RGB-TROCR.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Download data"
      ],
      "metadata": {
        "id": "aGAvRa4H25Nm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !wget \"https://drive.switch.ch/index.php/s/MmdUlDCSo7qhngD/download\" -o test.zip -P \"/content/drive/My Drive/SSDA2023\""
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !wget \"https://drive.switch.ch/index.php/s/4Hrdys8syEW1glE/download\" -o train.zip -P \"/content/drive/My Drive/SSDA2023/data\""
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "!unzip \"/content/drive/My Drive/SSDA2023/data/download\" -d \"/content/drive/My Drive/SSDA2023/data\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5P0-0pJP37pF",
        "outputId": "a248fc24-f080-4581-afb7-e3d52bb6306d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unzip:  cannot find or open /content/drive/My Drive/SSDA2023/data/download, /content/drive/My Drive/SSDA2023/data/download.zip or /content/drive/My Drive/SSDA2023/data/download.ZIP.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installs"
      ],
      "metadata": {
        "id": "AeVYMFFZ27ve"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "mHgim1R50l-P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bdbf144-d217-44e8-d5e0-0a8db15f25ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m80.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.5/268.5 kB\u001b[0m \u001b[31m27.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m110.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m80.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.2/486.2 kB\u001b[0m \u001b[31m29.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m21.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m36.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q transformers\n",
        "\n",
        "\n",
        "!pip install -q datasets jiwer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "SwKE-jiFZXBS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcf1f9b7-c358-4ab9-dfd0-8b7f8be707ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "train_df = pd.read_csv('/content/train_new1.tsv', header=None, sep='\\t')\n",
        "train_df.rename(columns={0: \"file_name\", 1: \"text\"}, inplace=True)\n",
        "\n",
        "# del df[2]\n",
        "# some file names end with jp instead of jpg, let's fix this\n",
        "# df['file_name'] = df['file_name'].apply(lambda x: x + 'g' if x.endswith('jp') else x)\n",
        "\n",
        "val_df = pd.read_csv('/content/test_new1.tsv', header=None, sep='\\t')\n",
        "val_df.rename(columns={0: \"file_name\", 1: \"text\"}, inplace=True)\n",
        "\n",
        "val_df.head()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "g6j_P84D3FXM",
        "outputId": "26985e66-d296-43cc-e606-f0157d6e67f2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               file_name  \\\n",
              "0   images/0-2000-out/la/633_01_r1l1.png   \n",
              "1  images/0-2000-out/la/633_00_r1l16.png   \n",
              "2   images/0-2000-out/de/633_00_r1l8.png   \n",
              "3   images/0-2000-out/la/633_00_r1l5.png   \n",
              "4  images/0-2000-out/la/633_00_r1l23.png   \n",
              "\n",
              "                                             text  \n",
              "0                             D. Osvaldo Myconio,  \n",
              "1  et preces, ad constantiam et patientiam. Roga,  \n",
              "2                   duos, zwen weybel. Causam non  \n",
              "3      occidisse praesidem Mendresii, qui Svicius  \n",
              "4                               Bullingerus tuus.  "
            ],
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              "\n",
              "  <div id=\"df-a92b857d-1845-40a7-8d74-5d8734aa6879\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file_name</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>images/0-2000-out/la/633_01_r1l1.png</td>\n",
              "      <td>D. Osvaldo Myconio,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>images/0-2000-out/la/633_00_r1l16.png</td>\n",
              "      <td>et preces, ad constantiam et patientiam. Roga,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>images/0-2000-out/de/633_00_r1l8.png</td>\n",
              "      <td>duos, zwen weybel. Causam non</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>images/0-2000-out/la/633_00_r1l5.png</td>\n",
              "      <td>occidisse praesidem Mendresii, qui Svicius</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>images/0-2000-out/la/633_00_r1l23.png</td>\n",
              "      <td>Bullingerus tuus.</td>\n",
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              "  </tbody>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "          document.querySelector('#df-a92b857d-1845-40a7-8d74-5d8734aa6879 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a92b857d-1845-40a7-8d74-5d8734aa6879');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from PIL import Image\n",
        "\n",
        "class SSDADataset(Dataset):\n",
        "    def __init__(self, root_dir, df, processor, max_target_length=128):\n",
        "        self.root_dir = root_dir\n",
        "        self.df = df\n",
        "        self.processor = processor\n",
        "        self.max_target_length = max_target_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # get file name + text\n",
        "        file_name = self.df['file_name'][idx]\n",
        "        text = self.df['text'][idx]\n",
        "        # prepare image (i.e. resize + normalize)\n",
        "        image = Image.open(self.root_dir + file_name).convert(\"RGB\")\n",
        "        image = image.resize((300, 50))  # Resize image to 50x300\n",
        "        pixel_values = self.processor(image, return_tensors=\"pt\").pixel_values\n",
        "        # add labels (input_ids) by encoding the text\n",
        "        labels = self.processor.tokenizer(text,\n",
        "                                          padding=\"max_length\",\n",
        "                                          max_length=self.max_target_length).input_ids\n",
        "        # important: make sure that PAD tokens are ignored by the loss function\n",
        "        labels = [label if label != self.processor.tokenizer.pad_token_id else -100 for label in labels]\n",
        "\n",
        "        encoding = {\"pixel_values\": pixel_values.squeeze(), \"labels\": torch.tensor(labels)}\n",
        "        return encoding"
      ],
      "metadata": {
        "id": "oeEzVN5hkbrd"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrOCRProcessor\n",
        "\n",
        "processor = TrOCRProcessor.from_pretrained(\"microsoft/trocr-base-handwritten\")\n",
        "train_dataset = SSDADataset(root_dir='/content/drive/My Drive/SSDA2023/data/train/',\n",
        "                           df=train_df,\n",
        "                           processor=processor)\n",
        "val_dataset = SSDADataset(root_dir='/content/drive/My Drive/SSDA2023/data/train/',\n",
        "                           df=val_df,\n",
        "                           processor=processor)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KoUJWEpWkooG",
        "outputId": "ad0d20e7-238b-4673-d969-9fe8da646232"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check images"
      ],
      "metadata": {
        "id": "q-0TLIcxlRF4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoding = val_dataset[0]\n",
        "for k,v in encoding.items():\n",
        "  print(k, v.shape)\n",
        "\n",
        "\n",
        "\n",
        "labels = encoding['labels']\n",
        "labels[labels == -100] = processor.tokenizer.pad_token_id\n",
        "label_str = processor.decode(labels, skip_special_tokens=True)\n",
        "print(label_str)\n",
        "\n",
        "\n",
        "\n",
        "image = Image.open(val_dataset.root_dir + val_df['file_name'][0]).convert(\"RGB\")\n",
        "image\n"
      ],
      "metadata": {
        "id": "iLifcnOqlE5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "outputId": "da972c85-5f1d-4d99-fe85-b2ff9eee716d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pixel_values torch.Size([3, 384, 384])\n",
            "labels torch.Size([128])\n",
            "D. Osvaldo Myconio,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=752x103 at 0x7FCCFA5A5B70>"
            ],
            "image/png": 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w19AgRdPNM5T/QWfe65C2w8SRhi3rxxMoVr9Ht3baKw60oqvVCLHOfZuNNYPbeJpLI3jTgZoZqWRKh0jzYP7qfFXiGEBm3kpFEiQaLIk+yQvLF5XQuesCmAuM1l+qSnGKmWgiAMfWVGAyryPaP9OhX8wFZETlk/TqODpmPtydxgLr7KT+MAk1JYcKBcURLANoUKuq2Q8rGSZUasVS3VaSDTMrldsREcmCIwfcKKau0YycRJqZUkZDHVErhNo25YeAVDdMfSwb20RvuLsgfApYIHr9PcCW98MNx+BJ2rV4NSxroPmKkYlVkr5FgeW9W+nXFVU7TntmGg8mvg7GrXgDY2YFBdVBX764RdRlOU1TlETUDC7AJdnCNCtEtKtd5phYOooGXbwhMB30RkGlNplMoSQebAlbE9MsoF7v39lnh6nFEOswNjMXYLnCcB7+5MDIVAmiI3CIopqxWHDHViqM5sHskOJKYi4xMkr11u/eZhaLMu0ny8woKW6amczRHvMG3Ro/CWDWtpTHFSA2llprJ6lCf9sxj2RtpRXtce32pyg4blU5ixX9cBRPHMuMs9Kvjhh24AhlMd2Hj65zj4VHuaqQBaZI0NzMh48B0lLJKK1iLsXl8WHZebzWD/PLNUmI+Pjl8s396cvXB82YmTQnmEgk3Ya8pPZ69fvHP//lh3owA+AsXLPCwIKKne6jnQPKLz05eNaUJnAIMNbDHW6+F23BxAVqepWb4LG3b98nQMXH92/HGJe575HDz0bNuJToPBTaY9hwP3U6Jp62UygETiU3e//hyw/fPT9U820wZnzCD/V23rJqGVQiPRy71RS6uNJrM5MZD7UvCiqtnAErfCab91mpoAOiNfynFYXWuXn7b64D3uJjlv3Via2vMzq8HyDLYuywh6fVf0lcHINZHgzQEXVpVjplCIqjPFKxufQJ66C1M496KqAWI4kiAtkZmTklK93JjeZAvexiTkRmf7n3dpQpr7/RcKOcKksJ3iEYncWhmUiIlta5Xu9I+7S1tBJLAn50MhRv0TxinYTiQrESX62AVJWyWjEYUe9lNF9kCUi4kV2iWRG8gkOuOtcRvEjKTHJTpQ+EGdKEKMpkJT9tsiK6G6DVjnaUVko/LJSZHBnTWjEcFPiR2a0k40CHx5/XFpVfU2aW8gH1ReXzj/SEt8ieBxKt3zDkoRJsrMj1se50rxyyKlYJ8olEG8uzQ8hoYkrAkQKYNZ88Fjrss8HamxUOuJw+mmozmoPWDEMkVtETMi6hloplO9Zj7dkRog+P2Oiin3ZxjFL5tqxixDLF+szay1r7w/v09zQRajeEshzJLR/sNzwy1Cfwng3Ay0tzHbw/ECCZXSrnsoY60+5d671xEsW4Y6EC72dWSV/W+amcQIqM5ZcOCq0EIDpww2GaCzccxl0cIpUkDlzUa8VVyU4JcWPs+9BlA+Cu5/SfWDmV2m4CJZ0FioQnymUc56IZk+73qQfXKup11YoruHa1y8ydoJRMW94CqwhZuyUGoDw4jgozve8H/CqDXunsOjC3FKRT/kIzbcXVnEVJiiiH1UD5ia8VoIxicEjAB0Gh/uNJuvbk4B0fAsDcQTJS3fzVMQa99ll9NFwftTjdBFQW03oT6+ywOwtKa8m2nAPyAF3AUm1rHGdTj1+1Dd/jMiEf492nLxn7Zl5LMWj79SJw5oyckGXC7fT7r5/mnH/91x8a3q21bRFVZyQqVThxW/+SjNRfZUe2qmD3qS8aBEty5z7MBrv8tKTiR+ZpZu7vXr9ViDAO7LNrbGNsJ9sUYWNLxvUaY5i7GWzbhvtpn/s+LyRnRMTO6t3SfP/u0/fff9OodMVPAUQ0aFh45jh0C9/cfq3UYWWEsM5t3BDrKFlH/YaEZm2IFae7YutZZlW72B/auimDqGoyOihSHebXhG8mW4VhaHq6DxqJrLx29TByvUcT0e59fLig+3LUty89Us8ulC1/ylu6txz1Su/XoumG0hxNHxHoQv5/+3w+8Q8FTySZuMj8w+G1Z0NlZPUJdazQJilkmo44oaOmc+BLQTKIyTxkYUBr/6SMvbquEuqgLpoQxA1ErnNRdIBVRbiiWKWRIo/etF605v2eGFSDq+W4QP43rTmERDIZUWgS7fa5AlFhFR5CsKMepS7plrKpO1WXHGp9lIBmXPtJCoyjol3rJUwws9bRd+acErKjR8WrggS5lvSARpnWRts7TrJCTFlpG5x1n0WfK3C1cvWudSdnFomaIozdKCVwLKyzTLw8Uf38YasHY1CBmc42UmSfZoj4+PUjOb5//u06ieuB0MlKbcxC2OufhQK4kEqFAxF089bBZUuHjn07jnF3fdcZ7rC2cMmR9K/ojcYBJMyd3TTRVtRwzAS0UrLpxif+a2FPoMub9d9H/UsELcsQUM6y2/kW76XFcxDFfB6St/WBufL0jozrgAKrpnZ8CBexVxFPkKIQ+YLtZWduABv9RbDdizX86s01oljlRvI0pznM1w4+8RdPNhQ3aqw7IDrsLU9oletIXLX/GyxTarXCooHnwfh0InXsX5n6wkN+/GV0Y7+g5O0nFnF34DY0tCTZ21Gl1Tz0rgu+L1NHteinIkIRS8zOW3fI8YR/+E+qNNE4ItChajgaj6tbe2+O87DO7tOO3vA8XuAPYCIZJfd88+o3Rfzw8ke2IRnNfv/9lfv2w8vvwaYKHr7MMcwMswT+oYIQTo8552W34YqMnNVW3rLPefHhZvj1n2/+8tefy9Q6hQWQSbPFTrMt2YxLHZ3d5a7DRHEL1VonoCsRNS1CALQQ3gqM5dBe/fqrmc89ZfRtK7RWPLhi369XISjlzNN2dvMIRQa5x3yMOWmeSYkMifLh7v75y5T04putjY0lmJdky+dh5Vl2PP8SJd0iEQQVC20HO19YubtkCzp7RcTFlYTmUr7X8VdGMJuTlcq5RX3Z4eBv0WuBZh1yQ7bHAVjoqguv1Zbc0rqDXlSd2EOPVse6/31tdAefoknQtHfJutmt11Jmt30ZvCHPATgqIzo8LdBpi5aycCV2KkYcjWNq0gQAwahcMscFELuGhS7c3MhgdbQsxQ4Mx1iHJSpcPbDHkReOZgoc+afAjFjzKQp0mgAZsnsAoKPvvUBDlTX6EQk0K4lUa5g7trGOP3UosYDuXuiNbqdEHG0p6KWOkPIgF2hGJa2phyfuy8jiHdpX08zdfVT7fQWCmqRQfwiIoh3sc0niWcgKS+tVqqxWuqAws9vSW95koFphexlWIe9u1gGWBABcNNsRhlpEsAJTO4dKAus3rV1gfX4lojjg9YiI43yy6PRsxqT2aj3Xbb/pXmErqiKUlPLLw+PGYbSvn79er/v3L19EdiJ7fFj5u4ZjbPzdW7Uk5CtQGlbLfkONpwxKJ7KhY+IIcJx1M4P5gSDruVVmEgfBj1uc7kNeihMTIHTl+EYFLlsFWjyg5bZrx4zs1odDSFmHE0V2GEuhdgAaoPTobftLUVjerhznyorXI7TGEmQxnCtD6hNkFLprBVDzQI2sAR7sX7ulJ73ryzMiu1zVDPOyvwUIsNjqW9sayaWIzGYqjxXOta8Lc2R3ExTBMKcaOq/3wxG4F7FCAg4tcE0WVWAGkx/slVKZUzhA+DK5Jwo+kOYGFbexuklvQWvJRRpZWYFjHMgMK4Wo/KWTzEZCR/oldasW4kDwyMhyRM1GjEH3csSF1Ms94eZn65OtKq0NydbL/vaP32mKeTn5nfkpZe9fvzdwZsjlZoODsLev30bMH3/5+f27D+4nWSYxfGToiEYzAgknlOktUimeXlDOiAxuw8359vX7n/78cz3BP/7+uzsZ+unn7whWZ+fKqmuvcUj6nqgh40m6icKnaAxmR5TVYv9xWwu8ef12nDYiaR6i8+RO5i5NpipkXh9nRtI8JpQKBbiv9FoxJ828eWnLxB47bbqNt+/mTz+96Pxs8XdYNApb6VKxc1HCyOO16nBqNeM1A91kc6fFyx2TpJJQUtndl3oSYiW1Rq9N4MjkV+i7HTodka/JsKr1ZO2vujxqrdA/cpj6l+UYG3OU02uD1GJolsU/zejqXNoyV1CZMB2uWp2J2IHk1q4GjvoDut1Z6OjYMbDeroN14xWYmOShCKpz7G7WY3uWz1rHtI7ODfzfjmeDCbXQZ4lk+y/wQDO9JlSV1sxLXrrmCZUfRtb7xK39j2YDRgZacdIwH6VCaBduqyAFSTx6LI48EkQBju6fVQcpHIsnZqKnVXWOdoTzY6c6CUJvphlrxhVpTQJ3fbDDbyPX5mNuueqxkojI7mDAQcgd1thbZl0BzSyCAH2iFsJjY5lqZ8hGAGTNj+vXXwHRCo14T4ADuSiENDCBaC4FN2MlRsyJJxjWlvs/0OSx2odNvPn9vwYdES9++gXKhL5+2k22x1VMwuH89PXxm/tzVijA0nNJZsgmQvu43IpTt8CmhfIO8N6JEtZ0tVzntGq6vaQtAKqhZL1rC6mVi80+byw9WvHe9UGdTDwJHmWHlbX2pMGSW2V251djqexOrV7UZUaN0Ky9Ri2hGZRZAIg3In9FscYPjZraJS7jXfzWeqXlC5785BEy2qwPfGA1r+xJgP9vYUNNH64VQwhk2mFiC2gXfdCWCu8KCMEEqKeQcYWF3rOK6yo9q1nYStDZ+uUFIFZgKKSxrHM5pmWmC0BEKKpdlQvXLp+MxXHU03MFWCmTEpCVIfIAZ1gAz8xoEs3dMpcezA4iZB3PA9oWlAEIxoEJrdSZ2clx025FDMvM3ZcEsIpKMHrJyZvZeTK1T8Djx6/3d+fgJFXSA8EiLVI2TmYZec1I7BAx3D68fpOgufYMSC4zgsaJAMzN9zmvl8vmY4wtISDFXDxmeV83ZE78/p+/c+O+i2nmDMxff3vzp19+pKn1uk/6OwwuGpRVOmqLPLCxson19tP9zerejVVBJgm8efslBOWV1Pl03niqSYgnjuvjA6A9Qumn8ewxHmmWKShsaAxkd+sGQAqRU8oxTmMMg0UGGWZ88+6zEL/88jMXYjCyXGVtOsgOL+vJn6DYw3SIFSsWWGAJibWyhxU5gEp23Ts/XtnnKsGtXKuCDZcm40nEVju0J4cDpQRdGcJYIlYtq1vulYfIaSGeYru1Zp2tN1rn4uD8eSMCGwZ1Z2eu7ouSPPFQyOXR7J3VgdwZpK3Xg5bARQuK/DHbWH+r0j8vp1/FXHSDpFf54XC83W9TUwGqmgyaw5JZIyWac+qC+LL2wmELwg4CNIYUfPoXyMyaddiJSmWJtko55nmUhjsD60hAut2mp2YCmaHDnRqLvAEyFWW34IIVuJHZTavdVokrPX8SBdZvrCBSwZHMXCikNWsLGbW1NETEavpuLgrR/sCMOEjxZf5r6VDEKgs2rz67o0peUHLhKdxSQpl76WybbrJGRD219VBZV2sqxRpl0ZBkZUTQ0HLwklgsUv1qFe8yKZDg69/fAnIrqdv27tWr717+8P7jV0cqZkSMcfKxFWK/PMzz85P6tQ/mGYU2KhvVkyj+RBu+4EzZXbY5VhGa7KHCKZVacXEEVR+vYlBmzBoa2+i1vWizZLUE/dZ9BEseVAgtq4CyIijWn7Lm6VbOvdIZQUgLooY3NEPSvQCrdeFIHBoqLrsjb+NVVm7YBVoBtqYbs+XueVjbsqVcowNvGoVl9oel9191f4IbBOGo/pTPefJGIDIYYUsPViWztYkG9MTVUg5KuYTvt6809hdIyNthK1l+/6o+RB47LtXgIxKmYQt8g0t+gQSMchpLrVxHJTHbsd7iCxeZWQeyvrz97pGwoeEey5ZWxFh/UG/u3VR5ZN1VxXsyy7lJ3BVzQKIdSDcIOSTrkRkVCGDWhO0KkBVi2ilk1sDkFvd8+fSgqJQx09LcZhZA3zKhGQ4zQ5KgoyWfNLi5leq7rKeOgJBouG5uw8cofaz7mJpQjbmYWmMZ9pjIcDPjuURAQsL05t1H7ZIieE3EX/78bygpiR8kw2oEbMpBqVQPv1FWE5dkKdoKz4vVI+3t20/VUpsyN1JuBnHaIBNKTUWkLJwoiJgkBJgQcYUTlA0OHwrNmQmFZo/0VtH3Dtoe8evff/vTX37qXtaK5tIBDxY6da+gCR0V7RUCmQWvS65hXqglY4XoypC0frDJgJ7woRTzkCgLNSB7dYsCyIwn5tv52nG016FesKD0xVjtkEfUW/0pjT6NALOg80F4A9VmUD92UMjrNcAlQlmfZFLJRG79CAsWHmexICVw1Gu7fwqHxvmG8wxqovl4oyOz7ZRm5Ytm7uWyEqEU0Y20LY+dOkSWKTO7jVbKLgH39L/SOdvKVVgfASapogKOFJWkCSV5RD/W0niHUow0955Kz6UjXpF/9Vcn1vujzvjqM6rCj9AU97H4i7Fr4h5H9RNd8WNlUQelxuVTDhOpMNkqiCofNBwxq/7fQrbHqBEsPkYVnoBhAqwnED3pyFmQZdFtR+5dz9rKdbbZFA/RRFl/S825WaX6hW2l7oEtSpyqymnF6AOUH3AKo0YGVdnooGU+fnwQrt99+x1uv/j+3TsfZvScdctAyPDx0ydaqbs0BlNzT4M5MgPx4e2n73542WdgUSNaeo4bcVXRws1Vw7XWkjw5iuhoI6yHZLfqrFNVgIg3UuuQYtW+N4ZnD5XB+o4KzM2WSMhQzCNpIKmq5rLD0ZN42afcF8FcNpcpZRAwk4FVe133CCxsuH762IyUPJFLBk3mOrA1GJ6VajYtJK0DYn0wDp663W1ny0/MuygKPbVzHCQcWjixzDGRIFNmNNcqsnKZcfkkrOSyXhsLbh9HqLBaqsU5vYXV3MEjn+/flJQxoxoKzMYo67flNLoYhxYLciXBtwyujKFMbRXvIUXGmkdW6bOadetqh1nVxldzE3tFl0NZOdyKLUQTKmuuiZ4+4Xp5wEgvR1mFj2aUm69p66pNqYFSaElVUR3VPuCQ3rx964JCtJEIH5bg9Xo1s3F2doPdVbnBxxhevmoi94TRkNlycWUyM7FHEjHggMY23G3G1YwQ9ocvHz9+fPj08dPD1+fff//zyz8//+YbbtTU9Tq301bD2owgnUAgpNh8o53e/Pb7T3/5c6/hE9yIFQwLr4LLiNCWI6vGn3adH9593DMAMxsETFPQadwr9Xj5QveMK2ZeLteZ4T5oiNwzL18/fK5hevfPnj3/5gWNMXfjIAikd/MmMhUxCdINhiJ8fYxXv7796ZfvVgReXD9J8te//Z8y/umv/+PISHiQNFxkQMSMiBmSzFIpO4aA3bJINeSoGnfxMZ0D9YoU0iwMhQOp5BGGG4BITVwvuHHEko5NVDVTlQjmj3P9lxJFAJcGUdUr0hGUx6PiD5kAqrp+sA+HQ17s0w2A1EOtLIi3by8jB5S3IWcVAkWaJCajP16LNjscr9qZWxMbbpApszQDNYEGZNZg1oMGyDK+m+vDStHavS8jRS3y4b54ZN9NpzGTZuYmuHpw2cqBun1JyrSMtbnl7oTqjOUR45busL1Ypes0Olm3X6gz4RufzrX/fe/HDf8fkGWdN3IVA1c0aJu+Mf0gelYLyOwZRRk1mGed3n5CEYx+AjSLzAX7hTJRLP3cIiq0sFj5/CUDALp7ZemBCvlVW/8TaI7oi0QOI9LixYEDfS4fg+HDURlxlRFpnz58LW/19fPnOeeL774F8PH9eyPdpNwzA+ZJbD6sGy0YkU5lztTFfVSjuxu/fHzzzYuXtxBOoss2FZi7tFoYjZDyaCC8ReiDtUEm7EbBdSbFDo/rzCwvZKSewsZVIGx0q9VEALbEnThGLS16pgVyR0v+EzjFbiiArSGnzaHUNRbFrwAmN8sVDCs4dvcjOk9ruaBAFJu5DvBxJxCOF1v1pcOHLaezDAhPMqqbL2zUUFbyB47CrNqPzNe7rnRp+ZDCG2u46koBqxmxk6knK3bbvDa1ppez5lNqqWzMjNa6ufr5iFlXKQFeXXxCzeFtL75OyJF3tAHcnAkWui1RodVsYC2PXERUETkyc7jMtboQ+7mbUY06oOzxyuDt1W5+3Fax8TCLXlrrQVU0wwE3y1tmMlflgivxqC1lk0LoLFgE/v7rfzi3bWxyFjU05y4QiW1sBtAz8yqk6ZQTdBvuosDc98ucc9BEztgHOchMbT6kJMIg45DmjGvsl+uXh1e//fbp07v4+vXT14fr459fvvh++Iu6pCZc3KQMBbbqS5IunBHT/DRgSbz++69mPA978cuffv/7f5D281/+WmtVCkyTBD92DDeTrciJN2/fQXQjfewzLTVjD2rjHZC7Qpf98ct1iPt1XubjyU+f9svHLx8+fXj/6f370+l0fvb857/89fzsxZhEwofN627AyT3nlXIfS4OAsc+ImdswMAP56u27X16+LNsVuj3i1X/9r2SK9o9//o3iv/1v/8frf/5zRvzLv/21jFSAggnUvqpLxhMqV1vCPlvR4dayc4PD7W1Wzt+Ae4XwVNKEo+B10K+LBS4v1yFFFfJ0kDprBNsKagtSlHHyMDyy4x8O7Wce8aOesHgIJbt9bUGTG+BZ0agu/dBB+j5JBdcc7eNUahGwFdhaP0Fo6fy5KCKVUGjeQBLXcYPUl+55TbtMM2q1Vi1mA4f/e/ofN98VKU0Wl19+oDwbDx97cEze3NCRPa7OB/VAzQVGC8CaWaalVxnzhkqPtJYrtrEFTysqoY5PcxhHFW/ZzGqiMKRqwEY5S65HRcOAqvJER5GG0bcwBNCUjDCllhi0cZ5UMDkUzHLntR81ML/GRqycZIGv5a2F1XJqZhKyu2ZFwtwXk5XHgtyw+9oqM/bU6ltOUHTXTZMEafz66u15jLwGDICqKzzmBHU+b5vbh08fiRhuCu3XCxCZKQyYZ7oybThpJiimtCvmnDnc3RyiDJ8+vv7u+19wmNPtH+3Sjzev5sN63IXjjhCIhi65VKW1zbrp6dpvLvuyY2RIg/pemxsPoQKUYCS5mg6WgS4vy2p9UrVm/tGhrP78m7AutRRPWF/pq1bbNMWtb7qJu9Z/H0C8T0t/crfDgTT3irZVKevgzQX+FzfYgb/OQyd+HS2e5CcL29aTmwlwmnUdvi6/dHsK1A5YANCs+XOuT15fui7OaLTRrhatYy75ZF+iBh49Dtm0Wi8djcoUGVxwZXmGlVscT6Pesi6+kkuQOEAySUuD11SnOt21ObhN4L1BNQFM1NCaw49aczYrFC2DXeDplsmujlbgyKw7InR7l+okr+Seti7q4woPT+RlZvbrP/9z0LfTaWzn2CNnznkN7BAdw+kQc99jhrkXHr8KcmXOXdc9Z0bA/O7uDNJkOSWKLikSSdSeI2J//frVh99ff/jwzj03pcOdp9N2B8LdNCnECgEGmELX67XykX1Gqm6QdXNL5Zt//A3QzHz92z9+/OVfeklodFVJ7Ob1iHevP5pt3/34/PfffycI0djXaiYMPFmPn0hBM2ZcrjPy65evjw9fjfj11W9v3r27XnZL3d/f3T3qh5/arw7fEhLDbcCAmcpUZPUcpShhmNdcvu10EuLNu7cDpsSL718IeP37P+BmGlmMSur3//VfMFPq1//6+5//7d+4HJmZW7Vqcc0XeRJBFyI4kv01aXsNrcQCNBDMVhmhU7UDUDe/iEbZi+rvM25Pbp7LsjgtuEByFYSr5WQdgBXb1nHlQh6rGQcsUrEfXnV1xK1PbWXoKyWov5OhJjw6XyfKALwjUMaBcFZ0WC0FT/+5jnv9ym7AjuOMsA9pUa51AyixfOA6TFwMe6dPK0mqlBo3MSVrltrqCTjIgHapWrcc1EeyEMdNa5UEc32Dbme/8ttMpdMsj16hld9xDdHuhPVIXpenqvqgDmlLu1ncijmHkxQOgFh/nkpELKiT6/JUHDqcw9uZ0at5Qk8xaD1IPqn7HyMVSDDXHDQcSV1nmn9wsCyW5+BOSK6kHdV+tIDaAaPrnUiau7vWkIp6s7K9pblAAhhODDONmmdEYWYmpZyxkxw2xnARIdGTIAwWM2fOCbkNIMQwzUhVg36iKl17VF7hY3z4+O6HH3461vyICbVQPOxY660k9Xt2JOVqWAFZU5sWMuoi8SJRli13NGqhLltDi1Ygdp/hGjitzIjGQk80TTjOWhtfrJPZDsRa/lb3iguLe6xMZ/1gKmuIfSHoPkiNnrr7f5UOVzdjC1duDOKSzreRd69v/WO5mVyYbPnQW1/CTaBUKGglZOsgkE22AZL6ZzqLPTI0HSemxvEI5biP/SzMaQueVQUOwJPrxFruVAwKJaHuTF4cezW/c7V+SLrVVStPsppA2Je0L0xTJuTkke6smn2rlExdPa7pVX0k1GWj5SuPjKScRiegB4EArFJx7cQhEOlXw3H4ayWzirmls+mttCf4r5XChiPR5gGa9evf/4ng+XwH+j6DMBtmumbCiG14DRnIasPeTmaDkdd5veSeEft+Xfdxg7k7YHYy21La504DYMi6/IHXx8vv//jnp3evjOkcoTzdn5999800PO6776l9hyc4qpA9lRAusacmnXDLug+EvM6dGRU8ZuwJ/frP/0rQ3S0z5qRtv/z558MLfHz/WcSu+PXXt87tvHlkhjD3nVTGruA43UGWEwZ3F89n7ZPGrw9f3r199dvvr/Y5nfbsfO/Dn33z/JsX37izL+soSd91kiA2wkknGDNEDWNmCCFQmNViMjMhvH39OpVjbNXXwoSbm1EJKOGayv/8n//nv/6P/9stiTJbp64D3s1NHTYM3MbYafHB68Q2N3OrhqzS1kLwbF/epvg09eJTg8RqhNAxM66wx7pwINVDrJ/k639IqsrnVKaetnSW/eiqgVSZrfPBEfOXYjljOS/h+EHeiM1snrnzn0XKtKTjQDoCjg6pPEakrMUq+e1S4Nrr12+MmJo///xLXyK9ltew7paqtoQDcWDla52ekXWjaGcqK6yu+jIyVtr4pB69qjakGTO5+kUPZV9vS/mzXH2yItCpSLuUXGnbgUzREWeRTJVPrFHqjQNvHvjI1bWqNp2LQo3o2lrBmhdf+CexHsJImPWiLcJYUsoScXxYMYKWLBGAln+ujWif2IgcC7A2RdH/dovpOP5GLlivlQYcafTxunmDTlwjutpsx7NxYipB91MiZpWTOdwYcwI2tjEj5nWezy9o2355jMgZocS2eWQgJtMigpZO2BiBTGGMzQ0ZV+Mg+frtrymR+vnlv+jo8a1hi8sXEMyMJyfscALJ7hl7EkAX7CgHwjawVlEfgAI3QLuSwgb0hwlgrekxcLLXbrF5lTHFIXBkb1uPFTHvvmjWBTRNKaJxxHH9UCYNdV3CjV5jcxEdyGrr+ngSq5dpwRa0APf2Sjx2+rCytWaLzyRlbroRylLNYz2+Elgz8ZpzsNs7QgITi/NcyLxh21Oc05mMqR/dYAfM1AGr1wfXicpskqf/cF2Ybk1oHR2Mfbhp5nJ1D/QTSLbK27eUIht5ZR7dKzDIHPDlzdrFNp3aWW1/jAAkk6urvx64CfYqN8chOURDmsZhqQVp2sCAlR3XUT2kPDV5CyYrTfp609fv3sLN4HVOyFP5FsdgSMrInXC37XQ+Vwqz0auXdiK37exWzCmNSsbc9+00bHPNGDbAOa+x7zJgG369XB8+f1HO++fn83baLw888f6bO/fhZoNm3FSMhhYfbWbOmOnuPpx0A4WZEaDGdjptJ879er0IafC8hhTVPfaPX//x55//BOn9x08QYE50Py6NwxyhzF2akfuw+7phZ7gPH9edCQPGu+vvb9+9+fr18vz+xXY6IzJyfvvyuz//b3999vxZxCQEudFnXpGkHIZx3rbtZMJ1fwS0xzVD93fPkpqxj2EYPq8xyPN2mnMiOMYJbsFwsqc8JubczW0bp7//7X/+y//+P3roDtAX2dR8CuvrnzpROIL+ivSq6yRjRc3ST5gt3Lx8YHeFqA6kdbJhOGqh5eWseVCswlH2bZV9zYvVRILSEeJooKukq65/Qres6VAoVyNqrjpDg56DsMkbQ9BYPBdBZQfnU+BghfQjtVp+QeuElqbNb900FaIb1WUdavSq1dCQlDt80Pjx6xeZ7XEF+e7N2xffPNcNqy1Hb0YYuG6pxcpe+gVWLljf2NNuE6i8ZqGpJ80ZK3atRhPvFvBGYFnerjoDVr6i1jV1Ok0xFrrDQXRYe8hamdVs2mtFrGsqnzBR/YYESzJ31D/7ztI12Kpz7qY/UPYV7KsuVwVtEVtkTbokITNbKsRyU3k4c2Xpu4vBykY2ZrZm2JTWcm3E+mQskulmE/1itubTHBRjtUjV49Zjm6XcrTRHRgIjFYPmzlRE7DBt29DOJv0LTNBoDIRY1D1BG5ubWyiqNXJsHqF9TncKMh/bNmYGhNJ8tZYk8u2b32ieyu+++f6IuDX2BwSi3lBcTBTA6gq8HYm16we2KZoFVXxdUPHQV6JH+hBIVKGx0L3dFlUL9x2M7cpqQLLSLjY2Uu9xk8xWHQ2MFdjQJIGt9oeDt0SlGFrCsY73q1aigyCxBOu+ukXkLt6n/0q3GByJjwFwaypEWCduNZZ3lW09PKAqthKHRnida1Z7BW8Zkg5t70oEaEe3QFGq6xvL14BhVZXict3rV7/Hcm39Mku3iEMe7sdlnFTPfChOuBx7b8Ot9Yh2q/neZM43Uu147go1nWIeyH8do360tdHr5HQoKL+c2aKrEp7XB5Q5HJICWwwttJ7rRsCsceX9XAEm02QyNxFvXr1LRGgaUtpDNvxsTWjVdZRy84J+7kYfM2UY5f8cIzKGDw7RfEpSmNMTgD1cvsY+784nKaEAPZNz6vHhsu87RJMhaxPG/fbsvJ3cbLhRJsTj9VGA8YweopEGaebUPpwJhPYukNLdTyO5YwLhw0ns+4QZfTv79v79R5glRLr5drlelBH0QvypNEOmncbJuB0YEDCD79f94cun337758OXzwObBk/bZsOw+U+//Pnb774vEqtH0yJ9IJDzmmYOZkXmgqbmNmyMcScKs7nLPWKf4XTWbQkzOsoCl8vl8eExZ4zTOD87GTHc//m//uNPf/3X8lPo1uU/0AzoqdNt8ocloGerHuPO/RhEXhGj/NHtaouleK+Qf7Ahbb3odKoOsiCEVMqejjSsC1pIVKvIepI+BtnIHUudcPD54OEn+pR1GsI+Qy1EU2dAddfqAl41MSEl5WKvjmpOi4n7/HS9uD1n0RRZsk/O1SiWycY0feEa4t2HT2PYnBdkDt8i9Onjw/MXd7Q1GHhFaPSEsHUVfPFRxyCh9rC3MQI1tNDWsOZbFrhGM3Chmf70WtK1ebddu+mN/pjdLYS0PGr9yDFGZLWiLPDBJ/66k/kGjkfTXF9OVGlzP0Mjai3xebsuWvWfrNFix18m15qBKHxhopG3mhBUEtKCwIm+qqLoxkZ6ffs6qdJwN5qiuj2tA0G3By/0aQfLfuCH1cbRmE7NCtQa0MzoBMc1Ymo6h9nmNNBTEQzjtm13Rd9Vxfw6r8PP522bFskToMf9M2AUhw3SjBJHarq7g0LMvEo5MCgoETkpRXON/PDp/XcvvkO99NhK1YVOD9aYk/rP4OoMecpJVOzsFc8/ZA9YuLkhUfbJ6WymZrmuRtqm9/qMrhC/8BxoQCSO6k+B1z6lR9XzqZUTpPdEoDXOsvmSlk0JTz1Q72qF9Xovc7EGZBauQl/0inUuD57kgCIHcBCK5awPzYOEWL8lRdQlHLiNNzsmHjZFcquxAH3Xy3oFropxLWoRKV0vx8HX5GGly2qx9rQ69QJVm1wETLFjVon6cOsroROsbvnEsbhciQ7EXOLv+gpYzcNB0e/SjaDmqnatC5KwSrUHPl7lqWrBaKJlpY/l50rp16M1GufSW8R/2081gO7dXu6u9cXlQA9DXqAz9dtvb9zG2AZFZGBgbJv5ZjaMY86rRHJs23kbp6Nx02BUXUiSAIZ5hoybwJhXHxUFicRwN1lOy9wlnsaIyP3y9c2r366Xy/P7857YL0HgmY+7sWGKwwCbWbf7jvLqyVKvR22EQbQk6XDCqox8vewJjXFyOoyJ/TzuQfaFtoSmxskTzExz13AjI1JzN8Pwk7jBZGZzj7knDIE0mg97ePxyfXi0tMi0zS/749jO//ov//b9y59pbj6qPDQVqdxOW+jCk42xZcS0CRBwI06DEhOhyOqHE3Luu6VdLpGUDbPhMkohleqmOCogc79cZAYfr3/7/ccfXx49Cg3ce9dXbkBl5gqEfboqqOVhpnVybhPhOtNfbrubQpr6JYvfQapH0rYZN6wn7ZhRevNd6zOrh59uBGE9F694jOMRF65agbZfiEbH04sPsdos+i+2O0ysfoh1ztnoCsU7HWGdPNRvT7DUyk3Yw8AkoJxbaAlqMr8+JoC5X51GGKboBvrnD9dvf7irgQAHiHyCOm7CuMpC0bwuGyTmgVvqLQt2PKH+0RzD0ySzytoNuKo5nNZEfoH94m2ah0C79VvEkSRF0pL0Zqu7fb514YezP6zhid8u/wStJv2lQ2jwthB0a4TropB2bhUOcBjtLf9cxDzIdpvoylEDVrZGsi/KLvTO5t+lmiLBm3HWJ+eaotkZ8Qqgt5v+luEcR0WCtbyiR6tUCCk106C3uACDzIGEMH2DkcNPkGLfrzPnniDPmzFn5u7DMpXax3Y3eGYamOfzmbTIacVyzotiB9xPA7KpXYIxDXA/ERY5v3798u13P9TTF1ZZA5cOlAAj5VYNzcf8obLrJ1yiOsqUaS5C8wjwtZO3w97XzUirPoK+Nr4bwCwzig3op1ipB3A0+wkNVmt2RXEMNRDPvE8ne2Rk78TiI3ikN2s2STcYd3hHplS6NC1AI6kn6JT09Bh3exzRtRyEycjBIPq2oHUlU0OpJcWXVF1av7/76IM+PAJ//vkXHQlZGfFhgri5tTVJijAg+57YNTNtaWv0h18ojkRuAg/IpacyFxS8tWqgA8g0Y6wBVs2E31KMgpzdndX05cK4nagWhGjLcR65Tvn8mqHRB6l6YFFfnYWAD5C9PA7aYSxJgbGSmUzVpD6iE4unr8+lyQft3es3Re/++PMv6MkKIPH7r28cg7QZkNzMaAM0cIOYkdqBNFFToOTm14dHUe4blAWhlTORKW5+VsLg1tEbGUn57HsEUbPZh0Xg4Xr9DEz6vcgUNPG4z2BMxYgBk9GCpFv1wBN7GScwEkKmKxpd1US1QEYk0802P11jT+TpdJYgRkSEYHDKjAhka+DN6JlOKcyKD54xL4owGLkNDhCh/Pr4MGeY5HAn3n999/zF9999/+J0PoMEbEYOmLnl5H4N83F3vusBjLQpuTsjh42r5uX61WAAZkTEpGOcN8Eg+LZtd6fIHRHKfPbsfHe6u1yuQsx5yUiMWnz+9ttvZifaiIg//eVHdDq8SI1y7B1TVu5UJAZGMQK3RqjyW3kUWdDXqx1J2uHhyssft3CTBqOtxhcaErRkBqDmdwAdtyqSuA3ObjpYK3ofIV9dHrVVRmoq5TjkC6xhSUfX7+Zx+ZEWMyEhail4UxZzfeATJVl92tF2uggfmVXEiYh8DMHPp8Hg3BExITnHsBMSiv3D24/f//QDcAMit4VbTw+pZm+gBiOVegQA4X3x7UpTimDDQnK1xZmSwWW1/KWDwJJCV/1hGxXjqgkFOAqU62V1oNd64xQCzXEUOK8C+1q0hi9rCssildGJZ5F2h+SoNeZlS1lDOjJBKI+0PCt37eHGt7/fP9S0YU1n7vlYq4RCAMUaDmtGvaqcYNcFV2njCQLrKmKbS4MsEUZfFzXYeqcVhzs/PGiCdacHupAxIq+SIZWk0xGgUcBESjuEDCIwfDNjzKDS3a3QoyOBqaBys4qa3MbZMq/XhzmvOefYLGLPVFKn0wmScjIRkTQX4+vHd9+8/FkrkSdbq1vxo98GzSEU2O3RLsdCtLalSpL9Zihk0eD5Bv1uv7AWd1GF/e/1Ayso3h7s4B9av1HHs3ngVYFpJqbbxfs3by+AVggf3EwXpoDVG65AxtpCQYcwFp019DVytsSlFZpL3NWnol/QjRzZF+P67dj0I0QEK1f68OnLGD42p5mQ/3zzar+G2/jLLz/cqOUF72sZFr4huUbft8eqdXI2ybVU612NFk1mOuQF6ybebrlSca1lyg1Fmj1anQm9ayjs0N6+R3GsE6LaQDN6pT+doTKrGNTWUrsRXRjq/W81NkA6jjmJ6Hwyn4hmOkhxpUSByFxVrYoZB4zuWoD/9o+/P3v2jZnPfZf2d7//Uw7YNnjeL5GJ4bTBCSJEI5h76ARE8a6SFBFXKWJezqe7cgsROwQzl4YUCWVixu4c2xiokTYElPOy7zFpw5bsfM7Yr4+KmTn3/eruj/uOiBfnl6f7O98sNQHaGD4V+8zMVqeDxg02oJCuWoRmN59W7x6Vcd0zZoSfPKGI63W/mA/JJFoY++KjjD0S3Ia5IVJAiNjjUbEj0sZW5d0Z+fHT54cvX5STltIEndBwRg0FhiHRZIOZb54RxkaNw93GKfY9FayeqZxKjeF+Gq68Xq5WqjMYJzTz8es1c6eJzIirabg7OB5z0gkbpbOuS0tjvwr26z9e/+nPP9ZJgVWmhvZROkLPgvi0HvvcGKcDr25Op88JltRd0ofXrxA55/Th3/74cxtbSURo5LrD0MBMBOsq4zbOVFb8bi3OAlp1KiJyHe8b/je7XeOA5QDaP2tF+v5OlDerZ69B+CtgV7EWmeWi3Bul1ZNo0ULlcHrOW6Tq6k10ExNNtHx8fCSMyIzpNPi4ZiRCMY2zKrtu/v7d2x9e/ljPmVqXkT35tVJdY8mLjvhJHY+GdQVSe+N1otBaBUlSX9u1WI2lo/Kx+dhsOICIVrisvb6BydotkURC8YSwSculfG7ctaoJCwjyiC492uco3zQfeIiAUrJESvVVC6o1jyAc+3UktcUok2xA0zPegCIB6zs6HQR0SwN7XW5ypcWLsbQhN/ibWB0tq7J003IsX13Qpy6Nrvd25a3IVa85INtnUvQIuJQTEXTCZl1Xh/RB9+ECLpdHEmZjJiMhM9Iz05j0bUKxT0EmXS8PXx8+mJmPUdOTOAY56mgEIuqCvTkT+fk//7+C/emv/0fXTddK3LJj8FDDdC/JzeC5DKtZkOPdKdUg9CZO0B1iZRd2HGa7TbtrmuMwBRzRqDFPfVKxLrVjnXeQbO38ql8cxn78heXRtA7Vip+r65sBklGdkrekACuALiNpEqr4NkJpXQJuUpd0d5K/v3o/fNs1YWm0ly9frvywOaJPD5eMkMKHQ1TAwFRsToivXn/84ft73rLBhShwOwhNlZUQu5ww18LqSGWeFOZUCshk3VhhN3jpnVMQq4G/11fS0tFweZ46+uvuEeMBu2pib0856i62hTmSWi2D9ZdrJmYHDCz/U8J+88aMvWs9qZuQrWSovd5B3iSgjk8Egd9fvxVhyJjT7SQwgK/Xz+QwDsHE7kaeuphxGzJOtw1gQBkBgWbSNDJjl8k389gyAsjL49fNTqfTeS83Gfvj/iga6Ey4SdpD6TSgm8PNeKKbu9mIyBn7njvHdj49I2xertdUAEaczndjG2LMhIMmow3DLsjNgci5KzHnI50CIn2YZwYnMqOn1CeRAC9nd0hxfYRC8/r18QL4/f1zFORkzojMIHi9iqC5ha6JlMJA20btYCjfvXv/H//n/5yPH8/D0sfD5ZLEdy9/+eXnv5zOp8S0IIe200nMBM19jKG8QqKZcu6PDwoJKQcEt23zM4CYAZcPsCTTMwdPbqd5uUZeUwHOzMuwbRvny5yJdHczDB+27Lt0tQTev36fCIGhoNI5fv7TTwcvI9TMklzUHdgUeh+iPjjtlFAZTZWVXr95B03GzLk73TA+vn0/Q2L+9NNP6wPZXgM4jvDtLFf4MR6XRx7Rrzza6uztPstbxmfGRQXr6J/qNClvGMFq0N76STSmAYBK8fs8+YEumhmqNpH2ra1orgT/9e+vIJn5L//2V4rvP3yMmBKc5mnDTyr+AyI14xIZZiA2o795/eaHlz+Uf8CxCLfUrIVJK+a2gyrhx635QEVmaDmMzrkKzeQqe5gRh6rJrNpgzY64ltXHfpvWt3akcInRU7C6mvR2v3Kls8wneGwpA1oIjvJ+s1StOoJnZ4w9GRgl6+VxKcBhKet1EiqytRzfgiq18RXKilBvag23QsF60lYuNwa6SRX7QtP+nMOJrpArLGZsVUbbwPqPu2MfkFUxDtb372JF61GzRTU53OnYI2leVYQxPI05te+XmBMJN6ObaHNO+skJM08GMGnWao3UjGsC8FGTis3o2yZjSKQn+PB4geu0DQKhMIfbePvq7z/89C+2YN6NBmAfjAUiFoJueuZAi13iW/pxmVrCEH0vhuQ+MBbPd6QmLQXjgpPlaHQ08vVjGFDCqFa0rEJSb9fN+Hpy6Oo4XxDniLhaSnI72hmKz11cslabM6p+sQbrZdVOlOtNi4qQ0xc3pY/vP5UAM0Pb2GhmaTMRmf/8x6ttDEVVV6fXqDOKrIJGGGr8B4SUrhLfvn/87psXxLpAvA7RgmVo3iIqiyqjsr4yymgmM6SYCXPaVD51X2ubF7ndKE3KObXkzb3lzfSsrSN75EvRBAe2Uib7wiYdbdjkosPUE8fbPd1kyAtZ3tDVkbYcZHnx0eWol5decFY4Ll+nsm4N+fD+fbY0EmaDw2OGgESahWFDDjo2HxG87hfbME7QDCSGn80VSu41FiL3y2WfVwwOt9xnRI5tkyBLMw36jJnaCQz3FGg+zJMRkVUbLQ0xmG4uOkCnwXHa7onk5nPKBjYbd75JiYh5vWDcCcYZrhgaBpOlhMzJ1DBeYp/79PNp2AhS2j1IYZxyKq+P14w0m6dTnscwOc2x6bpfAFA5himUc+bcDdzGiNTlejnxFBl7Xje626ggWjqhy8P18/vPll+//WbAt135MPHy+59/+su/Pnv+zflum5cLOAEOP9XdiSyvnMyYmRMSEzK4+/AT5YQ9XB72/eKW7j62k5mFac59AmMb2hX7XsclM67zEeBpO6dyDB9mMdN8SyWg0hAJGTnhThplEfznr29P5/HjD9+VLL2Mmsya5NJhXkXq2GGVT1MkSa9ev3cbpCW0nTZFXq5X2hAVyF9f//4vf/pLm6SOsL10YP3PhTn6/kh2bX3NAl/ikRWrAUImKK30LH/406qLdbJRThWdmHQOevBMlYD176ygpU7gpdYwL9aZWG6GfPzyWKIxN3/1j1+zSpXUtm1Oxh6h3Ydv242B3TYYlLnTtrvT6dObj1Lcf/dNCZOfIL5miHWLrtkE441+au1tl/s7mN80jouYqpGciaNrrPm2iklR0ehAM4crbB9lJL0mHytNbgsrZg15Wln0YrtpXvIRBg4CPkLrksFFBVRLea1M5Yyq3rWnYBeson8P2lgoQQJqnscyHRKG7sGo+TGV6ILdc91mbLfwDZqENQcPxwd3V0qSa3BbP2MqVZdM3kDMETXasqxvq1ghWAIwgFSIhA2wRPY2kIQGwXWly74EnV5h2sxI76Y0IKHrPmOGiU5kxBjjxd13MScxKvMFGVnVdhAOCUFFRkwz3Z82cXv/7vfvv/8JXDZ9jClbG7kMXN0QlLkKGUeQqySi0vNmUntC4jIFFDNrNbMwj5On21nu8Fk4pg9lPYit6VQEyQFfhNDS26wtXF3ZC38uvqf+88gJ+KQxGz3JA0+u7WqMfQCj5dmWklUrpwIAvHr15rzdQ+zzBBV1aaJsKGbss77U6adhIlAjgN3JQeWMS887pZS5R358ny++XdcRgy3bRNTwvV6r7sKoypctLGqAaCJKJup1X8faysrDkLeRVqa6EGdJgP3o+lvH0xYF3HTnATeFOohWDIp7iw+aR9WqB2czXIv+4ZHELBAjiNVy0GwfFlzJtb0Hx1sLUBQ6uBrlAHz9/IkyQ0bOlNxdOaEc5qftHkaFbFQA8bLa0Bx0GJU4bbZHJmQGRfRo/9gpPFymGd3HGAMAGA+Pn/eAcRhpVOwTNnxsBN3GIGsARfnefUbs0zee74cNv87rZY/r5fLw+HU7+XmcBznGOeZ1XuflcZ6eu293QpLWPrDq8TQfMtl5OzEc5jNnxBzK4QbG18cH0iWD2TifzZCqTgRJ8zzOxeHN/arZE40p8byN0zDs+3Xe33+zYczr9XJ9TOl8uhubO8fpfLq7P+m6mQ/b7Hw6zwzfxna+O4270xiDvsaQmxllyqoGmKVEnoxDiWtM0swtrnPO3OdOczPLqUC607cx5x6xb8Ye/EPOqT12ZZ6252M7J5KmTO1zLh5QJ3MfQwI5ysQMVpMqrtfLr7//9qc//Xmd5pSOBtuqnuMgHrH0gs3NKH77/ZVbdedrbL45H7/uooGxz93HIP31q3cvf/zuCUtcvvIYsrL8yjL8wivtV5uMqEC58EpdIVA+OWt0U5+Ffuw6al07q7t0CpkJh+ddTX8E3Ex2KEQrIIWe3H1Rf2TAq1evmXl/ujN3QjBw+MYRmkZlTs09ZIJlRt2NNjNi6jwGWDcu2cxd6jB3+fLl9Oz+w5uPoH748bubGiaPlhkcbuFp1nXQGejRPEdD8tGQ1v7s0HYAglnXsLJpp/4+LuCFW9XvyLGpNQCgeqCTljdyvEhl1d2WXbw62tyWpypH1DzZMoUDfVa6nmppzQozzcJQMPaY4xWDsGi6taN/dI8QK9sDgKcpYiE6S1Wh6JipmEtjhM5JgyHUMEZJMQOIpr2O5p0n1EZjtXa6QqcQGPDNLCMu+5xGj5AooydMsrqxbCt5ueUeu0LgMBviVCpUNxUheoEnaN3nq7oyfjZ6NWc5d9rYToAy9xl7SVIue0SEyDdvfnv58ufaeGgV/pp3PYDYkR2suLgwRnO1XsJqGJCCuwFeRpLKnCCTcklP8vcbOYoDb5eqdzFkC1dymXnSrKabHa0vjUmbaTsMQPWxN3JvcZYrwefxGyTqpqmGwCt7KObu9gEHz1bMQ+bbNx+cm4QSrM7Y64aJ4ZvTI2Kz6nkAjIJHIDXNffiYqOKGD2yu2HOmaj/nHvlhvyZlxPc//Pzh7dvI/OGnn5+ACS3iE0stSPBoxCvAvlTxtrCh1mz15UoKMKUUGTom+vRJq/ZMLvbrsASyB29Ia5TOGgbjhy8t+2MNPF35YDvohVa7qsToGQqHHz7gZu3aUyFeWcuymyQovH/9TylQkSzhgFdS0h6CuVtC8GLeedlz+GZje7w+Xi+Pw4ZjS1wLP4gkc8Z1xmNxRfP6ME7j+fNnw3zu+5wzNEMc2zZsy7nX/PfIGQiDL5VeNaHEdV7n3D0z8tE3L73a/nC9PjwKgiGYUkTG5XJRxCDdOkWssYNjGwkpt74e1rCZyUFPNyAsGgHCoDFYSqKQXy/XTBmhjMyAwLCcO6WYO6vAt6eAIbvOGT59OwlOKg0BQbFt2yUuXy5fRl75dR++IfPZ6fzTjz8/e/aNj20WJwVD5owJC6TVYVJKMdGhwmiIzLw+IhAznSbkzBQi57QpQdUXH7qaaWxbdbo3GKayA4nJ4KezZY7BPUIwqUhcKJEzkzHG8M0f9weFXr96HTlf/vASwh+LFnmbeNkJrlVeCYvf/vG7IRlIydzAatR0sw1uCREbNSLizW+vv/vxexx9nct2V9RctALRw7eK6gaYoBuXLl7SrJPWwz+5Jmol1sio9r4VaXh0kksZdaKB5bwWbdB9TIv1PWQrPOISAODNh4++nQeQmXO/hgJUIn2cAGXMjF2Saq7BZim5bTkjY0/MRBhzKmaAsoHhNKa+fnoAmRm/v371p59/AZTRtY0CLnZMD+PyMStNxXol9hRWNm5YaaVS8YRq6LVqvFMf7qTdKjGrrez4im6fzCCwer2t0ahZlYyCs3aiHlELjxZyWmiSDdcyUKBaWrT0cbljee8197R2sWbArli1gNqRU7cssoLkISSyYhOsiZMKnlxt/7aWkTQo09AUfF14hDabI7blUSdRtdGuGZLFPJDMtAXvipEqJzxAunvKyj7K44zNk0LGyXy3fd/3asNWJERoT8wxNrrN/Tojhp/NRmImmXBAM3bMyMzTthmtVmPOKwiOCnfMec3Yfbj5MPPhIyOCevXmtx+/+7GBw0J5OFb8yIwXeLxhAalx31E0qMuO67ZqLaeBVEnTF4t2AMqVpy/YslBgIadbyt9/ob655MqWksmPrs1lCrXflHAbtY9i1aqrrWyjpU6Ne7C23wzebEQiqXUBJI4T0v/+5u1HJ2ZN5wzusRMYsEQMipxmIrpjaO5wPwNw52nbQOtGfwDwiBwy1Ulz0pgR0uTJPn54tc9pw9+9f/3tix9acfLEZa5XRy90KciaoLoV1RdIQUrrut/1AQuYrvdcAKgObWasjLK8j9YV6+URjzyql/WW8xFk1Uu4WjzKS9RZyu4DbBnySl4L5eD4TfB2YzluPboVJfnmzSsYr3MncD7de4TDQUYKZhlTymptHMbTdiZ9JwAOZ/jpYUZGbqbM8OFGj5xumvsjFDPCN7dhhoEJnrtVzDm27XQ+nWMmCRtWab73XT0BwOg0lyHuTmmpOWNGCr6dh51PfreN7fFyIe3ZsxPN8jofH7/mNQboyKncr9e04WNQ1YZwnjlnXFLTB20MkJDTSAwAQ15yCUSapJyGMfe93ggK0lxDmcaUB2FIzXnNDB8nk12/XsYWY3A73WP4jpkpxf7lw9svH98/v7MBd/pm/hDzusflmka5CcBmTtrMayi0AyC3TbErwjaHlRY4TyfHmh5lxjRl5Ol8jpixh9FFzJhAGqVE3cziPhThjmHY97SxiT4MI+N88kvuqUGZG0OR6kC/nUcoMtJpkJz27s0rKV/+9Esrchdxv2DAylmkf/ztb+iLIy2gZJx5Jm2P6badxhlOukd4SnG9Gu3z24/Pvn++jmPhBqjHNd3c0JFSSAmhGl5p1SlFCe7ohr4jE6nkQaWh1PLPN66llLwdMlcpZ0XA9ms85vRAoh0StTqvH96/mYLgKQwzuPbrdc5J0/0YipjzCgWRGXKvdEd0K/cwxrhcH1N52kYCw51wiEEhJ8z95EzlvH789OHbF98LxxXT6KaTG5fcwYaN/1aW84S0WKiOgGpeC4rT0cqXbvW/tfB1V2bNnqnKOb2YfWW2owAMo5p9ulZorU+QJFt+rL6s17eosFVaqhLhLCH/zWt18OTBAyyNYwZWjsoj2evUDk2y5QFo2rlqJe0GgQ4sKLX+6Ihw/d462PEnHrsu/TRH/0hqXVO/QnL/QtBSKbkSx8Ra9MzmcZ0PEE7mIZtxAXQamxFz7vucJx9gCHG5XjLj/v6FYVz3K7JOV864KhL0qUirMXo1Yfq0bWeEzEcxYUW4ggIzZiAy9msP3JGZ2Xn4TpGmsE9fPuU1Xnz/fYWaNoEbwl0ZxrIxQMp+JhB9AdD6v+rlF5ApZnSfNdHLUctuJeRgQ5/sKfidxXfVclE3jWztcAvZhC0WXOdhMKjMGlyJ/tqZQsodx7vTcplnbXQ3463UBQc7VFa5qmR8//kTLK/Xx7nvJ9xVRW8bZ8uAYY9rRijj4fHj9fowZ7if7u+/P53u7p7fyVJzGi2BOSeiNGX9+L5tBldMk0XuEVdBJjOzD5/fM+1+u3MiYz9/80zdSlT+pQpH0WztErpwOb9exn7DNZym3J/VLJnOB3OdpKfrX6U268sr+qySPaLw2JG1WmsvV5np8A61U1li4oiur/dNWv1FRV7d6LWVay6CqP/zzavfatfctnG6cxuZAWYEaBvcmZlT+9w5jOaQg2MMKgOZm51451LmzG2MlGaE4lrdjJRdL5ez+d3dM6au14s7MyITY9vcXUiSME4FVVPlzAz7PvNomNovRrh7hE7bqeCaO+/uTmOYw4kRUTxFzhkzkRqDA5mIpGVxwtvYarDezHC302mzsZG+73vE9J6Sn2Nz0geljIggubnBGDmdHNvY5wSy61i9EXPOeRo83d/FTs3rNWK7I+YGpzkz5tfPnw0Yvq1JV/h6iU+fHr7/SXcsGTCIuhYj2QS4OYcM4KD5NXCdqZBbKqJYPUFk1PgIo8lBMZRuDtiMPVuWyTGMGzLnGHci5x50ZuLx8TEmMWycNoOprusjYDC3FPaZw7bSCUGp3El78+rVzz//jMbjXXtCHikQX//973Vle80iEmFWF9VYzAgqFfW6M64ZJcN2kI+fPj/79tv2jUL1Xjog3ZoyD3TCFejKJfZwRMKMMq8WX7aDW8H7Fp7Lwys1l/PUwTzVoVt0bh+g969f008//vQDabBkjU4lCH58+5v7KSYk5Lw+ZmzOebnCbIxBsyqS0xjXOS+Js7GuNdO8XB9ZXbTzMnPG3MZ2ytyHy8dpZgLwAh7m2zjFzI9vP3z74/eVfmGtyq3VVChJbK3H4bqFotKsRBxPuXeS5t7Uw8KkR3a7kuMaM17dXskws+xmqu4DSxLdK1LgaRV4Fo1kq1u5LpdZCfYhYEbzSN1o7etC1v6UlX2uXpNchbY17ozLPa8969BKdh9jSalWK1a1fDP5367jXahnpZr/LRU8klzQB6173RXsMLjultBaFknMsMwI77IAwNb1YEBTmakTQjnlbqCucQmRVnU+O2/3E9hzhzlkxi0Vs8bT7PuwkXmdChtu5u5Jco/rdb+ebAychYHUfnkUpzlaWOW007nGLCkiiGumyIjsJnjjh/dvX/70y4J4udb8D1HkMBIpDxSAtUxHGn5Ukcy8KxTAUVQQpDigYjXOpyREgrHYRxwcXdcw1p0GuVpr8ARRvn37jhwvf3jRD9kGp4VMkFiVZpCUweKIn2iOsFXNy6TY2PBgGUXy05dPqLmo7tr3mNe6ruXh4evl69c5r3O/7I/79fHhcf98ja/KcJ6//e6X73/6k92N3DGq9XFPJYYNbmOPvT4/qqdr0DD2h6nEaTuNsY2xCTYjZnUtzcyPl+35VolClXyXnovu3R1xe5UD1uM4jG3pJN1g5gtQHi3fzeGovS5YNSQlG5ksllwofLLAD3m0EWY7iHLVN2lgd15IuoHO26USQFXFSN5GM2h9FCDg1W//sfm2+d2+B8M19RhfiWwBZUI5JZiT0jg5zK4paRo1EKpeJFmSNgzGmBkZNRKNtg2zc7oJDocDq6MNLowU83J9lDaYAWYcNIQiosxIewQyLpevbg53G9iZpdcOXC/Xr5Dut7PbiGsk09zpflVccmLSkNtmJHyY2RAkBodG905Y7BnKfc6MuDuP4Z5R0ksTp0CYRSZ9nE5naS+20QhYjZSbgsT00+YATWka98Nk+/UhdcmY+57j7jzMz+d7s9PlMu/OzIyPD58S9zW5iEb3U4+IyjQfpINAau4TxDBPmAgflUqrbq+QJyiFzPw6Zwniagb6adxR2ONKt+HM66R8206ROesahMBpG6Qeqcf9evJzRtJdQFYPpklCxGRfIy9pZuaMSSVpv/32z59//pNWLM0EGNWs9P7166SU4XAJObidN68qYISNIeZEDm5UjmJ+7JShfc7h/uXDwzff32cflJbFLHLkGJWt5d/aSS4PpXWu2vHUKS0FpcoZ1v7rcEiq3hzeXGauWYL8+vCFbnJS2LYtkG/fvq1w62bffv8dgN//62/bdobS0zF3MmxgGO10FtzcZyRp2/kOuc/L9XR6tp3OHKBx3ydTTgtd3PJ8Pl+n9n0HxDsokAn3weLtzSy368NjxP7u1RsY576nQB8/fP9NLJdb/ilr0ikXIFlebPEQWLxCYYU1rGcBkJVMFSQp0zqQnsBgZlp2kQCHzhrMyKzK5R96nwtNybBmFFZKvAbkaaXhWgq/xdngENJo9WFIaCo9CRlNOEadtVHo6N7lYqDaKyohxJofWt0dC2f9t4UyGpm3rLPyLdotQUTNy2hLgiwXA1bRL1E2G0oBxXYfjB9rdYanKZnKnGEY7l4jzODbZr75yDkduZ04Hz59+fpp8zujXy4Pj18/ffn6ZSoYYHI7ubk9e/b8fP8slRN5mQ9XH8/uvj9t39AYmvPyuI3hchsbaGmKSBoz97hM+WmcTiW5CJDU2Lb379+9fPmjVOIH1KF8wpnyCC4q1Zut2iJwCJ6Ow3ZUc26Qc3Efa00XNcgFayDEU4qhDdrM5V00iajweXQs8/2HN/UsH95/IvHtd9/2DzewhSTWUAO0neWaHXfLZrK7mdqEgWNc5HEaPn35RDL2aT7cN3cF4ro/Xr5+/vT+9eXhy2W/KoS9XPm+x5453XS5fK0XQ7WVBga8FCgYZoacF4SsXCGrrnGnMaEMKfdJuhKJHDSzzc3jEX7fN68VfChcWQ34Byw7zPwY+LC4myX+b3+AgrGwo1bFJ7uZNy+S3eWuHhHVcB6qgTeHSzlou6p51qd2X6oszVrOXElOHiNDqjQ2sy2HXGRrA5rff//bNoYy9nx8fJwhzcev+2U/nU7b2ccYJEMppI+Ty6s7E8Yk5rxe991pwzd3GDylSJmZwuc+zex0Om3bwLbF5THmNTLcT+YUI1k9AlO5nbaB4aS7b8mY8UDBt9PYbO7zet1lmEqnn+7Ooet+mQgB+Pz45fFyhabbcDp4iplVZyLTBxHIOYVRPHfkpBmQHCWBc05ohlrvT6UyGBngzAg3FxgZPu52mBTDhoBxdjAiLoBvwyODoI9RtQpRAYzzfUTEvKYir2an7Yfvfzid7r5+fu/up/MwGxmlqAhChi32yTP95CecMjGv+/ARljN7RtvYTjZGTjGDwpwzJToJEVZlQbRj7nLk5XLh8NP5pC75b8NV/LLDyibH3Qk8m3n11xpHFXgBzTLYTDMbw0FMwu5PJQ9xs/dvX7/47mWu/srCsV8/fvDtlHvOude1nGZ05zBLMSxCIcDc5FSa1bxTKBkgjYOyz+8enn9/3yfv5tXaiLH0YY1r+og0gVMJoZkL1YzSGfzyvexBgQvQtEhV62QCioyMa+i8jdN2epzX68z70zOJtackzA3GL5++7rH7+dl+vRr24UNmtm2oKAiLmYoc20lkagIa5/vN7gBThsLmNc1Y054rd4VlKtx6kuO23aVERE0bYYSTYZJlZOwxU9x8fPj4lVBk/vB9p6OCInPlpOLNB6Sq34Alpk30lBTrOlQFlCfUFJqKq2Ziu0UmrQ6VI0OXGBFkSVBqBA7BHghUHi8P18gesnwkYWh8WQ/aXg6dgNd4SUO1mq/kE00p8iAFuqajquWtmTxcDIJ6mFh2jOptL6tYRfz66uxVEggvkGY8hj3XABoeNF5xUMinRE5ahhmiNdNP/7+eR9IYY4tIBSMTNaaPLR265JzxGPsl5+V6uXz49OHT1y935+fPn33z5cunX//zP9++fguJAg3Pnp9Je/7ti29efBspnIdv293pLvP0/LzVfWrudRWAu59odok9lTRzv2Pk8M3oZp167nMag2av3/z6849/UgPDBp8r8e7C3Dp7WPG+qmqo6yO0CpZYvOcqbqrKHQJ6+uIaVFPEQJ+D26SlPrgNptLAAJAZeczGhd69f8dGo41IPn76pFB0K1D+/Oe/NM2QzBt8V0ZkRkHu1eJ/DAtYPA4I4POHzwQf94dxt6WCTHdGRFJSfvr46dObN18+vAtd5DSMzYxmuWtoizTSnW4wSD1bEx7zSsJLiwqmugsLEM1yZiUDU2lJKhqgSTSctjEzxZyPkXGdEe4n9zH3fPHts5q1wifmunDIAhnsgn0nNA0jSNbUSnXGYOusZ2aymiaEbvlqy8+VRdVEELScbAFglkNZqp0EyrPQ3Ieyr/87hI1YAGgxnuXIl5xfgt6/e0NzCPu+X+fDnOk+2KSdaEYbgCuy5l76MBIRe8xMiEazjaJEcxKcU6SbESGOQXKY1/UjdO2Pjx8+fRrjfP/sfvMxzqf9mgbebWdzJqMwdU0tdt+GbZWixQwKbjaGCaEqqw8f23a6e4YxLvM6AqDb2GZoXh+/fPrww/XBn92HLNPcR6guUDwpkohQkBYBcpzOYyD360TL+UrsBLONNOZOyocX/eEbfWP5IKMpwp1mo29grEtF0qq4Atj5TOzXYVtGfnn4DITR5p5jxGmraxl2ICDFnnOPZGIDDBGRSMHceuCCMoU9Q+5DRE6R5k4aIua66w2VxBOcM2v2WwZIP53vh21iBVEmKNmcCYW5uZ0ISySsulGCNTUi82gOJknR3ekjYu6XncIY4+Hz5/M331TeG5mxP9gYKUZqZoohxeDcZyQNYGqmkhx7CKxeRSvCFI6eaC+E9PnTp29/+OHgl7UksIsiOFxbtZP7LelS2SdBO4RnTYGvo1R9KPVpBJD2cLmUvbk53Sds6uKRynQfrsw0yh30Mfd5uV4eNc5ykyjzUOacY/D+2XMwr/GQGULV4UCDEdc9AJzOp7lLCocBPnwIV7jljki6rFhAM8sIN47hidz36zZOoMU+FZGSY5j7+c73HZmqcW4G+/ju6w8/f7dQyAIwPbTTbgFhQcBage5ptidTeZpzR6O8A4H0xz5RTB8ZNQAgMzEjLSs6CDW6b2ABn0pzgSe6P67vqi4EOwbEY0W++lvF0S3o9KTf8wBeC78VG7hKGO1Omw9gDyEtzshXp0RnnMzby+S6SgaAquN6vWMDOfOq9eNIG61LmdWHcQjVj5aR2/s27aQh82I5x7m7SPeHx33fd+nz45f37958/vDh65fPj18eYp+ZcTrd+dgu14fP794/fvo8DOfzmSd792Gi0sHTM4m7NM7n07a9uL9/droj8POff3r500thiCfuDjMRY9uMNujDzYy7prkrQzPP2wndO20f3r76/uXPJN+++Yfx7uWPP/WrVItXJGnq6b5a6miYqYodeSiwutluiawWh1rgGICZ+XA3B5GZ0dCypyNkW142FKXVfn388JaEwSELKZhm3GAEBZuh1IQSpNtw8d3vry/z8edf/kToy8ePiWtmmvzZi2+7FA325D0kUcO0SeDDx09IZGbkpJPbiAgjTttZaSX+ev/+3e//+Pt8+Kp9+ulkp7v7Z9+ex+nx4UvAn40h6PHxcX/M68NVL9I4IOwRmXkeGzLjesk6PmOYmeae+zX3ObbNtzuLXdCwTclZvaSZ+x6hucd1DNvMnUYw5hxul6/X0/2Wcy2+sUpDWXZZ8xzcn+QwC+ZXVQlG82ZRsNC4WUQm+7LrdWiI2+1M3VF1oM8FgNlL2ze1FDFMI93MxlB3punAW6tnCmBoRlU21fVd/fbrP07baYxzzv3xcgkGAE24Dx+UBYwypCZMY2xmCERGZArwSrggkxip6wwpQjIbOcNT93cbB2NGeeQEv172zw9fh+/bdj6f7qihuNCGJi771TaTQVGNTSmugeIZzHSYGxCToss3PwmWqfP9t8++/f7Nq1fEFrFf59d9TiHevX394uVPz569oGSwnJHZNNVwG9qQkeCMsEH3ggIzMkvjZ6xLRgGKYUzf3AhGDuFwboM+jJYxBZptSlOKdNomWCLp7tv90BXml+vju/evHx+/zgzLRJrRjbpeHuf1MuPOyx0GwnPGVYLhdJ3RtWLS3I2eoSjvvvnwkscIyITgQ0E3QBH7NDiEzUf5BOcgLDPLs9dPVYogKGdKsoG6RXQMP4/TPufc5+AYY4NyT2Tl3FcVLZCFHTAvb98BosPdejaU8nG/XK7zfDIfzJnzMt1p7iJOYwPHHrlfJcmojADMxybgmrtziATs04cvP/z4PQhlvn/3JRVAasa3394f5EoTK9UgQKyb8vQkinXoWmF6DT1esdmIz18+04fEimZGM4jpD49XA0/ncT5tGYrczaoAdxIHaHMPsrOTyNhz5g43GWnc5IAdtx9g9JUgji0JzMh5uSoShmI69/nw5eHifj6f7zKmZko5zP10xsMX5qRbWlJ0OpZvEjIqWEIlb/348fP337/oJQJ6mGl32xONHxeRu65DJyD4EdrL/RxsFnAE4+XUpMVndC5nLZTuUX4luCEga8e05gM2lZICVTUjLsSBoyKxmOWj2chyobS8zeJCiWNSiVBjF7OD0iElW9Wm5VAXNY4q9z6pGdVk1L7KqJZsFbZ6EWJV+ZWi0V3EWPqDskD2mjSP0N+3rrA6MtVOPSGMfVYLZW5ji4yPn96/++3X2OPLw8Pbz+/fvnrz6eOnTA3b7rZtG/b168frFM0o++ab5/fbaTudpnF++ew+yIFp2xhnt2en+z2u717//b8+v0fgt99e/uVf//rtt9//8qc/KzVT293d3eneYENutOu8TsthwwBZug8z7terAmn27v1rIM/jLibfvXn9p3/5aznWzCSiXrxRYCU4rYNx8942LZIA1JFjr0CoQ+BB1qUBxeQIECdumtRlvYcpfP7wLnnZxkZYAoUyh1VqSdiQ9nKXxXYrTRTt9ObdBwO2zQS/7DtoDw8PXx8efvzxx3YkACCCj1++JFre5TVlh9y2Tcp9T982Sfvcr5fHx6+f3/3+6vL18xg+then07Nn3/7w8seXZjav18vDV0cSev/+fSh9bBEzU5tvEsxPw0+K3REmIcFMIA0x90vhEB9jjFPErrCMpMGrS234CX7OERkQjIzM67y60SyunyZN3zx/Vmfo46evA2O7p1TeqpTtFR5s0Zmq82Ddr9cyxoOnAY1JRKzjVAkPMjPWZALJlJkLyx/1SB2inFLUsfOYnmy4ctiaN+FmNCvxDVzvX38K7jPn4GDCuEkYvu2Z43wexIAzkdKZkuUws+LUt3u2gNA4MEDHXYqhnW4z4nq9IKa7jUG35DBmxrwASDFiDh+x08f9t9/9gNTd3QtgzNA43detfSZnMJXJ63ba3E8RerxcUjPnnkoiI6jgGNs4nbZx2i9zf3yk+O23L58//2HmpSU8CaQePn38+uH99eUvPnwYiDS4MsQMOEIhwmTDjFFTjhABA4jTuGNin1cM82Egcyq4m480q4t6My1DSJz85L6RjNpzc4PZOO1IN23mGel2H5zXuM59v9u2dBsem28Zqdzfvn/1w/sf7p+d7eR1WzMUrIqKMGeQ2rYN5uYbYHtepVkJpTJ6fWhmrO5fN8vrQ0rbtkUEzU9nn8qMmaKR4zQIG05CSEq6ZsKmY5QZJSLE4ZsstpOf7SRAdMI152V/ROo0TihKHLrOi3MbZkLMDCV8+DAXBdndtt2d7jRzznAwMmzzGtFxurt7eHycieucThiZV5Iwgw2zsSkVGW9evzevOU1E2syMxMcvQeLFs3HI2vIPTRhNoFbLAlc5QVoDnQiSv//9v0Cwi2IjZ0Iaw9y9BO8uN94Np8GcHpzXvIpknja/qwZ481TMeVWGrnv42DPDNt9O2xjnGYGIKtTANLaTwD0zIt1dqX2/UnKYzLfhkh6vV/exjTP9lDYiZ8yg2XmcY+773GdOwk6+paCYFQQ2N1LGAeJyvWzu7z98DMWP3/1AIGn5tH6+hJGlTiK6ap5HdLmxvFhFmJ6atjgVlONS1rykyrvWyL5K0CNU94pJWlNGVz5uNBO7vcSyLjF+MvFvKSlqI6stjZa2uG20AhxHxKtG8+476moUbq9zFCgOcbjWDaSHpy30Z7evLgkk0TeAsCQvkU17S8wDHWG1kdY0bUhiroJAJSH1N248OVu4bRpz3+fcBzVjfrk+/Od//s+///u/e+LL4/44p8MGnM7z+Xx/f8qce8wyl8u8gvL7O5qlksYxzIl9PhrHBufUyam7LfNOk5ev8b/+/ddvXnzMyB9//AVm42zEfa3OjAjJ6DXbRYyMQO81MwTAne6eyDG2D2/evfzl5z6C3rSURFFYg0kagaoImMzMXqeaw3LIKY7yh0rs4Qv79vbDwbqokCyu7LC5y6cvYzjHCUmCEZGB09j6YkMXLMwFqhJxC7/OazJ4chqMSuXw02lwzhTCN35++HA6n2Lms7tv4/ExMkjGjBlhZn4axsxrxJQIN1PmZV4+f/z84e37L58/z8fH+2fPz/fPn92/3Lb7+2fPTqczlKdvzvfPzgZlZsCv18cxhiIEwM7bsD1ySswa5ARWuolUTkLDXMoZu9twG5kmC0kObHebEXPfs5px9oAhmWOMGtIEJCK+fvlAG9cp5ymRl88pi7v7s5RKq/Rr5St9dq0jEoofO6rPqKmIlTCskc1Nz3RVuQDkygZKMuc6xserFTBYwrYufeN2RA5e2MjS0NuHt5+kMHLYMNvGVi57ZoYod88pcngNtiVs8DTuY2ZS22kjhwTQbDC1KzJmzNjNRyKAIJNk8WIuaGqfQJA+rG5wAYdvxjvldINyTug0TsNc15odgJS8klj3yIi5R0wiRMi24SeKoswGYMV/zet8frp/dnfe9x2rkUeBnI9fv3y4Xr6ex3MOdzfCU7he9+t+MaOfKqULAoTVXW/DPaWIfb/soXke92MM20x1MzbDvTnPBK4xnUgz8zNAZdhw1s67+igjSsiVysvler3MTI1RtbtBBPT45dO7N69/e/nyh22cZoKiI2uk0Iy5Xy/mOJ1PIIu82vdJS7hXQxi5mWqoQYbkbupLE3wqaLy7u0/NGstSKLcGtxhcdb8fWdPSBcUs0TRpluXxkakZEM2GuxOckYgUSUbTipmVaS8J+nCa8bRthA0zWIpxuhtExtwzY7/maRuhPfZrUkAMH8N9zkIgiozYYTZoPuMaMzY3Q9duS1+cU58+7SS++fZcOV3xk1AeKXA1rL979WbcnUD75pvnR5PzxzdvxzhL6QM+eL3uhalLBpqqqr25O0yrDsZt28y9LgfmoGVKOaVtnHg28pKq5M3dhgyxp8HNBpQRIcJ8bF5PBnefOcsZEDC3IX9mZ8IJjtPA5jP3SO0P15Ob2WYwt23O636dynjz/s3D1y/Pnz178f332/mZj7MPlxApKAR9+PT5h+9esAmJXPVwlsqQkcrohqNFEpfKuwQEpC2o0HWBW82kprZRknXYWS3WR5m7xDoFEFrku6gXFiLRys3LYVpnblpjSwuhwlZqV2GRaVYuugtSwhpO0gWpVQ5bVEhGSUrX9ApJeVyceFORomqDFW9rurzXCI888GBKDLBdsDKSDEhpZk8mrZREUmvewBJY3jAbUBtvRo54/LrP6+O+v/n19a+v/vnrr/81v34+jfF4jSl7fvfN/fk5qNOwsW2P1xynu5y6Pl6ve2DwMSaUe2QRuUoZ3M2l/PzlM93HaXtx96NSkfnw8Pjhzbu/Xa//vPs7nf+X//v/9d/+9/8HMS66GEEfTERehbQ0AxJTTNJr7+e8ZqbZWbJ9zr//r/9053D7/sef1h416P3y9ZGAZ8zcX/zwTWu0y0vUrixoXEMUegZJ3SxwU2bkjR40k2i21FYkwM/vP8ScPmg8z4yxjZPher3sl0duA4apIB20zBiOzT00eZYbMwNJIa9z3+lmdb23NmKw8+Pr4xf2Te8yM5PoLveoWyjIgAh9+fTh3dvXr399dXl4vOzx8scff/7zn093z0/n51XVdHoCsPRtuFHSt/7d49fTdb9Kur//Zt8vSIr4+vjodDcZlVTEvtbpBDrBmPsej6ftuWEj4czM+PrwuUTlSLqfUkQmh3w4YEpCMcwvj19lV/Oz3UqF8XCdSn73jbFSPKHn0qwKEW0ZdqaUdVrYGVGVhLogJQERucr89QlqoQv6dnkrQFNT/lo0vsJHb/BxSHhoBQgKr//+uygqXYSPGRGaPmy/RO4CoYDbKSRSp204sy4653DTnHMKZcyIECw6Y1PWVU3nbcuJmHOaxrYRvM4riOHnOeee1zG2u2d3p7ldr77vD8gw+jDNuGQYk5Dte9oYNL9crlYz/LchO43T+Tqvl8d9Rg53Oq+RjAnBhp/t/O2Lb7779tmXTw97YIbO7siRe16+fP786e04n3ezBNwE2PAzETDRLeaecU3jye8Au87HlMEHaRh+8o2wfd9llCliasbAiaRCETSeztsA5tfIDNFgLmqWsGYW+42kmYDHx4f3b959+fRZubudUjPo5n5/Ouvy5dPbN49fvnzz7Luyi0wpAlAybYhk3QqUOSUodojum3o+S1OgXh37rLQRCTBi+EYgQwrVvVgpzOvV7OQ1faP6DI0JpUKC005ji4xUOEyKHdcEc2qf08nNndug2XWf2AvJy7yIykGzUGZ22U7JEimnIyMQ1+v+WTu38Vzbed+vyNy2kdRwJzk2oxSzvtS2gRapmipFZB0IZMwkSKcCXz49fPvDi0ytq9BUMbUuTv708SNPda2hPr7/vJmnZnWzG1gJz75PIIVI6XKRxyCpbLlSQO4WmTCetueR2vfpxuGM667YYwb9DoZxdppvY4h83Kf2XQkpTElyn7vIDT6cUEjp2915nJnJZHWqjnGmknSZX2JGhhIIRijm9f7+3saGOa/X6+Xj+9/++fdff3v15u3bv/7rX/+f/6//t2+6xM55PfkQYkJu7rIvny7PX9xVslKSgxqzacrETKSyJ7gsKqV4LJhBK3YsNcjRZLIoC4K5iJJjZoygvvipUNFKxbAUS0sHpZZtN3Ix2eqdUNWAALKn7va8s4pj1s2eaNCQaab+tsoaj+pWcUURAMyHDa+0MrNoS1nXQEa/2uJbYIVOUnKtOw5SqlnzRQqp3zV7ekqEVV5Y1grw6Exv5NbSgKP91NxIGzkvj58+/e3f//Y///3fvz5+fX539839t8qwM2fYaXu23T/bzvzh2xd5vcR1jsFLTDO7O9+5W/J8ururZTyP03VOzSnzOR8m6LKB88l9av8SX1N7JK6PcX34+Obdr/t8fPnix7v7F9yGvErR4zqvNN77uZSeYxs0j5kZEbmDmxtFmNtAiwVe//7bDy9/RAn3zT5+/Cj43IPubvbh7ftvf/hu9XIfALpG065LLg4NVBE2KXGt7nHj2sLT9R/vXr+txTZ3M/fhnd0CYiSRSqbuxqgtiRmwvdqaNtsqNMY+tSfclHXFTA7bEJBy8wFyZggy95PGZtCg3OYU7WTGPXbE/uHt23/8r789PDxsdn9/9/zHX/7yy1//LROXfQfCrLs23ChFTT8w5ziPt+/fODnsTgaNHY5913m7cx9uA5j7DNJ8O9NPkQSUVEgZMIPBQgJjmEUyJXdnDwqmbT4G3XxOpWDkhmdg1t0UHE6AE8PMfHz+sif3b1+8wLrf+uvDI2J3o2G8+P5bNFsbpRwuxvJgQmssU1EuZkZItsZaFHVZvG/3qnYzPJZfKN3AYt2a2yNbGVAl6l//4z8y09y3uv0jdk7B5uN+LdlFJbbbtlVLNTMHTJlO+nDNIPA4d5m2QSmvlytIt+18PregO5FJM5vXKXk4p9LAx+sXib5mhrp7TYAb46xM4f9H1Z81SZIkV7ogH2YWUTUz3yI8ItcCqtAAbs/09AwNzf//GfdSL0AVqiq32H0zN1MV4WUeRD0Sl/IhiTLDPdzM1VRZDp/zHevrCuIiap5JXKSM7mbrTUWL1kAy0aS8Zlv7WfWCR98TUWtGHKI8X+wurq+eHj97JBNXqQm2jLasnz992F+9hlYmoUwFQ2sTd3KPQGI37YgyDKJ1acfltE67w/4wiZTe1vNylgKoqBRFGXU9lCSsldm3z6UIWJDNW28+1znCW2ssqqREFhkE9NPp4eP7KpguL60tJLzfFUrRgjXa8fT8+Ph4e/tdECR5tA10925dlQ+7WUh6WxKppdK2gqQMGum47iuYqs5DqSWGd6PEVCeKNG8iQihJTixmZh6aIUTMQmRJwSSqbBFatGoZOgqDIyIgkCxarEdfG4ELCw3nAUFUQOytq4iw9B6UmHQK8hfsu7kHaxEVw2pura8MZbRuKzGzcBGFaFHplq339ETmaA9h4YgI8sIszN7NzZOy906Jqaiodg8IL+dlnqfYYDMM5rvPdwBGCl2SaEwnwi1jWPwIyIjWzXPN8PW8etK02zFq9hhk01KYS8nM9BQwgbx3J6gWICN6925rY2infu5nrjJLLaW4h1myMA3TT1ISiXKY+OqYsvczKMGCZA6KSId7giiZorKM5wKrIkbVTVgaGMr6/rdf/uM//v388Onzh18mvSwphWWus8pklJFuYawiyYV1WK4en9rVVX35QTaVJfPFnLelnWlLq+CFePZ1dNmGlW33PUJhnEL/acU0INc0zCjxAvYl+k/g3c2hjBdH4AgK5XY4Q2Zu9WD59Yk3hJRgERrrpPGNXiJZL9WVY16ir8aXDW9HL2uOFx4bi0sUlm3Lv+k42105kjY9+f+2iIIMfMMW5o0Yt2aSBDRz8zlvb8smZfPXke9FfPqKTdlewFB6+GXJpZ8/373/9eeff/ppWZ9FQKDVvAh2u30p+6vXt7vr66tX+1cX+7/9+c/v3r9TERWOqQAAyZtvv7t+/cYjW2+7aU6i87J4W54e70/PSySINYnWZmaRBC2DcFH2lxfL6fl//8//8+bNN9/98AMJSy3Ks2rRUjkpfA0PKgUkLNR7K8NTKmDFqPhS0RFB/PTxEzMiXBg6Td3HNlciTESeH4+HywNDxxX1MuVuKgz469VJ45Jw9+3aiBd85NdrkQjAxw8fq2oOLORwUSFaOyFZ61zKZGGROakmjeZhlLInYveVvKGAQcvaKFz5K6F/AFgLbYR+hjBYRuwJzEzoaevayKlICfcwt3V9eHg6n09EqVVf3b6+eXWrMvXoNHrvyFtzACTc+vnxeB/mF/uLTJidn07nInPdH5RjPa/ntfW5I3fBwkjmTHhmz82VjFqKkxIJBMKg0DEOZnQe50veeAlI2T6PFJ6RxCwygj+iWxkKEbW+IntGJsfT09P1zasMuj/eM0cRDPXl6fHLfn/18Pm+93j95ioz6eWXOEbY7bczogegjJfPzFj0DlD1EClzdInh62ciX+yQL5PRiww8PjsUAbz/6VePNtcqLJmpzB5OcPfWfSUeAR1lit5PiVSW7lRLFQrr7tZtPe3qYSxE4+WsIzrIKUOcgry83RSWIT2yLYswkyBjoL3YA2Aq0yTMyurWzVy1qhQCdLu8I9MHCtt6CAOQwThUrikiZRIBhXlvbV3KXBPMpe4OVyxzOz5NKkABoDXXvvR13WySFEybbpdIQgoDNDhD5CnuXuokWliUyGPr7Q2QDENnqRP1MA+Cs2ZkJwsHD1YAEcGyiCrreW0UozxSCGlBwrQ8P52Od6LBKElSwDzcc0gtSmGtnbqv0JlVhRmB5msEGJqe3ddIF4XqyM4RKDnBrJmeWZjSencPnYpoFVUaoDAmCop0LTLUXgLXUjIysoOYYBHD4a3kudVSjtN1BJh1qgEHSARUKrbIIKKFW4xicuVpHIeTCJnCY9Bj2hbugySbIoI6h9vYznskM2lhCKkKkUQ40bhJxlSmgXQSZQ+ljN5suCIgsnWUsEQQGCIanvd3Tx6e7nBSVeViObqJjIJrqZxCXMIbEalOAlhb7u6+HJ/vGbmcW5n2r7Tu9hye1m2qU4KSYriqKZOFWm8eUac9McgtASlFIc2asKjUl9gzjc69jHFPYe/Ww6oeiMWjE4Ag8nTvRHA3S0oGOItk75FUiKWoEsjTkdCs7mnr0/vf/vb+t7+zxaSTSK2Ew+FKZRolzhAhyODwiaqlOQWLPD2sh6v6e84ot8Uov0wz9HURFPx1N5TbOJYvD5mNTQyQiLAoXswPQHAyckjTsU0GeDHEYlhktv+WL0mor6bijXxBTltSlrfnC+WLHJIvK6qxxMdLlnYoMxsabWhM44a57X42ZWXkhYeiI9sJcIQwMtMjyInjRUt5Ge02Ft/2HB0++gAPLt0LiZHo9w7E8TPx1/Fts5S+3Ni3tNt2AM7wABmS9c9//svj3SeWuLm5br2X6eLNm2/fvH172B+mspsOu7Kv86RFkv7GrXcZqR+m1pfLw+U//eufbl5/Y5aELEXcjQBr9vjw9Hxqjw+Pp7sv7fn4vKxBKGVmpoBHEJfpvPSf/vbXj58/npanH//xTzsBpRWeKDLCx43Gmjk6UabnGPEivC8NTso6PposqiIRPvKw0TtDM9l6TzhEIv3h/svtm28+/vITCrPqAHIWnXeHy3FxnB9OlC6q88X+69rya4hmXEwsDJanx4eiHORjd8EuSER2MCadSQoRkSUzLNx6p8yB1jiva+9NlSFQrSwVnApOonNbzLLW0sLDvWplMEjAQeTdzQkIrLYEUdWJgtKjn86//vIfd58/pBOLXF5fvrp9LayPd4/Wl2V5WJeH9NbXGFvSp+enh8d7IbrcXx4uLp5Pj18+3z9+eJ72B6l2ejoen4/7y/1+v8/M66uLV6+uuJZpvpiGKoPand2J1aGNMokJImHOrFNlJ2LZxvxwtzDiDIdAY5ghgoKCwjLAlJmeoEyLBKdwyOOXux4tmbzFePnJSeT97hNDi/Lj/fHi+uJFJ8tRUJI5EG0D+MUZWwB+fJC2SC5tCI7N1/hS2PCyjdzMA8hBt+LBEv/4/oOKWnbicT8epvBgZiJbz+cefdrNTAmPJO/WWDhEGeoQZ2REpCUokEUnJ0R4Rgi0kEoizbtZ5DB4ITNUpKh07z09zCmVuRil9Qbqottxp0cwKCM5wCoEcBHv3daOKmAmUPM1LYUZQekpqqTSvQ8LSiBKLSAK66xyuHl1+/b787Gta1tXE0kW6rGuffHoRUSCPB0EiEtCWdzduvdwFpVx+HNmFEDMnRFTUcVs5sKFQtZzQySrgGtkMHIuxcxHlxMihSEqvZ/Dl3RyDOeIE9Px+enTx/fH42NQFLGqExdOgrsFpQKF+cv7D4fL27ff/ZBkNixnU01zcK5tJQJEwDJcBkTwCAEPMxOASB+5SICYiFU9fW2NNgfdVlls1jmZWblIUHo4Bmy6N2ZSCBLRzcO9u7AA6G2hbdGQXHVUsKZnElTR+kKRTGpuyTTpDhHWzygKSlDqOEMneVhmMgrr3PuqtdSpepCNrvvI3k6ZNE2Th+e2jqX0JLgwZSCy07BCkwPsZn1dhj2xNw+L7gHJMe0MPxaFefax3jEy8oB2T1MRhvbejo8Pf//rXz58/DXNprr7wx//2Y3SkRkBc1ZFDIskCEFhbpmNMtf1DJUqCkgyIp3Cd+WgZQYy3ZEERO+Nk8BsvSOJIz06ON0aKBkRfQnfPEPDVhmObsRFpEgEhTllAF4KInht/Xh/f75/uGDKWkintVHdzYfr2xAJsiQqpYDZzCPZAgFwglGg+fx0YsTu4vrFmMqMTKaXYebl/CubsrDtBNwHTNUzM9Nt4LNJRVnjJf48tufJXwES4yA9Dt25bYL4hXKeL0ZASo7xdS8eniH2bNVzm+dkjB8jSZkvK3vmJP7dq/hiW9yml62DT5iTkMyjdgAvW7JNf8oXDZ1sjPDbLXrMdxwv88nLmgNMDN7qMbfw1Lgjc0ZuFmMmIGNENr9SHcePPU6co1Jp/KgelEhWdr++uo7sT08P1/ubf/nX//79j3/SubIqEh7msfS2WNqyLrbl0TuLrKfT1dXFbl9FoFpLlUjrPaYye6XdxZVwsdY//Pbrp9/e755PT09P6+kRYd1WEZ5Yg4Qin+/u/315nib9/sc/CsOcRWpm0lajmExwT0aFbG2TnNvGLUmSMuCioilu3rqxQ5koyXpnFREhECof7z/u9rMUJdbezbpbz9PD08Xr6/W0QtgjMu14Os+1fDWHjs3GeFw+Pt0BVLia50BcuNsscxi6paACxSOJUaYa2d23u0C0FTCkOa0RLFEoCFwliREiiCh9aX11UgigpJUrQVos6U5BquwUACprYbXWrfvDp08ff/2ZMwTska2vDw9fnk/n+7uHtpz68tDbXURjFBpzgYWQFtGn5cN6vGfWnWg7L6fHp6fzh+hnJB4+BIpA5DDv5v0+VL/99vu3t2+JpEwXpe6nacdJcHOzYOKqYBVSynTvoEhvxFzrHCTJGZwcnFseOSIdIrXu0h0xhpxkllprRgA5q5h1JgiJma/ZpbCqIlgA8/b89HC4vF6ej5nk0V/dvs2M3zvTfz+GjGtoOxRg+9SN/8QDT0Njj+tfY4wvfxKUGZ8+vAdTghIu0K0OJnK1Nqksy/r0+OAZBVynYmbBJLUyS1IhLQaQaCYhy1xr1UrgDCciLcWNvRkiWKHMHtmXxgxB6W0hb1z4sN+ZuXViKTrruL8wRdgGLxKVAVtT2Zj5XMoWywIrY8D3COwjBV9YNgcZD6IWBKBAwnuvqm9u3zy8+/Th6ckp9/vdwHj23pbn5WJnBHb3OlVsdB+KcIQXqQBHts25xFKkdmsk4gmwTvPkRBnMGUPGI2IQE7kKmXu4S6mDPWi9EUg4l2V19949MqDx27uf37//rXcjot1cd4d9Wlt6eGSGD+Pew+PTpw8fX726zSokhUBzrdLNvRlTglUrWJAixO7k3gmOTAo4eZLXKmWePWLYX0YeO4KSKAjmpFqZQe5g+l1ZjxinSUSqgIiMMsOZMc3Vs7W+TuUgwZ5GEmCmIM9gEoAznJOEKUExQpYcHkFJbVmFWIrkxppgCYRFGgcxKWut6OkUGeiUlkDYujwfT+dpPtR5iE/kYbmFYxwYAqq5m8rEtQZ5ZGckq1RopjEoKLv5qPIuEgXSzv689gSJQhhadww+PTz+/a//9uHDL5xhzbTg8uKq1rnIJCVaUIT33rWUkcF2pA1OEBfoFGBhlcpEbsspIrVMKnPE6i/PSCCkiLLa0plomnYh0sOzR1inzWk4+NrdfZFaQTOxsKhHmjvFZgYgZNXJW3769Lk1m+rcLM+rndvyzdvXh4uL3o1l5MugRRkwo9YbE4mquw/scRI93H98ffsdhiGF6aXXepM+8EJ8wOYKp8zhnt/mGw8fYBCHD1jc19sPj/XJy1gwZJnfheQX6eN3Q8kI0vB/8lT4pnNgg2J8Na58PafH14FGJDjzhaxDQ+vevvVw6vDXMPiYH75WCuJFLMf4nhTE+N0XvOWw4mWWYeZNjtpcjjRqt2mTigiE4BHLGbe4jQsS/5k9ncNRM+I5+fUlRyJDJwWhnFdn1tdv3n7/43f7w75vBWPBnCrcFl/P52VpMc4R45aJzLTee0TMsxLILRBMzgALE0DTNP34hz9cXV0+H08ffn3/01/XbgEoEyskYI2MiGJtf//rX0n0xx//wDkroKWMQVxVRXj71XMGordGboRosez310y82upJVSYMFSUpvId7b73KFIFIF2w9rt46mAZrMSyTsDw/O6WlA2lp6d5P/bCbsVlKhwRA/XwWFiLygEcKmMGsMgJjRSdlHYtKJd6e3JFISBHrkRTzXBEwSwaG31tUt5gktpUTICqUFIlwy97NIkBk1JKyKGV46wt5rqfzl093bWnzToD0tX/5+PnpeM6kh7tHRO4mVgFStFSAe+8qXMY5MaWtMU0kShOBWZtPjZoETRBUhQhlPj+eTtaOj6f7jw/7w+Xtd9+9el1ANTu5ExhmLa0xl6q7EdFsbmkmtbBi0l233mNlDjDSsDQPcgwqDyPNWjtHhEhZshNoqkVEKNy7WZATFSlDJROmSHfvnDgfH0bjqRK+fHz/+u23wTEm+q9y7vDGUyTxi9K77ZnAzB8+vCs6C3O35tEj8s2bbzbbTdLx4Wh9BTCEpoEgNOujrdDJzRPg7tHb0qdJdQqCSLHMdlqnWqJwRtZSIny0Ihm5FGwFg0gnt+hBrJDCRZgdBkAEx3WtdZqKBgG0If0FIlLSKcNSIiIHPQ8iUoQoem+UKiigrRdZRVlg6RnJ4GAlsozIIEhGOrGDPN0VYhaZvr+Yp8PkQd2DRXe7oo610/Hu6XJ/M+2mUiuLZAYQlMJCMDJr40YEYoFYZCAEKlKG31RVwnpmqsrGa3F3c8re4mwUG+TGnYjBKiw9LfopEYlOzBn2ePfl6eFBmXe73c319c2b67vPX9blKGDPBEF0yrDHu/vldDpczB4x2iS3bT0LeBKdsKGrMfbJkPEui5knsZkz9QTcfaQ2RJTBFsmiyayqzLNZS3LrFhHKGH6uoiVzkGo9JSHERMmdkBMKIruZR0CYEZFRpCI5kms9IIJAu6nYaoP3OZKsWiunJDa/BViIkJFF51InrRpBYTGWnqLFzKIv5/Nz732eR+YziCDCAbJwd9dAEW5ukcY6Tzp5ugeDwaKxZl+dy5D3oy3paMxmIWlapY5ExaylcFlO599+/eXdu1+8nXfzPrHWSV+9up6vLgRC1BnCUsk5I5d+Iu9JoVqKTqoTiUaCwYmIdCKITih1/KhIgIRfPBiRnmkeJFopiJyQAlJQEjQirdnj3ePTw8f5sLu5fTvvLiIsCMPxoqiqEyGY0fr57v7T4/PxZr8DR481xeuEiNaWc52nopqeln2MAZQWSWGAaA6vHauQf/7y0+2rP7xsg75CH7bCmvy90RPMoJSxgMltQ04xtMAxuOTLCZoG8DzBG8/p5aTFv4dAc/Oyb2jpcUZhHjJGZo5bBGWCmeWFyLtJ1y9GmP/kIaTtJxh/lwydCSMIs5mpvqLCXsaH7evHNLLZdDJooHlHqJswNoJE5CDCUHFfqLFJvz9hX36GcUjYGAIjEvX1nImXqqvM30e7F3/zZkFQ906pCBwOF/urAymbLSM1SQhRFSo8wz0YRaWOSUeFJ61kZusylnYZKRBiDGYZEcLdI1j44vpwuNzPk1LGr7/+0tZnVXBGMs91h7r3bk+fjn+2P+/2lz98d0OciREPY4KAWYkSablatEy35qCkMkoSFdbJeuQaQ//N5CTeTOHOrJwcbkbUrXvEpDxPE1H3dBaYW46d1vhlRVDS6fksw3ihnJFCGPiNxHYf9DBVqlq8G6SIMGUwUEXDTUgE1clFOZJo1MB6CoqUgS92YaXoFsEirDwdJqAEgThJ2NK7hScBhYnIKTJfrgNLss+ffvn84dfWFoKC2Fo0f9K+FBbua1AQZtZpaZTOtUxg8eydkikMaGZt8VpqESHOMl1IqWQ2qZZaLMzTVXSfF8+n5Zeffq51Oj8/LY8P+4vr29tv6zSZdWTupzkhxNzdKSFgriWQ5gttNPwgSib9uicdBl43Cw9mZpAUJea2Lo/LsaiqqHuOYuxCc+UKpiAnzqnW2LiDG72XgeP94+7iMGTQ/LrQ3eRfGs6zrWiBkoD7D1+q7BLRowdzUEn4/f397e1tZn7+9AlBLJCEWyAgRc3NvTMUMvpviaNeXb9xWwlu4XU66LSrzM49c5CMCifSIqwnONOR1L0LM2tQRq3qkSM2zPC1HT2Q5p8+fbi+fRVjvwyGsMh2emIov2CHwGTeE2Rmo0IjIpKNmZVBjOSwCIzC5K8UC6f0sDSWVMAC67JCtJSaMoXL1TffvDqu91/u19ZLJWGF2cf3P087/e6HH7Xsc9jMmnlEIN060mGQMuYtgCUZzDKAKoHo3iMCIs0smqsIE2cmgpmoiFCIWWawlqI6RTqL7C8vBoPbzB7uHtrD+WZ3mWQXV4c//PH7q5vXYXz8ckKGgDjZWmT0x7v3v/70l+mgh6tXwuKjllm1SA1obBXR6eZjvO1mygANVxOCuFkEIRICdnKHb118gy9tDuJMMu8UNI6kquKgZCYaGRxPH0dO6r1zEbBmymCxI1+OwImk8dCunmZpFKmlKknP9G7EWYrmsGJEuFtSCgpEKotONSnXdfW1M3OplAKQi6CIXMw7FazrWZg4ISSU5NbdvWo1a603sJilIMFCFJFhbgRKydHJ8+n9uw/vfjFfVVVZ99PF1fWrejhM825N6768+/Drn//tf52Px4vdHCZB0+H6bZkvwOKRMWzqyglxN+udqQvLIJ906+kJVk/36G4NAYis1iKDos86hROgwurk5i1BELS0NKOkCGvdfD093H95uH96Pi3n42M7nXaHw3/5b3XaH0b3qm2YUBpuodPp+cvd+8enj4+PdwqAazKXaT5cXewOMxDWzkIQFDd3b9hOmcMnIx7uPTrFpFJQHh/eHS7eblHrzTKCkdr5msDBNhRsz/pxj2fZFjq0paVzs60Q0Sg92igkwO++WrzIMLSxLV4MuV//73hCJEbd6u+bmu3PxgvrBlsN+qBg/L7E/+p9+V0v4mFe3VZLX3dX9BJOog2aSi+z2tZJ9eIK+grO4c2dMzrrvu61RkCHxmhHL1/zdWYa1tUXgv8W1vn64+XLfX4MRpruIxgSvaU5fLzpjkLDfpQkOpU9c90dQJIUhUrF1PzJLYW1lBqRLFLqROm9ryAqWrr1pS8cVFkAOlzs/4//x7/e3r795defn48fo63uQcxhMAdROT+2L+8+v331PREgYJZIj2Tf6CHWeuvei/DhsA9PiAomNxdWAROFZXOKTNKUSWsRCfGgrDq5IdJFhYKZlRI8xgNkRBAKEXo7M6eqjum0sHiSI1mFItycJGkwm8E+dnaBCCIy0RQV5cnTiVC4dutOCQZlFS6IFtFGv9QwnyjnMPx6JKtM0zRCzhEurAzhGZqbjicstpU9OcHP6+Pd4/vmRwFlS7MOEUDrtK9aGXMiS9V5KqVeEGmtMxjdm3BOdW7mp7YCYqs9n9uwyrEKyJkpI9yaRxSZmHjSXbmcWOj+07tPH36r+4t//a//zz/88Z+4iKrKVDK1eQaChRVgptbXNOuGU2uqUjbzNItIlUo84j8idXYTTqaMDKPW7PzsLKXutO5GHDIJA4fdvYuKinhvEeke7unpREmIp6fneTeNgwK2JgMZC96XeXsDYn7+8FtSRWaas6iNz1pyT//08ZO7SQKMEb8KUClTcwtvqiJcRKaxpmneLg41yCxbLbXUnQUTM0kgo6oQuPfm0SE83BvMWoiA7L2N0BWLUMK6RSwPjw+qU19aN+vmeTrPWupUxj5sYCORXqdCItQkvClpJ+vdixQQS1EWHncuxPjkr0WLTrD0rfHJAgSzGBmQAjWUzIRKBMzo1atv7AeHx/F0v7TGid7Xp/Nj2enr29e73RUBzBrs0X0bpYDMmEAMiegUARUgPMzMhb2UUoSD3N0iwkFOIaIyXHEIaKEQ88ig7M1sMVoxpNSkvpw//frr493TNJcMjsTu4qLsLm9ef/Px1w+nh88qpFWcYn8oz+fT45f37fzjq9vXAlhwpnoSb6gLG9MzeDiQgiHbtjSTkkWnyDRzgD2oh1FaVSUmD3N3ypynvRS2IBpltwRL9gyPLsgi6knndiYwSR3CeNGSxP6CdQRzLVOGmzcmFC0QCWeLPvLmRFnrnITeO0FGclu1DgCIU4/M9DR3yhRBui+tc3ItU1KQyKQV4Eyn9LFujiTvnYm9mcNqncDKVEjYwpZ1YYbWygydq3vcfb77X//7f3747e9CMc0HCJiw2x2ubt/88O33u12xWH7663883t2HZYGuvE77y8ubb7vDzmtSZPSihUEhLFJZM0O9e1hUiVpKM2+9A+weIBZVZFJ6hhPSyMcxqVYJkA8FSdWshzchWp+fPr7/eHq8f7r/cH4+f3l6mubdXPZ3d/fv3324/fYHF6JIBApYFISwsIfHh19//qmdjsp4en4mWnvk9eXNfPFqPlyExXo6eX9OKCkzQ1i7uWphFjNvtka4qPSe6a4qT0+P9pIdRubr1xebb4YII4Q8ggvjgfwiRTAzfd0BudM2zeTXeWJTI77CXfhFw8Cw+OaLyvJ16KGvGxn8zmqhrzIMABZhwssEM8aR3yeDx6d1OH8urvcvpo6vriC8rJm2l0Av1mEa326IFCPAIhslIAY06EUNikwKz8yB4P9KxtucwgBYKJPoJWs+NtjjH97WVPkiFL28HZusNNzHOtqKzG1t7XQ89d72u4txLEoOEmbhbuFBqqW7zwX73R4korXonGMhDAEUXFhqJKwZJY/DR1FVaBLJTrBDmXdc+D/+2o72Jd17M189g7gKwR4f7o7Px0sp0zSQIRFp0W2YfTm1MogMnALyNO89CETingADBWREwYVJwckDdUUQFqzrKTNFJCkjXZWDOCmZC0HDjZIZKQx3Q1IkmUWKiMgwSikzRIKSKQUqqsxIDwoCjyxGNzfKbNGJklUhBVwpFEkCuPccWzvhgXdglQBG+H48+t3G9Z7bNQSCYCSTfeCqjO7v7p+fngUynuwQTuX9dPHq9nYqcyRxKUVKYWJApbIUYvbwUmSe5ojsaQl6uH88PR7tvK7rc8JsWb2t574CwSyZQgSQz0Wkcjr70h/vP/30819e3d5cXN3KXJtF+Bob+CAsUnK40Bhc5aUgdHxqKYNFPZOCGFvxujdb++m8PHx+/3E/1de3b7QIMUbJorKa9TR36pQpTGbeuqlWcNmyiSzEeDo+H/bzpoLy5qvJZMr4299+miuqDLL4FBQ00M2Za1vNbSoCIEe0YGRWCcxFtYgWiyxaRUSliFYCh3WGOblFSqkjm2ZhHmnWZpXeGokkYsuRg7VWZvZurTczE+HTeWGRuRzMQcn76YpEQHpFvNcLCLo1VRKZtNQy1bHKaNGRlpnWV2ZiqKioFAqC6Dgbec8IUJD1VCFg8GqCGVqEEhQU4awQ1h0fxrDkPUCiCpGAmhSqpYQFiGYtz0+nh8fjxeVtEm8HTSYGSplGJaXogFsxBSkXMGeyVvLu6QA4M4tMlJYZUAkiy6iqIxjHQgS2tT0+Py3r0+on0TrNl0Xk7tOXz58+LuuJywUA8CS8Z94dLuNwdTgeP0HY051sniciSo92WvraUiujEEiVM3mc1oaIzzJ+X11TBi9VSyEwkUAprZMHCTODSZQVTIb0+AoBGbf0cZDWIAQxZfTekz2I3BzjkwvOGCefoAyRgeRMFvRIT1IpLAq30dQGlrBwcyiYYd4pzRzKilQGexiEFVgXM2uixEIJREJEVUu3VClaZjeLcMpMHgdZmoqYRTcvUyll2rIpiAE4o6QNpEOe5HdfHp4eloJ5qlmnydwiKJwevtz18/Lq5sK9Pz8ea+HkARENnaTOJdMHuNGRxJ7RM5k2wZHrrgiVwqhFzc+RTVKVBSwbbGds21i7e6QheV0bhCDkYWR+Pp0eHz5HXx7u7z+++0zdq/h+2p2WrqIEB6/L+Wi9l7on5v1UmNmRwcmC59Py+dNDNsx1ak4ZBuLD/nq/v4JMRUhV2mqJZIZqYZJR5dzdunUPG861TBctogVUMszch0L8+dPTq1eDij5uQr+PIl9XJS+Si4zrKQeRBRtKAF8VjnwZRvL3Mw29uGW/un//b8ug8dh4EXQof08GDaVlMOi2gWbbPkVEPj93AUOYIh4+3znl9dUhx9LqZXe2OVZe5gjQ1mww5p3NK8wycp+ZQaMyFi9254369lK28MLEGcbpTZjJr3Gc8VowyD3DxTOmt8G/eZGURpRVxsClTjBbRMo07Y7H46eHD/UwC9jNmClBx9M5gkotBEonFDUK691Alnhe13k5H/YioN6bpAY7sWcQJ81lFh7NLABLNzPExe31m/P369qsu3KGug8wudCXTx9+/flvf/ov026eRBSCdCPfOjeVBWDL8KAh7RItrCXJx4ODMpGbQ2ZoYwxGKT2zWTMPhVJQspkHg0XhHm4mEh4GzQhvrRORJxsFkSAz0wYqTVU3aW+E40aFMogBdwvH6CI2684kUqBzDK0/engTDiYQK0ESyZyR6TG+Aw8UWKQlBxFb7zQeG5QZaT0ACXASdYuHzw/np4WoEltyHm6urm+/ub65vb6+FmGwDiYMB2WEimSSWSfGeERvlCmyy6tLaz0j3SzT1ufl7svD8fjobT09PzmkiOZgyy+dCId5V1yOd5///X//n//wT//1rf7okUJcyo4YgHlfW2vMnKygriIMID3SB4NyXRfiomW2yKSgxGlt1sOtrGvOk0gphPDolGM9FZyRnO7xvNxlW8y6Tod5f11LFdRMI0kiFsjz8/PF4UBE6/Ma1J2MWQGdd7MKItYIYwotgkRrZpEeKWPtNYBElORJ0AAiUkKip2yqUgI1At06MiCc6R6J1CT1IRUz1aIAx1Z7Ke4gH/b3SLPe1oxAsq1JwSA4GZIpgwmimOcDX10BMhgEAHs3cCfbsgUbBM4DCQoImDiI4eHZeykTCyXcKFiER+Z2WYkzI4WFOcNDtuCDWRCxEDF5jOZnptxNB0u25F3RiFammonz+fz0eNe//R4QG2WPOmsZn0oEMEiMEVlLBSScXhrPOIkHvb1UVbBn/yqXL60xRFm9rUtbvnz8eP/5k0fz8MPFFb+uzvz505fz6VTrsATRq9tvd7trItZS3n73/Xk5t+djeO89iEhZrLf3P/98cXV9ef0qBQBXKlImTMqUaz/16OOGm5TJqSiQ6mk01pDNOKEsSQOjXwtrUozocAx8C8UGP6JIGshgeBDTqF2geTqMIiu8FOiOTpTkZGa3WNdlcFOXvmYNYigjSdKSgqvsPM2sbw5uD+LiFM1Wc2NmgRBcCwjBAs6RmCzWLd2KaIRFWOsLyJmFJUuZpqkoR0cXKaCaEeBMX4EoTExCMZrSo0i5mC9ev3rTimYuu3kuWtraxi3r+fGxPZ8IoGDVYtndg1N760/HL/N+mqaJSSCIoMgMb4GAhDIlEkA6twgiVK6Fx1J9c1YNF0oCnn2Ieut6yliX9bQ+n4Xk+Hx6PD4gbV3OvZkwLMDEpe5LUVUqlfe73Us8ICDiaZnESRJ+eng4PS37WpS9VGEhC76+vtztLplrZspUd8WsLdZXyopkYc0gcNYiZgFGhjNBRJFDSHZIgtK7EXD/sFxd1jGNjN3Oy2yxfSJGlJK26qNtENk2Ry/o89x8xC+EvG0JBXAC+E+rnJfc0wsxLf/TYmp4oemroQUMlg11QZlBIIuI53OMvpH0HA5Ez7x/OF9e1LG7z43jt13ML7upF50nX4SlIQJpGW9RxjD3CGSITEKUz/fHYVvb7+v2Q45hP+Lrq6avDugt4ZUv/t+XPo6v4xvRsOwOoUanqx2dU0N3Zfaw5fFuvb2pZSeiotKjn56fJj3IVFV4mnZgsnAPHwwoEWhBURaEeY8IQqZ3FlItmWQbExfhvZsTMM+7H//hHzLir//72fqqpUiVyOjR+uqff/v15ub64novKMLa2ioQFhl6RhF1691CRIkYCJUgRuVJWNd2Wvpi1t27yAQwz1REQIh2ZlZmYYkMMzcmKaTWF0+vcDBZX2E+lUqJCGJREc0MbOVzEpGeEFGAwBQId2cZJGNhVoBh3Z1YEZkUkEQRWe0U3pghqoAOFrFR9m6ZKKUwS3g42vhNCtgyq3IRDbNmBujQ/qSU0/HRet9Nu24eoMPN4Q9//OObb/9Q6wWBzDukJFCKlKE8UeRwOfDIjGPxtZvR4Jnu91DhVMqgV3n7Ns7n9f7z54+ffn16uGutDSG0eedMLjyX/bouH375earz1eXNbn+lvNHbCBS0EsVAFoIJ4VUKoBYeCQ6qZTdqo4Mz0jKiFKGYFdN335ZSQpg5ERHBTpQWncmX1u4fHn/++W/nx4+Hw+5P//rfp/2lSFYRQJMpU7oFMXczkBBLRgdhoNYFzEAkB+Bh0r2IFhWVidmFolZOznERW7TuLYMpWKuUWpKyLx658WCFBcPQR+AUYibVcUZQhuhWFaaldA/P8HBV5W3+cVYRSIYI9mHNw4rCzJIaUSmlguBuAM27fUZ6d/JwMyPnrEVmSfQMgiKZAWK3aJnEwRTGrDQou5SQbQe9MSTMk5woAEylCFO3zsIZ7GYQRyYZz/vLeXdzXtelrWamwsQiGe18jNbm/UUPd48qExiNVmUGZDtyqRDDx8kPMeikVMAkAgYo0sOcx6OeYZ7Nmke09enL/bt3P/8W3cs0Qeo0H6Z5/nL3+eH0JNN02M8iqPurt99+m4C1Ja3f3Lw63r59vyzerDcvnPNcO9rD0+fHx8fd5bVKCKR3K7WqcnRr67r0c6m1aNFSt1V9QmUGKDwiZNgMRJSATOpuBGJIUY6M7m2o+cPWttG+IsIDLCRFmAsrQL03ymBWJhrN4QClZ1sbE/a7C/NY1rOTY0iWhG4GqHBRFDKQZM8IksEfWJeGjWbmIB+OPyGZdGoW3S3cyFcuL4aE5LAgTyazTOVZIGVfHYhE0QnUzXtSR6ZIHQldkRmJN998K4UfPr378Nsv1paRHm/dzRtrcWIkalGK6G15el60zKTlp//4y9Pd3cXF5cXlxf7iQBCkEqLsKgPdjCLOa5/KTuqcXMFi3t1WDzMzs4Fa5LX1p6eH7H58Pt8/PoDsfLpvp/N+2olULtOWjZwmFV6fz+FxdbjY76fu/eH0dLi82e0vwdKsezbQ6PdOBVWiq/1exdd2HOdFJy6TsoBZRZkpkZy5pBmSMwCGyCxaQEnkQCJFuTBnDqOjbqo6ilhE9/bh00lrKVr2kyC3ESQzt6NE/m5S2XZDo18Z+E+LJSLPl9h3JuUINPy+bdpkjBiPfLzA8eil0x0vaSL6XUNJGkWoL6LO49MKIgR5koXx6EURFWYhLKc1My6uLoYvNl9WYpl0fG5g2u/q8eGJIvZXh83u8sLSGxrOywJqqJ389PmBWQYh7XhaLw/TtnT7Tz7fl/0RAFBEjtvXy2sblp1NJN3erg15ASI9XF6DEOfoFuDo6xLeMO20CBGn027eXRwuRbiqiHDra3ggE2BRlDpQtCGixHBPZoKkh4loJjxjbC/NmrAmVFlV9dWb61//VpZnB6kWBad7MPvDw6cPn367/eEbrQcEMyaWDQObRJ5JgEWLdFUhpiQjz7EbA6hb770XLRkGkGpt1EcBHo1wn1u6M1hFPCKCVJQAVkIkxiaiR/eQQoSMME5mru45wCbuAUrWsa5COlFmKbI16GaOCrOI4emi8A5ErYW2HBNH0oCeJbEMZokTDZkmUkg4lcLdrbJuc0JabMwhPh2f1uUZ6OFtMbuWm/28rzIaApOggAKMJLPuZqwIChZRrRSeTKRQlkEm4lI8s7dQJhbRipmnV3I7X8hvP9Pjl8/tzOY9IwmbU7KW6ot9+Xj//OPp4uLViNaBEZEMIRQMcS8SRGaNKAPCUsEhImERaRBQmlsDxoUT06xg89i2GebuYWR9OT29+/D+w8fP91++RD8tS//mu/X6BpQePlAvGTFuxeo+iJmsOoVbBgX1ZiuDahEOOCgyPEOkCGuQBzlUrVs4IQgBdwfS+3rOBj0QFKJptqn9RZm4mRORMHnaeHonDQlVQMjhA4gY9zFQeroyVKV5Ey3zPFoVo1C6LxYGYWFl4jb48QlyptFumLkua2qCcz2vQklMwjtyikgp6h5AAOlhEgwiASy991WFFVubj/dAEoQzkxJm3tZWK4gkwghRJyUJC9/N5YvH0jpFUuV5LhR+/+X+8eGx1EMy1rY4GYuQBEEHVWxQnBNIgJAR4b2DoBoiTJkeTggwJSJByqooID8/nT58+OX+yzsKm+Y5CPN+f3lzKUqn52NrZx4nUsLl9X4+1N6X5XQKb8I071QV3R1DkOcENB3H4+lV71IqWCnzvDyTP9vqRubp6KsCEPHeg7zWPSBEJAoQtW5BRuFJZJlJKcLCRQDKECaQZHpimOTSyQlUigAcEZ5k1txs7Nwz3CJKKTJUPesUSUIEYuE6TeAc2WMbWDhBJHFCUIKdInU0d3owi4hQAhluMWID3V1LRERyZjqBXi5C2k17l8jW23Ls1ufJy7yvKKLa1k7sVbhw6Z5mBqIhGatWImjq7ZtXl4eqlD//9NO59VqqlCrhazcWrgqzhZFFeZqEC4K8L/7lw4fHu8/7/Tzv91zqaL0/XF3sdrs0721py2mu+/3VjdZDEmfaeXlazmePoEhvlunPp+Pz47FweV7a6dyLFoAI7BGsEenDoHZ1ud/v5xzmXebTsjR3M631chTHUqZFYylKEpagFADkbV0YECmRfcRLI5JGODZcIEUmVW3rmhG95TRPQ6MQCFEybXWd4/mtrPEStCEaeHeAwmx9ari8nOml/Xjcnb4uiF6eyIwhWL8knIYlZvw76av5dZTZDHvK+G6ji2IoCUNBSQLBib76c5m/xqcoIkGnpxVIKeLuPPQ/IqKMsOGLEZXCEuZFNDLOpwYCBdUdE9HxtABlfOIf7x8Hgmc5LrvL3ctmbdCLf69yADOzPN59+fqWufeMOB5zv9NtffZ1nGEgty3UcA9kbP7osWUa49xX3ellgAIR6Y8//nPrq7d2/+XL8/OTztciFwxND7cenrXuWEQAFZQay7qSo0rt0dZmbQlvlJLOZEOW2jL3GIqiFB5zlrATSXcLBgsurq9evf3m+PTcWuursSIT1vNpeeLf3v/wp+f9HpkZ6cohzJkJjL27Y2uo4iCEE5O0yGaNiapOGcHMpQoze1CMcnCSTHM3UVYpqkpJyKjTjog8emstnSrXJCSDWT2JAS4FgBNADBEChVtGUMtMiKoyMnu3vvXII0iGsQZJ5vAYDbus437PKtR95P9ZlFkzKRAUSRaAKDSsk7fciLQAtuGJiHprp+Pz+fkc6zOTDMQoUChd2AGQQQcw1wxkTBHGPmo8WiciEbyY33kw+IOYggJs3UFUCpdpd3k5XV9cHB/uf/rrX3/77efwUTFWWKtkOz+vH3/79PGHd2+++RYBXxepQpkgAU9JIFKidArv5tGkVOEUUcv0cShKUJYqhRSho1Ahh8/GLM6n893j548fP9j5TLE8Pj6ezt2NCAqpzJoOd0oEYrR+yFjfOHyQlslZSClX9yXdRKeCGRKUPRHmbt5FlMBEOC0rJaU5ezBIXk4zvS0nkNQd82g49hZmpCIamW7OlAqWAYIgELE7WIRA6cGBibnUyqosAkLCgWDW8OjrGWlhvVlzkEhNym59CwJsIWZt1pflbNnLbicTdbMQ0ipEDkL3Th1BECixOYW5cZKIgjPNibKtK1HPDUcs5p3Gq+2ezo70bJmhoswCyZr23Q9vz6fnu7tPXII5FSCS5+fTTz//jWq5uLpmFsph856AbY43MxrqWg76LQGsUkSEaOieBganvkyBXqQI6HR8ePj80Ze1liKlAOXq5qaU6fHu/uHDlxIRSMu4ubg9XL9hLuQ51SlDwvurmzftzfrz018BzcS6pnBNXz99+LnM+u13/zDPDJboDYgkYpGdzspMpN5TqCKzD6NW+AgLECiDKbJ796RaC4+DDoRAwkyE5bw+Pd2fn+5P50Vqnafd9cXVYX+ITNLCUrZURgwXMNw6p46zuWohQus9Ryx2FJTnSBGAMfICG0StooLVs4d3YU53EDMkuQgxQQjUtj0URwKQAYIjAjkVkihIuiglBgIkMqJ3QrQwAiszeKp14kSGufXWxtWCIjwfys3bi1L/6f5pObfFbNlVkW5cJo5cjktVuTgcdu7EsvYmXKqI9356asv55ASLcO+l1uv9dVqczk9gR9C8u9gdLj3Rze4eH9bWBDoVRTqRERlRBiuTHuZKoECRIszMdWKpyrtS9Icfvr+4vHw+npdnOz8d7x8/zAf59tV3t2+/88jIYFYoj+EUhHVdW2uATRWqk2gNx+IgGsjiBjgzKDlIMymSVBSkzTx9qUUI4GEk2I5suoXdmJPcMymgPJ4M3L172PnZ9oeyPa2xSS7hiUwKHirgsAaPEWerRwAz8/G8ZNhhnodtY6ASM4kzRqH1aP+ODeiCMeVsj3xgbFpOJ+f03b5Q0vFpYUYSrGcmD5J1IhUswt17N49GUpQHxms0h2YAtK4A5zTv3LA5XTBQNESg5+f14nK/vYpNZ8mv/p4vHz8N8YWRIlxRM6z1flqxn5Q2PYdetKnf91jbSm6D32DguHKcsPHVarp9mb5+800giPL67e0oYZkOOwZ5XzwoicHVPJxsd5gvL3bL6ZEHxy5rW+x4v9zcJHaipYDZ3SNCRNNhzcCkVSJ7RNfBxKUQpdYdJN9+98P5uH5+/+HpdJ9raFFK3e2vMmQ5G7kzs2fvLTplgSZrwK1njuczaDRiIjUykjKCqs4Z6WkEkDCPKtZS+rqGjwqPmoNmMEpYGN16WCBEoaWUoPTslMxgLZVVBjGIcuBUqKhadCIgCpFAxq7NlEtiuBFgLcKJhaSUIbeBGMKe4RkJZo5BYyEdDhr0symxliLC5mfACs/jNGA0MBEAeF1PXz4/3D8cK/p+N+/K7vLyRuo0RMIt0tO7imZEcjIjfPCzkxNFB9ZFR+iXkdtXiahoEtydYKUIXObL68N+zyLEeP/bL9ZcdefpoFAVprWfj4jOXI08LIVYVaddsYy0YHByUinRicDEaeOT4TFwtxTqCREWbpnpllovw/uXuw9//fd/++nnf/vy6dNhml69umAtF7v9skYovvvjH9/++O00jZp3A0G4iJSgIOZwo6RSZNIprLlndkIWlZ2UQ6b1TGSCRx8YK0sGh5soL8/ndVlVhBVcRApRp2FnIaKpFEOaxXBoRSIiCVFYh3d6mwsjk4kZTIVAECePcDBLUIKLkmbQ0lZbu2pSJG2L7TIeYsxyPi3prlpERjY7JqlKZZJKkhEWFqnGAoE3Nw84KXMGhULLVJycSETmdT1FdhUVqEVHBoMxPB2FpUwMCm/pTszJsG49cnd19erb7+6entxOyE5eM3KWcrr7fPdxN+9qrRejzCsjihaWEd3U8HQfDndhSHJ40tIdCZYc3ctFay0zUbZuHkG9+/k8o67U3VJT9oerq8sbW/vnD+/X06loWXzd7S//4R//ZXd9A55ZyLwjVWQG05sf+OH+sb1bKITLVKpwa+/e/Xo8LVXmt293MpHUQuSi7DY2yHWYlARE6e427NStNxAYQhFMSSizzgJyX8BYzZgLc/S1vfvlt7/+x78fP39+Oj66Sq3TD99888N3304Xh6s33+wO16ISFiOcSA7STpROmWS1ThTavUd2ZmaUSEpi0UpJFBzphFE3D3cbfHuAmJAsEKFkZVZwgs0a5VZPUspEmdBKROkZYaLKXOq0Q5C7CasAiA6KPvLMKcSiwr018kRQugdItDBzdIf4P/7TP/7g03E5Pj7en4/HqUy7w9WXjx8+trXIIJKdwpuygFhqKTyFGRGnubXFIsljpYVBMrrjmXxdnlp7XtrT6XRe4/JwWaoqFCogbf3UI8KjQpBc65RIKA6XF7dvvjkcLkUA8NXlTanTxY17T1/W4/IDT7w/XFaerJmF12kWLQyEZZgt3Y2SlUWSWS28t+XUM+Fl4ggbE8GwFWQyqDBjFDN7JKSEh8WwFyPBbp6WpRSLTuJaVLhmElGJGEtprOHrc4+0IqSMi91+UPL4ZR/zYojxLQUy3Cmg++c2HmC9xe6wGx3UtBVuj6Tz2Mf4VmexBZhzlPYM487plEAQ8um4UvK43IUFwSAGa7c1EIHsPYaVL53W3pQZARIBa5IPEidAEbTVZxARq4DCDRQgOT6edvs6rC8DV8dAZh4/H4XKoG8IC0OTMpmJkcnnhsuLStuo99UdHC+umQEt2DzV+fsbhbFbo0ENHgpNkJm7sFwcLq8ur5xG/yaFyTTNQGEpFo2QqhOTCgGaUmCdPGLtjTKZE5nCnEiQpqeZUebgZXU3Ygg0nLbqPkomXFxdfP/j97aup9ORELXOpXCn3ntr53PzRVmJDD42LsYSXFR0suxJ6RQy/GVh3VpSVikqEiwZLiKABMYTglSAIhmbTmUeg4Vo3TxDVIRYklm4t2bmoCi1AhnhYCmlplO4D9gM+Zg0OQmZEFHmYaWyTAIrKSGckkACIDKEg4heWlppzOfhTkEJWPc0T5YkCkoWLpiYiltkjjArrBuzWrfTefHEtNtJgRRcXV/uLi7qbqcsZj2SKABG0MgtR1qDQIsIFMCwnZdSoRzeB86dksenY/i23II8m1m3VnfT7dvb9fz8+f39sjTWLIW0lv2hRqzLcpp3yqKDGJRADNsEJROSGUyq+yDvzcI3lQvI8BbBkcjuoMX7el593sXpdPr73/76t7/+9XQ6CrSUWWQHFmuWHNe3b3788ccyVbBvS2KGZQePsvoky+iexKnFwkFlqpPZwK5mMosWBCEZIoFMpm0dOTwnWkADMapLaxFW59lHCkarcBHxOk+e1Ah13pMbMltrAQhLeiQwUG4sRaWMsoR06mEsSciidV1766RatSqFS3qC3TmRU5ksPYmkqKgCPOk85+RurGAZ5cfO4IwMjsieYUiMXU9EBLxnBoXwlCTCRRgqrMLNbKzz0iOdksjSgAQnnNxCCieh6C5ZL6/74XDx/LQS+XAdZnRf1rv3n/eHq9tvFChBFO5AFlYVIbClDWGGQaODl0iGNuTeRgFUJI21r0CtLcvx8Xx6EkaZ5tWi7i5vXr0tdf70+ePj8SGyu7k5Lg7X8/4gwsKD7EeAaKlJOe/j8tX18/HOWoMQStntLq87osfx8eHtmwYigqiOkcog4uNN1iJgJBQKkDkzk5s9PX759Om9iH73zY9FKiWTx9qbJ7Tk5y93n9/99tvP75/u7tiJUm3x5fm4Hs8Pd3dXr2//xKqlMkp4MgjCyoWZiSHMp/XskXM5EAUjVIZom0kkKATuzcNdhDwtX4IlAmEwQ0TVh3tUto5d8+QMUoiIAmBirenuYQkO8LDvpAcgoqMI3rt7phExsxKyWSNzEEWksKiqlhLmx6OBdqSTsOx5v9/v3YMJlGjr+eluzt5bN08EURVIrSilFj0/u5mH9fCYpp1A196qctXtfDcoKDUweVjv67LuJqUkbySFm7GUaSrTXHdzmff7fRKkluvb1zc3N3UqxNFbT8qg0CKeVvb19eEWCqmTNweTMJdJy1TcvJNz0bqbpJZSqnIS4Xk5LsvSDOv5HNGFIVyGWaV7UISwdG/jrMZQ8AvYdlT4JQU5CUkRDw8QIbaOaogbxgLWM5CkkNEBcDwtkbmbSjALCW+CbNCLh2Y45j+dugDePCnBeno6Xby6Hpaazem7GWY24WeYjIcCR76tbyKFxCKIiUU1h1eSHAiEJyWxJmF0dQ0b4jxPEe5hyCxcR4hvzA9JRKDeu3mMv1hZx+7J3ZhSUPo5e18cfnN9ObreHu4Xyu1pBOZRw+RmEVG0JMHD7x+emMgjIfz65jKA9CE1bVLNmG9yG2ji5TVvNqF8ARZq1cKhSaDRCT6mReucUqe51Dki2HtCMnldDZBSNTkgZD3uH+5P6/miHzIFzE4UGRiWlQhmhDkRI3m1jsyiBRGInKTojv3Nm6en4+PxPqOpTIE4P53MfDmdMKaehAoTODw8V8aUbu4doqVMntmsIVMF5oTk8ATAoPDOlObBNKqyIseujxksDEryCE+3UascHgQMYrSwDjhiUKaTkoDgIxLDkQAn9d6pgKUSEVKGYhzuFCBWiJTKnkGCJFjrAVJVp9Gl7ZmdKABCMkzhgwktntHXjiBKbr15GpELJUgjAiXCV/dVGR7xeDzP+wHIFgsau/nRbWhmDEnrCqqqAaZAs5VA4646IEBjd8cs7uRwUMDTvZk3Bkd4s7PHOh/Kdz9+czqenp6PF/VAsdkSP335/OXxy9t5X6BBYeYRyY2EkzNYJieGFLBSIKKbrUwySiMoSViFxcnD0rstp+dff/r57uOXh7s78uWwO6zSuUzm2teutf7jn374/sd/eHV1Sx6991omLZXIW7e2uIiwsJJw0aA8r8+USQ6l4TPuo/Qn3dOREUyAcO8tRkNFkFHyXJUoo/elPT+dzFopU5l2CW1OQpLpy/kEZkoqZZI693UdAE6KVIxVOgXy3NaCraUElOGNIKqFMhhZ5wJWCNIYIUU1C7k7K2lirhMAVtk+wMhSp4xsrdepqkycFL2HB4kIgxKR1FsnkEtSRGFNS8qYaiUa1o0ASQSCEmDzjBxQGNvN81zFzBBeVUnUg26ur/7xH/7x/a90fP7saZ16wpPw/PTw+d0vRWh3uNhdXrNo5IjTJgAzy6Sha2aGuzNIBZ6dOSVoiDrmjoSv/f7zh4cvvy2no+qEMs/73eXrt5evbk/L8Zfffnl8uC/ERDzVaTpclKkWreHm3YrIOG1SQrV+8+Mfyq78/Le/PJ/PyxI70Tevbpa2Pt1/fPdbvXnz9ur1DbNaJLOCJDw9OsDJnASPUby2Pj3dPX35/NuvP/3228+F+fEf/8uf/st/u7i8DO9tXdbWPn759Le//fnx8+dCupt3pNjpXCyIYL1/+XL38e7T8Xj8b//v//7q9esy7XhzkhpzHRFagUZEpx4wHevkiNGmPth2TKQsxAMo72BQBGIr3OktwlOYRdOtdc/cXKEJUBLIydNoiLIqyRiVEiONYwmQemazbulMad6JmElICJmiEuMemLz2tvTc7a5QpnRn1lIni/TebG0QYeVuPSWdcmlddSpQJiESYoGSyrwvs8q8LK21liVRtHk4MiM9LDNYqk7Z27KsJ29S665cHL65fXPz6vXF4VJF57qfavXoLCqlcFEIggKS1gJICBiScME08EIEJ7bwaOs50liYBUS83x128743S5gWVdVpmmsthSW76WEnUjFqV0nhlGHEyCAWTcrW1iqVC5tZpjNlwrSUHi3cabRf5SgktsKaSCFJ5owAIT3IfPglnpc87IZPZJhFfnfFZuT7u6NnMAsoQkCUori7u6PI16+vXxYsIAzeH4hl+FTAGNkiJ1rWztIZkpTuI3k5mI9WBKUoEpbmlIAKg8WCEmGUDvYg9+SMTBbkaHi13jsDhccnHVQ0g5jgucWPzHtqEnB3POaYmFBGmxxRENgzNYm1wj1zkFedfMu0Z/jd48NWApIUHu6mpVxdH5JoYGzG7IKv79bWzp1EpJPu2EcSJiwhAGUKc6lTKTXSLTtxJCiFedJpt0/zvq6QnGtVEfN2/3Tc73hXZ1ER5cEj2dDj4ImLZ/goZHPh5FECzMr7w+Hq1dX8q66ns7XzYn1tNn69hUsGxgiSBBUJi9ZPYCpMCOIYFJatT5hBmehm7taskRtUABWplAEGRMxidYcngKKV0sOMksiTAimIpOGggDCLxsAfJ7m5W4w3iQZ4kBOUHiZQhiaZaAZNFoGRkBDJaISXLqjMMDuvLTaOVkCGk5l4PIPdiZGZhBQp7mHekreSj+xGosxpbYm2Tsr7Iiv1Wuog16ZHRDKlFOYKD0IgVlIR0TqOdCGUIDcbqmN6KvN4Y8fdUHjQzxqYBKhlUuXl7BRFry+vXl08r0eL1SM9OEnaamszG1VqDBU5nxcizFONWJx6ZiFPDzLrAtS5ginNGUIqEWG9gZiJ3fzuy28//eXvvEIJNxdXqvzwfFx7O3rO+8s//NM///O//MvlxQXBzVv3bNZSCJBIHrd4xtDiiMIH0iEoQfDwCKuoQiCwVvUIC48WKiKyUcZZRVXT7PHL4/2Xh/uHhzKVuiuXrKza3MiDyZM6QKqTsI6GF/JCEmODyAmSeJHfQkUTkUzdsnsjMCHrrBOXyA6iHrbhaxlBttrCqYrq7kYuVRhobfWwdFbRLa1AyQKCEiG6UxAz1VoIGLb8CAwHgorkJk/BA91cVDySmBgiySDNoGSoyrhBsAQoSfmHH7+9PMy//PS3p8eP4ZbJ02HHpfTT8y//8b8PN6/+8Md/3l+89nCP3DprV6NMTi7Clat7ZJggizCA3johARViAc7L8dPHn8/Pj8rwnETkzbffXt68LrW0h/X56cla288HqMo07S8upNQw83AmDrPR++7mLPzq1e3hMH989+vdh8+lzGU2lPK8Pvlp1LYs88Ve5nmkWAqYgZZuFM99ieExPa0//e0vP/36b+vT43paR6vL/3r+v4LyH//0L7VM6+n4268///TzT+v5KJHzXFQ9eSRqY6o73e2DL83Xj7/+8j9p/e//3//fm29+pK0+E8Mj5WFVFJOGpBslcSTSExyRAUoRKiKM0sKcdOwQhrEskqa5EEmA0jMtM5woprJjYmKrtXgPspQkQuYgozKiJTyKiFEQR2THqJcJuEVbF9Va6mTRCFG0IDnBDpDw/vJKmJO0TBN5MyKLMLckENjDl74QGCCRYhE3+4s333xn6bfMoDw+P7NOovvj0/PD3ReBEURIGNTXVdLKpGtfdpO+vfhOCBPrq9evLl7dXL66necdRVqPqlWEuy8qLFxbRHdngFGcYm1eiyhLsgzbJUUIIGXKWM17P/c6TVXqEEOmeZeJ8J5CU637aWdBbu18Ou12V1qYEy2ItQ7Vumi1HiKS1MYRNB1VJ5FIOOVAwgRAOraqQwflVMnMHNB/4gSzc5KnELtHc+ukBd360aIjStVZZZg7sdfqkYuZU4LFASBV1Hr/9OnTmze3w3mVgd+tvyIsPBJL68PJ4KyAU2SQFCrh3ZHBSq0bEXOyMlNk+lCgRQqCzNMpQ5jNusUCVqIiss8AORcu0OzeMgMk4cEFIkyGsLGW8gGFGbglB0lBOnWz6K5ZVAsFZQazWnpr56oqLGEJ9oCviwmKSHHqhPSk1k/tbmXim5vrgd17wdIgX/ZTo49TidnNRt5GioT7QDhndN6MVAwuDJRpLvPcTsfu1vpaiTOSwjkxOoICwelMI1MDIvaIMsJE6SqaoDTyiFH+6eFg7PeTKIU5GKXgsuxbDxb2MGbRzQOVI98+2nBTIdBh8QYJiCkZTOPc3y09cj/viNU8Wx+RS4QP0JUIhm8kMxJUKDwpRQurAiCEx0qbrZwSEWnDmysqkd7dMh2URTkT45BMZGa9cAHDwlVqKSU9FzNhzFNJtwhXIQ8aesDAk5mtDCNmz0xXGs82ShESopESr6LOwSyqm5aW5sYuTKUyK/uYosa4Di5lhNOpTKVwATTCmUkETmlO2+z/4i1nYU6PTRoHMEPAyQph4pwOu/nQ16blN8oI74MrTDqt3fqSEUIoGKxi6SzJzCKTR3IyJcydOYV5LlNSOngqJTiPz8eMIIr7u/v/+Nuf3/321zj1WQ6spZYpqWdaUVzcXL5++/2P//Dj5eWNqpifh8WdMZRrCESUdLRPsHhmJDEoRgUxLMkInAFKjIBXMqFHOA2cF9EIviUzO9HptPzy87u7h/v9YTLv7nz9quZoHwQjlUAR4euZlSkJpEIIJMEZnBTCnCIcmeRr66zMjKIaERiGtnQii7RIg0hQpMF6mGcR3hgBvYFdlUH9+HhuLa+urlhzbSZgpsHUEvLNgacqSWTmxgGWWouqprt7MJIiQVxqUaj7yspgIMCJcGvmqhiULw+jAEOo8uWrq9vTN8/HJxYj5DQVkXpu58fHx9N6nueL72SC6uBA2nD8s/gYrmkwFpmI0iMJMYKj5CCk++n5obWTKI3agN1+nneTigzcYl+aELTw6jZNF5cXl5lEyOFqjy2B4OPbgyDEVUsts4A9QhKZSpnn8+n+7n49L/PhEgMnk5nMnDzY7azsSXdfPv3lL//+8PxuJt3VuXOlyJ7x62/vWOcfvvnm8/2Xn9792luba2FiEFrrBItE0VoVyTZNLI1rocfHz58/fbi+ejNf7EQnbIu9cXpy6h7Dt0ZKScIA4D2SSBnu5kEeQaLChdKoyFzEbbgwnYRBZN0JKCyjsCxG4aE7MpWZCFVqs7X3VlCUFTROE0ZMFCRgkkreu1uOEFl6eAtgKnsRJUotUmvh5AxyR8YABYKxg9I8H1iLlNJXtz66COjq1evv//EfgmLUZx/PJ66T1N2ytKfPt215hojq5Gm2riKsUzmen6TgzdtbJtbkeZp0qvN8ScSdjMjOy1mVRaT39LTcKAGZlPNuMnMGCVMS2UAfejIwTZPWqfuYJ2HdCJYi036+urzycxKyR4KzWb+/v7u6ef3qxvtqDIGU0fnsEeybu4OFRdhtIFU4yBkQSPegpHCzJGEhRKRlqntio4FnIkRBKSySnu6tWysMT2aeCkY2mNa+qkBVkimDihQfIj+N3mnPTIg8Hc9w9TAg5n3BUGlGbDvz+Plc6oVqdj/laN3bgofIHG+egHh1i0iMq4/BAuIclmK3CAhQIn0gHz1MsGnBZj0yRRTQCO69EbGIeDiAUqYk8rZG2LDKufUM8gywqFSBnJez9VbrDC3MhYeqpMwQMEUnhozFNYFGFxsRZdL90/31/vJl2zRMuaMLwUf0Ww2UymlOScPICc7eOgWZ2ch+B4VImcvEwGpLkCWlR1iz0+OdLad6+6pWZmSzlt2ZmUVIwMQ50lvDvcMcZG5rqXVAVcx7KRuOWWTIXtOTr+5GDC1ThAOkRaw1cmJWCCdjVFAUlCH1g0EUTu7RSVF1X+ocxIGMdCeKwTkDV1WKtN6adSISALJFJtNjWJ5qKTpNxMj08LRwEEoZkpuTJ0sdxIokMgs349FcL1lKrVyFmcLDDGHMmp4ZgaRa62h4H72D6cSkLOhukQ6EoFSehAsA6ty8EeDJpAXgIN4frm9e3z7dvT+tXVWxxLKs5fSsWkstrIMs7iwCImbx7XTXkZSOwRAakpGyxCirU9U6wuRBSaozAUqcZuEkstOqzP1wcSNQCpu4RlC4zfPhcLgWmYGSng6fpkKZvRszMLrQw1hQdLK2ttaSk7JbUB/AyKSnx6f/8T/+r3fv/j7JVOru5I3MCZlkzdqbb777r/+v/35x83q3v0j0bj3SwVwnFSglhcfYbDNlJnk3Igp3i0SSIAOuwpFwt/RBQEAQMQqXQZWTJFh3dyfq3rs1d+t9Pd+dj/t5Xi/Xdb+UHUQwZNugzMjxIHEzGTFoJgg25JolpTustVjXrqFaRLgwRVCu61lYmcLDW/OgXtQi0twZEmSR7tkj+uP9yfvSl+eP7798fni8ubl5+/ZNmafD4ZKh5oNgyaXotJtG6Y8Kl2kH5sjszTeoISiDVmtJ7GwRISyAUNooJ2KQp0fYV9bXOCcQ4/L29q3Z/ad37fQQTr03y0ii9XR+//e/eVuv37y5vLqBSEYUmQvUwsK8BQEaQee+glOlEIuHZTiZ+fn8fP8lrXMhYj1cXL66fbObJ5FYjnd3H3+pkjrNMpVvr2+vbr7bH67LpJnsPZgHsM6TAoRwO6/P6eubb14TyfH+2M5H7z7rfrVzoB+f7t7/8tNu2g1/KIr05tZAwlI0Iz9/+fLbr39dT0973u91J9B5km6mbv20/vyXP/ty3h0udtM+uqtm1Zqp4T2im3upu1Imp9bcm/nFxVVk//lv/yEy/dO//jdozaRaIVLS2cMYCeeCrflvOCEj3N1QCyEpQmQsspGhMUqJZADNzcnNvC+rCErRqUyR4d7XJdxCmCnDCeoFg5mSME8ggi01wOLdAa11kqmAdBgS6lSsm7XWfS0korUWRBpHItNHumrL4SqSd7uLUvYnf8iIIhzpr26vbn+4rRcHYfVmvrbDZZWiTilld7W/SLOg1KkCgQhiDvC6rsQ+72YQwow8Sp0iwsxsHB1l0GcLDYw7oERuFhS1VtGJIikyOYXBWcI90po5QTg1M9wDkJQM4emwu7i6elqe+tqd0uBPp0d7+Hx5ffP9j38qIoMQQpGGJEQkZ6TDMp2dBQVAsx7RBZxOgIgqqXquW4tyarMAdQJJKUQ0XArm3T0JbIg6l8IlewgKMQcFq2QiKRbvSM9UZVYQKAsgLO59eHeWZUVGKULA89Npf7HHFsym+893BInsFBzjRsdISk5JJnNDUCm6naqQSKpzpZczR9C4h6h7BhHKlulShDCHd/NumcNlkdsqK3u4DvAJuUUQhIUzkT4gt6YFkkzEQWnrGu7gBDxisIOTKCKdWZjH4UAyycIAmmqJBCUlsUV/PJ+u93tsuhSSQZudMpJIT+tZmYUQrXvStJuIORLhuVITkQRFpHkvpV5cXn14j9UsM9dz8x7t08d373969e3tga6EOHJ0G2VSMI90DyVvkSyRsnqM8FAmA+wZWrROM0olMGW3vpAnszIXQbEIM6tE0V2gNIgaxACUC4LhHmTMERkskFJEtwLsdBKQQDODmbCt8TzDKE0liDZWwIjw927mrlymeb+VhSYLkYjwcNFkc0+VmViII9yJUpSZ1d2Ya1C0cGbyWHmYclUpqTenhIqSZDAByCBVFhRKtOhrWwQ0T8LEDB1IDIJq4aSwSIaAEJFlnuo8e6ZCQOo92rnxNQaVExmDxWJhDE7nCIv05I5MRd0XNWKCEqVHF1bhod+kspiFe9BAUQ7aNrNAQSQFF5ev5unC+xEgs57Mr9++fX17e9jtVQsyksx9cXMSAUt4IsnD3GhdXZFTKURh0c+ndWwBH5+O//Zv/+Pzl48X82V2svClrYULT7Wbcamvv/3+zXc/apmG9SnCddjKCAgO84zh4Y+hw6WPFNJG9og0RI4oLDLDu8FZmIgnqVzF3AjKkIhtzsz0i6vDv/zrn1593D2flre3b6qqtfM0q3ANwnBtZ8+Rnu3Zl/MzQ0opbkFEogwksbu3sOTNc5zRzK2POB6yIInAa/OlPc1V3GxZTgLZXVxq2TPJ3d2nP//l39vphL6SxxL++Pnjr3/98/Wb2z/+0z+/fv0dixqZZ0xT8YzobZ6mWucy7U9ttR6cQemjJYWEvPfWl7nMBEaikHpa856ZWmR0vo3yGhbJhFOGG7S+/e57Zf34W7bTg7U2TbXur9a2Wlvf/fL34/PDt99/Px9uUjiqhUxFKqWIVkq2bhYhPEKlADRtPT3cn57u+nrS5OjhksnC00wqzZan+0+nh8/KdFrPl29f/+t/+/+oHqBKEunEWjh0tAkzgzKz2/Pjp7acDvuL/T+9/fXvP3/89eQRxMIQAVlb//7nf+P061ffXlzc0G42zyITc/T1dP/4+Of/9T+ePn86lB2zKlO3RpbpJJBSD2s/ffr4/o+7/b/8l39d/Yzs+92FB7fF+tKenp/62sg8I9IzgnZlUuzuHj7/7S///vqbb65u3zKLRaMAa0GQCGjYk8HpGBQiCABJkSrCkeQROSxHOW76mekenFGKeKxMEGiaJxozq8LDAC5FRIQ8MpyFK3So8SzSe6OxUUgMW4MjpZYqk5t5GqRwSSLyNEkBJWeArEwihHYOcxtAnUjUud6+fWvn5zVPa0ZLO1zvyiS9myEj3MkFwsSqGRjXfH3pcfSxEQ6SqVRL9+hAMhgimWEZHgHm8CiiYAkmFa3T3GyxNChmrQRqbWHSdJDkOOOrco8090GISRqXStmkOVWdB9Y3hRkis5az2/P5eGrnursQVu9reEgpxNzNrZsWzHNlYkrOSCYQCTkpl6AcOYThhd+aXXjEkTxtJUJG9n4awiJEp3mnUqxRgJgpiVjBiki1Ptgi6hHA9qTPzQ8+gZpFEpGZgVIIyfz8vFDE5c3F89NJpZiZe8vkUnUURhHFsFGMTl8Kp+zpCZmYmYXNe5jN0zSoC0Hu5MlIHrbPYE6QE3woe6P5dsSHJp0Y3K0t65k5CqnqJCxUJF0oSaRokQjv3s2dwKVMBCFkpmdmt5irqFBkMEjmapZwrpi6L5SxNQEAFujhD+v6ar9n5o8fvkBiV3dJ4s4K6Pn5XKtW5hEIzEzvdj4/n58frw77/cUVhqRJYC6vrm+vLm4/nT8krRE5Qi3r2hk6nAoZA1sED0reqogit4aAJOHxBHULchTWqt6KlOJJEcEUbTX3UstuDEEi7JZ9scHRzPCB4wTBnWiktdPX9ZREqpMUGRmOSVRU29IoAgSKIB7HHUoi0eFMNcgYgj2i97YQRIq4W2tLRFKQahmdTc3dA1O9RBYnSxrp9BgBS5YhgxXzCA9KU+Yh80K0DLtxhJsbB7MI69D2KAXJU5mZBuFCMn1ZV2KABS+d7u7OQEZ/erp7fLyTURtK7m7dGsBTqZEWuaEkAjTa6YgDIJKw3pideESsNj95KRPl0GkJkcIggXuMFCDIIcJcIjtlFi1Fqp2pI5irkBwurmutERZOutmotyyhiER4ejAkkBFGQglKAnPt2TLgae8+fHj3/l14N89uzqK7eiiqTtSJ6nRx++bb3eE6idkhlMyeZEnZu1FylTquMpFMCgsjxOjhDiPzbr7OU+VUAadvRSHuyZQBK0V7D/OmwkiKaJ4tsmuhaaeXV4erq8uLq8sg4jROYyipDGnePUZXuoePsuvWw8OJLKJJAYGsrefjSpbT1R5TieX88HjnYaVMytPoSbXw4+k++mLrsjw/q/z/mfqvJjmSbFsTXJuomjkJhgiQRJJih9x+6v//E+ZhREZmpHv6nHtPsSTgQDB3N1PdZB7U8/SUSD1lFRJwhJup7r3W95UXb767e/0Wnu9+++3jb+85aC602ZSZmTPXdf326ctm3qts5osdK01aRcjWnhm1VgKtp7W1RkI9LG0tzMQK4lILiLSMGzkyHRFIA4e5qVTVKTLCR+9heFNEiVDl6u7WY/38bj21hdpaxqmnins83z945stXCJK6m/YXQ+I2i6g3ByCFeby8AIIfH+8//vZb70cKZ7BbMAtzSS7EZX1+Ph6OCFinpaNMWylTAOmONFByjgHc+UoqwunR19aazfvp4urudLN8eP8zeZ9qpVA4hUdb+v/669/u7o4//fiXbhEZdeoJu3+4f/frx4ePXyQJzD0jzxlFiOrYaqbz2pZ37399mfTm+1c68XazRdbeIp2enx4/fnh/eLyPxSkBS8+oUxHI/den3379uNleztuZAhnOQ4eQGPTzCB+Hb4CYVGV0NsBjR5C/10WFiQIEkBNBRatOSiRV+3o4nY5ai240AGIW1aIlfB3wa7MeGSxj+SfjIQ1GAmtvSSiiQMbw4UmRgR9J7/2UnGOCP0gXmSwjW5Bh0S16mSadqvvaLDkpHN7MtBEr8UAsMnPVs2C7DPakh2WQCGWaZ2OoCFmmZxZWJnLEYL0QiwDmPb2VaU4qZ3gdUbibZ+FCQMC1TMQjk9pFJEEJiIybNA9HFDOZe4ZLkRwQjW7CtSoHqVs7nQ7b3ZVwEZbCCsq1NZCqKiKzBxXOCE4BOOEBU1FGcFC3kN/XwIMlnhFESMSoI4swk2QyeExyU0khmnASxeCIJwYllpGByKRSKghjzYEc9VgnEhDW1qeiYxIvVI5Pi2dGWOaQqZGHJVlScsLNQXRWnWdblxMnubKDYEYIpHsykMwchPGD6hlgGZN7IgYJEIP2LUzCGQiiUcI9ej+M7ASSOYP5XD5q3TxVmKw7sZY6wcdXOH7H5GGo6kdQ3KlbBqdUGXdyVzpjOTkzBEl2//QkAx/DZ/ieR2hRnadpaKRonMHCw8N6QzoJksb4SCjZw29fvu69R2vHp3sNTrTgcMtuCWWkI4hJibg38+wYDQwGQK2HwOcyIXxMCwEjUaY6+M3jv5EO0TqJMjJNmKdaRvXVrBGxWZMyjQ0CiyixZ/QwldFJTndnSHB4LhEmycwcoLCeQAgSThkg0lIi070rA5HKKlKVBTLkPuqgJPb0tvYxvTMz+Z38k8RIjBoyWJzSkZlBoKIVAUs5K7mJSBGRAkUMsiAhwt1YUFgKzxGZSX4uLWHk3ikCGbBkZkry1e4/vDt8+zRpSe+UkciHh/vLFw+bzQzKpa0sY46tYAEJwSMdxKzFwn1tg1SRAREZXyLw4Dpm0YKggAMOHmTlNOujg+3h51WjrRbr1avXr96+nra7SLhnhCU7MebtJsERxJRSFSB2lzpnjlw1RVIQEv7bu1//+l//2VcrpC2cOC+uL15998O69HVpL6a6v7q5e/WWgEzL823VPZwULDSutMnItB7pEeCI8HU53H/5cnpe+mrPh8d5N93c3N1c307TDETrnYPnOh2fnh6+3rMWLeIED2t9bW0VYoQfDs8WPtXSvLtZRrZ1iQBxWW01t+PhtB6eL/f7DDqcVhFZV4dSUW6nAygy8Xw4LYd1kiobJVUNfHv8ev98f7m/utpfMdjMevTWl346MY0rtUDL7vL6/uv9t0+ft6UyZJ4mrdytaxGVCuT9l8/Hw8qT7vfzxWY3zdNms582u+Px6Ba1TFW1h1tvnDB3JLHKUH9YdJES5N0azKqUHBCJUkfziYUTDpyZJ1QUQqT0sv5Q6rz5+vn0+MVPiyN7szBf+mqRtuay9svrS/7hdWx3MQPdOMaTsgkyszVbj89PD58/Hx4fzK0WZWLLmJJ3m5kR6+Hw5f2Hr5/erb0FcqqbzeaiW6f0BHn0pBBWpUoiSKHxPZQ6b69ksyu7HRW+/e71H/u/fnr3m/dWC7FIQsC8en96ePjZ/0GiiQSzhR2OT4eHJ/YstUDlcnc51fl0fF6eD0Lk4d2iR05Vej+9+/Wvz4f7aTvtLi5e3L3ZzHsWXN1dTlt9/0t+fH9Ib1MtVVUL7/a708Pxl7/9/fb69oeffmKVzIi2jgZlN0eGiiBDlJJ42Iy6eQAp424OIqHCSAOciLVoBiwCzIlcvYnyJBOGkFDE3U69mXPvSZIcOShKDLLeiUlSMkfTIiO8MNek3k/EXFWTzo6MAdVnSBJZWm8WkYJa6zTatEwkpK1Ti5Jl5rpusFmfl4cvn9/ur6WIcJFJPTIC5jh3S9M80rwhiaUQs4KAtEwRpsze2oh0hDl+p8yFJRAeKzlZskV4OCVzZSBrKWdlEFOYe66RmgODzpSRLAUJpEW0dK+q+8u7b7v7w9PJs/duRUhJj0+n0/Mhr804UKoQAcmULCnE41JEw6g0qFMsrOMHPJWEmDx8bSszl1JZOCQDmRkCUmItOyKy1SMC3Z1CRYrI6j2QKjUjMoy5qHCEZ1AAa+9MpCw9LcxWb9ChGISMTkBmuI0PM9w8XUjCTzB3ilBnEg0evFtoQmQIs6QWYkrzNJfClra2YAgzWw9EyuDICIPczAyeHBY9IsYOBIjwvnbL7I9fPz0/fOmtPZ86a724uNR5M28vpzqDnEXmaRYp4OIe3jsljc2UFnJz6+ZkxESg3psTVa0ZrkRcCghmPto6PRyeBGYFHEmwbL0bnA2pm+02M6y1RIpKBDHRxfbCCysYEbUIo5g7VHYXF9fX13MtqEWJHXRcl+NxOa2n1Zuq6jwhYCNgCho0+pEv4SJaKoOW1oySVQGuUoLAyMvNFKDToTes03arRSJjXFNG13bIFoBY1lZorGC4iKZbIHgcTTJhsIBKrnFyd3jKNAXB3Lo1YdLCCENS4Rmkazt5uLAEsdaNkBAJsXDRIlM4WRgLgMgUMyOy0YB1EBcpWpiVmAEkWDmIJZ2KTghubs1P6UGRSC9ahXWUnkREpa5YLTxjCFRZSgVRtyaixAiLkeSGpGphaLTWlhOZZUYP1zpl0NPDt9Pxwf36rGoSKUUTGubdmhZRBnFx0GouwiCKIEqKHsFGPALXQmBOBoYoPYli2swWfjp1b+jr8vnrh5MfMUnrjoJXP7y8e/1irlMmR0eSW64e1j0jnFJVlCgygygEFKwkwk5rPzrSfH3/2y+nx+d5mifV1jrg8zzfvXrjxtvd7urmCkzC4r0NFMpAT1bRs42W4ZEJZ0ri4taCcTw833/59Nvf/9GPx9aNWZ8e8PG3n+9uv7u8ugZlM9Mi+93Wm7U16zwVHbv7/nQ6HtelUJHE8fi8nk6iJTPhaG09LYcCnafJ04OjWXt6eNzUWnhalmWzrWClAeQCE3BcDu4o2IS1bkcmMRLNsuGtOsXa17WZrVmSkmfdiESEQ3m327Xnw7uff3l6PpyJiVUzM50yODJYOdKfn77lMz19af9cl2mar2/u3v7w025/mfNWdpdB7MTKNawDKaJBsfa+rqcAapmUWRnMIVojeNhM4PAeoiyqkoQgMwOgkCpKZfP67Y83L18+fv10//7j8+H56el+PZ10Kqry9PiVoMsz//Zzv7q52V92KYUz2tK6N++tLe10PD4/PhYtda52SnMQ3LyLGyXg9uXLp19//utA4m7229c3d7cvXtZpSs/eOzPcc11bTpjqBgkGM8m82c/zZGQ9wqNvdps//8u/17L59Os/18MXKVkGFqhPsHx++DJIhmvvIFzsdphKgvaX+9uXL9989xORfvr0+Z9//Ws7PGc6EpPWeeJN1WVZvr5/Z9FI9eb1h7c/vr19cTtPm3lXS52VS8o6tPKLryR8vduBYz0+htMwfQ/1U0RSpEiJ8MystYAJnZJitE+7hYURaCobECJBoGZdpGRks84IFUIks9apBtLJkUFgN5caEHY3qUVEkQ7HSEw4bIgUhEspnL2ta2veRQtxsAiILNLMAREwCSLhfZhNx5A1PZy1FCJWRQpCVebNNLW2fnj37vLmxeXNa9aZVYEglsoFSe7j3mGJYC7moUJE5GHmPvKqGIPs3pZ1KVOtvBFIeqiyEgvD3JlZaAIggkQqsbJGIq0JEsI9XWga8m4tRaSExdKsW0OSSN3tb69eHA73z3TKDjOEA9Z8PRzhJjIpK1EAw0joCBIuTHyGu53RMaAkELv19MZSIpKYRDjciZhlzO0hme7emhEx4rwLH8xydyEhJo6wKsUM5g08KiglCau31lthHZWeQTAh5BBeth5hDkTjNk+zkkYiEB5HIRItNvxhwyCVaWHJBgotM6smUgoYYrH21ovOnm5kRMMqPDqkeb6ukuiA/yIHr4iIzNrp8Pj89O2//vP/+PTu52YtOoO0TrVuL169/v72xYvrVzfz7no1q1IKg5OCEzmc8eclo0dr3uBRMQuLpZ3dnQ4K9OiLNYJEJJhUJAOWluFjusDMSjXClQnmSaBIF8pSdIAaLNvvjYQkIRHxtEiEp1tzX0iIEEQwa8tyDLMkHQRWwH9Pzoy//GAQI9yagQPDNsIE6n05rYfwFtHBhYncTFiKTpH4b1YKSDKdCKJaRioNSfBux/HqpSFqV+Yx4BiRd6IQd3iGeSYzC4icIjEWchlAMKNYgEtRrtFstKclkendLNKZirBGQBhCCQtVAkVyQoSoIIdhFx5NwCwSSaNiQ8jIJCYORkTChDNG2J2LMqK1kTNNxqgqmFskMWSo3Fly2MhBJIXnzQzmZW2EwGD/WF8Oz60t02Y3MjHCJZzcw71lQAQs4uGIJMHAtSJpXNhBGR6U0BT3tLCAEZKJIj2zp6/h1PvpcHgIGKkkl+12u9vtwr23hVFEKqmEkXWEJUbPJQPwCGdQH0pxZQIRBNA0xOpVpIiwZJkIwdNU9/uLUjelSJ3ELAhELL9n2FNpFBo0kL21/xaAMAUxtXX98P7Dlw/v+6kJ0VSZwOF5WJZP/cPXz596GhGEZTPXqjrVPR6l91UozfrTempuAubIvq7L2qd5Gx7LqXkGwzaq3hcQWLkUvdjs0lNJdptJlbSKZXpkYUVASIih4ERORQhJ5EWEWRFufXG3UpWE2uKlFGLz9KkUmH189/7w+JVgGZqI7r33Fj2UiwfSz4zNOpWMiK7r6p8/fW2t73fb/cX1i7u3dbMTnaZSR/Yr+5rEMZ4ehPH3O0gOkTYmauGpxCpVpIhKukeMrU4MM0WMb46WFy9fT2WzefgG4bUZhC0Dkps6lVLXkz3k4/G4lqpCtJ5Oa1t6b96DwUJaau3mRCkySs/RrK+t03J6erx3MwYD9er65rvvv99fXmopyQj3zCBVZHbvbKSqHpmRzAW/0yGVhFmguLq+Xp4eluWhpw2RZ5jDgzVVCgtZhLlnNBXaXd68fPPdi7vb3X7PLCl4fn748m59fjqCICSUHEGcMskkjNXXD7/9+nx4/OmnP7x+9SZCT6fVIwC4wx2ZMRXRkka29pN7Q5kJqEWZyrI0Sqiwg0aqYlDdVTmJYmS9KYVBbGYjwU1ENA5A7pbpTJoOIiWuY/xM4x0EN29jq8jEI3pPv2M7IoILqaroFBHrEkhiUgAWRmFCpZQpoUgCyM0yIVSEZJDRMt0RMkjzSuDsYcJI8iD3xT58/MS6vXmxDQQ4mJmVMigs3SNBwjWTzLt70GArjKtPpIqQcHgLuKioCCK1CEMEJYMzbRQ2M0dfDT2cKElg0YQwFC3AeBdEeLT1kBHdLIOVS4aWabq4upq322NbZtGAZ3rhNDsSm8imaIlsBCqskcwkyGx2YibWCqLxrRSAR6YCJAwlyXBWoTyfZjhi0Np7NE8731wDGO0CV9aqPHmYZ1t611AKmHWtE5M0MwEn0WrrZpqVCvHo5aSyug/IHzMTmDOJwUXVw6QWiugWlJyEpS1IVylSxNyFiERZOMIojYha67/D6SzCiFmkyPBjM2ztBZOImi1VCzHW3seLo/f2+fPH337+x6cPH9feQbqZZ1vj9LSupzg9n57ub37AH199t2dMrmB2yhROT9DItp5t5DyYtzm8nSOEroVSrPelLQYXwe97CxvvFhFiZQeKVE5t5gq3NKckZh1sbHMLa0QpSoFce6eAqEbG2k6ndkxKy+iW5t2N+uq9NcAzeoLjbIXk0aBJGhsWiYxRdyuq3c36qqzhvi5tNEEgYJHtXPZznWoBkYdx6igkRjhUwLUWYOwSrSUlMQuzQESVVS1y0sLMbkkj4WJRSymFrPvECiYEZaa5E7LW2o0DyaI+SMaZGegna2hcWVWJzqYtJiYOKipKwhZEHj0jJQWQ8LARjEdQWniqFKGS2YQG/4xofNGQzZpxEotodY9IJ0LrLWy8Rc4IxHQTIiV1j8zOqhcvXpX9B126hod7IJ8fln/87dd5e/3ybSlaMplpNNRyror0br21E4BSKiKtG7EOhGXvlgQCB6J7M/MyE9iB7Bat++CoDt5wP5y4xak3D7x8/ePV9etMWduRwNO08UwLI2YEaKwVOYvKWXBFFG4w16GSzuSkqdZ5rkTp1jIACkYWoTqpu1vv5zFsIpMjzd0MoiJhLRDhpkrCupxOX79+XNfj89Pj1y9f+rJstJSq7uaeKnqh1bv1flraSajMpbjZY+8sTxYR3qc6td4tQxQ+SHaelFmEy1w9+kanqezSe1BkAiTEZbOdhwE0LIexFMycZEbmPZlFGTlG05Lm5mZuUgo813X1yHCiHkhytzJNl/vLzbx9fjp8u/8UGZVrmoEaF55mNZg1Q1J4ahVS9QiCbHaX3Vu6nR4f+uH48HD48OW+1HnSabudknN7OU1TEZ4205VWFZailUQjegLNViYlnsx8nH0z2FtE2LmdJ0TKMNdhByBE8vbmarrYba6u9jevluXY1qOy1lJZyOzEFIC7J6uKaKG51Ilm2V1c7S8ul3b85R9/hwdEmFWkrK29//hunud2Ou7nLUvdX9++fPv24vqKRPtwahIjoKoi7OkqZ/KusIaFuSWj1ImTMyKJdtfXb2uR3fz47fP6fDo8PWdEwAlCpKfltEbbbi+2V9f7y+vr65cXV5es3LxzRpnK6x/fbHfz+3+++/r1Q/eea4QzBUUSRDbTXnpvz6df/vqPpy+PReb7x6e+tnL2zLN38R7GcXn74vblm81mJinN27qajAq9yMiskKP3ngxJgpMjxh5VR/PMmoAzcrWGBFM9N0KJk8mGXru1cekXKYQksoheVCglLBM4l8CRCGdPb0akAQvv7k2IVTjJMhEB4lSVUgWZ6dk7EXiuGxoimvRMgEo4CdHl5dXbH376+vXT45dfT6dnM2tOz0+Pbj28exoLW8bKHQT40CKfM7AkYj1BpCweRjhzAUFj6GQ48zhi+FV7GAWPFjQSCPKInh7pWplJqKhlhLmQevRlqK9irO9HfuvMa9E6y6vbrx8uT4fHy+3ObX14emy2Hh4fng8PV9Pc+oLMKjrG6UFAGuTMAfdoHjZgckHwdJVKjAz3MF+t6gyIZwhRRCZcWDPQvScxiWZCWJmVGe7dIzwiw0REVIb62iMyXSBcpqCITBIBJYta6w4GiIm4SGECI9w9XUQi/eHb56fHb0AN8Om0np4OgM2benF1Weda61TqzFooM8NZSvOROUf03vpSijip66SSkIx1aR6OU/OjCAN5eH5KT+/28f2vj/df2rrMZR7Ynt28L7u6tm7Rmi2Hh69//8/+eH+6e/nmxeu77W4LKQRSGjlDZp2ConsKChNY1DNVpgxaVlMWcwtLUS2krDIMWJ4mysrn8nZmBLqnqVlHJIsSCwJmbbU1M0phFgJxGJk16p2YUsklVo/D4hnEKeF4Pq7dLNzN21R1UBY9PaIVEUro4LskSATM3nq3LqxM4pnrycNEaHJkD4fQ/nK338/KxKBIX9cWEWUqddp4gMZdCTzImQxFshYVYgsXURX1TC4ipURPIszTJr2PidFwaGTGUCKQpLDCgUgmTLMiKRzr2sHJIHPnMVARbm3t4UUrs7R+SsBBkiE8t7aaxzDSJcY8Ic0tEcIiFDTGfWDhQsMUk6kswdL7kQC33ror1xF0cgQlOGNtDhq9tUihze7q+sUbe1789EgCKHyN+y/3Xz5+vL57weBSpvFo8BymSYWAIsNTVQcRQSu5R7iZOZGUMjGJI81dWTLTehMRkSKQEH8+Pn/5+tlWn3k62WmeN3cv38ybC62TW0/vFkSqSRk9YVGEp8JDexckmUkM0PgzBSFrFi3b7fYi8T7MKdy6A2mr9fUEYZFN4ZmZLPq4m5o5iGLosgiZIcrM7nZ8vP/4t//6/56eHwQqKLsy1bkQwXuEOSkDpFxE2IKVdVOqConW7pbeI/yMCsxMdyDdwwIWuZyOyvPVfkNgs0ZTnff742J9sXCqVd1bz06sk84ekaDW7XTqF5fz3d3d1X4vUHNf1+X+0/26PH47PomWi82OiFkikRC+2F3sLq6uX1wn8PHju6enx8J6sEXr/mJ/vd9tL672RPr8eLh/eFxPDZYZ3bsloVRhzm2de1s5uJQ5WY/Hp+XrR0RE9Nb9+++/u7q+htP19d32creZNrS54MJDltLaIlI226myTMx9tcEbHK4V4TI2GU7QBAcI3LI3s/DcXVxdXb5wjwF6yIyUHrEgzEe4pRSB0MJCwsrzdlfm6cu3j9P7D9mRmeuyAiyK0+G5HY/7qc7ThGl6+f2b69tbEjFzb11ViwqkUpEAl5yVlUHElBTdG42NP9iskwiLSp2m/X5zdXl4fPj28dPH3z61dQFQ5lJq2bRFK7/+7vuL6xckhSHbaTYyM/KECi4u9tvNbrd5Uf62+fL1fV8bwns/kclmMyOzlLqhotDDx69U2JiS2YKI0/rRe2b47Xdv/v1//O/Xt3ckokJJpfUGxHazsUxDKBMlwyLDx/2GUyLWTPOwjEZO07QJzoCRqKgiiFKYAQ4RhGeQiwqnIGjY30RQS7Ge3UagmJs1IDhNRd27Zk23dAvOhFfRtTcCCVdhAcFtBWXRiWWK7EExxCGR4enMJQJEPG8uXn23q3U+3H/u7UhAuPe1LafDui5SigeTDAYMMxXyFCRx5rBKlUpgZfa1j34iy3A6ZIaaZa0QVhEQpa0+RoWqGkFm3swyU4qAKCKLaiQnEJ7N1yWaOCup0CRcIRbZwUGUGVFExksdw10Nj7Rv94+//fpe62azySKbsOy+UpFSCwgsYm5mbUiymckj0sM8ODMo3XtGmkVGL0U8AqMRRAJQxGgVcHJBCrOOm0ykgapQocIJGs0GobDsTFSk9jTVUkr1GPdk5lKRMuIsqnWu2vsoEAXcj4env//9f31+/67IRFysma1NNXXij++SpEgpu+31PO/buoKwvbg4ay092rpm+n6/rVo32x1A6X54vm8tlm6H4yHCWlufHx576+79dDzud9uLi4tShI4jAV6qzBDup6a1llrd7Lef/+vLx3/+m/1vu7/8jxahUlXV05GoSBWNtNQaEcHULKNH0SJcg9wldJ6ZpbBwjuaLkFJEtzPHPxNOHKKsrdl43o+sgEiZRMd+hzmYxTq5x3j7z1O9vLiQafaBVnMbwApKmnQCyMILszCbWY74NY3AOROj9VZEM2XAMkS09VhO3S2ATHhzV6rbzX5ocViVCCoSxEUrsxJFUgycvEAtDMKDGu7h5lZEIuHuA24GArGMzQcIntHNIoOItAgJEZOwRLow6ei6AKBRmg/3jiwsOvCLhqAk60mUGUKDhoHhRk/J8/OVIMKcFK13cBJThHOeR8CihUnSnUYvZ5TcMzJtrlVlAiE4A4H/e+afw9rR1y4sNzfXDx+KLcFEwrmZ1a0fDl+8najOgx4t55IURwLgWqdh8IjwoQoRljAkAumZnmBmqpWAoGQGM0h4EOLb6Xj/9dNH9y4iSXT94uLq5kKUw0NIdZIRJgWzKEU0cKowkq1HhI8PgQuPPsVyXDm1lGm7v4oUgtWi4eGWBA4kMVQUkASBPHNsppVVFMJJjIjubV2eH78+P377+uVTtONcqndyz+12KtPUW0pRSIhoW3uQz/PGSZhRixAygiwipTAEIgSmYBbOzMwUkh79uLZSuNYpvAfT5eX1zes3rVE/rtGdNQ/PD3WeNtNWIOu6uqBusLvkm9vLm9vr3bwpVDz829fP958fE8JU3ABwVXWKMk/73dXd3avtfu99/fjp07enb762WXQz725ev3393ZvdvN1uNuG53CxXT4+PD4/r4Xg8PizLEoAFzGyCME/NWloTdkUowQEhrUoP357Ww0LI+/sv291mv7u6un11dfNaoKREUgCYt8LqnpGeFINnnZyUQMAyWJQC5MiE92zNiVg5I01E5stdt0yEFhJJJLpZZJLKpBUdGBd7KhEkovN2Pnw7ucdpacxcJ6VEuC/N68T73VRn7dajNRq3egoIEzSABBdVJc3hmGEC6YjCu7tFCDOBKKU3R+pmt9fv6/7izloXZlImpbRWhLcXlzqVtdsQXYuUWWpYEIwLQlDuNnWSF1+u3v3y7tvDhyCoSqT2biQOamGNc+ASCagJzhxjE0rSq+vb/cUVjeP8KHAM6RYDQeaWucIZGcnJqiRI93ADEIFlWTlIZNJa5rrRWgnauyHh4WEGZK2FWMxsrDiZMTCbAJn7aNwkUoUy8hzlREb0DE4ksyYiEpTiYYgmwshhfQ8pKlKIZFnX6DaV6Xe8J5AxIqOjo8zEZ9CGL+vp+PT4eHPzepp3JJTEqlNCMiTDCG7WA0l1SF2JEsIMBQGB4erVUidiQgpBzddEEElv5tFpsxmXWhJV0VIU1D3MmoNyDIEINGkV5t5MiYVLMiUs88xyT5Iy7UTr2lfri1ZQmVvQ8+NheV62m0ucmVQmBGah3wl07lm1iqrZGjB3G8pIt+7mokJKbg3jY+zOIgmstjY7CQtSnFLLBIIyR8KsqUpRjfQE8izvGWklGtQDIvK0BJjFrDORqDgQFons1rs3cPa+dmBtLTpg1KzXypIBClWI0rparGFYn++PSmXtKwnNm61HClP8TsOf53me5u3lNoHT4/Pj47feaVmtWyei3qyt3TxUeTNNzBJhwpjqNKCVp76O2JFZ8AC3WhweH9/98uvt7du628uc3S0yq7D1hrOOU5m9ec8AApSkJIGUUnhkTCJbWyFcZM7kFu4RzLQujYBpUwbyhAN5xq5mihQVYaJM92ggsISAiJMifPXKdbvd92RRJmIlRPhyeACSdcoRjg+QD4UsQxRMY2IBhDKlg1gDtJp16x5u1nvrazSnvL17dXP1UrkCcDdkKlOSIKitKzFlOHg81sCqI2SeBg8nhvU+VjNEEh5ChVibubsRRSKHWmOoiISUmDNNxGRkMvOMbCAij3B3EfXhwSFiViGSSIpBRQYJpYRHIxBREhxB4Z5pUnWaNMM9ekZwqaolScJjhFQS3b35AIyzVCJiQdoobSIjLJDJqpG0rKfMVJKicnW5r7M+PxEAibiYZ3Nenh9++/Vv13frixevtvNOSMJzGHqJRmxJAB5HGVt9jCWYNeEOHzrswuTdCKyYkDDL3vvz4/Nvf//58eNH9+Yil69f/uV/+9erF5eiykMBwyypkaAY4HwOyu6R3mJMVIgzMgnGLsRBPEZoZbMFhIg3m00tm8Pz8fH56f3HX78rfyhld2on6wZgqgN8ykhG4vj88PT0tbXl+enh0/v3T89PRWWe5ghwnW6ubl7c3nGVx8fD4emJbGEw+lrnenF9uwNvNtNuOzHJ/dPp29MTA2jr2k4wu9xfb7c10kuZQfJ8eGai/W5mZGu2v7q6vHmxvdhzsi/9dDg1W0l+KIWLVm+99U6iSIhynYtTjmM3hR+X0+H0FPDtdvaAWdte7t78+H3d7pjV3b89fHv68uXw+Jxrbnb7ly9fXF7dbvYXJKSs82YTFp52t7178+MPbVmfHx8f758Op8Px+LA8P4VDi5S5RosYwMjQqiQsbr605en4WISnU3389uj5rtSfv/vhp7ff/3h9d1vKPKxzbplEMXonQzRLmeFEyhBinFevGVyw4YlIVJgyzKyFc1FmSaQnubk30aJMkglkJMPc041FRWS7vXz6+nQ6rOlABZhERXmqdTft55tXL8d6lMbudRC7cK7cj017wDMpADhlpsiIq4WWiVkRnITwoCSRgplVt5JSRDwDREXIfEkmIilCEHW3SEdqDANbeETKvLnevHxxe3txefPl622pFS6Hx9NxfXx6/IDW1t7NOgmBlQlKJeCb7fbN938g3d7e3pJIBtwBoUgREeZcrQ2jM2USQCQjAdbCwg2IUou5gaXOE5QzWUmpI7KlY5R73f28byeM+EOOSmN6eC5uwxfr1uCmRGC2dE8QlXDKTIvIpGku6UYkqkyZFp7pPEqr4Rmxrgvr2LTzoEgk/X6rSEKECBJh3eq83W34uCwPH95/mabXP/xJ64aFBByRmc4qFHAnD2veCczRSVhFtBSPcCCIIZjnzegomfVIzxGqFqhORGIZxEwUrJnkZh1hkUnEVWeSrFwjXYTKplAKJRKkXJWpNwsggN3FJZf6/PBI4ZzMlPDTt0+/fr3YX1xc0kYzksGS7GtPTg8SQrTWupPw8fRkfnTrGYHkdlojg0T62uC52cys0vtKCTdrfc3opRRiAZcyb6bNdtJNdCMJVHjviRjeJYSbA8y9WYaxlmge4WWuyenpQQwy8zXS3fnYwzNEuLcW4UXl7vYV3JblRCkJR/ooWzORahFQW9Z1Xdy8bopG18zj0wGgaZ679+NTPzw8vn9vXJC2LqdjZo0QItZStEqEkZbNtCF4730a75bgEfGSwlUkITKpWXQn1YkiP314///6f/4/vvvhzz/9+c+bi924fiT3iGDmoVZU4XkzpVNEhvffWcEWSdaHZCLRkyiVy9qX3ttQG5j3cOi8nQPIzExPCsqgQDBlUia79d7XTFcIBa3HE9w380QiKURE6Zl9vf/68fn5aXNRVSu5ZWbVwqqJcRRGRB8woggjploYjsh0W8PXCKMk6ri8uvjLn/9y+/IOLBFeVCMzfFwFAArC+DLnEJKJUILt1L33Ok110oAD7HGmOWdi5M5K0ZFtFCEhDuvLumhCwaBQirAezgnq6RmDjkiFKxMTE7G4MyEiuoyegiMRVUtmsIZ7Q3KpW2Fe1/b0fITLxXaXHhlJtWTRJIEj3IAU8JAqWJdSVKT0buu6jMEIB6pUiPZRkaRwX4S1TtV7bHabm5evnh8fEcbsYcYkvS2//PV/no7H3W47z7swC3MRcgpkCBdWsFBIiQxyqkLGo9A5Njg0ClYIzHXq3a131kjzj7+8//Dbu/SFFPvL25/+9Ofv334vZRoDKUIwa7oihkjRRCTHu1yZmCLSLQWacG8NnCByODEpi4gKiopUloVPy+H53T/+qVznP16yFHNTLukIT6ns0U+n5eO7Xz5/+rnbydZ+eF5BKnUfMlOhV69f//Djj5t5A+G77/rx+fl0ePJmYVG3ZX91mxAtWktl0v3i34URYX16ODx9hejV9UudBQQthVSQnD4+PcrgUmYQOXp6K7OalaC4uLlQRl+6Srm8meftJnpvfWXlHuEWZHF8erz/9BHep6lq4XXxtq6llv1+a4HHL/eP918eH7720wpIEL38409//rc/F61h6P2YcNEQIVrRzXcX09X1i5vbl31t3fvT/ZfT/X2G141a+JfP3758eV6XhZKiN1HWJEnUqtt5w64RFNG/fPr49HjYbPev377RquHNLCNHPJOrVi2SsPCePiSHlImIQUPmGgoXC8igYBB5wEd2VTgBJnGK3kf8ECOsz0JuFmkq5cXd6+PxZC0qb6DRspc6vXz1w/WLN1y0TpKBqczMoyxJFgNcSkwJUHojKpli6REpwrVwEXZP4kJUM8CSpXBGRnRYBjkjVQROrYfSGNBKJkUOTuo5lsscAx0S3mvZTKUG+t3LVy9evyaRDI61R6wff/vb/Ycva7eH0xNYpjoXFQoOt5tX13/8y19SimjVwr23DJrrrkh1N8BImZOYZZirlSQp1vRmPdxFUThrqbUWrSUcqzuHK3OEI1kgqiWYkFmoJHI44zycAGJ4+LAC0tkSEQHy3kilyGwWdB4vMzErj3ZOyaEJSycAnJbZmhUpvZsktDDSWUoQ3CzcMuFgYbm4uri+vfvYToZAJpMc7r9+Krh9/ZpVR6gnuoN5W2YhIczkfIZYu3cwc0XGWOSd2RA9wlKYkK6lErOHM4MSbilFiTLNrHcSTFXh4uGZIEYtc+/LsppwqVJ6jx5di1IqgwieZpm8313e3L05ng7ZAuYI1/RlXb58/Of11eXt6z9IqSxKRJRwb89Pz9ntdHhee2eVx6f74+FbZQ4zT/fuIiRK2aEoh5o68bIsh6fltCzC4x2W3dySZao3Ny92m8v1tG5321o2SQRQKbzdbtfl6OFSCgVP05aLZkBYyjwG/eIZa+vupyKFIwDRUomCygaETNtezJvDbNYoOQLeGQAcETwge3OtwdqWRcfILftUqMpMokqIYOvhhljbPM1lV1jq8AhFQkVnkrq9UJXHh4/Z1kx180xGYJrnH/74J63l09ePz08P3rItlkidZj+0z59/BuP19293V9cFEhFmnUVPyxK+bmqdpE4qVKRZNLO2NiEfYPAOOCSDMwJpKsksTKmFwdlai3BloUSEeaYNdnx6pBNIPGI9LW1ZSgVQMyjcGCFCu+0c1j1chTtnb723qE7CQGZSengJpmFS97RumQSCRzKzUAaCMk7Hp4eHr621Qdm5uNhd3VxwlfEriKNIZaYEBTzRM8BUmAsQHj4AxBG5LouZEU0jb8TEAUtiDCTdyDISASAWFbGwgVLrvRdhR+ZQw2QA4QEmFik6WriMJLDALZggIm4pGGqLwapqmcEikaOg4SoCkUCKiKqEUAQyLdyRPqbomcSkRQXp1h15pheAEd6IMuPsB8q0SYqWkgkPEplvbl593HxYT/egdA+AIqIf+pd3H/cXvxXdbuYNEBEIRCbcM8OSeoJbj8oKAJwqJYjMOuXQffHoBgCBiDR7fPj2/sNv3Q61yLTdvv3uh9vb18yTsETSyLIV9QwQC505MUEM5oFXpvGEHSK1sGAFckxTg5W2m2rruvZViEmzZCxPj08Pj9a8bqbNdiOkFM6czdav3z4fnx4+f3y3HA910mnelrKVaZ62V7v99TzNu4udaI3IUpi0Xl5f31y/gBMTDE1kshhJphx6qTopMySnWq8AmbaTTJLp3bwk11rdfV0XN9SpOCLMIvy8uRASod5aR2RPIvaw1Y5FRVG6ewIZ/fHbw/uf//n47UGZi3BvrTdL5Ol0+Pt//bV1W49mbe1tdbdk2+73l5f7WiYSRYBJSTMSyrLZ7qWHG9raM71UqayT3LbtBhHTVFZbpnlm/fLl4+dobVkiLVRKAHOd57o5PLUR7yp1evHi9uLqWnXiTLOkpJR06wgSTnBhEXAhzgwadpEzCZUyzkfgs/RWlGHZFwehbiYSRHakj7kxsWgpEUkew2mVRsJ8e/syu9l66L5OPL+4eXn38vVmdxU5sH6eoAgIKwgiSKLLy823r0+ZwUzEnEEZQQAT5Wi8YLTrLJPk/NMsSFERhw+iJAuzw9zP+47huA5jImFhQSQzlRFZozPAJEEqzEngwkUrp37//R+u9y9b78e+ci3CUoTCM9z3+41oHRJO8zViJUS4ppN5yEQCdvNkSmJweJi5EbmqcK1EzkwinJFpwyVMQcnKkmjdBm9PSYg1MgikRYPCPcJC5Fww9nSMRD9FBjxyAo2pxkgJRwSltDAhkBSAhNn7aA9FjC+1aJ1qZotop75OtJdSE7Dw0ZuyjFrK3d13D1/urT8R5TzV1a2t3td12gRJMiuTEMHCAmCWytXCeu+JAFGAGBzR++kIgAHrRhASjkEeZOZMAJnJyKrMzCro3UApUBIQsbu3dbEeTFxkI1TiHAo+v3EsyD16a0QsRV9/99b76fP7v6sGcqoQf356fvj24d2vl1e3otdJY4PpT4/fvnz4BI/TemzNSPhwfG7rkeZNRow1X6QX1ggQh/mSnRHkDbYgJVWJmDKEILbG/edvj3js1nfbHVNp1j18qry/2J+Oz+aWCU66urhGKUG03+6s3zhyNYKoFKF0OLbTxeX1DRcJ96lOYLr/+vm3X9893n+Obowyag61DGoXDdAcRKRSpUJCrbdmrYiylkRSYU2JSGJhnom59yPSRIhLKpWqm/28u7p7qZq//OP59NDDgzkhSPj+Yn77w3dat1rnv69/pVzSvfU+svKUDs4R06BIEEstolNvSHOaajfPWOo8ndVnkeAUZkKM3UHi3J6PHOxeC1Cat+UUBA1rkSGsJDr4lEAkkXtE79kjPRiFwCyyu9hR5na6KFROdlIiLfV5eQwPZQglZSorcUTaYk0H6n5tiBQRVs3E4Mwx07qsH397//X9e1gnIRa9vL2RufY0RQVrktgZJh7EYEFEcAZDiIST0pEASyW17rY2L2DPhWRiYUR4JIg8I3sULQh3b91HewgszCTumVmIK4Z8KIM0BujRM88Sc3LiJCGGCDEoqDB4yCbYPZmEmSPPrUStDMDa2jOnOiEp3YVESDxJWJPQDcKqqkP6TcLQmolIZ5LunSBchAAJStagHHBcJrl+cfv2+x/f/+pPT5+ZSEXmsmWzZTn9/T//w9b2+rvvLoyt/AAAnopJREFULy+upqmIciRFRliCEEgzYxnNdY6wgDMlyMxdSEXYozOFsn/89OH/+o//8+uXj0UErDcv7t7++P20ncdd3s0soVojJAeGBimCwQL0TA/HyJ+PRyej1m1VYetB6RHzdt5fXnz5+JieA1QtiVj94fPD8+PjzTQHhXn3fnI73X/59Onjh+Pp4L1vN7vLqxfbi6tpUzfbuU7bIluVOhw3RJJBHj5w7yQSFMIzghkuICKyNBHVUXGcKpdRiTNOBlhJ0NFtCUq3zizhRoKqSM9SNcJ1Ilvs9HTa7OeiFUkjZY3UMDYPMIfHh3e/vP/t5/CECNyXZQUEwh5xen5a1+aW283u6upa6nRxdXl1df3i7oWKJgsU5GreLKKUqlORCgLM+4hPnv1lZKIcQNH64sXtbnNxd3W9LMtqPkjACJ6miZKen07NWngrMv305z/fvX7j5siQ1FHWYBox4YZw5lK1IAlEET3DmCkd3gdHZEywRiAjKFN4YLsH2CimiWBIZESceouIcaAn5sqlaLm9e1knPRzuM3OeL66ub+s0ITqP3EfhCBQRY+/ehOj2+iqBmxf7z5/uRcvqBogKASmcQK6tZSaN/ipLawEi4ULEOWTwlcdwe94Us27uhBiKyNGWAg0WSmYEgYqUQPRoEBYgxl4HmUBrnXS+erXLyB4mVXGmUAcRlaJEGZmtt9bWwsxlOJmRlBEYCYkMQAfBanyAUmQmooyVwh3GIAUzQKokIYgAgg0RAHvSCDsmkiJHyCMzo5kW9QweaNTwUR6qtRCRWw/GOHATM9wGvE6IxuYPnggngAGQNesiLDRl+qBymw+brwhLJHr0JN9d7HYXu4evTyKsXGKz7Ymff/nndxH7m1tWqlLOicZwD6ORErLOkgTq3TIzPUR4mCDHbKlHDKUeyFU4DZHOQjwMx0wpkpHtuPK5LQBkmjflQsnNVgiJiEoZdtZu1ruLKgs3i83F7qc//yuFPX1732wNwzxtPfr9588f37+7/U72VzcATofD4+dP7fAw4sCCSOuViaYJQqqVzBCdSFTnU1u7uYCk1oF9g5RkBFhISFxAVZQQwqw0R/eOZt4IEVYfvp16a0y5rCsHR1taZu+53+weP306LMvjctJp3mw26dHX9e3bH/80/zu6FtVJZGnHd7/9/eNvPyNSuDq8MEthcMqAQHsmSHSatnvVwuNqipinUrRi4OnTM7C09OTCcnx+DrLdbq6TTHWepm2ZNyB6+PqlZE2eoztNorVm9ORcW+Oyv755+d3qH97/0talKHczzwyncHI39z7qyUlCyKkWzq0FmNM92/FElEXKPBX3sOgEVhCpJGXvS567zyFElmF97bamkFo0glAIAR5GCJIcKJmBrQazQ84lAma3mLc3RaY1sxYtpWymaZ4KIzLWZHCdVSqh9W4D+ygQUYCoThNLaUsDXIp8/PTh3YffVLRu8un4tNntb+9e7q8uPZFZapnmMi2nZV0OLFykZMKshfdSsmgZ500QC9Fmtw+ECllvmcjsAiUVYhBx705g67AIogj3cIuMOkmZBZxME0BtPTFxxlBxt3QDi2gFE9IjjYjSaE1jZrC7G5KRCZYBe2CwO2VSjn5nsnus60lLEZEikjm4dh4C1kpEnhnehxqQwpWVABZhlAACPjqe5giQzMTC7bSC8PrtWyCW/3puy7FK5SLBtJfSFvv17397fLj/87/868tXr9iFtQCgJJUCkqmmRQcIxDykZcPtRsU9Az3T+np6fvzyH//5f/7jH/8oXsp2h0mvb2/3+wsuZVhKMiMhyRLEHDHIP0lCRZd1TXf5vSmjKiOmx6lpTBkI90jVabPdgWrvBsAsYd1b2v3T89PX/dVs3R/v779+fr8en2w9DsDr5c3t929/3F+9mLe7JK+lFFFAevcM1qlyIKiXUpjI+hnxHAbOICYVZabwGHmADGKtwoUylrWb2VR35pnUp6l062CkZEoKk0eLtGmaiKr3k5YwClGkrd5DMJVa088o7zAfNovFW+vhvWVaZd1tZ/Ow5oAsht3F5R///Kebm9tps5u2W8rxO+QeziWkbHsXYsYk5AkzDlJWFY3okcYgSrawUs5qiWlTX7y8i+TNdq+TElOCukVr3d2ZoQR4lHkjhQATLkh0H9ygMs40biEFFOQ+yrwGRDgxnRGXCbgHhWZEwgnQyhAQQ4hYlFP6sXULZllbY4ZIurkwhaaHJdH++qpu5vCc6q7UiowIUxWiwRdnQrIkF72YNxEx2Cq3L68eHpfAiPiksriZURQtguHYjXAw68Cq0yBsgMMHGcSHgyQjpJQkZ05KzkhSpAcL9ezpvWoV4QCN42MQLDwyVTSHsFEkrBMncwjLwLae0yxh3rv3nOpGz/bTPr4g4ZmDvi1FiIVIUijdM5lk/PRERDKJlCrqHs3dbEGCSVlZihRWpASxU1rvyDj/W8zCI7gmxhhLRhJFlZlgA9iXQcKqlMhAEigQ5EHC3TsRlUnNFvbzlzqapWqyJmuwDKwND5UwJxyFqezml29ePTx9Ph2f9rNqqZT58bdfo60/adld1jH0nmoNz24rKDIyPBisrB5Y4yxvEqLW1kxo0UjPsN6a0CxceoxOYmSypIKDIcRi5L0tA8kCGrq/gJtnp2ShWUjdQEyOlKoqkpmErqLb6xfH1z9+u/9ieUrEZr9BzM9Pp4/vfp72u/3ldct8OByW1lSLedZ5LsM5czhQCmsRGBybza6bPR9PgLDIaelJkDKVmbj0xLou6/PhVEpV1XQrpeg09WbCXLR0IyYUFgSxs6VFmpbaLcBcSw33w+PjaTktpwNX9bWux9V7bKf6/Pi6bi7qpYbmcjg8Pn6aBFo21qVFhECYWm9rX+bNhljKXO+++/H6xatapuHAHvUB4TJoJqIIBNPMWeDwcJCLImDuSVKmUg6Px5+//uPwfGQLyVK41Gk2o6enx1//+Y/Lm9P1zd2r16+tL8vhESpP69IC01Q327lqkUymaJ7d27zdcInKqqVkePRIN6BHRJxfU3APpEe2FqfWW9WKJAvKESaHap17dh1DPGQmIsKJRJIBUWHSjoyUkFoQo3LCJCFV66bUNsCRKQL33nubMsAJRvfe+zqixsTgJBIOH83EBFEm2toeH59aX/ZFgzLZd7vtdrcnLgoGKhElpRQtPo2rDTKHhppIPACOWurZhs2pyozop+7upASOaBBhlqoKTiYoJwADEYsM+JLZUE90EAfc0/0MGMqRCyR2hiSi98YqDIF7EpSESJIkPcNBZ0MLPMxgLFyEMiEySIuOIKcEMD7GosMsEOaWSJLC4EBGGNKIedxRIjwskUKiAmQMeXYgebPb39zevvtte3x+Nglmai1UVIiXY/uwfJh3u3k7z9O2ThwZRXh4FYSFRFsza02ZhStzSRBzMV8enx/b8vz08O3D+18+ffwgSGaWWq9f3u6vX6hutZSI5tZUWESa9yCq/51uJAFzqZmZGeFmjEEuhncPSzBIRhQARaaLy9t5++3h2xeRiEwtEwuB4+nxM7+35bQ83D8cH576uqry1cvLqc6vXr69ffldmWsQwhFBOTyoCnECjfPZuEyzcAWQSZFhCEYKkYiIeCAtrPfGQlKESavS2pu7jRx1kCdlqZWYkPG7wqkDKaxuPv7HEe3+/v70tN69fFNmFZVEmvfWO6VfXl1cXl7/+u7z2jsieTMRFRW3cJ3KfLH//scfvvvx+7lUYnVCN6dQpqJagjyDIfDsp/UUgSrKIpSRw1oVySyXu+tua5KfEcVE2zqls2gVSh5VPmFmREpVUaK2rGYts5UqOk3REp5FawLpWbW0bggiZdXsYSKKyN1mAvLp+Qkpg92dyUgnEhUBJxBMhDGzZBWFeQd4qhNTWK4jS8MiEMqwzCxag5FEnoE8Y9aG1A+elLKZlYit90HjZGYivr7cvP/w+Pr15bdvp3PLl5DJESAeiRLiHE/CyDGgyMyxgkXmaDfWKirdV9CwZ6N5a2sXZWZJdo/Uwsw8BAKiFcKttUjXcv460+ByDPcVQKIR0fvp6flzRuy3NwRi0cj89vCFiS4uX3hkesuIeVJ4BqVKFRHKcHd3yxw8T85zN4oyyDwJAEFCALLxfBjIKgjR4OImMAw2SeSRVLR4MMDMAmA4ocJMSEdchkGRQUKZkdaDwQlHxmgXJhHR0tdx4BJRIaIEkyY83DOMRtoUenl5d3X58uPhNAI9YW4n+/rp/ubucX/5YkgVjs9P0Z2UyqYSmEgRjBCinCd2Ze/euoe7SCFAiQyRsPTVsq99PcvxhMwNEToMB1JZM8NzqBzdFCrMTBnoIzgy/iEL6VSR0lv/3fhL07QB67LapGIe3nvvy7evn7efPu4ub6ftpZZp3u3FzTykFi4cmalFZWah9XS/neusm9b7+vhwJt11IS27y8vdrvT1eFy/6kR05El3QkoM4lxae3o+zKVs501bQULGkWHhvdZShz/BA55aOeAqoeqTUBIEORXtGSAl0lImopIJEKuWMk2c0nuqiijceyR7R9SSTMy63e8vri6EpDdXnUiH5KACrfupiFpzEhMRUCqHhXk6wOPUbhaqRUtdzUqYSMnMoYixbl+/fj0c+/F4qvPU+kmrRngpJcNJQqvOU01KZ2SSnInvkcPtnC5cGJLhwnI2Ulkk8PT4eDg+NDsmYp73tWy0TD1dlQXEJBmmnNU9xp+HhIl0eMWZXRSl1gkFo9mTke7jhWhhluae3twjlt6eTs/zxbVWdDN3D+QQ3SaiJ1GQkHbztTdiMWvfPn9++va4kS2zWdL28urm5Z1MpfdQKTTkTdnNY3ymOMvhASGP3novtYaNdEgGovV+fHzItWlVKinGlFSmrRYtXKw7szOomzNBpHDCelvamgDxwBnlOJhk/O4Q8ZawUeWlcxgZY1uJ4KnOa+9gKlAgmIKA4DMKAmAgIwOZMfT1YcxJIixMQZlAZjrMguBShBN5ZjGF2UpCkWQRRFwKZ4S7EzBXjSAiuri+++FP/+KRp6cnMZl09vAeaR7Hw+nv//N/ent+eftye3GdJPM86TyZERFrgZ2W6DZN03Z7RTp7xHK8Pz3ev3//z6fnh+VwfHj4DOTF7pJ18+YPf/zuDz9d3FwbIt299UwXrUKa5N26MfMwpyTBiFkGKSXck9WGKzJyTPW7W6SRcJXpzY9/aJEesT5/yxg4GxXWrx8+f/vyeV0X92QprHV7fffqh592+8vd5mIQFJmhonHOFek0ibj17j0aCxBjwC+R4W7mwZVZCzJjYJJzPOFLho8jp5AUKdablokhrXcQVCqDe18dmclp4o4QuNOyriLcDuFHnsp22syeYWsHUamFhNPj9u71JDPRf319erq8erHfbIsocWrF7mK+vLzZX17rJORhLZ2CmZMpJFkQtlISQ8waLIQlz+8mtr6u/djdCk8sBajEPtcNiHp6IkWYI3tb2SEsCUKGEKehu1HSZq4s3MNPy6pUWCiZMyPJuvdMMKnHCiIAu6mO5VFm7rb7x0MDkggePa3P8yQqkUYi5pFBDIKGVlUCiKVIhkVnpiSKJBMp4jDzUgugEWABj8diOIH3+1EBDet9hN9Gwguqw9Lx3Xc3Cby43RMSOX97OBAh3dOTKIk4YgiXQydBgiLlvAEFFZ43EhGrRZwDQZFEGU7pvXXiYND4IwNQUCTShtkWdMbXW+SIKHGOxXR6kiHHZ8YJkDCYzR2i03ZbhIngvSFCICP+BpbF18ggpBYhynDOThFYrA2wllal3JqbUOVR5YWznmm4zIUThEjycwhuVFbBQpoULNHdR05Oi6rUSDRb3H3EHAMgDmFR0cx0b5EgiAdZhouCaCgHKZMyBu02I5kpgNVX4Wne7n76478J+OHLh+7NvZdJPdrD1w+vvnsVHMwazWHJqLn6yMMkeFSWxi6YlKyjTjqAMVVL790y3Ja19x5epompDMn675jpsx6LhJhRRETUeovwhDAEyECPHPag6fxXQ2Dm0Tq7vLz+4Yc/fxB5evj6cP9ISBZZ1vbx/afb13+Ypv1UK1/dFCVmZdZM9vSra8zzzsNPy0MVZuZwf7GszVpEACRFS50qb9xs7U+gaC04FZEi3L09PR62D/eUxsTOjbWy8roey+TbqUzzIGgvCDouJ0KEqLehI0BrJqL766u7Nz9uL2/rNGUkmDabq+//8O/r8ZAeH99/enp8RA43LSXidGyknLF8fPd+t7/ezDuwJiCg0T7RokkVQUKUlhYrgzIcEnQuFvMgSLNif73TqnbolO5wjj5qOr2F98P9t68eBiJEllI2usk1d7vty7s3UhVVQYweMjhM6UAwJDOTzMnW5ZjW0/356f7+/j7Cn56el9OxqJh3z5inDaSUufzxTz9d7C+JdBbVEacCc48uWpQUcGhQBs5UAMmUYJibpQFcJxWRNPJwEG2mHUEevn69uXzJ2wsP01IchLQi7JmUXMtmPB85ydyeD4f7x8fT6cCUIkpZ7u5ev/7+x+1uV3QiSLjT8DxmsBD47DcenmxRqIh5rOuqWkWkLaf7Lx///p9/W5fjdDGXmS/38/XViyuZuRCQvXciT4IKigg8urXemqqOu0Qp8zC1K5cc361068kShAjLSQtIARKFEyfl76ilxsQiAiBJCBBKHj8iCSYjpKRa0GDCMeeo/kV0keIW3ZKVJpGIWNvqxJwOpFkkqyGZg2AZjnBiYeFA9oRudm9//LMHfvnHf62Hw1w1UoRyYt5USfLnL5+onfz9r4fFL/ZX+5urw6n1tU2F3frD45effvjxX/78726nx+enf/z1f337+K6tp1KmAbQV0WmzffPDn378w182+z0XjnB0D3MSNreMHEuczPA0Ig5PEKV3gFVLLTOJrutSdIxmMxAgMDNkVLz4zfdvl+enX57vJxXmFBURMQtrHQlw3b24vXv55vXr7y6uriYuwhpkiRBiGXtRKhQSkYmgtCLMg/0zRAsjcEgQLkQqglEOFRWCuq0UOTgFLDxr6Z1HyJmIGSAnj04MpsLJnsWRqoVS9vvLeVNspesX+/3lBWv2dnKLF6/frqcnzKW1hPa5TlLm1TBfbLeb2Ru6WZJJjXmehcuYlJeSnOAzQBbMkd5FN828eyjXYZMpZUr3bgt4YGJyNcuMqRYtM7MiWotVq2gAKweimXkka2ECBY1sVpkqiHrrzSLEkhJDI0FpsXhGpU2CL3YX4zQBYOBKkXl9MT8+H4kiyBJJlEBYRLZM8KSqrK0vTjEgpkyZRKJzliCO5r13F5GR0geoVGYiEk5HGF1fbtw9MyJ8JEHH7yHP30weppH/m+ePvLnc/j6Ww8Ba3D88U3JEBFxoSIZjBHt3F5ObEdFccllLwswNhMoyz9y8G2K871QVyT7uI2Mkw8TCmV4gGTzmeUqlFEl0R7j5NG8209vTegpKUChPQbzbXc6lrqeFEsxaip7dh6AIJzhxMvLMI+8SnhDSosLSvTvckzJTiUGqLEVFa8nx3GE2W737GBgRcSaItAcyiUU8Isa9IyWRLFpl8pQIZESOOZKosFp6N8oQLRROArCUjFARIaIYh7pkYkhJshjhELBSuby9Sfz5+enYnr4SohQBw/3Q10ORApE6K5KYJlYBEixMHAnLWLsNIM08bd0Wj15LJXh6X5fFe2NmlcpBQ+cixN3gCUMWliLk2USKsLhZBGmpHjEwY+HmGSri4ZxQZdY0zwgX0TJP3//0p4vLq//4//y/D4/PXIgnlfRJt5NMtUhPks2mTAM/q5SFWChClVr0afum1pIZfTlttslVIjIiPQORSrWZTbRXpdF9ycxa1MLu3kCJrR+btWXtkSpU3S3ilL6sa3t4OuTh1JajeF7VOdwt1bS3bGv0/fXNjz/929u3f9xsNsPqrawA7l7/yJnuPWRyeWfLks1rLchlWU9b3YDx6dO7ebd79eanebtLTiYKoIcTi8hESE6XqiqKcPOWmZWLSkny8UhJxPXtzR/+9c+//fWf/dDCWngkxWbebqSatd5OCIsI4pKeIFxc3/3b//j3tz99n4V777VsinDaQOP1tS1OrFLQu/f12+d3Hz/8Gm3ty/Pjw4O7E0067TkAREZ/PD3ePx82l9evvru7ub6mrGFdgQbW3+eM6RQRnSOnWpyHyGNgeaOtCzJKqZu53L24ac8Px9OzFlXWvtrnT19evz7s48ojx+Ahw5zBrCTKY8BDhDEWdvTjejw+mR+6uUzT3cvX+4sXjCmMMkMAjz5wanGmT0QQFWERApF1NxspUu7r8ddf/vbul38+fLo/LKt9DRa8vL2ep+01w9NwlgoFFxFGhkVYt1aLishqNp7I4R4RQSLEHsGUWgoLwZMZ5zA+s/l4BiRFJGBuETFPEzGYz8YGoiBCeDIREyNH0p4yc7SuEhawMPfIeZ61FPfsrXVrTCIMUeFki/RuSEIVFjJHBpnDEilsmSC+u3kZrb1798vaW5qVQlBiIeYqykhuazx+ezo8HueH++OpRe9TUYA/f/tUuV7uLhzx9f7rl4+/nQ5LVbZu3Y2lhNSrly9/+PGH7Xafw6XiHeFDjZYgGQRlIjPv3bwDoKAgplJUuTCxeQy5DgM9PZNIxMNGmDTDSiGt1M0qS1FirQCHr+5Npdzc3fz0r3+5ub6bpo3q4EkYKEdtn0mSaDy+bQxtiClAyTnGaMQgQIaObDQnz24bIABXJWth3UgZRB4jwBCiBQEzUwGQnt3DFZVVx99pkmy3W5Ho60pFeqwaylxevH0dGfP2kojacgALQW9eXJNMg61Qt1UarwYtXKbNOQiWEWmFhbWOVzkgKptI8aBBhUmMHI+lO0CiWivSKYIAScrTelTR4CxFVMTWNlIoce5eD8X6eTsIjMUEvfnuu69fvgxJKhGrVOJ4eXf39fPnq6vrEQgZsGYa4SOPBK4udo/PT3UqKUnExCJJrXctPFiIF1e74RB9fHgiAmUIsQgliFmck6VIIHzsniDCYyV4e3sRo0EzDr4DPInzeetcv3EnShr8HAA4v2XHKWe0r64ud0T4cn/v3VlEVa9udhj1SDdiHubAveJwCndHQEUzXUSKTu6IcM4sWpaEOQbkehySzycqSIxFECUpmIVG7CRybENFuRRVcI8kcARlspCqEiu0CKd6y2Gpc+tra6zCxKBkTi5CDFBSwMw9UUTBLOOgPbhMRN3d3cz76H1EWJWJWQOUIApEBhOCGeC2dohUrcKqUpDUWrNBe2RhoTAHJSuApIAQPL2IymiYjCQlkBms7OECHrSpIaDaXu5u7m6sn9aTtdVAbtu0QE1OQ4vGQgPYI8wREZnd3QI5drFuZ73xCPgiemt9HRksUpVx08hwHj4ireRByUWYsgDZ+9KspYNQwORBRIwsqgqK7k2JM4KIVKs5ZYZH1jq9uHl5d/v68eFbim93m4tLeXn3cr/fZHjvnZUkEjkseGV8KXp6t1ZlzkRrtq5WpAhVkmAGE3ESJVsOiDATkAQeK3IWZplLCeM5YhcUIQwBpUfrfkrK7eNyfVxPx+fHj7/hdKASRZhN1+UYoLvXb+5evdJJAp2SZHRXh7eLWYTvXr2ep823jx/f/fz3YCMmEerWhIUo3r975ylvf/h+mjdBoqUIaZznGJ7IESAJYjeSYBYlnDENTEjyed798OMfotn9p0+PD54gj1i9aTu21nnsCjlFJhCR1Lff//Tq7fd1szm1xaxRgkFFZwLSB/2azI3SH759+/mff39++FpEq2Izb9x7c5GiXNSsMxPANy+uv/vhD9t578aMiAhlEnNDQEGZ1n1x96J1HH6TyMyQGAtsESLKWuju9d3nL++eDg8bntwzTIjKaV179zLXDIugyOw2MmQR6JHoaYSEt+fHr/df362nR7MeST9+/+bm1UutFVlVpszM7BGAeyREuYhYgoSEOYncqfVgEMy/fHn38f0/P7375+P9I4UWpUS0Zm6UOSWVBCJDqyhpZpitHkM3SkXVvINjCMaYhFUj+nj3ETgTbjRE6kiLdCGNAawgrN2IGcTdO3qLjPCWxFollCncLVSLgGOsOeg8wfYeyBRhdxcpTAXJSzuaNeZSVJO9hwlJIdZSHch0MLNq60gkcbqv3VtluXpxvdlteK6fP39eD8dYjoxIouZBSRadWPb7TTud+uEwk4oWFjjiandxeHz6j//r/2DlDBPktCkEhqdEopS3P/344x/+tNtfqhCElTgAUKaeiyiFKyLcuwhr3bh7MEQhIpRs3YDwDGYGhQcSjhExsRiKjCJk1t28G1goM8Rt9NGY6fruxZ/+/d9vX70dFXAhjnTz4JSiJQZYITPDhIpnhg2e2ijhc2QSIYJiUMzg4UZjnShCSCXROnFKa88AzIN/1+WOkfYZd8QlPM3d0iQwchxCcJTIKCUB8uxIfvnqZZ6PCwng6nr39dt9FTF3swYWYiS7TqLzTooKC5DwQXELHfpZ+MAmB9jcI8MttUCGKRCdMz2WpCxSA27wZCFKs2ZppWh0Oq3NIgjCokyChHvnUcWzBCSdwv369i6B25cvafx0umcG8gKEFy9fjuHHWbBDxABGmTIjMy/3F0DePy5jgrrfTaA583dJ4vjeE11eXQxl7P3XbwMtfn11+/uOgMevmjHSa/n7+YnPvZsBrBwZP+Tvp6pzJj3Ox5nzSec8sjmzZ8dvgjPy1e3d/ePzfj+F+/hHIDrz6AAA+2399O1eSFtmhCcl5yDBu4dwtvMngDNuIVoEYmAVoEIhvffmCzExOJMpEqMLDQhJJhiEoOYtkNM0lyLd27IY3MfcAUzJSiCP8HRiGYyJ8zgkcwIZJ7GfXbeRvZk1k1KSuDULEvexhy0Rgz/jc00uclxJuAhHWohqsoxDYcYAcDMTpcMze29rX3PoipGqSiDOQcqAoyPixd3V1y9PYPIIBjO0myfSJYVEy+a7P/yx7OZ3//hrOx4CbgH30Ucd5WmwEM4vTJyb/YxRXGXSMaWyjKUdKzPAddoQU0RzhOi4xIaM9EW21lp68m5LLBEWaWOHFuGUTCRJIGYWYQ52QcTz87dI320vp7plZrgzcZmnn/7lX/a3d+a9Tlwrpmkqm2KjFiSlTBOQkVRVEOTmRKhlhsNaHz3nAenxOB90q5SEMweSlJWZLbunj+2Dhy/NEWPgNe6GA4XAddpBWHV3sV2eDkL96TEObsZCYEjML69e3d39cdpeU5Xxf0mg9ZWJiAfunba73VymbZl87Z8/fyR4rTNxAlBItPb1/a/b3fT67U+GTPe5Tsl8rqFRdmureURQBlGx6GAVFiKoMJMK282Lu1rL45vvfvvnu2+PX9d1oXSjTgUUGuzTvH316qfrFy/LtH1x+3ra7NwDroPJ1sOCncCUWbiwsGcsqz8+Pd1/+wp3pRrMNJWNbPZ170S1UF9PSpNO9epuf3PzgkCtt7nOU53UAiR6/vJaN++lTCCs4ckoRZiDXIiENK03CiLWOm/qvBOtyoykJfrx6dBOhiDlQlKWWDJHYEkTcOtJwsmEeHz+9vOv/+vx+RNlT/ft/sWb1z/Omw0lexIiB70bICYhzqGj4+BIQpJnJIuWbVr/9vn9f/3nf7x/9zOT9xaTTvtNjTJLvfjLv/7b7as3LJWUw40oVQnOGRIcJDpEUSJUy0TErNodHpi0BGVGMqRZH6PngOXQ2gFaJiL4iOMlcAZ/Q1iSDEimEo7s0cN6UJF61r8KC5N7tm5KqsTETlKDyMM8k4iHVwtMgQARPIqwgHq4m43Y12iIiycGKke01PKnv/zLm+9+Ojwe3r97/+nd++ZHswOh6bQpzKWIhjZnJPiMcBIO0zLKGwmQe6gqIsqkq+Tly9d//vO/3FzdDLMdj1ht5motBEIqCEL3sAgTcJ22tRaHEzOB19PS15WZgjIEySqoJDxq+IWKpYW7E50O7fR45BRzZ2UwZ7qHbS533//0x5e3b4glESPBDVDS+bk+Jgqs0q2bn1ikKAtKUgyU9ni5MlJIVYQoEpGJSApPIQaXxZqoTLwDxXhDjbFNEgWTsmSiuSnXaWIC6q58/fLt4uXd80OjLL8vtmJty+vvfjznNs/ngATy5ub6+eFrszVJROdSKpdRYi9a5t5X68v1dhulZEpfOxDpzgTPsBYsKIUiJUFuARhxgDLhybUnEbLU6oTCInPpgwkb/dQWrbs6bZDp2c2ckpWZFT08U7b77cySGfCMsV+OSPf//1XOIOjjv8vK453O40V0/nNeX87jhDCSI+Pu8/tncAZ2JpyJLq/2Y9IyCGmDZZ4U589qaAvzv/8lSnQmomdm5AB4M51b1ef//Pfk5nfK1CCe0/mXEby4uYmI/XYy8/MxaRBnz2zdUSpCUX5xfX1//1hKTaHWVrcuJAC309EQ07wtdaJxGPfRdsLIrKSbtUWrKrNZeKayeOT4XsBxHmEpNTMVnrRGOoiZKzOD0sLCvEgRKGeI8Mj0IiUsI/qQDSEDgST0cRDJEu7uIUWqzh7EYuCohRmYC2L4lkSuLurD43Hg9MtUA+Tmbq0tnbWwQEtZu/W2inBvXrgQ8+vXN+MH+dvnR/f0tJcvb8bf64vbi28PT9ZdVZQ1hfzM6iOwXuyv6lyK0LePn++fH3ZXl/O0jbBSp7luE/Bwy4CZkAoNGkBEUgYnCGSqJb17mIWDpczqYdQVoO698qbIbN3SAxHt/0fWn27JktxIgrAAUDNfYr9brkwWWct0nfPN+z/JN2e6e7qKxS2Z690j3M0UkPkBqHlkTxSLybw3wsNMF0AgAASPnyNdujZrSsLEpt0EpYRE57mftUnDBKrK5LF8/niK6Pt2JbNEeCDc+4w2HQ+vr28VFv0JfMrBUuw+t4PldGsGwTZFm2dtSpfz+dzU5jbHempTamtTAPcVFDETjWZ0UVWbbYLTew9GRKxB6q7pTIGZgQGEqog1aRqUtZ9//fX77//x19PHT42xLIsoYPvd3cs//tu/v3z5FQ2ATZOlnqHkbcmZ6Ig1Vp1w98XLbyinhZ/e/2omqlHldCEOAWXXdtoaGUIQZITC2mQa63ntU2sSgghCV/eVdbUtmcQuNzf3+8Ptw4uvP3/+8Pbdr713Ue/r0p9Wtbi5u//qqz9c3d6sEZPtm+2cvU0Ag+jOMwG1ydTgtMkUKsDr1198/Pjh15/fWpvvXtzd3j+ITLe3Lyl0nhmc5NB2k87O6NHXZgppQWnSTAQm6tERgsC6drVUp8tEbLYjRpCt7Q2tL2trNzc3rz8fPtBPYmgTPj9+fP/x3Xk9zX3fmkpKO4XTXSTbk91Eez//+I/vP/76Vh1L9I5+++Lq+jYLv1s41vUkSoFn8JWh/bqsRDSboUqYiDZFSLx7/+GnX35el0VVn1YPLOp6c3P71be/f/PlV/vDPuespgTY4muDTW0GG1RAKn1qqiKllNJBiE4KxsLI4v/UhQNE2VQVSmQ5L0JEvXfvq6+LirZ5VhMxazohiAl0RnLRzRBWc5QUbTalZb+Tu2eOxsRW7xFdTmJNpJma9X5y76ZJnHSKz/NeVSL6NO0CRKomQfbTfr45HHe3+/1tm/Zvf/rh8yf29SnEFmf3LlSInWOlmhC7aT8f9k1ihq7eH0/nvmJWC/JzX/aHm2++/f00H3uw6QzNMrSV0WlmTdkD2h1rtgyJQhRZkx9rWGuqppZNNtbZ4VCV1qaln6N7E5Pwcz+p2OPj56enz4e95Lpw8cV7BG7vXtzcPqg1pwMhJqhFi0h/EmKigMKFHjabGuBuOZIhLAdPZqlo49SmWRC9dzroffGOJsHV11UJInsjV7CHd7EmYqku7e7Xh/16Wm3Xwv3h4S68X91cff741NcV5JtvvmYlO1IZXlGdViIq59PyxTff/fTT31893L/99AlBEr6s69rvrw+Yr5DqFpQ2N5Lqk5gyQrVDVkbM04xQZCOqr2iKMG37eZ778nlqs4kIZDftIUv0c/Rz9MVsx+giEb4amtic0qowub6+SdeezWgxKtezKCp7XyGVJx0vVv9T1VKjBKWsEqkA8BwFVVNRqgsX01WpoOwxygpyRCT0k0G1FOcimgMYpFqgiyCp31J1vKMqdEAukUwogpkRTo3+fEX3YGgomqhY/bYLpOHdzU2E393dpNz1cZ7efv6g0ibbQcXXsxl2s0Vw7ejRiQChyIG+PcsMJtEmbSvvaVOOqk1+QAJOWU0b2XsCTJsa1Nlf3t7lcj5+/pxLFEFpzZox9UPNYFCGaiOMQTMVKFQg0fspOkjTphD66jd3x3CXUVIE4O726J7dqE6ICbqQCDAAy+r41Xs4Vdu8393fXoVHrtPdi+vkvsa6EcT97fXb9+9XX03309z66cTeScuScNP51csvJ9m1w/Hu/mbe7SE6talNEwR+7utydudkO5l2mWsQE6UyEKkiaI1oibAjgoSqRdAjorEzxxIqRI7HQ6/eeolwkiZCuJIMoRCSowSc1pqG92VuEzABkm3rhKcMDCjLeoIT0fd7m+cpaFjPyPmHPSfjaV/FVK0BKtKBgIaIiyDnljOCmn0eTfM0qkjQu2emY9YISDSBhkHVLPutwqObmdoUXUh+/vDpb3/7x9u3v8wUZ3z4dDLbffnFw/2bL27urlVi7QHE03pWkXk6CkxN3fvy9AhjdoTZtLt+uHvz9RfC/vT4wdcFVDTVSV+8fvnm1RulZe3j03kRBYDJWjN1uoCW+VyqtQli3RnR1bLqIJzeOKnq/ri7ub26f7g/n1ZoeJCri6yittvt13VdY21iNCU6NCZrohqPC4JqEggi0mqqyt3t3bfffrc73M7z/uXLlze39+GuZs3UESLaV5cG6LqcPbqoTsJGkSZqDKZ2s4mh7TrJANdVQzI0YHiwq8KmKQIByZ7hX7//y3l9XIMRfV3Wv/7lry9evd7tDt0tuWn27rHoJNaUayzn9f27X3/6+/d+WkSoOn3z7Xe//+O/HW+u1EzFmkoYwQhokGT0CLXmDFEJ0aDALKcDtWl/c/Py4eGLt6GB9eZGbm7vXr344os3X796/Xra7QQM1BQpFbVmQvRO1damScS9nwh2x1qZQ80xbwTU2rp2U2vCCFfApCWNEeyqRoF7pFzVUz+va9gkKioEfVWomapYkkoCCUWJt5OqyuBKWmtwB7uENjFptqydsYrMIuo+8jXaBM3MgBQwVBftVeYmszVnhIdom/ftfr47HP/4+cuHT58/nc/99HR6/+m9mtwebyJkiW7Cuc2397f744799Pjx4w8//vDp/Is0Wpv2+9v93fXr118/vP4yBL2HNqoRCGkQTtM0qarjDK4CZMcEkJrEPSvhPVXLzJrNRItYJQKpE8poqpPoclp/+ekvnz59/PDh09PTu3lmNiSpCel3tzfffPvd8XhDCAPNGijrsooINBJjRhAmpKtGM1NFeI/uqbjlEiQESpXu/bSegnO4Q2jTZNrc12V9Umu9Y3U/r49PE+Yms6rZRCgp3nl/e2QwItrOhvNNHy03t8e7+xsU2yCZF8lUC0dHrUDefPMtiNevvo6Iu6vrIjvyK4h051mWAyG5gIcZ/Wz3L3ai+uOP/xDw1ZuX6f5//P7P4VxXlzg1E5Xmnr1Dy+n0ufd1nkRi3U0TRJbzk2WntBroEX59/aCq2ppa5nqyjCGi8k1ZIJhgoJ5RnkOakQZKQBPeWTHhM2Jn+3r2opmlrSXKMCOw/clvnGWF+lXtlOPuErAM0FPwBjkaNvV+Czdops4kgsihq2QwwhkMhUTk+9Qv5uVJma1KyOpoeXF99+7jo0uIiFkTqq++Zle/QsXoTFG71qTJnozuqypb26lIsJUCTn009vvDHPvz6dO6rkHmmGtTubs5Sq0P9/u9eydJBUSdvTWFtSV6kE2baaOYIrz78SpxYVMzUXn768c2CcGHmxsmScUCnQlNGEFS1QEQU5tms4kBs4mE0neqEN6+vJMxR28Dipm42fBmbsPdze37jx8d6h49OrhK6Dwd6aKww/5m/2b/8PqNWBZ1NBGNHhBKhIlCczS1rS4ersYmpmgU9HU1NZM9pVOD0bURwUYVm0PaGm6m3demrU2HphQzk3Z+Ond3GlJwHegh0aZmsjcxSwppntuLVwmqQxgR4as1EeUkBveVq002TbNZQ8g0a3Q3g3Ohd42J5MnPIj5PrTXjGos/tZaUsAeZyl4edDcyoodpE8m4SadpUgKZ12OmI3ztzrSkZEKbz58//vXP/8+Hn39qsNawdIftv/j6D//yL//H4fook0T0Zk0IUkzbuqxEV01tU1FKa43Q82kRwxdfv7m7ufn86dOvP/9I+PH6sL/aP7x6dXW8ZwgAJ/u6NrOUfOwhDpjtFOKxWl1JTirUBo1I3VRoDzhFIhAyTcfdrCUcGXRfVu8UAftsAp4jGIx1WTHNbZ4n23ENcQ14D/alK9C0kThe3X13/WLe7WHikSN6mDW7vfc2URpE53k6ntoZ3QVQRVuWFaQgVMQZMQKdZgrIsq5BCjVNXD8vIgbhvG/HwxzwNaITHj7JdHr/9OGXn+Xrr2w6Aq0vKyLU0ow4qR8/fPjH3/72+eMnhj+dHu9evPm3f/n/3b95hclMzMzcVyCEImG7yYAgOhBmpm3KJkgymTVxysuXX8z/vv/L3/7X4/Lu4eX9w4uXLx6+3k0HVTZpRPSc8VvzSw0CaBdFygj3dTExtUnUPEedhgtkaq1ZowudqbNuKoxg4OHlQ7ZPP67runYRmefJ1Bbv2qxZNNW+IKvcQffVAbU92txSPwEey3qGmkojqcroq6+h075NkzVluJCS409VTafMOFibTIIea7gYnO4Mpa3BFD5BuDYDYr/XZne39w9ND2r66fQBwPXxuvdY+tKyebaZmHhfDsdPbXez292cTp+vrq5fvP7i5sXLw/6YHiTTAN5XazYd9ll/imyI7gJVGlSsr+F0M+t9gZBqZkZBCCN6jhcmQiTaZNZleXz8/q//+V9//u/nx4/usLBoVNUg14jH89PDV1+9ev3F7nBEU+kx2bT29XR+EpVpNrNZRKhO7a21fjpRILYDckI5IOHqUEy6Q9hkU5ZEsK80Mbamwianz+fwU5vn/eEgTR7Pn9Rk1kNre1JF5OZ6D6S0B5VaxbGbmy8HGxz+fxAewciGXE3lBub4WEgCosqk/Mbbp6CIiODqCgSPR03o8OWXX0WONIsQ4M2X34rqT//4uzTt65Noc4goKH31J+/LpLOK3b7+7vT+7bl7733eNVGR4P39SwKqY65MqiMyykuVnypOOV8nywA3L1Y5JSD1+vItUn9AhL8BMDXrixARVYiAqbUfhVxKgvA3YAWAqEZWBBDBYFwal8bjZfaruCDNqoFcLHm+M1Ha2OTwzbVBI12W+6hjEzEqbwQgBC/urj+8f1KBifY16KuAImyTkkKlsJuZiCUR3+MkCm0wE0FK9UDUHLHfHQQQ1cPxNl9fwLpfz6BjFSsBuzn9h0bEeu4iqtpyQF14XB1nZrcMQKlVFODm6vj21/e8SSYNSkZg8FioAcE1EEL8ZKoSiEzxLXFi9Ie76+hr4UUyuUNh1ibrKFLaLgFub67Ppy6uuttFiIrNbe/CQOzmRp/Qzx4yTS0JO2aDbghoQQ9AMWqW19Wxis3QsDFmi9Tz6RHkft7ZhElnQk9LmLQI793b1LjWuMBzP4EmatcHW1YFaQaBms4qE6KeneY0arPKz/QQiGnLhOUkVJ3NZkacz6u0Nk1Np72KexBh7LmnIHw9pzh1jiuL6KtIExWBNFMSjBCBiriHTI1Nxdjmua/dROZpOq/n5dy5Ogk1s2lngn4+PX38+PMPf/nlxz9POoXzw6eP++u7f/m3f//uD3/YHY866tjgncHD4aitdY8gu/feu6nM0x6CxaNHMHya2+2L+9u7h9v7G1XuD0ebWpumHtIjpyXSdFIKsfacrqg6tR3onUvQLQeRirZmnvletbr1JIl14Typ7XTaNagsyyo62TwpoWjBdVnPnU7AVE2sn1aSRIhHTpElM6MSwVCzabcz04hY/XR6epqm3TQ1ESAiUzcRdjgcrO2Xp8/wM4A2BNxC1ATifSU5TfPUJg+YhjCmNvfV3VdNDUEJKHa7Ns3a0T2PvCmxfPrw8+n0/jC1JJS1iRgE4d3d+e7DLz/9/I/1fPK1L11u7++Pt0dp2iyJH1LodE0IkdEWsKw9y5xCQieN3hniq7u7Trx/eaW7b1d/uZun3W4/z0Kcl2WVaSfaqGitYe2M6N1T7FgQjDUb9prW9F2xpkLvHcLeCZiK3r3YAVDVD+/eu8SrV69Zo9uwV+0BEfHgPO+FvvrZfW06t2laFva+MNxUGTyfHmc9AOY1pqaG0UDI6PSuZlAldJqmiLUva2RCJOjLQri1Nk07U3PP0eMeZW/SXNEjIlxcm5mZWZtE1ASmuD7O3UMV85y/s/ceERPZInSaDq9f76+ON8v6dDwedoe9TTsxCXI3T4zwNaaSTCXB6B0BpcCamPRYSizeJaJHrDoJhKatBzwi3L2fM9WbPR/n06f/+Mv//PN//U8s66EdHd6jC6lQiHw4PX5+5Ly73R9urbVzPDldKJQw09iKTgUgheIdnnr73gFVqJl59DkVKMC5texjoy9iEIV7NwBcRDrha3ebZX84zPs9w4VCCtTn3QGSg88pwsLFqiN3kqmBzd8Wo7BRHVITXotiCF78fTquHLharjeinIfIxWnn/1OqQnn7aeDNV9+++/Xnxfu6upk01YhAsNksmK4fviJ5uH9x/ulHUQvg+nh4/2GpHpURbEUUXBDVphJUppiuaqoxbTggwS1Q4IeqSk3QMsDBcMX1lpIj4+s9RTJtLSzWhoxcl21Rxq8RlYzhNb9plNWk645LvxUr7/WsYOeCw7IzKv/8mQMmtkrixD7Iapv60frO8UkU3L24/vj+nUr4skZYapYGEJTJjD188TZPYo2kyWRmKV7g4dZUQkkcExZvv/MZdJGtkLlgYk0sym0CEMnjAp8/n47Xh8+Pj1f7PRmyQUuSpFDuHm4IPLy4H+k2gaSYDkfiMD+eqmrWrm/k3a+fxaT3LqJtale7OXIkQm0da/FTf6c2Indo4Elit2tPn59MrLWDWWOIKBDs3j18jTCbqHT3SWd4jUbp59XJtt9Za7NmOkcQ7H4mmPKI4Xk+xNSyiiqMwq6INs8BU0HvHu6TTMHovbdmL++v3PtOA2znc+q+Rshq0hgaPUIDTUhG90yYqjXVCYSatmliQESX9QwExEVN6IzYTTuFdHHRKaPdvqzeu6jYNImKmDSdIijhMJCxLutkIqD3npoe0TsE7sEQca4pYJz1L6Ziwujv3/3yt7/8+cd//O38+UmVUMLsqy+//vqbb+fDzukizVqLdQWdDCY9IgjvjE5Q206b9b4CoWrB6O5NqJDj8QhFs5mAh6rN7Ev6kEmnprZ4zzuQtYiEpMJCBE0QsfQuYlObcvh0aqyEmGKoO/buSVgHA6XmP5m0HH8L0Kga2ZfKyF6NrERVQYSvvq6hNgU717Wv6y9vf356/Hx1db923+/38zSbWYRA5OnppJZWVAXSEG6mBNe+TGbT1ESbia6+ErTZmjSQvuT8xayGCbq32eb9niHeqSI2Syzx4f37X3/98GK6bkoVYlI29fBlPf/5v/7rr//559OnJ1Bsnr/85uuv//mP89VeBeIw6Lo4oVPbmyhTAgcKSJssnB60nUG6aFc0OMEUNYmbuzvKDdybTZOZqnoqAYb3iFYGUKfWmHrgCAhVFWzhDJJmotZEmhjDIyQ6714cKsEA3D3cP0siQwSicnu9f3wMIHp4SI43wtIDlhOnqGY2tXCHC1cGsuYLOpkQJsJEGVK1CSR6d1Vp087XdV1XUkHd73bWVESDQoGquC/UTBwyokNkbrssCIiIFSsgsxnA7kv6x3V5MhVwiXDVKYKUDgaE09Tudg+Ch9YMzPl6HTm0ElQVkRSPT/zsTXSaW/QehEoziDYR1TVLT5Qp/AjRpgYyVBg0KFZ8+vDhP//j//6vP/2P5fFpZ3M72LxrrWn0LkAHD9e3b7559fXv/+DQcBe1Ns9BUPRwnDxCqhOeClVakDZPpkqoQOjuHmKWHIipCVpwBZghblZreoT3TsJ0EtW02M12UIavTj/MR24BqWZiaAM0GSREZvqyjGPzmhyOemu+oXsUrgkA9TmiAk3qgoHNuZuUKtpWhSubpyuPX07wxes3IvLn//y7h6uIyqSGu/uX6eNURFVffvlVJpMYcXt7J1k0KwJBPSaqBURVDRZhqGL3IptA5mDo5JnS1yfLPV46l2jU0Gx/mBgkMc+GWQopcFuQDc4wE4Rqg0TJ3iY+Q3bVjyoVDaSm8PgAjgOaP1VPpyXzoFUolJhzy6iNZGlSEdsNr9dXCsmbu/sP7382nRCNASiF7t5bCmRxjUDkMF2pAdpABfFQOR73+M3XxmKJZC11pc9Uxp8VoEmKZoC+29srMq4Oh3z+/BpFTgN3psOIZ2uyAUaR1BoBmadORB9eXLv7uw+fbq6mp7XApfxmB4tNHOvMnBianH3CGlVc3Vx/fv+JZhyiHaSfz51KbabKiE7mwFuuvio4TTqJSjP0pcR+pFHckYwz2ox5t+vL2jgDPcIpKvTZ2jSB0kGlyjkWBBWtWTtIe3h5HR5KExES+yOfTr70s5reXjWpxVcAT+eotc1OuljzQSOsWfPoon1qid0o9BBCdj1iibOEB6SnErqqCNf13LuICN0lsp9LADEx70sKX3U/wQ2RWo4WwdPqUINISERQVbDw8/v3/+u///dff/5bBCE4+efj7uYP3/zrP/3zv9tugurcLGtz0ESEHr70ztVxqVLrnfq0oImZqBrXDrM2tSlWF+ycsSLSChg473aMHoFpNlP1Rem0ede0KSCmcztG59KX3a6JzMtyYlAVva8iMZmEGEJSNTu4+pKZHu19jcCks1SGu2V2RSLcezONRJvZqBJd1IDocYYgHJ8/vVseP/348z/+8cMPDByvbu/uX331zbd39/cuU1Ixvq6qKZMiImi99/CULUkraypNyXU566wQIEj32UC1YISLQHVSndrN/cv211/Pp492QJt1t7taF/7tz3/rLrtpvnu42+2OIbKu/Yd//P1//F//f39cdvtDuz4+fPnqmz/84fXrLxStrz24Tm0WapumqSkYSyoMQpQ2tdbFe3iqzYJsTWQ3n0+n5bwwQlrD1JbVk/q3pvO0S1UZMVpT9xWR1xIgHaBjavNEhndISq7DTESmHmqTHm+PqSGWmbmKnUUBlrKJqojePeind49gN1HVJlMD0A2wTEvq6h6M2WaTiQxVTHNz+LKsFJDiQZknBOhszTLzKJISL7tpd+hEBLuHx6piYEh4wNPehRPeobLfX6nYui4IULR7/3Q6ZTFaS6HtLDyhaNsB0+PTEkJRNIOKm9o8Hwicz+fFvdOn1ur1hd2XNURag4qZNBMhWpsiZymLrd47fBUxm0A2KGnMggaw6dQZAn36+Onv//Uff/3P/2hu83RL8TZNbZLlqWszQZzX9fXvvvunf/5vD3cvUtZGzVSaWSMj52yYJvphzt6b51mtTdN0ds8ByYwQMwdVG4HH05n01tBsFrHWuK5L7w5MbUqBkonizui+snfCr47HCBfJFm/bvJA8OwbI6XmZXRKomlwIHCGpWv9WCaPk1AQEVEYJSNXeKuhxmV+dPMFwb/kvhUNkFHlUaP67776I8FRQyi8A1nJ0uomKZlmscPjL4ZiKDhnOMTM3qgVkPNx7dMflxQePMRBKpZgEW1+11LdWMqh4qeCgJNLkykB6OQq6GAsMZid5r4324ca4FOuzsRomulFGvHAwTOZAAVGz2heGuLNCfo5KqCwyiWS/1IzEhUEnM3upItfXD08fz4yJQNN2/bDb3gjAx3dvGZIt9+7hpFmjc137/d1V8VYbaHuOMzQB2rZqIjWb8QIlVC4/k0Q7kiGpGY4FBLHhx4jwLPUdz8hCrCIiKhHV/S6SmkC8v70ieZgi27Gef23H7AItI88wc0ze9p/r+5us1vn4+XR1mNdVDtPu0+Nna6bQ6NTUgBZpU/N+hiDgConeI6KpBdTh1qZ5ykJGBlNWEcnmaSg7unsIzuspAmbT4TjTo5mp2M39vp5URMTyKO133M9XidiLFBUAcjOB5Mf3j2pNFasv7meVppjQvcdKdaggjAwRqNka0d1P69KESX+31nKp3d3drRlJ05Z3RbXNE9a1Bxeir6cObwZdc/lMmZNHTbKrMVxPT49//fN//vrLj4fdjiLLujaZ3nzzT1///l90t7d5KmEB8Wam0gBAWxbtCUStORcoSAen6BHhOguUOW8ENKXM1kK6h6/usZ5m2zedVsbKldKgVnciKOGqEHFArq5yBhH3+8PaIxldIlRbphNFA4KIDggJRZhaBgs9oqUCaiF4kmvAIRAxZrAUqMhZVE1+/fmn//U//p/184fHpw/d43w6vf3l52k+UkSbmkgwQBjEoNZMTIhobZ4ki2zhrRkp67KiRwTRoIh1WRVhNmnOTdSGEEhv0+7u/vXh+ENfT6LdVITo/ennH//+8eO7Nk1ff/ftS//yeHX984+//Md//9PTxydVkVgfHl59+903t/d3SaKr1vxqFckB2amDByAFKBkd2bW/UtVULFwRTIwDBnoXaRESqqpTBNa+mkKEk6lNDYkC3MMdCrOJkOXc1WnFrgcZEswE4e7mUBc5OLLykrWVRaFHzloCIDcPVyQZ/uHXzyKwyQCPvioFqRIuzQlFIAdQd/aI3qOzQ+BYM5WdkQIkEKs7o0eziXQIPCIoBHqs4YuIWzMVjYCvXeim2v1sNomSpEmjCMXFMlInfWAbE1XtpGjAIxuUqKRCTHv31XtqywdzqBlSK4iEuGdbRQ2Hg1LRmdN6JeGkaKMn9gc93Ff3dW6TiD49Pv75z3/663/9r9Pnj8f9tTZjyYFIwNzjdFr0eHj5+vXNzZ1q637uvbdoxhTfBoNwiqVb03mycIR79qOqAaKT2bIsS19tMlH27hE9QUPvzHMVHiLW5iaQNjUEe18DaNZWcj8376uqQo2kFJWR+pSsKhhccEAWYBaa0dSECDL18qobqHj2Z8jotxE7B6M/gnXZpFRqgnDppySg4KBzOdgODjiQSRlAy+VVpcv4fXyOPapUIkFWTV4rIfzMzXMU9qA+kiXrjy0vE8kKElFuMstlqj4nNp+kpclfKxZRLVbDlQ80gsrbpTMvYJfPLRTqhscKkUGH2wW2bqnBS1zya8x0GeHZ6T1QALlhgvpfaZsjfOQNVVQE015Txlqt1aYMxuz2xUsC7395H3QocmyjztNVKjlm30jRXVlfNSaiiKhqaNJIEMvqWcsZExlLFKwQSaqsSrAvmKeIk2ePHwzfNHoKY4+lyIaEfK86FapWLWN6SbrVSRTJnvmIrRgnl1okgx2K8IJ6wt39MFuENxMGrw9XT+tKSng+K5uqQLwTknX03prlpN8QUNVUvYd79KU342QTZlFyOZ2W83k/77pqh8OgGZiDlL4s/eXLV4l78821Hr6Sclo6NFqZvpJJ4s3dkcHH03lqzV1ACUae4XXJ4WiavKAHui9BTm0CIqGoAMvZGWbTbOoeDoU2cQaDashZj6ri0RHOjtVhzaQkh3qENmnpks/n09///ue//u1P8GVut2f3dZ2/+OJ3v/v2X/bXdyLSrOV4PwjWWNWFgWxEnubma/Sli2rTScVM4LGaCgJTU1X29STQ7iICbUXoC5D9vQx0dyQOm60Lva9GHg/NdM4rXyK3jKYQ0zYfIHp6/MwgRTK+Q0ikiXJONhnZCTVVbZF6fZW6lix2QQhguSkiKtL28/7Dhw//+ac/f//3v+91UmnLcib0/uHh2999c3t/B0h0R0CbajMTRM/g2lqbWl9dZBKRvq5OV1VtCEdOUfHVBeJMSZ/mEU2MDpX5+v7ueH/9ePqF0ZXN12icfVnfff6xCz9+/vDV24/zfvfjjz99eveO0qLJ3avX3373x4e7L3oHRdo8Qal5bAABm5aFlWT6gZT6AtjMIIhA76ECKKfJsvCCgFqbbDJrPXpfV7TsqXQ/97SNPToEAl3XrqYRzh4Khcnheo8RMmlrMgIidx8FlONrRDd1IYZfIXG83UcEiKbt6ZEGoUgIMtO0updsRjhzmrEgeUgJM22ALssCummQvnqnmJ9X1eYOKJsooN4gwjaZmfTVNRTwabJyCKqIAGSy1iCmOdqwM7V0nb0v3rvZpCIwTSjtQeeyPLF3n7TN0+z0XjO5EITZTCgkp2T3dNaiDOmRwzmZbdoGApqjmL0vqxjFzEWF+OHHf/yvP/33x/e/TiI9M0Zmj+ezP/lyOlOwv7/77p//5ZtvfnfczeHk1ChsYir0WF0QHo2qNFWl0EzNJKIHGN6hmiCvd3fvauh5V6ws8eIrvWiOLHynMICILipG1VhmY6TQQI6YE2yuiyQi84blhTULJkaaZuvNgYqyAMWoAklwU4UoMjILhWRiQzy61dmg7kFWf0Z6a9moD17KdyGqzRTpLn18PoDqIxrcyjN3NHBR+sUgpTgbzXRTipnmZ1dom3BmAwxZRTs8dD2vIgP/yMawUmtB5ThUdXjg4ScJIlFJ/uLwRBIxXGpSXLExMAV3pG7fIIU2L1xuegOhaUoUgixJQfVPypZS0cguj9ru8StyUCTAtACqCjQNTbWU8uNSa6bA3cs7996XNbs/rOQMshQmQ6bcRo5Fi0yFqVnyQMZkUTMXF8Cw87IhaFQargBEZZqKI0NBsVrTy/m4MH2SEhVZBD3g37Bn8ezksdZesnZ5vO+22EUcDnCAMpf0HI61PbHup/bhw1lUzMxI7919FZGWMwqI7t4RWWyhItc3RwDv3328uz9+/vyYxYH3D3e8uf3HP75f4Ow9iMamTRRhtJubBym6SuoJS5uxnpBg0o/5tONWx9hp7iY7nVcVy5R6iLjAQwQQo6lkzlKISZuoOUtIZjk/+drFZrpObcoi69XP3p1CD1NAaYKd4AyuQCPE2bNpqFXGuROioj//+Ne//+l/cDkddvvVGbQXr19984d/vnt4qU3BEIaA1kwES/fQEAg7FCJqDOSos/AlvPt6XtenkztUr65vJjs0a92xsDO8UdVao4Jo1pxQs70dcwaeqJsEjNe7qZi9Mhl16Qlmij9N3P54PJ+WHA8nah8+fvIIBj3F7FUhrcfqvpoIVKnBiLQYuzZHtr8h4yLtjh9++On7v/1dJGxSD7z44usXr1+/ev3miy9/N89776sQai0ieqzM3GEXuDVGN8nBhwEiNHSSaZ5lYazdRLVZUExbDwek6RTeo3eY7vbTfJi6+zRZ0zbPUzvcntfztJxWhlI+/voTXcL7JP4U66uHr//wr//+5ZffmjZ1N52stSba185saRNCNRs6zAymq9MpZhN9rUsU3oOmsBkWWiPzIto0tWaimGzqAoeryrKuCO6nfay+LitU5nkCsohamx36slzd3aAi3tjcTJodjFKG/IbKW2GLuMKT2ilwqYM4l+PV1XbvP3367IyObIhoEGum8wyA5/PjelrEm0za2qQqHjnngTrNkEYx5uxW6QYCFtEkpfXUm6nuJgGEoRoUW7urmmkL7+uywAwUh89tT4r70qMDcKfDKTHp3GSGINT7ujIEinVdCDYzSeUzTkADUvW31PHJFhJmai4SDHWPPkkD1Mw6PaT3fpptt5t3qsq+fHr/69Pjx3me7q+O8+6KkMfT4+Py+en0tJ+vv/rud198+82XX347t5kMekzadvO10LufmhmgvQXcs97eWnO6wKd58jXW03lxV20QI7Db7Uxt6es0mU0WDCa3qTBTUTKoVDJi7R4ukP2Ucw8uxRUyMjKQItuzyllLuNzUDAqtozJAT7n2kc0JqFIqoEv7VeU4AiG3Mzb0hUlWXWVVm3jUEVcZpfLlShOfa3KHSObJq3IWW7PuQN4ZU18yINjelYkAlOWENJMmBC3GMsgAYxVgXVih8ReXPAlZ4CriAk0uCSmBQPMvki/J35H4KfUNo5CCmkFEsg07YgCO8txZZFB5mwukqQLWqtcmbLjhLLN97poBZN0EUDVBY1VYYsQREW5qaNjScVJIKncViupHq0HiAw8M568iGolow0e1iwylH6nUdgQlEvpE75mdyenpl7RgrmJwaAqOtFVl6pJ6E5WWBTl5tMb+4YJrq99pbEdCbcjgrrhVbl3+oVoH9dlKjr0fKz6yU8jEJyBKJVtrV9fzp8fz/rjLJ/j84WP0ULPru2M1w423yH8+vHwI95ub6+3UkfHmzZtfP35oOhnV17jaT1m+XjSbJIsp4yiyjqyO7O3zB8a4QnlERPb7XV971k0lz93mCcGmMk2y9AViCsvmi4wHl2V5+vQ4T9O0U2i4r04PrlPWa6p4RMBy8PuuHVeA9Gnes9C7+MpsqA0/fXj/9i9/+p8f37+9Oh6naQ7q7cOL3//r/3H38CpzCBlDC5l1xybN4auv7DGpUa3ZpFCAK6P3xQRPT58/fX5/OFzv50M0EHo6rRwpaWX+F92dIoe99BN2Rzs/BdUZ/bjfV4HXs68RjOV5N1G9urkDebw2HeD79vrq/edPRFaXZdToEAq8WQMYCqpGdBNOis6gBBHI8oaVp76SYraz/fzwcP/7f/q3h1df7HZ7AcM7g1Bb0RPF9HCxBlEgGultEl97DmmlpBWiqM7TZKJeXkyjuwKqSCIHNFG5ub09XN1G/xRY1dTmyTBp7xI9ix3hSJ+xv9p/+7uvHu5fTPO1CK15CNxDJRUtAYWIhIt3IRpzeTQnqEiVfBKgo5TA1uX8ObMN3WOeafMsFqC0NlEahWAqCbr3MLWQ6OvJBKJ2dbyFyOHmkM4gNQBR5G5KflFV1FTSypDVElGEeoSnKcxtHZwu63Zv9uzm9vrdxw9NtWkLhwfpsFmJENX97qAUtWZqgaSstVmjNO9wXygm0gRUZs2fO7mulO6AIkcImAQIhrtP1hQpxxUd6ItPU4MgvHdfp0lVLEJJrr0zUpZUM9cFQXHtArNWCis0pN7Lmr5ASY2IlavBlWVuenRwajoHpNxAE1XE2ldfBT0iO8Zj6avq4pSnpwWqk80PL198993vjzcvwNm7NJVgNDFTda4gQTfNKDHzFS5iCuvucVrhWPrSY7V5r6opJT5NR3Khs6kK+7KuNpnO5iWamC8S9/c37t27RzjUMBz8liHZjN6w1oIUGBERsZGB0eqiHEa5fAC2oT9Zj5udosnHFGrOeDz9FqVwU5Q5FtauunuoCpolqkm6f/wmy98l4hiRNsmsFrtkZ575WAwOY5QGA2Bk/0p+VA6ACOGI82V4QI5SmkG6XFyaFKoHiI2MAUaRDYmo+H+jKRgUkSKDRGAwtir2LY5Gk2Lh8NDllVl6H6EiOStxCzaKAMrFRbIsBZHM8NsvUqqla6Cx9IfFUI2sFLb/HV4d08kqJWMoSZxlT75EufQQEVRFuQx/mjhNC0xkc+czxpcRET2Dq6peHhgiJb7jsmmig0chKTGUUFXVqi0flZMAxrD0rLiqpF7WTEYBuRjzJEQElRKUsdX18Bz7+YymKUygojQDJMIjHMMGHg/G8OPOeu8gBXJ1faXbYw8KcGwGAOSYrTGIo06oqL68fygWKnw81QVaXzCfMIeCQ7LqMoEvVLdSsstmiBbD0ab58dMjkbg5uoeQTTT6ObyLNg+PbGNfI+i+dpU2z3sTW3pf1yXorVFkbm0OBXsiWBE1lUlk3/tCYGotIJFQk8o4v333088//O3x87tmAnANXt0c718+HI5H7yEJyxCWEzREVCWWrJ6WvMo9bLLWfYFCVaY2Tc16v/aIeZ4joruLqrbmvvZ1TXnPvBfz3lQUlPlQOHU3z/6YLwstA5I1+5Il1dv1LyZMREdIlwDi5nDMlX48PfkagNcsx1wNIktDz6fzqT+JibYmzUj4ShN79fLhzeuXXPzu1YtXX7568ep1azNEiY7oEZ7FLxAXhK9hBIHWpIVz4cJQiYkksz03StwgUbfHyoBJSzfoEdRst5m/+fZ37Ot//sf/OK0nC55OS+XP4OfF19XhXH29ebj753/7b7//4z/v91ehrBYtOMllWUhqin3U7LscAqDhoOcMSEIZ9Lvbmw/vVxEnCZVpvpLAuqxiirYPbapCD5JXVwd3n1o7PT5REC1UmzAaRWK9uX8T45JI0ZUaW4xJJgksKXdbieSLfjGTRPLICymSJlOzFSWyIJmU0TH6cHv38fOjiqx9gSo0Jxu5Um0+ZIzh2XeX1RcxiZoK1RIalaEUoRlJzxgy61cUKWfbm5WynxAN6qo0zNOkKt0XRggpatpMSISYzpPNCYqFasjpZkoFRChGhzMiFtVMtUbmPZf1RBEK3buxS0DDdvOkllPRO0BTa4edinoP0gK4unnY764+vn/r3ilN2nT9cPvi1Yubq5vr2/ub+5dt2geiA4Darona0tdAV1UPKnOMBlWUwmAnASU9BBR4KnGDNLPzsswTdrNaoirMe+wIfHr/SK6qCmhIvHh1H+FCy/ob3aiPyu8M3j7FWipCzXk0IWVSNf8+ORCQW5iCEc3oFgcnooBSkQWqm2JNWYe0zBEYZ6cY3iz/ooRW6uQ3SAuXZp8iDQbkSFjwrAcpf6D+UznTUpWkp1+teZCXPiQOlkY16/lyLCQhlyLWAiuJsoCSkFQdXED9moQdHBFBhgla/FA5xdaAyoxIEBrlGKH6jDQtL8stMycj+XLBS17qejBGZNHzpojDCxUhIkYZH0uRTPUYlW3bIEuLBdlk+oDqF2BoDG4hIVZsySBViRwVN3gZUdHs/2tpeJjwhUwnnek/wcjKJVZTjQHmGBzyyykrmHLmuNQkZQtdxl4xNJWDQ9ZnfAGaFZuI/G0JndOmgQqzRMcDPkkhxcTfpqZJBQkYmvAqLCzcO7qQoaI2+s99HDkIGOnOKxXJrRQHGwdUtzHrjhIqShrkDB5cGb69WiQSqv3NCyF+wcCACGzkTYAqLMs22tKXUgBXN8ePHz+BUKAZhBQB3TWgAkcPoak1beHR177bzaKWam0qYrAmFCkN1dYmqDJEM7vrmGTKLIRQmzZtYCw//fz9n//0Pz+9fx+diOZnvbm9++Kffn/78HKedobmolAh10SKHqeMmybdwQA7S7CTq3dROqOJzfOBEcfj/by/Crpog6mZ7pTshoju7h5ocjzYsHiSl2l31CB3+10Bxa3fOLG4qhRDuNmtmn2IUWEzjqoA3M+7z/6U9KoHuwSQU/WeKHE6n5fT0/X1cdKWWZo2T7tp13avdu3/lBVtv9ddm3Y7hUb0iLOGg6Fqc9t5V6drg4p5eDgbRHrWY0lK6cg8T6qxLmfPadGWOXvMOgF07/vdXqfmEYyYb+Tb7373408///j3v0zFTsnUbGey0xYeZ/j9qxf/+t/+z2+//X2bZgKE02Wld5xBWk3vFFMNdrGQJuFOKGjh3RSmurg/vHoJ+v3D3c8/fy8Cbbu57eEUPWubtM3u/vT09Pr1iySkM0+wO+w/fPhAwxd3t2nTMtqUlHC5wLZCH1oxqACi1jKkC/dMjY/Q0nM09xbjMQIjoE/bVORNhlYqd7c37z+8d3bVJirikfcligoySQDEnDJjIg1awyiYhquYpBV0oKmZ5ljvCEKErdn84uWNu5/ePxKAEk3EJHowqKoWDVWOQdITGThXNRPP6k0LVgETKQYjJHCOfmpoEqRBTXaqpDytJxWq4LycWjsoGykeDsTUJlIIOEVsajZ1Xx9efPny1Ufo7ubmcH9/N+1317fXd/cP+90OMGLWpt5PEHSG6SQarYlTq+oFqmJeNX+RE3BEME2N0RFQmKEFdO3L1aHlSBVqShhXBH683qkeVGv09rD+mTOI9DfFQpAZ3VbuouBvZkxEME5YdcJeihui2kAlYkS+KZIbRd7JczZ/JCDS8GcczcH0IMuNAdW8oBupn4Jk+V4b3pCRmtBCGKzYXFSLTGJgiOkxhmPcEId7J3XEylWGMmgWVn9WpbfKd+rz3/WMnpbhl6qouVjPbDge1atZaprkc5TATZFESiKC4T6KjeujWJSGysYAFYLcWJDhgasdBzmTMi8ZGKDEKOBRETNLPgODRkqIphgCQL/Zkc1DbosGwC8pIdk6nJHpD45+8SSK0vbkwBBVBSMcgUsHVhI2th0S0SrEEQwQWARN0RFVw1NHigQZ9AuDUoTWxWeJpkDpQO1FMuICBbGVBiY+RRWlEBjoAiKEQVJtD8hybTUEKV0lmw1Fslgk40OmGONg2TDYr4RuY82EoKJQu4pGwYrBFAoECg0MA54rcsHsA63m58W2VtUqYRyxQJJZ27fmTbi5vv7l13ci0loTgWVZWA+GTEaDQzhPEl3WM0Wk98WFKtp0ir5CIE2D3TCZmveCiqJQawOaE2Jw0E/RP//09//69R8/zHbcyZ6i8+H6q+/++OrVF1Obmsxms6l29kyUgKIqUAmqSCuyAz7IaxAS4NI9PKZ5alNDyv0T7iESOnNd1u6AtuOxutpQt1cyVAE5msyyAjznyF6ILUjqVBWJUwCUJCmhKlF5TFEBb66uP777bJmSMg3Qw9e+UGJ/PO52u7mptSY2iVnvS5e1NXt4eFAq1DogIqbi3Z2uKiS6RzytpvN82CELuNYeZCM9vJtmAYoGl7Uv7isZKiZiAmk2I2BqwVClmRH0dWGEGa6ub968+eLXn3+OWOERne5Q7UJZe5+P++9+992XX7xRFSAyeRG9B9y5mmqbGhG0wKRKdQ8yC//1+ubw7pcztO9vb4+qdS/Jly+/APH+4/vzuiiojVCuvtLj1auHvMgU5NBpEA/396paML8aNkegkz5mMLXJs0iZdRFrSeOOW8sIr4i5ONsypqleUyejMA1R8W6Jwz7c3//400+k0B10bdVwINpUJPpqYgo9HvYnjyA9ulKzF8I91GiTmWqsKA0Od6QHoD68utMRu0/Xu/72KbiEA2y+UqA6NVYAatm/H76osc2zioQEUv1HzIP0UDFpagBpHivEKxByTNaqjJQIQThDoneXiHTNIhKM7gGgqazL0rkej4d/+uMfv/j668Nxt5tNm7SpzdPebAoAYiJi2pwBuvsq4SY6JeXkBKuYJGcJMURoAmFXhs3TQcxEp04e9jMGB81ypMz6mLSzTHmxwhDjllauqepdtPBJ3ugMTQbc2XBIHp9Rrjn+IMIFQRmJoXQiA71w+6yUbahOYVExkyTrwkdRTnkde1Y5k2W/qqQggskFVu2umWUr3gWTDcRSjkmf/2l+DQ6+gm86N78wmKAN4Agh0srsseBX0S0g6lenn80aaR3l6nzuNYCaC1BcUnj0oEZYzd8WVMt3VJVMtqiojjJnGdUQ4x+FMzIfEYgoYD6IkwEXnrE3QW7VyhXZE/lU3JRsNoZjZAkveZbNX5Z+oF7gXYHegXiGpcjcWTFkpdWhMBR2Tr5ksyrAJfWZ+NULTgXB/JEQEK2N12QBsLzniZkgW8purNiwUVWxPvik8ZQAIwD6CPKS+hyR32h1Gwgk6wFqgwXMIJB0IQoRZQekKBXPwEYuZX50dcPl38SQPBhZLclTJhwZuoI29R8VUVPNeYmFIaMw0KBosugejDoalzfARlxBoKqvXr14+8u7SacAiL5GpzBdoUDc/dGfVGBNevfsTpAAfV2Xx/208967xzSRkyKaiQbYu3ssCLE2NzMBel9Pjx8/f/rl47u36B0SAbu9f3j59VcvXr9WTLECk4igR4f4ui50Hne7Nlkg6EF62vZk0oVmrbWm4aEUVVKyVE1LEKo1ha/npfcu2u6upqK1hm3Zdn9Yw8qtslKR1dY/bEr91WWFE9eoqlnqkBVJDbx4fffp3YnRe+9Bmup+Pq59mdq8P8x9XTxgk4HoHnl0YRYkQBNb3QUyT/M0N5BYeiLfEIdaeHgPYQtnQ5cGi3CPZdKpBDMgzeaUDTedIFNI7fY0TR7RV+/L6n21CWrtuz/8sc37D+9+/emHH9798rYvp+ur493Da5unly9ffPdP3+2OBxHdzXsn2HNeGI2cDJWpVzrD+3p9uDEzawbRcB5vr8MjekfLw3qJlm5vbkn88uuP1iahRPD1y9uyzs/swlbfXgdXsv8WHqPLgpQKnoq1vOTSNyenItAszN6oNtmURlBcM5RbnDZu+GbHgyEvX7xgxLt3HwjpgYCoWGtzxEq4QK9vrsjYazqNmeTjx76/0o8fng63x8dPTxS21jQEzkY7HK/TILtXOiz7Mwg/Xj+cT59ubl+21r7/+w+i8eaLV1X6Q8r1zft3P4RHk+aQkA64CCZrbTJGgE6uaQlaa8j5aoCn3Ivqcdr1iNV9N80QqGXXj4loOsndPKe9VcVOzcyuDq8WJyBcF9EQVdVJINHP1kKgQtfUOfXo6GIGwgOru6nO0741cy+TrRTDLIBLqDWY9OiHY0v9a4yMT5rszeCScO8REN8SUtxC9nQdRYVDIRc9PV7M/fAdMcyr5LCKxCKVnkyp9UHsVGYTYNYIq1UQTvathrKaiVwIL0uBKjlPQM7cPgkNhUiQWV4DEVUDIyed1WEmSboHQGi2xOb9qNqPgiXbkS0ng2Hd9Lf1Lix+KVIEefxhtfplMJ91QrXySUZU1VmMHFOR1eU/qm4omMUwDFMzVLUcCCAYMsZCqGSd7DOkxsSMm+saW8ktu1+gaeCkIl0K0ZRrNbNta7ekjIiKFXyRTHglR2Uygthggowq+CtzH4V+IsaAyIQ7Zd0TCUbta6LlrPvgABYbbq4jV8mS8YwydiUrwCNFC7KrtqaujOiqLKBsfN2ARxt0HkCloJ0MUqewzkCoVYic2bQcaR6RAduI6GtLkLVKHMF+sZvIGT8bok4gOV609m6sPKimxQrkJqYf9Uzy6zjEhbdSsEpVVJLDDedIxW6EWWKXSogMJ56Wm+G1aGhQ05evXzx+PJO+eAQ94+F1dVVZe/Tw1lpEnvjWzGxS7+vUjvNuWt1FTam+LCpwUe/uPTpXArOan5fw5fHdh7//5c//+PEvXJbj7uYccfVw880//f7+5ZvdYc+IIn/DBQHGx7cfPn/88OrNq/uHOy1CpKj0oItSkfk9MxXARaUv0ddIy0z6ujxNahIy63xzf13XJ48bcVmOkgwAZNCemUuKyHB1LGr6VEREuCew3NCzyJaTKgmtm/v9L79+ig4R00kBmVRMZspEZWCVUk6x1poAjp4BXHGXKqYNmM7rKXtqJZUWV6byP6Aq2vbzERLn/ihY6K6QebcjxH15ePm6uz99eIQ2lQiuAguw01VkN+980o7VwePN9T//67/2vv78048//v17uh+url68fn11c5O9R22ezGaBxRJcHcLwXvUpJVmoErqzXV/XYWQRVdnHHAR0qUYqtpUAX736smgVgKP7dLN3G6SRipyYlb3BLFdP5daynJLHFsXNR5B0iCOLJYt0LyijEG1N1UQyy1sqI6IwVZrl/VRBSuUlQUAwGNfXhwguS4cGHezd2V++fvPp13dSKn7Y3ujqZiJ4d38lIte3Vx/ev0+m73B1NUKyfPwcyVAST9gdImJ/uMkV/PrrL0tDJ9miNDFze3n96sMvb23aQaeySX2FNjIMYoilr1scK8HdNJFY1h7BaWraMKsZva+rNtVp6p3ZjmJT27WZxHI+i2KamkSvRhZVrzZyQCgNkKAvEVSxZua9k92miRLnXuIIyDR8rVZQHaA2sWnqEee+CvXm5uaZzxgObwOgwOZvGJFGPvtoniVflFX9lDoTCgE3udbNCubuyhaV5m0mhI6LYWcBYtmOaLJ8uQiqG99b7knUKiRVTbNCKqSiifzRoDPCZbhxsuh4engefMveII5DTmbzPUsPUjL3MiKtZ89QbuwZxNm8QrgTtCJtjEEPrw+vqECK70wOgBhdVYVGolgXjkRWMqlCMlO62fthgJRiQw32IGuQpZYGUZrRAi4q0K0H52JrK1LcAkpB9UIllKitBcM9XftggDCaiKDGpEhGgkKAHBNeejBZ6zQsgpoqBIlmi7dCnYBtoAEGHAYjuyrLQJliO7OXb7rYtPL6IiJSGgrPWBm5dH75EBAavkQaBgs3AubhalLmLKkpjYxK6nwP85jLUh+e40tFSsE4ItxTeik3Kts7pCpgrIioMSar4FHS43VDYhztYclGTk3NjW2QPcN4p/0egkbPbH4VeankjC0Kg3GhYYp9R23vyHBJ4teRyMuK+OzxluPt/sP7zwQjJz0FbZoINrVGjQhaByWcoKTseGu7vjrUcgKRqC39TGC2/dRm+pry9d6fTp8//PCXP//0/ffr2mdrC6RdXX/1T7//6nffqE6mhvDIdkijikTI8eowGYQ9fG1thrNNE0S9h5KqAkrAVcyXfu4fpYnGLGLTZB49tGdSCNN0f3+FemtnNRpW6DfgJqQq6IlntVd5AWq70yIiQOm1Za3Q8ACoEMnO2fQ5D/dHQj5/eFqje6ymFmRfe5uaCNzjuNs9nlZr07ouETq3lh0b2kSbdXd4iJgIZcpJgSLk3HboJwJtam13s4PIQa8vZYODac4X2F3t1hO8u8cyTTunibbwbsL9fg5t67pG76K6389fffXlw90dnKKG2UYfo8RKumfKGOLakBJ2aohwZKE7s4hcRCVcRRnB6D2ylAcUjChKBgwZuF6ZWlEVqA3oMugZCMeW5NZt3mbjTvN+5mchh6D6QDw51DYxjaSMoXAAGgAQ5yiwIUtLnmYyogH3njPgRpAQERTR3dTOp/VwczRTQK4e7usxhju8eMvNt7je3N98/vRZLKeq5oS/ICVz9e49vGcRVnHr0gFLAAtclu72+DIibu7vRPDh/aPoLI2+dgk0MyElAGdnz/4K0Eklq1VATQcdUVKIokBED++L7+a57A0XYfhqmqWHAjOEIpNMzp4ZOQ82yfUUafj08al1t93kAdUJbCq6rEF2gQY6fRXl6dx51jZPATzc3T6LYjdibCxjBqgVtuZaFiqpWLMiigCBAKCpjlLYOivTnxHVUqn9Cm1k1GGihi4OvjtpU6Ycez1GhTfW6tA9ryEV21BO7T0ECXJZ+m/jfCNPmlRdxjhbAFNA7dLYAkjNZ6iGcrHsQt5+9Ti+Y62GJeD2VWFCJLnA+E3itY4rR/aC42RAlFv6QC/fWXRUTnoLVkc2CwMKM/0ew9dFhIjDN+OYvtuTGhpedryhpQ+zC1p9liCWCu5LAHr0kKXiVBrrUYCXb8MxnqAK7AqqbfiPw5nismuCsjnP7nK9ujw7kYzB2A1+LOXLLrucZ3EDJBCRHGkmSZhyA6OFFKMoZ2T9sgQktF6WGAI8+V95gMocZDlE/Qk2y5g8NKPqrjJoK2i+deSJREbqGQvo2Ir0f1p80iC3MQBQhJT4HTEc4TOJItRSS4HXkUpLoDbyxaOIrdDf802OKgBRUTNrTarYPLYbBFyQcdGBKfghqorrm/3PP3/OyMf72tqUPruvEfS8R4RnSyYAd5KBQCCc3M32bI05a1vWZV3O/fz06d27d+9+3e1sf5iXpc/Hq+uHV7e3L1R3AjVpYuroBDiEKuf9tN+p93VZzuvSAdN5zlK4plPyZkR4rIEOwNdOsabKvnZf29xU1AMvHm42w1QYdPAqpclcN6XiLTLEs8YyD/xgcpI5ARhCjQxFVG2YXNYZKYZUNrrv+v4aW1Y8PF18o2mbROXqMLt3CAKakz3blDoZDY4Ata7JkBwHIpziagrt7RIUYiD4YdtYfQwm+/j4ybOxHmoq2oEeXWJWaTuzxc8eC6M1m45XTSHO6PBEEt1paOIRE8SoLWMLcSI6RU3U5ql57xGh4zwia1V8DfcE4yJIYdlNKXVwy9n+h+cNJlmOlNIQSIJHRmNlWTZWzCyXxY9wCYFo1v2GZwWtgLSUEZKcNoOBqkZpgoxyy6gwKNssI3M8mQ+S6h5NYy9g9NjN5usqbKjqARCSiBgb57chTeL2/hqqN3f3QMqQoaKNjZXK+TuDxULx3gIisRqHi6jDJiKi9y9u3r79kMfaICkN7AiImkwQgB1ShbamhMjSF7UJMq19EZkA6d5D0Ka89hpOumuwma6994jsgU+TkgBTELECMGu7Nk3R3X3J/fDu2maVNre96kRg7ScREB7iTUyhy3mJ8L7i9ZtXw+JxSwI+i2xJlBGsg1RaajbWfPgvVGTN7M5G0bHJ6g36AcO3SNWNZq1jFRgrqRm8l6mvAonYXHGikQQJo/kfyOOaaQiVjS/nxQNRUe373AZIIZlLqTKDgTDzl5YqbYIf5gS7lv53OMj8Rmw34eKAt/UTkZKJucwYulyAcQ22ODt3wUcnBJnt65J5MRoQtTT1ocxSs1SqxbBCuUGIkaSICIhv1nIj1SItwPO6aFRUlh+O/++XJFStOsboazhDo9AhWZuVl0qfqybKZi4qYcSklzfCIO95TXIO79V7HDng9Dl8HHm7gcNyxWMgGdYKbxDwshkwpI7iRgwX2IwgB3TDgBxS+z/wRC3ScFi1A/URgI4sav33BagN4P6MEa/SIhmYI2P2PKfZFJd3J2qg2bNnG+HfOMWillYTALA+g3TgUDoYkUgmYnV0eJTlq/uxpf8wolaVMfdAJMdQiVRnV967Z6em3I5oOuuHh/v3bz8oRabJdPII5xKEGWAqEF/o0ds8GbWvq/sZBHQGxN1VoNoyyFQhe3z++POvP/3w7qcfP336MM1HE9zdv/r693883t7vrq4ZYqmPj0wHBtdu2kAoGkRsKu1ND19z3A98agaIBBhY/ayqU9unpU2nNdm0n5u1Ked7YPBSOXSvjkEd7+zYyyXUwajV/RvhdcF6jg7NQkFQtLo1lwNLFnsLlNm5HLk6D6P6M9OdWWWI48E+fj41NTMTSEmamKzhChs5blGxHj2kkratTsuoeE9KGqN6VkShYsqH+9twd+8R8XhyE2nTno6+BhCiupsta5JPywqBqTLQfWnN9javy1rVogrPizq19OGr+93trXvHaHnN3GmWvCWboSRVIvIVMrdY1jthF0qdtTpiatmHUcsXr8J5lNkU04hhuvFs07qLRER4LyCiKjAFLeckbLeUDPolNGRV60flxaU0wdCL3RMEOIYP5roTRATUAaIlgbI1hJf/0GfaWYWUbCQN0h8NizfilAuPLcmpRmr3yCWPgM2PjRojfXhx//aXd94DUziFOSytmVJMYWgRq+lM8LQ8qoqqzbt9RBRdDq/QTidVo4dSk61Y+mntS5usTbuU5jWxCD/3MxkNjSIBrr42QVNzcne4luTdaEIDwAgJN8S5r6EuNjfbzfsJ4M3tTcVYmT4fm5TnInFAUmIpZK8q21XEaEAeByWjwvLKiFLZck/wWNdvLK5uZQJAquGV88uwsRzBb/qyC85673mfI7cbeTRQnVEiCUQygSCyFcZoRdscUfCGTkjSJQWpyxtgGK10UWSkwJXRKPlisXUU14eh6jYL06QBqnTMsD6iQrHimUaRZv5Od0dsvb5Jdqa6slW1SL7dgHdlzkxVGpN3GDGVXKxdvQ3ro1CE9oizw0MgHLq6HPybe7Wis9g0efZcuqUzwh0Y9bbD2W5XJLKNGeOWD/eP3BUriDx+z1aKogBD4ED4AMvDwgPAKLOs81cvxeqo21zKgBPcNrWqpgWGUsTJ3dr+IxvWJBnwDQTXjpWBAlG4tuxs1uGRNV+q2PKRRJAaiyVlxy8hFIkaalbnhQoqxpmJYLFgwe385NthEJaVtFVDtmjQmcHtRoxpZcYGQgpIaGHwaveTmvOh415g4FkZNpL5a7QSK0R2xbNAdPUzkuE9IyMCN7dXp3PvfU31hNXPpMzT7iJaFUKPoDPWHktfvTW23dXSHeDU1N2X0yPX88ePb//x9z89fny7Lkv36Gvc3tw8fPXN3asvmu0ky/oB5qwaSuZ1RcWk5Wmc5p1JU+WyrgAmawRTVZWAUUxnj2DIvJsJEt7M6Eu4145pYcQYtHTey23T+SyPkUV2uf9kJL1YXig0FANiJhegUpmK4MgzEsIUj95wTNmcPFb5u2qmUNrgrOMQkYf76cPHx4WdjrnNHo4IEQ2GI3a7w9RCW2vtYG3O/HKL7JwkATHbxEIhyKk0VczIss4U4Pognz9TWyPChREwsaaGoBMQzdPbuwe7IeU8QxstdS+hJVvVnRH39zcJYqSB4SKpNSASkBHDjbo2CbIMQRmtgoaRSFJGcLHBxoQr5Uxy8hWLAaOIbBd9QAEADCYXEb3C3JGjALANV+EwD8yZydmqz+oVH+l+VUGEifglqC34Vbnrsjrh9LLEPhid4douCm8bMVDVPCid1A0Rc7OFeFaiSGavxUYoXWBezQqrH725u37/ywdPEXpQRW1q4USEQKbWtE1BTpxGvYaDmVuLYBhMRNfVhQ5GsylbvDxF/IRq5ourtBAE3ZpmP2NQlnUlRefd6GchnAw2mRWtB909+lqRbODm5oYA0EQ1S9JYRn8o846AQJL6EG5Z9oFjNiyS37uJn9ZFY0KkgT967wQ0h4eYZVnP+JwMGzmY/BHgArYFOpngz3oR99oTQYxiBWaS1Stkz5C3p66lqpkBVqZkGJ2MoEfNewlzyHDzgzAov1kGPkdXMEfaF2GEUV+ialtWa7iqdCkpezlOj+iQ7/AREVQ7ODe57c1/D+aMtfhJbTOCoqweHrNLjmPc4IIMgtCa0LNd7XEfh9Qi8tNQVFRxE5t6U8EDyYOdCVpJm5YvnaTrqP6O1CTmYMmGNDH92eKkE7Bq6hPJBUCmlrO5PTXEa+lrSDgvdTUV8w5nCoysTRreKu6UyhQHkuSPUs/Ji69FKOWPK3Wj2sbqs4KmMYVDRJSESoCirPsUCTgiXRATfcogpTCOwWCqxtGq65M09CVIYJX3yoDsA89UwG9iG7jdrNZwbyMe08p21btuS5SkYzDnskl1s3t49ZgnwExgMij8+mjdzpWaDk98OXVbdFEUWZkGkvtZzwtZXTINUKHRmdWBHYye8zpgauc4nT6vt/NOtTVrakLxZfn04/d/ffvLL34+MdR01+Z29+LN6zdf3j28EDMnJxVtCa0lQjSnTuYMjF2DwJ3NWo5oaCbuOZbBln6mhHImoU3FrCMn1vZTf7o7HpOfdTIYiUtJZqffqLuuAq8IppQSY0OHiE0hgpvHU5nEWLF6RQJMiSJUaU5lCVJ6YqQWRwcAR6mebqh1aHJurg7k3c3x1w+fCXiEsIDXPG86olI0ZHg6+papTW6ubjhQJNzFAFLPWU/g6nr+9PFJmyKzGJTVXQU51Ach3hdxmdteESk7TKL3VSBNJwa8rxBeX18lVkvW6TcJXoVamyQxVjFVg5MZFm8TsqxQ6iKNNiB4Gp5hV3C5hZsb4hDnwKBa848FxeXnQX9mk1XkIm8Ak2wsIYQhdZdBHQSpDGNQfk5TRWpoKwyul+HJC2RlgAwXuxGA4/Jjq2UuuFwhDjC04YKXkoWIkMgXpI9M1tagjsuvL7tzuDl8+vRZNMckOZYaLKYiBNa1MzyiC6T3jnhqotJk9Z5xX3d6D3YKRHciogGEOpR0RoewhYAK0WYMohu0tV0qYyCwrCsQgEesU2sR7GtfJUSiTSZA03ZzvOKGX7xCufTNucFZibL5MZIhF50SybbN8sKAQE0B3Wj/YebKKIe79zV6B0BrUduIwfMXkaEVatb9HOzsMKGjPiOPZ5Tc/sYkVaskh3kYZtqD0GEctlqUBFN1NYYnwPZ64zbrSJFkzWoaLHjpEuHSgQkia79VZRTqDmmQyzoW+C2QoAZmkB2RiHlzAcVi1hUQStYVlTpFohn3kBAazCw1Ap7joIImHJ1Qo+s4sUGaVkKFZXsjQrIhbNAqmf7VhBKDLcClxyqklv9ZR2T2OatK6ss9s6xbWpBBZ/aom0wQ3SQTB57K7fht04+qiWUDQU4FGmv+DDWOqLkiJwpCoM6wpA7rRGwczebrOY6yQpLXzIM54pvk20VGTTaEZbhjhN/DsD479wM7imyiQrWnua8xgkNk4W+9SPHgCcxyNHdWO2VzZbL3MhC0bNLDo3JJUJxfIfVq6JHMOwAR7jlmLou7VbdUK4jwRJQ6zEFWK+ZpEWZl8IBI/9u7lp0uaz8IBjIb7O+u90+r++pJ1atIpoS0tSbwBQi4Lx/evv3b3/72/tP5q9999/Llm8N+Z42fPnz48W/fv/31B9Jtah7NdHrz6usvvv56t5/b1LLWnWR3b9qi9xw1pRBrMyHuXSkmE70vWAm9uT6MFIXN85F1ZyAinx6XbII6HndHmcuqjNgsxoRScFM8xyCz5BLJbXFOqgOLVrkSAEBzjA/LOFZtBrZDneUEMrAvYwu5o7LimVoyVaYerNV8ygHOR2qevL/ePz6ePXrQKHo8tnHF6mGCDgdDIWhV3jFafIq+lKrnyJrCcdxJSMFdkdu768fHRxHpERDtKXctYlnpZ9ZsZ2Iuve2nIPraBaECsIuYie2PM4dIaMUDeRsHE6FmpaeZlsQd6KxAqtIQWzFBrWOIaFUTpHXIibU5BVeeaY5lkKt5GwPbCS5QKdtjpcXmZuI1EWt4IpqW1KRZWr8Ll4nI9CPH7LsR32TwcakMGOmSgJShu8T5Y3NzQfLDt7BDLkVAF9STMSYHHh6eSACmJDsHmV/Ydshv+Aik9vu9aEl/PX1+7OGBgJgQTUREneyktCmidxENLb/jmdkxSqiaQAFasxYqUzPbKyYyoKGTZf0wnYsvu3Y4HvZkrOclej5KF3HvnSGqswDWtFlbe7++upFLc2xs74+NxkwbJLaBY9S5TwhY1MpGVqUrqyiw9jkKpyAG5K3jMVr/nXLxScGqy9XtU5PQeMatVQjoMsB1ETymVVssBUYjJU757GxU+Y4wCxyr/iMdzCibQMVgrMucPIqBbBgBKElNFdPyuCh+BZKzPghSxlURjQIGVCbLNYIcjFeSLUiPEWZsIDI235eGMjwLjQp9MX9IqAqYVAcTtmCDG0DaPGe6OSkxNKEwImQI/CezorrVcxBERaB4Xo88TGaSSTkFI3kitQzdL5YVF5SMcV+3P4pQDc1fugV8Zc7LemZdtGmxegKJwiBZYzuu6sWhckRojHB2qm0zspNASIE1EGjNMlhGRSSSEjVDsRAkoaO+MLKmOV00U4F9nMstas2/TpYrU6SD5sro6HkINrilpK2LxBt81biJFzQ8evEDWYsHVupsY72QBS4ZyxVyLySqG/rpiExsoIiUcuTbHeh9Y1vB1GsYwQcKXW4IZgA0Ih9PRmJ4vEogtv05NH3yUKQOZPcUtkmEbUbwfFq//+v3f/6PP4VMa+cvP/10PLarw+708Wl5PDVrbbdHO+wOx7ubly9evoGkEYR7tyYK60t0OYNUUaOatZym6r6CrVTJBXcPV3m20nXoGOmWAc7NzbF2E8MxAghkI3j6lPJyaalyHbZZrttJBkhmO6CoqhlGWUjykwCiV1Eknn0JBJph8zM3PU5NgtEgVBUtVR6yq0cvJiU27idAHI67z58fA3p/dyiLOiz1s7tMAI3hAxBIvnRWisJydaqKpYgQGb1vIipyc3ebp+79u08iyr5KBvJKgzE0nK4RfQHFbDaDSXhfBdgdprQ4mQYa1FNxknxuGovD9GGLpHLLteKbO+OY/ZYFc+GpWFL9ys95DhlWPCMrHQHThZ4e3IxksCFFwdVlKEuU3SOCyHHSo59ioL/KTrGYk3zqzAuXDtgWq20AdsTzImNmD0cpgIhANBPSGZ2YUX8j4KhjsWSYy6SIBKqSslJ5RjURN7YzIci+lfDeCRgsE+nXN9fv333u7lAYqMrWdLaDUqDq3kVUmjWir+H0nKBrTU3Z+wlgawB1slnVevd1PS1xnrgz1b4s0UNgKQskDBGYCqF0JHBXU7OWR3JZltuHlyOYuGxWLukGXmTAyhj54vIDSYuqoCgn0Sz7HSlkyQJDBzHSmlvsrkrYcP1BQseojIx9KqbWkVUe9U+b6RxIN8GopmRRQYDhrccJH4dBhvsHh/Umq61k6NBv1378sFasM3APBs+bDbSiYuN6lfPafAEjQsM0N1+AYTFT6JVFMo2VQT0eR4ohmapKgQUAf8YhybMosAJxRvhI9MDGamRkXEc6Njw6ML6KWJE2uZLo43kohaAyZVrB5DM7W9tZ7Gh9Q25uspZWTPGljyeA8NGhI0WwiqQ0biaLwaq3YP3MmLk5at7UxGwUP1W6MzwDLVWWpDiGLFBBnDwuW0cVKlnp7oCQ6V0muxyytMx13wFcslvDcpWdZJURjpwsISK+/aIqic8HSGaTlwPM4U3Gh6gqJHXrJTM+qFAYNf1HQNA4zPfF7mzt9iNIU1pWutkzYnCrOihw8mw/R+grMkZ2MLLSQit4HO+SckZZe1a2o9DzVoa9/coxhU1ElRIymGzEYdeenpbWJg/42gODShcj0Luezn1qBpXz50+fPryzJjfHQ3Nak24xtcPrL393vLo+Hq7m+XA+LyBUtHsQvpt3JtqjEzXTj4jTeVWTpqq0DAKu7w8ctz2vpwz3ghG/lUPL5eGWW90UpSXPHgZgJUm/6DWIyFDVQwRG5s/4rKsORQpxI+cuYKfG22tRZYDE+J7ceh8yQSEUCfGktrFV5WcoN0p8ROTm9i6v54ZFI6oJcsM3eC5BlucsGPA0x+X+n0Fb0YvRGUGfQCAPL25J/PL2nZnQI2IdRjiyV0Zp9HXt7OKHw1FkCBwBqFYsrbxtOtc6XiOaGICtAP9GIMsIOZ5Z/LGbJVUBICTUg+LEheeoCAPDWtQRKQJZCuUBgCVfKwVo8mRf/p5Ju1V5X/qsvM0bu1mFcPa8IxcMl+HiCl5hVDoMNWkAqf0w4BAZDO/uWfmaY1y20qzx4sPyDBMCoSAplxEzlQdLIfmL8cCozqEkugXv744fPp4oEe4nP4tDZEKDABF9DRFvJk0AawYx9kjpFGEEoruABhFffV2WHquI0Amxvup6Xg+HffS+LikMU8e6tVkYKd/V2Xvw5Rdfp0huuZli9SwvkAAhHGJOZcCUhXcj3HuiwJRQayKKkZ8c+5j9bMWPPTdxak1U1WO7Z/ndUirQG4UpHgMr531SrZEHI34n0zYXDL9gmrp+o7ISyeEpk7ERjEpKjAwD5WIcqktf68sKUozDV4AjiGpdT8ODZ0TwxgYhyCZAyte6e/cgS7Y8DySfG5p6iPp4Dn82zKds0BPbb0/3pGOUZoVMojHo33JXfOaLE0uV55DRtSDMIkHVrfC5SPPf2KgkJJjOHhejRqim2TXZ+IDkc0qkJmfTlMROfk/iLw4xlIEUR8fNIFFQ7YMXko7DeaDYxNxOrTh5ZABl1HCQo6tkxGsoXq1yK46Rk5GtGOWZObd8ztgMZh71Ypgw2IhhxESYBUCbTUwWp6xslqd6/uu4NWXYbJi+EAxT/CwgBUQNBtNNsm8jmyuuY7gHoLr1KA0ij2RNjx9eK+22XAYw1cuiYr8sp9AyppK8JUhKJgPy9MYmf7MVsWV2ssjaPA6ipJa8drqfiMNh/vy4KKzJLoQm6iRE2rRbVy7LutvtyOjoKdDx+VOfxab9fPvw+ouvvnv58o1NpmAB87orQAQQ0zxr1Bji3v3metrPmi9FLhCoWXRPiZ/x5GRinQ3h5f/kdhXGlSxzduGouIVJz1iu4YU3dLvd6LJbiRJqHzNNOK5nGeTiuvKCUCREFBd+qE7rZnkGMQhrgGhtSQ4FSlOhqtqeUWvPTOr2ciCyprKiiOHkspKtMiDBUpMsVajBzD/zmSyRDNzfXL19/5HeJzMAOonAPE+jBxFXtzfZZzzeqixjgJIJ4+ctgsgoKypQKV+QaF03LQQIEVvjaV2B3I9Mt2dEEBFwENAxITmPcvm08l0jyE/6tOq0K8c1VIAl2XelEFuaKSreGirRXrk6UU0XWhbysvwkSxv0clysEOjFFOfiR6rApfhHlP6HQlPCX+pygmOS3LP9TlMgqG4mkWqcE0Zkt2qudJpUMyWNkbVImf4AlTc3O1V9+/YtJG7u3rCqCzlsZc5F0g/v3kNoqoZ2uJ4+/vLRdJIGJ3K41by3nc4i5oEgD7ubJjtTILpiTnk1gYKizYRZIEE1e/PlV5HCnNsdrazNuJ85OzvvxjY8pzY30a3THZIallRShgojs0R05NU3L4pUGKkdMdUId0H3AsbIKVCEJPIb7AAxDmrekfQgsn3psM8FNgvuggz0tPHPUIGMfvN8ks0QxIZmvHeyPOoQTBrrdCkPqD+qDFsSjRsxPyp5I5JqQhVs9BqbzCxWieQGxsW/vEZ5RwyMkK9v6fgxViEfqd4qdDS5JCoKD3DLLMiASRRAzIZ+d16xIkOeOc0NvT1HRAMAkhFe+1WEkrs7IdtUCdlQB8ZtSZ8ghBoyLEpnIHKp6GHWP0YIa9BhBCPKTRXiS3QQQzbjUlcMqWmdGf5KyU8EPXLZMUoc0mSM4zMqoxjeO3JMl1QDXVQGTYdJZ90VuajLODfIVx+f+FCpW1yXnPj43Qh37z08IGI6FDIKQW5e82LPBuU0nKNYu2hdV0ZOhheKGIJGCSWGGX9WMJl2pq6F5pCQXAxcLlKCk3qSpDEhQ924SD8GnY4YS8iSjBYRsY3Gv6wMJLOxyKRdlsPf3BweP63NjMA8tdO6rBHNME12nOd4pO0btanoqcfqMU3t1Zsvv/vDv+2OR4CCyMKgaWpTa5T0brH2s9MFzaxd37a3vz6Ok69wCe+FYirjJsOTbhQan51+jIXhoMUwouLsYEcV/2b0Pap0Lw7oGSB9BoxGrt8jDe8I+Uw2TuxCVW7RTl3HdHw6WvMLxCfFrNudqexK/GZOVD1VdgTVZ8dImtetF5CtTVMdF6mqtPL04Shtu0L0wSojFx3QZ0STm1W6ub4SkdOnJzA8VgEQ8AhVu31xX62qHIn0wYwMfjMrMJBVuRdqZuyIjDqy8glSlRSQ7P/EKEsaVEprMtaStRaRmYy0cd6djGFDk3GNkUCuCzru6W8AoUgpwG0gvwzUEAIdrR0QKEWokhRrRXgypNOrNV5HjtKygLk8Unh4VvRTt7OaYKIotBHlJAhDkWIcaON5ziMNulgGBqB7Jktyu7ObPDWnh1Eo+6QRFGHE3e2dR6zLmZm1zVlIZJJIInF9czUCAUTweH/88MvHZhZwsGtLy7gKYTZxdQrneYro3bu6p2R2liMQ3uGMaNruXr3ZMAo5nGIFv2P6ROIBL++CcWHS+qdtS4BbpSrPZC1QlyJkC1lFRA1DhQxbDWEWaQ0PI4PGdOBZd9HFguTi9u5RnUqaEvt5dKPAd+UKISKhlOprGL951G/r4Nzr9uSV9tH5QoyO4ggfpZZlW2Sc2nF7qtxy4KNKmA1An24nTUGWf20xwTiDCZZG5Vk+m1kr6zpuaN3GjL/zmkiRCZKNjbXWuVaJZkULmtYPqV6+s56M6V/qR9UUaByWqD59OL86xXmG9TdROSAJ1HLDLk1RKhrjDuZ6KVkJIilWpGyPI5LpLPCRlEOlmcrOBKpttRBT8QWjZ6O2VxIRQiQCnrHMKLOst9JqdiuYwgw/3dOXhEfKaiQEhNioGiy/TGAA2vTaGyqpyyIAS24xs2zcqL4K5TcdptribX3LBmK7mrLhmc0/1GnYlAU2W1nHU0W3cQcxmK6LpvTwtlmZMRRS8izWzSiRAYx/jshg49/zUG9kYXZgRZA0VdVpcEsXo7/B92H46+1v7w+/vv0Y3c32dQEj5slubq6ePrdzj3k/7eZjm7EKb29vvvzyq8P+6AIVac0yFzZbE8qyLoHeWuvhy3J+/eo61+zhxRWQF0by7tTxz1HlEKkUjW42raBIete8nfIc96uMRAFIZkllXLgyGfh5O/UXZ1RrsclMbG1+JYXx7Lrj+cL97yfHzKSEwNMKptDDBn/AAmpV8z6KNFBpmgqNNnf47J8k0No0b1g53MsAgeIiisGBB7IEz0NQtURbvchwMJnhFohePdyp2vufvyeIEJV29/JlSqejCubq9aoVJMsXBmwYC8iIYWxErWQsdJy2Z6duXCSMUzei3tTUkw3sXwxifbqTjAGNRKBgoLLB3G7BCPJHuJNvO6xjvlds7Rh1GJDK26NTl1VkHKOcU+qaqCoLOY8Ufpm+tLkVnBXVlIshhrKYuc2bq8pmpsSFmmM1N8Nehxx5JymKy8SfDK1FrLRzipXl9rN13sL7CNkvfQR1firVrSq6VY7fvLwR4N37twqBqTNvUBdSotyts4fQg5PMhuStszmCED3e3XrvUqIpz42mqia/WGEdxo0fp0BUqu2fNGMTjbohSJ2JzaRiMAeyOZjN/pKi5T8xmo0EGzWU0epwA+XACnSMMLOyEiWlxupwjKobUJV4FkBviD7xjGkt6uDnuGmRpRMFrPheQSUoa0B1eonSEBz2fjizQdclPfMsIkZFGwmmxr+nnIzHOJzCmosZIgKz1gqoXXDncy+WEWi54vR3KiNSGzQZtzM6Ln+Vp0SMDotaXogItZrYhQqJLeuFCvStuJjUsU64ulExUqNqZHym1Iul85ZNDWgQHJaQEdvSbejpQiRgq71QNbVGILxnJV+FLUAENtBjraagpi8aOlkYmBioCpZifCCWY+FHVP0sNs/lyrGmgqhJBrKB2gGkYjzn5uFQv0A3lBZMZQHQUPpBhSYw0HDxLxeeI3up61aI6uVX1FGraCs43nFY60S3ZmqtTS1rmJJ6zA7q8fAUVTVt1tSsKIZnvnMc8RFwJyklokZIy7O/sYXDmpeDHJuZTn7Yu7GuGPZBZfgqEai8fHX/609vu3eSioBHs7Y7Xu+vXsU5bJ7uX7+6vrvV3bTb7Q/TztQAN5PWZgLZaUEinL2vEFHomzdvhk0qCzAcmsgmppx/OVDj8HyM4MjZD9sMAmK21RPlz2cCNmJMxcsPKTG6iuZMYzvYwzRU80ra2I30qvxvfuOoR8r/HlMVUMLfzxFVYaoKQcuEZyVqXiJseTGUQFD+M9UANzdcZ7COEltRAsCWsIjIYXC+lU9JiUyn+3atw42ChRy7fQFqBEPFrh9evv/l59sXL56jCXK83mBcKhodukZ1Tksut4xzwkBgdDON+5ArJ1JmbHuSNOYybA7GLLo0krUPw4xxs5SEwDOhsoG/sQyjBKDcycaGxCBFRoA36nvK8tZnB5mEQcEUjNgl/2c9aAZzI6rKoIwsLwtQaFbMRKFWDA5ByhduVjEfUDcLSaTIBFRSeCMZVCDbGremeUm1chEZVNW21NsDJ4+y+dehkiokE36VpYKIvHz5+v3btyCbtrDoEapSBLQS4KTTPO0lEGB0mkzzNdyV5LosHHnsLauqpsLK8mQjkhCj0l5G7LaFiCoNUtKfYHn9koHGMBMYacWB9TkYbGFIibAl1NISzZVhNE01gqKRRQbj5mceh6KiUSU7aTNTCeyieCHDY1VPbma7NnpGCs8x5VaLScqtqpBd8vp4scGQIT+5HbN85JGm5AAeQyYkhUQK1xYTALJthEae9K1PiVvqUxUpx1dtwVEEilbtQ916EUGeXB0FehvmqyCnuIhy1dSs3pU0vrx8M1ChCkbOMfXxA+mrkkQBM0IbPk9GeVzyqwXWK46SgcpHFDOOxBZ6VjhQQRlGb+CwpABGpDpYfQazbLFaCTFuYhU6p4MfGB1Q0yJ1Sjy6LANyHfJfii0D6BWkYPBPDBndFHnC4FGjXsc9qZcta2GX6HfryOVwRrkj4w5Zjs+0gqPcTk9euohLkP1sSccyYsC0yz6XhRQRyQFSZq2ZmQDu68WqY/Sr16ht8wJRugEYjmWoA6RDpU014x39jfuJDEGH3eXWsFFhRuGF3LhBtYuksoAOPjhf4MXrF+9++RDrKbBqm+f97tVXX928eC26D4mr406nptMMEfoKYtd2pKuqtbb4Gjly0VQ8FHb38g2QgCNJCMm6k1qrbBEZVhV59/PQMuij1SjXpOgGZL3XOKQEPVxQlXCxxThVC5hmTVXVqKAHvVc5yyWYTIXkLQkuJOnDIxMbAB1pCwigwshKkZGWAiARlNgSCQO91BWFKFij6y7xRjF3UehofCtHpNU44tTBvZfScUns5Q7W7SM3CZ3kLoejy59ASX2kI42r+4eIuLl/kXRWYbet5lrVzAbtjYxS8ndsF1XyzFsick2EdjGB48ETzVSuTQrN1FEvaySqVaXKQWkAw0sx6d3tNv/m4+vwDu883ngsh3t97wV2qUDE0lOPT0tWI2rWd9omVRuOP5GJpx53HtZCZ6YmEzC43/FQBcPCozzi+Lks66j4LRe9bxaXJCXUQzUgFZqPxPWFx8oPlGoN5UXlRkTNUAg4PbJn+lmpBdLTxQ1FMSkuincPDwL58OvPWd5qKmCs6+Li6Z2jd3rA7P7lVUT03gvUwmMoo+Q6o8J1EXRJ3DmQIQafRwlUIhOiWdWba5IxgSdzgpFoL99lKqoDM+fmeiKMcWaGmxOtK3f5w3q6zY5znBpVHQ6zmq62EIYk3J0I3/SNR1A2cGoOQI1qPcnEjGxNGKUBUNiWg/fbzigKuIps+JTAyDEVfRg+hOkHhs7BNwPMOED2AZ/HiUUxSsMUVXlv7kFIbEi62mHULtkuuaQJB1t88ZF1YzPIH3doq4viMPpZTCMFSTc7yhwnJMM01ArkkuRO54wnbCYkd277lGJxRVRDUzFzuMsEE7LNmCJEtaUB0gqFtfi2GvCWwVtaKEhU5JOMpoTHtpIRWUVdyb26bmVYxr2vz6ua1nGrKUPMYjQCDWJ4aLTG4AoBiFZrVDbhDls24PUwT9shEzUBxEbpMYpovUQ721XHJTCT7S/Ira2MHAUpFx+WZRKtjtwoHqoLozKsaxHGEYHOGDcEZWHGwRhuTjdfUOcsIjZEx+E4sxwzHUf2RGdBx0aCXhYNyXVeAp7CRLcPNz9+/ylnXXjEfNjtr47aZoJALGs0VSjOfZ1EhcbQ03ltHkGHNAhUbLL57sWrcgRVUbThzlKEUGvclMl0o8oUHI2ZJEqydYxBHc17gKAGugC1FxEjnffMVkOfgdy80+7pv6oo/lIblUlV5HyPwdlgSE5wW2pAkCMlh32G1O3LKENGM3idpToZgpJQdOelLCzVRSvsAUDEiJwEgta7i1wc65bUkUuxpxTaGOxb5gKeO/rCSWkySNbQsXKr4xSNaD7vfbNMI0GeFcskeZNRyNYgkUnTCnyHKRn0e4JrlQsdjMq81D0fUi2iol5enChSy4Chgm/EmKJ3eVpkWXfGpQoNQAczUbNngTGR1qxG2MszCJwQSi9ivmkaBzqS6veKIQ08pD64AbRB+Q6LzAzT0rtwuMakRspaVe9FDGGSqpIePisrOTLEyafQAnER6blH4Vgex3LHTGHS4b28r/ByCsGtM217pownU8FLAaHI9f0LgB/evfXs1Qo6qZJhdTeVu5e32PTcRHS8ar4pnim2uXf4hjEGFzS4LVQzWt6aPMzlnpnDSjCQbrqWPJZmmlc0R6ZtCodUCb1Y+o1ZzGpaMPVLn9/gEXxqGiSOP0t3gHHFtLYvCo1sOQBsS3mRu0ExasMqqeYctLIOzmL66qe3RxkfloYmSTVuVFTi0mp7yVzJFnsJ030HsFV9jTFhRQ5BrdmYAelVbwdACGeJ+0NrIItux2NrBgY3lcmBk2TwnOljGOGe/ELu77C+gpF0yCuZSgoseZvRbzac7XaJ0txLqqrkgefzZuBarg26xbhBAxYhBgzLTSzeKSthnifyRq1bCu8mAaPjk1ldKlsAOlS/iu4VNZPBB2zbWpyJqrBolbIpoirKHPheCCULBMcJwMgADANX2bhq1s6KqYELRsYon0FG2ZBs2zEOZS6piEBtNEiCHD1Bzy7mJcDMjMazfMKlt+j5BRqeM0uTNi48GN4ho7pps2PYdFFxsZZZVrLF02PPijvfQOjocmfQ63yOZU9VucTkA2qiDFFhcnn5+vX7dz8tywmY5mmeJvPwHkPfiESgqRlERVyih1/PU9rs03mN6Hf3D+XWPHMqkR5QqgQqH3ZIsl2OtcrF0XCQOrV7qhYD4KVNYzX8CHARKE83W1oPSNIgIy5kRFc0PMr0DYyroi0ZE8lKlW3R0sFxu0AXmDviqXF6aqNtOBopcdPavsiokl6s3EUJI36LLjB+OdG8dxHh4IC3s6uW2hyGFGKpFuo6FREcOXq13LfRn8oIKFI3c9yaysqg4FeOrLCKPAbkGbmrchnYliLJQDJx4jMCM54ZokucsUU56f5jy4+Vs+DAEwVdY2DP9BFSaGp88AjNSJehnT3agHNQQzNrWc+7lV+QsdUjQ1Vj+0R5hkAvwgkZ0GiBwry8Sc5XA9EzqIBnCrF527zWWZhXPL8nwCyw2mxELlAe4arLyuUPQhB5nTJboONRL2q2CRaLkwj02FzvMBMKVMUZRppCsp+oGjUBCHh1ffP580ebJlXtS8+uGBe5e/UiX34UTolUdq0WrZJbkp5riMgNCyZbae2ozcZIXYIQ6Ab+yl4qJXn+asypAqZk/N3HAAodtysXTFEpp+0uDPZyfHZemmTrKuYe2ZSyReWxEnVdjPj41FINzpkb9ZoVUYmYta0iO+f5ZbyVlqL8yzDbMkzvtlEcce720Bluo4KzPGeJ1HS8JLe4HSO2LeWeZKogW3kTNmYvUjSBF2SMnNgpCMRYD2R+aaRQx6WvXF7FYc9AecKCSvYBxAjjxminWqsKMElALOONyzRsqUsCH7OsCt3V8qkIKENJOTq4TZ8oJYl6dBlsXVWYYPukjXuSZE3ry7LBlYC7R3GEZcM5PNPG0w3wxwxAR0iUy48Y1WYjYs6MVSabvGwxq7gSxVJjXGG6d4+sxCjfP0YXCESstQIeZdMuM6XyFmMctLp744Rl+uNZKm2YUtZaqDUZ2uMcBF6ajDpudYWHkRZDqxJs75050bpWIRMTHKdSNpdPEr7B9hETQUSzDgmaQl+WJR0pbeDb+o4Pl6xRH8g5vA+B7xHq5wm9unnx7u0vZlNr1qyt6+IeNrUkV53RbGqq4V0ML6+vtlj0eITIlPelqpdyXxNADdpjw15jqQa3EOPMAGOKwMh44rIpKTieV1m0hp7UaitIBSkqqDzJwB0XCsc065x0c6ajtHegYNl2BCmhsUXwyBXTysY8M5Vpisd07DxMdYHrGzcZPAhEyRwIYspgLlXmmbNbWSHS3LtUL9UIjHQ0VWDL3QykJQLmPBQaRvuBaIb4cGe4jHoOCsSZZDk2ekaKyJBNtaU8eB7gUTew9W9XuFOJ5dyPtM95vTbTFh7J8SbrFWVPkQlL05aGh2ROPqPIoHsi08sYKcpnMRAGwIgBKEHQu/e+kqFqWdlnLbvkhw8YXipvxshw56JbZSswKJyoc1ZEWd5PVrsRR6UAIAnqyaGVo00MCKnIbdxESLUmDftdOAAjriwoMjxafkthSoIKOMukl1HezmUa/4GtpTTK8gDl1ooqIxydVfCVryRbpZGqHq5uTp8/QjBP1ldfur/58ivZ5NTG7chfnTFiRjiQrOh1RAqEAFusdDFlUacVOgAuRiddQgsVa2kb1ETVUNlPyb2gF7WmWxyUUuTDXRKILfB9tt+lsy+VVJG6n4l5Ls+wmW3RZzU72x1mUY9CRgxlvITvoy8mffTWVodkKCtCKJiftD4HRAEvXLpUn1353QElMEKSrF2L7W0pRaympR18ZMOWtBqoyAYN7gOxFD3+zKfUcILnxvpZ+d0AcBwOvtrx6iTXGljW7jM8Ja45eK+6PB3byiT6Gknb4a8qMtGtxTINRzLTGEjU+5qtEjagIsdejbCkljOP1iVBW/A3xXa1AIdlXJvkkGMEshuQlMtIAEGlR4Mlz6LbCSxDaUmrFaTYmJjSqolUksliAJTvLwI7u7A7GFqwu2biAlLFAHaZVLVFUxseRuV9R2QlVk6UDO+IFVuoKcPScMN7+YT1LvnhCXVrVdWqiK+iAkMmjF0iIsfiVWJ0nMDyUnVRqpckN6l+yeBkRERbKypt2z0Z+elEkBnbp2cp0osRET1bIrjZJl4kAeXu/uHpcV2jS0BVjc1EyTWBRlMVSA/eXh22Y7mFeRlU5HZLXt1EnwMYjoERl4NyoZ3SoI2vBO+Jt7ICPQddcjznhQNA+dxx54UcKQVufzdQqGprhahyqQprjHnyAshm1BTAJl+Zsp/cyIBxjDGM1AgHMlTmOGhlHrT6hdLJpSOAwCPYt7owDKDeMASPL9WjWlHHxbrkwaqyEBA1t7lGE6gBkjsdjIpYzNJd1mXfnOsGpEVGNe0IZXQUCj+LpS6Rywj4SBmCiIOpSNI/IgU5csei2BqaGQmZ1cTS0mwJP2RSiSO/HBQRQ+aYS64mGAKns2i0KgYstFTgxwYIS+c15ILHVQFYomGiCm1iU5kVrhxXqAI4kBSWak3WW3nOwBFVZoBLRO6SikKoKmBJhJXL3yzNgDPJi3uEuIz2+9RAzSKxjLMzSJBIReFIGrjU44ojKQydYLDsbvHGYq1Zm0QTRmDUw+d3ZNAWiYFM5Or24f2PP6hJiL/54psNim1sDMM36Tc1szZnFj+8ZxnUoAo0RyrmadnQhVBYY162idKbJbGMmnO4tZqhwlAJ8QEouZ3VCh5QdQsxUp/lQUe6FBBRts07FwE7btA4chyyxQqMMrK0KpnmHi6zjNUlVoGIDf9fLq/uQUXB43q6JAJWzR4Pkoye92xIyY1+K5FBhJRHztII2YTHMBDvYKXz16o2USPgVTbYGZGpl5RIv/hpkYKqkfkNIbJ19xJvSAXtOji1YHDIsbAuVQaO+RT5auMdeFl/EWSDHDfsshHlQBINFQIJoMpQyABDCZ/FmtqEUZ2aETOsaN1RuT1sWbl6jrg2L1PCHxXJ6tXAhjlkxAcsPepxuqS4qurFzQEOTtK0csSxFSDKYNJH0JuuohxBtSAEQYmRiS5qxABx9+6ZFfGcCizDzJJUGFVEWuKZDYZX/U2iGRm+oUyMik2iJhUg9S2+wuh7k+FykCndIRtY2J/bWdaxP9vZEcmZdOPCIcGhWRVBZvW9MPuQPTwGpikoNi6yZhSulfLC4Eu3EqNBNGZUV8d+wNWAcoujuPmOZAdFRfXm9vjr23eA3N/d5m/99e3PJm46wZ2Q29urDXnnA1HEcxxx7xFdUhlJTTXLADaLFkGXkDE8sY5PcjgyMp5VWiOS8BdgimMyOnjpvqyt0cL3ZLhvNidGBamMcqRMmJpZy9nyo7ywu/eswMsTTkYJO1mDGqCRJdjRUwUeo1iDzxmly1edrZAo06Rb3V8kJyRiog1jU1lkVQoliGr+3Tinz74KT2xRVNW2MOOiRE6ax0rV0qhcrGzZPcpvPpbl2rZYtGLBLbtTp+7/+zTbjw+GoaDqaOnJpUqY6xvQS/ZMILSB8IafSExcMdfoSRlBKodNyvltuJSaX56lgtrLfujAXsMRXV4nsQMSAYxKLtHBGv7m5evqV2CT/qlggUK2BLk+M9dAalpguMasj3q2i2X/MoFPjC6L3I68+VreevCSeZeiChp5+cxNxI/bb6lPynfL4ACIrbgs/xPbh9cRydt3//qLX3/+G4pdyPYEyWN26cHO9c11Qw7DqSs5AqnhFMqpZ2Yti0bzQza6vkLVEW/rdmAHQBjnSbb/q9XaduSZKa8Dk74AkhH6ZkhHZLpFkNyeLmGK8pIRKAzK/+2wDboiAw+BAdsjjSXIXVaTICQuB0g0RddyvMm4RIWGimR4Fizh2fHdaKO6OPL/NnUFSwrFICzg1dmDs+v/f6Pemz0kgepNZ14LlEAD7Ut7sqox8J7Q7sz0kw67UPAtbWMXrv+GBvIbefokeDjSL3uk0m0+Mz+Lh2QUenzl2li5wHXlyhIY8xsKxZ7ik5uKLJjIhliRc6ayIsGtMOGqAE08QJNJmXYfF/XHpsmbkX0ZM8aYlSbUXQWOFqqqi7rYNlIxeTN2vS0b6D9FPqFytII8ZB3Mosr87igTe10SWJ8dmk3aNGCKUMbif1ng9ZfDOR8MFyTNx+TrgbMiRt1wcj3BVoCZcZIopikT6/jyMhJXJ3tcKwnCeGvMUSsp86SZUAJQlKa1837/WkQQwOv19/18tBt+/jx5zvKhhp8eTNAmmZ1sjsumIMZiIFWQkOr8gGSFsZMfnZiZYcgiggHQNp2K7WRPKKol3r33602wCMwddXsMHsqZqrr6weo+emOYyCVy1WUdbl1hEv3YPeHVZcPKIYzBrmsXtuH2H7lYxzmHXhEHAAAAAElFTkSuQmCC\n"
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.nn.utils.rnn import pad_sequence\n",
        "\n",
        "def custom_collate_fn(batch):\n",
        "    # Separate the input features and labels in the batch\n",
        "    pixel_values = [item[\"pixel_values\"] for item in batch]\n",
        "    labels = [item[\"labels\"] for item in batch]\n",
        "\n",
        "    # Pad the sequences to have the same length\n",
        "    pixel_values_padded = pad_sequence(pixel_values, batch_first=True, padding_value=0)\n",
        "    labels_padded = pad_sequence(labels, batch_first=True, padding_value=-100)\n",
        "\n",
        "    # Return the padded batch as a dictionary\n",
        "    return {\"pixel_values\": pixel_values_padded, \"labels\": labels_padded}"
      ],
      "metadata": {
        "id": "cMqm3mZmYfx_"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataloaders"
      ],
      "metadata": {
        "id": "Eu-a_SWhrEu7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True, collate_fn=custom_collate_fn)\n",
        "eval_dataloader = DataLoader(val_dataset, batch_size=4, shuffle=True, collate_fn=custom_collate_fn)\n"
      ],
      "metadata": {
        "id": "zgHWQDcEnUlT"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Config model"
      ],
      "metadata": {
        "id": "WpmbD91LtV0b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import VisionEncoderDecoderModel\n",
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = VisionEncoderDecoderModel.from_pretrained(\"microsoft/trocr-base-stage1\")\n",
        "model.to(device)\n",
        "\n"
      ],
      "metadata": {
        "id": "E-SmmAjLtVps",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77e6120c-c34a-4bf6-9c5c-1b10e92e57a0"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-stage1 and are newly initialized: ['encoder.pooler.dense.bias', 'encoder.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VisionEncoderDecoderModel(\n",
              "  (encoder): ViTModel(\n",
              "    (embeddings): ViTEmbeddings(\n",
              "      (patch_embeddings): ViTPatchEmbeddings(\n",
              "        (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
              "      )\n",
              "      (dropout): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (encoder): ViTEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x ViTLayer(\n",
              "          (attention): ViTAttention(\n",
              "            (attention): ViTSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): ViTSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ViTIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ViTOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "    (pooler): ViTPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (decoder): TrOCRForCausalLM(\n",
              "    (model): TrOCRDecoderWrapper(\n",
              "      (decoder): TrOCRDecoder(\n",
              "        (embed_tokens): Embedding(50265, 1024, padding_idx=1)\n",
              "        (embed_positions): TrOCRSinusoidalPositionalEmbedding()\n",
              "        (layers): ModuleList(\n",
              "          (0-11): 12 x TrOCRDecoderLayer(\n",
              "            (self_attn): TrOCRAttention(\n",
              "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            )\n",
              "            (activation_fn): ReLU()\n",
              "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): TrOCRAttention(\n",
              "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
              "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
              "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (output_projection): Linear(in_features=1024, out_features=50265, bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set special tokens used for creating the decoder_input_ids from the labels\n",
        "model.config.decoder_start_token_id = processor.tokenizer.cls_token_id\n",
        "model.config.pad_token_id = processor.tokenizer.pad_token_id\n",
        "# make sure vocab size is set correctly\n",
        "model.config.vocab_size = model.config.decoder.vocab_size\n",
        "\n",
        "# set beam search parameters\n",
        "model.config.eos_token_id = processor.tokenizer.sep_token_id\n",
        "model.config.max_length = 64\n",
        "model.config.early_stopping = True\n",
        "model.config.no_repeat_ngram_size = 3\n",
        "model.config.length_penalty = 2.0\n",
        "model.config.num_beams = 4"
      ],
      "metadata": {
        "id": "T9OO-Ti5tX6T"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Metric\n"
      ],
      "metadata": {
        "id": "yhMJ92gHul_9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "wer_metric = load_metric(\"wer\")\n",
        "cer_metric = load_metric(\"cer\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "367a4a81ce114a939d53d4c41d356299",
            "4cac948f4630443ea2b553d731c66a1d",
            "3160dc815eac46b8ae5b1f5db62cb08e",
            "b81e63b81d504023aad7bfc1eb5520ca",
            "64e39919961245edae68cb6362309246",
            "f31d0f7308d94344b983ac23b0a00726",
            "2886f43a283342e99aa5704fc6a099b2",
            "0195339b115b4fec86ab1e5f5728d640",
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            "afa0ef6e142e49519f98fea8f8d6b056"
          ]
        },
        "id": "q3ulz9ortmVx",
        "outputId": "f786acbc-d0b4-4d75-975f-602f5903b4e9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-fb0986f8b54e>:3: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  wer_metric = load_metric(\"wer\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.90k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "367a4a81ce114a939d53d4c41d356299"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.16k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "192dc0bfaffc406c9a23fb82094b38a3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_wer(pred_ids, label_ids):\n",
        "    pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)\n",
        "    label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
        "    label_str = processor.batch_decode(label_ids, skip_special_tokens=True)\n",
        "\n",
        "    wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
        "\n",
        "    return wer\n",
        "def compute_cer(pred_ids, label_ids):\n",
        "    pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)\n",
        "    label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
        "    label_str = processor.batch_decode(label_ids, skip_special_tokens=True)\n",
        "\n",
        "    cer = cer_metric.compute(predictions=pred_str, references=label_str)\n",
        "\n",
        "    return cer"
      ],
      "metadata": {
        "id": "lSQX23pPtnqH"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Finetune"
      ],
      "metadata": {
        "id": "ZNavrn0nunO4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime#, strftime\n",
        "\n",
        "datetime.now().strftime(\"%B_%d_%Y-%H:%M:%S\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 42
        },
        "id": "K94Jo-JTuvLf",
        "outputId": "d37fc571-d434-48dc-f3bd-77750eb76356"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'July_07_2023-03:30:27'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 50\n",
        "\n",
        "from transformers import AdamW\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "\n",
        "accumulation_steps = 4  # Adjust this value based on your desired effective batch size\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    model.train()\n",
        "    train_loss = 0.0\n",
        "    for i, batch in enumerate(tqdm(train_dataloader)):\n",
        "        for k, v in batch.items():\n",
        "            batch[k] = v.to(device)\n",
        "\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        loss = loss / accumulation_steps\n",
        "        loss.backward()\n",
        "\n",
        "        if (i + 1) % accumulation_steps == 0:\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "        train_loss += loss.item() * accumulation_steps\n",
        "\n",
        "    print(f\"Loss after epoch {epoch}:\", train_loss / len(train_dataloader))\n",
        "\n",
        "    # evaluate\n",
        "    model.eval()\n",
        "    valid_wer = 0.0\n",
        "    valid_cer = 0.0\n",
        "    with torch.no_grad():\n",
        "        for batch in tqdm(eval_dataloader):\n",
        "            # run batch generation\n",
        "            outputs = model.generate(batch[\"pixel_values\"].to(device))\n",
        "            # compute metrics\n",
        "            wer = compute_wer(pred_ids=outputs, label_ids=batch[\"labels\"])\n",
        "            valid_wer += wer\n",
        "            cer = compute_cer(pred_ids=outputs, label_ids=batch[\"labels\"])\n",
        "            valid_cer += cer\n",
        "    print(\"testing wer:\", valid_wer / len(eval_dataloader))\n",
        "    print(\"testing wer:\", valid_cer / len(eval_dataloader))\n",
        "model.save_pretrained(f'./datetime.now().strftime(\"%B_%d_%Y-%H:%M:%S\")/')"
      ],
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        "colab": {
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        },
        "id": "z15BtGMoueQB",
        "outputId": "cbf88ae0-4c27-424e-c021-add676e15e31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
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        {
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              "model_id": "0f3e947e3fc64653a788c57848c218d6"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss after epoch 0: 5.999457565091905\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1259: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1353: UserWarning: Using `max_length`'s default (64) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "testing wer: 1.1014730473397099\n",
            "testing wer: 0.8237357023076822\n"
          ]
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        {
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        {
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          "name": "stdout",
          "text": [
            "Loss after epoch 1: 4.35032659910974\n"
          ]
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        {
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          "text": [
            "testing wer: 1.005600636320112\n",
            "testing wer: 0.6426351288998976\n"
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          "name": "stdout",
          "text": [
            "Loss after epoch 2: 3.1554906662730944\n"
          ]
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        {
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          "text": [
            "testing wer: 0.7626234720201874\n",
            "testing wer: 0.43540951837455016\n"
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        {
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss after epoch 3: 2.7164918464564143\n"
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        {
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          "name": "stdout",
          "text": [
            "testing wer: 0.7856190386506944\n",
            "testing wer: 0.48605584593466683\n"
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          "text": [
            "Loss after epoch 4: 2.296719267254784\n"
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        {
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          "text": [
            "testing wer: 0.7854373474872195\n",
            "testing wer: 0.4580954502659531\n"
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        {
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          "name": "stdout",
          "text": [
            "Loss after epoch 5: 1.7125828044400329\n"
          ]
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        {
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          "name": "stdout",
          "text": [
            "testing wer: 0.6783636511519042\n",
            "testing wer: 0.33741240114609217\n"
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        {
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          "name": "stdout",
          "text": [
            "Loss after epoch 6: 1.0482586234630573\n"
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        {
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          "text": [
            "testing wer: 0.5927372343200055\n",
            "testing wer: 0.27280514162787717\n"
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          "text": [
            "Loss after epoch 7: 0.7954523407277607\n"
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        {
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          "text": [
            "testing wer: 0.600104804272645\n",
            "testing wer: 0.29010558025530725\n"
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          "text": [
            "Loss after epoch 8: 0.5874228522713695\n"
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        {
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          "name": "stdout",
          "text": [
            "testing wer: 0.5935952517751923\n",
            "testing wer: 0.2738067747191976\n"
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          "text": [
            "Loss after epoch 9: 0.7149426884771812\n"
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        {
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          "text": [
            "testing wer: 0.6729552448711997\n",
            "testing wer: 0.33094938059410905\n"
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          "text": [
            "Loss after epoch 10: 0.9293960582110143\n"
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        {
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          "text": [
            "testing wer: 0.6450378040338707\n",
            "testing wer: 0.31153385280177703\n"
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        {
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          "text": [
            "Loss after epoch 11: 0.518411126874742\n"
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          "text": [
            "testing wer: 0.6212274565266658\n",
            "testing wer: 0.3086843184568465\n"
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          "text": [
            "Loss after epoch 12: 0.33454980696773245\n"
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          "text": [
            "testing wer: 0.5647502403840265\n",
            "testing wer: 0.2673866589044246\n"
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          "text": [
            "Loss after epoch 13: 0.2386622118674928\n"
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          "text": [
            "testing wer: 0.5695037033367812\n",
            "testing wer: 0.26085379025032546\n"
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          "text": [
            "Loss after epoch 14: 0.2110161637399523\n"
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          "text": [
            "testing wer: 0.5744375539105833\n",
            "testing wer: 0.2664365095232171\n"
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          "text": [
            "Loss after epoch 15: 0.22258675912217724\n"
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          "text": [
            "testing wer: 0.5555559134941395\n",
            "testing wer: 0.25030895532439\n"
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          "text": [
            "Loss after epoch 16: 0.38430375259901794\n"
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          "text": [
            "testing wer: 0.6353792666536711\n",
            "testing wer: 0.3240187438542431\n"
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          "text": [
            "Loss after epoch 17: 0.6369125519746116\n"
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          "text": [
            "testing wer: 0.6341862609388529\n",
            "testing wer: 0.30082156943148325\n"
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            "testing wer: 0.28016810034193373\n"
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            "testing wer: 0.2540902406906329\n"
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            "testing wer: 0.24273538172142178\n"
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            "testing wer: 0.2543993420152473\n"
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            "testing wer: 0.38655375491780686\n"
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            "testing wer: 0.6259229718733512\n",
            "testing wer: 0.32535963898518405\n"
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