MultiModal_RAG_with_llamaIndex_and_LanceDB.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **MultiModal RAG App for Video Processing With LlamaIndex and LanceDB**"
      ],
      "metadata": {
        "id": "Yn8jv85EiZn_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. llamaindex framework\n",
        "### 2. Lancedb Vector DataBase\n",
        "### 3. LLM MultiModAl GPT-4V or Google-gemini-pro-vision\n",
        "\n",
        "\n",
        "# **Steps Need to follow:**\n",
        "#### 1. Download video from YouTube, process and store it.\n",
        "\n",
        "#### 2. Build Multi-Modal index and vector store for both texts and images.\n",
        "\n",
        "#### 3. Retrieve relevant images and context, use both to augment the prompt.\n",
        "\n",
        "#### 4. Using GPT4V for reasoning the correlations between the input query and augmented data and generating final response."
      ],
      "metadata": {
        "id": "5ZHVe_qkiYkg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install llama-index-vector-stores-lancedb\n",
        "%pip install llama-index-multi-modal-llms-openai\n",
        "%pip install llama-index-embeddings-clip\n",
        "%pip install git+https://github.com/openai/CLIP.git\n",
        "!pip install llama-index-readers-file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sY9xSK0SihIG",
        "outputId": "22e8e2d4-aa21-4706-8660-76cb79a39caa"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
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            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch<3.0.0,>=2.1.2->llama-index-embeddings-clip)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
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            "Requirement already satisfied: pydantic>=1.10 in /usr/local/lib/python3.10/dist-packages (from llamaindex-py-client<0.2.0,>=0.1.18->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-clip) (2.7.0)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-clip) (3.7.1)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-clip) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-clip) (2023.4)\n",
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            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-clip) (1.2.1)\n",
            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses-json->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-clip) (24.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=1.10->llamaindex-py-client<0.2.0,>=0.1.18->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-clip) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=1.10->llamaindex-py-client<0.2.0,>=0.1.18->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-clip) (2.18.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-clip) (1.16.0)\n",
            "Installing collected packages: pillow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, ftfy, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, llama-index-embeddings-clip\n",
            "  Attempting uninstall: pillow\n",
            "    Found existing installation: Pillow 9.4.0\n",
            "    Uninstalling Pillow-9.4.0:\n",
            "      Successfully uninstalled Pillow-9.4.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "imageio 2.31.6 requires pillow<10.1.0,>=8.3.2, but you have pillow 10.3.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed ftfy-6.2.0 llama-index-embeddings-clip-0.1.5 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 pillow-10.3.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              },
              "id": "4b0c9e9fdb32483381501f71fbeed4ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/openai/CLIP.git\n",
            "  Cloning https://github.com/openai/CLIP.git to /tmp/pip-req-build-i2bmz3mr\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git /tmp/pip-req-build-i2bmz3mr\n",
            "  Resolved https://github.com/openai/CLIP.git to commit a1d071733d7111c9c014f024669f959182114e33\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: ftfy in /usr/local/lib/python3.10/dist-packages (from clip==1.0) (6.2.0)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->clip==1.0) (12.4.127)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision->clip==1.0) (1.25.2)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->clip==1.0) (10.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->clip==1.0) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->clip==1.0) (1.3.0)\n",
            "Building wheels for collected packages: clip\n",
            "  Building wheel for clip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for clip: filename=clip-1.0-py3-none-any.whl size=1369499 sha256=0dec45dfb1bd27829c86245f9e5d0e3e15b5aa3fa618dc4c8906052d77c880dd\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-txu2nl_n/wheels/da/2b/4c/d6691fa9597aac8bb85d2ac13b112deb897d5b50f5ad9a37e4\n",
            "Successfully built clip\n",
            "Installing collected packages: clip\n",
            "Successfully installed clip-1.0\n",
            "Collecting llama-index-readers-file\n",
            "  Downloading llama_index_readers_file-0.1.19-py3-none-any.whl (36 kB)\n",
            "Requirement already satisfied: beautifulsoup4<5.0.0,>=4.12.3 in /usr/local/lib/python3.10/dist-packages (from llama-index-readers-file) (4.12.3)\n",
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            "Collecting pypdf<5.0.0,>=4.0.1 (from llama-index-readers-file)\n",
            "  Downloading pypdf-4.2.0-py3-none-any.whl (290 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m290.4/290.4 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting striprtf<0.0.27,>=0.0.26 (from llama-index-readers-file)\n",
            "  Downloading striprtf-0.0.26-py3-none-any.whl (6.9 kB)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4<5.0.0,>=4.12.3->llama-index-readers-file) (2.5)\n",
            "Requirement already satisfied: PyYAML>=6.0.1 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-readers-file) (6.0.1)\n",
            "Requirement already satisfied: SQLAlchemy[asyncio]>=1.4.49 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-readers-file) (2.0.29)\n",
            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.6 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-readers-file) (3.9.5)\n",
            "Requirement already satisfied: dataclasses-json in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-readers-file) (0.6.4)\n",
            "Requirement already satisfied: deprecated>=1.2.9.3 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-readers-file) (1.2.14)\n",
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            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-readers-file) (2023.6.0)\n",
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            "Installing collected packages: striprtf, pypdf, llama-index-readers-file\n",
            "Successfully installed llama-index-readers-file-0.1.19 pypdf-4.2.0 striprtf-0.0.26\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install llama_index\n",
        "%pip install -U openai-whisper"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qNZ4yrIMpa9S",
        "outputId": "cad849f2-6b95-4e56-b3c1-da66eb4a89bc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting llama_index\n",
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            "Collecting llama-parse<0.5.0,>=0.4.0 (from llama-index-readers-llama-parse<0.2.0,>=0.1.2->llama_index)\n",
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            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=1.10->llamaindex-py-client<0.2.0,>=0.1.18->llama-index-core<0.11.0,>=0.10.30->llama_index) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=1.10->llamaindex-py-client<0.2.0,>=0.1.18->llama-index-core<0.11.0,>=0.10.30->llama_index) (2.18.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->llama-index-core<0.11.0,>=0.10.30->llama_index) (1.16.0)\n",
            "Installing collected packages: llama-index-legacy, llama-parse, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, llama-index-readers-llama-parse, llama-index-cli, llama-index-agent-openai, llama-index-program-openai, llama-index-question-gen-openai, llama_index\n",
            "Successfully installed llama-index-agent-openai-0.2.3 llama-index-cli-0.1.12 llama-index-embeddings-openai-0.1.8 llama-index-indices-managed-llama-cloud-0.1.5 llama-index-legacy-0.9.48 llama-index-program-openai-0.1.6 llama-index-question-gen-openai-0.1.3 llama-index-readers-llama-parse-0.1.4 llama-parse-0.4.1 llama_index-0.10.30\n",
            "Collecting openai-whisper\n",
            "  Downloading openai-whisper-20231117.tar.gz (798 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m798.6/798.6 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: openai-whisper\n",
            "  Building wheel for openai-whisper (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openai-whisper: filename=openai_whisper-20231117-py3-none-any.whl size=801358 sha256=397b83347b361a46eb32640b2e64cf144eb463a09d29cad06af5c2a446e7b7fe\n",
            "  Stored in directory: /root/.cache/pip/wheels/d0/85/e1/9361b4cbea7dd4b7f6702fa4c3afc94877952eeb2b62f45f56\n",
            "Successfully built openai-whisper\n",
            "Installing collected packages: openai-whisper\n",
            "Successfully installed openai-whisper-20231117\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install lancedb\n",
        "%pip install moviepy\n",
        "%pip install pytube\n",
        "%pip install pydub\n",
        "%pip install SpeechRecognition\n",
        "%pip install ffmpeg-python\n",
        "%pip install soundfile\n",
        "%pip install torch torchvision\n",
        "%pip install matplotlib scikit-image\n",
        "%pip install ftfy regex tqdm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Y6xmSWjkppBJ",
        "outputId": "637cab2b-a090-47e5-b4e3-b88428ef65c2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: lancedb in /usr/local/lib/python3.10/dist-packages (0.5.7)\n",
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            "Requirement already satisfied: pylance==0.9.18 in /usr/local/lib/python3.10/dist-packages (from lancedb) (0.9.18)\n",
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            "Requirement already satisfied: overrides>=0.7 in /usr/local/lib/python3.10/dist-packages (from lancedb) (7.7.0)\n",
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            "Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from pylance==0.9.18->lancedb) (1.25.2)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31.0->lancedb) (3.7)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31.0->lancedb) (2024.2.2)\n",
            "Requirement already satisfied: decorator>=3.4.2 in /usr/local/lib/python3.10/dist-packages (from retry>=0.9.2->lancedb) (4.4.2)\n",
            "Requirement already satisfied: py<2.0.0,>=1.4.26 in /usr/local/lib/python3.10/dist-packages (from retry>=0.9.2->lancedb) (1.11.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from deprecation->lancedb) (24.0)\n",
            "Requirement already satisfied: moviepy in /usr/local/lib/python3.10/dist-packages (1.0.3)\n",
            "Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.10/dist-packages (from moviepy) (4.4.2)\n",
            "Requirement already satisfied: tqdm<5.0,>=4.11.2 in /usr/local/lib/python3.10/dist-packages (from moviepy) (4.66.2)\n",
            "Requirement already satisfied: requests<3.0,>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from moviepy) (2.31.0)\n",
            "Requirement already satisfied: proglog<=1.0.0 in /usr/local/lib/python3.10/dist-packages (from moviepy) (0.1.10)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from moviepy) (1.25.2)\n",
            "Requirement already satisfied: imageio<3.0,>=2.5 in /usr/local/lib/python3.10/dist-packages (from moviepy) (2.31.6)\n",
            "Requirement already satisfied: imageio-ffmpeg>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from moviepy) (0.4.9)\n",
            "Collecting pillow<10.1.0,>=8.3.2 (from imageio<3.0,>=2.5->moviepy)\n",
            "  Downloading Pillow-10.0.1-cp310-cp310-manylinux_2_28_x86_64.whl (3.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from imageio-ffmpeg>=0.2.0->moviepy) (67.7.2)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.8.1->moviepy) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.8.1->moviepy) (2024.2.2)\n",
            "Installing collected packages: pillow\n",
            "  Attempting uninstall: pillow\n",
            "    Found existing installation: pillow 10.3.0\n",
            "    Uninstalling pillow-10.3.0:\n",
            "      Successfully uninstalled pillow-10.3.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "llama-index-embeddings-clip 0.1.5 requires pillow<11.0.0,>=10.2.0, but you have pillow 10.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed pillow-10.0.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              },
              "id": "f74407c854394029baf918b916b01d7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pytube\n",
            "  Downloading pytube-15.0.0-py3-none-any.whl (57 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/57.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.6/57.6 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pytube\n",
            "Successfully installed pytube-15.0.0\n",
            "Collecting pydub\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
            "Installing collected packages: pydub\n",
            "Successfully installed pydub-0.25.1\n",
            "Collecting SpeechRecognition\n",
            "  Downloading SpeechRecognition-3.10.3-py2.py3-none-any.whl (32.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32.8/32.8 MB\u001b[0m \u001b[31m40.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.10/dist-packages (from SpeechRecognition) (2.31.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from SpeechRecognition) (4.11.0)\n",
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            "Installing collected packages: SpeechRecognition\n",
            "Successfully installed SpeechRecognition-3.10.3\n",
            "Collecting ffmpeg-python\n",
            "  Downloading ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ffmpeg-library enables you to use FFmpeg in Python to manipulate various media files for different purposes like building comprehensive multimedia applications, preprocessing media files.\n",
        "\n",
        "MoviePy is a Python library for video editing, enabling cutting, concatenations, title insertions, video compositing, and effects like animations or color grading.\n",
        "\n",
        "Pytube is a Python library used for downloading videos from YouTube. It supports downloading in various formats, resolutions, and also direct audio extraction.\n",
        "\n",
        "\n",
        "Pydub is a Python library for audio manipulation, enabling easy loading,\n",
        "editing, and exporting of audio files in various formats with minimal code.\n",
        "\n",
        "The SpeechRecognition library in Python allows you to convert spoken language into text using various engines and APIs, such as Google Speech Recognition, IBM Speech to Text, etc.\n",
        "\n",
        "\n",
        "SoundFile is a Python library for reading from and writing to audio files, supporting many formats through the libsndfile library, ideal for high-quality audio processing.\n",
        "\n",
        "FTFY (Fix Text For You) is a Python library that fixes broken Unicode text and mojibake (garbled text due to encoding issues), making text legible again.\n",
        "\n",
        "OpenAI Whisper is a robust, multilingual speech recognition model developed by OpenAI. It converts speech into text and supports various languages with high accuracy.\n",
        "\n",
        "pprint is a Python module that provides a capability to \"pretty-print\" complex data structures in a well-formatted and more readable way than the basic print function."
      ],
      "metadata": {
        "id": "tMlqUqibp0ji"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from moviepy.editor import VideoFileClip\n",
        "from pathlib import Path\n",
        "import speech_recognition as sr\n",
        "from pytube import YouTube\n",
        "from pprint import pprint\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "igmrjXU6pwhu"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import userdata\n",
        "OPENAI_API_TOKEN=userdata.get('OPENAI_API_KEY')\n",
        "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_TOKEN"
      ],
      "metadata": {
        "id": "ukX3ASTKqNDw"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "print(os.getcwd())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjxaH7FwqRGQ",
        "outputId": "ebaf140c-15b1-40db-e50f-69d441bb9aa1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "video_url=\"https://youtu.be/3dhcmeOTZ_Q\""
      ],
      "metadata": {
        "id": "0dA6Lv4Hqp26"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_video_path = \"/content/video_data/\""
      ],
      "metadata": {
        "id": "0TzZx3dbqrwq"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from the video i am going to collect images,audio,text\n",
        "output_folder = \"/content/mixed_data/\"\n",
        "output_audio_path = \"/content/mixed_data/output_audio.wav\""
      ],
      "metadata": {
        "id": "bTx05t7bqcFv"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir mixed_data"
      ],
      "metadata": {
        "id": "PTzo50Y6qtmA"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filepath=output_video_path + \"input_vid.mp4\"\n",
        "print(filepath)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9SkpcGgq--g",
        "outputId": "4fc30558-b6f9-493b-8a5a-403d6f1e10ae"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/video_data/input_vid.mp4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pytube import YouTube\n",
        "def download_video(url,output_path):\n",
        "  yt = YouTube(url)\n",
        "  metadata = {\"Author\": yt.author, \"Title\": yt.title, \"Views\": yt.views}\n",
        "\n",
        "  yt.streams.get_highest_resolution().download(\n",
        "        output_path=output_path, filename=\"input_vid.mp4\"\n",
        "    )\n",
        "  return metadata"
      ],
      "metadata": {
        "id": "dwfB_9uhrB2F"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from moviepy.editor import VideoFileClip\n",
        "def video_to_images(video_path,output_folder):\n",
        "  clip=VideoFileClip(video_path)\n",
        "  clip.write_images_sequence(\n",
        "      os.path.join(output_folder,\"frame%04d.png\"),fps=0.2\n",
        "  )"
      ],
      "metadata": {
        "id": "-lOX4wuBr8N6"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def video_to_audio(video_path,output_audio_path):\n",
        "  clip=VideoFileClip(video_path)\n",
        "  audio=clip.audio\n",
        "  audio.write_audiofile(output_audio_path)"
      ],
      "metadata": {
        "id": "0HPUIQSFsMkh"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def audio_to_text(audio_path):\n",
        "  recognizer=sr.Recognizer()\n",
        "  audio=sr.AudioFile(audio_path)\n",
        "\n",
        "  with audio as source:\n",
        "    audio_data=recognizer.record(source)\n",
        "\n",
        "    try:\n",
        "\n",
        "      #recognize the speech\n",
        "      text = recognizer.recognize_whisper(audio_data)\n",
        "\n",
        "    except sr.UnknownValueError:\n",
        "      print(\"Speech recognition could not understand the audio.\")\n",
        "  return text"
      ],
      "metadata": {
        "id": "_p39w53ZsRb5"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "video_url"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "RZnTqV_fslb2",
        "outputId": "e043f6f5-f3d3-4031-dae2-e260004fcb02"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://youtu.be/3dhcmeOTZ_Q'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output_video_path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KU1B6rEGsnVt",
        "outputId": "0bf343b3-b008-491f-c705-1d2d363476aa"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/video_data/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metadata_vid = download_video(video_url, output_video_path)"
      ],
      "metadata": {
        "id": "RblUwfbJshSJ"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metadata_vid"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwZJpGH8ssiM",
        "outputId": "3051ee60-9337-43ec-8698-9c4ccf6d9e6b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Author': '3-Minute Data Science',\n",
              " 'Title': 'Linear Regression in 3 Minutes',\n",
              " 'Views': 7286}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "video_to_images(filepath,output_folder)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JbtVwXvgsqD8",
        "outputId": "5e51c2e6-cbea-4ad8-e76e-c444eb027bfc"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Writing frames /content/mixed_data/frame%04d.png.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "                                                            "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Done writing frames /content/mixed_data/frame%04d.png.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filepath"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "XeIiBcBXs9fu",
        "outputId": "2dc35766-49b3-43e5-fc58-3128d316c97e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/video_data/input_vid.mp4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output_audio_path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "hhf-ckBDtAAe",
        "outputId": "4a97db0f-737a-440a-d555-e11f2135e538"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/mixed_data/output_audio.wav'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "video_to_audio(filepath,output_audio_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhfIDRJLswtx",
        "outputId": "4d3e1a7e-c431-4590-ceb3-bbd4d680db13"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MoviePy - Writing audio in /content/mixed_data/output_audio.wav\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "                                                                      "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MoviePy - Done.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_data=audio_to_text(output_audio_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGIEgadAtCaq",
        "outputId": "49523efa-e6e1-4d56-9fcd-f25c4b377f10"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|████████████████████████████████████████| 139M/139M [00:01<00:00, 133MiB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "TCEnFoCPtHq8",
        "outputId": "df721248-ec0e-4747-c6bc-34f254354d27"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\" Lanyard regression is a statistical technique for modeling the relationship between an output variable and one or more input variables. In layman's terms, think of it as fitting a line through some data points as shown here, so you can make predictions on unknown data, assuming there is a linear relationship between the variables. You might be familiar with the linear function y equals mx plus b, where y is the output variable, also called the dependent variable. You may also see expressed as f of x, the function of the input variable. x on the other hand, would serve as the input variable, also called the independent variable. It's likely you'll see the coefficients m and b expressed as beta 1 and beta 0 respectively. So what do the m and b coefficients do? The m or beta 1 coefficient controls the slope of the line. The b or the beta 0 controls the intercept of the line. In machine learning, we also know it as the bias. These two coefficients are what we are solving for in linear regression. We can also extend to multiple input variables, so x1, x2, x3, with beta 1, beta 2, and beta 3, and so on, acting as slopes for each of those variables. In these higher dimensions, you would visualize the linear regression as a hyperplane. So how do we fit the line to these points? Well, you'll notice that there's these differences between the points and the line, these little red segments, these are called residuals. They are the differences between the data points and the predictions the line would produce. Take each of these residuals and square them. These are the squared errors, and notice that the large of the residuals are, the more amplified area of the squares are. If we total the areas of all of these squares for a given line, we will get the sum of the squared error, and this is known as our loss function. We need to find the beta 0 and beta 1 coefficients that will minimize that sum of squared error. The coefficients can be solved with a variety of techniques ranging from matrix decomposition to gradient descent, which is depicted right here. Thankfully, a lot of libraries are available to do this for us, and we will deep dive into these topics in other videos. To validate a linear regression, there are a number of techniques. Machine learning practitioners will often take a third of the data and put it into the test data set. The remaining two thirds will become the training data set. The training data set will then be used to fit the regression line. The test data set will then be used to validate the regression line. This is done to make sure that the regression performs well on data it has not seen before. The tricks used to evaluate the linear regression vary from the R square, standard error of the estimate, prediction intervals, as well as statistical significance. These are topics we will cover in future videos. If you enjoyed this video, please like and subscribe. Look at my two O'Reilly books, Essential Math for Data Science, and getting started with SQL. Chapter five of Essential Math for Data Science actually covers linear regression and much more depth. If you want live instruction, I also do teach on the O'Reilly platform. Promotional link below. I teach classes including machine learning from scratch, probability, and SQL. Comment on what topics you would like to see next, and I will see you again on 3 Minute Data Science.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(output_folder + \"output_text.txt\", \"w\") as file:\n",
        "        file.write(text_data)\n",
        "print(\"Text data saved to file\")\n",
        "file.close()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgEEv89ptdHp",
        "outputId": "d9ceef12-1119-4595-d077-925034701218"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text data saved to file\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.remove(output_audio_path)\n",
        "print(\"Audio file removed\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zse424_3tl9a",
        "outputId": "240eb1ac-2384-4ef4-e11b-3f31e8af11a5"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Audio file removed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#process the video\n",
        "#image\n",
        "#text"
      ],
      "metadata": {
        "id": "7KB6YBHJuCLt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core.indices import MultiModalVectorStoreIndex\n",
        "from llama_index.core import SimpleDirectoryReader\n",
        "from llama_index.core import StorageContext"
      ],
      "metadata": {
        "id": "vj4OUtZluIGG"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.vector_stores.lancedb import LanceDBVectorStore"
      ],
      "metadata": {
        "id": "kBBDEuXUutl5"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_store=LanceDBVectorStore(uri=\"lancedb\",table_name=\"text_collection\")\n",
        "image_store=LanceDBVectorStore(uri=\"lancedb\",table_name=\"image_collection\")"
      ],
      "metadata": {
        "id": "xUv_t8vMuxYK"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "storage_context=StorageContext.from_defaults(vector_store=text_store,image_store=image_store)"
      ],
      "metadata": {
        "id": "Ua0JXObmvRYN"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_folder"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KCicDH2WvZvQ",
        "outputId": "8a22d126-c409-49fe-e68b-bde9f5edd5d0"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/mixed_data/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "documents=SimpleDirectoryReader(output_folder).load_data()"
      ],
      "metadata": {
        "id": "_B-UYzwtvXKq"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = MultiModalVectorStoreIndex.from_documents(documents,storage_context=storage_context)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BbaU5Noqvdyk",
        "outputId": "6ea2eefc-b4fb-4eb6-f93b-17044a47b3fc"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████| 338M/338M [00:05<00:00, 69.7MiB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retriever_engine=index.as_retriever(similarity_top_k=1, image_similarity_top_k=5)"
      ],
      "metadata": {
        "id": "v5vZLg_-vm2o"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BQ2viUQuvv8K"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core.response.notebook_utils import display_source_node\n",
        "from llama_index.core.schema import ImageNode"
      ],
      "metadata": {
        "id": "3hTtlvjav2fw"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def retrieve(retriever_engine, query_str):\n",
        "    retrieval_results = retriever_engine.retrieve(query_str)\n",
        "\n",
        "    retrieved_image = []\n",
        "    retrieved_text = []\n",
        "    for res_node in retrieval_results:\n",
        "        if isinstance(res_node.node, ImageNode):\n",
        "            retrieved_image.append(res_node.node.metadata[\"file_path\"])\n",
        "        else:\n",
        "            display_source_node(res_node, source_length=200)\n",
        "            retrieved_text.append(res_node.text)\n",
        "\n",
        "    return retrieved_image, retrieved_text"
      ],
      "metadata": {
        "id": "3c5AB1KWv3yv"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query=\"can you tell me what is linear regression? explain equation of the multiple linear regression?\""
      ],
      "metadata": {
        "id": "LZHW-10jwEla"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img,text=retrieve(retriever_engine,query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "byH2Aq95wK1B",
        "outputId": "048af396-0548-4ae2-ccb6-cfc00751a975"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**Node ID:** aec6ceb6-129d-43e5-b1c1-921016db203f<br>**Similarity:** 0.7475849986076355<br>**Text:** Lanyard regression is a statistical technique for modeling the relationship between an output variable and one or more input variables. In layman's terms, think of it as fitting a line through some...<br>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def plot_images(images_path):\n",
        "  images_shown = 0\n",
        "  plt.figure(figsize=(16, 9))\n",
        "  for img_path in images_path:\n",
        "        if os.path.isfile(img_path):\n",
        "            image = Image.open(img_path)\n",
        "\n",
        "            plt.subplot(2, 3, images_shown + 1)\n",
        "            plt.imshow(image)\n",
        "            plt.xticks([])\n",
        "            plt.yticks([])\n",
        "\n",
        "            images_shown += 1\n",
        "            if images_shown >= 5:\n",
        "                break"
      ],
      "metadata": {
        "id": "HnCjdTmnwSVJ"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_images(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "ovyDQLk-wkcS",
        "outputId": "12fd36b6-c601-4f0b-e306-69d441fb3b31"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 5 Axes>"
            ],
            "image/png": 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76K3aRfOWVYyYvQDpcNPyxks4PXZGLvkChtNBy/b1bP/7b8kqKmbCF76CfUQR/s5Wdj3wG9p3rIf+bU8dPZbJS6/FyMnGDEvsDje+jjoq/v0gvoN7AQvPiHxOvvHb4Mki0tlG1StPM/lzX0IbWYwZjnDw6Yc4+MoTWAMyIJKzchl3yQ14SsYRCfkxnC5CXV7qXn+epvVvgDRB0ymYdTpjz78Uy+ZAkxaRcJB9zz1G65a3+zMq3jkTTkrJgdeepWD6dEZf+Dma1r1CtK+H+K1p3oSZYHPRvn0FJWctZWD2XeyeVSNv2smUXXQVlt2NhYUOVL/4b5rWvoE0TYpOPp3Si79MxJVMwZkXkjN+MkJK9jz5D9wugzHnfRHpdNO0YSXRrmZKz/8i+ogswl4v+/91H8ULL8Byp+Ldv5ftf/0ZphUBBIbDzdjFF5F36gLCEQ1d19Exqd+8hpoXH8cM9uJMSWfy568ntXQywUgEw7AR8nZQ/fIjtG1f/x//ZSiK8skmh/wcC9jJWFvZxOBHEhP6M/AEum4nLTuTtb/6H6xggMlLr6Hws1ey/8mH8LfUoaFhRvrY9L934qupQiLorNzByT+4h+b1b9FdvYOmbZsItLez8LePkzyqiPW/+gHhPi9jFy1lyle+ReWoInr37wJiWV79QzShaQampdG1ZSNb//EHIj2dSCs6aBui0RDbH/o/3KmZuMqnsfvZh4hGw3ibm2hY9Tohn4+Kf96LZtg56cZb8Eclm373/7CCvex3uFj4g58z7eu3seq2r3Bo21pCgT4W/OxBLD3WP1vDzg2EI2HO+On9CE0ftBcDPV1EersH1ekd978mEQ47wuHEZndQcvpZuPJHU/P4A0T8fWhabKviTUh1BAVTZjP+S19n7R9+QdvbzwPQVbWbOT/4GW1V22nfvBJN0+mqrmbLX36PGexAaDpTr/wG4676Oi27t0LQx8hJU0kqm8G6b19DqKMeNB3dH8Dl8SCA5PxRzLjpVipefYbqR/4AZpTGTctZeNdf6Fr8GfY/849htyna18eeR+9n0ld/yJzb76O7upLWLW/Svnkd3TV7kGE/oFG/cwsB/73k3/MIon/fNlduw/r77zntnn9iabHMwoYta+nt6mD+Pf/ElZXGmnt+SNTfizsrl4U/vZ/JX/omG3793+x75TnCXV2c9KP/Iyx0tvzpTmQkTHpxKYvu+hO9n/k8u/7xB+xuD3P+6//R1trOjt/fjQz5qX8tmdNv+w2TbvgeG+76Fm17drPxr7/jnHvnkFlWzoaf/zfBznbKF11EWJpsXLWc8yfMomnt61Q+9NvYw1TryAeOijIc7aOugKIoiqJ83MQuhuPPfXVA779x0cgrm4judtC1rxLCEcovvIKJ1/03+15+ktU/uZnVP/0e9LQx55ZfkTy6HAE0bFnHvuf+QXppKYUXfA53RgZbf/8zqv/xRxzp2XTW17LjoT8iu9rx1uxm+4O/Zuc/f02wswWbJ5U5N/+E9CmnsvF3d7Pmru+w9d4fMXLCdGZ/6y6M5EywJJWvP0vt2hdImziD0s99GZ+vm42//iG1Lz+OIyefQ1vWUfPcI2SNncyosy9DanY2//pWap75O3kLzqb8K7dQeNoSdjx0Hzt+/z/YXToTrvsBWlJKYm9klU/EVlDA+t/fxcq7vsHqX3wbXbOY8d0fY08ZgUQQ7Gpn2z/vwwoGSDv5DIrP/AwVj/+L7T//EdGWBiZc8x3co8Ylbrhc6dnM/f7duItL2PCb/8fan3yTTffcQrLLQfkl1yBssSy3MfPOYMa3f0zDtg2sufNbrL7rW/Ts3cns7/6MzKlz+o/Z0fszkmggJb11VRx68zUySkoZcdJ84pdKNruLsecvZc8rz2ImgngDlxcUTJ/N7O/fTcv+A6z56XdYe9e3aFn/Oid/40eMmHseoNO8Zyu7n34AGeqledMqdj1wDzsfvIfu/TtoqdxKxWP3kTpqJPmLLyJt3DQ2/+0edv3vL3G7U/F2dbPt4f/D5nbgKCnHEnpsqwwnU794AyWXXsfOxx9mzU9vZs3PvkXnrnWUX34t9hF5CKEx+dIvkzVtLqt/9/9YfdfX2fCb/8butFFwxgXQX5aiKMq7GfToQwiEOHIEz/g8wrI4uGYVEZ8XGY3QsmcHDpcbR0YGEknUirDh/j8SaWshaeQYUkomYWo2hNBILiyOlSQlmmWhIWnbvIpwTwfSjNBevRuJhisje/D6Jeg2F1MvuY6UojFsvv83hLvb4CiBMWlGOPDGS6TmF5IzaTagoxsORp+2gIMrl0E0QlZRKclT5tCyaTVWMJahFw0FqF27gqxxU0gpnYyQse2V0hrwbE8isTDFEXuOkM9HuNuLNI+t6wxHUiZzbv4x8279Lafe9ltGLTyPbX//A/uefABkrMGpEPH/SaTQGH36uYRDJt27NyMMA2HodFXvxOztZfQp54HQCXS2sfEvv8bhNEgtHEvq6PGYgRBJBaOwp6THNsMysaUlU3DG2bgy8xDAzicfoH7ly0ghGDXrDERKFm0b3gRNRxhOeltb6KmvIv+0s9FsjuH3PZL9bz7P2z+4hkPPP0SSx035pV/llF/cz+zbfodn9MTYdZaUYJmxDMNEp3axz8bQpsJYUTQpqV23hqi/B7Dwt7fQuvltsmbOw5GRB9JCWCamGaZpzRvIcAAhTbprD9C9Zw+58xZhuDyMGDsRR/kUWta9hRWKZcubfh9tG9eSP20eKYWlsasVaYEmaNq4gUBrMzIaYc/yp6h543kwTXRkbJAyM6qCdcpxURl2iqIoinKcYhdngBCkl47FIaJYhk52cTmln7mS7roG9jzxIK7MbEo/dyVNFTs5tGoZMhoi4u9l5+P3s3DeORSedym7/nQnfd1dGA0H0MwI4YCP7f/6M2awF2/1dkSgi77m+lgz1bCfqK8X78G9yP4LvtIFF5B60mmsu+dW+ur2IBH01FVT9dT9TP/+3Yw8/RwOvvgQvW3NuNoaCQtBV9MB9jz9AJhReg/V0Fe/D39bM3ZpYbckkT4flc89ihX20XVwN/nzF5Iz41TW//fV+Gp2IgSkFY6h6EvfxJWZR5+vC4Cu6t14GxrwNxwALILBPqqee4zT73qA5PLJhDY0E42E6K6txvR1IRw29r38LJ271yKwMJ4WzLvjVDJLx+Ov3Y1EULbofFLKTmLLnd8g0LgfgECzn6on/sLYi64AaeFISmbCZdfS29rCgZefhL7u2E3A8/+i6IwLGHfRl1i3a9O79hcjpAXRIPtefpy8MxYz/vwv0rLhbcxALyPGT0VzJdOyeQ25E2f2LzGgaZLTRfkXrsfXF2Lfsw8S7e0CJNWvPEvBwosovfiLtG96i2CPl+6GA2CaRDrb6D6wBwY2Wq2rhkgEIS12PPwHwt42hKajyyi+hmqiPm+sqavQ0ITEQiezaCxFF1xC9evLaNu6EqxY06jKF54kb+Y8hGmi2+zklk2hu6OTYFsrMuInGPSz5/H7SR5d2p8hoyiKcnw0IRBHfxaCaZn4e7oTPZiZkRBCmvSngyE0nbIF51J43sV427uxfD4cDhuWywn2w0EeiQRpEurpjjXDlWBFAiBNhD6gmwUsTE1SvvRL5E+ZQe2WtViRILHR3I+utboC34HdjDzzXBrXv0HaqNEYriQ6qyoASB0xArvdINTeNmi5ns5WNE0nJSuH7kRdB+a5xX62httJERMrFHnX7994NmPY18XG//slZk8HSAsZ7MMMBgYMEKEhBFiiPy9c1/Hk5YMnlZlfvRW9vw5S0zA1A2eyA3QNm9vDlCv/i7Sy8fQ2NWKFTZLz8hG6DWEYxDIFt5D56rOMveRqyi68nO6dG6l983mat64DKUjKLyBi05nypRuIhsL9DzMFtuwiRCSC7nTFjtewG2jRXVPB1gOV6I/8iZTCIkoWXkje4s8z5SvfZ93tX0OGIoOGkTjSkJ58LQh5OwdMk/R2dFDgcuFJTSPYGlvGtCThvp7EfhZmBH9nGxmTZmAkJ5OUPxIpdHydHUgOD8rhbWtF2D3YcvKgemesR0ehEepuTxyxeHcgUrcd0a+gohwrFbBTFEVRlOMU778HISiYtYBo2WQ0IbAiIfY88Tfq1rxOsLOR0aechZFWgLf+ZexpGVj9TUejup1odzc546dT6XBiBn2JUr2VVUT7n+JakTA1K19DomE4XbELfkH/6GMCNI28k08hHAnRW7O3/3ZEAhpd+/dAKMSIk+ZQ+/Kj/ZkFJg4rSOeOLbGnvEDY18uBla9Cf4YZSHprdmOF+wCIRsKEeroxutroa2lECh0pJb7ODuyGjsPtpq9/v3Qc2Ic7M59RpyzBmZWFEDZcmSPQdCeezEzaB+xBgU6ouwvvoX3Em5ZG29uQkQjOpORYpoBhJ2vm6fgCfvy1VYmlJdCwawut9QeQkRCpRSUkFxTTsnYNdncSuGI3edIw6OlsI61kPPbUTILth975uAqJFAJv/X5aVi5n5FmXMmLmPFrWv0HJuUupevVFzEAvhxsoHL4A92Rkk1Q8juY9u7E5POhZNkAihE6ktY2UaXNxZWbjOzSwH58jL+ElEkuYdNXsI9zrBQTSsqhZ/WrsE2LYY0kFiWwNi5ETZ2A40uio2IWwzESZ4d4u3vzp9wl5u5HSouNQFXmLPs9JX7uNuhXP01Gzm/bd22jdvQWG7f1IURTlXcSDQEd7GwYEpGT/acbqP5dBztiJTLrhO2x97EFqn/w7kWgEd2YOZ0+ezdDGYAIZy7BKlGf1/zw4hGPoNnoOHqR57XKm3fQ/dJx5CQdefgiso3/PWUE/ta89z4Srv45ndCmF8xZSu2ENVn9/d2Y0gpTWEcFJITSkBNM0+8/M/ds4YEYhNKQ4cmioYFcz3toK4BgzrqwopreDSHfLsG/HHyYOOrNICHR2sOOff0AGYmdrKfrPHqEgmCYTzr2QvAXnsOrH36KrYiOalJSedhbjv3kHQvRft/h9bPm/n+N+7lFGzTydvAVnctItv6bqkfuoeup+hGlhhQPseuwfhNsaMAceO8sk2ucbfpt0G5mjx9FVWwNmEOn30b1nJ5uqKjnZcJA9/yxsqSmEWg8H1QaNbavraIj4SXHAnoj/IxLThKYBEivRv6+INaEeclA1TWBZEmlaWGYUISVCH/hZFOiaQMcESyb2t4YY1Mfg4bklwwcZFeXdqYCdoiiKohwnCYkr3l2P3kf3ro3Em+3EBnqIXbC50tMRuk7BvDPJKR+PlGas6SWCUNhPuKcDYTMgGG9CpBEN+RI3I7FL+Nj8AhFrbiPjz9IlQhPY0tKJRCOJIF/8vWAwRCRq4U7LjPX30t8USAqNSGi4ZkGxegshEx0+079maYGMmlhmpP9iVCIti9goCf3NMtEonLuEidd+g97WJlp27MAKB4jqttiFuzh8sSvjT5+j0cNPoIn1IWeK2NN/AE3XcCWnE4qamKHBnXJb0QjBjlYAHKkpSJub7AkzOfXmnwy4mDeRDheh1iZ02/ADRAyuU3/H6ZbJvpeeYOT8sxl34RVo4RB6UjJNG9/oPyr9c2uHt8mRnILdkUTKmAnMvfknSBlBItGkhCQPgab9aE6Dw7caFkOzKmJrB03EmlrFj3X8ZmDwbenh45OUkQnSINwXYGCWgZSSQEdLovSt//grMiTInbeQ3EXn0dfSSMvK19j//MMEOxvfcf8oiqIMlBjYwbSQljhipPJEq8X+/0R/w8VYAE8gpIFEIzW/CBwpdO3eRSQSG4xJ0/T+WF10QHk6Eh0hNWJvWrHvSCHQBkRDpDSRkQANa5bRW7+PrOJyplx6Dd3VO+jau31ILQcsh+TQhreY8vkvU3rBZWTk5VH56zsS83cfqkcGekkqGMXhx3YWmSMLiYbDdB+qBQSmaaEbArvLlSg7bUQeQuhH7J/26j20H9gPg8I5w9Uv1pzy8LYPlXiM2L9fYtOkGaVzfxXZRROJ+HxE2uuJjzVbcMoSrEiIxs0rSR49nmC3H9+BKoQVC7VphmNQ0DFz9FhM3U53TSV76g9S89pTzPre3eSctpB9zz9CV/VucnUDJPQ2NyQ+H5llE0krLmf/a88Ou98Nl5s537yV5T+/lVDjwcN7wooQbGtEREKJAaasqERIMBxJie11ZfdnAgr6903sM4EhSMotoD3+yRSCjIJCgj0d+Dpb+veahWEYuLOy8VbHVqvZbCTnjsTf0ki0t4fu2gOISIjU/FF0bI6VJdDIKCgk1NdFoOHAgKOnocn4WfjwXwCAFf/wCzCcyUjM/oG7FOWdqYCdoiiKohynRN88UiCkibAiAy6xD1+6RUJ+LCvK/pcf5+DLj6AR69cmnp0npYUZDCSWk4DUzGFufA73wCbiT/D7/4uGQrg1weGHv7FyDM2GLjTC4VB/cA2QsSYbgy/4B6xJSGIBKTNR1uB54h2LH/5JiljtHMnJTLniBrr9Ydb/7HtEvV1IJGnFE5hw7mVHPl8WRz4p7w8Z9s/bv3/CITShoWtDlofEdkTCIaKapG79G+x+4B4Gjo4r+y+uRegoTXEG7YnY0CES6Dy4j0MrXyVv8VImf/nb7PrXfYmRBWPlx/qrSVyMhyJIU6dj22a2/uF/Yn0YJQJtIlbTUCB+lPuDcYOzPUTiCMdvcN8p7+3w/vCH+4gYAs1uP+JWT0PEshWRBDqbWXffT3E+9mdyp86i8IxzKVt6FWljylj305sxQ30oiqK8Ow3NsGH3pAICbDbsnmRCfm/iS123OdFcHqLCRHe70A0nGhKbKxkB6K4kNJuL7kN1iGiE7Jmn0LV3O8KMMmrGbCxPOobDjeFwI80IhjsVDQ3N5cKwu7DMKHa3B4SO7k7BsLsQSOzOJBAC3Z2EEBp7n3uY7MkzmXHdD1n7i+8R7m4iepTRw30drTSue5uiCy6n9qXHCXU0xvroA7oP1dH05kuMnL+A2hXPEmxvwZ0xgpFnfIa61a/RV1sJgL+zlai3g1FzF+HbW4HT7WTk3FMxpYyN/m53xEaF1XRmfOmruMdOZt2vbiPc2cpwwTohBI7+fWWgYXMnI4M9RIMBDp8hZGw+TzKmoSMsieH2EO3rYd/yJ8iZv4jCxRey/8m/YkWjpOWNYvIXvsyG+/+IZpn0Hagh6+TFpJdNomP7Ogynh6xZ84kKDZs7jYhuZ8SE6WRMn8u639yOGezDsiwQJsGWRoiGqN+4ipLqSkYvvgDvgUqiAR/O5DSmfekGardsOOrAGkJaONKSKbvw8+x7+K+E+7wIoZGSP5JRc+fTsnENYW9H7Ph0t2N2NJM3dwFde3dgdxgUzltCSGgYLjeG3UE0HIx9JoVg1Cln0LF+BYH2RjLHTiDjpNnse+MFIt6O/jOohoVG/oKz6a7aSdTXTf6s03CUTWXvA7/FDPnp2L+b9lWvkn/G2RxatYygt4OkEflkzz+T/W+8RF9jHULXsbuTkEKg25243CkEAr7+6wCBZZn4/d3oyckIw87JV95ES/0Bal56fNhjrigDqYCdoiiKohwnMeAHKePdHR/Z3MV7cD9aoI+UnHxkMIiU0cS8I2fMw5FfQPVLTxzuCFtConOehMPNLeIrTQS0TIuuql1kTD8VV84o+ppqE5liKdk52Jwuuqv2Ik0rEaIb1EJkKCkQR6x/+NnjGXFxDo8HW2YWvu07MXt6Y/UWIAy9PxnxHRtMJX4Wif+DFYnStb+S7JLxGFkjoLs1MWda3mhGnXo2lS/8i57mQ5jeDpKz85HhMNIMJ0rNLh1PzvTZ7H7mEYi800ix8aBof0DUirDvxUfIP3UxkWCIlk0rE02RpTCJj4wr+qf6OtsItTfhyc4EK4IMBvsDoJBeUEbh6UuoeObvRONBPyET2QuazYlht/c3Gervh4h4sPHIGsc/cfF3W/ftoSgaJWXkSJoHzGc43Ew671JqN6+ht6GGkjnzObhtA4GOFg688Tx1q5Yz9xu3kTl7CUZGJlZTn7p1UBTlmORMnE7ZxVcQ8vWSVjyGaV/9Dlvuu4dwbzcA+dPmMvazl9Pd3sSUz36efWYYIxqh9HNfoLv9EOMv/iJVdoOWDSvZ9c8/Unz2haSPKYOuLvxth/Dt303hqWchTIvg/grKLv8aPd1tjFqyGCPSTU9DA1O+dCO97e2MPWcpNsNCj5oUnbWUvrYWJn3xRvY++TcyszKxzAju5HROufl/2P3sP2nc8DbDngukRdVbL5G18AIOvPki0ozEJgMyGmHzA79jyuVfYe7Nd+BrbyJ5xAha9u1m5z//hOwPAga7O9n18H2Mu/R6sn72f4Sb66ld/QZjR06kbOlVHEzxcOiNl0DTcOXk48wtRHPaOdrjGYfHw+wbv4tt1HgivR3M+Mr36N29me2P/XnAIgJHcipzvvIdtOQcPNLkpBu/zab77qWrtpoNv7yV8suuY0Tx3YT6/HjSU9n9/BN07FgNSHa/8gz2wiJm3PBdvPX7we+jp70Fd3sDU675Cvv++Wc6avYxYt5iTvneHfi93XhSMrFCJhV//yOWaRLwdrHmV//D5Cu/yrzv/YxgVyfulDQaqqqoWfYsw10jAURDQfY99xQZs+cz65ZfEPF60TUb7oxMmisrqHj0r4hoGAmEe71se+SvlF55I6fe9UdCLYc4+NZy3EWjmbD0UhpTXFS9+jQAhilo2bKWaVfehM2Tij1nBPXr11D92N8Sgz5IBGY4QG/VHuZ89VZwu7BljaDymX9y8LVnY/l6kTAb/vwLJl9+E7O++xN629tIHZFN3aZV7Hn0b2CGyRg7gWlXfJO+7g5GzT+TEWXjWP9/P0X2n++tSIT9r73IqPM/y0nfuAN7Wjotzz91nH9xyqeVCtgpiqIoynHQAE23YWkCpIWm62i6A2lGkUP6oek+uJ/2NcvJnXMa1S+MIdCwHyFNHJlZjL/iK1S9vRwsCyF0dN0GIoqGjmHYiJqRRJaWJsBm2GKBL2ED3QUiBFaUmhUvMmrRZxm16AK6KrcSDfkx7A5GL7mQPm8X9cufAszYqG2aPRYO0iw03Y5lRkg0rRQ6mmEnqgmkZqBrNiwritB1dEATFrpuw0SgaRpCM5ACdM1A0wz8vj5CnV7SRxViy8wm2NGM0A3yZ8zGdNgRNntsnVYETbfFMgzR0HUHptBj26nb+utnYOg2TDPKvlefYMRpZ1Bwxnn4Du7Dioaw2eyMv+hStIxCrOiDBNv7qF/2DHnnXE7auOl07d6ABBzuNCZ/4QbaWxsxo1GGu1mItaLR0HQ7pqajGQZC0xBWlI4D1az+ybfx93gx+7pjmXK6HmtiLKMICXbdTsgME+ntovblRyj84lfJmnIKHRtXgJQYDg9TLvkyAR3MUGzQCxkOg2nGMkaEjdIFnyFzyjTW//Z2NMOOLmONv4RhR0ZDIPs/V0JDGA40YcOGBF1HmtC6azuBnVsZNedMal95kkh3KwhB3sxTyTv381StXoGw2Rl/6Q34sNG45tVYU13TxIoECPW2YammOYqiHIemXVtp3xcbkCH+ACcSPJyl27h1Fa0Vm7EEIC3McBiBpG3v1sTjjmgkghUNs/vJv3Fg+VOIpFQsv59wdzu2J+4HoWNGw0grQvtPv93/4ClKNBJGWhZr7v4+Eg1TgBUJgYS6tcuJ56OboRAdOuxftYLYGUcSCUf6H7MMFQsoWRI6K7fQU737iDlCvV42/fmXuNOyICkJq6+PYFf7oOwxKS2qX32SxvVvormTCHV3YgUDHFq1HClEIrtPRiOs+/1dGC43oc72I9aVWGdfHxv+9AssoSceyFlmZEh/fJJQr5e1f/gpAj320EuamME+kJKWHRvo2LsDe0Y2aHai3R1E+7yJvRD0trPhN3fgzMxEOJxEOlqxQkH2PvL7RCa/ZZq0//i/cKZlIJxOrGCIYHdHbL/Hmw037GP1L36AMyMb4XAT9XYS6u16hyQygRWNsuPJB9GfewRbUhq6JwmkxOrpJtjnTQywFd+3B1Y8zaGtq7B53ES6O4kE/DStWxbLSI+YAxoHSzpr9lL77D+wZeRghQOEOtuxzHD/uyD7c+pbt66h4dUn0NLSMQMBgl2tSOtwa4dAVweb/vRTHOnZCHcSlt9HuKsdy4o9BOzav4dVd38rET/VJMjA4a5FJJLql/9Nw/o30F0eQp3tRPt6eYcdoygJQg7XM+IQPT09pKamfhj1URRFUd4nXq+XlJSUj7oaJ6T/5LzmSctk1vXfQ4yZgCO3gL7qSmRXK9v/9lt6WmJ92AxoNEtSZh4zb/wmWl4xLRvXoEXD5EyaTmdjIzsfuBezr4vRc+Yz5oIrSCqfga+1HfPQXrY//L90H9gNCEZOnsXYS64mZdxUQoEIvtpK2rdvoOqZf2ABudPnMOW679BbX0NPdTWpJeNIKhrDjgd+S/P6t9CEyYQLLyPz9M+SPmYCvXV7CTfWs/7PPyXcFctaK557JqUXfgl3+TT8re20b1zOvqcfZPY1/4V7ymykw4l/52Y2PvBbxsw+nbzTz8NRWIp3/x6aVi6j+oWHKTztHMbf8F0C7S107NyKy+XBdLjJm3sa4eZGuja+xc7n/slJV3+NpMlzEe40eiu3suOBX5OVk0PRZ6/GXT6NvqZGvNtWseX+3yHMCAVzz6D8iq/SXb2f7tp9ZBWPxZmZzqY//JTehhoAnJ40Zlz9X6RMOJn6zaswAwFyyqcSCfnY9L+/INLVjBwug0EIplxwKRlzFuMpKadnfyVNq1+j+sVYU5VYk9LYcrrNwZxrvoGr/GQ8xePwNdbSd7CSzffdQ6SnDcPpZurlX2HErEU0bl5NtKeLnJJJRO0uNv7pDgItBwHQDDvzvvMzHMUTaVn9GgUnz6by5aeINFRT/rkvkzz+ZAI9XgL1e9j91D/p2LEBkIwoLmXCVd/EVXYyuozgrVrH9r//Hz0H95NeUMLMr9yCaWg079iKKzmFjInT2f3UwzS+/RK6Aad+63acY6fQumEVkc5OUkYWkVY+mZ2P/Y1Dbz47oCNuRTnxqHPa0X3Y92oiERI5/P+hczDkvcG9tIlh5zncU6dADBjV9WhZ3sdS0+Eyxocum1FQiHCn0rm/kpOu/y6tB/dS+8rTRynv+GsiBoUIj61OA9/V4tndQ7Lth+s18GhHRrzr8kdb++GjMnDuWC+C77TUwBr09y03TL744PmONu3d65X4VwhSx4znzF/+gzd/fivtG5YjpNZ//h9QY6ExetbpTPnBr3jru9fgq9kV62du2Hq+f45tryufFsdyXlMBO0VRlE8odXNzdP/JeU03bKQWFGPpBokutGWU3kN1/X2nDLOM3UnGmHEk5Y0Cy6S7/gDd/SOcCgTO1DQ8WTlIqfWXaOJtaiAaiPXF4kpKwp09AoQg3jNa1NeHr7URsEAInKmZ5JSOw0jPJujtpn1fBaGuduK3RknZudiTM2IXrgIsK4L30AGsSKzJjzM1A09mVuJGygwF8be2kJZfAHosAw5L0tXUgCslFWdyGhIbEpNQbyf+thakEKTkFZI6pgzdYcdbW0dP40HScnLQhUYk4Mfb1kxa/iik4QFMkBF6mg5hc9hwZ45INBmOhoL0NB5K9EfnSs8ku2wSRlI6gc522qt3Eu31DgrCCcNG5qgSkopKkZqgr6GBroPVmCEf73SJnJSdiz0lJXETGu7pwdfWekTGpBAa6QWFCIcrcUkvo2G8h+qQ0dh+1HSD1IISUorHotsM/I2NtNXs6R8J+HBd7Z5UciZMxZaahrfxIJ1Ve7G7XCSPyOHwLZdJb2sL4d7Y6Hg2l5vk/JGAFhsJUFr0NNX392UEhstDVukEXHkFmH1+2qur8LfW9W+6RLc7SM0fRUr+aAyXm7Cvl7aa/QTaW0EezpJQlBOROqcdnbpX+8+UzTuT4suuo6ViFykFBaz95W1Eeo6e9aacuApnnULR+V8iY/xs+mp2UfvaY1Qtf5nBgThB+ZnnU3DmUuylk+nbvYX9zz/EoY1vfYQ1Vz6NVMBOURTlU0zd3Bzdh39eE/29ksWaYAzXQ5wYMnVoXoLeH5p6p97l4s/Pj/YEVwyac+CUwxeyR8sfGP6ZtxhmjsPvCMASEuTArYzPqzNwBMB3dzin41jCSrEQo8BK5AG821LaoGMwXOZIfJsGZzoMN0SI6F+rHLC9R5Y3sNwjBuU4yvwCMaRuA/MLSJR0tJrH3h84vIXWv4/eqX8/RfnoqXPa0al7tf+Mw5NKRvkkNJuLjqpKQp2Nx3imUU409uRUjLRMBLEuNiK9nYS648HXw+dNZ1omIikVhI6Qkkh3BxFfF+o8qHyYjuW8pvqwUxRFUZQP3LCNMRPvHf7/0d833+G9gb+JozZgGfibPGLK8CW+2/ShTZlgYIMcOWDy4K2UHF+wLrbM8VxGH95fxxKsi813OGR59K098mgNnTcWpDOHNAB7J+/2CRha+nBLD3d0B88z8H05ZO7jPRaKoiifFIJQn5emzasHfPsfbWQm5UQX7vUS7vW+y1ySYHc7dA/Oojz8QExRThwqYKcoiqIoH4oP5zIwthaND2t9R6vBieG91OX9qv+JtB/eycelnoqiKB+U+Ljccep78dNIZVUqJyIVsFMURVGUTxR1wakoiqIoiqIoH3cqYKcoiqIonzgqaKcoiqIo706dLxVFOXFpH3UFFEVRFEVRFEVRFEVRFEU5TGXYKcogx95J+LuXoZ7YKYqiKIqiKIqiKIpy/FSGnfKJJgQIIYgF0Y5lxKfBoxwqiqIoiqIoiqIoiqJ82FTATvmE6g+4SZBScnzZbhKwhi9PURRFURRFURRFURTlA6YCdson2rGH6VRATlEURVEURVEURVGUE4MK2CmfEiogpyiKoiiKoiiKoijKx4MK2CmfaOIdfnunZY6n1ztFURRFURRFURRFUZT3kxolVvnEGxp0O7KZrHiX9xVFURRFURRFURRFUT48KsNO+YQaGHY7nCsnj0iZO9YcOhXGUxRFURRFURRFURTlw6ECdsonmky85OEJiqIoiqIoiqIoiqIoJzDVJFb5FJCAOBy0O+I9RVEURVEURVGUT7J4yyJ1/6MoHxcqYKcoiqIoiqIoiqIonwoiFrt7T3G797zgCeiTsh3KJ5kK2CmfErEsO0VRFEVRFEVRjt+oUaNYvHgxlmUlpgkhEEIM+v1opJRIKTFNEwBN0xLzSykHlftJomkamqbx6quv0tDQ8IGuy+VyceGFF5KUlNQ/RWIJMBtbsG3chM0ChECXOlJamEgQoCGwAB0LEwspBoazBEJaSCEQUuvvHlyAlLG7KyH655VIGXvryGw+ceTPw8XL+heT/e+J+GLxMuXghY7pDk/EtyFeS4hqTlynzyGY5hmwvKCpqZmXX375A/8sjho1ipEjR2JZFpZl0dbWRm1tLVJ+NEFEwzAoLy8nLy+P9vZ2KioqCIfDuFwusrKy6OjoYOLEiWharEe1QCDAvn37CAQCR5Sl6zrnnXcepmmyevVqLrroIrZu3cr27duPun5d1znnnHMAePHFF497PyQlJXHRRRdRVVXFhg0bjmvZE50K2CmKoiiKoiiKoijvaMKECfzyl78cdDM9NFgXD8INd8MdD8rFgyFD548H8j5pdF3HNE1qa2s/8IBdUlISP/zhDykoKADAQhLBpPON1Zg17aRqBhFDYAuHkA4bGAamL4BwOTCEhhUJIpAY4ShRu44wQeo60jKRQqBFLaQ0Y4EyAaI/W++DiDNJKd8xAHw8hDgcsBNSI2hPIe9b3yFQNhpN0P8ZhNWr1/Lqq69+4AE7u93O7bffzqOPPkpLSwu33nor9957L2+99dYHut7h2Gw2vvGNb9Db28umTZuYMmUKF110EXfffTeFhYVcdtll/OpXv2LJkiUUFRXxyCOPsGjRIjIyMvjBD35ANBodVF48AHn11Vfz6quvkpKSwkknnfSOATvTNGltbeXGG2/k5ZdfPu7vgkAggKZpnHrqqSpgpyiKoiiKoiiKonw6GUbsFnK4YMo7BVjiAbrh5pdSouv6+1jLE4eUkkgk8qGuM3EcLBPdsrCZEEn1oJ0xCyFN+oIWzrR0fFhIf4jU7Ax8dY0YGgiXHd/zb5A9dwYtlftwnjQJSxPou6rQOrrQ/X0IZCxKF1+NiEfsTpxmprF9EKugAIQEhIa0QCOKaUURQsMw9HjC4IDlPlgHDhygs7OTLVu2sHPnThYsWMCECRNobm5mxowZPPfcc0yaNImMjAw2bdrEueeeS1tbGytXruSss86ioaGBdevWAZCfn8/ChQupq6tj6tSprFq1itzcXEaPHs3jjz9OQUEBM2fOZMOGDfj9fubNm8eKFSs4dOgQAHPnzmXq1Klcd911hMNhtm/fzu9+9zvOOussdu/ejWma+Hw+ampqsNvtrFixgrq6Oh544AFcLhfhcJiFCxcyYsQI9u3bx7p16/B6vViWRTQapaenB8MwmDt3LuXl5bz11lu4XC6mT5/OSy+9RGdnJwBerxen08lnPvMZ0tLSeOGFF2htbWXUqFGcdtppSCl58803aWpqIjMzkyVLlmCz2Vi3bh379u2jp6eHrKwsRo4cyaJFi6iurmb//v0sWrQIXdfZvHkzFRUVH/ixfb+pUWKVT53/9CtYNaxVFEVRFEVRPq2OpdnrwJ8HvoaWMXT60PV83F/Hut/eb4n1S/pfEj1/BO1VNXS/tRHLikJfEGd6OuFIFDxJBKVEs9mJelwENIF0uxF9YXRvgGivD8fBQ+h9AXSIvYRAk6BZYFii/6VhWBq61NAGvHSpJd472jya1DDiL/T+14Bpx/nSpcDof+lSoBGrryFBSIlEIoQWyxIk3pT3w2MYBp/5zGf45je/yZgxY1i+fDkdHR0sXbqUlJQUfD4fV155JcFgkPLyckaPHk04HGbSpEl0dHQkyunu7uaMM87g5JNPZteuXfzqV78iGAzidDq5/PLLaW9vZ+nSpViWhWmalJaW0t3dnVh++vTp7Nu3j3A4DMSy3fbu3cvs2bOpra3lz3/+c2LekpISzj33XK644gqef/55/H4/l19+ORMmTOCVV17hiiuuYPr06Udsq5SSjo4OLr30UsLhMJqmUVBQQF9f36D5CgsLqaqqoru7m9tuu43MzEx+8pOfsHv3bmpqarjzzjtJTU3lRz/6Ea2traxfv57bb7+d7OzsRBnl5eWkpKRQU1PDDTfcQE9PDxs2bODkk09+vw7dh0oF7JRPnRPnuY+iKIqiKIqifPzEm7Ee7WVZFrK/+evgV+xK3Bo2qDdMwA917f5eJQKh/U1XZc0hUseUkL7wdCLbdqPV19P95Ask+XzIjg5cmsDq7kYPh0k+fwEBuw3PeQuwAhHsB9swLAvNstAsiYi/Bh0cQSy8EAvpiQEvjvISQ+eTOkgNLAFSQ8R/H/QaMJ/UEUd7WTpYGlg6WDrS0pCWSHzOYnU/nIUXn/5hMU2T5cuX8+CDD/Laa6/x5S9/GZ/PRzAYBKCzsxPTNAkGg/zrX/9i9uzZ5Obm0tLSQnV1daIcv99Pd3c3a9euZcuWLXR0dLBp0yY2btzIiBEjaGxsZPny5cydO5eJEyfy2muvDQqUmaaZyJqN0zSNaDRKOBymtbUViO2bvr4+Ojo6KC8vZ9OmTQghOPvss8nOzub888/HMAzGjx8/qKx4oLq6uprNmzczdepUJk+ezPPPP08oFBo07759+6isrOTtt9+mvLyciRMnkpaWRkVFBdu2bSMvL49JkyYxfvx4Nm3aRFVVFaFQiAkTJiCEYO7cudx00008+uijNDU1sWvXLq666iouuOACVq5c+f4dvA+RahKrfIq8P1/A716KuqxQFEVRFEVRPlmOCGYIiWmZCCkxLSvW5LV/PmEJhBRIXSAtq785okATFgKIGjo6AilNLCnRpIYpBHp8+AJNA00MCKcoxyoSiSSymHTdAKljpiQRyslG27EbSwO7zYm/sxeP3Y1V14y/vqW/TWgU2XAIqUHUskDTwZToQG/RaITQ0BI5PxoWAlMAQhuUoiaPOHISMeQe6ch56B/6YsDv8sjlQMOSFlZ88JJEjtygwgeNeyE0gdRiGYcCCNqcZDs9selCxAKGgg914BPLsujr66Orq4u9e/dyzjnnJIJbuq5js9mw2WwAVFZWYlkWX/7yl3n66aeP+FuMNz9NBMvj/fX1l/f888/z85//nK1bt3LvvfcOWnbdunV85zvfwe124/f7sdlsTJw4kUceeWTQfFJKmpqaWL9+PX/84x/52te+xoYNG+ju7mbVqlWsXLmSt99+m1AohMfjOSKj1rIsnn76ab797W+zfft2Hn/88SP2idvtRtM0HA4HlmXh8/kQQqDrOrquI4Sgt7eXaDSKzWZD13UcDgeBQID09HSqq6tpbGzkxhtv5O6772bz5s2sX7+e8847j5/85CdceeWVH3rz9P+UCtgpnyJDRytSFEVRFEVRFOX4xUb51KTEt6eGQ8vfBqeN1KKRRAJe/NX1aDYX+SdPpW7zdgwTUqeMI9QXwDIjjD77TPydPfiqqnFlpRFtakemJhH1hyAzgxHjypB2dat6vIYGcuJB1KyTppL1119hiCgGELVsxEIBcsByEgwrdstkkQhuxRqPDhjjVYr+EJmMBc36M9UGr3lwAC8WDLSGhPCOZZ7hciy1QYtZloXQtP6WvwMCVYeHrE2EBmX/IBlOQ8NyehKhx6MNlPJBGT9+PKmpqZx22mmMHTuW+fPn849//INgMMju3bu54YYb6OrqIikpiXHjxrFr1y6ee+45LrvsMnbv3j2orNzcXAoKCpg0aRKGYZCSksKECROYNGkSI0eOJCcnh9raWjo7O2lubj6iGerWrVt59dVXuemmm9ixYwfl5eVs27aNVatWJfZJSkoKEydOpKCggKKiItavX89VV13F5ZdfzqOPPsrZZ59NMBhkzpw5PProo0ybNo3s7GwmTpxIWVkZmqaRnJzMzp07cTgc7N69e1DgTNd1pk2bRmZmJp/73OcYN24czz//PLt27WLjxo1ceuml2Gw23nzzTfbs2cMzzzzDF77wBVpaWqirq2Pfvn0sWrSIpKQkVq5cya9+9Svq6uoYOXIkVVVV9Pb2UlVV9bEciVrIY/hk9vT0kJqa+mHUR1E+QIe/qmOOaSDwYRxtORUIVE4sXq+XlJSUj7oaJ6QP4rwmhMAwXEhpIxoNEbvaDb/jMjabDSEE0Wj0A7uIiNXL+Ng9UTxWhmFgmuZxX2jHRyYcbiQym82GaZrHdUzinaUPfLL9cRB/Yh0f5e14Py+GYRwxQtzHgWEYg0arVE586px2dB/WvdqSJUv417/+1d98TiKlhRkMsO+P/2DcpRfg6+yg/a3V5MyaSttrq3Hl5RI0I/gbmsmaMgFXUQEtq7cgLIuS66+it7GZ7o2bGX3hEqof+BdWVy+jv3IlptuDPSkJMWAQio9rll28iW8oFOLyyy9n+fLlH+j6srKyWLZsGaNGjULTNAzDiAWjAFPK/pFdwRp0mpKJ2JY2cNTf/sidJS0sKRPHQO/vH08ClmXGsieHNCeVQuOIDDs5+Pv2yHlIjOQ6YEJ/0G6ggcvF6h7/VfZvyMA7vviciea7moama+jxQSniTYal5PXXX+eyyy5L9Of2QcnIyCAtLQ2IXTd4vV66u7uRUuJyuSgtLaW1tRWn00lfXx+BQIBp06aRlZXFs88+O6gst9tNbm4u4XCYUChEcnIyPT09OJ1O7HY7nZ2dBINBbrzxRp588slhRyrWNI3c3FyysrLo7u7m0KFDmKaZCGTa7XYKCgoQQtDW1kZvby/p6emkpqZy6NAhMjMzyc7Oprm5mfb2dgoKCrDb7XR3d5OamoqUks7OTsLhMN/85jf585//TFdXV2L9Qgjy8/MxTROXy4Xdbmf//v1Eo1HsdjtFRUVAbLCOSCSCrusUFRVht9s5ePAgkUiEgoICNE2js7OTzMxMotEoXq+XgoICpJQcPHiQQCDwgR3T9+JYzmvqsYXyKRT/Co8/cenvt2DIHHFDk7cPLzHwSdLH5+ZMUZQPRkqKi/ETCygYmcqbKyrpaHMBUWD4gEBhYSGlpaX09fUxZswYHnvsseMexv6duFyuRLOEG264gfvuuw+fz/e+lJ2VlcWMGTN49dVXj3jP4/HQ19fHqFGjKCkp4c0333xf1jkcXde5+uqree2118jJyaGjo4P9+/cPmsftdhMIBI4IopWVlTF37lwefPBBpJTYbDbOOecc3nrrLa6++mqeeOIJysrK2LNnD62trei6/o4X8CUlJZx66qmJ8oYSQuB2u494sv1hSUpKYsGCBbz00kuDglSjR49m8eLF/OUvf8GyLFwuF9deey0PPfTQoIvp4TidTq6//nqeeOIJmpqahp0n/jl8PwNjhmEwefJkNE2jsbHxqOs+GiEE559/Pm1tbaxevToxPTMzk5kzZ/LWW28d0a+OoiiHyUTwRoK0iAgTXdfQbAKcDjRPErpm4D1QT9FZCzjQ0IKuCwzAinX1j/QHEZEQVjSMJSD1pOnIpg5sWRkYmg007WMbpDsRxAOF8VF5BQzKZtOPsnOHdnAv0dDE4Okah5taav3BsaG3QscUsBuaYQdD+sQDiXXkRN7baMKi//ZPAnJA5t1HobOzMzE66lCBQICdO3cmfne73Xz/+98nEAhw3333HTG/3++npqYm8XtbW9ug96dPn85nPvMZ6urqjnq+tCyLxsZGGhsbB02PX8+Ew2EOHDgw6L2urq7EdUJzczPNzc2J9wYGBePbuWDBAk477TQqKyuPuL6QUiZGrR0qHA5TVVU1aJppmkdc79XW1iZ+9nq9w/78caQCdsqn1ICnMsME294p/Hb4ic3AuVTATlE+LeK5uuXjsxmRZ+Hrgj6fpL0jSlN9B6MKPGRl2Olqix4lVBdzxhlnsGnTJvbu3UtWVlYis2lgYMjhcBCNRhMdAg/sjNtmsyX6qLHb7YTDYaJRExAIIZk/fz4bN26ks7MTTdNwOp2EQiEikQhCCJxOJ5FI5IjMqoGBlXi/IeFwOJGJBRAMBhMXSvE+REKhEElJSSxcuJAXX3yR3t5eamtrE+UahkEwGEyUo+s60Wh0UHZWvK+W+DoHjrRnmmbi/VAolMgcCAQCuN1umpqaEgFJu90OxC7oFi9ezPLlywkEAui6jqZphMNhWlpayMrKwm63J+pQXV2deJJts9mora2lt7eX4uJi0tLS2LZtG9B/s9q/L+LLNjc3k5WVhc1mSxyzgcfG4/Fwxhln8NJLLw3av1Z/v0+WZSXqF9+++LLxbMDhsjHjnwuIPSGXUqLreiJDzmazJbL+9u/fn3jfMAzC4TBNTU2kpaXhcDgIh8P4/X4sy8LhcCCESEwXQmC327EsKxHMCgaDBINBkpOTaW9vH7TO+DE77bTT2LJlC93d3Uc0ORp4XCORSCJwKqVMPFWPb+vAjL+ysjLGjRvHsmXLuPrqq/nd736XmC++3+J1tNvtiSyBeBmGYdDZ2UlKSsqgOgkhWLRoEevXr1cBO0V5B/HvPyE1DIeDwkWnsuffL6DpUHLeQgK6huf02WSMyKR57wHkiGzaGtrITknHU1yIr62DPY89Q1ZxAb6uTvY++gzZUyZAUQ6WsJBYiCGhIxW8e+/i2XAa736PM/Dfw7Qj5jliKTF0qhjS3JVYP3fvOs/QcrRhAnbD1+SdstvF0EXk4bu5gaP3fpgj+R4rv9/PPffcQzQafU8ZYjt27ODAgQP4fL739cHw8Vq9ejXbt2+np6fnI6vDx5EK2CmfMgMz64YLvL2zgenViqJ8+ogBr9wCnTGlHl54rJEvXVHEirda6Om20dHcQWNdAEGUd/rGqKio4Ac/+AEVFRU888wzJCcns3jxYjo6OggEAjidTjo7O8nPz2f79u1cccUV7Nu3j6amJnJycmhoaCA9PZ1QKERLSwvFxXksW/Y2UmqkpSVx4YUXEgwGWbt2LXa7nXHjxlFcXMyLL77IzJkz6e3tpaSkhMcff5xIJILT6eTaa69ly5YtQCwjy+Px4Pf7aWxsRAjBkiVLaG1tpaKigmnTptHc3MyiRYuIRqPU19fjdrtZvHgxlZWV2O12TjrpJHp7e5k9ezZtbW04nU78fj+nnXYaq1evJi8vL9G0Q9d1zj//fNLT06mpqcHlcnHo0CHOPPNM2tra2LJlC2PGjCEajbJv3z5GjRpFb28vo0aNYtu2bcyaNYtDhw7h9XopLi7G7XZTXV3NZz/7WZqbm9mzZw+LFi2ip6eHjo4OampqyMrKory8nLKyMt5++23OOeecRJBR0zROP/10NmzYwOmnn47T6aSjo4MLL7yQN998k5ycHDo7O9m0aVPimGZnZzNu3DjGjBnDa6+9xmmnnUZHRwe5ubl4vV4WLVrE3r17mTZtGuFwmLy8PNauXcukSZPYs2cPubm5BAIBDh06RHl5OfX19WRlZeHz+ZgwYQLbt28nMzOTl19+ORF4W7p0KX19fRw8eJCxY8cCsafelmVRW1vLl770JWpqaqiqqmL+/PnU1dVx7rnn0t7eTjgcZvPmzWRkZDB+/HgKCwt5/fXXgVjg69xzz6WlpYX8/Hw6Ozvp6upi1KhRvPbaa4mLfsMwKC8vZ9SoUbS1tdHT08PEiRMBqK+v57zzziMajdLU1MQFF1zAmjVrmDRpElu2bCE/Pz/xVL2goIDq6mpyc3NJSkpi7969LF26lNdff52ysjKeeuqpREB27Nix9PX14ff7GTVqVKLD6fhode3t7YwYMYK3336br3/967z66quUlpaybNkyJkyYgGVZFBYW0tnZOShg197e/r5loSrKJ9HAh0YQy6KSukHK1ImklJfH8p4MDbeQ2EbmowmNgjFjELK/I/3+5o0WgBXL/so+ayESiabrWFJiavGRRg878UIoHw+DAlFD/j3mMoj3V3e0dRxzZd597cO+fezDjhxrsO3jOJBJb2/ve17WNE26u7vfv8q8R5FI5F0z95UjDc16VZRPsIF91w18HX8JiqIopWPdVOzsjI0uZjOJRE3OvSCFsgkGyak68e8Yu93GggXzOffcc0lKSkosv3v3bm655Raqqqq46aabOOuss/D7/WzdupW+vj5OP/10duzYga7rTJkyhWg0ypo1awgEAsyYMYO+vj5KSkqYMGECF1xwAb09ff19v0To7u5m//79bNu2jUgkgmVZVFdX43K5GD16NBdccAE9PT243e5EnQKBAGvWrKGsrIxAIJAIRAkhmD17NvX19ei6zhNPPMGBAwdIS0tLXBwbhsFJJ51EbW0t+/btS4zSlZKSwuLFi/F6vWzdupUzzzyTjo4ODMOgsrKSSZMmJZrrmKZJc3MzPp+PtWvXcsYZZ9Dd3Y3NZuPJJ59kwYIFiUy+OXPmJDK3mpqaME2T1tZW0tPTufjii9mxYwebNm2irq6O2tpaduzYkcgmNAyD2bNnI4TA5/Oxa9cuPB4PJSUlWJaVyOIzTZPOzk48Hg/V1dVUVFRQV1fH5s2bKS0tpb29nR07dgz6THi9Xnbv3o3H42Hx4sWcc845+Hw+cnNzaWlpoaamhn379iWCVT6fj5ycHCorK5k+fToul4tgMMi8efM47bTT8Pl8FBYW0t7ejsfjYd++fUycODGx303TZM2aNZSWliKlZOXKlYlsuFNOOYW2tjYcDgcvvPAClZWVib6M4lmWp512WmI/7NixA03TmDt3LhDrX+ess87C5/ORl5dHYWEhn/3sZ49o2mqaJlVVVezYsYOlS5dy8cUXI6UkEAgk+rPZvn071dXVCCHw+/1kZ2fj9/vZu3cv8+fPp6+vj/z8fK644opEdmJqamqiyYvL5SI9PT2xzvhnJv75HHiTFh/hbu7cuYmM0rq6OtxuN6NHj+a8885j27Zt1NfXv9c/fUX51IqPRBnPDNYAHR1N2BEuJzidSMOB0J3YDDeG7kK3uRB2J5rdATYH2B1oNgeawwk2B8JwohkuLM1A6DY0YcSaWb5LRCUePBz4UpT3Sn1+lBOVCtgpnzKHA3XDNYVVFEU5FroBo4syCYfg4ssKWPZKCxW7+vjTn/bx178cpLnZj9XfINYyY/2CxDvKjZs/fz5+v59nn32Wffv24ff78Xg89PT04PP5kFJiGAZ2u51AIIBlWYTD4US/JwcOHOCFF15g586dPPfcc0ybXoY+IG9eSklWVhZZWVmD+rKRUuL1emlsbOT5558f1KdaRUUFRUVF6LpObm4u2dnZtLS0kJSURGZmJn6/n2AwmGi+WVZWRlZWFk1NTTgcDlJSUtB1neLiYjRNQ9d1+vr6cLvdicEJhBCJoNLAZrZwuAluvElnvPllIBCgo6ODrq4u1q9fz6ZNmxIZZna7HU3TEs1GfT4fTqeTtra2xPbm5+czZcoUCgsLaWhowOl0JgKOuq5js9kSzWDj5cTLllImmm2OGTOG7du3M2PGDILB4KA+7eIX+/H6e71evF4vdXV1vPDCC4kMsAkTJlBXV0dWVhYHDx5kxowZdHV1JY7rli1bWL16NT09PdTW1vLiiy9immZinw0MVgE0NjZit9vJycnBbrczadIk6urqMAyDrKwsgsFgonmnpmnk5OQwZswY6urqYtkt2dmJcm02G36/P9FEt6urK1H/xsZGHnnkESZMmIDL5Uqs37KsRDNUv9+P1+uls7OT9evXs3PnTsLhMBkZGaSmprJjxw7mzJnDgQMHKCsro729ne7ubg4ePMjzzz9PU1MTXV1drFy5kpqaGkzTTPzNDNzueAAu3qF1/Dg4nU7mz5/PgQMHME0zkcE3cPlQKIRhGDgcDnRdP6KJbrxZshCCsrKyxGAiiqLEDArai8P9OWtoIA43349lMcWuuQ/PJRBo/c1d++cQIAX9zSXFkOU/fplQiqIo7ycVsFMURVGU4yJISTEIhXxUbPPT26uTk6ehaTZME0wr1qF2XCwDaR+VlZWD+sXq7e1l2rRpnH766axdu5bXXnuNzs5Opk2bRk9PD08++SSzZs1KNEHt6OggJyeH+vp6Vq9ezZQpU7Db7TgcDpxOJ2+8vh4ZjXXSLaXk7bffJi8vDyEEzc3N5Obm0tfXh2EYPPzww0yaNImMjIxB/ZmEQiFeeeUVGhoaaGtro7m5GSEEBw8eJC0tDa/XS0ZGBunp6bS1tdHd3U1LSwtSSpqamujo6KCqqgqXy0VmZiadnZ2sWbOGSCTCtGnTeOKJJ3C73XR1dVFQUEBTU9OgzCnLssjMzGTatGk89thjeDweent7yc7O5sUXX0TTNMaPH09zczMvvPAC06dPJxAIkJubi9PpBOCxxx6jqKiI8vJygsEgK1asICsri9raWhoaGrDZbDQ0NBAKhVi1ahUTJ05kz5491NfX097eTm5uLt3d3YkRxlwuF5WVlei6nuizbf369UdkaEUikUTz1urqalavXs2///1vJk2aRFpaGm1tbezbty8x77Jly6isrOTtt9+mqamJZcuWYVkW48ePp7W1laeffpopU6bgdDrxeDx0d3cnMvUGZmpalsWyZcuoq6ujp6eHvXv3kpKSQmVlJRkZGYljn5qaSnt7O1JKqqurE81OTdNk5cqVjB8/nsbGRnbv3o3X60XTNJ566immTJlCUlISQghSUlJ488038fv9ifWvW7cOl8vF2LFj+cc//pE4xmVlZYRCIV5//XVyc3Px+/2sW7eOjRs38tZbb7F//37a2tp47rnnmDp1Kh6Ph0ceeYTs7GzGjBmDw+Ggo6ODgoIC/H4/mZmZieDuzp07aW1tZcqUKTz11FOJfgQjkQjbtm1jxIgRbNy4kfT0dFpbW8nPz6evrw+Xy8UjjzzC5MmTgdgAKfH+DiE2cMihQ4cYN24cNpuN8ePHD3pfUZTBzQ7jAe945p1pmgNG7Y4/HB/8sFzKgQ/NZeI/ZP/UQYv0n02lTPRzOTSbzrIsuru7E311xh+yDPw93i/mcFl5Q18DRwhXGXyKonzUhDyGb58Pa6hwRTnxxPvRGNp1vDppKye+Yxkq/NPqvZ/XDBx2FwsXjWPsBJM//6GSz32hhG5vF5vX+Wlu6j7mxvaJTrv7s5kGTh/YCf7QDCA4POjBwPliKxXx3oGOuvzRyj5aHd/LPO9W7/jPA8X7TMvKyuLhhx8elI14tLKHu3Ecur6ByxxLXd9pO/Pz85k9ezZ79uyhsrLyqKPBDq3f8dzovdsxOtb6vlPZ71buO33Ojrbu9/pZG24fHevnbuhyx7Kdx/K+cmJS57Sj+7Du1ZYsWcIjjzySGFgmHsj6+9//zsqVKxFC4PF4+J//+R+ys7OPWs5w39cD34uXHe+3KxwOc/fdd/P973+fzMzMQcsHAgFuvvlmbr/9dgoKCohEIvzyl7+kvLyciy66CE3T2Lp1Kw899BC33nrroIdE8Wy+YDCIz+cjMzOTv/3tb8ycOZPp06cfkaH7XgYkiO+jUCjE5ZdfzvLly4+7jOORlZXFsmXLGDlyJJqmJQYQUt6dlJI33niDSy+99B1HhVeU98uxnNfUoBPKp9bggcaPZW51Ua8oikU4EmDlyl2sWm0RikR4/NE9JCUbdHWEj+tbIn4jMDRgMKhD72N87/DPR5//3cp+pzoe7zzvZb1SStatW5cYPfZY1vdOZQ33+7HU9Z3W29TUxMqVK+nq6jqmdb+XYNC7Lf+fBJiOdT8cy8/vVq/3uty7zX88Zb7bMVLBOkU5fsMF2Hbu3ElRURGnnHIKuq7jcrno6uqir6+PESNGYBgGHR0dWJZFVlYWmqbR09OD1+slKysLl8tFX18fQgg6OzvJyMhA13XuuecepkyZwhlnnMEpp5yCy+UiHA7T1taGy+VKjILd1taWyOyTUrJ582Zee+01Zs6cSUZGBvfeey+7d+/m29/+NpZl0d7ejhCCpKQkpJSsWLGCdevW8f3vf5+dO3eSm5tLVlYW6enpuN3uQQ+bFEVRPkwqYKco70gSy65TF/WKogBYSGnh85nER50OBk2CwXg/Pcp7JaWkvb39o67GO4rfGCqKonzaDBeoi9M0jdTUVHJycnA6nTQ1NfHDH/4QKSULFixg6tSp/PrXv0YIwRVXXEFRURG/+MUvgNiI5Lfddhv33nsvHR0d9Pb2kpmZyZlnnsny5ctZu3YtoVCI5cuXM3v2bP7617+yadMmUlJSCIVCfP3rXz+iL1TDMCgqKuJvf/sb48aNo6+vj/T0dEzT5O9//ztPP/00ycnJiQGT3njjDdra2khOTkYIwcMPP8yDDz6I2+3mN7/5DZmZmR/OTn6fqSCjonz8qT7sFOVdDWk2NMxLUZRPm6EZbUObzSuKoijKJ0e8f7f4gEADaZrGM888wx133MHf/vY39u/fTyAQ4MYbb2Tx4sXs2LGD9PR0br75ZqZPn84f/vAHZs+ezc0330xSUhKPPfYY7e3tLFmyhPvuuw+v10tOTg6zZs3i2muv5fTTT6ezs5O1a9dSVVXFAw88wB133EFnZyfBYPCIelqWxWWXXca2bdu47777+MpXvoKu69TV1fHss89y77338rvf/Q5N00hKSuLyyy9nzpw53HTTTViWxYIFC/jb3/6Gruvs2rXrYxn4+sDqLId5KYrygVEZdsqnViw3RjWHVRTl/RP/XlEURVGUT5p4MGzg7xDLPr722mu5+OKL0TSNUChEe3s7Dz/8MFlZWdx8880A/PGPf2TatGl0dnZSWVlJS0sLuq6Tk5ODEILRo0eTmppKamoqQgjsdjtutxuHw4GUku7ubgoKCkhLS8Pj8QzKfBvap2lmZibXXHMNe/fuZcqUKUgp8fl8uFwu8vPzcTgcjBo1CiEELpcLwzBwu91omsbo0aNJSUkhNTV10GBRHydHdFNwxIPG2I+Duwjq/21gsO+IeeDwlP6IXX+RR8w3zC3UsPMMqdZR5xmmPonJw9b5GO7fPobBWOXTRQXslE+1YwvWHbnM8F/tKrCnKIr6FlAURVE+PeKBoRdffJE9e/bgcrmYMGECW7duZdasWSxfvpxly5bR2trK+PHjOXDgAEuWLGHjxo1MnjyZzZs3M3XqVF5//fVBgzEBZGZm8vLLL5OWloZpmsycOZNnn32W3/72twSDwcTI28Nlk0kpOe+881iyZAk+nw/LsigqKsLpdPKLX/yCrKws3nzzTcaMGUNaWhqVlZWsWLFiUBlDB4X6OBl+UKbB3fwIQByOt8XuZIQOcvA8hzPpYiE9K1G0dbicwWvvHzhk4JSjBNrk4F/EkPupoeUc1VHnkYj+oOLhgOThWqm+TJUTnQrYKcoRp49j6Oz6g6mIoiiKoiiKopzQBo4ArWkaF110EVVVVQDYbDZmzZpFKBSipaWFH/3oR+Tk5PD8888TCoW48soryc3NZdSoUVRVVbFw4ULGjBnDFVdcwZgxYxBC8PnPf56SkhLGjh3LK6+8gtvt5qtf/SplZWXcfffdrFixgtGjR3PXXXdRXl7OddddR3p6OkIIdF3nuuuuo7CwEJvNhmEYCCH4r//6L0aOHMndd9/NCy+8gMPh4M477yQ3N5fi4mKuu+46pJQsXbqU0aNHI4Rg6dKlFBYWfqwGnYjvA03TEqPbWpaFsCx0M3p4dF9LovWPkkv/YB1SSqSILWv1D+IBEiGs2N1S/z7QEoG/AX0HDqyEBAsd2d/7lqZpiP5Q3ODKDlym/zMlh4zwjux/6/DdlyXidYkF94S0QJqDio7No6GjgdSA2DwSsDQQmoYw7EihgegPE6rgnXICEvIYPpkf1lDhivLhG/q8Z2hnDMPkab9jWeqLXjlxHMtQ4Z9W6rymKIry8aLOaUf3YZ3TFi5cyEMPPYTT6UTXdYBEoGdodtxQ8fkG/j5wmUQgacitaTzoJAcElYZbT3y++HtDR4PWNA3LsoYNysTXP7S+A+ukadp7CtrFlw+FQlx++eUsX778uMs4HtnZ2SxfvpyCgoLYABy6jjQlbdu30/OPf+NAizVtNk2EpvU3lY3dw0gp0YSOpumYkTBCA6HFgmYD990wobfDPePHd6+mQyy3DU1oIIbJsBOHM/GkjJV/OGCn9Qfs+osdkq4nRH8IUAikZWJZUSwZy6ETWuw9XQh0YvWwABMrERiM2g1KvnYt0cICDN1A6z/WK1as4NJLLyUcDv+HR0JR3t2xnNdUhp3yKXY8wThFURRFURRFUQaKB7Gam5tZu3YtZ599Nm63+6jzxVVVVdHQ0ICmaaSlpVFeXo7T6Rx2mUEZVwMCccMF2rq7u1m1ahWRSITTTz+d9PT0I8qoqKhACEF5efmw6xi4rqFBxqH1eLft/KhJICLA7OjEvW4dqVIgLQszaoKmYWHFMiVFLJCnEcuGM00LXdcR/fNEoybSsmJBNnk4aJdIV9BkvNVsbLoej9DFAoJCgDY0ZNefKXc4YBeL+0kZy+ATAiRaIlMvts9jZQ089pYliUajiUI1EXvpCPT+3AwJROXh1IygXcd21SVEyT9cnRPs2CkKqICd8ql1ZPep/zkV+FMURVEURVE+uYYLkkUiEW677TaysrJYsmQJMHzzQsuyEss++uijbNiwgaKiItrb28nMzOTOO+8kIyPjiKy2QCDA/fffz+WXXz4oG2VgQE3TNCKRCLfffjttbW243W5eeOEFfvOb35CUlJToi05KydatWzEMgwkTJiQyxzRNOyIgN7D8gX3ZDZ33RDMoyAhY/YEuBAgp0YVG0G5gZaYSjUTQAyGc4Qi6ACEkEonQY3lpmibQpABDJ6w70aMRNGnGmrgK0KRACokV730uHrATOuHMNEKWie4P4AxHYoFBGQvBISRSSKQW388gpYZAImWsroaURLVYmUI3sDweZG8fmgzHsvZic6PpYBOxZaOawERgj4axdINIchpRfwCHGUZL7BaBIUBaVuy4ajLR3Fc1i1VONCpgp3yKCeKdpR79fdXMVVEURVEURVEGGpiZ9tJLL7F7927OOeccenp6ePzxx6moqGDGjBlccMEFrFy5Er/fTzQa5ZJLLkkse9FFF3HdddcRCAS4/fbbeeyxx/jiF7/Iv//9b6qrq5k1axbhcBi/38/DDz9Md3c31157LatXr2bDhg1MnDiRUCjEpZdeSkpKCoFAgIqKCu644w6mTJlCRUUFdrudiooKHnvsMQCWLl1KQUEBNpuNjo4OHnnkEerr61mwYAFnnHEGTzzxBJZlsXv3bubPn8+ZZ57J/v37efzxxwmHw1x88cWMGzeOp556ip07dzJt2jQuvvhiXC7XR3Ysjo1ASAHSIqiBfu7pRCIRNKFhy8+h7+9PkBG2sEQsczHgcSB0A80XQuRkQXM7tsnjCNTX4m7rBGKDOBiahhSCiISolIkgnjl1HJEJYzC6exFuB31vb8Lo9WMPRQArFjjUwJRRhBREpYbMycZq7cSwQggpiRLrgy6ChhQ2nOctpuPlV0jvjmBaAiElQkhMYv3xaZaFNTKHXhNExMTq6EY/fRa92yuxHaxH6498yAFDzZ6IAVdFGUgF7BTlqIYZ/lxRFEVRFEVRlEQ20ogRI3A4HBQVFXH//ffT3NzMeeedxyOPPILP56O+vp6tW7dy0003HdEEVdM0PB4P5513Hv/4xz/o6emhsbGRCy64gCeffJK2tjauvvpqnE4nRUVFvPnmmzz99NN8+ctfZt26dbzyyitcdNFFACQlJfGFL3yBO+64g/Lyci655BLa29v54Q9/yPnnn49hGLzwwgvY7XYcDgfPP/88SUlJnHXWWfz1r3/F4XDw5JNPMmPGDObNm8fvf/97cnNzufPOOzn77LNxuVw888wz5OTksGvXLi699FKefvppwuEw11xzTSIz70QOAklAjsojgIV92UosBOa58wkjCY4vI5yXRaSmAff4UoI56Vjb9pB23kLaXlpOqsNAYmDOOYU+omi6RmTTXvTiPCJ5aYQDfhxdPRh7DqAVF9Kzcy+pe5vQTp2G/XPnEtxfT3BTBa7xRUgEoW278UwswtQNol4/6eefRfvytwnVNWAbXwI9AcSuamzjRhMakYFl6FiahmnYkDOmEmhuweYP4bOBE9CLRmIGguAL41p6Fh3L3oDGNsyJYwmOyUds2YW7rw8ptcE9ln8Exys7O5vMzEzC4TAHDx5MZHCmpqaiaRpOp5N58+bx5ptv0t7e/h+vLz09nYULF7J582YOHjz4H5f3fhFCMGvWLNLT03nttdcwTfPdFxpg5MiRzJ07lzfeeIOOjo7/qC6appGbm0tbWxunn346oVCIVatW/Udlvh+0d59FUT7NPvhg3eDxlbRhXifuSV9RFEVRFEX5dJswYQLJycmcfvrpbNu2jRtvvJElS5Zw7bXXsm7dOizL4gtf+AJnn332oOXiTU4ty6KlpQWXy8XmzZu57rrrOOOMM7jmmmswDINp06bhdrs588wz2bRpE5dddhlLlizhxhtvJC0tbVB5l1xyCX/5y1+YO3cut99+Oy+++CI5OTlcddVVXHHFFXzve99D13VCoRDbtm3j0KFDvPjiiwSDQXbv3o3dbuezn/0s55xzDunp6VRUVOByubjmmmu44ooruOWWW9iwYQNer5dnnnmGrq4uduzYgZkYVfVEJ7GPyCLS0IJD1/Hk5WHzm+jjS+meWkbr7n3YZs+gtdtLqKENUd+I2dhOtKYWstLoc7qwRo+kq9tL1OagbWoZ4Wnj6d24g7RJ4+gNBNB1DWvlJnImT0Gev4DenbsxGzsI19Thys+mvbuLqMuNGFuMPSMDn2YgDzQQaO0ieLAe26JTaahpIJieRufsiUTHFdO7bTdOjwspBDIaIeqw02sYiJlTYM5J+IQg5HQS0Q0CaR4CLd2EapvQNBB9fsz2HsKTJmBZOrJ/MIz+oW8/kqOQm5vL73//exYtWpQYwAXg5JNPZs6cOfT29rJo0SJKS0vfl/X5/X6mTJnCSSed9L6U937yer1ce+212Gy297TskiVLKCoq+o/r4Xa7ueqqq7DZbFiWxZVXXjloMJuPisqwUz7lPvomr/Idfjv6NEVRFEVRFEX58AzNRBo4+IIQArvdzsiRI1mxYgWZmZmsWrWK4uJiwuEwHo/niOXb29uprKykrq6Ohx56iG9+85u89NJLvP3224zIHsHKlSuJRqOJEV6bm5spLi5m9erVzJkzh7Vr19LT05Mot62tjVtvvZXrr7+eqVOn4vF4cNgdNDU2sbuiAk3X2bNnD6ZpYrfbycnJYfr06cydO5fdu3cza9Ys3n777dhgC0KgaRojRoygp6eH7du34/F42Lp1a2IE1s9//vPs37+fsrKyxI39iZxdFx8mItznR09JJmpFiPZ0YSsuINzXiys5icwZ0wlFIliBAFrERPMHEYEgDq8f/EGEZWJ1ebG1tGNzuCEvg3BbO+neAOGaRghFMaVE5ObQ9fTzuArz8Cyej7+zHXp70VxJJJWMxpGaTsDXg+ULIg514Q5EEYEgRiCEKz2TrPHj0O0GhtPA19RKRmcfZkMzJhq6tAjtrUafMhGfGUV4o8ixZQQr9+NOT8G0Gej+EI6eIIYlCdcfwhmK0pOeGutjb+AxEh/NMauoqKCtrY3169cTiUQAsNvtWJZFbW0tfX19ic/2+PHjKS4uTixz6qmnJoLbDQ0Nw5Zvs9mYN28emZmZHDhwgK1bt9LZ2cmIESP4/Oc/T21tLRs2bMDtdjNv3jw8Hg979uxhz549jBs3joKCAnp6eigrK2P58uW4XC6mT59OT08Pq1evJhgMDrvegoICTjrpJKSUrFmzhq6uLmbPno1pmqSnp2MYBq+++irhcJjRo0cze/Zs2tvbh82sKysrY+LEifT29rJhwwYmT56Mz+ejqqqKefPmsXfvXhoaGujt7aWoqIiSkhKqqqrYvn07KSkpzJs3D8Mw2LFjBwcPHqSgoICTTz4Zy7LYsGEDGRkZ5OTkEAgEGDNmDPv372fr1q2YpklraysOh4MzzzwTt9vN66+/Tm9vLyNGjEhsz6pVq+jp6Xn/PhRH8dGHDBXlIxEfI+ijNPTkII/yUhRFURRFUZQTy8BmrWPGjMFms/Htb3+byspKvv71r9PZ2cn1119PXl4eqampg5bNy8tj3bp13HrrrTz66KPcdNNNLFiwgG98/b/YvnUb3/jGN2hvbaNkzBjS0tJYuHAhv/rVrzjjjDPQdZ2vf/3rrFy5kgkTJiQCbNnZ2SxZsoSf//znfPtb32LGtOlc/NnPctWVV3LXT+7ijjvuQEpJfn4+I0eO5L//+7/ZuHEjP/vZz9i4cSNOp5Pi4mIcDgcAo0ePpri4mOuvv55f/vKX3HbbbViWxU033URfXx8///nPefnll3E4HCd8dp0AdBlruROtqcdVOJLOkiKio0djpKRCfRuiuRtzyy4cvX3YAgFEXibWuDGEdNDmzyWYlUUoNRkz3UPU40KmpUA0hJGTQ8f4IuyTx2NIgYxK9IllmNMmE0hJxwwH0EMRrPHlyFlT6KisxvJ6iaYmE0xNJZCeRNS0kKaJedIk/B1thCqrsZrbsRpbcY/OoycvHX1MAeFUFxg6tHeQWlRAtKUTvb6Z9MljsXV0QWoyemoyERklPGcyvQV5hDLSCacnY6UmEdUkIn6s4v8MOHYfdvBu4Posy6KwsJDPfe5ziWm6rnPqqacyYsQIQqEQ3/ve98jJyaGuro4f//jHiVGQh1q4cCFz585l165dnH322TgcDoQQTJo0ifb2dm655Rays7MpLi4mMzOTiooKbr75ZgoKCgiFQvzoRz9i4sSJuFwuZs6cye23386+ffsoLS3l+uuvP+r2nHLKKbS0tOD1ern11lsxDIOsrCx+9rOfEQqFOO+885g/fz4ZGRncfffdtLS0kJOTc8R2pKSk8IMf/IDKykpGjRrFuHHjGDFiBNdccw1SSmbPns3ChQuRUmK32xk3bhzV1dV897vfZfz48dxwww04nU4OHTrEOeecQ1ZWFnfeeSd1dXVEIhEuvfRSTNPkrrvuori4mNTUVFJTU/nqV7+aGNhmzJgx9Pb2kpOTw/e+9z1SU1O56667aG9vR9d1brnllkHZkR8UlWGnfIqd2CdWRVEURVEURTlRDBeUklKSlJTEz372M2w2g4yMDO69915CoRAulwtd1/nyl7886MZW0zSuu+46rrrqKiAWlDAMAyEERcVF/PGPfyQYDOJ2uzEtE5vdzs0330woFMLpdHLnnXcSCPhxu11YFtjtsQCbYRhcdtllfOYzn8EyLVxOJ7quc8kll3DuuediIWNl9jdftdvt/OlPfyIcDifqeudPfoLdZkMIwY9+9CNsNhtjx47lrLPOwrIsXC4XQgjuuecegsEgDocDu92e2DcfdR928SbGAzMf46PtCgECgSsYIvjyCpLHj8XqDeB7/EUy21uJ7tyJe2w+fbt2k9blxZaeRqC+AT0QRBga0Z5uksJB5L4qUkUYvaUBp91O2B8m6rTjbW7FNMPohiD49krc6RnYUtxEX1mBx27gGlNM8KldjCkfQ++ePdg1HdOQpIYjaHoUuW4zqckeIlv3MmLCGPw9PaTtPYjW001GUQ4dy14jNRxAmmEc6AReXI6zqxcjGiX4rBdXuA+rvYlUS0McOEhSqhuruxszFIKIiau2FqFrEO0fNXeYffdRikajNDQ0MHr0aCD2d/H5z3+eUCjELbfcQlJSEosXL+bBBx+kpKSEjIwMxowZw+bNm48oq6Ojg1mzZuHz+Xj22WcJhUJIKVm1ahVvvfUWX/ziF0lLS6OtrY309HQWL17M6NGjGTlyJNu3b6etrY2XX36ZtrY2Lr74YkaMGMGECROw2WzMmTOH++67j3A4fMR6a2pqOO2009A0jcmTJ2Oz2airq2P//v289dZbFBcXU1hYSCAQSNQnMzOTpUuXHrEvTNPkuuuu45VXXmHXrl2JUaZDoRCNjY2Jv7NoNMqKFSvYsmULu3btYv78+bS2trJ06VJeeeUVnn/+eaZOnQrAtm3b0DSNDRs2EA6HaW1tZdmyZXi9Xmw2G1/+8pcT5e7fv58NGzZw4MABHnzwQaZPn87EiRMpLCzE6XQyZcoUUlNT6ezsfF8/B0OpgJ2iKIqiKIqiKIryjo4WiBJC4HDYkNJCShObTcNmi42aKqWJ3a4jxOGGXVKCYdgwDBvx7mmkBCktkBY2m4bd7onNZ7PHRiM1DAwjduuqaRqGkYSuC0AHeXiwByEEbrc71kuZjLdWiU1DE4nl4/W22+2xpohIzP4gnuh/L96nlhACp9MJkBgcwDAMkpOTE9ukadpHHvAB3jFwKEX//hACZ18f2oatWFKi6zqWtNAbGtDq6knTNTQE1qatGIDV3U2KACH7y9U0EGDRjtQE9tkzcZcWE21qw9XjQzM0nK1tOFpbQUocSPADm7bjFgKxpoNMJBYCS4CFxNB1tKZ6OGQREaBt2ESKAA2BrK1HHqwnXRzeLkOAp7EJGTtYOBoaEZqA6jpcUiCRyPrYdgshEj2FIwRRIbBiR/nDOixHFT9eHo8nEagbaOfOnZxyyinMnTuX7du34/f72bx5M62trRw8eJCamppEUHZgs9Lq6mq+/e1vc+qpp/LLX/6SK6+8EogFt+JBXYArrriCUCjEAw88wLhx4xKfGcuyME0Ty7ISAbI1a9ag6zrr1q0jEolgGMagvhsdDge33HILP/vZz2hoaODUU09N1CccDiOlxDRNDMPAsqzE36FhGEf0FReJRLj99tspLS3lpptuIi0tjZqamsR8Ho+Hvr6+xPzxBwI2m41IJMKyZctYv349F198MXfccQePPPLIoL97p9NJJBIhGo1iWVbiNfCYxMvUdR0pJaFQiK6uLtatW4dpmuzZs4fe3t7jPOLHTzWJVZSP1AdxsvjoTz6KoiiKoijKJ59lWezZs4dt27YSDoU4eLAmcRMspcTv72P79u309fn6A3oW0jLp8/VimVEsM0I0Eqa6ugppmViWSWtLK5WVlTQ3N9HZ2UFvb2//ABWxMqVl0efrZfOmDbS1NhI1I/j9fiKRCGbUBAnSiq3H6+2is7OVgwer8ft7kDLC3r0VHDxQjWmGsMwQUTNE1ArTWHeAvt5epLQwzcPbYJpm4uX3+zFNk1AoRE3Nfj5uLXakANkfxLREbPCFgBWlD0lYSqJIhGUhrVjwVEoJmkAKsDSwNIGpSTTAZlokRSTm25vxP/Mq+lvrSDLBsOJ3OGLIrY7EtEyiUhKRkogEUwo0NGKxRInVH1yTmoYeX7A/ECuI/SsRWMQDkHGx6UJosUzC+DL9L+Kv2KyxQN9w++dDCrqWlJSQmZnJwoULWbp0KV/4whc455xzGD9+PKNGjWLs2LHk5OQQiUR46qmnuO222ygvL+fZZ59l3rx55Ofnc+655yKl5JRTTklkq8YtXLiQBQsWUFVVRUNDA263m+LiYkpLSyksLExkzLW2tlJcXMzkyZMpLi6mvLyc0tJS0tLSmDZtGrqus2bNGsLhMKWlpcycOZPy8nI0TeO73/0uhYWFiXWapkl7ezuTJ0/mpJNOIisri7KyMsrLy8nOzqawsJAxY8ZQWlrKgQMHCAaDLF26lAsuuICMjAxKSkoSZaWnp/O1r32Nnp4edu3ahd/vp7m5mYKCAi655BJOOukkSkpKKCkpITc3lyVLlrBkyRLKy8tZsWIFV111FYWFhWzbto329na2bNmC1+vljDPO4JJLLmHu3LmUlJSQmprK1KlT0TSN0tJSMjIymDx5MhMnTqSoqIhzzjmHL33pS7zxxhts376dyspKpk2bxvjx4znllFMSQb4PkpDH8Kns6ek5ot8BRVH+U/EzxcfrRK98fHi93kQ/DMpg6rymKIry8aLOaUf3YZ3TFi1axEMPPYSzv6lpLFMnSvW+alqaDhENBwj5A2RmZNDb20fh6CJa29oYOXIkff4+2trb0XSNcChAa2sLubm52GwGkUiYSDjCSTNPYuvWHXi9PYwbPy5WdnU1qSlpuFwusjKzaOvsxLDbyUhPp6O9hbaWFjIzs2lqaqJ07FiaW1qYM3cOB6r3EQwEiEYj1NfX4fEkYXe4mTNnLq+/8QZjxozB7XbS0tJEMBggyZPEoYN1FJeMJRQOEwgEMU2TlJRkvN5ukjweotEozc3NzJo9m/0HDuByJ3PK6fORGAghEcMEgeIj4YZCIS6//HKWL1/+gR6jzMxMXnvtNUaOHBnLIHQ4CJsWna+/hfY/P8YTyz3DlDphwwYzJ5F66ixIS8OyLIIN9fS8/jbu/fUY48vx+3x4GhrR+4NjQkoiQmImp6HPmol7+nhITSaCwPIH8e/cRWjdFjJbupCahRSxPuMiSW6cZy5AulKJajqWjGWD2aWFb+tO7Hv3YWDG9mH/rVE8HpcIrsWzBweNGSEYHLY7uv78KqJo+AyDgj/+gsCkCdh1WyKz7PXXX+eyyy4btrnn+yk/P5/8/PxB01pbW0lLS8MwDFpaYn8fwWAQr9dLXl4enZ2d1NXVJUZl3rt3L11dXdxyyy288cYbrF69OlGWx+Nh4sSJOJ1Oqqqq8Hq9jB8/HtM0aW5uprCwkJ6eHg4ePMiUKVMQQuD1ejEMA6/XS25uLj6fj7179yKlJCMjgwkTJtDX10dFRQW5ubl85zvf4ZZbbsHv9yfWm56ezpQpU2hvb0fTNLxeL263G4/HQ319PXl5eWiaRmVlJUlJSUycOJHGxkZcLhcdHR0cOnQodqz6A2gFBQV0dHSwe/duTNOkrKyM7Oxs6uvrSU9Pp7W1ldzcXDo6OiguLubAgQPU1taSnZ1NWVkZUkp27dpFT08PaWlpTJw4Eb/fz65du8jNzSUnJwev10t1dTV5eXkUFBTQ3d2N3W7HNE1cLhdOp5OtW7cSDAZJSkpi8uTJSCnZuXPnoCy/9+JYzmuqSayifKRUsE5RFEVRFEX5OIoFqdxuF/4+H51tjQjLpK+nk1DIJD8vHywTYVk01Tfg9Dhoa2smNSWZ1GQPMhIiGOxD1wysQJj25jYcdgfBUJCszEw8bicHa2oQpknLoQZCvh6aO9qYOXsumgZISSQcou5gDZYl6evt4VB9HaFpUzlYU43f10NKchLJToMktx1XUip+Xy8Om4GQFo31dbQ0N1JaWsKuXRWMGTOGuroaQqEQI0eOQkpB1Z5dJCd5iIb6GDEim5QkF82Nh2hrbqGoJCkWRDoBG7ccvS89iWZJ+txukm+8HH1cEe0vvomsqMKwJFrRaDK++EU6V60l4/+zd95xdpzlvf++M3N63V61Rb13uVvu4IJxAwwBwk2uSQwXQnLDBZIQQgk3CZBAEpyQC4QUum2wwTaucpMsWZLVu7Srbdq+e3qd8t4/5pyjXRXjKlnyfP053lOmvDNzNO95f+/zPL+FyzD7urD6h3AJWxrLCTAvu5yqW99Btv8YY89tQY6OIyWotdWELlhB5MZrGXv4cbTHniFgGkihIE1JbjJNIWBSdcsNxMZjqM+8SBEDI5PBIwRCltJuyy09sfmi7HN7wtuv5sQIAAUhLM7mOGxwcJDBwcGT3u/r66s8L4tXwDQ32J07d1aeq6rKT3/6U7q6uqZtJ5PJsHnz5mnvbdu2rfJ8ZGSk8nzLli0nteNE99nJyUnWr18/bftf/epXp4l1ALFYjGefffak7ZUZHR2tPM/n86dd1rIsDh06xKFDh6a9f/DgQQ4ePAhAb28vQOU8ll+D7Rg9NjY2bd14PD5N1Ozv76e/v7/y+nTXZCrpdJqNGze+7DJvNI5g5+Dg4ODg4ODg4ODg4PCqENjCUHU0yrJly0jGWwiHolgSspkstfX1dM6ZTXdXF3PmzyOXzzJ77hyE0EimUvi8XoSAbDZHIBSiqqqGQlc3VyxYRCAaRtMULrjsClLJFJ2aRjaRon3OQmrqawFJOpXmksvWEpuM4fcH0HWdcLSacKSKNZesteuyKSpGsYBhmkTCUfx+HxdceBHhcIjDh7uYOXsBoVCQhqZOUqkUjTNmo6oqgUAQgNnzlpKMx4hEo2guN7VNKSSS+hmdRKuioLx1K0xJKStRaVCSpyQUJfivvwbXwk56v/B1aocn0ABVgn70KKktmwj/jw9QmN2C1X8EKSSKBAMF9V1XEHjHVfT/238S2XeUqLTsfUgFunvRt2wnO6uV1k/+ASPhMNmfPkjENHHlipjPbUIxJOb8xcjBfjyPPIai2asqim2KcUYyUqWFROUtqbS+SkzT5PDhw2d8vxMTE2d8n29XHMHOwcHBwcHBwcHBwcHB4WWZ6jhaRrGgkMtTX1dHwO8lGCwZMSgKdiQTLFy6BIDJ8VHcbheZTJaZMzsQSMxSmmlTfR1CKCxdMB9TscWl4cEBzEKBWW1tIARWk8Xw0DDRYACkRVtrM16vh4DPy8REjJqaanyBAJpbo6GpibICJLBNKRRpR/hURcKMjo7S1NhIKBLGpbkIh8NE81E8HjeqplIsFDEtk5AvRHNjA5ZQkEKlqrYRRaNUS60co3VuZMyUvVENv5ead17J4QceoWp4BJdQ7LpwAlQgnMmSvv9hGv/pctJCABZFAdn5c6m77RZ6v/otarq6UIRt7iCEsFNfATcSV1cvk3//XRr/6o/pOtiNd+tOXIqFIi00RQF0hLBQFImm2LX0kGfoLEoQQiLPA7HO4e2BI9g5OJw3TCn68Ko+c3BwcHBwcHBwcPjtqKp6XLAr/bTcsWMHF164hr1791Mo5IlEIiRSKaprasnnclimyepVq3nmyadob5vB2PgoikslFAximQaDg0N0ds5CWhLN5SFaW0t7Zzs7t7+Ens9x6MB+otEohm7Q19/HnDmzEIpgeHgYyzKpraljdGQczWW7zs6aPRu3z09rWxtbtmxBU1WSySRVkSj79+/nmiuvYOuLLzKjrZV4IkYkEmFsbAyXy0Ug6MelaQwMDNDR0UE+X0SgMHvhIqrrGkuq1xQDg3OISqqpx00x6EV29+OWAkuoJRsHe7wghIJrdBTrQBfSzj0mL1Sit7yD4fUbiXT34AHMkqmElFO3XnLf7e0j8fBz1L7vNlK7D1Kl55HCrp8npWUbX5QcXs840pHrHM4d3roxvA4O5yViyuNs7NvBwcHBwcHBwcHhdSJL8VpCxTAs0sk0Pd09eFweNm7YSGJ8nP6uw+zbuR2f20UunSKXy9Hb10sikWBibJyD+w+Qiqeora6hv7cPUzcZPHaMriNHMA0daeq0tc3AsAwmYxNkCxl8AR/HBgfpPnqUY8cGqa6uZc++veSKWbL5DA0N9Wx+cRPSNMlnsgz09LB31w4CHo2d27bQVF+NpgkWLlrAocOH8Ho99Pb2kIjHsSyTiYlxxsZGCIdD7Nmzi/q6atLZJMPDQ9gRg9N/T5+tX/WvFglIIZAoYOioho6MBBFSIEpRiJQMHCwh8ZgC2XsM1dBxSdBqa/EunEPx6fX4pcQUSuXAbf3SLkAnSw+hSXLrnyPcUEOhvQFhSYRUKFehEyiV/Z261t6bdA5KzwXW8Qt3DoqvDm8fnAg7BwcHBwcHBwcHBwcHh5dFSolpmmiaNkVkEbTOmMHw6CgrV68mEglTXVtLLpfDkhYt7Z00NzdjAjfdcguTkxMcOLif8fFxOmbPo6mpmVQqTdOMWYTDYVzDw+imQTyRYfmKC2msr2dg8Bi6rpPNZolEdXxeL5lslrnzfKRTKa648jpM00DTNDLpNNGaauKJBLX1Dczo6CAUClEVjRKOVJNIJvEGwmQHR1ix+gKKhTwur59IJEomk8HrdSMEFAoF2jpnEU8maWhuJVpj182bKs+9lWUeIQRCCKxSUTihCGQpNdibzlPcdZCa264ldmAf1fEcqrQFN6uc5Css0r/6DW4jj4aF1d5JLlNAGxqhFFhnR8mdgrIwpsSS6KNxXPNnYRzqRaucMFFp45mknL58YqtFqS3yjBTQc3B4dTiCnYPDWcHpEBwcHBwcHBwcHM4tTNO0hQ2h2JaeimDBokVTgpUkjS0tSCkwJahC2NFXgCkloepa6ltaScRiNDQ2obrcAEjLTpFsbmu3RSYpUUqCzoyZsyv7nyryTHVCldJCSolhFGlq7yAaqQKhsOrCixEoCEWhsbWjso3la2wBbno6Z7mETEnakcffRYhSFNq5g5QSy7JTXaeeN1UK0v9xL9G/uBv1q59l4qeP4XlpO75MDpelgCLRVQtlYoQQYAJGWzNyeBR3sYgFL69Wls6bUjRR+0bxzmiloCiodkLs8cXOrdPp4HBWcAQ7BwcHBwcHBwcHBwcHh5dlWgSSAKRtNkDJgAGYVszfXkTY4h5ljUcS8IcJ+EMgwJJ2ETSJQEiQQrETJcVxcUeIU1dxEnajKq+EAE3zUFXlxqJselFO9iwtNS2qS5SyIaeKgMdfnzoA7BxTmaaUsbavn0QKC8/EIBNf+Abud6yl4QPvxvzQbWRe2kfy2Rfwdx3BqxeRGojSOoGaaoqpLJppi7SvZLeKAD2ZQWkOYkrplNR2cHgNOIKdwzmJQJw2DPutzW/rqd64nqzclR53rzrHfmA4OLwlUQGF9vYQwYALy8oxcCxDOmUBUxMt7NosNgJ7fvr4v21FgSVLqxgZyTM8lKNS6Ln0+dS7gADmzq0hX8zS15NHIlFVhXnza/EFdDIJDx6PwtDwBKOjhdO0W6kMlE6fxDIdAXTOChOtVtm5PY5pSBRAQ2Ah6ZxVDaLA4SOZKcd8fMhW3kokHGXBQi/5jMQwdNxujT17Ryka8qT9nci5eJd3cHBwOJ+ZJngJKg6sxz8HKe2cSeUEcYyKOHY8ik0gStFrNrISDTalBzhdKFZ5MYEdRVcyQJBSYhaLaC7lhDa8yuObyjnUIZ0+tbOc7qqiAN5sBu5/jPRDzyEXz8F31cXU/MXHyE+OErv/ScSL26gq5kFaCE3DsCysSu2633JCpB2RKBBIq2w2IZDi9KMdeYpnwJse2WgHWZ5DF9jhbYVjOuFwjnKqm6oy7VEOXD/eXZzvTA3nP9XxOh2Rg8PrR1Bd6+eCixvpPTrOvDkRGhvcNDcGqKnyTZHpys9UQENRTpgfs6C+1sv8eTUlKc0uwCymrFW+m6kCAkGLCy8Ml+5sgJQ0tfi44upqEpN5MHQWLKhBEXYZZ/uvUinrrOJGxYOKUhLdju+jjIKCihsNBRUI+lWuurYDKZXKZLpHU1g8ux63EPi9gsvWRlEBFRUNDxq+0trHz1ehmOeO99Xj1iTJyQKtzVGqo1403KU2QmdnPX6fe8pazhSDg4ODw7lCuV6aLXYJhFBKDzHlYUdcKaK8vL2MIsQJj7KJwZR1FU79EFP+VvYtUBSFVCqFaRhTrBRe70Ge8DgnkQghEcLCEMI2odAkXj2NZ9tWrL//F+L/+yukX9hB8x98FPX3f5e05kFKKObySI9WmVr87fXnBCYWatiPTGURloUUx6VaS8rTamTlqUVpJ9+evOVp36up371XxqmWPdP19BwcXilOhN35wrSpivIQbKoXzsmLIxQUKTHPQSFHThkWHy8UKko3dgFYU8Lo7TXOwcN8GU7sVE4+OFmeZnRwcHgDMVCUPCtWe+jtdrFt2xiFguB9H5jJ4f0ZNm3sI1zlJxwJkEqnmJgwWb1qFoNDY3h9Kvv3DeH3u2hrqSMYcJFImUQjHpqaolhS0NM9yuIlHRT1FAIv3T2DtM9oIByUmJZ+fJxgSZJxHdOUBPwWiXiesTGdlSs60fU8muZGSomUOTQ1wJHuYZqbA1iml4H+FMtXzCaRmETXBQcOjtjFoTXJzM4QLpfJ+FieqqogTY2SY70Gum4PpFataueyS6qI/cQgkyng8XiYN7eJVDbD6EiWWbOqUV3Q158mES8AkkIhT7GYJZ/PUFPj4cVNfVgS5s0N4vKojI1n+MjvtfPwgy4OHYyxctUcNm3aS6FgnNUr7eDg4OBwMm/FwvxSSiYmxlFVFY/HS6GQR9NUBvr66JzZaY8FhACUSvvflgJNaWygC4WC14U3X7DHgsJCCHBJiRYbR//ZrxnfdZCmL/05R3fuxLVhM9bwGO75HaRUBSHN0ubK6cwnI4VEF0BTHYWXjhLgleucAoFhWQiFyhTg8XHdy2ylNB5+ubwiIU6Wb9+G3wSHcwgnwu48QGgq4YYGIi1thFtnEmnpIFo/A02cWo8VCEK1TQRr689RxdbucFV/gHDLDELNbQQa2wk1dRJu6iTc3E6gvgV3KFKqsXD6GZxzm9NP9WkBP8EZLQhNcXohB4c3FMHEmMUv7+3hltvr+Nzn5+NyS9LZLIOjSbIFkzUXNpPLp7jtvTMwzTwXXu7BNNMsWe5lRruP939wHpOTk+gGKKpC56waQlG49PIaWloDRKvzzF+ogtT5nQ8uIp2Nkc+DKvylFpQeUlJb62LRiiiBqEkymSUYzrFoeRhD5ghGYf5yDcvK8L73zSA+qeP3erjm2g7a2hSaWi0ExcqRLVtaz9y5YY71ZnjPe2ehF9Jk4wlSCRMFDSEF8WSGZCHJRCwFKPj9XmKpNDfeOouLLmlkwcIgmtC47roOxJSf2Zpq0DHLz6o1HeQyReprXaxcWY1LFQhLUEzHGR6cpFjUWbrSi9tn3+cdHBwcHBxejrIA5/O4sQydZCLG2OgIh/fu5qUXnuOFp59k15bNPHjvz9m9a+dbUnA8U0hh/3aQtVX4Pvn7JIXdtwuh2n22sGUxn6KgHDiIvnUP4UtXYygWHO0lVF9FMRwE7O2cLuBQCDu+Tob8aE21FLt68ArF3vdvaaMApFRRrruCVFUVQtplkISkkjqtSQtVWiAtW6CT5Yi812ZkYU9wvn2/Fw5vbc5NvcZhGprfy+Jb3g2NixD1MxFDvRi9XWz+2X9AMcHU6CsBaG4fyz/yBxQ9Hjb/w99APs25F34miESrWHbreyjMWIDwBCgM9iL1ApbiQvF68Hothrdt4ejjjyKzSc69Y/xtnHw8CqApks7rb6R17VVs/Pu/I9vfe+ab5uBw3qISjXoY6M/xV3+W4ZrrisydHyafc1EsmoRCPoSQBIMawZAHr8dHLqMQjxtYpo4/4KaxRWNiskgyXUDVVFwuCAQ1XO4c0agfU3czPpbj0MFxbnvfTCbGi4QDRY6X6y5HSQtGBk0efaQfl+Zi9rxGisUC46MGu3dkWLXax8SYYP++GLe+p4p0KkfK66FzpkZ/t4vRYYUjBxOUp6RntAdIJ/PkcjpVUQtNk1iGRNdLM+lAoWCQy1sEgl4QCqmUSSEPmqZR3+ClmNeJT2R5aXMWgV6ZES8WNQ7sSbFp8gCBsIdAyEMiVeC6G0M8/GCRQl5HSoHL4yaXL5SOU1Ta5uDg4ODgcCJlkcWyTPw+HwLY8syzbNr4Aof27qYq5KO6uoZ8Lk9RNzjS28eChYtQVXtC6e0RZTf9GKWUKG433lmzmfAGEJmELdqVe2wpMACPVGAyjac1QlKoaD29qJks5tJ5FJ/dhBuY6gVyYu1dxbJwLZhLXppYR3sRwiplSJWWntqsqW68QMGlUnvtNYz1D8NkClH6RWAKhbywkFJBtbALa0gLgbRTbBXbuMTB4XzCEezOA/RUho3f/yFtV9xE+51L2ffwL5nYvR5pGJw40FEByywyuG0jlseHZRZPWuZURc+n31XtlFNO+fmJ7lHHn5Yf1knbPo6CQAOsUlHzcgD09OUlCgaxoX6e+X//wuLf+V9ULb+A/T/5AbmRQRQkitfLvOtvZv4734dS0Dj86E8Bc3qZt1Jzp8ZxVNomsJ3qOT44Lv+dOnw8VdJxeVvlXRilNdTSHuRp1jt+bCf3YSef7elDWTllOd2C7IEDJDU3xXj8hEsyxaxDUOqgp+7p+L6mtsNkeqzLqStKODic/whMQkHJ5VfV8+L6CTRNY/++SRB+Zs2qRSVNa2uEHdtHyGVUFi2sJeiJ0NQQIhIO4vWmeWlLnDnz62hu8SGQaE0hDh+eIJFQmTkrihAWiqZiEWPjC8dYsKCaurowdfUeVNcIpm6hqBrNLUECAUHnzAaam+uoqRNkUiki1W5c7gLRWhdV1V5MJnnm2UkWLI4SDod4YUOMRYtaaCz42KvG7X/gwIubhrn00gbmLqji4KEiihImGG4lFBwjmTKwkCRSeSSChqYqXG6DQNBPbW2aQMDNtm0TrFhRg8uvUSyChQboRMIRXK5aWjvy+Pway1aHGByYIJUpsO6pArquMDzqoWN2LT09WfyBIKGwl0Q8iyPWOTg4ODicClmK1pJSYlqSQ3v38qMf/ZCjXUcIBYN4PR7cHj+Fok4oFEZ1uejr62HPnp3MmjWXUCh0tg/hTaMsZMpSFJxSMWi1RzqWtHDVNGDNmwUv7UBgYJVShgUSKQwMIRGNETJjkyhIfLkME488Tc0t15PcuouGdM52fi2PEMQUmzsJaa+L6ltvYOjxR4ikM1iKiu1QW1q8Utqo/FcBKbEEKFKgqCpCWqhYuC1JD4Kf5xOM1tXS2t6O0ItUHx3m4qJBGIGGjkfasf1SHh9vvT1EWYfzGUewOx+QIA0TTN2uKWAWkUbxFAuWbtWmSd+z60CCKq3yWI2p0RsAQlERQsEyy1LQyQMnIVSEYpcwtywDME4KdxYIFHHcuan0P/tuXlbHpjzK9fWEIkFaSOtU+5YIKbGMPIpVRKIj9CRCT9qipJ6i+7lnqbvs3TQvW8XRJ+/D0M1KiwBQhF0cXUqwbAdHgUAqU2rfScVuuyKQpoVVrntR+p+U5Rp5x2s4yNLKdl09WTomq3ReBIqi2uKaVa6zN13+shCoiooiFCzLwpLl4z/BVEIRqIqCaZpYksoyUgj69+1laN/+Ukc6FVuGUzTVPrd2Q05ow3FpTiJLxXvt5S3LemMK9zo4nLOoDA9JHvrFMNXVKlteHGZiAsbGBgmHFSYndAYGkrjdku/963YsCw4fHEE3BL/4eZpcwWD71jjRaIBj/ZN43Aqx2CjRiMLePUNomkBVBKqqYpoWTz0xQE21h64jCXZsVzBNAwmYluCF5wbY9IKJJRUmY2myGRO3piJUHcMyeHFTL5pLwTCLPP2kQXXUhWHGyaR1jnanQZpY1vF/+0NDOR59ZBCfT3DwYA8et8a///suCgUTicDCYmwsx4P3H6NYBE2V9PXFyeWLfP87u0glC4wM5vB4FWLxIuV7SS6X55vf2Is0iyAUBh8ZJp0q4PG4UFWVZLxAT28Wrx+S6SL/9R8vkc0UYUrv5ODg4ODgcCJSGliGwWO/+jX3/eRHmKZBZ2cnL27Zxspli8lls1SHI1RFo2zdtoPm2bMxTZNCIX9eC3Zlpo5FSva52LKdQjqZxLt2NUOHD9KQTFcGNxJwmxqFme2IJZ1Mfv1xmqQ9lpFPPE1w+QKsP7iTxD0/JZDPgyIRArTSOMdEknf7CP7uneRQ4KHn8FIa31AaiSj27wPFUlClghQSS1iVgAlRGWS5yOBjm5rnvlyMLW7wWpKMolHdUsczE6M8ksjTFAoREgq+ZI45lsLFhkqVM2ZxOE9wBLvzhldyQ5IIj5dFl19L0Rclq+sMPf84ZGK0zF2Ad+FyLLzkD+whZ+jMXHs1bl+I0b6j9D37FFZyqBSfJZBCxV/TQuNl1xBsakUIjcnBg4xvfIHcyDHAwIVEDVdTtXwttTMXoHncKHqByZ4DDGx6HjOXQVLEFaqibe07KWpeRGKcsd27abz8Kppa2ilYeY4+8xvi+3ec5phs8c1Ujz8vS2pmwQDdQFVddsx2eYJFuAm3ttF00SVEq5spGJJYfw9DLz5LMT4K0gQBltSo7pxPw2Vr8UdrSU2MMbRhPYGGBlxN7QQsGNi6kUhVFO+sTrJAZt9hXJpC20Vr0YIuMhMJDv7iXqxiHE80QtPFVxJtnYVQBMnxCQY2Pkd26DBCmghUhMdD44qLaZi3AuH1kC7kSfT2MLlzM4XJAVTAHayh+uILqG6fi0cIcpks44eOML7vJcxcmrYLL0K2tIDlY3zT8xSHjthh5ChogTqa11xG7bxOhKKRTqcY3vQCia7dSEtH8wSYd/lNGIEwppFjeNN6mldfSGjOYkzDoH/jc8T2bASz8Bq/pw4O5zoGhmGRSgtS6fLEiKBYkIyNAVik0unSu/b9KJMxARdgYZXE8YmJTClS1v7ROjp24sRE2UDHYHSs/No6Ht0rdSYm9JJMf1xkz06J4k3Gj0/cCCSTMQNZ6vbHJ/KVFh6fYpFks0WyWdWOYzZ0Mlmz9IlV+gvxRKHUEwiSaQkYyHQR0NF1SSarlrZom0YUigVGR3XKMdYSFYEkXygAbsDCKOTJlm4rk+O5V3oxHBwcHBzeRkyrMyYtwGR4sJ8Hfv5Tgj6VfMEin8sxd85MqmuqsKojVFfVsO6Z5zl69CiXvfOdNDU1EQ6HK2LWeR+BVRbsSv+XdoE5lFiM5I69NHzsd5m871G8/QMohonidpFfNI/oXR8h8dwmIrv3oAmBJUDTDSb/+ftUfeKj5L/8GcbvfQj3gUO4szlcQMHnwZjXSfTOO8hXR+j51veod3kwC3kUKdEDIfJCIxCOoGcSxKoieEq/Q+yACds9uKi58CsqG6RBT0hhY1EnrfoJ+wPols7w8CCZTJJAMERcNym6PJiGSdOcNkZcCkeHRnjXRJq2olnKlnKEO4dzF0ewe5shLIVMKk10ziW0zF3E2JatmJkEhVwRn+5m5vW3kO2cTz42zHj3AL6aWua880bckSoO/vhfUCwdiSDa3MGaP/g0xwYH6H7sV0hLMvu6a5j1ic+w5d/+kfTAQZDQsOJCmq99F4d++UMKI+P4q1pY8K53UjNzLtv/63tYRQvLNMjEJmi++laUUJDqectJDw0zfKSHzmuvI3T4yOkFO1HqeLBnaypdrhBEWxpRfR5G9g1hGrpdhVSBxkVLWPo7H6J72072PvIrXH4vy991G62LF7Hlu/+MkRxFCkHdvEWs+v3/Q09fN73PrUNRVeZdez16RxuqJ8zwo49hFYsY2TTSEMy58Q5GO44QLGYY6+rC11hN7fI1qI88iDdcxyUf/WMyyRwHH3kA1czRdMmNXHr3n/Dif36TXPchDBTmvPNGGlZdyb77fkR2fAxPYwMr3/8RemurOHzfv6O5Aqz6wCfJ+TUOPfxzSCepWbCMCz74u2z6ryJj2zeST6YJdHiZcfWdZHv6kUNHsAA1GGbx//gYWjhI9wM/o5CYJLJ0JRd89GPseejnDD6/DsuySMfTNHUuoGbJYqrqm8jHYxzbt4e2pRey4n/czdZ/HiLZfRgnMdbh7ctx8QooTWTIE947MfVdP2Eb5U/Maesc/+R43TiJcdJa059b0947Xbq9nNIOedroNVnZ39QkeTnt86nPyqJgYcpyJ29bTrtfGFO2d6pocAcHBwcHh9NjAUKajA0O8NiDv8TIpbCEG7eqoSmScMBHdVWU5tYWurp7aWho5IILL6S9rZ1UKoXPF8Dt9pztw3jTKacNl39fKNhincznMA4dwr1hM0YiTdOHb6fo9yMMCy0cwVIsJh59BvH4OkKGeTyNVQgCmRzZr30b47ILabr9RszonZiZHIoUuNx+hN+D6XVDKsXsj7wPkcmS+Jfv49OLBO7+fbzhKqxomOqqEOIzrRXHWSk0hFBAmqQtnb959CH2pSZICp2cohHx+/B4vVjFPEIVZAt5IpEIQQSWYRKJVKN5fDTMaOGAZZAQBh8cLdBuWgjhjFkczl0cwe68oTRMk+XqZnBCxTJAYOo6fS8+j1Y9k7o5CxClFNSJ/h6yaZ3OtdehhoPs/fd7MROjCFWhoTZEcOF81EAAKxUHl4vOW24l4fVy8L6fYEz0AIKD94+z6M/+L3Ouv5kd/96LSYFcfJSjD/2Y9Pb1mNIgc+wQmivHkt/7KIG2NpJH9mBmUwxveprmJavxNF/O0a0/JrHtGVR0XLlBjMnYaY/Y7nVACg+R+Rfgb5qNFAaRugbaV65hcu9Wjvz6XrBMBOAKRpn/7jtJxRIc/c19mOk0Qgh26HlW/fGnab38CnoeuQ/V7aP15vcwYeTp+tF3ITaARJAf7WXFH/85uckkQ88/gquYZmLcIpdOM++62wlEguz+p3/CSA6iBIK4c+NAlvk3vZ9MVYQd//4t8iMjCAHJX/+Amtmfof2m29n3nW8ihKB2yWIGj+xhfO9WkAaZ4SN0R/yooQAKEk80gmvRPPrv/2/SXfsAk9RYHzX1PqSeQmAxuW8XQpq4r3hPKZpHYCGYefl1+OcvZv+3v8rkgR1IJKmRAepqGlh80/tIHjpIdriPka1PUBV1o65extjIAAOP3o/LMjhyeBfLP/0VahYsJ9ndjSPYObzdOZUwdnLy/suvf2oB7rev/9vWe6XbORHnX7WDg4ODw1uTUukX2xKU7RvW84sf/j96eo6SSOSYedEaLFRGxsZpammhsakJfyBAbW0ts2fPY2h4hEWLFtPQ1ECxaE9MnffRdUyti22X6xEItPE4+f/3Y2oxsLbvJrHrAFZ9LSLgQ+QL6JMxAukCmrBs51gpykMukBINA/e6jSSe3YyoqUJEolgIZCJOPp3EJQXSsK+ViiQki2hCkvnmPRRLzrT2dSxnF1gIFHKqj51uyRNhlQmPh/F4ktqaGkincHt9CKHg9waYMWMGbo8bKcEsGkwmJzCLBooQ7Nu1G1OBTFsr3RNHaDNlxdFWlpNkxam8bc88Pp+PpqYmTNNkfHycTCZztpv0liMYDFIsFikWTz3BG41Gyefz5PP5M9yyM4fy2xdxODcQx+vDyenxD8dvSXZdAdujx0BBBwzbpAETlSJ5FcaOHKCYHMNCx7R04qPD+AO1uDxBJAJ/dRW1s5ehd49AMoVX9aBqLgrpBMqxLtzzl+AK1YAUjO7exuiOTSg1Tfg75xHqaMPlUTEVH+7aOjQMVCyEVHCZOiKeIHZoF7rMk5cGh597nKE9W086WnnCCylUom2zqZ6/mPrVF1P3jtsY11X23PtDcmPdKKVbdHX7TETLAsb290JB4FI9KIqLzMgIuWSGquWXgOYhWN9ItHMx+a5uzMQQBhIDi/RwH4nBMSy8CEwsLEwEhtARWCT37MNKjCIwMdJJ9tz/UzTFS9X8C4kPJcjHknYkoNAw8nnGB8YJzlqFUhVFSJO8IYgsuoT6C67EFahCRWVg/TP0P/qwXW9QzyILaRovuYrqOctxeYNgWez+5f1MHtiDxMQo/7OWEksxyCNQ3EHql1+IlcwQ7+/CEqXwcyPP6MF9yEgd1YtXAgIDnYIqyBmSiX37UC0DE0kuPoGa0fFFWt8KfZyDw1mhXHXSOuG9U4lvb2YCxqn26eDg4ODgcH5j10vTLZ2Xnl/HsjoPS5sDeJQ8o+OjBMMh0pkMbq8HwzRwu91UVVWBopAr5HF7vAih4fF4z07rT6otfWYQEhQBSuk/DQiYOi4LPAL8pk54aITQkR4CfceIZHKowsKwLHQDTBNMwy57bVl2HV1DSLyWgWd0DPfhw3gPH8I7MkI0myOQyxLUM4TMLAFZQFHAEuAydEJ6jqCeJ2QUCBtFQqZO2LTIWyY/MuL8PCjoNQocPXqUOXPnkMrYwRUoCtV1tTQ1NTE6Mkrv0V76eno52tVNJpPFF/CDKujv68PM5fF5QwhfAIGFIm3jjYplnyw9zuKAprq6mq985StceumlXHvttfzTP/0T4XD4jO1//vz5XHfddSe9/3pEbCEEv/M7v0N1dfVJn9100010dna+6m2+5z3vYfny5afd35e//GWuvPLKV73dcwknwu684eW8V8s3pKn6bNn4ofzc/muioGdS2DMdJVGnWAShgbBlL38wiN8VwNPezMqP3Y2UGgUh8UkVd20nUtNwBbyYSYjO6GDJre9FRprIxBMIo4DqD1JwB1FUF2VvIfvWoCDzeaSen9amVzIkVYp5eh75KZmxAXyKoGb5hcz9vU/QcsMtdP/4O2DkkEiC0QhoXoJrLmdN+wwQGgXFhVBA81WDlQO3B28wiFRdJONxhLSOn0XLQs9k0bzRUuvK5UwFhpTo6TTlobxAgjRx+YOoHj+RhnZW/+FfgDQwVYkpTdzV9bjNAv5gkPTIMN0P/pqO93yIJR/6KEbsDka6uxjc8hzJg7sQFMkn4hz+1YO033ATiz/25+RjgyT3bWN4y2Zi/YcA3Z5BkiAVEMJ2e3K5PfjDUUbTGYxiYYqoC9nkJIYEb11jJSZTAoZpIbMZlPK3yzIRFqC4HJXAwcHBwcHBwcHhjFLWEhQEUlPxelx01FfjDQTZP2Hg8/pQNQ0pJT6fj4mJCVpb2xmbiBEOhYhEIm+LunWnOr6pE31SgK4qyJLJg4JAlPJeFUWhbNcgFYFEsZ9b5RIZ8nj9P0TFVPD433JtQAWERFHVyhgyY5oUFEkEDU2CisBActgNT/pUnkjnMMZG8Pr9NDQ00NPTg8fjYf78+QghOHDgAIVCAVVVKRQKZDIZ0uk0EohEI1hS0tjWyprVazh25BB+EyyhYJQEQ7tIuYX2FhjHLFy4kGAwyI9//GMsy2JkZAQhBMuWLSOZTDI8PMzixYvp7u5mYmKC6upqVq5cydDQEC6Xi2QySW9vL0uWLCESidDf3091dTV79+7F7XazbNkyCoUCO3fupFAo0NHRwcyZMxkbG+PgwYNcddVVLFu2jLGxMfbs2YNh2FGn8+bNw+VyoWkakUiEjRs3UigU8Pv9LF++HEVR2LFjBz6fj1mzZjFmF3AmGo0yPj7Ohz70IbLZLFu2bOHYsWMAhMNh7rzzTp5//nmefvppurq6mDlzJu3t7fT19dHV1cWsWbMIBoMYhkFDQwMvvfQSxWKRQ4cO0d/fX9nH0qVLAdi9ezfxeLxkInN+11Z3BLvzhtMlMqmAfXO2XQdfznXPAlSE6ioJNzZSCKRVmpMQ9vspTWWi6yDDv/lvu9MUIKRidxDSpDg2iuoNMP93PooRqmL3PV8nPTyMIiX+9g6W/Z/5x3tdABSkUNEp2u0oF4B6pUevSKRqIjHJWTC4ayvV+/fRdOFljL/wFKmuXXZnIyVCUZjc+QLxjU8iy2KcEKBoSMNE5rKlcyawpMT2Ryx1XFJBMXUE5QLqdilTIQQmCqYsnuJaCISikhzsZuAX/w5WEVlyyJWWbWGeiw9jYhE/sJ1df3+Q+s651Ky+jJolFxC96BIGHr6f/od+CpbJsRceZ3LfFhrmLaBuyYW0X3oLkUtupPe//4Wh7esq4qepWICChrTjKi0LU8qKADeldfaxmKbdCTOloxdKpXMXCDsyT5Rrdjk4ODg4ODg4ODicWRRFcNlV1/CbH36bpmAQ1S9p0FwcOtyFx+3B4/FQVYry0fUC3d1HqIQHKMpZi3Q7UyiKPSabKtwJWRbnQHrceMLB0hgH1CnRZkKWh2EC09SPu8wy3byhHHRRfnHSGRV2NKSqahSEm01e2BbV8FdFaM0Y1A6nUBDsxWBrIUNfPkvBtHBrbooFg2xmnI6ODubMmcO+ffvo7+/H6/Xi8Xgq6ZF+v59UKmULdpEIrS0t6IaBQNKgqMwIhsGrIoRElaBaEjOZRjGs6TErZ4G+vj7mzZvHX//1X/PII4/wzDPPkM/nWbVqFQsWLOBLX/oS733ve9m6dSsPPfQQX/va13jqqadoamri7rvv5pOf/CQLFy7k+uuv59577+Wv//qvOXLkCH19fXzhC1/g17/+NTNnzuSKK67gv//7v/nMZz7D9773Pd71rneRz+fx+Xy4XC6CwWDleyKEoKWlhS996Ut8+9vf5sILL6SlpYV7772XL3zhC7z00kuYpslf/uVf8i//8i989KMfZdeuXWSz9tj52Wefxe12EwgE8HiO14hUVRW3243f78fj8XDhhRdy55138sMf/pC7776bBx98EMMw+Na3vsU//MM/4Ha7ueOOO/jCF77AzTffzKFDh7jvvvv4m7/5G37xi18QjUZZs2YN//AP/8D4+DiJROKsXMMzhSPYnTeUk16nhvfasWsScIVCLLzpHez71W8wsraDoazk75edAsuBV6KSSisBy7KwLKPyfiaRpFDME/B6SI0eQxgWlrDdDgPhCJHmRhIjOsFQDf7Gdrp37Sc92AvSREUgZHGKHDg1Hk0paXhTj+G33ElleRsSqViVtSyjSO+637Bk4TJar7iW/T17wTRIjI+jmEVUaZIc6gdplGaEBFWNLSjBAHJEkk0msQp5qqpr6RcaUtqRa6gCfyCArLRaAUz7vClKxcPxuMgFhVScYjaBxwXJod6Sw6q9nCcQoq6jk9zkIIqiUFVfS2x8lOF9WxnfvxNX3QMs+YNP0XTpWoaffgjyRQLRMInxUfo3DXFs8wainY+z5GOfouHyyxjZ9SyKKbAsA8sqO1Oq6MUihWSCUFUzbm+Agl6oRAYGq2pxYZIZGSx9D0RlpgwxPWvemva9cXBwcHBwcHBwcDizqIrG/FUXEahu4MF7f8aOl7ZiFAvkckVmtM8gFAphGAbRaJRsLkdtXZSu7qMUiwVU7fjwd6ohw5vJmRQIhRAoioKqqtP2L6XEFGAKkPkCMle0xTtRShWVdhSdJUTpfQULC0XagRSKoiCtUi270qBRSokqbGdXE4klLYQl7Sg9YdpCgxB0Cy8vLJhBLODHZWp0C4ukqpPLZrA0jclcFgyT2upa8oUCLpeLtWvXkkwmWb9+Pbqu09TURCQSYXJyklgshsvlQlVVAoFAJQKst6+PeXPnEZucpHUyQdXQCCpFrFIkoJACtRyTcMauyKnp6+vjd3/3d7npppv41Kc+hZSSu+++m/3797Nw4UIymQxdXV0oikJnZye1tbXcf//9BAIBbr/9do4cOcLHP/5xfvrTn/Lss8+yevVqxsfHaWtrY+HChfzmN78hl8tx9dVX84tf/ILGxkYuvfRSfvOb39Db28v+/fvxeDysX7++0iYpJfv27WNwcJAHHniA8fFxLrvsMpqbm1m7di0bN25ECMGKFSvQdZ2/+qu/4p//+Z/ZuHEj3/zmNzFNk+HhYTZs2EBPT09lu7FYjL6+PrZu3crevXv5xje+wYYNG3jppZdobm7mve99L1/4whcYGhrikUcewTAM7rzzTqLRKIcPH0ZRFJYuXUo4HOapp55CVVWampoAGB0dJZ1On+nLd0ZxatidLwgFRYLHKiWYCjdCaIjS7IaiBQnNXgOuAGAXADWEgkRDQdih0CgIaSCliSJNpGLfygxVoShMOxpNCvKxBOldW4nObsZdW1+ZoVAUjearbqPl2ptBUTENHakXCYeDKG7NFhOFi+rmObhUN4pi79fCTt20pGoLeqhosiw+KhxPmj3+EChIVEAFxbBtFeTxaDCQJLr2U9y3k+alS/DPmIWCh3hPD4XuvUQWrEb1VwMuVOlBdQVZfOsHmLH8IgSC7Ogw2e6DeOctQKtrQcGeqaqa0UaopQlDlOPULLs+nlAoaKCrqt0RSrvtEkExNcnkjg1UNVcRam0BoYIQqIpCy9rL6bzpZqTmQngDXPj7/4NQQxMS0KVOdqyPQn8Pimlf43B9C6v+x/9CC0SQQqBbMDlwhOLkGHlLKZ03HaSJJk1UaWACup7h2Mb1BHx+wrMXoODGhRvF5aduyUqKyUni+3dhoWDhAamiYCGFiVm5Agq6BroCSnn6zcHBwcHBwcHBweGMYFfeLkdvtTQ1k0ymSGey5Ap5fH4f9Y31NDU1USgUSGfTpLNpotEI8clJnnvmWUzdOmuRVWcqFXfqfhRFqRyvKiWKFOD3k5vRiKyvx2xrIzNnDpkFC0gvWkhh0Tzyi+aRCwQxF86juHwx+Y4OssEI2bkzKaxaSn7VMvKrl5NeupDMgjnE53aSm92B0dlOsbOFggIIBUsILCE45HeRDHhJxeOMjgwzPDLKcCLOaDqN6vEya+YsWlpnIBQFv9/PbbfdxujoKDt37sTlchEIBEin04yMjJDJZDBNE03T8Pv9zJo1izUXrEHTFGpra2hpn4FXShandbzodlYTdo13IexKxGX99GwOZZqamvB6vXznO9/hQx/6ELlcjgULFiClrERIer12rUVFOS7ZSCmxLKsizFqlVOWp72WzWQYGBti3bx9f+cpXGBwc5E/+5E+YmJjgq1/96rSacOV9TaVYLGJZFoZhoCgKiqJQLBY5duwYXV1dfPWrXyUejxOPxzl69CizZ8/G5XJV2gdME4ynRnuqqoqqqpXlpJTTlp26jRO/xxXh2TTJZDJIKdm2bRvj4+Ov/UKcAzgRduc4AnD5vMy/7GrcM1ejKiYd195Iw+oLsUwTodgpkNIVoBCoQ/OGaV+2nMj8pUhXkI611zGxy4OaKxK94BLcPo3aWXMRl1/F0RdfpG3lagKzZ+PzeOm8+j2MbH2Uye5DHHnwXlZX38XyD3+M7qdfwEjGaFgwn6plC9n9o//EMhXMRJqxF56j7rp30fn+j5Lavo+qSD0Nq1agS53IshV4CzkG9+6g7fJrEC3NeAN+Ot/5DvI9BxjYuat0lBIwpxyxRKLgr6qi/eKrCHUsxO0O0HzluykcO0rfhqeQZhGMAj3PPsUF8xcz/5aPEju4j2MvPMaun/8/Fn3kD1jwoQ8zuGETmBbtK1dghELsv/c+u2Zd0aTrwZ+x5OP/m7n/838RW78Jt1ulYeZM0rEsQjVB2JYTNbPmUL96LS5NoX7JEoLqrQxtfJZ8LIZEQZUGBx+9j+VNNcz94B8y8PijFJNxIrMXMOOiNez62X+g5LNYXj9ZJcLM9/8v+tc9jpFK4Z/RRvWCZRx59ilcmRQEQojGFjrf83uMbdmMNPJUL1iAN1TN6MMPYljQuuIS/EuWoQiVhtWX4nIbjGxZT++L6wh3tDDnjttRPQH0WJzQ0mV4Z81k173/QX50AEX10nnRdUTmLkGVgsa11zL6ooqSjTHjoqvweD1Ut7RgXfdOep9/Cit3/jryODg4ODg4ODg4vFUoDd7lcVO9ZCrOwYMH8Pl9SFOjsbGeltYWotEoPr8fEwtFUfB7fbS1tLLx+We45KKLCEUjFPIGHq/3hBI9b1LLz0LNvFNF9ZXfsaIhlLpqCskcrmgUzeunaOpInweZTCMtC6MqguL1oWoqNPopWgJ3SyPZiRhuqaD5PFghP1I3UPweKBoQT6N6/VgDQyhSQxcK+3yC0fmtpDIpDN1AEeDSNKKRCB6vj3A4Qj6XxzBN1q5dS21tLU8//TQjIyOk02kikQi6ruPxePB6vTQ2NuJyuZicnMTtdlNTU8PAsX6qa6pob+/k8P4DNI6M0T6RRBFKKTCkHM4hp52Hs0lTUxN33XUX99xzDz6fDyklvb29uN1uOjo6uPLKK1m9ejW7du3iySefJJVKcc011xAoOR9blsVjjz3GzTffTCKR4KKLLuKRRx7hwIEDDA4OEgqFME2Tiy++mImJCW6++Wbuv/9+lixZgtfrJZPJUFtby0c+8hGeeOIJBgYGAJgxYwbRaJSGhgZmzJhBQ0MDyWSSnTt3Ul9fz7Fjx7j88ss5cOAAH/7wh3n44Ye57bbb+KM/+iO+853vkMlkWLx4MVdffTU/+MEPKiJbPB6ns7OTJUuW8PDDD3PDDTewe/du1q5dywMPPABAbW0tV155JYqiMDY2xuTkJC0tLQA88sgj5PN5LrjgAqqqqqivr+e///u/ueSSSxBCMDk5ebYu5ZuOkK8gRjeZTBKJRM5EexxeJQrg9rhpW3UhWa0KwwJFCITwYJoGKKXqa9LCNPLE92yjfuFSDE8YS6q4lAKZ3gOouoV/zgKKgIKFkkswtH0bzUuXo4fCCDy4hJv00e3EersQwoXLH6Fh2Rp87XMwNUF2ZIDYjhcojg4jZSm6z+WiZsXlRGbOwY0g0XeMycN7CM3tBLcHbTLG6MHd1K9aRUH1oUmBohQwRocZ3b9/mt23TblCm4InGqVhyWqKit+eVVBApmIM79gEll3bQHUFqV96EYo3itAsRre/QDE5gremmsjKi/FVt6LiIjfcx8SOzRSSkyillF0TL+GGJuouugh3pInMxBgTWzcz+30fxRM22Pq1L2DpRSJtswm0z0NaoCoKiigwvHMLxUTCbqmQWNLE5QtQt+wifO3zUFUvxdgo4zteID3Si2aZGELF39BOcP5Coo0zQQjMfI7Ygb3EjuxEGGmkqhJum09k5lJ8jY2YpomRijGxawup/i6QgvoFyxB1DSDtGREzOczYjs0gJYrHR/XCFQTmLEbz+MkmJpnc9iL5Y0cQ0kAobpqXXogRCoNlW2okjx7CKsSoXbAYQwRRhMCyCoxsWY9VcAS7tzKJROKMOk6dSzj9moODg8O5hdOnnZ4z1addffXV/PCHP8Tn850UFfNmM3XIamGCZfDi+g18+S//klDQRyDgo76+nksuuYTWGTOQgC8YoJDLcfjAftLJFAeP9DJvwUKqamo40tPLsuWrWHvFFfh8vjdNVCtHRBWLRT74wQ/y5JNPvin7KVNbW1updVaOZjJ1i9hz6zE//yUiUlII+Mh7vbg0DRSBFcugVYUxpYVu6XilwLQkJmBYFj6XC9O0MLFwGxI1HgcFTFVgqQJRNEDTEFLiNkE1TSYUL9tbatjeWU/csIsGpVIZFEUhk0njcbvIZHPUNzSQz+VZvnw5oVCIDRs2MDQ0RC6XIxKJoGka6XSacDhMsVgkEAiwatUq9u/fz8DAAOFwGEURpDNJ5i9YQFAvcvneLlZOplGwjguyJaHXfihkNRfN93yd7OIFuFVX5fo/9dRTvP/976/UyXuzcLvdLF++nKamJqSU7N69m6NHj6IoChdddBH19fUcOXKEmpoatmzZQltbGwsWLCCZTPKHf/iH/N7v/R6apnHRRRdV/u7cuZP77ruPuro61qxZg2EYbN68mWw2y4UXXkhNTQ3Dw8Ns3boVTdO4+uqryWQybNiwoWI6sWTJEpqamjh06BBNTU2EQiG2bNmCZVlcfPHFKIrC1q1bMU2TFStWcPDgQRobG4lEImzbto3q6mqWLFnC9u3bp6XFNjY2ctFFF3Ho0CEOHDjAkiVL6OjooLe3l127dhEKhfj3f/93/vEf/5GamhpeeOEF0uk0F1xwAVJKtmzZQjAYZM2aNWSzWTZu3Egul+Oiiy6ir6+PwcHBN/V6vVm8kn7NEezOcRTsKmUS0HGV5CzLThGdEuhbfmZU5hbKNe9kJeHUqqwNU6uwiSkWFBrTZycUFHTsumYKoEhZ+RxVxTSNUvqqvYZyQm06Wdrz8f1QqgMnbGFImlOWPn7UrlI2t1XyNFUqW5VT/HLtugpWpXjq8WXsxFqlIgPa59Hellk6F4HaVgQW6cnhkh24xO3xs/h/f518rIed/+/vUaWBJe29u0rbKZbOnS13CVShYEkTgbTPsRBoCBQpMZBYYmql1nLKL1ByWTp+vuSU1paEWVk+ovJ5s2sJKlPOwfHrebyILKUU37LpxlQbCqXy3YCya6xZOWcnfkcc3so4g5vT4/RrDg4ODucWTp92et5Ogp1lWUgsUokYf/Z//g9H9u8jHA7idmksXrKY+fPnU11djcfro7m1lVwmzcb1z1LIZknGExwbHCaRzeEKRGia0cH1N9zIpZde+qYKdqZpouv6GRPsnnzySZqbm48LdoZF7Nn1GJ//IiEpUWa2kQkFKaqCcFMDkzsOElg8j1yhgEgk0FSFQDhKAQs8LoxcASuXx0SiZQpoe/bhl2CWRo9CTBmlWIJ+b4B7o25Gaquom9HGZCxGJpultqYG0zSJTcaoikYYn5jAlBYXXHAhkxOTvLT1JUzLqAhz8XgcRVFwu90IIWhoaKCtrY2GhgY2bNhgO8RKyaxZMzH0PPUNdczpH+WKvYcJm9bx4EkhmDJCRUpbsGu65+vkzpJg92rQNI0///M/Z+vWrbS3t2NZFt/97ne5+OKLueiii9izZw833ngjX/va1yrOrOcaV1xxBX/+53/O5z//ebZs2XK2m3PGeCX9mpMSex5wXMoxS5KYLVmVhakTC6oet5iYzqmdP6cKRVNFLxurbLMgy1uegnXSk2lmE+UtTbdpON5GpJwmtJ3YJlvgs4/VPGm5kptRaRun2sLpnHVlScZsXH4B1XPnsu0/v4uVS2ApCnUrV+OrC3HokRfAssVEuyQrFEuNPdGSwZJWSbor7fnENskTX5RC/U+rh5VqFUjbA/j4OSoLbdYpj+74cnaK8eml+vK36aTGneY74uDg4ODg4ODg4PDGc2JsiRAwOjLKP33rHxgfHSXg9+NWNTxeDwKIx2OEQiFAIZ8voOsGlhQkk2nyusFEIo5ugqkW6WjvYMWKFW/6MZyNlFg4uRaY7RQrkceGkJaFPxJGP9xDqGjC9j34AKGbKG6NQuYoQlORloXmcUOhiCIUVCFs8wlZMrgQ9nbtt1SO+Ly8sGwWejCEORmnf3CIQMCHz+9jZGQEgPr6eqoiUdo7Omjv7OSlrVvZu3e/Xa/O4ycWi2FZFjU1NWiaRqFQYHJykomJCYrFIn19fcTjcbu+eFUVx44dw60JZhQMVuwfImyWK6GLE8appe9SuVa6OB6M8FbGMAzuueceWlpaOHz4MEePHsWyLDZv3szIyAjBYJAvfvGLxGKxs93U18y+ffu4++67SSaTZ7spbzkcwe4cxwKK015ZpxRUzMqzcuzVVOnm5T1ZJUpFprGmvHscW6KaWmnOXsTiuIhUbsfp5R457fnLyUISffqeTrM18bKRYFPftYDCtE8kmbER2i+9nOV3f5J8fBKPz4e/ppauR35FbN9OykLhiRucfnbllNfH0V+27RVp77dwwv5fVoacHmP3crzc+ubLflMcHBwcHBwcHBwc3niklAhpsnvnLv7m/36VhtpqNClRNTvvRlom+XwWQy+iKCoer59MJovf5yedybPvUDcpUyCVIHf9wV34/T4uu/QyXG73GT2GM0G5yL8oObgqioUlJQUp8EogU0DVTYxEFhCgaZDKIwBTysp4rSxqydLPfwswAEWCWVLCVFWxhxeq4Jjq45nZLQyoKkGXG7/fi2EY1NXUEovFMCwTt9tNrpBnTkM9tTU1PPnEE8RiMdLpJA0NDYRCIRRFIZfLVeqwVVdXk8lkGB8fx+/3UygUCAaDFAr26K25tZV5tTUsev4lGgoGernOoQBFLY99JJadwoSCsMc0qgsVDSmnZs6+Ncc4ExMTTExMTHtP13WOHDlyllr0xjI2NsbY2NjZbsZbEkewe9txKvnotUo407dwum2cvdve69mzxeiuTYwf2YU7HMXnC2JJk+TkKDKZPEcizX67OOfg4ODg4ODg4ODwVqZcuH58bJzPfe6ztDY1YhTy5LIZAj4vQoFcJsPE2CguVaG+vhFFcYEiKBQKLFi4kKeeeoa5y1bymc9+jrq6OmC6++b5SLl+npSS0OIFuP7v51GkiZC26FbGEuWiQnb2zknbAYSi2KKWBMWUCFkqnSQUEILhkUF++MCvSLsVjGyRoZEj1NRWV6LjAsEAroQLr9fLmjVrSKVS/OKXvyAei1NbW0t1dTXFYpFYLEZzczPFYpFMJkM6ncblctHc3ExNKaV2aGiI1tZW3G43F110EUJIalQ3Cy6+HMUsIq2iPVITCih2PtLUbCSJwKe4yDc1oohyGam3fqSdw9sTR7A7bzh94udvf+/UW3rlUs/5KQpZ0sDKJNGzKTKlXFc7lPzNPuLz83w6ODg4ODg4ODg4vFrKgt0jD/8GTBOf28XI0DHC4SBGsYCUoCqQSaeJxeMcGxxEKhoNfh9+nx+XqrJ69Wru/pM/pb6+HpiSJnqW0lXPFFJaSAnu+lq0+rV2mZ7KeMYqCVsSKUpRaKZVKqskSoPC4+epcqrMcsVwgaIoFAs6//W3f8cxnxuRShMKhPF63MTjccLhCG6Pj0JBRy8UWb50GQqC3bt24Xa5iUQiFAoFisUilmURCoUwDIOBgQG7/p5pMjw8TDAYxOv1cujQIQzDwOPxEAqFePrpp1m5aiXXf+ADSGmgquWaeuWa4EqlnrpVqtQtSiWMpLBrpjs4vJVxBLvzhlOJPK9UxHM4FZUzVQ6NLsVWS1Gq/3CW2uXg4ODg4ODg4ODwdkEIgaEbbNrwAgvnzGF0+BgBrxtpWeQKeRRVEImG8fu8mPkcw8e6UVRJTW0tE5OTKFKSTqc4dmyQxqaWk2q7vdmc6TTLqfsTQqAoZSM7UTGJUEqRCHZknVKq4Q3qKwg2U+xi45Q2yfbt29ixfQehYIhsOouhG/iCAcKBMKqqMjo6iqZpzJw5k2AwyOOPP04+n8fj8eD3+0mn03i9XkzTxOfzcfToUUzTxOWyzSB8Ph9+v5/R0VECgQDBYJBIJEJ3dzeDg4Ok0ikuu/xyHnjgAf7n//x9IpEwlEwBy8diZ/CWa9uVT46t3L1V02AdHGDK99XhfESe4vHK13qjea1ftmkzOtM/OeHxZiOQtmfEKzo/r7xFJ7b/la554nInbue1RVi+8v05ODg4ODg4ODg4vLkIoJDPMzY6gktRKGQyGIUCQkp8Xi/BQJBQMEgmnaFYKJBKJDhy6DDSsvB5veQLeWbOnElVVdTenjhuTndGj+MMRnOV96UoSkm0U1AVBUVRUIRiq25CIIRaGWspApTSRy/3EAoIRSAUKBYLPPXUOny+AP5AiEhVNYFgkHyhgNvjJp1JMz42SiQcoq2tjXXr1gEQDAZRVRVd13G5XCSTSUzTZHJystL2SCSCz+fD7Xaj6zqhUIhIJEIkEsHj8RAMBlm6dCnpdIaHH3qYkaERkokUYKfpIhQo2RQKRKX9Jw4fp16X8z3i0uHcw4mwc3jTsO+DAqnYRU/VUg2FV9Q1TsvLPR6WfbxjPZVId6KL1Mk33FPNqAkhpnXYp++8X12nXm7hywugr0RkezV7fK3rv9J1nbp4Dg4ODg4ODg4OZxAJIyMjaJqCYeioQkFaErfLjWUaNDc1Ew4H0Ys66WwWv8uDbpgM9PVT39CIqqpomkY8Hn9bRFOdVoCS0n5deWvKcqfYzmnHR1PSiQ8cOMgLL7yA1+Mlk8ni9XrxBwMEIyFUTSOTSTNv3lyWLFnMo48+Sj6fp6XFjnJMpVKk02mklLagWIrG83g8VFdXk0gkME0Ty7IIBoPU1tZimiYjIyNUV1cTDodpa2sjn8/z0tathMMRxifGmdE2g/IIrJL6zKkOe/pRvx2+Gw7nHk6EncObhkQglHLNANARSOyZjpeLihMIFCnsgqj2huxb7ilvotO3M7Uexak6q/L7U4W7cl2MN4szc+svn9epnOo8O0VVHRwcHBwcHBwcXh9vzG/n354JVLYJ2L17Fz6fj1hsEo/HjcfjQUpJTU0NkUiE+sYWfIEImbzO8FicTCZPf18fqqoSCoZwuVxkMpk3oM3nBuXrc2J67KtZv2xaoes6qVSKXC6HrusVEU1KycaNGwFBLpcjEAhQXV1NOp1maHiYiYkJWlpa6ejoYN26dRSLRQDi8TjxeBzTNFFVlXA4jBCCwcFBOjo6qK6uJpvNoqoqqqpSU1NDW1sbk5OTZDIZotEo8+bNo6GhgUQigdfrJRgM4vP66O7ufkPPo4PD2caJsHN4UxGKyoK5c+jr7SWTyZbfPWGpEztoUaqwYBc09Xp9tLS20NnZiZSSrq4uenv7sKxy53485LtcH8HtdpPJZOjp6al0KFM7qZkzZxIIBCgUChw5cgRN05g9ezaHDx+udCavF1WUhcYzIdrZ9SdsThToJKcW85xZJAcHBwcHBwcHh7PFVKHudHEk9tT/0e4j1NZWM9LbQzGfhTxEIuGSi6ibUCRCQ1MTk8kEQlGwLMnRoz3UNTTSUF+Px+PBMIwzc1jnCZZlMTo6yrZt2xgeHkbXdaqrq2ltbWXWrFkAbN26lWAoiDTt8VZZVEtnM9TV1TFz5kxe2rqVWCyGrusEAoFKwIRhGOTzeQqFAolEAsuy6OrqIhqNVlJm8/k8brct0A4ODgJ2quzY2Bjr1q1jwYIFTExM4PF48Hg8DAwMYBgGqqpW9uOkuTqcyzgRduc1Jybpn+mblSAcjvCVr/xfli5dXnrvVHX1yuU/7YdEwRQKFhpSuPjoH3yMP/3TT+N2uwmFQnz5y1/mwx/+3ZNuvlJKNFVjzZo1fPGLX+Q73/lOReQrfy6lpLm5mX/+53/mM5/5DIsXL0ZRVVpbW/m7v/s7Wlpa3rCjX7l6NbfddiuKcqJZePlZ+Z/fibOKr1VIO5Uw93Lbei2puI7I5+Dg4ODg4ODg8Oqo1IGWx3+TH39enuCWx0cI5WVK6yuqiqKoFPUi2VzONqIwDOrr6wmFwqiKi2i0ikAwyNGeHhKJJJdcegnRaBSfz1sS9lxTWnTmxkZnSzB6vfvVdZ0XXniBvXv30tXVxb59+1i/fj2bNm3i3nvv5Xvf+x6JRAJN03C73eTzeRKJBKlUikg4wpIlS9i0aSMDAwMAuFwuIpEIlmVNE9XKgpyqqvj9/oqYl8lk8Hq9tLS0VCL7FEVhYmICoQjmz59PIpHA5/fZQRhdR+g60sXQ0FAlYMPB4VzHEezOa16b6cQbuf9YIs4ffeoTbNu+bUqbjld2szuSkrd2qfgpmCANwABM0pkU377nHh5++GF++ctf8uCvHuL297yPQCiMXVS0JPRJQS6X5yc/+Qn9/f0IIXjPe96DqqqVFgkhuOmmm3C5XBw9epRf/OIX6LpOT28v/+sTn6Cvv79Sl2Eqv7XDEwKEWmqLzcJFS7jyyuuwfZimynVKqcBrOY11+vUR4pV2sCdez1Nd76nX/fV+H5xOz8HBwcHBwcHh7czrKSUjAauSfmKBtJCWhWkamJZZehhIaVY+x7KwkFTX1JDL5VBVcLk1hKYiFQWP34/q0kgkY3S0t3Pt1dfxjuuup6Ojk2AwSH1dLbFYjMHBQRrr6ux9Q8kV9cwKdmdKQCqnkpYNJ14rIyMjDAwMkMvlSMTjxCdjuFSN0eEReo/20H2kC8swURCEw2GklBUziLlz5zI8PEw6naGhoQGwz4Ou6xQKBRRFYWRkBIBCoUBtbS3BYBBFUXC73aiqimVZmKZJOp1GVVVyuRwer4dcIY/mcuHyuHF7PBQKRTKZDC0tzbhdLp59+hksy3pDzqWDw9nGSYl1eNPQNI0bbryR2to6Nr/4Ivv27eWqq6+lvaODg/v3U1NTw+o1a+jq7ub+++4lk81NydQ8HhX3ox/+N7phUJ4FEwLy+dxpbsR26HMul+NnP/sZH/zgB/n5z3/O0aNHAWhsbGTGjBns2rWrsr7P6+Xmd7+bQCDAY489xvjYGO9617uIRCK89NJLLF++nJkzZ7J161Yee+wxTNPkHe94B83Nzby4eTN79+5h1QUXsHjJEg4fOMjG9c+zdPFiVi1fQWNjE7/74Y8Qi8d4+OGHMC2T1atXctlllyGEpLu7m8cee5JsNouiKFx44RrWrFmFlJLR0QmefeZZhkud2evjjfqBcKooPgcHBwcHBwcHB4eTkdP+Z0tkhl7gWH8/27dvo7v7CIlEgpbmGWQyGVSXRktrK0uXLKWtrQ23xw1CMHPWTB5/+CEURRAI+DFMiaZpxGJx5s2bRzaXQ1FVvF4fc+bMob29nUgkQjabJRIOY1mWHdFV+i0rz/PftFNFwtcq2o2MjLBz5040TUPXdfL5PMeOHbNjE4WwgyKkxO12YxhG5VFVVcWs2bN48gl7jJPLZjEMg46ODpLJJJlMxs6M0jQURcGyLNLpNF6vF5/PRz6fJ5PJ4HK5UFWVvr4+CoUCpmmSSqYqIh5AMpUEKYlEImQyGYaHh3F73JimiaY5UofDuY8TYefwpmFaJkeOHObqa65m6bJlSKCnr5flq1byhx//GIoiWLfuKW6+9d3ccNNNQGkm7QRxqVDII6XFnHlzuendN7P2qiv59rf/iVwuWxL4yutYSKzKzN/zzz/P6Ogot99+e8Vs4oYbbphW9FRKiW4YdHd38573vpcZM2ZgmiZHjhzh3e9+N3fffTcjIyO8+OKLfOpTn2LlypVIKTl69CjXXXcdy5Yvt52rhgZZvHgJ115zLRIYHh1lbGKcVCrFzl07OXjgIFJKbrj+HfzJpz7J4489yr33/pxLLl7DJz7+B7g0NwvnL+LjH/skjz76BD//+f20t3Vwwzuvt+3H31I4kXYODg4ODg4ODm9HXrX4I7FN5yRI0+Dwof387d/+X/7u7/6GFzY8z8ToMEGvm3R8nGxykrGhQXZu3cJfff4v+D//+9O8+MKL6IUilmGSSafRdZNUKks+V8DQTeKxBP39g0SjUSQSwyxSXVNFfUMdQgG/38vw8CDbtm9jcnISyzJLkW5nLvvoTKdmHk85fn37zeXsAInh4WGGh4cpFouVunC6rpNMJpFAwO+vmFMYhsGyZcs4cOAA6UyaYCCA1+ulvr6e8fFxisUiuVwOn8+Hv7SebSYSq5hcTE5Okk6nKRQKdHR0EA6HMQwDt9tNa2trxaBi9erVXHfddTQ1NRGLxaiqqqpE4pmm+cacTAeHs4wj2Dm8aUjL4tDBg6RSCYSwZ9T6urrJTE4wMjDAIw89xPrnn2PT+vWsXrkCVYjjWbEnoCiCjs52Zs+ZjWnqqAooWCDNinsUU/6apkkymeQnP/kJN9xwAzNmzKChoYHOzk5efPHFaS6yhq6zf98+CoVCZd2DBw+SzWbZvn07jz/+OI8//jhdXV0sWbIEgK6uLpLJJIoERQgG+voZHxqGUtTeyOgox44dI51KsWPHdg4ePIDX7eH3Pvw/Wffks+zbd4BjA0Pce+8vuO4dV1NXX01LSwsBfwjTkExMTPL9f/8+zz6/ASnV11yF8OXWs8/Bq9ygg4ODg4ODg4ODw6tBWhRyWX72wx9y349/hFfARatWsHDuHGoiVWBKCrkcfq+PebNncdnFF3HV5ZcQDbr44uc/w5e/8Hli46OYhkmxYGCZ4PF4yecLFIs6Bw8c5NjgIEKR6IZOOp1CSotIJMz4xDjbd2zn0ksu5jePPGz/3helLJ0z9Dv4TAt2hmFQLBbfkLTQQqFAZ2cnmqZVtlfUdZKpFKl0mmQyyfDICKOjo7jdbjo6OvB4PBw6eBCfx0tjYyNCCFKpFMlkkkQiAdhmgdlsFo/HQyKRIBQKoWkaY2NjmKaJaZoIIejr68M0zUr9O7Br4fX29pJKpdi7dy/r129gzpw5+Hw+gsEgS5Yuxev1ThvvOTicqzhxog5vOuUKbqIUDi8swfDQMEZp5iOXyeJuciElJefXkzENkycefZwnH3ucyy69jC9+/vP84dEeunp7kaJUA88SCCkQqCA1QLBu3Tp+53d+h9tvv514PM66devI5XJ2u0p1HSqd6AkzUVJKhoeHQYJlWqTTGVwuN3bare3LKoRSWUdIW6ScsgFkSVCUSEKhIA2NDaxevZqauihCQCgcAalQW1PN1pe28I6j7+Cf/+nbHDx0kCeeeIxnnn225Jf72lxdy2uc1FUJu01OsJyDg4ODg4ODg8Mbjjz+xCgWeOLRR5gcHaY2EiEen2T9008zMjKKadp1yjSXXXPaHwiguTSaGhtZumQJSxcv4dnnN/Dtb34TTdNsIUda5PN5/H4/pmWQTqfYuWMHbe3tFItFwj4f6VSKZCLB5hdfJBAIsGjhQjZt28nYyAhtnZ1n9FSURaMzJR6VI9Ver1Coqiq6ruNyuWhva+fwoUOk02k0TUMIgaIoFAoFO5W5VDN87ty5bNiwgbHRMSzLYmxsDF3XiUQiGIaBy+WiUCgwNjZWWaexsZGRkRFGRkYoFAqEw2H8fj9er5dsNkssFquk3Q4ODqKpKolEgmeefobu7i4CgQDRqijbtm0nGAxw/fXXoyhOXJLD+YEj2Dm8DqaKSCcLSoKSqYIUIG1zCCnAFAqmtJew11AwrVNt0+7YamurGR+frLhI7du/H+HSmDl3Dkd6ekCRoCoICxRhGzqAipQKiUSC++67j4997GO89NJL/OxnPzttmLiASiHa8r5N07QPQYJlypJAZ7fQlAKhqLaJBBaapk2rlWBhYgkThL0vQ1qYwuLp559l3bqnKvt0u1yMT0xQKBT5i7/8Mzo7ZnLVldfwyU9+kmXLl/J3f/d1Wx18RaLdK+yYHaHOwcHBwcHBwcHhTcKep7cwikU2PP8sPYcPUcilOdjTRS6XoyoaZfmyxYQj0ZKZgJvJyUn27tvPwLFjJOJx0sk0M2fN4sILL6S9vZ2nn3yCXD5HNBzG7XYjlOMT8JrLjbTsjB6vx0OmlFI5OTnJJRdfjMvlYnJigsFjx2hrb0co9gT8mUg3OdNRXq/XbAKouLdGo1F6enpYvHARx/x+8vk84XCYQqFQieRzu91EIhGWL1+OZVkMDAyg6zp+vx9d10tmIbYJRlnwMwwDRVEwTZOJiYmKC6zX662IbWUXWb/fj9vtrkTixSZj+P1+VEVBUzXa29vYs2cPVdVV3HXXXTQ1NTmRdQ7nDY5g5/A6kKd5fhyl5JoqFJBY2K6ugCg7M9kG7sdDtqdvx+v18ld/9SW+9KUvMjY2DkB9fT1uj5fJyZi9kAVIiZASgYlEwdLsYrJCCB577DEuvfRSHnzwQbLZLEIILMuaFiZeLpw6dTamPHNUbpElSroZEiktsskEtTVVCCFxe7x0dLbb7rSlozBNC1VREIrC6pXL6e/vZ9++fbS0tDA2No6Ukmgkyh998lN881v/wKpVy5ASNm7czIGD+4knxrj+hnegqorjdOTg4ODg4ODg4HBOIKVESItcOsVTTzzO4QP7SU5O0NV9iJmdHUTCIUKBAJFwmPGJSTtSrpAnGgxw2YVrMK1VbHxxCyOjQ6TSKXK5HMuWLSO1eiV79uxBQaBqtotooVDE5/dTU1ODUBSGhobw+fwIIThw4AAdbW3MaGmloBu2K6kAqzRxL4QoZQCVOE80HkVRKk6xrxUpJZOTk3g8HiYmJujv72fu3Lns2bOHbCaDYRhoLhdgGw12dnYihODZZ59FSonb5SKVTOL3+3G5XCSTyUqEZDlCTwjB0NAQqqoSiURQFIVkMkkkEqGxsZGenh68Xi+appHL5dA0jeHhYT7wOx9gz+49DAwM0NraSmNjI8tXrGDNmjVUV1efE2Kdx+PB7XYjpayYcICd7gug6/pJ6wghmDVrFqZpVgwVXwllI49XGnEphGDmzJlYlvWq9vPbUBQFj8dTyXb7bbjdbizLwjCMN6wN5yKOYOfwpqGoCu9453XUN9SxcuUKdu7cQVVVFTNndlBfX8vKlcvRdZ0lSxZTU1PLNddcw5NPPjntZmJZFm63mz/900/z6KOP4vF4uPXWW3niiSfYvXu3vVDJHl4CqqZy/c030DZzBrfcfgsPPfggxwYG+NM//VMMw0BVVa6//no6OzuxLIubbrqJ5557jltuuYVwOMw111zDxMQEixYtIhqNcvHFF7N3316aZ7TQ0t5MJBpg1eoVvLR1G888/RSf+NQn0DFwaRqGpdPe2cbaKy7nuefW09PTww03Xs8VV6zlPe+5gy9/+Svcc889fO5zn+ODH/wgfX19XLH2So50HSUWT+D3B3j/++8k4A+jGwYXXrCKJx57HMuQp4yte21Jsg4ODg4ODg4ODg6vjVfqOppNp/jRf/6A8dFRMskEmWScWR3thIMBTL1AIpZnfGQY07QYMwy8Xi+ZTJr6hnqC0Souu/wyRkfH2L5tO1s3b2J46BiXrb2MVDZF95EurKLt+mpKieZy0dLSQndXF6qqkk2n0TQXsfFJli1dRG/PUfYePETA76d1xoySQ6z9K/p80+vKQtgbEWWXy+VwuVwEg0E7ilHT8Pv9GMUimqJS0HVUVSUajbJ69Wp+/etfk8vlKBQKuFQVv89HOp1GLdW/y+fzCCHQdR1N00in0xXRKplM4na7aWlpYe7cuUxOTuJyuSq16DweD+l0mqVLl3Ln+9/Phz6kkc8X8Hg9aJqGqqonZ0+9hYW75cuX87nPfY5169bxb//2bxVDxGuvvRZN0/j1r3990jpCCBYuXMgtt9zCRz/60VcU0OH1evnc5z7H3//935NMJl9R24QQLFq0iFtuuYW77rrrDavB2NzczIc+9CG+9rWvnbLtLpeLG264gccff5x8Ps+73vUustksjz766Buy/3MVR7A7rzhVrv7ZjMyy3VS/+tWvoigKsViMbDbLP/7jP2KaJiMjIxiGwfe//32EEJUipFMpFAp8+tOfZt68eTQ1NWFZFt/5znfYt2/fSTMPEjAsi64jXXz7G99ACEEmnQGOz1JIKenp6eFb3/wWlrTIZrMUi0V27drFZz7zGaSUJOJxDh06xBf+8gsIRZBOpRg5NsQ/fv0bKKrK2NgYAnjqiScZGxulqbWF/Xv3kcsXaGxsIB6LA5KNmzaRzeVobGzgm9/8FiMjowwPj/LpT3+aJUuWEI1GefDBB9m/fz+WZfHMM+sZGhyhra0dr9fPd7/7Hxw8dBBLmshSHcATj9fBwcHBwcHBwcHhrUB5YC+lZNOmTeh6kcHBAcxigeWLFzJ0rJ98JoXb7cbUC1imhcfrIxbLEAgFCIT8qKpKMpHA5fWxcME8qqIRjnYf5aVt2ynqeVZdsAbLMOk60oVlWYTDYXw+H7t378bv8+H2eLAsi1wuT0N9PcODg+zbt59ULs/7PvQRqqurSUzGMA2LQCSCz+crZcWKKZV+3rpCz6vh1QotJy7v8Xhsscztpn9gAI/HQ3VVFWOjY+RyWXTTpFgsEovFePbZZxkcHMQwDAKBAEaxSDAQxO8P4A8GCAaDTE5O0tvbS7FYxOVyVRxdwRZr/H5/ZYzY1dWFx+NhxowZhMNhvF4vQ0NDfPzjH8fn9YGAkNtdEeXK5Y5EKTvqrSzWAWzZsoV8Ps/69esrYp2qqvT09JBKpfD7/TQ3N5NIJGhtbWVgYICxsTF2797NbbfdVhE3Lcuiv7+f1tZWotEoAwMDjI+PV/bj9/v55S9/ST6fp6mpqVLCKRgMcuDAgZPG03ZwzUyGhoaA4wJwZ2cnoVCIrq4uUqkUPp+POXPmYBgGR48eJZfLEYlE6OzsJJfL0dPTQ11dHWBf22g0SiwW41e/+hWKotDZ2Uk6naampoZisUh3dzeNjY187GMfY3BwkO7ubg4cOEAsZmfUeTweZs+ejZSSI0eOIISgtbWVZDJJc3Mz4+PjHDt27ExcujOOI9idJ6iAeVbFuZMxTZN9+/ad9H53d/e0DmFkZORlt5NMJtmyZcsr26dlsWf3npPeL9+yLcti3969wHTBa+fOndOWHy2JcuV1x8fGKxspN71gGGzZug22brOXE4KB/oHScygWdbZs2Vr5zE6lhdHRUZ566qnSOSjvRVLUdfbs3ceevfuY/sn01jpCnYODg4ODg4ODw1uVQjbNcH8PfYcPko1N0NTUwEDvUQIBP8lkgkg4RLFYJB6PEwqGkIbO+Mgwbo8bQ9eJRKLk00kO7NmF1+tlwdzZZFIpjg0OMj46xvz5CwiFwuzd8RKJeAzLklSFw8RzWYLhIF2H44AgNjFBW3WYlXM62HnwCI/96n6ef+ZJkokUZkGnoaGBW26/gyuveyeq2wOK4NQBEG8MZ8ItVkqJaZqva1+6rrNh/QZUoTA+MoZHdWG43GRSaQJ+P6m0LdgY+TyaULAMk0P7D2CZJpZuEK2KUijYIpTH66mYTrS0tNDS0sLevXtJJpMVka0cxefzehGW5FhfP2ZRp76lleamJjw+H+MT49z10Y8yc/asU4px56Ij7IntdblcvOtd70LTNL7//e/zZ3/2Z8Tjcbq6urj44ou5++67K8tWVVXx2c9+lscee4zW1lbe+c538uyzz/K+972PL33pS7YbMnYpqS996Uv80R/9EQsWLOCzn/0sP/vZz1i+fDnr1q3j5z//eWWb1dXVfOMb32DdunW0t7dXohtvv/125s2bx86dO/nIRz7CF7/4RT7+8Y/T1WUbfixdupSnn36av/iLv+A3v/kNK1asqASkfPKTn+T+++9nwYIFbNq0iVtvvZW77rqLO+64gwsuuIAf/vCHXHXVVTz55JMMDw8TjUZZvHgx6XSa2267jUwmwz333MNnPvMZenp6cLlcvPvd7+a73/0un/jEJwiFQmzevJnrrruOu+++m4mJiTNz8c4gjmB3nmD7rZ7YwRwP934rcaatzeGVVNt7+fXkK1hxusPsy312+tac7pNzq/txcHBwcHBwcHA4n3k5cWRifJz9e3dTLGQJBf1YepHauhpyuRxCCPK5HPF4jGAgCNi1pYvFAql0Cq/PC9iik2VZJGImQ0PDtM1opbW1FQNJPJEkNjZGS1UASzcZTieoamtF1/N43RrFQoF4LI5bFXjcKsIsEvJ5SVkK42MTeL1+PEEfk7EE//av/8p4Isl77/wAitDOix/drzRt+XQIIXhx0yZb/DMMLNPEMAyymQx6sciSpUs5evSobdBnmZWBj7QsfD4fqWQKy7Jsx19Nw+v1YhgG4+Pj+Hw+Vq5cSU9PDz09PRXBzuPx4PF6GRo4hq7rVFdXIy2Lrq4uauvreM9738uKlSumtfF8I5/Ps337di677DJGR0cZGBhg+/btPPLII1x++eVEo1GklITDYf74j/+Y733ve2zZsoVrrrmGOXPm8PTTT/P973+/ErEHcPjwYeLxOAA7duwgmUzywx/+kEOHDnHttddOE+wuvfRSstksP/rRj1iyZAlz587F6/Vy11138YMf/IBYLEZ7ezsrVqygoaEBRVF46KGHGBwc5B3veAfJZJJHH32UjRs3Eg6HKxl0P/jBD1BVFZ/Pxy233EKxWGTHDrtU1gMPPMDg4CB/8id/wt13382xY8f41a9+xeTkJDt27GDRokXMnDmTVatW8fWvfx1VVfn5z3/OT37yEw4cOIDL5eJ73/seF1xwAY2NjY5g5/BW560VYXeuI0/4ezaR0349TI2/44T3HRwcHBwcHBwcHM4OerHIww/9mkQigYawI32kSTrjIZPJUyzkySFRFA3TtCgUs6XC8hZF3cAiT9i06O8/Rjgcpra2lkQsyeT4OC2trWT1IqPjE+TSSVZ2NOIxdI6l8iSzKUKRIB6Pi2jQT8Drwa250Mwier5AJBrh1ts+QF1zKw0NjbjcXroOHeD73/kXHn7oVzQ0NnHlNded9lf2ucTrFbMURWHRokU88cQTSCAQDFIoFklnM7hUjaUNDTQ1N/Hww4+gulykMml8Hi+qqlacY6WUZDMZ3G434+PjDA0O4nF76OjoIBaPkYjHueCCC9i2bRtCCLLZLNlslkw+h8/nQ3FpCE2ltW0G77vzTubNmzfNHPB8w+VyUVVVNe09y7JIp9NIKdF1vXJdq6qqyGazXH/99Wzbto0NGzaQzWa5+eab6ejo4GMf+1hFpIPp34dsNlu5Rid+T/x+P4VCASklxWIRy7JQFAW3213Z/7/9279x8OBB/vZv/5YrrriCz372s2zYsIFisVgxtkiXHJrr6+vJlAxK8vk8brd7WpvKUYCFQgGPxzOtLVPb5vV6sSwL0zQr75W3lU6nATsq9PWYrLyVOX+/9Q4O5w1iyoMT/k5938HBwcHBwcHBweHN5VTZMuX3xkaG2LJxPS5VIRabxOPx4vWGyKSLjI5OUFVVg0BBSsgXilhSoBsWpiXweAOYhiQ2mcDvC6AIFb1g4Pf5cCkwMjhAPh7DSMdoqvYTdisEA0EKuoFeKKIISTAYwO1x4fW6MKwiRWkh3R4aWlq57rp3snjJMurqm6iqruWCiy7hT/70Twl4Pby44VmkebIz57nIVOOJ18q8efMYHBwEAaa0MKWFy+VCYos5N910E7fediuqS0NRlIq5X9mYwuPxoCgK8ViMYqGAUBSymQzjY2NI08Lv9eH3+Vi6dCmWZVVqlc2ZO4doVZS6hnouX7uWT/7RH7Fw4cJpYsz5EF1XVVWF3+9nzpw5LF++nCuvvJI777yT+vp6otEo1dXVVFVVUV9fTyQSIRgMUldXR11dHRMTE3z/+99n8eLF3H777dx44414PB7+9V//FSklPp+vsp9IJEIgEKCuro6amhr8fj+RSITa2tpK/ccyL730Eu3t7SxcuJDLLruM2tpa3G43Tz31FF6vlyNHjrBs2TJ8Ph8f/vCHefHFF/mv//ov2tvb2bhxI+3t7bS2tnLzzTezdu1aamtrK+0GKq9ramoAWLlyJXPnzuW6666riH6GYTB//nxuvfVW6urqqK6uZnBwkEQiwdKlS1mxYgVDQ0OMjIxUzlMoFCIUClFXV3defDdOxImwc3B4SzO9kt10XnlEnRN75+Dg4ODg4ODg8Ho55YBY2rkgUlqkkwksvUg2ZZDP5dGESi6bx+1yEY5UMXBsELdLxeXSyOULIATJZBKf34/XpVFf34Cu65glQ4N4Io5LBZ/PRyKRoL62jpDXA4UMApWdR/vYerCX+oZmFLdKOBJBIMjl8wQDfvSiwXgiydIFi1AUFSktBgcHMS1JR9sM5i9cyIzWVuKTkxRyWTxB13lhOvF6hQt/wE82m6W+vr6SYimlxOv1cvDQIY729PCHf/CHVFVV8fOf/gxhSXTDwO1yVRxls9ksqqqiKipS2pF7Q0NDRCIRFixYQK6Qp729HcMw2LNnD5FolFA4zPIVK/jABz5AU1MTqqqelyJMY2Mj9913H5qmsWDBAjsN+cUXaWpqYvPmzTQ2NrJ582ZSqRQ1NTX84he/IBQKIYTgwQcfJBgM8pOf/ASXy8X+/ftZsmQJLS0tfOtb32J4eLiyn9raWh544AFCoRBut5t7772X6upq8vk8GzZsIBgMVow/urq6+NrXvsall17K4cOH+fGPf0x1dTXf+ta3uPrqq7nyyit5/PHH6e3tZf369Vx++eXous7f//3fMzo6yj333MPatWuZmJjgueeeY/Xq1fzyl7+kpaWF4eFh6urq+NnPflYR8A4fPszKlSvp7+/noYceIpfLcc899zBv3jxefPFF5s2bx65du5BS8pd/+ZdcfvnlWJbF5z//eaSUdHd3Y5omNTU1PPLIIyiKgqIo0yLxzgccwc7B4S2NI7U5ODg4ODg4ODi8xZEWSIvdu3ZRLOgYRpGqSIR4PIElIRAI4LHAsCx8bj+5QpFsoUgg6KeqthaPy8Po6CiaomKaBkIIdF2nWDQxVDtaLBgIMzo2gc/nY3RskupQhL7RSeJ5g7HDPbyjcwbJdAZpWfh9PizLwuPxEgjB6PgYuWKefYe7sKRk7uy5CFVFKC4CwTDj45Ooih3F9XprwJ2OMyU8vRH78ZZSXEOhEL29vXg8HmKxGB6vF7fHTTgcxu1x8973vhe/z8+vf/UrRoaHsaRVqT9oGAa6rmMYBpZll26yLKvyntvrxevzMXfePDK5LDfeeCOXX345HR0dqKr6pl2Hl+NM7W/v3r3sLRkhno6p5o0HDx486fMDBw6cctmpHDp0iEOHDlVev/DCCwDs37//pGWllGzevJnNmzef9NmvfvWraa83btzIxo0bp723Y8cOduzYUXn9/PPP8/zzz0/b9wsvvIDL5eLKK68kFArx2GOPVaIrT1znxGP66U9/WmknwL333lv5rLu7+xRHf37gCHYODg4ODg4ODg4ODg4Orx0hMXWTgYEBNE0jHA4yMjqMZVlIU6KqKulMmra2Fvbs2UMgEKCmtoaGhjqKuk4ilrRTK6UEVUEv6hi6TigYoKmpntjkJLlcDtO0UDQPWihKfzJNVnHh9vsJVfuwFAVLQjadRQkGCPi9FItFioUCR4508fjjT1Df3MqyFctxqZotKulF0pk0EmlXAz//grleNeV0Wk2zz5FlWeTzefx+PwDBYJClS5agCIHX6+WO99zBqpUr+fY997B3715C4SCZVBqhKPi8XiKRCD6/j2i0ilhskng8Ti6XIx3L0tvbSyQaZcWKFXzoQx9C07SKaHbi3zPB2TBHfLshpWTdunU899xz6PorT0N/u14bR7BzcHBwcHBwcHBwcHBweE1I7MH0ju3beWnzFnLJFHohiwR8fh9m0cTj8WAaJsPDI7jdbrxeL6qikM9mcbldLFqykA0bNjE6Po6iKHhcLnxuD6YpyeWLhKPVFIsGmqaRyWRobW5hzvx5LMgVUDUXCEl/Xx+ZZBJLaKRzRVKZDCG/n2AgyMjEJEPDQ7zzXTcjFA0pLZAmsclJxicmiVZXo7ldZ/tUviUQ2KJcY2Mj8Xgcj8eDYRhEo1FM02TFihWEwxGEIkqp0JKOzk6+/JUvc+TIEXbs2MkLG9YjsF1Ch4aGsCyLjg5JIpFA1TQsKYlEIixatAiPx03rjBkoijJNpHu7CjTnO4ZhsGvXrrPdjHMGx3TibUy5IOn5xvl6XHB+H5uDg4ODg4ODg8M5hJRIKZHSIhWL85/f+z6WYWAYBolEgkwmTSqZwuVyMTk5STKVpFAo4nK5MAwDj8dDIZ9HEQqRcBif308gEKzULTNMk2wux/DwMOPj4xSLRSYmJsjn82QyGXZu304mESMTnyAxMYmlm3j9IVo75zB74VIKUsVCweVyUVtdw8T4xElGDAcOHCCWiHP7HXegaCpv5xC7qWMMTdO46667CAQDBAIBpJRkMhk0TWPt2rUIxV5Wlr4DlrSIx+Ns3bqVxx59lMFjgwwODjIxMUGxWKSmpoZZs2bR1taGoig0NDTQ3NxMXV0dAwMDlTpuU9vhjHscHJwIu/MGZxbiOGej3sGZwrnGDg4ODg4ODg4OZ4MTo58EIEydXCrJf3//ewz2HSUQCOB2u8jns/j9PlRFkEolsSyLQqFANptBKAr1DfXkcjksQycUqcKwIBwJo6kquVyOyYkJCrqOpijMaGwmk0kDAk1zMTg8DNKipiaKVawiXyxiWJLxkRFUX5ADR7qZjCUIhwNEOmZgGRK9oDNybIBiIY/L4wUkhw4d4r777uMjv/f7LFi6jLJY92aNI87U7/jXJXQJgQSEAitWruAb3/gGBw8eZHBwCFVTWbhwIW1tbdP2ZRgGmzZt4qc/+Sn7du9GURR8gQDzFyygubmJrq5uAgE/vb29dHV1VVxKa2tr2blzJ7Nmz2bBggWvy9X2jaAsPjo4vJVwBLu3MefzDel8PjYHBwcHBwcHBweHM01ZBCr/zrYsk0wqwY//8z958Jf34ff6SKcMisUiQoiSQ6hCLpMhFArhcmnouo6iaSAEQlVoqG8mFIkyPj7OqtWrcXu8jI+PMzw8zEB/P9lUChBICVKamKZOKBwmm0kR9PuJxybx+XyYhoHXo2EKSUdHO5PJvZhSEK2qJjYyTC6bI5UqMDo6QsuMGRw+fIRvfvMfuOWWd7P2yitBUZGIt3F83RTKgp+UhMJhVq9ZU/loamBE2Rjkqaee4rHHHqO/rw/Lkrg01Y6A3LWTRDJBIBBA13W8Xi+zZs3C4/EgpSSRTHLtdddy/Q034Ha7K/s+GzhjR4e3Ko5gd57g3GQcHBwcHBwcHBwcHN5s7EgkyGWzPPXEkzz4wAO4XS6S6TQutwe3241bc6GbFqaUFA0DBCiKwO12Y0qYnIwhhKBpxgxyhkFVtIqWGe14vQE83hCK6sHtCbB/906y+RyqqtDYWI+u6xwbGqFY0BgZG6OuphqX242u67hcHoRU8LhdXHPlWhobG3FZBt2HDqMbJhJBoVBgx44d/Mu3/4Xly5ZyySWXAgKl5BB7PlCOFHs9kYKSU2dwTY2Ck1LS29vLE088gaqqNDU1UczlMU0TwzBwuV2Mj44xYg0TjVbR3tZGV3c3/oCfyy9fy5VXXUl9fX0lIlAKRzB1cDgRR7BzcHBwcHBwcHBwcHBw+K0IIbCkAMOgv+co9997LwowEYuzfNWFrFizGp/PRzqTIp5MIi0Lo5hjuLcbq5AjnUqjSAXN5aKuoRFLuGloaSWTzbJxy3Zqa+ooFnVi8STZgkEqV0QfHmHezA50XSefz+PzeEggSOdNGrwRXIEwwWoXhUKBeDJFKpXkWN8AfUeOIIB8sYimuTHyRfr7+9i8dRt33HEHC+bPR1FUhFARKGctuuuNRlZqC76xZYJOtS3DMEilUng8HsLhMH6/n9HRUSwklpQU9CKLFy/G4/FQW1vLje+6idmzZxMOh9E0DUVRbNOSUrtF6fnp9vdm4gTAOLwVcQQ7BwcHBwcHBwcHBwcHh1dMJpthy4ubiE9Mkk6lef8HP8JNt70H07LQDQPdMLCAZCpBX/chRvv6qK6twetx0z8wRHPrDBobm2mfOw/F46PKMsGyMEyJxzTxVUUxigZNM2awaf06uo8NURMKUl0VIRAIEAqbLFuziOr6ekzTQlHA5dZoNXSKuTxHDh7iWF9/SfxRSMaTFBDkcnnuuusuouEIiqKgCAWhlOq2ndUz+sZiWdZrFrxezVoNDQ20trZy6NAhhAS3201rayt9xwaYMWMGd9xxBzNnzqSlpYVAIEAqlWJ8fLziOGtZlh21V063ZrpQVxbRztf65A4Ovw1HsHNwcHBwcHBwcHBwcHA4LVPTI4WEZHySl7ZuRkqTBYsWcPfHP4YWCGOapr2cAKvkLFoTDuC2irzw3NNoQlBdFSSTnOTAgT10HxviwksuYfUFa9A0F5YlKRSLZLM5xscnSSWTXHXdu3n6yd/QPzaJ1+vFpboIhiL4Q2EQKh6/j4JeJKebZFI5xkZHcflDJLJ5Mv+fvTuP06us7///us6599m3TJJJJpN939hC2AkQNhEEKlgEqiIotdVabUV/VipatWpLF/utbV1qFalUBRFQiexLCIQQsu/7ZPZ97v2c6/fHPfedmSwQEJI78H7yuMnMPWe57nMm+ZzP51zXdRJxorEo6axHVU0dZaXlhEMR3EAw185jOBT2WBadDn7i6h+6nSMpLy/nPe95D7/4xS/YuH4DXiZLWWkpM2fOpKSslIULFzJmzBgg90TeFStWEIvFCAaDvPTSS5SXl3P++efj+z6ZTIZAIEA4HD5kv8fioYIqCkoxUsFOREREREREjihfrLPW4lufgf4Buro68TyP8y64gEhZKT7O0HxkFIY5hoJBykpmMq6hgXCsnJ//7z0YAlRUV2KNS92oOnbv3kV1bS0XXHghxjhYa8lksvT29tPa0kp7WyvvueJK7v3pT+gdTFFVGWUgkaKzu5ve3XuoHz2aGbNm0tXVRTyRJFJaSltbGzNPPoVUMklNdTXhUJiTTzqJuXPn0dy8n/HjxxONRnMf7h02d1p+TrjC03zfQCHqjR4HYwxz5sxh69attLe1Mdg3QCabpbqslpKyUu677z4+/OEPs379elasWEFPTw9jx47lf//3f0mn05x88sk8+uijdHZ2smvXLkaNGsVVV13FqFGjRvzOvZnPIvJOoIKdiIiIiIiIvKbCHF8WMpkM2UwGz8syZdp0fAyWA8Mw848msMYQDEWorInw3quvI1pazkB/L62trSx/YQXjA2GyWY/Vq9dw4UUXFx7+4DhBRo2KUl1VTcPYsSTig9TV1/PCc8+wfs0afGsxoVYC4RDbd2xj2swZVNfUECstobS0lFishGQyzcSJExk/bvzQU08BA+ls7qEI79TijzEGx3GOWY+0UCjEJZdcQltrG889+yx9/X34Bq5afBVr167lS1/6EgMDA4wZM4aSkhLWrFnDxIkTiUVjPPP0MwwODhCNxZgzZw5Lly4lEAiwb98+9u/fTzqdpqysjFGjRlFfX68hsvKuo4KdiIiIiIiIHB0DrusSDgUJBFwyXm7+OWOGynSFYoph+OxwbsDl0ssuw1qP+x94gI8tPInZM2ezes2rNDZNyj38YWgb+U2EwiFqa2uxtoaGceM444zF7Nm5k/Xr1oHjMBDPDXkdO3YM1TU1hEIhXNclnc5grWHMmDEHepv5YC0jem+9Uws/b9WQ2KNVXl7O5ZdfRnxwkNWrV9Pe0cHzzz9PTU0N3d3dzJ8/n2QySTqdBmDr1q2UlZYxY/p0SkpLweQKwr/4xS/Yt28fwWCQ7u5uampqWLBgAbW1tSP2d7jzd6SHRrxTz7G8O6hgJyIiIiIiIq/JWpt7SICB6qpKxo+fQCqR5H9/9EMmT5xI7agx2MKccEPFOjP03M+hP3xrAJf3XHEVWItxHM4+7/xhhbrDFFdcgwNY62BwmTxtBlOmzyCb9ejs7sJaS1V1Te7hBYDjGHp7+4jFSgrbKxRzTK5ledlsdmido++Rlh+a6fv+iDZba3NPPR36+fEuFL2Vw0iPpsA5YUIT77v6fWSyGVavXs327dvp6+vDGENnRwe1tbWkkil8zyOZSIKFtevWMhiPk0wm8TyPUCjEggULOOWUU2hqaqK6uppgMAjkHqSR/x00xuC67iHtOvjpuPmf5x9uMbyod6x6Ior8IVSwExERERERkdflOA6uAzXVVVx06aXs3LmD/Xt2csdnPsUFSy9j6aWXU11Ti3FcLPleXrmebZAblYoFxzhYY/BNrsA2vIx2pAJKoQhD7oEWuIbqmrrDFl0qKioP3Y7J9/XLFbLS6TT3338/vu8zYcIE5s2bRzKZJBQKkUql8H2fSCRCJpMpFANDoRD79u2jr6+PRCLB6NGjqaysJB6Pk0qlCIfDdHV1sXDhwkPaf6QeYG+14cWqYzUsNr+vKVOn8vHbb+fpp5/mwQcfZNu2bYTDYfr6+hhVW0dpaSlTp0wlFouRSCXZuXMnFZWVnDFrFueffz5VVVVEIpFDeghaa0mlUjzyyCNEIhFKSkpYsGABAwMDlJaWEo/HAQiHwwCkUilKS0t59NFHmTRpEoFAgLq6Ovbv309JSQn79+9nxowZ1NXVFUVxVeRIVLATERERERGR1zS8qBGORJi34CSuvf4GfnbP/5BOJLjv3p/w9JNPcuGFF3LqosXU1o8lHIngAV7WI5vN4Di5nlGBYLAwXNZYsMZiATc3NpKDS1uHK6i8VqHlaAowwWCQiRMn4vs+69evLxSXstksmUyGwcFBKioqCIVC9Pb24vs+CxcuxBjDypUrCYVC7N69m+7ubsrKynBdl4qKCgBOOumkozyq7xz5Y15dXc0VV1zB7Nmzue+++1i/fj0Gw8DAAPF4nFgsxubNmwmGQ8TjcSoDAXbu3MmqVau49NJLC8XR/DbzxcBoNMqYMWMIBAJs3ryZzZs309DQwI4dOygtLSUUClFVVcWuXbuIRCJcdNFF9Pb2snLlSmKxGH19fTiOQygUyhUOp05VoU6Kngp2IiIiIiIi8rqstbmec45DWUUF511wIeMbx/P0E4+x6qWXaWtt5d6f/pSHfv0g5eVlVFVVgXHp7u4mnUnhOFBaUkpFZSWVVVWMHj0292CICY2UV1WBG8UO9b6zQ7UU5zDtyBdy8l+/0c+QV15ejud5LFiwgN7eXrq7uyktLaW2tpZsNks2m6W3t5d0Ok1lZSV9fX2MGzeOs846i02bNlFZWYm1lgkTJpBKpejv7y88HOF4F4OO9f7zz/WAXE/MyZMn81d/9Ve53oqeT093Nz09PYWCWTAUIhAM4DouoXCIYDCI67rDtmcO2r6hoqKCYDDItGnTaGlpIZvN0tjYSE1NDa7rsnXrVsaOHUsymSSTybBgwYJCQS8YDFJTU0M8HiedThMKhUb8LhyrHpAib4QKdiIiIiIiIvK6Dsz7lXuSa3lFDXMXnMLk6TO57KpW1rzyCtu3bqWjZR99XW3s6mwlEg3jOA6lJWVDQxZ99u7YyvrV/fT09REKhYmVxJg0bSrnLrmck085jdLy8sL+ACyWQgWPXHHoDy1IGWOYOnXqiPeGzz+XnystP1fdwcdg5syZQG5+tHyhKZvNjughVgzeiuLh662ffwpvbqrCA4VU13WJxWIYoKysjPGNjflWFf4/7KxyaN/KA6y1TJs2rVBYy89XN/x4L1iwAMidk2AwyJgxY5g1a1Zh/rr8sbDWEgioFCLFT7+lIiIiIiIi8roOFNCcQqXFCQQoLSunpKSMxsYm0skUyfgg8YFeEokExhgCgSC4ARwn96CAdCbNwMAgHR2ddHa1s3XbZnbt3sU/fvvbjKkfzVlnn8NlV76X6tqa3NBZa/DJz3X35ntCHdwr77WKa/kiXH6Zgx9YMHwb+a/zD0g43D7fLY7mya350p4Z3i2P1y8M5otsR+pdebiHUByuTce796PI0VLBTkRERERERF5ToUiVfw1xhp7wao3FGIdAIESstIyqunosB+oxdqjGZ6zFWPA8n3QqTdbPMhjvo6urg22btrLi+eX87L57WfXKi3zkw7cwa948jOvmHzR7TB1ccDq4RxeMLAYdbhhnvkfesXa4p6Uemx0f6S1b6E1XWOTgdh1lM4927sIjzX34RrcrcryoYCciIiIiIiKvyXVdwuEwruseubCR73hnDbYwTDI30NGaXPnOGRo66QUsbjDXi620PEZNXR3jx09g9ry5rHn5ZZ587Pfc+Tdf5KaPfISLL72UUCgyVCjMv95+RyrAHa4Qdrhjki/wHe5Jtm+H/HDP4UM/i0lxtWak/DDZYjtm8u6mgp2IiIiIiIi8Ls/zXrOgYV6j1pGfgq6wjAEfO/QfOG6Q0rJyYpEodVXVTJsxg4cffogf/uAHJBJxrr7mjzCuC8bhWBXsDudIvbher9BzLAtB+aJdsRWfjkcvyTei2I6XiAp2IiIiIiIi8po2bNjAZz7zmdce3mkPX0qzwPDxkIahAp7J9b/zrR163889FdZCJusx0NdH8/4WvvWtf+CJp54lGAzmeu4VUcHuaFhr2bRp09vQmpH6+/v5yle+QjQafdv39WYUc8HOGENLSwue5x3vpogUGHsUZeS+vj4qKiqORXtEROQt0tvbS/nQU9ZkJMU1EZETi2LakSmmiYiceI4mrhXXM6dFRERERERECvQgABF5d1LBTkRERERERIpUsQ6iFBF5e6lgJyIiIiIiIiIiUkRUsBMRERERERERESkiKtiJiIiIiIiIiIgUERXsREREREREREREiogKdiIiIiIiIiIiIkVEBTsREREREREREZEiooKdiIiIiIiIiIhIEVHBTkREREREREREpIioYCciIiIiIiIiIlJEVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7ERERERERERERIqICnYiIiIiIiIiIiJFRAU7ERERERERERGRIqKCnYiIiIiIiIiISBFRwU5ERERERERERKSIqGAnIiIiIiIiIiJSRFSwExERERERERERKSIq2ImIiIiIiIiIiBQRFexERERERERERESKiAp2IiIiIiIiIiIiRUQFOxERERERERERkSKigp2IiIiIiIiIiEgRUcFORERERERERESkiKhgJyIiIiIiIiIiUkRUsBMRERERERERESkiKtiJiIiIiIiIiIgUERXsREREREREREREiogKdiIiIiIiIiIiIkVEBTsREREREREREZEiooKdiIiIiIiIiIhIEVHBTkREREREREREpIioYCciIiIiIiIiIlJEVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7ERERERERERERIqICnYiIiIiIiIiIiJFRAU7ERERERERERGRIqKCnYiIiIiIiIiISBFRwU5ERERERERERKSIqGAnIiIiIiIiIiJSRFSwExERERERERERKSIq2ImIiIiIiIiIiBQRFexERERERERERESKiAp2IiIiIiIiIiIiRUQFOxERERERERERkSJyVAU7a+3b3Q4REXmL6d/uI9OxERE5sejf7SPTsREROfEczb/dR1Ww6+/v/4MbIyIix5b+7T4yHRsRkROL/t0+Mh0bEZETz9H8223sUZT1fN+nubmZsrIyjDFvSeNEROTtYa2lv7+fsWPH4jia+eBwFNdERE4MimmvTzFNROTE8Ubi2lEV7EREREREREREROTY0G0qERERERERERGRIqKCnYiIiIiIiIiISBFRwU5ERERERERERKSIqGAnIiIiIiIiIiJSRFSwExERERERERERKSIq2ImIiIiIiIiIiBQRFexERERERERERESKiAp2IiIiIiIiIiIiRUQFOxERERERERERkSKigp2IiIiIiIiIiEgRUcFORERERERERESkiKhgJyIiIiIiIiIiUkRUsBMRERERERERESkiKtiJiIiIiIiIiIgUERXsREREREREREREiogKdiIiIiIiIiIiIkVEBTsREREREREREZEiooKdiIiIiIiIiIhIEVHBTkREREREREREpIioYCciIiIiIiIiIlJEVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7ERERERERERERIqICnYiIiIiIiIiIiJFRAU7ERERERERERGRIqKCnYiIiIiIiIiISBFRwU5ERERERERERKSIqGAnIiIiIiIiIiJSRFSwExERERERERERKSIq2ImIiIiIiIiIiBQRFexERERERERERESKiAp2IiIiIiIiIiIiRUQFOxERERERERERkSKigp2IiIiIiIiIiEgRUcFORERERERERESkiKhgJyIiIiIiIiIiUkRUsBMRERERERERESkiKtiJiIiIiIiIiIgUERXsREREREREREREiogKdiIiIiIiIiIiIkVEBTsREREREREREZEiooKdiIiIiIiIiIhIEVHBTkREREREREREpIioYCciIiIiIiIiIlJEVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7ERERERERERERIqICnYiIiIiIiIiIiJFRAU7ERERERERERGRIqKCnYiIiIiIiIiISBFRwU5ERERERERERKSIqGAnIiIiIiIiIiJSRFSwExERERERERERKSIq2ImIiIiIiIiIiBQRFexERERERERERESKiAp2IiIiIiIiIiIiRUQFOxERERERERERkSKigp2IiIiIiIiIiEgRUcFORERERERERESkiKhgJyIiIiIiIiIiUkRUsBMRERERERERESkiKtiJiIiIiIiIiIgUERXsREREREREREREiogKdiIiIiIiIiIiIkVEBTsREREREREREZEiooKdiIiIiIiIiIhIEVHBTkREREREREREpIioYCciIiIiIiIiIlJEVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7ERERERERERERIqICnYiIiIiIiIiIiJFRAU7ERERERERERGRIqKCnYiIiIiIiIiISBFRwU5ERERERERERKSIqGAnIiIiIiIiIiJSRFSwExERERERERERKSIq2ImIiIiIiIiIiBQRFexERERERERERESKiAp2IiIiIiIiIiIiRUQFOxERERERERERkSKigp2IiIiIiIiIiEgRUcFORERERERERESkiKhgJyIiIiIiIiIiUkRUsBMRERERERERESkiKtiJiIiIiIiIiIgUERXsREREREREREREiogKdiIiIiIiIiIiIkVEBTsREREREREREZEiooKdiIiIiIiIiIhIEVHBTkREREREREREpIioYCciIiIiIiIiIlJEVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7ERERERERERERIqICnYiIiIiIiIiIiJFRAU7ERERERERERGRIqKCnYiIiIiIiIiISBFRwU5ERERERERERKSIqGAnIiIiIiIiIiJSRFSwExERERERERERKSIq2ImIiIiIiIiIiBQRFexERERERERERESKiAp2IiIiIiIiIiIiRUQFOxERERERERERkSKigp2IiIiIiIiIiEgRUcFORERERERERESkiKhgJyIiIiIiIiIiUkRUsBMRERERERERESkiKtiJiIiIiIiIiIgUERXsREREREREREREiogKdiIiIiIiIiIiIkVEBTsREREREREREZEiooKdiIiIiIiIiIhIEVHBTkREREREREREpIioYCciIiIiIiIiIlJE7dqF6wAAsXpJREFUVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7ERERERERERERIqICnYiIiIiIiIiIiJFRAU7ERERERERERGRIqKCnYiIiIiIiIiISBFRwU5ERERERERERKSIqGAnIiIiIiIiIiJSRFSwExERERERERERKSIq2ImIiIiIiIiIiBQRFexERERERERERESKiAp2IiIiIiIiIiIiRUQFOxERERERERERkSKigp2IiIiIiIiIiEgRUcFORERERERERESkiKhgJyIiIiIiIiIiUkRUsBMRERERERERESkiKtiJiIiIiIiIiIgUERXsREREREREREREiogKdiIiIiIiIiIiIkVEBTsREREREREREZEiooKdiIiIiIiIiIhIEVHBTkREREREREREpIioYCciIiIiIiIiIlJEVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7ERERERERERERIqICnYiIiIiIiIiIiJFRAU7ERERERERERGRIqKCnYiIiIiIiIiISBFRwU5ERERERERERKSIqGAnIiIiIiIiIiJSRFSwExERERERERERKSIq2ImIiIiIiIiIiBQRFexERERERERERESKiAp2IiIiIiIiIiIiRUQFOxERERERERERkSKigp2IiIiIiIiIiEgRUcFORERERERERESkiKhgJyIiIiIiIiIiUkRUsBMRERERERERESkiKtiJiIiIiIiIiIgUERXsREREREREREREiogKdiIiIiIiIiIiIkVEBTsREREREREREZEiooKdiIiIiIiIiIhIEVHBTkREREREREREpIioYCciIiIiIiIiIlJEVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7ERERERERERERIqICnYiIiIiIiIiIiJFRAU7ERERERERERGRIhI4moV836e5uZmysjKMMW93m0RE5A9graW/v5+xY8fiOLovcziKayIiJwbFNBERebc6qoJdc3Mz48ePf7vbIiIib6E9e/Ywbty4492MoqS4JiJyYlFMExGRd5ujuk1VVlb2drdDRETeYvq3+8h0bERETiz6d1tERN5tjqpgp+FCIiInHv3bfWQ6NiIiJxb9uy0iIu82mghCRERERERERESkiKhgJyIiIiIiIiIiUkRUsBMRERERERERESkiKtiJiIiIiIiIiIgUERXsREREREREREREiogKdiIiIiIiIiIiIkVEBTsREREREREREZEiooKdiIiIiIiIiIhIEVHBTkREREREREREpIioYCciIiIiIiIiIlJEVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7I6aGXqJiIi8EyiuiYiIiIgUq8DxbsCb5boB3EAQgEwmhfX9t32fgUCQoOsCPql0Gt/at32fxcLFEAqHsDhk/SzZTOZ4N0lE5B1Fce3YUlwTERERkWJWVAW7aEU14045CxsIgnGwGLAOxvpYckmEwZLq6SQaiTFxyXsIVlXx1L/eRd+WdW9z6yxzL34vNWddTKCkjGe/8Xn6921/m/f5B3Acxp98JsGyWvaseJzMQM9hFwuEIkw4/VyIlOHj49ggDga8LAOdHXRsfRV/sItweSWnf+wvCDVMpWPdOl76z6+DzR7bz/QmGeNSNWEKoxacTqCmFtdzGNi/i66dm+jdvQ0/McDIXibHOmE9uIfLG9u/wWAM+IVV3z0Jt0ixU1x7CymuFSiuiYiIiLzzFdWQ2NIxDcz649uITZhOrGEyZZNnMPfjf824qz5IpHEakcZpVM09lenv/wi7X36BtQ/8mGjjFIiUHJP2rX3sYbY+/AvCjdMxkejQu8U5nMjBMOu9f8TMj92BW1Zz5OUMOGVlTLjoKqZ/7P/DrR+HLa8iMn4y82/9FGf/7XeINc4gMdDL8/9xN168H2f0eDBF9atzBC7g0HTuxZz+hW/il5bRumYlLZtXUz55Gufc9e9Muux6cufweCYD9qDX0ckPZnOCIRbd+HEaFy+lWH8fRd6tFNfeOoproLgmIiIi8u5RVD3srIW+fdtZ+/1vYbMZYuWVTFx8Hn3rXmHTf30DsJQ1TWHBR/+CTGqQdLwfx4Ix5jCXpoe/WD3w7utfzBrM0BK5/2cScbzB3qEqp8vxu4g8XNtHvmetJdM/QCBr8dKJI24pnUqy9aGfUz1uCoGGyex46B4y3S1gYPcTU1jy9e8z808+ycq/+ySZ/j78rMUc8rlfvz35o2k5unPwWmfn6NMQj1BJBbP++KNsf/E5tvzku2AzWKDjhceJxqIESkoLn+aNtST/fu7Po23Tm02hRq534FiCwbhhRp99Ob3OE/Dco29i6yLydlFcO1qKa4primsiIiIiwxVVwW5g307W/fT74GUx2FxaYQ02PywCGGxrZv2P/x0/m8YCWcfBmhBlDU2UTpxBsreL3s1r8VJx8olHMFJCbFwjpfXjMNant3kXyT17yHopLBAqryRcOxpjIdXWSrCsjIqpM0j39dG9eR1+YhCLl7uQNA7DLzNL6sZAaUVuT16Ggb07wPcKn8kYh9jY8TjhGD5g00ni+3bhBoNEGyZgTQg/nSCxbydYj3CsjNLJ04lW1pHs7aF322Yygz2AR6i8mnDtWIx1GNy/Ey/ZT7CsglDdWIzvk9i/Cy+VhKHWxft6CCfj2KH3juTAJfqwS25r6WveTX/LPmqmzSZYUkp6oA9wCpfUeY5xKBkzjoqmKVjj0rtnB/G9O/H9/HxADpHKKmLjGimrriOdSNG9YzPpjtah85o7T240SsWkKZTW1JEYTBLfs5NERwu+nx1ayiFWM5rKidNwIlF6W/czuHMrfmZwqPUj53syQLgkRrhqNPT3Y+2B+Yk8L8Pm+3+MEysDIFRWQbBuHFhDcv8essk+QqUVhOobML5HYv8evGScYEUFwZrRONaQaWslVFlN+aRJZBKD9G5aT7q/GwuEa+txy6vB90i17KNkTCPl4yYS7++hZ9MavMQAwxOiYGkF5ZNnEimvItHTRd/WjWQT/YBPsLScaN1ofBySHW0EQkGqp87AGIeundsJVVSTDZUSrBpFyaQZ4HvE9+3CZl77vIvI209xTXFNcU1xTUREROTNKKqCXWqgn7YNqwpDIixgDUPfOVh80vFBOja8Sv6i0MWl4eQzqRs/jnSklLpJk9n15G9Y891vYr00jhtg0W2fITRuCs3PP0EoHGDB9R9h6wvPs+l//wMnm6Jm8lQmX3ETNQtOZ9vD91M9uoZMKELV5Ol0rH6JlXd/GT89AOQSlcKdeGOYvPg8xl59E/G2FuI7t7Pqh/+IF+8n32rXDTL1wvcyfulVpAbitDyzjPU//X+UVNcw5/rbqJhxErueeJj1//NPlI2ZwKm3/3/0d/fSu3EtExadT2zUGFb8v7+jb8dG6ibPYOpVN1M9+yQev/NP6V77IvUTpzP52lupmjGXp/7m43RtfDnXNmuJ93RTOtiDn04d1fG35How5LmBAMFwlEwyiZf1ONz0544bYNrlf0TjhVew74XnMFjmXXcrO59/kk33/Sd+NkmkupYld/4DHXv20rNxHaNmNjH35k/w6vf/hbaXn8bHEiqr4ow/v4NENkvnhnVUzxzHmI99gVf++9/Y/8yvwRgaT7+QWX/8MVrXrCTZ3sH0yz9IT+tOVv/nt8gM9h7286QG+0l3tdFw4Xvp2PgKXRtewU/GAUvX1o2FniajJk6h6ZpbqZtzMs9++ZO0rn6O6saJTLv+o1TPOY2nv/TndK1Zzqgps2i64kZqZp3K7id+Q1nMZaBvgNqZ88h6sPwf7iC+fwfjT1nM2POvomTiNFp//zDBaIx0MkHVgnnEW5tZ+Y9fJ93TggVqJk/npI99lo7WffRt2crksy4kVFrF8n/7Owb2baVyXCOzrrmZygVns23Zr6kZU08qVMKoqdPZ8uivqCqJ4ZRVUDN9AeH33wqpQV794b+Q7G45qvMuIm8fxTXFNcU1xTURERGRN6OoCnb2oK9ziY3NjSnCH3pZPBjqqWBw3RCVdTU8/+3/Dz+ZYO41H6LxfTex7ec/Jt66GwcHLzPIS//vLga2b8Zi6NrwKqd+7lu0vPAkPVtfZf8rL5Ho6GDJP91H2fgmXvj250gP9jL1gmuY97G/YMP4Jvq3rQVyd8PNUOrlOAE836H75RdZ9aN/JdPXhfWzIz5DNpti9Y//nVhFDdEZC1j/wI/JZtP0tuxn7zO/JzUwwLr/uRsnEOKU2+4gnrW89M9/g5/sZ1s4ypLPfYMFn/giz3zxY+x75XlSiUHO+9oP8V0XsOxds4J0Js35f/d9jOOOOIqJvm4y/T0j2vSax9+xmHAIE44QDIWZfM7FRMdOYPt9PyATH8Rxcp8qP9TGxdAwbxEzP/gJnv/Xv6f9qQcB6N68ntM/9zXaN6+mY+XTOI5L99atvPyf/4KX7MQ4LvNv+nOm3/wJWtevguQA4+bMp3TaSSz/9IdIde4Bx8WNJ4iWlGCAsrHjOen2z7Pud/ez9Z5/BS9L80vLWPLV/6T7oivZdv+PDvuZsoODbLz3+8z5+Bc4/c7v0rN1A20vP0HHyuX0bN+ITccBhz1rXiYRv5ux37oHM3RsWza8gv/f/8LZ3/offCfXA2Pvy8/T393Jud/6H6K1lTz3rS+QjfcTqx3Nkr/7PnM/+ElW/MNfseW3vyLd3c0pX/p30sbl5X+7C5tJUzVxChd89d/ov/L9rP3RvxKKlXD6n/0N7W0dvPovX8em4ux5tIxzvviPzLn1s6z46l/QvnE9L/7XP3Pp3adTM20GK77xVyS7OphxwVWkrceLzyzjPbNOY//zv2fDj/8JrD+iN4yIHD+Ka4primuKayIiIiJvRlHOsDxiYIoxGHPok87yyxjfZ+dzz5AZ6MVmM7RufJVwNEa4uhqLJetnWPH975Bpb6V03CTKJ8/Bc4IY41DWODG3JWtxfB8HS/vKZ0j3dWK9DB1b12NxiFbXjdy/BTcYZf61t1DeNImV3/9H0j3tcIQEwnoZdjz2MBVjG6mfswhwcQNhJpx9Hjuf/g1kM9Q2TaFs3um0vvQsfjLXkyGbSrDr+cepnT6P8ilzMTb3ea31DxwAa7H4eOaQI0dqYIB0Ty/WO1wfgkOFS2s4/VN/y5mf/yfO+uI/MX7J5bzy3//Klp//AGxuYI4x+f9ZrHGYcM5lpFMePetXYgIBTMCle+savP5+JpxxORiXRFc7L/7nPxCOBKhonErFhJl4iRSlDeMJlVflPobvEawso+H8S4jWjMEAa37+A/Y8/QjWGMafdj6mvJb2FU+A42ICEfrbWunbs5mxZ1+CEwwf/thj2fbEgzz1uQ+x78EfU1oSY8Z1H+eMv/8+i774z5RMmI3FzSXPvpfriVGY/Cf3u3HwkCr8LI617Fr+HNl4H+AT72ilbeVT1J58JuHqMWB9jO/heWn2P/cYNp3AWI+eXTvo2biR0WdeQCBawqipswnPmEfr8ifxU7k5mbz4AO0vPs/YBWdS3jgll0ZbHxzD/hdXkGhrwWYzbFz2C7Y/9iB4Hi4WYy14WSU1IkVIcU1xTXFNcU1ERETkjSiqHnaH4xjDiLzmIJ7vEe/rKcz04mVSGOsxdNsc47hMO+8yGi+/mt6OHvyBAcLhIH40AqEDF8MWC9Yj1deTG65kwc8kwHoYN1hYzsfHcywzrvkgY+edxK6Xn8fPJAH/NSddbtu6joEd6xl34WU0v/AYleMnEIiW0rV5HQAVo0YRCgVIdbSPWK+vqw3HcSmvraen0Nbh/QFyX/uHO0gZDz+VGerJcWT5Xh/pgW5e/Pdv4vV1gvWxyUG8ZIIDE2k7GAO+GRra5bqUjBkLJRWc/PHP4w61wToOnhMgUhYG1yEYK2HeTX9G5bSZ9O9vxk97lI0Zi3GDmECAXI+Kl6n53QNMvfZPmPbeG+hZ8yK7nniQllXLwRpKxzaQCbrM++CtZFPpXDKCIVjXhMlkcCPR3Pk67Af06dm+jlU7NuDe82+UNzYxecl7GXPR+5n3sb9m+Z1/ik1lRky3faiDptX2IdXbNew9S39nJw3RKCUVlSTbcut4viU92Fc4zsbLEO9qp3rOSQTKyigdOw5rXAa6OrEcmLy8t70NEyohWD8Gtq7JzXxlHFI9HYUzZr1cIm3d4CHzL4lI8VJcU1xTXFNcExEREXk9RV+wy2c1r/VktQMX7jb3pfVh6OK7fups5tz6l6z62Q/Z9fP/JpPNEKup55K5izi4g6HB5u5EF7bnD3098lI34Abp27mTlueXseD2/4/OC69lxyM/Bv/Id/z9ZJxdjz7IrD/5BCUTptB45hJ2rXgOf2heIC+bwVr/kCTOGAdrwfM8bO7ydqhJZuQy5tDntCW7W+jdtQ44yjvTfhavt5NMT+thf5y7I87Ii2gLia5OXv2ff8UmBnNvmVxL/FQSPI9Zl72XMeddyjN/+xd0r3sRx1qmnH0xMz/5ZYzxMUA2PsDL//4NYr+6l/Enn8OY8y7klDv+gc33fJfNv/g+xvPx0wnW/uxHpNv34g0/d75HdnDg8J/JDVIzYTrdu7aDl8TGB+jZuIaXNm/g1ECYunMvJlhRTqrtQPIx/Cga18XBMDTp1LAjkf/DFN4zjgNYfOsfeA/noJ404DgG37dYz8f3shhrMe7w30WD6xhcPPBt4Xg7mBFzMR1Y2h4hGRORoqS4primuJZbT3FNRERE5IiKckgscGACbM/H+uaQC7fC6I6h//Lz7+QSHYOxASwOFWObIFxO9/q1ZDLp3DAhxx365Nlh23OxuBjrkD8sxlowBmfYzq31sJkEe5/7DbueeoS9j/wf8677EFVT53FwAjScxbJvxZME4nGmXHE91bPns++Fp8lfRvfs24NN9FPaMJ78XX+AmnGNZNNpevbtAgye5+MGDKFotLDtylFjMMYdeUEOdGzdyOZHfjb0Xf51+NYZa0d89kOPdn5dPzeMBYP1snRt20y0pJTMwACJ1t0kWveQaNlDzaSZ1EyZhTGGsgkzSfbEGdixGeN7ONbHCYRHJGc1E6ZSMWE6g3t2sPH+H/HsF26n+9UV1J+9BBOM0L11PUE3ABb6W/aSaMntJ1ZeztiFizlS6huIxjj9k58nWD9qqK9Kbr4o/AzJ9mZMJoX1comfn7UYC4FwaeEzR+uGekwYho6NybU7YCgd3XDguBhDdUMjyb5OBrpah46aTyAQIFZ7YOiZEwxSNnoc8dZmsv199OzagcmkqBg7HnNgFimqGxpJDXaT2Ltj2FlzcAojm0aeT98eeCsQLcONxo5wrkXkeFFcU1xTXFNcExERETlaRVqwc3ACIUIlFYCBYJBQSdmI63I3GMGJlpA1Hm4sihuIEAiECUbLMIAbLcUJRunZtxuTzVB38hk44RhuIMT4kxbhl1QRCMcIhGO4gQCBWAUODk40SiAUxXHDhGIlYFzcWDmBUJRgKEIoUgrG4MZKMcZh069+QqarnZNu+QKxugkEjjDnDMBAZxvNy5+iaenVdO3ZS6qzmfxcRj37drP/iYcZd+55REeNxTgOJbWjGXf+lex+9lEGd20AIN7VRra3k/GLLyBSNYbKhomMW3wWnrW4sVKc/HAox+WkD36cs/7mnwhVj+KIA2KMIRwrwwlGCeAQjJURiJQw8lfDYgyES8rwAi7GdQjESjAYtiz7P5xsH40XvRdCYXzHoaJhAnM/8GFSWR/H9xjcsZ3Sykqqps3BcUMESqqoPe1cssYhGKvEdUOMmrWQ2dffghstA2PwfR+MR7K1GbIp9rz4DOmtG5hw0RUESiqwjkO4opoFH7wVE40ccQJyY33ClWVMe+/7iZRV5+YJcoNUjJ/I+MXn0vric6R7O3Pnp6cDr7OFMYvPo6SmgaqxE2g8cykp4xCIxggUhprlkpvxZ5xP2ZgmAsEw9TPnUX3KInY+/msyvZ1DR9vBx2HseZcQrR5NMBRh3KJzCU+bz+7fPYCXitO5bT0dz/yOsedfQrhqFNYJUDJ6LHXnXsi2xx5msHk3xnUJxUqxxuCGIkRj5RjjFNri+x7xeA9uWRkmEOLUm25nwvnvOeI5F5HjQXFNcU1xTXFNRERE5OgZe7ixCAfp6+ujoqLiWLRniEPD/NOYdvWNREc3kkwmie/ZxMvf/Rbp/h7A0HjqeUx93w0wagzZzv1s+b8fE8hmmPL+m3BrRpPu6GDzr39G64qnmXHVDUy45L307G+B7m7i7fuoO+UcMukUe558hOS2dUy74U+JjGlgoGc/+x+6j769e1nwwdswoyeR6etl56P34WY9mi6+hkBVLfG2/Wz6+feoqa2h8eLrCJZUkehsZv0D/0Pziqc40p3xupkLOeNv/x9P/u2f07NuxYifhcsqmXfDxyhrnMpAx37KRo2ie18za/7n38j0tAG5YUKTl17N9Os+SjqdJN2yhz3PPsbUq/6Egb5Odj76f+x77GFMIMBZf3kX0WkLeeqLHyXZvPuw7YmUlrLots8SHD8TJxIj3raf/vUrWf2z/xg2FMoQKa/k9I99huCEuWStR3LHWl767t1kBruon3sqM66/BW8gTmowTklVBTufe5Jdv/8FeFnCFaNY8NFPUTllDr17tkF8gMH+QUYvOoN4ezNb/uc/wLfMvPF2/HSceG8PJeU1+J7Hqv/+Zwb37QCgYtxk5t70cZxQhGR3F7HySvbv2MyW//sBXnKQXC+Dg36TAkHmXPlBqhedi81myfT24jpBYtU1dGzfyLp7/4tU9/7cHDvGYeJ5VzHlptswqSSp1n3sfHIZM665nmR8kOZHf8Xm3/2SionTuOhb9/DqfT+gfsJEgiUVhOpH0bpuDWt/8A9kBrox1jLh1HOZ+/m72fQ//0bDzLkQixKsHcWuZx5jy89+AKl+wBKuqmHuDbcTGdNIf0c7FaPq6Ni+iY33fg+vv4eaqbNYcOMnCYwZT7Kni9T+Pbzw739XGHZmMEy5/HrGv+d99G/dTqyyipf+3zcZbN56xN9DeXfo7e2lvLz8eDejKCmuKa4primuyYlFMU1ERN5tirZg57hBguEQMDT/irVkkoOFuXcCgQCBUBTfANbHS6cxWIKhIP7QGIpsJoOXSYMxRCuqMKUV+PE46Z4OguEoGBcvm8b6GdxQbOjee5ZsJo31fSLhKBYHz4CfSYEFEwySH6PhpVIYF9xACIPFwZJJZ8hm04e5nMzNxVMzYyGzPvBRnv27v8JPHTw/TW6+mFhlLZSW4g8OkuzuOPQuuzHEKmpwYqWkerrwkwkC4SjWGLKZVK6t2FyvgmiMZFcH1h5+vh9jDJFIDN+4hamhfS9DNpU4aDmHUDSKwc0NcLEeqeQg/tCvTyAcIVRdB06IbE8n2cHeYdOVOzhuiEhNDSYcIdPZhp9K4oZDWCzZVArf83CDESKV1ZhIBD+ZItnTWfgshd+MQIhIdR0mHCPb20Wqv3vYjw9ObA7ciXeDIYKllbglpbknA/b1kBzsxR705DmDIVRZR7AkRqani0wiTjASwgB+xiOTSVExcRoXfusnPPGNO+hf8wLB6nr8dIJUVwe+lz/3lgmnns38z/8TT336RtLtzTiVVXiJBMnuNqzvMXx2JsdxCVfVYWKl+PEB0t0d+L4HWFzHJRiJFD6dYyGViI+Yc8k4LpHqOtxoCamuDrKD/UNDpeTdTMnNkSmuKa4primuyYlFMU1ERN5tivKhEwaL9dKk4mmOdCc1m82SzfYNWyfHy2aGpioeMUEPiZ4u6Okkn5SkE/2FLRvAz/aO2L4FBuOHmfA5kxrRUjxLNp0a/s4hLa5uaMTEKujatoFJ517M7ucePUxSM7Rf32ewqw262oa15OCFLPGeDujpGNqnIZXIH4sDLcjE+8nE+19zAIm1kEoM5qf9HvnZhn1vrSUVjzN8qvThS3upFPH9ew5aP8/H95LE2/aN2Hc2PvIJdl4myWB7M5AbuHS4y3I/mybe1pxv1dCfDkf6Pckv52VSeN2t0P0ax5XcnEypnjbSPQfalYpnD7TTGHACufl+jEMqOUh6385h8wjlP74DTi5Z9B2X5GAv/kAvw6f/Ht4C3/dIdLZA56Ft8nwPLz74Gp8PrO+R6Gg57O+fiBx/imuKa4prByiuiYiIiLy+oizY2de9NDv05/aQ7460DXvIHt78heDrtSOntmkaE6+/hdZ1a4k2NND80/866u0dXSte+9O89lbz96sPXurw3x/5qL7e+kda7/BLv/Y99KNd+nBteHPtyv85/tTFNL3ng2Q9w8Krb2BXWZDNyx45aLuGGRdcTsOF15D1YMHNf8a2B3/MvhefPKp9/yGU1IgUJ8W1N9oKxbWjW+5I7x15KcU1ERERkRNDkQ6JfWcJl1RQPWMOTjBK5+YNpLqajyJ5k2IUKqsgUFmDIYgBMv1dpIZ6hBxIKwyRyhpMaQUYF2MtmZ5OMgPdKPWQY0nDh45Mce0Po7j2zqG4JicKxTQREXm3UcHubXdgMMfhvpJ3D3PI8CyRt5eSmyNTXPtDKK5JjuKaHEuKaSIi8m7jHO8GvDsMnyFn5Ffy7qGkRkTeORTXRHFNRERE5O2kgp2IiIiIiIiIiEgRKcqHTryz6O6ziIi8kyiuiYiIiIi83dTDTkREREREREREpIioYCciIiIiIiIiIlJEVLATEREREREREREpIirYiYiIiIiIiIiIFBEV7ERERERERERERIqICnYiIiIiIiIiIiJFRAU7ERERERERERGRIqKCnYiIiIiIiIiISBFRwU5ERERERERERKSIqGAnIiIiIiIiIiJSRFSwk7eQGXqJiIi8EyiuiYiIiMjxETjeDZB3IpPLb+ybXPfNrViE3imfQ0Tk3U5xLeed8jlEREREil9RFezGjx/PRRddhO/7hfeMMRhjRnx/JNZarLV4ngeA4ziF5a21I7b7TuI4Do7j8Lvf/Y69e/e+rfuKRqO8973vpbS0dOgdi2/Aa24l+OJLBH3AGFzrYq2PhwUDDgYfcPHx8LFm+GW/wVgfawzGOkMdGgxYm+vXYMzQshZrcz860OPBFrZxyNeHyyuGVrNDPzP51fLbtCNXshxF3wqT/wz5VkLWiRA953SSlSXD1jfs39/CI4888rb/Lo4fP55x48bh+z6+79Pe3s6uXbuw9vgkW4FAgBkzZjBmzBg6OjpYt24d6XSaaDRKbW0tnZ2dzJ49G8fJdfpNJBJs2bKFRCJxyLZc1+Xyyy/H8zyeffZZrrrqKlatWsXq1auPuH/Xdbn00ksBeOihh97wcSgtLeWqq65i8+bNrFix4g2tK+9uimtvjuJa/nvFtTzFtUPXUVwTERF5Zyuqgt2sWbP45je/OeKi4+CkJp+sHO7CJJ+85C8aD14+n/C807iui+d57Nq1621PbEpLS/nCF75AQ0MDAD6WDB5djz2Lt72DCidAJmAIplPYcBACAbyBBCYaJmAc/EwSgyWQzpINuRgPrOtifQ9rDE7Wx1ovl1AYMEO9Gt6O63Fr7Wsmym+EMQcSG2MdkqFyxvzFX5KYNgHHMPQ7CM8++zy/+93v3vbEJhQKceedd3LvvffS2trK5z//ee6++26efPLJt3W/hxMMBvnzP/9z+vv7eemll5g3bx5XXXUVX//612lsbOT666/n29/+NkuXLqWpqYl77rmHCy64gOrqaj73uc+RzWZHbC+fqP3Jn/wJv/vd7ygvL+eUU055zcTG8zza2tq47bbbeOSRR97wvwWJRALHcTjrrLOU2Mgborj25iiuvTmKa8eG4pqIiIgcC0VVsMsLBHLNOtxF52tdiOYTmcMtb63Fdd23sJXFw1pLJpM5pvssnAffw/V9gh5kKkpwzj8NYz0Gkz6RyioG8LHxFBV11QzsbibggImGGHjwMeoWn0Trhi1ETpmD7xjctZtxOrtx44MYbC6bye/G5DOb4hmOkzsGuQYawFjAOFgfHLJ4fhZjHAIBN9+xYth6b68dO3bQ1dXFyy+/zJo1azjvvPOYNWsWLS0tnHTSSfzqV79izpw5VFdX89JLL3HZZZfR3t7O008/zcUXX8zevXtZvnw5AGPHjmXJkiXs3r2b+fPn88wzzzB69GgmTJjAfffdR0NDAyeffDIrVqwgHo9z5pln8vjjj7Nv3z4AFi9ezPz587nllltIp9OsXr2af/7nf+biiy9m/fr1eJ7HwMAA27dvJxQK8fjjj7N7925+8IMfEI1GSafTLFmyhFGjRrFlyxaWL19Ob28vvu+TzWbp6+sjEAiwePFiZsyYwZNPPkk0GmXhwoU8/PDDdHV1AdDb20skEuHKK6+ksrKSX//617S1tTF+/HjOPvtsrLU88cQT7N+/n5qaGpYuXUowGGT58uVs2bKFvr4+amtrGTduHBdccAFbt25l27ZtXHDBBbiuy8qVK1m3bt3bfm7lxKS49sYorh0fimuKa4prIiIixaMoHzpxNMODhn89/HXwNg5+/+D9nOivoz1ub7XC/i1DL4s7dhQdm7fT8+SL+H4WBpNEqqpIZ7JQUkrSWpxgiGxJlIRjsLEYZjCN25sg2z9AeOc+3MEELuRexuBYcHwI+Gbo5RDwHVzr4Ax7udYp/OxIyzjWIZB/4Q69hr33Bl+uNQSGXq41OOTaG7BgrMViMcbJ9aYgP+Tp2AkEAlx55ZV88pOfZNKkSSxbtozOzk6uueYaysvLGRgY4KabbiKZTDJjxgwmTJhAOp1mzpw5dHZ2FrbT09PD+eefz6mnnsratWv59re/TTKZJBKJcMMNN9DR0cE111yD7/t4nseUKVPo6ekprL9w4UK2bNlCOp0Gcr0CNm3axKJFi9i1axf/8R//UVh28uTJXHbZZdx44408+OCDxONxbrjhBmbNmsVvf/tbbrzxRhYuXHjIZ7XW0tnZyXXXXUc6ncZxHBoaGhgcHByxXGNjI5s3b6anp4cvfvGL1NTU8JWvfIX169ezfft27rrrLioqKvjSl75EW1sbL7zwAnfeeSd1dXWFbcyYMYPy8nK2b9/OrbfeSl9fHytWrODUU099q06dvAMprimuKa794RTXFNdERETeTYqyYAf5oRaHJi75l+/72KFhQiNfuSTGP2zyc5jEiGK6t31iKSSMQ0N87PZ9VEyaTNWSc8i8sh5nzx56fv5rSgcGsJ2dRB2D39ODm05T9p7zSISClFx+Hn4iQ2hnOwHfx/F9HN9i8q8RJ8eQ+5XNpT5m2IsjvMzBy1kXrAO+Aetg8t+PeA1bzrqYI718F3wHfBd8F+s7WN8Ufs9ybT/QWyH//rHieR7Lli3jhz/8IY8++igf/vCHGRgYIJlMAtDV1YXneSSTSX7605+yaNEiRo8eTWtrK1u3bi1sJx6P09PTw/PPP8/LL79MZ2cnL730Ei+++CKjRo2iubmZZcuWsXjxYmbPns2jjz46IqHwPK/QuyjPcRyy2SzpdJq2tjYgd2wGBwfp7OxkxowZvPTSSxhjuOSSS6irq+M973kPgUCAmTNnjthWPqHfunUrK1euZP78+cydO5cHH3yQVCo1YtktW7awYcMGnnrqKWbMmMHs2bOprKxk3bp1vPLKK4wZM4Y5c+Ywc+ZMXnrpJTZv3kwqlWLWrFkYY1i8eDG333479957L/v372ft2rXcfPPNXHHFFTz99NNv3cmTdyTFteKnuKa4primuCYiIlIsimpI7CEXfcbi+R7GWjzfxxiDM7Sc8Q3GGqxrsL4/NGzD4BgfA2QDLi4Gaz18a3Gsg2cMbn6aZ8cBxwy77JSjlclkCnd7XTcA1sUrLyVVX4fz6np8B0LBCPGufkpCMfzdLcT3tA6Nncli9+7DOpD1fXBc8Cwu0N80AWMcnEId2cHH4BnAOCNu5dtDzpzFHJSiHroMQ1OED/veHroeOPjWx89P8l7oSzBi4yPmBzeOwTq5nhkGSAYj1EVKcu8bk0usDMd0gnjf9xkcHKS7u5tNmzZx6aWXFpIA13UJBoMEg0EANmzYgO/7fPjDH+aXv/zlIX8X88N0CkWF/LxGQ9t78MEH+cY3vsGqVau4++67R6y7fPly/vIv/5JYLEY8HicYDDJ79mzuueeeEctZa9m/fz8vvPAC3/nOd/jTP/1TVqxYQU9PD8888wxPP/00Tz31FKlUipKSkkN6Hvm+zy9/+Us+/elPs3r1au67775DjkksFsNxHMLhML7vMzAwgDEG13VxXRdjDP39/WSzWYLBIK7rEg6HSSQSVFVVsXXrVpqbm7ntttv4+te/zsqVK3nhhRe4/PLL+cpXvsJNN910zIfxSXFTXDsxKK6huKa4prgmIiJSZIqqYDdS7mlojrUMbNzOvmVPQSRIRdM4Mole4lv34ASjjD11PrtXribgQcW86aQGE/hehgmXXEi8q4+BzVuJ1laS3d+BrSglG09BTTWjpk/Dhor44xepgy9488lm7Snzqf2vbxMwWQJA1g+S+/Wyw9azEPBzV/4+hSQgN8hm2LPwrBlKJWwuuRi6oz9yzyMTnVzS5B+U6hzNMofri+KMWM33fYzjDI2QGnZBf+DRfoUUyg5NJh4JOPiRkkKKlu9Zc6zMnDmTiooKzj77bKZOncq5557Lj370I5LJJOvXr+fWW2+lu7ub0tJSpk+fztq1a/nVr37F9ddfz/r160dsa/To0TQ0NDBnzhwCgQDl5eXMmjWLOXPmMG7cOOrr69m1axddXV20tLQcMlxn1apV/O53v+P222/n1VdfZcaMGbzyyis888wzhWNSXl7O7NmzaWhooKmpiRdeeIGbb76ZG264gXvvvZdLLrmEZDLJ6aefzr333suCBQuoq6tj9uzZTJs2DcdxKCsrY82aNYTDYdavXz8iwXBdlwULFlBTU8Mf/dEfMX36dB588EHWrl3Liy++yHXXXUcwGOSJJ55g48aN3H///XzgAx+gtbWV3bt3s2XLFi644AJKS0t5+umn+fa3v83u3bsZN24cmzdvpr+/n82bN79jn9gpbxXFtWKkuKa4primuCYiIlKMjD2Kq62+vj4qKire9sYsXbqUn/70p0PDDCzW+njJBFu+8yOmX3cFA12ddDz5LPWnzaf90WeJjhlN0ssQ39tC7bxZRJsaaH32ZYzvM/mjN9Pf3ELPiyuZ8N6lbP3BT/G7+5nwsZvwYiWESksxwybrPlF7I+SHQqVSKW644QaWLVv2tu6vtraW3/zmN4wfPx7HcQgEArmLdsCzFmOG8pYRv1W2kAM4wzIGM5Th+NbHt7ZwDtyheYQs4PterpfJQcNurHE4pCeCHXlReegyFJ54N+yNoeRmuOHr5dqe/9YOfZBcEpP/HIVNDa3u4LgOrhlKd/JDq6zl97//Pddff31h3pu3S3V1NZWVlUAuMevt7aWnpwdrLdFolClTptDW1kYkEmFwcJBEIsGCBQuora3lgQceGLGtWCzG6NGjSafTpFIpysrK6OvrIxKJEAqF6OrqIplMctttt/Hzn//8sE90dByH0aNHU1tbS09PD/v27cPzvELCFwqFaGhowBhDe3s7/f39VFVVUVFRwb59+6ipqaGuro6WlhY6OjpoaGggFArR09NDRUUF1lq6urpIp9N88pOf5D/+4z/o7u4u7N8Yw9ixY/E8j2g0SigUYtu2bWSzWUKhEE1NTUBuUvNMJoPrujQ1NREKhdi5cyeZTIaGhgYcx6Grq4uamhqy2Sy9vb00NDRgrWXnzp0kEom37Zy+Gb29vZSXlx/vZhQlxbXipbh2YB+KawcorimuKaaJiMi7TVHeireFi1wL1idjPFzXwQkaiIRxSkpxnQC9O/bQdPF57NjbiusaAoCfmxIZG09iMin8bBrfQMUpC7H7OwnWVhNwguA4J2wyUwzyCVX+6YUGRtz1d49wcA+eNNHi4JiR7zscGJLiDCURB+ceR5XYHNwTgWHJR2EZ/9A3eXNPXTRDCZAF7LAeCsdDV1dX4SlyB0skEqxZs6bwfSwW46//+q9JJBJ897vfPWT5eDzO9u3bC9+3t7eP+PnChQu58sor2b17N/v37z/sPn3fp7m5mebm5hHv55PVdDrNjh07Rvysu7u7kJy0tLTQ0tJS+Nnw5Cn/Oc877zzOPvtsNmzYMCKpye8n/3S/g6XTaTZv3jziPc/z2LZt24j3du3aVfi6t7f3sF+LHIniWvFTXDuU4primoiIiBw/RVmwy1/UGusQCIdpvOAsNv7vr3FcmHz5EhKuQ8k5i6geVUPLph3YUXW0722nrryKkomNDLR3svFn91M7sYGB7i423Xs/dfNmQVM9vvGx+JiDLrGV5Lx5+V4DDocOwhmx3EF/HuAcsswha5mD3zUHDQsiNx/Q6y5z8HacwyQ2h2/Ja3VGNQevkp8AngO/zxz0dbGIx+N861vfIpvNvqk76a+++io7duxgYGAAb2h+pOPh2WefZfXq1fT19R23NogcieLaiUVxTXFNcU1ERESOt6Iq2A1/0h3k7jZbN0D5/NmUz5iRuz8ccIgZS3DcWBzj0DBpEsbmLhbdoWEgPoCfu0ted/ESLBbHdfGtxXMMB98PL75LzRPDiAv2g/486m2Qn9fnSPs46sa8/t4P++OjWK+wi6Nc7qi3WDz6+/vf9Lqe59HT0/PWNeZNymQyh/RAEDneFNdOLIprR97NifY7pbgmIiIiJ7qiKtjln9hVeLKVtYCLNS42aofm685dBgfzF8Ru/gbz0IQwxuIUciMzNNEz+ENP2XMO/Og1He6uczHeRZYTw7GcmFtEiofimrxTKa6JiIiIvL2KqmAHjHwa1bAnkDk4+XmOc2wumRk5GuTAXe38T2zhFrkDQ5NHi4iIHCuKayIiIiIi8kYVXcFu+N3+fFLj+35uwu78hM1DE0IffG/XHvLmsOek2cP/PP80r0w2O/QUv5F836evr4/y8vLCfn3fxww98S3P8zxc9/Undc6v6zjOIXen1dNBROSdR3FNRERERETeqIMfbnZcua5LMBgsJBCQS25+9KMfceutt3LrrbfyF3/xF4c8zetg+TmDDjdcI/+etZa+vj56e3vp6Ojgc5/7HO3t7fi+j+d5hVc8Huczn/kMzc3NWGtJp9N87Wtf4xe/+AWe52Gt5eWXX+azn/0sHR0dI9bNJ2SJRKKw7e9973usWrUK3/cLrxOVhsMcPSWtIu9OimsnFsW1o6e4JiIiIvL2KuoedpC7eF6zZg1NTU2cccYZuK5LNBqlu7ubwcFBRo0aRSAQoLOzE9/3qa2txXGcQtJSW1tLNBplcHAQYwxdXV1UV1fjui7f+ta3mDdvHueffz5nnHEG0WiUdDpNe3s70WiUYDCI7/u0t7cXkhhrLStXruTRRx/l5JNPprq6mrvvvpv169fz6U9/Gt/36ejowBhDaWkp1loef/xxli9fzl//9V+zZs0aRo8eTW1tLVVVVcRisRG9LERE5J1FcU1ERERERN6ooinYHS6hyXMch4qKCurr64lEIuzfv58vfOELWGs577zzmD9/Pv/wD/+AMYYbb7yRpqYm/v7v/x6A0tJSvvjFL3L33XfT2dlJf38/NTU1XHjhhSxbtoznn3+eVCrFsmXLWLRoEf/1X//FSy+9RHl5OalUik984hMjnxpnDIFAgKamJr73ve8xffp0BgcHqaqqwvM8/vu//5tf/vKXlJWVEQgEOOWUU3jsscdob2+nrKwMYww/+clP+OEPf0gsFuMf//EfqampOTYH+S2mZExE5MgU1048imsiIiIiUiyKZkistRbP88hkMocMp3Ech/vvv58vf/nLfO9732Pbtm0kEgluu+02LrroIl599VWqqqr41Kc+xcKFC/nXf/1XFi1axKc+9SlKS0v52c9+RkdHB0uXLuW73/0uvb291NfXc9ppp/GRj3yEc845h66uLp5//nk2b97MD37wA7785S/T1dVFMpk8pJ2+73P99dfzyiuv8N3vfpePfexjuK7L7t27eeCBB7j77rv553/+ZxzHobS0lBtuuIHTTz+d22+/Hd/3Oe+88/je976H67qsXbv2hEwQ3rY228O8REROQIprJxbFNREREREpJkXTww4OJA3Dv4fchNYf+chHuPrqq3Ech1QqRUdHBz/5yU+ora3lU5/6FADf+c53WLBgAV1dXWzYsIHW1lZc16W+vh5jDBMmTKCiooKKigqMMYRCIWKxGOFwGGstPT09NDQ0UFlZSUlJyYgeAgdPGl5TU8OHPvQhNm3axLx587DWMjAwQDQaZezYsYTDYcaPH48xhmg0SiAQIBaL4TgOEyZMoLy8nIqKClKp1LE5uG+xg+f5OTANuh3+JmbEMkPfDU+KDlkGDrwzlNkMbfKQ5QyHJD6HXeagZh1xmcO0p/D2Ydt8FFnXCZi0ishbR3HtxKG4dtBGj0RxTUREROSYKKqC3eHkL6AfeughNm7cSDQaZdasWaxatYrTTjuNZcuW8Zvf/Ia2tjZmzpzJjh07WLp0KS+++CJz585l5cqVzJ8/n9///veF5CT/Z01NDY888giVlZV4nsfJJ5/MAw88wD/90z+RTCbZsmXLiOUPbtfll1/O0qVLGRgYwPd9mpqaiEQi/P3f/z21tbU88cQTTJo0icrKSjZs2MDjjz8+YhvGmBN2cu7DPfUQfIZf7BvAHMhLcnmIccGOXOZAj4Nc6uMXNu0f2M7IvQ89BXH4O0dISOzIb8xB2dDB2zmiIy5jMUPJ14HE7UCrNIG5iBxMca04Ka4d+IHimoiIiMjxV5QFu/zFoDEGx3G46qqr2Lx5MwDBYJDTTjuNVCpFa2srX/rSl6ivr+fBBx8klUpx0003MXr0aMaPH8/mzZtZsmQJkyZN4sYbb2TSpEkYY3j/+9/P5MmTmTp1Kr/97W+JxWJ8/OMfZ9q0aXz961/n8ccfZ8KECXz1q19lxowZ3HLLLVRVVWGMwXVdbrnlFhobGwkGgwQCAYwx/Nmf/Rnjxo3j61//Or/+9a8Jh8PcddddjB49mokTJ3LLLbdgreWaa65hwoQJGGO45ppraGxsPKEm584fA8dxCk899H0f4/u4XnYoUbBY3+KYXALCsKcbWpNb1x+a7Bwsxvi5XGDoGDiFBGnYHEvDG2HBx8UOjeh2HAczlLKMbOzwdYZ+p+zI3y+LHfrRgQTEN/m25JIgY32w3ohN55ZxcHHAOkBuGQv4DhjHwQRCWOOAGUqnlOSIvGsprhUvxbXhyyiuiYiIiBQLY4/iaquvr4+Kioq3vTFLlizhxz/+MZFIBNd1AQoXxAf3IjhYfrnh3w9fp3DBfdDHzV+c22EX34fbT365/M/yy+X/dBwH3/cPe/Ga3//B7R3eJsdx3lRyk18/lUpxww03sGzZsje8jTeirq6OZcuW0dDQkJuo3HWxnqV99Wr6fvS/hHFyQ8A8D+M4Q0OKcgmCtRbHuDiOi5dJYxwwTi65GH7sDpOiHJhtMX94HRdyfQBwjAPmMD0RzIEeC9bmtn8gsXGGEpuhzR7UrcGYoVTJGKzv4ftZfJvra2Cc3M9cY3DJtcMHPPxCApUNBZj8px8h29hAwA3gDJ3rxx9/nOuuu450Ov0HngmR19fb20t5efnxbkZRUlxTXMtTXFNckxODYpqIiLzbFGUPu+HyF/stLS08//zzXHLJJcRisSMul7d582b27t2L4zhUVlYyY8YMIpHIYdcZcWd6WMJyuISkp6eHZ555hkwmwznnnENVVdUh21i3bh3GGGbMmHHYfQzf18HJ2MHteL3PebxZIGPA6+witnw5FdZgfR8v64Hj4OPnepSYXMLjkOs14Hk+rutihpbJZj2s7+eSEXsguSkM9HFsfnRR7n03n8nkEidjwDk4tRnqUXAgscnlR9bmejoYAxan0KMhd8xz2xp+7n3fks1mCxt1TO7lYnCHOixYIGsPjIJKhlyCN19LlrEHmlNk505Ejg/Ftdf+nMeb4hqKayIiIiJFoOgKdodLJjKZDF/84hepra1l6dKlwOGHYfi+X1j33nvvZcWKFTQ1NdHR0UFNTQ133XUX1dXVh9z9TyQSfP/73+eGG24YcedueOLhOA6ZTIY777yT9vZ2YrEYv/71r/nHf/xHSktLC3P2WGtZtWoVgUCAWbNmFe6wO45zSOIyfPvD5/w5eNliMyIZA/yhhAADxlpc45AMBfBrKshmMriJFJF0BteAMRaLxbi5+/eOY3CsgYBL2o3gZjM41ssNBTLgWIM1Fj8/S08+sTEu6ZpKUr6HG08QSWdyCZTNpSoYizUW6+SPM1jrYLBYm2trwFqyTm6bxg3gl5Rg+wdxbDrXuyG3NI4LQZNbN+sYPAyhbBrfDZApqyQbTxD20jiFw2IIGLC+nzuvji0Mi9LwIZF3H8U1xTXFNRERERF5o4quYJc3/A7+ww8/zPr167n00kvp6+vjvvvuY926dZx00klcccUVPP3008TjcbLZLNdee21h3auuuopbbrmFRCLBnXfeyc9+9jP++I//mP/93/9l69atnHbaaaTTaeLxOD/5yU/o6enhIx/5CM8++ywrVqxg9uzZpFIprrvuOsrLy0kkEqxbt44vf/nLzJs3j3Xr1hEKhVi3bh0/+9nPALjmmmtoaGggGAzS2dnJPffcw549ezjvvPM4//zz+b//+z9832f9+vWce+65XHjhhWzbto377ruPdDrN1VdfzfTp0/nFL37BmjVrWLBgAVdffTXRaPS4nYujYzDWgPVJOuBedg6ZTAbHOATH1jP43/9HddrHN7keHomSMMYN4AykMPW10NJBcO50Ent2EWvvAnKTXQccB2sMGQtZawvJjjd/OplZkwj09GNiYQafeolAf5xQKgP4uQTLAc9mMdaQtQ62vg6/rYuAn8JYS5bcXD0ZHKwJErn8Ijof+S1VPRk832CsxRiLR27eIsf38cfV0++ByXj4nT2455xG/+oNBHfuwRn622SHPZKvGBNTETk+FNcU1xTXRERERORoFW3BDg7ctR01ahThcJimpia+//3v09LSwuWXX84999zDwMAAe/bsYdWqVdx+++2HDNVxHIeSkhIuv/xyfvSjH9HX10dzczNXXHEFP//5z2lvb+dP/uRPiEQiNDU18cQTT/DLX/6SD3/4wyxfvpzf/va3XHXVVQCUlpbygQ98gC9/+cvMmDGDa6+9lo6ODr7whS/wnve8h0AgwK9//WtCoRDhcJgHH3yQ0tJSLr74Yv7rv/6LcDjMz3/+c0466STOPPNM/uVf/oXRo0dz1113cckllxCNRrn//vupr69n7dq1XHfddfzyl78knU7zoQ99qNCDoZgvli1gx48hgU/oN0/jY/AuO5c0luTMaaTH1JLZvpfYzCkk66vwX9lI5eVLaH94GRXhAJYA3ulnMEgWx3XIvLQJd+IYMmMqSSfihLv7CGzcgTOxkb41m6jYtB/nrAWE/ugyktv2kHxpHdGZTVgMqVfWUzK7Cc8NkO2NU/Wei+lY9hSp3XsJzpwMfQnM2q0Ep08gNaoaP+DiOw5eIIg9aT6JllaC8RQDQYgAbtM4vEQSBtJEr7mYzt88Bs3teLOnkpw0FvPyWmKDg1jrjHj43vE4X3V1ddTU1JBOp9m5c2ehp0tFRQWO4xCJRDjzzDN54okn6Ojo+IP3V1VVxZIlS1i5ciU7d+78g7f3VjHGcNppp1FVVcWjjz6K53mvv9Iw48aNY/HixTz22GN0dnb+QW1xHIfRo0fT3t7OOeecQyqV4plnnvmDtiknHsU1xTXFtTdHcS1HcU1EROTdw3n9RY6/WbNmUVZWxjnnnMMrr7zCbbfdxtKlS/nIRz7C8uXL8X2fD3zgA1xyySUj1ssPzfF9n9bWVqLRKCtXruSWW27h/PPP50Mf+hCBQIAFCxYQi8W48MILeemll7j++utZunQpt912G5WVlSO2d+211/Kf//mfLF68mDvvvJOHHnqI+vp6br75Zm688UY++9nP4rouqVSKV155hX379vHQQw+RTCZZv349oVCI973vfVx66aVUVVWxbt06otEoH/rQh7jxxhu54447WLFiBb29vdx///10d3fz6quv4hWePlfsLKFRtWT2thJ2XUrGjCEY93BnTqFn/jTa1m8huOgk2np6Se1tx+xpxmvuILt9F9RWMhiJ4k8YR3dPL9lgmPb500gvmEn/i69SOWc6/YkEruvgP/0S9XPnYd9zHv1r1uM1d5Levpvo2Do6errJRmOYqRMJVVcz4ASwO/aSaOsmuXMPwQvOYu/2vSSrKulaNJvs9In0v7KeSEkUaww2myEbDtEfCGBOngenn8KAMaQiETJugERlCYnWHlK79uM4YAbjeB19pOfMwvdd7NCk4UOPCDwuZ2H06NH8y7/8CxdccEFhonuAU089ldNPP53+/n4uuOACpkyZ8pbsLx6PM2/ePE455ZS3ZHtvpd7eXj7ykY8QDAbf1LpLly6lqanpD25HLBbj5ptvJhgM4vs+N91004hJ/+XdRXFNcU1x7Y1RXDtAcU1EROTdoah62B18x3b4JNXGGEKhEOPGjePxxx+npqaGZ555hokTJ5JOpykpKTlk/Y6ODjZs2MDu3bv58Y9/zCc/+UkefvhhnnrqKUbVjeLpp58mm80WnoTX0tLCxIkTefbZZzn99NN5/vnn6evrK2y3vb2dz3/+83z0ox9l/vz5lJSUEA6F2d+8n/Xr1uG4Lhs3bsTzPEKhEPX19SxcuJDFixezfv16TjvtNJ566qncpNTG4DgOo0aNoq+vj9WrV1NSUsKqVasKT6p7//vfz7Zt25g2bVrhAqiYeyHkp9NOD8Zxy8vI+hmyfd0EJzaQHuwnWlZKzUkLSWUy+IkETsbDiScxiSTh3jjEkxjfw+/uJdjaQTAcgzHVpNs7qOpNkN7eDKksnrWY0fV0//JBoo1jKLnoXOJdHdDfjxMtpXTyBMIVVSQG+vAHkph93cQSWUwiSSCRIlpVQ+3M6bihAIFIgIH9bVR3DeLtbcHDwbU+qU1bcefNZsDLYnqz2KnTSG7YRqyqHC8YwI2nCPclCfiW9J59RFJZ+qoqcnMRDT9H5vics3Xr1tHe3s4LL7xAJpMBIBQK4fs+u3btYnBwsPC7PXPmTCZOnFhY56yzzioUAfbu3XvY7QeDQc4880xqamrYsWMHq1atoquri1GjRvH+97+fXbt2sWLFCmKxGGeeeSYlJSVs3LiRjRs3Mn36dBoaGujr62PatGksW7aMaDTKwoUL6evr49lnnyWZTB52vw0NDZxyyilYa3nuuefo7u5m0aJFeJ5HVVUVgUCA3/3ud6TTaSZMmMCiRYvo6Og4bA+EadOmMXv2bPr7+1mxYgVz585lYGCAzZs3c+aZZ7Jp0yb27t1Lf38/TU1NTJ48mc2bN7N69WrKy8s588wzCQQCvPrqq+zcuZOGhgZOPfVUfN9nxYoVVFdXU19fTyKRYNKkSWzbto1Vq1bheR5tbW2Ew2EuvPBCYrEYv//97+nv72fUqFGFz/PMM8/Q19f31v1SyHGhuKa4prj21lBcU1wTERF5tynq22DDh/9MmjSJYDDIpz/9aTZs2MAnPvEJurq6+OhHP8qYMWOoqKgYse6YMWNYvnw5n//857n33nu5/fbbOe+88/jzT/wZq1e9wp//+Z/T0dbO5EmTqKysZMmSJXz729/m/PPPx3VdPvGJT/D0008za9asQiJSV1fH0qVL+cY3vsGn/+IvOGnBQq5+3/u4+aab+OpXvsqXv/xlrLWMHTuWcePG8Vd/9Ve8+OKLfO1rX+PFF18kEokwceJEwuEwABMmTGDixIl89KMf5Zvf/CZf/OIX8X2f22+/ncHBQb7xjW/wyCOPEA6Hi74XggFcm5vQOrt9D9HGcXRNbiI7YQKB8grY045p6cF7eS3h/kGCiQRmTA3+9EmkXHDOXUyytpZURRleVQnZkii2shyyKQL19XTObCI0dyYBa7BZizt7Gt6CuSTKq/DSCdxUBn/mDOxp8+jcsBW/t5dsRRnJigoSVaVkPR/reXinzCHe2U5qw1b8lg785jZiE8bQN6YKd1ID6YooBFzo6KSiqYFsaxfunhaq5k4l2NkNFWW4FWVkbJb06XPpbxhDqrqKdFUZfkUpWcdi8ucq/8ewc3esk5zh+/N9n8bGRv7oj/6o8J7rupx11lmMGjWKVCrFZz/7Werr69m9ezd/+7d/W3ha5MGWLFnC4sWLWbt2LZdccgnhcBhjDHPmzKGjo4M77riDuro6Jk6cSE1NDevWreNTn/oUDQ0NpFIpvvSlLzF79myi0Sgnn3wyd955J1u2bGHKlCl89KMfPeLnOeOMM2htbaW3t5fPf/7zBAIBamtr+drXvkYqleLyyy/n3HPPpbq6mq9//eu0trZSX19/yOcoLy/nc5/7HBs2bGD8+PFMnz6dUaNG8aEPfQhrLYsWLWLJkiVYawmFQkyfPp2tW7fymc98hpkzZ3LrrbcSiUTYt28fl156KbW1tdx1113s3r2bTCbDddddh+d5fPWrX2XixIlUVFRQUVHBxz/+8cIDACZNmkR/fz/19fV89rOfpaKigq9+9at0dHTgui533HHHiF4k8s6guKa4prj2B54XxTXFNRERkXeJouphd7iLd2stpaWlfO1rXyMYDFBdXc3dd99NKpUiGo3iui4f/vCHR1wAOI7DLbfcws033wzkLt4CgQDGGJomNvGd73yHZDJJLBbD8z2CoRCf+tSnSKVSRCIR7rrrLhKJOLFYFN+HUCiXiAQCAa6//nquvPJKfM8nGongui7XXnstl112GT42t82hYT6hUIh/+7d/I51OF9p611e+QigYxBjDl770JYLBIFOnTuXiiy/G932i0SjGGL71rW+RTCYJh8OEQqHCsTnec/3kh2IN7yGSfyqhMWAwRJMpko88TtnMqfj9CQbue4iajjaya9YQmzqWwbXrqezuJVhVSWLPXtxEEhNwyPb1UJpOYrdspsKkcVv3EgmFSMfTZCMhelva8Lw0bsCQfOppYlXVBMtjZH/7OCWhANFJE0n+Yi2TZkyif+NGQo6LF7BUpDM4bha7fCUVZSVkVm1i1KxJxPv6qNy0E6evh+qmejp/8ygV6QTWSxPGJfHQMiLd/QSyWZIP9BJND+J37KfCdzA7dlJaEcPv6cFLpSDjEd21C+M6kB16uuBhjt3xlM1m2bt3LxMmTAByfy/e//73k0qluOOOOygtLeWiiy7ihz/8IZMnT6a6uppJkyaxcuXKQ7bV2dnJaaedxsDAAA888ACpVAprLc888wxPPvkkf/zHf0xlZSXt7e1UVVVx0UUXMWHCBMaNG8fq1atpb2/nkUceob29nauvvppRo0Yxa9YsgsEgp59+Ot/97ndJp9OH7Hf79u2cffbZOI7D3LlzCQaD7N69m23btvHkk08yceJEGhsbSSQShfbU1NRwzTXXHHIsPM/jlltu4be//S1r164tPI0zlUrR3Nxc+HuWzWZ5/PHHefnll1m7di3nnnsubW1tXHPNNfz2t7/lwQcfZP78+QC88sorOI7DihUrSKfTtLW18Zvf/Ibe3l6CwSAf/vCHC9vdtm0bK1asYMeOHfzwhz9k4cKFzJ49m8bGRiKRCPPmzaOiooKurq639PdAji3FNcU1xbW3j+LayGOhuCYiIvLOUlQFuyNdsBtjCIeDWOtjrUcw6BAM5p4uZ61HKORizIHOgtZCIBAkEAiSu8C0WAvW+mB9gkGHUKgkt1wwlHtqWyBAIJA7HI7jEAiU4roGcMEemBTbGEMsFsvN5mItudvNufdwTGH9fLtDoVBuyAYWbyjZMUM/y889YowhEokAFCZRDgQClJWVFT6T4zjH/cIYeM0Ey5qh42EMkcFBnBWr8K3FdV186+Pu3Yuzew+VroODwX9pFQHA7+mh3ICxQ9t1HDDg04F1DKFFJxObMpHs/naifQM4AYdIWzvhtjawljAW4sBLq4kZg3mukxosPgbfgI8l4Lo4+/fAPp+MAWfFS5QbcDDYXXuwO/dQZQ58roCBkub92NzJIry3GeMY2LqbqDVYLHZP7nMbYzAMdVc1hqwx+LmzfKxOyxHlz1dJSUkhoRluzZo1nHHGGSxevJjVq1cTj8dZuXIlbW1t7Ny5k+3btxeS1+HDb7Zu3cqnP/1pzjrrLL75zW9y0003AbkkIJ/8Atx4442kUil+8IMfMH369MLvjO/7eJ6H7/uFROK5557DdV2WL19OJpMhEAiMmOMqHA5zxx138LWvfY29e/dy1llnFdqTTqex1uJ5HoFAAN/3C38PA4HAIXPqZDIZ7rzzTqZMmcLtt99OZWUl27dvLyxXUlLC4OBgYfl84SQYDJLJZPjNb37DCy+8wNVXX82Xv/xl7rnnnhF/7yORCJlMhmw2i+/7hdfwc5Lfpuu6WGtJpVJ0d3ezfPlyPM9j48aN9Pf3v8EzLsVGcU1xTXHtraW4prgmIiLyblHUQ2IhdwG0ceNGXnllFelUip07txcuFqy1xOODrF69msHBgaHEx8f6HoMD/fheFt/LkM2k2bp1M9b38H2PttY2NmzYQEvLfrq6Ounv7x+ayDu3Tev7DA70s/KlFbS3NZP1MsTjcTKZDF7WAwvWz+2nt7ebrq42du7cSjzeh7UZNm1ax84dW/G8FL6XIuulyPppmnfvYLC/H2t9PO/AZ/A8r/CKx+N4nkcqlWL79m0Uxp+cIKwBO5Ts+SY3SXXCzzKIJW0tWSzG97F+Lsm01oJjsAZ8B3zH4DkWBwh6PqUZi/fUSuL3/w73yeWUehDwGUobDCPzB4vne2StJWMtGQueNTg45HIuiz+UhFjHwc2vOJSwGnJ/Wgw++UQtL/e+MU6ux0V+naEX+Vdu0VxCdLjjc4yS08mTJ1NTU8OSJUu45ppr+MAHPsCll17KzJkzGT9+PFOnTqW+vp5MJsMvfvELvvjFLzJjxgweeOABzjzzTMaOHctll12GtZYzzjij0Ksnb8mSJZx33nls3ryZvXv3EovFmDhxIlOmTKGxsbHQs6CtrY2JEycyd+5cJk6cyIwZM5gyZQqVlZUsWLAA13V57rnnSKfTTJkyhZNPPpkZM2bgOA6f+cxnaGxsLOzT8zw6OjqYO3cup5xyCrW1tUybNo0ZM2ZQV1dHY2MjkyZNYsqUKezYsYNkMsk111zDFVdcQXV1NZMnTy5sq6qqij/90z+lr6+PtWvXEo/HaWlpoaGhgWuvvZZTTjmFyZMnM3nyZEaPHs3SpUtZunQpM2bM4PHHH+fmm2+msbGRV155hY6ODl5++WV6e3s5//zzufbaa1m8eDGTJ0+moqKC+fPn4zgOU6ZMobq6mrlz5zJ79myampq49NJL+eAHP8hjjz3G6tWr2bBhAwsWLGDmzJmcccYZhWRI3lkU1xTXFNfeOMU1xTUREZF3G2OP4kqrr6/vkLl03g4XXHABP/7xj4kMDcnJ3dHMsnXLVlr37yObTpCKJ6iprqa/f5DGCU20tbczbtw4BuODtHd04LgO6VSCtrZWRo8eTTAYIJNJk0lnOOXkU1i16lV6e/uYPnN6bttbt1JRXkk0GqW2ppb2ri4CoRDVVVV0drTS3tpKTU0d+/fvZ8rUqbS0tnL64tPZsXULyUSCbDbDnj27KSkpJRSOcfrpi/n9Y48xadIkYrEIra37SSYTlJaUsm/nbiZOnkoqnSaRSOJ5HuXlZfT29lBaUkI2m6WlpYXTFi1i244dRGNlnHHOuVgCGGNHzPmcl39iYCqV4oYbbmDZsmVv6zmqqanh0UcfZdy4cbmeFuEwac+n6/dP4vx/f0tJ7h49nnVJB4Jw8hwqzjoNKivxfZ/k3j30/f4pYtv2EJg5g/jAACV7m3GHkghjLRlj8coqcU87mdjCmVBRRgaDH08SX7OW1PKXqWntxjo+1uTm1smUxohceB42WkHWcfFt7q55yPoMrFpDaNMWAni5Yzj0G5/PWwpJSL6XxYi5tQ0j05sjG7oPTRaHgUCAhu/8PYk5swi5wcId+N///vdcf/31hx0W81YaO3YsY8eOHfFeW1sblZWVBAIBWltzfz+SySS9vb2MGTOGrq4udu/eXXh65aZNm+ju7uaOO+7gscce49lnny1sq6SkhNmzZxOJRNi8eTO9vb3MnDkTz/NoaWmhsbGRvr4+du7cybx58zDG0NvbSyAQoLe3l9GjRzMwMMCmTZuw1lJdXc2sWbMYHBxk3bp1jB49mr/8y7/kjjvuIB6PF/ZbVVXFvHnz6OjowHEcent7icVilJSUsGfPHsaMGYPjOGzYsIHS0lJmz55Nc3Mz0WiUzs5O9u3blztXQ4lGQ0MDnZ2drF+/Hs/zmDZtGnV1dezZs4eqqira2toYPXo0nZ2dTJw4kR07drBr1y7q6uqYNm0a1lrWrl1LX18flZWVzJ49m3g8ztq1axk9ejT19fX09vaydetWxowZQ0NDAz09PYRCITzPIxqNEolEWLVqFclkktLSUubOnYu1ljVr1ozoDfFm9Pb2FuYWkpEU1xTX8hTXjkxxTXGtmOKaYpqIiLzbFNWQ2EPlLuZjsSjxwQG62psxvsdgXxeplMfYMWPB9zC+z/49e4mUhGlvb6GivIyKshJsJkUyOYjrBPATaTpa2gmHwiRTSWpraiiJRdi5fTvG82jdt5fUQB8tne2cvGgxjgNYSyadYvfO7fi+ZbC/j317dpNaMJ+d27cSH+ijvKyUskiA0liIaGkF8YF+wsEAxvo079lNa0szU6ZMZu3adUyaNIndu7eTSqUYN2481ho2b1xLWWkJ2dQgo0bVUV4apaV5H+0trTRNLs1dbB//ESiHOPKcQxbHtwzGYpTddgPu9CY6HnoCu24zAd/iNE2g+o//mK5nnqd61ny83dvw9+wnaHIpRMKAd9bZVF21lPiefbQ/9SK2rQNrwa2tpuy0hVRcdiHtD/2OwG+foMTLYo2D9SyJrgFSJR5VV15Kd0c37hMvkCZLdnCQsDEYOzQ8Kd/Sg5tv8s8DPOjtN3JgDICDMT7HsxdJc3Mzzc3Nh7y/e/fuwtf5i3xgxFPzVq9eXfjadV3uvfdetm3bNmI7g4ODrFixYsR7L7/8cuHr1tbWwtcvvvjiIe04+Cl9XV1dPPPMMyO2/9WvfnVEUgPQ3d3Nk08+ecj28tra2gpfJ5PJIy7r+z6bN29m8+bNI97ftGkTmzZtAmDXrl0AheOY/x5yT9Zsb28fsW5PT8+I5G/Pnj3s2bOn8P2RzslwAwMDPP/886+5jJzoFNcU1xTX3gzFNcU1ERGRd5uiLtgZchfQ1ZWVzJ8/n76eBsrLKvEtxAfj1I4axcSpU9i+bRtTZ0wnkYwzZdpUjAnQ199PNBLBGIjHE5SUlVFVVUNq23bOnTmbkspyAgGH0846l/6+fiYGAsR7+5kwdRY1o2oBy0D/AGecdQ7dXd3EYiVkMhnKK6spr6ji1DPOyc1f47hk0ymynkdFeSWxWJTTFp1OeXkZW7ZsY9KUmZSVlVI/ZiL9/f2MHj8F13UpKSkFYMr0efT1dFNRWUkgGKJ2TD8Wy6jxE6msqgSneEctW2sLd+9h6DLeQtpC7JILCM6ayK6/+Sa1LZ0EANdCZscO+l9cTvmffIDUlAb8PVuxxuJYyOLgvudcSpaez57v/jcV63dQaf3cPqwD23eReXEV8cnjGPdnt9JaXk783geo8DyCiTTeU8txshZvxhxs8x7CD/8WJ5Bb1XFyk4cfk5E71sfiUpQZ6RvkeR5btmw55vvt7Ow85vsUORYU1yoV1xTXjivFNRERETlRFFXBbviT2fIcH1KJJKPq6iiJRSgtHZqw2nHI3fGFWfPmAtDV0UYoFGRwMM6kSU0YLN7QcJwxo+owxmHezBl4Tu4ivKV5L14qxeTGRjAGf4xPy/4WKktLwPo0jhtLJBKmJBqhs7ObmppqoiUlBEIB6seMIX+lbMhN3u3Y3J3Qqopy2traGDN6NGUV5QQDQcrLy6lMVhIOh3ADLulUGs/3KIuWMXZ0Pb5xsMalqnY0ToChOWfy97JPjPl+8s+Qy8Yi1Fx8Hlvuf5iqllaCxsnNn2PABcoH4wz8/CFG//PZDBgD+KQNxGdMo+59V7Lrq3dTs20bjslNgm2MyQ0RAkJYgtt20fXt/2T0lz7Ftk3biby0mqDj41ifgOMAGYzxcRxLwMnNOYQ9RkfRgjEW+w5IakTkD6e4primuCYiIiIib0ZRFewgN1ShkNgMXYm+8sorLFp0KuvWbSCVSlJRUUFvfz/VNbUkEwl8z+OUk0/hiWW/Z0LjeNo72nCCLmWlpfhelubm/UycOBnrWwLBMJW1tUyYOIHVq1aSSSbYvHEDlZWVZDNZdu/ZzdSpkzGOoaWlBd/3qK2po621g0Aw93S+yVOmEIrGGNfYyIsvvkjAdenr66OqopINGzZwwXnn8tILLzC+cRw9vd1UVFTQ3t5OMBikpDRGMBBg7969NDU1kUymMThMmTWb6rrRQ9nBsImeTyCFITnhEOnSCHb7HkLW4Bt3aLrr3EQ7xjgE29rwN27D5sZokTQulVcupeWZ56nYvpMw4A1Nvm3t8K0PPaVw1256H3qK2ve/j/41m6jKJLEmN8+QtX5ugvChJ+Edc1ZpjYgcoLiG4primoiIiIi8QcU8LgUwYFyyWZ+BvgF2bt9JOBjm+Wefp7ejgz3btrB+9SqioSCJgX4SiQS7du+it7eXzvYONm3YSH9PP7XVNezZtRsv49G8bx/btm7Fy2awXobGxvFk/Sxd3Z3EU4NES6Lsa25m+44d7NvXTHV1LWvXryORjhNPDlJfP4oVLyzHeh7JwTh7d+5k3auvUBIOsPrlFxkzqppAwDBr9kw2b9lMJBJm166d9Pb04PsenZ0dtLe3Ul5extq1rzKqrpqBeB8tLfvJ9awYeUk84mFxRcwC1hgsDmQzuNkMtqIUYw1mqLcGQxNd+8YS9gx21z7cbIaghUBtLZFZU0k//gwxa/GMU/jguTwvN1GPHXqZgCXxzFOU19eQmlCP8S3GOuRn6zE4hf0dfk6it+kYDH1t8A+cuBMwSRWRt4HiGqC4BoprIiIiIvL6iqqHnbUWz/MIBALDLkYN48aPp6WtjZNOOYWKinKqa2tJJBL41qdhwkTGjh2LB1x+5ZV0dXWycdMGOjo6aJoynTFjxtLfP8CY8ZMpLy8n2NJCxsvS0zvIgoWLGD1qFHub95HJZIjH41RUZohGIgzG40ybHmWgv59zz7sIz8sSCAQYHBigsqaant5eakfVM76pibKyMqoqKymvqKa3r49ISTnx5lYWnnIa6VSSYCRGRUUlg4ODRCIhjIFUKkXjxMn09PVRP3YclTW5+YWGpzHFfDlsjMEYgz80eY5xDHZoCFVkIEn61U3UvO9Cujeup7ongWtziYmfHwxlfAZ+9QihbJIAPv6EiSQGUwT2tzLUASHXm+Aw8gmE091Hpq2H4IzJZDfvIlA4YKbQxmMpP8zr4FabobYcxQOZReQdRnFNcU1xTURERETejKIq2EFuMmBrLRgn9+gzxzBz9uxhN3UtoxsasNbgWXCNyd2lBjxrKauuZVTDOHq7u6kfPQY3GALA+rmhJGMbJ+Quxq3FGbrwHT9pSmH/wy+Ghz8xzlofay3ZbJoxE5qorKgC43DyosUYHIzjMHpcU2EbC07NJSojh70Yht+vzl/nmtxKQ3frTxzWWnw/NyRo+HFzrWHgh/dR+YWP4X71r+m897eEV64iOpgg6DvgWDKuj9PZShngAdnGsdiWNkLpND68dlY3dNyctIe7u43I+HGkHAc3N3DowGIn1uEUkXcoxbUTh+KaiIiIiBSLoirYjbhTawCbm5SZoYmqgRGTHucWMbkkiPy1sKUkVk5JrAwM+DY3WYzFYCxY4+QGlJgDF8HGHH5ksMk1qvCdMRAIhKmqCuGTnxw8PyhmaKkRd7/N0KiR4cnSge8Pf6P8BLsaz+dq5M+fxRqfcGcznX/zLUJLz6H+A+/F++D7GFy5nr4nnyO2bSuRTBobADO0TklNNen+OAEvl8wezW4dA5m+QZyxpXjWjmiLiEgxUFwbuewJQXFNRERERIpAURXs4KDEwFB4Ut2Bn4O1ubElzkFJBIUk4sDdfoMZusufYwt3zYddAR/plnV+MUOut0EuR8oNcUqnCQSdg9rwBj/fcCfQBfmRh8DkhwW5OEAkPgg//y0Dv34KO2cq0fMXU/OFj5PsaqP758swL7xMVToJ1scEAmR9H78wx8/rHBCb67lhMFg/Pym3wZoj5zf2MF8Bb3sPkFxnlBPoBIvIW0pxrfgprr0ximsiIiIib7+iK9gdzv/f3p1HyVVdh/7/njvUrbmq51mtAUloQEhiMmPAgA0GDMbTS+LE6+fgPOyV+CXr/ZbtrCSOncRJnpfznF8SkjjPrMR5tmOMjfEAZsYYhEAIDWhuzVPP3dU1T/fe8/ujugtJSESAkErS/rBa6q6uuvfWvYX22eees88bClafIDk4eoyCOuaxEz/7Tdu1b6gfU9t/NpslHo9jBgIcW6PnbTnLBiAcn64X0XanE0osTbCaw1+3Fn/deqZa23DfewXdv/tpRpesJvetbxOsFKgUS+hYYnqkiT6J2jgKDx8zHkZnMyjfR1tmPWXxtT5hLqHrf2rUcdZdOdHn62Rr9Rzv9VLrRwhxJIlrZwuJayd6vcQ1IYQQQoh3V8OtEtuIjT+tNePjY6RSkxQKBVKpSSzL5NCBA2jfA+1DrUINWuuGfA+nxXSDvqoMiiEHjUZpH0/5YICtNMHUOM4DP2P8q39D1w1XU7hsOR4W1eExAs1NuKZRz/GUOvEYAa00VQPoaqM8MoHNyeeGCoXn6+krNr29mTpBJ5Hkvmm6dbyk5iSPSwhxbmrEmCBx7SRJXJO4JoQQQghxhjRch12jmUlSQk4A362SSacYGx1h55ZNvPrir3jx2ad47ZU1/OTBH7DptY3nb1JDbQaW0hrd2kTo9z9FRlkorVDKRGFO11eCkGFgbN9Bde1m4ldfimv4sHc/sfYmKvEoUNvOzHiSYxMDpWrjEHQsjNXVSmX3PoLKqO37vzhGBWhtYtz8a2SbmlBaTSdg1KeYWdrH1H4tYdXT08WmK0O9nYLf53WyK4RoOBLXTp7EtROcF4lrQgghhBDvurNiSuyZMtMY9X2PcCiEAl755XO8tPpFBrZsoikWorm5hVKxRKXqsmv/ARYtXoJpmsCb1PU5pxz9HrXWGIEAwXkXMBGMoPLpWnIzUwpdK1zA0QZM5nB6E2SUibVvP2a+gLdsIZXnXiJAveZ6bbvH7NHwfexFCyhpD3/vfpTy0fX+52NGFBy5aiFQtk1ab7qRsYPDMJlFTU9Y8pRBSflobWD6EMDA0D4KXZuKZNQKvAshxNlK4trJkLgmhBBCCCHOPOmwOwE9fVdba43nawa2bOG73/0Oe3fvIhaNEnQcAk6YcqVKLBbHtG0OHNjH5s0bmTdvAbFY7Ey/hXfNTMKnp0cLGPWF7Pzan9rHbunAXzgPXt2AwsVXCqitZKiVi6s0qjNBfmwSA02omGfi0WdpufMWMmtfoyNXrK2QN5OsqNcr9CgNuaBN8123MvTEoyRyeXzDpLaS3/TT67WeZv42QGt8BYZWGKaJ0j4mPgFfsw/FD0ppRtta6e3vR1UrNO8d5sqKSxyFRRVHK0xAa79e/v38SF6FEOcCiWsnJnFN4poQQgghRKORKbFvQmsX3y3z+E9+zN/8xZfZt3M7c/r72L1nL21trXi+TyyeoLm1lZ279xKNRPE8j3K5dKYP/bQ4quC0np5jg8bHIJfJELzuUobiITyt6qWwfcDyLPw5F6AumsPky2sJaoXCRz/5LJGJFNHf/ThpJ4j2ZravsbTG1LUpRaVAkOhv/xZFDPj5rwgC/vTKegYKZdQ+1oZvYLomhmeAP726op5OdrQGZZMnxEumwT9WpnjEctnka3YYFqM9PTwUN/hTu8TfNFv8fy0O/xDw+LmtmTRM3v01+IQQ4tSTuPbmJK4JIYQQQohGISPsjnBUPRbtAx7Dgwd5+AffJxoyKZV9SsUiC+bPpbmlCb85QXNTC8/88nn27t3LNe9/P11dXcTj8Xqj/5y/U61fX0lQAbpWiAcjlSKzYQsdn/ltJn/4GMGDhzBcDyNgU1qykOQ9nyT9q5dIbNqMpRS+AqvqMvkP99P0e5+m9OefZ/zBnxPYPkCgUMQGyiEHd+Eckh//MKXmBPv+7lu02w5euYShNdVIjJKyiMQTVPNpUk0JnJmy20btOJUyqFg2YcNklXbZFzNYXamSM8PEwxGqfpXh4UHy+QyRaIypqkfFdvBcj675sxixDfYOjXD7RI5ZFQ8D+K8rDAkhxJkhce1tkLgmcU0IIYQQogFIh91x+IDSHmODh3j8Jz/GLWbxVYCAaWEZmngkRHNTku7eHnbv2U9HRyeXX3EF/bP6yWazhEIRAgHnTL+Nd93M9KqZ1M2gltToUhF3YIDAqjW46Rxdv3U3lXAY5fpY8QS+4TPx2C9RTzxDzPVen+6jFJF8kcLX/hH3mivouvsDeMmP4+WLGFphB8KosIMXDEA2ywWf/BgqXyD9T/cTqlaI3PspgvEm/GSc5qYY6vO9KO3VjlVZKGWA9sj5Vf76sZ+zNTtBRlUpGhaJcAgnGMSvlFCmolAukUgkiKLwXY9EohnLCdHR18N23yWtXH5ztEy/56OUf9zzI4QQjULi2smRuCZxTQghhBCiUUiHXV1tioquLZ3G+lUv8NB3/pV9+/aSTheZ+57L8DEZGRunq6eHzq4uwpEIra2tXHDBQoaGR1iyZCkdXR1UKi5wftSBUUd8p1Rt+o41PkXpX79HKy7++k2kX9uO396KioRQpTLVyRSRXBlL+bUV9vR0QgSgNRYugWdWk35uDaqlCZVI4qPQ6SlKuQy2Vmi3dq1MNDFdwVKa/DfuozK9gl/tOtZqD9UmFhkUzRAbA5on4yYTjsP4VIbWlhbIZQkEQyhlEA5G6OvrI+AE0Bq8istkZgKv4mIoxdbXNuEZkJ/Vy56JXczydH3lP01tihTqeGsAnn6hUIiuri48z2N8fJx8Pn+mD6nhRKNRKpUKlUrluL9PJpOUSiVKpfNjOqA410hcezskrklcO5tJXBNCCCHOHVLD7igapaDqV3n1+We4uM1hWXcExygxOj5KNB4jl88TCDq4nksgEKCpqQkMg2K5RMAJopSF4wTPzNHrMzOFRWkwFBjT/1lAxKti++AoCHtV4kMjxHbtI3LgMIl8EVP5uL5P1QXPA88F3699eb7CVZqg7+KMjhHYuZPgzgGCIyMkC0UixQLRap6YVyCiyxgG+Apst0qsWiRaLRFzy8TdCjGvStzzKfke33Wn+EFUsd8ts3fvXuYvmE82n6sloIZBc1srXV1djI6Msn/vfg7s28/e3XvI5wuEImEwFQcPHMArlggFY6hQBIWPoWsFymvpjQI9/XUGk5vm5mb+4i/+gquvvpqbbrqJv//7vycej5+2/V944YXcfPPNb3j8nST7Sil+4zd+g+bm5jf87rbbbmPOnDlveZsf+chHWL58+Qn39+d//udcf/31b3m7QjQOiWtvh8Q1iWvHkrgmhBBCiNNNRtgdYabNZaDQlknQsZnd3kwwEmXbhEsoGMK0LLTWhEIhJiYm6O3tZ2wiRTwWI5FInBf1fY73/vSRXwqqpoFW00kPCjU9P8gwjNqoAkAbCo1R+973p7ejX6+ThELN1NGu/z1TQ8kApTFME5SBRpH3PMqGJoGFpcFE4aLZGYCnQiZP5oq4YyMEw2E6OjrYt28fjuNw4YUXopRi+/btlMtlTNOkXC6Tz+fJ5XJoIJFM4GtN56xeLrv0Mg7vGiDsga8M3OnEChT4PlYDlP5ZvHgx0WiU733ve/i+z8jICEopLr74YjKZDMPDwyxdupQ9e/YwMTFBc3MzK1euZGhoCNu2yWQy7N+/n4suuohEIsHBgwdpbm5my5YtBAIBLr74YsrlMhs3bqRcLjN79mzmzp3L2NgYO3bs4IYbbuDiiy9mbGyMzZs347q10TkLFy7Etm0syyKRSLB69WrK5TLhcJjly5djGAYbNmwgFAoxb948xsbGgNqIgPHxcT7xiU9QKBR45ZVXOHz4MADxeJyPf/zjPP/88zz77LPs3r2buXPn0t/fz4EDB9i9ezfz5s0jGo3iui4dHR28+uqrVCoVBgYGOHjwYH0fy5YtA2DTpk1MTU1NF9svn4ErKMSpIXHt5Ehck7gmcU0IIYQQjUZG2B2HYSiuueFGBsYzuFYUM9xER0c3Azt34wQcHMehqbmZjo4OqtUye/bsIp/PTr/23D+lhmGglDrqvSo9k8SA4QRwWpLYzQnslgSB5gR2Sxy7JY7VnMBpjuM0J7CTEexEhEAiitMUJ9AUq/2djBFoiuM0xaYfi2FPP1b/u2nmOXFoaualria+vbSTn159Ic8un836rhbWdbXwn11J/iVq80y1RNnzMc0AlbLL2Ng4LS0trFixgr179/Lcc8+RTqfRWtenkYTD4Vo9I61JJBLMmzOH/r4+FJoOw6QvGoeWJKolgdmUIJCIYZomBtNjEM5ggnPgwAEWLlzIX/7lX3LNNdfwy1/+knw+zyWXXMJnP/tZTNPkox/9KDfeeCPhcJivfe1rtLW1sXLlSu677z6SySS33nor99xzDwB/+Zd/yQc/+EFisRhf/epXCYVCLFq0iM997nN0dnby+c9/nqmpKW6//Xb6+/sJhULYtk00Gq0nwkopenp6+Od//mcWLlzInXfeyUc+8hECgQBf+tKX6Onpob29nT/90z8lHA7z6U9/mttvv533vve9rFy5EsdxCAQCRCIRHOf1WlqmaRIIBAiHwziOwxVXXMHv/d7vkU6nuffee7nmmmtoa2vjm9/8JkuWLKG7u5u/+qu/IhwOc8cdd3DLLbcQi8X467/+axzHoaOjg0996lMAjI+Pk06nT/8FFOIUk7j25iSuSVyTuCaEEEKIRiMj7I7DNCwuvOQ9RJo7+MmDD7Dh1bW4lTLFYoW+/j5isRiu65JMJikUi7S2Jdm9Zy+VShnTev2UHlm4+t10OqcMzSQ0pmketX+tNZ4CT4EuldHFSm2lOTU9pUbXRhv4Sk0/buDjY2i/tvqeYaD96Zo/GpjepqlqK+B5aHzto3xdG82gvNqHVyn2qCAvLuojFQljexZ7lE/GrFIs5PEti8liAVyP1uZWSuUytm1z3XXXkclkeOGFF6hWq3R1dZFIJJicnCSVSmHbNqZpEolE6nfK9x84wMIFC0lNTtI7maZpaASTCv70iAmlFeZMofHTdkWO78CBA/z2b/82t912G//jf/wPtNbce++9bNu2jcWLF5PP59m9ezeGYTBnzhxaW1v50Y9+RCQS4e6772bXrl189rOf5fvf/z7PPfccl156KePj48yaNYvFixfzi1/8gmKxyHvf+14eeughOjs7ufrqq/nFL37B/v372bZtG47j8MILL9SPSWvN1q1bGRwc5OGHH2Z8fJxrrrmG7u5urrvuOlavXo1SihUrVlCtVvmzP/sz/uEf/oHVq1fzjW98A8/zGB4eZtWqVezbt6++3VQqxYEDB1i7di1btmzh61//OqtWreLVV1+lu7ubj370o3zpS19iaGiIRx99FNd1+fjHP04ymWTnzp0YhsGyZcuIx+M8/fTTmKZJV1cXAKOjo+RyudN9+YQ45SSunZjENYlrEteEEEII0Yikw+4oxnRxZR+loKerm0wmSy5fQPtVQuEI7Z3tdHV1US6XyRVyFIpFkskEU5OT/OqXz3HTzbdgWsYZadmerilLR+7HMAw8rzbtx9QaQyu8cIhic4JQ2ccLBSk6QTAtfDSm8lGAv38Iq78L1zbx0zmYTKM7WzHjUXxdm1rklstYrovr+1i+j+l6aL+KOjiEjYFPbU7RQNgmEwmSnZrCdV0q5TKZTIZyuUxHRyfz2jooFYpkslnC4TC3334727ZtY8eOHdi2TSAQIJfLUSqVqFQqeJ5HKBQiFArR1tZGJBrBsgxaW1vo6e9jcMcAS3NVglTRStfnTClVm/qkdW2ExplMbrq6uggGg/zLv/wL//7v/86//uu/smjRIlzXrY8kCQaDpNPpo0aUaK3xfb+ewPrTU7qOfKxQKHDo0CF83+cv/uIvGBwc5A//8A+5+uqr+epXv8pXvvKV+vYMw6gnvjMqlQq+79ePxTAMKpUKhw8fplwu89WvfpWpqSkMw2Dv3r1ccMEF2LaN53n17ZimiefVVkpUStU/k6ZpYppm/Xla66OS8JnHZl53vOP0PI98Po/WmnXr1jE+Pn7KrosQp5/Etbe6H4lrEtckrgkhhBCiEZz781xO2kxh5emaMkAmO8WOHdsJhWuN3M6udnp6e0gmk7VCx9MNvXAwxKyeXlY//0sK2SnQHuVSafqW+mk48jNQW+jYxiq8PlPGT8Yw2popxyOothas9jb8ZBTd1YoOh/GDQdymBH4whOGEMDrbcZNJzJ5OCpUqfsWtTcGJhdHRMEZnC7Q2QTCI2ZyojVzAoqocNoYcRi/sJZvP4lZdDMC2LJKJBLNm9dPW1o72Na7ncd111/GhD32IF154ga1bt5JOp+sNZMdxiEQizJo1i6VLlxKPx4lEIrS0tDA+PkahkKe5uZmd27YTGxqhfyKDoQzAqC+ep6fPQAOU+qGrq4s//MM/ZMmSJSxduhStNfv372dsbIzZs2dz/fXXc+mll9LX18fQ0BDZbJYbb7yRm266idbWVnzf5/HHH+eOO+7gkksu4T3veQ9aa7Zv387g4CCxWIxoNMqVV17JrFmz+OhHP8rq1at57bXXCAaD5PN5Wltb+eQnP0lPT0/9uPr6+kgmk3R0dNDX10dHRweZTIaNGzfS3t6OYRhce+21xGIx7r33Xh555BEqlQqf+9zniMfj5PN5li5dyic/+cn6515rzdTUFHPmzOHTn/40jzzyCFdddRULFizguuuu4+GHHwagtbWV66+/nve///2MjY0xOTlJT08P3d3d7Nq1i1KpxOWXX8773vc+brvtNpRSXHXVVcydO/dMXEIhTgGJa2+FxDWJaxLXhBBCCNFIZITdtCMb6bW7yT47dgwwPj5BLBoiEgmhTIN4LEYoFEJrTSQaxTJMdm7fRsA0KOYyPPj979DU0sKuffu5ePklXPdrv0YoFHpXk4/TvYrezN3aY+/wQm3qj5/J41Y8bMvCLRbxU3mcpjie9qn6VYJaYfga7+Ahyr5PyLaxPJ/q5u1EXI05NQUGKFPhmwpVccGyUFpje2D6MGHYrO9pYf2cdqZcn2QgSDabxzAM8vkcoWCIfKFYv9M901hetWoVqVQKz/Po7OzEsixyuRzxeLx+h3zp0qVs27aNQ4cOoZQiGokyOTFBe3s7UbfCiuFRmrwSGn/6utbmO80UJ5/Oj8+ozZs38+///u9ccMEFaK358pe/zMGDBzEMg69//eu0t7fzv/7X/6KlpYVCocBXv/pVFi1aRCqVYmRkBN/3eeyxx5iamqKzs5OBgQHy+TyZTIY//uM/5rLLLsN1Xb71rW9RKBR46aWXWLZsGQ8//DBr167Fsiyi0Sj5fJ7h4eH6cRWLRf7+7/8e0zTZtWsXIyMjeJ7Hl770Ja688kp6enr413/9VzzPY8OGDQwMDPDtb3+bRCJRP/aLLrqIZ5555qjP/f3338973vMe1q1bx/bt2xkbG2PRokV873vf47XXXiMWizEyMkI6naalpYUvfvGLlEolXnjhBbTWR72vQqHAgw8+iNaaV155hcHBwTNxCYV4xySuvbX9SVyTuCZxTQghhBCNRDrsjuH7PhpNNp3hP/7jP3DdKp7vUCqViScSuJ5HJpPBCYZoDUcxNORzWSqlAi3RABtffoF0oYgdSZDNFYjGYlx99dXv6jGficTmKNM5m0bjoQl0tFKNRSmZinhXB5MbdhBZupBiuYxKpymbBpF4kjI+pmNTKZbxiyU8NDpfxkqnCPugfA9VnZ6SU63WduUr9gcjPJgMMJJwaIsk8FIp8oUCra2teJ6H7/k0JRNoPUE6nebyy69gYmKSJx5/Es93qVQqRKNRJiYmMAyDQCDA1NQUHR0dzJo1C8uyyGQyGIZBNptl3ry5JBNRwsEA88ammD+VQ03XKJpZ6k8fmczoo/46IyqVCmvWrHnD477v8+KLLx71mGVZfOxjH2Pt2rUsWLCAp59+mmKxyJVXXsnSpUvZvHkz8Xic1atXAzA2Nsajjz561Daef/75o352XfcNz4HaKnWbNm0COKpeD8Bjjz121M9PPfUUQH21O4DJyUl27dr1hu0ODw/XRxwAbNy4kY0bN9Z/Xr58OdFolGKxyI9//OP6488++2z9+3w+z89//vOjtvvSSy+9YV9CnG0krr2N/Ulck7gmcU0IIYQQZ9h532F3bCNdKRgdGeXv/+5/Mz46SiQcJmBaOEEHBUxNpYjFYoBBqVSmWnXxtSKTyVGqukykp6h64JkVZvfPZsWKFe/6ezgTU4fgjTVTaivqafThIbTvE07Eqe7cR6ziwfrNhABV9TACFuX8XpRlon0fywlAuYKhDEylakW69XQhcFXbbu0hk12hIC9ePI9qNIY3OcXBwSEikRChcIiRkREA2tvbaUok6Z89m/45c3h17Vq2bNmGbdtEnDCpVArf92lpacGyLMrlMpOTk0xMTFCpVDhw4ABTU1MopWhqauLw4cMELEVf2WXFtiHinkIBBrWaRPXhB0dkNGp6VIY642W6/2uu63LffffR09PDzp072bt3L77vs2bNGkZGRohGo3z5y18mlUqd6UN927Zu3cq9995LJpM504cixLtO4trbJ3FN4trZQuKaEEIIce477zvsZmitUdpj08bX+Ou/+iodrc1YWmNadm31N9+jVCrgVisYhokTDJPPFwiHwuTyJbYO7CHrKbQR5Z7fvYdwOMQ1V1+DHQic1vdwOswUQ1bTK90Zho+vNWWtCGogX8aserjpAqDAsiBbQgGero1WgNcb/3q67e8DLmBo8KYzBtOcLnRuKg6bIX55QQ+HTJOoHSAcDuK6Lm0traRSKVzfIxAIUCyXmN/RTmtLC089+SSpVIpcLkNHRwexWAzDMCgWi8RiMTzPo7m5mXw+z/j4OOFwmHK5TDQapVwuA9Dd28vC1haWPP8qHWWX6vQ0IaXAMGcSGo2vau/KQOGhwLQxsWYGK5zWa/RWTUxMMDExcdRj1Wr1uHf9z0ZjY2OMjY2d6cMQ4rSSuHbyJK5JXDvbSFwTQgghzn3SYcfrhabHx8b54he/QG9XJ265RLGQJxIKogwo5vNMjI1imwbt7Z0Yhg2Golwus2jxYp5++pcsuHgln//CF2lrawM4apWyc9HMymtaa2JLF2H/1Z9gaA+la8nJDF9pNNMr7h1vO4AypotcazA8jdIaqCVOKMXwyCDfefin5AIGbqHC0MguWlqb66MIItEIdtomGAxy2WWXkc1meejHDzGVmqK1tZXm5mYqlQqpVIru7m4qlQr5fJ5cLodt23R3d9PS0oLneQwNDdHb20sgEOA973kPSmlazACLrrwWw6ug/UqtELcywDBhZkTC9KgDjSJk2JS6OjGUPz0aofFHJAghzh0S194eiWsS14QQQgghGoV02PF6YvPoI78AzyMUsBkZOkw8HsWtlNEaTAPyuRypqSkODw6iDYuOcIhwKIxtmlx66aXc+4f/k/b2duCI6TRnaFrP6aK1j9YQaG/Far8OhZ6eQgQafzoB0Gg1fbfeqz02MwphpsFfG9UwvVHPZzrdwTAMKuUq//E3/4vDoQAqmyMWiRN0avV54vEEASdEuVylWq6wfNnFGCg2vfYaATtAIpGgXC7XC2/HYjFc1+XQoUP1lfSGh4eJRqMEg0EGBgZwXRfHcYjFYjz77LOsvGQlt/z6r6O1i2lO1x6amRSkDBQGGvAxainM9GqMWimMc/z6CyEak8S1t0/imsQ1IYQQQohGIB121BrVbtXlpVUvsnj+fEaHDxMJBtC+T7FcwjAViWSccCiIVyoyfHgPhqlpaW1lYnISQ2tyuSyHDw/S2dXzhho477YzWZxbKYVhqOnHpu+5q1odHOojEAz09N168yRuyhsKQDO9SdavX8eG9RuIRWMUcgXcqksoGiEeiWOaJqOjo1iWxdy5c4lGozzxxBOUSiUcxyEcDpPL5QgGg3ieRygUYu/evXieh23bKKUIhUKEw2FGR0eJRCJEo1ESiQR79uxhcHCQbC7LNddey8MPP8zv/M6nSCTioKdHmdQLkwN6pgbQzMnR0wvtNeZ0ISHEuUvi2tvfn8S16XMCEteEEEIIIc6gc3tuy0lSQLlUYmx0BNswKOfzuOUySmtCwSDRSJRYNEo+l6dSLpNNp9k1sBPt+4SCQUrlEnPnzqWpKVnb3nRCc7obtKdz1MPMvgzDmE5uDEzDwDAMDGXUshOlUMqsjzIwFBjTv3qzL2WAMhTKgEqlzNNPP0MoFCEciZFoaiYSjVIqlwk4AXL5HONjoyTiMWbNmsUzzzwDQDQaxTRNqtUqtm2TyWTwPI/Jycn6sScSCUKhEIFAgGq1SiwWI5FIkEgkcByHaDTKsmXLyOXyPPLzRxgZGiGTzgK16Uwog1oyV/tv5vg58uuY63Kuj0wRQjQGiWtvf18S1ySuCSGEEEI0AhlhB6BhZGQEyzJw3SqmMtC+JmAH8D2X7q5u4vEo1UqVXKFA2Haouh6HDhykvaMT0zSxLIupqanz4q7zCRvqWtd+rj90xPOOs50jz9VR5+2IaVfbt+/gxRdfJOgEyecLBINBwtEI0UQM07LI53MsXLiAiy5aymOPPUapVKKnpzYaJJvNksvl0FrXEq/pUQuO49Dc3Ew6ncbzPHzfJxqN0traiud5jIyM0NzcTDweZ9asWZRKJV5du5Z4PMH4xDh9s/qYKchdnyLG8d720e/6fPhsCCEahMS1t0TimsQ1IYQQQohG07Aj7E5NI1Af8/VGM+WUN216jVAoRCo1ieMEcBwHrTUtLS0kEgnaO3sIRRLkS1WGx6bI50scPHAA0zSJRWPYtk0+nz8Fx3x2mLk+x04jeiuvnynuXa1WyWazFItFqtVqPdnQWrN69WpAUSwWiUQiNDc3k8vlGBoeZmJigp6eXmbPns0zzzxDpVIBYGpqiqmpKTzPwzRN4vE4SikGBweZPXs2zc3NFAoFTNPENE1aWlqYNWsWk5OT5PN5kskkCxcupKOjg3Q6TTAYJBqNEgqG2LNnzyk9j0KI84fEtcYmcU0IIYQQQjSSc3yE3ZEJzYn6Jmtllffu2UVrazMj+/dRKRWgBIlEfHq1tQCxRIKOri4mM2mUYeD7mr1799HW0UlHezuO4+C67ul5W+cI3/cZHR1l3bp1DA8PU61WaW5upre3l3nz5gGwdu1aorEo2qslOjPJR66Qp62tjblz5/Lq2rWkUimq1SqRSASlarWHXNelVCpRLpdJp9P4vs/u3btJJpP1qUWlUolAoJbIDg4OArUpRWNjYzzzzDMsWrSIiYkJHMfBcRwOHTqE67qYplnfj0wHEkKcPhLXGpnENSGEEEIIcaqcMx12Rw9c0Md8P1N05YjvjxnpYJgmhmFSqVYoFItEwmFc16Wnp5tYLI5p2CSTTUSiUbZs3Uoy0cRtt91ONBYnFAoyPDyMbdtHbPH019053d7pfqvVKi+++CK7d+9mamqKXC4HwOzZs1m3bh3pdJp0Oo1lWSgTSqUSxWKRYrFIsqmJiy66iOd/9StGhkcIBoPYtk0ikaBUKh2VfMwkLpVKhXA4fFTSE4lE6OnpoVQq1Qt2T0xMoAzFhRdeSDqdJhQOUSwU2bV7F1OZKYaGhuju7q7XORJCiHeDxLXTT+KaxDUhhBBCiEbRcFNiZ6aUvK3XAr6mlrRoH7SP9n08z8XzvekvF629+u/xfXw0zS0tFItFTBPsgIWyTLRh4ITDmLZFOpNidn8/N733Zt538y3Mnj2HaDRKe1srqVSKwcFBOtva6glTbfW409PoPd3FwGem3LzThv3IyAiHDh2iWCySnppiajKFbVqMDo+wf+8+9uzaje96GCji8Tha63rR7AULFjA8PEwul6ejowOonYdqtUq5XMYwDEZGRgAol8u0trYSjUYxDINAIIBpmvi+j+d55HI5TNOkWCziBB2K5RKWbWM7AQKOQ7lcIZ/P09PTTcC2ee7ZX+L7/ik5l0KIc5/EtbdO4prENSGEEEKI891ZP8JOH/VHLZVwq2UOHzzI+vXr2LNnF+l0mp7uPvL5PKZt0dPby7KLljFr1iwCTgCUYu68uTzxyM8xDEUkEsb1NJZlkUpNsXDhQgrFIoZpEgyGmD9/Pv39/SQSCQqFAol4HN/3a3e+p0c+6CNHQJyDjkym3m5yMzIywsaNG7Esi2q1SqlU4vDhw7VC10phmiZoTSAQwHXd+ldTUxPzLpjHU08+RaFQoFgo4Lous2fPJpPJkM/n0bp2/QzDwPd9crkcwWCQUChEqVQin89j2zamaXLgwAHK5TKe55HNZOvJDkAmmwGtSSQS5PN5hoeHCTgBPM/Dss76/32EEA1I4tqZIXFN4poQQgghRCNpuJbZW24ka9AYKO2hfZddu3bygwe+z+ChQ0QjYWxTkUwkyE2NUywWKVVcxoeH+NEDD9Da2sFv/OZvsuLSlfiuRz6Xw8GjXCyilEHQCTGVSnPw4CDzLpiDRuN6FZpbmvB9H9d1CTtB9u3dy7r167hzcpL+OR7KUKc1pzndq7S9k9EiRyoWi/i+X6/z49gBQqEQevrc5vN5DNMkEg5TOKJw98UXX8z27dvJ5XNEIxE8zyMcDjM+Pl7fblNTEwCe5xEKhRgcHKS9vR3f95mcnEQpRTgcZvHixQwODjI0NITjOPT29rJj5wCDg4NceeWVLF68mM2bNjGwY4DZs2czNjZGsVjE87x3/P6FEOcHiWtvncQ1iWtCCCGEEOe7huuwe1u0T7lY4KEHH2Dvnl0ELZP3XLKi1ogdn8DzPMrlIuFgiP5Z7bS3t9PX3cnBgwf48p98nksufw+XX3EFnutR8Vx8D8KRIKVSGcuy2LF9B8Gww6KmBFW3Si6XJRQKkUjEOXjwIOs3rOfqq67kF48+wuKlSwmGDcA8bcnN6U5sXNelUqlgWVZttMA7UC6XmTNnDnv27KlPx6lUq+TzearVKkopDMOgVCoRi8Xo6OjAcRwGduwg5ARJJpMMDg6SzWbrIxAADMMgl8sRj8dJp9PEYjEsy2JsbAzP81BKoZTiwIEDKKXqdYIAbNtm//79LF26lO3bt/PCC6u4+qqrsCyLaDTKRcuWEQwG69sQQohTTuLa6dnRNIlrEteEEEIIIRrN2dthp1//xq2UefKxR5kcHaY1kWBqapIXnn2WkZFRPK9Wz8Wyaw3wcCSCZVt0dXay7KKLWLb0Ip57fhX/+I1vYFlWrdGrfUqlEuFwGM93yeWybNywgVn9/VQqFeKhELlslkw6zZqXXyYSibBk8WJeWreRsZERZs2Zc1pPxUzj+nQ1srXW+L7/jhMq0zSpVqvYtk3/rH52DgyQy+VqxbinE5pyuVyb8jWdQC1YsIBVq1YxNjqG7/uMjY1RrVZJJBK4rott25TLZcbGxuqv6ezsZGRkhJGREcrlMvF4nHA4TDAYpFAokEql6tOTBgcHsUyTdDrNL5/9JXv27CYSiZBsSrJu3Xqi0Qi33HILhtFw5R+FEGc7iWt1EtckrgkhhBBCnO/O2g67WnkfH7dSYdXzz7Fv5wDlYo4d+3bXpo4kkyy/eCnxRHK66HKAyclJtmzdxqHDh0lPTZHL5Jg7bx5XXHEF/f39PPvUkxRLRZLxOIFAAGVQL0Bt2QG0X6slFHQc8rkc5XKZyclJrrrySmzbZnJigsHDh5nV348yVK1O92lINk733fBTsYrczCp3yWSSffv2sXTxEg6Hw5RKJeLxOOVyuT7iIRAIkEgkWL58Ob7vc+jQIarVKuFwmGq1Ol1U3ayPPrAsC9d1MQwDz/OYmJioTzsKBoP1pGRmtb1wOEwgEKiPWEhNpgiHw5iGgWVa9PfPYvPmzTQ1N3HPPffQ1dUlIxCEEKecxLXXSVyTuCaEEEIIcb47KzvstNYo7VPMZXn6ySfYuX0bmckJdu8ZYO6c2STiMWKRCIl4nPGJydqIgnKJZDTCNVdchudfwuqXX2FkdIhsLkuxWOTiiy8me+lKNm/ejIHCtGqrrZXLFULhMC0tLSjDYGhoiFAojFKK7du3M3vWLPp6eilX3drqbQr86Vo4SinUkTfrz5G2sGEY9RX13i6tNZOTkziOw8TEBAcPHmTBggVs3ryZQj6P67pYtg2AZVnMmTMHpRTPPfccWmsCtk02kyEcDmPbNplMpj6SZGYkg1KKoaEhTNMkkUhgGAaZTIZEIkFnZyf79u0jGAxiWRbFYhHLshgeHubXf+PX2bxpM4cOHaK3t5fOzk6Wr1jBZZddRnNz81mR1DiOQyAQQGt91JQqe/qcVqvVN7xGKcW8efPwPI+9e/ee9L5mCp6f7MgUpRRz587F9/23tJ//imEYOI5DsVg8qecHAoF6zS4hzjSJa2eWxLXGv5AS1/5rEteEEEKIc0vDddid7OpshVyW73773xgfHSWfSZPPTDFvdj/xaASvWiadKjE+Mozn+Yy5LsFgkHw+R3tHO9FkE9dcew2jo2OsX7eetWteYnjoMNdcdw3ZQpY9u3bjV2qr43laY9k2PT097Nm9G9M0KeRyWJZNanySi5ctYf++vWzZMUAkHKa3r296Jb1aI+9cy2tmEoZTMRqhWCxi2zbRaLQ22sOyCIfDuJUKlmFSrlYxTZNkMsmll17Kz372M4rFIuVyGds0CYdC5HI5TMvC92vTvZRSVKtVLMsil8vVG/eZTIZAIEBPTw8LFixgcnIS27brNXscxyGXy7Fs2TI+/t/+G5/4hEWpVMYJOvWaRsc23Bs5wVm+fDlf/OIXeeaZZ/jmN79JpVIB4KabbsKyLH72s5+94TVKKRYvXsydd97Jpz/96XrtpTcTDAb54he/yN/+7d+SyWRO6tiUUixZsoQ777yTe+6555TVquru7uYTn/gEX/va14577LZtc+utt/LEE09QKpW4/fbbKRQKPPbYY6dk/0KciMS1xiZx7ehz0agkrklcE0IIIc43Dddh92ZmGkBaa1566SWq1QqDg4fwKmWWL13M0OGDlPJZAoEAXrWM7/k4wRCpVJ5ILEIkFsY0TTLpNHYwxOJFC2lKJti7Zy+vrltPpVrikssvw3c9du/aje/7xONxQqEQmzZtIhwKEXAcfN+nWCzR0d7O8OAgW7duI1ss8bFPfJLm5mbSkyk81yeSSBAKhaZnD6nXs5wGbhC/FW+1QXrs8x3HqSUVgQAHDx3CcRyam5oYGx2jWCxQ9TwqlQqpVIrnnnuOwcFBXNclEongVipEI1HC4QjhaIRoNMrk5CT79++nUqlg2zamadbvStu2XRuR4nmMjIywe/duHMehr6+PeDxOMBhkaGiIz372s4SCIVAQCwTqycvMCoLKMKjNCGvsa/jKK69QKpV44YUX6kmNaZrs27ePbDZLOBymu7ubdDpNb28vhw4dYmxsjE2bNvGhD32ongT6vs/Bgwfp7e0lmUxy6NCh+qqFAOFwmB//+MeUSiW6urqwLKtexHz79u1vGPHQ1NTE3LlzGRoaAl5PlOfMmUMsFmP37t1ks7Xi9/Pnz8d1Xfbu3UuxWCSRSDBnzhyKxSL79u2jra0NqF3bZDJJKpXipz/9KYZhMGfOHHK5HC0tLVQqFfbs2UNnZyef+cxnGBwcZM+ePWzfvp1UKgXUPosXXHABWmt27dqFUore3l4ymQzd3d2Mj49z+PDh03HpxHlG4lpjkbjWuCSuSVwTQgghzjdnVYfdjHIhx/DBfRzYuYNCaoKurg4O7d9LJBImk0mTiMeoVCpMTU0Ri8bQbpXxkWECTgC3WiWRSFLKZdi++TWCwSCLFlxAPpvl8OAg46NjXHjhImKxOFs2vEp6KoXva5ricaaKBaLxKLt3TgGK1MQEs5rjrJw/m407dvH4T3/E8798ikw6i1eu0tHRwZ13f5jrb34/ZsABQwHvXlHn07GqntYaz/Pe0b6q1SqrXliFqQzGR8ZwTBvXDpDP5oiEw2SnVyt0SyUsZeC7HgPbtuN7Hn7VJdmUpFyuNdadoFMvzt3T00NPTw9btmwhk8nUk5GZ0Q6hYBDlaw4fOIhXqdLe00t3VxdOKMT4xDj3fPrTzL1g3nGTlrNx5bxjj9e2bW6//XYsy+L+++/nj/7oj5iammL37t1ceeWV3HvvvfXnNjU18YUvfIHHH3+c3t5e3v/+9/Pcc8/xsY99jK985SuUy2UA2tvb+cpXvsLnPvc5Fi1axBe+8AUeeOABli9fzjPPPMMPfvCD+jabm5v5+te/zjPPPEN/f399FMjdd9/NwoUL2bhxI5/85Cf58pe/zGc/+1l2764VRl+2bBnPPvssf/zHf8wvfvELVqxYwbZt2/B9n9///d/nRz/6EYsWLeKll17irrvu4p577uHDH/4wl19+Od/5zne44YYbeOqppxgeHiaZTLJ06VJyuRwf+tCHyOfz3HfffXz+859n37592LbNBz/4Qf7P//k//N7v/R6xWIw1a9Zw8803c++99zIxMXF6Lp4470hcOz6JaxLXjiRxTeKaEEIIcT5p2CXB3qwROTE+zrYtm6iUC8SiYfxqheamBL7nopSiVCwyOTlJOBQG1HTNnhITExNMTk6QTqfIpifJZ6cYGTrM9u3bmNXXyxWXX07AtplKZ0iNjdHTFGFOUwS3mKYpGScWDREKWNiGJp9OETAVTsBEeRVioSAKg/GxCQzDIhyNM5lK881//md++MMH8T3v6HlEZ7GTnd51IkopXn7pJdavX4/nunieh+u6FPJ5pqamuGjZMszpej2e78HMCBTfJxQKkc1kKRWLGIaBZVnE43Fc12V8fBzHcVi5ciWzZ8+uJ2C2beM4Dk4wyMTERL3ej/Z9du/ezejoCHfffTcrVq446hjf9Osdn8XTr1QqsX79ehzHYXR0lEOHDvH888/zrW99C8uySCaTaK2Jx+P8wR/8Ad/61rd46KGHcByH+fPn4/s+999/f31kA8DOnTuZmpoCYMOGDWQyGb7zne/wgx/8gOXLlx+1/6uvvppCocB3v/tdfvazn+FOT+m755572LVrF6lUiv7+flasWEFHRwfz589n3bp1PP3009x0001kMhkee+wx/umf/olXX32VDRs2kE6n+bd/+zf+6I/+iCeeeAKtNZVKhQ0bNrBz504efvhhvvvd7/Ibv/EbDAwMcPjwYX7605+yfft2NmzYQCAQYO7cuVxyySU8+OCDPPDAA1x77bX1kRQbNmzgW9/6Ful0ms7OztN4tcS5SOJa45K4JnFthsQ1IYQQQjSKs26EXbVS4ZGf/4x0Oo2Fqt0R1R65vEM+X6JSLlFEYxgWnudTrhSmC/D6VKouPiXins/Bg4eJx+O0traSTmWYHB+np7eXQrXC6PgExVyGlbM7cdwqh7MlMoUssUQUx7FJRsNEgg4By8byKlRLZRLJBHd96Ndp6+6lo6MTOxBk98B27v+Xf+KRn/+Ujs4urr/x5tq0kzN9Et+hd3pH3jAMlixZwpNPPokGItEo5UqFXCGPbVos6+igq7uLRx55FNO2yeZzhJwgpmnWV9jTWlPI5wkEAoyPjzM0OIgTcJg9ezapqRTpqSkuv/xy1q1bh1KKQqFAoVAgXyoSCoUwbAtlmfTO6uNjH/84CxcurK+ydy6ybZumpqajHvN9n1wuh9aaarVav65NTU0UCgVuueUW1q1bx6pVqygUCtxxxx3Mnj2bz3zmM/VkBo7+PBQKhfo1OvZzEg6HKZfL9eTD930MwyAQCNT3/81vfpMdO3bwN3/zN/zar/0aX/jCF1i1ahWVSqVeADw3vZJle3s7+elC7qVSiUAgcNQxzYyWKJfLOI5z1LEceWzBYBDf9/E8r/7YzLZyuRxQGz3zTorRC/FmJK6deRLXzj4S1ySuCSGEEOe6hmzJHW9aysxjYyNDvLL6BWzTIJWaxHGCBIMx8rkKo6MTNDW1oDDQGkrlCr5WVF0fz1c4wQieq0lNpgmHIhjKpFp2CYdC2AaMDB6iNJXCzaXoag4TDxhEI1HKVZdquYKhNNFohIBjEwzauH6FivbRAYeOnl5uvvn9LL3oYtrau2hqbuXy91zFH/7P/0kk6PDyqufQ3htXMDsbHVmg++1auHAhg4ODoMDTPp72sW0bTa3Re9ttt3HXh+7CtC0Mw8B1XUzTrBfwdhwHwzCYSqWolMsow6CQzzM+Nob2fMLBEOFQiGXLluH7fr2my/wF80k2JWnraOfa667j9z/3ORYvXnxUo/VsmyJ0PE1NTYTDYebPn8/y5cu5/vrr+fjHP057ezvJZJLm5maamppob28nkUgQjUZpa2ujra2NiYkJ7r//fpYuXcrdd9/NBz7wARzH4Z//+Z/RWhMKher7SSQSRCIR2traaGlpIRwOk0gkaG1trdfJmvHqq6/S39/P4sWLueaaa2htbSUQCPD0008TDAbZtWsXF198MaFQiN/6rd/i5Zdf5j/+4z/o7+9n9erV9Pf309vbyx133MF1111Ha2tr/biB+s8tLS0ArFy5kgULFnDzzTfXkyPXdbnwwgu56667aGtro7m5mcHBQdLpNMuWLWPFihUMDQ0xMjJSP0+xWIxYLEZbW9s58dkQZ4bEtcYmca3xSVyTuCaEEEKcbxpuhN1xGw5aowCtfXKZNH61QiHrUiqWsJRJsVAiYNvEE00cOjxIwDaxbYtiqQxKkclkCIXDBG2L9vYOqtUq3nTh56n0FLYJoVCIdDpNe2sbsaAD5TwKk417D7B2x37aO7oxAibxRAKFolgqEY2EqVZcxtMZli1agmGYaO0zODiI52tmz+rjwsWL6evtZWpyknKxgBO1z4ni3O+0gReOhCkUCrS3t9enomitCQaD7BgYYO++ffz33/3vNDU18YPvP4DyNVXXJWDb9ZX3CoUCpmliGiZa10Y4DA0NkUgkWLRoEcVyif7+flzXZfPmzSSSSWLxOMtXrODXf/3X6erqwjTNc7Kx2tnZyQ9/+EMsy2LRokW16Vovv0xXVxdr1qyhs7OTNWvWkM1maWlp4aGHHiIWi6GU4ic/+QnRaJT//M//xLZttm3bxkUXXURPTw9/93d/x/DwcH0/ra2tPPzww8RiMQKBAA8++CDNzc2USiVWrVpFNBqtF0jfvXs3X/va17j66qvZuXMn3/ve92hububv/u7veO9738v111/PE088wf79+3nhhRe49tprqVar/O3f/i2jo6Pcd999XHfddUxMTPCrX/2KSy+9lB//+Mf09PQwPDxMW1sbDzzwQD3R2blzJytXruTgwYP8/Oc/p1gsct9997Fw4UJefvllFi5cyGuvvYbWmj/90z/l2muvxfd9/uRP/gStNXv27MHzPFpaWnj00UcxDAPDMI4asSDEyZC4dnaQuNbYJK5JXBNCCCHONw3XYXdC2gfts+m116iUq7huhaZEgqmpNL6GSCSC44Pr+4QCYYrlCoVyhUg0TFNrK45dq29iGSbedE2garVKpeLhmrW76tFInNGxCUKhEKNjkzTHEhwYnWSq5DK2cx/vm9NHJpdH+z7hUAjf93GcIJEYjI6PUayU2LpzN77WLLhgAco0UYZNJBpnfHwS06jd7X6ntXJO5HQ10E/FfoLTU4FisRj79+/HcRxSqRROMEjACRCPxwk4AT760Y8SDoX52U9/ysjwML6uTfGoTQdzqVaruK6L7/tAbTrMzGOBYJBgKMSChQvJFwt84AMf4Nprr2X27NmYpvmuXYc3c7r2t2XLFrZs2fKmz9m6dWv9+x07drzh99u3bz/uc480MDDAwMBA/ecXX3wRgG3btr3huVpr1qxZw5o1a97wu5/+9KdH/bx69WpWr1591GMbNmxgw4YN9Z+ff/55nn/++aP2/eKLL2LbNtdffz2xWIzHH3+8Pgrl2Ncc+56+//3v148T4MEHH6z/bs+ePcd590K8QxLX/ksS1ySuzZC4JnFNCCGEON+cPR12SuNVPQ4dOjRdkDnKyOgwvu+jPY1pmuTyOWbN6mHz5s1EIhFaWlvo6GijUq2STmVqU1C0BtOgWqniVqvEohG6utpJTU5SLBbxPB/DcrBiSQ5mchQMm0A4TKw5hG8Y+BoKuQJGNEIkHKRSqVApl9m1azdPPPEk7d29XLxiObZp1Rrf1Qq5fA6Nxoezv9DPKTAz7ciyaufI931KpRLhcBiAaDTKsosuwlCKYDDIhz/yYS5ZuZJ/vO8+tmzZQiweJZ/NoQyDUDBIIpEgFA6RTDaRSk0yNTVFsVgklyqwf/9+EskkK1as4BOf+ATWdNHvmeM48u/T4XSseHi+01rzzDPP8Ktf/Ypq9eSn68m1EaedxLVzhsQ18W6SuCaEEEKcn86KDjtNrdGxYf16Xl3zCsVMlmq5gAZC4RBexcNxHDzXY3h4hEAgQDAYxDQMSoUCdsBmyUWLWbXqJUbHxzEMA8e2CQUcPE9TLFWIJ5upVFwsyyKfz9Pb3cP8CxeyqFjGtGxQmoMHDpDPZPCVRa5YIZvPEwuHiUaijExMMjQ8xPtvvwNlWGjtg/ZITU4yPjFJsrkZK2Cf6VPZEBS15KWzs5OpqSkcx8F1XZLJJJ7nsWLFCuLxBMpQ01PGNLPnzOHP/+LP2bVrFxs2bOTFVS+gUKTTaYaGhvB9n9mzNel0GtOy8LUmkUiwZMkSHCdAb18fhmEclcxIQ/bc5Lour7322pk+DCHelMS1c4vENfFukrgmhBBCnJ8actGJOq3RWqO1TzY1xbe/dT++6+K6Lul0mnw+RzaTxbZtJicnyWQzlMsVbNvGdV0cx6FcKmEog0Q8TigcJhKJ1uu7uJ5HoVhkeHiY8fFxKpUKExMTlEol8vk8G9evJ59OkZ+aID0xiV/1CIZj9M6ZzwWLl1HWJj4Gtm3T2tzCxPjEGwpWb9++nVR6irs//GEMy+R8Hopw5B1/y7K45557iEQjRCIRtNbk83ksy+K6665DGbXn6unPgK99pqamWLt2LY8/9hiDhwcZHBxkYmKCSqVCS0sL8+bNY9asWRiGQUdHB93d3bS1tXHo0KF6vZsjj2OmyLgQQpw2EtfOKRLXhBBCCCHEu6WhRtgde5dYAcqrUsxm+L/3f4vBA3uJRCIEAjalUoFwOIRpKLLZDL7vUy6XKRTyKMOgvaOdYrGI71aJJZpwfYgn4limSbFYZHJignK1imUY9HV2k8/nAIVl2QwOD4P2aWlJ4leaKFUquL5mfGQEMxRl+649TKbSxOMRErP78F1NtVxl5PAhKuUSthMENAMDA/zwhz/kk//Pp1i07GJmkpp3qzF9uu6sv6OEQCk0oAxYsXIFX//619mxYweDg0OYlsnixYuZNWvWUftyXZeXXnqJ7//n99m6aROGYRCKRLhw0SK6u7vYvXsPkUiY/fv3s3v37vpqbq2trWzcuJF5F1zAokWL3tHqf6fCTJImhDh/SFx7ZySuSVwTQgghhDhfNWSH3Uzjz/c98tk03/v2t/nJj39IOBgil3WpVCoopaZXUjMo5vPEYjFs26JarWJYFiiFMg062ruJJZKMj49zyaWXEnCCjI+PMzw8zKGDBylks4BCa9Daw/OqxOJxCvks0XCYqdQkoVAIz3UJOhae0sye3c9kZgueViSbmkmNDFMsFMlmy4yOjtDT18fOnbv4xjf+N3fe+UGuu/56MEw06jweh3CEmcRIa2LxOJdedln9V0cWzJ4poP7000/z+OOPc/DAAXxfY1tmbaTIaxtJZ9JEIhGq1SrBYJB58+bhOA5aa9KZDDfdfBO33HorgUCgvu8zQRIaIc5PEtfOExLXhBBCCCHEKdZQHXYzandsoVgo8PSTT/GThx8mYNtkcjnsgEMgECBg2VQ9H09rKq4LCgxDEQgE8DRMTqZQStHV10fRdWlKNtHT108wGMEJxjBMh4ATYdumjRRKRUzToLOznWq1yuGhESpli5GxMdpamrEDAarVKrbtoLSBE7C58frr6OzsxPZd9gzspOp6aBTlcpkNGzbwT//4Tyy/eBlXXXU1oDCmV9I7F8zcUX8nIyo0x6+3c+RoAa01+/fv58knn8Q0Tbq6uqgUS3ieh+u62AGb8dExRvxhkskm+mfNYveePYQjYa699jquv+F62tvb6yMntJLEUghxZkhca2wS14QQQgghRKNpuA47pRS+VuC6HNy3lx89+CAGMJGaYvklV7DisksJhULk8lmmMhm07+NWigzv34NfLpLL5jC0gWXbtHV04qsAHT295AsFVr+yntaWNiqVKqmpDIWyS7ZYoTo8wsK5s6lWq5RKJUKOQxpFruTREUxgR+JEm23K5TJTmSzZbIbDBw5xYNcuFFCqVLCsAG6pwsGDB1izdh0f/vCHWXThhRiGiVImCuOM3QU/1WYSm3ea3BzreNtyXZdsNovjOMTjccLhMKOjo/hofK0pVyssXboUx3FobW3lA7ffxgUXXEA8HseyLAzDqBV3nz5uNf39ifb3bpLRCEKcnySuNT6Ja2+PxDUhhBBCiHdPw3XYzcgX8rzy8ktMTUySy+b4b7/5SW770EfwfJ+q61J1XXwgk01zYM8AowcO0NzaQtAJcPDQEN29fXR2dtO/YCGGE6LJ98D3cT2N43mEmpK4FZeuvj5eeuEZ9hweoiUWpbkpQSQSIRb3uPiyJTS3t+N5PoYBdsCi161SKZbYtWOAwwcOTjeSDTJTGcooisUS99xzD8l4AsMwMJSBMqbr25zRM3pq+b7/thODt/Kqjo4Oent7GRgYQGkIBAL09vZy4PAh+vr6+PCHP8zcuXPp6ekhEomQzWYZHx+vr8zn+35tdMPMtDSOTmhmkg0p0i2EeLdJXGtsEteEEEIIIUQjaZgOuyOnkSgNmalJXl27Bq09Fi1ZxL2f/QxWJI7nebXnKfCnV2BriUcI+BVe/NWzWErR3BQln5lk+/bN7Dk8xBVXXcWll1+GZdn4vqZcqVAoFBkfnySbyXDDzR/k2ad+wcGxSYLBILZpE40lCMfioEyccIhytUKx6pHPFhkbHcUOx0gXSuSLBULhEBXXo6mljVg0jhMIYlp27ThP45Sh09k4P3Zlune6nROJx+PcfvvtPPTQQ2zfug2v6hKLRlm0aBGRWJQVK1bQ1dUF1FYuXLNmDeFwGNu2Wbt2LfF4nBtuuAHf96lWq1iWheM4b9jvqR5VcTySPAlxfpG4dmrO4enel8S1kydxTQghhBDi3dMwHXYzSY3WGl/75LI5Jicn8DyP62+8kWAsio8xXbeF+nSQgG0Tiyyit6cHJxznRw98D4VFojmJViZt7W0cOLCf5tZWbrzpJpQy0FpTrbqk01lGhkcYGx3h9jvu5Pv/+V3S+TJNyRC5YpmJVIr0gYN0dHZy4eJFTE5OUiiWCEajjI6OsuiSSymXSrQ0N+MEHC5ZuZKLLlrG4OAQfX19hEKh2ps7x2rMzNTOqa96+BYa7G/1PCilWLp0Kbt27WJsdJR8JkfVdWmOtRKJRXnwwQf51Kc+xdatW1mzZg1TU1N0d3fzwAMPUKlUuOSSS3jyySeZmJhg//79tLe3c9ddd9He3n7UZ+7tvBchhHgzEtfOHhLXhBBCCCFEo2mYDjs4ohaKhmq1ilut4nkuFyxYiI9C8/p0lZkSzlop7ECQZEuQD979cULROLlsmpGREV56eQ19loPremzcuImbbn5/vUi2Ydi0t4dobmqmp7ubYiFPW0cHL7/4Als3bcLXGhUYwXIC7Nm7mwWLLqS5pYVwNEI0GiUcjlAqVZgzZw59vX3Tq8MBCipurXj0udpIVkphGMZpu3MfCAS45ZZbGB0Z5cVVq8hkM/gK7rryLjZv3syf/dmfkcvl6OrqIhKJsGnTJubMmUM4FOaF518gn88RCodZunQp73vf+7Asi8OHDzM0NESlUiEWi9He3k5HR4dMJRJCnFIS184OEteEEEIIIUSjaagOuzoFpmniBGwsy6Tq1er0KDWdztQbnYojq+iYlsmtH/gAWns8/JOfcO+KlSxZtISNm15j1uy5tSLZ09uY2UTACdDa2orWLfT09nLVVVdycN8+tm7ZAoZBrlCbGtTd3UVzSwuBQADTNKlUqmit6Orqev2uvA9ac9Rd7nO1gXyqpg6drHg8zm23fYBCPs/GjRsZGx9n9erVtLS0kEqluPjiiymVSlQqFQB27dpFLBrjwoULiUSjoGqJ80MPPcThw4exbZtUKkVLSwvLly+ntbX1qP0d7/qdqLj2uXqNhRCnkMS1hidx7XXn6jUWQgghhDibNFSHnda6VkxZQXNTkr6+fsrFEg/8x78zb84cWtu70PXaOdNJjZpeH236L18rwOT2O+4CrVGGwbXX33BEQnOcRqipMACtDRQm8xZcyAULL8R1PSZSk2itaWpuqRV5BgxDkU5nCIcj9e3VG72qdmQzXNedfs3J37mfmcLi+/5Rx6y1rq0ON/37M92gPpXTbU4mEezvn82H7v4QVbfKxo0b2bNnD5lMBqUUE+PjtLa2Ui6V8T2PUrEEGjZv2Uy+UKBUKuF5HoFAgOXLl3PppZcye/ZsmpubsW0bqBUcn/kMKqUwTfMNx3XsKoIzv58pAn5k8nO6RmwIIRqXxLXXz4PEtTeSuCaEEEIIIU6koTrsoJYAmAa0NDdx8623sm/fXoYO7uOP/t8/4Mb3fYD33XobzS2tKMNEM3M3vDYCAGqzd9BgKAOtFL6qJSJHphsnamjWG6vUCn9jKppb2o7bOE0kkm/cjpoZE1Fr8FcqFR5++GF836e/v59ly5ZRKpUIBAKUy2V83ycYDFKtVutJUyAQ4PDhw2QyGYrFIp2dnSSTSQqFAuVyGcdxmJycZMWKFW84/hPdKT/VjmzUn67pQzP7umD+fD7z2c/y/PPP87Of/Yzdu3fjOA6ZTIb21jai0SjzL5hPOBymWC6xb98+EskkVy1ezA033EBTUxPBYPANIym01pTLZX7xi18QDAaJRCIsX76cXC5HNBqlUCgA4DgOAOVymWg0ypNPPsncuXOxLIu2tjaGhoaIRCIMDQ1x4YUX0tbW1hBJqBDizJG4JnHtzfYlcU0IIYQQQhxPQ3XYHdn4c4JBli1fyUf+22/yg+/9XyrFIg9+/7s8/9xz3HTTTVx2xZW0dnTjBIN4gOd6uG4Vw6jdQbZsuz6tSGnQSqMBszaHhGNTgOM1PN+sQXoyDVXbtpkzZw6+77N169Z6I9x1XarVKvl8nkQiQSAQIJ1O4/s+K1asQCnFq6++SiAQ4MCBA6RSKWKxGKZpkkgkAFi5cuVJntVzx8w5b25u5o477mDJkiU8+OCDbN26FYUil8tRKBQIh8MMDAxgOwEKhQJJy2Lfvn2sX7+eW2+9tZ5EzmxzJmkKhUJ0dXVhWRYDAwMMDAzQ09PD3r17iUajBAIBmpqa2L9/P8FgkJtvvpl0Os2rr75KOBwmk8lgGAaBQKCWYM2fLwmNEOc5iWsS196MxDUhhBBCCHEiDdVhB9NTM1BgGMQSCa6/8Sb6ZvXx/C+fYf3adYyOjPD9//xPHvn5z4jHYzQ1NYEySaVSVKplDAOikSiJZJJkUxOdnd21Atr9s4g3NYEZQk+PUtDTbU7jOMcx0+Cd+f6tvocZ8Xgcz/NYvnw56XSaVCpFNBqltbUV13VxXZd0Ok2lUiGZTJLJZOjt7eWaa65hx44dJJNJtNb09/dTLpfJZrP1ItJnutF8uvc/U/8caiNW5s2bx+c///naqA7PZyqVYmpqqp5Y2IEAlm1hGiYBJ4Bt25imecT21DHbVyQSCWzbZsGCBQwPD+O6LrNmzaKlpQXTNNm1axfd3d2USiWq1SrLly+vJz62bdPS0kKhUKBSqRAIBI76LJyukSJCiMYicU3i2on3J3FNCCGEEEIcX8N12L1eH6W24l080cJFyy9l3sJFfOCuETZt2MCeXbsYHz5MZnKU/RMjBEMOhmEQjcSmp3b4HNq7i60bs0xlMgQCDuFImLkL5vNr772NSy69nGg8Xt8fgEZTz3SoNaLfacNdKcX8+fOPeuzIOj0zNWVmavocew4WLVoE1OrIzDTIXdc96k56IzgVSdZ/9fqZ1QprJZ1eTzhN0yQcDqOAWCxG36xZM0dV//OIq8obx6C8TmvNggUL6gnITF2fI8/38uXLgdo1sW2brq4uFi9eXK/zM3MutNZYVsP97yWEOAMkrklcO+HvJa4JIYQQQogTaLiW1+uJhlFvkRqWRTQWJxKJMWvWbCqlMqVCnkIuTbFYRCmFZdlgWhhGraBypVohl8szPj7BxOQYu3YPsP/Afr7xt39LV0cn11x7HR+484M0t7bUphhphc9MTaC3f8f42NELb5aEzCQrM885trDzkduY+X6mkPTx9nm+OJkV7mZSoJmE6HivPZ6ZZOREo1COV6z7eMd0pkeJCCEah8S118+DxLXjk7gmhBBCCCGO1VAddvXG/MzXNGN6JTytNEoZWFaAcDRGU1sHmtfbrXo6F1JaozR4nk+lXMH1XfKFDJOT4+zesYs1q1/iBw9+n/UbXuF3PnUPi5ctQ5nmzIJ8p9WxDfNj73zD0Y3m4013mRm5cLodb1W507PjEz2k66MO6k859rhO8jBPtsbTiWpEvdXtCiHOTRLXJK6d3I5P9JDENSGEEEKI81VDddiZponjOJimeeIG4MwABa3Q9ekktQkhWtXSHGN6iolnaUy7drc/Gg/T0tZGX18/S5ZdxKZ163jumaf58pf+lN/+nd/h/bfeSiAQnE6oZr7efSdKVI6XMBzvnMwkQsdb8e/dMDMt5sgpMo2ksY7maDPTiRrtnAkh3j0S1ySuvVONdTRHk7gmhBBCCPHuaagOOwDP89604afepE04U6qn/hwFPnr6PzBMm2gsTjgYoq2pmQUXXsijjz7Cv//bv1EsFrj7wx9FmSYog9OV2BzPie52/1cN4tPZYJ5JbhqtkX4mRpO8FY12voQQ7z6JaxLX3gmJa0IIIYQQ5yelT6KllclkSCQS7/rB9PX1ceONN775NBh9/JRDA0fOG1FMJzqqNk7B13r6cb+2ep6GquuRy2QY2LENz/O49PIrsG27NsKhgRKbk6G15qmnnuLQoUPvwhG9LhgMcueddxIKhd7V/bxdjZzYKKUYHh7m8ccff0NBdiHeDel0mvj0QgTiaBLXTi+Ja2+fxDUhaiSmCSGEON80VIedEEKIU0eSmxOTuCaEEGcXiWlCCCHON6e/onNDkoLJQgghziUS14QQQgghhDibSYcd0LiTTYQQQoi3Q+KaEEIIIYQQZzPpsBNCCCGEEEIIIYQQooFIh50QQgghhBBCCCGEEA1EOuyEEEIIIYQQQgghhGgg0mEnhBBCCCGEEEIIIUQDkQ47IYQQQgghhBBCCCEaiHTYCSGEEEIIIYQQQgjRQKTDTgghhBBCCCGEEEKIBiIddkIIIYQQQgghhBBCNBDpsBNCCCGEEEIIIYQQooFIh50QQgghhBBCCCGEEA1EOuyEEEIIIYQQQgghhGgg0mEnhBBCCCGEEEIIIUQDkQ47IYQQQgghhBBCCCEaiHTYCSGEEEIIIYQQQgjRQKTDTgghhBBCCCGEEEKIBiIddkIIIYQQQgghhBBCNBDpsBNCCCGEEEIIIYQQooFIh50QQgghhBBCCCGEEA1EOuyEEEIIIYQQQgghhGgg0mEnhBBCCCGEEEIIIUQDkQ47IYQQQgghhBBCCCEaiHTYCSGEEEIIIYQQQgjRQKTDTgghhBBCCCGEEEKIBiIddkIIIYQQQgghhBBCNBDpsBNCCCGEEEIIIYQQooFIh50QQgghhBBCCCGEEA1EOuyEEEIIIYQQQgghhGgg0mEnhBBCCCGEEEIIIUQDkQ47IYQQQgghhBBCCCEaiHTYCSGEEEIIIYQQQgjRQKTDTgghhBBCCCGEEEKIBiIddkIIIYQQQgghhBBCNBDpsBNCCCGEEEIIIYQQooFIh50QQgghhBBCCCGEEA1EOuyEEEIIIYQQQgghhGgg0mEnhBBCCCGEEEIIIUQDkQ47IYQQQgghhBBCCCEaiHTYCSGEEEIIIYQQQgjRQKTDTgghhBBCCCGEEEKIBiIddkIIIYQQQgghhBBCNBDpsBNCCCGEEEIIIYQQooFIh50QQgghhBBCCCGEEA1EOuyEEEIIIYQQQgghhGgg0mEnhBBCCCGEEEIIIUQDkQ47IYQQQgghhBBCCCEaiHTYCSGEEEIIIYQQQgjRQE6qw05r/W4fhxBCiFNM/u0+MTk3QghxdpF/t4UQQpxvTqrDLpvNvtvHIYQQ4hSTf7tPTM6NEEKcXeTfbSGEEOcbpU/idpXv+wwODhKLxVBKnY7jEkII8TZprclms3R3d2MYUvngeCSuCSHE2UFimhBCiPPVSXXYCSGEEEIIIYQQQgghTg+5TSWEEEIIIYQQQgghRAORDjshhBBCCCGEEEIIIRqIdNgJIYQQQgghhBBCCNFApMNOCCGEEEIIIYQQQogGIh12QgghhBBCCCGEEEI0EOmwE0IIIYQQQgghhBCigUiHnRBCCCGEEEIIIYQQDeT/ByTRqX33t5dNAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qa_tmpl_str=(\n",
        "    \"Based on the provided information, including relevant images and retrieved context from the video, \\\n",
        "    accurately and precisely answer the query without any additional prior knowledge.\\n\"\n",
        "\n",
        "    \"---------------------\\n\"\n",
        "    \"Context: {context_str}\\n\"\n",
        "    \"Metadata for video: {metadata_str} \\n\"\n",
        "\n",
        "    \"---------------------\\n\"\n",
        "    \"Query: {query_str}\\n\"\n",
        "    \"Answer: \"\n",
        ")"
      ],
      "metadata": {
        "id": "97bGd7wcyKTZ"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYVgHILHy5-X",
        "outputId": "81c6e61e-9acb-4942-87bb-06dea9d0f0aa"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/mixed_data/frame0003.png',\n",
              " '/content/mixed_data/frame0040.png',\n",
              " '/content/mixed_data/frame0044.png',\n",
              " '/content/mixed_data/frame0042.png',\n",
              " '/content/mixed_data/frame0045.png']"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "metadata_str=json.dumps(metadata_vid)"
      ],
      "metadata": {
        "id": "P-oEG3Q2zC0F"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query_str=\"can you tell me what is linear regression and equation of linear regression?\""
      ],
      "metadata": {
        "id": "MrgFXHJIy_XU"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "context_str = \"\".join(text)"
      ],
      "metadata": {
        "id": "6VC4mg79yuMZ"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_documents = SimpleDirectoryReader( input_files=img).load_data()"
      ],
      "metadata": {
        "id": "tNyOcu2fywnO"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.multi_modal_llms.openai import OpenAIMultiModal"
      ],
      "metadata": {
        "id": "IUGGglIMwtHB"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "openai_mm_llm = OpenAIMultiModal(model=\"gpt-4-vision-preview\", api_key=OPENAI_API_TOKEN, max_new_tokens=1500)"
      ],
      "metadata": {
        "id": "qRrclz5dxlaj"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result=openai_mm_llm.complete(\n",
        "    prompt=qa_tmpl_str.format(\n",
        "        query_str=query_str,metadata_str=metadata_str\n",
        "    ),\n",
        "    image_documents=image_documents,\n",
        ")"
      ],
      "metadata": {
        "id": "UdzMuacuyWMR"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pprint(result.text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PGjzok8zKDS",
        "outputId": "039f8566-0095-4095-bd9c-1e261e2d359c"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('Linear regression is a statistical method used to model the relationship '\n",
            " 'between a dependent variable and one or more independent variables. The '\n",
            " 'method assumes that the relationship between the variables is linear, '\n",
            " 'meaning that it can be represented by a straight line.\\n'\n",
            " '\\n'\n",
            " 'The equation of a simple linear regression, which involves a single '\n",
            " 'independent variable, is:\\n'\n",
            " '\\n'\n",
            " '\\\\[ y = \\\\beta_0 + \\\\beta_1x + \\\\epsilon \\\\]\\n'\n",
            " '\\n'\n",
            " 'Here, \\\\( y \\\\) is the dependent variable, \\\\( x \\\\) is the independent '\n",
            " 'variable, \\\\( \\\\beta_0 \\\\) is the y-intercept, \\\\( \\\\beta_1 \\\\) is the slope '\n",
            " 'of the line (which represents the effect of the independent variable on the '\n",
            " 'dependent variable), and \\\\( \\\\epsilon \\\\) represents the error term, which '\n",
            " 'accounts for the variability in \\\\( y \\\\) that cannot be explained by \\\\( x '\n",
            " '\\\\).\\n'\n",
            " '\\n'\n",
            " 'In multiple linear regression, where there are multiple independent '\n",
            " 'variables, the equation is extended to:\\n'\n",
            " '\\n'\n",
            " '\\\\[ y = \\\\beta_0 + \\\\beta_1x_1 + \\\\beta_2x_2 + ... + \\\\beta_nx_n + \\\\epsilon '\n",
            " '\\\\]\\n'\n",
            " '\\n'\n",
            " 'Each \\\\( x_i \\\\) represents a different independent variable, and each \\\\( '\n",
            " '\\\\beta_i \\\\) represents the partial effect of that independent variable on '\n",
            " 'the dependent variable \\\\( y \\\\).')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qa_tmpl_str=(\n",
        "    \"Based on the provided information, including relevant images and retrieved context from the video, \\\n",
        "    accurately and precisely answer the query without any additional prior knowledge.\\n\"\n",
        "\n",
        "    \"---------------------\\n\"\n",
        "    \"Metadata for video: {metadata_str} \\n\"\n",
        "\n",
        "    \"---------------------\\n\"\n",
        "    \"Query: {query_str}\\n\"\n",
        "    \"Answer: \"\n",
        ")"
      ],
      "metadata": {
        "id": "t8Y8VyRbzYuB"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X24-KE-UznCS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}