Speech_recognition_fulltrial.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Speech_recognition_full.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Maelaf/Swahili-Speech-To-Text/blob/main/Speech_recognition_fulltrial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j235TEbtT8Qm",
        "outputId": "4a2a0b59-1199-47f0-e4d8-eb1cfa999a96"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.activity.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fexperimentsandconfigs%20https%3a%2f%2fwww.googleapis.com%2fauth%2fphotos.native&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "4/1AX4XfWiskbjC_lFsQ9fqgCf9tlveHvc2eLkm74AuMC2AXW1k_Q2TZ_PgSE8\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfD1-k3D6fea",
        "outputId": "8ebf7bb0-2d1f-4ae9-e473-1e9107cff047"
      },
      "source": [
        "#list Directory inside SWAHILI\n",
        "import os\n",
        "os.chdir(\"/content/drive/MyDrive/Week-4/speech_data/ALFFA_PUBLIC/ASR/SWAHILI\")\n",
        "os.listdir()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['LM', 'README', 'data', 'kaldi-scripts', 'lang']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_c0Z9ppF6fhF",
        "outputId": "734bbeaf-b771-43df-b4df-f36993e87844"
      },
      "source": [
        "!pip install python_speech_features"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting python_speech_features\n",
            "  Downloading python_speech_features-0.6.tar.gz (5.6 kB)\n",
            "Building wheels for collected packages: python-speech-features\n",
            "  Building wheel for python-speech-features (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-speech-features: filename=python_speech_features-0.6-py3-none-any.whl size=5888 sha256=98089faac64089807399b42bc9125aeffa427032092c053ae8a476a91ecad845\n",
            "  Stored in directory: /root/.cache/pip/wheels/b0/0e/94/28cd6afa3cd5998a63eef99fe31777acd7d758f59cf24839eb\n",
            "Successfully built python-speech-features\n",
            "Installing collected packages: python-speech-features\n",
            "Successfully installed python-speech-features-0.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H8G4KENI6fjk",
        "outputId": "913b32e7-4ddc-48e1-9f69-3c3d04284955"
      },
      "source": [
        "!pip install mlflow"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting mlflow\n",
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            "  Downloading Mako-1.1.4-py2.py3-none-any.whl (75 kB)\n",
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            "\u001b[?25hCollecting python-editor>=0.3\n",
            "  Downloading python_editor-1.0.4-py3-none-any.whl (4.9 kB)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from alembic<=1.4.1->mlflow) (2.8.1)\n",
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            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from databricks-cli>=0.8.7->mlflow) (1.15.0)\n",
            "Collecting websocket-client>=0.32.0\n",
            "  Downloading websocket_client-1.1.1-py2.py3-none-any.whl (68 kB)\n",
            "\u001b[K     |████████████████████████████████| 68 kB 6.4 MB/s \n",
            "\u001b[?25hCollecting gitdb<5,>=4.0.1\n",
            "  Downloading gitdb-4.0.7-py3-none-any.whl (63 kB)\n",
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            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.0 in /usr/local/lib/python3.7/dist-packages (from gitpython>=2.1.0->mlflow) (3.7.4.3)\n",
            "Collecting smmap<5,>=3.0.1\n",
            "  Downloading smmap-4.0.0-py2.py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.17.3->mlflow) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.17.3->mlflow) (2021.5.30)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.17.3->mlflow) (1.24.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from sqlalchemy->mlflow) (4.6.1)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.7/dist-packages (from sqlalchemy->mlflow) (1.1.0)\n",
            "Requirement already satisfied: Werkzeug<2.0,>=0.15 in /usr/local/lib/python3.7/dist-packages (from Flask->mlflow) (1.0.1)\n",
            "Requirement already satisfied: itsdangerous<2.0,>=0.24 in /usr/local/lib/python3.7/dist-packages (from Flask->mlflow) (1.1.0)\n",
            "Requirement already satisfied: Jinja2<3.0,>=2.10.1 in /usr/local/lib/python3.7/dist-packages (from Flask->mlflow) (2.11.3)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->Flask->mlflow) (2.0.1)\n",
            "Requirement already satisfied: setuptools>=3.0 in /usr/local/lib/python3.7/dist-packages (from gunicorn->mlflow) (57.2.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->sqlalchemy->mlflow) (3.5.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mlflow) (2.4.7)\n",
            "Requirement already satisfied: prometheus_client in /usr/local/lib/python3.7/dist-packages (from prometheus-flask-exporter->mlflow) (0.11.0)\n",
            "Building wheels for collected packages: alembic, databricks-cli, prometheus-flask-exporter\n",
            "  Building wheel for alembic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for alembic: filename=alembic-1.4.1-py2.py3-none-any.whl size=158170 sha256=54cc341ab710bc11124feab773a4b634ba6b94c5a517a363036b9d3ab08b6f98\n",
            "  Stored in directory: /root/.cache/pip/wheels/be/5d/0a/9e13f53f4f5dfb67cd8d245bb7cdffe12f135846f491a283e3\n",
            "  Building wheel for databricks-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for databricks-cli: filename=databricks_cli-0.15.0-py3-none-any.whl size=105259 sha256=c091040b0da74f6e4135f58e61eaecc2b5c12ac5ce7ccbacdff07bbbbb639f5b\n",
            "  Stored in directory: /root/.cache/pip/wheels/e7/ba/75/284f9a90ff7a010bb23b9798f2e9a19dd9fe619379c917bff4\n",
            "  Building wheel for prometheus-flask-exporter (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for prometheus-flask-exporter: filename=prometheus_flask_exporter-0.18.2-py3-none-any.whl size=17416 sha256=282d99344866f1953416380e034e5d477975fb01328f01f5136f1a2f8ed83876\n",
            "  Stored in directory: /root/.cache/pip/wheels/6a/1e/1c/c765920cb92b2f0343d2dd8b481a407cee2823f9b4bbd2e52a\n",
            "Successfully built alembic databricks-cli prometheus-flask-exporter\n",
            "Installing collected packages: smmap, websocket-client, python-editor, Mako, gitdb, querystring-parser, pyyaml, prometheus-flask-exporter, gunicorn, gitpython, docker, databricks-cli, alembic, mlflow\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed Mako-1.1.4 alembic-1.4.1 databricks-cli-0.15.0 docker-5.0.0 gitdb-4.0.7 gitpython-3.1.18 gunicorn-20.1.0 mlflow-1.19.0 prometheus-flask-exporter-0.18.2 python-editor-1.0.4 pyyaml-5.4.1 querystring-parser-1.2.4 smmap-4.0.0 websocket-client-1.1.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93WrCqQr8t9U"
      },
      "source": [
        "import numpy as np\n",
        "import soundfile\n",
        "import json\n",
        "\n",
        "import random\n",
        "from python_speech_features import mfcc\n",
        "import librosa\n",
        "import scipy.io.wavfile as wav\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "\n",
        "from keras import backend as K\n",
        "from keras.models import Model\n",
        "from keras.layers import (BatchNormalization, Conv1D, Dense, Input, \n",
        "    TimeDistributed, Activation, Bidirectional, SimpleRNN, GRU, LSTM)\n",
        "from keras.utils.vis_utils import plot_model\n",
        "\n",
        "\n",
        "import _pickle as pickle\n",
        "from numpy.lib.stride_tricks import as_strided\n",
        "\n",
        "from keras.layers import (Input, Lambda)\n",
        "from keras.optimizers import SGD\n",
        "from keras.callbacks import ModelCheckpoint   \n",
        "import os"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cdb2ff7o7AXJ"
      },
      "source": [
        "###Utility functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mymcuAo86UI"
      },
      "source": [
        "supported = \"\"\"\n",
        "ሀ ሁ ሂ ሄ ህ ሆ\n",
        "ለ ሉ ሊ ላ ሌ ል ሎ ሏ\n",
        "መ ሙ ሚ ማ ሜ ም ሞ ሟ\n",
        "ረ ሩ ሪ ራ ሬ ር ሮ ሯ\n",
        "ሰ ሱ ሲ ሳ ሴ ስ ሶ ሷ\n",
        "ሸ ሹ ሺ ሻ ሼ ሽ ሾ ሿ\n",
        "ቀ ቁ ቂ ቃ ቄ ቅ ቆ ቋ\n",
        "በ ቡ ቢ ባ ቤ ብ ቦ ቧ\n",
        "ቨ ቩ ቪ ቫ ቬ ቭ ቮ ቯ\n",
        "ተ ቱ ቲ ታ ቴ ት ቶ ቷ\n",
        "ቸ ቹ ቺ ቻ ቼ ች ቾ ቿ\n",
        "ኋ\n",
        "ነ ኑ ኒ ና ኔ ን ኖ ኗ\n",
        "ኘ ኙ ኚ ኛ ኜ ኝ ኞ ኟ\n",
        "አ ኡ ኢ ኤ እ ኦ\n",
        "ኧ\n",
        "ከ ኩ ኪ ካ ኬ ክ ኮ\n",
        "ኳ\n",
        "ወ ዉ ዊ ዋ ዌ ው ዎ\n",
        "ዘ ዙ ዚ ዛ ዜ ዝ ዞ ዟ\n",
        "ዠ ዡ ዢ ዣ ዤ ዥ ዦ ዧ\n",
        "የ ዩ ዪ ያ ዬ ይ ዮ\n",
        "ደ ዱ ዲ ዳ ዴ ድ ዶ ዷ\n",
        "ጀ ጁ ጂ ጃ ጄ ጅ ጆ ጇ\n",
        "ገ ጉ ጊ ጋ ጌ ግ ጐ ጓ ጔ\n",
        "ጠ ጡ ጢ ጣ ጤ ጥ ጦ ጧ\n",
        "ጨ ጩ ጪ ጫ ጬ ጭ ጮ ጯ\n",
        "ጰ ጱ ጲ ጳ ጴ ጵ ጶ ጷ\n",
        "ፀ ፁ ፂ ፃ ፄ ፅ ፆ ፇ\n",
        "ፈ ፉ ፊ ፋ ፌ ፍ ፎ ፏ\n",
        "ፐ ፑ ፒ ፓ ፔ ፕ ፖ\n",
        "\"\"\".split()\n",
        "\n",
        "char_map = {}\n",
        "char_map[\"\"] = 0\n",
        "char_map[\"<SPACE>\"] = 1\n",
        "index = 2\n",
        "for c in supported:\n",
        "    char_map[c] = index\n",
        "    index += 1\n",
        "index_map = {v+1: k for k, v in char_map.items()}"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thUQsF_K6fmG"
      },
      "source": [
        "def calc_feat_dim(window, max_freq):\n",
        "    return int(0.001 * window * max_freq) + 1"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4i2BAHb76foo"
      },
      "source": [
        "def spectrogram(samples, fft_length=256, sample_rate=2, hop_length=128):\n",
        "    \"\"\"\n",
        "    Compute the spectrogram for a real signal.\n",
        "    The parameters follow the naming convention of\n",
        "    matplotlib.mlab.specgram\n",
        "\n",
        "    Args:\n",
        "        samples (1D array): input audio signal\n",
        "        fft_length (int): number of elements in fft window\n",
        "        sample_rate (scalar): sample rate\n",
        "        hop_length (int): hop length (relative offset between neighboring\n",
        "            fft windows).\n",
        "\n",
        "    Returns:\n",
        "        x (2D array): spectrogram [frequency x time]\n",
        "        freq (1D array): frequency of each row in x\n",
        "\n",
        "    Note:\n",
        "        This is a truncating computation e.g. if fft_length=10,\n",
        "        hop_length=5 and the signal has 23 elements, then the\n",
        "        last 3 elements will be truncated.\n",
        "    \"\"\"\n",
        "    assert not np.iscomplexobj(samples), \"Must not pass in complex numbers\"\n",
        "\n",
        "    window = np.hanning(fft_length)[:, None]\n",
        "    window_norm = np.sum(window**2)\n",
        "\n",
        "    # The scaling below follows the convention of\n",
        "    # matplotlib.mlab.specgram which is the same as\n",
        "    # matlabs specgram.\n",
        "    scale = window_norm * sample_rate\n",
        "\n",
        "    trunc = (len(samples) - fft_length) % hop_length\n",
        "    x = samples[:len(samples) - trunc]\n",
        "\n",
        "    # \"stride trick\" reshape to include overlap\n",
        "    nshape = (fft_length, (len(x) - fft_length) // hop_length + 1)\n",
        "    nstrides = (x.strides[0], x.strides[0] * hop_length)\n",
        "    x = as_strided(x, shape=nshape, strides=nstrides)\n",
        "\n",
        "    # window stride sanity check\n",
        "    assert np.all(x[:, 1] == samples[hop_length:(hop_length + fft_length)])\n",
        "\n",
        "    # broadcast window, compute fft over columns and square mod\n",
        "    x = np.fft.rfft(x * window, axis=0)\n",
        "    x = np.absolute(x)**2\n",
        "\n",
        "    # scale, 2.0 for everything except dc and fft_length/2\n",
        "    x[1:-1, :] *= (2.0 / scale)\n",
        "    x[(0, -1), :] /= scale\n",
        "\n",
        "    freqs = float(sample_rate) / fft_length * np.arange(x.shape[0])\n",
        "\n",
        "    return x, freqs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x30Os-Pi8bdT"
      },
      "source": [
        "def spectrogram_from_file(filename, step=10, window=20, max_freq=None,\n",
        "                          eps=1e-14):\n",
        "    \"\"\" Calculate the log of linear spectrogram from FFT energy\n",
        "    Params:\n",
        "        filename (str): Path to the audio file\n",
        "        step (int): Step size in milliseconds between windows\n",
        "        window (int): FFT window size in milliseconds\n",
        "        max_freq (int): Only FFT bins corresponding to frequencies between\n",
        "            [0, max_freq] are returned\n",
        "        eps (float): Small value to ensure numerical stability (for ln(x))\n",
        "    \"\"\"\n",
        "    with soundfile.SoundFile(filename) as sound_file:\n",
        "        audio = sound_file.read(dtype='float32')\n",
        "        sample_rate = sound_file.samplerate\n",
        "        if audio.ndim >= 2:\n",
        "            audio = np.mean(audio, 1)\n",
        "        if max_freq is None:\n",
        "            max_freq = sample_rate / 2\n",
        "        if max_freq > sample_rate / 2:\n",
        "            raise ValueError(\"max_freq must not be greater than half of \"\n",
        "                             \" sample rate\")\n",
        "        if step > window:\n",
        "            raise ValueError(\"step size must not be greater than window size\")\n",
        "        hop_length = int(0.001 * step * sample_rate)\n",
        "        fft_length = int(0.001 * window * sample_rate)\n",
        "        pxx, freqs = spectrogram(\n",
        "            audio, fft_length=fft_length, sample_rate=sample_rate,\n",
        "            hop_length=hop_length)\n",
        "        ind = np.where(freqs <= max_freq)[0][-1] + 1\n",
        "    return np.transpose(np.log(pxx[:ind, :] + eps))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9nJ78TV8X-b"
      },
      "source": [
        "def text_to_int_sequence(text):\n",
        "    \"\"\" Convert text to an integer sequence \"\"\"\n",
        "    int_sequence = []\n",
        "    for c in text:\n",
        "        if c == ' ':\n",
        "            ch = char_map['<SPACE>']\n",
        "        else:\n",
        "            # print(\"checking character \" + c + \" in map:\")\n",
        "            # print(char_map)\n",
        "            ch = char_map[c]\n",
        "        int_sequence.append(ch)\n",
        "    return int_sequence"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4y39Q5Bq6frJ"
      },
      "source": [
        "def int_sequence_to_text(int_sequence):\n",
        "    \"\"\" Convert an integer sequence to text \"\"\"\n",
        "    text = []\n",
        "    for c in int_sequence:\n",
        "        ch = index_map[c]\n",
        "        text.append(ch)\n",
        "    return text"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XI0GcAAT-Rrj"
      },
      "source": [
        "# Code adapted from https://martin-thoma.com/word-error-rate-calculation/\n",
        "def wer(r, h):\n",
        "    \"\"\"\n",
        "    Calculation of WER with Levenshtein distance.\n",
        "\n",
        "    Works only for iterables up to 254 elements (uint8).\n",
        "    O(nm) time ans space complexity.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    r : list\n",
        "    h : list\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    int\n",
        "\n",
        "    Examples\n",
        "    --------\n",
        "    >>> wer(\"who is there\".split(), \"is there\".split())\n",
        "    1\n",
        "    >>> wer(\"who is there\".split(), \"\".split())\n",
        "    3\n",
        "    >>> wer(\"\".split(), \"who is there\".split())\n",
        "    3\n",
        "    \"\"\"\n",
        "    # initialisation\n",
        "    import numpy\n",
        "    d = numpy.zeros((len(r)+1)*(len(h)+1), dtype=numpy.uint8)\n",
        "    d = d.reshape((len(r)+1, len(h)+1))\n",
        "    for i in range(len(r)+1):\n",
        "        for j in range(len(h)+1):\n",
        "            if i == 0:\n",
        "                d[0][j] = j\n",
        "            elif j == 0:\n",
        "                d[i][0] = i\n",
        "\n",
        "    # computation\n",
        "    for i in range(1, len(r)+1):\n",
        "        for j in range(1, len(h)+1):\n",
        "            if r[i-1] == h[j-1]:\n",
        "                d[i][j] = d[i-1][j-1]\n",
        "            else:\n",
        "                substitution = d[i-1][j-1] + 1\n",
        "                insertion    = d[i][j-1] + 1\n",
        "                deletion     = d[i-1][j] + 1\n",
        "                d[i][j] = min(substitution, insertion, deletion)\n",
        "\n",
        "    return d[len(r)][len(h)]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUNs2EnD-bp4"
      },
      "source": [
        "\"\"\"\n",
        "Helper functions for plotting\n",
        "\"\"\"\n",
        "\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_hist(p):\n",
        "    hist = pickle.load(open( \"models/\" + p + \".pickle\", \"rb\"))\n",
        "    plt.plot(hist['loss'], label=\"train\")\n",
        "    plt.plot(hist['val_loss'], label=\"valid\")\n",
        "    plt.xlabel(\"epoch\")\n",
        "    plt.ylabel(\"loss\")\n",
        "    plt.legend()\n",
        "    plt.show"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ycuZ1cv9NSL"
      },
      "source": [
        "###Input data generation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Ppo9xbg9yGB"
      },
      "source": [
        "\n",
        "RNG_SEED = 123\n",
        "\n",
        "def make_audio_gen(train_json,\n",
        "                   valid_json,\n",
        "                   minibatch_size=20,\n",
        "                   spectrogram=True,\n",
        "                   mfcc_dim=13,\n",
        "                   sort_by_duration=False,\n",
        "                   max_duration=10.0):\n",
        "    return AudioGenerator(train_json, valid_json, minibatch_size=minibatch_size, \n",
        "        spectrogram=spectrogram, mfcc_dim=mfcc_dim, max_duration=max_duration,\n",
        "        sort_by_duration=sort_by_duration)\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hpMCkLEP6ft2"
      },
      "source": [
        "\"\"\"\n",
        "Defines a class that is used to featurize audio clips, and provide\n",
        "them to the network for training or testing.\n",
        "\"\"\"\n",
        "class AudioGenerator():\n",
        "    def __init__(self, train_corpus, valid_corpus, step=10, window=20, max_freq=8000, mfcc_dim=13,\n",
        "        minibatch_size=20, desc_file=None, spectrogram=True, max_duration=10.0, \n",
        "        sort_by_duration=False):\n",
        "        \"\"\"\n",
        "        Params:\n",
        "            step (int): Step size in milliseconds between windows (for spectrogram ONLY)\n",
        "            window (int): FFT window size in milliseconds (for spectrogram ONLY)\n",
        "            max_freq (int): Only FFT bins corresponding to frequencies between\n",
        "                [0, max_freq] are returned (for spectrogram ONLY)\n",
        "            desc_file (str, optional): Path to a JSON-line file that contains\n",
        "                labels and paths to the audio files. If this is None, then\n",
        "                load metadata right away\n",
        "        \"\"\"\n",
        "        self.train_corpus = train_corpus\n",
        "        self.valid_corpus = valid_corpus\n",
        "        self.feat_dim = calc_feat_dim(window, max_freq)\n",
        "        self.mfcc_dim = mfcc_dim\n",
        "        self.feats_mean = np.zeros((self.feat_dim,))\n",
        "        self.feats_std = np.ones((self.feat_dim,))\n",
        "        self.rng = random.Random(RNG_SEED)\n",
        "        if desc_file is not None:\n",
        "            self.load_metadata_from_desc_file(desc_file)\n",
        "        self.step = step\n",
        "        self.window = window\n",
        "        self.max_freq = max_freq\n",
        "        self.cur_train_index = 0\n",
        "        self.cur_valid_index = 0\n",
        "        self.cur_test_index = 0\n",
        "        self.max_duration=max_duration\n",
        "        self.minibatch_size = minibatch_size\n",
        "        self.spectrogram = spectrogram\n",
        "        self.sort_by_duration = sort_by_duration\n",
        "\n",
        "    def get_batch(self, partition):\n",
        "        \"\"\" Obtain a batch of train, validation, or test data\n",
        "        \"\"\"\n",
        "        if partition == 'train':\n",
        "            audio_paths = self.train_audio_paths\n",
        "            cur_index = self.cur_train_index\n",
        "            texts = self.train_texts\n",
        "        elif partition == 'valid':\n",
        "            audio_paths = self.valid_audio_paths\n",
        "            cur_index = self.cur_valid_index\n",
        "            texts = self.valid_texts\n",
        "        elif partition == 'test':\n",
        "            audio_paths = self.test_audio_paths\n",
        "            cur_index = self.test_valid_index\n",
        "            texts = self.test_texts\n",
        "        else:\n",
        "            raise Exception(\"Invalid partition. \"\n",
        "                \"Must be train/validation\")\n",
        "\n",
        "        features = [self.normalize(self.featurize(a)) for a in \n",
        "            audio_paths[cur_index:cur_index+self.minibatch_size]]\n",
        "\n",
        "        # calculate necessary sizes\n",
        "        max_length = max([features[i].shape[0] \n",
        "            for i in range(0, self.minibatch_size)])\n",
        "        max_string_length = max([len(texts[cur_index+i]) \n",
        "            for i in range(0, self.minibatch_size)])\n",
        "        \n",
        "        # initialize the arrays\n",
        "        X_data = np.zeros([self.minibatch_size, max_length, \n",
        "            self.feat_dim*self.spectrogram + self.mfcc_dim*(not self.spectrogram)])\n",
        "        labels = np.ones([self.minibatch_size, max_string_length]) * 28\n",
        "        input_length = np.zeros([self.minibatch_size, 1])\n",
        "        label_length = np.zeros([self.minibatch_size, 1])\n",
        "        \n",
        "        for i in range(0, self.minibatch_size):\n",
        "            # calculate X_data & input_length\n",
        "            feat = features[i]\n",
        "            input_length[i] = feat.shape[0]\n",
        "            X_data[i, :feat.shape[0], :] = feat\n",
        "\n",
        "            # calculate labels & label_length\n",
        "            label = np.array(text_to_int_sequence(texts[cur_index+i])) \n",
        "            labels[i, :len(label)] = label\n",
        "            label_length[i] = len(label)\n",
        " \n",
        "        # return the arrays\n",
        "        outputs = {'ctc': np.zeros([self.minibatch_size])}\n",
        "        inputs = {'the_input': X_data, \n",
        "                  'the_labels': labels, \n",
        "                  'input_length': input_length, \n",
        "                  'label_length': label_length \n",
        "                 }\n",
        "        return (inputs, outputs)\n",
        "\n",
        "    def shuffle_data_by_partition(self, partition):\n",
        "        \"\"\" Shuffle the training or validation data\n",
        "        \"\"\"\n",
        "        if partition == 'train':\n",
        "            self.train_audio_paths, self.train_durations, self.train_texts = shuffle_data(\n",
        "                self.train_audio_paths, self.train_durations, self.train_texts)\n",
        "            self.train_length = len(self.train_texts)\n",
        "        elif partition == 'valid':\n",
        "            self.valid_audio_paths, self.valid_durations, self.valid_texts = shuffle_data(\n",
        "                self.valid_audio_paths, self.valid_durations, self.valid_texts)\n",
        "            self.valid_length = len(self.valid_texts)\n",
        "        else:\n",
        "            raise Exception(\"Invalid partition. \"\n",
        "                \"Must be train/validation\")\n",
        "\n",
        "    def sort_data_by_duration(self, partition):\n",
        "        \"\"\" Sort the training or validation sets by (increasing) duration\n",
        "        \"\"\"\n",
        "        if partition == 'train':\n",
        "            self.train_audio_paths, self.train_durations, self.train_texts = sort_data(\n",
        "                self.train_audio_paths, self.train_durations, self.train_texts)\n",
        "        elif partition == 'valid':\n",
        "            self.valid_audio_paths, self.valid_durations, self.valid_texts = sort_data(\n",
        "                self.valid_audio_paths, self.valid_durations, self.valid_texts)\n",
        "        else:\n",
        "            raise Exception(\"Invalid partition. \"\n",
        "                \"Must be train/validation\")\n",
        "\n",
        "    def next_train(self):\n",
        "        \"\"\" Obtain a batch of training data\n",
        "        \"\"\"\n",
        "        while True:\n",
        "            ret = self.get_batch('train')\n",
        "            self.cur_train_index += self.minibatch_size\n",
        "            if self.cur_train_index >= len(self.train_texts) - self.minibatch_size:\n",
        "                self.cur_train_index = 0\n",
        "                self.shuffle_data_by_partition('train')\n",
        "            yield ret    \n",
        "\n",
        "    def next_valid(self):\n",
        "        \"\"\" Obtain a batch of validation data\n",
        "        \"\"\"\n",
        "        while True:\n",
        "            ret = self.get_batch('valid')\n",
        "            self.cur_valid_index += self.minibatch_size\n",
        "            if self.cur_valid_index >= len(self.valid_texts) - self.minibatch_size:\n",
        "                self.cur_valid_index = 0\n",
        "                self.shuffle_data_by_partition('valid')\n",
        "            yield ret\n",
        "\n",
        "    def next_test(self):\n",
        "        \"\"\" Obtain a batch of test data\n",
        "        \"\"\"\n",
        "        while True:\n",
        "            ret = self.get_batch('test')\n",
        "            self.cur_test_index += self.minibatch_size\n",
        "            if self.cur_test_index >= len(self.test_texts) - self.minibatch_size:\n",
        "                self.cur_test_index = 0\n",
        "            yield ret\n",
        "\n",
        "    def load_train_data(self):\n",
        "        desc_file=self.train_corpus\n",
        "        self.load_metadata_from_desc_file(desc_file, 'train')\n",
        "        self.fit_train()\n",
        "        if self.sort_by_duration:\n",
        "            self.sort_data_by_duration('train')\n",
        "\n",
        "    def load_validation_data(self):\n",
        "        desc_file=self.valid_corpus\n",
        "        self.load_metadata_from_desc_file(desc_file, 'validation')\n",
        "        if self.sort_by_duration:\n",
        "            self.sort_data_by_duration('valid')\n",
        "\n",
        "    def load_test_data(self):\n",
        "        desc_file='test_corpus.json'\n",
        "        self.load_metadata_from_desc_file(desc_file, 'test')\n",
        "    \n",
        "    def load_metadata_from_desc_file(self, desc_file, partition):\n",
        "        \"\"\" Read metadata from a JSON-line file\n",
        "            (possibly takes long, depending on the filesize)\n",
        "        Params:\n",
        "            desc_file (str):  Path to a JSON-line file that contains labels and\n",
        "                paths to the audio files\n",
        "            partition (str): One of 'train', 'validation' or 'test'\n",
        "        \"\"\"\n",
        "        audio_paths, durations, texts = [], [], []\n",
        "        with open(desc_file, encoding='utf-8') as json_line_file:\n",
        "            for line_num, json_line in enumerate(json_line_file):\n",
        "                try:\n",
        "                    spec = json.loads(json_line)\n",
        "                    if float(spec['duration']) > self.max_duration:\n",
        "                        continue\n",
        "                    audio_paths.append(spec['key'])\n",
        "                    durations.append(float(spec['duration']))\n",
        "                    texts.append(spec['text'])\n",
        "                except Exception as e:\n",
        "                    # Change to (KeyError, ValueError) or\n",
        "                    # (KeyError,json.decoder.JSONDecodeError), depending on\n",
        "                    # json module version\n",
        "                    print('Error reading line #{}: {}'\n",
        "                                .format(line_num, json_line))\n",
        "        if partition == 'train':\n",
        "            self.train_audio_paths = audio_paths\n",
        "            self.train_durations = durations\n",
        "            self.train_texts = texts\n",
        "        elif partition == 'validation':\n",
        "            self.valid_audio_paths = audio_paths\n",
        "            self.valid_durations = durations\n",
        "            self.valid_texts = texts\n",
        "        elif partition == 'test':\n",
        "            self.test_audio_paths = audio_paths\n",
        "            self.test_durations = durations\n",
        "            self.test_texts = texts\n",
        "        else:\n",
        "            raise Exception(\"Invalid partition to load metadata. \"\n",
        "             \"Must be train/validation/test\")\n",
        "            \n",
        "    def fit_train(self, k_samples=100):\n",
        "        \"\"\" Estimate the mean and std of the features from the training set\n",
        "        Params:\n",
        "            k_samples (int): Use this number of samples for estimation\n",
        "        \"\"\"\n",
        "        k_samples = min(k_samples, len(self.train_audio_paths))\n",
        "        samples = self.rng.sample(self.train_audio_paths, k_samples)\n",
        "        feats = [self.featurize(s) for s in samples]\n",
        "        feats = np.vstack(feats)\n",
        "        self.feats_mean = np.mean(feats, axis=0)\n",
        "        self.feats_std = np.std(feats, axis=0)\n",
        "        \n",
        "    def featurize(self, audio_clip):\n",
        "        \"\"\" For a given audio clip, calculate the corresponding feature\n",
        "        Params:\n",
        "            audio_clip (str): Path to the audio clip\n",
        "        \"\"\"\n",
        "        if self.spectrogram:\n",
        "            return spectrogram_from_file(\n",
        "                audio_clip, step=self.step, window=self.window,\n",
        "                max_freq=self.max_freq)\n",
        "        else:\n",
        "            (rate, sig) = wav.read(audio_clip)\n",
        "            return mfcc(sig, rate, numcep=self.mfcc_dim)\n",
        "\n",
        "    def normalize(self, feature, eps=1e-14):\n",
        "        \"\"\" Center a feature using the mean and std\n",
        "        Params:\n",
        "            feature (numpy.ndarray): Feature to normalize\n",
        "        \"\"\"\n",
        "        return (feature - self.feats_mean) / (self.feats_std + eps)\n",
        "\n",
        "    def train_length(self):\n",
        "        return len(self.train_texts)\n",
        "    \n",
        "    def valid_length(self):\n",
        "        return len(self.valid_texts)\n",
        "\n",
        "\n",
        "def shuffle_data(audio_paths, durations, texts):\n",
        "    \"\"\" Shuffle the data (called after making a complete pass through \n",
        "        training or validation data during the training process)\n",
        "    Params:\n",
        "        audio_paths (list): Paths to audio clips\n",
        "        durations (list): Durations of utterances for each audio clip\n",
        "        texts (list): Sentences uttered in each audio clip\n",
        "    \"\"\"\n",
        "    p = np.random.permutation(len(audio_paths))\n",
        "    audio_paths = [audio_paths[i] for i in p] \n",
        "    durations = [durations[i] for i in p] \n",
        "    texts = [texts[i] for i in p]\n",
        "    return audio_paths, durations, texts\n",
        "\n",
        "def sort_data(audio_paths, durations, texts):\n",
        "    \"\"\" Sort the data by duration \n",
        "    Params:\n",
        "        audio_paths (list): Paths to audio clips\n",
        "        durations (list): Durations of utterances for each audio clip\n",
        "        texts (list): Sentences uttered in each audio clip\n",
        "    \"\"\"\n",
        "    p = np.argsort(durations).tolist()\n",
        "    audio_paths = [audio_paths[i] for i in p]\n",
        "    durations = [durations[i] for i in p] \n",
        "    texts = [texts[i] for i in p]\n",
        "    return audio_paths, durations, texts\n",
        "\n",
        "def vis_train_features(index=0):\n",
        "    \"\"\" Visualizing the data point in the training set at the supplied index\n",
        "    \"\"\"\n",
        "    # obtain spectrogram\n",
        "    audio_gen = AudioGenerator(spectrogram=True)\n",
        "    audio_gen.load_train_data()\n",
        "    vis_audio_path = audio_gen.train_audio_paths[index]\n",
        "    vis_spectrogram_feature = audio_gen.normalize(audio_gen.featurize(vis_audio_path))\n",
        "    # obtain mfcc\n",
        "    audio_gen = AudioGenerator(spectrogram=False)\n",
        "    audio_gen.load_train_data()\n",
        "    vis_mfcc_feature = audio_gen.normalize(audio_gen.featurize(vis_audio_path))\n",
        "    # obtain text label\n",
        "    vis_text = audio_gen.train_texts[index]\n",
        "    # obtain raw audio\n",
        "    vis_raw_audio, _ = librosa.load(amharic_path(vis_audio_path))\n",
        "    # print total number of training examples\n",
        "    print('There are %d total training examples.' % len(audio_gen.train_audio_paths))\n",
        "    # return labels for plotting\n",
        "    return vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path\n",
        "\n",
        "\n",
        "def plot_raw_audio(vis_raw_audio, title='Audio Signal', size=(12, 3)):\n",
        "    fig = plt.figure(figsize=size)\n",
        "    ax = fig.add_subplot(111)\n",
        "    steps = len(vis_raw_audio)\n",
        "    ax.plot(np.linspace(1, steps, steps), vis_raw_audio)\n",
        "    plt.title(title)\n",
        "    plt.xlabel('Time')\n",
        "    plt.ylabel('Amplitude')\n",
        "    plt.show()\n",
        "\n",
        "def plot_mfcc_feature(vis_mfcc_feature):\n",
        "    # plot the MFCC feature\n",
        "    fig = plt.figure(figsize=(12,5))\n",
        "    ax = fig.add_subplot(111)\n",
        "    im = ax.imshow(vis_mfcc_feature, cmap=plt.cm.jet, aspect='auto')\n",
        "    plt.title('Normalized MFCC')\n",
        "    plt.ylabel('Time')\n",
        "    plt.xlabel('MFCC Coefficient')\n",
        "    divider = make_axes_locatable(ax)\n",
        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
        "    plt.colorbar(im, cax=cax)\n",
        "    ax.set_xticks(np.arange(0, 13, 2), minor=False);\n",
        "    plt.show()\n",
        "\n",
        "def plot_spectrogram_feature(vis_spectrogram_feature):\n",
        "    # plot the normalized spectrogram\n",
        "    fig = plt.figure(figsize=(12,5))\n",
        "    ax = fig.add_subplot(111)\n",
        "    im = ax.imshow(vis_spectrogram_feature, cmap=plt.cm.jet, aspect='auto')\n",
        "    plt.title('Normalized Spectrogram')\n",
        "    plt.ylabel('Time')\n",
        "    plt.xlabel('Frequency')\n",
        "    divider = make_axes_locatable(ax)\n",
        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
        "    plt.colorbar(im, cax=cax)\n",
        "    plt.show()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsbKvhQ39WcV"
      },
      "source": [
        "###Model defination "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AydqDJi6fzH"
      },
      "source": [
        "def model_1(input_dim, units, activation, output_dim=29):\n",
        "    \"\"\" Build a recurrent network for speech \n",
        "    \"\"\"\n",
        "    # Main acoustic input\n",
        "    input_data = Input(name='the_input', shape=(None, input_dim))\n",
        "    # Add recurrent layer\n",
        "    simp_rnn = GRU(units, activation=activation,\n",
        "        return_sequences=True, implementation=2, name='rnn')(input_data)\n",
        "    # TODO: Add batch normalization \n",
        "    bn_rnn = BatchNormalization()(simp_rnn)\n",
        "    # TODO: Add a TimeDistributed(Dense(output_dim)) layer\n",
        "    time_dense = TimeDistributed(Dense(output_dim))(bn_rnn)\n",
        "    # Add softmax activation layer\n",
        "    y_pred = Activation('softmax', name='softmax')(time_dense)\n",
        "    # Specify the model\n",
        "    model = Model(inputs=input_data, outputs=y_pred)\n",
        "    model.output_length = lambda x: x\n",
        "    print(model.summary())\n",
        "    plot_model(model, to_file='models/model_1.png')\n",
        "    return model"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lEo7vgFx--bC"
      },
      "source": [
        "###Training "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utKJlnH9_JvC"
      },
      "source": [
        "\n",
        "def ctc_lambda_func(args):\n",
        "    y_pred, labels, input_length, label_length = args\n",
        "    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDxm843f-97p"
      },
      "source": [
        "def add_ctc_loss(input_to_softmax):\n",
        "    the_labels = Input(name='the_labels', shape=(None,), dtype='float32')\n",
        "    input_lengths = Input(name='input_length', shape=(1,), dtype='int64')\n",
        "    label_lengths = Input(name='label_length', shape=(1,), dtype='int64')\n",
        "    output_lengths = Lambda(input_to_softmax.output_length)(input_lengths)\n",
        "    # CTC loss is implemented in a lambda layer\n",
        "    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')(\n",
        "        [input_to_softmax.output, the_labels, output_lengths, label_lengths])\n",
        "    model = Model(\n",
        "        inputs=[input_to_softmax.input, the_labels, input_lengths, label_lengths], \n",
        "        outputs=loss_out)\n",
        "    return model\n",
        "\n"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsaTC--p-9Kw"
      },
      "source": [
        "def train(audio_gen,\n",
        "          input_to_softmax, \n",
        "          model_name,\n",
        "          minibatch_size=20,\n",
        "          optimizer=SGD(learning_rate=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5),\n",
        "          epochs=20,\n",
        "          verbose=1):    \n",
        "    # calculate steps_per_epoch\n",
        "    num_train_examples=len(audio_gen.train_audio_paths)\n",
        "    steps_per_epoch = num_train_examples//minibatch_size\n",
        "    # calculate validation_steps\n",
        "    num_valid_samples = len(audio_gen.valid_audio_paths) \n",
        "    validation_steps = num_valid_samples//minibatch_size\n",
        "    \n",
        "    # add CTC loss to the NN specified in input_to_softmax\n",
        "    model = add_ctc_loss(input_to_softmax)\n",
        "\n",
        "    # CTC loss is implemented elsewhere, so use a dummy lambda function for the loss\n",
        "    model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=optimizer)\n",
        "\n",
        "    # make results/ directory, if necessary\n",
        "    if not os.path.exists('models'):\n",
        "        os.makedirs('models')\n",
        "\n",
        "    # add checkpointer\n",
        "    checkpointer = ModelCheckpoint(filepath='models/'+model_name+'.h5', verbose=0)\n",
        "\n",
        "    # train the model\n",
        "    hist = model.fit_generator(generator=audio_gen.next_train(), steps_per_epoch=steps_per_epoch,\n",
        "        epochs=epochs, validation_data=audio_gen.next_valid(), validation_steps=validation_steps,\n",
        "        callbacks=[checkpointer], verbose=verbose, use_multiprocessing=True)\n",
        "\n",
        "    # save model loss\n",
        "    with open('models/'+model_name+'.pickle', 'wb') as f:\n",
        "        pickle.dump(hist.history, f)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrGMfX2KAUJO"
      },
      "source": [
        "###Training RNN model \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SpWXh2agATmZ",
        "outputId": "c2dd2d7e-ff1c-48a2-a90e-0d59a878cad4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "TRAIN_CORPUS = \"train_corpus.json\"\n",
        "VALID_CORPUS = \"valid_corpus.json\"\n",
        "\n",
        "MFCC_DIM = 13\n",
        "SPECTOGRAM = False\n",
        "EPOCHS = 5\n",
        "MODEL_NAME = \"RNN_model\"\n",
        "\n",
        "################ Reminder MINI_BATCH_SIZE=250 \n",
        "MINI_BATCH_SIZE = 250\n",
        "\n",
        "SORT_BY_DURATION=False\n",
        "MAX_DURATION = 10.0\n",
        "\n",
        "audio_gen = make_audio_gen(TRAIN_CORPUS, VALID_CORPUS, spectrogram=False, mfcc_dim=MFCC_DIM,\n",
        "                           minibatch_size=MINI_BATCH_SIZE, sort_by_duration=SORT_BY_DURATION,\n",
        "                           max_duration=MAX_DURATION)\n",
        "# add the training data to the generator\n",
        "audio_gen.load_train_data()\n",
        "audio_gen.load_validation_data()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-55ef38d93948>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m                            max_duration=MAX_DURATION)\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# add the training data to the generator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0maudio_gen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_train_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0maudio_gen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_validation_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-15-d6e2f280f55e>\u001b[0m in \u001b[0;36mload_train_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    154\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mload_train_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m         \u001b[0mdesc_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_corpus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_metadata_from_desc_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesc_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_by_duration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-15-d6e2f280f55e>\u001b[0m in \u001b[0;36mload_metadata_from_desc_file\u001b[0;34m(self, desc_file, partition)\u001b[0m\n\u001b[1;32m    178\u001b[0m         \"\"\"\n\u001b[1;32m    179\u001b[0m         \u001b[0maudio_paths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdurations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtexts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesc_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mjson_line_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mline_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson_line\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson_line_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'train_corpus.json'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVMzDuhmARk_",
        "outputId": "240b51a6-89ae-495b-84b5-ab53a20394e1"
      },
      "source": [
        "model = model_1(input_dim=13,\n",
        "                units=5,\n",
        "                activation='relu',\n",
        "                output_dim=len(char_map)+1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "the_input (InputLayer)       [(None, None, 13)]        0         \n",
            "_________________________________________________________________\n",
            "rnn (GRU)                    (None, None, 5)           300       \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, None, 5)           20        \n",
            "_________________________________________________________________\n",
            "time_distributed_2 (TimeDist (None, None, 223)         1338      \n",
            "_________________________________________________________________\n",
            "softmax (Activation)         (None, None, 223)         0         \n",
            "=================================================================\n",
            "Total params: 1,658\n",
            "Trainable params: 1,648\n",
            "Non-trainable params: 10\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6JqjwczA-gq",
        "outputId": "384ee605-c447-44e6-98ef-bac657770fe1"
      },
      "source": [
        "\n",
        "train(audio_gen, input_to_softmax=model, model_name=MODEL_NAME, epochs=EPOCHS, minibatch_size=MINI_BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1915: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "37/37 [==============================] - 321s 9s/step - loss: 1079.9997 - val_loss: 296.9860\n",
            "Epoch 2/5\n",
            "37/37 [==============================] - 364s 10s/step - loss: 189.9148 - val_loss: 229.7775\n",
            "Epoch 3/5\n",
            "37/37 [==============================] - 356s 10s/step - loss: 185.1066 - val_loss: 227.6811\n",
            "Epoch 4/5\n",
            "37/37 [==============================] - 362s 10s/step - loss: 182.7528 - val_loss: 216.8543\n",
            "Epoch 5/5\n",
            "37/37 [==============================] - 369s 10s/step - loss: 181.6490 - val_loss: 220.8959\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFDycFII_yr-"
      },
      "source": [
        "###Prediciton"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqavDf-W_6n-"
      },
      "source": [
        "def predict(data_gen, index, partition, model, verbose=True):\n",
        "    \"\"\" Print a model's decoded predictions\n",
        "    Params:\n",
        "        data_gen: Data to run prediction on\n",
        "        index (int): Example to visualize\n",
        "        partition (str): Either 'train' or 'validation'\n",
        "        model (Model): The acoustic model\n",
        "    \"\"\"\n",
        "    audio_path,data_point,transcr,prediction = predict_raw(data_gen, index, partition, model)\n",
        "    output_length = [model.output_length(data_point.shape[0])]\n",
        "    pred_ints = (K.eval(K.ctc_decode(\n",
        "                prediction, output_length, greedy=False)[0][0])+1).flatten().tolist()\n",
        "    predicted = ''.join(int_sequence_to_text(pred_ints)).replace(\"<SPACE>\", \" \")\n",
        "    wer_val = wer(transcr, predicted)\n",
        "    if verbose:\n",
        "        display(Audio(audio_path, embed=True))\n",
        "        print('Truth: ' + transcr)\n",
        "        print('Predicted: ' + predicted)\n",
        "        print(\"wer: %d\" % wer_val)\n",
        "    return wer_val"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMKgtDAG6f1o"
      },
      "source": [
        "def predict_raw(data_gen, index, partition, model):\n",
        "    \"\"\" Get a model's decoded predictions\n",
        "    Params:\n",
        "        data_gen: Data to run prediction on\n",
        "        index (int): Example to visualize\n",
        "        partition (str): Either 'train' or 'validation'\n",
        "        model (Model): The acoustic model\n",
        "    \"\"\"\n",
        "\n",
        "    if partition == 'validation':\n",
        "        transcr = data_gen.valid_texts[index]\n",
        "        audio_path = data_gen.valid_audio_paths[index]\n",
        "        data_point = data_gen.normalize(data_gen.featurize(audio_path))\n",
        "    elif partition == 'train':\n",
        "        transcr = data_gen.train_texts[index]\n",
        "        audio_path = data_gen.train_audio_paths[index]\n",
        "        data_point = data_gen.normalize(data_gen.featurize(audio_path))\n",
        "    else:\n",
        "        raise Exception('Invalid partition!  Must be \"train\" or \"validation\"')\n",
        "        \n",
        "    prediction = model.predict(np.expand_dims(data_point, axis=0))\n",
        "    return (audio_path,data_point,transcr,prediction)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxMvCjeu6f4J"
      },
      "source": [
        "def calculate_wer(model, model_name, data_gen, partition, length):\n",
        "    start = time.time()\n",
        "    def wer_single(i):\n",
        "        wer = predict(data_gen, i, partition, model, verbose=False)\n",
        "        if (i%100==0) and i>0:\n",
        "            print(\"processed %d in %d minutes\" % (i, ((time.time() - start)/60)))\n",
        "        return wer\n",
        "    wer = list(map(lambda i: wer_single(i), range(1, length)))\n",
        "    print(\"Total time: %f minutes\" % ((time.time() - start)/60))\n",
        "    filename = 'models/' + model_name + '_' + partition + '_wer.pickle'\n",
        "    with open(filename, 'wb') as handle:\n",
        "        pickle.dump(wer, handle)\n",
        "    return wer\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbWtNP386f7c"
      },
      "source": [
        "def load_wer(model_name, partition):\n",
        "    filename = 'models/' + model_name + '_' + partition + '_wer.pickle'\n",
        "    return pickle.load(open(filename, \"rb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hVNHfFk46gAK",
        "outputId": "c2925b1d-4f62-4c0a-ac3a-039320fbee1b"
      },
      "source": [
        "def plot_raw(name):\n",
        "    raw_audio = librosa.load(\"test/\" + name)[0]\n",
        "    plot_raw_audio(raw_audio, size=(12,2))\n",
        "\n",
        "def plot_spect(name):\n",
        "    plot_spectrogram_feature(spectrogram_from_file(\"test/\" + name))\n",
        "\n",
        "def plot_mfcc(name):\n",
        "    (rate, sig) = wav.read(\"test/\" + name)\n",
        "    plot_mfcc_feature(mfcc(sig, rate, numcep=13))\n",
        "\n",
        "plot_mfcc(\"speaker1_4.wav\")\n",
        "plot_mfcc(\"speaker1_2.wav\")\n",
        "\n",
        "\n",
        "plot_spect(\"speaker1_4.wav\")\n",
        "plot_spect(\"speaker1_2.wav\")\n",
        "\n",
        "\n",
        "plot_raw(\"speaker1_4.wav\")\n",
        "plot_raw(\"speaker1_2.wav\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Rxtmqx4fCockPlaVvkukr6/R1jzCGlOk+E4ljTNCH1geJU3kt4hjaFseu3GirNprwbF+PM8eYbGjfJWyzcKjWMA7khpQwSd4tJvJ+AGSoFKlip1riLgdbGS+P11HwbqAhXXn67QafPtEVCdufrGqJPJ3zPADwknYAXkm5bqDaJ/FSRz40IaA8aO81dDB/yIY3hDH6pGEJ4njpoxt+/951HgKGSkX3NW4LDeYPzch437hDMNbry5gVgYfebzy0Vaax5XVsKe5QyXPIC1EonkbetbQOVa9SDIJTx4gUQ+eHNEbsJp13FY+8DMzRO4MxsLqZ4UYrjVU7kt6povA4BYANCpRYY42yMW7tDFaz5g4yYgWALPPrvHLSrBHpkIEqV4sh0t6XyKcZr3Z5GKcyE0PsO+x7XSKls6Y4vgHZNwDxsnHjFM41TU4ytlxlZz2W+KUOcCEjwWOoEQDDpYdDVkFSnuE7BvbTFvbX3U3w+lAVshTVqYcAKUiR14CH/w4PBWMnE3ycmfDw8BC/TYE753ySvFs8POb7QKHNxum8H7ZfsZ5iKd19dl/nWJKivnTRxrNqPES8aXjcOufceDVF6n5MlZmxOuyp5T9W0jL2nYd0tsfumGP1NlVCPYTc+9JNub7v0n3MIDR+/5BvN36zFf9Ewb8RjzaZHuttxr0+fDIoMbadHtq/fvxZ6SsAKRLb+/LDfegVkNi1lDRjiHkK0vqbFLUaHm/ICts+3yLUptJWaffrp1KvHwsbIcKYJO8WUxmMgOZpBujrvsc8zVTQdd4ryM2ZujDf7oCa3rtElnU+dPsDd0e8Nc8wFEJS+O5cXqfzHH1pe0ji7ifuIYnZIXDqJXN9EH78TXEoKRkiZQ9NArRnHRr7eJpxn3OYATBNH3k/A88xKwqpefMhRmJDagsh8sWPUyeUvjyFMLTMYyswPnWHoas/x4QvrVPlIVUg4/vuOlHf9OoVxSsCcSieXCU9RLvftz3r52nEPpSHY2PSedfx+BnDCeCTuhM4cQ7Fo2uS1IdIPt/ASRJ3h8QLQ1VpsJrlVRuW5Q3ZzjqSHYOv45ASdbmZkyZtT5G+A8Ok0Ycg4A+NQA81YBsj4T2VmlJMEjWWRA0hVsfeOOUhr9yk6rUOVflJCU+FphrG1QB5OP8NGRm6ApW4kaJMQ0vXhyHlkqqDrK18pNyXEu5DiOD5PIOFVnHoesirWGr/n/ouh2hvY1YUhqQ9BKnfdkwdSekH7hPf2+ygTJjIu8XDYisPHGMkwMeSGA/OxzZDlg/vxDXSLdcQ+vFc4h72PtPfdpvSHlIGctCX8El8h0h495XKpSJVTWOMPrbvWRIpE6dUffPUMk5Ro7mPbzKWlB5yAByqVjVE4pYmCU6TAPvChmDoCo69vn/dOcTkKwVj1VCG5iUmdJKg70bxu/NOwEMqkHxt1v7aOPI6h+9eN8/9tOQ7jV11ks8Bwu3qvo2ExxDt1NWZIZPCUNqnQC503m3YhKkMjgR3AOk2b0q5b7SOoUfqzq+TBD7LrTca7oWG55k62Q52kyYAWAMosWkMZ9cArO67vVIgYx0jddLaRk08zga69GVfxAZC34Alj22c43deY6V3QLqk6hCDH2EIqRlDjk6tpzxktSOlbIdItQ5p9zBEesqPtUE9FF8+m8cfCp8EnEvIufScixPoPlfEEI9L18lIN+ZN5BCI+daO9X9D+sfUtjHEaNtXDziB78KoX6WyHq6KNZQ0hiZ6Ia8yQ1dotMmRpkYj001FqhQ9tIKhkfnYKkeqEO1U0vklXnHOJ8m7xUTeDwBe8e3xujmjXUkrrBujC1fj3f4WKLFp9jC1pLjv750GmI3i371ChirLsM2azqT0f9YUN5EZ4v7cNS8zhaoyo3uckWnLPN0njrVacgjcd9n44BswQ5LxoYOmFieU9kPBKSR4YSnbOJWGVJUILY4vXz6kEC6NLEmSrv2G4vC0Yt50fHn1xYthn7Z8yn4gddJtj/3+kR6CwfCYVUAZ7z5WAoeS6EOpuz0E9Zlb/D3nfDJYtZjKYA+kuIQcC73j6QYgabAq4/if6y5vyrS0MFoWledcOs+PZbn0pfh9ifchcWg9xlgeT6k6c6j4Y+/RMLTs/ORnK86JJPL4YTeHwOF8UKcM2EMNI4f6gNfSBNKXtoeR+DSyvq/Ebl8carWHoBOd8aRlaD1KqUOHmMDGVyGHbxg3RLUq5T4+kdTzmKYOoyGlTEJtMlbGY3XkTwVfPsL528dZ7WFQ48w5nyTvFhN5TwApesTCtPvGEHxLdjkZJv3ALUjxRMIlzj4Jki51p2udlF2XupNknXTYebiUxIcMV8uGgBXNCoVuuBof/IdKVoDhOtahe0L5GIJDkBHfcm0/3uGb/Nj3H3LfKaTp/cmDPZfeI+5/OLtfuO3m+PUpZVVnKPH13SPTGIrYZCbNPadvJSRtUhWzSRiCIYQ1NGn1xU1Jg2ONMiXbKoaQVy1s6J4Sx5K47+O9aMj9obBTomw1GSwmybvFVAYjwD0g9HX+qkZ/0iXfRMipEw4RgA3TJc9QtX5OaTOndaM6w/2oU/r2199phAYRqULjDijde3bvR9WnaNLtfLpzV5EblM5zb9tnz70D0liMvT8kNTwVWUsZRGJLuwU71p4T07cdp5sZXkUZKrFKkUiP9eUfw1hD3qGDXqpk91R68FpaMUl6CrHYB2M9Do2RkO6z8ubDqdViuv7fNfoccm8/vE+yU1SaYqpwQyZW2nkKhvRxh5a4H5q4p3n80Sd4sUlhpaSTO9czdv1hqm1+NGIi74nopN9pHmdk45duILlvd+4OcoPS0XN3j+2vz8+7TctP4EODd0pj90nqu+u6Trv2LPd3uHvINHIydkk3bNynnw8jLSkkMCS5A+BM8Prx4wZLWjry/hBS1AN8agKhwT420MekfTzuEAyRoMmwtZjijZmQpFzT4Puesj6k6rxr4b77gPQ28tGIod95iLrLODUIH7p9mVPyFZOiH8OLVCh/IcTa7yHzeohv4m/PoVWbvtc2LozTr+nhWlpavk6FXIxFk9qMxSMn7/Vg0jQUQ6R4pNZCUnagU4HxSd4rZL1lIS1t7rGGpPGUBifyfOMIfz7jJDdknObmsT+h0dyFZcgcjzI8LZ5eClI7kTGSz7FussZ0bEMkK6kd8NjOWU4UUgeb2ACnSd58RD2FyGvGh7609fymu+ELkZJUI0otTgpxkPF88LVdbQCX4a4LVzdOyjmlye1nYhPIU+PUhMP3zTTCF1ux6lZ3+6qQIclw6Jk+xCYNY9rCoaXvsfAhGFovwqtQGxwSvryNWSmrRNmm2KsMycMpIOvmpDZj8ajL4KyuUa4P23AkKuYbvfXmghxkRGc9w2ROGHXMXBrXyd7t7wZF2xCIkHMz1ALrVn2maCTzJdYgjzN0HJJU+pAqqQP8xE+X1KVL2feV1oUkmfsMKkNJWpd+2tJpiLBrqxjdeZhQFcK7T4xw+dSixk6oQpCDSnfeb7vDB9X9BpMUUpRCQrQ4Q0mOlh+ZJx8OOeDz+7bNu9n7il68oenHkJK/IcKG2CRVy3uszIZgX7UVN046ad2X4Mr7Uw1mx7pTTNWHH4NYfR2jQ66fD9utdAhSVYBC46J2PsawWXvOMXGG2jmfJO8Wj5u83wCLH93FI+4F9vz8riuxDPZ4xo5LoJ4B2wzYzM6wLl01mA0KLDFPPufh3E2kT30G0DvGIeB6k/zXjROSbIbT3afR+wgSlUd7XGXYrAqsVyXuVgWwKoGVAVZw/24SjrW/rfLL/6D8Al3dycXfrPmj40vx+1Q5vpTHK8wul7h6coM5lrjCNeZY4gK3mGPpHBdYY45bVXGLGy1LSQ1HGsnyS4Lpen+CEl5hoNruphMYiKvw4EkTco7h3kLSiA2/R6azj5TR1y7kaoVtH0V73JV417dQ3FvMnft8qnryWb78hCC/X4qqXmjlIObmNjTJ5enSsetpS28PQ3TG5fFQYizTi5HdoX2ufyKWTl7lanKYBB9usnSfOIVR/b4qVGNsnU5J1DXI+vIYJO/GmAzA+wB8oK7rdxljPgHAdwB4EcBPAPiCuq73kjw/9DIIYwfg+QnSoVKqYF24E1mvxHUAJm8ubXfI8wobT73vBj87ABKF0oi7pFgaYaVnpkrfh2BIp3Qo7xMpEwQtXxm2QAYUM9susnyLaraxJH6bAatznXzz3xhBl+dQjqEcA34C7yPy2t8lP69xdrlEMVtjfnnbDoRavVijxDUum1q0cciPJEJtWWL/wTQmTQ9dT5HcBvMXqbohQpLqGYTiaJONkCelFL3SQlmdkHgzeb/xSQpTjCRDkweNLPP2oaU/tA8dIx0dQ+K1tMaoXPkQawO+uh+elPcnYDKt0OqIPN4XY8pnyCT0GPcPmTwNXWHz3ZeSr2NjIUjeI5G8/2EAPwPgheb8TwH483Vdf4cx5q8A+GIAf3mfBB43eT+DJTDHBrU5IlgZXMJVdr93pSt1X+Ki+Z23knUuYSe5qAzjJJ6fEwkjEs+lZkBf3zb9FdN1wFOXzYfqlQ9FcEDPMmDR15GmMqN7qipzdqbdbjNU27w939FutdsMaMKxNZQB91ceh9Aj8TWQb4G8wlleIcsrlLM1srxCUbobYXFXneSGU0rPpftOTs77kkhdtcZmL/1b7bsJy6FWbVLyQe9Vgdtl+ElJSFobi8PdpgJoXaX2VhuqCtm2ycPWXVHMlHpVeYqyys/YMSN8WaC99IivS4BDcWU8HmdfaCRPqlvtSy4Ooceboh+uTSb4Cga/poXJZ6VMWrQ8xZAyoQX6ZJvaklYXuu+YtV9Ptjf5XC0vh0CoLMbayxxbOp1K1LX2EnPAMGRV5D6wxa855w+dvBtj3g7gswF8HYAvN8YYAL8DwOc3Ub4VwJ/ARzV5N8Aebl+HQarLJBB3uUS9Fb9hbzF5G4eD7u2Oc+faWPiX4/LmtbtGHErHJXthmeCYjjkkqZIDYhveEHSVnK+aPG5zS8qlCoxPwh5Sj4n1d1R36HgGIDdAfg7MzrHLgd0MuJvVwGyN89kG5WyNi8Vt+4g5lgDQkvE5blFijYv2d+lI2LvzdRvOiaWmgmCz9ziXsIFDkLq0CWlIysXPqS3p5ulAllWdoGBgv9aXWOtkOkb0fNe1OEMkwUOQYlQXW8XQ48bjyPT7eevihcrcR7Y5Mdful9f5PVK1yfc8mbaWP/3dQlJ2fZIvd9K2/ZHcddu/h4hvwkvP51J6Wf4h9MvWN26E2wGPP8b+KUTq5bWUd0vpk1L7I9/9vufcF/Q+917xkjHmfez8PXVdv4edfyOArwJw1Zy/COBZXdf0Ir8C4G37ZuJxk/cznIa8SykpYAfZRt89pOdOkve+JL3ELS4cHfcl5rjFhaMmI88rZM4xSeI14vpmhtNBVp3knKToRNJV6XmMpMdIu4+4pxD4kNpMT13GALMZ7mYz3F2usF6VLYmvkKFo6s0cS5Dq1RZcP7nzqE9lxieNpJLRDdZ8EKV7xnXgMYnNGK8+Q8j4KSVGsr09pvbX2RLcv4RNImXVL0Zm5PWhagEpdS60ekGODGgPDH7ONxzqPMu4Ez1JzlPsF2LqQj74JLihFSa5h0eBDdYonPixlT65WZ8mPEgh8L4VDypPbbVDKzvfeerESfOIlboKEpugajZCY1YDQ3FkPvZ1LrEvVIPV+2SuW7xa1/U7tUvGmHcB+FBd1z9hjPn0Y2bjcZN3g9OozQC65D3rwvkSNjXnCt1uogRquNRgCmxaMm4lqEtV3901WHU7bU3nXcIX7pOYxTpCHu69XrFnMNWUNu2tSHubXh35c3b8OS1RD6i4xI4JWnZy6CTdx318Bqv0K0l7T9f9DueXtyhnaxSzDebZspWikxEqSdbnrUEqXeviFexXWlb0Je9uRx5DqpSKX9soddY3ENI9sr756p3MUww+qWtM192nB+yTXGrXU9If8x6+MEnStUG6n6eAjqxiEEyqP4eCVCGKx0+Pmx8gq9TlVLlVWaryDNtMkuysaXFNXFaHNTsnn+MC394gXIgTGhd8BF6v632Sp9nJ8FU8qbYnV/akqp8mnY8RytBEc4j3qP77ZiBXy0O9R8XS0fLmQ8jWpstrn5AD3UoIxfP163Lq0YxK9rgAACAASURBVD2XTZw8anxD2tchUIg2agyQ3y95D+G3A/j3jDGfBTuCvwDgmwA8NcbkjfT97QA+sG82Hjd5P5XknSAl7zlQ57zz7s+y0Uav2o4qnAR1jFaCQeSeJKkbFLhgJEc2Q1/6HKlkS5Kqog2vlI5JnCu65Pa4U1kB4ErFJVLIPJFzm9H4byws9R6ZpjwOQUrb+XMdw1VL3OeXS1yU5EHmlnmSWeIKnXeZK1w3YddtOBF8fk+JDebVEhc3dzhfw+pxrJpfet81yxMAVIBarfhEJGPHmn2IaDObmUt0unouiU9cGmaz2Cf4YxGTMqWodnBYakA7PvRV0bRn7ZPfUPgQHeMo+c+UdLP9Jh6SpOU5EYiOSBCByCvA8DZawW2z8nzbhEGJBwxv23mjf5s30sBsB+Q7IL9zVCoxg6NauSznDvmmlkqrtXx1di3CuO2UdSNsx4oMVdteuGthQoXOHz+Fh76TnAzb+tu5Nd40aXJiqLmsHeLtR06EeR6PJekd2leMXVkburpF6Wjphb5byiQ+Ft5cREYN7ZQ8S+D17L3OuTHA+QNd3Kzr+o8C+KMA0Ejev7Ku699vjPlbAH4PrMeZLwTwvfum9bjJ+ykl75yYoCMgUtqiS8f5kmdHTvxJuZs98QZWsP/de7qNoWJGN/Q86ojtsSXkNm0iF/HNevh75BqpZ6RcJe6O1NxTJpygq9c94SQl57++a1B+Y2G+tEN9tKY2o0ne89oRC5L7vu6804Ml9atrXGGOJZ4Jl5Cu5N0O/xfZEvmTvgEsp8bAsAGzN4FTJokyPKRTTbBvum0nkP1npK0qPQYcOs+8je8LmnykxBv6HltGLLvvaclh67Era+pjSadsAsVWAGLSQi5tN7Kt+tqurwgTxoV1xscEksD3HRCEnBRUyIXE2PbWdvWWC4fWYiLsV9/QX8c/eQvZwVA6W1xEJcb8GaFJG2Ef3etjEf7Hkv6bAUv8H875vUvex+GrAXyHMeZPAvgpAN+87wMfXxFwnFjyXpMmhkLatSVS3jET0ZKqMLeYiw5cV5Phxkpdhx7Xr9OQ4pJMWyLkz9e82sgBO2tGyWqbtdKzHprw3TZzR1VO6kO1dJu717emO+eEXSPoktSnxtGO+X2xVqXpu/M/gWqb2VWdDE490wYGPkkknfhNc3SLi3Y5e445uMGqppdqszpu8HkInmf2VTvxSZ3jRpR9net9pNtDEfNt36YTUW+Jqascevmcqx6SxxxazeQb5NG3p34SAKqM9YNZU4NLf99F4RSWovbQXdfrrXyGr+/UVpAOoW/N6xrVodTVWIJG1umZdN1HxjW1Dn4Pbx8+lTOeVj8/44lw6P21a/t8+zF5OBTGTv73MTA/NqTo7t513hNR1/V7Aby3Of5FAL/1kM9/BEVwv6hZCcV0G7lUnZMoH3HvS150w1QuReEuIsPeavaRsHSdskSqwVe7nJ7BmWANISY8La1TSnFj5xs4+TV+T8xrDYCe/v5WWV0IIcs7dYCM/jK59Ox6gil6NaTzJMN/6RpJ2ztdVK7jHibt+yxTU9nJ5fnuuq6XrhGmkA577HwodBLuX77XiAhds6tbuseM3jOrvkqIk6/E5qK5jYy5jNwywgsobahMXy2RxxIppCJ1shWql7wXLpzwcEHuKyHtyk7XkZZ9tRTAaMIZzbZJO9bGA56uzONQ6GoY/fLk9Ye7hNSeMVRl7L4l2KdOP1bmvpWLAnp/1Y+bZhDse+9TGrhfNJ7VWhhgZFV+U+Fxk/cNgH963CQM0JbSeSMZPc8AzHZAtkM9u8O6BDazcywzq9bACfk1rpz9Lfn5TXNsw/rXbzHHsppjeXOBzarE7mZudwqlHUC3cDcV4n8aNCmvZiR5eYez2Qbzy6VjJEkqGNqx1Kkm/WtpWKkRzQwVymqNbFv19Fk5aMUDsMRkXXaDm2bU5eqRzlv1Ene/Uff4GlfOvfQNNqsCy5t59w18O67Kb6AYrN5p38Apf+sicna5xPzy1il/q8/edbqkDuPqvN8Ivfhr93s9X2H2HK6uO9d557rvtkK7iOm2K7q+dQ4sF643Jq4aQKtQPtUBn/GeRnJ8K1LuK/SJeSaeoE2kpBGeZvjrreOK0V7VTNq0um+0OiSRd/GB8KqgLCuuH62RxhRDeZ8U2DeZ1l9BFySEJLxy8uSbfIbDdakx/+V1JIaYW80UoYEUMLjlsnXyrz1TS5uQKpVNMfrkz+N2HLHvH1OTG2vkyREzOqc4/dWCyhOu6ePrdYo/u8vPYXX2Y5Mwn2DDV6+6eA+TDm7wD92Ax7DF6gnwuIvgDHa3yWNC6DQSKeFGd9I9JB+2uybfl853SfSl3kQctlmGapah2ubYzdY2cTSqISt0hGkseZfkMa9xNtugoA2CMq4HvXU6KA6XMFmL/Q1K5LAu0XwdV4UMJTaossxKn8ttTxrZxvUsofPOqhv6slaHm/z5zLHEJa5VUkgGYP3wC2yyEtUiw3pRYvNyoRIgjTQCcDzuAN2KhEY6UiXtROS5lJ37dZ9j2RJFAO13AOzguFmUKBZrlOtNSxjPibBXCBP3rqAteLtgYXUOrMs+ieRGqTaqtdUgr0zWQHuDOTLMG1eXvIz5KpSPqKcQRonQYBtSDSDwL0kGhto99LweqSgrpsudpkaTQrJCBE8j3L5wrYxj3qc4xkjVrQS3atOzx7lTJqnfzReHx/PlM5V0hchyKmlN9uSlfGvfM4eiZ5QcmLwcYrVkn7jHQPr3znEowjt2Rdp9RuKquGfV46GufJxBqO9N5B3AVARx8Dqr6DpXeZ+YW4plDQk1yfs1k7hf4xLXuGolv8/w1FKz5xedpPdZI+kliTv9SumvT+qbi1+StmtS35nB7nKB1eUCq0vgjUYKf/X0GhdlJ3G/xRxXuBZGVbqEkwbgLqxTKOIbBuVVhWJ1Z+MI6bvVTNk1ZX7XkMKqJYXkl3yDsiWCgBUiZ+iMfylP3ed1SYqU7vpctUm1J74RFICedx2nTHJGFsiTRqM+A8BRodEkQvq5O+kLXeeEMSvDrgv5d+yH6dJMvnFLSGLNw+ZY4imeuROW9QbFamel0NrKAF8h4JMO6VFEA32WDG67oHNaPZjBuxGbvlLQGR/Kc20FwTcB5K40+W/ou8QmCbF64zvXvIXw52sTmpCOvwYf6QyRWW3CFpus+GyEUtW1YnkPvU/qu8h3Ctk1pZL60Luk1KFQ/dHcR8pzXodSvc8A7uRX5tWHlDKSZctT1sJDaqqh+hf7JmnfIryaFDrv+mL/dfe59+f1x4e/io/0Ayfm+siLIIP1onlsNKUkPQmQxJ0GYCLtLlElDwC28ZP+Vtd4rEcQIvdXuLbS3kWJ5WKOzcuFqzqzKoDVeVhdI0ZYguoz7q6exWyDIusoyJypvwBd57VsPKGQ+sMSFyhxpUqPfQQvyypgoUvMCDHXlrKT1Xwfy2N6JpFIO+jY1YMLttGRrxOvkKPKMiADqtIdOELwDUqhTjPUoct7U4zCYgNjrKPWXIaSzncOvonLRXDQVweaskJR6jsy8q/QvaOuRx6CtprTvUc3zUwxLpT2KPSOlL+yWVGISbGHDPYc/voU1oENXU+JI9MBfN5j/AiRZx+JDZGtlDDt+Vr6HEO+Rwq6vmfr/EqMkfCm5lW2cV+/E1Mx087HEHVf/eR58kEbH7iLY4oj21ms3FKMiWU8LT++5xNC38LXx8syi5H6lHvddPQ2fiqs8eGTp/kY8KjJe50BqyenSy9Fj5SDOi83rJOOzrHEstFjpk05NJWOTVZg/aTE5omi51tl2KysaRbtKgrAcdHYpt1KecMGkxq5Cm3IQY27QicxBNAQt3lAMqMTTDqPfo/AwMqPtQ6UEyv6RqHl/2MP7P1O26864ZNq+jrZkBQ0VM77LOXqkxE3f1tWzllTd0IDdmyy057Tsk3i50hVPwH8htB0LSZB5elROM9/Dr+bxbEEjtLZsDCOmDG4do8vLBSeAl99DNXbELmTE5FCuUe7LyVPb3Zo/Z8N11WAaFyS8WQ7Gdqv+sJSkFqfbFjciFa7z64ipqedAl/fm9LPyvu1STSfzGjpDVHNOTbu7E4KHSaDVQCPnLyfCtJtGYduWERz8U5dgwi7lQRs1Pv5QJM3xJ824KDNmi6wxAZlK8EjfWx6VtthenSt7fPD7r00iYncDS+0S562a2doB09Nwkf588GW7nBd4Nh5zGDqkANLKkkLTUrG6MJqae+juxmbKPgkRvJaTMrEn+97tpanIdDKIXWSCIz3liPv1TDmne5DUrYPYjrnKZPalEleClkZQ1T8ZDGcXvLENNI/hifj+rWU/jCkoqSvVunqJJoqEz3fN1Hm6YcQ64dT+m7ZBtdwfVHv244PoSMfes+hE+rHYMD6DD/mBkw67wAeeRGYDTB7/ylS2tmZXr5rDPPuUM/6BqtkpMa3pSbjNdezzLzVh/d7n7F67+tVibtnV66XGa7znqo6E1OZaQ1Wm99LJHmeoRUE7rNlC2tsCHQSbm5QyTV/uTQ/RNp8SDFYo/OQmoV01ZdtO317x+sHH/983kBCYz71lcLQk+pTlVvPRbThV8rW6NzY1tWzdvWvSZd6iKcW+zr9FaXuOF0fk6vFSF1YOeFzJ498Uqmr0fiW3CmPIaTUIa3uyHrlTE5EnerVJ2kQvG9dEvWI+iVS55F1Q3pikh6WyGaHrwh29czd5ZPXpU1VtrYftBLY2025wZlj77FFnlcoZhtkWd9rj+xruJcl12+Uz48U67dod2HyuPQcnbcl8rzEbSqqwPfh5U/fhNtM0DHZU8zQ23H19vIc68wtUyr31mjesavo20/w7yyN6W01com0W43C9T+m8+4KbXQB0AVue/dK/fhUVT+ZZ99kg8JpNSDmnpPbMmkel7TrFTInPDZ5obz5EFaVDAvYMnRqn7Jf5f1vyEbB971lXrTvcCz8HD7oBkzkHcBjL4IdLJE9NqgTpk48b+rPDKi2u95GUVXTeNcoxPDRGaxyY9UbHra+wvWzq85Q9QbAs+Y9+Z9msMpJfOg9HB139nfJftu/c+wuz3HzdI6zyyWqp1nr/5kTGOoICqwdV4U0gF7huiHxt4LMd0aLBdYt6fFtEOPzey3dSjo7J2qeU+RgHCPl/FiLi0gY0PfMwgZ3swbOy6ZONYa4NqmMESs7sPMJIJ/sXePScXmp0Zg1ym5SuCqAVem6vkyxn/BNBJ06ddd6LSpnGxSlu8Mrdx9KdUGbFAJ2P2EaiEKuGKUtBSfXPoQGo0GTO16PtMkeD/MRd3mcStyZcS3VI2CHKq+wyTrSJ43ln+Gp0xc9w1ucPun15vrN8yvcPLsCXp25/VFqv+SpS7um3uzyxn2q1g89Fb8vNcdPV7h8eo0XF6/hCtd4imd4ime4wjVexKvt8VvwDC+ii7PBNZAB2ewa5+tdR9yfK3+r5hvQb6g9KG26Jer8eNEcL+zfOYAqv8N2kYF2zt42xNs3BdEmV5LkazYYnEzqVUoj8XGXin0CP/y6Py2//ZMPKd6VtHON1Pu8XKW4p/UR9pRvELYj2LTlAmZTIy10dI9l7njL1V+1FXOffcIpSDuBO6BoManNPHLyfg7gY0+UljBa3cy0ra+7LayJSFS4dWbKJdatlxYaUJ2OurzA7ctzLF92JTC36znWq8I1WvVJ3YFhpKslXtZY9SyvHNLV95HRkXAeDthO6hYXqJAJo9UX0zqJrLKDaxnuHPqkK235PCzZ6Evq3XsV1R6xm2WqkaTm7tL1YMOlaWU7EK/bwbnTl+6MbNdY46atN600VHTf24V1F6kNQpreNh1rRDc2SMuNoVI2loq5yuxLlvRhWH6flG8joU0iaQLZCpIzqNt1G60NhhYBhmaPEXjNmJ6Tl6KZHBXYtHT9JbzargxK8udI5BdzbBYF1m/TPefEJI8+9QfNUFFKFiXRoDoiz32Sef4LWCnsMzzFuixw/es3KH595zK1XDdejThRl6Q9tKJJv5zEi9UQGjO4lFxK2n2rbLzNc0PoAgUqtuoZI5Xp1SteIXlfaNX4OhepY5+7LzHU7k9V7QM6G6iw4WZYzVPPw2EI71hbpArWmcRt41yi/9zTEfIheIbvcQMmyTuAx14ExnaMp4DcIKhP3PtSja7rzJoOd+MQ/Ap5S7x4fCIlpON+iznm5S02ZYHtkwybqsRmVWC7zSyZ32Y2g+SWcCs3FGbI6+a3Ex2eNUarZePbnQxYNaLE/YfbdyDS2UmNeNgGG0ijVf6O9r11lRkNoY45pv6g61nHrke8bGSiM83inWAnDfJLgVyvOPryeNj1YJ+4D5HC9cs4vFwqyT0929b1AgVIRoQ2vC8rsvYgdtLn91DkU7fidavNY+Z+qyHIsO2tqvnen3AoPeRU9CWLfeLsuDRFf+dmqiPdfR3pB9ASRZ5niklemXg6/NunkEZtYp15vilXSaR6n6PCNa6cfsWtJ7q9TvvssrJuUxfu5MHNW8KknH0L/qu1Of4eoUm07zw2SXL7sAxkrjykzVPe+++ZO8fymfw8ZFuj5SWUv5Q6xI9l/61PEHVVH7lCoHlJS1lpkPmReU19VylIAXSbAK3uha774sXycErUEHxmIu8AHnkR1IZJv44Evu04kXYAjlqD/e0XJXWtFTpJCcXnXS7RrzVKkDtGV+py40pqMis9rZBj86RwB8nGUDXobSbzd2pDfHRLVQZ+3afTrHWAPjI9BD5JT6oERKYXI2GyM3WvhZuVvFc3erZyLHpWDnI5qi/bxjaOkvqePhd6Mn/auU8CpS15+wa4kKGzTz9TXgM6YsmvyzxS+XHopMQdDId4lPEt1cfuk3lJXV5PRQpRyFDhslGYCWGI6sK+k5Ix2Ke8um+l+Q1Jg49M9SdY49Q5Du32MgW+78j7y5BqS3r/G5PGH9fwmoz/13t8f2BYG4nBJzAJrTL7Ji56nLgTgJRx8dhQ68akNvPIyfuZXYo8BThp73e0/K8/4O8D3pC6RtZ1zL2K7UnS521GgufZ7ue6aYhfjhLrhhyWKFGAJBckKZVkq29QIzeFGC/l6r3fyM59bGd0KHLiqyNDvd7YcrVlO2fXUrzKHEqiEpsU+ZaV5X0VcixhfcUvlWV436Cm52m4R6IUqVWIQIV0bn3Pk3mISTy19wqvPOkrTKFwOcHX7uHphr7pIcnXsSYR+7Rprf4AYf1rHu6rU7F6lipRdfN6+GF/SB9yXxLcxwZeThmq3re1YXlz7CfZ4XbZ5wW+59wXNpqryEfNXA+DqQgSIKXtzjXWgboqI91Std9sxFVrIJ3TkGcReqYjYfXs7qmB7+hJv9zHOwCHeJPv7RJrbBrCnqFqj7t4lrxzIk8b83CJqtzyCOiTgxiOIYV5CJ3UIeFzAaaV3aHLUy6r68fhATxlgI+5VkzBENeQKcvUWnztvWUa+7jujEnitFU2fs2GEz2w/vcpTA78nDj48+NfPUtZAQmBVkT64fpzQvUoJe1jufYL5YvWfFKJd+hZp1q9SY2X2tccuz8+RN9BOKZLRV9fRIit2vkEAr64sfTuA73yncg7gEdeBLUxqu/1YyKloZKqjB3+SOfd9+tKpUmaTtdJ2k0Saz5RIBK9zTJUWQ6UVm2mQF9tJmPuWEgK75O2SdUGkrD7jMm4xD2mMygl8VReIfJ+jCX4Q6nlTPAjdYBMaVOHGkRSnjNkwOPHIXKf+twh+eTwSbpjdiAUZ9i1PgHPGbHX3oHu8X3roQR06MpUKikZQ+pC9TeU7iGJl5aWLyyGlElDykpDLG5opWlImWo49urtvuPBoca0tAnTYVWSTj0W7iC0KybyDmAqgmRwqXtHPcMdpwYi9gQi6RTOz4E1No0e/AZlT20GsBqaJDXbIkOWWT1pribDEdJz42TevpNfUspVaUgil6FqDch0su7Xj9byNgTxDiquu0kIpT9uUBjWwcZUTWJqC9oyaYjMDcmnlkcNKfqvMdUKeT1VpzblGmF/0ugnHSmEMUQW95HopRC5YxDgsRJNbt8h0+DfkdL0SaZ5nLFE8RCrQrF09pn8HXMSOHSilJq/FDK/z+pTDGn9fKH2ndozaMLaT2fcOJQaP7TKxo/HjA08vp72/QiwpME8AK968EcTjkbejTF/DcC7AHyoruvf3IS9FcB3AngHgF8C8Ll1Xb9ujDEAvgnAZwFYAviiuq5/8lh5u288hKUoX+fjC++Wy7e9+DZc6i371DbcgZgTf7vqsHU68VQiP7ZM5X1cjzCEQ6uaDJkwpKxODJmUHKtTHvvcfcp26MQvRjaHqNWEnnkf6g4TOoRIGdmKHBKHIMHyniETw7GTqNSVkZDEf+gqwan08jl8fczQPvLQUu1YfsI6630BUEj4I/MXSysWfiycQbjrnSTvAI5bBN8C4C8A+DYW9m4Af7+u6683xry7Of9qAJ8J4Dc2f78NwF9ufh8ENF13iUyRBPHGwiXu/LyTuHeqMx2KNk63GUMnyeAS9wq5VZdBBjTu27Q89vPmM0rz76SnnQ8B96CiEXier311E6lDSl1qTSVN3WTmMB0ZTVyOBTkpAtLLJnViJiFXp/rX99ddPjbJ3af+HZuAHAJj6tyxls3TJqVh4jGEzITix/LF4ZM+x73I2L5b7vQpjyu4rmPHeo86hBSew7/yMUw1aki+tDRTvqmuItpfCR469oWe56av19sYhq5gDmsT/jydWtATQiEn1xN5B3DEIqjr+h8YY94hgj8HwKc3x98K4L2w5P1zAHxbXdc1gB81xjw1xnxMXdevHCt/h0TXdHPWmWat7+SQwSrfYjxmsMqf1eu4hdFqCJk0WlUMVmWHJT3HyPPOywxRYNcQjrvK5B2ARuBtPH/VTJEqhzrN8HJjmprJmM44RnqGSlV8+RsSN5T+EMTUNPbV7w1J+nyD/zGId+w9tXQPpeftQyrpPHS9GbKsntJGhk52DqHeEqpXqYgZMmueYkKbo2nol1/nNpYLcSgP5Nudk/lDI3USN7Rf2ZcgpqsdVk4/Hwr3XevC0saNQ7wfQes7+fGY9aSHuOK3xvvcAINJbQann7+8zAj5BwG83By/DcD7WbxfacKC5L1GmlT8UOAdMA/j0CTw7vU0g1VNz4vrylNn33bcwmjVm36mdzIp0nWZV3495Mtb3kvpxjq9EMJEQSOmfX3FEBGjFQ7eAcaeOxSnkKqcWp/xEOR2KFE/tIpASrpDVQN8k5NYXkKDqZ+0x8nLBv06wuuUnGTTdXeC3Y/jy0cKKP5QVaUhk8RQfO2+GGL1XSPPIXfCrmBj6RByeQ+Rfpnn2ITEtwInkdIXjbG3GTJZ1PKxDw7xrGOukEoMtb+K9esPQZ1yKHpcaJK8A7jHIqjrujbG1EPvM8Z8KYAvBYCP+3j0tqc/BvgEgQa27pwGHEvIK3DjU+peO7Iu1WVkxfR16jyMjLu4BIdUZipk0VlprIPlpNqHfv7I2KdzJ0nGq5KkaxILmS+ep6F4DOoKIQzRx0yVoIfK8lBqELE6k0I2OQ5tODwUqRL9VPWffSYefSFB+vccsoITWo3y3SPj+NIein0k6/saAu8jgeRjBC9DCpPGttR/y3SHTkpC+T6G5N1NNzQRzr1xjinpjfWDqeo2/fhF79mpY9fQvjaejn9C1b+eLtA59MrsPlDL8nEP8QfBqYvgV0kdxhjzMQA+1IR/AMDHsXhvb8J6qOv6PQDeAwDv/BRTv/BP746Z3waeNHjpZf2wujkmTRbarbXKz5rfjO3UmrL7YljKokGTGPK0aHrR+ZDvq+fE1H00f/T0q23FbtMpHVWf7TZDtc1btZ+dT/2Hh2+pgA2wRfeH5ncFN3yrhKccy3uhhEP5BTvndSVnvzmAmTjW/i7F8SUPr3F2ucT8comLxS1KrHGBJea4bb/EHEvny/Av5ls5SR38UldpOnei3VbjPH1nJ99qjWJ1h2wLnK+bMpS//BgAKs/30MYcXr3kt8lZHDrnx/K8Oa5zWz2rHO1uzOtMqrjp7S+mnzyW5A9ZCUhVZ+JkLJU8piBFqEDXNI9VPFwLk/fTsU8i3NMDPoKgKNuOe2a23cUjDQSNS/a4GX/Y+CQl/lVbem499p0DaHcw9akM8bjaGEj3Bt9Dqef9ups512S4vBaLL9MashLC4SPn/nZQ9a7xMHdTRH9bSFEBik0kjoUNfv4k6Tw2nJq8/20AXwjg65vf72XhX2aM+Q5YQ9U3kvTddwCeHyejKvigzsPY4L0u+wO3JcSZQ2qJwG6aazrB7Yg0dQj8WHYcMbdh3TW9c5QddEqHnBLH6ew8G0pVDRHP8grVNsNZrncM7ZC1zYB82xF4jhBh1359x6mE3kfi5XGu/HLyTr+cqPuIOyfvlwa71QI3qwLrVYlytsZ2YesRkfYKWfsrB94MVhJIJLozrHZdh1oUzbW+6oWmblW0qmDbNq3uN8caBUps2l17Nw2x32QFskWzn8B6g2y7c4l81XwPjbBX8H8DDll1ZPuW34mu8XDW9iWyrZ2w0K7MzuoY0H4HYJjag4SP3Mjn+AgMlwrTdRkWmmDogoa4+0UfMdHqlJzk0bGcjGaoMF8vkW13KNeAWcFO8Naw9YV+qf7wCWDFzoF+uwbCE8FQfZETvlI59oTxcWVdNrryZeaMETEiPFZVSxtfdCLfJ78+QVEKgfblw4dU49kUYq/lYUhcX37GrgiPWTUdIjk/xarsPniGH3cDJp13AEck78aYvwlrnPqSMeZXAHwNLGn/LmPMFwP4ZQCf20T/flg3kb8A6yryP05K5BTknXfMJEFlnXC9sDxyMzvDspy3UmkyPr3GFZa4wC3mbfg1rtpzed1eu2ji2uddr6+wXhXYrErsbubAynRE00coAZ048mM52MT+ZndAXuEsr1DM1sjzyu7QmlcoMr6h05oZsNouu9WRzypk2RYo/XrvNkvhTiPWOY7pdENSGv4r0x+yHBwiLJIAu1JxklRvnA2xgCw36wAAIABJREFU/GEdmXEk2kL6HfKgIPM8BjHVJ9+zqXxvMcdtObdkJvHesZDfTFv1ssfxehEytB1KcmP59F2L1eOh+tSp7WTIuwCuxFvWSd6PSNKewSXw9LssL1CWGxQLO/ErVjt39YYIvCTuK3bM+1E5GZRIJeolugn6wsapZ5aYLxcz501o/NBWMLVVTi7Y0TzRaN8v9bsMxZj7jqVHrgsg6NpwXX/+XA1DyC5/buo34Rjj5jYlrTF5OSYmnXcdRyuCuq4/z3PpdypxawB/cHAiOYC3Dr5rHIS0rZ51pJ2kIVydhKSKWkOQg1MlOljqcguscYs5srLCuiywuSyxuVxivSpxtyqAVdmpjEh1jiHvJN9P/W3ME7YZdgA2K7sgZ8n7FktctGQeFB4wjuW/hH068BgJSiHe3NCXe+3hx1tFnafSpP8RZPkWOS+rprxcA2D32EfUJWlX1VCaoT5kYCyXYNu8JgxIoQEvXbfan7bveTLdMZKikGravpJHX3wtXOZBy999oFMxOQ7BAvzfXJvYW4lzCVrNucUFK3VRp8sKWelXK+BrhG7arM4mqMtI9ZKuXvRXIR3VQXRqiTaMqy32STkP48/g9dBXB6nsYgi19xSDxxSp7xBC7EtnKMLvlZaffYw7U+KkTsyBwDim9ClDDbl9ebmPvmglJTcTeQfw2Iughr6MeWhk6C3DmzVwngPnz3dY5JY5W53XDzs6r7Rk3qm7dB0sX/KUy5+8M6Z7kQHVIke16OLY7PSlyvFX8usud8fx5Wyf1NYXxtOmc59O3VD4Or6+WoGuZkDBVVPWLcrDdWSpy7ipOpiSCPokbhUyLHHhxJVpxvLHEZp4hXSIffUhtQ7K52np+fLow9CJXmhwHKtaMlZqLZ8Ze/aYPGtxffF8+YkhVC9kP6N7unInpVxir0nvVaLvtJwuP5vmdXz1qT+RSyfsmiTdttW5ULEscIt5+ywf4ddIfIzAaxNjrS8HXB1qrSy7bxP6Vnq5x5wbaPnVvoMvLKUthybWY9oLj5+S30PCX2aH35TsmDjXJnWPm7keBI+7CLYAXjtxmppebBNmcuA8s6Qe+Q7ADijvVEM2IvXukOFu0gFokhx9iRtIHzRDks0YEZfxNFlPXlXItlVrVJVtgbwCjNRLpmP+C3bNB6nPrC1vy+VveS71XEPhMg5EGM9zSNea6o7UcV00f7Pm9wmAF2D12V8A8CJQPwGun5zj9ewpnuEpnuEteIanWOIprnGF1/AinuEpXod7/RpXeFY9xbNXn2L3bAE8Q/d3I/5W0NWxQgit2OSe8yF/s8j9UI5l3kL5bu9vVpbybasaRqtK5WzjrIz0VZBco1++ysFXRqT+tjb51Sa5Nov9RhFyP+iTwsY2AQpJdmWcqME5rVLFVqby5j1ZuQNILvvQ6pP2PbRVrTiR9HdKvNx95awRcU7ab5maJO350cZZF1jezO1q682s305lmw3Z5nSVhyoW+w7otylpTE+/3D6nDbOG88VsjXK2QVH2VZo0G4Xu+7gTLW3yZr+Fv1NKEY6EyHgKiQ9NiIYSeB+GCEPcMbmbaIVUIn1x5fN5+vzaKfEDuHUDJp13ABN5j0OWkCQJGdxOjxsdNRJ7IvX5DNhud6jyne3suKRXQYqhKMWTz/E9M0Olzrs16Yt9zZCag0dik8GqzJRpzw7lxXdNK5GyWiPbVihWOztZ8Bmr8V+ymSCSrsXxGbsRgadjeg6fnPRfoPv2RNb53xMAL8KSdjpe25WeK9xh+9Zrq0rVlKOV0lk7itfxFK/hJbyGF/GreBnPqqf48AdfBF6d2Z0TXm3+nrFfjchzIpACqvt8oA8N+OrAr8TxhfF0g6SeEXJv3u2HOpMblzG1JglqW6S6YcM6o98NCshBM4WQxNTKfJDt/hAG6Jx8qvEb4i7hGJznlSXxVI48PivbM0d9bKva0kgPRVyFzOdFyRdHksWYOpn8Ft03d1chZHlJdRcfeSfSTtJ2SeRvywusS1cP/nY97+ygVgWwOvcb2wNxWyhfu228WWG2xvlsYz1alZ0nqytctxZb9tj+zlmYFoeut8frpbVNeI5+n6sJUXzg7Z+PzfxYCE/uSitY28zOHScTtMLh+4baRDhmQMx/ffCRdArz9SMpKx2+yWpICAfAEcS1RX0iHv/t8ntPajMAHnsR1Og8AxwL8vkByTty2A5H6zBywDxvVG1yYFbeoc7vkl3LaVI1OvfN9n1IMbCUhMAnreBxU6QOoXRCeQqtELQDe7ZBkW1Qlu4g3bpJrNa4uLnrBojn7G+lnK+Vc43cl9Cl/EB/oKQ6MWvuoz9OVEtx3PxtMzh6r5WoHz0PRCT91AY7nyRuDLbNO23Zcc7CqF1IKd6NEuaT9snjJCm8aQ7O3fw60kZ7bdfct4NN504lMzWQb3HWSIPL2doSzFI3GKa6yfdzkPr6IZsDbcKsIdSmjma0mmVA1txfwomj5SkE+d4hNRhJyi+wVMl6F94n7dQfZNi2Bq3tZD+24uaDFOrwtg60RJGvwm5m5z21So0EqmoxZY5NWWD7RJ98+ewpfONDinTXRxK5qoy+GrVtrnVuYGlltudBisqahBvUh3QvkP4dAiTe5xlOlrcsSw7eJslDV9m09xT1m9B3cL9Bv19IEQ5oRF2ubPhIOwDnGwEuWTf7jBf7YiLvAB57EZyCvHM0knSHlEAcx+4niHu4z3cJ3tTsuUvSQoTa9zz+q3X2QyVyfHndd52/S4gY+PLvG1gKZSBRJXHZBvMnSxRPunAayLXzOW5bf+OO27mt8qtJhrQOTpP+XDa/C2C1sJ4nrnGFG1xhiXmn/sJ+SUXmhkncn+Epnq2f4vrZFXYfXLgSdilpj6nNOPDtpWa6Q/7ekvQSVuycjnmYVm78mdpSP48nw33tMtRetfCWEFiVGo24+7wAcWkx1VWpNuMOqGF9330xhFSPNU5LVRGQq3Da+0sin+6BiZF5tiJ3ThNw3nZTSPvQT8CLjhWHye1U8jwHZrgDsjs3jiYc0n61OL7rMXg+F3d/yhdNtP1KbJzwRJF8vF9nl0AGVGW43+/bKqXVK+15oUlsyjgo42rjcSg9mS8fYqviXZg7uMhVbN/EP+m+rD0ByvtRleF4Lf+7/cBJbeZNQN5POQOkQV9KVSlsjZ603elghTSe68jPctuR1w2R0XTkAV23leDrHLTlXZt9v9Rn08gKOfn2bdJEy7l86dfn3myLDJuqRLXNsF4VrZ5sqyPLN17SoOgmk9vKYrZBllXwLZMTMecbFtGybUvWhdRuni2ty7nFpr1XSpi4VKmVkDDpRVv2bJDjUjVeVp2ua+G4DyWXojftQvSVE37dEP31qrDL6G15ofMHL8M4YadjqTOrfQsp3VKl1PBvOOXzXy/DZgAu73A223j1aLmUj6RNQF8fVEIb0Hw6oZpkS5sw+pamU/Sqx0rdNcRIgu96iKyHiNOQCYEPnLQDfjIvxQNO2TNbm3LdSAc1+xXuEhLok3YeNhaV55gLm04tvfTZ4PBzNl4ZNmadM1W/81b4sANK4K7cYTM7wzbjHnEypQfWN+2L7WOiqZ5okNe0icAQL1IxUi+fF5o08Lgp8KmX9gVYer+htZsuflxVL5b+KXGLH3YDJsk7gMdeBKcm71L6V6GTxlMYlDwNGNvMVt/wBRimfhJTjeEdkk7cu86W62KuUbZ+6LswV09TxieDq2qbufqZMcMqdbAxze85kJ9jlwOrfNFXuZjdOeoNxWyDIuumEnz3UZ8U3qc3218WrvqdZOMmE5ntCMfox8ry5WVvpxzdd1jiApt1Q9rzCpidd4SdiPANgKfQyfrY/QJSyLtG5HskvdOrLWfrdqfYFGM3H0G2xd/vIFINtvWlaHei4FMh8Bmo0mQvxcibPuUQaBJSexzePdP++qSHfQKSKi3l92iIGebR9ViZOUbxGmGXKm0xwq6pvR1rrBnLh/bND59IaGMbV4ED++WraRmQ50C13SHPK1SZ/U5dXXJV+rbo+j5dZ1zXHbfPiqudhNQ5Y7Ygbn71sKGEnv/G8m6L2O2bQpPX7jzrkfNUnfeU/T7kBOHUmMml4Im8A5iKYBg0VRki8BQGuIR+6PNhB6Dz3A5GZOBqDek2PSk87drIYc/7C3B8MOaSXzrbNp2A1KC2r+QOqPZ33sSZt9eWmDvXCmywKQtkeYXNqsA6r1DNNh2JH0ze2bFXAlz3WE+1zVBldljZNCo8GxQNKbQ7ey4xdwzYYp4p+HlXNn7S2H0fV1qjqSj5/D4ThaX0inYEvkZZbpr9AG5tWdN+ANusP2GK7Rarlb/2LQYR+LAUPaSnHNqgxyfNDn2HfQg8D/NJ3emLtnUkhWjyMtfIYwjN9zhnktNz/p2yHZCTYGDXrOzdNSt7VU9lzyU3fTU9n6rAUPsWKcnTJPBuOCMXmZ0kZ2XXiugeSfIBXYfXpJT7mL48leOEnj322pC8aOo9yioxHXPjzpikXeu3tN3Dpa96ucmUfQVX71yrS27x+CecPsItw2U/nVrP+0XcTWZSyK9Peh6KL+O4Yzfvu2L9mu5Vjuf9PqTvDibyDuCxF8Gp/LwDfYJOkgoi8JLES5LvU62RajXrLszkjYEr0A6+wA51bvUkfVK2LuxMnDe6h8w45wKu32C5ZE+E3nkO8pZS0a6xfAfZ7njeuTm7uXAJZMi9GZTfQRJfA8xm2M2A3azG3azA+WxjDTgXdse2CrkjC0rRge+7oev7NdYmPL3vIkg7d8sn5f63uGjVaQgdnWWqNdUVljcXWN3MrTs5rseu6bVLrzI+6bsPvvKXbuS4pP3yHLvZOVaXC6wuKewO55e3jhcLqj0FNrjCNRuI7bSFE3fXe0j/e9isxlVn+HmYOOrP7QbzAlmT3wxVOzlGBuQN0eye5VexIgl8CkK6x25dy5SwsN2Kz8ZFhtn0wnYs8pyXf6jsh0yeNGliu/uzIPoxkm+P0Xyj5M/xaEGqmoCrrukazsbdYGpqMvKa5o40tNeJJN7ed+jVN78knpNxHjelPmvXALQb/fk2+evld5v1vFrxjQ4BOJv3AV2b0AQXJGlf4oK1DX48d+4rwT1j9duZTe/wO28PwQ5n/cD9NfUePR43eT8FfJWESLlcatTiVCIekXRJ3hEIY7+mud760MiFP432qwoCkDXneed7fjPrrO256obcnpv7He6rzbjqHI77s3KOTVlg/aKiA78uej6io/6hhU9oAD0Xc0Wp705KHZ50E+dzHSeNDvmAT+iTkzxIGrWlWCmF6uuFdoOaHSTz9hlEypLgW62gczIg5fE1Ai+f4fMS49Ntb/Xb/cSdu5YjWwMK16T1fAImJVGhQUa75g5QYbUOLV4FO+Gt2Le2qlNZ7z6SIANM1QpQ3axq8Ekj5Spbd6xLz+U7ZM1b0Ipc6Bk8bR/Jink54e+qEQcfuQgZ/6qbCK03aasfUgI/cAVEPfdJufm15lwzFvUZito4Yd3qlD6iT2b73z2kj969prVF4JMpSxvDO8L6HDLIfPnzH35vvX2E6+9go1SaOPOVLM8mfyHoE9v+BFdKyPuTXr0d8efIMqqUdLR8nQq9spsk7wAeexGcoR3k7gWxjtkHSfy164C+hCsl0EBfd9HngYcNEK2x7HqHOt+hKO0S+rosUDXEiVPbK0HkpU62DZ8LAuqXwlTIsC5LVGVfymJfPd7Z9SWnfomp+9t1chVoN0Prs/sal70OUN7bFbVODkMdnJTchEi8JeuuEXDPx/PNHFgZV8ouJevauc8ntCZ11+pfTPLOXUFyY1T+tzrH3eU53ri8wPJyjtvLJZblBeaYY45ls+pQoMSmnRxaiXxni0DnvtUQ+c3GYoix2ZCBeuigvu8zU3TStfu1sLEGrhwpknZXdQ1tPLBj6c2HuyVsvUVt0d+rYcuOpW48WFj30n1oRFxOjMm7FL9WsutMJYX6YU3FJGTYSfFstvuEmCNW78aS3n2v+9Jz85beFn19MYVXyvWYl5u+wEYvy33atm9M0QQGIbXNkOqrvNf3fB4vlMdjoZfORN4BPPYiyGA3sjlFOhwagdZUYDL0yY5Umck7378Aeh5m/MvS4eVpDp8ULEX32jdYSA81XAJsz7JW+lJgjQq33nTku3X51qunRsZk56J3RnECrnV+8v7Q8RD0JR66RLNXZqX92z7JUL1s4/HvQ+ealFWTcPG8pHpH8JVhSNIjXSJqUlWfGpJ23k28iuAg5Mu3ROxbp04OyQ5hbN0K5V9C+34+1ZhNQ319xoE04dYmkD4DQ9lHyDzwvGnQJIeae0hpQE6TNs1TlBOWNcbPi26VxvEN3/h5P+fuI7n7V00Kr0GToGt9foneqqemO+77Plq41l/L/sR+h/hwf4iJ55BJHf/+3fjR1f0Cw5Ay8Qzpw2vx7KpZP56vfo/1MDMUKUKJmI59aj9zHxJ3wnP8YD/w8DKPg8AY83EAvg3Ay7CK3e+p6/qbjDFvBfCdAN4B4JcAfG5d16/vk9bjJu8Gp5O8h6QrUprCjuXubdJFltQHlF5HeKcd0kEFhnXSQzBGctkZUlqkGOoMQUwi45PkpEiA7HGhL4uKdFOlMCFoHWNI8hIq29CkQn7HVF3woYhJu0J5GpOvIfntk4cuffreuSASIZJCcTP116rL8OfaZ9rzDeT3ihvXyrzK99ImgTyM3gmwk6mujttehVQcNihwgaVzf2gvB0o7pPMuy1Gb1IQmc32PT327B9cOgfrMgoXRRGaDDBcoy02nZldVKFZ3rkqNLUggVsU4aYe7GRPgbsjEyy9lUyZntdI51nfzDH2P0Hdww2IT1bBHEj5Jl9+2uycsHZZ5IQxpD/x9ZbmEVin4G9DzUwQgQ+q/ROpkKPRuqYa6BHJ+kDJ2hvJ0TNzJTfYetuR9C+Ar6rr+SWPMFYCfMMb8XQBfBODv13X99caYdwN4N4Cv3iehh1sEDw3cCJXXW0ncG3+49czu4MZJe0y9pAvvD0uxzhxwB26b5e44hRClShtTJJGalDueZvokQe/A7LOs3H/bdqxEqEKkRhv4Qp003ePLyxikkFvfCoN2/9hlz33I+77voJ/HB++heY7VdU3aT3EkOQkZjvkIKaUrp+FafmLwGdNVCuEIkfEhJFEKEShd2aZ4/vTv4FObkeQ9zW5FSukzVO3eDFzS7uyoKlVmSPIOpEneFak738MDebePBxfwhNRkDiV95/ViX8IVEsDw7y4npYQhE3V4npEC2QenENox6j4ayfVJ3YeOC2OIvLzPdxxaGUjN56GFcSEY7yaBDw91Xb8C4JXm+NoY8zMA3gbgcwB8ehPtWwG8FxN5PwGk1J2OScrONrAgaftmZjtk6aeb9MW737njUUQj8fKcd97aFs4pDdBHFFINXnzGMT7DmKF65CkISR+13xiZ4AY8PI8VOsM9fn/Wpr9J7myB4R3fGJItB0op/QX89USGpw6iVFbyPm7E63tuqC7EJpjdeRrZDalYxQi8FiYNI8M7qPaV0yjv5E4SgONtJvO8lvTjrm0Cxom87Tsq0G7HvLy0wb3LYV8iHPM+k9LmtD7HLTt7b9mo0dA9lKJUnSHvUI56zHqD+fNdtzvyc1iyTuoxK/HLybtmyOqDtgpL52J8QA5g0XgSKwHMdlgsVkC5aseOZen2+eTUNsMW5OqWvIPxfs+3Osu/cYjEDyXTQybaGfr9SspKWGr+fPcQeFrUV2n9vPasVJKckg/fM1OR2m/7wobaEQydeBwaPwnh9vr+Je8vGWPex87fU9f1e2QkY8w7APwWAD8G4OWG2APAB2HVavbC4ybvp/qIUlUGcNVjWAfNiTuXntPyLanGaLIiTtS5dJ7IvbYrnU8CRuAdFIdLgvzW6zxcXpNDt4yrEX+edorubwpiHdSQJUE+eMgOvvOp33UmsY4t1omfQoIxVoI19vla3QLCZNy3iuPGCT/Xl5+UfMvnyWf4viNRSd4ONnDdry1xoUx0lTaXVUDW5GWgt5lUryBywq/trszJekjPPaTCN3TCrE2OtI24fLsmux6ImEeico2Lku0fUK1xcXPXSd65jrsmiQf6BqzuC6B5ge5Xs22aieNG912uzkqVSrlCy+0RYmo1oVVD/VtUDk2S7YPUxHzX6TvSNa1t98eAtEm7lqYG+X7ahEUrD628pDDCp5omr2m/hGOomxx6EvDgYYB7zvqrdV2/MxTBGHMJ4LsB/JG6rj9iTLdLeV3XtTFm7+WEx03eTwUuTKhgKw6p0eSwnf1ze3z+EeA822GRW0kK8jfaTrrz5uIfNGW41iHHjD0PrfP+0Bt5KlmMTR6GTC5CEmAtLwSfvuI+hEtO6GKkTJf/uupDMo8piA1UKZMo76SrEs/ahs9TwX0qc3/KIQl70SOV/fCUjb78u8K65L7NK2glo1/OmuqKT01miIGqFlcj+RVyVFXWfgdyAWuP2XdswjLm25rcvAJAMds438Etx/6OxzHyzr0TXWCJMtugeLJ2vou2f4O2rhCD3+7A36775FsvY/28Py7IOkB5kHnstQPl/dx+sdsYTu8f7YSVuxns7ndtQOxqgW4nwiEnC/HyTxPixAh2CijPtCJLapq+dxmDof1nSI0zZVI9ZsJxTINcwsrZ0hcPQfIehDHmHJa4/426rr+nCf5VY8zH1HX9ijHmYwB8aN90HnARJGAHS5pPCVmPpSoN/22kLwbALAeAHRbZqi+l4c/1haHz/Sv9/hLkpkwS0i+wLq3rBhbfQNJz+cikQC5h1CcgcgCjPMQGGb9qT1/PWOq9cqJF1/hyuzPA02BeVbi4udM9UYyR0tlMo3kJV/WK7CWa33phJ3zLxcyzAdYFrnHVbdKEq/b6Ncp2xWbZXr9sV3aWuMCmKrG8ubDuJreZ3diJe9ggf+8aZ5FhUq1AKwvpgm8b+PNd9z0n9OuDbIP8T+4Oq/1J3/WzGmeXSxSzNeaXtygy1wMKJ50XWLb1Vaujsl4TOCEYS+BTJ4Sh9tvew/ZpAODu1QAgtF9DqxSUb3HX7NnAJ1JF2bXznLVb8iZD6jH8+ArXmOO2+e2uy/N5teza9XP4VWmk6sxQtRk65uoyvJ2XfdsoUp2kmiHPfa54+QRMqxdxolo49YuT7RCJ1tXh/KqTmSec0gwJTijOEPjeN1V1RN4fmyDEnheDRso1ws3bohYWU2kLCQG1FXz5nqcg7UE8UOZqrIj9mwH8TF3Xf45d+tsAvhDA1ze/37tvWg+0CBKxg7upzDFBJcUNV+U1fqx14oEOXnonOLS7SHk9tIw4hghwUAdL3iy4RILS9klBQu/hSiY7ciOln1Ihie+SSoN9e1wtUazuMKNBnAby59B1ZG/g6sZKMp9C3n3EfdEcPwHMApgtgNliBTxZAU8+DCyA50/O8Kx8imtcOX/P8LT95YPgFnbilaPCbXsl73ZjXZXAjdF9wMcISwgx476xPc/Y/MTyoZF4jcznAGZ3OJttUMzWyPMKxWyDIusbSvJ62Jcid1J3oFNLkOjsK8LXu/MuDtUBaa+R0tYBnfg5zykzZwOaIZJeeifKH6WsuYXkLiAlab/Cde/8kkj88xvbrt9A17bl+Yodc+LOyTs5d9Imhrz+cPWYhpwjh23XRNgX7p+ZNe38yR2wuAMWN1gtgOvFJZa4aHY2sJNzXsYVupURvlGeT70yROAlafd9GwA9ew5Jyvl1fkzPkt+f2gU/1wi+dn8KhrYZ33FICi51+ek+Lb8xtRk5GRqa1xSJemg1Qh6n5PnY+DlcuwEPW/L+2wF8AYD/xxjzfzZh/yUsaf8uY8wXA/hlAJ+7b0IPtwgeGjTSzut0SPIidR+bcO5tgHY51aTgmv9kOZDa37iUQF4bOqjHZvCxGb1GBELv4Ba3LsXxeZqYY4kNrDGpXULvD1pZZiV+db6D4asetnBckn6DsKEbSeJTyTsN8gt0xH+h3DNDuyNvtt01W7y7A5wsO5Ks2gHcSuLXNNg/b4j7zazbwIlv4rSF+x6HIsypOEWvRO1Z/lL6/G8lw86xy8+xyhcNWauBfIuz2cbZ7VeqgEhCQy3GJikJi64KFsJQyaFGToaqNmnpxoQIHHLlrESBW1S4aNruHEvneXxtkAsH6Bpgy7Ks1tbQl9orJ+0amZcTdc2AFei3BdmWeZumP/qUPPxF2D1K3mp/P/LiOV7PnuIGV3gdT/EaXupNyvkx/RKxv31+gZtnV7ZNaxu1hVbTfPVeXXWqgdkas8slytkGV2V/B+T4sZ1sZdjqdgpC7cyZKFRk0O12rnxVGUC742yKKhPgukzUxrJMCfONa5qQiq45eU5sJ2PVZ+T7++L77knN55D2Pgaqq8j7nU94Udf1D8PmUMPvPGRaj5u8n0EnO4eEVkJS2kLHmtQ9IGmXGzL5wKXWmkFld76FKxHos8ehDS22PDZmuU7eJ/Plm+n31WXsAC4nO/SrPauLwUhTuUW2XWHGpc0V+pJ0/ps312XxzNg9OdyBUg6SGfr38wkDxXveXZsBeHH7Boon62b1YNn+XeG6VZF5hrdgiQvc4MpRq7nGFW4XcywXnWH0GgU2VYnNqsB6VXaqD9vMVXnY+vokkX/feex4SNxYWkOhrZ5pqwSyDqwA5MYSepxjlwN37X2W1NtjW3fPmGqI/W3qoaMDrhjqKWHO9Sxux6GpKFCYtiutT8LK7+dxZXpO/jwfRz5TSm5juu7aZk3z9bLzMEPElcgogVa61rAkmtqctvtqaBJL31rs79GTtj+BS+hfRLuSdl12hFyupNHfslWBuwBt5sT7zy4/dVMfxftq/ZF8D9+qE/3lAGZrnM82dnJadmqKcjKqoevruz7aGndvWwEVB0m0OYXeZE3dCgxjPkmzOz70V5dCY1jMODu2SsXzE8ozh9f+xzNR4GWr5UEbb0Or4PsQ+kPisem8nwqPuwgynGaHVQ5tkM/EuY/Ii/puXcB1LuFyVK2aTNY2tE3bNQLZeRPFAAAgAElEQVRu47Tn/YYm4Wt4McJNpkSkRynT5V320N0Y5bnMlw+STHAJjbNzIjbNsvNtu3x80dvl9bYjGQsg265wToN4Jr4dkWm9gPu6sXQu42XoBtJc3LOCHSQrdJL+S1jyzqR25wvgrZcrvHWxwt0LwO3lOZaZK9/iOvCtnjt0b0YbFHbVZ5GhWvS3We+/ri6xlYOK71s7S/lVhs2qwHabodrmuFsV3aRha/oqPGN14H1IIe2aZBLiWK7M5WgmO3lH4Btw4p4LMt87ziKknb2gj0Cn6iCHCLsMo+fL1QMt/S6vmvcQd+VBJ+9h0t6GEWnXVGJCKjJ0rYIrcZdtOmTDwlZUuU47LtGqwjnk/cP2d/Fkh8XiDbz41jdw/WTW2rFI8s7/bptJ+HUzMV/iIjghp7blTMo5lIklTSpLWk1qVpB8NkRyskXfuoLdBXmLzlaqxBrP8FRVJwvVRV7HQtDqmRbum2DavKyT7pOTFb9tQDzfsl+VfWrKvgy8T/cZnfvdTceFcDKfpyDxW23gfdzM9SB43EVwBshJ2Ukg1WXkr5TGMwkr122P+WYGOmlBd9yfaRNiDUnrHPhzfYQr5dmErtPmndUa5JtYxqUJgu+d3PhdBy71h7XBvTNU6/RhSUfWHt9Yvdg3AHwElix/GPoSu1xq53rxQk+23gLbCrjzEMfzHMgzdGo6HmO2VnrHB/0ZgBdgSUFD5M8Xd3hh8QaweKONt3rSGbuuUeAaV17yzjv9WGdN8El8QtIq1btJlmGzcAcaPriQUWS7IrAq4sQeym8MoRU2fhwL66VpgO05kNtIOwC7hhjdAS1xIpxFJOxAXwovpfcZN/4MeNCR3m6k7FMSk1RpvEa0YoQq9myACEzhnFNdKrDBsrQ7ps6fWDuWci3cQUqVGE01JkXqTlny9fO8LWfoS+FnsGS+BFYL20658TlfMaN2yvv+rPl2F1g6ZVdgA9oZd4MS66xAtfCrWcpvS9/M/Z4+L0l9r0qSwGsefXwbbfHvLkm8zJesOyljkxanb/fVlQsfr0JkNaZClpo/GXeIUCTkGMK3yZcqSPHwAEr7vgxWd1IL5QGrzZwSj5u8V7Ck6lSQqg5yiZEZKfn8vZMENOWckxxJfGKz46HQljtDlv6pUgx6Tt4MNL74qdf6cd10KmS4xQU2KFrjTf7MnpRwUSFbVMDH6ioH8j55fUyeOXjnSNAmVjw8FJYSVz5XDhhd3sZ3D6Hl8wxbXGDbkg+OnuSKT2SS0h33HYDxUqShBl2HHvh8+ZZ1Skufx7GqC4VaH1J04eU9/fxwlT69rcWIoKomI6TwF9kS88Ut5gs7kb/CdXud/Lyf08R7342apNBGI+/cGL3xJLVcuJv4EXknw1MuVe+8RLkTb7mPSNcH6Kuz8htI+CZ4mk0RL3/SWec67ZpnoFJ8C9rx1tlEK/QdqOxDTVwbn+XqN62OZN0vef3x7XaruXImVZ/YKjN9A18/mwqfVH+u9KOhVa6UZ/qecR/4WnzQDZjUZgA89iI4tatI3glw/cEtuhUAvpTeoBIdqyTqvFPm5J06dt5Ja24aNeKWAp9kLEp4FVLbXzLv+7odsqQY1p3M1WOftCRFd9B3z1CDo9SyD6ky8V+fWpE2MDjSmsYverXNVJ/b5G+759YPgObaTwVXB2nc/QF9tRCSBMf8qOvn/jiArIv+idYYjP2W/evDJW9D4SPRnTFnV3dyVKJN+Oux5qXEp8s7hMgTpOE531XVPn/jkEhaSaNVNXc17cZRMJlXS1y9cQfzBtwVNLmKJt1FSjUaafvSf4mOGBIpJHUZ7kmKPMy06jKNJ6kFgCcfRP0EuH5iVeC4Meo1LltVms7165XjCpaO1yjaNkI76mrfRn4DAOI7+NWWiIjPsWw9+xBx5+c9953kolMr+1j50y8i30Ej6sINb0+lqfkmsxlQL3YANqjKvCXsNBbLyZTcGV2O0dIVq9anS/jUcOTqFsX1rZTI/pOeFV4189uuuPxgH+Oi4TiTffhE3gE89iI4JXnnS6T0m6HdnEl2HOclcJ7vsChXQL7qSeTJu4xvoxTpV923zAWEZ/UhYqFJZ2KDtqZq47smw4lch8hyKN8h/UJJ4niHRZI8AK0u41ByqHV2Mi2ZJx9kWfU3VHIHAClx06Rw17hChRy3zy+wXpW4u7kAbs51bzKaW8iQCoqGHEB+3kmx8nPsGsM2+r3j/tAv6c/6Q59fLnG5uG4kd30ioPno7iR37uY7fg8VW2fQiaE/OQtLm2MkdohLtjHEN/TckDpTyEAv1UZFPpOn5SsDDbJN8d1UpboFJ42cSBYNwad0bbu4ADJg+9Y1rrIVzhewffUT9FXeuBSe2oIk7kBCe0Bf+q5J4aVBa2MMarZAsboDZksgQ0vNn+J1R8jD3UP69Jjlhk5Dvgd9C/kbIoEVsmbyUOKZbwKeVcieNH8ijpaGlo8YfGOW1gY0ci37YV6WsR1tQ+2F50kea9AFWuHVbRnPR7hTVo5DaZ0aH8FP9QMntZlHTt4rnJ68A2n67bIDb8LPZ3YHVuQ7IL9rt8neZh2pJ/1315BE110cMlhqJIOOfbu4+gxQZYdG8cd0dDIvsffQpRHawNIfJORGODF9S003OCT52CA+0Mgy6G/6XvYkPCRVu22NURvd2OdXWN7MsXu26Ej6DYBncM/5n3Qj5/PvrhF4XvflZkY9og7gKft9CuCpwe7pAjcvLbB+qcTm6TWqzK7SXOAWABpiZon7UzxTzfY4uS+wxvz5yuo4P4d/ud2HkB47b8/snG+YJr1Gabqi1Eb490+RYKeQ+JTVJN9Kjc/uhZ4ba6sp5CREVNw27LZXuXeDrSOuUTr3A9/bfG29QbHauSoyVDc4Kc/hqmKQx6hMxEsh70B/DJB1CugmBxxbYLYFZs/vUCzewGZ2jqvs2rvy6pJ2fUM9Kv9U8m6zGSNyGlFMI4eSBFLeMpDntKJ3ry8fElqdk+8tVQn1Z69hHTbYsDn8pJw/yzc2y7yNbdO+9/KtzMp8aPUgZWVN5sHN5/FZ9Efwl9yASfIO4LEXQY10g7R9ITv6DK6HiYqdV+g8lnAf0RkLZx27yawR43m+AzJL6jXDVu67NkTa/Q2tP3hrludc8puhasnH2iGVuvcS/ssJ6e16/v+z93axknTdedBTs6u7+vTPe/p9Z5z3sz8b2zIREhdIiIgQcQEiIiK5wDcoiZACQZFygy+QuSDABYIL5EjIyBIQ9IkQsARyIn6EE9kCFBRQkIKCrUgGIpAJAX/+n/d7z3zd06eru6qLi12r9tprr7Wr+sycMzP2WaMzVbWrumpX1a69n73Ws9ZCfZyHjJ7HCjgWOlicCrbkAEl/izNQtpgtTnBlg2px6sOayTTrtlOVpc2VERJyERG0wUbr+HltJFCXJvIdNti1G59k6W4D3M08EH+NANjv2N9erEsQD8C7Tt73f2e2bNh+kln/kGcAbtjf0u/mQP2VWP8GwjeyANqtb6f+zmtssMNLvMYrfIUt7ob1l/32Fl9jizt8/p1joEJ8p7+X78JP5GUsfovyoAF2ORGnMmViXvTbs97K5ifjF8Cd04m7Nqk3JgVAmkmZi8yq7Mte9Mu0j+CTB82BOO4DYgCYy7bqrxH3OVNAvRQJ6vj3NMd8+NYOuEGFE2pWFn/HGzH9rTGvTphXNarbUwzmZfugSSt3Ws3xrHlb4u9DKnikkmdKW+gnhq7Xwruy9dQz1/ZUongyOGVyRe3hXeWxgJp23qllY+fTJshaG9UkTCjScus4WufUND4RoInKWP1pDGnghm+DzpN7Dvz81iTBAu7vS5nwvqWTBc/gHcDzI7heOEDXhIP4Rmzz32mdOULZAOiBHhT0mnqkg7sc0GkwT6omNINyYLe0vzXm8KEXl6hQ44AlHNoh8OIBN8pjCh1BiXYYhNqy9eC9bDztIgfULcDFJ01SSv+pE/+6ZJE3SsSh6KRDlpXsiUA7lUnNoNT007Xs2wrgqYVPIEURM6gOB9xEbymyEPTmZwA4lkugXITnI0FDNKkRf4P2fQbf0jbwwL1BAO8cuJMQgCfg3kcDIC07aeBljGj+/hrgcpzjdJzjfrXEEvc9QNskz65BoEKcUOH+ix02tzvPZ75FzJuVGXClwyEX2YYs6xrfZwDubLkc7zgg5EqBPjpd0f92SE3Cfi/SlfTXufR9wgWNO6uWgFPPhQ4gPvim+Kr4i9bDaVtwvrRDYwCq6RQaTXJgJuXrt0N/Ql8wcDLPTeLQDsl9AMRtwQLnErjL9sP7eFkmf0Ntg4eblf0/O64ogVnf/wPxpJCUOkBQ7Ph1pyYm4jIGfjUFkG5FCmUyrKwWTGEsmokE03nF1DTI8i4BGLQ+vMIJ1Na0Y8fOn9Yv3qdp13MWME0Bp02y5TuRE3Y+GY+vqUwKmR/VU0rXCTzzDN4BPD+C6yQ32AMpsLc6dw3Y1+J4CcLY9YbBnQ/yUV0uSGQY5M+RRr+uiOZC8NEvCZjTkneaPLJB02sJAnCdZzutcJIy1bbzDIAa2MqBdrrHxicouTQujpeNwH+WWnUJ3CmCggbkY817Srvx1WiTgUF2zjrHnXJMSqtAPPQN571tsC9b7AEkMVMt7aB8Xvt+eeyfHZYIJi2ufZc/JMDfSw6w87Yc1bFE09CzoLdQ9RaeZU+fWaLs799f2T/LxjnUX9xjU+2x4OD9M6T8Zf/QbZHfcamsSw1rDtxrS2u/NmHopcu19V74OEqTeC3xG283854WAJBmrx0A+rzX7mpg4BoQBtggkosEQjlfEzlhlt9iUEWENkvgpXUOzrVoyxqnRYv58YJy5XnmibZdo8pMsfDKdpObzJV62GBLuZKjTWqad21Jci1tQ55j7D1b4DtHr6Jj+LsL7ZJ/vOOTNS6pljzPE+fr1/DELUCvXcsSOUkJrb4d3q+DGybY2pjiv1ndcVaWRfvaqg/JO49zAhwrDMn5+Lj8lBT4+0Rl8cx5xzN4nyZTNXRjZvdSbF+xX3JsgVjrwsXK1qpzXsOMXHIp+QdOv/O3FwbP8EjaHsyeek193Ruzd/58rkK9muO0YjQdkaAnGwFleN6+E9QSisjIJhafncrDc/H14c+DNN9pLOw8/52eBRcLvEvqDAGNHI2GphRB8+ifxWXRAeu+kyXwvEVKk9H47o1Yb/rY5DAsI1KbT8u1WHJN/Fb8rY+4fXWHTcWig+DA4HsNhwZN/6yIwjXvB+9hMFrNcbM6xPxm6Wz4vgaaKeexBrfpfrODTMhnixnrc2b9MrLUsT7lXGmT9nhgl5NKms6H8pC0TROusY2386MtVxBoGkwJYS3Km0qpqU9wzcX7RJBFxsqkSpM+IAbzYGVc5KSOl2l9OlsfNOwL9O/rgnN1Tt5R6CPmA4Dj3z8X/rwt8DxVxt4Zn1Bzeoe8lnYeWZa71oegalh1eKq6WDx2yaUHwMalQN2JKVZpfPdk8u2c/6vGKVi8fk8h317exwXPmncAn/oj+BAvcSpwl9uZTtzaZznF+bKHmUhzfEltIOcgn5+DSwly1PRdOD9zmvQ6cPdqN4dbtWhbZ4Y01ERLSkNLCkcI6KZJhzhMngckJShMXYty4NW66G9auELtuun9h+epOZpRCDIr4swBS5zqOQ77pY8qc5ylIP0OMVCXgJ22E+COGPCOgXcYS7IiTfRp4M86XKYd/nxbCpFIIluF5ZgowVcOeI1JbszWzjNmIXqX61nnE/3OmGa3Ruxs3oh1IO5HuIbernbbg4iTOujr1U+/oykadzrW0moO99b3D01zQYkewC8QMhpTO6E+mLbpufJvwZq3aNYaTZmTVjL4RbXArPEW1a68YH48Ru/uRmjlfZViIP8uYEvTiPPzaJQWTesvJ3C8fMxRcsz5WdYpfz/6BEKbUOYsF9daGOS1rXvQ6zx9EnPt/U39nXYt+btn+fDy/Daukdz3x51YgbTzpw6cO7NaGnj4ZdFfzzuzAkCvUQMg+e8kOTqaZla3HNdkhAPS/pKm64QTXE9p0MAqffz+Vj2gbxHoD1M7s6kyJY64ZmZP031LTXvMa5fAQQPyfClFDnpa+mqyWtzjBnOc+jCQBIv8L9vmxlsqmjKNLnOHtEyLNqNp4Afh3uDSYRXwDbUIqzLKjBZphjuwbhd4841v4M12i9tXd9hVPoIORZihsHP8jyZawULRg8/KYVke0JYiq6ZFgSAZ6/20/bLZymNyVjrjnDl6zBR6Kfd5meq8CoTkMgCi79/S8PnbibWtdNwcDweOFj1BTubS8wUev0M7+OZ4Kw3zGXEtnGvgqpZ9/70CoG3hmhauucD11SjbHuD7G5EXt24iFXHb/D1r/krSksotqBI40n1r1r5UkXP9MD+mLX8oGJxCx8nVwZJcfeTkIB4d4m/B0kyPaq6Vb2Dsfsfug/8+N2HIOeOakyymMANwXR4QAJNzgbwPqYVP3bPmHcCn/gg+ZJrchz45TaOjbU809ReCBz5jv5OgoGzpG2SceEfBueZoaUBjQDs4trWDhp1r2unXdIwlWa2EonW3nGJc2aJtykH7LoV3zTzZCDenS0dVrYx+L6PNWKEm6f6vcVjl2nbOeddiuFNoyCECzWqDw+oG918usas32N1tcNkvgX2hR5fhmvhc3PdBY91TZxogcZPkmneLNmOGigx/s1ffxWa7w8YF6gwPDbkc7AwHFtc9PH9r4jjUUX5P1jc7BtI1rSo/pi/LgTPuRM5pbpLidg3QsoCADbJSABU/v5odO00LKa83BvqkaBYrqX2nMssCpkEyDsoHuozGbQfbls6rD+X3Glr4gpUN/kqRlv4SaE+Ad1Ltj7eCFEwB/FymAGJ5jLTCtCgHoOq30+gmNKGS2nbaN0VrfW1byp1HA7FWme+Xp8Vv5+dIAHLmHtP7yr+r3Hco74OXJ2BeJO8DMNBWqRx4x+R9TyEfCvd9RPIRvY1PRCxNmxjIIUzYSXluW/5WO18vGmDQxNK6h84qTvTBOdeSulGDZ4DVedkyiQhtD9eyHGSGTkNvmhcAKBuvCxZZPV3Z4L68wXxxgnNtwluvElCeAnQLrOc08oAO3HWtYaoZ0Z49f9Za+MgByLc+BOflOPehNxMNOlKwLZ2i5T5+jimcdwLwPNIMB+4KeH+xfTsAdwLsHLjLMsqmuR6AvX8Sm3aH+fHsnVat1OrWfWj3NXWf8q0V7Bjin4dpz4WdgzuUn68fiLR6Kv3QlFj0XBsvB3wOQnLOqnRMDuDTMVzk96E5fOuAXQfxkYXM1T4pUNXCrVJA7xqmYdcsNBbP3foerDJtEiitrRj3adJokunzfrjGd0ymguZAWdT57w8597V10xw/6Tir3abgPfY3yIF3XqZdk9fpWvqS3NYm5rnJQrLPsWU1fg75PON6PQ18/Hb1zHnX5PkRTJUx0E5lSseslk8B7UYnr0WY8Ot6iEi/L2hmZCeUo8tYsd15GQFKTq/RQXyFUz1P4703Srx3QAehAFDOhuWlf06XssO5bAZAP1/UqBYnuLJF44i247nt/tSuD//lxWuSAqeXNEf+8QeqgATuEjjw43PgnfRSlqaI15OuSXWKgIprPfe/bDFkPNU0f9qXLp+v5KNr78E6z0IcowH8QSt/xHJ9wNIdImCugfaXeN3v28eJmuodlm8vPikTDxHJU6sTgNfulcuY5l0eMwWYjR2nXTeHYaZMLET/wWPRly3QNBe05QWubFEz7jYHf7JP4OtSIzkGYOicJBpISwF8HOkjdQzPA3cAMS2GH+eaAOgRJtvSiiPrACAONTkiVsAA/wzyoDoHRO3f2I3jIcB4DAzL4yyAKvfngOwYQB2vs/1c+fVzGuscoNfKZVnu3uW6JlNBcg5g596PfP5afa3zflTyDN4BPD+C6aJRXEp4kMBBOGCDcm3d2DfmrDp1cPRVjY+Z4qxqOUxqYL5mWuP4/GVUBzLZjUrJnq+2D8Y+IW0fLpLq4UAutdSZtUO9o9/Bx17nDrslYm6tjETjz88BgI4WZaefi/DDaTNcA080mvu3N3F2VY33PpXvzp1XM46l0bPPZVjl0WZoSZz39QL77QL7V9+DX3vVa+CrvAb+ZgD4HsQvqwNuqgOWX9z3U0mWebOPLuIa+IQ8yNyLfxmxyEmLFTpQ0i40GobFu7eoGbnIJlIshcCCLUsAFVBUfXbnCsDiguXqiLoCTosZ4LzjtL+kiyxq/HvXkq/Jib8F6unccfV1yowG0KUFLLWiBcuZRZfzETjaYWJMNQ1ZlWPtPIBBQ09SZN5JsILGuRGkRTSXl8OiVF3DFQeuA13WZCAP4vMTODrHibWrayw314xvDxVNucL3+f1pbPfx817LswoydXKkfVtTLANTqT90nblSD7reu9znNZJE3XoG7wA+9UfQIR14n0o4wORx3Uks8DkRuE9Nu07rJJYWRusILGfVFmU0YNfKwD2WYbVGnOTp1FY4Heeoj54u42kesxTojNEcNGsGgOiL7nnxQzQaBgSsLKp2XPc002oc+SKm0/jrtdmOTeNW5p7zVlBlCLwPvPft0nPet0vgrvDAfI135LyL9yHfAdeq01LjvGd47y8YcOdadQLjBNppfYn7aD9RZwi4L9uDz0pJoF0CZy7W69HA81TgbUkOc7j+fFwpQFJlzi217fQ+KrZcsOXKrx9XHrAfXOxD4Z/mTbROk8b4uNjiJnMUxJN4BhCMibtz6Tdq+aXQlZa4H5beF+I0+ETwNkE9Jv02PvaEZX2YnnFVvhtFrNCeM9Elp8m3OI2K06vYJGCKdYjLtSO7Yb2h7W4RxqTTYobGcWf7MH4csBzagE8M1uAEysib+vnkrDwauASu1waPjYnXll0TieV9TzhIrplMhN80w981kvPfegr5bdwnZd1HaBB4avn0wfvTTP7Ck+JRZTSzOAmBGm1gFYMsB+vUMfLOzdKW++rYmi0Sy/tedpgSSHKNWgDldhZWUzvXA/emd5K5UJQUYBy4WxYPedxISx4D1HRMaTxtDi5yXFvNYZU6Wq0jp8gy4VpuoMi04lryOlV/3Kms4MoWl7IBFrNAY+HPdYG0XJNrwTsH8MmESpyTh6ksgUu5wg5Au3ZoVv7u62Hi52HYBjvsscNdD8ru+qnMTZ9Ii0D8HDUqd8J8lVKa+EBnDUQWZYLoEpo2tuDPRzpC8rY8pqXXlgCMqvJKp4oA0rqv4fuYygOvw+oFDhUPN+rBOQfl+8ExOt3mYJ6DfE6HO+yXYWLelN4HY2xCLttS2QGLOqK+zasYnPv3nnh/DPvlBG+HTdRONtihwgmH6gZV1U8I3h7DpI8DefkdfFgcE0uuLtfUk49ntBSW5KLp9VTK9x1AeZp0Tpvg6f5VMaDPaen9NafSafLAe0zDL7XNud9Nud5DReu3LMqZX48/Nm08mrod73sqwBVEPtOu0Nvh7zZ5fgRjIp+QM8qlJt0q5+vu4cBdM3H5pQXg4w5J48hxDmiFejiGrnbCHDeI48BvFPN5iJ7STwbcHM2qnyTc2uY7AJGGTkaciTKmXhkWkmvYSWNuad2t43NRZyR9hi/le+CTLcvfQPoXcB8CGX1m5zbYvdzg8FKh0rwv2oy0JCWgC7HzKtfEc208BwYNcDnOUdN7Xfn2eI8lbnDADpsIhGnZb5e4H95L6ougT6aiNqWA9pgn3b9bxpW2kn85tKjaGq7pM3i2LCmQBPbatgT92iSVC3+WkqrHzlE08FFXyhqt45PD++He6b7Dd+C/BQLGNSqssTMd0k9VhbqKqTPklA4gCUln5WjglBdpAZPa9TlOA1Cn/UscsGYOzdJCs8R90LiTkzP5SXDgzt+HRr2yLKsQ5U4pz72z3L6x4+W1H6Ctt+iateN+D3EfP0a5HDt+TPsOhAkCyTUa7RjUyn4gzdj6VBSZa+QaXwlrgiKPkxaNh048HttxtZHnfwbvAD518O7gzcFPKVpnKTtXDtK58O+8T8xRIDiUueaMtjyjLV+gKmvVuRQIvPWwbn+4XDRnGkvLoXWm13TCtF87H11P40PCsU5EuZVRsCUAFQdafDiRXFgO2K+NPCNBoqbJ0CZP3oHWZ6T1mSxPw5qfLMVA6sDi6hPUp3p6ULPEYbXEzeoeu0WN434J7BcpcNeSNmngXbZZLhp4lwCeg3cVzB8xW5ywXB967WrQpnNdXWzxCFNaq31r0qJUB1ruz+DflcMJafui92i97wEAOw9CUQVQTHWPz2Fr9v22eNzidnnIQAmwNP8JqTHn61EIUmzyx9VLnxzsOPdty8rYO6UtcQDKLTgLKO2mw4v1Acv1ATeroHWnCERD9KGeenXDNPBEvxq08uQvcctoV5Toi8C8NekKDUcfPTXgLu+T9vFyRpmk92v5Osn+m9an8MQfOj7QtqYZ18aIAPTTcuscU6K3PESu/e11x4cx7KnkoQ7KUxR7XLjCI3d/jz2BKdBF210BNM4OzvH4chk/5Ank0wbvBeyMd48lmmZDA/IkFt+2RBgQyj5VNvp05+UFXXlB08f5pU7crzvVkWnqRys7e63z5CCdA/E5o9XMGbTkmvkScfp0f+05WtYRBAfS9p065SQ8nMGV5WU3THMbA8Wg2fMax4MCfxiglynXpRY13CyvsBdhgTlXwGkRAJjUtGsAS25z0LXDBrvbDQ63IZzkYX8TwHwuSZOmedfoSTngZQKwM2bre1SLegBgXJu+7ukMXJNK72Hgtff7tPcrteDURnw1U7A/1YRO2+TUOSVShbWPlwNA6/p9feg2eX2rrrweEkRZzs8Jx52sNFZ+AC1XgGa94e1oaCvEaTyzJdgB1IBm/foNBta4miugwGW7wn67wp4n/HoV1l+8eovtqzts3R22+Hpwdn6Fr1Qn6Cj0aHWPTbXztJo3R50Hz30Q5LijfReKtfVc6VZWiwfOlSASCE9JHERtyYoYw8UCYWOUDYuuUSI4B2+wU4/XrjtmJbNEH+fS7zXfTAQAACAASURBVPJdnTu1Mn5dS7E2NpGa+pytvk32e3p5q/4ud15ZD237MeVX8d1ouysKtOWHhK6pteZDyKM9gaIofgDATwP4Er4n/1bXdT9VFMUXAP4igB8C8HcB/NGu674uiqIA8FMA/giAA4A/2XXdLz5W/R5NuEaG+PFg24AN8Fk5cQxJw+aaC9ryBVzDZsOOwhqW0cckO4lrPzSvDabwieFcDj7Siuy+CC4QWKD1GxyiQShoY9IYur7eqYYlaETjjiQXjYLWCehJZzcO4i1wOIDEtsbN/hwP5jyOuMaNzTm4yUG99BFAZtUFWFyA6gys9jivgPv1DLWrsMM6AerESd5hgw12uMN22B9A8RIH3GPuTpjf1tiXLerFCef9DYawkpyHznmvOc474vqbAD7SvAfgPl8QxKS8qTHcnLMlTZjI8ZC3Oxo6NeCu0Zik5CaPdvm7R3SwojwAKejI1Yn/RoZz1KIWcdBeHyuc7zYetI+B9Rz9ircdAB6knwHcI4B2CdxJSvQqC4TGt/Tn5Icc2TKjxefUG/8t32ODfeT0vMXd8BeFHm13/jun0KNa3gD+bXOxNOz0nZN/08IrZ2YlsKjO6BZn1JVXzBDliFo9V4Jwp09SlvjyFBxmKYlGm+a/9+eNwThZp2hf3iqVWqTC/nxkLmm1lKAyfexN9jt5KGCXloHUmpCPAW+NablJ+dQJizZRskG4DfK1/bIe1gTtY5A2E471d4s85vSlAfAvd133i0VRbAD8QlEU/z2APwngr3Zd9xNFUfwZAH8GwL8C4A8D+L393+8H8Of65cclkotKGnQSAj+53zGNu1pW+2VRh84eALo+817j/LItQzSCXIx3v59pB0SoyRQOOcxZB0W0Dl4eNPH3gyZeaoXqfr/V4YUoBHGH58vy2qE0ukvKReYDBJ2TqDp0XV8PT10p0WKHTaxtdye4W8mh9yHlKLJJkk7dAu/CZN6VSAZvLZY+17QH8L4cQD2P1UKOiDtssH+7CZrVuyIPwDTKA78HOcGUQGVU+z7DeT3Dud/+zpDQ6YwXPXXmZpXSZiyuO4en3OpyDXgn0fZPcfjiv5szbYw1WKbr0zSOlnCAIDW2kTOp1Lyvlj5K0cvYf2KHTaDFELDPAXi1Dc36KFLLtB1p34OkW0kfCRmp6BWiaEVc077BHlt8PWja6Y9yBWxxh88JuLc7bN6cUbwBQH8SuO8RtO5jIUclVZIch0u2TtF/SgArH75zsQBQXbBaHIHqCFTAeeUtcbJPsPJwWBHDNCDplzrwyWnEp2ppp2h5p4LKMXnfXGuNVy8nAmOTpXj/9Eg5U8ofcr9jE7b39ZvHlD3+82i7Q/HR1fFDyKOB967rfh3Ar/fru6Io/jaAbwL4UQD/eH/Yfwrgr8GD9x8F8NNd13UA/kZRFNuiKL63P49xEfCM3k8nvG+R3EeubedAnrc1J35jmFiB2KEViDmQAJLMe6EaeY0iB+sATG2DpM5ojknSXE+/t/jv8hpUL01DYWkmLK0OB29SE6tx17WY0PzYOCwkA4iuDZkbRV38Uh/lNY3NPW7QII6vrUX9IHB+h8+H6B87bLCrN3jzeuspMUR9eI1YeyopETnaQ1T1mG8YRATG0wC8Sp2Ryxku6xn2ixX2MkNrtPQgn0cgSZ2KU4dV611oGsMpFCze8rllh08uOLWH07Kqtg7ZYMlyQ1pdacGRFKwc8OXAsA8L2a1ChBma5H2NbZTq6jVeYocN7rDFDht8hVfYVRvcVVvcvdzirt76dnW38O3pNasLp1lRG+LhSDUqlibSwZn+6Dd0jw3b7tvDZrvDS/car/AVXuIrvMRrfInfwpf4TbzEV8Py9+A38ar+CqvfugC/CeC3+r+vAHyHLQnAE5ivgcNb4P4INK03B8tXwck/MwClA24WwE3VJ8kSoTqHdetvDcxWwOyzC1brI7A6Aqs3/p3e6lGDrGhgGrUGyIOzHHAem2zSb1tQCjrH1surzzMmmuVMu1c+tsnxitOWrImRFS1HZgofEg827mFhkMMDiZe0nihMOqBPTAgAL8p2yDRe9uvzKk5YZuUmkf0ffw9TaDSPKfJaHQpzsvO7SZ6EOFQUxQ8B+AcB/C8AvmSA/DfgaTWAB/a/wn727b7MBu9PLfxpaTx3h/Qj00C5HHSZJtaK7w6kGuoAdsc7ZQ0Q69r3ac6rdA4HnyWUfgnUw3nLfh/RaCy6wFTNUHj0uulQauItp0L+THyYxpBRlUzE91iaHRvv1B6iNZLPW06QZGIcCtFHUef5c3DwHfR6u8OhbHHBKgCcPbyWckzbnovzjmLc0dCi0OSAfA7cD3/eSVECdg1cO/GOc1p32X7GHJ4pYlHsOBvOkdOYtyiH1tU4nzTsXF18rG/+MwrhSX0EXyeRqFHrW9in45oLXOXvpUWJJYuXTPX3jp732GE/hFXc4mvcY4m7aovdNzfYfXODQ7vE3ettbMWxOPA5B2gpPMyolitAaN5nr76LzXYX8do/F1SYm5425h+x77MO1RLzz/aYNey6KwC3AL4A8F2ETL1M+748AstczgCt7+frPN6+Buar/v5oeyX299s0GasrDsY5pdH3wpQ12sH7IHFqiaYc0UTbr4dJTDXV9rG2YoYfl6vDFG2rNc7x6z+U4w6g7xMCja9192idQ1s5NLdsrG6dGWlpTEoeVa0H4QBGI6zJbS2wgtVXPhTEP4X8LeyTsseOcPMpyKM/gaIo1gD+SwD/Utd13/XUdi9d13VFUViqPet8fxrAnwaAv+clnmj6wYR/f9a1pTbeGrhoQOg7+6LkNJngtKpp3TWN+/XgPe7cxjoxTYMxZR8BU94JynNbdZRiaWm0DkYD82MaBx3wj4N1izeoCd130E95Tit/4r7sNPgY1JhHLocE8DfYocYcu9UGp1WFw5fMEfE4B/azfJSZBjZwn6IhytFnNE28CeZjzborW8xdSo/hYDq2rowPOnHV0/dI55Dnn5o0yHJwXtaH4NzMQxJyjftYXPEx0LiAB5w9UCyOnpJR1UcsqyNuVsEPYol7HHCDOWrc954RFJmF6FjkSzFQtNwG2y/vcPjSR5zZ3W2Co6vGf58K3nO0mSFLr/eX2Gx32LhAEuOAPV7uh4gzBOSrtk4i9gzXrdlz5vtq/ywjzjvEsRrfXdJlOGindanoIeHv32GwLBcAlrjANUeUC/9tlGiT0L3XZrnlYgFo+ducv0aLkJVTjkeyf7eAfW4SkJMxwC/vPf72HdBTP+W95upmRvhxAFy/Lpybp4xtcuzy62PAPR/GNtdf8r4vXHfaePfYshpsXl6eaTNeHhX6FkUxgwfu/1nXdf9VX/ybRIcpiuJ74Q2YAPCrAH6A/fz7+7JIuq77FoBvAcDv+5Gikx/Gk4ukzFiDrTx2wnaBHswPJReEDHxxCu7JdZQWA8UCIKOfmCZDA64QGPVjImefx3xMyc2cMsCEW9LBOd9HoCvcejMcn3DYGWjjHaDUVPBtOue1tBlAt3ZYZlyKPGNltK0xx55lwzytKhxWIgNuuxwy3J6Pc+BY+SQ6VpSZMROvNOnScopWvjf3vuhBOpl554tTol2S79C3DXKaDgDA0rbzdS7WJE27tp55N4DzE6oYrOMAHo/pVM0xr044LWrMq3MM4iu2lHQZqenV+BocLDLHyKEcwR+mZG24hVM18RSalDTyFNs94sxXS5y+nOPwZZ+oqZ4H2oCWoGkKeB9AvE/SNFuc1KhEcQbe/bBNf/6L8hdrezB7jyVK16L54oCNO3rlyBrAZ/ATni8QO6jKUJG0BFKQD/b86X7oPUg6UyXWV/E6cd0PVZrbgeh03CqnceGlJY/3M77604f83KQ3HDPuzzHVCZL7jYzVY6pY9zsG9K0yK+rTlHNNFe055hyAp2rRNXA/RckVL9/NT+chQlY0kmfw7uXRwHsfPebPA/jbXdf9JNv1swD+eQA/0S//G1b+Y0VR/Ay8o+qbLN8d8Oj2Q1tP3qUNyT5Jauy1Y4CY5z/l/ul4DtzbvpwGmDaszxqgbC+oqwuwAJwMOq2Ih1LedFuiBWUHbdEOptwTKlAkG1+Nh3/413dkYZoQc6PHgbsV1z3tUMPkgETeY4PACyUtFWnNSA/v0IDHfadY8CdUg9bda9zDAP457hK6TRTj2934RFmr2DehQUim0zauz4LbTzCYmffC1l8kKsz+nUTm3n7QUZLxAN4EzJ9ZzqGNnld4buMgQb6HpK6ZtiPfu08EdQOKSkTvhZyxQ730j5EmGkSbQX2hCsfftwXcJYAvofcVXHow7Gl43CdlDu5PQVlWNadn6WsxZGSVoUctGtaYJUe12hRAucB5scB5Ae8HMWjjj1isD6gWJx+nXcmmyuO6b7CP4sHf4B7L2wOWt0aGXvpa+pjvURZdjTLD70NQHy3lRxz5J0ywrbj72joPIVtjjvt6GU+eiG/9LhNyray8dtkb1QUvG+D9Aft+xT4Sua0J9SdJuQE2tUnEmAPtFAdeua7VQRNtLJTnnOIcbFmW4zJb8WQ9g4fe1/uQPf7Kk1znU5PHhL7/KIA/AeCXiqL4W33ZvwYP2v9SURR/CsD/C+CP9vt+Dj5M5C/D+wb9C49Ytw8jU9r6Y30PvGPto9nQgMOBO1p4szuARQO45ozTomVJZ2IgLD9+jUkYdKSBH9/CoUI9aIeAoKX363bTlJ1oHsRbQDusc+GaFVr31gSqNZ0rJPPJdXi0LTUFUuvOt7W4zpaVg37vTebxNl+mz5AlJnIt2sZFg2TblHBlOwB4DbBrg6o2GE8R8jWYolGh9pSux4mYcuZp/ruo/qKMnqYMJ8pB36aPT29l81y+vaCgLJ5aVk8ZflQCxjGtNX27pPUlrS45P1ZnfHb7Bli9wdvb4MQ6RCXql3fYYgmf2bYaWlwcN79/SGgXfpJ3bhzQzPLUwNw+TVlBk5Pkz1+zbVq0VaDr8S+J+hQ3TK3SqFMkvK1FPUZVoq5CLgcXsGeIKtVLLiMpj9/OteQcjJMGPYq/30+UJMDfYT2sU8KsAbTvl8GSlrOm5caXqUDdsq7JcwADcJfOlAAGPjefyOdB8bTB0VIKaf1yvD1uLUjrNA3QPkRRJfs5aq88iZx13YfWXzuXds6nlAP+h6TsWfP+iOC967q/jiQkxSB/UDm+A/AvPlZ9HkWuaT9TnvTUt/HQt5bjZ5ZiG2FgCj8nbTo5R8XmrDl4jtD5wMckAGrRZXL8R/028qbanDYhp3mQ1zz1NgPtHPza1w4wkjqjPRuLqqSlp+eDPNesDiC/noesmBZdxtLSAfqAX7I8c6w9nWl7MoUG4FETtIgJNMBb1g4+4EtQrr0Pa6JAZY04h39fp6j1x9NXbmuKp411NYdrjljQMyXHVI3ux58X5z1b37v8djktw7DgcSdW+tfiHg3CxNmJuwhZfuuBL7+pdthVG9QvqxA3fn/Th4qE3qZ4XSRQpG3uL8FoNC/KVnVc5t8ugXaC63X/Lch8D55ewzMux+cb1qv+T0zOpVgBAPgEnK+HMLou+cZpH70Hco6kepHVzbetCqdqjubW4fQly6fRO0uSJQ2Aak1Tm9TECbm0pAEYvtHwWuNJ05iGO10fp+2Y96Fed5q1T6OS8H1auXY/DwXJwMMdMqeC2ncBv08NnF+IjKbP0Wa8fGjSybsJJfH7UJIzQ8pvVaPIvMvxU/dxkWCKD/4yGkLl4xDPFj6BkI9DHPjwPFwZAfYDbtjtuOgD4+BUS9xkgXhNLMDO90mtu1aWatNjwMZ/S9fiAIefQeuccxLDoziEmaWlG8u2uqt9XPfz/ga4m8URQGQYv6lUByBtX5qGjtavAe0DUCvgk0bNcCmBSwkfD34U9CvXytVP3oOYOMwWJ7iyiQAiadB5Yq+BgsHeBuder7GLNfCreyxXsTZ+frz4hEAy+Rd3YCXtu/YO6B5yAL5fdiufT+CwWpjtaaDEMDoGWYAkkAj0M0+HaxfhWz2XLbBwHsTTREVrS1a7KQEszsP7oKRe5Lwsk6jJu/GWkH307rglREvQFoH4PpxnlL9h6nvo1yV1pnF25luZSEvbNqlwPQc+6VfdHK0r0Vbv3q/S+6b9YyD3odQN7fzy+rKO4/eQp4eM0Sw1/xdr/7iCp5lU/9gaFE8G5XvMjaHXBpTIReDh9eL1eyoALdus57x/2tD1fcin/QQ+FHifgtNypuIpQto3vm2d61qNvRz0pcObBAR9WVd60zCFK6sRwpZplI1Ymyw1zHqnQ9vhFh2rvqYh986L3NUpNwBYWvMp2qBrBhStTHaAWuzh3MAuM2Vy0/nxbuMBkxXHXYJ1Kxb3NeBdAucGabuBOMaKPsPLxo4RgBtAoqkHEPFgc9xXPhDHcePTWO305HMAktYlp3qJ+x4cXnzGXk6LyU2QWrEdbioVqeHuAeiiCpFnNosdaheDwthaEyaNsh2qANRVaFcOp5Ue8cTfgj7QawCL3keg7TQpyAaPqS+dheMIQInmvT1gfhSOw42y1EC7Bd7ZsuiDDMzKC1BeAHcGymPc34oAAW0JnBazITQwp9zwvjMHynhf6p95qYKuMc3plFCPGpDk5fz4XJ3I4qOdS6vrQyK1aJZXKxCBHchgmRzjIP2hpNUvDxR4nblSiO7bIc2eTs8wKI10QDEH4OAG27ivp9+mfdyKKLdbOMioQbJdzPE0IjXvwDNtBvjUwTswDUh/CMlp/D6UyEgzUvMuMwH2S6lt57QNK/JBLiugTJIhQTww3Wx4TVSDMaA+dX+6Ps28q5nXZYg3mRDEdHCjcJAUrm8sA+ZDYrxr1Jmcpt0KESmBufxr2HF0XVMzG6KRkJZ8jFZjvZt04A4OqhwcSgBOWnbpGBm2d9i8OXrNOiX94dk7+wRAkDz4HPedvwP+HkasZzyaSbECFitgsTgDqzNwu48SBBEfnmfzlc6UtG4BfMqqrIFKS6RGlL8jkhZpRma6Jgf4O6zBE6zJUJ8OLSpXY746wa04ONN1jwBQtn39GnuwySXOS8FtCnCv0YSOgWV+zvD88v2pfD8SsGn15eVaHa17s37P70fW4xqwZrUn3i+MxTvX9o+FWZTH03Vlna6Rh/D8icgHAEscRq97bZ3e9XfXyl/Fd6Lt52gzXj5t8P6hNO9TtGCW6V7yzq11Z+yfen1NNIqB0Lp3pTextyUlBLH51xyME5gnQMo5wXE16YOnJCotpOaFL6fImOb7GoDPJedE6bUeOlfel41r3mkQizWV+uBOdT/190Ma5suiBpqFrRHcI7zvIzyY00D7NZEpZDvigF2C9bWyvhZ/Q5mP571cHzCvuEbba765w6gM38jBmQbiAV37rmneafCWcd2Xgm5xgwOWb48+YyqBcZHkZ1jPOau2iIE7B+9ACt5lH5ED7pTsh08EWnGe/v3NjxfUlf7sfDVCGz31LHiL6sEBdu6blt+gfHc15tF7qUHRlypQyMvwnYY+x2stw3fk+n7GJ6wKv5njNKxbMPnk+npegRes/kv6Z/hjgxZTPqvReOLsGH7clLpMqXfuGhaYl/s0YG7dp7wHqz4PkfcBNkMreTzgOjZeaZOSUK5bsmSZdby8Tq4+TyWl6Lc97HsG7582eP8QIp+YBOPawCrpKvJ4hxQQTT13L9K5VAr3U6KoCH7dDaZaPnRJR9NUQxxz12WWUKlNDoP5dc6qfN3qfMc69Ph8+jmu9bB/l4FA0zxJTmIuJTdRZQYHVAJmWhp6C2gTwAZioK4Bd03ja51PprvnwN0C72ufSXW5PgxxvQmgb7ATwPk+oUqExE2pdgxI39UYYJSaeCu+u49GIp6RfLbhQmECRbHdJb9dy+SpUWq090Bcd7o2adQrtn4r1m8RRZ+5w+c99/1miD5DfhU8jOR+iIQSa+W5g3SSLr53mETDYhiIUILklMpTu/OWr03iJFXphHlvASGOcRNPtlj7Gag1fWjIGfc/kJQm/k5yYilftD5cUaJY0Wt8ma7VpvUcwAemA56xZE38XGNgXR5zzaRErmti3dOU5EraxIJP4bRyjeIzZXKSq6smug9XakWYagmw4rtbyo73aT14V6nxWpQ8c96BZ/A+TSzAzvfJTpnKNHDOO24J6MXx5PzEs6z6ZejQgbxZVNNeyE5f1znFERI0QG7RYMYShsjOU9PejEmOEkHaNn+cppmfQo1JTfg5bf3UTk0OdBr3nU+QJM/4UN3gVFWob+c4tRUO+5s0XFyOHjOF657TvPuHEpYWkJeAnoMVujYANAUuxxX2+yX2LEHPfHHC0snIICHbasw9Dev0LjTNUai+znmXoF3TwtPEoqrqwUKg8a2Xb4+BVy2pMbSuca0tLjxvXrxP4XQ3WnLg3m93t8BhFagx+wGQc4C+VuK+h9CS0snVbH9a2wLi+ymL/h6Cs/JxsWJtx0/qKMoMxXanGlAkGZpAEHUp8Kjvk3fNvXIC4FWSA/F616zM+sQdO2a4v35Zs/Xc2FH24dkcceYBn5Sv5/y6c3Q8B/okbcnXXxiV5cfEY4NF+ZF0H60fp76Lfgcg8X+g8WCMJgToYHiodwYIW0Cd9uV4+VZEtGvr3Lb9uZt4CWCIACSFh9bl0Xw4JRBA0t9RbXL94lgCQu18sj/1S53W9pjSCDzwsdNmiqL4pwD8FPzX/h91XfcTj3GdTxu8F9BDrj2FyI4XSEE6F8njzWlxaJBw3tlsVmKIM9w0viNv+w59jnPfSYcBSHbIw2lZx8ydURq0Q6dL+3014o5rLGKCpi2WzldSwyxBu6ax4GJpTcN22plZfEUNCGrAbUyDwa+d1jG8aEtLxSNGaDQlLcPiEDLSLXG4XeJwe4PTl2kM6ON+6bXz+yIG7gTuG2U5BbwDcXtWgIiqcZTXi47vE/SUC5z7335HTgoAcIdVYFriFyl8cKRU89okQQPlmz6ajJb1M9pe9ZFmaB+BeU6lkZFmNACfo81IzTtRZm4xgPbzZ8D9eoad20Sg/A7baJ221WW9wZvXW+CuT8p0x/6kj4XlS8HvYXjniCd7kbWmwGW7wnG9wnENvNkCeAVg22G23WGz3WHr7rDF19hgjy2+xgFLvOo1dSXaISwkvZcNdtjiDpt6h9Wbi38PXyH2SbB8EGT9uWjvRFPSaBMtSXmCf29wQLeIaYya5TMXRUQCWWBcOdJGx07TtvPlGD3mXakzgK4ooeNoYlaCgho3w/6pChZPr4odOMecPP21fRlcfz+uv5cKA6DPOXpaMe+1scdy6M4pO2RZsFqmTrtyPNUsmU+hhSfePpePFbwXReEA/PsA/kkA3wbwN4ui+Nmu6/6P932tTxu8v0Bqnn5skW0mB2As2otcJ2DPuahikCj6Y2Z9OcckBOiDxNukjeFAn9NlKCsqgUdfpbgj9R9LzcooE2i8LTOs+sfgonU6J//wubc972R5h0zCtQBaJyOBdxxBJKVbTNuXA/Whi4/rZ9N8+IBFg/DUSZIWRi5xGO6184fb4GSsplKv52gbh/ros6wOmVQ51eGhUmaQvwGqp2RvTffZ17G0XFxOqNA6msb658+jS5ATJD25rwwuvOThJ+WrE+arGtUX8WBqtScgtQyRxGBIc3hOIxXJEKNRmFGRXfUeS9y93Qan6LsiBeoSsMsIRpLKJV8T9XvU33EaVyPOwf/uCpzXn+E7289wt91is32JTRWmJb+G7xXpp8LfEgdscee1+L+nz8j6w7vIokKhPMu2t5poEyku1pggrKoWJUajLea03PT+LQ0ytQtflgfBXOT+KVFnxpQvY1FprHNqdZB1mSrafXOnzimThrEJTa5uw/ET8aal5dbAtIv2cSVVfIyleNLGYCu/Ca+bXH9Mke/vI9e8/8MAfrnrur8DAEVR/AyAHwXwDN4j+ZCad02maCtzcsXb4NmvZiPHzvi53aUfMCmEGWKtT6/tOayCpocnB+LpvDl4vMHBTBwUZN7DegqDlQ5MmhZGbvOJgIzTTpODcEzQuszF5IMPlHPUySDYDGfkMd29M5zP7Bi0FP5avDMNjUBORPy6PaDxCQvF0qbaEBj0gC1N9sIBnKbVp98N16oc2sqhWemDrCbaPmsQs7Rrk5zeDNNzmnimTJLPXEZAO9fWE/gvyxbzxQnO+efM24wGrnLPxKHFKaNj85q9EnW/Tg6YU0WjvTWsfmHCFoA8j+Puv9E4qgwH7/u3G+zvNqmmnQA7X5cRjSxrTqi8banJRSlap8vLeoU36xXeLL4h/CiC8zPRbdTQncqkq6pOfXKmFAA9tnwIUGJdcwzUaoA2x3O39lvn0urxsYtG0ZsSMcZyTKXlNbx2i06o+QXlwL5tWX4owHmYzEW/+JEnafomgF9h298G8Psf40KfNnj/nSaP+U3QgMlN8iVbJ9NwBRQ1sDpesCqPOFdHnBYvUFe7CbSZ8bCQFm1mCmWGS9rB2fSZfKc3rQPTBu8WpPG8zitft2qk2jT606J3xGBXAkdvAeF18gDRC8X8lSZg9JMCrY6W0PFzxAMxN1nzgZrKIY7nXFEO1pvGReB8cIAEkDhBXjF5vgBA2SeEKkMWz6ZxqBYnoEIP4D0nmvPdtSRNVC6TNmmJgt5LkiZJy5DRZW6B48onZpI6aKLDvMZLRpn5HEsccIctKpxQrvyzOBLlCgggnYD8a6SAXmrgB7FCg5VIEnFzP4nJTs/sbwtgPcN5PcOb9WfYbd9GztD3vYadljkHaAmQLJGAZsxPZsznJh/9w+Yfj2lGNeCVc1C1HDy1viqNnJXjjOv9AJDXynP5iAHcg8RyUpXg29M7wY4JT1d1rJ9odfYTgN4S2NZwTeszMvcMxUL2r09Ee68Ul5QP7LD6qiiK/5Vtf6vrum89dSWewfv7FAs0aI3cOlYrH/tIHhIFQfIwCQBwIFB5mk7ZXuCaI8pFC+daSGDqQZrekfLwbVxTLJ0zp3TYY4OgDrhjuo81yKZAXgf1vAMNv2uH81K9xsB7uMfYCm+UBAAAIABJREFUQYon1uDa1MRpdSSuvtzWJlvDJKqnznCgHGmuJ1BP4htUupWmUMqu3L6mbEwk175Z4FJ2OB7naNf3PlrKYo55dRqe/xL3wzvhkVY22CVOk/e4iTKyRjHjq3vMqxrVbf82eLQTzXHVukcO3PvlufL89oMLsdil0ykH8vueIkMTaakpRdkAi1kAzLIeCwTwngtDigJoFBthTvMu/yR4X6AH6mI/la2PWKwPWK7vsXTxRMpK6MStW9do3i1tpOwHLHCdA+JjgLxBoCXSuyNroZScYkTTnkvwbfHsc9HJtEhalnJCmxBQXYBgjRsTnqDNP49UsZKbDI1NlMYmVPzYUIf8QJ4f09IxSPPj4kt+LvlUteuOOaF2JQPw/odPI2Lo+AhoM6+7rvt9xr5fBfADbPv7+7L3Lp82eHfwjllPKTmgINt+Dozwtpf7ZpoJxyoc+ccSDihjEJpqk+Vx0gE2Pi7VvMsP1EPxNlkfr3No5p7GE8B8qv1p2e/i85diQJQD5lThwF2eCwhacp8JL3S6vnw+AA9OlTlgOQyE0kFYTpgS57aeOqMOliP3NqbdG9dA2oOeDl7Gzz1WP7l/fAJIz183S2tOzzKSAx0DcIuNBzv3VRvTNERSIJf4tEAN9SoBE5/kEW0mONa1PXXnnj2HFjc4YNsD/u3tnXeG/kHvAH3YL3He3wD7WUqZeUjeAOkDlPubmo13ccaLxWmIUOPKdohYRM++ZhMV18N5/t6rgaqWgp0cwBlTLvBzhnVdETClLFyrGQD82OTAEvJBon417s913rkE9FZ0LNkHyXDCA6WvdTgd56kSwbK0SeGhRwFQ+FEg0ON4GFL+TKdEaLEyq1pWmtxkQIrV//F2xM+fo8bIiDKxU+tp6GPmxzNcg6A0IIsft8ZzLfu70oIfKsd48yMA7zn5mwB+b1EUPwwP2v84gH/2MS70aYP3F/Bm4k9BHvqk3+cbklp3Kivj5Ext6XzKczbQ8U5ZgsR0nxZtphJdWxl9gJybzgcR/Tb08pjqQWdponP5qy+VznZ67NsxzY1/xOO0GYv3bZmbTfANO5TnFO0Wv7ZWr5xo7ykAxGY4J5/4SHAgz0N+Bfy3JNzCQ05VmkzlZeqman3AtAZoqQHLDeZT2hhcX3/X18/w67E0pNIPIuf0rGVLpYkgf0bz6jRQl84AUM6Cpp008lOiFjXDSeWLCEu5roH4BNSfocWK5xrYtm+FRHUjwEttj9rMffJOpmnVx+Sa/mxsUjylDmMTVy78fVsOqFrfYfVXFn2mRjVQ47K0OIsSJ9fDgwjHljMP5JsSFxaVCsAQfaptHZyLT8RHJg24y6gtfL8E0fL75u/jWiuN9nt+3lg5NY/aM/lm0bHkV+NcC7eK3xB/BsN1jOzCmkLhsaRTMN7HSpnquq4piuLHAPy38OjqP+667n9/jGt92uC9AfBbT3Ad/pQ4z1RyTtnyXKHniue1EeT4OZaYh3PEpwCxnEzpFKTmwDIjW9sVdthmjqHzahqJawadMZCmDW7y/O9joLTOI4XejQa80oFON0FLcFYPDMg5gBo+Wop0CM44rSrX53Udu19tUkP7ZNvRaElWmE45eGrtLDj05idW+fvIv2utbcqBOEd3yB2j1XHqxAOw2481meaUKh61iGg//JgkROnqBqdVhfqlzy9wOs5RHyufLKxxwHE2Dt414YA9AecelM8WJ7iyQbU4JcmbQhKn+6R8iMkPxTEVaXbeXChY7V35d6A7bub8WbTvz/Jtsfp8GWqXx1jPTcTldz1lUmFZELhDvbY/fnb9Ma6foFbj30muflPrP2bVy9fdAuD678au+9jCccB7ESeWJE8YKGT34hei7e4jT9LUdd3PAfi5x77Ox/sEpsgJwP/3RNeSHHEeU5llNqRILbsqRG7Qg5alsZb59gE32L/d+FBtd6t8iDYZns0yUWsDZI5b2me/xKLGersbHL6otpxDGspS5z2e0p4Ppjd9DGbNSQYAXGbAl5EDE2caIHX2y5kAc/us81jXGxM+6TN8DVAB55WfAFJSHQ6qtNB/6b62r+INSKckowQRMKCsmKq52hIWZ11GbZGmaQukWwmRLOAutdp6cqY8WE5fR55qkDs/DfQytFruuhIc8OOm1hkgEObBEw8vCCALDjUrjZUh2czV4BzaVYnTSk+6E+qURinRnke8PR1sWaBK/q5GBYrCs0+Ot0Hq1HfxUNEUCRwUp8c/DWfhGrD5UHAdH/PUXAwvU+9zqkLDksdqP9ee/yH1+FDvhuRj1bJ/aPm0wXuBp7sDrmnX2lIPmovGm5SW5QFwsUkr6FHafij0fwR2yfltAGGrJQ6rG+y+3NhJdzRzNaBruixzdMIdjXmjIYtkPQBxGqpJalRwaMG1vBU2quZL4wU7tHCuQenaQSNDz8ySqYM8r2ukVWvbiFesetVLMP+uQJ7aEKCCd2mx4RpQAtw7bAZ6QwzY43T1XJvKaRHDuXrtadO4kMr+WHnz8yR+4wwofeSWC+ATKwGTuKdAAPmc3qBpuiytvqX5kuv6a0gBG51Lu3bUTkesTznzudy+5p4s0aw5VM7BdM00s1KDOx4dJE/p4ufldbGeebpvOueX75tKbcpZ/rRn/9C2JM/D90+9lylltJ2jZRDlYahXM96WuIwl+xuOUwZhCXLlth6/PedMe91An7v+WLjKXLjL3DG5az3kPnKgeYpFWSubalF+7MnGNSLr95Fz3p9MnsH7VNGcRrm2m8K8vQUWFbAoz/iseoOXqzcJGOMmay3hDueiDrC3qtBWDvVtzDuXA6mv3vt7KFNm3cSLv8fNled+eAdhDljXgC/XhtTTVbo/nMMul9ceuzdNK5lGY4h9BSyuspWoaTimXcbUhmMV0tePORdOdU6STocAAJ723i8v/UQlBvnsD1PX04kBECd3yiV0CscwsBhNLPSJBL1/nd+e7tOoP1PApb9uAGj+9u37sYCGpG1co4nXoofI9eH3gr8MIOUwRxV20Xujd8cdCinWvkODJe6H5ykjw9DU1b8HXxZHlKmjMJ20n1sCoy+sPsE1F58Fl74BmrxLK1z8EtKlnPSTUyDt446BNHbw47Ql/438vVVfTeEgRfg/BYXCJbYGDqFIz35J29bfGsBn3oJIEZC4skFTKnCfCy28sH8FeWdaLhaQzp1rDNxrE9SpdZuqRX4IRXMMqD8U8F+7/33LV1HYdC/P4P1TB+9W6ODHkAb+abVsvY+LHm2/RUh9XQKzBTCrfMx0VEecKw/mySnUAmtTB1ANtOc1GHntwpgGLh3Aqe6VWTfeAdM1zbpPCAXGwRWA5Ip8W3MoklSNsfJp2lQbyKdNSX/GY1kyJTf5XgyANeaxhWa/SKOBjEUGOQ6VtOlXcv2a6CAmXSuNEjJ309J5T3kfUuRES9OKynNbESa4RSltT3nevtYSNCexsYgzALKZOqdQZaZEBEmSf7k5WlcO0Yr4XQHT6AbapGUKTUrGqZYWPW7xI+G88OSdVC1c1cCt8hrtpC2x95VY8HJAnC9lGY0nJPxb5BMAC9wzIN81AFe6n8VtzEqgdEAhfbkkcK/6a6z66+WCRbBJwKwE2vKMZlWjxpy1ypa98zihmdVu6BeayPIcuNbGQb6P77csS9eAe16Hx5b3SXN5Kq5+To6CYP+RJ2l6MhltTUVRfAng3wbwfV3X/eGiKP5+AH+g67o//+i1G5ML4g7usYW+CepYSwTgTuUV2+fgwTyjRswqYFZeeq3G2YzyIgGdNtT7Kk3XSFi/0RwZtWgxJ8wxR8iyyjurfPKmaoglXh+rwKnmFA0NLMo+qFSWw19ItEPRJuaLUwQAS7SD5i12dvN31cD1sbzjgTsXA1qLtUu/s8Sa2NDz43v58XEZva+q174vcf/2Jk5nb/lJyCWB9pwG3noPOcDOE+xoWTKjuOEzXJg2lt4b15paCXV0YBzeCZdUO5UCxpz2XIs6YZZxTa4EWjmN6Rg9a3gHl7B0AEqRMZkDMOarQ9SsQ0WTwvBN5yaN2nfNwX5uSuJvIe2TNGtVbjLFhSYR9P4oggx9Q3PUuMdN0iZ42M6pk3Dre3ZoAdcf75BY8KJ2obUHZrGNAPxbpACfH9P02xqIF22qaHy+jv4h6Rm5pR8O9+3i7Wgl1mmb/9H3z0/f+ElO5U4qAKeJF727MC7dQ2tXlvB9Y/SXuJ220JRL4bxldF4rWtfYJEGea4rkxnJ5r9r5uUJMZqKW243i59RqeTueUJpmGW1/7A6rTyVTnsB/AuAvAPjX++3/C8BfBPDhwftTSwvfqcmBlMqBAOZlZ3hE3DH260UJLByAxQVwF6D0gL5xQNu/HQL2XLPmLyu1B+OcQa0j1DKihoF7zuIgN+x8cecm6T73WMa0jf2Nj0ZhOdnKNOo5Ey+12igyRQEsFrgsgONipWpzb6oDKA39DQ44YY4bHIZT06DRwIFnsKNumiJacDN+LjtrOC+Bi/Qd8AmPBv7pWOIr8+ffDO9uHoD73SpOZS+XBNgpGybXyFsUGu0d8HchNesSqGsZMfcIyXSafonFoPh3ZeujUjDhrTaNxzQtWogUG7yH36ca9/EQct6fwmthC6sta1Q8q0cmRYE8xrEy2b/w88tttToNfLq1dGk5UFLf49AOk32evdffmm0R1L4Ti+OtvR9t8py2g3g/pVvXzsfLrfpJsfxtkskGWUiaC2byHdIYQcofsuhKrXyOWqNp3oH0O9YsabxfJcDOATwPzrDulwTeFfoMZffVnOtz1FEZdU3zuQAeTpuY8jttYsEnmVO1vu8CMDVF3JTJCO2PJs0cNzgx0agQZbUe7pFR4PwyveeEEvdY0r1Iih76/n8nyZTW9arrur9UFMW/CgxxLD+8LeVDiNZeOJ1GCqfYAOngCrb9NmwXJTBzvYakhNfS4wK4c/gNfGx2AElQkNZ4qzKxi+S4TuFYEzj3TpO6E+UA3t0Sp9Uc9arC4eVNFF6ubRwux3kIL6cBxqs07zDDyknOrAwbpw60/TOhTrthA0iJFiec4LCMQAIQNHl+PW0UvCPmVg2uwdQGN+mYeuqfMWnemyFCjPLMOMCm57tWGgi11dx7sJ69RoXRsmGqmTABrM9YbHdqNky5LqMXSY23pknlco1zpKbVH0spPj9ePG2CwBRfSi0715RC7AMr40LWRg7c277csW0CX/x9ljxr8h6nxQxzVw3tme6Tlqfe3nZCSBBGk95gnbvvy1KN+1Q/HOud6MvUogLEoIomu9qEmvI85K+V1seuuw7eEzpU72Mzr+J249AMWXaHdjOV825QZdQ2FT+sdPTXlE6VstSAfBUirdVVTOmTY4qmPQ+thSaLQZlyLXDP7dcdZYWmWhwzdr0p55wquQkuPY/cRCJ3L9Y6HIITMtMRTbmHpwDRvzw7RNvPDqteprSwt0VRvIRnmKMoin8EwJtHrdVUeYEnjTc6SE7zRWUWYNfAe2a/poX3yzCrBsY7IF7OubBcsyu18NI8zgdiD2D9wNYiaLO4+MF/PmguW3c/gPnJ2fQ0Yc6GuSx63HGRwHiLEgfcRFq4HFAbczCUg33OaVXToNAz57SlHOf9gBs98pCkxVgUGU3TLq0e1wgHARw0HBF/J3TutThu35etZzjuv8Bxfcbd4oTl+jCEJuWTLi2Wt/RZsMC7FWlhDHRJ+ozmG1HBO1lWrhYTxtMAziJnyDHKzJgFKtxAWGrUBwa8zpXvR06LWdbfRuaboP5BO172HxZof9fBloBL/D5zmvnxjJfTtO15J0BLqB8IGtA5O9cmbZc9ALb6D21SIY+Z6pCoSaxUiMcGi97H6ZVZyiRrS2PJ/Pj15PVN8HmF5LTmObBq+WJpGulr9ktxiuO9dKD361Y7f3hyOI36mZtUP5X8Nr4bbT+Ddy9TwPuPA/hZAD9SFMX/DOB7APwzj1qrqVLgw4B3QNdaACl9hg/CXDtGv5G/E9rlovQa+NnQVhnPFQDKs11H2b6VCcFpMRu08LITltFvPIj0gOUgIsv4jpeD5XLoeEnbQuEJPfd97sMTHuceqOe476L+QxSTEkA580+i7HBZ1GhLh7YH8dWiTyDiwpCkpYu26BfS+S1npn9fnHeZSIc7pA5WjmqDQ3WD+1tPTTrsb3C828Rp6+lv0S/JLE/rQJx2uhHbOdG07jnNu0ab4X9bAOsjFusDlut7bNzO1LhvsBveiwTy+uQrHoDSW9E5135p0ThsPnYzADXXt3mv5XVVi7JqgVWq5Qcw2UlVk5zjKjA+YQ/fqb7NAQ89C64ZBYAbHJJrhufR11MZbqwJVdg/7Z3IfU7ZHz0zVk9/H2VyblkHDHf+MJkKeqYf95DZti1aTP4w+Ui53hr3m2vNiW5lHW8BdaqLbEtyXZNcpBjN+VTWIUvV6cetRNlE45evgD6GPWQyLtcXnVdciUzC5NuVOmzT1Il/L2l+Delgr41zgP6tPYVQVlguzw6rE8B713W/WBTFPwbg74OHy/9n13UZxPiE8gIeGHxI0dqQ9VQlxUaaM0sEk7g8xzXXyR1XAoXzEQBmFbB4e8a5OuO0OGFeBfM4fbgnnHBA7DASOr5yAAXk8BkDpnn0m2G917ZfeMfH+daA3slpnN9B81sAjcMFMEMFBghjA/dUZ5R2hppG1urkNLEGLl6TjZhMWWnsD+4Gp9sKu9sNKNrMYb8MPgY5zfuYs6r1HkzaEnQQr9JoOrxYH7BcHzBfnCKqDAfmVMbDA2rviSZclqbVEkvL+lD+MxcChiREueL7CDQ2zqFs2wGAu6YdQPkUkZY4qi//7mSbrFAPbdD1deNtsuqBF9U9xz++FrRn7yUzMMt9EvTF+9LrWud+35o8+ayttpObqPBjx47Tt98dYOW+HckFv0YeQ3sr2961QD22JIns1q5C65wP1zxicZoaVGLs2WiWIiqXCgrp6yPHplyI27zmXqeXybo+prxArMR4dlj1MvoEiqJwAP4IgB/qj/9DRVGg67qffOS6fbxiaLSj9RGNOiR1ZupvlGt2I2+RLHecelNXcfhJafaUjkU8+Q/xr31WTz0x0HCcFrpwzGkVylJ7HglwnAHlDOfFAucFcBzApI9EM1ucUC3qKAqNTCKVS6dOnV6ephHzcWXnpoP22IwsOe87bCLgnmTg5dFlNAfVXMSZ3LvIiQbYZSQZyW1/xba3BS6vVthv51i/usN85Z9phRprlpd4i6+T/MRcK+818XH87zlqlG2L+fE8ZOmV8znLT4Scw/0xGmDNax99NttAgRqjAETg0zHA4Tj4nQ4sc4Optm9M8z123lQzHjTzU+sl5X0D6Wvlodd/KJB5bE37QwD2lEkt35cup/kUaKBw6iRZTk7HgjLIUKcarYf63PmgswbQW5XpnBYdKAL7vYWZ8h9E2nog7YAAWBmrKXSuc7qjfIUaXoEWfLjo+VjjWs7abCmmPpTmfYlnzrsmU6Yvfxl+KP8lANPsuE8pT/0OJVCXYFzblnzUKqauaNFkcmABsM2KljlJmkRt7W8a/k1+7nyfv9XAeSdT6RI+ssupmqOuKpxu004tChsJBBMkEMyQ6jtgiXp6E6LGd+emRN4h5TosW9MeazekpsM3B11LK8X1QwFREyrUoGgzLdpe41lGnTJpav0b67UnixOaxuHYOGDRT4zIQVE6Kk7RqEMcYx1nadx5+9fOyes3WABm2Jcbf9pVoJ60g17PPyM/KbzBDhtssBu08zRNTCZaroVbxe9HE4sqYH1nU03/Y1o/TUt4zfc8JlOdLMfKfPk02tE1Mnb9HAXmXTnuFq+X3k5al+vvUQMXsix1NtStFfJ3Y+eRv/8YJXwf76ee8tvRrJpSWaWNbXn+vt/XRK3Mfk9SuEVYA1Lcd4tkGM+cjggsi3EIr6tvy3J5rpxyCnha3rt2rWfwPg28f3/Xdf/Ao9fkofKUvhMOqZe+5rWvAXeWuIkcx6QGXHYyOWDAAYAEAr5Mf7VWJ0dlEsRzgO4HthjAEqdRixXPNRFDnZ3/ayqHdnUdR9bmIOvZLnMxuQNvWnZqOpVG4xP6ZxCuCaAPEUj8ZT2xjoz2I6MykEWDWzjSvzV2boO72y12txvcffNz7N6sPff9bga8RggZeae0UY3vLik0lnD6Evl1LMQ+vp/+uIafthcAeu1Ujap/3nEs6LCcbnom4cCTBgH+e4dmEoC4BrjncjNomvdcfOgp9zsOhFNtJ00Grf1x+XXaUqtO8X7dEU4D4tb3N3yvLMrPTEt4pJW1Ymk5ENNtaBNazeIq/ZloLKhYWS6CC9+mdeZwLBP8cTinWXp8dePvx6+P04esCaYFkOk3Y2OWdT65z6qPJnLc4OfU6iDpLjwpWeSQzQIrnI5VnJ/ECnVsKUtGlCKXMl6eLQVJRE8MVmVXNliu75PkdmNjm8Z7zyXAA55W8/4GfyXafk7S5GUKeP/5oij+UNd1/92j1+ZjlrG2MjaJIMDS+lBtsxLoygvmx2OkfZ+7GDSPad/9Mh3wpVghpa7teCVAyXEIc+fTrq3VUxNNw0Zlkt4iQfwYTSZJAtTWmB/PdqIdvs4HeqtvowRd7gyUxySLYbfy4dYOlQ7eD7jBHbaMWOKXd/Agfne7we4H++16g93dxtNqXoukTUSrGcu6CqT3olmftD8tyyoQR7p5DWAxw3HxBY6LL/Db0cAU4vRrjlnxpM3ih07XJud4xTnriqblHSuX56bjpHPWGAjm33yO6zs2cZjyzcpj6Fo58CXrqN0bf4/cQZxHGtIcmOkLWLoDNm6HTbXDDQ7Y4s5Tqt7usXgD4Lvwbc4/JP+dvu3L3rI/mTBJfufWq+DKGgnWeVhFWpfx0RvE3wyLm67FS8/HSI8jBFn9riXpJCw/YbMUKgAGa+xY25/K+58i17RvFaxDWJ3dHO2q338rqDHQue/ZycpIBnEZWUZXUCmRrqBr4Ze4j/ZRaN2Hgnf57p5C/gMR3PCZ8+5lyhP4GwD+66IoXgA4wzutdl3XffaoNZsqH4o2k+Owv4PwD4J3slM+FIeYAyj38XNSh6lpuiWgtjQwct8UUG4N6lq9tSgX/F40cGSlU7eS62ixugewzgdvDaxLLR3Y0hroeSxuopC0YVk0wKq5YF7tUa1rVC500ksccMASS9xjhz3ucTdo6e+wHfjw97jB19jivlri8OUSuy83OPyI58gPCbP2s3He+xjFBtC1j3zdopnJJV13z4+b4VLOcMTK/p11fSDQq8y6x4R4i35FJmu9TcUDqT6x0ClXQGjLOdCfEw4U6PvXLHm5sH0yulSNOU5thcP+JkSE0hKsyYleLkOvRb+ipZXga/Cd8A7Om20A6RvssMWdmMayspX/237fXWy3ane42Z8x+y48UH+DkNFUZjq1Eh+RcE271K7zjKRKMqO3t2GSvu+/4zBB/xz3PU2MfIh2vTfIaOKjeh6ieVFEFMqnob2L5Bu1aYnEv76GksgnZFpUKKvP9o+3jb4HX0X9m7Amr3xs0qwTCV+dWUSnlCdWjz7pEYWJJM47gNHQkWNc9wbekRzAQK8EQq4S6dgvc2PwCF38GE3RNUfICuwz5PZj4tgY956lUvLD5Sagv1tkCtz8SQB/AMAvdV03Mho+sRR4L4D5KpEdnRPlXPsitTFs3eK8y/BsOa27pnF/iDkppaek2o8WIeuofo78dd/nx2ZpPnMc2ARYMYfGWS5tPdeqE0CxzOpTOzXZHpQshrMFMFud8Vn1BtvbNzgtZji45cD75oM2bXO6zZ4N+oMT8conzTq8XCZALTENj/kfWMBYxOAnecHWh/jF/bGlwvXUYhwPp1Umcta2JhZlg5ZW25HgWwMuFkjRAL3VZkM97Pvk377U/Jn5AUTEoh024lwlasyDI/TdKrXW8DwCUyw3Fojn/SUH71pirzWAdYHLdoU3r1Z4s/4G8Ap4sX2L7as7bFwA7Fvc4SVeRyD+Fb6KgP3W3WFzu8PN7QGbN0fMVkg18ZJyM+UeOD2SwDoB97VfkmVtV22ib5esafReuHWNvuevsY2+8x02IbPyfhnyPYw5oVvgfZg0F/36DFjMcCmBy8LTOI5RBKk4EMDNSgLD1JHcjyNkXQqTWGtibFnSpDLL8l2RyiU+jtJYS8nFeJx5mcCQg3a5zrcBxNRQRaFFoimnrH7ICpjANejLUWAeb1s8+OF6LEt0BNzb6CYeXzq5+eywCkyDvr8C4H/76ID7U0qi0euXXNv+ANBOMdav4bwDcUcEXMePJbFAj+wcNTBhgQyqrfxdOI6BpneIa+1Eh8EjiRRygJWcVY3HKoF3TotO2vIKccclMWNO08gnfDTY82WvresWQF0FszkBL86Pp8RTdT/w0GSPS/x+3AA42QHAAnCNQ1m2A4gHTlenwOYgPaqDUQ5gSMXNhTRU4XfzYVtRxPh97jpVUI7jnZsMapSdnMl5LGxbzpKU1llG2NCpa5amXQJ4nsMhRIeq8tl66RvgZZblRoJ37XySMiK17lu23ALYdli8+hqbWx+N6HPcYY3dsHyFryIwT39++2tPq3l7DJQa0rxrNJox6oymeRdUOE6VKVbAan3B6rM3OK/e4H79FXZugy3uUssZo8xpmvcdNrhf3eCwWqL+sn+n7XLIYn0+zgM/O/ce+PuQywlaeLJMSf8fDohPqOD6vkt+U9pk1j9a3QI1ZYI+Ng6OOfnyOvC+csxCbJ1vqkjFQr5PSp+7FRJSKhMA4IRquCd6P9E1XAu3aobra9Spp5BztU/KnsH7NPD+dwD8taIofh4hCvnvrlCRvMMrETiKLSsDdI2reMJc2+5/knfsudZZdWqHITuJFsHs7veLONRww5XpfzoPL6cPmyJG0+/a/oz+Ny1aF+JbA4hiXJt17oE+hfUjsN+WAdDT3Rf+ofjB1XI600C7pk2fAuzluS2RE7safnBvEQZ/al/w7YSAF9fSHaKB3ZvZd1gPAz4/ZtDw8RjweyMjxbs+AAAgAElEQVQGvNSa5u5HmdReeHmZKS89By9o+9h6ogk0yqJyBjLUuuoDjozwkKPMTAXtGiVgLD9ADrBog6X87qWmMJf4KwbtN8NkcPdm7S0vXIs7NWtvo6xrbWhM875AcGTew4P1Izx4H65b+Gy82w3uX91gt9rgc9xhjw3W2GGPzQDWCdQHek0PgXtKzfL77rGpd1i+vaCwqDME4Hn9uUhFjQbeK+C88g6nh2o5fKvcOsYtZ5OtaUSZsWhOUyZR2vsY3ksxfFuUGIhTZ7i2V/Mj0jIi0376Frhm2JrcSiWRJQ/15dK06pIWo2nltSANmqKN6hHqmY51mgLN5r2n/Y2llaeoZTRGUxk9r1K5HtFJzb6pfToAL+O8P4uXKUjv/+n/5niX9HKPIRf4jvYpRVJlCMQzwBUdq5THoSGps+DmuXwSCDoO0DuHsVmppXX363lKAT/G3p8/Tr2mUeWI3+ji37oqdCakIRk6OUaLKdve2ZQ0hJwSIwfosUgUkkbTiG2w5RQtneTFrgDc+mVxC6xWF6xevsH5sze4u91jhzXmOOEO2+G0vmNu+kv79hSZ39sN7l5vPQ2CR6DR4r/nEjbpLyhfNrYul1P3JdfuTf2YGRWdqb+nScWFJhQSTHKNcETtOANli9ni5BNNVXYM5RSsWLSaGLCckHcOsxwQw6TB9eeucRpoCyW2uIsg0wD4b+eobyucvhTAX2ZFtiJu5Ggy14DG5NkHx2UZTYMcWDklYN5rSmtUw/M7YBlRCyJQWdWYVye4L+LQsSllIw8abWBYDeBvLL64Rnm6x03yO5q0DVY0B7jbFqfFaYgtPrwrIKbBqe+BtB9Bu14tag/SKw1op9SL9F3E9Bn53LXY4/RNJBPckShew3vIZBsO7yYt02gyfPy1oqlp4/SY/xega+ttv64pYH66ZTA3Qcr5G2jW8qeQQpA+nqPNeBkF713X/ZtPUZEHSQdmC3gCcYjBOAftEsRLrXxf5iHEBW156akSNAMOGjTKfCjXg9RDx+Fn017D7eDD3r2LWYs6HdK2A7ZZcMyZ7iH1GPuNpBVo5sMKNebuhPkqTtpzsz9jViGAdBIOvDlg1yJPWGBf09rzpa90rKXjTm38r4YH8HTOBTBzwHJxwKmaRyCCBhYfC30ZRZ+5wxZ3b7fYv94CvzHzgJ1CSL6Gnswp57g6JlNUAbljHrrvmmPksVLTKLXABNYTWofnA5/XC7xZfJZytddHlQtM4Uk1TZmmlfdVHP+OxhzP5WTfAvw8TOdwrHOAA9rKobnVgYnqkC4ia3Bnvci/IeMQLKNi8Jj+PPJMcMg7JOW8jPOwl2+PcRQp+p6tSXp42OhfTFjmtO9C687DwcrQsLHjatCwkwVNvlfupHrYL2OqzFQHdJr0lgDKGS4LP5k9lzB57Tc44NRPkuid00RxzupIShWZl8HKoEzvRfVFapF/DwBQ9sDeXfr7OWcpq5oTKp8gzftpL2mtKfu4Py4I75Mb5buj74k/Lylk5dZk6lhKx/Gpybh1MNayywkT8d4B6BTVR5YX4tafo814MZ9AURT/Xtd1P1YUxV9G4jIAdF33Tz9qzT5GaZECeCDuUJwol0C+Acr+t/PjGVgAbc/VraLOcA4C6XzdA3Y3AHY63g0XDinNp8g1fD153pNSJrffJx8QmA7eCYwMoMnVmN8ybU8fVWYhTeRvEYN3K270mBYeGDexS+c20sD3mvdBC9/z30mbRIPkEodogHBo+1RG931Emg12qzV2qw3ufrCPA79fAncLO/PqlFCR18hDAPX7Pjb3+xyI14C81MJnsvcOE8lIq6hH3NBoNJZFbExy35bWN2hlY5zg3G8HceI4x3fFWkZ539bz4jQNqe2VYHCJe1Rt7Sft9J3X8G19Sux3y6ImxQLvDMTPKmC2uGBV7fvv/TuDAyv3a9GiyAy0JqwT2hMt62qOU5XSpSyapf66UoqE7kiqRe3SNegyVCG3bPj6xLz4Oea4Xwle/EStO6Br3uk6Y9p3S5Pun0c8kZ4j+J+0oER6/vcbxFQd+dzH3oG+nvqZ6WVh0k/3jb7GVF+HFjXm7G6X0TWGNuBaONeA9Ia8PlMUCu9LzuWbpOwajPM7VXJD4D8H4McA/DtPVJeHyVO/Qz7YS4dV/sf3yeMQeO+hc4k7EF+WasikWI6rJGPmJTkga+eytGvy2ClhJ63j0nrlOIEagL8yDi5p5lciiYUW151AOgfu2gCvceOl8DbBnVQV57ZuEUemSB3YNgNX9r4fyMlC41uTfw4nnLDBDu62wf3ihMOC8d6HTKeIQftUrbu1X5ZrGrPc+ti+o9h+yARjCoCX4J0D+BKeE7xY4LxY4FzGQJ4oB1ra8dipNQWvY456Y9+69v3l4sDT73Nx23OadtKyk3ZdC4nHHZN5hCHp9GhRM5Y4YI0dZKz3Je775SFw2rHDCRU2bge32KFsLyj4pJucUo/wTqvS2sa/b/odkKfCKaCdNO9YI6bGkQPrGlisANcc/USdvT8PwufJeydwOSXmu+Ru8zYRbmG8X+V0jFxGToojTrXYJYSf9HuwaB5DHVwzOKOXVR40yiht/JlpfHVOZ5IaeCpPQ0WGePBqqMjo6aXP/RrwPkVZZWnXNU177pipfdFUK8D7khaxw+pztBkvOfD+fwNA13X/4xPV5Xp5Ad8JPraUmXUJzLn2hXfmzHRXV4H3Dtgf85i2zc+k/YdkmeSkk4K8Vss+REm9yX0guWOsOPC8fmOaCG2ACVoQOhfvaByCE63vMLlTjkx+I+tIg2XrHJpVjbY8Ye4umNF7JCsKOdSRgykf2KXmHYhBJQfutC2jzdAxrTdLuuaCeRXqTh0quasdsMQaO2xHtHZkficN3eHlTTQYWXGKtUgwwzNkHFoO1lSeLUW84M9J2x47RluXzzk3CQDSXk9+02Ognp+TWyrKAigXOJeLnjvfgaJ0AHoceQADeAVSDR+XawZNeazsS6xJAde+zsU3k4v4MUT6oWbAWH65emtAgQM6Gf5OxqzWWv0A8OuDd0SlaDL0R1FluKWN0+PeIp6gW1S4kq3zCXzFfs/7BCqv2d9bACtg9hb44rtHdKsjDqu7YcJO4JwoNTzsp8WTtzTvmpIl927Tpe4HYFmP/GMpUSNQfHRwmIJUeV2tnprIe7NCSPJj07Eg3k/+I1Vv1b7BITq39Wy1a0+RqZMpa3KVo+HpyoMw7eBWEU6bATD4j0l5bPrMQpz/mfPuJQfev6coih+3dn4U0WYcvEbjKa7DRYJ1WtfMpwZoJ0eaKSmutRk9EDRlwDSznKWZ49ehfWNOOVbM22u0ETlNnrwXrUOTndUUZyoyqxMHc4Odwsc8YFndY1keAJy9nwIHa2Pa+DHaDEQb4RkYS0QOrIsaqOojDqsTXNXiMDyHZfSM+PN3UUnc24ZSenfhiRFor4/zbFKRJHzkANZHgLoE5EdxjNzO/UFZ15ZTJTdBl8uxsmG9gOcSez7xBT2X2DqvVZ+p9c1ZENhEglKpU9ZaGeqPJ5Hig7wV756DLc2Ur4mmzdO0hZrWVnLZk7K3R0+HI0Au47dLapwWFtLiV8sy7T21CMDeifWabZMTPZU5oHDA3F1836N04xKwnTBXeNne3rYc6WulSGBsgeg0AkkK5LW6conHIgcZ3Syu03w4nxUmlp9LXsPyAdGsSlaZNobJ4+Q1rTHZVtblJlD5PCa8zEp6ZX3H5rlcr1So9IlUeD+PK/flt5OyZ857foggaKxkaPlIpECk4XlUkU9KgnZaz0wIvdMH4+w54s7R50L/6x0HYJu/+VKTsU7N6qQ4YPdOSn57zsrnzNRI3L8T5uDOtlM6QVlPvq51ZlpmSx7RgA/opK3bYDcA9Tlqn16dgfYN9kFjx03qHAiM8WUlhYa9bwDpJE+mUifw3vPgi1UfdWa1x3m1x+425sdusBuoNBQq7yB0krs+jB79boNdqqlzc7SuRFv5CRl/L1FbYo6InCYhMwkm2R35H4UD1OgvMrGM/N1DwLwmFoCWWnaLSgOlTDtu0p8Idcm09ZbESa16YCXoKAASh9BrNHhTwAIQ83D9Y7G19LJcO7/kSMuU7zHfvV9vD96P5Q1iaozUrpP23fJn4dGlJJC3rDhSaUPfL51zJc7V9GUtEuuxD2pwBtYBwHOA6Lf1obtmAyKBYvKP4kEOLIkBpD7BCus2Dzv3bnO/t66bqyfdqxRZNpX2mYsU8xDqqFW/MRl7F/FyfGKl7ecS38vcPI6f7ymkxW9E28+0GS858P7rXdf9W09Wk4fIC3wY8J7TvDNNu8ygWrs4nJQWkkoDuVyzDSA6hrb5Uq5LyWngab91PQm8cxON8NF7QO/pLqfhWHn9sbrnBgRpbuc14/ctBzcAAz+UYlLcVAdUVZ+R7uUB8+PFRz6Y6uAGXDfYW9z3KvDe62qepcNosZ+thDwEi7RwgUnbatN3IXnNzbBdxqC9KZGEE5QOsWMxwrUskVO18trznyq5nvEh+0Z/QzqSWbTMueddWFtKYuZbEwfNAZeWo2We0y9DCcp4+DkgJsX6pmUccM6pNmOKuwOq1Qk3Kz18YfIb6d9CwDoH3sHWtTGBA3j6xvvvu1v4z+K08N8zj3RC33O8HWrdohxCRtIxfF8uDjmQgv8pIkGbBib9rdsUrGuP1a6rXfMamUJBS7fH68y3aXmTqeP70FJPnXhJCe2g7I993Hq+Tzl7Bvcgz+DdS25I+Xg17iQtnibOuzBtgsylHIixjJg8DBVPQ88BF+cpasdoMYAJ5EuwZYF5TaQZjso0bZuWBp4PpGukId0kiL7GQW+Kto6E32NOG2KZN/kzO6DEDht9wKv8H11nqiMur8/U+5ii3dGOyZXL3/lWVaPFvXn96H60pkRlmYnztRFPcuVj+96F/5jjxNK5sxNc5qiZtT4AYTLjT5BfynVLRq0GOl1Gxu+Ov/c09vyUJFNS8z4FkMl9Np0mPu8Ua4D/tm8iKln0O9cCK8Ct4t9pGscc2Mm1Dy3RTxJfXxCEWjgcsEQLp5ABY0dUi8qoKVNku87RZKaMBVQeO63GCYM0B2TrHFpbG+PA595HWj7S1ynvUzv+Gu36Q0FmbhJkWSu0ibNUYg3PlcVsl6Egkyzl/kaCPJ3SHQDwZ5Vw4M/gPQ/e/+CT1eKh0sKntn4K0QZJQXsoSmBRAYvyDFRnoDziXL2JNC2a1lNLY861MtLLPeeIdC0XzOKw6Z2HPpjRoHTA0uxorHNa9fjQMvYctWcyNYOZNoEg0fwHpGOpFpNYtpNhXz3veex9unSNxqJlw3xX4KjRQhStrQUgNcCg8TlzoHFsAph7N7myaPB2/c07DJM8+ZtrJ3jvc2Caeq6xCdCB+Vi8r+tawENS4qxoJUscAAYeuS9LRKehhEF1b0XjYSM1SxrXvOcoM1z4hEnSZ/i6pMgJpU/t9LFAjht8bLAsZxzE+3eiK3e072QKTcOix4RtHVzy40noaIfg2MrrER6z/k1P8efSJjYy2ow2zvI+V5bTuaxnT/UYi/FuPf/8O9AntfbzZu/I9WUDp92evE6dxD6W/Fr1F6LtZ4dVL2ZL6rruO+9y4qIoFgD+J4Tu6r/ouu7fKIrihwH8DICXAH4BwJ/ouu5UFEUF4KcB/EMAvgLwx7qu+7vZi7QA3qmWVwh/UpIqQ2XSXFr28X3LC1blESiPUfQZMqPypBGaE2uuAwJs/p0lHOzT7zVAmev8JM1HZoK1aEB0jbFBRbsHCf6nOO1YvHjJp5X7ZBmdw4PKOJMr114QDdnywO/69sLfO9GpeBr7HC0mhIvUwkd6D+4T5mhb73x6vNv4sJBWuvtcZlWrr5aWJ0630KgXfUjFywK4LIDzkNDIzp7Zwg2x7N3wRehhQOP3Po2yIUUbECzN2rto5bS2PXbtqTI2+XhoXUu0k47PlY0Jf79W3HYeEpJ8VDbDF7DDsj3gs6/OXqnzFn75HbZOSxmBRvLiexDfNUDTAucGaBq/5DLrH0NZ+vXSeSVOBNxl2EiRVblY+HCRi+oMrM7+u+BZl3kit76M0+k0hY8WMGDKe3moxpeX0/FyFBmOF3HbZQKgUc2vXXmVslpXMQiXIwCnF/I+d96X0XPjv+N9tHVeGbmrPnpTJdENueO/9G0JkaiCL4uMTnWNH4tUiGj7wzsct3g8JbVGttnnJE1eHvMJ1AD+ia7r9kVRzAD89aIofh7AjwP4d7uu+5miKP5DAH8KwJ/rl193Xff3FkXxxwH8WQB/7BHrd50QVeZaacXv2HmKBsNEgDqz1qUZUnnjlTNfGd4xXxXXn8MK8Zjn28v1XBrpKVqJ/5+9twu15dvyg351Zq1V62Pv/zl9/92dj+4WA0kQRVDTdBAflBhFY2u/JCREJIlCI0QUVBLavOQheQiKMdCiXGiwEwzXGJQ0ImiapJ/sIPlAg1ExJjHp1tDce/v8795nfdSqWsuHWaNqzFFjzDlrfe2zb+9x2GdVzaqaNatqfvzmb44PCeBzwTvvTPgSfjxwiPfKEObX9Gw5XR+ETcdg9DrSkSXdWGmkqum9Mym6idusG+wX1QFYHHwUxmqIwsjdwT3hsXum/egbtyixxKZThpkHnWrbhUhHU4aAXQZmsgI0pcC7/J2qX70AfETHGXaLNXZSx7oD9jGmPqZ+dQkDn5JzwOmtJFaWFNMvr7XAfc61qbJowifWDm3Pkmseomo89V+ZxPtVmfcAatW58QMQsusE0L+Cp4a+A1/vv4MByO+A0yfg6ROw3QMbAFv4JnBgv1xmXdVa7n21nQFYAlg6YLkAlpUH5z34pvZEQJ62eVA2+mXbpzXw9H6GJzdEY6X4DtKuRa5VaGyw9S3oNwYEcwI2aWqWdLx13TXdmNeramgrHhLIx5pyB96LnjQDUB2xWuz6lY258zXGMVDO+4eBjJoH/a90TMqdAtSYY9OusHleot5VOO7mnizRbHu0ZwKzX4F/hmP3e9D6034/7B8pQJxGUnEaTX6TGNklJ2RT1OGuJdLNM/B59b8vJTcD76fT6QT03vVn3d8JwG8B8Lu79J8G8IfhwfuPddsA8GcB/GRRFEWXjy4OQWCLuwivM5J95Ow7nbcwjnUMATdopYBN41sODYQG1FijITCv5ZOq9NKFF9/WgLumfymXdiWYn6KXyUVjeLZsQNli2XVI5Dxt3+/xiYK15OZGHVPTL+E6+O/jyhZNc8SMJl70mpsgo2HSpoFf7dM1Phz4bH/EvHrGarHBvnrqWR4ZjMVi5+ncJzyirubY/KoV6l81NlSl/eDbCf/u3I97TIgdAhB4O6FfzYd5eolXV4OJsYH8NyifMQOZopd/Cbuec612z2tJrK+QvtxJUgamU4/Fztf17smwdAziaXvZAX2+Rtk4h1N5GAAzgSXqdwk0v4fKvBd74ItPwBeaByl4Jl5KIfr1nl3nqjJrmIGa8ADgC7bNQPzhC2D7MMPGhSAyFnFV6s/Lvtb+HuM2R4QH/0ZEEhDgI5sCz1QPwHELW0eeAi9VVT3YHHSM/Hx3DFl4Dbjz7yBXxQX7zm3QhjFpGEdplY/2aXV10630fMBH1geHfXEPj90c9fsK7ftwfEytllvfQH4HbUKl2ZFp7chajU4x8TYDfz/W3b+HsNG9Gax6uenaQ1EUDl415tcD+E/hAz99PJ36LvAXAPxAt/0DAP4eAJxOp6Yoiq/gVWu+ad8At/c2I9+QpiajqcwoHgbI44w0VpJ6zQSAJWsdM1Clypyz/C6Bsmb8mALaknGPMetTXV/25WSeTpxrg+Pj5dpVtPMZd1y6Sk2sc6uwh6ta/2csOVLZfFVJ62fK5+bvhX+fXK9AVI4P+IgWrv/V8hnVAYce4KSWJeX18jtq31uygMPkocITbXc6+jR56PX0Nc81GpOVa/hZRrat1YRRmjcGJfaLG4PmLG0Duq4w/yXRBkxrgqG1a0pPfScA5iQuaYBrvfOU6lUJYBFGp12ut716DAVe8qoxz/iAX+5/vwcf8aH7I6DVfu1b+OCe/QSbxw4gN5HfxqA68xUC9v3wCfjOM7BtPetOfxbzDnhWqoRn3HvmHZ59/+LBs8A9i24x7M/dL7HPXVucVUC9GCbALXwwua0ZokqASrFCahEXukrMGNxpzHrdA0ZPlJCCG00M5wBali/lTbWvavcBaA9YePp2MdsDhUArqkGlyTVtp5IaMurSQ9cTHgK2nSZLz0wtka94ENu+e14BHxfjVUypjki/2jNoz2L9xVYyF4Nt0XyxR7WogyjPMZXRFPN+jxVNTTb4n4L9N513LzcF76fTqQXwjxVF8QHAfwvgH7o0z6IofhzAjwPAP/D98BX2HkJ1xWLaJfNSejUIOfPXjFE1w5gxqB+rrwC2/jgd02QK4NJAuubhwAJmMvBPDwCAEJQB+YZhfJu8aQAmiOJLiSoLBNvThkyXTIe1tDhUG1sFiiTHd7AG3mOTodj5sXvJMqUkxh5bYBIgYFCDXIeusPHHK6CtIhMMcU/N57y1nyOuTA9IqXP6aKOgdhVOOnPl2nql1mA71N8tJQwsdX9OXrmnlFlrR7HAagTmH/HUg3kC7QTsHz89Y/FteNUYAuccrHM1Ga7fbqiI8Xkb7TdsGxjA+4ydP8MAHvsTtUnnDgM7/wnheAIALbBujpivn+HeN917WXWwcztETjZUZzTSRxPN0NRWlQntEix//CND43aP5fPBu90lgM7fvwyUpanPZKjN8DF4VgGzxRHragcsdv3Kx24NbNaLnk3vAbkA6IMt0eMI6Neuwub9Ctv3S2x+QLz7/Rz73XxQo6FxziIYeD3h23yMK1s1SrM2xqXsvqSNUI6+vH/F99d7f6c4zH3Teb8xeCc5nU4fi6L4iwD+SQAfiqIoO/b9BwH8YnfaLwL4IQC/UBRFCc9DfEvJ6+sAvg4AP/wbi9OEsfB6orHxgm23vAfwBk4uI2kpTvrh1rwJpNRQgHzgNX6s9FK5g9dLHTODhlcABgRSM/VLZ/LWZOWca2p4bfgtlqNj1571xzrBFAOrvTNfK5p+W8s/9q6nfgerQx+vSIwnONrAEDOWGulcOnYe+ywOTdaqXMxVZGzylJr0ynNTq028/fJ7k3y3Mk2WQZ2Mkky68I946oF9hTqoby0c6sUM1frg2VvOVBLL/QnA1xAGa2IgfrYHvvwEG0TqDzEARxmzoUKeugzXcV8Dn770ti/EBj8J9pcDS83lsPRMIyf1Ung/oYE7DaTzIFk84m3v3afzP9bfwzVoy4Nn1/m75V5/NFsiycLHVtKU1W8tfsbiAVhUO6Da4bD+CtuHb2HjQrVD/m6f8DhSWeRjOR/D96hQV3PsK69GM8XuwH8LW31QroLIbU4ycYN+G7inXb9q/bHcvqX8aTwF+29qM15uBt6Lovg+AIcOuC8B/HPwRqh/EcBvh/c483sA/Lnukp/p9n++O/4Xovru9xSuKmMdY/te1+5dv1Snq44Mv3Rs7GFmSNeAu+yYgbHh5xSpkQ+AU+edAzYunU1rZYqBoBzWWLvOut4sV+sCNjYl5kSoP57an3Z9Kj1WRo2xo+OaUekUZqhS9G05wNOWc3kZtHchJVdliadRoK9wiBuvOtE5PFqtvIan8zLkuJTMlSlsf65ci3WT31Eu5af03r1ajY+uunw+YMaZdfn7Sdnnxqya68gGOOwHLzONeJUjLzMcJNKkYY+xbRYB/RYhm1wS+TMfeZPifxss8RHfo3qjkkGeLJKHf4PhW0x328mN+jmYJ5gbOABYb1Gt95h/bRw0y6FBta9D9RkC78AwibIrk2pXlkukcbWjJzwKFZuxjZHmqUZz0asGrouJEm3ZYtxJNU+PwxBn3WV/TOf5aqiz7lOJoGvIM/6fm+b/WuWWzPuvAfDTnd77OwB/5nQ6/XdFUfwNAN8oiuKPAPhrAH6qO/+nAPypoij+JvxC5+9K3qEB8Eu3KLoiltoMbbPlulkJzFznHnKxm6QDzwd/uU1/BNqBuCqEFAlOY+yhZAum6ua7xHP0eYsgNySNofJQMpUFJ40jM9xnLZXlwJjRTsrq3mKI6Zyg7mBsXwAgoguu667KpfENVth+Wg6+3HcV8FzEdS8tLwhSjxxi29LJHHmKQecKkv1+OOHdw6Z3C/nongJARvrNXIP3oQNn3G0gvW/pHUgOXByuWMLbhGasbQHrHCaejueA9Fx3qamyx9TfYu1Z63+0AEMx71FSbx7AKBovF802QAJCC6xTXenVZtwTHt4/4fH9Ex5/iKnQSLUZ0nknxv07bJsAPIH6zmB11nReZTTWl7cHzac7Z+D5cSBUm/k0pBcN8LVmh+p9jVW17VSEBvWNoYX8UmBAydlfSfjwHiwmGrOqgTfZH1KdauFA9kfAl0kWd6RLXQGusvtU/6rH7VkjZeRknNdPjQ3X6ro2FgLe0Nv3QeQM4WP4niuvAtis3ag8Qzn1SVTqu8h3InXRw3c32Er497QM3p/FpOe883vJBv9NsP/GvHu5GXg/nU7/K4B/XEn/WwB+REnfAfgdk27S4j4RVnk9kXqMDmNAL8A8Ty8WwMx53++ojkB56NkB7nUmHCDHnQyQx9iRSIYxNsBbAaKsgFItSmzR9tcCQI0KJUIjU7p33yky40QAvU48EPrA5fJOAnbGRMhvJTsmy+BKWx6OLUWmByS9o7UGFT0oyPCeZWTeJTYB4+PQeoavk0PjgHIWvjgCHtKgSoJ5C8Bz4fVd+nbnwH3X/TZdGgocmzV2D/753fuWZem9WPAJ1VjdhAYgBy0glgVb6btI8UAjQ8cdgytWKkuJwe+563NyfTr/1q4vf9uXn47LY3Rckxj7LvuBEZiOtHfLyFwDONZEZTQJ6Txn0a+lwiTbkdSp5sz7HPuBaZfMO9t/xDNW+w0WBMZlPa4wsLj02qn+7uDbElfjsIA72LX0S/2/Eoyp3xY67aDo3V+x+7TA+tMR6/UzDoXKrycAACAASURBVNUztg8fsXExtndsP6V9X+BlVbAaVldyJAc0pvLT2o02SaZ7LUV/bZ0vty9ZnbXKljqWWqGLkQGpvHh+L6ljfsJ4peK1qhEWRfEfAviX4RUd/m8Av+90On3sjv0EvMv0FsC/fTqd/odYXm9a/znSYgCFpEtJv7G+hTr7PYZOnetidulFCSxKAO4IlEecygMatwvcSALogzjZ7F2s0ccHcRoEaHuDZX/GHhWaDoR4IMqjgA7sr3RZ1uvvtxXq3TxkhpvCBo383Qkhf7jH7h2qfnBZ4B/L2t4H/6Hqv++ebXg72jJwSoeQs/mArq6gMe5ygiR1Kb3bslX/1fhkjt+vaVz3fme6T3fLt/vUIE2SddeAO/3yv48APgB4WGD3sMDueYWHD0/YrD0I8c//JNTChje7xGb0TjXw7TB4umg6QJ1iaizWPdy3mXeeR4p9t/Ic7h8HGzlC9cKaFMR0/eWzyGMxwGIN8lr5JaPLJ27UxrSoqpY6TbWvsfp0REEsOmfUOav+jMFYklh2qWet9eucMef7tO0wHlEblr5TjvG/SpRr7X9nFTD7zgGP669MFRDef0gG2d/KZdcrqx5I0oHnq00aY5M9eS6/L7+GrkuVGbDtf0ji9jhjNcBzzovd0yojl5w2aL3T2MQ612aHp8l7crkneD4gJKJeeZCmPw/gJzqPin8MwE8A+INFUfzD8Nom/wiAXwvgZ4ui+I2d0xdVXu0b+CyEgLgUzubIczJWn4rukrIFmuaItjyiLd91WbY9iCcgR7+8o5CNaziXWETXqxJSw+TMIs+HwKI0WRq7KRv0AjdYYrtf+Qifz6uxKkdMfQPQQSO9U3qfFuu76AL/PMxRL2qsHjZoFw5wvmOdi2fm+rXjJxqW77kXDM7SS7UbyfpyGc4YBlg+NdAjqtbBJIF31r3+deu8S79dNYD0j+KPA/iPCEE8/eZ8ixhwD9RkEIL3D+z3A4APCzx/WOD5ww7bD0ts1t579yOesEeFRxaUZzC+864lafLVYojCSu+V/DYPANbW+bZAbI4aCp07xV1qWAfGabJcU0G7lJyVBX9eWi9eY0Fl2lSdWAngU95MZJtcYYvVfhMH7aQus2dpHMDzbc64E4gn0G09Bn0iag8OoZ93TY2GR06tunvQPrH/xMp324Nn5AOw8KsaQx0f+jPZ92sAWYo1IcsB7bzu0sprTluITQD4b2pie025tL1dmndue829JtWutQBI59znlvK3RRlfs9rM6XT6H9nuX4K37wR8nKNvnE6nPYC/3amP/wi8Dagqrxu8F9CNSG8lkmkBQjUZua8toxrpFFSCdOE5yy4BH2AzflxkBbc6XktdIwwHPWbVaXiVZSMhVZZ3ZYtj2QALNoPm7NUOw0SHjuV4FAjYdv7nfd0uHjaoFjWW1eCxQurQym3uzYKnS+ZPukbrGft9Ddd411ZOeQYZlIvrtgcTHzZ9WGGDDVZ4xBOe8IglNnjoAsP3vrDdBk9fPuLpw6P3PfzN7wE+FiGIv1aEVfkd5Degbanzzo/Td94BeF700dzatQfn284d2wofVMCmGrsxNahwBSQ96FhthW+n9Gr5OVMZdovZvlRSedG7ybknj098jYE8tB/xENSn2yuJQ0/TokbldY5Lh8YdMXMY97/8j1ZAwfZTotV/jXnnIscCDtwJrD+wbcsLDYvMSq4NuZcT6dd9i1W/kqfZNADXmxSmZGh/dfLca5fHWg3SJgcW22ytruWsFlyrXcs2ZrH/1HronCmrCLHVCH4vkpfQfZ8p0RVeK3gX8q8D+K+67R+AB/Mkv4AhBpIqrxu830ss0E77HIxr+5yJoe3uOPmCb8t32FfSwFMD8BE900RHEWNN+DKr9BE8lEUfTCknOYsvXQv3vkW9qAeft40b1GYsljdGFmjM+wJAGQZ5mS9qrBx3WbYJmLuY9wrL7RnXve25cs07Ai29A6NnmZUAymOnInUQ3hCeRxMkX4qPARvP3caNAom4FZ7eP+Lp/WO/+rF5XuHw8dGvfHDArqnO0DPs9PKr30JbCZGgXmPo+78D3pUtmsah3s+7IFGue94NPuJDoELB7RG0KIOxJe2YWCyVZeAl93OWzsMVmXp0XW6ZLpHPZclZM/7mkSEDLyXCaLlvu592Xrfd8iYj9y1VGq46s/eRVLd7oGm8p5mmtQM0AUDpfNteLvx2IXXdCZg3COxTAIztAWjsYMD9af0QBBHiQYWkBxQtnojGbI+/R46B6tDGrH2rTVoqhjJNu7csoybaRFkD5dzvvRa/xFJn5OOkNAS2otmmVtbG38AGy9r3oXNC8J42MJbpVr7aO783Ay/B+2fAvH9vURR/me1/vXNhDgAoiuJnAfxq5bo/dDqd/lx3zh+C7w3+y3ML8Xn04ucK6Ze8lHDgwtMygLvmdUYD7aaXFtYcAZt9J5HAXrIHsnMKO7ly1PmE3dSgY+yPNf2vQ4uq2mNeVagXdWeguhlHzwQGQO8fSH/f/XsfAlfMFjVc2WD1sPUhtzF4qwjBdxy8S5UZbZlegvYZLbdL4K49h2SsXWfvUAHV/oClO6BevMO8qlFjzpj9fbBN4IYUbJ7w2JXPB20hhv4Jj9hUS2yrFTYfnoZogClvNLngXT4Tfza+LUG+zH83w7GcYVeuscsISDL4drfCd5/HFsWBea7u7Piac7w7XKvssfLIgZ6Oac90qa6vVkaeN+/Rxq4i6wC49+uC+xoVtT8C3qk/fn4j0rtfcg253Q2gneCD/EoHdACeHm3nAfyM67pL3fY9/DhAmbXsj4vR9iSRojHI4RiRNliN2YWk6mAMuFtB7mKG/7y+UelpOybjsVAnqSrU/bjquufeo4KDN5r3Mgew71Z39n0aXcNZ75TksPKuz6/s9se2PRKU50yo8oMw2XZbOf3XLWUhjEVOeHGD1W+eTqcftg6eTqffGru4KIrfC+BHAfyzzB06xTki+UEMMZBUed3g/Z5iMe6UlmLcme6jFXm1xdgVY2pWn9MxcLHYCUqTwJ571PCdnu/W6Mw5arTYKuB/cJ3VH6tKtNW4zFqZ7M+gdzgaMyuBOzF4KSZeXkvGcPPdETPuwULqyHLgnstYd3WkqIDZonMxun7GaQFs1h+xr+ZdgJBBnUYL1kJpg578YzgVcUts36+wed95qujsEfoIgLvZZOPh4Hly0q19ulcJ+Bn5DChnOAI4ojNKDq5Twj+UqUJq5RsPPu8UV6R+uwOvI29H3a8bg2C/H2e1+DG5PaTl66NbkwltomOBAMrPGtj59fzcFrb3npTRsAdFvt36PrBlx4Y+g+wdalTYVEusqi1W607n/Qt4Fp2CMZE+u/TjrjHuLNLnbO/B94q3Z76aFj4YuhcytGnuFtLScX+PUGWGAjR16jSHL4Dtw6wPHCQjgPJtGj+k9xnNYxC9Ty4OLWrY6hT8PPqWYZ87TLYsX/D8l9s0kKphsHopyRBg3KdqdjjyV4zFRJjR2MsJM83FZi2eRDp14EGwUl5+cuwO5Hu2JtuUPsTBGNK12BrapOoc8P4SwB3wQcNCeb0Gq0VR/AsA/gCAf/p0Om3YoZ8B8KeLoviP4Q1WfwOA/zmW1+t8A/cU7Q1x4M512blKDAft3TmHyjPt+yoeQTXmqzfmnxaIq8twienm6kyObYAExA2VSDgIocG+RDsqC3EfGjNB+chSyMAUc9ZZaXk08O4GiZ2pMe9LrklblWjLPdrygGrPBpkKY9BuDfQkfGARUQAPlR+0926I2ieBOA/VPajPPATAfhzO+3HwBf+8BJ5ncT/wqcFSE+t8bVubGGQfK5QyzeL6yVz6Nj0L90s/WaD2fGTpgVcjqRaUm67+nSCDsQD6xCEmpZiIOC0ewpkTjNzjclvbl2KBDCsAkJxsP+IJy2qLVTUc6328p9RorABNDMijxeCFBtBBowTuMrqq1GmnaKpfoNdv//TlOzxVQxCmj/gQ/A5/D50zTCPiarsaPHs9L/2q5q7QJ+VUfr7d12FfL98tariyHakiagbE2sqlZkM0Ilaq2q/OCqhcti3muwOcX4jzfa72DVj5yXaM2xbJVW0ZoEl6+BoHYRpHQ7ei2fLffhxsXR/HpGmcdywQEU4UcJJAxkOwt8cem2hbGoXr4F4H9sDLAPgt/n6w/xmozVwiPwnfM/z5oigA4C+dTqd/83Q6/W9FUfwZAH8Dvob//pinGeANvKelZwSNdNmRyA6G1TEPFY4Aah+Mooe4DcgvOqXN+yU7r0BBANN70tABdM7MXh7LdR9nGelItjzXoNZigWLlD1m/MfNusz+DrvoQ8CfUoe0HmP1mUIfh7J30RsGCuPS/ErhbwDEy4M8qYPZwANYHYP3cs3RksEYDN2fX+WDOwTsB+H6QXy+xXa+w+bLTn5eBnXaFbqwaw44pwDwFpKd+rbScfSkaaOHbGhDXdPjpd3EAqXBViz1c2fYuSrWBM7akDdiMV47Ezs9l9nPVXlL3s8uhs4r8PWV5muFg3vI68xVCTzMyuqps13xlLdWmZXumgEvEtEt1Hk2FpgHWOMK9/wpuPTCrw+omOVIdHKr6NbZtZ9S9wqaDnFu3wn5doV7Psf/SR/rc7+Z9DI1Y/AyposZd7Do0Zt/JgfgjnpJOAQIQT5Fxef/6Sbx/zsQDY/WioVL5dbtybFsUrIQIskQj1DRwzm0KJEiP2Rm0rnNOUOUx7/Tt/a89kdZX03JYdW11Te93JGlGck/D1e8mg9XT6fTrI8f+KIA/mpvX6wbv99R5l3UldV8C9634BeCao2fKHED+xQms+84aIJ27AQz7RseXQn06Z7jraKXWrPBDlnu4XgJu8gbhNdwblj5eDpfHOLsek1SDbOHU+/m0ErQ033Zb4S+fKrmeaeese405XNUAqFG2RxQUzGVhFEgDmQ2GIDDEyoeFDcGhdCHHjdu6ZXUO3Dn7Lo1VrTDqI1Wb/QpPHx8HdZmYtxn+fNbzx94F346laX9AqBtv5aHdXysjFwus92BcbFt+7PvfGfAww2GxwOHBxxkgF6Urt+nrJ4BAnUCyY5wFA0L1lJhYnqZyJtc+7rCuxha/bqzmlqO6x0UDF5bOu1Rzq7DvwGLXKqqNZ+G/JkCkBeot1Rnp8z21mqZNxiULz73MkNqM4klGrpx5MDi8U1qV4N9hmMyssMdzrzZTo0JbOTSVvhI6/g55hAg32ie2XTUmVtj2gCz5tPOrmIrBcADYJ4D3/nvIb9KI3+57zhqgbI9wzQ7lwrPb1E416wFviURxN3xb3TPijfTp+TeSq9H+Eey2EVu9yrE94SqvZTdqSrUo/nxQjvF7aGW6p9T468H+CUUWnvhul9cN3u8pLUIAH2PkSch3L0ufNUC5AFxzQFseUC5azN2YZR/N3kUHMIV119JivnzpN6YPb+27Di7LawcDH31w59tWwyRwLjsSnsZL1RvMYt//0qATLt92rDuxQNInNB9Y6Bg3eNN03nkRqU7Qo3OWrsTA0hGQJ53dL3w+CwCu2fl09i3or2bAT7Il/PwaPmDWfjcfA3fLz7sG0GGkXRO4W2D+GgA+BdwpzwU7z2JcOaB/APCww8OHJyzXA6DhHowk4JFuRyU7r31PKVo7ymm7Oe05dQ5AE/sxgNcm2nr5Q3AaTsSHe9J9aJWSzo+9FwKwbbnBvDpgwdVZnuHbnsW8U3vO8fPO65BUn4z4d5cuILlnr0a8V3pesjVKHa87AGmpMY6vi0+kiH2XDoX5HYZ3P+RC34uMQntZA21ZY+6OmFGAKvqVwN3SfSeRK2nyu2jfqDuvaADngPnO+893zj/PHnMsO/sKPx6PI48PZFClvuvcMdr6JuF+CNz5ORYIP8f2xilpWjnuJWOD1der835Ned1vwJsd31b4GyIAz4G71aHT8UZsu053T7RfGuho8PJZtMlKmsNqh7NyN7ou1hhl/hKcD3rjOtin7Sn+r4ey8kF93BlxjwXWMnuWq0jubo4vsUtjtxyWLqYf6x9grO+uGbNxI7Yvgdl74Gufdli936Fak7HXAOxCMD/vBth5t6rAvj/pXDZl3CuHBpy5xEA7Z83lNv+TadLbTexctcrKDkFz7kfSO/kDePhti2nnLPsHkc6DUX1Y4PlhgecHAA8HzB62ePzg/fATePd6wGOdbsszh8aKcdH6CGtibLU3PnCTsWiuupsEI7H+SPOmwRlAOXFJqcPJSMjUE1E5XAe24IBmvfdgserU4iggklTPkEyvBI9cOFCkfcnAcxDfpZ8Wg4vYFmX/zmitkLx3cRWiZecYQLqDDON02ISP9q180cfAzrIp4qtE2uQzZay6wmbIh3vsSoFymkAz0K08yPgaSrcmV+XYgYT01APQZHg8+eFj3jXFAu50T0tthWMIh6Y/d0jXj4fXEi4IJ9/DWHNf2Ki4J8ieAH03y+sG7y8tEphz2bFjtE3trfUM/KwcGPi2fIeq3GPvusAjXcfLZ/66qoy+JB6T2GBu6azLe1qMnCxfzKuMBiRSHUOMGTpHR3b91XGsCyt9QVuh1a0ldvIsZg1GcgDh4L0Lid7/vg/vvfgEfPn9X2H+fg/Sh3XB++DqFyHAd/B6rPOqxlPZYLdYAYvFsEI0Bbzz58th3HP+tPvGgPtovwAaHggsDKs9kikqMw/KNo8ey6+jv07axhvNDd9hFbSPLau/vF77IobMe4qF18QC/Vw9IJW/zsDllSXF1KX0eSWAJOAowaTsC/h7BLo+qGxRLwDgiLL0Xp4CsC7125v+4tRD0sPobK+LuwfmetQWwKb1B/IyQu9usI/aYyn6ZI04sR9hIEjCbyHff2goKesBqSvWvVLmMF6QC1yHJaqqhqu679nu4ZoWrjnqBqoxQoSLMpkiI1YAapA8+gYt5P7wHqXHt5yoyvydxyZP4+8QAmhNR50f0wy+eUms1Tyr5GXbwjXdPbrvASBwzlWkvsMV5VHc65UbrF5NXjd4H+JF30esGT2lKR124OOdsTEUnEdaxGsdR2rZWusYctgvea01OUiVge/T+bGyTunYiA0g0Tol6rC4H/YVtuz+2yDPvkRVg3m18z6ZefTF4eYhgJSsOzd2yzVa5awcLRPTvfg5ZOxmsFLDu/Pvtu64L5qacL147sFi03p998M3v9CjrWoAXorFutNvLoC3wHrqWGpSEXA1MToPCFh3OkTvX/5afzKCbL8/w2Exw1eLL/TrSoy8ekif9gB6LzFSJCi2QDAdkyy+ZPdTXiZi6TzPoXy0Ha7+SGnZ9UNZCcI0bH9wJ8nPqDt1jKq7zxw1Nlh2YHOFCvtugrTHxi1ROj+J5UBlBBolaI+BFTEe8L6dAu/FolZbXk0sTyc5xpM9wOw8nQDofxthtMrdn1peTSxGnRMk3KtM6B1oa55PxErrHOZuj7L0HmZK/t6pD4x9D/oGShTdooSPvlv679s0R7TlEa5s0ZYtGsfHpgE40wozTYxoG0C/7+ukY1NEahNjlj5XtPaUC8gtjzIjlTzpycey8ThnAnVtCbVmPHg/voH31w3eHXo94LuJ1K3Tluos/Trxtv2M9gig6wwcdQY0bFGTHOuu+v0x057qMFIuIumcKXqwsckGN3SdogvLmYf+fQkAQPsNhqVAbqC67wZzbYmxf4/vgVW5w4IANTHgnP1ORGNUdTQBu5PTJncyGqPDoKK1h1fn6bZnn4Dve/+M71s/Y7f++6Poi9wLzcg3tFuh/nLee5wJjNswZpjo/YbvblwfuSF13VaebeZ+5Mk1JZ8k8ElDbFteJ/96oXA6DfuldClcbWY2/DalZ/BptYyAtjZhKI10/o1NBn+ICOzKBlUH3ucuVJkBdOZa1mmtndggOwTqWlocwNv5yvJay/uyzLwM/Dp7YjF2b6flCQz1t0bFmPi57zVcC+e6CVPT+TufyDaeWL/OcbFrjn6iUfp7LLFR+km9r9VcAueQKIBY0XRA67oCVvHxQXt3lu40bctvnppAtnDYdhBfrTcOKNdtH4XWmvTFJoOanAuk+f18nZM+x28v9jvQJ+/yGprg1kBnaNsMK75G/lPa7a3l8PAcJpzGE9BfifK6wfu9I6zye0nWPQbYXRgggjMyA/i1O+3UslyMuQZsFt5iviWIpzTtvhbgp2umlJM6BeItrOOS4bPAOf3y6HlDKdmAsi7RlhusyiMKzoxLMM/BOzHtErzHmHetvkjDNu4bmgzcmGcKrIHdex8unTzNbLDyrDrzUsHdRlpAXjKAHHhbPolb1mn2buf63w74Wuy5NIjlOvGc5S67d2CdbzLzM/T+3rXVAS5yxYxvS/eQOex7ipkvASy8F5r5omMYF3UfEdgCqUAavIdAKa23LPe5b3U9PbQtsRh6CfplOXlZcyQGJrX3oF2j5cH7qP44TZYBlNV0cBKq/oX9Jv3m9usp40c+bvi842qKvBwpsSaBQHr1RkvT68dYPWe4Z6iHrZUr/Qzj9pJSHdPzuQzAptTFcsW619S6P2o7rdK+GmOi0ByT5byVlOLWp1OR9JX/K0Fe9xto4RnJe4gE7pRmAXgB5otPnf/ZCkB5xKrc9fqPK6Z/N4D3NMMiwTVJqqOWx3OMSXMMUXNBe8wQLkck+6cFrJAGVlw3digXC4ddAW25x7w6oFp3y4iabrvlCSEF3Ekk68513gmsd+D9tAY26zB4Cw/UwgO40Paz2H/CI572j9415Mf1wGp/hM5wa8D4Uon1MtYxAsuAfx+5+V1yfw3I5/5pgZn6tC4QU0fj9gPPbt4t3XdA0umgNxdEWKw1HctVk5kCvLR76GWLs4cW064FnaH0ZIRPbhAp1dti6m5y9SymLsBVZmhftm+qCzzyKmvz5Gt8U43VaKxAQl53nBRYvMgxwxo39O8wZsHpnfLvJL+FFtWaJnncIHvUF/OoqtLDj/weuSobQn0p+A5EyJThO9cCJkoVpdi+FlSRCCPrO9C3yv0GlEs4id7r7zVhMByc4+pRPgBsdRp6/7wp30GFptinz/mVKK8bvJ8wGAfeWqQuHeArMaVTZ0EVXW7Tfndd0XSBJBDq38EBrm/wg99yXf3EpwGS+bE/K2e1eQcyVo2YzgzFOiyrA5OAPlV+jbWhO3IGcwguog02RufmaszX+86byxgQOAyGVUE4b01HMEbMCPsHOZBwfVfOrlMkVWLRw4BMQ+TFnolvV9g8L7H7+Ah8nIVg/SPS0VVzwXuZ8auBYrmfm5ZzT7kdK3e0rAPw1gLZcB3hnNDkAEbGqBpTLQfyXNFAgTVZ98eGl0BL6/KaWNvMuV9MNLAugYblKYp8uT/iGSv4SOMe+nof4guKpPoVgO9gMETn25ZL2FigJikacSNX1PgEnRunr7vAbO+PWK+f1cn7pgpjlxKQH6J9hupv3PuM1ofHvgX/Jn5bn7zxMYTyHBSS9mjh+i8o+2WHFvNqD1eFJeNGqwDGhquIfIPuO1gRVrknHmkzoNkXPOExeM+2TYK0X/B2BhTltm2cVxvkq5LWcwR9j1ere1e2/Urdcr2FAw+WNThj0CLchtu2D/66y8MXoful906TKT6+3Vv/XXSBnnl/U5t53eD9c5BUHeKgg7kO42o03EWVZN5jajQARh2y1jnHdJbpN7ZEy8sgtyWg5wD+Uj/18lkkk2exgBYLH4J6MsIaqwpYxj4OnU6ya3svCXIVIAd0We9DGrRpLI81gIeDzSr45u8WNY4PJVAWvv5x7zIE2C0D0VuJ7Hly7nfN3sqaYIzY8wIoZ8BihmMJHBfAQYmoOl/UXl8XPuQRgKA+nROanPIg4dsWCJPpGghPuWtNqdNp56burUkIFsftUYJ2crNJ4IPWoVbY4AFPPu3Tswft34EH7QTetW0C719Bj7bagfdTAzQtcOjqZ0P68N2jz7rf0mGsdidZd+4SVnqZEvvFGlivO1C/eO4B/b4afMNrbiPVKJ/iG4ffKb9hWZM1n+77J+u8lPomSHWp4sfyQRp/RmvckRFRNWPiFmUP0ul8Au8cmPeRqclGJqbWB4z7OEulskTXVy9wLIHdYg2UwHOviueB/eJhg2pRY1kZQbAiUW616OIagcUJCd9X0UqMrdJ3bWkeBEN7wht4xxt4v1xaxAG8rNsNgIVfjpqVtJJ+6Jl353gUVeLfbf1yXwS9Y9Y6ZW1A1jo778FhYPbJon6I/urTtPvK/HNYeZ7PVJdmFD2Og3ZapfDKMaGXCp7eIvR7K49TGpWcb+s6n+lOzfLKMwT+CPVVw2f3YK+FY8Z3wCPIGIlWGLzb0dX7DfbvK2wfNsOA0zgfoCnGtscYohw557p7XQPkqccEuu4DE7Z62ATh4zXvGdYKDzFnckXIUl8B8tl3beIbA+Q57mFlunVcK0PM4xWJtkIhvZlwEM8jqhJ4X2KDx692mH0HHoB/CyFQJ7BOjDtn3nlshz1w+ARsd8B2782ctwhNoAFmpsEwBTN5RglgBWBZeYC/rDwQHzHvBODp7wGhnUsljq29a8vFGlgsdjitd9isddUPa4VU+05crAmaNl7w861xJGfl1apr1ngQA/SpemqRSNL1o3x39X4e2AH1/SgJ9RPUd1KRU30pP5fysfqizmbGlS2qxb4PBMfjHEhvPxysU+2gbXoDFQaXn/zNkPA35vfva7Aq5XQq0BzewPvrB+8v+Q25egzfL+EHA1pCBUt38ANGl1aUwMIB3irjCJSHwBiWLwMCfinQ/3adk8tTN/HHZUerg3bqzIhZH2vU6WwP+fClwEDk55fKtWeUihzYU2Ag1mFYx/iT2fqBGrBqgmMcYElVHAtspcC7/BZ8sJADMF/eTbma47qxgY7seo79mj15Nxj1S7uN8ywSgMDodIrcGnxfk+SxwLsYIOeLGnO3DwZAyxUeZ7hirvL4YKupZjmMXRhakvJbbbVTvqLD1S9auKD+DHVL6mFrTO/ATHJj575+CeFqSORtZ+XG747Y9Uc84QM+jn4/4CM+vP+ID+9/mpcVjwAAIABJREFUGR/wEd/z7R2KbwH4NjyQp29LS//fwaDy2AH40yfg6RPwnb2/bMv+NhjAu/wUHLB35tJY0t+++/sEfPEJeOzY9D4Th1AFgVhnAuwyWNvXgMMa2D7MsHGDVymu0iFdSMq+BUirVcbSYisuFpkU69ctlSxZjlwGPjV5lGMFJ2Q4WCeih/Jpq9JH6uVQtu2ejxn4+/2h3gMI636MMe5sYqR6HjdslyOv5YbTAvDLvn8Kgb6qE7+vvcekvaIeCly3P45IOVKNLnBsXz90vVRe9xt4h8Go7V6itb2Ynq007iBgz891Yp9tF+gMXfv7k+n1sTvHiCKpfVl2H5oU8IAhXC+QA0W5XF/Cs93DUuRYb1AO/Bwc7DFH3Vb9IF/vKt/BNc7rBgIeQKrP1fnwFnrI1WLfs6ED+B46Ih7WmzosvlxInSCAYGmQ3FDSvqVaI/kcOl8TbRIFPMFSUbLAvJYW8/0cDOaVQ1M51OuQcQLCyQUvb0xiqz5yUOfn9wM7Gwj764Rfau5hQPV602eeOaPv6g+ApC47l97Iudv23owaVHgcDa6cSeaTRzmpHK3kuM7+pRobsMr3JxlFa9LNJ3NcZ1e2Wzrvaf+I/W6O3fPKT+yei7GrT6l6NWEV51j6v0MJ7Dp28dtWEKwPAB4OePjej3hYD6bbGpj/nq99xIevCXDfHX/cP2H9reNIraboAPYXz8APkvqMZqTuX7oXXs04OUNMqRO/Ffsl5p2z7YyR361D1RhuAzPF13uOwWouMM5VuUlNAKy8aJXZb+d5DRrnMejiD9uhXRjZkEEC9MxVgT4f153v4PtTo08D8tU8qN8BMCKIpHG2BrwlyUBgXvZJmqef0fstHVxzRON8lTZG5PvLCfl9/HexvG7wfu8gTTnCB6ideVa+XGuFSqwAFFU3KSiPQHX0OrzdIHKoBnaHBg4yjqQBnjoL7q93KLLrWXbemUrgvnleDYwv6Q3Kgb5fo6bcO73tcgaUnR5yecKhXAZu+OpFjbmjdQDfbVHntse8B/GDzmOFFTb9L12z6gx65phjCx8IaotlkoGXy46xpV85SGh2DlzKnjb0qkt8+XOOOdoujLpmm5DyYJRixKTE2DRrMOTH+nJ0vqhbx9Iqz27NQexWG/j3HQ2IAcOV17UdGweULY6NQ1s6r7oWgHjX67JTuTwrF6pcVdj3dYxqHAUK8u2E6sxKrTPcq0dsAkgMoXznErxrq2gaW96iZBNI9GVwcJhXw6SlLlscsfK6uATSSU0gpX5lMXXm6gfbpuueATQzPDffi83DCh8XH7B62KpMvVzxeMTTAGaqDVa/dovq14YrIZZtguYKUZOUpy7tO7WsFsQidl4StC+3HU+VXMBPohFAmsRcN+ZIDonAt2OrBKkVhGSf6di9Ml8Xr2fSlktzyKBFGJekFU/nNUYKv1uNCq1rgYV3H9k2R6AajIiBO0ZZle/uVLyBd3w3gPeXfoIc11Wp82L91TVUEUqEKj6cQVLynzUAcIBbPMFVq74TIV3qYZa/6qCsHyyJtdtiiUc89dsjV1suVONoEPoXB8ZsqyuHgloRASWjabqQg+1GK1SbGTrLXB3kmP6/lRZ2+rqP+0H2IOaoxOB9aN6xSCuM1XAA70mE8rdYpnFZ8tSw+LbGVFkThSzvRB271Tq/UsBZLVlfcpl5YtsBDFFMu/qlMe86mAu9bljvxoeJp3C6wyrWkJeH42Rn4eBQIxzEudA3D++l6/TyPOYge4nxBO8DBqO9EfivnI9J8b5E/avCSWUPKtm34O1WVRtQRH6P1LeQbCR/6hYuUP9xaPERH0wQpKu8Dd93ip7veKLF+iy0o/PkN5buMgF0/VJtlkeSBNr9cstv90lxe4mcCTrlqU1krHvIMslypZ8nriuvPW9qxTD1jrT7WGmahPUwtO2SfZCmyhk6Y9DdSWqsO3/+fXf/Ei1a53zbq5reLzz3BX8P/++ndzIB9sr8ryB5aej7+iX1BnPecE67vvRLcRUdznLRvuG2kC/L1qiCDmsYDOug86KB1IPhDfaY45F1JzTw1wy8Na4DatWYaZYiOziLobA6N+5GkvwSa0CeMxq887RcAnJGI4dBGjrM9GDHQZnGpHN2TgJ7b3uw77+RBzl61Fv6pWfIGSzlddpKg/bs8s1pS/0WQCRwGASKkipXxsS3H27Kbrs84UCTQ+ESknSx925weUdAe2DLh2XuIXx63Z/r302p1gmtHtE71AAf35bfid67dCFL34be5fCtB2P4qluP8qwbgXQP8JeReme1XV63gFCFIHh+NwawGrCWYF3z3iOBC5+4y3PH4F+/H5VJfoOYaOdNBfbjbV19yjrfuldKrHZ7LoA9B+ym9i2ZOuGIkQa5gbK0fe1+vH+UzzauG8M+/978eupT/PZAhvD9tisXOTjwwNyvOpMLT1ol1Or+qC24rmyOlesOmg/NOyWYz71Y/89YXjd4b+ANku4pmn67E/v8V6irjPY1UM33MeinAxgZrvpt1kE6u6PTGbrx8jrXkbb0LKWBZEoXk47399EMJi/QebeMe8b674nAFcY5mv7ymMXTgZcUaxDhE5tBnWdcGs1FnOXzOTBa3c+x380HbwlSXclyE3mvjvKeHXKgqlEAi1mnsjHD8WHX2VHUmFd1bzCpGVAuscEHfAxcFwZeUHp3hls87p+w+nREQV5OpK9xzb+4NBLj5Qd0HWvNLeEDgC90g0ceN4Cn8bgBFGMg9BzN6tzeq8EF3oy4Kg0izxBEtPUGw6uHDeaLGivHXd0N7/4hKOETPuCX2fbH4Dt8+PRV6D5Suo78hNDXu+YyMmaolxoTUsSJ3LaOVSI/Ak7W2MLLIcspRY5dqbJ19U26PN473oeFY4vDmLSJqQvxNCAP1GsrAmSjUsP2KCPda3KiSZJOwepUt2JM/epoLNN8u1v9nPwGizDGhBaZOWbAmre/CSa5kp2fqj52C2l6Dx+dnPAG3vHawTtwPZ3wS8VhUE0Bwjc7oYM+EXbtOkTuSQIYL1MCmp6fDuAttkAzetIDUoRGbdww9RmPwbkUIKjeVT5IxfNsHBRI840LjMEjl7ID9YHOuzd6C1z70a/hG/ccN3+SlecMoGQsfJWwjaxiOu7SIHWLVf9rgXgJ0nsgtauAXZE2LrT8EvPf/hto3yXjN3c7tR+7DzBM8DRh6lcWyz534WTOChJEX6kkZgrov5/Dqr+NH+wr7Ks5qqrG6v0Gy+dDGPlTRvwksJjy+hPrTwjId8DvtBgm/1R3fRZtX/cf8aTUKT3iZ+AbuypRV7ZtBX8X/v66qohc5ZI6u9JHtebbmgD+EhsfrInAOLr3QgaiBIglWNcmTjnelzQQHwPVueMCEudqecsy5JSZX2uUVxJJ0tPR0JeFKy/0vam9SHU/jRn32+MxLmYkK8c38nxGxwmkyz6UT0pr+LgZe8yx3a98X/q89GMYj0gto1NbY1usb5Xfgn+zBZEKMxwXnb93MuaWBt393wnvHjZYPWywXI+JBiIStL6Mu2WVhJYc67QVg1vJSZrKvoF3AN8N4D1fBe66IjtG8vduVSqucy6jslI+rrPoLpmHGXeEdyN5CIA9CfeYxNl4Kdy1pGYMFQPujxyUY9Vv0XbdLcVtOi34DbZYug1W71fYPKy8cepi5UH8bpbu3FIdHP3KbRminjo5Fq1u9F7gTRFJ3WFIG96TNEC0ltx9UdqgU5MdnDYoxZZl+fdpg3PLfq/tQU83bFaNjxvAGKGAaY8ZFwJjoMK3ZY+RAuvqXzpq6dzZvs/lCsewH9cDDos9vibUbw+/sYzYm7uCQ/chwNDdDPv3YUTJpDs2a5y0mFUJEmm3AZbPB8zLA1blRiUG+KqcpU/P03wxbZ1huT1+BEvdY7qqCYCec3dogDVQrtNqKTL/nPJxSem0k+tPAL37TzNCs5wwWMfkJA/Q6w6lpyQ1yai8a+NZt+1/u/GpPASBqUj9knsysxhu3t/VmMtSReuO/hjDw1JupD5WdiCUXBmssEULB7li2RMo1Rz7qsLmS+amt3O4MDluxrXGtp6c8uMaBYpzZdsHbLL6okGNEv33oHHtIz4EYD1lTK+971vIFn8/TDjBdLL3K0leP3h/KSEwLkEOB+gksaVLbcDVrkMXvQ/MbSRt99dFjEfIxWR5CDpj+qXOdlPp4bflkjkttdO+98Lil96p+6tQY+5qbNZLrzdMbufKCkG0T3p38s+qnTGAOOrkhg6O2FVi3WMAjIO0WHTMlJ6sJtJlGdc/9wMV+ccfgkXRdaG607iLVpn3pmPe+cASc+unDTC0bbGL1vdQvw1nlE44lg0OXbRS+kY1KsANjUgCq/HAoq96pPSApb2ENFCNqVFZ7kbV1Zx9jfnuGLLtxLJzNRnLLWGKffcPZX8Xx4AXgJlDB7pEbAnG7mqh5mny74smGdOQbc0F75fKueAhRw3j3DJIg+aSXH+i9V47eF9hAHuAgXtfwDRQtwB7DlOp1SNRh1J1ZF+FnnFinnLkMUv4uyXbjvE5ZO8x1i33xtr+CDDohFPepFZDpfP9O3fUUHf5e6IqcLgg1T9380FdRqqMaas3Vr+q9amKGo22WshtteSYRjZemooMfRnr3ZNI1v2WbRswmPfPRePiBeV1g/fPwdtM6v6yXksWMwaQZEPfQ7/fKIhBpBy882UTkFkDlO0RrnlGvZjBOd/At1hh3rG6g6LMqmPah/0Vth1L/zRy2lZjjk218kaw7zt/763D5nkZRquzdASlqACS6cIDAasLoGd2AW88t3dVxzzM+47LvybbcMcfH587vOK08RiJBmxi7srkwGelVdjDVQ3mVd0beQIIDD1pPzD2JOEuFlMW/Zp6ilBLAdLeXcL07teNJy2A/Y61AT02gdIYd/q2crJGKhklWgbMw6iGehAmpppV1ZhXA5gv2xbz3cEz7il1mSkDlUYUyF8L5As7myDbzquEa3wkaAABY09yCSBOeWqyDCJj7UfmkVohiJVZS7dYSN5HAFD6ENavOPTedFw1XofjAbsAjFl7Xzh7tWaKioEF4Nk+B+z1YjZazZW2VDHALo/LFUn+3mN1SR7TvjPg+0di4SkQ0yO7X6xc6jmV8y5t1+M+WZYrVc9y6pJUM0uREPa+Pt7J+1A5/dRneJdTvP5cKiPw7gvwK15eGvpeJi/p5z13QI11oBJ051bIc2adlu4kM3QrKmCxABbVAV+sv+r9ve9dhQ0+BmCc/L4TS/+ER8bYh/rw/Lp+vu88kPcd+thoCUDSQ0X4eOMldY19jbGzlncJ6zwpnJHIAS0aqBg/V+j9RROuQzp0tK0HV65ThygHf+lgv1oAJH58ijhFLWk4Fr6zUpzbNq5X9Qn2QbqqXT6Jb58jMZUZCd693/aqr7nkpWGOGlssscQWW6x6UL/Bsp/IEqgndTIC8xusvFrQusV8XffqM/PdcaxKAeire/qD8ZcSpGm6yn5bV5vxtxr4OH88H1zpQDw93FhgJxegpybAsTzktvUcmqTqlAaiNMNAvurXTybdHqUbAtARmJ/vjihL4W9bA+6WCtz4IcbkkiSMaAUHfhVngQPgDtF6JlU2ffFCsBumXTYZzKkjlotLzVYjBur5M8TUyeRz5Yrs22LqWfz81BionRPbtspzDxnd803nHcB3A3i/5xPwCsP7EGt5UlvCTHW08lhMb1GrwDFdOtpWgDtXoaFof7M1MKsOwPqAw/qZAfklHjvwLoM4cZWbgHlXPNBwK37OyvhHJeB5vRl+IzrUcwFGeP5l5YuBBW0SkwLfI9/a/W/3XJxNTw3qqQ5SvKqjkY6SqShytp6tkpBoLL3fZpOz0TmhnmxsEgH4iUDMRSFNgDxQ99qxFFxJLkdvupUpLboq9/CgeT3qVXRcDecalJUOEYJny5wchvVIAqA4aJ2ivz42ltfbz9R2Yp0/fh+DuoTfp7KHqhZa+XKBYarsGuDRmE1Kj4H6+P3nvma6Fs41PqCYxcj7B6cMQkmNEeMHNM/jKzTSI1p/OfMLTs/IvaLFvomVZolW760JQ449hwbq6T4p95DynrGyksRWC6Vo4Dp1jANhOQ6W0N1ZnnP/a8pRlukNvAN47eD9HTzwvIdojJem5uKgd5aULtkN7T4Q50hwL69PtSE67jA8By2zLth2hYGp4Wl7YLYHZvsDDtUB1cMelavxgKfOWt9bsmsuC0eMu/BUwTtHMqThnaD+OPryuX81egfMj2V1ypHAM5OBcUwsHUhrO6aPnvubuq+2L0UB6KNt7bf3FgQAnXvGctanHdm5fPsQUY8CbNCvSSzgFwDVWFYzVLZiAUg3pfJcmV9sOdtvnzdITmEp9evjw8M1J9bn5hs7Ny9GwXWHwJwVQEvdga5Pqen1+x2IR5XHtE57jnids0CmDRptFpeXL3h/7fR6n5oUyFUlOi8H2PNzU+MO/6XjUrS6d+4qoiVTvselZbmVzKR16ht4B/DawTsApU3c5z4ExLlIcFQp6RpLn2NkNAWwy3PlcrpUnSHGnX4X4rdzM7dZD8GbJEgnRp0CQfAOzhc3BOTkJcUX13VFCxmsGPs2XBMaKvUqIyxPKgs/phmL+vO7NAo8Uzq4Hry3RgRJ9jFi6iaN0dxSbDizTRhN/rQ0iOOx39h2TFLL7+ekpfaBAfz3Ztt+AsBNtVMx/w48X6r/fJv/CW9FlpGYZuR8TcPncwF8DgM3vIrLAdmU49e87tpAYwoDKkVn4tMqedo5scmczJvfN8fw8NJny7/mvG/TODcJwMsYJzQ2+O2xe0MaR/hY491X5qljacf5Ofy4lNik85x2FVOv0a6ZohJzSVu4hpBL2zcJ5XWD93sy7ynhYF2mS9HawhS1hUvGKQlWJJgnjyDcP3QJ7CugLV3QMREw4fuUJtViHvEEKzCGf6SBBSeJdXCxZX6LbefHsvUbOxAPYBRBkp+fU2Z+XfgsrLNvQ7UY2tbUY9QVAB5d9JpMfExyWHhrW/4GaYJlB0wVG8Bm3KW+fZ+VwrpT8BONddfYdSsirxb0RIvWW4ptjYEfXkv4fOcwyylVEbmfUovJbaO5Eruf1s4scDX1HK28U43yUu4i6RwJ2DWmPaVuw8+l/NM6zJezuikQGLLo+ZMJC0zGgg7miMXqUx0YO6YEOyenfV2XPcytc/akIN0+rWute1vGv/eSE97JhDfmHa8dvDsA71+6EBPkXqsEmlggyg37MddwIegNQfiQVdOzFgR2ltgo19tLjySxjkIbcK0AH5YXATqX+7zN8Tag3cNaHj1Lf54udyyNTQpzwY22n6ufHJwzwWhYPW8CmzOV4T2H0dNAhKaWwCeiHmyHxoXjCL51cK4E7ZKJd31e6SBf57KmU1i/nLoxBQhY+cYkBrxj7S9XVS51Lwuk5AK0GjpY9t/Te7Xi52jeQ+LpITsfmyAMZTifgbeAuZV/apVBO0ceD/O+3oCZ7gvTzHksj9z2MrWcUqZMpm9R9mtPVnJkj/93nPgG3l85eH8Hb1x5S4nVVfb2ZAAlMtrhXh388XCgoV/L4t4ChzlLc1JyGrA2aNK5KT1AWd6YHuC5zxEDytaAnwvoU0ZJOc8ly5V+nul6xeeCL8AG4wAmeZjRztWYb15WDvjDFRzfE8tnoIFcPq8z0rVzxuUpxXkcKNEC+gDiaSK6x7wH4sSrE/jeYuwX3vIckgLu9NyXAPdUnclZ4p96vnXfnLJZkqMnfC5ACepl952pHnAA6dUq0khhCqCNgfE0027lkw/gbyWyL6xjJ9+4DCmx+uhrAdl7gNxruWukOiNX1fVz769CU0C4JH4L0gTguwC8n24N3hXRIpzGXGJJcK4x0fJcy9XZFDdnZvkTg6IGSC3QPQWUxwDuFJY6Bk6se3AgBoR68xbw154nB+Cc80yXSvZgETttwlhwyeB06QCr5zntXacMCyV40vfHAJu3132Qt7+W6qBD008A5D15mWT+ec+W0pnVVQkqRbf03EH8WpMOKz23L0lNMF663cbE90svW5Zc8iD1Xq+1kqPlP1Une0qdnqoHfgm4zbnWWg2xypVaeZF9wS2e61J5J62Y3oI0AXjl4P1UxO0Dry3cBVbKTzKpl8RURrjet3U8pvJxa9Z3OO88huFW3h4IiMckNslIgfbY8RiY1/anPlcsLdZRa2yfdt0UdZVz5ZyVgdh7zAVY57x7S81BY0CH/XaUHi+Hd/FXi/wsRlUr2zky9TtMBbrm6s4FbSB1/VQ2fmqadY9z5BxPINcAllPKcK5c6x1pcu6EJRdop9R4eFunOiLPa+Cy+18rbarkfO+xU4jU+DQPyvYSqyUpOb7pvKvyqsH7PeVc4F5jPgLhmm9z7VgOoAfOU0G5jCG4njurcwcX+cwaoKclbw60pxiwWufx+/LvwMvFRVMR0SRnkOF5xAzkwjQbvF9zoJnybBy8Ti1Lqv5NqVOxdxxj5Ok+GpCPp+l6zeFvnE2LySUT5ltO0nPLkrMCJvs+2W6pzcdID3l97N5TnjP2rXKBeU6dvzZDmjvhl22Bn5uakKaukflrZYnJtRj42LFr9j2XyrXIsUsn23eXN/AO4LWD9wKjgBC3EgLsfjtUj4mx7SFQ99sc0FuBijRgn2Li/e+0hnhOp93CQXbC/PxUB5wDIHNEA80xdZ6YKlKuPr/Mk99TK9elMuXdTJlUXXOQSa8YpOtBrM5MMcCbWpdy2LpzJlHAoPbg0PR1ZQpbF0vPlXPBRuy9ymOWx47c+jjVOJ0kpYtvXXuJB51UGTU518gwV2ffKsOl/VCqXdM4wMsW1vP56JrYeDNcl1eWHMl9BzEgfI5dUkrkNVMmErl9aP7YnDdOp8p5N3kD7wBeO3i/g3DQ7vense20TeoxBMg5aOfb/LimViMDG/lyhGA0V6wBOgZQrA5C934QZ35zwEuOxAYy6Q3Hg6nwvt6rQZ01cOYMmvdadr+G3KIzPoc5PAeUT9VHzZVcIC2BOTAsp7dBvRu26z4tXsapq1vXlHuyh+dIbvu61I4mV9LfMqwP2vu9N3icmk/OBMQag64xcXopQuSWeaTvkd9nnkswTAHu8vx7Sa3RA2/g/XWD91MxBte3FGLcgXzgLgE6B+Uy8iiB91qcN2bwQ0Ye0Jd/LcmZycdAun5cXybV8tDOiZVPkxymaQozl1I9ymUAz5E8nczzJlR2HvFl6deoK3vuPS5ZWp6afglIy5Epqkv+/JedNKVXbS4jD3JVN/R7355QyPFalTKWnwKOp5Y7VSat7LFyO3GudBxA79xipi8F8Q2c2QanqpPllOWSlfAxyZSOFcBXxlMG9/56Lc0ey+X2PWQuNfHfmHcAdwDvRVE4AH8ZwC+eTqcfLYri1wH4BoAvAfwVAP/a6XSqi6KoAPxJAL8JwLcA/M7T6fR3bl2+WwgB6yZoNiF7ngLuMk1j5SWAl51nTM4BjHKQmwLs+Xmp+58rKVB9KTA/10iSzrGAyqC33yjHnPkdeO0CbHdz/JhMp2Na+V5qifQlWOecb5sLwHLO1fK30qZI7BvmDMhTzuHnpe79EsL7xnsb4uWC4OHY+S47c+6XK+e655wK8OW9cuy1brk6Ejs/ZyXumn1WajJp9+khIOfpqfJpfR2NTXJ18d4rcycUMuENvOM+zPu/A+B/B/BFt//HAPzx0+n0jaIo/nMA/waA/6z7/eXT6fTri6L4Xd15vzOWcXECXJMKhn6ZTGH2Y53OMMAPwB4gxjwO3CULr6nT8LxzRAYUAeJMFPeWQeeW7Ji/fwrM88467PjkoHFtIz1eLrk9lOn+C1HWO491xMO2bSSZB+Djy6u8PJZcCjY1uQc7nwLr2jZvZ2EIdQqz3ozyDfOxQf41VnO4uo4NOJpAtceqA9NW1NKTc0tSoCpnlVA7N3eVaSqJwI+nQKYEp/xba3VKpqcM5lOMt1WumOjAMT71Sb+z+xp55gL12HlTV5euMVG99mT3kvxi3+we/TOXk5bw5uf9tmilKIofBPAvAfijAP7doigKAL8FwO/uTvlpAH8YHrz/WLcNAH8WwE8WRVGcTqfRtyO5t9pMTKjDbILOdszAD3+eQR867fBczrhr6jMW+26xGXmGeWMQLgd6PuDzWflUDx8cZGjM8707CH7/ezMLcXAcGoOljTwvZ0cvZZGu4XEjVZZbfKNcNauYx6GU4XQMaMWY/pjQu4tH+CQgPrTxVNsN22l+Oz93VS0XqKcmDzmG8znqAFPbQW6dsUA6v0YD7xag5/lo9+b3/xzl2mXLIYLkGBQem2ajNaXe3EK1TLtnripYTtlf2gam0tRmXrZIF0tRFP8egP8IwPedTqdvdtj4TwD4bQA2AH7v6XT6q7E8bk01/icA/gCAx27/SwAfT6cT1ZBfAPAD3fYPAPh7AHA6nZqiKL7qzv/mjcuYJVYApjEw52otQxoBb6kus8EyYNzlvtSbt/TgR6AhI6x9bLDTVDFs9QyK0BZ6ocld1hvKc30gKWXqJOPakuqIc3R8Lf3dGEMfy5Pfl593jWc757ycfG6pWpOrHpUC2zn6xymAHjueW5eAPNb8fHsXvqIVlulaYCV2bARcWuWcRkvTV23dGVVLej1LRdYO+2ydhNE8jfHrrYkBnUNyjp1FbHXIut/UdO24de+cZwGm23Wk1AdzJoCpe2j3ypVcpj93AiLPzSm3zO/essffGSe+YrWZoih+CMA/D+DvsuR/EcBv6P5+Mzyh/Ztj+dwMvBdF8aMAful0Ov2Voij+mSvm++MAfhwAfuiHrpVrXLihap8mOpGBlW7Y/7zr9akj4wuMwXDVhWCfo0bd/RIb7ycHfn+JjdrpA0Drxp/Wmp3nAMQYoNdAeko3W7uv3L6H5LKbt5JrAvfciZIFymR5Ymk57806Z694D7hUh/cS9m4KgNHKMcXY+dpuClMydTCeojZ1i0nWVIZdbSNOISOc0h6qPGAmyyMlBjA1NtwCsxpw59f9jEobAAAgAElEQVRoeaVWfLTyaZKjg28B7Vg5pzyvvJdVNl7GvBXlvIlqPmkVrl7F7qOVR5OphEbuqlSqPLkTGy73ZuJH93v9Ou9/HJ7U/nMs7ccA/MlO0+QvFUXxoSiKX3M6nf4/K5NbMu//FIB/pSiK3wZgAa/z/icAfCiKouzY9x8E8Ivd+b8I4IcA/EJRFCWA9/CGq4GcTqevA/g6APwTv6kwVWruLUOnM+6guGpMinkPf73qTOhe0vYDD8T1HnMafIxdDzu0EKDTpEQ7T6ZL3XmrPDmSAzanL4PmG9amO928Xmaq/m4Oq8rPy11GtdKsc1LsM5DnXSM2gMu6reUTu7+81hILSGtAYqpaTM6zxMqvpae+XR7zPt1bi3Y9P4eXJQc48jLHVP2mqDDkMqo59zpHcidpFki9xMMMMJ0EGZ+fb+J7b8IlV26p/he/7/Xex+c+vt1DCqn1/orBe1EUPwbvvOV/8ZoyvfSaJ52QVsr9wfvpdPoJAD8BAB3z/u+fTqd/tSiK/xrAb4f3OPN7MMw+fqbb//nu+F+I6bsDwKkoVFb8VsKXPkk4OJceZTzAtoC5/5XAfYNVFMhTnpzVqNvKl6VxaJsOHDTj91KWrAEr285NAe/h/h6Vem2KFabz+7IkwbDWmcVBiudKwuv0yYvFyOTpyU4fMK/t1i+tryivIRAQAqj8NhUDz7mgnY7nAmHtHHm/sEzn+/uOAXdNqL6VCI1YtW9HedO5Pn34hiGQvQ7Lzd8bL4Plfz5XB/aSQd66NhdgTwUpU0C8dr9bybmkBe9TLxGt3U9ZZcqduKby0e47pU+aKpfq3N/C0cHUvjxG3PBrUxNdeZ/PAbxv8X+FCS9vsPq9RVH8Zbb/9Y5UBgAURfGzAH61ct0fAvAfwKvMXCwv4ef9DwL4RlEUfwTAXwPwU136TwH4U0VR/E0A3wbwu16gbKZQYCZgvBwopUTb/RsUaKzfOfY+/y6V9qXQFIHUcfgSJS0TN86hrVhn2Om9cz33Ib+4/+Mx4LaZd3t/nMbzp7z5r9xOybmdnLw21mFxT0EO09xmxZ/Fik0Zv9ZOn2ZsdO2OOZd1l+dOYRSl5DzDSxtckXwOAyGXc8tzS/3Xc3R8c5nI1HXy2ktlikejqe1E7ueooqXLmz9xzZ2E50zSpwL5S+Ua/Spwu77Vj+/jFShuMK6tktG51phOZY6tvsmyX0JOXUvKz49m/+bpdPph6+DpdPqtWnpRFP8ogF8HgFj3HwTwV4ui+BEMmickXCtFlbuA99Pp9HMAfq7b/lsAfkQ5Zwfgd9yjPNcWjZ0Y4GoZ/SXmXe5L5p2z+NJtZIsSbTuw7q3CupMMjPscruxUWFwrGv0wPUgxt0P6NHBPecVY+FyZ0qlYg8K5fqBv1aFdyspbaTn5X0umuKxLAf9UnrF8csRiGOU9NRbxmozjJc9AklMXzlFJSV2XKkNK4gA9T31n6vH49vlt5BxXpMB5geVy75krt5qED17F4quhtGIVlmkaVLk1qXPO6s01SIRUu7KOc+JJ61dvPe5eIo389q/U28zpdPrrAL6f9oui+DsAfrjzNvMzAP6toii+AW+o+lVM3x14Geb91Qln3aWEHVkIzLkaTQhhXQ/GOSi3f8e676QuQ6oyTePQNh1oyALvgCubXpXGla3/Y0BegnjXPVuFffcE/niFfTeZ8Ns5Qg1fduJT3U5SHlxSjG1sILAGu6mDYOz8qZ1eHIRPY4tIbslGn8/oXsJWNcHvOcLrRWxNJFUXUsc/Z7d9wOezUhETjWm25By1hnNAbxpYnW8knLNKMMVAMiW5KmipiacF+nOuzT2Wktx+5VxgHJ5zff/o/FvK9zBeJR5Piqxynav+9lmsIH52ZPzF8t/Du4n8m/CuIn9f6oI38J6QUF0m/boGeE7+0dv+16EN1GJ4QyRIH+Yx/Fad35nAFaXbonZzNBVbkmQuIgnEc8AOQPe8wKYWnC0fgLove6lu13BoUKHu8xi25X6eGs0lYnX0OUzuVPD+kkDsJUHWOQNdirXNY4xvq+rwWmXqhHQK6LLyv9ZE17qnvG8uI5yzOpLK+9qqGiTXnojn9gG3BFxT8o4z1lNJDXJRfD6MmTIRv7SO5I4VOd90iurPJWA+9763lNF7fsUGq1xOp9M/yLZPAH7/lOvfwPtEuWanngOAuP58LI/e4M21g657qTRaRf89JrzD4ew6bQ+Mpy8tAXXaHjzMVFngnT/TpZLzrc7xQnJuGc5hbs5Z6pXXpQbJ85nydA96iYrPlAHms2CDDLkVEMyVW6xEpY5dKp+LEeE1nvFcNbFz3ae+9MrOa1i5IblW/3/vNn7pKgDweUz8zrr/yxusfhbyBt4TUrZtz74PM/2QMac071mCzuVKMoOBqhR//TzYH1j70NsE10mPSvKwbqw6ZuJ1PXb7ON/P04GX5eC/OZIzoOUu3U7RPU2V4RZyLsN1/v3O99HNy6BNMnRvP7p+sp3X/XRNY3KZus/tB8bUd4ypun0Oy+jnTCDOCUyUc79z5RpEQu6E5KUniym5d/ly64g9NtRqPvz6nFWAKc89ZRVmilH2NVY/7y0qeH8988ObyRt4nyD60jG3nh9b2ksf73yf67LzX6n3vsUyMFIddOW7/ISxquYmkoTruAe/LgTX5PrRUqGh4z6g1LCduhawJwv895pyzmDBOzXZwb00s8XlFm7Kzs17ioHq9LLc9p2fM8DnTPxi11j3PfdZp6yw5LqR4+feejUnZ/Jl3We6kWFcP/yWgOVaK4I+r9u0/9w6OHXydM+JUwy4puqI60w7+bn6eXmrn7FypWTqc5zbjmPPIq+7p6zwKUz4LlGbuVTewHtCpLFqTuciB2vSXu/zZPvaYC4NXXmejXEdEDdU7e/dOJRli7ZxcOxXlluTPaoRS7eHjwhL24Me/Pha6a7KwSUDNuVISo/0EpWMKbqFUySXJbY7188DtOSscsi6PiXQUW7kxZxAR7nPEMvbKktMpzqmN5ujW36uTGXp5H6Msfettw32eR5Tv4G/xg7QxMtm9RdSFY+Oja9Lr/DI7WtK3hgSP+ccAuHceAexep5y/TilTee07alymdeZvH4zd+I6ZSUzDcrtKLH8eHhMbxN6e5iP3kWqfd5OinHSG3h/3eC9OJ3gmhsviXf5t2UXeIUFaqLK3YKnNf3gRQ1jjjlq1Nh3WxX2PbO+wlYEadqCWHXOwnOmnVxH9m4inUPrHFClA8nk6EbHBkf6lQO3f94BjJM3iJRfWq1MslwpSS31TWNc8piUS5cXc9gTcu1FnS2vU+OJkJ7G88p1ozflWXJVlC4B8LHz5D3lfaeWX6blgPLcsp4bIdYCcul6n27H8rh2LNWGc9pXTHIm1znsZ+5zpvKzypArqdWZnHoytV1o55BoAcBS5eZjm9xuEQYhi4lXJS2D/qvt8rPeg7+OP0NttgFLYsx/ruqkzOdcbznnTGS1/SntVY7V9thwm3HhmvIJ3woT3nTeAbxy8P6uBtZ/93j7G5UA0N2n7GqNG46dSqBxQFsCbfkObel6kN97hsFY7YVvW/7bR0C9b4phBx8DMyTagGDlXYsyyvJtsFSjyLatQ72bo2kc6l2FY+OAXQU0BbCDnzFrfxDbwHh2LWtrydK07dHfCSgbvFvUnWtM7yrTlS3mVR10fPQm4l5ydDaDjmnCO8u58M7D4+6usMEce6ywRYUaS2xQdekPeMIKW6ywwRIbPOIJq087LL4C8Kn749v8b8e2990+/bbdO9+Lb6JJKX4de88OwEJs02/VpVeRfUqz9rt8Twu73QE2mCEZXLiO2xWv75b7Vt8OhojIWyyxR9W3DUqX+/yXXL5Sm2mbEm3jfLuhlbSGVfym8PW4/w7dBypbvCN3r6xeu7LF3HnPUNwrFJXAkwta3OdQmc+hDdLoOulxylcFHRxwsUC4bE/8Hny/f5Z9jfnuiLIFCqrLVH+1/db4bTDUf9oGxn2SfBRetXibkO2Btivxy+t+pK1QXa8Xvp7vHSd09DrMx4VcJlsDstqEgR+T6fK+smx0ruu3w2jEWpn4BCImOglz2YT2nONye0g7nzKOEVr2eJNfhs/VgHUuo7C86bwDeOXgHcB9noDfw4VpFnDnIFwC4aHTHcD5SI+9Pz/cj3WG/jc9+7c6Yzkp4DJ0nv6XVGWIVXFo/PqCm2O+3vtnW9SdDv5mDEqa0gMRCRKngnf6JXAOeECTAWZyQLoED2Oj3hCoDEWzO1N+PgdQBEoIuHMg74H6lm1vPIBvN1g+HzDTgDoBdALnEsxwwMKBC3//OZ1krA3GJlQSuK+VNAHqLRAz1N088K61G2vSLGMshHEXxmpx3tg8rFsk/C60Ale5Gu2ate3OhoXU4HJtWChOA4DAPoXXswF4c2DeQIJ2mcZBPgfyvE3QIKuxf/xXE6vtEFNKeRKQ22Dl32PVoqxE+2xbuKaFa45wvitAIet7099gTCDwYsb6I1/Y8TYfIzQA323T2EF1mY8b43FgPIbkjAmpCezwnu0J7nBO3ooCP9eaAOTklypbSmKTQq2vH+9PP0e7Hy+L3M6Ra3y78PzLbZPO/SbnyOh9vem8A3jt4P0d/KB+a2FviTpcIM608054zFBzIDCOoJoC+FqHrXWOJKllwqnMiRXdlQeLOuzmA0gHBqCuse3a7xnfBiXpxs2AcoYjgGPZrbBJprj/ZYAf8CM9gHcjw96uMxbpJFPccsoJwIhNVNjPJbb9PoH3CjVWboP5+xqr95uAnafzKY1Y/Dn2PVs5IxBPvxzISPaRi9ZriEmtycxTGtvnE2AgbFNAjOmzJ665gwtn+/ggMQcxfq77Nt49qv8GFVqm3qa3e3tFrY60Y8Db2bSuBKopz2EzgpIFzwEndC09i39Prnf5usFSZR3PXWrXmUtLr9deBSvhJzDOtXCVDqrojavvp2X371QmXZNe3W3Ld2x7+I7AeIII2Ks+dH5OP2+x67krT315I8f5dyRVGSCcbNH9uJ2TlqeWdolXICkx9S2tDfBj5wB5mR9Po20NvGv74XOn3104CdJVqKj9ateMr7XPu6VThBw5Sp33N/AO4LWD93stn9A9nGdwZgBQArM9cCqPAI5o3KEH8wACQB92xFpafMlTplMaENd91B8lvtxpAQ0bpMxRuwqtc9hXA2DRmCLpHYd7xiFmHoBn5y2Rx6RKgX8p498d2w7UdOiaWZfuf4/dOcfu/IOVL/+FsS9FsnVJdR8Maiij7bEqUEXbLvQQ1G9XFrCxdR01iQ1IFogbs7Fx/U2eB79eOy+nzLcU6TrOr0blRRy+pqTsOvy2DbJj9UAH2bIe5I2s8ltZ31Zb6cplUnlvOuQxsPMAeoYeYCw9iXwUU23myCajHuzzMUGOB1I4AAv6yr4PHa/A8kkg4J0CUF6x8YHfT753S7RvmqsORaK1hVQeOWL1Q3l1JEyz1LT4udW+Tq/qcOLDGie0x8wltDXklpum3SNn9fSF5FFozbzpvHt53eC9QH5lv5YIthDQGXirk42xcJJZjzF02tKo389f/uRAPjWRGHT3x4NLjF2MsfO9Gg1g6/de8I1QwneYsVqewhlTZ/ix87Ul9gC4n0YqP9ViP9LJlxFtpV4yDTo8yq2mp6wNXoAOimMyRRczBfRigC5n3y7HmCnURLYfrW3lepQ5x6gwp4xThOcbvlseWyL9TnPBWyqf1LnWZG4KO2qpwAU9GrHz6FTqOjWbtgTIwVjZAigFmDc+y4kWGCeOCTFSJJauA/wQvMtxgkuM4LFEuyZnpVe71rr/OeXiMu7D7HojbY74diyCeFXtPQGi9qd6nQNsFj7necI00We2ov0yJx5y1cixS+VicaF1958Dw63BgJfjaD4bed3g/YWlbEMiuG80ov9pRy1g33Vw836bD/hkzEPLlHLZNOaNIOVCLOXhwOat8sG8yhR1qgBtNV5NANBHhU25u4wdl7rBLZsIqKx+sM3Ue/o0DC0kpfua08nFjD8bwPdS/obU5dIz1Zhj3qmIkaEXuemkQVsCoBYlQv/6XvUhBn749bmgvGXXjVlSm+miQTHFdMVrZTuUl7GpQJ66gyVcDWJIU8DLyJVsfAUsFwClrpkiU3Vcp5TxlkvqUyZ24XZjnkd5UM1xcKgdfJ9dTWOhUyA17Ft1FTBeRqrXDRzmnYcVfq62+hrTb09NEKfKlFWtSwHqrWVKm9piCQB4zjj30ndsSfTdO/EOOSapLNXNWN/+OSD2Qb49+7kw4U1tBsAbeD9bimZgW/xsVgAF1oAq1GjQ9t3zoI3WBDBkzoC8xpzwzlwD8HJbCj+WMlaV4NwC66SPTzrBlI9DG+Trjw2TERo6e08DrnMp5toeyGtCvumD5zJAewDYObPP1WtGajSwj+X8wtgnsdj3frsAyk5nvwR25TpUkykBLA5A2WK2qHtj3PmittVkxLYFin1R4uyQBphSy9McpGurBiq7ZaTLbQ7ka4eeTQUG+wQAqktZDYxbIkF6kM+EbpTeGW+LKVDEdY1zJQSGY7AZY0JzjAq1/VsBF02uwfq/BGgksfrpYRI9zT3iNd99zmTP2j7HIDXHE84lE9hLJ6k5qxax8t2zXZC8tJ76NaXGz4cJb+AdwGsH74X3PnEv4Uui/vddYJxk6bfnqJxYDLZPG6vPALpuo0+Pf1atI5X52EZWYbpkj+iXrxr487dZ9+syUMuqHQfQs/kkueoKo2cV7D+fFHDd/ODeYuIQ1dfv5B0HlZ2eOoDAI46MeGst76aAeprJHtIAXaeZS4wNley9NOQinVdLv9SaVFjMO78nZ9/78iRiQMSOS2BP+caY9iEtrSIQN9S7zWDPv93nxq5J0dhyma6x7SmDU67bTjJSF7jk1QjnBsDYGNv/Duo0gFRdtNLHaX5/3FfT/vBI6TqVC5xTYD0F8mNjVU7byRXZh1mrM5pNzfhXPz+VNz//XDlnkpl7zUtOYHPlv5BrHm867wBeOXg/vQP29/A2wySn883RcT9Hp9FSbfH3H3eco7JndIS5OoyA7OwcwFziTQEs57AEVidvDSaxSUN/zHXv03XXVvoEKZgUsFWCnAi3UriXGvJOY6mttCix79PnvecPuiZmgKoPULZ3hmiZI4PWFLWZnH1NtUY+bzDQOvZc7rKB81L2NgWQLx3UX1Jegk3MEqdsZ44R12QrUyA6RizEiBTtuJWftX/OM3A5V8/dSrunStY17HjC/K7XhqcSJvoqqDKhNSYk2jlWvrllfZP7yasG7ziFBhi3lLbk22HHzBkTNXARBg8sMtDLOBzK2F2kBfgtrzQ5ojVWi22wgBOdSyCLHwtBZLwTGTMk9kdNDXq57tYoTXuX2jXhfsfCeqTo79e6pEqP5k6yP6YAd/kOc4Cx/AbWteH7z1OZiYl1jX9n427mWsD2GoNIDkNn1928CIWxOm9NorSyXUvO+8b3ZevPndxMfbZrvuMUcM4B7ynnAlreqZXXqQDeKrdV9luW/9zJx1QwrF0XA7I5DPu5dSv3XjqAt/vznBUF65qXklF9fgvSBOCVg/fiNLaYvoU0zk8S2nJYgpXL6i0DdzGQvsWK/U6Lxih9xo+AZYbRZ++33FDL0PSSwYweaYrBS0XXyBI7jIPCpBhZQO9EUoMgz4WOa+BbRqzVwHtOEBRpINaSOoUTA1E1NLGUygn9yndxLRbbAvjh/fPdLuYwZrkMpHZeLmNp7U8Re8BOTzxj50yZuF663B4bZLVjU0BMbh655dFEZ2rz1Sou1bfW7pdTRi5+au/U/VwmPWZ8mspDPlMue20911QQn9vec84dlyX+LCmAy+vjmAiJrejF03g+vByfKxj+rpDPW+vvLvKqwfvRAZv12CvELSSm2x4LyMTDp2v7OcB9i2V/H34Pyw1jX+ZOP5t0qkm0qIy8wyHwvu2eMuo2K2FwSH8x4A7oDGb2t1E6+9igLM8vMehQN3BYYpO8xkrT7q2JBcxymJ7UoKABcDm4Ojhw97nXHlBywYCWlrssrwGlS0QDIGHomVByV7muoRamSQpMx1fXYisG01YVZFlsxnOcnlMfpjC6KXBrnSfve8lE8CVF1snc1ZLP3QYiV6454Y0dpxXFc97brdTlcvu/c/rJl5xwHCEw3pvBKoBXDt7vJTJinhQH74KvhA+LTmkOrgOtpFTTsGMD/N5j3jPTkm3nEwFLN751Xn2jqZw6GFkgENAYCI3xzWd4SeQgQlEaed7DffPZ3pTksjbXBJGxPC2pcc6y7hwObQ+6bYZ43h/Xzo3f43KZwpzlsOdTv4OVzxQ5txyp58nN9xzJVYWz7B9SfYHM7zwAH9a7azPDJTrvVayvJe9WuXmmjk2VVF5x4+Vp9TwHZE2d8E5dtYmt2KT6IZlXbnk+Z7lXea+tzx/m/XLvvJTP9WawCuANvE+Ssm0DAO/Aw0VrjBRFlJujRYuq1wsfuHv5Syz1HHUP2MnnrwfsW9SYYymuAnSGSn0Oo3ONsWty0JZ5kCvIFEBPDfTXWWrPW1r3x6Yv415Dp5RLzuRKnhdj2bVlYg3oy/yt8qQkdwUiBnTP/WapMsRkKqi6VflT5bCOx77TJXWKb+caNecAvFzJqZNxgHgesLSuP0dy68o5aihT9cOntA39fcT7Dr3e5K/Q3Mvm46UA6Dn16VJj3an94ee80rTF/xkmvOm8A3gD72cLLdkPagn+VXr2veyBvWSx6NqBefcsEV+crzFHxYA/Oh/qFWoMzL4vgXQhRsdSZdf2p+ruWsengvYcNoYL79hc//7jg9Yl+qbaebIcOcxgSp1gqn411UFZv1LXaPfk942VNSaxAeCa/pNTk9Nre6k4F1znHic5l4HPMbjLr2PpupI7SeTX3EJu4Y1kehnyVgRifRE/nqv+k8onp4y5MmWSOKSH311v/5Q27/OpR2ddVr5YmYDpdh7XMFJNHffvJf0mpva3/hrbc9znCuALnMKEN7UZAK8cvL87AOtfOj+KYr4c0dfr8oBTOfjurRczNM6hRt3rowNeTYSAPOAbDddx3zDDVa4HT3rv9Cs90HBPNKRSw3XeAYwijUqROu8AsvyKD/rw42PcuBUYOqiU+o0/N0/fPd0pxjvn3OXcKcxdTrmkyEmBnICljGilSlULhw2WfR3j6TKYlmWoy8vit3V3mNwrjlZXQmsOX3tX2PS/K2yw7Gv9BktssOr2o9ufdqj2QPEJwCcAO/iq9gm+I6f9Bp6Vadg2MO7s6fEc2y+7fb5NAbIq9rtgxyrgUAH1wsd82DvduFx6npIuZC9xAesf47K2oeVxKUuduzwv2+EU1T0tYFfvXrSLuDvfHVG2nT/3Bno9ocdolX0S7VF5PZJ1yKgn+2o+6uP5OLAR2zKNOzzQvJbxdt+Kt5P+FjpJY30TzZZpLsYMh+kxHbS6oNWXSyUf+E+75yWT1lz1Qgm4z5kUaufw81LluoccMBsnvoH31w3ecYQfvG8tfIBvhyD2feAN1gVxEL4VcOUJj9h0v9sOnvi0ZZc2nLPBEtv9CpvnFQ7PS2A38/GZdxh+dxiAS4PxQIRum3/l0vhbKL/93wnvHjaYL/aoFjXmFQGyYfhYYdv/co80LVyf7m8/dOZyYiDZfPtz5HkVsFcD9MGIrrEGEAIH2r20cgEIgsaQypX0kCNjA2hehvhgbU32tlgBGMJ584kfTRb7Qb6t0DYO+90cbVPisJv7GSlFoKVHiQGWxQkoG1C012qxhyvbvn5w0D5HjUc8BcCc7z/iCY+ohKvUcgAe6xJtucG6EZP1RpTJv1i7zFw04A6EAIxv818DkPmJ1CoA6Pw78DRtUmWBrUtYsSnXTmH/rzGYx8CiBdS5hyvNYL7vW1yLudujqjoj+naP+e4A1wAzDuB3CEE7feeG/cYfQhe6bud/ZgDK9gjX7FAuWh9NGlvsMUeFGkts+johHRtoHsc2WPX9hTbp33frudZq4vgxbJWWS2yjxufpnlpyVnZ4ma4l1zZyvVQsoC37Bkm6aKSMnMTlxo6RKrm5qzy3kGd8I0x403kH8NrBe4HsABwXCxu8B+b9Xed9ZqjwTdeJUuWn7X03SIcNhP4y2DULTEmgbm2XSjqXHTwg2WFcK5oCR6ywaxzqXYX5Yo/2wWHuSDPfDyhz7LHFSvBA/vcjuGeaYfDlnTkQquNISel3y3y0AD+SHRryHjNFWrdGbJ5rjnAetwJgURo1xq6XwwAMMNQjAn9y4Ka36zpQTuIH5XlfdwaeuwomhZyxo8ngfjdHvatwfF4BuyJvEsjrTMl+ywIoZ0A5w2GxwCGY8B3wblFj9bDBcu0ndI946sH8I55GIP4BTz3bTlPeAPBXW6y+32/3k0Zi5Dnzvhflj421HLD3zwUVvPPvRUHaUsHVaDu8ZdurwJFBJa9l/pWPB0tNeJ9hsXDE0PG8c12syjR+jVZOqzyyXHHj2rDNht6uwr6D9ync0xWRBwHIdy3m65hXrDGUofIA4UQ8eCY2Kadn3rP2yesBn8CdE/vDih0iCYAYAOPfK7YiYzHgkmSJMejTQP14JVarHymZusqkETBpW6O8ldop5eZitWV+nJ9D95x3f3ScolrHVljlxC5WX7Ty3Vr+D5r9krzpvAN47eD9He4K3rm6TFv6AVwbeHkDn6NGA9cbqzZweETI4g7Qq+75+g1W2FdP2FYrbD6sUO/m2O8qz5DuqgF4EUih+i2Zd+U5Rr8qE+9Z1XeLGq5sA1aVM1waAzYMiLre49ARkK7jXB0sx0XXO0VrMNhjPmJ4xsu38Wieo/3/v71vD5rlqO77nTt7d+/3XWSIIiKDhCMoS3YUFQ+XRIgJtmQwBTaFSFXsoAIbCBXKFA+TxLFF7D/ipAhKoFzBFQqjwgRcyDxKgaDCDiATkTgxGIFCMBI4KBgLPbCkOIErvnt3787t/DHTO2d6+jnPXfb8qr5vZ3p6us/M9OPXp0+fztaF6cii2eXTcV0AACAASURBVJFx2WM7+vpAblbrnHnHrQeGObIakTHfBzdVOcIhHoVTVee/WGC1mGP56AVWF843JlerM3Os1xny9Qz5OsO5dVYUdKDQxDc+RPl9ZjmOlaZXvHyY1MM0m9HHpmnMeTi10UCa5jMLLDfHcyyxWK4wP3MOx7+LSnvKSbvP/IH3OzN2PUM10J2h0UFQeW+2Plfb56Eq1/Xv6PIOZWq/bGQrxVe3qyO1EQBbelqKHHoqPg/e5yPpqbb7sWTRVndtJLxO8LlpTW6ch9bW6PdRuAR0eRvz2aX7UH3pbPOr+wl73KqkaNn0sxdlbWUtS1wm37dJWWvjIumhvTzM6wBqjhxsJpQp5D30TV1adjPt5nl4QGC7L+W6rdz4SLzr27oIue96yiC8L+9YsThuqtnF5h3AjpN3dQw4e3KcvDYmMrPKtrXqiOvT46aWndu9mzDpW4b1hvCzSMhOFiRpOVtjNctx7kRWkPg1VVpzII28Nwh8nbBnszUW5fE8a3aI5gZNtg7TpjWzaWT0u+C/9fcUtmc3yb9talbD1lAWWu4M3N2nzwbTq4UJtG8uLQr/7sXzVYOiQxxZpkG5uY37mnN6NJshPxm2izQRb+7gJlm6xvBwU7M6Q76R9TQOURCpA2SLHLNFjuzRLN9yVgTAZjM13w7MvE4Xv9lmHwf+XVxaTZvGs7EeBeE1DPqd+7RgJvowbzHTtuU1pLvU6h635rNe9w6sdTqs+XWbadhm/FLtrM1vqMPMssLLkk8Tn6MyiXEN5usDQ/uGfebeH7ltIM6g9wNx7QPSZeBkhttmXl3fz/VNfEhdIG8jyeY9MfFt+Zjx2yI8MGi3/iR2wKF/3TtfDINjMEwlhbwD2HHyDqo64CHBO3cATg0Mh6ltAJpTXTzujMU9wiHmWG4a6sPSPSTXnOrGWdss68WqNa2pDWXDfKzWQK8x0w21pbH2Nbwu0mVqWEztl48U8/cSA1e8eiOclfHmtXtsxDs0ZRuaJo2FrZMwSZvLZMFHFEKkkJdBW4NfDF+y6Aafd7C+wZmWR8fXOkegWOC9KMu89qK0KDWJWp4MOfR+ATa9NTJgphfULsLaOltnvGbvx/cuzW/A36eOr99tUScycO9Q2v2rTkfnx2Ux2wqTJLQtdy7E1re+7mumE7dQ3EXozLoaG8/Mu4vZgzaDaobzNHR/oOWoZmx5WS9M55absjJH5So4ZCazzrJiYL7wlyXfbKZ+nhSTGFNBY56bm/eFBlom1oF2ScMWZ4a8VodCWnoTYy/Y7IPw940pZLJ6mxGb990m7+eOEVYnxnsEm1YuN8iRD7ox0030ojx2xTUbPW0DfYAjrLIF8izDwaKuaQGw+bWmy0g7AK/3EF+j6yPntqlT3ztxwXw3rrhmvIqo20wE2k9/xmoo2iK2DGXlE4bciaU2tG3NHUyY78FGvIrjJmHixHWFRTTBsuUbCxexMeu6vmYL9w2odFpmXDMPlzxDd5h9ke+h0KXetTWjaIPQYNw3oLbNjJlxbeUGaNapYnCYXo5iB0hZ7bj+fm0zirwvWDHlCb/fNpPpqvtdETsT5QrzhY9pUtK2X42p79vUJqxt32t7xJsMO03ex4KpaTcbUhPFVH+1IIjbNGrt9CZtSxq8YdOEfVkSGb0Lq9ZKbhr3LCt2WgWwXoTtGnU+VX5um0YAwSnO2vsKNGCVfol3DPXdQ7l8Y6PPjiIGqY1wkxyHZwJi0rFNh9ruiyGTNjIa0iSHFkXZ8m3TWbbVoPme23fNJ6Nr5qMtUqfOfQMtV3qxg7YUpBDwkGbcNViMSSskSwi8HMTMrIUGhX3ZKacMALsMvn3E24wTk55PLht8bYzNBCb0Ll3fR/dgtjzNvFxx2sKlmCqurTfyapjvUsuyTQTdBxUduF8Q8h4AJ+6xI3bTkwz/1dfWxnV9zr0O8AWMZphvCp/75TaxQl3bDmjCrhsiTtA5ydaLSjNrnGpTqTB4w5dhvZkKNRuVtg1eaqOUQtb7NlUIIU4bNLYVoh+u9x8iUraB4BQzIF0wtJa8D6TKOOYzxQzGzDhdtJ1DmULE1NsY22rzPHYhM68DfCayDdyDgma7swvlH2ivmTZnHprX+1f8pPQ5fbWNYyuwfJhtkSzbBCHviWg2rMUrrJNpF1GvL2x1bbrR9PHb3PDFtTCJL0rS5jPaREYjdWGSae+eIW8027pTcTVopp2kGW7e23fj4V9kF18N9CZcYyG2M4whMGPbbMZiqo4itlNMGUB0MctqE6cLUt/7GANXl7cVE+3JV/iZ+1jDAvg1vSkaX349Y9e5Dbd2CcrvNY/boksabQdWbdsq1/cNfVOfyZB5f9eZnTZIXYBrO0+d6XSFjY21bZMmwZb25pFQiFs8uq3QGm1bODe1KcLqmmlXo5jN1hsCb5J2fR2oE/c+YdPy5Jg1bJptmiF+r3bP5s+re/Ft2zj10ajFaKhtdqUxHUnsPSFZhkLX9zdlp7Jt083pM03D24j3aUKTSqLakq8UEyEbYsg7Pw4R91SC78uzLlc/My+xz2s7T/FitA0E0oXQc8YNRfuVIRa2mc96ujNsQ1vXWLAqALDj5H1MmA0T90rRByqS1iTz2oZex9Fal/LGZJjEnWvdY5GXg4vqvEnQObiXkHo6pQ9lNE1nhm60+SxAm3u75x+vJfLboba3CfaFpaAt6Ys1sYnJZ2yTpl1AeP1JOmGKqZfLlqZcM2u9r9KKLbtj2eqHYHtXqSTfdl+sTfWQbajPe4vpdSfOjGc1mLxjrY0ZQv42gyfbfaFzW5qutMfENg/epsROk3eCezOcPrHOMmdnoC299Y6JQEXEi8Wmq5qLrwPt9nHjE/5Uze/vaRzA5gtYm8wUv8YmL1kGZNVC1VBFi9HKNn/DPpFtGneAu/eaN9K1yWPKxBHS2HTVcKVot2IawxBcxDrsBs/uHz/lfn7dJosrLCRvXZ52uy+6XIvymQgexmXgMg85bR2LoTufVL/rPKxv7WlInhgZXXXYVjfNBZ0x12IWgbqeI+Vb9vXuXfeMReBTBjdx5mDpZnJ91eM+lAVtvbx0lcucSbJ76PFtchXeACtmtiokc9/4JL5jhIivSGDHyftU4LaFZgGue4OpKkaxAVDdCn5REvsVVjhEtvF1HbPxS85SKmTxexxo0zjw6yHyF0McbenY8o5FyKa02XkXefo8B9jScLkG1GmZsvDrPsSYB6S8/7Tr8Zp7n+xmx8DDXTti5uy4+bw+F6X13TGzGi1j97MBvd60aWroTaP6hN5/ojpnJC+ztwv1emEv27b7XOS5az2I0S6nkPKYMFsa5rOaMvSBNrNGvGzrc/7bJq9UpLyDmHav7WDCN4COJdRt+zseP8VrTkg2n6yx+aco2tr2Ky75xsC8YXwkuzQBO07ex7R5d3WCugN07XrHF56ukZULUyuNut5Jz1ycWl+kWh37Nufg8mm4NrXwVdp6hQ9rTwFsiFpxTzWosWuHw5umcJlc38L9rfwaL6C/xTt9aFVDDaRPK2Lv2OPiVeftbX3dHZhfI6+vNTX1zY5GI8dsM9PldU3Ht1Vt8XlSpp9j7I1zZODrnENlz5VvLLqW/1SNe6rmP4TYembOtPC9D1K/kU/WvhddhvJt875daQzRPvniuJVEwy0UNmWwnccoNXwzenYyHCbFPE+OLs8ao4gLx40z2XSVqzFJfNPiXTTvwI6T97Fg68xDxN1GxDVZ5+GnA+fmttlWd5HM4wyAzXbYLths3gHUvM0AVUPVxvRBu460xeHhxbF96+uuDURYO+XX1ozR4YQQ+w54vDpB4Xan/WorfN+rrcbIH89v6tOHliiW7KRqlE30uZlLG5IYPejw3GfLuwtZbHNv7Hu0+8QOl5ccXdwsNpcr+p7R9yx9D5C6oqvmf4yFz6F0fKRa91gZinnC1eae/hQfgjicxv1GiGjeASHvQYRW74e0JF1QLN6qiG8Ovaizvgo8R7Yh4Pk625BzTeJnDVeR8aSdmylwcs5NIkJbYvvIPs9vI98WV8y+ptHbdG4xU6qu+1O0Mi6kxI/p1Lo8j+1+Xhd1pzsVugwc09GPT4sppsRNpA44fAMMX5yYgYxNni4YcnBiz6+ftmqImczTEWn60nchpd2MUTDU4zfjxGiit4XMb0P97ge7rXknotcBeA2AHMDvKaV+uQx/I4BXluGvV0p9wpeOkHcPYho/XSHW5Sidk2tNvjkJnxubGbk0Qnolvya6hTZ7heXGSr7SvC+ycpV+Vt9d1aV1dU0Z+gi3jazzXVcX1uOmzbJv4SGXqQ1Spp99nYtP0xiTbwxMosmhve8A7XYudGmsbXFdMpj3paAfF56z2jsaw+XaGNilDnTKwU8sbDLqMp6y6L0fWdJnQEKDiCwhri/PELp+a/Ndbwth1YhRXnT12tWn0inWMUPqoDVmjVyKQnIMLzRH+EMjZHfJOxFdA+BaAE9RSi2J6K+V4ZcDeDGAvwng8QD+gIguU0o5K9KOk3capfAATVt3Hu7T7DQbbk1VZ9YGs26/Xo/ruk+f80VltnRtcGkofQ2RdgvZbUq5QKwJhFsWNwk2wZ+V51sMkupuLzXmmzCdT9pUeApCZjwurNn3n1m+p+veMYik79100bSN7bqsqxnAkLMcNvRVJsci8DGea1JIb6qWPTTwb4PYNLqW9SG/Uaq5ie2eNqR3CJtw3/V2DhPq/Z9LWdbXmgmTU4QWd9uIuiuNlHoTkrNv2Mv39s7OB/BqADcopZYAoJR6sAy/FsAHyvA/I6K7ATwdwGdcCe04eR8HrmlD7gqxfsz15XUbeFe4ueNqG5t3AF67d24+o3dVBWDdWXWJuWHDPt8cz7Hc+MnR8Valtn2BJVZYsON57Y3YtPvAMKYz/WiAww1vXyTYT97b+4SPS7/7M9gHgfHmK7Ebt7QdEPgQ0szp/Qg4YgaeXU2BhsI2zgDYiEOIgDS9QekZy8LPuElA2hIZl5wpSHGtmZr3UAQ+TmMdV8Z9ypmU9i0GKW2gT5a+tO+pz9HFJTKP61MshgYVLmWXee/QZq7NTZp2V/MO4DIAzyKiNwE4A+CXlFK3A7gIwGdZvHvLMCcGJe9E9A0Ap1DY8KyVUlcS0fkAPgjgEgDfAPCzSqn/S0QE4G0AfgrAEYCXK6XuGFK+VLgaSG0qo6GJaN3sRWtFKzOaBZbIWfjCMKnRaXNCnaFyO7nRyGdZ5RpuUVba3EKkMrvJBSfONoJt+tx2mcjwZbzcE4SNuNfk6kAkuvi4BvrfMrqLbSYQR/xsHaLPnEZf13KHZjjaNsa6TDbD7c9qvkdzW5/iethgpk/i0maA00Y7mZJvLFIGf+mkJL6ctCFcrnpoko4YUp/q1tKn0eTxYpCilQ7Vb1vcmDbBjNcVXdpM13FMOrZ7+kLb97MN67G2zSRpaHwajxghky9YvYCIPs/Ob1RK3ahPiOgPAHy/5b5fRcG5zwfwDABXAfgQET2pjRBjaN6vUUo9zM6vB/AppdQNRHR9ef4rAJ4P4NLy728BeEf5u3MIaQa5t3fb9aZWv6kl4qY0tc4qr2vgnbBcztggpK51rI+yKxLIj/NNXNu9QzV6LsI4FfRahRBMm3YNs7Pj71Vjhf7I1FAaWFcdGFL7GJOPDymdYpup+fr1YTuf1A4+pRyMobWP/fbbsMmUidiBdn2QPWd1vXldtwVm+6rjuVwC1+OlmyLG3Kfz1e/TZYJopm8frMeh3ifFPVeM1r0I675jtS2dLkhxD6qRUjdSwqbH5Jr3h5VSV7ouKqWe47pGRK8G8GGllALwOSI6B+ACAPcBeAKLenEZ5sQUrOdaAFeXx+8F8GkU5P1aAL9TPtRniegxRPQ4pdQDroQUaPTC1awQFQnnpFsf81/uXpL/Lg3TmMqsxm42s6rFS3MV2XQTmSE3zGYAYAmtlc/KM/28FbmvH1d24/zYpmkvwm0+vt2aJBdMe/WuMMm3bXBgI9S260MiraPbnsFNCtqY/2iYxCAt3ziteYoZQKxmtI+y05b4FnHCPu5jSbArzRTEEoo+Z9D66lPiyWK8aVWXGZExMVSb08e3GVKxELrWFantRsoMYFeFxFBYN97/5Jr3LviPAK4BcBsRXYaiq3oYwC0AfpeIfgPFgtVLAXzOl9DQvboC8EkiUgDeWU4tXMgI+bcAXFgeXwTgm+xebfPjJO+7CHdjG083uAlELXyWb1xFhmzedfwY6AWqxbEeqFTaoUorZPMvXmiUbE+n7YgrzVKlxR+q8TeJd1wH6zbd6NJQp5podHW/6Muza4cf2yF0JbmxhKhvhL5zl858Cp/vtrBUAjwkgelKpLpq2Yeo1/3U37jdO4cYBMaaNmm5UhYS+9LfRoyhbddo42K3ramWLX1X2Fg43tCyT65574J3A3g3EX0ZBbF4WamwvpOIPgTgLhQjk9f4PM0Aw5P3v6OUuq90h3MrEX2VX1RKqZLYR4OIXgXgVQBw8Q/Q4IXKbGCamllNBmcb7bEms9rW3bR5X9Q02UV4cwvgKj+tva5Pnfpt3oG63bvL3l0/g5bX5S6ycAu53shaP/ZtY2+e2/3Jm/IMhb60In0QrpSON1bLm+b1IX27dhfavI+i/JoEcXi719j02mqmXddiF+T60k9BjIbaJkMMSR/K5KQtptI22+qJy6TDNHmxuYTlcdoQrS4zOjZzGd+3132QLV7KuoIYYt+mHsS+i9j21Izbl+liLNpqxlPbeVv5ndJ8RoEaIbuqeVdKrQC81HHtTQDeFJvWoC2tUuq+8vdBIvoICtc3f6HNYYjocQC0q5wom59Se38jADz1yiyJ+LeBqwHjcNmu61/TbCb0azepqXuZsZnOAKiZzzSeZaNpnxda+qzSnmsTmbxG2rPNYKNYJKtNaOYbTbrWljdNTZq28BxTLbpxDZLS0xkers7M7AhNjOlmMc18p70GOlbmMYl+n9rcoTrHLvbA9etxnaVt0f3YmFJL2FYGXpbsLl/nm3RDLVhfz59iVhSrNbfJZtrM+2VytwOp7lz7IvG28xh52iDVQQMArCy91bYNvn2wm83srOa9Nwz2pYjoJIBjSqlT5fFzAfwLFLY9LwNwQ/n70fKWWwC8log+gGKh6rd99u7A9ti882PTT7uNkOtfTcBzzBr27fXfynWkzU3k6kxROdfrDPmaad4Zic9qbiJLTTezf/e5jTzCAdOyH9Su2XZTNbXuLm0+ULeJb2Pz7vouPvSp7Ryz/HXJa6iNaTjS7cLDmsVQGua9sWYF+44hy0NX7ywmYlxqdjXHCoW3gU97HONv3hbf3MvDp6225TUE+qpjcQvr+92uzfe9qzVd68Y7NM/tsver4mlbNm0Kq1jJtmH9RPO5d1fz3ieGHGZdCOAjhQdIzAD8rlLq40R0Owr3OK8E8OcAfraM//so3ETejcJV5CtCGRDU4FqWkNmMDVxbr01lKvOQeS0eT1fH09dsLiaLaxnPLGi7rsn6Jq/GotXcStrrZjNLJ2m37bhqmtdkjnT5s+r3EEKbb+67J1Vj0yUvoJ0GOtYdm+3+mMV7MXK5EDbziXWDF2evmUrUpsIYA6YYdBmsjjlb0NcAXLcyHCvEyTvUN+tz5ij1vcdqttvWm5j2OKXNHtpLS3WtWU50eB3zZJO3vupGSFmRYtueagdvy2MKfI/5ee8Ng5F3pdTXATzFEv5/ADzbEq4AvGYoedoiZDaTMTKvSag2OLHZvJvh5qg4R4YFllhigYVlxKyJe2FrWEq3KO3fZxnm4Bp3y/1Mw16k1/T37iPx5hyCz6bT3/FXi1X5va5d6jhCWg8fMeTxbWQwlUiGZO2KNh1EG1OPLnK0Sy99dsNtLtRew5XayXYxSUrNa+xZxakR69pOH8dsSmPGtaWZOvhti76Ied9l1oYhiXybuPuCPgY5bddSpeQxJo5w29QibCW2z8BpC2ESUrNyLLBCjrxGuM0Gv661nmO12aO02KW0HlK3azfdQ1bmNszmHRmQYfML1BvtWIJrLiatju3acptW3YZ6R8v9l1dTk01Z00hZuCHqZ4FRH51O6kxA2kJUtyalvXeKdpsluYiQOSgOmQuY9/vy9IW3RVd73CpO/4S8i0a5rfeWlLxDMowJn4nVVNhGO2ON1JmXFA310HW3jZmeqdCJnTX0pW/LZ2rs0sDpuLWebkfdnRLb22pEQQ3eANt8fLsaF611n5UaZE2rtUGMNpvhZjD8V5PfgrLOod0x6uuVTHYSaq789xNE97Sa6z4+w2Cz58zK/1pGU6vPNxdKcVMVizbkvbrW3kvAkGQ+xXa3L7vf2Ot9gufV16LiPjCG67e+0J/5Uvo9tvg2jEFg+i63vvRCRNOn1U+xdY+NHyOTSx4bYj0RheTse3DeF8aa3epj4JrqFSqm7MScu9IcC2cbNFXMZoAdJ++zI4Xzv3hmxAxRvbGsPF4Uv2oGLBfA6sRxrLNss8DUXIR6hEPvecxiVX1s1b6jXYPkJo91Dbk2d6m/ljApTNF6h5CyMNJlBuPSsKSY1Oj4/H33ZY/ve8ddNEohOXdJI8PRJ8lu41eZ3+ePP6x2Lsb0xGeC4nPb5zM9MY99YSG5XTLz/M3w+uZ4WU1m3lbyMDN927Oa8sQi1ad7bBlpV6fj6oZLA23OzPIw7V1Ih9VdBtsdGrhma820Q6aLJmyDAldZqedeD1uymW1zttuMq8+1Rxdff9zGHCs0OxkyHQu57HS9M1uevrAhcQrvMUKEvAM7Tt6RA/jLkfLS5VUTeH1+ojinDDhxAjixOAs1O4vl4gxWJ45jmdVJ+AGOasTePDcJfI4MRzhEjsztJtLSYbVBSicVU4FDHbvPLCZuNX+BUMfnnxZtR+7NvGzn+4TUBr2LqYc9vf6bsj4Hl+3yT9f4xmpAbZ26L04MuW2rAQ6ZX4R8hcdc5650+S9Q7Ujt8tRl4pxx7VjkZncAvM4FTMcCGubmerHppcZLXQfFvYpp0m46L3CF1+/3r7EqZGoqTmzwkVSzPPvIeCx5dw0eYwbDMQjVDZ6HL582Mzhjuhz24Xtsh9XesNvk/TiKjWTHgkngdVipeV9nwOrEMeSzbKN9L0byXFPOF2TmGxt53iRou/cDHCHHDIc4qo3wbSN7H3FPWUTnG4nbKrqvI82QRzUgNjlMtPFt3Mb9YFffvjFytUVbt3rNdIYjlzELg2MGTHEzIXYPRanPFyrzLjJranLN+mjr1Hl8nrdPExZCaHBqI2NzNDWpPiJVpWUPq+fp9hzVps7E2yfbZ8YARkz1ZnWL+Fkql2yuttNF5ura3apP4LOqJlnkWuD6sXv21VVmbTK7nq0K66c+V+195a/+NA6t6bjyTUGonNna8rmxR0Fs+XXl1WU2MCbv1D7MFn8XTCtfja8bIaJ5B3advBMKs5WxwAl7ea7KsHUG5OxtmiRd26NzTzI5a7SKTZDWbOHqctMYm4TdRRqKfONH6j6yUU0D1jsEV2fiu8Z90utNpLhPeq3l2mi0PFqv4r1XDcaxhtvLdcN/vekGU7/zGK86vvv0AEWH81+OlEY8xSTITLvtota2spp58nRdJNvW4dvJu5sQ9E3gQwNM20DVpbH2TdeH0jTzdcnoekYX0XK9axexr667yz7PP4Y8h9CG1Ffh8eZH7jT6JYgzmF7Iil5Al50M1SqnOZZYYYEFlqxNP40Yku4j7TF2zjHvI5bA++pqqA1wpW+TJwW2emOGpWiyfYPr2HobQkzbaZudANDY6Tx2cG6mrc99ypOxcIDTRoho3oFdJ+/HMB55L98UJ+tAQdjzWaVtr2tTinNOvm2eZHzhPjIM+Ke7bQgRknq1zpwyxJD11XJeI+r5OisI+oakz4A1VfWQ10dX3ZwBxZRLcXyu9qtwdrbekPtjmryXhF6T+Xmm7TB1F9rcYdbmprMpSp3A6/dpNnD1gVzYhtVGqqrwJoEKES0bKeN5+wj3NqMom9VajH7SjJ+J0shYOdDHnLyZ95iEwEzfLVvTMxNHrJbNTqB8Zc43QxKnLe2j03cRIRsR7VoeYsu/j7CZ39al/S7eVaGsKRb8rzbtqF687SPnrva/zeLP2AGhGT9Utsw4tuuu/FyyueB6zqHMTWLOXTJwhBQhqYNwF1lvxltv0goOrvLxybv4ebdjt8k7AerEeNn5CLuPlNsWn7p3U63i8vtzhG3egfRpd18D7+sMQh1GnmeV5ntddEiA3dZ0k+Y6rjjadojVv9xuM2SzmaKV8BFnnR/QzlTA14nFaKvD4e0Ie0hrau/E/R2MS8vTeO95jmydI1ufQ1aNx0BrFAM7nbU+hxHGYZ7PjOPS9I0f6wXo+ewYlgu+aDzDaRxu6uQS8/K8imOe8199D1+Ezuu2qVUtHitcL1zfzvxGMUTcPRsSv6jbJpMNPsLiIxpd6m6oPHI5fPXZRtCqb2Y3v4qxuzbD9P08jGvyM6y9ZaQa9NWVDGZY9Xx+Dbr5Xu3h/Wt9zffffM6Ygbd7Bixm1szs92z11NaHchlC8A3C/d/G/U6bSpyqNDXTKfMoiXq2ZjKsz0U9Q5841shSNO/ArpN3lJ35CFCzgkCYFh26Qq9Zk2R22Ec4sHbsRzjcXNMkXZ/ze/h1c2BgmqQA8C624uCLmHybNxXn9crNvQxU11nHmrGGN2PXF2ENYVDuaKLi1wr5tNv6Pvc0cZonBB/MTsdluqH/+I5/bbwIuDoXM28fXB2EuSCNL1TTpVkPUQ9xVDvnv4fZacyz4jxbnyvq+RLAGRQk/Ux5vma/nNjbZnPqD1D9lh6jNscLgE4AJxYATpzD4ckzODq5wnxR1N8cs3IQXa/Hp3GIIxziFM6rnduOeRuwefJypmp5ZlGfpQoNavVCR2PGCUBj1sm2k7Jtqt3cLTmDzXOInUwXZSLNnMlXL83ZMfdgIa+VPdfz8YWSJm3WspvExUda8tmx8rdQ5gBgCpbKPAao+gvXYC5GqcPt4bWdvNk31NsLH0omUgAADWlJREFUtza+enf29t5VBmwLUvV75cd8+JqxdsCWlvltfMTS+S08sxGhAZN19tiyDiF2katLFv49ONIHsv4BUag++wZZADDLyveeMVkW45PmM8fuMUJE8w58D5D3sUDrymTGh7xWke2/XIvHtetmx+7T4mnCvjozr5mlAE1vCBwhG3FAk/Nmg8obYDO8i3cBnadL0+KCGa95njblG4rXPA6bL9gQ8vQRa65kW/TGO4+Yjb1iOhob3MS9Om5Sj3opPsARFlhtfjmZP8TpIs7iCItFeW++xPzMWSyWALnIuz4uHiJM3rnWfcF+TxS/ZxfFIvTlgtfF4gnW7LvV301VvhdYQm++xuPyL7DZnG0xx3qR4eDk6Q2RB+zeULhnEtcMlL8jtw+2bPXcd48t/apcxGvhU0wGeHzfgEG/72VpW6mvn27IZrk/21wsrnvMM20EOYYkmjMvPDyGoHNiyeXgz6TDZ6i09L73H0PafQNy81fXbVcbYCP+tlm3zYxb8UB26MdnJq56LZprplz3x+b7dc2g62/lal9dRF6XC/09Qn2cq28y77Obbc3Yt8ugZ2hsSqrYmWRXHzgG1rjfCBHNO/A9QN5jCHUfsJnMLDNewbNah641c7amjWvdTuMARyUp0No5He80DuxN4nKO5Zl5ZUd+pnS5aNqQm2C24cXvGmeZtm5+YrnR0q1mi41t+AoLzBkJWWKOYsFt0Ujo550hZ81FYa+pw+csvn3kn8G1iVMMXIQ5TPDdhN+XdkwD1mxo3YunXFOzZuPvIgX2PAs72jn0LsD26XibHDo/+3OFtXTmAM+mjTM7o4JcVJ1rVY+Kkr/K5shO5pifLDr4+ZmzVeeut3tYbhLTD+GGx1yG79dgzoLpOsp/qxmzunZdm9jwumxqV9fmN82bpmWmSZk+z2ZrrNcZZrMc+TrbEPg8zwCjuOnvpr+1rq/6mq67Jhnk8fixC2acoj1Ia6h5e8LT4d5K+C9/PttsWGw8jhSSYqtHMdpZFwG0rX2KMbOymVGG6nSGtfV98/dQmd4UefC4+Sbf5vvS4fy6rudrVCRWH+u0rZihqNdaRB3NUxyryWU2c5IVpajaVrDqrypnEfYFwzFeflxtbErbWjxu3EyyTZHiCjPTiVGipQzCh4DpBUg07wV2mryfy4Cjk8dGySuflRUwq1dWs3E17Vt9i1x0NS8ai2pxUj1O1RxkWG80dAeLDKt84fXeYgPXuANoTKkD9hXrNs2bT8ueMlK3TSHGaCfq8WeN9xt/b/p9seYlaXLYq6OP9HMU733N4sQvWGvzDlzayyapd9suV5qvWUl2bQO75n2zLEd2Mk5T5INJtnSYrWP2ESkNbU6mB0tLPLKJYy5ad5k35NkMyIB8EZ4FcX0D/r7Nb+HSlvs06rbvZ0uf58+/RxfY24dmXVmV6vHVJo7bBtqXti8Pd9ymKVpMmbKFhYg+T9uMo+U2iWPoWW2ETX9jW520Dc65Nj4z4tT7iIBZU7YqZjwWzfKnYavf5veNMS302bnbTRbtrmFt39smV0q/EVIauUi16z2F6qf5Tl1taEq/3Ae+i08ZIaJ5BwBSylzJuzsgoocA/PmAWVwA4OEB0xfEQb7DdkC+w/SQb7AdkO8wPeQbbAeG/g5/XSn1WH1CRB8v85wKDyulnjdh/gB2nLwPDSL6vFLqyqnl2HfId9gOyHeYHvINtgPyHaaHfIPtgHyHaTCOzYlAIBAIBAKBQCDoDCHvAoFAIBAIBALBjkDIux83Ti2AAIB8h22BfIfpId9gOyDfYXrIN9gOyHeYAGLzLhAIBAKBQCAQ7AhE8y4QCAQCgUAgEOwIhLw7QETPI6I/JaK7iej6qeXZRxDRE4joNiK6i4juJKJfnFqmfQURZUT0P4joY1PLsq8goscQ0c1E9FUi+goR/e2pZdo3ENE/KtuiLxPR+4noxNQy7QOI6N1E9CARfZmFnU9EtxLR18rfvzKljPsAx3d4S9kmfYmIPkJEj5lSxn2BkHcLiCgD8HYAzwdwOYDriOjyaaXaS6wB/BOl1OUAngHgNfIdJsMvAvjK1ELsOd4G4ONKqR8G8BTI9xgVRHQRgNcDuFIpdQWKvT1fPK1Ue4P3ADB9a18P4FNKqUsBfKo8FwyL96D5HW4FcIVS6skA/heAN44t1D5CyLsdTwdwt1Lq60qpFYAPALh2Ypn2DkqpB5RSd5THp1CQlYumlWr/QEQXA/hpAO+aWpZ9BRE9GsCPAfhtAFBKrZRS/29aqfYSMwAHRDQDcAjg/onl2Qsopf4rgL80gq8F8N7y+L0AXjSqUHsI23dQSn1SKaW3PP0sgItHF2wPIeTdjosAfJOd3wshjZOCiC4B8DQAfzytJHuJfwvglwGcm1qQPcYTATwE4N+X5kvvIqKTUwu1T1BK3QfgrQDuAfAAgG8rpT45rVR7jQuVUg+Ux98CcOGUwggAAP8AwH+aWoh9gJB3wdaDiB4F4D8AeINS6jtTy7NPIKIXAHhQKfWFqWXZc8wA/AiAdyilngbguxAzgVFR2lRfi2Ig9XgAJ4nopdNKJQAAVbjNE9d5E4KIfhWFqetNU8uyDxDybsd9AJ7Azi8uwwQjg4iOoyDuNymlPjy1PHuIZwJ4IRF9A4X52E8Q0fumFWkvcS+Ae5VSeubpZhRkXjAengPgz5RSDymlzgL4MIAfnVimfcZfENHjAKD8fXBiefYWRPRyAC8A8BIl/sdHgZB3O24HcCkRPZGI5igWJd0ysUx7ByIiFDa+X1FK/cbU8uwjlFJvVEpdrJS6BEU9+M9KKdE2jgyl1LcAfJOIfqgMejaAuyYUaR9xD4BnENFh2TY9G7JoeErcAuBl5fHLAHx0Qln2FkT0PBRmlS9USh1NLc++QMi7BeXii9cC+ASKxvlDSqk7p5VqL/FMAD+HQtv7xfLvp6YWSiCYCK8DcBMRfQnAUwH8q4nl2SuUsx43A7gDwJ+g6D9ld8kRQETvB/AZAD9ERPcS0SsB3ADgJ4noayhmRW6YUsZ9gOM7/DsA5wG4teyjf2tSIfcEssOqQCAQCAQCgUCwIxDNu0AgEAgEAoFAsCMQ8i4QCAQCgUAgEOwIhLwLBAKBQCAQCAQ7AiHvAoFAIBAIBALBjkDIu0AgEAgEAoFAsCMQ8i4QCAQAiEjxDaiIaEZEDxHRx8rzl5fn2m3p77C4v0REXy3Dbyeiny/DjxPRDUT0NSK6g4g+Q0TPt+QdFS/yOZ5FRHeWshwQ0VvK87cQ0S9o2Rz3Pp6Ibm6Tb3n/G4josO39AoFAIAhjNrUAAoFAsCX4LoAriOhAKXUawE+iubPyB5VSr+UBRPQLZdynK6W+Q0TfB+Dvlpf/JYDHAbhCKbUkogsB/Lgl79h4MXgJgDcrpd5XyvcqAOcrpfLQjUqp+wH8vZb5AsAbALwPgGzWIhAIBANB/LwLBAIBACJ6BMBvArhDKXVzqVm/E8CzlFIvKLcAv9JC3u8BcLVS6utG+CGAbwJ4olLqO558vfGI6DoA/wwAAfg9pdSvlOHPBfDrABYA/jeAV6DYBfffAPg2gD9CsXnKT6PYVOjNAP4GgEeUUm8loh8E8FsAHgsgB/Az5e/HlFJXEFGGYuObq8s83q6UeicRXQ3gnwN4GMAVAL4A4KUoNpF6K4A/BfCwUuoa1zMLBAKBoD3EbEYgEAgqfADAi4noBIAnA/hj4/rfZ2Yzryi17OeZxL3EDwK4x0fcQ/GI6PEA/jWAn0Cxq+pVRPQiIroAwK8BeI5S6kcAfB7AP1ZKvQvFtvH/VCn1EqXUCwGcVko9VSn1QSP5m1AQ8qcA+FEADxjXXwng20qpqwBcBeAfEtETy2tPQ6FlvxzAkwA8Uyn1mwDuB3CNEHeBQCAYDmI2IxAIBCWUUl8ioksAXAfg9y1RamYzJXkfElcB+LRS6qEyv5sA/BiANQri/N+JCADmKLYtjwIRnQfgIqXURwBAKXWmDOfRngvgyUSkzWgeDeBSACsAn1NK3Vve80UAlwD4b62eUCAQCARJEPIuEAgEddyCwvzjagB/1RextHF/hIieZNG+3w3gB4jo+wLa99h4HATgVqXUdZHx24AAvE4p9YlaYGE2s2RBOaQvEQgEgtEgZjMCgUBQx7sB/LpS6k8i478ZwNu1Fp6IHkVEP6+UOgLw2wDeRkTz8tpjiehn+M2BeJ8D8ONEdEFpg34dgP8C4LMAnlnarYOIThLRZbEPqJQ6BeBeInpRef/C4iXmEwBeTUTHyziXEdHJQNKnUNjZCwQCgWAgCHkXCAQCBqXUvaX9dizeAeA2ALcT0ZcB/CGAc+W1XwPwEIC7ymsfA2DTrlvjKaUeAHB9mf7/BPAFpdRHSzOalwN4PxF9CYXJzA+nPSl+DsDry/v/CMD3G9ffBeAuAHeUMr0TYQ37jQA+TkS3JcoiEAgEgkiItxmBQCAQCAQCgWBHIJp3gUAgEAgEAoFgRyDkXSAQCAQCgUAg2BEIeRcIBAKBQCAQCHYEQt4FAoFAIBAIBIIdgZB3gUAgEAgEAoFgRyDkXSAQCAQCgUAg2BEIeRcIBAKBQCAQCHYEQt4FAoFAIBAIBIIdwf8HWQsi5eZNXZUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8lCSllLjoTEm35pw3SnoupTRX0usl/X5X29onBulb1FFLtONnh/trSVh+zHOws3ACMmaaH/NUDZvL+2L8ab+3tz0eoyfCsoErXwq5V8+XQx4D3cImdQr5CR0bcqM2hdx50vKQN3wcP598fowVP7FOQ8AUZBoKaAk5D5kz0Ys/K07LBsvShNwBBoKpUE+MwTT4Zryes9xZNv5EOmfZ8yfRW5FPR641y57WjmLdT8Ey/QL572KkHYGyFRoJuG+2K60bS5fgCczK55x29ovuMBrQ+sOfrqbxpGgRGYmD489cT4FtYTAufNy2YHNhXS2AhWMBjp11z3PoEbx1VDOQSNIi9Ovh6NcV5p2ClWRUDXMM65nmiNtxcfs4fhKdx9aKzLoTbC60xTQhsx89wrecb2B/tEXANlHsd4PxQ9UbkFkW9mGev6zLDTBfDO4fc4V1CP1oLnMNK9AiWjqakLsgozxFQ9MKmmZQFm6a7cxrXSsyr+2sy0fYMdA2nXHs3N4UWEFOxXsFz9GZZ8e8hkaTAq1T8AROmpGo14r3SNhdWh+LuVczXoD9bcHyyTSsPBVjB6y/Bdur6AegO6xGa4rGKPYpWLkqjEmrkfk+wu3x4oa6052Fx7ET7suD9CoMUnQTLSg/t0v2iUG6McYYY4x59dDOA9S+KaXiV3k3ln9kU5KUUpqsnXxFJOnzOeefvlKF8CDdGGOMMcbUDXXwTfrSap70nDP/7Lc7vCCFH/oZXH5ul3iQbowxxhhj6oZ9fOLorrhb0o9SSteqNHF0hKTHqr1gP6wDY4wxxhizr1IH36T/1aSUzlZpws3Bkn6eUpqecz4t5/ynlNJPVPp54i2S/qma2UXyIN0YY4wxxtQR+/I36TnnOxVnxRaXfVnSl3d3W/4xI2OMMcYYY+qMlHOuvVY702vs4bm5ZccHj09A2/WWB6CYhGWsgsuQ1yK/C3lllW3B/LRoRM+QV6hXyDfooyEP07yQt6oh5HXQdE2H6+4nD14YC8CPnpciU11FbSKXD0euULIVoEaLZZmMTAViLW0fdZPUErYiX83lVJxh3scl0IJxykgTMo+Pirfi8VHjRSYhU5nI17OumpBnQ2EmKMyo1ur7mpgr1HxQRp4AZSTbiv2E5S/WXROW9UJmWaYjT14VczM0Ytx3C9av0I6h7jqj7qhcXLEZT9T4Iy37LfWZxbpshUJxDC5urOdWluXxGEdBhTcT7Toc7XoWNsey1tKYrvk6nmBdc+4VK4cqvSmFx+dgGVR2A6Cm46Zph6t2bZMqz/ctj+IJ1K2ujbE71KDUZT6CzPKw/LzeFM/p7jXO50XU/hGofjugT1M/ybK01th8E9fHOXke+gnrfgr3X+scLJ5HeFNrgtKV9cw+vgEH2xcv4Hsm9ZSt6NMdoEjcwmOhBvF45BqK2oprcbHte2DhcuQpyOhXFUpFHHxnHNsGaki/X3h8tXJ+vq3wR6eUb1H7cbw0rdrE0b3FvvrXBGOMMcYYsx+yL9/u8kriOjDGGGOMMXXDvjxx9JXEg3RjjDHGGFM3+Jv0Eq4DY4wxxhhTN/ib9BIepBtjjDHGmLrBg/QS+8QgvUHb1LUwi7iTNsYVOPv618i0tzyEPBD5WeSiFITmGLx2wPKoglkxLqoqPqd/D3mhDgn5YZ0Y8jhFgwDtL02n/FvI8zQs5DuugBKF5owJyLQ5cKY6e0zRSHAFltEU04R8LvIYZBoJuG/aGTiLnvaE86DtwbEN+HBs+GXLomJl81QYB2j54P7vKjxuxTJ9NcYOn4mZ9hbOMaexhHU3AEYS7v9UzNKn5Yfn1KmwftDUMwG5wgKCefoDCv2S7UymILOsNIZMoXXjuBhPxvo06dyDumM/Ji2teAIXCZ5DbLsNsDOsKNgZBmNbfG2FAQRvawu6xNyE9c9Du1az8EiVFh8anWYiVxhPqAHBxbj7e2OmHWJpIXPfY2BzuQvL2Y68PtAatADGkS0wWXTHsdEKsgE2l1Ys5/ViBa1DaLu+aFvub3bhnF5xR1y2ZkbMJ0yMme06FbkJmdc+ZsJzYO6v8ERzjDTd8PUs74Aaw7kOhfOI50ytzOvDBlQ81+c5U/G+hj7N1zfhWObC5rIB6ws2F753sN/3KpwnFdciGM62YPzAa+8G6r6PwnKaafjGcVLhMfatfWSAuodxHRhjjDHGmLohSerYniNUfthpJzxIN8YYY4wxdUNKUgcP0j1IN8YYY4wx9UNKUseG2uvt73iQbowxxhhj6oZ2/ya9TmiXKkgptUpaLWmrpC318NOrxhhjjDGm/Wn3e9LrhPasgjfnnOmA2Cmb1EnzNaQtH61ZcYWVeMEyZEym3gzbS0eu/+MYF8/f8bj/4XHZ8vPjtPbeC+PU62Ern4/7gkHg4MOfCblxyCZVg2abXtoa8iFaGF8Ac8aIt/4x5GWtcer66IZoAZijI0Je9BAqoPjxirPgOZGbvW3ipJinT4i5O6wXkzGLnbPcaa65KMbXnhKVAW/Ub0PuryUht/ZpCvnuM6Md5qW3HRp3QFvN6YXHNE0sgvmBVh2aG5hptuGse5ovTkDmrP6pi2OmKYMGgjOQaWOYjbajgaVoZ+C2KQBhvxK3/VSMnWHd4LHTJsO6oEmiCbmibWBguWtzzHNha+DxDEe/njttx+MzYHZorVEW9oPOsPjwinslLn5X4WJZy6hUYZqAoaQX2mI4Mq0gU2GDYPlVXN4cF7Vi1SbkyetiPgE2iYp3I/ZZZPYrGpFq9asKQ9OB1ZezbU+tsv0t58RlTchsVx47rp0VZaEJpwKacQ7c+Wpt4PrTAR2b5eW1nuVZyovICzseDkAfbMKqPFZaeLh+c42y8X2wFdevMTj/eU5P5bGwLmkFwgYWoW7Vr/AY1h9eSysOlrYmdpRaZWOn7V14jOtkkuTbXXy7izHGGGOMqSP8k6OS2q8KsqT7U0pZ0g055xvbqRzGGGOMMaae8CBdUvtVwck55xdSSv0k/SqlNDvnHO49SCldLOliSWo8tN/OtmGMMcYYY/ZHPEjXAe2x05zzC+X/l0i6U9Lrd7LOjTnnsTnnsR0P7rm3i2iMMcYYY0y7sdc/p6SUukk6IOe8uvz47ZK+uLfLYYwxxhhj6hBPHJXUPn9M6C/pzpTS9v3/KOd8X7UXdNE6jdETbXm1eoTlvakYgKCAE5Y345ekqPlZBUFKa+Fx/ygAUe/pcd/Lx8D2sgRlW46yQZZCe0ujou2lL9Q1W9GL+2Ka/ntO+37IYzANfmHDIdherIyBsMX8uDUWuPd5O2bNr1vTJSzbcEbvkDU1Ri04L8Q3DHog5HWK9oVlw2LDLlw8MORtt3QL+bBTog6hdWNTyE2Nz4U8XPNCHqL5IY9WNE888O0hIWsFZukXDSZx11ILZrLT/nIpMg0DNBAMQKaFg4YBbv982Fxuwaz8sZi1T4MBobGgKZpxdFfRcPBgXDagufq+RmHbM5ti5i/FUY7A7bXgWDvgWHmVpEmCbaH1yNh+Z1ygaJsZUDC6sN2akXksTcg0goxBvghloWmnltWD26fdgdtjeSt+1Q82F9pjmj+x4zENRuzzNA7RZPEITBXNWJ3mGZ5jhMdKiw/Ly2OvZVyp1Y+L27sPBpHpqJwmGIko3eD1ZgoyDSXNOCe7ox/wHGt6W8w8dppw2A9mIlf0o827zqw3tjP3RYsPl9Pqw3ZqRr4cdcVj53tBd9haeA4PRl3fiuVNuLYX+3ErzrdFcWxV+2JKFQ2Xo5+pC/KUwmPYl3xPuqR2qIKc87OSXru392uMMcYYY/YBPEiX5CowxhhjjDH1hm938SDdGGOMMcbUEf4mXZKrwBhjjDHG1BMepEtyFRhjjDHGmHrDt7vsG4P0l1YP0PUPfrotf+KUb8QVOAF5o6ou38L1oWGn/SW4GrBsAyYv91iJqd9rq5dFjTE2aGvINJwQ2lq4fheYJmiDma9oKGlSNJ7QpNP9rJfi+g071n988slh2QEnx4Pvcf7qkFfeEnUJM84bHfKapZiGvyAaUbqPiWXRJXF2+DDoHpY1Rt3C0zoy5KOhAdqkTiEvUZwl/9phj4bciI732JpTdoRvx6LqdOSPI9OEQ9tBhaEEmWYI2hvYD2ntGANjwFgsb0Km2YLlb0W+smA46NtcvSw0nJyM3BfnCF/PqxztDBNwrDSa3A5jwQk1bAu0w9BdxfLTShLKhsyy34LMftSETMNIKzKtHmz3S5DZNux3NJywn9AOw+23IhdNHNw390UrxyODYuY5RGPI0lkxNx0dM+uS/aCWbYa2GJ6j3D7hOXZ+4XETDUh4o+K+2S5NyGcg34ftT/lVzCfA3sLryfRrYx78qZh5vWP5eI5z+aihMRfbln2OdcFrM/sFr7WjkCk8ob2J/YDnNOE5zfVZF83ItNcU+z37bCvq7T6aqdCOFWWHQUmLkam3W7+Lx/I36WVcBcYYY4wxpn7wIF2Sq8AYY4wxxtQTHqRLchUYY4wxxph6w/eke5BujDHGGGPqCH+TLslVYIwxxhhj6gkP0iXtI1UwsMcC/eMpl7flIWthW6DNZSU3ECPtLfyTyvqtyMUAW0vnKEOp2Hc+KubEyc3PxNi1X5zhvAIKAuYjt84JeW5DnBY/RPNDHqiFIdNgsgWVsRVd5I3dfhezduQ3nzOlalmZf37u+JDP6XZ7yE91izaFPoctC7mros1l8saoRxiueSGP1bSQZwm2BtAF2y8eqyR965fRSNCzGWqMKYXHV2PjVyLTMMAZ+TRZNCPjlKiAlg5u/yxkGk5oKKBVhEaEc5FvRS6aOGhPoK3gOuRadgOWhTYFWj14rKybidA10LpBswSP5ypkXnVvnRLzgOYdj3msNFMsjeeEFvWJmdc6thth3V2EfDky+xWPvbXG+qxLvp5tUby88ZzhOUDLxql4giYanmNjcH3g/tgWTchsu5bNMTdHW1VNKxH7cTPy9YXHtHbQBEOLDuu5loGE59BVsLmwbu5CbobNZQqWtyKzfDT30IzDc/qEgrWE5wD7CWGf5LGzbqainc9CO9P604TMay37dStyLTMPrwHFuqx1DpxOU1XGCrD86KQYB+P1LOua7xQC6inJt7toHxmkG2OMMcaYVwn+Jl2Sq8AYY4wxxtQbHqHqgPYugDHGGGOMMSbizynGGGOMMaZ+8D3pkjxIN8YYY4wx9YTvSZfkKjDGGGOMMfWEB+mS9pEqyEraWFAFLuwWXViHd4D6jkcFbWIX6pyw/mBowpa37Hi8LhoM9cLomJt6xtzxc9jXj5GxrzknHRHyScsfD/nohuhs7AgFZNOY+MRn4f4brRkhz9eQkM/WnXF/W2eF/P6GH4Y8cOVLbY+X9YwHz22vVo+Qt/SMf8s6T7eFvBDuzGFQKlIf2dAY3ZnjdW/V/R+teGxj9ETIy4InsLI8y06Lurvf6o0hrzyjEKjho8pqNlRWVKANRz6jRr4deRQyNWJnoCNdNTRmKtXuiZqx1x4W9ZZ//GN0pB12YdxAse7XqWtYRrVmH0XN4C03fCTkc/4zOtWo1uykTapGhdZUUWu6VLGd2Q/Ipq2NIS+HFrH3gHg8y8c0xw0MKNTtFlycHkE/uQTKRerg2I8mxXjUSfH6wutDr29EZ1qD4jm2DHWzWP1D5jVg4cpDYnEf6R0LFE+5Soo6OurcqHdsRqbajlAbireVitdTX8f3nZYamcfajMxzjtcA7r+oEmRZT0amao/KQqoxWTaWndunInFivN4NGBavN1QD90Lj9teSkNkPe2g1lkfvYIfC+g3fgWMZbMU9Fuu+Eq9P3DepeF+qsT6vf9w/X78ajcv3tU2K1x9e34rXsyWL4/m67cpusXC8fozC9Yfa0MlwSnc4Pmb2mzVF7SJ1jtpHRqh7FleBMcYYY4ypH3xPuiQP0o0xxhhjTD3h210kuQqMMcYYY0w94UG6JFeBMcYYY4ypN3y7y577MaOU0vdSSktSSjMLz/VOKf0qpfRM+f+D9tT+jTHGGGPMPsj2b9Lb61+dsCeLMknS9ZJuLjx3haRf55yvTildUc6fqbWhpeqrSfpQW75EN8QVtuAF/PQFu0tHHvXK6vvvUnjcdURcNiJOOtdzsL8MxWTpZzAbehAmQ5/86WhbgKBEHd+Kwi2Psc0oSe8AACAASURBVPcDUe9w7lui5oMzwdeFo6ucCT6/IdoZ5ujIkA978RdtjwfMjBXZcFL1Wenf0z+EfNDyWPZ13eJnyJsbPxjy0Xoq5Kt0ZcicJd9JG0OeBh0ErSDHYuo6jQO36b0hs1+2HF/YfpTmVNgIfrwl1oVaY6yYFQ9DUfdTXwp5zYaD4wo8R25BPh82l/OjvUULOsa8IeZ5a4eFfPhr/xTyRbop5CGa3/aYNhcaQ36u8SFP+ei4kNejD9+td4U8XHNDpi2hnxaH/CLsLS/jnGiELWaGouJpdUM8x2YNiv3q+QehzqBZY0uhbgfQAoR1eS2joYSWDuzrRD0c8pgKXUNknmI7N6GjPqwTQ164ONZlp87xHGQ/rjCgnIpcbArW2wDkWvaVwejja9DHB8eo+5BpTGLVNSN3xvZ5Tk5Eps2Fbcu6K5aHx3oXMuvuPGTWJfdFkw7rmmacSdHcseL6eE4N7dka8nCYvFoULSG8Fq/QUSHTBlM0Mo3To2HZjc9/LOT3HfaDkHk94nsgjUh8j12oaDSiVYw8rJNC7o/r08z/+7qQz/lSvJjPRceZ+fyYkDv32vHes+0mDFDOQmHY7jQq0f5yBmwutIixX7T+fSH8d1zm210k7cEqyDn/NqXUhKfP1I5L1/clTdFuDNKNMcYYY8yrBA/SJe39Kuifc36x/HiRBKmuMcYYY4wxvie9/T6n5JxzSinvanlK6WJJF0tSw6HVfzzEGGOMMcbsJ/ibdEl7vwoWp5QOyTm/mFI6RMKNYwVyzjdKulGSOo0dvcvBvDHGGGOM2Y/wIF3SHrS77IK7JV1YfnyhpJ/u5f0bY4wxxph6p6Ed//0NpJTenVL6U0ppW0ppbOH5ppTS+pTS9PK/b9fcVs575kvqlNKPVZok2lfSYklfUGmO+U8kHSrpeUnvyTkv39U2tjN4bL/8iZYdJo336raw/LDPRLMFjSfCBOYcJ28r9cP6q2KcVTC2HB1lJ9qMdZfDFNO7Z8y3YTkFAs3/iid4pw9tDbQh4JPn8n+J07On6M0h02gyRk+ETJPFUsx0P02/bHu8FTvvtTIqBDpOQ1k3ILPdaN2BgGTDKTG3dDsu5GM3Rt1Ct4XbQn5maKx9GgTWq2vINLIsROM06zchF00XNMWwHpdB3zJfQ7A8rl+0o+ysLLRu0NpzO1QR46ERmoa6eD2MCAdhmv+TMJzQoMC+UTQg9IUagsd2FGwIA1fG830r+vy6brHPs25pNBqoF0PepE4h05ZASxEtQWwLtl0L1qcdYol2XJDWoQ/OWhzP12334OJGCxDPMRhDDj7+z1gcTTjDYNnopZdD7gFNCPsN+/njL0ALsgb6iMko763IzYXHPDYaSWieuCbaXA7oHK9t22aiLmlrodmi1v5oypiLzO13R6ZZgwYVrn/T3TseT4iGo4p1aQmaHA1LOjf2u4q6pmmmqXrZOjbFN8o+feLBLJpzeHwB3tc6nh5fv/n6A+MK6Nedz4hvJhvWFI6HfY6mm6nItOw0Ic9EZjvTAtSKzLZg3dLMcwIyv22uMPGg8YrHz21f8eWYb/98zLSzsF8wsy7Zh4uGozljlde1tGmAxh6Wcsvn1G6kSzQt50yP0e69NqWjJG2TdIOky3POLeXnmyTdk3Nmr9gle9Lu8r5dLKJE0BhjjDHGmH2enPNTkpRSqrVqTXzHjzHGGGOMqR/233vSh6aUnlDpno0rc86/q7by/lkFxhhjjDFm3ySpvRWMfVNKxRuvbiwLTSRJKaXJqrzpTZI+n3Pe1XzLFyUdmnNellI6XtJdKaVjcs6rdrG+B+nGGGOMMaaOaP9v0pdWuyc958zfQ65JznmjVPrp85zztJTSPElHqHJ2YRsepBtjjDHGmPpiPxuhppQOlrQ857w1pXS4pBGSnq32mn2iCtaoezBlnKvb4wpb8YItyJi0X4uMGcrriwG/kbpqYcw9sK8FMJRQDF/xk6trkZ9CHo/8ADImvc/Qa2L5YCihBYQWjoFYThvMJjW2PZ6s+MHy/CV3xMIsRlkxoV93I7MuMB96frdoZ6mwuTwTbS48FYb0WxDyo93GhdxHy0Leir+9fWL5jarGrN476moxWrqrok3hjfptyEX7iST9SB/A+vE2thmwq2yEoWQsPqjfi470JuyfFo+h6Ac0sNBGMxxWENbdxkK/OXVjVHosbKz+42UdcRJ1iAIRdb43nsC9FdtZsALRsrHhpJhXd4sd98SVj4X8Ys9Y3jHYII/9jnmxLc8educu13/s+RPDsvcc9qOQ53z4yJBppukEO9MfPwQ1xP/EyLLzejFPw0LugH5AG00vKFHeMOjhkH8/L9qmNBwTrSbBPja3sJxmil7ItHbc2jHEbWNj1gSsfx3yghrL+b3a9cgnI9PURRPG5Hj90Rh09OksUKHjTsKiio2vR47XvgqLB+0wHDlchXxpjJsHxH65bBk0RB3QzqNiP9g8G29sNKDAkLLhnt7xieJNCbTq8IaFMciU5OHYKmwtrGraX2hg+vYk5Hjt1xnnxMy6b0Kmheh86F5Y/iL3RJvLAWPjm/C2qXGA0/30aNpaM/vg6mWhmeamwmMOVdv/m/S/mpTS2ZK+IelgST9PKU3POZ8m6U2SvphS2qyS/eWSWobDfbQKjDHGGGPMfkn735P+V5NzvlPSnTt5/g5Jd1S+Ytd4kG6MMcYYY+qHffib9FcSV4ExxhhjjKkvPEJ1FRhjjDHGmDpiH77d5ZXEg3RjjDHGGFM/+HYXSftIFXTUFvUtmDYaYSyooBH5uRgTjxqT5hOsIh2Lc2+HxmVbYLfcQrMMoLGec+xpZ6mYGf6/yDSmoOyNJSVnG6P1ZMitOCDaITaiMmkpGfDcDn3N+cswHyLuWsIk87VjDgi52xLYWGh3QR5xP+wGbHfOmYYVpAFtdaTmhExTRV8tjcu7xfLPaIyGlV8XdA8ToNHoACXRkJVRp7CwZ1w+WjOqlq1oP5Kkj+qGkDuhMT6mb4ZMCwfPsSNQN1x/vO4NmdYg2mr6FOqSFp5eo1CWrbHsj484KuTjnoMCiQqlIcg8p/DyzrAIjZz5vKoxZlzURfTDSfkYzBk3Drug6vaKdXviYdGG0gqVwxg9EXJ/HPwcHRHyvOujnaU/yvozvTNk9rtm/SbkFToo5IGKuquf/PzCkI97R1RfvH5YtAq1dI9a4m1rYj8PlhFIvipsKTRLLEWmsYQWD8pTWpFpquD2m5EHI9MCwvKcjzcmHk8HbLDYDbnvRVDL8BxAn68w1bBsNNcM5/6Q74oX/83fxpvBlTW2x7q9BZnGFB7fXbN2PP54NJRV2F34Ht6KTKM193Uf8uXI3N8lE6rvj21Bi9EU5CZknieF5R37xhHJ5ulxALJtUbS5DD7nmZAX/HFE3Pat2Bct47Dw6KLC46exzIN0Sa4CY4wxxhhTb/h2Fw/SjTHGGGNMHeFv0iW5CowxxhhjTD3hQbokV4ExxhhjjKknPEiX5CowxhhjjDH1hu9J9yDdGGOMMcbUEf4mXdI+UgVJ29RF69ryFn68ojJpKzIsQbCWVar7wKDi7qJhTP16x5wOjXlZtLMJq2sQd9aZT4AaikdFY1JNFd5HdGPIn9R1IU/T8SG/V7eF/MzQHRqwKUObw7L3b/xRLNqyqNrr9giUizCO6XBkqvX+jPw+ZNYl2nkrev/r7odLC235p5GxQFQuroAb66bp/7zLsmYY0W7u/Z6QqTg8UQ+F3IBO/s/6esgvamDID0HR+Bqo9Zai8vsUlKdSpdqP/ah4fkrSfHgPl2H7RfXfC6PiWdFj6+qQD3xqc8jDj5ob8qpDO4bcaUJc/5pul4V85aT/CrlCE/YMMvrRP4/7asif1NdCXoO6ocb0KM0K+WkdGXKxLtmOVKCyXqfhYOaujMrFDRNjXc+86OCQe498IeRftJ4d8pHDooqT/YCKyIPfETt+X/Sr+/94ZsgVaj9qFYtt0YxlrchzkdnOU5CvgKv3Uvh2qflj2bi9DcjUz1EryGs7VXtU/52BXDQyclssO+G+JiI3I69BZl1zfV6LL0Jm3TBTEVmrLdjWZxW0i6x3lv06ZCoQ2Q5UYzbVKBvVm+wn3B7bhsvHINdq68L+Nl8N5zPaqeeV0aW5YA4GU+wH1JJejXxFleXUdhpJ+8gg3RhjjDHGvErwN+mSXAXGGGOMMabe8D3pHqQbY4wxxpg6wt+kS3IVGGOMMcaYesKDdEnSAXtqwyml76WUlqSUZhaem5hSeiGlNL38b/ye2r8xxhhjjNkH2T5Ib69/dULKOe+ZDaf0JpXm/t6ccx5Vfm6ipDU552v+km11HDs69235aVt+bm00FnS+DC9Yi8wZx5OQMcG5wu6wsfAYxg/aXgQhwDrYXRajbENpnmEmnBWP7UOqIZ0S4wsjot3hdp0b8pGK9gbaJLrC4jFM89oeb8UNZLSdjIZRZMDdK2Ph2G4bkXmsnBV/GjLq8vFRR4V83Oyn4grsByjPd0acH/JHpt+iqvQrPEafuvaUfwz5oq03hbyuoWvM6hLyCh0U8kB0RFo/aIuZq+Ehs+1oj+HyIZof8iZ1Cvk3enPIY6BUWlZQELx562/CsgP/HO0stexNfxgZ1Q+vWwhLD20M7Ge8IPdEHoiMbrvqhGiXebkh9nvW3Q90Qchj0LGnF3QNLdBUDIeKYg7MMGz3Q9AvaN0Zr3tD3gQTTR+oMGhneQ4qiw5onHWK/XgKtB+PzXtTyN0HxP2tuT7aZ9S98JgGEBpEaPG4D/kSVQfdSLcj09pBCwjtK7WMKOznV8a61snQX1W7HtY6Nh4L7Sk8dhpEuG++L3F9Gk14Tk6dFPOlE2Km0YR1x+2zLYpvc7Sf1No2M8t+JTLrhmU7H5n2FvbbbyOzrVj+85AnIl9VZd/o0wNe+2zIy5bFytt8fXzT7H1ltEOR5TOjz664/aVjz9Tmlhlpex47JuU/PFB1c3uUA/poWs6ZnqC9zh77vJBz/m1KqWlPbd8YY4wxxux/5FSpSX410h5V8PGU0gUqfR69LOf8cjuUwRhjjDHG1CMepEva+4P0b0n6kqRc/v+/JP3DzlZMKV0s6WJJOuBQ/r3ZGGOMMcbsj+QkbWnYY9Mmd4NttVfZC+zVQXrOue1nBlNK35F0T5V1b5RKP4fZcezoPXPjvDHGGGOMqStyStraoT2/St/UjvvewV6tgZTSITnnF8vxbFVOTzHGGGOMMa9ytjb414z22CA9pfRjSc2S+qaUFkj6gqTmlNIYlW53aZX00d3Z1iFapMv01bbceQlWoK2BPIfcG7lfjBmzs1PRGvBveO3HkXFnTleYIIYux/qUUP4aebSqw1n07NOwigxaHgvQPC6aNT6t/wyZdoZzoQW4uWCqOFEPh2Xj9fOQN8IAkk+OZUuPxKxuyA8h89ifqp6PexZPsN9AnvDHt0c9zEdaos1lM+wx3+x5ccj/suTGtsffOuXCsOxTj34r5FVjoyFktXqE3AqLBm0tfVaio8FQcpveG/Lv9MaQz9JdIfeFYuCXUOf0UzwJb9c5Id+q94XctHJByE/3PLzt8d0N7wrL5gw9IuT16IMf1Q0h00Cytk/8E2mnU+KfLR/u+fqQT3nwsZAhNKo8p1C3TzQcG3IPrQ6Z5psuMCSx/EsLHZFWHBqT2A9o3XlYJ4b8Rv0u5B/pAyGP06NVy85+ME6x7li+p2GfoS3q4GGxvC99CPosGluKf3ulJaMJmfYXLqcVg/n22Gd18uCYm7F+K19fY//X1chX4IKE4kDQFO0u3PeixTFfik5OywczrtUVxjRy7iTsb0LMbFf9XYw03QyosT8aT2hUKfYFWndYFlrDOEpCN6j4qrGWzaWWXWYScjMy64I2GW6/mlmH7YxjXfTQ4fGJ7jFqYnxPXHdpHNBs2hBtUbTFHFq4Xq3CN9dZqcKM9WpkT9pd3reTp7+7p/ZnjDHGGGP2fbKStniQXk/KdmOMMcYYY6StHqK6BowxxhhjTP3g211KeJBujDHGGGPqBg/SS7SnhNIYY4wxxhizE1LO9a8gT4eOzfrMjinJeXyKK1yGF2xE5m8hPYt8U4ybj0UuzDTvOhavXYiMWebr7ldVun4aT9yAzFnr766x/79Hhrlmdr/DQp6vISG3KB5gE9Q4tDdMK6w/ByaHr+iKkE9a/njIc3rHsoz8f8/HwtKEswb5Hcj/g8x2Z79g3XI5LB4vvCVqgQY9hwLCIrToLTs2MGAm7Ct/xrrj484GLIzr01gypzHWNb9x6ISD+Z3eVHV9tvt6dQm5j5ZVzQ3QMUzSh0K+Vp8K+QntOMmeVrS50GzzSX0t5EPQ6bk+7Srs42dMfyBkniMQ12jz0Jhn9Yxan2UVOoVIV9hcbtJFIY/WjJCLbTNLR4dlR2hOyGxXbovt+Bu9OeR5a4fF7XeL2z9ST4dMW0yzoh1qCrbfX9EqQrvL838aGbLmxigan4pmDV6LaelYhEyLBpfTksGyTI3mG40cF/N5WB+HVtVUI0k3PRnz6a+JuQnrs26Kthd2SVo8WJaJsY/q0mhUqqgLvp7H2lpj/6xrWkNod6Fxhddumm5YvuL+m7BsQBwDdR8QbUtrph8c16fNhZnHwn7GuqAthuuznfl6mna4f9ZNcfkELLsFme+57LPcVxPyGWw4cGqhoVrHKq9vaRvcjR7bKd/ZQtXW3mNEWjAt58yrzF7Ht7sYY4wxxpi6wXaXEh6kG2OMMcaYuqF0T7qHqK4BY4wxxhhTV3jiqAfpxhhjjDGmjrDdpYQH6cYYY4wxpm7Iku9J1z5id2ka2yd/vmV8W/7IA5iC/FW8gB89/gn5/yH/K/LJyN0Kj2H8WPdUzF2xvMIcASGA1iJz1jvnFr+r+vannnRc3N3KaFQhD/d8PXY/JmTaJY7WrJBv1gfbHp+oh8OyaSj8f+j/hHyb3hvyNx74TMibj49l7XhvzHoIme1+PjLsLRvioWp6t9eG/HldFfLNujDkQS3R7rJoLBu/uOtOIa9XtCeMfABmG9gSnh8YDQN8/aPoWEfCAtKgrSGvw+tpAXkIFg8aU/rC7nK8WkLuBZXGPCgGiuXhtmkEITSE0J6yEFqfYVBT9N0Yy97tuW1xB7A/PT8+1n0H1CXfSGiT4fG9iPK9DF1D0RbDb5IGwmzDfbNsc1HvNOGshp6BphrWHe1OS9AWXE4bDbd/58qzQt5wVzQoVZgpiuaLu7CMmddOnO8VBhEaUWiuoGXjdmTqnU6HFoivp23mEmRaQ1h+GkxmFx7TEEJLB+0oLAu3zbqkkWQAMqUe3P9kZNpjzkJmeVuRP74g5tNRwOJ7Aa6thx//p5B5vg7R/JDX4Vr5wB/PiBuELa4C1sW5yHwfm4RMmwvrfmmNXDxFJ9YoC9uZFh8eC9dnv7oPeVJx8PYN5bygze5y1Niu+X9ajlR78YY03XYXY4wxxhhjivh2lxIepBtjjDHGmLrCg3QP0o0xxhhjTB3hb9JLeJBujDHGGGPqBv+YUQkP0o0xxhhjTF3hHzPaRwbpL2iQPq8vt+WP9IbdBdYO5hfGR2PAoJnRyqH/xeshGAi1dGBc1KUR645AfkuN5Y/WWM65xdzfMzGOGxVtLjf0/HDIDVAaPK04e5qmiRkaHXITptUPLeQ36Xdh2Zs1JeRvQrNzPbU7OLanex4e8jFrod3oo+osRI5SDXVG3bWMiZX9f3RNyDRjqH/sRwOeWxny5UO/1Pb4qrX/t+q+IQTR1IHHqRqv2Toj5FMboi7hi1AW9YMx5Qe6IORm/Sbk4ZoXt69fh8w/Q9JYsgmN+Tu9MWQaFIqcA23GYUteCnlLv7hvGkPYTjSOHLYwbi/Ym6QKgxPXf3BgNCLRuELzDstHO8T7n4+6qfMPm9T2mJaeLjDZsJ5rmWU2wTLUiIvlsRV6qQjrksdWq3w89qE9W0N+ani8+HYcuSqWd8yO8q5pitYdnY7C0jwBAUiFIYRGFNpeaBihbWUFbC61zBc01zTVWE6uRy4e/0VYxnd6NjPNOBOQaXuhGWcqcmuN19N0Q7vLqcjfRqY95nZUbi8sL+4fxpFeejnkI/V0yLRH8fryhtc+EPLvL8KbPuqq8/D4vrFhAQYctPrwWKcg34N8DTLbumjOwVCqwlhEwxHLRnPMX9rOTQWj28J4HfTtLiX2iUG6McYYY4x5deBBegkP0o0xxhhjTF3he9I9SDfGGGOMMXVE6Zt0D1EPaO8CGGOMMcYYYyL+mGKMMcYYY+oG35NeYo8N0lNKQyTdLKm/pCzpxpzzf6eUeku6TaW57K2S3pNzfnlX2zHGGGOMMa8uPEiXUs55z2w4pUMkHZJzfjyl1EPSNJXkPxMkLc85X51SukLSQTnnz1TZlIaO7Z0ntrytLV9490/iCv9eozDvRn4R+XhklGZzwQLWEdbAVf8V84EnYVuHIlNhRFUWDk1HIb8V+c/I42P8txGfDpkKxV7wUVEv9fGN0fP1vcZ/0K5YB/Uc9W9L1C/kI6C6GruxJeSWxqhEPGXhYyHPHnhYyCP/6/lYoK0o4Dhk1i21YjB18iPt5kNi7vgclheMbJs6xzvLtnaIF5+Whnisb1z5+5Cn94z+N2r/OMFmGtydY/REyFRvTobzjNtnvzhesa3IDL0m5CbFynlYJ7Y9vkQ3hGXsR8z9oZO8F53+vbotZNYNL/yHz4/uvUVDooORxz5iSXT5fbJfvAB9TN8MeZ6GhXwJfHJHa1bIwwqeshP1MMoeO+Ftem/IpyqqODdCuUjmwfN3+9ZzQz6xIe7/nhfeGfJnB8Vjp7KVCkkqHL87DxfUzlEJOWBQ7IeL7ihoWXm+UhdHPdxsZOriasH1qXSc9Bdun9f+ZuRW5FpfqV1aeHwClp2BXEupSHVeE/JVUUuoDvEc1clYn+Wh3pJ1Sb0ly0tdJrSKFXVV3B+1xtQQXro55ikdY1HOgkJxERSK7HeEukvW/WRklpd6Sr5+IvLlyCMLj6lvZJ+8VdWhHpIqTvY7tlMx3zVW+aWWtD1y3Le3mZB+Mi3nzNrf69S8Jz2l1D+l9N2U0i/K+eiU0odrvS7n/GLO+fHy49WSnpI0SNKZkr5fXu37itZOY4wxxhjzKmb7xNH2+ve3kFL6z5TS7JTSkymlO1NKvQrLPptSmptSmpNSOq3WtnZn4ugkSb/Ujp+CeVrxM/vuFLhJ0rEq/XRP/5zz9u+yF6l0O4wxxhhjjDGSSn/1bK9/fyO/kjQq5/walcbMn5VKX3JLOk/SMSr9/Ng3U0pVd7Y7g/S+OeefSNomSTnnLaq8kWCXpJS6S7pD0qU55/Dzcbl0r81O77dJKV2cUmpJKbWsfok/KWqMMcYYY/ZHtk8c3RcH6Tnn+8tjZal089j2n8Q9U9KtOeeNOefnVPoN1tfvbBvb2Z3v9NemlPqoPJhOKZ0gaWX1l5RIKXVUaYD+w5zz/5afXpxSOiTn/GL5vvUlO3ttzvlGSTdKpXuTdmd/xhhjjDFm32Y/srv8g9Q2UWqQ4oyPBeXndsnuDNI/JeluScNSSg9JOljSudVfIqWUkqTvSnoq53xtYdHdki6UdHX5/5/uRhmMMcYYY8yrhHb+xdG+KaWiIeHG8pfHkqSU0mRJA3byus/nnH9aXufzKk3t/eFfW4iag/SyneUUSUdKSpLm5Jw313iZJJ0k6YOSZqSUppef+5xKg/OflCefPi/pPbU21PrnwzXhH3cYGy78JyhQeBSc7Uyby1PII2LcvCrmBYW/GwyF1GI97sQ58BlsuycybSwPIs9XdS5DXogMw8hnBv5HyIu7HRzydB0b8mooEs5tvD3kVkzzL5o3aMGYoyNCHoiGOASF77RhW8hjaRCZHuPIVdHmMvWy40Jeg/Kc3oLK/mKMbbMuyrzwuThrf9BzcVb/wp6xLg8b+NIulw9cGZd1fDEea9PI2HDzekZzDW0rtHYcviTqDjb1mxHyNxUtGucqtiutID/UB0Ier5+H3FXr4/5QnvkaEjItJe/QvW2Pb9YFVctCS1AXRbPEO/WzkBeiIV83G0qlZTGqT4ydtBEZhhT87e8z/b4a8v+nj4X8Jv0urq+rtbs0ahOeiXl4wQQjVdZVL9yZyHaifWVIQ7wAcf33DfpRyDQ2jdOjIc+BReiWOz4S8qhz/hAyTT7LNqJxim+JNEUMRqZJiwYRXE8qjCMjkWnCoIGEDEdm+ShIakUehTwRmTPDiuUbjD8+d4hvir0Hx048sAFvJJDuzF0ZDUUbToDRpBVloUmHBpVm5CbkCcisa7YN+8J5yMW3NdpTTkdeGm0u3PeGVhw7j43GE/ZD2lnYD6nS4HjmOmTWxRrkalYi9kHum32QVp1eyOzjXE7zzU0TCyH2wTr4xdGl1ewuOWe2ZCClNEElv81b8w6N4gtSeHMcXH5ul9SsgfJN7eNVOo06SHp7Skn4dryCnPNUlQb1O4MiQWOMMcYYY/bp211SSqdL+rSkU3LOxW+V7pb0o5TStSp9LThC0mM72UQbu/Mx5WcqfTacofLkUWOMMcYYY/YU++ogXdL1khol/ap057ceyTlfknP+U0rpJ5JmqfQ3kn/KOVcVsezOIH1wWSNjjDHGGGPMHiUrtfc96X81OWfe7FZc9mVJX97dbe3OIP0XKaW355zv392NGmOMMcYY89dQB/ek1wW7UwOPSLozpXSApM0q3Weec84H7tGSGWOMMcaYVyX78O0urxi7M0i/VtIbJM0ozFDdqxx+6Fxd/a137niCRpRaR8EZyr9EjuIKbcZs6uCxgD2lRze8FmaYjg/FvAxmiT60szSqOmtjfPafogGInzyHL49Kg8PWRsvIpn6zYobNoVlTQp6m40PuVZhW31BhkogHQ0PJEKhsOuLYlvWMdpZuA6vr+U+++/H4BK0+mJRf0W/QtoMejTaXH487M+Qj9XTIjf2ieYMGlCI5SjG0QgeFzLrqvzwe+8u945T9jGMZsSS2+1n992kZdAAAIABJREFU7gyZhhSaeS7QzSHTJvOoxoXMtuyDafwP68SQi+Yemma4LUJjSCNsLDw24RzlObbq0Ghz6P1Q1DP07hnz8lGx7tnvx2oaixwYrWjeeRrHUzS00FRDu9JGHMwVMMcMRafuDvvLUGg5uD/2Q1p6uJyvb6CaAvaHmfOiPKFz35dVlaKBhecvrRb8gzOXU55GCwatGlch08rB7Z2MXMv60R2ZJgxaPHh8lxcenw9nw8jYx7cOiIOf1Q3x/KfhqGv3eC3bQKMJy8q6oAeDbUEbzPnIrCs6N9h2dyE3Fx7DUHLwaVG51kuxD/J9jAalFa+NCpMFX4UujmaauchnINNUcwVyMzKP/RJk1sUJVdalAYntSmjKuWUqnmhCxptw08QdjxdGfdK+PHH0lWR3BunzJc1srwG6McYYY4x5deFB+u4N0p+VNCWl9Atpx8frWgpGY4wxxhhjzF/H7gzSnyv/61T+Z4wxxhhjzB5hX7a7vJLszi+O/tveKIgxxhhjjDG2u5TYZQ2klK7POX88pfQzSRX3o+ec37VHS2aMMcYYY16V+J706t+kXyDp45Ku2Utl2SUr1Et36uy2/O6BcRZwTSNKCzIsIooSD3WlDaK4PqwcW/6MDCNAR0xmXr8E26awBBPDK0DZh6yMU9U3dT4g5PQsXg+DwMP9onWDs/ppzuCfn5aqT9vjHpiy/zn4+vsqqm1m6eiQj+z5TMgrFGfN9z80VlZiu/ZHpt0F9hb1RObZABvDect/GvKfex8cMq0j69Wl7fGQntFY0rSxNWRadXjsA56Jx967IRbuhbGxoy1GZbRqaMi12rVR0VTzRv0uZFpBnsMsfhpbZij+Hto6dd1lWXlhHr0x2lC2NsblNJwcoTkhf2vIhSH/Y8v3Qz7wuc0hV9hgcM7S9nCvxod8PC44tLewbYt1IUlNhbrth53Xsr2cqKiT6o/Xj9ETVbf3Lt0dcif0g39+4fqQLx30tZDZFkvQtm8fF8+hRzdGS9DKBTiJN8BSUjRj0IJxK/JZyCcg07JBWwu3T4sGrxc34busDig7jSe0tXB/VyNPRKZhpWh36auqrGyJhVm5AoWjaYb2FdpZYEypqFu2BeuSdc9rezMyj53bZ91WWfdYnBO0NfF9atbimLfNxQWD+2Zb8NhZl6y7i5BZ171UHdplWguPaTBiWWgkYr9gn26G0mjK4ph7xWudWu8ohGjVsd2lRLVB+jxJyjlTeGiMMcYYY8wewfekl6g2SD84pfSpXS203cUYY4wxxuwJfE969UF6g0p/3EhV1jHGGGOMMeYVw7e7lKg2SH8x5/zFvVYSY4wxxhjzqseD9BLVBun+Bt0YY4wxxux1PEivPkh/614rRQ26aF20EmzFCrQx0PYCAwvEFpWvR+5YDLCvLIsyhbiupAPHxryKhhGaZg5F5uzrKOnQlT0nhny27gy5YWysrBYdH/J87PDXaPZzYOn4maJ5s2jxoGliit6saozXvbEs3QaH3Au6g6W949Ty1W+PeT0sGcfMgNoGZh6NQqZhAG3xXO84lX3g2jjNflm3OI3/aR3R9vgQLQzLuj24LeQTNvwx7owmiiExbkYffVjR0jNE0SYzEPuvdfGj9WNdwVQjSUuhLJiPAq7QQSFPh2LgRD3c9rivloZlczU85KbG1pCXFYxC3JZUaV85Tb8MmefchigY0bxuh4d8zMLYj/qsjKadrT1jXT6muMEnNTpkmnNok7imoOngOdJDq0NmO41BJ+Y5SXvT9c9/MuS3HBbr6mjNCvmyQVH2RfNFi+IFj5YfWoxmNcbXr5wCXUTsGlJRHgHJl85FppliIjKtF8ORpyLX2t+38b0Wr92tyLS5cH+nIvPdeipsMn0L++ex432oouy0dtCUw+U0jjyCfB/ydb+IecDfxTwB65+PPBOZhpTbkU9HvrTw+ONx0cNj47WzV7fYMAsexJs+j5X2FhpS7kJm29AGQ1sL14dApaJf0IxDiuWt1afZh698MuYx0dpVcQ41wdZUYUg6Z8fjFV8JizxxtMQuB+k55+W7WmaMMcYYY8yewD9mVMI1YIwxxhhj6grf7uJBujHGGGOMqSM8cbSEB+nGGGOMMaZu8D3pJTxIN8YYY4wxdYXvSd+Dg/SU0hBJN0vqLylLujHn/N8ppYmSPiLppfKqn8s537vzrRhjjDHGmFcTvt2lxJ78mLJF0mU558dTSj0kTUsp/aq87Gs552uqvDawWgcGnd8nhlwflnfuiRdQsUglI7VC0aimjBzkc79m2SJN2Nfm+2Nej/VhUKvUQaKFXujXO2QqzrpqXchU/1GV1xUlGqdHQ6aykSq/oj6OKrwjNSdknnBU6Q3fOjfkLms2h7ykZzz2h3VSyO9deUfIFRrDZaoO2p3qzsO3ROWi0O+6dot1X4TqPKo8nx0a1XNUGB73zFMhd4DK6vVot1a4Oln3ozUj5Dk6MuTV6hHyTXCuUXPIfkh9Zi+9HPL5G3/Y9vi0xqj9+4quqFqWYZoX8go4y9jHqSV8/JSjQqZG8PyFsR9tQDtP6XZKyL+BapTnyLmK26OGkOVfXHCF9oGDkErEJkWnaw/4396s34T8dX0i5IsP+yZev7pqpmKRyz+gH4Z8p84O+fkPwjc3ERrB5ph7N8W6XLdmx9V4w0XxelCh4WtFpkZwMDIViFTtUdnI/Z2HPBeZitdHpsQ8tjlm6uqot7seysfi8fHYeCxU7/HYqWykVvAmZJaVdUXlIrd3NTIVjywfTJ0Vx8vyFMuLy/iayQfHPDLmCv0jR01cTpqRaykWqebk+qy7BTX2x/IW+8JZqs5E5FOhXGRZeU40Ic/G+a5/KzxeKFPJHhuk55xflPRi+fHqlNJTkgbtqf0ZY4wxxph9H3+TXmKv3PCTUmqSdKykRyWdJOnjKaULVNLuX5ZzfnnXrzbGGGOMMa8mPHFUOmBP7yCl1F3SHZIuzTmvkvQtScNU+oPYi5L+axevuzil1JJSatn0Eu9DMMYYY4wx+yPbf8yovf7VC3u0JCmljioN0H+Yc/5fSco5Ly4s/44q745Teb0bJd0oST3HDueNTMYYY4wxZj/Et7uU2JN2lyTpu5KeyjlfW3j+kPL96pJ0tmpPuzDGGGOMMa8iPEjfs9+knyTpg5JmpJS2z2v/nKT3pZTGqKRlbJX00Vob6qJ1FTaKQC17C+0vXB82mCXLYy46RlYtictoa1m/sXqGj0AQUQjCEml+jAuhg3nd7PgZZ/nIOK29x8o4dXxcz2gB+aL+NeQ+UKB85aEvhjzgpGe1K2iG+Zo+GfJ/6v+EfLxadrktSVrfvWPI69Q15LM33hny6p7x2Huvjcc+ddRxIZ/8xcfjDmkFgnknRxmNnuwdFS3rUb6iVaTv1livm9ERfqc3hXxhy0+qli3hDrD+jS+FfG+38SHTttKKafej9WTIjdgh7S4XQe9QtPxI0pC1UTnQq1vc/7WNn2p7/FZNDsvWwOZyzGz0OZzfa0fEu/aOWRjXf2ForGwakM5dCyvQqhg743rQZ1RsyzF6IuSDatT1uuiL0hL1D3lI4aS/6sF/D8u+dMrlIffX4pDZbrxe8NjZbjTdDMEF6ALdHPJknRpyd9hePqT/iev/IK4/9advC5ltu3xNFdfA+cgTkZuQ+XUQDSU0kvDdkbaWk5GnIC9FhthGvZqr75+XRxpQaKspGllo2aARhPuimWanf98uQNPMdVzhVzH2Qjufi9VpZ2Fdz47vLdoyLmbaYGh3ubXweBSW8di/jcz1aZaheeaqGsv5ehpW2G+uRO6LzLZkP2f57yo8pjmGffoKZG6b1iDCftcdRqI1ny6Eu8Mif5NeYk/aXaZKSjtZZCe6McYYY4zZKVmeOCr5F0eNMcYYY0xdkepqAmd74RowxhhjjDF1g293KeFBujHGGGOMqSs8SPcg3RhjjDHG1BFZyfekax8ZpG9Wp2Ap6MzfNtqKzKP6c40d0LAyPcaicOBA2FfWPxBzP1g7aHdZtxb76obM2dMsG1g7NJot7tLZIb+z58/i/mEgaVJryKth1njtSXH69tGaFfImdWp7/LBODMuugI6gJegHKs0T4xriDP6jN8Z9jXgkGkNoX+kGm4tg4jl5OWwu7Cc12iKhLeb3jk8M17yQexRMFwc+szksg5RDC0/BwbBssBU8OyoqAgauXRRyfxw8TTo9YOG4A7qFZqgqTtMvQ67VjxZ2i+U7fGEs3+qBO/rZYQujmUarYr5n5FtiWVbGk+7qxs+E/KVlXwl5UGPUs6zqH4+9M873zbAhPNozWoFoPJmh14T8SX0t5IU6JOR7dUHINCoVjS3fOCVqK+bjgsBz7kjNqbpttus0HR/yYvULeQwuhrS/3PjTfwl54pmxLQi3T6tH5+GxrTbMxQW1eDGmUWQyMg0huHxUnGO1DCi0btyKDTbjYLj9+5CbkXH5qjCULEKmRaQoFWrFMlo8aByhEY2WD+6LRpQTkFtgcyEV1g9kGkx0VIxnYDH7Ak06xbZjvZ6OfCvyqciXfz3mmz4RM+uCmW3B/bEfNyPXeG+osALx+C4tPJ6CZa3IuDbqkW+hLP8YcxPW57GvQL6veK2Mb4rZ96RL2kcG6cYYY4wx5tWDb3fxIN0YY4wxxtQRnjhawoN0Y4wxxhhTN2Qlbd3mQboH6cYYY4wxpn7I0pYtHqR7kG6MMcYYY+qGnJO2bvEQdZ+pgeK9SZth4ej4l28sgonjm3+mXbMwxvVYnDDTej1MNJxEr1XItL9E2YHGLo/KkQQjwLiB0ZBCG8PTOiJk2lz6Y4b1O3V3yAdhenbR8nGEng7LntPQkIdDtzCsig1Fkrot2xay+sT4wNA3hPyWu38fV2A7s/LR7j89++0hn3n3/XEFtD0NKjRtjNaMtseDeqMhIa1g3aw9Klp7ZjXGdnzdQqhn0I+GjYzbG7gyGlNae0YTxYSVt4S8vns8qw5piAc/XceGzH70ruWoO1iOihYSns/zB0YzzDuWR5tLgrXnNT1nxCdoisC+b2+Ayeak34R8kz4S8rF6IuQZGh3yEhhLfqZ3hsxz8I36Xci0HC0tdHTaXO7cGu1NH224IeT16hLyHB0ZciMq40kcyzg9FvJqVOav9daQ33fm90JeAXUFy8/rxxuOj207Y20sj26KMQiimrCMxg/aXGiqaEWmeWJyNOPofFyAmmBzid1WGq7q3ILcjDwWmbaZKVX2dy6W1Xqn57Hfg9xUY3u0sfBauwI6mS3Qx9ACwnN4+IHVt38JMu0xRTMO7SeE/YhWn5Nhc2E/YzvVKusEZLY7bS/6MvLnY6TZ5iK8OZxXqEsakGgQYlnHwuYyCctZ79w+TTSDC2VffKdMJfvMIN0YY4wxxuz/lL5J9+0uHqQbY4wxxpj6IcuDdHmQbowxxhhj6oick7Zs9iDdg3RjjDHGGFNHJG3b6iGqa8AYY4wxxtQPWZJvd1HKObd3GWoycOwh+cMtH2rLX3rmK3GFL+AFtdr1QeT+Mc7C7O/i5OzoTpCeRD6Om6aJBh+LOk7AC/ohQygAcUSFDWb5iDh9en3BviJV2hYm69SQWzGNn8aVQ6A4maHXtD2ep2FVX/t6RfMMTQ+dtCnk8fp5yL2wfu+ZUNvA+vH8Ww4O+bDZ0XAiSEH+9O7DQz7mhmfjCuxXI5DR1kHaAbmLUPSKj8s0AtB2gOXLR8V2770k7mBtz2iL+WXjaSGPRmU0YAd9t0bTRcOWqM7h9rpqXcjsN0eufabtcednFEGfXz4kHtuLsKEM2Tq/atkWNsb1B25cWHU5y05osuGv4tGYsgbrN0A7xO09UdBD8LUvw57Cuni/fhQyzTJNUJoM1XMhL8bFkHaYXno55DdDMcLrxxc3/mvIvMd044bGkIf0iW25ZG28IK65pnBO85yhteOuWTGfG+uiwihCQwnPORpP7kOG7EUQmFQYUmgNgbCpwvJB8wavIUWrCLdNewutOawLGkWakFthvrmEb1SAZX2k+uo6q8b+WVfM7BuTp+14fMnxcRmtPKznXsiTapTtVGS2I7fHtoIIp2J9lpf9lgYVtv3swmOaabgvZtYr6/1Wjid/GGPT+bve3vyxyhta0vaYRo/N+ukf1G4MO2Bazpmtt9fxN+nGGGOMMaZ+yJK2pJqr7e94kG6MMcYYY+qLih+WefXhQboxxhhjjKkfsjxIlwfpxhhjjDGmnvAgXZIH6cYYY4wxpp7Ikja3dyH+OlJK/6mS5mOTpHmSPpRzXpFSapL0lKQ55VUfyTlfUm1be2yQnlLqLOm3khrL+7k95/yFlNJQSbeq5HCYJumDOedNu95SyZ4QLAe1Sr0VuRGZs59XxrgeizsWHq/CMq7L3AX7TrS3jEPmDsbH+MLA3iHfixWOUjQa0O7yT7o+5A/ABvFEUARIp+rXIdPYUrQ5HI19L4WmgzaXftCxrMBU8hU6KGTaXWhz0Z9jPOxB2Fx6Yn0ICY55ADYXcgoy+yGEB4sG7thh68CmsGwjOuUpCx+LL6apAvt6fjzMNQtxrGBxY+x4p26N+oZfNkQ7C9uyy5qoPFjSM/bDYZobMttuGRQEM7rtOP6mMa27KHWJxq0bQ36yIdpTbm84N+TRDdFUMw5WodWN0ZgyYmFUHBTbTZJmwNbCfkhj0lAYVB7WiSF/XZ8I+TT9cpfbWwLbykZ1Cvls3RXyo7igjNETIbMd/kcfCpmWHx7bJux/LoxOvN6c23h7yBsbY7+/t3O8fj07DwaWazBxjOaMInchX4dt8bpfy65yOvJ5yCORadGg0WR6jf3VMprQiHIP7DVXFI6X5hlaOliPLPsVyDSOdMfFk7YYGki4v+HIY5Bp1pmLTKPJqBqv31IwukzCMrYzfR5sxzXIj8DxtuA1MfP1zLQAsW4uRz4DmWaeW5CbkVsLj3ksk34V89i3xcx24DlFLdAo2Fxm0pz17cLjxXFRVuVYbt/hV5I+m3PeklL6qqTPSvpMedn/396Zh0lVnev+/WigGW1kphlsJlFEQW2VozGiEjWIQm7I0RgTMYPDVRNzExM95iQ5Hn0Sj95ojCbqMRG9MaIXI6ISUVQcAwoyiqIgjTILCDI22n7nj9pN7+8tukpam66W9/c8POy3dtXea6+9du1V1Wv9aom7c4uvlSb5n1JnKgGc5O6DkbkETzOzoQCuB3CTu/cD8AGA79VjGYQQQgghRGPj4wb89xlw9yfdvXor05EtaP3U1Fsn3TNUf05rlvxzACcBqP565R5kf38ghBBCCCH2VarHpDfCTjrxXQD/SOXeZjbbzJ4zs+Pzvbhex6SbWREyQ1r6AbgNmbE5G1OfMJYD6F7Lay8AcAEAtO3Ff6sTQgghhBBfSBp+4mhHM0sPPL3T3e+sDmY2FdkDuwDgand/JHnO1cgcRfWvOq0C0Mvd15vZkQAmmtkh7s4DnXdRr510d68CMMTM2gF4GNmj+HK99k4AdwJAl/Iehf+zqEIIIYQQ4rPT8J30dbl+cdTdc82QgZmNRWYGwcnu7slrKpEZCg53n2VmSwAciOxZaLuozzHpu3D3jQCeBfAvANqZWfWHgx4AVuyNMgghhBBCCFGfmNlpAH4G4Ex335Z6vFMywgRm1gdAfwA5jRX11klPCtMuWW4J4CvIqGeeBVCtYzgPwCP1VQYhhBBCCNHIaNxj0m8F0BbAU2Y2x8yqNTZfBjDPzOYgMzfzInffkGtD9TncpRuAe5JPDU0APOjuj5nZQgDjzexaALMB/DnfhpphJ7phZc0DrN5jjRDD64soR8sZtr8dc8vUchTPZRuJBrJikSE11Ovf7BPyIc/FD1VP9Izev+MrXwh5WPG0kNcgFqAlovKIlYtTENV7rFxchANDXoioNRuwS/cJlJF6bjG5pFg7uJkq4284J2TWAGbBJ4PrnpSMeC/Guef2D3nwk3Tio30uW4PYO2oQ9+8VPWZpXR6fh9CeAXzYpVnI+x1Mgtg3Yjzg/qhcfOSbp4Q8asGTIfduERWKRtrRQ3tH9d5UcqZdU/LLkK/FL0IeuCnW3YSSUSF/GbHdbkup+ibS3PFhmBbyLUWXhfyHlT8PeWuH+F3DD4r/O2Rul+eSo+z+0ujWY00haw9ZS8jawxGYHHJzRMMsaw6bIyomT6TjT8PXSBmWhtyP3pE+IPdeR/KEsl6yiJxn3ye3Hh/7ZJwecvr9AABWojRkvqbLimL5N2ykKUpX0nVwY+o6YXUd24aXU+Y/KD9GoyjHkO5xHD2f1XdllFdT5vsOT63imwer+Xg951+QYjL9h3nWNfLI2fHjYh46NmZWLrJqjwcBDKPMGkHu9LDikZWJXFe8npWR4yhvoQMoSzUWlt9NnBbzjmEx87HeTLkHKRcZshJiDOU3KVdQ5nbO53ICZT5XN8f3Q1z0g5plruchpFxkbWgZclNGr5/O9/D5lI9LLVM5gYYe7lJnEnvh7h5/CMBDe7Kteuuku/s8AIfv5vF3ABxdX/sVQgghhBCNmEb8Y0afJ/rFUSGEEEIIUTg07h8z+txQJ10IIYQQQhQODW93KQjUSRdCCCGEEIWDOukA1EkXQgghhBCFhDrpABpJJ70JHK2wveYBPnH5xi21pswzxU+g/FyMXVI2mA9pXywYwTcoT8pdlkOeIUVmFI5gM9rGl2/6JORWnaM1pClVBltEjsXLIS/CgJAraPp2KVaFvBLdQv7z05fuWr7g5N+HdadiCnJRgd4hX4ZbQm5HU/j/E9EwcuyQeCxHY0bIBzSNBhSQ0SS0KQAkn8myAGwtiRaRXhvi9i1WNfYrrlnfq0N87sr2WS0nwm2WDERsnhl1R7S54Gsxbmsdy76ufYeQ78L3Q2arRxesyfn8ESXRaMLtsF1lPJdFxTXr+2JJWHcNnefbqi4JeUdJiJhUfEbI/xu3hbyRFAZn4NGQ2ViyEfuHzMaStJkGAKpIF/UWGZG2BT8UMAKPh8ztMF3Xa+jE8/XMJpqepDDaSY16O5X9fNwdMptqZuHIkNn+Ukxmmg5kj1mP2M5mkiqDr/GvHvn3kNkOM3f00JrAho8elNm2wne70WRzYdPFIMr5bC2ceX/rKHP5+b5WRpmtHpzT+zuX1rHt5faxMbMRhI0kf2VVDv9Q+IMxnntWzMPo6bR7ervJNqDwuf0rZT5XPUiJsiC1zOehKxWOzxtbdRZQnkj515Rp83ltdEMps42G23W+dtTjB7Wv522x1WcxnfeRdCL4vHAzGUIGoo6U02VfyDc9qJOORtJJF0IIIYQQ+wj6Jh2AOulCCCGEEKKQUCcdgDrpQgghhBCikJAnHYA66UIIIYQQopCQJx2AOulCCCGEEKLQ0HCXxtFJb4JPokWALB1ZJ5KPqhdlnkT8RowHR1kDOnw7tTwzruu9Abk5n/J+Mb59Upwe3f+lOD36G0WPhfxY6Ukhs5niLbK1sK1hGKaFzLYGzg9jdMgdyd4w+OQabQCbHdgUsxh9Q+6NipD3p2nqbOUoo+ezyeL/4Tsh/wL/N2QcHmNpJelYeNY+rX7hm8eH3KE4Hm9R+1h3O9F81/LAqoXxtVtjw6lofUDIi0ujhWNnafOQ2YjSqScpA+gauanzj0NmqwbbXPhcsmXkeDwf8kIcHPKJ1M6qmsaLKm3tWIg44/8FxHp+vGhEyHz9cjthWwof64l4NuQP6PXbycYyFcNDZtPNcFJh8PaWksXoD/hhyGw1Sm+fj60YO0Nm0wzboObj0JD5vL6MY0Pm9xOGr8GPyWzzm7nXhNxp8Lshv/9AfDPuela0W61+vU/c4c1UgPT7Lwk80I/yjZTZRMFWDDZTsGGknDLdC7JsLHwf4jySMt/H2CrCBha2lExLLbNBhPfFx8rvfWdT/hJV3kW0fjTZXMpo/ZuUuXxsZ+HyTPgw5qF0I+Vz0xG1w/vmunhsXsz9DouZbStjKbN55td/otdfHDO3Mz52bmd8rNwuyiizwaUitZxlsuHxJXTeuY2OR274+XMeogfmp5bphqsx6QAaSSddCCGEEELsI6iTDkCddCGEEEIIUUho4igAoEn+pwghhBBCCCH2JvomXQghhBBCFA6yuwBQJ10IIYQQQhQaGpMOc/eGLkNeepe391/P/MqufN7DD8Yn3J5nAydTnk+ZDS3tKZemlt+mdR0oX5hn2zsob43RT435pfZHhPwozgy5FbZRcaK9oQxLQ2b7y1KaCn7rimieGNn90ZDn0NT2tGGFzTFs/Kiiz4RT1seDvaTDbSGzOeJ0TA65M9aGvI2sHLy/gYiGlU7vxWn+y3p2CvmAOe+HPH3IYNpfNGtwXU9BzfG1JaUAG0TOQKznVlXxvLbcEgfnbSxpE3K7TXH7TekbiEntTwm5lGbS83llyw9bQrgu7yWzzrF4OWSum4n42q7lUzElrGuLzSFzPbOthcvKthi2uVRRu2pOxhS2uWxGrGu2CrEhha1F3I7Xk3qC6zZdvkPpzYrrhq9fXr8TxSGznYXP+z+XRbNOm46xrlu2juac95d1C5kb3sHdYzth3rgtvr9lGVsqKKdPBVs6OLNR5LeU2ZJRRvkg5IbtKk9QZqsGG1GGUmaby62U2VjCr0+v522Nvy7mMVfHzKYbZmq8r+BSuvHxsbbLk9lgwucia/+U21Dm7fO5T9tt2PrDtha2BLEViM97vrLw8yuQm6551nM75+2x7YXrNn2uJtK6HXSeszo4/02ZLDtNyfLDnWxuw+l293E53GdadbTO5Y4xrLbZi/zJZrl7vpZZ7+ibdCGEEEIIUTho4igAddKFEEIIIUQhoTHpANRJF0IIIYQQhYQ86QDUSRdCCCGEEIWGOun110k3sxYAngdQnOxngrv/yszGATgBNT9cPtbd59RXOYQQQgghRCNCY9IB1O836ZUATnL3LWbWDMCLZvaPZN0V7j7h027o3U964dKtNVPdz2tNdpdiegFPSO7PG6TM457YyFKSWu5D69ZQ5lnrx1GOkgtCjI1iAAAfuklEQVSsPrMk5MXoG/I0nBjyMZgR8kaaSs5mig40tZxNGYeRPeIX3a8J+doVvwy5CdkbyrpU7FpmU0Qlmod8ItlfSjtEw0glnUi2frRENEsw/bAk5K4rN4X8dmmcpt9pR5wmf8DMaHN5pvxfQu5LyoQlpAEYicdDHpGy0ZyPu8O6Q9a+E/JH1Iar6Mrc3qZZyMVVlSHPKImWjC1oGzIbSdjusp4umklkEToU80JmY8nX8HDIEzAGuUif23y2ljXoEjK3q1KsCrk3XWSLyGiUbUCJ2ytGrNv30DNkrsuz8EDIbFBi+wu/fjTVXdPU8a+j85I2BgHA2/dE49DR5z0Xcjc6z5MxIuRl95HCZGi0ffVsHcu6piqeCyyP7RITY35jJNlb2LrBZg2+I/E3aeNzPJctX2w/+T5ltnLwvtjExdYMtmywuYINK3xvyGW6ALItIXy8LL5Ib38QretHNhc+Vj4PXHbQeb6VrHCXW8xcV/w1HNtauDx8rGw84XPHViB2ckxPLQ+nddwmuezj6abdo3fMy6kT8CW6RrisfN5Oo8zH/hjl1WRgOY06PFy33BbS55pNNtNoWy9+SE84GDnJZ6ZZzr3u36WWSRGkMekA6rGT7hm3Y/XbWLPkX+H7HoUQQgghRMOhMekAgCb1uXEzKzKzOQDWAnjK3au/Br7OzOaZ2U1mxt+DCyGEEEKIfZXqTnpD/SsQ6rWT7u5V7j4EmZ8DONrMBgG4CpmfiTgKmZ8N+vnuXmtmF5jZTDOb6etYsC+EEEIIIb6QVI9Jb6h/BUK9dtKrcfeNAJ4FcJq7r/IMlQDuBnB0La+5093L3b3cOvIgcyGEEEII8YWlqgH/FQj1aXfpBOAjd99oZi0BfAXA9WbWzd1XmZkBGI3s6TRCCCGEEGJfRWPSAdSv3aUbgHvMrAiZb+wfdPfHzOyZpANvyMz5vqgeyyCEEEIIIRoT6qQDqF+7yzwAh+/m8ZP2dFvdm6zAT1r/ouaB1vQEViYRH9Eem71NT2CrEKu30trFIlo3g3LnGD/sE9VV+9FgJ1Yosh7uWLwc8hraAevlWLk4H4eFzHo51t99QOXp331RyG8/EJVvL3b9yq7lTidEt2UX8lP++4prQz6ve9QS3rPi/JArupeFzApEVtlx3XUpXRvy8CpyU22NkaoGx2/6Z8jXlFwVMuswL8MtIW9OaRCfJZVm8847Q16IgSE/ijNC/jFuCvnATVHheFibqNKcWRQdZKzHnLH7UWa7YCUjq0BZFbqd1J5vLDs05A4HxHkl9+Lbu5a5HgfgrZBZybgSpSFPxukhcxsfRupPbjcM6yMPJU3penLnjUNst6w9fGZZ1CZiC+ns2sT3hEEH1PjqWMG4+vrogO3689gO+HpmfeSyGaRcHBbf7Fix+sYMUiiyFvBGyldSXkeZ31snUmZdHO8vbWljlR4PJJ1eEfNycvFSVWQpFVmNx7o6vu+wUpEVkKxY5LriYx2W5/XLKafrlsvKx8Jlz6d/nLZfzGNp/V8pn0u5B+VhlKeS+G04KR35XHO7upUyn9tcnb1cukYAKCPlIuseP2YtKa2v4O3l2R+fGz6W1fT+we2WVaO3Xx/zmNQ0QNZB8r6b0nn/mDpLZTQUmc8z6y1J5YugVf4krpInHcBeGpMuhBBCCCGE+PTU53AXIYQQQggh9gz9mBEAddKFEEIIIUShoTHp6qQLIYQQQogCQhNHAaiTLoQQQgghCglNHAUAmLvnf1YDU1Lez4+b+V+78uQFX49PGEcvoAnJILkCnqZcSvnkGOf2rLECDL6D1DBv0GtPiPG5r0WLRk9EAwpbMTogWjCmUOHZ3sAGlZdxbMjXVV0d8veL7gqZjShsi/nbmnNC/uQJUuukizMsruJZ7s2GfhjL0iGWpR/ZWz4mlQ4bRti6wSacKno9120rbEMuuK7TRhIA2InikHNZQwYgWnLyGUvYeMKWDmYIZudcvxbRQMBWoaWkHDhnxfiQL+8e7TKdEc05XNcM103aUjSHJFDcplsGAwCwlgxHs8lcw/aVNZvi80tLVoU8Ao+HzHXNhqSBWBjyNrQM+Zm5I0M+YvCLIXO7WTDrqJDTza7TyfH9gs01y5eQxmMiWTHY2sFWDDZBLKbchjLZWFrcuCHkpmSH2bKONCEVZKYYTdu/FrkZl1rmso+jzFaMrpSnUV5NuYwyf6vHphp+/RDKfC7YKsJmG34+74+/Yku/nQ6jdfxrJLyejST5DCP8/AmUz6bMdfdYnvJwXcyhPI76LaOp3XP50u2Yj43tKFzW6XSf6NgKOWGpNLcL3n8+GwzXBT+fz+26pTFfSnaaNGyWmUkbb0G6Fq6bj7kXvZ0yd8ZYA7QstfxHuK/YdSKtuNzRnfUze5GlNsvd+Srd6+ibdCGEEEIIUThouAsAddKFEEIIIUShoU66OulCCCGEEKKA0Jh0AOqkCyGEEEKIQkKedADqpAshhBBCiEJCY9IBNBK7i/Uqd/y8ZpbvG5eUhfUH3b8svoA/ffEE4w8p8+xpnn2dFlfwx5onKR9JuQvlTTG+PqhPyDNpyv9ONA/5cYwIuRTRVMGWjakYnnN9JW1/e2Wcub5pPCkRojgj1gdNBG9/0IqQN8zsHvLXj4kzvdmmshltQy6jae8jMDlkNtXwsbK141lSCpRjVsilWBnyQgzMub0xpDhIG03YVMOvrUCcgb8S3UJuSo16DTWsc3BfyGw4uQMXhsz2mHxwu3x4U9RyDC+JyqSpm6Ii6Vslfws5XTd8nhejb8gzcEzIfJ7m49CQN1fF7R1TFI+1ORlSuG4fuu3ckE+45ImQuV1xebdsjfvfMqdTyG2GvB/yga2j+Sddnnmb4rHteKJ9yFlmh/GUb6VcQflKypdT5vc7vv7ZYPIiZbbFsJmC959l8aCcboa351i3u23xsfH7Pls+nqBcQZne7/LeR/j5XJd8Ltm8wZA4J1hEeN3EeM1gCN2o5syLuUc0GiFeEtntgm0tfJ6HUuZjZ4nHArpJD6GbOFuK2D7DdZneH7dZNhgxXFYWlHBZxlIuo8ztgu0v0yjzNURCp6xjHU6Znz81tcwGocXUDoZQO+Cyc9nWLaQHXqFcRvm11PJNcH+vxu7SpNzRogHtLttldxFCCCGEECKiMekA1EkXQgghhBCFhsakq5MuhBBCCCEKjMIfjV3vNGnoAgghhBBCCCEi6qQLIYQQQghRYDSO4S4fIcwWvw/nhNWXfPOPIXddSgoVPsqtMU4/aHDIx2yYG3Jlce1Fa0Ez+leUR/tC500bQm5GRVtKs53ZKMLmi9PJaPIuWUKepqndx+P5kO95/eKQOx3ybsiVO6LtBTtizJrNzbPqUxQV0YAyfm2UdmTZU9g8cyQpAKbg1JD70Q64LiuorrtgbcgDEWem8/4XYUDIh2N2yM2xM+RtqDHlrEFn5GIqog3lSlwfctnWaDBa0jpagdiA0hLbcpaNbS35zDgDEA0kN5T8LGQ2rLQr2ZhzfdpucyBt+1i8HPKX8ULIbLZZy3Ubi45/XPW/Qr74N78LuQPpGU66JKoqnpk1MuQeR74d8gC8FfKhraMhYfNx8Rp+YNNZIS+pjPqFTRNSRqUKREi2lGWe+C3lfNaNuyiz4eRNytMos+mCyzch1hV+2j9mNqhwedh8Ed4CYhvH4mimyrJ2sPnmS5S5rrbQwQ0hxQnbXFhEwYaVCXnWsyVkMRlZWB02hlanzRu87bH0WjaCDCKLB5toKihz3Z5NmdslHztbRbjdrCObCzs2eP9sFVpN4yR2WM0yG0q4LHxeb78u5qFXU6bn8zU0jTKfZ27jFZT5XPCxcjvm42ON4bAc++J7NOctPP7EKA+k3JJyb8pp85bGtuwOfZMuhBBCCCHE54CZ/aeZzTOzOWb2pJmVJo+bmd1iZouT9Ufk25Y66UIIIYQQooCodjA21L/PxA3ufpi7D0Hmb3S/TB7/KoD+yb8LAPwp34Yax3AXIYQQQgixj9B4f3LU3dO/xtUaNWN5RgG41zO/IjrdzNqZWTd3X5W1kQR10oUQQgghRAHRuH/NyMyuA/AdZH5n/sTk4e5A6ue2M7OEugOotZNe78NdzKzIzGab2WNJ7m1mM5IxOQ+YWfN82xBCCCGEEPsK1d+kN9Q/dDSzmal/F6RLZ2ZTzWzBbv6NAgB3v9rdewK4D8Clda0Fy3zrXn+Y2f9BZm72fu4+0sweBPB3dx9vZrcDmOvuOcflWNdyx7drps5PuyGaLE54+5WcZXixfxyb3zTPz1gNfSnaXdA6tVxJTyaTxKvlcWp4Z6wJuWPl+pAnF48ImW0uszEk5J7hQ1i2oeRhjA55PuKs/SL68xFbPZjXlhwXH1hNs7nTm+NZ8tNj7Pqbd0Lm88DH9h6Za0bj4ZDb0jT6noimmgmkP7gFPwq5A+K5aFsZ66KyOH5+fIi2x2adwzA/5LRxZTH6hnU/xB9CfgHHh1xEddOXptmzUWQRDgyZ67YSUVE0i0wRbH/pQu12JUpDfhhfC5mNLB1JYcDHkzbnrCVbyzp0CLmCjABsmmFzDpd19ZL4+j59o8WH2x2bbbjsm0ktUVEVt19aFK1CCx4/KuQsk8XNMTa5sUY/9cny1nFlPvvJWMq8LzaQVFBmUwRbQOiazrJ4sA3mNMr9KE+jzFYSZkEty7vbF1s62FTFsDGE/85ckef5XBdsk+FzxWasMsp8rtjyMYRy+pLjY52a57VsDOF9sRUo33nluhmXZ/tsJMlnl2ErEO+P6zq9PW5jbGdh+8qcPGVhy9CYPJYhtgit+5AeeIle/9WY2XTD55rbLe8/fXxcF1zvfD1PXxpzR7K18OiUjWR3akF2px3p/udRcJ+5q4NhNtiBJ9FwdJ3l7lzbe4yZ9QIw2d0HmdkdAKa5+/3JukUAhuUa7lKv36SbWQ8ApyO5tZiZATgJNUKmewDqVQohhBBCiH2Yxjtx1MzSn0ZGoebjziQA30ksL0MBbMrVQQfqf0z6zQB+Buz6ergDgI3uXv15q3o8jhBCCCGEEAmNc+IogN+a2QAAnwBYBuCi5PHJAEYgY6DfBuD8fBuqt066mY0EsNbdZ5nZsDq8/gJkFDVA216fb+GEEEIIIUSB0ngnjrr712t53AFcsifbqs9v0o8DcKaZjUBmxNl+AH4PoJ2ZNU2+Te8BYMXuXuzudwK4E0jGpAshhBBCiH2Axqtg/DyptzHp7n6Vu/dw9zJkfjT4GXf/FoBnUfODxucBeKS+yiCEEEIIIRobjXdM+udJvdtdACAZ7vLTxO7SB5l57+0BzAZwrruzMyXQrPww339mzZToq3FdWP+jN++MLyDjyu/7B3MORmByyNvQMuTBb9OM5DTvUY6CECz7RqeQD1jwfshb+8fPReuKo8mCrRwbafr1mZgU8oW4PeQ5ODzkYzAj5OZZeprcrCdlwTbEmetpA8tCHBzWVdEfatiawXaVdviActTFtML2kMtJf1CKaNXoRrmUVKQX0499jcXdIR9O0/oXYQBywcd3By7ctTwaE8M6tqec8vQLIT90crT+8L6/T5qP9WREuWbXD5xlYCMK1xXbXSYj7v9Aen1vUhxwu1hCNpuTSS+RPpdcdm7zzEs4NmS2s3A7KUM0EuxP7epjOm8dqV2yGWcnovWH7S98PGwp4sztvCxVt2xf4n2xrSnfNcfnaTu99/F6Phds3uFjSRuNAGD1dX1CzjJNsBWEjS1Mu1qWgWzJ2VjKbK5gi0c++wt7HsiYUnJrVIq0LY7nriVZQNZXxffWnTtiu9oyJ95LsowmXFcda1ne3XMvp1wW+wEH952NXOSyNQHAkjXxxH6ygCxFv6YNct2zToJtMgy3qwrKf00tX5ln32SaadY12lc+WrxffALbX/jL39sp87Hw/vma4Hb+V8psp+Fzz+WblloeRuvY8jOc8mLKZZTZ/rRgGj1wAuUNqeWT4T4nZXcZ6NkHuzc58nOxu3xW9sqPGbn7NCRNw93fAXD03tivEEIIIYRobDTeMemfJ/rFUSGEEEIIUUBoTDqgTroQQgghhCgo9E06oE66EEIIIYQoKPRNOqBOuhBCCCGEKCj0TTpQjwpGIYQQQgghRN3YKwrGz0rL8oO938x7d+VHcGZY32dmVF991D++flbJ4JBZufalla/FF4yjAqQVRrRtDKG8gfIIyu9Q3ko5WgzxVO8vhcyqvJdJR8dqvTJyUbEabyEGhswKtg7kKWPF21p03rU8BaeGdcuvosoaGeMpx0VF/qGYT/uOKrxWpDBjxmBCyFxXrSrj668oviHk/8CvQt5MXq9pODFk1tutI/fVtJTf6iw8ENaxjo3VmazxuxB3IBcDqqIisfmO+A3E1NYnhcyqT74mWK3XD0tCriD3FmsRWdF4Bh4NeU7qwmGtXzGdt77k/dpJSkRu81y3fCw34oqQf4lrQj5+0z9DXl9SEjK3c9Yknlo5JeTWaz8J+SMyuC0sidfJKpTWui/mHPwtZL7eH8UZIbOak3W0rKvka4qVrDfipyEvpveX1+6I71/oGiNrDDGI8ps51lfQurHxXtan78KQO9J7WRs6b6xoZSUsqz35/Si7Hcbns86SX8/6yhdwfMjvbY3teMuLpGhMW1lZnUf3qa7HxBtRL9KYstaUM79fsEaUr4mN2D/kfNpVVjoOwFsh832Jz81mtK11f7yO74Gsl3zukehMHDTq1ZA7k06X75GsbOX9v1cZz2tR07j/nkWx7otJo8z3nXeWxOPBXRbz2TWLPQZH3XRfep/na4DLvoR8kcteOijuiw2KrGhM6ycryuHbZ6YUjP0d+B0ajjP3HQWjEEIIIYQQnw4NdwHUSRdCCCGEEAWFOumAOulCCCGEEKKgkN0FUCddCCGEEEIUFPomHVAnXQghhBBCFBT6Jh1oJJ30FqjEANTYK17Al8P6ovJnQ96/Ks4055noh1VFiwhNls42DJyQWi6mdWQfWHZhnHHPZWnbOn4yXNo+6g6epmn5a9AlZJ7VzrOtb8FlIZ+IaXH/NOueZ7KzDYLrrgvWhpyelb98BtlcymKkSeh48r5RIU8dHo+9b5do9RiOp0PmWe48S59tMRuLo0GAjSs34cchv4UDQ2ZLyGX4Q8jpNgoAE/D1XcuLaRY82ww487amUrvoQkYBOk14r3Us65qUhQfIbjdse+H1nLeTBeihrWNC/k3rq2J5qO7SZhw22wzB7JDZDMG2hJkoz7me2/wxmEGvPzLkJSXRULIIA0KeTaoMNk9sLo51VdQz1u1aOhdDgj4qXnNsIGGLBZeN8/eW3RPypQfcFDJbePia4WtqLb0f8Xl97dvR5tLi1qi72jGzfcjtb14R8oaK0pCxhcwUT6SW2fxydnwun3e2rWzJMlXENs12KLY9sfGEr+FuZHvha2wbWobMthc2bW15jGwu8dYCXFmz2OnId8Oq9WuiTWX1n/vE3CPmV/qR9W01nYfYZLPe6w84PZ4cPhd8bPz+8v7jveIGua/WjvKNMZZMiMa3vsU195J8Jpi5K0iFQ0aSBcuPQk7YB7KOMhtPSIgS2jiADRd1jw+8SM/nuhhGme67uLlmcfml8Z69eVCsi02LSccUqzW7B1lBmdtoP8ppu8sqWqdv0gE0kk66EEIIIYTYV9A36YA66UIIIYQQoqDQN+mAOulCCCGEEKKg0DfpgDrpQgghhBCioNA36YA66UIIIYQQoqDQN+kAYO6e/1kNjJm9D2AZMvOUea60+HSo7uqO6q5uqN7qjuqu7qju6o7qrm6o3upOdd0d4O67FEZm9gSy3TR7k3XufloD7h9AI+mkV2NmM92dBUfiU6C6qzuqu7qheqs7qru6o7qrO6q7uqF6qzuqu9w0aegCCCGEEEIIISLqpAshhBBCCFFgNLZO+p0NXYBGjOqu7qju6obqre6o7uqO6q7uqO7qhuqt7qjuctCoxqQLIYQQQgixL9DYvkkXQgghhBDiC0+j6KSb2WlmtsjMFpvZlQ1dnkLGzHqa2bNmttDMXjezHyWPtzezp8zs7eT//Ru6rIWKmRWZ2WwzeyzJvc1sRtL+HjCz5g1dxkLEzNqZ2QQze9PM3jCzf1G7y4+Z/Ti5VheY2f1m1kJtrnbM7C9mttbMFqQe2207swy3JPU4z8yOaLiSNyy11NsNyfU6z8weNrN2qXVXJfW2yMxObZhSFwa7q7vUup+YmZtZxySrzaWore7M7LKk7b1uZv+VelztLkXBd9LNrAjAbQC+CmAggG+a2cCGLVVB8zGAn7j7QABDAVyS1NeVAJ529/4Ank6y2D0/AvBGKl8P4CZ37wfgAwDfa5BSFT6/B/CEux8EYDAydah2lwMz6w7ghwDK3X0QgCIAZ0NtLhfjALC/uLZ29lUA/ZN/FwD4014qYyEyDtn19hSAQe5+GIC3AFwFAMk942wAhySv+WNyL95XGYfsuoOZ9QRwCoB3Uw+rzUXGgerOzE4EMArAYHc/BMCNyeNqd0TBd9IBHA1gsbu/4+47AYxH5uSK3eDuq9z9tWR5MzIdpe7I1Nk9ydPuATC6YUpY2JhZDwCnA7gryQbgJAATkqeo7naDmZUA+DKAPwOAu+90941Qu/s0NAXQ0syaAmgFYBXU5mrF3Z8HsIEerq2djQJwr2eYDqCdmXXbOyUtLHZXb+7+pLtX/6zjdAA9kuVRAMa7e6W7LwWwGJl78T5JLW0OAG4C8DNkfh6zGrW5FLXU3cUAfuvulclz1iaPq90RjaGT3h3Ae6m8PHlM5MHMygAcDmAGgC7uvipZtRpAlwYqVqFzMzJvup8kuQOAjakbmdrf7ukN4H0AdydDhe4ys9ZQu8uJu69A5lukd5HpnG8CMAtqc3tKbe1M949Pz3cB/CNZVr3lwcxGAVjh7nNpleouPwcCOD4Z0vecmR2VPK66IxpDJ13UATNrA+AhAJe7+4fpdZ5R+kjrQ5jZSABr3X1WQ5elEdIUwBEA/uTuhwPYChraonaXTTJ2ehQyH3JKAbTGbv6sLj49amd7jpldjcxQyfsauiyNATNrBeDfAPyyocvSSGkKoD0yQ3KvAPBg8ldrQTSGTvoKAD1TuUfymKgFM2uGTAf9Pnf/e/Lwmuo/uSX/r63t9fswxwE408wqkBlWdRIy46zbJUMRALW/2lgOYLm7z0jyBGQ67Wp3uRkOYKm7v+/uHwH4OzLtUG1uz6itnen+kQczGwtgJIBveY2TWfWWm77IfLCem9wvegB4zcy6QnX3aVgO4O/JkKBXkPnLdUeo7rJoDJ30VwH0T2wHzZGZVDCpgctUsCSfRv8M4A13/11q1SQA5yXL5wF4ZG+XrdBx96vcvYe7lyHTzp5x928BeBbAmORpqrvd4O6rAbxnZgOSh04GsBBqd/l4F8BQM2uVXLvV9aY2t2fU1s4mAfhOYtwYCmBTaljMPo+ZnYbM8L4z3X1batUkAGebWbGZ9UZmEuQrDVHGQsTd57t7Z3cvS+4XywEckbwPqs3lZyKAEwHAzA4E0BzAOqjdZdE0/1MaFnf/2MwuBTAFGfPBX9z99QYuViFzHIBvA5hvZnOSx/4NwG+R+ZPS9wAsA/CvDVS+xsjPAYw3s2sBzEYyOVJkcRmA+5IP0+8AOB+ZLwLU7mrB3WeY2QQAryEz3GA2Mr/A9zjU5naLmd0PYBiAjma2HMCvUPv722QAI5CZgLYNmTa5T1JLvV0FoBjAU8log+nufpG7v25mDyLzgfFjAJe4e1XDlLzh2V3duXtt16TaXIpa2t1fAPwl0TLuBHBe8lcctTtCvzgqhBBCCCFEgdEYhrsIIYQQQgixT6FOuhBCCCGEEAWGOulCCCGEEEIUGOqkCyGEEEIIUWCoky6EEEIIIUSBUfAKRiGEKCTMrArA/NRDo929ooGKI4QQ4guKFIxCCLEHmNkWd29TyzpD5n31k71cLCGEEF8wNNxFCCE+A2ZWZmaLzOxeAAsA9DSzK8zsVTObZ2b/kXru1Wb2lpm9aGb3m9lPk8enmVl5stwx+alxmFmRmd2Q2taFyePDktdMMLM3zey+5AMCzOwoM3vZzOaa2Stm1tbMnjezIalyvGhmg/daJQkhhNhjNNxFCCH2jJapX/NdCuDHyPx89XnuPt3MTkny0QAMwCQz+zKArQDOBjAEmffe1wDMyrOv7yHzs+JHmVkxgJfM7Mlk3eEADgGwEsBLAI4zs1cAPADgLHd/1cz2A7AdmV8sHQvg8uRnuFu4+9zPWhFCCCHqD3XShRBiz9ju7ulvpcsALHP36clDpyT/Zie5DTKd9rYAHnb3bcnrJn2KfZ0C4DAzG5PkkmRbOwG84u7Lk23NAVAGYBOAVe7+KgC4+4fJ+v8P4N/N7AoA3wUwbk8PWgghxN5FnXQhhPjsbE0tG4DfuPsd6SeY2eU5Xv8xaoYftqBtXebuU2hbwwBUph6qQo73c3ffZmZPARgF4F8BHJmjLEIIIQoAjUkXQojPlykAvmtmbQDAzLqbWWcAzwMYbWYtzawtgDNSr6lATcd5DG3rYjNrlmzrQDNrnWPfiwB0M7Ojkue3NbPqzvtdAG4B8Kq7f/CZjlAIIUS9o2/ShRDic8TdnzSzgwH8M5nLuQXAue7+mpk9AGAugLUAXk297EYAD5rZBQAeTz1+FzLDWF5LJoa+D2B0jn3vNLOzAPzBzFoiMx59OIAt7j7LzD4EcPfndKhCCCHqESkYhRCiATCzXyPTeb5xL+2vFMA0AAdJESmEEIWPhrsIIcQXHDP7DoAZAK5WB10IIRoH+iZdCCGEEEKIAkPfpAshhBBCCFFgqJMuhBBCCCFEgaFOuhBCCCGEEAWGOulCCCGEEEIUGOqkCyGEEEIIUWCoky6EEEIIIUSB8T+ygcUEp9ECRwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DXy3fb6CitXfMuwt3YMA/zz2P6/afQLMHpmP5ruM+79Ox9OYL8zbj9R+3ImNilt9daxyvw/cX7cRHS3Yia7Vxh+ydBTvC5jolorO8SbIfBTATQGMR+RDAPAD3BzUqoihx1asLccNbrpO7UDhRYAwM+2XHUcxcc8DWWAJ17euL8cPGQxXWeaqrfMu72eesO3zyDJ7IWoenZqwPeCIXbwZI+urQCf8SsSXbgnvHxFWCOHf9IafrI9VnAQyeLePYkP3cnE3ldyQe/N9qfLhkJzImZiFjYpZfLd4rdh9HfuHZSXS2H87H41nRUSudKJrEedpAVeeIyK8A+sDoJnK3qrq+70pE5bJ32jf1clFJKeJjY3Clw+CsOz5YhpWPDkONKvG2xRWIRduOYOWe41j0wODyn+HiZ+b7tI/NB/MwNICW61Dwt8U4e+cxdGxUA1UTPX60+2xbzkn86qJFd5eFU8yHQ/fi+/+7KuB9uJpEas66g5iz7uxsod6c6/wzxRV+9yt2H8eK3RXPReVlIrKfy5ZsEele9gDQFMB+APsANDHXEVGYWrbzKFo9NANPTl+PzZUG5w19zrekNAxyngpOFZagy+TZ6PPkPEyatrbC7Jie7DpyKigJ9h8/DI+uHf+atREdHp0VlH3vdJNI+3IOPBnnZ7emcKKqePG7zV5tO/jZ+R4HLv71i5VWhEVEIeauu8iz5uNlAEsAvA7gDfP5y8EPjYj8tXibUQf4dSc1tg/ZVG7NagdOFODdhTt8es8QH79geCtr9f6gzlToq4MnfJua3BueEkHH2RQDEYzuN6H2667jKCjyro/0tsP5HktBbsvxPBnRqj25eDeIM3ESke9cJtmqOkhVB8Fowe6uqpmq2gNANxhl/IjIjYKiEs8bRQBXt70jUWEQB6Fa2Sd2RoB9572ZIdJXeQXuW6s58O4su34Xk75Zh90Wdt0hosB4M/CxjaquLltQ1TUA2gUvJKLocM3ri+0OwRLfb4iuQW3BMnvtwQrL6/adwN++WOV10nP8VGH5ILhAaywH43vR3Z+scPv6hf+yvspKpFIbO1l5MzsoEYWGN0n2KhF5U0QuMh9vAAh8VAhRlIuWgUhsofTNJ7/swn2frsBlL/yET7N344Knv/fY1eJofiG6PjYHzR+cDgAY3aVhQDH8/sNlyJiYhfwzxThy0n33oMMnzyBjYhYWbg3tePZw6l5jORt/tOJo/r0SRRhvkuwJANYCuNt8rDPXEVGY4gxwoVeWrE78cjW+XF6xR92Xv7rvYXc0v2Ii3KBmlYBiOX7KKNs47Pkf0ePxuW63vfdTo4X6sW9cd3cpDkI3G2fjBaKF1XnuMR9nXY2WrmpEkc6bEn4FAJ43H0Rkg+mr9+OyTg3sDoPcOOOmxf/Pn6/EsA5pqJ7kuXRiQVGJZVPquhpEuHpPLqbO3YTth/Oxzey/veFAHrYcykPLetUrbJt7ughdJs+2KKKz9lkwwDEcWsN3Hz2FxqnJFdb52l3k113H0L1JLZevF5f4tr+2f5+J3s1SsefYaSyYeLFP7yUi63hsyRaR7SKyrfIjFMERRapcsyXRKn/wsUScp8lB2AUk9HYecd03e+Xu3PLnD365GjEWT63Y+uEZ5c/3HT+Ny1/6GfM2HCpPsM++dm6FkKlzN3l9nDPFoW1BPelhMGYoOJvx09cbSWNfWVh+t2DdvhN4cvr6CnejjuT71pINAEu2H42KSi1EkcybGQsyHZ4nARgHIDU44RBFvpJSxQdLdtoag6f+4K/8sAX3DGkdomgIAOZvykHHRjWcvvbUjA3lz79cvhf3WnxuHL9UDZ/quk64s9zel5KPn2XvwY19mvoUWyDCdZp3f9rXx766EKv2nP2ylZIUhzsvbhVwLKcKi5GcYP3kRETkmceWbFU94vDYq6pTAYwIQWxEEemFeZvxr1kb7Q7DraM+tIzZf0M+Orj7P3G40uDEYFWnWLztCE64af298a1fAAClpYrSUsWB3AJkrdrv9f79mSI8EJ7qS4dKp0dnVei64k/ZS8cEGwCemb0JL323Gb9sPxpQbA98udrj4FciCg6PX28rze4YA6Nlm1+LiVxYvz+w8muuZEzMwuIHBqN+jaSA9+UpNVm7LxfbcvJxeYBVLqiizQfz0CqtuucNg2DVnuNelZXMmJhV/vxfV3X26Ri+VLZwlx+XlCpiYzwn0F5sEhJ5Z4qxYvcxtKhbDTWTEyz7ZvrMbO+76rjy9Yp9+HrFPqx8dBgS42KQFB9rQWREgdl5JB+1qiYgxYtxKpHMm+oizzo8ngLQHcDVwQyKwsOGAycwfbX3rVjnu29W7sOavblBLaH19Qpr5oF6b9FOZEzMwrack05fH/HCz/jTx8tZpcRir/6w1avtThVa37d51EsLfH7PX7/wrVrrPx26vQSix+NzvNrO6r7rgbjy1UXo+pgRt511sl3JWrUfbf8+Ewu2hLZUI1FlBUUlGPivH9D/qe/sDiXovGmRvlVVKwx0FJFmQYqHwkRBUQmGT/0JALBjCnsHeeNPHy8HADSpVGnASk/N2IBayQloU786GtasgrrVEwPa39X/XoTsh4dWWLfxQF7588luyrqR75ylXs6mQI/U8na+zKjpLj0+fqoIQ56bj39e2RntGlSPqD7Fc9YdxKo94Vcj/8H/GXPKvf3zdvxn0Q7cfmFz9GjK4VUUek9krQdg3AGKdt60ZH/h5TqfichwEdkoIltEZKKT1xNF5FPz9SUikmHFccmztn+fWf58IVs+fLIryNMa3//fVRj98gL0fGIulu86FtC+Dp88t2/2JQ4D495duCOqplW3m7PfZV6BtZVo7JYxMQtT525C7unAfq4th07iylcXov0jsyyKLDQ+/mUXXvxui91huDRvwyHMWnsQd3zgW8UiIqvszz1/qt64TLJFpK2IXAmghoiMdXjcDKPKSEBEJBbAywAuBdAewLUi0r7SZrcCOKaqLWHU6f5noMclz37z70UVlq97cwmO5hdiyyHnXQvImBLbDle8shB/+XwlZq89gA0HTqC4pBRLd/g2UGrOuoP4PHs3TheW4JCTVlXm2Nb5esU+LNl2BOv3nyhPQqPx9zt17mZ0mTwb7y8+t8pOWVm5Pce8/0ObMTELXyzbg4yJWeWPz7N3Y18Y/rH+boP78pnhIifvTFjUGSeKZuKqz6WIjAYwBsAoANMcXsoD8ImqLgzowCJ9AUxS1UvM5QcAQFWfcthmlrnNIhGJA3AAQF1101E0MzNTs7OzAwntvOc48MmZJ67oiBgRXNurCQ6fPIO4GDEG+3iQf6YY1725BFPGdkK7Bik+xfTz5sPYcOAEbruguU/vs8LIF39CXkEx5v91ENbuy8XqPbm4pleTCtsMfvYHbM3Jd7EHIqLwlFo1Ae9O6InO6TXtDsVWa/bmon6NJNSpFlgXPPLsilcWYPkuo0vVOxN6YlCbejZHFBgRWaaqmU5f8zSwSUT6quoitxv5F9RVAIar6m3m8o0AeqvqnQ7brDG32WMubzW3cdl/wa4ku3JiOqhNXeSeLkKb+imolhiLmBgBFCgqUcTHCopLFXExgrhYQVGJlg8wO3aqCKWqqFMtsXxQT6kq8s8Uo2piHGJEjG0FRgdPs2NjSYkaxzCV7U/1bD9QVWNEf5y5XUmpIvd0Ef776x7c1DcDVRKMUefeDs4Khh5Na2HnkVNonFoFTVOTcaKgGD9tzkGRlzOe9W9ZGyt2HUe+OXBsROcGaFwrGWv35eKnzYfRv2Vt1ExOwPacfFzQqg4A85zECUpKFMWlisS4GJSUKkrUKGP23iJ7a14TEYWDUV0aYtrKfeXLdwxsgfcW7sBph2ncb7+weYUW8ncWbEepAtWT4pBnlo8c37cpThWWYOmOo2jfMAUbDuRhaLs0vL94Jwa3S0ODGknGnzbzT5pAyqvRCIBlO49hyfajuLFPU8QIEB8bg4N5Z/Dz5hzc0Kep5d11rs5Mx/Jdx7HZxd3cOtUSMLJzQ7y7cAcAoEvjmmiamoza1RLwzoIdbvedlpKIqolx2JaTj1b1qqFBzSr4cVMOWqdVw6C29fDR4l0Y0j4N9VISoWrUvE+Ii4EIUFBYgtiYGCgUJaWKeesPoVGtKuiSXqM8H5iz9iCqJ8Whd/PaOHG6CJsO5uF0USn6NE9FQqzZmcHMJ8rOWvl44vIVxjkwVp3d8GxuoeV35BRweH42D4HDtlb/Tb2yezoS4mJwfe8mLuciCCa/kmwRuV9VnxaRF+FkvI6q3hVgUJYl2SJyO4DbAaBJkyY9du4MbVJUUFRSoQ9zZfGxUv4hIWIkdXExxvKZ4lIkxsUgRgSlalwoZYm4wOFTBWcvLmccS1mVvU/k7OCisnqyqlqesJYdv0zZBefL4KVI5vjzJsTFIEaAgiLjeVyMIFYEMTEScN9SIqLzRVJ8DOJijM9WVS1v8PAk3mxwKtuHY9IGh6RNtWKpyLJGo7J1jvuxW7XEOJy0aHBfQlwMBCjPFcra2BLjjN9VbKzguDnTcFJ8DMp+RWUTUVX+vSTExqC4tBTxsTHlCV5ZHlH23rL8oVTVyCscUpKzX3qc5xvlKUmF9xgLwfqbOvU3XTGmW6Og7Nsdd0m2uyHb681/g9UsvBdAY4fldHOds232mN1FagA4UnlHqvo6gNcBoyU7KNG6kRQfiy1PXIpSBWas2Y9LOtQ3W6m9GVcafjx1F3Gmbf3qSKkSj0Ft6mFs90Y4crIQjWpVQaL5pSAxLgYr9+RizMsLcG2vJnjyio5QNfovJifGol+LOl7FtGDixUirbrTyG19agl/Cq+zYz4zrgr98vhIA8PiYjujVLBVNUpNRUqro8GhkDc4iIioTI0Dn9Jr4y7A2aFAzCSlJ8UiIjUG1pDjExgiKS0qNu6g4m4iFy0RAzpwuLMGwqfMxoV8zDGpbD2kpiYiPNe5Qrtmbi8+z92DCgAykVk1AckIcqsTH4to3FmNY+zRc3bMxkuJiUWLe1XT8O1Nq3uGMd/O3XVUr/G6O5hdi1toDGNO1EUrMbw7JZq1yq/+GVT52uHKWY4zt1giJ8bG4vncTpNeqgvhYo6a7u1r4kfCzeuwuErQDG0nzJgCDYSTTSwFcp6prHbb5I4BOqnqHiFwDYKyquq3RzT7ZgXN2AXRomIL//aE/juYXIrVqgssWdU8KikoibjKEgqISxIi4/Zk3HczDsOddT1cdCjf0aYJ7hrRG5uNz/Xr/Lw8NxqrdubjtP7x+QuUvw1pbMuFIOPvH6A74ZOluPH1VZ3RoWAOfZ+/GwDZ1MXnaOmSxDr+tNvxjOAqKSrwaU0NkFcccIxpKBPvVki0i38DNvFWqOiqQoFS1WETuBDALQCyAt1V1rYg8BiBbVacBeAvA+yKyBcBRANcEckzyzpB2aZi7/mD58qe390Hv5rUBIODZBiMtwQa8i7m1TbP4AcDdg1vh3qGty5dXPjIMXR6b7fX7HT/khrQ3Bv44TvOdEBdTfsuRArd60jAcPFGA9FrJSIqPxS0DmkVcmTpv1K6agL8Nb4urezbGjX0zytePyzRuYHZrUtPrJPuj23qjX8s6KCopxbQV+3DgRAH+OKglfvd+NmatPeh5ByHUM6MWlu4IrLRmqCTFx0bkZzJRpHDXXeSZYB9cVacDmF5p3SMOzwsAjAt2HFTRv2/sgRYPnj0tMeEyd3GEuGNgC7w2PzSDRy/rVL9Cgg0ANZK9m6b2o9/2dtpNZ+lDg9HsgbPn/7KO9fHVin3nbEf+qZ4Uj+oOUwlH0kQr3tj8xKVub6d7a8eUEZiz7iDqpyShU7oxmCk+NgZX9kgv3yacZnws8/6tvd2O0QkHHRul4K6LW9kdBp2nOjRMwdp9J+wOIyRcfrqr6vyy5yKSAKAtjJbtjapqT1FgConYGMGL13Yrn8Gwca3gzWAYTa7t1QR7jp1y24fMKmO6NsTUa7r5/f6x3Rq57AdfuZ9bpNT9jQQjOjWwO4Sg8zbB9qY/5dD2aR724dWhQubnvw1CUnwsVk0ahs6TvL+bFAoJsTEoLCnFDX2a4PExnewOh85jPTNSsXbfCTwysvLUKNHHYxOKiIwA8BqArTAGiDYTkd+p6oxgB0f2ubxLQxzNL0T7hikBdxE5Xzw11pRmSwIAABfkSURBVPjD9fWKyuN3rfHW+Ez0apZaoRXUXw+OaOf29ddu6IE7PlgGADhREP1T34bKtZXqq7vy/q29cONbvwQ5GntVTQi8m0K1xPC5C+DY7SrMcn8AwEvXdcOZ4lKPX1yIyDreNDk8C2CQql6kqgMBDIIx+yJFufH9MtAzI9XuMCJOsPpnD26XZkmCDcDjhAvDO9bHc1d3wcx7LrDkeGRokurdXaHYIDTR3n5hczSvW9Xr7VvWq4ZGNav4dIx+LWr7GlZAbujTNKTHc6Xy9WRl1YMRna25+yEiuLxLQ/bBJttlZtQCALRv6NukdJHIm2aAPFV1rOy+Dcasj0TkRLsGKbihTxN8sHiX3aEEZGz3dM8bkdeu790ETWp7TrKDlaj+4aIWGN8vA2//vB1pKYl4cvoGt9vPvudCxMSITyU9fZkIwoq6VmX1mO2W/fCQCstWfkWadHkHLNp6BEfz/eulOahNXTStXRWD2tS1MCoi/43s3BA9M1KRlhL9d8m9SbKzRWQ6gM9gfC6OA7BURMYCgKp+GcT4iCLSrQOaW5pkf/unAZbti+xR082A1Cu7p+O/v+4BALw7oReW7jga0LG+/8tFGPTMDxWWayYnoGYy8HezH+SE/s0wb/0hTJ27CRm1q2Lm2gOY+puuaNcgBScKisoHPP/+ohZezwJ764BmAcVdZulDQzxvFEViYwQlpYpBbepiyfajmPfngVi5+zgu6VC/4sQefnhnQi/L4iSyyvmQYAPeJdlJAA4CGGgu5wCoAuByGEk3k2yiSprV8f62vDfqVXffvYPCn7tU6YY+TcqTbH9r0DtqVqcqdkwZgVYPTUdRiTr9/xgfG4PhHetjeMf6bvd1/yVtvE6yrWrBdfeFJBL42ltkbLdG+HzZHtw3tE15JZUGNc521Qm3AZ5E5B2PSbaqTghFIETRJr1WFew5dtruMALWJq06Nh5kD7FAXd6locvXgjVz2Xd/vghbDp0MaB8igjWTL0FHL2Y1jfWhtE5TN/3Tve2THq7Jp69tzzf1zcBdg1uhsZd99okoMnhTXaQZgD8ByHDcPtDJaIiinZ01fB8e0Q6PZ60HYFRxyC8sKX+trAqKtzo0SonKJPtfV3VGp/Qa+PNnKy2r2dq0djJ2HjkFAFj8wGDkFxbj+KlCdE6v6ba0XeX/KVZNxNs4NdmSxM3bKh6+9JHu19J5CUnA++Q5bJNsH+MSgaUJ9tNXdcbUOZuwL7fAsn0Ske+8+eT8CsbMi98A4LRvRF7q27w2dh09ZcuxezQ1Rm93Sa+Bf9+YiT5PzSt/zeeBdRYlfOGmbObBrLsuQEFRiSUTiMy9byC+Wr4X7Rr4VvqyclKmEfpLT4y3ZiBisFr2I1WLutVw+KR3/fTjYwVXZzbGFd0a4XRRiec3EFHQePOJWKCqL6jq96o6v+wR9MiIItw/xnS07di1qxp9uLs1qXVOslc1jGoL+2rWPReibX3rSyT60s3BnfjYGIzLbOxTlQ3gbPeCDmZJK6taskPpdwObW1IezteygXb7/UUtgn4MX34nix8YDMD4v5hiUclPIvKPN0n2/4nIoyLSV0S6lz2CHhlRhLNiAFsZX5PAJrWTMePuC/BQpUlnkhNiPdbIrqxBzfAZBV67WgJm3nMhpt/le/3unmZtVmfsbjcta7i1Irn+89DWge/EiWl39nf7erPa1gz2vX94G0v2Eyp/G972nHWhaIh39vlSNSEWtX28vokoeLzJAjoB+C2AKTAmpnkWwDPBDIqIKvLnD2e7Binl/YDH9zUm7cjwIxEa16Oxz+8JlrKWufYNUzDj7gvQpXFNr973ye198LsLz7Y43jW4VYXXrWg4/uKOvn6/Ny7WyMqqJcUFHE+jWsFpCe7Q0H3rfCuLJmHypatIYMXtgsfXuPypHvTLg4NxXe+zM4he1qk+1j423Of9EFHweHPfeByA5qrqXyV8ovNYo5pVsPe4/RVGbrugOd5btBOd033rxgCE1+Ayx9a7dg1S8PUf+yOvoAidJs12+77MprXw85bD5cuV6znHWdBdJDOA2VHbpFXHA5e2xRXdGgEANIAm7bJ9WM3dbyijdnL5OADy7Zp5d0JP1PNQM3hYhzR8uXxv+XKPprVQMzkBT17RCU9e0Qm7j55CXZb5JAo73rRkrwHgXXMREVUw854LsOTBwXaHgcapyZh2Z39MHt3B5/cmJ4R3H25PU80/cGlbxMXGoE/z2hjRqQF+un8QalSp+B4RwT+v9K3qipVEBL8b2KI82fI3xR7brVHQBg26261jTeeAj2PZnnzTom5VPHd1F6ydfElIj1srOcHjNsM7NqjQzavyl8LGqcmcLp0oDHnz17MmgA0ishTAGXOdquro4IVFFB2qJ8V7TAJDpXO6f9+VI72FrLvZwpoUH4uXr3c9nCSQ7g5jLW497t0sFT0zamHpjmM+ve+pIH5RCMeKH7UsmrRm/l8vQlpKUlgnqm3rV8fPW4w/wc//pqvN0RCRN7xpyX4UwBUAngTwHIClAFoGMyiiaNPJx2oTZI21ky9BTy+7cXRvUgvpfvZnfs7ipCc5IQ6f39EPjVN9iycxzp4k0cr825d91UtJ8vl35EzT2lVdJtiTR3Xw+UtUML6OdDXHH/zfNV3RMMIqsBCdrzwm2Wa5vhMARgJ4F8DFAF4LblhE0cXXCWDIGr6WK2zgQ21rwOgXHooSbuRaszrVAnr/oDZ1z1n3ye19cHHbenjjpkyM75dRPtV5Zd/cOcDp+rjYmAqDEp15ZGR7NKtTFa29vIPyp8Et8eQVnXB5Z9czhxJReHGZZItIa7N03wYALwLYBUBUdZCqvhiyCImi2JXd0+0OIWLcM6SVy9esakm918fyd5/+ro/TEm7R6rcXNPO8UYBCXTHknQm9zlnXp3ltvH1zTwxtnwYALhNmV8k3AFzVw7i2M2qfO5PjJ7f3wS0DmuH7v1yEKgne3X1IjIvFdb2bIMaimu5EFHzuWrI3wGi1HqmqA8zEmtNHEVmIfy+9525a8tWTKg5Wu2twK7x+Yw+fj9Gvheupviu7dUCzsJrsY1SX4Ldwtqpn/URAlfk622Ugl9B7t5ybYDuTGBeLC1p5/3/DUc3kBOyYMgKPm5NTzbrnQvRp7uOsq0QUkdwl2WMB7AfwvYi8ISKDYf+cDUQRqV5KZA8eDHfVHLqFjOzcAPcNbY1hHer7ta83b8r0artQfBj6UsmvpkWDAP1hZZ/sstbjUBjY+tyuIq40rFRBJc3DNV353N3Qpyl2TBmBNkGYsZSIwpPLJFtVv1LVawC0BfA9gHsA1BORV0VkWKgCJIoG9aonYeUjwzA1QqsCtKoXWL9XVzo0TMEvFpc4DHSK9CHt07xKWC9qUy+g41gtzUOtZStUbmV+7uoulu7/kg5pQRu8eXWm0X2jVb1q6NM81efSmo+Oao9/jOmIZ8Z1wQe39saSB4cEI0wiiiLeDHzMV9WPVPVyAOkAlgP4W9AjI4oyNZLjMaZSlQJvEiOrypQFYuY9F2Jsd+vK1L07oSfevCkT79/a2+NEHGXaN0ix7PieZD/kPoHa+uRlGOBn9wF/zLzH8zTyl3VqEPQ46lU/e67ev7UX2pnnxN9z06uZ/xP4lClrRX9mnPuE/2/D26JVvWp446ZMfHJ7X5+/lCQnxOHGPk1xVY/0kJ57Iopc3pTwK6eqx1T1dVW1f3YNoihw39DWHvsOB9oya4XYGMETYzqdMx25v5rWrooh7dOQWtWYiOPaXu6nbv/p/kEY1DZ0Lcdxbvp/A6E7J/cMMQZiNk2tGpLjeTKobT2M79sUgFFZpV2DFHz5h36438/Bn/+5pReyH7amRTi1qvsvo7WrJWLOfQORUSc0v8uy5D8+1v7rl4js4VOSTUTWiokRj32HHxrRLkTRuFclIRbjelhTDaVepQlu7hhYsQze30e2r7DcOPXcCg3BNuueC0N+zMqu6pGOHVNGeFWBItnLKhWBmjy6I3ZMGVE+A2H3JrXcDkp1Jyk+tsJMhv5UFvHmHcGaat6druk18dsLmmHqNd1CfmwiCg9MsolC7MVrffuje0W38CnzV9/HOtJlhrRLw/S7LsC1vRpjwcSLz6lf7ZhcffTb3rh1QDP0zKjl17Hutqi1vU396tgxZQS2PXkZvvpj//L1//tDP0v2b7VQ9MkOllfdzMTpyWOjO2JEpwZuK8PYMUNiTIzgoRHt0YgTxxCdt5hkE4XY5V0aYu59AytMUPPeLb1wc78M+4Lykr8tli9d1w3tG6bgqbGdnSYdjVOr4Jb+zTD3vgvLk6XXbvC9BB8ANK9r7SDNmBhB18Y1UTUhFknxMejWxL/k30rDO9THT/cPCouuRFaoa97ZaF7X964cjVOT8fL13ZEUH4tv/3Tu5DDO1hERhYJv06ERkSVa1quGlg4VOwa2rouBreti0qgOyJiYZWNknnVOr4FVe3K93n5o+zSXU1aXERE8cnnFLiK1qyVi9aRhKCwu9StOqy1/xN6iSo1qVsHe46cBGK3sjVOTkRwfi7wzxaju48yW4SYzIxUf3dY74IGQHRvVwJ2DWuKl77dUWEdEZAdbPplFJBXApwAyAOwAcLWqHnOyXQmA1ebiLlUdFaoYiexy24BmePPn7XaHYZk3vKw77Ux1HyZ76ZJeA1dluh9AGYiEOHtv/C2YeDFUFVmr9+MSsx//gFZ1MGPNAfy90heUSNSvpTUVO/y920JEZDW7Po0mApinqq0AzDOXnTmtql3NBxNsOi88PLI9ejS1v0uCK6EspeeLr+8cgBv7NLU7jKASEYzs3LA8kbymlzHdd3+LEtRo4FjL259ZP4mIrGJXkj0awHvm8/cAjLEpDqKw1Mm8xf3Brb1tjuRck0Z18Hrb6Xd5ru9M/hvYui52TBnBwXUOHCezacjfCxHZyK4kO01V95vPDwBwNY9ukohki8hiEWEiTueNBy9rh6//2D8sJ73w1L+6rTlt9P3D26B9w/Bs9aboNaF/RvlzX6alJyKyWtCSbBGZKyJrnDxGO26nqgrA1UdhU1XNBHAdgKki0sLZRiJyu5mMZ+fk5Fj7gxDZICEuBl0a17Q7DL/cP7wNAOCGKO+6QeEpKT4WA1vXBWB/P3oiOr8FbeCjqrqcxktEDopIA1XdLyINABxysY+95r/bROQHAN0AbHWy3esAXgeAzMxMtl0Q2aRfi9q4uG0adkwZYXcodB6b+puumLHmANqYd1WIiOxg19f8aQDGm8/HA/i68gYiUktEEs3ndQD0B7AuZBESkc+GtHPV84sodGpVTcB1vZvYHQYRnefsSrKnABgqIpsBDDGXISKZIvKmuU07ANkishLA9wCmqCqTbKIwlVE7uUJ/WCIiovOZLXWyVfUIgMFO1mcDuM18vhBAp8rbEFF4emdCL4hExwyEREREgeKoECIiIiIiizHJJiJLsA2biIjoLCbZROSzL+7oW2H53iGt0bR2sk3REBERhR9b+mQTUWRLS0mqsHz3kFY2RUJERBSe2JJNRERERGQxJtlERERERBZjkk1EPouP5UcHERGRO/xLSUQ+q18jyfNGRERE5zEm2UTklzsHtbQ7BCIiorDF6iJE5Jd7h7ZGszpVUbd6ot2hEBERhR0m2UTkl9gYwZU90u0Og4iIKCyxuwgRERERkcWYZBMRERERWYxJNhERERGRxURV7Y7BUiKSA2CnTYevA+CwTccm93huwhvPT/jiuQlvPD/hi+cmfFl5bpqqal1nL0Rdkm0nEclW1Uy746Bz8dyEN56f8MVzE954fsIXz034CtW5YXcRIiIiIiKLMckmIiIiIrIYk2xrvW53AOQSz0144/kJXzw34Y3nJ3zx3ISvkJwb9skmIiIiIrIYW7KJiIiIiCzGJNsCIjJcRDaKyBYRmWh3PNFKRBqLyPcisk5E1orI3eb6VBGZIyKbzX9rmetFRF4wz8sqEenusK/x5vabRWS8w/oeIrLafM8LIiKh/0kjm4jEishyEfnWXG4mIkvM3+mnIpJgrk80l7eYr2c47OMBc/1GEbnEYT2vNT+JSE0R+UJENojIehHpy2snfIjIvebn2hoR+VhEknjt2ENE3haRQyKyxmFd0K8VV8egilycn3+Zn22rROR/IlLT4TWfrgl/rjuXVJWPAB4AYgFsBdAcQAKAlQDa2x1XND4ANADQ3XxeHcAmAO0BPA1gorl+IoB/ms8vAzADgADoA2CJuT4VwDbz31rm81rma7+Y24r53kvt/rkj7QHgPgAfAfjWXP4MwDXm89cA/N58/gcAr5nPrwHwqfm8vXkdJQJoZl5fsbzWAj4v7wG4zXyeAKAmr53weABoBGA7gCrm8mcAbua1Y9v5uBBAdwBrHNYF/VpxdQw+vDo/wwDEmc//6XB+fL4mfL3u3D3Ykh24XgC2qOo2VS0E8AmA0TbHFJVUdb+q/mo+zwOwHsYfp9EwEgiY/44xn48G8B81LAZQU0QaALgEwBxVPaqqxwDMATDcfC1FVRercRX9x2Ff5AURSQcwAsCb5rIAuBjAF+Ymlc9P2Xn7AsBgc/vRAD5R1TOquh3AFhjXGa81P4lIDRh/mN4CAFUtVNXj4LUTTuIAVBGROADJAPaD144tVPVHAEcrrQ7FteLqGOTA2flR1dmqWmwuLgaQbj736Zrw82+WS0yyA9cIwG6H5T3mOgoi8zZNNwBLAKSp6n7zpQMA0sznrs6Nu/V7nKwn700FcD+AUnO5NoDjDh9+jr/T8vNgvp5rbu/reSPPmgHIAfCOGF153hSRquC1ExZUdS+AZwDsgpFc5wJYBl474SQU14qrY5BvboFxhwDw/fz48zfLJSbZFHFEpBqA/wK4R1VPOL5mtgywZI4NRGQkgEOquszuWOgccTBur76qqt0A5MO4HV2O1459zL63o2F8GWoIoCqA4bYGRS6F4lrh9egfEXkIQDGAD+2OBWCSbYW9ABo7LKeb6ygIRCQeRoL9oap+aa4+aN6Cg/nvIXO9q3Pjbn26k/Xknf4ARonIDhi33i4G8H8wbp/Gmds4/k7Lz4P5eg0AR+D7eSPP9gDYo6pLzOUvYCTdvHbCwxAA21U1R1WLAHwJ43ritRM+QnGtuDoGeUFEbgYwEsD15pcUwPfzcwS+X3cuMckO3FIArczRqAkwOsNPszmmqGT2fXoLwHpVfc7hpWkAykZujwfwtcP6m8zR330A5Jq34mYBGCYitcwWpGEAZpmvnRCRPuaxbnLYF3mgqg+oarqqZsC4Dr5T1esBfA/gKnOzyuen7LxdZW6v5vprzJHczQC0gjFQiNean1T1AIDdItLGXDUYwDrw2gkXuwD0EZFk8/dXdn547YSPUFwrro5BHojIcBhdFUep6imHl3y6JszryNfrzjVvRnLy4XGk62UwKl1sBfCQ3fFE6wPAABi3z1YBWGE+LoPRJ2oegM0A5gJINbcXAC+b52U1gEyHfd0CYwDEFgATHNZnAlhjvuclmBM28eHzuboIZ6uLNDc/1LYA+BxAork+yVzeYr7e3OH9D5nnYCMcqlTwWgvonHQFkG1eP1/BqHjAaydMHgAmA9hg/g7fh1ENgdeOPefiYxh944tg3AW6NRTXiqtj8OHV+dkCo790WW7wmsP2Pl0T/lx3rh6c8ZGIiIiIyGLsLkJEREREZDEm2UREREREFmOSTURERERkMSbZREREREQWY5JNRERERGQxJtlERFFIRGqLyArzcUBE9prPT4rIK3bHR0QU7VjCj4goyonIJAAnVfUZu2MhIjpfsCWbiOg8IiIXici35vNJIvKeiPwkIjtFZKyIPC0iq0VkpojEm9v1EJH5IrJMRGaVTf1MRESuMckmIjq/tQBwMYBRAD4A8L2qdgJwGsAIM9F+EcBVqtoDwNsAnrArWCKiSBFndwBERGSrGapaJCKrAcQCmGmuXw0gA0AbAB0BzBERmNvstyFOIqKIwiSbiOj8dgYAVLVURIr07ECdUhh/IwTAWlXta1eARESRiN1FiIjInY0A6opIXwAQkXgR6WBzTEREYY9JNhERuaSqhQCuAvBPEVkJYAWAfvZGRUQU/ljCj4iIiIjIYmzJJiIiIiKyGJNsIiIiIiKLMckmIiIiIrIYk2wiIiIiIosxySYiIiIishiTbCIiIiIiizHJJiIiIiKyGJNsIiIiIiKL/T8IEKzxkVeSvwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7QaQXfi6gC7",
        "outputId": "d294e642-7c0d-4742-efd4-f0a4f73c6651"
      },
      "source": [
        "model = model_1(input_dim=13,\n",
        "                units=5,\n",
        "                activation='relu',\n",
        "                output_dim=len(char_map)+1)\n",
        "model.load_weights('models/RNN_model.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "the_input (InputLayer)       [(None, None, 13)]        0         \n",
            "_________________________________________________________________\n",
            "rnn (GRU)                    (None, None, 5)           300       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, None, 5)           20        \n",
            "_________________________________________________________________\n",
            "time_distributed_1 (TimeDist (None, None, 223)         1338      \n",
            "_________________________________________________________________\n",
            "softmax (Activation)         (None, None, 223)         0         \n",
            "=================================================================\n",
            "Total params: 1,658\n",
            "Trainable params: 1,648\n",
            "Non-trainable params: 10\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAC69T9QbWBh"
      },
      "source": [
        "index_map[0]=''"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8XbGviNbmg6"
      },
      "source": [
        "from IPython.display import Audio\n",
        "from IPython.display import Markdown, display"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_4I6ABkGEaM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "e02b34a6-480c-4366-ab13-1ccdcf4864e2"
      },
      "source": [
        "predict(audio_gen,14, 'train', model)\n",
        "_,_,_,raw_pred = predict_raw(audio_gen,14, 'train', model)\n",
        "raw_pred_char = np.vstack([sorted(char_map.keys(), key=lambda k: char_map[k]) + ['BLANK'], raw_pred[0]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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6PADQADv4n/DH89v2t/+D/7/4X/vD9Of4d/1IAwQAdAC3/yf7M/uf+C/9K/53/qP8U/zP+AP6b/jH/Jf+f/h7+Cf6U/mf/9P/s/3v/Ef8B/2v/1f/i/8j/3P/9/+P/uv/H/wsAQwBFABoACwBEAJ0A4QDiAI0AlP8V/kD9Nv4IAHAAyf62/Dr8mf1i//f/Ov9e/ib+dP4H/7X/KAAZALb/Wv8n/xH/IP9V/4v/cv/V/h3+/P2m/mX/gf/+/mL+If5y/jP/6P8BAH7/Af8O/5T////z/67/oP/A/7r/fv96/9v/RwBfACkAAwAsAIUAxADKALoAkwDp/4X+Yv3f/Y//YwA6/y79Qfwz/fD+2P+C/8r+dv6R/vL+kf8gAEAA8v+F/zX/Cf8G/zX/ff+M/xP/U/77/Wz+Nf+L/y//ov5+/tn+Uf+Y/7D/tf+r/5H/fP+J/6n/zv/w//r/zf+C/3P/yf9FAIAAagBMAGYAogDVAAMBGwEBAWUA//56/XH9Nf+gAOr/zP1a/Nb8oP78/+//Lf/F/tn+H/+X/ysAdQBLAOn/ef8O/9r+CP9z/7n/af+R/uf9Fv7p/oL/f/8W/87+4P4//63/7v8HAPP/t/92/2D/fv+y/+//CwDT/2T/Rv+x/0EAkQCQAH0AhACiAM0A/wAoASABoQBc/7P9Ef1K/gMAKgCP/tL8ifzM/VT/5v+M/x3/+v4D/0H/zP9IAFUAAACA//v+oP6c/uv+Vf9o/9b+Bf7H/WP+MP+D/0j/3f6Y/rz+UP/h/wQAsf86/wL/J/9n/3T/d/+i/8D/g/8w/z//t/83AGsAWABHAHIAswDTAOYA/ADeAGAAUv/P/dH8hP1J/wEAy/7n/Cr8Mv0F/wIAvv8i//3+MP9v/8P/GgA7ABMAqf8I/27+Qf6a/hr/S//j/hf+pP0K/uj+af8y/7j+o/4g/6//w/93/1b/i/+r/3r/I/8I/0//xP/s/57/PP86/6n/LwCKAJgAggCLALUA3QDqAOkA3gClAM7/Ef6C/N387v5WAHD/Rf0Q/O387f43ABIAbf86/2T/lP/P/xQANQAeAMr/MP92/gj+Lv7P/l3/P/9j/pL9pv2E/lz/nP9j/x7/JP9l/6D/tf+0/6//jP9U/yb/E/8z/4X/3P/i/5T/S/9m/+v/jADZAMcAsQC2AL8AzgD/ACMB9QAvAH7+mfxQ/C7+HQDz/xD+bPyM/Er+AwBmAN//i/+a/6P/sf/t/yMALAAAAHP/kv7k/eP9ev4s/2P/zP7y/cj9Y/4L/1f/gP+j/6j/j/9t/2//qv/5//v/pf9T/yv/K/9o/93/HADk/4L/b//G/1EA1gAUARUB9wDEALEA8QBQAVAB0wDD//39b/y6/MT+SAC4/9T9g/wf/SX/ogCIANH/pP/h/wYADAAOAA8AEwDl/z7/Xf7i/Q/+qP5D/1H/pv73/RD+wf5c/6X/x//X/9r/zv+m/4v/qv/c/8n/h/9V/y7/I/9q/9X/9v+4/4j/pf8OAI0A6AAFAfsA5ADLAM0A/QAnARYBtACd/5r9APyd/Ob+SQBw/3j9ffyJ/ZX/mAATAG//nP8OACEA7P+9/7P/xv+Y/+b+Cf6b/dX9f/4y/1b/pf7O/cL9e/5W/8z/y/+m/6T/rP+F/0j/Sv+Q/7H/g/8u/9v+1f49/8D/4/+i/1X/Wf/L/24A4QDpAMMAtgC5AKoAnQCoAMcAxAD+//795vvM++n96P/U/yf+0Pwu/eD+MwAvAJ7/kP/i/wIA7//M/6D/gv9d/+3+Mv6l/aj9Pf4N/2H/0f72/c39cP44/6v/xf/G/9X/3f+d/zj/F/9M/4j/jP9a/wb/zf4E/4f/2//I/4r/ff/J/1oA2AD4AN0A0QDAAJwAkQC4AN0A2ABnAAT/2fyW+5v82P4GAEL/nf3o/PT9tP92APX/aP+N/wsAUwA3AMj/Vf8o/wX/jf72/bj98P2L/jD/QP+Q/uD99/3A/pn//v/v/8P/x//Q/4f/FP/u/ir/dP+C/1H/Af/i/in/mf/M/7z/pf+5/xgApAABAfAAqACCAIwArwDOANYAtwCCAAUAqv6y/L775Pzy/t3/DP+k/Uz9g/4EAFUAnv8x/5//TQCOAEMAr/8x//v+0P5u/gf++f1D/sT+OP8z/5D++P0j/vH+wP8cABAA8f/k/8X/av8O/wj/V/+X/5L/Yf8p/xz/Wv/B//n/2P+o/8//YQAMAVUBGAG5AJYAqgDAAM4A5QDxAMAAFgCc/sP8+/sZ/fH+wP8g/wz+0/3P/v7/OACf/03/s/9dALoAhQDn/0P/6P61/mv+Jf4Y/lr+2/5M/zX/j/4L/kH+Cv/J/xEA9f/Z/+z/3f+C/x3/+P4j/2f/iP94/07/RP9x/6v/uv+k/6j/9v95AN4A7gDMAL8AtQCIAFcAYACqAOwAzQAbAOX+Xv1N/IP8z/38/jP/o/4g/mX+Rv/Y/5r/Lf9L/+X/dACIAA8AXv/X/on+RP73/dT9+/1s/u7+Fv+4/i/+Cf5q/hD/mP/R/9D/wf+s/3D/F//c/tP+8v4o/1H/UP9B/1D/bf+C/47/nf/H/xYAeAC9ANAAugCTAFcALwA9AHIAmwCiAGQAnv8w/qH8Dvz5/HH+Lv/Z/jb+N/4H/9D/xv8o/+L+V/8kAJwAaAC7/wr/m/5I/ur9pv22/Sv+1f5I/yX/jv4a/jT+wf5R/5j/ov+p/8D/v/+H/yz/6P7d/gX/Qf92/5j/s//I/87/xf+y/7f/5v9HAKgAyQCuAIMAcQBxAG8AcABzAH4AWgCm/zj+zPyD/Hr9lv7h/nf+H/56/lH/zv+G//D+0f5Q/xEAjQBoAL3/Bf+V/lP+Ff7l/ef9NP6y/hD/+v6b/mL+gv7V/in/X/99/6H/zf/m/7v/XP8C/93+//5M/4v/rP/E/+L/7//m/93/3//1/y8AggCzALQAsQC8AK0AfABLAEIAYQBZAMj/g/4u/dH8kf2A/s7+iv5M/pT+T//S/5//Gf/u/lT//v90AF0Ax/8p/9z+tP5s/hz+/P00/rf+Kv84//L+uf7B/u3+Kf9h/4v/sv/m/wgA9/+z/2b/P/9R/5D/xP/T/9v/DQBVAHQASQD+/9j/DwB7AMIA0gDEAMEAxQDNAMAAkgBIANf/E/8U/nH9nv1J/rz+rv5u/nH+5P5n/3v/Kv/7/jP/rv8gAEcADgCU/yL/2f6Z/l3+PP5P/pb+7v4W//z+yP62/sv+9v4s/1v/gf+g/8X/2v/M/6T/df9d/3L/nf+9/9j/CQBNAGoASwADAM7/2P8eAHAAkgCQAJUAnQCkAJkAgAA+AMH//P4R/n79oP0k/nT+Z/5O/nf+2P4j/xz/3f7A/vH+UP+r/+T/5P+g/z//5f6U/kz+Jf41/mz+qv7R/sz+uv67/sr+0v7b/vb+Hv9L/3z/qP/B/8T/sf+Z/4b/hv+U/7D/3f8dAEYAOgAMAOz/9f8bADYANgA2AFUAiwClAJkAeQBHANH/7v7v/Wz9o/0m/mb+XP5c/qX+D/88/w7/wP6p/tb+Jf96/7n/yv+p/2L/D/+4/m7+Qv45/l7+ov7e/vH+5v7q/vj+BP8D/wP/Dv8r/1z/mf/L/+T/5//Y/8r/zv/R/9b/6v8LACIAIQAWABkANABeAHEAXAA3ADcAWwCLAKMAgAAIADX/VP7f/e79Jf4s/hH+KP6V/hn/Vf8n/9f+tv7b/hr/RP9R/1L/UP9a/1z/Nv/k/oz+Yv51/qT+wf64/rP+1f4I/yj/J/8a/x3/Nv9f/4P/l/+i/7X/4v8UAC4AIAAIAAQAFgArACgAFAALACAAVAB+AIMAcgBkAGIAZgBiAD4Ax/8M/2b+LP5Q/mz+WP4+/mH+yf4n/zb/BP/V/t7+Df8+/1b/UP8//zj/Sf9d/0//E//K/qn+uf7g/vX+9v4D/yH/O/9D/0H/Sf9e/3b/jP+j/7b/0f/x/xcANwBBAD0APABHAFIAUABLAEgATwBZAGsAhQCbAKYAoQCSAIEAWwD3/03/pf5M/kz+Y/5h/l7+f/7J/g3/H//5/s7+xv7n/hX/Nv9E/zX/JP8v/03/U/8t//H+y/7H/tP+0P7H/tH+9/4i/zb/Nv85/0r/YP9w/3j/gv+V/7f/8P8hADYALgAgABwAIAAjACAAKAA6AEoAVABXAGkAhACSAIIATQD3/3L/3f5u/kj+Pv4m/g7+I/5v/sb++v7u/sT+pP6g/rH+yf7g/u/+8v7//hn/Kv8b/+z+vf6m/qT+pv6e/pz+tv7o/hL/HP8b/yb/PP9V/2b/c/+B/5D/q//U//3/HwAwADEAIgAZACAAIQAcAB8ANQBWAG0AcgBvAG8AZgArAKL/7v5u/kP+P/4r/hb+Kf5j/qP+xf7K/sP+wf7D/sb+zf7W/t/+3f7j/vj+EP8g/xf/BP/s/tT+uP6g/pv+rP7K/ur+EP81/0//YP9w/4b/n/+u/6//uP/Z/wAAIAAuAC4AJwAlACsALgA0ADwASABRAFsAcACEAIUAXAADAIj/Dv/A/on+Vv4m/hT+NP5t/pX+q/65/sP+zP7J/sT+w/7K/tT+3v7x/gr/H/8e/w7/Af///vz+5P7E/q/+sP7J/uv+C/8m/0P/Xv9//6n/x//W/9f/3//3/xEAIQArADQAPgBIAEoAQQBBAEsAVwBbAF8AZgBuAGUAMwDQ/1n//v7I/pv+a/5U/mL+gP6Y/qb+tv7H/tX+3P7c/uP+5/7h/tj+4f4I/yn/OP8w/yr/M/80/yv/Fv8B//L+6P7s/gL/Lf9h/4f/ov+8/9f/9f8KACIAPQBMAE8ASwBKAFUAaQB5AH8AgQB+AHwAfwCHAJQAkwB1AC0Ayv9o/yv/A//S/pn+d/58/pj+sv7D/s/+1P7Y/tb+0/7Y/tv+1/7S/t7+9f4J/xn/If8w/zr/Nv8h/wr///7+/gb/C/8V/x//L/9F/2r/mP/A/9r/5f/z/wkAJAA5AEYARQA/AD8AUwBrAHEAbwBsAHEAdABrAFYALgDy/53/O//z/r/+kP5l/lL+Xf5z/n3+gP6M/p7+tP67/rf+sf6u/qj+o/6u/sP+1f7g/uL+8P4C/wf//P7t/uf+5P7l/uP+8f4I/yP/RP9n/4n/p/+3/8L/1//1/wgACQAEAAgAEgAiADwAUABaAFwAWQBZAGIAZwBaACgA0/9t/xT/1v6j/nP+UP5M/l3+Z/5v/nn+hv6V/qP+q/6o/qH+n/6p/rv+y/7P/tL+3/7x/gP/C/8L//7+7v7l/uf+6f7w/v/+Gf88/2T/jP+p/8X/3//7/woACgADAP//BQARACAALAA6AEQARwBUAGcAegB6AGcAOQDv/5L/Q/8N/+L+sf5+/l/+Xv5p/nP+fv6C/oX+h/6E/or+lP6d/qP+rf65/s3+5/7//hf/JP8k/xj/Ef8J///+8v7r/u/+/v4Z/zP/UP91/6P/0P/v/wEABwAKABAAGAAgACUALAAxADgARQBTAGEAZwBmAFsASQAuAPv/s/9m/yj/8/7A/pn+if6P/pf+l/6Q/oz+if6J/o7+lP6T/oz+hP6M/qn+0f7u/gT/GP8q/zf/PP9B/z3/Lv8a/w7/EP8g/zH/Q/9f/4H/rP/T/+//BgAYACQAKgAwADUANQA8AE4AYgBzAH4AgwCFAH0AdQBpAEwADgC3/2P/LP8M/+/+zf61/qf+pv6q/rP+uP62/q3+p/6l/p3+kP6M/qH+xf7f/u3+8P4D/x//NP86/zL/KP8l/yr/Lv80/z3/Rv9V/2//lf+1/8v/2//s//7/CAALAA4AFwAjAC0AOQBOAF0AYQBjAGcAagBkAEsAGwDW/4X/Rv8f//3+0v6n/o3+h/6N/o7+h/6A/m/+av5v/nT+dv5z/nv+jv6p/rv+yP7Z/vH+B/8F//T+7/71/vv++/73/v7+DP8m/0X/Zf+E/57/sf/C/9D/2//c/9r/4v/t//b//f8MACMAMwA2AC0AKwAvACgADgDd/5//Yf8p//r+0f6y/p7+lP6P/oX+ef5q/lz+UP5T/lT+Uv5L/kz+Y/6D/qT+vv7X/vL+CP8U/w//Bf8A//v+8v7s/u3+//4e/zz/WP91/47/pf+7/83/2f/g/+f/7P/2/wEADwAfADAAOwA9ADkAMwAvACUADQDm/67/bf81/w7/9v7g/sf+uP6w/qz+nv6H/nL+Yf5V/lH+TP5H/kr+Xf58/p3+uv7S/uX+9f4A/wf/C/8M/w7/Dv8M/xD/Hv83/1H/bf9//47/nf+1/87/2P/W/9b/4f/0/wgAGQAqADkAQgBGAEYAQgA5ACQACQDe/6n/bf81/w//9v7l/tf+yv66/qn+nP6L/nT+Yf5W/lP+WP5d/mT+dP6K/qP+wP7Z/ur+9/4D/wv/DP8I/wL/Av8O/yT/QP9h/4P/ov+1/77/y//Y/+H/5v/t//3/EwAmADcASABUAFcAWQBcAF0AVwBLADYAFgDi/6X/bv9E/yX/E/8G//n+6/7b/sv+t/6h/or+f/54/nr+gf6M/p3+tf7O/uT+9v4C/wz/Fv8f/yP/Jf8q/y3/N/9K/13/df+L/6D/tP/D/9X/5//2/wYAGgAsADcARQBSAGUAdQB6AHkAcgBkAFYARgAnAPr/uv96/0v/Kv8S//3+8P7o/tn+wf6n/pH+f/5x/mv+cf5//oz+j/6c/q7+vv7L/tL+2f7h/ur+8/75/gP/Dv8X/yf/OP9P/2X/fP+S/6b/t//B/8b/zv/f//f/CQAaACgAOABOAGAAbQBqAFoAPgAgAAEA2P+j/2j/Of8U//v+5P7P/rf+nf6J/nP+Wf49/ij+L/5O/mb+cf5x/nv+kv6t/r7+w/7E/sr+0v7b/uX+6P7x/vv+Dv8j/zn/U/9t/4T/m/+y/8H/x//Q/+3/EAAmACgALABAAFYAYgBmAGEAVQA+AB4A+//N/5b/Yv8+/yT/Df/5/uL+yv6z/pn+eP5Q/jL+L/5C/lL+Y/5q/nT+gf6Q/p7+qf62/sf+3f7t/vf+BP8S/yL/Lv82/0D/UP9i/3n/lP+s/73/x//U/+n/BAAaACkANABIAFgAYABoAHAAbQBSAC0ABwDe/6v/eP9N/zD/Hf8R//z+1v62/pv+g/5o/lH+Sv5Z/nT+iP6Q/o7+i/6N/pb+nP6j/qj+uf7P/uf+/P4K/xD/GP8v/0v/Z/9//5r/tP/L/9v/5P/o//f/DgAiADAARABeAHEAegB7AHkAZwBIACkABwDY/6L/dv9b/1P/S/87/xj/5/7B/qb+kv5y/lr+XP54/pn+p/6j/pn+l/6i/q/+vf7P/ub+B/8h/y//MP8o/yP/K/9D/2D/f/+e/8P/6f/7//v/9/8AABsAPwBkAH8AkACgAK4AtQCtAJUAdwBZADgADQDa/6X/hv92/2//Wf8v//v+0P64/qb+kP6A/oT+o/7A/r/+rv6f/qD+p/6s/rD+v/7W/vb+FP8f/yL/GP8X/yz/UP95/5T/p//A/9v/6v/m/9v/5f8FACoASABdAHgAkACaAJcAjABxAE4AKQAJAOr/vv+V/3n/bP9h/0P/DP/K/pv+if57/mr+Zv53/pT+m/6N/nv+cf51/n7+jP6i/rj+yf7X/t7+2/7W/tb+5v4G/zD/U/9v/4f/n/+t/6j/oP+p/9D/BQAwAE4AXgBnAGwAaQBfAE4ANgAbAPv/zf+X/2j/Tf9F/zz/IP/w/r/+n/6W/oj+b/5Z/lv+eP6R/pD+fv5s/mr+dP6D/pf+rP7D/tT+3/7c/tH+zP7Z/vr+JP9F/1z/bf+G/57/pv+h/6v/0P8GADEAQwBNAF4AbABqAF0ATABBADUAHQDr/6//fv9e/1b/Sf8v//z+wf6c/o3+gf5j/kv+Vv6F/qj+nv55/mT+cv6K/pj+oP6v/sb+3v7r/uj+2f7T/uT+Df9B/2f/df94/4b/nP+g/5f/ov/L/wYAMgBHAFQAXABbAFoAWwBaAEwAKAD//8z/l/9w/1n/Tf89/yH/8P68/pj+kP6L/nP+Y/56/pz+pf6R/nL+bv55/on+l/6r/sX+2P7m/uX+3P7Y/uL+A/85/23/if+N/47/nf+l/6H/qv/Z/xgAPQBBAEUAXgBsAGQAUQBMAFEARgApAPb/tf9+/2L/X/9j/1D/If/o/sL+vP66/p3+ev6F/rv+2f6//pH+fv6T/qn+tP7G/uL+/f4M/xH/Bf/1/vj+G/9W/4j/oP+j/6r/vf/R/9H/z//v/y0AYQBzAHEAdACDAIwAhwCAAHgAaQBLABMAz/+Z/3j/cf9w/2L/Pf/+/sj+sP6p/pr+if6T/r/+3P7F/pT+df5+/pr+tf7Q/uv+/v4F///+8v7n/vH+Gf9S/4v/pP+i/6P/uP/M/8n/wP/d/yUAXgBeAEQAQgBeAGsAYQBZAF0AWQA1APz/vf9+/1D/Rv9e/2//Rf/x/qz+k/6P/oH+av5y/qH+xv6x/nf+Tv5R/mv+hP6e/rv+z/7W/tX+zv7B/sT+6P4u/3H/i/+G/4P/mP+s/6L/ov/H/wsAQABDADIALAA2AEIATQBdAFsARgAfAPL/uP9w/zT/Kf9N/2H/PP/s/qv+if6D/nL+X/5m/o3+tP6q/n7+Uf5E/lr+fv6b/rT+yP7U/tf+0P6//r7+4f4i/2L/gf+A/3//j/+n/7H/sf/F//z/NwBKADQAJAAwAFAAZABoAGoAYgBGABkA4P+b/1//S/9a/2r/Vf8W/9T+qf6S/n/+af5i/oP+sf64/of+TP45/lX+ef6R/qn+xv7Z/t3+1/7M/s3+7v4r/1//eP95/3j/i/+l/7X/u//L/+//HQA/AD8AKAAeADEAVwBuAGUAUgBIADsAEADE/4D/af9v/3T/Zv8//w3/3f63/p/+jf59/n3+mP6t/qD+dP5S/ln+ef6R/pn+pP7A/tL+0v7E/sP+3/4L/zf/Vf9o/3T/gf+S/6z/wv/S/+D/AwAxAEMANAAmADoAYgB1AGoAYQBwAGwAQQAFANf/s/+U/4X/hf99/1r/I//8/ub+zf6w/qb+tv7L/sz+tf6b/pP+mv6p/rb+wv7X/uj+7v7p/uX+7/4I/y3/UP9t/37/iv+b/6v/wP/V/+X/9/8KACQANgA1AC4ANgBXAHEAbwBkAFsAWABMACoAAADd/77/pv+Y/4n/av8+/xj/Bf/1/tv+uv6l/qz+tP6r/pj+jf6Q/pn+ov6o/q3+vP7R/uH+5f7m/vf+GP81/0n/Wf9r/4D/jv+Y/6P/tv/C/8L/yP/h/wQAFAAIAAIADAAeACYAIQAYAA0AAADn/8n/qf+I/3X/df9w/0//Hv/4/uj+3v7F/qT+j/6S/pP+h/54/nL+cv52/n7+jP6X/pf+mv6q/rz+xP7P/uf+BP8c/yz/OP9H/1P/X/9w/4f/mP+h/6D/lf+a/7X/4P/s/9j/0P/n//L/2v/Q/+T/4//E/6r/m/+B/1r/Sv9N/0T/IP/3/un+5f7O/qf+mf6m/rX+qP6N/o3+nf6k/p/+nf6u/rz+yP7U/t/+3v7e/vP+Ff8l/yH/I/89/1f/XP9U/13/gv+v/7v/nf+d/7//0P/F/77/0v/f/9j/v/+p/6j/qP+R/4b/kf+N/2X/RP8//zn/Hf8F/wX/Ev8D/+T+0/7P/r7+tP6//s/+0f7F/sP+xf7J/sv+0/7l/vf+Bf8N/wz/Bv8C/wb/Jf9P/2//Wv9E/3P/gP9I/1f/pv+7/5j/hP+X/5f/kf+Q/5f/r/+y/5j/jf+V/3//XP9X/3z/d/9G/zj/PP8w/x//EP8Q/xH/Cf///vL+8f73/vj+8v71/v7+/v76/vT++v4E/wr/DP8V/yL/Hf8m/0D/NP8y/03/Vf9c/33/if9t/3X/o/+K/13/i/+2/5j/lf++/7H/e/+E/7r/sP+C/4X/qf+b/3T/d/95/2z/Z/93/2v/P/9J/2n/Wv8p/yL/T/9V/zL/J/87/z//N/82/zv/Nf8z/0b/Uv9A/zb/S/9Q/0j/WP9X/zj/WP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type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Truth: ኢትዮጵያ ውስጥ የ ነበሩት ኤርትራውያን ም ተመሳሳይ እድል ነበራቸው\n",
            "Predicted: \n",
            "wer: 42\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11U40F6GKMav"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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