Semantic_Complexity.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pierrelabendzki/Text_Complexity/blob/main/Semantic_Complexity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "OVXv2FBEdPFN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Installing Librairies"
      ],
      "metadata": {
        "id": "ndtKikCn07Zv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pydub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8u0HTq7e5eLk",
        "outputId": "b922b305-7739-4308-b1c0-4786b42b5dea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pydub in /usr/local/lib/python3.10/dist-packages (0.25.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pip install whisper-timestamped"
      ],
      "metadata": {
        "id": "ffFy6Gm751jr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install whisperx==3.1.1"
      ],
      "metadata": {
        "id": "TwV71zn_HTfn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install openai-whisper"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17zNxJbKWc3E",
        "outputId": "8a3e024f-0c20-48f6-da7a-d9a2d27a7fe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting openai-whisper\n",
            "  Downloading openai-whisper-20240930.tar.gz (800 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/800.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m798.7/800.5 kB\u001b[0m \u001b[31m27.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m800.5/800.5 kB\u001b[0m \u001b[31m17.4 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: numba in /usr/local/lib/python3.10/dist-packages (from openai-whisper) (0.60.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from openai-whisper) (1.26.4)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from openai-whisper) (2.4.1+cu121)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai-whisper) (4.66.5)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from openai-whisper) (10.5.0)\n",
            "Collecting tiktoken (from openai-whisper)\n",
            "  Downloading tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\n",
            "Collecting triton>=2.0.0 (from openai-whisper)\n",
            "  Downloading triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.3 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from triton>=2.0.0->openai-whisper) (3.16.1)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->openai-whisper) (0.43.0)\n",
            "Requirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.10/dist-packages (from tiktoken->openai-whisper) (2024.9.11)\n",
            "Requirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.10/dist-packages (from tiktoken->openai-whisper) (2.32.3)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper) (1.13.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->openai-whisper) (2024.6.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken->openai-whisper) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken->openai-whisper) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken->openai-whisper) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->tiktoken->openai-whisper) (2024.8.30)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->openai-whisper) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->openai-whisper) (1.3.0)\n",
            "Downloading triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (209.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.4/209.4 MB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m62.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: openai-whisper\n",
            "  Building wheel for openai-whisper (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openai-whisper: filename=openai_whisper-20240930-py3-none-any.whl size=803321 sha256=d3d5201ee4a4b8ce6cd6b3e6d5afd57f22f878e837ec99b89160254ed47182e6\n",
            "  Stored in directory: /root/.cache/pip/wheels/dd/4a/1f/d1c4bf3b9133c8168fe617ed979cab7b14fe381d059ffb9d83\n",
            "Successfully built openai-whisper\n",
            "Installing collected packages: triton, tiktoken, openai-whisper\n",
            "Successfully installed openai-whisper-20240930 tiktoken-0.7.0 triton-3.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/m-bain/whisperx.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "blLy4laaSMiZ",
        "outputId": "35bb80af-9de5-440b-ca01-3999a7c6164a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/m-bain/whisperx.git\n",
            "  Cloning https://github.com/m-bain/whisperx.git to /tmp/pip-req-build-6y62k54i\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/m-bain/whisperx.git /tmp/pip-req-build-6y62k54i\n",
            "  Resolved https://github.com/m-bain/whisperx.git to commit 9e3a9e0e38fcec1304e1784381059a0e2c670be5\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch>=2 in /usr/local/lib/python3.10/dist-packages (from whisperx==3.1.1) (2.4.1+cu121)\n",
            "Requirement already satisfied: torchaudio>=2 in /usr/local/lib/python3.10/dist-packages (from whisperx==3.1.1) (2.4.1+cu121)\n",
            "Requirement already satisfied: faster-whisper==1.0.0 in /usr/local/lib/python3.10/dist-packages (from whisperx==3.1.1) (1.0.0)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from whisperx==3.1.1) (4.39.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from whisperx==3.1.1) (2.1.4)\n",
            "Requirement already satisfied: setuptools>=65 in /usr/local/lib/python3.10/dist-packages (from whisperx==3.1.1) (71.0.4)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from whisperx==3.1.1) (3.8.1)\n",
            "Requirement already satisfied: pyannote.audio==3.1.1 in /usr/local/lib/python3.10/dist-packages (from whisperx==3.1.1) (3.1.1)\n",
            "Requirement already satisfied: av==11.* in /usr/local/lib/python3.10/dist-packages (from faster-whisper==1.0.0->whisperx==3.1.1) (11.0.0)\n",
            "Requirement already satisfied: ctranslate2<5,>=4.0 in /usr/local/lib/python3.10/dist-packages (from faster-whisper==1.0.0->whisperx==3.1.1) (4.4.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.13 in /usr/local/lib/python3.10/dist-packages (from faster-whisper==1.0.0->whisperx==3.1.1) (0.24.7)\n",
            "Requirement already satisfied: tokenizers<0.16,>=0.13 in /usr/local/lib/python3.10/dist-packages (from faster-whisper==1.0.0->whisperx==3.1.1) (0.15.2)\n",
            "Requirement already satisfied: onnxruntime<2,>=1.14 in /usr/local/lib/python3.10/dist-packages (from faster-whisper==1.0.0->whisperx==3.1.1) (1.19.2)\n",
            "Requirement already satisfied: asteroid-filterbanks>=0.4 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (0.4.0)\n",
            "Requirement already satisfied: einops>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (0.8.0)\n",
            "Requirement already satisfied: lightning>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (2.4.0)\n",
            "Requirement already satisfied: omegaconf<3.0,>=2.1 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (2.3.0)\n",
            "Requirement already satisfied: pyannote.core>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (5.0.0)\n",
            "Requirement already satisfied: pyannote.database>=5.0.1 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (5.1.0)\n",
            "Requirement already satisfied: pyannote.metrics>=3.2 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (3.2.1)\n",
            "Requirement already satisfied: pyannote.pipeline>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (3.0.1)\n",
            "Requirement already satisfied: pytorch-metric-learning>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (2.6.0)\n",
            "Requirement already satisfied: rich>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (13.8.1)\n",
            "Requirement already satisfied: semver>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (3.0.2)\n",
            "Requirement already satisfied: soundfile>=0.12.1 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (0.12.1)\n",
            "Requirement already satisfied: speechbrain>=0.5.14 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (1.0.1)\n",
            "Requirement already satisfied: tensorboardX>=2.6 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (2.6.2.2)\n",
            "Requirement already satisfied: torch-audiomentations>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (0.11.1)\n",
            "Requirement already satisfied: torchmetrics>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from pyannote.audio==3.1.1->whisperx==3.1.1) (1.4.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=2->whisperx==3.1.1) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=2->whisperx==3.1.1) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=2->whisperx==3.1.1) (1.13.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=2->whisperx==3.1.1) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=2->whisperx==3.1.1) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=2->whisperx==3.1.1) (2024.6.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->whisperx==3.1.1) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->whisperx==3.1.1) (1.4.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->whisperx==3.1.1) (2024.9.11)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk->whisperx==3.1.1) (4.66.5)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas->whisperx==3.1.1) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->whisperx==3.1.1) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->whisperx==3.1.1) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->whisperx==3.1.1) (2024.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers->whisperx==3.1.1) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers->whisperx==3.1.1) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers->whisperx==3.1.1) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers->whisperx==3.1.1) (0.4.5)\n",
            "Requirement already satisfied: lightning-utilities<2.0,>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from lightning>=2.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (0.11.7)\n",
            "Requirement already satisfied: pytorch-lightning in /usr/local/lib/python3.10/dist-packages (from lightning>=2.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (2.4.0)\n",
            "Requirement already satisfied: antlr4-python3-runtime==4.9.* in /usr/local/lib/python3.10/dist-packages (from omegaconf<3.0,>=2.1->pyannote.audio==3.1.1->whisperx==3.1.1) (4.9.3)\n",
            "Requirement already satisfied: coloredlogs in /usr/local/lib/python3.10/dist-packages (from onnxruntime<2,>=1.14->faster-whisper==1.0.0->whisperx==3.1.1) (15.0.1)\n",
            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.10/dist-packages (from onnxruntime<2,>=1.14->faster-whisper==1.0.0->whisperx==3.1.1) (24.3.25)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from onnxruntime<2,>=1.14->faster-whisper==1.0.0->whisperx==3.1.1) (3.20.3)\n",
            "Requirement already satisfied: sortedcontainers>=2.0.4 in /usr/local/lib/python3.10/dist-packages (from pyannote.core>=5.0.0->pyannote.audio==3.1.1->whisperx==3.1.1) (2.4.0)\n",
            "Requirement already satisfied: scipy>=1.1 in /usr/local/lib/python3.10/dist-packages (from pyannote.core>=5.0.0->pyannote.audio==3.1.1->whisperx==3.1.1) (1.13.1)\n",
            "Requirement already satisfied: typer>=0.12.1 in /usr/local/lib/python3.10/dist-packages (from pyannote.database>=5.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (0.12.5)\n",
            "Requirement already satisfied: scikit-learn>=0.17.1 in /usr/local/lib/python3.10/dist-packages (from pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (1.5.2)\n",
            "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.10/dist-packages (from pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (0.6.2)\n",
            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (0.9.0)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (3.7.1)\n",
            "Requirement already satisfied: optuna>=3.1 in /usr/local/lib/python3.10/dist-packages (from pyannote.pipeline>=3.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (4.0.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->whisperx==3.1.1) (1.16.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=12.0.0->pyannote.audio==3.1.1->whisperx==3.1.1) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=12.0.0->pyannote.audio==3.1.1->whisperx==3.1.1) (2.18.0)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from soundfile>=0.12.1->pyannote.audio==3.1.1->whisperx==3.1.1) (1.17.1)\n",
            "Requirement already satisfied: hyperpyyaml in /usr/local/lib/python3.10/dist-packages (from speechbrain>=0.5.14->pyannote.audio==3.1.1->whisperx==3.1.1) (1.2.2)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from speechbrain>=0.5.14->pyannote.audio==3.1.1->whisperx==3.1.1) (0.2.0)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=2->whisperx==3.1.1) (1.3.0)\n",
            "Requirement already satisfied: julius<0.3,>=0.2.3 in /usr/local/lib/python3.10/dist-packages (from torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (0.2.7)\n",
            "Requirement already satisfied: librosa>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (0.10.2.post1)\n",
            "Requirement already satisfied: torch-pitch-shift>=1.2.2 in /usr/local/lib/python3.10/dist-packages (from torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (1.2.5)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=2->whisperx==3.1.1) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->whisperx==3.1.1) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->whisperx==3.1.1) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->whisperx==3.1.1) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->whisperx==3.1.1) (2024.8.30)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile>=0.12.1->pyannote.audio==3.1.1->whisperx==3.1.1) (2.22)\n",
            "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<2026.0,>=2022.5.0->lightning>=2.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (3.10.5)\n",
            "Requirement already satisfied: audioread>=2.1.9 in /usr/local/lib/python3.10/dist-packages (from librosa>=0.6.0->torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (3.0.1)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from librosa>=0.6.0->torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (4.4.2)\n",
            "Requirement already satisfied: numba>=0.51.0 in /usr/local/lib/python3.10/dist-packages (from librosa>=0.6.0->torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (0.60.0)\n",
            "Requirement already satisfied: pooch>=1.1 in /usr/local/lib/python3.10/dist-packages (from librosa>=0.6.0->torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (1.8.2)\n",
            "Requirement already satisfied: soxr>=0.3.2 in /usr/local/lib/python3.10/dist-packages (from librosa>=0.6.0->torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (0.5.0.post1)\n",
            "Requirement already satisfied: lazy-loader>=0.1 in /usr/local/lib/python3.10/dist-packages (from librosa>=0.6.0->torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (0.4)\n",
            "Requirement already satisfied: msgpack>=1.0 in /usr/local/lib/python3.10/dist-packages (from librosa>=0.6.0->torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (1.0.8)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=12.0.0->pyannote.audio==3.1.1->whisperx==3.1.1) (0.1.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (1.4.7)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (3.1.4)\n",
            "Requirement already satisfied: alembic>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from optuna>=3.1->pyannote.pipeline>=3.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (1.13.3)\n",
            "Requirement already satisfied: colorlog in /usr/local/lib/python3.10/dist-packages (from optuna>=3.1->pyannote.pipeline>=3.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (6.8.2)\n",
            "Requirement already satisfied: sqlalchemy>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from optuna>=3.1->pyannote.pipeline>=3.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (2.0.35)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.17.1->pyannote.metrics>=3.2->pyannote.audio==3.1.1->whisperx==3.1.1) (3.5.0)\n",
            "Requirement already satisfied: primePy>=1.3 in /usr/local/lib/python3.10/dist-packages (from torch-pitch-shift>=1.2.2->torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (1.3)\n",
            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from typer>=0.12.1->pyannote.database>=5.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (1.5.4)\n",
            "Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.10/dist-packages (from coloredlogs->onnxruntime<2,>=1.14->faster-whisper==1.0.0->whisperx==3.1.1) (10.0)\n",
            "Requirement already satisfied: ruamel.yaml>=0.17.28 in /usr/local/lib/python3.10/dist-packages (from hyperpyyaml->speechbrain>=0.5.14->pyannote.audio==3.1.1->whisperx==3.1.1) (0.18.6)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning>=2.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (2.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning>=2.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning>=2.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (24.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning>=2.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning>=2.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (6.1.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning>=2.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (1.11.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2026.0,>=2022.5.0->lightning>=2.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (4.0.3)\n",
            "Requirement already satisfied: Mako in /usr/local/lib/python3.10/dist-packages (from alembic>=1.5.0->optuna>=3.1->pyannote.pipeline>=3.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (1.3.5)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.51.0->librosa>=0.6.0->torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (0.43.0)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.1->librosa>=0.6.0->torch-audiomentations>=0.11.0->pyannote.audio==3.1.1->whisperx==3.1.1) (4.3.6)\n",
            "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from ruamel.yaml>=0.17.28->hyperpyyaml->speechbrain>=0.5.14->pyannote.audio==3.1.1->whisperx==3.1.1) (0.2.8)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy>=1.3.0->optuna>=3.1->pyannote.pipeline>=3.0.1->pyannote.audio==3.1.1->whisperx==3.1.1) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install syllables"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GOrA-FvS6fnS",
        "outputId": "8b7d7dfa-0f88-4b91-c4dc-620500a85152"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: syllables in /usr/local/lib/python3.10/dist-packages (1.0.9)\n",
            "Requirement already satisfied: cmudict<2.0.0,>=1.0.11 in /usr/local/lib/python3.10/dist-packages (from syllables) (1.0.31)\n",
            "Requirement already satisfied: importlib-metadata<7.0,>=5.1 in /usr/local/lib/python3.10/dist-packages (from syllables) (6.11.0)\n",
            "Requirement already satisfied: importlib-resources>=5 in /usr/local/lib/python3.10/dist-packages (from cmudict<2.0.0,>=1.0.11->syllables) (6.4.5)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata<7.0,>=5.1->syllables) (3.20.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pylzma"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AiMIjeJo6rH-",
        "outputId": "b12c9f4e-35e6-4867-deb6-6cf069291266"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pylzma\n",
            "  Using cached pylzma-0.5.0.tar.gz (4.2 MB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pylzma\n",
            "  Building wheel for pylzma (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pylzma: filename=pylzma-0.5.0-cp310-cp310-linux_x86_64.whl size=222324 sha256=c6482cb875ae6a84ac056c18e055f978b24f70a6e8e4986dae985500196a421a\n",
            "  Stored in directory: /root/.cache/pip/wheels/74/c9/02/91112815e838f544c1d46fda071241e454694579d022751d2b\n",
            "Successfully built pylzma\n",
            "Installing collected packages: pylzma\n",
            "Successfully installed pylzma-0.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mount drive"
      ],
      "metadata": {
        "id": "u_fmdS3d9RCi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('content')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "znqbUtiD9Qne",
        "outputId": "51a85f76-afa9-4060-a625-e7a4353a2b62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.mkdir('Temp_Folder')"
      ],
      "metadata": {
        "id": "Bq1rYBWqc3xi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you want to import your google drive"
      ],
      "metadata": {
        "id": "rXiFEV4AcscB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "id": "60sDqb7ncsuK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Inporting librairies"
      ],
      "metadata": {
        "id": "I_xmpK5n5sLZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "# import librosa\n",
        "from pydub.utils import mediainfo\n",
        "#import audiotools\n",
        "import subprocess\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from scipy.io import wavfile as io\n",
        "import math\n",
        "import json\n",
        "import re\n",
        "\n",
        "\n",
        "import whisper\n",
        "#import whisper_timestamped\n",
        "import whisperx\n",
        "\n",
        "import syllables\n",
        "\n",
        "import pandas as pd\n",
        "from csv import writer\n",
        "\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "import pylzma\n",
        "from pylzma import compress\n",
        "\n",
        "import torch\n",
        "from transformers import AutoModelForCausalLM , AutoTokenizer\n",
        "\n",
        "import itertools as it\n",
        "import string"
      ],
      "metadata": {
        "id": "ISyY6-endB25"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from Text_Complexity import *\n",
        "\n"
      ],
      "metadata": {
        "id": "kHg7_DwK1H95"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Defining useful functions"
      ],
      "metadata": {
        "id": "1ho2pjOQ1MkG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4icR8HRU06Re"
      },
      "outputs": [],
      "source": [
        "def generate_text_info_timestamps(audio_dir,file_name,output_dir):\n",
        "    # result = model.transcribe(\"trash_syllabic_rate_PK.wav\", language = 'English' ,verbose = True)\n",
        "    model = whisper.load_model(\"medium\")\n",
        "    result = whisper_timestamped.transcribe(model,audio_dir+file_name, beam_size=5, best_of=5, temperature=(0.0, 0.2, 0.4, 0.6, 0.8, 1.0),language='en')\n",
        "    result_output = os.path.join(output_dir,file_name[:-4]+'.json')\n",
        "    with open(result_output,'w') as f:\n",
        "        json.dump(result,f)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def json_to_df(input_dir,file_name,out_dir_csv):\n",
        "    with open(input_dir+file_name) as f:\n",
        "        d = json.load(f)\n",
        "\n",
        "    df = pd.DataFrame({\n",
        "        'filename':[],\n",
        "        'onset':[],\n",
        "        'offset':[],\n",
        "        'text' : [],\n",
        "        'syllable' : []\n",
        "        })\n",
        "    df.to_csv(out_dir_csv+file_name[:-5]+'.csv')\n",
        "    print('csv output initilased')\n",
        "    total_segs = d['segments']\n",
        "    nb_segs = len(total_segs)\n",
        "    for s in range(nb_segs):\n",
        "        seg = total_segs[s]\n",
        "        words = seg['words']\n",
        "        nb_words = len(words)\n",
        "        for w in range(nb_words):\n",
        "            word_info = words[w]\n",
        "            word = word_info['word']\n",
        "            onset = word_info['start']\n",
        "            offset = word_info['end']\n",
        "            syllable = syllables.estimate(word)\n",
        "            row_contents = [0,file_name,\n",
        "            onset, offset, word, syllable]\n",
        "            append_list_as_row(out_dir_csv+file_name[:-5]+'.csv', row_contents)\n",
        "\n",
        "class LMHeadModel:\n",
        "\n",
        "    def __init__(self, model_name):\n",
        "        # Initialize the model and the tokenizer.\n",
        "        self.model = AutoModelForCausalLM.from_pretrained(model_name)\n",
        "        self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "    def get_predictions(self, sentence):\n",
        "        # Encode the sentence using the tokenizer and return the model predictions.\n",
        "        inputs = self.tokenizer.encode(sentence, return_tensors=\"pt\")\n",
        "        with torch.no_grad():\n",
        "            outputs = self.model(inputs)\n",
        "            predictions = outputs[0]\n",
        "        return predictions\n",
        "\n",
        "    def get_next_word_probabilities(self, sentence, top_k=50000):\n",
        "\n",
        "        # Get the model predictions for the sentence.\n",
        "        predictions = self.get_predictions(sentence)\n",
        "\n",
        "        # Get the next token candidates.\n",
        "        next_token_candidates_tensor = predictions[0, -1, :]\n",
        "\n",
        "        # Get the top k next token candidates.\n",
        "        topk_candidates_indexes = torch.topk(\n",
        "            next_token_candidates_tensor, top_k).indices.tolist()\n",
        "\n",
        "        # Get the token probabilities for all candidates.\n",
        "        all_candidates_probabilities = torch.nn.functional.softmax(\n",
        "            next_token_candidates_tensor, dim=-1)\n",
        "\n",
        "        # Filter the token probabilities for the top k candidates.\n",
        "        topk_candidates_probabilities = \\\n",
        "            all_candidates_probabilities[topk_candidates_indexes].tolist()\n",
        "\n",
        "        # Decode the top k candidates back to words.\n",
        "        topk_candidates_tokens = \\\n",
        "            [self.tokenizer.decode([idx]).strip() for idx in topk_candidates_indexes]\n",
        "\n",
        "        # Return the top k candidates and their probabilities.\n",
        "        ###\n",
        "        return [topk_candidates_tokens, topk_candidates_probabilities]\n",
        "\n",
        "def append_list_as_row(file_name, list_of_elem):\n",
        "    # Open file in append mode\n",
        "    with open(file_name, 'a+', newline='') as write_obj:\n",
        "        # Create a writer object from csv module\n",
        "        csv_writer = writer(write_obj)\n",
        "        # Add contents of list as last row in the csv file\n",
        "        csv_writer.writerow(list_of_elem)\n",
        "\n",
        "def derivative(x):\n",
        "    step = 10\n",
        "    out = np.zeros(len(x)-step+1)\n",
        "    for k in range(step,len(x)-(step+1)):\n",
        "        temp_sum = 0\n",
        "        for i in range(step-1):\n",
        "            temp_sum = temp_sum + (x[k+step-i] - x[k-step+i])\n",
        "        out[k] = temp_sum\n",
        "    return out\n",
        "\n",
        "def get_words_list_up(df_total,offset):\n",
        "    df_temp = df_total[df_total['offset'] < offset]\n",
        "    df_temp = df_temp['text']\n",
        "    df_temp = df_temp.to_numpy()\n",
        "    return ' '.join(df_temp)\n",
        "\n",
        "def get_words_list_up_index(df_total,index):\n",
        "    df_temp = df_total.iloc[:index]\n",
        "    df_temp = df_temp['text']\n",
        "    df_temp = df_temp.to_numpy()\n",
        "    # print(df_temp)\n",
        "    return ' '.join(df_temp)\n",
        "\n",
        "def get_next_word(df_total, t,index):\n",
        "    df_temp = df_total[df_total['onset'] > t]\n",
        "    # print(df_temp)\n",
        "    df_temp = df_temp['text']\n",
        "    print(df_temp)\n",
        "    df_temp = df_temp.iloc[index]\n",
        "    # df_temp = df_temp.to_numpy()\n",
        "    # print(df_temp)\n",
        "    return ''.join(df_temp)\n",
        "\n",
        "\n",
        "def get_next_list_dataframe_prompted(dir_results_df,file_name):\n",
        "    df_total = pd.read_csv(dir_results_df+file_name)\n",
        "    model = LMHeadModel(\"gpt2\")\n",
        "    t = 0\n",
        "    list_words = []\n",
        "    gpt_score = 0\n",
        "    list_gpt = [np.nan]\n",
        "    list_entropy = [np.nan]\n",
        "    list_complexity = [Kolmo(df_total['text'][0])]\n",
        "    list_info_rate = [np.nan]\n",
        "    prompt = ' The following text is about a mother and her infant playing together, as they would at home, with a toy panda, a book and a rattle, while wearing EEG hats and facial electrodes. '\n",
        "    gpt_surprisal = np.nan\n",
        "    ent = np.nan\n",
        "    N = len(df_total)\n",
        "    for index in range(1,N,1):\n",
        "        print('\\n\\n')\n",
        "        list_words_previous = prompt + get_words_list_up_index(df_total,index)\n",
        "        print(list_words_previous)\n",
        "\n",
        "\n",
        "        next_word = df_total.iloc[index]\n",
        "        next_word = next_word['text']\n",
        "\n",
        "        complexity = Kolmo(get_words_list_up_index(df_total,index)+ ' ' + next_word)\n",
        "\n",
        "\n",
        "        table = str.maketrans(dict.fromkeys(string.punctuation))  # OR {key: None for key in string.punctuation}\n",
        "        next_word = next_word.translate(table)\n",
        "        next_word = next_word.lower()\n",
        "\n",
        "        print(\"next actual word is = \", next_word)\n",
        "        print('\\n NUMBER OF WORDS === ', count_words(list_words_previous))\n",
        "        if(count_words(list_words_previous)>600):\n",
        "            list_words_previous = prompt + get_words_list_up_index(df_total,index)[50+(5*(count_words(list_words_previous)-600)):]\n",
        "            print(\"words > 600 => shortening the list of words\")\n",
        "            print(list_words_previous)\n",
        "\n",
        "\n",
        "        list_words,list_proba = model.get_next_word_probabilities(list_words_previous, top_k=50000)\n",
        "        list_words = [words.lower() for words in list_words]\n",
        "\n",
        "        ent =  - np.sum( list_proba * np.log2(list_proba))\n",
        "\n",
        "\n",
        "        try:\n",
        "            print('Entropy = ', ent)\n",
        "            print('next predicted words = ',list(zip(list_words[:20],list_proba[:20])))\n",
        "            index_match = list_words.index(next_word)\n",
        "            proba = list_proba[index_match]\n",
        "\n",
        "            print('probability of actual word found = ', proba)\n",
        "            gpt_surprisal = -np.log2(proba)\n",
        "            print('Surprisal = -log2(p) = ', gpt_surprisal)\n",
        "\n",
        "\n",
        "        except:\n",
        "            print(\"Words not found, setting surprisal to NaN\")\n",
        "        # else:\n",
        "        #     gpt_surprisal = np.nan\n",
        "            # print(gpt_surprisal)\n",
        "            print('\\n\\n')\n",
        "\n",
        "        list_gpt = list_gpt + list([float(gpt_surprisal)])\n",
        "        list_entropy = list_entropy + list([float(ent)])\n",
        "        list_complexity= list_complexity + list([float(complexity)])\n",
        "    df_surprise = pd.DataFrame({'Complexity':list_complexity,'Surprisal':list_gpt, 'Entropy':list_entropy})\n",
        "    print(df_surprise)\n",
        "    print(list_gpt)\n",
        "    return df_surprise\n",
        "\n",
        "def get_GPT_surprisal_text(text):\n",
        "    words = text.split()\n",
        "\n",
        "    model = LMHeadModel(\"gpt2\")\n",
        "    t = 0\n",
        "    list_words = []\n",
        "    gpt_score = 0\n",
        "    list_gpt = [np.nan]\n",
        "    list_entropy = [np.nan]\n",
        "    prompt = ' The following text is about a mother and her infant playing together, as they would at home, with a toy panda, a book and a rattle, while wearing EEG hats and facial electrodes. '\n",
        "    gpt_surprisal = np.nan\n",
        "    ent = np.nan\n",
        "\n",
        "    for word_index in range(1,len(words),1):\n",
        "\n",
        "        list_words_previous = words[:word_index]\n",
        "        next_word = words[word_index+1]\n",
        "\n",
        "        print(\"past words = \", list_words_previous)\n",
        "        print(\"next actual word is = \", next_word)\n",
        "\n",
        "\n",
        "        table = str.maketrans(dict.fromkeys(string.punctuation))  # OR {key: None for key in string.punctuation}\n",
        "        next_word = next_word.translate(table)\n",
        "        next_word = next_word.lower()\n",
        "\n",
        "        print('\\n NUMBER OF WORDS === ', count_words(list_words_previous))\n",
        "        if(count_words(list_words_previous)>600):\n",
        "            list_words_previous = prompt + words[100:word_index]\n",
        "            print(\"words > 600 => shortening the list of words\")\n",
        "            print(list_words_previous)\n",
        "\n",
        "        list_words,list_proba = model.get_next_word_probabilities(list_words_previous, top_k=50000)\n",
        "        list_words = [words.lower() for words in list_words]\n",
        "\n",
        "        ent =  - np.sum( list_proba[:16] * np.log2(list_proba[:16]))\n",
        "\n",
        "\n",
        "        try:\n",
        "            print('Entropy = ', ent)\n",
        "            print('next predicted words = ',list(zip(list_words[:20],list_proba[:20])))\n",
        "            index_match = list_words.index(next_word)\n",
        "            proba = list_proba[index_match]\n",
        "\n",
        "            print('probability of actual word found = ', proba)\n",
        "            gpt_surprisal = -np.log2(proba)\n",
        "            print('Surprisal = -log2(p) = ', gpt_surprisal)\n",
        "\n",
        "\n",
        "        except:\n",
        "            print(\"Words not found, setting surprisal to NaN\")\n",
        "        # else:\n",
        "        #     gpt_surprisal = np.nan\n",
        "            # print(gpt_surprisal)\n",
        "            print('\\n\\n')\n",
        "\n",
        "        list_gpt = list_gpt + list([float(gpt_surprisal)])\n",
        "        list_entropy = list_entropy + list([float(ent)])\n",
        "\n",
        "    return list_gpt, list_entropy\n",
        "\n",
        "\n",
        "def find_proba(next_word,zip_word_proba):\n",
        "    return 0\n",
        "\n",
        "def find(it, pred):\n",
        "    return next((x for x in it if pred(x)), None)\n",
        "\n",
        "\n",
        "def count_words(text):\n",
        "    words = re.findall(r'\\b\\w+\\b', text)\n",
        "    word_count = len(words)\n",
        "    return word_count\n",
        "\n",
        "def df_to_numpy(input_dir,file_name):\n",
        "    df_total = pd.read_csv(input_dir+file_name[:-4]+'.csv')\n",
        "    #t_max = np.max(df_total['offset'])\n",
        "    # t_max = librosa.get_duration(filename = audio_dir+file_name[:9]+'.wav')\n",
        "\n",
        "    t_max = float(mediainfo(audio_dir+file_name)['duration'])\n",
        "    print(mediainfo(audio_dir+file_name))\n",
        "    print(t_max)\n",
        "    N = int(t_max*50)\n",
        "    print(N)\n",
        "\n",
        "    list_complexity = np.zeros(N)\n",
        "    list_InfoRate = np.zeros(N)\n",
        "    list_surprisal = np.zeros(N)\n",
        "    list_uncertainty = np.zeros(N)\n",
        "    list_segment = np.zeros(N)\n",
        "\n",
        "    N_df = len(df_total)\n",
        "    print(\"N_df = \", N_df)\n",
        "    for index in range(N_df):\n",
        "        onset = df_total['onset'][index]\n",
        "        offset = df_total['offset'][index]\n",
        "        list_complexity[int(50*onset):int(50*offset)] = df_total['Complexity'][index]\n",
        "        list_InfoRate[int(50*onset):int(50*offset)] = df_total['InfoRate'][index]\n",
        "        list_surprisal[int(50*onset):int(50*offset)] = df_total['Surprisal'][index]\n",
        "        list_uncertainty[int(50*onset):int(50*offset)] = df_total['Entropy'][index]\n",
        "        list_segment[int(50*onset):int(50*offset)] = 1\n",
        "\n",
        "    return list_complexity,list_InfoRate,list_surprisal, list_uncertainty,list_segment\n",
        "\n",
        "\n",
        "def demo_audio_semantic_complexity_pipeline(audio_dir,file_name,temp_folder):\n",
        "\n",
        "    # ## AUDIO TRANSCRIPTION (can take 20min for 1min file with large model on laptop)\n",
        "    # generate_text_info_timestamps(audio_dir,file_name,temp_folder)\n",
        "\n",
        "    ### FROM JSON format to CSV format\n",
        "    # json_to_df(temp_folder,file_name[:-4]+'.json',temp_folder)\n",
        "\n",
        "\n",
        "    ### FROM CSV TRANSCIPTIONS to COMPLEXITY SURPRISAL and UNCERTAINTY ####\n",
        "\n",
        "    # df_surprisal = get_next_list_dataframe_prompted(temp_folder,file_name[:-4]+'.csv')\n",
        "    # df_total = pd.read_csv(temp_folder+file_name[:-4]+'.csv')\n",
        "    # df_total['Complexity'] = df_surprisal['Complexity']\n",
        "    # # df_total['InfoRate'] = df_surprisal['Complexity'].diff()\n",
        "    # df_total['Surprisal'] = df_surprisal['Surprisal']\n",
        "    # df_total['Entropy'] = df_surprisal['Entropy']\n",
        "    # df_total.to_csv(temp_folder+file_name[:-4]+'.csv')\n",
        "\n",
        "    # df_total = pd.read_csv(temp_folder+file_name[:-4]+'.csv')\n",
        "    # df_total['InfoRate'] = df_total['Complexity'].diff()\n",
        "    # print(df_total['InfoRate'])\n",
        "    # df_total.to_csv(temp_folder+file_name[:-4]+'.csv')\n",
        "    df_total = pd.read_csv(temp_folder+file_name[:-4]+'.csv')\n",
        "    # print(df_total)\n",
        "    ####\n",
        "\n",
        "\n",
        "    ## FROM SURPRISAL and UNCERTAINTY to NUMPY TIME SERIES ####\n",
        "    # path_output_complexity = 'Semantic_outputs/'\n",
        "    # path_output_InfoRate = 'Semantic_outputs/'\n",
        "    # path_output_surprisal = 'Semantic_outputs/'\n",
        "    # path_output_uncertainty = 'Semantic_outputs/'\n",
        "    # path_output_segment = 'Semantic_outputs/'\n",
        "\n",
        "\n",
        "    list_complexity,list_InfoRate,list_surprisal, list_uncertainty, list_segment = df_to_numpy(temp_folder,file_name)\n",
        "    # np.save(path_output_complexity+file_name[:-4]+'_SC.npy',list_complexity)\n",
        "    # np.save(path_output_complexity+file_name[:-4]+'_IR.npy',list_InfoRate)\n",
        "    # np.save(path_output_surprisal+file_name[:-4]+'_SS.npy',list_surprisal)\n",
        "    # np.save(path_output_uncertainty+file_name[:-4]+'_SU.npy',list_uncertainty)\n",
        "    # np.save(path_output_segment+file_name[:-4]+'_SpSg.npy',list_segment)\n",
        "\n",
        "    #### PLOTTING ####\n",
        "\n",
        "    text = df_total['text'].to_numpy()\n",
        "    df_onset = df_total['onset'].to_numpy()\n",
        "    df_offset = df_total['offset'].to_numpy()\n",
        "    df_complexity = df_total['Complexity'].to_numpy()\n",
        "    print(df_onset)\n",
        "    print(text)\n",
        "    # text = \" \".join(text)\n",
        "    print(len(text))\n",
        "\n",
        "    time = np.arange(0,len(list_complexity)/50,1/50)\n",
        "\n",
        "    plt.figure(figsize= (16,9) )\n",
        "    for k in range(len(text)-1):\n",
        "        plt.text(df_onset[k],df_complexity[k],text[k],size = 6)\n",
        "\n",
        "    plt.plot(time,list_InfoRate,label = 'Information Rate')\n",
        "    plt.plot(time,list_complexity,label = 'complexity')\n",
        "\n",
        "    plt.plot(time,list_surprisal,label = 'surprisal [bit]')\n",
        "    plt.plot(time,list_uncertainty,label = 'uncertainty [bit]')\n",
        "    plt.xlabel('time [s]')\n",
        "    plt.ylabel('complexities')\n",
        "    plt.legend()\n",
        "    plt.savefig(file_name[:-4]+'.svg')\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DEMO with toy files"
      ],
      "metadata": {
        "id": "M0I5023o1djP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ORDI = 'pierre'\n",
        "\n",
        "### DEMO GPT2 TEST\n",
        "# sentence = \"I enjoy walking in the\"\n",
        "# model = LMHeadModel(\"gpt2\")\n",
        "# list_proba = model.get_next_word_probabilities(sentence, top_k=10)\n",
        "# print(list_proba)\n",
        "###\n",
        "\n",
        "\n",
        "\n",
        "#### DEMO WITH PLOTS FOR TEXT\n",
        "# text = 'Hello my name is Pierre and I am a PhD student at the University of East London'\n",
        "# list_gpt, list_entropy = get_GPT_surprisal_text(text)\n",
        "# plt.plot(list_gpt)\n",
        "# plt.show()\n",
        "\n",
        "\n",
        "\n",
        "#### DEMO WITH PLOTS FOR AUDIO\n",
        "audio_dir = '/content/'\n",
        "temp_folder = '/content/Temp_Folder/'\n",
        "\n",
        "# file_name = '1015_1_pk.wav'\n",
        "file_name = 'Demo-audio.wav'\n",
        "# file_name = 'Demo-Deutsch.wav'\n",
        "\n",
        "full_audio_path = os.path.join(audio_dir, file_name)\n",
        "print(\"Full audio path:\", full_audio_path)\n",
        "\n",
        "demo_audio_semantic_complexity_pipeline(audio_dir,file_name,temp_folder)"
      ],
      "metadata": {
        "id": "0umetMhz1dSm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5f2ade1c-7ff5-44af-e32c-487de7123690"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Full audio path: /content/Demo-audio.wav\n",
            "{'index': '0', 'codec_name': 'pcm_s16le', 'codec_long_name': 'PCM signed 16-bit little-endian', 'profile': 'unknown', 'codec_type': 'audio', 'codec_tag_string': '[1][0][0][0]', 'codec_tag': '0x0001', 'sample_fmt': 's16', 'sample_rate': '44100', 'channels': '2', 'channel_layout': 'unknown', 'bits_per_sample': '16', 'id': 'N/A', 'r_frame_rate': '0/0', 'avg_frame_rate': '0/0', 'time_base': '1/44100', 'start_pts': 'N/A', 'start_time': 'N/A', 'duration_ts': '3093615', 'duration': '70.150000', 'bit_rate': '1411205', 'max_bit_rate': 'N/A', 'bits_per_raw_sample': 'N/A', 'nb_frames': 'N/A', 'nb_read_frames': 'N/A', 'nb_read_packets': 'N/A', 'DISPOSITION': {'default': '0', 'dub': '0', 'original': '0', 'comment': '0', 'lyrics': '0', 'karaoke': '0', 'forced': '0', 'hearing_impaired': '0', 'visual_impaired': '0', 'clean_effects': '0', 'attached_pic': '0', 'timed_thumbnails': '0'}, 'filename': '/content/Demo-audio.wav', 'nb_streams': '1', 'nb_programs': '0', 'format_name': 'wav', 'format_long_name': 'WAV / WAVE (Waveform Audio)', 'size': '12374504', 'probe_score': '99'}\n",
            "70.15\n",
            "3507\n",
            "N_df =  58\n",
            "[ 1.78  2.04  2.28  2.44  2.72  3.08  3.52  4.4   6.24  8.6   9.08  9.5\n",
            "  9.86 10.04 29.5  34.76 41.44 41.62 41.8  41.86 42.12 42.3  42.46 42.8\n",
            " 43.68 43.84 43.96 44.52 46.68 46.72 47.04 47.7  48.36 48.64 48.78 49.12\n",
            " 49.72 51.8  51.82 52.08 53.4  53.54 53.84 55.08 55.24 55.54 56.82 57.04\n",
            " 57.42 59.02 59.16 59.9  62.16 62.32 62.76 64.34 64.48 64.84]\n",
            "['This' 'is' 'an' 'audio' 'file' 'to' 'demonstrate' 'the' 'complexity,'\n",
            " 'the' 'surprises' 'and' 'the' 'uncertainty.' 'And' 'now' \"I'm\" 'going'\n",
            " 'to' 'try' 'to' 'say' 'very' 'unpredictable,' 'like' 'for' 'instance'\n",
            " 'elephants,' 'and' 'then' 'panda,' 'and' 'then' 'for' 'some' 'reason'\n",
            " 'swinging,' 'and' 'then' 'computer,' 'and' 'then' 'smoothie,' 'and'\n",
            " 'then' 'plants,' 'and' 'then' 'window,' 'and' 'then' 'constitution,'\n",
            " 'and' 'then' 'books,' 'and' 'then' 'bag.']\n",
            "58\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABSMAAAL0CAYAAAD3KiKlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzde3zO9f/H8ce12dFsNmFkjCgrpzlTmEMOoYSkhH0RlaWSQqkkhfJzjs7mVA6FUGFhyClnyiFkOW1OGTZ2vK7fH8uV5bTDde06Pe+3227Xdn1O789h3K7nXu/322AymUyIiIiIiIiIiIiIWJmbrRsgIiIiIiIiIiIirkFhpIiIiIiIiIiIiBQIhZEiIiIiIiIiIiJSIBRGioiIiIiIiIiISIFQGCkiIiIiIiIiIiIFQmGkiIiIiIiIiIiIFAiFkSIiIiIiIiIiIlIgFEaKiIiIiIiIiIhIgShk6wbYA6PRyKlTpyhSpAgGg8HWzREREREREREREXEoJpOJy5cvU7p0adzcbl3/qDASOHXqFCEhIbZuhoiIiIiIiIiIiEM7fvw4ZcqUueVyhZFAkSJFgKyL5e/vb+PWiIiIiIiIiIiIOJZLly4REhJiztluRWEkmLtm+/v7K4wUERERERERERHJozsNgagJbERERERERERERKRAKIwUERERERERERGRAqEwUkRERERERERERAqExozMoczMTNLT023dDBEAPDw8cHd3t3UzRERERERERERyRWFkDiQlJXHixAlMJpOtmyICZA0GW6ZMGfz8/GzdFBERERERERGRHFMYeQeZmZmcOHECX19fihcvfscZgUSszWQycfbsWU6cOEGlSpVUISkiIiIiIiIiDkNh5B2kp6djMpkoXrw4Pj4+tm6OCADFixcnLi6O9PR0hZEiIiIiIiIi4jA0gU0OqSJS7ImeRxERERERERFxRAojRUREREREREREpEAojBQREREREREREZECoTBSzBISEnj44YcpXLgwRYsWtXVzciw6Otqh2isiIiIiIiIi4qoURjqpyMhIOnTokKttxo8fT3x8PLt27eKPP/6wTsPyKTQ0lAkTJmR778knnyyQ9kZERGAwGDAYDHh7e3PvvfcyatQoTCZTrvZzs3MQEREREREREXEFmk1bzI4cOUKtWrWoVKlSnveRlpaGp6enBVt1Zz4+PgU20/mzzz7LiBEjSE1NZfXq1fTt25eiRYvy/PPPF8jxRUREREREREQcmSojc8lkMnElLcMmX7mtwLteREQEAwYM4PXXXycoKIjg4GCGDx9uXh4aGsp3333HzJkzMRgMREZGAnDs2DEee+wx/Pz88Pf3p0uXLpw+fdq83fDhw6lRowZffPEF5cuXx9vbG8ia7fnTTz+lXbt2+Pr6EhYWxqZNmzh8+DAREREULlyYhg0bcuTIEfO+jhw5wmOPPUbJkiXx8/OjTp06/Pzzz9nO4a+//uKVV14xVyjCzbtpT5s2jXvuuQdPT0/uu+8+Zs2alW25wWDgiy++4PHHH8fX15dKlSqxZMmSO15HX19fgoODKVeuHP/73/+oVq0aMTEx+T4HgF9++YVGjRrh4+NDSEgIAwYMIDk5+Y5tEhERERERERFxFKqMzKWr6Znc//YKmxx734hW+Hrm/ZbNmDGDgQMHsmXLFjZt2kRkZCQPPvggDz/8MFu3bqVHjx74+/szceJEfHx8MBqN5iBy7dq1ZGRk0L9/f5588kliY2PN+z18+DDfffcdCxcuxN3d3fz+e++9x7hx4xg3bhyDBw/m6aefpkKFCgwdOpSyZcvSq1cvoqKi+OmnnwBISkrikUce4f3338fLy4uZM2fSvn17Dh48SNmyZVm4cCHVq1enb9++PPvss7c8z0WLFvHSSy8xYcIEWrRowbJly/jf//5HmTJlaNq0qXm9d999lw8//JCPPvqIyZMn061bN/766y+CgoLueC1NJhO//PILBw4cyFZJmtdzOHLkCK1bt2bkyJF89dVXnD17lqioKKKiopg+fXqO7q+IiIiIiIiIiL1TZaQLqVatGu+88w6VKlWiR48e1K5dm1WrVgFQvHhxvLy88PHxITg4mICAAFatWsXevXv5+uuvqVWrFvXq1WPmzJmsXbuWrVu3mveblpbGzJkzCQ8Pp1q1aub3//e//9GlSxfuvfdeBg8eTFxcHN26daNVq1aEhYXx0ksvZQs1q1evTr9+/ahSpQqVKlXivffe45577jFXLAYFBeHu7k6RIkUIDg4mODj4puc5duxYIiMjeeGFF7j33nsZOHAgHTt2ZOzYsdnWi4yM5KmnnqJixYp88MEHJCUl8euvv972Gk6dOhU/Pz+8vLxo3LgxRqORAQMG5PscRo0aRbdu3Xj55ZepVKkSDRs2ZNKkScycOZOUlJQ73VoREREREREREYegyshc8vFwZ9+IVjY7dn5cHxQClCpVijNnztxy/f379xMSEkJISIj5vfvvv5+iRYuyf/9+6tSpA0C5cuUoXrz4bY9XsmRJAKpWrZrtvZSUFC5duoS/vz9JSUkMHz6cH374gfj4eDIyMrh69SrHjh3L1Xnu37+fvn37ZnvvwQcfZOLEibdsX+HChfH397/t9QDo1q0bb775JhcuXOCdd96hYcOGNGzY0Lw8r+ewe/du9uzZw5w5c8zvmUwmjEYjR48eJSws7I7nLSIiIiIiIiJi7xRG5pLBYMhXV2lb8vDwyPazwWDAaDTme7+FCxe+4/GujY14s/eutWHQoEHExMQwduxYKlasiI+PD507dyYtLS3fbbxT+661507XIyAggIoVKwIwf/58KlasSP369WnRokW+ziEpKYl+/fplq7K8pmzZsrk5LRERERERERERu2XTbtqjRo2iTp06FClShBIlStChQwcOHjyYbZ2UlBT69+9PsWLF8PPzo1OnTtkmUIGsSVbatm2Lr68vJUqU4LXXXiMjI6MgT8UphYWFcfz4cY4fP25+b9++fSQmJnL//fdb/HgbNmwgMjKSxx9/nKpVqxIcHExcXFy2dTw9PcnMzLxjuzds2HDDvi3dZj8/P1566SUGDRpknlwor+dQs2ZN9u3bR8WKFW/4KujZyUVERERERERErMWmYeTatWvp378/mzdvJiYmhvT0dFq2bJltBuFXXnmFpUuXsmDBAtauXcupU6fo2LGjeXlmZiZt27YlLS2NjRs3MmPGDKKjo3n77bdtcUpOpUWLFlStWpVu3bqxY8cOfv31V3r06EGTJk2oXbu2xY9XqVIlFi5cyK5du9i9ezdPP/30DZWKoaGhrFu3jpMnT3Lu3Lmb7ue1114jOjqaadOmcejQIcaNG8fChQsZNGiQxdvcr18//vjjD7777rt8ncPgwYPZuHEjUVFR7Nq1i0OHDvH9998TFRVl8TaLiIiIiIiIiNiKTcPI5cuXExkZyQMPPED16tWJjo7m2LFjbN++HYCLFy/y5ZdfMm7cOJo1a0atWrWYPn06GzduZPPmzQCsXLmSffv2MXv2bGrUqEGbNm147733+Pjjj63WvddVGAwGvv/+ewIDA2ncuDEtWrSgQoUKzJs3zyrHGzduHIGBgTRs2JD27dvTqlUratasmW2dESNGEBcXxz333HPTcSoBOnTowMSJExk7diwPPPAAn376KdOnTyciIsLibQ4KCqJHjx4MHz4co9GY53OoVq0aa9eu5Y8//qBRo0aEh4fz9ttvU7p0aYu3WURERERERETEVgyma/1L7cDhw4epVKkSe/fupUqVKqxevZrmzZtz4cIFihYtal6vXLlyvPzyy7zyyiu8/fbbLFmyhF27dpmXHz16lAoVKrBjxw7Cw8NvOE5qaiqpqanmny9dukRISAgXL17E398/27opKSkcPXqU8uXL4+3tbfFzFskLPZciIiIiIiIiYk8uXbpEQEDATfO169m0MvJ6RqORl19+mQcffJAqVaoAkJCQgKenZ7YgErJmYU5ISDCvc22m5uuXX1t2M6NGjSIgIMD8df1s0SIiIiIiIiIiImIddhNG9u/fn99++425c+da/VhDhw7l4sWL5q/rJ2gRERERERERERER6yhk6wYAREVFsWzZMtatW0eZMmXM7wcHB5OWlkZiYmK26sjTp08THBxsXufXX3/Ntr9rs21fW+e/vLy88PLysvBZiIiIiIiIiIiIyO3YtDLSZDIRFRXFokWLWL16NeXLl8+2vFatWnh4eLBq1SrzewcPHuTYsWM0aNAAgAYNGrB3717OnDljXicmJgZ/f3/uv//+gjkRERERERERERERuSObVkb279+fr7/+mu+//54iRYqYx3gMCAjAx8eHgIAAevfuzcCBAwkKCsLf358XX3yRBg0aUL9+fQBatmzJ/fffT/fu3fnwww9JSEhg2LBh9O/fX9WPIiIiIiIiIiIidsSmYeS0adMAiIiIyPb+9OnTiYyMBGD8+PG4ubnRqVMnUlNTadWqFVOnTjWv6+7uzrJly3j++edp0KABhQsXpmfPnowYMaKgTkNERERERERExHkcXQcx70BG6s2X31UJOn0J7nYx+p84GIPJZDLZuhG2drupx1NSUjh69Cjly5fH29vbRi0UyU7PpYiIiIiIiFjNd8/C3vm3X6ffOihVvWDaIw7hdvna9RRhi4iIiIiIiIjIv9KvZL3Wex7ubZV92Xd94Mo5MBkLvl3iFGw6gY24ntjYWAwGA4mJiRbbZ2hoKBMmTLDY/kRERERERERcWvrVrNdS1eCeptm/Cql3nuSPwkhxeFu3bqVv377mnw0GA4sXL7Zdg0RERERERETyKTY2lkGDBuVp28jISH777bccrx8XF8fKlSsBqF279r9jRd4keOw84zhxiaqKlLxTGCkOr3jx4vj6+tq6GSIiIiIiIiIO6fowEoCMfyojVQUpVqAw0okZjUY+/PBDKlasiJeXF2XLluX9998HYO/evTRr1gwfHx+KFStG3759SUpKMm8bGRlJhw4d+OCDDyhZsiRFixZlxIgRZGRk8NprrxEUFESZMmWYPn26eZu4uDgMBgNz586lYcOGeHt7U6VKFdauXXvbdv7yyy80atQIHx8fQkJCGDBgAMnJyQDMnDkTPz8/Dh06ZF7/hRdeoHLlyly5kjWGxfXdtENDQwF4/PHHMRgMhIaGEhcXh5ubG9u2bct23AkTJlCuXDmMRv1FR0REREREROzPnj17aN++PXXq1GHv3r3MnTuXevXqUb9+fVasWAHAmjVrqF+/PvXr12fmzJnZtt+5cydt27blwoUL/O9//6NRo0ZEREQQFxcHwOnTp2natClPPPEEU6dOpV69euzbt4/QIRvxef8Sb0ycA8CsWbPw9/enePHibDia9Vk8et4SOnXqZG5ffHx8wV0YcWgKI3PLZIK0ZNt85XLi86FDhzJ69Gjeeust9u3bx9dff03JkiVJTk6mVatWBAYGsnXrVhYsWMDPP/9MVFRUtu1Xr17NqVOnWLduHePGjeOdd96hXbt2BAYGsmXLFp577jn69evHiRMnsm332muv8eqrr7Jz504aNGhA+/btOX/+/E3beOTIEVq3bk2nTp3Ys2cP8+bN45dffjG3pUePHjzyyCN069aNjIwMfvjhB7744gvmzJlz02rIrVu3AjB9+nTi4+PZunUroaGhtGjRIltwem2dyMhI3Nz0ayAiIiIiIiL258qVKyxZsoSZM2fyxhtvMGrUKNauXcvKlSt58803gazP/suWLWP9+vVMmjSJq1ezqhq3bNnC22+/zTfffIOfnx8HDx5k3bp1xMbGUrZsWQACAwOJiYlhwYIFVK5cmT59+pCamsqOQfeyLtKXT75ZCsDLL7/MmjVrOHHiBEmpJvaczgQgICCApUuX0qtXLxYsWGCDKySOSLNp51b6FfigtG2O/cYp8Cyco1UvX77MxIkTmTJlCj179gTgnnvu4aGHHuLzzz8nJSWFmTNnUrhw1v6mTJlC+/btGTNmDCVLlgQgKCiISZMm4ebmxn333ceHH37IlStXeOONN4B/w85ffvmFrl27mo8dFRVFp06dAJg2bRrLly/nyy+/5PXXX7+hnaNGjaJbt268/PLLAFSqVIlJkybRpEkTpk2bhre3N59++inVqlVjwIABLFy4kOHDh1OrVq2bnnfx4sUBKFq0KMHBweb3+/Tpw3PPPce4cePw8vJix44d7N27l++//z5H11NERERERESkoIWHh2MwGAgLC+PAgQNUrlwZb29vvL298fDwICMjg8zMTO666y4AKlasyKlTpwB4++23+e677/D39wegf//+dO/enWLFivH+++/j5+fH+fPnef755zl69CjHjh3j1KlT+Pv7E+SVgWdxd9IzskLHq1ev8uqrrwLg7gYJSSY8MREeHg5ASEgI27dvL+jLIw5KJWFOav/+/aSmptK8efObLqtevbo5iAR48MEHMRqNHDx40PzeAw88kK1qsGTJklStWtX8s7u7O8WKFePMmTPZ9t+gQQPz94UKFaJ27drs37//pu3cvXs30dHR+Pn5mb9atWqF0Wjk6NGjQNZfar788kumTZvGPffcw5AhQ3J5NaBDhw64u7uzaNEiAKKjo2natKm5W7eIiIiIiIiIXfj7T9i3BOI2sGvjaky/f8/BH6ZS+e5A/vpjLyl7vufS2VOkpaVRqFAh3NzcOHfuHOnp6Rw6dIjSpbMKqGbNmsWQIUM4dOgQmZmZdOnShdmzZ1OyZEkWzp9Lxr4f+fS9V2hXswxTX3uaciUDKVfob8hMh9TL2Zrk4+PDpEmTWLFiBQHebjQr7w5kTSB7jSmXvTnFdakyMrc8fLMqFG117Bzy8fHJ/+E8PLL9bDAYbvpefsZcTEpKol+/fgwYMOCGZdfKxgHWrVuHu7s78fHxJCcnU6RIkVwdx9PTkx49ejB9+nQ6duzI119/zcSJE/PcbhERERERERGLS0+BT5tA6iWIyyDgchrtH+/M6WQjXz7qw2/FjDRu0xE3v7sYOSEagA8++IC2bdtiMBiIiooy5wElSpRg9uzZdOvWjZkzZ9KjRw8MBgMGg4E5vcOI++QVtv6cwncXjYT4Gzh1KpOPp04hPcUIqe7/NCgrbBw3bhwPPfQQXl5eZKQaOZ2k4FHyTmFkbhkMOe4qbUuVKlXCx8eHVatW0adPn2zLwsLCiI6OJjk52VwduWHDBnN37PzavHkzjRs3BiAjI4Pt27ffMB7lNTVr1mTfvn1UrFjxlvvbuHEjY8aMYenSpQwePJioqChmzJhxy/U9PDzIzMy84f0+ffpQpUoVpk6dSkZGBh07dszlmYmIiIiIiIhYUeqlrC8gonEjIhpnX1wt+SxPVz0MFRpAmzYANG/enC1btmRbLzo62vz9tUlls00uO+8Z5sdn8nKLsjxcu9JNm+JXOpzL738AQM+ePc1DwDG+Clw8zoNdHoW7awLQrl072rVrl6dTFtejMNJJeXt7M3jwYF5//XU8PT158MEHOXv2LL///jvdunXjnXfeoWfPngwfPpyzZ8/y4osv0r17d/N4kfnx8ccfU6lSJcLCwhg/fjwXLlygV69eN1138ODB1K9fn6ioKPr06UPhwoXZt28fMTExTJkyhcuXL9O9e3cGDBhAmzZtKFOmDHXq1KF9+/Z07tz5pvsMDQ1l1apVPPjgg3h5eREYGAhkhbD169dn8ODB9OrVyyLVoyIiIiIiIiIWY8zIenXzgF7Lb1y+Zz4sfNYih+rygAe0fQPq9LbI/kRySmNGOrG33nqLV199lbfffpuwsDCefPJJzpw5g6+vLytWrODvv/+mTp06dO7cmebNmzNlyhSLHHf06NGMHj2a6tWr88svv7BkyRLzYLr/Va1aNdauXcsff/xBo0aNCA8P5+233zaPcfHSSy9RuHBhPvgg668xVatW5YMPPqBfv36cPHnypvv8v//7P2JiYggJCTEPpntN7969SUtLu2U4KiIiIiIiImIzmelZr26qHRPnZTBphFEuXbpEQEAAFy9eNM8ydU1KSgpHjx6lfPnyeHt726iFjiEuLo7y5cuzc+dOatSoYevm3NR7773HggUL2LNnj62bki96LkVERERERJzQ+SMwuSZ4FoE3Tty4/FplZIWm0GNx3o8z7xnYvxTajst9ZeQ/3bR5do25m7YI3D5fu54qI8UlJCUl8dtvvzFlyhRefPFFWzdHRERERERE5EbGf+Y/cFdlpDgvhZHiEqKioqhVqxYRERHqoi0iIiIiIiL2yehI3bRdvqOt5JEjPN3iIEJDQ7HXXv/R0dHZZhMTERERERERsTvmCWwU14jzUmWkiIiIiIiIiIg9yLxuNu3bymchUL4KiQz5O7a4PIWRIiIiIiIiIiL2wFwZ6W7bdohYkcJIERERERERERF7cMdu2hauSjSoylEKnsJIERERERERERF7cG0CG/c7ddMWcVwKI0VERERERERE7IG6aYsLUBgpIiIiIiIiInIH0dHRpKWlmb/ftGkTiYmJzJ8//7bbxcbG8scffwCwa9cupk2bBkBcXBwrV64EoHbt2lkrGzOzXv/TTbtz587ExcVZ6ExEbEthpBSI4cOHU6NGDYvtLzQ0lAkTJtxyeWRkJAaDAYPBwOLFi4Gsf+gNBgO7du265XaxsbEYDAYSExNve/yIiAjz/m+3PxEREREREXEO14eRkZGRNGjQINdhZI0aNXj++eeB7GGkWeY/3bTvOJu2HcjnhN7ium41IqqIRQ0aNIgXX3yxQI/ZunVrpk+fTmBgYI63adiwIfHx8QQEBABZ/9m8/PLLN4STCxcu5MiRI9StW9eSTRYRERERERELMplMREVFsWfPHgoVKsSIESMYOnQoJpOJdu3aMXToUIYPH86RI0c4f/48ycnJLF++nFOnTtG9e3e8vLy49957iYyMZNeuXbRp04bHH3+cS5cuUbt2bdavX8/atWuJiIhg6tSp9OjRg23btgGYl0dHR/Pdd98xf/58evXqxbx58zhw4ADbt28nKSmJTz75hPT0dEJDQzl+7Bi1ShnwLrwdz7kt+PXXX/Hy8qJQoaz4Jvr7WJbOv0Ka5xYSJtdhyZIllCpVypaXWCTXVBkpOXLtrz+5ZTKZyMjIwM/Pj2LFilm4Vbfn5eVFcHAwXl5eOd7G09OT4OBgDHeYUSwoKIjixYvnt4kiIiIiIiJiRUuXLsXNzY3169ezZs0aPvzwQz7//HN++eUX1qxZY+76XKlSJX788Ufq169PTEwMsbGxPPPMM6xZs4Zp06bRoEEDatSowU8//cTAgQPN+3/++edp0qQJsbGx3H///Tcc38fHh8jISEaNGsXMmTMB8Pb2Zvny5RQvXpxq1arxwQcfkJaWxo4dO6h+XygHzhlZ90ZD1q5dy9KlSzlx4gRJSUns2bMHgAAvA0tfqUuvXr1YsGCB9S/if2kCbsknhZFO6ttvv6Vq1ar4+PhQrFgxWrRoQXJyMpDVxfjll1/Otn6HDh2IjIw0/xwaGsp7771Hjx498Pf3p2/fvuZuznPnzqVhw4Z4e3tTpUoV1q5da97uWjfnn376iVq1auHl5cUvv/xyQzft2NhY6tatS+HChSlatCgPPvggf/31FwBHjhzhscceo2TJkvj5+VGnTh1+/vlni12bAwcO3LH9iYmJxMbG8r///Y+LFy+au2QPHz7cYu0QERERERER69q/fz9NmjQx/5yQkEBYWBgGg4GaNWty5MgRAMLDwwEICQnhwoULdOnShaNHj9KtWzdmz56dp2ObTDfvx3z16lUee+wxzp49y19//cWFCxdwd3fH39+fwt6eWb2f3T0wmUwMHjyYVq1a4e7uTkJCAhggPNg9W1vzR8miFDyFkblkMpm4kn7FJl+3+ofsv+Lj43nqqafo1asX+/fvJzY2lo4dO+Z4+2vGjh1L9erV2blzJ2+99Zb5/ddee41XX32VnTt30qBBA9q3b8/58+ezbTtkyBBGjx7N/v37qVatWrZlGRkZdOjQgSZNmrBnzx42bdpE3759zdWISUlJPPLII6xatYqdO3fSunVr2rdvz7Fjx3LV/lvJSfshq8v2hAkT8Pf3Jz4+nvj4eAYNGmSRNoiIiIiIiIiV/f0nYYUvsm5RNPy+GH5fTHEfI/uXTMb02yJ2rFvOPZlH4MwBDMc2Z60TvwfTie0U+uNHPop8kDnDnmLM6FEYjUY8PDzIzMzMdoj/vufu7s7ly5e5fPkyf/75Z/Z1UpPg2CYObP+FjvXLUzzAl5ASAZTIOAUmE4UOLoPUy1k7ciuEm5sb77zzDj/88AN+fn40a9YMgOs78uX2c76IPdCYkbl0NeMq9b6uZ5Njb3l6C74evndcLz4+noyMDDp27Ei5cuUAqFq1aq6P16xZM1599VXzz9fK16OioujUqRMA06ZNY/ny5Xz55Ze8/vrr5nVHjBjBww8/fNP9Xrp0iYsXL9KuXTvuueceAMLCwszLq1evTvXq1c0/v/feeyxatIglS5YQFRWV6/P4r5y0H7K6bAcEBGAwGAgODs73cUVERERERKSAZKbDZ01pf/UCy/9I4aE2y/Fwh+FNvOgT9QomE7StVIjQja/BvhRIdIezHrAzDTxhyc4vmLI1a7iyVtUq4+bmxqOPPkqXLl3MnycBSpUqxdWrV+ncuTOjRo2if//+NGrUiLp161K6dGkg67P14MGDWf35Wzx+1xHKpqUxedp+ihhM/H7IxGtjPsPDDVjQE5KSsnbs7kn16tXp0qULHh4eJCUlcfr06YK+iiJWoTDSCVWvXp3mzZtTtWpVWrVqRcuWLencuXOuJnKBrMF2b6ZBgwbm7wsVKkTt2rXZv39/jraFrPEWIyMjadWqFQ8//DAtWrSgS5cu5kF3k5KSGD58OD/88IM5WL169arFKiNz0n4RERERERFxYGnJkJKIwWBg6nNNwfBvx9ANjbOvOrzHv99Hlf33+ycjzsG5P+CfIp8XX3zxphOzLl++3Px9pUqV6NGjR7blDRo0YN26dTDrcTjyFxG1KkPhEjdt9vrhgLsH1H+erVsfAmD+/PkEBgby4IMP8qB/PGRkdRtv164d7dq1u9OVELE7CiNzyaeQD1ue3mKzY+eEu7s7MTExbNy4kZUrVzJ58mTefPNNtmzZQvny5XFzc7uhlDs9Pf2G/RQuXDjPbb3TttOnT2fAgAEsX76cefPmMWzYMGJiYqhfvz6DBg0iJiaGsWPHUrFiRXx8fOjcuXOeJ9ERERERERERF2My/vv9/34EN/fc7+P3RbAg0mJNMmv8OlR/Mserd+nSxfJtsAh1EZe80ZiRuWQwGPD18LXJ151meP5vOx988EHeffdddu7ciaenJ4sWLQKgePHixMfHm9fNzMzkt99+y/G+N2/ebP4+IyOD7du3Z+tmnVPh4eEMHTqUjRs3UqVKFb7++msANmzYQGRkJI8//jhVq1YlODjY3EXcEnLTfk9PzxvGBBERERERERE7Z7zuc5xB0ccNNNak2JAqI53Qli1bWLVqFS1btqREiRJs2bKFs2fPmgO3Zs2aMXDgQH744Qfuuecexo0bR2JiYo73//HHH1OpUiXCwsIYP348Fy5coFevXjne/ujRo3z22Wc8+uijlC5dmoMHD3Lo0CFzKXulSpVYuHAh7du3x2Aw8NZbb2E0Gu+w15zLTftDQ0NJSkpi1apVVK9eHV9fX3x97zxup4iIiIiIiNiQ6Z8w0uCWfcYXp2HLMNEZr6cUJIWRTsjf359169YxYcIELl26RLly5fi///s/2rRpA0CvXr3YvXs3PXr0oFChQrzyyis0bdo0x/sfPXo0o0ePZteuXVSsWJElS5Zw11135Xh7X19fDhw4wIwZMzh//jylSpWif//+9OvXD4Bx48bRq1cvGjZsyF133cXgwYO5dOlS7i6ChdrfsGFDnnvuOZ588knOnz/PO++8w/Dhwy3WFhEREREREbGCa5WRhjx0zxYRq1IY6YTCwsKyDaD7Xx4eHkydOpWpU6fecp3bdYsOCwtjy5abj5sZERFxw3iUAMOHDzeHeCVLljR3Gb+Z0NBQVq9ene29/v3757h9t9vvtbY99dRTN13nZu2fNm0a06ZNy/XxRERERERExEauVUbmZaxIV+KUVaNi7zRwgjitZcuW4efnx7Jlyyy+7zZt2vDAAw9YfL8iIiIiIiJiARatjLRQl2iN0ygCqDJSnNSHH37IsGHDAChVqpTF9//FF19w9epVAMqWLWvx/YuIiIiIiEg+XJtNW5PXiNgdhZGSY9d3c7Z3JUqUoESJElbb/9133221fYuIiIiIiEg+XauMdFMYKWJv9FspIiIiIiIiIs7FXBmZn27aVhpP0VnGaXSQYiWxPwojRURERERERMS5OPsENvkOAhUkiu0ojBQRERERERER52LRCWwkG2ep7BSbURgpIiIiIiIiIs7FWSsjLR4EKliUgqcwUkRERERERESci9ESY0aKiDUojBQRERERERER52Ky4GzaFpuoReM0ioDCSHEgsbGxGAwGEhMTC/zYw4cPx2AwYDAYmDBhgvl9g8HA4sWLb7ldXFwcBoOBXbt23Xb/kZGR5v3fbn8iIiIiIiKSA+YxIxV7iNgb/VZKgYuOjqZo0aK53q5hw4bEx8cTEBCQ420iIyPp0KFDro91Mw888ADx8fH07ds3x9uEhIQQHx9PlSpVgFsHqhMnTiQ+Pt4i7RQREREREXF5JgtMYGO1iVo0TqO4tkK2boC4lvT09Dxv6+npSXBwsAVbkzuFChXK9fHd3d1ztE1AQECuQlYRERERERG5DaOTTmBjKRbpeq5u55I3qox0UqGhodm6EwPUqFGD4cOHm382GAx88cUXPP744/j6+lKpUiWWLFmSbZvff/+ddu3a4e/vT5EiRWjUqBFHjhwxL//iiy8ICwvD29ubypUrM3XqVPOya12U582bR5MmTfD29mbOnDn873//4+LFi+ZuydfaNGvWLGrXrk2RIkUIDg7m6aef5syZM+b9/beq8FqF5YoVKwgLC8PPz4/WrVubKwyHDx/OjBkz+P77783Hio2NpVmzZkRFRWU7z7Nnz+Lp6cmqVatyfa3j4+Np06YNPj4+VKhQgW+//faGa7Br1y7i4uJo2rQpAIGBgRgMBiIjI3N9PBEREREREbkDS1RGiohVKIzMJZPJhPHKFZt8mSw2aO6/3n33Xbp06cKePXt45JFH6NatG3///TcAJ0+epHHjxnh5ebF69Wq2b99Or169yMjIAGDOnDm8/fbbvP/+++zfv58PPviAt956ixkzZmQ7xpAhQ3jppZfYv38/TZs2ZcKECfj7+xMfH098fDyDBg0Csqom33vvPXbv3s3ixYuJi4u7Y1h35coVxo4dy6xZs1i3bh3Hjh0z72/QoEF06dLFHFDGx8fTsGFD+vTpw9dff01qaqp5P7Nnz+buu++mWbNmub6Gb731Fp06dWL37t1069aNrl27sn///hvWCwkJ4bvvvgPg4MGDxMfHM3HixFwfT0RERERERO7g2mzalpjARv5D3cwlf9RNO5dMV69ysGYtmxz7vh3bMfj6WnSfkZGRPPXUUwB88MEHTJo0iV9//ZXWrVvz8ccfExAQwNy5c/Hw8ADg3nvvNW/7zjvv8H//93907NgRgPLly7Nv3z4+/fRTevbsaV7v5ZdfNq8DWV2SDQbDDd2Xe/XqZf6+QoUKTJo0iTp16pCUlISfn99N25+ens4nn3zCPffcA0BUVBQjRowAwM/PDx8fH1JTU7Mdq2PHjkRFRfH999/TpUsXIKvK8tokMrn1xBNP0KdPHwDee+89YmJimDx5crYqUcjqsh0UFARAiRIl8jRupoiIiIiIiOSA01ZGWjgItNq4mCK3pj8RuLhq1aqZvy9cuDD+/v7mrtG7du2iUaNG5iDyesnJyRw5coTevXvj5+dn/ho5cmS2btwAtWvXzlFbtm/fTvv27SlbtixFihShSZMmABw7duyW2/j6+pqDSIBSpUpl69p9M97e3nTv3p2vvvoKgB07dvDbb7/luct0gwYNbvj5ZpWRIiIiIiIikj9xcXGsXLkSuMNnTZORzvOvEPd3mgWOaqFeilbo7SjiiFQZmUsGHx/u27HdZsfOKTc3txu6dd9s8pj/Bo0GgwHjP+XsPrc5XlJSEgCff/459erVy7bM3T37X54KFy58x/YmJyfTqlUrWrVqxZw5cyhevDjHjh2jVatWpKXd+j+Pm7U/J93Z+/TpQ40aNThx4gTTp0+nWbNmlCtX7o7biYiIiIiIiO1cCyNbtmx5+xWNzloZKeL4FEbmksFgsHhXaWsoXry4eSIXgEuXLnH06NFc7aNatWrMmDGD9PT0G0K/kiVLUrp0af7880+6deuWq/16enqSmZmZ7b0DBw5w/vx5Ro8eTUhICADbtm3L1X5zeiyAqlWrUrt2bT7//HO+/vprpkyZkudjbN68mR49emT7OTw8/JbtAW7aJhEREREREbm506dP07VrV3777TeSk5NZv349+/btIzQ0lNOnT/PKK6/wwQcfMGvWLPr374+XOxTKyASDG9HR0SxdupS0tDQSEhJYsmQJpUqVysFRrdSFWV2jxcWpm7aTatasGbNmzWL9+vXs3buXnj173lCxeCdRUVFcunSJrl27sm3bNg4dOsSsWbM4ePAgkDX5zahRo5g0aRJ//PEHe/fuZfr06YwbN+62+w0NDSUpKYlVq1Zx7tw5rly5QtmyZfH09GTy5Mn8+eefLFmyhPfeey/P53/9sfbs2cPBgwc5d+5cturQPn36MHr0aEwmE48//niej7FgwQK++uor/vjjD9555x1+/fXXG2brvqZcuXIYDAaWLVvG2bNnzRWmIiIiIiIicmuBgYHExMSwYMECKleuTJ8+fUhNTWXHjh2sW7eOTz75BMias2DNmjWcWPU5SWkm9pxMBrLmLli6dCm9evViwYIFtjwV56Fu55JHCiOd1NChQ2nSpAnt2rWjbdu2dOjQIdvYijlRrFgxVq9eTVJSEk2aNKFWrVp8/vnn5irJPn368MUXXzB9+nSqVq1KkyZNiI6Opnz58rfdb8OGDXnuued48sknKV68OB9++CHFixcnOjqaBQsWcP/99zN69GjGjh2b5/O/5tlnn+W+++6jdu3aFC9enA0bNpiXPfXUUxQqVIinnnoKb2/vPB/j3XffZe7cuVSrVo2ZM2fyzTffcP/999903bvvvpt3332XIUOGULJkyVuGliIiIiIiIo5s+fLlLFq0KEfrDho0iNjY2Nuuc/78eTp37sxLL73E0aNHOXXqFP7+/gQFBREWFmYuPLl69SqvvvoqrfqNwN0ACZeyhv261nstJCSECxcu5P3E7EW+g0AFiWI76qbtpPz9/Zk7d262966f4Rq46diKiYmJ2X6uVq0aK1asuOVxnn76aZ5++umbLgsNDb3l+I3Tpk1j2rRp2d576qmnzDN736yNERER2X6OjIy8YdKZDh06ZFunePHi5sGN/+vcuXOkpKTQu3fvmy7PiWvHeuGFF266/GbX4K233uKtt97K8zFFRERERETsXevWrS26v6+//pp27doRFhZG//79bznmv4+PD5MmTeK+1D3c26I7ze4vwS9kDbl2TU7mGZDbUDdzySeFkeJy0tPTOX/+PMOGDaN+/frUrFkzR9vt3bsXPz8/Pvzww1uGj3n13HPPMXv2bIvuU0RERERExNreeOMN2rVrR8mSJalSpQoXL17km2++wWg0kpycTFRUFGFhYdStW5fdu3fz2muv0a1bN3bv3k2fPn0oWbIkaWlptGvXjszMTHr27Mnx48fx8/Nj9uzZLJg3F7/fZ/Og5wUaDtpPRMUixJ24whsv9SU9JROm1IXUTEi/AlPqMq6NPw/VDcerkIGMDDh9OR1K2PoqWZDFg0AFi1LwFEaKy9mwYQNNmzbl3nvv5dtvv83RNgMGDOCZZ54BsqotLW3EiBEMGjQIIIcDKYuIiIiIiNheo0aNWL9+PSVLliQiIoKtW7eyfv16GjRoYF4nISGByZMnA/Dwww/TrVs3hg0bxuzZs6lUqRIPPfQQAIsWLaJMmTLMnj2bWbNmMXnyZJ5oUoVJX2yldBUPWlV055mqGTQLKUSDMu40r+AD5w7iB1we4gfnDtKzIvQc4vdvA+vU5cG2keYf27VrR7t27XJ3kharpFRFpggojBQX9N/u3jkRFBREUFCQlVoEJUqUoEQJZ/pznYiIiIiIuIKGDRsybdo0goODGTJkCOvWrePIkSPmgBGgQoUK+Pv7A5CZmQlkBZT33XcfALVq1QLg8OHD1KlTB4A6deqwcuVKKvdqy/5zRtad8uKNkWP4esnPHE88w8Ah74LPHcb+d/eAu2tb+pRFJJ8URoqIiIiIiIhI7v22kIB9i7l85Fd8znnzUPwVhs/+meCiPvDr55CSDvN/xZB0Oqu68LouxiVLluTQoUNUrFiRHTt20KlTJypWrMivv/5Kp06d2Lp1K5UqVcJgMBDkY2DDsTTefjKKcXNXkepeGN/7HwbgxIkTlClTxjrnp7ERRaxCYaSIiIiIiIiI5N5PgyH5DFX9UihkAvcDS/FOS6ZRUQ84lQBpwL6DcDkJLhyFoArmTd977z2efvppSpQoQWBgIJA1IenChQtp3LixecxIrsbxUFl3fol3AyA4OJgiRYoAkJGRwVNPPcX69esL/NRFJO8URuaQZtsSe6LnUUREREREbC4tGYBJY0aAb9awVj898p91Yt5hW18gIw2Abdu2ARAeHs7WrVtv2OXXX3+d/Y2TcQxs4MXAgBAApk2bZl60Y8cOevToYYETcUEW+Uypz6WSNwoj78Dd3R2AtLQ0fHx8bNwakSxpaVn/kV97PkVERERERApcZmrWa/gzEHD3zdeJHQXpyVY5fN26dalbt65V9m3/FASK41IYeQeFChXC19eXs2fP4uHhgZubm62bJC7OaDRy9uxZfH19KVRIv8IiIiIiInJrtWvXNlcj5tRnn31G3759b7+S0QjGjKzvC3nlsXXimDSWpuSPkow7MBgMlCpViqNHj/LXX3/ZujkiALi5uVG2bFkMGlBZREREREQsLEdhZGbav9+7e1ixNU5UAWiRrtEW/gyoz5RiAwojc8DT05NKlSqZu8aK2Jqnp6eqdEVEREREJJvY2Fg++OADvLy8SEhI4KuvvjIvi4mJYeTIkVy5coVOnToxZMgQoqOjWbp0KWlpaSQkJLBkyRIWL17MwYMHiYiI4O2332bWrFkcPnwYd3d3oqOjCQ0NzdphtjDS2SsjnSgQFbEDCiNzyM3NDW9vb1s3Q0REREREROSWrly5wooVKzhw4ACDBw82v//ggw+ydu1ajEYj9erV46WXXgIgICCAr776imnTprFgwQIGDBjAl19+SWxsLOnp6QwbNowNGzbw119/ceDAAUJDQ7O6fq9d/u9B3T3N33bu3JmxY8f+G1pajCr4RJyFwkgRERERERERJxEeHo7BYCAsLIz4+Hjz+9u3b+fdd98lPT2duLg4zpw5Y14fICQkhO3bt2fbl4eHB/3796d79+5cvXqVu+++m9atW2ctvFYZ6VYIctRryxGrC60UgKprtLg4hZEiIiIiIiIijizlElw8AX/HsWvrRkwJv/PHkThKBflxKuEsnN7Hh6NG8sknn1ChQgVq1qyJ6Z/xC68fh/6/7506dYrPP/+czMxM9uzZg8FgYMaMGRiNRgLLhlHM7TLvNven5M8/89JLL3Hu3DmuXr3Ke++9x5dffpnVDXzW6axu4N93YcmPKylVqlTBXx+xDouMgSmuyKaDzq1bt4727dtTunRpDAYDixcvzrbcYDDc9Oujjz4yrxMaGnrD8tGjRxfwmYiIiIiIiIjYQOplmFAVpjWAJf0J+HsP7RvX4JkujzKywlY4ux+mNaCT1wYef7QtzzzzDEWKFLntLu+77z46derEH3/8gdFoxN3dHU9PT6pXr06xYsVITU1l5fyvCLvLjUmbrzJs2DAqVarEmjVrCAwMJDU11TyDd4C3G0uf8qXXUx1ZsGBBQVwR15DvIFBBotiOTSsjk5OTqV69Or169aJjx443LL++pBzgp59+onfv3nTq1Cnb+yNGjODZZ581/3ynf1hFREREREREnMLFk5CSCBjAK4DKwVcY266YefG2l4GrF4isDpGjJ8J9rc3LIiMjzd+3a9eOdu3aATB79mwg6zN5UFAQFy5cwNPTk/T0dIoXL467uzth95QhKR0yMWDIzOTPP//khRdeICkpid27d3PixAkAwkt7AemElA5m+5Ez1r0WUjDUzVzyyaZhZJs2bWjTps0tlwcHB2f7+fvvv6dp06ZUqFAh2/tFihS5YV0RERERERERp5eZmvVaJBgiPoFly+D1sdnX+bwZnNx+47a3YzLx9Zw5tGvblj59+tC5c2cuX75MEX9/MjMzISPtn+I8A+7u7pQvX56PPvqIli1bsmTJEkJCQpg9e3a23MqUl2o+FfBlZ/EgUMGiFDybdtPOjdOnT/PDDz/Qu3fvG5aNHj2aYsWKER4ezkcffURGRsZt95WamsqlS5eyfYmIiIiIiIg4nMz0rFd3DyIiIhg7duzt18+Jgz/BB6Vpfng4E4c9x2Nhnpj2fU/cjp/ZvHIhiX/ugBlts9Y1GBgxYgSHDx+mcePGJCYm0r17d06dOpX/dtgLjY0oYlEOM4HNjBkzKFKkyA3duQcMGEDNmjUJCgpi48aNDB06lPj4eMaNG3fLfY0aNYp3333X2k0WERERERERsa6Mfyoj3b0st88jayD9CjWC3dn7vN8tV4uNLAy1IqFlS37//fcblkdGRsKZ9+DKOdq1jKDdM2F5b5MK+ESchsOEkV999RXdunXD29s72/sDBw40f1+tWjU8PT3p168fo0aNwsvr5v8YDx06NNt2ly5dIiQkxDoNFxEREREREbGWzLSsV3dPy++7fn9o9OqtlxsM4BuUs305YnWh1cZGVLIqrs0hwsj169dz8OBB5s2bd8d169WrR0ZGBnFxcdx33303XcfLy+uWQaWIiIiIiIiIw7gWRhayQhjp6QuFi915PRGRXHCIMSO//PJLatWqRfXq1e+47q5du3Bzc6NEiRIF0DIRERERERGRgjV37ly++uorAO5r1YtBK1NuqIzs168fAJ07dybu3NV/3rVBdaJmXrZPFqlUdcBqV7ELNq2MTEpK4vDhw+afjx49yq5duwgKCqJs2bJAVhfqBQsW8H//9383bL9p0ya2bNlC06ZNKVKkCJs2beKVV17hmWeeITAwsMDOQ0RERERERKSgLF++nIsXL9KrV69/Q6X/hJGffvqpDVomBUdBoDgum4aR27Zto2nTpuafr43j2LNnT6Kjo4Gsv/iYTCaeeuqpG7b38vJi7ty5DB8+nNTUVMqXL88rr7ySbTxIEREREREREVvYvHkzL730Er6+vjRp0oQlS5bQoEEDNm7cSJ8+fdi8eTN79uzhrbfeonPnzqxZs4ahQ4cC8MILL9CjRw/27t3L888/j8lkokmTJmzYsIFffvkFk8nEXXfdxeVLF/n4ZAbjN/9AsddLsHr1anbu3ElkZCRBQUG4ubnRd186x89cJvnTHlSrVY/Tp0+zZMkSSpUqZeMrlBMK3eyPql0lf2waRkZERGC6Q2lw37596du3702X1axZk82bN1ujaSIiIiIiIiL58sMPP/DOO+/wyCOPYDQamTlzJkOGDCEwMJBSpUpx5MgRPD096dChA507d2bo0KEsW7aMgIAAGjRowBNPPMEbb7zB559/TuXKlXn44Yf58ssvGTlyJOvXr2fIkCH069ePB0MMjHu2Oc3GbOarr75ixowZ3HvvvezatYsiRYrQuP7dlLn3KlMOBxIZGcnZs2dZsGABAwYMuEXLFQBmZ6HrYZGu0RYOAtWNXmzAISawEREREREREXE0/fv3Z+TIkcyZM4du3boRGBhISEgIAPfee695roOUlBQAMjMzueuuuwCoWLEip06dIiEhgbCwMAAqV65Mz549OXLkCElJSZw6dYrC3p7ULW0irOxdGI1GLly4wNWrVzlx4gStWrXCZDIRvSmBQsZUrnolkpCQQGhoKNu3b7fBFckPhWYizkJhpIiIiIiIiIglpV6G7TMIuHiOKR1LkZZenFrP98LLwx1Wvw+AISkBts+A8O7mzdzc3Dh37hwBAQEcOnSI0qVLU7JkSfbv2ETlK1tY+f1cerWuyZ+BIcRsP0y55D1gzMBgcAe3rDEjTSYTPj4+lCxZkhUrVlC8eHHeeyyE1LN/klS1M/369WPFihVcvHgxBydiyQDQEastrRSAqhpRXJzCSBERERERERFL2vUNrHyTTzensnB/BhlGiAwrxDe/pcO6D7PWuZwESwdA8fvMm33wwQe0bdsWg8FAVFQUPj4+vP/++/R55jFMiSdoGezGnCWr8POEE2eMDP54IRmZAO7gHWDez7hx4+jVqxdlypTBYDAwbe1Jzv6dSuau76n/WC8yMzP59ttvGTduXMFeFxERFEaKiIiIiIiIWNbVCwC83L4GLw9oZH771etW2ea7EK6cg5RL5rkQmjdvzpYtW7Ltqnr16mz4v2dg42QIrgZl6994vELeUPdZkpMnmd/q2bPnv8s/bw4nt0HXz6FybYxGI2+99Vb+zzNHVAVonxyxUlWchcJIEREREREREUsyZmS9hj4Ej3x083VObM0KI3OjQgS0fC9fTQOoW7cudevWzfd+CoRFJn1xQvZwXeyhDeKQ3GzdABERERERERGnci2MdLOT+h+NUSgidkRhpIiIiIiIiIglmTKzXt3cbduOvLLHijdbBqoWux4W2I/Fr0Me9qdwW/JJYaSIiIiIiIiIJRkdPIy0BnsMOEXEJhRGioiIiIiIiFiSvXXTzitVwImIFSiMFBEREREREbEkc2VkTsLIHFQMqqrQNqwWxirkFdemMFJERERERETEkq5VRhrsrZu2DUJNVVeKyH8ojBQRERERERGxJHM37duFkZo4JGdUFWoVFqm21b2RvFEYKSIiIiIiImJJJmPWq92MGemKIaazUxAojkthpIiIiIiIiIgl5agy0p7ZY9DlBIGq04z96QT3QmxKYaSIiIiIiIiIJTnLbNoW5chBnD213cJBoEt2/RdbUxgpIiIiIiIiYkm5CSPtulpOQZWIWJ7CSBERERERERFLMmZmvTpsN21LcuRA00ptVzWiuDiFkSIiIiIiIiKWdC2MNNwmjMxTIJXPEMuuqzBFxFUojBQRERERERHJp7i4OFauXAlA7WErst68RTftzp07E3fuakE1zbEpQLUSXVexHYWRIiIiIiIiIvl0fRhpznnsZQKb3FZhKgC0f/Zwj+yhDeKQFEaKiIiIiIiI5NHp06dp2rQpTzzxBFOnTqVevXrsO3mJ0AmX8anbnTfeeAOAWbNm4e/vT/HixdmwYQMA0bvS6PTiSNq3b0+dOnWIj4+35anYN6cYZ1HhnQgojBQRERERERHJs8DAQGJiYliwYAGVK1emT58+pGYY2dGvMO9GdWXq1KkAvPzyy6xZs4YTJ06QlJTEnhNJAAQU8WXp0qX06tWLBQsW2PJUrMuRq+jsqe0WD2XzsD+nCIbFluykZlxERERERETE8Zw/f57nn3+eo0ePcuzYMU6dOoW/dyGCfNzYvf0v0tPTAbh69SqvvvoqAO7u7iRcSsMTCA+7B4C7776bnTt33vwgtgrDFDqJiBUojBQRERERERGXYDKZiIqKYs+ePRQqVIgRI0YwdOhQTCYT7dq1Y+jQoQwfPpzDhw9z/vx5AB599FHmzZtHyZIlmTdvHtHR0SxevJi01BQunz1B01qVaVejFGN3b6VcyQDKuZ/hckoGRy8Yidm0h5SUFF5//XW8vb3x9fXlypUrpKamUifUn1VbTHwyazG/HPobb29vypcvb+0rYOX934QjB5pWa7sDXxMRC1A3bREREREREXEJS5cuxc3NjfXr17NmzRo+/PBDPv/8c3755RfWrFlDXFwcAGFhYfz0008EBgaSlpZGbGwsaWlp/PnnnwD4+vry47BHeLPyUQ5t+J6J0z4j/tQpTh3/i4+nfYIJKB/oxsMNa+Lt7c2HH37II488wpo1a/j9999xc3Pji1+yxodMvJTMrFmzeOKJJ+58Ao4c7ImI/EOVkSIiIiIiIuIS9u/fT5MmTcw/JyQkEBYWBkDNmjU5cuQIANWqVQOgdOnS5u/vvvtuLly4AECtWrXgcgJ1Srsxcbs3ez9sSu3h69g2vHHW8nfWQY2n8fjzMlu2bAHA39+fKlWqULhwYdLT0ynifYLO93mwkap4enrSrl072rVrZ6Uzd+QQ047GaxQRi1BlpIiIiIiIiDg3oxHO7Ccs2Jd1yxdBwm+Q8BvF/b3Zv24Jpvi97Ni0jnv8MyDpDIbEY3AhDgDDddWIpn/Gbrw2tuO2U0Yq3hcGPZeQUrgMmc8s4ljTKVxwC4IOU/Hw8iYzMxOAypUrM2DAAGJjY9mwYQPvPVoBALfr9n/58mUuXrxYEFfkDhQAOj17mpRHXI4qI0VERERERMS5LXsJdsykvcnE8t0pPFR3Hh7uMLyJF32e6YjJBG0rFSL0hy6wLQVOucORN+BMI+DGasW0tDRaD5tL0plUvhnVEIBu3brRoEEDGjduTNGiRQFo06YNL7/8Mi1atODVV1+lb9++TJ8+HYBXH/ibB9yz73fevHkYDAZ69+5tzavhoBy5uvMfFg0A7SFMtIc2iCNSGCkiIiIiIiLO7fTvABh8ijL1Se9sizZUyb7q8HZA6mVIv8LY7rUgIgKAKVOmAPDbb7/RuHFjonpVhM0fQ4kAAIYOHcrQoUOz7atjx4507NjR/PPMmTP/Xfh5czgZx7eT3jS/9ccff9ywjyy2Cn0sGQA6cnDlyG0XsT8KI0VERERERMS5GTOyXjt9CZUevvP6K96ETVOs26ZrrquW+/DDDwvmmOLALFwhmqdJkZygSlVsSmGkiIiIiIiIODdj1riNuLnffr0ciIyMzPpm+Rv53leBs8k4gY4cXFmp7ZoVXVycJrARERERERER55aZnvXq5mHBneYz2FNFmoi4KIWRIiIiIiIi4tyuddN2c9HOgY5ciadZn0WcjsJIERERERERcW7WDCMdOei7FQWALkD3WGxHYaSIiIiIiIg4t2thpHtuw0gFNnbDGUPf/LCHwNge2iAOSWGkiIiIiIiIODe77qadg0DHVqGPJQNABVco3BbJojBSREREREREnJtdh5Fi9+wpSLV4hWge9qcqVcknhZEiIiIiIiLi3DKvhZEWnE073wGVLUIgG4RqCq5E5D8URoqIiIiIiIhzM1dGutu2HeJYrBakKqAV16YwUkRERERERJybVbtpO0KwlNs22lG3ZLtqi4hYgsJIERERERERcTpxcXGsXLkSgNrT/s560/323bQ7d+5MXFzcv2/Y01iBIiJOQmGkiIiIiIiIOB1zGGk0Yq6uc9gJbBSKOkYFqgNR0C425Kj/EouIiIiIiIjc0vjx41mxYgWff/45phQjHeddYcVH5ahatSrJyckMHjyYWbNmkZCQQHx8PA899BC7d+/m+eefp9CFwyQcT2LJfZcpZe2G2nUoZMkA0J7Ps4BY9F7bw/W0hzaII1JlpIiIiIiIiNhMv379rLJNVFQUL7zwAhfPxpOWCRHl3CkVHIzRaGT+/PkMGTKEqVOn4uPjQ9u2bRk0aBCJiYkUKVKEpSOepFcNTxas33+bIyiIcR261yKWpMpIERERERERsZlPP/3UKttcunSJJUuWsH3rrwBcSIHq1ary1/EThISEkJiYSO/evfn9999JT0/nzJkzhISEcP/99wMZhAQY2J6Ukuu25VieZmrOZ6WiTaow1b3asix8PW3xHIrLUxgpIiIiIiIiFrV582ZeeuklfH19KVWqFH5+fnz22Wc0btyYPn368OSTT/LYY4+xfPlyateuzbZt24iMjMTb25sjR45QuHBhFi1aRGZmJl27diUxMZH77ruP5ORkoqOjzdsMHz6cI0eOcP78eZKTk1m+fDk+Pj688Oz/WLx0GR6FPDCkJ+PvZaBcUQN73dzNbfTx8WHGjBk8+eST/Pjjj3h5edGnTx8M14UzppyEd3kKc8QxWOne6pkRF6du2iIiIiIiImJRP/zwA++88w5r1qxh9uzZ/PHHH6SmphIYGMiGDRvYunUrderUuWG7hg0bEhMTg5eXF3v37mXx4sXce++9/Pzzz1SvXv2mx6pUqRI//vgj9evXJyYmhm2rl5C45WvmP3KF0wnxbNq6i6sZ/4SK14VAFSpU4LnnnuPq1auUK1eOTp06UaxYMatcD5vLbfhlT+NY2lNbRMQiVBkpIiIiIiIiFtW/f39GjhzJnDlz6NatG8WLF2fZsmW0bt2a1atXs27dOho3bnzDduHh4QCEhIRw4cIFDh8+TK1atQCoVasWGzduvOM2V/48RK1gAw+V8+LkW6XoNPMUsc+HQFh7ItuONW+3ZcuWW5/AymG0u9eDdg1vbKOIiOSPKiNFRERERETEcjIzCPDzZcrE8Uz/4jMGD36dhxo24IMP3qdRw/qULFGc7xcvpkHd2pCZnm3T/3aRrlixIjt37gQwv/7XDduUK83OBCOUvJ+dD34CIXVh0EH+bvAGV65cyeFJFGQ32hxU/qk6UF2bLU7PlNiOKiNFRERERETEMjZPgxVv8ummKyzcn0GGESLDCtHowNuM2H+FBxY2odHpDH49mYrf+NCsbU6nQfK5m+6uQ4cOzJ07l+bNm1OhQgU8PDzu2ITaVe/D3wsa/99ewh9Zat5m3LhxNG3alObNm1vqbJ2fJfM/BapYNAC0h+tpD20Qh6QwUkRERERERCzjUAyYMnm5vhcv1/fKtuj860UAeLKKB09W+TdU3NbbExL2EB0dbX5v7Nh/u1N/8803eHh48Nlnn3HhwoWsbbZtA2D48OHm9aKiorK+ObyKia298bi7KisrtCUtLS3r+OfP06RJE4udav6DmDwkffmuDlR4lCcK3UQsSmGkiIiIiIiIWIYxI+u1/US4v8Od15/eBs7su+0qjz32GElJSXh5eTFv3rwcNaPfshSOJO/FWPI9ZsyYAcC0adNytK1YmLpXi8h/KIwUERERERERyzBmZr16+YNP0Tuvb3C/4yo//vhjLhth4qvHfCC4Kjy3Ppfb5oUjhG25baMqAQEr3tp87Ngewl17aIM4NE1gIyIiIiIiIpZxrTLS7c4ho0NQ91w7oHsg4mwURoqIiIiIiIhlmP6pjHSzh054zlS9pUBORJyHwkgRERERERGxDHNlpD2EkflQkN1Q7br60p4CXXtqixOw6+dOnJ3CSBEREREREbGMvHbTdshgxBHbbEu6Xo75nItYnsJIERERERERsQyjMes1BxPTuLw8VV/mszpQYVge2eN1s4c22UMbxBEpjBQRERERERHLyG03bWv0vC3ofEQzC9+Bro+IZKcwUkRERERERCzDWcaMdDa5DUxVQWnHLBzu2qJCV1yewkgRERERERGxDLuaTVucgk2DUSuFbqqmFRenMFJEREREREQsw3gtjLSDj5r5CnzsLCxSpaKIOBE7+B9CREREREREnEKeu2k7YNhmsYDQjs/dnir47KktIpIvCiNFRERERETEMozqpi23YMeZa8HRRRABhZEiIiIiIiJiKbmujHSK6bQLbrt8VwcqDBMR21MYKSIiIiIiIpZxrTLS4G7bdliKxmrMP2foXW2Pj4E9PJv20AZxSAojRUREREREJNeMRuNN3rxWGekkYaTLUsgkItajMFJERERERERyJDY2lvbt2/P444/zySef0KhRIxo2bMg333wDwKydSUREJ1Oz6aPMmjULgE8++YS6devSrFkzFi1aBMCrr77KQw89RLPxe4lLzAo1w8LC6NmzJzVq1GDOnDkWaG0+SvI0WYodsWEwarXnwJ6ezbwMF2DhJojLsWkYuW7dOtq3b0/p0qUxGAwsXrw42/LIyEgMBkO2r9atW2db5++//6Zbt274+/tTtGhRevfuTVJSUgGehYiIiIiIiOu4ePEiCxcuZM6cOaxatYr169czZcoUMjMz6VS5ELGRhdmw8nvGjx8PwPz58/n5559ZvXo1jz32GNu2bePkyZP88ssvvNuuLCPWpoIJEhISmDx5MuvWrWPSpEk2PssClKOurqpUFBHnYdMpzpKTk6levTq9evWiY8eON12ndevWTJ8+3fyzl5dXtuXdunUjPj6emJgY0tPT+d///kffvn35+uuvrdp2ERERERERV1S7dm3Onj3LH3/8QcuWLQFITEzk7NmzbDqUwsQtaZh+fobDhw8DMHr0aF566SVMJhNDhw7l8OHD1KlTB4A6oUV44++sysgKFSrg7+8PQGZmpg3OLLdcISC0pxI4e2qLiOSHTcPINm3a0KZNm9uu4+XlRXBw8E2X7d+/n+XLl7N161Zq164NwOTJk3nkkUcYO3YspUuXtnibRUREREREXJmbmxt33XUXlStXZuXKlXh6epKeno6Huzsj16eyLrIwhoHzqVAl6zNa1apVmT59Ohs3bmTMmDG88MIL5l5xW/9KolJQVoc9g6W6nzrKpBp5Ol9HDuQc5L64Ckf5PRGnZNMwMidiY2MpUaIEgYGBNGvWjJEjR1KsWDEANm3aRNGiRc1BJECLFi1wc3Njy5YtPP744zfdZ2pqKqmpqeafL126ZN2TEBERERERcWSXE2B+T9h5BPZdwm3yzwx74BIP3x+EmwGK+xVifmQ5Olb2oNH0ZGrGv0FgYCAAzz//PHFxcaSmpvL+++9Tu3ZtSpUqxUMPPUShM38xvY3XHQ5u5xxpfEkFULal6y8C2HkY2bp1azp27Ej58uU5cuQIb7zxBm3atGHTpk24u7uTkJBAiRIlsm1TqFAhgoKCSEhIuOV+R40axbvvvmvt5ouIiIiIiDiHP9fC8c1E3AURjYELR2kVDK2euTZrtgkuxPFmYy/efKQcvPIVuGd93IyOjr5hd9fGk+TTxhC/G4Bt27aZl1//vW25aHhk0dN2oLD2luzxObCHNtlDG8QR2XUY2bVrV/P3VatWpVq1atxzzz3ExsbSvHnzPO936NChDBw40PzzpUuXCAkJyVdbRUREREREnFbmPz3LQupDy/duv+5dlcxBpE05UsWitelaiIgdsYP/IXKuQoUK3HXXXRw+fJjmzZsTHBzMmTNnsq2TkZHB33//fctxJiFrHMr/ToQjIiIiIiIit5CZnvVa+C4IqWuFA9hbhZWdhXeu3L3XpudupecgX+GwhdvkcmOXij1ws3UDcuPEiROcP3+eUqVKAdCgQQMSExPZvn27eZ3Vq1djNBqpV6+erZopIiIiIiLiXIwZWa/uHrZthz2xWEjmwkGjiLgkm1ZGJiUlcfjwYfPPR48eZdeuXQQFBREUFMS7775Lp06dCA4O5siRI7z++utUrFiRVq1aARAWFkbr1q159tln+eSTT0hPTycqKoquXbtqJm0RERERERFLuVYZ6WbpMNIaFVYK9yzGkt278xveqqu5iNOwaWXktm3bCA8PJzw8HICBAwcSHh7O22+/jbu7O3v27OHRRx/l3nvvpXfv3tSqVYv169dn62I9Z84cKleuTPPmzXnkkUd46KGH+Oyzz2x1SiIiIiIiIs4nMy3rVZWRN5HXkCwP2ymQExEnYNPKyIiICEy3+evIihUr7riPoKAgvv76a0s2S0RERERERK7nqt20XXmsRktRgHode3qe7Kkt4mocasxIERERERERsQGrddO2JoVgeaegKhuF0iIWpTBSREREREREbs/4TxhprcpIewt7nK6az9nOR+zid8YOmiCOSWGkiIiIiIiI3J65MtKmI33ZmYJMYlw59bHhuVstlFY4LK5NYaSIiIiIiIjcXqaVKiOtEfbYQ8VYbth1exWa2R2L3xJNpCQFT3/WEhERERERkRts3ryZl156CV9fX5qUukrltHTGz/scQ8CPvPvuu7Rq1YqIiAhq1qzJxo0bad26NefPn2fTpk0888wzvPzyy/z55588//zzpKamEh4ezvjx4219WpanYCaH8hu66jqLOAtVRoqIiIiIiMgNfvjhB9555x3WrFnDW51rMuqXVNZOeoGVK1fy5ptvmtfr1KkTGzdu5IsvvqB3795s3ryZWbNmATBkyBCmTp1KbGwsKSkpbNu2zVank0dWrFrMU4iZz0DOrqswRcRVKIwUERERERGRG/Tv358ff/yRbt26MW/9QcoGuOHt64u/vz8eHh5kZGQAUK1aNdzc3AgODqZ69eoUKlQID4+s7twHDhygd+/eRERE8Ouvv3LixImCOwFVLNoJ3QczewqD7akt4nLUTVtERERERESyy0glwNeLKePHkpaWRq2wchhSjaSkQ9qlS6SlpVGoUNbHScN1oZ/hPwHgfffdx9ixYylXrhwmk4nMzMxbHNDeghEnC9ByG8wqqBIRK1IYKSIiIiIiIv9a8SZsmsKnm1NZuD+DDCNEhhWiVBEvGvefgFuRbxg5cmSOdjVmzBiee+45UlJScHd356uvvqJs2bJWPgEnpHDQxuzx+ttjm0RyRmGkiIiIiIiI/OvQSgBeru/Fy/W9/n3f04+nJy+C0jXMb8XGxpq/v348yM2bNwNQoUIFfvrpp9sczBoViAUU0lgsIFSodFsKYu2Y7o3kjcJIERERERER+Vd6StZrz6VQqsa/7xfyyvoS1+Hy425a6fzzdV0t3CaXv8diCwojRURERERE5F8ZV7NefYuBt79t2+IQCjDMceTgKL8Vjo587k5H90LyR7Npi4iIiIiICAAmk4nHvjpJ0xnJnE1MtnVz8skCgYnTdRF2tvMREUekykgREREREREBICEhAUyZrOlZGILvvuV6RqMRNzcL1rY4Xejn6Cx4P1TReB095yKgMFJERERERET+8dKAAWw8nsHj865Q+MDLHI8/g5+fH7Nnz+bixYv06NGDUqVKUaNGDYYOHWrr5lqP0wVoznY+kn8KRsV21E1bREREREREAPhw5HCalHOnW1UPyoSUZe3atXTt2pXJkycDcPLkSWbNmmW5INIaoV+BVVkWZJij4MimVLkrYlEKI0VERERERCRLZhoAh/82UqdufQDq1KnDoUOHAKhevTqenp42a55TUtB1B7o+N2UPl0XPruSRwkgRERERERGB/ctg88cAVCzmwa/btgGwdetWKlWqBGDZcSKdhdN16b6eM59bDljt3rr4dRWXpzEjRUREREREXFRcXBx//PEHn037mD82/sCSp3wA6FCzFAt3H6dx48bmMSMvXbpk49bmkkWCJCtWfuWpfY4cYuX3WjryuVuQxQPSPOzPqQN4KQgKI0VERERERFxUXFwcK1eu/Kd7tonQIE++HT8A7m3F14MbZ1s3KCiIb7/91kotUXfPAqFutSJiBxRGioiIiIiIOJHTp0/TtWtXMjIyKFmyJGPGjKFHjx6UKVOGffv2MXHiRJo1a8bPP//ME088QWpqKoXc3SjhYaLn92nsXvgDr712P93Kw59//snzzz9Pamoq4eHhjB8/nujoaJYuXUpaWhoJCQksWbKEUqVK2fq0LczFK78sGlq6+LW8nsJgEUBjRoqIiIiIiDiVwMBAYmJiWL9+PXfffTerV6/m3LlzzJkzh/nz5zNs2DCGDx/OsGHDmDFjBv369SMwoAhnr5iY3CWUdevWMWnSJACGDBnC1KlTiY2NJSUlhW3/jCMZEBDA0qVL6dWrFwsWLMhHa60RVBVQ4FOQwVJ+j6VutfJfCkbFhlQZKSIiIiIi4kTOnz/P888/z4ULFzh16hRBQUFUqVKFQoUKERISQlJSEgCZmZn4+/vj7u5OtXtDiUs/g39AAPj7k5mZCcCBAwfo3bs3AJcvX6ZVq1YAhIeHAxASEsL27dttcJYiBUnBnYglKYwUERERERFxALGxsXzwwQd4eXmRkJDAV199xfTp09m+fTtXr17ls88+o0bFu3mofl0qlLsbU1oaPoWgnL+JHUnnaB3RkIzMTBJOnYBL8bhnpjB86KscOnqc839fINQf8CyS7Zj33XcfY8eOpVy5cphMJjIzM5k9ezaG6yrtTKqwcl72UFGp5+sWdF3EcSmMFBERERERcRBXrlxhxYoVHDhwgMGDBzN37lx8fX3ZuXMnH416jzlVNxCYfpYje09StaQ7+45lcuXnMRzdlUZUXU+erurB/XuSYEc0Ix4oxKsrrpKaZMLTAPFJgJdftuONGTOG5557jpSUFNzd3fnqq69sc+J5YoEgLUdBWF6Pk5dZjPN4KMkjK11wp7mPCkQlbxRGioiIiIiIOIjw8HAMBgNhYWHEx8fz0Ucf8fPPPwNQKD0Jwq7i5+XGmqh7KOLtTucZx2kbXpI9F85Tq2JR/O7yYfzjl9gbn0Lz6sVpcjSBPfGpXE03UqWUD9ToBmAeG7JChQr89NNP2doQGRlp/r5du3a0a9cu/yem6jfnl+9xLy3TDMdn4QthD9Wv4nI0gY2IiIiIiIi9S0+BjFR27dyBKe0KB3/bTXCJ4sSsXMH61SuZMHY0poyUrHW9AjAM2AYDf4dKLTH1iaFixzfY+cAbMPB3tpV6Bhr0Z1fTWSSGPsK6I8kMGT8bU9ij8EAHzpw5Q1pamm3P19acNqBR6CuW4Ky/H1JQVBkpIiIiIiJiz35+F34ZB3EZBJxJo32VAE4nG/nyUR+GHU4lolIR6pdxh3OZQGEo5HnDLvr06UOXLl2YP38+pUqVonz58lSuXJm//vqLhx9+mMqVK5vXHThwIO+++y733HOP9c/NGqGfqiwtwILX0GmDXRHJK4WRIiIiIiIi9uxwjPnbyne5Mbalt/nnJU/5Zl/XrRCx30wCv6yxH7/99lvzopUrV96w6/Xr19/wno+PT8EEkQ6vIEPP/B5LgaB9sKeg3J7aIq5GYaSIiIiIiIg9y8zIem32DgSegKEf3Hpdt0Lg4X3r5Tnw+eef52t7ySVVcto/3SMRi1IYKSIiIiIiYs+M6QBENHmIiB4NbdwYB1JQ3YOduujQHk5OQaCIs9EENiIiIiIiIvYsMyuMxM3Dtu2wKnsNnKzYrjyFpfYQDopdULWmODCFkSIiIiIiIvbM+E83bXd1bJN8smmA5YDjXlqtutZJQmUFopJHCiNFRERERETsWWZa1qtTVkbaayhjr+0qIAqZ7JfFA1IXf9bFJhRGioiIiIiI2LNr3bTdnTGMtIYCCtIKMrDL77EKavzMmx/chscWq7Dp8yTOQGGkiIiIiIiIPTN301YYKeLQVHAqAiiMFBERERERsW8uMYGNNRRU9VZ+j6OE6rZ0eaxDXfHFhhRGioiIiIiI2JG4uDhWrlwJQO3atcHoAt207TUYsdd2FRR1x/2Hiz8HIhamMFJERERERMSOXB9GAmAyZr2qMtLJ5CHoc+Vw0JXP/aYUkIrjUhgpIiIiIiJiR6ZNm8a8efOIiIggOSmJnouvUuOTJObMXwjAn3/+SatWrYiIiOCVV14BIDo6mk6dOtG+fXvq1KlDfHy8LU8h5+w1YLLXduWbDQMsh6wytdJz4DTPlyPeU7EHCiNFRERERETsyPPPP8+TTz5JbGwsCadPM7mNN+v+V5hJ0z4DYMiQIUydOpXY2FhSUlLYtm0bU6ZMwd3dnaVLl9KrVy8WLFhg47OwIYcMveyNrqH9snCQ6TTBqDiSQrZugIiIiIiIiNxchdBy+HsdBSDTmNVd+8CBA/Tu3RuAy5cv06pVKwDuv/9+AEJCQti+fbsNWutqCjKwy++xbBg4KexyQrqnkj+qjBQREREREbEXaVe4HLeTeV/PolG9cP788whH/jby4FfJ/PnnUapWrUpgYCAzZsxg2LBhGI1GoqOjuXDhAobrQh+TqgMdJwTTvXIhutcioDBSRERERETEfkxvTYN9bxPqcZ7gpN8wpSax+mgG565A+fLlmT9/Pt7e3jz33HN07NgRf39/PvroIxITE23d8nyy15AmB+1ylNDTYdnrs+HodF3FdtRNW0RERERExB5kpkP8btIzoVgRH85cNVK8iBun0otQpXIQ3/2ynaSkJNLS0lizZg116tRh7dq1ADRs2JCePXsC0K5dO9q1a2fLM5GcyFOIWdDBp4JWQNWrIhamykgRERERERF7kHQagK9/M9LupXGsPZJM6659Kdf1QwzBVcyrXeuC7e7uzoULF0hNTWXv3r02aXL+2WvYZa/tcmW6JyLOQpWRIiIiIiIi9mDnHACaVy1F90mTWLpsGT4+PrdcfcSIETRv3pzQ0FDKli1bUK10AKpiuyWbVvg54H2xWhd8C+xX1ZriwBRGioiIiIiI3ETt2rXZtm2b1be5JjHhL1b+nk6Xmmns3Xs427LIyEgA/Pz8iI2NBaBly5a0bNkyT8cSC3CkybRzfTwFXZIDek4kj9RNW0RERERExA4kJqcy//d0aPCCrZsizsamk+yoe7VFWfxe6v5IwVMYKSIiIiIiLs9kMvHiiy/StGlTWrRowYkTJ8zL/vzzT1q1akVERASvvPIKANHR0XTo0IFHHnmERo0acfLkSQCSk5Pp2bMnNWrUYM6crG7Xs2bNIiIigpo1azJr1iwAhg8fTvfu3XnkkUdo0qQJV69eZdpPe1j7VyYRL3/G7t27ad++PREREURERJCSklLAV6SAWaXCqqC6wub3OKoucxnOUkmoGeQlnxRGioiIiIiIy/vhhx8IDAxkzZo1vP/++4wePdq8bMiQIUydOpXY2FhSUlLM3bB9fX358ccfefPNNxkzZgwACQkJTJ48mXXr1jFp0iQAOnXqRGxsLBs2bGD8+PHm/VaqVIkff/yR+vXrExMTw/NtqtGknDuxE/ri5+eHr68vsbGxrFmzBm9v7wK8GiL/sIfQyVkCPBEx05iRIiIiIiLi8vbt28eiRYtYt24dJpOJkJAQ87IDBw7Qu3dvAC5fvkyrVq0AqFWrFgB16tRh4sSJAFSoUAF/f38AMjMzAVixYgUTJ07EZDJx+PC/Y0GGh4cDEBISwoULF7K155577qFhw4Y888wzlCtXjhEjRuDu7m6NU7ctewi7bCYP5+7S18uWnDAQVcgrNqQwUkREREREXFf6VVg+hMoJ2+hyn5G3Hi+e9XbGVRps/BPmPMF9Pn8z9vEylLurMCbTXWRemsXsTcfY+dsZCN7Mtj0JVMyMhzlPYLh4DJLOgl9x8yFGjhzJunXrMBgMVKhQwfy+4bpgyWQy4VHIjcx/8oHU1FRefPFF3Nzc6Nu3Lxs2bKBOnTpcuXKFYsWKFcy1cVSWCFkU+tkf3RMRp6EwUkREREREXNfRdbA9mvZFTKw+kUrTYQcxAN2qekBKGhxayZj6Rp6bspKUDHA3wFeP+cDpDNISM2j9ziKS0uCbTj5waCVcSYL9S6BOb/MhOnbsSKNGjahZsyaBgYG3bEqpwMJcTTfR+Z05vP/Z4/Tp0wd3d3cKFy5MzZo12bBhA2vXruW9994rgAsjd+aIlWU5bbMVzs0hK/GsFIBaJFh1xOspkkVhpIiIiIiIuK6MrIlhDMUqMmHaq9kWXYsTKwA/Rf5nuyXraHxfClFdW/773rYv2dZ3Oxgzsn78Z2zJN998kzfffDPb5sOHDzd/HxUVlfXNsldY/kxhiHgG7ruP9evXZ9tm165dPPPMM7k9Q3EKCp7EHum5lLxRGCkiIiIiIq7LmDWuI0WCIbxbzrfbnQ5JSdm3OfwznNxu2fZdZ9CgQVbbt+1ZIdQoqG69DtF92IZtdIjr40gsfD11f8QGFEaKiIiIiIjrMhmzXg1uudosMjLS8m0RO1QAlV8O2X1Z8sZZ7rUCTMmf3P2PKyIiIiIi4kyuVUa62cFM1S4ZSinUsG/2cH9c8fdCxLkpjBQREREREddl+ieMNFgwjMxvqKhuk/lkifCqAO5Bnu6zng2xFIW8YjsKI0VERERExHXZU2WkiNyGDYNYl6xaFrEehZEiIiIiIuK6LFkZqYpG1+KIAVVO22yVc3PA6yUiVqEwUkREREREXNe1CWxUGWlbVgm/nCgcdsTg0xlY7Q8MFtivnglxYAojRURERETEdV3rpp3L2bStQ+GC3clR4JPfYKkA7rtNq3adKBSW7BSISh7Zw/+4IiIiIiIitmGVysj8fkB3ofBGXdtFcsfivzP6HZSCpzBSRERERERcl9EKs2mLbVmiWqtA8pk8HKSgw1t7CIudqfrOWc7FHp4LcWgKI0VERERExHWZLNlNWx/QRURE7kRhpIiIiIiIuK5rlZGawEZyzRGr3GzY5vxWBdq0Gs8R7/UdOEuVpjgkhZEiIiIiIuK6THbUTdulwwErnLtTdSUt6GfDlZ9FEbE2m4aR69ato3379pQuXRqDwcDixYvNy9LT0xk8eDBVq1alcOHClC5dmh49enDq1Kls+wgNDcVgMGT7Gj16dAGfiYiIiIiIOCTjtQlsLPjRKN8VYJZphlhCDu6lQ4SeNmyjQ1yfW7FS2y1yTRQYi+OyaRiZnJxM9erV+fjjj29YduXKFXbs2MFbb73Fjh07WLhwIQcPHuTRRx+9Yd0RI0YQHx9v/nrxxRcLovkiIiIiIuLors2mbQ+VkS7JkYMqC3HpilgRcUWFbHnwNm3a0KZNm5suCwgIICYmJtt7U6ZMoW7duhw7doyyZcua3y9SpAjBwcFWbauIiIiIiDghkwXHjHToCjBnYolwT/dS7JWFn818/bulIF3yxqHGjLx48SIGg4GiRYtme3/06NEUK1aM8PBwPvroIzIyMm67n9TUVC5dupTtS0REREREXJDRjsaMFNeSpxCooENSewhlnSnwcqZzEck7m1ZG5kZKSgqDBw/mqaeewt/f3/z+gAEDqFmzJkFBQWzcuJGhQ4cSHx/PuHHjbrmvUaNG8e677xZEs0VERERExJ5ZsjIy3xRUOBR1rxaXZQ8htTgyhwgj09PT6dKlCyaTiWnTpmVbNnDgQPP31apVw9PTk379+jFq1Ci8vLxuur+hQ4dm2+7SpUuEhIRYp/EiIiIiImK/zJWRluw0lt+QygU/6CvYKxg5vc5WuR8O/HvhlM+nM56TOAq7DyOvBZF//fUXq1evzlYVeTP16tUjIyODuLg47rvvvpuu4+XldcugUkREREREXMi1CWzsojJSHFMBhGROGYaJiKuy6zEjrwWRhw4d4ueff6ZYsWJ33GbXrl24ublRokSJAmihiIiIiIjYu127dt3Qw8rsP2NGJiYmMn/+/DweyQUrGvPL3if9KZAQsACOYdPrbOf3+Hasdt0c+JqIWIBNKyOTkpI4fPiw+eejR4+ya9cugoKCKFWqFJ07d2bHjh0sW7aMzMxMEhISAAgKCsLT05NNmzaxZcsWmjZtSpEiRdi0aROvvPIKzzzzDIGBgbY6LRERERERsSM1atSgRo0aN19oyt5N+1oY2aVLl4JpnFieqgjFFeg5Fwdm08rIbdu2ER4eTnh4OJA1/mN4eDhvv/02J0+eZMmSJZw4cYIaNWpQqlQp89fGjRuBrO7Wc+fOpUmTJjzwwAO8//77vPLKK3z22We2PC0REREREbEjsbGxDBo0iJo1axIVFUW9evUYM2YMAJ/8sJO6nyfRbFA0ixYtYtq0aaxdu5aIiAj27dvHwIEDadKkCXXr1mXXrl0AREREMHDgQBo3bkxUVBQAV69e5anxq2kSnUzzFycBWZ93mjZtSqNGjRg7duydG6pwwX7Ye8VmQdF1kNvRv1mSRzatjIyIiMB0m4f3dssAatasyebNmy3dLBERERERcUKJiYm89tprlClThurVqzN48GDmrz/Izz0K49+qL8bGjxEeHs6RI0f49ttvARg5ciS+vr7s3LmTjz76iDlz5gDQoUMHxo0bR4MGDbh48SIzZsyg9j138WqLcxhb9AdgyJAhLFy4kMDAQNq3b0/37t0pWbLknRuqAMhF5OE+u+Kz4UyBlyXOxeLPgAs+U2Jzdj+BjYiIiIiIiCUEBgZSrlw5ALy9vQEY3aMBL81ZjGnXAoaW6nDDRJcfffQRP//8MwCFCv378ela7667776bxMRE9u/fT+/7S8HVA7i5ZXVA27NnD48//jgAFy5c4Pjx4zkLI12SFQInqwd3ThSSieSGK4biYlF2PYGNiIiIiIhIviQeh7MH4XIChvSrcGJb1lfaFTixjap3ZTL9MR/6tm/AmDFj8PDwIDMzaxzJ8+fPExMTw/r165kwYUK2nluG6z6Mm0wmwsLCWLc/a4x7ozFrhu7q1avz/fffExsby44dO6hVq1YBnrg4F0sFnzndjxWC1vxWBdo0AHPC4NmZKk7F4agyUkREREREnNPpfTCtIcSlwx8ZcC4DvmietexcEnzRnOcXXyUu0Uiq3zLen/QlpUqV4urVq3Tu3Jn333+foKAgIiIiqF+//m0P9eyzzxLZYiJNYpMptOxjVm0bwOjRo+nYsSNGoxEvLy8WLVqEj49PAZy4WE4OAhtViYmI5IrCSBERERERcU7nDwMmIu4pTESN4GyLtr2e9RodCfgEQsfPofi9ACxfvty83pIlS27YbWxsrPn7a2NLAsx7tTnsmQctXwSgVq1arFq1KhcNVqWSSyqQCjUbBqYOHdZaqe0OfU1E8k9hpIiIiIiIOCdjRtZrSF2IXFaAB85vuKSgwvZ0D8Te6Y8X4rg0ZqSIiIiIiDgnY9bYjxj0sUfsUJ6q4xSSiojj0//KIiIiIiLinEz/hJFuBdUhTEFRnlmlq7KV74cmACkgznSdLXEuFn6u89Vl3JnujRQkhZEiIiIiIuKcrnXTLrAwUkRERO5EYaSIiIiIiDgncxjpbtt25JQrFhnZ+0QeObonBXAOlqrCzOl+rFL1qbFUnYfuheSPwkgREREREXFOtgoj1X3XtnT9xdKc8plyxnMSR6EwUkREREREnJPRmPXqaN207b1a0BUU6D0ogFDIps+UnmcRyU5hpIiIiIiIOKeCHjNSIaKIc7Ha77T+rRDXpjBSRERERESc07Uw0uAgY0aKZVk9HLbBGIgFHXgrYLdfTtl1XFyFwkgREREREXFOmk1bxPE5U+jmTOcikg8KI0VERERExDmZMrNeC3w27bwGDgoqHFKBVA9a6tnI6X70LNotiz9v+difwlXJI4WRIiIiIiLinIy2CiPzy5W6xlrjXC0ZkChssZj8BlfqMi7iNBRGioiIiIiIcyrwbtoKSyQPVF3mAJzwHuXnuVMwLPmkMFJERERERJyTxoyUPFPYYjG6lCLyHwojRURERETEKZkyM3hs7hWaDv6as2fP2ro5UuDsPAXLU3WZnZ+T07HS9VZlobg4/YlQREREREScUsL5SwCsGRsJxYvfcj2j0YibmwXrNPLa/dGVu+s64rk7YptzTaGZ/XKF50+clcJIERERERFxSi9NW8nG45k8/u4CCs84wvHjx/Hz82P27NlcvHiRHj16UKpUKWrUqMHQoUNt3dx/qWpK5DrOFLo507mI5J26aYuIiIiIiFP68H8P0aScO91a1KBMmTKsXbuWrl27MnnyZABOnjzJrFmzLBdEKkTMPWtcM0tWLOZoXwVw3y11Tjndj0tUfToqCz9v+dqdnhPJG4WRIiIiIiLinIyZABw+dYE6deoAUKdOHQ4dOgRA9erV8fT0tFnzRFxLfoMrhf0izkJhpIiIiIiIOJ/MDMhIBaBiSHF+/fVXALZu3UqlSpUALDtOpDiXAq1yLYDqMlXt5o8KAP9Dz5Pkj8aMFBERERER55J8HqbWh5MJAHRoVJ2Fn22ncePG5jEjL126ZMUG5DW5UOJhUQrg7ITug33SvzdiOwojRURERETEuZzeC8lnCC3qxrfPlIR7GvH1169mWyUoKIhvv/3WRg28E1cMbxwxGLFBt+OCDlhdPdC12um7+HUVl6cwUkREREREnMs/3bMpVQP6rAL3gvrYo4BBRETkTjRIioiIiIiIOJf0q1mvHr4FGERK3lgjwLVklWUO9lUg1YOOWDlqIZrZ++Z0XcSBKYwUERERERGnErtpB4NWptBv1n4Ahg8fzrJly2zcKhF7oADLphQgigAKI0VERERExNlkZnXT/rRvA9scP6+Bg4IKO6Iu9xaX7yE2dU8AK1yHfOxP/2ZJHimMFBERERER55KZDkDtYTHZ3v7rr79o1aoVf/31F8uXL6dRo0Y0bNiQb775xhatvDWFLhbiINexQAIdB7kWIuISNICKiIiIiIg4l38qIzH8W3tx+PBhJk+ezPTp0ylVqhRPP/00a9aswd3dncaNG9OlSxfc3d3zd1zlPXnniBVWjthmW3CKcN0J73V+Tskp7qnYksJIERERERFxLhlpWa/XfWD+6KOPGDduHKVLl+bMmTP88ccftGzZEoDExETOnj1LcHCwLVorripPgU5Bh0AKnUTE8hRGioiIiIiIczi1C/YugBNbs36+rjJy3LhxTJ8+nbJly1KvXj0qV67MypUr8fT0JD09HQ8PD9u02dVZo8JKFYtiMVYKY1VZKC5OYaSIiIiIiDiHnwbD8c1wPiPrZ7d/P+4ULlyY+fPn06lTJz788EOGDRvGww8/jJubG8WLF2f+/PkWbEhewzCFaHYnR8FmAQRLBR2w2lWga09tsSe6LuK4LBZGJiYmUrRoUUvtTkREREREJHfSkgCIaNuFiN5VoNb/ABg+fLh5lZiYfye1adWqVYE2L+dUNSVWYlchoyvS9ReBPM6mPWbMGObNm2f+uUuXLhQrVoy7776b3bt3W6xxIiIiIiIiuVazOzR6FXyDCvjAChHtSn66whZoN1pXCajye576/cpi4euQr2fdVZ5dsbQ8hZGffPIJISEhQNZfFmNiYvjpp59o06YNr732mkUbKCIiIiIiIs5OoYZVaYxCEbEjeeqmnZCQYA4jly1bRpcuXWjZsiWhoaHUq1fPog0UERERERHJEXVBlQJlg+fNIUNFR2zzf+jfFhGLylNlZGBgIMePHwdg+fLltGjRAgCTyURmZqblWiciIiIiIuJo8hpcuGTgYY2gyhWvo5U4ZPgpOaPfE7GdPIWRHTt25Omnn+bhhx/m/PnztGnTBoCdO3dSsWJFizZQREREREQkd3IfoEyZMoXo6GgSEhJ45513brpOYmLiHWfdjohOJulKarb3oqOjmTJlSs4bowDIjuQgsCmQ+6XgyCasdm/1Oy6uLU9h5Pjx44mKiuL+++8nJiYGPz8/AOLj43nhhRcs2kAREREREZH8MBqNOV43ODiYd99996bL7hhGKkQUp2FH4adLVgyLOLc8jRnp4eHBoEGDbnj/lVdeyXeDREREREREAGJjY1m2bBljx47lt99+Y+zYsezZs4eGDRuydetWOnbsyODBgxk+fDgHDx7kwu59kJbMgsevcD4ujh49elCqVClq1KjBww8/zGuvvUZGRgaPPfYYgwYN4vjx4zz99NP4+fnh5eVFhw4diIuLY9CgQXz77bf8+uuvDBw4kEKFCtG2bVvOnTvH2rVriYiIYOrUqXzxxRds376dq1ev8tlnn1Hjn3YP/ewndn+wgvDwcCZOnJjtnKKjo/nyyy/JzMxk5MiRNGvWrMCvq+SEgmWxAksGqwppxYHlKYwEmDVrFp9++il//vknmzZtoly5ckyYMIHy5cvz2GOPWbKNIiIiIiIiQFZ14muvvUaZMmWoXr06gwcPBqB8+fJ80yiMT37cxefzfqBj3wc4efIkP//8M56enrRo0YKFCxcSGBhI+/bt6d69O2PGjOGtt96iZcuWdO3a9YZjvfLKK8ydO5eQkBCMRiPHjh3jyJEjfPvttwCMHDkSX19fdu7cyUcffcScJ4MAaP9gGJNf/ZyuXbuyY8cO8/7Onz/P3LlzWbduHVeuXKFt27YKI6+xSrDiIIGiq4RK+T1PVR6LOI08ddOeNm0aAwcOpE2bNiQmJponrSlatCgTJkywZPtERERERMRFGa4LH0z/BBmBgYGUK1cOd3d3vL29zctr1aoFQJ3S7hz66wQA1atXx9PTE4A9e/bw+OOPExERwbFjxzh+/DiHDx/+d7s6dW44flpaGiEhIQC4ud340emjjz6iUaNGDBgwgFOnTv3blnvvNu/z0KFD5vePHDnC77//TtOmTWnbti1nz579zx5dJJRyFg4VIirIcxoWD2XzsT+H+h0Qe5KnMHLy5Ml8/vnnvPnmm7i7u5vfr127Nnv37rVY40RERERExHUFBgZy4kRWsLh7924ge0B5vZ07dwKw7VQmFcuVAbIHiNWrV+f7778nNjaWHTt2UKtWLSpWrPjvdtu23bBPLy8vTp48CWSNO+nh4WEuxDh//jwxMTGsX7+eCRMmmMNSgJ2HTpn3ef0EnxUqVKBatWqsWbOG2NhYdu3aBcDff//NlStXcnl1nIQ1qt0cJSDJ07k7YKjoFBWNDvJMiTiIPHXTPnr0KOHh4Te87+XlRXJycr4bJSIiIiIiLmzFm/Dbd1Q1mbiy9yQP31uYKsFecDUTTqfC/1XOWu90HMzvCaZQjh8/Tsvv9mNIT2fB+235+z+7HD16NB07dsRoNOLl5cWiRYt4/fXXefrppxk7diz+/v43NGPcuHF06dIFDw8P2rZty6uvvsrVq1fp3Lkz77//PkFBQURERFC/fv1s2/205QAjGjemevXq1KpVy1ywcdddd9G1a1eaNGmCu7s7VatWZdKkSYwbN46mTZvS3LwHZwhvnESOgk1nvl/OfG4uLj+hvVMEzGJLeQojy5cvz65duyhXrly295cvX05YWJhFGiYiIiIiIi5q65eQcRUDsOSJaz2xMv959YLL8QBs6+0F+xZDel+eeOIJ2tU9CGf3Q5HC+IeGmsd2hKxu3KtWrcp2mLJly/LLL7/ccPhr29WtW5cNGzZkW7Z8+XLz90uWLMm+4ZIBxEYWhmbtofFr5rcjIyPN33fv3p3u3btn2+z8+fM0adIEvv/mFhdEXJ6jVHs6HSuFbgrzxMXlKYwcOHAg/fv3JyUlBZPJxK+//so333zDqFGj+OKLLyzdRhERERERcSWmf4LHbt+BX4lbr/dpY8Dk8EHNtGnTbN0E+S9nDYty+rtiV79T9tQWEbGEPIWRffr0wcfHh2HDhnHlyhWefvppSpcuzcSJE286C52IiIiIiEiOmYxZryXvB//St17PYACTieFvvAZFSsLHI68tsHoTrcKuAqCCZoVzd5hA0ZXvu6ux5L3WcyOOK09hJEC3bt3o1q0bV65cISkpiRIlbvMXSxERERERkZwy/lMZaXC//Xr2ShmB2Bu7CGbz+4thD+cgIpaQ5zDyGl9fX3x9fS3RFhERERERcXUmE+bQwuB221Wv2+g/rw7OLoKjgmKNcy2o58BJnjdxMBb+nXGpf2/EXuQ4jKxZsyarVq0iMDCQ8PBwDLd5YHfs2GGRxomIiIiIiIu5vquym4NVRupDveRKHp6Xgn7GLHI8J/i9cOkhFEQsL8dh5GOPPYaXl5f5+9uFkSIiIiIiInlybfIayEEQcovl+qwiFpODEErPmzgkBaxiOzkOI9955x3z98OHD7dGW0RERERExNVdm7wGctFNW8TZFXRwpKAKsGLQ7OgBtqO3X2wtT/+7V6hQgfPnz9/wfmJiIhUqVMh3o0RERERExEVlCyNz2E3b7rpQ5rU99nYeBcgq9zA/gYmzhi0O+IzZ3e+3iORXnsLIuLg4MjMzb3g/NTWVEydO5LtRIiIiIiLioozXd9PO5ccVpwktnDUIk5tymuf2DlzlPG9H10AEyOVs2kuWLDF/v2LFCgICAsw/Z2ZmsmrVKsqXL2+51omIiIiIiGvJTTdtg+EWhV62CvMUIuaaNbrBFlTg41DBkhM8mxqbMztHevxE/iNXYWSHDh0AMBgM9OzZM9syDw8PQkND+b//+z+LNU5ERERERFzM9RPYONps2iK54Srhmqucp4jkWK7CSKMx66+U5cuXZ+vWrdx1111WaZSIiIiIiLio66vNctxNWyVCYiUOVf1oDQoS7Y7Fb0k+dujyvx+SV7kKI685evSopdshIiIiIiKSz9m07eSDcV4/oOuDvdyKng0b0/UXsaQch5GTJk2ib9++eHt7M2nSpNuuO2DAgHw3TEREREREXJB5AhtDDrp3OmnVlkt2a7VC2JOf6+iS9+A6dhV+2lNbnEh+7rGr/35IvuU4jBw/fjzdunXD29ub8ePH33I9g8GgMFJERERERPLmWmVkrqsir2OrD8r6gC72zq5CRkdgpd9p/VshLi7HYeT1XbPVTVtERERERKziWhiZm8lrFLA4MGuEMgX1POi5yx1dLxHJkqc/N6akpNxyWXx8fJ4bIyIiIiIiLu7abNp5qYxUKClyc6rEsxOW/DdK/96J48pTGFmzZk127dp1w/vfffcd1apVy2+bRERERETEVeWmm7bdBix5DQkULtifHNyTPD+HedjObp/523HENouINeUpjIyIiKB+/fqMGTMGgOTkZCIjI+nevTtvvPGGRRsoIiIiIiIu5NoENoZcdNO+gaOHH47efnEaDhl+OjsL3xPdY7GBHI8Zeb2pU6fStm1b+vTpw7Jly4iPj8fPz49ff/2VKlWqWLqNIiIiIiLiKq51tc5VN217qSjUh/o8s0oX+/zcD3u7l/byjLsoDQFxC7oukjd5nqKuTZs2dOzYkQ0bNnDs2DHGjBmT6yBy3bp1tG/fntKlS2MwGFi8eHG25SaTibfffptSpUrh4+NDixYtOHToULZ1/v77b7p164a/vz9Fixald+/eJCUl5fW0RERERETElszdtPMSBumDsYhl2NHvkoJAK9F1FdvJUxh55MgRGjRowLJly1ixYgWvv/46jz76KK+//jrp6ek53k9ycjLVq1fn448/vunyDz/8kEmTJvHJJ5+wZcsWChcuTKtWrbJNoNOtWzd+//13YmJiWLZsGevWraNv3755OS0REREREbG1axPY5Gg2bXurXpNcs0YXUUcLrwq0vQ52bWzNal2YHf3fLkdvv9hanrpp16hRg7Zt27JixQqKFi3Kww8/zCOPPEKPHj2IiYlh586dOdpPmzZtaNOmzU2XmUwmJkyYwLBhw3jssccAmDlzJiVLlmTx4sV07dqV/fv3s3z5crZu3Urt2rUBmDx5Mo888ghjx46ldOnSeTk9ERERERGxldxMYHMrth4DLa/hkqOFaK5O9yt38nu9bP17LSIWk6f/4adOncrcuXMpWrSo+b2GDRuyc+dOatasaZGGHT16lISEBFq0aGF+LyAggHr16rFp0yYANm3aRNGiRc1BJECLFi1wc3Njy5Ytt9x3amoqly5dyvYlIiIiIiJ2wBxG5mcCGwen0MU15Ok+F+DM3WJ5yq9FgDyGkd27dwcgLS2NgwcPkpGRAUCRIkX48ssvLdKwhIQEAEqWLJnt/ZIlS5qXJSQkUKJEiWzLCxUqRFBQkHmdmxk1ahQBAQHmr5CQEIu0WURERERE8sk8m3YuPqrYS4WaQkTnk6NnS/f9tvR7YR328u+eSB7kKYy8evUqvXv3xtfXlwceeIBjx44B8OKLLzJmzBiLNtAahg4dysWLF81fx48ft3WTREREREQE8tdNWx/OHZgV7p0zhWA2e7ad6BqKiN3IUxg5ZMgQdu/eTWxsLN7e3ub3W7Rowdy5cy3SsODgYABOnz6d7f3Tp0+blwUHB3PmzJlsyzMyMvj777/N69yMl5cX/v7+2b5ERERERMQOXAtd3HLwUeWWYZMCFMknZwoy80LB/n/Y0fWw+LPp4s+62ESewsjFixczZcoUHnroIQzX/SI88MADHDlyxCINK1++PMHBwaxatcr83qVLl9iyZQsNGjQAoEGDBiQmJrJ9+3bzOqtXr8ZoNFKvXj2LtENERERERAqQKQ/dtO1OXoMLOwo8Cow1ghBXvI4iNqDQWvIoT7Npnz179oaxGgGSk5OzhZN3kpSUxOHDh80/Hz16lF27dhEUFETZsmV5+eWXGTlyJJUqVaJ8+fK89dZblC5dmg4dOgAQFhZG69atefbZZ/nkk09IT08nKiqKrl27aiZtERERERFHlINu2kajEbdslZP6QCyOrACfX4VHuWSlqkF7KEbUsyA2lKcwsnbt2vzwww+8+OKLAOYA8osvvjBXLebEtm3baNq0qfnngQMHAtCzZ0+io6N5/fXXSU5Opm/fviQmJvLQQw+xfPnybF3D58yZQ1RUFM2bN8fNzY1OnToxadKkvJyWiIiIiIjYQFRUFN27d6devXqsWr+FlTEpXCl0kt8WNcXd3Z3o6GjKlCnD/fffT7169QgICGDHjh2sbGrC2wBvjPiQpm0e42Gbh5L2kDCI2Kv8/n7q98tuuPowBpJveQojP/jgA9q0acO+ffvIyMhg4sSJ7Nu3j40bN7J27doc7yciIgLTbdJ4g8HAiBEjGDFixC3XCQoK4uuvv85V+0VERERExH507dqVuXPnUq9ePeYtW0VZLwNeXh6s+WkNW7ZsYfTo0UyZMoUTJ06wYcMGAgMDGTduHEu2jOCJyibWbtjMyLGTYL+tz0ScTw4CtDwHM3nYLq/HUnhkJ2z9BxMR+5CngVgeeughdu3aRUZGBlWrVmXlypWUKFGCTZs2UatWLUu3UUREREREnNiDDz7I5s2bSUtLY9+ho3i6w6LdF4iIiOD1118nMTER4P/Zu+/4KKr1f+CfbWlAQmihBVAQQRSUIqICERQsWFEsqGBBRazoV+XayxXbVax49adyvTbUiwUVBNSAgA1EivQSeighvW2Z+f2xO7O7yWbL7MzOzO7n7ctXQnZ25kw/88xzzkGPHj2Qm5sLABg/fjw+WVuHpbs8GHJy/+Bm2wy8mI8mTUZ5HMRNlXOJ+0EbDGySeSnKjASA7t274+2331azLERERERElGpctbC4anHayQPw+EMP4MzBx6PXkW0YN6ANHp5b6J3E5QKAoIBjXl4eRAAv/+bEw/++OPHlDkdpYI19uBkIA2hERFqJOhhZUVER9Uyzs7MVFYaIiIiIiFLI7j+A/5wPuGtxRa0Hp7xajXW3NsOxPe348Wc3zjjjDFgsFowfPx433HBDo69f1TcDT/xUiX7HH+f9Q7IE81Ips1OLdU2W40BX3IbGpfI5k0rXGzKMqIORLVu2jDhStiiKsFgs8Hg8cReMiIiIiIiS3J4/AHctAGBgRxvcj0hJDRbMeGgKMOLBoMlXrFgR9G+r1YIJ/RyJKGl0+FCfWkwZ9DRhmY2wnY1QBkPidiFlog5G/vTTT1qWg4iIiIiIUo2n3vuz31XAha8Ff2a1hf3qW2+9hf8ur8bXl2eE+JRBQTIRBrpIFzzuSD9RByOHDx+uZTmIiIiIiCjVeLx9QcKeHjH42NBNN92Emw49CrhqwIdq0kxUgUIzBL8NUMZ4g656ZB5rtkwD7I+4mL38pDfFA9iUlpbinXfewYYNGwAAxx13HK677jq0atVKtcIREREREVESc/syI21p+pZDdUqDLqkcVNVg3Y3ebF5R+Qy+ThQes2CJAADWyJM0tmTJEnTr1g2vvPIKSktLUVpaildeeQVHHXUUlixZonYZiYiIiIgoGXmc3p92pcHIhoEZPuiTSowWyNQtiKXCdjDatiQi3SnKjJwyZQouv/xyzJw5EzabtzmFx+PBrbfeiilTpmDt2rWqFpKIiIiIiJKQFIy0pas7X92CH2otN5WCN1qsK4PSlAKYZUkmpigzcuvWrbjnnnvkQCQA2Gw2TJ06FVu3blWtcERERERElMTUaqbNh3IidfGcIiINKQpG9u/fX+4rMtCGDRvQr1+/uAtFREREREQpQB7AJtn6jKTUoFbALoGBP1MGGY1QZiOUwUf1zO9UysQmo1DUTPuOO+7AnXfeia1bt+KUU04BAPz66694/fXX8cwzz2DNmjXytH379lWnpERERERElDR+/fVX3Pl/nyHLXY3huxeiYs5urFy5ErW1tXjrrbfQqVMnTJw4Ed9++y0AYOTIkfjiiy+QnZ3tn0nDh3KjBFqUlsMo5SciigavWaSQomDklVdeCQC47777Qn5msVggiiIsFgs8Hk98JSQiIiIioqRQVFSEzZs3Y9SoUfj222/x6KUn4Nz0FRDOvhB1fa9BVlYWVq1aheeffx4ffvgh0tLSsH//ftTW1qJdu3bBgciwmOljOoYNakRRLsWZagq+p3RZhhhEJt59rMc6aLRMI+wPw55zlAoUBSN37NihdjmIiIiIiCjJFRUVYcGCBRg1ahSmTJmCp679BB8W12B87ib88f3zWLRoEQDAbvc+plx99dX4+OOPUV1djfHjx4eZs0EeqtUKMBghUEFEGjDItSpevEZRnBQFI7t27ap2OYiIiIiIKMnNnDkTy5cvx4rli3FO/y5YuWU/LG4Bd/zrI7Tv0gNLly7FypUrcc899wAAzj//fJxzzjlwuVyYNm2azqUnTWgR1EjKjC+d1kmV/cPAFREFUxSMBIB9+/Zh6dKlOHjwIARBCPrsjjvuiLtgRERERESUXCZPnoz8zp1wb+ZnOO31leiUbUW1B7CmWdGqVSsUFBTIfdIDQFpaGnr16gWr1SpnSwZrGORIxiAUkR54Lhkf9xGZl6Jg5KxZs3DzzTcjLS0NrVu3hiXgbYnFYmEwkoiIiIiIQnPVoqj8CM49xoFX77kMaNEBgx75EV9//XXIya1WKyZMmJDgQsaLQYKUkJQZmERE2lMUjHz44YfxyCOPYNq0abBarWqXiYiIiIiIko2zGo6SjfCU7EC3Dlb8ddgK4bL3sWvXLrTM/SvkV2699VaUl5dj4MCBsS2L/ZmRmSQ0qGnCAKoRgr5GKINM5esbr5ekA0XByJqaGlxxxRUMRBIRERERUXQ+GIsTtizHtKU1uL25BReelI9TTz0VVqsVr776asivvPHGG9HN2zCBArUe6lMxOKDBPlQjyBLVsZWK+4uISDlFwcgbbrgBn332GR544AG1y0NERERERMno8GZkp1uw5O7jAEcWcPrduLff5XqXikg/ioKlSgOfBgiYxvvSQI8MPs2WaYD9ocoLAKO8CCKzURSMnD59OsaMGYP58+fjhBNOgMPhCPr8xRdfVKVwRERERESUJFx13p/Xfgm0OlqdeTYMFPC5mACociAYremqYbJ/iYjipzgY+f333+PYY48FgEYD2BAREREREQVx+4KR9swELEznZxIGjihpqHAuMUbglzTXBu5Tio+iYOS//vUvvPvuu5g4caLKxSEiIiIiomRRVFSEzZs3Y9TIMwDR4/2jPV3fQhlZ0gQqUkUS7y8ei0SkIUUj0KSnp+O0005TuyxERERERJREioqKsGDBAn9WJADBlsTBSLUywJhJRkSRMGBMJqYoGHnnnXc2OeIdERERERERAMycOROzZ89GwZln47jXq3DdV7WYet8/MGzYMNTVeQOU//jHP7Bw4UKFS2gYtOPDuWlpElhRI6gbRbniDh4n8Lg1ZQDLCGU2QhmIkoeiZtq///47fvzxR3zzzTfo06dPowFs5syZo0rhiIiIiIjIvCZPnoz8/Hy88NAdyM7rimXn5CL36Zfx4osv4uuvv8Zll12GxYsX46mnnlJ3wUwsJKJkpXrmNC+YlHiKgpEtW7bEJZdconZZiIiIiIgoGbnr0aOVFbnZWQCA8ePHY/LkyejQoQOGDBkCq1VRgy0/w2V7Ga08BqZFk3RVjodEBGgULEPp9jJE0/9494se66DRMo2wP9Q4Twx37SWzUBSMfO+999QuBxERERERJZOfpsOxaC48a4uBOSthtUAeSTsvLw+iKOLll1/Gww8/rG85DYUP9tQUnY4NIwTNiCjpxPUK8tChQ1i6dCmWLl2KQ4cOqVUmIiIiIiIys5ojKPrqaRzc+hdWbtyFy15eirI6EWjZRZ7kqquuwqZNm9CvXz/ly2nUZaTewTy1AjcMAFEy4fHsp/c1SiUMUlOcFGVGVldX4/bbb8f7778PQRAAADabDddeey1effVVZGVlqVpIIiIiIiIykboyFJUJWLYXWPLtbN8fLUC30+VJrFYrJkyYoFEB+KBMCaB78FtLybxuRKQ3RZmRU6dOxeLFizF37lyUlZWhrKwMX331FRYvXox77rlH7TISEREREZGZ1Fdh5gonZq9zoeC2V/Higp0YMul5nH72Jfjzzz/x1ltvYcaMGbjhhhtUWiADJ+Zn0H2YiIBjQoOaBt3OpAD3JZmXoszI//3vf/j8889RUFAg/+3cc89FZmYmxo0bh5kzZ6pVPiIiIiIiMhtnFSYPTEN+u+a496NPMG7cOCxbtgy7du3CpEmTsHDhQtx00016l1I7SZ0xZyIJa0rKTFxN8XwiSjqKMiNramqQl5fX6O/t2rVDTU1N3IUiIiIiIiITq6/y/rQ5UFRUhH79+sFqtaJbt24oKytTcUFJFgRKyaBLku3DWCgKlirdXim8ndVgqHNT5X3J/h9JB4qCkUOGDMGjjz6Kuro6+W+1tbV4/PHHMWTIENUKR0REREREJrRzKRxWwGNxoFu3bvjrr78gCAKKiorQsmVLDResc8BArYd6Bgf0Z7R9YKhgmELxroPR9gkRKaaomfaMGTNw9tlno3PnzvLod6tXr0Z6ejoWLFigagGJiIiIiMhEPC5g2cs4Ic+Gab94B7688MILceqpp8JqteLVV19Vf5kNgxwMWhCpJMXPJc2uJUbYrmoEuJMgSE66UBSMPOGEE7BlyxZ8+OGH2LhxIwDgyiuvxPjx45GZmalqAYmIiIiIyETqKwEA2ekWLFnwLZA/CABw77336lkqIg0kcSBGzUxMvhzwS4YMVyIVKApGTp8+HXl5eZg0aVLQ3999910cOnQI999/vyqFIyIiIiIik3HXe39abHIgUjOGDXIw4BAzBmmITMSo114yC0V9Rv773/9Gr169Gv29T58+ePPNN+MuFBERERERmZS71vvTEb7F1F133YXa2lp1l236gJbZy280CQqYxB0UT+B+N/05QkTJQFFmZHFxMTp06NDo723btsX+/fvjLhQREREREZmUlBlpTw872YwZM1RcaMMAi9mzdsxe/hhokd3KgFuS4f4Micc5mZiizMj8/HwsW7as0d+XLVuGjh07xl0oIiIiIiIyJ3dtJS79tAZn/r+DmDJlCiZOnIhPPvkEgwcPximnnILvv/8eAFBQUICqqirMmjULY8eOxfnnn49BgwbJyQ1PP/00hgwZgjvuuAP9+/fXc5UoJSUiIKxgGUqDt4bo0sDMwTMzl53IeBRlRk6aNAl33XUXXC4XRowYAQD44YcfcN999+Gee+5RtYBERERERGQeX363ED1bW/H0uGPxVno//Pzzz5g+fTp+++03OJ1OjBgxAqNHjw76Tk5ODt59913MnDkTn332GcaNG4fvv/8ey5cvx5YtWzB37twmlmaEAAtRIjAYxvPdR/XAMrcrJZ6iYOT//d//oaSkBLfeeiucTicAICMjA/fffz+mTZumagGJiIiIiMg8tm4vwoAONsCegQEDBmDOnDno0qULMjIykJGRAYfDAbfbHfSdk046CYC3BdbKlStRVFSEvn37wmKxoGfPnmjevHmUSzdIwIbNJ1NDKuxnQ2RU6kmj9TfCdlXj+E2Fc4A0oSgYabFY8Oyzz+Lhhx/Ghg0bkJmZiWOOOQbp6eH7hSEiIiIiouTWo3NbrPrNg7H2DKxatQpt27bF6tWrUVdXB6fTCafTCbs9+DHEEvBgLooiunXrhnXr1kEURWzduhVVVVXhF9rwgdgID/pK8MGeDEPNY9Gk56MmeI4TAQqDkZLmzZtj0KBBapWFiIiIiIjMasFDwF8f46L6OnxyWMDIl1bj6NOORkZGBh544AEMGzYMVqsVTz31VMRZtW/fHmeddRaGDBmCAQMGoFWrVglYARWYNQiarNTYH4kIEDMITWbDax3FKa5gJBEREREREQBg5ftAfTnsAD4emwnHaZPw1q6jUFpaiquuugpXXXVV0OSFhYUAgIkTJ8p/GzNmDMaMGQPA2zXUP/7xD2zevBk7duxI0EoYREo96GuxrokO7plpfzHwSUT6YzCSiIiIiIji5/H2JY+r5+DCW55E1eJlSE9fgdmzZyua3aOPPoply5ahtrYWb7zxRuiJUipoRwmViGNL0TJS8Jhn5mgTuF3IvBiMJCIiIiKi+Aku7892vfHdosK4Z/f000/H/iU5aKF3wIZBAjIavc8JmDuoaOayExmQVe8CEBERERGRyYkiIPhGyLY69CiADsvUQrKsB6mOwTBmQhMlEQYjiYiIiIgoPh6X/3dbKje+YrBEMVMG28xYZh2YOYioWdnjma/KZTLz/iHTYjCSiIiIiIjiIwQGI9MSuOBkfYhO1vVKNDW2Y4oGHE0ZHDYBQ21XNcpipPUhM2EwkoiIiIiI4hOYGalLM20JH4xNR4usrEQHfOJeBx63RJRaGIwkIiIiIqL4SP1FAoBNh2Bkw+CT3s0ODZX9RMokaXYqj01SRZKeH5QwDEYSEREREVF8PE7vT4tN/0CgmTFQlGIUnCspeX7xvCBKNgxGEhERERFRXIp2bMeln9agYFZVYhdstMCM0cpDSSTOgJwhDk0GFVXFlxdkYgxGEhERERFRfASP96fewbhkeTjXezvqwoT7LlmOt3AMdSzqWZYU2NdECWTXuwBERERERGRyHm+fke9f3g4A8Nhjj2Hr1q0oKSkBAFxwwQWYPXs28vLyMHv2bA0K0DBQYKQACunGUIE0IpWoflzzPKHEY2YkERERERHFR/SOpt2ldab8p969e2PevHnIzc2F0+lEYWEhnE4ntm/frlcpKWWomMUWVfYjgznJS6N9a4RAeSpk9pJhMRhJRERERETx8XiDkbD6R9Lu27cvAKBjx47y7506dUJpaamKCzbAAz3FifuQQaFUkmT7mscuKcRgJBERERERKVddAtQc8f5u8/cCZQnI/An8XdT04VXvB+N4A2t6l59kRshc0wSPMSLSH/uMJCIiIiIiZb65G1jxLlAmeP8dkBmZUA0DnKYPJJm9/BQVRcdpCh4bzL4zHtNfY0lvDEYSEREREZEyRcsAAN1aWvH55c2B3ucD8A5gI3nhhRfk31977bWEFo8oacQdkDNA8IhBRZVxe5J5MRhJRERERETKeOq9P29YBOQPSvzyjZqdw6BL7DTZZlofH6mwnw10jul5vqfCriZKIPYZSUREREREyrid3p82nZpnE4WiamAzRaNQam5Do740ICLdMBhJRERERETKeKRgZJq+5ZACRnpnJMYbdNG7/HpIhkBV3OuQgvvdLDQ7PuOZr1pl8h13yXAOkukwGElERERERMpIwUh7ur7laMTkD9cMDhhAku6DVAx4G0nSbf9kWx9KFAYjiYiIiIhIGY/ezbSTNGBEKULB8ctANRElAQYjiYiIiIgodqIIuH0D2NiMlhmpN2YLkdriPKZMHcTk+WQ8Zj6eyAgYjCQiIiIiotgJHshBAr0HsJGbPjJoYV4a7DutA3BJ1+SWiCgxDB+M7NatGywWS6P/p0yZAgAoKCho9Nktt9yic6mJiIiIKF6FhYU4++yzcfHFF6Nfv35Yt24dPvnkEwwePBinnHIKvv/+e9TU1OC8884DAFx77bV44oknAHjriKQxT73/d6P1GalbFhgHMjEGFbdjqgccDZVRqWdZDHgcpPqxSaZm17sAkfzxxx/weDzyv9etW4ezzjoLl112mfy3SZMmyRVPAMjKykpoGYmIiIhIGy6XC/Pnz8e8efPwzjvv4Mcff8Rvv/0Gp9OJESNGYMWKFairq4PH40F9fT3+/vtv7NmzB126dNG76MlP6i8S0G80bUMFStSUrOsVSjKsazKsQ0NqBrqScfsQUTwMH4xs27Zt0L+feeYZdO/eHcOHD5f/lpWVhfbt2ye6aERERESksRNPPBEAkJ+fj7KyMnTp0gUZGRnIyMiAw+GA2+3GSSedhK+++grdunXD7t278eOPP2Lo0KH6FjwVuAOCkVbDP1YQxSaRgW5muKWeeI4v1Y9NBosp8UxVa3A6nfjggw8wdepUWAJOwA8//BAffPAB2rdvj/PPPx8PP/wwsyOJiIiIzGrrIqBkO7B+EyzFa4Hf3gK27YW7eAN2btmDuiWvw+l2w1m6D/aV72JoXg2e+MfdeHzS+dhQU4+Xn34YHz5+g/d7pJ26Mu9PW7pxMhSNEtQxSjmIGuGxqS8DbX8DFYVSj6mCkV9++SXKysowceJE+W9XXXUVunbtio4dO2LNmjW4//77sWnTJsyZM6fJ+dTX16O+3t/HTUVFhZbFJiIiIqJolWwDPhjr/b3IDexwA/N+Aw56YNvvxAP97Bh25R2wWix4angaMO//cHqNgNWbq3D6offRttaDF/bUoteGfwEb9F2VlJHWTO8SoPFTtUGCo0ThKDpMeWyTgfDFCylkqmDkO++8g3POOQcdO3aU/3bTTTfJv59wwgno0KEDRo4ciW3btqF79+4h5zN9+nQ8/vjjmpeXiIiIiGJUud/7M60FCs47EwXesWlwfB9g1hne36+6IvgrrQG4Z3t/HwzgwOhEFJRkvS/QuwTGEW+GaCo/2Guy7loH7hK4v+LePiYOYqbyeUGUpEwTjNy5cycWLVoUNuMRAAYPHgwA2Lp1a5PByGnTpmHq1KnyvysqKpCfn69eYYmIiIhIGVed92erbsBls/QsCZmCiQMs4Ril2btZMXhlTNwvyYPXKIqTVe8CROu9995Du3btcN5554Wd7q+//gIAdOjQoclp0tPTkZ2dHfQ/ERERUbxOPfVUPPHEE1FNW1VVhYKCAm0LZEZuXzDSnqlvOYgoMQwf1IgigGb4dVBADhwaaN303M4MpBKpyhTBSEEQ8N5772HChAmw2/3JnNu2bcOTTz6JlStXoqioCF9//TWuvfZaDBs2DH379tWxxERERJRqdu/ejc6dO6OwsFDvopibFIx0ZOhbDjIXOVDg+6l7cIiBCyKZ7udjsuJ1hszLFM20Fy1ahF27duH6668P+ntaWhoWLVqEGTNmoLq6Gvn5+Rg7diweeughnUpKREREqerzzz/H+PHj8f3332Pjxo345JNPsG3bNpSUlKC6uhrz589HZmYmbr/9dqxZs4YvTpviqvX+tDMYSUR6SmQAjUElw9IskMoALaU2UwQjR40aBTFEWnR+fj4WL16sQ4mIiIiIgi1YsABffvklWrVqhc8++wwAcMwxx+C///0v7r//fixcuBAdO3ZESUkJFi9ejPnz52Pt2rU6l9qA5GbaDEZSFAyXcRVveRiUIo3xENOXKs291bruSZnkKs2OKAamCEYSERERGdmePXuwbt06XHjhhRBFEeXl5Tj77LNx0kknAfC+QC0tLUVNTQ0GDBgAABg0aJCeRTYuBiOJkJrRAQ2iZIYLVjcUS/mM0gUBEVH8TNFnJBEREZGRff7553jppZcwf/58fP/99+jfvz82bdoES8BDoyiK6NGjB1atWgUAWLFihV7FNTYX+4wkJXyBGg4yQQASlv5npuPN1EFME23nlMN9Q8owM5KIiIgoHr+/jf+9PA1fXtMG2HYvAOCM8mpcMfswxqf/APydBSyvANKsGHigObK3lWDYURkY1DkN2OsEnu2mb/mNRu4zkqNpUzx0DryYKUilO4MHyVJ9X6oaxEzxbUlEMgYjiYiIiOKx5lP8PCENQAXgi6Nddgxw2SPZAOqB2nrcdhIAeIDaUrwx2gog3ffldKC2VI9SG1+HfnqXgEzB4IEsShHJeBwaMXCYjNuZKDUxGElEREQUF98D27kvAEcN07coySKtGZDTWe9SECVeqmfhGUkimzUn/X5PhiCiAfdR0h83lMwYjCQiIiKKh/QwkN0JaHusvmUhSlXyQ7nOD+em7pePiIgoMTiADRERERERJRezBwXNXn4lNMnyMvh2VLSf410nZtPFRu1jSIVR0VW/Phj8PKGkxGAkERERkRpSMXhApDeedxRKwpqvJjCwxya5pDYeU6QjBiOJiIiI4sLKPBE1xOtC1AwfUE6WfWn07RwGg2bGxX1DCjEYSURJoaysDJ9++mnYae666y7U1tY2+flbb72ldrGIiIgoIZLlgThZ1iPFGD6gGg8V142BKyLyYTCSiExPFEWUlpZGDEbOmDEDmZmZTX7OYCQRxSeZH0aJTEKOdeh1PvI6kDxSfF8aMXCY1EFfk+G+oDgxGElECVNYWIh7770XALBu3TpMnDgR/fv3x2233YbBgwfj2WefBQAcOnQIY8aMwfDhwzF+/HgAwPz58zF06FCceuqp+PjjjwEAEydOxJQpUzBq1Cg89dRTWLx4MQoKCrB+/XpMnToVw4cPx8knn4y//voLAFBQUICqqirMmjULY8eOxfnnn49BgwZh//79mDlzJjZt2oSCggLMnj0b5513nlzukSNHoqKiIoFbiohMxYgPbEQpgw/ElAyS/D6SDIEr3uuJVGXXuwBElNrKysrwf//3f+jcuTP69euH+++/H9OnT8d1112HsWPHQhAEiKKIJ598Ej/99BNsNhuGDRuGcePGAQD69++P119/HUVFRSgvL8fnn38OAHjqqaeQlZWFVatW4fnnn8eHH34YtNycnBy8++67mDlzJj777DPccccdeOedd1BYWAgA+OSTT7B//37U1taiXbt2yM7OTuh2ISIiIkotGgR7kiEIpjYG1ZII9yWZF4ORRJQwloAKoeirCOXm5qJr164AgIyMDADAhg0b8I9//AMAYLVacfDgQWzevBmjRo0C4A1gHjp0CAAwaNCgkMt6/vnnsWjRIgCA3d74UnfSSScBAPLz87Fy5cpGn1999dX4+OOPUV1dLWdnEhGFxYdeIv0Y7ZmcAR+dJWj7x72fY7lv+JbFe01iaba9uR8ptTEYSUTaqy0D9v+F3Opt2LPxT2B7IVYvXAhUFsPirAK2F3qnq68Etheid8fmWPLZG7hkwm0QMlqiTZs26NWrFxYsWIC0tDS4XC44HA4A3mAlADgcDng8HgBASUkJFi5ciKVLl2LlypW45557GhUpVGA08G/nn38+zjnnHLhcLkybNk2DjUJEyYNBByLjMHnAJiWDmCbdV5RASXReqHKOq3XOmPx6SabGYCQRaW/WecCBdThBFFGzpRZnnXUmjm9rA+pEoMQDvH+hd7qSKuD9CzGtlYCJL32Jl194Fp1PuQgffvghHnroIZx11lmwWq1o27Zto8FqOnTogNraWlx66aX45z//iVatWqGgoACnnHJK1MU89thjMXbsWEydOhWnnXYaevXqBavVCrvdjptvvhn//ve/1dwqREREFC+jPUPzoT75JEuAmMcmERkIg5FEpL0jOwAAljbH4Os705ucbMVD3p9tBTe+vWqT9x+PfgAAGD16NEaPHh00/axZs+TfrVYr5s+fL//766+/bjR/qT/IiRMnyn8bM2YMxowZAwD44IMPgqa3Wq2YMGECADAQSURNkx9U+aBHRCph4Ih0p0XWXJIEdikA9ykpw2AkEWlPFLw/r54D5HaNPH11CfD80dqWKYJbb70V5eXlGDhwoK7lICIiIiIdJDIgzHgOEaUYBiOJSHuity9HWG0Kvivqkh3wxhtvJHyZRGRyzGQi0pEvmmOYJrVGKYeJGGbfkfqS4f7I4zNYMuxT0pNV7wIQUQqQMiMtvOQQUTLiAwoRkaGYpfsMXV5i8Z6VNLgrycQYGSAi7Qm+zEhLlJmRzC4iIiKiqBitzmC08pgA633RYeaoTjQ6PnncU4pjMJKItCWK8HeAreSSw4oXEZkFHyyISC28nhhHstRFTXxMJVUgVoV1UT2QaeJjg0yLwUgi0pbURBtQ1mckEZHRJdVDEpFJ8TwkPTHLjcyI103SEYORRKQtqYk2oCwzkjdJIiIiiprUGkPn4BDrL0kgxQOMWvS7Ge95ofd5TY3xWkcKMRhJRNoKzIzkADZElMz4jESUeAxOJJFUDmqk8roTUSpiZICItCUGZEZG20ybDxZEZCp8iCQin3jrMMwyUolBMmTJi/uBiBpgMJKItBV3ZiQr5URERBRJktUXUip4Y+J1jTt4rGDdGfDWB7dbsJS6RpEWGIwkIm0F9RnJAWyIKJmxYk6kOy36uaPUxiAUGRaPTTIvBiOJSFuKRtMOeIBgBZCIjI6XKSIdGTXoyAtDajHqcRjA1JlsOp5Pam83eVXMvD+I4sdgJBFpiwPYEBEREZHZmDp4pyJVtwOD9MbCvlVJP4wMEJG25GbaluhvdLwhEpGpsDJPpDvDtKSI9zpglPXQgWH2IXkZcX/wPkuULBiMJCJtSZmRUTfRbjQD1YpCREREyS5ZXg6Yvfw6M1vfoQzEElGKYTCSiLQl+jIj2USbiJKeSR56iZKJ6YOOZO59yCBidMy8j4lIC4wOEJG2pGbaMY2kzQoLEZkIM1qIqCFeFygasQRiVcv25LGpDLdbMD6vUXwYjCQibUnNtJVmRrIyT0RERBGxvkBaieLYMnV2J5kWn5PIxBiMJCJtxd1nJBGRSfBhlIjivQ4wuECaMfE9StfzQu3tliz92hLFh8FIItKWkszIoJszK+VEZHS8ThHpp8EDvdkGLmlKSgYqjHYtTcV9EECLc4nBdnWodX1IluslmRKDkUSkLYED2BARERFRIknZZ/qWInoM0kUlJYP0RMmJ0QEi0paiZtqsaBCRGfHaRaQbw2VcGa08RsZrZ9JjEJGIGmAwkoi0JcaZGWm4hwsiogZ4nSIiSk1xX/8VBOkY2CMjYR2IFGIwkoi0JTfT5gA2REREpLJGgRm9B4eId7l8sDccwwRbVCqHYdbHZLjdiFTFYCQRaapo1x4s2OYGrDYMHDgwui9xABsiMhW9gx9ERKQvXv8pxbDOQ3FiMJKINFW0e683GMkbFhEREWkm2V5epmC9yWiZZ8lWd0229UkUtbebqse5wc4ZohgwGElEmpr5/ueY/bcLBa/tQHV1NSZMmIATTzwRH374IQBg+/btGD16NAoKCnD33Xf7vsXKEhGZEa9dRORjtMBaqpG3P6/LymmR9c/zgoi8GIwkIk1NHn8RLu/jQOEdPVFcXIxXX30VS5YswSuvvAIAeOCBB/DGG2+gsLAQdXV1WLFiRfAMWJknIqPjdYpIRw0CJQxCmQ93Fe8jUePB4qXWdmA3M6Qfu94FIKJkJ3h/WG04+uijkZ2dDQDweLwD22zcuBE33HADAKCyshKjR4/GwL7H6VJSIiIiorjEPX4Ng1Lmwv0VHQa7iCgYg5FEpI3KA8DyV+DY8As8AgCLDRZL4wrbscceixdeeAFdu3aFKIreIKXoSnx5iYjixcwCIqIkpGHAMZb7hmpZvwygkpp4PJEyDEYSkTb+/A/wy2s4wSli2n4PLnuvCGVlzYMmufTSS3HHHXfglltuQV1dHWw2G95991106dA2YCre4IjI6HidItJdsmUU8uWGuXB/pYAku8YQ6YzBSCLSRvVhAED2scOw5IMzgN4XAG2OkT9esWIFLr30UnTp0gXz5s0L/q6rNpElJSIioqRhlD7QGLiIHbeZtvQ+Jyi58Hii+DAYSUSqOHDgAK644gq43W7k5eXh2ZHpuPbdanTusQfrD83Cyy+fhBEjjsGiRYtw3333oVu3bti/f38Tcwu4uSVbpgMRJTFWzIkSTvegIxmTUYLSycAA21DX5wGN1l+NY5PPSWRiDEYSkSpyc3OxcOFC2O123Hnnnfhx1W84XCNi8VO3YEvucDz44IMYMWIEHnroISxatAjNmjVDz5499S42EVH8+DBARDL25xc7AwS7qDEt7m28XyI1z3GixhiMJCJVlJSUYPLkySgtLcW+ffvQ6gQ7jm9nhb1ZS+Tn56O0tBSAdxTtVq1aAQD69u0bemZ8i01EREQx4QM+mRmP36jwGcFLre2g2qBIRLGz6l0AIkoCBzfio1cex5iTu2PxO4/g7CF90LVZvfe2luYdtEb03exsNhtKS0tRX1+PtWvXRjFzVs6IyOjYHJCI1MbriSkw0y86vD8SUQPMjCSi+BSvBd48HSNLPbjm3VrMff8VZNoB9PBdXtJbBE3+xBNPYOTIkejWrRu6dOmCQ4cO4b333sPjjz+e+LITERGRyTUIchgl04dBquSh6b6M5ThV6cUXj00iMgAGI4koPoc3AwBO7JKDtY+dGPTRteO6APmD0dyRgcLCQgDAqFGjMGrUKAD+bMlBgwY1mCkHsCEiM2LmBxGRYqau8/H6TynK1Oct6YnNtIkoar/++isGDx6MM844A4899hgGDhwI1FcBAAb+v1pg8lI8duBMTPytB86dm401x92HXieciCuvvBIDBw7ERx99BACYOHEipkyZglGjRmHFihW49NJLAQDXXXcdhg4dioKRZ6GoTECdW8TV19+EESNG4IILLkBFRQW2bduGU089FWeccQZuvvlm3bYFEZGMFXEi/RnlPGTWmjHImzGO/ZFsTYuTbX0SjecmkaqYGUlEUfv222/x6KOP4txzz4UgCPjmm28ApzcYCYtNni4/Px+zZs0CAOzZswfLly9Hs2bNMHjwYFx++eUAgP79++P1119HUVERAMDlcmHTpk1YtmwZLB4XhCfb4I0/XBhxwTBcf8ttmD17Nt566y3k5ubi6quvxq233gpBEBK5+kRERESkNgbJDMpI/SEzEGg4hjguyMyYGUlEUZsyZQq+++47jB8/HvPnz/f+sb4SACBa/JeTwGbXRx11FFq1aoX09HTk5+fj8OHDjaYBAIfDgSlTpuCaa67BnVPvQY0LWH/Ig5lvv4uCggK88sorOHz4MMaNG4cdO3Zg/Pjx+OCDDzReYyKiGLBiTpR4jc47IwVQ4mD28lNsUibrzoTrqfa5qGq/tibcnkQ+zIwkoqjl5OTgtddeg9PpxIABA5CVlYXK0sNAvYjtByrk6axWf2CyqKgIpaWlyMrKwu7du9GmTZtG0wCAx+PBuHHjMH78eDz91JOYs9KFXm2sGHLpTbjmhlsAeLMn3W43nn/+eQBAnz59cPXVVzeaFxFRYvFhgIga4nUhNXA/JxaD9ETJgsFIIopMFIHZV+PfH3yLOX/Xwi0AE/tkom0zK4beuhInd7SiY+vckF/Nz8/HHXfcgQ0bNuDee++FzWYLOV1lZSUuvPBCWCwWWAB8OMCOVpkW3PRjId778FMAwD333IOqqiq89tprAIDRo0czEElERERgUIhMKZasO6OMFK+IGctsZGptzyTJJCdTYjCSiCKrrwQ2foO7BgJ3Dczw/VEE4MG1fZoBsABX/RcA8NhjjwV9NSsrC//973+D/ib1JwkA3bp1w+effw4AWLx4sfePHjfwZGsAwPtvvwFktQr6vtTvJBGRsbAyT0TxXgdSOahq4nU3VTDHxNuZiJIGg5FEFJng9v8+5Q/A2iC7Mb0F0LxdYstERGQUKdPXF5ERNQgCmTp7jNSjRsYXjyEiIq0wGElEkQke/+9tjompYrdixYrYl2eqt8tEREREakululAqraueuJ3jwxePoXG7kDLsbI2IIhN9wUiLlYFCIqJG2OcSke6MlqFssOIQxcxIWcZGO7+JKG4MRhJRZFJmpCX04DPqM0Clh4iIiIgoHNWCZCkSbDNlUFHt5xIVX2Dquj35vEbxYTCSiCKTMiMb9hWZkGWbsdJCRERECdHUA71emcrMkE4+rIsaB88voqTBYCQRRSYNYGNlN7NERI0YqSkbUcozeeAolQNfpl51pdf/WL5n4i5BzFhmI1Nre7L+QjpiMJKIIhME789ENdNmhYWIiIhiYupIVmOsC8VHjSBLsu6DVA54E5FhMBhJRJHJzbT1uGSwwkREJpGsD65EpADrL1HjtZOIKOUYOhj52GOPwWKxBP3fq1cv+fO6ujpMmTIFrVu3RvPmzTF27FgcOHBAxxITJamED2BDRGQmDDoQ6YeBLKKoMOgbH2aUhsbtQgoZOhgJAH369MH+/fvl/5cuXSp/dvfdd2Pu3Ln47LPPsHjxYuzbtw+XXHKJjqUlSlKJ7jMysLLEGxwRERFFS/c+0BjwIQUMWd81Uh+VRtw+RBQPw49GYbfb0b59+0Z/Ly8vxzvvvIOPPvoII0aMAAC899576N27N3799VeccsopiS4qUfLSczRtIiLTMMIDGxGZG4Mu5sL9FRsTbi+1g7G6vzBRiSGC1GRmhs+M3LJlCzp27Iijjz4a48ePx65duwAAK1euhMvlwplnnilP26tXL3Tp0gW//PJL2HnW19ejoqIi6H8iCiPRA9gQEZmJCZ+tiJKOITPL4pGKD/pG3YcaliuWgE6yBLHiksrrHopRzxmiyAwdjBw8eDBmzZqF+fPnY+bMmdixYweGDh2KyspKFBcXIy0tDS1btgz6Tl5eHoqLi8POd/r06cjJyZH/z8/P13AtiJIAB7AhIiIiil7SBUfNJsFNjJklFgG3DxEFM3Qz7XPOOUf+vW/fvhg8eDC6du2KTz/9FJmZmYrnO23aNEydOlX+d0VFBQOSROEkus9IIiIz4rMWUeI1CgIZqZ87io5R95VRyxUvBsrNT61jk9dL0o+hMyMbatmyJXr27ImtW7eiffv2cDqdKCsrC5rmwIEDIfuYDJSeno7s7Oyg/4koDF1G0/bdFJlZQESGx+sUEfnwoZ6IiCgiUwUjq6qqsG3bNnTo0AEDBgyAw+HADz/8IH++adMm7Nq1C0OGDNGxlERJiAPYEBERkaElyUsBvoQlzcQYKGcflUSkIUO3ubz33ntx/vnno2vXrti3bx8effRR2Gw2XHnllcjJycENN9yAqVOnolWrVsjOzsbtt9+OIUOGcCRtIrXpkRlpsbBCTkTmwAc2IqLUpFpdlXXesAzxTGCEMhgRtwspY+hg5J49e3DllVeipKQEbdu2xemnn45ff/0Vbdu2BQC89NJLsFqtGDt2LOrr6zF69Gi88cYbOpeaKAkJemZG8gZHRERETWnqJYDeLwfirL+kYnNvQwScQjBqucyI2xJ8tiHyMnQw8pNPPgn7eUZGBl5//XW8/vrrCSoRUXIrKirC5s2bMWrUKAwcOBArVqzwfsBm2kREkaVi8ICIyIgSnrGudDmxfI+Djeiz7hotU4110TW4m8LHIanCVH1GEpG2ioqKsGDBgsYf6DmADRGR4THLgUh3hsm4Yv0lZkYNrhm1XPHS41xJ1m1JRIoxGElEspkzZ2L27NkoKChAdXU1JkyYgBNPPBEfzv0RALC9xNsdQkFBAe6+++7EFMowDxdEREREWmO9h4giUCu4yz6vSUcMRhIloYEDB8Y0/axZs/DLL7/gmmuuwYknnojCwkIUFxfj1VdfxZIlS/DKB3MBAA/8byveeOMNFBYWoq6uzt+Mm4iIwMo8kQ6a7DKS5yNREJ4TRGQghu4zkogSY+LEiQC8/bRu2rQJAHD00UcjOzsbAODxCACAjfurcMMNNwAAKisrMXr06JgDn1GzWHzJAcwQICKDYwY3kTEY6VyMuywMHJmDgY451Ul9VOpbCiJKTgxGEulEFEXcdtttWLNmDex2O5544glMmzYNoihizJgxmDZtGh577DFs3boVJSUlAIALLrgAs2fPRl5eHmbPno1Zs2bhyy+/hNPpRGVlJT755BN06tRJXsb27dsxefJk1NfX46STTsJLL72EBx98ED179sS4ceMwatQo/O9//8Mbb7yBgUe1xNz3ZmL3zu0oOL4TqstqcNOoPnhrSgFQfRhD3qlGz/Z5+Nd//oOuXbtCFEV4PB6dth4RERFRoGQOCqUKo+7DBJTLSIF0QzLA9uE+IlIVm2kT6WTu3LmwWq34+eef8dNPP+G5557D22+/jaVLl+Knn35CUVERAKB3796YN28ecnNz4XQ6UVhYCKfTie3btwMAsrKy8N133+HBBx/Es88+G7SMBx54oFGz6kcffRTvvvsuJk2ahLvuugvt2rXzTvzXx3ig9140t3nQVjwId005Nm3ehPo//ovKqioM72rDc9edhltuuQVnnHEGzjrrLOzbt0/DLcTXsERkMmwCR0S8DhhEgkeeTsRykiIYlgzrQEGS4rgkPTAzkkgnGzZswPDhw+V/FxcXo3fv3gCA/v37Y9u2bQCAvn37AgA6duwo/96pUyeUlpYCAAYMGAAAGDRoEF5++eWgZWzcuDFks+orrrgCL774Ij744AP/xO56tEi3YHi/o/DZc7cBAP71SSG+zWiFoadswNVXjMDR59+Gebd2QkLxBkdEhsfrFJF+kiz4l5L1HqPuwwSUS1EQ06jbKxF0WHe1A80cNIYIAIORRIlXWQz8/SV6YxsWzf4Ol3bcDwBoa6/Bho8eRK+uefjzxy9xy6AM/Lz7N1ia7QdaFwH7VsHytxNIXwcUr4VY4f3eqlWrAAArVqxAjx49ghZ17LHH4oUXXghqVl1SUoKPPvoIV111FWbOnInJkyfL0zusgKdZHjB0KgBg/DFXYdKkSaiqzsLxV08HABw8eBAtW7ZEWlqa1luKiIiIKHpBgTw+6BOFlooB72Sl475kFjjFicFIokRb8DCw9lOcL4qYX1SH069YBocNeGx4Om584FmIInDeMXZ0W/sSsK0OqLUB1Q6gqA5I+xU4bAd21QJLZwD5V8PpdOLss89GVVUVPv7446BFPfvss7jllltQV1cHm82Gd999Fw888ACee+45nHzyyTj77LMxatQo39QiOrSwoLbeiUsvvRTTp0/HMcccA0EQMGbMGHmeU6dOxeOPP47u3btru53kGxwrTERkcMxyINKf4aoLcRaID/pEpDnDXTgphTAYSZRoNd7BaCxdTsEb07oFfbTsguBJH+vr//0F6ffyvXjt3KVABwvWARg2bBhuu+22oO+tWLECgHdE7Hnz5gV99tFHH8m/L1y40Lucxx4D3v4Z2FuE+W8/CfQ6V54mLS0NV111lfzvzMxM7QORREREREREFILKLyv48oN0wGAkUaKJgvfnoBuBvuNi//7Gb4GdS6H+m6zGHX1ffPHF6N69Ozp06CD/7e2331Z5uU3hTZGITIaVeaLEM9x5Z7TymIhR+8sMVy7VymzAdZeLxGOaiNTHYCRRooke709L/IPZT5w4Me55hPPFF19oOv+oGLViSkQk43WKyHAMF6SMFq8nqmD9MbkYYn8aoQxEySP+aAgRxUa6mSoORlqC56MW9nlGREREpiWCwQKT0jRwnKh6rdLlxPK9xq2YTMcQQUVSF/cpKcNgJFGiSc20486MTPILv5krWkSUonjdIiKfuIMuvJ7ojnVR49Fln6i9zCQIKhOpgMFIokQTfM20rTZl39fsxmXUG2OSB12JyPyY6UGkI6PVW4gMTo97luGeL5KErvUP7lOKD4ORRIkWd2akRs20iYiIiCg+DLoQERFFxGAkUaKp1kxbZYbrM5JBVyIyGQYhiPQjislRZ0iGdVDMjOtuojLHfI8y0bqlGrXqG4Z7/qNUYrBoCFEKkEfTjreZttoVBFY4iIiU4fWTiMhY1LwuJ+Aab+QgNF+0EZEGGIwkSjSjZkZKjFLhMEo5iIiIiGIWZ3AppapBGq5souqTrLdGSel5YeBgLREpYtBoCFESE6RgpNJKi0bNlw2bps/KBxEZnGGvn0QpgEEg0kwCjq1Yjl/ea4goiTAYSZRoUmZk3KNpM0hHRERE5BdQN9ItSMlAEVFjap0XOpxfal9Lki2obOQuBsjQGIwkSjTDNtP23UgMc1/kADZEREQUrWSpLyTLepBx8RgjIv0ZLRpClPzkAWyUnn4M0hERGYv0Mscwb3OIiMzHjHVbM5aZkoiOxx/rPBQnBiOJEk3OjFTYTFsr8r2MNxYiIiIyC4PWW+IOUhl0vcxCzSBh0gQcYzymkq05MYWQLMc2mRGDkUSJFm8zbbk+oPbNw2A3I/aNSUSmwwc2Il0FBY14PhKQuOMg3uWwvkuxUPm4ZpYj6YDBSKJEE+Jtpq0x3oyIiGKTNFkzRCZmlPOQ9ajYGXWbGa5cBjnG46H0PDXK+U1EqjFoNIQoiUk3U2u8fUaqUho/wzXFMEo5iIiIiIgMwnBB0hTCoCiRahiMJEq0uJtpp1jzZd70icgs+IBIlHjJdt6x2kNa06NurdZ5mhTne7Kd5Mm2PpQoDEYSJVrco2lrhaPBEhEpw4o4keHoXp+J87qge/mJyEujc5HnOKU4o0VDiJJf3KNpS820k/zhlzdoIiIiipoIvhgwOzX3X6LmZaJjjnXr5KPr8yCPJ4oPg5FEiWbUAWwM12ckEZHZ8PpJRLwOGIpZAnCGTDJgqyki0o7BoiFEKcCwfUYarRLEig8RmYQhHyKJUgXrC+Zn1H0YQ7kYsKNEUut4k6svPH4p8RiMJEo0KRhpVdpMW2NGq0zxIZ+IiIhiZrD6TNRY70kpsdS7U7oVE88LomTDYCRRosWbGalVn5EpXcEhIooHm7IR6U4UjfUCM+6y8HpCZDhGusYQmRyDkUSJJgcjFVYyNWumbTCsgxMRERERJQG1KvZJ8IDAgCYRAAYjiRIv7tG0tWLUzB7esInILIx2/SRKAYart5BiagZpEhXwYWAp+Wl2jUmSaxfPAVKIwUiiRIt7NG2NmmkTEZEyvBwTkYTBUYNRYX8kTZ07xm2RNOtNREbEYCSlvKKiIixYsAAAMHDgQO0XGHefkRoxXJ+RDLoSERFRtEQEvRkwa1AwFes9Zt1XQeJdByPv92TYP8lKx+MmKc5b0pPBoiFEiRcYjEwI0ZcZqXQ0bc36jDRyJYiIyARYMSciSh6Gu6azrk5q4zFF+mEwklLezJkzMXv2bBQUFKC6uhoTJkzAiSeeiA8//BAAsH37dowePRoFBQW4++6741+gWqNpa8UoFS+jlIOIKCJW5on0Y9T6QpzXBdaDUoSC/WzmY0Np5m8qZgyHpfIxYOZjikyLwUhKeZMnT8bll1+OwsJCFBcX49VXX8WSJUvwyiuvAAAeeOABvPHGGygsLERdXR1WrFihfGGBN9J4m2mrfVM2XDNtCSsfRERERESkNz6XEKnFrncBiIzk6KOPRnZ2NgDA4/E2p964cSNuuOEGAEBlZSVGjx6tvG9JafAaQHkwUrNm2kREREQm1eglrV4vV432UtdM1KzbJqqebML6uB5Zhmpl3jGDjyhpMBhJqcvjAhY/C8evK+DZtB34YjIs5buBLyZ7Py/bBXwxGcc2r8YLV/ZF13YtIIoiPO55wBffK1um1EQbMN4ANlJlyjA3eQ5gQ0QmwesUkTEkxbmYDOtgIKrUa7lPSE1Ge+Yi0geDkZS6ipYCS57HCfUipq2pwWUPbUDZEQ+w+iPv5zVVwOqP8OwgAbe88Anq3IDNArx7YSa65MQZSLRnAvYMhV9mkI6IiIgIAB/ok0IS7MO4B9NOQL0+5nOFzxoUDR4npAyDkZS66soAANl53bDkw+sbfbziTO/PowHMu0zlZeefDDiUBiM1YrQ+I/lwQUSmwSwHImog7uASryf64z4AwHubkTE5hUyMwUhKXe5678/W3YHT79K1KDHRrM9Io97MjFouIiIiMiwGUCjZpHTgKZXX3ah4jaX4GK3TOiJVrFu3DhMnTgw/kavW+9Oeqdpyv/zySxw8eDDq6WfNmgWn0xnjUjS+8LPyTkSkEK+fRPoRYYiABetRFAtFx4uZjzEDnKPJQK3rTEoHuElvDEZS6nLXeX/a01WbZVPBSEEQQkytNBjpo/bNw3A3IzNXtIgopRju+klEppXK1xM11z2VtyMRkQmwmTYlDbfbjauuugpHjhxB165dAQDz58/HP//5T3g8Htx+++248sorMXHiRKSnp2PzHz+gO2rRdfPf+O7FITjttNPwwgsvYM+ePZg4cSKcTif69u2L1157DbNmzcLcuXPhdDpRXFyMr7/+Gunp6bjkkktgsViQnZ2NGTNmYP78+fj7779xxhln4LjjjsP8+fNRXV2NyZMnY9GiRVi5ciVqa2vx1ltvoba2Fn/99RfOOeccXHzxxbj22mtx4403oqKiAh06dMD7778Pm83WeEU1a6YtL0Cj+SrEyiQRERE1yWD1FjIYFY6PcHVRU1ZT9Si0Wuepzuc7n0uIVMPMSEoaX375JXr06IFFixZh0KBBEEURTz75JH744Qf8/PPPeO211+DxeAAAw4YNw0//ugHrDwnoe3QH/PLLL1i8eDFcLheeeeYZ3HvvvViyZAlqa2uxZMkSAEBOTg7mzp2L66+/Hp999hlWrVqFk08+GT/99BO++OILHHXUUTj77LPx3nvv4bnnngMAOBwOzJ07F+eeey6eeuopLF68GP/+97/x/PPPY8iQITjxxBMxb948TJ06Fc888wzuuOMO/Pjjj+jbty+++OKLBG9BDsBARBQXXj+JDETv8zHOoEUqXU9SaV2JGNAkAsBgJCWRrVu3YsCAAQCAQYMG4dChQ9i8eTNGjRqFkSNHoqysDIcOHQIA9O3bF3DXomMLC/r26AQAyMvLQ0VFBbZu3YpBgwbJ89myZQsA4KSTTgIA5Ofno7S0FMOHD0ezZs0wfvx4vPjiiyHLJM0HAJ5//nkMHToUd9xxB/bt29do2vXr1+PRRx9FQUEB5syZg+Li4ibW1FdhS/YbmeYZoEREauF1ikh3opj8dSNKrJiCpGYIqMZYRvl8MsO6aUizYHmSbFded0khNtOm5FBzBD3a52DVL4UYO2IgViz5Hm2yM9Grx1FYMPttpKWlweVyweGoB5xVsFTsB6oPAwAsDn+fkaIookePHvj9999xzjnn4I8//sCECROwfft2WAJuRKIowuVy4dFHHwUAjBo1CuPGjYPD4ZCzLwHAavXG+0tKSrBw4UIsXboUK1euxD333AMAQdP36tULF198MYYOHQoAcLlcGm6wEOT7SJLcGImIiIiIkgaDPkSUPBiMJPPbsQR4/yJc5HHjk+9qMXLOTPRsZYXVDTx0vANnDe4NqwVom2XBp5dlAetrgQ/nA+18/THaggewuf/++zFhwgQ8/fTTOP744zFs2DBs37690WL/+OMPPPjgg7BarejcuTM6d+6Mc845B3fddRfOPPNMdOrUSZ42NzcXrVq1QkFBAU455RT57xdccAHGjRuHsWPH4sEHH8SkSZPkAOdzzz2HgQMHNl7flMkYZFCUiMyG1y2ihEu6Jr7JXr+jYAqO32Q75KPB7Lsm6LhdUvE4JFUxGEnmt38NIHpgt9nx+fg2jT4e3Tv437Muy5J///yGDsDQywAA33zzjfz3H3/8Meg7EydOlH8fM2YMxowZAwD4+eefg6a75JJLcMkllzQqg9Vqxddff93o77fffjtuv/12+d9z5sxpNE1jUjPtKCaNidRnpNrzjRMrH0RkdLxOERGpQM1rKa/LhsT7pcFwf5B+GIwk8xMF78++44CL39S3LGbGygERERGZVoN6jN4Zk3HXq4z2dtikND8OWH9OKJ4WPipvCL2vl5SSOIANmZ/o66PRkiKHs3yv0KryY5CbUco0Ryci85Myyw1y/SRKWawzmJPRr51JdlzpkYCQNPfHJDsWiHSUItEbSmpSZmTS3OT0wodpIiIiMhuD1VtYj0pNSbnfGXjTBrcrEcBgJCUDQQpG2vQtR8JIfUbyRkZEZCzJ+DBKRAnF+p2BJPCanoj9rjRgmpSB1lhotP4pv10p1TEYSeYnZ0amyOGs1Y1LrgQZ5cbIoCsRmQSvU0T6a3QeGqU+Q6QS3mrIkHhgkjIpEr2hpJZqwUgZL/xEREREMkO9GIizLKmYNaXm/jPUsRCGov2cgscGn3uIkk6qRW8oGUkD2FjZTDs+Busz0ijlICKKFq9bRInH847IRBhUNJS4nid57aX4MBhJ5peymZEqM+wbZKOWi4hIwusUEUn4gB4zoweUw9WRDVt/DseMZZYY/FhJFNXPGW5XSjxGb8j8xBQbwEa++WhVkeDNiIiIiMymQb3I6AGuJpk5UETUFLOejw2YMvhMZEwMRpL5Cb5m2qatdBqFwZppcwAbIjIdo1w/iVIV6wykopjqxPFe/xNx7MZYRtbBiUhDDEaS+aVcM20G6YiIDIXXYyIdGfQlQNzXBYOuF+lIr3tNih+LmiVqpPh2pZSXKtEbSmZSZS9VBrDR6oYoV5oNcmM0TIYmEREREWlPzWCbWV4SKajvso5MREmAwUgyP2k07ZTJjJSYpZIVr1RZTyIyPT4gEumnUSaiTucjrwPGwv2RHNgCwbi4b0ihVIveUDJiM22VGK3PSCIis2BFnIgoeYW7xvP6HxMGrtTHbUomZejozfTp0zFo0CC0aNEC7dq1w0UXXYRNmzYFTVNQUACLxRL0/y233KJTiUkX8gA2KdJMWyuGu5ExKEpEREQRBL5ENVxdRoFkWAcyNjMfY0yaUFkcxwL3BcXJ0MHIxYsXY8qUKfj111+xcOFCuFwujBo1CtXV1UHTTZo0Cfv375f/f+6553QqMeki1TIj5Qu/VhUJg91YzFxhIqLUYLQ+d4nI/PigbwAx7APur/CSZvsY5blE5e2ZNPuHzMSudwHCmT9/ftC/Z82ahXbt2mHlypUYNmyY/PesrCy0b98+0cUjo0i1YKRmD7tspk1ERERElHCJePkecx3fKIG3JMIkCyKZqaI35eXlAIBWrVoF/f3DDz9EmzZtcPzxx2PatGmoqakJO5/6+npUVFQE/U8mJg1gYzXV4Ry/ZL+ZaZ4BSkSkMr7MIdJRg/qCbucjrwOKqVm3TcZ6sl7rlPL3No3WP+W3K6U6Q2dGBhIEAXfddRdOO+00HH/88fLfr7rqKnTt2hUdO3bEmjVrcP/992PTpk2YM2dOk/OaPn06Hn/88UQUmxJBujGnSmakVjcuNjMkIlIoCR96iUyJ5yI1ZPB6raJ6vcHXiYgoCqYJRk6ZMgXr1q3D0qVLg/5+0003yb+fcMIJ6NChA0aOHIlt27ahe/fuIec1bdo0TJ06Vf53RUUF8vPztSk4aU9upp1qA9gke4WbFS0iIiJKNclevwvBzBliyZiBaUjczsbFfUPKmCIYedttt+Gbb77BkiVL0Llz57DTDh48GACwdevWJoOR6enpSE9PV72cpBN5NO0UyYyUgnSqV34M2mck729EZBoGu34SkX4YpEoe3Jcq4rZUnSga7/mNKAqGDkaKoojbb78dX3zxBQoLC3HUUUdF/M5ff/0FAOjQoYPGpSPDSLkBbDTCihYRkTK8fhLpr9F5aPaHc7OXP9WYaX+Z+Z5lpu1sAnHVX7gvKD6GDkZOmTIFH330Eb766iu0aNECxcXFAICcnBxkZmZi27Zt+Oijj3DuueeidevWWLNmDe6++24MGzYMffv21bn0lDDyADYp0kxb84FdDHJjkYth5goTERERJYwRXgwwQyl5pPquVPV8SvWNqTLVrzPcP5R4hg5Gzpw5EwBQUFAQ9Pf33nsPEydORFpaGhYtWoQZM2aguroa+fn5GDt2LB566CEdSku6SbnMSK1uFgaowBMRmRmDEESJx/MuiaRyXTSadY93+yg9V3iOyYzwwoMoSRg6GClGONnz8/OxePHiBJWGDEsORqbYjVKrm6FhtqNRykFEFAkfTohIJQx2ECUvnt9EslRJJaNkJqTYaNpaBQvle6PBgoC8aRMREVGsdH+5yvpL9DTcV7ofB5EoKJ/h1ynJaLa9uR8ptTEYSeaXcs20JazkEhEZCx8siEglDDgZSLg6N+vjCcHNTJR0DN1Mm6ihoqIibN68GaNGjfL/UQpGpsoANtLDruo3Zd8MjVL5NUo5iIiIyMCMVl8wWnmIDESHFk9OjxPF1cVxzKAaNrsNHd0ent2hMFBMCjEYSaZSVFSEBQsWNAhG+kbTTrnMyFTBOxwRmQRfohARJZEYrulmuv6buQukGLezR/Dgkq8vwc6KnfEtN78TrimvwH3xzUUjJt6flNIYjCRTmTlzJpYvX44VK1bgggsuwGeffQZbyWa8coYH/VMlGCnfhFW+8Zi5YkJEpBdeO4mModG5aKLgUJAUvqaoeT0127XZkOVVsUw6BWtr3DVyILKZoxksCq4LbsGFOk891qelqV08A4hjH5spAE+GxGAkmcrkyZORn5+Pe++9F+PGjcOyZcuw66XRmPTGT1g4JVWaaWvFaJUgqTm60cpFREREFAHrL6S2eI8ppcEjEwedxIDnm58v/xkOmyPmeSzY+hXuWfYQRIt3jknJxPuYzCtFUsko2RQVFaFfv36wWq3o1jodZXVi6l1Etarkptp2JCJSDa+fRAkXWG9JqgBgCl1PNK17Gnw7Klp3g6+TgYiB1wSlsVhVt3cyXaOI4sPMSDIHwQPs+gWO/SvgObwN3dzb8NcvP0HY8C127d6LlhmW1OkzUqsKm3yzNkgFh0FRIjKDpAp+EBnHvB3z8NOunyJPKB5Av+zmGK99kaLD+kvyCTuYNu8BZqE0qGjx5W+pvqd5raAUx2AkmcOf/wG+uRsn1IuY9lMNbv/jG1zYyYZTz7oQVgvw6jkZgC0Z+/EIJ1UqP6mynkRERCR58tcnUemsjGraea1bYYy7Bjkal4mI4qW0Xq/se4IoyL9bFSauSDFD0SgJG0RJgsFIMocjOwAA2a07YMlDneU/3yv9kt0BOGpo4sulC636UvTNj2/piIiU4fWTSDV17joAwK39bkWLtBZNTvfcH89CBOAU3cEfmPV8ZKadccR0DJnpeEudYyywz8h4MyOFCNOlrtQ5nkhdDEaSOfgqpDjpamDkw/qWRW9mrVzHjAPYEJEZ8BpFpAWpr7exPceiXVa7Jqf71x/Pwg1AEEUY63w0UllSEbe/saj1/BLbfAL7jLQofIZS+r2E4bMSmRSDkWQOUjDSkaFvOQxF5RuP0fqMJCIiopTlET0Aom9amXSP4w0CIAeqD+DnvT8HNTttSqY9EyO7jESWI0ur0oW1dO9SvLXmLbgFd+SJAaB8Dzq0bY1/ih6kbk3fgEdwEgS5RBW2q3Qmmn9rhJAE+5jMi8FIMiRRFHHRRRehoqICI0aMwMPH1WHgW1VYMSpT76IZgFbBQoPdjIz+FpKICGBFnkgDoijKQYRIwUi5thBFkC4xtKm/PPrLo1i2d1nU09/a71ZMPnGyJmWJ5OONH2PVwVUxfWdt82YY5zyCwWoXJqnqk/Heb5RuC/NuQykzUml/kYC/ebdogXHu+aof1xzVnRKPwUgypOLiYgDATz/5RlGcfbX3JzMj/bS6GRqu0maQmz4RERElRGD2n81iCzutN1AQIv/JcPWZ+BypPQIAOLHtiWiT2abJ6baVb8OO8h0oqStJVNEakTIirznuGgxuHzm8OH3xfdjrqYHblHU+HQKESXZsa0m6MijtLxLwN9NW5eg0SjCTyAAYjCRDuvPOO7F8+XK0atUK119/PV44qd77gT0Dhw8fxo033oiKigp06NAB77//Pmy28BXVpKJVBYTNtImI4sMHRCJVBAYjI/XXJuU7CaKYJA/6oddBCqpM7jcZp3Y6tclvv/HXG5i5emZQX3mJJi27d6veGJ4/POL0r1sd2Osx8utn45aMwpOOxfiCkdIANrzHE6lJeb4ykYaee+45DB8+HHPmzPH+wVXr/WnPwDPPPIM77rgDP/74I/r27YsvvvhCv4LqKtkrRrzhE5EZJPu1mCjxBMSSGemlRt9wqlI5GCgFaCMFZ/1ZXPptD2n/Rd80Vv8yx40vo6Kj9LxQ+D35mIpj98QTyCSipjEzksxBHsAmE+vXr8dvv/2GJ554ArW1tbjmmmv0LVvCaTXKtHSzNtgNNymyHIiIiChaHsEj/x5tIMDUgayQgtdbbm4aKRjp+140A91oRo7/RLfvpJClqtmcqszLYHVitaRQ3VqdzEgVm2kTkYzBSDKW+krgf5OAHduA7TuArzcBa0uBlr7R+Ozp6NWrFy6++GIMHToUAOByuXQssA6MFiwkIiIfXp+J1BDUZ6Q1fGakHMgySqhAo3qaPBBHhIZt8QzUoZZYMyPlAUI0K5EJGDJAqGKZ1DovYpxPtANhhV0kj8/wDHnskhnof7ciClS0DNg8Dzi0EagrB45sB6oPAW5fM+3co/Dggw/ipZdewogRIzBixAisXr1a3zLrRuULv9H6jLT4G14RERkWK+FEqgtsph15NO3UCBRE3UzbAJmRcoZjlFVKbZvaG6Req4Z47zcpmNCgxgA2EsGwmy/Zr36UrJgZScbia47d7Zg++Py2pwAABb6PVkzpArTujtaAvy/JlKTVnZA3MiIiItKfIAQEIyPkTsiBLFFEUtRlmgg4ycHICPVAQ/QZ6StrpH0n8QdQNSsSxcPEQUy5mXYc6yC9EEnOwzOOtTLxcUHGwGAkGYvga47drA3Q40x9y2J0WmXjGObGYpRyEBFFyTDXT6LEqXBWYEXxCoiiCIvFggF5A5CTnhPXPGPJjPR/x2ihAm3KE3WmqAGytqMNAEmTGaapfUPhtqUu2Yq810RLjfMg+AVAvPNT6xhX+Rhg/YV0wGAkGYvH1/+jLU3fchiZVjcLozXTlhigMk1E1DReoyi13bfkPizbu0z+95AOQ/DWqLfimmdgFmCkgJYcmguqLxisLqNEg/WWsw0jBCP9WVzmy4zUusRrD63F/7b8L/om7JX70axVS1xnEdBO26KRRlTpM5ID2BBpgsFIMhZBCkY69C2HKST2lrjxyEasO7wu5GfZadk4I/8MOLjfiIiIUkpxVTEAoH2z9iiuLsaBmgNxz1MaTdtmCT94DaB1f4NKaBMIjbXPSD0zI6Md+VuizSBEjef18p8v47fi32KbTU42sp0uTI44YRIEwBNC6T5W9j01+oz0B8uZjUikJgYjyVikzEgrD82mSW1Z1K5kSp2NN74xujwuTJw/EdWu6ia//eRpT+KiHhepVxwOYENEZpBs2VhEMfKI3sDhZT0vw6urXlVl4JRYgllBWXVJ3Joi2qCKtM0Cm7onmtxPX9TXxMT0c1nrGxDz/KPPx9Etj444/c9bv8GfFdtQq2mp9JC850lD0fa1GpZ8ThGRmhjxIWPxMDMyIh3eotV6auVA5PDOw4MeDjYe2Yji6mKU1JYkvFxERESkL+lh367ii2QpwBlLZqSQNIHI0OshBfjM0GdktE3KJdJUmgR7AuqsUrBzdLfRGJ4/POJXKw6uw58V2xKUdWvA41fVY0it55fY5iPvuzgWb+WLRiJNMBhJxiI107YyGBmZypWWMH1GBlZoZ5wxI+iB49Hlj2LOljkGah5FREREidIwcKhGZmQswSw1mmm7PC7MXD1TlSbmOLIdLVu1xM2CG9nxz00mZTpG7ENT6jPSRM2048pai0G0Td0l8ijfholFxbtPla6IYTZA7HybTJU+I426GXR/EaP38smsGIwkY+EANlHQ6k7Y9I0k8MGi4c1crqip8PARTKvm6EREagq4RrH/J0pBDTMj9QpGxnMu/l78O95e+3ZM3wkrJxtHiZW4NK6ZNDGATYRBYfyDbeifGRltkDFR/X7GmrEZ3cA6OgQImzi+RVGUXw6EnkCAFYhyWKHkoG6fkUmIz1mkIwYjyVgEt/enLfKheemll+KFF17Axo0bUVtbi4svvljjwhmMVjePEBWccMFI6d/qByOJiIjI6KTgh8PXqkWNgJI0z2iCRlb5pagIpeECqS/Bjs064opeVyiah+Tbvz/AprqDqFM5dCH3wxjlADZGqJdFnYEo9cmncVwk1sCU1USjKLs8Loz7Zhy2lm0NO13b/I74RKhPmdHB1egzUtV+adV6flP95aeS+fEFLMWHwUgyFk/szbTPPvtsjQpjUJpn3jQdjAz1UKBZcyBmGBGR6fC6RamnYWakGvWBaPtHDPpOHMuT1qF9s/a47vjr4pgTsGHbfGyqO6h6ll+sA9gYITMyUhanRJPMyBDHYbQBXYnFV/6owro611v3Vu2NGIgEgEN2O9a7K1ImGBlrlwGh+AeF4j2eSE0MRpIhHDhwAFdccQXch7chz1ODZ7tU4f5LL8Xnn3+OqqoqjBkzBoWFhVi0aBHuu+8+dOvWDfv37wcAzJo1C1VVVbjtttvw4osv4rPPPoPNZsMrr7yC/v3767xmWkj8KNPRVCq1G7XRDO+jiShlsYkTpTipjiD3GalCfSCWzEi5VhTHuSiVOZ5+5SRypmbccwoWbRNjqa5mhD4jo43dSNtM6zLHup8TX+NWTlq3FmktMO+SeSGnuXnOBfjbeUTf9VG6jxV+L/aR3RuzqHBdaGLOGs2XyBwYjCRDyM3NxcKFC2H/4VHc+cgL+HHNbgCZjaZ76KGHsGjRIjRr1gw9e/YM+qy4uBhffvklli1bhl27dmHSpElYuHBhgtbA5AJv8CHeHIZ7q8hm2kRERKlLChxq0WdkLKNpB4eMYnvIFwQVg5HxZiY28bWoR9M2UmakIYJ+AUeItA2jzNi0WrQJLGtBWje7xY6c9JyQ0zjkQabU3dLfF32PBUULwk9UsxHHtMzGzaKoSwgurszIZO4zkkhHDEaSIZSUlGDy5Mko3bYS+/a50b8gXf4s8C2px+NBq1atAAB9+/YNmkdRURH69esHq9WKbt26oaysLCFlTzgdmoGEeyjQbtRGDmBDRCbD7iUoBUmBPDWbaccy6rEUVhLiCBVEO1J1NCxqZUY2KIpcxiibaRvhJXG0QT8kKNgT+2javjpuIsJQ0Zw3YaaJZt2kvRH9+kQ33bO/P4tDtYciTrcgtyXOc1ciP8qlhxTjOapKn5FGH01bQ/N2zMNjyx+D0+Ns/KHoQbdO7fGR4A6RQkQUGYORZAgfffQRxowZgxvbH4Xbn3gdZdUu7N17GACwevVqeTqbzYbS0lJkZWVh7dq1QfPo1q0b/vrrLwiCgF27dqFly5aJXAV9iKI6D79i+GwCKeshVAXH/7aQQUMiSkW89lFqkzMjLb5gpArnRGyZkfEPLiEFUKNZXiRaNdOOdQAbI2RGRhv0s/omiyegHI1YB7CRih+2VAZ5aR5NNqo/UB5jmSPsxzpPHQDg5r43o3Vm65DTzPjtWdTAgzppsNIEUWc0bWleqadwdyFq3DVNfr41LQ3bnWXok7giURJhMJL0teR5YMkLGLm3HtfMqcLcXAsy7cCAFlk46aSTMHToUAwfPlye/IknnsDIkSPRrVs3dOnSJWhW7du3x4UXXohTTz0VVqsVr776aqLXJirl9eVYsmcJ3Epvxs5q2Jo3w+k1tWilWqnC317DNQ3SrJk2M4yIiIgMr+EANmo2045pAJs4lhtr8CwcNeYRSrTNtLVrsRK9mIN+CWq4G+vASFoFloMoOl6a7lIpXDaq3Oxc5UND2q4XdL8AXbK7hJzmrd+fR43oSXiTd3UGsIlhICMVldSWYHv59tAfOqtgT0/D8fVOOOIKk0b3DHhrv1txyTGXBH02fs55OCDUp2SQltTBYCTpa/1XgLsOJ+YBayc38/7NagfG34yJ3Uc0mnzUqFEYNWpU0N8mTpwo/37vvffi3nvv1bLEcXv+j+fx1bav4ptJ29YYUV2Dl9XKjAwUYn66jKYt4y2OiMyCL1Eo9TTsM1LNZtrRBI1CNj1V2JQz+mbF4cqjMPssgqibaRsgMzL2Uat931O1zI3nFXtflrE0H9f3+h9dM21tjg09lx2RNJaSCn1GBs1QYy6PCxd9dRHK6suanqhje1xWUYlHNCyHdN3JTs9GXrO8oM/svuulh89qpBCDkaQvjy878NL3gPzB3t/TmgGZLXUrktYO13qbnx+be2yji3o0SqoP4O/STThsi78pkSzCg0O40QeN1DcREVHCGaSJHpFeGmVG6jWadhzLU5KJGak8yoORob8XbfamEeplcpZejEE/rZtpx7pNpIw4M1zlY8n6VL0LgSj2d/znhTKq9hmpSomim0ulq1IORB6dc3Sjz6ucFThYexi7HSqFc5q4roQdN8D3k111kVIMRpK+pM5wW3QAcjopmsXuIzV4YM4alFa7Yv5udqYd0y/pi6PaNFO0bCUOV3n7VTm0ZwhqXCfH/H3R9jvQYhMEC5Co6pE8ymSIjAHpb+EePg698grK534T20IrytAiLxt518T2NSIiIkocKXAoPayqmRm5t7QO5778c9hpnc0EwCHViJQtO9xL11hZVQ1chJp/hNG0DZAZGWsASNM++QKCLLEGSc00mnY0AXXNsnajyCyWMyMVXx8U9gerSp+R0jmlfWs0SeA59NVFjVvUzds8B/f98qj6ZWogbFdd0vHEl7KkEIORpC/BF0C0pSmexcL1B7Bsa4ni789btx+3FvRQ/P1Y7SuvASzA3rJ6uCsqYv5+drM6oIWvYqTaxT980yYBAiCKSHMBQm1t0Gc2pxs2jxi2cnHkP+9DqK6OuVRHypujncfDho9EZGDKm4YSmV1gppkWzbTrXcD6/eHrSkd39y6vqj72l9KSWPsSDEfOpot7MwRfT6JtSm6IPiNjbKadqCa8se7nxA5cEt9SomnGb411jaKcLJaRvBOdGalGn5GJGu09kCAK6L9FwLH7LDhYOqPR57lHNuHSfQLKNH6ETauowzl/CGi7bylK2gZfiwv+rMffuQKEdtqWgZIXg5GkL6mZtk35oejyeG+Ap/dog5uGNU5jb8p7y3bgp02H4PYk+KYoCoAFOKt3e1zZJ/bMyPkbDmFumQZv58IQPB489b4HPfcdwqZ/9g/67EwApzuA3+/bDzSxOqLL+4DQ+bVXYW/TJuLyPFXV2H3jjb4v820bERGREUlZkQDgsDoAqNNM2x/ktODlK05EblbTL62fWBwqO0dhn5GqNtNWV6yD7JhqABtp1GqNiywH7KIe5TvxQajwmi5JNIFWabW1OjbDL9sCiGoEI2M7t0VRxIi/BAzadxh7f71P0RLFujLcWuzBr/0SdyR4qqtx7xwBdgEoWf7vRp/nABgHYGOxFdBwuIT+87ahb6EAYCEOYmHQZ+f5/q/uFXvCCRHAYCTpTcqM9FVglfD4br4dcjIwrGfbqL+3YH2x9/tqDykXQbMaJ07eKuCU3PU4oS4z5u+Xbf0bNSUCyrsAqlWPIlTgxZIj6Lmv6a9nuIBWmw40+bnH7YYVwMTNH6Jif3rE4mTWufGs9A/BDI1jiIgAvQcwIEq0UJmRsfbLt+nIJkxeNDlooAZ/IM2CU7u3QdsWYeoOixuXJVZq9Csn0aoprJxtGKGMVkvk7nO0pu1AMcrF3nxc2pbhSpbY4Jr3KyFaMQkeXL7Yg95HirF7weSQXzt/3yEc28wD8QJPyM+VljG6JuJeie4+QKyrw03zBVjFalSsnqt4PgUA2lYB+D+1Shaep6Yadt8pnHtN4z6r9v/9GzL+3Iz0em3LkVHp7VKt9pjOaN9nYNBnB7/9EukuQKxxalsISloMRpK+PFIz7TiCkb7MRps1tpu5zXcjT3Qw8oofD+C0vwUA36IY38b8/WN8/2/obgHuVLt0oQll5bACqMqyYsDSP4I+K7xrPDos2Qg0sR1FUYTVF1Dcb/kDlULk/ZQREBz1uD28UBGRcTF7m1KYR/AHNUINcBCNX/f/ikO1h0LPv7YL7BHqd0GZiArPRzUzI63xBtaaWIdo+7WUA206XppibRqryeAmIbaj0mbaZngtLu4/gLHLRQA1qNpYGHKao3z/7+qrXiabKIoY+7MHw9cJKHl/HMqauA5Mq6rG9raAcFtit6ZY74TVdyi0+7//A6yxn+OH1/wGYV4h0pT3BBEzwe1tPeixAO0f/Eejz7e/909k/LkZFtGiaT3E4nuGqxhxEvrfPT3os6KfvkZ6uaBrFjaZG5/xSV9SMNKq/FCUMiNjDkb6bkaeBF9Ac6q861zWrRPye/aJ+fv7tm1Ei2270KwGCeszUiwrBwBUN7PCmpUV/Fmad99ZmspgdPnv3L2aj0FGy8gdi7hrKgF84v264OaFiogMzQVgfXoahEOrAbvyPpDJL8uehZ65PePs54u0pkZmZI2rBgAw5ugxuLO/9y2ryyNi2HOFEN0tYI02GBlHSwpVm2lbNM6MjHBOGCkzMvoMxAT1GRlj8/GYjgedr1VivTc7rT7Ngm6PPhlymo3/egw5R9yAS71jQ4SIc1YIaFEHCGX7mzzqWgNoXQbUHKhSbdnREANemLS6/jpF95Qj/xMhzCuERUTCXkCKHm+5hSYOQalvWku8xYmwPhZfsonF1jjILJVN9JghXE9GxGd80pcKA9gIgtJgZPD3E8XmW17ROcMx5M6HY/7+ijefxbEzZsGi5nVfFFG6JQuH/m4BfH9OowqVtd47aE11Voi3nVZ5Q4aetcdfCbj91GtwUvfIAdjdB/ajyheM9HhibUpCRJRYj7RtjW+aNwMW3qh3UZLKPwb/A1f2ulLvYlAYgX1G2qzeOkLMwUi3NxjZJrMN2jdrDwCoc3kgurMBIHJmpK8aF08gy+Mr85YD1Xj9p62K5wMAu49460xxV9MCR4EWxegDaXL/i7qmRgJQMFCMNsNpy78pbT6udF/Wbd6MfQ88AKGiMsxEZXBYWqNzHxeU5RZ7ib5MOmeaFS3Hjg05Tc2/n0TOEbeq21kQBdik5sT/eho5nUP3379+whXIqgMQR3cKirh9QT0L8EbhNkWzyNp+BKcA6jx7RbnxPb6EnSaDkTaVgpH+OYb+s/ScHCKjVDTCtYZMjcFI0pcazbTlJhexBSOlN+2JbqYtBSMtdmWnn9X3Zsp781Gv7OVFWfDU2YC60kafSVt2V35GiAJJwcgmmmn7KkcA4EiLLujsSPMfD24GI4nI0ETs9F3P22a2QZajmc7lMb+y+jKU15djW5myB0dKnKDMSIuy0bSlzMgsh7/lhTugThHty2ZpPEIBwC6HHUL5dmTZs+QAZzhFJd5srfX7KvHnyk1Rljy0PnnVQCug2umOPHGUAgOtkQJp0mjbSvvQdO3fj/rNmwEAGccfD3vr1jHPI5qRnQNZLAnKjIyy302JnOWqMOmx6scfUb9+Q8TpXEhHbVEZmitbDABA9A0KKtiaLqwonUsqPvuIoig3g04/7jhkHnVsyOncdgsAEYLSer3CgFe904ksAB4r8Pz3ys7t0w4f9gYjVT88m95Xgtv7jOxp4vpn8b380bo7BqnlW6jMSDkYmeBnaUoeDEaSfgQP/K9OlR+KbqWZkb4KhjvRwUhPvMFIX7NoVZsKiPKs8h64F1mnDg36dH3Jejzwy0NI79YGjbrElt7MNZUZGRCMtNkjD14DAPaAG57bpV5lnohICx7f7eexwQ9hWNeR+hYmCby37j28uPJF1Lpr9S4KRRAqMzLWgJKUGZll9wcjpf7AvfONspm2L/j2UJvWmNuiGfDlhQCAh095GGOPGSuXL5TKOm8T10yHHRcNyo+p/A1t3e+tp3lUrF4GBhYj9hkZR3NhwenE9gsvglBRAQBIO+oodJ/3XczzibZJuUTOjIx5SbGJtt9NiVXJ4DIBRF8dtsVZZ6H1jTeEnGb/3begfl9ZdE1dw9T7RV8GoBhum8ccPIo8nQB/ZqTV3nRyiT9wFeWiVeKu9wVpLRaMG9g55uQVALCvaAEActA1ITzS/gz9sdUamJyiHX8z7RDPrdJLBGZGkkIMRpJuDuzbgyv+Uw23AOStuR7PPv8vXHvttejcuTPWr1+Pl19+GSNGjJCnr3HVoN7TeMiwWnc5YHFFbMbTkDS9kOALqBSMtDqUNU232DS6+fjml9a5EzJ69gz6yFNcjv2bLWjnEvD938VBn1XUutERTfcX4vH1YSMAyIgyM9IesG3cAjMjicjYRN8TntIBPChYM192abVLvUEWSBuBzV7lvgqjjDZUOCuw+chm7KvaB6BhZqR/HrYIwYPgkZhFbEgPDog8+euTeGnlS3jzrDfRr22/kPOo8wVyWjVLxzNj+0ZV/qbc+I53+crrlyL2/tISVXNvBAICqO853VhxjAW4Ivy348mMFCoq5EAkADh37ED91q2wNvNnfFubN4etRYsIaxBj34wJGk1bFEVkV4tASSlczoMRp3eU1SKrToRgC1OycAFCX7aiPS8Pmf1CH3vWDG+dN97sMlHwBd3CPA/JgUoVn30Cm2mHyp7zL9v3M46+Xb0Lie15z+PbBx4r8MwlfSP2QRvK3LLfvItO4COjR8qMbGqTavU82IAlTDNtuc/IePcppSwGI0k3udnNsPCaLFgFK+46nIdF383D4YOH8NPChdi2cycefPBBORi5ePdi3PXTXXCLobPkmh+TjirheQC9o16+Xs207dIbJoey08/fR4i3uYMqRBGiVEsIkTng8r2d21/mxM3/XRn02Y2HqnE8gKoaZ8hZO52+YKQVcISppARyBGTKetwMRhKRgYkipKuUlcFIVWTaMwH4m++ScQUGIwMHIRFFMWJW3OVzL8eeqj3yv5vZ/QEvfxc8iGEAG+93BN9fpg96HDX3P4bcIy5UZZTh77aL0O+s0AEhp68VR7T1lHCkwJrSpr2lG0RU7MwCEHz8NwMwfJ2I4oJzcLh1qya/39ZVjafr3Vg6Lkw/hU2oqi4DADhtQFUm0KoK2D7m/OCJHA50/c9/kNX/pCbno3TUanWbaTee12XfV2P0rx7Uv3I1oukZtDeAdy3AwvMV7kxf/TlckA5WJQHCxuURpL4Rwx3CSlNQw6y+IAhyxqAtTGakN0gqQkxwaqQQ0GekkkAk4G+VlsjMSNETPtPValW7z8jQwg1gA/Vj25RiGIwk3ZQcOojr33bhcLkFB93vQcjOQdf6Omw/9TS0mvESSkv9fRf+efDPJgORAGCx1aPUvR3AGVEvX3rTnvjMSO9P5ZmRGt0QRWn+jSuOtXK/Rxb079Iy6AHDttN7c3I3ETR0u7zBSLctuPl1OA6bzVtpEAGPwGbaRGRsUtBBSfMvakzOjHQzM9LopGbaNostKPAkQgybFSeIghyI7JrdFXlZeRjScYh/vjF0wSMHI31NcKVQR6ctZcjY4pKn2/7bJuCs0POo99Vh1AhG+utIyipqdYf9vx/tayLt9DixfOJF6HIYEEpL4QyoIzfkANADQPkfJTEvu77O23em2wb81NeC838XkW7xB5hEtxtwuVC7enXYYGTMfUYmKDPymF2+OqXFEl2GnS/Q1m5/NAG0xvOTmk7D3vRxZZGDkVEsIhxfBmC4ZtpSn5FqNquV+jYEAKs9zLON1F1lgpNABDkzUvn92eLrhzORmZFSN1dNDmCT6GbaIZJVVMt2pZTFYCRpzrVvH8o+/1/AX0Rk9uiIj375GUPTW+DiLq3x1IFidHTYsaXeArGuDjV//hl0o5SaZ086YRJuO+m2oPmP+mgiDrhXQUDjJtzhSG/H3Gp26hMFKTPSqjAz0u4b7EerPiNDZUb6m0pb8Pktpwa9WXxzlXdQm6b6jHQ6vfvFYwUcYfprCmS3Wv3ByGj60CEi0o0Ij+8pyxpH/8fkJzXXLSovwiPLHtG5NBSO1JQ+sJk24At2hHn29wR0wfLReR8hOy076HOpbhZLMFKqx3gsQGa9CNue4G5laqvrsONw6AC31GdkmiqZkVIzaYUz8H2v7fXjkH7UUQAAj6sGD06w4ehi4M2RbyDdFmJAQZ+Nn7+DrLlL4HDG3rLEE/ACefZwG2YPB9Zcu1oOsBY/+RRKP/wQnvKy8Kvg2xl7y+pgieKlgssjBZI1qJMHBOccbu/8M16djqPOvDDiV5fefRlaz1unOOAjNdMud4qoaeLYk5Ii4m6m7ZEyI8OcM1IwUsWAoMftbxllDRN0lfs+THQwUgrqxfGuUOqf0ZrARxLB44YVYZrdB42mHc82Df9df2ZkqNG01T+eKLWw1kyacxUX4/AbbwT/0SKiYMheXFnsxCJHLVoPG4b2F10Ex4yXvU0a6uqCJq9ze/+dYc9o1OTDBu+gKAJCNxNuitRnpEenPiMtSjMjpTdhat4QRdF/Lwpxs3EFBCMbNXGQmgk0cSNy+fqM9MTQTNvqC0YCgNvNzEgiMjbpemWLskkihdcuqx0Ab5+CX2z9QufSUDRy03ODWk0IEGCDDYdefx01f6zwT2gBWl5yCdLP8acohuprVcqMtIfop6whf4xDBEQRF/xkwfCVHgD/DZpuzc4S3PtCYch5pLUpR3pbIC3UIA0xkjaD0mqa3Iq1wbrXp1mwoQuQefIguSuDUDx/fg8AcNQrCEbWe18guwN2iVtww+F7EW7L8QaNA/uVDKXWN3DLte/8AdG1I+JyB3SsBnKAere20R4pGGnNiG5ARVGqEyt8VNh9uArNAfz39934b2VhyGneL69DW3hbIeUoWwwAf+BTDNtnpPSLmpmR/nq61RaumbZv0QkOXEl92ofrSzMSqxT4884x3hJFNZXg8cCKpjNdbYEDmmpIDkaGGHhVlPYp22mTQgxGkubsbdog96orAQDr9u/H0rlf4vLmLZFf3hpfdfNWpnp+9BFsOTkYU1ODkplvItPtQWFhoTyPOo83GJlua1x5sPqCkZ4YMyOlt+2Jbi7gz4yMcwAbAGo2aJH6jAyVhu8RpOY2IR4KpOBoUwPYuPzByFgq+VKFSeAANkRkcHLsgH1GquLonKPxYsGL2FmxU++iUJRO7XhqUJNcURThqajA4VdfazSta89edDh7BK4s9GDgFhH7Px0HR7t26PTii7Dn5gII7jMyMl99zncmHhNw2FgyMlDa3I6Wh6vgEAS0yGiiHuLwzqNzblboz2Mg9xmptI4md5vjv57EMpq2mOmtF6c5FQxg42ocjHQKzoBgpDdcVvv33yj99NMm53PXZ9U4Zp8HFvfLoeuODdgsNShuKWLb+Njq8rGy++Jm1rSmM0sDSQF2pQkApRW1aA5AtNmaPvZ8y6itj/Pluy/ZIXwwUoNm2h5/ucP1GQm5eyyl9XplZRZc/j4jlZK7yEpgZqTo8TZ/b7IPULWbaTcR9Iymz8iED5FOSYPBSNJcWpcuaP+It5lVewA9d/yC6qJ6lPztDUS602x4aPV0wGLB8Qc2oz+A2qqyoHnUu72Vk4wQzVKs8Ab1Yg1GSn17JbiVtjzinM0R5oYd7vv2gBuiis20/ZmRIQawkZtgh7hRyc0EQpfFHRCMjCbDQSK9QXV7GIwkIgMTRXnADI6mrQ5PZSWOvuUldN61S++iUJRyzt8G65MPyf8WRAFWp7/FSsd/vQDXvn049K8XIdbVweV24sJfRFgBuA5vh2vbdlQvW46cMecBCMiMDNFaoyFpCundsvRwLs54BL3OGoePplyK/oUbkZNmwdrHRoecx4yVG/DOOqBN8+iCVOHLE9/r4lCZkUJAnqU1QnDPkuldB0d97AECj9S1TsB2d3qccj+utlatAQB1q9egePWaJuczSP6tNupldy8GDu1TLxjpqXFi57y2yK5Ziza+LuXlzMj06DIj5Tpu2E0ZZk/76rB9OufiqSaOvV+XPemdS1TJEZFH7g6bAdjwZIm4uMjTBfYZGSp7Tp6Vv3PXhBKi2S4R2LQauTpc/56RBrCRy6RtX9Xy812IZBVBCm4n+mGakgaDkZRQhYWF+KJsP9aXVKLK1hyVnjoMvaoTlu74FgDgqhTQH8CeQ9twVMD35MxIe4jMSI8DfbcL6Gndhor0eVGXpe2mQ+h/4DAEd9t4VilmdqkfpHRlmZFSfyyq3xBDvImXSH07hXy7bQvfTNvjexjx2AB7DP2pSXMTGIwkIoPzcAAbVdX++SecOyI37STjKP/mG+Q8/qD8bxGi/DANhwM5552H+m3bvMFIpxNCfb1co0g/7jjUr18Pocbfp57bI6JNTRlOPrIPFfPDRy+O3+xBbUcRQq4v0OSb3JqeAYvNBsEX1LOFGWRBCvZFO/pzOBbf8hRX00IMKBiYyRZplHIxwxuMVJQZ6Wum7bFbYLPY4BE9cHr8QeXmZ5yBlpdfDvfhw03NAgDw065CHMwRcMLV/0JBj2MiLve3CRejbYm7yVY2ShxZuhP15Q4c+vovtHnO+zeHL4nvill/Yn9m04MASSbvLMW5iKNpcTSjaavU75404IkY7sW/4j4EwgTNArtTCrNsfzPtePdxbPdZaRAhTxy3Z6tWwcgwPC5vkLdOFNHjH981+rxf5QY8lYAyWaUWfWEyIxM9GCwlDwYjKeF2VdeiVZ4LC7+fh/N+ewRLK3fhql5XoWPzjijd+T8AW+Cprgr6jtxnZIjMyP7rinHF1wKA37H349+jLkcPAP8EsDDXDVxzsvIVipGUGWlVmhkZlJav0sVfFOVm2uEGsAk5KqLcZ2ToyoXL6YQV3szIWCr5/mbaTP0nImPrvQ248U8PrPOfxE5fBhEp5z50CADQfPhwtH/yCZ1LQ2EJAraeMQJwuyGWlct/FkUR8AUp5O5lfPUe0eWC2+nvGzytc2fUr18PsdafRSeIIp5b+gY61BzB3iXhi3A1gEsdwMaHXQBEfzDSl6Xl8dVr7GHqE6LcLDz+YKScfKawjuYfULCJYGSkYEyWt67c+lA9ii6/IuLy0rp3R4cnn4DFZvN3rWOzIM2Whlp3LZyCPxhpa94MHR5/LOI8n3+vP2B14ZUeRyO9R4+I0zsdclQj4rTRChWzdfji47UWO9xRLEuwRJMZGWaBUkA+XDdFSoKRoZblO77FcAPYaNhM222NECiXR15OdJ+RvgzDuPqM9A1gk8CiV9c5kQlvEDXUser2XWm0LlO4Ztpy1iaDkaQQg5GUcN1zgfwsG66+83H8LfyN5uc3x0U9LkLv1r0xd/lGAFvQesN+bBntb85wQ3UxxtrdaL/wbezK+F/Q/Eav/RsAUNwSOJwd/Y2mTYWI9mVA613L4Dp4UIU1i4II2OVm2lE2EWnAZvdmVKp+8wmTGekO02ekf0Cd4ALVrl6Nvf93HywlJQC8wchY+JtpcwAbIjK2s34DeuwVgaK/UaN3YZJI5oABcLRrp3cxKAJbbi48R47g0D334/5SD37qZ4EgCnIQIFQw0lPvD0Zas1sAAISAYKTb6UKHmiMAgMyTTgqbWVazYgUyXIClxhs0k+pH0mAagq9Vhi1MH9QeXz92agQjbdJo2gq/LzXTDuozMrCZdqQ+I9u3hcfibZJcu3p1xOXVrl6N3CuvROYJx0OQm2lb4LA6UItauDyuCHMIUQaIsABolh7do6bchFbF98+WtOBjRhQEORg59dzjMfLkARHnMf+B97zzUlrn9tVhLWFGmVZrhGspQzFcc2TRHylXjRjlaNX+/ioTm2QgDbATX2ZkYgaLCeRx+wck+u0fIxt9/t3ne4CFCRzAJlRAXadBiSh5MBhJ2nPXA/+7AThSBGwuh8dZj9vPSIf1njfR9sITYd1ixe0frYdDOIycChseBWB3CXDv9PcV1dr3P4rXo7rB7HN9Pz8/vwuO9GsVdbHaL9mKm76uQv91m7F12PC4VlEJW5qyzEipPxaLCFX7jJRnFTIY6au9hXrjKTddCC5L5aIf4Nq1S35/v7NdbBV8KTPSwwFsiMjgpKZ/1qsvQYf+p+lbmCRhycxEs9O4Lc0gvXt31Bw5groVKzEAwFEHRPyf8D7yKipxOQBI9ZYQwUiXDbBmeQeNEWr8wUhPhT/LsusH/w0bjFx9fG+kuf3NPxtlRvqaO9vCBEHUzIy0WCxBXXHHTBQBWORucIDgAWzOfWVp2K/X2fci/UYb+tS0w2OnPRR22oPPPgfnzp1wl3ibXQsub2sWwe7NjAQQlBlZUlWPu2b/hcNVzlCz88v2rn3zKOu6ctaaiq1hrAHBSNHpDOr2p2XL5sjLjtw/qF3qGklpsaT1iaKZdrxZoVFlAFrVz2ST+owUIpw6/pGXVVt0VKTtEtdo2r4s5UQOYONxSUFUS8hjtXmm99yyqDqGQGOWMMkqonw4MRhJyjAYSdrb+yewYa7391I3dpYLGP6+E7bCK1BXVoe2Xdtg224nRHcFLPbeuHVSC7SuC84tsYhAblkGrj36JhzfMThL4t1lRVhaImDsyEsw/pRuURfrjvKpOFQ4D60r1al8RksQBazpZkGXFi0UfV+TG6LorzVbQtyspdG0Q3aaLjfTDr4Rib63686zz8L9+T9iX64dk2IokiDXS9lMm4iMTboe2wadhOzR5+pbGKIE6/jCC6hevhweVz32P/oYWlUBd3z5qvy5nBmZ5u8r2+PrH9JtA6yZvmBkYDPt8goAQK0jI3x/ewgIgngaBiN9Axz6WnCE7TNSFNBjr4i82h2oOLwg7PIi6baxBL0zRYhtldVfRLk+1riZtihasGF/Rdjv27JqkNXVggprGlqMGBF22tKPPoZz5054jnj7TxSkrnXsVqRZvdsvMDOycNMh/LwlfH+RANA8x1venKwog5FS1VPFgTCsDv9xs+f2O4KCw/aMKEfTluq4CotlkTMjwzxyS5mR8a667/iPZjRtVYORvnWM1AJKDlyp2C9oNKRgpCeeYKQ9oJl2vNsuyu/7B94J/bnNqlK2ZoTySH1GWkJ046VWVi+lLgYjSXsuXy5j7lEouOZFFABAu+NQn5WLgR8MBADce9YJ6NsxDwAgiCPhFoKb5j4zbwP+qKrDJcMGoeXx7YM++7vsD/y58SAuj2LExUDOltmYMsWOtp7R+PH6F5SsmSLHv3syLLZavB2u/5gwgptpq3fx9/cZ2bhc7jCjaUsPCdYGlXzBN3CNq01r7G1jAYTYRpmV+4x0MzOSiAxMFOW+gMM2xSNKUo68dmh58UU4Ul2HOQsex6nrRbR2t0RGRZl3Ammgu4C+sj1V3r7B3TaLnBlZu2oVDv/7LQCAfctOAEBNelbE5TcZjHT4MiMtUjPtpoMgOdsO4un3PQC+x158H3GZ4Yzw/T9vjML6S4hMJFGu71kw7ZxeOK5jdpNfn7WyEr/XRzeohK2Vt33RoZdewpH33oOtzNe1js0qZ0Y+v+J55KTnAAB2H6lBegcBx2dchbtGnNjkfG9b7m0JnBZlXVfUoJl2oKrFi+Xfy7MAuyPKQSTlF+4KFxzNADZyQCeKhYTZp9Jo2mEHsIl1NO0onjOEgObEYecUbwaowiCgf1RqZYsFAJvvOLaK3hcDiRiqTsqMFJroh9MmZe1qXI7wfUZKvzAYScowGEnac/n6BWrWBug+AsXVxZi/Yz4qXZXyJKce1QEndWnd5CxmLduLLQecqK5v3H+gR25aE9vl2C71JSQmuk9Cb2XDEeoNUxRsNm2aaUtCPUyHG01bGu2xYTNtsd4bjBTk+bGZNhElJyn48f7v+1B6cJW+hUkSWWk2TDmjB/JbRQ5Gkb6WbjmMz1fuRmW9C78PteGzocAbwq3IePZpAECluxqPLn8UFo/gbbYNyAMVuu2AM6s5AKBu3TrUrVsHAJDy1qqymg66SYKCkaIo9xn5WmERbFtaoKU0hkiYYE+zYm+2oatZOrJ7HR/dijehdMMaZNS40LxSYfDEV8x99SXYf2QjAKCktsT3qQUDuuZiYLemuyVatD0LvxcHBjCblnHssajAXLgPHYL70CE5sFHS2oF2We1QVFGEVQ2uaWktAUdaXww95qym12G5L4ARZd3c32ekii/ZA/Z3+8cfh0f04Klfn8KmzhZMi5BtK7NFkRkZpi4uDe4Y7kWVRaVm2lLgs8LpwR0fh74PnV7vC8ypGDuSmml7LGhyuQBwtpzdm9jAlZTUEF9mpP/ZS+oPVR1hslg94fsAtfletqjXUi70cvyjaYcIG6l17FLKYjCSNFNbW4tzzjkHqD6ElWsqMOCoVcAHBTjS7AjEcf6LlujJwDMP/h9m//fdJufV3NcBdlWoYKTvAmiL8SbjsHiDkR7E3jF3XHw1Glu40e7CsNv9D43HSgAAK8JJREFUb+fU5BItsAG46vvrUNI6+NJQ7fQ2nQpVqZTS9q0Nm2nXe5tpu+MMRrKZNhEZmz8z8rfdldhYvk/f4iSRFhl2PHjecXoXgyJ48pv12HTA+4K5RW/v39Iy/FmQVUIt5myZAwC4zOKtvwjVvmbadguWdOqHzT0KkO0M7qJHsFhw4NQzcXaE5XuDDKLc/FM6H3/eVob9e3fh3HLvy1F7mOahtlpvnaWsT2f0ff+DiOsczrpxBei+5gAsCpscH7FYkAHgpW2f4pe5nwd/KFqQZg9fn3L4Am3RDBTS6rrrkDVwoNxEfkPJBjyz6l8Qj2uPN05/Gsv3LQ/qr/L1FR/jkGsTrLa6pmbpXbYvEBpx5G9pet8qNezyJx5S09HmfTsj9/JxcHqc+KHeGyCPtA0l/mbaCsslZ0aqPJp2iO1aXes9zms8Ar5eHfo+NNB3DjhdMb7oDxNUlptpW9DkcgHgLN82rHfGmQQSY/KJFBCOr89IfzPtRAVTBXeEzEhfUo32A9j4foYIqLPPSIoXg5GkmczMTBQWFgKrPsDA8yag8KEzUNj5Ntz+2u0AgMHtB+PXzUBdeU88+9KUsPNqpkUwUsqMROIyIwVBhJwZaVF2+lntgTcflS7+oghBBGwASlylOFwbels6PB0b/U3uC6qJPiMFqZ+cGNtH6NW3DBFRrKTMhKHHdsC4AQyexWvlzlJ8u3Y/9pWFD3iQMewr8way7hjRA7P2WyFCQHpGQP+QVuDO/nficO1huG3/QZrbH4z02KwotWbg3ePHoF9+S1zYz1/PsFqAccflRVx+YGakKIry+ZjfJhv7qwC3xRdICFOfsNf4sruyoutLMBzRl01nVdiwowLezFDBCrTL9PeTfrCyHq7y4+GI0C2R9Hk0mZEWqxWZ/frJ/3buE7DpsAU97TbkNcvDxcdcHDT9f1YuwyFsgtXW9Iv8wMBEtH2yCxoMrCL1Pym14AkMqtqjbJ0kt/6Jqioa4oW9FIyMZjTteBMjXf5m2o+MCX0fss5Qvzm8v09G7zWgZVboJvDin95le1TsFzQaoi8zsqmgXjRsDTIjE0HK6GwqiKpVckpDVlHqMzLUaNrsM5Liw2Akaa6swvu2fGupG3MO7MG+LQfheqUWtbW1qD9tKmytu+LCs4Zh9aqVePPNN/Huu++iefPmuP3223Hxxd5KkJQZuWTzIV9Az2/XEe+b9JiDkb6OuWuc9Xj1hy1xrWO0hIAAoiPaJiIN2Ow2eOB9+FWz3xLpzdrtJ92OPn0Kgj77c1cpHpyzAR1bHd34e0000xZ8mZEuuU8TZcFINtMmIqOTMrFO7pGHs04/St/CJIH2ORn4du1+rNlblrD7MynjEUVU+l4U3zS8O96fbYFHBNIy/ZmRgtWCG0+4ETvKd+CILxj5x/q9GATvADZ1viyt4ztm43oF548UjBQFEQIE+eG8T34r/L5BDOgzsukHZocvq0zISo95+Q2Jdm8FpmGLkWhJga/zOg3FxePelP/e7/EFqK91RZEZKQXfYl++VOeyWWzYerAS3/99IKjeXVIJIB2Atb7JeQQGa6LPjPROpzSbNOQ8pXJbGwcjo+0qyRKQEdcUT72I8i1ZED75Brb8bci58EJYMzO935e6OYpiAJt4RxKXgleizdLkefTRq+oPaS3Ko2lbcOXgLuiQkxlyuq9UbPEkiiLEuuheVll8fdhHGu07HKl/RqugZm/9XjV/roJz+7ZGf2++3vu3JgewkZJBEpQZ+f2GQyisCb4fd5H2JTMjSSEGI0lzlVXeYOTag058um036uqdOHpqV4wXr8PTry9Aq5GT5Pvwp59+ikWLFiE7OzvoZtWqmTdw+NuOI/htx5GQy2mWFtvhnOXrvLrW7cS/Fm6OdbUUa97Le8HOCFcxCcNud8ADwCYCbsGD6MYpjEyqaHVp2RXHtjo26LN9hw5CcJaEfBvvH8CmYWakL8tAYTNt/2jaDEYSkYGJImy+y1RaRpSDIlBYnXO9D7O7j9Qm9P5MyuVkOtA83e7tzkUEMrLSIYWrpL7a0m3pcPuqAjk//QwAqLdaUO/21vcyHMpe0koP6xaPBx7BDXtAZiRQDrcvoNThQDX+uunqkPPosKHIW9ZmKgQjpQCW0uqLrzplb1BPdPq2U1qEzMg0qZm2grCJFLCzWWy497M1+Gt3WfC824hIbwvYwmRGBgb9ou0z0t9MO7byhp2nPAqwL3srYHs4omymDXvkzMgj6wQcXtkSWPkpAECoqUXr66/zfs+XNWgNU+e3yM20oyuS5OCMGahavET+d7d9u7zLDzuATXyZbFXLlqF21V9Bf/MUbfAu1xL+2BQDXhrEa8/tt6Nq0Q9RTdtHWn5cmZE2iJCaaat3kLoOHMTOa66Rm/MHkoZrdTWRVWv3tZTTOjNSavn29doD2LU5+H78nO/lQczN/ol8GIwkzTW3ed+Yd27TEiMc7bDgsLcz+oyWeRDqvM10pBvxM888gzvvvBOiKGLatGk49lhvUOzKk7ugss4Vspk2AORlZ+DUHk0PgBPKMW1bAtuBzrkODOqUr2TVFPnG16F5VrqyMKLd7pAr+C6POsFIURBg9aUi2u2NK+LSW3FbqD4jpWYCTfYZ6WvaoLjPSL5tIyLjEkURNil4EO0IrRTWCZ1yMO2cXigqqda7KBSlM3t7m1Nbffd6R5rdH4z01R3sljSUNQOya4GeRw4CAMqyrHJmZIZDWeqSnBnpEeHx+OuJ/bu1xX3N2mPfH52BJUCLGgFYsjLkPKSaj6tNS0VlCCT6AliKB5aQrie24Bqey9fMPOpm2gqyldy+QR1tVpt8/p3XtwOyM7x1ue3OPKyvB9pmhxl4IzAzMupgpAYDYTRoph24PRzhAnYB/H1GhlmMr6tTS0Y6xLp6HHzuORx87jkA/oBS+NG0o99f1XsF7FuYB/cnExt9JnUwcDgnTEBdHv044qIAAO5aEWV/N4fw9idwOz9H+f/mNDltbZolfJBXDrrGF8wTBRFVP/4U03cEAOu6NpcH0IqV1Wb3tkoTATHuNu7eje+qtaJmxUrA44E1OxtZAwYETbW9dB+2127GdwNbY2KIuQRnRsZz3oT/rhTsPLpdNk47tsHz8t/eHx52qUUKMRhJ6hMEYPevQI135L/cik0AgEHHdMJNWd2xcLm30rl2dy2kC6BUBznhhBPw3nvvYfny5Xj22Wfx7rveQW3atkhXvRP7DIf3Zt0jLxPTz+qr6rybIooivnlfWmdllW6pz0gA8DhVGnwnoAKUZm/cX5I7TL+cVt/NMLfCjWXPvCb/PXvXHtgBbKuoB1oDigewCfG2kIjIKDyiIGdGpmfE398ceQMYNw/vrncxSAE5+OTwP2J4rBbMX1eMwzXl+PAiG07aLmJ43hj8dPAb/NG9JU72NS/NCNevXhiCbwAbiyDIgz4A3vPx1oKuqBxyF962rYNw6HD4srdogXFX36aoDIFEuZ9BhQEC33O9J6DeJAiiXBeL2ExbboKsPDOyvMaDMl8/mk9fdAJysrx1z083bcT6X4E1h1fhwaUPhp0H4A9ORyJlrak7gI2vHL66qxAQRLJH2VWS1RcoDJd9Jri9H2YXnIKKJX9ArAkeiKnangFn18bdHPkXEn0gtqrIA3etv+xpXbsi7+GHAQAfrP0WP5R8jd1tOyH0ngnIDoxyOx9ZC5SszQbWfh3095ZX+MN6h6qP4Me9i7Dk2GYYGS4zUuoWVPE+9n7PU+OUm7T3/P238IFeAC/98hk+LHoBNksbPKlwyXa71R+MVOEQPbimBUrWtwC+egQAkHXyIOS/9lrQNJ8t/wTvbfkn0l1ZIefhSPNlRqoUB6wv2oW9D90MT0VF0N+z6r0rfPqxebjxkuDn5c/+wz4jKT4MRpL6Ns8DPrlK/ufq9DRsTEvDqYd/QPmW7yBavBXF+Wu9za0zA5rlTJ48GUVFRaivr8c///lPTYvpsHov4k7BqelyAimpoDVkd/iDkW5BpWBkQMDP4Wj8RlUaJMge6k1ypnf6dhUuYNbrjT7+bnsp0Fp5ZqTIZtpEZGAiPHKfkY60+Jt4EpmZ9KJVCAhKeCwW3P7BSsDiRoteFuxpa0GvE4fi27++A5wO1Lu8J1C6CpmR7oDMyPR07/nYIr0Fpk6drWjeSog2bwXGprT64rue/LKjHGN8f3IGZB45bOGzDR12qZl27FGKWpe3Xrn1oDegluGwIjvT/7iYl+XNgN1fvR9fb/u68QwCZNozkWaLLltc2mZqZkYKvm1WBic6IjjzMD3KYKQ08Ey4Ztqibz9nHNUZ7Z+ZIQ/OBAB3ffIXvi+qxD/bNj0Qk9xMO4ool1Ttb3XxKOTeei8cnTrJ2Zt7nWuwcbcFzV1Nr5tFHigo4qIAAG5fXDXz+J7IHHAKYLMj56ILkdGzpzzN9t2r8M6PP0FwpWneTNvjtKB2ZykAwJqTA1t2dsTvuNLT4HJYYHfH00w7oA9cFZ5Lag/5zgu7DdasZsgZM6bRNG6pv1FLE820VRxNWxSA8vmLUL9pU8jPqzIAtM5t/D0OYENxYjCS1Ffi64S3WVugVXcIqMdRj5egUnTB2sOKDj06wCG2xiknDYHlpNNxzgkdcMOTKwAAs2bNSlgx03wD2Ows34kX/nghIcv0iP4bmDXKJiINBfYh5HarE4wMDPilOZrOjAxV5LbD++Gr5Ra0rrDBJuYEfVaa0xLo4+1Eu2WMncJ7396KijpgJyJKFLfgkfuoy8hgMJJSmzxgSWAfeTYLBnbNhQgRUo9josXbiFsQLaiTMiOV9hnpW6TFI8Dj8r9gzkzTp9sE0aZOn5EHq/yB1cBgZOQBbHzBSAXVp/JaqXG9FQO75mJM3w5BTa1P63Qa/nn6P3G4NnyWKQD0a9sv+mCknBkZc5GbtN1VhRwA39ftxnEIHhAx2sxIS1SZkb5p09NgzciANSBDviKzBVy2OtjDDbIZQ0BHWpa9XWuk5Qc3mZWCV2FbXsU4armv1T6yh5+CVrdPCzmN1H0ARAusYQcTja+ZtruyDlu/zoPo/h0AYM9tHBwLxSMH9eIIRtoCMr3d8R+kgsdbls4z/oUWZ44OOY3bt52aGgRKeulgE+Mb0PTIliwc+DMHEN8HALS6/npkn3OO/PlV30zE/tw6TM5q1vjLcWe7UqpjMJLU56zy/jzuQuC8f6G3uw5zq4uDJmnfrD0yQjQHTqTsdO/btIO1B/Gf9f9J6LIdVoccDI1V4Ns5j0v9zMj9VSLch6qCPi4urwUQOjOyQ24H3HeGVKkrb/BpOSadUIW31wLZMQ7swGbaRGQGgc1C09IZjKTUJmdGBmQ5pqXZ8fnkUwEAA/6bBqfghGjxBg1FwYrDld7fFTfTlrPqBDgDuq/JSNepnukLFtoUxiykTCcBdmw5UAmr1SI3mQYi93coZacJtjL0/U/kboi6tOiKT8Z8jOZpzeXMSIfVhs9vObXRtHarHRd0vyDaVYmalBmpdATyUMo8TuQA8Pg2lzsgCBZtn5FWucl909NIeQZlbhGuijq0y/Yfdx7fMkN1cySxxBAgFH1Nwq0hXny55WVF7rcx2gxUOdAa5kVbve8eGCnYF29mpLO4AqLb6g2AtWyFnEsvjep7UlJDtCO7h2J3qJsIIviyNK1ZoUceB/zHTlPB5cAyCaIAZVdPoGpfhvzQZW3WDC3HXoL07v5uUratsEK0WpDhCBE2kg9dBiNJmaQJRr7++ut4/vnnUVxcjH79+uHVV1/FySefrHexUlO9L5CV5n2DkmHPQLecbvqVpwkD8gZg2snTUFxTHHlilfVv119xMNYRMECCW6VAneD2z+fa91bBiaKQ04WqTPVr2w+vjngVxQ0Czl9t/QrrStbhux3fAYi9Wbq/mTY7RSYi43I66+XfM9hnJKU46YH/ph8n4xXpjwFZaOm2dDgFJypdR3x/seL3Iu/viptpB2RGOgMyI9N1Gt1elJr2KqyiSYEvEVac9dKSoM8ctkjZZ0B+iy4Q3C1gtVdGNaL2zsoiPLz0cXRr2Rl/7POOSGFrommoZqR9qGKVz+YL+AhW4JKvLwmqMzuiDHxLg9+E65fP7fRu43d+34sP9vyAdyYMxEjfgE6eMH2uy6TgYRTrLgUHremNj21XNJmRUsAwytiR1Cw8VPBT4vR1jRCpOyZRHsBGWeCqprIOAFCZm4Vxw/6Bi5p3xIwovudvYaU8GGkNaH6uxrOX4PIdV1lZEEUx5HnqEsJvV4fNASksKrhdioORgtM7/45PPYTsCy6DpVFGubdsoYORbKZN8UmKYOTs2bMxdepUvPnmmxg8eDBmzJiB0aNHY9OmTWjXrp3exUs9zkrvz7QW+pYjAqvFiqt6XxV5QoOxW/23G0GlzEiXu07+PSMtAxnWxpcGh82K8/t1bPR3i8WCgvyCRn+vcFZgXck67K3aCwBomdEytkLJiQ4MRhKRcdXX+6+f6SEeEIlSSZ82ffDr/l9RDX+QHgGBmDRbGuAC3lr7JgDAZrEiO8OOdtkZGHxUa0XLFHzVItEjwun0BiM9lsjNmTVjlTIjlT2gB2ZGSqNYS8aEqIc11LdTOxzrfAZb9h6MOK0r5xs4Wq7Eot3zgd3+vzusoQfN0IoWo2nX1HvryB4rsKV0i/x3wdkqfLPpAFZf89xw/fK53CIAC8qy98KRuxQfbNiIYt842ocsO+DIrcVvh/egcmM2LNJ/AVmEDmsFeiHKZtq+av8Rax1+2TEfaw+vlT/bVOHt8iqaZtrRDhQkBT/daTbUumsxd9tcrC9ZHzTNjlJvMkKkzEM5DqpwH5eXejuw9DisSG/3LX6s/htn/6/pzEJJaW2lb/nKg5GOwD4jA1pDKCU10663i7jiq4uwvXx7k9OGy4yUngI9bg8cIaeKzOPylsXepg0OukpRXVsd9LnU92yGvfGzIfuMpHglRTDyxRdfxKRJk3DdddcBAN588018++23ePfdd/HAAw/oXLoUJGVGpjfXtxxJypGmfmaky+MPan588zD06RR/EH987/HISctBtbsaVlgxosuImL4vxFlpISJKBCn4AQCZmZEfjIiS2Ztnvol91fuwdc8a4OV7AQS3jBjdbTQ+3fQpRIiwWWy47bSxuO740H2mRUuUs+pE1DudsMKbDWcPM5iGpnwZnkr7jJQCX5cM7IpZ18a+bdLtNsyZPCyqaS97uzlWH26Bgl4t0b1tcxSVVOPHDSXolnVmzMuNhxhjkCwagjT4omjHmyPfRK3Lg5s/WAlPXcfwmYoBpAFswvUZWSpakAPgSJuNyGi/GSsqgRW/+z5MAzLaA1/u9v4fylibgF7wDrQTyRFBRCaAh7d/gNWWj0JOk2Zt+j4U6wA2FR4gDcBtG1/Fug8bD1IZyCqG6FMwgBhjf5UN1VXVwwpgT44Taa1/hghgb1Wkb/ll+ALEStgCsrs9KiRJuN3efMcLf7oOR7LDH4st0DPk39PsDtT6fnd5XFDaLsPpy9J8ZNMbWLAzRFDUdxkNlRkZb4CZyPTBSKfTiZUrV2LaNH+nularFWeeeSZ++eWXkN+pr69Hfb3/jW1FgyHsk0lV+REUnXKaDkvuiI2eH3HpKZN1WHZyswW8maq9dCLWqTBPC+R7DTrlRh6ZLhrNHM1wea/Llc/Ad4M79v3lWPff3qqUiYhIbYHXzzSdBswgMgqb1Yb8FvlI72THAStgF4CqVv7H5GmDp2Ha4NADYSgl9RnZd+5W4BtvEoKgUxwS8A9gc0yRgHW9Y6+/5Pqe67MS0Adt5+z2+GPb2Vh4GPjBYoEgihBFILdnW82XHUgKVPVdVaNom4XSy7cds12ZuOZ17wjMgngMAESfGemLtrQvQZPlyvEtp7k9D65yb+aqxRdRlprfDuqWi7YtvPszsFmuKIrIwmIA9Wi7rjziumf6luV0WCB6MiHUdYZQ19G3LEAU7Dg6//xwKwQA6LO2LqrtnOZbXr3Dv72E+jy4K04MmEqEKFrQzjYg/Mx8szi+8ICifSwF6fc3cwGwQRRtqNt5S8SMR0EUIQpWnNg1cv+pTbEGPHuVnnMpjoSZNho23xuUel86o+vIcLhKhgdN4z0Xbeh8bJeQ83Ck+cu06/Qzo40vhyiL93q1qm4bkOHLdPQEZEaLgKeuEzq3aJyVLZ+3y4+odt6mkk1XDsLYR97Xuxi6Mn0w8vDhw/B4PMjLywv6e15eHjZu3BjyO9OnT8fjjz+eiOIZgk2nlxX1acqa21BkOzta0HWfGPZNrdL59so0xgAM5Z1bAPsqvA/5fOFGRAa3s6MFvfXKxCIymDYt8vDMfSegdsMGDL/sGk2XVds1D9i6N6i+sKejFSdqutSmdR58Fuq/WIV0l/I6eHkW0GfIOZEnjNNpPdrgy7/2QhQBT0DG2uCjWmm+7EAZx/WF+9efYBfUfW5xW4HtLbsFtf4e0DU36ia7x55+PkrfnINmdeHLVZkJTDjrcSz9tho1zuCU2ObpdjxzzTB0ahk6Y/HLtXfAvWxh1OtelQHsq5iEquoeIT8/bejRTX63eZ/+cP88L6btXJEF3DR8Bvr0GYChzxYCYuiXbkMGdA47H6F7NwjrNsZVrxcAlHRpiTz3SGzfeRQEZ17E70hO7qr8uTQzsxn25FnQ+YB6z1672gKH994Jj5gF0d2yyekGdA19Lua26YzVbYFOh8Jn7kZjXy5wwDUYzgNt4DpyGtCgB8pOLTPRqWXjrhssPXpA+Gs1rKJ+8QYz4yYDLKLJhz/at28fOnXqhOXLl2PIkCHy3++77z4sXrwYv/32W6PvhMqMzM/PR3l5ObKz1ckKMwqP240tq5dEnlBloiML7fOPQW4uA5JacDnrsW3tMtXne/QJpyEtzRjBSADYtnY5XM66yBMSEems+wmnwWGg6yeR3kRRhFt0w2FV2ptZ9Io2rEBdjb+lk97n4+H9O3B43w7F3+949PHIzk1Mv/flNS7UBwxkaLdZ0apZ4rO89+5Yj8oj6g4q2bxVe6S1Dg7OtWmeHnEQoEBlJftRXLQh7DTtu/VGy9YdUOv0oLIuuD/3FhkOZKaFH15k384NqDi8P6ryZOX1QEZ26GMjzW5Fy6zw+27/zo0oP7wvqmXN+GELlhzOxjPjh6N722Y475WlaNUsDfPvHBo0ncViQZvmaRGDvLu2rEZNRUlUyw4lK7s1uhzTD4Ig4nBVfeQv+KhxTNfX1mDH+l/jmkeg7Px+sGeEb9oeqdxqlal55+ORltl0DKRlVlqTffDu2rIGNRWH4y5DKsrrcixy23bSuxiaqKioQE5OTsT4mumDkU6nE1lZWfj8889x0UUXyX+fMGECysrK8NVXX0WcR7Qbi4iIiIiIiCjZ3fHxKny9eh/ystORZrdi95FaHNchG981CEYSEQWKNr5m+vZEaWlpGDBgAH744Qf5b4Ig4IcffgjKlCQiIiIiIiKiyPp09AYRDlTUY/eR2qC/ERHFy/R9RgLA1KlTMWHCBAwcOBAnn3wyZsyYgerqanl0bSIiIiIiIiKKzo1Dj8bAbq1Q6+sH02a1oH/XlvoWioiSRlIEIy+//HIcOnQIjzzyCIqLi3HiiSdi/vz5jQa1ISIiIiIiIqLwbFYLBnTN1bsYRJSkTN9npBrYZyQREREREREREZFyKdNnJBEREREREREREZkDg5FERERERERERESUEAxGEhERERERERERUUIwGElEREREREREREQJwWAkERERERERERERJQSDkURERERERERERJQQDEYSERERERERERFRQjAYSURERERERERERAnBYCQRERERERERERElBIORRERERERERERElBAMRhIREREREREREVFCMBhJRERERERERERECcFgJBERERERERERESUEg5FERERERERERESUEAxGEhERERERERERUUIwGElEREREREREREQJwWAkERERERERERERJQSDkURERERERERERJQQDEYSERERERERERFRQjAYSURERERERERERAnBYCQRERERERERERElBIORRERERERERERElBAMRhIREREREREREVFCMBhJRERERERERERECcFgJBERERERERERESUEg5FERERERERERESUEAxGEhERERERERERUULY9S6AEYiiCACoqKjQuSRERERERERERETmI8XVpDhbUxiMBFBZWQkAyM/P17kkRERERERERERE5lVZWYmcnJwmP7eIkcKVKUAQBOzbtw8tWrSAxWLRuziqq6ioQH5+Pnbv3o3s7Gy9i0MpiMcg6Y3HIOmJxx/pjccg6Y3HIOmNxyDpKZWOP1EUUVlZiY4dO8JqbbpnSGZGArBarejcubPexdBcdnZ20h/4ZGw8BklvPAZJTzz+SG88BklvPAZJbzwGSU+pcvyFy4iUcAAbIiIiIiIiIiIiSggGI4mIiIiIiIiIiCghGIxMAenp6Xj00UeRnp6ud1EoRfEYJL3xGCQ98fgjvfEYJL3xGCS98RgkPfH4a4wD2BAREREREREREVFCMDOSiIiIiIiIiIiIEoLBSCIiIiIiIiIiIkoIBiOJiIiIiIiIiIgoIRiMJCIiIiIiIiIiooRgMDIFvP766+jWrRsyMjIwePBg/P7773oXiZLUkiVLcP7556Njx46wWCz48ssvgz4XRRGPPPIIOnTogMzMTJx55pnYsmWLPoWlpDN9+nQMGjQILVq0QLt27XDRRRdh06ZNQdPU1dVhypQpaN26NZo3b46xY8fiwIEDOpWYks3MmTPRt29fZGdnIzs7G0OGDMG8efPkz3n8USI988wzsFgsuOuuu+S/8RgkLT322GOwWCxB//fq1Uv+nMcfJcLevXtx9dVXo3Xr1sjMzMQJJ5yAFStWyJ/zeYS01K1bt0bXQYvFgilTpgDgdTAQg5FJbvbs2Zg6dSoeffRR/Pnnn+jXrx9Gjx6NgwcP6l00SkLV1dXo168fXn/99ZCfP/fcc3jllVfw5ptv4rfffkOzZs0wevRo1NXVJbiklIwWL16MKVOm4Ndff8XChQvhcrkwatQoVFdXy9PcfffdmDt3Lj777DMsXrwY+/btwyWXXKJjqSmZdO7cGc888wxW/v/27j+m6nqP4/jrdOAQCAIacEAHkoFITEcwSMmZ4R8x14ISmLEBmX9UkKDRWn84qjX4o7WF/UFR7egsY9XCSlMiE1bNFGk0zAaRIK1QZgtDZeA4n/tH89x7tHsvJed77oXnYzsb5/P98D1vzl77sO973x9dXTpx4oTuuece3X///fruu+8kkT9Yp7OzU6+99ppWrFjhNU4G4Wu33367hoeHPa8vv/zSs438wdd+++035eTkKDAwUAcPHtSpU6f00ksvKTIy0jOH4xH4Umdnp9ca2NbWJkkqLCyUxDroxWBWy8rKMhUVFZ73U1NTJi4uztTX1/uxKswFkkxLS4vnvdvtNk6n07z44ouesdHRURMUFGTeeecdP1SI2W5kZMRIMh0dHcaYP/IWGBho3nvvPc+c77//3kgyR48e9VeZmOUiIyPNG2+8Qf5gmbGxMZOUlGTa2trM2rVrTVVVlTGGNRC+V1tba1auXPmn28gfrPD000+bu+66699u53gEVquqqjJLly41brebdfAanBk5i01OTqqrq0vr16/3jN10001av369jh496sfKMBcNDAzo7NmzXnkMDw9XdnY2eYRPXLhwQZK0YMECSVJXV5euXLnilcGUlBTFx8eTQcy4qakpNTc369KlS1q1ahX5g2UqKiq0YcMGr6xJrIGwxg8//KC4uDjdeuutKikp0dDQkCTyB2t89NFHyszMVGFhoaKjo5Wenq7XX3/ds53jEVhpcnJSb731ljZv3iybzcY6eA2akbPY+fPnNTU1pZiYGK/xmJgYnT171k9VYa66mjnyCCu43W5VV1crJydHaWlpkv7IoMPhUEREhNdcMoiZ1NPTo9DQUAUFBenRRx9VS0uLUlNTyR8s0dzcrG+++Ub19fXXbSOD8LXs7Gzt2rVLhw4dUmNjowYGBrRmzRqNjY2RP1ji9OnTamxsVFJSklpbW/XYY49p69at2r17tySOR2Ctffv2aXR0VOXl5ZL4P3ytAH8XAADATKuoqNDJkye97lUFWGHZsmXq7u7WhQsX9P7776usrEwdHR3+LgtzwE8//aSqqiq1tbXp5ptv9nc5mIPy8vI8P69YsULZ2dlKSEjQu+++q+DgYD9WhrnC7XYrMzNTdXV1kqT09HSdPHlSr776qsrKyvxcHeaaN998U3l5eYqLi/N3Kf+TODNyFrvllltkt9uvezrTuXPn5HQ6/VQV5qqrmSOP8LXKykrt379fR44c0eLFiz3jTqdTk5OTGh0d9ZpPBjGTHA6HbrvtNmVkZKi+vl4rV65UQ0MD+YPPdXV1aWRkRHfccYcCAgIUEBCgjo4O7dy5UwEBAYqJiSGDsFRERISSk5PV39/PGghLxMbGKjU11Wts+fLlntsFcDwCq5w5c0afffaZtmzZ4hljHfRGM3IWczgcysjI0OHDhz1jbrdbhw8f1qpVq/xYGeaixMREOZ1Orzz+/vvvOnbsGHnEjDDGqLKyUi0tLfr888+VmJjotT0jI0OBgYFeGezt7dXQ0BAZhM+43W5NTEyQP/hcbm6uenp61N3d7XllZmaqpKTE8zMZhJUuXryoH3/8UbGxsayBsEROTo56e3u9xvr6+pSQkCCJ4xFYx+VyKTo6Whs2bPCMsQ564zLtWW779u0qKytTZmamsrKy9PLLL+vSpUt6+OGH/V0aZqGLFy+qv7/f835gYEDd3d1asGCB4uPjVV1drRdeeEFJSUlKTEzUjh07FBcXp/z8fP8VjVmjoqJCe/fu1YcffqiwsDDPvVfCw8MVHBys8PBwPfLII9q+fbsWLFig+fPn64knntCqVat05513+rl6zAbPPPOM8vLyFB8fr7GxMe3du1ft7e1qbW0lf/C5sLAwzz1yr5o3b54WLlzoGSeD8KWamhrdd999SkhI0C+//KLa2lrZ7XZt2rSJNRCW2LZtm1avXq26ujoVFRXp+PHjampqUlNTkyTJZrNxPAKfc7vdcrlcKisrU0DAP1turIPX8PfjvOF7r7zyiomPjzcOh8NkZWWZr7/+2t8lYZY6cuSIkXTdq6yszBhjjNvtNjt27DAxMTEmKCjI5Obmmt7eXv8WjVnjz7InybhcLs+c8fFx8/jjj5vIyEgTEhJiCgoKzPDwsP+KxqyyefNmk5CQYBwOh4mKijK5ubnm008/9Wwnf7Da2rVrTVVVlec9GYQvFRcXm9jYWONwOMyiRYtMcXGx6e/v92wnf7DCxx9/bNLS0kxQUJBJSUkxTU1NXts5HoGvtba2Gkl/mivWwX+yGWOMf9qgAAAAAAAAAOYS7hkJAAAAAAAAwBI0IwEAAAAAAABYgmYkAAAAAAAAAEvQjAQAAAAAAABgCZqRAAAAAAAAACxBMxIAAAAAAACAJWhGAgAAAAAAALAEzUgAAADMuPb2dtlsNo2Ojlr+2TabTTabTREREdOaf7VWm82m/Px8n9YGAAAw19GMBAAAwA25++67VV1d7TW2evVqDQ8PKzw83C81uVwu9fX1TWvu1VqLiop8XBUAAABoRgIAAGDGORwOOZ1O2Ww2v3x+RESEoqOjpzX3aq3BwcE+rgoAAAA0IwEAAPC3lZeXq6OjQw0NDZ5LnQcHB6+7THvXrl2KiIjQ/v37tWzZMoWEhGjjxo26fPmydu/erSVLligyMlJbt27V1NSUZ/8TExOqqanRokWLNG/ePGVnZ6u9vf0v1/ntt99q3bp1CgsL0/z585WRkaETJ07M0LcAAACA6QrwdwEAAAD4/9XQ0KC+vj6lpaXp+eeflyRFRUVpcHDwurmXL1/Wzp071dzcrLGxMT3wwAMqKChQRESEPvnkE50+fVoPPvigcnJyVFxcLEmqrKzUqVOn1NzcrLi4OLW0tOjee+9VT0+PkpKSpl1nSUmJ0tPT1djYKLvdru7ubgUGBs7IdwAAAIDpoxkJAACAvy08PFwOh0MhISFyOp3/ce6VK1fU2NiopUuXSpI2btyoPXv26Ny5cwoNDVVqaqrWrVunI0eOqLi4WENDQ3K5XBoaGlJcXJwkqaamRocOHZLL5VJdXd206xwaGtJTTz2llJQUSfpLjUwAAADMHJqRAAAAsERISIinESlJMTExWrJkiUJDQ73GRkZGJEk9PT2amppScnKy134mJia0cOHCv/TZ27dv15YtW7Rnzx6tX79ehYWFXrUAAADAGjQjAQAAYIlrL4u22Wx/OuZ2uyVJFy9elN1uV1dXl+x2u9e8f21gTsezzz6rhx56SAcOHNDBgwdVW1ur5uZmFRQU/I2/BAAAAH8XzUgAAADcEIfD4fXQmZmSnp6uqakpjYyMaM2aNTe8v+TkZCUnJ2vbtm3atGmTXC4XzUgAAACL8TRtAAAA3JAlS5bo2LFjGhwc1Pnz5z1nNt6o5ORklZSUqLS0VB988IEGBgZ0/Phx1dfX68CBA9Pez/j4uCorK9Xe3q4zZ87oq6++Umdnp5YvXz4jdQIAAGD6aEYCAADghtTU1Mhutys1NVVRUVEaGhqasX27XC6VlpbqySef1LJly5Sfn6/Ozk7Fx8dPex92u12//vqrSktLlZycrKKiIuXl5em5556bsToBAAAwPTZjjPF3EQAAAMBMsdlsamlpUX5+/l/6vfLyco2Ojmrfvn0+qQsAAACcGQkAAIBZaNOmTVq8ePG05n7xxRcKDQ3V22+/7eOqAAAAwJmRAAAAmFX6+/sl/XF5dmJi4n+dPz4+rp9//lnSH0/pdjqdPq0PAABgLqMZCQAAAAAAAMASXKYNAAAAAAAAwBI0IwEAAAAAAABYgmYkAAAAAAAAAEvQjAQAAAAAAABgCZqRAAAAAAAAACxBMxIAAAAAAACAJWhGAgAAAAAAALAEzUgAAAAAAAAAlqAZCQAAAAAAAMAS/wDO6Kjz/nkavQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_text_info_timestamps2(audio_dir,file_name,output_dir):\n",
        "    device = \"cuda\"\n",
        "    batch_size = 8 # reduce if low on GPU mem\n",
        "    compute_type = \"int8\" # change to \"int8\" if low on GPU mem (may reduce accuracy)\n",
        "\n",
        "    # transcribe with original whisper\n",
        "    model = whisper.load_model(\"medium\", device)\n",
        "    result = model.transcribe(audio_dir+file_name,language = 'en')\n",
        "\n",
        "    print(result[\"segments\"]) # before alignment\n",
        "\n",
        "    # load alignment model and metadata\n",
        "    model_a, metadata = whisperx.load_align_model(language_code='en', device=device)\n",
        "\n",
        "    # align whisper output\n",
        "    result_aligned = whisperx.align(result[\"segments\"], model_a, metadata, audio_dir+file_name, device)\n",
        "\n",
        "    print(result_aligned[\"segments\"]) # after alignment\n",
        "    print(result_aligned[\"word_segments\"]) # after alignment\n",
        "\n",
        "    result_output = os.path.join(output_dir,file_name[:-4]+'.json')\n",
        "    with open(result_output,'w') as f:\n",
        "        json.dump(result_aligned,f)\n",
        "\n",
        "def json_to_df(input_dir,file_name,out_dir_csv):\n",
        "    with open(input_dir+file_name) as f:\n",
        "        d = json.load(f)\n",
        "\n",
        "    df = pd.DataFrame({\n",
        "        'filename':[],\n",
        "        'onset':[],\n",
        "        'offset':[],\n",
        "        'text' : [],\n",
        "        'syllable' : []\n",
        "        })\n",
        "    df.to_csv(out_dir_csv+file_name[:-5]+'.csv')\n",
        "    print('csv output initilased')\n",
        "    total_segs = d['word_segments']\n",
        "    nb_segs = len(total_segs)\n",
        "    print(\"nb_seg = \", nb_segs)\n",
        "    print(total_segs)\n",
        "    for s in range(nb_segs):\n",
        "        word_info = total_segs[s]\n",
        "        print(word_info)\n",
        "        try:\n",
        "            word = word_info['word']\n",
        "            onset = word_info['start']\n",
        "            offset = word_info['end']\n",
        "            syllable = syllables.estimate(word)\n",
        "        except:\n",
        "            word = word_info['word']\n",
        "            syllable = syllables.estimate(word)\n",
        "            word_info = total_segs[s-1]\n",
        "            onset = word_info['end']\n",
        "            word_info = total_segs[s+1]\n",
        "            offset = word_info['start']\n",
        "        row_contents = [0,file_name,\n",
        "        onset, offset, word, syllable]\n",
        "        append_list_as_row(out_dir_csv+file_name[:-5]+'.csv', row_contents)\n",
        "\n",
        "\n",
        "def demo_audio_semantic_complexity_pipeline(audio_dir,file_name,temp_folder):\n",
        "\n",
        "    ## AUDIO TRANSCRIPTION (can take 20min for 1min file with large model on laptop)\n",
        "    # generate_text_info_timestamps2(audio_dir,file_name,temp_folder)\n",
        "\n",
        "    ## FROM JSON format to CSV format\n",
        "    # json_to_df(temp_folder,file_name[:-4]+'.json',temp_folder)\n",
        "\n",
        "\n",
        "    ## FROM CSV TRANSCIPTIONS to COMPLEXITY SURPRISAL and UNCERTAINTY ####\n",
        "\n",
        "    # df_surprisal = get_next_list_dataframe_prompted(temp_folder,file_name[:-4]+'.csv')\n",
        "    # df_total = pd.read_csv(temp_folder+file_name[:-4]+'.csv')\n",
        "    # df_total['Complexity'] = df_surprisal['Complexity']\n",
        "    # # df_total['InfoRate'] = df_surprisal['Complexity'].diff()\n",
        "    # df_total['Surprisal'] = df_surprisal['Surprisal']\n",
        "    # df_total['Entropy'] = df_surprisal['Entropy']\n",
        "    # df_total.to_csv(temp_folder+file_name[:-4]+'.csv')\n",
        "\n",
        "    # df_total = pd.read_csv(temp_folder+file_name[:-4]+'.csv')\n",
        "    # df_total['InfoRate'] = df_total['Complexity'].diff()\n",
        "    # print(df_total['InfoRate'])\n",
        "    # df_total.to_csv(temp_folder+file_name[:-4]+'.csv')\n",
        "    df_total = pd.read_csv(temp_folder+file_name[:-4]+'.csv')\n",
        "    # print(df_total)\n",
        "    ####\n",
        "\n",
        "\n",
        "    ## FROM SURPRISAL and UNCERTAINTY to NUMPY TIME SERIES ####\n",
        "    # path_output_complexity = 'Semantic_outputs/'\n",
        "    # path_output_InfoRate = 'Semantic_outputs/'\n",
        "    # path_output_surprisal = 'Semantic_outputs/'\n",
        "    # path_output_uncertainty = 'Semantic_outputs/'\n",
        "    # path_output_segment = 'Semantic_outputs/'\n",
        "\n",
        "\n",
        "    list_complexity,list_InfoRate,list_surprisal, list_uncertainty, list_segment = df_to_numpy(temp_folder,file_name)\n",
        "    # np.save(path_output_complexity+file_name[:-4]+'_SC.npy',list_complexity)\n",
        "    # np.save(path_output_complexity+file_name[:-4]+'_IR.npy',list_InfoRate)\n",
        "    # np.save(path_output_surprisal+file_name[:-4]+'_SS.npy',list_surprisal)\n",
        "    # np.save(path_output_uncertainty+file_name[:-4]+'_SU.npy',list_uncertainty)\n",
        "    # np.save(path_output_segment+file_name[:-4]+'_SpSg.npy',list_segment)\n",
        "\n",
        "    #### PLOTTING ####\n",
        "\n",
        "    text = df_total['text'].to_numpy()\n",
        "    df_onset = df_total['onset'].to_numpy()\n",
        "    df_offset = df_total['offset'].to_numpy()\n",
        "    df_complexity = df_total['Complexity'].to_numpy()\n",
        "    # text = \" \".join(text)\n",
        "    time = np.arange(0,len(list_complexity)/50,1/50)\n",
        "\n",
        "    plt.figure(figsize= (16,9) )\n",
        "    for k in range(len(text)-1):\n",
        "        plt.text(df_onset[k],df_complexity[k],text[k],size = 6)\n",
        "\n",
        "    plt.plot(time,list_InfoRate,label = 'Information Rate')\n",
        "    plt.plot(time,list_complexity,label = 'complexity')\n",
        "\n",
        "    plt.plot(time,list_surprisal,label = 'surprisal [bit]')\n",
        "    plt.plot(time,list_uncertainty,label = 'uncertainty [bit]')\n",
        "    plt.xlabel('time [s]')\n",
        "    plt.ylabel('complexities')\n",
        "    plt.legend()\n",
        "    plt.savefig(file_name[:-4]+'.svg')\n",
        "    plt.show()\n",
        "\n",
        "    plt.figure(figsize= (16,9) )\n",
        "    plt.title('Dynamic Semantic Complexities')\n",
        "    plt.subplot(411)\n",
        "    for k in range(len(text)-1):\n",
        "        plt.text(df_onset[k],df_complexity[k]+2,text[k],size = 5)\n",
        "\n",
        "    plt.plot(time,list_complexity,label = 'complexity')\n",
        "    plt.ylabel('cumulative complexity')\n",
        "    plt.subplot(412)\n",
        "    plt.plot(time,list_InfoRate,label = 'Information Rate')\n",
        "    plt.ylabel('instantaneous complexity')\n",
        "    plt.subplot(413)\n",
        "    plt.plot(time,list_surprisal,label = 'surprisal [bit]')\n",
        "    plt.ylabel('Information Surprisal')\n",
        "    plt.subplot(414)\n",
        "    plt.plot(time,list_uncertainty,label = 'uncertainty [bit]')\n",
        "    plt.xlabel('time [s]')\n",
        "    plt.ylabel('Information Uncertainty')\n",
        "    plt.legend()\n",
        "    plt.tight_layout()\n",
        "    plt.subplots_adjust(hspace=0)\n",
        "\n",
        "    plt.savefig(file_name[:-4]+'_all_4.svg')\n",
        "    plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "KL4kYxTmMaKA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#### DEMO WITH PLOTS FOR AUDIO\n",
        "audio_dir = '/content/'\n",
        "temp_folder = '/content/Temp_Folder/'\n",
        "\n",
        "file_name = '1015_1_pk.wav'\n",
        "# file_name = 'Demo-audio.wav'\n",
        "# file_name = 'Demo-Deutsch.wav'\n",
        "\n",
        "full_audio_path = os.path.join(audio_dir, file_name)\n",
        "print(\"Full audio path:\", full_audio_path)\n",
        "\n",
        "demo_audio_semantic_complexity_pipeline(audio_dir,file_name,temp_folder)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "cMTH0gErQb9q",
        "outputId": "ccb6beff-911c-4a85-f0f8-d21f124758d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'os' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-364cefa9f78e>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# file_name = 'Demo-Deutsch.wav'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mfull_audio_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Full audio path:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfull_audio_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined"
          ]
        }
      ]
    }
  ]
}