Model_Training.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gi6fjnJ4ZCIQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "train = pd.read_csv('/content/drive/MyDrive/580project/train.csv')\n",
        "test = pd.read_csv('/content/drive/MyDrive/580project/test.csv')\n",
        "\n",
        "train = train[~train['Summary'].isnull()]\n",
        "test = test[~test['Summary'].isnull()]\n",
        "train = train.sample(30000)\n",
        "test = test.sample(1000)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_count = []\n",
        "summary_count = []\n",
        "for sent in train['Text']:\n",
        "    text_count.append(len(sent.split()))\n",
        "for sent in train['Summary']:\n",
        "    summary_count.append(len(sent.split()))"
      ],
      "metadata": {
        "id": "5_LVRo0AYcuX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import rcParams\n",
        "\n",
        "# Plot parameters\n",
        "rcParams['figure.dpi'] = 300\n",
        "rcParams['font.family'] = \"Serif\"\n",
        "rcParams['axes.labelsize'] = 14\n",
        "rcParams['xtick.labelsize'] = 12\n",
        "rcParams['ytick.labelsize'] = 12\n",
        "rcParams['figure.facecolor'] = '#E5E5E5'\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.hist(text_count, bins=100, color='g', label='Text')\n",
        "plt.hist(summary_count, bins=100, color='b', label='Summary')\n",
        "plt.yticks(np.arange(0,600000,100000), np.arange(0,600,100))\n",
        "plt.xticks(np.arange(0,1001,100), np.arange(0,1001,100))\n",
        "plt.xlim(0,1000)\n",
        "plt.ylabel('Frequency/1K')\n",
        "plt.xlabel('Words')\n",
        "plt.legend()\n",
        "plt.title('The Distribution of the Document Length',\n",
        "          fontsize = 16, fontweight = 'bold')\n",
        "plt.legend(fancybox=True,framealpha=1, shadow=True, borderpad=1,\n",
        "           fontsize=12)\n",
        "plt.savefig('/content/drive/MyDrive/Distribution.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "RvMZqybIb8IY",
        "outputId": "f973f7bc-1b44-475c-dd91-819454b17186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_text_len= 150\n",
        "max_summary_len=50"
      ],
      "metadata": {
        "id": "Y1EKKSZCsMV_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "cleaned_text =np.array(train['Text'])\n",
        "cleaned_summary = np.array(train['Summary'])\n",
        "\n",
        "short_text=[]\n",
        "short_summary=[]\n",
        "\n",
        "for i in range(len(cleaned_text)):\n",
        "    if(len(cleaned_summary[i].split())<=max_summary_len and len(cleaned_text[i].split())<=max_text_len):\n",
        "        short_text.append(cleaned_text[i])\n",
        "        short_summary.append(cleaned_summary[i])\n",
        "        \n",
        "post_pre=pd.DataFrame({'text':short_text,'summary':short_summary})"
      ],
      "metadata": {
        "id": "izs3vLm-tSVX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "post_pre.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuo9b2Q_7dt7",
        "outputId": "96a7ef08-6bea-48ea-a6c2-acbe75d31744"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17334, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post_pre['summary'] = post_pre['summary'].apply(lambda x : 'sostok '+ x + ' eostok')"
      ],
      "metadata": {
        "id": "81lZKmbot02e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_tr = post_pre['text']\n",
        "y_tr = post_pre['summary']"
      ],
      "metadata": {
        "id": "7Xrypf83ugHg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.text import Tokenizer \n",
        "from keras.utils import pad_sequences\n",
        "\n",
        "#prepare a tokenizer for reviews on training data\n",
        "y_tokenizer = Tokenizer() \n",
        "y_tokenizer.fit_on_texts(list(x_tr))\n",
        "thresh=4\n",
        "\n",
        "cnt=0\n",
        "tot_cnt=0\n",
        "freq=0\n",
        "tot_freq=0\n",
        "\n",
        "for key,value in y_tokenizer.word_counts.items():\n",
        "    tot_cnt=tot_cnt+1\n",
        "    tot_freq=tot_freq+value\n",
        "    if(value<thresh):\n",
        "        cnt=cnt+1\n",
        "        freq=freq+value\n",
        "ymax = tot_cnt-cnt\n",
        "ymax"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPkBk6tfcyo_",
        "outputId": "4e92d6a3-5691-4eb9-9a8a-8745a84dcc9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8133"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.text import Tokenizer \n",
        "from keras.utils import pad_sequences\n",
        "x_tokenizer = Tokenizer(num_words=8140) # 196053\n",
        "x_tokenizer.fit_on_texts(list(x_tr))\n",
        "\n",
        "#convert text sequences into integer sequences (i.e one-hot encodeing all the words)\n",
        "x_tr_seq    =   x_tokenizer.texts_to_sequences(x_tr) \n",
        "\n",
        "#padding zero upto maximum length\n",
        "x_tr  =  pad_sequences(x_tr_seq,  maxlen=max_text_len, padding='post')\n",
        "\n",
        "#size of vocabulary ( +1 for padding token)\n",
        "x_voc   =  x_tokenizer.num_words + 1\n",
        "x_voc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_IW7y3qvBE6",
        "outputId": "0d134a6b-3bd8-4335-d65d-59ee82d154aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8141"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_tokenizer = Tokenizer(num_words=1330) # 49147 \n",
        "y_tokenizer.fit_on_texts(list(y_tr))\n",
        "\n",
        "#convert text sequences into integer sequences (i.e one hot encode the text in Y)\n",
        "y_tr_seq   =   y_tokenizer.texts_to_sequences(y_tr) \n",
        "\n",
        "#padding zero upto maximum length\n",
        "y_tr   =   pad_sequences(y_tr_seq, maxlen=max_summary_len, padding='post')\n",
        "\n",
        "#size of vocabulary\n",
        "y_voc  =   y_tokenizer.num_words +1\n",
        "y_voc"
      ],
      "metadata": {
        "id": "7la2yXKobwJt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75b6b945-ef6b-4dc6-f1f0-b36248ed5a31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1331"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, LSTM, Embedding, Dense, TimeDistributed\n",
        "from keras.models import Model\n",
        "from keras.utils.vis_utils import plot_model\n",
        "\n",
        "latent_dim = 300\n",
        "embedding_dim=200\n",
        "\n",
        "# Encoder\n",
        "encoder_inputs = Input(shape=(max_text_len,))\n",
        "\n",
        "#embedding layer\n",
        "enc_emb =  Embedding(x_voc, embedding_dim,trainable=True)(encoder_inputs)\n",
        "\n",
        "#encoder lstm 1\n",
        "encoder_lstm1 = LSTM(latent_dim,return_sequences=True,return_state=True,dropout=0.4,recurrent_dropout=0.4)\n",
        "encoder_output1, state_h1, state_c1 = encoder_lstm1(enc_emb)\n",
        "\n",
        "#encoder lstm 2\n",
        "encoder_lstm2 = LSTM(latent_dim,return_sequences=True,return_state=True,dropout=0.4,recurrent_dropout=0.4)\n",
        "encoder_output2, state_h2, state_c2 = encoder_lstm2(encoder_output1)\n",
        "\n",
        "#encoder lstm 3\n",
        "encoder_lstm3=LSTM(latent_dim, return_state=True, return_sequences=True,dropout=0.4,recurrent_dropout=0.4)\n",
        "encoder_outputs, state_h, state_c= encoder_lstm3(encoder_output2)\n",
        "\n",
        "# Set up the decoder, using `encoder_states` as initial state.\n",
        "decoder_inputs = Input(shape=(None,))\n",
        "\n",
        "#embedding layer\n",
        "dec_emb_layer = Embedding(y_voc, embedding_dim,trainable=True)\n",
        "dec_emb = dec_emb_layer(decoder_inputs)\n",
        "\n",
        "decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True,dropout=0.4,recurrent_dropout=0.2)\n",
        "decoder_outputs,decoder_fwd_state, decoder_back_state = decoder_lstm(dec_emb,initial_state=[state_h, state_c])\n",
        "\n",
        "#dense layer\n",
        "decoder_dense =  TimeDistributed(Dense(y_voc, activation='softmax'))\n",
        "decoder_outputs = decoder_dense(decoder_outputs)\n",
        "\n",
        "# Define the model \n",
        "model = Model([encoder_inputs, decoder_inputs], decoder_outputs)\n",
        "\n",
        "plot_model(\n",
        "    model,\n",
        "    to_file='./encoder_decoder.png',\n",
        "    show_shapes=True,\n",
        "    show_layer_names=True,\n",
        "    rankdir='TB',\n",
        "    expand_nested=False,\n",
        "    dpi=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "kK_S7JGfeA8Q",
        "outputId": "f9e38809-44fb-4c75-9fad-7b09464ebedd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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vr69EIjE2Nh47duzly5f1G3gnVCrVnj17PD09n62Ki4vj/Ldx48apNygoKJgxY4ZIJLKzs4uMjGxvb2eqTp48uXPnTqVSyZRkZ2cz/VhZWbF3RgAAMMgh2za86dOn//Of//T19SWE3Lx5c9OmTXoOICgo6Pbt2y4uLmZmZo8ePTpy5IieA4D+GzJkSF5e3s2bN1NTU5nCLVu2JCUlbdy4kXmLLS0tjxw5kpuby7Q5c+bM8ePH582bV1xcPHnyZEPE/h8lJSWvvPLKmjVrWlpaentscXGxr6+vj49PTU1NVlbWZ599FhYWxtT6+fkJBAIfH5+6ujq6xN/f//79+xcuXJgzZ47OTgAAAOAZyLYHqdbW1k4vH4JuZ0Zv8ywUCt96662RI0fy+Xy6ZMeOHRkZGceOHZNIJEyzpKQkIyOj0NDQ+vp6PUTVK9euXduwYUNYWNjEiRO7anP48GFKzY0bN5iq2NhYW1vbrVu3isViDw+PyMjIzz///LfffmMarFq1asKECXPmzFEoFIQQDodjb2/v5eU1YsQIVs8LAAAGOWTbg1Rqamp1dbWhoxiIdDszhprn0tLSzZs3b926VSAQqJd7enpGRERUVlauW7dO/1F1b8KECZmZmYsXL2b+YNCeQqHIzc319vbmcDh0yezZsymKysnJUW8WExNz9erVxMRE3UQMAACgBWTbA1FKSopYLBaJRDk5ObNnz5ZKpQ4ODunp6UyDpKQkgUAwdOjQ5cuX29nZCQQCT0/Pixcv0rXh4eEmJia2trb0yxUrVojFYg6HU1tbS5dERESsXbu2rKyMw+G4urpqGdX333/v5uZmZmYmEAjc3d2/+eYbunzp0qX04lcXF5crV64QQt5//32RSGRmZnby5ElCiFKpjI6OlslkQqFw/PjxR48eJYR88sknIpFIIpFUV1evXbvW3t7+5s2bOpg7QiiK2r1795gxY/h8voWFRUBAgPoFzu4nR2Nmup/n3vZGCDl9+rRUKt2+fbtOzrQbSUlJFEX5+fk9WxUXFzdy5MhDhw6dPXu2q8O7mcMeP5yki3ecVbdv325qapLJZEyJi4sLIaSoqEi9mYWFhbe3d2JiIkVRbIcEAADwLxSwafHixd7e3tq0fPPNNwkhT58+pV9+9NFHhJBvv/22vr6+urray8tLLBZ3dHQw7UNDQ8Vi8a+//trW1lZcXDxt2jSJRFJRUcGMa2NjwzROSEgghNTU1DAlQUFBLi4u6gHQ67a7ifD48eMxMTFPnjx5/Pjx9OnTLS0t1XszNjaurKxkShYtWnTy5En6v9etW8fn80+cOPH06dONGzcaGRldunSJOcdVq1bt3bt3/vz5//znP7ufog0bNjg7O3ffhqKo6OhoExOTw4cP19XVFRUVTZ482crK6tGjR0yD7idHY2a6n+fe9vb1119LJJJt27b1eBY0ExOTffv29dgsNDTU3t5evcTZ2dnNzU2jmYuLy507dyiK+uGHH4yMjBwdHZuamiiKysvL8/f3V2/Z/Rz2+OHs6h3X0ssvvzxhwoRny2NjYx0cHMzNzXk8nqOjo7+//08//URXnT9/nhCSkJCg3l4oFPr4+Gh0EhUVRQi5cuUKU7Jq1Sr1D3M3tP86AwAAMHBte0Dz9PSUSqXW1tbBwcHNzc0VFRXqtVwul7766ObmlpKS0tjYmJaWxl4wf/jDH7Zs2WJhYTFkyBA/P7/Hjx/X1NTQVWFhYUqlkhm9oaHh0qVL9M1nbW1tKSkpgYGBQUFB5ubmmzZt4vF46nHu2LFj5cqVmZmZo0eP7n+Qra2tu3fvnj9/fkhIiJmZmbu7+/79+2traw8cONDnPnU4z3Pnzm1oaNi8eXOfg9FGc3PznTt36Iu7nfLw8Fi9enV5efmGDRuerdVyDrv6cPb4jvfZe++9d/LkyXv37jU1NaWnp1dUVHh7excXFxNC6O1HjI2N1dvzeLzW1laNTuhV2tevX+9/PAAAANpAtv18MDExIYTI5fKuGkydOlUkEqkvmWAVj8cjhDD7qb322msjR4787LPPKIoihGRkZAQHB9Opz82bN1taWpid2oRCoa2tLXtxFhcXNzU1TZ06lSmZNm2aiYmJ+vKP/tDzPPdNdXU1RVEikaibNnFxcaNGjUpOTi4oKNCo6u0canw42XvHhw0bNmnSJFNTUxMTk+nTp6elpbW2tiYnJxNC6OXp9O2PjI6ODqFQqNEJPS1VVVX9jwcAAEAbyLZfHHw+n7nYzIbc3NxXX33V2tqaz+d/+OGH6lUcDmf58uW3b9/+9ttvCSF/+9vf/vSnP9FVzc3NhJBNmzYxexvfvXu3D/u7aYne383U1FS90NzcvLGxUVdDsD3P/dfW1kYI6f5eQ4FAkJaWxuFwPvjgA40LwP2cQ7294+7u7sbGxrdu3SKE0EvnGxoamNqWlpa2tjY7OzuNo+j8m54iAAAAPUC2/YKQy+V1dXUODg667fbChQt79uwhhFRUVAQGBtra2l68eLG+vn7nzp0aLZcsWSIQCA4dOnTz5k2pVDp8+HC63NramhCyZ88e9QVMhYWFuo2TYW5uTgjRyAt1ODMszbNu0Qml+pNcOuXh4bFmzZqSkpLY2Fj18n7Ood7ecZVKpVKp6D8qnJycJBLJ3bt3mdrS0lJCyPjx4zWO6ujoIP+eIgAAAD1Atv2CyM/Ppyhq+vTp9Esul9vNshPt/fLLL2KxmBBy/fp1uVz+5z//2dnZWSAQMPusMSwsLN5+++3s7Oxdu3YtW7aMKR82bJhAILh69Wr/g9HGuHHjTE1Nf/75Z6bk4sWLHR0dU6ZMYUr6Mzka89zP3lgydOhQDoejzY7asbGxo0ePpneSYWgzh91g7x2n7yRm0Hdeenh4EEK4XO6cOXMuXLigUqno2ry8PA6H8+yuLPS02NjY6Dw8AACATiHbfo6pVKqnT58qFIqioqKIiAiZTLZkyRK6ytXV9cmTJ9nZ2XK5vKamRv2aH23IkCEPHjwoLy9vbGzsNFmUy+VVVVX5+fl0tk3vrXb27Nm2traSkpJOl/CGhYW1t7d//fXX8+bNYwoFAsH777+fnp6ekpLS0NCgVCrv37//8OFDHc2BJoFAsHbt2qysrCNHjjQ0NFy/fj0sLMzOzi40NJRp0/3kPDsz3cxzb3vLy8vTww6AIpHI2dn5/v37Pbak15No3FyozRx232dX73hwcLCNjU2fnw9fWVmZkZFRV1cnl8sLCwuXLl0qk8mYB0Zu3ry5qqpqy5Ytzc3NhYWFCQkJS5YsGTVqlEYn9LS4u7v3LQYAAIBe09vuJ4OTNluG/fjjj2PHjjUyMiKE2Nrabt++PTk5mb6Xa8SIEWVlZQcOHJBKpYSQ4cOH37p1iz4qNDSUx+PZ29tzuVypVBoQEFBWVsb0+fjx41mzZgkEAicnp7/85S/r168nhLi6ujJb112+fHn48OFCoXDmzJl//etfu9m/Iisriz4kMjJyyJAh5ubmCxYs2LdvHyHExcVFfS88iqImTZoUFRWlcYLt7e2RkZEymYzL5VpbWwcFBRUXF+/cuZP+NX/YsGEaDwjsipY7AKpUqoSEhBEjRvB4PAsLi8DAwJs3b6o36H5y1Gfm0aNH3c9zb3s7deqURCKJi4vT5nypfuwAGB4ezuPxWlpa6JdZWVn0W2xlZbVy5UqNw9evX6+xA2A3c6jNh7PTd5yiqMDAQEJIdHR0p2dRWFg4Y8YMZqW1ra2tp6fn+fPnmQZr1651cXERi8VcLtfBwWHZsmUPHjxQ7+H8+fMvvfQSn8+3s7Nbv359W1vbs6PMnTvX3t5epVIxJdgBEAAAWIVsm13s/fMcGho6ZMgQNnrujzlz5ty+fZulzrXMtnXLsPPc52y7pKSEy+Vq+WeM3iiVSi8vr9TUVEMFUFtbKxAIdu3apV6IbBsAAFiFlSTPsR5vg9MPZiFKUVERfYnXsPHo3ACZ5+61trZ+8803JSUl9F2Arq6u27Zt27ZtW1NTk6FD+xelUpmdnd3Y2BgcHGyoGGJiYiZOnBgeHk4IoSjqwYMHBQUF9P2UAAAALEG2Df0VGRlZUlJy69at999/X2N3C9CbJ0+evPXWWyNHjvzggw/okqioqAULFgQHB2tzu6Qe5OfnZ2Zm5uXldb8ROHt279599erVU6dO0bvF5+Tk2Nvbe3l55ebmGiQeAAAYJJBtP5c2btyYlpZWX1/v5OR04sQJwwYjEolGjx79+uuvx8TEuLm5GTYY3RpQ89yN/fv3Mz9XHTlyhCnfvn17eHj4xx9/bMDYGD4+Pl9++SW9Mbb+5eTktLe35+fnW1hY0CUBAQHMpNXW1hokKgAAGAw4FEUZOoYXWUhIyP379/Pz8w0dyIsgKirq2LFjZWVlhg5Ef/h8/u7du1esWGHoQIAQfJ0BAKBPcG0bAAAAAIAtyLYBAAAAANiCbBsAAAAAgC3ItgEAAAAA2IJsGwAAAACALVxDB/DiKysrW7NmjaGjeBEUFBQ8fvx4UE2mUqnMysoaVNuwDGS//PKLjY2NoaMAAIDnDLJtdtnb20skktOnTxs6kBeBUqm0trYegJOpUCiqqqqsrKz4fL5ue3Z0dKysrHz48KFuu4W+4XA4jo6Oho4CAACeM9hvG6C/SktLR4wY8d1337366quGjgUAAAAGFqzbBgAAAABgC7JtAAAAAAC2INsGAAAAAGALsm0AAAAAALYg2wYAAAAAYAuybQAAAAAAtiDbBgAAAABgC7JtAAAAAAC2INsGAAAAAGALsm0AAAAAALYg2wYAAAAAYAuybQAAAAAAtiDbBgAAAABgC7JtAAAAAAC2INsGAAAAAGALsm0AAAAAALYg2wYAAAAAYAuybQAAAAAAtiDbBgAAAABgC7JtAAAAAAC2INsGAAAAAGALsm0AAAAAALYg2wYAAAAAYAuybQAAAAAAtiDbBgAAAABgC7JtAAAAAAC2INsGAAAAAGALsm0AAAAAALYg2wYAAAAAYAuybQAAAAAAtiDbBgAAAABgC7JtAAAAAAC2INsGAAAAAGALsm0AAAAAALZwDR0AwPPn4cOHf/vb35iXjx8/JoSkp6dfvHiRKQwICBg1apQBggMAAICBhENRlKFjAHjOdHR02NjY1NfXc7lcQgjzJeJwOIQQpVLJ4XAePnxobW1tyCgBAABgAMBKEoBeMzExWbBggbGxsVwul8vlin+jX3I4nNdffx2pNgAAABBk2wB988477ygUik6rVCpVSEiInuMBAACAgQkrSQD6QqVS2dvbP3r06NkqExOTmpoaqVSq/6gAAABgoMG1bYC+MDIyWrRoEb1uWx2Xyw0ICECqDQAAADRk2wB91OliEoVCsWjRIoPEAwAAAAMQVpIA9J2Li8vt27fVS0xNTWtra/l8vqFCAgAAgAEF17YB+u7dd981NjZmXnK53LfffhupNgAAADCQbQP0XUhIiFKpZF4qFIp33nnHgPEAAADAQIOVJAD9MmHChBs3bqhUKkKItbX1w4cP1a92AwAAwCCHa9sA/fLuu+/S/8HlchcvXoxUGwAAANTh2jZAv9y/f18mk9Hfox9//PHll182dEQAAAAwgODaNkC/ODg4zJgxgxAik8leeuklQ4cDAAAAA4vmszmgoaHh3r17ho4CnievvfZaQUHB7Nmzf/31V0PHAs8TW1tbS0tLQ0cBAADswkoSTceOHXv77bcNHQUAvPg+/fTTNWvWGDoKAABgF65td+7s2bMikcjQUQAhhHh6em7YsMHPz8/QgXTn4MGDy5Yt00lX6enpqampZ8+e1UlvMGC9/vrrhg4BAAD0Adl256ZNmyaVSg0dBfyLi4uLh4eHoaPoztSpU3k8nk66KiwsNDY2HuDnC/2H7WsAAAYJ3CUJoAO6SrUBAADgBYNsGwAAAACALci2AQAAAADYgmwbAAAAAIAtyLYBAAAAANiCbBsAAAAAgC3Itg1p2rRpxsbGEydO7Gc/S5culUgkHA7n6tWrWjY4deqUmZnZV1991c+htQBkUjkAACAASURBVLFt2zY3NzepVMrn811dXT/88MOmpia2B9XnCT7Xzp49GxUVlZmZ6ezszOFwOBzOu+++q97A19dXIpEYGxuPHTv28uXLhopTnUql2rNnj6enp0Z5XFwc57+NGzdOvUFBQcGMGTNEIpGdnV1kZGR7eztTdfLkyZ07dyqVSn2cAAAADCbItg3p0qVLs2bN6n8/hw4dOnjwYK8a6PMZoufOnVu5cmV5eXltbW18fHxiYuKCBQvYHhQPSdXGli1bkpKSNm7cGBQUdPv2bRcXF0tLyyNHjuTm5jJtzpw5c/z48Xnz5hUXF0+ePNmA0dJKSkpeeeWVNWvWtLS09OrA4uJiX19fHx+fmpqarKyszz77LCwsjKn18/MTCAQ+Pj51dXW6DhkAAAY1ZNuGx+Fw9D/o3Llz6+vr582bp4exTE1NQ0NDhwwZIpFIFi5cGBgYePr06Xv37rE6qD5PkBDS2tr67KXWAW7Hjh0ZGRnHjh2TSCRMYVJSkpGRUWhoaH19vQFj68q1a9c2bNgQFhbW1S9Chw8fptTcuHGDqYqNjbW1td26datYLPbw8IiMjPz8889/++03psGqVasmTJgwZ84chULB+pkAAMCggWzb8HTyYJQeU3bd5vQURR0/fvzAgQPaNP7666/VH5tnZWVFCOnthckBLjU1tbq62tBR9EJpaenmzZu3bt0qEAjUyz09PSMiIiorK9etW2eo2LoxYcKEzMzMxYsX8/n8Xh2oUChyc3O9vb2ZL8Ls2bMpisrJyVFvFhMTc/Xq1cTERJ1FDAAAgx6y7T5SKpXR0dEymUwoFI4fP/7o0aN0eWJiolgsNjIymjJlio2NDY/HE4vFkydP9vLyGjZsmEAgMDc3//DDD9W7Ki0tHT16tFgsFgqFXl5eBQUF2gxEUVRCQsKoUaP4fL6Zmdn69es1IuymQUFBgUwm43A4+/btI4SkpKSIxWKRSJSTkzN79mypVOrg4JCenq4RRnx8/KhRo4RCoZWVlZOTU3x8/MKFC/swdZWVlUKh0MnJqQ/HaknjBElP55iUlCQQCIYOHbp8+XI7OzuBQODp6Xnx4kW6Njw83MTExNbWln65YsUKsVjM4XBqa2vpkoiIiLVr15aVlXE4HFdXV0LI6dOnpVLp9u3b2TvHfkpKSqIoys/P79mquLi4kSNHHjp06OzZs10dTlHU7t27x4wZw+fzLSwsAgICmIvEWn6cOv1Us+f27dtNTU0ymYwpcXFxIYQUFRWpN7OwsPD29k5MTMRKJAAA0BkK/hv9D399fX33zdatW8fn80+cOPH06dONGzcaGRldunSJrtqyZQsh5OLFi83NzbW1tW+99RYhJDc3t6amprm5OTw8nBBy9epVurGPj4+zs/OdO3fkcvmNGzdefvllgUBw69atHgf66KOPOBzOp59++vTp05aWluTkZELIlStXmAO7b0Av5Ni7dy/TmBDy7bff1tfXV1dXe3l5icXijo4Oprft27cbGxvn5OS0tLT88ssvNjY2r776ah+mt7m5WSKRhIeHa38IIeTgwYO9HUjjBKmezjE0NFQsFv/6669tbW3FxcXTpk2TSCQVFRV07eLFi21sbJiuEhISCCE1NTVMSVBQkIuLC/Py66+/lkgk27Zt623YFEV9+umnEomkDwf2irOzs5ubm0ahi4vLnTt3KIr64YcfjIyMHB0dm5qaKIrKy8vz9/dXbxkdHW1iYnL48OG6urqioqLJkydbWVk9evSIru3x49TN10dLL7/88oQJEzQKY2NjHRwczM3NeTyeo6Ojv7//Tz/9RFedP3+eEJKQkKDeXigU+vj4aHQSFRWl8VViiUQi+fTTT9keBQAADA7Xtvuira0tJSUlMDAwKCjI3Nx806ZNPB4vLS1NvY2bm5tIJLK0tHznnXcIITKZzMrKSiQShYSEEELUV4tKJBJHR0culzt27NiDBw+2tbUxKzS6Gqi1tXXPnj2vv/76mjVrzM3NhULhkCFD1EfvsUGnPD09pVKptbV1cHBwc3NzRUUFU5WdnT1lyhQ/Pz+hUDh58mR/f/8LFy50dHT0duri4+Pt7Ozi4uJ6e6CudHOOXC6Xvljr5uaWkpLS2Nio8Z5qb+7cuQ0NDZs3b9ZR1DrW3Nx8584d+uJupzw8PFavXl1eXr5hw4Zna1tbW3fv3j1//vyQkBAzMzN3d/f9+/fX1tZqrCzqaqq1+fr0zXvvvXfy5Ml79+41NTWlp6dXVFR4e3sXFxcTQujtR9RXNBFCeDxea2urRicjRowghFy/fr3/8QAAABCsJOmbmzdvtrS0MJuLCYVCW1tb9QRanYmJCSGEue+KXqUtl8s7bezu7m5mZsb8ut3VQKWlpS0tLT4+Pl1F2GOD7tExqwfZ1tZGqf22rlQqeTyeRu7So6ysrGPHjn3zzTfqt+UZyrPnqG7q1Kkikair9/R5V11dTVGUSCTqpk1cXNyoUaOSk5M1ljYRQoqLi5uamqZOncqUTJs2zcTEhFl7o0Fjqnv19emVYcOGTZo0ydTU1MTEZPr06fTfpfSvOvTydI3bHzs6OoRCoUYn9LRUVVX1Px4AAACCbLtvmpubCSGbNm1itvW9e/eurm774/F4TF7S1UD3798nhFhbW3fVSY8NemvOnDm//PJLTk5Oa2vrzz//nJ2d/fvf/75X2XZGRsaOHTvy8/MdHR11FRWr+Hx+TU2NoaNgRVtbGyGk+xsNBQJBWloah8P54IMPNC4A03vkmZqaqheam5s3NjZqMzqrXx917u7uxsbGt27dIoTQy+4bGhqY2paWlra2Njs7O42j6PybniIAAID+Q7bdF3QWu2fPHvVFOYWFhf3vWaFQPHnyhLmXq6uB6At16s/m0NBjg96KiYl57bXXlixZIpVK58+fv3Dhwu53+Nawd+/eI0eOnDt37ne/+52uQmKVXC6vq6tzcHAwdCCsoBPKHp/k4uHhsWbNmpKSktjYWPVyc3NzQohGbq39dLH39dGgUqlUKhX9R4WTk5NEIrl79y5TW1paSggZP368xlH0+qhnr3kDAAD0DbLtvqB3F+nqwY398d1336lUKuYZIl0NNG7cOCMjI/rGr0712KC3iouLy8rKampq5HJ5RUVFSkqKhYWFNgdSFBUZGXn9+vXs7GyNq6EDWX5+PkVR06dPp19yudyu1pw8j4YOHcrhcLTZUTs2Nnb06NFXrlxRLxw3bpypqenPP//MlFy8eLGjo2PKlCnajM7e1+fNN99Uf0nfeenh4UEI4XK5c+bMuXDhgkqlomvz8vI4HM6zu7LQ02JjY6Pz8AAAYHBCtt0XAoHg/fffT09PT0lJaWhoUCqV9+/ff/jwYd966+joqK+vVygUly9fDg8PHz58+JIlS7ofyNraOigo6MSJE6mpqQ0NDUVFRRo3qPXYoLdWrlwpk8n68MT1X3/99ZNPPjl48CCPx1N/pPauXbv6Ew8bVCrV06dPFQpFUVFRRESETCZj3ghXV9cnT55kZ2fL5fKamhr1S6S0IUOGPHjwoLy8vLGxUS6X5+XlDeQdAEUikbOzM73cqHv0ehKNJUMCgWDt2rVZWVlHjhxpaGi4fv16WFiYnZ1daGioNqN38/UJDg62sbHp8/PhKysrMzIy6urq5HJ5YWHh0qVLZTIZ88DIzZs3V1VVbdmypbm5ubCwMCEhYcmSJaNGjdLohJ4Wd3f3vsUAAACgieU9T54/Wu4A2N7eHhkZKZPJuFwundoWFxdTFJWYmEjfZeXo6Pj999/v2LHDzMyMEGJjY/Pll19mZGTQ18wsLCzS09MpikpLS5s1a9bQoUO5XC69gcndu3e1GaixsXHp0qWWlpampqYzZ86Mjo4mhDg4OFy7do0+sJsGe/fupZexikQiPz+/5ORkOuYRI0aUlZUdOHBAKpUSQoYPH87sRXju3DlLS0vmY8Pj8caMGZOZmdnjfHa1t4PGXmzdIL3fAVDjBCmK6vEcQ0NDeTyevb09l8uVSqUBAQFlZWVMh48fP541a5ZAIHBycvrLX/5Cb17u6urKbBF4+fLl4cOHC4XCmTNnPnr06NSpUxKJJC4urldh0/SzA2B4eDiPx2tpaaFfZmVl0VuUWFlZrVy5UqPx+vXrNXYAVKlUCQkJI0aM4PF4FhYWgYGBN2/epKu0+Th19akODAwkhERHR3cVdmFh4YwZM5jF1ra2tp6enufPn6dr165d6+LiIhaLuVyug4PDsmXLHjx4oH74+fPnX3rpJT6fb2dnt379evreXw1z5861t7dXqVTaT2bfYAdAAIBBAtm2Ji2z7cEmOTk5IiKCedne3r569Wo+n8+ka+zpQ7bdB/Sz5dkeRRv6ybZLSkq4XK7Gc84NTqlUenl5paamGiqA2tpagUCwa9cuPYyFbBsAYJDAShLo2aNHj8LDw//0pz8xJSYmJjKZTC6Xv0irmXu8a/BF4urqum3btm3btvVhdRBLlEpldnZ2Y2NjcHCwoWKIiYmZOHEi/QgqAAAAnUC2DT0TCoU8Hi81NbWqqkoulz948ODQoUPR0dHBwcEPHjzgdM2AaRP0KCoqasGCBcHBwdrcLqkH+fn5mZmZeXl53W8Ezp7du3dfvXr11KlT9Kb4AAAAOoFsG3pmZmZ25syZGzdujBw5UigUurm5paWl7dix44svvhg9enQ3P51kZGQYOnatbNy4MS0trb6+3snJ6cSJE4YOR3+2b98eHh7+8ccfGzoQQgjx8fH58ssv6QX3+peTk9Pe3p6fn6/lZjsAAABa4ho6AHg+eHl5/eMf/zB0FGyJj4+Pj483dBSG4evr6+vra+goDM/f39/f39/QUQAAwAsI17YBAAAAANiCbBsAAAAAgC3ItgEAAAAA2IJsGwAAAACALbhLsnM//fSTWCw2dBTwL2VlZYWFhYaOQk/Ky8uVSuXgOd9BS6VSGToEAADQB2TbnXvjjTcMHQL8x44dO3bs2GHoKPTHyMjI09PT0FEAu4yNjQ0dAgAA6AOy7c79+OOPpqamho4CCCFk3LhxW7duDQoKMnQgevLFF18kJyf/9NNPhg4E2PXyyy8bOgQAANAHZNudGzNmjFQqNXQU8C+/+93vxo4da+go9MTW1tbY2HjwnO+gZWSE22YAAAYF/O8eAAAAAIAtyLYBAAAAANiCbBsAAAAAgC3ItgEAAAAA2IJsGwAAAACALci2AQAAAADYgmxbxzIzM52dnTmdcXR07Fuf06ZNMzY2njhxYj9jW7p0qUQi4XA4V69e1bL21KlTZmZmX331VT+HBvacPXs2KiqK/Pdn791331Vv4+vrK5FI6I0FL1++bKBI/0OlUu3Zs+fZJ/jExcVpfGvGjRun0aagoGDGjBkikcjOzi4yMrK9vV3LQbdt2+bm5iaVSvl8vqur64cfftjU1NSrzrupPXny5M6dO5VKpZbBAADA4IFsW8eCgoJu377t4uJiZmZGURRFUQqFoqWlpaqqSiQS9a3PS5cuzZo1q/+xHTp06ODBg72qpSiq/+MCe7Zs2ZKUlLRx40ai9tmztLQ8cuRIbm4u0+zMmTPHjx+fN29ecXHx5MmTDRcvIYSUlJS88sora9asaWlp6e2xxcXFvr6+Pj4+NTU1WVlZn332WVhYmJbHnjt3buXKleXl5bW1tfHx8YmJiQsWLNC+8+5r/fz8BAKBj49PXV1db08KAABebMi2WWdsbCwUCocOHTpy5Mj+9MPhcHQVkvbmzp1bX18/b948/Q+tZ62trTp8WLpue+vKjh07MjIyjh07JpFI1MuTkpKMjIxCQ0Pr6+vZjqG3rl27tmHDhrCwsK5+qzl8+DCl5saNG+q1sbGxtra2W7duFYvFHh4ekZGRn3/++W+//abN0KampqGhoUOGDJFIJAsXLgwMDDx9+vS9e/e07LzHoVetWjVhwoQ5c+YoFIpezwsAALy4kG3rT3Z2dn8O5/F4/Y+h+5Rdtwk9RVHHjx8/cOCADvtkT2pqanV19cDsrVOlpaWbN2/eunWrQCDQqPL09IyIiKisrFy3bh2rMfTBhAkTMjMzFy9ezOfze3usQqHIzc319vZmPqizZ8+mKConJ0ebw7/++mtjY2PmpZWVFSGEub7efedaDh0TE3P16tXExMTenhoAALzAkG0bQGJiolgsNjIymjJlio2NDY/HE4vFkydP9vLyGjZsmEAgMDc3//DDDzWOKi0tHT16tFgsFgqFXl5eBQUFTJVSqYyOjpbJZEKhcPz48UePHmWqKIpKSEgYNWoUn883MzNbv369ep/d1xYUFMhkMg6Hs2/fPkJISkqKWCwWiUQ5OTmzZ8+WSqUODg7p6enqYcTHx48aNUooFFpZWTk5OcXHxy9cuFCHU9cNiqJ27949ZswYPp9vYWEREBDAXHcMDw83MTGxtbWlX65YsUIsFnM4nNraWrokIiJi7dq1ZWVlHA7H1dU1KSlJIBAMHTp0+fLldnZ2AoHA09Pz4sWLfeuNEHL69GmpVLp9+3Ydnm9SUhJFUX5+fp3WxsXFjRw58tChQ2fPnu20QTfTRbR7r7v6yLHn9u3bTU1NMpmMKXFxcSGEFBUV9aG3yspKoVDo5OSkTedaDm1hYeHt7Z2YmIglWAAA8B8U/Dc6b6ivr+9PJ+rrtimKWrVq1fXr19UbbNmyhRBy8eLF5ubm2trat956ixCSm5tbU1PT3NwcHh5OCLl69SrT3sfHx9nZ+c6dO3K5/MaNGy+//LJAILh16xZdu27dOj6ff+LEiadPn27cuNHIyOjSpUt01UcffcThcD799NOnT5+2tLQkJycTQq5cuaJNLUVR9O/se/fuZdoTQr799tv6+vrq6movLy+xWNzR0UHXbt++3djYOCcnp6Wl5ZdffrGxsXn11Vf7M400QsjBgwd7bBYdHW1iYnL48OG6urqioqLJkydbWVk9evSIrl28eLGNjQ3TOCEhgRBSU1PDlAQFBbm4uDAvQ0NDxWLxr7/+2tbWVlxcPG3aNIlEUlFR0bfevv76a4lEsm3bNm3O99NPP5VIJD02c3Z2dnNze7bcxcXlzp07FEX98MMPRkZGjo6OTU1NFEXl5eX5+/szzbqfLqqn97qbj5yWXn755QkTJmgUxsbGOjg4mJub83g8R0dHf3//n376iak9f/48ISQhIUH9EKFQ6OPj06uhKYpqbm6WSCTh4eFadq790PQdq+pfoq5IJJJPP/20t5EDAMBzB9e22VJfX8/sq/A///M/nbZxc3MTiUSWlpbvvPMOIUQmk1lZWYlEopCQEEKIxmpUiUTi6OjI5XLHjh178ODBtrY2epFGW1tbSkpKYGBgUFCQubn5pk2beDxeWloaIaS1tXXPnj2vv/76mjVrzM3NhULhkCFDmA67r+2Gp6enVCq1trYODg5ubm6uqKigy7Ozs6dMmeLn5ycUCidPnuzv73/hwoWOjo6+TF8vtba27t69e/78+SEhIWZmZu7u7vv376+tre3POhYul0tf+nVzc0tJSWlsbKRntQ/mzp3b0NCwefPmPgejobm5+c6dO/Tl1a54eHisXr26vLx8w4YNGlXaT1en73U3H7l+eu+9906ePHnv3r2mpqb09PSKigpvb+/i4mK6lt4DRH01CCGEx+O1trb2dqD4+Hg7O7u4uDimpPvOtR96xIgRhJDr16/3NiQAAHhRIdtmi8a17e4bm5iYEEKYm6voJdpyubyr9u7u7mZmZvSv2Ddv3mxpaWE2ShMKhba2tnSmXlpa2tLS4uPj02kn3ddqgw6bibOtrY1S+wFdqVTyeDyNBIUlxcXFTU1NU6dOZUqmTZtmYmLCLP/op6lTp4pEIi3vxtOD6upqiqJ63OUmLi5u1KhRycnJ6uuOSJ+mS/297uYj10/Dhg2bNGmSqampiYnJ9OnT09LSWltb6Z9cCCH0CnWNexA7OjqEQmGvRsnKyjp27Ng333yjfndp951rPzT9plRVVfUqJAAAeIEh29aHxMTEZ7cN7icej0enPs3NzYSQTZs2MZfS7969S9/7df/+fUKItbV1pz10X9sHc+bM+eWXX3JyclpbW3/++efs7Ozf//73+sm26W3XTE1N1QvNzc0bGxt1NQSfz6+pqdFVb/3U1tZGCOnxRkOBQJCWlsbhcD744AP1q7D9nK5uPnK65e7ubmxsfOvWLfolvVa+oaGBadDS0tLW1mZnZ6d9nxkZGTt27MjPz9fY/777zrUfms6/6TcIAACAINt+TikUiidPntD3bNHp8p49e9RXCBUWFpJ/X5Dr6vEf3df2QUxMzGuvvbZkyRKpVDp//vyFCxd2s723bpmbmxNCNJLFuro6BwcHnfQvl8t12Fv/0SmdNs9S8fDwWLNmTUlJSWxsLFPYz+nq5iOnWyqVSqVSMX9UODk5SSSSu3fvMg1KS0sJIePHj9eyw7179x45cuTcuXO/+93vNKq671z7oemlU7293A4AAC8wZNv68/Dhw/fff18nXX333XcqlYp+TAm9jUmnj4ccN26ckZERfYNXb2v7oLi4uKysrKamRi6XV1RUpKSkWFhY6Krz7o0bN87U1PTnn39mSi5evNjR0TFlyhT6JZfL7WZlTo/y8/Mpipo+fbpOeuu/oUOHcjgcLbfTjo2NHT169JUrV5iSHqere9185PrpzTffVH9J33np4eFBv+RyuXPmzLlw4YJKpaJL8vLyOBxOVxuzqKMoKjIy8vr169nZ2RoX9bXpXPuh6TfFxsZG+7MGAIAXG7JtfaAoqrW1NTMzUyqV9rmTjo6O+vp6hUJx+fLl8PDw4cOHL1myhBAiEAjef//99PT0lJSUhoYGpVJ5//79hw8fEkKsra2DgoJOnDiRmpra0NBQVFSkfhtc97V9sHLlSplM9uzTsPVAIBCsXbs2KyvryJEjDQ0N169fDwsLs7OzCw0NpRu4uro+efIkOztbLpfX1NSoX6SkDRky5MGDB+Xl5Y2NjXQmrVKpnj59qlAoioqKIiIiZDIZPeF96C0vL0+3OwCKRCJnZ2d6LVCP6PUk6kt6epyuHjvs6iMXHBxsY2PT54fDV1ZWZmRk1NXVyeXywsLCpUuXymQy9Uc2bt68uaqqasuWLc3NzYWFhQkJCUuWLBk1ahRd283ov/766yeffHLw4EEej6f+ZPhdu3Zp2Xn3tQz6TXF3d+/bDAAAwAuI9V1Pnjf93AEwKyurm50iNm3aRFFUYmIifSuVo6Pj999/v2PHDjMzM0KIjY3Nl19+mZGRQV8Ys7CwSE9Pp7tNS0ubNWvW0KFDuVwuvYfJ3bt3mUHb29sjIyNlMhmXy6Vz6OLiYrqqsbFx6dKllpaWpqamM2fOjI6OJoQ4ODhcu3atx9q9e/fSy1VFIpGfn19ycjId9ogRI8rKyg4cOED/8TB8+HB6L8Jz585ZWloyJ8vj8caMGZOZmdmPd4OitN4BUKVSJSQkjBgxgsfjWVhYBAYG3rx5k6l9/PjxrFmzBAKBk5PTX/7yF3pncVdXV2ZTv8uXLw8fPlwoFM6cOfPRo0ehoaE8Hs/e3p7L5Uql0oCAgLKysj73durUKYlEEhcXp835arkDYHh4OI/Ha2lpYUqYz56VldXKlSs12q9fv159B8Dup6vH97qrj1xgYCAhJDo6uquwCwsLZ8yYwSx3trW19fT0PH/+PF27du1aFxcXsVjM5XIdHByWLVv24MEDjR7Onz//0ksv8fl8Ozu79evX0/fm0roZvatNQjQ29eum8x5raXPnzrW3t1epVF3NAAM7AAIADBLItjXpZL/twSk5OTkiIoJ52d7evnr1aj6fr54R9oGW2bZu0Y/41vOgNC2z7ZKSEi6Xq/Gcc4NTKpVeXl6pqamDcHSKomprawUCwa5du7RpjGwbAGCQwEoS0I1Hjx6Fh4f/6U9/YkpMTExkMplcLjfsEuc+0+YeRANydXXdtm3btm3bDLJ0p1NKpTI7O7uxsTE4OHiwjU6LiYmZOHEi/XQqAAAAGrJt0A2hUMjj8VJTU6uqquRy+YMHDw4dOhQdHR0cHNyf1erQjaioqAULFgQHB2t5uyTb8vPzMzMz8/LyetwI/MUbnRCye/fuq1evnjp1it4vHwAAgIZsG3TDzMzszJkzN27cGDlypFAodHNzS0tL27FjxxdffGHo0Hpt48aNaWlp9fX1Tk5OJ06cMHQ43dm+fXt4ePjHH39s6EAIIcTHx+fLL7+k1/oPttFzcnLa29vz8/P1tg8PAAA8L7iGDgBeHF5eXv/4xz8MHYUOxMfHx8fHGzoKbfn6+vr6+ho6isHO39/f39/f0FEAAMBAhGvbAAAAAABsQbYNAAAAAMAWZNsAAAAAAGxBtg0AAAAAwBbcJdm5b7/91lD7iMGziouLv/nmG0NHoSc3b95UKBSD53wHrQG+oTsAAOgKsu3OzZ8/39AhwH8kJiYmJiYaOgq9euuttwwdAgAAAOgAh6IoQ8cwsLS2ttbW1ho6CnielJeXv/LKK0ePHvXw8DB0LPA8sbCwMDU1NXQUAADALlzb1iQUCocNG2boKOB50t7eTggZOnQoPjkAAACgAXdJAgAAAACwBdk2AAAAAABbkG0DAAAAALAF2TYAAAAAAFuQbQMAAAAAsAXZNgAAAAAAW5BtAwAAAACwBdk2AAAAAABbkG0DAAAAALAF2TYAAAAAAFuQbQMAAAAAsAXZNgAAAAAAW5BtAwAAAACwBdk2AAAAAABbkG0DAAAAALAF2TYAAAAAAFuQbQMAAAAAsAXZNgAAAAAAW5BtAwAAAACwBdk2AAAAAABbkG0DAAAAALAF2TYAAAAAAFuQbQMAAAAAsAXZNgAAAAAAW5BtAwAAAACwBdk2AAAAAABbkG0DAAAAALAF2TYAAAAAAFuQbQMAAAAAsAXZNgAAAAAAW5BtAwAAAACwBdk2AAAAAABbkG0DAAAAALCFa+gAAJ4/jY2Nt27dYl7eu3ePEHLr5id0agAAIABJREFU1i2JRMIUOjo6WlpaGiA4AAAAGEg4FEUZOgaA58zjx4/t7Ozkcnk3bYqLi93c3PQWEgAAAAxMWEkC0GuWlpa+vr7Gxsad1nI4nHHjxiHVBgAAAIJsG6BvFi9erFQqO60yMjL64x//qOd4AAAAYGDCShKAvmhpabGysmptbX22isPh3L5929HRUe9BAQAAwICDa9sAfSESifz9/blczfuMjYyMPDw8kGoDAAAADdk2QB8tWrRIoVA8W/7uu+/qPxgAAAAYmLCSBKCP5HK5tbV1fX29eqGxsfGDBw+GDh1qqKgAAABgQMG1bYA+4vF4b7/9tvpiEmNj4zfeeAOpNgAAADCQbQP03TvvvKO+mESpVIaEhBgwHgAAABhosJIEoO9UKpW9vf2jR4/ol3w+v7a21tTU1LBRAQAAwMCBa9sAfWdkZLR48WJ6MQmXyw0ICECqDQAAAOqQbQP0C7OYRKFQLFq0yNDhAAAAwMCClSQA/eXi4nL79m2JRFJTU8Pn8w0dDgAAAAwguLYN0F/0Bttvv/02Um0AAADQ8F/XtsvLy2fOnGnAaPRGpVIplUoej2foQPRHoVBwOBxjY2NDB/ICUigUVVVVVlZWyLYBeuv48eMeHh6GjgIAgEX/9dxphUJRWVn53nvvvfDPnS4qKvr73/++adOmwZN9/u///q+Dg8PcuXMNHciLKT09PTg4mMPhGDoQgOfGkydP9u7d297ebuhAAADY9V/XtktLS0eMGPHdd9+9+uqrhgtJHw4fPvzHP/6xra1t8FyMnDx58vTp01NSUgwdyIupqqrKxsbG0FEAPE8Gz784ADDIYd02gA4g1QYAAIBOIdsGAAAAAGALsm0AAAAAALYg2wYAAAAAYAuybQAAAAAAtiDbBgAAAABgS1+y7V27dg0dOpTD4ezfv1/nAfVBW1vb6NGjN23apIexTp06ZWZm9tVXX+lhLHghnT17NioqKjMz09nZmcPhcDgc+lGUDF9fX4lEYmxsPHbs2MuXLxsqToZKpdqzZ4+np6dGeVxcHOe/jRs3TqNNQUHBjBkzRCKRnZ1dZGRkr3ZW3rZtm5ubm1Qq5fP5rq6uH374YVNTk/add1N78uTJnTt3KpVK7YMBAADos75k2+vWrfvhhx90HkqfffTRRzdv3tTPWOrbkwP01pYtW5KSkjZu3BgUFHT79m0XFxdLS8sjR47k5uYybc6cOXP8+PF58+YVFxdPnjzZgNESQkpKSl555ZU1a9a0tLT09tji4mJfX18fH5+ampqsrKzPPvssLCxM+8PPnTu3cuXK8vLy2tra+Pj4xMTEBQsWaNl597V+fn4CgcDHx6eurq63JwUAANBbLK4kaW1tffZ6mM798MMPN27cYHsUxty5c+vr6+fNm6eHsfQzgaA3O3bsyMjIOHbsmEQiYQqTkpKMjIxCQ0Pr6+sNGFunrl27tmHDhrCwsIkTJ3ba4PDhw5QajW9ibGysra3t1q1bxWKxh4dHZGTk559//ttvv2k5uqmpaWho6JAhQyQSycKFCwMDA0+fPn3v3j1tOu9x6FWrVk2YMGHOnDkKhaLX8wIAANAbLGbbqamp1dXV7PVPCGltbV2/fn1iYiKroxiKHiYQ9Ka0tHTz5s1bt24VCATq5Z6enhEREZWVlevWrTNUbF2ZMGFCZmbm4sWL+/DIVYVCkZub6+3tzTzNfvbs2RRF5eTkaNnD119/bWxszLy0srIihNCX2LvvXMuhY2Jirl69+qL+3wMAAAYO3WTb58+ff+mll0QikVQqdXd3b2hoiIiIWLt2bVlZGYfDcXV1TUxMFIvFRkZGU6ZMsbGx4fF4YrF48uTJXl5ew4YNEwgE5ubmH374YW/H/eijj1asWGFtba2Ts+hRQUGBTCbjcDj79u0jhKSkpIjFYpFIlJOTM3v2bKlU6uDgkJ6ezrRPSkoSCARDhw5dvny5nZ2dQCDw9PS8ePEiXRseHm5iYmJra0u/XLFihVgs5nA4tbW1hBCNCaTbnD59WiqVbt++XT/nCzqUlJREUZSfn9+zVXFxcSNHjjx06NDZs2c7PZaiqN27d48ZM4bP51tYWAQEBKhfpu3xc6hUKqOjo2UymVAoHD9+/NGjR3V+ds+6fft2U1OTTCZjSlxcXAghRUVFfeuwsrJSKBQ6OTn12LmWQ1tYWHh7eycmJmJ5GAAAsEoH2XZzc7Ofn98f/vCHJ0+elJSUjBw5sqOjIzExcd68eS4uLhRFlZaWRkRErF+/nqKov/71r3fu3Hn06NErr7xy5cqVqKioK1euPHny5L333ktISLh27Zr24/6///f/ysrKFi1a1P9T0NLMmTPVF6z/+c9/Xr16dWtrq0QiOXr0aFlZmbOz87Jly+RyOd0gPDx8yZIlLS0tq1atKi8vv3z5skKheOONN+hfw5OSkhYuXMj0lpycvHXrVualxgTShfR9XSqVSg8nC7qVm5s7atQokUj0bJVQKPz888+NjIyWLVvW3Nz8bIOYmJioqKiPPvqourr6woUL9+7d8/Lyqqqqomt7/Bxu2LDhk08+2bNnz8OHD+fNm7do0aKff/5ZJycVFRVlYWFhYmLi5OQUEBBw6dIlpurRo0eEEPU1MwKBQCgUMmH3SktLy7lz55YtW2ZiYtJj59oPPWnSpMrKyl79bwcAAKC3dJBtl5eXNzQ0jB07ViAQ2NjYZGZm0r/5dsrNzU0kEllaWr7zzjuEEJlMZmVlJRKJQkJCCCHar+lsbW2NiIhISUnpf/z95+npKZVKra2tg4ODm5ubKyoq1Gu5XC59VdLNzS0lJaWxsTEtLa1vA82dO7ehoWHz5s26iBr0p7m5+c6dO/QV1k55eHisXr26vLx8w4YNGlWtra27d++eP39+SEiImZmZu7v7/v37a2trDxw4oNGy089hW1tbSkpKYGBg0P9n787jmrj2//GfhIRsgKAgUBaFgCKKCuotoFRbH8WqVUBqpe569SJd0IpKXVBUtFoUvVqpD28t12oLauWKG63X1qVUar1VKsWKuCJSBVH2LZD5/THfT35pjMkkZLLA6/mXmZmc8857kjlvh5kz0dH29varVq3i8/l6fwOVzZo169ixYw8ePKivr8/MzCwtLR05cmRRURG9lp4DRPlSEEIIn89vamrSo6+NGze6urqmpKQwaZx5176+voSQwsJCPUICAABgyADVtre3d8+ePadPn56cnHzv3j2G76LPUSluUeLz+YQQxdk4rVasWPGPf/zDzc1N53DZRH8oDZ9i6NChYrGY+X8qoHOoqKigKErtiW2FlJSUvn377tq1Ky8vT3l5UVFRfX390KFDFUuGDRtmbW2tuCTpecrfw+Li4sbGRsXcfCKRyMXFxSDfQA8Pj8DAQBsbG2tr6+Dg4IyMjKampl27dtFr6cvTVe5BbG1tFYlEunaUnZ196NCh7777TnG6WnPjzLum94h+p9sBAAAYMkC1LRKJfvjhhxEjRmzYsMHb2zsmJka/01fM5eXlFRYWzps3j9VeWCIQCCorK00dBRhVc3MzIUTzvYZCoTAjI4PD4cydO1f5F0TPUmdjY6O8sb29fV1dHZOu6UtTVq1apZgV+/79+3pM56dVQECAlZXVzZs36Zf0DQm1tbWKDRobG5ubm11dXXVqNisra9OmTefOnevdu7dioebGmXdN19/03gEAAGCJYe6S7N+///Hjx8vLyxMTEw8ePLhlyxaDNPsie/fu/f7777lcLl090HdJbtiwgcPhGOqCVJbIZLLq6mp3d3dTBwJGRVd1Wh+nEhISsnjx4pKSkvXr1ysW2tvbE0JUamvm3yL617Ft2zblqfry8/N1/QhayeVyuVyu+B+Fl5eXra3t/fv3FRvQtx8MHDiQeZs7d+48cODADz/88NJLLykv19w4865bW1vJ/+0dAAAAlhig2i4vL79+/TohxMnJ6eOPPw4KCqJfsicjI0O5dKBPFa9cuZKiKOU/uJuhc+fOURQVHBxMv+TxeMwvngHLRT97lcmM2uvXr/fz87t69apiyYABA2xsbJT/G3np0qXW1tYhQ4Yw6Zqe86egoECPsDUbM2aM8svLly9TFBUSEkK/5PF448aNu3DhguKm3tzcXA6Ho3ZWludRFJWYmFhYWHj06FGV8/paG2feNb1HnJ2dmX9qAAAAXRmm2l6wYMGNGzdaW1uvXr16//59uprs3r17eXn5vXv36urqunJNKZfLnz171tbWdu3atUWLFnl6es6ePZte5ePj8/Tp06NHj8pkssrKSuWzcURdAnNzczEDoCUSi8Xe3t5lZWVat6SvJ1G+w08oFCYkJGRnZx84cKC2trawsDAuLs7V1TU2NpZJ10KhcM6cOZmZmenp6bW1te3t7WVlZX/++Se9NiYmxtnZWb/nwz98+DArK6u6ulomk+Xn58+bN8/T01P5kY1JSUmPHz9es2ZNQ0NDfn5+amrq7Nmz+/bty6Tr69evf/LJJ//617/4fL7yw+EVfzfT3LjmtQr0HgkICNDj4wMAADClfJK4pKSEEHL27FlKo61bt9JngyQSyaRJk+7duxcaGurg4GBlZfXSSy+tXLmyra2NoqgrV6706tVLJBKNGDFi+fLl9A1JvXv3/vHHHzdt2tStWzdCiLOz81dffZWVlUU36ODgkJmZqbn35ymf22boyy+/JIQ0Nzfr1NHOnTvpS0LFYvHEiRN37dpFfyhfX9/bt2/v2bPHzs6OENKrV6+bN2/Sb4mNjeXz+W5ubjwez87OLjIy8vbt24oGq6qqXn31VaFQ6OXl9cEHHyxdupQQ4uPjU1paSv01gY8ePaIo6tSpU7a2tikpKTqFTQsMDIyLi9PjjWAQ8fHxfD6/sbGRfpmdnU1PUeLo6Pj++++rbLx06dKIiAjFS7lcnpqa6uvry+fzHRwcoqKiiouLFWu1fg9bWloSExM9PT15PJ6Tk1N0dHRRURH93qioKELI6tWr1cacn58/fPhwxeXOLi4uoaGh58+fp9cmJCRIpVKJRMLj8dzd3efPn19eXq7SAj0Tv0AgcHV1Xbp0qfIvTnPXL5onJDU1lUnjWtfSxo8f7+bmJpfL1cYAbGM44gAAWDoOpfRkh1u3bvn6+p49e3bUqFGGr+vNyf79+2fOnNnc3KzHQ/J0smDBgsOHD1dVVbHaCxNBQUHBwcFmMmdiF3Tr1q1+/fplZGTQk12aCblcPmrUqNmzZ8+dO7frdE2rqqpyd3dPSUlJSEgwSQDQdUYcAOjiWHxyO9C03hsHXYGPj8+6devWrVtXX19v6lj+n/b29qNHj9bV1cXExHSdrhWSk5MHDx4cHx9vqgAAAKCLMMdq+8aNG5wXM+HwDNARy5cvnzx5ckxMDJPbJY3g3LlzR44cyc3N1TwReCfrmpaWllZQUHDq1Cl6pn8AAAD28EwdgBp+fn7K17dYrhUrVmRkZLS2tnp5eaWmpr711lumjghMbMOGDadPn/744483bdpk6ljI6NGjR48e3dW6JoTk5OS0tLScO3dO5XmTAAAAbDDHarvT2Lhx48aNG00dBZiX8PDw8PBwU0fRpUVERERERJg6CgAA6CrM8UoSAAAAAIDOAdU2AAAAAABbUG0DAAAAALAF1TYAAAAAAFtQbQMAAAAAsEXNnCTjx4/v9BNjyWQyQoiTk5OpAzGe1tbW33///cCBA6YOBACAEELkcrmpQwAAMAY11fbs2bOlUqnxQzGmX3/99euvv161ahWP11XmQNy+fbuHh0d0dLSpAwEAIISQJ0+efPzxx6aOAgCAdRzl58jcunXL19f37Nmzo0aNMl1IxrB///6ZM2c2NzcLBAJTx2IkQUFBwcHB6enppg4EAICQrjTiAEAXh+u2AQAAAADYgmobAAAAAIAtqLYBAAAAANiCahsAAAAAgC2otgEAAAAA2IJqGwAAAACALfpU20eOHPH29uZwOBwOx8XFZfr06S/a8rfffouJifHy8hIIBI6OjoMGDUpJSVGsjYmJ4Wh04sQJ5b6SkpLU9pKWlsbhcLhcrp+f34ULF/T4RKCwYMECRf5V9uyZM2eWL1+uvEdmzJihvEF4eLitra2VlVX//v2vXLli3MDVk8vl27ZtCw0NVVmekpKi8mUbMGCA8gZ5eXnDhw8Xi8Wurq6JiYktLS3MO928ebOfn59IJJJIJH5+fklJSbW1tSrbaG5fw9pjx45t3ry5vb2deTwKlrUHtabRJDkkFpXGDuZQwwbP5/Do0aOKX5OjoyPbHw0AwJJQSkpKSgghZ8+epRiQSqXdunXTsMG1a9fEYvHChQvv3r3b1NRUXFy8bNmy0aNHKzaYMmXK6dOnq6urZTLZn3/+SQiZOHFia2trQ0NDRUXF/Pnzjx8/ruiLEOLi4tLa2qrSS1tbW69evQghyi1r9eWXXxJCmpubmb/F0gUGBsbFxWndLDY2tnv37rm5ucXFxcr5Wb169YQJE2pra+mXUqm0R48ehJATJ04ovz03NzciIsKwkevt5s2bw4cPJ4QMGjRIZdX69etVfgj9+/dXrP39999FIlFSUlJ9ff3FixcdHR3nzJnDvN/x48dv2bKloqKirq7u0KFDfD7/9ddfV95Ac/tae9++ffvIkSOfPXumQy4scA9qTqNJckhZWho7kkOtG6jkUC6Xl5WVXbhwYdy4cT169GASnk4jDgCA5WKx2p45c+ZLL72kvKSlpeXNN99UvIyJiWloaKD/TVfbyuPT7t27lavtIUOGEEIOHTqk0svBgwfpM5dmWG03NjaGhISYSWvMq203NzeVhR9//HGfPn2ampoUS6RS6VdffcXlct3c3KqrqxXLzaTIoCiqoKBg0qRJBw4cGDx4sNpqe//+/S9675QpU7y8vORyOf0yNTWVw+H88ccfDLuOiopSztXkyZMJIeXl5QzbZ9J7fHx8SEiITCZjGJIl7kHNaTR+DikLTGNHcshkA7U5XLhwIaptAABlLF63XVVVVVNT8/TpU8USa2vr48ePK15mZmaKxeIXvT02NvbNN99UvHz33XcJIZ999pnKZmlpaQkJCQYL2qD27t1bUVFhnq0xd+vWraSkpLVr1wqFQuXloaGhixYtevjw4ZIlS4wflVaDBg06cuTItGnTdH1caFtb28mTJ0eOHMnhcOglY8eOpSgqJyeHYQvZ2dnKuXJzcyOE1NfXM2mfYe/JyckFBQXbt29nEo+F7kENaTR+DollplHvHDLZgOieQwCAronFanvYsGENDQ2vvfbaTz/91PHWXnvttX79+p09e7a4uFix8KeffmpsbAwPD+94+2pRFJWWltavXz+BQODg4BAZGXnjxg3F2vj4eGtraxcXF/rle++9J5FIOBzOkydPCCGLFi1KSEi4ffs2h8Px8fHZsWOHUCjs2bPnggULXF1dhUJhaGjopUuX9GuN3ubbb7+1s7PbsGEDSx+ftmPHDoqiJk6c+PyqlJSUPn36fP7552fOnHnR2zXkMD09XSKRiMXinJycsWPH2tnZubu7Z2ZmKr+9vb199erVnp6eIpFo4MCBBw8eNOyne96dO3fq6+s9PT0VS+gLma5du6ZfgyUlJfb29vT1TlrbZ9i7g4PDyJEjt2/fTlGU1gA6xx5UTqPxc0g6lkbNRxKtaTR+DplsQHTPIQBA18Ritb1s2bKhQ4f+9ttvI0aM6N+//yeffKJ8nlsPCxYsIITs3r1bsWTr1q2LFy/uaKAvlpycvHz58pUrV1ZUVFy4cOHBgwdhYWGPHz+m1+7YsePtt99WbLxr1661a9cqXm7fvn3ChAlSqZSiqFu3bsXHx8+ePbuxsXHhwoX37t27cuVKW1vb66+//uDBAz1aoxfStyjJ5XLWEkAIISdPnuzbt6/av0KIRKJ///vfXC53/vz5DQ0Nat+uIYfvvvvuhx9+2NTUZGtre/Dgwdu3b3t7e8+fP18mkyne/tFHH33yySfbtm37888/J0yYMHXq1P/9738G+VzLly93cHCwtrb28vKKjIy8fPkyvfzRo0eEEFtbW8WWQqFQJBIp9jtDMpns4cOHn3766ZkzZ3bu3Gltbc2kfea9BwYGPnz48LffftMaiUXvQbVpNH4OScfSqPlIojWNxs8hkw1oOuUQAKBrYrHaFolEFy9e/Oc//+nn53f9+vXExMR+/fqdP39e7wZnzZolkUj27dvX1NRECLlz587ly5enTp1quJD/oqmpKS0tbdKkSdOnT+/WrVtAQMDu3bufPHmyZ88evdvk8Xj0+S1/f//09PS6urqMjAy9Wxs/fnxtbe2LpmoxiIaGhrt379LntNQKCQn58MMP792799FHHz2/lmEOQ0ND7ezsnJyc6Ev5S0tL6eXNzc3p6elRUVHR0dH29varVq3i8/kdyZjCrFmzjh079uDBg/r6+szMzNLS0pEjRxYVFRFC6FkXrKyslLfn8/n0t445Dw8Pd3f35OTkTz75ZMqUKYrlmttn3ruvry8hpLCwUHMYlr4H1abRyDkkHUsj8yOJ2jSaJIdMNqAxzyEAQJfF7nzbfD4/Pj7+jz/++PnnnyMjIysqKiZPnvzs2TP9WuvWrdvUqVOfPXuWlZVFCNm2bdu7776rOGVocEVFRfX19UOHDlUsGTZsmLW1tfLlHx0xdOhQsVis/AdlM1RRUUFRlIbL6wkhKSkpffv23bVrV15ensoqXXNI703FKb3i4uLGxkbF3HwikcjFxcUgGfPw8AgMDLSxsbG2tg4ODs7IyGhqatq1axchhL7Ota2tTXn71tZWkUikUxcPHjyoqKj4+uuv9+3bFxgYqLjmXnP7zHund4rWM+6WvgfVptHIOSQdS6MeRxLlNJokh0w2oDHPIQBAl2Wkp9u8/PLL//nPf+Li4iorK8+ePat3O/S9krt3766urj58+DB9bQlLqqurCSE2NjbKC+3t7evq6gzVhUAgqKysNFRrbGhubiaEaL7RUCgUZmRkcDicuXPnqpz36mAO6T/Kr1q1SjGP7/379xsbG3X9FFoFBARYWVndvHmTEEJfOq88LXFjY2Nzc7Orq6tObfL5fCcnp/Dw8KysrKKioo0bN9LLNbfPvHe66KF3kAaWvgfVptHIOSQdS6Ml5pDJBjTmOQQA6LIMXG1fuHBh27Zt9L+jo6NVzovQz4DoSLU0ePDg4ODgX375JTY2dvLkyQ4ODh2JVjN7e3tCiMqIWF1d7e7ubpD2ZTKZAVtjCT2Uan0OSEhIyOLFi0tKSlTmse5gDp2cnAgh27ZtU55GJz8/X6ePwIRcLpfL5XQt5eXlZWtre//+fcVa+kL5gQMH6te4j4+PlZUVfZmK1vaZ997a2kr+bwdp0Gn2oHIajZxD0rE0WmIOmWxAY55DAIAuy8DV9q+//iqRSOh/t7S0XL9+XXktPZ2I3lULjT69/c0333z44YcdaUerAQMG2NjYKN+NdOnSpdbWVnrmbxqPx1O+IUwn586doygqODjYIK2xpGfPnhwOp6amRuuW69ev9/Pzu3r1qvJCJjnUwMPDQygUFhQU6Bq2VmPGjFF+efnyZYqiQkJCCCE8Hm/cuHEXLlxQ3H6am5vL4XDUTkbxvKqqKpV7CUpKStrb2z08POiXmttn3ju9U5ydnTXHY6F7UHMajZxD0rE0WmIOmWxAY55DAIAuy2DVtkwme/z48blz5xTVNiEkKirq0KFD1dXVNTU1OTk5H330UURERAer7bffftvR0TEqKsrb27vDUWsiFAoTEhKys7MPHDhQW1tbWFgYFxfn6uoaGxur2MbHx+fp06dHjx6VyWSVlZXK54EIId27dy8vL793715dXR1dRsvl8mfPnrW1tV27dm3RokWenp6zZ8/Wu7Xc3Fy2ZwAUi8Xe3t5lZWVat6T/jK5yTxWTHGpuc86cOZmZmenp6bW1te3t7WVlZfSDkGJiYpydnfV+LPbDhw+zsrLo55jm5+fPmzfP09MzLi6OXpuUlPT48eM1a9Y0NDTk5+enpqbOnj27b9++irdr6F0ikZw+ffqHH36ora2VyWRXr16l7+5VnjxHc/tae6fROyUgIEBzPBa6B7Wm0Zg5JB1LI3s51Bx2B3PIZAPy1xwCAIB6yn+dZPhkr+zsbA335mdnZ9ObnT59esqUKVKpVCAQWFtb9+3bNzk5+fnHN9bW1r7yyivdu3cnhHC5XB8fnw0bNjzfl6Oj4/vvv08vXLZs2cWLF+l/r1q1ir6+kMvl+vv7//jjj5qDpzF8lqRcLk9NTfX19eXz+Q4ODlFRUcXFxcobVFVVvfrqq0Kh0MvL64MPPli6dCkhxMfHp7S0lKKoK1eu9OrVSyQSjRgx4tGjR7GxsXw+383Njcfj2dnZRUZG3r59W+/WKIo6deqUra1tSkoKk4+s97Mk4+Pj+Xx+Y2Mj/VLtHlFYunSpyiP0NORw165d9C1Wvr6+t2/f3rNnj52dHSGkV69eN2/epLdpaWlJTEz09PTk8XhOTk7R0dFFRUUURUVFRRFCVq9e/aIPkp+fP3z4cOWLUENDQ8+fP0+vTUhIkEqlEomEx+O5u7vPnz9f+VmPFEWdP3/+b3/7m0AgcHV1Xbp0qcpXRXPvEydO9PLysrGxEQgEUqk0JiamsLBQZRvN7WteSxs/frybmxv9nD/N8VjoHtSaRmPmsINp1Hwk0ZrGF+VQa9gdzCGTDZRzSMOzJAEAVOj/5HaLZpwnt6uIjY3t3r27MXtUpne1XVJSwuPxNDzn3CTa29vDwsL27t3bBXunKOrJkydCoXDLli1M4sEeVEunHFJIozoqOaSh2gYAUGGkOUmApvUuK3PQ1NT03XfflZSU0Pc/+fj4rFu3bt26dYpnj5tce3v70aNH6+rqYmJiulrvtOTk5MGDB8fHxzOJB3tQLZ1ySJBGdZRzSFFUeXl5Xl6e4vFbAABAQ7UNqp4+ffrGG2/06dNn7ty59JLly5dPnjw5JiaGyV1iRnDt9QCUAAAgAElEQVTu3LkjR47k5uZqnv+4U/ZOCElLSysoKDh16hSfz2cYD/agCj1ySJDGv1LJYU5OjpubW1hY2MmTJ40fDACAOeNQFKV4cevWLV9f37Nnz44aNcp0IRnD/v37Z86c2dzcrHkCXQNasWLF1q1bW1tbe/funZqa+tZbbxmnX4WgoKDg4OD09HS9W6DvuNq0aZMBowJd5eTkXL9+fdmyZSq3MzKBPUjrSA4J0kgI6XAOaV1nxAGALg7VtpGqbZPreLUNAGBAXWfEAYAuDleSAAAAAACwBdU2AAAAAABbUG0DAAAAALAF1TYAAAAAAFtQbQMAAAAAsEXNnCQmjAYAALoUzEkCAJ0eT/lFz549//Wvf5kqFAALVVFRsXLlyiVLlvTt29fUsQBYGPxqAKDT+8u5bQDQA6YNBgAAgBfBddsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWVNsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWVNsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWVNsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWVNsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWVNsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFg5FUaaOAcDClJWVhYWFyWQy+qVcLq+urrazs+PxePQSHo93+PDhYcOGmS5GAAAAMAuotgH0ERgY+Ntvv73o59OzZ8/y8nIrKysjRwUAAADmBleSAOhjxowZHA5H7So+nz9t2jSU2gAAAEBwbhtAP+Xl5R4eHnK5XO3aX375BZeRAAAAAMG5bQD9vPTSS8OHD1d7ArtXr15Dhw41fkgAAABghlBtA+hp+vTpz5/b5vF4s2bNetFFJgAAANDV4EoSAD09e/bM2dlZMTOJQlFRkb+/v0lCAgAAAHODc9sAenJwcAgPD1fM+kcI4XA4AQEBKLUBAABAAdU2gP6mTZvW1tameMnlcmfOnGnCeAAAAMDc4EoSAP01NjY6Ojo2NTXRLzkczt27d3v16mXaqAAAAMB84Nw2gP7EYnFkZCR9MQmXyw0NDUWpDQAAAMpQbQN0yNSpU+mLSSiKmjFjhqnDAQAAAPOCK0kAOkQmkzk5OdXU1FhZWT169MjR0dHUEQEAAIAZwbltgA7h8/kxMTGEkDFjxqDUBgAAABU85RcVFRXLly83VSgAFurRo0eEkIaGhr///e+mjgXAwixbtqxv376mjuIv/vvf/2ZlZZk6CgAwqpiYmNdff52lxv9SbdfW1n7xxRf+/v52dnYs9Wcmqqqq7t69O2TIkK7zzL/r16/b2Nh4enqaOpDOSSQSNTY2Xr9+3dSBAFiM5ubmgoKCGTNmmFu1XVhYuG/fvmHDhpk6kM7g2bNnt2/fDgwMtLKyMnUsRnLjxg2hUNi7d29TBwI6uHz5cv/+/Y1UbdN27do1atQolvozE/v37585c+b58+cFAoGpYzGSoKCg4ODg9PR0UwfSOf3888/BwcGmjgLAkty6dcvX19fUUagnFovz8/NNHUVncOjQoSlTppw5c6bTn8VTCAkJ8ff337t3r6kDAR2w/f3EddsABoBSGwAAANRCtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWfartLVu29OzZk8Ph7N692+ABMZSSksL5qwEDBhih31OnTnXr1u348eNG6As6pTNnzixfvvzIkSPe3t70V3fGjBnKG4SHh9va2lpZWfXv3//KlSumilNBLpdv27YtNDRUZTmT32BeXt7w4cPFYrGrq2tiYmJLSwvzfjdv3uzn5ycSiSQSiZ+fX1JSUm1tLfPGNaw9duzY5s2b29vbmQcDoDdLGTWam5v9/PxWrVpl6kD+H0vJm94WLFigOHhOnz5deRU9TBBCzH+kMHmEao/nR48eVeTWTJ7xrE+1vWTJkosXLxo8FItAUZSpQwALtmbNmh07dqxYsSI6OvrOnTtSqbRHjx4HDhw4efKkYpvTp08fPnx4woQJRUVFQUFBJoyWEFJSUvLKK68sXry4sbFR1/cWFRWFh4ePHj26srIyOzv7iy++iIuLY/72H3/8cf78+aWlpY8fP16/fv3mzZvfeustho1rXjtx4kShUDh69Ojq6mpdPxSArixl1Fi5cmVxcbGpo/j/WUreOqJ79+65ubnFxcXKk3MrhglCiPmPFCaPUO3xPCIioqys7MKFC+PGjWOpX12xeCVJU1PT8+fDDGj//v2Ukt9//529vhTGjx9fU1MzYcIEI/TFdgLByDZt2pSVlXXo0CFbW1vFwh07dnC53NjY2JqaGhPGptZvv/320UcfxcXFDR48WO0Gmn+D69evd3FxWbt2rUQiCQkJSUxM/Pe//33jxg2GvVtbW7/33ntOTk42NjaTJ0+OjIz873//++effzJpXGvXCxcuHDRo0Lhx49ra2nTOC4AuLGLUuHjxonHGUOYsIm8dJBKJ3njjjT59+igetKd2mCDmPVLQTBjh88dzDofj5uYWFhZmPs/PYrHa3rt3b0VFBXvtd3pIYGdy69atpKSktWvXCoVC5eWhoaGLFi16+PDhkiVLTBXbiwwaNOjIkSPTpk3T45GrbW1tJ0+eHDlyJIfDoZeMHTuWoqicnByGLWRnZyvnys3NjRBSX1+vtXGGXScnJxcUFGzfvl3XjwZgtvQbNZqampYuXdqVfwtmMtq+aJgg5j1S0Ewbofkfzw1TbZ8/f/5vf/ubWCy2s7MLCAiora1dtGhRQkLC7du3ORyOj4/P9u3bJRIJl8sdMmSIs7Mzn8+XSCRBQUFhYWEeHh5CodDe3n7ZsmUGCYY9eXl5np6eHA7n008/JYSkp6dLJBKxWJyTkzN27Fg7Ozt3d/fMzEzF9jt27BAKhT179lywYIGrq6tQKAwNDb106RK9Nj4+3tra2sXFhX753nvvSSQSDofz5MkTQohKAultvv32Wzs7uw0bNhj1Y4Mh7Nixg6KoiRMnPr8qJSWlT58+n3/++ZkzZ9S+l6KotLS0fv36CQQCBweHyMhI5dO0Wr+H7e3tq1ev9vT0FIlEAwcOPHjwoME/3fPu3LlTX1/v6empWCKVSgkh165d06/BkpISe3v7Xr16aW2cYdcODg4jR47cvn17V/iDNZiKTqOG5iGD6DVqMLFy5Ur670gG+9gd1jVHWw3DBLGEkcKEEVrA8Vz5D8ElJSWEkLNnz1La0Ft+9tlnFEXV19fb2dlt3ry5qanp0aNHkyZNqqyspCgqOjpaKpUq3rJmzRpCyKVLlxoaGp48efLGG28QQk6ePFlZWdnQ0BAfH08IKSgo0No1bf369e7u7vb29nw+v3fv3hEREb/88gvD91IU9eWXXxJCmpubmb+F9uDBA0LIzp076ZcrV64khHz//fc1NTUVFRVhYWESiaS1tVWxfWxsrEQiuX79enNzc1FR0bBhw2xtbUtLS+m106ZNc3Z2VmycmppKCKGzRz2XQIqiTpw4YWtru27dOl3DpigqMDAwLi5OjzeCQXh7e/v7+6sslEqld+/epSjq4sWLXC63d+/e9fX1FEXl5uZGREQoNlu9erW1tfX+/furq6uvXbsWFBTk6Oj46NEjxQaav4dLliwRCATffPPNs2fPVqxYweVyL1++rFPwL7/88qBBg1QWav4Nnj9/nhCSmpqq/BaRSDR69Gidum5tbS0rK9u5c6dAIFBcuKK5ceZd0/chXb16VaeQwFCYjzhGtnXrVltbW0O1ptOooXnIoHQfNbTKy8ubOHEiRVGVlZWEkJUrV3bkwz6PLolqamp0faPljrbBwcFz587VullsbKybm5vyErXDBGUJI4U5RKj2eL5w4cIePXow+Qi2trZbt25l+Hn1YIBz2/fu3autre3fv79QKHR2dj5y5IiGO0D9/f3FYnGPHj3eeecdQoinp6ejo6NYLKZvyGV+TeesWbOOHTv24MGD+vr6zMzM0tLSkSNHFhUVdfzj6CE0NNTOzs7JySkmJqahoaG0tFR5LY/Ho/8n5+/vn56eXldXl5GRoV9H48ePr62tTUpKMkTUYDwNDQ13796lz7CqFRIS8uGHH967d++jjz5SWdXU1JSWljZp0qTp06d369YtICBg9+7dT5482bNnj8qWar+Hzc3N6enpUVFR0dHR9vb2q1at4vP5en8DlWn+DdJzgFhZWSm/hc/nNzU16dSLh4eHu7t7cnLyJ598MmXKFCaNM++avqSvsLBQp5AAOk7DqGHAIUOrpqamRYsWpaens9S+wXXi0VbrMEEsYaQwYYRmfjw3QLXt7e3ds2fP6dOnJycn37t3j+G7rK2tCSGKS9r5fD4hRCaTMXy7h4dHYGCgjY2NtbV1cHBwRkZGU1PTrl27dI7eoOgPpeFTDB06VCwWM/9PBXQOFRUVFEWJxWIN26SkpPTt23fXrl15eXnKy4uKiurr64cOHapYMmzYMGtra+W/L6tQ/h4WFxc3NjYq5uYTiUQuLi4G+QZq/g3S1x2q3IPY2toqEol06uXBgwcVFRVff/31vn37AgMD6WsrNTfOvGt6jzx+/FinkAAMSPOowfaQsWLFin/84x/0TRGWpfONtkyGCWIJI4WpIjTz47kBqm2RSPTDDz+MGDFiw4YN3t7eMTExup6+6riAgAArK6ubN28auV89CAQC+g920HU0NzcTQjTfaygUCjMyMjgczty5c5V/QfSsRjY2Nsob29vb19XVMem6oaGBELJq1SrF5KP379/XYzo/rVR+g/QlksozZDc2NjY3N7u6uurULJ/Pd3JyCg8Pz8rKKioq2rhxo9bGmXdN19/03gEwT+wNGXl5eYWFhfPmzWOjcXNgWaMtk2GCWMJIYaoIzfx4bpi7JPv373/8+PHy8vLExMSDBw9u2bLFIM0yJ5fL5XK5HjMnGJlMJquurnZ3dzd1IGBU9FFA6+NUQkJCFi9eXFJSsn79esVCe3t7QojK8Yj5t4i+82nbtm3KF5Dl5+fr+hG0UvkNenl52dra3r9/X7HBrVu3CCEDBw7Ur30fHx8rKyv6ShXNjTPvurW1lfzf3gEwQ6wOGXv37v3++++5XC5du9DHig0bNnA4nP/9739s9GhMFjfaMhwmiCWMFCaJ0MyP5waotsvLy69fv04IcXJy+vjjj4OCguiXrBozZozyS/pK+ZCQELb77aBz585RFBUcHEy/5PF4zC+eActFP3uVySyk69ev9/Pzu3r1qmLJgAEDbGxslAe/S5cutba2DhkyhEnX9Jw/BQUFeoStmebfII/HGzdu3IULF+RyOb0kNzeXw+G86HZ7FVVVVVOnTlVeUlJS0t7e7uHhobVx5l3Te8TZ2VmXzw1gPCpDBjHoqJGRkaFcuCjfJan8534LZXGjLfNhgljCSGH8CM38eG6YanvBggU3btxobW29evXq/fv36e939+7dy8vL7927V1dXZ/Bv+cOHD7Oysqqrq2UyWX5+/rx58zw9PXV6Up3RyOXyZ8+etbW1Xbt2bdGiRZ6enrNnz6ZX+fj4PH369OjRozKZrLKyUvlsHFGXwNzcXMwAaInEYrG3t3dZWZnWLem/wSnf4ScUChMSErKzsw8cOFBbW1tYWBgXF+fq6hobG8uka6FQOGfOnMzMzPT09Nra2vb29rKyMsUzYmJiYpydnfV7pq7W32BSUtLjx4/XrFnT0NCQn5+fmpo6e/bsvn37MulaIpGcPn36hx9+qK2tlclkV69enTVrlkQiWbx4MZPGNa9VoPdIQECAHh8fgCUahgyi+6jRpVj0aMt8mCDGHSn0GyaMPJYR8z+eK//XluF8TFu3bqX/9yCRSCZNmnTv3r3Q0FAHBwcrK6uXXnpp5cqVbW1tFEVduXKlV69eIpFoxIgRy5cvpy9g7927948//rhp06Zu3boRQpydnb/66qusrCy6QQcHh8zMTCZzqSQkJEilUolEwuPx3N3d58+fX15ezuSNNP1mANy5cyd9SahYLJ44ceKuXbvoD+Xr63v79u09e/bY2dkRQnr16nXz5k36LbGxsXw+383Njcfj2dnZRUZG3r59W9FgVVXVq6++KhQKvby8Pvjgg6VLlxJCfHx86EmLlBNIT5Fz6tQpW1vblJQUncKmYQZA04qPj+fz+Y2NjfTL7Oxs+t5zR0fH999/X2XjpUuXKs+aJJfLU1NTfX19+Xy+g4NDVFRUcXGxYq3W72FLS0tiYqKnpyePx3NycoqOji4qKqLfGxUVRQhZvXq12pjz8/OHDx+uuNzZxcUlNDT0/Pnz9Fomv0F6Jn6BQODq6rp06VLlX5zmrimKmjhxopeXl42NjUAgkEqlMTExhYWFDBvXupY2fvx4Nzc3uVz+ohiAVV1hBkBdRw3NQwal+6jBnFnNAGjRo63eMwCqDBOUeYwUmo/V5hAhTe3x3HxmANRzvm1Lp/d827qKjY3t3r07270wgWrbtEpKSng8nsqjzk2uvb09LCxs7969Xapr2pMnT4RC4ZYtW0wVAJjtiGPY+bZ1Yj5DhqHoPd+2rswndXpX2xgm9Pai47n5VNssPrkdaExueoBOz8fHZ926devWraOfPW4O2tvbjx49WldXFxMT03W6VkhOTh48eDD9XC0A84EhQ28Wl7qmpqbvvvuupKSEvsMPw4TeVI7nFEWVl5fn5eXRt8ibA3Ostm/cuMF5MTPf5QAvsnz58smTJ8fExDC8D4Zt586dO3LkSG5urtYZXjtT17S0tLSCgoJTp07RM/0DdD4YSc3f06dP33jjjT59+sydO5degmFCD88fz3Nyctzc3MLCwk6ePGna2BTMsdr28/PTcDY+KyvL1AEytWLFioyMjJqaGi8vr2+++cbU4YDpbdiwIT4+/uOPPzZ1IIQQMnr06K+++oq+OLLrdE0IycnJaWlpOXfunIODg0kCAFDLsENGpxlJmbDE0Xb37t2K3XHgwAHFcgwTOlF7PI+MjFTk9smTJyYMT4Fn6gA6s40bN9IP4wBQCA8PDw8PN3UUXVpERERERISpowBQhSFDb50sdRgmmLOU47k5ntsGAAAAAOgcUG0DAAAAALAF1TYAAAAAAFtQbQMAAAAAsAXVNgAAAAAAW9TMSZKcnOzk5GT8UIzp/v37hJCpU6dyuV3l/xulpaW1tbWTJ082dSAAAIQQYj6P8HheS0sLjpYGUVZWRgiZOXNm15nb/vbt248ePcL3x7K0tLSw2r6aaruhoUEgELDaq8k1NTURQmpra7tOtd3e3t7a2lpbW2vqQAAACCGksbHR1CFogqOlQdB7ua6ujsfrKjMOt7e3y2QyfH9AmZpvf2pq6qhRo4weiVHt379/5syZJ06c6PT/r1AICgoKDg5OT083dSAAAIQQcuvWLV9fX1NHoZ5AIPjuu+9MHUVncOjQoSlTpvznP/+xs7MzdSxGEhIS4u/vv3fvXlMHAjpg+/vZVc7sAgAAAAAYH6ptAAAAAAC2oNoGAAAAAGALqm0AAAAAALag2gYAAAAAYAuqbQAAAAAAtuhTbR85csTb25vD4XA4HBcXl+nTp79oy99++y0mJsbLy0sgEDg6Og4aNCglJUWxNiYmhqPRiRMnlPtKSkpS20taWhqHw+FyuX5+fhcuXNDjE4HCggULFPlX2bNnzpxZvny58h6ZMWOG8gbh4eG2trZWVlb9+/e/cuWKcQNXTy6Xb9u2LTQ0VGV5SkqKypdtwIAByhvk5eUNHz5cLBa7uromJibqNO/95s2b/fz8RCKRRCLx8/NLSkp6fuJVze1rWHvs2LHNmze3t7czj0fBsvag1jSaJIfEotLYwRxq2OD5HB49elTxa3J0dGT7o5nczz//3K9fPy6Xy+FwnJ2dlYc242A+EIN50jrakr/uZfM8zpg8QrXHc3M8HFFKSkpKCCFnz56lGJBKpd26ddOwwbVr18Ri8cKFC+/evdvU1FRcXLxs2bLRo0crNpgyZcrp06erq6tlMtmff/5JCJk4cWJra2tDQ0NFRcX8+fOPHz+u6IsQ4uLi0traqtJLW1tbr169CCHKLWv15ZdfEkKam5uZv8XSBQYGxsXFad0sNja2e/fuubm5xcXFyvlZvXr1hAkTamtr6ZdSqbRHjx6EkBMnTii/PTc3NyIiwrCR6+3mzZvDhw8nhAwaNEhl1fr161V+CP3791es/f3330UiUVJSUn19/cWLFx0dHefMmcO83/Hjx2/ZsqWioqKuru7QoUN8Pv/1119X3kBz+1p73759+8iRI589e6ZDLixwD2pOo0lySFlaGjuSQ60bqORQLpeXlZVduHBh3LhxPXr0YBKeTiOOMW3dutXW1pbJlmPGjCGE6PFFMhStA7HJHTx4kBBSU1Nj6kCMJzg4eO7cuVo3YzjaUmZ/nKFMHeHzx3M9Dke2trZbt25lLUaKxWp75syZL730kvKSlpaWN998U/EyJiamoaGB/jddbSvvld27dytX20OGDCGEHDp0SKWXgwcP0mcuzbDabmxsDAkJMZPWmFfbbm5uKgs//vjjPn36NDU1KZZIpdKvvvqKy+W6ublVV1crlpvPj7+goGDSpEkHDhwYPHiw2mp7//79L3rvlClTvLy85HI5/TI1NZXD4fzxxx8Mu46KilLOFf383vLycobtM+k9Pj4+JCREJpMxDMkS96DmNBo/h5QFprEjOWSygdocLly4ENU2S54fBVBt08xqtGVebTMZbSmzP85QZhDhi47nzA9HbFfbLF63XVVVVVNT8/TpU8USa2vr48ePK15mZmaKxeIXvT02NvbNN99UvHz33XcJIZ999pnKZmlpaQkJCQYL2qD27t1bUVFhnq0xd+vWraSkpLVr1wqFQuXloaGhixYtevjw4ZIlS4wflVaDBg06cuTItGnTdH1caFtb28mTJ0eOHMnhcOglY8eOpSgqJyeHYQvZ2dnKuXJzcyOE1NfXM2mfYe/JyckFBQXbt29nEo+F7kENaTR+DollplHvHDLZgOieQ+ggU40C5q9zj7bEvI8zNNNGaP7HIhar7WHDhjU0NLz22ms//fRTx1t77bXX+vXrd/bs2eLiYsXCn376qbGxMTw8vOPtq0VRVFpaWr9+/QQCgYODQ2Rk5I0bNxRr4+Pjra2tXVxc6JfvvfeeRCLhcDhPnjwhhCxatCghIeH27dscDsfHx2fHjh1CobBnz54LFixwdXUVCoWhoaGXLl3SrzV6m2+//dbOzm7Dhg0sfXzajh07KIqaOHHi86tSUlL69Onz+eefnzlz5kVv15DD9PR0iUQiFotzcnLGjh1rZ2fn7u6emZmp/Pb29vbVq1d7enqKRKKBAwfSp0lYdefOnfr6ek9PT8US+kKma9eu6ddgSUmJvb09fb2T1vYZ9u7g4DBy5Mjt27dTFKU1gM6xB5XTaPwcko6lUfORRGsajZ9DJhsQ3XPY6WnelZpHAc1DAHnBKMDEjz/+6O/v361bN6FQGBAQQD+Rft68efRVrVKp9OrVq4SQOXPmiMXibt26HTt2jLz4W/fJJ5+IxWJbW9uKioqEhAQ3NzflQVlvBhxtScdSbZ6jLbGE44wJI7SAY5HyiW7DXknS2Ng4dOhQuhd/f//NmzdXVVW9aOPnryRR6evu3bv//Oc/CSGLFi1SLI+KisrIyKirqyPsXEmyevVqa2vr/fv3V1dXX7t2LSgoyNHR8dGjR4oNpk2b5uzsrHiZmppKCKmsrKRfRkdHS6VSxdrY2FiJRHL9+vXm5uaioqJhw4bZ2tqWlpbq1xpFUSdOnLC1tV23bh2Tj6z3lSTe3t7+/v4qm9F7hKKoixcvcrnc3r1719fXU+r+bKQ5hytXriSEfP/99zU1NRUVFWFhYRKJRPnq/CVLlggEgm+++ebZs2crVqzgcrmXL19m8nkVXn75ZbVXkri7u9vb2/P5/N69e0dERPzyyy/0qvPnzxNCUlNTlbcXiUQ6fcEoimptbS0rK9u5c6dAIFC+akVz+8x7p++huXr1qtZILHoPqk2j8XNIdSyNWo8kmtNo/Bwy2YD2fA67+JUkmnel5lFA8xBAqRsFmFxJcvjw4eTk5KdPn1ZVVQUHByv2TnR0tJWV1cOHDxVbTp069dixY/S/NXzr6M+4cOHCnTt3Tpo0SfNVdgyvJDHsaEt1LNUdHG31vpJE7UGGsoTjjDlEqPZ43iWuJBGJRBcvXvznP//p5+d3/fr1xMTEfv360Udw/cyaNUsikezbt6+pqYkQcufOncuXL0+dOtVwIf9FU1NTWlrapEmTpk+f3q1bt4CAgN27dz958mTPnj16t8nj8ej/1fn7+6enp9fV1WVkZOjd2vjx42tra180VYtBNDQ03L17lz6npVZISMiHH3547969jz766Pm1DHMYGhpqZ2fn5OREX8pfWlpKL29ubk5PT4+KioqOjra3t1+1ahWfz+9IxhRmzZp17NixBw8e1NfXZ2ZmlpaWjhw5sqioiBBCz7pgZWWlvD2fz6e/dcx5eHi4u7snJyd/8sknU6ZMUSzX3D7z3n19fQkhhYWFmsOw9D2oNo1GziHpWBqZH0nUptEkOWSyAY15DruUF/0iiKFHASbeeuutNWvWODg4dO/efeLEiVVVVZWVlYSQuLi49vZ2Re+1tbWXL18eN24cYfat27Rp0/vvv3/kyBE/P78ORsjGaEsMmmpzGG2JeR9nTB6hmR+L2J1vm8/nx8fH//HHHz///HNkZGRFRcXkyZOfPXumX2vdunWbOnXqs2fPsrKyCCHbtm179913ra2tDRry/6+oqAQ0iRsAACAASURBVKi+vl5xep4QMmzYMGtra+XLPzpi6NChYrFY+c8oZqiiooKiKA2X1xNCUlJS+vbtu2vXrry8PJVVuuaQ3psymYx+WVxc3NjYqJibTyQSubi4GCRjHh4egYGBNjY21tbWwcHBGRkZTU1Nu3btIoTQF8y1tbUpb9/a2ioSiXTq4sGDBxUVFV9//fW+ffsCAwMVVwFqbp957/ROefz4seYwLH0Pqk2jkXNIOpZGPY4kymk0SQ6ZbEBjnsOuSeUXocL4owCfzyeE0NOlvfbaa3369Pniiy8oiiKEZGVlxcTE0P+/Yu/Yqxbboy2xhAGXyUGGmPFxxuQRmvmxyEhPt3n55Zf/85//xMXFVVZWnj17Vu926Hsld+/eXV1dffjw4QULFhguRlXV1dWEEBsbG+WF9vb29IUrBiEQCOhzDGarubmZEKL5RkOhUJiRkcHhcObOnaty3quDOWxoaCCErFq1SjFx5v379xsbG3X9FFoFBARYWVndvHmTEEJfzKc8LXFjY2Nzc7Orq6tObfL5fCcnp/Dw8KysrKKioo0bN9LLNbfPvHe66KF3kAaWvgfVptHIOSQdS6Ml5pDJBjTmOQS1jDAKnDx5ctSoUU5OTgKBYNmyZYrlHA5nwYIFd+7c+f777wkhX3755d///nd6ldGOvTQjjLbE7AdcJgcZYsbHGZNHaObHIgNX2xcuXNi2bRv97+joaJXzIvTM5x35xQ4ePDg4OPiXX36JjY2dPHmyg4NDR6LVzN7enhCi8j2orq52d3c3SPsymcyArbGE/vpqfQ5ISEjI4sWLS0pKVOax7mAOnZycCCHbtm1TvvgpPz9fp4/AhFwul8vl9GHOy8vL1tb2/v37irW3bt0ihAwcOFC/xn18fKysrOjLVLS2z7z31tZW8n87SINOsweV02jkHJKOpdESc8hkAxrzHMLzWBoFlAfi0tLSqKgoFxeXS5cu1dTUbN68WXnL2bNnC4XCzz//vLi42M7OTnEzt9GOvTS2R1tiCQMuw4MMMfvjjKkiNPNjkYGr7V9//VUikdD/bmlpuX79uvJa+s5lvasWGn16+5tvvvnwww870o5WAwYMsLGx+d///qdYcunSpdbWVnrmbxqPx3vRnwi1OnfuHEVRwcHBBmmNJT179uRwODU1NVq3XL9+vZ+fH31vuwKTHGrg4eEhFAoLCgp0DVsr+t4mBfpOi5CQEEIIj8cbN27chQsX5HI5vTY3N5fD4bzoPnEVVVVVKvcSlJSUtLe3e3h40C81t8+8d3qnODs7a47HQveg5jQaOYekY2m0xBwy2YDGPIfwPJVRwFBDgPJAXFhYKJPJ3n33XW9vb6FQqJjPkebg4DBlypSjR49u2bJl/vz5iuXsHXvVYnu0Jayl2oCYH2SIOR1nzCdCMz8WGazalslkjx8/PnfunOJHTgiJioo6dOhQdXV1TU1NTk7ORx99FBER0cFq++2333Z0dIyKivL29u5w1JoIhcKEhITs7OwDBw7U1tYWFhbGxcW5urrGxsYqtvHx8Xn69OnRo0dlMlllZaXyeSBCSPfu3cvLy+/du1dXV0f/sOVy+bNnz9ra2q5du7Zo0SJPT8/Zs2fr3Vpubi7bcxKJxWJvb++ysjKtW9J/PFK5p4pJDjW3OWfOnMzMzPT09Nra2vb29rKyMnr6mpiYGGdnZ70fBvvw4cOsrCz6Oab5+fnz5s3z9PSMi4uj1yYlJT1+/HjNmjUNDQ35+fmpqamzZ8/u27ev4u0aepdIJKdPn/7hhx9qa2tlMtnVq1fpu3sXL16s2EZz+1p7p9E7JSAgQHM8FroHtabRmDkkHUsjeznUHHYHc8hkA/LXHAITGkYBzUMAUTcKqHh+IKbncDxz5kxzc3NJScnz18jGxcW1tLScOHFiwoQJioWav3UGx8ZoSzqQajMfbYlxjzP6jbZGPhIS8z8WKZ+TZzgfU3Z2tobbZrOzs+nNTp8+PWXKFKlUKhAIrK2t+/btm5yc/Pyke7W1ta+88kr37t0JIVwu18fHZ8OGDc/35ejo+P7779MLly1bdvHiRfrfq1atoq8v5HK5/v7+P/74o+bgaQxnAJTL5ampqb6+vnw+38HBISoqqri4WHmDqqqqV199VSgUenl5ffDBB0uXLiWE+Pj40NMMXblypVevXiKRaMSIEY8ePYqNjeXz+W5ubjwez87OLjIy8vbt23q3RlHUqVOnbG1tU1JSmHxkvWcAjI+P5/P5jY2N9Eu1e0Rh6dKlKvPHacjhrl276NsafH19b9++vWfPHjs7O0JIr169bt68SW/T0tKSmJjo6enJ4/GcnJyio6OLioooioqKiiKErF69+kUfJD8/f/jw4coXoYaGhp4/f55em5CQIJVKJRIJj8dzd3efP3++8rMeKYo6f/783/72N4FA4OrqunTpUpWviubeJ06c6OXlZWNjIxAIpFJpTExMYWGhyjaa29e8ljZ+/Hg3Nzf6OX+a47HQPag1jcbMYQfTqPlIojWNL8qh1rA7mEMmGyjnkNZFZgD8+eef+/fvz+Vy6cMLPWxp3ZWaRwHNQwD111Hgs88+YzIQUxSVmJjYvXt3e3v7yZMnf/rpp4QQqVSqPPlsYGDg8uXLVT7gi751mzdvpv9Y7+HhoeGJvAoMZwA07GjbwVR3cLTVewZAlYMMZR7HGc0HGXOIkPb8sYgypxkA9Z9v26IZ58ntKmJjY7t3727MHpXpXW2XlJTweDwmR1Vjam9vDwsL27t3bxfsnaKoJ0+eCIXCLVu2MIkHe1AtnXJIIY3qqOSQ1kWqbf2YdhR4kXHjxt25c4elxo3z5PbnmTDVelfbOMjoTe2xiDKnattIc5IAjckNECbX1NT03XfflZSU0Pcc+Pj4rFu3bt26dYpnj5tce3v70aNH6+rqYmJiulrvtOTk5MGDB8fHxzOJB3tQLZ1ySJBGdZRzSFFUeXl5Xl4efTMlvIiZjAKKyy2uXbtGn+I1bTxsMJNUa4DR1lCUj0XELA9HqLZB1dOnT994440+ffrMnTuXXrJ8+fLJkyfHxMQwvIGDbefOnTty5Ehubq7WqUk7X++EkLS0tIKCglOnTtFT5zKJB3tQhR45JEjjX6nkMCcnx83NLSws7OTJk8YPBnSVmJhYUlJy8+bNOXPmqMxEBEaD0dYgVI5FxDwPR8onus3273oGZ/wrSZYvX07P0967d+/Dhw8brV8FhleSaPDdd98lJiYaKh7Qz9GjRzdu3NjW1qbHe7EHaR3JIYU0UhTV4RzSzHbEYelKEpOPAspWrlzJ5XI9PDwUj2pniUmuJDFtqhleSaIBDjLMGeRYRLF/JQmHoihF5X3r1i1fX9+zZ8+OGjXKBIW/Ee3fv3/mzJnNzc1aZ5LvNIKCgoKDg9PT000dCAAAIWY84qSlpSUnJys/2Qf0dujQoSlTptTU1NB3vHUFISEh/v7+e/fuNXUgoAM7O7vk5GTl2cMMC1eSAAAAAACwBdU2AAAAAABbUG0DAAAAALAF1TYAAAAAAFtQbQMAAAAAsIX3/KJly5Y5ODgYPxRj+vPPP7lc7ptvvkk/d7cruHPnTlVV1ZgxY0wdCAAAIYQ0NTWZOoQXamxsxNHSIB4/fszlcqOiong8NfVGp1RcXFxWVobvj2VpbGxktf2/zAD46NGjDz74gNX+ADqfxsbG8+fPv/zyy927dzd1LAAWZu3atf7+/qaO4i9OnDixb98+U0cBJvDrr78SQoYMGWLqQMAEZs2a9eabb7LU+F+qbQDQg9lOGwwAAMxFREQQQnJyckwdCHQ2XeU6CgAAAAAA40O1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWVNsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWVNsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWVNsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWVNsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWVNsAAAAAAGxBtQ0AAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWDkVRpo4BwMI8fPhw3bp1ipd1dXXZ2dljxoxxcXFRLPzggw8GDBhgiugAAICR7du3//HHH4qXP/zwAyHktddeUyzp16/fokWLTBAZdC6otgF0JpfL3d3dHz16xOWq+euQXC4XCASVlZU2NjbGjw0AABjaunXrkiVLrKys1K5tb2/fsmVLQkKCkaOCzgdXkgDojMvlTps2zcrKql0dKyuryMhIlNoAAGbunXfe4XK5ao/k7e3tHA4nJibG1DFCZ4BqG0Af77zzTltbm9pVbW1tU6dONXI8AACgq5deemn48OFqz21bWVmFhYW5ubkZPyrofFBtA+gjKCjI19eXw+E8v8rW1jY8PNz4IQEAgK6mT58ul8ufXy6Xy6dPn278eKBTQrUNoKfp06c/f902n89/5513BAKBSUICAACdTJ48We25bS6XO2nSJOPHA50Sqm0APU2bNu35MyIymeydd94xSTwAAKArBweHMWPG8Hg85YU8Hm/s2LE9evQwVVTQyaDaBtCTVCodNGiQyuntnj17hoWFmSokAADQ1bRp01Tuw2lra5s2bZqp4oHOB9U2gP5mzJih/JLH482YMeNFk0kBAIAZioiIEIlEykuEQuGbb75pqnig80G1DaA/lYtG2tracBkJAIBlEYvFkZGRiotJeDzepEmTMIsrGBCqbQD9ubq6jhgxQnEyu1evXkOGDDFtSAAAoKupU6cqLibBLK5gcKi2ATpEMXsUj8ebPXu2qcMBAACdjRkzplu3bvS/7ezsXn/9ddPGA50Mqm2ADnnrrbfoc9ttbW146hgAgCXi8/lTpkzh8/l8Pj8mJsba2trUEUGngmoboEMcHBzeeOMNQsigQYP8/PxMHQ4AAOjjnXfekclkMpkMl5GAwf1lgsn6+vpTp06ZKhQACyWVSgkhAQEBhw4dMnUsABbm1VdfdXJy6mAjGLyg4yiKoi8mefToEQ7m0EHjxo37y422lJKSkhLTBQYAAF3O2bNnqQ7D4AUAZqWkpET5GMV7fouTJ0+OGDHC+JEZU1ZWVmxsbEVFRdd5wnZYWNiwYcPS0tJMHUjntG/fvlmzZpk6CgBLcufOncDAQAM22BUGL0thoSNOQUEBh8MZNGiQrm/My8sbP3781atXvb292QgMLAj9ZVBZqKbaFovFdnZ2RgnJZOh57O3s7LpOtW1lZWVtbd3p96ypfPDBB6YOAcDCGHw+464weFkKCx1xXnnlFf3eKBaLCSE2NjYW95HB4OgvgwrcJQkAAAAAwBZU2wAAAAAAbEG1DQAAAADAFlTbAAAAAABsQbUNAAAAAMAWfartLVu29OzZk8Ph7N692+ABMSeTyTZu3Ojj42NtbW1vbz9gwIB79+6x3empU6e6det2/PhxtjuCzurMmTPLly8/cuSIt7c3h8PhcDgzZsxQ3iA8PNzW1tbKyqp///5XrlwxVZwKcrl827ZtoaGhKstTUlI4fzVgwACVbfLy8oYPHy4Wi11dXRMTE1taWpj3u3nzZj8/P5FIJJFI/Pz8kpKSamtrmTeuYe2xY8c2b97c3t7OPBgAZcOGDbOysho8eHBHGpk3b56trS2HwykoKGC4gZEHIK2/QTZgkGWCHkcIIeY/lJhDhOZwzNen2l6yZMnFixcNHoqupkyZ8uWXX3711VeNjY1//PGHVCqtr69nu1OKotjuAjqxNWvW7NixY8WKFdHR0Xfu3JFKpT169Dhw4MDJkycV25w+ffrw4cMTJkwoKioKCgoyYbSEkJKSkldeeWXx4sWNjY26vreoqCg8PHz06NGVlZXZ2dlffPFFXFwc87f/+OOP8+fPLy0tffz48fr16zdv3vzWW28xbFzz2okTJwqFwtGjR1dXV+v6oQAIIZcvX3711Vc72Mjnn3/+r3/9S6cNjDwAaf4NsgSDrFaKcYQQYv5DiTlEaA7HfBavJGlqanr+fJihZGVlHT169PDhwy+//DKPx3N1dc3JyXn+1JrBjR8/vqamZsKECWx3RFhOIBjfpk2bsrKyDh06ZGtrq1i4Y8cOLpcbGxtbU1NjwtjU+u233z766KO4uLgXncPbv3+/8rOyfv/9d+W169evd3FxWbt2rUQiCQkJSUxM/Pe//33jxg2GvVtbW7/33ntOTk42NjaTJ0+OjIz873//++effzJpXGvXCxcuHDRo0Lhx49ra2nTOCwAhhBAOh2PkHo05ABFtv0GWYJDVTO04Qsx7KKGZNkKTH/NZrLb37t1bUVHBUuOfffZZUFBQQEAAS+2bA1YTCEZ269atpKSktWvXCoVC5eWhoaGLFi16+PDhkiVLTBXbiwwaNOjIkSPTpk3T4yFQbW1tJ0+eHDlypKIiGTt2LEVROTk5DFvIzs5WzpWbmxshhP77lebGGXadnJxcUFCwfft2XT8aAI3P53ewBa31umELeoqiDh8+vGfPHobba/gNdg4WN8i+aBwh5j2U0EweoWmP+Yapts+fP/+3v/2Nfo5XQEBAbW3tokWLEhISbt++zeFwfHx8tm/fLpFIuFzukCFDnJ2d+Xy+RCIJCgoKCwvz8PAQCoX29vbLli1j2F1ra+vPP//cwWvm9JCXl+fp6cnhcD799FNCSHp6ukQiEYvFOTk5Y8eOtbOzc3d3z8zMVGy/Y8cOoVDYs2fPBQsWuLq6CoXC0NDQS5cu0Wvj4+Otra1dXFzol++9955EIuFwOE+ePCGEqCSQ3ubbb7+1s7PbsGGDUT82GMKOHTsoipo4ceLzq1JSUvr06fP555+fOXNG7XspikpLS+vXr59AIHBwcIiMjFQ+Tav1e9je3r569WpPT0+RSDRw4MCDBw8a/NM9786dO/X19Z6enoolUqmUEHLt2jX9GiwpKbG3t+/Vq5fWxhl27eDgMHLkyO3bt+Mv112K2p+DfiPUrVu3/Pz8JBKJSCQKCwvLy8vT3AshhKKo1NTUvn37CgSCbt26LV26VKVNDRvoOgDRYWzcuLFv374ikcjR0dHLy2vjxo1vv/22fqlT/g2yRKfPqHmEJboPsuY/wmoYR4glDCWmjdDEx3zlPwSXlJQQQs6ePUtpQ2/52WefURRVX19vZ2e3efPmpqamR48eTZo0qbKykqKo6OhoqVSqeMuaNWsIIZcuXWpoaHjy5Mkbb7xBCDl58mRlZWVDQ0N8fDwhpKCgQGvXFEXdvXuXEDJ48OBRo0a5uLgIBAI/P79PP/1ULpczeTtFUV9++SUhpLm5meH2Cg8ePCCE7Ny5k365cuVKQsj3339fU1NTUVERFhYmkUhaW1sV28fGxkokkuvXrzc3NxcVFQ0bNszW1ra0tJReO23aNGdnZ8XGqamphBA6e9RzCaQo6sSJE7a2tuvWrdM1bIqiAgMD4+Li9HgjGIS3t7e/v7/KQqlUevfuXYqiLl68yOVye/fuXV9fT1FUbm5uRESEYrPVq1dbW1vv37+/urr62rVrQUFBjo6Ojx49Umyg+Xu4ZMkSgUDwzTffPHv2/7F35wFNXenfwE8gIRu7ICLIjiIuda1CdazaoVZbFK1KrWPVqUUdi6hV1AqlilZFEXWgjsswHVtxqRbcqE7rVqu1VsEFN8SVUgERDBAgIbnvH+f9ZTIBsi8Evp+/vPee3PvkueSeY3LucyuXL19uY2Nz+fJlnYIfNGjQK6+8orJy1apV3t7ezs7OHA7Hz89v7Nixv/76q2Lr2bNnCSEpKSnKL+Hz+SNHjtTp0BKJpLi4eOvWrVwuVzFxRf3OtT80vc0oLy9Pp5DAWLTvcYy4q5Y+Drr2UCNHjgwICHj48KFUKr158+agQYN4PN69e/fUH+XTTz9lsVgbN26srKwUi8Xp6ekqf4HqG+jaAa1evdrW1jYnJ0csFl+5csXDw+P111/XNbfNfgY10rvH0ek9qu9hGR07WUN62NOnTxNCCgsL9Xit9prtRxhr6EpaSYTmueY3+8dghO+2Hz16JBKJevTowePxPDw8Dh486Obm1lLj0NBQgUDQoUOH9957jxDi4+Pj5uYmEAimTp1KCNFyTif9Jcvd3X316tUFBQWlpaXjxo2bN2/enj17DH87eggPD3d0dHR3d4+Ojq6trX3y5InyVjabTf+jFhoampGRUV1dnZmZqd+BxowZIxKJEhISjBE1mE9tbe3Dhw/pN6zNCgsLW7BgwaNHj5YuXaqyqa6uLjU1dfz48VOnTnVycurVq9e2bdueP3/e9OfgZv8O6+vrMzIyoqKiJkyY4OzsvGLFCg6Ho/dfoLIPPvjg8OHDT58+rampycrKevLkybBhwwoKCuhWWgPE1tZW+SUcDqeurk6no3Tp0sXb2zspKWn9+vWTJ0/WZufaHzo4OJgQcuPGDZ1CAuul8eOgUw/l4ODg5+fHZrN79OixY8eO+vp6+qls6Sh1dXWbNm164403Fi5c6OzszOfzXV1dlXeosUGz1HRA2dnZ/fv3j4yM5PP5/fr1Gzt27Llz5yQSiU5Ja/YzaGZq3mP76WE19iPEGroSy0ZowWu+EUbbAQEBHTt2nDp1alJSkvY1+Ozs7AghiunqdAKcVCrV5rV0FmmPHj3Cw8NdXV2dnJw+//xzJycn7aejmQh9U2rexYABAwQCgfY3ikHbUFZWxjCMQCBQ0yY5Oblbt27p6enKv0cTQgoKCmpqagYMGKBYM3DgQDs7O+UfTFUo/x3evXtXLBYrbiDm8/mdOnUyyl9gly5d+vbta29vb2dnN3jwYDqYoF/FEULotEKV+1EkEgmfz9fpKE+fPi0rK9uzZ89XX33Vt29fOslS/c61PzQ9I6WlpTqFBNZL+4+Drj1Ur169nJyc6Gyllo5y//59sVg8cuTIlnaisYF6TTsg+vutYlEmk3E4HJX/iGrU7GfQUtR3sm27h9WmHyHW0JVYMEILXvONMNrm8/mnTp0aMmTI6tWrAwICoqOjdf36Sleenp6EEDr1irKzs/P19S0qKjLpcY2Cy+WWl5dbOgowq/r6evJ//0tsCY/Hy8zMZLFYM2fOVP4E0YpF9vb2yo2dnZ2rq6u1OXRtbS0hZMWKFYqq2I8fP9ajnJ9GvXr1srW1vXfvHl2kcyWVq/OKxeL6+nr64dUeh8Nxd3ePiIjYu3dvQUHBmjVrNO5c+0PT8Tc9O9AemPTjwOFwaK/f0lGKi4sJIe7u7i3tQWMDXY0ePfrKlSs5OTl1dXW//fZbdnb222+/retou9nPYKvVhntYbfoRYg1diQUjtOA13zh3Sfbo0ePIkSMlJSXx8fH79u3bsGGDUXbbEnt7++Dg4Fu3bimvbGxsdHJyMulxDSeVSquqqry9vS0dCJgV/YRrLK0fFha2cOHCwsLCVatWKVY6OzsTQlQuN9r/FdGee9OmTcoTyC5evKjrW9BILpfL5XJFT+Dv7+/g4PD48WNFg/v37xNCevfurd/+g4KCbG1t6UwV9TvX/tD0J3Vdv24H62W6j0NjY+OLFy/ovbktHYX+6qLmGU8aG+gqKSlpxIgR06dPd3R0HD9+/KRJk9RX+FZP+TPYOrXtHlbLfoRYQ1diqQgteM03wmi7pKSEDnzd3d2/+OKLfv36qYyDTWHy5Ml5eXkPHjygi2Kx+PHjx62/IOCZM2cYhhk8eDBdZLPZWk6eAatGn72qTZHRVatWhYSE5OXlKdb07NnT3t7+t99+U6y5dOmSRCLp37+/NoemFRVaelidId58803lRXozSlhYGF1ks9mjR48+d+6cXC6na3Jzc1ksVkt306uoqKiYMmWK8prCwkKZTNalSxeNO9f+0PSMeHh46PK+wYqZ7uNw+vRpuVxOn9DR0lF69uxpY2ND7+JtlsYGuiooKCgqKiovL5dKpU+ePMnIyHBxcdHyteo/g62TSg9L2lYnq30/QqyhK7FIhBa85htntD179uw7d+5IJJK8vLzHjx/Tv3VXV9eSkpJHjx5VV1cb/c994cKFvr6+06dPf/LkSUVFRXx8fF1dXdNJ962BXC6vrKxsbGy8fv16XFycj4/P9OnT6aagoKAXL15kZ2dLpdLy8nLlb+NIcwnMzc1t5fWJoFkCgSAgIID+TKwe/YlN+adeHo+3aNGiQ4cOff311yKR6MaNG3PmzPH09IyJidHm0Dweb8aMGVlZWRkZGSKRSCaTFRcXK55PER0d7eHhod8jc3///fe9e/dWVVVJpdKLFy9++OGHPj4+yo9sTEhIKC0t/eyzz2pray9evJiSkjJ9+vRu3bppc2ihUHjy5MlTp06JRCKpVJqXl/fBBx8IhcKFCxdqs3P1WxXoGWn9/0sHY1H/cdCVRCJ5+fJlY2Pj1atXY2NjaZek5iju7u4TJkz49ttvd+3aJRKJrl+/rnKvkcYGupo3b56Pj49+FbI1fgZbCTU9LNGxk23lPaz2/Qgxb1eiXz9i5s6OsuQ1X/krdy2LKG3cuJH+z0AoFI4fP/7Ro0fh4eEuLi62tradO3f+9NNPGxsbGYa5evWqr68vn88fMmTIsmXL6OR0Pz+/n376ae3atXTWh4eHxzfffLN37166QxcXl6ysLPVHV3j69Ol7773n4uLC5XJfffXV3NxcLV/I6FsBcOvWrXRKqEAgiIyMTE9Pp28qODi4qKho+/btjo6OhBBfX19FHaiYmBgOh+Pl5cVmsx0dHceNG1dUVKTYYUVFxfDhw3k8nr+//8cff0xLqwYFBdECRsoJpBVwjh8/7uDgkJycrFPYFCoAWlZsbCyHwxGLxXTx0KFD9NZyNze3efPmqTRevHixclEkuVyekpISHBzM4XBcilQGowAAIABJREFUXFyioqLu3r2r2Krx77ChoSE+Pt7Hx4fNZtPuvKCggL42KiqKEJKYmNhszBcvXnzttdcU0507deoUHh5+9uxZunXRokWBgYFCoZDNZnt7e8+aNaukpERlD7QSP5fL9fT0XLx4sfInTv2hGYaJjIz09/e3t7fncrmBgYHR0dE3btzQcucat1Jjxozx8vLSvnIoGJdFKgA2+3FIS0vTtYfKzMwcPnx4x44d2Ww2rWHy+PFj9UdhGKa6uvrDDz/s0KGDvb39kCFDEhMTCSHe3t7Xrl2jL1TTQI8O6NSpUx06dFB09xwOp3v37gcPHtQyqxo/g2ro1+Po+h7V97CMjp2sIT2seSoAqvQjTOvoStRfzFtDhArmueY3+8egZ71ta6d3vW1dxcTEuLq6mvoo2sBo27IKCwvZbLb21WrNQyaTDR06dNeuXe3q0NTz5895PN6GDRssFQBYZLTdrqSnp8fFxSkWGxoaFixYwOVylYdrJmKeHqf19LDmGW2jHzGE2a75pqq3Deppc08DtHlBQUErV65cuXJl63nusUwmy87Orq6ujo6Obj+HVkhKSurTpw99aglA2/Ps2bPY2Ni//vWvijV2dnY+Pj5SqbTNTGUm7ayHRT9iCMte81vjaPvOnTuslrX+MwrQrGXLlk2cODE6OlrL21xM7cyZMwcPHszNzdVYwLUtHZpKTU3Nz88/fvw4raMM0Pbw+XwOh7Nr167S0lKpVFpSUrJz587ExMTo6OiSkhJ0slYK/Yh+LH7NZ1vkqOqFhIQwFnmKvbEtX748MzNTIpH4+/unpKS8++67lo4ILGz16tUnT5784osv1q5da+lYyMiRI/V+job1HpoQkpOT09DQcObMGV0LDwNYEScnp5MnT65cubJr1661tbX29vY9evRYu3btRx99xGaz20An2257WPQjumoN1/zWONpuM9asWdPKHwQA5hcREREREWHpKNq1sWPHjh071tJRAJjc0KFD//Of/1g6ClNpzz0s+hGdtIZrfmucSQIAAAAA0DZgtA0AAAAAYCoYbQMAAAAAmApG2wAAAAAApoLRNgAAAACAqTRTk+TYsWP37983fyjmdPHiRUJIZmYmm91eqrK8ePHi9u3bO3futHQgAACEEFJWVmbcHbaHzstatLce5+7du4SQ/fv3d+zY0dKxgIXRPwZVyg+WpA+/hTap/fy/AgCsiBGf3A6tB3ocaOdUntzezOfh9OnTr7/+utkDM6vdu3dPmzatvr6ey+VaOhYz6dev3+DBgzMyMiwdCAAAIYTcv38/ODjYiDtsD52XtWhvPc6ZM2eGDx9eWFgYFBRk6VjAwugfg8pKzNsGAAAAADAVjLYBAAAAAEwFo20AAAAAAFPBaBsAAAAAwFQw2gYAAAAAMBWMtgEAAAAATEWf0fbBgwcDAgJYLBaLxerUqdPUqVNbannt2rXo6Gh/f38ul+vm5vbKK68kJycrtkZHR7PUOnr0qPKxEhISmj1Kamoqi8WysbEJCQk5d+6cHu8IFGbPnq3Iv8qZ/eGHH5YtW6Z8Rv7yl78oN4iIiHBwcLC1te3Ro8fVq1fNG3jz5HL5pk2bwsPDVdYnJyer/LH17NlTucH58+dfe+01gUDg6ekZHx/f0NCg/UHXrVsXEhLC5/OFQmFISEhCQoJIJFJpo37/arYePnx43bp1MplM+3gUrOsMakyjRXJIrCqNBuZQTYOmOczOzlZ8mtzc3Ez91oxL+Wyq8PPz02+fAwcOtLW17dOnj4Gxffjhhw4ODiwWKz8/X8utx48fd3JyOnLkiIGHBpOylitJ64+QELJy5crQ0FBHR0culxsUFLRkyZKamhqVNvp1GQb2F//V9AEBWj5rIDAw0MnJSU2D69evCwSC+fPnP3z4sK6u7u7du0uWLBk5cqSiweTJk0+ePFlVVSWVSv/44w9CSGRkpEQiqa2tLSsrmzVr1pEjRxTHIoR06tRJIpGoHKWxsdHX15cQorxnjf79738TQurr67V/ibXr27fvnDlzNDaLiYlxdXXNzc29e/eucn4SExPfeecdkUhEFwMDAzt06EAIOXr0qPLLc3Nzx44da9zI9Xbv3r3XXnuNEPLKK6+obFq1apXKB6FHjx6KrTdv3uTz+QkJCTU1NRcuXHBzc5sxY4b2xx0zZsyGDRvKysqqq6v379/P4XD+/Oc/KzdQv3+NR09LSxs2bFhlZaUOubDCM6g+jRbJIWNtaTQkhxobqORQLpcXFxefO3du9OjRHTp00CY8nXocM+xKuVNrbGwUi8WlpaXdu3fXe4cjR45sevHRQ1ZWFiEkLy9Py61Hjx51dHQ8fPiw4YfWm5Y9Tptx+vRp0uSBJmpY15WEafURDhs2LD09vaKiQiQS7du3j8PhjBo1SrmBIV2Grv1Fs38MJhxtT5s2rXPnzsprGhoa3n77bcVidHR0bW0t/TcdbSufs23btimPtvv3708I2b9/v8pR9u3bR7+5bIWjbbFYHBYW1kr2pv1o28vLS2XlF1980bVr17q6OsWawMDAb775xsbGxsvLq6qqSrG+lXzwGIbJz88fP378119/3adPn2ZH27t3727ptZMnT/b395fL5XQxJSWFxWLdvn1by0NHRUUp52rixImEkJKSEi33r83RY2Njw8LCpFKpliFZ4xlUn0bz55CxwjQakkNtGjSbw/nz57eB0baCIedx5MiRffr0MSQkStfRdmtgntF26+lkdRptW92VhGn1EY4ZM6axsVGxOGnSJELIkydPFGsM7DJ06i+a/WMw4bztioqKly9fvnjxQrHGzs5O+betrKwsgUDQ0stjYmLefvttxeLcuXMJIV9++aVKs9TU1EWLFhktaKPatWtXWVlZ69yb9u7fv5+QkPD555/zeDzl9eHh4XFxcb///vsnn3xi/qg0euWVVw4ePPj+++/r+rjQxsbGY8eODRs2jMVi0TVvvfUWwzA5OTla7uHQoUPKufLy8iKEKH7VUr9/LY+elJSUn5+flpamTTxWegbVpNH8OSTWmUa9c6hNA6J7Dq1Rdna2IS/ncDiGx6A4BXps1RXDMAcOHNi+fbsR92k61tjJWuOVhGrNER49etTW1laxSCezicViumh4l2H4tc6Eo+2BAwfW1taOGDHi559/NnxvI0aM6N69++nTp+/evatY+fPPP4vF4oiICMP33yyGYVJTU7t3787lcl1cXMaNG3fnzh3F1tjYWDs7u06dOtHFv/3tb0KhkMViPX/+nBASFxe3aNGioqIiFosVFBS0ZcsWHo/XsWPH2bNne3p68ni88PDwS5cu6bc32ub77793dHRcvXq1id4+tWXLFoZhIiMjm25KTk7u2rXrzp07f/jhh5ZeriaHGRkZQqFQIBDk5OS89dZbjo6O3t7e9HsaBZlMlpiY6OPjw+fze/fuvW/fPuO+u6YePHhQU1Pj4+OjWEMnMl2/fl2/HRYWFjo7O9P5Thr3r+XRXVxchg0blpaWxjCMxgDaxhlUTqP5c0gMS6P6K4nGNJo/h9o0ILrn0KqlpaUJhUIbG5v+/ft7eHhwOByhUNivX7+hQ4d26dKFx+M5OzsvWbJE5VX3798PCQkRCoV8Pn/o0KHnz59X3trSmaXfrnXr1o3L5To5OS1evFj5Veq3nj9/3sfHh8Vi/f3vfyfa/XWtWbOmW7dufD7fzc3N399/zZo19KtBM1Dz0VDfJxIdO1ld90ZM1sNa9ZXE1F2Gsa51v//+O5/P9/f3p4uGdxlGuNYpf9Ft3JkkYrF4wIAB9CihoaHr1q2rqKhoqXHTmSQqx3r48OHmzZsJIXFxcYr1UVFRmZmZ1dXVxDQzSRITE+3s7Hbv3l1VVXX9+vV+/fq5ubk9e/ZM0eD999/38PBQLKakpBBCysvL6eKECRMCAwMVW2NiYoRC4a1bt+rr6wsKCgYOHOjg4KD8S4dOe2MY5ujRow4ODitXrtTmLes9kyQgICA0NFSlGT0jDMNcuHDBxsbGz8+vpqaGae5HJfU5/PTTTwkhP/7448uXL8vKyoYOHSoUCpVn53/yySdcLvfbb7+trKxcvny5jY3N5cuXtXm/CoMGDWp2Jom3t7ezszOHw/Hz8xs7duyvv/5KN509e5YQkpKSotyez+fr9AfGMIxEIikuLt66dSuXy1WetaJ+/9offdmyZUS7n4+t+gw2m0bz55AxLI0aryTq02j+HGrTgGqawzYzk2T+/Pk3btxQbvDZZ58RQi5dulRbW/v8+fNRo0YRQo4dO1ZeXl5bWxsbG0sIyc/PV7QfOXJkQEDAw4cPpVLpzZs3Bw0axOPx7t27p2jQ0pn99NNPWSzWxo0bKysrxWJxenq6cpLVb2UY5unTp4SQrVu3Ktqr+etavXq1ra1tTk6OWCy+cuWKh4fH66+/bkgaKS17HPUfDfV9IqNjJ6vr3nTqYbWfSWKlVxLzdBmG9/gMw9TW1jo4OMTGxirWGKXL0L6/MPdMEj6ff+HChc2bN4eEhNy6dSs+Pr579+70Xenngw8+EAqFX331VV1dHSHkwYMHly9fnjJlivFC/h91dXWpqanjx4+fOnWqk5NTr169tm3b9vz5c0N+YmOz2fQ/fKGhoRkZGdXV1ZmZmXrvbcyYMSKRqKVSLUZRW1v78OFD+v+8ZoWFhS1YsODRo0dLly5tulXLHIaHhzs6Orq7u9Op/E+ePKHr6+vrMzIyoqKiJkyY4OzsvGLFCg6HY0jGFD744IPDhw8/ffq0pqYmKyvryZMnw4YNKygoIITQO5GVf5MihHA4HPpXp70uXbp4e3snJSWtX79+8uTJivXq96/90YODgwkhN27cUB+GtZ/BZtNo5hwSw9Ko/ZWk2TRaJIfaNKC0z6FVePnypaIaCf1+p6nQ0FCBQNChQ4f33nuPEOLj4+Pm5iYQCGgRJ+XvGgkhDg4Ofn5+bDa7R48eO3bsqK+vV5z3ls5sXV3dpk2b3njjjYULFzo7O/P5fFdXV8UO1W9Vo6UPaXZ2dv/+/SMjI/l8fr9+/caOHXvu3DmJRKJ78nTWyjtZU/SwVn0l0Rih9kGaNMI1a9Z4enoqV8AzSpdh4LXOtPW2ORxObGzs7du3f/nll3HjxpWVlU2cOLGyslK/vTk5OU2ZMqWysnLv3r2EkE2bNs2dO9fOzs6oIf9XQUFBTU2N4ut5QsjAgQPt7OyUp38YYsCAAQKBQOXS3NqUlZUxDKNmej0hJDk5uVu3bunp6So/khLdc0jPplQqpYt3794Vi8WK2nx8Pr9Tp05GyViXLl369u1rb29vZ2c3ePBg2sPRr4joXLrGxkbl9hKJhM/n63SIp0+flpWV7dmz56uvvurbt69iOqD6/Wt/dHpSSktL1Ydh7Wew2TSaOYfEsDTqcSVRTqNFcqhNA0r7HFoFle+21Temp0mRIjpFW/HH31SvXr2cnJwUP0+3dGbv378vFotHjhzZ7E7Ub9WGyoeU/sar2CqTyTgcjsrIw0TaYSdr1VcSjRHqEaTRIzx06ND+/ftPnDjh4OCgWGmULsPAa52Znm4zaNCg7777bs6cOeXl5fQ7dv3QeyW3bdtWVVV14MCB2bNnGy9GVVVVVYQQe3t75ZXOzs504opRcLnc8vJyY+3NFOrr6wkh6m805PF4mZmZLBZr5syZKv8XNDCHtbW1hJAVK1Yovm16/Pix4r4HI+rVq5etre29e/cIIXRin3JZYrFYXF9f7+npqdM+ORyOu7t7RETE3r17CwoK1qxZQ9er37/2R6cXAnqC1LD2M9hsGs2cQ2JYGq0xh9o0oLTPodVJS0tTKcNvOA6HoxjmtnRmi4uLCSHu7u7N7kH9Vj2MHj36ypUrOTk5dXV1v/32W3Z29ttvv22e0XY77GSt+kqiMUIDgzQ8wr17965du/bMmTMqZfKN0mUYeK0z8mj73LlzmzZtov+eMGGCyv8VaF10Q0ZLffr0GTx48K+//hoTEzNx4kQXFxdDolXP2dmZEKLyJ1JVVeXt7W2U/UulUiPuzUTon5fGuu5hYWELFy4sLCxUqWNtYA5pj7Jp0yblyU8XL17U6S1oQy6Xy+VyegX09/d3cHB4/PixYuv9+/cJIb1799Zv50FBQba2tnSaisb9a390+lOvxm/c28wZVE6jmXNIDEujNeZQmwaU9jmExsbGFy9eKG7GaunM0m/aWnqilvqtekhKShoxYsT06dMdHR3Hjx8/adKkHTt2GGvn6rXDTrZtXElaitDAIA2McOvWrV9//fWpU6c6d+6ssskoXYaB1zojj7avXLkiFArpvxsaGm7duqW8lZYT0XvUQtGvt7/99tsFCxYYsh+NevbsaW9v/9tvvynWXLp0SSKR0MrfFJvNVvO7oXpnzpxhGGbw4MFG2ZuJdOzYkcVivXz5UmPLVatWhYSE5OXlKa/UJodq0Dv9m32CmoHefPNN5UV6H0ZYWBghhM1mjx49+ty5c3K5nG7Nzc1lsVjN3kLeVEVFhcq9BIWFhTKZrEuXLnRR/f61Pzo9KR4eHurjsdIzqD6NZs4hMSyN1phDbRpQ2ufQSv3xxx8zZswwyq5Onz4tl8v79etHF1s6sz179rSxsWnpHif1W/VQUFBQVFRUXl4ulUqfPHmSkZFh0q+xlGn8aBjYJ6p0sq2hh7XGK4n2ERoYpN4RMgwTHx9/48aN7Oxsla/VKaN0GQZe64w22pZKpaWlpWfOnFGMtgkhUVFR+/fvr6qqevnyZU5OztKlS8eOHWvgaHvSpElubm5RUVEBAQEGR60Oj8dbtGjRoUOHvv76a5FIdOPGjTlz5nh6esbExCjaBAUFvXjxIjs7WyqVlpeXK//fiBDi6upaUlLy6NGj6upq+iGXy+WVlZWNjY3Xr1+Pi4vz8fGZPn263nvLzc01dQVAgUAQEBBAf7tUj/60pPL7ozY5VL/PGTNmZGVlZWRkiEQimUxWXFxMy9dER0d7eHjo/ajY33//fe/evfQ5phcvXvzwww99fHzmzJlDtyYkJJSWln722We1tbUXL15MSUmZPn16t27dFC9Xc3ShUHjy5MlTp06JRCKpVJqXl0fv7l24cKGijfr9azw6RU9Kr1691MdjpWdQYxrNmUNiWBpNl0P1YRuYQ20akP/NYRvDMExdXd3BgwcdHR313olEInn58mVjY+PVq1djY2N9fX0V1/yWzqy7u/uECRO+/fbbXbt2iUSi69evK99hpn6rHubNm+fj49P0MddmoPGjob5PJDp2srruzRQ9rDVeSbSP0MAg9Y7w1q1b69ev37FjB4fDYSnZsGGDoo3hXYah1zrlb+y1LKJ06NAhNXfUHjp0iDY7efLk5MmTAwMDuVyunZ1dt27dkpKSmhbdE4lEf/rTn+hd1TY2NkFBQatXr256LDc3t3nz5tGVS5YsuXDhAv33ihUr6JwbGxub0NDQn376SX3wlJYVAOVyeUpKSnBwMIfDcXFxiYqKunv3rnKDioqK4cOH83g8f3//jz/+mNY9DQoKoiWHrl696uvry+fzhwwZ8uzZs5iYGA6H4+XlxWazHR0dx40bV1RUpPfeGIY5fvy4g4NDcnKyNm9Z7wqAsbGxHA5HLBbTxWbPiMLixYtVigGpyWF6ejq97SA4OLioqGj79u20V/P19VUUyWpoaIiPj/fx8WGz2bSbKSgoYBgmKiqKEJKYmNjSG7l48eJrr72mPAk1PDz87NmzdOuiRYsCAwOFQiGbzfb29p41a5bysx4Zhjl79uyrr77K5XI9PT0XL16s8qei/uiRkZH+/v729vZcLjcwMDA6OlqljpjG/avfSo0ZM8bLy4s++0p9PFZ6BjWm0Zw5NDCN6q8kGtPYUg41hm1gDrVpoJxDyhorAKrv1FasWMEwTFpaGj1Nfn5+P/3009q1a52cnAghHh4e33zzzd69e+mXXi4uLllZWXS3mZmZw4cP79ixI5vNpjVMHj9+rHzcls5sdXX1hx9+2KFDB3t7+yFDhiQmJhJCvL29r127pnHr1q1baZ8oEAgiIyM1/nWdOnWKPpGb4nA43bt3P3jwoP4ng2EYrXsc9R8N9X0io2Mnq+vedOphta8AaHVXEnN2GfpF2FKdEJWifgZ2GU2vdS0x8pPbrZp5ntyuIiYmxtXV1ZxHVKb3aLuwsJDNZqt5zrlFyGSyoUOH7tq1qx0enWGY58+f83i8DRs2aBMPzmCzdMohgzQ2RyWHlDWOttuz9PR05adYNDQ0LFiwgMvlKoaD+jHPk9tVWLCT1X60jSuJflrhta4l5q63DU1pvDeiNairqztx4kRhYSG9JyAoKGjlypUrV660yE+NzZLJZNnZ2dXV1dHR0e3t6FRSUlKfPn3o0zQ0xoMz2CydckiQxuYo55BhmJKSkvPnz9MbjMAqPHv2LDY29q9//atijZ2dnY+Pj1QqtfgUZ/20/k4WVxI9WDxC5WudfjDaBlUvXrwYNWpU165dZ86cSdcsW7Zs4sSJ0dHR2tzbYQZnzpw5ePBgbm6u+qqlbfLohJDU1NT8/Pzjx4/T+r7axIMzqEKPHBKk8X+p5DAnJ8fLy2vo0KHHjh0zfzCgHz6fz+Fwdu3aVVpaKpVKS0pKdu7cmZiYGB0dbchsdVAPVxJdtaprnZ6Uv+huPz/GmX8mybJly2gVdz8/vwMHDpjtuAqG/6534sSJ+Ph4Y8UD+snOzl6zZk1jY6Mer8UZpAzJIYM0MgxjcA4pzCRpDc6dO/fGG284Ojra2to6OTmFh4enp6dLpVIDd2v+mSSW7WS1n0migCuJVdDjWtfsHwOLUXqI1P3794ODg0+fPv3666/rP363Brt37542bVp9fb36IvNtSb9+/QYPHpyRkWHpQAAACDFqj9N+Oi9r0d56nDNnzgwfPrywsDAoKMjSsYCFNfvHgJkkAAAAAACmgtE2AAAAAICpYLQNAAAAAGAqGG0DAAAAAJgKRtsAAAAAAKbCbrrqwIEDzT6Jvi25cuUKIWTz5s1sdjMZaJPKysquXbuWmppq6UDACjQ0NNjZ2bFYLEsHAm1ZRUWFcXfYHjqv1qa+vp7H4zVd3956nKKiIkLIP//5Tzc3N0vHAhZG/xhU/E8FwAcPHvTp08eMIVkMwzByudzW1tbSgZiPTCZjsVg2Nvg1AzSrr6+XyWQcDofWrwUwnePHjw8ZMsTAnbSfzqv1kMvl9JGTAoGgac/S3nqcdjioAPXy8/MDAgIUi/8z2gYAIIRcunRp7dq1hw8f7ty584IFCz766CN7e3tLBwUArUJ+fv769esPHDjg6uo6f/78uXPnOjs7WzoogFYNo20AaF5RUdGWLVu2b99uZ2c3ffr0pUuXenp6WjooALCY8+fPr1u37tixY76+vnFxcbNmzWq1z/oGaFUw2gYAdUpLS7/88svNmzfX1dVNmjRpxYoVXbt2tXRQAGA+crn82LFjX3zxxcWLF3v37r1o0aIpU6a0n7ueAAzXXuZUAYB+PDw8kpKSHj9+vG7duh9//LF79+7vvPPO5cuXLR0XAJicRCL597//3bNnz8jISBsbm8OHD+fn50+bNg1DbQCdYLQNAJo5OjrOnz//wYMHmZmZ9+7de/XVV4cMGXLkyBFLxwUAJlFTU7N58+bAwMAZM2YEBgZevHjx/Pnz77zzDkoVAegBo20A0BaXy502bdrt27cPHz4skUgiIyP79ev373//WyaTWTo0ADCO8vLypKQkX1/fJUuWDB8+/ObNm0eOHBk8eLCl4wKwYpi3DQB6ordMHT16NDAw8OOPP46JiWm28i4AWIWHDx+mpaXt3LnTxsZm5syZixcv9vb2tnRQAG0BRtsAYJC8vLxNmzbt2bPHzc1t9uzZcXFxKAcGYF2uXbu2cePGrKwsFxeXuXPnxsbGurq6WjoogLYDo20AMAKVcoHx8fGdO3e2dFAAoIGiqJ+fn9/8+fM/+ugjPp9v6aAA2hqMtgHAaFTKBX766afdunWzdFAAoIoW9VuzZs0vv/zyyiuvLFy4EEX9AEwHd0kCgNHQcoFPnjyh5QJDQ0PfeeedX3/91dJxAcD/p1zUz9bW9vDhw3l5eSjqB2BSGG0DgJE5ODgoygUWFhYOGjQI5QIBLK66uhpF/QAsAqNtADAJWi7w1q1bhw8flkqlKBcIYCllZWXKRf0KCgpQ1A/AnDBvGwDMQVEuMCAgIDY2FjdjAZgBLeq3Y8cONps9Y8YMFPUDsAiMtgHAfPLz81NTU1EuEMDUFEX9XF1d58yZM3/+fBcXF0sHBdBOYbQNAOb24MGDzZs3K75vW7JkiZeXl6WDAmgjUNQPoLXBaBsALKOsrCwjI2Pz5s1isXjy5MkoFwhgCFrUb/Xq1ZcuXUJRP4BWBXdJAoBldOzYkZYLXL9+/alTp1AuEEA/tKhfjx49IiMj2Ww2ivoBtDYYbQOAJaFcIIDeaFG/gICAGTNmBAUF/fLLLyjqB9AKYbQNAJZnZ2enKBfY2NgYGRnZt29flAsEaIlyUb8RI0bQon6DBg2ydFwA0AzM2waAVkdxm5e/vz/KBQIow03GAFYHo20AaKVouUCUMAOg8IkAsFIYbQNAq4Zv8gDwaw+AVcNoGwCsAC0XuGXLltra2smTJy9fvjwkJMTSQQGYlnJRvz59+ixYsOD999+3tbW1dFwAoBvcJQkAVoCWC3z8+DEtF9ijR4933nnn0qVLlo4LwCRoUb/Q0FCVon4YagNYI4y2AcBqKJcLvH///uDBg2m5QPxGB22GclG/4ODgS5cu0aJ+lo4LAPSH0TYAWBlaLrCgoEClXGBjY6OlQwPQX2lpaVJSko+PDy3qd+vWrSNHjrz66quWjgsADIV52wBg3RQ3kPn5+c2fPx83kIHVUbkVOD4+vnPnzpYOCgCMBqNtAGgLrl27tnHjRhRHA+uSl5e3adOmPXv2uLm5zZ49Oy4uztnZ2dJBAYCRYbQNAG3Hw4cP09LSFN9zCGtPAAAgAElEQVQRLl682Nvb29JBATSD/iZz9OjRwMDAjz/+OCYmhsfjWTooADAJjLYBoK1BuUBotWhRv+Tk5F9//bVv375xcXEo6gfQ5uEuSQBoa5TLBZ4+fZqWC/zll18sHRe0aw0NDYqifhwO5/Dhw1evXkVRP4D2AKNtAGibaLnAoqKizMzMoqKisLAwlAsEixCJRMpF/X799VcU9QNoVzDaBoC2jJYLvHnz5uHDh2UyGcoFgjnRon6+vr7x8fEjR468ffv2kSNHBg4caOm4AMCsMG8bANoRlAsE8ygqKtqyZcv27dvt7OymT5+Oon4A7RlG2wDQ7ly/fn3Dhg1ZWVkuLi5z586NjY11dXW1dFDQRqCoHwCowGgbANopWi5w586dNjY2M2fORLlAMBCK+gFAszDaBoB2rby8PD09fevWrTU1NZMnT162bFn37t0tHRRYE1rUb9WqVZcvX0ZRPwBoCndJAkC75u7urlwusGfPnigXCFqiRf26d+8eGRlpZ2eHon4A0CyMtgEAiL29va7lAouKiqZNm4baJm3SnTt35s6dq6aBclG/rl27oqgfAKiB0TYAwP+nXC5QLpdHRkb26dOnpXKB69ev37179wcffCCTycwfKpjO9evXw8PDt23bdvv27aZbUdQPAHSFedsAAM1TlAv09fWNi4ubNWuWQCCgm/744w8/Pz+JRGJjYzN+/Pg9e/ZwOBzLRgtGcfXq1REjRtTW1hJCpk6dmpmZqdikUtRv6dKlnp6elosUAKwGRtsAAOo0Wy4wPj5+48aN9FttGxubUaNGHTp0iMvlWjpYMMiVK1dGjBghFovprxm2trYPHjzw8fG5evVqWlranj173N3dY2JiFixY4OTkZOlgAcBqYLQNAKDZgwcPNm7cmJmZyWazp0+f/s9//pN+/UnZ2tqOHDkyJycHFd+s14ULFyIiIurr6xVTg9hsdmRk5PPnz8+dOxcaGrp48eIpU6bY2dlZNk4AsDoYbQMAaIuWC0xJSamvr5fL5cqbbG1thw8ffvjwYTyc0hqdP3/+zTffbGhoUJmFb2tr26NHj0WLFqGoHwDoDaNtAAAdNDQ0eHt7V1RUNL142traDh48+Pvvv7e3t7dIbKCfs2fPvvXWWxKJpOkNr7a2tklJSStWrLBIYADQNqAmCQCADv71r381O9QmhMhksl9++SUiIqK6utr8gYF+Tpw4ERER0exQmxAik8k2bNigPGsIAEBXGG0DAGhLJpOtW7eOxWKpaXD58uU33nhDJBKZMzDQT25ubmRkZGNjo5oyjiKR6F//+pcZgwKAtgYzSQAAtHXgwIFJkyZpbMZms3v37v3DDz+4uLiYISrQz3fffTdp0iS5XK4yBb8pb2/vhw8fstls8wQGAG0Mrh3Qlsnl8pqaGktHAW1HUVHRm2+++fTp09LS0hcvXii+rbCxsWGz2SwWSyKRMAzT2Nh49erVP/3pT0eOHHF1dbVszNCs77777q9//aviK20bGxsbGxuGYZS/5Lazs3Nzc/P09PTx8bl27VpwcLCFgoW2hs1mK4r3Q3uA77ahLSsoKOjZs6elowAAAPiviIiIEydOWDoKMB98tw1tX3Jycpv/UurUqVP/+Mc/9u3bZ+lAzGfhwoV9+vSZNm2apQNpnkwme/ny5YsXL7hcbpcuXSwdDvyXTCa7c+eOk5OTi4uLUCi0dDjQ7mzatMnSIYC5YbQNbd+IESPCwsIsHYVpiUSif/zjH9pMKW4zkpKSunbt2q7eMgC0AQcOHMBd1O0NapIAAAAAAJgKRtsAAAAAAKaC0TYAAAAAgKlgtA0AAAAAYCoYbQMAAAAAmApG2wAAAAAApoLRNgDZsGFDx44dWSzWtm3bLBXD66+/zmrC3t7e1Mc9fvy4k5PTkSNHTH0gS5k9e7Yin1OnTlXe9MMPPyxbtuzgwYMBAQG0wV/+8hflBhEREQ4ODra2tj169Lh69ap5A/8fVhHkypUrQ0NDHR0duVxuUFDQkiVLVJ7kev78+ddee00gEHh6esbHxzc0NKjsoaUGhw8fXrdunfJTHnViRSd63bp1ISEhfD5fKBSGhIQkJCQ0LRWnPo3IoelySJpLY3Z2tuIK4+bmZtK3BlaMAWi7bt68SQi5cOGCxpaFhYWEkC+//NIMUTVr2LBhTT+eb775ppYv37Fjh34f56NHjzo6Oh4+fFiP11pW9+7dFyxYoLFZTEyMq6trbm7u3bt36+vrFesTExPfeecdkUhEFwMDAzt06EAIOXr0qPLLc3Nzx44da9zI9dbKgxw2bFh6enpFRYVIJNq3bx+Hwxk1apRi682bN/l8fkJCQk1NzYULF9zc3GbMmKH8cvUN0tLShg0bVllZqWtU1nWix4wZs2HDhrKysurq6v3793M4nD//+c/KDdRnCTlkTJxDpkka5XJ5cXHxuXPnRo8e3aFDB20ifPfddyMiIgx+o2BNMNqGtsy4o22xWBwWFma86P7Hm2++qejMqJiYmB9//FHLl+s92jYn4yZQ+9G2l5eXysovvviia9eudXV1ijWBgYHffPONjY2Nl5dXVVWVYn0rGUBQrTzIMWPGNDY2Khbpg4eePHlCFydPnuzv7y+Xy+liSkoKi8W6ffu2or3GBrGxsWFhYVKpVPuQrO5ER0VFKUc7ceJEQkhJSYlijfosIYeM6XPItJDG+fPnY7QNLcFMEgBt7dq1q6yszEQ7//777x0cHBSLT58+vXnz5ogRI0x0OIswaQK1d//+/YSEhM8//5zH4ymvDw8Pj4uL+/333z/55BNLxaZRaw7y6NGjtra2ikX6q7pYLCaENDY2Hjt2bNiwYSwWi2596623GIbJycmhixobEEKSkpLy8/PT0tK0jMcaT/ShQ4eUo/Xy8iKEKCbkqM8SckiZOodE9zQCYLQN0IyzZ8+++uqrAoHA0dGxV69eIpEoLi5u0aJFRUVFLBYrKCgoLS1NKBTa2Nj079/fw8ODw+EIhcJ+/foNHTq0S5cuPB7P2dl5yZIlegewdu3a+fPnG/EdNev8+fM+Pj4sFuvvf/87ISQjI0MoFAoEgpycnLfeesvR0dHb2zsrK0vRfsuWLTwer2PHjrNnz/b09OTxeOHh4ZcuXaJbY2Nj7ezsOnXqRBf/9re/CYVCFov1/PlzQohKAmmb77//3tHRcfXq1aZ+p8q2bNnCMExkZGTTTcnJyV27dt25c+cPP/zQ0ssZhklNTe3evTuXy3VxcRk3btydO3foJo0JJITIZLLExEQfHx8+n9+7d+99+/bpGr/GINVEqE2QhkdI/f7773w+39/fnxDy4MGDmpoaHx8fxdbAwEBCyPXr1+mixgaEEBcXl2HDhqWlpTEMo00AFjzRxsphYWGhs7Ozr68vXVSfpTaWQ2KkNBo9h0T3NAK09p+eAQyh30ySmpoaR0fHdevW1dXVPXv2bPz48eXl5QzDTJgwITAwUPGSzz77jBBy6dKl2tra58+fjxo1ihBy7Nix8vLy2tra2NhYQkh+fr4eYRcXF4eGhspkMu1fovdMkqdPnxJCtm7dShc//fRTQsiPP/748uXLsrKyoUOHCoVCiUSiaB8TEyMUCm/dulVfX19QUDBw4EAHBwfFbIH333/fw8ND0TglJYUQQrPHNEkgwzBHjx51cHBYuXKlHpHrPZMkICAgNDRUpVlgYODDhw8Zhrlw4YKNjY2fn19NTQ3T3I/jiYmJdnZ2u3fvrqqqun79er9+/dzc3J49e0a3akzgJ598wuVyv/3228rKyuXLl9vY2Fy+fFnLt6xlkOoj1BikIREq1NbWOjg4xMbG0sWzZ88SQlJSUpTb8Pn8kSNHatmAWrZsGSEkLy9PmxgseKINzKFEIikuLt66dSuXy929e7divfostbEcMoal0aQ5ZJpLI2aSgBr4bhtA1aNHj0QiUY8ePXg8noeHx8GDB9XcaR4aGioQCDp06PDee+8RQnx8fNzc3AQCAS1/ofydovbWrl378ccf29hY7OMZHh7u6Ojo7u4eHR1dW1v75MkT5a1sNpt+WRUaGpqRkVFdXZ2ZmanfgcaMGSMSiRISEowRtVZqa2sfPnxIv69qVlhY2IIFCx49erR06dKmW+vq6lJTU8ePHz916lQnJ6devXpt27bt+fPn27dvV27WUgLr6+szMjKioqImTJjg7Oy8YsUKDoejR/bUBKllhC0FaawI16xZ4+npmZycTBdpVQfleSaEEA6HU1dXp2UDKjg4mBBy48YNjQFY8EQbnsMuXbp4e3snJSWtX79+8uTJivXqs9SWckgMTqNJc0h0SSMAwUwSgKYCAgI6duw4derUpKSkR48eafkqOzs7QkhjYyNd5HA4hBCpVKrr0UtKSg4fPjx9+nRdX2gK9E2peRcDBgwQCAT6/afCIsrKyhiGEQgEatokJyd369YtPT39/PnzKpsKCgpqamoGDBigWDNw4EA7OzvFdBoVKgm8e/euWCzu2bMnXeTz+Z06ddIvey0FqWuEKkEaJcJDhw7t37//xIkTilsR6DxaxaeDkkgkfD5fywYUPXGlpaUaY7DgiTY8h0+fPi0rK9uzZ89XX33Vt29fxd0O6rPUlnJIDE6jSXNIdEkjAMFoG6ApPp9/6tSpIUOGrF69OiAgIDo6uukXG6azbt26WbNmqdyT1Jpxudzy8nJLR6Gt+vp6QgiXy1XThsfjZWZmslismTNnqpz6qqoqQohKHXRnZ+fq6mptjl5bW0sIWbFihaJA7+PHj+l9hLpqKUiLR7h37961a9eeOXPGz89PsZLO5lcueywWi+vr6z09PbVsQNFBDz2J6lnwRBueQw6H4+7uHhERsXfv3oKCgjVr1tD16rPUlnJIDE6jSXNIdEkjAMFoG6BZPXr0OHLkSElJSXx8/L59+zZs2GCe4z579mzPnj1z5841z+EMJ5VKq6qqvL29LR2ItmgfqfEZH2FhYQsXLiwsLFy1apXyemdnZ0KIynBB+wy4u7sTQjZt2qQ8n+/ixYs6vQX1QVo2wq1bt3799denTp3q3Lmz8np/f38HB4fHjx8r1ty/f58Q0rt3by0bUBKJhPzfSVTPgifaiGc5KCjI1ta2oKCALqrPUlvKITFeGk2RQ6JLGgEIRtsATZWUlNy6dYsQ4u7u/sUXX/Tr148umsG6deumTp3q6upqnsMZ7syZMwzDDB48mC6y2Ww9Js+YE31o6MuXLzW2XLVqVUhISF5envLKnj172tvb//bbb4o1ly5dkkgk/fv31+botF5Nfn6+rmFrH6SlImQYJj4+/saNG9nZ2U2fgcpms0ePHn3u3Dm5XE7X5ObmslgsRbELjQ0oeuI8PDw0xmPBE613DisqKqZMmaK8prCwUCaTdenShS6qz1JbyiHRN43mySHRJY0ABKNtgKZKSkpmz559584diUSSl5f3+PFjOpp0dXUtKSl59OhRdXW1KcaUpaWl//znPxcsWGD0PRuXXC6vrKxsbGy8fv16XFycj4+PYpZ5UFDQixcvsrOzpVJpeXm58ldEpLkE5ubmmrkCoEAgCAgIKC4u1tiS/kSucr8Uj8dbtGjRoUOHvv76a5FIdOPGjTlz5nh6esbExGhzdB6PN2PGjKysrIyMDJFIJJPJiouL//jjD0JIdHS0h4eHro+8bhqkpSK8devW+vXrd+zYweFwWEoUvwslJCSUlpZ+9tlntbW1Fy9eTElJmT59erdu3RR70NiAEEJPXK9evTTGY8ETrSaH6sMWCoUnT548deqUSCSSSqV5eXkffPCBUChcuHChoo36LOmaQ/XxWOOHxTw5JE3SCKCBSSqdALQOWlYA3LhxI/2KQigUjh8//tGjR+Hh4S4uLra2tp07d/7000/pE/KuXr3q6+vL5/OHDBmybNkyepeMn5/fTz/9tHbtWicnJ0KIh4fHN998s3fvXrpDFxeXrKwsLaNduHDh1KlT9Xun+lUA3Lp1K52nKBAIIiMj09PT6ZsKDg4uKiravn27o6MjIcTX1/fevXv0JTExMRwOx8vLi81mOzo6jhs3rqioSLHDioqK4cOH83g8f3//jz/+ePHixYSQoKAgWiJQOYG0Ctjx48cdHBySk5P1eMt6VwCMjY3lcDhisZguHjp0iFZdcHNzmzdvnsrLFy9erFLUTC6Xp6SkBAcHczgcFxeXqKiou3fv0k3aJLChoSE+Pt7Hx4fNZru7u0+YMKGgoIBhmKioKEJIYmJis+9CpyDVRKhNkPpF2FJxBuViarSMPZfL9fT0XLx4cX19vcpONDYYM2aMl5cXfc6f+ngYi57olnKoMezIyEh/f397e3sulxsYGBgdHX3jxg2dsqRTDjXGY40fFjPksGkaGVQABLUw2oa2TPt629bObE9uj4mJcXV1NcOBNNJ7tF1YWMhms5VL8LYGMpls6NChu3btsnQgLbJ4hM+fP+fxeBs2bNAyHpzoplRyqDEe5LBZTdPIYLQNamEmCQDoRuNdU61NXV3diRMnCgsL6Y1NQUFBK1euXLlypeJhzhYnk8mys7Orq6ujo6MtHUvzWkOESUlJffr0oc+N0iYenOimlHOoTTzIYbOU08gwTElJyfnz5+n9lADNwmgbwOTu3LnDalmrHWC1GS9evBg1alTXrl1nzpxJ1yxbtmzixInR0dHa3AFmBmfOnDl48GBubq762sYWZPEIU1NT8/Pzjx8/TivZaxkPTrQylRxqGQ9yqEIljTk5OV5eXkOHDj127JhF4gGrwGIYxtIxAJhKQUFBz549L1y4EBYWZulYTGvnzp2zZs0y9cd5+fLlGzdulEgkfn5+KSkp7777rkkPp15oaOioUaNSU1P13gO9m2rt2rVGjApMIScn59atW0uWLFG5D09LONEEOTQSA9NITZw4USQSnThxwoiBQSuH0Ta0ZRhtt2GGj7YBAMwPo+12CDNJAAAAAABMBaNtAAAAAABTwWgbAAAAAMBUMNoGAAAAADAVtqUDADC50tLSp0+fWjoK03rx4gUhpM2/TWVSqbS6urpdvWUAaAPEYrGlQwBzQ00SaMtoTRJLRwEmweFwpFKppaMAANDZG2+88Z///MfSUYD54LttaPs2bdrUvXt3S0dhWt9//31aWtr3339v6UDM56OPPhowYMBHH31k6UAAAHSwevVqGxvM421fMNqGtm/QoEFtvt42nVDx5ptvWjoQ8xEKhb6+vu3qLQNAG7Bz506RSGTpKMCs8L8rAAAAAABTwWgbAAAAAMBUMNoGAAAAADAVjLYBAAAAAEwFo20AAAAAAFPBaBsAAAAAwFQw2gYgBw8eDAgIYLFYLBarU6dOU6dObanltWvXoqOj/f39uVyum5vbK6+8kpycrNgaHR3NUuvo0aPKx0pISGj2KKmpqSwWy8bGJiQk5Ny5c8Z/w+3J7NmzFflXObM//PDDsmXLlM/IX/7yF+UGERERDg4Otra2PXr0uHr1qnkD/x9WEeS6detCQkL4fL5QKAwJCUlISFApc3b+/PnXXntNIBB4enrGx8c3NDSo7KGlBocPH163bp1MJtMvMCs60RpzSDSlETk0XQ5Jc2nMzs5WXGHc3NxM+tbAijEAbdfNmzcJIRcuXNCmcWBgoJOTk5oG169fFwgE8+fPf/jwYV1d3d27d5csWTJy5EhFg8mTJ588ebKqqkoqlf7xxx+EkMjISIlEUltbW1ZWNmvWrCNHjiiORQjp1KmTRCJROUpjY6Ovry8hRHnPGu3YsaO9fZy7d+++YMECjc1iYmJcXV1zc3Pv3r1bX1+vWJ+YmPjOO++IRCK6GBgY2KFDB0LI0aNHlV+em5s7duxY40aut1Ye5JgxYzZs2FBWVlZdXb1//34Oh/PnP/9ZsfXmzZt8Pj8hIaGmpubChQtubm4zZsxQfrn6BmlpacOGDausrNQ1Kus60epzyGjKEnLImDiHTJM0yuXy4uLic+fOjR49ukOHDtpE+O6770ZERBj8RsGatK/uGdob4462p02b1rlzZ+U1DQ0Nb7/9tmIxOjq6traW/puOtpX7nm3btimPtvv3708I2b9/v8pR9u3bFx4e3jpH22KxOCwsrJXsTfvRtpeXl8rKL774omvXrnV1dYo1gYGB33zzjY2NjZeXV1VVlWJ9KxlAUK08yKioKOWUTpw4kRBSUlJCFydPnuzv7y+Xy+liSkoKi8W6ffu2or3GBrGxsWFhYVKpVPuQrO5Eq88hoylLyCFj+hwyLaRx/vz5GG1DSzCTBEBbFRUVL1++fPHihWKNnZ3dkSNHFItZWVkCgaCll8fExLz99tuKxblz5xJCvvzyS5VmqampixYtMlrQRrVr166ysrLWuTft3b9/PyEh4fPPP+fxeMrrw8PD4+Lifv/9908++cT8UWmpNQd56NAh5ZR6eXkRQmpqagghjY2Nx44dGzZsGIvFolvfeusthmFycnLoosYGhJCkpKT8/Py0tDQt47HGE60mh0RTlpBDytQ5JLqnEQCjbQBtDRw4sLa2dsSIET///LPhexsxYkT37t1Pnz599+5dxcqff/5ZLBZHREQYvv9mMQyTmpravXt3Lpfr4uIybty4O3fuKLbGxsba2dl16tSJLv7tb38TCoUsFuv58+eEkLi4uEWLFhUVFbFYrKCgoC1btvB4vI4dO86ePdvT05PH44WHh1+6dEm/vdE233//vaOj4+rVq0309qktW7YwDBMZGdl0U3JycteuXXfu3PnDDz+09HI1OczIyBAKhQKBICcn56233nJ0dPT29s7KylJ+uUwmS0xM9PHx4fP5vXv33rdvn67xawxS/VnWGKThEVKFhYXOzs50WtSDBw9qamp8fHwUW+lkquvXr9NFjQ0IIS4uLsOGDUtLS2MYRpsALHiiTZFDoilLbSyHxEhpNHoOie5pBMBMEmjLjDuTRCwWDxgwgH5wQkND161bV1FR0VLjpjNJVI718OHDzZs3E0Li4uIU66OiojIzM6urq4lpZpIkJiba2dnt3r27qqrq+vXr/fr1c3Nze/bsmaLB+++/7+HhoVhMSUkhhJSXl9PFCRMmBAYGKrbGxMQIhcJbt27V19cXFBQMHDjQwcHhyZMn+u2NYZijR486ODisXLlSm7es90ySgICA0NBQlWb0jDAMc+HCBRsbGz8/v5qaGqa5H8fV5/DTTz8lhPz4448vX74sKysbOnSoUChUnp3/ySefcLncb7/9trKycvny5TY2NpcvX9bm/WofpMazrD5IQyJkGEYikRQXF2/dupXL5e7evZuuPHv2LCEkJSVFuSWfz1f8kWtsQC1btowQkpeXp00kFjzRpsghoylLbSyHjGFpNGkOmebSiJkkoAa+2wbQFp/Pv3DhwubNm0NCQm7duhUfH9+9e3d6ddbPBx98IBQKv/rqq7q6OkLIgwcPLl++PGXKFOOF/D/q6upSU1PHjx8/depUJyenXr16bdu27fnz59u3b9d7n2w2m35xFRoampGRUV1dnZmZqffexowZIxKJWirVYhS1tbUPHz6k31c1KywsbMGCBY8ePVq6dGnTrVrmMDw83NHR0d3dnU7lf/LkCV1fX1+fkZERFRU1YcIEZ2fnFStWcDgcPTKmJkjtz3KzQRoeYZcuXby9vZOSktavXz958mS6klZ1sLW1VW7J4XDoX742Dajg4GBCyI0bNzSGYcETbaIcEk1Zaks5JAan0aQ5JLqkEYBgJgmATjgcTmxs7O3bt3/55Zdx48aVlZVNnDixsrJSv705OTlNmTKlsrJy7969hJBNmzbNnTvXzs7OqCH/V0FBQU1NjeLreULIwIED7ezslKd/GGLAgAECgUB50kIrVFZWxjCMmun1hJDk5ORu3bqlp6efP39eZZOuOaRnUyqV0sW7d++KxeKePXvSRT6f36lTJ/0y1lKQepxl5SANj/Dp06dlZWV79uz56quv+vbtS6fm03m0jY2Nyi0lEgmfz6f/1tiAoieutLRUYxgWPNEmyiHRlKW2lENicBpNmkOiSxoBCEbbAPoZNGjQd999N2fOnPLy8tOnT+u9H3qv5LZt26qqqg4cODB79mzjxaiqqqqKEGJvb6+80tnZmU5cMQoul1teXm6svZlCfX09IYTL5appw+PxMjMzWSzWzJkzVb7TMjCHtbW1hJAVK1YoCvQ+fvxYLBbr+i7UBGnxCDkcjru7e0RExN69ewsKCtasWUMIodP3lcsei8Xi+vp6T09PuqixAUUHPfQkqmfBE22iHBJNWWpLOSQGp9GkOSS6pBGAYLQNoN65c+c2bdpE/z1hwgSV7zzo8x30Gy1Rffr0GTx48K+//hoTEzNx4kQXFxdDolXP2dmZEKLS1VVVVXl7extl/1Kp1Ih7MxHaR2p8xkdYWNjChQsLCwtXrVqlvN7AHLq7uxNCNm3apDyf7+LFizq9BfVBtp4Ig4KCbG1tCwoKCCH+/v4ODg6PHz9WbL1//z4hpHfv3nRRYwNKIpGQ/zuJ6lnwRJsoh0RTltpSDonx0miKHBJd0ghAMNoGUO/KlStCoZD+u6Gh4datW8pbaTmRphdindCvt7/99tsFCxYYsh+NevbsaW9v/9tvvynWXLp0SSKR0MrfFJvNVvySq6szZ84wDDN48GCj7M1EOnbsyGKxXr58qbHlqlWrQkJC8vLylFdqk0M1unTpwuPx8vPzdQ1b+yAtFWFFRYXKLQeFhYUymaxLly6EEDabPXr06HPnzsnlcro1NzeXxWIpil1obEDRE+fh4aExHgueaBPlkGjKUlvKIdE3jebJIdEljQAEo22Alkil0tLS0jNnzihG24SQqKio/fv3V1VVvXz5MicnZ+nSpWPHjjVwtD1p0iQ3N7eoqKiAgACDo1aHx+MtWrTo0KFDX3/9tUgkunHjxpw5czw9PWNiYhRtgoKCXrx4kZ2dLZVKy8vLlb/jIYS4urqWlJQ8evSourqaDqPlcnllZWVjY+P168f5cLAAACAASURBVNfj4uJ8fHymT5+u995yc3NNXQFQIBAEBAQUFxdrbEl/Ile5X0qbHKrf54wZM7KysjIyMkQikUwmKy4upuVroqOjPTw8dH3kddMgLRWhUCg8efLkqVOnRCKRVCrNy8ujNwEvXLiQNkhISCgtLf3ss89qa2svXryYkpIyffr0bt26KfagsQEhhJ64Xr16aYzHgidaTQ7Vh60xh0RTlnTNofp4rPHDYp4ckiZpBNDANKVOAFoFLSsAHjp0SM1994cOHaLNTp48OXny5MDAQC6Xa2dn161bt6SkJOWHgVMikehPf/qTq6srIcTGxiYoKGj16tVNj+Xm5jZv3jy6csmSJYogV6xYQecO2tjYhIaG/vTTT9q8Uy0rAMrl8pSUlODgYA6H4+LiEhUVdffuXeUGFRUVw4cP5/F4/v7+H3/88eLFiwkhQUFBtK7f1atXfX19+Xz+kCFDnj17FhMTw+FwvLy82Gy2o6PjuHHjioqK9N4bwzDHjx93cHBITk7W5i3rXQEwNjaWw+GIxWK62OwZUVi8eLFKUTM1OUxPT6f3TgUHBxcVFW3fvt3R0ZEQ4uvre+/ePdqmoaEhPj7ex8eHzWa7u7tPmDChoKCAYZioqChCSGJiYrPvQqcg1Z9ljUHqFyHDMJGRkf7+/vb29lwuNzAwMDo6+saNG8oNzp49++qrr3K5XE9Pz8WLFzf97GhsMGbMGC8vL/qcP43xWPBEt5RDjWFrzKHGLOmUQ43xWOOHxQw5bJpGBhUAQS2MtqEt06netlUzz5PbVcTExLi6upr5oAp6j7YLCwvZbLZyCd7WQCaTDR06dNeuXZYOpEUWj/D58+c8Hm/Dhg1axoMT3ZRKDjXGgxw2q2kaGYy2QS3MJAEA/Wm8g6o1qKurO3HiRGFhIb2xKSgoaOXKlStXrlQ8zNniZDJZdnZ2dXV1dHS0pWNpXmuIMCkpqU+fPrGxsVrGgxPdlHIOtYkHOWyWchoZhikpKTl//jy9nxKgWRhtA0Ab9+LFi1GjRnXt2nXmzJl0zbJlyyZOnBgdHa3NHWBmcObMmYMHD+bm5qqvbWxBFo8wNTU1Pz//+PHjHA5H+3hwopWp5FDLeJBDFSppzMnJ8fLyGjp06LFjxywSD1gFFsMwlo4BwFQKCgp69ux54cKFsLAwS8diWjt37pw1a5Y5P87Lly/fuHGjRCLx8/NLSUl59913zXZoKjQ0dNSoUampqXrvgd5NtXbtWiNGBaaQk5Nz69atJUuWqNyHpyWcaIIcGomBaaQmTpwoEolOnDhhxMCglcNoG9oyjLbbMMNH2wAA5ofRdjuEmSQAAAAAAKaC0TYAAAAAgKlgtA0AAAAAYCoYbQMAAAAAmArb0gEAmNzDhw/pU8rasJKSEkJIQUGBpQMxn4aGhoqKinb1lgGgDRCJRJYOAcwNNUmgLaM1SSwdBQAAwH9FRESgJkm7gu+2oS0LCAi4cOGCpaMAaAtGjx4dHR09bdo0SwcCYPWcnZ0tHQKYFUbb0Jbx+fw2X2kbwDw4HI6Pjw8+UAAAusJdkgAAAAAApoLRNgAAAACAqWC0DQAAAABgKhhtAwAAAACYCkbbAAAAAACmgtE2AAAAAICpYLQNAAAAAGAqGG0DAAAAAJgKRtsAAAAAAKaC0TYAAAAAgKlgtA0AAAAAYCoYbQMAAAAAmApG2wAAAAAApoLRNgAAAACAqWC0DQAAAABgKhhtAwAAAACYCkbbAAAAAACmgtE2AAAAAICpYLQNAAAAAGAqGG0DAAAAAJgKRtsAAAAAAKaC0TYAAAAAgKlgtA0AAAAAYCoYbQMAAAAAmApG2wAAAAAApoLRNgAAAACAqWC0DQAAAABgKhhtAwAAAACYCkbbAAAAAACmgtE2AAAAAICpYLQNAAAAAGAqGG0DAAAAAJgKRtsAAAAAAKbCtnQAAADQ6jAMU1VVpbKmrq6usrJSsYbL5QoEArOHBgBgZVgMw1g6BgAAaF0YhgkODi4qKlLTZuvWrfPmzTNbSAAAVgozSQAAQBWLxXrvvfdYLFZLDWxtbSdO/H/t3XlQU9f+APCTjWwkLBKQssliRQG1qFUR2jJ0mForq7ax0Knto4OOFhW1uLEUREEUGKhMR0VeR1vQCqO1is9aK0+nDs+OIBY3oAJayqZAICwh5P7+uO9l8st6E5LcQL+fv5p7T875nnPPGb+93Jy71pwhAQDAFAXZNgAAADXi4uI0/fGTRqOFhoY6OTmZOSQAAJiKINsGAACghq+vr5+fn9rb2zKZLD4+3vwhAQDAVATZNgAAAPU++ugjKlXNPxMMBiMqKsr88QAAwFQE2TYAAAD11q1bJ5PJlA7S6fT33nvPxsaGlJAAAGDKgWwbAACAeu7u7suWLaPRaIoHpVJpXFwcWSEBAMCUA9k2AAAAjeLj45Vub3O53JUrV5IVDwAATDmQbQMAANBo7dq1io9u0+n0999/n81mkxgSAABMLZBtAwAA0EggEISFhdHp/33xsFQqXbduHbkhAQDA1ALZNgAAAG3i4uKkUin+3/b29qGhoeTGAwAAUwtk2wAAALSJjo62srJCCNHp9Pj4ePl9bgAAAERAtg0AAEAbHo8XERFBoVCkUqlQKCQ7HAAAmGIg2wYAAKDDhx9+iGGYm5vbsmXLyI4FAACmGPiDIAA6vP3222KxmOwoADATmUwmk8mUHheRyWQ0Gk0mkwUFBZEVmOmMj4/TaDS1b80EQFVCQsI//vEPsqMAUwlk2wDo8J///Gfu3Ln+/v5kBwKAOdTW1v75558xMTFKx/v7+/38/Ozs7EiJyqROnjwZHBz86quvkh0ImAIqKiqePXtGdhRgioFsGwDdPvjgg+TkZLKjAMAckpOTr1y5UlpaqnS8paXF29ublJBM7eTJkx9//HFCQgLZgYAp4F//+hfZIYCpB/5wBgAAQLfpmmoDAICpQbYNAAAAAACAqUC2DQAAAAAAgKlAtg0AAAAAAICpQLYNAAAAAACAqUC2DQAAAAAAgKlAtg2ASVy+fNnGxubixYskxpCQkMDj8SgUSn19vXGjUqxnyZIlNBpt4cKFkw1XT6q9I0ImkxUUFGh/Rcu1a9d2795tlObMRlO/fvjhh9zc3ImJCTPEYAlznhTyCVNZWenl5UWhUCgUykcffaRYJjw8nMfj0Wg0Pz+/u3fvkhTpFIgQIZSbm+vr68tms7lcrq+vb2pqqkgkUixw69atFStWcDgcZ2fnlJSUsbExpRo0FTDncgBAEWTbAJgEhmFkh4BOnDhx/PhxxSPGikqxnjt37oSGhhqlWr2o9k6npqamN954Izk5eXh4WFOZ9PT0oqKiPXv2TL45s9HSr4iICBaLFRYW1t/fb+owLGHOm5/ihImNjf3jjz+8vb1nzJhx+vTpS5cuyYtdvXr1+++/X716dWNjY2BgIFnRWn6ECKGbN29+9tln7e3tXV1dWVlZubm5a9askZ9tbGwMDw8PCwvr6empqqo6efLkxo0bFb+upYA5lwMAiiDbBsA4RkZGFO8srlq1amBgYPXq1SSGpIpgVEp9IVIPhUIxOCqdzRnFvXv3du3atXHjRi234XNycioqKs6ePcvj8Uwdj7Ho7NeWLVsWLFjw7rvvSqVSk0Zizjlvnjmjk6YJU1RURKVSExMTBwYGyIpNO0uO0MrKatOmTQKBwNraeu3atVFRUT/99NNff/2Fn83Kypo5c+aXX37J5XKXL1+ekpLyz3/+89GjR/Kvay9gtuUAgCLItgEwjtLS0u7ubrKjUGZYEmxAXxgMhgENGdwcTq/eLViwoLKyMi4ujslkqi3Q3Nycmpr65ZdfslisyTdnNjr7hRDKyMior68vLCw0Z2AmZQnLTcuECQoK2rp1659//rljxw5SYtPJkiOsqqpSHFIXFxeE0NDQEEJIKpVeunTpzTfflC/GlStXYhh24cIF/KPOAmg6Lgdg+SDbBsAItm7dun379paWFgqF4uPjc+vWLXd3dwqF8tVXXyGECgsLuVwulUpdtGiRk5MTg8HgcrmBgYEhISFubm4sFsvW1vaLL75QrHBiYiItLc3d3Z3NZs+fP//MmTMEI8EwLC8vb86cOUwm08bGZufOnfJTSlEhhGpqal5//XUOh8Pn8wMCAvCHI5X6cujQIQ6Hw+Pxuru7t2/f7uLiUlpaqlQPQqi5udnX15fL5bLZ7JCQkFu3bslPJSUlWVlZzZw5E/+4adMmLpdLoVB6e3tVm9Pedy29m7yioiIMwyIiIog3pzbUkpISLpfL4XAuXLiwcuVKPp/v6upaXl4u/5baYdfe8Umys7N78803CwsLTfewh9Ls0j4IRUVFLBbL0dFxw4YNzs7OLBYrKCiotrZWXpu+c+bKlSt8Pj87O9tEvVNLdcIo2r9//6uvvnrixIlr166pLYBhWH5+/ty5c5lMpp2dXVRUlOINWp2zaPKzxfIjxDU1Ndna2np4eCCE/vjjj6GhIXd3d/lZ/BWnDQ0N+EedBZBZlgMAyjAAgFY8Hu/IkSM6i8XGxnp7e8s/Pnv2DCFUXFyMf0xPT0cI1dbWisXi3t7ed955ByF06dKlnp4esViclJSEEKqvr5d/fceOHUwm89y5c319fXv27KFSqXfu3CES7d69eykUypEjR/r6+oaHh48ePYoQqqurU41qaGiIz+fn5uaOjIx0dnbGxMT09PSo7cvevXsRQlu2bCkuLo6JiXn48KFS78LCwry8vJ4+fTo+Pv77778vXbqUxWI9efJEXkNcXJyTk5P8Y15eHkJIU3Na+q69dwQtXbp0wYIFqse9vLzmzZtHfDC1hIqP2M8//zwwMNDd3R0SEsLlciUSifZhN/iia+8XDv8ZH5Gx2rZt29y5c4m3K6c0K7QMAoZhiYmJXC73wYMHo6OjjY2NS5Ys4fF47e3t8tr0mjM//vgjj8fLzMw0IGwMwxBCx48f1/dbqhMG5+3t/fTpUwzDfv31VyqVOmvWrKGhIQzDqqurIyMj5cXS0tKsrKxOnTrV39/f0NAQGBjo4ODQ2dkpL6B9ACczWyw/QgzDJBLJ8+fPi4uLmUzmqVOn8IM1NTUIoby8PMWSbDY7LCyMYAEc8eWgysXFJT093YAvgr8zuLcNgPnMmzePw+HMmDFj3bp1CCF3d3cHBwcOhxMfH48Qkt83Gh0dLSkpiY6Ojo2NtbW13bdvH4PBKCsr01n/yMhIQUHB22+/nZycbGtry2az7e3tNRVubW0ViUR+fn4sFsvJyamystLBwUFL5Tk5OZs3b66srPT19VU9y+PxZs2aRafT/fz8jh8/Pjo6euzYMZ0Bq9LSd716py+xWPz06VP8NhhOZ3M6L1NQUBCfzxcIBEKhUCwWt7e3I83DbvBFJ2j27NkIofv37xurQoLUDgKOTqfj903nzZtXUlIyODhocH9XrVolEolSU1ONFLVuqhNG1fLly7dt29ba2rpr1y6lUyMjI/n5+TExMfHx8TY2NgEBAV9//XVvb6/qqlE7gMaaLZYcoZubm6ura0ZGxqFDhz744AP8IL67CI1GUyzJYDBGRkYIFsCRtRzA3xZk2wCQwMrKCiEk/5kO/tDz+Pg4/vHx48fDw8P+/v74RzabPXPmTMW/4WrS3Nw8PDwcFhZGJAYvLy9HR8f4+PiMjIzW1lb9O6FRQECAjY2N4p9uidPSd716p6/u7m4MwzgcjvyIzuaIXyb8cuPXV9OwG3zRCcK71tXVZawK9aU4CKoWL17M4XCM2F9TU50wau3fv3/OnDlHjx5VfLYKIdTY2Dg0NLR48WL5kSVLllhZWSk+TqNEcQCNOFssNsJnz551d3d/991333zzzWuvvYY/po8/z630A0eJRMJms/H/1lkAR/pyAH83kG0DYHHEYjFCaN++fZT/aWtr07Jpndzz588RQgKBgEgrbDb7+vXrwcHB2dnZXl5eQqFQ6fbPZDAYDE15lXZa+q5X7/Q1OjqKEFL8oaHO5gy7TJqG3eCLThCebeDdtExMJrOnp4fsKIhSnTBqsVissrIyCoXy6aefKq4vfAc6a2trxcK2traDg4NEWjfibLHYCBkMhkAgCA8Pr6ioaGxsPHDgAEIIf5Rfcfvt4eHh0dFRZ2dn/KPOAjjLXw5gmoFsGwCLg2d4BQUFik993b59W+cX8fs6qu960MTPz+/ixYsdHR0pKSlnzpw5fPjwZMKWk0qlL1++VPyhEnFa+q5v7/SC/+ur+NoLnc0ZfJnUDrvBtREkkUjQ/7ppgcbHx/v7+11dXckOhCjVCaPJ8uXLk5OTm5qasrKy5AdtbW0RQkqZK/ERMO5ssfAIfXx8aDRaY2MjQsjT05PH47W1tcnPNjc3I4Tmz5+Pf9RZAGfhywFMP5BtA2Bx8I1KDHhnob+/P5VKxX8npFNHR8eDBw8QQgKB4ODBg4GBgfjHyfvll19kMpniCzLodDrBW91a+q5X7/Tl6OhIoVAU9x7W2Zxhl0nTsBt80QnCu+bk5GSi+ifpxo0bGIYtW7ZMfoT4nCGF6oTRIisry9fXt66uTn7E39/f2tr6t99+kx+pra2VSCSLFi0iUqHRZ4vlRPjixYsPP/xQ8UhTU9PExISbmxtCiE6nv/vuu//+979lMhl+trq6mkKhyHeG0VkAZ+HLAUw/kG0DYBz29vYdHR2tra2Dg4OTzBJYLNYnn3xSXl5eUlIiEokmJiaeP38uf7mDFgKBIDY29ty5c6WlpSKRqKGhQctPFTs6OjZs2PDo0SOJRFJXV9fW1ibPdQzoi0QiGRgYkEqld+/eTUpK8vDwWL9+vfysj4/Py5cvz58/Pz4+3tPTo3jnSak5Go2mqe969U5fHA7Hy8sLf3oEp7M5wy6TpmE3+KIThHctICDAWBVOnkwm6+vrk0qlDQ0NW7dudXd3N2zOjI+PV1dXm3kHQNUJowX+tIbiT/dYLNb27durqqpOnz4tEonu37+/ceNGZ2fnxMREghVqmi1CodDJyUnfV6+bM0LtQXK53KtXr16/fl0kEo2Pj9fV1X388cdcLjc5ORkvkJqa2tXVlZ6eLhaLb9++nZeXt379+jlz5shr0FkAWeRyANOcEfY1AWBaI7gD4N27dz08PNhsdnBw8L59+/DHBzkcTkRERGFhIf6jnFmzZt28eTMnJ8fGxgYh5OTk9O2331ZUVOC3WOzs7MrLy/HaxsbGUlJS3N3d6XQ6nvY1NjYSiXZwcDAhIWHGjBnW1tbBwcFpaWkIIVdX13v37hUXFytG1draGhQUZGdnR6PRXnnllb1790qlUtW+JCcn439vdXNzw/fhUqoHw7CysrLQ0FBHR0c6nY7vuNLW1qYY1YsXL0JDQ1kslqen5+eff45vXO3j44Pv+KbYXGdnp5a+a+mdzpG5ffv2ihUrFJ/vDAoKqqmpkRdISkpiMBjDw8NEBlPLZTp69Ch+uWfPnt3S0nLs2DE+n48Q8vDwePLkiZZhN+yi6+wXbtWqVS4uLjKZTGeFhu0AqDQrtA8ChmGJiYkMBsPFxYVOp/P5/KioqJaWFsUK9Zozly9f5vF4+/fv1zdsHDJoB0DVCVNVVYXvUuLg4LB582al8jt37lTcX08mk+Xl5c2ePZvBYNjZ2UVHRz9+/Fh+VucAapot0dHRCKG0tDS1MVtChDqDjIiI8PT0tLa2ZjKZ3t7eQqHw/v37igXwHeuZTKazs/POnTtHR0eVatBZgPhyUAU7AAIDQLYNgA4Es20w1TU1NdHpdPnOvtNJb28vi8U6fPgwkcIG77etl8TERHt7e1O3QpBh2bZlTpiJiYmQkJDS0lKyA9GG3CD1Wg6qINsGBoAnSQAAACGEfHx8MjMzMzMz8XdETycZGRkLFy7EX6JkOYj8xNCSWeCEmZiYOH/+/ODgoFAoJDsWjUgP0jKXA5jeINsGYGp49OgRRTNL/sfVDIw1OLt37167dq1QKCT46zdTM0q/8vPz6+vrL1++jG/rDozI0ibMjRs3Kisrq6urdW4ETiJyg4TlAEhBJzsAAAAhvr6+GIaRHYWFMuLgZGdnX7169eDBgzk5OUapcDIm368LFy6MjY3duHFD6e165NqzZ09ZWZlEIvH09MzLy1uzZg3ZERnOoiZMWFiYiV7/ZEQkBmmZywH8HUC2DQAA/094eHh4eDjZURhHZGRkZGQk2VEoO3DgAP6ykulhOk2Y6c0ylwP4O4AnSQAAAAAAADAVyLYBAAAAAAAwFci2AQAAAAAAMBXItgEAAAAAADAVyLYBAAAAAAAwFQrsKQaAdlwul8fj2dvbkx0IAObQ1dU1Ojrq4eFBdiDm8/DhQ2dnZ1tbW7IDAVNAa2vrF198kZGRQXYgYCqBHQAB0IFCocyZM2fRokVkBwKAOdTU1LS3t7/zzjtkB2I+Dx8+9Pf39/f3JzsQMAWcPHmS7BDA1APZNgA6UKnUyMjI5ORksgMBwBySk5OvXLmSn59PdiDmU1BQ8P777yckJJAdCJgCzp49S3YIYOqB57YBAAAAAAAwFci2AQAAAAAAMBXItgEAAAAAADAVyLYBAAAAAAAwFci2AQAAAAAAMBXItgEAAAAAADAVyLYBIJNQKKRo9eOPP16+fNnGxubixYtGbLeystLLy0uxISsrK0dHx7feeisvL6+vr0+pvLFiUKxnyZIlNBpt4cKFk6xTLwkJCTwej0Kh1NfXq569du3a7t279R0cM/jhhx9yc3MnJibM3zRQpTpPPvroI8UC4eHhPB6PRqP5+fndvXuXrDgtP0I5mUxWUFAQFBSkeio3N9fX15fNZnO5XF9f39TUVJFIRPCs9vphWQGzgWwbAJJdvXq1v79/fHz8r7/+QghFRERIJBKxWNzd3f3ZZ58hhEzxwtfY2Ng//vjD29vbxsYGwzCZTNbd3X327FlPT8+UlBQ/P7/ffvtNsbyxYlCs586dO6GhoUaplrgTJ04cP35c7an09PSioqI9e/boOzhmEBERwWKxwsLC+vv7zdw0UKI6T2bMmHH69OlLly7Jy1y9evX7779fvXp1Y2NjYGAgWaFafoS4pqamN954Izk5eXh4WPXszZs3P/vss/b29q6urqysrNzc3DVr1hA8q71+WFbAbCDbBoBMFAplxYoVNjY2dDpdfoTBYHA4HIFAgL/ActWqVQMDA6tXrzZpGLa2tm+99VZZWdnZs2e7urrwRuUFiMQwMjKi9taUItV6KBSKwWETaZGgnJycioqKs2fP8ng8pVM6B8c8tmzZsmDBgnfffVcqlZq5aVMw4rUzem1aqJ0nRUVFVCo1MTHR/LOCIEuO8N69e7t27dq4caOmP3NZWVlt2rRJIBBYW1uvXbs2Kirqp59+wu9N6Dyrs/5ptqyAxYJsGwAylZeXczgcTWcTExPfe+89c8aDEFqzZs369eu7u7u//vprvb5YWlra3d2tb3MMBkPfr0yyRdX8vrm5OTU19csvv2SxWNq/a/DgGEVGRkZ9fX1hYaH5mzY6w66deWrTRNM8CQoK2rp1659//rljxw5Tx2AYS45wwYIFlZWVcXFxTCZTbYGqqirFAXdxcUEIDQ0NETlLpP7ptKyAxYJsGwCLduvWLXd3dwqF8tVXXyGECgsLuVwulUpdtGiRk5MTg8HgcrmBgYEhISFubm4sFsvW1vaLL75QrGFiYiItLc3d3Z3NZs+fP//MmTM6G12/fj1CqLq6Wm0MCKGamprXX3+dw+Hw+fyAgACRSLR169bt27e3tLRQKBQfHx+E0KFDhzgcDo/H6+7u3r59u4uLS2lpqVI9CKHm5mZfX18ul8tms0NCQm7duoUfT0pKsrKymjlzJv5x06ZNXC6XQqH09vbiR1Rb1NRTDMPy8vLmzJnDZDJtbGx27typ1N+ioiIMwyIiIohcEaXB0dRuSUkJl8vlcDgXLlxYuXIln893dXUtLy+Xf0t1DLXUhrOzs3vzzTcLCwtN8XCRvjAMy8/Pnzt3LpPJtLOzi4qKevTokfys9sundO2KiopYLJajo+OGDRucnZ1ZLFZQUFBtba1htSGErly5wufzs7OzjdtlLfNk//79r7766okTJ65du6bp61pGTOdsMWAVmzlCowRJRFNTk62trYeHhwFn1bKoZQWmLQwAoBWPxzty5IgZGsL/+hkZGal0/NmzZwih4uJi/GN6ejpCqLa2ViwW9/b2vvPOOwihS5cu9fT0iMXipKQkhFB9fb386zt27GAymefOnevr69uzZw+VSr1z5w5+Sv5oshI883Nzc1Mbw9DQEJ/Pz83NHRkZ6ezsjImJ6enpwTAsNjbW29tbsZ69e/cihLZs2VJcXBwTE/Pw4UOlvoSFhXl5eT19+nR8fPz3339funQpi8V68uQJfjYuLs7JyUleW15eHkIIbwun1KKmnu7du5dCoRw5cqSvr294ePjo0aMIobq6OvkXvby85s2bpzQIxAdHS7sIoZ9//nlgYKC7uzskJITL5UokEi1jqP16YRi2e/dupeCNbtu2bXPnztVZLC0tzcrK6tSpU/39/Q0NDYGBgQ4ODp2dnfIC2i+f0rVLTEzkcrkPHjwYHR1tbGxcsmQJj8drb283rLYff/yRx+NlZmYS7DJC6Pjx4zqLaZonT58+xTDs119/pVKps2bNGhoawjCsurpaaS1rHzEtswXTNSu0M0+EkwwSw7ClS5cuWLBA01mJRPL8+fPi4mImk3nq1Cm9zuqsX69l5eLikp6eTqQkAHJwbxuAKWnevHkcDmfGjBnr1q1DCLm7uzs4OHA4nPj4eISQ/I7U6OhoSUlJdHR0bGysra3tvn37GAxGWVmZ9srxXTsGBwfVnm1tbRWJRH5+fiwWy8nJqbKy0sHBQUttOTk5mzdvrqys9PX1VdvWrFmz6HS6n5/f8ePHR0dHjx07RmQElGjq6cjISEFBwdtvv52cnGxra8tms+3t7RW/KBaLnz596u3tTbAhpcHROcJBEwX1aAAACPRJREFUQUF8Pl8gEAiFQrFY3N7ejjSPoc7aZs+ejRC6f/++AUNkRCMjI/n5+TExMfHx8TY2NgEBAV9//XVvb69h1w5Hp9Pxu6rz5s0rKSkZHBzUOVE1WbVqlUgkSk1NNTgYVTrnyfLly7dt29ba2rpr1y7VswRHTO1sMWwVmzNCIwapiZubm6ura0ZGxqFDhz744AO9zupkIcsKTGOQbQMwtVlZWSGE5D/xwR+DHh8fxz8+fvx4eHjY398f/8hms2fOnKn4F3+1xGIxhmF8Pl/tWS8vL0dHx/j4+IyMjNbWVmN04r8CAgJsbGwaGhoM+K6mnjY3Nw8PD4eFhWn6Ynd3N4ZhWp6eV6I0OMRHGL9S+KXRNIY6a8Pj7OrqIhitiTQ2Ng4NDS1evFh+ZMmSJVZWVoqPf0zG4sWLORyOzolqTkTmyf79++fMmXP06FH5A1Fy+o6Y4mwxbBWbM0LjBqnWs2fPuru7v/vuu2+++ea1115Tekxf+1mdLGRZgWkMsm0ApjOxWIwQ2rdvn3zr6La2NrXbbCl68uQJQkjtrWiEEJvNvn79enBwcHZ2tpeXl1AoHBkZMVbADAZD/u+3XjT19Pnz5wghgUCg6Yujo6MIIU2/oFKlNDiGjbCmMdRZG5vNlsdMInzHNGtra8WDtra2mv4eYgAmk9nT02Os2iaPyDxhsVhlZWUUCuXTTz9VWhSTGTHD5pg5IzRukGoxGAyBQBAeHl5RUdHY2HjgwAHiZ3WykGUFpjHItgGYzvAss6CgQPEBstu3b2v/1pUrVxBCK1eu1FTAz8/v4sWLHR0dKSkpZ86cOXz4sFGilUqlL1++dHd3N+C7mnqK71cwNjam6Yv4P7TE33ChNDiGjTDSMIY6a5NIJPKYSWRra4sQUsrD+vv7XV1djVL/+Pi4EWszCoLzZPny5cnJyU1NTVlZWYrHJzNiBs8xs0Vo9CC18PHxodFojY2NBpzVxEKWFZjGINsGYDrDNypR+95ETTo7OwsKClxdXT/99FO1BTo6Oh48eIAQEggEBw8eDAwMxD9O3i+//CKTyeTv2qDT6cTvc2vqqb+/P5VKramp0fRFR0dHCoVCcB9i1cExYISR5jHUWRsep5OTk17NGZ2/v7+1tbXiW35qa2slEgm+QzxOr8un5MaNGxiGLVu2zCi1GQXxeZKVleXr61tXV6d4kMiIaWLYHDNnhKYIEvfixYsPP/xQ8UhTU9PExISbm5vOs8RZyLIC0xhk2wBMZywW65NPPikvLy8pKRGJRBMTE8+fP1d89QOGYUNDQzKZDMOwnp6eM2fOrFixgkajnT9/XtNz2x0dHRs2bHj06JFEIqmrq2tra8OzInt7+46OjtbW1sHBQeKJkUQiGRgYkEqld+/eTUpK8vDwwLfYQwj5+Pi8fPny/Pnz4+PjPT09bW1tSt9VbJFGo6ntqUAgiI2NPXfuXGlpqUgkamhoUPrVF4fD8fLywh84UUJkcHSOsF5jqLM2PM6AgACCw2siLBZr+/btVVVVp0+fFolE9+/f37hxo7Ozc2JioryM9sunOltkMllfX59UKm1oaNi6dau7u7t8JuhbW3V1tdF3ANQyT5TgT2vQaDSlgzpHTEuFWmaFUCh0cnLS6+3rRo9Qe5AGRCjH5XKvXr16/fp1kUg0Pj5eV1f38ccfc7nc5ORknWeJs5BlBaazSe1oAsDfgBl2ABSJRG+88Qa+VwaVSvXx8cnOzsZPFRcX49sMcziciIiIwsJC/Ac9s2bNunnzZk5Ojo2NDULIycnp22+/raiowG/P2NnZlZeX4zWMjY2lpKS4u7vT6XQ89WxsbPzhhx/mz5/P4XCsrKyoVCr63xsTX3/99czMzBcvXiiGpxRDa2trUFCQnZ0djUZ75ZVX9u7dK5VKMQy7e/euh4cHm80ODg7u7OzMzc3F/zLr5uaG78mlVA+GYWVlZaGhoY6OjnQ6Hd9fpa2tTd7uixcvQkNDWSyWp6fn559/ju+T7ePjI98YTqlFtT3FMGxwcDAhIWHGjBnW1tbBwcFpaWkIIVdX13v37uH1JCUlMRiM4eFh/KNeg6NphI8ePYpfqdmzZ7e0tBw7dgxP0D08PJ48eaJpDDXVJm9r1apVLi4u+P8AmAjBHQBlMlleXt7s2bMZDIadnV10dPTjx48VC2i/fErXLjExkcFguLi40Ol0Pp8fFRXV0tJicG2XL1/m8Xj79+8n2GVEbAdApXlSVVWFb1Hi4OCwefNmpcI7d+5U2l9Py4hpny2Y1lkRHR2NEEpLS1MN2JwRaglSS4QYht2+fXvFihXOzs54TjJz5sygoKCamhp5gYiICE9PT2trayaT6e3tLRQK79+/T/AskfoxPZcV7AAIDADZNgA6mG2/bUCWpqYmOp2uaZtey9Hb28tisQ4fPmzSVghm28aVmJhob29v5kblCGbbljlPJiYmQkJCSktLyQ5EIwuPUN9lBdk2MAA8SQIA+Lvz8fHJzMzMzMxUfOGzBcrIyFi4cCH+AqPph/gPVcligfNkYmLi/Pnzg4ODQqGQ7FjUs/wIp/eyAhYCsm0AAEC7d+9eu3atUCgk+HNJ88vPz6+vr798+TK+pToghaXNkxs3blRWVlZXVxPfMN7MLDxCWFbAPCDbBgAAhBDKzs5OSko6ePAg2YGoceHChbGxsRs3btjZ2ZEdi/Ht2bOnrKxsYGDA09Pz3LlzZIejg0XNk7CwsG+//Rb/OYRlsuQIp/eyAhaFTnYAAABgKcLDw8PDw8mOQo3IyMjIyEiyozCVAwcO6Ps6EnJZ7DwBepneywpYFLi3DQAAAAAAgKlAtg0AAAAAAICpQLYNAAAAAACAqUC2DQAAAAAAgKlAtg0AAAAAAICpUDAMIzsGACwan8+nUqnW1tZkBwKAOQwNDUkkEnt7e7IDMZ/u7m4+n89iscgOBEwBnZ2d+/bty8jIIDsQMJXADoAA6LB79+6xsTGyowAAAGAR3nrrLbJDAFMM3NsGAAAAAADAVOC5bQAAAAAAAEwFsm0AAAAAAABMBbJtAAAAAAAATAWybQAAAAAAAEzl/wC6DG3yGGCcggAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import EarlyStopping\n",
        "model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy')\n",
        "es = EarlyStopping(monitor='val_loss', mode='min', verbose=0,patience=2)"
      ],
      "metadata": {
        "id": "rUB9lX-yeTa9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit([x_tr,y_tr[:,:-1]], \n",
        "                  y_tr.reshape(y_tr.shape[0],y_tr.shape[1], 1)[:,1:],\n",
        "                  epochs=30,callbacks=[es],\n",
        "                  batch_size=1024,\n",
        "                  validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXT9dqhCegmO",
        "outputId": "96c5e670-abdb-498d-e38d-f44cd6a29999"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "16/16 [==============================] - 633s 39s/step - loss: 1.6561 - val_loss: 0.5494\n",
            "Epoch 2/30\n",
            "16/16 [==============================] - 618s 38s/step - loss: 0.5482 - val_loss: 0.5340\n",
            "Epoch 3/30\n",
            "16/16 [==============================] - 592s 37s/step - loss: 0.5261 - val_loss: 0.4980\n",
            "Epoch 4/30\n",
            "16/16 [==============================] - 610s 38s/step - loss: 0.4865 - val_loss: 0.4603\n",
            "Epoch 5/30\n",
            "16/16 [==============================] - 598s 37s/step - loss: 0.4624 - val_loss: 0.4504\n",
            "Epoch 6/30\n",
            "16/16 [==============================] - 596s 37s/step - loss: 0.4528 - val_loss: 0.4446\n",
            "Epoch 7/30\n",
            "16/16 [==============================] - 614s 38s/step - loss: 0.4462 - val_loss: 0.4375\n",
            "Epoch 8/30\n",
            "16/16 [==============================] - 604s 37s/step - loss: 0.4400 - val_loss: 0.4325\n",
            "Epoch 9/30\n",
            "16/16 [==============================] - 601s 37s/step - loss: 0.4347 - val_loss: 0.4298\n",
            "Epoch 10/30\n",
            "16/16 [==============================] - 610s 38s/step - loss: 0.4314 - val_loss: 0.4280\n",
            "Epoch 11/30\n",
            "16/16 [==============================] - 605s 38s/step - loss: 0.4285 - val_loss: 0.4253\n",
            "Epoch 12/30\n",
            "16/16 [==============================] - 613s 38s/step - loss: 0.4258 - val_loss: 0.4238\n",
            "Epoch 13/30\n",
            "16/16 [==============================] - 623s 38s/step - loss: 0.4230 - val_loss: 0.4214\n",
            "Epoch 14/30\n",
            "16/16 [==============================] - 610s 38s/step - loss: 0.4204 - val_loss: 0.4194\n",
            "Epoch 15/30\n",
            "16/16 [==============================] - 602s 37s/step - loss: 0.4177 - val_loss: 0.4176\n",
            "Epoch 16/30\n",
            "16/16 [==============================] - 614s 38s/step - loss: 0.4147 - val_loss: 0.4153\n",
            "Epoch 17/30\n",
            "16/16 [==============================] - 610s 38s/step - loss: 0.4118 - val_loss: 0.4129\n",
            "Epoch 18/30\n",
            "16/16 [==============================] - 613s 38s/step - loss: 0.4091 - val_loss: 0.4116\n",
            "Epoch 19/30\n",
            "16/16 [==============================] - 613s 38s/step - loss: 0.4057 - val_loss: 0.4085\n",
            "Epoch 20/30\n",
            "16/16 [==============================] - 620s 38s/step - loss: 0.4026 - val_loss: 0.4076\n",
            "Epoch 21/30\n",
            "16/16 [==============================] - 621s 39s/step - loss: 0.3992 - val_loss: 0.4032\n",
            "Epoch 22/30\n",
            "16/16 [==============================] - 618s 38s/step - loss: 0.3963 - val_loss: 0.4013\n",
            "Epoch 23/30\n",
            "16/16 [==============================] - 618s 38s/step - loss: 0.3932 - val_loss: 0.3992\n",
            "Epoch 24/30\n",
            "16/16 [==============================] - 624s 39s/step - loss: 0.3901 - val_loss: 0.3969\n",
            "Epoch 25/30\n",
            "16/16 [==============================] - 617s 38s/step - loss: 0.3873 - val_loss: 0.3946\n",
            "Epoch 26/30\n",
            "16/16 [==============================] - 628s 39s/step - loss: 0.3841 - val_loss: 0.3922\n",
            "Epoch 27/30\n",
            "16/16 [==============================] - 623s 39s/step - loss: 0.3816 - val_loss: 0.3918\n",
            "Epoch 28/30\n",
            "16/16 [==============================] - 613s 38s/step - loss: 0.3791 - val_loss: 0.3886\n",
            "Epoch 29/30\n",
            "16/16 [==============================] - 630s 39s/step - loss: 0.3764 - val_loss: 0.3876\n",
            "Epoch 30/30\n",
            "16/16 [==============================] - 624s 39s/step - loss: 0.3739 - val_loss: 0.3868\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reverse_target_word_index=y_tokenizer.index_word\n",
        "reverse_source_word_index=x_tokenizer.index_word\n",
        "target_word_index=y_tokenizer.word_index"
      ],
      "metadata": {
        "id": "InW9oXgYOkwN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoder_model = Model(inputs=encoder_inputs,outputs=[encoder_outputs, state_h, state_c])\n",
        "\n",
        "# Decoder setup\n",
        "# Below tensors will hold the states of the previous time step\n",
        "decoder_state_input_h = Input(shape=(latent_dim,))\n",
        "decoder_state_input_c = Input(shape=(latent_dim,))\n",
        "decoder_hidden_state_input = Input(shape=(max_text_len,latent_dim))\n",
        "\n",
        "# Get the embeddings of the decoder sequence\n",
        "dec_emb2= dec_emb_layer(decoder_inputs) \n",
        "# To predict the next word in the sequence, set the initial states to the states from the previous time step\n",
        "decoder_outputs2, state_h2, state_c2 = decoder_lstm(dec_emb2, initial_state=[decoder_state_input_h, decoder_state_input_c])\n",
        "\n",
        "# A dense softmax layer to generate prob dist. over the target vocabulary\n",
        "decoder_outputs2 = decoder_dense(decoder_outputs2) \n",
        "\n",
        "# Final decoder model\n",
        "decoder_model = Model(\n",
        "    [decoder_inputs] + [decoder_hidden_state_input,decoder_state_input_h, decoder_state_input_c],\n",
        "    [decoder_outputs2] + [state_h2, state_c2])"
      ],
      "metadata": {
        "id": "D-ekw9g1Ouuc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def decode_sequence(input_seq):\n",
        "    # Encode the input as state vectors.\n",
        "    e_out, e_h, e_c = encoder_model.predict(input_seq)\n",
        "    \n",
        "    # Generate empty target sequence of length 1.\n",
        "    target_seq = np.zeros((1,1))\n",
        "    \n",
        "    # Populate the first word of target sequence with the start word.\n",
        "    target_seq[0, 0] = target_word_index['sostok']\n",
        "\n",
        "    stop_condition = False\n",
        "    decoded_sentence = ''\n",
        "    while not stop_condition:\n",
        "      \n",
        "        output_tokens, h, c = decoder_model.predict([target_seq] + [e_out, e_h, e_c])\n",
        "\n",
        "        # Sample a token\n",
        "        sampled_token_index = np.argmax(output_tokens[0, -1, :])\n",
        "        sampled_token = reverse_target_word_index[sampled_token_index]\n",
        "        \n",
        "        if(sampled_token!='eostok'):\n",
        "            decoded_sentence += ' '+sampled_token\n",
        "\n",
        "        # Exit condition: either hit max length or find stop word.\n",
        "        if (sampled_token == 'eostok'  or len(decoded_sentence.split()) >= (max_summary_len-1)):\n",
        "            stop_condition = True\n",
        "\n",
        "        # Update the target sequence (of length 1).\n",
        "        target_seq = np.zeros((1,1))\n",
        "        target_seq[0, 0] = sampled_token_index\n",
        "\n",
        "        # Update internal states\n",
        "        e_h, e_c = h, c\n",
        "\n",
        "    return decoded_sentence"
      ],
      "metadata": {
        "id": "-mqyF1pwakDY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decode_sequence(x_tr[101].reshape(1,max_text_len))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "VH-BJP0NVCBt",
        "outputId": "cdeed241-1009-4277-aa7e-0720a2fd55d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 365ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' great product'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "summarizer1 = pipeline(\"summarization\", model=\"facebook/bart-large-cnn\")\n",
        "summarizer2 = pipeline(\"summarization\", model=\"facebook/bart-large-xsum\")"
      ],
      "metadata": {
        "id": "TRJmC2--9ZKP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from rouge_score import rouge_scorer\n",
        "scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2','rougeL','rougeLsum'], use_stemmer=True)\n",
        "\n",
        "def calc_rouge_scores(candidates, references):\n",
        "    result = scorer.score(candidates,references)\n",
        "    result = {key: round(value.fmeasure * 100, 2) for key, value in result.items()}\n",
        "    return result\n"
      ],
      "metadata": {
        "id": "F535ni9dDoC4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rouge_cnn = pd.DataFrame(columns=['rouge1','rouge2','rougeL','rougeLsum'])\n",
        "rouge_test = pd.DataFrame(columns=['rouge1','rouge2','rougeL','rougeLsum'])"
      ],
      "metadata": {
        "id": "gLqwGHoZVyiR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(test.shape[0]):\n",
        "    # print(i)\n",
        "    txt = test['Text'].tolist()[i]\n",
        "    if len(txt.split())<60:\n",
        "       maxl = len(txt.split())\n",
        "    elif len(txt.split())>900:\n",
        "       txt = ' '.join(txt.split()[0:900])\n",
        "       maxl = 60\n",
        "    else:\n",
        "       maxl = 60\n",
        "    try:\n",
        "      summ_cnn = summarizer1(txt, max_length=maxl, min_length=1)[0]['summary_text']\n",
        "      summ_test = decode_sequence(test['Text'][i].reshape(1,max_text_len))\n",
        "      output_cnn = pd.DataFrame(calc_rouge_scores(summ_cnn,test['Summary'].tolist()[i]), index=[0])\n",
        "      output_test = pd.DataFrame(calc_rouge_scores(summ_test,test['Summary'].tolist()[i]), index=[0])\n",
        "      rouge_cnn = pd.concat([rouge_cnn, output_cnn], ignore_index=True)\n",
        "      rouge_test = pd.concat([rouge_test, output_test], ignore_index=True)\n",
        "    except:\n",
        "      continue"
      ],
      "metadata": {
        "id": "9F3CiJGobvgY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "078ce68f-4355-42f3-9f8c-029ca6e843de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (1097 > 1024). Running this sequence through the model will result in indexing errors\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(np.array(r),axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrOe26uzHpUa",
        "outputId": "35b1a187-dc81-4e59-b12f-8b1538e60f3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([22.15110515,  8.23171674, 17.06806867, 18.5925    ])"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "rough1 = pd.read_csv('/content/drive/MyDrive/580project/pretrained_rouge.csv')"
      ],
      "metadata": {
        "id": "YnoLKo9MUJD_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "N = rough_cnn.shape[0]\n",
        "x = ['Rouge1']*N+['Rouge2']*N+['RougeL']*N+['RougeLsum']*N\n",
        "y1 = rough_cnn.iloc[:,0].tolist()+rough_cnn.iloc[:,1].tolist()+rough_cnn.iloc[:,2].tolist()+rough_cnn.iloc[:,3].tolist()\n",
        "y2 = rough_test.iloc[:,0].tolist()+rough_test.iloc[:,1].tolist()+rough_test.iloc[:,2].tolist()+rough_test.iloc[:,3].tolist()"
      ],
      "metadata": {
        "id": "8dTVufyo8Xk8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Box(\n",
        "    y=y1,\n",
        "    x=x,\n",
        "    name='Pretrain',\n",
        "    boxmean=True,\n",
        "    boxpoints='outliers',\n",
        "    whiskerwidth=0.5,\n",
        "    marker_color='#00cfcc'\n",
        "))\n",
        "fig.add_trace(go.Box(\n",
        "    y=y2,\n",
        "    x=x,\n",
        "    name='Selftrian',\n",
        "    boxmean=True,\n",
        "    boxpoints='outliers',\n",
        "    whiskerwidth=0.5,\n",
        "    marker_color='#e898ac'\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    boxmode='group',\n",
        "    title = 'The Evaluation of Two Models',\n",
        "    title_x = 0.5,\n",
        "    width=800,\n",
        "    height=500,\n",
        "    titlefont = {'size':26,\n",
        "                 'color':\"darkblue\",\n",
        "                 'family':\"Serif\"},\n",
        "    xaxis = dict(\n",
        "        title = 'Metrics',\n",
        "        titlefont = {'size':20,\n",
        "                     'family':\"Serif\"},\n",
        "        tickfont={'size':20,\n",
        "                  'family':\"Serif\"}\n",
        "    ),\n",
        "    yaxis = dict(\n",
        "        title = 'Value',\n",
        "        titlefont = {'size':20,\n",
        "                     'family':\"Serif\"},\n",
        "        tickfont={'family':\"Serif\",\n",
        "                  'size':20}\n",
        "    ),\n",
        "    margin=dict(\n",
        "        l=30,\n",
        "        r=30,\n",
        "        b=50,\n",
        "        t=50\n",
        "    ),\n",
        "    paper_bgcolor=\"#F3F3F3\",\n",
        "    plot_bgcolor=\"#F3F3F3\",\n",
        "    legend = {'bgcolor':'lightgray',\n",
        "              'orientation':\"h\",\n",
        "              'bordercolor':\"Black\",\n",
        "              'yanchor':\"top\",\n",
        "              'font':dict(\n",
        "                  family=\"Serif\",\n",
        "                  size=18,\n",
        "                  color=\"black\"\n",
        "              ),\n",
        "              'borderwidth':2\n",
        "              }\n",
        ")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "VKj7Cd14-W6n",
        "outputId": "2df7e2b5-b1d0-49ab-a634-99b220bc27b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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