PJT_partie_Francois.ipynb

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      "cell_type": "markdown",
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      "source": [
        "# Librairies"
      ]
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      "source": [
        "#@title MEL SPECTROGRAM\n",
        "#!rm -rf /content/melspectrogram/ #supprime le dossier melspectrogram si problèmes\n",
        "!pip install librosa # installation de la librairie librosa\n",
        "!git clone https://github.com/Jakobovski/free-spoken-digit-dataset #téléchargement du jeu de données\n",
        "!mkdir /content/melspectrogram #création du dossier qui contiendra les mel-spectrogrammes\n",
        "\n",
        "import glob # librairie spécialisée dans la recherche de chemin\n",
        "list_of_files = glob.glob(\"free-spoken-digit-dataset/recordings/*\") #permet d'obtenir le chemin relatif de tous les fichiers du dossier recordings\n",
        "print(list_of_files[:2])\n",
        "\n",
        "n_files = len(list_of_files) # nombres de fichiers audios\n",
        "print(n_files)\n",
        "\n",
        "import librosa\n",
        "y, sr = librosa.load(list_of_files[3], sr= 8000)\n",
        "#y est le signal sous forme d'array numpy, sr est\n",
        "#le taux d'échantillonage (vaut 8khz  d'après les indications github)\n",
        "print(y, sr)\n",
        "\n",
        "import numpy as np\n",
        "# calcul du melspectrogramme\n",
        "sp = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128)\n",
        "#conversion de l'amplitude en dB pour une meilleure comprehension\n",
        "s_dB = librosa.power_to_db(sp, ref=np.max)\n",
        "\n",
        "print(type(s_dB))\n",
        "\n",
        "from IPython.display import Audio\n",
        "Audio(y,rate=sr) # permet de lire un fichier audio sous forme d'array numpy dans un notebook jupyter ou dans colab\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import librosa.display\n",
        "import numpy as np\n",
        "\n",
        "def plot_spec(S_dB):\n",
        "  plt.figure(figsize=(10, 4))\n",
        "  librosa.display.specshow(S_dB, x_axis='time',\n",
        "                         y_axis='mel', sr=sr)\n",
        "  plt.colorbar(format='%+2.0f dB')\n",
        "  plt.title('Mel-frequency spectrogram')\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "plot_spec(s_dB)\n",
        "\n",
        "\n",
        "n_mels=128\n",
        "sr = 8000\n",
        "\n",
        "\n",
        "def compute_spectrogram(path):\n",
        "    \"Calcule le melspectrogramme du fichier audio situé à path dans le repertoire de fichiers\"\n",
        "    file_name = os.path.basename(path) \n",
        "    s , _ = librosa.load(path, sr= sr) # sr est le taux d'échantillonnage du signal.\n",
        "    \n",
        "    melspec = librosa.feature.melspectrogram(y=s, sr=sr, n_mels=n_mels)\n",
        "    melspec_db = librosa.power_to_db(melspec, ref=np.max)\n",
        "    melspec_db_image = melspec_db.reshape(melspec_db.shape[0],melspec_db.shape[1],1) # on rajoute la dimension 1 à la fin pour forcer la représentation en tant qu'image (hauteur, largeur, nombre de couleurs ici égale à 1)\n",
        "    return (path,melspec_db_image)\n",
        "\n",
        "def get_duration(spec):\n",
        "  \"Renvoie la dimension correspondant à l'axe temporel d'un melspectrogramme\"\n",
        "  return spec.shape[1]\n",
        "\n",
        "def get_longest_duration(list_path_spec):\n",
        "  \"Renvoie la dimension temporelle du melspectrogramme le plus long\"\n",
        "  list_time = [get_duration(spec) for path, spec in list_path_spec ]\n",
        "  return max(list_time)\n",
        "\n",
        "def save_melspec(path_melspec): \n",
        "    \"\"\"\n",
        "    Permet d'enregistrer les spectrogrammes dans le dossier melspectrogram\n",
        "    \"\"\"\n",
        "    path, melspec = path_melspec\n",
        "    file_name = os.path.basename(path)\n",
        "    melspec_path = os.path.join(\"/content/melspectrogram\", os.path.splitext(file_name)[0])\n",
        "    np.save(melspec_path, melspec)\n",
        "    #print(\"{} saved\".format(melspec_path))\n",
        "\n",
        "\n",
        "import os\n",
        "from tqdm.autonotebook import tqdm # bar de remplissage pour visualiser l'évolution du calcul et de l'enregistrement des spectrogrammes\n",
        "import sys\n",
        "import tensorflow as tf\n",
        "n=n_files\n",
        "\n",
        "\n",
        "\n",
        "print(\"Conversion des fichiers audio en melspectrogrammes\")\n",
        "list_spec = []\n",
        "for path, spec in tqdm(map(compute_spectrogram, list_of_files[:n]), total=n):\n",
        "   list_spec.append((path, spec))\n",
        "   \n",
        "\n",
        "max_time = get_longest_duration(list_spec)\n",
        "print(\"La plus longue dimension temporelle est {}\".format(max_time))\n",
        "def spec_padding(path_data):\n",
        "  path, spec = path_data\n",
        "  min_spec = np.min(spec)\n",
        "  spec_padded = np.pad(spec, ((0,0),(0,max_time-spec.shape[1]),(0,0)),\"constant\",constant_values=((min_spec,min_spec), (min_spec,min_spec),(min_spec,min_spec)))\n",
        "  return (path, spec_padded)\n",
        "\n",
        "\n",
        "\n",
        "print(\"Traitement des melspectrogrammes pour qu'ils aient la même durée\")\n",
        "list_padded_spec = []\n",
        "for path, spec_padded in tqdm(map(spec_padding, list_spec[:n]), total=n):\n",
        "  list_padded_spec.append((path,spec_padded))\n",
        "  \n",
        "\n",
        "\n",
        "print(\"Traitement des melspectrogrammes pour qu'ils aient la même durée\")\n",
        "for spec in tqdm(map(save_melspec, list_padded_spec[:n]), total=n):\n",
        "  pass"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: librosa in /usr/local/lib/python3.6/dist-packages (0.6.3)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.22.1)\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (2.1.8)\n",
            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.14.1)\n",
            "Requirement already satisfied: resampy>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.2.2)\n",
            "Requirement already satisfied: numba>=0.38.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.47.0)\n",
            "Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.12.0)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (4.4.1)\n",
            "Requirement already satisfied: numpy>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.17.5)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.4.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (42.0.2)\n",
            "Requirement already satisfied: llvmlite>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (0.31.0)\n",
            "Cloning into 'free-spoken-digit-dataset'...\n",
            "remote: Enumerating objects: 9, done.\u001b[K\n",
            "remote: Counting objects: 100% (9/9), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 3166 (delta 3), reused 5 (delta 0), pack-reused 3157\u001b[K\n",
            "Receiving objects: 100% (3166/3166), 23.92 MiB | 5.57 MiB/s, done.\n",
            "Resolving deltas: 100% (72/72), done.\n",
            "['free-spoken-digit-dataset/recordings/6_yweweler_21.wav', 'free-spoken-digit-dataset/recordings/6_yweweler_13.wav']\n",
            "2000\n",
            "[-0.015625  -0.0078125 -0.015625  ... -0.015625  -0.0078125 -0.0078125] 8000\n",
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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DoUMoxGIWhg6hEKmMup5I5HelbXRwLWUjWtZcbmY/By4Gfkk2fVEJOE/SeyWtJetveYuk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\n8kT62y/qSaNG6JD+NEbRPzS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            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Conversion des fichiers audio en melspectrogrammes\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "La plus longue dimension temporelle est 36\n",
            "Traitement des melspectrogrammes pour qu'ils aient la même durée\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "Traitement des melspectrogrammes pour qu'ils aient la même durée\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WlzGG5vrs-FR",
        "colab_type": "code",
        "outputId": "36f37275-08e5-400c-eb39-e5844847da44",
        "cellView": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#@title LIBRAIRIE CONFUSION MATRIX\n",
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"\n",
        "plot a pretty confusion matrix with seaborn\n",
        "Created on Mon Jun 25 14:17:37 2018\n",
        "@author: Wagner Cipriano - wagnerbhbr - gmail - CEFETMG / MMC\n",
        "REFerences:\n",
        "  https://www.mathworks.com/help/nnet/ref/plotconfusion.html\n",
        "  https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-classification-report\n",
        "  https://stackoverflow.com/questions/5821125/how-to-plot-confusion-matrix-with-string-axis-rather-than-integer-in-python\n",
        "  https://www.programcreek.com/python/example/96197/seaborn.heatmap\n",
        "  https://stackoverflow.com/questions/19233771/sklearn-plot-confusion-matrix-with-labels/31720054\n",
        "  http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py\n",
        "\"\"\"\n",
        "\n",
        "#imports\n",
        "from pandas import DataFrame\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.font_manager as fm\n",
        "from matplotlib.collections import QuadMesh\n",
        "import seaborn as sn\n",
        "\n",
        "\n",
        "def get_new_fig(fn, figsize=[9,9]):\n",
        "    \"\"\" Init graphics \"\"\"\n",
        "    fig1 = plt.figure(fn, figsize)\n",
        "    ax1 = fig1.gca()   #Get Current Axis\n",
        "    ax1.cla() # clear existing plot\n",
        "    return fig1, ax1\n",
        "#\n",
        "\n",
        "def configcell_text_and_colors(array_df, lin, col, oText, facecolors, posi, fz, fmt, show_null_values=0):\n",
        "    \"\"\"\n",
        "      config cell text and colors\n",
        "      and return text elements to add and to dell\n",
        "      @TODO: use fmt\n",
        "    \"\"\"\n",
        "    text_add = []; text_del = [];\n",
        "    cell_val = array_df[lin][col]\n",
        "    tot_all = array_df[-1][-1]\n",
        "    per = (float(cell_val) / tot_all) * 100\n",
        "    curr_column = array_df[:,col]\n",
        "    ccl = len(curr_column)\n",
        "\n",
        "    #last line  and/or last column\n",
        "    if(col == (ccl - 1)) or (lin == (ccl - 1)):\n",
        "        #tots and percents\n",
        "        if(cell_val != 0):\n",
        "            if(col == ccl - 1) and (lin == ccl - 1):\n",
        "                tot_rig = 0\n",
        "                for i in range(array_df.shape[0] - 1):\n",
        "                    tot_rig += array_df[i][i]\n",
        "                per_ok = (float(tot_rig) / cell_val) * 100\n",
        "            elif(col == ccl - 1):\n",
        "                tot_rig = array_df[lin][lin]\n",
        "                per_ok = (float(tot_rig) / cell_val) * 100\n",
        "            elif(lin == ccl - 1):\n",
        "                tot_rig = array_df[col][col]\n",
        "                per_ok = (float(tot_rig) / cell_val) * 100\n",
        "            per_err = 100 - per_ok\n",
        "        else:\n",
        "            per_ok = per_err = 0\n",
        "\n",
        "        per_ok_s = ['%.2f%%'%(per_ok), '100%'] [per_ok == 100]\n",
        "\n",
        "        #text to DEL\n",
        "        text_del.append(oText)\n",
        "\n",
        "        #text to ADD\n",
        "        font_prop = fm.FontProperties(weight='bold', size=fz)\n",
        "        text_kwargs = dict(color='w', ha=\"center\", va=\"center\", gid='sum', fontproperties=font_prop)\n",
        "        lis_txt = ['%d'%(cell_val), per_ok_s, '%.2f%%'%(per_err)]\n",
        "        lis_kwa = [text_kwargs]\n",
        "        dic = text_kwargs.copy(); dic['color'] = 'g'; lis_kwa.append(dic);\n",
        "        dic = text_kwargs.copy(); dic['color'] = 'r'; lis_kwa.append(dic);\n",
        "        lis_pos = [(oText._x, oText._y-0.3), (oText._x, oText._y), (oText._x, oText._y+0.3)]\n",
        "        for i in range(len(lis_txt)):\n",
        "            newText = dict(x=lis_pos[i][0], y=lis_pos[i][1], text=lis_txt[i], kw=lis_kwa[i])\n",
        "            #print 'lin: %s, col: %s, newText: %s' %(lin, col, newText)\n",
        "            text_add.append(newText)\n",
        "        #print '\\n'\n",
        "\n",
        "        #set background color for sum cells (last line and last column)\n",
        "        carr = [0.27, 0.30, 0.27, 1.0]\n",
        "        if(col == ccl - 1) and (lin == ccl - 1):\n",
        "            carr = [0.17, 0.20, 0.17, 1.0]\n",
        "        facecolors[posi] = carr\n",
        "\n",
        "    else:\n",
        "        if(per > 0):\n",
        "            txt = '%s\\n%.2f%%' %(cell_val, per)\n",
        "        else:\n",
        "            if(show_null_values == 0):\n",
        "                txt = ''\n",
        "            elif(show_null_values == 1):\n",
        "                txt = '0'\n",
        "            else:\n",
        "                txt = '0\\n0.0%'\n",
        "        oText.set_text(txt)\n",
        "\n",
        "        #main diagonal\n",
        "        if(col == lin):\n",
        "            #set color of the textin the diagonal to white\n",
        "            oText.set_color('w')\n",
        "            # set background color in the diagonal to blue\n",
        "            facecolors[posi] = [0.35, 0.8, 0.55, 1.0]\n",
        "        else:\n",
        "            oText.set_color('r')\n",
        "\n",
        "    return text_add, text_del\n",
        "#\n",
        "\n",
        "def insert_totals(df_cm):\n",
        "    \"\"\" insert total column and line (the last ones) \"\"\"\n",
        "    sum_col = []\n",
        "    for c in df_cm.columns:\n",
        "        sum_col.append( df_cm[c].sum() )\n",
        "    sum_lin = []\n",
        "    for item_line in df_cm.iterrows():\n",
        "        sum_lin.append( item_line[1].sum() )\n",
        "    df_cm['sum_lin'] = sum_lin\n",
        "    sum_col.append(np.sum(sum_lin))\n",
        "    df_cm.loc['sum_col'] = sum_col\n",
        "    #print ('\\ndf_cm:\\n', df_cm, '\\n\\b\\n')\n",
        "#\n",
        "\n",
        "def pretty_plot_confusion_matrix(df_cm, annot=True, cmap=\"Oranges\", fmt='.2f', fz=11,\n",
        "      lw=0.5, cbar=False, figsize=[8,8], show_null_values=0, pred_val_axis='y'):\n",
        "    \"\"\"\n",
        "      print conf matrix with default layout (like matlab)\n",
        "      params:\n",
        "        df_cm          dataframe (pandas) without totals\n",
        "        annot          print text in each cell\n",
        "        cmap           Oranges,Oranges_r,YlGnBu,Blues,RdBu, ... see:\n",
        "        fz             fontsize\n",
        "        lw             linewidth\n",
        "        pred_val_axis  where to show the prediction values (x or y axis)\n",
        "                        'col' or 'x': show predicted values in columns (x axis) instead lines\n",
        "                        'lin' or 'y': show predicted values in lines   (y axis)\n",
        "    \"\"\"\n",
        "    if(pred_val_axis in ('col', 'x')):\n",
        "        xlbl = 'Predicted'\n",
        "        ylbl = 'Actual'\n",
        "    else:\n",
        "        xlbl = 'Actual'\n",
        "        ylbl = 'Predicted'\n",
        "        df_cm = df_cm.T\n",
        "\n",
        "    # create \"Total\" column\n",
        "    insert_totals(df_cm)\n",
        "\n",
        "    #this is for print allways in the same window\n",
        "    fig, ax1 = get_new_fig('Conf matrix default', figsize)\n",
        "\n",
        "    #thanks for seaborn\n",
        "    ax = sn.heatmap(df_cm, annot=annot, annot_kws={\"size\": fz}, linewidths=lw, ax=ax1,\n",
        "                    cbar=cbar, cmap=cmap, linecolor='w', fmt=fmt)\n",
        "\n",
        "    #set ticklabels rotation\n",
        "    ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, fontsize = 10)\n",
        "    ax.set_yticklabels(ax.get_yticklabels(), rotation = 25, fontsize = 10)\n",
        "\n",
        "    # Turn off all the ticks\n",
        "    for t in ax.xaxis.get_major_ticks():\n",
        "        t.tick1On = False\n",
        "        t.tick2On = False\n",
        "    for t in ax.yaxis.get_major_ticks():\n",
        "        t.tick1On = False\n",
        "        t.tick2On = False\n",
        "\n",
        "    #face colors list\n",
        "    quadmesh = ax.findobj(QuadMesh)[0]\n",
        "    facecolors = quadmesh.get_facecolors()\n",
        "\n",
        "    #iter in text elements\n",
        "    array_df = np.array( df_cm.to_records(index=False).tolist() )\n",
        "    text_add = []; text_del = [];\n",
        "    posi = -1 #from left to right, bottom to top.\n",
        "    for t in ax.collections[0].axes.texts: #ax.texts:\n",
        "        pos = np.array( t.get_position()) - [0.5,0.5]\n",
        "        lin = int(pos[1]); col = int(pos[0]);\n",
        "        posi += 1\n",
        "        #print ('>>> pos: %s, posi: %s, val: %s, txt: %s' %(pos, posi, array_df[lin][col], t.get_text()))\n",
        "\n",
        "        #set text\n",
        "        txt_res = configcell_text_and_colors(array_df, lin, col, t, facecolors, posi, fz, fmt, show_null_values)\n",
        "\n",
        "        text_add.extend(txt_res[0])\n",
        "        text_del.extend(txt_res[1])\n",
        "\n",
        "    #remove the old ones\n",
        "    for item in text_del:\n",
        "        item.remove()\n",
        "    #append the new ones\n",
        "    for item in text_add:\n",
        "        ax.text(item['x'], item['y'], item['text'], **item['kw'])\n",
        "\n",
        "    #titles and legends\n",
        "    ax.set_title('Confusion matrix')\n",
        "    ax.set_xlabel(xlbl)\n",
        "    ax.set_ylabel(ylbl)\n",
        "    plt.tight_layout()  #set layout slim\n",
        "    plt.show()\n",
        "#\n",
        "\n",
        "def plot_confusion_matrix_from_data(y_test, predictions, columns=None, annot=True, cmap=\"Oranges\",\n",
        "      fmt='.2f', fz=11, lw=0.5, cbar=False, figsize=[8,8], show_null_values=0, pred_val_axis='lin'):\n",
        "    \"\"\"\n",
        "        plot confusion matrix function with y_test (actual values) and predictions (predic),\n",
        "        whitout a confusion matrix yet\n",
        "    \"\"\"\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "    from pandas import DataFrame\n",
        "\n",
        "    #data\n",
        "    if(not columns):\n",
        "        #labels axis integer:\n",
        "        ##columns = range(1, len(np.unique(y_test))+1)\n",
        "        #labels axis string:\n",
        "        from string import ascii_uppercase\n",
        "        columns = ['class %s' %(i) for i in list(ascii_uppercase)[0:len(np.unique(y_test))]]\n",
        "\n",
        "    confm = confusion_matrix(y_test, predictions)\n",
        "    cmap = 'Oranges';\n",
        "    fz = 11;\n",
        "    figsize=[9,9];\n",
        "    show_null_values = 2\n",
        "    df_cm = DataFrame(confm, index=columns, columns=columns)\n",
        "    pretty_plot_confusion_matrix(df_cm, fz=fz, cmap=cmap, figsize=figsize, show_null_values=show_null_values, pred_val_axis=pred_val_axis)\n",
        "#\n",
        "\n",
        "\n",
        "\n",
        "#\n",
        "#TEST functions\n",
        "#\n",
        "def _test_cm():\n",
        "    #test function with confusion matrix done\n",
        "    array = np.array( [[13,  0,  1,  0,  2,  0],\n",
        "                       [ 0, 50,  2,  0, 10,  0],\n",
        "                       [ 0, 13, 16,  0,  0,  3],\n",
        "                       [ 0,  0,  0, 13,  1,  0],\n",
        "                       [ 0, 40,  0,  1, 15,  0],\n",
        "                       [ 0,  0,  0,  0,  0, 20]])\n",
        "    #get pandas dataframe\n",
        "    df_cm = DataFrame(array, index=range(1,7), columns=range(1,7))\n",
        "    #colormap: see this and choose your more dear\n",
        "    cmap = 'PuRd'\n",
        "    pretty_plot_confusion_matrix(df_cm, cmap=cmap)\n",
        "#\n",
        "\n",
        "def _test_data_class():\n",
        "    \"\"\" test function with y_test (actual values) and predictions (predic) \"\"\"\n",
        "    #data\n",
        "    y_test = np.array([1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5])\n",
        "    predic = np.array([1,2,4,3,5, 1,2,4,3,5, 1,2,3,4,4, 1,4,3,4,5, 1,2,4,4,5, 1,2,4,4,5, 1,2,4,4,5, 1,2,4,4,5, 1,2,3,3,5, 1,2,3,3,5, 1,2,3,4,4, 1,2,3,4,1, 1,2,3,4,1, 1,2,3,4,1, 1,2,4,4,5, 1,2,4,4,5, 1,2,4,4,5, 1,2,4,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5])\n",
        "    \"\"\"\n",
        "      Examples to validate output (confusion matrix plot)\n",
        "        actual: 5 and prediction 1   >>  3\n",
        "        actual: 2 and prediction 4   >>  1\n",
        "        actual: 3 and prediction 4   >>  10\n",
        "    \"\"\"\n",
        "    columns = []\n",
        "    annot = True;\n",
        "    cmap = 'Oranges';\n",
        "    fmt = '.2f'\n",
        "    lw = 0.5\n",
        "    cbar = False\n",
        "    show_null_values = 2\n",
        "    pred_val_axis = 'y'\n",
        "    #size::\n",
        "    fz = 12;\n",
        "    figsize = [9,9];\n",
        "    if(len(y_test) > 10):\n",
        "        fz=9; figsize=[14,14];\n",
        "    plot_confusion_matrix_from_data(y_test, predic, columns,\n",
        "      annot, cmap, fmt, fz, lw, cbar, figsize, show_null_values, pred_val_axis)\n",
        "#\n",
        "\n",
        "\n",
        "#\n",
        "#MAIN function\n",
        "#\n",
        "if(__name__ == '__main__'):\n",
        "    print('__main__')\n",
        "    print('_test_cm: test function with confusion matrix done\\nand pause')\n",
        "    _test_cm()\n",
        "    plt.pause(5)\n",
        "    print('_test_data_class: test function with y_test (actual values) and predictions (predic)')\n",
        "    _test_data_class()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__main__\n",
            "_test_cm: test function with confusion matrix done\n",
            "and pause\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:164: MatplotlibDeprecationWarning: \n",
            "The tick1On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick1line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:165: MatplotlibDeprecationWarning: \n",
            "The tick2On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick2line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:167: MatplotlibDeprecationWarning: \n",
            "The tick1On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick1line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:168: MatplotlibDeprecationWarning: \n",
            "The tick2On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick2line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:63: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6nIab78afk9tv90Hsm9viJmAM8PWor7sdXz5uOSktKWilyanPIaUtBY/FQ6u9\nlYb4BlLaUggSRCuNQRtQKBIdiSQ6EgFIbU1lR9IO2mPacVlc+7zOMRuPIcYTg81jA6AquYqgCpJd\nn81n4z8jszGTjMYMvhj3BcltySS3J/dZ+yM2wdFau3f+rZTKB1KAdeGN6vCmgbdqv6agI/RI6pys\nY7k4ayZPlS8B4PeFL+MJ+vZ5jouzZrKlvZL7a1cy0pbF9UNPZ96mZ/dZlh2VTLo1kd9sfp7zMqYy\nM3kMS+rXcnb6FJY1rB94yQ0Qfc/v0FFRtK9YC0qh6nb0Ws87aw7uu+/rcdw6/xF0bBztX63HsK0Y\n+1kn07ZyPeggln+/Svvn32D+96tYn/4n7rvvw/zKS/iPOlqSm0OGouXSubgnTwMg+dF7SX78fupv\n//MBb7uQ8OITBG12tr/+GebtpWT+7EK2v/4pOsa21zKCQezvL6bilY+wf7CY+NcW0Hjj74h9ZxHu\nCZMluRlgMhszSejY9bj/+vz1AEwomUCLvQWA0vRSPGYPlSmh/5dtrlAisnnIZmpSahhbNpbMxkwK\nBhdgDBixuW04ohy0R7dj9puxeWz7vI7Vt2sistfkpTizmMlFk1EotNK02FvQnb/YaqX7tP0ROQen\nF2cAy7TW7RCaoxPmeA5LjoC7K7kBKHLUkLrbOjYHYmriCD6qD33zFDqq8OkAeTFp+ywL6CAmgxEF\nWA1m/DpAujWRvJg0ljcV7O1S4dPRgeW1l3H/+o6uJ5/0oG83Ec/y39fxXnYlAMG8YQQmHYF52RIw\nGCEQAJ8v9JSVxYJqasS68Dk8P/9lnzdFHBzB+ISu5AbAPe4HmGorv9W2C/YP36LtvEsA8A0eimf0\neGK+/HjfZUYjKhhA+X0YXC602YyhtZnYN1+l5dK5B7PJ4juIdcWS1pLW9WentJY08qvzyWzIxBnl\npGBwAX6jn9ya3F7n0ADYnXYa4xvZkrOFqpQqktqTOKLoCIxB4z6vYwzumoy8NXsr2Q3ZXU9hjdo+\nCpfVRcWgCjIbMklu67veG4jgHhzo9nh4JrBeKXUOcDXwR2BlWIM7zCngpNSJrGrZ9WDbHSN+jEEZ\nWNdayus1X+Lf48l+uzEKULQHdu0W3uhtI9kSS52nda9lX7cUs7m9gntHX8oOTzOLa77kF3ln8ULF\nwHzSyFBWSjAxiai//gnT8k9Du4H/5k4CU6f3qGt549+YP/mI4KA03L/6LYGjjg6do7KSYPau5fiD\nWdkYqqrAZsMz9+fEnvYjgoPScD72BFH33IHrtjvBYum3Noo+FAwS958XcM486Vttu2DaUd2trj89\nC9OOmn2W6egYWmb/lKyrzgQ2EIkAACAASURBVCWQnErdnQ+R/Nh9NM29Fczy+Rnodq5LA2AKmhhb\nPnavdceVj2Nc+biu1zkNOeQ05Oy1/t6u0+2cZeO6vd4zIeprEZ3gaK0DSqk4Qo+K1wGrgUe01pLc\nhNnlOSfgDvpYUr8GgJ+vf5ImXzvRBgvXDj2N8zKmsqi673a/XlS9vOt8M5PGUOyowa+DXD/0dMzK\nyJL6tWxur+iz630fKhjAWF6Ke/xE3Hfdi3HV19gunRUaYord1ePlufxq3L/8FZjNmD5Zhu3yi2j/\nfBU6ad+/BXmvvAbvldcAYPzyc1CK4MjRRP9iLqq9Dd855+M798KD2kbRd1L+eic62kbrrCuwffL+\nQb9e24WX0XbhZQBErVkJSuHNG0HqPTdjcHTQceIZOE46+6DHIcT+HA5DVBp4BLhQa/1jrfWH4Q7o\ncDcn64ekRyXw6La32TniunMrBlfQyycNGxhhz+zxvo7OuTKxxuiuY8mWOBq97fss253NGMVxKeN5\ns/YrZmUew7L69fyz7H2uyDm+L5v4vQSzstEmE77zZwEQmHwUOikZY0n3vbd0WhqYQ2tW+I87nmBm\nNoYtm0PnyM7GULkrYTNUVRLMyup+Ia+X6D/dg/uOe7A++TiB6TNwPvU8UQ/eDy4XYuBLfuSPmCtK\nqb33cTAY8KdnHfC2C/60zG51TbVV+HdOWN9HWRefl6R/PkDjz28j/uVncB8xlR33Pk7SM4+g3ANv\nXps4/ER8gqO1btda36G1XhvuWARclDmDobZBPFT8364hKJvRirlzWpQBxZTEEZQ763t9/8rmQk5I\nnQDASFsWFoOJUueO/ZbtNCf7WBZVLyegg1gNZjSg0VgNB2clze9CJ6fgP+ZYTJ8sA8BQUoRqqCc4\nNK9bPVVT3fW1ccN6DBXbCeaHHrP0nXUeludDk68N24oxrlmF7/ju3cbWxx7CM+fyUI+Pw4lGheb8\n+Pzg8x7MJoo+kDT/z1i3bKD2gafBEprI6Rk1HuVxE7X2K4B9brvQccIZxC1+CQDz9lKsm9fjnHrc\nfst2Snz+H7SffRHB+EQMbmdo3Fkp8PtRfvn8iPCL6CEqMbBkRSVzTsbRVLubuGvUbADqPa28veNr\nrhp8EhqNURkpclR3DSclmG38atj5XSsZv1L1GdcPPZ1jk8fiDfqZX/peVy/QvsoARtqzUCi2dE50\nfqv2K64echImZWRxzYr+ug0HxPXAI8TcdC3qrtvQJjPOx59Gxydgm30+7l//jsCkI4i+9y6M69eA\nwYi2WHA+/lSoVwdwX38TMb/4GbFTJoDRiPPBx8Ae23V+w7YSTN98hePm3wChYauYuT8h6u8P4Z11\nMcTte7EvEV7mbYUkPvc43sF5ZF19HgC+zBx2/OWpfW67kP1/p1Lz8AICqem0/N9cBt0zj8EXzEQb\njNTfdj/aFpr8ua8yAFNFGdYNq2m+6kYAWi+8jLQ7biDh+X/Qcdr5BO3f7uEBIQ4GpXXfPpYVKdwO\nr9yYPUTZQpMI56x6MMyRDCwLJ98MQEu9I8yRDDwJqTbcDvltfk87v5dKvhoY874GivwpoUms8pnp\nKcpm4eSzTw53GAPOkjeXAL3sDsphMEQlhBBCiMOPJDhCCCGEiDiS4AghhBAi4kiCI4QQQoiIIwmO\nEEIIISKOJDhCCCGEiDiyDo4QQggRoZ7/8DPSXd1Xlp577FSMWnPDhgLy2tqxBDUvjMjjhZH5+z3f\nMTV1/P6bdQA8MGksS3NCq86bA0Gu2FLMj6prifN6abJaeWrMCD7LTGNabR3XbygkOuBn8dDBvNh5\nnXGNzdy+egM/PW4aDnPfL7YqCY4QQggRwcrtNl4asWsl9B0xUQxt66A81o7TZOKIhqYDOk+S28NN\n6zbjMhqJDnTfDPn6jVs4fXsVKwelsDxjEMluN6ZgEICfb9hCpd1GpS2Gy7Zu48PsDBqio7hxfQHz\nx448KMkNSIIjhBBCRLQWq4WVaSm4jMbQdhrAxuRENiYncmlhyYElOFpz89pNbI+1URsTzUmVNV1F\nqU4Xp2yvojommruOmohRa7xGY1d5tD9AeayNLQnxnFVeSYw/wEXFpVTZYvg8U3YTF0IIIcR3ML6x\nmf++9zEeg4HPMwbxyIQxuE3G/b9xN2eXVTC6uZW5P5zKZYUl3cpy2zswAuZgkFeX/I84n5/iuFj+\n8oOxlMXF8mZuDnOKS4EK1iYn4jMozimt4Npjp/ZdI3shCY4QQggRoT4YnEmVzYbPYGBWSRknVNVS\nFx3Fv0YPP+BzJLk9XL25iFeH52LSmmh/aHgqweMl1uvD0jkUlez28Nj40SR4vVxeWMKtazdx/bFT\nWTB6GB/mZBDt91MSF8v9K1bzwsh8cts7+ONXa7D5/HyWkcZTY0f0adslwRFCCCEi1Esjdk0cDii4\n5+t15LZ37Pd9pmAQpTV+g4EEj5eoYJDLC7dxeeG2rjo/LSjC5vfzv85hpmarhXdyszEHAlxeWEKm\nw9lVt9JuA+Dk7VWYg0HeGpLNwg8/44OcTJZmZ7Dg4y/4ZlAya1KT+6rpkuAIIYQQkWhoWztXby7i\n60EpuI0GzikLbe66KSmBJLeHKTvqGdbaBsCw1jZOLa9kfXIi1XYbf1qxmomNzdwybTLF8bH8YfKE\nrvOeVVbBpMZm/pubw8dZ6ZTH2tmYmMC45hZmb91GvNcHwNqUpG7xxHu8/GRLMb+eNhmUwhjUjGtq\nwdC56bexjzf/lgRHCCGEiECtFgt+g2J2USk2v4/GqCheGJHHv/NzGdfYzLz1BV11p+1oYNqOBh6Y\nNJbqzt6WnZxmM5/tNhn46B31AGxNiKM81g7An44Yxw0btjC7qBS3ycjS7AyeGNN9yOlnm7by7pBs\ntne+57Hxo5i7aSvDW9p4b3Amq/qw9wZA6T7OmCKF2+GVG7OHKJsFgDmrHgxzJAPLwsk3A9BS7whz\nJANPQqoNt8Mb7jAGnJ3fSyVfVYQ5koElf0oOgHxmehFls3Dy2SeHO4wBZ8mbSwBUb2WykrEQQggh\nIo4kOEIIIYSIOJLgCCGEECLiSIIjhBBCiIgjCY4QQgghIo4kOEIIIYSIOJLgCCGEECLiSIIjhBBC\niIgjCY4QQgghIo6sZLx3cmOEEEKIga/XlYxlL6q9cHXIUuF7iraHlpd3dXjCHMnAEm23ArKFRW8W\nTr5Zlt3vxc6tGuTedCf3Ze9kq4bedW7V0CsZohJCCCFExJEERwghhBARRxIcIYQQQkQcSXCEEEII\nEXEkwRFCCCFExJEERwghhBARRxIcIYQQQkQcSXCEEEIIEXFkoT8hhBBC9HDt1dcyY/oMUlNSAbot\nNHjxrIs5/eTTSUpMonx7Of985p9s2LQhXKH2SnpwhBBCCNGDMiiWfrS0x/EzTzuTKy+9kuqaav7x\n9D9IS0vjnt/dQ2xsbBii3DtJcIQQQgjRw/wn57PgpQU9jh95xJEA/PuNf/PO+++wes1qbDYbxx97\nfP8GuB+S4AghhBDigDU3NwPwgwk/ICM9g/y8fAAyMzLDGVYPMgdHCCGEEAds4WsLmTBuArPOn8Ws\n82fR0dEBgNc3sDZJlQRHCCGEEAesvqGea264htwhueig5uwzzub0U06ntKw03KF1IwmOEEIIIXqY\ncuQUkhKTul6fetKpuFwutpVu44QfnUBVdRV5uXmccuIplG8v55PPPglfsL2QBEcIIYQQPcw6bxYT\nx0/sej3vhnnU7qjl9rtuZ+b0maQNSsPn87F8xXKeeOYJgsFgGKPtSRIcIYQQQvRw629v3WvZVddd\n1Y+RfDfyFJUQQgghIo4kOEIIIYSIOJLgCCGEECLiSIIjhBBCiIgjCY4QQgghIo4kOEIIIYSIOPKY\n+ABluv3XGN9cjKG8HPfK1egxY8Md0sDQ2IjlmitR27aBxYLOH4b3kb9Damq4IwuLOVk/ZEricAZZ\n4/nVpgVUuhsBMCsjl+Ycx7jYIXi1n+KOGp7e3nNXYCGEiFTSgzNABc88G+/7HxEcPCTcoQwsSuG/\naR6eNRvwrFxFcGge5t//LtxRhc2qlmLuKXyVek9rt+Ozs4/FGwwwb9Oz/Gbz87xWvTxMEQohRHhI\ngjNABacfg87OCXcYA09SEsGZP+x6GZwyBVWxPYwBhVeho4omX3u3Y1aDmZlJY1i0W1LT5nf2d2hC\nCBFWMkQlDl3BIKannyRw+pnhjmRASbMm0BFwc0HGNMbE5uAO+lhUtZxCR1W4QxNCiH4jPTjikGW+\n5ZdgsxP42bXhDmVAMaBIsyZQ5qzjd1te4uXKT7kp/2yiDZZwhyaEEP1GEhxxSDLd/htUSTHe514E\ng3yMd9fobcevA3zRvAWAEmct7X4X6VGJYY5MCCH6z2ExRKWUsmitveGOQ/QN0113YFi7Gu+/3wCr\nNdzhDDjtAReb2ysYHzuEDe3lpFsTiTfHsMPTEu7QhBD9rDK5ku1p23FZXRiCBhI6Ehi1fRTRvmgA\nXBYXK8aswG/0k9GQwbjycb2epyq5iopBFbisLjSaOGccIypGEOeKA6A6uZqytDIcUQ5QMLlwMkkd\nSQA4rA42DN2AM8pJUlsS40vHY9RGfEYfX4z9goklE0lwJPR52yP6V1+l1HSl1KvAn5VSvf+rDVDm\nW39J1Mg8VFUl1rNOw3rUpHCHNCCogs2YH3wAVVOD9cTjsE6fgmX2j8MdVthclvMjHht/DUmWWG4f\nMYu/jLkcgGfLP+ScjKO5f8xl3JB3BvNL38UZ8IQ5WiFEf3JanBTkFuAxexheOZyk9iQaEhooyi4C\nQKPZmLsRjd7vuVptrdhcNoZXDie9KZ3m2GbWDlvb9d6AIUBSWxLRnuge7y3NKMVn8pFXk0d9Qj21\nSbUAbM3eSlpz2kFJbiCCe3CUUv8ApgCPArnA/Uqpi7XWHWEN7AD5HngY3wMPhzuMAUePHoOr3R3u\nMAaM5ys+5vmKj3scr/O28setr4UhIiHEQKFVKPkwBUwktSfhN/qpS6zD7DcDUJZehiPawdDaoRRn\nFe/zXKMqRmHQu/pEdiTuwGPx4DP5sPgt5NSHnvr9JuYbXFGubu/1G/1YfVaS25IpySwhYAzQZG+i\nKa6JaZum9WWTu4nkHpyHtNaTtdbPAY8A/nAHJIQQQvQXm8fGqPJReMwevhz7JSVZJcQ6YhleNZy2\n6DZKMkoYUzYGq2//Q/27JzdN9ib8Jj92px2Lf/8PL2TVZ9EW08aKMSsw+U2ktKRQMKSAUdtHYQoe\nvH6WiO3B0VoXASilLgCeAlYBc5RSC2Q+jhBCiEjnM/ooTy/HFDAxqmwU7dHtlGWUUTC4gI7oDpLb\nk7G77dQl1AEQMAZwm91E+aL2es4mexPr8tdh9VqZsG3CAcWR2pbKzA0zcVld2F12ytPKiXXGYnPZ\nWDV8FY4oB3GOOMaWj8UcMPdJ2yGye3B2qgHOB64EjgL6/ZliVbQV6/HHYp00Fuvxx6KKi3rUMd33\nB6KGZmOdfhTW6UdhnveLXYVOJ+bLL8E6cTTWI8ZjeO+dXe/7831Yj5qE9UczUdvLu45bLjgbVbLv\nLsdwM93+G6zjRhIdG4XavKn3SoEA5nk3Yp0wGuvEMRgXPHtAZcbnF2A9chLWY45GrV3Tddx87TUY\nln9+sJrUZ34Qn8d9oy/lvtGX8qfRl3JUwrAedRSKK3JO4OFxV/HQ2Cs5Lnn8AZX9MHkcfxlzBfeN\nvpTc6EFdx68Zcgoj7VkHt2FCiH7TFNuEy+oiqT2J9OZ08mryAKhPqMdtcdMQ38Dyccu75uTUJdax\nIW8DAEGCBFSg2/yc2sRaVg9fjdlv5sjCI7F5bAcci9VvJcGRgMfsoTK1kpEVIynJLEErzTEbj6Ej\npoPytPL9n+hbiNgenJ201l/s/FoptRqYqJQyaa37bcjKfNMN+K+ZS+DiORhfWYj5xp/jfeeDHvX8\nsy/Bf9+fexw3PfowxMbhWVeAKi7CesoJuNdthmAQ4ysL8XyzDuOrL2P853z89/0Z40svEDx6Gjq/\n5w/FgSR45lkErrseyykn7LWO8dWXUdtK8KzdBI2NRM2YSvBHx6OH5O6zzPSXP+FZuRrDmtWY//oX\nvC++jOGz/4HRSPCYGf3Yyu/mutzTuLvwFSrdjeREp3DXyNl8s/axblMBZySNJt2awLyNz2A3RXPf\n6EvZ2F5Og7dtn2XnZUzl15sXMDQmjXMyjuaRbW8x2p5NUAcp7JDFAIWIFDGeGAAa4xqpSK2gPTq0\n6rndZSe3Nrdrjk5zbDMVgypIbE8krzqUBG0espmalBrGlo0lszGTquQqNg/ZjCFoYHDdYNpsbbTZ\n2khqT8Lit9AW3UZbTBsec+hhhob4BpxWJ+nN6V3DUBrN5iGbya/Kx+q3opXGaXVSlVqF1+Ttiqev\nHA49OLsbBNT2Z3JDfR2GdWsIzLoIgMCsizCsWwP19Qd8CuPri/BfeTUAethwgj84AsPSD8BohGAA\nfD5wOsBigcZGjM8vwH/TzQelOX3pQLajML7+b/xXXBla6yY1lcCZZ2Fc/J/9lmE0gtsNTifaYgav\nF9Mf78F3z70Hu1l9IogmxhgaF7cZrbT4Ono85zA1aSTLGtajgXa/i1UtxUxNHLHfsqAOYlYmrAYz\nfh3AqAxcmHkML1d91n8NFEIcdLGu2K45Nluzt1KXWEdKSwpjy8YyqHUQaS1ppLWkEecMPeod5Ynq\nerR7T832ZlAQNAYpHFzIhrwNbMjbQEdU6Lmd+oR6CnILcEaFtoUpTy+nILcAn8nXdY7q5GoUiqzG\nUE9xXk0eFr+Foqwi4pxxDN4xuE/bH/E9OEqpQYSGpy4AdgDz+vX6lZXojMzQD1wAoxGdnoGqqkTv\nsQO26fVFGJd9iE5Lw3/7nQSPntp5jgp0zq5/eJ2Tg6GygqDNhv/nN2I9fiY6LQ3vE89gvuN2/Hfc\nFUp2IkCPtmfnoKoq91vmu+c+rOecgbbb8T3yOKaHHiBwxZWQ1Ps370Dz6La3uXnYubgDPqKNZv5S\nvLhHnWRLLA3etq7XDd52kiyx+y17uepTbhtxIe6Al2fKP+Ts9Cl83LAeR0CeThMi0mQ1ZnUlFHuT\n2ZhJZmNmt2Pjysd1WxNnz9d7yq/JJ78m/1vFYnfbmVowdZ/v+T4iPsEBHIAT+JnWenW4g9kb/1U/\nxX/rb8BsxrDsQywXX4j7m3WQnLzP9wV+OpfAT+cCYPj8M1CK4OgxmOf+FNXeRuD8CwlcMKs/mjCg\nBM85F8855wKgiosxfP013ptuxjzvRlR9PYHpxxC49vowR9k7A4pz0qfwYPEbbHVUM8KWyS/yzuTW\nTQvwBH37P8F+fN1SzNctoflZ6dYEhtkyeLv2a67IOYF4czRb2qv4oH7Nfs4ihBADW8QPUWmtHVrr\nR8OV3OjsbFRNNQQCoQOBAKq2Bp2V3b1iWjqYQ7PHg8efiM7OxlCwqfMcOd12zFYVFQT3HNrxejH9\n4S5899yLaf5jBGfMxPvcS5juvxdc3dckOJT0aHtlRde921fZ7sy/vgXf/Q9gfPVldEoK3hcWYnzr\nv6jSbQe/Ad/BkJhBJJjtbHVUA7DVUY0n4CMrqnvvU6O3nRRLXNfrFEssTd72/Zbt7tKcH/FCxScc\nkzSadr+TR7a9zZGJwxhkiT8YTRNCiH4T8QlO2KUOIjh+IsZFrwJgXPQqwQmTYI/hKap3Te5U69eh\nyssJDg/NmQicdwGmZ58OlRUXYVi9iuCJJ3d7u+nhvxK47ApITkY5HKBU6I/PB95D96n4wHnnY1rw\nLASDUF+P8e23CJx73n7LdjK+/BLBI49EDx8OTmfonkDob6ezv5tzQJq87SRZ7GRYQ3tHZUYldW61\n0Nqt3srmrRyfMgEFxJqimZwwjJXNW/dbttOMpNGUOGqp9TRjNZh3zfHRYDX03aOaQggRDofDEFXY\n+f72GJa5V4d6UxIT8T7xDBB6lNv329+jj5iM+a47MaxdHZqrY7HgferZUK8O4L9xHpa5V2OdOBoM\nRryPPg6xsV3nVyXFGL5agffXt4fqXzMXy5WXhZKe2ZdA/MD8bdx86zyMb/4XdtRiPet0dFISnq/X\nYLngHHy/vRN9xGQCsy/B8M3XWCeNBcD369vQuUMB9lkGQFMTxgXP4n3rvVD9i2ZjmfNjrIv/Q3Dq\nNPTYgbl7R6vfyb+2f8iN+WehdSjteLJsCY6Am18NO49F1V9Q6tzBZ42bGWZL56FxVwGwuOZL6jvn\n3eyrDMBmjOJHKRO4r2gRAJ83FTAv/2yOThzB1o4qKtwN/dlkIYToc2rnf6CiO1eHV27MHqLtoYnL\nrg7Z02h30fbQ005zVj0Y5kgGnoWTb8btOHR7EA+WKFvoe0nuTXdyX/Yuymbh5LNP3n/Fw8ySN5cA\nqN7KZIhKCCGEEBFHEhwhhBBCRBxJcIQQQggRcSTBEUIIIUTEkQRHCCGEEBFHEhwhhBBCRBxZB0cI\nIYSIUNdu3MKMmjpS3aHlPU4+6yQAJjQ08dcvV3WrWxJn59ofTuv1PM9/+Bnprp771V16wgx2xEQz\ntrGZqwuKyGvrwGky8s6QbF4ckQdKMaaphZvXbiLZ7eGTrHT+NmE0KEWmw8kjn33FDcceTW1MdB+3\nXBIcIYQQImIpDUtzMplTVNpr+VtDstmQHFo1vd2895Rg/rhRRHVuOZTmdHHVlmLqo6w0RFlJ8Hj4\n41drCSh4csxwZtbUcdnWbTREWXl/SDZXFRThMRpZnDeYS4pK+SQrnbUpSfxifQGvDM89KMkNyBCV\nEEIIEbHmjx/FglHD9lpelBDHirRUPslKZ9WglL3WW5EeqvNJVjqJntBCjG/m5hAwGBjb1IrN72d1\najLv5Obw7/whAJxVXglAjN9PbUw0q1NC++lF+/2cWFFNrM/H4rwhfdXUHiTBEUIIIQ5TN63bzJvv\nLWPh0k85ZXvVfuvH+PycUlGFyxgahgJosoZWoM5vbSfD4eQHDU0AZDpCGz2/PSSHabV1PPjlKsrt\nNkriYrmqoIiHJo4hqHpdhLhPyBCVEEIIcZhptlp4dtQwymLtpLrdXLW5iJvWbWZzYjwVsfa9vu/U\n7VXY/AHeyM2hwxLalLcgKYG3h2RxZnkVzy1bTocplFp4DaE+lHdys/l6UDJJHi8lcbHcuH4zH2Vn\nYNCaRz77imS3h7Upifxt4hj8hr7rd5EERwghhDjMVMTaeWW3ROYH9U3MqK1jcIeDilg7pmAQpTV+\ngwHd2cti0JpzS7cTABbnDe52vkcnjOGVYUNJdnuI8fv508o1lMbtOn9dTDR1MdFMamhibFMLc384\njftXrKLSHsPtU3/Ay0s/ZWNSAu939gr1BUlwhBBCiAg1ZUc9Se5dGySfWl6Jy2Qip8NBgtdLcXwc\nSW4PR9U14DEYKI6PA+BPK1YzsbGZW6ZNZn3n3JnpNXWku9wsT0+lxhbT7TpXFBSzIyYKayDI+dvK\nCQIvjMjrVsccCHDD+gIemzAaj8mIUWvyW9s5vbwKSyCIsY83/5YERwghhIhQs0rKmdjY3PV63voC\naqOjeHLMCC4uLuXEihoAiuNjeX5kPjv28UTT+du2A/B6LxODM5xOzimrwBwMsN1u559jR7Kp8+ms\nneYUlbIlMZ7VqckAPDlmBL/8f/buPD6q6m78+OfMnsxk3zcSspCwI8gmi9YFdxSpP0UfrY+KW7Uq\nrrWttfUpj1qrVatWrRa1buWxtu6CUq2iCLKELQIhkH3ft8ls5/fHhEBMAqgJE8bv+/XiFe499958\nz7xmkm/OPfd887dzyc7drEmI48PU5O/d3wMpPcgZU7DobHPJC/MNIQ7/RLLOtq5DHPnDEuKwAnDR\n+j8EOJLh5+Upt+BsdwU6jGHHZvd/luS16U1el4HZ7BbmzZ8X6DCGnRVvrgDod6ayPEUlhBBCiKAj\nCY4QQgghgo4kOEIIIYQIOpLgCCGEECLoSIIjhBBCiKAjCY4QQgghgo4kOEIIIYQIOpLgCCGEECLo\nyEJ/A5MXRgghhBj+ZKE/IYQQQvwwSC2qAchS4X3JMur9k9dlYDa7RUpY9OPlKbcA0Nkm75kD7SsH\nI5+lvqRUQ/+6SzX0S0ZwhBBCCBF0JMERQgghRNCRBEcIIYQQQUcSHCGEEEIEHUlwhBBCCBF0JMER\nQgghRNCRBEcIIYQQQUcSHCGEEEIEHVnoTwghhBB9XHvltcw+bjZxsXEAfRYaTIhP4M+P/Bm73c6K\nj1bw4CMPBiLMAckIjhBCCCH6UAbFyo9W9t+mFLfffDsGw/BNI4ZvZEIIIYQImCeefoJlLy3rt+2C\nhRcwIm0Eryx/5YjG9G1IgiOEEEKIw5aVmcUliy7h4ccepr6hPtDhDOigc3CUUksO1q61fmhwwxFC\nCCHEcHbrz25lw6YN7C3Zy6yZswAICQkhJjpmWCU8h5pkHNb9NReYCrzZvX02sHaoghJCCCHE8JQQ\nn0BWZhbTp07v2TfnuDlERUax5M6DjoscUQdNcLTWvwFQSv0HmKy1bu3evgd4Z8ijE0IIIURATDt2\nGtFR0T3bp51yGp2dnTzwxwcwm8wATBg/gXPOPIdNmzfxt1f/FqhQ+3W4j4knAK4Dtl3d+4QQQggR\nhM5fcD4Tx0/s2V5ywxKqqqu4dPGlPftsNhsANbU1bN66+YjHeDCHm+C8AKxVSr3RvX0u8PzQhCSE\nEEKIQLvtF7cd8piVq1ayclX/j5IH2mElOFrr3yml3gPmdO/6b631xqELSwghhBDiu/s2j4mHAi1a\n60eAMqXUyCGKSQghhBDiezmsBEcp9WvgDuDn3bvMwPCaTSSEEEII0e1wR3AWAPOBdgCtdQX7HyEX\nQgghhBhWDjfBcWmtNaABlFL2oQtJCCGEEOL7OdwE5+9KqaeASKXUYuBD4C9DF5YQQgghxHd3uE9R\nPaiUOgVowb+q8d1a6+H5XJgQ4gfvopTjmRaVQ7w1gtu3LaPM6V8+fknWOcRZwtGA0+vi+dJVFHfW\nBjbYADLddQfGN9/AMmZ8kgAAIABJREFUUFyM88sN6DFjAx2SEIPmsBIcpdT9Wus7gJX97BNCiGFl\nfVMh79ds4Ne5F/Ta/+Se9+j0+dcsnRKRxVUZp/KLgh/u8xK+s+bjve56LKeeFOhQhBh0h3uL6pR+\n9p0+mIEIIcRg2dFeToO7tc/+fckNQKjRin9q4Q+X77hZ6NS0QIchxJA4VDXxa4HrgCyl1IFrMIcB\nnw9lYEIIMRQWp89jfHg6CsV9u14PdDhCiCFyqFtULwPvAf8L3HnA/latdcOQRSWEEEPkmeIVAMyO\nHs3FqXN5oPCNQ5whhDgaHaqaeDPQrJR6BGg4oJp4uFJqutb6yyMRpBBCDLbPGgq4Mv0UHEYbbV5n\noMMRYkg02hvZlbqLtpA2jF4jqXWpZFZm0mxvZlfKLtpt7XiNXkK7QhlZOZLExsR+r+PDx67UXVRH\nVeMyuTB7zMQ3xZNblotB+2e71ETUUJRcRLutHYPPQFxzHOP2jsNlcrE5czPNoc1EdEQwoWgCFo8F\nn/KxZvQaMiszB/y+38fhzsF5Emg7YLute99RQSkV2f3125SmEEIEEavBTLR5//qkkyMyafM4JbkR\nQavL1MWmnE2029rJKcvB4XRQlFxEeUw5HdYOFIrMqkwyKzNpt7azdeRW2mxt/V6rMqaSkoQSzB4z\nuWW5WDwWyuLLqIipAKDR0Uh+Vj4uk4vs8myyy7Mxef1jKCXxJbSGtJJdkU1raCslcSUA7E3YS0hX\nyJAkN3D41cSVPmA2ntbap5Q63HMDSin1DDBKKTW/e0RKCBHkLk37EVMjc4g027lr1Pm0eTr5n53L\nuTHzLKxGMz6tafc4ebDwn4EONaDMt92M8c1/QXUV1rNPR0dH07VuU6DDEoOk2dGMx+ghoSGBtLo0\nQlwhNIQ3UBZXxrQd00huSN5/bGgztVG1tNvacTgdfa6l/ev8EtIVQnRLNPVh9bSFtvUkMUVJRaBg\n3J5xRLRHYNTGnnO9Bi8mr4mYlhhKEkrwGr20W9spiS9hRsGMIev/4SYpRUqpn7F/1OY6oGhoQhoc\nSimD1toHWLp3XQg8dcB+IUSQeqH037xQ+u8++3+945UARDN8uX//MO7fPxzoMMQQsbj9v/5aQ1vp\nsHTQEOafOttp6+y5rQTgMrlodjRj8BqIaI/o91rJDck0O5qpiK2gNsq/dlRKbUrP6EtriP+pxR1p\nO/yJj8dEVkUWI2pHkFSfRHlsOV+M/QKT10RSfRIFIwrIrMzE5rYNWf8PN8G5BngU+CX+cg0fAVcN\nVVCDoXuUKR2wA/cD1yil/qa1bg9waEIIIcSQi2yPJKU2hfK4claPX43J4/+Vb/DtT26cZicbczbi\nNrqZsGfCgAlHS2gLVVFVRLRFkFGVQXlsOeVx5YR3hJNal4rP4B83sDvtZFZmsiNtBzvSdhDVFkV4\nZzhztsyh3daO3WmnNrIWn8FHYmMim7I20RLagt1pZ0zxGEJcIYPW/8Oak6K1rtFaX6i1jtdaJ2it\nL9Ja1wxaFEOnHqgDSoAa4OTAhiOEEEIcOWNKxjB7y2ymfj2V8XvGA+Do9N+CarW1sjZvLZ2WTibt\nnkR8U3zPeRqNV3nx4U9cqqKr8Bl9JNUnEd8cT2ptKgC1Ef7RnNCuUACS65NJaEogsi0SFHRYOwAw\ne81Etkei0exK2cXo4tGUxJfQZmtj1tZZ+JSPwuTCQe37odbBuV1r/YBS6jG6C20eSGv9s0GNZvCd\nDJRrrbcqpb4G/qCUWghcrrX2BDg2IcS3ZFZGLkk7gXFh6bi0h8K2Sv5S0rtqzMKkmZwcN4lGt3+y\n5M62CpaVfgSARZm4OuM0RobG40PzUtknbGz2321fkDiDmdG5dHpdPLbnbepc/iH327MX8Hzpv6nu\najqCPf121K6dWK6+EhrqIToG19PPorNzeh1jWnovpmeeQiclAeCbMRP3Q4/6Gzs6MF+7GMOmDWA0\n4f7dffhOP9N/3v1LMf7f38ERhuv5v6FHpANgWTgf9wMPobOyj1xHxbdWmFyIzWXDp3wUJxSDhsyK\nTFpDWvlq1Fd4TB5GVI/AY/RQFVWFo9OBw+mgMqaSbRnbSKpLYlzxOEKd/gSmLL4MgzZQGVMJ0DNf\nJ7UmlYKMAkriS3BanDSEN2D0Gvvc8tqZtpOUuhTCnGFUqSpcZhflceV0Wjuxuq2D2vdD3aIq6P76\n1aB+1yOnFhivlPoHMBloB1ZorT1KKaPW2hvY8IQQ38ai1Lm4fF6WbHsOgHBTaL/HfVq/nZfLP+mz\n/8zEY+n0drFk23MkWiO5O/dCbt76LAYUs2JGc9u2ZcyOHs28uMm8XP4Jc6LHsLOtYlgnNwDmm27A\nc9U1eC+8COOrL2O+8Xpc73zQ5zjPoovxLL2/z37Tow9DWDhd+QWowl1YTz0JZ/528PkwvvoyXV/l\nY3ztFYx/fgLP0vsxvvQivukzJbk5CnRYOyiNL8WrvDicDnJ35xLVHkVFTAUek//v/JKEkp7jMysy\ncVT2nWScWptKp7WTmqgaCkYUYPaYSalNIbMiE4CU+hS6LF2UxZXR6GjE4XSQU5bT65ZXfVg9zfZm\nRhePBmBE9Qia7E3sStmFvdNOVkXWoPb9UOvgvNX99flB/a5HTgfgwD9n6DJgAXAe8DdJboQ4ulgN\nZuZEj+H6LU/37GvxdHyra8yMyuXJve8DUNXVRFF7FZPCR7KppQgDCpMyYDWY8WovDqONE2LHs3TX\n8kHtx6CrrcGQvxHXm+8C4D3/Asy33gS1tRAXd1iXML6+HNdTzwKgs3PwHTMZw8oP8M07DXxecLuh\nox0sFqivx/jCMlxvvTdkXRKDZ8KeCf3uT65PJrk+ud+2/toNGMgtyyW3LLff4xWKrMossioHTlJi\nWmOYtW1Wz7bVY2XqzqmH6sJ3dqhbVG/Rz62pfbTW8wc9osG1SWt9xr4NpdSnHL2jUUL8oCVYI2nz\nOlmYNJMxYWk4fW6Wl69mR3t5n2NnRucyITydJk87r1d8zq52/3B6jCWcOldLz3H1rlZiLGF0+Ty8\nW7Oe3+RdRLO7nSf3vs+i1Lksr/gM7zB/6FKVlaGTksHY/Viu0YhOTEKVl6G/keCYXl+OcdWH6IQE\nPHfdjW/6jO5rlKLTRvQcp9PSMJSV4rPb8Vx/I9YT56ATEnA99SzmX92F51f3+JMdIYaxQ92ierD7\n63lAIrCv7O4ioHqoghos+9buUUqZtNYerfWwfrRdCDEwA4oEayR7O2p4ufw/ZIUmcmv2ApZsfbZX\nEc0Pa/P5Z+WXePExLiydJVnnctu2vx5yQb8Pa/P5sDYfgDxHCgBlnfVcnX4qIUYLaxp3sqZxx9B1\ncIh5rliM57Y7wWzGsOpDLBf+GOdX+RATc9DzvIuvwbv4GgAMn30KSuEbPQbzNYtRrS14z/sx3oXn\nH4kuCPGtHPQpKq31J1rrT4BZWusLtNZvdf+7CJhzZEL8/mRCsRBHv3pXKx7t5fPGrwHY3VFFq6eT\nRFtUr+OaPR14u5/82NpaTIOrldSQ2O5rtBBrCe85NsYSRr2rd9VxozJwfvJsXin7D6cnTKagrYxH\ni97mvKQZmIfh+qY6NRVVWQHe7rvuXi+qqhKdktr7wIREMJsB8J14Mjo1FUPBtu5rpKFK98/DUKWl\n+L5ZZdzlwnTvPbh/+ztMTzyGb/YcXM+/hOm+30Fn55D1T4jv6nBLF9iVUpn7NpRSI/GvLyOEEEdE\nq7eT7a2ljA/zP8WTaI0iwhzaZwJwlHn/BMn0kDhireFUOv0LnH3ZuJOTYid0nx9Jlj2R/JY9vc6f\nnzCNj+u20OZ1YjWY0dq/hqtRGTANx2ovcfH4xk/EuPw1AIzLX8M3YVLf+TcV+2/lqc35qOJifDmj\nAPAuWIjpub/42wp3YdiwHt/J83qdbnr4QbyXXgYxMaj2dlDK/8/tBpcLIYabw/1z5GbgY6VUEaCA\ndODqIYtKCCH68Vzxh1yVcSoXpx2PV/t4Ys+7dHi7uD17AcsrPmdPRzUXpMxmZGgCPu3Do308uec9\nmrsnI79dvY5rMk7jobGX40Pzl+KVOH3unusnWCPJcSTxRuEaAFbUbOKGzDM5O3Ean9UX9LoVNpy4\n//gYlmuu9I+mREX1TBi2LJyP+xe/Rk+egvmeu7sfAzeCxYLrmef8ozqA58YlWK65EuvE0WAw4nr0\ncQjbX7dL7S7EsHYNrjvu8h9/1TVYLr/Un/Qsuhgi+l/9VohAUgeUmDr4gUpZgbzuza+11l1DFtUw\n4Gx3Hd4L8wNis/snFTrbh+cP+UCR12VgNruFi9b/IdBhDDsvT7kFgM42ec8cKMQhn6WB2OwW5s2f\nd+gDf2BWvLkC/AMvfRzWeKtSKhS4Dbhea50PjFBKnTVoEQohhBBCDKLDvaH8V8AFzOzeLgf+Z0gi\nEkIIIYT4ng43wcnSWj8AuAG01h0MMCQkhBBCCBFoh5vguJRSIXQv+qeUygKCeg6OEEIIIY5eh/sU\n1a+B94E0pdRLwCz8pQ+EEEIIIYadQyY4SikFfI1/NeMZ+G9N3ai1rhvi2IQQQgghvpNDJjhaa62U\neldrPR545wjEJIQQQohBNqGugQe/WN9nf35MFLcdd2yf/b/4ajMT6xuIdLmpCrFx6cl9CxjkNjbz\n8Op1mLTmxVGZvJibhcXr5a71WzimroFSRyhLJ4+nwuFfG/i+L9azPi6G5dkZg96/bzrcOTgblFJD\nV/JTCCGEEEOqJMzB0snje/6ti/PXIdsRGd7v8T4Fq1KSBryezePljo1bcRt6pxInllcxvbqWF3Mz\niexyc/Eu/2rhJ5VWEO5y83rmiP4uN+gON8GZDqxRSu1WSm1WSm1RSm0eysCEEEIIMXiarBY+Tknk\n45REVifGk9XSikcp/jmy/4Tjf6dM4I2DJCNXb/MXn307o3fdsxCPB7fBwPq4GBpsFkI9HsJcLq4s\n2MXDE0fjMxyZkieHO8n41CGNQgghhBBHzAkVVUR3uViVkkhdiO1bnz+9upZ5pRXcPHsq06t7T8n9\nNCmBCwr38tQna3AZDLw4Kourtu/i3ymJ7Io8cmU9DprgKKVswDVANrAFeFYqcwshhBBHt/OKigF4\nPTP9W59r9vpYsmk7K9KSaTObCXP767mFud1EdnVRF2LjshNnMbKljarQEEa0tTOhvpHr50zjto1b\nmVjXSL3NymPj8ygc4PbYYDjUONHzwLH4k5vTASkqI4QQQhzFJtU1kNXSxuboSHZ1JxhKa8xeLyaf\n75DnW3xeolwuziwpZ9mq1Zy7pxSAc/eUcvW2nQA4TSYKoiNpM5v52eYCHhufx+zKGmZX1nDVCTOo\nsIdw3dYdQ9dJDn2Lakz301MopZ4F1g5pNEIIIYQYUj2jN1n7R2/G1zfy4Bfrez1RdXx5FQmdnQCE\neLycVlxGg83K+rgY7p0yoefcuRXVHF9ZzSdJCX3m7CzaVcTOyHC+io/lrL2lWL1ezigpJ6ullU7j\n4c6S+W4OdXX3vv9orT3+JXGEEEIIcTRKbmtnWnUdZfZQ1iTEHfTYKwp2kdjpBCDC7WbJ5gLyY6JY\nmxDHp8kJPcdltLZBJZSE2dl5wBybtNY2Ti8p55rj/WUsP0xNZmpNHZfu2E2tzcZTE0YNQQ/3U1rr\ngRuV8gLt+zaBEGBfHSqttR66m2cB5mx3DfzC/EDZ7BYAnO2uAEcyvMjrMjCb3cJF6+XO9je9POUW\nADrb5D1zoBCHfJYGYrNbmDd/XqDDGHZWvLkCBqiNedARHK21cSgCEkIIIYQYSkfmYXQhhBBCiCNI\nEhwhhBBCBB1JcIQQQggRdCTBEUIIIUTQkQRHCCGEEEFHEhwhhBBCBB1JcIQQQggRdA660N8PnLww\nQgghxPDX70J/MoIjhBBCiKAztJWujmKdbV2BDmHYCXFYAWhp6AhwJMNLeHQoIMvL98dmt0g5gn7s\nK0kgZSx621fCQj5LfUmphv51l2rol4zgCCGEECLoSIIjhBBCiKAjCY4QQgghgo4kOEIIIYQIOpLg\nCCGEECLoSIIjhBBCiKAjCY4QQgghgo4kOEIIIYQIOrLQnxBCCCH6cNgd3HrTreRk5RARHkFjUyMr\nV63khZdfwGAwsPiyxcydPZeIiAhaW1pZvWY1Tz37FG6PO9ChA5LgCCGEEKIfdrudEakjePeDd2lq\nbuLCH1/If134X9TV1+H1ell47kJ2F+3m1eWvcsZpZzD/zPns2buHdz54J9ChA5LgCCGEEKIftXW1\nXPnTK/H5fACYzWauW3wdmRmZFBYVAlBVXcX6TeuZPGkyWSOzaG1rDWTIvUiCI4QQQog+9iU2AEop\nZk6bCcDG/I18+dWXjMkbw2mnnMasmbMAePeDd/nP6v8EJNb+yCRjIYQQQgzIbDLz81t+zjETj2H5\nP5azes1qRuWM4oQ5J1Cwo4C7/+du1qxdwxmnnsEZp54R6HB7SIIjhBBCiH7Z7XaW/mYpJ8w9gedf\nep5nlj0DwI/m/gibzcbKVStZs3YNb733FgAzps0IZLi9yC0qIYQQQvRhs9l4+L6HyUjPYN36dZRX\nlHPCnBNoam6ivLwcgLNPPxu3283JPzoZgL3FewMYcW+S4AghhBCij4jwCDLSMwCYOmUqU6dMBSB/\nSz53/OoO4uPjmTtrLjdcewOtLa288/47vPTqSwGMuDdJcIQQQgjRR3VNNfPmzxuw/ennnubp554+\nghF9OzIHRwghhBBBRxIcIYQQQgQdSXCEEEIIEXQkwRFCCCFE0JEERwghhBBBRxIcIYQQQgQdeUx8\nuKqvx3LV5aiiIrBY0FnZuB75E8TFBTqyI856988xv/UvDCXFtH22Ft/osQAYCndh++lVqMYGdFQ0\nzieewZeVHeBoxXBjuusOjG++gaG4GOeXG9BjxgY6pIC5KOV4pkXlEG+N4PZtyyhz1gPwyLgrcWsv\nLp8HgFfL/8PmluJAhirE9yYjOMOVUnhuWkLXxi10fbke38hMzL/+ZaCjCgjPGWfT/vYKfGkjeu23\n3fIz3FdcRfvafNxXXIVtyQ0BilAMZ76z5uN6/yN8I9IDHUrArW8q5Lc7XqO2q7lP2x93v8ldBS9y\nV8GLktyIoCAJznAVHY1vzvE9m75p01ClJQEMKHC8M45Dp6T22qdqazBuzse98P8B4F74/zBuzkfV\n1QYiRDGM+Y6bhU5NC3QYw8KO9nIa3K2BDkOII0JuUR0NfD5Mf3ka7xlnBTqSYUOVl+NLSgaj0b/D\naMSXmIQqL0PH/vBu4wnxff105BkoFDvaynmt4jM6vF2BDkmI70USnKOA+dabwe7Ae/W1gQ5FCBGE\nfrPjNRrcrZiUkUvTTuCytBN5Yu97gQ5LiO9FEpxhznTXnajdhbj+/g8wyB3FfXRKCobKCvB6/aM4\nXi+Gqso+t7KEEIe277aVR3tZWZPPLdnnBjgiMVg+HfcpTquz174Z22fQbmunMKWQLnMXBm3A3mkn\npzyHqLaofq+zO2k3RclFvfaNqB5BblkuFTEVbMvY1uecpLokxhWPoyy2jMLkQgCyK7JJrfP/nK6M\nrmRvwl6mF0zHMAQzZn4QCY5SKklrXRnoOL4t0z2/wrBpA67/+ydYrYEOZ1jRcfF4x0/A/Prfcf+/\nRZhf/zveCRPl9pQQ35LVYMKAgU6fC4CZ0bkUd9QEOCoxmOyddjIrM3u2bS4bTouTlNoUrG4rraGt\nlCSUsGXkFuZumXvQa+WW5GLxWAAIdYYCENUaxfii8T3H7E3YS6u9lYiOCLzKy9dpXxPfFA/A12lf\nk1SfhM/gY2fqTiYVThqS5AaCPMFRSs0EbgbSlVInaK07Ax3T4VIF2zH/4ff4snOwnnwCADo9A9cr\nfw9sYAFgvfNWzG//C1VTTeh5Z6GjYmj//CucDz5CyE+vwvLgfRARSecTzwQ6VDEMmW+7GeOb/4Lq\nKqxnn46OjqZr3aZAhxUQl6b9iKmROUSa7dw16nzaPJ08WPhPbsqajwGFQSnKnfX8teSjQIcqBpHF\nYyG2ORajz4hCARDXHEd0SzReoxez10xJQklP28HEtMQQ4grBoPcnJSGuEEJcIQC4TC62ZWzD7DGT\nXJeMz+BDGzThHeFoNNXR1fiUP7lJbEgkoiNiaDoNKK31kF08kJRSfwLOAn6ltX7x257f2dYVnC/M\n9xDi8I8itTR0BDiS4SU82v9XjLPdFeBIhh+b3UJnm7wu3xTi8P8FfNH6PwQ4kuHl5Sm3APJZ6o/N\nbmHe/Hnf+rxPx32K0+IEBQafgfjGeMaUjMHoM1ISV8KOETsAMLvNTNo9icj2yH6v03OLqvs3o6PT\nweiS0X2O33fcyMqRZFf41yXbkrGFqpgqAJLqk0iuS2ZbxjZmbp+Jyff9xllWvLkC6D8zC+YRnK1A\n9r7kRimVqLWuCnBMQgghxBGTXJ+M3WnHoA3sTdhLVUwVNpeNnIoc4pviCe0KpSW0haLkInak7WDq\n11P7vWUU1hFGXnEeNreNRkcjxYnFbM7c3OuWlk/5KIsrQ/kUaTX7l2YYv3c8GdUZANiddr4Y8wV5\nJXnURNawJ2kPWmlSa1LJqMkY1L4HbYKjtf6zUupGpdTDwFSgQimVDzyitW4LcHhCCCHEkMuqzNq/\noSE/O5+2EP+vQJvbhs1tI7YllpqoGlrsLbSFthHeEY5P+dBoDNqAQhHfHN9zmbjmOMpjy+mydOE2\nujF7zYB/0rDL7CKpPgmrp/e80bDOMMA/whPWEUZ0azT/nvRvRpeMxuaysWHUBuKb/QnXYAn2x3Ke\nBMYANwJ3d///iiMdhOmuO7GOyyUkzIba3nemOQBeL+YlN2KdMBrrxDEYlz13WG3GF5ZhPXYS1lnT\nUZs29uw3X3sVhtWfDVWXBoVj0mjs04/BfvwM7MfPwLhqZd+DOjoIueJSHMeOxz79GEwfvHdYbZYH\n78N+3LGEzjuh1wKJIRcsQBXtHspuiSGkdu3EeuJcrJPGYj1xLqpwV59jTEvvxTYyFetxU7EeNxXz\nkp/tb+zowPyTi7FOHI118ngM772z/7z7l2KdOgnrj+agSvav5GtZOB+1u3BI+/V9xFrCWTr6kp5/\nj4y7kqcnXtfnuIVJM3lywrU9x12WdlJPm0WZuGHkWTw09nIeHPvfHBOxf0LqgsQZPDDmJ/wmdxGx\nlrCe/bdnLyDB2v/tDDE8tIa0siF7AyXxJZTFlLE72f+zL7Itki0jt1CUWERFdAU7UnfQGtKK0Wsk\nxOmfS7MhZwOrJq+i0dEIQH5mPoVJhVTEVLB9xHY8Jg/2TntPcgNQEu//WZte3f+q4e3Wdsriysgr\nzUMrDQrqIuqojqoGQDO4M0OCdgSn22PAk1prN4BSajUwTill1Fp7j1QQvrPOxnvdT7GcetKAxxhf\newVVtJuuTdugvh7b7Bn4fnQiOj3joG2mB/6Xri83YNi4AfODD+D62ysYPv3Ev/DdrNlHqovfWeey\nv/XUluqP5fFH0GFhtH21BcPuQkLPOoW2dVvA4Ri4zefD/PdXaf9iPeblr2J55km6fvu/mF/5G96p\nM9CZWQN+PzG8mW+6Ac9V1+C98CKMr76M+cbrcb3zQZ/jPIsuxrP0/j77TY8+DGHhdOUXoAp3YT31\nJJz528Hnw/jqy3R9lY/xtVcw/vkJPEvvx/jSi/imz0QP4xpnda4W7irYP83wktQTMKr+/3b9tH47\nL5d/0mf/mYnH0untYsm250i0RnJ37oXcvPVZDChmxYzmtm3LmB09mnlxk3m5/BPmRI9hZ1sF1V1N\nQ9Yv8f1Z3BaUVuxJ3IPb6MbmtpFZkUlGdQZfp31NaVwpbpMbk89EdGs0WRVZmH3mfq/l6HRQFV2F\n0+LE6DUS3xhPTnlOT3t9WD1toW1Et0T3jNYcSKPZnr6d7PLsnqewcktzKUoqwqd8jKwYib3LPqj9\nD+oER/tnULsP2JUIVB3J5Ab8S8UfivH1/8Nz2eX+tW7i4vCedTbGN/6B56YlB23DaASnEzo60BYz\nuFyY/ue3uF5ZfgR6NvTMb7xO5+NPA+DLysY7aTKmj1bgOee8gdtOPhXl84LbDR0dYLagGuoxv/QC\nHf94O5DdEd9HbQ2G/I243nwXAO/5F2C+9SaorT3sIrTG15fjeupZAHR2Dr5jJmNY+QG+eadBz3um\nHSwWqK/H+MIyXG8dPQveGZWBWdGjuW/X69/qvJlRuTy5930AqrqaKGqvYlL4SDa1FGFAYVIGrAYz\nXu3FYbRxQux4lu4Kjp8xwczqsXLM7mP6bRtdOprRpaMHPPfYncf22s6qzOp9u+sbYlpjOGX9KQO2\nKxRTd07ttS+tNo202qEroxLUCQ6AUsoB/BRYCNQCNwU2ov6pslL0AcUkdWoaqrzskG3u3y7Fes6Z\naIcD9yOPY3ro93gvuxyio49sB76jkKsvB63xTj8O56/ugYjeQ96GslJ02v4PgE5Jw9Dd9wHb7Ha6\nrr0B+6knoOMT6Hz8aaz3/JKuu+72/+ISRyVVVob+RnkOva88xzcSHNPryzGu+hCdkIDnrrvxTZ/R\nfY1vfJbS0jCUleKz2/FcfyPWE+egExJwPfUs5l/dhedX9xxV75kpEVk0uNvY29n/OjYzo3OZEJ5O\nk6ed1ys+Z1e7f3mwGEs4da6WnuPqXa3EWMLo8nl4t2Y9v8m7iGZ3O0/ufZ9FqXNZXvEZXu07In0S\n4rsK+gQHcAJtwDVa6w2BDmaw+c45l65z/KuOqsJCDOvW4brpFsxLbkTV1uI9bhbea38a4Cj71/7O\nSv/Kw11d2O66HdvtS3A+9dyhTzwM7iuuwn3FVQAYP/8MlMKXNxrb9VejWltxn3sengU/HpTvJYYX\nzxWL8dx2J5jNGFZ9iOXCH+P8Kh9iYg56nnfxNXgXXwOA4bNP/e+Z0WMwX7MY1dqC97wf4114/pHo\nwnd2Quw4Pqnb2m/bh7X5/LPyS7z4GBeWzpKsc7lt219p8zr7Pf7A8z6szQcgz5ECQFlnPVenn0qI\n0cKaxp2sadyNM98jAAAgAElEQVQxuB0RYhAE+yRjtNYerfXjwz250alpvSbDqrLSnrIDB2s7kPmO\nW3Hf93uMr72Cjo3F9eLLGN/6F2pPUZ9jh4OePlituK5YjGntmj7H+FLTUKWlPduqvBRf93kHa+vh\ncmFd+lu6fn0vlj8/jve42XQ++wLW398HnUfNuo8C0KmpqH3lOQC8XlR/5TkSEsHsn0fgO/FkdGoq\nhoJt3df4xmeptBTfNyuNu1yY7r0H929/h+mJx/DNnoPr+Zcw3fe7Yf2eiTI7yHOksbqhoN/2Zk8H\nXvyjLltbi2lwtZIaEgtAvauFWEt4z7ExljDqXb2rjhuVgfOTZ/NK2X84PWEyBW1lPFr0NuclzcCs\nfgh/K4ujTdAnOEcL74LzMC17Dnw+qK3F+PZbeM9dcMi2fYyvvITv2GPROTn+eSeqe90jpfzbw017\nO7Q0+/+vNeZ//B/ecRP6HOY5ZwGW5/1zJgy7CzFuXI/nxFMO2baP5dGHcF98KTo6xj+3Qin/P48b\n3LKY2FElLh7f+IkYl78GgHH5a/gmTOo7/6aivOe/anM+qrgYX84oALwLFmJ67i/+tsJdGDasx3dy\n78XTTA8/iPfSyyAmBtV+wHvG7QbX8H3PzI0Zw6bmogFHZKLMjp7/p4fEEWsNp9LZAMCXjTs5Kdb/\n+Uu0RpJlTyS/ZU+v8+cnTOPjui20eZ1YDWa09j/zYlQGTANMahYikORdeQSYb1uCLTcLVV6O9ewz\nsE71T/qyLDwHtWE9AN5FF6MzRvoffz1pLu47fo7OGHnINgAaGjAuew7PLbf7j79gEYZPPsY6bTI6\nKxs9dtyR7fBhULU12Oefhn3ONOyzpmLYXYjz9w8DYD9+BqrSPzeg6/qbUM3NOI4dT8iiH+N86E8Q\nFnbINgBVtBvjui9xL/ovAFxXXI3l2aexz56G+/8tgvChWyJcDA33Hx/D9NQTWCeNxfTUE7j/+BjQ\n/Sh392fJfM/dWKcdg3XmsVhuuBbXM8/5R3UAz41LUM1NWCeOxnL+AlyPPt77PbO7EMPaNXgvvsR/\n/FXXYHrmz1inT8a76GKIGL7vmbkxY/m4vvftqduzFzAyNAGAC1Jmc/+Yn/C/oy/hyvR5PLnnPZo9\n/j9+3q5eh91k5aGxl3Nr9gL+UrwSp2//8xkJ1khyHEl82rAdgBU1m5gXP4n7x/yEz+oLeupYCTGc\nBG2phu9LSjX0JaUa+ielGgYmpRr6J6Ua+ielGgb2XUs1BLuDlWqQERwhhBBCBB1JcIQQQggRdCTB\nEUIIIUTQkQRHCCGEEEFHEhwhhBBCBB1JcIQQQggRdGT5SSGEECLI3bN2E8dV1wIw72z/gqgZLW0s\n3r6TcQ3+qvCljlBunzmFDnP/FcUBchubeXj1Okxa8+KoTF7M9RfgvH3DVo6trcPu9tBqMbM+LobH\nx+XRYTYxpqGJWzZtI8bZxccpifxxwmhQiuT2Dh75dC03zJ1OVWjIoPdZEhwhhBAiiJ1eXMYxdQ29\n9tndbh744itCPF5ez0ynyh7CmIYmjAdZG8/m8XLHxq24DQZM+0qmdKu0h/BCdBZdRiNnFpdxSlkl\ntSE2luVlc0XBLrqMRt7IHMHFu/bwcUoim2Kj+dnmAl7NyRiS5AbkFpUQQggRtJLbO7hm207+PHZU\nr/3z95QS6XLz9+wMXhqVyYq0ZB6aNJZWi2XAa129zV9U9e2MvrUQX8zNYlV34lLqsAOwr958qMdD\nVWgIG2KjAQjxeDi5tIIwt5s3MtMHoZf9kxEcIYQQIggZfD7u2LCVjbHRvJeeys2b9xdizWxpA2BO\nZTUX7SzCa1CsSknijxNG4zP0HfuYXl3LvNIKbp49lenVdf1+vwe+WM+oZn+R1vWx0SzPzgDg7fQ0\nrt9SwOyqGooddnaHh/HIZ2v55fRj8Kl+FyEeFJLgCCGEEEHonL2lpLe28eS4ySS37y+xk9zeQajH\n07P926kTWVBUwmmlFeyOCONfI0f0uo7Z62PJpu2sSEumzWwmzO2vUxbmdhPZ1UWT1V/G57Hxo4lx\ndnFGSRnTauo5raScNzLTeScjlXXxMUR3udgdHsaNm7fzUWoSBq155NO1xDi72BQbxR8njsHTT3L1\nXUmCI4QQQgShhA4noV4vj362rtf+ZatW81liPABrEuL4IjGeaGcXk+obSelOhIw+Hwat8RoMWHxe\nolwuziwp58yS8p7rnLunlDCXm/snjwdgR5S/GG2lPaQ7wanouQVVExpCTWgIk+oaGNvQxDXHz+S+\nNespc4Ry14xjeGXlf9gaHcn76X1vf31XkuAIIYQQQWhFWjLboiN7tn+1fjMA906ZQEmYnZnVtcyp\nqKbcHsppJRUAbOyeJ3Nz/nbmlVXy+0ljWZWSyL1TJvRcZ25FNcdXVvNJUgJvZI4go6WNn+woZENs\nDC6jgXml/msVhTt6xWP2erlhcwGPTRhNl8mIUWuymls5o7gci9d30AnO34UkOEIIIUQQKooIoygi\nbP+O9f4vnyYnAPC7yeP5768LuWFLATUhITw2Po8vukd2DuQ1GHrOAchobYNKKAmzszMygviOTqKd\nLi7/uhCzz0uj1cqbGak8l5fT6zoX7drD11ERbIiLAeDpMaO4OX87l+zczZqEOD5MTR7U/is9yBlT\nsOhs65IX5htCHP77rC0NHYc48oclPDoUAGe7K8CRDD82u4XONnldvinE4X9S5aL1fwhwJMPLy1Nu\nAeSz1B+b3cK8+fMCHcaws+LNFQD9zlSWx8SFEEIIEXQkwRFCCCFE0JEERwghhBBBRxIcIYQQQgQd\nSXCEEEIIEXQkwRFCCCFE0JEERwghhBBBRxIcIYQQQgQdWehvYPLCCCGEEMOfLPQnhBBCiB8GqUU1\nAFkqvC+b3b+8vLw2ve17XTpauwIcyfATGmaV90s/5LPUv32vi5Sw6OvlKbdIqYZ+dJdq6JeM4Agh\nhBAi6EiCI4QQQoigIwmOEEIIIYKOJDhCCCGECDqS4AghhBAi6EiCI4QQQoigIwmOEEIIIYKOJDhC\nCCGECDqy0J8QQggh+nDYHdx6063kZOUQER5BY1MjK1et5IWXXyAlOYVf3vFLkhOTMRgMVNdU88ab\nb/DOB+8EOuwekuAIIYQQog+73c6I1BG8+8G7NDU3ceGPL+S/Lvwv6urr2LR5E1+u+5LyinLCw8K5\nZNEl3PjTG9m4eSMVlRWBDh2QBEcIIYQQ/aitq+XKn16Jz+cDwGw2c93i68jMyOTdD97lhZdfwGF3\nEBsby3nnnIfNZmM4FfCWBEcIIYQQfexLbACUUsycNhOAjfkbARiROoKn//Q0AB6Ph8effpzKqsoj\nH+gAZJKxEEIIIQZkNpn5+S0/55iJx7D8H8tZvWY1AFXVVdx595089NhDNLc0c8miS0hKTApwtPtJ\ngiOEEEKIftntdpb+ZiknzD2B5196nmeWPdPT5uxysmHTBt5f+T4f/+djIiMiOW7GcQGMtje5RSWE\nEEKIPmw2Gw/f9zAZ6RmsW7+O8opyTphzAk3NTeTl5hHmCKO4pJiI8AhOOekUAIr2FAU46v0kwRFC\nCCFEHxHhEWSkZwAwdcpUpk6ZCkD+lnw++vgjTj35VM458xzcHjcVFRU89exTPfNzhgNJcIQQQgjR\nR3VNNfPmzxuw/f2V7x/BaL49mYMjhBBCiKAjCY4QQgghgo4kOEIIIYQIOpLgCCGEECLoSIIjhBBC\niKAjCY4QQgghgo48Ji5EELEsOh9D8V4wGNB2O67fP4yeMDHQYQkxrDmMNq4beTrx1kg82kuVs4ln\nS1bS6ukk257EFSNOxmIwUetq4Yk979Li6Qx0yOIwSIIjRBBx/fkvEBEBgPGdt7D+9Gqcn64JcFRC\nDG8aeKtqHQVtZQBclDKXC1Pm8JfiFVyXcTpP7f2AHe3lnJs4nQtT5vJ08QeBDVgcFrlFJUQw6U5u\nAGhuRhvkIy7EobR7nT3JDcCu9kriLOGMDE3Arb3saC8H4KPazcyIGhWoMMW3JCM4QgQZy/XXYFj1\nIWhN1z/eCnQ4QhxVFHBK3ETWN+0mxhJOnaulp63V24lSCrvRRrvXGbggxWGRP++ECDKuP/0Z5/ZC\n3Hf/Fssvfx7ocIQ4qvwk7SScPjcraodPTSXx3cgIjhBByrvoYiw3/hTq6yEmJtDhCDHsXZRyPIm2\nSB4s/CcaqHe1EGsJ72kPM4agtT6qRm/KYsooSSih09qJwWcgsi2SvJI8QtwhtNna2Jm6kyZHEwCh\nzlCm7JyC2Wfucx2f8lGYXEhVdBVuk5uI9gjySvJwOB37v1dsGSXxJXRYOzB5TaTUp5BTnkO7tZ0t\nI7fQYesguiWa8XvGY9RG3EY3n4/9nIm7JxLZHjnoff9BjOAopdKVUoP/6gkxnLS1ocpKezaN772D\njoqG6OgABiXE0eGC5NmMtMfzUOG/8GgvAHs6qrEYTOTaUwA4KW4CXzbuDGSY30qHpYOCjAK6zF3k\nlOUQ3RpNXWQdu1J34Ta6+WrUVzSGNTKiZgS5pbmEdYahle73WruTd1OcWExkWyTZ5dk02ZvYlL0J\nHz4AKqMrKUgvACC3NJeRVSMx+Pwpxp6kPbhNbjIrM6mNrKUqugqAnak7SWhMGJLkBoJ8BEcpNQG4\nF8gCLlNKbdBa+wIclhBDo6Mdy6UXozrawWhER0XR9drroFSgIxNiWEuxxXBO0nQqnA3ck7cIgNqu\nZh4uepMn9rzHFeknY1H+x8Qf3/NugKM9fPuSFZPXRHRrNB6jh5qoGsweM6VxpbjNbjIrMhlZNRKl\nFSn1KQNeqy68DoCsiizsXXaqo6ppdjRTH1FPXHMcu5N2AzCpcBJWtxWjNvac6zF6sLqtxLTEsDt5\nN16jlwZHAw3hDczcNnPI+h/UCQ5wOfCV1vocAKXkJ70IYvEJdK36T6CjEOKoU+6s56L1f+i3bVd7\nBXduf+EIRzQ47F128orz2JG2gy/GfgFAWHsYOeU5bE/fDkB1VDVFSUUorUhqSGJ08WgM/dzcsbqt\ntNFGfUQ9ukXTYesAoMPagdfg7bkFtjFnIx22DixuC3kleSQ0JZBSm0J+Vj5rxqzB4rIQ2xTLxpyN\n5JXkYfINXRoStAmOUioLSNZa39S9fQywKbBRCSGEEEeG2+imOLEYk9dE3t48WkNa2Zu0l4IRBXgN\n3p7jJu6eSElCCRWxFYR1hDGidkSfa2WXZ9Ma0sqOtB2Af1QIwOAz4FM+UP55OjEtMWRWZFKQXsDW\nkVuJ2hxFXEscc7bModPaiaPTQXFCMWEdYdg77azPWU+7rZ3w9nDGFo/F7O07/+e7CuY5OEXARKXU\nIqXUv4CHgT8qpc4JcFxCCCHEkGsIa6DT2kl0azSJjYlkVmYCUBtZS2hXKABxTXHEN8eT0JAA0DMy\n48OHV3nR+G9zhXeGM3vrbKYVTOO4rccR0eZfcyusMwyz14zJ40940mrSSGpMwtHpwGfw4bT4J2Rb\nPVYi2yPpMndRFldGbmkuu5N3o5Vm1tZZtIW2UZxQPKj9D9oRHK21Vkq9BNwK3ASsBv4buFIp9ZHW\nui2gAQoxAPMv7sT45hsYiovpXLMePWZs32OW3ovpL0+jk5IA8E6fifuhR/yNHR1YrrsKw8YNYDLh\n+p/78J1+BgCm+5dien052hGGa9mL6BHpAFgXnoPrgYfQWVlHppNCDIGLUo5nWlQO8dYIbt+2jDJn\nPQCJ1iiuzTgNh8lGm8fJk3vfo6qrqc/5CsVP0k5kYkQGWmverFrHx/VbDtl2fMw4zkw4Fo/28vTe\nD9jbWQPAVemn8kn9Vna0lR+hV6C3fUlMfXg9pXGltIa0AuDodJBam0ppfCnVUdWEdoVSEVsBQHSL\n/6GE7enbqYytZOzesSTXJ9PoaKTR0YjNbaPR0Uh9RD3RLdE9E4TTatPYk7SHoqQiItojaA1txeKy\nYHfae+LRaLanbyerPAurx4pWmg5rB+Vx5bhMrgEnOH9XwTyCA/BvYDxQ0z25+N9AI5AW0KiEOAjv\nWWfT9d6H+Eb0HSY+kGfRxThXr8W5eu3+5AYwPfowhIXhzN9O12v/wHrDtdDWBi0tmF57BecXX+G5\n8ipMTz0JgPGlF/FOnyHJjTjqrW8q5Lc7XqO2q7nX/itGnMyK2k3csu2vrKjdxBUjTun3/NnRo0m0\nRrJk67P8escrLEye2fOY+MHaFiTN4Fdf/40XSldxTtJ0AEY7UvFpX8CSG/CProzZOwar28rO1J3U\nRNUQ2xTL2L1jsXfZGV80HoWiYEQBHqOHvJI84pvjB7xeeWw520dspzayluS6ZCYUTehpy6zMJK0m\njfqIegpTColsi2Ry4eRek40rYipQ7J/MnFmZicVjYVfKLsI7whlRffCfed9W0I7gAGitP1VKPQfc\nAFwPnAWYtdYFgY1MiIH5Zs76Xueb/vF//ppUgM7OxnfMZIwrP8A77zTwesHtRnV0gMUM9fWYXlxG\n15vvDUboQgTUvpIKBwo3hZARGs/nu74G4POGr7ks7UTCTCG0fqNo5ozoXFbVbUYDrZ5O1jcVMiNq\nFG9Xf3XQNp/2YVYmrAYzHu3FqAz8OHkWD+3+1xHo9cGl1KcM+HRUQlMCCU0J/baNKx7HuOJxPdtR\nbVHM2TpnwO9j0AbySvPIK8077FgcTgczCmYcqgvfWbCP4ADcCTQppT4B5uGfiyPEUc/4+nJsM4/F\nes6ZGL7cX1BTlZX2Gv3xpaahysvAbsdz/Y3YTpqL8Z23cF97PZa778L9q3vAYglAD4QYejGWMBrd\nbT1zSTSaRnc7Meawfo89sDRDnauVaEvYIdteKf8PPx/1Y+YnTuONijXMT5zGv+s2H1ULAgajoB7B\nAdBaNwG/VErFa61rAh2PEIPBfcVi3LfdCWYzhlUfYl10Pp3rNh1yxWLP4qvxLL4aAMPqT0EpfHlj\nsFy7GFpb8S5YiHfh+UeiC0IEjXVNhaxrKgQg0RpJtj2Jt6vWcVnaSUSYQ/i6tZwPpPTDEfdDGMEB\nQJIbEVQSEsHsf5zSd+LJ6NRUDAXbANCpaRhKSnoONZSVolNSe5/vcmG+9x5cv/kd5icewzt7Dq5l\nf8N8/1Lo7D1sL8TRrN7VSpTZgcK/DJpCEWW2U+9u7ffYA0szxFrCaHC1HrLtQJek/YgXSz9mVvRo\nWj0dPFL0NsdGZRNviRjsrolD+MEkOEIEE1Wxf66B2pyPKi7GlzMKAO+552H6q38OjiosxLBhPd6T\n5/U63/Twg3guucw/4tPRASj/isduN7hcR6obQgy5Fk8nxZ21HBftnxtyXHQeeztr+sy/AfiycScn\nxk5AAWGmEKZEZveUZjhY2z6zo0ezu72Kqq5GrAYzPc8EabAaBm99F3F4gv4WlRBHG/NtSzC+9S9U\ndRW2+Wego6Nxrt2IdeE5uH9xN77JUzD/5m4Mmzb6SzKYLXQ9/Zx/VAdw37gEy7WLsU0cA0Yjrkcf\nh7D98w3U7t0Y135J1x13Af7bVpbLf4L5j3/Ac+FFECF/aYqj06VpP2JqZA6RZjt3jTqfNk8nt29/\nnueKP+SakadxXtIM2r1dPLln/6T627MXsLzic/Z0VPNp/Xay7Yk8NO4KAN6o/ILa7nk3B2sDsBtt\n/Ch2Akt3LQfgs4YClmTNZ3rUKHa2lVPqrDtSL4PoprQe3OfOg4Wz3SUvzDfY7P6JqM52+Qv/QPte\nl47WrgBHMvyEhlnl/dIP+Sz1b9/rMlDZhB+yl6fcwrz58w594A/MijdXAPRbhkluUQkhhBAi6EiC\nI4QQQoigIwmOEEIIIYKOJDhCCCGECDqS4AghhBAi6EiCI4QQQoigI+vgCCGEEEHq2q1fM7uyhjin\nfxmLeWf7K6mPr2/kpvztxHU60UpRbg/lpVEjWZ3Uf/FNtGbRrj2cVVxGhMtFUXgYfx6by/boyF6H\nXbf1a87dUwrAJSfNpjo0hDENTdyyaRsxzi4+TknkjxNGg1Ikt3fwyKdruWHudKpCQwa97zKCI4QQ\nQgQppWFlWnKf/R6lWJWaxGMTRvP37AxGtrZx1/otWLzefq9zSlkl/71jN8VhDp4am0tSeyf3rt1I\nqNvdc8yUmnrO2ltGl6F3anFFwS66jEbeyBzBGSXlTKpvBOBnmwt4NSdjSJIbkARHCCGECFpPjM9j\nWV52n/0F0ZG8mp3B2vhYNsZG4zYofEphGGCJ2/ndozJ/GpfLWxlpvJeeQpjbw0nlVQCEuVzckr+N\nv2dl0Gi19Do31OOhKjSEDbHRAIR4PJxcWkGY280bmemD2Nve5BaVEEII8QN0bG09967dBIDTaOC+\nyeNwmoz9HpvS3gFAbYh/tKW6+2tKm3//TZsLqLdaeTE3kxPLK3ud+3Z6GtdvKWB2VQ3/n707j4+r\nKhs4/juzT2ayTib71ixNurO0UApFEKhsFlyqFAQBWVVEQX0VN3ZZVEReReDFhUVAQAQEtUBRWVq6\nr1mbPZNJJslkz+xz3j9mMk3apKA2mTQ9Xz580tyZuXPuk3Pufe45597bbLVQn5TIg+9u4vsnHktY\nTHgT4sNCJTiKoiiKchSqSknmuyceR/7QMJdX7+Oayjp22tIYMnz4g0HF/keJssLpYkVHFz9ctoRM\njxdt9BFQGSMeeo0GXivKY3OGjTSfn/qkRG7cVclbedlopOTBdzZh8/rYkZ7Kz5fMJ6g5fANLaohK\nURRFUY5CA0YDWzNs/Lm4gG12G9kjHo7pdgOgDYfRh0JoosmKw5IAQIbHE/3pjSy3JpDp8aCVkrs2\n7eB369+LTWj+6YatLHD3AeBKMFOdmsyC3j4WuPt4cm4JX95TQ5s1gWtPW86pzk7ObG0/rNunenAU\nRVEUZZY6obOLNO/+BwGf3dyGR6djfm8fIzod7ZYEMkc8nODqJiQEzYlWAL6xs5JVbU7uP2YBb+Tn\n8GpRHuU7K/nKnho2ZNk5p9nBkE7H+twskv0Buk2m2HfcsLuKFH+AXyyqoDEpMbZcHwpxw64qHlo8\nD59Oi1ZKSvoHObfZgSEUjvX8HC4qwVEURVGUWWpNfTNLolctAdy0q4oOs4m/FeRyTouDVJ8Pn1ZL\nXXIiz5XOoTXRMuF61uXnYPd6Ob+pjSXdbhqTrPx6QTnDej3Dej3t0R4egKsra4EAmzPS6Rsz4fji\nukaqU5PZZrcB8Oj8uXxjZyWX1tazMdPOm3kHX+313xDyMGdMs4V32K8CcwCTJVJRvcP+OJdkZhmN\ny8ig70PeefRJSDSq+jIB1ZYmNhqXi7f+NM4lmXn+cPzNrFq9Kt7FmHHWvbIOYMKZymoOjqIoiqIo\ns45KcBRFURRFmXVUgqMoiqIoyqyjEhxFURRFUWYdleAoiqIoijLrqARHURRFUZRZRyU4iqIoiqLM\nOirBURRFURRl1lE3+pucCoyiKIqizHzqRn+KoiiKohwd1LOoJqFuiX2w6C2xVWwOoOIyuXWvrFNx\nmYCqMxNTcZnculfWsXDpwngXY8bZs2XPpK+pHhxFURRFUWYdleAoiqIoijLrqARHURRFUZRZRyU4\niqIoiqLMOirBURRFURRl1lEJjqIoiqIos45KcBRFURRFmXVUgqMoiqIoyqyjbvQ3Q1x/1fWcsuIU\n7Ol2YPyNrooKirj6iqtZOD9yk6fWtla+/YNvMzIyEpeyTje9Ts/ll17O6aeeTlJSEm63m8d+9xgB\nf4C1n1tLbk4uBr2BppYmfvPEb9ixa0e8izwt7rvzPkqKSzAZTfT19/Hu++/y6G8fZdlxy47quByq\nLQFkZmTy6wd/jcViYd1b6/jJgz+JRzHj4onHniArM2vcsutuvI6GxgYWzl/IFZdewdzSuQSDQerq\n6/j2978dp5JOH6vFyje//k3KSspITkqmt6+XN9a/wRN/eAKA5Scs58rLriQ3O5funm6eef4Z/vbG\n3+Jc6qmRlJjEnbfeyfyK+aSmpNLj7uHlv7zMLx/5JQCnnXoaX//K1yksKKSjs4P/++3/8eLLLwKg\n0+q4+cabOe+c80gwJ7Bz907uuvcuGpoa4rY9KsGZIYRG8MZbb3Dx5y8et9xisXDfnfdhNpt58eUX\n6ejsYH7FfLRabZxKOv2+cu1XOPcT5/LB5g94b+N72NJs6LQ6CooL6O3r5c31b2K327nosxdx2/du\n45IrL2FoeCjexZ5yDY0NvP2vt5FS8tkLP8unVn+KxuZGbGm2ozouk7UlACEE3/7Gt9Fojt7O6+aW\nZp5+7unY752uTrKzsrnn9nvw+X08/cenGRgYYMG8BXEs5fSxWCwU5BXw+t9fp6+/j4s+exFfuOgL\ndPd0s2PXDn74nR/S2dXJrx77FeecdQ433XATjnYHu/fujnfRDzur1UpxUTHP/+l5evt6ueryq7j+\n6uvpdHXyweYPeOC+B3A6nfz4Jz/mMxd8htt+cBvNrc1s2baFq6+8mksvvpTX/vYalVWV3HD9DTz0\ns4dYvWY1oVAoLtujEpwZ4leP/grgoJ3y6nNXk5KSwpPPPMmzzz9LKByatWcPE7Gn2/nEmZ+g3dnO\nrXffilarxe/3A5GenUAwEHvv0uOWUlpcSk52DrX7auNV5Gnz68d/TaI1EYvFwsqTV1KQX4CUkj++\n+MejOi6TtSWAz3/m8xTkF/DM889w5WVXTnfRZoS+/j4+2PIBHo8ntuzqy6/GYDDwi4d/wdv/eptA\nIMDrf389jqWcPl3dXVz1lasIh8MA6PV6vnz1lykuKiY3OxedTscfX/wjf133Vzpdndx9692sPm/1\nrExwOl2drF6zOhYLg97Ad775HcrLyinML0Sv0/P47x/nxT+/iKPdwSMPPcJFay5iy7YtrP3cWgKB\nAD+640d4fV4WLljIOavOYcXyFbzz3jtx2R6V4MxwxXOKAVi5YiUXf+5iQuEQ6/+5np//789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n2/x+SJHeRHY6wL69AH9fj1fgKawIyLiyEQqeeDCYOMGEZwJ7pj2zK2bfh1fvqt/WhCGpKHkydc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\nCIaEWsHU4Q0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "_test_data_class: test function with y_test (actual values) and predictions (predic)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dTqDD1ZLV96hjhtgTh1wLbTVul+E9USnqdzeo390RlQLNFW5X4T2x6SxYtMDt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\nZL7U3MG5g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G50/iAiZbNaXfOwehmcetNzdB1wrbmNg681nwI+Y4wZiGQ5lxMRSQPuEpG4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\nWgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Irt3qqnQPPMm",
        "colab_type": "code",
        "cellView": "code",
        "colab": {}
      },
      "source": [
        "#@title fonction gerer data pour avant le modele, fonctions adapter pour confusion matrice\n",
        "import os\n",
        "import random\n",
        "\n",
        "def importer(nom_fich):\n",
        "  \"\"\"nom_fich = nom du fichier à importer\n",
        "  sortie = couple [data, [chiffre, speaker, occurrence]]\"\"\"\n",
        "\n",
        "#charger le fichier .npy dans \"data\"\n",
        "  data = np.load(\"melspectrogram/\" + nom_fich)\n",
        "\n",
        "#retirer l'extension \".npy\"\n",
        "  infos = nom_fich[0:-4]\n",
        "\n",
        "#transformer \"0_jackson_4\" en [\"0\", \"jackson, \"4\"]\n",
        "  infos = infos.split(\"_\")\n",
        "\n",
        "  return data, infos\n",
        "\n",
        "        \n",
        "def gerer_data(pourcent, quoi_tester, random_ou_pas=0):\n",
        "  \"\"\"quoi tester : (0=chiffre) (1=speaker)\n",
        "  pourcent : pourcentage données d'entrainement demandée\n",
        "  random_ou_pas : mélanger les données ou garder l'ordre par défaut\n",
        "  output : X_train, Y_train, X_test, Y_test\"\"\"\n",
        "\n",
        "#Création de nos listes vides\n",
        "  X_train, Y_train, X_test, Y_test = [],[],[],[]\n",
        "\n",
        "  list = os.listdir('melspectrogram/')\n",
        "#Mélanger ou non les données\n",
        "  if random_ou_pas == 1:\n",
        "    random.shuffle(list)\n",
        "  \n",
        "#Importation des données dans X_train et Y_train grâce à \"importer()\"\n",
        "  for i in range(len(list)):\n",
        "    X_train.append(importer(list[i])[0])\n",
        "    Y_train.append(importer(list[i])[1][quoi_tester])\n",
        "  \n",
        "#Transformer le nom du speaker en array (lisible par les réseaux de neurones)\n",
        "  if quoi_tester == 1:\n",
        "    for i in range(len(Y_train)):\n",
        "      if Y_train[i] == \"jackson\":\n",
        "        Y_train[i] = np.array([1,0,0,0])\n",
        "      if Y_train[i] == \"nicolas\":\n",
        "        Y_train[i] = np.array([0,1,0,0])\n",
        "      if Y_train[i] == \"theo\":\n",
        "        Y_train[i] = np.array([0,0,1,0])\n",
        "      if Y_train[i] == \"yweweler\":\n",
        "        Y_train[i] = np.array([0,0,0,1])\n",
        "\n",
        "#Transformer le chiffre en array (lisible par les réseaux de neurones)\n",
        "  if quoi_tester == 0:\n",
        "    for i in range(len(Y_train)):\n",
        "      indice = int(Y_train[i])\n",
        "      Y_train[i]=[0,0,0,0,0,0,0,0,0,0]\n",
        "      Y_train[i][indice] = 1\n",
        "\n",
        "#Mettre une partie des données dans X_test et Y_test\n",
        "  X_test = X_train[0:round((1-pourcent)*2000)]\n",
        "  Y_test = Y_train[0:round((1-pourcent)*2000)]\n",
        "  X_train = X_train[round((1-pourcent)*2000):]\n",
        "  Y_train = Y_train[round((1-pourcent)*2000):]\n",
        "\n",
        "  return X_train, Y_train, X_test, Y_test\n",
        "\n",
        "def adapter(L_entree): \n",
        "  \"\"\"transformer [ [0,1,0,0],[0,0,1,0], ... en [1,2,3,1,0...]\"\"\"\n",
        "  L_sortie = []\n",
        "  #pour chaque liste de L_entree (de la forme [0,1,0,0])\n",
        "  for i in range(len(L_entree)):\n",
        "    #empiler l'indice du maximum de [0,1,0,0] dans L_sortie\n",
        "    L_sortie.append(list(L_entree[i]).index(max(list(L_entree[i]))))\n",
        "  return L_sortie\n",
        "\n",
        "def remettre_0_ou_1_dans_Y_pred(Y_pred):\n",
        "  \"\"\"transformer [0.001, 0.001, 0.9999,...] en [0, 0, 1,...] \"\"\"\n",
        "\n",
        "  #Pour chaque liste de Y_pred (de la forme [0.001, 0.001, 0.9999,...] )\n",
        "  for i in range(len(Y_pred)):\n",
        "    \n",
        "    #Remplacer la valeur maximum par 1\n",
        "    imax = list(Y_pred[i]).index(max(list(Y_pred[i])))\n",
        "    Y_pred[i][imax]= 1\n",
        "\n",
        "    #Mettre à 0 toutes les autres valeurs\n",
        "    for k in range(len(Y_pred[i])):\n",
        "      if Y_pred[i][k] != 1:\n",
        "        Y_pred[i][k]=0\n",
        "\n",
        "  return Y_pred\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MI6homcGOLJI",
        "colab_type": "text"
      },
      "source": [
        "# Réseau qui identifie les chiffres"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-YYSok7OQXR",
        "colab_type": "code",
        "cellView": "code",
        "outputId": "5635cc35-a916-4dcf-c05c-615bbc9262c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#@title Le modèle pour les chiffres et son entrainement\n",
        "\n",
        "        \n",
        "# On utilisera Keras\n",
        "import keras\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout\n",
        "\n",
        "\n",
        "# Création du modèle\n",
        "model2 = Sequential()\n",
        "# input: images de 128x36x1\n",
        "# On applique des conv2D\n",
        "model2.add(Conv2D(32, (3, 3), activation='relu', input_shape=(128, 36, 1)))\n",
        "model2.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model2.add(Dropout(0.25))\n",
        "\n",
        "model2.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model2.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model2.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model2.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model2.add(Dropout(0.25))\n",
        "\n",
        "model2.add(Flatten())\n",
        "model2.add(Dense(256, activation='relu'))\n",
        "model2.add(Dropout(0.5))\n",
        "model2.add(Dense(256, activation='relu'))\n",
        "model2.add(Dropout(0.5))\n",
        "model2.add(Dense(10, activation='softmax'))\n",
        "# Enregistrer le modèle\n",
        "model2.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.SGD(lr=0.01), metrics=['accuracy'])\n",
        "\n",
        "\n",
        "\n",
        "#Séparer nos données entre \"train\" et \"test\"\n",
        "X_train, Y_train, X_test, Y_test=(gerer_data(0.5,0,1))\n",
        "\n",
        "print(np.asarray(Y_train).shape)\n",
        "\n",
        "#Entrainement du modèle\n",
        "model2.fit(np.asarray(X_train), np.asarray(Y_train), epochs=100, batch_size=10)\n",
        "\n",
        "print('shape de nos fichiers .npy importés:')\n",
        "X_train[3]\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1000, 10)\n",
            "Epoch 1/100\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 2.4876 - acc: 0.0900\n",
            "Epoch 2/100\n",
            "1000/1000 [==============================] - 1s 702us/step - loss: 2.3025 - acc: 0.1090\n",
            "Epoch 3/100\n",
            "1000/1000 [==============================] - 1s 688us/step - loss: 2.2990 - acc: 0.1140\n",
            "Epoch 4/100\n",
            "1000/1000 [==============================] - 1s 723us/step - loss: 2.3011 - acc: 0.1350\n",
            "Epoch 5/100\n",
            "1000/1000 [==============================] - 1s 711us/step - loss: 2.2871 - acc: 0.1390\n",
            "Epoch 6/100\n",
            "1000/1000 [==============================] - 1s 705us/step - loss: 2.2790 - acc: 0.1490\n",
            "Epoch 7/100\n",
            "1000/1000 [==============================] - 1s 702us/step - loss: 2.2467 - acc: 0.1570\n",
            "Epoch 8/100\n",
            "1000/1000 [==============================] - 1s 708us/step - loss: 2.2059 - acc: 0.1750\n",
            "Epoch 9/100\n",
            "1000/1000 [==============================] - 1s 702us/step - loss: 2.1292 - acc: 0.2330\n",
            "Epoch 10/100\n",
            "1000/1000 [==============================] - 1s 704us/step - loss: 2.0295 - acc: 0.2580\n",
            "Epoch 11/100\n",
            "1000/1000 [==============================] - 1s 708us/step - loss: 1.8467 - acc: 0.3010\n",
            "Epoch 12/100\n",
            "1000/1000 [==============================] - 1s 717us/step - loss: 1.7296 - acc: 0.3700\n",
            "Epoch 13/100\n",
            "1000/1000 [==============================] - 1s 709us/step - loss: 1.5494 - acc: 0.4150\n",
            "Epoch 14/100\n",
            "1000/1000 [==============================] - 1s 714us/step - loss: 1.4327 - acc: 0.4430\n",
            "Epoch 15/100\n",
            "1000/1000 [==============================] - 1s 756us/step - loss: 1.2917 - acc: 0.5180\n",
            "Epoch 16/100\n",
            "1000/1000 [==============================] - 1s 703us/step - loss: 1.1842 - acc: 0.5250\n",
            "Epoch 17/100\n",
            "1000/1000 [==============================] - 1s 716us/step - loss: 1.0429 - acc: 0.6190\n",
            "Epoch 18/100\n",
            "1000/1000 [==============================] - 1s 705us/step - loss: 1.0013 - acc: 0.6220\n",
            "Epoch 19/100\n",
            "1000/1000 [==============================] - 1s 715us/step - loss: 0.9194 - acc: 0.6400\n",
            "Epoch 20/100\n",
            "1000/1000 [==============================] - 1s 699us/step - loss: 0.8472 - acc: 0.7060\n",
            "Epoch 21/100\n",
            "1000/1000 [==============================] - 1s 710us/step - loss: 0.7309 - acc: 0.7290\n",
            "Epoch 22/100\n",
            "1000/1000 [==============================] - 1s 698us/step - loss: 0.7203 - acc: 0.7420\n",
            "Epoch 23/100\n",
            "1000/1000 [==============================] - 1s 720us/step - loss: 0.6687 - acc: 0.7570\n",
            "Epoch 24/100\n",
            "1000/1000 [==============================] - 1s 709us/step - loss: 0.5975 - acc: 0.7920\n",
            "Epoch 25/100\n",
            "1000/1000 [==============================] - 1s 702us/step - loss: 0.6289 - acc: 0.7550\n",
            "Epoch 26/100\n",
            "1000/1000 [==============================] - 1s 695us/step - loss: 0.5351 - acc: 0.8000\n",
            "Epoch 27/100\n",
            "1000/1000 [==============================] - 1s 708us/step - loss: 0.5393 - acc: 0.8030\n",
            "Epoch 28/100\n",
            "1000/1000 [==============================] - 1s 696us/step - loss: 0.4678 - acc: 0.8420\n",
            "Epoch 29/100\n",
            "1000/1000 [==============================] - 1s 690us/step - loss: 0.4512 - acc: 0.8400\n",
            "Epoch 30/100\n",
            "1000/1000 [==============================] - 1s 702us/step - loss: 0.4738 - acc: 0.8330\n",
            "Epoch 31/100\n",
            "1000/1000 [==============================] - 1s 697us/step - loss: 0.4483 - acc: 0.8430\n",
            "Epoch 32/100\n",
            "1000/1000 [==============================] - 1s 708us/step - loss: 0.3464 - acc: 0.8920\n",
            "Epoch 33/100\n",
            "1000/1000 [==============================] - 1s 697us/step - loss: 0.4302 - acc: 0.8380\n",
            "Epoch 34/100\n",
            "1000/1000 [==============================] - 1s 734us/step - loss: 0.3394 - acc: 0.8820\n",
            "Epoch 35/100\n",
            "1000/1000 [==============================] - 1s 689us/step - loss: 0.3301 - acc: 0.8790\n",
            "Epoch 36/100\n",
            "1000/1000 [==============================] - 1s 701us/step - loss: 0.3534 - acc: 0.8740\n",
            "Epoch 37/100\n",
            "1000/1000 [==============================] - 1s 694us/step - loss: 0.3008 - acc: 0.8990\n",
            "Epoch 38/100\n",
            "1000/1000 [==============================] - 1s 694us/step - loss: 0.3524 - acc: 0.8880\n",
            "Epoch 39/100\n",
            "1000/1000 [==============================] - 1s 699us/step - loss: 0.2887 - acc: 0.9020\n",
            "Epoch 40/100\n",
            "1000/1000 [==============================] - 1s 697us/step - loss: 0.2605 - acc: 0.9110\n",
            "Epoch 41/100\n",
            "1000/1000 [==============================] - 1s 705us/step - loss: 0.3118 - acc: 0.8930\n",
            "Epoch 42/100\n",
            "1000/1000 [==============================] - 1s 696us/step - loss: 0.2529 - acc: 0.9220\n",
            "Epoch 43/100\n",
            "1000/1000 [==============================] - 1s 700us/step - loss: 0.3039 - acc: 0.9030\n",
            "Epoch 44/100\n",
            "1000/1000 [==============================] - 1s 702us/step - loss: 0.2305 - acc: 0.9240\n",
            "Epoch 45/100\n",
            "1000/1000 [==============================] - 1s 684us/step - loss: 0.2077 - acc: 0.9370\n",
            "Epoch 46/100\n",
            "1000/1000 [==============================] - 1s 719us/step - loss: 0.2553 - acc: 0.9170\n",
            "Epoch 47/100\n",
            "1000/1000 [==============================] - 1s 700us/step - loss: 0.2156 - acc: 0.9260\n",
            "Epoch 48/100\n",
            "1000/1000 [==============================] - 1s 697us/step - loss: 0.1718 - acc: 0.9410\n",
            "Epoch 49/100\n",
            "1000/1000 [==============================] - 1s 702us/step - loss: 0.2023 - acc: 0.9330\n",
            "Epoch 50/100\n",
            "1000/1000 [==============================] - 1s 703us/step - loss: 0.1922 - acc: 0.9340\n",
            "Epoch 51/100\n",
            "1000/1000 [==============================] - 1s 699us/step - loss: 0.2145 - acc: 0.9240\n",
            "Epoch 52/100\n",
            "1000/1000 [==============================] - 1s 698us/step - loss: 0.1709 - acc: 0.9460\n",
            "Epoch 53/100\n",
            "1000/1000 [==============================] - 1s 721us/step - loss: 0.1786 - acc: 0.9450\n",
            "Epoch 54/100\n",
            "1000/1000 [==============================] - 1s 686us/step - loss: 0.1704 - acc: 0.9330\n",
            "Epoch 55/100\n",
            "1000/1000 [==============================] - 1s 698us/step - loss: 0.1700 - acc: 0.9470\n",
            "Epoch 56/100\n",
            "1000/1000 [==============================] - 1s 709us/step - loss: 0.1856 - acc: 0.9390\n",
            "Epoch 57/100\n",
            "1000/1000 [==============================] - 1s 741us/step - loss: 0.1868 - acc: 0.9450\n",
            "Epoch 58/100\n",
            "1000/1000 [==============================] - 1s 686us/step - loss: 0.2076 - acc: 0.9360\n",
            "Epoch 59/100\n",
            "1000/1000 [==============================] - 1s 708us/step - loss: 0.1585 - acc: 0.9520\n",
            "Epoch 60/100\n",
            "1000/1000 [==============================] - 1s 704us/step - loss: 0.1334 - acc: 0.9600\n",
            "Epoch 61/100\n",
            "1000/1000 [==============================] - 1s 686us/step - loss: 0.2015 - acc: 0.9330\n",
            "Epoch 62/100\n",
            "1000/1000 [==============================] - 1s 718us/step - loss: 0.1220 - acc: 0.9570\n",
            "Epoch 63/100\n",
            "1000/1000 [==============================] - 1s 770us/step - loss: 0.1065 - acc: 0.9590\n",
            "Epoch 64/100\n",
            "1000/1000 [==============================] - 1s 723us/step - loss: 0.1387 - acc: 0.9510\n",
            "Epoch 65/100\n",
            "1000/1000 [==============================] - 1s 706us/step - loss: 0.1365 - acc: 0.9540\n",
            "Epoch 66/100\n",
            "1000/1000 [==============================] - 1s 722us/step - loss: 0.1365 - acc: 0.9540\n",
            "Epoch 67/100\n",
            "1000/1000 [==============================] - 1s 712us/step - loss: 0.1644 - acc: 0.9450\n",
            "Epoch 68/100\n",
            "1000/1000 [==============================] - 1s 702us/step - loss: 0.1591 - acc: 0.9480\n",
            "Epoch 69/100\n",
            "1000/1000 [==============================] - 1s 715us/step - loss: 0.1055 - acc: 0.9620\n",
            "Epoch 70/100\n",
            "1000/1000 [==============================] - 1s 711us/step - loss: 0.1183 - acc: 0.9560\n",
            "Epoch 71/100\n",
            "1000/1000 [==============================] - 1s 708us/step - loss: 0.1006 - acc: 0.9690\n",
            "Epoch 72/100\n",
            "1000/1000 [==============================] - 1s 724us/step - loss: 0.1023 - acc: 0.9650\n",
            "Epoch 73/100\n",
            "1000/1000 [==============================] - 1s 708us/step - loss: 0.1134 - acc: 0.9680\n",
            "Epoch 74/100\n",
            "1000/1000 [==============================] - 1s 706us/step - loss: 0.1064 - acc: 0.9710\n",
            "Epoch 75/100\n",
            "1000/1000 [==============================] - 1s 722us/step - loss: 0.1406 - acc: 0.9580\n",
            "Epoch 76/100\n",
            "1000/1000 [==============================] - 1s 747us/step - loss: 0.1468 - acc: 0.9590\n",
            "Epoch 77/100\n",
            "1000/1000 [==============================] - 1s 700us/step - loss: 0.0747 - acc: 0.9720\n",
            "Epoch 78/100\n",
            "1000/1000 [==============================] - 1s 689us/step - loss: 0.1286 - acc: 0.9610\n",
            "Epoch 79/100\n",
            "1000/1000 [==============================] - 1s 708us/step - loss: 0.1531 - acc: 0.9570\n",
            "Epoch 80/100\n",
            "1000/1000 [==============================] - 1s 695us/step - loss: 0.1335 - acc: 0.9580\n",
            "Epoch 81/100\n",
            "1000/1000 [==============================] - 1s 683us/step - loss: 0.0856 - acc: 0.9730\n",
            "Epoch 82/100\n",
            "1000/1000 [==============================] - 1s 694us/step - loss: 0.1240 - acc: 0.9580\n",
            "Epoch 83/100\n",
            "1000/1000 [==============================] - 1s 697us/step - loss: 0.1133 - acc: 0.9650\n",
            "Epoch 84/100\n",
            "1000/1000 [==============================] - 1s 695us/step - loss: 0.0857 - acc: 0.9700\n",
            "Epoch 85/100\n",
            "1000/1000 [==============================] - 1s 694us/step - loss: 0.0859 - acc: 0.9720\n",
            "Epoch 86/100\n",
            "1000/1000 [==============================] - 1s 692us/step - loss: 0.0825 - acc: 0.9740\n",
            "Epoch 87/100\n",
            "1000/1000 [==============================] - 1s 691us/step - loss: 0.1254 - acc: 0.9640\n",
            "Epoch 88/100\n",
            "1000/1000 [==============================] - 1s 691us/step - loss: 0.1051 - acc: 0.9760\n",
            "Epoch 89/100\n",
            "1000/1000 [==============================] - 1s 700us/step - loss: 0.0787 - acc: 0.9730\n",
            "Epoch 90/100\n",
            "1000/1000 [==============================] - 1s 711us/step - loss: 0.1087 - acc: 0.9650\n",
            "Epoch 91/100\n",
            "1000/1000 [==============================] - 1s 789us/step - loss: 0.1082 - acc: 0.9670\n",
            "Epoch 92/100\n",
            "1000/1000 [==============================] - 1s 722us/step - loss: 0.0873 - acc: 0.9710\n",
            "Epoch 93/100\n",
            "1000/1000 [==============================] - 1s 706us/step - loss: 0.0637 - acc: 0.9780\n",
            "Epoch 94/100\n",
            "1000/1000 [==============================] - 1s 692us/step - loss: 0.0972 - acc: 0.9670\n",
            "Epoch 95/100\n",
            "1000/1000 [==============================] - 1s 720us/step - loss: 0.1052 - acc: 0.9630\n",
            "Epoch 96/100\n",
            "1000/1000 [==============================] - 1s 694us/step - loss: 0.0734 - acc: 0.9800\n",
            "Epoch 97/100\n",
            "1000/1000 [==============================] - 1s 687us/step - loss: 0.0633 - acc: 0.9760\n",
            "Epoch 98/100\n",
            "1000/1000 [==============================] - 1s 698us/step - loss: 0.0693 - acc: 0.9760\n",
            "Epoch 99/100\n",
            "1000/1000 [==============================] - 1s 705us/step - loss: 0.1134 - acc: 0.9650\n",
            "Epoch 100/100\n",
            "1000/1000 [==============================] - 1s 740us/step - loss: 0.0987 - acc: 0.9760\n",
            "shape de nos fichiers .npy importés:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[-43.28439095],\n",
              "        [-44.66571752],\n",
              "        [-50.32324929],\n",
              "        ...,\n",
              "        [-66.68075381],\n",
              "        [-66.68075381],\n",
              "        [-66.68075381]],\n",
              "\n",
              "       [[-41.60676242],\n",
              "        [-44.76745793],\n",
              "        [-51.08802257],\n",
              "        ...,\n",
              "        [-66.68075381],\n",
              "        [-66.68075381],\n",
              "        [-66.68075381]],\n",
              "\n",
              "       [[-45.4472857 ],\n",
              "        [-47.85611858],\n",
              "        [-55.09675264],\n",
              "        ...,\n",
              "        [-66.68075381],\n",
              "        [-66.68075381],\n",
              "        [-66.68075381]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[-41.10349932],\n",
              "        [-44.51462282],\n",
              "        [-40.63713197],\n",
              "        ...,\n",
              "        [-66.68075381],\n",
              "        [-66.68075381],\n",
              "        [-66.68075381]],\n",
              "\n",
              "       [[-49.86809573],\n",
              "        [-53.33717718],\n",
              "        [-52.00746249],\n",
              "        ...,\n",
              "        [-66.68075381],\n",
              "        [-66.68075381],\n",
              "        [-66.68075381]],\n",
              "\n",
              "       [[-66.68075381],\n",
              "        [-66.22391692],\n",
              "        [-64.37199182],\n",
              "        ...,\n",
              "        [-66.68075381],\n",
              "        [-66.68075381],\n",
              "        [-66.68075381]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "12113a2a-e2d5-4753-e9da-5c7aab8a4198",
        "cellView": "code",
        "id": "3QWpoQEoOwLL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#@title générer X et Y des chiffres, faire prediction, adapter resultats, afficher matrice de confusion sklearn.metrics\n",
        "\n",
        "#Calculer les resultats (Y_prediction) avec notre modèle entrainé\n",
        "Y_pred = model2.predict(np.asarray(X_test))\n",
        "\n",
        "#Transformer les résultats : [0.001, 0.001, 0.9999,...] -> [0, 0, 1,...]\n",
        "Y_pred = remettre_0_ou_1_dans_Y_pred(Y_pred)\n",
        "\n",
        "#Transformer [ [0,1,0,0] , [0,0,1,0]]... ] en [1,2,3,1,0...]\n",
        "Y_test_mat = adapter(Y_test)\n",
        "Y_pred_mat = adapter(Y_pred)\n",
        "\n",
        "\n",
        "#Afficher matrice\n",
        "from sklearn.metrics import confusion_matrix\n",
        "Matrice = confusion_matrix(Y_test_mat, Y_pred_mat)\n",
        "\n",
        "print(Matrice)\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[102   0   0   0   0   0   0   0   0   0]\n",
            " [  0 101   0   0   0   3   0   0   0   1]\n",
            " [  1   0 100   1   0   0   0   0   0   0]\n",
            " [  0   0   0 101   0   0   3   0   2   0]\n",
            " [  0   0   0   0  98   0   0   0   0   0]\n",
            " [  0   0   0   0   2  87   0   0   0   0]\n",
            " [  1   0   1   0   0   0  89   0   4   0]\n",
            " [  0   0   0   0   2   5   0  89   0   2]\n",
            " [  0   0   0   1   0   0   1   0 102   0]\n",
            " [  0   0   0   0   0   2   0   0   0  99]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McW98WwGPM4x",
        "colab_type": "code",
        "cellView": "code",
        "outputId": "00eed9e9-96e7-4e00-80b6-16921c3f8502",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 845
        }
      },
      "source": [
        "#@title Afficher pretty_plot_confusion_matrix des chiffres prédits\n",
        "\n",
        "plot_confusion_matrix_from_data(Y_test_mat, Y_pred_mat, columns=[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"], annot=True, cmap=\"Blue\",\n",
        "      fmt='.2f', fz=11, lw=0.5, cbar=False, figsize=[8,8], show_null_values=0, pred_val_axis='lin')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:164: MatplotlibDeprecationWarning: \n",
            "The tick1On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick1line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:165: MatplotlibDeprecationWarning: \n",
            "The tick2On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick2line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:167: MatplotlibDeprecationWarning: \n",
            "The tick1On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick1line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:168: MatplotlibDeprecationWarning: \n",
            "The tick2On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick2line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:63: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a6Yx2nhmF0dqluRk67fp9wJoGr78NkzKH7O1LIkrYG72XFp8WTA4ToU2hTNg7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UlTHl\nlczIK+LDxHgsRgM37tNef2RFFRNKK3g1dcgvzsXeI4AWRVG8aJsUWlGUJLpNnS6EEEIIoV9K27iJ\nBjc3trgdLP4AACAASURBVIUF49XayrSSUurdex6RP7yiisuzcnl+ZAq/39V1FoAXRqTQYtSOw305\nCKYVHyas2UyApQXvVm2qnO1hISTU1RPR2IybzcbCXem8MCKFRnd7u2+9s/cVHgS+BuIURfk3MAW4\n7he/uxBCCCGEiyjy9WHJyBRu23OAV9b+BEBGgB+vpHU/IufV2sq9O/by6eA4toaFdNt+pPMHMLK8\nEr+WVrL8fanxMLEtLJh8H2+e/vFnrMCSUalceTCHQ74+/Bh17HJx9jpuB1BRFAXYj1YNZDLaqd+F\nqqrqZPiSEEIIIcTx+VlauDzrEA3ubrw4IoXE2jquzMxl4a50Hh83ssu+1+7Pws1m4+v4GMIbtQmo\njapKdEMjRT7e7fuNKq/kwZ93Uu7pwcPjtbrDzW5u3PKr00mqraPSw4TJZuPpDT9z25mncfPeA0wp\nLqXB3Y2X04ayrYfOpT2Oew2gqs0T86WqqhWqqn6hqurn0vkTQgghxKlmTHklUY1N7AgNZm1MJP8e\notXIOL2kDEVVcbdacbPZAIhsbCKs2czydT/x9E9bAQj7f/buOzyqMnvg+PdmMum9dyChJSEg0puC\ngMIqsFhXd0FEZdW1ouvq2nZXrGv3J7q2XcVedl37oiIqCIj0kgLplfQ6SSaZub8/biohkMQMM3M5\nn+fJk7llZs7JnZm8c+/7vqepmX9t2IxL2z6zC0t4cNtOat2MrJ4+kUKfzhobLQYX0gL9KfXy5Ka9\nqbw+KoEh9Q1ckJXHn6ZNYFtYKLfuPjDgXPp6CXinoiiTVFXdPuBnEkIIIYRwYoVtZ+4mllawKDuf\n+FqtdGOOrw8pFVU8tmUHe4ID+eP0ibw7fCjfxEQC4G82c+O+NKrdjDybkohVUViQV8jNew5iNrjw\n0bA4RlfXMrq6lt0hQVS7u3U85zl5hbioKp8NiWZSaYW2Lr+IlMoqDH2cy/lY+toAnAL8TlGUHKAB\n7TKwqqrq2AE/sxBCCCGEE8ny9+WJcUmcn5nL1QczMBsMbA0L4YXkkYQ0dR8bmxoU0HE7vO0ScJPB\nwA9RWpWgMRVVuAAeFit/2J/ese9t0yZQ7a7VFPdvNrMi7TC3T5sIisLPYcF8GRvFr7PyqGtrTA5U\nnyqBKIoy5FjrVVXNHfAzOzqpBOJ49JQL6K8SgJ5yQSqBOCS9vc70lAtIJRAHdLxKIMc9A6goigdw\nDTAc2Ae8oqpq6+CGJ4QQQgghTqYTDQJ5DZiI1vhbCDj/V2IhhBBCiFPcifoAJqmqmgKgKMorwE+2\nD0kIIYQQQtjSic4AtrTfkEu/QgghhBD6cNxBIIqiWNBG/YI28tcTaK8DrKqq6mfzCO1HH4NAhBBC\nCHGq6nUQSJ9GAZ+i5A8jhBBCCGc2sFHApzTdDc/XQT56ygX0Nw2EHqaAgI5pIHRxbHQ0pQ20TWuj\nh+MC+nv/gz7yactFR9PA9LrthKXghBBCCCGEvkgDUAghhBDiFCMNQCGEEEKIU4w0AIUQQgghTjEy\nCEQIIYQQwgbGjhnLYw8+1mP9nn17eP2t17n5DzcTGhqKalUpLC7kzXffZPOWzSclNmkACiGEEELY\nQF5+Hg/+/cGO5flnzWfShEmkH0qntbWVDd9toLSslLDQMH73m9/x59v+zNJLl2I2m20emzQAhRBC\nCCFsoLqmmo0/bATA6GrkmquuobW1lY8++YjyinIyDmfg4+1DdHQ0l1xwCYqi4KKcnN550gAUQggh\nhLCx2WfMJigwiA0bN1Beoc2ZOHH8RO6/934AmpqaePiJh2lqbjop8cggECGEEEIIGzt/8fkAfPjf\nDzvWpaancue9d7L2xbVYrVZWXbEKH2+fkxKPNACFEEIIIWzotLGnkRCfwN79ezmUeahjfW1dLTt2\n7+CjTz9i556dREZEctrY005KTHIJWAghhBDCho519u/aq67F1GiiqLiI8LBwJk+YjMViITcv96TE\nJA1AIYQQQggbiYqMYvLEyRQUFrD1p60d62tqa1h49kICAwNpbm7mUOYh3v3gXfIL809KXNIAFEII\nIYSwkaLiIhb8ekGP9W+99xZvvfeWHSLSSB9Ae7lkOUyZDdPmwPzzYM8+e0c0cIcyYc5CGDdF+304\n094RDcyd90HSBPAOhQOp9o7ml9NbPokTYPx0mDpH+/lqg70jGjgnfs8YFQMr4+byRPJKHk5azlVx\n8wGIcA/kr6Mu5fHkK/jrqEuJcA+wc6QDIJ/Ljktv+TgAaQDay4v/B9s2wpZv4aY/wLU32Tuigbvx\nNli1EvZs037fcJu9IxqYRQth/ccQF2vvSAaH3vIBeOMV2Pqt9jP/LHtHM3BO/J65NOYMzFYLqw+8\nyh0HX+e9Iq1qwZVx81hftptbD/yT9WW7ubKtYehU5HPZcektHwcgDUB78ffrvF1TCy5OeihKy2DP\nXrhY6+DKxedry2Xl9o1rIKZPhZhoe0cxePSWj1448XvG3cXIrKAk3i/qLFVV22rCz9WToV5h/FiZ\nBsCPlWkM9QrD19XTXqEOjHwuOya95eMgpA+gPV13M3yzEVQVPnrX3tEMTGERREWCwaAtGwwQGQEF\nhRAaYt/YhP6svFZ7v0yfAn+5CwL87R1R/znxeybcPYB6SxMXRE4jyTeWJmsL7xduxqy2UNVSj4oK\ngIpKVUsDwUZf6lob7Rx1P8nnsuPRWz4Owkm/3ujE2qcgfbf2j+yuv9g7GiEc21cfa5fnfliv/XNe\nfYe9IzrluKAQ7h5AjqmUu9Pe5O2C77k5YTHuLm72Dm3wyOeyOEVIA9ARXHYxfL8ZKirtHUn/RUdB\nUTFYLNqyxQLFJXLpUQy+9teUuztcfQVs3W7feAbKid8zFeY6WlULP1Zpl3ozTSXUtTbSYm0l0OiD\nggKAgkKg0ZuKljp7hvvLyOey49BbPg5CLgHbQ309VNd0vng//x8EBkBQoH3jGoiwUBg7Bt77N1x6\nkfZ7XIqclheDq6EBWi1aHy1VhQ8+grHJ9o5qYJz4PVNnaeRgXT4pvkPYV5dLhHsg/kYvipuryG0s\nY3rQaDZXpjI9aDQ5jaXOdflXPpcdl53zKQguIC88j0b3RlysLgTUBzA6bzT1XvVkR2TT4NGAVbHi\n0+TD8MLhBNcFH/NxGo2N7B+2n1qvWqwGK5HlkYzJHdOxvcnYRHpsOlW+VaiKSkh1CKPzR2O0GDG7\nmtkbv5carxr8Tf6MzRqLW6sbVsXK1sStxBfHE1EV0a+8TokGoKIoQ4AaVVWr7R0LAA0m+N2V2m+D\nCwQGwvtvgKLYO7KBefoxWHU9PPwYBATAS8/ZO6KBue1O+O9ncKQUzrsAgoPg5032jmrg9JRPaRlc\ntlL75m+1wuiR8OSj9o5q4Jz4PfNq7tesGnoOv409E4tqZW3255gszbya+zXXDFvA+ZFTabA083z2\nF/YOtX/kc9mx2Skfk5uJ1KGpuLa6MqJgBFW+VZQGlnLIegjvJm/cWt2ILIqkya2JnIgc9iTsYda+\nWRgtxh6PZXWx4tHigVutG6WBpT2270nYQ613LfFF8bS4tpAfpk0InZKTQl5YHnWedQwvGk5WVBZ5\noXkMLx5OTngOns2e/W78gc4bgIqijAXuBxKAFYqi7FRV1WrnsCA8DDZ+ae8oBs+oEfDd/+wdxS/3\n2EPaj17oKZ9hQ2GLE8/7dzQnfs+UmmtYk/Fej/VFzZXcm2a/SW1/Mflcdmx2ykdVtIFNrhZXguqC\naDW0UhpYirHVyLCSYbionT3pKvwqqPOqw+Ruwt/Uc4Cad7M3KdkpFAUX9WgAthhaqPWuxa3FjYTi\nBCyKhfywfEqCShidPxqLiwVXiyvBtcHkhedhMVhocG8gLyyPqalTB5SbrhuAwErgZ1VVlwAoirN+\nlRNCCCHEyebd7M3o3NGkx6azJXkLAL4NvowoHNGt8dfg3kCDRwNuLW74NPr0+3kMFgMGi4EW1xaq\nvatpNjZrGxRodGsksiKSwpBCtiRvwdXiSmRFJKlxqcQXx+PR4jGg3HTbAFQUJQGIUlX15rbl8cBu\n+0YlhBBCCGfRYmghNyIXV4sro3NGU+dZR05kDqlxqaTkpABQ71HPzhE7cVFdGJc5DoNq6PfzuODC\n6PzRpMalsn30dhRVwcXigtVgxUV1wafJh1n7ZtHg0YB3kzdlAWVYXaxEVEWwO2E3tV61eDd5k5Sb\nhKe5b/Nv6nkUcBYwTlGUSxVF+S/wJPCUoihL7ByXEEIIIZxApW8lje6NBNUFEVEVQXxxPABlAWXa\ndp9Kto/ajqqoTMiYQEBDZwlEK1YsigUrfet5FlURxay9s5iUNonpB6bjorpgsBjwavYCwGgxEtAQ\ngIrKoehDJOYmkheWR71HPTP2z8CqWDkcdbjPuem2Aaiqqgq8CdwGPAacBewFrlIUpf/nZ3vTl/qE\nr78Fk8+EqbNh0hmw9sXObRYL3HI7jJkEKZPgX+s6t732JkyYodWl3LWnc/01N8LmLYOWwjH1JS9H\njb2v8X/9LcycB4HRWt3crhwpNz3l0i2fqW35ZPXc54FHYUhSZ93fW/7Uuc1kguVXQ8pkrTbwF+s7\ntz38OEycBbMXQl5+5/qll0LmMZ5nMDn5e6a3Gr9dKSisiJ3Lk2Ou5InklcwOTunTtjODx/Bo0goe\nTFzGUM+wjvWrhpzDKB8bTOWh189lcPrXWTdOkEt746vCr4L80HzSY9MB8Gn0odyvnJ0jdmIxWIgt\njcXkbqIksIRGN230e3ZkNhtO30B2ZDYArS6tFAQXUOVTBYDJw0RBcAG1nrWANtq4JLiEBo8GDgw5\nQKtrK0NLhna71AyQEZtBdHk0vk2+qIqK2WimMLSQRvfGjj6LfaHbBmCbb4EUoLRt8Me3QBUweMVR\n+1Kf8NeLtAlst26EDZ/DM8/DvgPatnc+gMxs2LsNNnwBD/wdcvO0bY88Ad+vh78/AI89pa37frM2\nC/qMaYOWwoDzctTY+xr/sCHw3JNw8x96bnOk3PSUC8CNf2zLZ+vxa3pedlFn3d8nH+lc//Ra8PWF\nfT9pozSvu0WbwqO2Tst120a4egU8/7K2/xvvwNRJkBBvm3w68nLu90xvNX67mhmUSIR7AKv3v8J9\n6W9zQdQ0Qtz8TrhtaeRU7kl7g9fzN7AkcgoAiT4xWFUr6fWFg5+MXj+X+5qbI8fflRPk4tvoS1JO\nEu4t7mTEZFAaWEpIdQjJOcnUeNeguqioikpmdCb74vexL34fVb5Vx3ysFtcWUoemUhRSBECNTw2p\nQ1M7ziZaDBayI7JJHZJKk1sTwwuHM6xkWLfHqPCtoMa7hmHF2vq4I3H4mnw5FH1IG0BSlNDn3HTd\nAFRV9QfgVeCGtlXnAUZVVVMH5Qn6Wp/Qz7dzKgGTCVpaOpc//AiuWKbVnAwNgUUL4d8fa9sMLtDU\nrE1LYHQDsxnWPAz33zso4f/ivBwx9v7EnxCvzSXleoyusI6Sm55y6U8+x/Phf+HK5drt4fFw+mmw\nfoOWi8Wivb9MJnBz0ybxff0tuOX6wc+lKyd/z/RW4/doU4NGsaF8LypQ19rIjurDTA0cecJtVtWK\nUXHF3cVIq2rBoLhwYdQM3i78YfCT0evncn9yc9T4nTSX6Ipoph+cztxdc5m9ZzbjM8fj3exNQnEC\n83fM7/ETVREF0LE9oVhrlHmaPY+5f/v2IaVDOHPvmczbOY9Z+2cxrGRYx+Tq7YLrgplxYEZHP0P3\nVncmZUxi7q65TE2binezd5/z0u0gkC7uAG5TFOU7oA7426A9cn/qE372Jdy3BrJy4K93w5gkbX1+\nIcR1OSEZEwOFbd+I19wHiy4EXx945nF4/BlYscz2E5P2NS9HjL0/8R+Po+Smp1zgOPkU9czng4+0\nmqzhYXD37TBlUls+BRAb07lfbLT29/D2hhuu0S7/hofBi8/C3X+De+/UGoO25OTvmd5q/KY3dD87\nF+zmS7m5tmO53FxHkJvvCbe9Xfg9d468kCaLmVdyv2ZxxGS+Ld9Lg6Vp8JPR6+cyOP3rrBs95eKk\ndN8AbJv8+W5FUcJUVe058+LJcu4C7Se/AC5ZDufMg5HDj3+fJedpP6D1jdi+QzuTccvt2rek6VPh\nulW2j30gnDn2E9FTbo6ay5WXw+23gNGoNQIvvhx2btImsz6eVSu1H4BNW7QzOomj4Pc3Ql0dnL8E\nLvy1zcMfEDsei641ft8q/J4ErwhuG76U1ftfodFq/sWPv736MNurtc7pEe4BDPeO5NOS7ayInYu/\n0ZO0ukL+V7brFz9Pv51qn8vg/PF3padc7EDXl4C7sknjbyD1CWNjYOLpnR3XY6O7d1YvKIDoY9z/\n9rvhkTXw7gcQEgJvvAqffA7ZOYOWToe+5uWIsfcn/uNxlNz0lAscJ5+o7vtFhGuNP4C5s7XtB9Pa\n8onR/mG3yy/s+fcwm+FvD8H998BzL8LM6fD6S9ogkUYblCdz8vdMbzV+IzwCe+zX3q8PIMTNl0pz\n3Qm3dbUsdg7r8jcyIyiRulYTT2d9ysTA4YS59Zw4d0D0+rkMTv8660ZPuTipU6YBaBNd6xNC7/UJ\n0zI6b5dXwPebIDlRW166GP65TitvVVYOn3wBSxd1v/9b72kfTiMStL4OHfNZK9qyvfJyxNj7E//x\nOEpuesoF+p5PUXHn7T37tH8AI9o6Ny9dBK+8rt0+nAU7dsH8s7rf/4lnYfll2hnDhgZQ0HJqaQFz\ny+Dl09+8HOlYdNG1xi/QUeP3SHP36pnbqjI4K2QsCuDr6smEgOFsq8o44bZ2M4MSyWwooaS5CncX\nIx3jFVWtH+Kg0OvnMjj966wbPeXipBRtthTRg6m8b3+Y9ENafcLq6s76hCOHw9LfwD13aB3Ub78b\nNmzUOuirwIrfwrVXa/e3WGD1HdplLoDVN8DK5Z2PX1kFv1kOn36o9WOqrIJLL4eKKpg2GZ59/Pjx\neYW059Of7PuWl61j/yW59CX+H7fC5au0S4OqCn5+sPYprTFxMnLzCtFZLmUn3q8jnxu65PN/bflc\nCvf8Scvn6uth116tk7ebG9x1OyyYp92/oUG7pLtnn9ZvaM29cN7CzsfPzILb7oL/vK0tZ2XDimu0\n+120FO649QS5hGq/dfSeuWzHie8X5ubPqqHn4OPqgUW18l7hJvbU5nD78KW8X/Qj2aYjKChcEXcW\nKX5DAfik5Cc2lO8DOO42AG+DB6sTlvDgofexqNa25cX4uHqSUV/IK3lfnzDGtybcOnjvGXt+LkPf\n3/8Dyc2RP5udJJezF5/dv/s5qPUfr++1Apo0AHvT1wagoxvoG9MR6SkXGPg/AEfUnwagoxtoA9AR\n9aMB6Az63AB0Bnp7/4M+8jmFGoC6HwQihBBCCDFQ1+5PY2ZxKaFNWn3esxdpk6Tftms/ZxcU99j/\n76cl81VsVI/1QU1NXL8vnZSKKjwtFko9PfhoWCwfD4sD4JJD2SzMKyTKpPVTXjZ3Jke8tLJuSZXV\n3Lr7AMFNzWyMjuCpsYmgKEQ1mHj6h5+44YwplHj1rQRcO+kDKIQQQgjRC0XlmA26T4fG8uDpKTx4\negoPjR9DY9uUNhkBfj32Bbgy9TAzS0rZGxzIy4kj8DebuX5/OjH1DQAYrVY2R4ZRa+zZH/bK1EM0\nGwz8Jz6OX+UVclqFNtn0jXtTeWfE0H43/kAagEIIIYQQvVqbMpp/je45PVBaoD8boyPYGB1Bk6sB\nT4uF7aHB5Poeu9qsS1uXu8P+vuwIDaLB1ZUWRaGpreH4xqgEXkoaicnV0OO+Xq2tlHh5sjNEmwrL\ns7WVeflF+La08J/4IQPKSy4BCyGEEEL8AudnaeXpPkzovTH2UtIIohtMXJGeyRXpmbQqCk+MS6Lc\n0+OEj//pkFiu35fKzJJScn28yfTz5elNP3H3lPFYlV67+R2XNACFEEIIIQYooaaWcRVVZPn6sDM0\nuNf9ZhceYXR1LV/GRrEtPJQrUw9x495Usvx8yfL3Pe5zfDY0hu1hwQQ1m8n08+WmvQf5JiYSF1Xl\n6R9+Iripmd0hgTw1LolWl75d3JVLwEIIIYQQA9R+9u/f8XHd1husVowWS8el3wX5Wgm79xOGsDky\njB8jQvGwWjm9vKJPz1Pq5UlaoD/JVdUkV1azbmQC1+1Pp8DHi9/PnsoZxUeYl1/U57ilASiEEEII\n0YvJR8pYkNtZfWhBbgFnFpYAENTUzJlFJVS6u/FtdGS3+92y5yCffb6BuW0jhQu8vQFYkZbJgtwC\n5rQ9Rk5bn8GUiioW5Bbg2apVRzmzqKTb8wIYLRZu2JvKs2MTaXY1YFBVEmrq+FVuIW4WK4Z+TO0n\nl4CFEEIIIXpxUWYu49pG3QKs3ptKiacH30VHsDgnHzeryidDY2kxHP+c2nNjRmFVYGxFFVNKyynz\ndOcfSSP5OUybe/CcvMJu08pclarVz/5ySEzHussOZZMW6N9xqfnFpJHcsucgyzIy2RoeytdHl9U8\nDpkIujcyEbTj0VMuoL+JYGUiaMcjE0E7Lr29/0Ef+ZxCE0HLJWAhhBBCiFOMNACFEEIIIU4x0gAU\nQgghhDjFSANQCCGEEOIUIw1AIYQQQohTjIwC7p38YYQQQgjhzHodBSzzAPZGD8PZQZfD83WRC+hv\nGgg95QL6yEdPuQB4hciUNo5IT68z/U0D0+s2uQQshBBCCHGKkQagEEIIIcQpRhqAQgghhBCnGGkA\nCiGEEEKcYmQQiBBCCCGEjSQnJnPViquIHxqPyWTis/99xhvvvAHA1MlTWbl8JdGR0ZRXlPP2+2/z\n5VdfnpS45AygEEIIIYQNBAQEsObeNcTGxPLiP18kNz+X5ZctZ8H8BURFRnHvHfdiNBpZ+9Ja6urq\nWH3DalKSU05KbNIAFEIIIYSwgeTRyXh7e7Nz104++/IzPvjoAwAWLVzEueeci6urK+99+B6fffkZ\n/3rzXwAsPnfxSYlNLgELIYQQQthAZVUlAAnxCURGRDJ+7HgAoiKjKCsvA6C0rBSAkiMlAERHRZ+U\n2KQBKIQQQghhA6npqXz6xaect/A8XnvxNerr6wEwt5h77KsovRbtsAlpAAohhBBC2Mgzzz/DOx+8\nQ3BQMF5eXjz014fIzsmmsKgQgPCw8G6/29fbmjQAhRBCCCFsZMXvVnCk9Ajubu6cv+R8rFYr695e\nR1V1FecvOZ+Lzr8IBYWFZy8E4JPPPzkpcUkDUAghhBDCRiIjIlly7hKMbkby8vN44eUXOJB6AIA1\nj6xhxe9WcN2q66iorODp555m7/69JyUuaQAKIYQQQtjIQ4891Ou2zVs3s3nr5pMYTSeZBkYIIYQQ\n4hQjZwDtoaISrroOsnPA6AbD4+GZxyA0xN6R9d+d98F/P4XcPPjpe0hOtHdEv8yhTFh1PVRWQlAQ\nvPR/MDzB3lENjOTiuPSUj5PnMt4/nouiZgCgAP8u3kK2qZTVCUs69vE2uONpcGPVnrV2inIAnPy4\n9KC3fByAnAG0B0WBW26A3Vth+/cwbCjce7+9oxqYRQth/ccQF2vvSAbHjbfBqpWwZ5v2+4bb7B3R\nwEkujktP+Th5LtcNXcja7M/5c+o61uZ8wTVDF1JhruXPqes6fn6uPsyPlWn2DrV/nPy49KC3fByA\nNADtISgQzpjRuTx5AuQV2C+eX2L6VIg5OZNW2lxpGezZCxefry1ffL62XFZu37gGQnJxXHrKRwe5\nWFHxMrgD2pm+6pZ61C7bDYoLM4IS2Vi+3z4BDoQOjks3esvHQcglYHuzWuGlf8G559g7ElFYBFGR\nYDBoywYDREZAQaHzXZ6XXByXnvLRQS7PZH3KrcN/TZOlBU+DkUcP/6fb9gn+CVS21JPTWGqnCAdA\nB8elG73l4yCkAWhvt94BPt5wzVX2jkQIIU4pLigsiZjM44c/IqOhiJHeUdwYfx5/PPAvmq0tAMwO\nGcN3znT2T4g+kgagPd15HxzOhg/eABe5Gm930VFQVAwWi/YN02KB4hLnvMQtuTguPeXj5LkM8Qoj\nwOhDRkMRABkNRTRbWoj2CCLLdIRAow+jfWJZm/2FnSPtJyc/Lj3YOZ8q7yoOxRyi3rMeg8VATHkM\n8cXxKCg0GZvIiMmgwq8Cq4sVr2YvxmaOxbvZ+5iPVe9RT0ZMBtU+1QB4NXkxIWMCRquRryZ8dcz7\nzN8xnwb3BvYN24fJw0RQbRAp2SkYVAMthhZ+TP6RcZnjCGgI6Fdep0SrQ1GUYYqiDLd3HN3ctwZ2\n74F3XwN3d3tHIwDCQmHsGHjv39rye/+GcSnOeYlBcnFcesrHyXOpNNcR5OZDpHsgAFEeQfgbvTjS\nXAPAGcFJ7K7Jot7SZM8w+8/Jj0sPdsyn2bWZ3SN20+DRwIiCEfg0+ZAVlUVhcCFWrOwcsZMjgUeI\nrIxkdN5oAmsDURX1mI/VYmjh55E/U+VbRVxpHKPyR+Hb6Nuxf0pWSsdP3JE4APwa/ADIjsymxbWF\n+OJ4ygLKKAkqASAjJoPwqvB+N/5A52cAFUWZBtwCXAhcDxy2b0RtDqbBY0/DiAQ461fauqFD4J3X\n7BvXQNx2J/z3MzhSCuddAMFB8PMme0c1cE8/pk018PBjEBAALz1n74gGTnJxXHrKx4lzqWk18c+8\nr7kpYRGqqv0TfjFnPQ1tDb4zgpN5Lf9be4Y4cE58XI7JTvnU+NTQamglvDKc2PJYPM2eVPpVUhBa\ngEE10ODZQGRFJCPzRwIQTe9nJfND82kxthBfFM+wkmEoqkJ0Ref+EVURHbdLArUG3pAjQwBoNbTi\n3uJOcG0wmVGZWAwWKn0qqfSrZNqBaQPKTWl/0euJoigewD+AMcC9wLmAWVXVm/v8IKZyffxhvNq+\nIZl0MFpKT7mAlo/k4nj09DrTUy4AXiFctuNxe0cxKN6acKuujgugj3zacjl78dkAVHtXs330drya\nvBh/aDwFoQXkRuTianEluiya3IhcvBu9MbmbQIGQmhCSs5MxWo09HnrvsL0cCTqCd6M3DR4NKKpC\nhYhE+wAAIABJREFUZGUkibmJuHS5IGtyM7F5zGY8zB7M2D8DF1wo8ytjT8IeVBcVN7Mbk9InsWvE\nLkYWjCS0JrTXdNZ/vF7pbZtezwA2A4+oqnoQQFEUV+BSRVEMqqpa7BuaEEIIIZxBQEMA0WXRFIYW\nsjllM66tWrPJxeqC1cUKaGfnUrJTOBJ4hCNBR8iKymJUwagej2Vx6Wx+jMscR154HkUhRfiafIkr\ni+vYlheWBwrElsZ2NAxDa0OZtW8Wje6N+DT6kBuei6/JF+9Gb3aM2EGDRwN+DX4k5yZjtPRsfB6L\nLvsAqpqDXVZFAMWqqloURem1NSyEEEII0VVSXhIz981kUtokUrJTAPBp9MGryQuAgPoAwqvDiaqI\nAtDOBgJWrFgUC1a0hqJXs7Z/aHUoYTVhhFeGa/t7mDqeq8WlhaKQoo7BJl25t7oT0BBAs7GZgtAC\nRuWPIjMqE1VRmbF/BvVe9eSG5/Y5L102ANspitKeXzqwCLTG4aA+yaFMmLMQxk3Rfh/O7LnPw4/D\nxJkw+UyYMRe+2tC5zWSC5VdByiQYPw2+WN/zfrMXQF5+5/qlv4HMrEFNo8+5fP0tzJwHgdHaKOau\nLBa45XYYM0nL51/rOre99iZMmAHT5sCuPZ3rr7kRNm8Z/FyO1pfcHDn+riSXzm2Si+04cT7j/eN5\nMHEZDyYu46HEZUwK6DkG8NqhCzr2eTBxGW+cvprT/bXSYgoKK2Ln8uSYK3kieSWzg1M67ndm8Bge\nTVrBg4nLGOoZ1rF+1ZBzGOVzEkalOvFx6cFJcjkcdZgKvwpqvWpJjUsFFeKL4omojMDV4qr1CQwp\n0M7cAcF1wYA2cGPD6RvIjswGIKYsBkVVOBJ4hMLgQopCtNHnQbVBHc9VGFKIxWAhujwaV2vPi7Qq\nKgeHHCShMAH3VndURcXkbqIwtBCzq7nXASjHousGoKqq1rabGUC6oihJg/4kfSlPM2E8fL8efvoO\nnn8aLr8aGhu1bU+vBV9f2Lcd3n8DrrsZ6uuhtg7eeR+2fQdXXwHPv6zt/8Y7MHUSJMQPeip9ymXY\nEHjuSbj5Dz23vfMBZGbD3m2w4Qt44O9ajWCAR57Q/gZ/fwAee0pb9/1mbUj/jIF1YO2XvuTmyPF3\nJblILieDE+dzrPJuR1/6eT7ny45Sby/kfEmDpYm9tTkAzAxKJMI9gNX7X+G+9Le5IGoaIW7aaMyl\nkVO5J+0NXs/fwJLIKQAk+sRgVa2k1xfaNC/AqY9LD06Si8ndxKGYQ2TEZGC0GBmXOY7AhkDcLG6M\nOzwOj2YP0mLTaPBoIL4ontjSY5dG9W72JiUrBQWF1LhUWg2tjM4bTViN9kVCRSU/LB9FVYgrjTvm\nYxQFF6HQOXgkvjget1Y3DkUfws/k1zF6uC903QDsIgSoBaoH9VH7Wp5m/lngpZ36JSUZVBUqq7Tl\nDz+CKy/Xbg9PgNNPg/XfgMFF++bT0qKdJXQzQkUlvP6mVkd4sPU1l4R4bfi96zG6j374EVyxTJvT\nMDREqxP874+1bQYXaGqGBhMY3cBshjUPw/33Dn4uR+trbo4av+QiuZxsTp7Picq7HW12yBg2V6bS\n2tZFfGrQKDaU70UF6lob2VF9mKmB2ihPq2rFqLji7mKkVbVgUFy4MGoGbxf+YOOscPrj4qy5jM0e\ny5zdc5i3ax5TU6d2NNgAguqDmJo2lXm75jFr/ywSihNQ2r5uJBQnMH/HfBKKEzr2D68OZ8aBGczb\nNY8ZB2YQW9bZWFRQmLV/FvN2zsPT7HnMWKIropmYMbHjOXyafJiaOpW5u+YyMWMi7q19n1ZOr4NA\njpYG/Aq4a1AfdSDlad58F4YN1Sa2BMgvgNgu1/ljY6CgCLy94YZrtcu/4WHw4v/B3X+Fe+8EN7dB\nTWPAuRwtvxDiunzziYmBwrZvxGvug0UXgq8PPPM4PP4MrFim1UW2tb7m5qjxdyW5SC4ng5Pnc6Ly\nbl211/p9MOP9jnXBbr6Um2s7lsvNdQS5+QLwduH33DnyQposZl7J/ZrFEZP5tnxvx9QxNuXkx6Ub\nPeXipHTfAFQURVFV1awoSrKqqgV2DeaHzXD/w/DJB33bf9VK7Qdg04+gKJA4Gn5/A9TVwflL4MKl\ntot3MC05T/sBrZ/H9h1wy/Va346ycpg+Fa5bZd8Yj8fZ4+9KcnFMesoF7JZPX8q7dTUxYDjl5lpy\nG8v69Pjbqw+zvVqbUjbCPYDh3pF8WrKdFbFz8Td6klZXyP/Kdg1qToNKT68zPeViB7q/BNw+6MMm\njb+u5Wng+OVptm2HK6/TJnse2aVDcmyMdhawXX4BxER1v6/ZDH97SDut/dw/YOZ0eP1lbZBIe1/C\nk5lLb2Kjuw9WKSiA6GPc//a74ZE18O4HEBICb7wKn3wO2Tm/KIVe9TU3R42/K8lFcjkZnDif45V3\nO5bZwT1r/VaY6zr6/AGEuPlSaa7rcd9lsXNYl7+RGUGJ1LWaeDrrUyYGDifMzX8QM+rCiY9LD3rK\nxUnpvgFoU30tT7NjFyy/Gt58FcaP675t6WJ4pa0CyOFM2LEb5s/tvs8Tz8Ly32pVNhpM2plARdH6\nB5p7fqO1aS7Hs3Qx/HMdWK3at69PvoCli7rv89Z7MPF0rQpKey4AKNqyLfQ1N0eNvyvJRXI5GZw4\nnxOVd+sqyOjDKJ8YNlemdlu/rSqDs0LGogC+rp5MCBjOtqqMbvvMDEoks6GEkuYq3F2MnX0MVXB3\n6ds8bP3mxMelBz3l4qR0WQlkUPS1Ekj6Ia08TXV1Z3makcO1qVruuUMb1DFrvvYNJrKzzAsvr4Ux\nSdDQoF3S3bNP6wOx5j44b2HnfplZcNuf4T/vaMtZ2bDi99r9Ljof7rj1+PH1Z4b2vuTy41a4fJV2\nCVpVwc8P1j6lDXSxWGD1HfDNRu3xVt8AK5d3Pn5lFfxmOXz6odaPsbIKLr0cKqpg2mR49gQz/P+S\n2eb7kput4z9WPpKLY+YC/c9HT7k4cD59qQQyI2g0iyImd5R3+7BoCz/XHOb24Ut5v+hHsk1HAFgS\nMYU4zxCezf6s2/0VFK6IO4sUv6EAfFLyExvK93Vs9zZ4sDphCQ8eeh+Lam1bXoyPqycZ9YW8kvf1\nCWMccCUQBz0ugK7eM+2VQJzd8SqBSAOwN1IKzvHoKRfQX/k0PeUC+shHT7mAlIJzVHp6nZ1CDUDd\nDwIRQgghhBgsj/74Mwm1dXi0Wqh2d2NTZBgvJo3E4tK9V513SwsPb9lJTEMDRquVCg8P1sdG8eZI\nbR7fM4pKWJl6mJCmZlpcXMjx9ebVxBHsCw7EzWLhzzv2Mb68knwfLx48PYUiH28AHt6ygx2hwbw/\nfOgvykP6AAohhBBC9FGWny8vJ47g2ZTRNLq6sjQ7n/n5Rcfcd29wIP9IHsU/kkfhZrFweXom48sq\nADC7GPh8SDRPjU3ky7gokqtquGOn1tXgrMISphwpY92oeAKaW/jtIa2ayNz8IvzMLXwY3/cJn3sj\nZwCFEEIIIfrohTGj8DW34N3SwqziUuLqG1CVnldaG4xGXkoeiV+zGX9zCwvdCwluNnfsuzUilB2W\nILxbLdQbjVyQlYfaNsGzZ2srLS4u7AgN5oyiI3i1tuJrNnNV6iHunXwaVpdffv5OGoBCCCGEEP3w\n6obN+Ldos3B8FRPJ1zGRx9zPo7WVD9Z/17H8zvCh7A7pnJJoYV4h1+9PB6DazciDE7S60z9EhnPJ\n4Rz+8d1WzC4urBuZwKqDh/g2OoJDAYMzzZA0AIUQQggh+uFvk8YR0Gzmwsxc5hSWsCU8lE1R4T32\nMxsM/Gnq6QQ1m7ksI4tfZ+exNTyUg0EBAPwYEUaRtxcjq2v5XUYW1xxI55YZkyj39GDFWTMYVltP\niZcncfUNjK2o4vpZk/njrv2MK6+iwsOdZ1NGczjAr8fz9oX0ARRCCCGE6Id9wYH8EBXO2yOG4qqq\nnF1QhKKqGC0WXK3Wjv2sisKu0GC+iYnkk6GxeFiszC4s6dhe7unBz2EhvDUynmxfH0ZX1xJfWw9A\nk6srqUEB1BuN3Lg3lWdTRjOzuJSZxaWsmj2VIm9Prms7ezgQcgZQCCGEEKIPJpaWM6ewhIOB2hm8\nxTlalZIsP19SKqp4bMsO9gQH8sfpEzknr5CR1bUc8vfF3Wrlgqzctn19ALhj5z7yfLwp8/RgWG0d\nCbV1NBoMFHp7dnvOSw9lkRHgx89hIZyXk4+7xcKv8grb9h94M04agEIIIYQQfVDjZmRYbT0ziksx\nqCrlHu68M3wo60bGk1xZ3W3fKnc3xlZUMT+/CFVROOLpwUuJI/hySAwA9UZXFuXk42duweTqyq6Q\nINaNSsBk7KwkE1tXz8K8Qq45cxoAX8dEMam0nOXpmZR5ePCPsSMHnItMBN0bmQja8egpF9Df5Ml6\nygX0kY+ecgGZCNpR6el1dgpNBC19AIUQQgghTjHSABRCCCGEOMVIA1AIIYQQ4hQjDUAhhBBCiFOM\nNACFEEIIIU4xMgq4d/KHEUIIIYQz63UUsMwD2Bs9DGcHXQ7P10UuoOVTX2zvKAaHT6S+jgvoI5/2\nXGry7RvHYPGP1cdxAf1NaQP6ODb6mwam121yCVgIIYQQ4hQjDUAhhBBCiFOMNACFEEIIIU4x0gAU\nQgghhDjFSANQCCGEEOIUI6OAhRBCCCEGwbVXXcvM6TMJDQkFuo8mHho3lKuvuJoxSWMAyC/I5/Z7\nbsdkMuHl6cUffv8Hpk2ehsFgYNeeXTzzwjNUVlbaLFZpAAohhBBCDALFReGrb77isksu67be29ub\nR9c8iqenJx/+90NKjpSQNDoJg8EAwLVXX8v8s+bz0ScfUVdfx28v+S1eXl7cfvftNotVGoBCCCGE\nEINg7YtrAXo0ABf/ajEBAQGse3sd77z/DharhS+/+hIAL08v5s2ZR3lFOWtf0u4/8fSJnDb2NOJi\n48jLz7NJrNIHUAghhBDChuKHxQMwa/osPn7/Yz5+/2NW37gaFxcXIiIiMBgMlJaVduxfcqQEgOio\naJvFJA1AIYQQQggbcjO6ddz+28N/42DqQRbMW8CihYuOub+i9FrBbdBIA1AIIYQQwoaKiosA2Lp9\nK1u2beG7Td8B2hm+kpISLBYLYaFhHfuHh4UDUFhUaLOYpA+gEEIIIcQgmDxxMkGBQR3LC+YvoLGx\nkc//9zm/XvRrZs2YRWFRIQvmLwBg195dmBpNbPhuA/PPms+1V11LfUM9o0aMYs++PTbr/wfSABRC\nCCGEGBQXLb2IcSnjOpZX37CakiMlLL96OQ88+gBXLLuCG669gdLSUp59/lm2bNsCwHMvPoeiKJw9\n92wMBgPbtm/j2ReetWms0gAUQgghhBgEf7zrj71u27RlE5u2bDrmNpPJxKNPPmqrsI5J+gDay6FM\nmLMQxk3Rfh/OtHdEAye5OIaKSrjgdzB+Jkw5Cy67EsoqtG0/7YBp8+C0mbDkN1BWbt9Y+8uZj8vR\n7rwPkiaAdygcSLV3NAP38JMQEAcH07Xl7Tthxjkw4UxY+lvne40BXLIcpsyGaXNg/nmwZ5+9I+qX\n0/yG8UDi73g4aTn3jLyYUDc/AIyKgZVxc3kieSUPJy3nqrj5do50APT0GeAgpAFoLzfeBqtWwp5t\n2u8bbrN3RAMnuTgGRYGbr4Ndm2DbBhg2BO57AKxWuPoGePwB2L0JZkyFex+wd7T948zH5WiLFsL6\njyEu1t6RDNzuffDzLoiN0ZatVlh1Ezx2P+z4DqZPgb88bN8YB+LF/4NtG2HLt3DTH+Dam+wdUZ95\nG9y5dthCns36jDsOvs6G8n2sjJsHwKUxZ2C2Wlh94FXuOPg67xVttnO0A6CnzwAHIQ1Aeygtgz17\n4eLzteWLz9eWnfEbs+TiOIICYdb0zuXJEyC/AHbtBXd37Z8ywMpl8J9P7BPjQDj7cTna9KkQY7u5\nvWyuuRn+eI/2haLd7n3g4Q7TJmvLK38HH31qn/h+CX+/zts1teDiPP8iw90DqGlpoKS5CoDdNdmM\n8x9GkNGHWUFJvN+l0VfbarJXmAOjt88AByF9AO2hsAiiIqGtBAwGA0RGQEEhhIbYN7b+klwck9UK\nL78Gvzpbi7/9TA1ASLC2vbJKazQ6Oj0dFz148HG4eCkM6XIGM/+o11hwkPYaq6qGwICTH+Mvcd3N\n8M1GUFX46F17R9NnxU1V+Bu9ifcKJ8t0hBlBiQCEuQdQb2nigshpJPnG0mRt4f3CzaQ32G56kUEn\nnwE24Txfb4QQfXfbXeDtDb9fae9IhJ78tEM7o3zVcntHYjtrn4L03fCXu+Cuv9g7mj5rtJp5NutT\nlsXOYc3o3+Jv9KShtQlQCXcPIMdUyt1pb/J2wffcnLAYTxe3Ez6m0Dc5A2gP0VFQVAwWi/ZNxmKB\n4hLnvCwkuTieP/8VMrPhvde0S1gx0dql4HblFdp6Zzj7B/o5LnqweStkHIaxM7TlomJt4NHvr+j+\nGquo1F5jznb2r6vLLoYbbtVyCQ468f4OYH9dHvvTtXnj/Fy9ODd8EuXmWlpVCz9WpQGQaSqhrrWR\nCI9Ask1H7Blu38lngE3IGUB7CAuFsWPgvX9ry+/9G8alOOepbMnFsfzlIa0/1tuvav3+AMaPhaYm\n+HGbtvzqOvj1efaLsb/0cFz04pY/QNrPsO9H7ScqEj58A268BhqbYMtP2n6vvgFLzrVvrP1VX69d\nUmz3+f+0BqyzfFEC/F29AFCAS6Jn8k3ZHsrNdRysyyfFdwgAEe6B+Bu9ONJcbcdI+8nOnwFWxUpG\ndAbfp3zP1+O/5ocxP3AkQGs8FwQXsGnMJr4e/zU/Jv1IqX9pr4+jopIVkdXxONtGb6Pau/M4NBmb\n2J2wm29O+4Zvx33L/qH7aXVpBaDau5rNyZvZcNoGDsYdREUFwORuYuO4jTS6NfY7L0VV1X7fydko\niuKrqmpdv+5kKrftHyb9EKy6HqqrISAAXnoORg4f/OfxanuDmGzYWVZyGRivEKgvHrzHS02HyXNg\neDx4emjrhsRpjcGt2+GmP2kd+ONi4OX/0z5UB4tPpL6OC9gun9vuhP9+BkdKtTNLwUHw87HnBvvF\n2nOpybfN46dMh3f/CUmjYNvPcMud0NT2Gnvx6cF9jQH4x9ruuBwp1aaBaTCBwQUCA+HBv8D4cSe8\n64B4hXDZjscH9SGvHnI2I72jcHUxsK82h3X5G2lRLYS5+bNq6Dn4uHpgUa28V7iJPbU5g/a8b024\nVbuhh8+AtvfM2YvP7lh1MO4ghaGFhNSEEFYVRpNbE15NXri3uLNj1A78GvyIKo8iNyKXJrcmpu+f\njpfZq8dDFwUVcWDYAYJrggmtCSUzKhMVlZn7ZmK0Gvl55M9U+VQRXxxPi2sL+WH5RJVHkZybzPaR\n27EYLITUhJAdmc2EjAkE1QWxY8QOgmuCGVo69JjprP94fa9FhXXdAFQU5QzgesADWAt8r6pq34Y/\n2boBeLKcjEbTyaKnXGDwG4D2ZOsG4Mmkp9eZrRuAJ5stG4Anmw0agPZyUhqAJ8tRDcBGYyObUjbh\nafZk+v7pqIqKQdUGo+yJ30NpYCmnZ5xOcF0w+SH5pA1JY0jJEEYWjuzx0NtGb6PWu5bp+6fj3ezN\noehD5ETkMDpvNIF1gWxJ3oJ/vT+T07XR9N+N/Y4W1xZm757N9lHb8Wr2IrY0lh2jdjDu8DhaDa3k\nheUxJW0KCsdu5x2vAajbPoCKokwD7gdeAHyAq4AsIMOecQkhhBDCOdR71oOiXQb+btx3tLq24mvy\nJTk7GZO7dj7Jw6xdcfE0ewJ0rD9a+/r2/TybO/d3N7t3e6z27WajmUb3RmLLYkmNS6U0sBTvRm98\nG335afRPjD80vtfG34notgEIjAAaVVV9W1EUT+AcoOAE9xFCCCGEAMDqYgWg2dhMYl4iZlczmdGZ\nHBh64Bc/dns/vl63K53bY8pjCK4NxuxqxrfRl4NxB4msiERVVH4a9RPNbs0E1gaSlJeEi9q34R16\nHgTyDTBKUZQn0c78RQJ/VBTFRh06hBBCCKEnXs1aXz63VjdiymMYckQbTGPyMHVsa3JrAugYiNG+\n3ooVi2LpaOi1r2/fr8m9qWP90Y/VfltRlY4zhZ5mT/xN/lT7VFPtU01CUQLpsel4NXsx9eBUjgQe\noSioqM+56bYBqKpqIfArIBy4tO22K3B12xnBwdeXWoUWC9xyO4yZBCmT4F/rOre99iZMmKHVody1\np3P9NTfC5i02CblXesoFnDef49X37cpkgst/D+Omw+mz4Iuv+rbtkSdh0myYcx7kdTlBfsHvtKlk\nbM1Zj0t/4v/6W5g5DwKjtTrAXTlibkfX+O3K1AhXXAfjZ8GkOfDl133b9ujTMHUuzFvS/XV20eWQ\nlTP4OfTluLz+Fkw+E6bOhklnwNoXO7c52HHprcbv0aYEjuThpOU8knQ5Dyctx69jVLDCiti5PDnm\nSp5IXsns4JSO+5wZPIZHk1bwYOIyhnqGdaxfNeQcRvnYeJoVJ3j/+zb6ElAfgNloJisii8PRhwEI\nqg0itlSbDP1w9GEKQgrIjchFURViyrVJ0Q8OOciG0zdQHKz19Y4p09anx6aTH5pPYUghrq2uRFRG\n4NPkQ0BdADXeNWRGZpIWm4bZaCaiIgJXa+fFWotiITUulcS8RAyqAVVRqfOsozCkEKuLtdtZwxPR\nbQOwTR5aA/BnVVVrgO8BT8DfJs/Wl1qF73yg/WPduw02fAEP/B1ytXmbeOQJ+H49/P0BeOwpbd33\nm7V5j2ZMs0nIvdJTLuC8+fRW3/doz7wAvr6w50dt/r/rb4P6huNvq62Ddz6Erd/A1ZfDC69o+7/5\nHkyZCAnDbJdXO2c9Lv2Jf9gQeO5JuPkPPbc5Wm5H1/g92rP/AF8f2PUDvPMq3PinztdZb9tq6+C9\n/8Dm9XDlMnjxX9r+b72vlSuMHzr4efTluPx6kVb3d+tG2PA5PPM87Gu7rOdAx+V4NX67GuYVzgWR\n03ko4wP+dPA1/pr2Do2WZgBmBiUS4R7A6v2vcF/621wQNY2Qtkbk0sip3JP2Bq/nb2BJpFYuMtEn\nBqtqJb3extVCnOT9PyZrDCHV2ujb4qBiIisiScpNIqg+iMRc7bJwWmwaLlYXxmaO7Tibd7Soiiji\ni+Kp86wjPSYdj2YPTss8DaPFCEBKdgohNSHkhOdQHKw9z6j8Ud0eIzsyG/8Gf4LrggEYWTASq4uV\nzMhMQmtCiaqI6nNeem8AegDpaANAACagjXwuGfRn6mutwg8/giuWaZOkhoZoheH//bG2zeCiTaHQ\nYAKjG5jNsOZhuP/eQQ/3lMkFnDuf3ur7Hu3Dj7Uav6BNAzN+HHy14fjbDAatXFdLi3aW0M1NO+P4\n+ttao9PWnPm49Cf+hHhtzjLXY3S5dqTcjlXj92j/+QSu+K12O2EYjE/RznAeb1v7xL0tLdpZQjej\nVoZw3btw0zWDn0dfj4ufr/YFC7TXf0tL57IDHZfeavz6GrpfyPpV2AQ+O/IzNW11fhutZlpUCwBT\ng0axoXwvKlDX2siO6sNMDdRGqVpVK0bFFXcXI62qBYPiwoVRM3i78Aeb5NPBid7/ni2ejM8cz9xd\nc5m9dzZjcsbgZtEqqcSUxzBr/yzm7ZrH9IPTCavpPIs6JncM83fM72iUKSgkFCdw5r4zmbdrHlPT\nphJY3znPpEeLh/Y8u+cyZ/ccxuSMwWg1dotleNFwxuSM6VgOrA9kxoEZzN09l9MyT+sYodwXeh4E\nAlAJ/Be4S1GUy4Fi4C6bPFNfaxXmF0JclxqaMTFQ2PYta819sOhC7Vv0M4/D48/AimUnfyJSPeUC\n+smna33fox1d7zc2GgqKjr/N2wuuXwVnnQfhYfCPp+GeNXDPn7TGoK05+3EZjPqkjpTbsWr8Hq2g\nqPtrKSYaCoqPv83bC/5wNcz7NYSHwvNPwL0Pwt1/tM3rrD/H5bMv4b412mXov94NY5K09Q50XHqr\n8Rvs5ktdY+fkv9GewZSaa7hn5CV4GIxsrzrERyXbOvYtN9d27FturiPIzReAtwu/586RF9JkMfNK\n7tcsjpjMt+V7abA0YVPO/v7XAV03AFVtksP/KYqSAbioqnqMDgYOZMl52g9ofSG274Bbrtf6P5SV\nw/SpcN0q+8bYV3rKBRwjH1vU9716hfYDsGmrdgYkcSRcczPU1cP5i+CCJYP3fIPNEY6LrZzM3Npr\n/P7lzsF5vKNdtbyzfvDmbZ2vs+tu1V5nS8+F8xfb5rmP59wF2k9+gTYJ9DnzTjy58El+zXWt8WtU\nDOypzaahtQkL1m77uaAQ5xnKQ4c+wFVx4U8jLqDCXMcPlQeP+/jbqw+zvVrr1xbhHsBw70g+LdnO\niti5+Bs9Sasr5H9luwYtn0Gl5/f/SaD3S8AAqKqabfPGX9dahdB7rcLYaMjrMilrQQFEH6Oj7e13\nwyNr4N0PICQE3ngVPvkcsnNslkIHPeUC+sinvb7vay9ol0KOdnS93/xCiIk68bZ2ZjPc/wj87S5Y\n+zLMnKY918NPQmP/Swz1ibMfl77GfzyOklvXGr8p0ztr/G74vvt+MVHdX0sFhRATeeJt7cxmWPN3\n+OudsPYVmDEV/vmcNkikcZDOOA3kuMTGwMTT4Yv1bcsOclza7K/L46/p73B32pv8r3Q3RhdXSo8q\n5VZuruWnqgxaVQtN1hZ2VGeS4B0BQIW5rqPPH0CImy+V5p7FsZbFzmFd/kZmBCVS12ri6axPmRg4\nnDA3G3Sbd/b3vw6cEg3Ak6KvtQqXLoZ/rtMu55WVwydfwNJF3fd56z3tw2hEgta3ob1fCoq2bGt6\nygWcP59j1fc92tLztBq/AIezYOdumDfnxNvaPfkcLL9UK0vWnpeiQEsrmFtsk5ezH5fBqE/qKLn1\nVuP3rDO677fkXPjnm9rtzGzYuRfmzj7xtnZPPQ/LLtEu0ZlMWtHajteZeXBy6etxSetSE6DqdQKs\nAAAgAElEQVS8Ar7fBMna5VWHOS5tjlXjt9na2m2fHyvTSPHTpigx4MIY3zhyG8sA2FaVwVkhY1EA\nX1dPJgQMZ1tV95oIM4MSyWwooaS5CncXY+cMdSq4u3TvhzYonP39rwPSABxMTz8GL7ysDWl/4WVt\nGWDpb7R/ugCXXQzDhsLYtmHvd94GQ4d0PkZllTbM/babtOXfXATf/QATZ2kd+Nv7qEgup0Y+qenw\n+LPaN+N5i2H6PLi07RLw9HnaeoCbroOaWm2ql4uWwzOPav1iTrQNtH/WP+2A316sLa9aAS/9S5t2\n5tILwP/YU04MCmc9Lv2J/8etMGIsPPs8vPqadrt9gI4j59Zu5oLO19mN12ivpfGz4JIr4KmHO19L\nx9sGWj+77Tvhsou05auXw8uvw/T5cMn5g/s668txefV1mDhTmwbm3Avg91d2fjFysONycfRM/p60\ngifGXIlFtfBO2wCN24cvZZhXOABbqtKobTXx96QVPJS0jIKmcjaW7wPgh4qDlJqreWLMlfxt9GX8\np3gLZV36BHobPJgTMpb/tvUZ3FSZSrJvLI8kXU5JcxX5TTYq8ebs738np+tawL+I1AJ2PHrKBaQW\nsKPS0+tMagE7LqkF7JiOqgXs7E7JWsBCCCGEEIPlLz/tZvoR7bL62YvmA7Agt4BLD+cQ0thEobcX\nryYOZ2tE2LEfQFW59FA25+UW4G82k+XnywvJozgYFABAUFMTN+5N4/TyCloVF7ZEhPLcmNGYjK4k\nVVZz6+4DBDc1szE6gqfGJoKiENVg4ukffuKGM6ZQ4tW/GhdyCVgIIYQQ4jgW5hYwvryy27qU8kpW\n702l1mhk7ZhRuFmt3PvzXiJ76Xc4v6CYK9IzyfX14R/Jo4hsaOT+n3bh1aL1s75j536mHCnjvYSh\nfB0TyfyCYq49oFXkuTL1EM0GA/+Jj+NXeYWcVqHNC3nj3lTeGTG0340/kAagEEIIIUSvohpMXHMg\ngxeSR3ZbvyRH61rxz8ThfPb/7J15fFxV3f/fd/bMksmeSSb70qQ7S4GWlrK1CLIJWJBVFNAHFFBA\n4KfiA4ogDygCioCIbAoCilpApIDIWihd0ybNvk8m+zJLZr+/P25mmrTZWtJm6Xm/XnlN5tx7z3w/\n9yz3e8+al83LhbloZJkzG0dZrB84p145/zeLSlifl82/cu1YgiFObXWS63JzRHcvuxOtPF9SyKOL\nS+nW61jT0oYxGMIYCuE0xrElJQmAuFCINc0OLMEgrxbkjvp7EyEcQIFAIBAIBIJRUEUi3LZlJ1tT\nkvhX7sgtEu1DLX0dcQaAWCucfYwWwGh4Z5xyXvvQp93txe4eGRdAuzEOtSxj8w7yWm42K5wd/PKT\nzTSaTdTGW7iqoppfLV1ARBpzmN+4iDGAAoFAIBAIBKNwbkMzuS43v1t0FJnDHLtMjxd1ZORcUdV+\nTh2VGP8C1bBJuq/nZbEpLZkkf4DaeAs37ijnnawMVLLMQx98RrLPz7aURH69dAGh0daKHS3+/TNX\nIBAIBAKB4PAg3evDGA7z8IebePrdj2LhT7/7Ea1mY+wcgLShRfNbTUq4OhJBGw7HHLloePS8tKHF\nz1vNxlhcacMWRE8d9BGWpFjLYocxjt2JVhb29rGwp4/n5hVy3c5KWsxGvn3Scla3tbOm2TFpbaIF\nUCAQCAQCgWAU3srOZNfQLF2AOzbvAOBnRy/Br1ax0tnJlbtrsHkHWVfbSEiSeGOoq/j728s5raWN\n+49YyIbsTNbnZVGyvZzv7KzkE1sqZzS24tZoeNduw6PVsiMpgYU9fVxWWUt8IEiyP8BbWRl4tXtc\nNW04zPU7KnhkyXz8GjVqWaaw38WXG1vRhSOo92NpP+EACgQCgUAgEIxCndVCndWyJ2Cz8vFBprIA\n90OL53NRTT3X7dyNw2Tk7qOX4Bhq6dubt7IzSfX5OKuhhaVdPdTHm3lsYQkerbLTyi+OWsz1ZRVc\nWNtAWFLxtj2DRxeVjIjjkup6dida2ZKaDMATC+bx/e3lXF5Vy8b0VN7ee5vPcRALQY+FWAh65jGX\ntIBYCHqmMpfymVgIeuYiFoKemRxGC0GLMYACgUAgEAgEhxnCARQIBAKBQCA4zBAOoEAgEAgEAsFh\nhnAABQKBQCAQCA4zxCSQsRE3RiAQCAQCwWxGTAIRCAQCgUAgECiIdQDHYi5MZ4c9y0DMheVGzBnK\n51xKG6Fl5jEXl4GZC1pgKJ91TrcVU4MxdW6lC8yJZW2iS9rMoWVgxjwmWgAFAoFAIBAIDjOEAygQ\nCAQCgUBwmCEcQIFAIBAIBILDDOEACgQCgUAgEBxmiEkgAoFAIBAIBFPAtVdfy6rjV5GakgrsmUyy\n/JjlXHzhxdgz7ei0OhqaGnjq2afYtmMbAF85+yus+8o6rAlWfD4fNbU1PPbkYzQ0NRw0W0ULoEAg\nEAgEAsEUIKkkNryzYZ/wwoJCevt6eeb5Z/j7a3+ndF4pd/3oLswmMwAej4e//fNvPPjIg3y66VOO\nOuIobrjuhoNqq2gBFAgEAoFAIJgCHn3iUQAuueiSEeEv/fUlgqFg7Puyo5ZRVFBEZkYmVTVVbHh3\nA3qdHqPRSFxcHGtPWcvB3qhDOIACgUAgEAgEB5Hhzl+WPYtsezbdPd0junivuPQK1p23DgBnu5Nf\n/+bXB9Um0QUsEAgEAoFAcAjIy8njvp/eRygU4qf3/pRAIBA79vqbr/Oju37EK39/BVu6jW9e8c2D\naotoARQIBAKBQCA4yCxZtIQ7f3gnwVCQW398K9W11SOOO9ocONocbNq8idNOPY2VK1ZisVhwuVwH\nxR7hAAoEAoFAIBBMAccuO5akxKTY99PXns7g4CAer4c7f3QnapWaV154BXumHXumnd1Vu3G2O7nn\nznvYun0rff19LF60mHhLPB2dHQfN+QPhAAoEAoFAIBBMCevOW8fSxUtj32+6/iac7U42vLsBnVYH\nwJWXXRk7fv+v78fZ7sTv97PuvHWYTCbcHjcfffIRf3z+jwfVVuEACgQCgUAgEEwBP/jRD8Y89twL\nz4157K577zoY5oyLmAQyXVTXwslnwNLjlM+a2um2aHJ098AFl8GRq+C4U+CSq6CzWzn22WZYsQaO\nWAXnfg06u6bX1gNhtqbLaAgtM5eLroDjToIVJ8Pas2B72XRbdODMpbTx+eDGH8CS4+CYE+G7N0+3\nRQfOLE+XI+Lz+fn8y/jFgiu4Y96FpOriRxw/P2MFfz76ZrIMydNk4exHOIDTxQ23wLe+Cds/VT6v\nv2W6LZockgTfuw62fgifvgv5ufC/P4dIBK65Hn75c9j2IaxcDj/5+XRbu//M1nQZDaFl5vLEb+DT\n9+CT/8CN34Frb5xuiw6cuZQ2P/4p6A2wfSNs+i/ccdt0W3TgzOJ0Man1XJt/Bo/Uvc7t5c/yblcZ\n38xZEzueF5dGkSmDTn//NFo5+xEO4HTQ0Qnbd8CF5yvfLzxf+T4bWsySEuGE4/d8P/ZoaG6BrTtA\nr4fjj1PCv3k5vLp+emw8UGZzuuyN0DKzsQ5rzegfANUsrYrnUtq43fDnl+AntykvugDpadNr04Ey\ny9MlXZ9Af9CD098LwLb+epZa87Go49BIar6RcypPNb09zVbOfmZprTPLaXVAZgao1cp3tRoybNDS\nOr127S+RCDz5DHz5NMX27Kw9x1KSleM9vdNn3/4yV9IFhJbZwHXfg5Ij4K57lBbB2chcSpu6BuUF\n954HYNVaOP0r8PHG6bbqwJjl6dLm68WqNVFgTAdgZdJ8AJJ1Fr6aeTwf9lTQFRiYThPnBMIBFBw4\nt/wITCb49sFdrFIgmJM8+muo3AZ3/gh+dOd0WyMIR6C+EZYuhg83wM/ugIu/CQMHbxkOwegMRgI8\nUvcal2efzN2ll2LVxuEJ+TCodRQY09nQuW26TZwTiFnA04E9ExxtEA4rb2bhMLQ5Ics+3ZZNnh/e\nBbX18NIzSvdVll3pCo7S1a2EJyVOn437y1xIlyhCy+zhkgvh+puVCVbJSROfP5OYS2mTbQeNZk+3\n6TFHQ0qSMnniqCOm17b9ZQ6ky05XEzsrmwCI1xg5M/0YSsx27IZkHlp0NQBJOgu3F1/A4w3/pszV\nOJ3mzkqEAzgdpKXCkkXw0t/g4nXK59LFkJoy3ZZNjjvvhW1l8Mqzyrg/gCOXKDPoPv5UGQf41HPw\nlbOm1879Zbany3CElpmL2w19/Xsexm/8GxITZtfLUpS5lDYpybB6JbzzHqw5WZlF29kFBfnTbdn+\nMwfSxaox0h/yIgEX2VfxTud2/uH8lH84P42d89Ciq7m/5lVafN0HzY4ecw+bSzbvE57oSmRZ1TLc\nBjdVWVX0mfsAMPqMHF11NNqIdsw4+439bCrdhCzJFDgKKGwrBGBH/g76Tf0EtAE0IQ1pfWnMa5mH\nWlbj0Xsoyy/Da/CSNJDE4vrFqGU1QXWQjxd+zNLapSR4EvZL22HhAEqSpJFlOTTddozgoQfgW9+F\nXzwACQnw+99Ot0WTo6ISfvkIFBXAmnOUsNwceOEpeOJhuPE28PshJwuenIXjmmZruoyG0DIz8Xjh\nsquUT7UKEhPh5ef3TDyYbcyltHn4frj2e/D/7gStRtGSYJ1uqw6MWZ4uF9pXMc+UiUalpmyggRdb\nP5gWO8w+M4vrFse+O5IddFu7iffEE1QH+Xze54TVYXLbc4nzx9Fn7kOW5DHjC6vC7MzfiSqiIqwO\njzjWa+7F3m3H4DfQnNZMS1oLmrCGYkcx9Rn1BDVBCtoKqLZX40xyYu+2U5VVRXpv+n47fwCSLI9t\n6GxHkqTVwLVAJfCELMuOSV/s7ZobN8Y49MbnbpteO6YCc4by6Z0dM9kmxJgitMxEomVmLuiZS1pg\nKJ91TrcVU4MxdW6lC3DJ5l9OsyFfnD8fraz9eNo5p+1zLCJF+GDxBwQ1QVaVrcKR7KDWXkuBo4B8\nZz6SLCEx/otceU45vZZeUvtSabQ1jmgBjEgRVLIyNaPD2sH2ou2k9KdwZM2RbCvcRkATYH7TfD4r\n/Yzi1mLMXjO78nexYtcKNJHR2/Pe+udbYxo0ZyeBSJJ0HHAX8HcgCNwqSdLJ02uVQCAQCASC2Ygz\nyUlAGyC9Jx1D0IA7zg1Ae2I77xz5Du8c+Q67cncRITLq9Z3WThzJDhbVL0IdUe9zPOr8AXQmKC86\nSQPKuGB7p50B4wAbF2xEE9KQ0pdCRW4FpU2lYzp/EzGXu4AXAx2yLP9FkiQjcC9wkSRJm2VZFvPH\nBQKBQCAQTJrGNGWiSW5HLqB050ZZWruUpvQmHCkOLF4LOZ05I66NSBHKc8vJ7M5EG9YS1AQBCGqC\n+DV+9CFlPL2MTFVWFY4UB+k96eR0KPGkDqRyQtkJDOoHMQ+aaUxvxOK1YBo0sbl4Mx6Dh3hPPAsb\nF6INjz3+cDhztgUQaAAGJUnKlmXZi+Ls2oB923UFAoFAIBAIxqDH0oPb6CbBlUC8V1nI3eg3ApDa\nl0pafxrpPcq6hV6DF4AIEcJSGBmZsCpMQBugNbWVjxZ9RHNaMwDNac1UZVUp50sRyvLLaEpvwt5p\nZ3H94hFdyvqQngRPAn6tn5bUFkqaS6jNrEWWZFbuXInb6KYxffKzoeeyA/gZ4AF+K0nSeiAd2AKk\nHrRfnMzei+EwfP9WWHQMLD4Gnh62OfQzf4KjVyr7g27dvif8f26Ajz45aGYD4+/xOxyvF77+bVh6\nPBx1Avxrw+SO3fcgHHMSnHwWNA1bLuaCy5TlZA42szlt9kZo2XNsurVMxv5n/wzHngjLT4JjVsOj\nT+w5Ntu1zVT7Y7YvH7K9bvTz/voPZc/fZauVz/YOJTwchu/fNqTrWHj6+T3XPPNnOHoVrDhF2QEp\nyv/cCB8dgoWjZ3O6MPEev8PJ0CfyxyNv4BL7ibEwnaTh+vyz+NXCb/LAwm9wpLUgduw823L+b8HX\nuavkYlJ0llj4rUXnka7f/0kSw4m1/rXnxsKyOrOQZIn2xHZak1txpCjTDKLdtuW55bx71Lu0Jbeh\nDqtZUrsk9hd1Foe38m0u3kx7UjsWj4UkVxLtie10WkeOeZWRKc8tp7C1EH1IjyzJePVeWlNbCWgC\n405A2Zs56wAOdfPeBPwS+KMsy18F9IAVQJIOwpS7yey9+OIrisOz41N491/w8/uhUVnriPt+Be+/\nBff/HB74tRL2/kfKOk4rV0y5uSMYa4/fvXn4MbBYYPvHyhqA370F3J7xjw244MW/wsZ34Jqvw2N/\nUM7/00tw3DIoPATLLMzmtNkboWXmaJmM/V85W9n3d+N78O4b8PDvoGzX3NA2U+2/4QdDtm8c2/Yt\n2+Ce+2H9S/D5+/D2+j1b9I3Q9cYouv49hq7lB1cXzOp0mWiP3+FISFyVu5bP+2pGhJ9pW8Zg2M9N\nu57igZpXuSb3NPQqLXEqHSuT53Nb+bO83bmd01KPAuCEpAVUuR20+/sO2G6P3kOXtQujz0hq/542\nJJPfxOI6pZWuIqeCkDpEaVMpaf37biGoQkV6X3rsz+QzKXH4TFi9ykzzPotio8vkoqygjLKCMnZn\n7x4RjyPZgYSEvVtZRqqgrQBdSEe1vZp4bzw57SO7nsdjzjqAALIs+2VZ/q8sy38bCooDyoeOTe0s\n38nuvfjXv8M3LlcWSU5NgbPPgL/9UzmmVoHPrywPodVBIAB3/wJ+9pMpNXVUxtrjd2/++k9ln19Q\nloI5cilseHf8Y2q1si1cMKi0Eup0Sovjsy8oTufBZranjdAyM7VM1v54y54lXrxepRxEv892bTPR\n/sna/shjcMN1YFNaYrDGg8EwpOsf+6nrPmXnkIPNbE4Xxt/jd2/OsR3L1v46nL6R24muSCzhnS6l\n5dXp76PO4+SI+HwiRFAhoZFU6FVawnIYs9rASSmLWd++6QvZbfKbWLtlLSt3rdxnlm96Xzord61k\nzdY1rNy1kuzO7NixRY2LWLt5LZndmfvEWdhWyNrNa2MzgAHWbl67z98JO08YcZ29286yqmUxO8w+\nM8srlnPq1lNZVrUsNpZwMszlSSBArKXvNuAiwAGM0qw1BYy39+LwxTebWyFnTwYhKwtah/ZnvPt/\n4eyvgsUMD/8SfvkwXHn5oV8gdvgev3uz956/2XZocYx/zGSE734LTjlL2Vz98YfgjrvhjtsUZ/Bg\nM5fSRmiZOVomaz/A62/C/96t7Dd7149h0QIlfLZrm4n2j2m7Y6Ttu6sgLxdOO0fpqTj3TLj1+4pz\n3tyqrGUa02VX4gW4+ydw9rohXQ8M6bpMpMskGL7Hb523fcQev67Bwdh5OXGpLInP4+6qlzg/Y2Sr\narIufsQ+wN0BF8k6C/5IiDc6NnNX6SX0Bz38ruFNLs5azcuODwnLo8/KPdyZ8w6gLMuyJEl1wFWy\nLG+ZbnvG5dyzlD9QxnVs2gzf/64ylqOzC45fDtd96+DbcTD2+L3mSuUP4MONSiU7fx78z/fA5Ybz\nz4YLzp2635tqZkraTAVCy6HnzNOVv+YWuOgK+NIamFc0/jWzRdtYzHT7w2HYWQ7rX4ZAEL5ykeLo\nXXrR+NeN0FUHm7YM6bptmK5rDr79B8o0psvwPX61kprtA/V4Qj7Cw5ZNUaPi6ty1PN7wJjL711H3\ndud23u5UxjWWmpUu0pbBbr6d+yXi1Do29laxsbdy6gTNcuZ0F3AUWZZfOujO3/C9F2HsvRez7dDU\nvOd7SwvYR9mf8dYfw313w19egZQUeP4pWP8G1DccNAnAnj1+n3lM6T7Ym733/G1uhazMiY9FCQTg\nZ/fBT38Ejz4Jq1Yov/WLB2HYG+CUMlfSBoQWmDlaJmv/cLKzYNlR8K+3hr7Pcm0z0f4xbd+rLsrO\nUrar1OuV1rAzT4fNW4fpGlaXtbQq8Y6q62fwl78qW8k9/weRLhOw09XEXZUv8uPdf+LfHdvQqjR0\nDBufl6A1ka5P4Nai83lo0dWcnnYUp6Qu5uqctQB0BwZIGTZxJFlnoTvgGvEbaknFusxVvNDyPmek\nH0WFu4WH617j/IzlaKU53+41aQ4LB/CQMHzvRRh778XzzoE/Pqd0s3Z2wfp/wXlnjzznzy8pD4ni\nQmWcRmy+iqR8P1hE9/h94ak9e/zuzXlnKfv8gvL2u2Wbsm/mRMeiPPhbuOJiZdP7qDZJgmBIeQs/\nGMyFtIkitMwcLZO1f3fVnv+7uuH9D2Gh0vU167XNRPsna/uF5yv7/sqyMi7zvQ9g8cL91HWkSJf9\nxKoxRn8ltsevP7Jnp9buoItvb3+UG3c+yY07n+TNji2821nGk03KqhKf9lZxasoSAGz6BApNNrYP\njFxJ4pz0Y3mvqwx32IdepUWWlbZEtaRCIwm3J4q4E1PJQw/AY08q0/Mfe1L5DnDe1xRnCOCSCyE/\nD5YMTeH/f7co41Ci9PQqU/ZvuVH5/rV18N8PYNkJysSK6NihqSa6x2+bU9nj9/g1cPFQF/Dxa5Rw\ngBuvg/4BZamXdVfAw/+nvD1PdAyUlsXPNsOlFyrfv3Ul/P5pZdmZiy/YMwPvYDCb00ZomblaJmP/\nU8/CslXKMjBnXgDfvmrPi9Fs1zZT7X/ofmW1gaXLlc+H7h+y/eI9tq87T3Gcoku6zC+Br186pGud\nshJCTNfNo+h6fpiur8J/P1SWkynKF+kyDhfaV3H/giv51aKrCMvh2B6/txadR74xfcLrX2vfhEmj\n51cLv8ktRefxZOMGfJE9jQfp+gSKzRl80FMOwFsd2zgt7QjuW/B1PuyuYDASODjCZiFzei/gL4TY\nC3jmIfYCnrnMNS0wN/TMJS0g9gKeqRwmewHPRsbbC1h0hgsEAoFAIBCMgTkQ5JZtuyjuH8AaCNKr\n17EhK4NnSwqxe7z8ePMOMj2DqGSZdmMcr+bn8Hpe1qhxLenq4YFPNo8Iq403c+2JyhqMK5wdXFZZ\nR7bHQ1hSUW218NjCEuqsFhb09HHztl0k+/y8Z7fx6yXzQZLI9Hh56IPPuH71cTiN+y6pMxbCARQI\nBAKBQCAYA1MoRI7bwxu5WfTptHytpoHLquvpijOwLTmRT9NSaTUbiQ8EubyylhvLKtiamoTDZBwz\nzvW5WZQlK0vvuLSKK6YLh/nR5h2oZPhjaRF2j5cvN7VyQ1kF31t1LFdVVONXq3m1IIdLq+t5z25j\nW0oSN+yo4MXivP1y/kA4gAKBQCAQCARj0mnQc/XJxxMZmiijjchct6uSggEXb+Rm8WxpIeZAkBSf\nn/PrGjGEwxMuYFOdEM/G9FR8GnUsTCXLgERAJbE1JYk+vY4vN7XiHnIQjaEQbUYjW1KSuLS6nrhQ\niDXNDizBIK8W5I7xS2MjHECBQCAQCASCMYgMWxJNkmVWtCvjULemJAOQ4/LwxH+VfZRDksRvF5XS\nNk7rH8D3tpdz0/Zyugx6nikp5N85dnwaDfcctZhbt+7k0Q8+BcBhjOPXS5RJOa/lZvPdsgpWOTto\nNJuojbfw0Ief8ePjjow5p/uDcAAFAoFAIBAIJkAbjvCDbTs5squHlwtz+ShD2fPXaYzj9uVHkeYd\n5OuVtVxeVcvnacmjOoG9eh1PlRbRYDGT6vNxVXk139teTnmiFYfJyMXV9ahlmQeWLsAcDPGt8ip+\nuLmMm1Ydw+t5WWxKSybJH6A23sKNO8p5JysDlSzz0Aefkezzsy0lkV8vXUBotHV890IsAyMQCAQC\ngUAwDqZgkHs+3cJJjnaeKSnk9wvmxY75NGq2pCbzZm4W79ltJASCHO/sAEAViaANh1FFlN1Omi1m\nXizOZ6MtlfV52WxJTUYN5Lg9FAy4KOkfoD7ezFs5dv5WmMuATsui3j7i/cryNR3GOHYnWlnY28fC\nnj6em1fIdTsraTEb+fZJy1nd1s6aZsekNIkWQIFAIBAIBIIxMIRCPPjRJvJcHjalJtNqMnJSq5M+\nvY7S3n4sgSCNFhPWQJC1Q85XXbwFgEur67m8qo7n5hXwXEkhl1XWkhAIUGONJ8nn55iOLvwqFTXW\neHxqNQGVRFG/i3U1DZiCIRICQbr1OgZ02pg92nCY63dU8MiS+fg1atSyTGG/iy83tqILR1BPcnk/\n4QAKBAKBQCAQjIE1ECTP5QHgmM5ujunsBmB7ciLvZGXwpaZWzm3wE1RJOExGHs/PYWtq8qhxNVjM\nfK2mnjXNytq8NVYLz5YU0j40g/fuo5dwWVUdl1XVEZEkticn8uT84mE7tcAl1fXsTrSyZeg3nlgw\nj+9vL+fyqlo2pqfy9t7bHo6BWAh6LMRC0DMPsRD0zGWuaYG5oWcuaQGxEPRMRSwEPWMZbyFoMQZQ\nIBAIBAKB4DBDOIACgUAgEAgEhxnCARQIBAKBQCA4zBAOoEAgEAgEAsFhhnAABQKBQCAQCA4zxCzg\nsRE3RiAQCAQCwWxmzFnAYh3AMZhDU8CBuaFnLmkBRY/QMvOYS/lsLmmBuZfP5pIWmBv5LKpl0bJF\n02zJ1LDz851jHhNdwAKBQCAQCASHGcIBFAgEAoFAIDjMEA6gQCAQCAQCwWGGcAAFAoFAIBAIDjPE\nJJCDwLVXX8uq41eRmpIKjBwYe/ra07l43cWkJKfQ2tbKU88+xcbPNo64/oJzL+DbV30bgFt+eAs7\ndu44dMZPwML5C7n6yqspyCvA6/Xy+r9f5/kXn0elUnHNldewetVqrFYrrgEXH238iMf/8DjBUHC6\nzQYOPF2McUa+8+3vsOLYFajVarZu38rDjz1MT0/PtOiI8kXy2RmnncEF516AzWbD6/Hy5ttv8tSz\nTx1yDWMxVj4rLSnl6q9fTU52DnGGOBxtDv780p/574f/nW6Tx2Q2a5lrddmB6rnq61ex5uQ1xFvi\n8Xg9lFeU88hjj9Dd0z0tOkZjrHx2+cWXc/nFl+9z/nMvPMdzLzw3DZbuy2zPZ7fffDtrTlmDLd0G\njJxAcsG5F3D1N67Glm6jsamRX//217z3/nsAmEwmfviDH3LyiSejUWv45LNPuPsXd0zysdYAACAA\nSURBVNPZpex3feTSI7ntptuYVzyPnt4eXvrrSzzx1BNTZrdoATwISCqJDe9s2Cd88cLF3HT9TQy4\nBnj094+i0+r4ye0/IcOWETsnLyePb1z+DQYHBw+lyZMiISGBu39yN9lZ2TzxxydobG7kikuu4PS1\np7P2lLVc8JULGBgY4PEnH6dvoI9zzjyH006dObPCDjRdrr3mWtaespa3//M2r/z9FZYfu5zbb7r9\nUJu/Dweq55QTT+H73/0+sizz2JOP8cIrLxAMzgwnHcbPZ9n2bCKRCH9+6c/86aU/Ybfbuf3m28nO\nyp5us0dltmuZa3XZgerp6e3hxZdf5MHfPEhlVSXHLz+eq6+8+lCbPybj5bMPPvqAe+6/J/bX3tEO\nwO6q3dNs9R5mez6TVBL/eO0f+4QvO2oZd91xF/39/dz7wL3o9Xoe/L8HybYrZfz2m2/n3LPOZf3r\n63n6+ac5efXJ3Hf3fQBYzBZ+8+BvyMzM5L5f3UddfR03XHcD55x5zpTZLRzAg8CjTzzK0396ep/w\nc886F4A/PvdHXn/zdV5+9WU0Gg1nnn4mAFqNlttuvo0PP/6QqpqqQ2jx5FhYuhCTycSWrVt4/c3X\neeXvrwBw9hlnI0nKUkPOdiebt23G6XQC4HK7ps3evTmQdDHGGVlz8hq6urt49PeP8twLz1FZXckR\nS44gJzvn0ArYiwPNZ9HWgDt+dgcb3tnAq/98dca0BMD4+ey999/j1h/fyt/X/50XX36RTZ9vQq1W\nk5ebN71Gj8Fs1zLX6rID1fPqP1/lrXffYsv2LdTW1wIQiUQOldkTMl4+a2hq4L0P3uO9D96jqbmJ\n9LR0Gpsa2bR50zRbvYfZns/uvf9eHvndI/uEX3zhxQA89OhDvPTXl/jjc39Eq9Gy7oJ1mEwmzv7y\n2bR3tHPvA/fy6BOPUrarjGOXHUtBfgFnnnEm1ngrr7z6Cn955S/ce/+9I+KcCoQDeAixZ9gB6Ojs\nABRnaXj4lZdfidlk5uHHHp4eAyegp1fp8iwsKCTDlsGRS44EIDMjk7f/8zZvbniTlStW8vTjT7Ny\nxUre+PcbvP/R+9Np8qQYL11sNhtqtTp2bMTxTPshtnRyjKfHoDeQYcvA7/dzz533sP6V9bzw9Auc\nsPKEabN3b8bLZ8OHEyRYE1gwfwE+n4/yivJpsXUi5pKW4cz2umxvJtIDcMsNt/Di0y9yyYWXUFNX\nw5NPP3noDR2D8fLZcM4/93wA/vbPvx1aAw+Q2Z7PcrNzAWhztgHQ0toSC8+yZ6HRaGLHAFodrcrx\nnNx9r3Uo105lw4NwAKcRlbTn9hcVFHHBuRfw/IvPk2BNQK/TA5CSnIIxzjhdJo6gorKC1/71GtlZ\n2TzzxDOccdoZAASCAeYVz+OkE06iorKCn9z9EzZ+tpEvf+nLfPlLX55mq/ef4ekyGtHWztnCcD1a\nrRaVSoVer2fzts3c+8C9GOOM3Pb924i3xE+jlXsYL59FSUlO4b6f3YfZZOa+X903o8ZiDWcuaRmP\n2VaXTcRodcBzLz7HHT+9gw3vbqCooIiLLrhoGiwbncnks8SERE5afRJ9fX28/Z+3p8vUL8Rsz2cq\n1fjPlvGePRNdeyAIB/AQ0tqmePfpqekApKWlxcLTUtNQqVTcfMPNPP3405SWlALKGIHVq1ZPj8Gj\n8PDvHuayqy7jxh/cyM/v/zkA9Q31nLz6ZAwGAxve3cDGzzay/l/rAVh+7PLpNHdSjJcuTqeTcDhM\nWmpa7Pz0NOW86NvaTGM8PS63iwHXAAD/eO0f/Of9/9DQ1IBOpxuhcboZK58B5OXm8dD/PYQt3cad\nP7+TjzZ+NJ2mTshc0hJlLtRlwxlPT5SGxgY+/fxTfvv4bwFlcsJMYrx8BnD2l89Gp9Wx/l/rZ9SY\n3/GY7fmssbkRgEyb0hIbbZFtbG6kpbWFUCg0YjxjRobyf2NT477XDn02NTdNmX1iFvBB4Nhlx5KU\nmBT7fvra0xkcHGT9G+tZvXI1V15+JbZ0G+vOW0coFOKNf7+Bz+/jZ7/4Weyayy+5nLycPJ7987Ns\n3b51OmSMypWXXUl7Rzt6nZ7zzz2fSCTCcy88R3FhMaCMOQkGg6w5eQ2gVJozhQNJF++gl3f/+y5r\nT1nLtVdfi9vjpqS4hO1l26e0IB4IB6IH4LU3XuOSiy7h0osupbKqksL8Qrq7u2lqmV49wxkrnxXk\nF/DAzx/AbDbz17//FZPJxEknnERDYwMNTQ3TbfaozGYtc60uOxA9RqORn9z+Ez79/FPcbjcrV6wE\noK6hbrpkjMpY+QyUlv+zTj+LQCDA+jfWT7Ol+zLb89nqlatjM5hBmfnr8Xp48eUX+dKaL3HDdTdg\nt9v5xuXfIBgK8vLfXsbj8fD6m69z7lnncvvNtzPgGmDxwsVs2ryJuvo6urq6uOG6G7jgvAtwdjg5\n9aRTAXjx5RenzG7hAB4E1p23jqWLl8a+33T9TTjbnVxxzRU89NuHuOirF3Hdt67D0ebg7vvuxtHm\nAOCDjz+IXROd6bNj547YrK2ZQIYtg3PPPBetTktTcxOPPfkYuyp2UVFZQVpaGqtXrub6a6/HNeDi\n9Tdf508v/mm6TY5xoOny2yd+iyRJnHbqaajVaj7d9CmPPLbvgN9DzYHq+dNf/oTJbOLkE05mxbEr\n2FWxi8efepxAIDDWTx1yxspna09Zi9lsBuCCr1wQO/+5F56bMU7T3sxmLXOtLjsQPXqdHo1Gw6UX\nXUqcIY7+gX7e/s/b/OGZP0yjkn0ZK58BnHrSqSQkJPDmhjfp6++bZkv3Zbbns29c8Q2OOfqY2Pe7\n7riLVkcrXzrnS9x1z11c9fWr+OEPfkhTcxM333YzzS3NgDJ5RKVSce7Z56JWq/nvh//l7l/cDcCA\na4Dvfv+73HbTbdx+8+309vXyyO8eGXW28YEiybI8ZZHNJU4757Q5cWPm4ibdc0ELzL3N4OeSFpgb\n+WwuaYG5l8/mkhaYG/ksqmX4Wn6zmZ2f7xxz0LoYAygQCAQCgUBwmCEcQIFAIBAIBILDDOEACgQC\ngUAgEBxmCAdQIBAIBAKB4DBDOIACgUAgEAgEhxnCARQIBAKBQCA4zBAOoEAgEAgEAsFhhlgIepK0\nJLfQlN7EoH4QVURFgjuB0qZS4oJxAAzqBtm4YCMhdYiMrgwWNY69hlCvuZeazBoGTAOoZBUWr4Vl\nVcsA2JG/g35TPwFtAE1IQ1pfGvNa5qGW1Xj0Hsryy/AavCQNJLG4fjFqWU1QHeTjhR+ztHYpCZ6E\ncXX0mHvYXLJ5n/BEV2LMhv3RAtBv7GdT6SZkSabAUUBhWyEAboObyuxKBkwDSLKErcfGvJZ5qGTV\nlGiJ8sGiD/DpfSPClpcvxzJoGde+vYkQoTqrmrakNsLqMFa3ldLmUsw+c0zP7uzd9Jv7UYfVZPRk\nUNxSjAoVHdYOKnMqCalC5HTkxH6j19xLWX4ZK8pXoA1rx9UxXtoY/AbaUtr2ObawYSGZ3Zn7hDsT\nndTYa/Br/ahkFaZBE8WtxSS6EwGQkWmwNdCS0oJf60cX0pHnzCOnM4c+Ux+78nbh1/qx9diY3zQf\nCQmv3stnpZ9xXMVxxAXixtUC0GvqpTqrGnecG3VYTVZXFgVtBUSkCNuKtjFgHCCkCY3Ie6PGY+6l\nPLccn86HJEsY/Uby2/JJ71O2hwqpQuzO2U2ntRNZkklyJVHaVIohaJiyfDaWFgllia2gOsjG+Rvx\n6X3j6hktjc1eMysqVjCoG+TDxR/uc43Bb+CEnSdMWbrA2GWmMruSXkvvPucfXXk0Se6kfcIBOqwd\n1GXW4TF4UEVUpPansqhBqTPGu29TpWcq62YYvb44VGkz1v2SkanOqqY9sZ2AJoA2pCWtL42SlhJU\n8r7tOMPLd0AbwDRooqSlJFb+R0t/gFVlq1BH1Owo2EG/sR+r18qSuiXoQjoiUoSN8zdS0FaArdd2\nwFokJDqsHdTYa/DqvRiCBvLa8sjqzho9ngnKf5+pj6qsKtxxbgBMPqWuS3IlTV35z+mlq6CLoDGI\nKqwirieOjJ0Z6AZ19Ob00lnUSSguhM6jI60ijfj20fdV783qxXGkY0SYxWkhZ1MOAD6zD+diJ95E\nL6qQCmurFVu5DUmWGEgfwLnYSVgdJrk+mbQqZVs8T5KHlqNaKHqvCHVIPaGW4QgHcBJ4dV4q8irQ\nhDQUtxTTa+mlI7GD6kg1S+qXICOzM28nMhOvHe3VedlSvAVVREVBWwHakJY+856V2XvNvdi77Rj8\nBprTmmlJa0ET1lDsKKY+o56gJkhBWwHV9mqcSU7s3XaqsqpI702fVEY2+8wsrlsc++5IdtBt7Sbe\no2TY/dECEFaF2Zm/E1VERVgdjoVHpAhbi7bi1/opbi2m39RPc1ozmrCGIkfRlGgZjmnQREFbQey7\nIWAY177RqM+opym9CVuPDYvHQq29lm2F2zh+1/EAbCvchl/np6i1iH5TP03pTWjCGgrbCtmdsxuT\nz4TRZ6Qus46M7gwMQQMVORWUNJdM6PzB+GmT3ptOykAKoKRRRW4FYXU4lm57o46osXfa0Qf1uIwu\nmtKbKMsvY3WZskdmva2eWnstVreVfGc+QfWevUGr7dWoI2pyOnKoz6jH1msjyZVERU4FeW15k3qQ\n+TV+thVvAxmKW4rpSFScBH1Aj63XhiasvNw4UhwTxiXJknI/AwZ8Oh91mXWU5ZeRsi0FtaymMruS\ntuQ2sjuy0Ya01GXUEcoPsaxq2ZTks/G0RB9au7N3E9RMfn/VrM4sEl3Kw1gTVqphXUg3Iv27rF20\nJbdh9ViBqUmX4YxWZgraCgh0KjvChNQhKnIqUMkqzIPmUePoNfeyvXA7+qCeotYiVBEVnjgPMPF9\nmwo9U1k3w9j1xaFIm/Hul4REU3oTZq+ZfGc+LSkttKS1YBm0kNW1r+PUktJCjb2GpIEk8px51Nhr\n2Fa0jZVlK9GFdZQ2lxJWKfoGdYPUZNWgD+jRB/TUZdbhinNR5CiiLrOOptQmitqKaEhvIM4fNynn\nbzwtSe4kdhTuwOA3UNJcQmtKKxV5FRj9xlFfMiYq/2UFZfh0PvLa8pCQqM+opyy/jBN3nDgl5T9g\nDOBY6kAVUJFekY4n2YMrw0V7uJ2kxiQcSx0Y+gyk1KbQVdhF87Jmiv9TjM6rGzPO1KpU9C49ABqf\nUv5lSab5mGaCcUHSdqcxmDBIT0EP6qCatKo02ha3oXfr0Xl0dJZ0ktCSgGZQQ9uSNjJ2Zey38weH\nQRewJElLhj7HXA17ImRJqTw0YQ1JriQsXqVlSRtSHuoNtgY8cR7ynfkTxtVgayCiijCvZR457TnY\nu+yxt2WAE3aeQJGjiKzuLAodQy1pRuXNJqQOoQ/qSR5IRiUrFVSPuYee+B6KWosmpUUX0mHrtWHr\ntZHWl4bL6EKSJXI6cvZbC0BlViWgPNCG4za48el9xHvjye3IjWlpSW2ZMi1760rpTyG9Nx1bry3m\ncI1l32g0pzYjRSQWNCwgryOP1L5UBg2DdMd30x3fzaBhkLTeNHI7clnQuABJlmhOVbb0CavCmHym\nWIUSVoept9Vj9Btjb6qT0TBW2li91tgxdUStvAX2J8daJ/cmtT+VnI4cUgZSSHIplWq0tSoiRWiw\nNaAOqzmy+kgyujPIb88np1PJAyF1iDh/HEkDynUhVQhHkoOgOkhuR+6ktPSb+wmpQyQPJJPdlU1u\nu3JdS2oLmoiGpXVLyejOmCAWhQRPAnnOvJgWKSIpWiTFtrbkNvQBPaXNpRS2FWL1WOm19OI2uKck\nn42nBZTW1o7Ejv3Kt/GeeFL7U7H12mKOvTqijqWxrdcWa9GIls2pSJfhjFZmklxJsd+PqCIgQUZ3\nBrrw6A+zuow6kGBR/SKyOrPI7sqmtLl0UvdtKvRMZd0MY9cXhyJtxrtfUQc2+htx/riY7tHotnYD\nkNueS3ZXNskDyYTUIZxJToBY3rP12ghoFYc/uyMbFSrCqjCasIbkgWQ0YQ1hdRiP3kNTWhPzm+Z/\nYS0tKS3Ikkxeex7ZXdkUOYpix0ZjvPIPxO5NkispVtdF038qyn80j6lDakydJuL6lXuvDqrpyesB\nIL0inaTGJFJqU0AFPbk948Zp7DZicVqwOqyYekwAuFPdBMwB4tviSalLIXN7JkSI/UZEE0Hv0mPs\nMca+dxV1ofPoiG8bvSFgIuZ0C6AkSccDv5Qk6UJZlpsPNB6T30RpYymV2ZV8svATACweC8WtxQzE\nDVCbUcuSuiWENKEJ43IZXQA0pTdRnluOKqIipyOHYkcxwIjm/M6EToBYpWLvtLO9cDsbF2xEF9CR\n0pfC1uKtlDaVoonsf1I6k5wEtAFs3TYMQcN+a+m0duJIdnBM5TF0WbtGHNOFdCCD1+DFbXDTE69k\n4qAmSFAdnHItveZe/nPkf1BFVKT1prGgaQE9lp4x7duboDpIUBtEF9ShlpU3qWglO2gYjFUy0ZZF\nTUSDNqQloA0QVAXJ7symPqOeZppJdCXGnMPlFcv3WwvsmzbDaUpvAohVqmPRmtJKZY7yQNMGtbEW\nDK/eS1gdRhvS8snCT/Dr/MT54ljQuIAkdxLZndlU5FTQkdiBadCEZdDCZ6WfcWT1kTEnciJ0QcVh\ncBldeHVeeixK+g8aBid/E4bRbe1mW9E2AFRhFYvqF6GOqHHFuZAlOZYuoKRRP/149d4pyWfjafFp\nfVTkVFDUWjRmK9lolOeWU55Xjj6gp9BRiL3bPuJ4j7kHl9GF1W2NvVRMRboMZ7Qyo44oeV9Gpimt\nCeTx85krTqnPKrMrcRvdaEIaCh2F5HTmTJgHpkLPVNbN49VnwzlYaTPe/crsyaTf3I8jxUFnovJc\nsHfax2yNi8bVY+nB6DPGnjtevXfEeSFViNaU1lgXLSgOf2tKK58s/ARNWENGdwYVORUUtBXsUxcd\niBZvULEhWmaj9azX4B0lJoWxyj/AkrolbC/czpZ5W4Chuq5+cewefdHyr/foydiRgXORk9qTaxXb\n+wykl6fTsLJB+c1BxeGMtvoFTOPvq964vBEk0Ll12HbZsHRYYtdE41KH1WgCGkKGEGFNmKSGJLqK\nlXxp7DIiRSR68nsofH/0IU2TYc45gJIkqWVZjrbd1wKfAF8D7pckSSXLcmR/4wyqgzTaGtGENZQ2\nlOKKc9GQ0UBFTgXuODfJLqUlpiOhA1Baf3xa36iFJSIpP68NaVlau5Qaew0NGQ0keBJI7U8FlMq3\nKqsKR4qD9J702Ftm6kAqJ5SdwKB+EPOgmcb0RixeC6ZBE5uLN+MxeIj3xLOwceGkuhwb0xoBYm+o\nu/J2TVpLRIpQnltOZncm2rA21v0V1ATxa/wYggYKHYXUZdbxycJPUEVUSBEJWZKVcUJTqCWzOxOT\nz4RKVtGQ3oAz2YkhYMCR4hjTPn1IP+H92Z9napGjiIzuDELqEBavhS3ztlDYVog7zs3Woq2E1CHS\ne9OZ1zpvUvHtnTZRBuIG6LX0YvaaSXYljxtHWl8aRr+RAeMAdZl1VGZXcszuY2J5MKgJUthaiC6k\noyKngh0FOzhxx4lkdWWRPJBMQBPAMmihPKecjO4MZEnms5LP8Ov8JA4ksqBpwajjj0B5a7d32mlN\nbeWjxR+hCSlVjSpyYJ0OVreVo6qOwmPwUGOvoTqrOva2Px5Tkc/G07I7ZzdxgThS+lMYMA0ASmvw\noG5w1G4/XUgXcxZ9Oh/V9mrKc8uxeqwjWnNHc/KnIl2ijFVmoi+inQmdDOoHSelLweQ3jRlPRKXk\nJZNP6U6uzK6kMruSRHfihHlgKvRMVd08UX02vL44WGkz3v0aMA7gTHRidVvJc+bRmtJKa2or8d74\nUbuAC9oK6LX00mhrVO7PUFzRl9sorSmthNVhZfjEUP6PH4znhLIT8Bg8mHwmOhM6iagi2HptbCtU\nxu6afCYWNC4Ys2t7f8p/tIVtPMYq/9qwlgZbAwFNgHnN89CENezO2c32gu2sKF8xJeU/pA3RVdiF\nKqgiY2cGvngfXcVdtC3Zd0z2RFp0Xh3pu9LRu/X44n10lHTQfHQzJRtKRj1/eHzpu9NJaE4goolg\nGDDQsLyBtMo0/BY/jcc2EtFGiG+Lx1Y+cRd9lDnTBRzt4h3m/AF0Au8AqyVJsh6I8wfKW9SgfjDW\nPRIdN9OZ0IlP56PL2sVHiz6iOqsagI7EDsoKygBlYkFYCsdakIx+pfnW1mMjrT8t5vRF38wiUoSy\n/DKa0puwd9pZXL94xBukPqQnwZOAX+unJbWFkuYSajNrkSWZlTtX4ja6aUxvnJQmt9FNgiuBeK/S\nfLw/WsKqMAFtQCngiz6iOU1pYG1Oa6YqqwqAAmcBJ+w4gWN2H8Oxu49FlmTi/HGxSmiqtBS2Fca6\nTfOceQC449wT2heR9ujRhrVog0rFP3xsDECcLw6jT0m36MDpkCpEUBNEG9SijSgVh8lvwuq10pbc\nRkSKkNWZRXluOan9qRxVfRSNtka6Ld0HlDZRog+f6EtBlL3zGYAhaCBlIIUCZwHmQTMDpgHcRrdS\naQ+dlteeR1ZXFrqQjqA2SEittJTEBeKweq30mfvoM/dR6CikMrsSo9/I8vLltCe240gaf/zegqYF\nrCpbxTG7j4m9kU+mlSyqJcKe4qoL60h2JZPTmUPyQDKD+kF6LD3E+eOQZAmfbs+A9uj/0bI2Ffls\nLC2DukFcRhcfL/qYnfk7ARgwDbCpZNOoWsw+ZQxXan8q2Z1K1xwSeAye2G959V46rZ3E+eNI60sb\nYcdUpAuMXWaixF5A9mr9G15mht/jzO5M0vvSSXAngLSnPpsoD3xRPVNVN0+mPjsUaTPW/XImOYmo\nI2R0Z5DWnxbrou60do7QEs1nhqCBFbtWcFz5cSwvX056b/o+9354K29O+8j6RBvWkuBJUCaf2KuZ\n3zifprQm3AY3K3euJCJFqMmsOSAtsbp0qJzGyutQ+P6U/4A6QGdCJ7qQjtyOXOzddqVcGgZj+fmL\nln9PioegKYipy4TVYSW1Wnlmu2wudB6lxS9oHHphiFM+o+GyJBNRRWLlxdRjIqUuBUuHhdSaVAwu\nA7JGJmAK7IlrKI6wOkxYG0btV8fG9+k9euL64+jL6kNWySQ2JtK6tBVLu4Xcjbl0F3bjTtlTjidi\n1rcASpIkyUMMfT8duA54G3gN+A/wFeBS4NHo+fvzG9FKrju+m+bU5li3h3nQTJ4zL+al91p6aU5T\nugALHEpFVJ5bTltKW2y2ZlZnFh2JHbSmtCLJEu2J7UiyFBsQvrl4M32WPiweC0muJNoT21FH1DFH\nEZSCW55bTmFrIfqQHlmS8eq9tKa2EtAEJvVGNVoFv7Bh4aS12LptLKldEru2PbGd9qT2ES2WDenK\nODMYGt8hsc8M3C+qxRXnotpeTcpACqqwKlZxJ7gTRsyOHc2+LcVb6LX0xmY4ZndmU5dZR3lOOfHe\neDoTOonzxSkPaRRHsCOhg8a0RvpN/ciSTHZn9gh7ApoANZk1HF19NBJKi2efuS9WARxo2oAysNqZ\n6EQX1JHRM3L83N75rCy/DNOgCUPAgMvowhXnQh1WE+eLQxvRYuux4Ux2Um2vjnVlW7yWEW/BYSlM\nRU4F85vmo5bVyJKMK85Fa0qrUqlNoKUmswZDwEBEiigVrEwsL7Ukt8S6fPxaPy3JLZj8JhLdidRn\n1FOXWRebgbk7azeaiAajz8igfpAuaxeSLGEeNKOJaLD12GhLbmN31m60YS39pn4SXYkjWtS+aD4b\nS0tEFYk5ze44N3WZdcqM6xalJW1vLbUZtQQ0AeK98fi1frqsXagiqhGOflNaE0iKkz9a9+EXTZfx\nygzAgHFAqYO8ln0G5e9dZrI6sqjIq6AprQmfzkdPfA/qsDo2OWK8PDAVeqaqbp5MfXYo0mas+xXt\nwm1Ja0Elq2hLVlqfonl873zm0XtoS26Ldf86UhyYBk2k9+wZj9yR0IFP7yO1NxVjwDiqPVXZVdi7\n7Fh8FpySM+YkD+oH0QfH70UZS4supKMpvYmG9AZFU4oy9i/q1O5P+deGtbH6qzqzGm1Yi8voQhVR\nxfIGfLHyr/coOt1pbrrzuvHH+5Vwl56khiQGMgdoL20nYAzQVdgFEUhsVJ7njqUO+rL7yNyaSWJL\nIo7FDlQhFXq3Hr/Zjy/ehzqgRu/WYxgwoPPoGMgYoKu/i8GEQVBBUsPIMhjShego7SD3k1zlGaOS\n8SZ594yJnMQzJsqsdQCjXb3DnTlJkr6G4vw9CKwAnkDp/v0XcIUkSU/Ksjx+5/woWAYtLGhYQGN6\nI1VZVagjalL6UpjXMm9E90h01pjBbxhzyYRkVzKlTaU0pDewO2c3Rp+RxXWLiR9UHgB9FmVGsMvk\nir2pGvyGEQ6gI9mBhBQbN1TQVsDO/J1U26uxeqz7vM3tjUfvocvahdFnHBFvWv+eN9qJtKhQjZjc\nMHwKvtWrVP4BTYBWm9LFEOePY37j/H0cly+qRRfUIckS9bZ6guoghqCBAkcBee15Iyro0ezbm3xn\nPkFNEGeSMqjf6rFS2lSKaqih/IjaI9ids5saew3qiJrs9ux9BpdXZVWR1ZUVq5hLm0qpyq5iwDhA\nZldmzJkci7HSBpTWCFklk92WPWEXnyakoTm1maAmiCaiDJAvdBTGWiujA/WjLyKpfamUNI/shqjP\nqMfqsca6mue1zKM8t5zajFpS+1NHXX5mOF69l+a0ZsJSGLPPTEltCYkepWKsyKvYc55BmcmZ0ZUR\nW6ZiOLqQjtaUVvxaP+qIGovXQr4zP1b2SpoUu9tS2ogQIaU/ZZ/B6l80n42nJUp04LkupCN1IHW0\naDAPmqm31cce4BavhUJHYawrLagO4kh2oAlpyOwa/f5+0XQZr8zAnheQie4JgL3bjl+ntKz0mnsx\n+8wUtxTHulgnc9++iJ6pqpsnU58dirQZ635ZPVYG9YN0JHZQkVOBNqTF3mnfSYrpwgAAIABJREFU\nx5keTntiOz6dD5WsipVv1bBOv1hX9hiTVLot3fSb+pnfqJSlnHZliahqezWmQVNsYt/+agFl3F5N\nZg27s3ejD+qZ3zh/zGfmROV/aY0ylKolTZksEy1Tw19mv0j5NwwYyNyWSXdhN+0L2lGFVZjbzdh2\n2WLjA7uKunAucqLz6MjenI3eO7pzbBgw0JPXQ09eD1JEwtRlIr0iHVVYSZfsTdm0LWqjo7QDVVhF\nUl0SKdUpI+JwLnSS2JiIwa2UscyyTJwLnfQk9JDQmIC5c/JjkaX9bAybcUiSdAOKjockSboV0Miy\nfM/QsVeBp4HPgZ8D78qy/OxkWgFPO+e02X1jhnjrn28BcNo5p02zJV+cuaQFFD1Cy8xjLuWzuaQF\n5l4+m0taYG7ks6iWRcvGXy9ytrDz851jjmaftWMAJUkqliRpM7Ac2DIUnAF0SJIUdYE/Ba6QZbkV\n2AScA7C/XcACgUAgEAgEc4lZ2wUMnAn8SZblXw0L+wQ4C6gC3gfqAMvQsReBZw6phQKBQCAQCAQz\nkBnpAO61lMvex6Ldt5lAwlCYWZZltyzLL0mSlAvcKEnSD4B5wJUAsix373W9QCAQCAQCwWHJjHQA\no86fJElaWZaDw522Yc7bq8BdkiSly7LcPnR+vizL90uStBTIlmX5tVHiFs6fQCAQCASCw5oZMQZQ\nkiTVXt9XS5L0b+DBIQdvNKdtE9AM/E6SpO9IkvQx8EMAWZa3R50/SZL2f4M8gUAgEAgEgjnMjHAA\nows0S5J0piRJRwDnAY8BeuBaSZKyho5Lw64JATcCfwCWAP9PluVrRol71K5kgUAgEAgEgsOVaekC\n3nscniRJJwI/AGyAD6iQZflVSZI6gPNR1vR7eZSWQI8sy68Drw+L64C2e5uIa3fuZlVbB6k+ZRHI\n085eC8CSrh4e+GTziHNr481ce+KKUeP5Sl0T62obsAYC+NRqaqzxPLawhIZ4ZeLy6Y0tfLWuiXTv\nIH61ivKkBB5ZVEqnMY4Vzg6+U1ZJXDjEq/k5PF+irMO0qLuXH24p45qTVuDRTrwFnDkQ5JZtuyju\nH8AaCNKr17EhK4NnSwpJH/Tx3DsfjjjfrdFw/hknjxrX2mYHP9i2a0TYx+mp3HnsEePeN2DK9ADc\n+dk2jm/vHPEbeQNurimvYlGPsrZis9nIrSuOxjtanLLMxdX1nNXYgjUQoC7ewmMLSyhPUhbHTfr/\n7d13YF1l+cDx75PkZt3snTRp0qRN0pFSWgplgwxFGcqQIavIlCUucCGIVFB/yJItw8oQUERAZVMZ\nLdA9Mpu92+w97r3v749zkiZtkrZp0kvo8/mn6cnNve97z3nf85x39vRww8Z8FjY04hIfVibE8qd5\n2XQ5/JjT1MIP128huqeXD6YlcO/82SBCUmcX9334Gdcfcxh1wSNvmTTUWOclu6WVy3OLmN7RSZDL\nTY0zmOdmzWDFtNG3/Ynt7uHKLYUs2t6Iw+OhxhnMrw+ZT3XIjvXRjqzdxq9WbwDg9wvm8nZKEhG9\nffx8zUayWlrJjwjnzkXzaQ3wx8/j4eEVq1ielc7/kvZ8u6EBY13bQ410TQG8lZzIHw6eN+a58ne7\n+dmaTRzc0ERlSDDLFuZQY+f3rpVrWBMbzUsz0/Y67Tv7yzsfktDdM+zY1ccsoSQ8dMTXO/v7eWTF\nKuK7e9gQHcmPjzgEGLs+mKzzMGDnMrOkbjvnF5UyrbMTf4+HstAQnsyeyfrYkdeuzGls5vsbcont\n7sGIUO0M5tnMGXycaK2nd1JlDd/eWkZKRyc+wI8OX8TGGGutt+SOTn66dhPJHV2sjY1i2cIc+n19\nCe3r57EPPuH2xQeRHxmx2zxMVL18UUExFxWWDDv2jxnTeWReFs7+fu5auZbkzk4cHg+NgYG8lZLE\ns5nWGnzfKKvikgJrV4ynsmfyn1RrQeMTqmo5u7iMa48+DI/P3re1jFV37mxOUwvXbC5gRns7rf7+\nvJ6azPN2+o6pqeOyvK3E9PTS7+NDWaiTJ2fPYlN05H4rLwBzG5u5PK+I9LYOuvx8eSM1mb9mpoPs\nukqJw+3h0vytHF9TR1hfH00BATw+J5MPk+J3e51enL+VM8oq6fb148GcbFYlWGtyXlBYwpzmFn5x\n2MK9TvstlbWc2NxGQr+16Pu8RXOtXxjDFXUNnNXQTGy/i61BAfwuOYE1oSNvn7i4vZOnCsuGHcsP\nCuTsOda9L7avn19U1HJ4ewcuEd4PD2XZ9EQ6fX1Z0NHFHWXVxPW7+HdUOLdPTwQRUnp6eS6/lPNm\np1Md4L9X+dpvLYBDu3mNMUZEgkQkXEROAG4H1hljDgH+BHzVfulKYBswR0QS7PcREfGxAz2z8/tP\nRvAHIAbeThl9Ec/XUpNZtjCHZQtzeGL2rFFf1+nw4x/pqfxx/hw+jY9lYUMTN2y0FsVN7OziBxvz\niOzt5c+zZ7EhOool9Q1cnmdtY3TdpnyqQ4L5ICmBiwtLSOjsws/j4caNeTw0N2uPgyWny8X0jk7+\nnZrMw3OtvWkvLCrl6xXVg6/5MDFuMD//t2DObt/zr7NmDL7+pYwdC4uO9b1NVH5OKa/i4Iam4Xns\n7+d3K1czv7GZV2ZM5+F5WZSEheI7yhDQk6pqWVpQTHloCI/OzSKxs5s7PltHcL+1Lc8tazdzWP12\nXsxI453kRE6qquWaLQUAfDeviF5fX15Jn87XK6pZ0NgMwA0b83hhVtoeBX8w9nlJ6ejCI8Jzs9J5\nNjOdaZ2d3LJuMyntI2/74+vxsGzVWo6qreedlEQezMlmfUwkDs+O/Ef19PL9Dbl0+w4fJXFGaQUZ\nre08nTWTWa3tnFFqLRh77tYyap1B4wo6dndtD7UpOnLwWlq2MIdCO6gqiLQW5B3rXH2luo7D6rez\nPCudiN5+vlNUCsAJlTWE9fXz9/TdL268p8pDnMPSWR+8697fA67flE9YX/8ux8eqDybjPAwYqcxk\ntLXRHODPM9kz+eeM6WS3tHH75xsIGSHdAC4R3ktO5IH5s3lxZhoz2jv42ZpN+LvtRZfdbtbGRlHr\n3PX6v6CwlLC+fv6amc6Suu18pboOgCtyC/kwMX6Pgj+YuHp5wJ/mZQ2+/u2UHYvWb4yO5NG5WTw6\nNwt/t5tLCoo5eHsjDreb723OZ31MFBuio7h2cz4Ot5vQvn4uzy3k3vlzxhX87UneBjj7+7njs3XE\nd3fz6NwsKkKcLC0o5qRKa9u5Ph9f/p06jXvnz+a/05OY29zKLWutTQb2V3mJ6O3lN5+tJ6Wjk8fm\nzKI8NISLC0v42pB7zlDXbs7nnJJyisNCeTBnNm9OT8LPY93ax7pOEzu7uLColPemJVLjDOK6TfkA\nJHV08q2SCu7PmT3i5+2OGHg1etdr8tyGZm6s2UZlgD93pySQ3NvPg1srCHe5xny/v8VE8uMZyfx4\nRjL3TNuxAPndpVUc19rOU/ExvBYVwRlNrdxSaZWNm6rr6fHxYXlcFOc0NHNou7V95K0VtTyRGLPX\nwR/shxbAgRa5oYGZiDiAW4FQY8x1InIW4G/P/n1eRH4jIucYY14SkXVY27gtBP4Nw7qM04FsYIUx\npnPnz55ID+VYOydcYBeQnRVFhLEqPpYev7GHHL6dkkSAy02wy0WQ281JVbUY+wFI7OCk08+PtbFR\nBLlcHF23jQ47EApyuSkPdZIfEc5p5VUEu9ycu7WUamcwHyXFj/aRu9geGMDlxx+Bx37ycngM39tS\nQHpbO2vsp6iy0BBWxcfQ47dnl8jm6Eg2R0XQt1MwMdb3NhH5Sers4uothTwyN5ObNu7YXeL00koi\n+vpZnpnOCzNn4PYR/jt92qjvc3qptR3Wg/OyqAlxEtvdw3lbyzihuo6N0ZEsaGxmS2T4YCvl0bX1\nnFhVy8Nzswh2uagNDmZtTBTfKSolyOXixMoaQvv7eSV95FX2RzLWeXl0Ttawm0FWSytH1m0nrb2T\nytBdV34/tqae1I5O3k5O5JE5mQjg9hmSf2P44fotVIQ6qQsO4qSqHRubB7ncdDj8WBMXzZklFQS7\n3Ezr6OSM0gquPWbJHudnqN1d20PVBQcNBs0RvX38aP0W2hwO3kq28j/WufLzeOj38WFNbDTH1NQT\n7HIR2tfH5XlF3HrognHfjEfSEuDPp/ExVgA9QivGgOOq6ziqdhuPzZnFdZsLhv1urPpgMs4DjF5m\nXsyYQb/vju/nkG2NzGxrJ6mri0L/XXfPyYuKoDAijJB+F9M6uzh3ayliwMd+xngtzdomMf2T1Uzr\n7B72t8EuF00BAayOi+aigmKCXG5yGppYuL2JK44fuZVuJBNVLw9YExtNfVDQsO+h0+Hg8bmZhPX2\nEd7XzykB1UT39mFE8Pd4cBhDUXgYguHY2nocHg9X5BayIilh8KFlPHaXtwFfqaojtN/FczNn8Fpa\nCuujo/jzB59wWlklb6cksSohljXuKJwuNx0OB2eVVGDsXZKCXK79Ul7mNrXidLn4ICmeN9JSqA8O\nYmFDE6eVV/Ffu8V0QGxXN1+tqKYmOIjbFh+ErzHD7itjXacuu51pS5T1vWe0WVvo3bgpn+cyZ7Bt\nDx/Gd/bb6dbDwFV1DcOOH9lqPYA/HR/Nx+GhLG7v5JTmNr7R1MpzcaPv+rTFGcQH4aF0D8lHRncP\nh3Z0sc4ZxMNJ1q5cJze3cVpjC3elJOB0e6gKcLAqLISr6xpwejyc3thCmNvN8jE+ayyTFgAO2aN3\nIFiLAy41xvzOntn7T2CZiEQD72Et6LwAWAPcBdwNvASsANqxFnUeaD1cANwAzAWunezgb098f0Mu\nP9iQS0NgAM9kZfDmGMHGxQXFnFNibbdUFxTIvfOtFraaECf35WTzvc0FPPHBSgAKw0N5Yo715Pqv\ntBQu2FoKVLI+OpJ+H+GM0kqu2cubwdBCLcZwuN0NtC5mx0X0ncISLiosoc3h4OWMVF6YNWOX9xlq\n2aq1+ADVziAemZvFp/Ejb4U11L7mx8fj4ea1m1kXE8V/UpOH3czS26yCeXRtPRcUluD2Ed6blsi9\n82ePWKlN67T2pd0eZFUQ9fa/0zq6aAywtvXZFrSjhac+OIjo3j4Surp5PTWF6zblcVTdNspDnBSH\nhXLfR5/xi8MOHgzm9sRY52VohRfR28ecplZ6fH3IHeUGk25XfFktbbz27/cQ4LO4GH538Fy6HA5O\nL6tkdnMrVx+7hIsLiof97TvJiXy1sprHP1hJp58fb6ckcuPGPJ7NTGd70OitXGPZ3bU9mtPKKvH3\neHg5PZVe+yY+1rl6OSOVc7eW8eiKVfT5+LA8M4Mrc4t4f1oCRRHjvxmPJKexmVf/8z69Pj58lBjH\nffPn7BJoxHT3cP2mPP48eyZlIwTqMHp9MBnnYawyM/QaS+7oJKWjk8YA/1HTDXDI9kbu+Gw9AD2+\nPty1cN4eBVv/Tp3Gras38OiKVTQG+LMqPoY7P13HgznZdO/hQ+ee2Jt6GeCJ9z8BoDQshPtzZpNn\nDwEJdLl4+a0Vg697YWYa6+3u7HenJXCF3ZL9dnIiM1vbWdDQxJXH7Xkguy92lAfrmhh4eBo4DnBK\nRfXgw0eLv4Nli3IA+DAxfr+Ulya7dSqjtZ3Ezq7B1ueknR4MANLaO/AFHB4Pf3trBWH9LraGhfK7\ng+dSFhY65nXq8vFhY1QEP1u7GbB6pk6qrCHI5eKfMyau9X9Ag8O6Vpe0d1IRGEBWlzUsZHrv2DvO\n3lZew6/La6h3+PFgUhyvxESSav9Nrf+Oh+LqAAexLhfJvf38LTaSX1TUcmJLO8WBAeQHBfJcfinX\nzJq+V/eZoSYtABzonhWRo4BrsJZt+amIrDXGvAPkAbnAd4GHgCOwWvnWGGMeF5GlIpJgjKnD6grG\nDhb/BAQCvzLGbJis9O+p5gB/nsy2KvfYnh6+m1vE9zfkkhsZPmLLDMAbqdNYHxPJwQ1NnF1SwWX5\nRdy+eAGhff18u7icTocfD83LJr2tnfO2lnHjxjzuWpjD07Nn8k5KIkEuF8Vhody1ai3LszJIa+/g\nN5+tw9nv4sPEeB63uw93x+H28OP1mzm4oYmXMlL5ODGO8N4+lmemUxwWitPl4rK8Ii7L30pReBhr\nRnjKqAkO4pE5mVSHBJPW1sElBcX8fM1Gzj/pmN124e5rfs4oqyS1vYOH5y0kaUhll9TZRfCQJvhf\nLz6Ib5VU8LXKGorDQ3l1DyoCYezVgnyGdCW/kZbM53HRRPX2URwWyo0bc3k3OREfY7jvw8+I7ull\nfUwk9x40B9cePFGPdF4GxHT3cOen6wjp72fZovk0jhII+NvdJcEuF79dmMOxNfUcW1vPhYUlvJyR\nxuW5RfxtVhp+xhDksrrsInr7CO3rZ2tEGBefcDQpHZ1UhjhZUr+dALeHFUkJ3PbZema1tlEV4uSP\nB83Z4+7t3V3bo30Pp5ZV0ecj/GtGyqjvPfRcNQQFculXjmRGWwd1wUFM7+hkfmMz1x19KD9et5mD\nGpppDAzggZxstkaE7VHaR/Lm9CSqnU76fXw4p9hqfdwWFMhTO3UzXrcpn/qgID6NiyG7pQ2AALeb\n+K5u6u3vbrT6YDLOw1hlpj4oELePD2ltHdz56VpcPj78+pCDdmnVHyovIpyfHraQlI5OLs3fypW5\nRWyIjqLDf+yy/1l8LBedcDQJXd2UhoVwTnE5JWGhlIc6uXvlGpI7OimMCOOeg+bSvpv3Gsne1stF\n4aHcn5PN9qBAchqb+XZxOb9cs5ELTjoGgD5fX25espCo3j4uKCzhm6UVrIqPJTcqgrsX5vCiPU6u\nyunkkRUreSAnmyNrt3FBUSm+xvBaWjJ/z0jb63yMx0h11ycJcdQ4g8lsaePCwhKu3lLATUcu3m/l\nJS8qgtdTp3FqeTXPvPcxHXaQ3zdCfThQd0X39PJAzmwi+vq4pKCYH6/fMqzle7Tr9CdHHEJGaxtd\nfn60Oxw8umIlPz9sIWcXl/ON8ircIjybmc57yYm7fPbeeiwxlsXtnSytb2RpfSNtdnDaO0pA1ujn\nx71JcRQFBZLQ188Pquu5rbyGDc7gEV/vM+RUvhQbxUdhIcS4XOQHBfKrilpeiw7H18Cz+SXE9bn4\nNMzJbdMT9+g+A5M8BlBEbsFqzXvTGPMy8Cvg+/avu4F3gZOBfqzdOxba3boYY46wg79B9mLONxlj\nvvlFCP4AKkNDeGHWDFYlxPJaWgprY6PxBaZ3WI2Sfh4PDrd7sAsMrNaQz+NjeWxuFm0OP46s205o\nXx8LGppI7OpmfUwUH0xL4NlZ1iDew+u2D/5tVYiToohwTqyqxeHx8FpqMj/ckMuq+FhuWbKQc0rK\nOXh7427T7ezvZ9mnazmupp5nsjJ4fI4VZLUG+LM8K4NPEuN4OyWJD+xJBmn2WDNfOz8DAdCW6Ej+\nkZHKp/Gx/G3WDEpDQwh0e0gc4cluJPuSn/iuHoLdbu7/6HOefu/jweNPv/cxvXZlsCo+lpUJcayw\nu5QHnop3zke1XQDjurvtf60nueqQYKpDgocdA2uChVtk8Ma7LTiI/Mhw5ja3MLepheWZGXxvcwFV\nIcFcddwSjqmt50R7TM5YRjsvAGlt7dz30WckdHVz2+IFwwJDH2NwuN342pVnTbCV5k1REXyUFM+b\n05MG8x/R20egx8MlBSU8/d7HHF23DYAr8oo4y26Javd3kBsVgRjDZXlF/PGg2XyzpILU9g4uO/5I\nfD0eLsnfutv8DBjr2hY77QNjfAZ8pbqWyL4+ViQl0BS4Y3P1sc4VQI+fH3lREXQ4HNywMY8HcrI5\nqnYbR9Vu48rjllDjDOJ7O3XF7q1nMzP4YFoCHyfG8YJ9809r79jlPMR3dzOzrZ2n3/+EW9ZZrRLZ\nLW3c8/Hng+81Wn0AE38exioz8d09zG9o4p6PP8fPY/jJ4YsGW8DAaj10uN34DDlPbQH+rImL5p/p\n01kbG01iVzcLdhpbOJrmwADyoiKI7e7lG+VVPDQvi0sKiq28Hm8FJQPX497a23p5VUIcr6el8Gl8\nLE/MyaTN4UdMTy+h9vhHjwjrYqN5NzmR19JSCHR7OK56x+2pNCyU0rBQzttaytbwUDZGR/GDDbm8\nNDON++fP5qrcomEB977YubzsXB7i7Vao6iFBRUNQIKvjYnguM53S0BCyW9oGe0n2R3kBuH/+HC48\n4ShuPHIxd9otkKVhIWDnx+G26y473c0B/ryRlsyL9pjyod/fWNepR4SiiHCqQ5xclVvI2ylJ9Pj6\ncEVeEffOn83LGan8cP2WwbGq+6LO38G35s7k7NnpnDU7gzftHplC+8Hc1xj8PZ7BseclQQE8kRjL\niohQ/hYXxcowJ75Aek8v5XYradKQMbeJff24gKoA6yGoNsCfTc5gDu7oYmFHFw8lxXFLZS3lAf6c\nOSeDk5tbOaOpdY/TP9ljAN3Av4A3RCQFawzfDSKyyBizRkT8gVTgYuB54B1jzOBULBHxs5d7GWSM\nqcULDq3fTpQ9GwusGY3dfn6kdHQS0dfH1vAwonp6WbytgV4fH7aGW09Lv121loMamwdnwC1btZZ1\nMVG0BPiT09hMWL+LbYGBtDscgwX2kG2NnFZaOdiNt3MXTHhvH0vzt3Lz4YtABF+PYV5Ty2AwM9pE\nhwGBLhd//Phz0to7+Tw2mmpnMMdV19ES4E9iZxdzmlvJiwwnyOXmpMpaPDDY1XjThlxOrqodnDF6\n/cY8evx8KQ9xMr2jk4y2dlodDqrsmWSjfW9DZ6+ONz9vpSSxZUjB/+WajQDcsWg+FaFODq/fztE1\n9VQ7g/lahRV8rbO7bXbOx2tpyWRtyOXazQWsTIjllPJqOvz8eG9aAp0OBxujIpjb1MKFBcWE9fUT\n3dvHW8mJdDl2FCGH2831G/N4YP5sev188TWGjNZ2vl5ejb/bs0/npc3h4A+frCbE5eLv6dNxulwc\nV11HWWgIZWEhnFBVy4/XbxmcKftucgKXFBSzcHsTXy+v4sjabXb+o6kLDuSORfMHP/e0skoWNDbz\naloK7+80q/iq3ELenD6NsrBQfKvqiOjr45SKapK6uocFZbsz1rWd09jMH1auGTY7FuBbJdakh7/v\nNI5yrHM11PlFJRRGhLE6LoZTyyoJcLv5ekU1GW3tdPuOv+qb0dbO5blFfB4XQ4+vD2eUWWMSt0RF\n7HIeHpuTSYg9czC1vYOLC0soC3HyuN31PVZ9MNREnYexykxyRye3rt6Ar4GXM1KZ1tnFtM4u8iPD\nqQsO4jtFpVxUWMLyzHSWZ2VwzeZ8uvz8qHEGE9/VzaHbGnCLUG7XVzNb2pjZ2jZY/g/b1kBSZxcf\nTEvYMbbYGG7cmMvTWRk0BwbgYwzTOrs4paKayN6+YS3tI5moevmXn2+gPNRJrTOYuU0thPW7qAhx\n0u7v4KsV1WS2tFEUHkqAxzMYlJaEDa+XU9o7+Xp5FdcceziCwQdYvK1h8Fz6esbOy57mrTnAf1h5\neX9aAkvzt3JKRTUNQYEcYZf1gTGYt6zdREWIk+1Bgcxoa7evf1+qd5qcM1nlZcCleVupDw4kwO3h\nzJJyPMDyzPTBlSfqggK5+MSjKQ0LZXNkBPOaWzi/sIRwOyAa6HI/ZFsDt32+ftTrdMCChiZmN7dy\n1bFLSOiyguNjauuJ6O3Hh+Gta7tzTGs7sf07QpGzGprp9PGhIDiQ0xpbKLO7f7/V0ExxYAD/tccg\nXlW7ne/VbuehxFgeSorjmpptRLlc5AUHEdPv4qjWDnpEyA0OpCbAn89DglnY0cU1NduIcLmJdbn4\nZ3QEnUNa4f09Hn5ZUcsd0xPp8fHB10B2dw9nNzQT6DG7vc8MNdkB4GfAw8AsIBmoAVzAUhG5AYgG\nfg18ZIxpBYaFrjsHf950TnE5B9mzOwF+sDGPuqBAHpuTyXlbSzmx0opLt4aH8pesjMHunZ31+lpd\nRs5+Fx0OBx8nxPJU9kwQoSQ8lHsOmsOZxeVckVtIn68vq+JieGSnLtCrthTy79RkKuyK9oGcbK7e\nUsisljb+Mz1pcCLHaML7+kmzZxAt3t7IYruFbUN0JH/JTOekylqOrN2Gn8dDVYiTB3Oyhz1hDVUa\nFsJpZVWcVlZJv48P62Oi+PPsWYPjgEb73oYGgOPNT0l46PBlN+xVHz60W/vuXJjD0vytXL8pj21B\nQTyQk83KhLgR3sm6Mcb29HBqWRUHNTRRGhbCI0NmIt+1MIfrN+Xx7eIy3OLDO9MSeWhe1rD3uKCo\nlPzIcNba6X1sTiY3bcjlosJiVsXH8k7y2DP6xjovb6UkEWJ3a59lB0ZgVaBlYbt2abX7+3Pr4oO4\nOreQ723OpzkggOWZ6bw6IwUjMvgdARxmjzUsjAgbvHkDLNjeSHZzq7WsDfBK+nRmN7dwWV4RlaFO\nlmdmjJmfoca6tmOG3OQGHLy9kfT2DtbFRO2ytMruzhVASnsHp1RUc7W97Mc7yUks3tbAxQXFbA8M\n5NH5ezZMYiSt/v64fITzi0pxuvppDAxkeWY6L2ekcULV8OfTdUOu3dYGK32tAf58bo+RHas+GDDR\n52G0MnNRQTH+dpBy6ZBxob9fMHfELuZWf387UOul19eXovBQ/jZzBpX2EhiH128ftrTKOcXlg9/J\nQAD4tcoajOyYoPXXzHRuXruZy/KKKIgM5x+7mYU6UfVyWVgIx1fXEdvdQ7efLx8lxA2OT20O8Gd+\nYzMn2WmtDwrk8dmzhk9eMIbvb8zlqeyZtNgtOQ/NzeLCwhL8jIe/ztrxveyp0fL2hwVzh72uw9/B\nrYcu4OotBVy9uYA2fwfPZGXwtt3F2eHw47SySsL6+uny82NdTBTLszKGLYc1meVlQGJXF2eUVeLw\nuKkIscrsluhI4rt27S367cJ5XL8pn/OLSunx8+Xt5EQetXtDZje37vY6dbjd3LAxj/vnz6bf15fK\n0BCemzmD08orcYkPD87L2uOJQQBL6xpY3LGjBfL28hqq/R1cNSuVE5t+JIGRAAANSElEQVTbmNbX\nT58I70aE8fuUBNyjdAEXBQVyRd12Tm+0Qp384EAeTIqjxr5mbpmRzC8qalla34Ab4bWocO5KGf5g\ne2XtdjY6g1hp1/t/SI7n9vIarqndxvvhofxrhNnKo5HJ3hlNREKNMe0iEgachxV0bgROAR43xpRN\nagLG6eTTT/5SbBn31r/eAuDk00/2ckr23ZcpL2DlR/PyxfNlus6+THmBL9919mXKC3w5rrOBvMw7\nZJ6XUzIxNq/ePOoMkf2yELSILAGWAkcB5xljNgEf2b8T0D16lVJKKaX2l/2xEHQAcD5QAiywgz9g\ncI1Ao8GfUkoppdT+M+ktgMaYBqw9ewGwF3t227+blF07lFJKKaXU6PbbXsD2Vm1mIPhTSimllFLe\nMemTQKYw/WKUUkopNZV5dxLIFDW+vVWUUkoppb7g9sckEKWUUkop9QWiAaBSSiml1AFGA0CllFJK\nqQOMBoBKKaWUUgcYDQCVUkoppQ4wGgAqpZRSSh1gNABUSimllDrAaAColJoyRETX5/wCERGnt9Mw\nkUQkQa8xdaDQAHA/E5EsETlcRBwi4uvt9OyrL0MeAERkpogcIiIB3k7LRBCRuSJyrIhEezst+0pE\njhKRi8DaS3Iq36BF5DQRuXH3r/ziE5EzgLtFJM7baZkIIvJV4BUgxdtp2VciskRELrL/9fd2evaF\niMyy62afL8v95otCA8D9SETOBF4FfgP8GbhWRMK8m6rxEZFMAGOMe6oXShE5FfgH8Hvg6YG8TVUi\ncgrwPHAT8BcRSfByksbFrvBDgEeBn4rI1TAYBE65uktETgbuAHK9nZZ9JSLHAncDrxpjtnk7PfvK\nPjd3A4nAD72cnH0iIqcDjwEnAj8CUr2bovETkW8CLwM/Be4BrvqytTp705SrRKcqEXEA5wLfNcac\ngBUIpgA3T7Ug0A6Y1ovIczC1g0AROQIr8LvEGHM80Azc4t1UjZ+IHAfcB1xujPkm0AfM82qixskY\n4zHGdADPYD0wHSEiNw38zquJ20v2dbYcuNIY87aIhItIqogEeztt47QIeMLOS5KInCQih4lIuLcT\ntrdE5ETgIeA7wCxgtogc491UjY/d4n8tcIEx5hKgDVggInEiEujd1O0dOy9XAecbY84CNgJLgR+I\nSKhXE7efTHZvhwaA+1cYVgUDVlfD64ADuGCqdGvZT1/XAd8H+kTkrzC1g0DgbmPMOvvnXwFRU7gr\nuB64yhjzmd3ydxhwnYg8KiJnT5XrbCcurIelZ4BDReQeEfmtWKZKHdYI9AOJ9o3tn8DDWC3OU/G8\nuIb8/DJwGVa98CcRifROksbNF7jYGLMFcAIFwFyYkmNOXUAQkG03LBwHXAzcC/xiirWeuYAQIAHA\nGPMkUAbEAKd6L1n7j93bcYSInCkis3b/F3tnqlSeU54xph+rCftMETnabsH4CFgPHOXVxO0FY0wn\nVmX/HFb3QuDQINCbaRunT7G6fwfGMwZgdZmE2cem1Bg6Y0yeMeZ9+7/fBR6yWwJXAmdjVZ5TzatA\nnTHmXWA1cDUQZixToiXQGFMAfAP4I7ABq/ycCvwXOAuYakHT+8AVIvIC8Lgx5nysh6cO4FCvpmwv\nGWPeNMZ8IiI+xpgW4A3gVyKSY4wx3k7f3jDGtAL3Y3WZvgU8ZYw5DXgCSAZmejF5e8XOy7PAZfZ4\nxjuBXqwhFCd6NXGTbODBQ0SOxHrwvRz44cBY6ImiAeD+9SFWobxIRI4xxriNMc8BScBB3k3anjPG\n1BhjOowxDVhN9EEDQaCILBSRbO+mcM/Z56DN/q8ALUCTMWa7iHwH+I2IBHkvheNnjLnTGPMb++en\nsYLaqTjAvRvIEpErsIK/u4DpInKVd5O1d4wxG7CCvruMMY/bXdxPYgV/072bur1jjNmE9QB4GDDD\nPlaC1ZoW68WkjdvAw4Qx5r9YY+hOnWKtzAAYY17GCpA+BNbZx94DQpl64wGfB/4DHA8EGWMuNMY8\nCsRPtaFTe8IeKjbQ8nc41jjuU40xXwfWAItE5MKJ+jy/iXojtXvGmB4ReRYwWIPas7GeaOKBWq8m\nbpyMMY32jfj3IpKPdQM43svJGhdjjAvoEJFKEfktcDJwqTGm28tJ22siIkNbL0TkLKzrrMZ7qRof\nY0yNiFQCvwSuNca8JiLHA1u9nLS9ZozJZcgkEPu8xDI1y/9/sFr9bhORcvvYwVgB+lS3Aevm+7up\n2LNhjGkWkfeAb4tIHxCIFahv9G7K9s5AK6CIPD8QoIvIxUAUMOXOy1hEJAZ4UESW2vecbOBMrN6C\nAuBFrF6co0XEz36o37fPnGIt3F8K9rT8I7Faz3qA+4aMQZuS7MH5NwMn2a0DU47d7O4A8ux/TzDG\nFHk3VfvGHst4IfAD4FxjzGYvJ2lcRCQFiDPGrLH/7zNVun9HYl9rS7Fa0c6xx59NSSKyEOvGFAA8\nPVXL/85E5EXgJ8aYMm+nZTxEJAJr/N9ZWPeZn9it0FOWiFyGVWbO/bJcZwB2QOcSkVSscoQxplCs\nJaOuBK6whymEY00m/ch+mNy3z9UA0HvsMWdTZhzTaOxB3y8CPzTGTKknzJGIyKXA51P5pjzA7lI4\nCSi2x6FNaTu3bE5VdgB4LNbYxnxvp0ft8GW5xgbYM2ZlyFCXKcsOkBzGmCnX+j8asdbRXAbcY4zJ\nFZHbgUuAE40xW8Va/uoK4CZjzP9ExHeiWqU1AFQTQkQCjTE93k7HRPiy3QCUUkp9cYnIk1i9TrcZ\nY4pF5JdYrepnG2OKROQGrB7DI4G2iWo00gBQKaWUUmo/GzqURUTuw1ry5ud2y9+vgNOx1nQsEJHp\nxpiKCf18DQCVUkoppfa/UYLAn9ktgcuAM4DFxpiuCf9sDQCVUkoppbxjhCAwBrjdnggya7ImI2oA\nqJRSSinlRTsFgY9hLXVzEdAzWWPSNQBUSimllJpkI00wHHpspyBwzkQs9TJmejQAVEoppZSafCKy\nBIgANhhjau1jQ4PAwWVeJntFCt0JRCmllFJqkonI0Vj7MpcD+SLyjjHmX/bWbzvv3hQKLBGR9yZr\nN5optcehUkoppdRUYS/6jogEA4uAi40xJwMVwHEichoM7v/rZ4xx2zt+vAO0TOZWhBoAKqWUUkpN\nAjuw+ybwAtb2j/PsX/0FqAJOEZFv2eP/XPYWfn8HfmSM+Xwy06YBoFJKKaXUJBCRHOAG4F7gSeBe\nETnKGLMN+CtQBuQbYzx2t+8rWEvAfDjpadNJIEoppZRSE0tEpgF3ApHGmDPsY98F7gK+bYx5X0Qc\nxph++3ezAacxZvX+SJ+2ACqllFJKTSARmWGMqQZWAL4icqGI+Btj/gzcCrwuIpHAwLIvYozJ21/B\nH2gLoFJKKaXUhLG7cp8HVhtjbhORy4CDgU+Al40x/SKSbIyp8mY6tQVQKaWUUmofDMz2tXUBtwHz\nReRmY8yTwFrgBOBc+zW1I/zdfqUBoFJKKaXUPrBn+x4hIvPtpVvWAbcDR4rI9caYp4BP7eMMLO8y\nmQs9744uBK2UUkopNQ4DCziLyAzgUuBkETnDGLNBRPKA14Gf2JM97vFqYneiLYBKKaWUUuNgB3+n\nY63d90vg/4DnRWSeMaYPa5mXV4GPvZfKkekkEKWUUkqpcRCRBcDTwPnGmDz72HJgOvA/4DL7d//z\nWiJHoQGgUkoppdQ42Gv33QysBOKBo7EmeBjgDaDBGPOe91I4Og0AlVJKKaXGQURCsMb+XQD8AcjH\nCgLbjDHPezFpu6UBoFJKKaXUPrAXee4TkcXAU8CNxph3vZ2usegkEKWUUkqpfeMWkUXAg8DPv+jB\nH2gLoFJKKaXUPhMRJxBnjCkdWB7G22kaiwaASimllFIHGO0CVkoppZQ6wGgAqJRSSil1gNEAUCml\nlFLqAKMBoFJKKaXUAUYDQKWU2kci8k0RMSKSvZvXXSoiSfvwOceJyOvj/XullBqgAaBSSu2784GP\n7H/Hcikw7gBQKaUmigaASim1D+ytoI4CvgucN+T4zSKySUQ2iMhdInI2cAjwrIisF5EgESkTkRj7\n9YeIyAf2z4eKyEoRWScin4hI1v7PmVLqy8zP2wlQSqkp7gzgv8aYQhFptHcDiLOPH2aM6RKRKGNM\nk4hcB/zIGLMaQERGe8984GhjjEtETgSWAWdNflaUUgcKDQCVUmrfnA/cZ//8gv1/AZ4yxnQBGGOa\n9vI9w4FnRGQWYADHBKVVKaUADQCVUmrcRCQK+AqQIyIG8MUK2F7aw7dwsWMoTuCQ43cA7xtjviUi\nacAHE5FepZQaoGMAlVJq/M4GlhtjUo0xacaYFKAUaAWWikgwDAaKAO1A6JC/LwMW2T8P7eINB6rt\nny+dnKQrpQ5kGgAqpdT4nQ+8stOxvwOJwL+A1SKyHviR/bungUcGJoEAtwP3ichqwD3kPX4H/FZE\n1qE9NUqpSSDGGG+nQSmllFJK7UfaAqiUUkopdYDRAFAppZRS6gCjAaBSSiml1AFGA0CllFJKqQOM\nBoBKKaWUUgcYDQCVUkoppQ4wGgAqpZRSSh1g/h8cPDyK/3PxLgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1pSGDjldN1hp",
        "colab_type": "text"
      },
      "source": [
        "# Réseau qui identifie les noms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUtV07asfAcE",
        "colab_type": "code",
        "outputId": "f800f337-5084-49a6-ddc6-b042df61c69a",
        "cellView": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        }
      },
      "source": [
        "#@title Le modele pour nom et son entrainement\n",
        "\n",
        "\n",
        "# Création du modèle\n",
        "import keras\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout\n",
        "\n",
        "\n",
        "\n",
        "model = Sequential()\n",
        "# input: images de 128x36x1\n",
        "# On applique des conv2D\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(128, 36, 1)))\n",
        "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(4, activation='softmax'))\n",
        "\n",
        "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.SGD(lr=0.01), metrics=['accuracy'])\n",
        "\n",
        "\n",
        "#Séparer nos données entre \"train\" et \"test\"\n",
        "X_train, Y_train, X_test, Y_test=(gerer_data(0.5,1,1))\n",
        "\n",
        "print(np.asarray(Y_train).shape)\n",
        "\n",
        "#Entrainement du modèle\n",
        "model.fit(np.asarray(X_train), np.asarray(Y_train), epochs=14, batch_size=10)\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:44: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:46: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:48: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(1000, 4)\n",
            "Epoch 1/14\n",
            "1000/1000 [==============================] - 2s 2ms/step - loss: 1.4787 - acc: 0.2490\n",
            "Epoch 2/14\n",
            "1000/1000 [==============================] - 1s 722us/step - loss: 1.3353 - acc: 0.3390\n",
            "Epoch 3/14\n",
            "1000/1000 [==============================] - 1s 707us/step - loss: 1.2125 - acc: 0.4390\n",
            "Epoch 4/14\n",
            "1000/1000 [==============================] - 1s 721us/step - loss: 1.1703 - acc: 0.4960\n",
            "Epoch 5/14\n",
            "1000/1000 [==============================] - 1s 715us/step - loss: 1.1087 - acc: 0.5380\n",
            "Epoch 6/14\n",
            "1000/1000 [==============================] - 1s 708us/step - loss: 1.0564 - acc: 0.5420\n",
            "Epoch 7/14\n",
            "1000/1000 [==============================] - 1s 707us/step - loss: 1.0271 - acc: 0.5700\n",
            "Epoch 8/14\n",
            "1000/1000 [==============================] - 1s 712us/step - loss: 0.9137 - acc: 0.6300\n",
            "Epoch 9/14\n",
            "1000/1000 [==============================] - 1s 724us/step - loss: 0.8305 - acc: 0.6570\n",
            "Epoch 10/14\n",
            "1000/1000 [==============================] - 1s 698us/step - loss: 0.8302 - acc: 0.6620\n",
            "Epoch 11/14\n",
            "1000/1000 [==============================] - 1s 731us/step - loss: 0.7836 - acc: 0.6730\n",
            "Epoch 12/14\n",
            "1000/1000 [==============================] - 1s 720us/step - loss: 0.7469 - acc: 0.7050\n",
            "Epoch 13/14\n",
            "1000/1000 [==============================] - 1s 719us/step - loss: 0.6687 - acc: 0.7300\n",
            "Epoch 14/14\n",
            "1000/1000 [==============================] - 1s 718us/step - loss: 0.6756 - acc: 0.7380\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7feffa97ffd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-wSj62sEd3T",
        "colab_type": "code",
        "cellView": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "292df748-f124-4d18-b85d-043084e5db5a"
      },
      "source": [
        "#@title générer X et Y des noms, faire prediction, adapter resultats, afficher matrice de sklearn.metrics\n",
        "\n",
        "\n",
        "\n",
        "#Calculer les resultats (Y_prediction) avec notre modèle entrainé\n",
        "Y_pred = model.predict(np.asarray(X_test))\n",
        "\n",
        "#Transformer les résultats : [0.001, 0.001, 0.9999,...] -> [0, 0, 1,...]\n",
        "Y_pred = remettre_0_ou_1_dans_Y_pred(Y_pred)\n",
        "\n",
        "#Transformer [ [0,1,0,0] , [0,0,1,0]... ] en [1,2,3,1,0...]\n",
        "Y_test_mat = adapter(Y_test)\n",
        "Y_pred_mat = adapter(Y_pred)\n",
        "\n",
        "#Afficher matrice\n",
        "from sklearn.metrics import confusion_matrix\n",
        "Matrice = confusion_matrix(Y_test_mat, Y_pred_mat)\n",
        "\n",
        "print(Matrice)\n",
        "\n",
        "\n"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[246   1  12   1]\n",
            " [  6 228   5   5]\n",
            " [ 44   5 191  12]\n",
            " [ 12   2  37 193]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUsL4x15xj_i",
        "colab_type": "code",
        "outputId": "97131bb1-c8be-4a18-8af6-ff8283fc3033",
        "cellView": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 845
        }
      },
      "source": [
        "#@title Afficher pretty_plot_confusion_matrix des speakers\n",
        "plot_confusion_matrix_from_data(Y_test_mat, Y_pred_mat, columns=[\"jackson\",\"nicolas\",\"theo\",\"yweweler\"], annot=True, cmap=\"Blue\",\n",
        "      fmt='.2f', fz=11, lw=0.5, cbar=False, figsize=[8,8], show_null_values=0, pred_val_axis='lin')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:164: MatplotlibDeprecationWarning: \n",
            "The tick1On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick1line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:165: MatplotlibDeprecationWarning: \n",
            "The tick2On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick2line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:167: MatplotlibDeprecationWarning: \n",
            "The tick1On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick1line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:168: MatplotlibDeprecationWarning: \n",
            "The tick2On function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use Tick.tick2line.set_visible instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:63: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5ylr7pltHBkVERPq6+Pp40svSKRhSwJIJSwhpc+JLkC+IbVHbKB5UTFxdHJnFmRQkFlAw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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Gh5CtCYjdcy",
        "colab_type": "text"
      },
      "source": [
        "# Faire varier le Dropout"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-3AAA_f3R_k",
        "colab_type": "code",
        "cellView": "code",
        "colab": {}
      },
      "source": [
        "#@title Création du modèle\n",
        "   \n",
        "# Création du modèle\n",
        "\n",
        "\n",
        "import keras\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout\n",
        "\n",
        "\n",
        "def creation_modele(val_dropout):\n",
        "\n",
        "  model = Sequential()\n",
        "  # input: images de 128x36x1\n",
        "  # On applique des conv2D\n",
        "  model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(128, 36, 1)))\n",
        "  model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "  model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "  model.add(Dropout(val_dropout))\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "  model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "  model.add(Dropout(val_dropout))\n",
        "\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(256, activation='relu'))\n",
        "  model.add(Dropout(val_dropout))\n",
        "  model.add(Dense(256, activation='relu'))\n",
        "  model.add(Dropout(val_dropout))\n",
        "  model.add(Dense(4, activation='softmax'))\n",
        "\n",
        "  model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.SGD(lr=0.01), metrics=['accuracy'])\n",
        "\n",
        "  return model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umciz0Sr30Bk",
        "colab_type": "code",
        "cellView": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f0e1196e-9f3b-4241-a723-613c40a27850"
      },
      "source": [
        "#@title Faire les essais\n",
        "\n",
        "#Séparer nos données entre \"train\" et \"test\"\n",
        "X_train, Y_train, X_test, Y_test=(gerer_data(0.5,1,1))\n",
        "\n",
        "\n",
        "val_dropout = 0\n",
        "Donnees_acc = []\n",
        "acc_test = []\n",
        "for i in range(11):\n",
        "  val_dropout = i/10\n",
        "  print(val_dropout)\n",
        "  modele = creation_modele(val_dropout)\n",
        "  Historique = modele.fit(np.asarray(X_train), np.asarray(Y_train), epochs=50, batch_size=10)\n",
        "  Donnees_acc.append(Historique.history['acc'])\n",
        "  acc_test.append(modele.evaluate(np.asarray(X_test), np.asarray(Y_test)))\n",
        "\n",
        "\n",
        "for i in range(len(Donnees_acc)):\n",
        "  plt.title(str(i/10))\n",
        "  plt.ylim(0,1)\n",
        "  plt.plot(Donnees_acc[i])\n",
        "  plt.show()\n",
        "\n"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:44: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:46: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:48: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.0\n",
            "Epoch 1/50\n",
            "1000/1000 [==============================] - 3s 3ms/step - loss: 1.4869 - acc: 0.2690\n",
            "Epoch 2/50\n",
            "1000/1000 [==============================] - 1s 676us/step - loss: 1.3171 - acc: 0.3670\n",
            "Epoch 3/50\n",
            "1000/1000 [==============================] - 1s 683us/step - loss: 1.1800 - acc: 0.4380\n",
            "Epoch 4/50\n",
            "1000/1000 [==============================] - 1s 693us/step - loss: 1.0371 - acc: 0.5520\n",
            "Epoch 5/50\n",
            "1000/1000 [==============================] - 1s 720us/step - loss: 0.8799 - acc: 0.6610\n",
            "Epoch 6/50\n",
            "1000/1000 [==============================] - 1s 686us/step - loss: 0.7665 - acc: 0.6930\n",
            "Epoch 7/50\n",
            "1000/1000 [==============================] - 1s 694us/step - loss: 0.6426 - acc: 0.7420\n",
            "Epoch 8/50\n",
            "1000/1000 [==============================] - 1s 687us/step - loss: 0.6236 - acc: 0.7460\n",
            "Epoch 9/50\n",
            "1000/1000 [==============================] - 1s 686us/step - loss: 0.5413 - acc: 0.7950\n",
            "Epoch 10/50\n",
            "1000/1000 [==============================] - 1s 702us/step - loss: 0.4432 - acc: 0.8370\n",
            "Epoch 11/50\n",
            "1000/1000 [==============================] - 1s 686us/step - loss: 0.3877 - acc: 0.8620\n",
            "Epoch 12/50\n",
            "1000/1000 [==============================] - 1s 691us/step - loss: 0.3249 - acc: 0.8750\n",
            "Epoch 13/50\n",
            "1000/1000 [==============================] - 1s 718us/step - loss: 0.3302 - acc: 0.8760\n",
            "Epoch 14/50\n",
            "1000/1000 [==============================] - 1s 723us/step - loss: 0.2748 - acc: 0.8940\n",
            "Epoch 15/50\n",
            "1000/1000 [==============================] - 1s 685us/step - loss: 0.2526 - acc: 0.9110\n",
            "Epoch 16/50\n",
            "1000/1000 [==============================] - 1s 687us/step - loss: 0.2204 - acc: 0.9330\n",
            "Epoch 17/50\n",
            "1000/1000 [==============================] - 1s 693us/step - loss: 0.1437 - acc: 0.9490\n",
            "Epoch 18/50\n",
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            "0.1\n",
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            "0.2\n",
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            "0.3\n",
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            "0.4\n",
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            "0.5\n",
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            "0.6\n",
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            "0.7\n",
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            "0.8\n",
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            "1000/1000 [==============================] - 1s 1ms/step\n",
            "0.9\n",
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            "1.0\n",
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            "1000/1000 [==============================] - 1s 692us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 32/50\n",
            "1000/1000 [==============================] - 1s 681us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 33/50\n",
            "1000/1000 [==============================] - 1s 695us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 34/50\n",
            "1000/1000 [==============================] - 1s 689us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 35/50\n",
            "1000/1000 [==============================] - 1s 692us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 36/50\n",
            "1000/1000 [==============================] - 1s 685us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 37/50\n",
            "1000/1000 [==============================] - 1s 694us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 38/50\n",
            "1000/1000 [==============================] - 1s 695us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 39/50\n",
            "1000/1000 [==============================] - 1s 685us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 40/50\n",
            "1000/1000 [==============================] - 1s 693us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 41/50\n",
            "1000/1000 [==============================] - 1s 692us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 42/50\n",
            "1000/1000 [==============================] - 1s 684us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 43/50\n",
            "1000/1000 [==============================] - 1s 690us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 44/50\n",
            "1000/1000 [==============================] - 1s 703us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 45/50\n",
            "1000/1000 [==============================] - 1s 678us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 46/50\n",
            "1000/1000 [==============================] - 1s 689us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 47/50\n",
            "1000/1000 [==============================] - 1s 689us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 48/50\n",
            "1000/1000 [==============================] - 1s 679us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 49/50\n",
            "1000/1000 [==============================] - 1s 682us/step - loss: 11.8790 - acc: 0.2630\n",
            "Epoch 50/50\n",
            "1000/1000 [==============================] - 1s 692us/step - loss: 11.8790 - acc: 0.2630\n",
            "1000/1000 [==============================] - 2s 2ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kIBToIiIBoUAXEQkIBbqISEAo0EVEAkKBLiISEAp0EZGAUKCLiASEAl1EJCAU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\n6CIiAaFAFxEJCAW6iEhA/H81ONBGXxEbuwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fAznATzt73jn3hHMuxzmXk5GREcy3FhGJeIHcsagIGNpuO9u/7xRmdhXwj8Bl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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BJXTekviUc26jmd0LFDrnFgG3AY+b2a10XiD9snNdf00l0jnn+PHrmxmUFMcvr51GSkLn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\niEf8L+YnK1VKLoGWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TPBm4Nsnn+p98JK/z1XaGPsxtCFr21SRPBej+PLLC8yyJJCfRi/lfV9Xfd7vH4tgBquob\nwPXA84HTu9tpQJuv9/OBHUnuoHcJ9UXAH9L+cVNVh7s/j9D7D/i5LPJ1vtqCPsxtCFrWf4uFS4F/\nWMFZlkR3/fQ9wMGqemffU00fe5KJ7sycJI8HXkrv/YPr6d1OAxo87qp6Y1VtrKrN9P59/mhV/QyN\nH3eSk5Oc8tBj4MeBz7LI1/mq+6ZokovoXXN76DYEb13hkZZEkiuBC+jdTvOrwFuADwP7gKcBdwKv\nrKq5b5yuakleCHwc+Az/d031TfSuozd77EmeTe9NsDX0TrT2VdUVSX6A3pnrGcDNwCVV9b2Vm3Tp\ndJdcfq2qXtb6cXfHd3W3uRb4YFW9NcmZLOJ1vuqCLkma32q75CJJWoBBl6RGGHRJaoRBl6RGGHRJ\naoRBl6RGGHRJasT/ALug+jgbTvfKAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qkRzL5p37qKh0Bz23qXooRnPQRbxLgV6HT1fnMK5fB4amtp6VoL7omRRHaUUl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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dos65hcDCk/Y92uj1FUGuS0REzlCnvFNUREROpUAXEfEIBbqIiEco0EVEPEKB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\nFxHxCAW6iIhHKNBFRDxCgS4i4hH/DUcOp3KuBXcPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LLVt3I3Rdai1fwIbHFw+fC/ktF0kqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCtFV\noEfEgYg4HBFHIuKeRZb/ZEQ8ERGfjYiPRMSre1+qJOlylg30iKgB9wG3APuBOyNi/4JmnwJGM/Nr\ngQ8Cv97rQiVJl9fNCP0m4EhmPpWZDeBB4PbOBpn5aGZOVpOfAPb0tkxJ0nK6CfTdwLMd00ereUt5\nF/CPiy2IiLsiYiwixk6ePNl9lZKkZfX0Q9GIeDswCvzGYssz8/7MHM3M0Z07d/Zy05K04XVzc65j\nQOeNe/dU8y4SEd8O/DzwrZk505vyJEnd6maE/hiwLyJuiIgB4A7gYGeDiHgd8IfAbZl5ovdlSpKW\ns2ygZ2YTuBt4BHgSeCgzD0XEvRFxW9XsN4AtwF9FxKcj4uASq5MkrZKu7oeemQ8DDy+Y996Ox9/e\n47okSVfIX4pKUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmF\nMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgD\nXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmF6CrQ\nI+JARByOiCMRcc8iywcj4i+r5Z+MiL29LlSSdHnLBnpE1ID7gFuA/cCdEbF/QbN3AS9l5pcDvwX8\nWq8LlSRdXjcj9JuAI5n5VGY2gAeB2xe0uR340+rxB4E3R0T0rkxJ0nLqXbTZDTzbMX0U+Mal2mRm\nMyLGgWuAFzobRcRdwF3V5NmIOPxyigZ2LFz3BrFR+w0bt+/2e2Pppt+vXmpBN4HeM5l5P3D/StcT\nEWOZOdqDktaVjdpv2Lh9t98by0r73c0ll2PA9R3Te6p5i7aJiDqwDTj1couSJF25bgL9MWBfRNwQ\nEQPAHcDBBW0OAu+sHn8v8NHMzN6VKUlazrKXXKpr4ncDjwA14H2ZeSgi7gXGMvMg8CfA+yPiCPAi\n7dBfTSu+bLNObdR+w8btu/3eWFbU73AgLUll8JeiklQIA12SCrHuAn252xCUIiLeFxEnIuLzHfNe\nGREfjoj/qv6+Yi1rXA0RcX1EPBoRT0TEoYh4dzW/6L5HxFBE/HtEfKbq9y9X82+obqdxpLq9xsBa\n17oaIqIWEZ+KiL+vpovvd0Q8HRGfi4hPR8RYNW9Fx/m6CvQub0NQigeAAwvm3QN8JDP3AR+ppkvT\nBN6TmfuB1wM/Vj3Hpfd9BnhTZn4d8FrgQES8nvZtNH6ruq3GS7Rvs1GidwNPdkxvlH5/W2a+tuO7\n5ys6ztdVoNPdbQiKkJkfo/2NoU6dt1j4U+CtV7WoqyAzn8vM/6gen6H9It9N4X3PtrPVZH/1L4E3\n0b6dBhTYb4CI2AN8F/DH1XSwAfq9hBUd5+st0Be7DcHuNaplLVyXmc9Vj48D161lMautumvn64BP\nsgH6Xl12+DRwAvgw8N/A6cxsVk1KPd5/G/gZoFVNX8PG6HcC/xQRj1e3RYEVHudX9af/6p3MzIgo\n9junEbEF+GvgJzJzovNeb6X2PTPngNdGxHbgQ8CNa1zSqouItwAnMvPxiLh5reu5yt6Ymcci4lrg\nwxHxn50LX85xvt5G6N3chqBkz0fEqwCqvyfWuJ5VERH9tMP8zzPzb6rZG6LvAJl5GngU+CZge3U7\nDSjzeH8DcFtEPE37EuqbgN+h/H6Tmceqvydon8BvYoXH+XoL9G5uQ1CyzlssvBP4uzWsZVVU10//\nBHgyM3+zY1HRfY+IndXInIgYBr6D9ucHj9K+nQYU2O/M/NnM3JOZe2m/nj+amd9P4f2OiM0RsfX8\nY+A7gc+zwuN83f1SNCJupX3N7fxtCH51jUtaFRHxAeBm2rfTfB74ReBvgYeALwGeAd6WmQs/OF3X\nIuKNwMeBz3HhmurP0b6OXmzfI+JraX8IVqM90HooM++NiC+lPXJ9JfAp4O2ZObN2la6e6pLLT2Xm\nW0rvd9W/D1WTdeAvMvNXI+IaVnCcr7tAlyQtbr1dcpEkLcFAl6RCGOiSVAgDXZIKYaBLUiEMdEkq\nhIEuSYX4fy4pS52E2OyIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jKtDjvAxBKB4EpjVYNwt4xd0zgVdiy6GpBb7v7mOA04DvxP7Gofe9GjjL3ccD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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/q6r6RLd7SawdoKq+DewBfhJY3d1OA9p8v78S2JTka/Quob4a+CPaXzdVdbD78xC9v8Av\nY47v88UW9GFuQ9Cy/lssXAv83QLOZV50108/CDxQVe/vO9T02pOMdWfmJPkh4HX0Pj/YQ+92GtDg\nuqvqrVW1tqrOp/ff82er6pdofN1JViZZdewx8LPAl5jj+3zR/VI0yVX0rrkduw3Buxd4SvMiyceA\nK+jdTvNR4O3Ap4CdwLnAQ8Abq2rqB6eLWpJXAf8CfJH/u6b6u/Suoze79iQX0fsQbBm9E62dVXVz\nkpfQO3M9E7gHuKaqnl64mc6f7pLLb1XV61tfd7e+T3aby4GPVtW7k5zFHN7niy7okqTpLbZLLpKk\nGRh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRvwv2D7N1h8AZdQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNXSmZgXUjtC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "4b8869b9-3f3d-4b1f-a43b-d96eeb91f2fc"
      },
      "source": [
        "for i in acc_test:\n",
        "  print(i[1])"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.947\n",
            "0.958\n",
            "0.958\n",
            "0.957\n",
            "0.254\n",
            "0.864\n",
            "0.389\n",
            "0.237\n",
            "0.263\n",
            "0.254\n",
            "0.237\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xJVrX1MFP9O8"
      },
      "source": [
        "# Chargements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IvSjU3GyD88c",
        "colab_type": "code",
        "outputId": "a1be472a-604f-4180-e3d6-26e0bf4db203",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        }
      },
      "source": [
        "#@title CHARGER LE DRIVE\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "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&response_type=code&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\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APUc23RT19iO",
        "colab_type": "code",
        "outputId": "087b34a0-4818-4aa0-af36-ee1f959e42f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#@title CHARGER LE MODELE\n",
        "\n",
        "from keras.models import load_model\n",
        "model = load_model('drive/My Drive/Colab Notebooks/modele 1') #modele nom\n",
        "\n",
        "from keras.utils import plot_model\n",
        "plot_model(model, to_file='model.png',show_shapes=False, show_layer_names=False,expand_nested=True )"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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lZUy3MvmxwsxDxl1evnw5Pj6e6VYwgPB6XqYQBKFUKpl9p/IkAI+X6IBdogN2iQ7YJTpg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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1W5_au68f67",
        "colab_type": "code",
        "outputId": "159aea74-09b2-4554-e577-1bde6af1e6da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "def mat_conf(cm): \n",
        "  df_cm = pd.DataFrame(cm, index = [i for i in [\"jackson\",\"nicolas\",\"theo\",\"yweweler\"]],\n",
        "                        columns = [i for i in [\"jackson\",\"nicolas\",\"theo\",\"yweweler\"]])\n",
        "# df_cm = pd.DataFrame(cm, index = [i for i in range(0,10)],\n",
        "#                      columns = [i for i in range(0,10)])\n",
        "  plt.figure(figsize = (10,7))\n",
        "  return sn.heatmap(df_cm, annot=True,fmt='g')\n",
        "\n",
        "from sklearn import metrics\n",
        "cm = confusion_matrix(Y_test_mat,Y_pred_mat)\n",
        "mat_conf(cm)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feffb9d3d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pR5IkFUM9rfxazjomkiRJncIeE0mSCq5R1jGRJEkFUD9piaUcSZJUQ+wxkSSp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\nngAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}