXAsWJQ0BQuFh6A9eMtAMpDlloIcYgUtJHYJlXDhorcuZ4xubOujo5BxxwQHo8+YABA1LtnNmzZydBxFOyerqArz4h+IQPYkMWVYgIsEZkO1qQUlgTG8SYxX7EFfMIQ2IfpxT8xIfaMObNXNuO4EZMMEfIqbknepgj/TC3RIiBI0bFcZd9oulizNbHX4+lG85LBXzFBqJNrPJqPPpcdYK1hREcOKacH8YkTi3Gw/nlO+eoGIQdXMSsc1bsw4qwmUUW+MBMewRLYoL0pLK0j4AYIoxIh+Ngcf0gWsOdyNvRAn+Cl3PZ/BOHza241a5z3bXA/xyunVKROPFc/5wXrgOuW/ZxPOeca5FrpALgYkEfOe6cix/e/aOx+TOPBtE0iybNep22eF1MmjotxVNHOJTfCwIFgYJAFYEixlTRKO8LAgWBdyGQxJjW1vjs2efHwxdfGY9celU8cslV//Oa3+fv8+f8mr/Pr76vrPdfdnXcfs218bdXb42Hr7o2Hrn6r2t+7zW/91v1c+331d//ut2Xr742Pn35lfHJTZvjS0ed1PD1ljXHxGmHrU7/3L8LvPJFtxDY0WJM7jSSwBFDOEAg3AlG7pBBNVGQi1zvxPfIAUKBCCKIxAjkARm2XRY6cvv2RwL9TsBxDPVgkHuvSAaS7lgdLQgKIo5cIjD57rJ+IPPacscY8URcCEeIDFcFcmy/WuEA4SEoeDoT4mXlPiGauDOdCZhjSzGCgcduuyuN7HItOB7XS77DDVPH0VeYEBeIOTAlYBGdpFNJidAn4o0xwAbBgs2+++4b++yzT1q9t+6///7JKTNyZEvssfe+ccWD34vbvv2HppGw9Gjrm+6L/Q4cmEQicwhXGEkB4/4xBwQ7Y+yo0ClRguDlzj9iKlY4oAhTYoqjA3aEN3ghrZwdHCHm1jEs5sT5Q3wg3uSYc3y/6VPL6LEx94jTmu4Y8GjrSXOWRf/+/dP8ij+4cDyYS30xz+oJZaGkvTgn/hFdnCtiiGBK0OMS4ojIGCPjiLxjwcg+jiFOLfAlyMBUHFrgCl/zALOy1EdADJkHojGHC3GPiGhOCLgdic9EFo43YqFrG8wJNM5x7bhemKcc574TK65TrgVZqHRNFDvERv+jaMd+YsD8u2Z95KN7xNpzrwkOrWaJMNrVPgF46bJl6dpVH7nybUGgIFAQqI9AEWPq41K+LQgUBP6KgH90Ro8aFa/ecVf84Z4H4k/3PtTQ9RdfvC++dOtt8drDD8Wfnn4y/uHppxq6/uobX4/7tt0c9yw9Nh4fMr/h65eGz4lNhxZnTCNPmJ1FjDEm/+D7x1+fuDek5PhHn+WemFAlkQQJ4g2ygiAj1oggEYctn4CBYCPOiCR3jbv4fideIKcIB6Ltbr7vCRra7eyCXHJO6CcRRL/VXiG8cKZkhwoCy7VC/HCX2vGQVGPNpNUxkV5jRb48ivrUU09NfTYGY7ctAmtfYyAccGsQhAgoXrVNqEGYEF13xI0JMUPgEOUsxnhMLUcOIYa4JY0JDsQcjiGP9N5rr73iox/dI3bf/aPpdY899kyCzMSJE1P/PvihD6Wn+DSziC+hZ8HRm6L/oCFpvEggogh7IoD+Gr+xEwzEBSJbxbZ2ThFR4guhDB5iwTxJBbFvXhyLG4Fgp2itQrU5RsyffsDUyhFF5OJCgauYnD5jRgwdNzWue/yNppFU+CgSPHBYS4obc23eCU5i0SpGxQfXjD4aY3tk3jmT3RPi1nkm5pD7LMRkjOAjXtTSgWe1XbELG6Td+ZjrKolhooK+tjdP+Ri74ms+38UmIcZ8mEexzokk3sxzW4vzn3ApjYyrxry7nopLcV47j65fasXk604WyryaO3NInHRcoo5rFVHUdWPAwIExYfbSuObrrzU1zs/55CPRq0//JDLqR1kKAgWBgkBXEChiTFfQKtsWBHZBBLIY88e77on/9eBj8b8feryh6/9538PxlTs/EX986on4r++8FP/fd15u6Pp/feuZ+PpNt8WDBx8eL+02seHrV3ebFOdPm5/I1y4YHk0ZchJjVq2OZ265I/79K1+P/3jg0Yatb3323th2+qaUStLVziO5CGAWGZBs50e221fbI2IgHUQId3cVlczpR4gHEintxl1fThpEGbFAMrL4I42CmOPuL8GjswSRIIP0OgZRiAhEcJFCk58+pE/ZGaCfSBWCTCDKd5n1w2dkyZ1sAkMm0gQehJazB4nVT+PgxiAMEAi4ZBQVJkg4RhagOF/8Dj/CE7EFoUPQ1IXxSoSw+p5AYRt9kOay3377JSGGGLPHHnskIaalpSXVQ9E2l8zkuYfFlQ//KG5/+U8NJ2K3v/THJGQcOGRkzJ03L7mfzL35MUaY66s790ir8RsDom9uqgJeNWb8Rpwj5hiv+CDiZaElb0tYQETVUDGfjue4vretWDJn5iPHHYcI145tPfmqz8ChcfLWzzccm+xA8Ejhw8+6Knr37p3EkEzQ1Ssy7+YVeec6E1PiBz7ZQZbHWvsKHwKOc0gbxmOf2gU+fpc2p33HNT9WMUIIcA7YBt5Sy6xqldRrr7b9XfmzOHMtFNOuha41Oc5dR7gBXa+qAlgVLzEKf/GtDeeHVKTaOLcPsYb7y9O0XMMc1+La5Bpi/sVCNc69l9akhtU+vfaPzXc9Hre+8FZTYt0Tw+asPSn69BuQREHXzrIUBAoCBYGuIFDEmK6gVbYtCOyCCBQxpn0Bp4gxjT8piDFrVq6Ke7Zui1c/d3e89vl7GrY+e+snYstJJ6d0m+70HJnjWkAkkTiCAcEFkaglH7b1HeIgRQP5QA4VwFy1alUSR5DBTF4IMbbP+xEwuHHcgUZe3LGvPUZbY0DspUkh3p7Uw+avLf2QwoGgEj0QY3eUOQkIIYiN8SHPihUjW0QdLhr9I8AQUPSJYMB9k8k09wUxhpvHb1wsHmnMUaMfatkQpAhQ1uwgQaiQcqITYYcA5LqjT9JtpEUREqRTeSLOhAkTYu+994k999wzevXqlQr6qlNjTpBud9H32bdXbLzpvrjl+d81nIRx3Bxx3tb46F57p/nPKULmgtCi7wQDuMHDmJBU+CL7hIK25pFAJhWMowNRNY/iobogurCCp+OIKzgRxswRdxExiABTXTlEkFsCz7ARI2P0tLlNqachheucT30jho2ZnOaKuFEdg7jmXBBb+soRRDgyx9xQMMuuq+q4vRebyDZybuzaql0cS5wj+XCAuzjNoqnUO8erYkOIcfx87No2y+d3I+A8JdC6rsBUnJvTfL1yLtYTHsUop5haVVIaCSeE6NrFPHLZme8s0BJ+uGeIHm+++WY6Vu08ul44B2279957x+yVxydhNguFjXq97du/j0vu+3bsve/+sWLFyuQyrB1D+VwQKAgUBDpCoIgxHSFUfi8I7OIIFDGmiDE9fQoQYw5ftSo+cd318fjnPh9PfuELDVu/fOttcfaGDSntprvjQqSlVEiVUOuAsCDFiBjBEWOxDRLKeo+sIHq2czdeCg6iQIRAFv2GbBBcjN1+9rdK45GGYV8kQ0oFMtPeggAh9bbnJOFOQVgIKZnIIOVEGcclpji2OhrZlUFwITQRVRBmghKyz4afnTCK/yqcu2LFinT3mihgLAQTjxImKBBhOHI8zhZJU0NF3/SFqKBdxMrqM4IHG6KQvmTBBnmGleKhxJ2hQ4emIr7qkUyePDnVaUHwjA+GnrbUOmVWXHjPcw0VYxAwbe7Xd0BMmTo1xUBVaDAvxsKNpO/mWBoF5wpxDLHMLpDaedQOrOHn8czSLWrTNrTvmsw1BRNOAS4YwgMBwrGz+6RKUsVP/l0MXXDBBbHXx/aLo7bc2HDB6uqvvRrzjzwjBg8ZkpwntfiIa+QdcXfewIbQCB8xBx9jFw+1i7gxLrFl3NqqXYxf6pPYFQtSx8Qm0dT5CVPnoO8yRvBxnuXzt7bN8vndCMCeC8t8uZaYG3OpiDlsCc3wr8VU3KsXQyQm2Lqe1Io22taWuDaXzidCnWO5plrMI4HR3OV5JICqH2R/ArknnPU+sH9suPqu4GJplBCjnWsf/WlMmb8q9thzryQa1YvXd6NWvikIFAQKAu9EoIgx78SjfCoIFARqEChiTBFjakKi6R/9A7/u8MPj6c/eFf/6/Dfj/3nxubfXf3/xubBWv/O+9rvaz3mb333tkbhh83ndSlOqDhzBRAYQPwIDYok4uAMvPSUX/UUuCQvcCMQCQgshx0rcQNSlZ9gPebG9u80cFO78+t0xkB6/IeBV0afaJ+9tj1QiLfrELeLYhBDEBcFBfBAirguuBGSHewCpufDCCxOBIeCoL4P0E3WMQZvGSFCQgrRs2bLkfPHYYp9to99q0xAiOGscD5EiQiFoiJpx6wMM9YEDRtoTYq594pC+IucEoryvxzt7dDdxZ+7cuSE1acyYMUkM4iJB+rRJ4OEa6T9wcCpUe8VD32sICZOedOlXXonxMxfFXnvvne7o63/tog9cGFIvzBnizz1jW64h4zFGuLu+ZlGG64MIZpyKo3KP5PSefAyEj1ChXeKKfTlttGmuxJ5UHnPE7ZFJKuFCbOibeJJWMnzEiOjVZ0CcfefDcfNzjSGqNzz9Zqw68/IYMHREiiPzX28xV+LDODgjnA/GJlaMO59Lzhl9ttiHk8uYFEkWI8TK2gUuYp8IYx/Ya5PwiPjDyLmW8fGKxHNnIPHih1jg/HO+lqVtBMyb64trg3OYAOL8VrvFtco8OufFOWytcOYUU4dKqp1rZS3OREXnvzkj3DkPCLW5mLdUM9c51xluO3PonCfYZAehV9euQYMGx5DWCXHmrV8JYmojBBlCzMqNl8Te+/ZK1z7nYI7TttEqvxQECgIFgXcjUMSYd2NSvikIFAQqCBQxpogxlXDokbfEmCPWHB7Pf+Hz8b9e+XZDawj94+OPxa1btmy3GJOB8A84AoIAEjw2btyYrPfcDQifu7TcJIhBJt15X6/ICcKIVEq5IMwQNBBrogyRwWdpKcgO0kP08DkTGG0g+gQb5MN+nC6EFttwT6ivgKxoFwlGlohICA0RhOPkqKOOiunTp6dHRUsFcucaGUJ6kXnCDqKPSBELEGmf85NrtK8dOCC9iJoFseLGQJSIBPqGTBEpuIaMR58RLeILQg5PApXvCRdcMVKtzjvvvDjzzDMTIXd8jhtYcz1UyZDjues+ZERrLDxmU1z+4PYJMoSYi+59IQ5esi769h+YBKdM3qvzmd/rC1JqzMYgDogNSKV4yC4Q82us0mN8BwupGUQHOCKgVXeM7cyHOTPnjiOuCAfELqkf3EnmyzFzmg4Bz7Wcq8BvSDLRDFEdNm5qbLrtgdjegsfqxKw999oY3DI2Fi9ZkmK5OicZG6++F5tiWh+lWTkPCCH66XtYiDXik7Gq1yQ1CTZiCK7Ol+riPFNbxHlA5LPAhxMMYc/pcsQreDsfCJGO7xhij1Dl2FlcqLZf3r8TAfNIcIMZAUwsE1rNo1g1V+YJ3s5537lemHPz6nuiDbdcjhX7a9N5T6hzbfSbc8d8uw4Qf8W744oLYprjOIZ9XdtcW8wtwWdES2u0Tp0dZ9z65e1Ozbv66z+Ow8+6MgYMa4kFCxYkd1z1HH0nQuVTQaAgUBBoH4EixrSPT/m1ILDLI1DEmF1XjEFo69VCafZJ8V4QYxAGZJvgQZBw590//gQJd3ythBMCSSbNncEN4UDytYusIBsIKaLjlQBB8EC4iSPIOgLJZYHgEE+yeIOMcwEgnQg48q2oJcLujnImu0QjhEU9m5NOOimlNq3+/9m782C9ivPe9zg4jsEGBJpnaWvYmgckoRnNAoGEJIwIo5iMmMxgMGBGm3mw8QSeh8TG2BiCg8EGA47jxMeO7SSOE18njp1KVU5y7rl/3qpzb6VOnVv13Po0PJzF63dP0t4bwe5VtbTevd61enU//XTr/X376V7bt5epRqJlpKMfIHJAD0IeEDBiDbiAPc57u48oGSJJZEEKFDBGVAYgBOJY00WECzBDMLEfOCQKwtSvjIaRP9CByDL67ZXIIiLcB/aAGtayIZpFlbRuhJnpPlNnzomVJ59V3u6zPwt53vmtv4uLH/hyLFq/I6ZMm1nsR9zzgZ42eWQrU2YAEWDAfUAEKAYasL96A1/UHTsDc/yJcOU/NrBCedmDb6R4db06ttCpV3/zQdOdtF3Ay7NFiRC2RKs69Tx2Zt/pM2bEtPlL47T33hOmGAFPfY0eeN8XX4jNZ18RE6bNjs1btpZoCfntbmMHvp75yikt4CL7qGPTvOSZ74uI0c70S9oHIQ/gATC5gVLaiqlu2Xdpp8S5tXj4Az8i6j2bb7sejAEO+Jy2xd/4azuAms+qx5ctoB61d3XDnxKC8U9RSWyvroA0gIVtRcOoH5Fi7M9P1ZNNn8L2+hFtBtjxDNEnQKXoPeBYOtqh/sd5EE47EF2mjwLf7OpYW5q/cHFMX7Qizrjhwbj9iZ/02cf1HaYogruTZ86JjRs3ln6v+khtCdUC1QIHYoEKYw7EevXeaoEhYIEKY4YmjCGIjFgSvoQKMe0H8WBsBzOMITCJAqJPVInIBmKCACAgwARCkdAltIk7AsEiuUBLCuje2BHEIBgJEml4FqFIjBDdwAoAIjLkoosuKtOKiA715loCljiyywvh6nswBhQhgoEQQISAJ5aIfXAHFBCFYqFU5TPiDSwBB4SVciqPLaMWpEnwgkLgD8ELsgBCoJIoBP4ECl1yySXlOukqI5u63n0glrwCTPItz0bDQSggwoLHBJoN8CHe8u9y8pV/+LA02Wbm7Hkxa+na2HPdfXHdF56Pe779f4SFZruDDnc9/XdxzaefiZ2X3RozFq2IWXPmFdsbxe/tJg/qT30om8iA9AHtiXAlHIEwo/18CohQJtEfvuM7yglCgBPsD8Aos7y4Th2lv7lHtIBIHL7ID9SJOs5pS2wM5BHEvl++fHlM6ZwXa3efX6IHQJnup3T8tqzBccMffy/OvfXhOHbjzhKFtHPnrgJ+pNubLaNWtBPlyrK6V/n87bw1YoBCZdUXATCmgSkH6GZTXvZha+Xmn+wN1vFb5eQ/fNA5Ih70cr10CPe0D5/LtUl6U46hfg1b67e1cVBGm+b7Nn2mfgAAVI/avv9T+AgAox8CDPlyQhd9mHr3fUYsiTJLP/e9vhBokb66Uo/qtlmPoDMg5HeMul++YmVMnjk3Np15Wex78NGy7kv3fv4vxc+v/9KLceb7H4oFa0+MaZ1zylvM9PdZxqFe/7X81QLVAvtvgQpj9t929c5qgSFhgQpjhiaM8aOY0B89enQsWbKkCDmimmgeaChzsMEY5SX+CDujvEZgiUeCADAgBIjj5ggpgUDsEhCEHrEBPLgOZMmNMJc+4UmcEOKEjHZHKEqDkBEJYdqO6AmL8p5wwgkxf/786OjoiM7OzrJ+i6gR4oPoJFqJBXkDTuTds+2iBuSJeFEWEAaQIWCzbuVffYsiAAr4AohjPRMRHYSvzXXqi9BhE2X0fAvEuj7Xj3FOlId7PcdnEQ+EG1FNkBHM8gRImIbFZsSVSA6j5IQeW/RFALGv5wERS5YujQlTpsXC47fFnuvuj0sf+loZ6b7xK9+Pmx/7i/LGlRv++M/KuUs+/FjsvuL2mL3s+BINs2bNmpLfdhE4WZddHdUtyMVf1Is6zi1FIpBlpJ/YVD/AC3FKwIoiUG+ij9gY6OMjbO1vO1iXPqNO2ZKt3GfNDuk0Raq8gBE2/uh+USMzOmdFx5xFcfypF8V5H/x0vOcTTxZ4xS7sBL6IDmC7M278cBy39dQYN3lazJ03r/ilaBbl7cvG5wAVfggC6mPUsZ2Q5hsiqQA5PiOKgj+LzOJXwB14BXpJw/fsJ+oCAHNOOwV2gByf+ZK2rD6VXcRZghhH9aDd1K33FgBNtFsw0Doy/laH6lc/xuesQ6UP0//wZ/UBmPpO9Ba/lgZ4qK9QP/o+9azOwGN+7j59hX4H0FS3Ipv4edYjf3dfRk5pdyJoRP2ZtjRl9qLYcs574rw7PlOi5kDa/+3nL/cDl37k6/GH1z9YYOPk6bPKot3KoG3VrVqgWqBaoD8sUGFMf1ixplEt8Ca2gB8+nTNnxm8+/tn4H19+Iv7frzzZr/u/feGr8Ucf+nD85pt/Ev/P917o1/VB/vN7L8a/P/t0fP3u++PLS3fEC4d0D1b25/v+eLW1UW8/XP1YJJAcm3tfz7k+789dM9sAACAASURBVMljX9MzT99CqUceeWQccsgh8ba3va28plbkA/HtR3MTPvRnEzgYYAwRYbSX/fzQBwgIBmJQpAnhYFpNd8ITCABYiA3imAgBPfyQJybY0BtIhPgTsaCEqUfEp6kt+Tyh/cQigQKGqBdRAo4gicV2wRlrqbiOECJQLNQKmuR0oawjvkCQinqxLgwx3w4yKD+I4BnemuR13KJT2ML0G+koC0EEJEgTMCK0cjqSqBRAhTAGbmzEmX4FbDFlSR7AEmIKgMgIHPdJy5GoErGTUSVZlt4e1QNRZ02fmTNnxrhx44K46lyyOhatPzmWn3h62Res3Radi1fGhMkdMX7ChJg7d26ZGkUYNiFab5+b17EVSMAPwBS2VTdspozKbGoR+/ExUQFgAxv5Xt5BB3YAYcAswtPfIETTtvyVcAUU+Bk/ELkFaLGznX8leMs8AhwE7qZNm2Lq1KllAeSO2QsLkFpw/Illmtb8NVtj1pLVMXn67Bg7bnzMmDEjdu3a1XYR1ky3N0d5AaGU3ZGA1icS7KJUAClwhL1cox2xCXHte5Fq7OM77U3ki/LzbeKerdN3QE7Xsi1/Yx/ppG2Ieem3axO9KctQvoafA476OvUAkunnQDb9BMCbU5r4L3AIGKdvq28+CKDpw3yvTn2v/Ws3Nv/3+N53gK7+iP/yDxAm61I7SXDsPv26+ucb+rRJkya97OdzFsWc5etD+zcdcd7qLdF57KqYNK0zxo4bV/xcP5W+OZTruJa9WqBaoH8tUGFM/9qzplYt8KazQIUx3QOc/oAxRnGN4BpRtPuB2fq53bnWa/OePLqneV+eb70vr8ljXrd69eo47LDDCowBZOxvfetbY8qUKWVNFBEHfmz7gduf2+sJY4hCgMWIvBB4oky0SU69MYJPcORG4OXoL4EAfLjfTiALs1e/hKN0ABQQRBQLcZxRH8SL74EHgp3AIBABGuKb2CRofE98gjbS9RyCUlrAB3AigkB6hDoAYFSaIAET8npAybWmxvhMtLfWo7J4rugWa8FYk4S4lWfQBzDwLNOlTJXyTOCIGFOHYIJ8JjQAG/gLkeu8cshDAhvPIp6k4zkEmOeYfqOM/bEpk7SIMVO9vBbba7CHDx9edp9FgqkjwAJwa4VZ+5sPfSnQpn3xI5FLbAAcpE/xF77iGv6gbgjLFJjOyzs/4B9gYWu9EawgmbT5EbHLp4hhR/eKlulqk6a8eaZXlM+ZM6dEyI0YMaJArGOPPbZESxHMytEKdbpKt6fzgBkYJV3TrABK5eUH/EZb06aAKzbgG4Q7EMhWQKXyJcAj5IGphDDN5/NPaahjMAv45Kfu1ZbYhx3q1jcL8EUgHwzUFwAo2rr/W4AMvpn1aKqSOlTfvtevAGH8Xd2oD7toGGm2btIBdzxHW5A+H+czjqYx+T4BTvN+92proLo+7KSTTioRhiJBR44cGRMmTIhly5aVvk1e+Ei7dJpp1s/VAtUC1QL7Y4EKY/bHavWeaoEhZIEKYwYexhA+S5cujUMPPfSg2t/ylre8BsQkkHFeXv1oJcj7W7S8XjAGrDDaSjz4gU8cEAnEMoDg+6awS1EhugWYInpFPYAR7EJoSCPFNKEhxH3v3r1lSoioECLSaLBRf0CE6CRuiRo7AQBigBPSASZADlAl8+I6o/uEN2FK3BD9hGoCH+UQXWDtFiKd6CRUPJs4B24AKGnZpE2AeC4QRVx5vnSJE2UgykWOWEzTWh2uz/y7P+0jqsTziR4Cich3BF7YDXyw4LG3H4FUImSIK0KMEDeSnmXtj65XWsoJntmBKmUn+Ih957IO+vu5FhwFDIAt0UPs1io0PZ+/AYFsDhpt3ry5rFNhoWV1wdZZV+1sokz6Fc8wXYw/g1CmADkP8HW3KTcbyAsYlSDPcaDsIz/+v1FuviIKi68516wH5RYJwZdvv/32Eu20ZcuWEiHGrsS4iC5wp3lfs7zKwe/Y19pI2om1iPgB/9OWq/huWqz3n/kNUKz9muYompJN9VHNTT2CnVnfTT8H1gAyIFlddbXxRWnrPwBhbUN/YXoesKc/7w6mykOrn6t390jb7pqu/KirfNXz1QLVAtUCvbVAhTG9tVS9rlpgiFqgwpiBhzHgA0Gwbdu2/d5PPPHE/b633XOlJ4T793//938HyLz97W8vC34K828K+P5qIoMJY/zI9sOdMAM6CAgwww/5XLOAgAYNiDUAwzQd0z6ACsCF0AU4QAv3E5BG3IXMmzLkHmUSHs9ewvaJYQAErBH9QFw3AQsRIOwe2Mk8iQgAvgiFVnFAMBjptf6F/BAyBKVnijIAZkSgiHARbk+0iGogakUAuUc+iHXPlh9/AwfSBEdMg3K/NWvAGBBGmsQWO5gO0iqACR3lEKVgapURaPeDQMrCHka0iTbpWRvEM0XHuIft5WcgN7Zkv8EQXWCVspvac8YZZxS/aicW2Y0tgRN2mTx5clkXCJziP+3uadpIWfj1ZZddVqJbRFWxt3SJzYMVNMif9iLfopNETzjXujlnKlNeO3bs2Fi8eHFpezl9sLWNNNPwHWDAb3fu3FnarvbHbkAWKNfd/c206ufftYA2C5IArBYc13eybevmOlFhADg/93+OaXJArfO+76keABdrVHkWWJz1qK0BOT3d35qn+ne1QLVAtcBgWqDCmMG0dn1WtcAb0AIVxgw8jPGj0Ui4ET1h9Y7N/fU45wcu8XzEEUe8CmOGDRsW69atK9BCJAcx1O4H9oG6+UDDmHuvuqpEJogwAUzADiPsRuNBFZDEDmiAFuCKSBIj7o7OiXwxtYxoIBiBG8AG3BApAzIQE9a9UL8EAgFJGNiJYZEwQIn0ASCChB9oc6YneT4wYcqE8wRid/aWLiBjrYbMP0ACrABHphsBRkL4Pc8OuABL4BEbAGwigcAQAodN2EfZARujz0awiWVggA09jz1cA2iBPHyDHYAHeREZw24gl7SVC+ARzeOz+zwXqNm9e3eJVOhpVPtA/Wyw71c/6lxZTQ8DntS5Omvd+Io6ZzOw1GLNRCp4pz57gjGAAnuKGLjqqquK/UXGELeep3/x+WDb5E2b4numsPEPUKp10w7AJpBpz549ZYqJ6EJg0PQm5e9u0x75rQgtdSEyJu3qO1CyXb10l2b97mULqBvRXvoZtuWDgKu+sHVzLV/UF1mTat68eSUKTD8sKqYnP9ePgj6i7UTV6Uvdp63pL3uKrGnNT/27WqBaoFpgsC1QYcxgW7w+r1rgDWaBCmMGHsYcjC5BDBJDAIzpSCJl/LgmtHN9i4HK90DDmBvPO6+sC5LRHd4WBC4krABBCGawgdgTGQJqEHnWQCAeRHuADULviQyiIGFLb+1CiBCaIA4BCnoAFYShz2APW3gGcdHbjRAlLAESU6MAFelae0G+tWnCXJly2hGRbz0bwp/ot1YIcQMGiO4xsg0KglfEr4gY9xM8dukCL+COaU/y7wjQ5LolBC7fAa6An/POO6+INSBI+mxgGg6/kx+gCtQZqA0kA89MA1O3dnCJfYjAvti8N3lUj9bwAaZEV7E9EQoKJDzwTBDAOT4ISrA1aACEsQ14BQACfu3yqD6svyNtzxBRpe2CbmzPn53vaXohf/YM/skP2cdaNCJP1GO7Z/fGDl1dI4qBTwGHfF9UmDKY+tcER+pNO1Qe/g0aazNAHh8W7UL8t1sHybO1OzCZfUWMmdLEz+3qXzSSKVDaed36ZoH0X20ZvAUE9UNsKxowpxzldSISwVq+bTqdaZaOII579AHaDV9s3dSjuuMjnqd/0/+Y+qdNg9h8pB0EaqYlbWAvox/5uQXHReW5t7/9vPns+rlaoFqgWqDCmOoD1QLVAt1aoMKYoQlj/NA12mgqBWFtystACuOmEw40jLnmjDNixMiRJdpg+fLlZZpCjuBaw4UI8EpqIjAjXrQDP8z7W6T7oQ/kECqACWEp8sR6GQRl63oiTTt195m4IGYJVPDE2hgEaI40E0UiL4gZYsg0Ia98NTI9bdq08qYR69sYdXYf4Q64EDsZRUQwm+rBV6QrPXZzH8ClLMQykWRqjesIKyCB6AIaPBe0IYr5GfjFFkAYKCZCyD39sRFdIIJpWSKAjNgDQsCTNUfspqyYHmPNCqKOL/L7AxVkYAsookyEvnrlU4AA+4MAIIxpZWAJeAK6sKUjWMXeIj9EDTjHVuoGfMn8qQfRANJVV+rMs9ncPeCY+0V4gWutG9DhfhE72oJ24ZXXYKx1a0wz0ycQz9KQrnI0YUlrmr35W/4JaG3P9DTAR7rgnzyDQCJWMtpK/fEb9aSfYgf3WSNHvpXVvdJM0JX54KeuZXfQChAFmQEv56QLJDhXt75ZgC0BNRF4IK72JsJNewZlAA/9Hd8DS9QTeKa/0weoD9cBwfwLaNEHSYNv5sbPAWXXaKf6IX0Uf1R3ImTUp3y0g5baDJ/RFl13ySWXFD/3Vrr0c/2wKVbaIigE7h2on2f+67FaoFqgWiAtUGFMWqIeqwWqBdpawA/i+mrrroFMf7xNqa3hX+eTxBHx6Edwq5gZ6KwNNIy5+cIL4/j1G8oPbcJbhIw1ewgCERnEH1FI8InaINxMqfFDn5A3akoIGDnVPvzYJ9iJBZADdOjNRlCAJuCDZxEmjoS0z4ShZxIa0k3B3V3ariH0iQdlUBbChOAhWohagghkIoY8LxfX9VYk4luEEJtYYBg0scCmxX+JEuIXJPK3CAYj0oQVYeMcceR5BFWWSTmMkAN6AJHnGbFmS4KZ7a33IG3imeBhW9Eb4IXntRNU3dmh+R2bADr8mVAX+dMxY1aM7+iMjvnLYvZx62Luqi0xd9XmmHXcupgyd0mMmTw9OufMi5O3by9lMlq+vzBSPSuXeiVK1WduIpjSXmwkioidTeciQglFEUdAjTpLH+GP7gNcfvzjHxf7gBX8ERBzHYCj7CCitVX4AjhmJ1jVR278S1vn6+p94eIlMX7qjJjYuSA6l6yJeas2x7xVW2LO8g0xbeHymDB9bkye3hnLV6ws7QhAEvmkrH3d5FG++QtBLupFOnZwShmVRxQFu/A7diKk+TU/cS2IRLyzM1DF79wLOGpnYJw6ZDtpaCPgAWEOhhHuwA778Hvft4vI6Gv5hsr1IAs/57/6DP0Q+/EtYEtbVh8Aq35VX6H/ABnVGz/nq/xWO+WL+gt+7nrRYtLMaWT6GN8l4AF+1BmonX6uPptT8vTR+m7+AxjPmbcgxk2ZEZNmLSx+Pnflppi3euurfj52amdMnjYzVq1eU0Cfvoif97aPHyp1X8tZLVAtsP8WqDBm/21X76wWGBIWqDCmaxDzwiHz480KY15P5x5oGHPf1VcX8GKEPNc0IcREjzgSzAQtMeAzUUdIEIRAgp0IIByJDkDBK1KJxQxv94NdNAgIQAASfcRKikyig3j0HIKSSCE2iG7wiyAlUggWIhVEMfLb3cgs4UN0imogiDJcn6BVFpEo1ikR2SI6wjVEDjFLwIhMIfjlWTQLOGJajKlDF154YYkaMVJMkBBMRK8pUIQrmxBURDA4o6wprNgP4CHyTQdjN7CFjYgx+RFpATywh6lDBJej9OSTgCbECPe+bOwFfhBuq9esjWNGjYuJnfPj+FMvjPPv+Ey89zPPxq1f/1Hc+c2/jTue+pu45Ws/jGs+/Uyce9vDseKkM2L89LkxduLkOOHEbUX8Gx3vy5agQV2CY/yjWQYgAHRhFwskqwPgRNnVjbKLBiH++BN7SAc4kRbfAFnY0d/spB5yao/71Ck/4t8pUoldkQs29cT3RDJ1zOiM0ZOmxfzVW+O0994T7/n4k3HTo38edzz118VGH3jiJ3H9F1+ISx78Spz07utj+qIVcfToCTFz1uxXYVozgqEnW8kfn1fv8i2vzfu1BW3U1BXglI/INwEOACmXtmJT12AN+wBNQBcbSpePE+WmILkHOORPQIF2yEcTxKSNwErttm49W4DtRTOlz/rcBFlgKrDCx02747NgrMgtPqwfTUipn5OOetIO+IS+RlvQVtS3tPgwwKoN2fW/+uisvzyClXxKv6b+zzr77Bg3cXKMnjQ9Fh5/Upz+vvvjiuLnP4gP/snPXuPnF9/3R7F179UF2o4YNykWLj721fWs+E7dqgWqBaoFDtQCFcYcqAXr/dUCb3ILVBhTYcxgu/hgwBjCK7ccGfcDnwAgiIkzQIIYIA6IcACBECBcAQnCmJAg9kQauA/MATrACWmIACGUpUUgGoEXRULouY64NMIPTAAYBAyBSrCDOIQEESJd1xEv8uua3FwL8oBL4JFriVLinC09X7nAGBEo+TYlo9jyQ/gQR/InHRsY4tmmbAA41uIAVIAj0UPsRyCbtrJ169YCbIx+EygJG5TFaLWym44k7F8UjKgEokhe2QxAABeIa88DusAnQphdfC9/BLu6aJY9bdDu6PnAkAVsx02YFEa5d1xyS4EvD7z4m3jwpX/pfn/xtwU8bD77ihg5YWpMmzGzTBPqS5QO0CHf7C9CIwUcG6lvo/RAHJEKNoBm6h1IALlAlYQTymMah7QACX8TmMACXwLK7Eb92Z2d2IuveQZbZ905AlRszD8WLFwYx4waG8u27I7LHnos7v3Or7q3zUv/Emx459N/F3tveySmLVwRRx49PHbt3v3qWkLt6qR5jg2UDWwBAwFNZbf5jg/m1DcC3nQpedYW+a32BXK5Ljdti/9Yk0dagIuII/YxxYsv8D9t2TN8ry0T+3wv7UPIa7tAWN26twA7gsD8SL8n0i83PqiO9UPqhY/b+ST/1yb4M5CS9eioL+H/zrvf7xD9AD8xhUlfpq3oCz1ffVqPSX+srtVjwhhwWr8JAh+7ZGkMGzk2jtt6Wlz5iafiwRd/26Of6ydAmrPe/5GYtuC4GDlmXOzatbv0UdlfZnnrsVqgWqBaoK8WqDCmrxar11cLDDELVBhTYcxgu/xgw5gsnx/1hDYBQJz5QW9hUIKPCCasiQvXNbeEDiJQQASRDcSg+wg/I7iiEkSaWMPFGiXW4LBeCQFImBCkok2ICgKaaPcsI87yRIgAN4QGgd6MEnEtUQ+UECBEj/wDRJ7r+YQlkcS2RAlgI4LASLL7AKbWjQgCgLzOWl6BKM81it0sh7VW/vAP/7A8g+ghUORd3+F+zyeiCWWAx7O9xtYOEhDXbKocABFBl+Jc2QEHglo6AE6CBvl1X2t9ZDmANPDpqKOPiYXHb4v3fubbvRJerZDmgRd+E5d+6Ksxa+naOOaY4QWcEJI9bfJF6KsHQlEEiI1t+BL4oEzszwfYSH2Ymqa+2En5XZ8bOCDKAwD0nWfwPxEDIlus56J+QSyi1jNESIEabMsn+RGYxreBmxEjRsTIcZPijBs+XOBKa/l78/ed3/x5bDh9Xxx5zMg4/vjjS7m7qpcsi/rmU+o669V37hMRw1/5gvwS1HxFe7EmifYAUmpvzU2a4JdINkDLxg6iK/iCN/ZoH9qZOtSupcte/JN95Id9nBc1U7fuLaCfSD9PSOgO9ZgwUL+lvtSbfodvA2rO6av0nc1N/WsLrgV6bMAhP/e2NetgaSP6BkDG+YQuILQ+CsxRj9IHcCZPnlIi3UCV+7/76/3qC2569Aexfs++OGLYMXHccceVMjTbZ7MM9XO1QLVAtUBvLFBhTG+sVK+pFhjCFqgwpsKYwXb/1wvGKKcf1mCCH/6iKkQdEIREsxFcopqQI/qaG+HhXvDE/b4nAgk+aQnJl4bIEIuMEhrEN1hDrBOcRKBrCBUCnRg0BUrEiLUQCHZilMAgNkWyABkiT0zzIcZFRvhenkXuGGEmWORF3uyAiqgCayaYfkQgOd+6EafE0vXXX1/yLLqFgCdyjEDn+jlEEVgD2lgUNwVWwiP5J8rYhbDyvWk5bCGvhDgBxWbyATIQ1CI52IhgVwbAin3knXgGIUAG67m4p7kBHyJxRo0ZF8efekHc9NW/2G8BBkbc9/w/xbWf/XYs3bwrhg8fXmyiXrva+AK4ph7VofzyEfWQ0TBs6TvTM/iJ88rGhqI4gBTXNzc2BBnYhi+yFxvwFeeABkf1z3fVgfpSVwAQ4Wy6DqCj7pVlfMesuOzDj8Vdz/xDiXbpDXxpveblKJlfxCmX3hxjp0yPjRs3Fv9s5r35mX0IcPkFlvix+mQj5/kuXweWiHZ2AKL8zTZ2tiPam5v7+RIRz68JdREygJTpcNobMKAt87GEpdoW23oO+4iS8yxtrG5dW0A9miqkDtkzI5uc14/rp9SjqBlwVH3oV/mofkU95lSj5lPUBXCrroBi94meU6/6ANF82o++RF1lZKJIG/fyCyCNjwDFo0aNis4lq2PfA1/uVdRXq3/n38Ds7U/8NHZdfnscM2J0rFy5spSL79atWqBaoFpgfyxQYcz+WK3eUy0whCxQYUyFMYPt7q8njMmyEnWELgFrrQ6CgKAj4ggPn422Z3RC3tc8uh98ECVjmg2BTGCLHgEnRHiADNInVEAHAoTIJkZFLRCEwA2xAdKIdLGOiykbRvlNEVq3bl1ZlNa0IeJf5AxgJH3ikjBqbkQKEWzKRr61RISBMucmP6IEiCZ5AAi8aQjgMAqtXwAQABTPIVoJKxEzppSceuqpRQQBT6JjRGUQU+CKNEV/yAeQRGCxjWkHAJA8E3WEFchAcCkTaAEqpEgHmtwDYPCZ3JQXhBg/fkKs3nF2XP+llw4IxKQQu++5f4xrP/tszF+1uUSTEIH8o3VLQKLe5M36GQSiehZBwId8x6f4gettyi2qyjQOYI19WkWeOpIOAKau+ZJ6kh7b8EcwQjQAuJdTNdgY5HC/emPbzlmz4uiRY8r6L3c98/f7FSmQtsnjB/7kZ3HSRTfEhCnTi4/Ka7sNnFIG9Q4usQEBzf/lVZ2DeO5P8MkW/BR45GN8p9W3PUudZCSNNuo6At5Re9TORE5oTzktKW3pGepKGtoA0FW3ri2gvaov/WFGxfFBIEyfx//VgT4i/RxYEX2oj/AWIxCytR75KdgJlukzpJERTz7rN/mCfkzd6hft+kx9U/q59tbR0RFT5iyOC+/+Qtz97C8P2M8BmVsf/1GcfPGNccSRRxVfzOidri1Vv6kWqBaoFmhvgQpj2tulnq0WqBZ4xQIJY/76ww/Hf/38o/HvX/xq3/YvvHJ96/GVdH756S/FZx94MP72a4/Fvz/7dPy3b3+rX/dfPflE/PFd98YX1p8WTw9f1e/758ctj8tXbypiqDpN/1jgYIAxrSUhIPzgNkpPJIoMIYAJhVxQlHggKggBIpvY9j3BCa5YdFTERqvA9iz3EZ0iF1xDbIgEIAiN1LMJgQPKiCrxCta5c+eW11B7HTUoA66AEAQMsUm8yCuB737iHsDwPSBAqBiVVhaiRuQJQUxMgRxAinViwA/fE/0iWdyvLOCBTXmJLdAFJDKNwIg2sa387GQU29ozRLS8gUVsSvQqH0hlFN1zwB7n5INQs/6NNERzEO7Aj/yzhTya/iWygf3sImVmzpwZs5asLgvQgigJCw70eM+3fxmXf+TrMWLspFh7/PHFDk0h6bO6Yyv5A0b4gnw7p3xG65VP+dkuN2JV2dJO6ps/NK9xLburTxEv7EyMglXqwLX8hu0TxDiyPwDGt4hmQOOYkaPjXVffFf0FYtK2N37lz2Pt7vNjytSO4itZvjyyh/oGXLxZRxn5A7ilnglqUUXK07St+7U/YNAaRuwprdbNPewjfcAlQZ9rfccGooaa64r4rL00I2200wQIrc+of0eJ9hLVBtaydwIS7RvcAsSAyFYfBgP1Oxb0BpZNQVQnrRtf1nfpf/i4Ng+oqaOsR7BWO0hfd61+Tb3pW0xNGztxSpmCd8ef/rzf+oH7v/vP8f5HfxDHbtwZo0aPKZE/nle3aoFqgWqBvlqgwpi+WqxeXy0wxCwAxhA2X7rr3nj64U/Ftx75TDzzyGd+59junGu7u949j3/s4fjIvffGY598JJ750hfimS99sezPtjm2O9fT9d/47Kfj4bvvjofefXl8evc58Znd55Rj83Oec2x+bl7T/Ny85t7te+LCE7eXUcAh5hoDVlw/7HefsiO+88lH4n+89N34z++92G/7vz75RHibEqiwP5sf+UQisCG6ACAgsI0AgwjOGfXNUVwwwmg/kNMXYUcI+nEvmgHUIcwJD+LS4pWmBZnmQ/ATIM5dd911RbR4JkEkOoJQch/QQbgTPyIvlF8eiWKj2kaVQQ0QhiC2oCzoQ8gSS8pLzIIFRK70jYoTVvIJgHhGvhUIDDAdAXRgF9On9u7dWxb6lSa7gQOiFERJAAiiZwg46QNERBXoI6LGZ+krk/uMuosEsu6Mo/PSUTfscvSIUXH2TR/d7zVQEiy0O1q0dtuF74sjhx1T8mraV24iekQ2sbVFipVffvmHMgFeytoKGdwPPpnOQXi6XhrWTQFtmhuRyp8IUNEFWXZpAjUiBojhFKh5BNHAIZFaXuk757j1IZLFSH+7cu7vOetxXPLhx2LavCWxfPnyUi9ZXsJc+xBNwf9AGP5t2ol2BB46x69aNxErIIqy8Xt+wsbtrmVjvu917QBlRgVJkz/5ThtI2/jM7mBPRuK0Pn+g/1av+ght32ebo7+b55z3t2vTrgOdt2b68gQ8a9fqwBRK0BQo0dfY1bGoptY+z73aKIij39LXaNcgcbt6FJkEvuk73JORVupIX6SNNOtRP+b5+ibt0DS8lSedUcDJ/vpzV/dZ5NrbxiwIvHPXrtJXNe1UP1cLVAtUC/TGAhXG9MZK9ZpqgSFsgYyM+fZXHouffOeF+Onz33tlfyl++vxL8dPnWnbnyv7Kdb5/9e/fvef73/xWPPzQR+K7Tz0ZP3nphfjZn73Ur/v3n/1WfObBB+PTp58Tj6/e2u/7Iys3xiWbTyhTV4awm/Rr0Q9mGNMsKEFgrYsMoRd9ADoIvQdHCBWQop3IaKaTUsJZvAAAIABJREFUnwkXYl7kgoVFgRIilVghMIh5IhQ0IWKMLgMBhDlBRMD7Hlwh+glvERkAkXMghbzJZ6aZU0KIUmBj/fr1sWjRojL1SaSN6APTX8AY04SII2AEDAEBiC5RGSnCiGOgRtnlQfk9G4AhtMEFMMA1ni3PRLkRcGAGfGlCLtdZsNV30nOPfFnvxMLBGzZsKGvfmJKTaUycOLGABiPXXQmpAzlvVPzmx/6yvBp385atry4ia8Re+UxjE91BLIrqARkcwaZm5EXWuyNxLRpE+URRARLqWz2BBxmF5FpQwjkiVRQSUZxrdfA1de6cZ7KfuuEvIgvUp6iYkeMnxzm3fHxA7MO21tUArEaOGlWeKV/8W96UCwxRz3xcvgBEftYuGibtxP8AJfbRPtK2+osmlCD4wRc+ar0QvtcELNoFqAM8ZvvKSCvRXaCjNAZzU//KBzRpV6JMbPoC4FI+1Xlu6lG52KFZ9vx+oI7soi61SXmVN5/VhbrRlgEtPtrOhvyY/YEvkJI/8AWgRdtnh9zcr92bSmnaHiCd7SBhjHOe7f70c/lgH8B62PBRcdlDXzugdWK66ysAmZUnn1lelc2netvXZxnrsVqgWqBaoMKY6gPVAtUC3VqgwJjOzvjXH/51/M9//q/xv377H/26/59/88v48mc/H//687+O//nv/xb/33/7937d//uvfhlPfOwT8fhxm+NHh0zo9/2pQ6bENUtXFfHZrSHrl722QIExO3bEs488HP/3C8+V6BgRMv2x/8sTX497r7pqvyNjWgtBPBBvRDggAHiIPrn11luLyPQDnTgxUi8qoCmciA2iRYi+dua6BDBgBlEBZBhxlr6pOIQ+YUbYN3/4y4cpAaZ6uJfgJXbBE8KeaLJWi7QSqhA/gA4xY3FTa9CY+rRw4cLyymqi3cgzIJCCSTqEPqGbUTbAk4gdYAYcUg7iP6MOCF1COwWxMoNYQI205FWePUdeXGukXHnZIOGG82CPSJhNmzbF/PnzyxtNtm3bVuyeafzeoYfGnmvvizuf/sWAwQbRJKbiTJg8tQh7dUs4KgNbiHBhA/kXWdRVNEz6EwiR65wAX2zEjspOWJqupo7tbMdO/M06PYAW/+FPvhdJA85I07X8ggBW12zk9b6T5xxbgEl3QvNAvmOfqx75ZoyZOKVAPvnhi3yXH4qcIqCVD1zJ9UbSHq1H7UbbYFP9gzKyE79kc2ln21JuUwfVg3YIGrBfwgFAzDUikdwnHVADzAMaRV/kta35GKi/tWVTtn7v934vJkyYUCI7PMs50UXTpk0r0wDz+drooYceWoAMXxmsTRvml/oFfYC6Y38QEVxh0+423+tD1Ak7yzuYrZ2YvqedsL26BJn1DZ6lL9EOfG/zvYg8u/5XVJxIPH6uHsG9iZMmxdzlG+LWx388YP0AMHvpg4/GUSNGlymd2mHdqgWqBaoF+mKBCmP6Yq16bbXAELRAhTHdA5wKY/q/URBbO7dvj69+6EPxj994PP75ySde3X/9xDei7K+c+/WTT0TZnW+ee+Xv1nP/5XOfi5sv3leiSw4k58QAQah9GJ0lLghd0RtGsMEEgtPotWgVEQqmKxHHhKmdMBHeL2Iko1pcT7ASYUQ0UEOUgCwpYlOYt8s/Ucd+0vBc6cmbdRWAGHkmdowwAyeiNwiem2++ubwW2Si0kXnXi8CRL1BExIA0iWGj2kCN13MvWbIkNm/eXKI0lNF1ykOoWfDXGjLsQEQpCyEsD54vUoK4Ywd5ToChnJ4jcsGzHQlr8EHe5EMEz+zZs2Px4sUlOuacc84pYgiYedvbD4/3f/n7/T79pgknvD3ogjs/U6YoyJf8Kz/xb1oMAQkUEK5NaNauztRHRsUAMnm98/xJ/YkmUl+EJ7jDRqZhiErw2VQfdrWDQkAX4MDO0skN3Js1d36s2XXegAnUtJPIpAWrt8bYsWOLUCam+ZXILL5JgAN48tzdpq3xEb7MF7QHGzuZksXXlEt5nRMlJhqJLwJ4vme/hBbsA2aKPmm1T3f5GMjvtAN9hvWfRHsBqDbnvK7cGlHab27a1YIFC0oEVutUoLymv4+eA16xrags/YCpRqLATAtK2NrVc9mf3fmzPiLzrc6AHG1eX2oKFMiiz3OtftN5/gIwA46e5bz2pU34O/0cGNVvvPVtb4udl906IFMV08cd73n2lzGuozO2bNla2l1X5a/nqwWqBaoF2lmgwph2VqnnqgWqBV61QIUxFca86gyD9IGwPenEE+P9l78n7r3y2rivuV91bdyXu/PNz83rWr975bpb910ep+84pSz8ur/FIQYIAMKAQCS+CT9tJQWGtIkP4CSn9VjPgIAxigwquNc5Yh4QIbxdn8LC/USZawlYghLE6c1GuIMbQvW3b99ewAogQnwSK8S/yATPljYYY+0FEQLEcU4fIGTlH0RRbs8nkoAa4MG91omxGKfoGrYwjchrrr3lCTTxBqYESUQV0AQW5CK98iNfdgLZd+xJiEkf2GAri6xK+9hjj41JkybFlClTCowxVcdrco2ez5k7LyZMnxv9uVhnU3g1P9/06J/HqIkdBTipVwvvitoBjrKu2IxI7G5XftM7RBgQq81r2YS9wQvTwYAGn0UXELEAF3EMUIkuUT/qi62ANLZUd/wS1FDfY6fMiDNvfGjAYcztT/40Np9zZYn2kGdT+ERTqSeRPbmOS7O87T6zARDBriIf/J3XEe2EPMgDUokgYw9whj0yEoMPicRQH9KSH20OjCT+m+22N+1rqF3D3iAYoOaNaSIA+RLba7NZH90d3c/PtWWRLM22ob8CIn2nHkFGfaPIQj6tHxTtAvx4Hp8WSaN/0I+BcnxBmvo5/vD7f/D2uPLjT0Z/LuDdbP/Nz4s37Iz5i44t0VtDzTdqeasFqgUOzAIVxhyY/erd1QJvegtUGFNhzGA7ORizfcOm+NjSbfHtQxbHc4cs6nZ//pCFbb9vd/6xQxbFFfNWlB/6+1MuoASoIAwSoohuSYDSmiYBTDATfUaRrX0gekNUic9EISEBvLiWmLERnMCIaRwZ+ZAitPUZ7f42gi2KADQBWYhzgpSIJ/rlnWgFR4gafxOpnmGTF9OZfEcEi24xrYnwAVyknW9KyQVjXUdQgTKeByJ4ljffWKsGXHENoWR3TcIW18mb6SWEGFjEziInpGPUHPwBfawJM2LEiBg5cmT5vHTp0nKe2J82Y2YsXndy3PmtgZuilCLs/ud/HeM6ZpU8gE4iGNgYpCMIgQAis7vdNUb4+QabAgiuz3sdRVqpKwLYzidEIvjOLgpAnRCxAJe/AQ/1zs5sC1KIynrf+94X4zpmxyUPPjrgMMY0sTNv+FC85S1viZUrV8auXbtKvkAledEuCOjudtfwQ+WzJgiA07ze94Q9sOLNX3xAHZj64jvC3WftgK8DAqIy+CH78D8RFyJoQMhsf+3a1FA+x+batogcbyjSJ7Gr3wfpq+nnrX+nP2vX+hmgDBhrXuezNq+dA652/QV/di1/8XzQE2wErTPSKusRtBZJpG3oc9522OFx+xM/GdAIuewLtpx7ZUydMatAxqHsJ7Xs1QLVAn23QIUxfbdZvaNaYEhZoMKYCmMG2+ETxnz82BPj+UMWxAuHzO+3/euHzIv3zD6uCLG+lItIIwSBAdEiolusTWBqBJDS3IzOEiDKITrFmikEIvACZoiacJ4IJAadN2IMPhA9om6MOBslNtUF7OkLiNFmQRTChkAnXDzTa6UJYpEqoiZAFGuwED2eL0KjKUZFCxBJ7s1FNEXQgDbK53vXi9BQVm9z8nYfAMD0AREHrjOdRjmJJ2JY/ggmU2sIKzYl0OQJaGArcMLuM+HMRoScSB8Q5uijjy7H8ePHl+lKomPkcfS48bFq+9n9/rrmFF2txylzl8TwESPKG+cANq/all82be58prn7zt9ErTLt2bOngBLlbL3ONSJKVq1aFStWrChgy7lMnxglkME9IIydwJjc1Yc88ScgcGLngvIWmNay9Pffdz/7y7jwzs8VGDNu3Lgy/UYdpX2a5Wx+ls/c+QBhftJJJ5X65Q/Na31mM2vnsI3FpwlxNvAdG/E9Pp++CeyIumi1D2ilDfLnur3WAmCzdrxmzZqythQ/Uxfpg81j1o9z+VkdWHTbIuH6kOb1eZ30+Lmpj3a+ov7zWlOjLMisLkEZaWYdOiZw5u/g6OFHDBu0fmDHpbfEuMkdZariay1X/6oWqBaoFujeAhXGdG+f+m21wJC3QIUxFcYMdiM4mGCMCBFwxNQaI/MiFEQeADNC5RNeABOiIUSzWJQWWABDAJdclDajHgAXo/ZGe4k/MEK6oA0YQWQQFkLy3UMI9XaTpggT6YEo8mPKD1FEhHqG9IkZ4sc5I8ry3ropu/TkjyAGDEwHMsKtDLn57JzviCXCyHQSolYaRraNahPNptk4r0wiEQCbXEzVs4AjC3HyAbDLNAUQB/yS7x07dsQRRxwR73jHO8px2LBhJUqms7OzCMURo8fE6kGGMZ2zZhWRSWyKPBGlQbgCYT3tfOrSSy8t8IDftLseWJMu24IuFkwWRZPXWtg2gQ2/ARVaRao6BtEAm8GFMZ8tMIY4ln9Tufgmn9KO7OrYnn/nUSSLdXi8Zt0aRdpRftc8AokgmKgN/iEKS/vLa9iV/yg7cEXcN4U88a6tAqDaOphat9daQD8nMg4QAQ+1df2TPib9sKtjvjFLVBcIJjKp9VrtBZAV2QTY8BWf9V3udz0f0Y+pX32XOmtCNX0a2K2+QeeXYcw/DHgEGIi58/LbKox5rcvUv6oFqgV6aYEKY3ppqHpZtcBQtUCFMRXGDLbvHwwwBkQACoTOE74ghiO4YeqDaBhQQfuwLgehQDgSwoQggWi6CgEjGqTdFAgCRzqiR0SKEMyEJ8Ft7RFRMcLuwQz56W6TFqBCuBObplGBMtIgfglb0SqAhyNIZNFb037k21SP5gaWeMuMcliYGBAxvUT57CJq5BtIUU7peRZBSyQBEiJvQCewhh2AByIYuLEGBNBkGoKyu9a0B1N2rJ1DsBFiBJ/7REoYjbdY8OGHvyMOO+zwV3dg5phjjikLxY4cPSYWrR+caUr3PfdPMX76nDIKDy6IdgKcADURP3yDfdig3c6PrPWivtQzENB6Hd9hBxECYITPhKjP6tt6Gu4xlQlwIFb5EeggOkTdsqmpPqKcRBuMmzY79j34lQEXqaaKnXnjh8uaMWyi/YBG8mg6GujGPspt9zn/djTVjh8AS3xGO8hr8shv+aJr2DLtQ9izSaanLvix+tE+CXcgks1AKn7IT3tqZ802MtQ+i5wTpecNSmzHnuAqgJp+2Oq//gau1Yd+IfuE1utck36uzwFUtHcQV9253jPAa+loD/o4MEZ7kL5IOz6jrxEF+AeHvyM+8Cc/C4tt93fUV2t6W/deHVOmd5Z2OdT8opa3WqBa4MAsUGHMgdmv3l0t8Ka3gB9CRp3rq63bQ5n6NqX+bwKvN4wBTqyHYrQWeAADQAaCloD2gx+kIbhBirwGCCEMLcJKRPdmugOI4nnSI24ICyAjIQSxSJx4LbE8EYytI/f+9jwiiYAhUAhegMS9hL58e5ZNOZTJ1A6iFGhyXQolAtY9xI7vCCIChxgm7J0j9IlfYt/z7AQUQQQGGNW2xkuOnvvOfRdccEFZ8BeAIIqVFcSx+7u5y6NdOcAYgtt0FTDm7W8/rBzf+c53lilLM2bMKK+7XrT42JjUuWBQFvC95bEflgV8RVwQqWzMPuyirEb0Aadch6fZUtQZIMUOQJl7WzdgAOiTlrrVF4NpoA97sHVOkQPP+Kj01L08gFr5fHXvmWDY+I7OOOumjw64QCWETzjvmnjrW99a7AIeWTuJT2gzACafzjI0yy+/2hkf5APtQIkyAzp8R3oEPeAkIsbfQIHoNZuyg33sA8i4R/vm19KuW+8swI581VpObKhucg0X9dG6qUd9m+vUd9ZH8zr9pHajr+Pn+jKgV98KvPj/QB9pU1f6WBFg6hGEAYP5Sva37tdX/P4fHBZXf/JPAzRthSf9/feyLbtj3sLFBTI2y1Y/VwtUC1QL9GSBCmN6slD9vlpgiFugwpj2EOZHh7x8vsKY/m8grxeMIQoBByJXuDshILqEgCScRZQQAglgQAJigKjzulwCop2w7MpChA3wYfFQkQPEDQhiBFikAKhDwIAbBDlhKjpFJIl8Eh+eJzrAArumcpgGApS4hxgFXggYu3T9DaAY2VY206881wKndt+517oOpiIARPJgmoApCQkCjDx73a7pJyJ5iB+Ciwh2D9ADUoi+MbXBOeAFbAJkTDVIyCBKQnSC/IJOhBmB5W9Cj0Aj1EwnkZ4oGFOVTFEaNWpUADGmNaizc845Nw4/4qi46dEfDOjCnQ+8+Nt4971fiqNHjy/5Uh824IWvGOUHRfgHyMX26js3dQdygSoikFr9xrQ3IEVUk/oALZyThjoEW/iMiI68F4xgYyLVNDHX2xw9T6SX9KbNnB0bz7gkHnzxtwMqUr3aeuHx28raPsQyuCSv8glAaT/Kpr2zTzMqhfjO11IT/gkS036u9X+TCCE2JPizvJ7Ft4E9aXum+7UB9gGkpMkeNt+5RuSHc816yufV42stwEZAFojLp0Q96a9EsDQ39gSp9TH6z9ZNOhkVqO/RdmzO+6zd6/eyjah39avvAGQS5qnD9HPAUvt429sPi9OuvmvAF/O2NtKkzvmxYeOmslB2axnr39UC1QLVAt1ZoMKY7qxTv6sWqBYoP3hrZEzXQKbCmP5vJIMNY/yQJxqMshMXRs8JPFMkhMUDIIQCAUAAE3SiHogRInJ/NsKBgAcbiGdpej7RTIgQh0aaCUiCw3UpQAAN4AGwEDlBDFkjwVoLwIfIA9+ZUgUkWXtFGQhgEAYMMV0FbCJ03A+eSGPZsmXlddFAC7hjsVXPda3rCGjPt0iqKJUtW7aUNSR8D9YQuZ5JHLMRMGQHsYgoo+rAi+kqomVESwAYRswBKH+zOduzuXRNSZBv0Tdg0/z58wuEsXjvzJkzCxQCgYg31x/61rfG2Td9bEAX77z/u/8cm868LEaPm1DKob5y40+Evak1bAZEqT9lU6fqly1EJYE2gFxzIziBPSAH0CFy+UVu0uB7vuMHIgGk6Rp25k+iZJwH6kzXAfnALXk5bvmK6Jh/XNzxzZ8PGIx54IXfxNWfejomdMwsiwa3thOinZ8Q29oboElE50LVgFNGh/GL5sa+bAYQsgHfboIc3xP4YAwIYBqNczb+yfc8j23Uie/BVBBQ9Iy6q1vvLKBu+JV+wc7v+K7+jc1zSqT23ApqPIH9+bD2C1o269H3/Fb/A8jyD9/rE/Ut+qx8oxMwp03wKYCZ74waPToWrTspbvvGXw2on7/n40/GsJFjS98EoNatWqBaoFqgLxaoMKYv1qrXVgsMQQvUyJiuQYzomApj+r9RDBaMIRaIaIDAj3siLRdAFf0CVAAPdkKDgCNsc0S9teTSIxYIYztBIv0EK4QmwQw8AC9Gk0V7ADF+xBM2BDQhaiQYlCBGRIsQ2bkILiAhEgQwOfbYY6Ojo6MAlLPPPru83lcZCH0gxS7/YIYFNE855ZSyToxzohIIfsKFIN63b19IY+/eveWtJiAKEWUKjn6AyCKecm0U0TiiWgh8kEokg/Kl8GUDZSC0QAnCjE1EPQAx8iYt4IaIY3PiWt7lzz3gGGAhDRENFrIFgebMmVN2kTmiePiMtIGLYcOOjsXrT46bH/vLARFhQMMd3/zbGDtlZqxavaYAsVZfyL8BolwrhZ0zgordlCdH/PN6tgMu+JrvCct2cICdgQV1BILxHfcStCKa1CEb8i9RS3aQxhQekG3khClx0T1fHBD7mAJyx5/+PN519V0xfPjw0nZymkmW01F9ASKiWNS5dqD+lB9MJMIJ/dZNO/I9n+Mf7aa+aHNApWvYPdus55k+Z9Fk9gX6QD72cZRuO3u35qH+/b8toB75sf5AHyoqhd+zuXP6znZRMXxY/QHfQJi/Wzd9CJDpGm0CiPE8/afIOxF26lGfpw6tlQTIajcWdD7qmBEFCt7/3V8PiK/f+51fxfrTLo6jR4wqfVm7MrSWqf5dLVAtUC3QtECFMU1r1M/VAtUCv2OBCmMqjPkdpxjgE4MFY4g6gg2IsMYJkWq01Q96P+6JCEKAsCDuutsIBOIDsNBmRLMQIKJTQAqi0ki+6BSQgYgQyeIISoAlnpvCufWY38mXN8d4s5EIkcmTJ5fX+RIeYAvR4jkgDmFrGotpIUaQpeHZ8pfARISByALPJ9bBAhE70vA3sWqaEmBFaAAjAACYkhEZykpIE0XEPjtk+sSUKAXCWnrgjDwRVqDS9u3bCxySN3mXrrzLI0gDFkmXPdQN0EA0myblVdLeYOSZKaDZH1A64qhhse+BL8d9z/1jv4uwu575hzjtmrvKAqHqLqcodeUfQASRr3wACbHKhsrWChLYWPl9D+K0gxj5HD6pPuQBsHOt+gQf2AZca/oRO7EPqDd+4uSYv3rLgE3luvLhp2La/KVlCpl1XFojHrIMjtrOT3/60wLilJt9CGqw0L2tm/6BH4uc4oNdbfzQujnsw4c9xyZ9bc9bgayxxEZ8Sprtoje6Sr+ef60F2E4UHntrs/xQvwI8tkZ/6RcAZtfqC1q/b6asjfBzoFb6GR2jb+Ln3vRmiqV61D9aTFte+Plhhx0W6/dcHLc+/qN+7wdA2Zu+8oM48phRsWHDxtLfNvNdP1cLVAtUC/TGAhXG9MZK9ZpqgSFsgQpjKowZbPcfaBhz+cylZaqOH/dEfUKRG2+8sYhlIk+kBoBh/RhC15QT0EIUgh/7hLLIGREbRLYRYeKA2LYTGUb7icvmDkAAKSIXCEDPEoJPOIhC8VzC3bOt+yH0XtSMiBSgxHNBCcCBmDQNw3WENqhEjMiL9ES0GEk2DQNYct6IMhFP0Pico9nSJWzzO6DK9/KW5VEO0ISwbQpswgqQAFJcAzKYhuM8QAC+GAE3PerUU0+N6667ruSTjQAwIEya8goWsLcpJp4rsiHzrmzKK1/KxPauY6OEP6bwsN2YMWOic8nauPZzz/WrCDPCft3nn48R4ybH/AULSp10Bxq0HXmTL9CKbUzx4nOisZqwJcGVSCPr72S0S1ftT7qiSPgh2KXdeI7oGL4hCkoUCJHKR9kp691bu444ekR525HR/f5c0NS0kC3nXBkTJk4q/tCTfZSP34la4SfaCP9WLn7Y3HK9HH6gPXSXNvvwU+1VWwS5pKceQL3du3eX6Xrsw2/BP/ar2/5ZgL3BRPD65fWbzokzzzyzALFmxAgbA9Xat/5H39ZTPWrvuVYXyAys6d9MrbRm1Q033FD8XH+gf8i24e1ro8ZNjAvv/nz0t5/f/uRPY8W2M+Lthx1e+mX9V92qBaoFqgX6aoEKY/pqsXp9tcAQs0CFMRXGDLbLDzSMOWvEtJgwYWKZ4nPccceV6Azi1HQXgIQwAy7AGoBDRAcxSyQnADBqD6QQCPnKYaBGJA1wA+Ioh6gQ4sDRiDzhJw0ROUSIEWGC2g95I/lGgQl04oVo8VkbzOlAgISRYHk1WgxeELLuBVOsmUDgiCwAMhzdA6J4pnRFkbiO4BcNADIR/q2CSLrSF/LPPsT9NddcU6IYPK+5AQnSUH7PynIqq3wAVc4DEV7brTxEG3BAQAEzhLjP7C1v4A7bulca0pYfQiwBEFCVUTHyQ4SxGWA1dsLE2HD6vrKYb3/ABq/IvfHL348Fa0+Id7zznUVw9iWSQn3yCTDKQsTEKCCjTMojgomIBZ9Mh2utj6a987PysiN7sZ0ILBEJ7Lx169YyHUfUANtlXgEJ0VoLFi6KEeOnxNWffLrfhOpdz/x9nHrVnTF+6oyynhAR3duNDQAjU6vAJG0RpMvII/k3pYnPdjWtpfVZfMVaI+ANHwJk+JUospNPPrlAUe1DW+Y37Fm3/bcA++ljRARedtllBXZrv/qQbLv6Mz6uP9V+9TM9ba4RVace9SkiqbQV0G7btm2lPvXb1qNKgMefPHfa9OnRMW9pXPaRr/dbJJg3he1+zwfjyKOHlzyAhL1prz2Vs35fLVAtMPQsUGHM0KvzWuJqgT5ZoMKYCmP65DD9cPFAw5gzRk4r0zSsN2L9kTPOOKPABtN/QBjQxHQiIfFGy43iGoW1LoJRdaKZyMwFQI2+m8aTUCWBClFAmPih7o0igA4h6P4UDF2Zi/gAa0yzECHgXoudCvvP6UBEaqt4JChBGXn2TFEGojBAFPAGJBIpQAgRoGydIr01L8SMvANOpnPIA2FFSIniIaTAJHYQnWNqEyBglNqaNoQSKOK5nsN+RJoojhRU7jPFSaSQHaAgoExhILZcCy4RYuzb3IgfeWy1gWvUk8iHSdM6Y/PZV4Q3+xwIkDEl4X1ffKGMhI8aO77Yk509v7cb4CbySRnZD2gCCJTP+i/+ZhegoSf/aD4T5GEz/ksAA1tsZ5FmkUjqjb8286reXDNq1OjoXLImLELqrTAHYqM7v/WLOP19D8SUWQtizdq1JU99EajqUZtjExFEorLUP7jEb4AZ3xHc3U1radrGZ/bhP9YbAmG0IXZmL20fjBFl086PWtOqf/dsAX1QRskBveoPQOPf/Jy/62f1CX3xcwBY+xFNd8UVV5S+SFqmeuo79I/6q9zUJ1DLZ2Z0zo65KzfF5R95PA50/Zjbn/xJ7Ln23pgwbVaA+XyzL+XI/NVjtUC1QLUAC1QYU/2gWqBaoFsLVBhTYUy3DjIAXw40jHn31IVx1jl7i7gTjSHKhejLKBifCVWCgsgFZES3EN8EQa490VPRiRIRMWAGQQxWACzd3U8wew5oYuoQ8AGaiKAALUSNiMDpKbSfWHWPaBTi3IgyMELEiJogQEXwiGZpt8kHaEQQE1IiK0w9MiInKGoVAAAgAElEQVRNyJr+IoIhp9QQQnZ2sygvAENwGa32t36EOJKuCB1AybQwx4yaAYwIZeUDdaRnPQnQh6Du6yb6QzTP1M65sXb3BXHNp761X0JMtMelD30tlm55V0yeNqOsU2PqVRNu9CZv7smIKmBJxIZIFv4GFJiuRVx2VSftnsGXRMYQuiKmLGoqogtcAC/ABtEkrfACJAER+cakyVNi9rLj49zbHo7bn/jJfgGZm776F3HKpTfH5M75sXrt2pKf1vVw2uW/eU7kBP/h78CddgDO8AO+CzTxi75EIbCPdPgvEW+KIL/lg4AYm/FRbbVuB24B9tZetWl9AEit3wBl+Lk2rh70u2zeWwAGams/+lJ+cNZZZ5WoP2mD54CMfqYZJZel0feAybPmzo+5yzfEeR/4VFlguq/g0RvUrv+jl2LbRe8rwHHFihWljfUmsifzUo/VAtUC1QKtFqgwptUi9e9qgWqB11igwpgKY17jEIPwx0DDmPfMPq5ACoLUj3cRJiIpCDQjryAMUQxggCGmgBDRIk2IOkCC2AUr3CuyBCxIYeHonPTcB1gAH851FSngPOEiUoSIATxExHiec6b1ADHWgiEuu0pH9RilNVor/xn9AgRZJwOMIfyJJSPWIm9MifHshESOykbgA1TKDUiZUiRiBZwx1eb8888v021EG5gOJS1CTDnlgU3BHDYklDyHoAK4QCJTdawj43sj5mzlb2+6cQ6QAhHSrn11PTay/o4Inemdc2Luio1x9s0fK4KqN+tH3PWtX5Q1Z057790xa+mamDFrdhF91qzo68bP+BbbWdMmN9Ey7GztIosRgw3s1E5U5j2OysbO6gXA4C/q2lFdWHeH/YlQUSZAR+smDfYFZBYsWBjT5y2OE857b1z+sSfi9id+2uOUjvue+6e45Ws/jHff90exZud5MbFjZmzctKkI7Vb40/rsdn+DZ+lHWX720QZEPgBL7McPne/OL3wHBoloEwkGhhLt2mIuBCtaBugBDfsCwNrlvZ572QLqBeQCuEw5S1ABHutTwUJ+ru/RF4AsPdWjvkmb4efqnz/rH/Rh+kb/X/BhfU1r9FzWi/4MhFuwaHFMnb0oTr74xrjqkT+NDz71N73yc5F11p1ZfuLpMXXmnLJwsHW6ss/M59RjtUC1QLVAXy1QYUxfLVavrxYYYhaoMKbCmMF2+cGCMV2VC1ghzvyABzWIQVEqIlOIACO8RAF4IOrA90Sx67UXxxwJJvZ819WPdtEVBITIm3zTEkGda6oAL6ZlpNggqrsDMdIjwglPeQWBQABiFFQCOExhkmf5J37lEeQBmExncVQ+0S85bQvYEe5PzLKDkW15BAAAG88S5UF0ATEElt3INZgDCtx0002vrscjDRCCMJY3glleCDXwhC3UQXdCrav6a553v/KakjJv/vwYP7kjlmzaFefc+om46pFvFjDjFdjetnLr138UN331L8t0JG8DOv3a+2Leio0lGmbJkiWlDprTIJrP6elz1omR/RT+8sZX2J8d2ITd/a2eiNDW6Bv3sK+pYfwi69XUJunyIxBPOuqRHdUPKJHCuDWvIhTUxZYtW2LKtBkxY8Fxse3C95VooOu+8HyZ4nXL1/5LsRH4csMf/1lc8+lnCoRZf9q7Y0LHrJg2fUaJOgG/EqS0Pqe7v4ET5beL9LEpO//X9kRQAVXK6yhiRmRPuzJpa9oJm/BbPgsQOKd9AgHSAKz4rDRde6C+1l35hsJ36guM1Y4Bk4w2UkfaoLrQn/BPfaj+CLDmt639o7rQD6sv0IM/8w0gXLvwHPWqHvm2flh6AFtX9Qicq/u1a9fGpKkdMWPhijjl0lviso8+Htd+/rni5/oAfUHTzy+46/Ox+pRzY/KMOdE5a3aB0NpnV88ZCnVdy1gtUC3QfxaoMKb/bFlTqhZ4U1qgwpgKYwbbsV9vGNOuvH54G/UlAsAVMMNIL+FB7Bqltfts+o1QeovYivgAW0xvIk4IDKLF7m/ti1Akxt0LShDuRoxFF4A60iVIpdMdiJFHgiMBibTcRwARRwS859pcS5wSN8SRxVLBEtNBRKx4S4npTCJYCCcQAdgxfYQdMh/KQGiBP55DMBFE8p72EmkkksYaLtaSkZa8sAn7mLbg1dwid0xRAiMIZBEQ7NAfm7yIyPF2l4kTJ8aIESNi0vRZMWf5+li29dRYs3NvEVxAzeyla2P8pI4YOWpUTJs2rdQlsa7u9mcjRvkLQSkdG/uLbFFX7Mun1C/bEZiioAAv/pFwi81BC/YWJWDtGfULECWUSOHLL4lTkQPS4gvqu7uNqBXRADyNGTM2Ro2dEDMXLo9F63fEyu1nx9rd58fybX8YC1ZviamzF8aoMeNiwoQJRdwqB99Trr5ufBL05P98jF9Ihw+J6AIiAcVm2bUXkE97bPqIz86JLkvAxabp9/Jm6iAbyzMgw058ji/vT/77Wt436/X8GSDUDwCBbM6e2jo/BWz17eqIv/vbtWALCNz0cz4gAk098QuRdc3padqiNNyvnWTUGfioHrvaQB/1r2+eO3dujBo1KsZOmBwzF6142c93pJ/viQWrt8aUznkxYuSo4ucWCuZz/Lxu1QLVAtUC/WWBCmP6y5I1nWqBN6kFKoypMGawXftghDHtbEBoEAXEBjEJMli7YOfOnSUcH9wwog8sADfWP3Ed4UEwEujEhPVbMmxfetIlbAgMgtu9hGhPQpHIEeHgNb4EPjFKiHtOCk2Cnkgi2o1IE6rWFvEcr4hdsGBBzJkzp7yhBBwRMWNtkxRW7ezgnPwBK8pjOgCBC8wASQS+KByghbCSPxEJwITRcqBGRAzwY6RctIhRb2KNcErw09Wz+3KefYk8drG47cyZM2PYsGFx+OGHxzve8Y446qijSvlBGwLdVIoEHX15TvNaIhLwMnovmsOmrohQthDlQXza1LF6AejSd7z5CkwiAsEJNlW37K08rZvIFCP37A7CgA58kEDujS3Vt/u89coCpcAV2xx22GHxzne+s8CsTZs2FXDHR9vloTVP3f3NFkS3+hZJZXNOuRNENutAZES2HdFaonFcbwdXgCh+yIaubd3YgH3cy0ddx1ba5oGWpfVZQ+VvfqutqENgNuEXX+RL/M80xoyAcb26AVn0PdqaN125HjTUV6T/O69/ad2koU/lI/opfah09Bk9bfKnPelb9dlLly6NkSNHln5AX3DEEUfEpEmTSrSYqD5tLdtoT2nX76sFqgWqBfpigQpj+mKtem21wBC0QIUxFcYMttu/UWBM2oW4M0pLcBAGBCqgAHRYY8XIragIgk/kyb59+0okCPhhAVGCQMSJH/vScjTCDBgQ8CImCI/uNiLHgr0iXEzJMfJLaIouIWQzXUJFtA04QAQRPHb3Wa/FIrKADmGjLECRvIsqIdK7yofzgA8xbJHO7du3l8VzTXUCXYx6E7qmnZiGBb5Yc0YeCB35IqREQRBf7AnIiAARKdSfGyEmP4QfWwMkgAlIRiA65zv5ZbcD2VIwAgTs4G/QwBoYbEuMgi9Nu+Y1IlXYhN+AL+qDvdQPaJGCtzV/7gfzQDbPAAnZFfxpByda71dmPqM+2YL/+X8AQFR/0iaOlSPFdWsavf2bjUXv8EFtxfM8X9tR/2Bda0SP8qk/9uEf7nWt3QLQYADglpEW7fKifrVTbUxbAGYAIdCrbn23AH9Qf/pA/bdNPelrAFjAi980N9+rfxCM7dWd+uDjfF50E3/jY65tt/FRUTeeAQCrR/f1xi+l2fRzkVhNP+cLysWPtLWu8tAuX/VctUC1QLVAby1QYUxvLVWvqxYYohaoMKbCmMF2/TcSjCEGjPgSy8QA+OEHPDFAaPjej3pwxujtzTffXBaoNTVHBAnRQYS4n2AnPp2zyK7oluYimF3VA7FguhEIc/HFF5f1WSxmSZCKhhB5QZBLj9jxLKBGSH+umyFCwy7/xLc8E/ymjRC37iO0gIC8hmgmUFzrnAgMEQmggfQJI2UBX8AH6RLergFi7NJ1jp3YiA0JK/ki5Iy0g1lA0EBtyqAsWZ7+fI6oKUITVFMG4s+6LmyjrMRmO6iSQhFwuO2228rryb1Bhn90B2Iy78oCvIhIYm8+5plsvT/bQNmHQAf++GS+IYwf8CX24f+e3W7Tvqwvwy9NgRPpxJ+ATd91t7GvyAhT6MAcAAC40vdoT3XrmwXYnG+CY/o/9mVLPmdqj98RXdUjqCcijp+rR2tG6XP4b7u20cyZ52hjrgcc7eCx/PR1k9ZA+Xlf81KvrxaoFhg6FqgwZujUdS1ptcB+WcCPqBkzZsS+vefHNfsujWsuuaxf98svuCj2nHpq7Lvg/Ljmskvjva375ZfFe+15vvXv5vn8nMfLL4vL331RnLlrV5y+dHnsnTE3zpsxtxx9zt255vnWv5vX5ec87pkxN05asbIIgv0ycL3pdyzwRoAxRAIxJ9oAPAA7iGzCwo96mx/2xKbpFkQK0QhSgCOuFYlBTIoCMG2HABWZYl0VUSqiVQhSaVssF3AxFcBzwQvpE46gC3hj3ZUrr7yy5AcAcC9hAvJ4LkADckgDGCBipAmWuA64kV7mXxkJK9fKI+EKjkhPNIGyG/H2WVSCdEyjEcki2kT6okE8G+gRraNMrhVp417pEXGiYUAd9pB3+THS7pk+A0dgzhttA5TYi535BpCSvkAwdjWCrx7YnMhkO74DcoENRv4Bi6ynrmyi/tiTvU21sLOjenGezV/PDTDhcwQ0vxGtwm9EC/ElvtoVGEkQk9Py2Aj4kxbwpX31BGTYnh3Z2BQ50/K0QZE4fF79vBF9brDrVF8kkk9bBl3ZlZ+rQ22fHUHIdpt7RcGBhunf+lNwTNvnq13dm+l5nj5QfycST13yIfUPZlpjiW/VrVqgWqBa4GC0QIUxB2Ot1DxVCxxEFiAm/cAlBuxGuuwf/chH4mONv/O8c+W7V6579ZpuzpdRd+m5p5Fmfz3DqOtDH/pQfORDH4qPigx45Zifm3871+58XuOYn10n3U8+8kiZanEQVdsbOisHO4zxIx9wADf4LIHbXEQVJCHiRIu4hhgmSkTQEH+EQQppR2JDhIQRZMCCiLEmBiFvvQWwg9Bw3tFivAQ5GCJEXzTMihUrYseOHXHttdcWwQ5oEKqmYhA7CUgS4oAsxLh8uZbw7070+I5IJZ6lKwrHAr/nnXdeWVtE32C6kdFs5c9NuYAkU5f27NlTpieZekMsE96mKIATygZImbJDvGWZTbVRRjZUFkCjPzZ293xijYC07oj8s4lXS6u/Zjn6+sxMX7nANOIUROMrfAakYtP0g0yfsGSzjApS3+Ad/2IXtpQmgGDdEwCru3wCMsqnjwXqCFVwyHpGxHNXm3zxFdCG/8gvG3kmXwHpe4IdXaWd5/kTn5c3oltZUtSbcmfaSLuNj8gHv1EGUI9/260hkvaR556mZfEnPu3/CK9oF53BtqJkAAL1VrfuLaAta6/aqTalfgAxfYL2zY9aNz7Lh7K+3A++pZ9r9+pE1JSoGb9DWttKM019Kv8EcUz9VI8gr/Wo9MHy1NVmahw/l9emn/MLUE6brFu1QLVAtcBAWaDCmIGybE23WuBNYgE/mvyg9WNmIHZpD2T68jzQzyDc2v3gfJO4wKAX42CGMSCGUVgCW8QG8ci/tBNigS9Y/4RoJqTtxCKBz0daBYX7iIF8xa4pOSJmCF3XE6wADkEsHQLRtArC2pozy5cvj46Ojli2bFkJ7wdJ5MsCmOxI5DajdVSmMojWAWKIIOXpDsTIszTki1iRB9NeRLmIRsjIDcKaoCLOtDtCFoAiiggz4tlnMALQIX4IMqKHeAMJiDN5ZkPXsx8xJa++z5H3/XFKZTQVRp4AIUININq4cWOsWrUqVq9eHZs3b44zzjijvBFLOf/qr/6qCExQoy+b63P6mjwDLOpY3RCa6rx1c44tlNeUMjZSNwnv+Aq7yhPbuAa0sJZLVxEknsGHXC/aylQe6/mcdNJJBcy05oHoBVvUJSDhHtd6HTAbrVu3rixQ7W1hIAo/600USutz1IWIBb4AKvIvohtc0Qb4ReumTWgL2pb6UyZQpglttD/2JuJBGbYmtLvzb/eIpDBtTlSaBbi9OYd/s23durYAv0ugom9QF0CiARzHVj/nw/ofPpaQFVgGRTN6Rdvh5+CKdq+u9bPZL7bLjT4KyHH93r17y7S+k08+OSw07Zw2kJs8AH/6VOt1AeCg8oknnhhr1qwpfr5+/foyZeqKK64oUEg743v8tG7VAtUC1QL9aYEKY/rTmjWtaoFqgWqBaoEDtkCBMZu2xAObTo3Hp2+Kb3RsjCc6Npajz/m3c809z+ex+V3e//npx8e+JWvLD/C+ZpRoS4hBIAAPKSAIDMKNaBBpQlQSGaBCVz/gUxSIyBCiTxgAEUZi7dI00kwEWOtD2uAJkXjuuecWEOMtIIQEKCK8H/Qg+AkQ6QE4pshYe4NYsYu0EGkCNhDEXY38pnAigoheUziIZ/eJ3CFywQ1pEOWe6Xu2ARI8A6hxXv7ZSnQIkQayEMuENVBlZy9lACNEORBsnqk8YA34AMh0N+2hXZ0qh+da/8ZUnZWrVsW4SR0xrmNWdMxfFrOOWxdzV20ue+ey42PK3CUxetL0mDxtRqzfsKGMtrMhgdibzfOIUvkWZcEvgAZ+oU7YrLmxv/wRn2CV+0zbUffSat0IYD7BXuobeGBf/tkOGpmWJmLA68qJTPsJJ5xQgFOmDfypR/l912mnReeceTFqYkdMmr0oZi5ZE3NWbIo5KzfF7OUbYvqilTFhxrwYO3laLFi0uERmqX9+0h0Uymc5KptpKPwlo1q0Gb6lfpvlSMipjPLnOvfyv3a+6xz7WKOJLaWpT1Enrfb0N1AmXZFlGzZsKPZxtNi2Z9Stawuoc/YF79gcYOWT2iuw17Q3mMb3tUN9g/Yv2g0oadZ3Ps05/UL2JeANONkuSsZz9Bf6QSAt/dzRs7QvGzjkOn3Sjh2nRMeMWTF60rSYNOtlP+ff+oJZx62PaQuWx/jpc2LMxKmxeMnSAsD1V8rcLr+Z73qsFqgWqBboiwUqjOmLteq11QLVAtUC1QIDbgHC6eRt2+KD198Qn/rg3fHpD97zyt78nOeax56+vycevOGm2HvanrKQbm8LkmIQgEiIQRQQF0ALYQlwAAyEiKgZwhFM6WqTptFZo8kiDIhwYobglx5BKrKCqCFaXGMnNnLKjygHz3O9Z2WUiftEF8gPgFGm6T30UPksqsarXEXQEELEEZFKpCgLIUtoACdC9JWL2AJJ5MNaL+5pFcH+BpKMIEtbxMlpp51W1uFwjuD1jEyfAJdHYlzZgQjCVySDcoJO4EAKcGUwPQtUAHuUtTUP7WwtIoJd2WHJ0mVx1IjRRXideMG1ccmDX4mbHv1B3PPsL+OBF35T9rue+fu48Y+/F+++70ux8YxLCnQYPnpsrFl7fInEYBN1190GSIheET0E2CXkIABBseYGgoA1aQvRHnyrp7LJg3vVD+AAUvFPdcMvba7xGewTyWLkP2HDli1byrQc1xG3BPKuXbtjwuSpRZwu2/KuOPfWT8R1n/tO3PHU38T93/11PPjib+O+5/4xbnv8x3HlJ56Kd733npi7YmMcPXp8TJoyNa6++poCPbrze8/jX+pVvgElkWXKzRecT3iZ+Sek+RBfB2KAQeCpp025RMlk+wEm0z5Zh2xInFtzCaBK+xDx1pABA+rW3gLqUVSMetR++TngqH1qJwli2Fp/kmBF+wa/9HHpq+2f8PJZ/VNGlal/MBfAabYRfYvzl156aWzduvU1MEafIT/8jA/sOOWUGDF6bAGuK08+K877wKfius89Fx9s+Pk93/5V3Pr1H8V7Pv5knHrVHdG5ZE0cM2ZCzJozL2644YYSydNb8Nhd2ep31QLVAtUCFcZUH6gWqBaoFqgWOKgsAMbs3rkznvrSo/EfP/uH+O9/+6vX7P9Xy9+t33f1t/v+/sW/iA9c//Krn3tTaIKDGDDy6600RmmJeyKfIBQxQSCIAAEVhL4TBt1txAnBSiQL5wcKlNnfxGlGlPiOQCXUiW7fEzrWRPBqaCKouzUtiAUQhLgXaWEdBQDHVIwbb7yxgB3pi6wQ2UDkmnKifMSvfIE3nuPZ7UaklVN52IPQZSfAyO4+wks5lEmaInMIYEKKQGMvkIJgIsgJOmAAfPF8UINAB4TYWPmte2KUnEgn+NQRUdcUZ/LlPPhhbZvhI0fHxBnzYs+198WdT/8iHnzpX3q13/K1H8bJF98YYybPiAmTJpfRcc9NMd9az84DavKp/KATOKcswEAKVEd2YFcghZ0yGqY1zZ7+Vl/sIbpKJJGpTuyhTkz/cN5UDHYQNQA4mJplWo46UP8zOztj2IgxsfqUc+PqT/7py/ClFzYCZ0Ct2cvWxTuOHBZbtm4tUKg7oco+1s7hE+qfXwBXIGK2HfaRN/6gXfEnbU9UQms9d2cfPgASulca0tIepC0d9cP+ng9SNoGVdubaurW3gL4F6BLtxbczek17zY399Rvqlp21axF+6rmrNpT3No/SAW88S18B5oC3/Mx32hF/4ucXXXRRATIJ1kTD8SPP7pw1u8DDde+6MG740ku96gP0Ffd8+5dx/h2fiVlLj49hw0fG1q0nlDL3xReb5amfqwWqBaoF0gIVxqQl6rFaoFqgWqBa4KCwADBx6q5d8dzXnoz//PW/xf/67X/02/5vP/p53H/LB9qul9FaeD+0wQOimlgjlglJIEU4vlFgr6cmtEEZAq83AsN10vLmpH379pVpRwCD6UeAD8EBvhAbogTspkcR1UbriUR5IrR78zz5FdEifVDD1Cm7Z1ggmKCyPojoAK+VJditmyGagngVOSCKp6tN+vInekY6RsvZiUgCsnwHtPiOvQgp9vId0e1aI9/KDv5Yv0PEEKBBeBnNJorlXRmcczRlwL2m4YjyaV1nBFiyPsqRRx8Tx53wrnj/l78fD7z4m14LsAQ2omaufPipWHj8tjjqqKPKmjJd1bW6sgiwsoJKCfGUD3yxsQtAAJwoLxilPsCT/d0S7PBFfgKusQeByl7EssgZQC6n45xyyikl2mTYsGExbmpniRC465l/6Lt9XvxtuO/E86+NY0ZPCFPnTCPqatO++a9IJwCQDdip6WOivUAaAIVf8aGMmOkq3e7Oa8vSEF0D6vEdNrdeDXtpH3Zinu8T8tomP61bewuAKuqGb6tvsMMb0BI4ugvs4ufqUR8gOqY3fVa7J7oP2NFv8Wl9sjqTpn5KP6bN8RvReSKdRDhZ98W0znHjxsXUeUvigjs/Fw+88M/74ee/idu+8VdxwnnXxFHHjIj58+eXdqU9161aoFqgWmB/LVBhzP5art5XLVAtUC1QLTAgFhhwGHNrzzCGuJUP4lbUAlFmJNhIvkgC4AAsEJ4PKohE6Epk+LHuXm9XIv6sReE11BaKBRWkByYQFeACoW/ElzgXhWHtC9ElRKNRXgLIGiS9Ee/yRjS5l/iVD/cRp57heRmdYkHbyy+/vEzhIlgJKEKZ8PFM8ElEA1Air9ICQkTzsJHvCVz5T0HmyDaEdkbcEOLyw7amJYFKphCI8iHq8nnSBSo8W3QJMQ3ksBkbyp9n3nzzzQV+NEfkiXlRQKPHTSjTjUS4lKk2vYj2SAjTPN733D/FdV94PlbvOCeGDx9e7NSEB9kQ2EQe2QOUUk42y2ga97CfiCTfqRt5VR9d+U+m3d3RveoTwLPmDhvKA1sBfaKr2Ff0EhBoAejFixeXskyZtTCu+PgTcbfpWvsBq9JOd37rF/Guq++OSTPmlgV/CePWTflzLRd1L1qCiDbtJH1FOwHeiHuQBlTqrn21PqOrv6UBgrGPtNmfnwOE2rK2oi5MdwFKvaWMrer2WgvwNVGB6k49gcf8mZ9nxIs+AJhxXjv3WRvn5wey8RF9Mz/PaYvqEfy0A7l8iZ8DbqJk+DngOH/1lrjiY9+Ie7/zqz6DmPRxYPYDf/KzOO2998TIMeNjyZIlpQ880HIdiE3qvdUC1QJvbAtUGPPGrr+a+2qBaoFqgTedBV5vGANQGHFNoSgyBPwgOggLAAA8IezaRUgQfUQdMEHYGREGIAAc672cc845BSYI3SeQiRTRJSBJCnJRAKbYEPZghGkonk14EPM5naO7yidKjBITRKZpyFOO4hLFok2IdOWUNpFqigvxS8wonzyYAgCSKAfhLLpAPkAQU19EEBilNmItkqgJYzJ/ykWweK60c1oDu3i20XP3AzOgijVjCGZll39wwbPZnQAEY6wPYcqVt6aYYpLCmWATATJu/IRY966L4savfD/u/27fR8JTgOWxAJnPPx/HbtheIIY8G6nPTZ2JmALO5JGNXGNqDQEpSgBcUF7lU1b2kN/+2viQ+iOMRXnkLi8gRwI4kTNjxo6N4WMnxaUPfa2AmCzngRytu7Hz8tti0vRZJQKndRqdtqQe+Zy6dwT2EliKgnJOpAM/1xbTZ/vDRtKSJqHOZ9M+wJ71fdShtggc8rUaGfO7VudjQBuf0idoo/oX/QX78XOAGQwEUfUh+qv+9HN9if4NkAFhsh75Dj/3LMBI/kTEWKh73wNfLtONDsS/3QvI3P7ET2LXFbfHEUcNi7POOqusSfO7lqpnqgWqBaoFerZAhTE926heUS1QLVAtUC0wiBZ4vWCMH/DEIyFLKBLUBD5I4W8L4op2sHZBimiQgTgBOohKQMHobIILwpPwlgYRftNNN5VReNADtGi3EYsAiKgQ94ueIHYAiZwClNCm3f3OAR4Et3yLMMmFZwkYYEl+RAMok+usqeC71hFef8unPBH5ykjIitABYUTS+CxvnqWs1o6QZi5e6z7Qopk20SaKxHNNn3GfKTWOwA+IJYpI+cEN0R12YAbg8GyvrV24cGF5rbcFgwl4eSXip0+fHnOXr4V26t8AACAASURBVI+rHun9+ie9EWlG1S3qOWby9FixcmWBVwkLiFHikDAkROUVcGF7tuBPysOnAJomfOuqHvt6ng8DYoBhClRHtgP05JFYNgXt6BGj4/TrHug3EJP2u/mxv4z1e/bF5KkdRahnGdQNXwQ+RKSob3WlLWnzbOec9sPPQJGB2LRVUExETNpIBJFn80k21L48vzfQcyDyeLCmyTb6ArYSNacu+bX+TJ8DMGuH+jrRMNo+ON3fmzanr/YcdZf1KE/8XB5FOpmKN27ytDjnlo/HnU//3X5HxKRv5xGQ4efLTzw9ho8YWZ7Zm0Wl+9sONb1qgWqBN74FKox549dhLUG1QLVAtcCbygKDAWP8gG9uhDEBCHoY6SU2ABWi1mdTK4zq+4FPVIpmIWqtDwImEHeuBThAFBEnRtaBD4JchIp0jRR39RYRQodIFx0gHaPL1tQgcETVeIbpLin+m/lvfib+E2TIN3FOZIpGIYYBAXnMtVgIF8/ubvM9Yaq87EIEyY802UUEAYACpIAlbAH0OCqH8xlpIGKIkPJctiRirO8CZEnTgsjudZ+0UviJxDF9xBoQ69ati46OjjLqPXv27LI+BCFm2pTpJcNHjYm9tz8Sps6kgOqvozcunXLpzfHOo4aVOiXuCU7TqERbWBwZQFJvoo+yLpSHvfjQQG0iX3I9I5FF8pHQQSSDiC++2TlnbsxfvbUI1AOZmtTOpoTq5R99PGYsWFamcfBpPqsdiKpSj+Adn9DmiHZ1TsSzGV9tgrv+tpXIK34FmIEJKeYBKz5aAUzXFgdRATR1KDqNj4mS0Wfra9QjqGbqY39HwzRzpb3Jh35CHnK6GSijn7WOjXZnetKanXvDNMV2vnog5yxeffWnno5jxkyMbSedVMByM4/1c7VAtUC1QG8sUGFMb6xUr6kWqBaoFqgWGDQLDCaMMQIOCBBoAAhhRqD5QS/k3SirqAbTFryCGEjwN+EhigPYsBPazoloAVtEmRDG0gYwCAYCEHRoHSmWByKHWAVrCBpCVXi/EWf5AjVAle5EqnSAGJEF0hD9wpbEOQEOoBC8uU6LiAT39LTJL0EtQke0h3IrJyCV9zvKG7ACtIh4cY0oGoCraSt5YC9iCYAxrQBEYd+cFmVhYWUGYjwTjDL1CIjxJqBJkyYVoXX00UeXzytXrixROso+ZszYmL9qcxm5PhCx1dW9YIPXO3vD0rr1G4rwFBWgTBZXJlJ9bkY3ibayzgXoN5CbugLs+A04RJSKUuILptnZTe8aMW5yeTtMV2U80PPW1dhxyU0xctSoUvf8AmjzWmCLq/IBeeOX2oZoKj5jOtdAb9oIgEjMA4jqRt5ABG246dcDnZc3UvraON/S3/Eh8Eq96RdMp3Ser+krsl8YqPLpa/Qz/BwIBvT0a/xJfwMYm8J49MjRJZINODlQn253v0i5NbvOK2tT8R39eN2qBaoFqgX6YoEKY/pirXpttUC1QLVAtcCAW2CwYIxoD9NniDIRBN4mRChagwAAICz82DfK6zPBkXCA8CDeiAAQpXWKDzECxCgLsQksADqtYlweCFXAxjQfe0YHuJ6oB1HkozsQIx0RBcSRqAPgx3QYcEdZ5JdYAQRAot5sGQ0DQCmrSAJwCjRpBUpdpScNdiCAE7QATQlnlE3+ACd59h2IAY55rXVGKuX6HqZFWTTTiPcRRxwRYMzYsWPLm01MSfDK79879NA488YPD0hUTAoyQGb9/9/emQBZVZ1rmwzGCRAElVFGmWUUND8ODcqgKIM4xDl4kWhEGVREQEZF0DgkRo1iIgjCdUCMQxBMrteLQxQNf0z8RVGj3lwzlkkqVambqpt8fz0rd1PHTnfT3Zxz+nT3s6t2nWnvtdd69uoD6z3v932Tp0bbDocn8Yt7Rgga10fMQ/SAF7/cI4rk5papjFU+3seBwv0tf394zTxDHBt29NHRue+QIL9LNp58P8IH10Dbw7umUBEEQYROcgzhXuBviXuO4Mk8xS3EXCnGBp/cEDGuyyIapxZhNQhChRYTijHOfF8DRjj1pk+fnhLj4rTCYcd3IqFKfIcVKrSs/Fi4Z1yL78TsXvHIPOc+8h1yWJs20f/YMakCUr7nd9Yeuai+ftuGaHFI25g9e3YSosr31dcSkIAEqiKgGFMVHT+TgAQkIIGiEyi4GDN/USxYsCAJKIgshL5Q0pnkuoR0IJwgOiBAEFaBuMHCmoUH4Ub8qs4vxFWJIyxccHwgNiCwIGCUP57XhLgQLoQYxMIdYYfFIMIKwgRCDguLqjYW4LTDApeFLm1xHn3m2ix2WWTWZGOxSpuEbSCWMA6cDPlaqGbtI9AghvGrMtxxJLHIgznXZEyINCzcCUVgwUNCToQYdkSZNm3apNwxiDH9+vWLffc/MK5b90JKtJktmvL9uHLrrrh42apocWi7oAoVAh7VsUgkjGOA/iMkwb38fa/JfajpsYSGIGThMmGxmis60BYse/Y5MsrOmFowISZjPXftv8eA48akZMfMoXPOOSfl+sHhBB8EOfqZLaZrOtbaHs/fJfMOZ1h5PrVts6Gfxz3CicJ9pDQ61bj4rsSxhsONeV7M+4igRiU7xGXuI39j2fWZ93x3fvFL+8akK5YUVJRlruOOad+9T4w88cQUhtjQ54Ljk4AE8ktAMSa/PG1NAhKQgAT2kkChxZjl8xbGJZdMS0LFpEmTYvTo0anC0YwZM5JIw2KaxTViACIBv+pn4gv/4d/TzgIPNw2CCL8aI8TwS27ueRxDaA4CD9ciTAJ3DcIKC2pEHxY6iEXZgjH3/Ow5ixBcJ4QlUcaVxKyEyXBdxBMElezY6j7SB9wwCCE4UljAs+ip7vm1PY7r4iAhzAsBigUVAhUiDPcE5wn364ADDoh9990v9ttvv2jatGla7Pfp0ydVVerVu0906jUwlmz6ScHFhvnrXohDO3aN/v37p9CpUaNGpfANBDwcBLkLxL38k6j26YTYsDgmfw73jfvIwjW7J7xP8uFz5t5WcD6EKo2+8Mr43Oc+F2VlZTFixIjkHGJOZW6Yag8sjwfCB8EP1xnCFW4K+LhVTgBG5Ibh74/E2YTj8f3Gd3UxQsvK94zvSEKRmEuInuQeQgDnPhIyyPfFPvvuH1feuTGogpYJhIV6HDpqcvQbMCgJ6+X76msJSEACVRFQjKmKjp9JQAISkEDRCRRSjPlg2/aYfenl0alr97SIZiF98sknp6SvhCixSMMRQ6JTBBUWayRh5Rd8Frbld97PPuMR1wwCDr/+U2UI1wvvEWbETlsk9UV8QVy45pprdlv8+cWesXN9ksDSF/LT0Ac+yx55zk47hB4RukNC24kTJyaXBiED9J/wGI5hHOV3BCZ2Ps/dCQ3ifEK2cKFkog55GQhDYCcJLTvtV7RzLO/zmLsjUOTuXCt3xyHDjhDDwo/QH8ZBBSnCr3D8jB07Ng488MDYb7/9d++IM1numDbt2sfA408p+K/hLOpWPLsz2nfrE82bN4+ePXsGFZ0Qj7jPOJsQRoq9k1Sa/DA4pHAuEDLFopW5Q59w8bTr1ju+dsu6gi9QSZ78lTm3RJMmTZKb6ZRTTknOJ+YOwiOOBhbVxd7JR8QiHj7MKRw6/H0oylT+VY/YgYOQ70scTgi1uPgQPriXxZ7n5KVByOYeZvcRNyPfr/QVYXrfAw6MRY++FvlOUF2RoDPmopnRuXvPJAxVTtFPJCABCfwzAcWYf2biOxKQgAQkUIcECinGfPTSGzHjX74WBx/cKgYPHpws9/zHHVEEASVL4IsQwi+/hM1UdycsCBHjrLPOSskjaZPFXu75/GJLtZ/JkyensBaOwa3Audn5Z5xxRpx77rnJ4UI/ss+yR0I8cL9ccMEFMXz48Bg0aFBy95DclvAr2svOw13DeTxme/nXvE+biEOEayHq8JiF39BWdm5VjxyX7RyXPd/TY1VtZu3gjqGCC+4TxBgEGHaeI4a0bt065Y5p0ap1fHncuUHFo4oWTfl+j9wrTZs1i4EDB6Z8KLBn3pALhR13FXtlz8t/lh2f+1jZuRVdg2vDCjEm23Fecc8J9SKUq2PPAXHFnRsLzueGp34WFy+9NzljOnbsmOYUfeLvAZGvrvbMaUVfMhccfBBlcKshyrh9lgBhXQjExxxzTMoZg5iFAyx3blY0l3M/L/+8/Otszue+X1mbhEvxt5bNce5j9rfHd9lJJ50UBzZvWZzvga27YsLlC6N95+7JjfZZcr6SgAQkUDUBxZiq+fipBCQgAQkUmUBhxZifxMJZ1wSCB8lMScBLRR8cGZlrI9fNUdHzitwgnMtiDjGGxS+OFd7DdYITAAcIjhcEDxY1JNQljIi2MpcKv9hnCYTpGy4UfrHncx5xyVAthPanTJmSHD2INjgfGAPXgl3mnMENUdGOUyfbOZ5zWRhT6QZBimvQd87NHD2Zq4fHzA2U+1jeMcRrfjnPdhZze9r5RTs7hvMYN+4YWPJLPC4PhK5WrVrFwQcfHIcccki0b98+uVImTJiQ+t+2Q8eiijFd+g2Jvn37pYUgYVXct7rcmUPkCcoWqTyySGVukv/nwgsvjI49+6cKM/kWpsq3hxgzZcl34vOf/3wKT2KeUu0KTnW5s9jHTZEx4u+J+c93AQ6PYiUSLvLX6l5djiTc/L3j6KP6VF3Oca6N+xABDREmu48I34TmMbcQlA9odlAse/LNgouOzPsJly2I9p27Kcbs1SzzZAk0TgKKMY3zvjtqCUhAAiVLoNBizE3zFiYHCTkGcndya1R359fzbCdnAqINv+JinUcMyZKD0n6WCJdFIGEsCAzY+7kW+WB4JDkmC2YW0og3WVJKPuN8hAkW2lwDMYdEsSxCCOOhEg35SbKd3CtV7Sw2+ZxrsMBCfKFdKgAhwGTX5rjye5Z7pDqP1Z1gMKDSD0lAEXEQrsgpwnhZXMGMxTKOBsbdtWvX6NSpUxJhjj766JTvh6pRsOjVp28MPP7kooQpUS6XMCXcOoRWcR/qeic/EWFKLFLhBT/6Rll0wkmyMKVpNz9Y8EXq0if+b5x99cr4whe+kPKzEFpS7HCWiq4HHwQq3B24hbZs2ZJCW5j3CjGV/9Xy/UJIWV3Pca7/ySefpO9anIw47xC6EdSZYwi6VO0iTGnhIz8uaCLvTIAcdf4V0eWIXoYpVT59/EQCEqiEgGJMJWB8WwISkIAE6oZAocWYFQsWpXCkfIwOsYQ8BSTQJdcKz7OElog1LIL5JRdLP4IBjpTc8q8s/hAi+LWZY8iVwqKQcxFhEHZIOEr7CCa4Q3DPYNNHAOIX65puXJO2cdrgOKFt8rIghhRyMYqAAy/GS9JhEvVmzhzcLyyo7rvvvrRAZpGMOEX/qLLE2BEXCM8aN25cDB06NCXNJcSM81ic4aY57vgT4vAeR8aSTW8UXGyY978JfC+66KI0lpreh0Icz0L1ueeeS24pxCnmH3MJ9ghwzJ/2XXuk0t/ZQrJQjwsffTVGXXBF7LPPPmmu0o9S2BBfmFfkc2LOIDC41S8CfO8hIiMg852JmM13Cxu5kXA+7bPvfskBVowEvkNOHB9HDhycEq/XL5L2VgISqGsCijF1fQe8vgQkIAEJfIZAfRFjcL3g5MDNgniAy4T3WPgiuLDQY7FAjgxcHggQ/LqcbRzHQhCnDEIDoguOGYQSRB1COmibnXb4RZ8cKlyLsKVM9Mnaq84j/cM9gZWfdgkdwXVTm7aquh4Lf65FdSQqUfGLNWFICEiIU5nwQh9wvSC4ZCEGhIsgFOHooJoUIld2DAxgQb6c6dOnJ8GG8cAS4YEwnP0PbB5zH3y+oL+IkxT04htWRcvD2iWHBfesFDYWqSTxZXFakbAGY0pbnzD54oInNqW0df/jxsZhhx2WxDLuUSls/J1SHaw2QmYp9N8+RHIU8h2ZVbnLZcJ3L9+dX9pv/5g0fVHg0CqU4Ei7hEJ1PKJfnDRqVHI15vbF5xKQgAT2REAxZk+E/FwCEpCABIpKoD6IMfwKi7hA7hccLSzw+OUfEQLnB6E2fIajg9wwiB3lF6OIFYwVgQUxgkU0ThgqB+F84dxHH300uWl4n/dwsZC3JfsVuLo3hoU5wg/CC9eiz4g/uGH2ZmNMjJn+sAhCfCE0BIGEnDeU6EY8QUBClCLPA+PgNcIKv2rDDvGA8z799NMkcLGYQoAhIS2hXzDkGMQsjuFcnDMwy0QHXCEw/8IXvxjnXntrQZN3Ukmp7Mxp0aZ9x+RqyhXZ9oZnoc9F4Dr+hBOCxMOFdA+t3PJuXHnXppTU9LTTTkt/E4Uem+1LAAJ8HyCEt2nbNvofOzoW/OtLBRNjmOfTb384WhzSNlWfQxB2k4AEJFATAooxNaHlsRKQgAQkUHACpS7GsPDmP/ss/BELEEd4D1ECizwhRwgsiAgcV9HGseQ2QJgg8SRhQhzPc9rEPYKggcCBEEOFEHLOUO2FHCvV3RBL6Bv5QnDD0D5ulNo6a1jo0Hf6gPiEwINIQvJghBccL4yJcBhycnA9RBUSf+IAglX2a3aukMN7JBJGKCKfB+NFcOHXbxiU33AZMaZcEYR+4VRq0aJF9B8+KuY99B8FWYSxAFu8cXu0bt85jjv++MSyfP9K9TXiG0mmW7frFF9d8p2C8MEtsPjxN2Li5QtTomXmNcKjmwSKRQCH4Ve+8pVo1uLguPLbm1IZ+kK4Y5Y/81Ycf/rF0bL1oUncLpVQvGJx9joSkMDeE1CM2XuGtiABCUhAAnkkUMpiDAICTg6cHThXyAGTOTN4n5wnCAm4ThALOL6ijTAJxBzKUZMDhkSUiBhUvEHQQWQgjAJ3CQkquRbiTK74UFG75d/DkYOYw/lUjUEoQvhAuKjJxjhwvxBulDleCHHC6UK75GhAfEHoIZcDThZ4wCBzDMGJPWPCAh3xhBAuxogIw/nkOqGPCD4Z2/J9pY2sndzPcNeQvLNZ8xYpjIjFUr4XYZTNnnDpgvjS/gckhxFjrC8bPMnP06lL1+gzrCxWbN6Zdz7w/vodj0TnXgOid+/eyclU0b2qL8zsZ/0jwPce4vD+++8f/+e082L++m15n+e44+Z877lo1qJ1jBkzNn3f1T9S9lgCEqhrAooxdX0HvL4EJCABCXyGQKmKMSwocXYguFDhB5EDgQJRARGF0B8EFspCV1aZhcUwAgSOkbPPPjuJMZxD+Ah5R1hEIJTgOKE6CG3iFslCdD4DqooXXAcXBKIQ4UFcg/7Srz0JMZxLuNGHH36YHC9UNlqzZk0SixBfEJvoF44H3CuIJwgvCCich5MFJhWJKTDE1YILhvNpD8dPFo7F+ZxLH2qzcU3mT4cOHaJ7/2Ex854n87oIIxkobR7cpkMMHTYsjXtPPGszjkKeg6hHvp2mLVrF5Jk3RL4FKxa+hHB17NQ5zZPa3stCMrDthk2A7xnCFsePHx+Htu0QFy78dt7LXF//8CsxeMT4OLBps/T9xXe2mwQkIIGaElCMqSkxj5eABCQggYISKEUxhv/cE3JEMlnEGHKvIGxQxYcQHASPhx56KFXVwdHC8bkbjhYWwZs2bUrlhc8777yYN29ebNu2LSXtzT0HQYKQHlwiuE84ryYLfsQQ+kdOFUKECE/C0YJwVL5fiBfkYEF44RwqzZDjhTFyfRw1PDJuxklyXdxA5EYgFIBzuR7ul/JtZ+NnMQ4rBBv6gqsIFxD9oz1cQrRD//Z2ow+0g0unfcdO8eVx58Y1DzyXF0FmxbPvxNXf3RJ9v3xiHNSiRSpjy7jq28a9IpEyYlKrtofHpbesixue+lleGC3Z9JOYcNmC6NitZ5ArBhHRTQJ1QYDvTETunj17RZe+g2PayjV5C1eiZPa4qddE85atkxOPea7oWBd32WtKoP4TUIyp//fQEUhAAhJoUARKUYzJhBjcHITpEEr05ptvJscIQgzuEZwouTkDEAYQWUj0Sxld3DQzZ84MyjEjctAmTpjcjV9zcdngZEHcQSSprhDD9VgUYM/nfMQOXDqE0bBQwHGC+wbxg5wxOFoQhyi5nYkv9JHzcLxs3bo1XnvtteTkQRDiXH79RcCpTHjJHQvXoz84fBBy6BMJiGmbdhF0quPUyW2zOs/pG/2l4lLnI3pF2RlTY85eCjKEJMz6zlNx1EkT47B27eO6667bzbU6fSq1Y0gyTaLojh07Rrf+w+JrKx+Mpd/fu6ozize+HqdfsSQ69+ofI0aOTMmZqzNPSo2N/Wk4BPgOQvzt2btP9Bp6fExbsXqvnWALNrwUE77+D8GxrKws/TuQ+73fcOg5EglIoBgEFGOKQdlrSEACEpBAtQkgxkwcPz7+ddUD8f6L2+MXL/8kb/trz2yN+bOvjkWLFlWrPzhadu3alZwhCBWIG4gZVAFCwOA/+pRhZnGb/TKKeMJrwpFIzMtxiC/khZk9e3YSZd5///3dx2cdwR3y3HPPJdGCvCtcJ2szO6aiRxa8uFMQh3C1EPZDIl36RR9wsiCIIBjRLi4XxsKOuMSinBLShA7BngpFhBzRZk1z1NBfxoHQhOsFoQcRhmuSlJj2s3wwFY0ln+8hmpHEs1uvfskhc/k3H40bn/55jR0gix9/PaatWBNDTpwYnbp2j8suu6zGbqV8jisfbTFnuE+Em3Xr1j16DhkeZ19zc8xf/2KNS4KT0Bj30SlTr4lOPfrFCWVlKQ9QeaExH/22DQnUlADCOfmo+vTrHz2HHBvnzbs9CDGqaS4pwvlm3/dMjDp/enTp1S9OOOGE5CSs6XdkTfvv8RKQQMMmoBjTsO+vo5OABCRQ7wiwYD/t1FPjthUr4/F16+OJ9Rvytj9w9z1x2dRLUojQnsDwayfCBIIFoTqEFBFqQwJUXiN4UO0n+884j5kQgTuFXC+ECSGQ4Aa56aabkgCCUJGdQx8yAQMnCscjmOAa2ZMQw4KaBS8uEEQfEukuWLAgCT9cj3a+973vpWvSX4Qjwp54n2shVjC+LFfNnnhU9jn9pB/kgkH4Qdhh7LhgyDWD0wdhCZdQMTf4bN++PTmRuvfsHT0GD49z594WV63aHEuf2FGl6PCPikmvp/wwVAXqMfCY9Ov6lVdeGQhpDWHLRDyEwiFDhkTnXkfGiedcFpfdtiGVA75p89uVLlhXbn03hTZdu+bf4l9u/G4MG3NmHN71iBg9enTKU2T+jIYwQxrOGHAtUuFu0OAh0alH3xh94YxAnEWUwfVWlTBDCN+c1T+KCxfdHYPKxkWXI3qlEDxCOvkbcpOABCSwNwQUY/aGnudKQAISkEDeCSDGTBg/Ph66f1X8v5dfjJ0/fjl2/viVnJ3Xue+Vf82x5d/7x+uXfvB0zJ01K66//voq+00oDoIIITskmaU6EKE9iBksXqlKhPjAf8Y5lvAiFumE/SBCcAwiDOcgltAO7+FewTqfbZkQQxgT4U4bNmzY3W52DI/ZdRA0yCmDgEIIE24W8qOcf/75MWXKlCTGZNWXcKSsXr06hS1RehpBhGpD9DcfG+1keXNwDCG8UJYbZw4hVuSg4Xp7EpXy0Zeq2kBwQgjr169ftO3QKVUROmPWjTHj7ieSo2Pu2n9PjhBcITzH5XHltx+P8ZfOjx6DvhwdOnWNYcOGJQEOV09D2xAGcS2NHTs2VVnq0ntAjDhrWlx260Nx1f3PxrUPPr+bD+XCWZjOuvfpuGjR3XH0mMnR7vAu0bVbt7joootSwmdDNhraDGkY48GtSM6qESNGpOTSnXv2j5POuzwuvXV9zjzfluY68/zaB/8tzfMLrr8zBo+cEB26HBE9evRMSddJhu4mAQlIIB8EFGPyQdE2JCABCUggbwQQY06fNDE2P/ZI/PfHH8b//Nd/5m3/eMcbsXLxohQyVFmHCTNC6EB4oXIQuVUIH0JMYSfkh1/+WcTySE4YcqIg2rDjSiE8CLcICwCEG8QAnDW5joFMiMFJgqBCRaEsvwtts6ilDcKFEDVw1BAexXE4aK644oqYMGFCTJw4MS0QCDuiLdwgCBC5oVOVjbWm7yMK0S/69N5776VcOIhPVIdChMGhA7tSW5AjgJGnhhAj8qS0aNEiDm3bProfeVQMOG5sDB19RgwdfXr0Hz46uvUdHC1bHRKtWrWKHj16pPwwzMm6FpVqeq9qejxJnrmXxx57bLRu3TpaHtwqOnbvHX2PHpFCtIaOmRwDy06LXkOOjfadu0eLli2jbdu2ccoppyQ3DPPNTQKlTIDvL8RxROOBAwemv/GWB7dO4XX9jhkZR500Kbm8BpaNi15Dhkeb9odHixYto127dqn6HaJ5fUzaXcr3xL5JoLETUIxp7DPA8UtAAhIoMQJ1LcbgIEGIQWBA3MDhQs4BQn5wvyDW4FDB5YLzhFwwiCmILrhpshAkBAsEmKVLlybHDK9zN9wtGzduTOFFCCwINSz4cdm88847yfXC+yyQaWPx4sUp183VV1+dXDA4EXj/lVdeyUsloty+VfSchQx9RNTA6cOYly9fnhjgvEE4KvWNe4ejiXtJIuVjjjkmlcFu3rx5HHTQQUmoQYyg9DPhXDhhGHdj2rJqXvPnz09ume7du0fLli2jadOmceihh8aAAQPizDPPTE4u/lYbG5/GNBca6liZs3yHE2o0d+7cFF6XzfNmzZrtnufknEKARwgvNYG5od4bxyWBxkZAMaax3XHHKwEJSKDECdSlGIOjhHAbQn1IOkuCUwQHHB+4VtgpC8xnuGb4jzqJcVngI0aw2GfDiUGIEqFHCDsscHOdFeQwwE1DImHKPJPDBeGF0CJyGyAEsfMZuWkQhSh3zXNEEI7J+kS4UCEXxJSKRoSiD/yijMuHfmYuGMZd6D7kc8pyH+gz94T7ieBCmW52nuNC4tdv7mHuPctnH0q5LeYSC0+EN4RBRMOMEclQec37iIuZ8FjK47FvEqiIQO48J9cXc5z5zfcAj3w34PZCtGGeF/I7tqL++Z4EJNA4CCjGUrAy3gAAEvpJREFUNI777CglIAEJ1BsCdSXG4IhB7EB8ycQQwoEQVQjJIW8MyXwRaKiQRPgSIgyL0kyEATL/cSfRLwlz2SnvzH/o+U8+jhfyc1Aa+bzzzotp06YlZwyiDQIHQtD3v//9VLUJYYgFAjuJcRGAEInoI+1z3UKJBbTLohsXDNdDdGLc5IIh8S9jYbHuYrze/FnZUQlIQAISkIAESoyAYkyJ3RC7IwEJSKCxE6gLMYY8JwghOFUQShBjCGXBjYJ4QlgOwgxhSYQekXcAMaL8r6WIE+SLoXoRlY04j6S+hDoh5BDuNHXq1PjqV78aCxcuTKEwWQlqxCBEDlwHmduE5yTpJR8MOVnIXYO4g1ulEBsCD8IT9n0cPYwD4YnqUAhA/GKMY6T8uAvRF9uUgAQkIAEJSEACDZmAYkxDvruOTQISkEA9JFBMMQZRAWEF4WHOnDkxe/bslIcFl8ratWuTIIMrhOeZCIPLBecIoTnY2D/55JPkeMFFgvCCCEM+l1mzZiVHCWWeaR9nCe2Ti4OQpF27dqVS2BWJG4gxhAaRhwYRCKGI6kTlw53ycXsJSUEEIu8LrhzEFxw9uGBIGEw/EYUK5cLJxxhsQwISkIAEJCABCdQ3Aoox9e2O2V8JSEACDZxAscQYXCy4THCskLD1kksuSYIMrhiEE1whiDC4YxBGCBciLInnJO998cUXk2uGY2iDSkpU60GIIdnuY489lo6hDCruFkQd3C08r6zyDIIH+QuoiIQLByHmySefTIJRbijU3k4B2kLYwY1DAmDy1zBeRBiEIkpVIzIVyoGzt/33fAlIQAISkIAEJFDfCSjG1Pc7aP8lIAEJNDACxRBjcK9QyhfXyjnnnBNUzbj00ktj3rx5KWdMJoIgpCC+II4QqkO4Ec4RPkdcQbwgFAkXCcltOR8XDGFPCB64ThA8uA7nIHxUVhoV4YPEvoRFUUGJ6yCKEDqUr41rkKCWUCSSBpOHhnHQP0QfctoQfqULJl/EbUcCEpCABCQgAQlUTEAxpmIuvisBCUhAAnVEoNBizE0Lr0+JcxEhRo8eHWVlZUmMoWQ0yXsRKDZs2JAEFsQWKhexI7YQvoSLBLfMW2+9ldwjiCU8x/WSCTGEPyHEIORwDueS+JYQp/Ibwgfv//znP0/X5lhcNZTJzkduFkQhwp4INYIt/WfsJA0mL87LL7+ccsGU75evJSABCUhAAhKQgAQKR0AxpnBsbVkCEpCABGpBoNBizPzZs6NP3yNjwIAB0bdv3xgzZkxKqIsYQwLfZcuWJVEGwQJRhqpJCCWEKZEnJndD6CDfCsfifMFxgrjCcbhMeI8KSIQ1Ic6U3zifkCXKRmelrCmdvbduGEQc+oEThnAjEv/Sl6wsNrlt3n333ZSMt3yffC0BCUhAAhKQgAQkUHgCijGFZ+wVJCABCUigBgQKKca8v/3VuOTCC6Jp06ZxxBFHxMSJE1NSXcQUHCMIL1QNoqwz4gmiCrllEDbKu1R4TX4X8sWsXLkylZ9G/OAcxBecMnfccUdqr6IS0BzLtXDOIACRq4UwJY4tf60a4EuHEgpFiNX69euTyIMIQz+p3ESYEn1kTG4SkIAEJCABCUhAAnVDQDGmbrh7VQlIQAISqIRAIcWYj3a8HtfNmhkjR56YXC9UEMI5gqhCrpSspHR1hArCfjZu3JjcJiTzxc2CwEI4EslwKUe9c+fOJHzkDhWhBTfN5s2bU6JgHCvkpMEhg1OmthvnEtr0wx/+MOWAIQyJflCRCZcO7VO5qTpjq20fPE8CEpCABCQgAQlIoHoEFGOqx8mjJCABCUigSAQKKcZ8vOONWH79glQ1iWpCuF9q40Lh3Oeffz7lgiHxLa8Rcki4i9OFXCwIMYgzuRvH4JqhVDUiDG4cEvxWVN4697zKniPAIArhgnnkkUfStWmX9rNS3IgwOGHcJCABCUhAAhKQgARKh4BiTOncC3siAQlIQAIRKcns6ZMmxubHHon//vjD+J//+s+87YgxKxcviiVLltSaNcl2cb8gulBdiZAm3iMEiPcefPDBePvtt/9JiKEsNrlbqFxEyBAlrn/zm9/UyqnC9ajYRBvktaH6EsmGH3/88dQ3KkXh1KmN0FRrMJ4oAQlIQAISkIAEJFBtAoox1UblgRKQgAQkUAwChXbG7I0Yg8OEPC+Us6b0NAl7EUYIU8KRsnbt2hQSlDliEENww3AOLhhEk4cffjglBK5pkl5yyfz+979PbSHqrF69ercLh5AnkgyTD6ai/DTFuG9eQwISkIAEJCABCUig+gQUY6rPyiMlIAEJSKAIBEpVjCHXCjlZcMPgQtmxY0fKw4IjhnLUhAZ98MEHu0OCEEWowETpaHK34JrZsmVLyhdT3dwwXBPRhrw2hCKRo4ZrI+og/FBimz5VVKmpCLfKS0hAAhKQgAQkIAEJ1JKAYkwtwXmaBCQgAQkUhkCpijG4TsjLggMGEYTwJMKEqKREjpjcSki4YQgjokLSLbfcksKSCF0iN8yeNtw0OHCoiESbXAMnDgl5qdBEm7hgyAXjJgEJSEACEpCABCRQPwkoxtTP+2avJSABCTRYAqUoxhB2RKLe22+/PVVB+uUvf5kS+CLE3H///UmYwcWC4wURhUS+uFcQYp566qkknOypilEmwuCEIfkv+V+43k033ZQEoB/96EdJnDEZb4Od+g5MAhKQgAQkIIFGREAxphHdbIcqAQlIoD4QKEUxBncKwsqmTZti165dKdwIkYTQJMSXLFEuIUPkhFm+fHlK1PvWW2/t/mxP7HHekHuGktjLli1LjhuS8+KCqW3Vpz1d088lIAEJSEACEpCABOqGgGJM3XD3qhKQgAQkUAmBUhJjyPuCoEJOGKok0TccMrwmIe+nn36axBbCj7LcMHfccUc888wzyS1TlYslS+6LC+axxx5LbeK0odISLhjcNyQHrqqNShD6tgQkIAEJSEACEpBAiRNQjCnxG2T3JCABCTQ2AqUixiDEIIiQI4bku4Qe4XohbwshRFQ2QighrwvvE1L0wAMPpES7f/rTnyotWc05v/71r1MpbHLNfPOb30w74g5Jeil3TahSdZP8Nrb54XglIAEJSEACEpBAQyCgGNMQ7qJjkIAEJNCACCDGTJowIR5ftzZ+9dab8dudb1W+v13FZ5xX7vOfbXshFl83NxYtWlQlMYQQKiFROYnwpGeffTZVL6Iq0tNPPx2/+tWvUh4YBBpyxiCo/OAHP4iPPvqo0iS9JNwliS+uGUQYcsqQmHfr1q2pHDYJgclNk4U8VdlBP5SABCQgAQlIQAISqNcEFGPq9e2z8xKQgAQaHgHEmNPGjYvbliyOx++6K564+5544u67//cx9znvZe9X9Zxz/nHeAytvjq9deEHMnz+/UnCIIeRvIfEuYUPr169PIUo4ZDZv3pxKSVMpCVEFcQZB5dVXX40//OEPn3Gz0A7iCs6Z1157LeWbuffee1NpanLBbNu2LZXCzs05U2mn/EACEpCABCQgAQlIoEERUIxpULfTwUhAAhKo/wQQY8aNGhUrJp0V60acEuvLTs7bfs9xo2LqcWWxYMGCSkERYkTC3m984xtJbCH0iBAknC/vvfde7NixIzlmCFfauHFjvP/++0FIU7bxHGGG90nIu3bt2hTqdN9996XjX3/99eS6MRdMRsxHCUhAAhKQgAQk0PgIKMY0vnvuiCUgAQmUNIHkjBkxMu4ZckL8R5NO8VKTDnnbn2rSOWb3GRRLliypkAGJeBFLEFqoloTz5eabb05Je999990UrkT+mHvuuSc5W3C1sOGC4VwcNRyHawYXDCIO1ZEQcj744IP4y1/+YhhSheR9UwISkIAEJCABCTQuAooxjet+O1oJSEACJU+grsQY8sSQ0wURBbEGMYYwJcKVSKyLQ+bWW28NQoxwvXA8OwILIgyhSLhgqLTEcVRfQtjBJWMemJKfdnZQAhKQgAQkIAEJFJWAYkxRcXsxCUhAAhLYE4G6EmM++eSTWL16dcybNy+uu+663UIMyXsRZQhbIuwoq5REPpiPP/44uV4QX5YtWxZZWWtyyiDUuElAAhKQgAQkIAEJSKAiAooxFVHxPQlIQAISqDMCdSHGILDgeLn22mtj5syZyRWDMENI0g033JCS+FIpCQEGF8z27dtjzZo16bjly5cnRwwumE8//TSVu/7b3/5WZ/y8sAQkIAEJSEACEpBA6RNQjCn9e2QPJSABCTQqAsUWY/7617+mUKS5c+fG5ZdfHgsXLkzlrFesWJFKVuOGoZT1O++8k3LBINAQikTeGHLD/OIXv0hlrhFqFGEa1VR1sBKQgAQkIAEJSKDWBBRjao3OEyUgAQlIoBAEiinGUPmIykk4Yi6++OKYMWNGClMiaS/OmJdffjleeOGF5IxBhLnrrrvioYceildeeSWFKP3xj380HKkQk8A2JSABCUhAAhKQQAMnoBjTwG+ww5OABCRQ3wgUS4zBEcO1EGLOPPPMmDJlSlx11VVx4403xqpVq5LoQijSnXfeGZSlJpEvZa3JLUPSXjcJSEACEpCABCQgAQnUloBiTG3JeZ4EJCABCRSEQDHEmPnz58euXbtS1aSxY8fG6aefHtOmTUuJe2+55ZaUiBcXDGLMli1bUpUlXTAFud02KgEJSEACEpCABBolAcWYRnnbHbQEJCCB0iVQaDFmZu+BMXXqJUEo0nHHHRfDhw+P888/P6ZPnx5Lly6Nb33rW7Fu3bp46aWXUq4YqyKV7lyxZxKQgAQkIAEJSKC+ElCMqa93zn5LQAISaKAECinGPNmkU0zr3CsGDz0mjjrqqOjTp0+ceuqpMXv27JSUd9OmTbFz587485//3EDpOiwJSEACEpCABCQggVIgoBhTCnfBPkhAAhKQwG4ChRRjNjbpGBOatY7mB7WI7t27p1wxt99+e0ri+7vf/c5kvLvvgk8kIAEJSEACEpCABApJQDGmkHRtWwISkIAEakygkGLMk006xzkt20aPHj1S+WpcMCTypST13//+9xr31RMkIAEJSEACEpCABCRQGwKKMbWh5jkSkIAEJFAwAoUUY55q0jmu7HlkzJkzJ0jIS2lrNwlIQAISkIAEJCABCRSbgGJMsYl7PQlIQAISqJJAocWY2X0GpSpKVXbCDyUgAQlIQAISkIAEJFBAAooxBYRr0xKQgAQkUHMCijE1Z+YZEpCABCQgAQlIQAL1i4BiTP26X/ZWAhKQQIMnoBjT4G+xA5SABCQgAQlIQAKNnoBiTKOfAgKQgAQkUFoEFGNK637YGwlIQAISkIAEJCCB/BNQjMk/U1uUgAQkIIG9IKAYsxfwPFUCEpCABCQgAQlIoF4QUIypF7fJTkpAAhJoPAQUYxrPvXakEpCABCQgAQlIoLESUIxprHfecUtAAhIoUQKKMSV6Y+yWBCQgAQlIQAISkEDeCCjG5A2lDUlAAhKQQD4IKMbkg6JtSEACEpCABCQgAQmUMgHFmFK+O/ZNAhKQQCMkgBhzatmI+PbgY+OHTTrG80065G3f2KRTzOgzKJYsWdIIyTpkCUhAAhKQgAQkIIFSIaAYUyp3wn5IQAISkEAi8NOf/jRGjRwZo3r1jckdusXkDl3ytp/WsVuceOTAWLBggbQlIAEJSEACEpCABCRQZwQUY+oMvReWgAQkIIGKCPz2t7+Nhx9+OFatWhX3r1q1+zH3+Xfvvz+9nx3DZ7mf577mGI7PPl+zenVs27atokv7ngQkIAEJSEACEpCABIpCQDGmKJi9iAQkIAEJSEACEpCABCQgAQlIQAIS+AcBxRhnggQkIAEJSEACEpCABCQgAQlIQAISKCIBxZgiwvZSEpCABCQgAQlIQAISkIAEJCABCUhAMcY5IAEJSEACEpCABCQgAQlIQAISkIAEikhAMaaIsL2UBCQgAQlIQAISkIAEJCABCUhAAhJQjHEOSEACEpCABCQgAQlIQAISkIAEJCCBIhJQjCkibC8lAQlIQAISkIAEJCABCUhAAhKQgAQUY5wDEpCABCQgAQlIQAISkIAEJCABCUigiAQUY4oI20tJQAISkIAEJCABCUhAAhKQgAQkIAHFGOeABCQgAQlIQAISkIAEJCABCUhAAhIoIgHFmCLC9lISkIAEJCABCUhAAhKQgAQkIAEJSEAxxjkgAQlIQAISkIAEJCABCUhAAhKQgASKSEAxpoiwvZQEJCABCUhAAhKQgAQkIAEJSEACElCMcQ5IQAISkIAEJCABCUhAAhKQgAQkIIEiElCMKSJsLyUBCUhAAhKQgAQkIAEJSEACEpCABBRjnAMSkIAEJCABCUhAAhKQgAQkIAEJSKCIBBRjigjbS0lAAhKQgAQkIAEJSEACEpCABCQgAcUY54AEJCABCUhAAhKQgAQkIAEJSEACEigiAcWYIsL2UhKQgAQkIAEJSEACEpCABCQgAQlIQDHGOSABCUhAAhKQgAQkIAEJSEACEpCABIpI4P8DME7/6CxB+XoAAAAASUVORK5CYII=)![image.png](attachment:image.png)\n",
        "\n",
        "Ref : https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks#:~:text=Fully%20Connected%20(FC)%20The%20fully,objectives%20such%20as%20class%20scores."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VcbCQ6I2xYrz"
      },
      "source": [
        "Replay Buffer : records previous state,action and rewards. A training set is created when the agent is updated and a sample of batch is created from the replay buffer. These samples can come from different episodes if the buffer is long enough. \n",
        "This is very important in order to reduce the high correlation between training data. Neural network dont do well is training data is highly correlated.\n",
        "\n",
        "Ref : https://livebook.manning.com/concept/reinforcement-learning/experience-replay"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0bYJ5hCxYrz"
      },
      "source": [
        "#ref : https://docs.python.org/3/library/dataclasses.html\n",
        "@dataclass\n",
        "class ReplayBuffer:\n",
        "    \n",
        "    '''\n",
        "    considering a buffer size of 50\n",
        "    '''\n",
        "    buffer_size: int = 50\n",
        "\n",
        "    def __post_init__(self) -> None:\n",
        "        \n",
        "        #deque : Returns a new deque object initialized left-to-right (using append()) with data from iterable. \n",
        "        #Deques are a generalization of stacks and queues (the name is pronounced “deck” and is short for “double-ended queue”). \n",
        "        #Deques support thread-safe, memory efficient appends and pops from either side of the deque with approximately the same O(1) performance in either direction.\n",
        "        #ref : https://docs.python.org/3/library/collections.html#collections.deque\n",
        "        \n",
        "        self._state_queue = collections.deque(maxlen=self.buffer_size)\n",
        "        self._other_queue = collections.deque(maxlen=self.buffer_size)\n",
        "\n",
        "        self.queue = collections.deque(maxlen=self.buffer_size)\n",
        "\n",
        "    \n",
        "    def __len__(self) -> int:\n",
        "        return self.n if (self.n > 0) else 0\n",
        "\n",
        "    \"\"\"\n",
        "    flag to check whether the buffer is full\n",
        "    \"\"\"\n",
        "    @property\n",
        "    def full(self) -> bool:\n",
        "        return len(self._state_queue) == self.buffer_size\n",
        "    \n",
        "    \"\"\"\n",
        "    returns length of state_queue\n",
        "    \"\"\"\n",
        "    @property\n",
        "    def n(self) -> int:\n",
        "        return len(self._state_queue) - 1\n",
        "\n",
        "    \"\"\"\n",
        "    :param items: Tuple containing (s, a, r, d).\n",
        "    state queue contains state (s)\n",
        "    other queue contain action,reward and discount (a,r,d)\n",
        "    \"\"\"\n",
        "    def append(self, items: Tuple[Any, int, float, bool]) -> None:\n",
        "        \n",
        "        self._state_queue.append(items[0])\n",
        "        self._other_queue.append(items[1::])\n",
        "    \n",
        "    \"\"\"\n",
        "    returns a batch containing state, action, reward and discount and next state\n",
        "    \"\"\"\n",
        "    def get_batch(self, idxs: Iterable[int]) -> Tuple[List[np.ndarray], \n",
        "                                                      List[np.ndarray],\n",
        "                                                      List[float], \n",
        "                                                      List[bool], \n",
        "                                                      List[np.ndarray]]:\n",
        "        ss = [self._state_queue[i] for i in idxs]\n",
        "        ss_ = [self._state_queue[i + 1] for i in idxs]\n",
        "\n",
        "        ard = [self._other_queue[i] for i in idxs]\n",
        "        aa = [a for (a, _, _) in ard]\n",
        "        rr = [r for (_, r, _) in ard]\n",
        "        dd = [d for (_, _, d) in ard]\n",
        "\n",
        "        return ss, aa, rr, dd, ss_\n",
        "    \n",
        "    \n",
        "    \"\"\"\n",
        "    randomizes the batch\n",
        "    \"\"\"\n",
        "    def sample_batch(self, n: int) -> Tuple[List[np.ndarray], \n",
        "                                            List[np.ndarray],\n",
        "                                            List[float], \n",
        "                                            List[bool], \n",
        "                                            List[np.ndarray]]:\n",
        "        if n > self.n:\n",
        "            raise ValueError\n",
        "\n",
        "        idxs = np.random.randint(0, self.n, n)\n",
        "        return self.get_batch(idxs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1czVqUhxYr6"
      },
      "source": [
        "Epsilon Greedy\n",
        "\n",
        "Epsilon greedy enables the agent to explore the environment. \n",
        "Before an agent makes an action, it considers a random value between 0 and 1. if this value > epsilon , the agent take random action. \n",
        "Eplison is generally set at a high value initially at the beginning of the traning and it slowly decays over time. Hence Eplison is decay factor. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gt3Ts8y_xYr7"
      },
      "source": [
        "@dataclass\n",
        "class EpsilonGreedy:\n",
        "    \"\"\"\n",
        "    Handles epsilon-greedy action selection, decay of epsilon during training.\n",
        "\n",
        "    :param eps_initial: Initial epsilon value.\n",
        "    :param decay: Decay rate in percent (should be positive to decay).\n",
        "    :param decay_schedule: 'linear' or 'compound'.\n",
        "    :param eps_min: The min value epsilon can fall to.\n",
        "    :param state: Random state, used to pick between the greedy or random options.\n",
        "    \"\"\"\n",
        "    eps_initial: float = 0.2\n",
        "    decay: float = 0.0001\n",
        "    decay_schedule: str = 'compound'\n",
        "    eps_min: float = 0.01\n",
        "    state = None\n",
        "    \n",
        "    def __post_init__(self) -> None:\n",
        "        self._step: int = 0\n",
        "        self.eps_current = self.eps_initial\n",
        "\n",
        "        valid_decay = ('linear', 'compound')\n",
        "        if self.decay_schedule.lower() not in valid_decay:\n",
        "            raise ValueError(f\"Invalid decay schedule {self.decay_schedule}. \"\n",
        "                             \"Pick from {valid_decay}.\")\n",
        "\n",
        "        self._set_random_state()\n",
        "\n",
        "    def _set_random_state(self) -> None:\n",
        "        self._state = np.random.RandomState(self.state)\n",
        "\n",
        "    def _linear_decay(self) -> float:\n",
        "        return self.eps_current - self.decay\n",
        "\n",
        "    def _compound_decay(self) -> float:\n",
        "        return self.eps_current - self.eps_current * self.decay\n",
        "\n",
        "    \"\"\"\n",
        "    returns max between eps_min and new_eps value which is new epsilon value post calculation(compound or linear)\n",
        "    in case new_eps value falls below eps_min set at the start, eps_min will be returned\n",
        "    \"\"\"\n",
        "    def _decay(self):\n",
        "        new_eps = np.nan\n",
        "        if self.decay_schedule.lower() == 'linear':\n",
        "            new_eps = self._linear_decay()\n",
        "\n",
        "        if self.decay_schedule.lower() == 'compound':\n",
        "            new_eps = self._compound_decay()\n",
        "\n",
        "        self._step += 1\n",
        "\n",
        "        return max(self.eps_min, new_eps)\n",
        "    \n",
        "    \"\"\"\n",
        "    Apply epsilon greedy selection.\n",
        "\n",
        "    If training, decay epsilon, and return selected option. \n",
        "    If not training, just return greedy_option.\n",
        "\n",
        "    Use of lambdas is to avoid unnecessarily picking between \n",
        "    two pre-computed options.\n",
        "\n",
        "    :param greedy_option: Function to evaluate if random option \n",
        "                          is NOT picked.\n",
        "    :param random_option: Function to evaluate if random option \n",
        "                          IS picked.\n",
        "    :param training: Bool indicating if call is during training \n",
        "                     and to use epsilon greedy and decay.\n",
        "    :return: Evaluated selected option.\n",
        "    \"\"\"\n",
        "\n",
        "    def select(self, greedy_option: Callable, random_option: Callable,\n",
        "               training: bool = False) -> Any:\n",
        "        if training:\n",
        "            self.eps_current = self._decay()\n",
        "            if self._state.random() < self.eps_current:\n",
        "                return random_option()\n",
        "\n",
        "        return greedy_option()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNxL1hUOxYr7"
      },
      "source": [
        "@dataclass\n",
        "class DQNAgent:\n",
        "    replay_buffer: ReplayBuffer\n",
        "    eps: EpsilonGreedy\n",
        "    model_architecture: DenseNN\n",
        "    name: str = 'DQNAgent'\n",
        "    double: bool = False\n",
        "    noisy: bool = False\n",
        "    gamma: float = 0.99\n",
        "    replay_buffer_samples: int = 75\n",
        "    final_reward: Union[float, None] = None\n",
        "\n",
        "    def __post_init__(self) -> None:\n",
        "        self._build_model()\n",
        "\n",
        "    \"\"\"\n",
        "    Prepare two of the same model.\n",
        "\n",
        "    The action model is used to pick actions and the target model\n",
        "    is used to predict value of Q(s', a). Action model\n",
        "    weights are updated on every buffer sample + training step. \n",
        "    The target model is never directly trained, but it's\n",
        "    weights are updated to match the action model at the end of \n",
        "    each episode.\n",
        "    \"\"\"\n",
        "\n",
        "    def _build_model(self) -> None:\n",
        "        self._action_model = self.model_architecture.compile(\n",
        "            model_name='action_model', loss='mse')\n",
        "        self._target_model = self.model_architecture.compile(\n",
        "            model_name='target_model', loss='mse')\n",
        "\n",
        "    def transform(self, s: np.ndarray) -> np.ndarray:\n",
        "        \"\"\"Check input shape, add Row dimension if required.\"\"\"\n",
        "\n",
        "        if len(s.shape) < len(self._action_model.input.shape):\n",
        "            s = np.expand_dims(s, 0)\n",
        "\n",
        "        return s\n",
        "\n",
        "    \"\"\"\n",
        "    First the most recent step is added to the buffer.\n",
        "\n",
        "    s' isn't saved because there's no need. it will added in next step\n",
        "    \"\"\"\n",
        "    def update_experience(self, s: np.ndarray, a: int, \n",
        "                          r: float, d: bool) -> None:\n",
        "        \n",
        "\n",
        "        # Add s, a, r, d to replay buffer\n",
        "        self.replay_buffer.append((s, a, r, d))\n",
        "\n",
        "    \"\"\"\n",
        "    Sample a batch from the replay buffer, calculate targets using \n",
        "    target model, and train action model.\n",
        "\n",
        "    If the buffer is below its minimum size, no training is done.\n",
        "\n",
        "    If the buffer has reached its minimum size, a training batch \n",
        "    from the replay buffer and the action model is updated.\n",
        "\n",
        "    This update samples random (s, a, r, s') sets from the buffer \n",
        "    and calculates the discounted reward for each set.\n",
        "    The value of the actions at states s and s' are predicted from \n",
        "    the value model. The action model is updated using these value \n",
        "    predictions as the targets. The value of performed action is \n",
        "    updated with the discounted reward (using its value prediction \n",
        "    at s'). ie. x=s, y=[action value 1, action value 2].\n",
        "    \"\"\"\n",
        "\n",
        "    def update_model(self) -> None:\n",
        "    \n",
        "        # If buffer isn't full, don't train\n",
        "        #print(\"self.replay_buffer.full=>\",self.replay_buffer.full)\n",
        "        if not self.replay_buffer.full:\n",
        "            return\n",
        "\n",
        "        # Else sample batch from buffer\n",
        "        ss, aa, rr, dd, ss_ = self.replay_buffer.sample_batch(\n",
        "            self.replay_buffer_samples)\n",
        "        \n",
        "        \n",
        "        #print(f\"ss=>\",ss)\n",
        "        #print(f\"aa=>\",aa)\n",
        "        #print(f\"rr=>\",rr)\n",
        "        #print(f\"dd=>\",dd)\n",
        "        #print(f\"ss_\",ss)\n",
        "\n",
        "        # Calculate estimated S,A values for current states and next states. \n",
        "        ss = np.array(ss)\n",
        "        ss_ = np.array(ss_)\n",
        "        \n",
        "        y_now = self._target_model.predict_on_batch(np.vstack(ss))\n",
        "        y_future = self._target_model.predict_on_batch(np.vstack(ss_))\n",
        "        \n",
        "        \n",
        "        # Update rewards where not done with y_future predictions\n",
        "        #print(\"\\ndd=>\\n\",dd)\n",
        "        #print(\"dd shape\",dd.shape)\n",
        "        # dd_mask is an array of bools indicating whether the episode was completed on that step \n",
        "        # (this is for all the replay buffer samples, not just the current episode). \n",
        "        # It's used to index into the array rewards and set the final reward where it is the final step.\n",
        "        dd_mask = np.array(dd, dtype=bool).squeeze()\n",
        "        #print(\"\\ndd_mask=>\\n\",dd_mask)\n",
        "        #print(\"dd_mask shape\",dd_mask.shape)\n",
        "        rr = np.array(rr, dtype=float).squeeze()\n",
        "\n",
        "        # Gather max action indexes and update relevant actions in y\n",
        "        if self.double:\n",
        "          # If using double dqn select best actions using the action model, \n",
        "          # but the value of those action using the\n",
        "          # target model (already have in y_future). \n",
        "          y_future_action_model = self._action_model.predict_on_batch(ss_)\n",
        "          selected_actions = np.argmax(y_future_action_model[~dd_mask, :], \n",
        "                                        axis=1)\n",
        "        else:\n",
        "          selected_actions = np.argmax(y_future[~dd_mask, :], \n",
        "                                         axis=1)\n",
        "        \n",
        "        #print(\"\\nselected_actions=>\\n\",selected_actions)\n",
        "\n",
        "        # Update reward values with estimated values (where not done) \n",
        "        # and final rewards (where done)\n",
        "        rr[~dd_mask] += y_future[~dd_mask, selected_actions]\n",
        "        #print(\"\\nrr post dd_mask=>\\n\",rr)\n",
        "        \n",
        "        if self.final_reward is not None:\n",
        "            # If self.final_reward is set, set done cases to this value. \n",
        "            # Else leave as observed reward.\n",
        "            rr[dd_mask] = self.final_reward\n",
        "        \n",
        "        aa = np.array(aa, dtype=int)\n",
        "        \n",
        "        #print(\"y_now before put along axis\",y_now)\n",
        "        #print(\"y_now shape before put along axis\",y_now.shape)\n",
        "        \n",
        "        #print(\"aa shape\",aa.shape)\n",
        "        #print(\"aa reshape\",aa.reshape(-1,1))\n",
        "        #print(\"rr reshape\",rr.shape)\n",
        "        #print(\"rr reshape\",rr.reshape(-1, 1))\n",
        "        \n",
        "        # np.put_along_axis puts the values in rr which holds the rewards for the sampled steps, \n",
        "        # into the relevant indexes (in aa, which are the actions) of the y_now array.\n",
        "        np.put_along_axis(y_now, aa.reshape(-1, 1), rr.reshape(-1, 1), axis=1)\n",
        "        #print(\"y_now after put along axis\",y_now)\n",
        "        #print(\"y_now shape after put along axis\",y_now.shape)\n",
        "        \n",
        "\n",
        "        # Fit model with updated y_now values\n",
        "        # Runs a single gradient update on a single batch of data.\n",
        "        # Train_on_batch will also see a performance increase over fit and fit generator for\n",
        "        # using large datasets and don't have easily serializable data (like high rank numpy arrays), to write to tfrecords.\n",
        "        # Ref : https://stackoverflow.com/questions/49100556/what-is-the-use-of-train-on-batch-in-keras\n",
        "        self._action_model.train_on_batch(ss, y_now)\n",
        "\n",
        "    \"\"\"\n",
        "    Get best action(s) from model - the one with the highest predicted value.\n",
        "\n",
        "    :param s: A single or multiple rows of state observations.\n",
        "    :return: The selected action.\n",
        "    \"\"\"\n",
        "\n",
        "    def get_best_action(self, s: np.ndarray) -> np.ndarray:\n",
        "        preds = self._action_model.predict(self.transform(s))\n",
        "\n",
        "        return np.argmax(preds)\n",
        "\n",
        "    \"\"\"\n",
        "    Get an action using get_best_action or epsilon greedy.\n",
        "\n",
        "    Epsilon decays every time a random action is chosen.\n",
        "\n",
        "    :param s: The raw state observation.\n",
        "    :param training: Bool to indicate whether or not to use this \n",
        "                     experience to update the model. If False, just\n",
        "                     returns best action.\n",
        "    :return: The selected action.\n",
        "    \"\"\"\n",
        "    def get_action(self, s: np.ndarray, training: bool = False) -> int:\n",
        "        \n",
        "        action = self.eps.select(greedy_option=lambda: self.get_best_action(s),\n",
        "                                 random_option=lambda: self.env.action_space.sample(),\n",
        "                                 training=training)\n",
        "\n",
        "        return action\n",
        "\n",
        "    \"\"\"\n",
        "    Update the value model with the weights of the action model \n",
        "    (which is updated each step).\n",
        "\n",
        "    The value model is updated less often to maintain stability.\n",
        "    \"\"\"\n",
        "\n",
        "    def update_target_model(self) -> None:\n",
        "        self._target_model.set_weights(self._action_model.get_weights())\n",
        "\n",
        "    \"\"\"Value model synced with action model at the end of each episode.\"\"\"\n",
        "    \n",
        "    def after_episode_update(self) -> None:\n",
        "        self.update_target_model()\n",
        "\n",
        "    \"\"\"\n",
        "    Use this to define the discounted reward for unfinished episodes, \n",
        "    default is 1 step TD.\n",
        "    \"\"\"  \n",
        "    def _discounted_reward(self, reward: float, \n",
        "                           estimated_future_action_rewards: np.ndarray) -> float:\n",
        "        return reward + self.gamma * np.max(estimated_future_action_rewards)\n",
        "\n",
        "    \"\"\"\n",
        "    Calculate discounted reward for a single step.\n",
        "\n",
        "    :param reward: Last real reward.\n",
        "    :param estimated_future_action_rewards: Estimated future values \n",
        "                                            of actions taken on next step.\n",
        "    :param done: Flag indicating if this is the last step on an episode.\n",
        "    :return: Reward.\n",
        "    \"\"\"\n",
        "\n",
        "    def _get_reward(self, reward: float, \n",
        "                    estimated_future_action_rewards: np.ndarray, \n",
        "                    done: bool) -> float:\n",
        "    \n",
        "        if done:\n",
        "            # If done, reward is just this step. Can finish because agent has won or lost.\n",
        "            return self._final_reward(reward)\n",
        "        else:\n",
        "            # Otherwise, it's the reward plus the predicted max value of next action\n",
        "            return self._discounted_reward(reward, \n",
        "                                           estimated_future_action_rewards) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzmQN2eixYr9"
      },
      "source": [
        "Play Single Episode"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uvdPq_0uxYsA",
        "outputId": "25f4a1d0-399d-4df0-9536-f43e0c818684"
      },
      "source": [
        "\"\"\"\n",
        "env :the gym environment\n",
        "agent : the DQN Agent\n",
        "n_steps : total number of steps the model should be run for\n",
        "pr : a boolean flag that decides whether to print \n",
        "training : a boolean flag that decides whether training is true/false\n",
        "\n",
        "returns a tuple of episode_rewards,episode_actions\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "def play_single_episode(env: gym.Env, agent, n_steps: int=10, pr: bool=False, \n",
        "                 training:bool=False) -> Tuple[List[float], List[int]]:\n",
        "        \n",
        "    episode_rewards = []\n",
        "    episode_actions = []\n",
        "    \n",
        "    #reset the environment\n",
        "    obs = env.reset()\n",
        "    done = False\n",
        "    \n",
        "    #iterate for n_steps\n",
        "    for s in range(n_steps):\n",
        "        if done:\n",
        "            break\n",
        "\n",
        "        # Select action\n",
        "        action = agent.get_action(obs)\n",
        "        episode_actions.append(action)\n",
        "\n",
        "        # Take action\n",
        "        prev_obs = obs\n",
        "        \n",
        "        #on taking an action, env returns \n",
        "            #observation, \n",
        "            #reward taken by the action, \n",
        "            #whether done is true or false, \n",
        "            #info about the action(scoring reward)\n",
        "        obs, reward, done, info = env.step(action)\n",
        "        episode_rewards.append(reward)\n",
        "\n",
        "        # Update model\n",
        "        if training:\n",
        "            agent.update_experience(s=prev_obs, a=action, \n",
        "                                    r=reward, d=done)\n",
        "            agent.update_model()\n",
        "\n",
        "        if pr:\n",
        "            print(f\"Step {s}: Action taken {action}, \"f\"reward recieved {reward}\")\n",
        "\n",
        "        last_step = s\n",
        "    \n",
        "    if training:\n",
        "        agent.after_episode_update()\n",
        "                  \n",
        "    return episode_rewards, episode_actions\n",
        "                  \n",
        "\n",
        "env = gym.make(\"GFootball-11_vs_11_kaggle-simple115v2-v0\")\n",
        "#print(\"env.action_space.n-=>\\n\",env.action_space.n)\n",
        "agent = DQNAgent(\n",
        "    replay_buffer=ReplayBuffer(buffer_size=1000),  \n",
        "    eps=EpsilonGreedy(),\n",
        "    model_architecture=DenseNN(observation_shape=env.observation_space.shape, \n",
        "                               # Setting low value to make it easier to run in notebook\n",
        "                               unit_scale=0.25,\n",
        "                               n_actions=env.action_space.n))\n",
        "\n",
        "_ = play_single_episode(env, agent, n_steps=20, pr=True, training=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:2325: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
            "  warnings.warn('`Model.state_updates` will be removed in a future version. '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 0: Action taken 4, reward recieved 0.0\n",
            "Step 1: Action taken 17, reward recieved 0.0\n",
            "Step 2: Action taken 17, reward recieved 0.0\n",
            "Step 3: Action taken 17, reward recieved 0.0\n",
            "Step 4: Action taken 17, reward recieved 0.0\n",
            "Step 5: Action taken 4, reward recieved 0.0\n",
            "Step 6: Action taken 4, reward recieved 0.0\n",
            "Step 7: Action taken 9, reward recieved 0.0\n",
            "Step 8: Action taken 17, reward recieved 0.0\n",
            "Step 9: Action taken 17, reward recieved 0.0\n",
            "Step 10: Action taken 17, reward recieved 0.0\n",
            "Step 11: Action taken 17, reward recieved 0.0\n",
            "Step 12: Action taken 17, reward recieved 0.0\n",
            "Step 13: Action taken 17, reward recieved 0.0\n",
            "Step 14: Action taken 17, reward recieved 0.0\n",
            "Step 15: Action taken 9, reward recieved 0.0\n",
            "Step 16: Action taken 9, reward recieved 0.0\n",
            "Step 17: Action taken 9, reward recieved 0.0\n",
            "Step 18: Action taken 9, reward recieved 0.0\n",
            "Step 19: Action taken 9, reward recieved 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "id": "ZR9seBwPxYsA",
        "outputId": "c9a8b74c-7c42-4b37-fd10-56faa7fea8ab"
      },
      "source": [
        "def run_multiple_episodes(env: gym.Env, agent, n_episodes: int=10, \n",
        "                          n_steps: int=10, pr: bool=False, \n",
        "                          training:bool=False) -> List[float]:\n",
        "\n",
        "    \n",
        "    total_episode_rewards = []\n",
        "    for ep in range(n_episodes):\n",
        "\n",
        "        episode_rewards, _ = play_single_episode(env, agent, n_steps, \n",
        "                                          pr=False, training=True)\n",
        "\n",
        "        total_episode_rewards.append(sum(episode_rewards))\n",
        "        \n",
        "        if pr:\n",
        "            print(f\"Episode {ep} finished after {len(episode_rewards)} \"\n",
        "                  f\"steps, total reward: {sum(episode_rewards)}\")\n",
        "\n",
        "    \n",
        "    return total_episode_rewards\n",
        "\n",
        "\n",
        "env = gym.make(\"GFootball-11_vs_11_kaggle-simple115v2-v0\")\n",
        "baseDQNagent = DQNAgent(\n",
        "    replay_buffer=ReplayBuffer(buffer_size=1000),\n",
        "    eps=EpsilonGreedy(eps_initial=0.7),\n",
        "    model_architecture=DenseNN(observation_shape=env.observation_space.shape, \n",
        "                               unit_scale=0.25,\n",
        "                               n_actions=env.action_space.n))\n",
        "\n",
        "doubleDQNagent = DQNAgent(\n",
        "    replay_buffer=ReplayBuffer(buffer_size=1000),\n",
        "    eps=EpsilonGreedy(eps_initial=0.7),\n",
        "    model_architecture=DenseNN(observation_shape=env.observation_space.shape, \n",
        "                               unit_scale=0.25,\n",
        "                               n_actions=env.action_space.n),\n",
        "    double = True)\n",
        "\n",
        "'''\n",
        "nmber_of_episodes = 10\n",
        "# Both n_episodes and n_steps need to \n",
        "\n",
        "dqn_reward_history = run_multiple_episodes(env, baseDQNagent, n_episodes=10, \n",
        "                                       # Use 3000 for 90 minute games\n",
        "                                       n_steps=3000,  \n",
        "                                       training=True, pr=True)\n",
        "\n",
        "print(\"\\n-------------------------------------------------------------\")\n",
        "print(\"\\nThe mean of reward history over the {} episodes : {}\".format(nmber_of_episodes, mean(reward_history_baseDQN) ))\n",
        "print(\"\\n-------------------------------------------------------------\")\n",
        "\n",
        "plt.plot(reward_history_baseDQN)\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.show()\n",
        "'''\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nnmber_of_episodes = 10\\n# Both n_episodes and n_steps need to \\n\\ndqn_reward_history = run_multiple_episodes(env, baseDQNagent, n_episodes=10, \\n                                       # Use 3000 for 90 minute games\\n                                       n_steps=3000,  \\n                                       training=True, pr=True)\\n\\nprint(\"\\n-------------------------------------------------------------\")\\nprint(\"\\nThe mean of reward history over the {} episodes : {}\".format(nmber_of_episodes, mean(reward_history_baseDQN) ))\\nprint(\"\\n-------------------------------------------------------------\")\\n\\nplt.plot(reward_history_baseDQN)\\nplt.xlabel(\\'Episode\\')\\nplt.ylabel(\\'Reward\\')\\nplt.show()\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMTJhOnJeNF5"
      },
      "source": [
        "class RandomModel:\n",
        "    def __init__(self, n_actions: int):\n",
        "        self.n_actions = n_actions\n",
        "\n",
        "    def predict(self) -> int:\n",
        "        return int(np.random.randint(0, self.n_actions, 1))\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class RandomAgent:\n",
        "    \"\"\"A random agent that acts randomly and learns nothing.\"\"\"\n",
        "    n_actions: int\n",
        "    name: str = 'RandomAgent'\n",
        "\n",
        "    def __post_init__(self) -> None:\n",
        "        self._build_model()\n",
        "\n",
        "    def _build_model(self) -> None:\n",
        "        \"\"\"Set model function.\"\"\"\n",
        "        self.model = RandomModel(self.n_actions)\n",
        "\n",
        "    def update_model(self, *args, **kwargs) -> None:\n",
        "        \"\"\"No model to update.\"\"\"\n",
        "        pass\n",
        "\n",
        "    def update_experience(self, s: np.ndarray, a: int, \n",
        "                          r: float, d: bool) -> None:\n",
        "        \"\"\"nothing to update\"\"\"\n",
        "        pass\n",
        "\n",
        "    def get_action(self, s: Any, **kwargs) -> int:\n",
        "        return self.model.predict()\n",
        "\n",
        "    def after_episode_update(self) -> None:\n",
        "      pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "id": "TCkJ5xrSeWU8",
        "outputId": "ec302d03-ce39-4e30-f117-a3593b2d1b36"
      },
      "source": [
        "# for 10 episodes\n",
        "'''\n",
        "env = gym.make(\"GFootball-11_vs_11_kaggle-simple115v2-v0\")\n",
        "obs = env.reset()\n",
        "\n",
        "number_of_episodes = 10\n",
        "\n",
        "random_agent = RandomAgent(n_actions=env.action_space.n)\n",
        "\n",
        "\n",
        "ra_reward_history = run_multiple_episodes(env, random_agent, n_episodes=number_of_episodes, \n",
        "                                          n_steps=3000, training=True, pr=True)\n",
        "\n",
        "\n",
        "dqn_reward_history = run_multiple_episodes(env, baseDQNagent, n_episodes=number_of_episodes, \n",
        "                                           n_steps=3000, training=True, pr=True)\n",
        "\n",
        "double_dqn_reward_history = run_multiple_episodes(env, doubleDQNagent, n_episodes=number_of_episodes, \n",
        "                                           n_steps=3000, training=True, pr=True)\n",
        "\n",
        "\n",
        "plt.plot(ra_reward_history, label='Random')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='Random Agent for 10 Episodes')\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n------------------------------------------------------------\\n\")\n",
        "print(\"Mean of rewards for {} : Eipsodes : {} for RandomAgent \".format(number_of_episodes, mean(ra_reward_history) ))\n",
        "print(\"\\n------------------------------------------------------------\\n\")\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nenv = gym.make(\"GFootball-11_vs_11_kaggle-simple115v2-v0\")\\nobs = env.reset()\\n\\nnumber_of_episodes = 10\\n\\nrandom_agent = RandomAgent(n_actions=env.action_space.n)\\n\\n\\nra_reward_history = run_multiple_episodes(env, random_agent, n_episodes=number_of_episodes, \\n                                          n_steps=3000, training=True, pr=True)\\n\\n\\ndqn_reward_history = run_multiple_episodes(env, baseDQNagent, n_episodes=number_of_episodes, \\n                                           n_steps=3000, training=True, pr=True)\\n\\ndouble_dqn_reward_history = run_multiple_episodes(env, doubleDQNagent, n_episodes=number_of_episodes, \\n                                           n_steps=3000, training=True, pr=True)\\n\\n\\nplt.plot(ra_reward_history, label=\\'Random\\')\\nplt.xlabel(\\'Episode\\')\\nplt.ylabel(\\'Reward\\')\\nplt.legend(title=\\'Random Agent for 10 Episodes\\')\\nplt.show()\\n\\nprint(\"\\n------------------------------------------------------------\\n\")\\nprint(\"Mean of rewards for {} : Eipsodes : {} for RandomAgent \".format(number_of_episodes, mean(ra_reward_history) ))\\nprint(\"\\n------------------------------------------------------------\\n\")\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jADzhBIGLj2U",
        "outputId": "c2eebcb2-91e2-49c1-e834-974268eb5a96"
      },
      "source": [
        "# for 10 episodes\n",
        "\n",
        "env = gym.make(\"GFootball-11_vs_11_kaggle-simple115v2-v0\")\n",
        "obs = env.reset()\n",
        "\n",
        "number_of_episodes = 10\n",
        "\n",
        "dqn_reward_history = run_multiple_episodes(env, doubleDQNagent, n_episodes=number_of_episodes, \n",
        "                                           n_steps=3000, training=True, pr=True)\n",
        "\n",
        "double_dqn_reward_history = run_multiple_episodes(env, baseDQNagent, n_episodes=number_of_episodes, \n",
        "                                           n_steps=3000, training=True, pr=True)\n",
        "\n",
        "\n",
        "\n",
        "plt.plot(dqn_reward_history, label='DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='DQN for 10 Episodes')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(double_dqn_reward_history, label='Double DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='Double DQN for 10 Episodes')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.plot(dqn_reward_history, label='DQN')\n",
        "plt.plot(double_dqn_reward_history, label='Double DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='DQN vs DDQN Agent for 10 Episodes')\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n------------------------------------------------------------\\n\")\n",
        "print(\"Mean of rewards for {} Episodes : {} for Base DQN\".format(number_of_episodes, mean(dqn_reward_history) ))\n",
        "print(\"Mean of rewards for {} Episodes : {} for Double DQN\".format(number_of_episodes, mean(double_dqn_reward_history) ))\n",
        "print(\"\\n------------------------------------------------------------\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:2325: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
            "  warnings.warn('`Model.state_updates` will be removed in a future version. '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Episode 0 finished after 3000 steps, total reward: -9.0\n",
            "Episode 1 finished after 3000 steps, total reward: -3.0\n",
            "Episode 2 finished after 3000 steps, total reward: -3.0\n",
            "Episode 3 finished after 3000 steps, total reward: -4.0\n",
            "Episode 4 finished after 3000 steps, total reward: -2.0\n",
            "Episode 5 finished after 3000 steps, total reward: -1.0\n",
            "Episode 6 finished after 3000 steps, total reward: -4.0\n",
            "Episode 7 finished after 3000 steps, total reward: -3.0\n",
            "Episode 8 finished after 3000 steps, total reward: -8.0\n",
            "Episode 9 finished after 3000 steps, total reward: -3.0\n",
            "Episode 0 finished after 3000 steps, total reward: -1.0\n",
            "Episode 1 finished after 3000 steps, total reward: 0.0\n",
            "Episode 2 finished after 3000 steps, total reward: 0.0\n",
            "Episode 3 finished after 3000 steps, total reward: 0.0\n",
            "Episode 4 finished after 3000 steps, total reward: 0.0\n",
            "Episode 5 finished after 3000 steps, total reward: 0.0\n",
            "Episode 6 finished after 3000 steps, total reward: 0.0\n",
            "Episode 7 finished after 3000 steps, total reward: 0.0\n",
            "Episode 8 finished after 3000 steps, total reward: -2.0\n",
            "Episode 9 finished after 3000 steps, total reward: 0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ihkkweUgw+Hz2BoPtba0wsq8vjl7IxfQR3eDu0vnqvYT8E905WLB16zbi66934YcfEjBx4mS88srzSElJRk5ONlaufAWLFz+L77/fgx9+SICzszNWrvx3q9fb2tri++93sBS98WkYBr+fy8Zb31xEo0qDV+f2x9RhElYTQ7MHYpr27T5yIYflSIi54MSdw5o1a3D27FlYW1vD3t4eK1euRO/evdkOy2BOX8vHX0n5HT+xC4b18cHQ3j6dft3IkWNw/XoKdu36Fo6ODnjooWna6rN8Ph/PPvs8Zs6chosXz2s37pk373Fs3foZHn54JlxdTWNjna6qqG7A1l9TcS2jFP3CPLBwUrh2lzYu8HCxw4BwT/x5VY6pQ4Nhb8ud2Ihp4sSdw4gRI7B//3788ssvWLJkCV588UW2Q7JIvXpFIjMzA1JpGiIiWidnoVCI7t17ID39ztabYrEnJkx4CN98s9XYoRpVy+07Hx3XevtOLpkwMBD1DWqcuEIlNcj948Sdw+jRo7X/j4qKQkFBATQaDSdLLuvD0N5du7o3vKaihfeqxfjPefvz5j2GefNmYtasRw0aGRs0Ggbf/HYdP/2RBm93e7w4sy+nZwMFejmhV7AbjlzIwbiYAFgJzfPvhxgHJ5JDSzt37sSoUaO6lBj+WV2wqIgPoY5/ILo+z5wIBK3b5+bNVHTrFgo7OzukpiZjzJgx2sdUqkbcunUTixYthlDIB4/Hg0DAg7u7CDNmzMb27Z9rv8f1tuTz+RCLO/6Q33EoFbv/SMO4AYFYHNcbtkasftpVs8b1xKovzuJ6TjkeGKBb5dnO0qXtLIm5todRftvj4+Mhl7d/q3vmzBkIBE0Lhn799Vfs378fO3fu7NJ5/lmyW6PRdFiKG9CtZLc5UqvvtM+pUyewZ89uvPvuR3Bzc8FTTz2OAQMGo1+/aGg0Gnz00SZERfVHWFhPqFQaMAwDtZqBSqXBjBlzMGfOw+Dz+drvcZlGo+mwzLIsvwK7j6ZhVLQ/5owJRWVFLUyhMLO/yBb+Ykfs/iMNfYLd9L5C29RLVOubqbfHvUp2GyU57N27t8PnHDlyBO+//z6++uork9gY3hy8/vqr2qmswcESvPPOZkREREIo5GPduv/is88+Rnl5OcrKSjFo0FC88Ub7G/vY2dlh/vzH8f777xj5HRhGQ6MaXx64DhdHayyJ74Paqjq2Q9IZj8fDhIEB+PJA0+B5n270t0S6hhOb/Rw/fhxr167F9u3bERTU9VthQ272Y0n+2R7Xrl3FqlUr8Morr2Hw4GEsRqYfHf1e7DqahiMXcvDSrCiMGhBkcleGKrUGr356Fl5udvj33P56PbapXynrm6m3B+t3Dh157bXXYGVlhWXLlmm/99VXX8HNzY3FqEiz3r37Ys+eX9kOwyhSs8pw5EIORvf3M9laRUIBH+NiAvDjcSlk+RWQ+OhvHwliOTiRHP7++2+2QyAEtfUqbPs1FZ5udpg5yrT3Zh4Z5Yv9Z2Q4lJiNZ+Ii2Q6HmCBuTyvRAw70mhEOudfvw64/0qCorMOTD/WCjbVxqqoaip2NECOj/HDhZhGKy2vZDoeYILNODgKBEI2NDWyHQTiksbEBAkHbG+YraSX4KykfEwcGIdTfhYXI9G9cTAD4PB4On6eSGqTzzDo5ODq6ory8GA0N9XQHYeEYhkFDQz3Ky4vh6Ni61EdlTQO+OnQD/mJHTBsmYSlC/XNzssGgXl44lSRHVW0j2+EQE8OJMQdDsbNzAAAolSVQq1V3fR6fz4dGQ7OVmplrewgEQjg5uWl/L4CmpPHt4Vuorm3Ev2b2NbtVxeMHBuJ0cgGOX8rFlKHmk/iI4Zl1cgCaEkTLD4P2mPp0NH2zpPZITC3EhRtFeHhECKdLY3SVv9gRkSEi/HExFxMGBhpthzpi+szrMomQTiirrMeO32+hm68zJg4KZDscg5k4IBAVNY04k1zAdijEhFByIBaJYRhsP5gKlVqDRZN7QWCmRR4BoGeQG4K8nPD7uRxoaOyN6Mh8/yIIuYc/r8iRnKHAjNGh8BbZsx2OQTWV1AhEgaIGV9NK2A6HmAhKDsTiFJXV4IdjUoQHuWF0fz+2wzGKmJ5iuDvb4iDtM010RMmBWBSNhsHWX1PB5wOLHgoHX89VS7lKwOfjwdgASHOVkOYp2Q6HmABKDsSiHD6fg7RcJeY+0B0iZ1u2wzGq4X194GArxO+JdPdAOkbJgViM3OIq7DmZjn5hHhgS6c12OEZnay3EqH5+uHSrGIWKGrbDIRxHyYFYBJVagy8PXIedjRCPTeip901wTMUD0f4QCHj4nUpqkA5QciAWYf/pTGQXVmHB+J5wdrBmOxzWuDjaYEikN05fy0dFNdUdI3dHyYGYPVl+BX49m4XBEd6I7iFmOxzWPRgbiEaVBscu5bIdCuEwSg7ErLXc8vPRcWFsh8MJvh4OiAr1wLFLeahvVLMdDuEoTiSHLVu2YMqUKYiLi8O0adPw22+/sR0SMRM//5mB/NIaPDEpHPa2VmyHwxkTBgaiqrYRp6/lsx0K4ShOFN6bN28ennnmGQBAYWEhJk6ciKFDh8LFxTzq6hN2NG/5OcaEt/w0lDB/F4T4OuPwuRyMivIDn2+ZA/Tk7jiRHJyc7lTDrKmpAY/HM8uS0fdyOa0Y1zIUbIcBHoCxA4Pg62raawBabvk5w8S3/DQEHo+HCQMC8UlCMi7dKkZMT0+2Q9KJRsPgVJIc/mJHdPOji8dGlQZ/XMzF6H5+et+9kBPJAQB27dqFr7/+GgUFBVi/fj3c3Nw6fQx3d8cun18sZrdc8+4v/oZCWcd610d9oxrHL+dh+uhQzJsYDqGAEz2PnfbBD5dRVlmHjUuHw9/PteMX3APbvxuG8qC7I/aekuHopVxMGBai8/RettqjVFmL93dewrX0EvSSiLBx6XBW4vgnNn8/Tl3Ow4/HpRjW3x/+eo7DKMkhPj4ecrm83cfOnDkDgUCAOXPmYM6cObh58yZefvllDB48uNMJorS0ChpN56tOsr1/gbKqHgWlNZg5OhQTBrJbOrq+UY2E05n4+bgUl28WYcnUCIhd7ViNqbOupJXgyLlsPDQ4CO4OVvf1s2X7d8PQxkb7YcfhWzhzORfdAzpOomy1xxVpCbb9mooGlRph/i64kVmG7Nwy2Nmwe33L9u/HmaQ82NsIYS/gdSkOPp9314tqo7Ts3r17dX5ujx494OnpiXPnzmH8+PEGjIo7mmvdcGHvYhsrAZbOiEKItxO+OngDq7efw2MTemJAuBfboemk5ZafU2nnsw4N7e2DhFMyHErM1ik5GFujSoPdJ6Q4eiEXAZ6OeHpaBCqqG7Dxu8u4kVWGft0td2oywzBIkSnQK9jNIGNGnOgzkEql2v/n5OQgNTUVoaGW008szVNCKOAjiEM7kcX29MSahbHw9XDAp/tSsP23VNQ3cHvaI8Mw+Pb3m6iubcRTU3qZ3ZafhmBjJcCY/n64Ii2BvKSa7XBaKVDU4K1vL+DohVyMjfbH6wui4ePugG5+LrCxFiA5k/0xOjbll9agrLIekSHuBjk+J8YcPvzwQ0ilUgiFQggEArz++uvo1q0b22EZjTRXiWAfJ859mHm42uHVuf2x7y8ZfjubBWmeEk9Pi0SAZ9fHdgwp8XohLtwsxvSRIZyNkYvGRPvjYGI2fj+XjYWTwtkOBwBw+lo+dhy+BaGAh+ce7t3qDkEo4CM80A0pMstODs3vv1dw58dndcGJ5LB582a2Q2BNo0qNzIJKjIsNYDuUdgkFfEwf2Q3hQW74Yv91rP36AmaNCcWY/n6cqk9UVlmPHYebtvxke9zG1DjbW2Nobx/8lSRH/IgQuDrasBZLbb0KOw7fxNmUQnQPcMXiKb3arZ4bIRHhirQEReW18DSxMTF9SclUwFtkDw8Xw7x/bl2qWqDMgkqoNQzCOD4tr1ewCGueGIDwIDfsPHILH+25hqraRrbDAtBiy0+NBk+a+ZafhjI+NgBqNYM/LrJXUkOWX4E128/j7+uFiBsmwb/n9LtrWfXmdSuWevfQqNLgRnaZQdfv0F8Ry6S5TYPRpjBn29nBGs/P6INZY0KRlF6KVdvO4VZOOdth3dnyc1QovMx8y09D8RLZo393MU5czkNdg8qo59YwDH4/l431315Eo1qDV+f2x9RhknsOsnq52cHd2dZik4M0txwNjRpKDuZMmqeEl5udyVQK5fN4GD8gECvmR8NKyMfG7y5h31+yLk0h1ofmLT97BVvOlp+GMmFgIKrrVDh11XglNSqqG7B5dxJ+OCZFn27uWPPEAJ1mTfF4PERIREjNUkBtYQtmASA5UwEBn4eegYabYUbJgUUMw0Cap0SoCdw1/JPExxmrHo/FoF5e2PeXDO/sugxFRZ1RY7iz5ScPT0yynC0/DaWbnwtC/V1w+HyOUT5wr2cqsGrbOaRmleHRcd2x9OHecLTTfRFopESE2no1ZHLzXYdyNykyBUL9XGBrbbhhY0oOLCoqq0VlTSO6cWB9Q1fY2Qjx1JQILHooHJkFlVi17RwupxUb7fy/n8++veVnmMVt+WkoEwcEorSiDudvFBnsHCq1Bj//mY53v78Ce1shXl8QjbHR/p2e4BAe7AYeD0iWlRooUm6qqG5AdmGVweuFUXJgUfPiN64PRndkaG8frFoYC3cXW3z48zXsPHILjSrDronILa7C3pMZ6N9dbJFbfhpK3zAPeIvs8XtiDhhG/12FJeW12LjzEn49m4VhfXzwxmOxCOzi+h4HWyuE+DgjxcLWO1y//X4pOZixtFwl7G2E8PFwYDuU++YtssfK+TEYFxOAPy7mYt03F5FfaphFVS23/FwwoQenptSauqYxpQBkFVbiRlaZXo99/kYRVm0/D3lpNZ6eFoGFk8Lvu1hchESEDHkFauq4MXPOGFJkCjjaWRl80SwlBxal5ynRzc/FbPrKrYR8zHkgDMse6YOyynqs+eo8TiXJ9X4F2rzl52MTesLZ3jQG8k3JkEhvONtb4eC5bL0cr75Rja8O3sCWhGT4uNtj9cIBeivHEiERgWGayrNbAoZhkJxpuJIZLVFyYEl1XSPySqoR6ufMdih6FxXqgTVPDECIjzO2/3YDn++/jtp6/UyPzJA3bfk5JNIb/S24ro4hWQkFGBvtj+QMBXKLqu7rWLlFVVj79QWcvCrHxEGBWP5of70WcpT4OMPORmAxU1rzSqqhrGpARLDh9yeh5MCS9LwKADDJmUq6cHOywcuz+yF+RAjOpxZh9fZzyJBX3NcxW275OfcB2vLTkEb394e1FR+Hunj3wDAMjl/KxdpvLqCqthEvzYrCjFGhei8BLxTw0TPQDckyhUHGSLimOQkaY/MqSg4skeYpwefxIPE1vzuHZnw+D1OGBOPVR/tBo2Hw9o6LOJiYBU0X/4h/+jMdBYoaPPEQbflpaI52VhjexxeJ1ws7PUW5uq4Rn+xNxreHb6FHgCvWPDHAoB9mkRIRSpR1KCqvNdg5uCJFpoCPu71RZudRcmCJNLccAZ6OBp2nzBVh/q5Y/cQARIV5YPfxdGz68SqU1Q2dOkZqVllTdc7+/ka5pSbAg7EB0DAMjnaipMatnHKs2nYOV6QlmDk6FC/M7AsXAy/wtJRSGo0qNW7mlCNSYpgqrP9EyYEFao0GGfkVnNi/wVgcbK3wbFwk5o/vgZu3P0B0nZ/evOWnl5sdHhltOdV62SZ2tUNsT0/8eSWvwzEjjYbB/tMybPzuEgR8HlbMj8aEgYFGmWzh6WYPsastkjmwza4h3cpVolFl2JIZLVFyYEFOURUaGjVmO95wNzweD6P7+eE/j8XA0c4K7/1wFbuPS6FS33s17q4/0qCorMOTk3vBxkq/++SSexs/IBC19Wr8eaX9nRyBpoq4//v+MvaekmFguBdWLxwAiY9xu0sjJO5IzS7r8HfJlKXIFBAKeOhhpE2ZKDmwoLnYnqUlh2b+Ykf857EYjIzyxcHEbLy949Jd+4uvpJXgr6R8TBoUZBLFCc2NxMcZPQNdceRCTrsfvFekJVi17Rwy8ivwxKRwPDWlFytbd0YEi1DfoL7vSQ9cliJTIMzf9b7XhuiKU8khMTER4eHh2LFjB9uhGJQ0Twk3Jzh3GhMAACAASURBVBu4u1huyQcbKwEem9ATz8RFokBRgzXbzyHxemGr5zRv+Rng6Yhpw2jLT7ZMGBiIssp6nEu98/NpVGnw3dFb+OCnJIicbLDq8VgM6+PD2oLE8CBX8Hk8JJvpuIOyqh45RYYvmdESZ5JDVVUV/ve//2HEiBFsh2JwplpszxBabkf62S8p2HZ7O9KWW34+ObmX3qdAEt31DnGHr4cDDiVmg2GYVtt3PhDtj5W3t+9kk72tFUJ8nc12ULq5RIgxJ2Nw5i9uw4YNWLRoEdzcDLPlHVcoKuqgqKi3qMHojjRvR/rQ4CCcTsrHm1+fx/7TmbhwsxhxwyW05SfLeLdLauQWV+PzvdewZvt5KCrqsWx6H8wd1x1WQm6MA0VIRMjMr+DMJlT6lCJTwMneCgFexvtb4ERy+PPPP1FZWYkJEyawHYrBNRfbozuH1pq3I31pdhRq6lVI+EuGbn7OmDgwiO3QCIBBvbzh4miNA6dlCPZ2wprbU5O5JEIiAgPzK6WhYRikZJYhIlhk1FI7Rhk5io+Ph1ze/myHQ4cO4d1338X27dvv+zzu7l3PqmKxYYtYNcs7nQkbawH6R/hwuqvEWO3xTyPFTogK98YvpzIwfmAQPDmwsxtbbcE1L82Nhry4ChOGSCAwcF2frhCJHOBgl4T0/EpMGm68Kc+G/v2QyZWoqG7AoD6+Rv1dNEpy2Lt3710fu3DhAoqLizFjxgwAQFlZGY4fP47y8nIsXbq0U+cpLa3q0o5kYrETiouNs2HItbRiSLydUKYwTMVSfTBme9zNhBh/QK1mPQ4utAVX+Ivs0K+HJ6fbo2egKy6mFqCoqMIog+PG+P04dSkHABDgbq/3c/H5vLteVLO+PDcmJgZnz57Vfr18+XJERkZi3rx5LEZlGPUNamQXVmHioEC2QyHELEVIRLh4sxgFihrWB8n1JUWmgJ/YAW5ONkY9L3f7NcyQLL8CGoZBGA1GE2IQzbN5zGXWUn2jGrdylKyUjGH9zuGfNmzYwHYIBpN2ezA6xJeSAyGGIHa1g5ebHVJkCjwQE8B2OPctLaccKrUGkUZc39CM7hyMKD1PCR93+05tok4I6ZwIiQg3ssvNopRGskwBoYCPMCOVzGjpnncOc+fO1WlQZ+fOnXoLyFxpGAbpeUpE96ANaggxpAiJCMcu5UGaq0TPINNeN5WSqUCPABdWaordMzk0zyACgOzsbPz888+Ij4+Hr68v5HI5EhISMH36dIMHaQ7yS2tQXaei+kCEGFjPQDcI+DykZCpMOjmUVdYjr7gaQyN9WDn/PZNDfHy89v8zZ87E1q1bERZ2ZweuKVOmYMWKFVi2bJnhIjQT6bfHG8L8jX97SIglsbMRopuvM5JlCkwfabol3q9nGm/Xt/boPOaQnp6OwMDWUzD9/f2RkZGh96DMkTRXCUc7K3i56W//XEJI+yIkImQXVKKipnObSnFJskwBZwdr+IvZmZKrc3KIjY3F8uXLkZmZibq6OshkMqxcuRIxMTGGjM9spN0utsdW1UpCLEmExL2plEamaZbS0DAMUmQKRASLWPvM0Dk5NE8xnTx5Mvr164cpU6aAYRisX7/eYMGZi8qaBhQqaqjYHiFGEuztBAdbocmud8gprEJVbSMrU1ib6bTOQa1W4+uvv8aGDRvw7rvvQqFQQCQSgc+nmbC6oGJ7hBgXn89DeLAIKZkKMAxjcnfszVvo9gpmb0Bdp093gUCA7777DlZWVuDz+fDw8KDE0AnSPCUEfB6CvamAGyHGEikRoayyHvLSGrZD6bQUmQIBno5wcTRuyYyWdP6Ej4uLw65duwwZi9lKz1UiyNsJ1rT/MSFGY6qlNOob1EjLVbI2S6mZzuUzkpKSsGPHDmzduhXe3t6tbtNoEdzdqdQayAoqMbqfH9uhEGJR3F1s4eNujxSZAg/Gmk4pjZs5ZVBrGNNJDjNnzsTMmTMNGYtZyiqsRKNKQ+MNhLAgIliEk1flaFRpYCU0ja7wZJkCVkI+urM8gUXn5NByQRzRnTT39mA0zVQixOgiJCIcvZgLaW45wlmobNoVKTIFegS4sr79aqeqspaUlCApKQllZWVgmDub6jzyyCN6D8xcSPOU8HCxhSuLA0uEWKoega4Q8HlIlilMIjkoKuqQX1qDEX192Q5F9+Rw9OhRvPLKKwgKCoJUKkVoaCjS0tLQv39/Sg53wTAMpLlKhLM4HY0QS2ZrLUSYvwtSZArMGM12NB1rHjxne7wB6MRspU2bNmH9+vVISEiAnZ0dEhIS8OabbyIyMtKQ8Zm0EmUdlNUNNN5ACIsiJCJkF1VBWc39UhopmQq4OlrDz4P9Xex0Tg5yuRwTJ05s9b34+HgkJCTcdxDLly/HiBEjMG3aNEybNg1btmy572NyAS1+I4R9zVfhzYXsuEqjuV0yQ8JeyYyWdO5Wcnd3R0lJCTw8PODn54fLly/Dzc0NGo1+NtRYvHix2e0bLc1VwtZaAH9x+xt4E0IML9DLCY52VkiRKTA4wpvtcO4qq7AS1XUqTnQpAZ24c5gxYwYuXrwIAHj88cexYMECTJs2DXPmzDFYcKZOmqdEN19n8PnsXwUQYqn4PB56BbshRaZoNZGGa5Jvjzf04sjAuc53DosXL9b+Py4uDgMGDEBtbS26ddNPvfTt27fjhx9+QEBAAF566SW9HZcttfUq5BZXYcqQYLZDIcTiRUhEOJdahLziavh7cvNOPkWmQJCXE5ztrdkOBUAnksMff/yB2NhYODs7AwB8fXWfahUfHw+5XN7uY2fOnMGLL74IsVgMPp+PhIQEPPnkkzh69CgEgs7N83V37/oPXSzWb92jyzeLwDBAdISP3o9tDKYYs6FQW7Rmiu0xIjoQ23+7gcziavSL0O/Oavpoj5q6RqTnKRE/KpQz7atzcti2bRteeuklBAUFITY2FgMGDEBMTAxEoo5vgfbu3XvPx728vLT/j4uLw9tvv42CggL4+XWu5ERpaRU0ms7fNorFTiguruz06+7l4vUC8AB4OFjp/diGZoj2MFXUFq2Zcnv4eTggMTkfwyK8On6yjvTVHlfSSqDWMJB4ORq1ffl83l0vqnUec9i5cycSExOxYsUKuLq6YufOnRg7diwmT5583wEWFhZq/3/q1Cnw+fxWCcMUSXPL4Sd2hJ1Np9YZEkIMJEIiwq2ccjQ0qtkOpY0UmQLWVnxOzWzs1CeXWq1GY2MjGhoaUF9fDycnJ4SEhNx3EK+++ipKS0vB4/Hg6OiILVu2QCg03Q9VjYZBuryC0zMjCLE0ERIRDp/P4UTF039KzlSgZ6Abp+o/6fwJ/Mgjj6C4uBj9+/fHgAEDsG7dOoSGhuoliK+++kovx+GKvJJq1DWoOXUVQIil6x7gCqGAp11LwBUl5bUoVNRgDMcqN+ucppycnKBSqVBRUaH9p1KpDBmbyZLmlgMAulGxPUI4w8ZKgDB/V+0ua1yRksmdkhkt6XznsH37dqhUKqSkpOD8+fP4/PPPce3aNYSFhZndlf/9kuYp4eJgDbGLLduhEEJaiJSIsPtEOsqr6jlTDDNFpoCbkw183O3ZDqWVTnVwVVVVobi4GAUFBZDL5aioqEBdXZ2hYjNZablKhPq5cGIJPCHkjuarc67sDqfRMLieWcaZkhkt6XznMGXKFGRnZ6N3796IiYnB8uXL0a9fP9jZ2RkyPpNTXlWPEmUdxkb7sx0KIeQf/D0d4WxvhZRMBYb21u96h66QFVSgpl6FSI51KQGdSA6vv/46oqKiYGPDjVsxrkqnYnuEcBafx0MviQjXZQpoGAZ8lq/WU2QK8MCdkhkt6dytNHDgQNTU1CAhIQFffPEFgKb1CQUFBQYLzhSl5SohFPAR6MWNVY6EkNYigkWoqGlEblEV26EgRaZAsE9TYUCu0Tk5nDt3DhMmTMD+/fvxySefAACysrKwevVqQ8VmktLzlJD4OHFqvjIh5A6ujDvU1quQnlfBuVlKzXT+BFu/fj02bdqErVu3aheo9e3bF0lJSQYLztQ0qtTILKikLiVCOMzV0Qb+YgdtFVS2pGaVQcMwiOBglxLQieSQl5eHwYMHA4B2VN3KygpqNfeWorNFll8JtYZBKK1vIITTIiQipOWWo57FUhopMgVsrAXoxtGLSZ2TQ7du3XDq1KlW3ztz5gy6d++u96BMVfNgNFd/2ISQJpESd6jUDG7llLMWQ4pMgfBANwgF3OyC1nm20vLly7FkyRKMGjUKdXV1eOONN3Ds2DHt+ANpGoz2crPjTD12Qkj7wvxdYCXkI0WmQO8Qd6Ofv6isBkXltRgXG2D0c+tK55QVFRWFX375BaGhoZg+fTr8/f3x/vvv48svvzRkfCaDYRhI85TUpUSICbC2EqB7gCtrg9IpmWUAuFcyo6UO7xxqa2vx2Wef4caNGwgKCsJzzz0HhUKBjRs3YsuWLYiLizNGnJxXVFaLqtpGGowmxEREBIvw43Epyirr4eZk3PVbKTIF3J1t4eXG3UXEHSaHN998E9evX8ewYcNw8uRJ3Lp1CxkZGYiPj8fatWt12uzHEqTl3l785u/KciSEEF1ESkT48TiQLCvF8D6672x5v9QaDVKzFIjt6cW5khktdZgcTp06hX379sHd3R3z58/HqFGj8O233yI2NtYY8ZkMaZ4S9jZCzhXPIoS0z0/sABcHa6TIFEZNDjJ5JWrr1ZwsmdFSh2MONTU1cHdvGrDx9vaGvb09JYZ2SPOU6ObnwvpyfEKIbng8HiIkIlzPbFpvYCzJslLweEDPIDejnbMrOrxzUKvV+Pvvv8G0aLx/ft28/uF+fPvtt9i5cyesrKzA5/Oxb9+++z6msVTXNUJeUo2BvUx7a1NCLE2ERIQzyQXILqxEsLezUc6ZkqmAxMeZkyUzWuowObi7u2PFihXar11dXVt9zePx8Mcff9xXEIcPH8ahQ4fw008/wdHRESUlJfd1PGNLz6sAQMX2CDE1zQXvUmQKoySHmrpGZMgrMHlwsMHPdb86TA7Hjh0zeBDbtm3D888/D0dHRwCAh4eHwc+pT9K8cvB5PIT4GOfKgxCiHy4O1gj0dESKTIGHjPCBnZpVBobh9hTWZjovgjOk9PR0XL16FZs3b0ZDQwNmz56NmTNndvo47u6OXY5BLO56FdXsomqE+DnD3898ZirdT3uYG2qL1sytPWIjvLHvZDocne1gZ9P5j8TOtEf6nxmwtxViYF8/zq6MbmaU5BAfHw+5XN7uY2fOnIFarUZ+fj6+++47lJWVYc6cOZBIJJ0e+C4trYJG0/mBJbHYCcXFlZ1+HQCo1BrcyGqa7dDVY3DN/bSHuaG2aM0c20Pi5QiVmsHpSznoG9q5XovOtAfDMLhwvQA9AlxRpqjuSqh6x+fz7npRbZTksHfv3ns+7uvri8mTJ4PP58Pd3R1DhgxBUlKSScyKyi2uQkOjBmG0MpoQkxTm7wLr26U0OpscOqOorBYlyjpMHBhosHPoEyfuayZPnqwt6ldTU4OLFy+iZ8+eLEelG2ku7fxGiCmzEgrQI9ANKZmGLaXRXCLcFMYbAI4kh8cffxz5+fl46KGHMGPGDEyZMgVDhw5lOyydSPOUEDnbQORsy3YohJAuipCIkF9ag1JlncHOkSJTQOxqC08301goy4kBaVtbW7zzzjtsh9El0jwl3TUQYuK0u8NlKjCir/5XS6vUGqRml2FwhLfej20onLhzMFWKijooKupp/wZCTJyvuz3cnGwMtjtchrwC9Q1qzu761h5KDvdBentzHxqMJsS08Xg8RASLkJqp6NKMx44kyxTg83gIDzKd6e6UHO6DNFcJays+/MVdX19BCOGGCIkI1XUqZBbof6puikyBEF9n2Ntyu2RGS5Qc7kNanhIhPs6cX8xCCOlYr2A38ACkyEr1etyq2kZk5leYzCylZvSp1kX1DWrkFFbRzm+EmAkne2sEejvpfXe41KwyMDCdKazNKDl0UUZ+BTQMQzOVCDEjkRIR0uUVqK1X6e2YKbJS2NkIIfExrbIjlBy6qHkwmmYqEWI+IoJFUGsY3Mgu08vxGIZBikyBXkFuEPBN6+PWtKLlkPQ8JXw9HOBgQgNMhJB76+bnAhsrgd66lgoUNSitqDe5LiWAkkOXaBgG0lxa/EaIubES8tEj0FVvySHFxEpmtETJoQvyS2tQU6+i5ECIGYqQiFBYVovi8tr7PlayTAEvNzuIXe30EJlxUXLogvTb4w00U4kQ8xPZopTG/WhUaXAju8wk7xoASg5dkpZbDkc7K3i5md7VACHk3rxF9nB3trnvrqX0PCUaGjWUHCyJNK8CoX4u4PF4bIdCCNEzHo+HCIkI1zPLoNZounyclEwFBHweega66TE646Hk0EkVNQ0oVNRQPSVCzFiExB219SrI8rteSiNZpkA3X+cubT3KBZQcOimd1jcQYvbCg5pLaXSta6mipgHZBZUm26UEcGQ/h8cffxxlZU2LTtRqNdLS0rBv3z5O7gYnzVNCwOch2Nu0VjsSQnTnaGeFYB9npMgUmDZM0unXp2Y2l8xw139wRsKJ5PDVV19p/3/06FFs2rSJk4kBaKrEGuztBGsrAduhEEIMKEIiwm9ns1BT19jpaqopMgUcbIUmfRHJuW6ln376CdOnT2c7jHap1BrI8iupS4kQCxApEUHDMEjNKu/U6xiGQUqmAuHBIvD5pjtphVPJobi4GGfPnsW0adPYDqVdWQWVUKk1NBhNiAUI8XWGjbWg0+sd5KU1KKus166XMFVG6VaKj4+HXC5v97EzZ85AIGjqoklISMDw4cMhEnWtUd3du77pjljc8e3f6euFAIABffwgcrbt8rlMgS7tYSmoLVqzpPaIChPjRnbZPd/zPx87k1oEABjePwBikb1B4zMkoySHvXv36vS8PXv24N///neXz1NaWtWlLf7EYicUF3c8Ze3KzSJ4uNhCXd+I4uLGroRoEnRtD0tAbdGapbVHqK8zElMKkHKrEJ5ubT/o22uPxGv58BbZg6dWc76t+HzeXS+qOdOtdOnSJVRWVmLEiBFsh9Iu5naxPepSIsRyaEtp6DiltVGlwU0TLpnREmeSw549exAXF6ftYuKaEmUdlNUNVGyPEAvi6WYHDxdbJOuYHKS55WhQaUx+vAHgyFRWAFi3bh3bIdyTNLe52J4ry5EQQoyluZTGudRCqNSaDveLT5Y1lczoEWj6nxOcuXPgOmmeErbWAvh5OLAdCiHEiCIlItTWq5Ehr+jwuSkyBcL8XWBrzZnr7i6j5KAjaZ4S3XydTXreMiGk88KD3MDjdTzuoKxuQHZRlVmMNwCUHHRSW69CbnEVdSkRYoHsba0Q4uvc4XqH65mmu+tbeyg56CBDXgGGAQ1GE2KhIoJFkOVXoKr27lPYU2QKONpZIdDLPNaBUHLQQVpuOXi8phWThBDLEylxB8MAN7LK2n2cYRikyBToFewGvpns80LJQQfpeUr4ix1Nti47IeT+SHydYGcjuOuU1rziaiirG8ymSwmg5NAhjYZBuryCupQIsWACPh/hQSKkyBRgmLZVGJqTRkQwJQeLkVtchboGNUJpZTQhFi1CIkJpRR0Ky2rbPJaSqYCvh4NZ1Vyj5NCB5p3f6M6BEMsWcZdSGg2NatzKKTeruwaAkkOH0vKUcHG0hoeL+VwREEI6z9PVDp6udm2SQ1quEo0qjVmNNwCUHDokzVUi1M8FPDOZgUAI6boIiQip2WVQqTXa76XIFBAKeOgRYF7roCg53EN5VT1KlHXUpUQIAdCUHOob1NruZqBpMDrM3xU21twsGtpVlBzu4U6xPUoOhBCgZ2DTOobm1dKKijrkFleZRRXWf6LkcA/SPCWshHwEmcmKR0LI/bG3FaKbnzOSM5qSw5VbTbu+mdt4A0DJ4Z6keUpIvJ06LNNLCLEcERIRsgoqUVnTgMs3i+FsbwV/z65vUcxV9Kl3Fw2NamQVVKIbdSkRQlqIkIjAoGltw5VbxeglEZlNyYyWOFEPQiaT4Y033kBFRQUaGhowadIkPPfcc6zGlFlQCbWGocFoQkgrEm9n2NsI8fu5HJRX1Zvd+oZmnLhzeOeddzB+/Hjs27cPP/30E/bs2YOkpCRWY5LS4jdCSDv4fB56Bbshq6ASgHmONwAcSQ48Hg+VlU0NXVdXBx6PB5GI3QaX5irhJbKHk701q3EQQrinOSEE+zjD1dGG5WgMgxPJYcWKFfjtt98wfPhwjBkzBosWLYK/vz9r8TAMA2meEmF010AIaUdzcojqLmY5EsMxyphDfHw85HJ5u4+dOXMGP/zwA6ZNm4Ynn3wSRUVFmD9/PiIjI9G3b99OncfdveszBsTiO9NV84qrUFXbiKieXq2+b0ks9X23h9qiNWqPpjb4z6KB6BHoBhczvXMwSnLYu3fvPR//9ttvcfToUQCAp6cnBg0ahPPnz3c6OZSWVkGjaVtOtyNisROKiyu1X59LygcAeLnYtPq+pfhne1gyaovWqD3ukIgd4OJo2p8RfD7vrhfVnOhW8vf3x6lTpwAAVVVVuHjxIsLCwliLR5pXDgdbIXzc7VmLgRBC2MSJqaxvv/021q1bh23btkGlUmHSpEkYOXIka/FI8yrQzc/FLOcuE0KILjiRHCIjI/H999+zHQYAoLquEfKSagzq5cV2KIQQwhpOdCtxCW3uQwghlBzakOYpwefxIPFxZjsUQghhDSWHf5DmKhHo5Wh2tdkJIaQzKDm0oFJrkJFfQV1KhBCLR8mhhdziKjQ0amhzH0KIxaPk0EJaLg1GE0IIQMmhlfQ8JUTONhA527IdCiGEsIqSQwtpuUq6ayCEEFBy0FJU1KGssp6SAyGEgJKDVvPmPmH+rixHQggh7KPkcFtarhLWVnz4ezqwHQohhLCOksNt0jwlQnycIeBTkxBCCH0SAqitVyGnsAqh1KVECCEAKDkAANJyyqBhGBqMJoSQ2yg5AEjNVAAAQv2o2B4hhACUHAAAqTIF/DwcYG9rxXYohBDCCZxIDhkZGViwYAGmTJmCKVOm4PTp00Y7t4ZhcCOrDN2oS4kQQrQ4sRPcihUrMHv2bMTFxSEzMxMLFizA77//Djs7O4OfO7+kGtW1jQijYnuEEKLFiTuHGzduYMSIEQCA4OBguLi44OTJk0Y5t5R2fiOEkDY4cecQERGB/fv347HHHsO1a9cgk8kgl8s7fRx3d8dOv4bh8+Hj4YCI7p7g8Xidfr25Eoud2A6BM6gtWqP2aM1c24PHMAxj6JPEx8ff9cP+zJkzkMvlWL9+PeRyOUJDQ1FSUoKxY8diwYIFnTpPaWkVNJrOvR0Nw8DFxR6VFbWdep05E4udUFxcyXYYnEBt0Rq1R2um3h58Pu+uF9VGuXPYu3fvPR8PCAjAli1btF9PmjQJoaGhhg4LAMDn8WBrI4Tp/ngJIUT/ODHmUFpaiuYbmD179sDa2hqDBw9mOSpCCLFcnBhzOHbsGL744gvweDwEBATgo48+ov5/QghhESeSw4wZMzBjxgy2wyCEEHIbJ7qVCCGEcAslB0IIIW1QciCEENIGJ8Yc9IXP7/og9v281hxRe9xBbdEatUdrptwe94rdKIvgCCGEmBbqViKEENIGJQdCCCFtUHIghBDSBiUHQgghbVByIIQQ0gYlB0IIIW1QciCEENIGJQdCCCFtUHIghBDShkUnB5lMhlmzZmH8+PGYNWsWMjMz2Q6JNWVlZXjqqacwfvx4TJkyBUuXLoVCoWA7LE746KOP0KNHD9y6dYvtUFhTX1+PVatW4cEHH8SUKVPwn//8h+2QWHX8+HHExcVh2rRpmDp1Kg4fPsx2SPrHWLD58+czCQkJDMMwTEJCAjN//nyWI2JPWVkZ8/fff2u/3rBhA/Paa6+xGBE3JCcnM4sWLWJGjx7N3Lx5k+1wWLN27VrmrbfeYjQaDcMwDFNcXMxyROzRaDRMTEyM9vchNTWViYqKYtRqNcuR6ZfF3jmUlpbi+vXrmDx5MgBg8uTJuH79usVeLbu6umLgwIHar6OioiCXy1mMiH0NDQ148803sXr1arZDYVV1dTUSEhLw/PPPa3do9PDwYDkqdvH5fFRWNu08X1lZCU9PT/D55vVxalZVWTsjPz8fXl5eEAgEAACBQABPT0/k5+dDJBKxHB27NBoNdu3ahTFjxrAdCqs2b96MqVOnwt/fn+1QWJWTkwNXV1d89NFHSExMhIODA55//nnExMSwHRoreDweNm3ahGeffRb29vaorq7G559/znZYemdeqY7oxdq1a2Fvb4958+axHQprLl++jOTkZMydO5ftUFinVquRk5ODXr16Yc+ePXj55Zfx3HPPoaqqiu3QWKFSqfDZZ5/hk08+wfHjx7Flyxa88MILqK6uZjs0vbLY5ODj44PCwkKo1WoATX8ARUVF8PHxYTkydm3cuBFZWVnYtGmT2d0md8b58+eRnp6OsWPHYsyYMSgoKMCiRYvw119/sR2a0fn4+EAoFGq7YPv27Qs3NzfIZDKWI2NHamoqioqKEB0dDQCIjo6GnZ0d0tPTWY5Mvyz2r9/d3R3h4eE4cOAAAODAgQMIDw+36C6l9957D8nJyfj4449hbW3NdjisWrx4Mf766y8cO3YMx44dg7e3N7Zu3Yphw4axHZrRiUQiDBw4EKdPnwbQNMuvtLQUQUFBLEfGDm9vbxQUFCAjIwMAkJ6ejtLSUgQGBrIcmX5Z9GY/6enpWL58OSoqKuDs7IyNGzciJCSE7bBYkZaWhsmTJyM4OBi2trYAAH9/f3z88ccsR8YNY8aMwaefforu3buzHQorcnJysGLFCpSXl0MoFOKFF17AyJEj2Q6LNb/88gu++OIL7QD9smXL8MADD7AclX5ZdHIghBDSPovtViKEEHJ3lBwIIYS0QcmBEEJIG5QcCCGEtEHJgRBCSBuUHAi5T08++ST27t2r12N++OGHhI8D4wAAA8JJREFUePnll/V6TEI6w2JrKxHyT2PGjEFJSYm23hYAxMfH44033rjn67788ktDh0aI0VFyIKSFTz/9FEOGDGE7DEJYR91KhHRgz549mD17Nt58801ER0djwoQJOHv2rPbx+fPnY/fu3QCArKwszJs3D9HR0Rg4cCBeeOEF7fMuXbqE6dOnIzo6GtOnT8elS5e0j+Xk5GDevHno168fFi5ciLKyslYxXLlyBbNnz0ZMTAymTp2KxMREA79rYukoORCig6SkJAQGBuLvv//GsmXLsHTpUpSXl7d53ubNmzF06FCcP38eJ0+e1Fa2LS8vx5IlSzB//nwkJiZi4cKFWLJkiTYJvPzyy4iIiEBiYiKeffbZVmMYhYWFWLJkCZ555hmcO3cOr776KpYtW2axe48Q46DkQEgL//d//4eYmBjtvx9//BFAU/G5xx57DFZWVpg0aRIkEglOnDjR5vVCoRByuRxFRUWwsbHR7nlw4sQJBAUFIS4uTlvhNCQkBMePH4dcLse1a9fw/PPPw9raGrGxsa320ti3bx9GjBiBkSNHgs/nY+jQoYiMjMSff/5plDYhlonGHAhp4eOPP24z5rBnzx54eXlpi6wBgK+vL4qKitq8/pVXXsHmzZvxyCOPwMXFBQsXLsQjjzyCoqIi+Pr6tnqur68vCgsLUVRUBGdnZ9jb27d6LD8/HwAgl8tx6NAhHD9+XPu4SqVqtXMfIfpGyYEQHRQWFoJhGG2CyM/Pb3enPLFYjHXr1gEALly4gIULFyI2Nhaenp5ttl3Nz8/H8OHDIRaLUVFRgZqaGm2CkMvl2nP5+Phg2rRp2uMSYgzUrUSIDhQKBb755hs0Njbi4MGDSE9Pb7dk9cGDB1FQUAAAcHFxAY/HA5/Px8iRI5GZmYn9+/dDpVLht99+g1QqxahRo+Dn54fIyEh8+OGHaGhowIULF1rdJUydOhXHjx/HqVOnoFarUV9fj8TERO15CDEEunMgpIWnn3661TqHIUOGYOzYsejTpw+ysrIwaNAgeHh44IMPPoCbm1ub11+7dg3r169HVVUV3N3dsXLlSgQEBABomia7fv16rF69GkFBQfj000+1m0u9++67ePXVVzFw4EBERUUhLi4OFRUVAJruHD755BO88847eOmll8Dn89GnTx+sXr3a8A1CLBbt50BIB/bs2YPdu3dj165dbIdCiNFQtxIhhJA2KDkQQghpg7qVCCGEtEF3DoQQQtqg5EAIIaQNSg6EEELaoORACCGkDUoOhBBC2qDkQAghpI3/B3SlvrLgBv6/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "------------------------------------------------------------\n",
            "\n",
            "Mean of rewards for 10 Episodes : -4.0 for Base DQN\n",
            "Mean of rewards for 10 Episodes : -0.3 for Double DQN\n",
            "\n",
            "------------------------------------------------------------\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "i38BUfvdb9XI",
        "outputId": "a9568a8c-2538-4b0a-ade0-0c8eeaee175a"
      },
      "source": [
        "# for 20 episodes\n",
        "\n",
        "env = gym.make(\"GFootball-11_vs_11_kaggle-simple115v2-v0\")\n",
        "obs = env.reset()\n",
        "\n",
        "number_of_episodes = 20\n",
        "\n",
        "dqn_reward_history = run_multiple_episodes(env, doubleDQNagent, n_episodes=number_of_episodes, \n",
        "                                           n_steps=3000, training=True, pr=True)\n",
        "\n",
        "double_dqn_reward_history = run_multiple_episodes(env, baseDQNagent, n_episodes=number_of_episodes, \n",
        "                                           n_steps=3000, training=True, pr=True)\n",
        "\n",
        "\n",
        "\n",
        "plt.plot(dqn_reward_history, label='DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='DQN for 10 Episodes')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(double_dqn_reward_history, label='Double DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='Double DQN for 20 Episodes')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.plot(dqn_reward_history, label='DQN')\n",
        "plt.plot(double_dqn_reward_history, label='Double DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='DQN vs DDQN Agent for 20 Episodes')\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n------------------------------------------------------------\\n\")\n",
        "print(\"Mean of rewards for {} Episodes : {} for Base DQN\".format(number_of_episodes, mean(dqn_reward_history) ))\n",
        "print(\"Mean of rewards for {} Episodes : {} for Double DQN\".format(number_of_episodes, mean(double_dqn_reward_history) ))\n",
        "print(\"\\n------------------------------------------------------------\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Episode 0 finished after 3000 steps, total reward: -2.0\n",
            "Episode 1 finished after 3000 steps, total reward: -1.0\n",
            "Episode 2 finished after 3000 steps, total reward: -2.0\n",
            "Episode 3 finished after 3000 steps, total reward: -3.0\n",
            "Episode 4 finished after 3000 steps, total reward: -2.0\n",
            "Episode 5 finished after 3000 steps, total reward: -6.0\n",
            "Episode 6 finished after 3000 steps, total reward: -4.0\n",
            "Episode 7 finished after 3000 steps, total reward: -1.0\n",
            "Episode 8 finished after 3000 steps, total reward: -4.0\n",
            "Episode 9 finished after 3000 steps, total reward: -2.0\n",
            "Episode 10 finished after 3000 steps, total reward: -7.0\n",
            "Episode 11 finished after 3000 steps, total reward: -5.0\n",
            "Episode 12 finished after 3000 steps, total reward: -2.0\n",
            "Episode 13 finished after 3000 steps, total reward: -4.0\n",
            "Episode 14 finished after 3000 steps, total reward: -3.0\n",
            "Episode 15 finished after 3000 steps, total reward: -2.0\n",
            "Episode 16 finished after 3000 steps, total reward: -1.0\n",
            "Episode 17 finished after 3000 steps, total reward: -4.0\n",
            "Episode 18 finished after 3000 steps, total reward: -4.0\n",
            "Episode 19 finished after 3000 steps, total reward: -3.0\n",
            "Episode 0 finished after 3000 steps, total reward: 0.0\n",
            "Episode 1 finished after 3000 steps, total reward: 0.0\n",
            "Episode 2 finished after 3000 steps, total reward: 0.0\n",
            "Episode 3 finished after 3000 steps, total reward: 0.0\n",
            "Episode 4 finished after 3000 steps, total reward: 0.0\n",
            "Episode 5 finished after 3000 steps, total reward: 0.0\n",
            "Episode 6 finished after 3000 steps, total reward: 0.0\n",
            "Episode 7 finished after 3000 steps, total reward: 0.0\n",
            "Episode 8 finished after 3000 steps, total reward: 0.0\n",
            "Episode 9 finished after 3000 steps, total reward: 0.0\n",
            "Episode 10 finished after 3000 steps, total reward: 0.0\n",
            "Episode 11 finished after 3000 steps, total reward: -1.0\n",
            "Episode 12 finished after 3000 steps, total reward: 0.0\n",
            "Episode 13 finished after 3000 steps, total reward: 0.0\n",
            "Episode 14 finished after 3000 steps, total reward: 0.0\n",
            "Episode 15 finished after 3000 steps, total reward: 0.0\n",
            "Episode 16 finished after 3000 steps, total reward: 0.0\n",
            "Episode 17 finished after 3000 steps, total reward: 0.0\n",
            "Episode 18 finished after 3000 steps, total reward: 0.0\n",
            "Episode 19 finished after 3000 steps, total reward: 0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "------------------------------------------------------------\n",
            "\n",
            "Mean of rewards for 20 Episodes : -3.1 for Base DQN\n",
            "Mean of rewards for 20 Episodes : -0.05 for Double DQN\n",
            "\n",
            "------------------------------------------------------------\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVSxNyDPlpLh"
      },
      "source": [
        "import zlib\n",
        "\n",
        "import pickle\n",
        "weights_bytes = zlib.compress(pickle.dumps(baseDQNagent._action_model.get_weights()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZbCBJQPwajb"
      },
      "source": [
        "import zlib\n",
        "import pickle\n",
        "import tensorflow as tf\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow import keras\n",
        "from typing import List, Tuple, Union, Callable\n",
        "from gfootball.env.wrappers import Simple115StateWrapper\n",
        "\n",
        "class DenseNN:\n",
        "\n",
        "    def __init__(self, observation_shape: List[int], n_actions: int, \n",
        "                 output_activation: Union[None, str] = None,\n",
        "                 unit_scale: int = 1, learning_rate: float = 0.0001, \n",
        "                 opt: str = 'Adam') -> None:\n",
        "        \"\"\"\n",
        "        :param observation_shape: Tuple specifying input shape.\n",
        "        :param n_actions: Int specifying number of outputs\n",
        "        :param output_activation: Activation function for output.\n",
        "        :param unit_scale: Multiplier for all units in FC layers in network \n",
        "                           (not used here at the moment).\n",
        "        :param opt: Keras optimiser to use. Should be string. \n",
        "                    This is to avoid storing TF/Keras objects here.\n",
        "        :param learning_rate: Learning rate for optimiser.\n",
        "\n",
        "        \"\"\"\n",
        "        self.observation_shape = observation_shape\n",
        "        self.n_actions = n_actions\n",
        "        self.unit_scale = unit_scale\n",
        "        self.output_activation = output_activation\n",
        "        self.learning_rate = learning_rate\n",
        "        self.opt = opt\n",
        "\n",
        "    def _model_architecture(self) -> Tuple[keras.layers.Layer, keras.layers.Layer]:\n",
        "        \n",
        "        frame_input = keras.layers.Input(name='input', shape=self.observation_shape)\n",
        "        fc1 = keras.layers.Dense(int(self.observation_shape[0] / 1.5 * self.unit_scale), \n",
        "                                 name='fc1', activation='relu')(frame_input)\n",
        "        fc2 = keras.layers.Dense(int(self.observation_shape[0] / 3 * self.unit_scale), \n",
        "                                 name='fc2', activation='relu')(fc1)\n",
        "        fc3 = keras.layers.Dense(self.n_actions * 2, \n",
        "                                 name='fc3', activation='relu')(fc2)\n",
        "        action_output = keras.layers.Dense(units=self.n_actions, name='output',\n",
        "                                           activation=self.output_activation)(fc3)\n",
        "\n",
        "        return frame_input, action_output\n",
        "\n",
        "    def compile(self, model_name: str = 'model', \n",
        "                loss: Union[str, Callable] = 'mse') -> keras.Model:\n",
        "        \"\"\"\n",
        "        Compile a copy of the model using the provided loss.\n",
        "\n",
        "        :param model_name: Name of model\n",
        "        :param loss: Model loss. Default 'mse'. Can be custom callable.\n",
        "        \"\"\"\n",
        "        # Get optimiser\n",
        "        if self.opt.lower() == 'adam':\n",
        "            opt = keras.optimizers.Adam\n",
        "        elif self.opt.lower() == 'rmsprop':\n",
        "            opt = keras.optimizers.RMSprop\n",
        "        else:\n",
        "            raise ValueError(f\"Invalid optimiser {self.opt}\")\n",
        "\n",
        "        state_input, action_output = self._model_architecture()\n",
        "        model = keras.Model(inputs=[state_input], outputs=[action_output], \n",
        "                            name=model_name)\n",
        "        model.compile(optimizer=opt(learning_rate=self.learning_rate), \n",
        "                      loss=loss)\n",
        "\n",
        "        return model\n",
        "\n",
        "    def plot(self, model_name: str = 'model') -> None:\n",
        "        keras.utils.plot_model(self.compile(model_name), \n",
        "                               to_file=f\"{model_name}.png\", show_shapes=True)\n",
        "        plt.show()\n",
        "\n",
        "tf_mod = DenseNN(observation_shape=(115,), \n",
        "                 # This needs to match the number from training\n",
        "                 unit_scale=0.25,\n",
        "                 n_actions=19).compile()\n",
        "tf_mod.set_weights(pickle.loads(zlib.decompress(weights_bytes)))\n",
        "\n",
        "def agent(obs):\n",
        "\n",
        "    # Use the existing model and obs buffer on each call to agent\n",
        "    global tf_mod\n",
        "\n",
        "    # Convert these to the same output as the Simple115StateWrapper we used in training\n",
        "    obs = Simple115StateWrapper.convert_observation(obs['players_raw'], fixed_positions=False)\n",
        "\n",
        "    # Predict actions from keras model\n",
        "    actions = tf_mod.predict(obs)\n",
        "    action = np.argmax(actions)\n",
        "\n",
        "    return [action]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVJYkXALxr2k",
        "outputId": "7dcf701d-5ec3-4848-c488-14edadce51cf"
      },
      "source": [
        "\"\"\"\n",
        "env :the gym environment\n",
        "agent : the DQN Agent\n",
        "n_steps : total number of steps the model should be run for\n",
        "pr : a boolean flag that decides whether to print \n",
        "training : a boolean flag that decides whether training is true/false\n",
        "\n",
        "returns a tuple of episode_rewards,episode_actions\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "def play_single_episode(env: gym.Env, agent, n_steps: int=10, pr: bool=False, \n",
        "                 training:bool=False) -> Tuple[List[float], List[int]]:\n",
        "        \n",
        "    episode_rewards = []\n",
        "    episode_actions = []\n",
        "    \n",
        "    #reset the environment\n",
        "    obs = env.reset()\n",
        "    done = False\n",
        "    \n",
        "    #iterate for n_steps\n",
        "    for s in range(n_steps):\n",
        "        if done:\n",
        "            break\n",
        "\n",
        "        # Select action\n",
        "        action = agent.get_action(obs)\n",
        "        episode_actions.append(action)\n",
        "\n",
        "        # Take action\n",
        "        prev_obs = obs\n",
        "        \n",
        "        #on taking an action, env returns \n",
        "            #observation, \n",
        "            #reward taken by the action, \n",
        "            #whether done is true or false, \n",
        "            #info about the action(scoring reward)\n",
        "        obs, reward, done, info = env.step(action)\n",
        "        episode_rewards.append(reward)\n",
        "\n",
        "        # Update model\n",
        "        if training:\n",
        "            agent.update_experience(s=prev_obs, a=action, \n",
        "                                    r=reward, d=done)\n",
        "            agent.update_model()\n",
        "\n",
        "        if pr:\n",
        "            print(f\"Step {s}: Action taken {action}, \"f\"reward recieved {reward}\")\n",
        "\n",
        "        last_step = s\n",
        "    \n",
        "    if training:\n",
        "        agent.after_episode_update()\n",
        "                  \n",
        "    return episode_rewards, episode_actions\n",
        "                  \n",
        "\n",
        "env = gym.make(\"GFootball-11_vs_11_kaggle-simple115v2-v0\")\n",
        "#print(\"env.action_space.n-=>\\n\",env.action_space.n)\n",
        "agent = DQNAgent(\n",
        "    replay_buffer=ReplayBuffer(buffer_size=1000),  \n",
        "    eps=EpsilonGreedy(),\n",
        "    model_architecture=DenseNN(observation_shape=env.observation_space.shape, \n",
        "                               # Setting low value to make it easier to run in notebook\n",
        "                               unit_scale=0.25,\n",
        "                               n_actions=env.action_space.n))\n",
        "\n",
        "_ = play_single_episode(env, agent, n_steps=20, pr=True, training=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:2325: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
            "  warnings.warn('`Model.state_updates` will be removed in a future version. '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 0: Action taken 6, reward recieved 0.0\n",
            "Step 1: Action taken 3, reward recieved 0.0\n",
            "Step 2: Action taken 3, reward recieved 0.0\n",
            "Step 3: Action taken 3, reward recieved 0.0\n",
            "Step 4: Action taken 3, reward recieved 0.0\n",
            "Step 5: Action taken 4, reward recieved 0.0\n",
            "Step 6: Action taken 4, reward recieved 0.0\n",
            "Step 7: Action taken 4, reward recieved 0.0\n",
            "Step 8: Action taken 5, reward recieved 0.0\n",
            "Step 9: Action taken 5, reward recieved 0.0\n",
            "Step 10: Action taken 5, reward recieved 0.0\n",
            "Step 11: Action taken 5, reward recieved 0.0\n",
            "Step 12: Action taken 5, reward recieved 0.0\n",
            "Step 13: Action taken 5, reward recieved 0.0\n",
            "Step 14: Action taken 5, reward recieved 0.0\n",
            "Step 15: Action taken 5, reward recieved 0.0\n",
            "Step 16: Action taken 5, reward recieved 0.0\n",
            "Step 17: Action taken 5, reward recieved 0.0\n",
            "Step 18: Action taken 5, reward recieved 0.0\n",
            "Step 19: Action taken 4, reward recieved 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "mzM8ZJUhx4wP",
        "outputId": "7dfa60c2-dac6-47ee-82e3-5c9601f87877"
      },
      "source": [
        "def run_multiple_episodes(env: gym.Env, agent, n_episodes: int=10, \n",
        "                          n_steps: int=10, pr: bool=False, \n",
        "                          training:bool=False) -> List[float]:\n",
        "\n",
        "    \n",
        "    total_episode_rewards = []\n",
        "    for ep in range(n_episodes):\n",
        "\n",
        "        episode_rewards, _ = play_single_episode(env, agent, n_steps, \n",
        "                                          pr=False, training=True)\n",
        "\n",
        "        total_episode_rewards.append(sum(episode_rewards))\n",
        "        \n",
        "        if pr:\n",
        "            print(f\"Episode {ep} finished after {len(episode_rewards)} \"\n",
        "                  f\"steps, total reward: {sum(episode_rewards)}\")\n",
        "\n",
        "    \n",
        "    return total_episode_rewards\n",
        "\n",
        "\n",
        "env = gym.make(\"GFootball-11_vs_11_kaggle-simple115v2-v0\")\n",
        "baseDQNagent = DQNAgent(\n",
        "    replay_buffer=ReplayBuffer(buffer_size=1000),\n",
        "    eps=EpsilonGreedy(eps_initial=0.7),\n",
        "    model_architecture=DenseNN(observation_shape=env.observation_space.shape, \n",
        "                               unit_scale=0.25,\n",
        "                               n_actions=env.action_space.n))\n",
        "\n",
        "doubleDQNagent = DQNAgent(\n",
        "    replay_buffer=ReplayBuffer(buffer_size=1000),\n",
        "    eps=EpsilonGreedy(eps_initial=0.7),\n",
        "    model_architecture=DenseNN(observation_shape=env.observation_space.shape, \n",
        "                               unit_scale=0.25,\n",
        "                               n_actions=env.action_space.n),\n",
        "    double = True)\n",
        "\n",
        "# Both n_episodes and n_steps need to \n",
        "'''\n",
        "reward_history = run_multiple_episodes(env, agent, n_episodes=10, \n",
        "                                       # Use 3000 for 90 minute games\n",
        "                                       n_steps=3000,  \n",
        "                                       training=True, pr=True)\n",
        "\n",
        "plt.plot(reward_history)\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.show()\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\nreward_history = run_multiple_episodes(env, agent, n_episodes=10, \\n                                       # Use 3000 for 90 minute games\\n                                       n_steps=3000,  \\n                                       training=True, pr=True)\\n\\nplt.plot(reward_history)\\nplt.xlabel('Episode')\\nplt.ylabel('Reward')\\nplt.show()\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hrmTaB0FdSX3",
        "outputId": "d0e8c6df-0122-4cf7-bb0b-51fbc64a1215"
      },
      "source": [
        "# for 10 episodes\n",
        "\n",
        "env = gym.make(\"GFootball-11_vs_11_kaggle-simple115v2-v0\")\n",
        "obs = env.reset()\n",
        "\n",
        "number_of_episodes = 10\n",
        "\n",
        "dqn_reward_history = run_multiple_episodes(env, baseDQNagent , n_episodes=number_of_episodes, \n",
        "                                           n_steps=3000, training=True, pr=True)\n",
        "\n",
        "double_dqn_reward_history = run_multiple_episodes(env, doubleDQNagent, n_episodes=number_of_episodes, \n",
        "                                           n_steps=3000, training=True, pr=True)\n",
        "\n",
        "\n",
        "\n",
        "plt.plot(dqn_reward_history, label='DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='DQN for 10 Episodes')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(double_dqn_reward_history, label='Double DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='Double DQN for 10 Episodes')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.plot(dqn_reward_history, label='DQN')\n",
        "plt.plot(double_dqn_reward_history, label='Double DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='DQN vs DDQN Agent for 10 Episodes')\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n------------------------------------------------------------\\n\")\n",
        "print(\"Mean of rewards for {} Episodes : {} for Base DQN\".format(number_of_episodes, mean(dqn_reward_history) ))\n",
        "print(\"Mean of rewards for {} Episodes : {} for Double DQN\".format(number_of_episodes, mean(double_dqn_reward_history) ))\n",
        "print(\"\\n------------------------------------------------------------\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:2325: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
            "  warnings.warn('`Model.state_updates` will be removed in a future version. '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Episode 0 finished after 3000 steps, total reward: -7.0\n",
            "Episode 1 finished after 3000 steps, total reward: -8.0\n",
            "Episode 2 finished after 3000 steps, total reward: -2.0\n",
            "Episode 3 finished after 3000 steps, total reward: -14.0\n",
            "Episode 4 finished after 3000 steps, total reward: -2.0\n",
            "Episode 5 finished after 3000 steps, total reward: -17.0\n",
            "Episode 6 finished after 3000 steps, total reward: -5.0\n",
            "Episode 7 finished after 3000 steps, total reward: -9.0\n",
            "Episode 8 finished after 3000 steps, total reward: -5.0\n",
            "Episode 9 finished after 3000 steps, total reward: -6.0\n",
            "Episode 0 finished after 3000 steps, total reward: -1.0\n",
            "Episode 1 finished after 3000 steps, total reward: -4.0\n",
            "Episode 2 finished after 3000 steps, total reward: -2.0\n",
            "Episode 3 finished after 3000 steps, total reward: -1.0\n",
            "Episode 4 finished after 3000 steps, total reward: 0.0\n",
            "Episode 5 finished after 3000 steps, total reward: -1.0\n",
            "Episode 6 finished after 3000 steps, total reward: -3.0\n",
            "Episode 7 finished after 3000 steps, total reward: -5.0\n",
            "Episode 8 finished after 3000 steps, total reward: -6.0\n",
            "Episode 9 finished after 3000 steps, total reward: 0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "------------------------------------------------------------\n",
            "\n",
            "Mean of rewards for 10 Episodes : -7.5 for Base DQN\n",
            "Mean of rewards for 10 Episodes : -2.3 for Double DQN\n",
            "\n",
            "------------------------------------------------------------\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "O53BVspid2Ay",
        "outputId": "28e71b98-9214-4537-a7e2-a2f812bee3dc"
      },
      "source": [
        "# for 20 episodes\n",
        "\n",
        "env = gym.make(\"GFootball-11_vs_11_kaggle-simple115v2-v0\")\n",
        "obs = env.reset()\n",
        "\n",
        "number_of_episodes = 20\n",
        "\n",
        "dqn_reward_history = run_multiple_episodes(env, baseDQNagent, n_episodes=number_of_episodes, \n",
        "                                           n_steps=3000, training=True, pr=True)\n",
        "\n",
        "double_dqn_reward_history = run_multiple_episodes(env, doubleDQNagent, n_episodes=number_of_episodes, \n",
        "                                           n_steps=3000, training=True, pr=True)\n",
        "\n",
        "\n",
        "\n",
        "plt.plot(dqn_reward_history, label='DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='DQN for 20 Episodes')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(double_dqn_reward_history, label='Double DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='Double DQN for 20 Episodes')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.plot(dqn_reward_history, label='DQN')\n",
        "plt.plot(double_dqn_reward_history, label='Double DQN')\n",
        "plt.xlabel('Episode')\n",
        "plt.ylabel('Reward')\n",
        "plt.legend(title='DQN vs DDQN Agent for 20 Episodes')\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n------------------------------------------------------------\\n\")\n",
        "print(\"Mean of rewards for {} Episodes : {} for Base DQN\".format(number_of_episodes, mean(dqn_reward_history) ))\n",
        "print(\"Mean of rewards for {} Episodes : {} for Double DQN\".format(number_of_episodes, mean(double_dqn_reward_history) ))\n",
        "print(\"\\n------------------------------------------------------------\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Episode 0 finished after 3000 steps, total reward: -8.0\n",
            "Episode 1 finished after 3000 steps, total reward: -6.0\n",
            "Episode 2 finished after 3000 steps, total reward: -7.0\n",
            "Episode 3 finished after 3000 steps, total reward: -11.0\n",
            "Episode 4 finished after 3000 steps, total reward: -4.0\n",
            "Episode 5 finished after 3000 steps, total reward: -9.0\n",
            "Episode 6 finished after 3000 steps, total reward: -4.0\n",
            "Episode 7 finished after 3000 steps, total reward: -6.0\n",
            "Episode 8 finished after 3000 steps, total reward: -9.0\n",
            "Episode 9 finished after 3000 steps, total reward: -10.0\n",
            "Episode 10 finished after 3000 steps, total reward: -6.0\n",
            "Episode 11 finished after 3000 steps, total reward: -7.0\n",
            "Episode 12 finished after 3000 steps, total reward: -11.0\n",
            "Episode 13 finished after 3000 steps, total reward: -8.0\n",
            "Episode 14 finished after 3000 steps, total reward: -6.0\n",
            "Episode 15 finished after 3000 steps, total reward: -5.0\n",
            "Episode 16 finished after 3000 steps, total reward: -7.0\n",
            "Episode 17 finished after 3000 steps, total reward: -10.0\n",
            "Episode 18 finished after 3000 steps, total reward: -4.0\n",
            "Episode 19 finished after 3000 steps, total reward: -6.0\n",
            "Episode 0 finished after 3000 steps, total reward: -3.0\n",
            "Episode 1 finished after 3000 steps, total reward: -3.0\n",
            "Episode 2 finished after 3000 steps, total reward: -1.0\n",
            "Episode 3 finished after 3000 steps, total reward: -2.0\n",
            "Episode 4 finished after 3000 steps, total reward: -4.0\n",
            "Episode 5 finished after 3000 steps, total reward: -3.0\n",
            "Episode 6 finished after 3000 steps, total reward: -2.0\n",
            "Episode 7 finished after 3000 steps, total reward: -1.0\n",
            "Episode 8 finished after 3000 steps, total reward: -1.0\n",
            "Episode 9 finished after 3000 steps, total reward: -4.0\n",
            "Episode 10 finished after 3000 steps, total reward: -2.0\n",
            "Episode 11 finished after 3000 steps, total reward: -4.0\n",
            "Episode 12 finished after 3000 steps, total reward: -1.0\n",
            "Episode 13 finished after 3000 steps, total reward: -2.0\n",
            "Episode 14 finished after 3000 steps, total reward: -2.0\n",
            "Episode 15 finished after 3000 steps, total reward: -3.0\n",
            "Episode 16 finished after 3000 steps, total reward: -2.0\n",
            "Episode 17 finished after 3000 steps, total reward: -4.0\n",
            "Episode 18 finished after 3000 steps, total reward: -1.0\n",
            "Episode 19 finished after 3000 steps, total reward: -2.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "------------------------------------------------------------\n",
            "\n",
            "Mean of rewards for 20 Episodes : -7.2 for Base DQN\n",
            "Mean of rewards for 20 Episodes : -2.35 for Double DQN\n",
            "\n",
            "------------------------------------------------------------\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "chAza_f6qN0Q",
        "outputId": "1bd08512-a9b8-4a90-bed6-e5c6fdc4cbb5"
      },
      "source": [
        "!python --version"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python 3.7.10\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}