colab_webui.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Build environment 环境配置"
      ],
      "metadata": {
        "id": "kGzjw-j308Ec"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 查看当前使用GPU的CUDA版本\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JD7qLbdAu4Rq",
        "outputId": "69c62dcb-ee1d-4089-d376-db795e1d38ef"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Jun 10 13:03:05 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   62C    P8              12W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Git Clone 克隆代码"
      ],
      "metadata": {
        "id": "449w7IYaAJiu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtztAfrnuxi_",
        "outputId": "f3a28198-20fe-4340-b374-44aeb2abad29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'Linly-Talker'...\n",
            "remote: Enumerating objects: 1431, done.\u001b[K\n",
            "remote: Counting objects: 100% (376/376), done.\u001b[K\n",
            "remote: Compressing objects: 100% (222/222), done.\u001b[K\n",
            "remote: Total 1431 (delta 191), reused 309 (delta 147), pack-reused 1055\u001b[K\n",
            "Receiving objects: 100% (1431/1431), 91.21 MiB | 32.80 MiB/s, done.\n",
            "Resolving deltas: 100% (610/610), done.\n",
            "/content/Linly-Talker\n"
          ]
        }
      ],
      "source": [
        "%cd /content/\n",
        "!git clone https://github.com/Kedreamix/Linly-Talker\n",
        "%cd /content/Linly-Talker/"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show torch"
      ],
      "metadata": {
        "id": "vFfb283CKIES",
        "outputId": "8c77923b-a89b-4788-a13c-f5b1df3839f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: torch\n",
            "Version: 2.3.0+cu121\n",
            "Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n",
            "Home-page: https://pytorch.org/\n",
            "Author: PyTorch Team\n",
            "Author-email: packages@pytorch.org\n",
            "License: BSD-3\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: filelock, fsspec, jinja2, networkx, nvidia-cublas-cu12, nvidia-cuda-cupti-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-runtime-cu12, nvidia-cudnn-cu12, nvidia-cufft-cu12, nvidia-curand-cu12, nvidia-cusolver-cu12, nvidia-cusparse-cu12, nvidia-nccl-cu12, nvidia-nvtx-cu12, sympy, triton, typing-extensions\n",
            "Required-by: fastai, torchaudio, torchtext, torchvision\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 安装torch\n",
        "!pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118\n",
        "\n",
        "# 默认存在ffmpeg\n",
        "# conda install -q ffmpeg # ffmpeg==4.2.2\n",
        "\n",
        "# 安装依赖\n",
        "!pip install -r requirements_app.txt\n",
        "\n",
        "# QWen安装\n",
        "!pip install transformers==4.32.0 accelerate tiktoken einops scipy transformers_stream_generator==0.0.4 peft deepspeed\n",
        "\n",
        "!pip install --upgrade protobuf"
      ],
      "metadata": {
        "id": "cugz7IdSKJk5",
        "outputId": "71a06e74-bd0c-4ca4-b27b-a08be06c270f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://download.pytorch.org/whl/cu118\n",
            "Collecting torch==2.0.1\n",
            "  Downloading https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp310-cp310-linux_x86_64.whl (2267.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 GB\u001b[0m \u001b[31m343.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchvision==0.15.2\n",
            "  Downloading https://download.pytorch.org/whl/cu118/torchvision-0.15.2%2Bcu118-cp310-cp310-linux_x86_64.whl (6.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.1/6.1 MB\u001b[0m \u001b[31m43.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchaudio==2.0.2\n",
            "  Downloading https://download.pytorch.org/whl/cu118/torchaudio-2.0.2%2Bcu118-cp310-cp310-linux_x86_64.whl (4.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.4/4.4 MB\u001b[0m \u001b[31m75.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (3.14.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (4.12.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (1.12.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1) (3.1.4)\n",
            "Collecting triton==2.0.0 (from torch==2.0.1)\n",
            "  Downloading https://download.pytorch.org/whl/triton-2.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (63.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.3/63.3 MB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision==0.15.2) (1.23.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision==0.15.2) (2.32.3)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision==0.15.2) (9.4.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1) (3.27.9)\n",
            "Collecting lit (from triton==2.0.0->torch==2.0.1)\n",
            "  Downloading https://download.pytorch.org/whl/lit-15.0.7.tar.gz (132 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.3/132.3 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.0.1) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.15.2) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.15.2) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.15.2) (1.26.18)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.15.2) (2024.6.2)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.0.1) (1.3.0)\n",
            "Building wheels for collected packages: lit\n",
            "  Building wheel for lit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lit: filename=lit-15.0.7-py3-none-any.whl size=90005 sha256=8d454a9dc3362ec66195014b04608b61c60c8c9e18e0179b460ba7946aef0b5b\n",
            "  Stored in directory: /root/.cache/pip/wheels/27/2c/b6/3ed2983b1b44fe0dea1bb35234b09f2c22fb8ebb308679c922\n",
            "Successfully built lit\n",
            "Installing collected packages: lit, triton, torch, torchvision, torchaudio\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.3.0\n",
            "    Uninstalling triton-2.3.0:\n",
            "      Successfully uninstalled triton-2.3.0\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.3.0+cu121\n",
            "    Uninstalling torch-2.3.0+cu121:\n",
            "      Successfully uninstalled torch-2.3.0+cu121\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.18.0+cu121\n",
            "    Uninstalling torchvision-0.18.0+cu121:\n",
            "      Successfully uninstalled torchvision-0.18.0+cu121\n",
            "  Attempting uninstall: torchaudio\n",
            "    Found existing installation: torchaudio 2.3.0+cu121\n",
            "    Uninstalling torchaudio-2.3.0+cu121:\n",
            "      Successfully uninstalled torchaudio-2.3.0+cu121\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",
            "torchtext 0.18.0 requires torch>=2.3.0, but you have torch 2.0.1+cu118 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed lit-15.0.7 torch-2.0.1+cu118 torchaudio-2.0.2+cu118 torchvision-0.15.2+cu118 triton-2.0.0\n",
            "Requirement already satisfied: numpy==1.23.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 1)) (1.23.4)\n",
            "Requirement already satisfied: face_alignment==1.3.5 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 2)) (1.3.5)\n",
            "Requirement already satisfied: imageio==2.19.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 3)) (2.19.3)\n",
            "Requirement already satisfied: imageio-ffmpeg==0.4.7 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 4)) (0.4.7)\n",
            "Collecting librosa==0.9.2 (from -r requirements_app.txt (line 5))\n",
            "  Using cached librosa-0.9.2-py3-none-any.whl (214 kB)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 6)) (0.56.4)\n",
            "Requirement already satisfied: zhconv in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 7)) (1.4.3)\n",
            "Requirement already satisfied: resampy==0.3.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 8)) (0.3.1)\n",
            "Requirement already satisfied: pydub==0.25.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 9)) (0.25.1)\n",
            "Requirement already satisfied: scipy==1.10.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 10)) (1.10.1)\n",
            "Requirement already satisfied: kornia==0.6.8 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 11)) (0.6.8)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 12)) (4.65.2)\n",
            "Requirement already satisfied: yacs==0.1.8 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 13)) (0.1.8)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 14)) (6.0.1)\n",
            "Requirement already satisfied: joblib==1.1.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 15)) (1.1.0)\n",
            "Requirement already satisfied: facexlib==0.3.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 16)) (0.3.0)\n",
            "Requirement already satisfied: gradio==4.16.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 17)) (4.16.0)\n",
            "Requirement already satisfied: edge-tts>=6.1.9 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 18)) (6.1.12)\n",
            "Requirement already satisfied: openai-whisper in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 19)) (20231117)\n",
            "Requirement already satisfied: scikit-image==0.19.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 20)) (0.19.3)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 21)) (0.31.0)\n",
            "Requirement already satisfied: transformers==4.32.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 22)) (4.32.0)\n",
            "Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 23)) (0.8.0)\n",
            "Requirement already satisfied: transformers_stream_generator==0.0.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 24)) (0.0.4)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 25)) (0.1.99)\n",
            "Requirement already satisfied: google-generativeai in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 26)) (0.5.4)\n",
            "Requirement already satisfied: tiktoken in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 27)) (0.7.0)\n",
            "Collecting protobuf==3.19.6 (from -r requirements_app.txt (line 29))\n",
            "  Using cached protobuf-3.19.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 30)) (1.33.0)\n",
            "Requirement already satisfied: google-api-python-client==2.126.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 31)) (2.126.0)\n",
            "Requirement already satisfied: g4f in /usr/local/lib/python3.10/dist-packages (from -r requirements_app.txt (line 32)) (0.3.2.0)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from face_alignment==1.3.5->-r requirements_app.txt (line 2)) (2.0.1+cu118)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (from face_alignment==1.3.5->-r requirements_app.txt (line 2)) (4.6.0.66)\n",
            "Requirement already satisfied: pillow>=8.3.2 in /usr/local/lib/python3.10/dist-packages (from imageio==2.19.3->-r requirements_app.txt (line 3)) (9.4.0)\n",
            "Requirement already satisfied: audioread>=2.1.9 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2->-r requirements_app.txt (line 5)) (3.0.1)\n",
            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2->-r requirements_app.txt (line 5)) (1.1.3)\n",
            "Requirement already satisfied: decorator>=4.0.10 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2->-r requirements_app.txt (line 5)) (4.4.2)\n",
            "Requirement already satisfied: soundfile>=0.10.2 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2->-r requirements_app.txt (line 5)) (0.12.1)\n",
            "Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2->-r requirements_app.txt (line 5)) (1.8.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2->-r requirements_app.txt (line 5)) (24.1)\n",
            "Requirement already satisfied: filterpy in /usr/local/lib/python3.10/dist-packages (from facexlib==0.3.0->-r requirements_app.txt (line 16)) (1.4.5)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from facexlib==0.3.0->-r requirements_app.txt (line 16)) (0.15.2+cu118)\n",
            "Requirement already satisfied: aiofiles<24.0,>=22.0 in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (23.2.1)\n",
            "Requirement already satisfied: altair<6.0,>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (4.2.2)\n",
            "Requirement already satisfied: fastapi in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (0.110.3)\n",
            "Requirement already satisfied: ffmpy in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (0.3.2)\n",
            "Requirement already satisfied: gradio-client==0.8.1 in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (0.8.1)\n",
            "Requirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (0.27.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (0.23.2)\n",
            "Requirement already satisfied: importlib-resources<7.0,>=1.3 in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (6.4.0)\n",
            "Requirement already satisfied: jinja2<4.0 in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (3.1.4)\n",
            "Requirement already satisfied: markupsafe~=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (2.1.5)\n",
            "Requirement already satisfied: matplotlib~=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (3.8.4)\n",
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            "Requirement already satisfied: typer[all]<1.0,>=0.9 in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (0.9.4)\n",
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            "Requirement already satisfied: uvicorn>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from gradio==4.16.0->-r requirements_app.txt (line 17)) (0.30.1)\n",
            "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from scikit-image==0.19.3->-r requirements_app.txt (line 20)) (3.3)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image==0.19.3->-r requirements_app.txt (line 20)) (2024.5.22)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image==0.19.3->-r requirements_app.txt (line 20)) (1.6.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0->-r requirements_app.txt (line 22)) (3.14.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0->-r requirements_app.txt (line 22)) (2024.5.15)\n",
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            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0->-r requirements_app.txt (line 22)) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0->-r requirements_app.txt (line 22)) (0.4.3)\n",
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            "Requirement already satisfied: google-auth-httplib2<1.0.0,>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (0.2.0)\n",
            "Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (2.11.1)\n",
            "Requirement already satisfied: uritemplate<5,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (4.1.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from gradio-client==0.8.1->gradio==4.16.0->-r requirements_app.txt (line 17)) (2023.6.0)\n",
            "Requirement already satisfied: websockets<12.0,>=10.0 in /usr/local/lib/python3.10/dist-packages (from gradio-client==0.8.1->gradio==4.16.0->-r requirements_app.txt (line 17)) (11.0.3)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->-r requirements_app.txt (line 6)) (0.39.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba->-r requirements_app.txt (line 6)) (60.2.0)\n",
            "Requirement already satisfied: aiohttp>=3.8.0 in /usr/local/lib/python3.10/dist-packages (from edge-tts>=6.1.9->-r requirements_app.txt (line 18)) (3.9.5)\n",
            "Requirement already satisfied: certifi>=2023.11.17 in /usr/local/lib/python3.10/dist-packages (from edge-tts>=6.1.9->-r requirements_app.txt (line 18)) (2024.6.2)\n",
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            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai->-r requirements_app.txt (line 30)) (3.7.1)\n",
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            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.8.0->edge-tts>=6.1.9->-r requirements_app.txt (line 18)) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.8.0->edge-tts>=6.1.9->-r requirements_app.txt (line 18)) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.8.0->edge-tts>=6.1.9->-r requirements_app.txt (line 18)) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.8.0->edge-tts>=6.1.9->-r requirements_app.txt (line 18)) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.8.0->edge-tts>=6.1.9->-r requirements_app.txt (line 18)) (4.0.3)\n",
            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (0.4)\n",
            "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (4.19.2)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (0.12.1)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai->-r requirements_app.txt (line 30)) (3.7)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai->-r requirements_app.txt (line 30)) (1.2.1)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (1.63.1)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (5.3.3)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (0.4.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (4.9)\n",
            "Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in /usr/local/lib/python3.10/dist-packages (from httplib2<1.dev0,>=0.19.0->google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (3.1.2)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->gradio==4.16.0->-r requirements_app.txt (line 17)) (1.0.5)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx->gradio==4.16.0->-r requirements_app.txt (line 17)) (0.14.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (4.53.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (1.4.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0,>=1.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0,>=1.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (2024.1)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.0->librosa==0.9.2->-r requirements_app.txt (line 5)) (4.2.2)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (2.18.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.32.0->-r requirements_app.txt (line 22)) (3.3.2)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.32.0->-r requirements_app.txt (line 22)) (1.26.18)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.19.1->librosa==0.9.2->-r requirements_app.txt (line 5)) (3.5.0)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from soundfile>=0.10.2->librosa==0.9.2->-r requirements_app.txt (line 5)) (1.16.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->face_alignment==1.3.5->-r requirements_app.txt (line 2)) (1.12.1)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton<3,>=2.0.0->openai-whisper->-r requirements_app.txt (line 19)) (3.27.9)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton<3,>=2.0.0->openai-whisper->-r requirements_app.txt (line 19)) (15.0.7)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer[all]<1.0,>=0.9->gradio==4.16.0->-r requirements_app.txt (line 17)) (8.1.7)\n",
            "Requirement already satisfied: colorama<0.5.0,>=0.4.3 in /usr/local/lib/python3.10/dist-packages (from typer[all]<1.0,>=0.9->gradio==4.16.0->-r requirements_app.txt (line 17)) (0.4.6)\n",
            "Requirement already satisfied: shellingham<2.0.0,>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from typer[all]<1.0,>=0.9->gradio==4.16.0->-r requirements_app.txt (line 17)) (1.5.4)\n",
            "Requirement already satisfied: rich<14.0.0,>=10.11.0 in /usr/local/lib/python3.10/dist-packages (from typer[all]<1.0,>=0.9->gradio==4.16.0->-r requirements_app.txt (line 17)) (13.4.2)\n",
            "Requirement already satisfied: starlette<0.38.0,>=0.37.2 in /usr/local/lib/python3.10/dist-packages (from fastapi->gradio==4.16.0->-r requirements_app.txt (line 17)) (0.37.2)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile>=0.10.2->librosa==0.9.2->-r requirements_app.txt (line 5)) (2.22)\n",
            "Requirement already satisfied: grpcio<2.0dev,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (1.64.1)\n",
            "Requirement already satisfied: grpcio-status<2.0.dev0,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (1.48.2)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (0.35.1)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (0.18.1)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client==2.126.0->-r requirements_app.txt (line 31)) (0.6.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio==4.16.0->-r requirements_app.txt (line 17)) (1.16.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio==4.16.0->-r requirements_app.txt (line 17)) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio==4.16.0->-r requirements_app.txt (line 17)) (2.16.1)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->face_alignment==1.3.5->-r requirements_app.txt (line 2)) (1.3.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio==4.16.0->-r requirements_app.txt (line 17)) (0.1.2)\n",
            "Installing collected packages: protobuf, librosa\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 5.27.1\n",
            "    Uninstalling protobuf-5.27.1:\n",
            "      Successfully uninstalled protobuf-5.27.1\n",
            "  Attempting uninstall: librosa\n",
            "    Found existing installation: librosa 0.8.1\n",
            "    Uninstalling librosa-0.8.1:\n",
            "      Successfully uninstalled librosa-0.8.1\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",
            "cudf-cu12 24.4.1 requires numba>=0.57, but you have numba 0.56.4 which is incompatible.\n",
            "cudf-cu12 24.4.1 requires protobuf<5,>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
            "note-seq 0.0.5 requires protobuf>=4.21.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "onnx 1.16.1 requires protobuf>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
            "paddleaudio 1.1.0 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.\n",
            "paddlenlp 2.8.0 requires protobuf>=3.20.2; platform_system != \"Windows\", but you have protobuf 3.19.6 which is incompatible.\n",
            "paddlepaddle 2.5.2 requires protobuf>=3.20.2; platform_system != \"Windows\", but you have protobuf 3.19.6 which is incompatible.\n",
            "paddlespeech 1.4.1 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.\n",
            "tensorboardx 2.6.2.2 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
            "tensorflow 2.15.0 requires numpy<2.0.0,>=1.23.5, but you have numpy 1.23.4 which is incompatible.\n",
            "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.\n",
            "tensorflow-datasets 4.9.5 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
            "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 3.19.6 which is incompatible.\n",
            "visualdl 2.5.3 requires protobuf>=3.20.0, but you have protobuf 3.19.6 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed librosa-0.9.2 protobuf-3.19.6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              },
              "id": "7a9db0a887c94145b88c8aac6da682ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers==4.32.0 in /usr/local/lib/python3.10/dist-packages (4.32.0)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.31.0)\n",
            "Requirement already satisfied: tiktoken in /usr/local/lib/python3.10/dist-packages (0.7.0)\n",
            "Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (0.8.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (1.10.1)\n",
            "Requirement already satisfied: transformers_stream_generator==0.0.4 in /usr/local/lib/python3.10/dist-packages (0.0.4)\n",
            "Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.11.1)\n",
            "Requirement already satisfied: deepspeed in /usr/local/lib/python3.10/dist-packages (0.14.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0) (3.14.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0) (0.23.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0) (1.23.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0) (2.32.3)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.32.0) (4.65.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
            "Requirement already satisfied: hjson in /usr/local/lib/python3.10/dist-packages (from deepspeed) (3.1.0)\n",
            "Requirement already satisfied: ninja in /usr/local/lib/python3.10/dist-packages (from deepspeed) (1.11.1.1)\n",
            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from deepspeed) (9.0.0)\n",
            "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from deepspeed) (2.7.3)\n",
            "Requirement already satisfied: pynvml in /usr/local/lib/python3.10/dist-packages (from deepspeed) (11.5.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers==4.32.0) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers==4.32.0) (4.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.32.0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.32.0) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.32.0) (1.26.18)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.32.0) (2024.6.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.4)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.27.9)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (15.0.7)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic->deepspeed) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic->deepspeed) (2.18.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (3.19.6)\n",
            "Collecting protobuf\n",
            "  Using cached protobuf-5.27.1-cp38-abi3-manylinux2014_x86_64.whl (309 kB)\n",
            "Installing collected packages: protobuf\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.19.6\n",
            "    Uninstalling protobuf-3.19.6:\n",
            "      Successfully uninstalled protobuf-3.19.6\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",
            "cudf-cu12 24.4.1 requires numba>=0.57, but you have numba 0.56.4 which is incompatible.\n",
            "cudf-cu12 24.4.1 requires protobuf<5,>=3.20, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-ai-generativelanguage 0.6.4 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-api-core 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-aiplatform 1.52.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-bigquery-connection 1.12.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.25.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-datastore 2.15.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-firestore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-functions 1.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-iam 2.15.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-language 2.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-resource-manager 1.12.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-translate 3.11.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "grpc-google-iam-v1 0.13.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "paddlespeech 1.4.1 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.\n",
            "proto-plus 1.23.0 requires protobuf<5.0.0dev,>=3.19.0, but you have protobuf 5.27.1 which is incompatible.\n",
            "tensorflow 2.15.0 requires numpy<2.0.0,>=1.23.5, but you have numpy 1.23.4 which is incompatible.\n",
            "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.1 which is incompatible.\n",
            "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.27.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed protobuf-5.27.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 测试pytorch的cuda是否可用\n",
        "import torch\n",
        "torch.cuda.is_available()\n",
        "\n",
        "print(torch.__version__)\n",
        "print(torch.cuda.get_device_name(0))\n",
        "print(torch.cuda.device_count())\n",
        "print(torch.cuda.current_device())\n",
        "print(torch.cuda.is_available())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVPi14TbYAk1",
        "outputId": "cdd4b8ab-a696-45dd-f28e-68c2e9bb93ab"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.0.1+cu118\n",
            "Tesla T4\n",
            "1\n",
            "0\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 若想使用语音克隆请，安装语音克隆对应的依赖\n",
        "\n",
        "# !pip install -r VITS/requirements_gptsovits.txt"
      ],
      "metadata": {
        "id": "5ghozl_z20rI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7be9338e-0695-41bf-a724-93a55dffb788"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: librosa==0.9.2 in /usr/local/lib/python3.10/dist-packages (from -r VITS/requirements_gptsovits.txt (line 1)) (0.9.2)\n",
            "Collecting numba==0.56.4 (from -r VITS/requirements_gptsovits.txt (line 2))\n",
            "  Downloading numba-0.56.4-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (3.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.5/3.5 MB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pytorch-lightning (from -r VITS/requirements_gptsovits.txt (line 3))\n",
            "  Downloading pytorch_lightning-2.2.5-py3-none-any.whl (802 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m802.3/802.3 kB\u001b[0m \u001b[31m40.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ffmpeg-python (from -r VITS/requirements_gptsovits.txt (line 4))\n",
            "  Downloading ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)\n",
            "Collecting onnxruntime (from -r VITS/requirements_gptsovits.txt (line 5))\n",
            "  Downloading onnxruntime-1.18.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (6.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m56.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from -r VITS/requirements_gptsovits.txt (line 6)) (4.66.4)\n",
            "Collecting funasr>=1.0.0 (from -r VITS/requirements_gptsovits.txt (line 7))\n",
            "  Downloading funasr-1.0.27-py3-none-any.whl (693 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m693.7/693.7 kB\u001b[0m \u001b[31m48.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting cn2an (from -r VITS/requirements_gptsovits.txt (line 8))\n",
            "  Downloading cn2an-0.5.22-py3-none-any.whl (224 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.0/225.0 kB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pypinyin (from -r VITS/requirements_gptsovits.txt (line 9))\n",
            "  Downloading pypinyin-0.51.0-py2.py3-none-any.whl (1.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m65.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pyopenjtalk (from -r VITS/requirements_gptsovits.txt (line 10))\n",
            "  Downloading pyopenjtalk-0.3.3.tar.gz (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m67.5 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",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting g2p_en (from -r VITS/requirements_gptsovits.txt (line 11))\n",
            "  Downloading g2p_en-2.1.0-py3-none-any.whl (3.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m94.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting modelscope==1.10.0 (from -r VITS/requirements_gptsovits.txt (line 12))\n",
            "  Downloading modelscope-1.10.0-py3-none-any.whl (5.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.4/5.4 MB\u001b[0m \u001b[31m80.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from -r VITS/requirements_gptsovits.txt (line 13)) (0.1.99)\n",
            "Requirement already satisfied: chardet in /usr/local/lib/python3.10/dist-packages (from -r VITS/requirements_gptsovits.txt (line 14)) (5.2.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from -r VITS/requirements_gptsovits.txt (line 15)) (6.0.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from -r VITS/requirements_gptsovits.txt (line 16)) (5.9.5)\n",
            "Collecting jieba_fast (from -r VITS/requirements_gptsovits.txt (line 17))\n",
            "  Downloading jieba_fast-0.53.tar.gz (7.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m69.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: jieba in /usr/local/lib/python3.10/dist-packages (from -r VITS/requirements_gptsovits.txt (line 18)) (0.42.1)\n",
            "Collecting LangSegment (from -r VITS/requirements_gptsovits.txt (line 19))\n",
            "  Downloading LangSegment-0.3.3-py3-none-any.whl (21 kB)\n",
            "Requirement already satisfied: audioread>=2.1.9 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2->-r VITS/requirements_gptsovits.txt (line 1)) (3.0.1)\n",
            "Requirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2->-r VITS/requirements_gptsovits.txt (line 1)) (1.23.4)\n",
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            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.10/dist-packages (from librosa==0.9.2->-r VITS/requirements_gptsovits.txt (line 1)) (1.1.3)\n",
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            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->pytorch-lightning->-r VITS/requirements_gptsovits.txt (line 3)) (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>=1.13.0->pytorch-lightning->-r VITS/requirements_gptsovits.txt (line 3)) (12.1.3.1)\n",
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            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->pytorch-lightning->-r VITS/requirements_gptsovits.txt (line 3)) (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>=1.13.0->pytorch-lightning->-r VITS/requirements_gptsovits.txt (line 3)) (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>=1.13.0->pytorch-lightning->-r VITS/requirements_gptsovits.txt (line 3)) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->pytorch-lightning->-r VITS/requirements_gptsovits.txt (line 3)) (2.20.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->pytorch-lightning->-r VITS/requirements_gptsovits.txt (line 3)) (12.1.105)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->pytorch-lightning->-r VITS/requirements_gptsovits.txt (line 3)) (2.3.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.13.0->pytorch-lightning->-r VITS/requirements_gptsovits.txt (line 3)) (12.5.40)\n",
            "Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime->-r VITS/requirements_gptsovits.txt (line 5))\n",
            "  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from openai-whisper->funasr>=1.0.0->-r VITS/requirements_gptsovits.txt (line 7)) (10.1.0)\n",
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            "Collecting crcmod>=1.7 (from oss2->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12))\n",
            "  Downloading crcmod-1.7.tar.gz (89 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pycryptodome>=3.4.7 in /usr/local/lib/python3.10/dist-packages (from oss2->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (3.20.0)\n",
            "Collecting aliyun-python-sdk-kms>=2.4.1 (from oss2->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12))\n",
            "  Downloading aliyun_python_sdk_kms-2.16.3-py2.py3-none-any.whl (98 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.1/98.1 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting aliyun-python-sdk-core>=2.13.12 (from oss2->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12))\n",
            "  Downloading aliyun-python-sdk-core-2.15.1.tar.gz (443 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m443.1/443.1 kB\u001b[0m \u001b[31m39.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (2024.1)\n",
            "Collecting beartype (from rotary-embedding-torch->funasr>=1.0.0->-r VITS/requirements_gptsovits.txt (line 7))\n",
            "  Downloading beartype-0.18.5-py3-none-any.whl (917 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m917.8/917.8 kB\u001b[0m \u001b[31m72.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime->-r VITS/requirements_gptsovits.txt (line 5)) (1.3.0)\n",
            "Collecting pynndescent>=0.5 (from umap-learn->funasr>=1.0.0->-r VITS/requirements_gptsovits.txt (line 7))\n",
            "  Downloading pynndescent-0.5.12-py3-none-any.whl (56 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.8/56.8 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata>=6.6.0 in /usr/local/lib/python3.10/dist-packages (from yapf->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (7.1.0)\n",
            "Requirement already satisfied: tomli>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from yapf->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (2.0.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.14.5->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (1.3.1)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.14.5->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.14.5->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.14.5->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.14.5->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (4.0.3)\n",
            "Collecting jmespath<1.0.0,>=0.9.3 (from aliyun-python-sdk-core>=2.13.12->oss2->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12))\n",
            "  Downloading jmespath-0.10.0-py2.py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: cryptography>=2.6.0 in /usr/local/lib/python3.10/dist-packages (from aliyun-python-sdk-core>=2.13.12->oss2->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (42.0.7)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile>=0.10.2->librosa==0.9.2->-r VITS/requirements_gptsovits.txt (line 1)) (2.22)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=6.6.0->yapf->modelscope==1.10.0->-r VITS/requirements_gptsovits.txt (line 12)) (3.19.1)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=1.9.1->inflect>=0.3.1->g2p_en->-r VITS/requirements_gptsovits.txt (line 11)) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic>=1.9.1->inflect>=0.3.1->g2p_en->-r VITS/requirements_gptsovits.txt (line 11)) (2.18.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->pytorch-lightning->-r VITS/requirements_gptsovits.txt (line 3)) (2.1.5)\n",
            "Building wheels for collected packages: pyopenjtalk, jieba_fast, distance, antlr4-python3-runtime, jaconv, oss2, aliyun-python-sdk-core, crcmod\n",
            "  Building wheel for pyopenjtalk (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyopenjtalk: filename=pyopenjtalk-0.3.3-cp310-cp310-linux_x86_64.whl size=5354578 sha256=ae2eaf820e39cefa31f249f2a6eaa7b2125a72329b03431a6647337e4543fba6\n",
            "  Stored in directory: /root/.cache/pip/wheels/64/a9/5f/19eedcf7741bcd1494dc8782b6842e433314793492cc9167c9\n",
            "  Building wheel for jieba_fast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for jieba_fast: filename=jieba_fast-0.53-cp310-cp310-linux_x86_64.whl size=7658020 sha256=568e2ef9cd28c8e7ac2c126e817fc407a0f7f2d98736b6fae7f2000f22ab28f3\n",
            "  Stored in directory: /root/.cache/pip/wheels/90/bc/2e/ec85cb24f946d722725b17fc7aa1afca0c2dcfd727d540a06a\n",
            "  Building wheel for distance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for distance: filename=Distance-0.1.3-py3-none-any.whl size=16258 sha256=dc51396627d657a02b9a70b6f9fe61a17885f44ed3bc2a0b00e83dc0a86be638\n",
            "  Stored in directory: /root/.cache/pip/wheels/e8/bb/de/f71bf63559ea9a921059a5405806f7ff6ed612a9231c4a9309\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=70f0e91c9f37aceef03bdd81f2f49bd7ffa54a77f5065aebf40d758fe682e5e6\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
            "  Building wheel for jaconv (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for jaconv: filename=jaconv-0.3.4-py3-none-any.whl size=16416 sha256=71267f48bf3eeedf9fa91087270fd7f2c13d905367a9125e1e7ebe1422661237\n",
            "  Stored in directory: /root/.cache/pip/wheels/46/8f/2e/a730bf1fca05b33e532d5d91dabdf406c9b718ec85b01b1b54\n",
            "  Building wheel for oss2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for oss2: filename=oss2-2.18.5-py3-none-any.whl size=118148 sha256=5b5492bc71f4d7d0250cfa6087f78076d450bafc0e3b649d2036b4410ca56bdf\n",
            "  Stored in directory: /root/.cache/pip/wheels/c5/ec/94/a908b823ad209d91fb3cb809c0553032e496dd3d36218e4596\n",
            "  Building wheel for aliyun-python-sdk-core (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for aliyun-python-sdk-core: filename=aliyun_python_sdk_core-2.15.1-py3-none-any.whl size=535325 sha256=6aa78cea23113152ff9cd2f686cae2ad89f15a83cc43714841e995fe24ef5dab\n",
            "  Stored in directory: /root/.cache/pip/wheels/69/4b/8e/0a28e00f4cf43b273c18cce083804738d41013e017da922ce4\n",
            "  Building wheel for crcmod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for crcmod: filename=crcmod-1.7-cp310-cp310-linux_x86_64.whl size=31411 sha256=31db22667aadf26b8cc0687abde2653c228b359de0f2cfa77c75bb8f845a66bc\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/4c/07/72215c529bd59d67e3dac29711d7aba1b692f543c808ba9e86\n",
            "Successfully built pyopenjtalk jieba_fast distance antlr4-python3-runtime jaconv oss2 aliyun-python-sdk-core crcmod\n",
            "Installing collected packages: jieba_fast, jamo, jaconv, distance, crcmod, antlr4-python3-runtime, addict, xxhash, torch-complex, tensorboardX, simplejson, requests, pytorch-wpe, pypinyin, pyopenjtalk, py3langid, proces, omegaconf, llvmlite, lightning-utilities, kaldiio, jmespath, humanfriendly, ffmpeg-python, dill, beartype, yapf, numba, multiprocess, LangSegment, hydra-core, coloredlogs, cn2an, pynndescent, onnxruntime, aliyun-python-sdk-core, umap-learn, torchmetrics, rotary-embedding-torch, g2p_en, datasets, aliyun-python-sdk-kms, pytorch-lightning, oss2, modelscope, funasr\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: llvmlite\n",
            "    Found existing installation: llvmlite 0.41.1\n",
            "    Uninstalling llvmlite-0.41.1:\n",
            "      Successfully uninstalled llvmlite-0.41.1\n",
            "  Attempting uninstall: numba\n",
            "    Found existing installation: numba 0.58.1\n",
            "    Uninstalling numba-0.58.1:\n",
            "      Successfully uninstalled numba-0.58.1\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",
            "bigframes 1.8.0 requires scikit-learn>=1.2.2, but you have scikit-learn 1.1.3 which is incompatible.\n",
            "cudf-cu12 24.4.1 requires numba>=0.57, but you have numba 0.56.4 which is incompatible.\n",
            "cudf-cu12 24.4.1 requires protobuf<5,>=3.20, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-api-core 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-aiplatform 1.52.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-bigquery-connection 1.12.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.25.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-datastore 2.15.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-firestore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-functions 1.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-iam 2.15.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-language 2.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-resource-manager 1.12.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-translate 3.11.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "rmm-cu12 24.4.0 requires numba>=0.57, but you have numba 0.56.4 which is incompatible.\n",
            "tensorflow 2.15.0 requires numpy<2.0.0,>=1.23.5, but you have numpy 1.23.4 which is incompatible.\n",
            "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed LangSegment-0.3.3 addict-2.4.0 aliyun-python-sdk-core-2.15.1 aliyun-python-sdk-kms-2.16.3 antlr4-python3-runtime-4.9.3 beartype-0.18.5 cn2an-0.5.22 coloredlogs-15.0.1 crcmod-1.7 datasets-2.19.2 dill-0.3.8 distance-0.1.3 ffmpeg-python-0.2.0 funasr-1.0.27 g2p_en-2.1.0 humanfriendly-10.0 hydra-core-1.3.2 jaconv-0.3.4 jamo-0.4.1 jieba_fast-0.53 jmespath-0.10.0 kaldiio-2.18.0 lightning-utilities-0.11.2 llvmlite-0.39.1 modelscope-1.10.0 multiprocess-0.70.16 numba-0.56.4 omegaconf-2.3.0 onnxruntime-1.18.0 oss2-2.18.5 proces-0.1.7 py3langid-0.2.2 pynndescent-0.5.12 pyopenjtalk-0.3.3 pypinyin-0.51.0 pytorch-lightning-2.2.5 pytorch-wpe-0.0.1 requests-2.32.3 rotary-embedding-torch-0.6.2 simplejson-3.19.2 tensorboardX-2.6.2.2 torch-complex-0.4.3 torchmetrics-1.4.0.post0 umap-learn-0.5.6 xxhash-3.4.1 yapf-0.40.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pydevd_plugins"
                ]
              },
              "id": "354d1befcb9d4d89adef5d335fbdf02d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 若希望使用NeRF-based等模型等话，可能需要安装一下对应的环境\n",
        "\n",
        "# pip install \"git+https://github.com/facebookresearch/pytorch3d.git\"\n",
        "# pip install -r TFG/requirements_nerf.txt\n",
        "\n",
        "# 若pyaudio出现问题，可安装对应依赖\n",
        "# sudo apt-get install libasound-dev portaudio19-dev libportaudio2 libportaudiocpp0\n",
        "\n",
        "# 注意以下几个模块，若安装不成功，可以进入路径利用pip install . 或者 python setup.py install编译安装\n",
        "# NeRF/freqencoder\n",
        "# NeRF/gridencoder\n",
        "# NeRF/raymarching\n",
        "# NeRF/shencoder"
      ],
      "metadata": {
        "id": "-PXWYeLhKrXF"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 若使用PaddleTTS，可安装对应的环境\n",
        "# !pip install -r TTS/requirements_paddle.txt"
      ],
      "metadata": {
        "id": "1ikKSSqtKmZZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d781c703-2c54-4405-e7b0-f3ae8ae63b8c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting paddlepaddle==2.5.2 (from -r TTS/requirements_paddle.txt (line 1))\n",
            "  Downloading paddlepaddle-2.5.2-cp310-cp310-manylinux1_x86_64.whl (126.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m126.5/126.5 MB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting paddlespeech==1.4.1 (from -r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading paddlespeech-1.4.1-py3-none-any.whl (1.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m66.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting opencc==1.1.1 (from -r TTS/requirements_paddle.txt (line 3))\n",
            "  Downloading OpenCC-1.1.1-py2.py3-none-manylinux1_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m66.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting matplotlib==3.8.4 (from -r TTS/requirements_paddle.txt (line 4))\n",
            "  Downloading matplotlib-3.8.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.6/11.6 MB\u001b[0m \u001b[31m69.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from paddlepaddle==2.5.2->-r TTS/requirements_paddle.txt (line 1)) (0.27.0)\n",
            "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle==2.5.2->-r TTS/requirements_paddle.txt (line 1)) (1.23.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from paddlepaddle==2.5.2->-r TTS/requirements_paddle.txt (line 1)) (9.4.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from paddlepaddle==2.5.2->-r TTS/requirements_paddle.txt (line 1)) (4.4.2)\n",
            "Collecting astor (from paddlepaddle==2.5.2->-r TTS/requirements_paddle.txt (line 1))\n",
            "  Downloading astor-0.8.1-py2.py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: opt-einsum==3.3.0 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle==2.5.2->-r TTS/requirements_paddle.txt (line 1)) (3.3.0)\n",
            "Requirement already satisfied: protobuf>=3.20.2 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle==2.5.2->-r TTS/requirements_paddle.txt (line 1)) (5.27.1)\n",
            "Collecting braceexpand (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading braceexpand-0.1.7-py2.py3-none-any.whl (5.9 kB)\n",
            "Requirement already satisfied: editdistance in /usr/local/lib/python3.10/dist-packages (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (0.6.2)\n",
            "Requirement already satisfied: g2p-en in /usr/local/lib/python3.10/dist-packages (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (2.1.0)\n",
            "Collecting g2pM (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading g2pM-0.1.2.5-py3-none-any.whl (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m77.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (3.9.0)\n",
            "Collecting hyperpyyaml (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading HyperPyYAML-1.2.2-py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: inflect in /usr/local/lib/python3.10/dist-packages (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (7.0.0)\n",
            "Collecting jsonlines (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading jsonlines-4.0.0-py3-none-any.whl (8.7 kB)\n",
            "Collecting librosa==0.8.1 (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading librosa-0.8.1-py3-none-any.whl (203 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m203.8/203.8 kB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting loguru (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading loguru-0.7.2-py3-none-any.whl (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nara-wpe (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading nara_wpe-0.0.9-py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: onnxruntime>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (1.18.0)\n",
            "Collecting opencc-python-reimplemented (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading opencc_python_reimplemented-0.1.7-py2.py3-none-any.whl (481 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m481.8/481.8 kB\u001b[0m \u001b[31m49.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (2.0.3)\n",
            "Collecting paddleaudio>=1.1.0 (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading paddleaudio-1.1.0-cp310-cp310-manylinux1_x86_64.whl (1.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m65.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting paddlenlp>=2.4.8 (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading paddlenlp-2.8.0-py3-none-any.whl (2.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m88.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting paddleslim>=2.3.4 (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading paddleslim-2.6.0-py3-none-any.whl (401 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.6/401.6 kB\u001b[0m \u001b[31m42.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ppdiffusers>=0.9.0 (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading ppdiffusers-0.24.0-py3-none-any.whl (2.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7 MB\u001b[0m \u001b[31m83.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting paddlespeech-feat (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading paddlespeech_feat-0.1.0-py3-none-any.whl (12 kB)\n",
            "Collecting praatio<=5.1.1,>=5.0.0 (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading praatio-5.1.1-py3-none-any.whl (77 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.5/77.5 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: prettytable in /usr/local/lib/python3.10/dist-packages (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (3.10.0)\n",
            "Collecting pypinyin<=0.44.0 (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading pypinyin-0.44.0-py2.py3-none-any.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m72.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pypinyin-dict (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading pypinyin_dict-0.8.0-py2.py3-none-any.whl (9.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.5/9.5 MB\u001b[0m \u001b[31m74.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (2.8.2)\n",
            "Collecting pyworld>=0.2.12 (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading pyworld-0.3.4.tar.gz (251 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m252.0/252.0 kB\u001b[0m \u001b[31m31.9 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",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (6.0.1)\n",
            "Requirement already satisfied: resampy in /usr/local/lib/python3.10/dist-packages (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (0.3.1)\n",
            "Collecting sacrebleu (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading sacrebleu-2.4.2-py3-none-any.whl (106 kB)\n",
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            "\u001b[?25hCollecting textgrid (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
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            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting timer (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading timer-0.3.0-py3-none-any.whl (8.0 kB)\n",
            "Collecting ToJyutping (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
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            "\u001b[?25hCollecting typeguard==2.13.3 (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
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            "Collecting webrtcvad (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading webrtcvad-2.0.10.tar.gz (66 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting zhon (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
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            "\u001b[?25hCollecting pattern-singleton (from paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
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            "Collecting pathos (from paddleaudio>=1.1.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hCollecting opencv-python==4.6.0.66 (from paddleslim>=2.3.4->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
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            "Collecting bce-python-sdk (from aistudio-sdk>=0.1.3->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
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            "\u001b[?25hINFO: pip is looking at multiple versions of pathos to determine which version is compatible with other requirements. This could take a while.\n",
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            "\u001b[?25h  Downloading pathos-0.2.9-py3-none-any.whl (76 kB)\n",
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            "\u001b[?25h  Downloading pathos-0.2.8-py2.py3-none-any.whl (81 kB)\n",
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            "\u001b[?25hCollecting pox>=0.3.0 (from pathos->paddleaudio>=1.1.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading pox-0.3.4-py3-none-any.whl (29 kB)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (2.16.1)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime>=1.11.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (1.3.0)\n",
            "Collecting rarfile (from visualdl->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading rarfile-4.2-py3-none-any.whl (29 kB)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from visualdl->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (5.9.5)\n",
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            "Requirement already satisfied: xyzservices>=2021.09.1 in /usr/local/lib/python3.10/dist-packages (from bokeh>=0.12.0->note-seq->ppdiffusers>=0.9.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (2024.4.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile>=0.10.2->librosa==0.8.1->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (2.22)\n",
            "Requirement already satisfied: Werkzeug>=2.2.2 in /usr/local/lib/python3.10/dist-packages (from Flask>=2.0->Flask-Babel->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (3.0.3)\n",
            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from Flask>=2.0->Flask-Babel->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (2.2.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (1.3.1)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (6.0.5)\n",
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            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (4.0.3)\n",
            "Requirement already satisfied: sortedcontainers<3.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from intervaltree>=2.1.0->note-seq->ppdiffusers>=0.9.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (2.4.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (0.1.2)\n",
            "Collecting mido>=1.1.16 (from pretty-midi>=0.2.6->note-seq->ppdiffusers>=0.9.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading mido-1.3.2-py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.6/54.6 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->aistudio-sdk>=0.1.3->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (3.3.2)\n",
            "Requirement already satisfied: pycryptodome>=3.8.0 in /usr/local/lib/python3.10/dist-packages (from bce-python-sdk->aistudio-sdk>=0.1.3->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (3.20.0)\n",
            "Requirement already satisfied: future>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from bce-python-sdk->aistudio-sdk>=0.1.3->paddlenlp>=2.4.8->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (0.18.3)\n",
            "Collecting jedi>=0.16 (from IPython->note-seq->ppdiffusers>=0.9.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2))\n",
            "  Downloading jedi-0.19.1-py2.py3-none-any.whl (1.6 MB)\n",
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            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from IPython->note-seq->ppdiffusers>=0.9.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (5.7.1)\n",
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            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from IPython->note-seq->ppdiffusers>=0.9.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from IPython->note-seq->ppdiffusers>=0.9.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (0.1.7)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from IPython->note-seq->ppdiffusers>=0.9.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (4.9.0)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->IPython->note-seq->ppdiffusers>=0.9.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (0.8.4)\n",
            "Collecting packaging>=20.0 (from matplotlib==3.8.4->-r TTS/requirements_paddle.txt (line 4))\n",
            "  Downloading packaging-23.2-py3-none-any.whl (53 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->IPython->note-seq->ppdiffusers>=0.9.0->paddlespeech==1.4.1->-r TTS/requirements_paddle.txt (line 2)) (0.7.0)\n",
            "Building wheels for collected packages: pyworld, pattern-singleton, textgrid, webrtcvad, ligo-segments, seqeval, intervaltree, pretty-midi\n",
            "  Building wheel for pyworld (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyworld: filename=pyworld-0.3.4-cp310-cp310-linux_x86_64.whl size=865328 sha256=dd6e729332d1d0bed926cd4f6d148e2a0e6cc93e83df663943f75374ec3b3193\n",
            "  Stored in directory: /root/.cache/pip/wheels/66/09/8a/a1d79b73d59756f66e9bfe55a199840efc7473adb76ddacdfd\n",
            "  Building wheel for pattern-singleton (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pattern-singleton: filename=pattern_singleton-1.2.0-py2.py3-none-any.whl size=4209 sha256=854369448824b19aa77c2a6dec969f8550428b0cab283e79440e6251b3e5f0d0\n",
            "  Stored in directory: /root/.cache/pip/wheels/23/be/db/6448f20352ff5bffd825add0eb58ca27c143393b9b4414a4ae\n",
            "  Building wheel for textgrid (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for textgrid: filename=TextGrid-1.6.1-py3-none-any.whl size=10147 sha256=8fc9b635ad275a0eb972f86914c7634d9bde159ce4128d5745928d7dd23f1bf3\n",
            "  Stored in directory: /root/.cache/pip/wheels/23/41/f2/e2ef1817bd163de3c21dd078966bdd71bd5c4455841f4ec016\n",
            "  Building wheel for webrtcvad (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for webrtcvad: filename=webrtcvad-2.0.10-cp310-cp310-linux_x86_64.whl size=73470 sha256=e8b009f450f3d6b261b921eb93ca481df638b44b860aa7bf3d485707072f3d0a\n",
            "  Stored in directory: /root/.cache/pip/wheels/2a/2b/84/ac7bacfe8c68a87c1ee3dd3c66818a54c71599abf308e8eb35\n",
            "  Building wheel for ligo-segments (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ligo-segments: filename=ligo_segments-1.4.0-cp310-cp310-linux_x86_64.whl size=99255 sha256=ef12b75d91f49558c5784d7e6bd2d6bf831b8a1bbb508c27358b6476e586c196\n",
            "  Stored in directory: /root/.cache/pip/wheels/6d/48/d1/3466977be4e41ba57f92ad0d5619f083df43cf319a151c4e06\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16161 sha256=48471c08abc7e07f03f80c312e6e586984bd77b1d04aa5a2a871b199b9ddd83f\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
            "  Building wheel for intervaltree (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for intervaltree: filename=intervaltree-3.1.0-py2.py3-none-any.whl size=26096 sha256=3f441ed5fbe0e91b1f5211fef641ebb94a11fda2a677cebf05eacb514b26b59c\n",
            "  Stored in directory: /root/.cache/pip/wheels/fa/80/8c/43488a924a046b733b64de3fac99252674c892a4c3801c0a61\n",
            "  Building wheel for pretty-midi (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pretty-midi: filename=pretty_midi-0.2.10-py3-none-any.whl size=5592289 sha256=c8ff022bddbeb54cfbfa222c274227b31a6fbf36d4561a684a84b58357600f8f\n",
            "  Stored in directory: /root/.cache/pip/wheels/cd/a5/30/7b8b7f58709f5150f67f98fde4b891ebf0be9ef07a8af49f25\n",
            "Successfully built pyworld pattern-singleton textgrid webrtcvad ligo-segments seqeval intervaltree pretty-midi\n",
            "Installing collected packages: webrtcvad, trampoline, timer, textgrid, swig, pygtrie, opencc-python-reimplemented, opencc, braceexpand, zhon, urllib3, typeguard, ToJyutping, ruamel.yaml.clib, rarfile, pyworld, pypinyin, pybind11, praatio, ppft, pox, portalocker, pattern-singleton, parameterized, packaging, opencv-python, onnx, mock, loguru, ligo-segments, jsonlines, jedi, intervaltree, g2pM, ftfy, dill, colorlog, bottleneck, boltons, bce-python-sdk, av, astor, tool-helpers, sacrebleu, ruamel.yaml, pypinyin-dict, paddlespeech-feat, paddlesde, multiprocess, mido, matplotlib, seqeval, requests-mock, pretty-midi, pathos, paddleslim, paddlepaddle, paddlefsl, paddle2onnx, nara-wpe, hyperpyyaml, Flask-Babel, aistudio-sdk, visualdl, librosa, paddlenlp, paddleaudio, note-seq, ppdiffusers, paddlespeech\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.0.7\n",
            "    Uninstalling urllib3-2.0.7:\n",
            "      Successfully uninstalled urllib3-2.0.7\n",
            "  Attempting uninstall: pypinyin\n",
            "    Found existing installation: pypinyin 0.51.0\n",
            "    Uninstalling pypinyin-0.51.0:\n",
            "      Successfully uninstalled pypinyin-0.51.0\n",
            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 24.0\n",
            "    Uninstalling packaging-24.0:\n",
            "      Successfully uninstalled packaging-24.0\n",
            "  Attempting uninstall: opencv-python\n",
            "    Found existing installation: opencv-python 4.8.0.76\n",
            "    Uninstalling opencv-python-4.8.0.76:\n",
            "      Successfully uninstalled opencv-python-4.8.0.76\n",
            "  Attempting uninstall: dill\n",
            "    Found existing installation: dill 0.3.8\n",
            "    Uninstalling dill-0.3.8:\n",
            "      Successfully uninstalled dill-0.3.8\n",
            "  Attempting uninstall: multiprocess\n",
            "    Found existing installation: multiprocess 0.70.16\n",
            "    Uninstalling multiprocess-0.70.16:\n",
            "      Successfully uninstalled multiprocess-0.70.16\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.7.1\n",
            "    Uninstalling matplotlib-3.7.1:\n",
            "      Successfully uninstalled matplotlib-3.7.1\n",
            "  Attempting uninstall: librosa\n",
            "    Found existing installation: librosa 0.9.2\n",
            "    Uninstalling librosa-0.9.2:\n",
            "      Successfully uninstalled librosa-0.9.2\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",
            "bigframes 1.8.0 requires scikit-learn>=1.2.2, but you have scikit-learn 1.1.3 which is incompatible.\n",
            "cudf-cu12 24.4.1 requires numba>=0.57, but you have numba 0.56.4 which is incompatible.\n",
            "cudf-cu12 24.4.1 requires protobuf<5,>=3.20, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-aiplatform 1.52.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "tensorflow 2.15.0 requires numpy<2.0.0,>=1.23.5, but you have numpy 1.23.4 which is incompatible.\n",
            "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Flask-Babel-4.0.0 ToJyutping-0.2.3 aistudio-sdk-0.2.4 astor-0.8.1 av-12.1.0 bce-python-sdk-0.9.14 boltons-24.0.0 bottleneck-1.3.8 braceexpand-0.1.7 colorlog-6.8.2 dill-0.3.4 ftfy-6.2.0 g2pM-0.1.2.5 hyperpyyaml-1.2.2 intervaltree-3.1.0 jedi-0.19.1 jsonlines-4.0.0 librosa-0.8.1 ligo-segments-1.4.0 loguru-0.7.2 matplotlib-3.8.4 mido-1.3.2 mock-5.1.0 multiprocess-0.70.12.2 nara-wpe-0.0.9 note-seq-0.0.5 onnx-1.16.1 opencc-1.1.1 opencc-python-reimplemented-0.1.7 opencv-python-4.6.0.66 packaging-23.2 paddle2onnx-1.2.3 paddleaudio-1.1.0 paddlefsl-1.1.0 paddlenlp-2.8.0 paddlepaddle-2.5.2 paddlesde-0.2.5 paddleslim-2.6.0 paddlespeech-1.4.1 paddlespeech-feat-0.1.0 parameterized-0.9.0 pathos-0.2.8 pattern-singleton-1.2.0 portalocker-2.8.2 pox-0.3.4 ppdiffusers-0.24.0 ppft-1.7.6.8 praatio-5.1.1 pretty-midi-0.2.10 pybind11-2.12.0 pygtrie-2.5.0 pypinyin-0.44.0 pypinyin-dict-0.8.0 pyworld-0.3.4 rarfile-4.2 requests-mock-1.12.1 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 sacrebleu-2.4.2 seqeval-1.2.2 swig-4.2.1 textgrid-1.6.1 timer-0.3.0 tool-helpers-0.1.1 trampoline-0.1.2 typeguard-2.13.3 urllib3-1.26.18 visualdl-2.5.3 webrtcvad-2.0.10 zhon-2.0.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              },
              "id": "37a638fa7f9641b699f5154ec255fa55"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 若使用FunASR，可安装对应的环境\n",
        "# !pip install -r ASR/requirements_funasr.txt"
      ],
      "metadata": {
        "id": "SgHZzd9uKpJP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64ccb2d3-097c-4326-ce32-5cd7a2e8911b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: funasr in /usr/local/lib/python3.10/dist-packages (from -r ASR/requirements_funasr.txt (line 1)) (1.0.27)\n",
            "Requirement already satisfied: modelscope in /usr/local/lib/python3.10/dist-packages (from -r ASR/requirements_funasr.txt (line 2)) (1.10.0)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (1.10.1)\n",
            "Requirement already satisfied: librosa in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (0.8.1)\n",
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            "Requirement already satisfied: PyYAML>=5.1.2 in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (6.0.1)\n",
            "Requirement already satisfied: soundfile>=0.12.1 in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (0.12.1)\n",
            "Requirement already satisfied: kaldiio>=2.17.0 in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (2.18.0)\n",
            "Requirement already satisfied: torch-complex in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (0.4.3)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (0.1.99)\n",
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            "Requirement already satisfied: rotary-embedding-torch in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (0.6.2)\n",
            "Requirement already satisfied: pytorch-wpe in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (0.0.1)\n",
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            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (4.65.2)\n",
            "Requirement already satisfied: umap-learn in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (0.5.6)\n",
            "Requirement already satisfied: jaconv in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (0.3.4)\n",
            "Requirement already satisfied: hydra-core>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (1.3.2)\n",
            "Requirement already satisfied: tensorboardX in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (2.6.2.2)\n",
            "Requirement already satisfied: openai-whisper in /usr/local/lib/python3.10/dist-packages (from funasr->-r ASR/requirements_funasr.txt (line 1)) (20231117)\n",
            "Requirement already satisfied: addict in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (2.4.0)\n",
            "Requirement already satisfied: attrs in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (23.2.0)\n",
            "Requirement already satisfied: datasets>=2.14.5 in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (2.19.2)\n",
            "Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (0.8.0)\n",
            "Requirement already satisfied: filelock>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (3.14.0)\n",
            "Requirement already satisfied: gast>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (0.5.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (1.23.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (2.0.3)\n",
            "Requirement already satisfied: Pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (9.4.0)\n",
            "Requirement already satisfied: pyarrow!=9.0.0,>=6.0.0 in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (14.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (2.8.2)\n",
            "Requirement already satisfied: requests>=2.25 in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (2.28.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (60.2.0)\n",
            "Requirement already satisfied: simplejson>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (3.19.2)\n",
            "Requirement already satisfied: sortedcontainers>=1.5.9 in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (2.4.0)\n",
            "Requirement already satisfied: urllib3>=1.26 in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (1.26.18)\n",
            "Requirement already satisfied: yapf in /usr/local/lib/python3.10/dist-packages (from modelscope->-r ASR/requirements_funasr.txt (line 2)) (0.40.2)\n",
            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (0.6)\n",
            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (0.3.4)\n",
            "Collecting requests>=2.25 (from modelscope->-r ASR/requirements_funasr.txt (line 2))\n",
            "  Using cached requests-2.32.3-py3-none-any.whl (64 kB)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (0.70.12.2)\n",
            "Requirement already satisfied: fsspec[http]<=2024.3.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (3.9.5)\n",
            "Requirement already satisfied: huggingface-hub>=0.21.2 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (0.23.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (24.1)\n",
            "Requirement already satisfied: omegaconf<2.4,>=2.2 in /usr/local/lib/python3.10/dist-packages (from hydra-core>=1.3.2->funasr->-r ASR/requirements_funasr.txt (line 1)) (2.3.0)\n",
            "Requirement already satisfied: antlr4-python3-runtime==4.9.* in /usr/local/lib/python3.10/dist-packages (from hydra-core>=1.3.2->funasr->-r ASR/requirements_funasr.txt (line 1)) (4.9.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.1->modelscope->-r ASR/requirements_funasr.txt (line 2)) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25->modelscope->-r ASR/requirements_funasr.txt (line 2)) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25->modelscope->-r ASR/requirements_funasr.txt (line 2)) (3.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25->modelscope->-r ASR/requirements_funasr.txt (line 2)) (2024.6.2)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from soundfile>=0.12.1->funasr->-r ASR/requirements_funasr.txt (line 1)) (1.16.0)\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from librosa->funasr->-r ASR/requirements_funasr.txt (line 1)) (3.0.1)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from librosa->funasr->-r ASR/requirements_funasr.txt (line 1)) (1.1.3)\n",
            "Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.10/dist-packages (from librosa->funasr->-r ASR/requirements_funasr.txt (line 1)) (1.1.0)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from librosa->funasr->-r ASR/requirements_funasr.txt (line 1)) (4.4.2)\n",
            "Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from librosa->funasr->-r ASR/requirements_funasr.txt (line 1)) (0.3.1)\n",
            "Requirement already satisfied: numba>=0.43.0 in /usr/local/lib/python3.10/dist-packages (from librosa->funasr->-r ASR/requirements_funasr.txt (line 1)) (0.56.4)\n",
            "Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.10/dist-packages (from librosa->funasr->-r ASR/requirements_funasr.txt (line 1)) (1.8.1)\n",
            "Requirement already satisfied: triton<3,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (2.3.0)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (2.3.0+cu121)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (10.1.0)\n",
            "Requirement already satisfied: tiktoken in /usr/local/lib/python3.10/dist-packages (from openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (0.7.0)\n",
            "Requirement already satisfied: crcmod>=1.7 in /usr/local/lib/python3.10/dist-packages (from oss2->funasr->-r ASR/requirements_funasr.txt (line 1)) (1.7)\n",
            "Requirement already satisfied: pycryptodome>=3.4.7 in /usr/local/lib/python3.10/dist-packages (from oss2->funasr->-r ASR/requirements_funasr.txt (line 1)) (3.20.0)\n",
            "Requirement already satisfied: aliyun-python-sdk-kms>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from oss2->funasr->-r ASR/requirements_funasr.txt (line 1)) (2.16.3)\n",
            "Requirement already satisfied: aliyun-python-sdk-core>=2.13.12 in /usr/local/lib/python3.10/dist-packages (from oss2->funasr->-r ASR/requirements_funasr.txt (line 1)) (2.15.1)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->modelscope->-r ASR/requirements_funasr.txt (line 2)) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->modelscope->-r ASR/requirements_funasr.txt (line 2)) (2024.1)\n",
            "Requirement already satisfied: beartype in /usr/local/lib/python3.10/dist-packages (from rotary-embedding-torch->funasr->-r ASR/requirements_funasr.txt (line 1)) (0.18.5)\n",
            "Requirement already satisfied: protobuf>=3.20 in /usr/local/lib/python3.10/dist-packages (from tensorboardX->funasr->-r ASR/requirements_funasr.txt (line 1)) (5.27.1)\n",
            "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.10/dist-packages (from umap-learn->funasr->-r ASR/requirements_funasr.txt (line 1)) (0.5.12)\n",
            "Requirement already satisfied: importlib-metadata>=6.6.0 in /usr/local/lib/python3.10/dist-packages (from yapf->modelscope->-r ASR/requirements_funasr.txt (line 2)) (7.1.0)\n",
            "Requirement already satisfied: platformdirs>=3.5.1 in /usr/local/lib/python3.10/dist-packages (from yapf->modelscope->-r ASR/requirements_funasr.txt (line 2)) (4.2.2)\n",
            "Requirement already satisfied: tomli>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from yapf->modelscope->-r ASR/requirements_funasr.txt (line 2)) (2.0.1)\n",
            "Requirement already satisfied: jmespath<1.0.0,>=0.9.3 in /usr/local/lib/python3.10/dist-packages (from aliyun-python-sdk-core>=2.13.12->oss2->funasr->-r ASR/requirements_funasr.txt (line 1)) (0.10.0)\n",
            "Requirement already satisfied: cryptography>=2.6.0 in /usr/local/lib/python3.10/dist-packages (from aliyun-python-sdk-core>=2.13.12->oss2->funasr->-r ASR/requirements_funasr.txt (line 1)) (42.0.7)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile>=0.12.1->funasr->-r ASR/requirements_funasr.txt (line 1)) (2.22)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (1.3.1)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets>=2.14.5->modelscope->-r ASR/requirements_funasr.txt (line 2)) (4.12.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=6.6.0->yapf->modelscope->-r ASR/requirements_funasr.txt (line 2)) (3.19.1)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.43.0->librosa->funasr->-r ASR/requirements_funasr.txt (line 1)) (0.39.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa->funasr->-r ASR/requirements_funasr.txt (line 1)) (3.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (1.12.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (3.1.4)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (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->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (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->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (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->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (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->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (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->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (2.20.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (12.5.40)\n",
            "Requirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.10/dist-packages (from tiktoken->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (2024.5.15)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->openai-whisper->funasr->-r ASR/requirements_funasr.txt (line 1)) (1.3.0)\n",
            "Installing collected packages: requests\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.28.2\n",
            "    Uninstalling requests-2.28.2:\n",
            "      Successfully uninstalled requests-2.28.2\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",
            "bigframes 1.8.0 requires scikit-learn>=1.2.2, but you have scikit-learn 1.1.3 which is incompatible.\n",
            "google-api-core 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-aiplatform 1.52.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-bigquery-connection 1.12.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.25.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-datastore 2.15.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-firestore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-functions 1.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-iam 2.15.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-language 2.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-resource-manager 1.12.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-translate 3.11.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "openxlab 0.1.0 requires requests~=2.28.2, but you have requests 2.32.3 which is incompatible.\n",
            "tensorflow 2.15.0 requires numpy<2.0.0,>=1.23.5, but you have numpy 1.23.4 which is incompatible.\n",
            "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed requests-2.32.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 若使用MuesTalk模型，可安装环境\n",
        "\n",
        "!pip install --no-cache-dir -U openmim\n",
        "!mim install mmengine\n",
        "!mim install \"mmcv>=2.0.1\"\n",
        "!mim install \"mmdet>=3.1.0\"\n",
        "!mim install \"mmpose>=1.1.0\"\n",
        "!pip install -r TFG/requirements_musetalk.txt"
      ],
      "metadata": {
        "id": "gjGwWWrkKykb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a055506d-80bb-4cf4-9ec8-791dd54800ba"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: openmim in /usr/local/lib/python3.10/dist-packages (0.3.9)\n",
            "Requirement already satisfied: Click in /usr/local/lib/python3.10/dist-packages (from openmim) (8.1.7)\n",
            "Requirement already satisfied: colorama in /usr/local/lib/python3.10/dist-packages (from openmim) (0.4.6)\n",
            "Requirement already satisfied: model-index in /usr/local/lib/python3.10/dist-packages (from openmim) (0.1.11)\n",
            "Requirement already satisfied: opendatalab in /usr/local/lib/python3.10/dist-packages (from openmim) (0.0.10)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from openmim) (2.0.3)\n",
            "Requirement already satisfied: pip>=19.3 in /usr/local/lib/python3.10/dist-packages (from openmim) (23.1.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from openmim) (2.32.3)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from openmim) (13.4.2)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from openmim) (0.9.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from model-index->openmim) (6.0.1)\n",
            "Requirement already satisfied: markdown in /usr/local/lib/python3.10/dist-packages (from model-index->openmim) (3.6)\n",
            "Requirement already satisfied: ordered-set in /usr/local/lib/python3.10/dist-packages (from model-index->openmim) (4.1.0)\n",
            "Requirement already satisfied: pycryptodome in /usr/local/lib/python3.10/dist-packages (from opendatalab->openmim) (3.20.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatalab->openmim) (4.65.2)\n",
            "Requirement already satisfied: openxlab in /usr/local/lib/python3.10/dist-packages (from opendatalab->openmim) (0.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->openmim) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->openmim) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->openmim) (1.26.18)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->openmim) (2024.6.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->openmim) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->openmim) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->openmim) (2024.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas->openmim) (1.23.4)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->openmim) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->openmim) (2.16.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->openmim) (0.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->openmim) (1.16.0)\n",
            "Requirement already satisfied: filelock~=3.14.0 in /usr/local/lib/python3.10/dist-packages (from openxlab->opendatalab->openmim) (3.14.0)\n",
            "Requirement already satisfied: oss2~=2.17.0 in /usr/local/lib/python3.10/dist-packages (from openxlab->opendatalab->openmim) (2.17.0)\n",
            "Requirement already satisfied: packaging~=24.0 in /usr/local/lib/python3.10/dist-packages (from openxlab->opendatalab->openmim) (24.1)\n",
            "Collecting requests (from openmim)\n",
            "  Downloading requests-2.28.2-py3-none-any.whl (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.8/62.8 kB\u001b[0m \u001b[31m849.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: setuptools~=60.2.0 in /usr/local/lib/python3.10/dist-packages (from openxlab->opendatalab->openmim) (60.2.0)\n",
            "Requirement already satisfied: crcmod>=1.7 in /usr/local/lib/python3.10/dist-packages (from oss2~=2.17.0->openxlab->opendatalab->openmim) (1.7)\n",
            "Requirement already satisfied: aliyun-python-sdk-kms>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from oss2~=2.17.0->openxlab->opendatalab->openmim) (2.16.3)\n",
            "Requirement already satisfied: aliyun-python-sdk-core>=2.13.12 in /usr/local/lib/python3.10/dist-packages (from oss2~=2.17.0->openxlab->opendatalab->openmim) (2.15.1)\n",
            "Requirement already satisfied: jmespath<1.0.0,>=0.9.3 in /usr/local/lib/python3.10/dist-packages (from aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim) (0.10.0)\n",
            "Requirement already satisfied: cryptography>=2.6.0 in /usr/local/lib/python3.10/dist-packages (from aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim) (42.0.7)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.10/dist-packages (from cryptography>=2.6.0->aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim) (1.16.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=2.6.0->aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim) (2.22)\n",
            "Installing collected packages: requests\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.32.3\n",
            "    Uninstalling requests-2.32.3:\n",
            "      Successfully uninstalled requests-2.32.3\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",
            "bigframes 1.8.0 requires scikit-learn>=1.2.2, but you have scikit-learn 1.1.3 which is incompatible.\n",
            "datasets 2.19.2 requires requests>=2.32.1, but you have requests 2.28.2 which is incompatible.\n",
            "google-api-core 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-aiplatform 1.52.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-bigquery-connection 1.12.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.25.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-datastore 2.15.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-firestore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-functions 1.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-iam 2.15.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-language 2.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-resource-manager 1.12.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-cloud-translate 3.11.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.1 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.28.2 which is incompatible.\n",
            "paddlespeech 1.4.1 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.\n",
            "tensorflow 2.15.0 requires numpy<2.0.0,>=1.23.5, but you have numpy 1.23.4 which is incompatible.\n",
            "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.1 which is incompatible.\n",
            "torchtext 0.18.0 requires torch>=2.3.0, but you have torch 2.0.1+cu118 which is incompatible.\n",
            "yfinance 0.2.40 requires requests>=2.31, but you have requests 2.28.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed requests-2.28.2\n",
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu118/torch2.0.0/index.html\n",
            "Requirement already satisfied: mmengine in /usr/local/lib/python3.10/dist-packages (0.10.4)\n",
            "Requirement already satisfied: addict in /usr/local/lib/python3.10/dist-packages (from mmengine) (2.4.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from mmengine) (3.8.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mmengine) (1.23.4)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from mmengine) (6.0.1)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from mmengine) (13.4.2)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from mmengine) (2.4.0)\n",
            "Requirement already satisfied: yapf in /usr/local/lib/python3.10/dist-packages (from mmengine) (0.40.2)\n",
            "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.10/dist-packages (from mmengine) (4.6.0.66)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine) (4.53.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine) (24.1)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine) (2.8.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->mmengine) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->mmengine) (2.16.1)\n",
            "Requirement already satisfied: importlib-metadata>=6.6.0 in /usr/local/lib/python3.10/dist-packages (from yapf->mmengine) (7.1.0)\n",
            "Requirement already satisfied: platformdirs>=3.5.1 in /usr/local/lib/python3.10/dist-packages (from yapf->mmengine) (4.2.2)\n",
            "Requirement already satisfied: tomli>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from yapf->mmengine) (2.0.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=6.6.0->yapf->mmengine) (3.19.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->mmengine) (0.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->mmengine) (1.16.0)\n",
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu118/torch2.0.0/index.html\n",
            "Requirement already satisfied: mmcv>=2.0.1 in /usr/local/lib/python3.10/dist-packages (2.2.0)\n",
            "Requirement already satisfied: addict in /usr/local/lib/python3.10/dist-packages (from mmcv>=2.0.1) (2.4.0)\n",
            "Requirement already satisfied: mmengine>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from mmcv>=2.0.1) (0.10.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mmcv>=2.0.1) (1.23.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from mmcv>=2.0.1) (24.1)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from mmcv>=2.0.1) (9.4.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from mmcv>=2.0.1) (6.0.1)\n",
            "Requirement already satisfied: yapf in /usr/local/lib/python3.10/dist-packages (from mmcv>=2.0.1) (0.40.2)\n",
            "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.10/dist-packages (from mmcv>=2.0.1) (4.6.0.66)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from mmengine>=0.3.0->mmcv>=2.0.1) (3.8.4)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from mmengine>=0.3.0->mmcv>=2.0.1) (13.4.2)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from mmengine>=0.3.0->mmcv>=2.0.1) (2.4.0)\n",
            "Requirement already satisfied: importlib-metadata>=6.6.0 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv>=2.0.1) (7.1.0)\n",
            "Requirement already satisfied: platformdirs>=3.5.1 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv>=2.0.1) (4.2.2)\n",
            "Requirement already satisfied: tomli>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv>=2.0.1) (2.0.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=6.6.0->yapf->mmcv>=2.0.1) (3.19.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine>=0.3.0->mmcv>=2.0.1) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine>=0.3.0->mmcv>=2.0.1) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine>=0.3.0->mmcv>=2.0.1) (4.53.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine>=0.3.0->mmcv>=2.0.1) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine>=0.3.0->mmcv>=2.0.1) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmengine>=0.3.0->mmcv>=2.0.1) (2.8.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->mmengine>=0.3.0->mmcv>=2.0.1) (3.0.0)\n",
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            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu118/torch2.0.0/index.html\n",
            "Collecting mmdet>=3.1.0\n",
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            "Installing collected packages: terminaltables, mmcv, mmdet\n",
            "  Attempting uninstall: mmcv\n",
            "    Found existing installation: mmcv 2.2.0\n",
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            "      Successfully uninstalled mmcv-2.2.0\n",
            "Successfully installed mmcv-2.1.0 mmdet-3.3.0 terminaltables-3.1.10\n",
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu118/torch2.0.0/index.html\n",
            "Collecting mmpose>=1.1.0\n",
            "  Using cached mmpose-1.3.1-py2.py3-none-any.whl (1.7 MB)\n",
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            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: chumpy\n",
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            "  Created wheel for chumpy: filename=chumpy-0.70-py3-none-any.whl size=58281 sha256=3bba4d735842dcdcdbb7f1ce393e00ca3dca95b9e4acd3a496303491c4ddd513\n",
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            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio->spaces->-r TFG/requirements_musetalk.txt (line 18)) (3.0.0)\n",
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            "Installing collected packages: opencv-python, tokenizers, diffusers, transformers, spaces, accelerate\n",
            "  Attempting uninstall: opencv-python\n",
            "    Found existing installation: opencv-python 4.6.0.66\n",
            "    Uninstalling opencv-python-4.6.0.66:\n",
            "      Successfully uninstalled opencv-python-4.6.0.66\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.13.3\n",
            "    Uninstalling tokenizers-0.13.3:\n",
            "      Successfully uninstalled tokenizers-0.13.3\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.32.0\n",
            "    Uninstalling transformers-4.32.0:\n",
            "      Successfully uninstalled transformers-4.32.0\n",
            "  Attempting uninstall: accelerate\n",
            "    Found existing installation: accelerate 0.31.0\n",
            "    Uninstalling accelerate-0.31.0:\n",
            "      Successfully uninstalled accelerate-0.31.0\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",
            "paddleslim 2.6.0 requires opencv-python==4.6.0.66, but you have opencv-python 4.9.0.80 which is incompatible.\n",
            "paddlespeech 1.4.1 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed accelerate-0.28.0 diffusers-0.27.2 opencv-python-4.9.0.80 spaces-0.28.3 tokenizers-0.15.2 transformers-4.39.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "安装可以后重启一下，才能使用新安装的库"
      ],
      "metadata": {
        "id": "dAaPl8kkAsP2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download pretrained models 下载预训练模型"
      ],
      "metadata": {
        "id": "0LqH8VZt1D6W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/Linly-Talker/\n",
        "\n",
        "# 在colab推荐使用huggingface下载，方式2\n",
        "!sh scripts/download_models.sh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LkFCAh4x1P4U",
        "outputId": "e1be0b05-acbb-4d2d-c10f-c6eec52a9eb9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Linly-Talker\n",
            "Please select a model download method:\n",
            "请选择模型下载方式：\n",
            "1. Download from ModelScope (No resuming capability)\n",
            "1. 从 ModelScope 下载（无断点续传功能）\n",
            "2. Download from Huggingface (With resuming capability)\n",
            "2. 从 Huggingface 下载（有断点续传功能）\n",
            "3. Download from Huggingface mirror site (Possibly faster)\n",
            "3. 从 Huggingface 镜像站点下载（镜像可能快一点）\n",
            "Enter 1, 2, or 3 to choose a download method: 2\n",
            "Downloading models from Huggingface...\n",
            "正在从 Huggingface 下载模型...\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
            "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
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            "Fetching 65 files: 100% 65/65 [01:17<00:00,  1.19s/it]\n",
            "Model download completed.\n",
            "模型下载完成。\n",
            "All models have been successfully moved and\n",
            "\n",
            " are ready.\n",
            "所有模型已成功移动并准备就绪。\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !rm -rf Linly-Talker/\n",
        "\n",
        "# # 从huggingface下载预训练模型，需要一段时间，因为比较大\n",
        "# !git lfs install\n",
        "# !git clone https://huggingface.co/Kedreamix/Linly-Talker\n",
        "# # 移动所有模型到当前目录\n",
        "# %cd /content/Linly-Talker/\n",
        "\n",
        "# # checkpoint中含有SadTalker和Wav2Lip\n",
        "# !mkdir checkpoints\n",
        "# %mv Linly-Talker/checkpoints/* ./checkpoints/\n",
        "\n",
        "# # SadTalker的增强GFPGAN\n",
        "# # pip install gfpgan\n",
        "# # %mv Linly-Talker/gfpan ./\n",
        "\n",
        "# # 语音克隆模型\n",
        "# %mv Linly-Talker/GPT_SoVITS/pretrained_models/* ./GPT_SoVITS/pretrained_models/\n",
        "\n",
        "# # Qwen大模型\n",
        "# %mv Linly-Talker/Qwen ./"
      ],
      "metadata": {
        "id": "BHTSiSwF3tLG"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# launch WebUI 启动WebUI"
      ],
      "metadata": {
        "id": "pVmRVq191Jmb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "需要修改一下webui.py 最后几行加上设置share = True，这样就能在网页上进行体验\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "    demo.launch(server_name=\"127.0.0.1\", # 本地端口localhost:127.0.0.1 全局端口转发:\"0.0.0.0\"\n",
        "                server_port=port,\n",
        "                debug=True,\n",
        "                share=True,\n",
        "                )\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "HNqzuahYFpHS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/Linly-Talker/\n",
        "\n",
        "!python  app.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "auS02lwhHdvB",
        "outputId": "739225fa-0b39-4512-f80a-710929babd7b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Linly-Talker\n",
            "Gemini模型加载失败，可能是因为没有google-generativeai库，但是Gemini模型不是必顂的，可以忽略\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:260: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  torch.utils._pytree._register_pytree_node(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:260: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  torch.utils._pytree._register_pytree_node(\n",
            "如果想使用FunASR，请先安装funasr，若使用Whisper，请忽略此条信息\n",
            "PaddleTTS Error:  No module named 'paddlespeech'\n",
            "如果使用PaddleTTS，请先安装PaddleTTS环境\n",
            "pip install -r requirements_paddle.txt\n",
            "ERNeRF导入失败，原因： No module named 'trimesh'\n",
            "使用ERNeRF前需要安装对应的环境\n",
            "error: XDG_RUNTIME_DIR not set in the environment.\n",
            "ALSA lib confmisc.c:855:(parse_card) cannot find card '0'\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory\n",
            "ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory\n",
            "ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory\n",
            "ALSA lib conf.c:5701:(snd_config_expand) Evaluate error: No such file or directory\n",
            "ALSA lib pcm.c:2664:(snd_pcm_open_noupdate) Unknown PCM default\n",
            "ALSA lib confmisc.c:855:(parse_card) cannot find card '0'\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory\n",
            "ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory\n",
            "ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory\n",
            "ALSA lib conf.c:5701:(snd_config_expand) Evaluate error: No such file or directory\n",
            "ALSA lib pcm.c:2664:(snd_pcm_open_noupdate) Unknown PCM default\n",
            "MuseTalk导入失败，原因： No module named 'ffmpeg'\n",
            "使用MuseTalk前需要安装对应的环境\n",
            "Loading checkpoint shards: 100% 2/2 [00:05<00:00,  2.63s/steps]\n",
            "using safetensor as default\n",
            "Running on local URL:  https://127.0.0.1:6006\n",
            "Running on public URL: https://a261bf5ac34031b46a.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 2233, in block_thread\n",
            "    time.sleep(0.1)\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/Linly-Talker/app.py\", line 193, in <module>\n",
            "    demo.launch(server_name=ip, # 本地端口localhost:127.0.0.1 全局端口转发:\"0.0.0.0\"\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 2129, in launch\n",
            "    self.block_thread()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 2235, in block_thread\n",
            "    print(\"Keyboard interruption in main thread... closing server.\")\n",
            "KeyboardInterrupt\n",
            "Killing tunnel 127.0.0.1:6006 <> https://a261bf5ac34031b46a.gradio.live\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/Linly-Talker/\n",
        "\n",
        "!python  webui.py"
      ],
      "metadata": {
        "id": "LdUowoDW2HDI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92361a94-a98c-49c6-c186-a4e1f7ae6584"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Linly-Talker\n",
            "Gemini模型加载失败，可能是因为没有google-generativeai库，但是Gemini模型不是必顂的，可以忽略\n",
            "/usr/local/lib/python3.10/dist-packages/_distutils_hack/__init__.py:36: UserWarning: Setuptools is replacing distutils.\n",
            "  warnings.warn(\"Setuptools is replacing distutils.\")\n",
            "WARNING:transformers_modules.Qwen-1_8B-Chat.modeling_qwen:The model is automatically converting to fp16 for faster inference. If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\".\n",
            "WARNING:transformers_modules.Qwen-1_8B-Chat.modeling_qwen:Try importing flash-attention for faster inference...\n",
            "WARNING:transformers_modules.Qwen-1_8B-Chat.modeling_qwen:Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary\n",
            "WARNING:transformers_modules.Qwen-1_8B-Chat.modeling_qwen:Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm\n",
            "WARNING:transformers_modules.Qwen-1_8B-Chat.modeling_qwen:Warning: import flash_attn fail, please install FlashAttention to get higher efficiency https://github.com/Dao-AILab/flash-attention\n",
            "Loading checkpoint shards: 100% 2/2 [00:21<00:00, 10.58s/steps]\n",
            "\u001b[1;32;40mSuccess!!! LLM模块加载成功，默认使用Qwen模型\u001b[0m\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
            "GPT_SoVITS导入成功\n",
            "XTTS导入失败，原因： No module named 'langid'\n",
            "使用XTTS语音克隆前需要安装对应的环境，请执行 pip install -r VITS/requirements_xtts.txt\n",
            "\u001b[1;32;40mSuccess!!! GPT-SoVITS模块加载成功，语音克隆默认使用GPT-SoVITS模型\u001b[0m\n",
            "ERNeRF导入失败，原因： No module named 'trimesh'\n",
            "使用ERNeRF前需要安装对应的环境\n",
            "error: XDG_RUNTIME_DIR not set in the environment.\n",
            "ALSA lib confmisc.c:855:(parse_card) cannot find card '0'\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory\n",
            "ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory\n",
            "ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory\n",
            "ALSA lib conf.c:5701:(snd_config_expand) Evaluate error: No such file or directory\n",
            "ALSA lib pcm.c:2664:(snd_pcm_open_noupdate) Unknown PCM default\n",
            "ALSA lib confmisc.c:855:(parse_card) cannot find card '0'\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_card_inum returned error: No such file or directory\n",
            "ALSA lib confmisc.c:422:(snd_func_concat) error evaluating strings\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory\n",
            "ALSA lib confmisc.c:1334:(snd_func_refer) error evaluating name\n",
            "ALSA lib conf.c:5178:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory\n",
            "ALSA lib conf.c:5701:(snd_config_expand) Evaluate error: No such file or directory\n",
            "ALSA lib pcm.c:2664:(snd_pcm_open_noupdate) Unknown PCM default\n",
            "[2024-06-10 13:48:09,585] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
            "\u001b[93m [WARNING] \u001b[0m async_io requires the dev libaio .so object and headers but these were not found.\n",
            "\u001b[93m [WARNING] \u001b[0m async_io: please install the libaio-dev package with apt\n",
            "\u001b[93m [WARNING] \u001b[0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.\n",
            "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n",
            "\u001b[93m [WARNING] \u001b[0m NVIDIA Inference is only supported on Ampere and newer architectures\n",
            "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.0\n",
            "\u001b[93m [WARNING] \u001b[0m using untested triton version (2.0.0), only 1.0.0 is known to be compatible\n",
            "Loads checkpoint by local backend from path: ./Musetalk/models/dwpose/dw-ll_ucoco_384.pth\n",
            "Downloading: \"https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth\" to /root/.cache/torch/hub/checkpoints/s3fd-619a316812.pth\n",
            "100% 85.7M/85.7M [00:04<00:00, 21.4MB/s]\n",
            "using safetensor as default\n",
            "\u001b[1;32;40mSuccess!!! SadTalker模块加载成功，默认使用SadTalker模型\u001b[0m\n",
            "\u001b[1;32;40mSuccess!!! WhisperASR模块加载成功，默认使用Whisper-base模型\u001b[0m\n",
            "IMPORTANT: You are using gradio version 4.16.0, however version 4.29.0 is available, please upgrade.\n",
            "--------\n",
            "Running on local URL:  http://127.0.0.1:6006\n",
            "Running on public URL: https://e629b62a903b99dbd3.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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7nQO1aJzukt2yEYRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBvVwgN4cyOXv5JcDTRwABBBBAYNcJqEJfhQrlQ2U3PnLsOufdtWWdQ1Xp0znVuaUhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCOx8gdydv0m2iAACCCCAAAIImG3ctDl0uVujGl21fteuB51Tdaesc0xDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEENi5AoT6dq4nW0MAAQQQQACBfIGv1q63KpUrGBX6vnuXhM6pzq3OMQ0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYOcKEOrbuZ5sDQEEEEAAAQTyBTZs3GgV9q2Ax3dUQOdW55iGAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIILBzBQj17VxPtoYAAggggAAC+QJbvt5qZcuWweM7KqBzq3NMQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDYuQK5eXl5O3eLbA0BBBBAAAEEEMgX2Nld7y5fvtz+cOe99uWXa3bYeP36DXbPvf+2devW7fC6sz+ZF6+nz05btmwp9qeoHbzef6A9/NiTRc3eoen3/ecRmzZ9Vto6EyZNjo8zbUZiZMnSZTbwnaH2+efLE1N3fHBnn9vkEbz7/njbsKHoKoDr/FwW1z6eOtveGDi8uEW+9bwNGzfZwsXL0rbzlV9bfd8YbN/m8/XAoaNswaKladudO3+hV0XclDYtc2TVF1/ahMnTbfWXazNnlbrxL9d8ZW8Nete+WL3t1/Hrbw23NV8Vfs2OnzTdRn8wodQ9Nx3Qpk2b7aVX3w6PyQOcPmtucrTQsK6bxUs/t7ETptjWraXv/9HmfbooPmYdX3H3weJeAwPeGWmTp82Ot7W9A3pd6LoprumYNm3eHP9s3ZoKHuu1OuDtkUW+NnVNrli12vQa1nJqny1YYps3bwnT3hg4ws/J9oWY3xk22j4cN7m4w8w6T89v6MgPss7bkYnfxetvTz0/8z9bZLrev0lbvOTzb3Q96LU5/7PF8S513Rb3Wo0XzBjQZ6bn+74VXhcZswqNfvDRxzb7k08LTdeEjZs2hdeS7mt6j0o2veYGDXs/nlTUccYLMLDLBfbU15pg3n1/nI2bNO1bG61bt96GjNjxe/F38d77rTHZAAIIIIAAAggggAACCCCAAAIIIIDAtxYom7Mrv5H91ofHBhBAAAEEEEAAgQKB6tWr2/sffGg59+fYb2/6ecGM7Rhau3ad3f/Ao3bV5RdbxYoVt7mGgoMVKuxr5cqVsz/9+V5bsXKlPfbff9nSZcusx1m9i1z/yst72+9u/VUIgEyZOsPy/L+orVixyrf1d2vTqqXts+8+0eTweESzJlamTBlbvfpLW7Q4PdRVu3Ytq1G9Wtryr77+ph3RvIk1a9oonv7Xe++3L9esCce5NW+rPfdCXw9AfuXTvvQA1Zc2adIUW7JkqdWpU9vuuPWXduopJ8XrlpaBqR5UHO9fylarWsW+3vK1bdyw2TZs2mjr122wz91PXzgrtHDumSdb3QMPCIf9vz6vpjlv2LDJvv76a3u8T7+0p5VjOXblxWeFaTNnzbOBQ0enzS9u5JQTjrEmjRrEiyxYuMQGDhllP7y64FrY5PtVMEeBpszP2PqSeKUH75Kt6n5VrErl9Gtx7ryFdlDt2lbvoNrxosPeHWub/flcdO5pYdujx06yjd71sb5A3uhhvzVr1toWD26UyU0V4W7Tomm8bmkbmDbjExsyckw4hxUq7muV81+LNapXtUMPqZt2uJ8vX2XzPl1o+5Q/Jm36ss9Xhi/vNfGgOgdYg4MPSpu/K0dW+Dlc7+cy2Q6oWcPKly8XT9ro52Xx0uXhGoymK2w5ePj7NmnKTOvVs1sI3Uyb+Uk4fwq9rPcQ69q168P51Tr1D6pjdQ7YP95mSQ8oSNv/7eFWy5+rjn/ke+PCcynquLp0amdtWzU1BYOW+vlKNt2TFMisXCn92i/n1T/r5l/38tLrP9l0LXziAVcFgTJbtf0qh/vnix6m1HUTtfZtmpt+XntzmOX5a+TNdwpCgQ0PrW+N81/T02bMtakz5liPU47zfSwwhWRffXOo9e51ui37fEW4DnPzX1/RtvWo5TZvTj/ONV+t9YDTAju4fuHrcv8aVcNxJrcRDeu6njlnvp1w7JHRpEKPe+v1t6ecn8wTVrlyJZsz9zObOHmGtW7RJHN2seNLl6+wGbPnFXs9JDeg98b9qlS2eX799vcgoa77Y45sY8+82L/YAPUVvXuG9XQtr/bXZrKtXPWlDfOgafs2RyQn237+vlWjRsFnEl23ej/WPfxxfz/W++/WLVttq78XRu+HFf0zT506tSz5/rRy5Rc205/jycd3Ctv/7+MvhveytJ35yFHtW9qR/kP7ZgIKS37h57a6f67SfUx/JKDPEPoMktn2lNfaav+MvuqL9D8MWOb3/mWfz7Ya1apmPq34c8LY8VP8j1oK3pOS7wPRSl/5e/GU6bPt+C4dgpem76333siERwQQQAABBBBAAAEEEEAAAQQQQACBkhMoW3K7Zs8IIIAAAggggEDxAvM/Vbjii7SFLr34fLvplt/b0Z3a2/7710ibV61qVWtwSH2vSqOKSgVhOi0UBUT0qC84k00BrMzAxik9zrdnn3rIDm1wsD3477/aLbf9yc654Arr36+PTZ04Kqx+2x13WyX/cvvXN/443lyZMqmPVwrX/eQXN4Xpc+fOt4qVKlntA2raoYceYr+6+Q7/gtW/kFyxMoxroZefe9y/JK9u740Za7/41e3x9tatXWu/+NkN9pMfXhNPK2rgsYf+abfc/udwnC/0eSSEhKrs5/utvb/1ee4VO7XbCXb9D66yOrVTYbiitlOS09/7YJLV2r+6B9XW2cfTZlpZ/wJ689dbQqitUcODreURjezQg+v7l9GV4sM85qg2HtjJsw/GfexVgTbbiYlQzBAPJNSotp+1aZkedNvioceyZcuaAg1qS/KrIp131ilpAS3NU2hQIclv02bMnm+jxoyPN6GgQ8PDD7HuJ3WOpxU1cPlFZ9pLr71jT7/Q3y48r7tt9ue4b/nyHqyo5KGP+R6U2N9O6HKUVffnWRrbkmUrwhfk8+YtsHUeXtuvSqVgv2Dh0hB+VcW+wxrUSwv1Pfnc6yEQpufz9POvh6dV14Nu3U442sNlI6zx4Q2sSpWK9rYHM79/6TmFglIKPCqcpWpXdfx11/CwQzygm+oOfIYHOtdvTFV7rLDPvnZArRqxnao8btmaHtLSzsv667pFs4Y2ctSHaZUUdR5P9cBnFA4LB5rll67XS84/w17oNzCEbE7s2incpypW2jcEWFW5rkObFh78aeSh4wpZtlCykyp6wPniXj3C8T/78pshYKqgjdpjT/e1jh1aWsumDeODjMKMqu43zM3kpPuugqc5+eG4N73qnsI/uv8q0FzBQz9R6Pb5vm+GwGO8wcRAHw8pZbbjuxxpLZv7/v223+qIxtalU1t74LEXPDRb2Z72a0n7UfiyWrUqoWrYsuUr7XAP9UWtY7sjbOrMOfaVB/LU9Lpq1vgw29/DppM8kKW/gxvuzyNqWlfB276vD7K1RVQOVZWzzHaav94b+es+W9vqPrrfFdf21utvTzk/ny5YnP/5o+As1vZw7iivPruf36/M0s+vQp4K4qmanl4jyaZQnCZlfl7RMnq9JJvCcc++8mYImR/m12b3k7vYW4NHhe1eeM5p4XGVB/tfevUdu8SDqsl7zL75f2DwwUeTwz1z69YtvnxeuEdrHwpKRa/V3Fx9Virjn7PqZn3v0nF1O/5o+2jiZDu6Yxt7zwPo++5T3k498Rjr89IAn9Y6Pmw9L4XR1TQcfQbTa6R+3Trxck+/1L/Q841nluCA3kfUkmF/jatK4qrVq9PCi5q+M9rnft9auGRZ1k3V8/fHmomgZXIhBYb1GeLiC86w/f1zwocebPt46iy7/nsXJBcLw3vKa23UmAn2iX+miK6b5BNJq46p9x6/zvQHGLp+Z3kAVUH6A/1zgc5VWf9ccPhhBe8F2s5mvx7VvvbPnmXL6prP2Wvf+wMEvxBAAAEEEEAAAQQQQAABBBBAAAEESlSAUF+J8rNzBBBAAAEEEChOYOiwd23chEk255N5Nnfep3byiceFxXue2d1efT0VmBg0ZEQI3h1+WAPr0K61f9l8oT3z7Ev2yqtvxJvesGGDTc/vqrZTl1OtqVe323fffeP5vc7taZf27hWPz5g5K1TpU6BPbR//Uvqeu35n02fMtv28sskfvdre8hUr7O1BwzyY1cBuvu3OsNwRzRrbNd+/PAwrYDj07VSluF/edEcIiv3pjpvDPP1SsK9K5Sp2682/iKdpoNtJx9vo4f096JOqNHLtD2/0EF4tu+LqH9ly/0I3arNnf2I33/ona9WimT30wN/D5PIe9Lrnz7fbhx9NsAO9Gt9Nv/pJtLh9NG6S1ffKUaU50KcuVRX6UrBOlfqOPrJVOP6Jk2fa+2MneniqcABOXV1+9tmSsJzCfwptjvIu2KKmsMBqr2AUTfvMK+xFVYEU1FFYabkHIgYOfc9yfcKbXuFI0YqVXg3sqA6trGPbI8IXutH2ko8KXCg0FrWoO1lNi75oVmBMQYdWHkZUKDGqTPeUB9WqerhNgUF9wRw1Vf0aOfrDUK1QYT41ra9ghro31BfyPbsfH6br1yLfV7X9qsahtHhGKRlQBbqBg9/1SpNVrYWHrVRdRwFEBexU/U3d1CpspRBKsnXu1CZ0y1j3oFpeYaeeVa5QwatblvdgSP/QterJxx8VjKd75b/Hn33VLj63exxUUZeXqsymsFglD8ipAtuI0R/ZFV6lUQGTYaPG2ha/VjSs601hmkP8tSHXDydMtq3hOtoa9lOuXFkr5/65XkVOob5TPFSo+4HOiYIv/3n0OVNA9L//ezE+/Cic86SfY4UVVeVRTdf0lb3PsiXLlltdr1hV97Su8ToK9R1Yp2b8HOIZpWhAodGrLj47VP1ShchXBwwNrxUFJqb68c/2IJxaSz/PslJr1uSw8COrBx97PoT/OuZX/QrTHn/BOnsoN7OK2bVX9Aq+CuNFbaJXOVQYRSHOqJUpk1s48OPnXV1W6xytXPWFHegVxA70e6hCtV/7/UBVS886/QQ7uN6B0WbsuVfeCmEsVThTmzxtVohfjZs41SsNrgjhQK2rqmu6JpJVJTu2a+n3ieZxQCneaP6AR0LC9X7/I89mzkobX+FV0VIRp7TJaSN76/W3p5wfdUO7ye/hq75Y7fenMh4+rxzOXzV/7bw39uNUuM4r4lXxQLaCr+1aNwuhPlWY/NIrrkZNwe0o8PafR5/3zyvplX1P7nqkX4MFQaTJfh880KuWRq3hYQdbb7/nVvB71WeLltgHH34ch6QHDimoUHuyB/CibZ96Uuo+peCYvC/wyrB6v1FT9cvnPDR4qYfCktXd9J6k4LzuB7q36722aaNDPSS/IryGNvl0/QwcPDp0o64KabonL1y8xAYN/yCEpvQ8H3umn7Vv3TzsS59jdExz53/mJuttw/qNIYwbZpaiX3ofUXj84Hp1wufF6NAUOtfnjmRFwmjet31UoO+jcVPCZta7q5rOsZr+7qCoUF9YYDt/7SmvNT0dVYfU+3Z/7zK9ub/X6LpXUxfqbVs3Dfd9Vdjr80LB/w9o/mGH1LOunTuEzx+qpq3rL9l0/tT+558t9Pnk7B4n7tXv/UkbhhFAAAEEEEAAAQQQQAABBBBAAAEEdr8Aob7db84eEUAAAQQQQGA7Ba68/CLTz2wP9Z186jn2u9/+MlSzi1ZfsnSZvebd0N7/z7tDhb5o+uWXXmD6idrDjz1p4z2sM+DNd0yBwObebe11V18RzS70OMTDhGecfkqYfsNPfhWCIBq57ab/C9N+cPVloYvcfq/2t7/edUcI+j3/Uj8b++H4ONQ3dPgoX2ZxWF7hsWf6vGjNPfSnpkDRCy/2s2v9GJ557qUwrfYBtTy02NVe7vu6PfZEH3vl+f+F4OG7o8bY96642J545N9huejXyd3Ps5t//TM78fhjw6TM49Rz/s9DT0SLh6qAbw0cbPf/93/xtKEDX4nDg/HEEhpQNbd3PYyniigKP21v63b8MSEooe48PTdUbOt8ZOtCASBV23ln2HuhWttyDy4092pjCvToGNp74KK4pvBW3zcGpy2iSmQKO0VNlfVU7WuMhz0+njIrBMv28TCHujms6V0qH3Nxm2jR8PhgCDm1S1Ud8ylP+5fRUUisR7djbciIMaZuW6OmYJS6TZzmVcaipvBaFB6MppXUo6qvXeFBNjV9Ua6uIJ967lWr70EIVUM7yL+UP8e/MM/srlhVdBSYaO1dCesL+h9ceb4pJKfXTu9e3ePz2Pv8HmGbT3g1th9c2Su4vjHQu4n1ao/nn31KWE5dDapLZ4X4oqaApa4dNV13CpSqmpFCa2pL/XpUVb2ze5yU1g2uwoJlPeDXy4OnizxgoXPT2Cuvqapb1NZ8tc7Dmv3s8gvPDCE9bVddNauV8+qQF/k1oVCnustMNh237hVq6p776kvPTY2U8O9sx6/KYqd5EFPdCet59Ox+QjjKfgOGeABoZXzEqlC2RelXb+Xdf/zEaR4u2hjG1dW4rglVLR3h3fmqHeLXhQKWaqp69vpbw8Jw8lcyQKnQ1OUX9UzODsMTPp5uDQ+tF7ot1+vj/Q8nhekK5OXk5NobA0d4WKO6NfXzpgBibz8n/foPsS/89fuVd9Wu66xjx1bhvI7+YGIIZ5503FHhmJo0bBAHAvf360xVGF/162Khd71dVLv68vP8nrJfWvgnc1kFEDd4yPSrdeuKfP3ujdefnPaU86P7gpqu+ykeJr7Yq+Il2zi//hVQV2A7Cn5rvkLbyfbkc6+F+bqXKLh64nFH2uEN6icXSRueM/dT69S+lXfzO9NfkzPCPHVNflLXo0JX3jVOrhqq7e3nr5eTvEqomt5bFKxW02t4gq8bNd2P3/ZKmnXzK+bN966vVdl2or+HRa21B9UVotKPtqWAlap39vXXUVV//5w+c160qH2+YlUIdo+dMMWq+/uhAonXXF4/VE17xwP11/jrQ002Uft04TLvJnVFCD4W99yj5UvqcZJXvIuqlqryWxQI2xXHo6BgFBZ8ziumqun9ZGe2PeW1pqqP6t5Z4e1D6h9ob3tYdfi7Y+00/5w0z4P9qqaqMHd1v+YV/NNny2wtx3Li6y+ar/c8VVq9+rJz49fp3nrvjUx4RAABBBBAAAEEEEAAAQQQQAABBBAoOQFCfSVnz54RQAABBBBAYDsFGnoVvq5dO9tTz75oP/3htfFaz73YN0xXl7tFtaXLPre//+sh7972sRDq+9H137cLL7vWzjitm9WrlwqPZK77poff7rj1N2Fy29YtPLiTY3fefa/d+NPr/Ny8AikAAEAASURBVEvw9d5dVzlbuHCRVffuf2vVqhmWU3WZjZs3hwo91atVtXHjJ9oU7z42aid6YG/w0HejUdO4wor6UVOVP4X6zjunp/V/a5D97MZb7Oc/vcHU/W6zJo1s2IjR1qTx4aECX1gh41fmcXY76QRr1jQVItSif/nbv+2I5k2tR/eT4zUrVVR3gCXf9CV4v/6DQ9ecqpqyo03hL4WyDvLqZwrtZDZV71u46HM70qvu6QvgZKtft7ad4l0D7u/d1qlK3wivkqd2sgcfMoNmyfXUNaK+JL7h6ouSk4sc7tShtc30ENsLfQeGgIbCYPV831O9gmRtr6oYVUTK3MCB/pxEompzqszTolmj8EV1tNxID6Spe+EjEl2fVtgnvapTtGxJPa7+cq13vzsrhOYUsFNVp+UrvrAuR7e1pUtXhOpnUYWd6BjH+flUtaYDau4fTQrPvVbNaqEKnypGqcnxLA+UqatdhWQUMlM758yT4nOtMF+y28ewQOJXe68cp+tHVRxVWbG4dvopx4ZAnsKVug+oap/2+75XwjrSu3BNBnWi7agilwI2CotpH2pdOrULAa5omUHD3/cgZ6NQFUjTFPwsLS3b8es8litfNgRKVXVMw2oKtW76enOoClbBq2DOmfdZ/DTK57825/m5ippCefO9i96oVfJ1olCfpuk1eOUlqVBotEz0OMYrny1cnD1Ip5BwWw/ljh032VT98sDa3vW5dxlarmyuB0FyQgBvjXe1qwCi7h+qQqaqkur68/W3hod7grocVYUyXWMK66itXbveK2MWhI5V8U+toXervnnzpjCsX+oGfOi7Y8J9p1WLJqFr4Us8gFpU0z5WeTfUalOnf+LXUousi+6N19+edH6ik9bB7wXjPViq0HiTRg2iyR5qnerjh2a9T0QLqQtwXYsd/RpQ5b9jj25ng4a+b4deUTfregrm6bps3PAQD50uCvcaBY5VEVRNr0e9PnW9N218aPxa1bz1XsVYvmt8/eTrUl2kb/YqmclplSrumzau0FSyap+2pwrICoUNHvG+rV23PnR9XlYVM71yoe5pPU45LmuwVeH0r/yzjpqq/6lqYQ3/HKUftcVLP0973wsTS8GvCm6i9+Yo1KdApe576xNdciscri6Yux7TIRyxAp96H1AYT599dL+J7mMH+2dSGemzhULNg4ePCe8bei883cNqCk4W1XTNKNSmQJu6jdWyJxx75Hb9kYTC2LqGzut5cviMsifcC1U5VuH7x556xdSd/dVXnGfveji8SuWKwbWyP6rpPTn5nqJpU6bPCV336hpV0FX3eZ1HVe8rqu2N996iLJiOAAIIIIAAAggggAACCCCAAAIIILB7BQp/67l798/eEEAAAQQQQACB7RL4/pWX2OVX3WBnndE9VOX7/PPl9tAjT4UqfcVt4K57/hkq3dWqmQrf1ahe3a656lL7/Z/uibutTa6vEOBnCxdbq5apruCi7nT/8e//hsXu/ecD9sijT8WrtO14fDysAY3PnDLGbvzZDWH6lKnT08I7aQv7iLroa9umIMChqjz/+OufrPuZF9q1N/wihBareoWnF19+NXzp/b+H78saNss8TgUdk2HHp/u8ZIcfdoh1OSZVpSfzOEpyXEE7dYPbzrvfG+5d2qnLy/fHpipr6bhUjUWhl/888lx8mGW8WpqqtyWbgjRRl4XJ6QrgFdUm+5e7quqloJm+jD/2mHa2yrvBHOpfjg/1bgVVyStb+8IrG6nCy/Y2fUnf6+xu9mSf10IoTUGoit4N4fiPZ9rmcVPtSq+ul62pOphaVJ1PobNk8Owjt6rhFf/UzWlpbXneL6CqFFaqVCF0s9rKu2f9wqunqfLNJg/CqpKhvpxX0xfyclHYSlXLnvSqfmoPP/mSh7Lqhap56uZRTeGXEaPHWu1a+4fKeZq2aPGyEDTZx0O229um+jWgpuDFttp+VSqHCnWqHqgAmwI6eg7jvALVVx6aibp2Tm5HVR9VMWuhd5UchfqS3bdqWYX6FDxocHD2oHFye7t7ONvxP+WVEfW8o/b40wXdF6oK5WcLloYqR6qip6p+8+YvjBbN+qhwjKoqZWtFVZ1MVl7MXK/m/tU8HDfHLvJumcd6l71TvHtgvc4VFqpcqWKofnaSdz2qbpAVKFI46YKzTw2BGIVFdH6uu+p8e7Hf234PaODhrPkhoKX7UPXqqS5Jk/tUd9LlylUIk1T1TN1E6zq+xCu11fDA8LaaQiYK+Bzf5UjvKnVikaG+vfH6U4W4PeX8ROdZ9x9dQ+qi9TCvsKfrY5p3kavuvo/0anZFNb2mRr73kR3v4SKF8dRUBVTvh8NHfRhCWpnrTvfKrbpm9T6qCnj6UfVRBVFV9TH52pykSn6JinyqktfIK+2p6qZeq1p+mQeti2u6nrNV050xe57N8kCeXu86Zxd5972q/qdu7bt5F+R9+w+yR5/ua8d4d9vtWjULu1CEX+/ZDz3xogdjU12nzpg1NwSsyntoWk0mOsai7g9hoRL6ddjBdUNATO9JVSpXDkE8nS+9nqMW3XOi8bX+hyFrPOCtpgq+CvSde+bJ/vr3+6RXRNRnhZUrvwjvj7qG9EcHo/3861wV9TlB21KQT/de/UGCKuSqGqkqGCsMWFzTdbVg4dJQLbJ8ufJ71GtNofBa/plIFV31/t2rZzcPgKbCrHWKeT9XaFWh1Og8bdy0KfxBQEOfVveg2lm59sZ7b1YIJiKAAAIIIIAAAggggAACCCCAAAII7HaBsvpH12wVJXb7kbBDBBBAAAEEEECgGIHjuhxtF114rl33oxvtlReesF//9o92XJdOdoJX8CuqPfn0CzbwnWH23vABoVpTtJy69D31+VfsD3fea7+96edpIblh3m3u6aecGEIf0fLJxxt/eoP9+Pqrk5PShlVZSlX7ovb9H/wsVPDLFkzRF+gb/Uv+qRPejRYPj6r098C//2rn9LrMLrm4V5imroe7djvbXur7hp1/7plpyxc1Mm/+Z/bO4KFh9uw5c0NVHoVS1C7pfb4H6VIhlDChhH9d5l2VKhCgdoh/UZ40/NTDCfqy+7hEFRUFqjJbI+8GVV9mZ7b1GzeG7v8yp2u8qoe0WnmFNIXinnlxgHcZODdUUTu+S8cwrMDBzDnzCq26YtXqEEZIdgWaXOj4zh3TKjRpnq6B7t06+5ftI61e/hfHZ5x6vFd+e83e824Hi6sml9y2uoZTF8Fqqv6mij6qHKfWuVNbr4hUcP2FiSX8SwGQH3hASt2gfjJvgb3hgbglXkkqau9516j6UWvdsqkd55WpTjyuU6h6uc4r6Qz256ZQSA0P/33l3VEqhKWuB9Wlo76A7+HV86KmsERN7xI1av/r82oIbGr8tJM7xxV75sxd4MHBAfbF6jUhTKXQSHROonWLelRXkApuKGjZ4JCDgvcJ3j3moGHve8XEw62yBza3p830Kl4LEpXmxk2a4j6f+TVcwY4+stX2bKLEllH3odG9JNtBKHQbtZV+3vt7FczMKpnRfP3/qK6RqPpiNF2P2scjXokpW1OFscoeFM3Wjmrfwga8k7qvTvbuMatWrWRtPaitLo6PObKNB3j3CaFMrasQjbqBVuhv/MczTIGPqBtQLVvTu+ldvfor71I11T14MlwUBUyTx6CqT7pG1SWxQtrJZSp4JTNVPsxsqijY3K+dls0b2phxk0I3qq1bNM5cLIzvbdffnnZ+opPWzUOjjz3T17tjHxyCVUNGjrFWfk4VXM7WFOp8+dV3vJvqcuFamDB5erxYtxOODl29K9zYLqNbeFUDbNywQbxsckDvOd+/9JzkpELDqjYatanT5oSgWVGvVR2jKk92PaZ9WGWeV9kcNWZ8uLermlyr5o1NQfON/p779PNvRJv1IGGqS+BKHnStXjUVflPgTBVb1dQF9lHtVc12nn/+Os76vz3cqyl3DUG+Bx973g7zsFVpbAoIK1g2we8bqhSsrr0PrndgHBbb1jHnWK7f48xWrFwd3jui96CPPRSof6NTSFx/PHC8e4dqol4BUH98kK3purrQg5S6Zyo8XsXvY1GIPNvymqagqarbqgpdLb/Pqe0p90Ld//Vp9mR/nS32MP/HHtzWH3bM90qFun43eYVWLzcZnpN+qVJk9O+e9b1baX3eirpMl3st/9ygcP0VvbP/gYW2sbfde/WcaQgggAACCCCAAAIIIIAAAggggAACJS9QNvqHrZI/FI4AAQQQQAABBBAoXuD2W35pZ557ifU4q7dX9lpu7w55vcgV7n/wMbvnb/dZ3xef8kDHfrbcK39FTWG2Pk8+YD17XWEbNm6wP9z+m/jLvoGDhtmlvc+LFi30qC9Uhw4bac975bxs7V9/u7PQ5H/f+2frfMyRhaZPmDTZLr78ukLTNeHjj6eE6f995Em79KLzQze/d/7+Zrv5tjvt1JOPD/O29euTufNC18Onn3qStWmVqgY41bsE7tuvv3U/pZtVrJc9ELOt7e6K+cmublW5TT9RUwUbfTmtL/6La2O96o1/q154kfwgY+EZlha8U7Bw5OiPQniik3/h29G761VT1aPMttqDSroWVHUus33oAR1165atfeoVcdRmzJ4bAnhV96sUKjepm8XWHpbYnrbUQ31T/ct4fQld3a9tNQX9FHJr49vYp0ZBSGN7trerl1EQRMEsfQmvMGadOvvbmacdn1aVrl//IbbIK9l1aNM8HI5c9LPGQ3xqB9SsYYt9fhRoUPeAZ5x6bKhKFRbI/6VwWFT1T5NOOLZjCPSqK9UNGwq6R1W45NCD63lQcL+4klZyO9sajrq2HPX+hPiL/o89PNZ/0Ei78OzTtrV6mD/Dw6ILFi0L3Sfr+W3etMXme0Bz/boNpT7Up/OgSlDL/ZrLbAra9TrrlMzJdvXl51pUfSs5U6FNndvMpi6xO7ZrGapWqRvfDv56VGjmowmTvWvJ2lbHK/vpGsnW6h5UJwTrhnnFTQVfVYnprcGjwqJDvAKnbhP7ejfVUeWrud4lsCp06lpVKEZVx1RhS9UhFRRWWGng0NGmMEjUFEZ80isWFtX6vNi/0KyoKlpyxhjvulnXs6qzqXU5qq1XCR0b7k1FVSPc266/Pe386DwqvHl2j5O82uNAe8KvE3WLftzR7TWrUFM1uudefiu8b1xy/umF5it0pCqOw979wCuyfR1X+9P1qoD5mU0OLbRONEGv1aeefz28HqJp0aPCcgpRJ1tFv96/f3n2z0BPPJv+uUfvOeryXq9rVTk7qkPLEMZe4/ew4zxgruCe3tsVoNJrsZq/X6mS4GQPYCmRpXUU9FZ35FHT8qq+On7SNPvy0K/CZL1uSmtr6feGseMnh/PQ6PD6oTLh9h7rUR1a+H1mTbAZ4Z89OnsVQ30O0OdbnduHn3g53pRc5K3QYLamIPEAD09/6qE2dT27ya+TLV69r6im7eveq1bGK0ZHbU95rfV5aUDoUjo6bj0+8mSBV3JY83p4xcLiwqEneYVDBScVklXly6La3nbvLcqB6QgggAACCCCAAAIIIIAAAggggAACu0+g7O7bFXtCAAEEEEAAAQS+nYAqwZx8Yld78KHHrX37NpbrVZAymwIZd//1vrDMi88+ao898bRteWyrh/dSgZ4bf3ObVfLqNWqP/Odeu+x7PwzV8v569x22zivnvT/mQ7v/H3dlbjaMKxv27uj3bc7c+V656Uu7+KKCL743+5fyt9z6pxAeyVz57r/9y2o+UTNzsgcNU92EZc6Y/+kCu8ufg47/d3/8q91z7312x22/trN7nm7169W1/bxaz7basBGjwyIHe5eeem5R2+ThFoX6vktNITeFxPSlt7psVRUudZ2rL2mjEJhCQwqI6RrKbAoYjPAu6Op4pZ2mDQ/2QFVrrwCWPSwUrbvau9hs7YG+I9sVdJ0czVPFr2xNXcqqG9rTTupsCha9OWiEnXtGt7CNmh5iVBfE22pTp6tCX46V8+4NVZUnavqC/j+PPh+NlqpHVc05xSvt6fmpGtDj3gXjkqWfh1CfglEK3Cm0qS4IIwNVqVr6+Qp/jaQqOSmUoopdPU7pGh6vuuTs+Dnq3Go7qtqn8ItCfQo5qptidWmrpmsj2erU3v8bB+fUPfSX3s1hz+7Hhy4Sx02cFqpnnXFqV78vrEnuJuuwztXsTz4L81Td6azTT4iXU6BgsFcLKs1NAbQl3sXhMg+eVPJKVQfXSxnrmJf6PW3xkuz3tT5eCVPnMLN9tXZ9qI6XOV2Vpzp5SGjU++NDaCiqZDnRX18NPeRTVMhXVcLmzE356rXduNEhdnSH1rbZw8F9XnjDerp3FQ8eJquBquvdR55+JQTpVKWz7xuDPaxULu6S+UCv5KeW7Ppafxx3Re+emYft1+yqUJlQIeHM55usiqYVVVXqg3Ef23FeiSuqjtbUu3Qe9d44e6HvwFC9MHMbe+P1tyedn+QFUb1alfAetN4rrKl7UH0+ybwX6T1LAdANHnpWd+vqllxNr7PNXmnsab9m1dT9p7ruVZetqiCr14Oq2yqEl60SsNbRvVEVytQta5NGDfy6r6zJoc306r3LV6yIRuPH9f5Z6cnnXovHkwNRyDqapuC7fqJjjKbr85HeBxU49LerMKzuyRXqU9NrVz+z5swPob5ovejxWA8/vuBhSHWleliDeoXMouVKw2OLZo1CtUJVfbv4/B6mwH+yqVqn/ngkanmhvlxqTPeQU/3zwAnHHhmq7SrYpy53969eLdxHrvvehYXuIdF2Mh9H+h8fKJB34TmnhfvUu37fjCokZi6rcV2Lqji7zq/Ntwa/a9d4kFP3oD3ltdbr7G729eb00OKH3tW6Kugee0w7a1C/btrTTlbI1HW82D+D6DW2n1daVNNnE/2RRraupaMN7Y333ui584gAAggggAACCCCAAAIIIIAAAgggUHICZbP9w3LJHQ57RgABBBBAAAEEsgusXLnK/u+mO2zIkOH2e6+s91Lf1+2EU8+1B++7x47qWFDlRdVKRr//gT39xAPWsUM7Gz9him3J22LrvFqT1m3VorlV9CCKWvNmTeypx+63vq8NCOPvjv7AunQ+Kg6ChYmJX7f/4S/e/edaO827523apJFdcF5BN1360l6hvmyt67HHePeujQrN+mTepzZ7zuNp0xcsWGQXXnK1/fF3N4Xjv/P3t9j/nno2XqZ9u9bxcFEDf/v7f+zd98bafX+/s6hFvjPT9YX0628NsxP9S3FVelMVFX0prRCnwlGXXdjTgzMLbIqHe1Q1SF0RRuGZCKF5k8M96FnBK7TMtZHvTfBu2zZ7BbAqXhmpQ9aqOApeqepcIw/fbG9bvvILr9j0tgcgWpi6CVZgKOriU9soroJMtI/Bw8d4VZ+vQgAomranPCqoELWOHkwZ7iHKrz1kpfOy1cMFCmXWzQ9OabkPJ0y1ch4w0HlRU/BNFYrU3WNmm+7nTSE+hRzbe6W/Kd51Yd/+gz1I2MkrPtX0Lm6XhQBD5nrfZHyqb3v0BxPtnDNOCserYIpe+2oKJOonM/iS3I/+30sBmLIeylQ3r3ta0/ErGHeAB5TUDvXATTLYqkBFUaG+5t7FddlEt7zRc9frSSGlbE2vs4nePeRhiesnc7mxHuQo42HBdq2bh1mquKfQpKp7NfHX6MQpM+yJZwtCSq+89k5YTmG/U0/oHIb1utq86WvT+V3m4VtdT5ddeEa8qwFvjwhVJlU9TF2oRiEqBa0y2/r1G8Mknd/iquKr22x1B3xg7ZqFqnSe27ObV416y7tuHeLX/olxsGdvvf72lPOTvBZ0fvt7V+N5fm23bdXMu1SeHgLNunfU8CqUUVOwaKOH93T/CpVMD0j9AYBC4Bv9faxO/njFivt6F7Ut/Y8P1ns1uNQfNKi7+MOLqGKnSpOvvDEoroLXwV8fyf2uWr06LWwWHY+u2aICs2PHpSoIR8sW+ehBPr3P6vmoZb7nFrle/gwFIPWjgHZjf78sza1cuTKhy1oFulVhODPUd6Cfv7n+vqVuuVes+sJmzf7UonCvPpOou3jdqxr4PU4hzY0bN1tTD2BOnTEnhO0UcFQwUKFBfXYoqn3t15C8FVLTvvS+GLXoPrQl0R2tllX3vgqVPuqBZnUfr4DhnvJai+7Beo4Kyuu1ppC0Qq4jR4+zefUWhc+F2e7RCj/qR03XWdS65ldL1f0/s+2t995MB8YRQAABBBBAAAEEEEAAAQQQQAABBHa/QNnMvxTf/YfAHhFAAAEEEEAAgaIF5s3/zF72AN+j/+tjHTzQ9u7Q/lbPK0Nd0ruXdy37X7vw4u/b6d272bVXXx66mNWXny8/93gITWmr1159Wdi4ut+97/6H7YpLL7Ka3tVl1BSSi4Jy7wwebqd1OzGaFT8qULhu7VqvwrXaHvvvv+zlfm+Yqum9OXBIvMzGRCWWeGL+wD7e7V7lLOGPbNXgrrzmx3bWmafbuWf3CGu3bnWE/f2eP6ZtUhUF9eVwZouOc9qM2fbqi0969bOFodrZk08XVHDb7F2y7QlNX+Yr7KCmLj71pW0y0FXHgzDqmnKUV6dR1brmTQ8PVX0U8tM1oO4/J348w7+w3uDBmZHeVWtXe9O7phs4eLSdfsqxYbtbPVQWbVOfiZt61Rr9rHTbmV5BaPmKL0KXhVouajqOod4FYpXKlWz/Yrpoi5aPHl969W2r5113qntCtQYHH5TW/aymKYio7Wc2hcQUplI3oud4NbuVK1d7IO5rGz76w3jR5DHGE0vJgI5d53KtV2Rb4udVVfnkrUpOh9Q/0E49sXPoqjJ5uFdfem4Y1XP/X59+cbBF6ym4u8KrIclfwws9tLe/ByPU9ilf3nr36m6v9h/qX/K/G4f56h5Uyw5x8+1t0f8jeTYlbl94aEPdFeqL/yiAqApLme2LjEpN0Xydt6gp2KPQqbpTTJ7HFX6vKa1thR+rWgUPWuqcPe3VExd40CQZ1FtSRJU+raf7YLksob4yPm3LpoLXmJZVU0DjrUGjQqAtCluEGX5SFuR3x63XyzR1Re1VGaOmipvJKpZ6rampmqOqWV558dleiTMVFo3WqexBb3V9OfK9j+Lum1U167hjOnpltImmc3+lV4dUF6IveDepvb0il7rlzdbU1e+22vve5e5Yr9DX+PAGHqQ5ptDi6v7x0gvOsD4v9feuW1+1Xj1PCfeGvfX62xPOT1QJbLYHs8Z5+FPvYequ+bSTu4T3qlZHNDJ1M/6MdxuqgHIX7/ZWITCd62uvSFVJ04WgoJWaqpW+P3ZSPB4m+q/kPWeR3/s6e3fNmW2lV8hTN71HNG1oLZs3Ml3LH3tgNTpGLb/Cg+aVKhWuDqt7n7qmztZyslTa1HKZnyvU1XC9urXDPnT/13DUbWlyu5s3F36taHkF2OWnyroDvJKrQvt6jy+t7Syv2hoFk/OSbxp+wOpOV92qq2tXhblVeVDvWWoKbb49ZLR/Lkl1DX54g/qhYpzmqbvlkf4eH1UdraXwXzLUp/3kaclU69C2uYcC53uXvS+Fe6bCoDJUUxU6hfheGzjM9N6qVXPyA5cy7uxdJSsI17plU+/qvnqpvxdG1/HqL9f662RaeA/QZwH9cYDu9+qufajfqxXmlndX/wONZAhQFfn0ntLPQ9P6/JjZvvgyveLu3v7en+nDOAIIIIAAAggggAACCCCAAAIIIIDA7hWgUt/u9WZvCCCAAAIIILADAj/86a+t/4C3vXpeJ7v3L3fYqR64i8I2qvzyfz+/wcNvp9ujjz9jZ593mZ14YlcP3f0jDvTtwK5SlT7efMd+feOPC62mqmrneMjuT3fcbBXzu+6dOGmKPZP7Yrxs9IVuPCEx0M8rAapb38z25Zr0Lw41/54/32GtWjbLXDSMP/zokzZk+ChbsmSprVqx0ho3bJi2nI6z55nd7Q9eybCqd3OnUN9GD1NN+nhqvNyWrYVDY/HMUjSgoFNmtRRVtYqavrxVIEzViNp5dTY1jau6zQOPveCVjFLfrE+ZPjt8ka5uWE8+4Wj/sjdVnUXL65wNHJL6Ml3jmW3s+MlhUvLcTpw8w8MSW+zscwuHPzPXT453830fenBBtbrkPAV15s/3c+VVdPTFdN066V01l/WQ4v5e2elsr1ZX0QNVCod57sGvg+XxZhSEKI1NX7o/6aEkNb12y5Ur61UQK9uxHmhp3qShj5fZocM+qM4BIbDwrHdXqXOsKn9q6jI1avry/hIPXSnEpW4nVXUx2YXpD648P1q0yEd1s/rjay9Om69u+bp7QEddv2ZrfTyso5DGWq+kpfuTzlWy7btPOTv80Pre/XKX+HjUvWbyPCqUWlqbQmwNPVTS3as5RU2hkWSXw6pyWVQbO2GylcmvMJZcJnSBmKhaqKDe431eDRUQFXI54diOadVTD/FAlCpazXqoT7yZrp3bx8PJAd0LFG5KNoVEo1a/Xp0QGFL3zjn+X9NGh4UuThXMHDZqrAeBR9hKH77k/NNDFcbTux1rz3sFvVdeHxSusWg7elzolUIHDR0dwqsK0ETVsZLLRMsp0HdUh1ZpVQ4zl1NwRV346lp/c9DIUIF0b7z+VDlsTzg/Z5xynAcwXw/Xm0JUUcAoOq+qGHb5RT29Iuw8Gz12QuhyV0E1dbm7o5XstM3PFiyxXH9v0L4ym0LnCoW1bN4w3Ac1f+bseVbW70tRU4XRbKE+vV7UxW+2psqZyaYuTF8dMDR0E1zDu4yNmu67HyS2oeGvPKBda/+C8K0qlq728FTFCun3yVFjxtsqf80p3KjuYYd7IFHvkVv8vVHPp7S05PuIzl90DnXPSr53HOghYy2r6zizapwCmvpRaEyv92gbeo56rvrRe2iFfcsXCr5fdG73NAq9z1131QVZ96N70Q+uOj+uIqsQn36i1qZFU9OPjvGBx57fI+6FCsjqDwTKe/fS6rK8k3dHrT/0UNM50I+q66lb4v8986r1OKWLd21c307015u6pFY726ugRk3vOw898bJ/TrHwGa+8f56O7uF7+3t/ZMQjAggggAACCCCAAAIIIIAAAggggEDJCOT4F4Cl8xvAkvFgrwgggAACCCCwkwQ+XbDUq9HU/lZbU3CuuleyObh+vW1uZ+myz01d10ZV9zJXUBdjA94aZKefdnKo/pI5f+yH4+2uv3oVPq/yt602Zcq0UHmsQ/s28aL6QvCFl1+zsz1UV8G74Iyaqvl1bN/KqwOmB7U0X5X1FPZTpcHtabNmfeLdSs4LizZv1tgrjxXvIpNZs+dYl2NSlX+0oj76vf3OEDvu2M5pxxk2uoO/dsY5Tu5SX2zPmvupdWxzRHLyDg2ra8LV/sV01BR0VDWkKAwaTde+VAlMVXS21RTkUyhQX+wqdLfYu/it61/UF9VUYamuh89qJSpCFrWspisMtNS3qVb/oNrbXE/HvtjXaebdmSab9tu88eGFvvxPLlMSw+oKsJJ3HZkZctueY9HravzEaSG4GX3BrmlL1D2e/jfGv4Gv4wG8ZBhie7a7K5ZRdUdVU1Sg6/DD6qdVBsq2P1W5y/PrScGyqH3lVTjnzV9UZPeX0XKl4XGSd4urrmOT17kqKM7z4Ky6QY6aKlBO8tdQx3YtCr0OtYyqVul1m6xC9cncz0zVOIu6ZhQ+UTeValWrVjYFMNRUoWk/DzRFx6RrxbOdRbYyZXJCcGOuh2r1Gk+GP7XS5s1bbI2Hr5PdlmqbKzxYq+BnsinwNHXmnBCIie4XyfnJYXXHXTPRBWtyXuawtqugVVEW0fLf5etvTzk/ChGpUti2zpXOmUJ56oY18zqKzudKv0YWenC7qCCbqs7m5W0NlQCjdbI96j1/9JgJ4fWnAFTUVKVW11UypKwgvSrjFlUV7//ZuxM4G+v2j+PXjLHv+14o2kUoJKFsWaIiJWvIlizJEpJ9KyllJ2VpIUUUCalEG8kWkfDY921sM/P8rvu477nPmXNmzowZMT6//2s69/K7l/M+yzyv+X9dl7bXzpYls9OyVD8L202r6/QmkGZXL9XnpUF4u0KmfT39fau/h7VKoQ6tsKkhYPd3pVY4zJ0zu6Qx/xvKXQlXvyP0u8b+HWCfk8fEF7hePmsaKDW/PmP932Kqo+//9Ru3md+pt8T5vxP+MZXBIyKiTBXDEKvKpv17JS7l5PzdG9dzZz8CCCCAAAIIIIAAAggggAACCCCAQOIL7N271+v/pxxi/hAbxR9HEx+aMyKAAAIIIHCjCyR24CupPbU9r7Zq1da+jOAErrfXOLhnxSwEEEAAAQSubQEN4KUz7XPdbUWv7Tvm7hBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTiEvAN9YUR6IuLjP0IIIAAAgggcCMI5AiyqtqNYMFzRAABBBBAAIFrVyBQhb9r9465MwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4CoTG9wDmI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBA0giERkZGJs2ZOSsCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCMRLIDQkJCReBzAZAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSSRoBQX9K4clYEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE4i0QGhUVFe+DOAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBJfICzxT8kZEUAAAQQQQAABBG4UgV17DtwoT5XniQACCCCAAAIIIIAAAggggAACCCCAAAIIWAI3FciNBAIIIIAAAgggkKQChPqSlJeTI4AAAggggAACyVeAP1wl39eWZ4YAAggggAACCCCAAAIIIIAAAggggAAC/gX4h87+XdiKAAIIIIAAAokrEBoSEpK4Z+RsCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQIIHQBB3FQQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkOgChPoSnZQTIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJAwgdCoqKiEHclRCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQqAKhISEhiXpCToYAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgkToP1uwtw4CgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFEFyDUl+iknBABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBhAkQ6kuYG0chgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkOgChPoSnZQTIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJAwgdCoqKiEHclRCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQqAKhISEhiXpCToYAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgkToP1uwtw4CgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFEFyDUl+iknBABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBhAkQ6kuYG0chgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkOgChPoSnZQTIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJAwAUJ9CXPjKAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQSXSA0MjIy0U/KCRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIP4CYaGhFOuLPxtHIIAAAggggAACCCCAAAIIIIBAIIH5Xy6WAwcOSIl775G777pD0qZNE2hqom2/ePGi/LTmVylTqmSs1zt69JgcOHjYum6GDGmlYIECiXYPwZ7o3Lnzsmz5Smd6pYcflHTp0jnrvgsRERHWphQpUvjuStD6vv0HJHeunJLUfxMKDz8nFy5ckMyZM8V5n2v/2CAZjEHRokVinRsVFSX/+98++XvHTvl7+3apWaOq5M+bJ9Zj2IlAchLQ74Of1vwia9f9KR3btZKQkJCr8vT0Mxp5+btIv9dTp06V5NeNiPD8Y/QUKf77v1/v2bNXtv69XbZv/0dCQkOkVYsmSf7847rAjfK7JC4H9iOAAAIIIIAAAggggAACCCCQXAXCkusT43khgAACCCCAAAIIIIAAAggggMDVFzhw8JB06tLTufCIoa9Jw6fqOetJtTB9xkcyaMib1umrVHlY3ho5UDJlyhjjcnM/XyCDh4525k2d8FaMOUm94dSpU9K+0yvOZVYsnS+FbvYf6jt79qw0e/5Fa+7k8aODCsg5JzYLGuDbvGWr/Llxs6xbt0HW/PK7nD1zRkaPGiT1H6/lnhrv5fPnL8iRI0fl4OHDcvjwETlowpL/7vqfbNu+QzZt2iL7zbULF75Zliz8VFKmTBnw/BrcadfxZWt+iRL3yMtd2kuF8mX9zj99+oxUqBx93+fPX5QObVv6nctGBJKbwOJvlkvPvoPkmPnc6Sh0801Sp1b1JH+au/fskfpPRYfYvl++IMkD0WfDw6VNu64Sfu6cTBg7UnLkyOE8z63btsumzX856/FdKPtAacmTO1e8Dhs3cZrMnD3HOaZu7ZqSK2f0PTk7ruJCcvldchXJuBQCCCCAAAIIIIAAAggggAAC15VAmP4L56v1LzqvKxluFgEEEEAAAQQQQAABBBBAAAEE4i3wxlvveR3zu6kmtX3HLq9t9kq9ujUlW7YsMm/+IntTrI9tWzXzu/+wCZW9OWaC1z470KeBtptvLijp0qb12h9oRatgbd22QzRMV+q+ewNNuyrbT58+K8+1aGvCeH9a13uyUQuZ8f44J4zyw6rV8tfW7XLahPROnTwjJ0+dFA29nTx5yiyfkt2mop0d/vG94QFD35Sqj1SWDBmiw4QayJw0ZYZcvHRBLl2KEK1+GGkCdxfM4+kzZ801Tsmp06fk+PGT5vynrXCg73l91//551/RwGVsVa2+/3G1FejTY/W5Hj16wvc0znrGjBnkucYNZcbMT6xtcz6bT6jP0WEhuQvcZ0Kv502lT3v0GzhCHqn8UKyVPu2519OjBvqatewov/z6u3Xbtes/J9MmvSN33F7UWl+wcLG88+6kBD+laZPHOt+jwZ6kXZuWXqG+z7/4Stq0ig46Bnue/2Le1f5d8l88R66JAAIIIIAAAggggAACCCCAQHIUCCPQlxxfVp4TAggggAACCCCAAAIIIIDA9SZw+PBRK+CW2C1RT5w4KRGRnhaGGmxLkyZ1ktFoFalPPv3c6/wfffyZ17p7pWDBfFalo2HDg6uW98LzTf3+w8RBw97yCpj17/Oy6PMe8cY7VghDQ2CD+vdyX9paDjfBkc1btsm2v3eYcNw2+XPDZln5/SprX9bs2eTXH5eIu+Xti6YC4d59B2Kcx3eDtsSsbFrqXumIiLhkWlxGv15/m/us+fgz8unMyXLrLYVlw6a/JFg733vJnjWL9ZzdwcVsZtusj+d6WfoeF9/1ggULmFDnv7Ee9sHlgJ5OUvea1atY8ydO/tAELP+Ocezmv7Y52zQ0qK9L6lTRTvbOTh1by03m+sEObRfctGUHqfd4TauKYbBB0NjOr/+Y9siRI15VxmKbH+y+SPOZPm7e4/bIbKpSut+r9vbr+VFbN2uYVIf+/TJrlsxeT0eDt+dMtUgdaUwr2NhaWHsdmIQr+nr/+NMamTJtlgmyPicPlr8/Ua+W01SGe2f0EHm+zUvWeTW0O27i+9Ktc/tEvc7VPNmOf3aKVtx0t0OPjIjyau+rVT811Dxz+ngpee/diX57a00V005dY/6OiO1CQ4a/KR/O8oSLY5s3dNCrMSqPJvffJbF5sA8BBBBAAAEEEEAAAQQQQAABBIIXoFJf8FbMRAABBBBAAAEEEEAAAQQQQCDRBTS40sQEidas+VXy5Mkt494ZJSVLJE5oQYOCpcs94tzz0MH95JmG9Z31xFz47fc/5IX2XRPzlEGda/Yn8+TzLxY6c1/u2tEKcul2u1WiVnV7pHLFGEG7n376WWrWaegc617QsMz3P66RShXLO5s3bNwiGiKLaxw7djyuKUHtz5w5k8wyIZbBw0bL1PdnWsfofdV9sonMfH+81KhaJahQX7r06UUrfN1z9x2m0tVtUtpUIMyXL0+Me9AWuXVrV5fYgpi+B2l73RzZs0uuXDkkf768UiB/XsmbN4/1qK1B06ZN43uI1/qePXtl2bLvnG1tnm/itOr97IsFssWELuMaC75c7HdKExPmjE+o77MvvrQqg2l1sLAUYfJ0g3p+zxvsxm2m4mPLF16S3bv3yF133SGzP5jgtyV0sOdzz1u6bKVpTdrF2bR08Ty5tUghZz05LAwfNdaE4z60noq+hzet+8HraennQIOuOgIFd70OuAorBw8dlueatbOupJUuEzvUpyfW77LGzzaQmbM+ta6jFeuerF/HtOItaK1fT//R339Vqnl+J7Vs3lj6vfqydftaQXT6lLEydOQYmTzF8x7QtuHaAnjepx9K2ftLy1Gf79lFi791KpNqmLjiQ+UCUuTNk8trn1Yg1c9pfEcwx5xzVVa0z5/cf5fYz5NHBBBAAAEEEEAAAQQQQAABBBC4MgEq9V2ZH0cjgAACCCCAAAIIIIAAAggg4AiMeXei7Ltcye32YkWledNGzr5AC8tWfG8F+nS/ViP6YOZHJtQ3KND0eG2PiIyI1/wrmTzlcujMPseA13pKkSI3yzvvTXGenwZvalSrbE+RwoULyUYTlLOHVmnr/Upne1X+/Xe3jH0vcIvFjZu2SK9XBzjzq1WrIu3atLDWGz5ZVz7+dJ7TurZrj36y7OvAVQOdk5gFvY/iJgAXmUA/U1RM9uzZJ5dMpT1/44gJ5rnHnv/9z1rVAFzuXDndu6zqaxp0KWICW336Dbb2abhl299/S8On6km/Pt1FK/qlSaNVGNNIenOOVKZqWaeurzoV917v10MaPFHH67yBVrp0aitPmYBQChNqSxEWKinDwiQ0NMQEHFfLoCFvOoc98EBped+0w7RDe1o57s0x42X5dz9Kx3bPm/BgMWdubAsfz/nca3ejBkkTOvW6iJ8Vbbs8fuJ0a4++/nVr13BmaUW4AUPecNZrVHvEK+zp7PBZmPnRHCcotHHjZvnKhI6uNChoX+KSCYxdzTHro7myfsOmeF/ysRqPSsUKgcNVsZ3w/IXzse021d08VfpinXSVd+rnt6EJg2rF0h/MZ0a/o+668/ag7kLDX5WqPi7ng3htfdtqV3q0rvW9FcyFfly2wKlquOirb+TfPZ7vn9iOPWFabrvHzNmfSeYsmdyb/C7nz5vXCgr73Wk2/rF+o7OrfLkyzrIuaOXJPj27yi1FCnt9z+/891+rkqZvYHKHCVxrUFvHk0/Uls4dX7CW/+v/6HdpQsf1/Lskoc+Z4xBAAAEEEEAAAQQQQAABBBBAIFogTP/omtitfaJPzxICCCCAAAIIIIAAAggggAACN47AV18vdSqLVa70UFChvnymupl7ZDeBoutxvNLtRdGAiI42rZpJtaqe8N7589HBnGK3FjFtY4t4PT13OCl3zuxe4TNtyxgo1KfXevX1YV7nqmlCfZ+YkNihw0dMy9Ojcvr0GWe/hmC0HW+Rwjc523ShePG75B5TRe22YrdKMfNzqwmQ5Mjh/zUYObS/HDKVuPyN92d87IQXs2bNaqovtguqqp+ey67s5a8amX2t5555SiIjIqXf60Olf98eVqBP97Vs9qw9xesxU8YMTqjPa0ccKxpK8g0WrvzhJ69AXwlT9W/axDFOoE/DVd179Zf5C76yzr7cBFXr16slo0fGHk7VKl1T3p/l3FGrlk2tFqvvvDdZbr+tqLxg3kdnzoZb+3XugEEjnbkVHiwrNao/Yq0fO3ZM3hj9njxWs6qULxfd7jR/vtzO/LgWln67wgngvWhCiXZYUY87f+GiV/XCW0x1QncFx0DnLlDA97OdNdDUa377T2t+kUAVEWO7+WJFb0lwqC+2817L+9q/0NJpQz552ow4Pwf2c9HKfhrsTujwDfoFOk+E+R6xx5Tps+S339bZq0E/jp84Lai5+jnV6p+BxirzvrJHmVIl7UWvR6u6rGlr3KvPQBk88FUr0KcTtBrrqZOnnbl2oE83aJVMbd/tO/KYCn3+7uf+0vfJqu883196TGRklNR56jmn8l8DE6Du8qL/kOC8+YtkpPndokO/w79e8JFV6dPaYP6j1/QdN8LvEt/nzDoCCCCAAAIIIIAAAggggAACCMRfIIxAX/zROAIBBBBAAAEEEEAAAQQQQACBxBIofvedVrW1b779Tm4zAZg2zzdNrFNf1fOMNUEse0ycPN0EKjxVz+xt+tjPhPD0xz3aXq6s594W17KGUl7u9XqM0FqXl/vEeuhsU22sSpWHnTm6PHXCW866Lmj7TK06WPWRh2O0bi1dqoSpihdhVZDyOsisLF2+UtZc3ugbivOdG2hdK/DFNpo+11DuL1PSCrzFNi+x9mmQTgN22t7THg8//KC88+ZQp9LXiRMnpU2Hbk6gUedpq9nuXTrahwR8XPzNcuc11DBMh3YtZe0fG0xA713rGN228PNZUrjQTTL5chtW+2RDB70qBQsUsNrltunY3dr8+9r1MnRgH9G2xfEd4yZFv18bPlkvvof7nd/gicfl8OFj5jn9KQ+ZcFPlhyv4ncfG5CWgbXD1u0XbSs/7fKF0e6mD+AY8k9czTvizWb7iB+tgDQrH9rl95ukn5IH77zOh7ELOxd4bP9UJ4jobLy8sXLRE9Md3aIVRf6G+NGlSx2hJ/qQJJtutf//aui3GfvvcGnq2R5Nng2v5faP9LrF9eEQAAQQQQAABBBBAAAEEEEAAgfgJJLz+f/yuw2wEEEAAAQQQQAABBBBAAAEEEAggoNXWAlVcC3DINbf5QhAtIxPrplOkCJXaj1V1qmEFOq+2Ub21sGnxu/kvJzymQZvYxu7d/5OBg0dZP76hv3927pJnmrSRju1biVbOc4+dplWwPXLnymEvxutRQ2z2CA8/J1Hm/3zHzTcVtDadDQ+XeV8sFFPAyu9wV/xa9dMa06o0umKi+4DKlSpI/rx53JusaoT9TVU8baXrGzS8r2Rxedu0RD5+7IQcPX7CCqy5K4Rphb4hA/p4VbrzOvnlFQ1Huls2v9ylvVWl76WuvZ3pt95SSG6+qYAVtHS3/u1gKulpoE9Hzpw5nWpa+px79R0k7709wjlHMAu/mkpl69b9aU2t9mglyZAhXTCHxTlHQ0o9u3eKc971MKFls+ekfNnoKoh6z7/9/ofM+Wy+c/tdOreTXDm83/sl7r3H2X8jLdQx30/2d80HMz+W3j06x/n006VLK9Mmj/WaN37S+05g9vbbi0qPl1/y2u9vRSva2Z//UiaI3LFdK69peh17VKr4oBS+2bt6qb1PHxeZltG+3wHu/Xny5JYK5R9wb/JavuXWwl7r7hUNBNufO/0eskeg7768eby/p+z5SfVYq0Y1J9S33rQJPnz4aIwqrv/u2uO8PnofT9avFdTt3Ci/S4LCYBICCCCAAAIIIIAAAggggAACCAQUoP1uQBp2IIAAAggggAACCCCAAAIIIJD0ApGRkV5tYjNkSC/uqvqnT581rQAjrBtx7ztw8JD8vvZPSZ8+rdU6Nj7V4TRQdebMWefJpU+fzm/1Ofe80NAUsYadcpkgW2HTltQ9/vnnX/dqjP26M5tpVZuQ8bRph6jtKrNmySKZs2SSHCbAlyNbVsmePbv5ySZ5cudygmWzTIW+3ibs9ULrFtZzsCvBHT4cs5Xurt17nNuJiLjkLGsr4Nr1n7MCLn36DTZV/PJ7tRXdYQJ/9siSJbMsX/K5vRrjUVv4lilf1dm+Yul80epe9tB7qFiljr3q9ajBnq8XfCL/7Ngpr/Yd7LUv0IpWC9Mff2PG9HExQn36fvBX5UqPH/3WOH+nsbb1ffVleb5544D77R36unXo9IoT6NHt//vffqvi38rvV9nTpF/vl63KiC91e9XZpiGiDm1bOutZjXVLc82pprqiDm3L/OlnC7zaODuTAyx8frltsO6uWePRALPivzm2z4/7c+/+bEWZlOZ289pu2rxV8uXNbX22M5pWysGOc+fOi1ZYtEemTBntRa9HbZlsBz1TpkzpfFa8JrlWSpa4W/THPbS6mTvUV6tGVdNeO3CIS4/VMKq+d7f/s0v03m4tUkjy588rISEh7lMnynIwFjpnw8bN8r/9+yV92rQmLJrPmBf1e31/38X6ev3x50ar1ba2F9cqlToquaoyzpg9R3q98lKczzFFihSmmuODXtfWAPOaNb9a27Zs2Wa9BlkyZ/aa417R7yk70Kfb27VpHuOc7vkvmoByoHH27Fkr1Bdov24/bz7LA/r3lHTGLr7jl9/WOoeUf6CMtRzMd599UO8eXeTAgYPWqoYf7eetVf/q1XnMniaLly4Xd2teZ8flBa209+Oq6DbA9v6IyOjvf93Wp/8QEzL2DkBu3LTZnm49zp3n/3u2RPE7ne+WG+l3iRcOKwgggAACCCCAAAIIIIAAAgggEG+BsKT4o1m874IDEEAAAQQQQAABBBBAAAEEELhBBQ4dPiIPPFjNefajRw2S+o9HV/upWfdpp8XgN4vmypEjR6VXv0FWiMQ5yCx0fqmtdGrf2isQ6N7vXt7y1zap9fgzzqY+vbtKqxZNnHV7YY4JR/Xo/bq9Kn/+/r0EChj16dlV9Mcek6Z+IIOHjrZXrcfChQtJr5c7SdGiRZztS5aucJaPHz9pVYezN2jVvECj1H33Svi5cPlq8TI5ePCw9RNo7nkT3Cle/C4TrOkksz+Z50zT6ktDR7wt+fPltradMSGW6R9+7OwvakI69kidJo2kNiEmu2pV0xbt5esvP7Fa4WpIw65Wp6EzdyjTPj4+j4Gq78XnHFcyN126dFY4aaMJOwUztMLg5HFvSvly98tSUwnx+x9Wy2t9ugd0uHQpQtyvu15jik973cdqVpViph11d9Nm2R3IyWICnLpNKyNqtSv79XDf52sDhkvliuVMVS3vqnHuOe7l1ZdDU7rtvpL3undd0fLiJcukvQkv2uP75QucCoPuz736bVz7vXww42N5y7Q6tt9L9nGTxr9ltYO212N7/PjTeaLP3x7z580UbfHtO3q8OkA+N5UedehnY/7cGb5TEm1dw40Lv1oq702YIhpM8x3aEvXNkQNjhEt958Vn/ejRY/KY+Y6zg15qvHzJPHGHn9V72Kh3YryH1OOd0cOsKpHua7q/i5eZcx04cEi6dO/rXONW832x9Ku51iEaNtUArj5ffY/q+1XbSMd3aBU8DUvbAekPZnwinTq0DniaGbPmOPv0u+hKWj5PnT7bsdHqmO+Om+KcW6syasBX36vvf/DuAzxTAABAAElEQVSRtH+hhbMv2IVVP0UH6e415jri891Xs3oV51IzZn/qLGugr3nTRs665kXd3yHOjssL2gZ85qzo43332+tfm6qFcY0Jk6b5ndL42QZOqO9G+l3iF4ONCCCAAAIIIIAAAggggAACCCAQtAChvqCpmIgAAggggAACCCCAAAIIIIBA0gucMxW0Ao1Bw9+U70xLVH/jrTHj5eaCBbwCgf7m6ba77rzdK7Sl4T1/ob7FrsBdxYfKBwz0+V5Hg12+gT6do+0o9aeZCVx07/JijMp/GsBp0aqj7+kCrv+1dXtQYQz3CW4t4l1NMFAIQ48pfrcnaKLL2qJ26vgxUr9BdPixeasXZf5nM6wQm87R8WD56Balf27YLD+sWm2qZcU/8OI5W+D/ZjRVzurWqRlwwnxX9TkNKRUK0GIze7bsfs8xqH9vOXb8mGQ0lSMzZMgomc31NESn1arsgJEeqJXJxo8dYYXVOrzUw6nwlzVbFunc8QW/506bNo1UeLCs/PDjar/7deMr3V6U3Xv+5wTP7IkakvIXDLP366OGqAYPf0tGjxzk3ux3WVuA/v33Dmdf3jy5nOXEXjh/wbvyl31+vd/n23Zx2rXa2+3H1m07izsQaG/391irZjWvUN+XC5fECPVppUR3qLJm9Uf8nSpRtmmg79GaT3q9Z3xPrJXoatdvLKuWL4yzYqDvsf7W9ZodOvd0wnY6Z9J7b3oF+iabEKm7pbP7PBr2bW6+hzToGCjEvGr1rzLEvMfcodKSJYq7TyM3mVbZ9nt13R9/JijUpwHh51s8J1odVMf4SdNNq/bGMb47dd/Jk6ecipW63vr5Jn4rsOq+uMZBU0101JueVsAaiHzcfNe4Q311Hqsm75sAtIb6Rox6W+rWqiEFCuSN67Re+7/59jtrXducp0qVymtfXCt79+43FWwjrWnnTGtx92c4a/YssmfPXucUx06ccJbDTaVIe59Wl43vdZ0TXcHCjfa75AqoOBQBBBBAAAEEEEAAAQQQQACBG14gTNtEUK3vhn8fAIAAAggggAACCCCAAAIIIHAdCLgDfXYVKPdtT542M6hQnx7z3DNPSa8+A63DNXjy9/Z/vFpnautFDeDZQ0MdcY1Tp07L2HFTxTco99QTdb3adE43lZ0WL1kubwwfENcpE31/6VIlpXKlh2T5iu9jPbdWuapRrbLXHG0/+qYJinXt3sfariHEViZw5a7MV6PaI6baVJRoaMgONmrFuAZP+G+n63WByyvaAnT9byudXRp+c1d80x03mQDn228Oceb4Lvz8y+9OqKlJ46fjdX09l7vVanj4OXl3/FQZ+94kr8t0N8G7tq2bOcGhu++63Qn1acj0jtuKSfWq3ob2CerUqi4pwlKY9qtFJEfObDJ8xBh7l7QyYSRtR6zhrLiGBo6KmcqPWgXt0qVLsuDLxdYh2m74JRMqdLc19neurdu2O5uzmrbN/0XIR2/A/VlzV2azb27m7M+kZ/dO9mrAxxw5sllhTzvU+ek8U23z5Red10gP/OXXtV5htJrVE6/lsO+NaUvZ8mXLOKE+/VzdX+Y+q/Xuwq+XOlUJNRw26+O5Ekz7Zt9r+K4PGT7GqzLb4IGveoVttVqnO9Cn36V1TChNv7/GT/RUWtPg6ksv95GpE97yPb21bofsdEXfNxlMdct7LrfetQ+4qUABe1E2bv4r6O9m56DLC088/pgTINQQ4bQPZom/trkfz4muQqqHPlU/+O8c9zUjIiLl5R6vOZvatGpiwpapnXVdSJkyTLp2ait9X/N8B3Xs0kPmfjTN633mdYDPyp49+5wKtA9XKOvsDea7b7P5fVWzTkPnGN+Flzr38t3krGtgs0LlWtb6vDkfSsl775ZapuW2tk5OyuGu+qrXuZF+lySlK+dGAAEEEEAAAQQQQAABBBBAILkLhCX3J8jzQwABBBBAAAEEEEAAAQQQQCA5CVSrVkVGDnlNMmfOJPtMqKxF6xedalDaLlWDUBqkiWvUNpWW7FCfzl349TfyUoc2zmE/rPrZWdaFR6tU9Fp3r2jFpHlfLJK+rw/zCgvpnFd7dZHWLZvKi6Zl5ItdeoqGKnRoIO7b5SulUcP6oq0dr3RoJcEe3byr/P1v3wFp066L16n1HzZONu1MV/7wk2jb3AsXvCsjpk6dWvLkyimVHn7Qb8DriXq1RINgdvjHfj72RbRdpnrMnfelvUm69+hnqqXdLrcVK+psi21BQ4KZTGU8e6Q1gaH/YqjNp5/Nl1GX22za96DV+d4Y/rrVetjepo8vtGomq3/+zakm+UL7rrJk0Ryrja57ni4/3aCe9aPLIy9XBNNlDUhpG2kd+j7u2rm97PnfXsmVM6f5yS45zP5s5idHjuySz1TV22VaNGuIMjQ0xIRSbzGv6S7Za95bY0wb67gCfXqNY8ejq3gVSUB7VD1HYo1uXTpYbUz1ea9bv0HqPRldFfKPPz2fm2Cu9Yz5TNmhPg3LacCznAnW2ePb5T/Yi1br3WCcnAMSsNCpYxv5e8dOaWPCmlVMoNb+x8VaybF0uUecMy4yIb8rDfV9ZsKc7lbOLZs3lsaNnnKuoa93L9N62B5aMXL6lLHOd2bVRx6WJ59ubu3WoKVWctTvWn9DA6XvjhkuFU0ozd93bkFX5bpjx477O0VQ27Qd9rONnpDJUz605r8x+l0pc18JKVu2tHO8VtG0Q8S68bnGDQPet3NQgIWJpnX6yu9XWXv189iqeRPzOTkaY/azTz8hH5mApP7eWbfuT/M5fjeo4KmeaM0vvznnK/tAKWc5mO++yAj/FS+dk8RzQduG648ODTSuXbfe+k6J52liTL/llsKSLWuWGNvtDTfS7xL7OfOIAAIIIIAAAggggAACCCCAAALxEyDUFz8vZiOAAAIIIIAAAggggAACCCDwnwmUM8GD8e+MdCrD5TVVr56sV1sGDxvt3JO2TdTtcQ1tK/lMoydl9kdzralz5i6wwlR24MZujag7NUgYKNii+/fuPSDdXumri15Dq3LlyJ7dBP4WWts1XGOH4DQQc8cdt8nGTVu8KgR6ncCspEgRJnVrV/fdHGNdQzVp06X12p4iRajXuq7MMs935FvvWdsfN9Xi+vd9JcacuDZ079pRtpqWre7qanpMHXOf2lpWx7h3Rknlqo9by/qfth27y1fzP5Y0abwrXjkTrpEFDT39uWGT/GFCZTNnz3Wq/dm3p618mzd5xgqKLV66XI4eOSb79u0XDVBqsMjdjlSPeaFDN1n0xUeOi30e+1Grbrnbeo4c0s8KNGo4dctf26STCYPqeKX36/LF5ZbCvXt0kbL3l7JCmTVqeyp2afho7epvrZCVBiJjC9PY19bHE6ZlqT2SOtxmX8ffY7cu7b2qr5UofrdXRUm7Zai/Y323PWBsCppKjrt377F2ffHl106oTwOQC79a4hyi3x9JPXKbkOzHM7yrPOo1s5kWze42zDvM++dKxh8mMGxX0dTzPGyCua/29A71LjLhZffo2L6VVyDvvpLFrWCphiF1bN32t5QpfZ/7EGd56sQx1vvQ2eCzkNe07LbH0aMJD/XpOdqbFt5zTWDRvq+WpkLo1ws+sip2ajvljqbdsD30u7VD25b2arwe15gAqLtq5pDXX7Va/foL9el37ojBfaVWvWeta2jQ+bZitwRVkVDbkuvQz22xorday8H+J9T8TtDjrmSkTplSsvgJa24wAcWnGrW4klM7x04wLZ8DVSq1JyXn3yX2c+QRAQQQQAABBBBAAAEEEEAAAQQSLhBm/7E+4afgSAQQQAABBBBAAAEEEEAAAQQQuBoCOU11MnerV71mLlOtzD20hWQwoT49Rqul2aE+DQCtM6EYbUeogaqFX0WHX+rVruG+RIzlAqYi1SRT/a61CZq4h1bj62JaWPobGv7SCnbBjGBCfdpON66WunqtM2fPOMGYXXv2+r28BiOPHTsWsLKehgXffmOI1GvQRP424T57lH0gunKWtoN9e/Qw6WSqE+rQdp7vjp8i3UzlucQcp0+flbtLPhjrKdXZ1/qF1i2k1ysx27mOnzg9Rvtk98k1lOkOTrn3+VvW5/2GqfTXx1Rs9B3a4rl9p+7OZg1F6nt3yPC3RNvGZjHhvOVLPrf2b9z0lxNSu3DJU10xMso51FmIbzDvxInosFWB/Pmc81ztBW3R7DvyuD7bR44e890dcF2/I5o2buCEfeeblsSv9+0hqVOnsoKS+rm0R43q0ZXyPv1sgezc+a+9y+vxmYZPSAHTFvpKxrlz52XFyh/k19/Wy8+//uYEfO1zamAt2Eqj9jH2414TKm3eJvr9rMG2sebz51tBb9PmrfYh1qNWZdMf9zhv7tMeGjr1F+p7wHzWNVga28ifLzrUd+x49PsstmMC7cuWLatpBTxG6j/lqd6o358t27wk8+d+KG+/O9mqlmcfq23Ng/0dYB+jj2vXbZCnn33e2aShyJrVqzjr/ha0amcrU4l1sqnup0O/71OnSiWP1azqb7q1TSuZLv5mhbX8yMMVnMqNAQ/w2XGHaZesAd6kGFHi50slKS50+ZzJ+XdJErJxagQQQAABBBBAAAEEEEAAAQRuGAEq9d0wLzVPFAEEEEAAAQQQQAABBBBAIDkKpApLleCnpdXAbjcBiS0muKLjy4VLrFDf72vXe1Vcq1SpQpzX0LaVA17rKefPn5fSpe6T+ibwdq2PKBMu0RbG/+7aI//+u1s2bf7LasurQTStMrjqu0UxQpT2c8qQIZ0UKVLIK9Sngbj6j9eSdGk9FQM1jKhtLOeYFrY63nl3ktQyYZfsJqCTWCMqKjJBpzp23H9I7NEqFWIN9cV2Ma2eVTB/XstOw6U//eRp4ayBn6qPPiwPlImueHb48FHp1rOfFXa0z7nAhM/0xx4a6rOHttS1R46sV1alyz6PPka5koGhfio7uude7eVUqVIm+JL16j7mhPo0APadeR9We7SSaXn9vXNOrfypVfTsscgEeQMFY7Vt7pWE+iaZ98Dotyd6fa/Y13U/Xrx4KUYQz70/0LJv1Ux9zn9v3yklS9ztdci+/Qe91oePfNtr3XflwMFDvpusdbeb3wlmY2hoCmeXBtmudGjgesBrvaTf60OtU2mgWKvk6feVPRqZlrhxBfHsue5HrXLYuHlbZ5OGIgf1j67+5+zws9C9awf59fe1Vgte3d2+0ysy4d1RUr3aI35mi/y1dbvzPniw/P1+5wSz8agJui5YuNi0yg1mdvSckiXukXtNxdFgRvOmz/htwx7o2ImTpwfaFXB7cv1dEvAJswMBBBBAAAEEEEAAAQQQQAABBIIWINQXNBUTEUAAAQQQQAABBBBAAAEEEEh+As8921D69BtsPbHP5i807So7yw+r1jhPtH696JCaszHAQtPnnrb2aHUr9+jSuZ2kMu0Ogx1rTahwydIVwU635mkI7wlzr+5x4sRJ00J2jnuT17IGmMo95L8KoVYz+/nXtQGrcX0y9wtZsmSZ1/m02qFWmRvUv5ezvbdp/7lo8bdOiGX5yh/lqavQ8tS5gXgu3FfyXtGwVwpT7U3b2GbIkF4yZcwkGTOll/SmvXGmjBnljz83ycxZnzpn/vP376157m4QWpWt6mNPOdX1tMWuO9TX4NmWXmEk52SuhUI332StRUREOtUVdUOOHIkXisycJbNzxT179jnL1/tCzpw5pNZj1WThoiXWU/lq8VIr1Lf8ux+cp1a/bk1nOa6FUNNqNaFj0pQPnIChfQ69N211q2Gy+ZfbKtv7Euuxe6/XTMvrjyRlPL57fK+dNWv0+8N3X1zr+/bvd6ZkzZrFWb6ShabPNTQBunWOmTvQpy2X+/XuFu/TLzPfgx0793K+o/QEU03l1YIFCgR1Lq0AOeHdN6RG3UbO5/SFDi/Lq6Y6Z2tTxc93rFrtCfvq9nJly/juDnpdv4deGzA86Pn2xB7dOwUd6tP2zfF5/2iFUbtFsn29uB6T6++SuJ43+xFAAAEEEEAAAQQQQAABBBBAIG6BsCjzzxndf3SN+xBmIIAAAggggAACCCCAAAIIIIBAchGoY8I1dqhPwwjrN2wSd/CnTq3qV/xUn3y8tmTImD7o82jryPiG+u43VeBe6fai1zV2mup7sYX6vCb7rBQ3lZyOGg9/Y9u2HfJKz/7+dsmMmZ/II5UrSmXTulJHNhPm0ZBfn/5DZfjgvqKeh0x73ysdP63+RUJM8O6uO27zOtXKZQskUBvZEW+MlfETp3nN913RdqWzP5jgbP58/iJZ98cGad+mhRNuyZUrl1eoL2PGDNZ8fe80a/Wi9H6lszR4oo6MGNJPnmnSRrR6WJ+e3mGjUiY86A4k2RfUcGbDpx6XOo9Vl6JFi1ib//l3l73beszpp1Wt14R4rGS+fO96yC4TykxO45mn6zuhPm13evjwYfntt3XOU6z6aGVnWRfeHDlQLly44LXNXsmRPbu9GK/HX831Bg8b7RyjbVkHvd7b+lzoxvcmTHMCas6kK1h4ptGTTktxrWQ3YfIH0rHd884Z8+XN7SxrZclff/zG/F3U2RRjwbfdeYwJsWxwh0T1eyCxxrBBfWTpspVeITw9d/euHSVdunRBX0ZbHb/1zgSrgqj7IP3u04C3/tjj9Okz9qL1OHTE21aQ172x5L33iLti4uCho031060yZEAfSZs2jTN15Q8/WcuFC98seXJ7t493JsWyMPvjz6RK5YdimZF4u0aNHmcqR8byBvG5VHwDfcn5d4kPDasIIIAAAggggAACCCCAAAIIIJAAgTACfQlQ4xAEEEAAAQQQQAABBBBAAAEEkolA5syZpGGDevLJp59bz+jTOV/IelM9S4e2YKxQ/gFr+Ur+U6GydwW9KzlXoGO12pcGXdxDW3DGNTRYcvddt8vttxWTYkVvkaK3FjEVqvIFbAGqgbw2Hbo6p1WjD6e+K01adnBCNl179JPvly4woRdPwKb+449JZdPCOKurKpxzggQsfPPtd9K6bWdp1qRRjFBfaEhowJbB8b3UBzM+cVp97jAtPt97e6TznHzPtW79Bnm2aVvLoLt5/htMwK9Pr66yZNEcy9V3fhXjoW2J1a/sA6WlTOkSUrZMaVNB607r/sPDz8maX363qvutclWO1CBWXFXPPv1sgYSfDZfHalQxVf1y+F7aaz1zluiwlbZgTk6j3AP3W62QteqkfhbeGjvJeXpVqjwc4/2YWO9P5yJmYfXPv7lXZfTIQaKV3ZJi9OzRWdq2aiZnzpx1goKj3hxr3gePSJHChaxL3nl7MefSGsD68ac1UrFCOWdbYi7s3eeq1Od6n13JNbTl7MChbzjfNe5zderSUw6Z4GZz872g4dy4xss9X5N5n0cH93R+WxPeDT8XLtM/+CjWw7VVs+/QVu6TTIU//W6yh55fg4aDTZBTh1bw/O67H63lRyo/bD3G5z/jJr0vw0eMkdGjBkkWl6l+j7Rr0yzgqT4z9+EvRBzwgMs7JkyKPQQd1/Gx7b+RfpfE5sA+BBBAAAEEEEAAAQQQQAABBBAILBAaeBd7EEAAAQQQQAABBBBAAAEEEEAgoQJaBenUqdMBf3R/Ug/f8Mypk6f9XvKZhk86292V7bQ9Z6pUSRPAcS6YiAsaXHL/xHVqDTYtX/K5vDN6mHRo21KqPuIJOs0ylaDcVbbs82jlvyeebuEVDnln9BApdd+90tcE2OxRxVTpS5s2tb1qdUhIrMDUFtPa2A7NLFsR3UrVuVgiLYw17XL7vT7UOduvv/8hBw8ddNZ9F1KGhUnqNNHPefqHH0kjU6VPQ6P+RuVKD8myJfNkw+8rZeqEt6Rd6+ZSssTd8tfW7TJg8CgpVe5R6fnqAOtQd9XGRx6uEGfHiY0bN1v3XrpcVfFtBe17L8VMiNMeGn4L5nN57vz5gJ9r/cxrV4ykHql8gnF6Xd+RIkWoNGvayNmsVSTtUa9O8K137WMS8qghLvfYZ4ztsXr1rzJp2gx7NcZjGtf7ST/Xsb02+lnWQJ+OXq+85HWuXn0HS2RkpLXtsZqPeu176eU+Md4j589fkHlfLLQCaF6T47nirvyogbcrHfO/XCwVqtSJEcRzn3eg+ezUb9hMtm7b7t7sd/nhh8p7bX/l5U7S07SmvZKh36HTJo91TmGF7Vq3cNY1/GuP8uVK24tBPep3nwb6dPy46mevYzKZipsvtm8d8Oe2Yrd6zf+vV26k3yX/tTXXRwABBBBAAAEEEEAAAQQQQOB6Fgi7nm+ee0cAAQQQQAABBBBAAAEEEEDgWhVY+f0quee+wC0CJ7w7SqpXeyRJbz9L5syilejsCkXjJr9vqmSltCq8uS+sYapbTbhJ21W6R23TmjcxxgOmEpuGvoIdh48cEQ1wxGdoeOQOn+CMBoo04BWfodW1+r42xDpEg0IaONOh52nQuLVXhSytaqWtdnU8baodzpm3QArdXNC0nX0tqEpZ1oFB/OfixYt+Z+027WJPno4Z5vI72WyMrc2ofczRY8fltQHDZIEJENlDbT+eMcmpdmZvdz/edeft8vX8j6R1uy5OpUdt9fr4k01k9ocTpXChm9zTrXacWj1Ng1oa9NGQzuJvljnHeibnkK+XfCs//LjaOfahinFXVTsbHu7M12BbbCNbtqxSsGABUUsd+/YdkAKmUmNs443R74n+BBq//vStqRCYLdDuRNle/J67vM7Tq88AqzJihfJlvbY/8XgtJwjl3vFolcDfTe55V7p86y2FvE5R/+nmUumhcrLXOK9Z86vXPt+VUiVLyGT50Nnc7PmOMnJYf9H23L7D3VZX9/cwwT47AKbX0aqQDZ+qJ/qdqFXeupgwnw6t1lezTkOpUf0RufWWInLg4CFZunyltV2/h1q3bOp7qaDX/93leU/pAffec2fQx/lO1IBxXxOwXb7ie69dWrVy4thRsmrNLzL6rXHOPq20Wu2xp6Rbl/byfPPGAVvyPm6CnXNMVUv9fKlJffNe0aHfably5nTOpwsnTpyUiZOnO9u0SmiuXN5zcpj70aGtx2dMHyfPNWsnE98bZT5PeZ3jVv0UHca7v3QpZ3ughUDffVv/3i5JX/9VZM5H08Q3QBvoXnV7o+faeP2O8Df3Rvld4u+5sw0BBBBAAAEEEEAAAQQQQAABBOInEPxf1ON3XmYjgAACCCCAAAIIIIAAAggggMA1IFDRBGjsUJ8GWGZ/8lmMUJ/eZtPGDU11s2HOHWtg5P4y9znrV7IwbeKYgMESf+f91oRqnm/jXW3L3zz3tkerVJS33/SE8ezt/+zcJZWrPm6vxniMuly9y71jpznGHvb+s2fPSq16z9qbrcd6JgDTvWsHZ1toaKhMNq0ntWJUXK0vT8UjiKcX0LCg79Ag2sT33pBMGTJ47frctCEukD86ROPeueWvv92rMZa1re8rpjqevk/soYG+2R9MkHvuvsPeFPAxtwn5fDJzivTqO9CpJqbV755o1EJmTx9vWhxHVytbu26DTJ72oSw3rTi1Cpu/cfzkKen6ymvOLr2XhwO0Sj1vApxaIS/EJBf37z8YfUzadM5yoIVyZUs7ob4/TdvguEJ9gc5zNbfnypnDK7CrIdgVK1eZdtneoT59TTSw9vXib53bq1O7erw+j86BCVioVrWyV2hS31vulq95TABP3yP+Run7intt1vDZxk1b/Ib6vCaalZZNn5VZs+c6r2v/QaOkkqnyqG4aXrsUESnaJtoeHp9oI90++u2J0rLZc+bzHHsw1D6H+/H06bNOoFjft7fecot7d5zLGkxd+u1Kq2Kgb5hPD2709BPSw1TW0wqgZUqXlEdN5UutOugOZmvwdNzE6dZ3u36/58uXx+u6+lkZPOBV2bd3v5Q1nwF7aDti35bEu/fs8Qr1tWrZ2LQpL2AfEuNR34drf1kRo8XzipWe1rulSpUI2MrbfTJ/33167Lh3Rpr3wl/O1JOmUuVfWwMHwd2tkJ2Dgli4t/hdkjJlyiBmeqZotdJA32c640b5XRI0GBMRQAABBBBAAAEEEEAAAQQQQCBWAUJ9sfKwEwEEEEAAAQQQQAABBBBAAIHrS8C3Xe4zDZ+Q+QuXOEEtDf9odTTf4FntWtW9Qn1P1K0VY05CJe6890HRYEuwI7ZQRDDn0OBP6tSpZeHX38SYrkEWe6z55Xd5/4OPJH0Gz72Fnw2XSVOjK4PZLRvTpUsnLU3Fq6nvz7QObWAqfg0b1CeGj78Wu9puVMN0GdKntYJUFy5ekmnTZ9m3YD2GpQj85xl9rZYsWeY1XysIjh4xwGpt69t2ddSbY73mBrsybOTbMn7iNK/pGux8f+LbosEWbZUqoSGSOVNGWfHdD17z3CvaMvXNEQOlkKnMZ1cP0yCXti3+aMZEKX63p2LZ5i1/ycJFS9yHOsuP1axqVfabZl4b93th2KC+VqU1e2JYihT2ojXv9UEjjXFa0SqZ9siYMe73nVak/OTTz61DFi1eKjVrPGoffs08ulsb2zfVpVM76dSlp70asLqlhti8Qn21ajjHJPVCurRpTbXLMdK5ex8n5KbX1O+Ddm2ay/MtGsudxaPbwLoDdDlNAE8rwmkrZ3tsM9VEHzXvf9+ROlV062fdp63HB7zWU1q06mhN1ffRmHcmWCE23dDgiTqSwbxX3hgzzisIZ002/2ne9Blp3aqp+YzHP9Cn5/j+h+j3oLYxD+Y8+llfveY3K8inlQX9Da2oOnRgHyvI595/1113yMLPZ8uYsRPkvfFTnV36vPVzrT91TWW+ls2flRLF73b233xTAdGfpBi+34fHjp9wKnFWqvhgnJf0992nYcYB/XpYbeE3SnSoT59n9VoN4zxnfCcUvfP++B4S6/wb4XdJrADsRAABBBBAAAEEEEAAAQQQQACBeAmE2f+KOV5HMRkBBBBAAAEEEEAAAQQQQAABBGIIfL3gkxjb4tqglbR2blsbcNr3y2JWaXNPrlm9SqzHa3W0H805/tq6w6oSpO1GfQN9er7t23e4Tyt1alfzWr/SFXc460rPFdvxEaYCV4NnW3mFwXT+w6YlpI57XW1L9Z76Dxxubff3n+Ku6nQvm6p8WslOKx8ONGEhrcwXzFhtWn/arT4Dzc+bN1egXdZr1fiZp2Tm7DnWnG5dOkiHti2Dvn7AE/vsKF+ujFeoT6u7DRscHaLr1W+QU/HRfahWzfIdGpx8qUMbuWQCjO+8O8nardbbt//jhPry5fOuJqihpBrVKkv9urUlZ87sUvKBKl6vYbVqVaSuqTDnHjcVzC8aPLQrC77/wWz3bqtCXObMmby2+Vt56MGyTvtpbTs8Ykh/qz2wPVfDSbF9Ru15vo8aTgx0XFyfez3X6317WD++57XX1aN82TKy899dcu78OSl00832Lq/Hrdu2O+sapvOtwubsjOeChgXtlq2xHVq0aBGZP3eG/Ltrl1VFMU+eXCa0ebNVVVGPC2Sk+143AS4N1O7du09CzP/dcUcx3WyNuL4btQ1sbOfW8Kb+aJtcDd/q+zZ/3jxWRTt/n++4rmfflz5qONQeLcz9BzP0M+6ulup7TI/unaRVi+cCVo7TIOMr3V60KjOOHTclRhh4vqniqT8vvfiCdOnU1vf0Sb7+swlR20Pft3EN/T3l/u4b8Fovafpc4gf34rqPxN6f3H+XJLYX50MAAQQQQAABBBBAAAEEEEDgRhYIc/8L9RsZgueOAAIIIIAAAggggAACCCCAQHIV0OpAJUtEV2fyfZ4ahNP2lPbQoJa7mpO9/Xp41IpY2opXwyvuUf3RytbqfSXvleKm8tz69Rvdu2Msa3vb6iZIZg+tODbno6lW+874/C2lxL332Kfw+9jq+SZ+Q5buyc8921Dmzf9Kxo8dGWcgS6v4ZUjvv+Wstpa1WzG7z6/LGvTq/FJbeWvMeBk+5DV5ukE9rykPln/A77F1HvMO2rkP6ta5vVw0wT6tEjbUVNlzB8AK3VxQqj1aSR4x96tBybymDat7jH1rqLRs3cnaVL9eLRk+OLpVqj1Pg1fdzTV69x1kb/J61EpwwQx9PTUoaYcvFyz8WhqaaozXw8iRI5voT6Cx/8BBeWP0u87uVqYynlZTvNpDP5dFCheyfuJ77aSsJqf3ktjnP3DwkGg4VIe+x28tUshajus/TzeobyqFznBaBut8/S5uZKqt1qz2SFDtavUYrYY58d03ZMc/O2XKtJlOIFj36WjwRF3PwlX+76qffnauWOLewL+PnElmwf7um2pauJe9v5R7V4xl/c4ONHbv3hNoV6zbYzunvwODuU5y/13iz4VtCCCAAAIIIIAAAggggAACCCCQMIGwhB3GUQgggAACCCCAAAIIIIAAAgggcL0LzJ33pYSfOyefzPncqzWmBpwSc2z640er9Wyw51z982/SqHGrOKenDIv+s0aqlCmd+bVMhTQN1ugobNrA1qtdU8qUuc9a1+pPX8z5UFb99Its2rJFoiJDrO32f1KEhUq+vHnl4YrlYoTttLpafIeG1zSYc+7cea9D8+fPK49UekieMm1A4xp33F5UVq/8SjKZ1re+Q8NShQtHV2gbMqC35Mntv/LfpCkfyKyPP7NO4e+5vNiulTQywSJ/x1eqWF7WrvvTuXz+vLmltmnjWvWRmK1QnUlmocfLL0pNU/VPW/i6h7pMHDfavclruYqx0cpk6tbZVBYLFKR8ttGTou1zf/hxtZwJPyepU6Uy77U0JpR6lwm75fA6Z2wr+p4ZMPRNq+rfmybY+GT9OjFe/9iOv5b2HT16TL4zLYh379krsz6a63VrTZ5t4LXOSuILTJoywzlp6+ebOstxLWilvcHm8/vSy33kOfO+rv/4YwkKQdrX0RDl4AGvWp+fD2d9KpNNwK+n+TwWKJDPnhL0Y4ik8GqhruvxHctW/GAdokFHf9Vi/Z0vtu8+9/w8JhQcWyXFth1f9mpB7T7WvZzS/H5wt4pftvizgJUR3cfZy5Wr1TO/ew5bqylTRv9+svfbj/6+f+19gR6vp98lgZ4D2xFAAAEEEEAAAQQQQAABBBBAIH4CIab9blT8DmE2AggggAACCCAQt8CuPQfkpgLeFVfiPooZCCCAAAIIIHC1BLSCV9kKMausaVW00SMHXa3b4DoIXDMCGnocPMwTNHzrjcFSr+5j18y9xedGFixcLC927hnjkBFDX7tuKhDGuPnrZIMGKu8zraN1aEVQbTt8rQwNyKZKlTLRW3cH8/x2mUp5Fat4AswDX+8thEuDUWMOAggggAACCFzLAvzt+1p+dbg3BBBAAAEErl+BvXv3ev1D5cD/ZPD6fY7cOQIIIIAAAggggAACCCCAAAIIxCGwdt2GGDOqmXazA1/rHWM7GxC4EQS0/emU92eZ4NvjUr7c/dftU/7193Ux7r1L53YE+mKoJP6GjBkzyBsjBsrU6bOko6l8eS2N/6Ltsv38U5pKqn16d7VWtVIfAwEEEEAAAQQQQAABBBBAAAEEEEAgbgEq9cVtxAwEEEAAAQQQSIAA/1oxAWgcggACCCCAwFUU2Lhpi8z74iu5ePGC5DbtWovfc4dUKF/2Kt4Bl0IAgaQQWLJ0haz84SfTiji1FDStVh+4v5RoG1MGAggggAACCCCAAAIIJI4Af/tOHEfOggACCCCAAALeAr6V+gj1efuwhgACCCCAAAKJJMAfNhIJktMggAACCCCAAAIIIIAAAggggAACCCCAAAIIXDMC/O37mnkpuBEEEEAAAQSSlYBvqC80MjIyWT1BngwCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC16tAqBnX671z3wgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggkKwESfcnq5eTJIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIXM8CoVFRUdfz/XPvCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCQbgdCQkJBk82R4IggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghczwJU6rueXz3uHQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIFkJUKkvWb2cPBkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHrWSA0MjLyer5/7h0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBZCMQakayeTI8EQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSuZwESfdfzq8e9I4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIJCsB2u8mq5eTJ4MAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIHA9C4SGhIRcz/fPvSOAAAIIIIAAAggggAACCCCAAAIIIIAAAteUQFSUSKT5YSCAAAIIIIAAAggggAACCCCAAAIIIJAQgTBCfQlh4xgEEEAAAQQQQAABBBBAAAEEEEDg2hP4as1ZeXXKMalaKq0MfyFbkt1gRESU7Nh3Sbb976Js3X1Rtu25KM1qZJT7b0+dZNfkxAgEEti484I8/fpBOX8xSppUzSD9mmUNNDXO7Q912iuHTkTInP655e7CqeKc72/CgOnH5JMVZ6Rv06zydOX0/qZ4bfvyp7Ny4kyk17b4rFS4J43cnDssPocwFwEEEEAAAQQQQAABBBBAAAEEEEDgGhcIizL/bJRg3zX+KnF7CCCAAAIIIIAAAggggAACCCCAQBACGiQ6dTZSjpyMiDH77IUo2WzCT/5G7mwppEAOTyho1YZzsu7vC3L6XKSEn4+SM+HmxyyfPqfLEbL7UKQc9XP+Tf9elGVv5jV/Z/J3BbYhkDQCO/ZedAJ9eoUPvzkt5e5OYwVbE3LFk+bzE2E+PhcuJbzMXooUIVbAcO7KU0GF+gZ+eNzvZyrY+x/RNhuhvmCxmIcAAggggAACCCCAAAIIIIAAAghcJwL8E87r5IXiNhFAAAEEEEAAAQQQQAABBBBAAAEVmDp9lgwYNNLBWDBvltxz9x1Wpa8f/jxnbf992wVp+Pp+Z44uNHg4o/SefNRrm73yZMX0MqyNp7KfVuAbPeeEvSvWx4zpQqVYgZRyX7FUcl/R1KItRzXU173X6/LpnM+dYzetXyXp0qZ11u2F9Rs2Sd36je1V6d+3hzRv2kjq990vm3ddlE71M0v7epmc/b4LHy8/IwM/OCZPmPsf0MJTnc33WA0z1uu9X46fjpRZfXPJrflTynd/nJMX3jxknW7L9IK+p3XWgz3W9z4+/WyBdO/RzznP7A8nSrmyZZx190KhoiWd1WrVqsjEd99w1u0FbeO682CE5MgYKpnSeacmt+6NkC5TTkuOTKEy7cWMEhpqH3X1Hs+ZCnnV+3veM+NeyCh33pTC78WDdQn0vvA96d4jEfJUf0+FvjwmmFq/QnoZN/+ktB99WMa+lEOql4n5nvM9R6D1VGHezv7mXTQVK0/6qbBXq2w6ef/rU7J220X598AlyZA25rnSpAqV9Gk82+s9mE4OHo8ZxNXP08LVZ61L33tLSimYK6W/27A+g//s3CWVqz7u7O/aub106tDaWWcBAQQQQAABBBBAAAEEEEAAAQQQQOD6EiDUd329XtwtAggggAACCCCAAAIIIIAAAgjc4AIRly5ZAh3bt5Y7by8q+fPlsdaHzjzuyGi1vrXbvNt51i7nqTyWwuStbivoCQftOxwhx0zYzT1qlU8nW0073TSpQiSd+UllftKanzATctK2ojrmvp5b7iiUUs6bwFyvScfkplxhUrF4GidQ1qRxA6lcsby8O2GabNy4WSIuxQws6XkiIz3X1jBbvdo1pOitt+hmU20wyqqWFm7OH9v4+uezVkW0L34844T6fI/938FL8s9+j9mvf523Qn0XTRU2rcYW1wj2WN/7eNAE+N57e4QsW/GDzPlsvpw7779Con39ggULSK/unSRbVk8w0d6+ZutFmbXSVE78J8JYebZmySDS4bF0UqOkpzXs2h0RctJ4nTwbIfuORUr+7J5U3+yVptri+QhpXiWtpHRl7GoNPGHN71grnTxdwbu97EO9PO+hkS3SS9li/gNk9r15PZqXyb6/87FUuAvWJdD7wn3NzaYyZPPhh6zKlFkzhMpnA3JLziwpRN8x402wr+OYw9LpCRMKfTyjaOW8YIY+h7OmIqWOdKnjPub7dSYcagKEsY1Hu+3zu7tAzjBZPjqvta9X4yzW4/5jEXLKFRI8ZIJ+dqivfd3MUtCnxW6RvGHOczt3Lrf1nvt39x4ZPvJtOXs23O912YgAAggggAACCCCAAAIIIIAAAgggcH0IhNF69/p4obhLBBBAAAEEEEAAAQQQQAABBBBAwC1Q7oHS8mD5+61Na01lvrkrz1jLzz+W0YS2IuVT04q3SL4wGdY6u7XdrgRWNH+YfDHIEwR8e+4JeWfeSfdpJWv6UGlRM6N0eOuQCfxFyY9j8kmoCYVFmKpkdqhPA0kpTVBq0pJTooG2levDTSXA9M55it99p+jP519+bYX6nB0BFu64rag8VrNqgL2BN/dtkkVmmHarVWOpyFbUVBLs2zSrHD0RIY+Xj77HwGeN3hPssb73kc8ELfXn8JGjVqgv+oz+l3LlyhHj+S/89aIMm+t5TfUorcCnobPjp0UGf3JW8mcLlXtuDpPaZVLK0dNpJG/WECfQp/Pf+8pT4a1RhTSS0k+luEsRl1OCOtlnXPRkIH22XvlqfF38vS9Oh0fKEBNg1fe3Dq0W+fngPFagT9e7NcxstZ/W/W9/dkJmLTstI1/IJhXuSaO7Yx3/mgCoPQ4ejzSfH3vN/2MWUx3xtoL+/830X7s958phgobZM8YMCN6SzztQqVdoNeKg2Mf5XtFfePCLQbnlzkKe86RJk9p6D23estUK9fkezzoCCCCAAAIIIIAAAggggAACCCCAwPUl4P+vTtfXc+BuEUAAAQQQQAABBBBAAAEEEEAAgRtaYP/RS6Zil0huEyB6pVEW0bCejoImfFeyqCf0s8JUFfMdgf6xZyFTEUwLAh42lcJmmlBUk6qmPJzPiDSV7iYs8AQC29XN5FQM85l2Rat2FMpu6+t7siL5Ukq/Zt7V7Xzn6HrTajHv33eetrgNtS/o2hnMscHeh+u0sS4eOxMlI+Z5Qmsa5hvVIoOUviXMqrA3fN5ZOXch0gr06UnSm4py7Wp4B9ZOX642F+tFkmintl/WEcjTszdh/53z3Rnp//4xqzqjniFf9hTyQe9c1mOrkYdko6nepwG+wc9nM22KQ2XKolPWe7iFqeinlSQHt8om2qY30Nj+v4vOLq3+WPbO1M66vwVtOf3lUE+1Pd/9D7+0V7Q9cH/z/oxvG2ANChYtGDP0Z1/jy1WewKa9ziMCCCCAAAIIIIAAAggggAACCCCAQPITINSX/F5TnhECCCCAAAIIIIAAAggggAACCNxgAuXuTCNDTJBJw1w/bzkvG3Z62r0eOxUhqzedtzS0wlmwQ4NZr7fMKk2HHJJRHx+XhpXSS5inq6tzinkm9KStSjVM2KxaRmd7Yi7sPnRJmg07JGs2nzPXD5HKJdPKiDbZJG0aT3JMQ1uzvz0tpYqlluEmzOVvnL8YJbV67rd2jeuSQ7T6nntM/eqUTFroCX9pSOw1U9WvSqm01pS4jrXPE8x92HODeVy54ZLTzva9FzLIXTd5/oSXOX2IDG6cXi5qYu7y+GPnJRk656yEhETJ7G6Z5au1F2T6t9EBzuZvn7La73avn05KmWBgfMahk5Hy4kRTGtCMt1plkDxZPG+CoXPOyB87I6TO/WmkcUXv8Nkvf1+UN74Il50HIkywLkSeLJ9GWj4SezgumHv6a9dF0+r5qDNVg6Sdn8xsVZHUjdtMy2gNoZ4x70l9//Z8NovUNa2kO4w5InvM+2jl+nPy6uSjMuWVnM45fBfWb49uk7xozVkZYkKAdkjRd25SrrepnVnqPpjOukTrUYdly64L1vu7/F2e8OY3v4Q7wcakvA/OjQACCCCAAAIIIIAAAggggAACCCDw3wmERZl/6hzoX2X/d7fFlRFAAAEEEEAAAQQQQAABBBBAAAEEghVYbUJvPSZGB57s49bvuChNhhy0Vp94KH6tZzUoeO8tKaXM7Wk8ATOfUN+9t6SSJyuml4K5wpyQnX3dxHr88qfoimTa/ldb/R44dlE+ec3TPljDWv8euCTp/bSXte9BW9bqHB1HT8UMNg41rVztoZXVtM3pR/1yWUHBuI61jwvmPuy5wTz+Zl43HSWLhDmBPvs4DZmlMq2P7XHybJT870j089p7xHtd28jqOBluH+F5DL8QJWfNjz1MJjDGuGBuwz63Bhzt8ff+SGv7vqOmXKPP+GCZJ0Sqm/Xepi0Nt4JxLapcWbDvtptSylOmxfNvf52XdzvHDGf63Ia1qq1pvx2VV8Z+fkImfnlKBpigaqChr/WMpZ4Ao87RwOr3f56zKvwFOuZqbN+6+4LsN86njCUDAQQQQAABBBBAAAEEEEAAAQQQQODGEQgj0HfjvNg8UwQQQAABBBBAAAEEEEAAAQQQSJ4COUzbXa0yZw8Np+nImiFU0pr2rDoypfdJ5Vlbvf+z2FQAGzLjmNfGRavPiv64isNJrd77TbDMM+2njSKfLPeEoTRct2iY/3akXicNckWf0/s9c0lOUyFu8Izjou1X1267KJtMJUINbCXG6NYws7SplUl+23peWr1xyApzvTXnuHzYO3dinD5B5/jjH08I8Z6boyvrLf/zkhxxhRLvLJBC7rwp+jW3L/T8o6lNRb4UToW9Rf0yS0Y/ocfpJnynP4k9yt1mWiI3SicnTAvhV2eeke37IuT9b8OlWaXUoq2Er2QMMqG8EFOx0V+b5EDnDTVEnUxFvxdMZb/UKaPDkL7zF5r3+KmzngDk0NbZrKqAfaccleVv5Yv1ep+sOCMDpnt/ZvTcdgjypbGHrSqT7utlzRgq37+dz72JZQQQQAABBBBAAAEEEEAAAQQQQAABBLwEov8y6LWZFQQQQAABBBBAAAEEEEAAAQQQQACB60WgtGk/+90YT0joyMkIKdt+r3Xr47rmsCrO6cqKdefk/a9PxfqUtF2vHQiMbaK2OfU3tBVvYo66D6aXwnk9f77q9ERmK9Sn5//zn4uJFupra8JeOsrckVoql0grGu7aaFra/pfjVLinKluaVNEhtMnfhMuuQ9Hu9culNqE+T5vghNyrBuxS+KTjLl668mpwz1RMLRlMe2T9afBgGhlmWvVqFbwDJyIlb9YrS/WlcFUojO9zji3Qp4HVMXNPWKesXS6dVRFw5tJTssG8zzSw2qhKhoCXCz8f6QT4/E2KMC+ZVpl0j0Mnol9H93Z/y8fPeIKG6S+3nPY3h20IIIAAAggggAACCCCAAAIIIIAAAslPgFBf8ntNeUYIIIAAAggggAACCCCAAAIIIHADC0wybUbt0XfqUZk3ME+sFcrsufr4aKm0kiOz/2RehMkWdRxz2Jo+om02U/0tZkArZVh0CM193sRYzuuqRKjBxaQYRQuktE571gS1/suhVfj+MMHCXaa9sIinbW2Tyqnl4PEoWbHhgmzbe+XPv021NNL44TReT/OhXtGtiL12JHClUM7o98OxU1Em1JfAEyXxYcNmHXdaNL9Y3xPyHNgym9Tve0AGfXhcHr43rbjff+7b0RbUFc1+3/HsoIOi4de+TbPKQ8W9nX2ylL6HOuv7TMVNbQOs464iiVOZ0jk5CwgggAACCCCAAAIIIIAAAggggAAC17QAob5r+uXh5hBAAAEEEEAAAQQQQAABBBBAAIHgBc6YAND7i6NDfdv2XJI2bxyWaT1yBnUSDfRpsM/fcFcbe7h4WsmWKWaoz99xibVty66LzqnuSqTWu84JLy/8vNnTjvbm3P/tn8xKFPGE+pasuyid60ZJetNCuUZJT6hLW/AmRqjP97n7W3e3yz0T7m9G7NvW74wORxbJc3XfL7HfWfTeL386K9O+8nxm2tTJJEXyeYKddxdOZcJ8aeS7P85Jw9cPyFfD80gGP0FW3eZvu92eOnfWFFI4T8LeT5+u8LS11jbaWYNonx39rFhCAAEEEEAAAQQQQAABBBBAAAEEELjeBa7Nv6Zd76rcPwIIIIAAAggggAACCCCAAAIIIPAfCGjFMW33qWN6z5yi7XBXbTgnfaYc9Wy8zv67c/8l0daoFy5GydRF0WHFErcGrlpmV0G75NPyNNBT//eAp9WuhgbXbDlnTbv/du/Kau5jU7j+mqZtZZNiVLzL8/z0/B0mnJb9xz0XijIWR0yL5LhGvmzRN/n3vrjnBzpf7iyhYgf7ftnucfrNPMZ2zl2HPJXljp6Jki9/9YQkc5nzuFsJB7re1d6+xoQ4u7x7xLqsBu+6NcjsdQtaZU/H/qMR0mTIQet96DUhkVeKFkwlGijNadoUb997Ud6Zd9K6QoPKGWTkxydkxjen5eyFKLn9pjBrXtrU0a9zIt8Kp0MAAQQQQAABBBBAAAEEEEAAAQQQ+I8FwqLMXwNDQqJbYfzH98PlEUAAAQQQQAABBBBAAAEEEEAAAQQSIDB89nH5aJmnslfzGhml/N1pZGynHNJu9GH5dMUZpwXvll2X5L42/7Ou8F+3mY3raX7981kp/cI5K0x13gT7dDSpmkEyx1K1TNuU/mPCgB8uOS2VS6aVe0zFtdjGo932ST7T2nevaXWqI3XKEHmhbsaAh9zhqhKo7Yjf65Ij4NyE7iiWL4UMaZJeen94RrabUF6D4SetUJy2QL54yeOQKpZWx7kyh0q2jKFy1FT16zzltBTLFyqtqqaVB4p5qtAFe18akLy3UJis3XFJJi8Jlxkrzsk5EyqLbYz6/IxMXx4qh05EJx471/Ff/TG28yTlPg1HjvvipIyec8K6jFbCm/FqLifAaF9bA3bTe+WUliMOyYZ/Lkr1V/bL+yYsm1SVHEe3z269198z9/bu555AX55sKaS4eU/bra8HzTgmjUzIr0XNjEl2H/bz5xEBBBBAAAEEEEAAAQQQQAABBBBA4L8TCCPQ99/hc2UEEEAAAQQQQAABBBBAAAEEEEAgMQQ0BDR5oaeSXdECYdL1csUxbaVb58F08tuW8/KgCflppS8dp85GB67iuv7v285LiPm/vUc8ldp0vlYATMphV9t7/rGMVjthrT6o16z3YHqxq6fp9e1/phrmKlj2dKUMsuz3cDlrWhGv3njOb6gv7PL93104pWjlvT+2e1r7aoDq7RdzSIEcgdul5jHtVDUsuHxtuHzzW7hVSdDffVypz0N3ppThzdLLhyvOy6bdl5wwnVa8e7pCKmlexVNN0F050H3NdjXTyOBPzopW+9uyJ1J+2R4R71Cfnq+Zuc4fO09b59FA3wO3pZR7TKW4yd+EW3bua6Y0QcOapVLJ/DWeCn16r+3NfehzuVbGiTOR0v6tQ/Lz5gvWLWmgb/6QPJLLvK7+Rvm70sgn/XLLs4MOyp5Dl0ywb5+M6ZhDqpdJK40HH5BjJjjpb9gh0f7Tj8mYucf9TZEuDbJIVfMZ1Xv61ryXvv4lXFauD3eqbWp4cHzXHJIpXai0q5tJZiw9bX12Z5pH/Sl9W2p5oU5GqVTi2gpN+n2ybEQAAQQQQAABBBBAAAEEEEAAAQQQiJdA4L9Qxus0TEYAAQQQQAABBBBAAAEEEEAAAQQQ+K8EtJKXjlLFUstUU0ksrQlTAczs5QAAQABJREFU2WNwy2xW+1C7zWyRfGEy01Ql0zHxy1My7avotrb2Me7HXpOOyo690YG+HFlSxFopz31sQpeXjMrrHNr96cyyY98lKZQ3TFKmiH5eOqFfs6zWjzPZLJS9M7WsnVBA/tpzUfLnTCFpU4fIthkF3VOsEJR728FjEVY4T0N97uHvWN0/sVsO2W+OORMeKRpA9Hcf7vMkdLn87SlFf7QF8b8HIySLCaBlTe9toPu/H5olxiVqlEwlle5KKbsOR0o6Y5D/ckvehX29W8y6D/R3nlK3hMnSAVlk9+EIyWPawqa7/N5qViW1c6iG99zHdjRBvoMnI+Vm43+tjeOnI51AX/EiKWVCt5ySI3Ps91n8llQyb2Buear/ASssumbTOSvU98ffF8WuIBnoeR4+HiGH/Wf6ZPdBz+dKK2yOMu117aEB1lefyyqNH81gvb90e9eGmU0IMLN890e4DPrwuOjn+de/zptAYAShPhuORwQQQAABBBBAAAEEEEAAAQQQQCAZCRDqS0YvJk8FAQQQQAABBBBAAAEEEEAAAQRuHIHpH86WJd8ulxqPN5UWIzzhoN+2npd7W+6JFUEDeuU67PWaM3flGdGfYIaGlIo+tzvgVA0Ntir7q2zYuEnWr98YcJ57x9JlK+X4iRNSo2oVeWVWfiuE6N5/vS1/1PWozF/4tTHYEtSt79i5S14bOFxuK3qr/Hymmvz2t6cVcFAHXyeT8ppAYI9H/4mXi/t90ffTAlaQLa6na7epjWue7l+/42KMz8L/2bsKeCuqr7vp7u5uRCSkU1K6Q0KQFEkF6e4WURCQLlFEaZEGaenuzkd3f2ud985l3n33JchfP/Z+v3vvxJkzM2tOzdvrrB2U42ZA8ZKf17VBs24JP+5GZcq+UPjjJzA7dv6ZqY/zukWQ336ZIl5eNwI7RPcrAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAI/AcQUFLff+Ah6SUqAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAImARSJ48mRQrWkiePnsuZ89ekLiRHkKJLrXd/a/4nTj5prm2TBnTCz9hw3r+F1TMGDHMvfCieS83b96SDWNy/yvu4XUuYuOmC+Z+okeLZu4vdiy/Sno2/+LFi8hLxMjl/UeLGk1Gt49md/2/+9246WGQcPFULlaO+O+Xi3/ygV65es1gy3OwfUidOsU/eTrNWxFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBReAfRiDUS9g/fA7NXhFQBBQBRUARUATeQQTOnr8iyZMmeAfvXG9ZEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBReD/KwL6v+//r09W70sRUAQUAUVAEfjfInDx4kWJGzeu6yJCu5Z0QRFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBP6nCIR+gfAeaoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIvC/RyA07H9/FXoFioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAiIMro00KgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCPxLEAj98uXLf8ml6GUoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAu82AqFDhQr1biOgd68IKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAI/EsQCEulPiX2/Uuehl6GIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCLxxBJ49uCH3D06RUC+fSaTMTSRclDhv/ByaoSIQXARevHghFy9flQdPnsvp0+fF6+pFKVwon4QPG0bix4vnITtG3dFJ+h6A0U2KgCLwlhB4/vy5HDt2TI4eOyFhw4SW1GlSS4b06dXP+Jbw19MoAoqAIqAIKAKKgCKgCLxbCIRVQt+79cD1bhUBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgXcNgYfnNkmkMKtEQos8PJdOwmWs+q5BoPf7L0Pg9p07cuX2I7l2O5QcOHhYTh0/Ko8e3JOYSVNK4tiR5dq1a5Ilc2a3q1ZCnxsguqoIKAJvCQEKhNy6fVtWr14tJ0+fwVlfCkKByYFDB+Vw2rRSsUIFCR0anayaIqAIKAKKgCKgCCgCioAioAi8MQTCcjagDrTfGJ6akSKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAj8CxB4/uK5PHn6VJ49fSGHTj2Xx2tuy4tnzyVC4ceSJekDCRsujIQNG1bChQnzL7havYR3CYEbN27KrcdP5fCx4zJ1wiQ5emgPyuljCRMuojx7/kKq1K0uVw7v8UDqe5dQ0nv9LyLwBCpuL1+IRED7qvb/C4Fnz57JwkULJeeHeSVq7IRycPd2uf/gvjzH9u3bt0saEPvey5Ll/9dN690oAoqAIqAIKAKKgCKgCCgC/2MEwvTp06f3//ga9PSKgCKgCCgCioAi8P8Qgdt37kuM6FH/H96Z3pIi8O4i8OjRY9mz94BcunTF38+jR48kVqyYbwykv3fukZGjx8mHH+aQiBEjmnw3btoiq9dslGzvZfY3xM/Dh49k+Kjv5dLly3CGZvRzPWfOnpdR34yTuHHiSIL43qHNeEzn7n3l4KEjki9PLl/HMMTQyVNnZMvWHbJy7QbJkT0bJkd5K6UQl87d+iESWihJkzqlOY7bOnbpLRAzkHRpU/nKiyunz5yTw0ePy+1bt2X46O/lg/ezypZtf5tr2bvvoMya84ukTZNKokaJ4udY5wYqJfToM0TWbdgkHxUr5NwV6PL8BYtl+qyfcFxhf3EMNBMkOHT4qPQdMEySJU3iwpLHrVi5VmLFjCGRIkXykw0nl/EZbNq8zeCZK8f7AV7DUxAyLly8JIeOHJOt23bKytXrgHUqiRz5Vd5eXjdk5JhxkjxZUomJ81qbPvMnmTRlppQuWdz1zOy+N/X7HM73AYNH4Z7X+PscWIYOoWxdu+bl53Pr9h2JEye2XLx4WUaNGS9JkiaW2AHUI+I3dtyPwntLnzYNjo31pm7FlQ/x5LMNqL5fueoliRLGdx3DhbdZj3yd2GflTZVrT3lzW98Bw2U12oBiRQr6l8TjdpbhQcO+EZaVVCmTu9L0GzhS7t+7J+nSpXFtcy78tnCpTMNzLlG8sHOzaUNYVhInSmDaMV87Paws+H2JfPv9JFPfd+3eJ5u27JAsmTJ4SBnyTRcuXZavOveScOHCov1KHaSMtu/YJT9OmSUZM6bz096xzjCs2+p1G3G92yTnB+/7m+fCxX/IxB9nSKGC+cz5PSUkgebylWtyC+2u+ydihAiu43bt2S/L/lhp+hg7CXbVmvWmb4uEfoj5nD13Xq5fv+HxEy5sOPRXEfxcwuPHT2TchMmyYeNWyZ8vt5/9zg3s+6bPnCe5cmY3hLMHDx5I/0EjJXq0aHjmCU3/5oXzZ0if1nXY69yfK5MAFpb9sVoGDhll2rgIEcIHkPLN7LqNdrFl644SAc/GUz8a2FkYoPTkubNy4uJFOXPlihw/f0FWrjoqa/bEkR1XUsilp6HlZYzQcv3uLbly3Qtt1wOJFSNmgH1RYOf0bz/b6qXLV0tun+fpX7qAti9a8odp84sWLuinL+NYYOOmrZI6VUqMO17KseMnxev6dY/l89btu/72L8tXrJJ+KGeZUR85PvLPbqIOrd+w2YxT/Evjvj2gPjBq1Ciof+Fch0ybMVfWbdwkeT/M6doW2ALb2NbtuwjLzXtZ3ZXtAjs6+Pt3/L1buvTsL+9jLBqSce8j1Onde/ZKhPDhZfrkH+TKFS/Jlquw5MhfTN7PXUiSpcokKVOnliiRQkvsaFFNPQj+VQZ+xJGjx2TI8LESPWpUSYqxR3CM7fNIjKUTJ0ok8eJ6Li/nz18yY3FP7WXoUKFdY0S2j/0GDpdHGIunS+vdfxw6fEyuXr2GEMRxzdji5KlTHss083769JlEwz14sjHfTZTvJ0yR8mVLSpgAyLvHT56WHyZOxzgyicSMEd1XVt98N0GOHj0h72fzS1Ziez362x8kU4b0Ej16NHMc+8PO3fqacm3fDXxlGMSVk6dOS7uO3SV50iSm7Q/iYcFO9rrtrT3hzTt3Zdexi7Lz2AU5fOaq3H3wUOJEiyThQJwOqrEu79y1V35ZsAiYpvPYnzrzetvtn/PcXOa4t0On7vIMZHFnn+xMx2s8ceKUr/F/HLSxd+/ele69BsotKHZ6el9lHnfv3sO73iCUpfCSMkUyZ7bCtnLzth2mb/G1w8MK303bdOhqxqHJUNfHT5wqaVKlwLtUZA+p/d909OhR2bRpk1y6cEnSp08nSZMnk3Nnz4iXl5c8eHhfokWJJpn9qIv6n5/uUQT+6wjo/77/609Qr18RUAQUAUVAEfh3IsB3BedYPehvVP/O+9GrUgQUAUVAEVAEFAFFQBFQBN4aAnSUkkwTkEPIXgzT0UEVPny4IKW3x73OL/9ZT6JZeDgIae7rr5M3jz137oJUq/VpgNkUKVJApk0aG2CawHbSWTx77nyTjKHIli77U2LHjiUx4GDLni2rzPtloWyA8/rTBrX9zap774Ey/9dFZj+fQ/26NczyX5u2weF7WxIkiG8cIXly55DMmdKDqPCN2U/SwoP79+XevQdmvWCBPCBZjZO9IDM6LQWcax/DOUjbf+CQ/PLrQsnjIALSwbfgtyWGkOE8zi4vWrIc5I5p0q9XZ5n3828gj2SR7j0HyPQp38uuPfvkOxC2alavbJO7fklofPDgkWudCytBNrkJhyaJcVHcSIAkPibEvXqy7X/vkrk//SqD+/fwtNts27p9J4hml3ztfw/XmtaHvMgd17yuyyIQaipX/BhrmUxaOnqbtWwvyUCwmz19nOzafUDmzPtV7sDZx885EGKclh155subWw4Ay04gSJLAQifZHTiy+Dw8WTjUrZZNP3XtWrr8T5kEUk+BfHnMsSR00kiWZBnK+UF2SZ48idlGx5klc5oNwfxinrw+p63b8JccB5EiT+6cEg0OeKexjIVB3SxXua5zs2s5LRzYK5fNF68bN2Ty1FlSvFhBXxi7EmKBzs5OXfuY8sXta9b9JdN/HOuxrP2Octa2XRfn4f4uN6xfW/r0/Nq1nySmr3GewOzE4R2mjSNh423XI0/XFpRyTfJk735DPB3uZ1vmLBml3RfNzXYS4/h8aPsPHja//GrSqL788edqQ/ZybcRC9y4dJL0PWY+EApbPbFmzmGf2/PkzEPWKgNA2Q+7eqywp4Kg9CALD/v0HpUWzT0GWTGCy2g2C2Ry0h4P6dXdmDXLaVdOGFQW5MAOcqoEZST6sB8MH95aJILmuWLHakBZKflQksEODvH/IsDEmXxI9I0TwJmG7HxwzRgz5IHtW12ZeFzGtWb2S6bNIVr0EMgRJvKdApLYWGW1bieJFfREi7T7+2na4V/eOzs2+lr8ZO8Fg5mujz8rUH7+Tn+f/LuXKlJDTIBwPHT5GqlepYAgaJHp81qytpILze8bkcfLr74tBNv/eUzZm27AhfaVqpY8NIdaZCCMIWYJncBjPuWzp4pj84ps0QpIy+zravv2HZDzKTKsWjQ1Z5DqIhD//8pscBV7p06WWadPnCtuNv3ftlhxo+yuVKyMhvT/nNfq3zPaua68Bhjjg3r75d8zrbmefz3ae5y1aOD/IP57LlH/nOXHmjJy7exPEqLQSJlRYeRkmlIQDKXJXsn3yAuO5HPmySYZMaSU01KSegIxx/vQJMO5PS/pUqfzLMsjb2R5eBhmJ9vTJU+nZZ7B5Xg3q1/SVR1SQKXifNLZLjZu38bWfKyNQnthn7d5zwBD++2Lc4G59+g8zfcLXndpKs8b1pdTH1d2TuNZjgUC+a8sqs07S8H30t9ZIWlqHPiVMmLAgb/omLrNv42QDGon8HON0+qqNfN68kZBM/WUnz2OJ5k0bSZdObeTmzdv+9oE/z5lsngnHG1UqlZNVazbIOZAw27ZqZi8t0N+5GEuxjWOdeRuWNUsm2Yt6ygkk348ZGuxT8j1hz66dEjN2bGnaqq3sP3xBDu0/IIf3bBevqxdBzgwNEk97iRc7vFy+dAnjCm+yWLBPFMgBUSJHNe3SDowJOQ4JyjuOzfL8hYumTH4E0nkmEEE9WbfeA0yZ8rSv69cdJMcH78mjx4/MZBkSlEhcKot2mGOdRk1by+XLV2TWtB8kTNgwUvuTJp6yMduqV60ow4f0MePHBSjXTiMBcz0mvwwZ8a2fPqTCx6VdRDxO3Plh4hSpVaOi83BDghqFCUYVK5SVBvV812EmJMmbY/mGn9R0ESP7YQLA8j9WGWL6HIy1aT98N9z1jmY2BOGL5YvlzD/CVxCyCFKS121vOYlpy77jsu/kVTl61gtt6ksQpKHUFz6snDrvJaU+zCBxYvpfhkmm58SDU6dO++r7SZJn3+xu2TEZatSw/mbz22z/SFLdf/Cgr8vhexnfg/juGB736zSOhYrh3Xj9hi3Spr3vtnvrXyukKd6V+I5JUvZKTIay1gvj8SQg8NMeo36wfKVD+8u+0GlLUcYePnzo633Iud+5/AKqtWwjSQxMmTKZjEDZWrt+k8xG/QoqUZ/nWrt2rWTImEk2//WXXL18QYoWLwnibUI5eeyYPMM57t674zytLisCioAioAgoAoqAIqAIKAKKwBtAQMPvvgEQNQtFQBFQBBQBRUARUAQUgf8/CJBEdvXadckPgpH7P7ir12ksJ0+flU1rlgTqXOY/5xs0+tyQZEiWCarRMXTi5BmPyRMniu8veeMB/smeOVt+KVWquEz4bgRmynuvVyhfWr4dNdhjfiHdSKdWwfwf+jm8d//hfraFZIMX8J/kQ5x5DKIibSaU6yJA+agxiHxUmeJ2KmZZq1a5nGSBg5VGpyAJfXRuX750RXpAASEy1OKqVSkvs3+aL5vgtJv0/Uh7qCFq/rpwiSHGkThChzeJNvylAhqdLVSRopMxHdThSMCzaiZU7CJJg0aFAjrIS5UoKus3bjHbSHjjNmu5cmQ3x5YDIXD5ijV41qfMrr937hYSIknMs4TGISCVMCQgrVKFMkZhq0v3AX4IcSYBvtp/5Zv0w+1j8Owrogx4smdQNQnMqBi4cNEyX8lI/IoHbKzdv+dNurvnQ8Qj8YJEph+AcfPPO0jdBi1xHYMMwZXKECRZktRX7uNSUq9OdZAqM7hIDWmhOkdHXVgfJRU+S1qbL5pBgSgF8DotBfLnAbHkAzieQ9tLgMP1uUyZPseQJujw6gByAQkHTnOSuOiAt6RMZ5qgLnfs3Nvf5+DutGOefA7lypYwv3zW7lahXGlD9KIiC+1vKJQ8ffrclAcngYZKH23adzXO6U9AVK1Xp4ZUq91IquMz7tthpoyaDHy+kidNKp+BcOafkeBKQiotoZvinj2G5/GkfrPhry2GhGnTkaDwtuuRPbfzNyjlmgQCtuVOu4i2ggRSErecpIb4CeKbZCSadus1SLzJZYVNvcgGxZ6MUJGJEiUSVKU2m3R5cn0AMspFg81dlFsayaokBNSsUVkqlCsllarXN+3KhjXexGM6a/mhMX+SjWOD8Hfw0FFD3uP2Xbv3y959+2XxUu/25uatW9wMhciR8v34yWa5RvWKUrNaJbMc0NfIIf2kAoi3naAitCl/4P1ZQHnZfSQA27aChDN+PBnJzz/NnGjUxKgCeeOm931chYIl2w0SS0hsiw8FVZL6ateqKp81/ETSpEkJ0vqrOu+e9x3MYKVZUrv7fq43alhXYsWOKaO/GS99e3WRHaiLvGYSRtKBKLdk6Qo8TxDAQGhyGknME8ePlqYt2kn9xi3lxx9Gm37g5s2bpt1ln1i1cnnXIRnSpzFloGgJ38QQVwIslKtUx7lqliuDzDR6eH/TRj59+sRso/otVQqp1EOjwivVPmkkEdPi+rTHIb0/k0kgXzPnzDN9ZEsQtKwFNF4Kyj6SgJykczv+SZYsiYvUzPOtWFEfY4B50rRxA3vqQH/ZHl25ext9QkaJDlJFOBzxHB+SajNm9h4rhEd5iiwg+oF48hiElPDpMsjZ48ckDfoTZxsQ6Mk8JGBf5U6s4/MqULisr9TsB7/7xptgHBp9GtW43C08SE6BWb9eXaE+eUeGDP1GEidOaCYIvHjx0hBmSWaaMG6Uq25EAFHSWp+BI8xztev2dzXUcPlxGsmqltRnz0fya9IkiaFQ5T2eYBmOA5Ia7fGTxzJz1jx5/sL3WOPTBnXQDnqPS0hetX0zSdOcaEBSX3CN7fMg3HvhQvn9kJ9Iwjl24iTU9GKYiSHOvDke2blrD5QNTxmSC8cWljzKSRTsh2nELFmyxLjXJC4lR6pxNvusvsGcKtaeFNyc53JfjoS2Pk3KVHLr9k3Zt2OLjP12rIQKEwEKfemh2FdI4iVILslSZ5E40Z+CIOVd/93zCMn6H3+ugSrgVV+Hvpc1k2zF2LgbxsuZM6b3tY8KfBz/Ul35xg3v9tomOH78tFk8fOQ4lJl9KwYTb5KH2rVuIZXRRnKMyokeJLsvXLzcqCfzvL36DsF71zX59aepNlvzS+XG2dPHS8PPWssnDZvLogWzheRr2udtOmFiRlzp1b2TWeeXnahxDSrD3XoMcG13LpBY726cCPIc7cUHuYu6dhUvVcUsf4IxKsfB1thf2H7Ovm+xDFPJm7b3wGGMZ2PI5GmzDaGP/V3SJInMOwn79uC2K7v37jckLL7PsLyxH9iBSUOejOWWxPDr12962m0mMBEjTpwiyZGYZ86YEWPuLK4yHdL29hnamom/rodC3zW0BU8FL1YSOtRLeYm29d6LULLi+h25eO22tKtd1F8MkiVJaMolVb/Z99epXU06tm9l0rOdIYnd1jFOwDqLiWbWbHv0Ntq/7X/v9Pi+xWtZg3dCfpyWEBM0tmxY7tpEYudUqPCyzWA5tpPGihUtaN5lOBZkWbHKhlR2t+8GXKayZvder97rD0ABnGPXGnU+M+dgPzITE8ScyqeukzsWskHNdOigXtKpSx/56ecFIKvWcuz1vMjJjdOmTZMjIO8l4gS5uHFl/6GDcuLHiRInLiZzRIwkd65dAVE3h+cMdKsioAgoAoqAIqAIKAKKgCKgCIQYgbChOG1KTRFQBBQBRUARUAQUAUVAEVAEDAIM00kyEGfPWweRhYbhk0iyovpeYEZnakiMYWWtg9P9eHclLV/7KZHgNPd1577XXN4G8gZVlNzNP0U193SBrZNcMahvVzkF58V+OMtItCD5i2QMKrpRWYBkmmsg0V2+fE2279gJoteHCPWTURgei4SNJnC0UsWNjn06sKggY8Plup+fjo/f5k2TIh9VkN7dvwL5r4LkKgDiDZzOVKChM6lggbweFWP6wSFO5xOJQBMmzzTO8bkzJhqlD55nGhw3NJIQiQ/JXSQElq9Sz6yT7EOzBDQqaFGZi04gXvvK1esNmbFs6RImHb+yZ39POn/ZRh4+9iY8unY4Fqji1QWEnYDs1BlvUhPDgToJcs5jOnb4XBpAeYThbBnqkSopdJi+n6uIM5lZpiJccRCRJoPwQitdsphxwhJ3huIk6Yx25OgJ4/SiI/F9EPhoVIGiQ5JE2p5dv4Ji2VEZA1UtOrZIIsyHEHxTZ841Smer126Un+f8aIia5mB8/fr7UvMcpkAlsn3HHiBZRZa5syaZ6+WzGT18gPRFWDeGzuzSsQ2egV/ihM0rqL8kkXZo20Lu338I5cd4cGD6fbdm6LieCJlFozM3HJRmSFQk8SAi7vUslIio2tWwfi1DzCORlDZn7q8y6vI4mQ8nd04QPYnPzNnzZNS3E0y56fhla2nZ7FNDclq8YKZxDDJMJYmhnaGclMnHKc+wzvy4Gx1zJJVSAYsYjxjc2w8h0B5D9SgnsdBuJ4nCaf+LeuQ8v10OSrlOnSqlUSSyx/B3KBR8SI77FW0Bw0a7WyiECSSxODVC59rQqlTGjB4tulFVY/qihfIZ5Zg/V60zBDBuu3T5itT77Asugox1Xeo2bCHnELKM1qptZ/ObL9+H0rrlZyCupTJEvnSoLyRuVanxipDJZfYB6UEYo126fNWQuhiKlSQoGlXwaDNm/+wifpkN+CLRnDbmu0mmDyOZln3ZOKjBlQexiIS6kNpeqAs2atrGHP7jhG9MKGlPeTWC2p0l5l0ELgWKvCI4kQTfEKRt4k8jAaoGiPRpUqeQuPHiyG0ofJLsy7q9DmE/3e0wQnPTNm3Z7r5LGO6Y5BKqAj584N1uPnj0EOpQ3st3792Vi1AHDMioaLjk9zm4/jBCktWU6bNNmE8esw/3fxuh82iFEf6XxGLWD7b3zJttZ0DGEJQpEL4uCchYtA/yFDf1nMu585eUbp3bS8xYsWTg0NFSs2olo2w1GKGc+Qzro322IUpf5/54Lv+MpOlRYyYY4rxTZTGg8VJQ9rmPaYYM/9b0940//UR6dvvKXA7Px7aW52/csJ6/fZX7tYcCYS8s2tsw2LH+8l1Zc/mmJEWbWyFZdIkSwXsMd/fhY/n5/G05+fCpFEoQU/LEjmDGCaECCM/pfp7A1kn4ZEhoT9bx656+NpPgSPKduzFMfaUaDVxl4r2cr/rfPdvXGPJG1KiRZdL4UcIQveXLlDL1mqqllgA0a+4vrmzZ3lv7a/Uio0I2AsqTU0GMX7ZoniEhcX9fjG+oDrl25UIQ9WL5CnfvPB8nKVhyeIumDQ3RmcebfgukPndLAvKOVXWz5GT3NMFdpzIbx1gktrrbmO8nGNI0+7p9f68zfTHTkCTfoHErF6mG20g6mzIRBCK0xa07dPNDeGQf+82IAa62tk7NKobUNwkTQYI7gYb/jw+D8ecekLVz5Mkjn7fvJhFjJEPbjVDRh3bJnu1r5fLFk1KpZnU5f+k4+p5UEimYYTJ5T+62eOkfRvWR6p/EjPdM4y/VrNdCqZHqeHYbx2ok9U2YNM0Xwc0k8PniONnd6qFt6t+7ixmDLF6ywuwe1L8bVPGim3rO/ock94CMffXv82eA1L4XIZUzmbEOlSV53eQlsh2mkdhMRUsaJ4/s27nBLNeo21iuoN9dt+I3Q1zjuLp8lU8MEX/erB/NNo5vqGzevl1LQ77n+0W+PB8Ky3iWzBkkCUirW7f/bdQG2UcliO9N9CeZlUalNYsXx90sZ7w+EnbbtGoC0ug+Q+rjmO0kJm2x/DtDKJlM/PliSHkayxmNxOdGTbzHE2aD44tjXSq5rVi51rH11SLH4MmTJ5UadZuY67N7+H4zYdxIM6YOaXt7/PQl2X7oirx8/ljCYsxPMh8tNF5NX4LcF+rlCzl++qpcunZDkiaM573T7Tt8+AhQEu4DRc+bZuIKlWyvXPVyEYarVa3gUqMjuc1pzvbon27/qI685LfZcg3kSSrf0ajQxwk9nNTFiQjWeA8cszgtVaqUhtBXpWJZOY93aU68oZI5VSJpfA+Yj3cchpym/YB6R7VmGgmmJPZzMom14z6TgezY0G4nEfC3hb4nZT1+DMIljOMkhs2meb9PXzfvJoFNQly0aJEcPHJYnjx5Lps3/YX3ntASNVZsuQdi8rPLF1G+UkihwoUla9asJm/9UgQUAUVAEVAEFAFFQBFQBBSBN4eAkvreHJaakyKgCCgCioAioAgoAorA/3MEhg7s5e8dkiTzJibMfAw1ryyZ05vzdOjUy6iBkcBEi4sZ8dbe1PkCy8fTfpJ3AlJDstf4Or8kSl0HESJRogSGoPcCDiGujwbRi8awlbuhclAdTh6S+jKkSyubNm83hD7uf/jgoXTv7U2mCo/rpVEVwZORdNPaJyQS1QYnwInGcLa/wzkex4dkNR9EzwMHj7gOJ0HEhsOk4huV36gyQyWSgcO8SW0kgNhwWVQtojKT5X3RIdS4WRtDRGOmdAI2b9pA3iN5AddDx9D4scMlb6EyUhROZIZqpNGpSdWUn+b/5iICmh1uX1TJo6MmZszobnterZJwRqNKTW6oi3my7r0HQ4Hwhvw8e5JRnmKaIoXySuNP67qSb4FziGoTIxEGi8QXqts0+9ybkMFEyxbOlTIVarru1R44YNAo4YfmVLJgGE1nmGcn8YHOr08b1PRF6GNoTJtm7LhJ8jcUSAYN6CkzfMgELUHMvHL1miHkboS63NVG9VyqjvZagvvbu0dHQ0Dc+Nc2EyJzz451LrVBZ14kqEaLFsUooTi3xwNJKRLUI+/e902M6/JVa6Oa0bPrlwZTHsOQZI2bt3WRC1hW6HTnx5qtjwybyI87Wcam4y+doa3afm3U44oVLSRDBvZ0OQ+d6aj4wudyCARL/4z7rf0v6pE9t/M3KOXamT44y3XhWGebaJXxeGyECOEkioNowf38WCNR5zMQTR6DQBYPbXiC+HGgQpcAhN21BneqtNwHASATCFr34Rimw57EgT49vpbTx3aB5D3UkG247DSGgKaSFkksDO3mNCpTnb9wwWzitTDcmjWGdKXzn4SwJ0+eGvVLtrchNRK+GjZp7SIJtIaSpLuNHT1IiqOsRUBbHAZkK1pilJ2DezcJwxIyTOFvIG7shEKlUy2J6ZztRM6c2WXO9B/8JTUwvSfCA8kaDOPZ9sturuscPMS7neYxLVp9ZeoMl93NU3sWGwqudWtVM2pMTG/UhED4pzKpDZtM0gZVUn+GaixVGgMy1umDuze6kuzYtELagZzMEMmrVyww6r1UCaTq09Fjx80nZYrkRt2TBO75c6eYY0Nyf66TBrCwd98hb5IK+ro3acSGql0cU5AUQQK/Jytb+iMZPnIsQlAe9EhS9nTMyTWr5NHdW7IpeiJZ5/VYMseNInuvP5BFF29LxZQxTF+89MItOfDgqXwQJ4qsuv5QnoZ6IfFPHJajZ05LxpJlyMT2lHWwtj2CarJVsw3sQJY1qqG5W0QoDc6dOUFGYcICVaBYB6bOmOMaA5CYZsl7VaFaXLnix+in80vsWDFNf0ByUcXyZWQZwjT+hnHKo0fexA62C8uh2kazEzW69OgnyX1IXlQkY9m05A+m4zj4c/QfloBiz8d9QTVnnXY/hucLiVkiUz7UEacxhCvVTUmU4biOk1LyQYmbtnTZSkPoqwLMmmBcwDHixMkzfE3o4XE/Y4IA1VeJOfvXZctXSR0f4g6J1CRlM/TmyKH9AlXHcl4bl2PFiyfJ0maSsxevyakTx2X3zp0g8p0zRKZIkaNJ2PDhoXgYypC5zp8/L+nSe78fuOcTnHVLPrRj1pFD+/oaA1KBujFI2lRmrIExtrUBfbsJPzSS46rW+lTOQC2NuBInEt5/nv2ji7htj+s3cKRRv+b65GlzMRY8bXZxMkv+fHnMsvsXSdG58pdybR40oLtZrlm9Mtq9DbIOaxwzZ0yfDoTfcXIESoHWxnw/0ZDnuM6+jdf3FQi0ETnmunvPvFexD7TvCClAdPuy3ecmLx6TEGp2DMFLMut2jCfXYCKJJU5RKTwCyGfWCmM8TBU2iyXH9uzLs+YobNozZ5s2YtR3mJT0nUzCxBe+wwRmLLs8nqQ7S9hPA7Vw+044HWNcljuOd9OC/E5SXMSI4aUpJjXdhYI2nyH7zM4YV9J4bK9+Q0xbTqJ4fpTbhYtX4Hmc8jWmDm57yzlsM5Zsk2dPHpl29Qk2eHP6sMPHOCR68PSl7Dl2UZImiCMCMhjtwo3bcuLsFSmcPT1Cbq/3pYDHsQo/uXPl8MnF88/bbv846YOfYqUq+3mvYTtk2yJeLRV+OTHHaavXbTTPgOFvb92+A7X3FWb91h3vcsXyyolfuXPx2bU175pfYgJRrnzeBP+mn/lWrD1+4pQJvzugj++x19279834zp57776DpuxzfTfKMT98f6UyIpUmH4LkHpD9BRLf1u3bMIEuq7z/XlaZPGmSrFq9RlKmSWMmJNyDWnI6qK3Xq0fy/ev3nQFdi+5TBBQBRUARUAQUAUVAEVAE3kUEwvIfvG/C+fgugqf3rAgoAoqAIqAIKAKKgCLwbiHQpkNX4/ikA52hi7r3HijNmzSU+QsWC8lCDFP5FUIFuas8/Y1QSd2giFW3VnWEt6lpQPsJ4XWoMEHFjwxwSlnjzHw7Oz969Ghms3VoMEzhMDi2ly5faRwJDAk7ZEAvVzgim4enXxJdvurcSzJhdv/wIX1AiNsPJ/l35rpJFOjcsa1xllNZpUrNBtKwXm38836ncQAv+HmGUL2BagMkm/ln3JcFpDNrdMp5Uk2y+z39khBBIhzJa3PnLfCVJE2alHD835cycPCTSEKncozoUY3Tmep3/HT+up2QrEE1A+scpmLGF583lfZtWkibDl185cmVl3BA0UlHnOm4o0IU1fDChQ0nYcOFNemptEdFPqpxMN+IEV459dwz/ADHkpwXEw51awztRSOh5Tycw+Uq1zXrfXt1hpLbYISb+xhOxOmS18chTacQiWg8n1Vv4gEkD9JYpooVKWSW+UVliyEjxhrHEJ14DCdGpTz/jAoOxIVGxTb/SH2f1q9jSDJUFLPKOumg6EUnZ44P3oe6VFKJCpUTWowY0YyiW5QoUU340HNQoSOWdP42alDXqOIwHcmZVKuj4k1O5EGLGvWVIz8rsKMjLFKkCAgJ9ZtxWtJhPBIOZhL+SpevLSuX/mJCDPLY6D7n5zIJfVR1oqqQdaZ26tybu1xGQtvqP371FfbRtTOICx8VK2xSktTnbnTEroRS2ywoa7Ro1tBXKEGGFKVKHxXbaAyvxXX3cJ/OPPn86eQjoTFa9CgIT7gABANvwpYzHQl2g/r3MOUpbtzYzl2+lo8cOWoIfV+irfoC6nDu/w9g3eqJcHh8bgwHFpjRMV4ECnUMa/k265Gn6wpqufZ0bGDbqCTJ9n3chKmupJuhoJUD9b0Wwup+AyIBSQpWddMmIr4M3UhSkruxbaXDnv2Hk8xGLN3tFMIFM8ThiNHfmV1sC2lduvdz1Z8/Fv+MshRaunTyVs3jfob/JnmHKkwMe21VmcaMHPhGHK903g5HCDe2I1T1YjtBVVsa2zCSYGwY9ceoGyRw0YgLw6JTPZPGukDiQm+QGWkkq9GhT0ISQ2LSEkLhhwTW7ZtekRS5fc++A9KkeTsumhDHJNk5zSrlHNi1Af1bQ+PMPrwP4S7H/ShjSQA58rchYrJdYtv+9Jm3+g7z8NSebd2204TIZVvG/uDZ8xf4PDGn5HMiOcG2EYUL5hWqtwZkEXwwsGlugOhAQh9tOJRpa/iEVOb5HjvaSpve/obk/uyxAf3a9iZNquQBJQv2PvZBDONNwieJRP5ZWvT9tPNo9zwpj5qdjq+XINCenThWruYvKjtC75KI+KuXMZeciRVdpm36W45degAFqRdy8WlUaZYvu2SOGEb6HNwhW848klznL0uoDaskY7ESb4TU507ycFymn0Xb37nvYBsxc+o48GAQLhjljSEoqdBJo/JlKjwX1kMS/kgQZt1hObwKNWMalcgYZpZtOm3VmnUon0Xk2MlTRg3YbMQX8z567KT5cBvXaVQMtkZlqtQpU2Dc9MJ1PrsvsF/28wz3SHVgtlu8LxJfaSkwUYJmxyVmJRhfVEBjeE6romoPpcIa8+zU4Qujgr38z9UuUt8JHEOjUinHnfywLXcSUqKCnEvVPn7ig4zP9mzXnn0uUh+Pz4AxEfsCKt85w0lzX0DG0NpXr1yWh/duY0y3U9asXimp0rwndZp2kXgJk0p4KEtGjBRdYsdLKHcTJoSa6KU3Quqz10QFaIaU/waTZVi+rE2E6jSfffkASLxUZiQpqB+IRD0QsrcjSHFde/SXn3753aUqZ/Pbss1bgYxjlIcguVLNlGqcEyZNx/huuU3m55dh6B8gPcMCsw/h5Jg7UGzlRBNaZPTJLNe0OyDrLQTBjRNL2D47J/HwXjZi0o81rlMB7opPubYhXW2Y3fegCM56exnh4U+fxrgf9YljMB538+Ztm435dVdlv4w+7wlCNlPVl4TIj4oXFqrQ8t0kZ/ZsRn3WVwYBrFiF1/QOFV2Wb/tOuArK7rRseF+yE4hsdsSJFgfvITY91+2kh+roU/iuyuM45nNacNtbvt9c9rqNfvOZhHZlZAl9pPd5L9P/9Muq3XIEJL66pXMb5c95y7ZKiZzeRFUq4C1cMAvjoa1GIZ1kW05aCIW/ClXqunJ2X3jb7Z/z/CSHftGyiXOTWeZY9DuMLzwZiZgsSwXz55XvJ0wzZYsTbEjEprL5TtQrvuPdxDu/8/2PeXFssn7jZpDp1stfaHNofM+ilShbzfw2BmZ1EbqY5dqGducz5n7nOJDjwdKYMFapHAjsgdjevXtl8eIlqDdX5OPSZeQciaBRokv0GNHl2pVLUrxIYaleozpIfel8tZ+BZKu7FQFFQBFQBBQBRUARUAQUAUUgGAh4e6iCcYAmVQQUAUVAEVAEFAFFQBFQBN5VBE4jXClVlWj3oLB1GEQ5KrNVr1rROE1mIeRhtqyZjVPQiVF2hL9k+CcqgNT/pIYhMzDUDrelxQz3oBqdNLNBdGP4vZdwSDMU1uhvfzChwgLKwwuO3UZNWwsJFZMRGpHKZnUbtIBTOIF8jVCkJCV+gpCQW9YvM+HqeF90uNJZQcdb4kTx5UufMHHRfIiG/p3vLFRmmLZg/jwID5TeRbTwL7379gpw8FJFhI5qJ9GL6aJEjiK/zJkM591d3PsM4zQr9VFRX2EjmzWub0h9JFhStYD3TnUDkv/8CzGbGKSpQQN6IGzUCxCxfjGqWF06tZPYMWNCUSuuCa1Wt3ZVafdFC8n6QQFp2rieIQjaaydhhs6S63DA0BrWqyVr12+SAoXLGvWp7NmyupzpoUKHgfpgQkPe64IwqYuh0EAnS/vWzeTLti2BXS+jjkYy3/SZP5n8kiRObH6dX1R+sOoPVHLrjOdFx3XP7h0NIdO/e7V5zJz9i1kkoYxlsVXLxkbVx+63v1QAsyHJSBigIs29uw9MuefyLIcj2B5D0gXD11GFySroWTIr0xw5esyQ+j6CE6sBsHI3huAlsfXrbn1MHaOSRC/cF+2hT9i1z9t2MupUdL4z7e4daxHq+CejiNWnRydJjbLbpHEDlJU7kg0OWjo2L8ExS7Ll9z9MQfhMb2KR+7lDuk61DSqn0dm2AKGALTGBDn467qwNR4hDhlV1N9Zl/4z35wwH3u6L5v4lNdv/WDLPOEEDTISduVCG3Al99hiqdpCkQDJlxvRpDVGEbR/LKsmz7kaM33Y9cr8Grge1XHs6NrBtVCLs0KmnwYU4zJjyvWl/6bilAt/nIHC6K51Y1Tab9wgoObFd2wNStVX7a9WiseSCIktEELtC44/qLVZ5j07w+1AdpRUrWUm+AjGFZD62DySwkMBLI5mVZpVAzQq+GFJw8LAxRrWzAOrrOJT9Tl9+Ia3bdTaEWTp+34SRwMayTlJfreqVhGpKNBLDSYKxRmIGyZFOewYiAI1KpgxVSrIhje0ESX0kgJBo47R4qBNOowIZjY7yeSACk9Tnnob7GRaZRBQSit3JP2xLWHe8vG4KlZ5oJHV7as/6DhjmS6GHYfeskYDCkNWW1EeCAsO/B2S8bqdS39SZc1zJ2b8whDytaOGChjxtd1pip10Pyf3ZYwP6taGFo0XznmgQUNqg7mPbEhPjCRJxSOpbvGS5i+Dqnke0qN7nZTsbFCOFZMete3L3+i0UikvA74mMvH1LLnhdl2hREEb7xB4JA3Lai7Q55IdDRyQJ6q/X5csSK3ZEOYjwkFcfPZdCyOMVQSUoZ/Wchv1ntUoVPO4sWOxjX9u/bNdKKqEOkKBaA3WoNso9iXjRMX6hXUEfRsI6zRL0uExVT9oHeT8yv7ugeOksk3OhhsmPtV4gbVPx16hXtmpmFB+pYBaQZXsvswlhzTTdu7Q3Se35nMcNQlhohpykuZOFSEqLjet/8OCR2R8DhBCu0zih5HWMhDo78cCZz7I/vMmxJO79DCLaQoSB7dm1oxkPFiyQxyjIfYaw4Bzr1qpRydckG2c+JJf98ecasylzxgzOXS6V3luop8Eh9bHNp5riw7s35fnTJyD4F5b8xcvLjdsPZcXvM+X86cMYS9yTj6vWxTgnhiTHePRNGtvAJo0+kTEg9TFsc4Vypc0Yhn0/FY7d22p7bk4woqo1FWUt6ShhwviYxNBI+iFk80fFCplxmU1PwmBUTAoiobvepy1lP8h5fXp+bSZ/MDT6Zz6EbJuev1Q65TiSysJFS1Q0u+aBMEjitTWr9GzXWeY5VqKS5ZMnTwzR3O7z75dtD0O30hiOmUY1PVrt+k3NPU3+4Rsp+XE1qPm1lM9QTjyZJdGxLLWDqhotJkhzts2kmjhD3wbHbvu0d9ExYeZNGUmPJIEVL1NVmn1WX6pXLu+nrwxuexsGA4/okcOh3N7H2JpuJkvo83vVt27fw+SpixIxfFjZ/PdBKZc/k6RPlcwkJMkwBvqELt37m3VOkCgJRcMCeJ8MyN52++e8liSJE0EZtaxzk1mmcqp/pD5OgjmACUoPMLbbsHKRea/kWInvWDSOBzjBazIUHa3d92kzGeaaE/oKF8zvIvPZceANtIGcfHYb78juNvfnBWbft6MHm/Ff+Y9LGlLgoMGjpfRHxfyMh5zHe93wkmXLlmEy0Tk5feq0jB3zjUSPGUeOHDtijqtRvZ60a9dOyXxO0HRZEVAEFAFFQBFQBBQBRUAR+AcQUFLfPwCqZqkIKAKKgCKgCCgCioAi8O4gYMP6WccTFSncCQgkCzSG82sEFHcYMpZkOhIL6DQj+Yoz+q3FihXLj9Kf3ceQrzvgsHr69BlCre0CQWGtcQLQ2eif0RHQ/IuOhgRCwkICKIXRMUbSUcf2n0OlIZNxwFJhgyoYVrGCDncqEtLhSmfvagcxw56LyhU0OiCoSuG0FMmTGUf96BEDnJsDXaaDkGH4Ro0Zb1QcnAdcvnLFhD9rCcfhKRAsef48H+aUsmVKOJOFaLli1XouIhYzqFX3M5NPf59QYwx/558RK4YdCxs2jLkmpiPZsG//YQj51UuWIgzvcx/lJ6ow8JmnS5Na5oJ88uOUGYbEN37iDJM9SZ98Rl+AdGMdQpa8Rwf5/gOH/VzG+o2bzLUznBaV76hmZ42Oe5KtnEbH5eSps0zYpcFQdssL8uF34yZLj64dnMlcy1+0aCLbt+8yamL5EbYuadJE0uaLZsYRTGWhJEkSudIGtMAQfidAKLXkAYYOoyOLNgIqfHQwWzUJOqaskSC5tEApu2p+WX9+gMrL56hDNJIdGOKSZJ1de/Ya9SdiWeSjClB9zAxyTjjzPJYt+ilQEqzJMJhfPI81kh3Llipuyr87Ae7w0eOSDHhRsSZ61Khy7fp1hI/eDcLhNXP4oOHfmt++cIw7jWWC5J7xAZD/mD46SDdOxR1nHkFdZntFpzhDiz1DuaXq2m2UGZL6yoFgeQhKf05LibpeB2FpaW+zHjmvgcvBLdfuxwe2TlU5lku2d1Q4qVLrU9chQ6HC594Gzpz8nZ+6N23mXENYvn79puvYOvWbucIlsy0/dOgoCDsvTVhxhrIlgY9GovIndapLscL5jdInlVE7fdnaKH8WLFbO1GeSoZ02Fefj8VTHsuEyqfDJ0Nz9B48yz5PEmjdpGzdtM4o9zPPosROurKmoxH4nCvCj0andqVs/88t1kshJKmmI8MMMpUjlJVofhOZlf0CbO3Oii1hkNuCLaoDzQSDmc5k9fbxUrlZfxkP11FN7RpIkjapdnuwvqDmx3aYRIz5zT0bF1Ec+ClGe9kdy9IcMwU2l2IAsig+phGlI5Jj0o3d/wPVqUHv6C5jSJk2ebn6dX9ahz21v6v6c+XPZEpUuXb7qvivE6yR41gSpZMjQb8zzoDLWwH7dTT/jnimVsmj2Otz3u6+/ePZUth4/Ic+ix5dUSVKDaPJCth84ILGg9BgLSkPXjhyS0KhjYaLFl8cxE2LffoSEjC1UnDp49KSEP3xEvkAeoQNQ5HU/p3/rDKl9917AhDl7LMd4R9BH0MpCQYkTQajS1QGEfxrV96hiTKPaHY3ttbvlRT+94JdXZch9P9c5ZrFGtWC2aQHZEITd5fX5Z6EwtqE5Sbw2bZjQ3v96/gX11JKZuY9qulZRt28vbxVl1uOQWPJkieUk8HEaxxMk2bOOUL2W6sUjQazftXuv5MI2kqfHYBLJwCGjzJiI4yK2s907d3AR2ogLyYt2vMt2qmyZj5ynkQsXLpn1BCA2B9c+zJNXrqINu3rjHkjZL2XimIGYIHJbEidJJclSZ5YUaTJJ6gyZ5MLxHWgbbpk67h8ZP7jnZvoWzT41hD4SvVm/2mKiEse0HGd7Mo7fmn/+pWnLm0Gp/MiRY65kDGHMcV2Dxp/LzCnjDebcee/eXSiM/2nSMVQoJxhxAg0taRDHj0zbpPEnUrlSOS76a3Fx7TQSoZ3EVv8OYHjzqFFTmt1doFjOOrb9711GcZfkvKKFC0Bx3Lv9HTt+svwKopk1TvYpjX64Rt0mLpI31ScTxEe7gvaN92rrxUGQh6m8RvNW2i7ose7avPlry9P5cxedm19rmWrDVLv9BhPC2Pbyw7rnnHgT3PaW7zV530suh05dldAow4bU5+T1sWnARLSXL7kQShLGjiYxoB6aOW0yeYj32QMg+SWMHUPixIiCyUzzXGMCtgWft+lk+oXg3PTbbP+ospwGKrDBsUoVyph2kIRjG26dkzsYwpnjpA4+pFD+byB1qpRQmO8qDIVO44QSPkOGb6aKJBUox387zBAzSdbmGLUYyqzTWGcHgLxHBXqr8M42pH2blkaJfQrCirds+qnzENcyFfcX/rZQLqPffIJQ1s8xCeMI+tZQoU+hfseRDh3aYzKGN+nWdZAuKAKKgCKgCCgCioAioAgoAorAP4JA2Df5z4B/5Ao1U0VAEVAEFAFFQBFQBBQBReBfjEBMH0USKrrR7vuERHS/ZIbPI6mPoaYYvpRWsXwZ81u8lDchhitUzvPPmUYlk649+xtnE0lBdIzRroMYFCe253Cbq1evM2n4FSWyt9rLPh/SV/NWX7n2cYHkCGu54fC0CioM3bZryyqzq1rtRlDmSGTUlKjwRwUiEoCo4mIJBU5iSeWKH9ssg/xLcg5JkrSLIKTQmUpVqghwsCdMkMBsZ/gz2oKFS8U/5YxxE6fKvn0HTbowYQOez9S7R0dDXNq8dacQM4YmfQ4lHxIwaQER1/KBWEiSBx0yVG6kkcxF1TwSRMZPnCb58uQ028MZFQuR3xctdZF1+Bxn/zTfOElzIMwxw2HVhNrVEChs8b4Z4pZGVaZK1eqZZU9fA0DScTeq1VlVHe6juo8NR0tCEEM8t/miqVE3pJINwyy72/Pnz8QLZYw2efoco0TYqsVnxhE8FIomIwb3NvsC+9q8ZYchKrHs8sP7WQOyKIk+g0DmIKmPBIX8IMWlTJHcqN9QtYNEQDrOOiC8Gwl6dNr2BdGHihX14YB/iBB2taE8SSOhgB8qxlBNhhY3Thzz+09+MURoGRD5qCoWGEmKxDuqd5HI2BVhualUQxUbqlRevXZdtqK8J0uaxCgxxooVw3XZXiCCkVhn64Jrh88CndH/hD1Cu0MHNS0s6hFJiDQSIkjIuAnyk7W3XY/seUNSru2xQYJMAiMAAEAASURBVP3lcyVBd+u2v6Vl646SneqEPu2DM48DcN6TdEpCpLvxuTLcLNsyJ4lmIXBkmEiqpNJ++H4k6kBS08azrpCYR+VRGtVsSDgjOaZqlQoyFMpYtE9qVze/9msl2jGGIiehheXSkvrI+6NaX72GLaVJi/YyHeRD/xSZbF7B+aXz2Tqg7XGLcH9zfMKpkxRMixQZJEmUWZJk6IgeB8d0RoSHZ3hhEvoK5sttD5ezCLtKbKyqn2sHFqg0Svvs07pCVVQq55IszXV3QjNJPdsQuteGuKcjnZYzf0lZAcJv5QpljTIl+wvm5Z9lyphe2BdaMo97ui0blrs2kaR5LxBSF/ez/yV5l8qrNIbb5n3wGhlOz4YKZdjL9Rs2GTIS08UGGYKqVCRqvKn7Y75OS5M6hVk9gXCtb8rY7lfB+MCQ+rr2MdmWgIoUJxi4mz2vvQ73/e7rHIOkTJNWtt+5JxERyjhyqNASLzJCF0IR7fSNW3IqUjwJAxL+U6gyRg4XWRLEjCRP0c9dun5bvECqzJEoEQhWqChvwKjCGpASq/sp+GxpeTGumDHrZ5k0ZZYhXnGcR8xyfpDN7LekPrPi9sU2YhsmmPTqN8xtj/dqK4Rddw9jzDZl/LfD/aQ/AmJuIxBtA7OaGON+XBr1aOUaqA2WMeRlhv7OnTOHmQjA46ngWQikp8UgqFPZekC/bmgPI0mHjt3NpIjAzhHQfipDHz68BHXtgUt5jWpZxIwhR8tVroux8g2TBUPwktRHY/jfcmVLyMpVa2UYxui8Lk5u4f3QSCyiotnsOfNNmz15whhTT81On6+jILkwHXEPrnHMkzxlKnn6ZKVEjRweCo1VJX6qbBI2WnIT1vLOjcty+dR+uX/zKpSxjksxtBVW/S245/KUnsRljl/LVqjlCg07HSq0HBu6G8dU9RFKloRsEsWTQHXaSeojcXLC98NN31KjbmNDwmabduToCenWw/cEH7teBIpjQbVkSZPKrDm/mpDyno6ZOG6UH6Ic21H2Be7284KFZiKIc3s+kGH5oUIrVeLix41rxlwPMMYkid6a7Y/uYgwbK1ZsuQUVdduPdcE7XH/0uyx3lrDK44aN8J6wwWUq5rYAiSqwsSLLE8sVy9ebMpa39m1aQHG8gbAv4YSEnn0GSSGEiedYlBbc9pbHfFw0h2zYcUSOnb+BvigsqHtg9RkS3yt23wtsjRElopQumBXtaygoyt2Xi9duyrOnUCONGFb27NppQmTXrlXVKIu2btXEEPZtmFmeJyj2ttq/qpXLyZkz5w2OfP4cZ9lxHCdgcLxMsrG7RUSbx3HZxk2bZf7vi81uZ/mw75EkUqdOldJMdmL7zPF/A0xWIqGPVgdjPpLhh2AyUIP6tYyKJfPlOMoa/2/QrJU3CfcrTORzzvsoigkiTM8+OE2qlL7KOI83pOgFCzAm2S1XL13GOO2E3MPEk9BQnE+bLq306N4DKtO57Kn0VxFQBBQBRUARUAQUAUVAEVAE/mEEAvZs/cMn1+wVAUVAEVAEFAFFQBFQBBSBdwUBKmAwjOgCEB5ywylLBwCJATSGi7KEuOgg0Pln3/8w2TiaqB5XDw6Dbj0HwMH1i3/JXdtt+FQSAqf9OFbSp00jK1aslskIbZU75weudJFBsnj82Ju849roWKC6BMM82uu2u9yJZju3rfEYytWmD+w3ZowY0goKbD/N/93cJ0MBV6tS3igMdv26rQlzalVTpk6fK40a1DEOZPd8Txw/ZdTN6BQr4CCIuKfj+kuo9jCUV7Gihczu3/GcYiH87oc++CTzIaJ4Ota/bQzDS9Uqquc9efLMJAsfPpz5pToIwwLnyZ3DhERu+2U3Wbl6vQk/yAQZEPKUljnTqzBvJN5QycjddiLUHp1zJLIlT5bE1+5MjuO5YyxChdIJ1AROaqu60+CTmkb17pNPW8jyRXORhzeJ0GY0ZPhYVxhcqsXRCdi/dxfjCGbYslOnz9mkHn+3QY3PEhWyIPztkt9mu9J9890EP45VkpcYuncqCIRTUUbXb9xiSH1FECp4BlRgqG7XFaqE9BmScEoyIElnxeHoKlOqmDAdSV5U+KOx7llSItfp4I8XNxZUJuJy1YSjJqkuqCpQ5iC3r4Gok54ctMuWrxSSaL/4vIlL9YvnocOX6kCJQdii0QnJcHR79x+UilU+kTJQaWIYTypeuhvDvrqHdWUatgcLFnqrebgfE9L1e1DpIGnSqpKlTZNSqO5IBRySkum0tqFWeY63XY/sfYWkXNtjg/NLorNV3nkB5ZlQTg+pT0bhwnrXcU/5JkqYEAqkEeWOG8mL7RmVsz5FW5Y5U3p80kHBKIlRZmU4Q9YFp5EEy3C1NaAoymNJCC2Q/0NXEpJlGcKThMAxowa5ttsFhoQmcZlhBqmEM+H7ESY0IvczPPvr1IevO7WVSuW8Cev2fBs3bzVkYjqmSeSipUqZQvb9vc6EAXYncrFdnACChjVnKG+7jb9Ul2UodvanJBrT2rVu4d0etv7KhK+z9Zz7WL8aNmnNRZfZvqRIycq+1BYtWduV0GfhD5CWEuM5WlUwprO2b/8hX2RNbl+zbqMs9wkPbNN5+q1Vo4ohC/HeayLsajIQw2kk8zVt0c7PIU4VKhIZhw/p80buz8+JsMG2Qwzb+CaNbSH7XIYdrINQ0Cx3nsye116HpzTObaEjRZZWQ0dIxwnT5DKIe7EQ0v3e2WOSIFY0uXvrjsRMmhD9yC1JEOqpXNy/WyIlSg7VvhjC9i5y/ETSrFknCYPwn7SnIN1fgZKqu+qq83wBLTOkNPskT+YelpkqTWzDCyM9CSE5PnjPYMMJFJYYmv3990xWjx4/8pSlCddNheA8CN9IMjB/bcheEjyItc3LmQEJI7XqNXVuMsvEJCBjeHCej+Vvxpx5hiASBc+RBCGSU0qB7D7huxEmC445+LGKsyTOnQNhl0alYxr7xpBYOkyAoJ05c0Y4zqCt+HOt+a0BtctIPqqbVJKbD8JW107tMC64gRDlj8yYp3SpjwxZrm6D5oa4bkl9xI4qVumRP0OrDkeb+d03Q0y+/OJYg9hRoS2kRpHDl48fSNJk8SRT1vTyy+IVGKcclzBQPeO+iLj2eAkTyRMP6qBeXjdAEg9vxkIhPX8shAC2pDT/8qBiV7VanxoCNpWaqSrMjyX8N27axij88Xgqk479fqJUr91YVi6bL1R4XrTAe9zXpkMXQ3gnkZBmJ0SZlQC+TqIdHAGVRS+E0Ga55vuUtfMXLppnQHKzu5H0TaKpuzFcs9N4LCfGcBLJTpDyaeyXqIy2YfUiWbVmPe7jQ9MeVKpe3+DFsTOVt7euXyZLMDa2ZKzuGJ8yHC/70h69BrpO8yVIVblyeJNJo0aN4toe0EIOqKtxUgvfEd+EKAPHvymSJzHE10+hIH/rzm0Z/c14qFfuM3WW1xLc9pbHREN72aNFZRk9fansOnJRQlNBFGMkY7h2XD0+L+XBo1Cy5+hFyZ4pNUJuP5YPMqeWeD6TVwYPHmFwbYPyw3DhkaCQznLCSRBOgqR3pp6/bXv0Ntq/1rjO35csN+MNTsTihLyhIG9yAkL3Ll/6O1li/oJFUOf0co0VWF/Y1g8EGZREuh5dvzQ3V9An7PCYkQOh8njTvDM675oTazihiO9/qzDOoPHdzBrb+6Yt2wvDaXNSFMd9rMfWWJ6Ib5mKtaUZ0s3GBD1OqqLxOpYsXSKbt26ReyzLx46asL4khebOnVu6desuGTK8ej+1eXr6ZRt17jye+ftZ3kgZ9nQO3aYIKAKKgCKgCCgCioAioAi8CwiEfRduUu9REVAEFAFFQBFQBBQBRUARCC4Ck6fOgdMjkjmMpCGqGgTHLMHnbxCu6kB1gCo6VFIiKYqkKhIfrFHJIihmFfAYnm8myHxOAg+dHrSTcCJR8Seaj8OI6hJtWzWTFwjVxnBLDB31UfHCxuFGsghDAEeLGg1KEMdNOv+ug//gtyEQmUfzJvVdSUlAcCoiUYnqdYznGgSVug/gzLL3RYct1ZxWrdkgU0BwIVFv1rTxUgXOtXETpho1I/dzUkmkHxwcJNJ91bkXVLUSmnCtdWq9Uka0x9Dxxzzfh9OBTu+SUAyimsbPcJbQSLDxz0iq6wLn33GQCJ1G58fkH0abTVTNotkwvjt3eSvI8FxlK9YyTiCSNamOQkcMnw2Nilf161aX3LlymDJUF8QHdyPZj06d0nCeu6vv2LRU9RkDAh3LQMaM6aR1yyZ2lyGbjYRqHNV4PoF61xQQ6dKmSWWciIOHfWvUouiM/ALH1IYjlI4kmiV3+kfqe+nj0PsaKkyWLOk6qT8L5tkPHWPCTFKR7glCPVkjmSpD+jTyy+CFcOzekGmTxphd4RBad/e21UYNho5Vlh0SZ6/C+UvLkjm9rNuw2Szzi4TNP6DUs2bFbyb0dYnS3uVhx+Y/XUQ/V+IgLvC6PdkWqK9NA/GUzj9rhxCujmWNSm0McxcHDnV3CwMlDLYTe/cd8qie6J7+n1rntSWCGp2t05fRtjQG8YzPpvnnHcx9UK3E2tuuRyEp1/ZaX/d3LwhcJBq4mztZwLmfqiwRUF5taFm7r2H92obUyfXe/YYilFo8kLqS+lEdsumNKgyIP5Ys27BebbvLEAlYl1m2GK6Wakqe7AuodZ05c860HaznDFfLcG+vWx9YZpz9Ac9NgkwNEM8egXybM18JGdi3q1DFlW1kSI19KQmJtJFDxxtyL5epBNj16w5wkI+U8lXqmZC8qVOl5C4TntqpAMhtDGnOvoXbnSGU0/so6jINzbZnVD+sBzIHzZ34x2fnTsBkOpIrSQzxZAsX/2EUb+0+tuf9enWROSA10Bieku0vIwfPnjvfpfDKfY0//QRKUTFc5G+qgIb0/piff8bnRKLUdvR1bOvcn1tA46WA9vF8JAWzH6xWubzH0/N8PC/P735ejwf4bIyfMbNEi5NAjl64IuFB4g4LAm44fMJAgS/Si2cS8dkTiRAmioQDKecRQkDevw/yMgh/6eLEl+TZc7iyZtj4aQgJOW3St+iHX02CcCUIZCFXjvfNpARPydxJfQxnT1LbvoOHJX2WPK5DJiDUPI39BpXuaCRncN3dOGGDlgzEHVqfHp1cyk1eXl5+iCEmEb6YV/GiBe2q65fEt4AINfZ8zZo0kG+/m2TKegmMMfmsGDqaobFJsKcqFI3LVNOtgefOvvvuXW8SX1Sc/1O0g6VLFnedOzgLmTCuoe1FfWZZYd/w68Il5npImLdGJWgShvZAxbk7CFdUnWP7mzBhfFnmQ77Nn9ev+tRHxQpLPhBeiEV91H8qKdIsuTerD5HQnic4v+FB4PS6elk23LguOw6fkls3vaRQnhwIwZtELiD09JmzF9B2PjGqWO4qfY2btzFKnVMnjQ32xAQSxaim2K3XIFPuSMDk82wANT6GIe78VRsXWZCkSLZjfD6fN28su30URa9eu2bGrxxXxke/RauHMSv4QiDAPTOEQ5HwrrDRfDcikZLq1/uB/cHDR6UtSILP/BlD2QlP4ydMMQQ7kp5ZVi0pkOdbvmKVtHBTHud2GsmlrIPuth91jJOUrPEdbdqMuWbV1qse3b5CeN3isgPpSOhkn8qJGOwvlkC114Zn57FOo9o2Q572QFhrGgnq34ydAKXruSC1l3cpVzqP8W+Z5YqkvhM4Z9rUKf1LFqTtl0CGpMIsJ4+RCB42XBjTtvHgnD4YhbS9ZR4J4seR/u3rysIVm2XirxvlKcbvhojIzgsWxox9HsnyNdulVuncEh3v2D/MWizlS+SX7BlTmXayASZD8Z3bGvthvvPS2Jbsx7iLRnKirfNmg8+XbY/eRvvHd7UpU73JqgzT7Mk2btqCCWkLpRsnIvnYDJ/JeP0wgekEQjSTAOs0EmRpB/duQs3xHGLdJMAX32fZJnFyAt+z+O5mrUfvQabsNG/aSNq0evUOYvfzl/9/mD11PJQ6awoJzZuhMsxJQ7//9jvem9bLdbQ/R48eMRNRqGDctElTad68uSFAO/Pxb5kTTKzSK4mWP2FMqqYIKAKKgCKgCCgCioAioAgoAiFDIOybmu0VstPrUYqAIqAIKAKKgCKgCCgCisC/E4EfJk5xXRgdPEEm9fkoN2XNnMGQp0jKatnsU0OAygdHoQ2lWKFsKVf+QV2oUbWSISMxTA+dSzb8ZSgJYxyodEzSKfUHnHLV4UylUeGDxlBPdBjTybQR5AI6mXpi2YZiLQKiFh0UnozvDCSt0bHEkEKccV+oWAVDNGP6D7Jnk6xwYjG0LB1CRxGujQ7SoJIV3c9JwhsJT1SY2oIQcrTMGTMYAsOgoaONQ63jl60Nge2rDl+YsFZ07hT3Udlj+k1QJti2bacJo8WwdX9AsYWOvXpwVJI0sewPb8d3KJCnjsOpwnCZdHI9gnIEjXnthvoKQ3BRXYrqMgzr5smoGrJm7UY/RB2mpePpBcL6kUhGI2mPeFIF0D4vG3LzxykzDXmKoXjpNBw3dgSU9SZBWaqNzILTxT00LrGmsuKxQNSTGDKtHZQASQCimgzDXFLhzmksS1RpoFOnRJmqxmFKlT/WAzrfSeijc242iJSWWHHu/Hk4bCPKUqjR0RiSyWmbNm83q3Sw9+3VCSH2ehkHequ2rxzsBxGq1BoJP82/+NI4/KlUNHJoP0MCOn3mrE0iTT9rIKnh2KSDtUrNRkLVOj6b0uVrGsyYkOGkSIijkfRChysdzDSG49u0ZZvr/qNFjWocxCRSHMSzKlzQW73PO3Xg37FjxTKJ2iMMtVP1khuvgUBBQh8JWCxz1sp/XBLXnEK+/Lqn/IW6WLFafaMYRJLS37v2mWRUQ6TxWt2fO8PfWbKrSeTzFVj43c1btkMlLjJCaR0wR3hSmeMOKg8NGDwa5GAvQ3KiQ/vMuQvmGBKaUvqEgzYb8PX7ouVSHUqaZ86ef6v1KKTlugDUT/IV8VaLs/fAX6sQVaDoK5Ki3U9Fns4Iq+e0j0GASONwoNp9VDT1jwSzevmvUF4KZUjZznBrh48eR1txFESdh4YUxnpVpFA+kOzOQI3uVR2hc3sWiF1UJuT1kmSyB+1UdTjq2X61bd1UUqE+sJ6TpMe64Z+xPg9E+EuSEGtU8+4vXrc+8FzL0damS5vW42l3/L3TXPcLKKNSAY19xe69+01aW0e5shXqnjXqfObK4zIcy9ZYPkeDnDwGJAla316d/dSRZiCdR4seVbp062uIfSQA54Lq6ln0K1Ure9+rzY/tA9vbsmVKSEy0y04jyTF1qpRmk23P2L60hYN8Cdo9Err7DRzpOmSdT+hU1wafBfYRI0aPc99s1o/g2bsblbesMdx98SIFjboZ86EyTy0Q6Um8Ydv79VetjQIP+5XXuT97Pv9+2Qe0RPvM8YVTJYvpAxovBbSPx5YqUUQ2rFlkiPMPEdrP3Xg+EhYG9unmvivA9XDYGz9WNLl/6Jg8gMqZxE4sXlQijhpPLt1/IaGjJZBLD15IuFhJJVQ44B02vNwF4SteykTinP1MAhOVu2rUaSw/z5kcbGLfYKiZUR0uKHb9xg2TjKqdherVlDwg8zvVvqjodBP9+ekzd2UtiOrO8N8sq1Z1cujg3ujzQpu8xk+cBqW+2Gb5zt27/l4GFelIIPVjPn2n+3bn+Uagrx4H0hXbpN7dv3KNETphnEYiDsklVGzbu++AIeJyTN28aUM5DIK7DZ+dAKqNVm2PCpwkNsbEeCmoVqRQAUM2Z6jjWlDN2nfgoMGD6qdOK4a6RFLfaiivDR/cR3r3H+qLzMUxJQnH1qyCINd7QkGLYWpJBvxz6S9mTDRj5k8maX08r5BaAoR7jhA9hnghNHSsxNEkFsZSV6AkuXPvQYyBHkrhIkWlXLlyIF75JZWOGNxXatZvKpWrN5DFC2YGaWIC2wmSiflOwLE230vGjh5kxr3nz1+Sjl17mzDEDEVMBWj2fyRqfzd6MEjEMc19M2w7bTUIuSQh8Vk7x+Ad2rbEWMKbzMU05/HecP7CJbT5J005yfFhMXN8a7SlVNsm4WzDRp/JF1AotEalZxrfeb7FNc5ESGqWM2e7e+z4CZvcz69/5fqsz5jGecCeHeuM4vI8qITz3YhETpLEEyWKLz27d5S+/YeZMsVrseNgHs93CE7gMoaObOXqdcCwr0nbrUt7E9Y0bZqUUrxUFSlVvgYIXu2kdo2qyMO7jnof6Pmb2JPQyHLmJKd6Th3wVpK1Rg3tI2yTLJGMz34KCKEpfMZ1IW1v7ZmJS7kSeWTRmp1y6vJtozZJQjzHm2GfPpSSOdNLvhyZJAbeQfj5uGguOYexY7YMqVzl54abiqLN++KlK+Ydk+t2vGb3Odujf7r949iFyo0Me8uxA9/l7CQnOzFg0pQZGH8lNxPpSFonQdba5B++kfffy2zG/WcRKvdDH7L4yDHjzf11hIoyjWGrs2XNJMRj+45d3of7tMfbd+yUb7//0bzXsT3Ngvc1nqdQ8QpmTEAlbSp+cyzRuWNr72P9+eb789QfvzNqkywjq1evhsr8JrlyCfX1yGG5e/8e3qXDS9s2baRRo8Ye3z/8yVpm+LQh/D/I+7mKmHGuxcq/Y3S7IqAIKAKKgCKgCCgCioAioAh4RiCsmTXleZ9uVQQUAUVAEVAEFAFFQBFQBN45BEYN6y/8eLKF82e6NlN15PQxn3+yYysVG5zrVCBZtvAnuQhHVUIfJT46VKnQxNn0QQnl5gxTyhPTubR80Ty5es0L4UPjGMeaMwwYHT6tP//MkJTcr4ekoi2YgW+NCn78XENeUaDqZxUnuN95H1y/f/+h/InQsLxuEihIqug/aKTLEUrihLt9C+dfhXKl3TcHaZ2qdMtwn917DzRKGnRYMOwliV6Tp84yxEKSFGnNoQ5DpZFFS/4wDiGqGWaHwh9JerXh6HRafoSnnDtvgVGrs9vfA+GMRAISr56/eG6cZzxftveyyOjh/aUYnHAtmjS0yT3+kmTB50ByzaIlK6CCEd2VbtS3443yCTfwuui0Zrg5hs6kVa5UTmrXrCI3QFoYhrBuW6HsRvUQhkamgzR3zmyGvDbuhx99haPksQzryBCb1lKl9EzgoUOIhD6Sfgb17+4Ks2mPs78Mu7TglxlQ62thSDUkbpAAWAKqhfa90enIrFGniS/VKIYJcxrXqZLYp8fXvkhtJ06eciUjccwanU9UhCNxlOWMCkEFi71yrqdAGDQaHa3TQeZr8UVHEBxuG+JSOpCr6tSsJiTMUdHvozLVjIrHV1C4olE5hc/VhkJr17aF2R4vXlxZsXgezlMOJFHf128SBPJVEoSUOfPmGyIi1YfcjeWq69evFDqc++mUzJW/lHFOkhhrVceoePNe1iyyb/8BONa2+lGhjAOHchTci7tdB17uCnDONCT02rBq3J7ZJ/y3Mw2XWYfoIKUijiHnJohnwv59P36yIfNQEYeOYIaCXvD7UkOcYgi+Aig/b7MeMUxeSMp1URBYq1Qs637bAa7TsepuQQl97n4M16nCSWVNGtsDqpcw1DLJItby5slplNlI+GQ5IXmPxmdIojhJe1+2a2XIbDdvIXwe2hkSSC9cuixzQM6dOdUzgczmb39JaKMyp7XXqQ8pkic32awGoYEf/4wqjwwVTBUf2w4yrT3eHhfNQei5e+8VGYlERxL6SKyY9P1Il8KQPc7+1kG7yvCuVIn5DcTT97NlNUQ4u9/914ZOdG5n6Hoq3dLYnkWJHEl6gdxhlZlIsrOkJKZxJxpwmzUSM0JiB0HqZF/G9otkdjrHWUfn/zQVClsDTMhHkpV7QVWKRD//LLD78+84u52hzUlw/w4KtpbUF9B4Kaj72KdQlZJG0rH7+IPn43l5/uBYeKhQlS+UVxav2ShXjh6RyCS2gejGEJDyMpSEeo6ll6HlKYh+LzEGeIjwhuEe3peqxQpKRPRb1kiAnwbSC58BQzevX7kQxCnkFURLniyxixjqfoizPea+Rg3rmDENcbD2/vtZZdDQbzBJIq6ZnEFlXxKxaJ1RHqwlTZLYkNqGDepj2gWGrKZZgrtN598v8xz1rTdR1r80zu08H/uqQf17SlRcL1UHSXitVqWiKxkndrDMksC8CaTyxUv/MHWE5Mj79+5LpWr1TFqOjajMzPDDZUDQt1axXCm7GOgvCVLdOncw9X3t+r/M+Mm9LDGT0iWL+SpjVI7ieangFx/jATvWYdpdW1bxx2UkozjzpPIhJ9KQ+OafIqrr4AAWYqNsVqpSTWbNmi33blzDmPu+3Lx1C2Og6yAj55TGjRqhb03pMYd06VLLArQFH2NM2RxjovlzA25nSP5t9nkHo67HfrwTyEYk1dmJB3zPmIX+gxNhxk2YbMbEHGP/tXaJGYd7vAgPG4ljmDDeDCQSmqmISOOzzggiE/u+NKlTYUJQekPstupkTPM+xt/WsoH8xGMmoK3n8yGpjxbUdpdjBLaP7uaprbYK6+5p2UY1blhXymFMPHjYN2ayT58Bw01fy7T9Bo0wY0Aus72oUbeJKeeDBvTEuLQKN5s2YCVI/S2++MpMmmAYemc9N4k8fJFMyfLF/oOKz862hxMN3Ccb2Cw4gchZVu320ggzzQ/DAz/Hu1ysmDHsLvMb0vbWmUn40OFAyI0soa/ccpHwwoUWaVi5oHxcshBC9b5S4suRNYOkxSQXE6rXh4jszMu53LoViOU+737OyUFM8zbbPxKlR+B9jfVnGFTOOanFWuEC+WUUyvuIUd/bTUY5lIrX1mKDGMs+nO+t/VCO3M2+C3D7ru1rDXm/V98hJll2jGNIIuWkB44JvkYZaAh1Q5YlquL1HTDMjAmIR4niRczHPX9P6/yfhlVTXbhwoVzDRAqG3L1z756EwzixV89eUq1aNX/fHz3lyW358+aE8vos4cQMXi+JjmqKgCKgCCgCioAioAgoAoqAIhAyBELhhR7/0VJTBBQBRUARUAQUAUXgzSJw9vwVSZ701T8w32zumpsi8N9DYDj+wU8iB0kAdOw4Q+T8F+7myNFjkiRxEoQkfuVs5qsEVfBugFj1CI7xJ3CM0kn0AgpydEQ7iYIhucct2/42ykNU2LNqSX+uWgcnRWFfjlc6OOj4suejgt0BKMA9f/bCddrEiRMY5UCqgVh1OBK9SPqg8RiqiCxbsUoK5P3QFSrsGFRFUqdOYVQ5qM5A1QE6HG2oKtcJ/Fm4ePEyVHXOSgw42DLCIWxJcVRNywxlBacT0b6a7YaSmlOdjQonYaGCaK/VnorKgbxeGh3iAYXm2rv/oJDA6HRY23zcf0noSpI4sVF4cN/nXF8PVRWS8sJHCCd5c+f0o8xITO398jiqlzwHkYcORWu8LiqR0SlGIuyTJ09chBmmWfD7EoRleyHZgTmd104z4Qcd5AfnPjqQ06RJ7SuMF9MzjF3MmNFNeCmmZxjfKjUbgiSVRwaBpBZSs+HBnMdTudLdaWvKDwgqiRPGhwLTGfM8qLb4IYisLCepQQK0hCEq39Exa52uLItboc5RtZJnZzCVPE6dPmPCWTqvwy5TFe38hYvy7NlzyY76SQd5UI2qeAwHWuqjolCGPGEIviQBsMwuX7EGBIqCpry87Xr0T5TrwDChctafq9ZKhbKlPYbRY1u5Gs7VqiDsWrVSqtFRWaVenZpy4eIltEFHDUmM5G5ixhCwVtUoWdKELrWlXbv3y5OnT4SOXJJeWSbYhtnQj85r5Xmp8OefOh/zOnDosCEZOOulM4/XrQ8kGLKc2bbMmTeX2f5ky5rZRSBhaLonT56a+yNxm0YV1ShRIkE1M59Z5xfbpK3bd0mFj0ubPojKTyTC2rrhSuhhgWqpxIQh/Vg/gmMxY8RwEVnc27PpCMmaAIRXkoSs8X724Vla4gG3k1RFNURL7rBp7S/xWrfhLxOW0UnY4PPcu/+wVK5QBmpYG6VQwbyuPs4ey2tasmylCRf/uvdn8wzol2qSa0GSqVuzqq++K6BjXmcf+7gZc+ZJURD6Q6rusx5E+QUIq3rk9Dm5BYLCC5D5bD/IckoeSQwotmZAGalYoph8BMIpGCb4eBOS7PVfQehx9mGf1K5uNwX4y7qwaPFyo+zn37UznHIihMe2Ie39y5BlmP0Jx418BmwDmCdVlZzmLKM8Zi3KVS2EvrZ16wHIi9Og+JU3dy5fYwwSAKOCsEq1Sndjm7BwyXKQM3K7wvjaNM7zsX1hKGj39ocKvHug0EeSOPtgKqSR1MXwuLv37DMhsdOmTeV6Jn9t2obx5CNh3cv+/nsYQ+ABBcOm4/4Y2pXk/3/aWHdXoS1i3eb1hsRug0y4efNmqEHukT//XCmPHz3AGCWGUee6D+XWFCmh8FasGN6pk0qJEh/5URO15+R4WaCMl9dD6GCbxv6ybNy+c8cohJFcFJBRNTUsSG3+jXmp+rcSk344Lk/uM/HCPT+23xEwYYOha23dc6ZhudgMYjstMVQL3cOqOssZFdLYD5JkZ832yZUrlHXVCeY5F0qEH7yfzVdZt8dQDZL3xrDfznE49/N6N23ZIRUxMYljMHdjXx4PoYZtWef5L1++jDF2ZpMX800MdT9LVnYeT0X07X/v8tW/Ofd7WibhdA4mJBUtVMAPNp7Sh3Tbm2hvee7rt+5KxyHT5fSFG+DqPcNMkVASJ0oEmTm6k0SMECbQy+O7FstDapR9kkxpJN5SNdO/sQvTOMvJP9n+8Tzsd0k0t+N1nt8aceTkLVoEvB+lSpnClPvTZ86Z+yoPcijJn3xvYD8RkLGMcWIWw0Wzftkyx3FSgXwIYexWPondBqjqO1Uznfnz2lmWsmTK6LFeMO2CBQukd+9ech91iOG+27VtJ3Xr1g02oY958bktXf6nISJWwZj4v/a/D96DmiIQFAT0f99BQUnTKAKKgCKgCCgCikBwEbh48aLr/8M8Vkl9wUVQ0ysCioAioAgoAopAkBDQf2wECSZN9A4h0BBqQfHixJE6tar66xx7h+DQW1UE/hUIUKmS4WVLI5RqYM7tf8UF60UoAv8gAlof/kFwNev/GQJPQOTjhAOjBuXpKsDso6pY+EAITp4O1W2KQPAReEUaNaS+v/6Sq1D9Yxm9C0IQ94YHCS5KpMggPT+WG15XMdEiqZQFkZyqfmqKwL8ZgSteN2X0hHlQmrxj2lXKDmfJmEpafxY0QvS/+d7ehWvj5Kpff50vVOxr0KChlCxZMkAy5buAid6jIhAYAvq/78AQ0v2KgCKgCCgCioAiEBIElNQXEtT0GEVAEVAEFAFFQBEINgL6j41gQ6YHKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAj8yxHQ/33/yx+QXp4ioAgoAoqAIvAfRcCd1Bewzv5/9Cb1shUBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBSB/yICoV9CBl1NEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAE/vcIhA4VKtT//ir0ChQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUA0/K4WAkVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUXgX4KAkvr+JQ9CL0MRUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBSB/2PvPsCjKNoAjr+5XHoPgdA7Sm82wIIgRQQRwa5YwV4+VFBsCGIXFQsqIooFexcFUQQLKtKk904glTTSyzcz8ZZLgyQkcHf5z/cct2V2d+a3eZ5vvH33HQQQQAABBBBAAAEEEEAAAQQQIKiPvwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEXESAoD4XuRE0AwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGC+vgbQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBFBGwFBQUu0hSagQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDtFrCpUrsF6D0CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACLiJARJ+L3AiagQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggICtsLAQBQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcAEBm5eXlws0gyYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggACZ+vgbQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBFBMjU5yI3gmYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggYCsoKEABAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRcQMCmigs0gyYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAARffwNIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOAiAky/6yI3gmYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggYPPy8kIBAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRcQICgPhe4CTQBAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAS1gKywsRAIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFxAwOYCbaAJCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCgBAjq488AAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAARcRsHt5eblIU2gGAggggAACCCBQXKCgsFAKCwokv0CksFD9Q0EAAQQQQAABBBBAwIMFvLxs4q1ewfWy2cRWw7/ZMdb24D8kuoYAAggggAACCCBQSuBYjrVLXZwNCCCAAAIIIIBAFQTsVTiGQxBAAAEEEEAAgRoXyM/PlzwdzUdBAAEEEEAAAQQQQKCWCOgXWfLyVWfVONiuovu8vb1rpOeMtWuElZMigAACCCCAAAIIuLDAsRpruzABTUMAAQQQQAABNxNg+l03u2E0FwEEEEAAgdogkEtAX224zfQRAQQQQAABBBBA4DAC+gUXPS6u7sJYu7pFOR8CCCCAAAIIIICAuwnU1Fjb3RxoLwIIIIAAAgi4toCtUE1rR0EAAQQQQAABBFxFQGcNKSBDn6vcDtqBAAIIIIAAAgggcBwF9LhYj4+rqzDWri5JzoMAAggggAACCCDg7gLVPdZ2dw/ajwACCCCAAAKuJ2Dz8vJyvVbRIgQQQAABBBColQIF6mUDptytlbeeTiOAAAIIIIAAAgiUI6DHx3qcfLSFsfbRCnI8AggggAACCCCAgKcJVNdY29Nc6A8CCCCAAAIIuIYA0++6xn2gFQgggAACCCCgBAoLCnBAAAEEEEAAAQQQQACBEgLVMU6ujnOUaBarCCCAAAIIIIAAAgi4vQDjZLe/hXQAAQQQQAABjxUgqM9jby0dQwABBBBAwP0EmHXX/e4ZLUYAAQQQQAABBBCoeYHqGCdXxzlqvqdcAQEEEEAAAQQQQACBYyvAOPnYenM1BBBAAAEEEKi4AEF9FbeiJgIIIIAAAgjUsEBhIZn6apiY0yOAAAIIIIAAAgi4oUB1jJOr4xxuSEeTEUAAAQQQQAABBBA4rADj5MPysBMBBBBAAAEEjqOA/Them0sjgAACCCCAAAKVEoiNSyqzfmFhYZnb2YgAAggggAACCCCAgKsJeHl5ldmk6HqRZW4/Xhtz8/IkKzNHsnNzJD/PvV++8bbbxM/HV/wDfMXHzs+hx+tviusigAACCCCAAAIIIIAAAggggAACCFRcwK4fgpf3Y2LFT0NNBBBAAAEEEECg5gWcH3TqMUxurnrQmJUjOXm5UqAeNBLaV/P3gCsggAACCCCAAAIIVE1Ah/LZVHCZr91H/P1VcJmP3WV/k0tLy5DsnBwJCQ6QsNBIsdu9VVur1u/jfZR+/ycvL18yszIlJSVd/Hx9JSQk8Hg3i+sjgAACCCCAAAIIIIAAAggggAACCCBwWAE7AX2H9WEnAggggAACCLiggA7oS0s7KDkqqE8/aAwPC3HrB40uSEyTEEAAAQQQQAABBKpZwDm4LFWNZX1VUF9ISJDLBfbFJ6RIgMpo17B+XbcN5HO+dToY0cfHW32C1X87BEtScqroPtaNCnOuxjICCCCAAAIIIIAAAggggAACCCCAAAIuJWBzqdbQGAQQQAABBBBA4AgCOqAvOSVNbN4286AxNCTYPKRz18whR+guuxFAAAEEEEAAAQQ8RMARXKbHrzpgTo9n9bhWj29dpegMfTqgr05EqEcE9JV01fdA9033UfeVggACCCCAAAIIIIAAAggggAACCCCAgKsKENTnqneGdiGAAAIIIIBAKQFHhj49VVmdiDCPfNBYqtNsQAABBBBAAAEEEPA4gaLgsjAzBa/OQO0KgX25eXlmyt3I8FCP8y7ZId1HPb2w7jMFAQQQQAABBBBAAAEEEEAAAQQQQAABVxQgqM8V7wptQgABBBBAAIFSAvpBZ4568Kan3I0MZ6qsUkBsQAABBBBAAAEEEHA7AT2u1eNbPc493oF9WZk5anragFrx4owOqtR91X2mIIAAAggggAACCCCAAAIIIIAAAggg4IoCBPW54l2hTQgggAACCCBQpkBWdu150FgmABsRQAABBBBAAAEEPErACi5T49zjXbJzcyTAP+B4N+OYXV/3VfeZggACCCCAAAIIIIAAAggggAACCCCAgCsKENTnineFNiGAAAIIIIBAKQGduSQ3N79WPWgshcAGBBBAAAEEEEAAAY8T0MFlepx7vDP15ecViN3u7XG+5XVI91X3mYIAAggggAACCCCAAAIIIIAAAggggIArCtgKCvjxyhVvDG1CAAEEEEAAgUMC+gGn/hTk164HjYcEWEIAAQQQQAABBBDwVAEdXKbHuY4x7/Hsp84ceCzKy9NmyPoNm4tdauWqNZKRkVFsW8mV/bFxMm/+LxIfn1ByV6XXj1VfK90wDkAAAQQQQAABBBBAAAEEEEAAAQQQQEAJ2G02kvXxl4AAAggggAACbiCgA/tUM3n45gb3iiYigAACCCCAAAIIVFhAj2/1OFdF9VX4GFetmJKSKjH7Yos1Lzq6rkRGhBfb9vW3P0iH9idKu7ZtrO3PPf+qpKalycw3XpKCwgL56JMvJTU1XW1LlWR13lWr1sr+/bFSv360THx4rAwccI51LAsIIIAAAggggAACCCCAAAIIIIAAAgh4mgARfZ52R+kPAggggAACCCCAAAIIIIAAAggggAACx0Hgz7//kRGXXWd9Bp1/ibw/+9MKtWTm9Kly4glt5MJLrpH8/Hw5eDBTQkKDpE3rFrJ581YZ2L+P/PX7PPnrt7kE9FVIlEoIIIAAAggggAACCCCAAAIIIIAAAu4sYNdTe3iR8sad7yFtRwABBBBAAAEEEEAAAQQQQAABBBBA4LgL9D/nbFm8aI6Eh4WZttx42z1SX2Xqu2bU7ZKQkGS1b8uWbfLAw49L547tZPprL5jtvr6+8uyTE2TpspXSQGXjGz/uTqv+suWrpEmThupc9axtLCCAAAIIIIAAAggggAACCCCAAAIIIODJAnYC+jz59tI3BBBAAAEEEEAAAQQQQAABBBBAAAEEjo3A519+KzNnzZYvPn5H/P395fc//pbrr7lCZs14pVgD+g0aIQ/c9z/pe/aZZvutd46TPJWdT5dHxt8rb858V6ZNn2XW9T8HEpNk7ryf5dU33rG2/TLvCyt40NrIAgIIIIAAAggggAACCCCAAAIIIIAAAh4iQKY+D7mRdAMBBBBAAAEEEEAAAQQQQAABBBBAAIHjKTDiwqEyZ+5P8r97HpQxd90qGQcPSrsT28jCXxerqXVbmQx8ZbWvW5eOUljoJU88/bzcc9fN0v+cPtKu7QlW1WemvCId2reVwYP6WduCAgOtZRYQQAABBBBAAAEEEEAAAQQQQAABBBDwNAEbmfo87ZbSHwQQQAABBBBA4MgCBQUFsmHjZqtivsqMcriPVbHEQnp6hkx84gXZHxtXYs/hV7OysmX6W+9LSmraYSvu2LVbcnJyyqzz2puzZPeemDL36b6sW7/J2pebmy/Z2TllfnJyc616zgv6/DEx+61NsbHxordREEAAAQQQQAABBMoW8Pa2yYvPPS6r1qyXG2+9W3r3Pl3CwkLl08+/lvsfekwF7hWWeeDoG66WG0eNlMCgILO/ebMmckavHtanYYP60qplM2td7/Px8SnzXGxEAAEEEEAAAQQQQAABBBBAAAEEEEDAEwTs+oGuzWbzhL7QBwQQQAABBBBAwKME4uITJDQkRE1d5nfU/dJBdKlpaVKvbpQ51+p1G+S6G++WRx4YI8OGDJRxDz0uvyxaXO51fvruQ4mMjBDdpgQ1/ZlzWblqjbzz3icydMgA580SER6usrHUK7bNseLn5yt/L10p+QX5csvoaxybS31PeuJFsdu9ZcpTj0hIcHCx/bM//kp6nnqyNGncsNh2vfLv6vUy6tZ75clJ42Vgv95y19iH5a8ly0vVc2xYvniu6OC+LVu3OzaJPr9+gNwmNdVs27Bpq9nW+8yeVp1WLZqL7gsFAQQQQAABBBBAoEggIjxMXnvlObnwopFy5RUXmY2PPjRWevcfJp99+Z1cPPz8ClHt2Llb5v/8i6mrx2iZWVlWUOCVl18sgQEBFToPlRBAAAEEEEAAAQQQQAABBBBAAAEEEHBHATsBfe5422gzAggggAACCFREICMjwwSOlVU3ul6UFOYXSFyJADVH3R6ndJeAAH8V5LVDBYitM5lAunXpUGYAma6ze2+MnHX6aeLt7e04hclet37jFumuphPTGUoqW0ZcPloeuv9/JiitsseWrP/r73/J/Y88KTp4TZcuHdvLzNemyB33PCQFKqvd4xPuE/2yhw7aG66uO+eLdyUsNMQ6jbbQ5dvvf5LvfpgvaWkHJelAsjRr2shs/3vpCmW9Qnbu2iuREeESEhIk5w08R0Zfd4UkJ6fKx198a+o5/xMeHipvvv2h2LztzpvN8tDz+puAwFeenyzjVbtH3TpWXnxmomzYtMWqezAjU5auWCUHUlLMtqCgQDn1pK5muXvXjvLa1Cfl3vGTxMdedP6nVIDf2b17yYqVayQ2LkHOHXC27N8fK8MuHWWO0Zn47rznkWLnf+7F11XQXlFQZXZ2tuhrOteZNWOqNGoQbR3DAgIIIIAAAggggIDI6tVrDcMbM96Vqy67WOqqF0uemPSAPPDIE2pse3aFiLZt3yEvvDTdjCm7du5ojtGZmL/8ao4MGtBfAhsT1FchSCohgAACCCCAAAIIIIAAAggggAACCLilQOknqG7ZDRqNAAIIIIAAAgiUFkhKTpH3Zn9udsQnJMnemH3StXMHs35y987i5SXyz7JVZn3lqrWiA/0a1C8K0GrTsoUsWb5SJj81Vdqe2NoEhj36+BQVZHeXDB86qNjFvp+3QN55/xN546Wn5JSTi4LKdIW3Vfa6T7/4Tma9+aJ0OkJQ39Llq+X9jz5XgWuPFjt3Ta50VUGKn37whgQGBspmlf3k48++Ee2ky6tvvGO+9T+3jr5a1Sl6aHrDNZeJ/uzZs0+GXnKdvP7SM8qtjqm7R/kOveg6+WjWNImKirSO9/W1S906Edb61m075Zs5P8qYO0bL6T1OtrY7L+ip23SJUUF3zz89wWTeK1RBh3oqN+eyecs2FWiXYTaFh4ZaQX16w2mndJP3Zr4k9aKi5DP18Dc3L1emTH3DTMubfvCgrF2/Uc4+61DWPZ3x76c5H1mnv/TqW2Tig3er+9/GbFu/YbM8/Niz8tkH0606LCCAAAIIIIAAAg4BnVnu54V/yPALBqmMxWGOzbXue+euPfLUcy/Lpx++JY9Ofk6eff5lmfjIfTJs6HnqBZlGEur04kh5OAt/Lcog3bRJQ3nu6YlWtZycHBPUZ21gAQEEEEAAAQQQQAABBBBAAAEEEEAAAQ8VYPpdD72xdAsBBBBAAAEERBo3bCAzX59iKHQQ2UvTZlrrDp9bRhct9Rt8mdw8aqRcoKaidZTRt4+Va6+6RO689Xqz6R81XWyEykJXXvl+/i9WUF9ubr58P/fn8qqW2p6QmCj7VABbTRWv/4Lk9Pl//f1v+XrOPHOpLp3ay9VqWrQ2rVqYfk599S256MLBMuray83+cy+4SmVXuVAaSn2Tmc95it4gFej38mtvSTeViVCXxX8vNZn7FqmsgI7S+4weJsBv+AXnOTbJb4uXmKx+ztusnU4Lu/fEyCVX3SwPq2yFFw491+y569YbrBqfqYDJyy8eJjo40bk4909nT3TcU39/fxl97ZXy+Tffyy71sFkvp2ekOx9q2vaPCubU5bRTu4m+p/rjKL16nizPv1IU1HfXLTcUy8zoqMM3AggggAACCNQugc2bt8vM9z6SeT8tMh1v3KhBtWRadkfFPWr8dumVo2Tyo+PVuLi7ys73oLzz3odWV07q3sVaLm9hygvT5Pc//5GXX3iivCpsRwABBBBAAAEEEEAAAQQQQAABBBBAwOMF7F46RQ0FAQQQQAABBBBAoJiAnoo2Pf2g6CldHcU5C59jm+NbZ/77+tt5ct+YW8Xf30/++meZNGxYX2Ji9juqmG/9sHfmux/J5i3bpf85Z8mD4+6UP1SQ20uvzTRTwurscGf2OlVuv/k6U3+nyvhy25gHZZWaAnjQwL5me2hIsHWuWR98Krt375VeKuPd3XfebGXNy1dT6r7y+ttmulwfH7uc0Lql1Y46EWGig/n0tMJr1RRmhYWFoqeW9fP1le3qesNUYKNedpRU5ZCVlW2m0dUBeY6iH8qmH8wwgXCObc2aNim2rgP+nLP26XrxCYlyUB23QGWyKVmaqul8W7dsbjbrzHl6Ct1b7hqvpj+2y4B+vdVUwQXFDslW2Vp02xzFV7Xb0b8//lwqW1RWQEfZoKZCXvjrn7JJZffLyMiSqdNmSDuVhdG5aLc8FZDpKMvU9L46I0zP0w5lFExLS5c5KmDzf7f+FxHqqMw3AggggAACCNQqgXUbNslbsz4S55ceNECBGk/U1nLt6DvkgvPPk+HDBhuCLipL9gvPTi7GkaGyLCceSC62Ta8kJR2QDJVNeb0as3396buye+9eiY1PlHff/9iqm5uXZy2zgAACCCCAAAIIIIAAAggggAACCCCAgCcLENTnyXeXviGAAAIIIIBAlQVsNptcfskwMw2tnkrtsouGSof2J5Z7vubNmphMdov/Wip9zz5dflTBe+cNPEemvjrDOua3P/6S8Y88KY9PGCe6/nNpmXJbAABAAElEQVRqKtivvp0rgwacraaB7aUCzhbLI/fdJWEq6M5R3vvwc3lETQF72cUXyAPq2G7qwagO7lv425/mXDep7IInde0ob779oYy69V75+L1pEhgQIC+qjHtfffODyo4yXupG1VEZ9WY6TikdOrQ1ny++/l5lzFspqSpIrc+gS6z9T6kHqU9NedVav/mO+0Vf56brrzTTA6ekpMqO3Xus/WUtNKxfT103qqxdsm7dJhMw+cbM94vt1wGQFw0fIs7Z+PQUui9PecwE+p03bKQxdj5IB/w5l/fUVMeO/h1UD4UzMg8F/Olp8Fo2byq9TjvJOiTgv2mFHRvOPrOn6I+j6PuXk5MrY8fc4tgkOoOgDuqz2Xg5xkJhAQEEEEAAgVok8O+adTJDjb3+UNnkKMUFnn1yonTu1K74xv/W3nzrXVmw6A/Zr7JTH0hMUi+dtC5Wz8fHR4aeP0gem3C/hIWFmqC+bPXyhn65xVHyC2pvwKTDgG8EEEAAAQQQQAABBBBAAAEEEEAAgdohYNeZWcjWVztuNr1EAAEEEEAAgcoJ3DL6aml7QmuZMetDGTnqLjnlpK4y6eGxVjY857PprCHDhw6SOfN+VlndTjJBX99/+W6xoL7vfvjZTAXbp/fp5tCLhp0nH332jZn+tmGDaAkNDTEBac7nfWDsHdK/z5lmk86Mt2LVWhPUN0edq8ep3U2gnd5Zr24dGXbpKFm7dqPoejqg73+3jzZZ//T+CwYPkD//XqYXSxWd+W/hvM9KbXfeoLMPOooOXHxw4jPSSE1vXFbZG7NP7rv3drlUBeiVVZatXCV33HK9XDLi/GK7n35+mnirYMqS5aRunUVn2Xv7jefF8SB31C1jZdj5A2XIef2KVW8QHV1s3XmlfnQ9+Wf5vxIbG282e6mgvDG3VT7bXm5unuiphykIIIAAAgggUPsEvpkzXx59fErt63gFe9xNvWxSXjn7rDOkcePGZnf7dieo5eJjyRA1Jn3p+UNT7nZo31amT3tOzujVwzql/h3z3P59pE6dCGsbCwgggAACCCCAAAIIIIAAAggggAACCHiigN0TO0WfEEAAAQQQQACB6hDQLz7orHv6s3zlGpkweYo8POlpmf7KM2WefsA5vU32vR9+XGAC+6Lr1S1Wb9Wa9WaK3Z8W/Gpt9/M7FCxnbXRasHl7W2t16kSqaWMzzfqadRtkmAoidJSmTRqbQLONm7dJUzUF7kFVr3Xr5o7dh/3W/QwOCpK7758oevrZkqX3GT3koguLplBz7OuqMgbOfL3sB9p33fuIo1qp7/T0DNm5a6+cefpppfblqGC5yDI8flEZXaa8NF1+/Ha2yo5nk6UqMC9JTdl25WXDRWffe2naTDmpWyc5vecppc7p2JCngi51EN9fS5bJpSMukOjoKLnn/kkquLBommNHvYp8H1RTxgUHB1WkKnUQQAABBBBAwMME2rc/Qdq0biGbt2z3sJ7VfHfatGkp+lPRosfSJcfTetw6cMA5FT0F9RBAAAEEEEAAAQQQQAABBBBAAAEEEHBbAYL63PbW0XAEEEAAAQQQqGmBLDXdlyNDXXeVdWTk5cOLTUtb8vpRUZFylgqAm/z0S/Kkmva2ZGndqoX0OKW7TFDT6ZZVygqoK6ue3qbPtWnzVmt3fEKCCeRr1qyxmvY2UiIjwmXz5u3SpWN7q055C9t37FL99Jdff/9Lbr/5Oolyynzy8y+/y9btO0sdunnLNvnfuEdLbdcblq9cLb16nVrmvs+++k7anthaGqjpeUuW7KwsNXWwf8nNMvuTr8z0wzqgT5f3Zn9usiLqgD5dAtQxr894r8ygPp3NZcmylbJvf7zqV6Sp3+O0btJcBT5Wtuhph7Oyc2Xuj7+ozIhlTy1c2XNSHwEEEEAAAQTcS6B1i2by0axp8ruaenf6W+/L2vWb3KsDtBYBBBBAAAEEEEAAAQQQQAABBBBAAAEE3ELAztS7bnGfaCQCCCCAAAIIHGMBnQ1ujApau//e2+TUk7tKXFyifP7199Z0tuU153w1HawOjtPZ7UqWc/qcIZOeeEEFvJ0ivVWmuriERBUkliP64XAzlWlv2/ZdooPzQkNCxc/Pt+Thxdb7qil8H3vqRZk7f6F07dxeXn1jlsnU17ljO5XNzkv0tT798jtp37aNREaGq7b/UOx4x4oODBx92zh56P67zKZBA/oUC7iL2RcrKalpjurWd3h4uAweVHaWlH37Y616zgs7du02WfWmPFl2Jj+dfU8HIzqX9Rs2m4flzzz+kNm88t+18tviJfL1JzOtapddPFTe/eBT+WfpSjlF3SvnMvvjL02QoZ4SuF7dosyJ945/TAJUEGNly4THnzf3Vk+9e989t1X2cOojgAACCCCAgIcI6N/SzlQvMOjP3/+skOkzP5AV/66xeuf134sI1gYWEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBSgqQqa+SYFRHAAEEEEAAAXcV8Dpiw51fdujWpaOMuu5KeeHlGWaqV33wKSd1lQkPjDnsefqc1Ut+/+lLk+FPZ4lzLsOGDJSEhCSZpILD9PS4uoy/93YT1NetawcVnNdBBg69ynw7pra1eRdlp9N1vVWwnqNcOPRcExT4+NNTizL0NW0kb057TsJCQ0yVKy8dJkuWrpCrbrjTrF9w/kAJWh/gONx8794TY6bCvefOG61gxd9VwJwOAnSUzVu3l5mVzm63qSl7Ax3Vin37+vgUW9cre1RQ3S13jpcLBg+QPr17ldofq4Im/1qyXMbcNrrYvs+/+t5k4NOZ/VLT0mX8hCdF35slKoDvky++kx07d8sW1UbtOWPWR8WC+lauWm+yAmr36XEJKlNfuDz7+MOSk5tjXSMrM8taPtLCE4+Ok+ycXBV0GSzeTtMiH+k49iOAAAIIIICA5wqcdko30Z/lK9fIjHdmm/FMWFjReMxze03PEEAAAQQQQAABBBBAAAEEEEAAAQQQQKCmBbzUw+biT5tr+oqcHwEEEEAAAQRqhcCuPbHStHF0pfqqA6bKKnq4UpCfLwlJqZU+Z1nnq+w2nUEuMCDAmoq3sseXrF9QUCCJSUkqK11EqeAwHdwWFhpc4WvpKXt1sJtjKtqS19KZ/8LDIsTHx7vkLtFZ7Jo0aWQC+vR5TjlzsHTq0FaCnIL19NS8Z6tAxfvuvtU6/od5C+TBic9Ij1O7W9ucF1avWS+333qDXDp8iNmck5MjF1xyvbRvd4KZltg56G/yU1NFBw6uXrtBzhvYVyZPGGedSk9/3KvvBaIz++lAQJ0J597xk6R5sybSuFEDadSwvjRsEC3NmjaW0OAQufSaW+T9t14y19En0fU7dWwrE1V2xLYntJLrRl5qnVsvjH1gsqxZt0FCVSDkx+++VmyfY2XqqzMkR/1djh1zi2MT3wgggAACCCCAQLkCycmpEh4eWu7+8nbosXNUZKjY1IsDzi+aONf38y394oTz/iMtlzfWdhwXG5d0XMbajusfj2/tHl0v8nhcmmsigAACCCCAAAIIuJBAZcfaVfnt24W6S1MQQAABBBBAwEUFYmJiJCoqymqdl3qoXFjej4VWLRYQQAABBBBAAIFKClTlh43yHjQe76C+SnbdLaurIaF8/d080dPv+vv7WX1Yt36TymyXa7IHOjbqDH86817PU09ybCr2rYPpGtSvK03VlMKO8u+addKx3Ymlghj1uWLj4iVMTTncpk0LR3XrOzY2XmUOjCwzKNGq9N/CkmUrpXOHdsXar3clJBwQXz8fk2HP+ZhlK1aJDhzsqAIZHRkOnffr5U2bt0mBCirVQYEUBBBAAAEEEECgpgQI6qsp2cOfl6C+w/uwFwEEEEAAAQQQqC0CBPXVljtNPxFAAAEEEHBtgVJBfWTqc+0bRusQQAABBBBwVwGC+tz1ztFuBBBAAAEEEEAAgWMtQFDfsRYvuh5BfcfHnasigAACCCCAAAKuJkBQn6vdEdqDAAIIIIBA7RQoGdRnr50M9BoBBBBAAAEEEEAAAQQQQAABBBBAAAEEaruAnnaYggACCCCAAAIIIODZAtH1Ij27g/QOAQQQQAABBDxSgKA+j7ytdAoBBBBAAAEEEEAAAQQQQAABBBBAAIHKCWTn5FbuADevzcNdN7+BNB8BBBBAAAEEEKiAAC9xVACJKggggAACCCDgkgI2l2wVjUIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgFgoQ1FcLbzpdRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcE0BW2FhoWu2jFYhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUMsEbF5eXrWsy3QXAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAdcUYPpd17wvtAoBBBBAAAEEyhDQryKQZLgMGDYhgAACCCCAAAIIuK2AHt/yyq3b3j4ajgACCCCAAAIIIIAAAggggAACCCCAQI0IENRXI6ycFAEEEEAAAQRqQsDmbZO8vPyaODXnRAABBBBAAAEEEEDguAjo8a0e51IQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGHAL8YOiT4RgABBBBAAAHXFvDyErt62JmZlena7aR1CCCAAAIIIIAAAghUQkCPb/U4V9R4l4IAAggggAACCCCAAAIIIIAAAggggAACCGgBgvr4O0AAAQQQQAABlxfwUg849cfX1y5p6ZlMwevyd4wGIoAAAggggAACCFREQE+9q8e3epzrGPNW5DjqIIAAAggggAACCCCAAAIIIIAAAggggIBnCxDU59n3l94hgAACCCDgMQL6IaePr4/JYpKUnOIx/aIjCCCAAAIIIIAAArVXQI9rdZY+Pc7V410KAggggAACCCCAAAIIIIAAAggggAACCCCgBewFBQVisxHbx58DAggggAACCLi+gM3LJoGBAZJ+8KAkHkiRyPAwZilz/dtGCxFAAAEEEEAAAQRKCOgMfTqgLycnR4KDgkSPcz2h5OblSWZGlupXvppOuFDy8ws8oVv0AQEEEEAAAQQQQOAYCuhXXWw2b5XN2lsCAv3Fx24/hlfnUggggAACCCCAgOsI2Anoc52bQUsQQAABBBBAoHwBnbnEMW4JUoF9GZnZErM/XkKCAyTAP0Dsdm8C/MrnYw8CCCCAAAIIIIDAcRbQgXx5efmSmZVpptz1Vhn69LhWf+txrrtn6ktNTVeBfDY1Pg8UXz9fFahI5sHK/snt2hMrTRrVc/u/hcr2m/oIIIAAAggggICzgB435+pxc2aWpKRmiK/dJqGhwc5VWEYAAQQQQAABBGqFAK821IrbTCcRQAABBBDwDAFHYJ/d29s8AM1VmU10cF9aeqYUqCwg6vceCgIIIIAAAggggAACLilgMo6oAD4dxBfg56Om3PUVb1vRiyvuHtCXdCBVwkKDJDDA3yXt3alRjllV3P1vwp3MaSsCCCCAAAIIuJaAfjfE10dl6vMJktCQIElKSRU93oyMCHWthtIaBBBAAAEEEECghgXshep1B34kqmFlTo8AAggggAAC1SZgAvtUUJ94FYiXv6/YdTCfGs/oDwUBBBBAAAEEEEAAAVcW0GPZovGsys6nstp5SoY+Avpc+a+OtiGAAAIIIIAAAu4roAP86oSHSmJyqujM0GTsc997ScsRQAABBBBAoPICdgL6Ko/GEQgggAACCCBwfAXMg1A1RZn+X6Htv4A+gvqO703h6ggggAACCCCAAAJHFvgvqM/xe5zj+8gHumaN3Lw8E6RIhj7XvD+0CgEEEEAAAQQQ8BSByLBQidkfr6blzRMfOxPRecp9pR8IIIAAAgggcHgBMvUd3oe9CCCAAAIIIOCiAo4HoPqbLH0uepNoFgIIIIAAAggggEApAcc4ttQON9yQmZElQUEBbthymowAAggggAACCCDgTgI6Y19QoL/o8adPaLA7NZ22IoAAAggggAACVRYgU1+V6TgQAQQQQAABBFxFwJMejLqKKe1AAAEEEEAAAQQQQOBIAjk5+RIZ4XOkai6zPzs7WzKzsiVcZXqpStHH5+bnS3BgYFUO5xgEEEAAAQQQQACBoxDQ2aHjD6YcxRk4FAEEEEAAAQQQcC8Be0FBgdjU9HUUBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgogIFBfni7e1d0epHXU8H1U16cqqMuvZyadG8iUx9daYUFBaUOm9zte/CIQNF/+6Zm5tr7f93zQZ5f/YX8szjD6hpg63N4uvra6YR1vWds4Drl4ecfzddsvRfWfHvOrnz1mvNwekZGTJx8gty641Xm/asXrtRMjOzJCMzQ9JSM2Tbjp2yfcdu6dnzZLl0+OBDF2QJAQQQQAABBBBAoNICPj52Nb7Lr/RxHIAAAggggAACCLirgN35hyl37QTtRgABBBBAAAEEEEAAAQQQQAABBBBAAIFjK1BYTZfLUMFxgRXIfufn5yeXXHS+PDVlmtxxy3Vy4dBzVQsKZNr0D6RP757S7sSWpkX+/v7me8PGbfLq9FmSnZ1jAvwCVHYXXe4ZP9kE3+kHw3a7XW4edZV06nCifPbl9/Ljz7+ZOjrAr1OntjLmthvMeln/6Ix9V10x3LTntpuukZWr1plMgEHqOgsWLZYbrrlURl5+oURGRpR1ONsQQAABBBBAAAGPEXB+MaIqnarITCy6TnWNP6vSRo5BAAEEEEAAAQSOtYCXGmQx/jnW6lwPAQQQQACBWiCwa0+sNG0cXameZuccyqJRqQOpjAACCCCAAAIIIICAhwv4+R7dNLdHGmvHxiVJdL3ISinqYyoz5tc/Q8bGxUtqSrokp6VJWtpBSUlNkzyVTW/IoL4VCuzTDdwfGy8R4aEyd/6vsnnLdlm3YbM0bdJIgoMCxdtukzG3jyrWj5279qjAu9dl2ouPWRn5bhvziEx44C6pH13XqpueniEBAX4m++D8X36TPbv3SXxikmpzgqmTkZFlMv9F1YmUpx67zzpu775YCQ0OkpCQYGvb9TePVdebLP7+fta2Iy3o/4Zq1CDKZAesyIPtI52P/QgggAACCCCAQE0L6PFdjvpNN1WN67KycioddKeTJ/v7+0poSJDKnuxjxmqHa7MeL1VlzHqkYyo71q7Kb9+H6xf7EEAAAQQQQAABLRATEyNRUVEWBtPvWhQsIIAAAggggAACCCCAAAIIIIAAAggggEBNCezYuUcW/71MZdRrLZER4dKiWRMJCw1W2fF+P+Ildba9J5+bZup1aNdGLlbT2Z7T93Rp1rSR7Nq9V+669TrJzcuV+x9+2jrX3PmLJFUFDeqip+59a9bH6oFxsCQmJZvgvF9//9vsCwkKkkHn9pGvvpun6qer6XRHysaNW6VVi2Zy3dWXmDr6n9/+WGJNv1uyPV06tZMx9z1m1dULt9/9iLX+1KT71I+ylQuatA5mAQEEEEAAAQQQcEEBR0BfXPwBiYgMkag64UcMyivZDX2Og5mZos9Rr25EhQL7Sp6DdQQQQAABBBBAwFMF7Lz16am3ln4hgAACCCCAAAIIIIAAAggggAACCCDgWgJtWrWQ7l07VrpRdru3nH9eX9m8dafs2hOjsvyly3oVeLd+4xaT4W/z1h2Sl5dnzrtk6b/SrUtHyc/Pl4L/JikZ0O8ss0+vR0SESb++Z1j7Cv7LKXPxhYPlgUefkbk/LpRNm7fLuQP7yJcq0K/Hyd2kQf16xdpcsj3Dzj9Xnpg41qqjgwsfvv8OlfmvaMpffU0KAggggAACCCDgSQI6IC9FZejTAX3BgYFV6pp+Tu04Vp8rKjKs0oGBVbowByGAAAIIIIAAAm4gQFCfG9wkmogAAggggAACCCCAAAIIIIAAAggggEBtFvD29paTunU2BHtj9ktC4gFZsPAPsx4eFmItn9C6hVlu376NDD63r5mq9+s5P5VLN6j/mWrq3sZmv5+fr9x7143y8KQp4uPjI61Vpr7VazfIa2++LxMfGlPsHCXb4+PjrbLLHJoeRVeOrle3UtPvFrsAKwgggAACCCCAgAsL6IA+/clWU+7WVRn6jrYEBQTIgaQ0c059XpLSHK0oxyOAAAIIIICAJwjYGRh5wm2kDwgggAACCCCAAAIIIIAAAggggAACCLiXwK7dMZKcklKlRrdo3kRuHn2V7Nm7v9TxzZs2tjK+xCckyVoVmDfs/IGl6s2Zt8Bk9HME9ekKOiNfUzWlb2xsvOTk5MoF5/WXxYuXyvwFv0mAf1HWvVIn+m/Dzl175IlnX7V23zX2UbN886ir1HU6WNtZQAABBBBAAAEEPEWgUHWkOgLw9Dn0uSgIIIAAAggggAAChwTshxZZQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEqlcgKytbVqxcK7lqelx/fz9z8i3bdsrGTVulmQrA27p9h/Tt3aNSF83IyJCVq9fLhx9/JfWjD02Nuz82TkZfd4Wc3L2TdT6bzSY2NX1vyWJTD49Llu9++MkE9NWNqiOffTlHrrh0mNx649WmrSXrOq9nZGRKbm6ehIWFykP33WHtmvbGu5KdnWOts4AAAggggAACCCCAAAIIIIAAAggggEBFBAjqq4gSdRBAAAEEEEAAAQQQQAABBBBAAAEEEECgSgJffDNPep7WTQX2rTHT2uqTxMUlSKcObdXUtw1lydKVlTpvcnKy3P/w0zJowNnSpVN7FXQ30jr+uRenW8uOhcysLNmyZbtj1frOyMyylvXCmnWb5Ktv58sj4+9QwYf+8uRzr8nll1wgOiug/vz2x5Ji9R0raWkHZcLk52Xwef3E5mWT0JBgxy7x8fWxlllAAAEEEEAAAQRqi0B2To7s2LlHTmzTsliXN6iXOnRpe0KrYttZQQABBBBAAAEEECgtYK+OlMilT8sWBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBATcmmEFo0ayIpKWkSn5hoSDq0byNz5i4U36V2EzAXGBhYIaqk5BTZGxMnl4wYIv4B/hIXnyjLVqyyjk1IPGAtOxZ0kF2PU7s7Vq3vbdt3W8sFBQXy8mtvqyx/l6lAw8Zm+zOTHyg2nVxKaqpV37FwIDlNdu3eK4PP7SuNG9WXtPR0WbBwsWO3xKrgRQoCCCCAAAIIIFDbBHRA33uzP5eRV4ywAvt0QN8zz78u4+6+ubZx0F8EEEAAAQQQQKBKAmTqqxIbByGAAAIIIIAAAggggAACCCCAAAIIIIBARQSaqyl2P/zkGylUlYcPHWgOCQsNlSsuGSr5+fni7V16atzyzhsZHiZ33X69dOnYThb+/rckJR2Q3xcvtaqnpKZZy46F1LR0+fmX3x2r1ndiUpK1rKfofWDsbWY6YMdGH5+idr0x80PZvn2XCiBMkEtVMKFziQgPkdtuGikndess23fsljw1xfBOFeTnKFklsgE6tvONAAIIIIAAAgh4soDO0KcD+hyBfYWFhVZAH1n6PPnO0zcEEEAAAQQQqE4BLzWI0r+nURBAAAEEEEAAgWoV2LUnVpo2jq7UObNzcitV31GZ4YxDgm8EEEAAAQQQQAABVxeo6qwZfkc5jeuRxtqxcUkSXS+yUnz6mMqO+St1gSNU1sF6WWpq3Xp1o6ya+/bHSVhosDgy/2VlZcsBld2vQf16Vh3Hgg7SCw4KUnUDHJvK/NbHZ2RkSaDKDBgREVZmHb0xNzdfZSNMkaioQ45JB5IlQB0XoKbzPVLR/w3VqEGU6ADDqv6dHOka7EcAAQQQQAABBKpDQP8eqzMd792XcNjx4MbN22TGOx9JosqmrDP0HS6gryJjIV2nKmPWIx1T2bF2VX77rg53zoEAAggggAACni0QExOjflc69DuXXQ+6+JHIs286vUMAAQQQQMATBfQYJjc3TzLVQ7ycnDwpLCg0mT88sa/0CQEEEEAAAQQQQMD9BfQUtF42L/H1tZsALx8fO7/JHeVt1dPq6o9zKRm85+/vV2ZAnz7GORjQ+RwllyNUdkD9OVLRmf2cA/p0/ciI8CMdxn4EEEAAAQQQQMBjBfRvuDqgr06dCMa+HnuX6RgCCCCAAAII1JSAnYC+mqLlvAgggAACCCBQUwL6x6DU1HTJVVN1hQQFSERYmNjt3uqHoZq6IudFAAEEEEAAAQQQQODoBNQQVk3Nmq9eSsmU1PSD4qOmnA1VGeX4be7oXD3laP3fOBQEEEAAAQQQQMCTBDZs2mpNuavHvI6pePXUvBQEEEAAAQQQQACBIwvYjlyFGggggAACCCCAgOsImLc7D6SIt8ps0jC6rsnMoTNiENDnOveIliCAAAIIIIAAAgiUFtDjVT1u1Znl9DhWj2cT1biWYK7SVrVxi85C7nuUUyzXRjf6jAACCCCAAAKuKeAc0Ken3NWBfCOvGGEC+/SUvBQEEEAAAQQQQACBIwsQ1HdkI2oggAACCCCAgIsIODL06Sm06oSHEsjnIveFZiCAAAIIIIAAAghUTkAH+OnxrB7X6gzUBPZVzs8Tax/MyBJflX2cggACCCCAAAIIeIrAuLtvFh3Q5yiOwL7mzRo7NvGNAAIIIIAAAgggcBgB+2H2sQsBBBBAAAEEEHAZAf2gMycnx0y5Wzcq0mXaRUMQQAABBBBAAAEEEKiqQGRYqMTExptxrq+vL1PxVhXSzY/LycmVtPQMqV8vwvwNMCWzm99Qmo8AAggggAACxYL5nDmYetdZg2UEEEAAAQQQQODwAmTqO7wPexFAAAEEEEDAhQQyM3MkJCiADH0udE9oCgIIIIAAAggggEDVBXTGPj2+1eNcSu0U0AF9sbFJEhjgJ97e3gR21s4/A3qNAAIIIIAAAggggAACCCCAAAIIlBKw66w3vP1ZyoUNCCCAAAIIIOBiAnrMkpuXJ5H+YS7WMpqDAAIIIIAAAggggEDVBQL8AyQ1LcNMwctvdFV3dKcjzX/b5OaJnnI3W2UjDwsPEn8/MjW60z2krQgggAACCCBQJKDeUamWcax5Xg0qAggggAACCCCAQDEBOz8WFvNgBQEEEEAAAQRcUED/qGM++YVit3u7YAtpEgIIIIAAAggggAACVRPQ49tCNc51jHnd7be6XXtiq9bxWn6Ur49dAgP9JCwk0GToc2Tpc7f7X8tvI91HAAEEEECg1gv4+fnIwcxMCQ4MPCoLfQ59LgoCCCCAAAIIIIDAIQH7oUWWEEAAAQQQQAABFxbQgX3q1U89RRkFAQQQQAABBBBAAAFPEdDjWz3OVVF9btml+vUiJD8/3y3bfrwa7Qjcs9ls1pS7eptj+/FqF9dFAAEEEEAAAQQqKxAc5C+JSWnmsKCAgEqPZ/SLLTqg74A6R53IkMpenvoIIIAAAggggIBHCxDU59G3l84hgAACCCCAAAIIIIAAAggggAACCCBQcwI6w5z+UCov4BzIR0Bf5f04AgEEEEAAAQSOn4BjHOPj4yMR4SFyML0oMK+yr6nod1t8fe3mHPpcjvMev55xZQQQQAABBBBAwHUECOpznXtBSxBAAAEEEEAAAQQQQAABBBBAAAEEEHArAZ1tjlI1AQL5qubGUQgggAACCCDgGgJ6LKNf7vDzE/Hx8VaJp9VMK5XMPu0I4nPOYOwavaMVCCCAAAIIIIDA8RcgqO/43wNagAACCCCAAAIIIIAAAggggAACCCCAgFsKEJjmlreNRiOAAAIIIIAAAkct4BgH6sC+o33RwxHc5zjnUTeOEyCAAAIIIIAAAh4gQFCfB9xEuoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggcSwFHMF5lM/SVbCPBfCVFWEcAAQQQQAABBERsBQUFOCCAAAIIIIAAArVCQI97Zr33mSxdvrpS/dU/Sr3/4ZcVOi4/P1927d5T7Pz7Y+Nl2YpVZtvb730qMTH7i+13rGRnZ8usDz6XrKxssyk3N1+ys3PK/DiO4RsBBBBAAAEEEEAAAQQQQAABBBBAAAEEEDieAo7gvqp+H8+2c20EEEAAAQQQQMBVBexHmw7ZVTtGuxBAAAEEEEAAgZICOqDujz+XSlZuruwtEVh3ztmnS3BwYMlDzLo+7vfF/0idiHBZvnKNVcdu95bOHdtZ63ph1ZoN8vJr78jNo66SU0/uYvZ9PecnSU5OlpO6dZYlS/+V007uJg0bFjvMrGRkZsmi3/6SYUP6i7+/nzw48RlJSEgqXVFtmfn6s2VuZyMCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgHsLMP2ue98/Wo8AAggggAACFRDQGfo++/J7E9B32qndxEcF4yUmHTAZ8P5Z9q8EBwVKn949S51JH/fJ53NMQF+9ulGy6I8lVp24+AQJCPCXV194zNqmF7p16WAC+qbPnK0y7mVJl07t5J+lK02d/42dqK6ZLS+//o74+fqYbU9Pvl/8/PzMcsl/Hp8wTh6Y8JScclIXuXDoIDmQfECeeOZVada8ccmqrCOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIeIiAXU8np1MhUxBAAAEEEEAAAU8U2L5jt0yb/q4K4kuW3qefJi1aNDHdzM7NkW+/+0l8fOxy/nn9ZOW/a8VfBemdqgLodNmydYe88dYHJvDvgbG3SetWzc12PXZ6d/YXkpKaJg/ff6fZVvIfnaEvsk64BKpgvRmzPpboenVk9HWXm2pPPjdNRgwbJK1bNjXrvr6+5nvKS2/K7t0xZnnC5Bfk5JM6y1WXXSi333yNPPHsq5KZkSVLlv8rYaGhcsPVl5p6/IMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4HkCdgL6PO+m0iMEEEAAAQQQOCTwxTdzpU5khAnqy83Lk63bdpqdf/y1TJo3byKN6teTPXv3mf1bt++ygvre/+gr8bLZJP1ghgmqO3TGQ0sPTHjGrHTq1FbG3HaDWU5LS5d8leEvSl0zJDhIhg7qJ3EJiZKpsvY5Sm52jrUer/bpLID33DlaZeJLkXvunywTHxojYWGhkp6eIZu2bpfAwECVJfBvc3jrVs1ktZrit0un9uVOF+y4Dt8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAu4nQKY+97tntBgBBBBAAAEEKiFw9x2j5MCBZLn3gSdk5BXD1VS3RZnxVq3dKAP6nik6q54u69ZvMdPiOk794Lg7VLBfjDz21EvyxMRxjs1q+ttXpF/fM9RxXc22r+f8JBkZB639jz7xorpeilm/WwXqrVu/SebOX2Tt1wuffvW9td5PteGKS4Za646FHTt3y6QnXzJT857Z62Q5pXsXyc3Pk6XLVokOOHz7vU9l+itPik0FHlIQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBDxHgEx9nnMv6QkCCCCAAAIIlCHgnJX42anTxf5fEJzOqPeZCq5bsPAPc1Rq2qHAPL3Bx8fbOlv96LrWsrfdbjL/ObaFhgQVC+p74tGxUqCm6B3/8NPmmEtGDBH9+VldZ9Fvf8ukh++Wm+4YL+PvvVWaNyuaCtg6+X8LU197RwL9/c30vs2bNRbdh0cee176nt1Trr5yhAlOTEw8QEBfSTjWEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQ8QsBeo6eHI8OIBd5IuIIAAAggggEC5Ann5+WZft87txe5dFKy3XWXCa6mm323xX2BdzL5YWaKy4JVV/lqywtqclZUtGzdvE+//zrN3737xth/Klufn52fq6ql7ncvOXXulUaP6zpus5UIVBPje7C9k6fKi6wcG+Mslw4eYLH9vvDXb1IuLT5BPv/he5v74q1kfd/dN1vEsIIAAAggggAACCCDgaQI5ubmyfcde2avG6fEJByRVvZSTnZ0jhZ7W0Ur2x0vV19nHQ0OCpW5UhDRqEC0tmjcSXx+fSp6J6ggggAACCHimQL76HXB/bIIkJiVLSmq6ZGZmix5X1PYxRE3dbT020eOQgAA/CQsNVi9Dh0v96Cjrt9Oaui7nRQABBBBAAAEEaoOASlZT/IFzbeg0fUQAAQQQQACB2iWQmHTAdHjwuX2tjs/96Vfp3rVTsel3ywvq++jTb6zj/Hx9ZNXq9ebj2NhFBQsermRnZ8uyFatl6OD+xarpKXbXrNsoQwb1k/1x8XLaKV1VRr/FMvrayyQsLFT0j5DBwUHmmM++/F5OaNNSOndsa9aDAgOKnYsVBBBAAAEEEEAAAQQ8QSBJPYBfuWajrNuwVb2E01iaqBdjOrRtbR4S62A250zcntDfyvZBvxCkgxt1kEJcfKJs3b5L5v+yWNq3bSVdO54okepBOgUBBBBAAIHaKKBn5di+c6/s2rNfoutHSV31/4lNmzQU/fKsj4+91o8haupvQo9NcnPzJCMzS5JTUiUmLkFWrNogTRvXVy9TN5IQ9SICBQEEEEAAAQQQQKBqAvaqHcZRCCCAAAIIIICA+wjs2xdvGnv9zWOLNfr1Ge+L/jiKI8ueY93x/eKzExyLZX7rB4/pGRkSHBhYar8O6Hv5tVlm+5m9TrH25+bly+7dMepHrvUmqG/cmJvlQHKKCerTlXJz882Dy7DQ1uYYX/UAs0njBuphXdF6psoYWF57rYuwgAACCCCAAAIIIICAGwn88dcKWbN+i3r5pp2MunqE+PsXZcF2oy7UeFN1UKN20Z/oenWkU4cTRGcTX71+s3z69Xzp2K61nN6jW423gwsggAACCCDgSgJrN24zv7O1atlEBvTtJb7qpVzKsRHQYxPtrT/hYSHSvGkjycnJlZ17YuSPv1dKExVY2eHElsemMVwFAQQQQAABBBDwMAGm3/WwG0p3EEAAAQQQQKC0wKYt26Rzp3Zy2UXnWzsfe+olkzmvi9quy4ZNW+Tjz+ZY+yuz8NMvv8umrTvkoXG3m8P0VLn67eB9aqqwT7+YIxkq4O/xR8dK4H/Z9SLCw+W5F99QmfgKpO/Zp5d5qfUbN8uLr7xVbN+X38wT/dFFT7d1pGDDYgezggACCCCAAAIIIICAiwrol2TmL/xLouqEyzWXDyWYr5L3SQf4ndKto3Rq10Y9PF8hH38xV/qf3YOsfZV0pDoCCCCAgPsJ6N/fVq7eKKFq2te+Z51GMJ+L3EId4NemZTNp1rihrN+0TX5bvEy6djqRrH0ucn9oBgIIIIAAAgi4j4C9tk9Z4T63ipYigAACCCCAQFUEktTUu0uW/iuTHrpb6kfXtU7h6+srEeFh1rbt23db+0ou7Ny1R5o1bVxys1nX2fWWr1xrZdDT6w9PmiING0TLx59/Jx1U5oyRVw43QXiOEzwy/k7Zsm2H+Pv5S+tWzRybJT09w1ru2P4EeW3qZGt90pNTpfeZPaT3GaeZbTab3drHAgIIIIAAAggggAAC7iqwd3+8fD9vkfQ4ubPJOueu/XCFduvgvnN695DVazfJ59/+JOcN7C2N6h/6byBXaCNtQAABBBBAoLoEEg+kyLLla+WENs1MdrjqOi/nqT4BHdzXpeOJsmPXXvlzySo5qXsHqRMRVn0X4EwIIIAAAggggICHCxDU5+E3mO4hgAACCCBQ2wXmzl8kLdWboY3V1LVlla+/my8rV62T3WpKiCbq7VHn0rBBPbHZbDLxianOm0st6zr9+hRl3Htr1sfSqFEDefi+OyQ+IVGmTX9fnnj6FesYXVeXgoIC8x0Q4C+vvvCYvP3uJ/LHX8vM9UJUFj5dz3l6XW9vu3nb2HmbOQH/IIAAAggggAACCCDgpgI6Q58O6Ovd62T1QL65m/bC9Zqtp+T1Uy8xadsR5/cjY5/r3SJahAACCCBwlAI6Q58O6OvQvrU0Ur/fUVxbQE/J6+PjY+5Zz1M7k7HPtW8XrUMAAQQQQAABFxLwUg+UC8nW50J3hKYggAACCCDgIQK79sRK08bRlepNdk5umfULCwulID9fEpJSK31OPfVtcnKqNGxYv9i5kw4kS5CaDjdBPUjcowL6dOa+die2LjXVV45qU2xcgjq2sNjxh1a8VFa+euLt7W027dsfpzLw+UmE01un+art6ekHJTc3TwoKi4L51BDM1NdvrEZGhIs+LiEhSVo0byrBwYGHTv/f0o6du0290NCQUvvYgAACCCCAAAIIIODeAnrsHBUZKjY1pizvdzo/NW48mlLeWNtxzti4JImuF+lYrdC3PqayY37nE+tpYtuf2JIMfc4o1bisM/at27hNLh1+bjWelVMhgAACCCBw/AX0dK5NGtcnQ9/xvxWVaoHO2Ld7z345s9dJlTrOubIeN1dlzHqkYyo71q7Kb9/O/WAZAQQQQAABBBAoSyAmJkaioqKsXczbZlGwgAACCCCAAAKeKBAYGCj6U7LoQDpdGqlpcvWnvKKD7pqUk+WvrGMa1C/9drAO+AsLCy2rurVNH1fWsY4KzZs1cSzyjQACCCCAAAIIIICA2wv88dcKiaoTTkBfDd5JnbEvTr04pK1P79GtBq/EqRFAAAEEEDh2AmtVwHpoaDABfceOvNqupDP2paSmi76HHdSLHRQEEEAAAQQQQACBwwsQ1Hd4H/YigAACCCCAAAIIIIAAAggggAACCCCAQDUK6Gl316zfItdcPrRSZ83cskJSF30umev+lJy43eZY33pNJKB9TwntPUICWlc9cE1n0s7IyDxse4KCAkplUtwfGyd1IuvIhk2bZfuO3TJ0cP8yz/HDvAUqq2FD6dChbZn7a2rj6ad1k1kffiPtTmjBNLw1hcx5EUAAAQSOmYCednf37hjpe9Zpx+yaXKh6Bdqd0FIW/Pq3NG1Yj2l4q5eWsyGAAAIIIICABwrYypvSwwP7SpcQQAABBBBAAAEEEEAAAQQQQAABBBBA4DgLrFyzUbp3bSf+/n4VbknsWw/JrkdGSPLPsyV733YpzM8zH72st+l9uk5Vy649e+SsAcMP+zl4sHjQX0FBgVx85U3y7+o1smnzNvn5l9+KXX73nhjZsaso+HDez7/Kxq3bi+0/FivaWFtrcwoCCCCAAALuLrB9515p1bKJ6Jk1jqb88OMvKmNcmnWKXbv3yK+//y25ufny++J/JCs7x9p3rBYWL1mqpqaNKfdyuk2zP/lScnIq1zZd/5MvvpWsrOxyz613FBYWmv5rg/I++fn5hz1HRXbqe6fvob6XFAQQQAABBBBAAIHDC5Cp7/A+7EUAAQQQQAABBBBAAAEEEEAAAQQQQACBahLIyc2VdRu2yqirR1T4jHueulYOrvr1iPV1cF9u/B5pfP87R6xbskLzpk1k+eK5JTeb9fiEBBk49KpS+5avXCsHVXa/x595SdLSDkp2drZccd3tpt6Y20bJ4r+Xis3mLXfccl2pY4/lhk7t2siMdz+XM3p2F1+fowuCOJbt5loIIIAAAgg4C+iAsl179suAvr2cN1dpee78hdK5UzsJCw0xx8fFH5AV/66R03ueLP8sX2mC8q+6bLh17t8XL5EFixZb60da6Nv7dDmj1yny/kdfyD/L/i2z+mUjhkqb1i2koLBA6tWNkqVLV4n9NLs0UZl9dVm8ZJkU5BVYx2ZmZsrf/6w0df39/K3t3jab9OxxklnPyMySHDUecS7p6Rnyx59Lpeep3SQkuKi/jv3+AQHi7+drVletXS8zZ33s2FXmd926deShcXeWua8yG5upPv64YLF0aNdavL29K3ModRFAAAEEEEAAgVolQFBfrbrddBYBBBBAAAEEEEAAAQQQQAABBBBAAIHjJ7B9x15p2bxxhbP06ex7FQnoc/RI19XHRN8w2bGpwt9vzPxAlq9YXap+VlZWqW06sGDiE8/LqGsvlwHn9Jb5Cxaph/ar5IGxd5i6DRtEm6C+Ugcehw06W5821/Yntml+HFrAJRFAAAEEEDh6gf2xCRJdP+qosvTpLLs6I50uBSrrr163qaA4u90m+sUDHWA28rIR8vWcH1Vmuyw1XikKnmvftrVER9ct1Ymvvp1ntg07f2CxfXUiwsz68AvOkyHnniNvvvOhNGvaSPqdfaZs2b5TTmjVXAICA2SeCi708rLJ+ef1K3a8Xtm3L9ZkzHPsyMvLNYv7Y+NVew8F6Qf4FwXl6Z0LFv6uxiTFMwc7jn9u6puOReu7X58z1LX7m3Uv8RI9frl19NXWfueFdRu2yPxfjvyShfMx5S3rbH36Xup72qhhdHnV2I4AAggggAACCNR6AbsevDIFb63/OwAAAQQQQAABBBBAAAEEEEAAAQQQQACBGhfYqx5QN2lUv0LXydyywkytW6HKTpV0xr7Q3iMkoHU3p61HXrzp+ivLrFRWpj790H/4BedKQECgLF+5WmX02SsHkpPNsj7J7r3lT59X5kVqeKM21/YE9dUwNKdHAAEEEKgxgcSkZKkbGX5U53/3g8/kQEqKOcf7H30lDerXk2uuvNisp6SkytLlqyUxKUn9/7ufvPHWB3LR8CHSSAW6RUZGmo/zxZevXCN79u6TMXeMEp3xt6wSGOAvGzZuEX3uIYOuNVn55vwwX0IuukDatW1T1iHWtt6n95AVq9da61kqC58u4WGh4qcC9h2lW6cOjkV1jX7mo6fPLVQZAHVJO5ghk9SLCBPGj5Hg4CCzzdvbVmaGPB+7XZLUeCYnK0fatGlp6ursgFu2bZeundqb9er6R99LfU8J6qsuUc6DAAIIIIAAAp4oYCegzxNvK31CAAEEEEAAAQQQQAABBBBAAAEEEEDA9QTiEw5IB5XtpiIlddHnFalWZh19bEWD+mapB/xffftDmefRG7Oyiqaxu/za20wmn6GDB8h1Iy81QQFr1m00x+mH+gkJSfL30hVm/cTWRQ/CzYoL/FNPTZe3buM2F2gJTUAAAQQQQKBqAimp6dK0SdHUtFU7g8i1Iy8xh941doJcfeVFEhocLP8b96h1uvUbN0njRg3kpG5dpL7KzOeYnteq8N9CbFy8zPrgU7NWJ7KOma63fnS0NZWto/6mzdtMvTPVVLxL/lkuCYlJKpjOLu+8/4k8eN/hp7H1U1kCG6mgQ0fJyMwxi/o6gQGHsvMFqox/Jcu9D0wquUkmPvlCsW1Tn51YbN2xkpOTJ69MnyX3332bNGhQTxb+tlj6qOmEq7vo4MTdajplCgIIIIAAAggggED5Avbyd7EHAQQQQAABBBBAAAEEEEAAAQQQQAABBBCoPoHUtHT1gDy4QifMXPdnheqVVakyx/bt3UuaNm4oeQX5EhFWlAEoNi5WHp70nEx/5Vnr9F9/N1fO7X+2CihoZLbdffuN1r4vvv5eFv32l0x58hFr29RXZ1jLx3tBm2t7CgIIIIAAAu4qkJmZrYLZiqbDra4+pKany/9uGyXh4aHy6OPPy7AhAyUkJFjWb9gsP/68SC6+cEipSyWrrHt6Ot0TWreSTVu2iqiMeF989YPJfHfTqJHFAvt++e1PlVkvRE0zGy95+YUSXa+OOudglRHwX/l+3kLVn0MZ95wv9NeS5bIvNs55k2Rn55r1LVu3q0x9h4L6Nm7ZLg3r15fTTularL7ul75eyaKnvJ067a2Sm631Nq2ay7nnnKn6OFuGDu4vSUkHTJa+zVuq9+UAfS/1PaUggAACCCCAAAIIlC9AUF/5NuxBAAEEEEAAAQQQQAABBBBAAAEEEEAAgWoUyM7OET+/Qw+iD3fqnLjdh9t92H2VObaJCuibM/dn9Vkg0158XPT6jl1FwX3durQXm80mL702Uxb+ulhGXj7C7NcX37Frt4y6ZaxpR3Z2thzMyJR+gy8z63feer35dpV/tLm2pyCAAAIIIOCuAjm5ueLjc/SPNQsLCw2Bnor3oJqadvKEcWY9RE1Nm5B4QAXn2eSDj7+U03ucXIpqswqom/H2bGnZsrlcOuJ8mTB5ioiXTW4efbWarvc9mf7W+3LTqKvEz7dorHPT9Vda55g7f6Hk5eaZaXd1NkDdlx9//tXa77zgo47XU+HqkquOWaiCA9u3O0H69z3TuZq17GXzspYdC0GBgRKoPiVLcFDRFLwlt2epsYy3t7fZPKB/H9mgggXffu8T6dfnTPH19REvdYmC/KIpfUseW5V13X99TykIIIAAAggggAAC5Qsc/ei3/HOzBwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcHmB0dddYTLZXaGm2H1v5ksyZep00+bJz7wk+uH3D/MWyMzXnpc2bVpYfcnLzZekA8kyY9pz8vMvv8mqNetlzB03ygyV2SaDzDOWEwsIIIAAAgi4isC33/+ksuStNM1p1rSRDB7Yz2qaztq7N2af/KKC+HU5p88Z1j4dCLhg0WL5Zs6P0qFtG7n2qkskO+dQQJrOOnfrjdfIGzPeNx9HYF92To6kqMx+uiSqjHc2L2+Ji08w68HBpbPomR3qn5O6dhRRH31dHWCoi26fv3/xzH6RkeHSpWN7s7/kP7Nmf2oFFzrv020qq6Qkp0pkRJjZpYP7OnZoKzt27pGI8P+22X0kPz+vrEPZhgACCCCAAAIIIFBDAgT11RAsp0UAAQQQQAAB1xZwvJXr2q30vNZ56dd6q7FwH6sRsxKn4j5WAssDq1b3/fdAIrqEAAIIIHAYAUfGuJIPpcs6xLdeE8net72sXUfcpo+tTNEPr8eNuUXatGohY8Y9qh5gF2Xqi4tLkK//niez3365WECf49xBgQHSXT1037Fzl+zZu88s14mMcOx2me/KZEh0mUbTEAQQQAABBJwEfH18TNY6nTWuqmWPCto7+6xe8tW386T3mT0lTE2N6yht2rSUb7+fL1kqs+2YO0ap7HSHMgvP/uQrWbJ0pQw+9xyTLU//d7FzUJ8+h87ON+ray+X5V96U92Z/bpa3bt0hb8z8wHEJ8/3XP8vM9+QJ9xXbXnIlIzNLPv/yO1m6YrWZ0lePSRxFZwheq6YI1lPulhXUN/Lyi1QAXr7M/uRLM5VunbpRskydx1u1u8dp3U1woeNcju/EAykSEVE0/lm7bqN8pwIgWzZvqkx+VOObDiZzYH41ZurTGQj1PaUggAACCCCAAAIIlC9g1w9CeShTPhB7EEAAAQQQQMBzBPS4J0e9RZuekaV+oMuW/LzqmzLCc5Rqrifedpv6EdJPggP9/5u2o2oBftzHmrtHFTkz97EiSp5bp7ruv+cK0TMEEEAAgSMJhIYES0pqeqlMM2UdF9C+Z5WD+vSxlSl6+rdPPvvWTJ130w0j5YzTT5Hhl42WF56eIOMnPCW33/2wjFVBfwPOOeuIv6Xm5RVlsTm952lq6t6qjXkr0/aK1NXm2p6CAAIIIICAuwoEBPipTLhZ5jelqvbhllEjzaE6qM+5ZGVlSWxcogno0wFxzZs2kYSEJPUb4kGz3P7ENnLqyd1U8H9z58NKLQeqYP+br79KDmZmmH3tVFa/5554xCx/8/080aOCIYP6m/XDTSW8PzZeXnn9bRNYOEQFEn4392dp2CBa+qnpd/W+t979SE7u3llNATzUnKvkPyd37yRxqj+69D7rdDUNb4DE7Nsv3jZvOfWkriWrm/VENfVw44b1Zceu3TJdTTHcT2UqPG9gX3lyyqvyzfc/q+mIu6mgyqLshNXxbFnfS31PKQgggAACCCCAAALlC9gJ6Csfhz0IIIAAAggg4DkC+sempOQ0ycrKlpDgAAkNjhS73Vs9kPOcPrpyTxS/5OXlS2ZWpiQkpZiHuJHhIUd8IFqyT9zHkiLHdp37eGy9Xe1q1XX/Xa1ftAcBBBBA4NgK1I2KUNPOJUp0vfKnnHO0KLT3CEn+ebZjtVLf+tiKln9U5p1JT01V/31gk6nPPWay7W3ZvtMcrrP0PDP5QXn7RX2WOAAAQABJREFUvU9k/CNPyudfzZEHxt4hzZuVnQnw6eenybyfFqksPmeph+2dzDkKCgokMTFJ1Bx6FW1StdfT5tqeggACCCCAgLsKhIUGS7KayjbcKbve0fZF/3+0zsD35Tc/SFBQkNStW0cyMooC8tas2yDrN20VHQjYTU+HW8ESFRUpURJpautnsD4+3mbZZrOpoDqbCUrUv2/pa7c9obX5fbLkqetERsoZvU6Rs8/sZX5Da6WCCd+Y8Z6sWb/RTImrA+6GDOp32N/V5v/yq7Rq0cwE9JU8v17fty9OVq5eK4MG9JG0tHTZtGWryvzXTV6b/q7JAOg4/6Uj/s/efYBHVaUNHH/TIQ0ICb13EARBKSICKmLvutYPu6Jr76uLdW27rmVta0VhbSjYu9gpUkRAQKSXQCghhQSSkOQ775m5k5nJTGZSCEn43+dJbjnlnvs7d4bLzJtzTjQBhhOlX+8eNuhRpw9++bW35Pabrq7w/IHO6X1M+1L7lAUBBBBAAAEEEEAguADT7wa3IQUBBBBAAAEEGoiAflC2dXuW6JRabVqlmatyjVTsfKem6fohm+5rkB/7e8dDgyh1dJBE8yHpjuxc2ydpzZuG/QFg+X4s6y+zRf/V0v2rX3a7+jHR9GNODfRj2f2mbzm8/so86uL7UXX7v4H8s8JlIIAAAghUQ6CtGWVm5ep10u+AHiFradztIGl65LmVDuzTMlo23GXlmvVy2snHyHlnn2a/bL/r3kdl7boNJvAw1Vah/4/Q6fR0yr0nnnlJ/vhzlQ3qS22eIpeY497LoWZKO/sz9BBZZa7zjrsfMqOFF5r6NsrNN1zpnbVWt9dv3Gy+2O9Qq+fkZAgggAACCNSkQPOUppJupqDt1KFttarVWTycZaUJ4teAvhOPGyPDhgyS1WvXy3+ee1XatGkpc+YvNFP0DnWyVmmtn3Hs3Jln/tA4y478l7kjSzZv3mKehdbK2DGj5IiRw229GuCX5w4m1AMaCDj2qFFm9MBtsvLXNfbZQ6cFzsrKtvmXmKl3te5uXTpJJzNFbnzjRva4/so2n7lN++gzWWryXHPlxZ7jOkrfho3pdlpeDTBc8sefsnLVGps+99eFNqCxf78+snVLhhw9ZrTn87ruXTvLJePOlt69u9tpgKd/P9OWqe6gMVszs6SN+1nL00g2EEAAAQQQQAABBHwECOrz4WAHAQQQQAABBBqagH7ApSP06RdxzZsluy/PNTyfBszo4nwIxX7teOgUZNoX23fk2L4JZ8S+wP1YO+3l/gj8ejGf/9ZQPwaun9dj3b6/q9L/riviNwIIIIDA/i7QuVNb+erbGXYE7UaNQk+51vKSB6Ro6wbJW/hDWHQJBx4uWqYyy9lnnOiTfcTwIXaKvQP79fI53rpVC3nk/r95jjVtmiwXnn+W3dcp8FqbgMVhgwd50tu3ayu3mil7dWnZMs1Madfak1abGzpa+ao1G8w0esNq87ScCwEEEEAAgRoVaNUyVX5duMwEyxdVeQren2bMkenf/2zb1axpU2nbupXcP+FWT30aJHfKiWNtYF9SYoIceEDval2DBghqAJ+OLtiubRs5wIx216JFmg3o0+eKjZsy5PX/TTHT2u6R7Zk7pJU7yG3u/EXyvgnMyzUBgTp6oI7od93Vl0hnM1Lwrl27RNN//W2xfPOd61qOGj3CBCYeZabJ/Uq++fYnOWRQf7nl+vGiowY6S4/uXeVrk3bj7fc5h+TMU0+w2wsXLZWRwwfbYMJjjznKk+5sHNjX5XD0kSPtOXR0v+os2ocZm7fJQL9nrerUSVkEEEAAAQQQQKAhCkSYL0j33bwPDVGUa0IAAQQQQAABK7BuQ4Z0aNeyUhoFXn8p611QH1dKiovNtK05lapTyxWYv2LV4LHW5oM/J0jIu262952APoVuythmA8Pi4mI9wZX+LaIf/UXq1j79WLf6o7ZbE27/13a7OB8CCCBQ3wT02Tk1JVkizR+iOH9Q4H8NcbEx/ocqtR/sWdupJGNLphmZruyLX+d4RWstU9lnfq1v+g+zJSkpQQ45KPyp7DJevivkiH06Ql9lA/oqur6Gkjbn18VmWr08OeLwIQ3lkrgOBBBAAIH9VGDh4j+kcXwj6d6lY5UEdpiR7nSkvBYtW5jPo5oErUOnho2NiTZT18YHzVNUVCxz5y2QgwcN8Eyx6585OydXGjdq5Aka9E8vNp936uiAuqQ0bSIpKc3strZz/YZ0G8SXZGa9CLZoGzZuSpfkxERbduu2bWYui0ifYD7vshro70wv3KhxY88If9rOmJgYz753Gf9tnX43LbV50GdW//yB9v9ctVZ25e+WA/v2DJRc4TF9bq7KM2uoMpV91q7KZ98VXhiJCCCAAAIIIICAEUhPTzfPcq6ZIxSEkfq4LRBAAAEEEECgwQpoMFjergJJSmjs/qDJMz+puWYdHYx9A6Pzne4Tj4iIUklKbGz7KNZ8SR3sC2yffjSj/O2r9nK/BH69mImP7WtMX2th96ONsA1cH/27b16PVb2/w+3/BvsPDReGAAIIIFAlgQHmC9wpH3wl/XQatzBG69OTaLBe8sjTJef792TXkplSuMX1BXhsi/bSuM8wm1aZKXer1PB6WEi/vJ+/YKmcefKYeth6mowAAggggICvQOeObeXn2QukY7s2QQPlfEv47jUzgXP6E2pp2sSZ7SN4Tp0id9jQshF6A+VskpwU6LDnmM4soqMD+i/htlPb0KlDe0/xNK8vgD0HvTb0uSvQs1eodnpVIS3Syr5k9j4e7raO0rdy1XoZPmRAuEXIhwACCCCAAAII7LcCBPXtt13PhSOAAAIIILB/COwuMEF9KU3NxWqgjK7ca/brhEejuDjJzcsybQn+V8faUE8/0n+ufqtj969+IJybWYl+rGPt5/2heu+P4fa/++ZlhQACCCCAgBlJpqn07d3NfCn/qxw5cmjYIhq0R+Be2Fw2oxqrtZqzIIAAAgggUN8FdNS69u3byNLlq6R/FUZ5q+/X3xDar32nfVjRCIQN4Tq5BgQQQAABBBBAoCYEIktKSmqiHupAAAEEEEAAAQTqlICO7qY/xXtKJDra++8YdIQ+74V9bw3XCIbeR/auT3R0jO0jp7+8z6zbzvGyfty77ant628o59PXmPaR01+h+9HJQX86Eq51/fQI1f++18geAggggAACLoHhQw+SbduzZNHvyyHZSwJqq8ZqzYIAAggggEBDETigZxfJydkpa9ZtbCiXtN9ch/aZ9p32IQsCCCCAAAIIIIBAaIHoyMjI0LnIgQACCCCAAAII1HMBzwBvZspX12h97uAZu+91caTXvo8Xf6hNE6pZ++1zBnfUxnF/VOwfqgNtugbc8vqzFA3t/Ses/icTAggggAACZQJjRg2V9z76WuJiY6VH905lCWxVW2D5n2tk1tyFcvqJR1W7LipAAAEEEECgrgkM6NdTZv6yUGJiYqRt6xZ1rXm0J4DAxk1bZPmfa2XY4AMDpHIIAQQQQAABBBBAIJAAEX2BVDiGAAIIIIAAAg1LwMQPRWgMka71ysyO7z7pvh617BPu3Ub/1e37N+x+rOX7S+8bfeFz/+zd+yfc/icfAggggAACXgI6JexxY0fK9zPmMmKfl0t1N3WEPjVVW6bdra4m5RFAAAEE6qKATt06aOAB8vuSFYzYVxc7yK9NOkKf9pX2GdPu+uGwiwACCCCAAAIIVCAQdffdd98TYb/lqiAXSQgggAACCCCAQCUFsnPypElyYqVKFReXBM2v03rm7yqoVJ1aJndnviQnxZt6NapHh1yr/Dovf5ds2rJdNqZnyIb0LbI5Y5tk7siWXbsLJDoqWmJjdHrfytfr3Z6nnn1RmjZJlubNm1fYzuLiUnn8qeekd68e0iguzue8X3z5jW1bh/ZtfI57n0fLP/b401K0p1g6dWgXNJ//9ajlE089LwWFRZUq519PoP2c3DzbR/pMGui5tC73Y1HRHsnN3Sm7zb1Q0U9UZJRE2RGyXfcJ/Vj118uCFQXy4P+y5YFJ2fLPt7LlmWk58sY3efLbikJpnRItrVL075aqXr++Xl7/3xTZuXOntG+nr5Hw3zc+/+Ib+fzL6TL44IFhl1v2x3L5acZs6dWjW9jtro+vY3NxLAgggAACFQjos3N84ziJMM8LgZ6HtGh0VFQFNYROquhZW0vn5e2SxITGoSvyyqFlKvvM71XcbiYnJkjnDm1k9rxFkrFlm7RplSY6tTtL5QX0efT7n+fK2vWb5ISxh0vLNP2/BQsCCCCAAAINUyC+cSNpkdZMlq9YK1nZuZLStIlEVfN5qWFK7burKjSfIy5eukK2bs2UQ0xAn372Wd1Fn5ur8swaqkxln7Wr8tl3da+d8ggggAACCCDQ8AVyc3MlPl6/13Yt0cE+KHQysEYAAQQQQAABBOq/gAnwsTE+lV+v27hZcsyHRc1TkqVd6zSJjY2xHPqhVJ4JMly9Lt0EpCVIh3atTOxP5evXdi1d+qc88eTzcuzRZmqsEO0sLimSZ559Sc48/STzQVgTmf79j5Kxeaucc/ZpMv27n6RlqxYy/NDBQet59N9PyYsvvWaC8wplzZp1PrFPp5x0nDTRD9cCXMcjjz0lL7z4mjxw353y84w5ppxOgxshUdGRMnTwwUHPF+p6bHrYN1jVfLWde6sfv/vhZ7niqhtDXsGdd9wkl1x4nseJfjRkAe6zUPfL+Ce2y6wluyVSSiQ3v1AiSlyBwFt3RMrPi/bIj4t2y9A+jeS5G8wX2FWoX8+ftSNH7rnvYfngvcme/grUrvxdu2Xz5gxzHnMt9nUrUlRSLP9+4lk5fMQw8wFzgue4prdp1VoamWAN/3btNEHDN958l6Q1T5Xhw81r17S7aE+RnHTa+fL4vx4wwX7dy7Wjfr+OQ75cyIAAAgggsB8K6GhyfzntGPl51q/y2psfysABvaVf7+7SqJH+EQtLKAEN5ltk/k8xf8FS6du7m7UMVYZ0BBBAAAEEGoKAjvo24tBB8vsfq2T6D7Ola5f20rFdG8/ndw3hGuvjNejnpms3pMvKVeulvfnjY+0jFgQQQAABBBBAAIHKC0TryCcE9lUejhIIIIAAAgggUH8E7POO01yNRTM/GoNjlwr2V6xeLzFmlJBunduUe16Ki4sV/WnWJFEytu6QFavWmXztbYBPuPU753/9f+/I0KGHSPdunW3jKiqv16JLdk6uFBYVypy5C6SkeI+rnCaY9EDXqyN7vfDSRBvQd9G4c+0Ig5tMQNKu3btl0uS3pVOnjnLyiceWO/8eM6Lf8y9MtAF9Awf2l6nvf6xnscv8+b+ZD+bayndffRiWpy0UwNtdXRgrLWybWKnzVbYfu3ZqH3b9pSa47IA+vWTau6972t+jz2D56vOp0tGOhChy060TTJ9o8FlZ+3360QRY2n4s8e1H7/zO/bpnTxj9eIKrH22D3F7FxaH78fuvP6ySr8/96lEIvhHo/nSur6L+PXXCFtm0rUjyd+4ycW+uEs5a7/vcvAITa1oqvy4vlVP/niHT7msR9vXMmfurXHz5tT6NPuf/LvfZ153XXnpaBh7U394fc81r78JLrpZmzVN88un+RZdf53Nsx/ZMmfjyM3L4YUO1qbJq9VqZOXOOnH/uGTJowIFy7923yxV/vVG++WKatEhNtSP3rd+wUTp36uDzem4I/e8Dww4CCCCAAAJ+AsOHHiS9e3SWBYv/kJdef0+6dGon7du2MqPwNLcjAurz9/7+OaY+SxUUFJr/D+yULVu3y3rzR0ir1myQPr26ypknj2G6Xb97il0EEEAAgf1D4ICeXaRDmxayeu1G+XL6DPNHr6mSZv5oQEeG0xH9YswsG/v7M8TeuhP02URnstA/fszKzpGtmVnmD5C32T+AHj5kANPt7i146kUAAQQQQACB/UKAkfr2i27mIhFAAAEEENi/BVzxPxoEZKJpTDhOhI4y51mcfd/0dRszbEBfqxYp9kM/54M/Z+0EZela82zekmlGg8uQDuZLx1D1T3rjbVm3boO7NSJvvzNVBg0cIA889G/bKlcrndaKdOrYwQT/nClP/Od56dK5k81z332PyvHHHy3z5i+Q9eYvX2fMmiOrzch7cY0aybff/2TzHH/c0XLlpRfKH8uXy2133i+LFv0u46+4WPr06WmDknSEsOf/+6oNSrrqiotkxsxf7JDOow4/1JZfvOQPuXPCP+z0sh9Ne8MEr/U0xyOk2ASoPW5GI9uYvlkmT3zeXK9m9/WzFdhfgX2908u2w9ly6nPyOvuBz1+Vflxvplnu0Lal+wQV1x9hxoz7fckyucQviOv2O+6V+ATX8Njzfv1N+vXt7anvyae1Hzva/fvu/6ccf9yYsn40gV7B+lGnaS3rx4tMP/YK2I8/z/pFEuIby6jDh5tzRMjvS5cF6Ecx/Wimcn7iOU8/uhroXK+7uaa8634O7Fv2erI3gVOowrU7Hs+dJ7z6xz+RaQP6MrN3m1Pq9Lpm8a3IvGBMQKs5ZvOY5PFPbJPnrk8Lq/0avKqvnQ/fnWRelyVed3Okez9Sxp5wpp1+2vu0PXt2l88+fEsbY368X7lm1y6u48eedLZnXz23my/gJ9z7kBxy8EHSs0dXueDcs2Tx4qXy9pRpcu1Vl8lHH38hJ51wjMTF6uhErnobSv+7IVghgAACCCAQVEBH7Tvi8CFy2LCB5rloo2zclCFLzOg7Obk7bTCb/su4Py/6dKHBjclmZKK01GbStXMHOWr0MImNcY0ovj/bcO0IIIAAAvu3gI7ad2DfnnKAGbV2c8Y22W6Cy9Zv2Cy7zEwbhUVF9n/X+7fQ3rl6fTbR55DGZnaCJsmJ0qZFqgzs14upkPcON7UigAACCCCAwH4mEF1ipqyKjHR/MbafXTyXiwACCCCAAAL7h4CJ9TGL6+s/Dahx7buuvWy/LH1nXr6dctcZoU+flZxgPue5SZ+hbK2mMt1umdZMVqxOl53NdkmC+QtgZwlUf/9+fW2gnuZ56pkXZED/fnLN1Zc5RWx9F192jdxx6/WiQUNNkpPtaF0a7NW7lwbWiZx26onyryefFR0BbPbPX0tq82Ym4Os+adUyTW64drynLr3uF16eJO3atrZBfbvN9BeLFy+z6RpceNihQ6Rzp05mOoy1kr5pk/xiRiyb8d2nNv2xx58xH8BFmml618qJp57rVWfZ5sgjT7Q7p5v2PPrQPTaAKpSvf3pZbRVvabkyT1fesv2y/nPqz8uvRj82zTeBcfFe5ytfv7bAGanvxecedzdIpFffofLQPyZIBzOKocZ63XjL303GCHvfaXttP5p+1eW0U0+Qf5kASacf9YvZW/9W1o8aQKbXo8Gjvv24J6x+1PPZfjT3cFj9eJrpxwfvsW3TXxX5Bkr3FKxgw+mfQOXLzueqQPd/XVFop9zNyzMBfQpqo+qcwD73ibR7TJKYgDx9qWtg36wlEaZsgQzoGmuOB+4/z/lMoGqBGbUyY8tWraSCpcT2h2ZIS20uo0cdJg//8ykz9fWPQcuMGD5UTj3lRPva1Hbo9R9yyEA59pij5MFHHpeJZvQ/Pf6wuWf00vLyd8v7H3wir7/yrD3u8mo4/R8UigQEEEAAAQT8BPTL4Z7dO9kfvyR2EUAAAQQQQACBoAJRUVHStk1L+xM0EwkIIIAAAggggAACCNQDgWjni+l60FaaiAACCCCAAAIIVEnAe2QtrSDUfmZWjjRPSbZ/+OAE9DnPTIGC+5w0LZO5I0sS41v5tNP/fP0P7GPTt2zbLjqF7YvPP2Gn5XQK/bZoid0877wzJd6MHuYsy/9cKReNO8/uDhk6SPpP72NG6YizI3Q4eXTtf75/PnKvmZprm3z2+ddyy/XjpVEjHf1L5LsffpKzzjxFjhh5mN2fa0aU06A+p/zzz/xLVptpQo8zo4x9942ZmtW9XHjJNTLugr/YgCY99NqktyU7O9tTzinv5A+17+QLtXbqcdZO/mD71epHcw8kukfaC1a/c/7Va9fJI4/9x9m16/+aqY6TTTCmLosWLzGBm309Pj79OMT0Y7+K+9E5f233o228+eWcP9x9J1+wdWXre/WznWY8RA2i1YC+KFutvg4njEuRUw5LsPvv/5Qn972WaQLmTLCfBveVFtsyWvapa3ynxw14flMm3wTzXnbVjba+QL80XRenfO9e3U2QbXfR1+vAgf1sWqmZ5nr8NbfIPRNuk1at0uyxVi1bmpECXK957/K33nSNjB5zsnw1/Vs5+sjRNq/+evHl1+zomUOHHmyPOedrKP3vuVA2EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEggpEB00hAQEEEEAAAQQQaCACrqlybaSPuaLQ69yd+Xb6Ve+APg0icgL6lEW3tV7N44x8nJjQWNabKXjDPd/kN6ZIezOa2+EjDrWjd7lGE4uQTz//Uo4eM1oam4A9Z5Su4uISM2XIRjPCXzvbK1EmuOnicedKpLsdZddlarGFyq7TjmnmGqhMrr/5Ts/0F1rf08+8JO9N/cjWudUEGerilNfRUVznF2nf1ow6Z0c7i7DTfbVqkWaOtTHHIqRp02TJzsr2lHPyhb/Ws4ZenHaFW29t9GPnjp3ksosvMI13eReakRDPPecME2iZao4peoScdsoJcpAZjVHbTz8aFb/703EKtp63vFBy84sNpRrrKkLuvjBFzjsqye7rr/PHJNnkeydqYJ9xN3m0jJYN63ymSLPmKTL3569Mba5+818fPHyMPeRfnzZr27Ydcs5Zp5r+dTVp5GGHSocO+ppx3Reu15FvvTqS4y03XmNGcpwgH0yZLF26dJDfFv4uTz39gg30jTLvLd7lGsrr2CXEbwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYoEmH63Ih3SEEAAAQQQQKCBCLiCgVwBNnpJFe9rYFZcbIwNHnIC+5yAPmetgT3OthPYp2UKTNlQ9TuBPh9/8qVsz9whL70ySf5yxqnSrFkTWbFytbz40iSZPPE5n3p06pB5M7/RxnuW4cOGyE23TZB/P/W8Pbbsjz8lzozsN2PWHLv/1/GXykgTMKjnKzVBgbocduhQiYlx/V3HrDnzpV+/A6SPGW1MlzVrN5jAwXSz5e8j8s23P9g8+isrO0d+/W2xREW76lmxYrXE2G3/cuHue6oOsRFufa58tdGPnTt1kKuvvMTT7mlm2tTJb74r7731qudY2UaEDaikHyvXj5k5JlLOBuo502BHmBH6Ei2r8xrU16Meu3fiDhvcZ0fsM8ds2XL3c7DzOz1VufR3p34subm5JqjvNFOBBu6JLF+xUnLz8uy2/tIA2ORkJwixrP7LLx0ni5csNSME3iD/e/2/cuOtE2wQ6BGjRnjKer8eG8br2OvS2EQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAgoEO18ERYwlYMIIIAAAggggECDEHAF2rguRQNqvPf9L9AVcOP9jORsO2stodtOYJ9r5C7vekLV70r/7KO3zKh8X8sLL70u/zTTt55/3ll2pK7jjh0jw4YeYir0rSfWEzSo59K0Ujli9AjJycnVA/L2lGkmMLCZHH3UKLvfzo6k56ojfUuGPXaeGUXOqXfipLfstL9HjHJNvzv/14Xy/kefetJtAXcbvKeXTUxMkG+++9H+uPKIHHXESHe5cHx9r8upI/Ray1Wufv8+03P4H6tcP5adf+HipXLqGRcEbHaPAwaXO758yS8SFRklrn4sdKfrNXn3Y4Tpx6mmH5uafhxt83j34+YtW+yx88453V1exLcfI2S+mUb5/Y8+sfV6MlW6H8tK+m6VXb/v8crsVdT/QeqPcPpe0/WnfD+67mtXmt22ZTSn9/kC1685dmzPlK69BmmBoIurprL6NFT2k8+/kltv/KvZKvWc6Qq/aXxffuEpGXW4K8DWuz1mMD55+B8mkO8vF8rww4+xowXe9TedArjsHN6NaRj9731FbCOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCAQSIKgvkArHEEAAAQQQQKCBCTiBPs5l+e87x11rDboqLCqW+MbRngAw70AwJ7d3YJ+O1re7wDXCnxN05OQrv3adPzYmVk458Tg56cRj5fPPv5Frrr/NZu3Tu6cUaF1xseWKlrVctyLk+GPMlKDuZYEZPa9VyzT3iGHOUdd6vRmFTxf/oKXLrrzelcH9u2Wrlmar7CzO9pcfT/HJ57+zectWycrZKU3taGTe5f1z6n6o9EBlvMuFKu9K33v96Kq/Z/duMv3L90UDu3Rq1JWr1splV15npk59Urp26eg5vvzPFXLl1TfbgD7XlZmpnD0GWpd/Py4K2o9r1663VXTtdbCrKvfvvdOPPqfw2gnl75U14Gao8r7pKclRsnWHnXzWVZtJ/uDnfDP9rmu0PucUesyyeuLhIkXLlr/ffOvX8nokPiFePp72hqffnH511qefc7G718rKL136hw0G3LJ1q63FhPpqdfLtlx+Y6XddU2XbAz6/ysrr4T1mFM327drIyhWrpElSkpnKN0uaJDfxKeHsNIz+d66GNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQDCBaGdkkmAZOI4AAggggAACCNR/AU+Uj70UJ0gn2HUlmuCenJ15JqgvLliWgMe1jJYtMYE9XiFI5fL6n/+bb36QCfc/In369JJz/3KaPPjoE7Jo8RJ56vGHpHPH9j7lPaMC6nSk5UbzMiOFmcOBzr9g4WI54/STRKfk1ZIaVnT+RePl6isuMaMCugLENCjwoX8+4VvenkebUGbo335Nnfb+x7Jw4e/y3NP/9ARF6fFAS6DygfKVP+ZqQ6jyTnpSNfvR+5q92+LUHxcXIx3dgVvbtmXKM8+9JKecdJyMGjXcp/83bcqwI7A59Wn5cPrRye99bu9+dI7796NOjfyw6Uef8l796LTfKe+99u5H7+Pe2xWV984XbDvQ/emd17/+gd1jZcbvxZKbv8eT7R4zza4upxyWYNfv/5QnzjF7wPxKSogWLevjYPb869f8paURdupq7c9A6ZpHF83ntL/EZNQRNgcO7C+PP/Gc9D+wnwwb7Hotufq37DXjKu367V3/vAWLbDBvTEyMCQZ9Qia98Y4cfdxp8sB9d9opuXUkP++lIfS/9/WwjQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFggOvBhjiKAAAIIIIAAAg1XwC9OptyFpjRtImvXb5IWzZuKjsDnPSKfd2YnMEvXxcXFsn17tgnyauUT0OWd39nW86ebQK/p3/5op+785Zd5cvll4+T6v15hR+c7YtQIueWOe+SosafKE489KCcef7RTtGzQMROVp4Fkufl5rpgls5+bu9NM7RordjQvsx9lWtKhQ1vJyNgqkya/LV9++p4dEcypLCE+XlJTUzzHli370yb5+Gj0n1mWm1HEenTrYrd90s2RraYd0810vEOHuAKa/NNtIa9fodK9sgbcDFXeSW9WzX4MeHJz0Klf03dk58iXX30rDzz0mPQ9oLfcdut1PumaZ/acedK/Xx/dtIst73bV6Mpg/bhaR+WrRj8657PrAP3ok252/PvRP93Z975+51hl1qHK+6dfdEyC/LR4t+c+d52r1Abx+QbymSA6rzi6EhOAd9FxrqA/7/b512/TzFS9Ov3u3fc/6p3VZ1vTxeRzyutU1fPMVMfffD5NFv2+RK665mbP1NebMjIk2YxamZScKNFROlqgCSY00Xw2SM+sZ86ZI69Peke+/PpbGwh6z4TbJMlMaz3STNH72qQ35a4J/5BXX3tTxl9xoRw39ij7vlDZ17FP4+tQ//u0ix0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGAAgT1BWThIAIIIIAAAgg0JAEdGctZNCAn1H58QiNJSoqXDZu2Soe2Le2oav6Bfd4BfVr3xs3bbJnE+MYh639t8jty3wOPSs+e3eW0k4+XRx68W9qZ6TedJc1MofvyC/+RVyZOlutv+psU7dkjp5x8nCvZHbSkA6899uRz8s6UaU4xz/qNN9+12zqd6KJ5P8hb775vgo1GS2czJawu3tev+xMnvS0//jRTfpk7XwYfPNAnvUOHDnZa0mNPOEuzBl30XHodunjXH4530Er9ErTecOpzzq/9mFzJfkx392O8ux8rOt8rr0yWh8yoiu3at5Xbbr5Ozj7rVBPAFWmvf/HipXLxFddJwe7dkp+XLy+98JQ97qnP3Y8ahBZOP/7m14/ONWp9umh1gfrROV/7Svajd/3Otp7HqU+3dfHfdx2t+Hdl6tP6+5vR9ob0jpUFJuZ0R64ZrU8vNkKj1OyG+2S6rYuuS820uzEywJQ7qGtsWPejjsCn93BaWqqtJegvO1KfK1VHZrz/7jskNa25jDaBuJ+bKarfm/qhHbnvsvE32H73rkdf759+8Kbc8ff75d33PpCTzaiOU995TfodeIDNpi5RJurvwnHnybFHj5HXJr8pN986Qd6d+pFMmvicz+vYMVQfXfSq60v/2wbzCwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoEKBCPOFtPMNWIUZSUQAAQQQQAABBCojsG5DhnRo17IyRaSgsChgfn1cKTEj4W3LzAm7TlvGDI21cdM2advaN1BHA2KcYJhAJ3TSV67ZILFmWsz2JrBPg/r0x3vRc+jP+o0ZUlhUJF07tbPJTnnvvN7b27ZnSVZWlnTr2sn7sGfbu/zyP1dJ504dJCbG9bcYe/aU2FHxRo4YZkfv8hTy2vAur4dzcnbK9sxMW4/uO+mbt2wzQW9JkrFli6xatUZi4+Jk4IB+0tgEtHn7FBQUyoaNm+y1anl9ePSWUJf2JigxNjZGkz31250Av5zzO0lOHzmjIjrHdR2oH/3Le+fXbf/0yvZjZ9OP3tcfrP7Va9bJTjPl8gF9ertGYHNn1POX7CmWFStX6wWYgM22kpgY76nGlV4i35rRDQ8P0I/+7XcKOv3Y0dwP3u0L1o8Jph+9F6cfS0ybvPvPyeP0Y7TpR+/6nXRnHax94fZja/N6rEr9p03YKpu273EF9ukVeL8e7f9o9FepNEuKltbNo2XqfWlOk33Wgdq/3bwmddS9o48aWe7+cQp/Pf0H6du3j7Ro4Wq/jqToPz225nXqz8vfJbt3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)"
      ],
      "metadata": {
        "id": "NE_pESA4aZOK"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NgSKg7g5acFs"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}